[
  {
    "path": "0.math/0.basic/README.md",
    "content": "\n大学数学基础知识\n------------------\n\n数据科学需要一定的数学基础，但仅仅做应用的话，如果时间不多，不用学太深，了解基本公式即可，遇到问题再查吧。\n\n以下是以前考研考博时候的数学笔记，难度应该在本科3年级左右。 \n\nmarkdown文件夹是文件的markdown代码，内容与pdf一致，是为了方便研究者写论文或者文章使用。\n\n"
  },
  {
    "path": "0.math/0.basic/markdown/README.md",
    "content": "\n数学基础知识\n------------------\n\n[TOC]\n\n数据科学需要一定的数学基础，但仅仅做应用的话，如果时间不多，不用学太深，了解基本公式即可，遇到问题再查吧。\n\n以下是以前考研考博时候的数学笔记，难度应该在本科3年级左右。 \n\n### 高等数学\n\n**1.导数定义：**\n\n导数和微分的概念\n\n$f'({{x}_{0}})=\\underset{\\Delta x\\to 0}{\\mathop{\\lim }}\\,\\frac{f({{x}_{0}}+\\Delta x)-f({{x}_{0}})}{\\Delta x}$    （1）\n\n或者：\n\n$f'({{x}_{0}})=\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,\\frac{f(x)-f({{x}_{0}})}{x-{{x}_{0}}}$           （2）\n\n**2.左右导数导数的几何意义和物理意义**\n\n函数$f(x)$在$x_0$处的左、右导数分别定义为：\n\n左导数：${{{f}'}_{-}}({{x}_{0}})=\\underset{\\Delta x\\to {{0}^{-}}}{\\mathop{\\lim }}\\,\\frac{f({{x}_{0}}+\\Delta x)-f({{x}_{0}})}{\\Delta x}=\\underset{x\\to x_{0}^{-}}{\\mathop{\\lim }}\\,\\frac{f(x)-f({{x}_{0}})}{x-{{x}_{0}}},(x={{x}_{0}}+\\Delta x)$\n\n右导数：${{{f}'}_{+}}({{x}_{0}})=\\underset{\\Delta x\\to {{0}^{+}}}{\\mathop{\\lim }}\\,\\frac{f({{x}_{0}}+\\Delta x)-f({{x}_{0}})}{\\Delta x}=\\underset{x\\to x_{0}^{+}}{\\mathop{\\lim }}\\,\\frac{f(x)-f({{x}_{0}})}{x-{{x}_{0}}}$\n\n**3.函数的可导性与连续性之间的关系**\n\n**Th1:** 函数$f(x)$在$x_0$处可微$\\Leftrightarrow f(x)$在$x_0$处可导\n\n**Th2:** 若函数在点$x_0$处可导，则$y=f(x)$在点$x_0$处连续，反之则不成立。即函数连续不一定可导。\n\n**Th3:** ${f}'({{x}_{0}})$存在$\\Leftrightarrow {{{f}'}_{-}}({{x}_{0}})={{{f}'}_{+}}({{x}_{0}})$\n\n**4.平面曲线的切线和法线**\n\n切线方程 : $y-{{y}_{0}}=f'({{x}_{0}})(x-{{x}_{0}})$\n法线方程：$y-{{y}_{0}}=-\\frac{1}{f'({{x}_{0}})}(x-{{x}_{0}}),f'({{x}_{0}})\\ne 0$\n\n**5.四则运算法则**\n设函数$u=u(x)，v=v(x)$]在点$x$可导则\n(1) $(u\\pm v{)}'={u}'\\pm {v}'$       $d(u\\pm v)=du\\pm dv$\n(2)$(uv{)}'=u{v}'+v{u}'$        $d(uv)=udv+vdu$\n(3) $(\\frac{u}{v}{)}'=\\frac{v{u}'-u{v}'}{{{v}^{2}}}(v\\ne 0)$       $d(\\frac{u}{v})=\\frac{vdu-udv}{{{v}^{2}}}$\n\n**6.基本导数与微分表**\n(1) $y=c$（常数）       ${y}'=0$          $dy=0$\n(2) $y={{x}^{\\alpha }}$($\\alpha $为实数)    ${y}'=\\alpha {{x}^{\\alpha -1}}$      $dy=\\alpha {{x}^{\\alpha -1}}dx$\n(3) $y={{a}^{x}}$      ${y}'={{a}^{x}}\\ln a$         $dy={{a}^{x}}\\ln adx$\n  特例:   $({{{e}}^{x}}{)}'={{{e}}^{x}}$             $d({{{e}}^{x}})={{{e}}^{x}}dx$\n\n(4) $y={{\\log }_{a}}x$   ${y}'=\\frac{1}{x\\ln a}$           \n\n$dy=\\frac{1}{x\\ln a}dx$\n  特例:$y=\\ln x$                      $(\\ln x{)}'=\\frac{1}{x}$       $d(\\ln x)=\\frac{1}{x}dx$\n\n(5) $y=\\sin x$         \n\n${y}'=\\cos x$        $d(\\sin x)=\\cos xdx$\n\n(6) $y=\\cos x$      \n\n${y}'=-\\sin x$       $d(\\cos x)=-\\sin xdx$\n\n(7) $y=\\tan x$  \n\n${y}'=\\frac{1}{{{\\cos }^{2}}x}={{\\sec }^{2}}x$  $d(\\tan x)={{\\sec }^{2}}xdx$\n(8) $y=\\cot x$ ${y}'=-\\frac{1}{{{\\sin }^{2}}x}=-{{\\csc }^{2}}x$  $d(\\cot x)=-{{\\csc }^{2}}xdx$\n(9) $y=\\sec x$ ${y}'=\\sec x\\tan x$     \n\n $d(\\sec x)=\\sec x\\tan xdx$\n(10) $y=\\csc x$ ${y}'=-\\csc x\\cot x$    \n\n$d(\\csc x)=-\\csc x\\cot xdx$\n(11) $y=\\arcsin x$  \n\n${y}'=\\frac{1}{\\sqrt{1-{{x}^{2}}}}$   \n\n$d(\\arcsin x)=\\frac{1}{\\sqrt{1-{{x}^{2}}}}dx$\n(12) $y=\\arccos x$ \n\n${y}'=-\\frac{1}{\\sqrt{1-{{x}^{2}}}}$     $d(\\arccos x)=-\\frac{1}{\\sqrt{1-{{x}^{2}}}}dx$\n\n(13) $y=\\arctan x$ \n\n${y}'=\\frac{1}{1+{{x}^{2}}}$     $d(\\arctan x)=\\frac{1}{1+{{x}^{2}}}dx$\n\n(14) $y=\\operatorname{arc}\\cot x$      \n\n${y}'=-\\frac{1}{1+{{x}^{2}}}$   \n\n$d(\\operatorname{arc}\\cot x)=-\\frac{1}{1+{{x}^{2}}}dx$\n(15) $y=shx$    \n\n${y}'=chx$       $d(shx)=chxdx$\n\n(16) $y=chx$    \n\n${y}'=shx$       $d(chx)=shxdx$\n\n**7.复合函数，反函数，隐函数以及参数方程所确定的函数的微分法**\n\n(1) 反函数的运算法则: 设$y=f(x)$在点$x$的某邻域内单调连续，在点$x$处可导且${f}'(x)\\ne 0$，则其反函数在点$x$所对应的$y$处可导，并且有$\\frac{dy}{dx}=\\frac{1}{\\frac{dx}{dy}}$\n(2) 复合函数的运算法则:若$\\mu =\\varphi (x)$在点$x$可导,而$y=f(\\mu )$在对应点$\\mu $($\\mu =\\varphi (x)$)可导,则复合函数$y=f(\\varphi (x))$在点$x$可导,且${y}'={f}'(\\mu )\\cdot {\\varphi }'(x)$\n(3) 隐函数导数$\\frac{dy}{dx}$的求法一般有三种方法：\n1)方程两边对$x$求导，要记住$y$是$x$的函数，则$y$的函数是$x$的复合函数.例如$\\frac{1}{y}$，${{y}^{2}}$，$ln y$，${{{e}}^{y}}$等均是$x$的复合函数.\n对$x$求导应按复合函数连锁法则做.\n2)公式法.由$F(x,y)=0$知 $\\frac{dy}{dx}=-\\frac{{{{{F}'}}_{x}}(x,y)}{{{{{F}'}}_{y}}(x,y)}$,其中，${{{F}'}_{x}}(x,y)$，\n${{{F}'}_{y}}(x,y)$分别表示$F(x,y)$对$x$和$y$的偏导数\n3)利用微分形式不变性\n\n**8.常用高阶导数公式**\n\n（1）$({{a}^{x}}){{\\,}^{(n)}}={{a}^{x}}{{\\ln }^{n}}a\\quad (a>{0})\\quad \\quad ({{{e}}^{x}}){{\\,}^{(n)}}={e}{{\\,}^{x}}$\n（2）$(\\sin kx{)}{{\\,}^{(n)}}={{k}^{n}}\\sin (kx+n\\cdot \\frac{\\pi }{{2}})$\n（3）$(\\cos kx{)}{{\\,}^{(n)}}={{k}^{n}}\\cos (kx+n\\cdot \\frac{\\pi }{{2}})$\n（4）$({{x}^{m}}){{\\,}^{(n)}}=m(m-1)\\cdots (m-n+1){{x}^{m-n}}$\n（5）$(\\ln x){{\\,}^{(n)}}={{(-{1})}^{(n-{1})}}\\frac{(n-{1})!}{{{x}^{n}}}$\n（6）莱布尼兹公式：若$u(x)\\,,v(x)$均$n$阶可导，则\n ${{(uv)}^{(n)}}=\\sum\\limits_{i={0}}^{n}{c_{n}^{i}{{u}^{(i)}}{{v}^{(n-i)}}}$，其中${{u}^{({0})}}=u$，${{v}^{({0})}}=v$\n\n**9.微分中值定理，泰勒公式**\n\n**Th1:**(费马定理)\n\n若函数$f(x)$满足条件：\n(1)函数$f(x)$在${{x}_{0}}$的某邻域内有定义，并且在此邻域内恒有\n$f(x)\\le f({{x}_{0}})$或$f(x)\\ge f({{x}_{0}})$,\n\n(2) $f(x)$在${{x}_{0}}$处可导,则有 ${f}'({{x}_{0}})=0$\n\n**Th2:**(罗尔定理) \n\n设函数$f(x)$满足条件：\n(1)在闭区间$[a,b]$上连续；\n\n(2)在$(a,b)$内可导；\n\n(3)$f(a)=f(b)$；\n\n则在$(a,b)$内一存在个$\\xi $，使  ${f}'(\\xi )=0$\n**Th3:** (拉格朗日中值定理) \n\n设函数$f(x)$满足条件：\n(1)在$[a,b]$上连续；\n\n(2)在$(a,b)$内可导；\n\n则在$(a,b)$内一存在个$\\xi $，使  $\\frac{f(b)-f(a)}{b-a}={f}'(\\xi )$\n\n**Th4:** (柯西中值定理)\n\n 设函数$f(x)$，$g(x)$满足条件：\n(1) 在$[a,b]$上连续；\n\n(2) 在$(a,b)$内可导且${f}'(x)$，${g}'(x)$均存在，且${g}'(x)\\ne 0$\n\n则在$(a,b)$内存在一个$\\xi $，使  $\\frac{f(b)-f(a)}{g(b)-g(a)}=\\frac{{f}'(\\xi )}{{g}'(\\xi )}$\n\n**10.洛必达法则**\n法则Ⅰ ($\\frac{0}{0}$型)\n设函数$f\\left( x \\right),g\\left( x \\right)$满足条件：\n $\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,f\\left( x \\right)=0,\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,g\\left( x \\right)=0$; \n\n$f\\left( x \\right),g\\left( x \\right)$在${{x}_{0}}$的邻域内可导，(在${{x}_{0}}$处可除外)且${g}'\\left( x \\right)\\ne 0$;\n\n$\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,\\frac{{f}'\\left( x \\right)}{{g}'\\left( x \\right)}$存在(或$\\infty $)。\n\n则:\n$\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,\\frac{f\\left( x \\right)}{g\\left( x \\right)}=\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,\\frac{{f}'\\left( x \\right)}{{g}'\\left( x \\right)}$。\n法则${{I}'}$ ($\\frac{0}{0}$型)设函数$f\\left( x \\right),g\\left( x \\right)$满足条件：\n$\\underset{x\\to \\infty }{\\mathop{\\lim }}\\,f\\left( x \\right)=0,\\underset{x\\to \\infty }{\\mathop{\\lim }}\\,g\\left( x \\right)=0$;\n\n存在一个$X>0$,当$\\left| x \\right|>X$时,$f\\left( x \\right),g\\left( x \\right)$可导,且${g}'\\left( x \\right)\\ne 0$;$\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,\\frac{{f}'\\left( x \\right)}{{g}'\\left( x \\right)}$存在(或$\\infty $)。\n\n则:\n$\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,\\frac{f\\left( x \\right)}{g\\left( x \\right)}=\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,\\frac{{f}'\\left( x \\right)}{{g}'\\left( x \\right)}$\n法则Ⅱ($\\frac{\\infty }{\\infty }$型) 设函数$f\\left( x \\right),g\\left( x \\right)$满足条件：\n$\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,f\\left( x \\right)=\\infty ,\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,g\\left( x \\right)=\\infty $;   $f\\left( x \\right),g\\left( x \\right)$在${{x}_{0}}$ 的邻域内可导(在${{x}_{0}}$处可除外)且${g}'\\left( x \\right)\\ne 0$;$\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,\\frac{{f}'\\left( x \\right)}{{g}'\\left( x \\right)}$存在(或$\\infty $)。则\n$\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,\\frac{f\\left( x \\right)}{g\\left( x \\right)}=\\underset{x\\to {{x}_{0}}}{\\mathop{\\lim }}\\,\\frac{{f}'\\left( x \\right)}{{g}'\\left( x \\right)}.$同理法则${I{I}'}$($\\frac{\\infty }{\\infty }$型)仿法则${{I}'}$可写出。\n\n**11.泰勒公式**\n\n设函数$f(x)$在点${{x}_{0}}$处的某邻域内具有$n+1$阶导数，则对该邻域内异于${{x}_{0}}$的任意点$x$，在${{x}_{0}}$与$x$之间至少存在\n一个$\\xi $，使得：\n$f(x)=f({{x}_{0}})+{f}'({{x}_{0}})(x-{{x}_{0}})+\\frac{1}{2!}{f}''({{x}_{0}}){{(x-{{x}_{0}})}^{2}}+\\cdots $ \n$+\\frac{{{f}^{(n)}}({{x}_{0}})}{n!}{{(x-{{x}_{0}})}^{n}}+{{R}_{n}}(x)$\n 其中 ${{R}_{n}}(x)=\\frac{{{f}^{(n+1)}}(\\xi )}{(n+1)!}{{(x-{{x}_{0}})}^{n+1}}$称为$f(x)$在点${{x}_{0}}$处的$n$阶泰勒余项。\n\n令${{x}_{0}}=0$，则$n$阶泰勒公式\n$f(x)=f(0)+{f}'(0)x+\\frac{1}{2!}{f}''(0){{x}^{2}}+\\cdots +\\frac{{{f}^{(n)}}(0)}{n!}{{x}^{n}}+{{R}_{n}}(x)$……(1)\n其中 ${{R}_{n}}(x)=\\frac{{{f}^{(n+1)}}(\\xi )}{(n+1)!}{{x}^{n+1}}$，$\\xi $在0与$x$之间.(1)式称为麦克劳林公式\n\n**常用五种函数在${{x}_{0}}=0$处的泰勒公式**\n\n(1) ${{{e}}^{x}}=1+x+\\frac{1}{2!}{{x}^{2}}+\\cdots +\\frac{1}{n!}{{x}^{n}}+\\frac{{{x}^{n+1}}}{(n+1)!}{{e}^{\\xi }}$ \n\n或 $=1+x+\\frac{1}{2!}{{x}^{2}}+\\cdots +\\frac{1}{n!}{{x}^{n}}+o({{x}^{n}})$\n\n(2) $\\sin x=x-\\frac{1}{3!}{{x}^{3}}+\\cdots +\\frac{{{x}^{n}}}{n!}\\sin \\frac{n\\pi }{2}+\\frac{{{x}^{n+1}}}{(n+1)!}\\sin (\\xi +\\frac{n+1}{2}\\pi )$\n\n或  $=x-\\frac{1}{3!}{{x}^{3}}+\\cdots +\\frac{{{x}^{n}}}{n!}\\sin \\frac{n\\pi }{2}+o({{x}^{n}})$\n\n(3) $\\cos x=1-\\frac{1}{2!}{{x}^{2}}+\\cdots +\\frac{{{x}^{n}}}{n!}\\cos \\frac{n\\pi }{2}+\\frac{{{x}^{n+1}}}{(n+1)!}\\cos (\\xi +\\frac{n+1}{2}\\pi )$\n\n或   $=1-\\frac{1}{2!}{{x}^{2}}+\\cdots +\\frac{{{x}^{n}}}{n!}\\cos \\frac{n\\pi }{2}+o({{x}^{n}})$\n\n(4) $\\ln (1+x)=x-\\frac{1}{2}{{x}^{2}}+\\frac{1}{3}{{x}^{3}}-\\cdots +{{(-1)}^{n-1}}\\frac{{{x}^{n}}}{n}+\\frac{{{(-1)}^{n}}{{x}^{n+1}}}{(n+1){{(1+\\xi )}^{n+1}}}$\n\n或      $=x-\\frac{1}{2}{{x}^{2}}+\\frac{1}{3}{{x}^{3}}-\\cdots +{{(-1)}^{n-1}}\\frac{{{x}^{n}}}{n}+o({{x}^{n}})$\n\n(5) ${{(1+x)}^{m}}=1+mx+\\frac{m(m-1)}{2!}{{x}^{2}}+\\cdots +\\frac{m(m-1)\\cdots (m-n+1)}{n!}{{x}^{n}}$ \n$+\\frac{m(m-1)\\cdots (m-n+1)}{(n+1)!}{{x}^{n+1}}{{(1+\\xi )}^{m-n-1}}$ \n\n或 ${{(1+x)}^{m}}=1+mx+\\frac{m(m-1)}{2!}{{x}^{2}}+\\cdots $ $+\\frac{m(m-1)\\cdots (m-n+1)}{n!}{{x}^{n}}+o({{x}^{n}})$\n\n**12.函数单调性的判断**\n**Th1:**  设函数$f(x)$在$(a,b)$区间内可导，如果对$\\forall x\\in (a,b)$，都有$f\\,'(x)>0$（或$f\\,'(x)<0$），则函数$f(x)$在$(a,b)$内是单调增加的（或单调减少）\n\n**Th2:** （取极值的必要条件）设函数$f(x)$在${{x}_{0}}$处可导，且在${{x}_{0}}$处取极值，则$f\\,'({{x}_{0}})=0$。\n\n**Th3:** （取极值的第一充分条件）设函数$f(x)$在${{x}_{0}}$的某一邻域内可微，且$f\\,'({{x}_{0}})=0$（或$f(x)$在${{x}_{0}}$处连续，但$f\\,'({{x}_{0}})$不存在。）\n(1)若当$x$经过${{x}_{0}}$时，$f\\,'(x)$由“+”变“-”，则$f({{x}_{0}})$为极大值；\n(2)若当$x$经过${{x}_{0}}$时，$f\\,'(x)$由“-”变“+”，则$f({{x}_{0}})$为极小值；\n(3)若$f\\,'(x)$经过$x={{x}_{0}}$的两侧不变号，则$f({{x}_{0}})$不是极值。\n\n**Th4:** (取极值的第二充分条件)设$f(x)$在点${{x}_{0}}$处有$f''(x)\\ne 0$，且$f\\,'({{x}_{0}})=0$，则 当$f'\\,'({{x}_{0}})<0$时，$f({{x}_{0}})$为极大值；\n当$f'\\,'({{x}_{0}})>0$时，$f({{x}_{0}})$为极小值。\n注：如果$f'\\,'({{x}_{0}})<0$，此方法失效。\n\n**13.渐近线的求法**\n(1)水平渐近线   若$\\underset{x\\to +\\infty }{\\mathop{\\lim }}\\,f(x)=b$，或$\\underset{x\\to -\\infty }{\\mathop{\\lim }}\\,f(x)=b$，则\n\n$y=b$称为函数$y=f(x)$的水平渐近线。\n\n(2)铅直渐近线   若$\\underset{x\\to x_{0}^{-}}{\\mathop{\\lim }}\\,f(x)=\\infty $，或$\\underset{x\\to x_{0}^{+}}{\\mathop{\\lim }}\\,f(x)=\\infty $，则\n\n$x={{x}_{0}}$称为$y=f(x)$的铅直渐近线。\n\n(3)斜渐近线   若$a=\\underset{x\\to \\infty }{\\mathop{\\lim }}\\,\\frac{f(x)}{x},\\quad b=\\underset{x\\to \\infty }{\\mathop{\\lim }}\\,[f(x)-ax]$，则\n$y=ax+b$称为$y=f(x)$的斜渐近线。\n\n**14.函数凹凸性的判断**\n**Th1:** (凹凸性的判别定理）若在I上$f''(x)<0$（或$f''(x)>0$），则$f(x)$在I上是凸的（或凹的）。\n\n**Th2:** (拐点的判别定理1)若在${{x}_{0}}$处$f''(x)=0$，（或$f''(x)$不存在），当$x$变动经过${{x}_{0}}$时，$f''(x)$变号，则$({{x}_{0}},f({{x}_{0}}))$为拐点。\n\n**Th3:** (拐点的判别定理2)设$f(x)$在${{x}_{0}}$点的某邻域内有三阶导数，且$f''(x)=0$，$f'''(x)\\ne 0$，则$({{x}_{0}},f({{x}_{0}}))$为拐点。\n\n**15.弧微分**\n\n$dS=\\sqrt{1+y{{'}^{2}}}dx$\n\n**16.曲率**\n\n曲线$y=f(x)$在点$(x,y)$处的曲率$k=\\frac{\\left| y'' \\right|}{{{(1+y{{'}^{2}})}^{\\tfrac{3}{2}}}}$。\n对于参数方程$\\left\\{ \\begin{align}  & x=\\varphi (t) \\\\  & y=\\psi (t) \\\\ \\end{align} \\right.,$$k=\\frac{\\left| \\varphi '(t)\\psi ''(t)-\\varphi ''(t)\\psi '(t) \\right|}{{{[\\varphi {{'}^{2}}(t)+\\psi {{'}^{2}}(t)]}^{\\tfrac{3}{2}}}}$。\n\n**17.曲率半径**\n\n曲线在点$M$处的曲率$k(k\\ne 0)$与曲线在点$M$处的曲率半径$\\rho $有如下关系：$\\rho =\\frac{1}{k}$。\n\n### 线性代数\n\n#### 行列式\n\n**1.行列式按行（列）展开定理**\n\n(1) 设$A = ( a_{{ij}} )_{n \\times n}$，则：$a_{i1}A_{j1} +a_{i2}A_{j2} + \\cdots + a_{{in}}A_{{jn}} = \\begin{cases}|A|,i=j\\\\ 0,i \\neq j\\end{cases}$\n\n\n或$a_{1i}A_{1j} + a_{2i}A_{2j} + \\cdots + a_{{ni}}A_{{nj}} = \\begin{cases}|A|,i=j\\\\ 0,i \\neq j\\end{cases}$即 $AA^{*} = A^{*}A = \\left| A \\right|E,$其中：$A^{*} = \\begin{pmatrix} A_{11} & A_{12} & \\ldots & A_{1n} \\\\ A_{21} & A_{22} & \\ldots & A_{2n} \\\\ \\ldots & \\ldots & \\ldots & \\ldots \\\\ A_{n1} & A_{n2} & \\ldots & A_{{nn}} \\\\ \\end{pmatrix} = (A_{{ji}}) = {(A_{{ij}})}^{T}$\n\n$D_{n} = \\begin{vmatrix} 1 & 1 & \\ldots & 1 \\\\ x_{1} & x_{2} & \\ldots & x_{n} \\\\ \\ldots & \\ldots & \\ldots & \\ldots \\\\ x_{1}^{n - 1} & x_{2}^{n - 1} & \\ldots & x_{n}^{n - 1} \\\\ \\end{vmatrix} = \\prod_{1 \\leq j < i \\leq n}^{}\\,(x_{i} - x_{j})$\n\n(2) 设$A,B$为$n$阶方阵，则$\\left| {AB} \\right| = \\left| A \\right|\\left| B \\right| = \\left| B \\right|\\left| A \\right| = \\left| {BA} \\right|$，但$\\left| A \\pm B \\right| = \\left| A \\right| \\pm \\left| B \\right|$不一定成立。\n\n(3) $\\left| {kA} \\right| = k^{n}\\left| A \\right|$,$A$为$n$阶方阵。\n\n(4) 设$A$为$n$阶方阵，$|A^{T}| = |A|;|A^{- 1}| = |A|^{- 1}$（若$A$可逆），$|A^{*}| = |A|^{n - 1}$\n\n$n \\geq 2$\n\n(5) $\\left| \\begin{matrix}  & {A\\quad O} \\\\  & {O\\quad B} \\\\ \\end{matrix} \\right| = \\left| \\begin{matrix}  & {A\\quad C} \\\\  & {O\\quad B} \\\\ \\end{matrix} \\right| = \\left| \\begin{matrix}  & {A\\quad O} \\\\  & {C\\quad B} \\\\ \\end{matrix} \\right| =| A||B|$\n，$A,B$为方阵，但$\\left| \\begin{matrix} {O} & A_{m \\times m} \\\\  B_{n \\times n} & { O} \\\\ \\end{matrix} \\right| = ({- 1)}^{{mn}}|A||B|$ 。\n\n(6) 范德蒙行列式$D_{n} = \\begin{vmatrix} 1 & 1 & \\ldots & 1 \\\\ x_{1} & x_{2} & \\ldots & x_{n} \\\\ \\ldots & \\ldots & \\ldots & \\ldots \\\\ x_{1}^{n - 1} & x_{2}^{n 1} & \\ldots & x_{n}^{n - 1} \\\\ \\end{vmatrix} =  \\prod_{1 \\leq j < i \\leq n}^{}\\,(x_{i} - x_{j})$\n\n设$A$是$n$阶方阵，$\\lambda_{i}(i = 1,2\\cdots,n)$是$A$的$n$个特征值，则\n$|A| = \\prod_{i = 1}^{n}\\lambda_{i}$\n\n#### 矩阵\n\n矩阵：$m \\times n$个数$a_{{ij}}$排成$m$行$n$列的表格$\\begin{bmatrix}  a_{11}\\quad a_{12}\\quad\\cdots\\quad a_{1n} \\\\ a_{21}\\quad a_{22}\\quad\\cdots\\quad a_{2n} \\\\ \\quad\\cdots\\cdots\\cdots\\cdots\\cdots \\\\  a_{m1}\\quad a_{m2}\\quad\\cdots\\quad a_{{mn}} \\\\ \\end{bmatrix}$ 称为矩阵，简记为$A$，或者$\\left( a_{{ij}} \\right)_{m \\times n}$ 。若$m = n$，则称$A$是$n$阶矩阵或$n$阶方阵。\n\n**矩阵的线性运算**\n\n**1.矩阵的加法**\n\n设$A = (a_{{ij}}),B = (b_{{ij}})$是两个$m \\times n$矩阵，则$m \\times n$ 矩阵$C = c_{{ij}}) = a_{{ij}} + b_{{ij}}$称为矩阵$A$与$B$的和，记为$A + B = C$ 。\n\n**2.矩阵的数乘**\n\n设$A = (a_{{ij}})$是$m \\times n$矩阵，$k$是一个常数，则$m \\times n$矩阵$(ka_{{ij}})$称为数$k$与矩阵$A$的数乘，记为${kA}$。\n\n**3.矩阵的乘法**\n\n设$A = (a_{{ij}})$是$m \\times n$矩阵，$B = (b_{{ij}})$是$n \\times s$矩阵，那么$m \\times s$矩阵$C = (c_{{ij}})$，其中$c_{{ij}} = a_{i1}b_{1j} + a_{i2}b_{2j} + \\cdots + a_{{in}}b_{{nj}} = \\sum_{k =1}^{n}{a_{{ik}}b_{{kj}}}$称为${AB}$的乘积，记为$C = AB$ 。\n\n**4.** $\\mathbf{A}^{\\mathbf{T}}$**、**$\\mathbf{A}^{\\mathbf{-1}}$**、**$\\mathbf{A}^{\\mathbf{*}}$**三者之间的关系**\n\n(1) ${(A^{T})}^{T} = A,{(AB)}^{T} = B^{T}A^{T},{(kA)}^{T} = kA^{T},{(A \\pm B)}^{T} = A^{T} \\pm B^{T}$\n\n(2) $\\left( A^{- 1} \\right)^{- 1} = A,\\left( {AB} \\right)^{- 1} = B^{- 1}A^{- 1},\\left( {kA} \\right)^{- 1} = \\frac{1}{k}A^{- 1},$\n\n但 ${(A \\pm B)}^{- 1} = A^{- 1} \\pm B^{- 1}$不一定成立。\n\n(3) $\\left( A^{*} \\right)^{*} = |A|^{n - 2}\\ A\\ \\ (n \\geq 3)$，$\\left({AB} \\right)^{*} = B^{*}A^{*},$ $\\left( {kA} \\right)^{*} = k^{n -1}A^{*}{\\ \\ }\\left( n \\geq 2 \\right)$\n\n但$\\left( A \\pm B \\right)^{*} = A^{*} \\pm B^{*}$不一定成立。\n\n(4) ${(A^{- 1})}^{T} = {(A^{T})}^{- 1},\\ \\left( A^{- 1} \\right)^{*} ={(AA^{*})}^{- 1},{(A^{*})}^{T} = \\left( A^{T} \\right)^{*}$\n\n**5.有关**$\\mathbf{A}^{\\mathbf{*}}$**的结论**\n\n(1) $AA^{*} = A^{*}A = |A|E$\n\n(2) $|A^{*}| = |A|^{n - 1}\\ (n \\geq 2),\\ \\ \\ \\ {(kA)}^{*} = k^{n -1}A^{*},{{\\ \\ }\\left( A^{*} \\right)}^{*} = |A|^{n - 2}A(n \\geq 3)$\n\n(3) 若$A$可逆，则$A^{*} = |A|A^{- 1},{(A^{*})}^{*} = \\frac{1}{|A|}A$\n\n(4) 若$A$为$n$阶方阵，则：\n\n$r(A^*)=\\begin{cases}n,\\quad r(A)=n\\\\ 1,\\quad r(A)=n-1\\\\ 0,\\quad r(A)<n-1\\end{cases}$\n\n**6.有关**$\\mathbf{A}^{\\mathbf{- 1}}$**的结论**\n\n$A$可逆$\\Leftrightarrow AB = E; \\Leftrightarrow |A| \\neq 0; \\Leftrightarrow r(A) = n;$\n\n$\\Leftrightarrow A$可以表示为初等矩阵的乘积；$\\Leftrightarrow A;\\Leftrightarrow Ax = 0$。\n\n**7.有关矩阵秩的结论**\n\n(1) 秩$r(A)$=行秩=列秩；\n\n(2) $r(A_{m \\times n}) \\leq \\min(m,n);$\n\n(3) $A \\neq 0 \\Rightarrow r(A) \\geq 1$；\n\n(4) $r(A \\pm B) \\leq r(A) + r(B);$\n\n(5) 初等变换不改变矩阵的秩\n\n(6) $r(A) + r(B) - n \\leq r(AB) \\leq \\min(r(A),r(B)),$特别若$AB = O$\n则：$r(A) + r(B) \\leq n$\n\n(7) 若$A^{- 1}$存在$\\Rightarrow r(AB) = r(B);$ 若$B^{- 1}$存在\n$\\Rightarrow r(AB) = r(A);$\n\n若$r(A_{m \\times n}) = n \\Rightarrow r(AB) = r(B);$ 若$r(A_{m \\times s}) = n\\Rightarrow r(AB) = r\\left( A \\right)$。\n\n(8) $r(A_{m \\times s}) = n \\Leftrightarrow Ax = 0$只有零解\n\n**8.分块求逆公式**\n\n$\\begin{pmatrix} A & O \\\\ O & B \\\\ \\end{pmatrix}^{- 1} = \\begin{pmatrix} A^{-1} & O \\\\ O & B^{- 1} \\\\ \\end{pmatrix}$； $\\begin{pmatrix} A & C \\\\ O & B \\\\\\end{pmatrix}^{- 1} = \\begin{pmatrix} A^{- 1}& - A^{- 1}CB^{- 1} \\\\ O & B^{- 1} \\\\ \\end{pmatrix}$；\n\n$\\begin{pmatrix} A & O \\\\ C & B \\\\ \\end{pmatrix}^{- 1} = \\begin{pmatrix}  A^{- 1}&{O} \\\\   - B^{- 1}CA^{- 1} & B^{- 1} \\\\\\end{pmatrix}$； $\\begin{pmatrix} O & A \\\\ B & O \\\\ \\end{pmatrix}^{- 1} =\\begin{pmatrix} O & B^{- 1} \\\\ A^{- 1} & O \\\\ \\end{pmatrix}$\n\n这里$A$，$B$均为可逆方阵。\n\n#### 向量\n\n**1.有关向量组的线性表示**\n\n(1)$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}$线性相关$\\Leftrightarrow$至少有一个向量可以用其余向量线性表示。\n\n(2)$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}$线性无关，$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}$，$\\beta$线性相关$\\Leftrightarrow \\beta$可以由$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}$唯一线性表示。\n\n(3) $\\beta$可以由$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}$线性表示\n$\\Leftrightarrow r(\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}) =r(\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s},\\beta)$ 。\n\n**2.有关向量组的线性相关性**\n\n(1)部分相关，整体相关；整体无关，部分无关.\n\n(2) ① $n$个$n$维向量\n$\\alpha_{1},\\alpha_{2}\\cdots\\alpha_{n}$线性无关$\\Leftrightarrow \\left|\\left\\lbrack \\alpha_{1}\\alpha_{2}\\cdots\\alpha_{n} \\right\\rbrack \\right| \\neq0$， $n$个$n$维向量$\\alpha_{1},\\alpha_{2}\\cdots\\alpha_{n}$线性相关\n$\\Leftrightarrow |\\lbrack\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{n}\\rbrack| = 0$\n。\n\n② $n + 1$个$n$维向量线性相关。\n\n③ 若$\\alpha_{1},\\alpha_{2}\\cdots\\alpha_{S}$线性无关，则添加分量后仍线性无关；或一组向量线性相关，去掉某些分量后仍线性相关。\n\n**3.有关向量组的线性表示**\n\n(1) $\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}$线性相关$\\Leftrightarrow$至少有一个向量可以用其余向量线性表示。\n\n(2) $\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}$线性无关，$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}$，$\\beta$线性相关$\\Leftrightarrow\\beta$ 可以由$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}$唯一线性表示。\n\n(3) $\\beta$可以由$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}$线性表示\n$\\Leftrightarrow r(\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}) =r(\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s},\\beta)$\n\n**4.向量组的秩与矩阵的秩之间的关系**\n\n设$r(A_{m \\times n}) =r$，则$A$的秩$r(A)$与$A$的行列向量组的线性相关性关系为：\n\n(1) 若$r(A_{m \\times n}) = r = m$，则$A$的行向量组线性无关。\n\n(2) 若$r(A_{m \\times n}) = r < m$，则$A$的行向量组线性相关。\n\n(3) 若$r(A_{m \\times n}) = r = n$，则$A$的列向量组线性无关。\n\n(4) 若$r(A_{m \\times n}) = r < n$，则$A$的列向量组线性相关。\n\n**5.**$\\mathbf{n}$**维向量空间的基变换公式及过渡矩阵**\n\n若$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{n}$与$\\beta_{1},\\beta_{2},\\cdots,\\beta_{n}$是向量空间$V$的两组基，则基变换公式为：\n\n$(\\beta_{1},\\beta_{2},\\cdots,\\beta_{n}) = (\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{n})\\begin{bmatrix}  c_{11}& c_{12}& \\cdots & c_{1n} \\\\  c_{21}& c_{22}&\\cdots & c_{2n} \\\\ \\cdots & \\cdots & \\cdots & \\cdots \\\\  c_{n1}& c_{n2} & \\cdots & c_{{nn}} \\\\\\end{bmatrix} = (\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{n})C$\n\n其中$C$是可逆矩阵，称为由基$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{n}$到基$\\beta_{1},\\beta_{2},\\cdots,\\beta_{n}$的过渡矩阵。\n\n**6.坐标变换公式**\n\n若向量$\\gamma$在基$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{n}$与基$\\beta_{1},\\beta_{2},\\cdots,\\beta_{n}$的坐标分别是\n$X = {(x_{1},x_{2},\\cdots,x_{n})}^{T}$，\n\n$Y = \\left( y_{1},y_{2},\\cdots,y_{n} \\right)^{T}$ 即： $\\gamma =x_{1}\\alpha_{1} + x_{2}\\alpha_{2} + \\cdots + x_{n}\\alpha_{n} = y_{1}\\beta_{1} +y_{2}\\beta_{2} + \\cdots + y_{n}\\beta_{n}$，则向量坐标变换公式为$X = CY$ 或$Y = C^{- 1}X$，其中$C$是从基$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{n}$到基$\\beta_{1},\\beta_{2},\\cdots,\\beta_{n}$的过渡矩阵。\n\n**7.向量的内积**\n\n$(\\alpha,\\beta) = a_{1}b_{1} + a_{2}b_{2} + \\cdots + a_{n}b_{n} = \\alpha^{T}\\beta = \\beta^{T}\\alpha$\n\n**8.Schmidt正交化**\n\n若$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}$线性无关，则可构造$\\beta_{1},\\beta_{2},\\cdots,\\beta_{s}$使其两两正交，且$\\beta_{i}$仅是$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{i}$的线性组合$(i= 1,2,\\cdots,n)$，再把$\\beta_{i}$单位化，记$\\gamma_{i} =\\frac{\\beta_{i}}{\\left| \\beta_{i}\\right|}$，则$\\gamma_{1},\\gamma_{2},\\cdots,\\gamma_{i}$是规范正交向量组。其中\n$\\beta_{1} = \\alpha_{1}$， $\\beta_{2} = \\alpha_{2} -\\frac{(\\alpha_{2},\\beta_{1})}{(\\beta_{1},\\beta_{1})}\\beta_{1}$ ， $\\beta_{3} =\\alpha_{3} - \\frac{(\\alpha_{3},\\beta_{1})}{(\\beta_{1},\\beta_{1})}\\beta_{1} -\\frac{(\\alpha_{3},\\beta_{2})}{(\\beta_{2},\\beta_{2})}\\beta_{2}$ ，\n\n............\n\n$\\beta_{s} = \\alpha_{s} - \\frac{(\\alpha_{s},\\beta_{1})}{(\\beta_{1},\\beta_{1})}\\beta_{1} - \\frac{(\\alpha_{s},\\beta_{2})}{(\\beta_{2},\\beta_{2})}\\beta_{2} - \\cdots - \\frac{(\\alpha_{s},\\beta_{s - 1})}{(\\beta_{s - 1},\\beta_{s - 1})}\\beta_{s - 1}$\n\n**9.正交基及规范正交基**\n\n向量空间一组基中的向量如果两两正交，就称为正交基；若正交基中每个向量都是单位向量，就称其为规范正交基。\n\n#### 线性方程组\n\n**1．克莱姆法则**\n\n线性方程组$\\begin{cases}  a_{11}x_{1} + a_{12}x_{2} + \\cdots +a_{1n}x_{n} = b_{1} \\\\   a_{21}x_{1} + a_{22}x_{2} + \\cdots + a_{2n}x_{n} =b_{2} \\\\   \\quad\\cdots\\cdots\\cdots\\cdots\\cdots\\cdots\\cdots\\cdots\\cdots \\\\ a_{n1}x_{1} + a_{n2}x_{2} + \\cdots + a_{{nn}}x_{n} = b_{n} \\\\ \\end{cases}$，如果系数行列式$D = \\left| A \\right| \\neq 0$，则方程组有唯一解，$x_{1} = \\frac{D_{1}}{D},x_{2} = \\frac{D_{2}}{D},\\cdots,x_{n} =\\frac{D_{n}}{D}$，其中$D_{j}$是把$D$中第$j$列元素换成方程组右端的常数列所得的行列式。\n\n**2.** $n$阶矩阵$A$可逆$\\Leftrightarrow Ax = 0$只有零解。$\\Leftrightarrow\\forall b,Ax = b$总有唯一解，一般地，$r(A_{m \\times n}) = n \\Leftrightarrow Ax= 0$只有零解。\n\n**3.非奇次线性方程组有解的充分必要条件，线性方程组解的性质和解的结构**\n\n(1) 设$A$为$m \\times n$矩阵，若$r(A_{m \\times n}) = m$，则对$Ax =b$而言必有$r(A) = r(A \\vdots b) = m$，从而$Ax = b$有解。\n\n(2) 设$x_{1},x_{2},\\cdots x_{s}$为$Ax = b$的解，则$k_{1}x_{1} + k_{2}x_{2}\\cdots + k_{s}x_{s}$当$k_{1} + k_{2} + \\cdots + k_{s} = 1$时仍为$Ax =b$的解；但当$k_{1} + k_{2} + \\cdots + k_{s} = 0$时，则为$Ax =0$的解。特别$\\frac{x_{1} + x_{2}}{2}$为$Ax = b$的解；$2x_{3} - (x_{1} +x_{2})$为$Ax = 0$的解。\n\n(3) 非齐次线性方程组${Ax} = b$无解$\\Leftrightarrow r(A) + 1 =r(\\overline{A}) \\Leftrightarrow b$不能由$A$的列向量$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{n}$线性表示。\n\n**4.奇次线性方程组的基础解系和通解，解空间，非奇次线性方程组的通解**\n\n(1) 齐次方程组${Ax} = 0$恒有解(必有零解)。当有非零解时，由于解向量的任意线性组合仍是该齐次方程组的解向量，因此${Ax}= 0$的全体解向量构成一个向量空间，称为该方程组的解空间，解空间的维数是$n - r(A)$，解空间的一组基称为齐次方程组的基础解系。\n\n(2) $\\eta_{1},\\eta_{2},\\cdots,\\eta_{t}$是${Ax} = 0$的基础解系，即：\n\n1) $\\eta_{1},\\eta_{2},\\cdots,\\eta_{t}$是${Ax} = 0$的解；\n\n2) $\\eta_{1},\\eta_{2},\\cdots,\\eta_{t}$线性无关；\n\n3) ${Ax} = 0$的任一解都可以由$\\eta_{1},\\eta_{2},\\cdots,\\eta_{t}$线性表出.\n$k_{1}\\eta_{1} + k_{2}\\eta_{2} + \\cdots + k_{t}\\eta_{t}$是${Ax} = 0$的通解，其中$k_{1},k_{2},\\cdots,k_{t}$是任意常数。\n\n#### 矩阵的特征值和特征向量\n\n**1.矩阵的特征值和特征向量的概念及性质**\n\n(1) 设$\\lambda$是$A$的一个特征值，则 ${kA},{aA} + {bE},A^{2},A^{m},f(A),A^{T},A^{- 1},A^{*}$有一个特征值分别为\n${kλ},{aλ} + b,\\lambda^{2},\\lambda^{m},f(\\lambda),\\lambda,\\lambda^{- 1},\\frac{|A|}{\\lambda},$且对应特征向量相同（$A^{T}$ 例外）。\n\n(2)若$\\lambda_{1},\\lambda_{2},\\cdots,\\lambda_{n}$为$A$的$n$个特征值，则$\\sum_{i= 1}^{n}\\lambda_{i} = \\sum_{i = 1}^{n}a_{{ii}},\\prod_{i = 1}^{n}\\lambda_{i}= |A|$ ,从而$|A| \\neq 0 \\Leftrightarrow A$没有特征值。\n\n(3)设$\\lambda_{1},\\lambda_{2},\\cdots,\\lambda_{s}$为$A$的$s$个特征值，对应特征向量为$\\alpha_{1},\\alpha_{2},\\cdots,\\alpha_{s}$，\n\n若: $\\alpha = k_{1}\\alpha_{1} + k_{2}\\alpha_{2} + \\cdots + k_{s}\\alpha_{s}$ ,\n\n则: $A^{n}\\alpha = k_{1}A^{n}\\alpha_{1} + k_{2}A^{n}\\alpha_{2} + \\cdots +k_{s}A^{n}\\alpha_{s} = k_{1}\\lambda_{1}^{n}\\alpha_{1} +k_{2}\\lambda_{2}^{n}\\alpha_{2} + \\cdots k_{s}\\lambda_{s}^{n}\\alpha_{s}$ 。\n\n**2.相似变换、相似矩阵的概念及性质**\n\n(1) 若$A \\sim B$，则\n\n1) $A^{T} \\sim B^{T},A^{- 1} \\sim B^{- 1},,A^{*} \\sim B^{*}$\n\n2) $|A| = |B|,\\sum_{i = 1}^{n}A_{{ii}} = \\sum_{i =1}^{n}b_{{ii}},r(A) = r(B)$\n\n3) $|\\lambda E - A| = |\\lambda E - B|$，对$\\forall\\lambda$成立\n\n**3.矩阵可相似对角化的充分必要条件**\n\n(1)设$A$为$n$阶方阵，则$A$可对角化$\\Leftrightarrow$对每个$k_{i}$重根特征值$\\lambda_{i}$，有$n-r(\\lambda_{i}E - A) = k_{i}$\n\n(2) 设$A$可对角化，则由$P^{- 1}{AP} = \\Lambda,$有$A = {PΛ}P^{-1}$，从而$A^{n} = P\\Lambda^{n}P^{- 1}$\n\n(3) 重要结论\n\n1) 若$A \\sim B,C \\sim D$，则$\\begin{bmatrix}  A & O \\\\ O & C \\\\\\end{bmatrix} \\sim \\begin{bmatrix} B & O \\\\  O & D \\\\\\end{bmatrix}$.\n\n2) 若$A \\sim B$，则$f(A) \\sim f(B),\\left| f(A) \\right| \\sim \\left| f(B)\\right|$，其中$f(A)$为关于$n$阶方阵$A$的多项式。\n\n3) 若$A$为可对角化矩阵，则其非零特征值的个数(重根重复计算)＝秩($A$)\n\n**4.实对称矩阵的特征值、特征向量及相似对角阵**\n\n(1)相似矩阵：设$A,B$为两个$n$阶方阵，如果存在一个可逆矩阵$P$，使得$B =P^{- 1}{AP}$成立，则称矩阵$A$与$B$相似，记为$A \\sim B$。\n\n(2)相似矩阵的性质：如果$A \\sim B$则有：\n\n1) $A^{T} \\sim B^{T}$\n\n2) $A^{- 1} \\sim B^{- 1}$ （若$A$，$B$均可逆）\n\n3) $A^{k} \\sim B^{k}$ （$k$为正整数）\n\n4) $\\left| {λE} - A \\right| = \\left| {λE} - B \\right|$，从而$A,B$\n有相同的特征值\n\n5) $\\left| A \\right| = \\left| B \\right|$，从而$A,B$同时可逆或者不可逆\n\n6) 秩$\\left( A \\right) =$秩$\\left( B \\right),\\left| {λE} - A \\right| =\\left| {λE} - B \\right|$，$A,B$不一定相似\n\n#### 二次型\n\n**1.**$\\mathbf{n}$**个变量**$\\mathbf{x}_{\\mathbf{1}}\\mathbf{,}\\mathbf{x}_{\\mathbf{2}}\\mathbf{,\\cdots,}\\mathbf{x}_{\\mathbf{n}}$**的二次齐次函数**\n\n$f(x_{1},x_{2},\\cdots,x_{n}) = \\sum_{i = 1}^{n}{\\sum_{j =1}^{n}{a_{{ij}}x_{i}y_{j}}}$，其中$a_{{ij}} = a_{{ji}}(i,j =1,2,\\cdots,n)$，称为$n$元二次型，简称二次型. 若令$x = \\ \\begin{bmatrix}x_{1} \\\\ x_{1} \\\\  \\vdots \\\\ x_{n} \\\\ \\end{bmatrix},A = \\begin{bmatrix}  a_{11}& a_{12}& \\cdots & a_{1n} \\\\  a_{21}& a_{22}& \\cdots & a_{2n} \\\\ \\cdots &\\cdots &\\cdots &\\cdots \\\\  a_{n1}& a_{n2} & \\cdots & a_{{nn}} \\\\\\end{bmatrix}$,这二次型$f$可改写成矩阵向量形式$f =x^{T}{Ax}$。其中$A$称为二次型矩阵，因为$a_{{ij}} =a_{{ji}}(i,j =1,2,\\cdots,n)$，所以二次型矩阵均为对称矩阵，且二次型与对称矩阵一一对应，并把矩阵$A$的秩称为二次型的秩。\n\n**2.惯性定理，二次型的标准形和规范形**\n\n(1) 惯性定理\n\n对于任一二次型，不论选取怎样的合同变换使它化为仅含平方项的标准型，其正负惯性指数与所选变换无关，这就是所谓的惯性定理。\n\n(2) 标准形\n\n二次型$f = \\left( x_{1},x_{2},\\cdots,x_{n} \\right) =x^{T}{Ax}$经过合同变换$x = {Cy}$化为$f = x^{T}{Ax} =y^{T}C^{T}{AC}$\n\n$y = \\sum_{i = 1}^{r}{d_{i}y_{i}^{2}}$称为 $f(r \\leq n)$的标准形。在一般的数域内，二次型的标准形不是唯一的，与所作的合同变换有关，但系数不为零的平方项的个数由$r(A)$唯一确定。\n\n(3) 规范形\n\n任一实二次型$f$都可经过合同变换化为规范形$f = z_{1}^{2} + z_{2}^{2} + \\cdots z_{p}^{2} - z_{p + 1}^{2} - \\cdots -z_{r}^{2}$，其中$r$为$A$的秩，$p$为正惯性指数，$r -p$为负惯性指数，且规范型唯一。\n\n**3.用正交变换和配方法化二次型为标准形，二次型及其矩阵的正定性**\n\n设$A$正定$\\Rightarrow {kA}(k > 0),A^{T},A^{- 1},A^{*}$正定；$|A| >0$,$A$可逆；$a_{{ii}} > 0$，且$|A_{{ii}}| > 0$\n\n$A$，$B$正定$\\Rightarrow A +B$正定，但${AB}$，${BA}$不一定正定\n\n$A$正定$\\Leftrightarrow f(x) = x^{T}{Ax} > 0,\\forall x \\neq 0$\n\n$\\Leftrightarrow A$的各阶顺序主子式全大于零\n\n$\\Leftrightarrow A$的所有特征值大于零\n\n$\\Leftrightarrow A$的正惯性指数为$n$\n\n$\\Leftrightarrow$存在可逆阵$P$使$A = P^{T}P$\n\n$\\Leftrightarrow$存在正交矩阵$Q$，使$Q^{T}{AQ} = Q^{- 1}{AQ} =\\begin{pmatrix} \\lambda_{1} & & \\\\ \\begin{matrix}  & \\\\  & \\\\ \\end{matrix} &\\ddots & \\\\  & & \\lambda_{n} \\\\ \\end{pmatrix},$\n\n其中$\\lambda_{i} > 0,i = 1,2,\\cdots,n.$正定$\\Rightarrow {kA}(k >0),A^{T},A^{- 1},A^{*}$正定； $|A| > 0,A$可逆；$a_{{ii}} >0$，且$|A_{{ii}}| > 0$ 。\n\n### 概率论和数理统计\n\n#### 随机事件和概率\n\n**1.事件的关系与运算**\n\n(1) 子事件：$A \\subset B$，若$A$发生，则$B$发生。\n\n(2) 相等事件：$A = B$，即$A \\subset B$，且$B \\subset A$ 。\n\n(3) 和事件：$A\\bigcup B$（或$A + B$），$A$与$B$中至少有一个发生。\n\n(4) 差事件：$A - B$，$A$发生但$B$不发生。\n\n(5) 积事件：$A\\bigcap B$（或${AB}$），$A$与$B$同时发生。\n\n(6) 互斥事件（互不相容）：$A\\bigcap B$=$\\varnothing$。\n\n(7) 互逆事件（对立事件）：\n$A\\bigcap B=\\varnothing ,A\\bigcup B=\\Omega ,A=\\bar{B},B=\\bar{A}$\n**2.运算律**\n(1) 交换律：$A\\bigcup B=B\\bigcup A,A\\bigcap B=B\\bigcap A$\n(2) 结合律：$(A\\bigcup B)\\bigcup C=A\\bigcup (B\\bigcup C)$\n(3) 分配律：$(A\\bigcap B)\\bigcap C=A\\bigcap (B\\bigcap C)$\n**3.德$\\centerdot $摩根律**\n\n$\\overline{A\\bigcup B}=\\bar{A}\\bigcap \\bar{B}$                 $\\overline{A\\bigcap B}=\\bar{A}\\bigcup \\bar{B}$\n**4.完全事件组** \n\n${{A}_{1}}{{A}_{2}}\\cdots {{A}_{n}}$两两互斥，且和事件为必然事件，即${{A}_{i}}\\bigcap {{A}_{j}}=\\varnothing, i\\ne j ,\\underset{i=1}{\\overset{n}{\\mathop \\bigcup }}\\,=\\Omega $\n\n**5.概率的基本公式**\n(1)条件概率:\n $P(B|A)=\\frac{P(AB)}{P(A)}$,表示$A$发生的条件下，$B$发生的概率。\n(2)全概率公式：\n$P(A)=\\sum\\limits_{i=1}^{n}{P(A|{{B}_{i}})P({{B}_{i}}),{{B}_{i}}{{B}_{j}}}=\\varnothing ,i\\ne j,\\underset{i=1}{\\overset{n}{\\mathop{\\bigcup }}}\\,{{B}_{i}}=\\Omega $\n(3) Bayes公式：\n\n$P({{B}_{j}}|A)=\\frac{P(A|{{B}_{j}})P({{B}_{j}})}{\\sum\\limits_{i=1}^{n}{P(A|{{B}_{i}})P({{B}_{i}})}},j=1,2,\\cdots ,n$\n注：上述公式中事件${{B}_{i}}$的个数可为可列个。\n(4)乘法公式：\n$P({{A}_{1}}{{A}_{2}})=P({{A}_{1}})P({{A}_{2}}|{{A}_{1}})=P({{A}_{2}})P({{A}_{1}}|{{A}_{2}})$\n$P({{A}_{1}}{{A}_{2}}\\cdots {{A}_{n}})=P({{A}_{1}})P({{A}_{2}}|{{A}_{1}})P({{A}_{3}}|{{A}_{1}}{{A}_{2}})\\cdots P({{A}_{n}}|{{A}_{1}}{{A}_{2}}\\cdots {{A}_{n-1}})$\n\n**6.事件的独立性**\n(1)$A$与$B$相互独立$\\Leftrightarrow P(AB)=P(A)P(B)$\n(2)$A$，$B$，$C$两两独立\n$\\Leftrightarrow P(AB)=P(A)P(B)$;$P(BC)=P(B)P(C)$ ;$P(AC)=P(A)P(C)$;\n(3)$A$，$B$，$C$相互独立\n$\\Leftrightarrow P(AB)=P(A)P(B)$;     $P(BC)=P(B)P(C)$ ;\n$P(AC)=P(A)P(C)$  ;   $P(ABC)=P(A)P(B)P(C)$\n\n**7.独立重复试验** \n\n将某试验独立重复$n$次，若每次实验中事件A发生的概率为$p$，则$n$次试验中$A$发生$k$次的概率为：\n$P(X=k)=C_{n}^{k}{{p}^{k}}{{(1-p)}^{n-k}}$\n**8.重要公式与结论**\n$(1)P(\\bar{A})=1-P(A)$\n$(2)P(A\\bigcup B)=P(A)+P(B)-P(AB)$\n   $P(A\\bigcup B\\bigcup C)=P(A)+P(B)+P(C)-P(AB)-P(BC)-P(AC)+P(ABC)$\n$(3)P(A-B)=P(A)-P(AB)$\n$(4)P(A\\bar{B})=P(A)-P(AB),P(A)=P(AB)+P(A\\bar{B}),$\n $P(A\\bigcup B)=P(A)+P(\\bar{A}B)=P(AB)+P(A\\bar{B})+P(\\bar{A}B)$\n(5)条件概率$P(\\centerdot |B)$满足概率的所有性质，\n例如：. $P({{\\bar{A}}_{1}}|B)=1-P({{A}_{1}}|B)$\n$P({{A}_{1}}\\bigcup {{A}_{2}}|B)=P({{A}_{1}}|B)+P({{A}_{2}}|B)-P({{A}_{1}}{{A}_{2}}|B)$\n$P({{A}_{1}}{{A}_{2}}|B)=P({{A}_{1}}|B)P({{A}_{2}}|{{A}_{1}}B)$\n(6)若${{A}_{1}},{{A}_{2}},\\cdots ,{{A}_{n}}$相互独立，则$P(\\bigcap\\limits_{i=1}^{n}{{{A}_{i}}})=\\prod\\limits_{i=1}^{n}{P({{A}_{i}})},$\n $P(\\bigcup\\limits_{i=1}^{n}{{{A}_{i}}})=\\prod\\limits_{i=1}^{n}{(1-P({{A}_{i}}))}$\n(7)互斥、互逆与独立性之间的关系：\n$A$与$B$互逆$\\Rightarrow$ $A$与$B$互斥，但反之不成立，$A$与$B$互斥（或互逆）且均非零概率事件$\\Rightarrow $$A$与$B$不独立.\n(8)若${{A}_{1}},{{A}_{2}},\\cdots ,{{A}_{m}},{{B}_{1}},{{B}_{2}},\\cdots ,{{B}_{n}}$相互独立，则$f({{A}_{1}},{{A}_{2}},\\cdots ,{{A}_{m}})$与$g({{B}_{1}},{{B}_{2}},\\cdots ,{{B}_{n}})$也相互独立，其中$f(\\centerdot ),g(\\centerdot )$分别表示对相应事件做任意事件运算后所得的事件，另外，概率为1（或0）的事件与任何事件相互独立.\n\n\n\n#### 随机变量及其概率分布\n\n**1.随机变量及概率分布**\n\n取值带有随机性的变量，严格地说是定义在样本空间上，取值于实数的函数称为随机变量，概率分布通常指分布函数或分布律\n\n**2.分布函数的概念与性质**\n\n定义： $F(x) = P(X \\leq x), - \\infty < x < + \\infty$\n\n性质：(1)$0 \\leq F(x) \\leq 1$ \n\n(2) $F(x)$单调不减\n\n(3) 右连续$F(x + 0) = F(x)$ \n\n(4) $F( - \\infty) = 0,F( + \\infty) = 1$\n\n**3.离散型随机变量的概率分布**\n\n$P(X = x_{i}) = p_{i},i = 1,2,\\cdots,n,\\cdots\\quad\\quad p_{i} \\geq 0,\\sum_{i =1}^{\\infty}p_{i} = 1$\n\n**4.连续型随机变量的概率密度**\n\n概率密度$f(x)$;非负可积，且:\n\n(1)$f(x) \\geq 0,$ \n\n(2)$\\int_{- \\infty}^{+\\infty}{f(x){dx} = 1}$ \n\n(3)$x$为$f(x)$的连续点，则:\n\n$f(x) = F'(x)$分布函数$F(x) = \\int_{- \\infty}^{x}{f(t){dt}}$\n\n**5.常见分布**\n\n(1) 0-1分布:$P(X = k) = p^{k}{(1 - p)}^{1 - k},k = 0,1$\n\n(2) 二项分布:$B(n,p)$： $P(X = k) = C_{n}^{k}p^{k}{(1 - p)}^{n - k},k =0,1,\\cdots,n$\n\n(3) **Poisson**分布:$p(\\lambda)$： $P(X = k) = \\frac{\\lambda^{k}}{k!}e^{-\\lambda},\\lambda > 0,k = 0,1,2\\cdots$\n\n(4) 均匀分布$U(a,b)$：$f(x) = \\{ \\begin{matrix}  & \\frac{1}{b - a},a < x< b \\\\  & 0, \\\\ \\end{matrix} $\n\n(5) 正态分布:$N(\\mu,\\sigma^{2}):$ $\\varphi(x) =\\frac{1}{\\sqrt{2\\pi}\\sigma}e^{- \\frac{{(x - \\mu)}^{2}}{2\\sigma^{2}}},\\sigma > 0,\\infty < x < + \\infty$\n\n(6)指数分布:$E(\\lambda):f(x) =\\{ \\begin{matrix}  & \\lambda e^{-{λx}},x > 0,\\lambda > 0 \\\\  & 0, \\\\ \\end{matrix} $\n\n(7)几何分布:$G(p):P(X = k) = {(1 - p)}^{k - 1}p,0 < p < 1,k = 1,2,\\cdots.$\n\n(8)超几何分布: $H(N,M,n):P(X = k) = \\frac{C_{M}^{k}C_{N - M}^{n -k}}{C_{N}^{n}},k =0,1,\\cdots,min(n,M)$\n\n**6.随机变量函数的概率分布**\n\n(1)离散型：$P(X = x_{1}) = p_{i},Y = g(X)$\n\n则: $P(Y = y_{j}) = \\sum_{g(x_{i}) = y_{i}}^{}{P(X = x_{i})}$\n\n(2)连续型：$X\\tilde{\\ }f_{X}(x),Y = g(x)$\n\n则:$F_{y}(y) = P(Y \\leq y) = P(g(X) \\leq y) = \\int_{g(x) \\leq y}^{}{f_{x}(x)dx}$， $f_{Y}(y) = F'_{Y}(y)$\n\n**7.重要公式与结论**\n\n(1) $X\\sim N(0,1) \\Rightarrow \\varphi(0) = \\frac{1}{\\sqrt{2\\pi}},\\Phi(0) =\\frac{1}{2},$ $\\Phi( - a) = P(X \\leq - a) = 1 - \\Phi(a)$\n\n(2) $X\\sim N\\left( \\mu,\\sigma^{2} \\right) \\Rightarrow \\frac{X -\\mu}{\\sigma}\\sim N\\left( 0,1 \\right),P(X \\leq a) = \\Phi(\\frac{a -\\mu}{\\sigma})$\n\n(3) $X\\sim E(\\lambda) \\Rightarrow P(X > s + t|X > s) = P(X > t)$\n\n(4) $X\\sim G(p) \\Rightarrow P(X = m + k|X > m) = P(X = k)$\n\n(5) 离散型随机变量的分布函数为阶梯间断函数；连续型随机变量的分布函数为连续函数，但不一定为处处可导函数。\n\n(6) 存在既非离散也非连续型随机变量。\n\n#### 多维随机变量及其分布\n\n**1.二维随机变量及其联合分布**\n\n由两个随机变量构成的随机向量$(X,Y)$， 联合分布为$F(x,y) = P(X \\leq x,Y \\leq y)$\n\n**2.二维离散型随机变量的分布**\n\n(1) 联合概率分布律 $P\\{ X = x_{i},Y = y_{j}\\} = p_{{ij}};i,j =1,2,\\cdots$\n\n(2) 边缘分布律 $p_{i \\cdot} = \\sum_{j = 1}^{\\infty}p_{{ij}},i =1,2,\\cdots$ $p_{\\cdot j} = \\sum_{i}^{\\infty}p_{{ij}},j = 1,2,\\cdots$\n\n(3) 条件分布律 $P\\{ X = x_{i}|Y = y_{j}\\} = \\frac{p_{{ij}}}{p_{\\cdot j}}$\n$P\\{ Y = y_{j}|X = x_{i}\\} = \\frac{p_{{ij}}}{p_{i \\cdot}}$\n\n**3. 二维连续性随机变量的密度**\n\n(1) 联合概率密度$f(x,y):$\n\n1) $f(x,y) \\geq 0$ \n\n2) $\\int_{- \\infty}^{+ \\infty}{\\int_{- \\infty}^{+ \\infty}{f(x,y)dxdy}} = 1$\n\n(2) 分布函数：$F(x,y) = \\int_{- \\infty}^{x}{\\int_{- \\infty}^{y}{f(u,v)dudv}}$\n\n(3) 边缘概率密度： $f_{X}\\left( x \\right) = \\int_{- \\infty}^{+ \\infty}{f\\left( x,y \\right){dy}}$ $f_{Y}(y) = \\int_{- \\infty}^{+ \\infty}{f(x,y)dx}$\n\n(4) 条件概率密度：$f_{X|Y}\\left( x \\middle| y \\right) = \\frac{f\\left( x,y \\right)}{f_{Y}\\left( y \\right)}$ $f_{Y|X}(y|x) = \\frac{f(x,y)}{f_{X}(x)}$\n\n**4.常见二维随机变量的联合分布**\n\n(1) 二维均匀分布：$(x,y) \\sim U(D)$ ,$f(x,y) = \\begin{cases} \\frac{1}{S(D)},(x,y) \\in D \\\\   0,其他  \\end{cases}$\n\n(2) 二维正态分布：$(X,Y)\\sim N(\\mu_{1},\\mu_{2},\\sigma_{1}^{2},\\sigma_{2}^{2},\\rho)$,$(X,Y)\\sim N(\\mu_{1},\\mu_{2},\\sigma_{1}^{2},\\sigma_{2}^{2},\\rho)$\n\n$f(x,y) = \\frac{1}{2\\pi\\sigma_{1}\\sigma_{2}\\sqrt{1 - \\rho^{2}}}.\\exp\\left\\{ \\frac{- 1}{2(1 - \\rho^{2})}\\lbrack\\frac{{(x - \\mu_{1})}^{2}}{\\sigma_{1}^{2}} - 2\\rho\\frac{(x - \\mu_{1})(y - \\mu_{2})}{\\sigma_{1}\\sigma_{2}} + \\frac{{(y - \\mu_{2})}^{2}}{\\sigma_{2}^{2}}\\rbrack \\right\\}$\n\n**5.随机变量的独立性和相关性**\n\n$X$和$Y$的相互独立:$\\Leftrightarrow F\\left( x,y \\right) = F_{X}\\left( x \\right)F_{Y}\\left( y \\right)$:\n\n$\\Leftrightarrow p_{{ij}} = p_{i \\cdot} \\cdot p_{\\cdot j}$（离散型）\n$\\Leftrightarrow f\\left( x,y \\right) = f_{X}\\left( x \\right)f_{Y}\\left( y \\right)$（连续型）\n\n$X$和$Y$的相关性：\n\n相关系数$\\rho_{{XY}} = 0$时，称$X$和$Y$不相关，\n否则称$X$和$Y$相关\n\n**6.两个随机变量简单函数的概率分布**\n\n离散型： $P\\left( X = x_{i},Y = y_{i} \\right) = p_{{ij}},Z = g\\left( X,Y \\right)$ 则：\n\n$P(Z = z_{k}) = P\\left\\{ g\\left( X,Y \\right) = z_{k} \\right\\} = \\sum_{g\\left( x_{i},y_{i} \\right) = z_{k}}^{}{P\\left( X = x_{i},Y = y_{j} \\right)}$\n\n连续型： $\\left( X,Y \\right) \\sim f\\left( x,y \\right),Z = g\\left( X,Y \\right)$\n则：\n\n$F_{z}\\left( z \\right) = P\\left\\{ g\\left( X,Y \\right) \\leq z \\right\\} = \\iint_{g(x,y) \\leq z}^{}{f(x,y)dxdy}$，$f_{z}(z) = F'_{z}(z)$\n\n**7.重要公式与结论**\n\n(1) 边缘密度公式： $f_{X}(x) = \\int_{- \\infty}^{+ \\infty}{f(x,y)dy,}$\n$f_{Y}(y) = \\int_{- \\infty}^{+ \\infty}{f(x,y)dx}$\n\n(2) $P\\left\\{ \\left( X,Y \\right) \\in D \\right\\} = \\iint_{D}^{}{f\\left( x,y \\right){dxdy}}$\n\n(3) 若$(X,Y)$服从二维正态分布$N(\\mu_{1},\\mu_{2},\\sigma_{1}^{2},\\sigma_{2}^{2},\\rho)$\n则有：\n\n1) $X\\sim N\\left( \\mu_{1},\\sigma_{1}^{2} \\right),Y\\sim N(\\mu_{2},\\sigma_{2}^{2}).$\n\n2) $X$与$Y$相互独立$\\Leftrightarrow \\rho = 0$，即$X$与$Y$不相关。\n\n3) $C_{1}X + C_{2}Y\\sim N(C_{1}\\mu_{1} + C_{2}\\mu_{2},C_{1}^{2}\\sigma_{1}^{2} + C_{2}^{2}\\sigma_{2}^{2} + 2C_{1}C_{2}\\sigma_{1}\\sigma_{2}\\rho)$\n\n4) ${\\ X}$关于$Y=y$的条件分布为： $N(\\mu_{1} + \\rho\\frac{\\sigma_{1}}{\\sigma_{2}}(y - \\mu_{2}),\\sigma_{1}^{2}(1 - \\rho^{2}))$\n\n5) $Y$关于$X = x$的条件分布为： $N(\\mu_{2} + \\rho\\frac{\\sigma_{2}}{\\sigma_{1}}(x - \\mu_{1}),\\sigma_{2}^{2}(1 - \\rho^{2}))$\n\n(4) 若$X$与$Y$独立，且分别服从$N(\\mu_{1},\\sigma_{1}^{2}),N(\\mu_{1},\\sigma_{2}^{2}),$\n则：$\\left( X,Y \\right)\\sim N(\\mu_{1},\\mu_{2},\\sigma_{1}^{2},\\sigma_{2}^{2},0),$\n\n$C_{1}X + C_{2}Y\\tilde{\\ }N(C_{1}\\mu_{1} + C_{2}\\mu_{2},C_{1}^{2}\\sigma_{1}^{2} C_{2}^{2}\\sigma_{2}^{2}).$\n\n(5) 若$X$与$Y$相互独立，$f\\left( x \\right)$和$g\\left( x \\right)$为连续函数， 则$f\\left( X \\right)$和$g(Y)$也相互独立。\n\n#### 随机变量的数字特征\n\n**1.数学期望**\n\n离散型：$P\\left\\{ X = x_{i} \\right\\} = p_{i},E(X) = \\sum_{i}^{}{x_{i}p_{i}}$；\n\n连续型： $X\\sim f(x),E(X) = \\int_{- \\infty}^{+ \\infty}{xf(x)dx}$\n\n性质：\n\n(1) $E(C) = C,E\\lbrack E(X)\\rbrack = E(X)$\n\n(2) $E(C_{1}X + C_{2}Y) = C_{1}E(X) + C_{2}E(Y)$\n\n(3) 若$X$和$Y$独立，则$E(XY) = E(X)E(Y)$ \n\n(4)$\\left\\lbrack E(XY) \\right\\rbrack^{2} \\leq E(X^{2})E(Y^{2})$\n\n**2.方差**：$D(X) = E\\left\\lbrack X - E(X) \\right\\rbrack^{2} = E(X^{2}) - \\left\\lbrack E(X) \\right\\rbrack^{2}$\n\n**3.标准差**：$\\sqrt{D(X)}$，\n\n**4.离散型：**$D(X) = \\sum_{i}^{}{\\left\\lbrack x_{i} - E(X) \\right\\rbrack^{2}p_{i}}$\n\n**5.连续型：**$D(X) = {\\int_{- \\infty}^{+ \\infty}\\left\\lbrack x - E(X) \\right\\rbrack}^{2}f(x)dx$\n\n性质：\n\n(1)$\\ D(C) = 0,D\\lbrack E(X)\\rbrack = 0,D\\lbrack D(X)\\rbrack = 0$\n\n(2) $X$与$Y$相互独立，则$D(X \\pm Y) = D(X) + D(Y)$\n\n(3)$\\ D\\left( C_{1}X + C_{2} \\right) = C_{1}^{2}D\\left( X \\right)$\n\n(4) 一般有 $D(X \\pm Y) = D(X) + D(Y) \\pm 2Cov(X,Y) = D(X) + D(Y) \\pm 2\\rho\\sqrt{D(X)}\\sqrt{D(Y)}$\n\n(5)$\\ D\\left( X \\right) < E\\left( X - C \\right)^{2},C \\neq E\\left( X \\right)$\n\n(6)$\\ D(X) = 0 \\Leftrightarrow P\\left\\{ X = C \\right\\} = 1$\n\n**6.随机变量函数的数学期望**\n\n(1) 对于函数$Y = g(x)$\n\n$X$为离散型：$P\\{ X = x_{i}\\} = p_{i},E(Y) = \\sum_{i}^{}{g(x_{i})p_{i}}$；\n\n$X$为连续型：$X\\sim f(x),E(Y) = \\int_{- \\infty}^{+ \\infty}{g(x)f(x)dx}$\n\n(2) $Z = g(X,Y)$;$\\left( X,Y \\right)\\sim P\\{ X = x_{i},Y = y_{j}\\} = p_{{ij}}$; $E(Z) = \\sum_{i}^{}{\\sum_{j}^{}{g(x_{i},y_{j})p_{{ij}}}}$ $\\left( X,Y \\right)\\sim f(x,y)$;$E(Z) = \\int_{- \\infty}^{+ \\infty}{\\int_{- \\infty}^{+ \\infty}{g(x,y)f(x,y)dxdy}}$\n\n**7.协方差** \n\n$Cov(X,Y) = E\\left\\lbrack (X - E(X)(Y - E(Y)) \\right\\rbrack$\n\n**8.相关系数**\n\n $\\rho_{{XY}} = \\frac{Cov(X,Y)}{\\sqrt{D(X)}\\sqrt{D(Y)}}$,$k$阶原点矩 $E(X^{k})$;\n$k$阶中心矩 $E\\left\\{ {\\lbrack X - E(X)\\rbrack}^{k} \\right\\}$\n\n性质：\n\n(1)$\\ Cov(X,Y) = Cov(Y,X)$\n\n(2)$\\ Cov(aX,bY) = abCov(Y,X)$\n\n(3)$\\ Cov(X_{1} + X_{2},Y) = Cov(X_{1},Y) + Cov(X_{2},Y)$\n\n(4)$\\ \\left| \\rho\\left( X,Y \\right) \\right| \\leq 1$\n\n(5) $\\ \\rho\\left( X,Y \\right) = 1 \\Leftrightarrow P\\left( Y = aX + b \\right) = 1$ ，其中$a > 0$\n\n$\\rho\\left( X,Y \\right) = - 1 \\Leftrightarrow P\\left( Y = aX + b \\right) = 1$\n，其中$a < 0$\n\n**9.重要公式与结论**\n\n(1)$\\ D(X) = E(X^{2}) - E^{2}(X)$\n\n(2)$\\ Cov(X,Y) = E(XY) - E(X)E(Y)$\n\n(3) $\\left| \\rho\\left( X,Y \\right) \\right| \\leq 1,$且 $\\rho\\left( X,Y \\right) = 1 \\Leftrightarrow P\\left( Y = aX + b \\right) = 1$，其中$a > 0$\n\n$\\rho\\left( X,Y \\right) = - 1 \\Leftrightarrow P\\left( Y = aX + b \\right) = 1$，其中$a < 0$\n\n(4) 下面5个条件互为充要条件：\n\n$\\rho(X,Y) = 0$ $\\Leftrightarrow Cov(X,Y) = 0$ $\\Leftrightarrow E(X,Y) = E(X)E(Y)$ $\\Leftrightarrow D(X + Y) = D(X) + D(Y)$ $\\Leftrightarrow  D(X - Y) = D(X) + D(Y)$\n\n注：$X$与$Y$独立为上述5个条件中任何一个成立的充分条件，但非必要条件。\n\n#### 数理统计的基本概念\n\n**1.基本概念**\n\n总体：研究对象的全体，它是一个随机变量，用$X$表示。\n\n个体：组成总体的每个基本元素。\n\n简单随机样本：来自总体$X$的$n$个相互独立且与总体同分布的随机变量$X_{1},X_{2}\\cdots,X_{n}$，称为容量为$n$的简单随机样本，简称样本。\n\n统计量：设$X_{1},X_{2}\\cdots,X_{n},$是来自总体$X$的一个样本，$g(X_{1},X_{2}\\cdots,X_{n})$）是样本的连续函数，且$g()$中不含任何未知参数，则称$g(X_{1},X_{2}\\cdots,X_{n})$为统计量。\n\n样本均值：$\\overline{X} = \\frac{1}{n}\\sum_{i = 1}^{n}X_{i}$\n\n样本方差：$S^{2} = \\frac{1}{n - 1}\\sum_{i = 1}^{n}{(X_{i} - \\overline{X})}^{2}$\n\n样本矩：样本$k$阶原点矩：$A_{k} = \\frac{1}{n}\\sum_{i = 1}^{n}X_{i}^{k},k = 1,2,\\cdots$\n\n样本$k$阶中心矩：$B_{k} = \\frac{1}{n}\\sum_{i = 1}^{n}{(X_{i} - \\overline{X})}^{k},k = 1,2,\\cdots$\n\n**2.分布**\n\n$\\chi^{2}$分布：$\\chi^{2} = X_{1}^{2} + X_{2}^{2} + \\cdots + X_{n}^{2}\\sim\\chi^{2}(n)$，其中$X_{1},X_{2}\\cdots,X_{n},$相互独立，且同服从$N(0,1)$\n\n$t$分布：$T = \\frac{X}{\\sqrt{Y/n}}\\sim t(n)$ ，其中$X\\sim N\\left( 0,1 \\right),Y\\sim\\chi^{2}(n),$且$X$，$Y$ 相互独立。\n\n$F$分布：$F = \\frac{X/n_{1}}{Y/n_{2}}\\sim F(n_{1},n_{2})$，其中$X\\sim\\chi^{2}\\left( n_{1} \\right),Y\\sim\\chi^{2}(n_{2}),$且$X$，$Y$相互独立。\n\n分位数：若$P(X \\leq x_{\\alpha}) = \\alpha,$则称$x_{\\alpha}$为$X$的$\\alpha$分位数\n\n**3.正态总体的常用样本分布**\n\n(1) 设$X_{1},X_{2}\\cdots,X_{n}$为来自正态总体$N(\\mu,\\sigma^{2})$的样本，\n\n$\\overline{X} = \\frac{1}{n}\\sum_{i = 1}^{n}X_{i},S^{2} = \\frac{1}{n - 1}\\sum_{i = 1}^{n}{{(X_{i} - \\overline{X})}^{2},}$则：\n\n1) $\\overline{X}\\sim N\\left( \\mu,\\frac{\\sigma^{2}}{n} \\right){\\ \\ }$或者$\\frac{\\overline{X} - \\mu}{\\frac{\\sigma}{\\sqrt{n}}}\\sim N(0,1)$\n\n2) $\\frac{(n - 1)S^{2}}{\\sigma^{2}} = \\frac{1}{\\sigma^{2}}\\sum_{i = 1}^{n}{{(X_{i} - \\overline{X})}^{2}\\sim\\chi^{2}(n - 1)}$\n\n3) $\\frac{1}{\\sigma^{2}}\\sum_{i = 1}^{n}{{(X_{i} - \\mu)}^{2}\\sim\\chi^{2}(n)}$\n\n4)${\\ \\ }\\frac{\\overline{X} - \\mu}{S/\\sqrt{n}}\\sim t(n - 1)$\n\n**4.重要公式与结论**\n\n(1) 对于$\\chi^{2}\\sim\\chi^{2}(n)$，有$E(\\chi^{2}(n)) = n,D(\\chi^{2}(n)) = 2n;$\n\n(2) 对于$T\\sim t(n)$，有$E(T) = 0,D(T) = \\frac{n}{n - 2}(n > 2)$；\n\n(3) 对于$F\\tilde{\\ }F(m,n)$，有 $\\frac{1}{F}\\sim F(n,m),F_{a/2}(m,n) = \\frac{1}{F_{1 - a/2}(n,m)};$\n\n(4) 对于任意总体$X$，有 $E(\\overline{X}) = E(X),E(S^{2}) = D(X),D(\\overline{X}) = \\frac{D(X)}{n}$\n\n"
  },
  {
    "path": "0.math/1.CS229/README.md",
    "content": "# CS 229 机器学习课程复习材料\n\n文件夹的材料斯坦福大学CS 229机器学习课程的基础材料的翻译版本，[原始文件下载](http://cs229.stanford.edu/summer2019/cs229-linalg.pdf)\n\n翻译：[黄海广](https://github.com/fengdu78)\n备注：请关注[github](https://github.com/fengdu78/Data-Science-Notes/tree/master/0.math)的更新，近期将更新完。\n\nmarkdown文件夹是文件的markdown代码，内容与pdf一致，是为了方便研究者写论文或者文章使用。\n\ngithub内容是方便用户学习研究使用，请勿用于商业目的。\n\n如果需要引用这个Repo:\n\n格式： `fengdu78, Data-Science-Notes, (2019), GitHub repository, https://github.com/fengdu78/Data-Science-Notes`"
  },
  {
    "path": "0.math/1.CS229/markdown/1.CS229-LinearAlgebra.md",
    "content": "> 本文是斯坦福大学CS 229机器学习课程的基础材料，[原始文件下载](http://cs229.stanford.edu/summer2019/cs229-linalg.pdf)\n\n> 原文作者：Zico Kolter，修改：Chuong Do， Tengyu Ma\n>\n> 翻译：[黄海广](https://github.com/fengdu78)\n> 备注：请关注[github](https://github.com/fengdu78/Data-Science-Notes/tree/master/0.math)的更新，线性代数和概率论已经更新完毕。\n\n\n\n# CS229 机器学习课程复习材料-线性代数\n\n[TOC]\n## 线性代数复习和参考\n\n### 1.  基础概念和符号\n\n线性代数提供了一种紧凑地表示和操作线性方程组的方法。 例如，以下方程组：\n$$\n4x_1 − 5x_2 = −13\n$$\n$$\n−2x_1 + 3x_2 = 9\n$$\n\n这是两个方程和两个变量，正如你从高中代数中所知，你可以找到 $x_1$ 和 $x_2$ 的唯一解（除非方程以某种方式退化，例如，如果第二个方程只是第一个的倍数，但在上面的情况下，实际上只有一个唯一解）。 在矩阵表示法中，我们可以更紧凑地表达：\n\n$$\nAx= b\n$$\n\n$$\n\\text { with } A=\\left[\\begin{array}{cc}{4} & {-5} \\\\ {-2} & {3}\\end{array}\\right], b=\\left[\\begin{array}{c}{-13} \\\\ {9}\\end{array}\\right]\n$$\n\n我们可以看到，这种形式的线性方程有许多优点（比如明显地节省空间）。\n\n#### 1.1 基本符号\n\n我们使用以下符号：\n\n-   $A \\in \\mathbb{R}^{m \\times n}$，表示 $A$ 为由实数组成具有$m$行和$n$列的矩阵。\n\n-   $x \\in \\mathbb{R}^{ n}$，表示具有$n$个元素的向量。 通常，向量$x$将表示列向量: 即，具有$n$行和$1$列的矩阵。 如果我们想要明确地表示行向量: 具有 $1$ 行和$n$列的矩阵 - 我们通常写$x^T$（这里$x^T$$x$的转置）。\n\n-   $x_i$表示向量$x$的第$i$个元素\n\n$$\nx=\\left[\\begin{array}{c}{x_{1}} \\\\ {x_{2}} \\\\ {\\vdots} \\\\ {x_{n}}\\end{array}\\right]\n$$\n\n-   我们使用符号 $a_{ij}$（或$A_{ij}$,$A_{i,j}$等）来表示第 $i$ 行和第$j$列中的 $A$ 的元素：\n\n$$\nA=\\left[\\begin{array}{cccc}{a_{11}} & {a_{12}} & {\\cdots} & {a_{1 n}} \\\\ {a_{21}} & {a_{22}} & {\\cdots} & {a_{2 n}} \\\\ {\\vdots} & {\\vdots} & {\\ddots} & {\\vdots} \\\\ {a_{m 1}} & {a_{m 2}} & {\\cdots} & {a_{m n}}\\end{array}\\right]\n$$\n\n-   我们用$a^j$或者$A_{:,j}$表示矩阵$A$的第$j$列：\n\n$$\nA=\\left[\\begin{array}{llll}{ |} & { |} & {} & { |} \\\\ {a^{1}} & {a^{2}} & {\\cdots} & {a^{n}} \\\\ { |} & { |} & {} & { |}\\end{array}\\right]\n$$\n\n-   我们用$a^T_i$或者$A_{i,:}$表示矩阵$A$的第$i$行：\n$$\nA=\\left[\\begin{array}{c}{-a_{1}^{T}-} \\\\ {-a_{2}^{T}-} \\\\ {\\vdots} \\\\ {-a_{m}^{T}-}\\end{array}\\right]\n$$\n\n\n-   在许多情况下，将矩阵视为列向量或行向量的集合非常重要且方便。 通常，在向量而不是标量上操作在数学上（和概念上）更清晰。只要明确定义了符号，用于矩阵的列或行的表示方式并没有通用约定。\n\n### 2.矩阵乘法\n\n两个矩阵相乘，其中 $A \\in \\mathbb{R}^{m \\times n}$  and $B \\in \\mathbb{R}^{n \\times p}$ ，则：\n\n$$\nC = AB \\in \\mathbb{R}^{m \\times p}\n$$\n\n\n其中：\n\n$$\nC_{i j}=\\sum_{k=1}^{n} A_{i k} B_{k j}\n$$\n\n请注意，为了使矩阵乘积存在，$A$中的列数必须等于$B$中的行数。有很多方法可以查看矩阵乘法，我们将从检查一些特殊情况开始。\n\n#### 2.1 向量-向量乘法\n\n给定两个向量$x, y \\in \\mathbb{R}^{n}$,$x^T y$通常称为**向量内积**或者**点积**，结果是个**实数**。\n\n$$\nx^{T} y \\in \\mathbb{R}=\\left[\\begin{array}{llll}{x_{1}} & {x_{2}} & {\\cdots} & {x_{n}}\\end{array}\\right]\\left[\\begin{array}{c}{y_{1}} \\\\ {y_{2}} \\\\ {\\vdots} \\\\ {y_{n}}\\end{array}\\right]=\\sum_{i=1}^{n} x_{i} y_{i}\n$$\n\n注意：$x^T y = y^Tx$ 始终成立。\n\n给定向量 $x \\in \\mathbb{R}^{m}$, $y \\in \\mathbb{R}^{n}$ (他们的维度是否相同都没关系)，$xy^T \\in \\mathbb{R}^{m \\times n}$叫做**向量外积 ** , 当 $(xy^T)_{ij} = x_iy_j$ 的时候，它是一个矩阵。\n\n$$\nx y^{T} \\in \\mathbb{R}^{m \\times n}=\\left[\\begin{array}{c}{x_{1}} \\\\ {x_{2}} \\\\ {\\vdots} \\\\ {x_{m}}\\end{array}\\right]\\left[\\begin{array}{llll}{y_{1}} & {y_{2}} & {\\cdots} & {y_{n}}\\end{array}\\right]=\\left[\\begin{array}{cccc}{x_{1} y_{1}} & {x_{1} y_{2}} & {\\cdots} & {x_{1} y_{n}} \\\\ {x_{2} y_{1}} & {x_{2} y_{2}} & {\\cdots} & {x_{2} y_{n}} \\\\ {\\vdots} & {\\vdots} & {\\ddots} & {\\vdots} \\\\ {x_{m} y_{1}} & {x_{m} y_{2}} & {\\cdots} & {x_{m} y_{n}}\\end{array}\\right]\n$$\n举一个外积如何使用的一个例子：让$1\\in R^{n}$表示一个$n$维向量，其元素都等于1，此外，考虑矩阵$A \\in R^{m \\times n}$，其列全部等于某个向量 $x \\in R^{m}$。 我们可以使用外积紧凑地表示矩阵 $A$:\n\n$$\nA=\\left[\\begin{array}{llll}{ |} & { |} & {} & { |} \\\\ {x} & {x} & {\\cdots} & {x} \\\\ { |} & { |} & {} & { |}\\end{array}\\right]=\\left[\\begin{array}{cccc}{x_{1}} & {x_{1}} & {\\cdots} & {x_{1}} \\\\ {x_{2}} & {x_{2}} & {\\cdots} & {x_{2}} \\\\ {\\vdots} & {\\vdots} & {\\ddots} & {\\vdots} \\\\ {x_{m}} & {x_{m}} & {\\cdots} & {x_{m}}\\end{array}\\right]=\\left[\\begin{array}{c}{x_{1}} \\\\ {x_{2}} \\\\ {\\vdots} \\\\ {x_{m}}\\end{array}\\right]\\left[\\begin{array}{lll}{1} & {1} & {\\cdots} & {1}\\end{array}\\right]=x \\mathbf{1}^{T}\n$$\n#### 2.2 矩阵-向量乘法\n\n给定矩阵 $A \\in \\mathbb{R}^{m \\times n}$，向量 $x \\in  \\mathbb{R}^{n}$ , 它们的积是一个向量 $y = Ax \\in R^{m}$。 有几种方法可以查看矩阵向量乘法，我们将依次查看它们中的每一种。\n\n如果我们按行写$A$，那么我们可以表示$Ax$为：\n\n$$\ny=A x=\\left[\\begin{array}{ccc}{-} & {a_{1}^{T}} & {-} \\\\ {-} & {a_{2}^{T}} & {-} \\\\ {} & {\\vdots} & {} \\\\ {-} & {a_{m}^{T}} & {-}\\end{array}\\right] x=\\left[\\begin{array}{c}{a_{1}^{T} x} \\\\ {a_{2}^{T} x} \\\\ {\\vdots} \\\\ {a_{m}^{T} x}\\end{array}\\right]\n$$\n\n换句话说，第$i$个$y$是$A$的第$i$行和$x$的内积，即：$y_i = y_{i}=a_{i}^{T} x$。\n\n同样的， 可以把 $A$ 写成列的方式，则公式如下：\n\n$$\ny=A x=\\left[\\begin{array}{cccc}{ |} & { |} & {} & { |} \\\\ {a^{1}} & {a^{2}} & {\\cdots} & {a^{n}} \\\\ { |} & { |} & {} & { |}\\end{array}\\right]\\left[\\begin{array}{c}{x_{1}} \\\\ {x_{2}} \\\\ {\\vdots} \\\\ {x_{n}}\\end{array}\\right]=\\left[\\begin{array}{c}{ } \\\\ {a^{1}{ } \\\\ }\\end{array}\\right] x_{1}+\\left[\\begin{array}{c}{ } \\\\ {a^{2}{ } \\\\ }\\end{array}\\right] x_{2}+{\\cdots} +\\left[\\begin{array}{c}{ } \\\\ {a^{n}{ } \\\\ }\\end{array}\\right] x_{n}\n$$\n\n换句话说，$y$是$A$的列的线性组合，其中线性组合的系数由$x$的元素给出。\n\n到目前为止，我们一直在右侧乘以列向量，但也可以在左侧乘以行向量。 这是写的，$y^T = x^TA$ 表示$A \\in \\mathbb{R}^{m \\times n}$，$x \\in \\mathbb{R}^{m}$，$y \\in \\mathbb{R}^{n}$。 和以前一样，我们可以用两种可行的方式表达$y^T$，这取决于我们是否根据行或列表达$A$.\n\n第一种情况，我们把$A$用列表示：\n\n$$\ny^{T}=x^{T} A=x^{T}\\left[\\begin{array}{cccc}{ |} & { |} & {} & { |} \\\\ {a^{1}} & {a^{2}} & {\\cdots} & {a^{n}} \\\\ { |} & { |} & {} & { |}\\end{array}\\right]=\\left[\\begin{array}{cccc}{x^{T} a^{1}} & {x^{T} a^{2}} & {\\dots} & {x^{T} a^{n}}\\end{array}\\right]\n$$\n\n这表明$y^T$的第$i$个元素等于$x$和$A$的第$i$列的内积。\n\n最后，根据行表示$A$，我们得到了向量-矩阵乘积的最终表示:\n\n$$\ny^T=x^TA\n=\\left[\\begin{array}{llll}{x_{1}} & {x_{2}} & {\\cdots} & {x_{n}}\\end{array}\\right]\\left[\\begin{array}{c}{-a_{1}^{T}-} \\\\ {-a_{2}^{T}-} \\\\ {\\vdots} \\\\ {-a_{m}^{T}-}\\end{array}\\right]\n\n=x_{1}\\left[-a_{1}^{T}-\\right]+x_{2}\\left[-a_{2}^{T}-\\right]+\\ldots+x_{n}\\left[-a_{n}^{T}-\\right]\n$$\n所以我们看到$y^T$是$A$的行的线性组合，其中线性组合的系数由$x$的元素给出。\n\n#### 2.3 矩阵-矩阵乘法\n\n有了这些知识，我们现在可以看看四种不同的（形式不同，但结果是相同的）矩阵-矩阵乘法：也就是本节开头所定义的$C=AB$的乘法。\n\n首先，我们可以将矩阵 - 矩阵乘法视为一组向量-向量乘积。 从定义中可以得出：最明显的观点是$C $的$( i，j )$元素等于$A$的第$i$行和$B$的的$j$列的内积。如下面的公式所示：\n$$\nC=A B=\\left[\\begin{array}{cc}{-} & {a_{1}^{T}} &{-} \\\\ {-} & {a_{2}^{T}} &{-}  \\\\ {} & {\\vdots} \\\\ {-} & {a_{m}^{T}} &{-} \\end{array}\\right]\\left[\\begin{array}{cccc}{ |} & { |} & {} & { |} \\\\ {b_{1}} & {b_{2}} & {\\cdots} & {b_{p}} \\\\ { |} & { |} & {} & { |}\\end{array}\\right]=\\left[\\begin{array}{cccc}{a_{1}^{T} b_{1}} & {a_{1}^{T} b_{2}} & {\\cdots} & {a_{1}^{T} b_{p}} \\\\ {a_{2}^{T} b_{1}} & {a_{2}^{T} b_{2}} & {\\cdots} & {a_{2}^{T} b_{p}} \\\\ {\\vdots} & {\\vdots} & {\\ddots} & {\\vdots} \\\\ {a_{m}^{T} b_{1}} & {a_{m}^{T} b_{2}} & {\\cdots} & {a_{m}^{T} b_{p}}\\end{array}\\right]\n$$\n\n这里的$ A \\in \\mathbb{R}^{m\\times n}$ ，$B \\in \\mathbb{R}^{n \\times p}$， $a_i \\in \\mathbb{R}^n$ ，$b^j \\in \\mathbb{R}^{n \\times p}$， 这里的$  A \\in \\mathbb{R}^ {m \\times n}，$ $B \\in \\mathbb{R}^ {n \\times p} $， $a_i \\in \\mathbb{R} ^ n $，$ b ^ j \\in \\mathbb{R} ^ {n \\times p} $，所以它们可以计算内积。 我们用通常用行表示$ A $而用列表示$B$。\n或者，我们可以用列表示$ A$，用行表示$B $，这时$AB$是求外积的和。公式如下：\n$$\nC=A B=\\left[\\begin{array}{cccc}{ |} & { |} & {} & { |} \\\\ {a_{1}} & {a_{2}} & {\\cdots} & {a_{n}} \\\\ { |} & { |} & {} & { |}\\end{array}\\right]\\left[\\begin{array}{c}{-}& {b_{1}^{T}}&{-} \\\\ {-}& {b_{2}^{T}}&{-}  \\\\ {\\vdots} \\\\{-}& {b_{n}^{T}}&{-}\\end{array}\\right]=\\sum_{i=1}^{n} a_{i} b_{i}^{T}\n$$\n换句话说，$AB$等于所有的$A$的第$i$列和$B$第$i$行的外积的和。因此，在这种情况下， $a_i \\in \\mathbb{R}^ m $和$b_i \\in \\mathbb{R}^p$， 外积$a^ib_i^T$的维度是$m×p$，与$C$的维度一致。\n\n其次，我们还可以将矩阵 - 矩阵乘法视为一组矩阵向量积。如果我们把$B$用列表示，我们可以将$C$的列视为$A$和$B$的列的矩阵向量积。公式如下：\n\n$$\nC=A B=A\\left[\\begin{array}{cccc}{ |} & { |} & {} & { |} \\\\ {b_{1}} & {b_{2}} & {\\cdots} & {b_{p}} \\\\ { |} & { |} & {} & { |}\\end{array}\\right]=\\left[\\begin{array}{cccc}{ |} & { |} & {} & { |} \\\\ {A b_{1}} & {A b_{2}} & {\\cdots} & {A b_{p}} \\\\ { |} & { |} & {} & { |}\\end{array}\\right]\n$$\n这里$C$的第$i$列由矩阵向量乘积给出，右边的向量为$c_i = Ab_i$。 这些矩阵向量乘积可以使用前一小节中给出的两个观点来解释。\n最后，我们有类似的观点，我们用行表示$A$，$C$的行作为$A$和$C$行之间的矩阵向量积。公式如下：\n$$\nC=A B=\\left[\\begin{array}{ccc}{-} & {a_{1}^{T}} & {-} \\\\ {-} & {a_{2}^{T}} & {-} \\\\ {} & {\\vdots} & {} \\\\ {-} & {a_{m}^{T}} & {-}\\end{array}\\right]  B=\\left[\\begin{array}{c} {-} & {a_{1}^{T} B} & {-}\\\\ {-} & {a_{2}^{T} B} & {-} \\\\ {\\vdots} \\\\ {-} & {a_{m}^{T} B}& {-}\\end{array}\\right]\n$$\n\n这里第$i$行的$C$由左边的向量的矩阵向量乘积给出：$c_i^T = a_i^T B$\n\n将矩阵乘法剖析到如此大的程度似乎有点过分，特别是当所有这些观点都紧跟在我们在本节开头给出的初始定义（在一行数学中）之后。 \n\n这些不同方法的直接优势在于它们允许您**在向量的级别/单位而不是标量上进行操作**。 为了完全理解线性代数而不会迷失在复杂的索引操作中，关键是要用尽可能多的概念进行操作。\n\n实际上所有的线性代数都处理某种矩阵乘法，花一些时间对这里提出的观点进行直观的理解是非常必要的。 \n\n除此之外，了解一些更高级别的矩阵乘法的基本属性是很有必要的：\n\n-   矩阵乘法结合律: $(AB)C = A(BC)$\n\n-   矩阵乘法分配律: $A(B + C) = AB + AC$\n\n-   矩阵乘法通常不是可交换的; 也就是说，通常$AB \\ne BA$。 （例如，假设$  A \\in \\mathbb{R}^ {m \\times n}，$ $B \\in \\mathbb{R}^ {n \\times p} $，如果$m$和$q$不相等，矩阵乘积$BA$甚至不存在！）\n\n如果您不熟悉这些属性，请花点时间自己验证它们。 例如，为了检查矩阵乘法的相关性，假设$A \\in \\mathbb{R}^ {m \\times n}，$ $B \\in \\mathbb{R}^ {n \\times p} $，$C \\in \\mathbb{R}^ {p \\times q}$。 注意$AB \\in \\mathbb{R}^ {m \\times p}$，所以$(AB)C \\in \\mathbb{R}^ {m \\times q}$。 类似地，$BC \\in \\mathbb{R}^ {n \\times q}$，所以$A(BC) \\in \\mathbb{R}^ {m \\times q}$。 因此，所得矩阵的维度一致。 为了表明矩阵乘法是相关的，足以检查$(AB)C $的第$(i,j)$个元素是否等于$A(BC)$的第$(i,j)$个元素。 我们可以使用矩阵乘法的定义直接验证这一点：\n\n$$\n\\begin{aligned}((A B) C)_{i j} &=\\sum_{k=1}^{p}(A B)_{i k} C_{k j}=\\sum_{k=1}^{p}\\left(\\sum_{l=1}^{n} A_{i l} B_{l k}\\right) C_{k j} \\\\ &=\\sum_{k=1}^{p}\\left(\\sum_{l=1}^{n} A_{i l} B_{l k} C_{k j}\\right)=\\sum_{l=1}^{n}\\left(\\sum_{k=1}^{p} A_{i l} B_{l k} C_{k j}\\right) \\\\ &=\\sum_{l=1}^{n} A_{i l}\\left(\\sum_{k=1}^{p} B_{l k} C_{k j}\\right)=\\sum_{l=1}^{n} A_{i l}(B C)_{l j}=(A(B C))_{i j} \\end{aligned}\n$$\n\n### 3 运算和属性\n\n在本节中，我们介绍矩阵和向量的几种运算和属性。 希望能够为您复习大量此类内容，这些笔记可以作为这些主题的参考。\n\n#### 3.1 单位矩阵和对角矩阵\n\n**单位矩阵**,$I \\in \\mathbb{R}^{n \\times n} $，它是一个方阵，对角线的元素是1，其余元素都是0：\n$$\nI_{i j}=\\left\\{\\begin{array}{ll}{1} & {i=j} \\\\ {0} & {i \\neq j}\\end{array}\\right.\n$$\n对于所有$A \\in \\mathbb{R}^ {m \\times n}$，有：\n$$\nAI = A = IA\n$$\n注意，在某种意义上，单位矩阵的表示法是不明确的，因为它没有指定$I$的维数。通常，$I$的维数是从上下文推断出来的，以便使矩阵乘法成为可能。 例如，在上面的等式中，$AI = A$中的I是$n\\times n$矩阵，而$A = IA$中的$I$是$m\\times m$矩阵。\n\n对角矩阵是一种这样的矩阵：对角线之外的元素全为0。对角阵通常表示为：$D= diag(d_1, d_2, . . . , d_n)$，其中：\n$$\nD_{i j}=\\left\\{\\begin{array}{ll}{d_{i}} & {i=j} \\\\ {0} & {i \\neq j}\\end{array}\\right.\n$$\n很明显：单位矩阵$ I = diag(1, 1, . . . , 1)$。\n\n#### 3.2 转置\n\n矩阵的转置是指翻转矩阵的行和列。\n\n给定一个矩阵：\n\n$A \\in \\mathbb{R}^ {m \\times n}$, 它的转置为$n \\times m$的矩阵$A^T \\in \\mathbb{R}^ {n \\times m}$ ，其中的元素为：\n$$\n(A^T)_{ij} = A_{ji}\n$$\n事实上，我们在描述行向量时已经使用了转置，因为列向量的转置自然是行向量。\n\n转置的以下属性很容易验证：\n\n- $(A^T )^T = A$\n- $ (AB)^T = B^T A^T$\n- $(A + B)^T = A^T + B^T$\n\n#### 3.3 对称矩阵\n\n如果$A =  A^T$，则矩阵$A \\in \\mathbb{R}^ {n \\times n}$是对称矩阵。 如果$ A =  -  A^T$，它是反对称的。 很容易证明，对于任何矩阵$A \\in \\mathbb{R}^ {n \\times n}$，矩阵$A  +  A^ T$是对称的，矩阵$A -A^T$是反对称的。 由此得出，任何方矩阵$A \\in \\mathbb{R}^ {n \\times n}$可以表示为对称矩阵和反对称矩阵的和，所以：\n$$\nA=\\frac{1}{2}(A+A^T)+\\frac{1}{2}(A-A^T)\n$$\n上面公式的右边的第一个矩阵是对称矩阵，而第二个矩阵是反对称矩阵。 事实证明，对称矩阵在实践中用到很多，它们有很多很好的属性，我们很快就会看到它们。\n通常将大小为$n$的所有对称矩阵的集合表示为$\\mathbb{S}^n$，因此$A \\in \\mathbb{S}^n$意味着$A$是对称的$n\\times n$矩阵;\n\n#### 3.4 矩阵的迹\n\n方矩阵$A \\in \\mathbb{R}^ {n \\times n}$的迹，表示为$\\operatorname{tr} (A)$（或者只是$\\operatorname{tr} A$，如果括号显然是隐含的），是矩阵中对角元素的总和：\n$$\n\\operatorname{tr} A=\\sum_{i=1}^{n} A_{i i}\n$$\n如**CS229**讲义中所述，迹具有以下属性（如下所示）：\n\n- 对于矩阵$A \\in \\mathbb{R}^ {n \\times n}$，则：$\\operatorname{tr}A =\\operatorname{tr}A^T$\n\n- 对于矩阵$A,B \\in \\mathbb{R}^ {n \\times n}$，则：$\\operatorname{tr}(A + B) = \\operatorname{tr}A + \\operatorname{tr}B$\n\n- 对于矩阵$A \\in \\mathbb{R}^ {n \\times n}$，$ t \\in \\mathbb{R}$，则：$\\operatorname{tr}(tA) = t\\operatorname{tr}A$.\n\n- 对于矩阵 $A$, $B$，$AB$ 为方阵, 则：$\\operatorname{tr}AB = \\operatorname{tr}BA$\n\n- 对于矩阵 $A$, $B$, $C$, $ABC$为方阵, 则：$\\operatorname{tr}ABC = \\operatorname{tr}BCA=\\operatorname{tr}CAB$, 同理，更多矩阵的积也是有这个性质。\n\n作为如何证明这些属性的示例，我们将考虑上面给出的第四个属性。 假设$A \\in \\mathbb{R}^ {m \\times n}$和$B \\in \\mathbb{R}^ {n \\times m}$（因此$AB \\in \\mathbb{R}^ {m \\times m}$是方阵）。 观察到$BA \\in \\mathbb{R}^ {n \\times n}$也是一个方阵，因此对它们进行迹的运算是有意义的。 要证明$\\operatorname{tr}AB = \\operatorname{tr}BA$，请注意：\n\n$$\n\\begin{aligned} \\operatorname{tr} A B &=\\sum_{i=1}^{m}(A B)_{i i}=\\sum_{i=1}^{m}\\left(\\sum_{j=1}^{n} A_{i j} B_{j i}\\right) \\\\ &=\\sum_{i=1}^{m} \\sum_{j=1}^{n} A_{i j} B_{j i}=\\sum_{j=1}^{n} \\sum_{i=1}^{m} B_{j i} A_{i j} \\\\ &=\\sum_{j=1}^{n}\\left(\\sum_{i=1}^{m} B_{j i} A_{i j}\\right)=\\sum_{j=1}^{n}(B A)_{j j}=\\operatorname{tr} B A \\end{aligned}\n$$\n\n这里，第一个和最后两个等式使用迹运算符和矩阵乘法的定义，重点在第四个等式，使用标量乘法的可交换性来反转每个乘积中的项的顺序，以及标量加法的可交换性和相关性，以便重新排列求和的顺序。\n\n#### 3.5 范数\n\n向量的范数$\\|x\\|$是非正式度量的向量的“长度” 。 例如，我们有常用的欧几里德或$\\ell_{2}$范数，\n$$\n\\|x\\|_{2}=\\sqrt{\\sum_{i=1}^{n} x_{i}^{2}}\n$$\n注意：$\\|x\\|_{2}^{2}=x^{T} x$\n\n更正式地，范数是满足4个属性的函数（$f : \\mathbb{R}^{n} \\rightarrow \\mathbb{R}$）：\n\n1. 对于所有的 $x \\in \\mathbb{R}^ {n}$, $f(x) \\geq 0 $(非负).\n2. 当且仅当$x = 0$ 时，$f(x) = 0$ (明确性).\n3. 对于所有$x \\in \\mathbb{R}^ {n}$,$t\\in \\mathbb{R}$，则 $f(tx) = \\left| t \\right|f(x)$ (正齐次性).\n4. 对于所有 $x,y \\in \\mathbb{R}^ {n}$, $f(x + y) \\leq f(x) + f(y)$ (三角不等式)\n\n其他范数的例子是$\\ell_1$范数:\n$$\n\\|x\\|_{1}=\\sum_{i=1}^{n}\\left|x_{i}\\right|\n$$\n和$\\ell_{\\infty }$范数：\n$$\n\\|x\\|_{\\infty}=\\max _{i}\\left|x_{i}\\right|\n$$\n事实上，到目前为止所提出的所有三个范数都是$\\ell_p$范数族的例子，它们由实数$p \\geq 1$参数化，并定义为：\n$$\n\\|x\\|_{p}=\\left(\\sum_{i=1}^{n}\\left|x_{i}\\right|^{p}\\right)^{1 / p}\n$$\n\n也可以为矩阵定义范数，例如**Frobenius**范数:\n$$\n\\|A\\|_{F}=\\sqrt{\\sum_{i=1}^{m} \\sum_{j=1}^{n} A_{i j}^{2}}=\\sqrt{\\operatorname{tr}\\left(A^{T} A\\right)}\n$$\n许多其他更多的范数，但它们超出了这个复习材料的范围。\n\n#### 3.6 线性相关性和秩\n\n一组向量${x_1,x_2, \\cdots x_n} \\in \\mathbb{R}$， 如果没有向量可以表示为其余向量的线性组合，则称称该向量是线性无相关的。 相反，如果属于该组的一个向量可以表示为其余向量的线性组合，则称该向量是线性相关的。 也就是说，如果：\n$$\nx_{n}=\\sum_{i=1}^{n-1} \\alpha_{i} x_{i}\n$$\n对于某些标量值$\\alpha_1,\\cdots \\alpha_n-1 \\in \\mathbb{R}$，要么向量$x_1,x_2, \\cdots x_n$是线性相关的; 否则，向量是线性无关的。 例如，向量：\n$$\nx_{1}=\\left[\\begin{array}{l}{1} \\\\ {2} \\\\ {3}\\end{array}\\right] \\quad x_{2}=\\left[\\begin{array}{c}{4} \\\\ {1} \\\\ {5}\\end{array}\\right] \\quad x_{3}=\\left[\\begin{array}{c}{2} \\\\ {-3} \\\\ {-1}\\end{array}\\right]\n$$\n是线性相关的，因为：$x_3=-2x_1+x_2$。\n\n矩阵$A  \\in \\mathbb{R}^{m \\times n}$的**列秩**是构成线性无关集合的$A$的最大列子集的大小。 由于术语的多样性，这通常简称为$A$的线性无关列的数量。同样，行秩是构成线性无关集合的$A$的最大行数。 对于任何矩阵$A  \\in \\mathbb{R}^{m \\times n}$，事实证明$A$的列秩等于$A$的行秩（尽管我们不会证明这一点），因此两个量统称为$A$的**秩**，用 $\\text{rank}(A)$表示。 以下是秩的一些基本属性：\n\n-   对于  $A  \\in \\mathbb{R}^{m \\times n}$，$\\text{rank}(A) \\leq min(m, n)$，如果$ \\text(A) = \\text{min} (m, n)$，则： $A$ 被称作**满秩**。\n-   对于  $A  \\in \\mathbb{R}^{m \\times n}$， $\\text{rank}(A) = \\text{rank}(A^T)$\n-   对于  $A  \\in \\mathbb{R}^{m \\times n}$,$B  \\in \\mathbb{R}^{n \\times p}$ ,$\\text{rank}(AB) \\leq \\text{min} ( \\text{rank}(A), \\text{rank}(B))$\n-   对于  $A,B \\in \\mathbb{R}^{m \\times n}$，$\\text{rank}(A + B) \\leq \\text{rank}(A) + \\text{rank}(B)$\n\n#### 3.7 方阵的逆\n\n方阵$A  \\in \\mathbb{R}^{n \\times n}$的倒数表示为$A^{-1}$，并且是这样的独特矩阵:\n$$\nA^{-1}A=I=AA^{-1}\n$$\n请注意，并非所有矩阵都具有逆。 例如，非方形矩阵根据定义没有逆。 然而，对于一些方形矩阵$A$，可能仍然存在$A^{-1}$可能不存在的情况。 特别是，如果$A^{-1}$存在，我们说$A$是**可逆**的或**非奇异**的，否则就是**不可逆**或**奇异**的。\n为了使方阵A具有逆$A^{-1}$，则$A$必须是满秩。 我们很快就会发现，除了满秩之外，还有许多其它的充分必要条件。\n以下是逆的属性; 假设$A,B  \\in \\mathbb{R}^{n \\times n}$，而且是非奇异的：\n\n-   $(A^{-1})^{-1} = A$\n-   $(AB)^{-1} = B^{-1}A^{-1}$\n-   $(A^{-1})^{T} =(A^{T})^{-1} $因此，该矩阵通常表示为$A^{-T}$。\n作为如何使用逆的示例，考虑线性方程组，$Ax = b$，其中$A  \\in \\mathbb{R}^{n \\times n}$，$x,b\\in \\mathbb{R}$， 如果$A$是非奇异的（即可逆的），那么$x = A^{-1}b$。 （如果$A  \\in \\mathbb{R}^{m \\times n}$不是方阵，这公式还有用吗？）\n\n#### 3.8 正交阵\n\n如果 $x^Ty=0$，则两个向量$x,y\\in \\mathbb{R}^{n}$ 是**正交**的。如果$\\|x\\|_2=1$，则向量$x\\in \\mathbb{R}^{n}$ 被归一化。如果一个方阵$U\\in \\mathbb{R}^{n \\times n}$的所有列彼此正交并被归一化（这些列然后被称为正交），则方阵$U$是正交阵（注意在讨论向量时的意义不一样）。\n\n它可以从正交性和正态性的定义中得出:\n$$\nU^ TU = I = U U^T\n$$\n\n换句话说，正交矩阵的逆是其转置。 注意，如果$U$不是方阵 :即，$U\\in \\mathbb{R}^{m \\times n}$，$n <m$  ，但其列仍然是正交的，则$U^TU = I$，但是$UU^T \\neq I$。我们通常只使用术语\"正交\"来描述先前的情况 ，其中$U$是方阵。\n正交矩阵的另一个好的特性是在具有正交矩阵的向量上操作不会改变其欧几里德范数，即:\n$$\n\\|U x\\|_{2}=\\|x\\|_{2}\n$$\n对于任何 $x\\in \\mathbb{R}$ , $U\\in \\mathbb{R}^{n}$是正交的。\n\n#### 3.9 矩阵的值域和零空间\n\n一组向量$\\{x_{1}, \\ldots x_{n}\\}$是可以表示为$\\{x_{1}, \\ldots x_{n}\\}$的线性组合的所有向量的集合。 即：\n$$\n\\operatorname{span}\\left(\\left\\{x_{1}, \\ldots x_{n}\\right\\}\\right)=\\left\\{v : v=\\sum_{i=1}^{n} \\alpha_{i} x_{i}, \\quad \\alpha_{i} \\in \\mathbb{R}\\right\\}\n$$\n可以证明，如果$\\{x_{1}, \\ldots x_{n}\\}$是一组$n$个线性无关的向量，其中每个$x_i \\in \\mathbb{R}^{n}$，则$\\text{span}(\\{x_{1}, \\ldots x_{n}\\})=\\mathbb{R}^{n}$。 换句话说，任何向量$v\\in \\mathbb{R}^{n}$都可以写成$x_1$到$x_n$的线性组合。\n\n向量$y\\in \\mathbb{R}^{m}$投影到$\\{x_{1}, \\ldots x_{n}\\}$（这里我们假设$x_i \\in \\mathbb{R}^{m}$）得到向量$v \\in \\operatorname{span}(\\{x_{1}, \\ldots, x_{n}\\})$，由欧几里德范数$\\|v  -  y\\|_2$可以得知，这样$v$尽可能接近$y$。\n\n我们将投影表示为$\\operatorname{Proj}\\left(y ;\\left\\{x_{1}, \\ldots x_{n}\\right\\}\\right)$，并且可以将其正式定义为:\n$$\n\\operatorname{Proj}\\left(y ;\\left\\{x_{1}, \\ldots x_{n}\\right\\}\\right)=\\operatorname{argmin}_{v \\in \\operatorname{span}\\left(\\left\\{x_{1}, \\ldots, x_{n}\\right\\}\\right)}\\|y-v\\|_{2}\n$$\n矩阵$A\\in \\mathbb{R}^{m \\times n}$的值域（有时也称为列空间），表示为$\\mathcal{R}(A)$，是$A$列的跨度。换句话说，\n$$\n\\mathcal{R}(A)=\\left\\{v \\in \\mathbb{R}^{m} : v=A x, x \\in \\mathbb{R}^{n}\\right\\}\n$$\n做一些技术性的假设（即$A$是满秩且$n <m$），向量$y \\in \\mathbb{R}^{m}$到$A$的范围的投影由下式给出:\n$$\n\\operatorname{Proj}(y ; A)=\\operatorname{argmin}_{v \\in \\mathcal{R}(A)}\\|v-y\\|_{2}=A\\left(A^{T} A\\right)^{-1} A^{T} y\n$$\n这个最后的方程应该看起来非常熟悉，因为它几乎与我们在课程中（我们将很快再次得出）得到的公式：用于参数的最小二乘估计一样。 看一下投影的定义，显而易见，这实际上是我们在最小二乘问题中最小化的目标（除了范数的平方这里有点不一样，这不会影响找到最优解），所以这些问题自然是非常相关的。 \n\n当$A$只包含一列时，$a \\in \\mathbb{R}^{m}$，这给出了向量投影到一条线上的特殊情况：\n$$\n\\operatorname{Proj}(y ; a)=\\frac{a a^{T}}{a^{T} a} y\n$$\n一个矩阵$A\\in \\mathbb{R}^{m \\times n}$的零空间 $\\mathcal{N}(A)$ 是所有乘以$A$时等于0向量的集合，即：\n$$\n\\mathcal{N}(A)=\\left\\{x \\in \\mathbb{R}^{n} : A x=0\\right\\}\n$$\n注意，$\\mathcal{R}(A)$中的向量的大小为$m$，而 $\\mathcal{N}(A)$ 中的向量的大小为$n$，因此$\\mathcal{R}(A^T)$和 $\\mathcal{N}(A)$ 中的向量的大小均为$\\mathbb{R}^{n}$。 事实上，还有很多例子。 证明：\n$$\n\\left\\{w : w=u+v, u \\in \\mathcal{R}\\left(A^{T}\\right), v \\in \\mathcal{N}(A)\\right\\}=\\mathbb{R}^{n} \\text { and } \\mathcal{R}\\left(A^{T}\\right) \\cap \\mathcal{N}(A)=\\{\\mathbf{0}\\}\n$$\n换句话说，$\\mathcal{R}(A^T)$和 $\\mathcal{N}(A)$ 是不相交的子集，它们一起跨越$\\mathbb{R}^{n}$的整个空间。 这种类型的集合称为**正交补**，我们用$\\mathcal{R}(A^T)= \\mathcal{N}(A)^{\\perp}$表示。\n\n#### 3.10 行列式\n\n一个方阵$A  \\in \\mathbb{R}^{n \\times n}$的行列式是函数$\\text {det}$：$\\mathbb{R}^{n \\times n} \\rightarrow \\mathbb{R}^{n} $，并且表示为$\\left| A \\right|$。 或者$\\text{det} A$（有点像迹运算符，我们通常省略括号）。 从代数的角度来说，我们可以写出一个关于$A$行列式的显式公式。 因此，我们首先提供行列式的几何解释，然后探讨它的一些特定的代数性质。\n\n给定一个矩阵：\n$$\n\\left[\\begin{array}{cccc}{-} & {a_{1}^{T}}  & {-} \\\\ {-} & {a_{2}^{T}} & {-} \\\\ {} & {\\vdots} & {} \\\\  {-} & {a_{n}^{T}} & {-}\\end{array}\\right]\n$$\n\n考虑通过采用$A$行向量$a_{1}, \\ldots a_{n}\\in  \\mathbb{R}^{n}$的所有可能线性组合形成的点$S \\subset \\mathbb{R}^{n}$的集合，其中线性组合的系数都在0和1之间; 也就是说，集合$S$是$\\text{span}(\\{a_{1}, \\ldots a_{n}\\})$受到系数$a_{1}, \\ldots a_{n}$的限制的线性组合，$\\alpha_1, \\cdots ,\\alpha_n$满足$0 \\leq \\alpha_{i} \\leq 1, i=1, \\ldots, n$。从形式上看，\n$$\nS=\\left\\{v \\in \\mathbb{R}^{n} : v=\\sum_{i=1}^{n} \\alpha_{i} a_{i} \\text { where } 0 \\leq \\alpha_{i} \\leq 1, i=1, \\ldots, n\\right\\}\n$$\n事实证明，$A$的行列式的绝对值是对集合$S$的“体积”的度量。\n\n比方说：一个$2 \\times2$的矩阵(4)：\n$$\nA=\\left[\\begin{array}{ll}{1} & {3} \\\\ {3} & {2}\\end{array}\\right]\n$$\n它的矩阵的行是：\n$$\na_{1}=\\left[\\begin{array}{l}{1} \\\\ {3}\\end{array}\\right] \\quad a_{2}=\\left[\\begin{array}{l}{3} \\\\ {2}\\end{array}\\right]\n$$\n对应于这些行对应的集合$S$如图1所示。对于二维矩阵，$S$通常具有平行四边形的形状。 在我们的例子中，行列式的值是$\\left| A \\right| = -7$（可以使用本节后面显示的公式计算），因此平行四边形的面积为7。（请自己验证！）\n\n在三维中，集合$S$对应于一个称为平行六面体的对象（一个有倾斜边的三维框，这样每个面都有一个平行四边形）。行定义$S$的$3×3$矩阵S的行列式的绝对值给出了平行六面体的三维体积。在更高的维度中，集合$S$是一个称为$n$维平行切的对象。\n\n![](images/fig1.png)\n\n图1：（4）中给出的$2×2$矩阵$A$的行列式的图示。 这里，$a_1$和$a_2$是对应于$A$行的向量，并且集合$S$对应于阴影区域（即，平行四边形）。 这个行列式的绝对值，$\\left| \\text{det} A \\right| = 7$，即平行四边形的面积。\n\n在代数上，行列式满足以下三个属性（所有其他属性都遵循这些属性，包括通用公式）：\n\n1. 恒等式的行列式为1, $\\left| I \\right|= 1$（几何上，单位超立方体的体积为1）。\n\n2. 给定一个矩阵 $A  \\in \\mathbb{R}^{n \\times n}$, 如果我们将$A$中的一行乘上一个标量$t  \\in \\mathbb{R}$，那么新矩阵的行列式是$t\\left| A \\right|$\n$$\n\\left|\\left[\\begin{array}{ccc}{-} & {t a_{1}^{T}} & {-} \\\\ {-} & {a_{2}^{T}} & {-} \\\\ {} & {\\vdots} & {} \\\\ {} & {a_{m}^{T}} & {-}\\end{array}\\right]\\right|=t|A|\n$$\n几何上，将集合$S$的一个边乘以系数$t$，体积也会增加一个系数$t$。\n\n3. 如果我们交换任意两行在$a_i^T$和$a_j^T$，那么新矩阵的行列式是$-\\left| A \\right|$，例如：\n$$\n\\left|\\left[\\begin{array}{ccc}{-} & {a_{2}^{T}} & {-} \\\\ {-} & {a_{1}^{T}} & {-} \\\\ {} & {\\vdots} & {} \\\\ {-} & {a_{m}^{T}} & {-}\\end{array}\\right]\\right|=-|A|\n$$\n你一定很奇怪，满足上述三个属性的函数的存在并不多。事实上，这样的函数确实存在，而且是唯一的（我们在这里不再证明了）。\n\n从上述三个属性中得出的几个属性包括：\n\n-   对于 $A  \\in \\mathbb{R}^{n \\times n}$, $\\left| A \\right| = \\left| A^T \\right|$\n-   对于 $A,B \\in \\mathbb{R}^{n \\times n}$, $\\left| AB \\right|= \\left| A \\right|\\left| B \\right|$\n-   对于 $A  \\in \\mathbb{R}^{n \\times n}$, 有且只有当$A$是奇异的（比如不可逆） ，则：$\\left| A \\right|= 0$\n-   对于 $A  \\in \\mathbb{R}^{n \\times n}$ 同时，$A$为非奇异的，则：$\\left| A ^{−1}\\right| = 1/\\left| A \\right|$\n\n在给出行列式的一般定义之前，我们定义，对于$A  \\in \\mathbb{R}^{n \\times n}$，$A_{\\backslash i, \\backslash j}\\in \\mathbb{R}^{(n-1) \\times (n-1)}$是由于删除第$i$行和第$j$列而产生的矩阵。 行列式的一般（递归）公式是：\n$$\n\\begin{aligned}|A| &=\\sum_{i=1}^{n}(-1)^{i+j} a_{i j}\\left|A_{\\backslash i, \\backslash j}\\right| \\quad(\\text { for any } j \\in 1, \\ldots, n) \\\\ &=\\sum_{j=1}^{n}(-1)^{i+j} a_{i j}\\left|A_{\\backslash i, \\backslash j}\\right| \\quad(\\text { for any } i \\in 1, \\ldots, n) \\end{aligned}\n$$\n对于 $A  \\in \\mathbb{R}^{1 \\times 1}$，初始情况为$\\left| A \\right|= a_{11}$。如果我们把这个公式完全展开为 $A  \\in \\mathbb{R}^{n \\times n}$，就等于$n!$（$n$阶乘）不同的项。因此，对于大于$3×3$的矩阵，我们几乎没有明确地写出完整的行列式方程。然而，$3×3$大小的矩阵的行列式方程是相当常见的，建议好好地了解它们：\n$$\n\\left|\\left[a_{11}\\right]\\right|=a_{11}\n$$\n\n$$\n\\left|\\left[\\begin{array}{ll}{a_{11}} & {a_{12}} \\\\ {a_{21}} & {a_{22}}\\end{array}\\right]\\right|=a_{11} a_{22}-a_{12} a_{21}\n$$\n\n$$\n\\left|\\left[\\begin{array}{l}{a_{11}} & {a_{12}} & {a_{13}} \\\\ {a_{21}} & {a_{22}} & {a_{23}} \\\\ {a_{31}} & {a_{32}} & {a_{33}}\\end{array}\\right]\\right|=\\quad \\begin{array}{c}{a_{11} a_{22} a_{33}+a_{12} a_{23} a_{31}+a_{13} a_{21} a_{32}} \\\\\\quad \\quad {-a_{11} a_{23} a_{32}-a_{12} a_{21} a_{33}-a_{13} a_{22} a_{31}} \\\\ {}\\end{array}\n$$\n矩阵$A  \\in \\mathbb{R}^{n \\times n}$的经典伴随矩阵（通常称为伴随矩阵）表示为$\\operatorname{adj}(A)$，并定义为：\n$$\n\\operatorname{adj}(A) \\in \\mathbb{R}^{n \\times n}, \\quad(\\operatorname{adj}(A))_{i j}=(-1)^{i+j}\\left|A_{\\backslash j, \\backslash i}\\right|\n$$\n（注意索引$A_{\\backslash j, \\backslash i}$中的变化）。可以看出，对于任何非奇异$A  \\in \\mathbb{R}^{n \\times n}$，\n$$\nA^{-1}=\\frac{1}{|A|} \\operatorname{adj}(A)\n$$\n\n虽然这是一个很好的“显式”的逆矩阵公式，但我们应该注意，从数字上讲，有很多更有效的方法来计算逆矩阵。\n\n#### 3.11 二次型和半正定矩阵\n\n给定方矩阵$A  \\in \\mathbb{R}^{n \\times n}$和向量$x \\in \\mathbb{R}^{n}$，标量值$x^T Ax$被称为二次型。 写得清楚些，我们可以看到：\n$$\nx^{T} A x=\\sum_{i=1}^{n} x_{i}(A x)_{i}=\\sum_{i=1}^{n} x_{i}\\left(\\sum_{j=1}^{n} A_{i j} x_{j}\\right)=\\sum_{i=1}^{n} \\sum_{j=1}^{n} A_{i j} x_{i} x_{j}\n$$\n注意：\n$$\nx^{T} A x=\\left(x^{T} A x\\right)^{T}=x^{T} A^{T} x=x^{T}\\left(\\frac{1}{2} A+\\frac{1}{2} A^{T}\\right) x\n$$\n第一个等号的是因为是标量的转置与自身相等，而第二个等号是因为是我们平均两个本身相等的量。 由此，我们可以得出结论，只有$A$的对称部分有助于形成二次型。 出于这个原因，我们经常隐含地假设以二次型出现的矩阵是对称阵。\n我们给出以下定义：\n\n-   对于所有非零向量$x \\in \\mathbb{R}^n$，$x^TAx>0$，对称阵$A \\in \\mathbb{S}^n$为**正定**（**positive definite,PD**）。这通常表示为$A\\succ0$（或$A>0$），并且通常将所有正定矩阵的集合表示为$\\mathbb{S}_{++}^n$。\n\n-   对于所有向量$x^TAx\\geq 0$，对称矩阵$A \\in \\mathbb{S}^n$是**半正定**(**positive semidefinite ,PSD**)。 这写为（或$A \\succeq 0$仅$A≥0$），并且所有半正定矩阵的集合通常表示为$\\mathbb{S}_+^n$。\n\n-   同样，对称矩阵$A \\in \\mathbb{S}^n$是**负定**（**negative definite,ND**），如果对于所有非零$x \\in \\mathbb{R}^n$，则$x^TAx <0$表示为$A\\prec0$（或$A <0$）。\n  \n-   类似地，对称矩阵$A \\in \\mathbb{S}^n$是**半负定**(**negative semidefinite,NSD**），如果对于所有$x \\in \\mathbb{R}^n$，则$x^TAx \\leq 0$表示为$A\\preceq 0$（或$A≤0$）。\n\n-   最后，对称矩阵$A \\in \\mathbb{S}^n$是**不定**的，如果它既不是正半定也不是负半定，即，如果存在$x_1,x_2 \\in \\mathbb{R}^n$，那么$x_1^TAx_1>0$且$x_2^TAx_2<0$。\n\n很明显，如果$A$是正定的，那么$−A$是负定的，反之亦然。同样，如果$A$是半正定的，那么$−A$是是半负定的，反之亦然。如果果$A$是不定的，那么$−A$是也是不定的。\n\n正定矩阵和负定矩阵的一个重要性质是它们总是满秩，因此是可逆的。为了了解这是为什么，假设某个矩阵$A \\in \\mathbb{S}^n$不是满秩。然后，假设$A$的第$j$列可以表示为其他$n-1$列的线性组合：\n$$\na_{j}=\\sum_{i \\neq j} x_{i} a_{i}\n$$\n对于某些$x_1,\\cdots x_{j-1},x_{j + 1} ,\\cdots ,x_n\\in \\mathbb{R}$。设$x_j = -1$，则：\n$$\nAx=\\sum_{i \\neq j} x_{i} a_{i}=0\n$$\n但这意味着对于某些非零向量$x$，$x^T Ax = 0$，因此$A$必须既不是正定也不是负定。如果$A$是正定或负定，则必须是满秩。\n最后，有一种类型的正定矩阵经常出现，因此值得特别提及。 给定矩阵$A  \\in \\mathbb{R}^{m \\times n}$（不一定是对称或偶数平方），矩阵$G = A^T A$（有时称为**Gram矩阵**）总是半正定的。 此外，如果$m\\geq n$（同时为了方便起见，我们假设$A$是满秩），则$G = A^T A$是正定的。\n\n#### 3.12 特征值和特征向量\n\n给定一个方阵$A \\in\\mathbb{R}^{n\\times n}$，我们认为在以下条件下，$\\lambda \\in\\mathbb{C}$是$A$的**特征值**，$x\\in\\mathbb{C}^n$是相应的**特征向量**：\n\n$$\nAx=\\lambda x,x \\ne 0\n$$\n\n直观地说，这个定义意味着将$A$乘以向量$x$会得到一个新的向量，该向量指向与$x$相同的方向，但按系数$\\lambda$缩放。值得注意的是，对于任何特征向量$x\\in\\mathbb{C}^n$和标量$t\\in\\mathbb{C}$，$A(cx)=cAx=c\\lambda x=\\lambda(cx)$，$cx$也是一个特征向量。因此，当我们讨论与$\\lambda$相关的**特征向量**时，我们通常假设特征向量被标准化为长度为1（这仍然会造成一些歧义，因为$x$和$−x$都是特征向量，但我们必须接受这一点）。\n\n我们可以重写上面的等式来说明$(\\lambda,x)$是$A$的特征值和特征向量的组合：\n$$\n(\\lambda I-A)x=0,x \\ne 0\n$$\n但是$(\\lambda I-A)x=0$只有当$(\\lambda I-A)$有一个非空零空间时，同时$(\\lambda I-A)$是奇异的，$x$才具有非零解，即：\n$$\n|(\\lambda I-A)|=0\n$$\n现在，我们可以使用行列式的先前定义将表达式$|(\\lambda I-A)|$扩展为$\\lambda$中的（非常大的）多项式，其中，$\\lambda$的度为$n$。它通常被称为矩阵$A$的特征多项式。\n\n然后我们找到这个特征多项式的$n$（可能是复数）根，并用$\\lambda_1,\\cdots,\\lambda_n$表示。这些都是矩阵$A$的特征值，但我们注意到它们可能不明显。为了找到特征值$\\lambda_i$对应的特征向量，我们只需解线性方程$(\\lambda I-A)x=0$，因为$(\\lambda I-A)$是奇异的，所以保证有一个非零解（但也可能有多个或无穷多个解）。\n\n应该注意的是，这不是实际用于数值计算特征值和特征向量的方法（记住行列式的完全展开式有$n!$项），这是一个数学上的争议。\n\n以下是特征值和特征向量的属性（所有假设在$A \\in\\mathbb{R}^{n\\times n}$具有特征值$\\lambda_1,\\cdots,\\lambda_n$的前提下）：\n\n- $A$的迹等于其特征值之和\n  $$\n  \\operatorname{tr} A=\\sum_{i=1}^{n} \\lambda_{i}\n  $$\n\n- $A$的行列式等于其特征值的乘积\n  $$\n  |A|=\\prod_{i=1}^{n} \\lambda_{i}\n  $$\n\n- $A$的秩等于$A$的非零特征值的个数\n  \n- 假设$A$非奇异，其特征值为$\\lambda$和特征向量为$x$。那么$1/\\lambda$是具有相关特征向量$x$的$A^{-1}$的特征值，即$A^{-1}x=(1/\\lambda)x$。（要证明这一点，取特征向量方程，$Ax=\\lambda x$，两边都左乘$A^{-1}$）\n  \n- 对角阵的特征值$d=diag(d_1，\\cdots,d_n)$实际上就是对角元素$d_1，\\cdots,d_n$\n\n#### 3.13 对称矩阵的特征值和特征向量\n\n通常情况下，一般的方阵的特征值和特征向量的结构可以很细微地表示出来。\n值得庆幸的是，在机器学习的大多数场景下，处理对称实矩阵就足够了，其处理的对称实矩阵的特征值和特征向量具有显着的特性。\n\n在本节中，我们假设$A$是实对称矩阵, 具有以下属性：\n\n1. $A$的所有特征值都是实数。 我们用用$\\lambda_1,\\cdots,\\lambda_n$表示。\n\n2. 存在一组特征向量$u_1，\\cdots u_n$，对于所有$i$，$u_i$是具有特征值$\\lambda_{i}$和$b$的特征向量。$u_1，\\cdots u_n$是单位向量并且彼此正交。\n\n设$U$是包含$u_i$作为列的正交矩阵：\n$$\nU=\\left[\\begin{array}{cccc}{ |} & { |} & {} & { |} \\\\ {u_{1}} & {u_{2}} & {\\cdots} & {u_{n}} \\\\ { |} & { |} & {} & { |}\\end{array}\\right]\n$$\n设$\\Lambda= diag(\\lambda_1,\\cdots,\\lambda_n)$是包含$\\lambda_1,\\cdots,\\lambda_n$作为对角线上的元素的对角矩阵。 使用2.3节的方程（2）中的矩阵 - 矩阵向量乘法的方法，我们可以验证：\n$$\nA U=\\left[\\begin{array}{cccc}{ |} & { |} & {} & { |} \\\\ {A u_{1}} & {A u_{2}} & {\\cdots} & {A u_{n}} \\\\ { |} & { |} & {} & { |}\\end{array}\\right]=\\left[\\begin{array}{ccc}{ |} & { |} & { |} & { |}\\\\ {\\lambda_{1} u_{1}} & {\\lambda_{2} u_{2}} & {\\cdots} & {\\lambda_{n} u_{n}} \\\\ { |} & { |} & {|} & { |}\\end{array}\\right]=U \\operatorname{diag}\\left(\\lambda_{1}, \\ldots, \\lambda_{n}\\right)=U \\Lambda\n$$\n考虑到正交矩阵$U$满足$UU^T=I$，利用上面的方程，我们得到：\n$$\nA=AUU^T=U\\Lambda U^T\n$$\n\n这种$A$的新的表示形式为$U\\Lambda U^T$，通常称为矩阵$A$的对角化。术语对角化是这样来的：通过这种表示，我们通常可以有效地将对称矩阵$A$视为对角矩阵 , 这更容易理解。关于由特征向量$U$定义的基础， 我们将通过几个例子详细说明。\n\n**背景知识**：代表另一个基的向量。\n\n任何正交矩阵$U=\\left[\\begin{array}{cccc}{ |} & { |} & {} & { |} \\\\ {u_{1}} & {u_{2}} & {\\cdots} & {u_{n}} \\\\ { |} & { |} & {} & { |}\\end{array}\\right]$定义了一个新的属于$\\mathbb {R}^{n}$的基（坐标系），意义如下：对于任何向量$x \\in\\mathbb{R}^{n}$都可以表示为$u_1，\\cdots u_n$的线性组合，其系数为$x_1,\\cdots x_n$：\n\n$$\nx=\\hat x_1u_1+\\cdots +\\cdots \\hat x_nu_n=U\\hat x\n$$\n\n在第二个等式中，我们使用矩阵和向量相乘的方法。 实际上，这种$\\hat x$是唯一存在的:\n$$\nx=U \\hat{x} \\Leftrightarrow U^{T} x=\\hat{x}\n$$\n换句话说，向量$\\hat x=U^Tx$可以作为向量$x$的另一种表示，与$U$定义的基有关。\n\n**“对角化”矩阵向量乘法**。 通过上面的设置，我们将看到左乘矩阵$A$可以被视为左乘以对角矩阵关于特征向量的基。 假设$x$是一个向量，$\\hat x$表示$U$的基。设$z=Ax$为矩阵向量积。现在让我们计算关于$U$的基$z$：\n然后，再利用$UU^T=U^T=I$和方程$A=AUU^T=U\\Lambda U^T$，我们得到：\n$$\n\\hat{z}=U^{T} z=U^{T} A x=U^{T} U \\Lambda U^{T} x=\\Lambda \\hat{x}=\\left[\\begin{array}{c}{\\lambda_{1} \\hat{x}_{1}} \\\\ {\\lambda_{2} \\hat{x}_{2}} \\\\ {\\vdots} \\\\ {\\lambda_{n} \\hat{x}_{n}}\\end{array}\\right]\n$$\n我们可以看到，原始空间中的左乘矩阵$A$等于左乘对角矩阵$\\Lambda$相对于新的基，即仅将每个坐标缩放相应的特征值。\n在新的基上，矩阵多次相乘也变得简单多了。例如，假设$q=AAAx$。根据$A$的元素导出$q$的分析形式，使用原始的基可能是一场噩梦，但使用新的基就容易多了：\n$$\n\\hat{q}=U^{T} q=U^{T} AAA x=U^{T} U \\Lambda U^{T} U \\Lambda U^{T} U \\Lambda U^{T} x=\\Lambda^{3} \\hat{x}=\\left[\\begin{array}{c}{\\lambda_{1}^{3} \\hat{x}_{1}} \\\\ {\\lambda_{2}^{3} \\hat{x}_{2}} \\\\ {\\vdots} \\\\ {\\lambda_{n}^{3} \\hat{x}_{n}}\\end{array}\\right]\n$$\n**“对角化”二次型**。作为直接的推论，二次型$x^TAx$也可以在新的基上简化。\n$$\nx^{T} A x=x^{T} U \\Lambda U^{T} x=\\hat{x} \\Lambda \\hat{x}=\\sum_{i=1}^{n} \\lambda_{i} \\hat{x}_{i}^{2}\n$$\n(回想一下，在旧的表示法中，$x^{T} A x=\\sum_{i=1, j=1}^{n} x_{i} x_{j} A_{i j}$涉及一个$n^2$项的和，而不是上面等式中的$n$项。)利用这个观点，我们还可以证明矩阵$A$的正定性完全取决于其特征值的符号：\n\n1. 如果所有的$\\lambda_i>0$，则矩阵$A$正定的，因为对于任意的$\\hat x \\ne 0$,$x^{T} A x=\\sum_{i=1}^{n} \\lambda_{i} \\hat{x}_{i}^{2}>0$\n2. 如果所有的$\\lambda_i\\geq 0$，则矩阵$A$是为正半定，因为对于任意的$\\hat x $,$x^{T} A x=\\sum_{i=1}^{n} \\lambda_{i} \\hat{x}_{i}^{2} \\geq 0$\n3. 同样，如果所有$\\lambda_i<0$或$\\lambda_i\\leq 0$，则矩阵$A$分别为负定或半负定。\n4. 最后，如果$A$同时具有正特征值和负特征值，比如λ$\\lambda_i>0$和$\\lambda_j<0$，那么它是不定的。这是因为如果我们让$\\hat x$满足$\\hat x_i=1$和$\\hat x_k=0$，同时所有的$k\\ne i$，那么$x^{T} A x=\\sum_{i=1}^{n} \\lambda_{i} \\hat{x}_{i}^{2}>0$ ,我们让$\\hat x$满足$\\hat x_i=1$和$\\hat x_k=0$，同时所有的$k\\ne i$，那么$x^{T} A x=\\sum_{i=1}^{n} \\lambda_{i} \\hat{x}_{i}^{2}<0$ \n\n特征值和特征向量经常出现的应用是最大化矩阵的某些函数。特别是对于矩阵$A \\in \\mathbb{S}^{n}$，考虑以下最大化问题：\n$$\n\\max _{x \\in \\mathbb{R}^{n}} \\ x^{T} A x=\\sum_{i=1}^{n} \\lambda_{i} \\hat{x}_{i}^{2} \\quad \\text { subject to }\\|x\\|_{2}^{2}=1\n$$\n也就是说，我们要找到（范数1）的向量，它使二次型最大化。假设特征值的阶数为$\\lambda_1 \\geq \\lambda _2 \\geq \\cdots \\lambda_n$，此优化问题的最优值为$\\lambda_1$，且与$\\lambda_1$对应的任何特征向量$u_1$都是最大值之一。（如果$\\lambda_1 > \\lambda_2$，那么有一个与特征值$\\lambda_1$对应的唯一特征向量，它是上面那个优化问题的唯一最大值。）\n我们可以通过使用对角化技术来证明这一点：注意，通过公式$\\|U x\\|_{2}=\\|x\\|_{2}$推出$\\|x\\|_{2}=\\|\\hat{x}\\|_{2}$，并利用公式：\n\n$x^{T} A x=x^{T} U \\Lambda U^{T} x=\\hat{x} \\Lambda \\hat{x}=\\sum_{i=1}^{n} \\lambda_{i} \\hat{x}_{i}^{2}$，我们可以将上面那个优化问题改写为：\n$$\n\\max _{\\hat{x} \\in \\mathbb{R}^{n}}\\ \\hat{x}^{T} \\Lambda \\hat{x}=\\sum_{i=1}^{n} \\lambda_{i} \\hat{x}_{i}^{2} \\quad \\text { subject to }\\|\\hat{x}\\|_{2}^{2}=1\n$$\n然后，我们得到目标的上界为$\\lambda_1$：\n$$\n\\hat{x}^{T} \\Lambda \\hat{x}=\\sum_{i=1}^{n} \\lambda_{i} \\hat{x}_{i}^{2} \\leq \\sum_{i=1}^{n} \\lambda_{1} \\hat{x}_{i}^{2}=\\lambda_{1}\n$$\n此外，设置$\\hat{x}=\\left[\\begin{array}{c}{1} \\\\ {0} \\\\ {\\vdots} \\\\ {0}\\end{array}\\right]$可让上述等式成立，这与设置$x=u_1$相对应。\n\n### 4.矩阵微积分\n虽然前面章节中的主题通常包含在线性代数的标准课程中，但似乎很少涉及（我们将广泛使用）的一个主题是微积分扩展到向量设置展。尽管我们使用的所有实际微积分都是相对微不足道的，但是符号通常会使事情看起来比实际困难得多。 在本节中，我们将介绍矩阵微积分的一些基本定义，并提供一些示例。\n\n#### 4.1 梯度\n假设$f: \\mathbb{R}^{m \\times n} \\rightarrow \\mathbb{R}$是将维度为$m \\times n$的矩阵$A\\in \\mathbb{R}^{m \\times n}$作为输入并返回实数值的函数。 然后$f$的梯度（相对于$A\\in \\mathbb{R}^{m \\times n}$）是偏导数矩阵，定义如下：\n$$\n\\nabla_{A} f(A) \\in \\mathbb{R}^{m \\times n}=\\left[\\begin{array}{cccc}{\\frac{\\partial f(A)}{\\partial A_{11}}} & {\\frac{\\partial f(A)}{\\partial A_{12}}} & {\\cdots} & {\\frac{\\partial f(A)}{\\partial A_{1n}}} \\\\ {\\frac{\\partial f(A)}{\\partial A_{21}}} & {\\frac{\\partial f(A)}{\\partial A_{22}}} & {\\cdots} & {\\frac{\\partial f(A)}{\\partial A_{2 n}}} \\\\ {\\vdots} & {\\vdots} & {\\ddots} & {\\vdots} \\\\ {\\frac{\\partial f(A)}{\\partial A_{m 1}}} & {\\frac{\\partial f(A)}{\\partial A_{m 2}}} & {\\cdots} & {\\frac{\\partial f(A)}{\\partial A_{m n}}}\\end{array}\\right]\n$$\n即，$m \\times n$矩阵:\n$$\n\\left(\\nabla_{A} f(A)\\right)_{i j}=\\frac{\\partial f(A)}{\\partial A_{i j}}\n$$\n请注意，$\\nabla_{A} f(A) $的维度始终与$A$的维度相同。特殊情况，如果$A$只是向量$A\\in \\mathbb{R}^{n}$，则\n$$\n\\nabla_{x} f(x)=\\left[\\begin{array}{c}{\\frac{\\partial f(x)}{\\partial x_{1}}} \\\\ {\\frac{\\partial f(x)}{\\partial x_{2}}} \\\\ {\\vdots} \\\\ {\\frac{\\partial f(x)}{\\partial x_{n}}}\\end{array}\\right]\n$$\n重要的是要记住，只有当函数是实值时，即如果函数返回标量值，才定义函数的梯度。例如，$A\\in \\mathbb{R}^{m \\times n}$相对于$x$，我们不能取$Ax$的梯度，因为这个量是向量值。\n它直接从偏导数的等价性质得出：\n\n- $\\nabla_{x}(f(x)+g(x))=\\nabla_{x} f(x)+\\nabla_{x} g(x)$\n\n- 对于$t \\in \\mathbb{R}$ ，$\\nabla_{x}(t f(x))=t \\nabla_{x} f(x)$\n\n原则上，梯度是偏导数对多变量函数的自然延伸。然而，在实践中，由于符号的原因，使用梯度有时是很困难的。例如，假设$A\\in \\mathbb{R}^{m \\times n}$是一个固定系数矩阵，假设$b\\in \\mathbb{R}^{m}$是一个固定系数向量。设$f: \\mathbb{R}^{m \\times n} \\rightarrow \\mathbb{R}$为$f(z)=z^Tz$定义的函数，因此$\\nabla_{z}f(z)=2z$。但现在考虑表达式，\n$$\n\\nabla f(Ax)\n$$\n该表达式应该如何解释？ 至少有两种可能性：\n1.在第一个解释中，回想起$\\nabla_{z}f(z)=2z$。 在这里，我们将$\\nabla f(Ax)$解释为评估点$Ax$处的梯度，因此:\n\n$$\n\\nabla f(A x)=2(A x)=2 A x \\in \\mathbb{R}^{m}\n$$\n2.在第二种解释中，我们将数量$f(Ax)$视为输入变量$x$的函数。 更正式地说，设$g(x) =f(Ax)$。 然后在这个解释中:\n$$\n\\nabla f(A x)=\\nabla_{x} g(x) \\in \\mathbb{R}^{n}\n$$\n\n在这里，我们可以看到这两种解释确实不同。 一种解释产生$m$维向量作为结果，而另一种解释产生$n$维向量作为结果！ 我们怎么解决这个问题？\n\n这里，关键是要明确我们要区分的变量。\n在第一种情况下，我们将函数$f$与其参数$z$进行区分，然后替换参数$Ax$。\n在第二种情况下，我们将复合函数$g(x)=f(Ax)$直接与$x$进行微分。\n\n我们将第一种情况表示为$\\nabla zf(Ax)$，第二种情况表示为$\\nabla xf(Ax)$。\n\n保持符号清晰是非常重要的，以后完成课程作业时候你就会发现。\n\n#### 4.2 黑塞矩阵\n\n假设$f: \\mathbb{R}^{n} \\rightarrow \\mathbb{R}$是一个函数，它接受$\\mathbb{R}^{n}$中的向量并返回实数。那么关于$x$的**黑塞矩阵**（也有翻译作海森矩阵），写做：$\\nabla_x ^2 f(A x)$，或者简单地说，$H$是$n \\times n$矩阵的偏导数：\n$$\n\\nabla_{x}^{2} f(x) \\in \\mathbb{R}^{n \\times n}=\\left[\\begin{array}{cccc}{\\frac{\\partial^{2} f(x)}{\\partial x_{1}^{2}}} & {\\frac{\\partial^{2} f(x)}{\\partial x_{1} \\partial x_{2}}} & {\\cdots} & {\\frac{\\partial^{2} f(x)}{\\partial x_{1} \\partial x_{n}}} \\\\ {\\frac{\\partial^{2} f(x)}{\\partial x_{2} \\partial x_{1}}} & {\\frac{\\partial^{2} f(x)}{\\partial x_{2}^{2}}} & {\\cdots} & {\\frac{\\partial^{2} f(x)}{\\partial x_{2} \\partial x_{n}}} \\\\ {\\vdots} & {\\vdots} & {\\ddots} & {\\vdots} \\\\ {\\frac{\\partial^{2} f(x)}{\\partial x_{n} \\partial x_{1}}} & {\\frac{\\partial^{2} f(x)}{\\partial x_{n} \\partial x_{2}}} & {\\cdots} & {\\frac{\\partial^{2} f(x)}{\\partial x_{n}^{2}}}\\end{array}\\right]\n$$\n换句话说，$\\nabla_{x}^{2} f(x) \\in \\mathbb{R}^{n \\times n}$，其：\n\n$$\n\\left(\\nabla_{x}^{2} f(x)\\right)_{i j}=\\frac{\\partial^{2} f(x)}{\\partial x_{i} \\partial x_{j}}\n$$\n注意：黑塞矩阵通常是对称阵：\n\n$$\n\\frac{\\partial^{2} f(x)}{\\partial x_{i} \\partial x_{j}}=\\frac{\\partial^{2} f(x)}{\\partial x_{j} \\partial x_{i}}\n$$\n与梯度相似，只有当$f(x)$为实值时才定义黑塞矩阵。\n\n很自然地认为梯度与向量函数的一阶导数的相似，而黑塞矩阵与二阶导数的相似（我们使用的符号也暗示了这种关系）。 这种直觉通常是正确的，但需要记住以下几个注意事项。\n首先，对于一个变量$f: \\mathbb{R} \\rightarrow \\mathbb{R}$的实值函数，它的基本定义：二阶导数是一阶导数的导数，即：\n$$\n\\frac{\\partial^{2} f(x)}{\\partial x^{2}}=\\frac{\\partial}{\\partial x} \\frac{\\partial}{\\partial x} f(x)\n$$\n然而，对于向量的函数，函数的梯度是一个向量，我们不能取向量的梯度，即:\n$$\n\\nabla_{x} \\nabla_{x} f(x)=\\nabla_{x}\\left[\\begin{array}{c}{\\frac{\\partial f(x)}{\\partial x_{1}}} \\\\ {\\frac{\\partial f(x)}{\\partial x_{2}}} \\\\ {\\vdots} \\\\ {\\frac{\\partial f(x)}{\\partial x_{n}}}\\end{array}\\right]\n$$\n\n上面这个表达式没有意义。 因此，黑塞矩阵不是梯度的梯度。 然而，下面这种情况却这几乎是正确的：如果我们看一下梯度$\\left(\\nabla_{x} f(x)\\right)_{i}=\\partial f(x) / \\partial x_{i}$的第$i$个元素，并取关于于$x$的梯度我们得到：\n$$\n\\nabla_{x} \\frac{\\partial f(x)}{\\partial x_{i}}=\\left[\\begin{array}{c}{\\frac{\\partial^{2} f(x)}{\\partial x_{i} \\partial x_{1}}} \\\\ {\\frac{\\partial^{2} f(x)}{\\partial x_{2} \\partial x_{2}}} \\\\ {\\vdots} \\\\ {\\frac{\\partial f(x)}{\\partial x_{i} \\partial x_{n}}}\\end{array}\\right]\n$$\n\n这是黑塞矩阵第$i$行（列）,所以：\n$$\n\\nabla_{x}^{2} f(x)=\\left[\\nabla_{x}\\left(\\nabla_{x} f(x)\\right)_{1} \\quad \\nabla_{x}\\left(\\nabla_{x} f(x)\\right)_{2} \\quad \\cdots \\quad \\nabla_{x}\\left(\\nabla_{x} f(x)\\right)_{n}\\right]\n$$\n简单地说：我们可以说由于：$\\nabla_{x}^{2} f(x)=\\nabla_{x}\\left(\\nabla_{x} f(x)\\right)^{T}$，只要我们理解，这实际上是取$\\nabla_{x} f(x)$的每个元素的梯度，而不是整个向量的梯度。\n\n最后，请注意，虽然我们可以对矩阵$A\\in \\mathbb{R}^{n}$取梯度，但对于这门课，我们只考虑对向量$x \\in \\mathbb{R}^{n}$取黑塞矩阵。\n这会方便很多（事实上，我们所做的任何计算都不要求我们找到关于矩阵的黑森方程），因为关于矩阵的黑塞方程就必须对矩阵所有元素求偏导数$\\partial^{2} f(A) /\\left(\\partial A_{i j} \\partial A_{k \\ell}\\right)$，将其表示为矩阵相当麻烦。\n\n#### 4.3 二次函数和线性函数的梯度和黑塞矩阵\n\n现在让我们尝试确定几个简单函数的梯度和黑塞矩阵。 应该注意的是，这里给出的所有梯度都是**CS229**讲义中给出的梯度的特殊情况。\n\n对于$x \\in \\mathbb{R}^{n}$, 设$f(x)=b^Tx$  的某些已知向量$b \\in \\mathbb{R}^{n}$ ，则：\n\n$$\nf(x)=\\sum_{i=1}^{n} b_{i} x_{i}\n$$\n所以：\n$$\n\\frac{\\partial f(x)}{\\partial x_{k}}=\\frac{\\partial}{\\partial x_{k}} \\sum_{i=1}^{n} b_{i} x_{i}=b_{k}\n$$\n由此我们可以很容易地看出$\\nabla_{x} b^{T} x=b$。 这应该与单变量微积分中的类似情况进行比较，其中$\\partial /(\\partial x) a x=a$。\n现在考虑$A\\in \\mathbb{S}^{n}$的二次函数$f(x)=x^TAx$。 记住这一点：\n$$\nf(x)=\\sum_{i=1}^{n} \\sum_{j=1}^{n} A_{i j} x_{i} x_{j}\n$$\n为了取偏导数，我们将分别考虑包括$x_k$和$x_2^k$因子的项：\n\n$$\n\\begin{aligned} \\frac{\\partial f(x)}{\\partial x_{k}} &=\\frac{\\partial}{\\partial x_{k}} \\sum_{i=1}^{n} \\sum_{j=1}^{n} A_{i j} x_{i} x_{j} \\\\ &=\\frac{\\partial}{\\partial x_{k}}\\left[\\sum_{i \\neq k} \\sum_{j \\neq k} A_{i j} x_{i} x_{j}+\\sum_{i \\neq k} A_{i k} x_{i} x_{k}+\\sum_{j \\neq k} A_{k j} x_{k} x_{j}+A_{k k} x_{k}^{2}\\right] \\\\ &=\\sum_{i \\neq k} A_{i k} x_{i}+\\sum_{j \\neq k} A_{k j} x_{j}+2 A_{k k} x_{k} \\\\ &=\\sum_{i=1}^{n} A_{i k} x_{i}+\\sum_{j=1}^{n} A_{k j} x_{j}=2 \\sum_{i=1}^{n} A_{k i} x_{i} \\end{aligned}\n$$\n最后一个等式，是因为$A$是对称的（我们可以安全地假设，因为它以二次形式出现）。 注意，$\\nabla_{x} f(x)$的第$k$个元素是$A$和$x$的第$k$行的内积。 因此，$\\nabla_{x} x^{T} A x=2 A x$。 同样，这应该提醒你单变量微积分中的类似事实，即$\\partial /(\\partial x) a x^{2}=2 a x$。\n\n最后，让我们来看看二次函数$f(x)=x^TAx$黑塞矩阵（显然，线性函数$b^Tx$的黑塞矩阵为零）。在这种情况下:\n$$\n\\frac{\\partial^{2} f(x)}{\\partial x_{k} \\partial x_{\\ell}}=\\frac{\\partial}{\\partial x_{k}}\\left[\\frac{\\partial f(x)}{\\partial x_{\\ell}}\\right]=\\frac{\\partial}{\\partial x_{k}}\\left[2 \\sum_{i=1}^{n} A_{\\ell i} x_{i}\\right]=2 A_{\\ell k}=2 A_{k \\ell}\n$$\n因此，应该很清楚$\\nabla_{x}^2 x^{T} A x=2 A$，这应该是完全可以理解的（同样类似于$\\partial^2 /(\\partial x^2) a x^{2}=2a$的单变量事实）。\n\n简要概括起来：\n\n-   $\\nabla_{x} b^{T} x=b$ \n\n-   $\\nabla_{x} x^{T} A x=2 A x$ (如果$A$是对称阵)\n\n-   $\\nabla_{x}^2 x^{T} A x=2 A $  (如果$A$是对称阵)\n\n#### 4.4 最小二乘法\n\n让我们应用上一节中得到的方程来推导最小二乘方程。假设我们得到矩阵$A\\in \\mathbb{R}^{m \\times n}$（为了简单起见，我们假设$A$是满秩）和向量$b\\in \\mathbb{R}^{m}$，从而使$b \\notin \\mathcal{R}(A)$。在这种情况下，我们将无法找到向量$x\\in \\mathbb{R}^{n}$，由于$Ax = b$，因此我们想要找到一个向量$x$，使得$Ax$尽可能接近 $b$，用欧几里德范数的平方$\\|A x-b\\|_{2}^{2} $来衡量。\n\n使用公式$\\|x\\|^{2}=x^Tx$，我们可以得到：\n\n$$\n\\begin{aligned}\\|A x-b\\|_{2}^{2} &=(A x-b)^{T}(A x-b) \\\\ &=x^{T} A^{T} A x-2 b^{T} A x+b^{T} b \\end{aligned}\n$$\n根据$x$的梯度，并利用上一节中推导的性质：\n$$\n\\begin{aligned} \\nabla_{x}\\left(x^{T} A^{T} A x-2 b^{T} A x+b^{T} b\\right) &=\\nabla_{x} x^{T} A^{T} A x-\\nabla_{x} 2 b^{T} A x+\\nabla_{x} b^{T} b \\\\ &=2 A^{T} A x-2 A^{T} b \\end{aligned}\n$$\n将最后一个表达式设置为零，然后解出$x$，得到了正规方程：\n$$\nx = (A^TA)^{-1}A^Tb\n$$\n这和我们在课堂上得到的相同。\n\n#### 4.5 行列式的梯度\n\n现在让我们考虑一种情况，我们找到一个函数相对于矩阵的梯度，也就是说，对于$A\\in \\mathbb{R}^{n \\times n}$，我们要找到$\\nabla_{A}|A|$。回想一下我们对行列式的讨论：\n$$\n|A|=\\sum_{i=1}^{n}(-1)^{i+j} A_{i j}\\left|A_{\\backslash i, \\backslash j}\\right| \\quad(\\text { for any } j \\in 1, \\ldots, n)\n$$\n所以：\n$$\n\\frac{\\partial}{\\partial A_{k \\ell}}|A|=\\frac{\\partial}{\\partial A_{k \\ell}} \\sum_{i=1}^{n}(-1)^{i+j} A_{i j}\\left|A_{\\backslash i, \\backslash j}\\right|=(-1)^{k+\\ell}\\left|A_{\\backslash k,\\backslash \\ell}\\right|=(\\operatorname{adj}(A))_{\\ell k}\n$$\n从这里可以知道，它直接从伴随矩阵的性质得出：\n$$\n\\nabla_{A}|A|=(\\operatorname{adj}(A))^{T}=|A| A^{-T}\n$$\n现在我们来考虑函数$f : \\mathbb{S}_{++}^{n} \\rightarrow \\mathbb{R}$，$f(A)=\\log |A|$。注意，我们必须将$f$的域限制为正定矩阵，因为这确保了$|A|>0$，因此$|A|$的对数是实数。在这种情况下，我们可以使用链式法则（没什么奇怪的，只是单变量演算中的普通链式法则）来看看：\n$$\n\\frac{\\partial \\log |A|}{\\partial A_{i j}}=\\frac{\\partial \\log |A|}{\\partial|A|} \\frac{\\partial|A|}{\\partial A_{i j}}=\\frac{1}{|A|} \\frac{\\partial|A|}{\\partial A_{i j}}\n$$\n从这一点可以明显看出：\n\n$$\n\\nabla_{A} \\log |A|=\\frac{1}{|A|} \\nabla_{A}|A|=A^{-1}\n$$\n我们可以在最后一个表达式中删除转置，因为$A$是对称的。注意与单值情况的相似性，其中$\\partial /(\\partial x) \\log x=1 / x$。\n\n#### 4.6 特征值优化\n\n最后，我们使用矩阵演算以直接导致特征值/特征向量分析的方式求解优化问题。 考虑以下等式约束优化问题：\n\n$$\n\\max _{x \\in \\mathbb{R}^{n}} x^{T} A x \\quad \\text { subject to }\\|x\\|_{2}^{2}=1\n$$\n对于对称矩阵$A\\in \\mathbb{S}^{n}$。求解等式约束优化问题的标准方法是采用**拉格朗日**形式，一种包含等式约束的目标函数，在这种情况下，拉格朗日函数可由以下公式给出：\n\n$$\n\\mathcal{L}(x, \\lambda)=x^{T} A x-\\lambda x^{T} x\n$$\n其中，$\\lambda $被称为与等式约束关联的拉格朗日乘子。可以确定，要使$x^*$成为问题的最佳点，拉格朗日的梯度必须在$x^*$处为零（这不是唯一的条件，但它是必需的）。也就是说，\n$$\n\\nabla_{x} \\mathcal{L}(x, \\lambda)=\\nabla_{x}\\left(x^{T} A x-\\lambda x^{T} x\\right)=2 A^{T} x-2 \\lambda x=0\n$$\n请注意，这只是线性方程$Ax =\\lambda x$。 这表明假设$x^T x = 1$，可能最大化（或最小化）$x^T Ax$的唯一点是$A$的特征向量。\n\n**线性代数和概率论都已经翻译完毕，请关注[github](https://github.com/fengdu78/Data-Science-Notes/tree/master/0.math)的更新，若有修改将在github上更新**\n\n欢迎大家提交PR，对语言进行润色。\n\n翻译：[黄海广](https://github.com/fengdu78)\n\n"
  },
  {
    "path": "0.math/1.CS229/markdown/2.CS229-Prob.md",
    "content": "> 本文是斯坦福大学CS229机器学习课程的基础材料，[原始文件下载](http://cs229.stanford.edu/summer2019/cs229-prob.pdf)\n\n> 原文作者：Arian Maleki ， Tom Do\n>\n> 翻译：[石振宇](https://github.com/szy2120109)\n>\n> 审核和修改制作：[黄海广](https://github.com/fengdu78)\n>\n> 备注：请关注[github](https://github.com/fengdu78/Data-Science-Notes/tree/master/0.math)的更新。\n\n\n\n# CS229 机器学习课程复习材料-概率论\n\n[TOC]\n## 概率论复习和参考\n\n概率论是对不确定性的研究。通过这门课，我们将依靠概率论中的概念来推导机器学习算法。这篇笔记试图涵盖适用于**CS229**的概率论基础。概率论的数学理论非常复杂，并且涉及到“分析”的一个分支：测度论。在这篇笔记中，我们提供了概率的一些基本处理方法，但是不会涉及到这些更复杂的细节。\n\n###  1. 概率的基本要素\n\n为了定义集合上的概率，我们需要一些基本元素，\n-  样本空间$\\Omega $：随机实验的所有结果的集合。在这里，每个结果 $w \\in \\Omega $ 可以被认为是实验结束时现实世界状态的完整描述。\n\n- 事件集（事件空间）$\\mathcal{F}$：元素 $A \\in \\mathcal{F}$ 的集合（称为事件）是 $\\Omega $ 的子集（即每个 $A \\subseteq \\Omega$ 是一个实验可能结果的集合）。\n\n  备注：$\\mathcal{F}$需要满足以下三个条件：\n\n  (1) $\\emptyset  \\in \\mathcal{F}$\n\n  (2) $A \\in \\mathcal{F} \\Longrightarrow \\Omega \\backslash A \\in \\mathcal{F}$\n\n  (3) $A_1,A_2,\\cdots A_{i} \\in \\mathcal{F}\\Longrightarrow\\cup_{i} A_{i} \\in \\mathcal{F}$\n\n-  概率度量$P$：函数$P$是一个$  \\mathcal{F} \\rightarrow \\mathbb{R}$的映射，满足以下性质：\n  - 对于每个 $A \\in \\mathcal{F}$，$P(A) \\geq 0$, \n  \n  - $P(\\Omega) = 1$\n  \n  - 如果$A_1 ,A_2 ,\\cdots$ 是互不相交的事件 (即 当$ i \\neq j$时，$A_{i} \\cap A_{j}=\\emptyset$ ), 那么：\n$$\nP\\left(\\cup_{i} A_{i}\\right)=\\sum_{i} P\\left(A_{i}\\right)\n$$\n\n以上三条性质被称为**概率公理**。\n\n**举例**：\n\n考虑投掷六面骰子的事件。样本空间为$\\Omega= \\{1，2，3，4，5，6\\}$。最简单的事件空间是平凡事件空间$\\mathcal{F} = \\{\\emptyset,\\Omega\\}$.另一个事件空间是$\\Omega$的所有子集的集合。对于第一个事件空间，满足上述要求的唯一概率度量由$P(\\emptyset) = 0$，$p(\\Omega)= 1$给出。对于第二个事件空间，一个有效的概率度量是将事件空间中每个事件的概率分配为$i/6$，这里$i$ 是这个事件集合中元素的数量；例如$P(\\{1,2,3,4\\}) =4/6$，$P(\\{1,2,3\\}) =3/6$。\n\n**性质：**\n\n- 如果$A \\subseteq B$，则：$ P(A) \\leq P(B)$\n- $P(A \\cap B) \\leq min(P(A),P(B) )$\n- (布尔不等式)：$P(A \\cup B) \\leq P(A)+P(B)$\n- $P(\\Omega |A ) =1-P(A)$\n- (全概率定律)：如果$A_1，\\cdots，A_k$是一些互不相交的事件并且它们的并集是$\\Omega$，那么它们的概率之和是1\n\n#### 1.1 条件概率和独立性\n\n假设$B$是一个概率非0的事件，我们定义在给定$B$的条件下$A$ 的条件概率为：\n$$\nP(A | B) \\triangleq \\frac{P(A \\cap B)}{P(B)}\n$$\n换句话说，$P(A|B$)是度量已经观测到$B$事件发生的情况下$A$事件发生的概率，两个事件被称为独立事件当且仅当$P(A \\cap B) = P(A)P(B)$（或等价地，$P(A|B) = P(A)$)。因此，独立性相当于是说观察到事件$B$对于事件$A$的概率没有任何影响。\n\n### 2. 随机变量\n考虑一个实验，我们翻转10枚硬币，我们想知道正面硬币的数量。这里，样本空间$\\Omega$的元素是长度为10的序列。例如，我们可能有$w_0 = \\{H，H，T，H，T，H，H，T，T，T\\}\\in\\Omega$。然而，在实践中，我们通常不关心获得任何特定正反序列的概率。相反，我们通常关心结果的实值函数，比如我们10次投掷中出现的正面数，或者最长的背面长度。在某些技术条件下，这些函数被称为**随机变量**。\n\n更正式地说，随机变量$X$是一个的$\\Omega \\longrightarrow \\mathbb{R}$函数。通常，我们将使用大写字母$X(\\omega)$或更简单的$X$(其中隐含对随机结果$\\omega$的依赖)来表示随机变量。我们将使用小写字母$x$来表示随机变量的值。\n\n**举例：**\n在我们上面的实验中，假设$X(\\omega)$是在投掷序列$\\omega$中出现的正面的数量。假设投掷的硬币只有10枚，那么$X(\\omega)$只能取有限数量的值，因此它被称为**离散随机变量**。这里，与随机变量$X$相关联的集合取某个特定值$k$的概率为：\n$$\nP(X=k) :=P(\\{\\omega : X(\\omega) =k\\})\n$$\n**举例：**\n假设$X(\\omega)$是一个随机变量，表示放射性粒子衰变所需的时间。在这种情况下，$X(\\omega)$具有无限多的可能值，因此它被称为**连续随机变量**。我们将$X$在两个实常数$a$和$b$之间取值的概率(其中$a < b$)表示为：\n$$\nP(a \\leq X \\leq b) :=P(\\{\\omega : a \\leq X(\\omega) \\leq b\\})\n$$\n\n#### 2.1 累积分布函数\n为了指定处理随机变量时使用的概率度量，通常可以方便地指定替代函数(**CDF**、**PDF**和**PMF**)，在本节和接下来的两节中，我们将依次描述这些类型的函数。\n\n**累积分布函数(CDF)**是函数$F_{X} : \\mathbb{R} \\rightarrow[0,1]$，它将概率度量指定为：\n$$\nF_{X}(x) \\triangleq P(X \\leq x)\n$$\n通过使用这个函数，我们可以计算任意事件发生的概率。图1显示了一个样本**CDF**函数。\n\n![](images/c89fd93c5d6dccce89762d57fcd66dac.png)\n\n<center>图1：一个累计分布函数(CDF)</center>\n**性质：**\n\n- $0 \\leq F_{X}(x)\\leq 1$\n- $\\lim _{x \\rightarrow-\\infty} F_{X}(x)=0$\n- $\\lim _{x \\rightarrow\\infty} F_{X}(x)=1$\n- $x \\leq y \\Longrightarrow  F_{X}(x)\\leq F_{X}(y)$\n\n#### 2.2 概率质量函数\n当随机变量$X$取有限种可能值(即，$X$是离散随机变量)时，表示与随机变量相关联的概率度量的更简单的方法是直接指定随机变量可以假设的每个值的概率。特别地，概率质量函数(**PMF**)是函数 $p_{X} : \\Omega \\rightarrow \\mathbb{R}$，这样：\n$$\np_{X}(x) \\triangleq P(X=x)\n$$\n\n在离散随机变量的情况下，我们使用符号$Val(X)$表示随机变量$X$可能假设的一组可能值。例如，如果$X(\\omega)$是一个随机变量，表示十次投掷硬币中的正面数，那么$Val(X) =\\{0，1，2，...，10\\}$。\n\n**性质：**\n\n- $0 \\leq p_{X}(x)\\leq 1$\n- $\\sum_{x \\in V \\text { al }(X)} p_{X}(x)=1$\n- $\\sum_{x \\in A} p_{X}(x)=P(X \\in A)$\n\n#### 2.3 概率密度函数\n对于一些连续随机变量，累积分布函数$F_X (x)$处可微。在这些情况下，我们将**概率密度函数(PDF)**定义为累积分布函数的导数，即：\n$$\nf_{X}(x) \\triangleq \\frac{d F_{X}(x)}{d x}\n$$\n\n请注意，连续随机变量的概率密度函数可能并不总是存在的(即，如果它不是处处可微)。 \n\n根据微分的性质，对于很小的$\\Delta x$，\n$$\nP(x \\leq X \\leq x+\\Delta x) \\approx f_{X}(x) \\Delta x\n$$\n**CDF**和**PDF(**当它们存在时！)都可用于计算不同事件的概率。但是应该强调的是，任意给定点的**概率密度函数(PDF)**的值不是该事件的概率，即$f _X (x) \\not = P(X = x)$。例如，$f _X (x)$可以取大于1的值(但是$f _X (x)$在$\\mathbb{R}$的任何子集上的积分最多为1)。\n\n**性质：**\n\n- $f_X(x)\\geq 0$\n- $\\int_{-\\infty}^{\\infty} f_{X}(x)=1$\n- $\\int_{x \\in A} f_{X}(x) d x=P(X \\in A)$\n\n#### 2.4 期望\n假设$X$是一个离散随机变量，其**PMF**为 $p_X (x)$，$g : \\mathbb{R} \\longrightarrow \\mathbb{R}$是一个任意函数。在这种情况下，$g(X)$可以被视为随机变量，我们将$g(X)$的期望值定义为：\n$$\nE[g(X)] \\triangleq \\sum_{x \\in V a l(X)} g(x) p_{X}(x)\n$$\n如果$X$是一个连续的随机变量，其**PDF** 为$f _X (x)$，那么$g(X)$的期望值被定义为：\n$$\nE[g(X)] \\triangleq \\int_{-\\infty}^{\\infty} g(x) f_{X}(x) d x\n$$\n\n直觉上，$g(X)$的期望值可以被认为是$g(x)$对于不同的$x$值可以取的值的“加权平均值”，其中权重由$p_X(x)$或$f_X(x)$给出。作为上述情况的特例，请注意，随机变量本身的期望值，是通过令$g(x) = x$得到的，这也被称为随机变量的平均值。\n\n**性质：**\n\n- 对于任意常数 $a \\in \\mathbb{R}$，$E[a]=a$\n- 对于任意常数 $a \\in \\mathbb{R}$，$E[af(X)]=aE[f(X)]$\n- (线性期望)：$E[f(X)+g(X)]=E[f(X)]+E[g(X)]$\n- 对于一个离散随机变量$X$，$E[1\\{X=k\\}]=P(X=k)$\n\n#### 2.5 方差\n随机变量$X$的**方差**是随机变量$X$的分布围绕其平均值集中程度的度量。形式上，随机变量$X$的方差定义为：\n$$\n\\operatorname{Var}[X] \\triangleq E\\left[(X-E(X))^{2}\\right]\n$$\n使用上一节中的性质，我们可以导出方差的替代表达式:\n$$\n\\begin{aligned} E\\left[(X-E[X])^{2}\\right] &=E\\left[X^{2}-2 E[X] X+E[X]^{2}\\right] \\\\ &=E\\left[X^{2}\\right]-2 E[X] E[X]+E[X]^{2} \\\\ &=E\\left[X^{2}\\right]-E[X]^{2} \\end{aligned}\n$$\n\n其中第二个等式来自期望的线性，以及$E[X]$相对于外层期望实际上是常数的事实。\n\n**性质：**\n\n- 对于任意常数 $a \\in \\mathbb{R}$，$Val[a]=0$\n- 对于任意常数 $a \\in \\mathbb{R}$，$Var[af(X)]=a^2Var[f(X)]$\n\n**举例：**\n\n计算均匀随机变量$X$的平均值和方差，任意$x \\in [0，1]$，其**PDF**为 $p_X(x)= 1$，其他地方为0。\n$$\nE[X]=\\int_{-\\infty}^{\\infty} x f_{X}(x) d x=\\int_{0}^{1} x d x=\\frac{1}{2}\n$$\n$$\nE\\left[X^{2}\\right]=\\int_{-\\infty}^{\\infty} x^{2} f_{X}(x) d x=\\int_{0}^{1} x^{2} d x=\\frac{1}{3}\n$$\n$$\nVar[X]=E[X^2]-E[X]^2=\\frac{1}{3}-\\frac{1}{4}=\\frac{1}{12}\n$$\n**举例：**\n\n假设对于一些子集$A \\subseteq \\Omega$，有$g(x) = 1\\{x \\in A\\}$，计算$E[g(X)]$?\n\n**离散情况：**\n$$\nE[g(X)]=\\sum_{x \\in V a l(X)} 1\\{x \\in A\\} P_{X}(x) d x=\\sum_{x \\in A} P_{X}(x) d x=P(x \\in A)\n$$\n\n**连续情况：**\n$$\nE[g(X)]=\\int_{-\\infty}^{\\infty} 1\\{x \\in A\\} f_{X}(x) d x=\\int_{x \\in A} f_{X}(x) d x=P(x \\in A)\n$$\n\n#### 2.6 一些常见的随机变量\n**离散随机变量**\n\n- 伯努利分布：硬币掷出正面的概率为$p$（其中：$0 \\leq p \\leq 1$），如果正面发生，则为1，否则为0。\n  $$\n  p(x)=\\left\\{\\begin{array}{ll}{p} & {\\text { if } p=1} \\\\ {1-p} & {\\text { if } p=0}\\end{array}\\right.\n  $$\n\n- 二项式分布：掷出正面概率为$p$（其中：$0 \\leq p \\leq 1$）的硬币$n$次独立投掷中正面的数量。\n$$\np(x)=\\left(\\begin{array}{l}{n} \\\\ {x}\\end{array}\\right) p^{x}(1-p)^{n-x}\n$$\n\n- 几何分布：掷出正面概率为$p$（其中：$p >0$）的硬币第一次掷出正面所需要的次数。\n\n  \n\n- 泊松分布：用于模拟罕见事件频率的非负整数的概率分布（其中：$\\lambda >0$）。\n$$\np(x)=e^{-\\lambda} \\frac{\\lambda^{x}}{x !}\n$$\n\n**连续随机变量**\n\n- 均匀分布：在$a$和$b$之间每个点概率密度相等的分布（其中：$a<b$）。\n$$\nf(x)=\\left\\{\\begin{array}{ll}{\\frac{1}{b-a}} & {\\text { if } a \\leq x \\leq b} \\\\ {0} & {\\text { otherwise }}\\end{array}\\right.\n$$\n\n- 指数分布：在非负实数上有衰减的概率密度（其中：$\\lambda >0$）。\n$$\nf(x)=\\left\\{\\begin{array}{ll}{\\lambda e^{-\\lambda x}} & {\\text { if } x \\geq 0} \\\\ {0} & {\\text { otherwise }}\\end{array}\\right.\n$$\n\n- 正态分布：又被称为高斯分布。\n$$\nf(x)=\\frac{1}{\\sqrt{2 \\pi} \\sigma} e^{-\\frac{1}{2 \\sigma^{2}}(x-\\mu)^{2}}\n$$\n\n\n一些随机变量的概率密度函数和累积分布函数的形状如图2所示。\n\n![](images/b958c16cfdce9e6bd2b810b10d71416e.png)\n\n<center>图2：一些随机变量的概率密度函数(PDF)和累积分布函数(CDF)</center>\n下表总结了这些分布的一些特性：\n\n|                分布                |          概率密度函数(PDF)或者概率质量函数(**PMF**)          |        均值         |         方差          |\n| :--------------------------------: | :----------------------------------------------------------: | :-----------------: | :-------------------: |\n|     $Bernoulli(p)$(伯努利分布)     | $\\left\\{\\begin{array}{ll}{p} & {\\text { if } x=1} \\\\ {1-p} & {\\text { if } x=0}\\end{array}\\right.$ |         $p$         |       $p(1-p)$        |\n|    $Binomial(n,p)$(二项式分布)     | $\\left(\\begin{array}{l}{n} \\\\ {k}\\end{array}\\right) p^{k}(1-p)^{n-k}$  其中：$0 \\leq k \\leq n$ |        $np$         |         $npq$         |\n|      $Geometric(p)$(几何分布)      |             $p(1-p)^{k-1}$  其中：$k=1,2,\\cdots$             |    $\\frac{1}{p}$    |  $\\frac {1-p}{p^2}$   |\n|    $Poisson(\\lambda)$(泊松分布)    |    $e^{-\\lambda} \\lambda^{x} / x !$  其中：$k=1,2,\\cdots$    |      $\\lambda$      |       $\\lambda$       |\n|      $Uniform(a,b)$(均匀分布)      |              $\\frac{1}{b-a}$ 存在$x \\in (a,b)$               |   $\\frac{a+b}{2}$   | $\\frac{(b-a)^2}{12}$  |\n| $Gaussian(\\mu,\\sigma^2)$(高斯分布) | $\\frac{1}{\\sqrt{2 \\pi} \\sigma} e^{-\\frac{1}{2 \\sigma^{2}}(x-\\mu)^{2}}$ |        $\\mu$        |      $\\sigma^2$       |\n|  $Exponential(\\lambda)$(指数分布)  |        $\\lambda e^{-\\lambda x}$   $x\\geq0,\\lambda>0$         | $\\frac{1}{\\lambda}$ | $\\frac{1}{\\lambda^2}$ |\n\n### 3. 两个随机变量\n到目前为止，我们已经考虑了单个随机变量。然而，在许多情况下，在随机实验中，我们可能有不止一个感兴趣的量。例如，在一个我们掷硬币十次的实验中，我们可能既关心$X(\\omega) =$出现的正面数量，也关心$Y (\\omega) =$连续最长出现正面的长度。在本节中，我们考虑两个随机变量的设置。\n\n#### 3.1 联合分布和边缘分布\n假设我们有两个随机变量，一个方法是分别考虑它们。如果我们这样做，我们只需要$F_X (x)$和$F_Y (y)$。但是如果我们想知道在随机实验的结果中，$X$和$Y$同时假设的值，我们需要一个更复杂的结构，称为$X$和$Y$的**联合累积分布函数**，定义如下:\n$$\nF_{XY}(x,y)=P(X \\leq x,Y \\leq y)\n$$\n\n\n可以证明，通过了解联合累积分布函数，可以计算出任何涉及到$X$和$Y$的事件的概率。\n\n联合**CDF**: $F_{XY }(x,y)$和每个变量的联合分布函数$F_X(x)$和$F_Y (y)$分别由下式关联:\n$$\nF_{X}(x)=\\lim _{y \\rightarrow \\infty} F_{X Y}(x, y) d y\n$$\n\n$$\nF_{Y}(y)=\\lim _{y \\rightarrow \\infty} F_{X Y}(x, y) dx\n$$\n这里我们称$F_X(x)$和$F_Y (y)$为 $F_{XY }(x,y)$的**边缘累积概率分布函数**。\n\n**性质：**\n\n- $0 \\leq F_{XY }(x,y) \\leq 1$\n- $\\lim _{x, y \\rightarrow \\infty} F_{X Y}(x, y)=1$\n- $\\lim _{x, y \\rightarrow -\\infty} F_{X Y}(x, y)=0$\n- $F_{X}(x)=\\lim _{y \\rightarrow \\infty} F_{X Y}(x, y)$\n\n#### 3.2 联合概率和边缘概率质量函数\n如果$X$和$Y$是离散随机变量，那么**联合概率质量函数**  $p_{X Y} : \\mathbb{R} \\times \\mathbb{R} \\rightarrow [0,1]$由下式定义：\n\n$$\np_{X Y}(x,y)=P(X=x,Y=y)\n$$\n\n这里, 对于任意$x$，$y$，$0 \\leq P_{XY} (x,y) \\leq 1$, 并且 $\\sum_{x \\in V a l(X)} \\sum_{y \\in V a l(Y)} P_{X Y}(x, y)=1$\n\n两个变量上的**联合PMF**分别与每个变量的概率质量函数有什么关系？事实上：\n$$\np_{X}(x)=\\sum_{y} p_{X Y}(x, y)\n$$\n\n对于$p_Y (y)$类似。在这种情况下，我们称$p_X(x)$为$X$的边际概率质量函数。在统计学中，将一个变量相加形成另一个变量的边缘分布的过程通常称为“边缘化”。\n\n#### 3.3 联合概率和边缘概率密度函数\n假设$X$和$Y$是两个连续的随机变量，具有联合分布函数$F_{XY}$。在$F_{XY}(x,y)$在$x$和$y$中处处可微的情况下，我们可以定义**联合概率密度函数**：\n$$\nf_{X Y}(x, y)=\\frac{\\partial^{2} F_{X Y}(x, y)}{\\partial x \\partial y}\n$$\n如同在一维情况下，$f_{XY}(x,y)\\not= P(X = x,Y = y)$，而是：\n$$\n\\iint_{x \\in A} f_{X Y}(x, y) d x d y=P((X, Y) \\in A)\n$$\n\n请注意，概率密度函数$f_{XY}(x,y)$的值总是非负的，但它们可能大于1。尽管如此，可以肯定的是 $\\int_{-\\infty}^{\\infty} \\int_{-\\infty}^{\\infty} f_{X Y}(x, y)=1$\n\n与离散情况相似，我们定义:\n$$\nf_{X}(x)=\\int_{-\\infty}^{\\infty} f_{X Y}(x, y) d y\n$$\n作为$X$的**边际概率密度函数**(或**边际密度**)，对于$f_Y (y)$也类似。\n\n#### 3.4 条件概率分布\n条件分布试图回答这样一个问题，当我们知道$X$必须取某个值$x$时，$Y$上的概率分布是什么？在离散情况下，给定$Y$的条件概率质量函数是简单的：\n$$\np_{Y | X}(y | x)=\\frac{p_{X Y}(x, y)}{p_{X}(x)}\n$$\n假设分母不等于0。\n\n在连续的情况下，在技术上要复杂一点，因为连续随机变量的概率等于零。忽略这一技术点，我们通过类比离散情况，简单地定义给定$X = x$的条件概率密度为：\n$$\nf_{Y | X}(y | x)=\\frac{f_{X Y}(x, y)}{f_{X}(x)}\n$$\n假设分母不等于0。\n\n#### 3.5 贝叶斯定理\n当试图推导一个变量给定另一个变量的条件概率表达式时，经常出现的一个有用公式是**贝叶斯定理**。\n\n对于离散随机变量$X$和$Y$：\n$$\nP_{Y | X}(y | x)=\\frac{{P_{XY}}(x, y)}{P_{X}(x)}=\\frac{P_{X | Y}(x | y) P_{Y}(y)}{\\sum_{y^{\\prime} \\in V a l(Y)} P_{X | Y}\\left(x | y^{\\prime}\\right) P_{Y}\\left(y^{\\prime}\\right)}\n$$\n\n对于连续随机变量$X$和$Y$：\n\n$$\nf_{Y | X}(y | x)=\\frac{f_{X Y}(x, y)}{f_{X}(x)}=\\frac{f_{X | Y}(x | y) f_{Y}(y)}{\\int_{-\\infty}^{\\infty} f_{X | Y}\\left(x | y^{\\prime}\\right) f_{Y}\\left(y^{\\prime}\\right) d y^{\\prime}}\n$$\n\n#### 3.6 独立性\n如果对于$X$和$Y$的所有值，$F_{XY}(x,y) = F_X(x)F_Y(y)$，则两个随机变量$X$和$Y$是独立的。等价地，\n\n- 对于离散随机变量, 对于任意$x \\in Val(X)$, $y \\in Val(Y)$ ，$p_{XY}(x,y) = p_X (x)p_Y (y)$。\n- 对于离散随机变量, $p_Y |X (y|x) = p_Y (y)$当对于任意$y \\in Val(Y)$且$p_X (x) \\not= 0$。\n- 对于连续随机变量, $f_{XY}(x,y) = f_X (x)f_Y(y)$ 对于任意 $x,y \\in \\mathbb{R}$。\n- 对于连续随机变量, $f_{Y |X} (y|x) = f_Y (y)$ ，当$f_X (x)\\not = 0$对于任意$y \\in \\mathbb{R}$。\n\n非正式地说，如果“知道”一个变量的值永远不会对另一个变量的条件概率分布有任何影响，那么两个随机变量$X$和$Y$是独立的，也就是说，你只要知道$f(x)$和$f(y)$就知道关于这对变量$(X，Y)$的所有信息。以下引理将这一观察形式化:\n\n**引理3.1** \n\n如果$X$和$Y$是独立的，那么对于任何$A，B⊆ \\mathbb{R}$，我们有：\n$$\nP(X \\in A, Y \\in B)=P(X \\in A) P(Y \\in B)\n$$\n利用上述引理，我们可以证明如果$X$与$Y$无关，那么$X$的任何函数都与$Y$的任何函数无关。\n\n\n\n#### 3.7 期望和协方差\n假设我们有两个离散的随机变量$X$，$Y$并且$g : \\mathbf{R}^{2} \\longrightarrow \\mathbf{R}$是这两个随机变量的函数。那么$g$的期望值以如下方式定义：\n$$\nE[g(X, Y)] \\triangleq \\sum_{x \\in V a l(X)} \\sum_{y \\in V a l(Y)} g(x, y) p_{X Y}(x, y)\n$$\n对于连续随机变量$X$，$Y$，类似的表达式是：\n$$\nE[g(X, Y)]=\\int_{-\\infty}^{\\infty} \\int_{-\\infty}^{\\infty} g(x, y) f_{X Y}(x, y) d x d y\n$$\n我们可以用期望的概念来研究两个随机变量之间的关系。特别地，两个随机变量的**协方差**定义为：\n$$\n{Cov}[X, Y] \\triangleq E[(X-E[X])(Y-E[Y])]\n$$\n使用类似于方差的推导，我们可以将它重写为：\n$$\n\\begin{aligned} {Cov}[X, Y] &=E[(X-E[X])(Y-E[Y])] \\\\ &=E[X Y-X E[Y]-Y E[X]+E[X] E[Y]] \\\\ &=E[X Y]-E[X] E[Y]-E[Y] E[X]+E[X] E[Y]] \\\\ &=E[X Y]-E[X] E[Y] \\end{aligned}\n$$\n\n在这里，说明两种协方差形式相等的关键步骤是第三个等号，在这里我们使用了这样一个事实，即$E[X]$和$E[Y]$实际上是常数，可以被提出来。当$cov[X，Y] = 0$时，我们说$X$和$Y$不相关。\n\n**性质：**\n\n- (期望线性) $E[f(X,Y ) + g(X,Y)] = E[f(X,Y )] + E[g(X,Y)]$\n- $V ar[X + Y ] = V ar[X] + V ar[Y ] + 2Cov[X,Y]$\n- 如果$X$和$Y$相互独立, 那么 $Cov[X,Y ] = 0$\n- 如果$X$和$Y$相互独立, 那么 $E[f(X)g(Y )] = E[f(X)]E[g(Y)]$.\n\n\n### 4. 多个随机变量\n上一节介绍的概念和想法可以推广到两个以上的随机变量。特别是，假设我们有$n$个连续随机变量，$X _1 (\\omega),X_2 (\\omega),\\cdots X_n (\\omega)$。在本节中，为了表示简单，我们只关注连续的情况，对离散随机变量的推广工作类似。\n\n#### 4.1 基本性质\n我们可以定义$X_1,X_2,\\cdots,X_n$的**联合累积分布函数**、**联合概率密度函数**，以及给定$X_2,\\cdots,X_n$时$X_1$的**边缘概率密度函数**为：\n$$\nF_{X_{1}, X_{2}, \\ldots, X_{n}}\\left(x_{1}, x_{2}, \\ldots x_{n}\\right)=P\\left(X_{1} \\leq x_{1}, X_{2} \\leq x_{2}, \\ldots, X_{n} \\leq x_{n}\\right)\n$$\n\n$$\nf_{X_{1}, X_{2}, \\ldots, X_{n}}\\left(x_{1}, x_{2}, \\ldots x_{n}\\right)=\\frac{\\partial^{n} F_{X_{1}, X_{2}, \\ldots, X_{n}}\\left(x_{1}, x_{2}, \\ldots x_{n}\\right)}{\\partial x_{1} \\ldots \\partial x_{n}}\n$$\n$$\nf_{X_{1}}\\left(X_{1}\\right)=\\int_{-\\infty}^{\\infty} \\cdots \\int_{-\\infty}^{\\infty} f_{X_{1}, X_{2}, \\ldots, X_{n}}\\left(x_{1}, x_{2}, \\ldots x_{n}\\right) d x_{2} \\ldots d x_{n}\n$$\n\n$$\nf_{X_{1} | X_{2}, \\ldots, X_{n}}\\left(x_{1} | x_{2}, \\dots x_{n}\\right)=\\frac{f_{X_{1}, X_{2}, \\ldots, X_{n}}\\left(x_{1}, x_{2}, \\dots x_{n}\\right)}{f_{X_{2}, \\ldots, X_{n}}\\left(x_{1}, x_{2}, \\ldots x_{n}\\right)}\n$$\n\n为了计算事件$A \\subseteq \\mathbb{R}^{n}$的概率，我们有：\n$$\nP\\left(\\left(x_{1}, x_{2}, \\ldots x_{n}\\right) \\in A\\right)=\\int_{\\left(x_{1}, x_{2}, \\ldots x_{n}\\right) \\in A} f_{X_{1}, X_{2}, \\ldots, X_{n}}\\left(x_{1}, x_{2}, \\ldots x_{n}\\right) d x_{1} d x_{2} \\ldots d x_{n}\n$$\n**链式法则：**\n\n从多个随机变量的条件概率的定义中，可以看出：\n$$\n\\begin{aligned} f\\left(x_{1}, x_{2}, \\ldots, x_{n}\\right) &=f\\left(x_{n} | x_{1}, x_{2} \\ldots, x_{n-1}\\right) f\\left(x_{1}, x_{2} \\ldots, x_{n-1}\\right) \\\\ &=f\\left(x_{n} | x_{1}, x_{2} \\ldots, x_{n-1}\\right) f\\left(x_{n-1} | x_{1}, x_{2} \\ldots, x_{n-2}\\right) f\\left(x_{1}, x_{2} \\ldots, x_{n-2}\\right) \\\\ &=\\cdots=f\\left(x_{1}\\right) \\prod_{i=2}^{n} f\\left(x_{i} | x_{1}, \\ldots, x_{i-1}\\right) \\end{aligned}\n$$\n\n独立性:对于多个事件，$A_1,\\cdots ,A_k$,我们说$A_1,\\cdots ,A_k$ 是相互独立的,当对于任何子集$S \\subseteq \\{1，2,\\cdots,k\\}$，我们有：\n$$\nP\\left(\\cap_{i \\in S} A_{i}\\right)=\\prod_{i \\in S} P\\left(A_{i}\\right)\n$$\n\n同样，我们说随机变量$X_1,X_2,\\cdots,X_n$是独立的，如果：\n\n$$\nf(x_1,\\cdots,x_n)=f(x_1)f(x_2)\\cdots f(x_n)\n$$\n这里，相互独立性的定义只是两个随机变量独立性到多个随机变量的自然推广。 \n\n独立随机变量经常出现在机器学习算法中，其中我们假设属于训练集的训练样本代表来自某个未知概率分布的独立样本。为了明确独立性的重要性，考虑一个“坏的”训练集，我们首先从某个未知分布中抽取一个训练样本$(x^{ (1)},y^{(1)})$，然后将完全相同的训练样本的$m-1$个副本添加到训练集中。在这种情况下，我们有：\n$$\nP\\left(\\left(x^{(1)}, y^{(1)}\\right), \\ldots .\\left(x^{(m)}, y^{(m)}\\right)\\right) \\neq \\prod_{i=1}^{m} P\\left(x^{(i)}, y^{(i)}\\right)\n$$\n\n尽管训练集的大小为$m$，但这些例子并不独立！虽然这里描述的过程显然不是为机器学习算法建立训练集的明智方法，但是事实证明，在实践中，样本的不独立性确实经常出现，并且它具有减小训练集的“有效大小”的效果。\n\n#### 4.2 随机向量\n假设我们有n个随机变量。当把所有这些随机变量放在一起工作时，我们经常会发现把它们放在一个向量中是很方便的...我们称结果向量为随机向量(更正式地说，随机向量是从$\\Omega$到$\\mathbb{R}^n$的映射)。应该清楚的是，随机向量只是处理$n$个随机变量的一种替代符号，因此联合概率密度函数和综合密度函数的概念也将适用于随机向量。\n\n**期望:**\n\n考虑$g : \\mathbb{R}^n \\rightarrow \\mathbb{R}$中的任意函数。这个函数的期望值 被定义为\n$$\nE[g(X)]=\\int_{\\mathbb{R}^{n}} g\\left(x_{1}, x_{2}, \\ldots, x_{n}\\right) f_{X_{1}, X_{2}, \\ldots, X_{n}}\\left(x_{1}, x_{2}, \\ldots x_{n}\\right) d x_{1} d x_{2} \\ldots d x_{n}E[g(X)]\\\\=\\int_{\\mathbb{R}^{n}} g\\left(x_{1}, x_{2}, \\ldots, x_{n}\\right) f_{X_{1}, X_{2}, \\ldots, X_{n}}\\left(x_{1}, x_{2}, \\ldots x_{n}\\right) d x_{1} d x_{2} \\ldots d x_{n}\n$$\n其中，$\\int_{\\mathbb{R}^{n}}$是从$-\\infty$到$\\infty$的$n$个连续积分。如果$g$是从$\\mathbb{R}^n$到$\\mathbb{R}^m$的函数，那么$g$的期望值是输出向量的元素期望值，即，如果$g$是：\n\n\n\n\n$$\ng(x)=\\left[\\begin{array}{c}{g_{1}(x)} \\\\ {g_{2}(x)} \\\\ {\\vdots} \\\\ {g_{m}(x)}\\end{array}\\right]\n$$\n那么，\n$$\nE[g(X)]=\\left[\\begin{array}{c}{E\\left[g_{1}(X)\\right]} \\\\ {E\\left[g_{2}(X)\\right]} \\\\ {\\vdots} \\\\ {E\\left[g_{m}(X)\\right]}\\end{array}\\right]\n$$\n\n\n\n协方差矩阵：对于给定的随机向量$X:\\Omega\\rightarrow \\mathbb{R}^n$，其协方差矩阵$\\Sigma$是$n \\times n$平方矩阵，其输入由$\\Sigma_{i j}={Cov}\\left[X_{i}, X_{j}\\right]$给出。从协方差的定义来看，我们有：\n$$\n\\begin{aligned}\n\\begin{equation}\n\\Sigma=\\left[\\begin{array}{ccc}{{Cov}\\left[X_{1}, X_{1}\\right]} & {\\cdots} & {{Cov}\\left[X_{1}, X_{n}\\right]} \\\\ {\\vdots} & {\\ddots} & {\\vdots} \\\\ {{Cov}\\left[X_{n}, X_{1}\\right]} & {\\cdots} & {{Cov}\\left[X_{n}, X_{n}\\right]}\\end{array}\\right]\\\\\n\n=\\left[\\begin{array}{ccc}{E\\left[X_{1}^{2}\\right]-E\\left[X_{1}\\right] E\\left[X_{1}\\right]} & {\\cdots} & {E\\left[X_{1} X_{n}\\right]-E\\left[X_{1}\\right] E\\left[X_{n}\\right]} \\\\ {\\vdots} & {\\ddots} & {\\vdots} \\\\ {E\\left[X_{n} X_{1}\\right]-E\\left[X_{n}\\right] E\\left[X_{1}\\right]} & {\\cdots} & {E\\left[X_{n}^{2}\\right]-E\\left[X_{n}\\right] E\\left[X_{n}\\right]}\\end{array}\\right]\\\\\n\n=\\left[\\begin{array}{ccc}{E\\left[X_{1}^{2}\\right]} & {\\cdots} & {E\\left[X_{1} X_{n}\\right]} \\\\ {\\vdots} & {\\ddots} & {\\vdots} \\\\ {E\\left[X_{n} X_{1}\\right]} & {\\cdots} & {E\\left[X_{n}^{2}\\right]}\\end{array}\\right]-\\left[\\begin{array}{ccc}{E\\left[X_{1}\\right] E\\left[X_{1}\\right]} & {\\cdots} & {E\\left[X_{1}\\right] E\\left[X_{n}\\right]} \\\\ {\\vdots} & {\\ddots} & {\\vdots} \\\\ {E\\left[X_{n}\\right] E\\left[X_{1}\\right]} & {\\cdots} & {E\\left[X_{n}\\right] E\\left[X_{n}\\right]}\\end{array}\\right]\\\\\n=E\\left[X X^{T}\\right]-E[X] E[X]^{T}=\\ldots=E\\left[(X-E[X])(X-E[X])^{T}\\right]\n\n\\end{equation}\n\\end{aligned}\n$$\n其中矩阵期望以明显的方式定义。 \n协方差矩阵有许多有用的属性:\n\n- $\\Sigma \\succeq 0$；也就是说，$\\Sigma$是正半定的。 \n- $\\Sigma=\\Sigma^T$；也就是说，$\\Sigma$是对称的。\n\n#### 4.3 多元高斯分布\n随机向量上概率分布的一个特别重要的例子叫做多元高斯或多元正态分布。随机向量$X\\in \\mathbb{R}^n$被认为具有多元正态(或高斯)分布，当其具有均值$\\mu \\in \\mathbb{R}^n$和协方差矩阵$\\Sigma \\in \\mathbb{S}_{++}^{n}$(其中$ \\mathbb{S}_{++}^{n}$指对称正定$n \\times n$矩阵的空间)\n\n$f_{X_{1}, X_{2}, \\ldots, X_{n}}\\left(x_{1}, x_{2}, \\ldots, x_{n} ; \\mu, \\Sigma\\right)=\\frac{1}{(2 \\pi)^{n / 2}|\\Sigma|^{1 / 2}} \\exp \\left(-\\frac{1}{2}(x-\\mu)^{T} \\Sigma^{-1}(x-\\mu)\\right)$\n\n我们把它写成$X \\sim \\mathcal{N}(\\mu, \\Sigma)$。请注意，在$n = 1$的情况下，它降维成普通正态分布，其中均值参数为$\\mu_1$，方差为$\\Sigma_{11}$。 \n\n一般来说，高斯随机变量在机器学习和统计中非常有用，主要有两个原因：\n\n首先，在统计算法中对“噪声”建模时，它们非常常见。通常，噪声可以被认为是影响测量过程的大量小的独立随机扰动的累积；根据中心极限定理，独立随机变量的总和将趋向于“看起来像高斯”。\n\n其次，高斯随机变量便于许多分析操作，因为实际中出现的许多涉及高斯分布的积分都有简单的封闭形式解。我们将在本课程稍后遇到这种情况。\n\n### 5. 其他资源\n一本关于**CS229**所需概率水平的好教科书是谢尔顿·罗斯的《概率第一课》(*A First Course on Probability* by Sheldon Ross)。\n\n\n"
  },
  {
    "path": "0.math/1.CS229/markdown/README.md",
    "content": "本文是斯坦福大学CS 229机器学习课程的基础材料，[原始文件下载](http://cs229.stanford.edu/summer2019/cs229-linalg.pdf)\n\n翻译：[黄海广](https://github.com/fengdu78)\n备注：请关注[github](https://github.com/fengdu78/Data-Science-Notes/tree/master/0.math)的更新，近期将更新完。\n\nmarkdown文件夹是文件的markdown代码，内容与pdf一致，是为了方便研究者写论文或者文章使用。\n\ngithub内容是方便用户学习研究使用，请勿用于商业目的。\n\n"
  },
  {
    "path": "0.math/README.md",
    "content": "# 机器学习的数学基础材料\n\n## 一、机器学习的数学基础\n\n[0.basic](0.basic)，这个是从大学教材里搜集的机器学习数学基础资料，分为高等数学，线性代数、概率论与数理统计三部分。主要内容是以前考研考博时候的数学笔记，难度应该在本科3年级左右。\n\n**数据科学需要一定的数学基础，但仅仅做应用的话，如果时间不多，不用学太深，了解基本公式即可，遇到问题再查吧。**\n\n\n\n## 二、斯坦福大学CS229机器学习课程复习材料\n\n[1.CS229](1.CS229)文件夹的材料斯坦福大学CS 229机器学习课程的基础材料的翻译版本，[原始文件下载](http://cs229.stanford.edu/summer2019/cs229-linalg.pdf)\n\n翻译：[黄海广](https://github.com/fengdu78)\n备注：请关注[github](https://github.com/fengdu78/Data-Science-Notes/tree/master/0.math)的更新，近期将更新完。\n\n文件介绍：\n\n[1.CS229线性代数（翻译完毕）](1.CS229-LinearAlgebra.pdf)\n\ngithub内容是方便用户学习研究使用，请勿用于商业目的。\n\n如果需要引用这个Repo:\n\n格式： `fengdu78, Data-Science-Notes, (2019), GitHub repository, https://github.com/fengdu78/Data-Science-Notes`"
  },
  {
    "path": "1.python-basic/Python-100/README.md",
    "content": "# Python练习题100题-带你轻松入门Python\n> 近日发现一个Python入门的练习仓库，作者收集100多道Python的常见练习题，几乎概括了Python初学要掌握的基本问题。\n\n如果您是python的初学者，那么这100多个练习可以帮助您轻松地使用Python。仓库给定的问题非常简单和容易理解。初学者每天可以尝试3-5个问题，需要一点时间来解决，但肯定会学到一些新的东西（不管你有多懒），经过一个月的常规练习，把练习做完看懂，只要能独立解决100多个问题，基本上Python就入门了。练习题作者地址： https://github.com/darkprinx/100-plus-Python-programming-exercises-extended\n\n## 仓库内容\n\n这个仓库一共有24个Jupyter notebook文件（每天一个），每个文件有3-5个练习，一共104题。问题模板格式\n\n- 提问\n- 提示\n- 解答\n\n在这个仓库是用Python3 语言。作者修正一些随机错误和变量命名，符合PEP8规范，仓库分为Jupyter notebook格式（文件夹名称:notebooks）和Markdown格式（文件夹名称：Status）两个文件夹，两者内容一致。\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day 1.md",
    "content": "# Question 1\n\n### **Question:**\n\n> **_Write a program which will find all such numbers which are divisible by 7 but are not a multiple of 5,\n> between 2000 and 3200 (both included).The numbers obtained should be printed in a comma-separated sequence on a single line._**\n\n---\n\n### Hints:\n\n> **_Consider use range(#begin, #end) method._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nl=[]\nfor i in range(2000, 3201):\n    if (i%7==0) and (i%5!=0):\n        l.append(str(i))\n\nprint ','.join(l)\n```\n\n---\n\n**My Solution: Python 3**\n- **Using for loops**\n\n```python\nfor i in range(2000,3201):\n    if i%7 == 0 and i%5!=0:\n        print(i,end=',')\nprint(\"\\b\")\n```\n\n---\n- **Using generators and list comprehension**\n\n```python\nprint(*(i for i in range(2000, 3201) if i%7 == 0 and i%5 != 0), sep=\",\")\n```\n# Question 2\n\n### **Question:**\n\n> **_Write a program which can compute the factorial of a given numbers.The results should be printed in a comma-separated sequence on a single line.Suppose the following input is supplied to the program: 8\n> Then, the output should be:40320_**\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndef fact(x):\n    if x == 0:\n        return 1\n    return x * fact(x - 1)\n\nx = int(raw_input())\nprint fact(x)\n```\n\n---\n\n**My Solution: Python 3**\n\n- **Using While Loop**\n  ```python\n  n = int(input()) #input() function takes input as string type\n                   #int() converts it to integer type\n  fact = 1\n  i = 1\n  while i <= n:\n      fact = fact * i;\n      i = i + 1\n  print(fact)\n  ```\n- **Using For Loop**\n  ```python\n  n = int(input()) #input() function takes input as string type\n                  #int() converts it to integer type\n  fact = 1\n  for i in range(1,n+1):\n      fact = fact * i\n  print(fact)\n  ```\n- **Using Lambda Function**\n\n  ```python\n  # Solution by:  harshraj22\n\n  n = int(input())\n  def shortFact(x): return 1 if x <= 1 else x*shortFact(x-1)\n  print(shortFact(n))\n\n  ```\n---\n```python\n'''Solution by: minnielahoti\n'''\n\nwhile True:\ntry:\n    num = int(input(\"Enter a number: \"))\n    break\nexcept ValueError as err:\n    print(err)\n\norg = num\nfact = 1\nwhile num:\n    fact = num * fact\n    num = num - 1\nprint(f'the factorial of {org} is {fact}')\n```\n---\n```python\n'''Soltuion by: KruthikaSR\n'''\nfrom functools import reduce\n\ndef fun(acc, item):\n\treturn acc*item\n\nnum = int(input())\nprint(reduce(fun,range(1, num+1), 1))\n```\n---\n\n# Question 3\n\n### **Question:**\n\n> **_With a given integral number n, write a program to generate a dictionary that contains (i, i x i) such that is an integral number between 1 and n (both included). and then the program should print the dictionary.Suppose the following input is supplied to the program: 8_**\n\n> **_Then, the output should be:_**\n\n```\n{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64}\n```\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input.Consider use dict()_**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nn = int(raw_input())\nd = dict()\nfor i in range(1,n+1):\n    d[i] = i * i\nprint d\n```\n\n**My Solution: Python 3:**\n\n- **Using for loop**\n\n```python\nn = int(input())\nans = {}\nfor i in range (1,n+1):\n    ans[i] = i * i\nprint(ans)\n```\n\n- **Using dictionary comprehension**\n\n```python\nn = int(input())\nans={i : i*i for i in range(1,n+1)}\nprint(ans)\n```\n---\n```python\n'''Solution by: minnielahoti\n   Corrected by: TheNobleKnight \n'''\n\ntry:\n    num = int(input(\"Enter a number: \"))\nexcept ValueError as err:\n    print(err)\n\ndictio = dict()\nfor item in range(num+1):\n    if item == 0:\n        continue\n    else:\n\tdictio[item] = item * item\nprint(dictio)\n```\n---\n```python\n'''Solution by: yurbika\n'''\n\nnum = int(input(\"Number: \"))\nprint(dict(list(enumerate((i * i for i in range(num+1))))))\n```\n---\n## Conclusion\n\n**_These was the solved problems of day 1. The above problems are very easy for the basic syntex learners.I have shown some easy ways of coding in my solutions. Lets see how to face and attack new problems in the next day._**\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%202.md \"Next Day\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day 2.md",
    "content": "# Question 4\n\n### **Question:**\n\n> **_Write a program which accepts a sequence of comma-separated numbers from console and generate a list and a tuple which contains every number.Suppose the following input is supplied to the program:_**\n\n```\n34,67,55,33,12,98\n```\n\n> **_Then, the output should be:_**\n\n```\n['34', '67', '55', '33', '12', '98']\n('34', '67', '55', '33', '12', '98')\n```\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input.tuple() method can convert list to tuple_**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nvalues = raw_input()\nl = values.split(\",\")\nt = tuple(l)\nprint l\nprint t\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nlst = input().split(',')  # the input is being taken as string and as it is string it has a built in\n                          # method name split. ',' inside split function does split where it finds any ','\n                          # and save the input as list in lst variable\n\ntpl = tuple(lst)          # tuple method converts list to tuple\n\nprint(lst)\nprint(tpl)\n```\n---\n```python \n'''solution by: minnielahoti\n'''\nprint(tuple(input(\"Enter a series of numbers separated by a comma :\").split(',')))\n```\n---\n\n# Question 5\n\n### **Question:**\n\n> **_Define a class which has at least two methods:_**\n>\n> - **_getString: to get a string from console input_**\n> - **_printString: to print the string in upper case._**\n\n> **_Also please include simple test function to test the class methods._**\n\n### Hints:\n\n> **_Use **init** method to construct some parameters_**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nclass InputOutString(object):\n    def __init__(self):\n        self.s = \"\"\n\n    def get_string(self):\n        self.s = raw_input()\n\n    def print_string(self):\n        print self.s.upper()\n\nstr_obj = InputOutString()\nstr_obj.get_string()\nstr_obj.print_string()\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nclass IOstring():\n    def get_string(self):\n        self.s = input()\n\n    def print_string(self):\n        print(self.s.upper())\n\nxx = IOstring()\nxx.get_string()\nxx.print_string()\n```\n\n---\n\n# Question 6\n\n### **Question:**\n\n> **_Write a program that calculates and prints the value according to the given formula:_**\n\n> **_Q = Square root of [(2 _ C _ D)/H]_**\n\n> **_Following are the fixed values of C and H:_**\n\n> **_C is 50. H is 30._**\n\n> **_D is the variable whose values should be input to your program in a comma-separated sequence.For example\n> Let us assume the following comma separated input sequence is given to the program:_**\n\n```\n100,150,180\n```\n\n> **_The output of the program should be:_**\n\n```\n18,22,24\n```\n\n---\n\n### Hints:\n\n> **_If the output received is in decimal form, it should be rounded off to its nearest value (for example, if the output received is 26.0, it should be printed as 26).In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nimport math\nc = 50\nh = 30\nvalue = []\nitems = [x for x in raw_input().split(',')]\nfor d in items:\n    value.append(str(int(round(math.sqrt(2*c*float(d)/h)))))\n\nprint ','.join(value)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nfrom math import sqrt # import specific functions as importing all using *\n                      # is bad practice\n\nC,H = 50,30\n\ndef calc(D):\n    return sqrt((2*C*D)/H)\n\nD = [int(i) for i in input().split(',')] # splits in comma position and set up in list\nD = [int(i) for i in D]   # converts string to integer\nD = [calc(i) for i in D]  # returns floating value by calc method for every item in D\nD = [round(i) for i in D] # All the floating values are rounded\nD = [str(i) for i in D]   # All the integers are converted to string to be able to apply join operation\n\nprint(\",\".join(D))\n```\n\n**OR**\n\n```python\nfrom math import sqrt\n\nC,H = 50,30\n\ndef calc(D):\n    return sqrt((2*C*D)/H)\n\nD = input().split(',')                     # splits in comma position and set up in list\nD = [str(round(calc(int(i)))) for i in D]  # using comprehension method. It works in order of the previous code\nprint(\",\".join(D))\n```\n\n**OR**\n\n```python\nfrom math import sqrt\nC,H = 50,30\n\ndef calc(D):\n    return sqrt((2*C*D)/H)\n\nprint(\",\".join([str(int(calc(int(i)))) for i in input().split(',')]))\n```\n\n**OR**\n\n```python\nfrom math import * # importing all math functions\nC,H = 50,30\n\ndef calc(D):\n    D = int(D)\n    return str(int(sqrt((2*C*D)/H)))\n\nD = input().split(',')\nD = list(map(calc,D))   # applying calc function on D and storing as a list\nprint(\",\".join(D))\n```\n---\n```python\n'''Solution by: parian5\n'''\nfrom math import sqrt\nC, H = 50, 30\nmylist = input().split(',')\nprint(*(round(sqrt(2*C*int(D)/H)) for D in mylist), sep=\",\")\n```\n---\n\n# Question 7\n\n### **Question:**\n\n> **_Write a program which takes 2 digits, X,Y as input and generates a 2-dimensional array. The element value in the i-th row and j-th column of the array should be i _ j.\\***\n\n> **_Note: i=0,1.., X-1; j=0,1,¡­Y-1. Suppose the following inputs are given to the program: 3,5_**\n\n> **_Then, the output of the program should be:_**\n\n```\n[[0, 0, 0, 0, 0], [0, 1, 2, 3, 4], [0, 2, 4, 6, 8]]\n```\n\n---\n\n### Hints:\n\n> **_Note: In case of input data being supplied to the question, it should be assumed to be a console input in a comma-separated form._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ninput_str = raw_input()\ndimensions = [int(x) for x in input_str.split(',')]\nrow_num = dimensions[0]\ncol_num = dimensions[1]\nmultilist = [[0 for col in range(col_num)] for row in range(row_num)]\n\nfor row in range(row_num):\n    for col in range(col_num):\n        multilist[row][col] = row * col\n\nprint multilist\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nx,y = map(int,input().split(','))\nlst = []\n\nfor i in range(x):\n    tmp = []\n    for j in range(y):\n        tmp.append(i*j)\n    lst.append(tmp)\n\nprint(lst)\n```\n\n**OR**\n\n```python\nx,y = map(int,input().split(','))\nlst = [[i*j for j in range(y)] for i in range(x)]\nprint(lst)\n```\n\n---\n\n# Question 8\n\n### **Question:**\n\n> **_Write a program that accepts a comma separated sequence of words as input and prints the words in a comma-separated sequence after sorting them alphabetically._**\n\n> **_Suppose the following input is supplied to the program:_**\n\n```\nwithout,hello,bag,world\n```\n\n> **_Then, the output should be:_**\n\n```\nbag,hello,without,world\n```\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nitems = [x for x in raw_input().split(',')]\nitems.sort()\nprint ','.join(items)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nlst = input().split(',')\nlst.sort()\nprint(\",\".join(lst))\n```\n\n---\n\n# Question 9\n\n### **Question:**\n\n> **_Write a program that accepts sequence of lines as input and prints the lines after making all characters in the sentence capitalized._**\n\n> **_Suppose the following input is supplied to the program:_**\n\n```\nHello world\nPractice makes perfect\n```\n\n> **_Then, the output should be:_**\n\n```\nHELLO WORLD\nPRACTICE MAKES PERFECT\n```\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nlines = []\nwhile True:\n    s = raw_input()\n    if s:\n        lines.append(s.upper())\n    else:\n        break\n\nfor sentence in lines:\n    print sentence\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nlst = []\n\nwhile True:\n    x = input()\n    if len(x)==0:\n        break\n    lst.append(x.upper())\n\nfor line in lst:\n    print(line)\n```\n\n**OR**\n\n```python\ndef user_input():\n    while True:\n        s = input()\n        if not s:\n            return\n        yield s\n\nfor line in map(str.upper, user_input()):\n    print(line)\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%201.md \"Day 1\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%203.md \"Day 3\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day 3.md",
    "content": "# Question 10\n\n### **Question**\n\n> **_Write a program that accepts a sequence of whitespace separated words as input and prints the words after removing all duplicate words and sorting them alphanumerically._**\n\n> **_Suppose the following input is supplied to the program:_**\n\n```\nhello world and practice makes perfect and hello world again\n```\n\n> **_Then, the output should be:_**\n\n```\nagain and hello makes perfect practice world\n```\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input.We use set container to remove duplicated data automatically and then use sorted() to sort the data._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ns = raw_input()\nwords = [word for word in s.split(\" \")]\nprint \" \".join(sorted(list(set(words))))\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nword = input().split()\n\nfor i in word:\n    if word.count(i) > 1:    #count function returns total repeatation of an element that is send as argument\n        word.remove(i)     # removes exactly one element per call\n\nword.sort()\nprint(\" \".join(word))\n```\n\n**OR**\n\n```python\nword = input().split()\n[word.remove(i) for i in word if word.count(i) > 1 ]   # removal operation with comprehension method\nword.sort()\nprint(\" \".join(word))\n```\n\n**OR**\n\n```python\nword = sorted(list(set(input().split())))              #  input string splits -> converting into set() to store unique\n                                                       #  element -> converting into list to be able to apply sort\nprint(\" \".join(word))\n```\n\n---\n```python\n'''Solution by: Sukanya-Mahapatra\n'''\ninp_string = input(\"Enter string: \").split()\nout_string = []\nfor words in inp_string:\n    if words not in out_string:\n        out_string.append(words)\nprint(\" \".join(sorted(out_string)))\n```\n---\n\n# Question 11\n\n### **Question**\n\n> **_Write a program which accepts a sequence of comma separated 4 digit binary numbers as its input and then check whether they are divisible by 5 or not. The numbers that are divisible by 5 are to be printed in a comma separated sequence._**\n\n> **_Example:_**\n\n```\n0100,0011,1010,1001\n```\n\n> **_Then the output should be:_**\n\n```\n1010\n```\n\n> **_Notes: Assume the data is input by console._**\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nvalue = []\nitems=[x for x in raw_input().split(',')]\nfor p in items:\n    intp = int(p,2)\n    if not intp % 5:\n        value.append(p)\n\nprint ','.join(value)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef check(x):                       # converts binary to integer & returns zero if divisible by 5\n    total,pw = 0,1\n    reversed(x)\n\n    for i in x:\n        total+=pw * (ord(i) - 48)   # ord() function returns ASCII value\n        pw*=2\n    return total % 5\n\ndata = input().split(\",\")           # inputs taken here and splited in ',' position\nlst = []\n\nfor i in data:\n    if check(i) == 0:               # if zero found it means divisible by zero and added to the list\n        lst.append(i)\n\nprint(\",\".join(lst))\n```\n\n**OR**\n\n```python\ndef check(x):                   # check function returns true if divisible by 5\n    return int(x,2)%5 == 0      # int(x,b) takes x as string and b as base from which\n                                # it will be converted to decimal\ndata = input().split(',')\n\ndata = list(filter(check,data)) # in filter(func,object) function, elements are picked from 'data' if found True by 'check' function\nprint(\",\".join(data))\n```\n\n**OR**\n\n```python\ndata = input().split(',')\ndata = list(filter(lambda i:int(i,2)%5==0,data))    # lambda is an operator that helps to write function of one line\nprint(\",\".join(data))\n```\n\n---\n\n```python\n'''Solution by: nikitaMogilev\n'''\ndata = input().split(',')\ndata = [num for num in data if int(num, 2) % 5 == 0]\nprint(','.join(data))\n```\n\n---\n\n# Question 12\n\n### **Question:**\n\n> **_Write a program, which will find all such numbers between 1000 and 3000 (both included) such that each digit of the number is an even number.The numbers obtained should be printed in a comma-separated sequence on a single line._**\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nvalues = []\nfor i in range(1000, 3001):\n    s = str(i)\n    if (int(s[0])%2 == 0) and (int(s[1])%2 == 0) and (int(s[2])%2 == 0) and (int(s[3])%2 == 0):\n        values.append(s)\nprint \",\".join(values)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nlst = []\n\nfor i in range(1000,3001):\n    flag = 1\n    for j in str(i):          # every integer number i is converted into string\n        if ord(j)%2 != 0:     # ord returns ASCII value and j is every digit of i\n            flag = 0          # flag becomes zero if any odd digit found\n    if flag == 1:\n        lst.append(str(i))    # i is stored in list as string\n\nprint(\",\".join(lst))\n```\n\n**OR**\n\n```python\ndef check(element):\n    return all(ord(i)%2 == 0 for i in element)  # all returns True if all digits i is even in element\n\nlst = [str(i) for i in range(1000,3001)]        # creates list of all given numbers with string data type\nlst = list(filter(check,lst))                   # filter removes element from list if check condition fails\nprint(\",\".join(lst))\n```\n\n**OR**\n\n```python\nlst = [str(i) for i in range(1000,3001)]\nlst = list(filter(lambda i:all(ord(j)%2 == 0 for j in i), lst))   # using lambda to define function inside filter function\nprint(\",\".join(lst))\n```\n\n---\n\n```python\n'''Solution by: nikitaMogilev\n'''\n# map() digits of each number with lambda function and check if all() of them even\n# str(num) gives us opportunity to iterate through number by map() and join()\nprint(','.join([str(num) for num in range(1000, 3001) if all(map(lambda num: int(num) % 2 == 0, str(num)))]))\n```\n\n---\n\n# Question 13\n\n### **Question:**\n\n> **_Write a program that accepts a sentence and calculate the number of letters and digits._**\n\n> **_Suppose the following input is supplied to the program:_**\n\n```\nhello world! 123\n```\n\n> **_Then, the output should be:_**\n\n```\nLETTERS 10\nDIGITS 3\n```\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ns = raw_input()\nd = {\"DIGITS\":0, \"LETTERS\":0}\nfor c in s:\n    if c.isdigit():\n        d[\"DIGITS\"]+=1\n    elif c.isalpha():\n        d[\"LETTERS\"]+=1\n    else:\n        pass\nprint \"LETTERS\", d[\"LETTERS\"]\nprint \"DIGITS\", d[\"DIGITS\"]\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nword = input()\nletter,digit = 0,0\n\nfor i in word:\n    if ('a'<=i and i<='z') or ('A'<=i and i<='Z'):\n        letter+=1\n    if '0'<=i and i<='9':\n        digit+=1\n\nprint(\"LETTERS {0}\\nDIGITS {1}\".format(letter,digit))\n```\n\n**OR**\n\n```python\nword = input()\nletter, digit = 0,0\n\nfor i in word:\n    if i.isalpha(): # returns True if alphabet\n        letter += 1\n    elif i.isnumeric(): # returns True if numeric\n        digit += 1\nprint(f\"LETTERS {letter}\\n{digits}\") # two different types of formating method is shown in both solution\n```\n---\n```python\n''' Solution by: popomaticbubble\n'''\nimport re\n\ninput_string = input('> ')\nprint()\ncounter = {\"LETTERS\":len(re.findall(\"[a-zA-Z]\", input_string)), \"NUMBERS\":len(re.findall(\"[0-9]\", input_string))}\n\nprint(counter)\n```\n---\n\n## Conclusion\n\n**_All the above problems are mostly string related problems. Major parts of the solution includes string releted functions and comprehension method to write down the code in more shorter form._**\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%202.md \"Day 2\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%204.md \"Day 4\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day 4.md",
    "content": "# Question 14\n\n### **Question:**\n\n> **_Write a program that accepts a sentence and calculate the number of upper case letters and lower case letters._**\n\n> **_Suppose the following input is supplied to the program:_**\n\n```\nHello world!\n```\n\n> **_Then, the output should be:_**\n\n```\nUPPER CASE 1\nLOWER CASE 9\n```\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ns = raw_input()\nd = {\"UPPER CASE\":0, \"LOWER CASE\":0}\nfor c in s:\n    if c.isupper():\n        d[\"UPPER CASE\"]+=1\n    elif c.islower():\n        d[\"LOWER CASE\"]+=1\n    else:\n        pass\nprint \"UPPER CASE\", d[\"UPPER CASE\"]\nprint \"LOWER CASE\", d[\"LOWER CASE\"]\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nword = input()\nupper,lower = 0,0\n\nfor i in word:\n    if 'a'<=i and i<='z' :\n        lower+=1\n    if 'A'<=i and i<='Z':\n        upper+=1\n\nprint(\"UPPER CASE {0}\\nLOWER CASE {1}\".format(upper,lower))\n```\n\n**OR**\n\n```python\nword = input()\nupper,lower = 0,0\n\nfor i in word:\n        lower+=i.islower()\n        upper+=i.isupper()\n\nprint(\"UPPER CASE {0}\\nLOWER CASE {1}\".format(upper,lower))\n```\n\n**OR**\n\n```python\nword = input()\nupper = sum(1 for i in word if i.isupper())           # sum function cumulatively sum up 1's if the condition is True\nlower = sum(1 for i in word if i.islower())\n\nprint(\"UPPER CASE {0}\\nLOWER CASE {1}\".format(upper,lower))\n```\n\n**OR**\n\n```python\n# solution by Amitewu\n\nstring = input(\"Enter the sentense\")\nupper = 0\nlower = 0\nfor x in string:\n    if x.isupper() == True:\n        upper += 1\n    if x.islower() == True:\n        lower += 1\n\nprint(\"UPPER CASE: \", upper)\nprint(\"LOWER CASE: \", lower)\n```\n\n---\n\n# Question 15\n\n### **Question:**\n\n> **_Write a program that computes the value of a+aa+aaa+aaaa with a given digit as the value of a._**\n\n> **_Suppose the following input is supplied to the program:_**\n\n```\n9\n```\n\n> **_Then, the output should be:_**\n\n```\n11106\n```\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\na = raw_input()\nn1 = int( \"%s\" % a )\nn2 = int( \"%s%s\" % (a,a) )\nn3 = int( \"%s%s%s\" % (a,a,a) )\nn4 = int( \"%s%s%s%s\" % (a,a,a,a) )\nprint n1+n2+n3+n4\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\na = input()\ntotal,tmp = 0,str()        # initialing an integer and empty string\n\nfor i in range(4):\n    tmp+=a               # concatenating 'a' to 'tmp'\n    total+=int(tmp)      # converting string type to integer type\n\nprint(total)\n```\n\n**OR**\n\n```python\na = input()\ntotal = int(a) + int(2*a) + int(3*a) + int(4*a)  # N*a=Na, for example  a=\"23\", 2*a=\"2323\",3*a=\"232323\"\nprint(total)\n```\n---\n```python\n'''Solution by: ChichiLovesDonkeys\n'''\nfrom functools import reduce\nx = input('please enter a digit:')\nreduce(lambda x, y: int(x) + int(y), [x*i for i in range(1,5)])\n```\n---\n```python\n'''Solution by: lcastrooliveira\n'''\ndef question_15(string_digit):\n    return sum(int(string_digit * n) for n in range(1, 5))\n\ninp = input()\nprint(question_15(inp))\n```\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%203.md \"Day 3\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%205.md \"Day 5\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day 5.md",
    "content": "# Question 16\n\n### **Question:**\n\n> **_Use a list comprehension to square each odd number in a list. The list is input by a sequence of comma-separated numbers._** >**_Suppose the following input is supplied to the program:_**\n\n```\n1,2,3,4,5,6,7,8,9\n```\n\n> **_Then, the output should be:_**\n\n```\n1,9,25,49,81\n```\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\n## The solution by the author is incorrect.Thus it's not included here.\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nlst = [str(int(i)**2) for i in input().split(',') if int(i) % 2]\nprint(\",\".join(lst))\n```\n\n---\n\n```python\n'''Solution by: shagun'''\nsquare odd no\n\nlst = input().split(',')     # splits in comma position and set up in list\n\nseq = []\nlst = [int(i) for i in lst]  # converts string to integer\nfor i in lst:\n        if i%2 != 0:\n                i = i*i\n                seq.append(i)\n\n\nseq = [str(i) for i in seq]   # All the integers are converted to string to be able to apply join operation\nprint(\",\".join(seq))\n```\n\n---\n\n**_There were a mistake in the the test case and the solution's whice were notified and fixed with the help of @dwedigital. My warm thanks to him._**\n\n# Question 17\n\n### **Question:**\n\n> **_Write a program that computes the net amount of a bank account based a transaction log from console input. The transaction log format is shown as following:_**\n\n```\nD 100\nW 200\n```\n\n- D means deposit while W means withdrawal.\n\n> **_Suppose the following input is supplied to the program:_**\n\n```\nD 300\nD 300\nW 200\nD 100\n```\n\n> **_Then, the output should be:_**\n\n```\n500\n```\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nimport sys\nnetAmount = 0\nwhile True:\n    s = raw_input()\n    if not s:\n        break\n    values = s.split(\" \")\n    operation = values[0]\n    amount = int(values[1])\n    if operation==\"D\":\n        netAmount+=amount\n    elif operation==\"W\":\n        netAmount-=amount\n    else:\n        pass\nprint netAmount\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ntotal = 0\nwhile True:\n    s = input().split()\n    if not s:            # break if the string is empty\n        break\n    cm,num = map(str,s)    # two inputs are distributed in cm and num in string data type\n\n    if cm=='D':\n        total+=int(num)\n    if cm=='W':\n        total-=int(num)\n\nprint(total)\n```\n\n---\n\n```python\n'''Solution by: leonedott'''\n\nlst = []\nwhile True:\n  x = input()\n  if len(x)==0:\n    break\n  lst.append(x)\n\nbalance = 0\nfor item in lst:\n  if 'D' in item:\n    balance += int(item.strip('D '))\n  if 'W' in item:\n    balance -= int(item.strip('W '))\nprint(balance)\n```\n\n---\n\n```python\n'''Solution by: AlexanderSro'''\n\naccount = 0\nwhile True:\n    action = input(\"Deposit/Whitdrow/Balance/Quit? D/W/B/Q: \").lower()\n    if action == \"d\":\n        deposit = input(\"How much would you like to deposit? \")\n        account = account + int(deposit)\n    elif action == \"w\":\n        withdrow = input(\"How much would you like to withdrow? \")\n        account = account - int(withdrow)\n    elif action == \"b\":\n        print(account)\n    else:\n        quit()\n```\n\n---\n\n```python\n'''Solution by: ShalomPrinz\n'''\nlines = []\nwhile True:\n\tloopInput = input()\n\tif loopInput == \"done\":\n\t\tbreak\n\telse:\n\t\tlines.append(loopInput)\n\nlst = list(int(i[2:]) if i[0] == 'D' else -int(i[2:]) for i in lines)\nprint(sum(lst))\n```\n---\n```python\n'''Solution by: popomaticbubble \n'''\ntransactions = []\n\nwhile True:\n    text = input(\"> \")\n    if text:\n    \ttext = text.strip('D ')\n    \ttext = text.replace('W ', '-')\n    \ttransactions.append(text)\n    else: \n\t\tbreak\t\n\t\t\ntransactions = (int(i) for i in transactions)\nbalance = sum(transactions)\nprint(f\"Balance is {balance}\")\n```\n---\n```python\n'''Solution by: ChichiLovesDonkeys\n'''\n\nmoney = 0\nwhile 1:\n    trans = input().split(' ')\n    if trans[0] == 'D':\n        money = money + int(trans[1])\n    elif trans[0] == 'W':\n        money = money - int(trans[1])\n    elif input() == '':\n        break\n    print(f'Your current balance is: {money}')\n```\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%204.md \"Day 4\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%206.md \"Day 6\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day 6.md",
    "content": "# Question 18\n\n### **Question:**\n\n> **_A website requires the users to input username and password to register. Write a program to check the validity of password input by users._**\n\n> **_Following are the criteria for checking the password:_**\n\n- **_At least 1 letter between [a-z]_**\n- **_At least 1 number between [0-9]_**\n- **_At least 1 letter between [A-Z]_**\n- **_At least 1 character from [$#@]_**\n- **_Minimum length of transaction password: 6_**\n- **_Maximum length of transaction password: 12_**\n\n> **_Your program should accept a sequence of comma separated passwords and will check them according to the above criteria. Passwords that match the criteria are to be printed, each separated by a comma._**\n\n> **_Example_**\n\n> **_If the following passwords are given as input to the program:_**\n\n```\nABd1234@1,a F1#,2w3E*,2We3345\n```\n\n> **_Then, the output of the program should be:_**\n\n```\nABd1234@1\n```\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nimport re\nvalue = []\nitems = [x for x in raw_input().split(',')]\nfor p in items:\n    if len(p) < 6 or len(p) > 12:\n        continue\n    else:\n        pass\n    if not re.search(\"[a-z]\",p):\n        continue\n    elif not re.search(\"[0-9]\",p):\n        continue\n    elif not re.search(\"[A-Z]\",p):\n        continue\n    elif not re.search(\"[$#@]\",p):\n        continue\n    elif re.search(\"\\s\",p):\n        continue\n    else:\n        pass\n    value.append(p)\nprint \",\".join(value)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef is_low(x):                  # Returns True  if the string has a lowercase\n    for i in x:\n        if 'a'<=i and i<='z':\n            return True\n    return  False\n\ndef is_up(x):                   # Returns True  if the string has a uppercase\n    for i in x:\n        if 'A'<= i and i<='Z':\n            return True\n    return  False\n\ndef is_num(x):                  # Returns True  if the string has a numeric digit\n    for i in x:\n        if '0'<=i and i<='9':\n            return True\n    return  False\n\ndef is_other(x):                # Returns True if the string has any \"$#@\"\n    for i in x:\n        if i=='$' or i=='#' or i=='@':\n            return True\n    return False\n\ns = input().split(',')\nlst = []\n\nfor i in s:\n    length = len(i)\n    if 6 <= length and length <= 12 and is_low(i) and is_up(i) and is_num(i) and is_other(i):   #Checks if all the requirments are fulfilled\n        lst.append(i)\n\nprint(\",\".join(lst))\n```\n\n**OR**\n\n```python\ndef check(x):\n    cnt = (6<=len(x) and len(x)<=12)\n    for i in x:\n        if i.isupper():\n            cnt+=1\n            break\n    for i in x:\n        if i.islower():\n            cnt+=1\n            break\n    for i in x:\n        if i.isnumeric():\n            cnt+=1\n            break\n    for i in x:\n        if i=='@' or i=='#'or i=='$':\n            cnt+=1\n            break\n    return cnt == 5               # counting if total 5 all conditions are fulfilled then returns True\n\ns = input().split(',')\nlst = filter(check,s)             # Filter function pick the words from s, those returns True by check() function\nprint(\",\".join(lst))\n```\n\n**OR**\n\n```python\nimport  re\n\ns = input().split(',')\nlst = []\n\nfor i in s:\n    cnt = 0\n    cnt+=(6<=len(i) and len(i)<=12)\n    cnt+=bool(re.search(\"[a-z]\",i))      # here re module includes a function re.search() which returns the object information\n    cnt+=bool(re.search(\"[A-Z]\",i))      # of where the pattern string i is matched with any of the [a-z]/[A-z]/[0=9]/[@#$] characters\n    cnt+=bool(re.search(\"[0-9]\",i))      # if not a single match found then returns NONE which converts to False in boolean\n    cnt+=bool(re.search(\"[@#$]\",i))      # expression otherwise True if found any.\n    if cnt == 5:\n        lst.append(i)\n\nprint(\",\".join(lst))\n```\n\n---\n\n```python\n'''Solution by: pratikb0501\n'''\nimport re\na = input('Enter passwords: ').split(',')\npass_pattern = re.compile(r\"^(?=.*[0-9])(?=.*[a-z])(?=.*[A-Z])(?=.*[$#@]).{6,12}$\")\nfor i in a:\n    if pass_pattern.fullmatch(i):\n        print(i)\n```\n\n---\n\n# Question 19\n\n### **Question:**\n\n> **_You are required to write a program to sort the (name, age, score) tuples by ascending order where name is string, age and score are numbers. The tuples are input by console. The sort criteria is:_**\n\n- **_1: Sort based on name_**\n- **_2: Then sort based on age_**\n- **_3: Then sort by score_**\n\n> **_The priority is that name > age > score._**\n\n> **_If the following tuples are given as input to the program:_**\n\n```\nTom,19,80\nJohn,20,90\nJony,17,91\nJony,17,93\nJson,21,85\n```\n\n> **_Then, the output of the program should be:_**\n\n```\n[('John', '20', '90'), ('Jony', '17', '91'), ('Jony', '17', '93'), ('Json', '21', '85'), ('Tom', '19', '80')]\n```\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input.We use itemgetter to enable multiple sort keys._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nfrom operator import itemgetter, attrgetter\n\nl = []\nwhile True:\n    s = raw_input()\n    if not s:\n        break\n    l.append(tuple(s.split(\",\")))\n\nprint sorted(l, key=itemgetter(0,1,2))\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nlst = []\nwhile True:\n    s = input().split(',')\n    if not s[0]:                          # breaks for blank input\n        break\n    lst.append(tuple(s))\n\nlst.sort(key= lambda x:(x[0],x[1],x[2]))  # here key is defined by lambda and the data is sorted by element priority 0>1>2 in accending order\nprint(lst)\n```\n\n---\n\n## Conclusion\n\n**_Before the above problems, I didn't even know about re(regular expression) module and its use. I didn't even know how to sort by multiple keys. To solve those problems in different ways I had to explore and learn those syntax.There are a lots of interesting stuffs in re module though I faced quite a bit hardship to understand many of them._**\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%205.md \"Day 5\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%207.md \"Day 7\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day 7.md",
    "content": "# Question 20\n\n### **Question:**\n\n> **_Define a class with a generator which can iterate the numbers, which are divisible by 7, between a given range 0 and n._**\n\n---\n\n### Hints:\n\n> **_Consider use class, function and comprehension._**\n\n---\n\n**Main author's Solution: Python 2**\n\n#### **_The solution code for this problem was not as reltive to as the problem mentioned and there was a typing mistake while calling the function._**\n\n---\n\n**Solution: Python 3**\n\n```python\n'''Solution by: ShalomPrinz\n'''\nclass MyGen():\n    def by_seven(self, n):\n        for i in range(0, int(n/7) + 1):\n            yield i * 7\n\nfor i in MyGen().by_seven( int(input('Please enter a number... ')) ):\n    print(i)\n```\n\n---\n\n```python\n'''Solution by: Seawolf159\n'''\nclass Divisible:\n\n    def by_seven(self, n):\n        for number in range(1,n + 1):\n            if number % 7 == 0: yield number\n\n\ndivisible = Divisible()\ngenerator = divisible.by_seven(int(input(\"Please insert a number. --> \")))\nfor number in generator:\n    print(number)\n\n```\n\n---\n\n# Question 21\n\n### **Question:**\n\n> **_A robot moves in a plane starting from the original point (0,0). The robot can move toward UP, DOWN, LEFT and RIGHT with a given steps. The trace of robot movement is shown as the following:_**\n\n```\nUP 5\nDOWN 3\nLEFT 3\nRIGHT 2\n```\n\n> **_The numbers after the direction are steps. Please write a program to compute the distance from current position after a sequence of movement and original point. If the distance is a float, then just print the nearest integer._**\n> **_Example:_**\n> **_If the following tuples are given as input to the program:_**\n\n```\nUP 5\nDOWN 3\nLEFT 3\nRIGHT 2\n```\n\n> **_Then, the output of the program should be:_**\n\n```\n2\n```\n\n---\n\n### Hints:\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input.Here distance indicates to euclidean distance.Import math module to use sqrt function._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nimport math\npos = [0,0]\nwhile True:\n    s = raw_input()\n    if not s:\n        break\n    movement = s.split(\" \")\n    direction = movement[0]\n    steps = int(movement[1])\n    if direction==\"UP\":\n        pos[0]+=steps\n    elif direction==\"DOWN\":\n        pos[0]-=steps\n    elif direction==\"LEFT\":\n        pos[1]-=steps\n    elif direction==\"RIGHT\":\n        pos[1]+=steps\n    else:\n        pass\n\nprint int(round(math.sqrt(pos[1]**2+pos[0]**2)))\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport  math\n\nx,y = 0,0\nwhile True:\n    s = input().split()\n    if not s:\n        break\n    if s[0]=='UP':                  # s[0] indicates command\n        x-=int(s[1])                # s[1] indicates unit of move\n    if s[0]=='DOWN':\n        x+=int(s[1])\n    if s[0]=='LEFT':\n        y-=int(s[1])\n    if s[0]=='RIGHT':\n        y+=int(s[1])\n                                    # N**P means N^P\ndist = round(math.sqrt(x**2 + y**2))  # euclidean distance = square root of (x^2+y^2) and rounding it to nearest integer\nprint(dist)\n```\n---\n```python\n'''Solution by: pratikb0501\n'''\n\nfrom math import sqrt\nlst = []\nposition = [0,0]\nwhile True:\n    a = input()\n    if not a:\n        break\n    lst.append(a)\nfor i in lst:\n    if 'UP' in i:\n        position[0] -= int(i.strip('UP '))\n    if 'DOWN' in i:\n        position[0] += int(i.strip('DOWN '))\n    if 'LEFT' in i:\n        position[1] -= int(i.strip('LEFT '))\n    if 'RIGHT' in i:\n        position[1] += int(i.strip('RIGHT '))\nprint(round(sqrt(position[1] ** 2 + position[0] ** 2)))\n```\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%206.md \"Day 6\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%208.md \"Day 8\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day 8.md",
    "content": "# Question 22\n\n### **Question:**\n\n> **_Write a program to compute the frequency of the words from the input. The output should output after sorting the key alphanumerically._**\n\n> **_Suppose the following input is supplied to the program:_**\n\n```\nNew to Python or choosing between Python 2 and Python 3? Read Python 2 or Python 3.\n```\n\n> **_Then, the output should be:_**\n\n```\n2:2\n3.:1\n3?:1\nNew:1\nPython:5\nRead:1\nand:1\nbetween:1\nchoosing:1\nor:2\nto:1\n```\n\n---\n\n### Hints\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nfreq = {}   # frequency of words in text\nline = raw_input()\nfor word in line.split():\n    freq[word] = freq.get(word,0)+1\n\nwords = freq.keys()\nwords.sort()\n\nfor w in words:\n    print \"%s:%d\" % (w,freq[w])\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nss = input().split()\nword = sorted(set(ss))     # split words are stored and sorted as a set\n\nfor i in word:\n    print(\"{0}:{1}\".format(i,ss.count(i)))\n```\n\n**OR**\n\n```python\nss = input().split()\ndict = {}\nfor i in ss:\n    i = dict.setdefault(i,ss.count(i))    # setdefault() function takes key & value to set it as dictionary.\n\ndict = sorted(dict.items())               # items() function returns both key & value of dictionary as a list\n                                          # and then sorted. The sort by default occurs in order of 1st -> 2nd key\nfor i in dict:\n    print(\"%s:%d\"%(i[0],i[1]))\n```\n\n**OR**\n\n```python\nss = input().split()\ndict = {i:ss.count(i) for i in ss}     # sets dictionary as i-> split word & ss.count(i) -> total occurrence of i in ss\ndict = sorted(dict.items())            # items() function returns both key & value of dictionary as a list\n                                       # and then sorted. The sort by default occurs in order of 1st -> 2nd key\nfor i in dict:\n    print(\"%s:%d\"%(i[0],i[1]))\n```\n\n**OR**\n\n```python\nfrom collections import Counter\n\nss = input().split()\nss = Counter(ss)         # returns key & frequency as a dictionary\nss = sorted(ss.items())  # returns as a tuple list\n\nfor i in ss:\n    print(\"%s:%d\"%(i[0],i[1]))\n```\n\n**Solution by: AnjanKumarG**\n\n```python\nfrom pprint import pprint\np=input().split()\npprint({i:p.count(i) for i in p})\n```\n\n---\n\n# Question 23\n\n### **Question:**\n\n> **_Write a method which can calculate square value of number_**\n\n---\n\n### Hints:\n\n```\nUsing the ** operator which can be written as n**p where means n^p\n```\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndef square(num):\n    return num ** 2\n\nprint square(2)\nprint square(3)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nn=int(input())\nprint(n**2)\n```\n\n---\n\n# Question 24\n\n### **Question:**\n\n> **_Python has many built-in functions, and if you do not know how to use it, you can read document online or find some books. But Python has a built-in document function for every built-in functions._**\n\n> **_Please write a program to print some Python built-in functions documents, such as abs(), int(), raw_input()_**\n\n> **_And add document for your own function_**\n\n### Hints:\n\n```\nThe built-in document method is __doc__\n```\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nprint abs.__doc__\nprint int.__doc__\nprint raw_input.__doc__\n\ndef square(num):\n    '''Return the square value of the input number.\n\n    The input number must be integer.\n    '''\n    return num ** 2\n\nprint square(2)\nprint square.__doc__\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nprint(str.__doc__)\nprint(sorted.__doc__)\n\ndef pow(n,p):\n    '''\n    param n: This is any integer number\n    param p: This is power over n\n    return:  n to the power p = n^p\n    '''\n\n    return n**p\n\nprint(pow(3,4))\nprint(pow.__doc__)\n```\n\n---\n\n# Question 25\n\n### **Question:**\n\n> **_Define a class, which have a class parameter and have a same instance parameter._**\n\n---\n\n### Hints:\n\n```\nDefine an instance parameter, need add it in __init__ method.You can init an object with construct parameter or set the value later\n```\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nclass Person:\n    # Define the class parameter \"name\"\n    name = \"Person\"\n\n    def __init__(self, name = None):\n        # self.name is the instance parameter\n        self.name = name\n\njeffrey = Person(\"Jeffrey\")\nprint \"%s name is %s\" % (Person.name, jeffrey.name)\n\nnico = Person()\nnico.name = \"Nico\"\nprint \"%s name is %s\" % (Person.name, nico.name)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nclass Car:\n    name = \"Car\"\n\n    def __init__(self,name = None):\n        self.name = name\n\nhonda=Car(\"Honda\")\nprint(\"%s name is %s\"%(Car.name,honda.name))\n\ntoyota=Car()\ntoyota.name=\"Toyota\"\nprint(\"%s name is %s\"%(Car.name,toyota.name))\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%207.md \"Day 7\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%209.md \"Day 9\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day 9.md",
    "content": "# Question 26\n\n### **Question:**\n\n> **_Define a function which can compute the sum of two numbers._**\n\n---\n\n### Hints:\n\n> **_Define a function with two numbers as arguments. You can compute the sum in the function and return the value._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndef SumFunction(number1, number2):\n\treturn number1 + number2\n\nprint SumFunction(1,2)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nsum = lambda n1,n2 : n1 + n2      # here lambda is use to define little function as sum\nprint(sum(1,2))\n```\n\n---\n\n# Question 27\n\n### **Question:**\n\n> **_Define a function that can convert a integer into a string and print it in console._**\n\n---\n\n### Hints:\n\n> **_Use str() to convert a number to string._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndef printValue(n):\n\tprint str(n)\n\nprintValue(3)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nconv = lambda x : str(x)\nn = conv(10)\nprint(n)\nprint(type(n))            # checks the type of the variable\n```\n\n---\n\n# Question 28\n\n### **Question:**\n\n> **_Define a function that can receive two integer numbers in string form and compute their sum and then print it in console._**\n\n---\n\n### Hints:\n\n> **_Use int() to convert a string to integer._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndef printValue(s1,s2):\n\tprint int(s1) + int(s2)\nprintValue(\"3\",\"4\") #7\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nsum = lambda s1,s2 : int(s1) + int(s2)\nprint(sum(\"10\",\"45\"))      # 55\n```\n\n---\n\n# Question 29\n\n### **Question:**\n\n> **_Define a function that can accept two strings as input and concatenate them and then print it in console._**\n\n---\n\n### Hints:\n\n> **_Use + sign to concatenate the strings._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndef printValue(s1,s2):\n\tprint s1 + s2\n\nprintValue(\"3\",\"4\") #34\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nsum = lambda s1,s2 : s1 + s2\nprint(sum(\"10\",\"45\"))        # 1045\n```\n\n---\n\n# Question 30\n\n### **Question:**\n\n> **_Define a function that can accept two strings as input and print the string with maximum length in console. If two strings have the same length, then the function should print all strings line by line._**\n\n---\n\n### Hints:\n\n> **_Use len() function to get the length of a string._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndef printValue(s1,s2):\n\tlen1 = len(s1)\n\tlen2 = len(s2)\n\tif len1 > len2:\n\t\tprint s1\n\telif len2 > len1:\n\t\tprint s2\n\telse:\n\t\tprint s1\n\t\tprint s2\n\nprintValue(\"one\",\"three\")\n\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef printVal(s1,s2):\n    len1 = len(s1)\n    len2 = len(s2)\n    if len1 > len2:\n        print(s1)\n    elif len1 < len2:\n        print(s2)\n    else:\n        print(s1)\n        print(s2)\n\ns1,s2=input().split()\nprintVal(s1,s2)\n```\n\n---\n\n```python\n'''Solution by: yuan1z'''\nfunc = lambda a,b: print(max((a,b),key=len)) if len(a)!=len(b) else print(a+'\\n'+b)\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%208.md \"Day 9\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_10.md \"Day 10\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_10.md",
    "content": "# Question 31\n\n### **Question:**\n\n> **_Define a function which can print a dictionary where the keys are numbers between 1 and 20 (both included) and the values are square of keys._**\n\n---\n\n### Hints:\n\n```\nUse dict[key]=value pattern to put entry into a dictionary.Use ** operator to get power of a number.Use range() for loops.\n```\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\ndef printDict():\n\td=dict()\n\tfor i in range(1,21):\n\t\td[i]=i**2\n\tprint d\n\nprintDict()\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef printDict():\n    dict={i:i**2 for i in range(1,21)}   # Using comprehension method and\n    print(dict)\n\nprintDict()\n```\n\n---\n\n# Question 32\n\n### **Question:**\n\n> **_Define a function which can generate a dictionary where the keys are numbers between 1 and 20 (both included) and the values are square of keys. The function should just print the keys only._**\n\n---\n\n### Hints:\n\n```\nUse dict[key]=value pattern to put entry into a dictionary.Use ** operator to get power of a number.Use range() for loops.Use keys() to iterate keys in the dictionary. Also we can use item() to get key/value pairs.\n```\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\ndef printDict():\n\td=dict()\n\tfor i in range(1,21):\n\t\td[i]=i**2\n\tfor k in d.keys():\n\t\tprint k\nprintDict()\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef printDict():\n    dict = {i: i**2 for i in range(1, 21)}\n    print(dict.keys())      # print keys of a dictionary\n\nprintDict()\n```\n\n---\n\n# Question 33\n\n### **Question:**\n\n> **_Define a function which can generate and print a list where the values are square of numbers between 1 and 20 (both included)._**\n\n---\n\n### Hints:\n\n```\nUse ** operator to get power of a number.Use range() for loops.Use list.append() to add values into a list.\n```\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\ndef printList():\n\tli=list()\n\tfor i in range(1,21):\n\t\tli.append(i**2)\n\tprint li\n\nprintList()\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef printList():\n    lst = [i ** 2 for i in range(1, 21)]\n    print(lst)\n\nprintList()\n```\n\n---\n\n# Question 34\n\n### **Question:**\n\n> **_Define a function which can generate a list where the values are square of numbers between 1 and 20 (both included). Then the function needs to print the first 5 elements in the list._**\n\n---\n\n### Hints:\n\n```\nUse ** operator to get power of a number.Use range() for loops.Use list.append() to add values into a list.Use [n1:n2] to slice a list\n```\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\ndef printList():\n\tli=list()\n\tfor i in range(1,21):\n\t\tli.append(i**2)\n\tprint li[:5]\n\nprintList()\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef printList():\n    lst = [i ** 2 for i in range(1, 21)]\n\n    for i in range(5):\n        print(lst[i])\n\nprintList()\n```\n---\n```python\n'''Solution by: popomaticbubble\n'''\ndef squares(n):\n    squares_list = [i**2 for i in range(1,n+1)]\n    print(squares_list[0:5])\nsquares(20)\n```\n---\n\n```python\n'''Solution by: yuan1z'''\nfunc = lambda :print([i**2 for i in range(1,21)][:5])\n```\n\n---\n\n# Question 35\n\n### **Question:**\n\n> **_Define a function which can generate a list where the values are square of numbers between 1 and 20 (both included). Then the function needs to print the last 5 elements in the list._**\n\n---\n\n### Hints:\n\n```\nUse ** operator to get power of a number.Use range() for loops.Use list.append() to add values into a list.Use [n1:n2] to slice a list\n```\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\ndef printList():\n\tli=list()\n\tfor i in range(1,21):\n\t\tli.append(i**2)\n\tprint li[-5:]\n\nprintList()\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef printList():\n    lst = [i ** 2 for i in range(1, 21)]\n    for i in range(19,14,-1):\n        print(lst[i])\n\nprintList()\n```\n---\n```python\n'''Solution by: popomaticbubble\n'''\ndef squares(n):\n    squares_list = [i**2 for i in range(1,n+1)]\n    print(squares_list[-5:])\nsquares(20)\n```\n\n---\n\n# Question 36\n\n### **Question:**\n\n> **_Define a function which can generate a list where the values are square of numbers between 1 and 20 (both included). Then the function needs to print all values except the first 5 elements in the list._**\n\n---\n\n```\nHints: Use ** operator to get power of a number.Use range() for loops.Use list.append() to add values into a list.Use [n1:n2] to slice a list\n```\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\ndef printList():\n\tli=list()\n\tfor i in range(1,21):\n\t\tli.append(i**2)\n\tprint li[5:]\n\nprintList()\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef printList():\n    lst = [i ** 2 for i in range(1, 21)]\n    for i in range(5,20):\n        print(lst[i])\n\nprintList()\n```\n\n---\n\n# Question 37\n\n### **Question:**\n\n> **_Define a function which can generate and print a tuple where the value are square of numbers between 1 and 20 (both included)._**\n\n---\n\n### Hints:\n\n```\nUse ** operator to get power of a number.Use range() for loops.Use list.append() to add values into a list.Use tuple() to get a tuple from a list.\n```\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\ndef printTuple():\n\tli=list()\n\tfor i in range(1,21):\n\t\tli.append(i**2)\n\tprint tuple(li)\n\nprintTuple()\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef printTupple():\n    lst = [i ** 2 for i in range(1, 21)]\n    print(tuple(lst))\n\nprintTupple()\n```\n\n---\n\n```python\n'''\nSolution by: Seawolf159\n'''\ndef square_of_numbers():\n    return tuple(i ** 2 for i in range(1, 21))\n\nprint(square_of_numbers())\n```\n\n---\n\n### Comment\n\n**_Problems of this section is very much easy and all of those are of a modification of same type problem which mainly focused on using some commonly used function works with list,dictionary, tupple.In my entire solutions, I havn't tried to solve problems in efficient way.Rather I tried to solve in a different way that I can.This will help a beginner to know how simplest problems can be solved in different ways._**\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day%209.md \"Day 9\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_11.md \"Day 11\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_11.md",
    "content": "# Question 38\n\n### **Question:**\n\n> **_With a given tuple (1,2,3,4,5,6,7,8,9,10), write a program to print the first half values in one line and the last half values in one line._**\n\n---\n\n### Hints:\n\n> **_Use [n1:n2] notation to get a slice from a tuple._**\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\ntp = (1,2,3,4,5,6,7,8,9,10)\ntp1 = tp[:5]\ntp2 = tp[5:]\nprint tp1\nprint tp2\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ntpl = (1,2,3,4,5,6,7,8,9,10)\n\nfor i in range(0,5):\n    print(tpl[i],end = ' ')\nprint()\nfor i in range(5,10):\n    print(tpl[i],end = ' ')\n```\n\n**OR**\n\n```python\ntpl = (1,2,3,4,5,6,7,8,9,10)\nlst1,lst2 = [],[]\n\nfor i in range(0,5):\n    lst1.append(tpl[i])\n\nfor i in range(5,10):\n    lst2.append(tpl[i])\n\nprint(lst1)\nprint(lst2)\n```\n\n**OR**\n\n```python\n\n'''\nSolution by: CoffeeBrakeInc\n'''\n\ntup = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)\nlt = int(len(tup)/2)\nprint(tup[:lt], tup[lt:])\n```\n\n---\n\n**OR**\n\n```python\n\n'''\nSolution by: AasaiAlangaram\n'''\n\ntp = (1,2,3,4,5,6,7,8,9,10)\n\nprint('The Original Tuple:',tp)\n\n[print('Splitted List :{List}'.format(List = tp[x:x+5])) for x in range(0,len(tp),5)]\n\n```\n\n---\n\n# Question 39\n\n### **Question:**\n\n> **_Write a program to generate and print another tuple whose values are even numbers in the given tuple (1,2,3,4,5,6,7,8,9,10)._**\n\n---\n\n### Hints:\n\n> **_Use \"for\" to iterate the tuple. Use tuple() to generate a tuple from a list._**\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\ntp = (1,2,3,4,5,6,7,8,9,10)\nli = list()\nfor i in tp:\n\tif tp[i]%2 == 0:\n\t\tli.append(tp[i])\n\ntp2 = tuple(li)\nprint tp2\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ntpl = (1,2,3,4,5,6,7,8,9,10)\ntpl1 = tuple(i for i in tpl if i%2 == 0)\nprint(tpl1)\n```\n\n**OR**\n\n```python\ntpl = (1,2,3,4,5,6,7,8,9,10)\ntpl1 = tuple(filter(lambda x : x%2==0,tpl))  # Lambda function returns True if found even element.\n                                             # Filter removes data for which function returns False\nprint(tpl1)\n```\n\n---\n\n# Question 40\n\n### **Question:**\n\n> **_Write a program which accepts a string as input to print \"Yes\" if the string is \"yes\" or \"YES\" or \"Yes\", otherwise print \"No\"._**\n\n---\n\n### Hints:\n\n> **_Use if statement to judge condition._**\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\ns= raw_input()\nif s==\"yes\" or s==\"YES\" or s==\"Yes\":\n    print \"Yes\"\nelse:\n    print \"No\"\n```\n\n---\n\n**Solution: Python 3**\n\n```python\n'''\nSolution by: Seawolf159\n'''\ntext = input(\"Please type something. --> \")\nif text == \"yes\" or text == \"YES\" or text == \"Yes\":\n    print(\"Yes\")\nelse:\n    print(\"No\")\n```\n\n---\n\n```python\n'''\nSolution by: AasaiAlangaram\n'''\ninput = input('Enter string:')\noutput = ''.join(['Yes' if input == 'yes' or input =='YES' or input =='Yes' else 'No' ])\nprint(str(output))\n```\n----------------\n```\nSolution by: Prashanth\n'''\nx = str(input().lower())\nif x == 'yes':\n    print('Yes')\nelse:\n    print('No')\n```\n--------\n\n# Question 41\n\n### **Question:**\n\n> **_Write a program which can map() to make a list whose elements are square of elements in [1,2,3,4,5,6,7,8,9,10]._**\n\n---\n\n### Hints:\n\n> **_Use map() to generate a list.Use lambda to define anonymous functions._**\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\nli = [1,2,3,4,5,6,7,8,9,10]\nsquaredNumbers = map(lambda x: x**2, li)\nprint squaredNumbers\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\n# No different way of code is written as the requirment is specificly mentioned in problem description\n\nli = [1,2,3,4,5,6,7,8,9,10]\nsquaredNumbers = map(lambda x: x**2, li)  # returns map type object data\nprint(list(squaredNumbers))               # converting the object into list\n```\n\n---\n\n# Question 42\n\n### **Question:**\n\n> **_Write a program which can map() and filter() to make a list whose elements are square of even number in [1,2,3,4,5,6,7,8,9,10]._**\n\n---\n\n### Hints:\n\n> **_Use map() to generate a list.Use filter() to filter elements of a list.Use lambda to define anonymous functions._**\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\nli = [1,2,3,4,5,6,7,8,9,10]\nevenNumbers = map(lambda x: x**2, filter(lambda x: x%2==0, li))\nprint evenNumbers\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef even(x):\n    return x%2==0\n\ndef squer(x):\n    return x*x\n\nli = [1,2,3,4,5,6,7,8,9,10]\nli = map(squer,filter(even,li))   # first filters number by even number and the apply map() on the resultant elements\nprint(list(li))\n```\n\n---\n\n# Question 43\n\n### **Question:**\n\n> **_Write a program which can filter() to make a list whose elements are even number between 1 and 20 (both included)._**\n\n---\n\n### Hints:\n\n> **_Use filter() to filter elements of a list.Use lambda to define anonymous functions._**\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\nevenNumbers = filter(lambda x: x%2==0, range(1,21))\nprint evenNumbers\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef even(x):\n    return x%2==0\n\nevenNumbers = filter(even, range(1,21))\nprint(list(evenNumbers))\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_10.md \"Day 10\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_12.md \"Day 12\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_12.md",
    "content": "# Question 44\n\n### **Question:**\n\n> **_Write a program which can map() to make a list whose elements are square of numbers between 1 and 20 (both included)._**\n\n---\n\n### Hints:\n\n> **_Use map() to generate a list. Use lambda to define anonymous functions._**\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\nsquaredNumbers = map(lambda x: x**2, range(1,21))\nprint squaredNumbers\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef sqr(x):\n    return x*x\n\nsquaredNumbers = list(map(sqr, range(1,21)))\nprint (squaredNumbers)\n```\n\n---\n\n# Question 45\n\n### **Question:**\n\n> **_Define a class named American which has a static method called printNationality._**\n\n---\n\n### Hints:\n\n> **_Use @staticmethod decorator to define class static method.There are also two more methods.To know more, go to this [link](https://realpython.com/blog/python/instance-class-and-static-methods-demystified/)._**\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\nclass American(object):\n    @staticmethod\n    def printNationality():\n        print \"America\"\n\nanAmerican = American()\nanAmerican.printNationality()\nAmerican.printNationality()\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nclass American():\n    @staticmethod\n    def printNationality():\n        print(\"I am American\")\n\namerican = American()\namerican.printNationality()   # this will not run if @staticmethod does not decorates the function.\n                              # Because the class has no instance.\n\nAmerican.printNationality()   # this will run even though the @staticmethod\n                              # does not decorate printNationality()\n```\n\n---\n\n# Question 46\n\n### **Question:**\n\n> **_Define a class named American and its subclass NewYorker._**\n\n---\n\n### Hints:\n\n> **Use class Subclass(ParentClass) to define a subclass.\\***\n\n---\n\n**Main Author's Solution: Python 2**\n\n```python\nclass American(object):\n    pass\n\nclass NewYorker(American):\n    pass\n\nanAmerican = American()\naNewYorker = NewYorker()\nprint anAmerican\nprint aNewYorker\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nclass American():\n    pass\n\nclass NewYorker(American):\n    pass\n\namerican = American()\nnewyorker = NewYorker()\n\nprint(american)\nprint(newyorker)\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_11.md \"Day 11\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_13.md \"Day 13\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_13.md",
    "content": "# Question 47\n\n### **Question**\n\n> **_Define a class named Circle which can be constructed by a radius. The Circle class has a method which can compute the area._**\n\n---\n\n### Hints\n\n> **_Use def methodName(self) to define a method._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nclass Circle(object):\n    def __init__(self, r):\n        self.radius = r\n\n    def area(self):\n        return self.radius**2*3.14\n\naCircle = Circle(2)\nprint aCircle.area()\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nclass Circle():\n    def __init__(self,r):\n        self.radius = r\n\n    def area(self):\n        return 3.1416*(self.radius**2)\n\n\ncircle = Circle(5)\nprint(circle.area())\n```\n\n---\n\n# Question 48\n\n### **Question**\n\n> **_Define a class named Rectangle which can be constructed by a length and width. The Rectangle class has a method which can compute the area._**\n\n---\n\n### Hints\n\n> **_Use def methodName(self) to define a method._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nclass Rectangle(object):\n    def __init__(self, l, w):\n        self.length = l\n        self.width  = w\n\n    def area(self):\n        return self.length*self.width\n\naRectangle = Rectangle(2,10)\nprint aRectangle.area()\n\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nclass Rectangle():\n    def __init__(self,l,w):\n        self.length = l\n        self.width = w\n\n    def area(self):\n        return self.length*self.width\n\n\nrect = Rectangle(2,4)\nprint(rect.area())\n\n```\n\n---\n\n# Question 49\n\n### **Question**\n\n> **_Define a class named Shape and its subclass Square. The Square class has an init function which takes a length as argument. Both classes have a area function which can print the area of the shape where Shape's area is 0 by default._**\n\n---\n\n### Hints\n\n> **_To override a method in super class, we can define a method with the same name in the super class._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nclass Shape(object):\n    def __init__(self):\n        pass\n\n    def area(self):\n        return 0\n\nclass Square(Shape):\n    def __init__(self, l):\n        Shape.__init__(self)\n        self.length = l\n\n    def area(self):\n        return self.length*self.length\n\naSquare= Square(3)\nprint aSquare.area()\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nclass Shape():\n    def __init__(self):\n        pass\n\n    def area(self):\n        return 0\n\nclass Square(Shape):\n    def __init__(self,length = 0):\n        Shape.__init__(self)\n        self.length = length\n\n    def area(self):\n        return self.length*self.length\n\nAsqr = Square(5)\nprint(Asqr.area())      # prints 25 as given argument\n\nprint(Square().area())  # prints zero as default area\n```\n\n---\n\n# Question 50\n\n### **Question**\n\n> **_Please raise a RuntimeError exception._**\n\n---\n\n### Hints\n\n> **_UUse raise() to raise an exception._**\n\n---\n\n**Solution:**\n\n```python\nraise RuntimeError('something wrong')\n```\n\n---\n\n## Conclusion\n\n**_Well It seems that the above problems are very much focused on basic concpets and implimantation of object oriented programming.As the concepts are not about to solve any functional problem rather design the structure , so both codes are very much similar in there implimantation part._**\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_12.md \"Day 12\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_14.md \"Day 14\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_14.md",
    "content": "\n# Question 51\n\n### **Question**\n\n> ***Write a function to compute 5/0 and use try/except to catch the exceptions.***\n\n----------------------\n### Hints \n> ***Use try/except to catch exceptions.***\n\n----------------------\n\n**Main author's Solution: Python 2**\n```python\ndef throws():\n    return 5/0\n\ntry:\n    throws()\nexcept ZeroDivisionError:\n    print \"division by zero!\"\nexcept Exception, err:\n    print 'Caught an exception'\nfinally:\n    print 'In finally block for cleanup'\n```\n----------------\n**My Solution: Python 3**\n```python\ndef divide():\n    return 5/0\n\ntry:\n    divide()\nexcept ZeroDivisionError as ze:\n    print(\"Why on earth you are dividing a number by ZERO!!\")\nexcept:\n    print(\"Any other exception\")\n\n```\n---------------------\n\n\n# Question 52\n\n### **Question**\n\n> ***Define a custom exception class which takes a string message as attribute.***\n\n----------------------\n### Hints \n> ***To define a custom exception, we need to define a class inherited from Exception.***\n\n----------------------\n\n**Main author's Solution: Python 2**\n```python\nclass MyError(Exception):\n    \"\"\"My own exception class\n\n    Attributes:\n        msg  -- explanation of the error\n    \"\"\"\n\n    def __init__(self, msg):\n        self.msg = msg\n\nerror = MyError(\"something wrong\")\n\n```\n----------------\n**My Solution: Python 3**\n```python\n\nclass CustomException(Exception):\n    \"\"\"Exception raised for custom purpose\n\n    Attributes:\n        message -- explanation of the error\n    \"\"\"\n\n    def __init__(self, message):\n        self.message = message\n\n\nnum = int(input())\n\ntry:\n    if num < 10:\n        raise CustomException(\"Input is less than 10\")\n    elif num > 10:\n        raise CustomException(\"Input is grater than 10\")\nexcept CustomException as ce:\n    print(\"The error raised: \" + ce.message)\n\n```\n---------------------\n\n\n# Question 53\n\n### **Question**\n\n> ***Assuming that we have some email addresses in the \"username@companyname.com\" format, please write program to print the user name of a given email address. Both user names and company names are composed of letters only.***\n\n> ***Example:\nIf the following email address is given as input to the \nprogram:***\n```\njohn@google.com\n```\n> ***Then, the output of the program should be:***\n```\njohn\n```\n> ***In case of input data being supplied to the question, it should be assumed to be a console input.***\n\n----------------------\n### Hints \n> ***Use \\w to match letters.***\n\n----------------------\n\n**Main author's Solution: Python 2**\n```python\nimport re\nemailAddress = raw_input()\npat2 = \"(\\w+)@((\\w+\\.)+(com))\"\nr2 = re.match(pat2,emailAddress)\nprint r2.group(1)\n```\n----------------\n**My Solution: Python 3**\n```python\nemail = \"john@google.com\"\nemail = email.split('@')\nprint(email[0])\n```\n---------------------\n**OR**\n```python\nimport re\n\nemail = \"john@google.com elise@python.com\"\npattern = \"(\\w+)@\\w+.com\"\nans = re.findall(pattern,email)\nprint(ans)\n```\n\n\n[***go to previous day***](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_13.md \"Day 13\")\n\n[***go to next day***](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_15.md \"Day 15\")\n\n[***Discussion***](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_15.md",
    "content": "# Question 54\n\n### **Question**\n\n> **_Assuming that we have some email addresses in the \"username@companyname.com\" format, please write program to print the company name of a given email address. Both user names and company names are composed of letters only._**\n\n> **_Example:\n> If the following email address is given as input to the program:_**\n\n```\njohn@google.com\n```\n\n> **_Then, the output of the program should be:_**\n\n```\ngoogle\n```\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n### Hints\n\n> **_Use \\w to match letters._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nimport re\nemailAddress = raw_input()\npat2 = \"(\\w+)@(\\w+)\\.(com)\"\nr2 = re.match(pat2,emailAddress)\nprint r2.group(2)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport re\n\nemail = \"john@google.com elise@python.com\"\npattern = \"\\w+@(\\w+).com\"\nans = re.findall(pattern,email)\nprint(ans)\n```\n\n---\n\n# Question 55\n\n### **Question**\n\n> **_Write a program which accepts a sequence of words separated by whitespace as input to print the words composed of digits only._**\n\n> **_Example:\n> If the following words is given as input to the program:_**\n\n```\n2 cats and 3 dogs.\n```\n\n> **_Then, the output of the program should be:_**\n\n```\n['2', '3']\n```\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n### Hints\n\n> **_Use re.findall() to find all substring using regex._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nimport re\ns = raw_input()\nprint re.findall(\"\\d+\",s)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport re\n\nemail = input()\npattern = \"\\d+\"\nans = re.findall(pattern,email)\nprint(ans)\n```\n\n**OR**\n\n```python\nemail = input().split()\nans = []\nfor word in email:\n    if word.isdigit():     # can also use isnumeric() / isdecimal() function instead\n       ans.append(word)\nprint(ans)\n```\n\n**OR**\n\n```python\nemail = input().split()\nans = [word for word in email if word.isdigit()]  # using list comprehension method\nprint(ans)\n```\n\n---\n\n# Question 56\n\n### **Question**\n\n> **_Print a unicode string \"hello world\"._**\n\n---\n\n### Hints\n\n> **_Use u'strings' format to define unicode string._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nunicodeString = u\"hello world!\"\nprint unicodeString\n```\n\n---\n\n# Question 57\n\n### **Question**\n\n> **_Write a program to read an ASCII string and to convert it to a unicode string encoded by utf-8._**\n\n---\n\n### Hints\n\n> **_Use unicode()/encode() function to convert._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ns = raw_input()\nu = unicode( s ,\"utf-8\")\nprint u\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ns = input()\nu = s.encode('utf-8')\nprint(u)\n```\n\n---\n\n# Question 58\n\n### **Question**\n\n> **_Write a special comment to indicate a Python source code file is in unicode._**\n\n---\n\n### Hints\n\n> **_Use unicode() function to convert._**\n\n---\n\n**Solution:**\n\n```python\n# -*- coding: utf-8 -*-\n```\n\n---\n\n# Question 59\n\n### **Question**\n\n> **_Write a program to compute 1/2+2/3+3/4+...+n/n+1 with a given n input by console (n>0)._**\n\n> **_Example:\n> If the following n is given as input to the program:_**\n\n```\n5\n```\n\n> **_Then, the output of the program should be:_**\n\n```\n3.55\n```\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n### Hints\n\n> **_Use float() to convert an integer to a float.Even if not converted it wont cause a problem because python by default understands the data type of a value_**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nn=int(raw_input())\nsum=0.0\nfor i in range(1,n+1):\n    sum += float(float(i)/(i+1))\nprint sum\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nn = int(input())\nsum = 0\nfor i in range(1, n+1):\n    sum+= i/(i+1)\nprint(round(sum, 2))  # rounded to 2 decimal point\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_14.md \"Day 14\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_16.md \"Day 16\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_16.md",
    "content": "# Question 60\n\n### **Question**\n\n> **_Write a program to compute:_**\n\n```\nf(n)=f(n-1)+100 when n>0\nand f(0)=0\n```\n\n> **_with a given n input by console (n>0)._**\n\n> **_Example:\n> If the following n is given as input to the program:_**\n\n```\n5\n```\n\n> **_Then, the output of the program should be:_**\n\n```\n500\n```\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n### Hints\n\n> **_We can define recursive function in Python._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndef f(n):\n    if n==0:\n        return 0\n    else:\n        return f(n-1)+100\n\nn=int(raw_input())\nprint f(n)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef f(n):\n    if n == 0:\n        return 0\n    return f(n-1) + 100\n\nn = int(input())\nprint(f(n))\n```\n\n---\n\n# Question 61\n\n### **Question**\n\n> **_The Fibonacci Sequence is computed based on the following formula:_**\n\n```\nf(n)=0 if n=0\nf(n)=1 if n=1\nf(n)=f(n-1)+f(n-2) if n>1\n```\n\n> **_Please write a program to compute the value of f(n) with a given n input by console._**\n\n> **_Example:\n> If the following n is given as input to the program:_**\n\n```\n7\n```\n\n> **_Then, the output of the program should be:_**\n\n```\n13\n```\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n### Hints\n\n> **_We can define recursive function in Python._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndef f(n):\n    if n == 0: return 0\n    elif n == 1: return 1\n    else: return f(n-1)+f(n-2)\n\nn=int(raw_input())\nprint f(n)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef f(n):\n    if n < 2:\n        return n\n    return f(n-1) + f(n-2)\n\nn = int(input())\nprint(f(n))\n```\n\n---\n\n# Question 62\n\n### **Question**\n\n> **_The Fibonacci Sequence is computed based on the following formula:_**\n\n```\nf(n)=0 if n=0\nf(n)=1 if n=1\nf(n)=f(n-1)+f(n-2) if n>1\n```\n\n> **_Please write a program to compute the value of f(n) with a given n input by console._**\n\n> **_Example:\n> If the following n is given as input to the program:_**\n\n```\n7\n```\n\n> **_Then, the output of the program should be:_**\n\n```\n0,1,1,2,3,5,8,13\n```\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n### Hints\n\n> **_We can define recursive function in Python.\n> Use list comprehension to generate a list from an existing list.\n> Use string.join() to join a list of strings._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndef f(n):\n    if n == 0: return 0\n    elif n == 1: return 1\n    else: return f(n-1)+f(n-2)\n\nn=int(raw_input())\nvalues = [str(f(x)) for x in range(0, n+1)]\nprint \",\".join(values)\n\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef f(n):\n    if n < 2:\n        fibo[n] = n\n        return fibo[n]\n    fibo[n] = f(n-1) + f(n-2)\n    return fibo[n]\n\nn = int(input())\nfibo = [0]*(n+1)  # initialize a list of size (n+1)\nf(n)              # call once and it will set value to fibo[0-n]\nfibo = [str(i) for i in fibo]   # converting integer data to string type\nans = \",\".join(fibo)    # joining all string element of fibo with ',' character\nprint(ans)\n\n```\n---\n```python\n\n'''Solution by: popomaticbubble\n'''\ndef fibo(n):\n    if n < 2: return n\n\treturn fibo(n-1)+fibo(n-2)\n\ndef print_fiblist(n):\n    fib_list = [(str(fibo(i))) for i in range(0, n+1)]\n    return print(\",\".join(fib_list))\nn = int(input())\nprint_fiblist(n)\n\n```\n\n---\n\n# Question 63\n\n### **Question**\n\n> **_Please write a program using generator to print the even numbers between 0 and n in comma separated form while n is input by console._**\n\n> **_Example:\n> If the following n is given as input to the program:_**\n\n```\n10\n```\n\n> **_Then, the output of the program should be:_**\n\n```\n0,2,4,6,8,10\n```\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n### Hints\n\n> **_Use yield to produce the next value in generator._**\n\n---\n\n**Solution:**\n\n```python\ndef EvenGenerator(n):\n    i=0\n    while i<=n:\n        if i%2==0:\n            yield i\n        i+=1\n\n\nn=int(raw_input())\nvalues = []\nfor i in EvenGenerator(n):\n    values.append(str(i))\n\nprint \",\".join(values)\n\n```\n\n**OR**\n\n```python\n# Solution by: StartZer0\nn = int(input())\n\nfor i in range(0, n+1, 2):\n  if i < n - 1:\n    print(i, end = ',' )\n  else:\n    print(i)\n```\n\n---\n\n# Question 64\n\n### **Question**\n\n> **_Please write a program using generator to print the numbers which can be divisible by 5 and 7 between 0 and n in comma separated form while n is input by console._**\n\n> **_Example:\n> If the following n is given as input to the program:_**\n\n```\n100\n```\n\n> **_Then, the output of the program should be:_**\n\n```\n0,35,70\n```\n\n> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\n\n---\n\n### Hints\n\n> **_Use yield to produce the next value in generator._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndef NumGenerator(n):\n    for i in range(n+1):\n        if i%5==0 and i%7==0:\n            yield i\n\nn=int(raw_input())\nvalues = []\nfor i in NumGenerator(n):\n    values.append(str(i))\n\nprint \",\".join(values)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef generate(n):\n    for i in range(n+1):\n        if i % 35 == 0:    # 5*7 = 35, if a number is divisible by a & b then it is also divisible by a*b\n            yield i\n\nn = int(input())\nresp = [str(i) for i in generate(n)]\nprint(\",\".join(resp))\n\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_15.md \"Day 15\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_17.md \"Day 17\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_17.md",
    "content": "# Question 65\n\n### **Question**\n\n> **_Please write assert statements to verify that every number in the list [2,4,6,8] is even._**\n\n---\n\n### Hints\n\n> **_Use \"assert expression\" to make assertion._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nli = [2,4,6,8]\nfor i in li:\n    assert i%2==0\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndata = [2,4,5,6]\nfor i in data:\n    assert i%2 == 0, \"{} is not an even number\".format(i)\n```\n\n---\n\n# Question 66\n\n### **Question**\n\n> **_Please write a program which accepts basic mathematic expression from console and print the evaluation result._**\n\n> **_Example:\n> If the following n is given as input to the program:_**\n\n```\n35 + 3\n```\n\n> **_Then, the output of the program should be:_**\n\n```\n38\n```\n\n---\n\n### Hints\n\n> **_Use eval() to evaluate an expression._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nexpression = raw_input()\nprint eval(expression)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nexpression = input()\nans = eval(expression)\nprint(ans)\n```\n\n---\n\n# Question 67\n\n### **Question**\n\n> **_Please write a binary search function which searches an item in a sorted list. The function should return the index of element to be searched in the list._**\n\n---\n\n### Hints\n\n> **_Use if/elif to deal with conditions._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nimport math\ndef bin_search(li, element):\n    bottom = 0\n    top = len(li)-1\n    index = -1\n    while top>=bottom and index==-1:\n        mid = int(math.floor((top+bottom)/2.0))\n        if li[mid]==element:\n            index = mid\n        elif li[mid]>element:\n            top = mid-1\n        else:\n            bottom = mid+1\n\n    return index\n\nli=[2,5,7,9,11,17,222]\nprint bin_search(li,11)\nprint bin_search(li,12)\n\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\n#to be written\n\n```\n**Solution by ulmasovjafarbek: Python 3**\n```python\ndef binary_search(lst, item):\n    low = 0\n    high = len(lst) - 1\n    \n    while low <= high:\n        mid = round((low + high) / 2)\n        \n        if lst[mid] == item:\n            return mid\n        elif lst[mid] > item:\n            high = mid - 1\n        else:\n            low = mid + 1\n    return None\n    \nlst = [1,3,5,7,]\nprint(binary_search(lst, 9))   \n```\n---\n\n**Solution by AasaiAlangaram: Python 3**\n\n```python\ndef binary_search_Ascending(array, target):\n    lower = 0\n    upper = len(array)\n    print('Array Length:',upper)\n    while lower < upper:\n        x = (lower + upper) // 2\n        print('Middle Value:',x)\n        value = array[x]\n        if target == value:\n            return x\n        elif target > value:\n            lower = x\n        elif target < value:\n            upper = x\n\nArray = [1,5,8,10,12,13,55,66,73,78,82,85,88,99]\nprint('The Value Found at Index:',binary_search_Ascending(Array, 82))\n\n```\n\n---\n\n**Solution by yuan1z: Python 3**\n\n```python\nidx = 0\ndef bs(num,num_list):\n    global idx\n    if (len(num_list) == 1):\n        if num_list[0] == num:\n            return idx\n        else:\n            return \"No exit in the list\"\n    elif num in num_list[:len(num_list)//2]:\n        return bs(num,num_list[:len(num_list)//2])\n    else:\n        idx += len(num_list)//2\n    return bs(num,num_list[len(num_list)//2:])\n\nprint(bs(66,[1,5,8,10,12,13,55,66,73,78,82,85,88,99,100]))\n\n```\n\n---\n\n# Question 68\n\n### **Question**\n\n> **_Please generate a random float where the value is between 10 and 100 using Python module._**\n\n---\n\n### Hints\n\n> **_Use random.random() to generate a random float in [0,1]._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nimport random\nprint random.random()*100\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport random\nrand_num = random.uniform(10,100)\nprint(rand_num)\n```\n\n---\n\n# Question 69\n\n### **Question**\n\n> **_Please generate a random float where the value is between 5 and 95 using Python module._**\n\n---\n\n### Hints\n\n> **_Use random.random() to generate a random float in [0,1]._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nimport random\nprint random.random()*100-5\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport random\nrand_num = random.uniform(5,95)\nprint(rand_num)\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_16.md \"Day 16\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_18.md \"Day 18\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_18.md",
    "content": "# Question 70\n\n### **Question**\n\n> **_Please write a program to output a random even number between 0 and 10 inclusive using random module and list comprehension._**\n\n---\n\n### Hints\n\n> **_Use random.choice() to a random element from a list._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nli = [2,4,6,8]\nimport random\nprint random.choice([i for i in range(11) if i%2==0])\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport random\nresp = [i for i in range(0,11,2)]\nprint(random.choice(resp))\n```\n\n---\n\n# Question 71\n\n### **Question**\n\n> **_Please write a program to output a random number, which is divisible by 5 and 7, between 10 and 150 inclusive using random module and list comprehension._**\n\n---\n\n### Hints\n\n> **_Use random.choice() to a random element from a list._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nimport random\nprint random.choice([i for i in range(10,151) if i%5==0 and i%7==0])\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport random\nresp = [i for i in range(10,151) if i % 35 == 0 ]\nprint(random.choice(resp))\n```\n\n---\n\n# Question 72\n\n### **Question**\n\n> **_Please write a program to generate a list with 5 random numbers between 100 and 200 inclusive._**\n\n---\n\n### Hints\n\n> **_Use random.sample() to generate a list of random values._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\n\nimport random\nprint random.sample(range(100,201), 5)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport random\nresp = random.sample(range(100,201),5)\nprint(resp)\n```\n\n---\n\n# Question 73\n\n### **Question**\n\n> **_Please write a program to randomly generate a list with 5 even numbers between 100 and 200 inclusive._**\n\n---\n\n### Hints\n\n> **_Use random.sample() to generate a list of random values._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\n\nimport random\nprint random.sample([i for i in range(100,201) if i%2==0], 5)\n\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport random\nresp = random.sample(range(100,201,2),5)\nprint(resp)\n```\n\n---\n\n# Question 74\n\n### **Question**\n\n> **_Please write a program to randomly generate a list with 5 numbers, which are divisible by 5 and 7 , between 1 and 1000 inclusive._**\n\n---\n\n### Hints\n\n> **_Use random.sample() to generate a list of random values._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\n\nimport random\nprint random.sample([i for i in range(1,1001) if i%5==0 and i%7==0], 5)\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport random\nlst = [i for i in range(1,1001) if i%35 == 0]\nresp = random.sample(lst,5)\nprint(resp)\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_17.md \"Day 17\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_19.md \"Day 19\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_19.md",
    "content": "# Question 75\n\n### **Question**\n\n> **_Please write a program to randomly print a integer number between 7 and 15 inclusive._**\n\n---\n\n### Hints\n\n> **_Use random.randrange() to a random integer in a given range._**\n\n---\n\n**Solution:**\n\n```python\nimport random\nprint random.randrange(7,16)\n```\n\n---\n\n# Question 76\n\n### **Question**\n\n> **_Please write a program to compress and decompress the string \"hello world!hello world!hello world!hello world!\"._**\n\n---\n\n### Hints\n\n> **_Use zlib.compress() and zlib.decompress() to compress and decompress a string._**\n\n---\n\n**Solution:**\n\n```python\nimport zlib\ns = 'hello world!hello world!hello world!hello world!'\nt = zlib.compress(s)\nprint t\nprint zlib.decompress(t)\n```\n---\n```python\n'''Solution by: anas1434 \n'''\ns = 'hello world!hello world!hello world!hello world!'\n# In Python 3 zlib.compress() accepts only DataType <bytes>\ny = bytes(s, 'utf-8')\nx = zlib.compress(y)\nprint(x)\nprint(zlib.decompress(x))\n```\n---\n\n# Question 77\n\n### **Question**\n\n> **_Please write a program to print the running time of execution of \"1+1\" for 100 times._**\n\n---\n\n### Hints\n\n> **_Use timeit() function to measure the running time._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\n\nfrom timeit import Timer\nt = Timer(\"for i in range(100):1+1\")\nprint t.timeit()\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport datetime\n\nbefore = datetime.datetime.now()\nfor i in range(100):\n    x = 1 + 1\nafter = datetime.datetime.now()\nexecution_time = after - before\nprint(execution_time.microseconds)\n```\n\n**OR**\n\n```python\nimport time\n\nbefore = time.time()\nfor i in range(100):\n    x = 1 + 1\nafter = time.time()\nexecution_time = after - before\nprint(execution_time)\n```\n\n---\n\n# Question 78\n\n### **Question**\n\n> **_Please write a program to shuffle and print the list [3,6,7,8]._**\n\n---\n\n### Hints\n\n> **_Use shuffle() function to shuffle a list._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\n\nfrom random import shuffle\nli = [3,6,7,8]\nshuffle(li)\nprint li\n\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport random\n\nlst = [3,6,7,8]\nrandom.shuffle(lst)\nprint(lst)\n```\n\n**OR**\n\n```python\nimport random\n\n# shuffle with a chosen seed\nlst = [3,6,7,8]\nseed = 7\nrandom.Random(seed).shuffle(lst)\nprint(lst)\n```\n\n---\n\n# Question 79\n\n### **Question**\n\n> **_Please write a program to generate all sentences where subject is in [\"I\", \"You\"] and verb is in [\"Play\", \"Love\"] and the object is in [\"Hockey\",\"Football\"]._**\n\n---\n\n### Hints\n\n> **_Use list[index] notation to get a element from a list._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\n\nsubjects=[\"I\", \"You\"]\nverbs=[\"Play\", \"Love\"]\nobjects=[\"Hockey\",\"Football\"]\nfor i in range(len(subjects)):\n    for j in range(len(verbs)):\n        for k in range(len(objects)):\n            sentence = \"%s %s %s.\" % (subjects[i], verbs[j], objects[k])\n            print sentence\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nsubjects=[\"I\", \"You\"]\nverbs=[\"Play\", \"Love\"]\nobjects=[\"Hockey\",\"Football\"]\n\nfor sub in subjects:\n    for verb in verbs:\n        for obj in objects:\n            print(\"{} {} {}\".format(sub,verb,obj))\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_18.md \"Day 18\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_20.md \"Day 20\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_20.md",
    "content": "# Question 80\n\n### **Question**\n\n> **_Please write a program to print the list after removing even numbers in [5,6,77,45,22,12,24]._**\n\n---\n\n### Hints\n\n> **_Use list comprehension to delete a bunch of element from a list._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nli = [5,6,77,45,22,12,24]\nli = [x for x in li if x%2!=0]\nprint li\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ndef isEven(n):\n    return n%2!=0\n\nli = [5,6,77,45,22,12,24]\nlst = list(filter(isEven,li))\nprint(lst)\n```\n\n**OR**\n\n```python\nli = [5,6,77,45,22,12,24]\nlst = list(filter(lambda n:n%2!=0,li))\nprint(lst)\n```\n\n---\n\n# Question 81\n\n### **Question**\n\n> **_By using list comprehension, please write a program to print the list after removing numbers which are divisible by 5 and 7 in [12,24,35,70,88,120,155]._**\n\n---\n\n### Hints\n\n> **_Use list comprehension to delete a bunch of element from a list._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nli = [12,24,35,70,88,120,155]\nli = [x for x in li if x%5!=0 and x%7!=0]\nprint li\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nli = [12,24,35,70,88,120,155]\nli = [x for x in li if x % 35!=0]\nprint(li)\n```\n\n---\n\n# Question 82\n\n### **Question**\n\n> **_By using list comprehension, please write a program to print the list after removing the 0th, 2nd, 4th,6th numbers in [12,24,35,70,88,120,155]._**\n\n---\n\n### Hints\n\n> **_Use list comprehension to delete a bunch of element from a list.\n> Use enumerate() to get (index, value) tuple._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\n\nli = [12,24,35,70,88,120,155]\nli = [x for (i,x) in enumerate(li) if i%2!=0]\nprint li\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nli = [12,24,35,70,88,120,155]\nli = [li[i] for i in range(len(li)) if i%2 != 0]\nprint(li)\n```\n\n---\n\n# Question 83\n\n### **Question**\n\n> **_By using list comprehension, please write a program to print the list after removing the 2nd - 4th numbers in [12,24,35,70,88,120,155]._**\n\n---\n\n### Hints\n\n> **_Use list comprehension to delete a bunch of element from a list.\n> Use enumerate() to get (index, value) tuple._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\n\nli = [12,24,35,70,88,120,155]\nli = [x for (i,x) in enumerate(li) if i<3 or 4<i]\nprint li\n\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\n#to be written\nli = [12,24,35,70,88,120,155]\nli = [li[i] for i in range(len(li)) if i<3 or 4<i]\nprint(li)\n```\n\n---\n\n# Question 84\n\n### **Question**\n\n> **_By using list comprehension, please write a program generate a 3\\*5\\*8 3D array whose each element is 0._**\n\n---\n\n### Hints\n\n> **_Use list comprehension to make an array._**\n\n---\n\n**Solution:**\n\n```python\narray = [[ [0 for col in range(8)] for col in range(5)] for row in range(3)]\nprint array\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_19.md \"Day 19\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_21.md \"Day 21\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_21.md",
    "content": "# Question 85\n\n### **Question**\n\n> **_By using list comprehension, please write a program to print the list after removing the 0th,4th,5th numbers in [12,24,35,70,88,120,155]._**\n\n---\n\n### Hints\n\n> **_Use list comprehension to delete a bunch of element from a list.Use enumerate() to get (index, value) tuple._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nli = [12,24,35,70,88,120,155]\nli = [x for (i,x) in enumerate(li) if i not in (0,4,5)]\nprint li\n```\n---\n\n**My Solution: Python 3**\n\n```python\nli = [12,24,35,70,88,120,155]\nli = [li[i] for i in range(len(li)) if i not in (0,4,5)]\nprint(li)\n```\n---\n```python\n'''Solution by: pratikb0501\n'''\nli = [12, 24, 35, 70, 88, 120, 155]\nprint(list(j for i, j in enumerate(li) if i != 0 and i != 4 and i != 5))\n\n```\n\n---\n\n# Question 86\n\n### **Question**\n\n> **_By using list comprehension, please write a program to print the list after removing the value 24 in [12,24,35,24,88,120,155]._**\n\n---\n\n### Hints\n\n> **_Use list's remove method to delete a value._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\nli = [12,24,35,24,88,120,155]\nli = [x for x in li if x!=24]\nprint li\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nli = [12,24,35,24,88,120,155]\nli.remove(24)  # this will remove only the first occurrence of 24\nprint(li)\n```\n\n---\n\n# Question 87\n\n### **Question**\n\n> **_With two given lists [1,3,6,78,35,55] and [12,24,35,24,88,120,155], write a program to make a list whose elements are intersection of the above given lists._**\n\n---\n\n### Hints\n\n> **_Use set() and \"&=\" to do set intersection operation._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\n\nset1=set([1,3,6,78,35,55])\nset2=set([12,24,35,24,88,120,155])\nset1 &= set2\nli=list(set1)\nprint li\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nlist1 = [1,3,6,78,35,55]\nlist2 = [12,24,35,24,88,120,155]\nset1= set(list1)\nset2= set(list2)\nintersection = set1 & set2\nprint(intersection)\n```\n\n**OR**\n\n```python\nlist1 = [1,3,6,78,35,55]\nlist2 = [12,24,35,24,88,120,155]\nset1= set(list1)\nset2= set(list2)\nintersection = set.intersection(set1,set2)\nprint(intersection)\n```\n\n---\n\n# Question 88\n\n### **Question**\n\n> **_With a given list [12,24,35,24,88,120,155,88,120,155], write a program to print this list after removing all duplicate values with original order reserved._**\n\n---\n\n### Hints\n\n> **_Use set() to store a number of values without duplicate._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndef removeDuplicate( li ):\n    newli=[]\n    seen = set()\n    for item in li:\n        if item not in seen:\n            seen.add( item )\n            newli.append(item)\n\n    return newli\n\nli=[12,24,35,24,88,120,155,88,120,155]\nprint removeDuplicate(li)\n\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nli = [12,24,35,24,88,120,155,88,120,155]\nfor i in li:\n    if li.count(i) > 1:\n        li.remove(i)\nprint(li)\n```\n\n**OR**\n\n```python\ndef removeDuplicate( li ):\n    seen = {}  # dictionary\n    for item in li:\n        if item not in seen:\n            seen[item] = True\n            yield item\n\nli = [12, 24, 35, 24, 88, 120, 155, 88, 120, 155]\nans = list(removeDuplicate(li))\nprint(ans)\n```\n\n---\n\n# Question 89\n\n### **Question**\n\n> **_Define a class Person and its two child classes: Male and Female. All classes have a method \"getGender\" which can print \"Male\" for Male class and \"Female\" for Female class._**\n\n---\n\n### Hints\n\n> **_Use Subclass(Parentclass) to define a child class._**\n\n---\n\n**Solution:**\n\n```python\nclass Person(object):\n    def getGender( self ):\n        return \"Unknown\"\n\nclass Male( Person ):\n    def getGender( self ):\n        return \"Male\"\n\nclass Female( Person ):\n    def getGender( self ):\n        return \"Female\"\n\naMale = Male()\naFemale= Female()\nprint aMale.getGender()\nprint aFemale.getGender()\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_20.md \"Day 20\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_22.md \"Day 22\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_22.md",
    "content": "# Question 90\n\n### **Question**\n\n> **_Please write a program which count and print the numbers of each character in a string input by console._**\n\n> **_Example:\n> If the following string is given as input to the program:_**\n\n```\nabcdefgabc\n```\n\n> **_Then, the output of the program should be:_**\n\n```\na,2\nc,2\nb,2\ne,1\nd,1\ng,1\nf,1\n```\n\n### Hints\n\n> **_Use dict to store key/value pairs.\n> Use dict.get() method to lookup a key with default value._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ndic = {}\ns=raw_input()\nfor s in s:\n    dic[s] = dic.get(s,0)+1\nprint '\\n'.join(['%s,%s' % (k, v) for k, v in dic.items()])\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport string\n\ns = input()\nfor letter in string.ascii_lowercase:\n    cnt = s.count(letter)\n    if cnt > 0:\n        print(\"{},{}\".format(letter,cnt))\n```\n\n**OR**\n\n```python\ns = input()\nfor letter in range(ord('a'),ord('z')+1):    # ord() gets the ascii value of a char\n    letter = chr(letter)                     # chr() gets the char of an ascii value\n    cnt = s.count(letter)\n    if cnt > 0:\n        print(\"{},{}\".format(letter,cnt))\n```\n\n---\n\n# Question 91\n\n### **Question**\n\n> **_Please write a program which accepts a string from console and print it in reverse order._**\n\n> **Example:\n> If the following string is given as input to the program:\\***\n\n```\nrise to vote sir\n```\n\n> **_Then, the output of the program should be:_**\n\n```\nris etov ot esir\n```\n\n### Hints\n\n> **_Use list[::-1] to iterate a list in a reverse order._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ns=raw_input()\ns = s[::-1]\nprint s\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ns = input()\ns = ''.join(reversed(s))\nprint(s)\n```\n\n---\n\n# Question 92\n\n### **Question**\n\n> **_Please write a program which accepts a string from console and print the characters that have even indexes._**\n\n> **_Example:\n> If the following string is given as input to the program:_**\n\n```\nH1e2l3l4o5w6o7r8l9d\n```\n\n> **_Then, the output of the program should be:_**\n\n```\nHelloworld\n```\n\n### Hints\n\n> **_Use list[::2] to iterate a list by step 2._**\n\n---\n\n**Main author's Solution: Python 2**\n\n```python\ns=raw_input()\ns = s[::2]\nprint s\n```\n\n---\n\n**My Solution: Python 3**\n\n```python\ns = \"H1e2l3l4o5w6o7r8l9d\"\ns = [ s[i] for i in range(len(s)) if i%2 ==0 ]\nprint(''.join(s))\n```\n\n**OR**\n\n```python\ns = \"H1e2l3l4o5w6o7r8l9d\"\nns =''\nfor i in range(len(s)):\n    if i % 2 == 0:\n        ns+=s[i]\nprint(ns)\n```\n\n---\n\n# Question 93\n\n### **Question**\n\n> **_Please write a program which prints all permutations of [1,2,3]_**\n\n---\n\n### Hints\n\n> **_Use itertools.permutations() to get permutations of list._**\n\n---\n\n**Solution:**\n\n```python\n\nimport itertools\nprint list(itertools.permutations([1,2,3]))\n```\n\n---\n\n# Question 94\n\n### **Question**\n\n> **_Write a program to solve a classic ancient Chinese puzzle:\n> We count 35 heads and 94 legs among the chickens and rabbits in a farm. How many rabbits and how many chickens do we have?_**\n\n---\n\n### Hints\n\n> **_Use for loop to iterate all possible solutions._**\n\n---\n\n**Solution:**\n\n```python\ndef solve(numheads,numlegs):\n    ns='No solutions!'\n    for i in range(numheads+1):\n        j=numheads-i\n        if 2*i+4*j==numlegs:\n            return i,j\n    return ns,ns\n\nnumheads=35\nnumlegs=94\nsolutions=solve(numheads,numlegs)\nprint solutions\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_21.md \"Day 21\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_23.md \"Day 23\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_23.md",
    "content": "# The extended part of the repository starts from this page. Previous 94 problems were collected from the repository mentioned in intro. The following problems are collected from Hackerrank and other resources from internet.All the given solutions are in python 3.\n\n# Question 95\n\n### **Question**\n\n> **_Given the participants' score sheet for your University Sports Day, you are required to find the runner-up score. You are given scores. Store them in a list and find the score of the runner-up._**\n\n> **_If the following string is given as input to the program:_**\n>\n> ```\n> 5\n> 2 3 6 6 5\n> ```\n>\n> **_Then, the output of the program should be:_**\n>\n> ```\n> 5\n> ```\n\n### Hints\n\n> **_Make the scores unique and then find 2nd best number_**\n\n---\n\n**My Solution: Python 3**\n\n```python\nn = int(input())\narr = map(int, input().split())\narr = list(set(arr))\narr.sort()\nprint(arr[-2])\n```\n\n---\n\n```python\n'''\nSolution by: mishrasunny-coder\n'''\nnum = int(input(\"Enter num: \"))\nL = []\n\nwhile True:\n    L.append(num)\n    num = int(input(\"Enter another: \"))\n    if num == 0:\n        break\n\nL1 = list(set(L[:]))\nL2 = sorted(L1)\nprint(L2)\n\nprint(f'The runner up is {L2[-2]}')\n```\n\n---\n```python\n'''Solution by: KailashS3 \n'''\nnum = int(input())\nscores = list(map(int, input().split(' ')))\nwinner = max(scores)\nlst = []\n\nif len(scores) != num:\n    print('length of score is greater than input given')\nelse:\n    for score in scores:\n\tif winner > score:\n\t    lst.append(score)\n\nrunnerup = max(lst)\nprint(runnerup)\n```\n---\n\n# Question 96\n\n### **Question**\n\n> **_You are given a string S and width W.\n> Your task is to wrap the string into a paragraph of width._**\n\n> **_If the following string is given as input to the program:_**\n>\n> ```\n> ABCDEFGHIJKLIMNOQRSTUVWXYZ\n> 4\n> ```\n>\n> **_Then, the output of the program should be:_**\n>\n> ```\n> ABCD\n> EFGH\n> IJKL\n> IMNO\n> QRST\n> UVWX\n> YZ\n> ```\n\n### Hints\n\n> **_Use wrap function of textwrap module_**\n\n---\n\n**My Solution: Python 3**\n\n```python\nimport textwrap\n\ndef wrap(string, max_width):\n    string = textwrap.wrap(string,max_width)\n    string = \"\\n\".join(string)\n    return string\n\nif __name__ == '__main__':\n    string, max_width = input(), int(input())\n    result = wrap(string, max_width)\n    print(result)\n```\n\n---\n```python\n'''Solution by: mishrasunny-coder\n'''\nimport textwrap\n\nstring = input()\nwidth = int(input())\n\nprint(textwrap.fill(string,width))\n```\n---\n```python\n'''solution by  : Prashanth\n'''\nfrom textwrap import wrap\nx = str(input(': '))\nw = int(input())\nz = list(wrap(x, w))\nfor i in z:\n    print(i)\n```\n\n---\n# Question 97\n\n### **Question**\n\n> **_You are given an integer, N. Your task is to print an alphabet rangoli of size N. (Rangoli is a form of Indian folk art based on creation of patterns.)_**\n\n> **_Different sizes of alphabet rangoli are shown below:_**\n>\n> ```\n> #size 3\n>\n> ----c----\n> --c-b-c--\n> c-b-a-b-c\n> --c-b-c--\n> ----c----\n>\n> #size 5\n>\n> --------e--------\n> ------e-d-e------\n> ----e-d-c-d-e----\n> --e-d-c-b-c-d-e--\n> e-d-c-b-a-b-c-d-e\n> --e-d-c-b-c-d-e--\n> ----e-d-c-d-e----\n> ------e-d-e------\n> --------e--------\n> ```\n\n### Hints\n\n> **_First print the half of the Rangoli in the given way and save each line in a list. Then print the list in reverse order to get the rest._**\n\n---\n\n**My Solution: Python 3**\n\n```python\n\nimport string\ndef print_rangoli(size):\n    n = size\n    alph = string.ascii_lowercase\n    width = 4 * n - 3\n\n    ans = []\n    for i in range(n):\n        left = '-'.join(alph[n - i - 1:n])\n        mid = left[-1:0:-1] + left\n        final = mid.center(width, '-')\n        ans.append(final)\n\n    if len(ans) > 1:\n        for i in ans[n - 2::-1]:\n            ans.append(i)\n    ans = '\\n'.join(ans)\n    print(ans)\n\n\nif __name__ == '__main__':\n    n = int(input())\n    print_rangoli(n)\n```\n\n---\n\n# Question 98\n\n### **Question**\n\n> **_You are given a date. Your task is to find what the day is on that date._**\n\n**Input**\n\n> **_A single line of input containing the space separated month, day and year, respectively, in MM DD YYYY format._**\n>\n> ```\n> 08 05 2015\n> ```\n\n**Output**\n\n> **_Output the correct day in capital letters._**\n>\n> ```\n> WEDNESDAY\n> ```\n\n---\n\n### Hints\n\n> **_Use weekday function of calender module_**\n\n---\n\n**Solution:**\n\n```python\nimport calendar\n\nmonth, day, year = map(int, input().split())\n\ndayId = calendar.weekday(year, month, day)\nprint(calendar.day_name[dayId].upper())\n```\n\n---\n\n# Question 99\n\n### **Question**\n\n> **_Given 2 sets of integers, M and N, print their symmetric difference in ascending order. The term symmetric difference indicates those values that exist in either M or N but do not exist in both._**\n\n**Input**\n\n> **_The first line of input contains an integer, M.The second line contains M space-separated integers.The third line contains an integer, N.The fourth line contains N space-separated integers._**\n>\n> ```\n> 4\n> 2 4 5 9\n> 4\n> 2 4 11 12\n> ```\n\n**Output**\n\n> **_Output the symmetric difference integers in ascending order, one per line._**\n>\n> ```\n> 5\n> 9\n> 11\n> 12\n> ```\n\n---\n\n### Hints\n\n> **_Use \\'^\\' to make symmetric difference operation._**\n\n---\n\n**Solution:**\n\n```python\nif __name__ == '__main__':\n    n = int(input())\n    set1 = set(map(int,input().split()))\n\n    m = int(input())\n    set2 = set(map(int, input().split()))\n\n    ans = list(set1 ^ set2)\n    ans.sort()\n    for i in ans:\n        print(i)\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_22.md \"Day 22\")\n\n[**_go to next day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_24.md \"Day 24\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n"
  },
  {
    "path": "1.python-basic/Python-100/Status/Day_24.md",
    "content": "# Question 100\n\n### **Question**\n\n> **_You are given words. Some words may repeat. For each word, output its number of occurrences. The output order should correspond with the input order of appearance of the word. See the sample input/output for clarification._**\n\n> **_If the following string is given as input to the program:_**\n>\n> ```\n> 4\n> bcdef\n> abcdefg\n> bcde\n> bcdef\n> ```\n>\n> **_Then, the output of the program should be:_**\n>\n> ```\n> 3\n> 2 1 1\n> ```\n\n### Hints\n\n> **_Make a list to get the input order and a dictionary to count the word frequency_**\n\n---\n\n**My Solution: Python 3**\n\n```python\nn = int(input())\n\nword_list = []\nword_dict = {}\n\nfor i in range(n):\n    word = input()\n    if word not in word_dict:\n        word_list.append(word)\n    word_dict[word] = word_dict.get(word, 0) + 1\n\nprint(len(word_list))\nfor word in word_list:\n    print(word_dict[word], end=' ')\n```\n\n---\n\n# Question 101\n\n### **Question**\n\n> **_You are given a string.Your task is to count the frequency of letters of the string and print the letters in descending order of frequency._**\n\n> **_If the following string is given as input to the program:_**\n>\n> ```\n> aabbbccde\n> ```\n>\n> **_Then, the output of the program should be:_**\n>\n> ```\n> b 3\n> a 2\n> c 2\n> d 1\n> e 1\n> ```\n\n### Hints\n\n> **_Count frequency with dictionary and sort by Value from dictionary Items_**\n\n---\n\n**My Solution: Python 3**\n\n```python\nword = input()\ndct = {}\nfor i in word:\n    dct[i] = dct.get(i,0) + 1\n\ndct = sorted(dct.items(),key=lambda x: (-x[1],x[0]))\nfor i in dct:\n    print(i[0],i[1])\n```\n\n---\n\n```python\n'''Solution by: yuan1z'''\n\nX = input()\nmy_set = set(X)\narr = []\nfor item in my_set:\n    arr.append([item,X.count(item)])\ntmp = sorted(arr,key = lambda x: (-x[1],x[0]))\n\nfor i in tmp:\n    print(i[0]+' '+str(i[1]))\n```\n\n---\n\n```python\n'''Solution by: StartZer0'''\n\ns = list(input())\n\ndict_count_ = {k:s.count(k) for k in s}\nlist_of_tuples = [(k,v) for k,v in dict_count_.items()]\nlist_of_tuples.sort(key = lambda x: x[1], reverse = True)\n\nfor item in list_of_tuples:\n  print(item[0], item[1])\n```\n\n---\n\n# Question 102\n\n### **Question**\n\n> **_Write a Python program that accepts a string and calculate the number of digits and letters._**\n\n**Input**\n\n> ```\n> Hello321Bye360\n> ```\n\n**Output**\n\n> ```\n> Digit - 6\n> Letter - 8\n> ```\n\n---\n\n### Hints\n\n> **_Use isdigit() and isalpha() function_**\n\n---\n\n**Solution:**\n\n```python\nword = input()\ndigit,letter = 0,0\nfor char in word:\n    digit+=char.isdigit()\n    letter+=char.isalpha()\n\nprint('Digit -',digit)\nprint('Letter -',letter)\n```\n\n---\n\n# Question 103\n\n### **Question**\n\n> **_Given a number N.Find Sum of 1 to N Using Recursion_**\n\n**Input**\n\n> ```\n> 5\n> ```\n\n**Output**\n\n> ```\n> 15\n> ```\n\n---\n\n### Hints\n\n> **_Make a recursive function to get the sum_**\n\n---\n\n**Solution:**\n\n```python\ndef rec(n):\n    if n == 0:\n        return n\n    return rec(n-1) + n\n\n\nn = int(input())\nsum = rec(n)\nprint(sum)\n```\n\n---\n\n[**_go to previous day_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/blob/master/Status/Day_22.md \"Day 23\")\n\n[**_Discussion_**](https://github.com/darkprinx/100-plus-Python-programming-exercises-extended/issues/3)\n\n# To Be Continue...\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_01.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 1\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program which will find all such numbers which are divisible by 7 but are not a multiple of 5,\\n\",\n    \"> between 2000 and 3200 (both included).The numbers obtained should be printed in a comma-separated sequence on a single line._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Consider use range(#begin, #end) method._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\\n\",\n    \"\\n\",\n    \"- **Using for loops**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2002,2009,2016,2023,2037,2044,2051,2058,2072,2079,2086,2093,2107,2114,2121,2128,2142,2149,2156,2163,2177,2184,2191,2198,2212,2219,2226,2233,2247,2254,2261,2268,2282,2289,2296,2303,2317,2324,2331,2338,2352,2359,2366,2373,2387,2394,2401,2408,2422,2429,2436,2443,2457,2464,2471,2478,2492,2499,2506,2513,2527,2534,2541,2548,2562,2569,2576,2583,2597,2604,2611,2618,2632,2639,2646,2653,2667,2674,2681,2688,2702,2709,2716,2723,2737,2744,2751,2758,2772,2779,2786,2793,2807,2814,2821,2828,2842,2849,2856,2863,2877,2884,2891,2898,2912,2919,2926,2933,2947,2954,2961,2968,2982,2989,2996,3003,3017,3024,3031,3038,3052,3059,3066,3073,3087,3094,3101,3108,3122,3129,3136,3143,3157,3164,3171,3178,3192,3199,\\b\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i in range(2000, 3201):\\n\",\n    \"    if i % 7 == 0 and i % 5 != 0:\\n\",\n    \"        print(i, end=\\\",\\\")\\n\",\n    \"print(\\\"\\\\b\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"- **Using generators and list comprehension**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2002,2009,2016,2023,2037,2044,2051,2058,2072,2079,2086,2093,2107,2114,2121,2128,2142,2149,2156,2163,2177,2184,2191,2198,2212,2219,2226,2233,2247,2254,2261,2268,2282,2289,2296,2303,2317,2324,2331,2338,2352,2359,2366,2373,2387,2394,2401,2408,2422,2429,2436,2443,2457,2464,2471,2478,2492,2499,2506,2513,2527,2534,2541,2548,2562,2569,2576,2583,2597,2604,2611,2618,2632,2639,2646,2653,2667,2674,2681,2688,2702,2709,2716,2723,2737,2744,2751,2758,2772,2779,2786,2793,2807,2814,2821,2828,2842,2849,2856,2863,2877,2884,2891,2898,2912,2919,2926,2933,2947,2954,2961,2968,2982,2989,2996,3003,3017,3024,3031,3038,3052,3059,3066,3073,3087,3094,3101,3108,3122,3129,3136,3143,3157,3164,3171,3178,3192,3199\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(*(i for i in range(2000, 3201) if i % 7 == 0 and i % 5 != 0), sep=\\\",\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 2\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program which can compute the factorial of a given numbers.The results should be printed in a comma-separated sequence on a single line.Suppose the following input is supplied to the program: 8\\n\",\n    \"> Then, the output should be:40320_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- **Using While Loop**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"9\\n\",\n      \"362880\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n = int(input())  # input() function takes input as string type\\n\",\n    \"# int() converts it to integer type\\n\",\n    \"fact = 1\\n\",\n    \"i = 1\\n\",\n    \"while i <= n:\\n\",\n    \"    fact = fact * i\\n\",\n    \"    i = i + 1\\n\",\n    \"print(fact)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- **Using For Loop**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n = int(input())  # input() function takes input as string type\\n\",\n    \"# int() converts it to integer type\\n\",\n    \"fact = 1\\n\",\n    \"for i in range(1, n + 1):\\n\",\n    \"    fact = fact * i\\n\",\n    \"print(fact)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- **Using Lambda Function**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"7\\n\",\n      \"5040\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Solution by:  harshraj22\\n\",\n    \"n = int(input())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def shortFact(x):\\n\",\n    \"    return 1 if x <= 1 else x * shortFact(x - 1)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(shortFact(n))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 3\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_With a given integral number n, write a program to generate a dictionary that contains (i, i x i) such that is an integral number between 1 and n (both included). and then the program should print the dictionary.Suppose the following input is supplied to the program: 8_**\\n\",\n    \"\\n\",\n    \"> **_Then, the output should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64}\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input.Consider use dict()_**\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- **Using For loop**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n = int(input())\\n\",\n    \"ans = {}\\n\",\n    \"for i in range(1, n + 1):\\n\",\n    \"    ans[i] = i * i\\n\",\n    \"print(ans)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- **Using dictionary comprehension**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n = int(input())\\n\",\n    \"ans = {i: i * i for i in range(1, n + 1)}\\n\",\n    \"print(ans)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_02.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 4\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program which accepts a sequence of comma-separated numbers from console and generate a list and a tuple which contains every number.Suppose the following input is supplied to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"34,67,55,33,12,98\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"['34', '67', '55', '33', '12', '98']\\n\",\n    \"('34', '67', '55', '33', '12', '98')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input.tuple() method can convert list to tuple_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"7767\\n\",\n      \"['7767']\\n\",\n      \"('7767',)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"lst = input().split(\\\",\\\")\\n\",\n    \"# the input is being taken as string and as it is string it has a built in\\n\",\n    \"# method name split. ',' inside split function does split where it finds any ','\\n\",\n    \"# and save the input as list in lst variable\\n\",\n    \"\\n\",\n    \"tpl = tuple(lst)  # tuple method converts list to tuple\\n\",\n    \"\\n\",\n    \"print(lst)\\n\",\n    \"print(tpl)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 5\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a class which has at least two methods:_**\\n\",\n    \">\\n\",\n    \"> - **_getString: to get a string from console input_**\\n\",\n    \"> - **_printString: to print the string in upper case._**\\n\",\n    \"\\n\",\n    \"> **_Also please include simple test function to test the class methods._**\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use **init** method to construct some parameters_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdin\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" abc\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ABC\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class IOstring:\\n\",\n    \"    def __init__(self):\\n\",\n    \"        pass\\n\",\n    \"\\n\",\n    \"    def get_string(self):\\n\",\n    \"        self.s = input()\\n\",\n    \"\\n\",\n    \"    def print_string(self):\\n\",\n    \"        print(self.s.upper())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"xx = IOstring()\\n\",\n    \"xx.get_string()\\n\",\n    \"xx.print_string()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 6\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program that calculates and prints the value according to the given formula:_**\\n\",\n    \"\\n\",\n    \"> **_Q = Square root of [(2 _ C _ D)/H]_**\\n\",\n    \"\\n\",\n    \"> **_Following are the fixed values of C and H:_**\\n\",\n    \"\\n\",\n    \"> **_C is 50. H is 30._**\\n\",\n    \"\\n\",\n    \"> **_D is the variable whose values should be input to your program in a comma-separated sequence.For example\\n\",\n    \"> Let us assume the following comma separated input sequence is given to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"100,150,180\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_The output of the program should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"18,22,24\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_If the output received is in decimal form, it should be rounded off to its nearest value (for example, if the output received is 26.0, it should be printed as 26).In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdin\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" 100, 150, 180\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"18,22,24\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from math import sqrt  # import specific functions as importing all using *\\n\",\n    \"\\n\",\n    \"# is bad practice\\n\",\n    \"\\n\",\n    \"C, H = 50, 30\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def calc(D):\\n\",\n    \"    return sqrt((2 * C * D) / H)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"D = [int(i) for i in input().split(\\\",\\\")]  # splits in comma position and set up in list\\n\",\n    \"D = [int(i) for i in D]  # converts string to integer\\n\",\n    \"D = [calc(i) for i in D]  # returns floating value by calc method for every item in D\\n\",\n    \"D = [round(i) for i in D]  # All the floating values are rounded\\n\",\n    \"D = [\\n\",\n    \"    str(i) for i in D\\n\",\n    \"]  # All the integers are converted to string to be able to apply join operation\\n\",\n    \"\\n\",\n    \"print(\\\",\\\".join(D))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from math import sqrt\\n\",\n    \"\\n\",\n    \"C, H = 50, 30\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def calc(D):\\n\",\n    \"    return sqrt((2 * C * D) / H)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"D = input().split(\\\",\\\")  # splits in comma position and set up in list\\n\",\n    \"D = [\\n\",\n    \"    str(round(calc(int(i)))) for i in D\\n\",\n    \"]  # using comprehension method. It works in order of the previous code\\n\",\n    \"print(\\\",\\\".join(D))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from math import sqrt\\n\",\n    \"\\n\",\n    \"C, H = 50, 30\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def calc(D):\\n\",\n    \"    return sqrt((2 * C * D) / H)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(\\\",\\\".join([str(int(calc(int(i)))) for i in input().split(\\\",\\\")]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from math import *  # importing all math functions\\n\",\n    \"\\n\",\n    \"C, H = 50, 30\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def calc(D):\\n\",\n    \"    D = int(D)\\n\",\n    \"    return str(int(sqrt((2 * C * D) / H)))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"D = input().split(\\\",\\\")\\n\",\n    \"D = list(map(calc, D))  # applying calc function on D and storing as a list\\n\",\n    \"print(\\\",\\\".join(D))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 7\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program which takes 2 digits, X,Y as input and generates a 2-dimensional array. The element value in the i-th row and j-th column of the array should be i _ j.\\\\***\\n\",\n    \"\\n\",\n    \"> **_Note: i=0,1.., X-1; j=0,1,¡­Y-1. Suppose the following inputs are given to the program: 3,5_**\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"[[0, 0, 0, 0, 0], [0, 1, 2, 3, 4], [0, 2, 4, 6, 8]]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Note: In case of input data being supplied to the question, it should be assumed to be a console input in a comma-separated form._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x, y = map(int, input().split(\\\",\\\"))\\n\",\n    \"lst = []\\n\",\n    \"\\n\",\n    \"for i in range(x):\\n\",\n    \"    tmp = []\\n\",\n    \"    for j in range(y):\\n\",\n    \"        tmp.append(i * j)\\n\",\n    \"    lst.append(tmp)\\n\",\n    \"\\n\",\n    \"print(lst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x, y = map(int, input().split(\\\",\\\"))\\n\",\n    \"lst = [[i * j for j in range(y)] for i in range(x)]\\n\",\n    \"print(lst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 8\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program that accepts a comma separated sequence of words as input and prints the words in a comma-separated sequence after sorting them alphabetically._**\\n\",\n    \"\\n\",\n    \"> **_Suppose the following input is supplied to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"without,hello,bag,world\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"bag,hello,without,world\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lst = input().split(\\\",\\\")\\n\",\n    \"lst.sort()\\n\",\n    \"print(\\\",\\\".join(lst))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 9\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program that accepts sequence of lines as input and prints the lines after making all characters in the sentence capitalized._**\\n\",\n    \"\\n\",\n    \"> **_Suppose the following input is supplied to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Hello world\\n\",\n    \"\\n\",\n    \"Practice makes perfect\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"HELLO WORLD\\n\",\n    \"\\n\",\n    \"PRACTICE MAKES PERFECT\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lst = []\\n\",\n    \"\\n\",\n    \"while input():\\n\",\n    \"    x = input()\\n\",\n    \"    if len(x) == 0:\\n\",\n    \"        break\\n\",\n    \"    lst.append(x.upper())\\n\",\n    \"\\n\",\n    \"for line in lst:\\n\",\n    \"    print(line)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def user_input():\\n\",\n    \"    while True:\\n\",\n    \"        s = input()\\n\",\n    \"        if not s:\\n\",\n    \"            return\\n\",\n    \"        yield s\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for line in map(str.upper, user_input()):\\n\",\n    \"    print(line)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_03.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 10\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Write a program that accepts a sequence of whitespace separated words as input and prints the words after removing all duplicate words and sorting them alphanumerically._**\\n\",\n    \"\\n\",\n    \"> **_Suppose the following input is supplied to the program:_**\\n\",\n    \"\\n\",\n    \"hello world and practice makes perfect and hello world again\\n\",\n    \"\\n\",\n    \"> **_Then, the output should be:_**\\n\",\n    \"\\n\",\n    \"again and hello makes perfect practice world\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input.We use set container to remove duplicated data automatically and then use sorted() to sort the data._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"word = input().split()\\n\",\n    \"\\n\",\n    \"for i in word:\\n\",\n    \"    if (\\n\",\n    \"        word.count(i) > 1\\n\",\n    \"    ):  # count function returns total repeatation of an element that is send as argument\\n\",\n    \"        word.remove(i)  # removes exactly one element per call\\n\",\n    \"\\n\",\n    \"word.sort()\\n\",\n    \"print(\\\" \\\".join(word))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"word = input().split()\\n\",\n    \"[\\n\",\n    \"    word.remove(i) for i in word if word.count(i) > 1\\n\",\n    \"]  # removal operation with comprehension method\\n\",\n    \"word.sort()\\n\",\n    \"print(\\\" \\\".join(word))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"word = sorted(\\n\",\n    \"    list(set(input().split()))\\n\",\n    \")  #  input string splits -> converting into set() to store unique\\n\",\n    \"#  element -> converting into list to be able to apply sort\\n\",\n    \"print(\\\" \\\".join(word))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 11\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Write a program which accepts a sequence of comma separated 4 digit binary numbers as its input and then check whether they are divisible by 5 or not. The numbers that are divisible by 5 are to be printed in a comma separated sequence._**\\n\",\n    \"\\n\",\n    \"> **_Example:_**\\n\",\n    \"\\n\",\n    \"0100,0011,1010,1001\\n\",\n    \"\\n\",\n    \"> **_Then the output should be:_**\\n\",\n    \"\\n\",\n    \"1010\\n\",\n    \"\\n\",\n    \"> **_Notes: Assume the data is input by console._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def check(x):  # converts binary to integer & returns zero if divisible by 5\\n\",\n    \"    total, pw = 0, 1\\n\",\n    \"    reversed(x)\\n\",\n    \"\\n\",\n    \"    for i in x:\\n\",\n    \"        total += pw * (ord(i) - 48)  # ord() function returns ASCII value\\n\",\n    \"        pw *= 2\\n\",\n    \"    return total % 5\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"data = input().split(\\\",\\\")  # inputs taken here and splited in ',' position\\n\",\n    \"lst = []\\n\",\n    \"\\n\",\n    \"for i in data:\\n\",\n    \"    if check(i) == 0:  # if zero found it means divisible by zero and added to the list\\n\",\n    \"        lst.append(i)\\n\",\n    \"\\n\",\n    \"print(\\\",\\\".join(lst))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def check(x):  # check function returns true if divisible by 5\\n\",\n    \"    return int(x, 2) % 5 == 0  # int(x,b) takes x as string and b as base from which\\n\",\n    \"    # it will be converted to decimal\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"data = input().split(\\\",\\\")\\n\",\n    \"\\n\",\n    \"data = list(\\n\",\n    \"    filter(check, data)\\n\",\n    \")  # in filter(func,object) function, elements are picked from 'data' if found True by 'check' function\\n\",\n    \"print(\\\",\\\".join(data))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data = input().split(\\\",\\\")\\n\",\n    \"data = list(\\n\",\n    \"    filter(lambda i: int(i, 2) % 5 == 0, data)\\n\",\n    \")  # lambda is an operator that helps to write function of one line\\n\",\n    \"print(\\\",\\\".join(data))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 12\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program, which will find all such numbers between 1000 and 3000 (both included) such that each digit of the number is an even number.The numbers obtained should be printed in a comma-separated sequence on a single line._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lst = []\\n\",\n    \"\\n\",\n    \"for i in range(1000, 3001):\\n\",\n    \"    flag = 1\\n\",\n    \"    for j in str(i):  # every integer number i is converted into string\\n\",\n    \"        if ord(j) % 2 != 0:  # ord returns ASCII value and j is every digit of i\\n\",\n    \"            flag = 0  # flag becomes zero if any odd digit found\\n\",\n    \"    if flag == 1:\\n\",\n    \"        lst.append(str(i))  # i is stored in list as string\\n\",\n    \"\\n\",\n    \"print(\\\",\\\".join(lst))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def check(element):\\n\",\n    \"    return all(\\n\",\n    \"        ord(i) % 2 == 0 for i in element\\n\",\n    \"    )  # all returns True if all digits i is even in element\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lst = [\\n\",\n    \"    str(i) for i in range(1000, 3001)\\n\",\n    \"]  # creates list of all given numbers with string data type\\n\",\n    \"lst = list(\\n\",\n    \"    filter(check, lst)\\n\",\n    \")  # filter removes element from list if check condition fails\\n\",\n    \"print(\\\",\\\".join(lst))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lst = [str(i) for i in range(1000, 3001)]\\n\",\n    \"lst = list(\\n\",\n    \"    filter(lambda i: all(ord(j) % 2 == 0 for j in i), lst)\\n\",\n    \")  # using lambda to define function inside filter function\\n\",\n    \"print(\\\",\\\".join(lst))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 13\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program that accepts a sentence and calculate the number of letters and digits._**\\n\",\n    \"\\n\",\n    \"> **_Suppose the following input is supplied to the program:_**\\n\",\n    \"\\n\",\n    \"hello world! 123\\n\",\n    \"\\n\",\n    \"> **_Then, the output should be:_**\\n\",\n    \"\\n\",\n    \"LETTERS 10\\n\",\n    \"\\n\",\n    \"DIGITS 3\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"word = input()\\n\",\n    \"letter, digit = 0, 0\\n\",\n    \"\\n\",\n    \"for i in word:\\n\",\n    \"    if (\\\"a\\\" <= i and i <= \\\"z\\\") or (\\\"A\\\" <= i and i <= \\\"Z\\\"):\\n\",\n    \"        letter += 1\\n\",\n    \"    if \\\"0\\\" <= i and i <= \\\"9\\\":\\n\",\n    \"        digit += 1\\n\",\n    \"\\n\",\n    \"print(\\\"LETTERS {0}\\\\nDIGITS {1}\\\".format(letter, digit))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"word = input()\\n\",\n    \"letter, digit = 0, 0\\n\",\n    \"\\n\",\n    \"for i in word:\\n\",\n    \"    if i.isalpha():  # returns True if alphabet\\n\",\n    \"        letter += 1\\n\",\n    \"    elif i.isnumeric():  # returns True if numeric\\n\",\n    \"        digit += 1\\n\",\n    \"print(\\n\",\n    \"    f\\\"LETTERS {letter}\\\\n{digits}\\\"\\n\",\n    \")  # two different types of formating method is shown in both solution\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"## Conclusion\\n\",\n    \"\\n\",\n    \"**_All the above problems are mostly string related problems. Major parts of the solution includes string releted functions and comprehension method to write down the code in more shorter form._**\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_04.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 14\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program that accepts a sentence and calculate the number of upper case letters and lower case letters._**\\n\",\n    \"\\n\",\n    \"> **_Suppose the following input is supplied to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Hello world!\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"UPPER CASE 1\\n\",\n    \"\\n\",\n    \"LOWER CASE 9\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"word = input()\\n\",\n    \"upper, lower = 0, 0\\n\",\n    \"\\n\",\n    \"for i in word:\\n\",\n    \"    if \\\"a\\\" <= i and i <= \\\"z\\\":\\n\",\n    \"        lower += 1\\n\",\n    \"    if \\\"A\\\" <= i and i <= \\\"Z\\\":\\n\",\n    \"        upper += 1\\n\",\n    \"\\n\",\n    \"print(\\\"UPPER CASE {0}\\\\nLOWER CASE {1}\\\".format(upper, lower))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"word = input()\\n\",\n    \"upper, lower = 0, 0\\n\",\n    \"\\n\",\n    \"for i in word:\\n\",\n    \"    lower += i.islower()\\n\",\n    \"    upper += i.isupper()\\n\",\n    \"\\n\",\n    \"print(\\\"UPPER CASE {0}\\\\nLOWER CASE {1}\\\".format(upper, lower))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"word = input()\\n\",\n    \"upper = sum(\\n\",\n    \"    1 for i in word if i.isupper()\\n\",\n    \")  # sum function cumulatively sum up 1's if the condition is True\\n\",\n    \"lower = sum(1 for i in word if i.islower())\\n\",\n    \"\\n\",\n    \"print(\\\"UPPER CASE {0}\\\\nLOWER CASE {1}\\\".format(upper, lower))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# solution by Amitewu\\n\",\n    \"\\n\",\n    \"string = input(\\\"Enter the sentense\\\")\\n\",\n    \"upper = 0\\n\",\n    \"lower = 0\\n\",\n    \"for x in string:\\n\",\n    \"    if x.isupper() == True:\\n\",\n    \"        upper += 1\\n\",\n    \"    if x.islower() == True:\\n\",\n    \"        lower += 1\\n\",\n    \"\\n\",\n    \"print(\\\"UPPER CASE: \\\", upper)\\n\",\n    \"print(\\\"LOWER CASE: \\\", lower)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 15\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program that computes the value of a+aa+aaa+aaaa with a given digit as the value of a._**\\n\",\n    \"\\n\",\n    \"> **_Suppose the following input is supplied to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"9\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"11106\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"a = input()\\n\",\n    \"total, tmp = 0, str()  # initialing an integer and empty string\\n\",\n    \"\\n\",\n    \"for i in range(4):\\n\",\n    \"    tmp += a  # concatenating 'a' to 'tmp'\\n\",\n    \"    total += int(tmp)  # converting string type to integer type\\n\",\n    \"\\n\",\n    \"print(total)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"a = input()\\n\",\n    \"total = int(a) + int(2*a) + int(3*a) + int(4*a)  # N*a=Na, for example  a=\\\"23\\\", 2*a=\\\"2323\\\",3*a=\\\"232323\\\"\\n\",\n    \"print(total)\\n\",\n    \"```\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_05.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 16\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Use a list comprehension to square each odd number in a list. The list is input by a sequence of comma-separated numbers._** >**_Suppose the following input is supplied to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"1,2,3,4,5,6,7,8,9\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"1,9,25,49,81\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lst = [str(int(i) ** 2) for i in input().split(\\\",\\\") if int(i) % 2]\\n\",\n    \"print(\\\",\\\".join(lst))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"Solution by: shagun\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"lst = input().split(\\\",\\\")  # splits in comma position and set up in list\\n\",\n    \"\\n\",\n    \"seq = []\\n\",\n    \"lst = [int(i) for i in lst]  # converts string to integer\\n\",\n    \"for i in lst:\\n\",\n    \"    if i % 2 != 0:\\n\",\n    \"        i = i * i\\n\",\n    \"        seq.append(i)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"seq = [\\n\",\n    \"    str(i) for i in seq\\n\",\n    \"]  # All the integers are converted to string to be able to apply join operation\\n\",\n    \"print(\\\",\\\".join(seq))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"**_There were a mistake in the the test case and the solution's whice were notified and fixed with the help of @dwedigital. My warm thanks to him._**\\n\",\n    \"\\n\",\n    \"# Question 17\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program that computes the net amount of a bank account based a transaction log from console input. The transaction log format is shown as following:_**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"D 100\\n\",\n    \"W 200\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"- D means deposit while W means withdrawal.\\n\",\n    \"\\n\",\n    \"> **_Suppose the following input is supplied to the program:_**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"D 300\\n\",\n    \"D 300\\n\",\n    \"W 200\\n\",\n    \"D 100\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"> **_Then, the output should be:_**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"500\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"total = 0\\n\",\n    \"while True:\\n\",\n    \"    s = input().split()\\n\",\n    \"    if not s:  # break if the string is empty\\n\",\n    \"        break\\n\",\n    \"    cm, num = map(\\n\",\n    \"        str, s\\n\",\n    \"    )  # two inputs are distributed in cm and num in string data type\\n\",\n    \"\\n\",\n    \"    if cm == \\\"D\\\":\\n\",\n    \"        total += int(num)\\n\",\n    \"    if cm == \\\"W\\\":\\n\",\n    \"        total -= int(num)\\n\",\n    \"\\n\",\n    \"print(total)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"Solution by: leonedott\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"lst = []\\n\",\n    \"while True:\\n\",\n    \"    x = input()\\n\",\n    \"    if len(x) == 0:\\n\",\n    \"        break\\n\",\n    \"    lst.append(x)\\n\",\n    \"\\n\",\n    \"balance = 0\\n\",\n    \"for item in lst:\\n\",\n    \"    if \\\"D\\\" in item:\\n\",\n    \"        balance += int(item.strip(\\\"D \\\"))\\n\",\n    \"    if \\\"W\\\" in item:\\n\",\n    \"        balance -= int(item.strip(\\\"W \\\"))\\n\",\n    \"print(balance)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"Solution by: AlexanderSro\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"account = 0\\n\",\n    \"while True:\\n\",\n    \"    action = input(\\\"Deposit/Whitdrow/Balance/Quit? D/W/B/Q: \\\").lower()\\n\",\n    \"    if action == \\\"d\\\":\\n\",\n    \"        deposit = input(\\\"How much would you like to deposit? \\\")\\n\",\n    \"        account = account + int(deposit)\\n\",\n    \"    elif action == \\\"w\\\":\\n\",\n    \"        withdrow = input(\\\"How much would you like to withdrow? \\\")\\n\",\n    \"        account = account - int(withdrow)\\n\",\n    \"    elif action == \\\"b\\\":\\n\",\n    \"        print(account)\\n\",\n    \"    else:\\n\",\n    \"        quit()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"Solution by: ShalomPrinz\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"lines = []\\n\",\n    \"while True:\\n\",\n    \"    loopInput = input()\\n\",\n    \"    if loopInput == \\\"done\\\":\\n\",\n    \"        break\\n\",\n    \"    else:\\n\",\n    \"        lines.append(loopInput)\\n\",\n    \"\\n\",\n    \"lst = list(int(i[2:]) if i[0] == \\\"D\\\" else -int(i[2:]) for i in lines)\\n\",\n    \"print(sum(lst))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_06.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 18\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_A website requires the users to input username and password to register. Write a program to check the validity of password input by users._**\\n\",\n    \"\\n\",\n    \"> **_Following are the criteria for checking the password:_**\\n\",\n    \"\\n\",\n    \"- **_At least 1 letter between [a-z]_**\\n\",\n    \"- **_At least 1 number between [0-9]_**\\n\",\n    \"- **_At least 1 letter between [A-Z]_**\\n\",\n    \"- **_At least 1 character from [$#@]_**\\n\",\n    \"- **_Minimum length of transaction password: 6_**\\n\",\n    \"- **_Maximum length of transaction password: 12_**\\n\",\n    \"\\n\",\n    \"> **_Your program should accept a sequence of comma separated passwords and will check them according to the above criteria. Passwords that match the criteria are to be printed, each separated by a comma._**\\n\",\n    \"\\n\",\n    \"> **_Example_**\\n\",\n    \"\\n\",\n    \"> **_If the following passwords are given as input to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"ABd1234@1,a F1#,2w3E*,2We3345\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"ABd1234@1\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"aaaaa\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def is_low(x):  # Returns True  if the string has a lowercase\\n\",\n    \"    for i in x:\\n\",\n    \"        if \\\"a\\\" <= i and i <= \\\"z\\\":\\n\",\n    \"            return True\\n\",\n    \"    return False\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def is_up(x):  # Returns True  if the string has a uppercase\\n\",\n    \"    for i in x:\\n\",\n    \"        if \\\"A\\\" <= i and i <= \\\"Z\\\":\\n\",\n    \"            return True\\n\",\n    \"    return False\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def is_num(x):  # Returns True  if the string has a numeric digit\\n\",\n    \"    for i in x:\\n\",\n    \"        if \\\"0\\\" <= i and i <= \\\"9\\\":\\n\",\n    \"            return True\\n\",\n    \"    return False\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def is_other(x):  # Returns True if the string has any \\\"$#@\\\"\\n\",\n    \"    for i in x:\\n\",\n    \"        if i == \\\"$\\\" or i == \\\"#\\\" or i == \\\"@\\\":\\n\",\n    \"            return True\\n\",\n    \"    return False\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"s = input().split(\\\",\\\")\\n\",\n    \"lst = []\\n\",\n    \"\\n\",\n    \"for i in s:\\n\",\n    \"    length = len(i)\\n\",\n    \"    if (\\n\",\n    \"        6 <= length\\n\",\n    \"        and length <= 12\\n\",\n    \"        and is_low(i)\\n\",\n    \"        and is_up(i)\\n\",\n    \"        and is_num(i)\\n\",\n    \"        and is_other(i)\\n\",\n    \"    ):  # Checks if all the requirments are fulfilled\\n\",\n    \"        lst.append(i)\\n\",\n    \"\\n\",\n    \"print(\\\",\\\".join(lst))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def check(x):\\n\",\n    \"    cnt = 6 <= len(x) and len(x) <= 12\\n\",\n    \"    for i in x:\\n\",\n    \"        if i.isupper():\\n\",\n    \"            cnt += 1\\n\",\n    \"            break\\n\",\n    \"    for i in x:\\n\",\n    \"        if i.islower():\\n\",\n    \"            cnt += 1\\n\",\n    \"            break\\n\",\n    \"    for i in x:\\n\",\n    \"        if i.isnumeric():\\n\",\n    \"            cnt += 1\\n\",\n    \"            break\\n\",\n    \"    for i in x:\\n\",\n    \"        if i == \\\"@\\\" or i == \\\"#\\\" or i == \\\"$\\\":\\n\",\n    \"            cnt += 1\\n\",\n    \"            break\\n\",\n    \"    return (\\n\",\n    \"        cnt == 5\\n\",\n    \"    )  # counting if total 5 all conditions are fulfilled then returns True\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"s = input().split(\\\",\\\")\\n\",\n    \"lst = filter(\\n\",\n    \"    check, s\\n\",\n    \")  # Filter function pick the words from s, those returns True by check() function\\n\",\n    \"print(\\\",\\\".join(lst))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import re\\n\",\n    \"\\n\",\n    \"s = input().split(\\\",\\\")\\n\",\n    \"lst = []\\n\",\n    \"\\n\",\n    \"for i in s:\\n\",\n    \"    cnt = 0\\n\",\n    \"    cnt += 6 <= len(i) and len(i) <= 12\\n\",\n    \"    cnt += bool(\\n\",\n    \"        re.search(\\\"[a-z]\\\", i)\\n\",\n    \"    )  # here re module includes a function re.search() which returns the object information\\n\",\n    \"    cnt += bool(\\n\",\n    \"        re.search(\\\"[A-Z]\\\", i)\\n\",\n    \"    )  # of where the pattern string i is matched with any of the [a-z]/[A-z]/[0=9]/[@#$] characters\\n\",\n    \"    cnt += bool(\\n\",\n    \"        re.search(\\\"[0-9]\\\", i)\\n\",\n    \"    )  # if not a single match found then returns NONE which converts to False in boolean\\n\",\n    \"    cnt += bool(re.search(\\\"[@#$]\\\", i))  # expression otherwise True if found any.\\n\",\n    \"    if cnt == 5:\\n\",\n    \"        lst.append(i)\\n\",\n    \"\\n\",\n    \"print(\\\",\\\".join(lst))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 19\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_You are required to write a program to sort the (name, age, score) tuples by ascending order where name is string, age and score are numbers. The tuples are input by console. The sort criteria is:_**\\n\",\n    \"\\n\",\n    \"- **_1: Sort based on name_**\\n\",\n    \"- **_2: Then sort based on age_**\\n\",\n    \"- **_3: Then sort by score_**\\n\",\n    \"\\n\",\n    \"> **_The priority is that name > age > score._**\\n\",\n    \"\\n\",\n    \"> **_If the following tuples are given as input to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Tom,19,80\\n\",\n    \"\\n\",\n    \"John,20,90\\n\",\n    \"\\n\",\n    \"Jony,17,91\\n\",\n    \"\\n\",\n    \"Jony,17,93\\n\",\n    \"\\n\",\n    \"Json,21,85\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"[('John', '20', '90'), ('Jony', '17', '91'), ('Jony', '17', '93'), ('Json', '21', '85'), ('Tom', '19', '80')]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input.We use itemgetter to enable multiple sort keys._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lst = []\\n\",\n    \"while True:\\n\",\n    \"    s = input().split(\\\",\\\")\\n\",\n    \"    if not s[0]:  # breaks for blank input\\n\",\n    \"        break\\n\",\n    \"    lst.append(tuple(s))\\n\",\n    \"\\n\",\n    \"lst.sort(\\n\",\n    \"    key=lambda x: (x[0], x[1], x[2])\\n\",\n    \")  # here key is defined by lambda and the data is sorted by element priority 0>1>2 in accending order\\n\",\n    \"print(lst)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_07.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 20\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a class with a generator which can iterate the numbers, which are divisible by 7, between a given range 0 and n._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Consider use class, function and comprehension._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solution: Python 3**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"Solution by: ShalomPrinz\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MyGen:\\n\",\n    \"    def by_seven(self, n):\\n\",\n    \"        for i in range(0, int(n / 7) + 1):\\n\",\n    \"            yield i * 7\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for i in MyGen().by_seven(int(input(\\\"Please enter a number... \\\"))):\\n\",\n    \"    print(i)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"Solution by: Seawolf159\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class Divisible:\\n\",\n    \"    def by_seven(self, n):\\n\",\n    \"        for number in range(n + 1):\\n\",\n    \"            if number % 7 == 0:\\n\",\n    \"                yield number\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"divisible = Divisible()\\n\",\n    \"generator = divisible.by_seven(int(input(\\\"Please insert a number. --> \\\")))\\n\",\n    \"for number in generator:\\n\",\n    \"    print(number)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 21\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_A robot moves in a plane starting from the original point (0,0). The robot can move toward UP, DOWN, LEFT and RIGHT with a given steps. The trace of robot movement is shown as the following:_**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"UP 5\\n\",\n    \"DOWN 3\\n\",\n    \"LEFT 3\\n\",\n    \"RIGHT 2\\n\",\n    \"```\\n\",\n    \"> **_The numbers after the direction are steps. Please write a program to compute the distance from current position after a sequence of movement and original point. If the distance is a float, then just print the nearest integer._**\\n\",\n    \"> **_Example:_**\\n\",\n    \"> **_If the following tuples are given as input to the program:_**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"UP 5\\n\",\n    \"DOWN 3\\n\",\n    \"LEFT 3\\n\",\n    \"RIGHT 2\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"2\\n\",\n    \"```\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input.Here distance indicates to euclidean distance.Import math module to use sqrt function._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import math\\n\",\n    \"\\n\",\n    \"x, y = 0, 0\\n\",\n    \"while True:\\n\",\n    \"    s = input().split()\\n\",\n    \"    if not s:\\n\",\n    \"        break\\n\",\n    \"    if s[0] == \\\"UP\\\":  # s[0] indicates command\\n\",\n    \"        x -= int(s[1])  # s[1] indicates unit of move\\n\",\n    \"    if s[0] == \\\"DOWN\\\":\\n\",\n    \"        x += int(s[1])\\n\",\n    \"    if s[0] == \\\"LEFT\\\":\\n\",\n    \"        y -= int(s[1])\\n\",\n    \"    if s[0] == \\\"RIGHT\\\":\\n\",\n    \"        y += int(s[1])\\n\",\n    \"        # N**P means N^P\\n\",\n    \"dist = round(\\n\",\n    \"    math.sqrt(x ** 2 + y ** 2)\\n\",\n    \")  # euclidean distance = square root of (x^2+y^2) and rounding it to nearest integer\\n\",\n    \"print(dist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_08.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 22\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program to compute the frequency of the words from the input. The output should output after sorting the key alphanumerically._**\\n\",\n    \"\\n\",\n    \"> **_Suppose the following input is supplied to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"New to Python or choosing between Python 2 and Python 3? Read Python 2 or Python 3.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output should be:_**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"2:2\\n\",\n    \"3.:1\\n\",\n    \"3?:1\\n\",\n    \"New:1\\n\",\n    \"Python:5\\n\",\n    \"Read:1\\n\",\n    \"and:1\\n\",\n    \"between:1\\n\",\n    \"choosing:1\\n\",\n    \"or:2\\n\",\n    \"to:1\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ss = input().split()\\n\",\n    \"word = sorted(set(ss))  # split words are stored and sorted as a set\\n\",\n    \"\\n\",\n    \"for i in word:\\n\",\n    \"    print(\\\"{0}:{1}\\\".format(i, ss.count(i)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ss = input().split()\\n\",\n    \"dict = {}\\n\",\n    \"for i in ss:\\n\",\n    \"    i = dict.setdefault(\\n\",\n    \"        i, ss.count(i)\\n\",\n    \"    )  # setdefault() function takes key & value to set it as dictionary.\\n\",\n    \"\\n\",\n    \"dict = sorted(\\n\",\n    \"    dict.items()\\n\",\n    \")  # items() function returns both key & value of dictionary as a list\\n\",\n    \"# and then sorted. The sort by default occurs in order of 1st -> 2nd key\\n\",\n    \"for i in dict:\\n\",\n    \"    print(\\\"%s:%d\\\" % (i[0], i[1]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ss = input().split()\\n\",\n    \"dict = {\\n\",\n    \"    i: ss.count(i) for i in ss\\n\",\n    \"}  # sets dictionary as i-> split word & ss.count(i) -> total occurrence of i in ss\\n\",\n    \"dict = sorted(\\n\",\n    \"    dict.items()\\n\",\n    \")  # items() function returns both key & value of dictionary as a list\\n\",\n    \"# and then sorted. The sort by default occurs in order of 1st -> 2nd key\\n\",\n    \"for i in dict:\\n\",\n    \"    print(\\\"%s:%d\\\" % (i[0], i[1]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from collections import Counter\\n\",\n    \"\\n\",\n    \"ss = input().split()\\n\",\n    \"ss = Counter(ss)  # returns key & frequency as a dictionary\\n\",\n    \"ss = sorted(ss.items())  # returns as a tuple list\\n\",\n    \"\\n\",\n    \"for i in ss:\\n\",\n    \"    print(\\\"%s:%d\\\" % (i[0], i[1]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Solution by: AnjanKumarG**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pprint import pprint\\n\",\n    \"\\n\",\n    \"p = input().split()\\n\",\n    \"pprint({i: p.count(i) for i in p})\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 23\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a method which can calculate square value of number_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Using the ** operator which can be written as n**p where means n^p\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n = int(input())\\n\",\n    \"print(n ** 2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 24\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Python has many built-in functions, and if you do not know how to use it, you can read document online or find some books. But Python has a built-in document function for every built-in functions._**\\n\",\n    \"\\n\",\n    \"> **_Please write a program to print some Python built-in functions documents, such as abs(), int(), raw_input()_**\\n\",\n    \"\\n\",\n    \"> **_And add document for your own function_**\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The built-in document method is __doc__\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(str.__doc__)\\n\",\n    \"print(sorted.__doc__)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def pow(n, p):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    param n: This is any integer number\\n\",\n    \"    param p: This is power over n\\n\",\n    \"    return:  n to the power p = n^p\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    return n ** p\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(pow(3, 4))\\n\",\n    \"print(pow.__doc__)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 25\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a class, which have a class parameter and have a same instance parameter._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Define an instance parameter, need add it in __init__ method.You can init an object with construct parameter or set the value later\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Car name is Honda\\n\",\n      \"Car name is Toyota\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class Car:\\n\",\n    \"    name = \\\"Car\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, name=None):\\n\",\n    \"        self.name = name\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"honda = Car(\\\"Honda\\\")\\n\",\n    \"print(f\\\"{Car.name} name is {honda.name}\\\")\\n\",\n    \"\\n\",\n    \"toyota = Car()\\n\",\n    \"toyota.name = \\\"Toyota\\\"\\n\",\n    \"print(f\\\"{Car.name} name is {toyota.name}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_09.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 26\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a function which can compute the sum of two numbers._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Define a function with two numbers as arguments. You can compute the sum in the function and return the value._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sum = lambda n1, n2: n1 + n2  # here lambda is use to define little function as sum\\n\",\n    \"print(sum(1, 2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 27\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a function that can convert a integer into a string and print it in console._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use str() to convert a number to string._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"conv = lambda x: str(x)\\n\",\n    \"n = conv(10)\\n\",\n    \"print(n)\\n\",\n    \"print(type(n))  # checks the type of the variable\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 28\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a function that can receive two integer numbers in string form and compute their sum and then print it in console._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use int() to convert a string to integer._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sum = lambda s1, s2: int(s1) + int(s2)\\n\",\n    \"print(sum(\\\"10\\\", \\\"45\\\"))  # 55\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 29\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a function that can accept two strings as input and concatenate them and then print it in console._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use + sign to concatenate the strings._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sum = lambda s1, s2: s1 + s2\\n\",\n    \"print(sum(\\\"10\\\", \\\"45\\\"))  # 1045\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 30\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a function that can accept two strings as input and print the string with maximum length in console. If two strings have the same length, then the function should print all strings line by line._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use len() function to get the length of a string._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def printVal(s1, s2):\\n\",\n    \"    len1 = len(s1)\\n\",\n    \"    len2 = len(s2)\\n\",\n    \"    if len1 > len2:\\n\",\n    \"        print(s1)\\n\",\n    \"    elif len1 < len2:\\n\",\n    \"        print(s2)\\n\",\n    \"    else:\\n\",\n    \"        print(s1)\\n\",\n    \"        print(s2)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"s1, s2 = input().split()\\n\",\n    \"printVal(s1, s2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"Solution by: yuan1z\\\"\\\"\\\"\\n\",\n    \"func = (\\n\",\n    \"    lambda a, b: print(max((a, b), key=len))\\n\",\n    \"    if len(a) != len(b)\\n\",\n    \"    else print(a + \\\"\\\\n\\\" + b)\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_10.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 31\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a function which can print a dictionary where the keys are numbers between 1 and 20 (both included) and the values are square of keys._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Use dict[key]=value pattern to put entry into a dictionary.Use ** operator to get power of a number.Use range() for loops.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def printDict():\\n\",\n    \"    dict = {i: i ** 2 for i in range(1, 21)}  # Using comprehension method and\\n\",\n    \"    print(dict)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"printDict()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 32\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a function which can generate a dictionary where the keys are numbers between 1 and 20 (both included) and the values are square of keys. The function should just print the keys only._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Use dict[key]=value pattern to put entry into a dictionary.Use ** operator to get power of a number.Use range() for loops.Use keys() to iterate keys in the dictionary. Also we can use item() to get key/value pairs.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def printDict():\\n\",\n    \"    dict = {i: i ** 2 for i in range(1, 21)}\\n\",\n    \"    print(dict.keys())  # print keys of a dictionary\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"printDict()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 33\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a function which can generate and print a list where the values are square of numbers between 1 and 20 (both included)._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Use ** operator to get power of a number.Use range() for loops.Use list.append() to add values into a list.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def printList():\\n\",\n    \"    lst = [i ** 2 for i in range(1, 21)]\\n\",\n    \"    print(lst)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"printList()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 34\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a function which can generate a list where the values are square of numbers between 1 and 20 (both included). Then the function needs to print the first 5 elements in the list._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Use ** operator to get power of a number.Use range() for loops.Use list.append() to add values into a list.Use [n1:n2] to slice a list\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def printList():\\n\",\n    \"    lst = [i ** 2 for i in range(1, 21)]\\n\",\n    \"\\n\",\n    \"    for i in range(5):\\n\",\n    \"        print(lst[i])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"printList()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\",\n      \"4\\n\",\n      \"9\\n\",\n      \"16\\n\",\n      \"25\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"Solution by: yuan1z && edited by: apurvmishra99\\\"\\\"\\\"\\n\",\n    \"func = lambda: ([i ** 2 for i in range(1, 21)][:5])\\n\",\n    \"print(*(func()), sep=\\\"\\\\n\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 35\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a function which can generate a list where the values are square of numbers between 1 and 20 (both included). Then the function needs to print the last 5 elements in the list._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Use ** operator to get power of a number.Use range() for loops.Use list.append() to add values into a list.Use [n1:n2] to slice a list\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def printList():\\n\",\n    \"    lst = [i ** 2 for i in range(1, 21)]\\n\",\n    \"    for i in range(19, 14, -1):\\n\",\n    \"        print(lst[i])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"printList()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 36\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a function which can generate a list where the values are square of numbers between 1 and 20 (both included). Then the function needs to print all values except the first 5 elements in the list._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Hints: Use ** operator to get power of a number.Use range() for loops.Use list.append() to add values into a list.Use [n1:n2] to slice a list\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def printList():\\n\",\n    \"    lst = [i ** 2 for i in range(1, 21)]\\n\",\n    \"    for i in range(5, 20):\\n\",\n    \"        print(lst[i])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"printList()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 37\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a function which can generate and print a tuple where the value are square of numbers between 1 and 20 (both included)._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Use ** operator to get power of a number.Use range() for loops.Use list.append() to add values into a list.Use tuple() to get a tuple from a list.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def printTupple():\\n\",\n    \"    lst = [i ** 2 for i in range(1, 21)]\\n\",\n    \"    print(tuple(lst))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"printTupple()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Solution by: Seawolf159\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def square_of_numbers():\\n\",\n    \"    return tuple(i ** 2 for i in range(1, 21))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(square_of_numbers())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"### Comment\\n\",\n    \"\\n\",\n    \"**_Problems of this section is very much easy and all of those are of a modification of same type problem which mainly focused on using some commonly used function works with list,dictionary, tupple.In my entire solutions, I havn't tried to solve problems in efficient way.Rather I tried to solve in a different way that I can.This will help a beginner to know how simplest problems can be solved in different ways._**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_11.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 38\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_With a given tuple (1,2,3,4,5,6,7,8,9,10), write a program to print the first half values in one line and the last half values in one line._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use [n1:n2] notation to get a slice from a tuple._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tpl = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)\\n\",\n    \"\\n\",\n    \"for i in range(0, 5):\\n\",\n    \"    print(tpl[i], end=\\\" \\\")\\n\",\n    \"print()\\n\",\n    \"for i in range(5, 10):\\n\",\n    \"    print(tpl[i], end=\\\" \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tpl = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)\\n\",\n    \"lst1, lst2 = [], []\\n\",\n    \"\\n\",\n    \"for i in range(0, 5):\\n\",\n    \"    lst1.append(tpl[i])\\n\",\n    \"\\n\",\n    \"for i in range(5, 10):\\n\",\n    \"    lst2.append(tpl[i])\\n\",\n    \"\\n\",\n    \"print(lst1)\\n\",\n    \"print(lst2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Solution by: CoffeeBrakeInc\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"tup = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)\\n\",\n    \"lt = int(len(tup) / 2)\\n\",\n    \"print(tup[:lt], tup[lt:])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Solution by: AasaiAlangaram\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"tp = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)\\n\",\n    \"\\n\",\n    \"print(\\\"The Original Tuple:\\\", tp)\\n\",\n    \"\\n\",\n    \"[\\n\",\n    \"    print(\\\"Splitted List :{List}\\\".format(List=tp[x : x + 5]))\\n\",\n    \"    for x in range(0, len(tp), 5)\\n\",\n    \"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 39\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program to generate and print another tuple whose values are even numbers in the given tuple (1,2,3,4,5,6,7,8,9,10)._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use \\\"for\\\" to iterate the tuple. Use tuple() to generate a tuple from a list._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tpl = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)\\n\",\n    \"tpl1 = tuple(i for i in tpl if i % 2 == 0)\\n\",\n    \"print(tpl1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tpl = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)\\n\",\n    \"tpl1 = tuple(\\n\",\n    \"    filter(lambda x: x % 2 == 0, tpl)\\n\",\n    \")  # Lambda function returns True if found even element.\\n\",\n    \"# Filter removes data for which function returns False\\n\",\n    \"print(tpl1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 40\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program which accepts a string as input to print \\\"Yes\\\" if the string is \\\"yes\\\" or \\\"YES\\\" or \\\"Yes\\\", otherwise print \\\"No\\\"._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use if statement to judge condition._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solution: Python 3**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Solution by: Seawolf159\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"text = input(\\\"Please type something. --> \\\")\\n\",\n    \"if text == \\\"yes\\\" or text == \\\"YES\\\" or text == \\\"Yes\\\":\\n\",\n    \"    print(\\\"Yes\\\")\\n\",\n    \"else:\\n\",\n    \"    print(\\\"No\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Solution by: AasaiAlangaram\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"input = input(\\\"Enter string:\\\")\\n\",\n    \"output = \\\"\\\".join(\\n\",\n    \"    [\\\"Yes\\\" if input == \\\"yes\\\" or input == \\\"YES\\\" or input == \\\"Yes\\\" else \\\"No\\\"]\\n\",\n    \")\\n\",\n    \"print(str(output))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 41\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program which can map() to make a list whose elements are square of elements in [1,2,3,4,5,6,7,8,9,10]._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use map() to generate a list.Use lambda to define anonymous functions._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# No different way of code is written as the requirment is specificly mentioned in problem description\\n\",\n    \"\\n\",\n    \"li = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\\n\",\n    \"squaredNumbers = map(lambda x: x ** 2, li)  # returns map type object data\\n\",\n    \"print(list(squaredNumbers))  # converting the object into list\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 42\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program which can map() and filter() to make a list whose elements are square of even number in [1,2,3,4,5,6,7,8,9,10]._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use map() to generate a list.Use filter() to filter elements of a list.Use lambda to define anonymous functions._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def even(x):\\n\",\n    \"    return x % 2 == 0\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def squer(x):\\n\",\n    \"    return x * x\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"li = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\\n\",\n    \"li = map(\\n\",\n    \"    squer, filter(even, li)\\n\",\n    \")  # first filters number by even number and the apply map() on the resultant elements\\n\",\n    \"print(list(li))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 43\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program which can filter() to make a list whose elements are even number between 1 and 20 (both included)._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use filter() to filter elements of a list.Use lambda to define anonymous functions._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def even(x):\\n\",\n    \"    return x % 2 == 0\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"evenNumbers = filter(even, range(1, 21))\\n\",\n    \"print(list(evenNumbers))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_12.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 44\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Write a program which can map() to make a list whose elements are square of numbers between 1 and 20 (both included)._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use map() to generate a list. Use lambda to define anonymous functions._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"code_folding\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def sqr(x):\\n\",\n    \"    return x * x\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"squaredNumbers = list(map(sqr, range(1, 21)))\\n\",\n    \"print(squaredNumbers)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 45\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a class named American which has a static method called printNationality._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **_Use @staticmethod decorator to define class static method.There are also two more methods.To know more, go to this [link](https://realpython.com/blog/python/instance-class-and-static-methods-demystified/)._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class American:\\n\",\n    \"    @staticmethod\\n\",\n    \"    def printNationality():\\n\",\n    \"        print(\\\"I am American\\\")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"american = American()\\n\",\n    \"american.printNationality()  # this will not run if @staticmethod does not decorates the function.\\n\",\n    \"# Because the class has no instance.\\n\",\n    \"\\n\",\n    \"American.printNationality()  # this will run even though the @staticmethod\\n\",\n    \"# does not decorate printNationality()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 46\\n\",\n    \"\\n\",\n    \"### **Question:**\\n\",\n    \"\\n\",\n    \"> **_Define a class named American and its subclass NewYorker._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints:\\n\",\n    \"\\n\",\n    \"> **Use class Subclass(ParentClass) to define a subclass.\\\\***\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class American:\\n\",\n    \"    pass\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class NewYorker(American):\\n\",\n    \"    pass\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"american = American()\\n\",\n    \"newyorker = NewYorker()\\n\",\n    \"\\n\",\n    \"print(american)\\n\",\n    \"print(newyorker)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_13.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 47\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Define a class named Circle which can be constructed by a radius. The Circle class has a method which can compute the area._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use def methodName(self) to define a method._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class Circle:\\n\",\n    \"    def __init__(self, r):\\n\",\n    \"        self.radius = r\\n\",\n    \"\\n\",\n    \"    def area(self):\\n\",\n    \"        return 3.1416 * (self.radius ** 2)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"circle = Circle(5)\\n\",\n    \"print(circle.area())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 48\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Define a class named Rectangle which can be constructed by a length and width. The Rectangle class has a method which can compute the area._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use def methodName(self) to define a method._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class Rectangle:\\n\",\n    \"    def __init__(self, l, w):\\n\",\n    \"        self.length = l\\n\",\n    \"        self.width = w\\n\",\n    \"\\n\",\n    \"    def area(self):\\n\",\n    \"        return self.length * self.width\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"rect = Rectangle(2, 4)\\n\",\n    \"print(rect.area())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 49\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Define a class named Shape and its subclass Square. The Square class has an init function which takes a length as argument. Both classes have a area function which can print the area of the shape where Shape's area is 0 by default._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_To override a method in super class, we can define a method with the same name in the super class._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class Shape:\\n\",\n    \"    def __init__(self):\\n\",\n    \"        pass\\n\",\n    \"\\n\",\n    \"    def area(self):\\n\",\n    \"        return 0\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class Square(Shape):\\n\",\n    \"    def __init__(self, length=0):\\n\",\n    \"        Shape.__init__(self)\\n\",\n    \"        self.length = length\\n\",\n    \"\\n\",\n    \"    def area(self):\\n\",\n    \"        return self.length * self.length\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Asqr = Square(5)\\n\",\n    \"print(Asqr.area())  # prints 25 as given argument\\n\",\n    \"\\n\",\n    \"print(Square().area())  # prints zero as default area\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 50\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please raise a RuntimeError exception._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use raise to raise an exception._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"RuntimeError\",\n     \"evalue\": \"something wrong\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mRuntimeError\\u001b[0m                              Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-1-5fe8f779d579>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0mRuntimeError\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"something wrong\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n      \"\\u001b[0;31mRuntimeError\\u001b[0m: something wrong\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"raise RuntimeError(\\\"something wrong\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"## Conclusion\\n\",\n    \"\\n\",\n    \"**_Well It seems that the above problems are very much focused on basic concpets and implimantation of object oriented programming.As the concepts are not about to solve any functional problem rather design the structure , so both codes are very much similar in there implimantation part._**\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_14.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 51\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Write a function to compute 5/0 and use try/except to catch the exceptions._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use try/except to catch exceptions._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def divide():\\n\",\n    \"    return 5 / 0\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"try:\\n\",\n    \"    divide()\\n\",\n    \"except ZeroDivisionError as ze:\\n\",\n    \"    print(\\\"Why on earth you are dividing a number by ZERO!!\\\")\\n\",\n    \"except:\\n\",\n    \"    print(\\\"Any other exception\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 52\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Define a custom exception class which takes a string message as attribute._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_To define a custom exception, we need to define a class inherited from Exception._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class CustomException(Exception):\\n\",\n    \"    \\\"\\\"\\\"Exception raised for custom purpose\\n\",\n    \"\\n\",\n    \"    Attributes:\\n\",\n    \"        message -- explanation of the error\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, message):\\n\",\n    \"        self.message = message\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"num = int(input())\\n\",\n    \"\\n\",\n    \"try:\\n\",\n    \"    if num < 10:\\n\",\n    \"        raise CustomException(\\\"Input is less than 10\\\")\\n\",\n    \"    elif num > 10:\\n\",\n    \"        raise CustomException(\\\"Input is grater than 10\\\")\\n\",\n    \"except CustomException as ce:\\n\",\n    \"    print(\\\"The error raised: \\\" + ce.message)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 53\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Assuming that we have some email addresses in the \\\"username@companyname.com\\\" format, please write program to print the user name of a given email address. Both user names and company names are composed of letters only._**\\n\",\n    \"\\n\",\n    \"> **_Example:\\n\",\n    \"> If the following email address is given as input to the\\n\",\n    \"> program:_**\\n\",\n    \"\\n\",\n    \"> john@google.com\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"> john\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use \\\\w to match letters._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"email = \\\"john@google.com\\\"\\n\",\n    \"email = email.split(\\\"@\\\")\\n\",\n    \"print(email[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import re\\n\",\n    \"\\n\",\n    \"email = \\\"john@google.com elise@python.com\\\"\\n\",\n    \"pattern = \\\"(\\\\w+)@\\\\w+.com\\\"\\n\",\n    \"ans = re.findall(pattern, email)\\n\",\n    \"print(ans)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_15.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 54\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Assuming that we have some email addresses in the \\\"username@companyname.com\\\" format, please write program to print the company name of a given email address. Both user names and company names are composed of letters only._**\\n\",\n    \"\\n\",\n    \"> **_Example:\\n\",\n    \"> If the following email address is given as input to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> john@google.com\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> google\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use \\\\w to match letters._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import re\\n\",\n    \"\\n\",\n    \"email = \\\"john@google.com elise@python.com\\\"\\n\",\n    \"pattern = \\\"\\\\w+@(\\\\w+).com\\\"\\n\",\n    \"ans = re.findall(pattern, email)\\n\",\n    \"print(ans)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 55\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Write a program which accepts a sequence of words separated by whitespace as input to print the words composed of digits only._**\\n\",\n    \"\\n\",\n    \"> **_Example:\\n\",\n    \"> If the following words is given as input to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> 2 cats and 3 dogs.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> ['2', '3']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use re.findall() to find all substring using regex._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import re\\n\",\n    \"\\n\",\n    \"email = input()\\n\",\n    \"pattern = \\\"\\\\d+\\\"\\n\",\n    \"ans = re.findall(pattern, email)\\n\",\n    \"print(ans)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"email = input().split()\\n\",\n    \"ans = []\\n\",\n    \"for word in email:\\n\",\n    \"    if word.isdigit():  # can also use isnumeric() / isdecimal() function instead\\n\",\n    \"        ans.append(word)\\n\",\n    \"print(ans)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"email = input().split()\\n\",\n    \"ans = [word for word in email if word.isdigit()]  # using list comprehension method\\n\",\n    \"print(ans)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 56\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Print a unicode string \\\"hello world\\\"._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use u'strings' format to define unicode string._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Question 57\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Write a program to read an ASCII string and to convert it to a unicode string encoded by utf-8._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use unicode()/encode() function to convert._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"s = input()\\n\",\n    \"u = s.encode(\\\"utf-8\\\")\\n\",\n    \"print(u)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 58\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Write a special comment to indicate a Python source code file is in unicode._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use unicode() function to convert._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# -*- coding: utf-8 -*-\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 59\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Write a program to compute 1/2+2/3+3/4+...+n/n+1 with a given n input by console (n>0)._**\\n\",\n    \"\\n\",\n    \"> **_Example:\\n\",\n    \"> If the following n is given as input to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> 5\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> 3.55\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use float() to convert an integer to a float.Even if not converted it wont cause a problem because python by default understands the data type of a value_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n = int(input())\\n\",\n    \"sum = 0\\n\",\n    \"for i in range(1, n + 1):\\n\",\n    \"    sum += i / (i + 1)\\n\",\n    \"print(round(sum, 2))  # rounded to 2 decimal point\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_16.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 60\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Write a program to compute:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> $f(n)=f(n-1)+100$ when n>0\\n\",\n    \"\\n\",\n    \"> and $f(0)=0$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_with a given n input by console (n>0)._**\\n\",\n    \"\\n\",\n    \"> **_Example:\\n\",\n    \"> If the following n is given as input to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> 5\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> 500\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_We can define recursive function in Python._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def f(n):\\n\",\n    \"    if n == 0:\\n\",\n    \"        return 0\\n\",\n    \"    return f(n - 1) + 100\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"n = int(input())\\n\",\n    \"print(f(n))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 61\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_The Fibonacci Sequence is computed based on the following formula:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> $f(n)=0$ if n=0\\n\",\n    \"\\n\",\n    \"> $f(n)=1$ if n=1\\n\",\n    \"\\n\",\n    \"> $f(n)=f(n-1)+f(n-2)$ if n>1\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Please write a program to compute the value of f(n) with a given n input by console._**\\n\",\n    \"\\n\",\n    \"> **_Example:\\n\",\n    \"> If the following n is given as input to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> 7\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> 13\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_We can define recursive function in Python._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def f(n):\\n\",\n    \"    if n < 2:\\n\",\n    \"        return n\\n\",\n    \"    return f(n - 1) + f(n - 2)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"n = int(input())\\n\",\n    \"print(f(n))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 62\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_The Fibonacci Sequence is computed based on the following formula:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> $f(n)=0$ if n=0\\n\",\n    \"\\n\",\n    \"> $f(n)=1$ if n=1\\n\",\n    \"\\n\",\n    \"> $f(n)=f(n-1)+f(n-2)$ if n>1\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Please write a program to compute the value of f(n) with a given n input by console._**\\n\",\n    \"\\n\",\n    \"> **_Example:\\n\",\n    \"> If the following n is given as input to the program:_**\\n\",\n    \"\\n\",\n    \"> 7\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"> 0,1,1,2,3,5,8,13\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_We can define recursive function in Python.\\n\",\n    \"> Use list comprehension to generate a list from an existing list.\\n\",\n    \"> Use string.join() to join a list of strings._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def f(n):\\n\",\n    \"    if n < 2:\\n\",\n    \"        fibo[n] = n\\n\",\n    \"        return fibo[n]\\n\",\n    \"    fibo[n] = f(n - 1) + f(n - 2)\\n\",\n    \"    return fibo[n]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"n = int(input())\\n\",\n    \"fibo = [0] * (n + 1)  # initialize a list of size (n+1)\\n\",\n    \"f(n)  # call once and it will set value to fibo[0-n]\\n\",\n    \"fibo = [str(i) for i in fibo]  # converting integer data to string type\\n\",\n    \"ans = \\\",\\\".join(fibo)  # joining all string element of fibo with ',' character\\n\",\n    \"print(ans)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 63\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program using generator to print the even numbers between 0 and n in comma separated form while n is input by console._**\\n\",\n    \"\\n\",\n    \"> **_Example:\\n\",\n    \"> If the following n is given as input to the program:_**\\n\",\n    \"\\n\",\n    \"> 10\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"> 0,2,4,6,8,10\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use yield to produce the next value in generator._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def EvenGenerator(n):\\n\",\n    \"    i = 0\\n\",\n    \"    while i <= n:\\n\",\n    \"        if i % 2 == 0:\\n\",\n    \"            yield i\\n\",\n    \"        i += 1\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"n = int(raw_input())\\n\",\n    \"values = []\\n\",\n    \"for i in EvenGenerator(n):\\n\",\n    \"    values.append(str(i))\\n\",\n    \"\\n\",\n    \"print \\\",\\\".join(values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Solution by: StartZer0\\n\",\n    \"n = int(input())\\n\",\n    \"\\n\",\n    \"for i in range(0, n + 1, 2):\\n\",\n    \"    if i < n - 1:\\n\",\n    \"        print(i, end=\\\",\\\")\\n\",\n    \"    else:\\n\",\n    \"        print(i)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 64\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program using generator to print the numbers which can be divisible by 5 and 7 between 0 and n in comma separated form while n is input by console._**\\n\",\n    \"\\n\",\n    \"> **_Example:\\n\",\n    \"> If the following n is given as input to the program:_**\\n\",\n    \"\\n\",\n    \"> 100\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"> 0,35,70\\n\",\n    \"\\n\",\n    \"> **_In case of input data being supplied to the question, it should be assumed to be a console input._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use yield to produce the next value in generator._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def generate(n):\\n\",\n    \"    for i in range(n + 1):\\n\",\n    \"        if (\\n\",\n    \"            i % 35 == 0\\n\",\n    \"        ):  # 5*7 = 35, if a number is divisible by a & b then it is also divisible by a*b\\n\",\n    \"            yield i\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"n = int(input())\\n\",\n    \"resp = [str(i) for i in generate(n)]\\n\",\n    \"print(\\\",\\\".join(resp))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_17.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 65\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write assert statements to verify that every number in the list [2,4,6,8] is even._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use \\\"assert expression\\\" to make assertion._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data = [2, 4, 5, 6]\\n\",\n    \"for i in data:\\n\",\n    \"    assert i % 2 == 0, \\\"{} is not an even number\\\".format(i)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 66\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program which accepts basic mathematic expression from console and print the evaluation result._**\\n\",\n    \"\\n\",\n    \"> **_Example:\\n\",\n    \"> If the following n is given as input to the program:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> 35 + 3\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> 38\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use eval() to evaluate an expression._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"expression = input()\\n\",\n    \"ans = eval(expression)\\n\",\n    \"print(ans)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 67\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a binary search function which searches an item in a sorted list. The function should return the index of element to be searched in the list._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use if/elif to deal with conditions._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\\n\",\n    \"\\n\",\n    \"### Solution by AasaiAlangaram\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def binary_search_Ascending(array, target):\\n\",\n    \"    lower = 0\\n\",\n    \"    upper = len(array)\\n\",\n    \"    print(\\\"Array Length:\\\", upper)\\n\",\n    \"    while lower < upper:\\n\",\n    \"        x = (lower + upper) // 2\\n\",\n    \"        print(\\\"Middle Value:\\\", x)\\n\",\n    \"        value = array[x]\\n\",\n    \"        if target == value:\\n\",\n    \"            return x\\n\",\n    \"        elif target > value:\\n\",\n    \"            lower = x\\n\",\n    \"        elif target < value:\\n\",\n    \"            upper = x\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Array = [1, 5, 8, 10, 12, 13, 55, 66, 73, 78, 82, 85, 88, 99]\\n\",\n    \"print(\\\"The Value Found at Index:\\\", binary_search_Ascending(Array, 82))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"**OR**\\n\",\n    \"\\n\",\n    \"### Solution by yuan1z: Python\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"idx = 0\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def bs(num, num_list):\\n\",\n    \"    global idx\\n\",\n    \"    if len(num_list) == 1:\\n\",\n    \"        if num_list[0] == num:\\n\",\n    \"            return idx\\n\",\n    \"        else:\\n\",\n    \"            return \\\"No exit in the list\\\"\\n\",\n    \"    elif num in num_list[: len(num_list) // 2]:\\n\",\n    \"        return bs(num, num_list[: len(num_list) // 2])\\n\",\n    \"    else:\\n\",\n    \"        idx += len(num_list) // 2\\n\",\n    \"    return bs(num, num_list[len(num_list) // 2 :])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(bs(66, [1, 5, 8, 10, 12, 13, 55, 66, 73, 78, 82, 85, 88, 99, 100]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 68\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please generate a random float where the value is between 10 and 100 using Python module._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use random.random() to generate a random float in [0,1]._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"rand_num = random.uniform(10, 100)\\n\",\n    \"print(rand_num)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 69\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please generate a random float where the value is between 5 and 95 using Python module._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use random.random() to generate a random float in [0,1]._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"rand_num = random.uniform(5, 95)\\n\",\n    \"print(rand_num)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_18.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 70\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program to output a random even number between 0 and 10 inclusive using random module and list comprehension._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use random.choice() to a random element from a list._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"resp = [i for i in range(0, 11, 2)]\\n\",\n    \"print(random.choice(resp))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 71\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program to output a random number, which is divisible by 5 and 7, between 10 and 150 inclusive using random module and list comprehension._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use random.choice() to a random element from a list._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"resp = [i for i in range(10, 151) if i % 35 == 0]\\n\",\n    \"print(random.choice(resp))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 72\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program to generate a list with 5 random numbers between 100 and 200 inclusive._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use random.sample() to generate a list of random values._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"resp = random.sample(range(100, 201), 5)\\n\",\n    \"print(resp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 73\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program to randomly generate a list with 5 even numbers between 100 and 200 inclusive._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use random.sample() to generate a list of random values._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"resp = random.sample(range(100, 201, 2), 5)\\n\",\n    \"print(resp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 74\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program to randomly generate a list with 5 numbers, which are divisible by 5 and 7 , between 1 and 1000 inclusive._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use random.sample() to generate a list of random values._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"lst = [i for i in range(1, 1001) if i % 35 == 0]\\n\",\n    \"resp = random.sample(lst, 5)\\n\",\n    \"print(resp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_19.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 75\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program to randomly print a integer number between 7 and 15 inclusive._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use random.randrange() to a random integer in a given range._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"8\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"print(random.randrange(7, 16))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 76\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program to compress and decompress the string \\\"hello world!hello world!hello world!hello world!\\\"._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use zlib.compress() and zlib.decompress() to compress and decompress a string._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"b'x\\\\x9c\\\\xcbH\\\\xcd\\\\xc9\\\\xc9W(\\\\xcf/\\\\xcaIQ\\\\xcc \\\\x82\\\\r\\\\x00\\\\xbd[\\\\x11\\\\xf5'\\n\",\n      \"b'hello world!hello world!hello world!hello world!'\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import zlib\\n\",\n    \"\\n\",\n    \"t = zlib.compress(b'hello world!hello world!hello world!hello world!')\\n\",\n    \"print(t)\\n\",\n    \"print(zlib.decompress(t))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 77\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program to print the running time of execution of \\\"1+1\\\" for 100 times._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use timeit() function to measure the running time._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import datetime\\n\",\n    \"\\n\",\n    \"before = datetime.datetime.now()\\n\",\n    \"for i in range(100):\\n\",\n    \"    x = 1 + 1\\n\",\n    \"after = datetime.datetime.now()\\n\",\n    \"execution_time = after - before\\n\",\n    \"print(execution_time.microseconds)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"before = time.time()\\n\",\n    \"for i in range(100):\\n\",\n    \"    x = 1 + 1\\n\",\n    \"after = time.time()\\n\",\n    \"execution_time = after - before\\n\",\n    \"print(execution_time)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 78\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program to shuffle and print the list [3,6,7,8]._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use shuffle() function to shuffle a list._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"lst = [3, 6, 7, 8]\\n\",\n    \"random.shuffle(lst)\\n\",\n    \"print(lst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"# shuffle with a chosen seed\\n\",\n    \"lst = [3, 6, 7, 8]\\n\",\n    \"seed = 7\\n\",\n    \"random.Random(seed).shuffle(lst)\\n\",\n    \"print(lst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 79\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program to generate all sentences where subject is in [\\\"I\\\", \\\"You\\\"] and verb is in [\\\"Play\\\", \\\"Love\\\"] and the object is in [\\\"Hockey\\\",\\\"Football\\\"]._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use list[index] notation to get a element from a list._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"subjects = [\\\"I\\\", \\\"You\\\"]\\n\",\n    \"verbs = [\\\"Play\\\", \\\"Love\\\"]\\n\",\n    \"objects = [\\\"Hockey\\\", \\\"Football\\\"]\\n\",\n    \"\\n\",\n    \"for sub in subjects:\\n\",\n    \"    for verb in verbs:\\n\",\n    \"        for obj in objects:\\n\",\n    \"            print(\\\"{} {} {}\\\".format(sub, verb, obj))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_20.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 80\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program to print the list after removing even numbers in [5,6,77,45,22,12,24]._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use list comprehension to delete a bunch of element from a list._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def isEven(n):\\n\",\n    \"    return n % 2 != 0\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"li = [5, 6, 77, 45, 22, 12, 24]\\n\",\n    \"lst = list(filter(isEven, li))\\n\",\n    \"print(lst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"li = [5, 6, 77, 45, 22, 12, 24]\\n\",\n    \"lst = list(filter(lambda n: n % 2 != 0, li))\\n\",\n    \"print(lst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 81\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_By using list comprehension, please write a program to print the list after removing numbers which are divisible by 5 and 7 in [12,24,35,70,88,120,155]._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use list comprehension to delete a bunch of element from a list._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"li = [12, 24, 35, 70, 88, 120, 155]\\n\",\n    \"li = [x for x in li if x % 35 != 0]\\n\",\n    \"print(li)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 82\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_By using list comprehension, please write a program to print the list after removing the 0th, 2nd, 4th,6th numbers in [12,24,35,70,88,120,155]._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use list comprehension to delete a bunch of element from a list.\\n\",\n    \"> Use enumerate() to get (index, value) tuple._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"li = [12, 24, 35, 70, 88, 120, 155]\\n\",\n    \"li = [li[i] for i in range(len(li)) if i % 2 != 0]\\n\",\n    \"print(li)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 83\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_By using list comprehension, please write a program to print the list after removing the 2nd - 4th numbers in [12,24,35,70,88,120,155]._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use list comprehension to delete a bunch of element from a list.\\n\",\n    \"> Use enumerate() to get (index, value) tuple._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# to be written\\n\",\n    \"li = [12, 24, 35, 70, 88, 120, 155]\\n\",\n    \"li = [li[i] for i in range(len(li)) if i < 3 or 4 < i]\\n\",\n    \"print(li)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 84\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_By using list comprehension, please write a program generate a 3\\\\*5\\\\*8 3D array whose each element is 0._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use list comprehension to make an array._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"array = [[[0 for col in range(8)] for col in range(5)] for row in range(3)]\\n\",\n    \"print array\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_21.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 85\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_By using list comprehension, please write a program to print the list after removing the 0th,4th,5th numbers in [12,24,35,70,88,120,155]._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use list comprehension to delete a bunch of element from a list.Use enumerate() to get (index, value) tuple._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"li = [12, 24, 35, 70, 88, 120, 155]\\n\",\n    \"li = [li[i] for i in range(len(li)) if i not in (0, 4, 5)]\\n\",\n    \"print(li)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 86\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_By using list comprehension, please write a program to print the list after removing the value 24 in [12,24,35,24,88,120,155]._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use list's remove method to delete a value._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"li = [12, 24, 35, 24, 88, 120, 155]\\n\",\n    \"li.remove(24)  # this will remove only the first occurrence of 24\\n\",\n    \"print(li)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 87\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_With two given lists [1,3,6,78,35,55] and [12,24,35,24,88,120,155], write a program to make a list whose elements are intersection of the above given lists._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use set() and \\\"&=\\\" to do set intersection operation._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"list1 = [1, 3, 6, 78, 35, 55]\\n\",\n    \"list2 = [12, 24, 35, 24, 88, 120, 155]\\n\",\n    \"set1 = set(list1)\\n\",\n    \"set2 = set(list2)\\n\",\n    \"intersection = set1 & set2\\n\",\n    \"print(intersection)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"list1 = [1, 3, 6, 78, 35, 55]\\n\",\n    \"list2 = [12, 24, 35, 24, 88, 120, 155]\\n\",\n    \"set1 = set(list1)\\n\",\n    \"set2 = set(list2)\\n\",\n    \"intersection = set.intersection(set1, set2)\\n\",\n    \"print(intersection)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 88\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_With a given list [12,24,35,24,88,120,155,88,120,155], write a program to print this list after removing all duplicate values with original order reserved._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use set() to store a number of values without duplicate._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"li = [12, 24, 35, 24, 88, 120, 155, 88, 120, 155]\\n\",\n    \"for i in li:\\n\",\n    \"    if li.count(i) > 1:\\n\",\n    \"        li.remove(i)\\n\",\n    \"print(li)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def removeDuplicate(li):\\n\",\n    \"    seen = {}  # dictionary\\n\",\n    \"    for item in li:\\n\",\n    \"        if item not in seen:\\n\",\n    \"            seen[item] = True\\n\",\n    \"            yield item\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"li = [12, 24, 35, 24, 88, 120, 155, 88, 120, 155]\\n\",\n    \"ans = list(removeDuplicate(li))\\n\",\n    \"print(ans)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 89\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Define a class Person and its two child classes: Male and Female. All classes have a method \\\"getGender\\\" which can print \\\"Male\\\" for Male class and \\\"Female\\\" for Female class._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use Subclass(Parentclass) to define a child class._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class Person(object):\\n\",\n    \"    def getGender(self):\\n\",\n    \"        return \\\"Unknown\\\"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class Male(Person):\\n\",\n    \"    def getGender(self):\\n\",\n    \"        return \\\"Male\\\"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class Female(Person):\\n\",\n    \"    def getGender(self):\\n\",\n    \"        return \\\"Female\\\"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"aMale = Male()\\n\",\n    \"aFemale = Female()\\n\",\n    \"print(aMale.getGender())\\n\",\n    \"print(aFemale.getGender())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_22.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 90\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program which count and print the numbers of each character in a string input by console._**\\n\",\n    \"\\n\",\n    \"> **_Example:\\n\",\n    \"> If the following string is given as input to the program:_**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"abcdefgabc\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"a,2\\n\",\n    \"c,2\\n\",\n    \"b,2\\n\",\n    \"e,1\\n\",\n    \"d,1\\n\",\n    \"g,1\\n\",\n    \"f,1\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use dict to store key/value pairs.\\n\",\n    \"> Use dict.get() method to lookup a key with default value._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import string\\n\",\n    \"\\n\",\n    \"s = input()\\n\",\n    \"for letter in string.ascii_lowercase:\\n\",\n    \"    cnt = s.count(letter)\\n\",\n    \"    if cnt > 0:\\n\",\n    \"        print(\\\"{},{}\\\".format(letter, cnt))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"s = input()\\n\",\n    \"for letter in range(ord(\\\"a\\\"), ord(\\\"z\\\") + 1):  # ord() gets the ascii value of a char\\n\",\n    \"    letter = chr(letter)  # chr() gets the char of an ascii value\\n\",\n    \"    cnt = s.count(letter)\\n\",\n    \"    if cnt > 0:\\n\",\n    \"        print(\\\"{},{}\\\".format(letter, cnt))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 91\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program which accepts a string from console and print it in reverse order._**\\n\",\n    \"\\n\",\n    \"> **Example:\\n\",\n    \"> If the following string is given as input to the program:\\\\***\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"rise to vote sir\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"\\n\",\n    \"ris etov ot esir\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use list[::-1] to iterate a list in a reverse order._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"s = input()\\n\",\n    \"s = \\\"\\\".join(reversed(s))\\n\",\n    \"print(s)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 92\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program which accepts a string from console and print the characters that have even indexes._**\\n\",\n    \"\\n\",\n    \"> **_Example:\\n\",\n    \"> If the following string is given as input to the program:_**\\n\",\n    \"```\\n\",\n    \"H1e2l3l4o5w6o7r8l9d\\n\",\n    \"```\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \"```\\n\",\n    \"Helloworld\\n\",\n    \"```\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use list[::2] to iterate a list by step 2._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"s = \\\"H1e2l3l4o5w6o7r8l9d\\\"\\n\",\n    \"s = [s[i] for i in range(len(s)) if i % 2 == 0]\\n\",\n    \"print(\\\"\\\".join(s))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**OR**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"s = \\\"H1e2l3l4o5w6o7r8l9d\\\"\\n\",\n    \"ns = \\\"\\\"\\n\",\n    \"for i in range(len(s)):\\n\",\n    \"    if i % 2 == 0:\\n\",\n    \"        ns += s[i]\\n\",\n    \"print(ns)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 93\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Please write a program which prints all permutations of [1,2,3]_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use itertools.permutations() to get permutations of list._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[(1, 2, 3), (1, 3, 2), (2, 1, 3), (2, 3, 1), (3, 1, 2), (3, 2, 1)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import itertools\\n\",\n    \"\\n\",\n    \"print(list(itertools.permutations([1, 2, 3])))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 94\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Write a program to solve a classic ancient Chinese puzzle:\\n\",\n    \"> We count 35 heads and 94 legs among the chickens and rabbits in a farm. How many rabbits and how many chickens do we have?_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use for loop to iterate all possible solutions._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"code_folding\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(23, 12)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def solve(numheads, numlegs):\\n\",\n    \"    ns = \\\"No solutions!\\\"\\n\",\n    \"    for i in range(numheads + 1):\\n\",\n    \"        j = numheads - i\\n\",\n    \"        if 2 * i + 4 * j == numlegs:\\n\",\n    \"            return i, j\\n\",\n    \"    return ns, ns\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"numheads = 35\\n\",\n    \"numlegs = 94\\n\",\n    \"solutions = solve(numheads, numlegs)\\n\",\n    \"print(solutions)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_23.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# The extended part of the repository starts from this page. Previous 94 problems were collected from the repository mentioned in intro. The following problems are collected from Hackerrank and other resources from internet.\\n\",\n    \"\\n\",\n    \"# Question 95\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Given the participants' score sheet for your University Sports Day, you are required to find the runner-up score. You are given scores. Store them in a list and find the score of the runner-up._**\\n\",\n    \"\\n\",\n    \"> **_If the following string is given as input to the program:_**\\n\",\n    \">\\n\",\n    \"> 5\\n\",\n    \"> 2 3 6 6 5\\n\",\n    \">\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \">\\n\",\n    \"> 5\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Make the scores unique and then find 2nd best number_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"5 2\\n\",\n      \"2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"arr = map(int, input().split())\\n\",\n    \"arr = list(set(arr))\\n\",\n    \"arr.sort()\\n\",\n    \"print(arr[-2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Solution by: mishrasunny-coder\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"num = int(input(\\\"Enter num: \\\"))\\n\",\n    \"L = []\\n\",\n    \"\\n\",\n    \"while True:\\n\",\n    \"    L.append(num)\\n\",\n    \"    num = int(input(\\\"Enter another: \\\"))\\n\",\n    \"    if num == 0:\\n\",\n    \"        break\\n\",\n    \"\\n\",\n    \"L1 = list(set(L[:]))\\n\",\n    \"L2 = sorted(L1)\\n\",\n    \"print(L2)\\n\",\n    \"\\n\",\n    \"print(f\\\"The runner up is {L2[-2]}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 96\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_You are given a string S and width W.\\n\",\n    \"> Your task is to wrap the string into a paragraph of width._**\\n\",\n    \"\\n\",\n    \"> **_If the following string is given as input to the program:_**\\n\",\n    \">\\n\",\n    \"> ABCDEFGHIJKLIMNOQRSTUVWXYZ\\n\",\n    \"> 4\\n\",\n    \">\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \">\\n\",\n    \"> ABCD\\n\",\n    \"> EFGH\\n\",\n    \"> IJKL\\n\",\n    \"> IMNO\\n\",\n    \"> QRST\\n\",\n    \"> UVWX\\n\",\n    \"> YZ\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use wrap function of textwrap module_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import textwrap\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def wrap(string, max_width):\\n\",\n    \"    string = textwrap.wrap(string, max_width)\\n\",\n    \"    string = \\\"\\\\n\\\".join(string)\\n\",\n    \"    return string\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if __name__ == \\\"__main__\\\":\\n\",\n    \"    string, max_width = input(), int(input())\\n\",\n    \"    result = wrap(string, max_width)\\n\",\n    \"    print(result)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Solution by: mishrasunny-coder\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"import textwrap\\n\",\n    \"\\n\",\n    \"string = input()\\n\",\n    \"width = int(input())\\n\",\n    \"\\n\",\n    \"print(textwrap.fill(string, width))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 97\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_You are given an integer, N. Your task is to print an alphabet rangoli of size N. (Rangoli is a form of Indian folk art based on creation of patterns.)_**\\n\",\n    \"\\n\",\n    \"> **_Different sizes of alphabet rangoli are shown below:_**\\n\",\n    \">\\n\",\n    \"> $size = 3$\\n\",\n    \">\\n\",\n    \"> ----c----\\n\",\n    \"\\n\",\n    \"> --c-b-c--\\n\",\n    \"\\n\",\n    \"> c-b-a-b-c\\n\",\n    \"\\n\",\n    \"> --c-b-c--\\n\",\n    \"\\n\",\n    \"> ----c----\\n\",\n    \">\\n\",\n    \"> $size = 5$\\n\",\n    \">\\n\",\n    \"> --------e--------\\n\",\n    \"\\n\",\n    \"> ------e-d-e------\\n\",\n    \"\\n\",\n    \"> ----e-d-c-d-e----\\n\",\n    \"\\n\",\n    \"> --e-d-c-b-c-d-e--\\n\",\n    \"\\n\",\n    \"> e-d-c-b-a-b-c-d-e\\n\",\n    \"\\n\",\n    \"> --e-d-c-b-c-d-e--\\n\",\n    \"\\n\",\n    \"> ----e-d-c-d-e----\\n\",\n    \"\\n\",\n    \"> ------e-d-e------\\n\",\n    \"\\n\",\n    \"> --------e--------\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_First print the half of the Rangoli in the given way and save each line in a list. Then print the list in reverse order to get the rest._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import string\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def print_rangoli(size):\\n\",\n    \"    n = size\\n\",\n    \"    alph = string.ascii_lowercase\\n\",\n    \"    width = 4 * n - 3\\n\",\n    \"\\n\",\n    \"    ans = []\\n\",\n    \"    for i in range(n):\\n\",\n    \"        left = \\\"-\\\".join(alph[n - i - 1 : n])\\n\",\n    \"        mid = left[-1:0:-1] + left\\n\",\n    \"        final = mid.center(width, \\\"-\\\")\\n\",\n    \"        ans.append(final)\\n\",\n    \"\\n\",\n    \"    if len(ans) > 1:\\n\",\n    \"        for i in ans[n - 2 :: -1]:\\n\",\n    \"            ans.append(i)\\n\",\n    \"    ans = \\\"\\\\n\\\".join(ans)\\n\",\n    \"    print(ans)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if __name__ == \\\"__main__\\\":\\n\",\n    \"    n = int(input())\\n\",\n    \"    print_rangoli(n)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 98\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_You are given a date. Your task is to find what the day is on that date._**\\n\",\n    \"\\n\",\n    \"**Input**\\n\",\n    \"\\n\",\n    \"> **_A single line of input containing the space separated month, day and year, respectively, in MM DD YYYY format._**\\n\",\n    \">\\n\",\n    \"> 08 05 2015\\n\",\n    \"\\n\",\n    \"**Output**\\n\",\n    \"\\n\",\n    \"> **_Output the correct day in capital letters._**\\n\",\n    \">\\n\",\n    \"> WEDNESDAY\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use weekday function of calender module_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import calendar\\n\",\n    \"\\n\",\n    \"month, day, year = map(int, input().split())\\n\",\n    \"\\n\",\n    \"dayId = calendar.weekday(year, month, day)\\n\",\n    \"print(calendar.day_name[dayId].upper())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 99\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Given 2 sets of integers, M and N, print their symmetric difference in ascending order. The term symmetric difference indicates those values that exist in either M or N but do not exist in both._**\\n\",\n    \"\\n\",\n    \"**Input**\\n\",\n    \"\\n\",\n    \"> **_The first line of input contains an integer, M.The second line contains M space-separated integers.The third line contains an integer, N.The fourth line contains N space-separated integers._**\\n\",\n    \">\\n\",\n    \"> 4\\n\",\n    \"> 2 4 5 9\\n\",\n    \"> 4\\n\",\n    \"> 2 4 11 12\\n\",\n    \"\\n\",\n    \"**Output**\\n\",\n    \"\\n\",\n    \"> **_Output the symmetric difference integers in ascending order, one per line._**\\n\",\n    \">\\n\",\n    \"> 5\\n\",\n    \"> 9\\n\",\n    \"> 11\\n\",\n    \"> 12\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use \\\\'^\\\\' to make symmetric difference operation._**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2\\n\",\n      \"1\\n\",\n      \"1\\n\",\n      \"1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"n = int(input())\\n\",\n    \"set1 = set(map(int, input().split()))\\n\",\n    \"\\n\",\n    \"m = int(input())\\n\",\n    \"set2 = set(map(int, input().split()))\\n\",\n    \"\\n\",\n    \"ans = list(set1 ^ set2)\\n\",\n    \"ans.sort()\\n\",\n    \"for i in ans:\\n\",\n    \"    print(i)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python-100/notebooks/Day_24.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Question 100\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_You are given words. Some words may repeat. For each word, output its number of occurrences. The output order should correspond with the input order of appearance of the word. See the sample input/output for clarification._**\\n\",\n    \"\\n\",\n    \"> **_If the following string is given as input to the program:_**\\n\",\n    \">\\n\",\n    \"> 4\\n\",\n    \"> bcdef\\n\",\n    \"> abcdefg\\n\",\n    \"> bcde\\n\",\n    \"> bcdef\\n\",\n    \">\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \">\\n\",\n    \"> 3\\n\",\n    \"> 2 1 1\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Make a list to get the input order and a dictionary to count the word frequency_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n = int(input())\\n\",\n    \"\\n\",\n    \"word_list = []\\n\",\n    \"word_dict = {}\\n\",\n    \"\\n\",\n    \"for i in range(n):\\n\",\n    \"    word = input()\\n\",\n    \"    if word not in word_dict:\\n\",\n    \"        word_list.append(word)\\n\",\n    \"    word_dict[word] = word_dict.get(word, 0) + 1\\n\",\n    \"\\n\",\n    \"print(len(word_list))\\n\",\n    \"for word in word_list:\\n\",\n    \"    print(word_dict[word], end=\\\" \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 101\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_You are given a string.Your task is to count the frequency of letters of the string and print the letters in descending order of frequency._**\\n\",\n    \"\\n\",\n    \"> **_If the following string is given as input to the program:_**\\n\",\n    \">\\n\",\n    \"> aabbbccde\\n\",\n    \">\\n\",\n    \"> **_Then, the output of the program should be:_**\\n\",\n    \">\\n\",\n    \"> b 3\\n\",\n    \"> a 2\\n\",\n    \"> c 2\\n\",\n    \"> d 1\\n\",\n    \"> e 1\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Count frequency with dictionary and sort by Value from dictionary Items_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solutions:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"word = input()\\n\",\n    \"dct = {}\\n\",\n    \"for i in word:\\n\",\n    \"    dct[i] = dct.get(i, 0) + 1\\n\",\n    \"\\n\",\n    \"dct = sorted(dct.items(), key=lambda x: (-x[1], x[0]))\\n\",\n    \"for i in dct:\\n\",\n    \"    print(i[0], i[1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"Solution by: yuan1z\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"X = input()\\n\",\n    \"my_set = set(X)\\n\",\n    \"arr = []\\n\",\n    \"for item in my_set:\\n\",\n    \"    arr.append([item, X.count(item)])\\n\",\n    \"tmp = sorted(arr, key=lambda x: (-x[1], x[0]))\\n\",\n    \"\\n\",\n    \"for i in tmp:\\n\",\n    \"    print(i[0] + \\\" \\\" + str(i[1]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\\"\\\"\\\"Solution by: StartZer0\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"s = list(input())\\n\",\n    \"\\n\",\n    \"dict_count_ = {k: s.count(k) for k in s}\\n\",\n    \"list_of_tuples = [(k, v) for k, v in dict_count_.items()]\\n\",\n    \"list_of_tuples.sort(key=lambda x: x[1], reverse=True)\\n\",\n    \"\\n\",\n    \"for item in list_of_tuples:\\n\",\n    \"    print(item[0], item[1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 102\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Write a Python program that accepts a string and calculate the number of digits and letters._**\\n\",\n    \"\\n\",\n    \"**Input**\\n\",\n    \"\\n\",\n    \"> Hello321Bye360\\n\",\n    \"\\n\",\n    \"**Output**\\n\",\n    \"\\n\",\n    \"> Digit - 6\\n\",\n    \"\\n\",\n    \"> Letter - 8\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Use isdigit() and isalpha() function_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"word = input()\\n\",\n    \"digit, letter = 0, 0\\n\",\n    \"for char in word:\\n\",\n    \"    digit += char.isdigit()\\n\",\n    \"    letter += char.isalpha()\\n\",\n    \"\\n\",\n    \"print(\\\"Digit -\\\", digit)\\n\",\n    \"print(\\\"Letter -\\\", letter)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Question 103\\n\",\n    \"\\n\",\n    \"### **Question**\\n\",\n    \"\\n\",\n    \"> **_Given a number N.Find Sum of 1 to N Using Recursion_**\\n\",\n    \"\\n\",\n    \"**Input**\\n\",\n    \"\\n\",\n    \"> 5\\n\",\n    \"\\n\",\n    \"**Output**\\n\",\n    \"\\n\",\n    \"> 15\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"### Hints\\n\",\n    \"\\n\",\n    \"> **_Make a recursive function to get the sum_**\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Solution:**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def rec(n):\\n\",\n    \"    if n == 0:\\n\",\n    \"        return n\\n\",\n    \"    return rec(n - 1) + n\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"n = int(input())\\n\",\n    \"sum = rec(n)\\n\",\n    \"print(sum)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# To Be Continued...\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "1.python-basic/Python_Basic.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#  Python语言基础\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"本章是python语言的基础部分，也是后续内容的基础。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1 Python数据类型\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.1 字符串\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在Python中用引号引起来的字符集称之为字符串，比如：'hello'、\\\"my Python\\\"、\\\"2+3\\\"等都是字符串\\n\",\n    \"Python中字符串中使用的引号可以是单引号、双引号跟三引号\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"hello world!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('hello world!')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"It is a \\\"dog\\\"!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"c = 'It is a \\\"dog\\\"!'\\n\",\n    \"print (c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"It's a dog!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"c1= \\\"It's a dog!\\\"\\n\",\n    \"print (c1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"hello\\n\",\n      \"world\\n\",\n      \"!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"c2 = \\\"\\\"\\\"hello\\n\",\n    \"world\\n\",\n    \"!\\\"\\\"\\\"\\n\",\n    \"print (c2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 转义字符'\\\\'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"转义字符\\\\可以转义很多字符，比如\\\\n表示换行，\\\\t表示制表符，字符\\\\本身也要转义，所以\\\\\\\\表示的字符就是\\\\\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"It's a dog!\\n\",\n      \"hello world!\\n\",\n      \"hello Python!\\n\",\n      \"\\\\\\t\\\\\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('It\\\\'s a dog!')\\n\",\n    \"print (\\\"hello world!\\\\nhello Python!\\\")\\n\",\n    \"print ('\\\\\\\\\\\\t\\\\\\\\')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"原样输出引号内字符串可以使用在引号前加r\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\\\\\\\\\\\t\\\\\\\\\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print (r'\\\\\\\\\\\\t\\\\\\\\')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 子字符串及运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"s = 'Python'\\n\",\n    \"print( 'Py' in s)\\n\",\n    \"print( 'py' in s)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"取子字符串有两种方法，使用[]索引或者切片运算法[:]，这两个方法使用面非常广\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"t\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print (s[2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"yth\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print (s[1:4])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 字符串连接与格式化输出\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The first word is \\\"hello\\\", and the second word is \\\"world\\\"\\n\",\n      \"hello world!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"word1 = '\\\"hello\\\"'\\n\",\n    \"word2 = '\\\"world\\\"'\\n\",\n    \"sentence = word1.strip('\\\"') + ' ' + word2.strip('\\\"') + '!'\\n\",\n    \"\\n\",\n    \"print( 'The first word is %s, and the second word is %s' %(word1, word2))\\n\",\n    \"print (sentence)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.2 整数与浮点数\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 整数\\n\",\n    \"\\n\",\n    \"Python可以处理任意大小的整数，当然包括负整数，在程序中的表示方法和数学上的写法一模一样\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"7\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"i = 7\\n\",\n    \"print (i)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"10\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"7 + 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"4\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"7 - 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"21\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"7 * 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"343\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"7 ** 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2.3333333333333335\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"7 / 3#Python3之后，整数除法和浮点数除法已经没有差异\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"7 % 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"7//3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 浮点数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2.3333333333333335\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"7.0 / 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"314.0\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"3.14 * 10 ** 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"其它表示方法\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"15\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"0b1111\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"255\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"0xff\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1.2e-05\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"1.2e-5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"更多运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import math\\n\",\n    \"\\n\",\n    \"print (math.log(math.e)) # 更多运算可查阅文档\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.3 布尔值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"True and False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"True or False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"not True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"True + False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"18 >= 6 * 3 or 'py' in 'Python'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"18 >= 6 * 3 and 'py' in 'Python'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"18 >= 6 * 3 and 'Py' in 'Python'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.4 日期时间\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"time.struct_time(tm_year=2016, tm_mon=7, tm_mday=20, tm_hour=0, tm_min=0, tm_sec=0, tm_wday=2, tm_yday=202, tm_isdst=-1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"now = time.strptime('2016-07-20', '%Y-%m-%d')\\n\",\n    \"print (now)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2016-07-20'\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"time.strftime('%Y-%m-%d', now)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"5\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import datetime\\n\",\n    \"\\n\",\n    \"someDay = datetime.date(1999,2,10)\\n\",\n    \"anotherDay = datetime.date(1999,2,15)\\n\",\n    \"deltaDay = anotherDay - someDay\\n\",\n    \"deltaDay.days\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"还有其他一些datetime格式\\n\",\n    \"![](images/09b1dbf9ccdb4b69b614a0875b4e5f4c_006tKfTcgy1fkr9s9113dj30g80i7weq.jpg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 查看变量类型\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"NoneType\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"float\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(1.0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"bool\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"str\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s=\\\"NoneType\\\"\\n\",\n    \"type(s)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 类型转换\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'10086'\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"str(10086)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"?float()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"10086.0\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"float(10086)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"10086\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"int('10086')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(10086+0j)\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"complex(10086)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2 Python数据结构\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"列表（list）、元组（tuple）、集合（set）、字典（dict）\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.1 列表(list)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"用来存储一连串元素的容器，列表用[]来表示，其中元素的类型可不相同。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0, 1, 2, 3, 4, 5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"mylist= [0, 1, 2, 3, 4, 5]\\n\",\n    \"print (mylist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"列表索引和切片\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[4]= 4\\n\",\n      \"[-4]= 2\\n\",\n      \"[0:4]= [0, 1, 2, 3]\\n\",\n      \"[:4]= [0, 1, 2, 3]\\n\",\n      \"[4:]= [4, 5]\\n\",\n      \"[0:4:2]= [0, 2]\\n\",\n      \"[-5:-1:]= [1, 2, 3, 4]\\n\",\n      \"[-2::-1]= [4, 3, 2, 1, 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 索引从0开始，含左不含右\\n\",\n    \"print ('[4]=', mylist[4])\\n\",\n    \"print ('[-4]=', mylist[-4])\\n\",\n    \"print ('[0:4]=', mylist[0:4])\\n\",\n    \"print ('[:4]=', mylist[:4])#dddd\\n\",\n    \"print( '[4:]=', mylist[4:])\\n\",\n    \"print ('[0:4:2]=', mylist[0:4:2])\\n\",\n    \"print ('[-5:-1:]=', mylist[-5:-1:])\\n\",\n    \"print ('[-2::-1]=', mylist[-2::-1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"修改列表\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"小月\\n\",\n      \"小楠\\n\",\n      \"19978\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"mylist[3] = \\\"小月\\\"\\n\",\n    \"print (mylist[3])\\n\",\n    \"\\n\",\n    \"mylist[5]=\\\"小楠\\\"\\n\",\n    \"print (mylist[5])\\n\",\n    \"\\n\",\n    \"mylist[5]=19978\\n\",\n    \"print (mylist[5])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0, 1, 2, '小月', 4, 19978]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print (mylist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"插入元素\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0, 1, 2, '小月', 4, 19978, 'han', 'long', 'wan']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"mylist.append('han') # 添加到尾部\\n\",\n    \"mylist.extend(['long', 'wan'])\\n\",\n    \"print (mylist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[0, [90, 80, 75, 66], 1, 2, '小月', 4, 19978, 'han', 'long', 'wan']\"\n      ]\n     },\n     \"execution_count\": 51,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"scores = [90, 80, 75, 66]\\n\",\n    \"mylist.insert(1, scores) # 添加到指定位置\\n\",\n    \"mylist\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"a=[]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"删除元素\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[90, 80, 75, 66]\\n\",\n      \"[0, 1, 2, '小月', 4, 19978, 'han', 'long', 'wan']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print (mylist.pop(1)) # 该函数返回被弹出的元素，不传入参数则删除最后一个元素\\n\",\n    \"print (mylist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"判断元素是否在列表中等\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print( 'wan' in mylist)\\n\",\n    \"print ('han' not in mylist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 55,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mylist.count('wan')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"8\"\n      ]\n     },\n     \"execution_count\": 56,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mylist.index('wan')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"range函数生成整数列表\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"range(0, 10)\\n\",\n      \"range(-5, 5)\\n\",\n      \"range(-10, 10, 2)\\n\",\n      \"range(16, 10, -1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print (range(10))\\n\",\n    \"print (range(-5, 5))\\n\",\n    \"print (range(-10, 10, 2))\\n\",\n    \"print (range(16, 10, -1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.2 元组(tuple)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"元组类似列表，元组里面的元素也是进行索引计算。列表里面的元素的值可以修改，而元组里面的元素的值不能修改，只能读取。元组的符号是()。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"('ming', 'jun', 'qiang', 'wu', [90, 80, 75, 66])\"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"studentsTuple = (\\\"ming\\\", \\\"jun\\\", \\\"qiang\\\", \\\"wu\\\", scores)\\n\",\n    \"studentsTuple\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"TypeError\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"try:\\n\",\n    \"    studentsTuple[1] = 'fu'\\n\",\n    \"except TypeError:\\n\",\n    \"    print ('TypeError')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"('ming', 'jun', 'qiang', 'wu', [90, 100, 75, 66])\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"scores[1]= 100\\n\",\n    \"studentsTuple\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 61,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"'ming' in studentsTuple\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"('ming', 'jun', 'qiang', 'wu')\"\n      ]\n     },\n     \"execution_count\": 62,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"studentsTuple[0:4]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 63,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"studentsTuple.count('ming')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 64,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"studentsTuple.index('jun')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"5\"\n      ]\n     },\n     \"execution_count\": 65,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(studentsTuple)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.3 集合(set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Python中集合主要有两个功能，一个功能是进行集合操作，另一个功能是消除重复元素。 集合的格式是：set()，其中()内可以是列表、字典或字符串，因为字符串是以列表的形式存储的\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{0, 1, 2, 'han', 4, 'long', 'wan', 19978, '小月'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"studentsSet = set(mylist)\\n\",\n    \"print (studentsSet)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{0, 1, 2, 'han', 4, 'long', 'xu', 'wan', 19978, '小月'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"studentsSet.add('xu')\\n\",\n    \"print (studentsSet)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{0, 1, 2, 'han', 4, 'long', 'wan', 19978, '小月'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"studentsSet.remove('xu')\\n\",\n    \"print (studentsSet)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a= {'n', 'g', 'm', 'c', 's', 'b', 'a'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = set(\\\"abcnmaaaaggsng\\\")\\n\",\n    \"print ('a=', a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"b= {'f', 'm', 'd', 'c'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"b = set(\\\"cdfm\\\")\\n\",\n    \"print ('b=', b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x= {'c', 'm'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#交集\\n\",\n    \"x = a & b \\n\",\n    \"print( 'x=', x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"y= {'g', 'n', 'f', 'c', 'm', 's', 'b', 'a', 'd'}\\n\",\n      \"z= {'g', 'n', 's', 'b', 'a'}\\n\",\n      \"{'g', 'n', 's', 'b', 'a'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#并集\\n\",\n    \"y = a | b\\n\",\n    \"print ('y=', y)\\n\",\n    \"#差集\\n\",\n    \"z = a - b\\n\",\n    \"print( 'z=', z)\\n\",\n    \"#去除重复元素\\n\",\n    \"new = set(a)\\n\",\n    \"print( z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.4字典(dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Python中的字典dict也叫做关联数组，用大括号{}括起来，在其他语言中也称为map，使用键-值（key-value）存储，具有极快的查找速度，其中key不能重复。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"guilin\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"k = {\\\"name\\\":\\\"weiwei\\\", \\\"home\\\":\\\"guilin\\\"}\\n\",\n    \"print (k[\\\"home\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dict_keys(['name', 'home'])\\n\",\n      \"dict_values(['weiwei', 'guilin'])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print( k.keys())\\n\",\n    \"print( k.values())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"添加、修改字典里面的项目\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'name': 'guangzhou', 'home': 'guilin', 'like': 'music'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"k[\\\"like\\\"] = \\\"music\\\"\\n\",\n    \"k['name'] = 'guangzhou'\\n\",\n    \"print (k)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"-1\"\n      ]\n     },\n     \"execution_count\": 77,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"k.get('edu', -1) # 通过dict提供的get方法，如果key不存在，可以返回None，或者自己指定的value\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"删除key-value元素\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'name': 'guangzhou', 'home': 'guilin'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"k.pop('like')\\n\",\n    \"print (k)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.5 列表、元组、集合、字典的互相转换\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"list\"\n      ]\n     },\n     \"execution_count\": 79,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(mylist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(0, 1, 2, '小月', 4, 19978, 'han', 'long', 'wan')\"\n      ]\n     },\n     \"execution_count\": 80,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tuple(mylist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['name', 'home']\"\n      ]\n     },\n     \"execution_count\": 81,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"list(k)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<zip object at 0x00000135BEAF2A88>\\n\",\n      \"{'A': 1, 'B': 2, 'C': 3}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"zl = zip(('A', 'B', 'C'), [1, 2, 3, 4]) # zip可以将列表、元组、集合、字典‘缝合’起来\\n\",\n    \"print (zl)\\n\",\n    \"print (dict(zl))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 3 Python控制流\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在Python中通常的情况下程序的执行是从上往下执行的，而某些时候我们为了改变程序的执行顺序，使用控制流语句控制程序执行方式。Python中有三种控制流类型：顺序结构、分支结构、循环结构。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"另外，Python可以使用分号\\\";\\\"分隔语句，但一般是使用换行来分隔；语句块不用大括号\\\"{}\\\"，而使用缩进（可以使用四个空格）来表示\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.1 顺序结构\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"36\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"s = '7'\\n\",\n    \"num = int(s) # 一般不使用这种分隔方式\\n\",\n    \"num -= 1 # num = num - 1 \\n\",\n    \"num *= 6 # num = num * 6\\n\",\n    \"print (num)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.2 分支结构：Python中if语句是用来判断选择执行哪个语句块的\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {},\n   \"source\": [\n    \"if <True or Flase表达式>:\\n\",\n    \"    执行语句块\\n\",\n    \"elif <True or Flase表达式>：\\n\",\n    \"    执行语句块\\n\",\n    \"else：        # 都不满足\\n\",\n    \"    执行语句块\\n\",\n    \"    \\n\",\n    \"# elif子句可以有多条，elif和else部分可省略\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"..........\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"salary = 1000\\n\",\n    \"if salary > 10000:\\n\",\n    \"    print (\\\"Wow!!!!!!!\\\")\\n\",\n    \"elif salary > 5000:\\n\",\n    \"    print (\\\"That's OK.\\\")\\n\",\n    \"elif salary > 3000:\\n\",\n    \"    print (\\\"5555555555\\\")\\n\",\n    \"else:\\n\",\n    \"    print (\\\"..........\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.3 循环结构\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"while 循环\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {},\n   \"source\": [\n    \"while <True or Flase表达式>:\\n\",\n    \"    循环执行语句块\\n\",\n    \"else：          # 不满足条件\\n\",\n    \"    执行语句块\\n\",\n    \"\\n\",\n    \"#else部分可以省略\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\",\n      \"2\\n\",\n      \"3\\n\",\n      \"4\\n\",\n      \"5\\n\",\n      \"Hello\\n\",\n      \"Hello\\n\",\n      \"Hello\\n\",\n      \"Hello\\n\",\n      \"Done\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = 1\\n\",\n    \"while a < 10:\\n\",\n    \"    if a <= 5:\\n\",\n    \"        print (a)\\n\",\n    \"    else:\\n\",\n    \"        print (\\\"Hello\\\")\\n\",\n    \"    a = a + 1\\n\",\n    \"else:\\n\",\n    \"    print (\\\"Done\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- for 循环\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {},\n   \"source\": [\n    \"for (条件变量) in (集合)：\\n\",\n    \"    执行语句块\\n\",\n    \"    \\n\",\n    \"# “集合”并不单指set，而是“形似”集合的列表、元组、字典、数组都可以进行循环\\n\",\n    \"# 条件变量可以有多个\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Yao 226\\n\",\n      \"Sharq 216\\n\",\n      \"AI 183\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"heights = {'Yao':226, 'Sharq':216, 'AI':183}\\n\",\n    \"for i in heights:\\n\",\n    \"    print (i, heights[i])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Yao 226\\n\",\n      \"Sharq 216\\n\",\n      \"AI 183\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for key, value in heights.items():\\n\",\n    \"    print(key, value)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"5050\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"total = 0\\n\",\n    \"for i in range(1, 101):\\n\",\n    \"    total += i#total=total+i\\n\",\n    \"print (total)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.4 break、continue和pass\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {},\n   \"source\": [\n    \"break:跳出循环\\n\",\n    \"continue:跳出当前循环,继续下一次循环\\n\",\n    \"pass:占位符，什么也不做\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\",\n      \"2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i in range(1, 5):\\n\",\n    \"    if i == 3:\\n\",\n    \"        break\\n\",\n    \"    print (i)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\",\n      \"2\\n\",\n      \"4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i in range(1, 5):\\n\",\n    \"    if i == 3:\\n\",\n    \"        continue\\n\",\n    \"    print (i)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\",\n      \"2\\n\",\n      \"3\\n\",\n      \"4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i in range(1, 5):\\n\",\n    \"    if i == 3:\\n\",\n    \"        pass\\n\",\n    \"    print (i)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.5 列表生成式\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"三种形式\\n\",\n    \"- [<表达式> for (条件变量) in (集合)]\\n\",\n    \"- [<表达式> for (条件变量) in (集合) if <'True or False'表达式>]\\n\",\n    \"- [<表达式> if <'True or False'表达式> else <表达式>  for (条件变量) in (集合) ]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['Apple', 'Watermelon', 'Banana']\"\n      ]\n     },\n     \"execution_count\": 92,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fruits = ['\\\"Apple', 'Watermelon', '\\\"Banana\\\"']\\n\",\n    \"[x.strip('\\\"') for x in fruits]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['Apple', 'Watermelon', 'Banana']\"\n      ]\n     },\n     \"execution_count\": 93,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 另一种写法\\n\",\n    \"test_list=[]\\n\",\n    \"for x in fruits:\\n\",\n    \"    x=x.strip('\\\"')\\n\",\n    \"    test_list.append(x)\\n\",\n    \"test_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[1, 9, 25, 49, 81, 121, 169, 225, 289, 361]\"\n      ]\n     },\n     \"execution_count\": 94,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"[x ** 2 for x in range(21) if x%2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[1, 9, 25, 49, 81, 121, 169, 225, 289, 361]\"\n      ]\n     },\n     \"execution_count\": 95,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 另一种写法\\n\",\n    \"test_list=[]\\n\",\n    \"for x in range(21):\\n\",\n    \"    if x%2:\\n\",\n    \"        x=x**2\\n\",\n    \"        test_list.append(x)\\n\",\n    \"test_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['AX', 'AY', 'AZ', 'BX', 'BY', 'BZ', 'CX', 'CY', 'CZ']\"\n      ]\n     },\n     \"execution_count\": 96,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"[m + n for m in 'ABC' for n in 'XYZ']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['AX', 'AY', 'AZ', 'BX', 'BY', 'BZ', 'CX', 'CY', 'CZ']\"\n      ]\n     },\n     \"execution_count\": 97,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 另一种写法\\n\",\n    \"test_list=[]\\n\",\n    \"for m in 'ABC':\\n\",\n    \"    for n in 'XYZ':\\n\",\n    \"        x=m+n\\n\",\n    \"        test_list.append(x)\\n\",\n    \"test_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['x=A', 'y=B', 'z=C']\"\n      ]\n     },\n     \"execution_count\": 98,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"d = {'x': 'A', 'y': 'B', 'z': 'C' }\\n\",\n    \"[k + '=' + v for k, v in d.items()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['x=A', 'y=B', 'z=C']\"\n      ]\n     },\n     \"execution_count\": 99,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 另一种写法\\n\",\n    \"test_list=[]\\n\",\n    \"for k, v in d.items():\\n\",\n    \"    x=k + '=' + v\\n\",\n    \"    test_list.append(x)\\n\",\n    \"test_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4 Python函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"函数是用来封装特定功能的实体，可对不同类型和结构的数据进行操作，达到预定目标\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.1 调用函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Python内置了很多有用的函数，我们可以直接调用，进行数据分析时多数情况下是通过调用定义好的函数来操作数据的\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2\\n\",\n      \"9\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"str1 = \\\"as\\\"\\n\",\n    \"int1 = -9\\n\",\n    \"print (len(str1))\\n\",\n    \"print (abs(int1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['Apple', 'Banana', 'Melon', 'Grape']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fruits = ['Apple', 'Banana', 'Melon']\\n\",\n    \"fruits.append('Grape')\\n\",\n    \"print (fruits)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.2 定义函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"当系统自带函数不足以完成指定的功能时，需要用户自定义函数来完成。\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {},\n   \"source\": [\n    \"def 函数名()：\\n\",\n    \"    函数内容\\n\",\n    \"    函数内容\\n\",\n    \"    <return 返回值>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"9\"\n      ]\n     },\n     \"execution_count\": 102,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def my_abs(x):\\n\",\n    \"    if x >= 0:\\n\",\n    \"        return x\\n\",\n    \"    else:\\n\",\n    \"        return -x\\n\",\n    \"    \\n\",\n    \"my_abs(-9)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"可以没有return\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"None\\n\",\n      \"['Apple', 'Banana', 'Grape']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def filter_fruit(someList, d):\\n\",\n    \"    for i in someList:\\n\",\n    \"        if i == d:\\n\",\n    \"            someList.remove(i)\\n\",\n    \"        else:\\n\",\n    \"            pass\\n\",\n    \"\\n\",\n    \"print (filter_fruit(fruits, 'Melon'))\\n\",\n    \"print (fruits)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"多个返回值的情况\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 104,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"4 5 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tuple\"\n      ]\n     },\n     \"execution_count\": 104,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def test(i, j):\\n\",\n    \"    k = i * j\\n\",\n    \"    return i, j, k\\n\",\n    \"\\n\",\n    \"a , b , c = test(4, 5)\\n\",\n    \"print (a, b , c)\\n\",\n    \"type(test(4, 5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.3 高阶函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 把另一个函数作为参数传入一个函数，这样的函数称为高阶函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"函数本身也可以赋值给变量，函数与其它对象具有同等地位\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"9\"\n      ]\n     },\n     \"execution_count\": 105,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"myFunction = abs\\n\",\n    \"myFunction(-9)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 参数传入函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"12\"\n      ]\n     },\n     \"execution_count\": 106,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def add(x, y, f):\\n\",\n    \"    return f(x) + f(y)\\n\",\n    \"\\n\",\n    \"add(7, -5, myFunction)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 常用高阶函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"map/reduce: map将传入的函数依次作用到序列的每个元素，并把结果作为新的list返回；reduce把一个函数作用在一个序列[x1, x2, x3...]上，这个函数必须接收两个参数，reduce把结果继续和序列的下一个元素做累积计算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<map at 0x135bea80438>\"\n      ]\n     },\n     \"execution_count\": 107,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"myList = [-1, 2, -3, 4, -5, 6, 7]\\n\",\n    \"map(abs, myList)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3560020598205630145296938\"\n      ]\n     },\n     \"execution_count\": 108,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from functools import reduce\\n\",\n    \"def powerAdd(a, b):\\n\",\n    \"    return pow(a, 2) + pow(b, 2)\\n\",\n    \"\\n\",\n    \"reduce(powerAdd, myList) # 是否是计算平方和？\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"filter： filter()把传入的函数依次作用于每个元素，然后根据返回值是True还是False决定保留还是丢弃该元素\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<filter at 0x135bea93b00>\"\n      ]\n     },\n     \"execution_count\": 109,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def is_odd(x):\\n\",\n    \"    return x % 3  # 0被判断为False，其它被判断为True\\n\",\n    \"\\n\",\n    \"filter(is_odd, myList)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"sorted: 实现对序列排序，默认情况下对于两个元素x和y，如果认为x < y，则返回-1，如果认为x == y，则返回0，如果认为x > y，则返回1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"默认排序：数字大小或字母序（针对字符串）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 110,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[-5, -3, -1, 2, 4, 6, 7]\"\n      ]\n     },\n     \"execution_count\": 110,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sorted(myList)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 返回函数: 高阶函数除了可以接受函数作为参数外，还可以把函数作为结果值返回\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 111,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<function __main__.powAdd.<locals>.power>\"\n      ]\n     },\n     \"execution_count\": 111,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def powAdd(x, y):\\n\",\n    \"    def power(n):\\n\",\n    \"        return pow(x, n) + pow(y, n)\\n\",\n    \"    return power\\n\",\n    \"\\n\",\n    \"myF = powAdd(3, 4)\\n\",\n    \"myF\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 112,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"25\"\n      ]\n     },\n     \"execution_count\": 112,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"myF(2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 匿名函数：高阶函数传入函数时，不需要显式地定义函数，直接传入匿名函数更方便\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 113,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"16\"\n      ]\n     },\n     \"execution_count\": 113,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"f = lambda x: x * x\\n\",\n    \"f(4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"等同于：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 114,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def f(x):\\n\",\n    \"    return x * x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<map at 0x135beaaa0b8>\"\n      ]\n     },\n     \"execution_count\": 115,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"map(lambda x: x * x, myList)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"匿名函数可以传入多个参数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 116,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"140\"\n      ]\n     },\n     \"execution_count\": 116,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"reduce(lambda x, y: x + y, map(lambda x: x * x, myList))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"返回函数可以是匿名函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 117,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"25\"\n      ]\n     },\n     \"execution_count\": 117,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def powAdd1(x, y):\\n\",\n    \"    return lambda n: pow(x, n) + pow(y, n)\\n\",\n    \"\\n\",\n    \"lamb = powAdd1(3, 4)\\n\",\n    \"lamb(2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 其它\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 标识符第一个字符只能是字母或下划线，第一个字符不能出现数字或其他字符；标识符除第一个字符外，其他部分可以是字母或者下划线或者数字，标识符大小写敏感，比如name跟Name是不同的标识符。\\n\",\n    \"- Python规范：\\n\",\n    \"* 类标识符每个字符第一个字母大写；\\n\",\n    \"* 对象\\\\变量标识符的第一个字母小写，其余首字母大写，或使用下划线'_' 连接；\\n\",\n    \"* 函数命名同普通对象。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 关键字\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"关键字是指系统中自带的具备特定含义的标识符\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['False', 'None', 'True', 'and', 'as', 'assert', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 查看一下关键字有哪些，避免关键字做自定义标识符\\n\",\n    \"import keyword\\n\",\n    \"print (keyword.kwlist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 注释\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Python中的注释一般用#进行注释\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- 帮助\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"可以用？或者help()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "1.python-basic/a.txt",
    "content": "time 2018-12-15 17:24:57 Saturday December PM 05 end action\ntime 2018-12-15 17:24:57 Saturday December PM 05 end action\ntime 2018-12-15 17:24:57 Saturday December PM 05 end action\ntime 2020-07-07 19:17:43 Tuesday July PM 07 end action\ntime 2020-07-07 19:17:43 Tuesday July PM 07 end action\ntime 2020-07-07 19:17:43 Tuesday July PM 07 end action\n"
  },
  {
    "path": "1.python-basic/test",
    "content": "\n阿斯达所，\n天安门上太阳升\n阿斯达所，\n天安门上太阳升\n阿斯达所，\n天安门上太阳升\n阿斯达所，\n天安门上太阳升"
  },
  {
    "path": "1.python-basic/两天入门python-第一天.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 一、Day1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 0.print\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"What is your name?Mike\\n\",\n      \"Hello Mike\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"name = input(\\\"What is your name?\\\")\\n\",\n    \"print(\\\"Hello \\\"+name )\\n\",\n    \"# 或者print(\\\"Hello\\\",name ),print中逗号分隔直接将字符串用空格分隔，若用+号连接，并且想留空格，则在前一字符串留空格即可\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.输入输出\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"username:111\\n\",\n      \"password:666\\n\",\n      \"111 666\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"username=input(\\\"username:\\\")\\n\",\n    \"password=input(\\\"password:\\\")\\n\",\n    \"print(username,password)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2.格式输入输出\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Name:Mike\\n\",\n      \"age:28\\n\",\n      \"job:worker\\n\",\n      \"---------info of ---------\\n\",\n      \"Name:Mike\\n\",\n      \"Age:28\\n\",\n      \"Job:worker\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 第一种方法\\n\",\n    \"name=input(\\\"Name:\\\")\\n\",\n    \"age=input(\\\"age:\\\")\\n\",\n    \"job=input(\\\"job:\\\")\\n\",\n    \"\\n\",\n    \"info='''---------info of ---------''' + '''\\n\",\n    \"Name:'''+name+'''\\n\",\n    \"Age:'''+age+'''\\n\",\n    \"Job:'''+job\\n\",\n    \"print(info)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Name:Mike\\n\",\n      \"age:28\\n\",\n      \"job:worker\\n\",\n      \"---------info of ---------\\n\",\n      \"Name:Mike\\n\",\n      \"Age:28\\n\",\n      \"Job:worker\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 第二种方法\\n\",\n    \"\\n\",\n    \"name=input(\\\"Name:\\\")\\n\",\n    \"age=int(input(\\\"age:\\\"))  #如果不用int()就会报错(虽然输入为数字，但是print(type(age))为str型)，因为python如果不强制类型转化，就会默认字符型\\n\",\n    \"job=input(\\\"job:\\\")\\n\",\n    \"\\n\",\n    \"info='''---------info of ---------\\n\",\n    \"Name:%s\\n\",\n    \"Age:%d\\n\",\n    \"Job:%s'''%(name,age,job)\\n\",\n    \"print(info)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Name:Mike\\n\",\n      \"age:28\\n\",\n      \"job:worker\\n\",\n      \"---------info of ---------\\n\",\n      \"Name:Mike\\n\",\n      \"Age:28\\n\",\n      \"Job:worker\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 第三种方法\\n\",\n    \"\\n\",\n    \"name=input(\\\"Name:\\\")\\n\",\n    \"age=int(input(\\\"age:\\\"))  #如果不用int()就会报错(虽然输入为数字，但是print(type(age))为str型)，因为python如果不强制类型转化，就会默认字符型\\n\",\n    \"job=input(\\\"job:\\\")\\n\",\n    \"\\n\",\n    \"info='''---------info of ---------\\n\",\n    \"Name:{_name}\\n\",\n    \"Age:{_age}\\n\",\n    \"Job:{_job}'''.format(_name=name,_age=age,_job=job)\\n\",\n    \"print(info)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Name:Mike\\n\",\n      \"age:28\\n\",\n      \"job:worker\\n\",\n      \"---------info of ---------\\n\",\n      \"Name:Mike\\n\",\n      \"Age:28\\n\",\n      \"Job:worker\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 第四种方法\\n\",\n    \"\\n\",\n    \"name=input(\\\"Name:\\\")\\n\",\n    \"age=int(input(\\\"age:\\\"))  #如果不用int()就会报错(虽然输入为数字，但是print(type(age))为str型)，因为python如果不强制类型转化，就会默认字符型\\n\",\n    \"job=input(\\\"job:\\\")\\n\",\n    \"\\n\",\n    \"info='''---------info of ---------\\n\",\n    \"Name:{0}\\n\",\n    \"Age:{1}\\n\",\n    \"Job:{2}'''.format(name,age,job)\\n\",\n    \"print(info)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 3.输入密码不可见\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"请输入密码:········\\n\",\n      \"3434\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import getpass\\n\",\n    \"pwd=getpass.getpass(\\\"请输入密码:\\\")\\n\",\n    \"print(pwd)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4.验证，python缩进\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"username:df\\n\",\n      \"password:i'\\n\",\n      \"Invalid username or password!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"_username='Alex Li'\\n\",\n    \"_password='abc123'\\n\",\n    \"username=input(\\\"username:\\\")\\n\",\n    \"password=input(\\\"password:\\\")\\n\",\n    \"if _username==username and _password==password:\\n\",\n    \"    print((\\\"Welcome user {name} login...\\\").format(name=username))\\n\",\n    \"else:\\n\",\n    \"    print(\\\"Invalid username or password!\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5.指向---修改字符串\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Hello World\\n\",\n      \"Alex Li\\n\",\n      \"My name is Alex Li Alex Li\\n\",\n      \"My name is PaoChe Ge Alex Li\\n\",\n      \"您好，我来了\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Hello World\\\")\\n\",\n    \"name = \\\"Alex Li\\\"\\n\",\n    \"name2=name\\n\",\n    \"print(name)\\n\",\n    \"print(\\\"My name is\\\", name,name2) # Alex Li Alex Li\\n\",\n    \"name = \\\"PaoChe Ge\\\"\\n\",\n    \"# name2=name指的是name2与name一样指向Alex Li的内存地址，name指向改了，但是name2不变\\n\",\n    \"print(\\\"My name is\\\", name,name2) # PaoChe Ge Alex Li\\n\",\n    \"print(\\\"您好，我来了\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 6.注释`''' '''`内涵\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'print(\\\"这是一行注释\\\")'\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 第一种情况就是注释\\n\",\n    \"'''print(\\\"这是一行注释\\\")'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"这是第一行内容\\n\",\n      \"这是第二行内容\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#第二种情况就是打印多行字符串\\n\",\n    \"str='''这是第一行内容\\n\",\n    \"这是第二行内容'''\\n\",\n    \"print(str)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"i'am a student\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 3.单套双，双套单都可以\\n\",\n    \"str1=\\\"i'am a student\\\"\\n\",\n    \"print(str1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 7.模块初始sys与os\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['', 'C:\\\\\\\\ProgramData\\\\\\\\Anaconda3\\\\\\\\python36.zip', 'C:\\\\\\\\ProgramData\\\\\\\\Anaconda3\\\\\\\\DLLs', 'C:\\\\\\\\ProgramData\\\\\\\\Anaconda3\\\\\\\\lib', 'C:\\\\\\\\ProgramData\\\\\\\\Anaconda3', 'C:\\\\\\\\ProgramData\\\\\\\\Anaconda3\\\\\\\\lib\\\\\\\\site-packages', 'C:\\\\\\\\ProgramData\\\\\\\\Anaconda3\\\\\\\\lib\\\\\\\\site-packages\\\\\\\\win32', 'C:\\\\\\\\ProgramData\\\\\\\\Anaconda3\\\\\\\\lib\\\\\\\\site-packages\\\\\\\\win32\\\\\\\\lib', 'C:\\\\\\\\ProgramData\\\\\\\\Anaconda3\\\\\\\\lib\\\\\\\\site-packages\\\\\\\\Pythonwin', 'C:\\\\\\\\ProgramData\\\\\\\\Anaconda3\\\\\\\\lib\\\\\\\\site-packages\\\\\\\\IPython\\\\\\\\extensions', 'C:\\\\\\\\Users\\\\\\\\haigu\\\\\\\\.ipython']\\n\",\n      \"['C:\\\\\\\\ProgramData\\\\\\\\Anaconda3\\\\\\\\lib\\\\\\\\site-packages\\\\\\\\ipykernel_launcher.py', '-f', 'C:\\\\\\\\Users\\\\\\\\haigu\\\\\\\\AppData\\\\\\\\Roaming\\\\\\\\jupyter\\\\\\\\runtime\\\\\\\\kernel-28ddb76e-a36b-4edc-ab46-0b848ae5812f.json']\\n\",\n      \"C:\\\\Users\\\\haigu\\\\AppData\\\\Roaming\\\\jupyter\\\\runtime\\\\kernel-28ddb76e-a36b-4edc-ab46-0b848ae5812f.json\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import sys\\n\",\n    \"# 打印环境变量\\n\",\n    \"print(sys.path)\\n\",\n    \"\\n\",\n    \"print(sys.argv)\\n\",\n    \"print(sys.argv[2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"##################################################\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 进度条\\n\",\n    \"import time\\n\",\n    \"for i in range(50):\\n\",\n    \"    sys.stdout.write('#')\\n\",\n    \"    sys.stdout.flush()\\n\",\n    \"    time.sleep(0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"---> 0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"cmd_res = os.system(\\\"dir\\\") # os.system()执行后直接输出到终端，然后结束，最后cmd_res保存的是os.system()执行后的状态码\\n\",\n    \"print(\\\"--->\\\",cmd_res) # ---> 0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"---> <os._wrap_close object at 0x000001B85DEF9940>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cmd_res1=os.popen(\\\"dir\\\")\\n\",\n    \"print(\\\"--->\\\",cmd_res1) # 得到的是内存对象值 ---> <os._wrap_close object at 0x00000000029187B8>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--->  驱动器 E 中的卷是 tools\\n\",\n      \" 卷的序列号是 E2B0-A3DA\\n\",\n      \"\\n\",\n      \" E:\\\\tools\\\\学术\\\\python\\\\两天入门python 的目录\\n\",\n      \"\\n\",\n      \"2018/12/15  11:10    <DIR>          .\\n\",\n      \"2018/12/15  11:10    <DIR>          ..\\n\",\n      \"2018/12/15  10:58    <DIR>          .ipynb_checkpoints\\n\",\n      \"2018/12/15  11:10             9,565 两天入门python.ipynb\\n\",\n      \"               1 个文件          9,565 字节\\n\",\n      \"               3 个目录 24,186,036,224 可用字节\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cmd_res1=os.popen(\\\"dir\\\").read()\\n\",\n    \"print(\\\"--->\\\",cmd_res1) # 读取数据必须再后面加个read()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"os.mkdir(\\\"new_dir3\\\") # 创建一个目录\\n\",\n    \"os.removedirs(\\\"new_dir3\\\") # 删除一个目录\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 8.三元运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 1.result = 值1 if 条件 else 值2\\n\",\n    \"a=1\\n\",\n    \"b=2\\n\",\n    \"c=3\\n\",\n    \"d=a if a>b else c\\n\",\n    \"print(d)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 9.python3特性\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {},\n   \"source\": [\n    \"python3中最重要的新特性大概是对文本和二进制数据作了更为清晰的区分。文本总是Unicode，由str类型表示,\\n\",\n    \"二进制数据则由bytes类型表示。Python3不会以任意隐式的方式混用str和bytes，正是这使得两者区分特别清晰。\\n\",\n    \"即：在python2中类型会自动转化，而在python3中则要么报错，要么不转化\\n\",\n    \"str与bytes相互转化\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 10.bytes与str转化\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"我爱北京天安门\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"msg=\\\"我爱北京天安门\\\"\\n\",\n    \"\\n\",\n    \"print(msg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"b'\\\\xe6\\\\x88\\\\x91\\\\xe7\\\\x88\\\\xb1\\\\xe5\\\\x8c\\\\x97\\\\xe4\\\\xba\\\\xac\\\\xe5\\\\xa4\\\\xa9\\\\xe5\\\\xae\\\\x89\\\\xe9\\\\x97\\\\xa8'\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(msg.encode(encoding=\\\"utf-8\\\")) # str转bytes,编码\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"我爱北京天安门\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(msg.encode(encoding=\\\"utf-8\\\").decode(encoding=\\\"utf-8\\\")) # bytes转str,解码\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 11.循环\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"第一种循环\\n\",\n      \"count: 0\\n\",\n      \"count: 1\\n\",\n      \"count: 2\\n\",\n      \"count: 3\\n\",\n      \"count: 4\\n\",\n      \"count: 5\\n\",\n      \"count: 6\\n\",\n      \"count: 7\\n\",\n      \"count: 8\\n\",\n      \"count: 9\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"第一种循环\\\")\\n\",\n    \"count = 0\\n\",\n    \"while True:\\n\",\n    \"    print(\\\"count:\\\",count)\\n\",\n    \"    count+=1\\n\",\n    \"    if(count==10):\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"第二种循环\\n\",\n      \"count: 0\\n\",\n      \"count: 2\\n\",\n      \"count: 4\\n\",\n      \"count: 6\\n\",\n      \"count: 8\\n\",\n      \"loop  0\\n\",\n      \"hehe....\\n\",\n      \"loop  1\\n\",\n      \"hehe....\\n\",\n      \"loop  2\\n\",\n      \"hehe....\\n\",\n      \"loop  3\\n\",\n      \"hehe....\\n\",\n      \"loop  4\\n\",\n      \"hehe....\\n\",\n      \"input your guess num:2\\n\",\n      \"Oops,think bigger!\\n\",\n      \"猜这么多次都不对，你个笨蛋.\\n\",\n      \"input your guess num:28\\n\",\n      \"Congratulations,you got it!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"第二种循环\\\")\\n\",\n    \"count = 0\\n\",\n    \"for count in range(0,10,2):\\n\",\n    \"    print(\\\"count:\\\", count)\\n\",\n    \"\\n\",\n    \"for i in range(0,10):\\n\",\n    \"    if i<5:\\n\",\n    \"        print(\\\"loop \\\",i)\\n\",\n    \"    else:\\n\",\n    \"        continue\\n\",\n    \"    print(\\\"hehe....\\\")\\n\",\n    \"my_age=28\\n\",\n    \"count = 0\\n\",\n    \"while count<3:\\n\",\n    \"    user_input=int(input(\\\"input your guess num:\\\"))\\n\",\n    \"\\n\",\n    \"    if user_input==my_age:\\n\",\n    \"        print(\\\"Congratulations,you got it!\\\")\\n\",\n    \"        break\\n\",\n    \"    elif user_input<my_age:\\n\",\n    \"        print(\\\"Oops,think bigger!\\\")\\n\",\n    \"    else:\\n\",\n    \"        print(\\\"think smaller!\\\")\\n\",\n    \"    count+=1\\n\",\n    \"    print(\\\"猜这么多次都不对，你个笨蛋.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 12.练习---三级菜单\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"北京\\n\",\n      \"山东\\n\",\n      \"广东\\n\",\n      \"选择进入1>>:2\\n\",\n      \"北京\\n\",\n      \"山东\\n\",\n      \"广东\\n\",\n      \"选择进入1>>:3\\n\",\n      \"北京\\n\",\n      \"山东\\n\",\n      \"广东\\n\",\n      \"选择进入1>>:q\\n\",\n      \"北京\\n\",\n      \"山东\\n\",\n      \"广东\\n\",\n      \"选择进入1>>:2\\n\",\n      \"北京\\n\",\n      \"山东\\n\",\n      \"广东\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"data={\\n\",\n    \"    '北京':{\\n\",\n    \"        \\\"昌平\\\":{\\n\",\n    \"            \\\"沙河\\\":[\\\"oldboys\\\",'test'],\\n\",\n    \"            \\\"天通苑\\\":[\\\"链家地产\\\",\\\"我爱我家\\\"]\\n\",\n    \"        },\\n\",\n    \"        \\\"朝阳\\\":{\\n\",\n    \"            \\\"望京\\\":[\\\"oldboys\\\",'默陌陌'],\\n\",\n    \"            \\\"国贸\\\":[\\\"CICC\\\",\\\"HP\\\"],\\n\",\n    \"            \\\"东直门\\\":[\\\"Advent\\\",\\\"飞信\\\"]\\n\",\n    \"        },\\n\",\n    \"        \\\"海淀\\\":{}\\n\",\n    \"    },\\n\",\n    \"    '山东':{\\n\",\n    \"        \\\"德州\\\":{},\\n\",\n    \"        \\\"青岛\\\":{},\\n\",\n    \"        \\\"济南\\\":{}\\n\",\n    \"    },\\n\",\n    \"    '广东':{\\n\",\n    \"        \\\"德州\\\":{},\\n\",\n    \"        \\\"青岛\\\":{},\\n\",\n    \"        \\\"济南\\\":{}\\n\",\n    \"    },\\n\",\n    \"}\\n\",\n    \"exit_flag = False\\n\",\n    \"while not exit_flag:\\n\",\n    \"    for i in data:\\n\",\n    \"        print(i)\\n\",\n    \"    choice=input(\\\"选择进入1>>:\\\")\\n\",\n    \"    if choice in data:\\n\",\n    \"        while not exit_flag:\\n\",\n    \"            for i2 in data[choice]:\\n\",\n    \"                print(\\\"\\\\t\\\",i2)\\n\",\n    \"            choice2=input(\\\"选择进入2>>:\\\")\\n\",\n    \"            if choice2 in data[choice]:\\n\",\n    \"                while not exit_flag:\\n\",\n    \"                    for i3 in data[choice][choice2]:\\n\",\n    \"                        print(\\\"\\\\t\\\\t\\\", i3)\\n\",\n    \"                    choice3 = input(\\\"选择进入3>>:\\\")\\n\",\n    \"                    if choice3 in data[choice][choice2]:\\n\",\n    \"                        for i4 in data[choice][choice2][choice3]:\\n\",\n    \"                            print(i4)\\n\",\n    \"                        choice4=input(\\\"最后一层，按b返回>>:\\\")\\n\",\n    \"                        if choice4=='b':\\n\",\n    \"                            pass # pass可以理解为占位符，表示什么都不做，返回循环起始位置，以后可以在此处添加内容\\n\",\n    \"                        elif choice4=='q':\\n\",\n    \"                            exit_flag=True\\n\",\n    \"                    if (choice3 == 'b'):\\n\",\n    \"                        break\\n\",\n    \"                    elif choice3 == 'q':\\n\",\n    \"                        exit_flag = True\\n\",\n    \"            if (choice2 == 'b'):\\n\",\n    \"                break\\n\",\n    \"            elif choice2 == 'q':\\n\",\n    \"                exit_flag = True\\n\",\n    \"\\n\",\n    \"    if (choice == 'b'):\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "1.python-basic/两天入门python-第二天.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 二、Day2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.编码变换\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"b'\\\\xce\\\\xd2\\\\xb0\\\\xae\\\\xb1\\\\xb1\\\\xbe\\\\xa9\\\\xcc\\\\xec\\\\xb0\\\\xb2\\\\xc3\\\\xc5'\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# utf-8与gbk互相转化需要通过Unicode作为中介\\n\",\n    \"s=\\\"我爱北京天安门\\\"  # 默认编码为Unicode\\n\",\n    \"print(s.encode(\\\"gbk\\\")) # Unicode可直接转化为gbk\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"b'\\\\xe6\\\\x88\\\\x91\\\\xe7\\\\x88\\\\xb1\\\\xe5\\\\x8c\\\\x97\\\\xe4\\\\xba\\\\xac\\\\xe5\\\\xa4\\\\xa9\\\\xe5\\\\xae\\\\x89\\\\xe9\\\\x97\\\\xa8'\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(s.encode(\\\"utf-8\\\")) # Unicode可直接转化为utf-8\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"b'\\\\xce\\\\xd2\\\\xb0\\\\xae\\\\xb1\\\\xb1\\\\xbe\\\\xa9\\\\xcc\\\\xec\\\\xb0\\\\xb2\\\\xc3\\\\xc5'\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(s.encode(\\\"utf-8\\\").decode(\\\"utf-8\\\").encode(\\\"gb2312\\\"))\\n\",\n    \"# 此时s.encode(\\\"utf-8\\\")即转为utf-8了，然后转为gb2312，则需要先告诉Unicode你原先的编码是什么，即s.encode(\\\"utf-8\\\").decode(\\\"utf-8\\\"),再对其进行编码为gb2312，即最终为s.encode(\\\"utf-8\\\").decode(\\\"utf-8\\\").encode(\\\"gb2312\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2.文件\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000我爱中华\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"f=open('ly.txt','r',encoding='utf-8') # 文件句柄 'w'为创建文件，之前的数据就没了\\n\",\n    \"data=f.read()\\n\",\n    \"print(data)\\n\",\n    \"f.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"f=open('test','a',encoding='utf-8') # 文件句柄 'a'为追加文件 append\\n\",\n    \"f.write(\\\"\\\\n阿斯达所，\\\\n天安门上太阳升\\\")\\n\",\n    \"f.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"f = open('ly.txt', 'r', encoding='utf-8')  # 文件句柄\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000我爱中华\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"0\\n\",\n      \"\\u0000\\u0000\\u0000\\u0000\\u0000\\n\",\n      \"5\\n\",\n      \"\\u0000\\u0000\\u0000\\u0000\\u0000\\n\",\n      \"utf-8\\n\",\n      \"<_io.BufferedReader name='ly.txt'>\\n\",\n      \"5\\n\",\n      \"None\\n\",\n      \"##################################################\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000\\u0000我爱中华\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i in range(5):\\n\",\n    \"    print(f.readline().strip())  # strip()去掉空格和回车\\n\",\n    \"\\n\",\n    \"for line in f.readlines():\\n\",\n    \"    print(line.strip())\\n\",\n    \"\\n\",\n    \"# 到第十行不打印\\n\",\n    \"\\n\",\n    \"for index, line in enumerate(f.readlines()):\\n\",\n    \"    if index == 9:\\n\",\n    \"        print('----我是分隔符-----')\\n\",\n    \"        continue\\n\",\n    \"    print(line.strip())\\n\",\n    \"# 到第十行不打印\\n\",\n    \"count = 0\\n\",\n    \"for line in f:\\n\",\n    \"\\n\",\n    \"    if count == 9:\\n\",\n    \"        print('----我是分隔符-----')\\n\",\n    \"        count += 1\\n\",\n    \"        continue\\n\",\n    \"    print(line.strip())\\n\",\n    \"    count += 1\\n\",\n    \"f = open('ly.txt', 'r', encoding='utf-8')  # 文件句柄\\n\",\n    \"print(f.tell())\\n\",\n    \"print(f.readline(5))\\n\",\n    \"print(f.tell())\\n\",\n    \"f.seek(0)\\n\",\n    \"print(f.readline(5))\\n\",\n    \"print(f.encoding)\\n\",\n    \"print(f.buffer)\\n\",\n    \"print(f.fileno())\\n\",\n    \"print(f.flush())  # 刷新缓冲区\\n\",\n    \"# 进度条\\n\",\n    \"import sys, time\\n\",\n    \"for i in range(50):\\n\",\n    \"    sys.stdout.write('#')\\n\",\n    \"    sys.stdout.flush()\\n\",\n    \"    time.sleep(0.5)\\n\",\n    \"f = open('ly.txt', 'a', encoding='utf-8')  # 文件句柄\\n\",\n    \"f.seek(10)\\n\",\n    \"f.truncate(20)  # 指定10到20个字符，10个字符前面留着，后面20字符清除\\n\",\n    \"f = open('ly.txt', 'r+', encoding='utf-8')  # 文件句柄\\n\",\n    \"print(f.readline().strip())\\n\",\n    \"print(f.readline().strip())\\n\",\n    \"print(f.readline().strip())\\n\",\n    \"f.write(\\\"我爱中华\\\")\\n\",\n    \"f.close()\\n\",\n    \"\\n\",\n    \"# 实现简单的shell sed替换功能\\n\",\n    \"\\n\",\n    \"f = open(\\\"ly.txt\\\", \\\"r\\\", encoding=\\\"utf-8\\\")\\n\",\n    \"f_new = open(\\\"ly2.txt\\\", \\\"w\\\", encoding=\\\"utf-8\\\")\\n\",\n    \"\\n\",\n    \"for line in f:\\n\",\n    \"    if \\\"肆意的快乐\\\" in line:\\n\",\n    \"        line = line.replace(\\\"肆意的快乐\\\", \\\"肆意的happy\\\")\\n\",\n    \"    f_new.write(line)\\n\",\n    \"\\n\",\n    \"f.close()\\n\",\n    \"f_new.close()\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"f = open(\\\"ly.txt\\\", \\\"r\\\", encoding=\\\"utf-8\\\")\\n\",\n    \"f_new = open(\\\"ly2.txt\\\", \\\"w\\\", encoding=\\\"utf-8\\\")\\n\",\n    \"find_str = sys.argv[1]\\n\",\n    \"replace_str = sys.argv[2]\\n\",\n    \"for line in f:\\n\",\n    \"    if find_str in line:\\n\",\n    \"        line = line.replace(find_str, replace_str)\\n\",\n    \"    f_new.write(line)\\n\",\n    \"f.close()\\n\",\n    \"f_new.close()\\n\",\n    \"\\n\",\n    \"# with语句---为了避免打开文件后忘记关闭，可以通过管理上下文\\n\",\n    \"with open('ly.txt', 'r', encoding='utf-8') as f:\\n\",\n    \"    for line in f:\\n\",\n    \"        print(line.strip())\\n\",\n    \"# python2.7后，with又支持同时对多个文件的上下文进行管理，即:\\n\",\n    \"with open('ly.txt', 'r', encoding='utf-8') as f1, open('ly2.txt',\\n\",\n    \"                                                       'r',\\n\",\n    \"                                                       encoding='utf-8'):\\n\",\n    \"    pass\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 3.全局变量\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"inside func ['金角大王', 'Jack', 'Rain']\\n\",\n      \"['金角大王', 'Jack', 'Rain']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"names = [\\\"Alex\\\", \\\"Jack\\\", \\\"Rain\\\"]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# 除了整数和字符串在函数内不能改，列表，字典这些可以改\\n\",\n    \"def change_name():\\n\",\n    \"    names[0] = \\\"金角大王\\\"\\n\",\n    \"    print(\\\"inside func\\\", names)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"change_name()\\n\",\n    \"print(names)\\n\",\n    \"\\n\",\n    \"# 当全局变量与局部变量同名时，在定义局部变量的子程序内，局部变量起作用，在其它地方全局变量起作用。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4.list操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"__author__=\\\"Alex Li\\\"\\n\",\n    \"names=\\\"zhang Gu Xiang Xu\\\"\\n\",\n    \"names=[\\\"zhang\\\",\\\"Gu\\\",\\\"Xiang\\\",\\\"Xu\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"zhang Gu Xiang\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 1.切片\\n\",\n    \"print(names[0],names[1],names[2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['Gu', 'Xiang']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(names[1:3])  # 顾头不顾尾，切片\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Xu\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(names[-1]) # 在不知道多长情况下，取最后一个位置\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(names[-1:-3]) # 切片是从左往右，此时不输出\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['Gu', 'Xiang']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(names[-3:-1]) # 顾头顾尾，取最后三个\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['Xiang', 'Xu']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(names[-2:])  # 取最后两个\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['zhang', 'Gu', 'Xiang']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(names[0:3]) # 切片 等价于 print(names[:3])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['zhang', 'Gu', 'Xiang', 'Xu', 'Lei']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 2.追加\\n\",\n    \"names.append(\\\"Lei\\\")\\n\",\n    \"print(names)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['zhang', 'Chen', 'Gu', 'Xiang', 'Xu', 'Lei']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 3.指定位置插入\\n\",\n    \"names.insert(1,\\\"Chen\\\") # Gu前面插入\\n\",\n    \"print(names)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['zhang', 'Chen', 'Xie', 'Xiang', 'Xu', 'Lei']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 4.修改\\n\",\n    \"names[2]=\\\"Xie\\\"\\n\",\n    \"print(names)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['zhang', 'Xie', 'Xiang', 'Xu', 'Lei']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 5.删除\\n\",\n    \"# 第一种删除方法\\n\",\n    \"names.remove(\\\"Chen\\\")\\n\",\n    \"print(names)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['zhang', 'Xiang', 'Xu', 'Lei']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 第二种删除方法\\n\",\n    \"del names[1]\\n\",\n    \"print(names)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['zhang', 'Xiang', 'Xu']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 第三种删除方法\\n\",\n    \"names.pop() # 默认删除最后一个\\n\",\n    \"print(names)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['zhang', 'Xu']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"names.pop(1) #删除第二个元素\\n\",\n    \"print(names)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(names.index(\\\"Xu\\\")) # 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Xu\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(names[names.index(\\\"Xu\\\")]) #打印出找出的元素值3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 6.统计\\n\",\n    \"names.append(\\\"zhang\\\") #再加一个用于学习统计\\\"zhang\\\"的个数\\n\",\n    \"print(names.count(\\\"zhang\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['Xu', 'zhang', 'zhang']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 7.排序\\n\",\n    \"names.sort() #按照ASCII码排序\\n\",\n    \"print(names)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['zhang', 'zhang', 'Xu']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"names.reverse() # 逆序\\n\",\n    \"print(names)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['zhang', 'zhang', 'Xu', 1, 2, 3, 4] [1, 2, 3, 4]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 8.合并\\n\",\n    \"names2=[1,2,3,4]\\n\",\n    \"names.extend(names2)\\n\",\n    \"print(names,names2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1, 2, 3, 4]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 9.删掉names2\\n\",\n    \"'''del names2'''\\n\",\n    \"print(names2) # NameError: name 'names2' is not defined,表示已删除\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['zhang', 'zhang', 'Xu', 1, 2, 3, 4] ['zhang', 'zhang', 'Xu', 1, 2, 3, 4]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 10.浅copy\\n\",\n    \"names2=names.copy()\\n\",\n    \"print(names,names2) # 此时names2与names指向相同\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['zhang', 'zhang', '大张', 1, 2, 3, 4] ['zhang', 'zhang', 'Xu', 1, 2, 3, 4]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"names[2]=\\\"大张\\\"\\n\",\n    \"print(names,names2) # 此时names改变，names2不变\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1, 2, 3, 4, ['zhang', 'Gu'], 5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 11.浅copy在列表嵌套应用\\n\",\n    \"names=[1,2,3,4,[\\\"zhang\\\",\\\"Gu\\\"],5]\\n\",\n    \"print(names)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1, 2, 3, '斯', ['张改', 'Gu'], 5] [1, 2, 3, 4, ['张改', 'Gu'], 5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"names2=names.copy()\\n\",\n    \"names[3]=\\\"斯\\\"\\n\",\n    \"names[4][0]=\\\"张改\\\"\\n\",\n    \"print(names,names2) # copy为浅copy,第一层copy不变，后面的嵌套全部都变,修改names2与names都一样\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1, 2, 3, '斯', ['张改', 'Gu'], 5] [1, 2, 3, 4, ['zhang', 'Gu'], 5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 12.完整克隆\\n\",\n    \"import copy\\n\",\n    \"# 浅copy与深copy\\n\",\n    \"'''浅copy与深copy区别就是浅copy只copy一层，而深copy就是完全克隆'''\\n\",\n    \"names = [1, 2, 3, 4, [\\\"zhang\\\", \\\"Gu\\\"], 5]\\n\",\n    \"# names2=copy.copy(names) # 这个跟列表的浅copy一样\\n\",\n    \"names2 = copy.deepcopy(names)  #深copy\\n\",\n    \"names[3] = \\\"斯\\\"\\n\",\n    \"names[4][0] = \\\"张改\\\"\\n\",\n    \"print(names, names2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\",\n      \"2\\n\",\n      \"3\\n\",\n      \"斯\\n\",\n      \"['张改', 'Gu']\\n\",\n      \"5\\n\",\n      \"[1, 3, ['张改', 'Gu']]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 13.列表循环\\n\",\n    \"for i in names:\\n\",\n    \"    print(i)\\n\",\n    \"print(names[0:-1:2]) # 步长为2进行切片\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5.Tuple操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2\\n\",\n      \"1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"names=('alex','jack','alex')\\n\",\n    \"\\n\",\n    \"print(names.count('alex'))\\n\",\n    \"print(names.index('jack'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Input your salary:8000\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:\\n\",\n      \"invalid option\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:1\\n\",\n      \"\\u001b[41;1m你的余额只剩[8000]啦，还买个毛线\\u001b[0m\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:0\\n\",\n      \"Added ('Iphone', 5800) into shopping cart, your current balance is \\u001b[31;1m2200\\u001b[0m\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:6\\n\",\n      \"product code[6] is not exist!\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:q\\n\",\n      \"-----------shopping list----------------\\n\",\n      \"('Iphone', 5800)\\n\",\n      \"Your current balance: 2200\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:p\\n\",\n      \"invalid option\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:no\\n\",\n      \"invalid option\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:4\\n\",\n      \"Added ('Coffee', 31) into shopping cart, your current balance is \\u001b[31;1m2169\\u001b[0m\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:4\\n\",\n      \"Added ('Coffee', 31) into shopping cart, your current balance is \\u001b[31;1m2138\\u001b[0m\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:4\\n\",\n      \"Added ('Coffee', 31) into shopping cart, your current balance is \\u001b[31;1m2107\\u001b[0m\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:43\\n\",\n      \"product code[43] is not exist!\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:2\\n\",\n      \"\\u001b[41;1m你的余额只剩[2107]啦，还买个毛线\\u001b[0m\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:1\\n\",\n      \"\\u001b[41;1m你的余额只剩[2107]啦，还买个毛线\\u001b[0m\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:2\\n\",\n      \"\\u001b[41;1m你的余额只剩[2107]啦，还买个毛线\\u001b[0m\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:3\\n\",\n      \"\\u001b[41;1m你的余额只剩[2107]啦，还买个毛线\\u001b[0m\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:4\\n\",\n      \"Added ('Coffee', 31) into shopping cart, your current balance is \\u001b[31;1m2076\\u001b[0m\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\",\n      \"选择要买嘛？>>:5\\n\",\n      \"Added ('Alex Python', 120) into shopping cart, your current balance is \\u001b[31;1m1956\\u001b[0m\\n\",\n      \"0 ('Iphone', 5800)\\n\",\n      \"1 ('Mac Pro', 9800)\\n\",\n      \"2 ('Bike', 5800)\\n\",\n      \"3 ('Watch', 10600)\\n\",\n      \"4 ('Coffee', 31)\\n\",\n      \"5 ('Alex Python', 120)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 购物篮程序\\n\",\n    \"\\n\",\n    \"product_list = [\\n\",\n    \"    ('Iphone', 5800),\\n\",\n    \"    ('Mac Pro', 9800),\\n\",\n    \"    ('Bike', 5800),\\n\",\n    \"    ('Watch', 10600),\\n\",\n    \"    ('Coffee', 31),\\n\",\n    \"    ('Alex Python', 120),\\n\",\n    \"]\\n\",\n    \"shopping_list = []\\n\",\n    \"salary = input(\\\"Input your salary:\\\")\\n\",\n    \"\\n\",\n    \"if salary.isdigit():\\n\",\n    \"    salary = int(salary)\\n\",\n    \"    while True:\\n\",\n    \"        '''for item in product_list:\\n\",\n    \"            print(product_list.index(item),item)\\n\",\n    \"        '''\\n\",\n    \"        for index, item in enumerate(product_list):\\n\",\n    \"            print(index, item)\\n\",\n    \"        user_choice = input(\\\"选择要买嘛？>>:\\\")\\n\",\n    \"        if user_choice.isdigit():\\n\",\n    \"            user_choice = int(user_choice)\\n\",\n    \"            if user_choice < len(product_list) and user_choice >= 0:\\n\",\n    \"                p_item = product_list[user_choice]\\n\",\n    \"                if p_item[1] <= salary:\\n\",\n    \"                    shopping_list.append(p_item)\\n\",\n    \"                    salary -= p_item[1]\\n\",\n    \"                    print(\\n\",\n    \"                        \\\"Added %s into shopping cart, your current balance is \\\\033[31;1m%s\\\\033[0m\\\"\\n\",\n    \"                        % (p_item, salary))\\n\",\n    \"                else:\\n\",\n    \"                    print(\\\"\\\\033[41;1m你的余额只剩[%s]啦，还买个毛线\\\\033[0m\\\" % salary)\\n\",\n    \"            else:\\n\",\n    \"                print(\\\"product code[%s] is not exist!\\\" % user_choice)\\n\",\n    \"        elif user_choice == 'q':\\n\",\n    \"            print('-----------shopping list----------------')\\n\",\n    \"            for p in shopping_list:\\n\",\n    \"                print(p)\\n\",\n    \"                print(\\\"Your current balance:\\\", salary)\\n\",\n    \"            exit()\\n\",\n    \"        else:\\n\",\n    \"            print(\\\"invalid option\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 6.Set操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{1, 3, 4, 5, 6, 7, 9} <class 'set'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 集合set  集合关系测试\\n\",\n    \"list_1=[1,4,5,7,3,6,7,9]\\n\",\n    \"list_1=set(list_1)\\n\",\n    \"print(list_1,type(list_1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{0, 2, 4, 6, 8, 22} <class 'set'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"list_2=set([2,6,0,6,22,8,4])\\n\",\n    \"print(list_2,type(list_2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------\\n\",\n      \"方法一\\n\",\n      \"{4, 6}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"--------------------------------\\\")\\n\",\n    \"# 取交集\\n\",\n    \"print(\\\"方法一\\\")\\n\",\n    \"print(list_1.intersection(list_2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"方法二\\n\",\n      \"{4, 6}\\n\",\n      \"--------------------------------\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"方法二\\\")\\n\",\n    \"print(list_1&list_2)\\n\",\n    \"print(\\\"--------------------------------\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"方法一\\n\",\n      \"{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 22}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 取并集\\n\",\n    \"print(\\\"方法一\\\")\\n\",\n    \"print(list_1.union(list_2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"方法二\\n\",\n      \"{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 22}\\n\",\n      \"--------------------------------\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"方法二\\\")\\n\",\n    \"print(list_1|list_2)\\n\",\n    \"print(\\\"--------------------------------\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{1, 3, 5, 7, 9}\\n\",\n      \"{1, 3, 5, 7, 9}\\n\",\n      \"--------------------------------\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 差集 in list_1 but not in list_2\\n\",\n    \"print(list_1.difference(list_2))\\n\",\n    \"print(list_1-list_2)\\n\",\n    \"print(\\\"--------------------------------\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 子集\\n\",\n    \"list_3=[1,4,6]\\n\",\n    \"list_4=[1,4,6,7]\\n\",\n    \"list_3=set(list_3)\\n\",\n    \"list_4=set(list_4)\\n\",\n    \"print(list_3.issubset(list_4))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"--------------------------------\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(list_4.issuperset(list_3))\\n\",\n    \"print(\\\"--------------------------------\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{0, 1, 2, 3, 5, 7, 8, 9, 22}\\n\",\n      \"{0, 1, 2, 3, 5, 7, 8, 9, 22}\\n\",\n      \"--------------------------------\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 对称差集 把list_1与list_2互相都没有的元素放在一块，其实就是去掉重复元素\\n\",\n    \"print(list_1.symmetric_difference(list_2))\\n\",\n    \"print(list_1^list_2)\\n\",\n    \"print(\\\"--------------------------------\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"--------------------------------\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 是否没有交集 Return True if two sets have a null intersection.\\n\",\n    \"list_5=set([1,2,3,4])\\n\",\n    \"list_6=set([5,6,7])\\n\",\n    \"print(list_5.isdisjoint(list_6))\\n\",\n    \"print(\\\"--------------------------------\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{1, 3, 4, 5, 6, 7, 'x', 9}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 基本操作\\n\",\n    \"# 添加一项\\n\",\n    \"list_1.add('x')\\n\",\n    \"print(list_1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{1, 3, 4, 5, 6, 7, 'x', 9, 10, 37, 42}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 添加多项\\n\",\n    \"list_1.update([10,37,42])\\n\",\n    \"print(list_1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{1, 3, 4, 5, 6, 7, 'x', 9, 37, 42}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 删除一项\\n\",\n    \"list_1.remove(10)\\n\",\n    \"print(list_1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# set长度\\n\",\n    \"print(len(list_1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 测试9是否是list_1的成员\\n\",\n    \"print(9 in list_1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# pop()删除并且返回一个任意的元素\\n\",\n    \"print(list_1.pop())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{3, 4, 5, 6, 7, 9, 37, 42}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 删除一个指定的值\\n\",\n    \"list_1.discard('x')\\n\",\n    \"print(list_1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 7.字符串操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Alex\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"name=\\\"alex\\\"\\n\",\n    \"print(name.capitalize()) # 首字母大写\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(name.count(\\\"a\\\")) # 统计字母个数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-----------------------alex-----------------------\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(name.center(50,\\\"-\\\")) #总共打印50个字符，并把nam放在中间，不够的用-补上\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(name.endswith(\\\"ex\\\")) # 判断字符串以什么结尾\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"alex                          name is alex\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"name=\\\"alex \\\\tname is alex\\\"\\n\",\n    \"print(name.expandtabs(tabsize=30)) # 将name中\\\\t转为30个空格\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(name.find(\\\"x\\\")) # 取索引\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x \\tname is alex\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(name[name.find(\\\"x\\\"):]) # 字符串切片\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"my \\tname is alex and i am 23 old\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"name=\\\"my \\\\tname is {name} and i am {year} old\\\"\\n\",\n    \"print(name.format(name=\\\"alex\\\",year=23))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"my \\tname is alex and i am 23 old\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(name.format_map({'name':'alex','year':23}))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('ab123'.isalnum()) #isalnum()包含所有字母及数字，如果不是这两个，则为False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('ab123'.isalpha()) # False  isalpha()包含纯英文字符\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('1A'.isdecimal()) # 是否是十进制 False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('1A'.isdigit()) # 是否是整数 False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('_'.isidentifier()) #判断是否是合法的标识符，实质是否为合法变量名 True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('aasd'.islower()) # 判断是否是小写 True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(''.isspace()) # 是否是空格 False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('My name is'.istitle()) # 字符串首字母大写为title，否则不是\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1+2+3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('+'.join(['1','2','3'])) # 对一列表中所有元素进行join操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"my \\tname is {name} and i am {year} old************\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(name.ljust(50,'*')) # 左对齐字符串，多余位用*补全\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"------------my \\tname is {name} and i am {year} old\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(name.rjust(50,'-')) # 右对齐字符串，多余位用*-补全\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Alex\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('\\\\n Alex'.lstrip()) # 去掉左边的空格/回车\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Alex\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('\\\\nAlex\\\\n'.rstrip()) # 去掉右边的空格/回车\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Alex\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('\\\\nAlex\\\\n'.strip()) # 去掉左边和右边的空格/回车\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Alex\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Alex')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1l5x li\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"p=str.maketrans(\\\"abcdef\\\",\\\"123456\\\")\\n\",\n    \"print(\\\"alex li\\\".translate(p))  #把alex li换成上一行对应的值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"aLex li\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"alex li\\\".replace('l','L',1)) # 替换 1表示替换几个l,从左到右计算替换个数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"alex li\\\".rfind('l')) # 找到的最右边的下标返回\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['a', 'ex ', 'i']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"alex li\\\".split('l')) \\n\",\n    \"# 默认将字符串按照空格分隔成列表，也可以在()中填写相应的分隔符，比如以字符l分隔，print(\\\"alex li\\\".split(‘l’)),而且分隔符在列表中不会出现\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['1', '2', '3', '4']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"1+2+3+4\\\".split('+')) # ['1', '2', '3', '4']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['1+2', '+3+4']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"1+2\\\\n+3+4\\\".splitlines()) # ['1+2', '+3+4']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"aLEX lI\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Alex Li\\\".swapcase()) # aLEX lI\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Lex Li\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('lex li'.title()) # Lex Li\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"00000000000000000000000000000000000000000000lex li\\n\",\n      \"---\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('lex li'.zfill(50)) #不够以0填充\\n\",\n    \"print('---')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 8.字典\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'stu1101': 'tengxun', 'stu1102': 'baidu', 'stu1103': 'ali'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 字典无序\\n\",\n    \"info={\\n\",\n    \"    'stu1101':\\\"tengxun\\\",\\n\",\n    \"    'stu1102':\\\"baidu\\\",\\n\",\n    \"    'stu1103':\\\"ali\\\",\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"print(info)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tengxun\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 0.查找\\n\",\n    \"# 方法一:确定存在\\n\",\n    \"print(info[\\\"stu1101\\\"]) # 查找若不在，则报错\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"None\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(info.get(\\\"stu11004\\\")) # 查找不在不会报错，直接返回None，若有直接返回\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('stu1103' in info) # True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'stu1101': '腾讯', 'stu1102': 'baidu', 'stu1103': 'ali'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 1.修改\\n\",\n    \"info[\\\"stu1101\\\"]=\\\"腾讯\\\"\\n\",\n    \"print(info)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'stu1101': '腾讯', 'stu1102': 'baidu', 'stu1103': 'ali', 'stu1104': 'zhubajie'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 2.增加\\n\",\n    \"info[\\\"stu1104\\\"]=\\\"zhubajie\\\"\\n\",\n    \"print(info)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'stu1102': 'baidu', 'stu1103': 'ali', 'stu1104': 'zhubajie'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 3.删除\\n\",\n    \"# 方法一\\n\",\n    \"del info[\\\"stu1101\\\"]\\n\",\n    \"print(info)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'stu1103': 'ali', 'stu1104': 'zhubajie'}\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'\\\\n# 随机删除\\\\ninfo.popitem()\\\\nprint(info)\\\\n'\"\n      ]\n     },\n     \"execution_count\": 96,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 方法二\\n\",\n    \"info.pop(\\\"stu1102\\\")\\n\",\n    \"print(info)\\n\",\n    \"'''\\n\",\n    \"# 随机删除\\n\",\n    \"info.popitem()\\n\",\n    \"print(info)\\n\",\n    \"'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'stu1103': 'ali', 'stu1104': 'zhubajie', 'stu1101': 'Alex', 1: 3, 2: 5}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 4.多级字典嵌套及操作\\n\",\n    \"av_catalog = {\\n\",\n    \"    \\\"A\\\":{\\n\",\n    \"        \\\"www.yo333.com\\\": [\\\"aaa\\\",\\\"111\\\"],\\n\",\n    \"        \\\"www.po333.com\\\": [\\\"bbb\\\",\\\"222\\\"],\\n\",\n    \"        \\\"333you.com\\\": [\\\"ccc\\\",\\\"333\\\"],\\n\",\n    \"        \\\"333art.com\\\":[\\\"ddd\\\",\\\"444\\\"]\\n\",\n    \"    },\\n\",\n    \"    \\\"B\\\":{\\n\",\n    \"        \\\"tokyo-lot\\\":[\\\"eee\\\",\\\"555\\\"]\\n\",\n    \"    },\\n\",\n    \"    \\\"C\\\":{\\n\",\n    \"        \\\"1022\\\":[\\\"fff\\\",\\\"666\\\"]\\n\",\n    \"    }\\n\",\n    \"}\\n\",\n    \"b={\\n\",\n    \"    'stu1101':\\\"Alex\\\",\\n\",\n    \"    1:3,\\n\",\n    \"    2:5\\n\",\n    \"}\\n\",\n    \"info.update(b) #将两个字典合并，存在key,则更新value，不存在key，则合并\\n\",\n    \"print(info)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dict_items([('stu1103', 'ali'), ('stu1104', 'zhubajie'), ('stu1101', 'Alex'), (1, 3), (2, 5)])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(info.items()) #把一个字典转成列表\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{6: 'test', 7: 'test', 8: 'test'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"c=info.fromkeys([6,7,8],\\\"test\\\")\\n\",\n    \"print(c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{6: [1, {'name': 'alex'}, 444], 7: [1, {'name': 'alex'}, 444], 8: [1, {'name': 'alex'}, 444]}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"c=info.fromkeys([6,7,8],[1,{'name':'alex'},444])\\n\",\n    \"print(c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{6: [1, {'name': 'Jack Chen'}, 444], 7: [1, {'name': 'Jack Chen'}, 444], 8: [1, {'name': 'Jack Chen'}, 444]}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"c[7][1]['name']='Jack Chen' # 3个key共用一个value,修改一个则所有的都修改了\\n\",\n    \"print(c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------\\n\",\n      \"{'A': {'www.yo333.com': ['aaa', '111'], 'www.po333.com': ['bbb', '222'], '333you.com': ['ccc', '可以在国内做镜像'], '333art.com': ['ddd', '444']}, 'B': {'tokyo-lot': ['eee', '555']}, 'C': {'1022': ['fff', '666']}, 'taiwan': {'www.baidu.com': [1, 2]}}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"--------\\\")\\n\",\n    \"av_catalog[\\\"A\\\"][\\\"333you.com\\\"][1]=\\\"可以在国内做镜像\\\" # 二级字典替换\\n\",\n    \"av_catalog.setdefault(\\\"taiwan\\\",{\\\"www.baidu.com\\\":[1,2]}) # 如果不重名，即创建一个新的值，如果重名，将找到的值返回\\n\",\n    \"print(av_catalog)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dict_keys(['stu1103', 'stu1104', 'stu1101', 1, 2])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(info.keys()) # 打印出所有的key\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dict_values(['ali', 'zhubajie', 'Alex', 3, 5])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(info.values()) # 打印出所有的value\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"---------------\\n\",\n      \"stu1103 ali\\n\",\n      \"stu1104 zhubajie\\n\",\n      \"stu1101 Alex\\n\",\n      \"1 3\\n\",\n      \"2 5\\n\",\n      \"---------------\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"---------------\\\")\\n\",\n    \"for i in info:\\n\",\n    \"    print(i,info[i])  #效率更高点\\n\",\n    \"print(\\\"---------------\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 110,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"stu1103 ali\\n\",\n      \"stu1104 zhubajie\\n\",\n      \"stu1101 Alex\\n\",\n      \"1 3\\n\",\n      \"2 5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for k,v in info.items():\\n\",\n    \"    print(k,v)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 9.函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 111,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"in the fun1\\n\",\n      \"in the fun2\\n\",\n      \"1\\n\",\n      \"None\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 1.无参函数\\n\",\n    \"# 定义一个函数\\n\",\n    \"def fun1():\\n\",\n    \"    '''testing'''\\n\",\n    \"    print('in the fun1')\\n\",\n    \"    return 1\\n\",\n    \"\\n\",\n    \"# 定义一个过程 实质就是无返回值的函数\\n\",\n    \"def fun2():\\n\",\n    \"    '''testing2'''\\n\",\n    \"    print('in the fun2')\\n\",\n    \"\\n\",\n    \"x=fun1()\\n\",\n    \"y=fun2()\\n\",\n    \"print(x)\\n\",\n    \"print(y)  # 没有返回值得情况下，python隐式地返回一个None\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 112,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"in the test1\\n\",\n      \"in the test2\\n\",\n      \"in the test3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"def logger():\\n\",\n    \"    time_format='%Y-%m-%d %X %A %B %p %I'\\n\",\n    \"    time_current=time.strftime(time_format)\\n\",\n    \"    with open('a.txt','a+')as f:\\n\",\n    \"        f.write('time %s end action\\\\n'%time_current)\\n\",\n    \"def test1():\\n\",\n    \"    print('in the test1')\\n\",\n    \"    logger()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def test2():\\n\",\n    \"    print('in the test2')\\n\",\n    \"    logger()\\n\",\n    \"    return 0\\n\",\n    \"\\n\",\n    \"def test3():\\n\",\n    \"    print('in the test3')\\n\",\n    \"    logger()\\n\",\n    \"    return 1,{5:\\\"sda\\\",6:\\\"zad\\\"},[1,2,3]\\n\",\n    \"\\n\",\n    \"x=test1()\\n\",\n    \"y=test2()\\n\",\n    \"z=test3()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 113,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"None\\n\",\n      \"0\\n\",\n      \"(1, {5: 'sda', 6: 'zad'}, [1, 2, 3])\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'\\\\n总结：\\\\n    返回值数=0:返回None\\\\n    返回值数=1:返回object\\\\n    返回值数>1:返回tuple\\\\n'\"\n      ]\n     },\n     \"execution_count\": 113,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"print(x) # None\\n\",\n    \"print(y) # 0\\n\",\n    \"print(z) # (1, {5: 'sda', 6: 'zad'}, [1, 2, 3])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"'''\\n\",\n    \"总结：\\n\",\n    \"    返回值数=0:返回None\\n\",\n    \"    返回值数=1:返回object\\n\",\n    \"    返回值数>1:返回tuple\\n\",\n    \"'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 114,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\",\n      \"2\\n\",\n      \"2\\n\",\n      \"1\\n\",\n      \"3\\n\",\n      \"2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 2.有参函数\\n\",\n    \"# 默认参数特点：调用函数的时候，默认参数非必须传递\\n\",\n    \"# 用途：1.默认安装值\\n\",\n    \"\\n\",\n    \"def test(x,y):\\n\",\n    \"    print(x)\\n\",\n    \"    print(y)\\n\",\n    \"\\n\",\n    \"test(1,2)     # 位置参数调用 与形参意义对应\\n\",\n    \"test(y=1,x=2) # 关键字调用，与形参顺序无关\\n\",\n    \"test(3,y=2) # 如果既有关键字调用又有位置参数，前面一个一定是位置参数，一句话：关键参数一定不能写在位置参数前面\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"'''\\n\",\n    \"比如加入一个参数z\\n\",\n    \"'''\\n\",\n    \"def test1(x,y,z):\\n\",\n    \"    print(x)\\n\",\n    \"    print(y)\\n\",\n    \"    print(z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 116,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"3\\n\",\n      \"4\\n\",\n      \"6\\n\",\n      \"3\\n\",\n      \"4\\n\",\n      \"6\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 关键参数一定不能放在位置参数前面\\n\",\n    \"test1(3,4,z=6)\\n\",\n    \"test1(3,z=6,y=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 117,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\",\n      \"2\\n\",\n      \"2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 默认参数,\\n\",\n    \"def test(x,y,z=2):\\n\",\n    \"    print(x)\\n\",\n    \"    print(y)\\n\",\n    \"    print(z)\\n\",\n    \"\\n\",\n    \"test(1,2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1, 3, 4, 5, 5, 6)\\n\",\n      \"(1, 3, 4, 5, 5, 6)\\n\",\n      \"1\\n\",\n      \"(2, 3, 4, 5, 6, 7)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 用*args传递多个参数，转换成元组的方式 *表示一个功能代号，表示接受的参数不固定，args可以随意起名\\n\",\n    \"def test(*args):\\n\",\n    \"    print(args)\\n\",\n    \"test(1,3,4,5,5,6)\\n\",\n    \"test(*[1,3,4,5,5,6]) # args=tuple([1,2,3,4,5])\\n\",\n    \"def test(x,*args):\\n\",\n    \"    print(x)\\n\",\n    \"    print(args)\\n\",\n    \"test(1,2,3,4,5,6,7) # 1 (2,3,4,5,6,7)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 119,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'name': 'alex', 'age': 8, 'id': 10, 'sex': 'M'}\\n\",\n      \"alex 8 10 M\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 字典传值 **kwagrs:把N个关键字参数，转换成字典的方式\\n\",\n    \"def test(**kwargs):\\n\",\n    \"    print(kwargs)\\n\",\n    \"    print(kwargs['name'],kwargs['age'],kwargs['id'],kwargs['sex'])\\n\",\n    \"\\n\",\n    \"test(name='alex',age=8,id=10,sex='M') # {'name': 'alex', 'age': 8, 'id': 10, 'sex': 'M'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 120,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'name': 'alex', 'age': 8, 'id': 10, 'sex': 'M'}\\n\",\n      \"alex 8 10 M\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test(**{'name':'alex','age':8,'id':10,'sex':'M'})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 121,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"alex\\n\",\n      \"{'age': 18, 'sex': 'M'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def test(name,**kwargs):\\n\",\n    \"    print(name)\\n\",\n    \"    print(kwargs)\\n\",\n    \"test('alex',age=18,sex='M') # 字典 {'age': 18, 'sex': 'M'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 122,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"alex\\n\",\n      \"3\\n\",\n      \"{'sex': 'M', 'hobby': 'tesla'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 默认参数得放在参数组前面\\n\",\n    \"def test(name,age=18,**kwargs):\\n\",\n    \"    print(name)\\n\",\n    \"    print(age)\\n\",\n    \"    print(kwargs)\\n\",\n    \"\\n\",\n    \"test('alex',sex='M',hobby='tesla',age=3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 123,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"alex\\n\",\n      \"3\\n\",\n      \"{'sex': 'M', 'hobby': 'tesla'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test('alex',3,sex='M',hobby='tesla')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 124,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"alex\\n\",\n      \"18\\n\",\n      \"{}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test('alex') # 后面的**kwargs不赋值输出为空字典\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 125,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"alex\\n\",\n      \"34\\n\",\n      \"()\\n\",\n      \"{'sex': 'M', 'hobby': 'tesla'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def test(name,age=18,*args,**kwargs):\\n\",\n    \"    print(name)\\n\",\n    \"    print(age)\\n\",\n    \"    print(args)\\n\",\n    \"    print(kwargs)\\n\",\n    \"test('alex',age=34,sex='M',hobby='tesla') # alex 34 () {'sex': 'M', 'hobby': 'tesla'}\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 10.高阶函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 126,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 高阶函数 变量可以指向函数，函数的参数能接受变量，那么一个函数就可以接受另一个函数作为参数，这个函数就叫做高阶函数\\n\",\n    \"def f(x):\\n\",\n    \"    return x\\n\",\n    \"def add(x,y,f):\\n\",\n    \"    return f(x)+f(y)\\n\",\n    \"res=add(1,2,f)\\n\",\n    \"print(res)  # 3\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "2.numpy/1.numpy-beginner.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 适合初学者快速入门的Numpy实战全集\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"本文由光城同学整理\\n\",\n    \"\\n\",\n    \"Numpy是一个用python实现的科学计算的扩展程序库，包括：\\n\",\n    \"* 1、一个强大的N维数组对象Array；\\n\",\n    \"* 2、比较成熟的（广播）函数库；\\n\",\n    \"* 3、用于整合C/C++和Fortran代码的工具包；\\n\",\n    \"* 4、实用的线性代数、傅里叶变换和随机数生成函数。numpy和稀疏矩阵运算包scipy配合使用更加方便。\\n\",\n    \"\\n\",\n    \"NumPy（Numeric Python）提供了许多高级的数值编程工具，如：矩阵数据类型、矢量处理，以及精密的运算库。专为进行严格的数字处理而产生。多为很多大型金融公司使用，以及核心的科学计算组织如：Lawrence Livermore，NASA用其处理一些本来使用C++，Fortran或Matlab等所做的任务。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 本文目录\\n\",\n    \"### 1.Numpy基本操作\\n\",\n    \"#### 1.1 列表转为矩阵\\n\",\n    \"#### 1.2 维度\\n\",\n    \"#### 1.3 行数和列数()\\n\",\n    \"#### 1.4 元素个数\\n\",\n    \"### 2.Numpy创建array\\n\",\n    \"#### 2.1 一维array创建\\n\",\n    \"#### 2.2 多维array创建\\n\",\n    \"#### 2.3 创建全零数组\\n\",\n    \"#### 2.4 创建全1数据\\n\",\n    \"#### 2.5 创建全空数组\\n\",\n    \"#### 2.6 创建连续数组\\n\",\n    \"#### 2.7 reshape操作\\n\",\n    \"#### 2.8 创建连续型数据\\n\",\n    \"#### 2.9 linspace的reshape操作\\n\",\n    \"### 3.Numpy基本运算\\n\",\n    \"#### 3.1 一维矩阵运算\\n\",\n    \"#### 3.2 多维矩阵运算\\n\",\n    \"#### 3.3 基本计算\\n\",\n    \"### 4.Numpy索引与切片\\n\",\n    \"### 5.Numpy array合并\\n\",\n    \"#### 5.1 数组合并\\n\",\n    \"#### 5.2 数组转置为矩阵\\n\",\n    \"#### 5.3 多个矩阵合并\\n\",\n    \"#### 5.4 合并例子2\\n\",\n    \"### 6.Numpy array分割\\n\",\n    \"#### 6.1 构造3行4列矩阵\\n\",\n    \"#### 6.2 等量分割\\n\",\n    \"#### 6.3 不等量分割\\n\",\n    \"#### 6.4 其他的分割方式\\n\",\n    \"### 7.Numpy copy与 =\\n\",\n    \"#### 7.1 =赋值方式会带有关联性\\n\",\n    \"#### 7.2 copy()赋值方式没有关联性\\n\",\n    \"### 8.广播机制\\n\",\n    \"### 9.常用函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.Numpy基本操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.1 列表转为矩阵\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 3 5]\\n\",\n      \" [4 6 9]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"array = np.array([\\n\",\n    \"    [1,3,5],\\n\",\n    \"    [4,6,9]\\n\",\n    \"])\\n\",\n    \"\\n\",\n    \"print(array)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.2 维度\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"number of dim: 2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('number of dim:', array.ndim)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.3 行数和列数()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"shape: (2, 3)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('shape:',array.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1.4 元素个数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"size: 6\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('size:',array.size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.Numpy创建array\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.1 一维array创建\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2 23  4]\\n\",\n      \"int32\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"# 一维array\\n\",\n    \"a = np.array([2,23,4], dtype=np.int32) # np.int默认为int32\\n\",\n    \"print(a)\\n\",\n    \"print(a.dtype)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.2 多维array创建\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2 3 4]\\n\",\n      \" [3 4 5]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 多维array\\n\",\n    \"a = np.array([[2,3,4],\\n\",\n    \"              [3,4,5]])\\n\",\n    \"print(a) # 生成2行3列的矩阵\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.3 创建全零数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.zeros((3,4))\\n\",\n    \"print(a) # 生成3行4列的全零矩阵\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.4 创建全1数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 1 1 1]\\n\",\n      \" [1 1 1 1]\\n\",\n      \" [1 1 1 1]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 创建全一数据，同时指定数据类型\\n\",\n    \"a = np.ones((3,4),dtype=np.int)\\n\",\n    \"print(a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.5 创建全空数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 创建全空数组，其实每个值都是接近于零的数\\n\",\n    \"a = np.empty((3,4))\\n\",\n    \"print(a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.6 创建连续数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[10 12 14 16 18 20]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 创建连续数组\\n\",\n    \"a = np.arange(10,21,2) # 10-20的数据，步长为2\\n\",\n    \"print(a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.7 reshape操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[10 12 14]\\n\",\n      \" [16 18 20]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 使用reshape改变上述数据的形状\\n\",\n    \"b = a.reshape((2,3))\\n\",\n    \"print(b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.8 创建连续型数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1.          1.47368421  1.94736842  2.42105263  2.89473684  3.36842105\\n\",\n      \"  3.84210526  4.31578947  4.78947368  5.26315789  5.73684211  6.21052632\\n\",\n      \"  6.68421053  7.15789474  7.63157895  8.10526316  8.57894737  9.05263158\\n\",\n      \"  9.52631579 10.        ]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 创建线段型数据\\n\",\n    \"a = np.linspace(1,10,20) # 开始端1，结束端10，且分割成20个数据，生成线段\\n\",\n    \"print(a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.9 linspace的reshape操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1.          1.47368421  1.94736842  2.42105263]\\n\",\n      \" [ 2.89473684  3.36842105  3.84210526  4.31578947]\\n\",\n      \" [ 4.78947368  5.26315789  5.73684211  6.21052632]\\n\",\n      \" [ 6.68421053  7.15789474  7.63157895  8.10526316]\\n\",\n      \" [ 8.57894737  9.05263158  9.52631579 10.        ]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 同时也可以reshape\\n\",\n    \"b = a.reshape((5,4))\\n\",\n    \"print(b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.Numpy基本运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 3.1 一维矩阵运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[10 20 30 40] [0 1 2 3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"# 一维矩阵运算\\n\",\n    \"a = np.array([10,20,30,40])\\n\",\n    \"b = np.arange(4)\\n\",\n    \"print(a,b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[10 19 28 37]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"c = a - b\\n\",\n    \"print(c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  0  20  60 120]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(a*b) # 若用a.dot(b),则为各维之和\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 4 9]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 在Numpy中，想要求出矩阵中各个元素的乘方需要依赖双星符号 **，以二次方举例，即：\\n\",\n    \"c = b**2\\n\",\n    \"print(c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-0.54402111  0.91294525 -0.98803162  0.74511316]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Numpy中具有很多的数学函数工具\\n\",\n    \"c = np.sin(a)\\n\",\n    \"print(c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ True  True False False]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(b<2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[False  True False  True]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1,1,4,3])\\n\",\n    \"b = np.arange(4)\\n\",\n    \"print(a==b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 3.2 多维矩阵运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 1]\\n\",\n      \" [0 1]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([[1,1],[0,1]])\\n\",\n    \"b = np.arange(4).reshape((2,2))\\n\",\n    \"print(a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1]\\n\",\n      \" [2 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2 4]\\n\",\n      \" [2 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 多维度矩阵乘法\\n\",\n    \"# 第一种乘法方式:\\n\",\n    \"c = a.dot(b)\\n\",\n    \"print(c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2 4]\\n\",\n      \" [2 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 第二种乘法:\\n\",\n    \"c = np.dot(a,b)\\n\",\n    \"print(c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"3.825517216750851\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 多维矩阵乘法不能直接使用'*'号\\n\",\n    \"\\n\",\n    \"a = np.random.random((2,4))\\n\",\n    \"\\n\",\n    \"print(np.sum(a)) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.09623355767721398\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.min(a))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.7420428188342583\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.max(a))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a= [[0.48634962 0.74204282 0.09623356 0.69074812]\\n\",\n      \" [0.60218881 0.52734181 0.41434585 0.26626662]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"a=\\\",a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"如果你需要对行或者列进行查找运算，\\n\",\n    \"\\n\",\n    \"就需要在上述代码中为 axis 进行赋值。\\n\",\n    \"\\n\",\n    \"当axis的值为0的时候，将会以列作为查找单元，\\n\",\n    \"\\n\",\n    \"当axis的值为1的时候，将会以行作为查找单元。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sum= [2.01537412 1.8101431 ]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"sum=\\\",np.sum(a,axis=1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"min= [0.48634962 0.52734181 0.09623356 0.26626662]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"min=\\\",np.min(a,axis=0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"max= [0.74204282 0.60218881]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"max=\\\",np.max(a,axis=1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 3.3 基本计算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 2  3  4  5]\\n\",\n      \" [ 6  7  8  9]\\n\",\n      \" [10 11 12 13]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"A = np.arange(2,14).reshape((3,4))\\n\",\n    \"print(A)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 最小元素索引\\n\",\n    \"print(np.argmin(A)) # 0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"11\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 最大元素索引\\n\",\n    \"print(np.argmax(A)) # 11\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"7.5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 求整个矩阵的均值\\n\",\n    \"print(np.mean(A)) # 7.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"7.5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.average(A)) # 7.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"7.5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(A.mean()) # 7.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"7.5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 中位数\\n\",\n    \"print(np.median(A)) # 7.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2  5  9 14 20 27 35 44 54 65 77 90]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 累加\\n\",\n    \"print(np.cumsum(A))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2 4]\\n\",\n      \" [4 2]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 累差运算\\n\",\n    \"B = np.array([[3,5,9],\\n\",\n    \"              [4,8,10]])\\n\",\n    \"print(np.diff(B))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(array([0, 0, 0, 1, 1, 1], dtype=int64), array([0, 1, 2, 0, 1, 2], dtype=int64))\\n\",\n      \"(array([0, 0, 1, 1], dtype=int64), array([1, 2, 0, 2], dtype=int64))\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"C = np.array([[0,5,9],\\n\",\n    \"              [4,0,10]])\\n\",\n    \"print(np.nonzero(B))\\n\",\n    \"print(np.nonzero(C))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[14 13 12 11]\\n\",\n      \" [10  9  8  7]\\n\",\n      \" [ 6  5  4  3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 仿照列表排序\\n\",\n    \"A = np.arange(14,2,-1).reshape((3,4)) # -1表示反向递减一个步长\\n\",\n    \"print(A)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[11 12 13 14]\\n\",\n      \" [ 7  8  9 10]\\n\",\n      \" [ 3  4  5  6]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.sort(A))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[14 10  6]\\n\",\n      \" [13  9  5]\\n\",\n      \" [12  8  4]\\n\",\n      \" [11  7  3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 矩阵转置\\n\",\n    \"print(np.transpose(A))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[14 10  6]\\n\",\n      \" [13  9  5]\\n\",\n      \" [12  8  4]\\n\",\n      \" [11  7  3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(A.T)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[14 13 12 11]\\n\",\n      \" [10  9  8  7]\\n\",\n      \" [ 6  5  4  3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(A)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[9 9 9 9]\\n\",\n      \" [9 9 8 7]\\n\",\n      \" [6 5 5 5]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.clip(A,5,9))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"clip(Array,Array_min,Array_max)\\n\",\n    \"\\n\",\n    \"将Array_min<X<Array_max  X表示矩阵A中的数，如果满足上述关系，则原数不变。\\n\",\n    \"\\n\",\n    \"否则，如果X<Array_min，则将矩阵中X变为Array_min;\\n\",\n    \"\\n\",\n    \"如果X>Array_max，则将矩阵中X变为Array_max.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.Numpy索引与切片\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 3  4  5  6  7  8  9 10 11 12 13 14]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"A = np.arange(3,15)\\n\",\n    \"print(A)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"6\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(A[3])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 3  4  5  6]\\n\",\n      \" [ 7  8  9 10]\\n\",\n      \" [11 12 13 14]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"B = A.reshape(3,4)\\n\",\n    \"print(B)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[11 12 13 14]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(B[2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(B[0][2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(B[0,2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[8 9]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# list切片操作\\n\",\n    \"print(B[1,1:3]) # [8 9] 1:3表示1-2不包含3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[3 4 5 6]\\n\",\n      \"[ 7  8  9 10]\\n\",\n      \"[11 12 13 14]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for row in B:\\n\",\n    \"    print(row)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 3  7 11]\\n\",\n      \"[ 4  8 12]\\n\",\n      \"[ 5  9 13]\\n\",\n      \"[ 6 10 14]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 如果要打印列，则进行转置即可\\n\",\n    \"for column in B.T:\\n\",\n    \"    print(column)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 3  4  5  6  7  8  9 10 11 12 13 14]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 多维转一维\\n\",\n    \"A = np.arange(3,15).reshape((3,4))\\n\",\n    \"# print(A)\\n\",\n    \"print(A.flatten())\\n\",\n    \"# flat是一个迭代器，本身是一个object属性\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"3\\n\",\n      \"4\\n\",\n      \"5\\n\",\n      \"6\\n\",\n      \"7\\n\",\n      \"8\\n\",\n      \"9\\n\",\n      \"10\\n\",\n      \"11\\n\",\n      \"12\\n\",\n      \"13\\n\",\n      \"14\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for item in A.flat:\\n\",\n    \"    print(item)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们一起来来总结一下，看下面切片取值方式（对应颜色是取出来的结果）：\\n\",\n    \"\\n\",\n    \"![](images/1.png)\\n\",\n    \"\\n\",\n    \"![](images/2.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 5.Numpy array合并\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 5.1 数组合并\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 1 1]\\n\",\n      \" [2 2 2]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"A = np.array([1,1,1])\\n\",\n    \"B = np.array([2,2,2])\\n\",\n    \"print(np.vstack((A,B)))\\n\",\n    \"# vertical stack 上下合并,对括号的两个整体操作。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 1 1]\\n\",\n      \" [2 2 2]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"C = np.vstack((A,B))\\n\",\n    \"print(C)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(3,) (3,) (2, 3)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(A.shape,B.shape,C.shape)# 从shape中看出A,B均为拥有3项的数组(数列)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 1 1 2 2 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# horizontal stack左右合并\\n\",\n    \"D = np.hstack((A,B))\\n\",\n    \"print(D)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(3,) (3,) (6,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(A.shape,B.shape,D.shape)\\n\",\n    \"# (3,) (3,) (6,)\\n\",\n    \"# 对于A,B这种，为数组或数列，无法进行转置，需要借助其他函数进行转置\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 5.2 数组转置为矩阵\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 1 1]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(A[np.newaxis,:]) # [1 1 1]变为[[1 1 1]]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1, 3)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(A[np.newaxis,:].shape) # (3,)变为(1, 3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1]\\n\",\n      \" [1]\\n\",\n      \" [1]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(A[:,np.newaxis])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 5.3 多个矩阵合并\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"------------\\n\",\n      \"(3, 1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# concatenate的第一个例子\\n\",\n    \"print(\\\"------------\\\")\\n\",\n    \"print(A[:,np.newaxis].shape) # (3,1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"A = A[:,np.newaxis] # 数组转为矩阵\\n\",\n    \"B = B[:,np.newaxis] # 数组转为矩阵\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1]\\n\",\n      \" [1]\\n\",\n      \" [1]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(A)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2]\\n\",\n      \" [2]\\n\",\n      \" [2]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(B)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1]\\n\",\n      \" [1]\\n\",\n      \" [1]\\n\",\n      \" [2]\\n\",\n      \" [2]\\n\",\n      \" [2]\\n\",\n      \" [2]\\n\",\n      \" [2]\\n\",\n      \" [2]\\n\",\n      \" [1]\\n\",\n      \" [1]\\n\",\n      \" [1]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# axis=0纵向合并\\n\",\n    \"C = np.concatenate((A,B,B,A),axis=0)\\n\",\n    \"print(C)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 2]\\n\",\n      \" [1 2]\\n\",\n      \" [1 2]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# axis=1横向合并\\n\",\n    \"C = np.concatenate((A,B),axis=1)\\n\",\n    \"print(C)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 5.4 合并例子2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-------------\\n\",\n      \"[[0 1 2 3]\\n\",\n      \" [4 5 6 7]]\\n\",\n      \"[[0 1 2 3]\\n\",\n      \" [4 5 6 7]]\\n\",\n      \"-------------\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# concatenate的第二个例子\\n\",\n    \"print(\\\"-------------\\\")\\n\",\n    \"a = np.arange(8).reshape(2,4)\\n\",\n    \"b = np.arange(8).reshape(2,4)\\n\",\n    \"print(a)\\n\",\n    \"print(b)\\n\",\n    \"print(\\\"-------------\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1 2 3]\\n\",\n      \" [4 5 6 7]\\n\",\n      \" [0 1 2 3]\\n\",\n      \" [4 5 6 7]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# axis=0多个矩阵纵向合并\\n\",\n    \"c = np.concatenate((a,b),axis=0)\\n\",\n    \"print(c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1 2 3 0 1 2 3]\\n\",\n      \" [4 5 6 7 4 5 6 7]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# axis=1多个矩阵横向合并\\n\",\n    \"c = np.concatenate((a,b),axis=1)\\n\",\n    \"print(c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.Numpy array分割\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 6.1 构造3行4列矩阵\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0  1  2  3]\\n\",\n      \" [ 4  5  6  7]\\n\",\n      \" [ 8  9 10 11]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"A = np.arange(12).reshape((3,4))\\n\",\n    \"print(A)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 6.2 等量分割\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[0, 1],\\n\",\n      \"       [4, 5],\\n\",\n      \"       [8, 9]]), array([[ 2,  3],\\n\",\n      \"       [ 6,  7],\\n\",\n      \"       [10, 11]])]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 等量分割\\n\",\n    \"# 纵向分割同横向合并的axis\\n\",\n    \"print(np.split(A, 2, axis=1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,  9, 10, 11]])]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 横向分割同纵向合并的axis\\n\",\n    \"print(np.split(A,3,axis=0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 6.3 不等量分割\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[0, 1],\\n\",\n      \"       [4, 5],\\n\",\n      \"       [8, 9]]), array([[ 2],\\n\",\n      \"       [ 6],\\n\",\n      \"       [10]]), array([[ 3],\\n\",\n      \"       [ 7],\\n\",\n      \"       [11]])]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.array_split(A,3,axis=1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 6.4 其他的分割方式\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,  9, 10, 11]])]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 横向分割\\n\",\n    \"print(np.vsplit(A,3)) # 等价于print(np.split(A,3,axis=0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[0, 1],\\n\",\n      \"       [4, 5],\\n\",\n      \"       [8, 9]]), array([[ 2,  3],\\n\",\n      \"       [ 6,  7],\\n\",\n      \"       [10, 11]])]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 纵向分割\\n\",\n    \"print(np.hsplit(A,2)) # 等价于print(np.split(A,2,axis=1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 7.Numpy copy与 =\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 7.1 =赋值方式会带有关联性\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 2 3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"# `=`赋值方式会带有关联性\\n\",\n    \"a = np.arange(4)\\n\",\n    \"print(a) # [0 1 2 3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[11  1  2  3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"b = a\\n\",\n    \"c = a\\n\",\n    \"d = b\\n\",\n    \"a[0] = 11\\n\",\n    \"print(a) # [11  1  2  3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[11  1  2  3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(b) # [11  1  2  3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[11  1  2  3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(c) # [11  1  2  3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[11  1  2  3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(d) # [11  1  2  3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(b is a) # True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(c is a) # True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(d is a) # True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[11 22 33  3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"d[1:3] = [22,33]\\n\",\n    \"print(a) # [11 22 33  3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[11 22 33  3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(b) # [11 22 33  3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[11 22 33  3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(c) # [11 22 33  3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 7.2 copy()赋值方式没有关联性\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 2 3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.arange(4)\\n\",\n    \"print(a) # [0 1 2 3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 2 3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"b =a.copy() # deep copy\\n\",\n    \"print(b) # [0 1 2 3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0  1  2 44]\\n\",\n      \"[0 1 2 3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a[3] = 44\\n\",\n    \"print(a) # [ 0  1  2 44]\\n\",\n    \"print(b) # [0 1 2 3]\\n\",\n    \"\\n\",\n    \"# 此时a与b已经没有关联\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 8.广播机制\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"numpy数组间的基础运算是一对一，也就是`a.shape==b.shape`，但是当两者不一样的时候，就会自动触发广播机制，如下例子：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0  1  2]\\n\",\n      \" [10 11 12]\\n\",\n      \" [20 21 22]\\n\",\n      \" [30 31 32]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from numpy import array\\n\",\n    \"a = array([[ 0, 0, 0],\\n\",\n    \"           [10,10,10],\\n\",\n    \"           [20,20,20],\\n\",\n    \"           [30,30,30]])\\n\",\n    \"b = array([0,1,2])\\n\",\n    \"print(a+b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"为什么是这个样子？\\n\",\n    \"\\n\",\n    \"这里以tile模拟上述操作，来回到`a.shape==b.shape`情况！\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0  1  2]\\n\",\n      \" [10 11 12]\\n\",\n      \" [20 21 22]\\n\",\n      \" [30 31 32]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 对[0,1,2]行重复3次，列重复1次\\n\",\n    \"b = np.tile([0,1,2],(4,1))\\n\",\n    \"print(a+b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"到这里，我们来给出一张图\\n\",\n    \"\\n\",\n    \"![](images/3.png)\\n\",\n    \"\\n\",\n    \"也可以看这张图：\\n\",\n    \"![](images/4.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"是不是任何情况都可以呢？\\n\",\n    \"\\n\",\n    \"当然不是，只有当两个数组的`trailing dimensions  compatible`时才会触发广播，否则报错`ValueError: frames are not aligned exception`。\\n\",\n    \"\\n\",\n    \"上面表达意思是尾部维度必须兼容！\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 9.常用函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 9.1 np.bincount()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 1, 2, 1], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 98,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 3, 3, 0, 1, 4])\\n\",\n    \"np.bincount(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"统计索引出现次数：索引0出现1次，1出现2次，2出现1次，3出现2次，4出现1次\\n\",\n    \"\\n\",\n    \"因此通过bincount计算出索引出现次数如下：\\n\",\n    \"\\n\",\n    \"上面怎么得到的？\\n\",\n    \"\\n\",\n    \"对于bincount计算吗，bin的数量比x中最大数多1，例如x最大为4，那么bin数量为5(index从0到4)，也就会bincount输出的一维数组为5个数，bincount中的数又代表什么？代表的是它的索引值在x中出现的次数！\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"还是以上述x为例子，当我们设置weights参数时候，结果又是什么？\\n\",\n    \"\\n\",\n    \"这里假定：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"w = np.array([0.3,0.5,0.7,0.6,0.1,-0.9,1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"那么设置这个w权重后，结果为多少？\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0.1, -0.6,  0.5,  1.3,  1. ])\"\n      ]\n     },\n     \"execution_count\": 100,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.bincount(x,weights=w)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"怎么计算的？\\n\",\n    \"\\n\",\n    \"先对x与w抽取出来：\\n\",\n    \"\\n\",\n    \"`x --->  [1, 2, 3, 3, 0, 1, 4]`\\n\",\n    \"\\n\",\n    \"`w --->  [0.3,0.5,0.7,0.6,0.1,-0.9,1]`\\n\",\n    \"索引 0 出现在x中index=4位置，那么在w中访问index=4的位置即可，w[4]=0.1\\n\",\n    \"\\n\",\n    \"索引 1 出现在x中index=0与index=5位置，那么在w中访问`index=0`与`index=5`的位置即可，然后将两这个加和，计算得：`w[0]+w[5]=-0.6`\\n\",\n    \"其余的按照上面的方法即可！\\n\",\n    \"\\n\",\n    \"bincount的另外一个参数为minlength，这个参数简单，可以这么理解，当所给的bin数量多于实际从x中得到的bin数量后，后面没有访问到的设置为0即可。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"还是上述x为例：\\n\",\n    \"\\n\",\n    \"这里我们直接设置minlength=7参数，并输出！\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0.1, -0.6,  0.5,  1.3,  1. ,  0. ,  0. ])\"\n      ]\n     },\n     \"execution_count\": 101,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.bincount(x,weights=w,minlength=7)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"与上面相比多了两个0，这两个怎么会多？\\n\",\n    \"\\n\",\n    \"上面知道，这个bin数量为5，index从0到4，那么当minlength为7的时候，也就是总长为7，index从0到6，多了后面两位，直接补位为0即可！\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 9.2 np.argmax()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"函数原型为：`numpy.argmax(a, axis=None, out=None)`.\\n\",\n    \"\\n\",\n    \"函数表示返回沿轴axis最大值的索引。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = [[1,3,3],\\n\",\n    \"     [7,5,2]]\\n\",\n    \"print(np.argmax(x))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"对于这个例子我们知道，7最大，索引位置为3(这个索引按照递增顺序)！\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"axis属性\\n\",\n    \"\\n\",\n    \"axis=0表示按列操作，也就是对比当前列，找出最大值的索引！\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 1 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = [[1,3,3],\\n\",\n    \"     [7,5,2]]\\n\",\n    \"print(np.argmax(x,axis=0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"axis=1表示按行操作，也就是对比当前行，找出最大值的索引！\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 104,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 1 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = [[1,3,3],\\n\",\n    \"     [7,5,2]]\\n\",\n    \"print(np.argmax(x,axis=0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"那如果碰到重复最大元素？\\n\",\n    \"\\n\",\n    \"返回第一个最大值索引即可！\\n\",\n    \"\\n\",\n    \"例如：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 3, 2, 3, 0, 1, 0])\\n\",\n    \"print(x.argmax())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 9.3 上述合并实例\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这里来融合上述两个函数，举个例子：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 3, 3, 0, 1, 4])\\n\",\n    \"print(np.argmax(np.bincount(x)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"最终结果为1，为什么？\\n\",\n    \"\\n\",\n    \"首先通过`np.bincount(x)`得到的结果是：`[1 2 1 2 1]`，再根据最后的遇到重复最大值项，则返回第一个最大值的index即可！2的index为1，所以返回1。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 9.4 求取精度\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-1.,  1.,  2., 10., 13.])\"\n      ]\n     },\n     \"execution_count\": 107,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.around([-0.6,1.2798,2.357,9.67,13], decimals=0)#取指定位置的精度\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"看到没，负数进位取绝对值大的！\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1.3,  2.4,  9.7, 13. ])\"\n      ]\n     },\n     \"execution_count\": 108,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.around([1.2798,2.357,9.67,13], decimals=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1.28,  2.36,  9.67, 13.  ])\"\n      ]\n     },\n     \"execution_count\": 109,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.around([1.2798,2.357,9.67,13], decimals=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"从上面可以看出，decimals表示指定保留有效数的位数，当超过5就会进位(此时包含5)！\\n\",\n    \"\\n\",\n    \"但是，如果这个参数设置为负数，又表示什么？\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 110,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0,  0,  0, 10, 60])\"\n      ]\n     },\n     \"execution_count\": 110,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.around([1,2,5,6,56], decimals=-1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"发现没，当超过5时候(不包含5)，才会进位！-1表示看一位数进位即可，那么如果改为-2呢，那就得看两位！\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 111,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  0,   0,   0,   0, 100, 200])\"\n      ]\n     },\n     \"execution_count\": 111,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.around([1,2,5,50,56,190], decimals=-2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"看到没，必须看两位，超过50才会进位，190的话，就看后面两位，后两位90超过50，进位，那么为200！\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"计算沿指定轴第N维的离散差值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 112,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1  2  3  4  5]\\n\",\n      \" [ 6  7  8  9 10]\\n\",\n      \" [11 12 13 14 15]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1 , 16).reshape((3 , 5))\\n\",\n    \"print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 113,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 1, 1, 1],\\n\",\n       \"       [1, 1, 1, 1],\\n\",\n       \"       [1, 1, 1, 1]])\"\n      ]\n     },\n     \"execution_count\": 113,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.diff(x,axis=1) #默认axis=1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 114,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[5, 5, 5, 5, 5],\\n\",\n       \"       [5, 5, 5, 5, 5]])\"\n      ]\n     },\n     \"execution_count\": 114,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.diff(x,axis=0) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"取整\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-1., -2., -1., -2.,  0.,  1.,  1.])\"\n      ]\n     },\n     \"execution_count\": 115,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.floor([-0.6,-1.4,-0.1,-1.8,0,1.4,1.7])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"看到没，负数取整，跟上述的around一样，是向左！\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"取上限\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 116,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 2.,  2.,  2.,  3.,  2., -0., -0., -0.])\"\n      ]\n     },\n     \"execution_count\": 116,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.ceil([1.2,1.5,1.8,2.1,2.0,-0.5,-0.6,-0.3])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"取上限！找这个小数的最大整数即可！\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"查找\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"利用np.where实现小于0的值用0填充吗，大于0的数不变！\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 117,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1  0]\\n\",\n      \" [ 2 -2]\\n\",\n      \" [-2  1]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 0],\\n\",\n    \"       [2, -2],\\n\",\n    \"     [-2, 1]])\\n\",\n    \"print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 0],\\n\",\n       \"       [2, 0],\\n\",\n       \"       [0, 1]])\"\n      ]\n     },\n     \"execution_count\": 118,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.where(x>0,x,0)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "2.numpy/2.numpy.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 1.Numpy简易入门\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.1 认识NumPy数组对象\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Numpy是一个用python实现的科学计算的扩展程序库，包括：\\n\",\n    \"* 1、一个强大的N维数组对象Array；\\n\",\n    \"* 2、比较成熟的（广播）函数库；\\n\",\n    \"* 3、用于整合C/C++和Fortran代码的工具包；\\n\",\n    \"* 4、实用的线性代数、傅里叶变换和随机数生成函数。numpy和稀疏矩阵运算包scipy配合使用更加方便。\\n\",\n    \"\\n\",\n    \"NumPy（Numeric Python）提供了许多高级的数值编程工具，如：矩阵数据类型、矢量处理，以及精密的运算库。专为进行严格的数字处理而产生。多为很多大型金融公司使用，以及核心的科学计算组织如：Lawrence Livermore，NASA用其处理一些本来使用C++，Fortran或Matlab等所做的任务。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np                     # 导入NumPy工具包\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data = np.arange(12).reshape(3, 4)  # 创建一个3行4列的数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0,  1,  2,  3],\\n\",\n       \"       [ 4,  5,  6,  7],\\n\",\n       \"       [ 8,  9, 10, 11]])\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"numpy.ndarray\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data.ndim         # 数组维度的个数，输出结果2，表示二维数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(3, 4)\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data.shape        # 数组的维度，输出结果（3，4），表示3行4列\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"12\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data.size         # 数组元素的个数，输出结果12，表示总共有12个元素\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dtype('int32')\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data.dtype # 数组元素的类型，输出结果dtype('int64'),表示元素类型都是int64\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.2 创建NumPy数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data1 = np.array([1, 2, 3])                   # 创建一个一维数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3])\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data2 = np.array([[1, 2, 3], [4, 5, 6]])   # 创建一个二维数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 2, 3],\\n\",\n       \"       [4, 5, 6]])\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., 0.]])\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.zeros((3, 4))#创建一个全0数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1., 1., 1., 1.],\\n\",\n       \"       [1., 1., 1., 1.],\\n\",\n       \"       [1., 1., 1., 1.]])\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.ones((3, 4))#创建全一数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 6.95312756e-310,  2.12199579e-314],\\n\",\n       \"       [ 2.12199579e-314,  4.94065646e-324],\\n\",\n       \"       [ 0.00000000e+000, -7.06252554e-311],\\n\",\n       \"       [ 0.00000000e+000, -8.12021073e-313],\\n\",\n       \"       [ 1.29923372e-311,  2.07507571e-322]])\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.empty((5, 2))# 创建全空数组，其实每个值都是接近于零的数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1,  6, 11, 16])\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(1, 20, 5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1., 2., 3., 4.])\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.array([1, 2, 3, 4], float)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1., 1., 1.],\\n\",\n       \"       [1., 1., 1.]])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.ones((2, 3), dtype='float64')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.3 ndarry对象的数据类型\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.3.1 查看数据类型\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data_one = np.array([[1, 2, 3], [4, 5, 6]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'int32'\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data_one.dtype.name\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.3.2 转换数据类型\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data = np.array([[1, 2, 3], [4, 5, 6]]) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dtype('int32')\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"float_data = data.astype(np.float64) # 数据类型转换为float64\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dtype('float64')\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"float_data.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"float_data = np.array([1.2, 2.3, 3.5])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1.2, 2.3, 3.5])\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"float_data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"int_data = float_data.astype(np.int64) # 数据类型转换为int64\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"int_data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"str_data = np.array(['1', '2', '3'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"int_data = str_data.astype(np.int64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"int_data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.4 数组运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.4.1向量化运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data1 = np.array([[1, 2, 3], [4, 5, 6]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data2 = np.array([[1, 2, 3], [4, 5, 6]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 2,  4,  6],\\n\",\n       \"       [ 8, 10, 12]])\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data1 + data2        # 数组相加\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1,  4,  9],\\n\",\n       \"       [16, 25, 36]])\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data1 * data2        # 数组相乘\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0, 0, 0],\\n\",\n       \"       [0, 0, 0]])\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data1 - data2        # 数组相减\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1., 1., 1.],\\n\",\n       \"       [1., 1., 1.]])\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data1 / data2       # 数组相除\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.4.2 数组广播\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"numpy数组间的基础运算是一对一，也就是`a.shape==b.shape`，但是当两者不一样的时候，就会自动触发广播机制，如下例子：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr1 = np.array([[0], [1], [2], [3]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(4, 1)\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr1.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr2 = np.array([1, 2, 3])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(3,)\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr2.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 2, 3],\\n\",\n       \"       [2, 3, 4],\\n\",\n       \"       [3, 4, 5],\\n\",\n       \"       [4, 5, 6]])\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr1 + arr2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"到这里，我们来给出一张图\\n\",\n    \"\\n\",\n    \"![](images/3.png)\\n\",\n    \"\\n\",\n    \"也可以看这张图：\\n\",\n    \"![](images/4.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.4.3 数组与标量间的运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data1 = np.array([[1, 2, 3], [4, 5, 6]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data2 = 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[11, 12, 13],\\n\",\n       \"       [14, 15, 16]])\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data1 + data2      # 数组相加\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[10, 20, 30],\\n\",\n       \"       [40, 50, 60]])\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data1 * data2       # 数组相乘\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-9, -8, -7],\\n\",\n       \"       [-6, -5, -4]])\"\n      ]\n     },\n     \"execution_count\": 51,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data1 - data2        # 数组相减\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0.1, 0.2, 0.3],\\n\",\n       \"       [0.4, 0.5, 0.6]])\"\n      ]\n     },\n     \"execution_count\": 52,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data1 / data2       # 数组相除\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.5 ndarray的索引和切片\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.5.1 整数索引和切片的基本使用\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们一起来来总结一下，看下面切片取值方式（对应颜色是取出来的结果）：\\n\",\n    \"\\n\",\n    \"![](images/1.png)\\n\",\n    \"\\n\",\n    \"![](images/2.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr = np.arange(8)    # 创建一个一维数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, 3, 4, 5, 6, 7])\"\n      ]\n     },\n     \"execution_count\": 55,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"5\"\n      ]\n     },\n     \"execution_count\": 56,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr[5]                  # 获取索引为5的元素\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([3, 4])\"\n      ]\n     },\n     \"execution_count\": 57,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr[3:5]                # 获取索引为3~5的元素，但不包括5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 3, 5])\"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr[1:6:2]              # 获取索引为1~6的元素，步长为2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr2d = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]]) # 创建二维数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 2, 3],\\n\",\n       \"       [4, 5, 6],\\n\",\n       \"       [7, 8, 9]])\"\n      ]\n     },\n     \"execution_count\": 61,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr2d\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([4, 5, 6])\"\n      ]\n     },\n     \"execution_count\": 62,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr2d[1]            # 获取索引为1的元素\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2\"\n      ]\n     },\n     \"execution_count\": 63,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr2d[0, 1]        # 获取位于第0行第1列的元素\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 2, 3],\\n\",\n       \"       [4, 5, 6]])\"\n      ]\n     },\n     \"execution_count\": 64,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr2d[:2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 2],\\n\",\n       \"       [4, 5]])\"\n      ]\n     },\n     \"execution_count\": 65,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr2d[0:2, 0:2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([4, 5])\"\n      ]\n     },\n     \"execution_count\": 66,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr2d[1, :2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.5.2 花式（数组）索引的基本使用\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"demo_arr = np.empty((4, 4))               # 创建一个空数组\\n\",\n    \"for i in range(4):\\n\",\n    \"    demo_arr[i] = np.arange(i, i + 4)   # 动态地为数组添加元素\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0., 1., 2., 3.],\\n\",\n       \"       [1., 2., 3., 4.],\\n\",\n       \"       [2., 3., 4., 5.],\\n\",\n       \"       [3., 4., 5., 6.]])\"\n      ]\n     },\n     \"execution_count\": 69,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"demo_arr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0., 1., 2., 3.],\\n\",\n       \"       [2., 3., 4., 5.]])\"\n      ]\n     },\n     \"execution_count\": 70,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"demo_arr[[0, 2]]        # 获取索引为[0,2]的元素\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([2., 5.])\"\n      ]\n     },\n     \"execution_count\": 71,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"demo_arr[[1, 3], [1, 2]]     # 获取索引为(1,1)和(3,2)的元素\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.5.3 布尔型\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 存储学生姓名的数组\\n\",\n    \"student_name = np.array(['Tom', 'Lily', 'Jack', 'Rose'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array(['Tom', 'Lily', 'Jack', 'Rose'], dtype='<U4')\"\n      ]\n     },\n     \"execution_count\": 73,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"student_name\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 存储学生成绩的数组\\n\",\n    \"student_score = np.array([[79, 88, 80], [89, 90, 92], [83, 78, 85], [78, 76, 80]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[79, 88, 80],\\n\",\n       \"       [89, 90, 92],\\n\",\n       \"       [83, 78, 85],\\n\",\n       \"       [78, 76, 80]])\"\n      ]\n     },\n     \"execution_count\": 75,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"student_score\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([False, False,  True, False])\"\n      ]\n     },\n     \"execution_count\": 76,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 对student_name和字符串“Jack”通过运算符产生一个布尔型数组\\n\",\n    \"student_name == 'Jack'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[83, 78, 85]])\"\n      ]\n     },\n     \"execution_count\": 77,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# 将布尔数组作为索引应用于存储成绩的数组student_score，\\n\",\n    \"# 返回的数据是True值对应的行\\n\",\n    \"student_score[student_name=='Jack']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[83]])\"\n      ]\n     },\n     \"execution_count\": 78,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"student_score[student_name=='Jack', :1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.6 数组的转置和轴对称\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr = np.arange(12).reshape(3, 4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0,  1,  2,  3],\\n\",\n       \"       [ 4,  5,  6,  7],\\n\",\n       \"       [ 8,  9, 10, 11]])\"\n      ]\n     },\n     \"execution_count\": 80,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0,  4,  8],\\n\",\n       \"       [ 1,  5,  9],\\n\",\n       \"       [ 2,  6, 10],\\n\",\n       \"       [ 3,  7, 11]])\"\n      ]\n     },\n     \"execution_count\": 81,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr.T      # 使用T属性对数组进行转置\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr = np.arange(16).reshape((2, 2, 4)) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[[ 0,  1,  2,  3],\\n\",\n       \"        [ 4,  5,  6,  7]],\\n\",\n       \"\\n\",\n       \"       [[ 8,  9, 10, 11],\\n\",\n       \"        [12, 13, 14, 15]]])\"\n      ]\n     },\n     \"execution_count\": 83,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[[ 0,  8],\\n\",\n       \"        [ 1,  9],\\n\",\n       \"        [ 2, 10],\\n\",\n       \"        [ 3, 11]],\\n\",\n       \"\\n\",\n       \"       [[ 4, 12],\\n\",\n       \"        [ 5, 13],\\n\",\n       \"        [ 6, 14],\\n\",\n       \"        [ 7, 15]]])\"\n      ]\n     },\n     \"execution_count\": 84,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr.transpose(1, 2, 0)   # 使用transpose()方法对数组进行转置\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[[ 0,  1,  2,  3],\\n\",\n       \"        [ 4,  5,  6,  7]],\\n\",\n       \"\\n\",\n       \"       [[ 8,  9, 10, 11],\\n\",\n       \"        [12, 13, 14, 15]]])\"\n      ]\n     },\n     \"execution_count\": 85,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[[ 0,  1,  2,  3],\\n\",\n       \"        [ 8,  9, 10, 11]],\\n\",\n       \"\\n\",\n       \"       [[ 4,  5,  6,  7],\\n\",\n       \"        [12, 13, 14, 15]]])\"\n      ]\n     },\n     \"execution_count\": 86,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr.swapaxes(1, 0)    # 使用swapaxes方法对数组进行转置\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.7 NumPy通用函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr = np.array([4, 9, 16])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([2., 3., 4.])\"\n      ]\n     },\n     \"execution_count\": 88,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.sqrt(arr)#开方\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 4,  9, 16])\"\n      ]\n     },\n     \"execution_count\": 89,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.abs(arr)#求绝对值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 16,  81, 256], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 90,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.square(arr)#求平方\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([12, 9, 13, 15])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y = np.array([11, 10, 4, 8])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([23, 19, 17, 23])\"\n      ]\n     },\n     \"execution_count\": 93,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.add(x, y)      # 计算两个数组的和\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([132,  90,  52, 120])\"\n      ]\n     },\n     \"execution_count\": 94,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.multiply(x, y) # 计算两个数组的乘积\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([12, 10, 13, 15])\"\n      ]\n     },\n     \"execution_count\": 95,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.maximum(x, y)  # 两个数组元素级最大值的比较\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ True, False,  True,  True])\"\n      ]\n     },\n     \"execution_count\": 96,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.greater(x, y)  # 执行元素级的比较操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.8 利用NumPy数组进行数据处理\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.8.1 将条件逻辑转为数组运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr_x = np.array([1, 5, 7])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr_y = np.array([2, 6, 8])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr_con = np.array([True, False, True])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"result = np.where(arr_con, arr_x, arr_y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 6, 7])\"\n      ]\n     },\n     \"execution_count\": 101,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"result\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.8.2 数组统计运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr = np.arange(10)     \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"45\"\n      ]\n     },\n     \"execution_count\": 103,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr.sum()      # 求和\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 104,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"4.5\"\n      ]\n     },\n     \"execution_count\": 104,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr.mean()     # 求平均值 \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0\"\n      ]\n     },\n     \"execution_count\": 105,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr.min()      # 求最小值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"9\"\n      ]\n     },\n     \"execution_count\": 106,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr.max()       # 求最大值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0\"\n      ]\n     },\n     \"execution_count\": 107,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr.argmin()   # 求最小值的索引\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"9\"\n      ]\n     },\n     \"execution_count\": 108,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr.argmax()   # 求最大值的索引\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0,  1,  3,  6, 10, 15, 21, 28, 36, 45], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 109,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr.cumsum()   # 计算元素的累计和\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 110,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 110,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr.cumprod()  # 计算元素的累计积\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 111,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1  2  3  4  5]\\n\",\n      \" [ 6  7  8  9 10]\\n\",\n      \" [11 12 13 14 15]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1 , 16).reshape((3 , 5))\\n\",\n    \"print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 112,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 1, 1, 1],\\n\",\n       \"       [1, 1, 1, 1],\\n\",\n       \"       [1, 1, 1, 1]])\"\n      ]\n     },\n     \"execution_count\": 112,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.diff(x,axis=1) #默认axis=1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 113,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[5, 5, 5, 5, 5],\\n\",\n       \"       [5, 5, 5, 5, 5]])\"\n      ]\n     },\n     \"execution_count\": 113,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.diff(x,axis=0) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 114,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-1., -2., -1., -2.,  0.,  1.,  1.])\"\n      ]\n     },\n     \"execution_count\": 114,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.floor([-0.6,-1.4,-0.1,-1.8,0,1.4,1.7])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"看到没，负数取整，跟上述的around一样，是向左！\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 2.,  2.,  2.,  3.,  2., -0., -0., -0.])\"\n      ]\n     },\n     \"execution_count\": 115,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.ceil([1.2,1.5,1.8,2.1,2.0,-0.5,-0.6,-0.3])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"取上限！找这个小数的最大整数即可！\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"查找，利用np.where实现小于0的值用0填充吗，大于0的数不变！\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 116,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1  0]\\n\",\n      \" [ 2 -2]\\n\",\n      \" [-2  1]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 0],\\n\",\n    \"       [2, -2],\\n\",\n    \"     [-2, 1]])\\n\",\n    \"print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 117,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 0],\\n\",\n       \"       [2, 0],\\n\",\n       \"       [0, 1]])\"\n      ]\n     },\n     \"execution_count\": 117,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.where(x>0,x,0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.8.3 数组排序\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr = np.array([[6, 2, 7], [3, 6, 2], [4, 3, 2]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 119,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[6, 2, 7],\\n\",\n       \"       [3, 6, 2],\\n\",\n       \"       [4, 3, 2]])\"\n      ]\n     },\n     \"execution_count\": 119,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 120,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr.sort()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 121,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[2, 6, 7],\\n\",\n       \"       [2, 3, 6],\\n\",\n       \"       [2, 3, 4]])\"\n      ]\n     },\n     \"execution_count\": 121,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 122,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr = np.array([[6, 2, 7], [3, 6, 2], [4, 3, 2]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 123,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[6, 2, 7],\\n\",\n       \"       [3, 6, 2],\\n\",\n       \"       [4, 3, 2]])\"\n      ]\n     },\n     \"execution_count\": 123,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 124,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr.sort(0)       # 沿着编号为0的轴对元素排序\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 125,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[3, 2, 2],\\n\",\n       \"       [4, 3, 2],\\n\",\n       \"       [6, 6, 7]])\"\n      ]\n     },\n     \"execution_count\": 125,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.8.4 检索数组元素\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 126,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr = np.array([[1, -2, -7], [-3, 6, 2], [-4, 3, 2]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 127,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1, -2, -7],\\n\",\n       \"       [-3,  6,  2],\\n\",\n       \"       [-4,  3,  2]])\"\n      ]\n     },\n     \"execution_count\": 127,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 128,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 128,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.any(arr > 0)      # arr的所有元素是否有一个大于0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 129,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 129,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.all(arr > 0)      # arr的所有元素是否都大于0\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.8.5 唯一化及其他集合逻辑\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 130,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr = np.array([12, 11, 34, 23, 12, 8, 11])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 131,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 8, 11, 12, 23, 34])\"\n      ]\n     },\n     \"execution_count\": 131,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.unique(arr)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 132,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ True,  True, False, False,  True, False,  True])\"\n      ]\n     },\n     \"execution_count\": 132,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.in1d(arr, [11, 12])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.9 线性代数模块\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 133,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr_x = np.array([[1, 2, 3], [4, 5, 6]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 134,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"arr_y = np.array([[1, 2], [3, 4], [5, 6]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 135,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[22, 28],\\n\",\n       \"       [49, 64]])\"\n      ]\n     },\n     \"execution_count\": 135,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr_x.dot(arr_y)   # 等价于np.dot(arr_x, arr_y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.10随机数模块\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 136,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 137,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0.90422833, 0.57874299, 0.36084718],\\n\",\n       \"       [0.46674697, 0.59189161, 0.88876503],\\n\",\n       \"       [0.51836003, 0.30765097, 0.79668824]])\"\n      ]\n     },\n     \"execution_count\": 137,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.random.rand(3, 3)     # 随机生成一个二维数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 138,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[[0.21438832, 0.58877977, 0.86120009],\\n\",\n       \"        [0.15222229, 0.53060997, 0.0562486 ],\\n\",\n       \"        [0.88035435, 0.32505223, 0.9045713 ]],\\n\",\n       \"\\n\",\n       \"       [[0.32907094, 0.88987195, 0.34523123],\\n\",\n       \"        [0.90645746, 0.61257549, 0.83944649],\\n\",\n       \"        [0.2015535 , 0.84522463, 0.87759584]]])\"\n      ]\n     },\n     \"execution_count\": 138,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.random.rand(2, 3, 3) # 随机生成一个三维数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 139,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 140,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(0)   # 生成随机数的种子\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 141,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ])\"\n      ]\n     },\n     \"execution_count\": 141,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.random.rand(5)   # 随机生成包含5个元素的浮点数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 142,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 143,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ])\"\n      ]\n     },\n     \"execution_count\": 143,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.random.rand(5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 144,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 145,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.19299506, 0.41434116, 0.90011257, 0.37469705, 0.69775797])\"\n      ]\n     },\n     \"execution_count\": 145,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.random.rand(5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "2.numpy/README.md",
    "content": "## Numpy简介\n\nNumpy是一个用python实现的科学计算的扩展程序库，包括：\n\n- 1、一个强大的N维数组对象Array；\n- 2、比较成熟的（广播）函数库；\n- 3、用于整合C/C++和Fortran代码的工具包；\n- 4、实用的线性代数、傅里叶变换和随机数生成函数。numpy和稀疏矩阵运算包scipy配合使用更加方便。\n\nNumPy（Numeric Python）提供了许多高级的数值编程工具，如：矩阵数据类型、矢量处理，以及精密的运算库。专为进行严格的数字处理而产生。多为很多大型金融公司使用，以及核心的科学计算组织如：Lawrence Livermore，NASA用其处理一些本来使用C++，Fortran或Matlab等所做的任务。\n\n## 一、适合初学者快速入门的Numpy实战全集\n\n本文由光城同学整理\n\n### 本文目录\n​      1.Numpy基本操作\n\n - 1.1 列表转为矩阵\n - 1.2 维度\n - 1.3 行数和列数()\n - 1.4 元素个数\n\n   2.Numpy创建array\n - 2.1 一维array创建\n - 2.2 多维array创建\n - 2.3 创建全零数组\n - 2.4 创建全1数据\n - 2.5 创建全空数组\n - 2.6 创建连续数组\n - 2.7 reshape操作\n - 2.8 创建连续型数据\n - 2.9 linspace的reshape操作\n\n   3.Numpy基本运算\n - 3.1 一维矩阵运算\n - 3.2 多维矩阵运算\n - 3.3 基本计算\n\n   4.Numpy索引与切片\n\n   5.Numpy array合并\n - 5.1 数组合并\n - 5.2 数组转置为矩阵\n - 5.3 多个矩阵合并\n - 5.4 合并例子2\n\n   6.Numpy array分割\n - 6.1 构造3行4列矩阵\n - 6.2 等量分割\n - 6.3 不等量分割\n - 6.4 其他的分割方式\n\n   7.Numpy copy与 =\n - 7.1 =赋值方式会带有关联性\n - 7.2 copy()赋值方式没有关联性\n\n   8.广播机制\n\n   9.常用函数\n   \n# 二.Numpy简易入门\n\n- 1.Numpy简易入门\n      1.1 认识NumPy数组对象\n\n  ​    1.2 创建NumPy数组\n\n  ​    1.3 ndarry对象的数据类型\n\n  ​        1.3.1 查看数据类型\n\n  ​        1.3.2 转换数据类型\n\n  ​    1.4 数组运算\n\n  ​        1.4.1向量化运算\n\n  ​        1.4.2 数组广播\n\n  ​        1.4.3 数组与标量间的运算\n\n  ​    1.5 ndarray的索引和切片\n\n  ​        1.5.1 整数索引和切片的基本使用\n\n  ​        1.5.2 花式（数组）索引的基本使用\n\n  ​        1.5.3 布尔型\n\n  ​    1.6 数组的转置和轴对称\n\n  ​    1.7 NumPy通用函数\n\n  ​    1.8 利用NumPy数组进行数据处理\n\n  ​        1.8.1 将条件逻辑转为数组运算\n\n  ​        1.8.2 数组统计运算\n\n  ​        1.8.3 数组排序\n\n  ​        1.8.4 检索数组元素\n\n  ​        1.8.5 唯一化及其他集合逻辑\n\n  ​    1.9 线性代数模块\n\n  ​    1.10随机数模块\n\n\n\n## 三、Numpy练习题100题-提高你的数据分析技能\n\n本文总结了Numpy的常用操作，并做成练习题，练习题附答案建议读者把练习题完成。作者认为，做完练习题，Numpy的基本操作没有问题了，以后碰到问题也可以查这些习题。\n\n网上可以搜到大量的Numpy教程和官方文档，但没有简单的方法来练习。教程是很好的资源，但要付诸实践。 只有实践，才能更好的加深学习。\n\n本站从github搜索到了一些Numpy的练习题100题，含答案，并进行整理：\n\n原代码作者：**Nicolas P. Rougier**（https://github.com/rougier/numpy-100）\n\n**本练习代码可以在github下载：**\n\n[numpy-100](numpy-100)\n\n**使用方法**\n文件夹有三个不同的ipynb文件:\n\n- **100_Numpy_exercises_no_solution.ipynb** \n\n没有答案代码的文件，这个是你做的练习\n\n- **100_Numpy_exercises_with_hint.ipynb**\n\n没有答案代码的文件，但有提示，这个你也可以用来练习\n\n- **100_Numpy_exercises.ipynb**\n\n有答案代码和注释的文件\n\n你可以在**100_Numpy_exercises_no_solution.ipynb** 里输入代码，看看运行结果是否和**100_Numpy_exercises.ipynb** 里面的内容一致。\n\n## 四、Numpy练习题\n\n整理了一个Numpy的练习题，总结了Numpy的常用操作，可以测试下自己对Numpy的掌握程度，有答案哦。\n\n### 试题目录\n\n  * Array creation routines（数组创建）\n  * Array manipulation routines（数组操作）\n  * String operations（字符串操作）\n  * Numpy-specific help functions（Numpy特定帮助函数）\n  * Input and output（输入和输出）\n  * Linear algebra（线性代数）\n  * Discrete Fourier Transform（离散傅里叶变换）\n  * Logic functions（逻辑函数）\n  * Mathematical functions（数学函数）\n  * Random sampling (numpy.random)（随机抽样）\n  * Set routines（集合操作）\n  * Sorting, searching, and counting（排序、搜索和计数）\n  * Statistics（统计）\n\n### 试题内容\n\n试题分为13个练习，每个练习分为两个ipynb文件，文件名带`_Solutions` 的是带答案的文件，建议初学者先练习下不带答案的文件,做不出来再看看答案。\n\n**本练习代码可以在github下载：**\n\n[numpy_exercises](numpy_exercises)\n\n### 作者及来源\n\n- 作者： Kyubyong\n- 来源：https://github.com/Kyubyong/numpy_exercises "
  },
  {
    "path": "2.numpy/numpy-100/100_Numpy_exercises.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"\\n\",\n    \" 100 numpy exercises\\n\",\n    \"\\n\",\n    \"This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"If you find an error or think you've a better way to solve some of them, feel free to open an issue at <https://github.com/rougier/numpy-100>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1. Import the numpy package under the name `np` (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2. Print the numpy version and the configuration (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1.14.2\\n\",\n      \"mkl_info:\\n\",\n      \"    libraries = ['mkl_rt']\\n\",\n      \"    library_dirs = ['C:/ProgramData/Anaconda3\\\\\\\\Library\\\\\\\\lib']\\n\",\n      \"    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]\\n\",\n      \"    include_dirs = ['C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl', 'C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl\\\\\\\\include', 'C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl\\\\\\\\lib', 'C:/ProgramData/Anaconda3\\\\\\\\Library\\\\\\\\include']\\n\",\n      \"blas_mkl_info:\\n\",\n      \"    libraries = ['mkl_rt']\\n\",\n      \"    library_dirs = ['C:/ProgramData/Anaconda3\\\\\\\\Library\\\\\\\\lib']\\n\",\n      \"    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]\\n\",\n      \"    include_dirs = ['C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl', 'C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl\\\\\\\\include', 'C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl\\\\\\\\lib', 'C:/ProgramData/Anaconda3\\\\\\\\Library\\\\\\\\include']\\n\",\n      \"blas_opt_info:\\n\",\n      \"    libraries = ['mkl_rt']\\n\",\n      \"    library_dirs = ['C:/ProgramData/Anaconda3\\\\\\\\Library\\\\\\\\lib']\\n\",\n      \"    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]\\n\",\n      \"    include_dirs = ['C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl', 'C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl\\\\\\\\include', 'C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl\\\\\\\\lib', 'C:/ProgramData/Anaconda3\\\\\\\\Library\\\\\\\\include']\\n\",\n      \"lapack_mkl_info:\\n\",\n      \"    libraries = ['mkl_rt']\\n\",\n      \"    library_dirs = ['C:/ProgramData/Anaconda3\\\\\\\\Library\\\\\\\\lib']\\n\",\n      \"    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]\\n\",\n      \"    include_dirs = ['C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl', 'C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl\\\\\\\\include', 'C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl\\\\\\\\lib', 'C:/ProgramData/Anaconda3\\\\\\\\Library\\\\\\\\include']\\n\",\n      \"lapack_opt_info:\\n\",\n      \"    libraries = ['mkl_rt']\\n\",\n      \"    library_dirs = ['C:/ProgramData/Anaconda3\\\\\\\\Library\\\\\\\\lib']\\n\",\n      \"    define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]\\n\",\n      \"    include_dirs = ['C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl', 'C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl\\\\\\\\include', 'C:\\\\\\\\Program Files (x86)\\\\\\\\IntelSWTools\\\\\\\\compilers_and_libraries_2016.4.246\\\\\\\\windows\\\\\\\\mkl\\\\\\\\lib', 'C:/ProgramData/Anaconda3\\\\\\\\Library\\\\\\\\include']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.__version__)\\n\",\n    \"np.show_config()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 3. Create a null vector of size 10 (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.zeros(10)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 4.  How to find the memory size of any array (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"800 bytes\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.zeros((10,10))\\n\",\n    \"print(\\\"%d bytes\\\" % (Z.size * Z.itemsize))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 5.  How to get the documentation of the numpy add function from the command line? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ERROR:root:File `'`python.py'` not found.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%run `python -c \\\"import numpy; numpy.info(numpy.add)\\\"`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 6.  Create a null vector of size 10 but the fifth value which is 1 (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"is_executing\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mNameError\\u001b[0m                                 Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-1-3ad7d656eb82>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m \\u001b[0mZ\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mzeros\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;36m10\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      2\\u001b[0m \\u001b[0mZ\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m4\\u001b[0m\\u001b[1;33m]\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[1;36m1\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      3\\u001b[0m \\u001b[0mprint\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mZ\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mNameError\\u001b[0m: name 'np' is not defined\"\n     ],\n     \"ename\": \"NameError\",\n     \"evalue\": \"name 'np' is not defined\",\n     \"output_type\": \"error\"\n    }\n   ],\n   \"source\": [\n    \"Z = np.zeros(10)\\n\",\n    \"Z[4] = 1\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 7.  Create a vector with values ranging from 10 to 49 (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\\n\",\n      \" 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.arange(10,50)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 8.  Reverse a vector (first element becomes last) (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26\\n\",\n      \" 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10  9  8  7  6  5  4  3  2\\n\",\n      \"  1  0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.arange(50)\\n\",\n    \"Z = Z[::-1]\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 9.  Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1 2]\\n\",\n      \" [3 4 5]\\n\",\n      \" [6 7 8]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.arange(9).reshape(3,3)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 10. Find indices of non-zero elements from \\\\[1,2,0,0,4,0\\\\] (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(array([0, 1, 4], dtype=int64),)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"nz = np.nonzero([1,2,0,0,4,0])\\n\",\n    \"print(nz)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 11. Create a 3x3 identity matrix (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1. 0. 0.]\\n\",\n      \" [0. 1. 0.]\\n\",\n      \" [0. 0. 1.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.eye(3)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 12. Create a 3x3x3 array with random values (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[[0.38623687 0.72860689 0.11861256]\\n\",\n      \"  [0.87583184 0.75984112 0.92027858]\\n\",\n      \"  [0.15569262 0.24601167 0.40514924]]\\n\",\n      \"\\n\",\n      \" [[0.23179978 0.41058658 0.97117928]\\n\",\n      \"  [0.76418077 0.5534301  0.52208481]\\n\",\n      \"  [0.6620278  0.92392492 0.58433957]]\\n\",\n      \"\\n\",\n      \" [[0.14777566 0.83096875 0.20888782]\\n\",\n      \"  [0.94630753 0.14862539 0.88969933]\\n\",\n      \"  [0.10930963 0.13027745 0.41575222]]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.random.random((3,3,3))\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.004334784341918696 0.9914238367397242\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.random.random((10,10))\\n\",\n    \"Zmin, Zmax = Z.min(), Z.max()\\n\",\n    \"print(Zmin, Zmax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 14. Create a random vector of size 30 and find the mean value (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.5720815414833132\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.random.random(30)\\n\",\n    \"m = Z.mean()\\n\",\n    \"print(m)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\\n\",\n      \" [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\\n\",\n      \" [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\\n\",\n      \" [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\\n\",\n      \" [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\\n\",\n      \" [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\\n\",\n      \" [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\\n\",\n      \" [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\\n\",\n      \" [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\\n\",\n      \" [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.ones((10,10))\\n\",\n    \"Z[1:-1,1:-1] = 0\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 1. 1. 1. 1. 1. 0.]\\n\",\n      \" [0. 1. 1. 1. 1. 1. 0.]\\n\",\n      \" [0. 1. 1. 1. 1. 1. 0.]\\n\",\n      \" [0. 1. 1. 1. 1. 1. 0.]\\n\",\n      \" [0. 1. 1. 1. 1. 1. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.ones((5,5))\\n\",\n    \"Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 17. What is the result of the following expression? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"nan\\n\",\n      \"False\\n\",\n      \"False\\n\",\n      \"nan\\n\",\n      \"True\\n\",\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(0 * np.nan)\\n\",\n    \"print(np.nan == np.nan)\\n\",\n    \"print(np.inf > np.nan)\\n\",\n    \"print(np.nan - np.nan)\\n\",\n    \"print(np.nan in set([np.nan]))\\n\",\n    \"print(0.3 == 3 * 0.1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 0 0 0 0]\\n\",\n      \" [1 0 0 0 0]\\n\",\n      \" [0 2 0 0 0]\\n\",\n      \" [0 0 3 0 0]\\n\",\n      \" [0 0 0 4 0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.diag(1+np.arange(4),k=-1)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1 0 1 0 1 0 1]\\n\",\n      \" [1 0 1 0 1 0 1 0]\\n\",\n      \" [0 1 0 1 0 1 0 1]\\n\",\n      \" [1 0 1 0 1 0 1 0]\\n\",\n      \" [0 1 0 1 0 1 0 1]\\n\",\n      \" [1 0 1 0 1 0 1 0]\\n\",\n      \" [0 1 0 1 0 1 0 1]\\n\",\n      \" [1 0 1 0 1 0 1 0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.zeros((8,8),dtype=int)\\n\",\n    \"Z[1::2,::2] = 1\\n\",\n    \"Z[::2,1::2] = 1\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1, 5, 4)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.unravel_index(100,(6,7,8)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1 0 1 0 1 0 1]\\n\",\n      \" [1 0 1 0 1 0 1 0]\\n\",\n      \" [0 1 0 1 0 1 0 1]\\n\",\n      \" [1 0 1 0 1 0 1 0]\\n\",\n      \" [0 1 0 1 0 1 0 1]\\n\",\n      \" [1 0 1 0 1 0 1 0]\\n\",\n      \" [0 1 0 1 0 1 0 1]\\n\",\n      \" [1 0 1 0 1 0 1 0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.tile( np.array([[0,1],[1,0]]), (4,4))\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 22. Normalize a 5x5 random matrix (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.60460987  1.57268218 -0.89575026  0.39473056 -1.03035535]\\n\",\n      \" [ 0.48811681  0.27544473 -0.44867393  0.29267064 -0.68241891]\\n\",\n      \" [-0.3952934  -1.05912252 -1.38505776  0.35666151  0.27154967]\\n\",\n      \" [-1.11291754 -1.55657621  1.41843066  1.9689818   0.90553902]\\n\",\n      \" [-0.50662642 -0.47622473 -1.01864733  0.07395656  1.94429034]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.random.random((5,5))\\n\",\n    \"Z = (Z - np.mean (Z)) / (np.std (Z))\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"color = np.dtype([(\\\"r\\\", np.ubyte, 1),\\n\",\n    \"                  (\\\"g\\\", np.ubyte, 1),\\n\",\n    \"                  (\\\"b\\\", np.ubyte, 1),\\n\",\n    \"                  (\\\"a\\\", np.ubyte, 1)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[3. 3.]\\n\",\n      \" [3. 3.]\\n\",\n      \" [3. 3.]\\n\",\n      \" [3. 3.]\\n\",\n      \" [3. 3.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.dot(np.ones((5,3)), np.ones((3,2)))\\n\",\n    \"print(Z)\\n\",\n    \"\\n\",\n    \"# Alternative solution, in Python 3.5 and above\\n\",\n    \"Z = np.ones((5,3)) @ np.ones((3,2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0  1  2  3 -4 -5 -6 -7 -8  9 10]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Evgeni Burovski\\n\",\n    \"\\n\",\n    \"Z = np.arange(11)\\n\",\n    \"Z[(3 < Z) & (Z <= 8)] *= -1\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 26. What is the output of the following script? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"9\\n\",\n      \"10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Jake VanderPlas\\n\",\n    \"\\n\",\n    \"print(sum(range(5),-1))\\n\",\n    \"from numpy import *\\n\",\n    \"print(sum(range(5),-1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"ValueError\",\n     \"evalue\": \"Integers to negative integer powers are not allowed.\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mValueError\\u001b[0m                                Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-27-4e3654d03fce>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m \\u001b[0mZ\\u001b[0m\\u001b[1;33m**\\u001b[0m\\u001b[0mZ\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      2\\u001b[0m \\u001b[1;36m2\\u001b[0m \\u001b[1;33m<<\\u001b[0m \\u001b[0mZ\\u001b[0m \\u001b[1;33m>>\\u001b[0m \\u001b[1;36m2\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      3\\u001b[0m \\u001b[0mZ\\u001b[0m \\u001b[1;33m<\\u001b[0m\\u001b[1;33m-\\u001b[0m \\u001b[0mZ\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      4\\u001b[0m \\u001b[1;36m1j\\u001b[0m\\u001b[1;33m*\\u001b[0m\\u001b[0mZ\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      5\\u001b[0m \\u001b[0mZ\\u001b[0m\\u001b[1;33m/\\u001b[0m\\u001b[1;36m1\\u001b[0m\\u001b[1;33m/\\u001b[0m\\u001b[1;36m1\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mValueError\\u001b[0m: Integers to negative integer powers are not allowed.\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z**Z\\n\",\n    \"2 << Z >> 2\\n\",\n    \"Z <- Z\\n\",\n    \"1j*Z\\n\",\n    \"Z/1/1\\n\",\n    \"Z<Z>Z\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 28. What are the result of the following expressions?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"nan\\n\",\n      \"0\\n\",\n      \"[-2.14748365e+09]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in true_divide\\n\",\n      \"  \\\"\\\"\\\"Entry point for launching an IPython kernel.\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in floor_divide\\n\",\n      \"  \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.array(0) / np.array(0))\\n\",\n    \"print(np.array(0) // np.array(0))\\n\",\n    \"print(np.array([np.nan]).astype(int).astype(float))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 29. How to round away from zero a float array ? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 6.  6.  8. -9.  7. -9.  9.  8.  2.  4.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Charles R Harris\\n\",\n    \"\\n\",\n    \"Z = np.random.uniform(-10,+10,10)\\n\",\n    \"print (np.copysign(np.ceil(np.abs(Z)), Z))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 30. How to find common values between two arrays? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z1 = np.random.randint(0,10,10)\\n\",\n    \"Z2 = np.random.randint(0,10,10)\\n\",\n    \"print(np.intersect1d(Z1,Z2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"SyntaxError\",\n     \"evalue\": \"invalid syntax (<ipython-input-31-0437b6d2f8fa>, line 8)\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;36m  File \\u001b[1;32m\\\"<ipython-input-31-0437b6d2f8fa>\\\"\\u001b[1;36m, line \\u001b[1;32m8\\u001b[0m\\n\\u001b[1;33m    An equivalent way, with a context manager:\\u001b[0m\\n\\u001b[1;37m                ^\\u001b[0m\\n\\u001b[1;31mSyntaxError\\u001b[0m\\u001b[1;31m:\\u001b[0m invalid syntax\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Suicide mode on\\n\",\n    \"defaults = np.seterr(all=\\\"ignore\\\")\\n\",\n    \"Z = np.ones(1) / 0\\n\",\n    \"\\n\",\n    \"# Back to sanity\\n\",\n    \"_ = np.seterr(**defaults)\\n\",\n    \"\\n\",\n    \"An equivalent way, with a context manager:\\n\",\n    \"\\n\",\n    \"with np.errstate(divide='ignore'):\\n\",\n    \"    Z = np.ones(1) / 0\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 32. Is the following expressions true? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in sqrt\\n\",\n      \"  \\\"\\\"\\\"Entry point for launching an IPython kernel.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.sqrt(-1) == np.emath.sqrt(-1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"yesterday = np.datetime64('today', 'D') - np.timedelta64(1, 'D')\\n\",\n    \"today     = np.datetime64('today', 'D')\\n\",\n    \"tomorrow  = np.datetime64('today', 'D') + np.timedelta64(1, 'D')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['2016-07-01' '2016-07-02' '2016-07-03' '2016-07-04' '2016-07-05'\\n\",\n      \" '2016-07-06' '2016-07-07' '2016-07-08' '2016-07-09' '2016-07-10'\\n\",\n      \" '2016-07-11' '2016-07-12' '2016-07-13' '2016-07-14' '2016-07-15'\\n\",\n      \" '2016-07-16' '2016-07-17' '2016-07-18' '2016-07-19' '2016-07-20'\\n\",\n      \" '2016-07-21' '2016-07-22' '2016-07-23' '2016-07-24' '2016-07-25'\\n\",\n      \" '2016-07-26' '2016-07-27' '2016-07-28' '2016-07-29' '2016-07-30'\\n\",\n      \" '2016-07-31']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.arange('2016-07', '2016-08', dtype='datetime64[D]')\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 35. How to compute ((A+B)\\\\*(-A/2)) in place (without copy)? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-1.5, -1.5, -1.5])\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"A = np.ones(3)*1\\n\",\n    \"B = np.ones(3)*2\\n\",\n    \"C = np.ones(3)*3\\n\",\n    \"np.add(A,B,out=B)\\n\",\n    \"np.divide(A,2,out=A)\\n\",\n    \"np.negative(A,out=A)\\n\",\n    \"np.multiply(A,B,out=A)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 36. Extract the integer part of a random array using 5 different methods (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0. 9. 9. 7. 4. 5. 8. 7. 9. 6.]\\n\",\n      \"[0. 9. 9. 7. 4. 5. 8. 7. 9. 6.]\\n\",\n      \"[0. 9. 9. 7. 4. 5. 8. 7. 9. 6.]\\n\",\n      \"[0 9 9 7 4 5 8 7 9 6]\\n\",\n      \"[0. 9. 9. 7. 4. 5. 8. 7. 9. 6.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.random.uniform(0,10,10)\\n\",\n    \"\\n\",\n    \"print (Z - Z%1)\\n\",\n    \"print (np.floor(Z))\\n\",\n    \"print (np.ceil(Z)-1)\\n\",\n    \"print (Z.astype(int))\\n\",\n    \"print (np.trunc(Z))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0. 1. 2. 3. 4.]\\n\",\n      \" [0. 1. 2. 3. 4.]\\n\",\n      \" [0. 1. 2. 3. 4.]\\n\",\n      \" [0. 1. 2. 3. 4.]\\n\",\n      \" [0. 1. 2. 3. 4.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.zeros((5,5))\\n\",\n    \"Z += np.arange(5)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def generate():\\n\",\n    \"    for x in range(10):\\n\",\n    \"        yield x\\n\",\n    \"Z = np.fromiter(generate(),dtype=float,count=-1)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0.09090909 0.18181818 0.27272727 0.36363636 0.45454545 0.54545455\\n\",\n      \" 0.63636364 0.72727273 0.81818182 0.90909091]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.linspace(0,1,11,endpoint=False)[1:]\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 40. Create a random vector of size 10 and sort it (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0.10181103 0.26965266 0.28134397 0.39484134 0.41394065 0.4810561\\n\",\n      \" 0.62636945 0.74893964 0.88528826 0.97455845]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.random.random(10)\\n\",\n    \"Z.sort()\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 41. How to sum a small array faster than np.sum? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"45\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Author: Evgeni Burovski\\n\",\n    \"\\n\",\n    \"Z = np.arange(10)\\n\",\n    \"np.add.reduce(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 42. Consider two random array A and B, check if they are equal (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"False\\n\",\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"A = np.random.randint(0,2,5)\\n\",\n    \"B = np.random.randint(0,2,5)\\n\",\n    \"\\n\",\n    \"# Assuming identical shape of the arrays and a tolerance for the comparison of values\\n\",\n    \"equal = np.allclose(A,B)\\n\",\n    \"print(equal)\\n\",\n    \"\\n\",\n    \"# Checking both the shape and the element values, no tolerance (values have to be exactly equal)\\n\",\n    \"equal = np.array_equal(A,B)\\n\",\n    \"print(equal)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 43. Make an array immutable (read-only) (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"ValueError\",\n     \"evalue\": \"assignment destination is read-only\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mValueError\\u001b[0m                                Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-43-dcc5e7f145b5>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m      1\\u001b[0m \\u001b[0mZ\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mzeros\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;36m10\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      2\\u001b[0m \\u001b[0mZ\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mflags\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mwriteable\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[1;32mFalse\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 3\\u001b[1;33m \\u001b[0mZ\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m0\\u001b[0m\\u001b[1;33m]\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[1;36m1\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n      \"\\u001b[1;31mValueError\\u001b[0m: assignment destination is read-only\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.zeros(10)\\n\",\n    \"Z.flags.writeable = False\\n\",\n    \"Z[0] = 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0.90871086 0.46968371 0.33413258 0.71420399 0.59422443 1.05019902\\n\",\n      \" 0.83054677 0.79518205 0.86608232 1.03318804]\\n\",\n      \"[1.39381095 0.5465867  0.38210814 0.60935469 0.126408   0.87938679\\n\",\n      \" 1.28469889 1.51565796 0.02201551 0.36169143]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.random.random((10,2))\\n\",\n    \"X,Y = Z[:,0], Z[:,1]\\n\",\n    \"R = np.sqrt(X**2+Y**2)\\n\",\n    \"T = np.arctan2(Y,X)\\n\",\n    \"print(R)\\n\",\n    \"print(T)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0.50581524 0.25651855 0.01674091 0.88347224 0.73571427 0.44169975\\n\",\n      \" 0.65173587 0.84419501 0.         0.70272942]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.random.random(10)\\n\",\n    \"Z[Z.argmax()] = 0\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 46. Create a structured array with `x` and `y` coordinates covering the \\\\[0,1\\\\]x\\\\[0,1\\\\] area (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[(0.  , 0.  ) (0.25, 0.  ) (0.5 , 0.  ) (0.75, 0.  ) (1.  , 0.  )]\\n\",\n      \" [(0.  , 0.25) (0.25, 0.25) (0.5 , 0.25) (0.75, 0.25) (1.  , 0.25)]\\n\",\n      \" [(0.  , 0.5 ) (0.25, 0.5 ) (0.5 , 0.5 ) (0.75, 0.5 ) (1.  , 0.5 )]\\n\",\n      \" [(0.  , 0.75) (0.25, 0.75) (0.5 , 0.75) (0.75, 0.75) (1.  , 0.75)]\\n\",\n      \" [(0.  , 1.  ) (0.25, 1.  ) (0.5 , 1.  ) (0.75, 1.  ) (1.  , 1.  )]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.zeros((5,5), [('x',float),('y',float)])\\n\",\n    \"Z['x'], Z['y'] = np.meshgrid(np.linspace(0,1,5),\\n\",\n    \"                             np.linspace(0,1,5))\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"####  47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"3638.1636371179666\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Evgeni Burovski\\n\",\n    \"\\n\",\n    \"X = np.arange(8)\\n\",\n    \"Y = X + 0.5\\n\",\n    \"C = 1.0 / np.subtract.outer(X, Y)\\n\",\n    \"print(np.linalg.det(C))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-128\\n\",\n      \"127\\n\",\n      \"-2147483648\\n\",\n      \"2147483647\\n\",\n      \"-9223372036854775808\\n\",\n      \"9223372036854775807\\n\",\n      \"-3.4028235e+38\\n\",\n      \"3.4028235e+38\\n\",\n      \"1.1920929e-07\\n\",\n      \"-1.7976931348623157e+308\\n\",\n      \"1.7976931348623157e+308\\n\",\n      \"2.220446049250313e-16\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for dtype in [np.int8, np.int32, np.int64]:\\n\",\n    \"   print(np.iinfo(dtype).min)\\n\",\n    \"   print(np.iinfo(dtype).max)\\n\",\n    \"for dtype in [np.float32, np.float64]:\\n\",\n    \"   print(np.finfo(dtype).min)\\n\",\n    \"   print(np.finfo(dtype).max)\\n\",\n    \"   print(np.finfo(dtype).eps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 49. How to print all the values of an array? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.set_printoptions(threshold=np.nan)\\n\",\n    \"Z = np.zeros((16,16))\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"39\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.arange(100)\\n\",\n    \"v = np.random.uniform(0,100)\\n\",\n    \"index = (np.abs(Z-v)).argmin()\\n\",\n    \"print(Z[index])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))\\n\",\n      \" ((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))\\n\",\n      \" ((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))\\n\",\n      \" ((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))\\n\",\n      \" ((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.zeros(10, [ ('position', [ ('x', float, 1),\\n\",\n    \"                                  ('y', float, 1)]),\\n\",\n    \"                   ('color',    [ ('r', float, 1),\\n\",\n    \"                                  ('g', float, 1),\\n\",\n    \"                                  ('b', float, 1)])])\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0.         0.70858175 0.65599073 0.78907021 0.03919281 0.58217916\\n\",\n      \"  0.33731228 0.7468501  0.81072821 0.34085638]\\n\",\n      \" [0.70858175 0.         0.59344529 0.09512034 0.71317592 0.25956344\\n\",\n      \"  0.80803137 0.53153655 0.36521195 0.63714484]\\n\",\n      \" [0.65599073 0.59344529 0.         0.68051235 0.62827887 0.33895441\\n\",\n      \"  0.46987665 0.13920127 0.34746751 0.3316422 ]\\n\",\n      \" [0.78907021 0.09512034 0.68051235 0.         0.79617326 0.35163036\\n\",\n      \"  0.90195864 0.60906462 0.4231078  0.73188727]\\n\",\n      \" [0.03919281 0.71317592 0.62827887 0.79617326 0.         0.5731727\\n\",\n      \"  0.29812293 0.72361492 0.79714149 0.30801574]\\n\",\n      \" [0.58217916 0.25956344 0.33895441 0.35163036 0.5731727  0.\\n\",\n      \"  0.58754608 0.30460826 0.24114521 0.40949401]\\n\",\n      \" [0.33731228 0.80803137 0.46987665 0.90195864 0.29812293 0.58754608\\n\",\n      \"  0.         0.59944019 0.75065963 0.17852171]\\n\",\n      \" [0.7468501  0.53153655 0.13920127 0.60906462 0.72361492 0.30460826\\n\",\n      \"  0.59944019 0.         0.22570191 0.44636568]\\n\",\n      \" [0.81072821 0.36521195 0.34746751 0.4231078  0.79714149 0.24114521\\n\",\n      \"  0.75065963 0.22570191 0.         0.57714969]\\n\",\n      \" [0.34085638 0.63714484 0.3316422  0.73188727 0.30801574 0.40949401\\n\",\n      \"  0.17852171 0.44636568 0.57714969 0.        ]]\\n\",\n      \"[[0.         0.21528274 0.6433255  0.33437612 0.28776324 0.14819688\\n\",\n      \"  0.34321379 0.54524259 0.68307656 0.0830019 ]\\n\",\n      \" [0.21528274 0.         0.44712729 0.27403285 0.32591639 0.28524061\\n\",\n      \"  0.33478698 0.35128389 0.49450395 0.16691601]\\n\",\n      \" [0.6433255  0.44712729 0.         0.41503631 0.74613255 0.73233626\\n\",\n      \"  0.49409743 0.41928663 0.49963719 0.61255203]\\n\",\n      \" [0.33437612 0.27403285 0.41503631 0.         0.57318081 0.47403049\\n\",\n      \"  0.08658329 0.56115111 0.70031091 0.35760984]\\n\",\n      \" [0.28776324 0.32591639 0.74613255 0.57318081 0.         0.16110392\\n\",\n      \"  0.60777831 0.46777708 0.57012915 0.22441559]\\n\",\n      \" [0.14819688 0.28524061 0.73233626 0.47403049 0.16110392 0.\\n\",\n      \"  0.49018431 0.54358417 0.66733094 0.12437018]\\n\",\n      \" [0.34321379 0.33478698 0.49409743 0.08658329 0.60777831 0.49018431\\n\",\n      \"  0.         0.64241387 0.78297501 0.38468325]\\n\",\n      \" [0.54524259 0.35128389 0.41928663 0.56115111 0.46777708 0.54358417\\n\",\n      \"  0.64241387 0.         0.14386424 0.470974  ]\\n\",\n      \" [0.68307656 0.49450395 0.49963719 0.70031091 0.57012915 0.66733094\\n\",\n      \"  0.78297501 0.14386424 0.         0.60610333]\\n\",\n      \" [0.0830019  0.16691601 0.61255203 0.35760984 0.22441559 0.12437018\\n\",\n      \"  0.38468325 0.470974   0.60610333 0.        ]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.random.random((10,2))\\n\",\n    \"X,Y = np.atleast_2d(Z[:,0], Z[:,1])\\n\",\n    \"D = np.sqrt( (X-X.T)**2 + (Y-Y.T)**2)\\n\",\n    \"print(D)\\n\",\n    \"\\n\",\n    \"# Much faster with scipy\\n\",\n    \"import scipy\\n\",\n    \"# Thanks Gavin Heverly-Coulson (#issue 1)\\n\",\n    \"import scipy.spatial\\n\",\n    \"\\n\",\n    \"Z = np.random.random((10,2))\\n\",\n    \"D = scipy.spatial.distance.cdist(Z,Z)\\n\",\n    \"print(D)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 2 3 4 5 6 7 8 9]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.arange(10, dtype=np.float32)\\n\",\n    \"Z = Z.astype(np.int32, copy=False)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 54. How to read the following file? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1  2  3  4  5]\\n\",\n      \" [ 6 -1 -1  7  8]\\n\",\n      \" [-1 -1  9 10 11]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from io import StringIO\\n\",\n    \"\\n\",\n    \"# Fake file \\n\",\n    \"s = StringIO(\\\"\\\"\\\"1, 2, 3, 4, 5\\\\n\\n\",\n    \"                6,  ,  , 7, 8\\\\n\\n\",\n    \"                 ,  , 9,10,11\\\\n\\\"\\\"\\\")\\n\",\n    \"Z = np.genfromtxt(s, delimiter=\\\",\\\", dtype=np.int)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(0, 0) 0\\n\",\n      \"(0, 1) 1\\n\",\n      \"(0, 2) 2\\n\",\n      \"(1, 0) 3\\n\",\n      \"(1, 1) 4\\n\",\n      \"(1, 2) 5\\n\",\n      \"(2, 0) 6\\n\",\n      \"(2, 1) 7\\n\",\n      \"(2, 2) 8\\n\",\n      \"(0, 0) 0\\n\",\n      \"(0, 1) 1\\n\",\n      \"(0, 2) 2\\n\",\n      \"(1, 0) 3\\n\",\n      \"(1, 1) 4\\n\",\n      \"(1, 2) 5\\n\",\n      \"(2, 0) 6\\n\",\n      \"(2, 1) 7\\n\",\n      \"(2, 2) 8\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.arange(9).reshape(3,3)\\n\",\n    \"for index, value in np.ndenumerate(Z):\\n\",\n    \"    print(index, value)\\n\",\n    \"for index in np.ndindex(Z.shape):\\n\",\n    \"    print(index, Z[index])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 56. Generate a generic 2D Gaussian-like array (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0.36787944 0.44822088 0.51979489 0.57375342 0.60279818 0.60279818\\n\",\n      \"  0.57375342 0.51979489 0.44822088 0.36787944]\\n\",\n      \" [0.44822088 0.54610814 0.63331324 0.69905581 0.73444367 0.73444367\\n\",\n      \"  0.69905581 0.63331324 0.54610814 0.44822088]\\n\",\n      \" [0.51979489 0.63331324 0.73444367 0.81068432 0.85172308 0.85172308\\n\",\n      \"  0.81068432 0.73444367 0.63331324 0.51979489]\\n\",\n      \" [0.57375342 0.69905581 0.81068432 0.89483932 0.9401382  0.9401382\\n\",\n      \"  0.89483932 0.81068432 0.69905581 0.57375342]\\n\",\n      \" [0.60279818 0.73444367 0.85172308 0.9401382  0.98773022 0.98773022\\n\",\n      \"  0.9401382  0.85172308 0.73444367 0.60279818]\\n\",\n      \" [0.60279818 0.73444367 0.85172308 0.9401382  0.98773022 0.98773022\\n\",\n      \"  0.9401382  0.85172308 0.73444367 0.60279818]\\n\",\n      \" [0.57375342 0.69905581 0.81068432 0.89483932 0.9401382  0.9401382\\n\",\n      \"  0.89483932 0.81068432 0.69905581 0.57375342]\\n\",\n      \" [0.51979489 0.63331324 0.73444367 0.81068432 0.85172308 0.85172308\\n\",\n      \"  0.81068432 0.73444367 0.63331324 0.51979489]\\n\",\n      \" [0.44822088 0.54610814 0.63331324 0.69905581 0.73444367 0.73444367\\n\",\n      \"  0.69905581 0.63331324 0.54610814 0.44822088]\\n\",\n      \" [0.36787944 0.44822088 0.51979489 0.57375342 0.60279818 0.60279818\\n\",\n      \"  0.57375342 0.51979489 0.44822088 0.36787944]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))\\n\",\n    \"D = np.sqrt(X*X+Y*Y)\\n\",\n    \"sigma, mu = 1.0, 0.0\\n\",\n    \"G = np.exp(-( (D-mu)**2 / ( 2.0 * sigma**2 ) ) )\\n\",\n    \"print(G)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 57. How to randomly place p elements in a 2D array? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Divakar\\n\",\n    \"\\n\",\n    \"n = 10\\n\",\n    \"p = 3\\n\",\n    \"Z = np.zeros((n,n))\\n\",\n    \"np.put(Z, np.random.choice(range(n*n), p, replace=False),1)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 58. Subtract the mean of each row of a matrix (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.08728985  0.34574179 -0.07096404 -0.35665903  0.27195823 -0.21226241\\n\",\n      \"  -0.05786562  0.46226838 -0.51113477  0.04162763]\\n\",\n      \" [ 0.15769693 -0.00687416 -0.23088468 -0.27531393 -0.15748372  0.49855641\\n\",\n      \"  -0.29182386  0.23153223  0.1335274  -0.05893261]\\n\",\n      \" [-0.19887161 -0.08551958  0.13483777  0.4249683   0.32140219 -0.01568819\\n\",\n      \"  -0.25863495 -0.19687856  0.0745396  -0.20015498]\\n\",\n      \" [ 0.05544274  0.23450517 -0.25309467  0.00991447 -0.50428326 -0.09024474\\n\",\n      \"   0.01610101  0.4324277   0.13499273 -0.03576114]\\n\",\n      \" [ 0.08014561  0.44307603 -0.54198293 -0.15386254  0.377373    0.24122398\\n\",\n      \"  -0.4620369  -0.03100136  0.21078297 -0.16371787]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Warren Weckesser\\n\",\n    \"\\n\",\n    \"X = np.random.rand(5, 10)\\n\",\n    \"\\n\",\n    \"# Recent versions of numpy\\n\",\n    \"Y = X - X.mean(axis=1, keepdims=True)\\n\",\n    \"\\n\",\n    \"# Older versions of numpy\\n\",\n    \"Y = X - X.mean(axis=1).reshape(-1, 1)\\n\",\n    \"\\n\",\n    \"print(Y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 59. How to sort an array by the nth column? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2 9 2]\\n\",\n      \" [5 7 9]\\n\",\n      \" [0 8 1]]\\n\",\n      \"[[5 7 9]\\n\",\n      \" [0 8 1]\\n\",\n      \" [2 9 2]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Steve Tjoa\\n\",\n    \"\\n\",\n    \"Z = np.random.randint(0,10,(3,3))\\n\",\n    \"print(Z)\\n\",\n    \"print(Z[Z[:,1].argsort()])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 60. How to tell if a given 2D array has null columns? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Warren Weckesser\\n\",\n    \"\\n\",\n    \"Z = np.random.randint(0,3,(3,10))\\n\",\n    \"print((~Z.any(axis=0)).any())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 61. Find the nearest value from a given value in an array (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.3388283390062511\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.random.uniform(0,1,10)\\n\",\n    \"z = 0.5\\n\",\n    \"m = Z.flat[np.abs(Z - z).argmin()]\\n\",\n    \"print(m)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1 2]\\n\",\n      \" [1 2 3]\\n\",\n      \" [2 3 4]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"A = np.arange(3).reshape(3,1)\\n\",\n    \"B = np.arange(3).reshape(1,3)\\n\",\n    \"it = np.nditer([A,B,None])\\n\",\n    \"for x,y,z in it: z[...] = x + y\\n\",\n    \"print(it.operands[2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 63. Create an array class that has a name attribute (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"range_10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class NamedArray(np.ndarray):\\n\",\n    \"    def __new__(cls, array, name=\\\"no name\\\"):\\n\",\n    \"        obj = np.asarray(array).view(cls)\\n\",\n    \"        obj.name = name\\n\",\n    \"        return obj\\n\",\n    \"    def __array_finalize__(self, obj):\\n\",\n    \"        if obj is None: return\\n\",\n    \"        self.info = getattr(obj, 'name', \\\"no name\\\")\\n\",\n    \"\\n\",\n    \"Z = NamedArray(np.arange(10), \\\"range_10\\\")\\n\",\n    \"print (Z.name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2. 5. 4. 1. 1. 4. 3. 2. 5. 3.]\\n\",\n      \"[3. 9. 7. 1. 1. 7. 5. 3. 9. 5.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Brett Olsen\\n\",\n    \"\\n\",\n    \"Z = np.ones(10)\\n\",\n    \"I = np.random.randint(0,len(Z),20)\\n\",\n    \"Z += np.bincount(I, minlength=len(Z))\\n\",\n    \"print(Z)\\n\",\n    \"\\n\",\n    \"# Another solution\\n\",\n    \"# Author: Bartosz Telenczuk\\n\",\n    \"np.add.at(Z, I, 1)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0. 7. 0. 6. 5. 0. 0. 0. 0. 3.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Alan G Isaac\\n\",\n    \"\\n\",\n    \"X = [1,2,3,4,5,6]\\n\",\n    \"I = [1,3,9,3,4,1]\\n\",\n    \"F = np.bincount(I,X)\\n\",\n    \"print(F)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"8\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Nadav Horesh\\n\",\n    \"\\n\",\n    \"w,h = 16,16\\n\",\n    \"I = np.random.randint(0,2,(h,w,3)).astype(np.ubyte)\\n\",\n    \"#Note that we should compute 256*256 first. \\n\",\n    \"#Otherwise numpy will only promote F.dtype to 'uint16' and overfolw will occur\\n\",\n    \"F = I[...,0]*(256*256) + I[...,1]*256 +I[...,2]\\n\",\n    \"n = len(np.unique(F))\\n\",\n    \"print(n)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[67 54 59 65]\\n\",\n      \" [71 59 49 46]\\n\",\n      \" [40 46 48 44]]\\n\",\n      \"[[67 54 59 65]\\n\",\n      \" [71 59 49 46]\\n\",\n      \" [40 46 48 44]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"A = np.random.randint(0,10,(3,4,3,4))\\n\",\n    \"# solution by passing a tuple of axes (introduced in numpy 1.7.0)\\n\",\n    \"sum = A.sum(axis=(-2,-1))\\n\",\n    \"print(sum)\\n\",\n    \"# solution by flattening the last two dimensions into one\\n\",\n    \"# (useful for functions that don't accept tuples for axis argument)\\n\",\n    \"sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1)\\n\",\n    \"print(sum)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset  indices? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0.36435063 0.49399621 0.41187814 0.55834713 0.50337821 0.59980105\\n\",\n      \" 0.54636651 0.62150694 0.46052147 0.55536347]\\n\",\n      \"0    0.364351\\n\",\n      \"1    0.493996\\n\",\n      \"2    0.411878\\n\",\n      \"3    0.558347\\n\",\n      \"4    0.503378\\n\",\n      \"5    0.599801\\n\",\n      \"6    0.546367\\n\",\n      \"7    0.621507\\n\",\n      \"8    0.460521\\n\",\n      \"9    0.555363\\n\",\n      \"dtype: float64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Jaime Fernández del Río\\n\",\n    \"\\n\",\n    \"D = np.random.uniform(0,1,100)\\n\",\n    \"S = np.random.randint(0,10,100)\\n\",\n    \"D_sums = np.bincount(S, weights=D)\\n\",\n    \"D_counts = np.bincount(S)\\n\",\n    \"D_means = D_sums / D_counts\\n\",\n    \"print(D_means)\\n\",\n    \"\\n\",\n    \"# Pandas solution as a reference due to more intuitive code\\n\",\n    \"import pandas as pd\\n\",\n    \"print(pd.Series(D).groupby(S).mean())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 69. How to get the diagonal of a dot product? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1.43740433, 1.30533245, 1.32379824, 2.46896817, 0.67545867])\"\n      ]\n     },\n     \"execution_count\": 69,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Author: Mathieu Blondel\\n\",\n    \"\\n\",\n    \"A = np.random.uniform(0,1,(5,5))\\n\",\n    \"B = np.random.uniform(0,1,(5,5))\\n\",\n    \"\\n\",\n    \"# Slow version  \\n\",\n    \"np.diag(np.dot(A, B))\\n\",\n    \"\\n\",\n    \"# Fast version\\n\",\n    \"np.sum(A * B.T, axis=1)\\n\",\n    \"\\n\",\n    \"# Faster version\\n\",\n    \"np.einsum(\\\"ij,ji->i\\\", A, B)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 70. Consider the vector \\\\[1, 2, 3, 4, 5\\\\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1. 0. 0. 0. 2. 0. 0. 0. 3. 0. 0. 0. 4. 0. 0. 0. 5.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Warren Weckesser\\n\",\n    \"\\n\",\n    \"Z = np.array([1,2,3,4,5])\\n\",\n    \"nz = 3\\n\",\n    \"Z0 = np.zeros(len(Z) + (len(Z)-1)*(nz))\\n\",\n    \"Z0[::nz+1] = Z\\n\",\n    \"print(Z0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[[2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]]\\n\",\n      \"\\n\",\n      \" [[2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]]\\n\",\n      \"\\n\",\n      \" [[2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]]\\n\",\n      \"\\n\",\n      \" [[2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]]\\n\",\n      \"\\n\",\n      \" [[2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]\\n\",\n      \"  [2. 2. 2.]]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"A = np.ones((5,5,3))\\n\",\n    \"B = 2*np.ones((5,5))\\n\",\n    \"print(A * B[:,:,None])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 72. How to swap two rows of an array? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 5  6  7  8  9]\\n\",\n      \" [ 0  1  2  3  4]\\n\",\n      \" [10 11 12 13 14]\\n\",\n      \" [15 16 17 18 19]\\n\",\n      \" [20 21 22 23 24]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Eelco Hoogendoorn\\n\",\n    \"\\n\",\n    \"A = np.arange(25).reshape(5,5)\\n\",\n    \"A[[0,1]] = A[[1,0]]\\n\",\n    \"print(A)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the  triangles (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[( 0,  9) ( 0, 11) ( 0, 59) ( 0, 91) ( 1,  8) ( 1, 47) ( 1, 69) ( 1, 82)\\n\",\n      \" ( 3, 40) ( 3, 97) ( 5,  9) ( 5, 73) ( 8, 47) ( 9, 11) ( 9, 73) (13, 53)\\n\",\n      \" (13, 56) (13, 58) (13, 99) (16, 62) (16, 82) (28, 84) (28, 87) (40, 97)\\n\",\n      \" (53, 99) (56, 58) (59, 91) (62, 82) (69, 82) (84, 87)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Nicolas P. Rougier\\n\",\n    \"\\n\",\n    \"faces = np.random.randint(0,100,(10,3))\\n\",\n    \"F = np.roll(faces.repeat(2,axis=1),-1,axis=1)\\n\",\n    \"F = F.reshape(len(F)*3,2)\\n\",\n    \"F = np.sort(F,axis=1)\\n\",\n    \"G = F.view( dtype=[('p0',F.dtype),('p1',F.dtype)] )\\n\",\n    \"G = np.unique(G)\\n\",\n    \"print(G)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 1 2 3 4 4 6]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Jaime Fernández del Río\\n\",\n    \"\\n\",\n    \"C = np.bincount([1,1,2,3,4,4,6])\\n\",\n    \"A = np.repeat(np.arange(len(C)), C)\\n\",\n    \"print(A)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 75. How to compute averages using a sliding window over an array? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Jaime Fernández del Río\\n\",\n    \"\\n\",\n    \"def moving_average(a, n=3) :\\n\",\n    \"    ret = np.cumsum(a, dtype=float)\\n\",\n    \"    ret[n:] = ret[n:] - ret[:-n]\\n\",\n    \"    return ret[n - 1:] / n\\n\",\n    \"Z = np.arange(20)\\n\",\n    \"print(moving_average(Z, n=3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\\\\[0\\\\],Z\\\\[1\\\\],Z\\\\[2\\\\]) and each subsequent row is  shifted by 1 (last row should be (Z\\\\[-3\\\\],Z\\\\[-2\\\\],Z\\\\[-1\\\\]) (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1 2]\\n\",\n      \" [1 2 3]\\n\",\n      \" [2 3 4]\\n\",\n      \" [3 4 5]\\n\",\n      \" [4 5 6]\\n\",\n      \" [5 6 7]\\n\",\n      \" [6 7 8]\\n\",\n      \" [7 8 9]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Joe Kington / Erik Rigtorp\\n\",\n    \"from numpy.lib import stride_tricks\\n\",\n    \"\\n\",\n    \"def rolling(a, window):\\n\",\n    \"    shape = (a.size - window + 1, window)\\n\",\n    \"    strides = (a.itemsize, a.itemsize)\\n\",\n    \"    return stride_tricks.as_strided(a, shape=shape, strides=strides)\\n\",\n    \"Z = rolling(np.arange(10), 3)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0.61458663, -0.83229834,  0.15687329,  0.86684934, -0.80418945,\\n\",\n       \"       -0.94400753,  0.68204938, -0.40034997,  0.56881214,  0.60763882,\\n\",\n       \"       -0.41558119, -0.96786492, -0.29382729, -0.43597838,  0.46867397,\\n\",\n       \"       -0.73113098, -0.31367014,  0.50218112, -0.18159377, -0.03321331,\\n\",\n       \"        0.93574029,  0.95764592,  0.83438669,  0.93493816,  0.27413529,\\n\",\n       \"        0.79686667,  0.21453622, -0.34477178, -0.90442065, -0.28982899,\\n\",\n       \"       -0.22376251,  0.7821683 ,  0.80871286,  0.09547893,  0.65898887,\\n\",\n       \"       -0.50329235,  0.70567483,  0.7260582 ,  0.29526807, -0.61692596,\\n\",\n       \"       -0.06398481,  0.38447694, -0.20192775, -0.776956  , -0.86298302,\\n\",\n       \"        0.62069504,  0.7156818 , -0.16731062, -0.67338785,  0.9475525 ,\\n\",\n       \"        0.78363151, -0.68028943,  0.37714017,  0.99798309,  0.09080988,\\n\",\n       \"        0.22431419,  0.44528906,  0.27134833, -0.17727835, -0.64134425,\\n\",\n       \"       -0.26404034, -0.10714442,  0.04077127,  0.76593247, -0.26830685,\\n\",\n       \"        0.81933781, -0.14372722, -0.78525057, -0.03657832, -0.0838801 ,\\n\",\n       \"       -0.9998223 , -0.57809221,  0.62441487, -0.91798214, -0.20347455,\\n\",\n       \"        0.89401068, -0.36615874,  0.35508649, -0.988561  , -0.42155252,\\n\",\n       \"       -0.79222461, -0.38832939,  0.65991297,  0.09981309, -0.26838381,\\n\",\n       \"       -0.61731762,  0.470328  ,  0.36521011, -0.04780786,  0.52860473,\\n\",\n       \"        0.44800137,  0.18834723, -0.90079054, -0.03385857,  0.72054287,\\n\",\n       \"        0.98253035, -0.93777418,  0.86997239,  0.98122332, -0.4601323 ])\"\n      ]\n     },\n     \"execution_count\": 77,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Author: Nathaniel J. Smith\\n\",\n    \"\\n\",\n    \"Z = np.random.randint(0,2,100)\\n\",\n    \"np.logical_not(Z, out=Z)\\n\",\n    \"\\n\",\n    \"Z = np.random.uniform(-1.0,1.0,100)\\n\",\n    \"np.negative(Z, out=Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i  (P0\\\\[i\\\\],P1\\\\[i\\\\])? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[3.70672797 7.69612039 2.25784974 4.60168732 1.86333395 0.34324433\\n\",\n      \" 0.81905363 1.00428102 4.4882624  5.52240104]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def distance(P0, P1, p):\\n\",\n    \"    T = P1 - P0\\n\",\n    \"    L = (T**2).sum(axis=1)\\n\",\n    \"    U = -((P0[:,0]-p[...,0])*T[:,0] + (P0[:,1]-p[...,1])*T[:,1]) / L\\n\",\n    \"    U = U.reshape(len(U),1)\\n\",\n    \"    D = P0 + U*T - p\\n\",\n    \"    return np.sqrt((D**2).sum(axis=1))\\n\",\n    \"\\n\",\n    \"P0 = np.random.uniform(-10,10,(10,2))\\n\",\n    \"P1 = np.random.uniform(-10,10,(10,2))\\n\",\n    \"p  = np.random.uniform(-10,10,( 1,2))\\n\",\n    \"print(distance(P0, P1, p))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\\\\[j\\\\]) to each line i (P0\\\\[i\\\\],P1\\\\[i\\\\])? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 3.51631169  6.42167595  4.90471349  8.92152608  0.64430522 13.1477316\\n\",\n      \"   4.82647115 14.35803776  2.83869333  5.78536237]\\n\",\n      \" [11.72333193  2.28525701  4.48035411 11.29872486 14.10963053  4.49070061\\n\",\n      \"   6.06173032  5.24835195  3.32272461 11.12005469]\\n\",\n      \" [ 7.68423685  3.99209265 12.25140797  4.74391157  4.67770228  5.40024792\\n\",\n      \"   5.31689533  8.63226702  7.63625936  2.4274878 ]\\n\",\n      \" [ 4.72217595  8.25441437  1.85343282  1.41186727  8.5013649   8.04308066\\n\",\n      \"   3.56471625  7.66668404  3.79347479  1.17033352]\\n\",\n      \" [ 4.61970509  1.82880232  0.88155845  3.87855982  7.49446718  1.13451433\\n\",\n      \"   3.69653461  1.60058913  2.96229578  5.89123061]\\n\",\n      \" [ 1.71064014  2.13236159  2.87428566  0.42743983  4.85508373  3.972235\\n\",\n      \"   2.17049563  4.87642258  2.29807957  3.20458539]\\n\",\n      \" [ 1.10951331  3.52006522  2.73748642  1.39980831  4.49782435  6.00139265\\n\",\n      \"   0.34929072  6.82585741  0.76526847  1.17624526]\\n\",\n      \" [ 0.56577927  0.63802769  4.85499764  1.49919907  3.40600949  1.94224115\\n\",\n      \"   4.85430884  3.39018238  5.08047535  5.36034047]\\n\",\n      \" [ 4.08155314  4.50758583  9.84132717  0.35258316  1.4288174   1.73395701\\n\",\n      \"   7.36540087  4.4413348   8.60583634  5.90988377]\\n\",\n      \" [ 7.15223448 10.7222427   4.64476979  0.81731108 11.09257472  8.63596863\\n\",\n      \"   5.38289103  7.54390954  6.10500134  1.95186145]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Italmassov Kuanysh\\n\",\n    \"\\n\",\n    \"# based on distance function from previous question\\n\",\n    \"P0 = np.random.uniform(-10, 10, (10,2))\\n\",\n    \"P1 = np.random.uniform(-10,10,(10,2))\\n\",\n    \"p = np.random.uniform(-10, 10, (10,2))\\n\",\n    \"print(np.array([distance(P0,P1,p_i) for p_i in p]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[4 3 2 8 2 6 7 3 0 8]\\n\",\n      \" [8 9 7 6 1 5 2 4 6 1]\\n\",\n      \" [1 6 2 0 2 4 0 3 4 8]\\n\",\n      \" [8 0 8 0 8 4 6 3 3 1]\\n\",\n      \" [6 0 4 9 5 3 0 5 3 0]\\n\",\n      \" [6 7 5 4 3 7 9 0 9 6]\\n\",\n      \" [7 1 8 4 1 9 0 8 8 0]\\n\",\n      \" [7 8 4 9 4 0 0 6 5 7]\\n\",\n      \" [9 3 5 8 7 2 6 5 5 7]\\n\",\n      \" [7 9 6 7 8 7 7 8 8 7]]\\n\",\n      \"[[0 0 0 0 0]\\n\",\n      \" [0 4 3 2 8]\\n\",\n      \" [0 8 9 7 6]\\n\",\n      \" [0 1 6 2 0]\\n\",\n      \" [0 8 0 8 0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Nicolas Rougier\\n\",\n    \"\\n\",\n    \"Z = np.random.randint(0,10,(10,10))\\n\",\n    \"shape = (5,5)\\n\",\n    \"fill  = 0\\n\",\n    \"position = (1,1)\\n\",\n    \"\\n\",\n    \"R = np.ones(shape, dtype=Z.dtype)*fill\\n\",\n    \"P  = np.array(list(position)).astype(int)\\n\",\n    \"Rs = np.array(list(R.shape)).astype(int)\\n\",\n    \"Zs = np.array(list(Z.shape)).astype(int)\\n\",\n    \"\\n\",\n    \"R_start = np.zeros((len(shape),)).astype(int)\\n\",\n    \"R_stop  = np.array(list(shape)).astype(int)\\n\",\n    \"Z_start = (P-Rs//2)\\n\",\n    \"Z_stop  = (P+Rs//2)+Rs%2\\n\",\n    \"\\n\",\n    \"R_start = (R_start - np.minimum(Z_start,0)).tolist()\\n\",\n    \"Z_start = (np.maximum(Z_start,0)).tolist()\\n\",\n    \"R_stop = np.maximum(R_start, (R_stop - np.maximum(Z_stop-Zs,0))).tolist()\\n\",\n    \"Z_stop = (np.minimum(Z_stop,Zs)).tolist()\\n\",\n    \"\\n\",\n    \"r = [slice(start,stop) for start,stop in zip(R_start,R_stop)]\\n\",\n    \"z = [slice(start,stop) for start,stop in zip(Z_start,Z_stop)]\\n\",\n    \"R[r] = Z[z]\\n\",\n    \"print(Z)\\n\",\n    \"print(R)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 81. Consider an array Z = \\\\[1,2,3,4,5,6,7,8,9,10,11,12,13,14\\\\], how to generate an array R = \\\\[\\\\[1,2,3,4\\\\], \\\\[2,3,4,5\\\\], \\\\[3,4,5,6\\\\], ..., \\\\[11,12,13,14\\\\]\\\\]? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1  2  3  4]\\n\",\n      \" [ 2  3  4  5]\\n\",\n      \" [ 3  4  5  6]\\n\",\n      \" [ 4  5  6  7]\\n\",\n      \" [ 5  6  7  8]\\n\",\n      \" [ 6  7  8  9]\\n\",\n      \" [ 7  8  9 10]\\n\",\n      \" [ 8  9 10 11]\\n\",\n      \" [ 9 10 11 12]\\n\",\n      \" [10 11 12 13]\\n\",\n      \" [11 12 13 14]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Stefan van der Walt\\n\",\n    \"\\n\",\n    \"Z = np.arange(1,15,dtype=np.uint32)\\n\",\n    \"R = stride_tricks.as_strided(Z,(11,4),(4,4))\\n\",\n    \"print(R)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 82. Compute a matrix rank (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Stefan van der Walt\\n\",\n    \"\\n\",\n    \"Z = np.random.uniform(0,1,(10,10))\\n\",\n    \"U, S, V = np.linalg.svd(Z) # Singular Value Decomposition\\n\",\n    \"rank = np.sum(S > 1e-10)\\n\",\n    \"print(rank)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 83. How to find the most frequent value in an array?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.random.randint(0,10,50)\\n\",\n    \"print(np.bincount(Z).argmax())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[[[0 0 2]\\n\",\n      \"   [4 3 4]\\n\",\n      \"   [4 2 4]]\\n\",\n      \"\\n\",\n      \"  [[0 2 0]\\n\",\n      \"   [3 4 0]\\n\",\n      \"   [2 4 3]]\\n\",\n      \"\\n\",\n      \"  [[2 0 1]\\n\",\n      \"   [4 0 0]\\n\",\n      \"   [4 3 3]]\\n\",\n      \"\\n\",\n      \"  [[0 1 1]\\n\",\n      \"   [0 0 0]\\n\",\n      \"   [3 3 2]]\\n\",\n      \"\\n\",\n      \"  [[1 1 0]\\n\",\n      \"   [0 0 1]\\n\",\n      \"   [3 2 4]]\\n\",\n      \"\\n\",\n      \"  [[1 0 4]\\n\",\n      \"   [0 1 3]\\n\",\n      \"   [2 4 1]]\\n\",\n      \"\\n\",\n      \"  [[0 4 2]\\n\",\n      \"   [1 3 4]\\n\",\n      \"   [4 1 1]]\\n\",\n      \"\\n\",\n      \"  [[4 2 2]\\n\",\n      \"   [3 4 4]\\n\",\n      \"   [1 1 3]]]\\n\",\n      \"\\n\",\n      \"\\n\",\n      \" [[[4 3 4]\\n\",\n      \"   [4 2 4]\\n\",\n      \"   [3 0 3]]\\n\",\n      \"\\n\",\n      \"  [[3 4 0]\\n\",\n      \"   [2 4 3]\\n\",\n      \"   [0 3 0]]\\n\",\n      \"\\n\",\n      \"  [[4 0 0]\\n\",\n      \"   [4 3 3]\\n\",\n      \"   [3 0 3]]\\n\",\n      \"\\n\",\n      \"  [[0 0 0]\\n\",\n      \"   [3 3 2]\\n\",\n      \"   [0 3 4]]\\n\",\n      \"\\n\",\n      \"  [[0 0 1]\\n\",\n      \"   [3 2 4]\\n\",\n      \"   [3 4 2]]\\n\",\n      \"\\n\",\n      \"  [[0 1 3]\\n\",\n      \"   [2 4 1]\\n\",\n      \"   [4 2 2]]\\n\",\n      \"\\n\",\n      \"  [[1 3 4]\\n\",\n      \"   [4 1 1]\\n\",\n      \"   [2 2 0]]\\n\",\n      \"\\n\",\n      \"  [[3 4 4]\\n\",\n      \"   [1 1 3]\\n\",\n      \"   [2 0 2]]]\\n\",\n      \"\\n\",\n      \"\\n\",\n      \" [[[4 2 4]\\n\",\n      \"   [3 0 3]\\n\",\n      \"   [2 4 3]]\\n\",\n      \"\\n\",\n      \"  [[2 4 3]\\n\",\n      \"   [0 3 0]\\n\",\n      \"   [4 3 0]]\\n\",\n      \"\\n\",\n      \"  [[4 3 3]\\n\",\n      \"   [3 0 3]\\n\",\n      \"   [3 0 3]]\\n\",\n      \"\\n\",\n      \"  [[3 3 2]\\n\",\n      \"   [0 3 4]\\n\",\n      \"   [0 3 3]]\\n\",\n      \"\\n\",\n      \"  [[3 2 4]\\n\",\n      \"   [3 4 2]\\n\",\n      \"   [3 3 1]]\\n\",\n      \"\\n\",\n      \"  [[2 4 1]\\n\",\n      \"   [4 2 2]\\n\",\n      \"   [3 1 2]]\\n\",\n      \"\\n\",\n      \"  [[4 1 1]\\n\",\n      \"   [2 2 0]\\n\",\n      \"   [1 2 1]]\\n\",\n      \"\\n\",\n      \"  [[1 1 3]\\n\",\n      \"   [2 0 2]\\n\",\n      \"   [2 1 3]]]\\n\",\n      \"\\n\",\n      \"\\n\",\n      \" [[[3 0 3]\\n\",\n      \"   [2 4 3]\\n\",\n      \"   [3 0 4]]\\n\",\n      \"\\n\",\n      \"  [[0 3 0]\\n\",\n      \"   [4 3 0]\\n\",\n      \"   [0 4 1]]\\n\",\n      \"\\n\",\n      \"  [[3 0 3]\\n\",\n      \"   [3 0 3]\\n\",\n      \"   [4 1 1]]\\n\",\n      \"\\n\",\n      \"  [[0 3 4]\\n\",\n      \"   [0 3 3]\\n\",\n      \"   [1 1 4]]\\n\",\n      \"\\n\",\n      \"  [[3 4 2]\\n\",\n      \"   [3 3 1]\\n\",\n      \"   [1 4 1]]\\n\",\n      \"\\n\",\n      \"  [[4 2 2]\\n\",\n      \"   [3 1 2]\\n\",\n      \"   [4 1 3]]\\n\",\n      \"\\n\",\n      \"  [[2 2 0]\\n\",\n      \"   [1 2 1]\\n\",\n      \"   [1 3 0]]\\n\",\n      \"\\n\",\n      \"  [[2 0 2]\\n\",\n      \"   [2 1 3]\\n\",\n      \"   [3 0 1]]]\\n\",\n      \"\\n\",\n      \"\\n\",\n      \" [[[2 4 3]\\n\",\n      \"   [3 0 4]\\n\",\n      \"   [0 2 2]]\\n\",\n      \"\\n\",\n      \"  [[4 3 0]\\n\",\n      \"   [0 4 1]\\n\",\n      \"   [2 2 0]]\\n\",\n      \"\\n\",\n      \"  [[3 0 3]\\n\",\n      \"   [4 1 1]\\n\",\n      \"   [2 0 4]]\\n\",\n      \"\\n\",\n      \"  [[0 3 3]\\n\",\n      \"   [1 1 4]\\n\",\n      \"   [0 4 3]]\\n\",\n      \"\\n\",\n      \"  [[3 3 1]\\n\",\n      \"   [1 4 1]\\n\",\n      \"   [4 3 4]]\\n\",\n      \"\\n\",\n      \"  [[3 1 2]\\n\",\n      \"   [4 1 3]\\n\",\n      \"   [3 4 0]]\\n\",\n      \"\\n\",\n      \"  [[1 2 1]\\n\",\n      \"   [1 3 0]\\n\",\n      \"   [4 0 2]]\\n\",\n      \"\\n\",\n      \"  [[2 1 3]\\n\",\n      \"   [3 0 1]\\n\",\n      \"   [0 2 1]]]\\n\",\n      \"\\n\",\n      \"\\n\",\n      \" [[[3 0 4]\\n\",\n      \"   [0 2 2]\\n\",\n      \"   [1 3 4]]\\n\",\n      \"\\n\",\n      \"  [[0 4 1]\\n\",\n      \"   [2 2 0]\\n\",\n      \"   [3 4 1]]\\n\",\n      \"\\n\",\n      \"  [[4 1 1]\\n\",\n      \"   [2 0 4]\\n\",\n      \"   [4 1 0]]\\n\",\n      \"\\n\",\n      \"  [[1 1 4]\\n\",\n      \"   [0 4 3]\\n\",\n      \"   [1 0 1]]\\n\",\n      \"\\n\",\n      \"  [[1 4 1]\\n\",\n      \"   [4 3 4]\\n\",\n      \"   [0 1 0]]\\n\",\n      \"\\n\",\n      \"  [[4 1 3]\\n\",\n      \"   [3 4 0]\\n\",\n      \"   [1 0 1]]\\n\",\n      \"\\n\",\n      \"  [[1 3 0]\\n\",\n      \"   [4 0 2]\\n\",\n      \"   [0 1 4]]\\n\",\n      \"\\n\",\n      \"  [[3 0 1]\\n\",\n      \"   [0 2 1]\\n\",\n      \"   [1 4 1]]]\\n\",\n      \"\\n\",\n      \"\\n\",\n      \" [[[0 2 2]\\n\",\n      \"   [1 3 4]\\n\",\n      \"   [2 0 1]]\\n\",\n      \"\\n\",\n      \"  [[2 2 0]\\n\",\n      \"   [3 4 1]\\n\",\n      \"   [0 1 2]]\\n\",\n      \"\\n\",\n      \"  [[2 0 4]\\n\",\n      \"   [4 1 0]\\n\",\n      \"   [1 2 2]]\\n\",\n      \"\\n\",\n      \"  [[0 4 3]\\n\",\n      \"   [1 0 1]\\n\",\n      \"   [2 2 0]]\\n\",\n      \"\\n\",\n      \"  [[4 3 4]\\n\",\n      \"   [0 1 0]\\n\",\n      \"   [2 0 0]]\\n\",\n      \"\\n\",\n      \"  [[3 4 0]\\n\",\n      \"   [1 0 1]\\n\",\n      \"   [0 0 4]]\\n\",\n      \"\\n\",\n      \"  [[4 0 2]\\n\",\n      \"   [0 1 4]\\n\",\n      \"   [0 4 0]]\\n\",\n      \"\\n\",\n      \"  [[0 2 1]\\n\",\n      \"   [1 4 1]\\n\",\n      \"   [4 0 1]]]\\n\",\n      \"\\n\",\n      \"\\n\",\n      \" [[[1 3 4]\\n\",\n      \"   [2 0 1]\\n\",\n      \"   [2 4 2]]\\n\",\n      \"\\n\",\n      \"  [[3 4 1]\\n\",\n      \"   [0 1 2]\\n\",\n      \"   [4 2 3]]\\n\",\n      \"\\n\",\n      \"  [[4 1 0]\\n\",\n      \"   [1 2 2]\\n\",\n      \"   [2 3 1]]\\n\",\n      \"\\n\",\n      \"  [[1 0 1]\\n\",\n      \"   [2 2 0]\\n\",\n      \"   [3 1 4]]\\n\",\n      \"\\n\",\n      \"  [[0 1 0]\\n\",\n      \"   [2 0 0]\\n\",\n      \"   [1 4 2]]\\n\",\n      \"\\n\",\n      \"  [[1 0 1]\\n\",\n      \"   [0 0 4]\\n\",\n      \"   [4 2 0]]\\n\",\n      \"\\n\",\n      \"  [[0 1 4]\\n\",\n      \"   [0 4 0]\\n\",\n      \"   [2 0 3]]\\n\",\n      \"\\n\",\n      \"  [[1 4 1]\\n\",\n      \"   [4 0 1]\\n\",\n      \"   [0 3 3]]]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Chris Barker\\n\",\n    \"\\n\",\n    \"Z = np.random.randint(0,5,(10,10))\\n\",\n    \"n = 3\\n\",\n    \"i = 1 + (Z.shape[0]-3)\\n\",\n    \"j = 1 + (Z.shape[1]-3)\\n\",\n    \"C = stride_tricks.as_strided(Z, shape=(i, j, n, n), strides=Z.strides + Z.strides)\\n\",\n    \"print(C)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 85. Create a 2D array subclass such that Z\\\\[i,j\\\\] == Z\\\\[j,i\\\\] (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1 16  6  5  6]\\n\",\n      \" [16  5 13 16 12]\\n\",\n      \" [ 6 13  4 42  2]\\n\",\n      \" [ 5 16 42  2 13]\\n\",\n      \" [ 6 12  2 13  3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Eric O. Lebigot\\n\",\n    \"# Note: only works for 2d array and value setting using indices\\n\",\n    \"\\n\",\n    \"class Symetric(np.ndarray):\\n\",\n    \"    def __setitem__(self, index, value):\\n\",\n    \"        i,j = index\\n\",\n    \"        super(Symetric, self).__setitem__((i,j), value)\\n\",\n    \"        super(Symetric, self).__setitem__((j,i), value)\\n\",\n    \"\\n\",\n    \"def symetric(Z):\\n\",\n    \"    return np.asarray(Z + Z.T - np.diag(Z.diagonal())).view(Symetric)\\n\",\n    \"\\n\",\n    \"S = symetric(np.random.randint(0,10,(5,5)))\\n\",\n    \"S[2,3] = 42\\n\",\n    \"print(S)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]\\n\",\n      \" [200.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Stefan van der Walt\\n\",\n    \"\\n\",\n    \"p, n = 10, 20\\n\",\n    \"M = np.ones((p,n,n))\\n\",\n    \"V = np.ones((p,n,1))\\n\",\n    \"S = np.tensordot(M, V, axes=[[0, 2], [0, 1]])\\n\",\n    \"print(S)\\n\",\n    \"\\n\",\n    \"# It works, because:\\n\",\n    \"# M is (p,n,n)\\n\",\n    \"# V is (p,n,1)\\n\",\n    \"# Thus, summing over the paired axes 0 and 0 (of M and V independently),\\n\",\n    \"# and 2 and 1, to remain with a (n,1) vector.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[16. 16. 16. 16.]\\n\",\n      \" [16. 16. 16. 16.]\\n\",\n      \" [16. 16. 16. 16.]\\n\",\n      \" [16. 16. 16. 16.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Robert Kern\\n\",\n    \"\\n\",\n    \"Z = np.ones((16,16))\\n\",\n    \"k = 4\\n\",\n    \"S = np.add.reduceat(np.add.reduceat(Z, np.arange(0, Z.shape[0], k), axis=0),\\n\",\n    \"                                       np.arange(0, Z.shape[1], k), axis=1)\\n\",\n    \"print(S)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 88. How to implement the Game of Life using numpy arrays? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0\\n\",\n      \"  0 0 0 0 0 1 1 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0\\n\",\n      \"  0 0 0 0 0 1 1 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 1 1 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  1 1 0 0 0 0 0 0 1 1 1 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\\n\",\n      \"  0 0 1 0 0 0 0 0 1 1 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0\\n\",\n      \"  1 1 0 0 0 0 0 0 1 0 1 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 1 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 1 0 0 0 1 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 1 0 0 0 1 1 1 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 1 0 0 1 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 1 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 1 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 1 0 1 0 0 1 1 0 0 1 1 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 1 0 0 1 0 0 0 1 1 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 1 0 0 0 1 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 1 0 0 1 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 1 0 1 1 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 1 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 1 1 1 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 1 0 1 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 1 0 0 1 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 1 1 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 1 0 1 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 1 1 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n      \"  0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Nicolas Rougier\\n\",\n    \"\\n\",\n    \"def iterate(Z):\\n\",\n    \"    # Count neighbours\\n\",\n    \"    N = (Z[0:-2,0:-2] + Z[0:-2,1:-1] + Z[0:-2,2:] +\\n\",\n    \"         Z[1:-1,0:-2]                + Z[1:-1,2:] +\\n\",\n    \"         Z[2:  ,0:-2] + Z[2:  ,1:-1] + Z[2:  ,2:])\\n\",\n    \"\\n\",\n    \"    # Apply rules\\n\",\n    \"    birth = (N==3) & (Z[1:-1,1:-1]==0)\\n\",\n    \"    survive = ((N==2) | (N==3)) & (Z[1:-1,1:-1]==1)\\n\",\n    \"    Z[...] = 0\\n\",\n    \"    Z[1:-1,1:-1][birth | survive] = 1\\n\",\n    \"    return Z\\n\",\n    \"\\n\",\n    \"Z = np.random.randint(0,2,(50,50))\\n\",\n    \"for i in range(100): Z = iterate(Z)\\n\",\n    \"print(Z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 89. How to get the n largest values of an array (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[9995 9996 9997 9998 9999]\\n\",\n      \"[9998 9999 9997 9996 9995]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.arange(10000)\\n\",\n    \"np.random.shuffle(Z)\\n\",\n    \"n = 5\\n\",\n    \"\\n\",\n    \"# Slow\\n\",\n    \"print (Z[np.argsort(Z)[-n:]])\\n\",\n    \"\\n\",\n    \"# Fast\\n\",\n    \"print (Z[np.argpartition(-Z,n)[:n]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 4 6]\\n\",\n      \" [1 4 7]\\n\",\n      \" [1 5 6]\\n\",\n      \" [1 5 7]\\n\",\n      \" [2 4 6]\\n\",\n      \" [2 4 7]\\n\",\n      \" [2 5 6]\\n\",\n      \" [2 5 7]\\n\",\n      \" [3 4 6]\\n\",\n      \" [3 4 7]\\n\",\n      \" [3 5 6]\\n\",\n      \" [3 5 7]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Stefan Van der Walt\\n\",\n    \"\\n\",\n    \"def cartesian(arrays):\\n\",\n    \"    arrays = [np.asarray(a) for a in arrays]\\n\",\n    \"    shape = (len(x) for x in arrays)\\n\",\n    \"\\n\",\n    \"    ix = np.indices(shape, dtype=int)\\n\",\n    \"    ix = ix.reshape(len(arrays), -1).T\\n\",\n    \"\\n\",\n    \"    for n, arr in enumerate(arrays):\\n\",\n    \"        ix[:, n] = arrays[n][ix[:, n]]\\n\",\n    \"\\n\",\n    \"    return ix\\n\",\n    \"\\n\",\n    \"print (cartesian(([1, 2, 3], [4, 5], [6, 7])))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 91. How to create a record array from a regular array? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[(b'Hello', 2.5, 3) (b'World', 3.6, 2)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Z = np.array([(\\\"Hello\\\", 2.5, 3),\\n\",\n    \"              (\\\"World\\\", 3.6, 2)])\\n\",\n    \"R = np.core.records.fromarrays(Z.T, \\n\",\n    \"                               names='col1, col2, col3',\\n\",\n    \"                               formats = 'S8, f8, i8')\\n\",\n    \"print(R)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"TypeError\",\n     \"evalue\": \"'float' object cannot be interpreted as an integer\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mTypeError\\u001b[0m                                 Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-92-4526b6c58168>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m      1\\u001b[0m \\u001b[1;31m# Author: Ryan G.\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      2\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 3\\u001b[1;33m \\u001b[0mx\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mrandom\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mrand\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;36m5e7\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      4\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      5\\u001b[0m \\u001b[0mget_ipython\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mrun_line_magic\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m'timeit'\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;34m'np.power(x,3)'\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mmtrand.pyx\\u001b[0m in \\u001b[0;36mmtrand.RandomState.rand\\u001b[1;34m()\\u001b[0m\\n\",\n      \"\\u001b[1;32mmtrand.pyx\\u001b[0m in \\u001b[0;36mmtrand.RandomState.random_sample\\u001b[1;34m()\\u001b[0m\\n\",\n      \"\\u001b[1;32mmtrand.pyx\\u001b[0m in \\u001b[0;36mmtrand.cont0_array\\u001b[1;34m()\\u001b[0m\\n\",\n      \"\\u001b[1;31mTypeError\\u001b[0m: 'float' object cannot be interpreted as an integer\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Ryan G.\\n\",\n    \"\\n\",\n    \"x = np.random.rand(5e7)\\n\",\n    \"\\n\",\n    \"%timeit np.power(x,3)\\n\",\n    \"%timeit x*x*x\\n\",\n    \"%timeit np.einsum('i,i,i->i',x,x,x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Gabe Schwartz\\n\",\n    \"\\n\",\n    \"A = np.random.randint(0,5,(8,3))\\n\",\n    \"B = np.random.randint(0,5,(2,2))\\n\",\n    \"\\n\",\n    \"C = (A[..., np.newaxis, np.newaxis] == B)\\n\",\n    \"rows = np.where(C.any((3,1)).all(1))[0]\\n\",\n    \"print(rows)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \\\\[2,2,3\\\\]) (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 1 1]\\n\",\n      \" [3 4 1]\\n\",\n      \" [0 2 4]\\n\",\n      \" [4 1 4]\\n\",\n      \" [3 4 4]\\n\",\n      \" [3 2 1]\\n\",\n      \" [3 0 3]\\n\",\n      \" [2 1 0]\\n\",\n      \" [3 0 3]\\n\",\n      \" [0 1 2]]\\n\",\n      \"[[3 4 1]\\n\",\n      \" [0 2 4]\\n\",\n      \" [4 1 4]\\n\",\n      \" [3 4 4]\\n\",\n      \" [3 2 1]\\n\",\n      \" [3 0 3]\\n\",\n      \" [2 1 0]\\n\",\n      \" [3 0 3]\\n\",\n      \" [0 1 2]]\\n\",\n      \"[[3 4 1]\\n\",\n      \" [0 2 4]\\n\",\n      \" [4 1 4]\\n\",\n      \" [3 4 4]\\n\",\n      \" [3 2 1]\\n\",\n      \" [3 0 3]\\n\",\n      \" [2 1 0]\\n\",\n      \" [3 0 3]\\n\",\n      \" [0 1 2]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Robert Kern\\n\",\n    \"\\n\",\n    \"Z = np.random.randint(0,5,(10,3))\\n\",\n    \"print(Z)\\n\",\n    \"# solution for arrays of all dtypes (including string arrays and record arrays)\\n\",\n    \"E = np.all(Z[:,1:] == Z[:,:-1], axis=1)\\n\",\n    \"U = Z[~E]\\n\",\n    \"print(U)\\n\",\n    \"# soluiton for numerical arrays only, will work for any number of columns in Z\\n\",\n    \"U = Z[Z.max(axis=1) != Z.min(axis=1),:]\\n\",\n    \"print(U)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 95. Convert a vector of ints into a matrix binary representation (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 1]\\n\",\n      \" [0 0 0 0 0 0 1 0]\\n\",\n      \" [0 0 0 0 0 0 1 1]\\n\",\n      \" [0 0 0 0 1 1 1 1]\\n\",\n      \" [0 0 0 1 0 0 0 0]\\n\",\n      \" [0 0 1 0 0 0 0 0]\\n\",\n      \" [0 1 0 0 0 0 0 0]\\n\",\n      \" [1 0 0 0 0 0 0 0]]\\n\",\n      \"[[0 0 0 0 0 0 0 0]\\n\",\n      \" [0 0 0 0 0 0 0 1]\\n\",\n      \" [0 0 0 0 0 0 1 0]\\n\",\n      \" [0 0 0 0 0 0 1 1]\\n\",\n      \" [0 0 0 0 1 1 1 1]\\n\",\n      \" [0 0 0 1 0 0 0 0]\\n\",\n      \" [0 0 1 0 0 0 0 0]\\n\",\n      \" [0 1 0 0 0 0 0 0]\\n\",\n      \" [1 0 0 0 0 0 0 0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Warren Weckesser\\n\",\n    \"\\n\",\n    \"I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128])\\n\",\n    \"B = ((I.reshape(-1,1) & (2**np.arange(8))) != 0).astype(int)\\n\",\n    \"print(B[:,::-1])\\n\",\n    \"\\n\",\n    \"# Author: Daniel T. McDonald\\n\",\n    \"\\n\",\n    \"I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128], dtype=np.uint8)\\n\",\n    \"print(np.unpackbits(I[:, np.newaxis], axis=1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 96. Given a two dimensional array, how to extract unique rows? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 0 1]\\n\",\n      \" [0 1 0]\\n\",\n      \" [1 1 0]]\\n\",\n      \"[[0 0 1]\\n\",\n      \" [0 1 0]\\n\",\n      \" [1 1 0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Jaime Fernández del Río\\n\",\n    \"\\n\",\n    \"Z = np.random.randint(0,2,(6,3))\\n\",\n    \"T = np.ascontiguousarray(Z).view(np.dtype((np.void, Z.dtype.itemsize * Z.shape[1])))\\n\",\n    \"_, idx = np.unique(T, return_index=True)\\n\",\n    \"uZ = Z[idx]\\n\",\n    \"print(uZ)\\n\",\n    \"\\n\",\n    \"# Author: Andreas Kouzelis\\n\",\n    \"# NumPy >= 1.13\\n\",\n    \"uZ = np.unique(Z, axis=0)\\n\",\n    \"print(uZ)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0.06728041, 0.05397444, 0.0764441 , 0.22359054, 0.0836961 ,\\n\",\n       \"        0.23600838, 0.01241854, 0.22915715, 0.07803804, 0.18796029],\\n\",\n       \"       [0.17717534, 0.14213557, 0.20130688, 0.58880039, 0.22040419,\\n\",\n       \"        0.62150136, 0.03270283, 0.60345942, 0.20550435, 0.49497216],\\n\",\n       \"       [0.27189413, 0.21812194, 0.30892651, 0.90357594, 0.33823335,\\n\",\n       \"        0.95375901, 0.05018592, 0.92607176, 0.31536797, 0.75958668],\\n\",\n       \"       [0.06789412, 0.05446677, 0.07714139, 0.22563006, 0.08445954,\\n\",\n       \"        0.23816117, 0.01253182, 0.23124745, 0.07874988, 0.1896748 ],\\n\",\n       \"       [0.11831111, 0.09491285, 0.13442526, 0.39317904, 0.14717774,\\n\",\n       \"        0.41501553, 0.02183773, 0.40296779, 0.13722817, 0.33052403],\\n\",\n       \"       [0.23778707, 0.19076019, 0.27017402, 0.79022918, 0.29580454,\\n\",\n       \"        0.83411717, 0.04389047, 0.80990307, 0.27580745, 0.66430229],\\n\",\n       \"       [0.03397511, 0.02725589, 0.03860257, 0.11290827, 0.04226467,\\n\",\n       \"        0.119179  , 0.00627109, 0.11571928, 0.03940748, 0.09491578],\\n\",\n       \"       [0.08646755, 0.06936696, 0.09824455, 0.28735447, 0.10756469,\\n\",\n       \"        0.30331365, 0.01596008, 0.29450857, 0.10029306, 0.24156312],\\n\",\n       \"       [0.22622413, 0.18148404, 0.25703619, 0.75180248, 0.28142037,\\n\",\n       \"        0.79355631, 0.0417562 , 0.77051968, 0.26239568, 0.63199907],\\n\",\n       \"       [0.00686845, 0.00551008, 0.00780394, 0.02282566, 0.00854427,\\n\",\n       \"        0.02409336, 0.00126777, 0.02339393, 0.00796666, 0.01918828]])\"\n      ]\n     },\n     \"execution_count\": 97,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Author: Alex Riley\\n\",\n    \"# Make sure to read: http://ajcr.net/Basic-guide-to-einsum/\\n\",\n    \"\\n\",\n    \"A = np.random.uniform(0,1,10)\\n\",\n    \"B = np.random.uniform(0,1,10)\\n\",\n    \"\\n\",\n    \"np.einsum('i->', A)       # np.sum(A)\\n\",\n    \"np.einsum('i,i->i', A, B) # A * B\\n\",\n    \"np.einsum('i,i', A, B)    # np.inner(A, B)\\n\",\n    \"np.einsum('i,j->ij', A, B)    # np.outer(A, B)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Author: Bas Swinckels\\n\",\n    \"\\n\",\n    \"phi = np.arange(0, 10*np.pi, 0.1)\\n\",\n    \"a = 1\\n\",\n    \"x = a*phi*np.cos(phi)\\n\",\n    \"y = a*phi*np.sin(phi)\\n\",\n    \"\\n\",\n    \"dr = (np.diff(x)**2 + np.diff(y)**2)**.5 # segment lengths\\n\",\n    \"r = np.zeros_like(x)\\n\",\n    \"r[1:] = np.cumsum(dr)                # integrate path\\n\",\n    \"r_int = np.linspace(0, r.max(), 200) # regular spaced path\\n\",\n    \"x_int = np.interp(r_int, r, x)       # integrate path\\n\",\n    \"y_int = np.interp(r_int, r, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2. 0. 1. 1.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Evgeni Burovski\\n\",\n    \"\\n\",\n    \"X = np.asarray([[1.0, 0.0, 3.0, 8.0],\\n\",\n    \"                [2.0, 0.0, 1.0, 1.0],\\n\",\n    \"                [1.5, 2.5, 1.0, 0.0]])\\n\",\n    \"n = 4\\n\",\n    \"M = np.logical_and.reduce(np.mod(X, 1) == 0, axis=-1)\\n\",\n    \"M &= (X.sum(axis=-1) == n)\\n\",\n    \"print(X[M])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-0.30976616  0.02882422]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Author: Jessica B. Hamrick\\n\",\n    \"\\n\",\n    \"X = np.random.randn(100) # random 1D array\\n\",\n    \"N = 1000 # number of bootstrap samples\\n\",\n    \"idx = np.random.randint(0, X.size, (N, X.size))\\n\",\n    \"means = X[idx].mean(axis=1)\\n\",\n    \"confint = np.percentile(means, [2.5, 97.5])\\n\",\n    \"print(confint)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  },\n  \"pycharm\": {\n   \"stem_cell\": {\n    \"cell_type\": \"raw\",\n    \"source\": [],\n    \"metadata\": {\n     \"collapsed\": false\n    }\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}"
  },
  {
    "path": "2.numpy/numpy-100/100_Numpy_exercises_no_solution.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 100 numpy exercises\\n\",\n    \"\\n\",\n    \"This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"If you find an error or think you've a better way to solve some of them, feel free to open an issue at <https://github.com/rougier/numpy-100>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1. Import the numpy package under the name `np` (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2. Print the numpy version and the configuration (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 3. Create a null vector of size 10 (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 4.  How to find the memory size of any array (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 5.  How to get the documentation of the numpy add function from the command line? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 6.  Create a null vector of size 10 but the fifth value which is 1 (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 7.  Create a vector with values ranging from 10 to 49 (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 8.  Reverse a vector (first element becomes last) (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 9.  Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 10. Find indices of non-zero elements from \\\\[1,2,0,0,4,0\\\\] (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 11. Create a 3x3 identity matrix (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 12. Create a 3x3x3 array with random values (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 14. Create a random vector of size 30 and find the mean value (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 17. What is the result of the following expression? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"0 * np.nan\\n\",\n    \"np.nan == np.nan\\n\",\n    \"np.inf > np.nan\\n\",\n    \"np.nan - np.nan\\n\",\n\t\"np.nan in set([np.nan])\\n\",\n    \"0.3 == 3 * 0.1\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 22. Normalize a 5x5 random matrix (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 26. What is the output of the following script? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"# Author: Jake VanderPlas\\n\",\n    \"\\n\",\n    \"print(sum(range(5),-1))\\n\",\n    \"from numpy import *\\n\",\n    \"print(sum(range(5),-1))\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"Z**Z\\n\",\n    \"2 << Z >> 2\\n\",\n    \"Z <- Z\\n\",\n    \"1j*Z\\n\",\n    \"Z/1/1\\n\",\n    \"Z<Z>Z\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 28. What are the result of the following expressions?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"np.array(0) / np.array(0)\\n\",\n    \"np.array(0) // np.array(0)\\n\",\n    \"np.array([np.nan]).astype(int).astype(float)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 29. How to round away from zero a float array ? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 30. How to find common values between two arrays? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 32. Is the following expressions true? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"np.sqrt(-1) == np.emath.sqrt(-1)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 35. How to compute ((A+B)\\\\*(-A/2)) in place (without copy)? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 36. Extract the integer part of a random array using 5 different methods (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 40. Create a random vector of size 10 and sort it (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 41. How to sum a small array faster than np.sum? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 42. Consider two random array A and B, check if they are equal (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 43. Make an array immutable (read-only) (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 46. Create a structured array with `x` and `y` coordinates covering the \\\\[0,1\\\\]x\\\\[0,1\\\\] area (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"####  47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 49. How to print all the values of an array? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 54. How to read the following file? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"1, 2, 3, 4, 5\\n\",\n    \"6,  ,  , 7, 8\\n\",\n    \" ,  , 9,10,11\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 56. Generate a generic 2D Gaussian-like array (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 57. How to randomly place p elements in a 2D array? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 58. Subtract the mean of each row of a matrix (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 59. How to sort an array by the nth column? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 60. How to tell if a given 2D array has null columns? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 61. Find the nearest value from a given value in an array (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 63. Create an array class that has a name attribute (★★☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset  indices? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 69. How to get the diagonal of a dot product? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 70. Consider the vector \\\\[1, 2, 3, 4, 5\\\\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 72. How to swap two rows of an array? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the  triangles (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 75. How to compute averages using a sliding window over an array? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\\\\[0\\\\],Z\\\\[1\\\\],Z\\\\[2\\\\]) and each subsequent row is  shifted by 1 (last row should be (Z\\\\[-3\\\\],Z\\\\[-2\\\\],Z\\\\[-1\\\\]) (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i  (P0\\\\[i\\\\],P1\\\\[i\\\\])? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\\\\[j\\\\]) to each line i (P0\\\\[i\\\\],P1\\\\[i\\\\])? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 81. Consider an array Z = \\\\[1,2,3,4,5,6,7,8,9,10,11,12,13,14\\\\], how to generate an array R = \\\\[\\\\[1,2,3,4\\\\], \\\\[2,3,4,5\\\\], \\\\[3,4,5,6\\\\], ..., \\\\[11,12,13,14\\\\]\\\\]? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 82. Compute a matrix rank (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 83. How to find the most frequent value in an array?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 85. Create a 2D array subclass such that Z\\\\[i,j\\\\] == Z\\\\[j,i\\\\] (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 88. How to implement the Game of Life using numpy arrays? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 89. How to get the n largest values of an array (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 91. How to create a record array from a regular array? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \\\\[2,2,3\\\\]) (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 95. Convert a vector of ints into a matrix binary representation (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 96. Given a two dimensional array, how to extract unique rows? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "2.numpy/numpy-100/100_Numpy_exercises_with_hint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 100 numpy exercises with hint\\n\",\n    \"\\n\",\n    \"This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"If you find an error or think you've a better way to solve some of them, feel free to open an issue at <https://github.com/rougier/numpy-100>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1. Import the numpy package under the name `np` (★☆☆) \\n\",\n    \"(**hint**: import … as …)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2. Print the numpy version and the configuration (★☆☆) \\n\",\n    \"(**hint**: np.\\\\_\\\\_version\\\\_\\\\_, np.show\\\\_config)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 3. Create a null vector of size 10 (★☆☆) \\n\",\n    \"(**hint**: np.zeros)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 4.  How to find the memory size of any array (★☆☆) \\n\",\n    \"(**hint**: size, itemsize)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 5.  How to get the documentation of the numpy add function from the command line? (★☆☆) \\n\",\n    \"(**hint**: np.info)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 6.  Create a null vector of size 10 but the fifth value which is 1 (★☆☆) \\n\",\n    \"(**hint**: array\\\\[4\\\\])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 7.  Create a vector with values ranging from 10 to 49 (★☆☆) \\n\",\n    \"(**hint**: np.arange)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 8.  Reverse a vector (first element becomes last) (★☆☆) \\n\",\n    \"(**hint**: array\\\\[::-1\\\\])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 9.  Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆) \\n\",\n    \"(**hint**: reshape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 10. Find indices of non-zero elements from \\\\[1,2,0,0,4,0\\\\] (★☆☆) \\n\",\n    \"(**hint**: np.nonzero)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 11. Create a 3x3 identity matrix (★☆☆) \\n\",\n    \"(**hint**: np.eye)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 12. Create a 3x3x3 array with random values (★☆☆) \\n\",\n    \"(**hint**: np.random.random)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆) \\n\",\n    \"(**hint**: min, max)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 14. Create a random vector of size 30 and find the mean value (★☆☆) \\n\",\n    \"(**hint**: mean)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆) \\n\",\n    \"(**hint**: array\\\\[1:-1, 1:-1\\\\])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆) \\n\",\n    \"(**hint**: np.pad)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 17. What is the result of the following expression? (★☆☆) \\n\",\n    \"(**hint**: NaN = not a number, inf = infinity)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"0 * np.nan\\n\",\n    \"np.nan == np.nan\\n\",\n    \"np.inf > np.nan\\n\",\n    \"np.nan - np.nan\\n\",\n    \"np.nan in set([np.nan])\\n\",\n    \"0.3 == 3 * 0.1\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆) \\n\",\n    \"(**hint**: np.diag)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆) \\n\",\n    \"(**hint**: array\\\\[::2\\\\])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element? \\n\",\n    \"(**hint**: np.unravel_index)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆) \\n\",\n    \"(**hint**: np.tile)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 22. Normalize a 5x5 random matrix (★☆☆) \\n\",\n    \"(**hint**: (x - mean) / std)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆) \\n\",\n    \"(**hint**: np.dtype)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆) \\n\",\n    \"(**hint**: np.dot | @)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆) \\n\",\n    \"(**hint**: >, <=)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 26. What is the output of the following script? (★☆☆) \\n\",\n    \"(**hint**: np.sum)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"# Author: Jake VanderPlas\\n\",\n    \"\\n\",\n    \"print(sum(range(5),-1))\\n\",\n    \"from numpy import *\\n\",\n    \"print(sum(range(5),-1))\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"Z**Z\\n\",\n    \"2 << Z >> 2\\n\",\n    \"Z <- Z\\n\",\n    \"1j*Z\\n\",\n    \"Z/1/1\\n\",\n    \"Z<Z>Z\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 28. What are the result of the following expressions?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"np.array(0) / np.array(0)\\n\",\n    \"np.array(0) // np.array(0)\\n\",\n    \"np.array([np.nan]).astype(int).astype(float)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 29. How to round away from zero a float array ? (★☆☆) \\n\",\n    \"(**hint**: np.uniform, np.copysign, np.ceil, np.abs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 30. How to find common values between two arrays? (★☆☆) \\n\",\n    \"(**hint**: np.intersect1d)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆) \\n\",\n    \"(**hint**: np.seterr, np.errstate)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 32. Is the following expressions true? (★☆☆) \\n\",\n    \"(**hint**: imaginary number)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```python\\n\",\n    \"np.sqrt(-1) == np.emath.sqrt(-1)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆) \\n\",\n    \"(**hint**: np.datetime64, np.timedelta64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆) \\n\",\n    \"(**hint**: np.arange(dtype=datetime64\\\\['D'\\\\]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 35. How to compute ((A+B)\\\\*(-A/2)) in place (without copy)? (★★☆) \\n\",\n    \"(**hint**: np.add(out=), np.negative(out=), np.multiply(out=), np.divide(out=))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 36. Extract the integer part of a random array using 5 different methods (★★☆) \\n\",\n    \"(**hint**: %, np.floor, np.ceil, astype, np.trunc)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆) \\n\",\n    \"(**hint**: np.arange)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆) \\n\",\n    \"(**hint**: np.fromiter)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆) \\n\",\n    \"(**hint**: np.linspace)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 40. Create a random vector of size 10 and sort it (★★☆) \\n\",\n    \"(**hint**: sort)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 41. How to sum a small array faster than np.sum? (★★☆) \\n\",\n    \"(**hint**: np.add.reduce)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 42. Consider two random array A and B, check if they are equal (★★☆) \\n\",\n    \"(**hint**: np.allclose, np.array\\\\_equal)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 43. Make an array immutable (read-only) (★★☆) \\n\",\n    \"(**hint**: flags.writeable)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆) \\n\",\n    \"(**hint**: np.sqrt, np.arctan2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆) \\n\",\n    \"(**hint**: argmax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 46. Create a structured array with `x` and `y` coordinates covering the \\\\[0,1\\\\]x\\\\[0,1\\\\] area (★★☆) \\n\",\n    \"(**hint**: np.meshgrid)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"####  47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj)) \\n\",\n    \"(**hint**: np.subtract.outer)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆) \\n\",\n    \"(**hint**: np.iinfo, np.finfo, eps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 49. How to print all the values of an array? (★★☆) \\n\",\n    \"(**hint**: np.set\\\\_printoptions)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆) \\n\",\n    \"(**hint**: argmin)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆) \\n\",\n    \"(**hint**: dtype)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆) \\n\",\n    \"(**hint**: np.atleast\\\\_2d, T, np.sqrt)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place? \\n\",\n    \"(**hint**: astype(copy=False))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 54. How to read the following file? (★★☆) \\n\",\n    \"(**hint**: np.genfromtxt)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"1, 2, 3, 4, 5\\n\",\n    \"6,  ,  , 7, 8\\n\",\n    \" ,  , 9,10,11\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆) \\n\",\n    \"(**hint**: np.ndenumerate, np.ndindex)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 56. Generate a generic 2D Gaussian-like array (★★☆) \\n\",\n    \"(**hint**: np.meshgrid, np.exp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 57. How to randomly place p elements in a 2D array? (★★☆) \\n\",\n    \"(**hint**: np.put, np.random.choice)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 58. Subtract the mean of each row of a matrix (★★☆) \\n\",\n    \"(**hint**: mean(axis=,keepdims=))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 59. How to sort an array by the nth column? (★★☆) \\n\",\n    \"(**hint**: argsort)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 60. How to tell if a given 2D array has null columns? (★★☆) \\n\",\n    \"(**hint**: any, ~)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 61. Find the nearest value from a given value in an array (★★☆) \\n\",\n    \"(**hint**: np.abs, argmin, flat)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆) \\n\",\n    \"(**hint**: np.nditer)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 63. Create an array class that has a name attribute (★★☆) \\n\",\n    \"(**hint**: class method)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★) \\n\",\n    \"(**hint**: np.bincount | np.add.at)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★) \\n\",\n    \"(**hint**: np.bincount)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★) \\n\",\n    \"(**hint**: np.unique)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★) \\n\",\n    \"(**hint**: sum(axis=(-2,-1)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset  indices? (★★★) \\n\",\n    \"(**hint**: np.bincount)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 69. How to get the diagonal of a dot product? (★★★) \\n\",\n    \"(**hint**: np.diag)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 70. Consider the vector \\\\[1, 2, 3, 4, 5\\\\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★) \\n\",\n    \"(**hint**: array\\\\[::4\\\\])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★) \\n\",\n    \"(**hint**: array\\\\[:, :, None\\\\])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 72. How to swap two rows of an array? (★★★) \\n\",\n    \"(**hint**: array\\\\[\\\\[\\\\]\\\\] = array\\\\[\\\\[\\\\]\\\\])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the  triangles (★★★) \\n\",\n    \"(**hint**: repeat, np.roll, np.sort, view, np.unique)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★) \\n\",\n    \"(**hint**: np.repeat)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 75. How to compute averages using a sliding window over an array? (★★★) \\n\",\n    \"(**hint**: np.cumsum)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\\\\[0\\\\],Z\\\\[1\\\\],Z\\\\[2\\\\]) and each subsequent row is  shifted by 1 (last row should be (Z\\\\[-3\\\\],Z\\\\[-2\\\\],Z\\\\[-1\\\\]) (★★★) \\n\",\n    \"(**hint**: from numpy.lib import stride_tricks)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★) \\n\",\n    \"(**hint**: np.logical_not, np.negative)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i  (P0\\\\[i\\\\],P1\\\\[i\\\\])? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\\\\[j\\\\]) to each line i (P0\\\\[i\\\\],P1\\\\[i\\\\])? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★) \\n\",\n    \"(**hint**: minimum, maximum)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 81. Consider an array Z = \\\\[1,2,3,4,5,6,7,8,9,10,11,12,13,14\\\\], how to generate an array R = \\\\[\\\\[1,2,3,4\\\\], \\\\[2,3,4,5\\\\], \\\\[3,4,5,6\\\\], ..., \\\\[11,12,13,14\\\\]\\\\]? (★★★) \\n\",\n    \"(**hint**: stride\\\\_tricks.as\\\\_strided)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 82. Compute a matrix rank (★★★) \\n\",\n    \"(**hint**: np.linalg.svd) (suggestion: np.linalg.svd)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 83. How to find the most frequent value in an array? \\n\",\n    \"(**hint**: np.bincount, argmax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★) \\n\",\n    \"(**hint**: stride\\\\_tricks.as\\\\_strided)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 85. Create a 2D array subclass such that Z\\\\[i,j\\\\] == Z\\\\[j,i\\\\] (★★★) \\n\",\n    \"(**hint**: class method)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★) \\n\",\n    \"(**hint**: np.tensordot)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★) \\n\",\n    \"(**hint**: np.add.reduceat)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 88. How to implement the Game of Life using numpy arrays? (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 89. How to get the n largest values of an array (★★★) \\n\",\n    \"(**hint**: np.argsort | np.argpartition)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★) \\n\",\n    \"(**hint**: np.indices)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 91. How to create a record array from a regular array? (★★★) \\n\",\n    \"(**hint**: np.core.records.fromarrays)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★) \\n\",\n    \"(**hint**: np.power, \\\\*, np.einsum)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★) \\n\",\n    \"(**hint**: np.where)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \\\\[2,2,3\\\\]) (★★★)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 95. Convert a vector of ints into a matrix binary representation (★★★) \\n\",\n    \"(**hint**: np.unpackbits)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 96. Given a two dimensional array, how to extract unique rows? (★★★) \\n\",\n    \"(**hint**: np.ascontiguousarray | np.unique)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★) \\n\",\n    \"(**hint**: np.einsum)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)? \\n\",\n    \"(**hint**: np.cumsum, np.interp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★) \\n\",\n    \"(**hint**: np.logical\\\\_and.reduce, np.mod)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★) \\n\",\n    \"(**hint**: np.percentile)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy-100/README.md",
    "content": "## Numpy练习题100题-提高你的数据分析技能\n\n本文总结了Numpy的常用操作，并做成练习题，练习题附答案建议读者把练习题完成。作者认为，做完练习题，Numpy的基本操作没有问题了，以后碰到问题也可以查这些习题。（文末提供下载）\n\n**NumPy**（Numeric Python）系统是Python的一种开源的数值计算扩展。这种工具可用来存储和处理大型矩阵，比Python自身的嵌套列表（nested list structure)结构要高效的多（该结构也可以用来表示矩阵（matrix））。\n\n**NumPy**提供了许多高级的数值编程工具，如：矩阵数据类型、矢量处理，以及精密的运算库。专为进行严格的数字处理而产生。多为很多大型金融公司使用，以及核心的科学计算组织如：Lawrence Livermore，NASA用其处理一些本来使用C++，Fortran或Matlab等所做的任务。\n\n网上可以搜到大量的Numpy教程和官方文档，但没有简单的方法来练习。教程是很好的资源，但要付诸实践。 只有实践，才能更好的加深学习。\n\n本站从github搜索到了一些Numpy的练习题100题，含答案，并进行整理：\n\n原代码作者：**Nicolas P. Rougier**（https://github.com/rougier/numpy-100）\n\n**使用方法**\n文件夹有三个不同的ipynb文件:\n\n1.**100_Numpy_exercises_no_solution.ipynb** \n\n没有答案代码的文件，这个是你做的练习\n\n2.**100_Numpy_exercises_with_hint.ipynb**\n\n没有答案代码的文件，但有提示，这个你也可以用来练习\n\n3.**100_Numpy_exercises.ipynb**\n\n有答案代码和注释的文件\n\n你可以在**100_Numpy_exercises_no_solution.ipynb** 里输入代码，看看运行结果是否和**100_Numpy_exercises.ipynb** 里面的内容一致。\n\n"
  },
  {
    "path": "2.numpy/numpy_exercises/10_Random_sampling.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Random Sampling\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"__author__ = 'kyubyong. longinglove@nate.com'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Simple random data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Create an array of shape (3, 2) and populate it with random samples from a uniform distribution over [0, 1).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.13879034,  0.71300174],\\n\",\n       \"       [ 0.08121322,  0.00393554],\\n\",\n       \"       [ 0.02349471,  0.56677474]])\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Create an array of shape (1000, 1000) and populate it with random samples from a standard normal distribution. And verify that the mean and standard deviation is close enough to 0 and 1 repectively.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-0.00110028519551\\n\",\n      \"0.999683483393\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Create an array of shape (3, 2) and populate it with random integers ranging from 0 to 3 (inclusive) from a discrete uniform distribution.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 3],\\n\",\n       \"       [3, 0],\\n\",\n       \"       [0, 0]])\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Extract 1 elements from x randomly such that each of them would be associated with probabilities .3, .5, .2. Then print the result 10 times.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"5 out of 10\\n\",\n      \"2 out of 10\\n\",\n      \"3 out of 10\\n\",\n      \"5 out of 10\\n\",\n      \"2 out of 10\\n\",\n      \"5 out of 10\\n\",\n      \"2 out of 10\\n\",\n      \"2 out of 10\\n\",\n      \"2 out of 10\\n\",\n      \"5 out of 10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = [b'3 out of 10', b'5 out of 10', b'2 out of 10']\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Extract 3 different integers from 0 to 9 randomly with the same probabilities.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([5, 4, 0])\"\n      ]\n     },\n     \"execution_count\": 66,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Permutations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Shuffle numbers between 0 and 9 (inclusive).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2 3 8 4 5 1 0 6 9 7]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[5 2 7 4 1 0 6 8 9 3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Or\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Random generator\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Assign number 10 to the seed of the random generator so that you can get the same value next time.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/10_Random_sampling_Solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Random Sampling\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"__author__ = 'kyubyong. longinglove@nate.com'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Simple random data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Create an array of shape (3, 2) and populate it with random samples from a uniform distribution over [0, 1).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.13879034,  0.71300174],\\n\",\n       \"       [ 0.08121322,  0.00393554],\\n\",\n       \"       [ 0.02349471,  0.56677474]])\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.random.rand(3, 2) \\n\",\n    \"# Or np.random.random((3,2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Create an array of shape (1000, 1000) and populate it with random samples from a standard normal distribution. And verify that the mean and standard deviation is close enough to 0 and 1 repectively.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-0.00110028519551\\n\",\n      \"0.999683483393\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"out1 = np.random.randn(1000, 1000)\\n\",\n    \"out2 = np.random.standard_normal((1000, 1000))\\n\",\n    \"out3 = np.random.normal(loc=0.0, scale=1.0, size=(1000, 1000))\\n\",\n    \"assert np.allclose(np.mean(out1), np.mean(out2), atol=0.1)\\n\",\n    \"assert np.allclose(np.mean(out1), np.mean(out3), atol=0.1)\\n\",\n    \"assert np.allclose(np.std(out1), np.std(out2), atol=0.1)\\n\",\n    \"assert np.allclose(np.std(out1), np.std(out3), atol=0.1)\\n\",\n    \"print np.mean(out3)\\n\",\n    \"print np.std(out1)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Create an array of shape (3, 2) and populate it with random integers ranging from 0 to 3 (inclusive) from a discrete uniform distribution.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 3],\\n\",\n       \"       [3, 0],\\n\",\n       \"       [0, 0]])\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.random.randint(0, 4, (3, 2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Extract 1 elements from x randomly such that each of them would be associated with probabilities .3, .5, .2. Then print the result 10 times.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = [b'3 out of 10', b'5 out of 10', b'2 out of 10']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2 out of 10\\n\",\n      \"5 out of 10\\n\",\n      \"3 out of 10\\n\",\n      \"5 out of 10\\n\",\n      \"5 out of 10\\n\",\n      \"5 out of 10\\n\",\n      \"2 out of 10\\n\",\n      \"2 out of 10\\n\",\n      \"5 out of 10\\n\",\n      \"5 out of 10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for _ in range(10):\\n\",\n    \"    print np.random.choice(x, p=[.3, .5, .2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Extract 3 different integers from 0 to 9 randomly with the same probabilities.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([5, 4, 0])\"\n      ]\n     },\n     \"execution_count\": 66,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.random.choice(10, 3, replace=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Permutations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Shuffle numbers between 0 and 9 (inclusive).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2 3 8 4 5 1 0 6 9 7]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10)\\n\",\n    \"np.random.shuffle(x)\\n\",\n    \"print x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[5 2 7 4 1 0 6 8 9 3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Or\\n\",\n    \"print np.random.permutation(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Random generator\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Assign number 10 to the seed of the random generator so that you can get the same value next time.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(10)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/11_Set_routines.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Set routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"author = 'kyubyong. longinglove@nate.com'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Making proper sets\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Get unique elements and reconstruction indices from x. And reconstruct x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"unique elements = [1 2 3 4 6]\\n\",\n      \"reconstruction indices = [0 1 4 3 1 2 1]\\n\",\n      \"reconstructed = [1 2 6 4 2 3 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 6, 4, 2, 3, 2])\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Boolean operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Create a boolean array of the same shape as x. If each element of x is present in y, the result will be True, otherwise False.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ True  True False False  True]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2, 5, 0])\\n\",\n    \"y = np.array([0, 1])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Find the unique intersection of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2, 5, 0])\\n\",\n    \"y = np.array([0, 1, 4])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Find the unique elements of x that are not present in y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2 5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2, 5, 0])\\n\",\n    \"y = np.array([0, 1, 4])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Find the xor elements of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2 4 5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2, 5, 0])\\n\",\n    \"y = np.array([0, 1, 4])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Find the union of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 2 4 5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2, 5, 0])\\n\",\n    \"y = np.array([0, 1, 4])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/11_Set_routines_Solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Set routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"author = 'kyubyong. longinglove@nate.com'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Making proper sets\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Get unique elements and reconstruction indices from x. And reconstruct x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"unique elements = [1 2 3 4 6]\\n\",\n      \"reconstruction indices = [0 1 4 3 1 2 1]\\n\",\n      \"reconstructed = [1 2 6 4 2 3 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 6, 4, 2, 3, 2])\\n\",\n    \"out, indices = np.unique(x, return_inverse=True)\\n\",\n    \"print \\\"unique elements =\\\", out\\n\",\n    \"print \\\"reconstruction indices =\\\", indices\\n\",\n    \"print \\\"reconstructed =\\\", out[indices]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Boolean operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Create a boolean array of the same shape as x. If each element of x is present in y, the result will be True, otherwise False.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ True  True False False  True]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2, 5, 0])\\n\",\n    \"y = np.array([0, 1])\\n\",\n    \"print np.in1d(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Find the unique intersection of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2, 5, 0])\\n\",\n    \"y = np.array([0, 1, 4])\\n\",\n    \"print np.intersect1d(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Find the unique elements of x that are not present in y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2 5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2, 5, 0])\\n\",\n    \"y = np.array([0, 1, 4])\\n\",\n    \"print np.setdiff1d(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Find the xor elements of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2 4 5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2, 5, 0])\\n\",\n    \"y = np.array([0, 1, 4])\\n\",\n    \"out1 = np.setxor1d(x, y)\\n\",\n    \"out2 = np.sort(np.concatenate((np.setdiff1d(x, y), np.setdiff1d(y, x))))\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Find the union of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 2 4 5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2, 5, 0])\\n\",\n    \"y = np.array([0, 1, 4])\\n\",\n    \"out1 = np.union1d(x, y)\\n\",\n    \"out2 = np.sort(np.unique(np.concatenate((x, y))))\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print np.union1d(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/12_Sorting_searching_and_counting.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Soring, searching, and counting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"author = 'kyubyong. longinglove@nate.com'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Sorting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Sort x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 4]\\n\",\n      \" [1 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1,4],[3,1]])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Sort pairs of surnames and first names and return their indices. (first by surname, then by name).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 2 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"surnames =    ('Hertz',    'Galilei', 'Hertz')\\n\",\n    \"first_names = ('Heinrich', 'Galileo', 'Gustav')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Get the indices that would sort x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1]\\n\",\n      \" [1 0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1,4],[3,1]])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Create an array such that its fifth element would be the same as the element of sorted x, and it divide other elements by their value.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x = [5 1 6 3 9 8 2 7 4 0]\\n\",\n      \"\\n\",\n      \"Check the fifth element of this new array is 5, the first four elements are all smaller than 5, and 6th through the end are bigger than 5\\n\",\n      \"[2 0 4 3 1 5 8 7 6 9]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.random.permutation(10)\\n\",\n    \"print \\\"x =\\\", x\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Create the indices of an array such that its third element would be the same as the element of sorted x, and it divide other elements by their value.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x = [2 8 3 7 5 6 4 0 9 1]\\n\",\n      \"partitioned = [0 1 2 3 4 5 8 6 9 7]\\n\",\n      \"indices = [0 1 2 3 4 5 8 6 9 7]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.random.permutation(10)\\n\",\n    \"print \\\"x =\\\", x\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Searching\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Get the maximum and minimum values and their indices of x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x = [[0 5 9 8 2]\\n\",\n      \" [3 7 4 1 6]]\\n\",\n      \"maximum values = [9 7]\\n\",\n      \"max indices = [2 1]\\n\",\n      \"minimum values = [0 1]\\n\",\n      \"min indices = [0 3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.random.permutation(10).reshape(2, 5)\\n\",\n    \"print \\\"x =\\\", x\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Get the maximum and minimum values and their indices of x along the second axis, ignoring NaNs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"maximum values ignoring NaNs = [ 4.  3.]\\n\",\n      \"max indices = [1 0]\\n\",\n      \"minimum values ignoring NaNs = [ 4.  2.]\\n\",\n      \"min indices = [1 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[np.nan, 4], [3, 2]])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Get the values and indices of the elements that are bigger than 2 in x.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 113,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Values bigger than 2 = [3 3 5]\\n\",\n      \"Their indices are  (array([0, 1, 1], dtype=int64), array([2, 1, 2], dtype=int64))\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 2, 3], [1, 3, 5]])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Get the indices of the elements that are bigger than 2 in the flattend x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 2 3 4 5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 2, 3], [1, 3, 5]])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Check the elements of x and return 0 if it is less than 0, otherwise the element itself.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 0 0]\\n\",\n      \" [0 0 0]\\n\",\n      \" [1 2 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(-5, 4).reshape(3, 3)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Get the indices where elements of y should be inserted to x to maintain order.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 2, 1, 3], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 109,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = [1, 3, 5, 7, 9]\\n\",\n    \"y = [0, 4, 2, 6]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Counting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Get the number of nonzero elements in x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 120,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = [[0,1,7,0,0],[3,0,0,2,19]]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/12_Sorting_searching_and_counting_Solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Soring, searching, and counting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"author = 'kyubyong. longinglove@nate.com'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Sorting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Sort x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 4]\\n\",\n      \" [1 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1,4],[3,1]])\\n\",\n    \"out = np.sort(x, axis=1)\\n\",\n    \"x.sort(axis=1)\\n\",\n    \"assert np.array_equal(out, x)\\n\",\n    \"print out\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Sort pairs of surnames and first names and return their indices. (first by surname, then by name).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 2 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"surnames =    ('Hertz',    'Galilei', 'Hertz')\\n\",\n    \"first_names = ('Heinrich', 'Galileo', 'Gustav')\\n\",\n    \"print np.lexsort((first_names, surnames))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Get the indices that would sort x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1]\\n\",\n      \" [1 0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1,4],[3,1]])\\n\",\n    \"out = np.argsort(x, axis=1)\\n\",\n    \"print out\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Create an array such that its fifth element would be the same as the element of sorted x, and it divide other elements by their value.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x = [5 1 6 3 9 8 2 7 4 0]\\n\",\n      \"\\n\",\n      \"Check the fifth element of this new array is 5, the first four elements are all smaller than 5, and 6th through the end are bigger than 5\\n\",\n      \"[2 0 4 3 1 5 8 7 6 9]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.random.permutation(10)\\n\",\n    \"print \\\"x =\\\", x\\n\",\n    \"print \\\"\\\\nCheck the fifth element of this new array is 5, the first four elements are all smaller than 5, and 6th through the end are bigger than 5\\\\n\\\", \\n\",\n    \"out = np.partition(x, 5)\\n\",\n    \"x.partition(5) # in-place equivalent\\n\",\n    \"assert np.array_equal(x, out)\\n\",\n    \"print out\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Create the indices of an array such that its third element would be the same as the element of sorted x, and it divide other elements by their value.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x = [2 8 3 7 5 6 4 0 9 1]\\n\",\n      \"partitioned = [0 1 2 3 4 5 8 6 9 7]\\n\",\n      \"indices = [0 1 2 3 4 5 8 6 9 7]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.random.permutation(10)\\n\",\n    \"print \\\"x =\\\", x\\n\",\n    \"partitioned = np.partition(x, 3)\\n\",\n    \"indices = np.argpartition(x, 3)\\n\",\n    \"print \\\"partitioned =\\\", partitioned\\n\",\n    \"print \\\"indices =\\\", partitioned\\n\",\n    \"assert np.array_equiv(x[indices], partitioned)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Searching\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Get the maximum and minimum values and their indices of x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x = [[0 5 9 8 2]\\n\",\n      \" [3 7 4 1 6]]\\n\",\n      \"maximum values = [9 7]\\n\",\n      \"max indices = [2 1]\\n\",\n      \"minimum values = [0 1]\\n\",\n      \"min indices = [0 3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.random.permutation(10).reshape(2, 5)\\n\",\n    \"print \\\"x =\\\", x\\n\",\n    \"print \\\"maximum values =\\\", np.max(x, 1)\\n\",\n    \"print \\\"max indices =\\\", np.argmax(x, 1)\\n\",\n    \"print \\\"minimum values =\\\", np.min(x, 1)\\n\",\n    \"print \\\"min indices =\\\", np.argmin(x, 1)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Get the maximum and minimum values and their indices of x along the second axis, ignoring NaNs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"maximum values ignoring NaNs = [ 4.  3.]\\n\",\n      \"max indices = [1 0]\\n\",\n      \"minimum values ignoring NaNs = [ 4.  2.]\\n\",\n      \"min indices = [1 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[np.nan, 4], [3, 2]])\\n\",\n    \"print \\\"maximum values ignoring NaNs =\\\", np.nanmax(x, 1)\\n\",\n    \"print \\\"max indices =\\\", np.nanargmax(x, 1)\\n\",\n    \"print \\\"minimum values ignoring NaNs =\\\", np.nanmin(x, 1)\\n\",\n    \"print \\\"min indices =\\\", np.nanargmin(x, 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Get the values and indices of the elements that are bigger than 2 in x.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 113,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Values bigger than 2 = [3 3 5]\\n\",\n      \"Their indices are  (array([0, 1, 1], dtype=int64), array([2, 1, 2], dtype=int64))\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 2, 3], [1, 3, 5]])\\n\",\n    \"print \\\"Values bigger than 2 =\\\", x[x>2]\\n\",\n    \"print \\\"Their indices are \\\", np.nonzero(x > 2)\\n\",\n    \"assert np.array_equiv(x[x>2], x[np.nonzero(x > 2)])\\n\",\n    \"assert np.array_equiv(x[x>2], np.extract(x > 2, x))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Get the indices of the elements that are bigger than 2 in the flattend x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2 4 5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 2, 3], [1, 3, 5]])\\n\",\n    \"print np.flatnonzero(x>2)\\n\",\n    \"assert np.array_equiv(np.flatnonzero(x), x.ravel().nonzero())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Check the elements of x and return 0 if it is less than 0, otherwise the element itself.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 0 0]\\n\",\n      \" [0 0 0]\\n\",\n      \" [1 2 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(-5, 4).reshape(3, 3)\\n\",\n    \"print np.where(x <0, 0, x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Get the indices where elements of y should be inserted to x to maintain order.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 2, 1, 3], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 109,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = [1, 3, 5, 7, 9]\\n\",\n    \"y = [0, 4, 2, 6]\\n\",\n    \"np.searchsorted(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Counting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Get the number of nonzero elements in x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 120,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = [[0,1,7,0,0],[3,0,0,2,19]]\\n\",\n    \"print np.count_nonzero(x)\\n\",\n    \"assert np.count_nonzero(x) == len(x[x!=0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/13_Statistics.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Statistics\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"__author__ = \\\"kyubyong. kbpark.linguist@gmail.com\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.3'\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Order statistics\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Return the minimum value of x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x=\\n\",\n      \" [[0 1]\\n\",\n      \" [2 3]]\\n\",\n      \"ans=\\n\",\n      \" [0 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(4).reshape((2, 2))\\n\",\n    \"print(\\\"x=\\\\n\\\", x)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Return the maximum value of x along the second axis. Reduce the second axis to the dimension with size one.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x=\\n\",\n      \" [[0 1]\\n\",\n      \" [2 3]]\\n\",\n      \"ans=\\n\",\n      \" [[1]\\n\",\n      \" [3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(4).reshape((2, 2))\\n\",\n    \"print(\\\"x=\\\\n\\\", x)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Calcuate the difference between the maximum and the minimum of x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x=\\n\",\n      \" [[0 1 2 3 4]\\n\",\n      \" [5 6 7 8 9]]\\n\",\n      \"ans=\\n\",\n      \" [4 4]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10).reshape((2, 5))\\n\",\n    \"print(\\\"x=\\\\n\\\", x)\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Compute the 75th percentile of x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x=\\n\",\n      \" [[ 1  2  3  4  5]\\n\",\n      \" [ 6  7  8  9 10]]\\n\",\n      \"ans=\\n\",\n      \" [ 4.  9.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1, 11).reshape((2, 5))\\n\",\n    \"print(\\\"x=\\\\n\\\", x)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Averages and variances\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Compute the median of flattened x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x=\\n\",\n      \" [[1 2 3]\\n\",\n      \" [4 5 6]\\n\",\n      \" [7 8 9]]\\n\",\n      \"ans=\\n\",\n      \" 5.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1, 10).reshape((3, 3))\\n\",\n    \"print(\\\"x=\\\\n\\\", x)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Compute the weighted average of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2.66666666667\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(5)\\n\",\n    \"weights = np.arange(1, 6)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Compute the mean, standard deviation, and variance of x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x=\\n\",\n      \" [0 1 2 3 4]\\n\",\n      \"mean=\\n\",\n      \" 2.0\\n\",\n      \"std=\\n\",\n      \" 1.41421356237\\n\",\n      \"variance=\\n\",\n      \" 2.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(5)\\n\",\n    \"print(\\\"x=\\\\n\\\",x)\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Correlating\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Compute the covariance matrix of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" [[ 1. -1.]\\n\",\n      \" [-1.  1.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2])\\n\",\n    \"y = np.array([2, 1, 0])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. In the above covariance matrix, what does the -1 mean?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Compute Pearson product-moment correlation coefficients of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" [[ 1.          0.92857143]\\n\",\n      \" [ 0.92857143  1.        ]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 3])\\n\",\n    \"y = np.array([2, 4, 5])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Compute cross-correlation of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" [19]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 3])\\n\",\n    \"y = np.array([2, 4, 5])\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Histograms\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Compute the histogram of x against the bins.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" (array([2, 3, 1], dtype=int64), array([0, 1, 2, 3]))\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAgsAAAFkCAYAAACuFXjcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\nAAAPYQAAD2EBqD+naQAAFVpJREFUeJzt3X+s5WWdH/D3h+XHKCljUsoMNKRUVlmsKbMzbhURB8sP\\nF0wwq2Z3b6XOojUiJksnbd2Yhmy6TSWE4MhqCTZkV4juTTdtzRJjhQXLEiqElBFJFJwmQEWBgdV2\\ncEWoDk//OGfo5Xrvc+d77p1z78x9vZJvZr7PeZ7zfe4zz5z7Pt+f1VoLAMBijlrtDgAAa5uwAAB0\\nCQsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0DQoLVXVFVX27qvaNl29W1W8u0ea8\\nqnqwql6sqj1VtWN5XQYApmnonoUnk/xBkq1JtiX5RpK/qKozF6pcVacl+WqSu5KcleSGJDdX1YUT\\n9hcAmLJa7oOkqupHSf5la+1PF3jt2iQXt9b+4Zyy2SQbW2uXLGvDAMBUTHzOQlUdVVW/m+S1Se5b\\npNrbktw5r+z2JGdPul0AYLqOHtqgqt6cUTjYkOQnSX6rtfboItU3J9k7r2xvkhOq6rjW2kuLbONv\\nJ3l3kieSvDi0jwCwjm1IclqS21trP1qJNxwcFpI8mtH5BxuTfCDJrVX1zk5gmMS7k3x5Bd8PANab\\nDyb5s5V4o8FhobX2iySPjVe/VVX/KMlVST6+QPVnkmyaV7YpyfOL7VUYeyJJvvSlL+XMMxc8d5IF\\n7Ny5M7t27VrtbhxWHnnkkVx22WVJ/m2Sv7/a3TmMXJ/kX6x2Jw4zjye52ufaQD7Xhvv/n2uj36Ur\\nYZI9C/MdleS4RV67L8nF88ouyuLnOBzwYpKceeaZ2bp16/J6t45s3LjReE3skowu8uHg/MeMvrRw\\n8HYnudrn2kA+15ZlxQ7jDwoLVfXpJP81yfeT/K2MPi22ZxQAUlXXJDmltXbgXgo3JfnE+KqIP0ly\\nfkaHLlwJAQCHiaF7Fk5KckuSk5PsS/Jwkotaa98Yv745yakHKrfWnqiq9yTZleT3k/wgyUdaa/Ov\\nkAAA1qhBYaG19s+WeP3yBcruyegGTgDAYcizIY4gMzMzq90F1g1zjenwubY2CAtHEP+pmB5zjenw\\nubY2CAsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0\\nCQsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0CQsA\\nQJewAAB0CQsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0CQsAQJewAAB0CQsAQJew\\nAAB0CQsAQNegsFBVn6qqB6rq+araW1Vfqao3LtFme1W9PG/ZX1UnLa/rAMA0DN2zcG6SzyV5a5IL\\nkhyT5I6qes0S7VqSNyTZPF5Obq09O3DbAMAqOHpI5dbaJXPXq+r3kjybZFuSe5do/lxr7flBvQMA\\nVt1yz1l4XUZ7DX68RL1K8lBVPVVVd1TV25e5XQBgSiYOC1VVST6b5N7W2nc7VZ9O8rEk70/yviRP\\nJrm7qrZMum0AYHoGHYaY58Ykb0pyTq9Sa21Pkj1ziu6vqtOT7Eyyo9d2586d2bhx46vKZmZmMjMz\\nM1GHAeBIMjs7m9nZ2VeV7du3b8W3M1FYqKrPJ7kkybmttacneIsHskTISJJdu3Zl69atE7w9ABz5\\nFvoCvXv37mzbtm1FtzM4LIyDwnuTbG+tfX/C7W7J6PAEALDGDQoLVXVjkpkklyb5aVVtGr+0r7X2\\n4rjOp5P83dbajvH6VUkeT/KdJBuSfDTJu5JcuCI/AQBwSA3ds3BFRlc/3D2v/PIkt47/fnKSU+e8\\ndmyS65OckuSFJA8nOb+1ds/QzgIA0zf0PgtLXj3RWrt83vp1Sa4b2C8AYI3wbAgAoEtYAAC6hAUA\\noEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtY\\nAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6\\nhAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6BoWF\\nqvpUVT1QVc9X1d6q+kpVvfEg2p1XVQ9W1YtVtaeqdkzeZQBgmobuWTg3yeeSvDXJBUmOSXJHVb1m\\nsQZVdVqSrya5K8lZSW5IcnNVXThBfwGAKTt6SOXW2iVz16vq95I8m2RbknsXafbxJI+11j45Xv9e\\nVb0jyc4kfzmotwDA1C33nIXXJWlJftyp87Ykd84ruz3J2cvcNgAwBROHhaqqJJ9Ncm9r7budqpuT\\n7J1XtjfJCVV13KTbBwCmY9BhiHluTPKmJOesUF9+yfbt5+foo5fTReg79thjV7sLAGveRL+Jq+rz\\nSS5Jcm5r7eklqj+TZNO8sk1Jnm+tvdRr+Dd/87okG+aVnjVeYLn2J7l6tTsBMLHZ2dnMzs6+qmzf\\nvn0rvp3BYWEcFN6bZHtr7fsH0eS+JBfPK7toXL6E/5xk68AewsH6RYQF4HA2MzOTmZmZV5Xt3r07\\n27ZtW9HtDL3Pwo1JPpjknyT5aVVtGi8b5tT5dFXdMqfZTUleX1XXVtUZVXVlkg8k+cwK9B8AOMSG\\nnuB4RZITktyd5Kk5y2/PqXNyklMPrLTWnkjynozuy/BQRpdMfqS1Nv8KCQBgDRp6n4Ulw0Vr7fIF\\nyu7J6F4MAMBhxrMhAIAuYQEA6BIWAIAuYQEA6BIWAIAuYQEA6BIWAIAuYQEA6BIWAIAuYQEA6BIW\\nAIAuYQEA6BIWAIAuYQEA6BIWAIAuYQEA6BIWAIAuYQEA6BIWAIAuYQEA6BIWAIAuYQEA6BIWAIAu\\nYQEA6BIWAIAuYQEA6BIWAIAuYQEA6BIWAIAuYQEA6BIWAIAuYQEA6BIWAIAuYQEA6BIWAIAuYQEA\\n6BIWAIAuYQEA6BIWAIAuYQEA6BIWAICuwWGhqs6tqtuq6odV9XJVXbpE/e3jenOX/VV10uTdBgCm\\nZZI9C8cneSjJlUnaQbZpSd6QZPN4Obm19uwE2wYApuzooQ1aa19P8vUkqaoa0PS51trzQ7cHAKyu\\naZ2zUEkeqqqnquqOqnr7lLYLACzTNMLC00k+luT9Sd6X5Mkkd1fVlilsGwBYpsGHIYZqre1JsmdO\\n0f1VdXqSnUl29FvvTLJxXtnMeAGA9W12djazs7OvKtu3b9+Kb+eQh4VFPJDknKWr7Uqy9VD3BQAO\\nSzMzM5mZefUX6N27d2fbtm0rup3Vus/ClowOTwAAa9zgPQtVdXySX83opMUkeX1VnZXkx621J6vq\\nmiSntNZ2jOtfleTxJN9JsiHJR5O8K8mFK9B/AOAQm+QwxFuS/LeM7p3Qklw/Lr8lyYczuo/CqXPq\\nHzuuc0qSF5I8nOT81to9E/YZAJiiSe6z8FfpHL5orV0+b/26JNcN7xoAsBZ4NgQA0CUsAABdwgIA\\n0CUsAABdwgIA0CUsAABdwgIA0CUsAABdwgIA0CUsAABdwgIA0CUsAABdwgIA0CUsAABdwgIA0CUs\\nAABdwgIA0CUsAABdwgIA0CUsAABdwgIA0CUsAABdwgIA0CUsAABdwgIA0CUsAABdwgIA0CUsAABd\\nwgIA0CUsAABdwgIA0CUsAABdwgIA0CUsAABdwgIA0CUsAABdwgIA0CUsAABdwgIA0CUsAABdwgIA\\n0DU4LFTVuVV1W1X9sKperqpLD6LNeVX1YFW9WFV7qmrHZN0FAKZtkj0Lxyd5KMmVSdpSlavqtCRf\\nTXJXkrOS3JDk5qq6cIJtAwBTdvTQBq21ryf5epJUVR1Ek48neay19snx+veq6h1Jdib5y6HbBwCm\\naxrnLLwtyZ3zym5PcvYUtg0ALNPgPQsT2Jxk77yyvUlOqKrjWmsvTaEPAGvCI488stpd4Ah3KObY\\nNMLCMuxMsnFe2cx4ATicPJ3kqFx22WWr3REYbBph4Zkkm+aVbUry/NJ7FXYl2XpoegUwVf8nyctJ\\nvpTkzFXuC0e2ryW5ekXfcRph4b4kF88ru2hcDrDOnBlfgji0Vv4wxCT3WTi+qs6qqi3joteP108d\\nv35NVd0yp8lN4zrXVtUZVXVlkg8k+cyyew8AHHKTXA3xliTfSvJgRvdZuD7J7iT/Zvz65iSnHqjc\\nWnsiyXuSXJDR/Rl2JvlIa23+FRIAwBo0yX0W/iqdkNFau3yBsnuSbBu6LQBg9Xk2BADQJSwAAF3C\\nAgDQJSwAAF3CAgDQJSwAAF3CAgDQJSwAAF3CAgDQJSwAAF3CAgDQJSwAAF3CAgDQJSwAAF3CAgDQ\\nJSwAAF3CAgDQJSwAAF3CAgDQJSwAAF3CAgDQJSwAAF3CAgDQJSwAAF3CAgDQJSwAAF3CAgDQJSwA\\nAF3CAgDQJSwAAF3CAgDQJSwAAF3CAgDQJSwAAF3CAgDQJSwAAF3CAgDQJSwAAF3CAgDQJSwAAF0T\\nhYWq+kRVPV5VP6uq+6vqNzp1t1fVy/OW/VV10uTdBgCmZXBYqKrfSXJ9kj9M8utJvp3k9qo6sdOs\\nJXlDks3j5eTW2rPDuwsATNskexZ2JvlCa+3W1tqjSa5I8kKSDy/R7rnW2rMHlgm2CwCsgkFhoaqO\\nSbItyV0HylprLcmdSc7uNU3yUFU9VVV3VNXbJ+ksADB9Q/csnJjkV5LsnVe+N6PDCwt5OsnHkrw/\\nyfuSPJnk7qraMnDbAMAqOPpQb6C1tifJnjlF91fV6RkdzthxqLcPACzP0LDw10n2J9k0r3xTkmcG\\nvM8DSc5ZutrOJBvnlc2MFwBY72bHy1w/WPGtDAoLrbWfV9WDSc5PcluSVFWN1/94wFttyejwxBJ2\\nJdk6pIsAsI4s9AX6y0kuW9GtTHIY4jNJvjgODQ9k9PX/tUm+mCRVdU2SU1prO8brVyV5PMl3kmxI\\n8tEk70py4XI7DwAceoPDQmvtz8f3VPijjA4/PJTk3a2158ZVNic5dU6TYzO6L8MpGV1i+XCS81tr\\n9yyn4wDAdEx0gmNr7cYkNy7y2uXz1q9Lct0k2wEAVp9nQwAAXcICANAlLAAAXcICANAlLAAAXcIC\\nANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAl\\nLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAA\\nXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLBxRZle7A6wb5hrTYq6t\\nBROFhar6RFU9XlU/q6r7q+o3lqh/XlU9WFUvVtWeqtoxWXfp85+KaTHXmBZzbS0YHBaq6neSXJ/k\\nD5P8epJvJ7m9qk5cpP5pSb6a5K4kZyW5IcnNVXXhZF0GAKZpkj0LO5N8obV2a2vt0SRXJHkhyYcX\\nqf/xJI+11j7ZWvtea+3fJ/lP4/cBANa4QWGhqo5Jsi2jvQRJktZaS3JnkrMXafa28etz3d6pDwCs\\nIUcPrH9ikl9Jsnde+d4kZyzSZvMi9U+oquNaay8t0GbD6I//kuR/DOzieva/kvyH1e7EYeTlOX//\\nWpJHVqsjh6EfJPnyanfiMPPfx3+aa8OYa8MdmGsHfpcu39CwMC2njf74d6vaicPTx1a7A4epq1e7\\nA4ehy1a7A4cpc204c21CpyX55kq80dCw8NdJ9ifZNK98U5JnFmnzzCL1n19kr0IyOkzxwSRPJHlx\\nYB8BYD3bkFFQuH2l3nBQWGit/byqHkxyfpLbkqSqarz+x4s0uy/JxfPKLhqXL7adHyX5syF9AwBe\\nsSJ7FA6Y5GqIzyT5aFV9qKp+LclNSV6b5ItJUlXXVNUtc+rflOT1VXVtVZ1RVVcm+cD4fQCANW7w\\nOQuttT8f31PhjzI6nPBQkne31p4bV9mc5NQ59Z+oqvck2ZXk9zM6W+UjrbX5V0gAAGtQja58BABY\\nmGdDAABdwgIA0LUqYcGDqCYzZNyqantVvTxv2V9VJ02zz6upqs6tqtuq6ofjn//Sg2iz7ufa0HEz\\n15Kq+lRVPVBVz1fV3qr6SlW98SDardv5NsmYmWtJVV1RVd+uqn3j5ZtV9ZtLtFn2PJt6WPAgqskM\\nHbexluQNGZ10ujnJya21Zw91X9eQ4zM6AffKjMaiy1x7xaBxG1vvc+3cJJ9L8tYkFyQ5JskdVfWa\\nxRqYb8PHbGy9z7Unk/xBkq0ZPX7hG0n+oqrOXKjyis2z1tpUlyT3J7lhznpldIXEJxepf22Sh+eV\\nzSb52rT7vprLBOO2PaMbaJ2w2n1fC0tG93a+dIk65tpk42au/fKYnDgeu3d06phvw8fMXFt4XH6U\\n5PJFXluReTbVPQseRDWZCcctGQWKh6rqqaq6o6refmh7ethb93NtGcy1V3tdRt+Af9ypY7692sGM\\nWWKuvaKqjqqq383oXkeL3ehwRebZtA9D9B5EtXmRNt0HUa1s99asScbt6YweFPH+JO/LaNfV3VW1\\n5VB18ghgrk3GXJtjfFfbzya5t7X23U5V821swJiZa0mq6s1V9ZMkLyW5MclvtdYeXaT6isyztfog\\nKZaptbYnyZ45RfdX1elJdiZZNydRceiZa7/kxiRvSnLOanfkMHJQY2auveLRjM4/2JjRHZFvrap3\\ndgLDsk17z8K0HkR1pJlk3BbyQJJfXalOHYHMtZWzLudaVX0+ySVJzmutPb1EdfMtg8dsIeturrXW\\nftFae6y19q3W2r/O6IT3qxapviLzbKphobX28yQHHkSV5FUPolrsoRf3za0/1n0Q1ZFmwnFbyJaM\\nduOxsHU/11bQuptr4196703yrtba9w+iybqfbxOM2ULW3VxbwFFJFjuksDLzbBXO2vztJC8k+VCS\\nX0vyhYzO5Pw749evSXLLnPqnJflJRmd0npHR5Vz/N8kFq30G6hoft6uSXJrk9CT/IKPjgT/PKL2v\\n+s8zpTE7PqNddVsyOsv6n4/XTzXXVnTczLXRbvT/ndHlgJvmLBvm1Pm0+bbsMTPXRmNybpK/l+TN\\n4/+Pv0jyj8evH5LPtdX6Ya9M8kSSn2WUbt4y57U/TfKNefXfmdE3658l+Z9J/ulq/4Ot9XFL8q/G\\nY/XTJM9ldCXFO1f7Z5jyeG0f/7LbP2/5E3Nt5cbNXHvlEtP547U/yYfm1DHfljlm5lpLkpuTPDae\\nM88kueNAUDiU88yDpACALs+GAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6\\nhAUAoEtYAAC6/h+sRyodSeNw6wAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xa3faa891d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0.5, 0.7, 1.0, 1.2, 1.3, 2.1])\\n\",\n    \"bins = np.array([0, 1, 2, 3])\\n\",\n    \"print(\\\"ans=\\\\n\\\", ...)\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"plt.hist(x, bins=bins)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q13. Compute the 2d histogram of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 127,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" [[ 3.  0.  0.  0.]\\n\",\n      \" [ 0.  2.  0.  0.]\\n\",\n      \" [ 0.  0.  1.  1.]]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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V6P48ePs2LFClauXDnsclptoa+5eCmQgFOzrHMVcGjKsgPAmqqKaqvD\\nh2fq7zQTM8tjbsWZ2fydOnWKG264kdWrVzM6OsqqVau44YYbeeaZZ4ZdWmstWHMREQH8MXA4pfQP\\ns6w6Ajw1ZdlTwIsj4kVV1ddGu3btGnYJjWNmecytODObv5tv3sihQ48Ae+mfgd/LoUOPcNNNbxly\\nZe1V6WmRKT4G/Fvg6gX8nZrF/fffP+wSGsfM8phbcWY2P71ejwMHHqLfWNxCfwzBUk6fThw4sJGj\\nR496imQIFuSbi4i4FxgFrksp/WCO1Z8ELpiy7ALgJymln8200ejoKJ1OZ9JjzZo10+6Qd/DgQTqd\\nzrTtt2zZwp49eyYtGxsbo9PpcPLkyUnLd+zYwc6dOyctO3HiBJ1OhyNHjkxavnv3brZt2zZp2fj4\\nOJ1OZ9rXnt1u96xjtDds2FDJfuzcuXNR7MdCfh5Lly5dFPsBC/t5nDx5clHsx0J+HkuXLl0U+wHV\\nfh5vfetbB8+uGfzvUvrjCPr1Hjt2rBH7sRCfR7fbfe5v48jICJ1Oh61bt07bpgyV37hs0Fi8Abg2\\npfSdeaz/QeDfp5ReOWHZZ4CXTr1AdPCaQ1ElqaV6vR6rV6/mF99cnLEX2Eiv1/Obi1k0cihqRHyM\\n/qd9M/BPEXHB4HHuhHU+EBET57D4OPCKiNgZEasj4j3Am4EPV1mrJKl5Vq1axfr1oyxZ8gf0G4on\\ngL0sWXIH69eP2lgMSdWnRd4FvBh4GPj+hMfvT1jn5cCFZ56klB4HbqR/Vc5j9Ieg3ppSmjqCRM/T\\n1K/bNDczy2NuxZnZ/HW7e1m79ipgI7Ac2MjatVfR7e4dcmXtVfU8F3M2LymlaSeJUkpfpj8Xhiq0\\nfPnyYZfQOGaWx9yKM7P5W7ZsGfv3P8jRo0f58Ic/zHvf+16/sRiyyq+5qJrXXEiSlKeR11xIkqT2\\nsbmQJEmlsrlosaljqjU3M8tjbsWZWR5zqwebixbbvn373CtpEjPLY27FmVkec6sHm4sWu/fee4dd\\nQuOYWR5zK87M8phbPdhctJhD3YozszzmVpyZ5TG3erC5kCRJpbK5kCRJpbK5aLGpd+bT3Mwsj7kV\\nZ2Z5zK0ebC5abHx8fNglNI6Z5TG34swsj7nVg9N/S5LUUk7/LUmSGsHmQpIklcrmosVOnjw57BIa\\nx8zymFtxZpbH3OrB5qLFNm/ePOwSGsfM8phbcWaWx9zqweaixe68885hl9A4ZpbH3IozszzmVg82\\nFy3m6JrizCyPuRVnZnnMrR5sLiRJUqlsLiRJUqlsLlpsz549wy6hccwsj7kVZ2Z5zK0ebC5abGys\\ntMnYWsPM8phbcWaWx9zqwem/JUlqKaf/liRJjWBzIUmSSmVzIUmSSmVz0WKdTmfYJTSOmeUxt+LM\\nLI+51YPNRYvddtttwy6hccwsj7kVZ2Z5zK0eKh0tEhGvBbYBVwAvB/5DSulzs6x/LfC3UxYn4OUp\\npadn2MbRIpIkZWjqaJHzgMeA99BvEuYjASuBkcFjxsZCkiTVzzlVvnlKaT+wHyAiosCmP0wp/aSa\\nqiRJUpXqeM1FAI9FxPcj4mBEvGbYBS1W+/btG3YJjWNmecytODPLY271ULfm4gfAO4E3Ab8HPAE8\\nHBGXDbWqRarb7Q67hMYxszzmVpyZ5TG3eqhVc5FS6qWUPpFS+npK6ZGU0q3AV4Ctc207OjpKp9OZ\\n9FizZs20LvbgwYNnHaq0ZcuWaTe8GRsbo9PpcPLkyUnLd+zYwc6dOyctO3HiBJ1OhyNHjkxavnv3\\nbrZt2zZp2fj4OJ1Oh8OHD09a3u122bRp07TaNmzYUMl+XHLJJYtiPxby83jggQcWxX7Awn4e99xz\\nz6LYj4X8PB544IFFsR+wsJ/HAw88sCj2A8r/PLrd7nN/G0dGRuh0OmzdOuef1ywLdm+RiPg5c4wW\\nmWG7XcDVKaWrZ3jd0SKSJGVo6miRMlxG/3SJJElqgEpHi0TEecAK+hdpArwiIl4JnEopPRERdwO/\\nmlJ622D9O4DvAn8PnAu8A3gd8Poq65QkSeWp+puL3wa+DjxKf/6KDwFjwF2D10eACyes/8LBOt8E\\nHgZ+E7g+pfRwxXW20tnOz2l2ZpbH3IozszzmVg9Vz3PxJWZpYFJKm6Y8vwe4p8qa9Avr1q0bdgmN\\nY2Z5zK04M8tjbvWwYBd0VsULOiVJytPmCzolSVKD2FxIkqRS2Vy02NRJWDQ3M8tjbsWZWR5zqweb\\nixbbtWvXsEtoHDPLY27FmVkec6sHL+hssfHxcZYuXTrsMhrFzPKYW3FmlsfcivGCTpXOf4DFmVke\\ncyvOzPKYWz3YXEiSpFLZXEiSpFLZXLTY1Fv5am5mlsfcijOzPOZWDzYXLbZ8+fJhl9A4ZpbH3Ioz\\nszzmVg+OFpEkqaUcLSJJkhrB5kKSJJXK5qLFjhw5MuwSGsfM8phbcWaWx9zqweaixbZv3z7sEhrH\\nzPKYW3Fmlsfc6sHmosXuvffeYZfQOGaWx9yKM7M85lYPNhct5pCt4swsj7kVZ2Z5zK0ebC4kSVKp\\nbC4kSVKpbC5abOfOncMuoXHMLI+5FWdmecytHs4ZdgEanvHx8WGX0Dhmlic3t16vx/Hjx1mxYgUr\\nV64suap681jLY2714PTfkmrn1KlT3HzzRg4ceOi5ZevXj9Lt7mXZsmVDrExaXJz+W1Jr3HzzRg4d\\negTYC5wA9nLo0CPcdNNbhlyZpPnwtIikWun1eoNvLPYCtwyW3sLp04kDBzZy9OjR1p0ikZrGby5a\\n7OTJk8MuoXHMLE+R3I4fPz746Zopr1wLwLFjx8opquY81vKYWz3YXLTY5s2bh11C45hZniK5XXzx\\nxYOfvjzllS8BsGLFinKKqjmPtTzmVg82Fy125513DruExjGzPEVyW7VqFevXj7JkyR/QPzXyBLCX\\nJUvuYP360dacEvFYy2Nu9VBpcxERr42Iz0XE9yLi5xHRmcc210XEoxHxbET0IuJtVdbYZo6uKc7M\\n8hTNrdvdy9q1VwEbgeXARtauvYpud28V5dWSx1oec6uHqi/oPA94DNgD/Je5Vo6Ii4DPAx8DbgbW\\nAp+MiO+nlP6mujIlVSVnroply5axf/+DHD16lGPHjrVyngupySptLlJK+4H9ABER89jk3cB3Ukpn\\n7pn77Yj4XWArYHMhNUgZc1WsXLnSpkJqoLpdc3EVcGjKsgPAmiHUsujt2bNn2CU0jpnN3+S5Knbh\\nXBXFeKzlMbd6qFtzMQI8NWXZU8CLI+JFQ6hnURsbK20yttYws/k5M1fF6dN/Qn+uisfpz1XxUQ4c\\neIijR48Ot8AG8FjLY271ULfmItvo6CidTmfSY82aNezbt2/SegcPHqTTmX5d6ZYtW6Z1vGNjY3Q6\\nnWnjpnfs2DHt5jgnTpyg0+lw5MiRSct3797Ntm3bJi0bHx+n0+lw+PDhScu73S6bNm2aVtuGDRsq\\n2Y/zzz9/UezHQn4e991336LYD6j28/jgBz84eHZmror3AR3g3wC/mKui7vsxzM/jvvvuWxT7AQv7\\nedx3332LYj+g/M+j2+0+97dxZGSETqfD1q1bp21ThgW7t0hE/Bz4Dymlz82yzpeAR1NK752w7O3A\\nR1JKZz1J671FpPrp9XqsXr2aybNsMni+kV6v57UUUg205d4iXwWun7Js3WC5pIZwrgqp3aqe5+K8\\niHhlRFw2WPSKwfMLB6/fHRGfnrDJxwfr7IyI1RHxHuDNwIerrFNS+ZyrQmqvqr+5+G3g68CjQAI+\\nBIwBdw1eHwEuPLNySulx4Eb681s8Rn8I6q0ppakjSFSCs5071OzMbP7OzFXR6/W48sor6fV67N//\\noLdMnyePtTzmVg9Vz3PxJWZpYFJK064+SSl9GbiiyrrUd9tttw27hMYxs+JWrlzJ+9//fk+FFOSx\\nlsfc6mHBLuisihd0SpKUpy0XdEqSpIazuZAkSaWyuWixqRPEaG5mlsfcijOzPOZWDzYXLdbtdodd\\nQuOYWR5zK87M8phbPXhBpyRJLeUFnZIkqRFsLiRJUqlsLiRJUqlsLlrsbLfn1ezMLI+5FWdmecyt\\nHmwuWmzdunXDLqFxzCyPuRVnZnnMrR4cLSJJUks5WkSSJDWCzYUkSSqVzUWLHT58eNglNI6Z5TG3\\n4swsj7nVg81Fi+3atWvYJTSOmeUxt+LMLI+51YMXdLbY+Pg4S5cuHXYZjWJmecytODPLY27FeEGn\\nSuc/wOLMLI+5FWdmecytHmwuJElSqWwuJElSqWwuWmzbtm3DLqFxzCyPuRVnZnnMrR5sLlps+fLl\\nwy6hccwsj7kVZ2Z5zK0eHC0iSVJLOVpEkiQ1gs2FJEkqlc1Fix05cmTYJTSOmeUxt+LMLI+51YPN\\nRYtt37592CU0jpnlMbfizCyPudWDzUWL3XvvvcMuoXHMLI+5FWdmecytHipvLiJiS0R8NyJ+GhGP\\nRMSrZ1n32oj4+ZTH6Yh4WdV1tpFDtoozszzmVpyZ5TG3eqi0uYiIDcCHgB3Aq4BvAAci4vxZNkvA\\nSmBk8Hh5SunpKuuUJEnlqfqbi63An6WU/iKldAR4FzAObJ5jux+mlJ4+86i4RkmSVKLKmouIeAFw\\nBfCFM8tSf8auQ8Ca2TYFHouI70fEwYh4TVU1tt3OnTuHXULjmFkecyvOzPKYWz1U+c3F+cAS4Kkp\\ny5+if7rjbH4AvBN4E/B7wBPAwxFxWVVFttn4+PiwS2gcM8tjbsWZWR5zq4dajRZJKfVSSp9IKX09\\npfRISulW4Cv0T6/ManR0lE6nM+mxZs0a9u3bN2m9gwcP0ul0pm2/ZcsW9uzZM2nZ2NgYnU6HkydP\\nTlq+Y8eOad3xiRMn6HQ608ZY7969e9qNdMbHx+l0Ohw+fHjS8m63y6ZNm6bVtmHDhkr2A6Z3+U3c\\nj4X8PO66665FsR+wsJ/Hrbfeuij2YyE/j7vuumtR7Acs7Odx1113LYr9gPI/j263+9zfxpGRETqd\\nDlu3zvnnNUtl9xYZnBYZB96UUvrchOV/DrwkpfTGeb7PLuDqlNLVM7zuvUUkScrQuHuLpJT+GXgU\\nuP7MsoiIwfOvFHiry+ifLpEkSQ1Q9WmRDwPviIi3RsQlwMeBpcCfA0TE3RHx6TMrR8QdEdGJiIsj\\n4tcj4o+B1wHOilKBs50m0ezMLI+5FWdmecytHiptLlJKfwX8IfB+4OvAbwHrU0o/HKwyAlw4YZMX\\n0p8X45vAw8BvAtenlB6uss622rx5rhHBmsrM8phbcWaWx9zqobJrLhaK11zkGxsbM7OCzCyPuRVn\\nZnnMrZjGXXOh+vMfYHFmlsfcijOzPOZWDzYXkiSpVDYXkiSpVDYXLTZ10hfNzczymFtxZpbH3OrB\\n5qLFxsZKu3anNcwsj7kVZ2Z5zK0eHC0iSVJLOVpEkiQ1gs2FJEkqlc2FJEkqlc1Fi53t1sGanZnl\\nMbfizCyPudWDzUWL3XbbbcMuoXHMLI+5FWdmecytHhwtIklSSzlaRJIkNcI5wy5A7dPr9Th+/Dgr\\nVqxg5cqVwy5HklQyv7losX379i3o7zt16hQ33HAjq1evZnR0lFWrVnHDDTfyzDPPLGgdz8dCZ7ZY\\nmFtxZpbH3OrB5qLFut3ugv6+m2/eyKFDjwB7gRPAXg4deoSbbnrLgtbxfCx0ZouFuRVnZnnMrR68\\noFMLotfrsXr1avqNxS0TXtkLbKTX63mKRJIWmBd0qtGOHz8++OmaKa9cC8CxY8cWtB5JUnVsLrQg\\nLr744sFPX57yypcAWLFixYLWI0mqjs2FFsSqVatYv36UJUv+gP6pkCeAvSxZcgfr1496SkSSFhGb\\nixbbtGnTgv6+bncva9deBWwElgMbWbv2KrrdvQtax/Ox0JktFuZWnJnlMbd6cJ6LFlu3bl3Wdrnz\\nVCxbtoz9+x/k6NGjHDt2rJHzXORm1nbmVpyZ5TG3enC0iObt1KlT3HzzRg4ceOi5ZevXj9Lt7mXZ\\nsmVDrEySlMPRIhq6xTBPhSSpep4W0bz0er3BNxYT56m4hdOnEwcObOTo0aONO8UhSaqG31y02OHD\\nh+e9rvNU9BXJTL9gbsWZWR5zqwebixbbtWvXvNd1noq+IpnpF8ytODPLY271UHlzERFbIuK7EfHT\\niHgkIl49x/rXRcSjEfFsRPQi4m1V19hW999//7zXdZ6KviKZ6RfMrTgzy2Nu9VBpcxERG4APATuA\\nVwHfAA67FcwfAAAKsklEQVRExPkzrH8R8HngC8ArgY8Cn4yI11dZZ1stXbq00PqLYZ6K56toZuoz\\nt+LMLI+51UPVF3RuBf4spfQXABHxLuBGYDNwtu+u3g18J6W0ffD82xHxu4P3+ZuKa22VnLkqFsM8\\nFZKk6lXWXETEC4ArgA+cWZZSShFxCFgzw2ZXAYemLDsAfKSSIluojLkqVq5caVMhSZpRladFzgeW\\nAE9NWf4UMDLDNiMzrP/iiHhRueW10+S5Kt6Jc1UUs23btmGX0EjmVpyZ5TG3enCeixaZPlfFKZyr\\nopjly5cPu4RGMrfizCyPudVDld9cnAROAxdMWX4B8OQM2zw5w/o/SSn9bLZfNjo6SqfTmfRYs2YN\\n+/btm7TewYMH6XQ607bfsmULe/bsmbRsbGyMTqfDyZMnJy3fsWMHO3funLTsxIkTdDodjhw5Mmn5\\n7t27p3XS4+PjdDqdaeOxu93uWW+6s2HDhlL2Y2zszMyuZ+aqOAnsZOJcFU3Yj2F+Hrfffvui2A9Y\\n2M/jDW94w6LYj4X8PG6//fZFsR+wsJ/H7bffvij2A8r/PLrd7nN/G0dGRuh0OmzdunXaNmWo9N4i\\nEfEI8F9TSncMngf9eaP/JKV0z1nW/yDw71NKr5yw7DPAS1NKozP8Du8tMk+9Xo/Vq1czeZZNBs83\\n0uv1/OZCklqkqfcW+TDwjoh4a0RcAnwcWAr8OUBE3B0Rn56w/seBV0TEzohYHRHvAd48eB89T85V\\nIUlaCJU2FymlvwL+EHg/8HXgt4D1KaUfDlYZAS6csP7j9IeqrgUeoz8E9daU0tQRJMrkXBXPz9Sv\\nLTU/5lacmeUxt3qofIbOlNLHUkoXpZR+OaW0JqX0dxNe25RS+ndT1v9ySumKwforU0r/ueoa2+TM\\nXBW9Xo8rr7ySXq/H/v0Pesv0edq+ffvcK2kacyvOzPKYWz1Ues3FQvCai3wnTpzwyuqCzCyPuRVn\\nZnnMrZimXnOhGvMfYHFmlsfcijOzPOZWDzYXkiSpVDYXkiSpVDYXLTZ1IhfNzczymFtxZpbH3OrB\\n5qLFxsfHh11C45hZHnMrzszymFs9OFpEkqSWcrSIJElqBJsLSZJUKpuLFpt6tz7NzczymFtxZpbH\\n3OrB5qLFNm/ePOwSGsfM8phbcWaWx9zqweaixe68885hl9A4ZpbH3IozszzmVg82Fy3m6JrizCyP\\nuRVnZnnMrR5sLiRJUqlsLiRJUqlsLlpsz549wy6hccwsj7kVZ2Z5zK0ebC5abGystMnYWsPM8phb\\ncWaWx9zqwem/JUlqKaf/liRJjWBzIUmSSmVzIUmSSmVz0WKdTmfYJTSOmeUxt+LMLI+51YPNRYvd\\ndtttwy6hccwsj7kVZ2Z5zK0eHC0iSVJLOVpEkiQ1gs2FJEkqlc1Fi+3bt2/YJTSOmeUxt+LMLI+5\\n1YPNRYvt3Llz2CU0jpnlMbfizCyPudVDZc1FRCyLiL+MiB9HxDMR8cmIOG+ObT4VET+f8nioqhrb\\n7ld+5VeGXULjmFkecyvOzPKYWz2cU+F7fwa4ALgeeCHw58CfAW+ZY7u/Bt4OxOD5z6opT5IkVaGS\\n5iIiLgHW0x/a8vXBstuBByPiD1NKT86y+c9SSj+soi5JklS9qk6LrAGeOdNYDBwCEnDlHNteFxFP\\nRcSRiPhYRPyrimqUJEkVqOq0yAjw9MQFKaXTEXFq8NpM/hr4P4DvAhcDdwMPRcSaNPNsX+cCfOtb\\n33reRbfN1772NcbGSpszpRXMLI+5FWdmecytmAl/O88t830LzdAZEXcD75tllQRcCrwJeGtK6dIp\\n2z8F/C8ppT+b5+/7H4DjwPUppb+dYZ2bgb+cz/tJkqSzuiWl9Jmy3qzoNxf/CfjUHOt8B3gSeNnE\\nhRGxBPhXg9fmJaX03Yg4CawAztpcAAeAW4DHgWfn+96SJIlzgYvo/y0tTaHmIqX0I+BHc60XEV8F\\nXhoRr5pw3cX19EeA/Nf5/r6I+O+Bfw38YI6aSuu2JElqma+U/YaVXNCZUjpCvwv6RES8OiKuBnYD\\n3YkjRQYXbb5h8PN5EbErIq6MiF+LiOuBfUCPkjsqSZJUnSpn6LwZOEJ/lMjngS8D75yyzkrgJYOf\\nTwO/BXwW+DbwCeD/Aq5JKf1zhXVKkqQSNf6W65IkqV68t4gkSSqVzYUkSSpVI5sLb4o2PxGxJSK+\\nGxE/jYhHIuLVc6x/XUQ8GhHPRkQvIt62ULXWRZHMIuLasxxTpyPiZTNts9hExGsj4nMR8b3B/nfm\\nsY3HWcHcPNYgIv4oIr4WET8ZzOL8f0bEqnls19rjLSezso61RjYX9IeeXkp/eOuNwDX0b4o2l7+m\\nfzO1kcHjpqoKHLaI2AB8CNgBvAr4BnAgIs6fYf2L6F94+wXglcBHgU9GxOsXot46KJrZQKJ/YfKZ\\nY+rlKaWnZ1l/sTkPeAx4D/0sZuVx9pxCuQ20/Vh7Lf1Rh1cCa4EXAAcj4pdn2sDjrXhmA8//WEsp\\nNeoBXAL8HHjVhGXrgX8BRmbZ7lPAfxl2/QuY0yPARyc8D+D/AbbPsP5O4JtTlnWBh4a9LzXO7Fr6\\no5xePOza6/AY/LvszLFO64+zzNw81qZncv4gu9+dZR2Pt+KZlXKsNfGbC2+KNoeIeAFwBf1uHYDU\\nP2oO0c/vbK4avD7RgVnWX1QyM4N+A/JYRHw/Ig5GxGuqrbTxWn2cPU8ea5O9lP5/90/Nso7H22Tz\\nyQxKONaa2Fyc9aZo9MOa66ZobwX+HbCdfnf2UERERXUO0/nAEuCpKcufYuaMRmZY/8UR8aJyy6ul\\nnMx+QH/uljcBvwc8ATwcEZdVVeQi0PbjLJfH2gSD/27/MXA4pfQPs6zq8TZQILNSjrWq7opaWMz/\\npmhZUkp/NeHp30fE/03/pmjXMfN9S6QZpZR69GeQPeORiLgY2Aq05qIxVc9jbZqPAf8WuHrYhTTI\\nvDIr61irTXNBPW+K1lQn6Z8zu2DK8guYOaMnZ1j/Jymln5VbXi3lZHY2X8P/4M2m7cdZmVp5rEXE\\nvcAo8NqU0oz3nRrweKNwZmdT+FirzWmRlNKPUkq9OR7/Ajx3U7QJm1dyU7SmSv3p0h+lnwvw3Fdi\\n1zPzDWq+OnH9gXWD5YteZmZncxmL8JgqUauPs5K17lgb/JF8A/C6lNKJeWzS+uMtI7OzKX6sDfvq\\n1cwrXh8C/g54Nf1u6tvAf56yzhHgDYOfzwN20b/g89foH2x/B3wLeMGw96eijH4fGKd/nckl9Ifq\\n/gj4lcHrdwOfnrD+RcA/0r+6ejX9IXL/H7B22PtS48zuADrAxcCv0z+f+c/AdcPelwXM7Dz6Q/wu\\no38V+v80eH6hx1mpuXms9b/Wf4b+8MoLJjzOnbDOBzzenndmpRxrQ9/5zMBeCuwFfjwI7hPA0inr\\nnAbeOvj5XGA//a/InqV/euVPz/zRWKyPwT+kx4Gf0u/Uf3vCa58Cvjhl/Wvo/7/3nwJHgY3D3oc6\\nZwZsG+T0T8AP6Y80uWbY+7DAeV07+ON4esrjf/M4Ky83j7XnhuxOzeu5/857vJWTWVnHmjcukyRJ\\nparNNReSJGlxsLmQJEmlsrmQJEmlsrmQJEmlsrmQJEmlsrmQJEmlsrmQJEmlsrmQJEmlsrmQJEml\\nsrmQJEmlsrmQJEml+v8Bev3xcJRJzrUAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xa3fbcfa7f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"xedges = [0, 1, 2, 3]\\n\",\n    \"yedges = [0, 1, 2, 3, 4]\\n\",\n    \"x = np.array([0, 0.1, 0.2, 1., 1.1, 2., 2.1])\\n\",\n    \"y = np.array([0, 0.1, 0.2, 1., 1.1, 2., 3.3])\\n\",\n    \"...\\n\",\n    \"\\n\",\n    \"plt.scatter(x, y)\\n\",\n    \"plt.grid()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q14. Count number of occurrences of 0 through 7 in x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 129,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" [1 3 1 1 0 0 0 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 1, 3, 2, 1, 7])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q15. Return the indices of the bins to which each value in x belongs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 130,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" [1 4 3 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0.2, 6.4, 3.0, 1.6])\\n\",\n    \"bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python [conda root]\",\n   \"language\": \"python\",\n   \"name\": \"conda-root-py\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/13_Statistics_solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Statistics\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"__author__ = \\\"kyubyong. kbpark.linguist@gmail.com\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.3'\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Order statistics\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Return the minimum value of x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x=\\n\",\n      \" [[0 1]\\n\",\n      \" [2 3]]\\n\",\n      \"ans=\\n\",\n      \" [0 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(4).reshape((2, 2))\\n\",\n    \"print(\\\"x=\\\\n\\\", x)\\n\",\n    \"print(\\\"ans=\\\\n\\\", np.amin(x, 1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Return the maximum value of x along the second axis. Reduce the second axis to the dimension with size one.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x=\\n\",\n      \" [[0 1]\\n\",\n      \" [2 3]]\\n\",\n      \"ans=\\n\",\n      \" [[1]\\n\",\n      \" [3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(4).reshape((2, 2))\\n\",\n    \"print(\\\"x=\\\\n\\\", x)\\n\",\n    \"print(\\\"ans=\\\\n\\\", np.amax(x, 1, keepdims=True))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Calcuate the difference between the maximum and the minimum of x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x=\\n\",\n      \" [[0 1 2 3 4]\\n\",\n      \" [5 6 7 8 9]]\\n\",\n      \"ans=\\n\",\n      \" [4 4]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10).reshape((2, 5))\\n\",\n    \"print(\\\"x=\\\\n\\\", x)\\n\",\n    \"\\n\",\n    \"out1 = np.ptp(x, 1)\\n\",\n    \"out2 = np.amax(x, 1) - np.amin(x, 1)\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print(\\\"ans=\\\\n\\\", out1)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Compute the 75th percentile of x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x=\\n\",\n      \" [[ 1  2  3  4  5]\\n\",\n      \" [ 6  7  8  9 10]]\\n\",\n      \"ans=\\n\",\n      \" [ 4.  9.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1, 11).reshape((2, 5))\\n\",\n    \"print(\\\"x=\\\\n\\\", x)\\n\",\n    \"\\n\",\n    \"print(\\\"ans=\\\\n\\\", np.percentile(x, 75, 1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Averages and variances\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Compute the median of flattened x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x=\\n\",\n      \" [[1 2 3]\\n\",\n      \" [4 5 6]\\n\",\n      \" [7 8 9]]\\n\",\n      \"ans=\\n\",\n      \" 5.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1, 10).reshape((3, 3))\\n\",\n    \"print(\\\"x=\\\\n\\\", x)\\n\",\n    \"\\n\",\n    \"print(\\\"ans=\\\\n\\\", np.median(x))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Compute the weighted average of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2.66666666667\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(5)\\n\",\n    \"weights = np.arange(1, 6)\\n\",\n    \"\\n\",\n    \"out1 = np.average(x, weights=weights)\\n\",\n    \"out2 = (x*(weights/weights.sum())).sum()\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print(out1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Compute the mean, standard deviation, and variance of x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x=\\n\",\n      \" [0 1 2 3 4]\\n\",\n      \"mean=\\n\",\n      \" 2.0\\n\",\n      \"std=\\n\",\n      \" 1.41421356237\\n\",\n      \"variance=\\n\",\n      \" 2.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(5)\\n\",\n    \"print(\\\"x=\\\\n\\\",x)\\n\",\n    \"\\n\",\n    \"out1 = np.mean(x)\\n\",\n    \"out2 = np.average(x)\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print(\\\"mean=\\\\n\\\", out1)\\n\",\n    \"\\n\",\n    \"out3 = np.std(x)\\n\",\n    \"out4 = np.sqrt(np.mean((x - np.mean(x)) ** 2 ))\\n\",\n    \"assert np.allclose(out3, out4)\\n\",\n    \"print(\\\"std=\\\\n\\\", out3)\\n\",\n    \"\\n\",\n    \"out5 = np.var(x)\\n\",\n    \"out6 = np.mean((x - np.mean(x)) ** 2 )\\n\",\n    \"assert np.allclose(out5, out6)\\n\",\n    \"print(\\\"variance=\\\\n\\\", out5)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Correlating\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Compute the covariance matrix of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" [[ 1. -1.]\\n\",\n      \" [-1.  1.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2])\\n\",\n    \"y = np.array([2, 1, 0])\\n\",\n    \"\\n\",\n    \"print(\\\"ans=\\\\n\\\", np.cov(x, y))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. In the above covariance matrix, what does the -1 mean?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It means `x` and `y` correlate perfectly in opposite directions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Compute Pearson product-moment correlation coefficients of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" [[ 1.          0.92857143]\\n\",\n      \" [ 0.92857143  1.        ]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 3])\\n\",\n    \"y = np.array([2, 4, 5])\\n\",\n    \"\\n\",\n    \"print(\\\"ans=\\\\n\\\", np.corrcoef(x, y))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Compute cross-correlation of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" [19]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 3])\\n\",\n    \"y = np.array([2, 4, 5])\\n\",\n    \"\\n\",\n    \"print(\\\"ans=\\\\n\\\", np.correlate(x, y))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Histograms\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Compute the histogram of x against the bins.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" (array([2, 3, 1], dtype=int64), array([0, 1, 2, 3]))\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAgsAAAFkCAYAAACuFXjcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nhAUAoEtYAAC6/h+sRyodSeNw6wAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xa3faa891d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0.5, 0.7, 1.0, 1.2, 1.3, 2.1])\\n\",\n    \"bins = np.array([0, 1, 2, 3])\\n\",\n    \"print(\\\"ans=\\\\n\\\", np.histogram(x, bins))\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"plt.hist(x, bins=bins)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q13. Compute the 2d histogram of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 127,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" [[ 3.  0.  0.  0.]\\n\",\n      \" [ 0.  2.  0.  0.]\\n\",\n      \" [ 0.  0.  1.  1.]]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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V6P48ePs2LFClauXDnsclptoa+5eCmQgFOzrHMVcGjKsgPAmqqKaqvD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\\nsrmQJEmlsrmQJEml+v8Bev3xcJRJzrUAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0xa3fbcfa7f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"xedges = [0, 1, 2, 3]\\n\",\n    \"yedges = [0, 1, 2, 3, 4]\\n\",\n    \"x = np.array([0, 0.1, 0.2, 1., 1.1, 2., 2.1])\\n\",\n    \"y = np.array([0, 0.1, 0.2, 1., 1.1, 2., 3.3])\\n\",\n    \"H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))\\n\",\n    \"print(\\\"ans=\\\\n\\\", H)\\n\",\n    \"\\n\",\n    \"plt.scatter(x, y)\\n\",\n    \"plt.grid()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q14. Count number of occurrences of 0 through 7 in x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 129,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" [1 3 1 1 0 0 0 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 1, 3, 2, 1, 7])\\n\",\n    \"print(\\\"ans=\\\\n\\\", np.bincount(x))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q15. Return the indices of the bins to which each value in x belongs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 130,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ans=\\n\",\n      \" [1 4 3 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0.2, 6.4, 3.0, 1.6])\\n\",\n    \"bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])\\n\",\n    \"\\n\",\n    \"print(\\\"ans=\\\\n\\\", np.digitize(x, bins))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python [conda root]\",\n   \"language\": \"python\",\n   \"name\": \"conda-root-py\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/1_Array_creation_routines.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Array creation routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Ones and zeros\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a new array of 2*2 integers, without initializing entries.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0, 0],\\n\",\n       \"       [0, 0]])\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let X = np.array([1,2,3], [4,5,6], np.int32). \\n\",\n    \"Create a new array with the same shape and type as X.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 2, 3],\\n\",\n       \"       [4, 5, 6]])\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X = np.array([[1,2,3], [4,5,6]], np.int32)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a 3-D array with ones on the diagonal and zeros elsewhere.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1.,  0.,  0.],\\n\",\n       \"       [ 0.,  1.,  0.],\\n\",\n       \"       [ 0.,  0.,  1.]])\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1.,  0.,  0.],\\n\",\n       \"       [ 0.,  1.,  0.],\\n\",\n       \"       [ 0.,  0.,  1.]])\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a new array of 3*2 float numbers, filled with ones.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1.,  1.],\\n\",\n       \"       [ 1.,  1.],\\n\",\n       \"       [ 1.,  1.]])\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = np.arange(4, dtype=np.int64). Create an array of ones with the same shape and type as X.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 1, 1, 1], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 59,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(4, dtype=np.int64)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a new array of 3*2 float numbers, filled with zeros.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.,  0.],\\n\",\n       \"       [ 0.,  0.],\\n\",\n       \"       [ 0.,  0.]])\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = np.arange(4, dtype=np.int64). Create an array of zeros with the same shape and type as X.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 0, 0, 0], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(4, dtype=np.int64)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a new array of 2*5 uints, filled with 6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[6, 6, 6, 6, 6],\\n\",\n       \"       [6, 6, 6, 6, 6]], dtype=uint32)\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = np.arange(4, dtype=np.int64). Create an array of 6's with the same shape and type as X.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([6, 6, 6, 6], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 79,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(4, dtype=np.int64)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## From existing data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create an array of [1, 2, 3].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3])\"\n      ]\n     },\n     \"execution_count\": 53,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = [1, 2]. Convert it into an array.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2])\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = [1,2]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let X = np.array([[1, 2], [3, 4]]). Convert it into a matrix.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"matrix([[1, 2],\\n\",\n       \"        [3, 4]])\"\n      ]\n     },\n     \"execution_count\": 62,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X = np.array([[1, 2], [3, 4]])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = [1, 2]. Conver it into an array of `float`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1.,  2.])\"\n      ]\n     },\n     \"execution_count\": 63,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = [1, 2]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = np.array([30]). Convert it into scalar of its single element, i.e. 30.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"30\"\n      ]\n     },\n     \"execution_count\": 67,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.array([30])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = np.array([1, 2, 3]). Create a array copy of x, which has a different id from x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"70140352 [1 2 3]\\n\",\n      \"70140752 [1 2 3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 3])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Numerical ranges\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create an array of 2, 4, 6, 8, ..., 100.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  2,   4,   6,   8,  10,  12,  14,  16,  18,  20,  22,  24,  26,\\n\",\n       \"        28,  30,  32,  34,  36,  38,  40,  42,  44,  46,  48,  50,  52,\\n\",\n       \"        54,  56,  58,  60,  62,  64,  66,  68,  70,  72,  74,  76,  78,\\n\",\n       \"        80,  82,  84,  86,  88,  90,  92,  94,  96,  98, 100])\"\n      ]\n     },\n     \"execution_count\": 85,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a 1-D array of 50 evenly spaced elements between 3. and 10., inclusive.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  3.        ,   3.14285714,   3.28571429,   3.42857143,\\n\",\n       \"         3.57142857,   3.71428571,   3.85714286,   4.        ,\\n\",\n       \"         4.14285714,   4.28571429,   4.42857143,   4.57142857,\\n\",\n       \"         4.71428571,   4.85714286,   5.        ,   5.14285714,\\n\",\n       \"         5.28571429,   5.42857143,   5.57142857,   5.71428571,\\n\",\n       \"         5.85714286,   6.        ,   6.14285714,   6.28571429,\\n\",\n       \"         6.42857143,   6.57142857,   6.71428571,   6.85714286,\\n\",\n       \"         7.        ,   7.14285714,   7.28571429,   7.42857143,\\n\",\n       \"         7.57142857,   7.71428571,   7.85714286,   8.        ,\\n\",\n       \"         8.14285714,   8.28571429,   8.42857143,   8.57142857,\\n\",\n       \"         8.71428571,   8.85714286,   9.        ,   9.14285714,\\n\",\n       \"         9.28571429,   9.42857143,   9.57142857,   9.71428571,\\n\",\n       \"         9.85714286,  10.        ])\"\n      ]\n     },\n     \"execution_count\": 86,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a 1-D array of 50 element spaced evenly on a log scale between 3. and 10., exclusive.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  1.00000000e+03,   1.38038426e+03,   1.90546072e+03,\\n\",\n       \"         2.63026799e+03,   3.63078055e+03,   5.01187234e+03,\\n\",\n       \"         6.91830971e+03,   9.54992586e+03,   1.31825674e+04,\\n\",\n       \"         1.81970086e+04,   2.51188643e+04,   3.46736850e+04,\\n\",\n       \"         4.78630092e+04,   6.60693448e+04,   9.12010839e+04,\\n\",\n       \"         1.25892541e+05,   1.73780083e+05,   2.39883292e+05,\\n\",\n       \"         3.31131121e+05,   4.57088190e+05,   6.30957344e+05,\\n\",\n       \"         8.70963590e+05,   1.20226443e+06,   1.65958691e+06,\\n\",\n       \"         2.29086765e+06,   3.16227766e+06,   4.36515832e+06,\\n\",\n       \"         6.02559586e+06,   8.31763771e+06,   1.14815362e+07,\\n\",\n       \"         1.58489319e+07,   2.18776162e+07,   3.01995172e+07,\\n\",\n       \"         4.16869383e+07,   5.75439937e+07,   7.94328235e+07,\\n\",\n       \"         1.09647820e+08,   1.51356125e+08,   2.08929613e+08,\\n\",\n       \"         2.88403150e+08,   3.98107171e+08,   5.49540874e+08,\\n\",\n       \"         7.58577575e+08,   1.04712855e+09,   1.44543977e+09,\\n\",\n       \"         1.99526231e+09,   2.75422870e+09,   3.80189396e+09,\\n\",\n       \"         5.24807460e+09,   7.24435960e+09])\"\n      ]\n     },\n     \"execution_count\": 88,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Building matrices\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let X = np.array([[ 0,  1,  2,  3],\\n\",\n    \"                  [ 4,  5,  6,  7],\\n\",\n    \"                 [ 8,  9, 10, 11]]).\\n\",\n    \"                 Get the diagonal of X, that is, [0, 5, 10].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0,  5, 10])\"\n      ]\n     },\n     \"execution_count\": 93,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X = np.array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a 2-D array whose diagonal equals [1, 2, 3, 4] and 0's elsewhere.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 0, 0, 0],\\n\",\n       \"       [0, 2, 0, 0],\\n\",\n       \"       [0, 0, 3, 0],\\n\",\n       \"       [0, 0, 0, 4]])\"\n      ]\n     },\n     \"execution_count\": 95,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create an array which looks like below.\\n\",\n    \"array([[ 0.,  0.,  0.,  0.,  0.],\\n\",\n    \"       [ 1.,  0.,  0.,  0.,  0.],\\n\",\n    \"       [ 1.,  1.,  0.,  0.,  0.]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.,  0.,  0.,  0.,  0.],\\n\",\n       \"       [ 1.,  0.,  0.,  0.,  0.],\\n\",\n       \"       [ 1.,  1.,  0.,  0.,  0.]])\"\n      ]\n     },\n     \"execution_count\": 97,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create an array which looks like below.\\n\",\n    \"array([[ 0,  0,  0],\\n\",\n    \"       [ 4,  0,  0],\\n\",\n    \"       [ 7,  8,  0],\\n\",\n    \"       [10, 11, 12]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0,  0,  0],\\n\",\n       \"       [ 4,  0,  0],\\n\",\n       \"       [ 7,  8,  0],\\n\",\n       \"       [10, 11, 12]])\"\n      ]\n     },\n     \"execution_count\": 101,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create an array which looks like below. array([[ 1,  2,  3],\\n\",\n    \"       [ 4,  5,  6],\\n\",\n    \"       [ 0,  8,  9],\\n\",\n    \"       [ 0,  0, 12]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1,  2,  3],\\n\",\n       \"       [ 4,  5,  6],\\n\",\n       \"       [ 0,  8,  9],\\n\",\n       \"       [ 0,  0, 12]])\"\n      ]\n     },\n     \"execution_count\": 102,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/1_Array_creation_routines_Solution.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Array creation routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Ones and zeros\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a new array of 2*2 integers, without initializing entries.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0, 0],\\n\",\n       \"       [0, 0]])\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.empty([2,2], int)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let X = np.array([1,2,3], [4,5,6], np.int32). \\n\",\n    \"Create a new array with the same shape and type as X.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 2, 3],\\n\",\n       \"       [4, 5, 6]])\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X = np.array([[1,2,3], [4,5,6]], np.int32)\\n\",\n    \"np.empty_like(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a 3-D array with ones on the diagonal and zeros elsewhere.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1.,  0.,  0.],\\n\",\n       \"       [ 0.,  1.,  0.],\\n\",\n       \"       [ 0.,  0.,  1.]])\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.eye(3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1.,  0.,  0.],\\n\",\n       \"       [ 0.,  1.,  0.],\\n\",\n       \"       [ 0.,  0.,  1.]])\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.identity(3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a new array of 3*2 float numbers, filled with ones.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1.,  1.],\\n\",\n       \"       [ 1.,  1.],\\n\",\n       \"       [ 1.,  1.]])\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.ones([3,2], float)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = np.arange(4, dtype=np.int64). Create an array of ones with the same shape and type as X.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 1, 1, 1], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 59,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(4, dtype=np.int64)\\n\",\n    \"np.ones_like(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a new array of 3*2 float numbers, filled with zeros.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.,  0.],\\n\",\n       \"       [ 0.,  0.],\\n\",\n       \"       [ 0.,  0.]])\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.zeros((3,2), float)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = np.arange(4, dtype=np.int64). Create an array of zeros with the same shape and type as X.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 0, 0, 0], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(4, dtype=np.int64)\\n\",\n    \"np.zeros_like(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a new array of 2*5 uints, filled with 6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[6, 6, 6, 6, 6],\\n\",\n       \"       [6, 6, 6, 6, 6]], dtype=uint32)\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.full((2, 5), 6, dtype=np.uint)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[6, 6, 6, 6, 6],\\n\",\n       \"       [6, 6, 6, 6, 6]], dtype=uint32)\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.ones([2, 5], dtype=np.uint) * 6\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = np.arange(4, dtype=np.int64). Create an array of 6's with the same shape and type as X.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([6, 6, 6, 6], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 79,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(4, dtype=np.int64)\\n\",\n    \"np.full_like(x, 6)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([6, 6, 6, 6], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 81,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.ones_like(x) * 6\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## From existing data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create an array of [1, 2, 3].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3])\"\n      ]\n     },\n     \"execution_count\": 53,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.array([1, 2, 3])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = [1, 2]. Convert it into an array.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2])\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = [1,2]\\n\",\n    \"np.asarray(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let X = np.array([[1, 2], [3, 4]]). Convert it into a matrix.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"matrix([[1, 2],\\n\",\n       \"        [3, 4]])\"\n      ]\n     },\n     \"execution_count\": 62,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X = np.array([[1, 2], [3, 4]])\\n\",\n    \"np.asmatrix(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = [1, 2]. Conver it into an array of `float`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1.,  2.])\"\n      ]\n     },\n     \"execution_count\": 63,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = [1, 2]\\n\",\n    \"np.asfarray(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1.,  2.])\"\n      ]\n     },\n     \"execution_count\": 64,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.asarray(x, float)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = np.array([30]). Convert it into scalar of its single element, i.e. 30.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"30\"\n      ]\n     },\n     \"execution_count\": 67,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.array([30])\\n\",\n    \"np.asscalar(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"30\"\n      ]\n     },\n     \"execution_count\": 68,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let x = np.array([1, 2, 3]). Create a array copy of x, which has a different id from x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"70140352 [1 2 3]\\n\",\n      \"70140752 [1 2 3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 3])\\n\",\n    \"y = np.copy(x)\\n\",\n    \"print id(x), x\\n\",\n    \"print id(y), y\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Numerical ranges\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create an array of 2, 4, 6, 8, ..., 100.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  2,   4,   6,   8,  10,  12,  14,  16,  18,  20,  22,  24,  26,\\n\",\n       \"        28,  30,  32,  34,  36,  38,  40,  42,  44,  46,  48,  50,  52,\\n\",\n       \"        54,  56,  58,  60,  62,  64,  66,  68,  70,  72,  74,  76,  78,\\n\",\n       \"        80,  82,  84,  86,  88,  90,  92,  94,  96,  98, 100])\"\n      ]\n     },\n     \"execution_count\": 85,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(2, 101, 2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a 1-D array of 50 evenly spaced elements between 3. and 10., inclusive.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  3.        ,   3.14285714,   3.28571429,   3.42857143,\\n\",\n       \"         3.57142857,   3.71428571,   3.85714286,   4.        ,\\n\",\n       \"         4.14285714,   4.28571429,   4.42857143,   4.57142857,\\n\",\n       \"         4.71428571,   4.85714286,   5.        ,   5.14285714,\\n\",\n       \"         5.28571429,   5.42857143,   5.57142857,   5.71428571,\\n\",\n       \"         5.85714286,   6.        ,   6.14285714,   6.28571429,\\n\",\n       \"         6.42857143,   6.57142857,   6.71428571,   6.85714286,\\n\",\n       \"         7.        ,   7.14285714,   7.28571429,   7.42857143,\\n\",\n       \"         7.57142857,   7.71428571,   7.85714286,   8.        ,\\n\",\n       \"         8.14285714,   8.28571429,   8.42857143,   8.57142857,\\n\",\n       \"         8.71428571,   8.85714286,   9.        ,   9.14285714,\\n\",\n       \"         9.28571429,   9.42857143,   9.57142857,   9.71428571,\\n\",\n       \"         9.85714286,  10.        ])\"\n      ]\n     },\n     \"execution_count\": 86,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.linspace(3., 10, 50)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a 1-D array of 50 element spaced evenly on a log scale between 3. and 10., exclusive.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  1.00000000e+03,   1.38038426e+03,   1.90546072e+03,\\n\",\n       \"         2.63026799e+03,   3.63078055e+03,   5.01187234e+03,\\n\",\n       \"         6.91830971e+03,   9.54992586e+03,   1.31825674e+04,\\n\",\n       \"         1.81970086e+04,   2.51188643e+04,   3.46736850e+04,\\n\",\n       \"         4.78630092e+04,   6.60693448e+04,   9.12010839e+04,\\n\",\n       \"         1.25892541e+05,   1.73780083e+05,   2.39883292e+05,\\n\",\n       \"         3.31131121e+05,   4.57088190e+05,   6.30957344e+05,\\n\",\n       \"         8.70963590e+05,   1.20226443e+06,   1.65958691e+06,\\n\",\n       \"         2.29086765e+06,   3.16227766e+06,   4.36515832e+06,\\n\",\n       \"         6.02559586e+06,   8.31763771e+06,   1.14815362e+07,\\n\",\n       \"         1.58489319e+07,   2.18776162e+07,   3.01995172e+07,\\n\",\n       \"         4.16869383e+07,   5.75439937e+07,   7.94328235e+07,\\n\",\n       \"         1.09647820e+08,   1.51356125e+08,   2.08929613e+08,\\n\",\n       \"         2.88403150e+08,   3.98107171e+08,   5.49540874e+08,\\n\",\n       \"         7.58577575e+08,   1.04712855e+09,   1.44543977e+09,\\n\",\n       \"         1.99526231e+09,   2.75422870e+09,   3.80189396e+09,\\n\",\n       \"         5.24807460e+09,   7.24435960e+09])\"\n      ]\n     },\n     \"execution_count\": 88,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.logspace(3., 10., 50, endpoint=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Building matrices\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let X = np.array([[ 0,  1,  2,  3],\\n\",\n    \"                  [ 4,  5,  6,  7],\\n\",\n    \"                 [ 8,  9, 10, 11]]).\\n\",\n    \"                 Get the diagonal of X, that is, [0, 5, 10].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0,  5, 10])\"\n      ]\n     },\n     \"execution_count\": 93,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X = np.array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]])\\n\",\n    \"np.diag(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0,  5, 10])\"\n      ]\n     },\n     \"execution_count\": 94,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X.diagonal()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create a 2-D array whose diagonal equals [1, 2, 3, 4] and 0's elsewhere.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 0, 0, 0],\\n\",\n       \"       [0, 2, 0, 0],\\n\",\n       \"       [0, 0, 3, 0],\\n\",\n       \"       [0, 0, 0, 4]])\"\n      ]\n     },\n     \"execution_count\": 95,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.diagflat([1, 2, 3, 4])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create an array which looks like below.\\n\",\n    \"array([[ 0.,  0.,  0.,  0.,  0.],\\n\",\n    \"       [ 1.,  0.,  0.,  0.,  0.],\\n\",\n    \"       [ 1.,  1.,  0.,  0.,  0.]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.,  0.,  0.,  0.,  0.],\\n\",\n       \"       [ 1.,  0.,  0.,  0.,  0.],\\n\",\n       \"       [ 1.,  1.,  0.,  0.,  0.]])\"\n      ]\n     },\n     \"execution_count\": 97,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.tri(3, 5, -1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create an array which looks like below.\\n\",\n    \"array([[ 0,  0,  0],\\n\",\n    \"       [ 4,  0,  0],\\n\",\n    \"       [ 7,  8,  0],\\n\",\n    \"       [10, 11, 12]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0,  0,  0],\\n\",\n       \"       [ 4,  0,  0],\\n\",\n       \"       [ 7,  8,  0],\\n\",\n       \"       [10, 11, 12]])\"\n      ]\n     },\n     \"execution_count\": 101,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.tril(np.arange(1, 13).reshape(4, 3), -1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Create an array which looks like below. array([[ 1,  2,  3],\\n\",\n    \"       [ 4,  5,  6],\\n\",\n    \"       [ 0,  8,  9],\\n\",\n    \"       [ 0,  0, 12]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1,  2,  3],\\n\",\n       \"       [ 4,  5,  6],\\n\",\n       \"       [ 0,  8,  9],\\n\",\n       \"       [ 0,  0, 12]])\"\n      ]\n     },\n     \"execution_count\": 102,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.triu(np.arange(1, 13).reshape(4, 3), -1)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/2_Array_manipulation_routines.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Array manipulation routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"Q1. Let x be a ndarray [10, 10, 3] with all elements set to one. Reshape x so that the size of the second dimension equals 150.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.]\\n\",\n      \" [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Let x be array [[1, 2, 3], [4, 5, 6]]. Convert it to [1 4 2 5 3 6].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 4 2 5 3 6]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Let x be array [[1, 2, 3], [4, 5, 6]]. Get the 5th element.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"5\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Let x be an arbitrary 3-D array of shape (3, 4, 5). Permute the dimensions of x such that the new shape will be (4,3,5).\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(4L, 3L, 5L)\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Let x be an arbitrary 2-D array of shape (3, 4). Permute the dimensions of x such that the new shape will be (4,3).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(4L, 3L)\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Let x be an arbitrary 2-D array of shape (3, 4). Insert a nex axis such that the new shape will be (3, 1, 4).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(3L, 1L, 4L)\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Let x be an arbitrary 3-D array of shape (3, 4, 1). Remove a single-dimensional entries such that the new shape will be (3, 4).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(3L, 4L)\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Lex x be an array <br/>\\n\",\n    \"[[ 1 2 3]<br/>\\n\",\n    \"[ 4 5 6].<br/><br/>\\n\",\n    \"and y be an array <br/>\\n\",\n    \"[[ 7 8 9]<br/>\\n\",\n    \"[10 11 12]].<br/>\\n\",\n    \"Concatenate x and y so that a new array looks like <br/>[[1, 2, 3, 7, 8, 9], <br/>[4, 5, 6, 10, 11, 12]].\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1  2  3  7  8  9]\\n\",\n      \" [ 4  5  6 10 11 12]]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Lex x be an array <br/>\\n\",\n    \"[[ 1 2 3]<br/>\\n\",\n    \"[ 4 5 6].<br/><br/>\\n\",\n    \"and y be an array <br/>\\n\",\n    \"[[ 7 8 9]<br/>\\n\",\n    \"[10 11 12]].<br/>\\n\",\n    \"Concatenate x and y so that a new array looks like <br/>[[ 1  2  3]<br/>\\n\",\n    \" [ 4  5  6]<br/>\\n\",\n    \" [ 7  8  9]<br/>\\n\",\n    \" [10 11 12]]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1  2  3]\\n\",\n      \" [ 4  5  6]\\n\",\n      \" [ 7  8  9]\\n\",\n      \" [10 11 12]]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Let x be an array [1 2 3] and y be [4 5 6]. Convert it to [[1, 4], [2, 5], [3, 6]].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 4]\\n\",\n      \" [2 5]\\n\",\n      \" [3 6]]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Let x be an array [[1],[2],[3]] and y be [[4], [5], [6]]. Convert x to [[[1, 4]], [[2, 5]], [[3, 6]]].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[[1 4]]\\n\",\n      \"\\n\",\n      \" [[2 5]]\\n\",\n      \"\\n\",\n      \" [[3 6]]]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Let x be an array [1, 2, 3, ..., 9]. Split x into 3 arrays, each of which has 4, 2, and 3 elements in the original order.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([1, 2, 3, 4]), array([5, 6]), array([7, 8, 9])]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Let x be an array<br/>\\n\",\n    \"[[[  0.,   1.,   2.,   3.],<br/>\\n\",\n    \"  [  4.,   5.,   6.,   7.]],<br/>\\n\",\n    \" \\n\",\n    \" [[  8.,   9.,  10.,  11.],<br/>\\n\",\n    \"  [ 12.,  13.,  14.,  15.]]].<br/>\\n\",\n    \"Split it into two such that the first array looks like<br/>\\n\",\n    \"[[[  0.,   1.,   2.],<br/>\\n\",\n    \"  [  4.,   5.,   6.]],<br/>\\n\",\n    \" \\n\",\n    \" [[  8.,   9.,  10.],<br/>\\n\",\n    \"  [ 12.,  13.,  14.]]].<br/>\\n\",\n    \"  \\n\",\n    \"and the second one look like:<br/>\\n\",\n    \"  \\n\",\n    \"[[[  3.],<br/>\\n\",\n    \"  [  7.]],<br/>\\n\",\n    \" \\n\",\n    \" [[  11.],<br/>\\n\",\n    \"  [ 15.]]].<br/>  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[[ 0,  1,  2],\\n\",\n      \"        [ 4,  5,  6]],\\n\",\n      \"\\n\",\n      \"       [[ 8,  9, 10],\\n\",\n      \"        [12, 13, 14]]]), array([[[ 3],\\n\",\n      \"        [ 7]],\\n\",\n      \"\\n\",\n      \"       [[11],\\n\",\n      \"        [15]]])]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Let x be an array <br />\\n\",\n    \"[[  0.,   1.,   2.,   3.],<br>\\n\",\n    \" [  4.,   5.,   6.,   7.],<br>\\n\",\n    \" [  8.,   9.,  10.,  11.],<br>\\n\",\n    \" [ 12.,  13.,  14.,  15.]].<br>\\n\",\n    \"Split it into two arrays along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[ 0,  1],\\n\",\n      \"       [ 4,  5],\\n\",\n      \"       [ 8,  9],\\n\",\n      \"       [12, 13]]), array([[ 2,  3],\\n\",\n      \"       [ 6,  7],\\n\",\n      \"       [10, 11],\\n\",\n      \"       [14, 15]])]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q13. Let x be an array <br />\\n\",\n    \"[[  0.,   1.,   2.,   3.],<br>\\n\",\n    \" [  4.,   5.,   6.,   7.],<br>\\n\",\n    \" [  8.,   9.,  10.,  11.],<br>\\n\",\n    \" [ 12.,  13.,  14.,  15.]].<br>\\n\",\n    \"Split it into two arrays along the first axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[0, 1, 2, 3],\\n\",\n      \"       [4, 5, 6, 7]]), array([[ 8,  9, 10, 11],\\n\",\n      \"       [12, 13, 14, 15]])]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q14. Let x be an array [0, 1, 2]. Convert it to <br/>\\n\",\n    \"[[0, 1, 2, 0, 1, 2],<br/>\\n\",\n    \" [0, 1, 2, 0, 1, 2]].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1 2 0 1 2]\\n\",\n      \" [0 1 2 0 1 2]]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q15. Let x be an array [0, 1, 2]. Convert it to <br/>\\n\",\n    \"[0, 0, 1, 1, 2, 2].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 0 1 1 2 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q16. Let x be an array [0, 0, 0, 1, 2, 3, 0, 2, 1, 0].<br/>\\n\",\n    \"remove the leading the trailing zeros.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 2 3 0 2 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q17. Let x be an array [2, 2, 1, 5, 4, 5, 1, 2, 3]. Get two arrays of unique elements and their counts.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 2 3 4 5] [2 3 1 1 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q18. Lex x be an array <br/>\\n\",\n    \"[[ 1 2]<br/>\\n\",\n    \" [ 3 4].<br/>\\n\",\n    \"Flip x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 120,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2 1]\\n\",\n      \" [4 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q19. Lex x be an array <br/>\\n\",\n    \"[[ 1 2]<br/>\\n\",\n    \" [ 3 4].<br/>\\n\",\n    \"Flip x along the first axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 121,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[3 4]\\n\",\n      \" [1 2]]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q20. Lex x be an array <br/>\\n\",\n    \"[[ 1 2]<br/>\\n\",\n    \" [ 3 4].<br/>\\n\",\n    \"Rotate x 90 degrees counter-clockwise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 122,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2 4]\\n\",\n      \" [1 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q21 Lex x be an array <br/>\\n\",\n    \"[[ 1 2 3 4]<br/>\\n\",\n    \" [ 5 6 7 8].<br/>\\n\",\n    \"Shift elements one step to right along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 126,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[4 1 2 3]\\n\",\n      \" [8 5 6 7]]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/2_Array_manipulation_routines_Solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Array manipulation routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"Q1. Let x be a ndarray [10, 10, 3] with all elements set to one. Reshape x so that the size of the second dimension equals 150.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.]\\n\",\n      \" [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\\n\",\n      \"   1.  1.  1.  1.  1.  1.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.ones([10, 10, 3])\\n\",\n    \"out = np.reshape(x, [-1, 150])\\n\",\n    \"print out\\n\",\n    \"assert np.allclose(out, np.ones([10, 10, 3]).reshape([-1, 150]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Let x be array [[1, 2, 3], [4, 5, 6]]. Convert it to [1 4 2 5 3 6].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 4 2 5 3 6]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 2, 3], [4, 5, 6]])\\n\",\n    \"out1 = np.ravel(x, order='F')\\n\",\n    \"out2 = x.flatten(order=\\\"F\\\")\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Let x be array [[1, 2, 3], [4, 5, 6]]. Get the 5th element.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 2, 3], [4, 5, 6]])\\n\",\n    \"out1 = x.flat[4]\\n\",\n    \"out2 = np.ravel(x)[4]\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Let x be an arbitrary 3-D array of shape (3, 4, 5). Permute the dimensions of x such that the new shape will be (4,3,5).\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(4L, 3L, 5L)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.zeros((3, 4, 5))\\n\",\n    \"out1 = np.swapaxes(x, 1, 0)\\n\",\n    \"out2 = x.transpose([1, 0, 2])\\n\",\n    \"assert out1.shape == out2.shape\\n\",\n    \"print out1.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Let x be an arbitrary 2-D array of shape (3, 4). Permute the dimensions of x such that the new shape will be (4,3).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(4L, 3L)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.zeros((3, 4))\\n\",\n    \"out1 = np.swapaxes(x, 1, 0)\\n\",\n    \"out2 = x.transpose()\\n\",\n    \"out3 = x.T\\n\",\n    \"assert out1.shape == out2.shape == out3.shape\\n\",\n    \"print out1.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Let x be an arbitrary 2-D array of shape (3, 4). Insert a nex axis such that the new shape will be (3, 1, 4).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(3L, 1L, 4L)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.zeros((3, 4))\\n\",\n    \"print np.expand_dims(x, axis=1).shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Let x be an arbitrary 3-D array of shape (3, 4, 1). Remove a single-dimensional entries such that the new shape will be (3, 4).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(3L, 4L)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.zeros((3, 4, 1))\\n\",\n    \"print np.squeeze(x).shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Lex x be an array <br/>\\n\",\n    \"[[ 1 2 3]<br/>\\n\",\n    \"[ 4 5 6].<br/><br/>\\n\",\n    \"and y be an array <br/>\\n\",\n    \"[[ 7 8 9]<br/>\\n\",\n    \"[10 11 12]].<br/>\\n\",\n    \"Concatenate x and y so that a new array looks like <br/>[[1, 2, 3, 7, 8, 9], <br/>[4, 5, 6, 10, 11, 12]].\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1  2  3  7  8  9]\\n\",\n      \" [ 4  5  6 10 11 12]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 2, 3], [4, 5, 6]])\\n\",\n    \"y = np.array([[7, 8, 9], [10, 11, 12]])\\n\",\n    \"out1 = np.concatenate((x, y), 1)\\n\",\n    \"out2 = np.hstack((x, y))\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print out2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Lex x be an array <br/>\\n\",\n    \"[[ 1 2 3]<br/>\\n\",\n    \"[ 4 5 6].<br/><br/>\\n\",\n    \"and y be an array <br/>\\n\",\n    \"[[ 7 8 9]<br/>\\n\",\n    \"[10 11 12]].<br/>\\n\",\n    \"Concatenate x and y so that a new array looks like <br/>[[ 1  2  3]<br/>\\n\",\n    \" [ 4  5  6]<br/>\\n\",\n    \" [ 7  8  9]<br/>\\n\",\n    \" [10 11 12]]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1  2  3]\\n\",\n      \" [ 4  5  6]\\n\",\n      \" [ 7  8  9]\\n\",\n      \" [10 11 12]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 2, 3], [4, 5, 6]])\\n\",\n    \"y = np.array([[7, 8, 9], [10, 11, 12]])\\n\",\n    \"out1 = np.concatenate((x, y), 0)\\n\",\n    \"out2 = np.vstack((x, y))\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print out2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Let x be an array [1 2 3] and y be [4 5 6]. Convert it to [[1, 4], [2, 5], [3, 6]].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 4],\\n\",\n       \"       [2, 5],\\n\",\n       \"       [3, 6]])\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.array((1,2,3))\\n\",\n    \"y = np.array((4,5,6))\\n\",\n    \"out1 = np.column_stack((x, y))\\n\",\n    \"out2 = np.squeeze(np.dstack((x, y)))\\n\",\n    \"out3 = np.vstack((x, y)).T\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"assert np.allclose(out2, out3)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Let x be an array [[1],[2],[3]] and y be [[4], [5], [6]]. Convert x to [[[1, 4]], [[2, 5]], [[3, 6]]].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[[1 4]]\\n\",\n      \"\\n\",\n      \" [[2 5]]\\n\",\n      \"\\n\",\n      \" [[3 6]]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1],[2],[3]])\\n\",\n    \"y = np.array([[4],[5],[6]])\\n\",\n    \"out = np.dstack((x, y))\\n\",\n    \"print out\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Let x be an array [1, 2, 3, ..., 9]. Split x into 3 arrays, each of which has 4, 2, and 3 elements in the original order.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([1, 2, 3, 4]), array([5, 6]), array([7, 8, 9])]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1, 10)\\n\",\n    \"print np.split(x, [4, 6])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Let x be an array<br/>\\n\",\n    \"[[[  0.,   1.,   2.,   3.],<br/>\\n\",\n    \"  [  4.,   5.,   6.,   7.]],<br/>\\n\",\n    \" \\n\",\n    \" [[  8.,   9.,  10.,  11.],<br/>\\n\",\n    \"  [ 12.,  13.,  14.,  15.]]].<br/>\\n\",\n    \"Split it into two such that the first array looks like<br/>\\n\",\n    \"[[[  0.,   1.,   2.],<br/>\\n\",\n    \"  [  4.,   5.,   6.]],<br/>\\n\",\n    \" \\n\",\n    \" [[  8.,   9.,  10.],<br/>\\n\",\n    \"  [ 12.,  13.,  14.]]].<br/>\\n\",\n    \"  \\n\",\n    \"and the second one look like:<br/>\\n\",\n    \"  \\n\",\n    \"[[[  3.],<br/>\\n\",\n    \"  [  7.]],<br/>\\n\",\n    \" \\n\",\n    \" [[  11.],<br/>\\n\",\n    \"  [ 15.]]].<br/>  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[[ 0,  1,  2],\\n\",\n      \"        [ 4,  5,  6]],\\n\",\n      \"\\n\",\n      \"       [[ 8,  9, 10],\\n\",\n      \"        [12, 13, 14]]]), array([[[ 3],\\n\",\n      \"        [ 7]],\\n\",\n      \"\\n\",\n      \"       [[11],\\n\",\n      \"        [15]]])]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(16).reshape(2, 2, 4)\\n\",\n    \"out1 = np.split(x, [3],axis=2)\\n\",\n    \"out2 = np.dsplit(x, [3])\\n\",\n    \"assert np.allclose(out1[0], out2[0])\\n\",\n    \"assert np.allclose(out1[1], out2[1])\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Let x be an array <br />\\n\",\n    \"[[  0.,   1.,   2.,   3.],<br>\\n\",\n    \" [  4.,   5.,   6.,   7.],<br>\\n\",\n    \" [  8.,   9.,  10.,  11.],<br>\\n\",\n    \" [ 12.,  13.,  14.,  15.]].<br>\\n\",\n    \"Split it into two arrays along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[ 0,  1],\\n\",\n      \"       [ 4,  5],\\n\",\n      \"       [ 8,  9],\\n\",\n      \"       [12, 13]]), array([[ 2,  3],\\n\",\n      \"       [ 6,  7],\\n\",\n      \"       [10, 11],\\n\",\n      \"       [14, 15]])]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(16).reshape((4, 4))\\n\",\n    \"out1 = np.hsplit(x, 2)\\n\",\n    \"out2 = np.split(x, 2, 1)\\n\",\n    \"assert np.allclose(out1[0], out2[0])\\n\",\n    \"assert np.allclose(out1[1], out2[1])\\n\",\n    \"print out1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q13. Let x be an array <br />\\n\",\n    \"[[  0.,   1.,   2.,   3.],<br>\\n\",\n    \" [  4.,   5.,   6.,   7.],<br>\\n\",\n    \" [  8.,   9.,  10.,  11.],<br>\\n\",\n    \" [ 12.,  13.,  14.,  15.]].<br>\\n\",\n    \"Split it into two arrays along the first axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[0, 1, 2, 3],\\n\",\n      \"       [4, 5, 6, 7]]), array([[ 8,  9, 10, 11],\\n\",\n      \"       [12, 13, 14, 15]])]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(16).reshape((4, 4))\\n\",\n    \"out1 = np.vsplit(x, 2)\\n\",\n    \"out2 = np.split(x, 2, 0)\\n\",\n    \"assert np.allclose(out1[0], out2[0])\\n\",\n    \"assert np.allclose(out1[1], out2[1])\\n\",\n    \"print out1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q14. Let x be an array [0, 1, 2]. Convert it to <br/>\\n\",\n    \"[[0, 1, 2, 0, 1, 2],<br/>\\n\",\n    \" [0, 1, 2, 0, 1, 2]].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1 2 0 1 2]\\n\",\n      \" [0 1 2 0 1 2]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2])\\n\",\n    \"out1 = np.tile(x, [2, 2])\\n\",\n    \"out2 = np.resize(x, [2, 6])\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print out1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q15. Let x be an array [0, 1, 2]. Convert it to <br/>\\n\",\n    \"[0, 0, 1, 1, 2, 2].\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 0 1 1 2 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0, 1, 2])\\n\",\n    \"print np.repeat(x, 2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q16. Let x be an array [0, 0, 0, 1, 2, 3, 0, 2, 1, 0].<br/>\\n\",\n    \"remove the leading the trailing zeros.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 2 3 0 2 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))\\n\",\n    \"out = np.trim_zeros(x)\\n\",\n    \"print out\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q17. Let x be an array [2, 2, 1, 5, 4, 5, 1, 2, 3]. Get two arrays of unique elements and their counts.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 2 3 4 5] [2 3 1 1 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([2, 2, 1, 5, 4, 5, 1, 2, 3])\\n\",\n    \"u, indices = np.unique(x, return_counts=True)\\n\",\n    \"print u, indices\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q18. Lex x be an array <br/>\\n\",\n    \"[[ 1 2]<br/>\\n\",\n    \" [ 3 4].<br/>\\n\",\n    \"Flip x along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 120,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2 1]\\n\",\n      \" [4 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1,2], [3,4]])\\n\",\n    \"out1 = np.fliplr(x)\\n\",\n    \"out2 = x[:, ::-1]\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print out1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q19. Lex x be an array <br/>\\n\",\n    \"[[ 1 2]<br/>\\n\",\n    \" [ 3 4].<br/>\\n\",\n    \"Flip x along the first axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 121,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[3 4]\\n\",\n      \" [1 2]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1,2], [3,4]])\\n\",\n    \"out1 = np.flipud(x)\\n\",\n    \"out2 = x[::-1, :]\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print out1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q20. Lex x be an array <br/>\\n\",\n    \"[[ 1 2]<br/>\\n\",\n    \" [ 3 4].<br/>\\n\",\n    \"Rotate x 90 degrees counter-clockwise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 122,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2 4]\\n\",\n      \" [1 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1,2], [3,4]])\\n\",\n    \"out = np.rot90(x)\\n\",\n    \"print out\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q21 Lex x be an array <br/>\\n\",\n    \"[[ 1 2 3 4]<br/>\\n\",\n    \" [ 5 6 7 8].<br/>\\n\",\n    \"Shift elements one step to right along the second axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 126,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[4 1 2 3]\\n\",\n      \" [8 5 6 7]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1, 9).reshape([2, 4])\\n\",\n    \"print np.roll(x, 1, axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/3_String_operations.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## String operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from __future__ import print_function\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"author = \\\"kyubyong. https://github.com/Kyubyong/numpy_exercises\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.3'\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Concatenate x1 and x2.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['Hello world' 'Say something']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x1 = np.array(['Hello', 'Say'], dtype=np.str)\\n\",\n    \"x2 = np.array([' world', ' something'], dtype=np.str)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Repeat x three time element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['Hello Hello Hello ' 'Say Say Say ']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello ', 'Say '], dtype=np.str)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3-1. Capitalize the first letter of x element-wise.<br/>\\n\",\n    \"Q3-2. Lowercase x element-wise.<br/>\\n\",\n    \"Q3-3. Uppercase x element-wise.<br/>\\n\",\n    \"Q3-4. Swapcase x element-wise.<br/>\\n\",\n    \"Q3-5. Title-case x element-wise.<br/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"capitalized = ['Hello world' 'Say something']\\n\",\n      \"lowered = ['hello world' 'say something']\\n\",\n      \"uppered = ['HELLO WORLD' 'SAY SOMETHING']\\n\",\n      \"swapcased = ['HEllO WOrlD' 'sAY SoMETHING']\\n\",\n      \"titlecased = ['Hello World' 'Say Something']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['heLLo woRLd', 'Say sOmething'], dtype=np.str)\\n\",\n    \"capitalized = ...\\n\",\n    \"lowered = ...\\n\",\n    \"uppered = ...\\n\",\n    \"swapcased = ...\\n\",\n    \"titlecased = ...\\n\",\n    \"print(\\\"capitalized =\\\", capitalized)\\n\",\n    \"print(\\\"lowered =\\\", lowered)\\n\",\n    \"print(\\\"uppered =\\\", uppered)\\n\",\n    \"print(\\\"swapcased =\\\", swapcased)\\n\",\n    \"print(\\\"titlecased =\\\", titlecased)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Make the length of each element 20 and the string centered / left-justified / right-justified with paddings of `_`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"centered = ['____hello world_____' '___say something____']\\n\",\n      \"left = ['hello world_________' 'say something_______']\\n\",\n      \"right = ['_________hello world' '_______say something']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['hello world', 'say something'], dtype=np.str)\\n\",\n    \"centered = ...\\n\",\n    \"left = ...\\n\",\n    \"right = ...\\n\",\n    \"\\n\",\n    \"print(\\\"centered =\\\", centered)\\n\",\n    \"print(\\\"left =\\\", left)\\n\",\n    \"print(\\\"right =\\\", right)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Encode x in cp500 and decode it again.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"encoded = [b'\\\\x88\\\\x85\\\\x93\\\\x93\\\\x96@\\\\xa6\\\\x96\\\\x99\\\\x93\\\\x84'\\n\",\n      \" b'\\\\xa2\\\\x81\\\\xa8@\\\\xa2\\\\x96\\\\x94\\\\x85\\\\xa3\\\\x88\\\\x89\\\\x95\\\\x87']\\n\",\n      \"decoded = ['hello world' 'say something']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['hello world', 'say something'], dtype=np.str)\\n\",\n    \"encoded = ...\\n\",\n    \"decoded = ...\\n\",\n    \"print(\\\"encoded =\\\", encoded)\\n\",\n    \"print(\\\"decoded =\\\", decoded)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Insert a space between characters of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['h e l l o   w o r l d' 's a y   s o m e t h i n g']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['hello world', 'say something'], dtype=np.str)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7-1. Remove the leading and trailing whitespaces of x element-wise.<br/>\\n\",\n    \"Q7-2. Remove the leading whitespaces of x element-wise.<br/>\\n\",\n    \"Q7-3. Remove the trailing whitespaces of x element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"stripped = ['hello world' 'say something']\\n\",\n      \"lstripped = ['hello world   ' 'say something\\\\n']\\n\",\n      \"rstripped = ['   hello world' '\\\\tsay something']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['   hello world   ', '\\\\tsay something\\\\n'], dtype=np.str)\\n\",\n    \"stripped = ...\\n\",\n    \"lstripped = ...\\n\",\n    \"rstripped = ...\\n\",\n    \"print(\\\"stripped =\\\", stripped)\\n\",\n    \"print(\\\"lstripped =\\\", lstripped)\\n\",\n    \"print(\\\"rstripped =\\\", rstripped)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Split the element of x with spaces.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[['Hello', 'my', 'name', 'is', 'John']]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello my name is John'], dtype=np.str)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Split the element of x to multiple lines.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[['Hello', 'my name is John']]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello\\\\nmy name is John'], dtype=np.str)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Make x a numeric string of 4 digits with zeros on its left.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['0034']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['34'], dtype=np.str)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Replace \\\"John\\\" with \\\"Jim\\\" in x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['Hello nmy name is Jim']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello nmy name is John'], dtype=np.str)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Comparison\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Return x1 == x2, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ True  True  True  True False]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x1 = np.array(['Hello', 'my', 'name', 'is', 'John'], dtype=np.str)\\n\",\n    \"x2 = np.array(['Hello', 'my', 'name', 'is', 'Jim'], dtype=np.str)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q13. Return x1 != x2, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[False False False False  True]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x1 = np.array(['Hello', 'my', 'name', 'is', 'John'], dtype=np.str)\\n\",\n    \"x2 = np.array(['Hello', 'my', 'name', 'is', 'Jim'], dtype=np.str)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## String information\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q14. Count the number of \\\"l\\\" in x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2 0 0 0 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello', 'my', 'name', 'is', 'Lily'], dtype=np.str)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q15. Count the lowest index of \\\"l\\\" in x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2 -1 -1 -1  2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello', 'my', 'name', 'is', 'Lily'], dtype=np.str)\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q16-1. Check if each element of x is composed of digits only.<br/>\\n\",\n    \"Q16-2. Check if each element of x is composed of lower case letters only.<br/>\\n\",\n    \"Q16-3. Check if each element of x is composed of upper case letters only.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Digits only = [False False False  True False False]\\n\",\n      \"Lower cases only = [False False  True False  True  True]\\n\",\n      \"Upper cases only = [False  True False False False False]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello', 'I', 'am', '20', 'years', 'old'], dtype=np.str)\\n\",\n    \"out1 = ...\\n\",\n    \"out2 = ...\\n\",\n    \"out3 = ...\\n\",\n    \"print(\\\"Digits only =\\\", out1)\\n\",\n    \"print(\\\"Lower cases only =\\\", out2)\\n\",\n    \"print(\\\"Upper cases only =\\\", out3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q17. Check if each element of x starts with \\\"hi\\\".\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[False  True  True  True]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['he', 'his', 'him', 'his'], dtype=np.str)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python [conda root]\",\n   \"language\": \"python\",\n   \"name\": \"conda-root-py\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/3_String_operations_solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## String operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from __future__ import print_function\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"author = \\\"kyubyong. https://github.com/Kyubyong/numpy_exercises\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.3'\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Concatenate x1 and x2.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['Hello world' 'Say something']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x1 = np.array(['Hello', 'Say'], dtype=np.str)\\n\",\n    \"x2 = np.array([' world', ' something'], dtype=np.str)\\n\",\n    \"out = np.char.add(x1, x2)\\n\",\n    \"print(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Repeat x three time element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['Hello Hello Hello ' 'Say Say Say ']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello ', 'Say '], dtype=np.str)\\n\",\n    \"out = np.char.multiply(x, 3)\\n\",\n    \"print(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3-1. Capitalize the first letter of x element-wise.<br/>\\n\",\n    \"Q3-2. Lowercase x element-wise.<br/>\\n\",\n    \"Q3-3. Uppercase x element-wise.<br/>\\n\",\n    \"Q3-4. Swapcase x element-wise.<br/>\\n\",\n    \"Q3-5. Title-case x element-wise.<br/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"capitalized = ['Hello world' 'Say something']\\n\",\n      \"lowered = ['hello world' 'say something']\\n\",\n      \"uppered = ['HELLO WORLD' 'SAY SOMETHING']\\n\",\n      \"swapcased = ['HEllO WOrlD' 'sAY SoMETHING']\\n\",\n      \"titlecased = ['Hello World' 'Say Something']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['heLLo woRLd', 'Say sOmething'], dtype=np.str)\\n\",\n    \"capitalized = np.char.capitalize(x)\\n\",\n    \"lowered = np.char.lower(x)\\n\",\n    \"uppered = np.char.upper(x)\\n\",\n    \"swapcased = np.char.swapcase(x)\\n\",\n    \"titlecased = np.char.title(x)\\n\",\n    \"print(\\\"capitalized =\\\", capitalized)\\n\",\n    \"print(\\\"lowered =\\\", lowered)\\n\",\n    \"print(\\\"uppered =\\\", uppered)\\n\",\n    \"print(\\\"swapcased =\\\", swapcased)\\n\",\n    \"print(\\\"titlecased =\\\", titlecased)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Make the length of each element 20 and the string centered / left-justified / right-justified with paddings of `_`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"centered = ['____hello world_____' '___say something____']\\n\",\n      \"left = ['hello world_________' 'say something_______']\\n\",\n      \"right = ['_________hello world' '_______say something']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['hello world', 'say something'], dtype=np.str)\\n\",\n    \"centered = np.char.center(x, 20, fillchar='_')\\n\",\n    \"left = np.char.ljust(x, 20, fillchar='_')\\n\",\n    \"right = np.char.rjust(x, 20, fillchar='_')\\n\",\n    \"\\n\",\n    \"print(\\\"centered =\\\", centered)\\n\",\n    \"print(\\\"left =\\\", left)\\n\",\n    \"print(\\\"right =\\\", right)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Encode x in cp500 and decode it again.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"encoded = [b'\\\\x88\\\\x85\\\\x93\\\\x93\\\\x96@\\\\xa6\\\\x96\\\\x99\\\\x93\\\\x84'\\n\",\n      \" b'\\\\xa2\\\\x81\\\\xa8@\\\\xa2\\\\x96\\\\x94\\\\x85\\\\xa3\\\\x88\\\\x89\\\\x95\\\\x87']\\n\",\n      \"decoded = ['hello world' 'say something']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['hello world', 'say something'], dtype=np.str)\\n\",\n    \"encoded = np.char.encode(x, 'cp500')\\n\",\n    \"decoded = np.char.decode(encoded,'cp500')\\n\",\n    \"print(\\\"encoded =\\\", encoded)\\n\",\n    \"print(\\\"decoded =\\\", decoded)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Insert a space between characters of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['h e l l o   w o r l d' 's a y   s o m e t h i n g']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['hello world', 'say something'], dtype=np.str)\\n\",\n    \"out = np.char.join(\\\" \\\", x)\\n\",\n    \"print(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7-1. Remove the leading and trailing whitespaces of x element-wise.<br/>\\n\",\n    \"Q7-2. Remove the leading whitespaces of x element-wise.<br/>\\n\",\n    \"Q7-3. Remove the trailing whitespaces of x element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"stripped = ['hello world' 'say something']\\n\",\n      \"lstripped = ['hello world   ' 'say something\\\\n']\\n\",\n      \"rstripped = ['   hello world' '\\\\tsay something']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['   hello world   ', '\\\\tsay something\\\\n'], dtype=np.str)\\n\",\n    \"stripped = np.char.strip(x)\\n\",\n    \"lstripped = np.char.lstrip(x)\\n\",\n    \"rstripped = np.char.rstrip(x)\\n\",\n    \"print(\\\"stripped =\\\", stripped)\\n\",\n    \"print(\\\"lstripped =\\\", lstripped)\\n\",\n    \"print(\\\"rstripped =\\\", rstripped)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Split the element of x with spaces.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[['Hello', 'my', 'name', 'is', 'John']]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello my name is John'], dtype=np.str)\\n\",\n    \"out = np.char.split(x)\\n\",\n    \"print(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Split the element of x to multiple lines.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[['Hello', 'my name is John']]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello\\\\nmy name is John'], dtype=np.str)\\n\",\n    \"out = np.char.splitlines(x)\\n\",\n    \"print(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Make x a numeric string of 4 digits with zeros on its left.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['0034']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['34'], dtype=np.str)\\n\",\n    \"out = np.char.zfill(x, 4)\\n\",\n    \"print(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Replace \\\"John\\\" with \\\"Jim\\\" in x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['Hello nmy name is Jim']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello nmy name is John'], dtype=np.str)\\n\",\n    \"out = np.char.replace(x, \\\"John\\\", \\\"Jim\\\")\\n\",\n    \"print(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Comparison\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Return x1 == x2, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ True  True  True  True False]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x1 = np.array(['Hello', 'my', 'name', 'is', 'John'], dtype=np.str)\\n\",\n    \"x2 = np.array(['Hello', 'my', 'name', 'is', 'Jim'], dtype=np.str)\\n\",\n    \"out = np.char.equal(x1, x2)\\n\",\n    \"print(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q13. Return x1 != x2, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[False False False False  True]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x1 = np.array(['Hello', 'my', 'name', 'is', 'John'], dtype=np.str)\\n\",\n    \"x2 = np.array(['Hello', 'my', 'name', 'is', 'Jim'], dtype=np.str)\\n\",\n    \"out = np.char.not_equal(x1, x2)\\n\",\n    \"print(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## String information\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q14. Count the number of \\\"l\\\" in x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2 0 0 0 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello', 'my', 'name', 'is', 'Lily'], dtype=np.str)\\n\",\n    \"out = np.char.count(x, \\\"l\\\")\\n\",\n    \"print(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q15. Count the lowest index of \\\"l\\\" in x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2 -1 -1 -1  2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello', 'my', 'name', 'is', 'Lily'], dtype=np.str)\\n\",\n    \"out = np.char.find(x, \\\"l\\\")\\n\",\n    \"print(out)\\n\",\n    \"\\n\",\n    \"# compare\\n\",\n    \"# print(np.char.index(x, \\\"l\\\"))\\n\",\n    \"# => This raises an error!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q16-1. Check if each element of x is composed of digits only.<br/>\\n\",\n    \"Q16-2. Check if each element of x is composed of lower case letters only.<br/>\\n\",\n    \"Q16-3. Check if each element of x is composed of upper case letters only.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Digits only = [False False False  True False False]\\n\",\n      \"Lower cases only = [False False  True False  True  True]\\n\",\n      \"Upper cases only = [False  True False False False False]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['Hello', 'I', 'am', '20', 'years', 'old'], dtype=np.str)\\n\",\n    \"out1 = np.char.isdigit(x)\\n\",\n    \"out2 = np.char.islower(x)\\n\",\n    \"out3 = np.char.isupper(x)\\n\",\n    \"print(\\\"Digits only =\\\", out1)\\n\",\n    \"print(\\\"Lower cases only =\\\", out2)\\n\",\n    \"print(\\\"Upper cases only =\\\", out3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q17. Check if each element of x starts with \\\"hi\\\".\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[False  True  True  True]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['he', 'his', 'him', 'his'], dtype=np.str)\\n\",\n    \"out = np.char.startswith(x, \\\"hi\\\")\\n\",\n    \"print(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/4_Numpy-specific_help_functions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Search for docstrings of the numpy functions on linear algebra.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Search results for 'linear algebra'\\n\",\n      \"-----------------------------------\\n\",\n      \"numpy.linalg.solve\\n\",\n      \"    Solve a linear matrix equation, or system of linear scalar equations.\\n\",\n      \"numpy.poly\\n\",\n      \"    Find the coefficients of a polynomial with the given sequence of roots.\\n\",\n      \"numpy.restoredot\\n\",\n      \"    Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS\\n\",\n      \"numpy.linalg.eig\\n\",\n      \"    Compute the eigenvalues and right eigenvectors of a square array.\\n\",\n      \"numpy.linalg.cond\\n\",\n      \"    Compute the condition number of a matrix.\\n\",\n      \"numpy.linalg.eigh\\n\",\n      \"    Return the eigenvalues and eigenvectors of a Hermitian or symmetric matrix.\\n\",\n      \"numpy.linalg.pinv\\n\",\n      \"    Compute the (Moore-Penrose) pseudo-inverse of a matrix.\\n\",\n      \"numpy.linalg.LinAlgError\\n\",\n      \"    Generic Python-exception-derived object raised by linalg functions.\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Get help information for numpy dot function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dot(a, b, out=None)\\n\",\n      \"\\n\",\n      \"Dot product of two arrays.\\n\",\n      \"\\n\",\n      \"For 2-D arrays it is equivalent to matrix multiplication, and for 1-D\\n\",\n      \"arrays to inner product of vectors (without complex conjugation). For\\n\",\n      \"N dimensions it is a sum product over the last axis of `a` and\\n\",\n      \"the second-to-last of `b`::\\n\",\n      \"\\n\",\n      \"    dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])\\n\",\n      \"\\n\",\n      \"Parameters\\n\",\n      \"----------\\n\",\n      \"a : array_like\\n\",\n      \"    First argument.\\n\",\n      \"b : array_like\\n\",\n      \"    Second argument.\\n\",\n      \"out : ndarray, optional\\n\",\n      \"    Output argument. This must have the exact kind that would be returned\\n\",\n      \"    if it was not used. In particular, it must have the right type, must be\\n\",\n      \"    C-contiguous, and its dtype must be the dtype that would be returned\\n\",\n      \"    for `dot(a,b)`. This is a performance feature. Therefore, if these\\n\",\n      \"    conditions are not met, an exception is raised, instead of attempting\\n\",\n      \"    to be flexible.\\n\",\n      \"\\n\",\n      \"Returns\\n\",\n      \"-------\\n\",\n      \"output : ndarray\\n\",\n      \"    Returns the dot product of `a` and `b`.  If `a` and `b` are both\\n\",\n      \"    scalars or both 1-D arrays then a scalar is returned; otherwise\\n\",\n      \"    an array is returned.\\n\",\n      \"    If `out` is given, then it is returned.\\n\",\n      \"\\n\",\n      \"Raises\\n\",\n      \"------\\n\",\n      \"ValueError\\n\",\n      \"    If the last dimension of `a` is not the same size as\\n\",\n      \"    the second-to-last dimension of `b`.\\n\",\n      \"\\n\",\n      \"See Also\\n\",\n      \"--------\\n\",\n      \"vdot : Complex-conjugating dot product.\\n\",\n      \"tensordot : Sum products over arbitrary axes.\\n\",\n      \"einsum : Einstein summation convention.\\n\",\n      \"matmul : '@' operator as method with out parameter.\\n\",\n      \"\\n\",\n      \"Examples\\n\",\n      \"--------\\n\",\n      \">>> np.dot(3, 4)\\n\",\n      \"12\\n\",\n      \"\\n\",\n      \"Neither argument is complex-conjugated:\\n\",\n      \"\\n\",\n      \">>> np.dot([2j, 3j], [2j, 3j])\\n\",\n      \"(-13+0j)\\n\",\n      \"\\n\",\n      \"For 2-D arrays it is the matrix product:\\n\",\n      \"\\n\",\n      \">>> a = [[1, 0], [0, 1]]\\n\",\n      \">>> b = [[4, 1], [2, 2]]\\n\",\n      \">>> np.dot(a, b)\\n\",\n      \"array([[4, 1],\\n\",\n      \"       [2, 2]])\\n\",\n      \"\\n\",\n      \">>> a = np.arange(3*4*5*6).reshape((3,4,5,6))\\n\",\n      \">>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))\\n\",\n      \">>> np.dot(a, b)[2,3,2,1,2,2]\\n\",\n      \"499128\\n\",\n      \">>> sum(a[2,3,2,:] * b[1,2,:,2])\\n\",\n      \"499128\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/4_Numpy-specific_help_functions_Solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Search for docstrings of the numpy functions on linear algebra.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Search results for 'linear algebra'\\n\",\n      \"-----------------------------------\\n\",\n      \"numpy.linalg.solve\\n\",\n      \"    Solve a linear matrix equation, or system of linear scalar equations.\\n\",\n      \"numpy.poly\\n\",\n      \"    Find the coefficients of a polynomial with the given sequence of roots.\\n\",\n      \"numpy.restoredot\\n\",\n      \"    Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS\\n\",\n      \"numpy.linalg.eig\\n\",\n      \"    Compute the eigenvalues and right eigenvectors of a square array.\\n\",\n      \"numpy.linalg.cond\\n\",\n      \"    Compute the condition number of a matrix.\\n\",\n      \"numpy.linalg.eigh\\n\",\n      \"    Return the eigenvalues and eigenvectors of a Hermitian or symmetric matrix.\\n\",\n      \"numpy.linalg.pinv\\n\",\n      \"    Compute the (Moore-Penrose) pseudo-inverse of a matrix.\\n\",\n      \"numpy.linalg.LinAlgError\\n\",\n      \"    Generic Python-exception-derived object raised by linalg functions.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.lookfor('linear algebra')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Get help information for numpy dot function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dot(a, b, out=None)\\n\",\n      \"\\n\",\n      \"Dot product of two arrays.\\n\",\n      \"\\n\",\n      \"For 2-D arrays it is equivalent to matrix multiplication, and for 1-D\\n\",\n      \"arrays to inner product of vectors (without complex conjugation). For\\n\",\n      \"N dimensions it is a sum product over the last axis of `a` and\\n\",\n      \"the second-to-last of `b`::\\n\",\n      \"\\n\",\n      \"    dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])\\n\",\n      \"\\n\",\n      \"Parameters\\n\",\n      \"----------\\n\",\n      \"a : array_like\\n\",\n      \"    First argument.\\n\",\n      \"b : array_like\\n\",\n      \"    Second argument.\\n\",\n      \"out : ndarray, optional\\n\",\n      \"    Output argument. This must have the exact kind that would be returned\\n\",\n      \"    if it was not used. In particular, it must have the right type, must be\\n\",\n      \"    C-contiguous, and its dtype must be the dtype that would be returned\\n\",\n      \"    for `dot(a,b)`. This is a performance feature. Therefore, if these\\n\",\n      \"    conditions are not met, an exception is raised, instead of attempting\\n\",\n      \"    to be flexible.\\n\",\n      \"\\n\",\n      \"Returns\\n\",\n      \"-------\\n\",\n      \"output : ndarray\\n\",\n      \"    Returns the dot product of `a` and `b`.  If `a` and `b` are both\\n\",\n      \"    scalars or both 1-D arrays then a scalar is returned; otherwise\\n\",\n      \"    an array is returned.\\n\",\n      \"    If `out` is given, then it is returned.\\n\",\n      \"\\n\",\n      \"Raises\\n\",\n      \"------\\n\",\n      \"ValueError\\n\",\n      \"    If the last dimension of `a` is not the same size as\\n\",\n      \"    the second-to-last dimension of `b`.\\n\",\n      \"\\n\",\n      \"See Also\\n\",\n      \"--------\\n\",\n      \"vdot : Complex-conjugating dot product.\\n\",\n      \"tensordot : Sum products over arbitrary axes.\\n\",\n      \"einsum : Einstein summation convention.\\n\",\n      \"matmul : '@' operator as method with out parameter.\\n\",\n      \"\\n\",\n      \"Examples\\n\",\n      \"--------\\n\",\n      \">>> np.dot(3, 4)\\n\",\n      \"12\\n\",\n      \"\\n\",\n      \"Neither argument is complex-conjugated:\\n\",\n      \"\\n\",\n      \">>> np.dot([2j, 3j], [2j, 3j])\\n\",\n      \"(-13+0j)\\n\",\n      \"\\n\",\n      \"For 2-D arrays it is the matrix product:\\n\",\n      \"\\n\",\n      \">>> a = [[1, 0], [0, 1]]\\n\",\n      \">>> b = [[4, 1], [2, 2]]\\n\",\n      \">>> np.dot(a, b)\\n\",\n      \"array([[4, 1],\\n\",\n      \"       [2, 2]])\\n\",\n      \"\\n\",\n      \">>> a = np.arange(3*4*5*6).reshape((3,4,5,6))\\n\",\n      \">>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))\\n\",\n      \">>> np.dot(a, b)[2,3,2,1,2,2]\\n\",\n      \"499128\\n\",\n      \">>> sum(a[2,3,2,:] * b[1,2,:,2])\\n\",\n      \"499128\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.info(np.dot)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/5_Input_and_Output.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Input and Output\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from __future__ import print_function\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"author = \\\"kyubyong. https://github.com/Kyubyong/numpy_exercises\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.12.0'\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2017-04-01\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from datetime import date\\n\",\n    \"print(date.today())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## NumPy binary files (NPY, NPZ)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Save x into `temp.npy` and load it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10)\\n\",\n    \"...\\n\",\n    \"\\n\",\n    \"# Check if there exists the 'temp.npy' file.\\n\",\n    \"import os\\n\",\n    \"if os.path.exists('temp.npy'):\\n\",\n    \"    x2 = ...\\n\",\n    \"    print(np.array_equal(x, x2))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Save x and y into a single file 'temp.npz' and load it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10)\\n\",\n    \"y = np.arange(11, 20)\\n\",\n    \"...\\n\",\n    \"\\n\",\n    \"with ... as data:\\n\",\n    \"    x2 = data['x']\\n\",\n    \"    y2 = data['y']\\n\",\n    \"    print(np.array_equal(x, x2))\\n\",\n    \"    print(np.array_equal(y, y2))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Text files\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Save x to 'temp.txt' in string format and load it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.,  1.,  2.,  3.,  4.],\\n\",\n       \"       [ 5.,  6.,  7.,  8.,  9.]])\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10).reshape(2, 5)\\n\",\n    \"header = 'num1 num2 num3 num4 num5'\\n\",\n    \"...\\n\",\n    \"...\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Save `x`, `y`, and `z` to 'temp.txt' in string format line by line, then load it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\\n\",\n       \"       [ 11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.],\\n\",\n       \"       [ 22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.]])\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10)\\n\",\n    \"y = np.arange(11, 21)\\n\",\n    \"z = np.arange(22, 32)\\n\",\n    \"...\\n\",\n    \"...\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Convert `x` into bytes, and load it as array.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 3, 4])\\n\",\n    \"x_bytes = ...\\n\",\n    \"x2 = ...\\n\",\n    \"print(np.array_equal(x, x2))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Convert `a` into an ndarray and then convert it into a list again.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = [[1, 2], [3, 4]]\\n\",\n    \"x = ...\\n\",\n    \"a2 = ...\\n\",\n    \"print(a == a2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## String formatting¶\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Convert `x` to a string, and revert it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1 2 3 4]\\n\",\n      \" [5 6 7 8 9]] \\n\",\n      \" <class 'str'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10).reshape(2,5)\\n\",\n    \"x_str = ...\\n\",\n    \"print(x_str, \\\"\\\\n\\\", type(x_str))\\n\",\n    \"x_str = x_str.replace(\\\"[\\\", \\\"\\\") # [] must be stripped\\n\",\n    \"x_str = x_str.replace(\\\"]\\\", \\\"\\\")\\n\",\n    \"x2 = ...\\n\",\n    \"assert np.array_equal(x, x2)\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Text formatting options\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Print `x` such that all elements are displayed with precision=1, no suppress.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.8  0.5  0.9  0.   0.6  0.6  0.5  0.5  0.9  0.9  0.3  0.6  0.9  0.8\\n\",\n      \"   0.1  0.5  0.3  0.9  0.1  0.8  0.3  0.   0.4  0.5  0.4  0.6  0.2  0.6\\n\",\n      \"   0.6  0.5  0.7  0.4  0.1  0.6  0.7  0.3  0.1  0.5  0.1  0.5  0.   0.8  1.\\n\",\n      \"   0.5  0.3  0.9  0.3  0.6  0.9  0.   0.4  0.5  0.9  0.3  0.9  0.2  0.9\\n\",\n      \"   0.4  0.8  0.5  0.2  0.1  0.2  0.7  0.6  0.3  0.6  0.8  0.7  0.2  0.2\\n\",\n      \"   0.4  0.6  0.4  1.   0.   0.2  0.4  0.8  0.5  0.4  0.4  0.6  0.2  0.2\\n\",\n      \"   0.8  0.1  0.9  0.7  0.3  1.   0.6  0.7  0.4  0.1  0.9  0.2  0.4  0.1\\n\",\n      \"   0.3]\\n\",\n      \" [ 0.5  0.9  0.2  0.1  0.   0.4  0.5  0.3  0.5  0.8  0.2  0.5  0.9  0.4\\n\",\n      \"   0.2  0.8  0.3  0.1  0.8  0.5  0.2  0.   0.7  0.7  0.6  0.7  0.1  0.2\\n\",\n      \"   0.6  0.2  0.4  0.2  0.2  0.1  0.4  0.4  0.9  0.6  0.3  0.4  0.9  0.1\\n\",\n      \"   0.6  0.7  0.7  0.2  0.5  0.3  0.3  0.8  0.   0.1  0.3  0.9  0.8  0.9\\n\",\n      \"   0.3  0.5  0.5  0.7  0.5  0.8  0.2  0.9  0.1  1.   0.5  0.3  0.7  0.2\\n\",\n      \"   0.9  0.3  0.6  0.4  1.   0.2  1.   0.7  0.8  0.6  0.9  0.9  0.4  1.   0.3\\n\",\n      \"   0.7  0.   0.3  0.2  0.4  0.1  0.6  0.2  0.4  0.7  0.3  0.   0.7  0.1\\n\",\n      \"   0.5]\\n\",\n      \" [ 0.7  0.1  0.6  0.2  0.9  0.8  0.5  0.6  0.9  0.8  0.4  0.1  0.1  0.7\\n\",\n      \"   0.7  0.8  0.1  0.8  0.4  0.2  0.6  0.8  1.   0.8  0.6  0.8  0.3  0.1\\n\",\n      \"   0.5  0.5  0.4  0.2  0.8  0.4  0.4  0.6  0.9  0.7  0.8  0.5  1.   0.3\\n\",\n      \"   0.4  0.7  0.5  0.3  1.   0.3  0.1  0.6  0.2  0.   0.2  0.8  0.7  0.7\\n\",\n      \"   0.6  0.4  0.7  0.7  0.5  0.1  0.9  0.   0.1  0.3  0.3  0.1  0.6  0.5\\n\",\n      \"   0.4  0.1  0.3  0.2  0.1  0.7  0.3  0.8  0.8  0.9  0.7  0.5  0.   0.9\\n\",\n      \"   0.5  0.6  1.   0.9  0.8  0.   0.7  0.3  0.4  0.2  0.3  0.4  0.9  0.4  1.\\n\",\n      \"   0.2]\\n\",\n      \" [ 1.   0.1  0.8  0.   0.4  0.2  0.2  0.8  0.1  0.2  0.1  0.9  0.9  0.9\\n\",\n      \"   0.5  0.7  0.8  0.6  0.1  0.2  0.2  0.5  0.4  0.6  1.   0.1  0.8  0.5\\n\",\n      \"   0.3  0.6  0.6  1.   0.5  0.4  0.3  0.8  0.7  0.4  0.1  1.   0.6  0.3\\n\",\n      \"   0.7  0.4  0.5  1.   0.6  0.8  0.5  0.6  1.   0.7  0.2  0.6  0.4  0.   0.8\\n\",\n      \"   0.5  0.5  0.2  0.8  0.2  0.4  0.5  0.2  0.   0.8  0.4  0.4  0.4  0.4\\n\",\n      \"   0.4  0.8  0.4  0.1  0.9  0.1  0.8  0.2  0.7  1.   0.3  0.1  0.9  0.3\\n\",\n      \"   0.1  0.1  0.4  0.5  0.5  0.6  0.2  0.2  0.4  0.5  0.6  0.3  0.2  0.6\\n\",\n      \"   0.7]\\n\",\n      \" [ 0.1  0.5  0.4  0.1  0.1  0.9  0.5  0.9  0.8  0.8  0.8  0.4  0.4  0.7  0.\\n\",\n      \"   0.4  0.   0.4  0.7  0.7  0.7  0.8  0.9  0.   0.6  0.8  1.   0.5  0.   0.9\\n\",\n      \"   0.9  1.   0.6  0.4  0.4  0.8  0.9  0.2  0.1  0.2  0.7  0.8  0.2  0.1\\n\",\n      \"   0.9  0.9  0.4  0.6  0.7  0.4  0.6  0.2  0.9  0.9  0.3  0.7  0.6  0.2\\n\",\n      \"   0.3  0.8  0.6  0.6  0.1  0.3  0.2  0.6  0.3  0.7  0.4  0.5  0.3  0.4\\n\",\n      \"   0.4  0.9  0.6  0.6  0.3  0.1  0.3  0.8  0.3  0.7  0.4  0.5  0.4  0.7\\n\",\n      \"   0.6  0.1  0.8  0.2  0.6  0.1  1.   0.8  0.2  0.6  0.9  0.2  0.4  0.4]\\n\",\n      \" [ 0.4  0.5  0.5  0.7  0.3  0.7  0.2  0.3  0.6  0.9  0.7  0.3  0.1  0.4\\n\",\n      \"   0.6  0.8  0.3  0.2  0.4  0.2  0.8  0.4  0.2  0.9  0.2  0.9  0.8  0.5\\n\",\n      \"   0.9  0.7  0.2  0.5  0.3  0.7  0.6  0.   0.3  0.5  0.6  0.8  0.5  0.9\\n\",\n      \"   0.2  0.1  0.3  0.9  0.3  0.3  0.6  0.5  0.3  1.   0.2  0.9  0.7  0.9\\n\",\n      \"   0.2  0.1  0.1  0.4  0.1  0.7  0.4  0.2  0.3  0.4  0.7  1.   0.9  0.5\\n\",\n      \"   0.1  0.9  0.5  0.1  0.4  0.9  0.7  0.3  0.5  0.3  0.3  1.   0.7  0.8\\n\",\n      \"   0.3  0.6  0.3  0.6  0.7  0.7  0.   0.7  0.6  0.1  0.9  1.   0.1  0.1\\n\",\n      \"   0.2  0.7]\\n\",\n      \" [ 0.1  0.1  0.4  0.6  1.   0.4  0.1  0.4  0.5  0.   0.3  0.3  0.7  0.4\\n\",\n      \"   0.7  0.8  0.   0.8  0.1  0.9  0.7  0.9  0.9  0.6  0.2  0.2  0.4  0.5\\n\",\n      \"   0.3  0.1  0.6  0.8  0.9  0.5  0.4  0.4  0.6  0.3  0.5  0.4  0.7  0.2\\n\",\n      \"   0.4  0.3  0.6  0.8  0.8  1.   0.5  0.7  0.6  1.   0.5  0.1  0.2  0.1\\n\",\n      \"   0.4  0.3  0.5  0.3  0.5  0.6  0.2  0.3  0.8  0.5  0.6  0.9  0.8  0.3\\n\",\n      \"   0.3  0.5  0.2  0.1  0.5  0.4  0.4  0.6  0.2  0.5  0.8  0.2  0.4  0.6\\n\",\n      \"   0.2  0.3  0.4  0.8  0.3  0.2  0.6  0.5  0.4  0.3  0.4  0.6  0.1  0.6\\n\",\n      \"   0.9  0.9]\\n\",\n      \" [ 0.6  0.6  0.1  0.4  0.6  0.8  0.   0.5  0.9  0.4  0.6  0.5  0.5  0.7\\n\",\n      \"   0.9  0.7  0.1  0.4  0.3  0.5  0.3  0.5  0.3  1.   0.4  0.5  0.1  0.4\\n\",\n      \"   0.3  0.6  0.6  0.3  0.4  0.8  0.5  0.4  0.   0.   0.8  0.7  0.9  0.5\\n\",\n      \"   0.2  0.2  0.1  0.3  0.2  0.6  0.7  0.2  0.9  0.2  0.3  0.5  0.4  0.2\\n\",\n      \"   0.9  0.8  0.1  0.2  0.8  0.3  0.9  0.6  0.3  0.   0.6  0.   0.1  0.7\\n\",\n      \"   0.7  0.2  0.8  0.4  0.4  0.6  0.2  0.3  0.4  0.5  0.9  0.3  0.   0.6\\n\",\n      \"   0.4  0.9  0.6  0.3  1.   0.9  0.6  0.9  0.   0.5  0.2  0.4  1.   0.5\\n\",\n      \"   0.7  0.9]\\n\",\n      \" [ 0.3  0.2  0.9  0.4  0.8  0.8  1.   0.1  0.8  0.1  0.1  0.7  0.5  0.6\\n\",\n      \"   0.1  0.5  0.5  0.4  0.2  0.8  0.   0.4  0.2  0.1  0.4  1.   0.1  0.   0.7\\n\",\n      \"   0.1  0.4  0.3  0.7  0.4  0.5  0.5  0.   0.2  0.2  0.9  0.9  0.8  0.1\\n\",\n      \"   0.3  0.3  0.   0.7  0.   0.8  0.5  0.5  0.3  0.3  0.1  1.   0.   0.9\\n\",\n      \"   0.8  0.6  0.9  0.5  1.   0.5  0.   0.   0.1  0.6  0.7  0.1  0.6  0.4\\n\",\n      \"   0.5  0.2  0.4  0.5  0.8  0.6  0.3  1.   0.9  0.6  0.1  0.7  0.   0.7\\n\",\n      \"   0.4  0.6  0.3  0.5  0.9  0.4  0.8  0.8  0.2  0.3  0.5  0.1  0.5  0.6\\n\",\n      \"   0.8]\\n\",\n      \" [ 0.4  0.6  0.6  1.   0.3  0.6  0.6  0.1  0.1  0.9  0.1  0.9  0.5  1.   0.6\\n\",\n      \"   0.1  0.8  0.1  0.4  0.1  0.7  0.9  0.7  0.4  0.3  0.5  0.5  0.4  0.   0.7\\n\",\n      \"   0.7  0.3  0.9  1.   0.7  0.   0.2  1.   0.6  0.3  0.3  0.1  0.7  0.5\\n\",\n      \"   0.3  0.7  0.1  0.9  0.6  0.9  0.3  0.1  0.9  1.   0.1  1.   0.6  0.7\\n\",\n      \"   0.5  0.6  1.   0.9  0.2  0.2  0.3  0.4  0.6  0.1  0.4  0.   0.9  0.4\\n\",\n      \"   0.9  0.7  0.1  1.   0.7  0.1  0.7  0.2  0.9  0.7  0.1  0.1  0.9  0.6\\n\",\n      \"   0.1  0.1  0.2  0.6  0.8  0.9  0.2  0.1  0.2  0.3  0.5  0.7  0.8  0.8]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.random.uniform(size=[10,100])\\n\",\n    \"np.set_printoptions(...)\\n\",\n    \"print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Base-n representations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Convert 12 into a binary number in string format.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1100\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Convert 12 into a hexadecimal number in string format.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'44C'\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python [conda root]\",\n   \"language\": \"python\",\n   \"name\": \"conda-root-py\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/5_Input_and_Output_Solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Input and Output\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from __future__ import print_function\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"author = \\\"kyubyong. https://github.com/Kyubyong/numpy_exercises\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.12.0'\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2017-04-01\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from datetime import date\\n\",\n    \"print(date.today())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## NumPy binary files (NPY, NPZ)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Save x into `temp.npy` and load it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10)\\n\",\n    \"np.save('temp.npy', x) # Actually you can omit the extension. If so, it will be added automatically.\\n\",\n    \"\\n\",\n    \"# Check if there exists the 'temp.npy' file.\\n\",\n    \"import os\\n\",\n    \"if os.path.exists('temp.npy'):\\n\",\n    \"    x2 = np.load('temp.npy')\\n\",\n    \"    print(np.array_equal(x, x2))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Save x and y into a single file 'temp.npz' and load it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10)\\n\",\n    \"y = np.arange(11, 20)\\n\",\n    \"np.savez('temp.npz', x=x, y=y)\\n\",\n    \"# np.savez_compressed('temp.npz', x=x, y=y) # If you want to save x and y into a single file in compressed .npz format.\\n\",\n    \"with np.load('temp.npz') as data:\\n\",\n    \"    x2 = data['x']\\n\",\n    \"    y2 = data['y']\\n\",\n    \"    print(np.array_equal(x, x2))\\n\",\n    \"    print(np.array_equal(y, y2))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Text files\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Save x to 'temp.txt' in string format and load it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.,  1.,  2.,  3.,  4.],\\n\",\n       \"       [ 5.,  6.,  7.,  8.,  9.]])\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10).reshape(2, 5)\\n\",\n    \"header = 'num1 num2 num3 num4 num5'\\n\",\n    \"np.savetxt('temp.txt', x, fmt=\\\"%d\\\", header=header) \\n\",\n    \"np.loadtxt('temp.txt')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Save `x`, `y`, and `z` to 'temp.txt' in string format line by line, then load it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\\n\",\n       \"       [ 11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.],\\n\",\n       \"       [ 22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.]])\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10)\\n\",\n    \"y = np.arange(11, 21)\\n\",\n    \"z = np.arange(22, 32)\\n\",\n    \"np.savetxt('temp.txt', (x, y, z), fmt='%d')\\n\",\n    \"np.loadtxt('temp.txt')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Convert `x` into bytes, and load it as array.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 3, 4])\\n\",\n    \"x_bytes = x.tostring() # Don't be misled by the function name. What it really does is it returns bytes.\\n\",\n    \"x2 = np.fromstring(x_bytes, dtype=x.dtype) # returns a 1-D array even if x is not.\\n\",\n    \"print(np.array_equal(x, x2))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Convert `a` into an ndarray and then convert it into a list again.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = [[1, 2], [3, 4]]\\n\",\n    \"x = np.array(a)\\n\",\n    \"a2 = x.tolist()\\n\",\n    \"print(a == a2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## String formatting¶\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Convert `x` to a string, and revert it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0 1 2 3 4]\\n\",\n      \" [5 6 7 8 9]] \\n\",\n      \" <class 'str'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10).reshape(2,5)\\n\",\n    \"x_str = np.array_str(x)\\n\",\n    \"print(x_str, \\\"\\\\n\\\", type(x_str))\\n\",\n    \"x_str = x_str.replace(\\\"[\\\", \\\"\\\") # [] must be stripped\\n\",\n    \"x_str = x_str.replace(\\\"]\\\", \\\"\\\")\\n\",\n    \"x2 = np.fromstring(x_str, dtype=x.dtype, sep=\\\" \\\").reshape(x.shape)\\n\",\n    \"assert np.array_equal(x, x2)\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Text formatting options\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Print `x` such that all elements are displayed with precision=1, no suppress.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.5  0.   0.8  0.2  0.3  0.2  0.2  1.   0.4  0.8  0.6  0.2  0.5  0.1\\n\",\n      \"   0.4  0.1  0.9  0.6  0.1  0.5  0.8  0.8  0.8  0.   0.6  0.8  0.4  0.3\\n\",\n      \"   0.8  0.2  0.7  0.7  0.2  1.   0.8  0.1  0.2  0.1  0.3  0.1  0.5  0.9\\n\",\n      \"   0.6  0.9  0.6  0.5  0.8  0.3  0.3  0.5  0.1  0.6  0.1  0.3  0.6  0.2\\n\",\n      \"   0.4  0.8  0.6  0.4  0.2  0.6  0.   0.3  0.8  0.5  0.7  0.9  0.8  0.6\\n\",\n      \"   0.9  0.8  0.4  0.4  0.7  0.8  0.   0.1  0.5  0.4  0.7  1.   0.1  0.2\\n\",\n      \"   0.6  0.3  0.9  0.1  0.6  0.4  0.3  0.8  0.3  0.6  0.6  0.3  1.   0.2\\n\",\n      \"   0.9  0.2]\\n\",\n      \" [ 0.9  0.2  0.4  0.9  0.5  0.6  0.1  0.7  0.   0.   0.1  0.8  0.8  1.   0.2\\n\",\n      \"   0.8  0.3  0.2  1.   0.6  1.   0.3  0.4  0.4  0.7  0.5  0.4  0.8  0.5\\n\",\n      \"   0.9  0.3  0.5  0.7  0.4  0.2  0.3  0.9  0.   0.6  0.8  0.3  0.5  0.2\\n\",\n      \"   0.3  0.   0.6  0.5  0.2  0.5  0.8  0.2  0.8  0.   0.9  0.   0.7  0.1\\n\",\n      \"   0.4  0.2  0.5  0.6  0.2  0.6  0.1  0.1  0.   0.5  0.9  0.4  0.5  0.8\\n\",\n      \"   0.5  0.1  0.7  0.   1.   0.5  0.4  0.2  0.   1.   0.4  0.1  0.7  0.7\\n\",\n      \"   0.4  0.8  0.4  0.6  0.6  0.5  0.8  0.8  0.2  0.2  0.3  0.2  0.5  0.9\\n\",\n      \"   0.5]\\n\",\n      \" [ 0.3  0.6  0.4  0.5  0.5  0.   0.7  0.1  0.   0.9  0.5  0.7  0.6  0.3\\n\",\n      \"   0.9  0.5  0.1  0.4  0.1  0.9  0.8  0.6  0.8  0.8  0.1  0.4  0.9  0.1  1.\\n\",\n      \"   0.7  0.4  0.3  0.8  0.3  0.8  0.8  0.2  0.7  0.2  0.8  0.3  0.9  0.1\\n\",\n      \"   0.9  0.2  0.8  0.9  0.6  0.1  0.3  0.4  1.   0.1  0.7  0.3  0.9  0.3\\n\",\n      \"   0.5  0.9  0.   0.6  0.   0.8  0.1  0.9  0.   0.8  0.6  0.5  0.5  0.2  1.\\n\",\n      \"   0.4  0.   0.2  0.   0.9  0.9  0.8  0.2  0.7  0.3  0.2  0.1  1.   0.4\\n\",\n      \"   0.5  0.4  0.8  0.8  0.8  0.7  0.6  0.4  0.7  0.6  0.5  0.8  0.7  0.6]\\n\",\n      \" [ 0.2  0.6  0.9  0.7  0.1  0.1  1.   0.5  0.8  0.3  1.   0.4  0.1  0.5\\n\",\n      \"   0.6  0.8  0.8  0.8  0.1  1.   0.8  0.   0.7  0.6  0.8  0.2  0.5  0.9\\n\",\n      \"   0.4  0.8  0.7  0.2  0.8  0.6  0.9  0.6  0.9  0.8  0.9  1.   0.6  0.6\\n\",\n      \"   0.7  0.1  0.5  0.3  0.   0.8  0.   0.5  0.8  0.3  0.8  0.7  0.1  0.5\\n\",\n      \"   0.2  0.1  0.7  0.   0.   0.6  0.   0.8  0.7  0.1  0.4  0.1  0.2  0.1\\n\",\n      \"   0.9  0.6  0.9  0.3  0.4  0.9  0.2  0.6  0.8  0.9  0.6  0.8  0.5  0.1\\n\",\n      \"   0.6  1.   0.   0.7  0.7  0.4  0.1  0.9  0.4  0.1  0.7  0.6  0.3  0.9\\n\",\n      \"   0.3  0.5]\\n\",\n      \" [ 0.9  0.3  0.1  0.1  0.2  0.4  0.3  0.5  0.2  0.   0.5  0.4  0.5  0.3\\n\",\n      \"   0.6  1.   0.1  0.7  0.6  0.2  0.3  0.3  0.1  0.5  0.6  0.   0.6  0.7\\n\",\n      \"   0.6  0.4  0.2  0.6  0.1  0.9  0.9  0.1  0.9  0.1  0.6  0.6  0.   0.1\\n\",\n      \"   0.6  0.4  0.3  0.1  0.9  0.8  0.1  0.2  0.8  0.4  0.7  0.8  0.6  0.4\\n\",\n      \"   0.9  0.3  0.6  0.7  0.4  0.8  0.3  0.   0.   0.9  0.3  0.3  0.8  0.5\\n\",\n      \"   0.8  1.   0.2  0.6  0.6  0.2  0.2  0.2  0.4  0.6  0.6  0.4  0.4  0.8\\n\",\n      \"   0.2  0.5  0.7  0.7  0.1  0.9  0.5  0.6  0.3  0.3  0.6  0.8  0.6  0.8\\n\",\n      \"   0.4  0.3]\\n\",\n      \" [ 0.3  1.   0.6  0.9  0.6  1.   0.7  0.9  0.4  0.3  0.9  0.9  0.3  0.8\\n\",\n      \"   0.3  0.6  0.7  0.3  0.1  0.1  0.4  0.3  0.6  0.5  0.1  0.6  0.1  0.5\\n\",\n      \"   0.9  0.5  0.5  0.6  0.4  0.4  0.3  1.   0.6  0.6  0.3  0.1  0.4  0.7\\n\",\n      \"   0.7  0.1  0.5  0.1  0.3  0.1  0.6  0.7  0.   0.1  0.2  0.4  0.1  0.4\\n\",\n      \"   0.7  0.3  0.2  0.9  0.5  0.   0.4  0.9  1.   0.4  0.   0.2  0.3  0.9\\n\",\n      \"   0.3  0.   0.8  0.9  0.8  0.6  0.4  0.5  0.   0.9  0.6  0.6  0.1  0.6\\n\",\n      \"   0.9  0.1  0.8  0.6  0.6  0.5  0.7  1.   0.5  0.3  0.3  0.4  0.6  0.6  1.\\n\",\n      \"   0.2]\\n\",\n      \" [ 0.7  0.7  0.9  0.2  0.6  0.3  0.9  0.2  0.9  0.8  0.5  0.3  0.9  0.5  1.\\n\",\n      \"   0.6  0.9  0.5  0.5  0.1  0.8  0.3  0.9  0.5  0.7  1.   0.6  0.7  0.1\\n\",\n      \"   0.7  0.9  0.4  0.8  0.9  0.4  1.   0.1  1.   0.5  0.1  0.4  0.7  1.   0.4\\n\",\n      \"   0.3  0.2  0.2  0.6  0.6  0.3  0.7  0.5  0.7  0.1  0.3  0.5  1.   0.8\\n\",\n      \"   0.4  0.8  0.8  0.7  0.1  0.2  0.4  0.3  0.4  0.3  0.5  0.4  0.6  0.3\\n\",\n      \"   0.1  0.7  0.8  0.6  0.6  0.2  0.7  0.9  0.9  0.7  0.3  0.9  0.4  0.6  0.\\n\",\n      \"   0.4  0.4  0.2  0.8  0.3  0.1  0.2  0.6  0.5  0.9  0.8  0.9  0.7]\\n\",\n      \" [ 0.8  0.7  0.7  0.6  0.9  0.1  0.4  0.9  1.   0.3  0.   0.2  0.1  0.5\\n\",\n      \"   0.8  0.1  0.7  0.7  0.6  1.   0.7  1.   0.4  0.6  0.2  0.4  0.4  0.6  0.\\n\",\n      \"   0.1  1.   0.5  0.1  0.2  0.8  0.2  0.1  0.4  0.7  0.5  0.4  1.   0.5\\n\",\n      \"   0.5  0.4  0.8  0.2  0.1  0.7  0.2  0.1  0.4  0.3  0.6  0.9  0.9  0.9\\n\",\n      \"   0.9  0.1  0.1  0.   1.   0.   0.1  0.4  0.6  1.   0.4  0.9  0.3  0.2\\n\",\n      \"   0.7  0.   0.3  0.2  0.7  0.4  0.3  0.9  0.3  0.   0.5  0.2  0.3  0.1\\n\",\n      \"   0.2  0.   0.1  0.6  0.9  0.2  0.5  0.8  0.7  0.   0.4  0.8  0.8  0.5\\n\",\n      \"   0.2]\\n\",\n      \" [ 0.2  0.3  0.   0.1  0.8  0.4  0.1  0.2  0.   0.7  0.   1.   0.6  0.7\\n\",\n      \"   0.3  0.3  0.7  0.9  0.3  0.7  0.1  0.1  0.5  0.6  0.3  0.8  0.7  0.1\\n\",\n      \"   0.6  0.6  0.3  0.2  0.3  0.3  1.   0.1  0.1  0.2  0.4  0.4  0.6  0.5\\n\",\n      \"   0.7  0.7  0.2  0.   0.8  0.3  0.9  0.1  0.1  0.4  0.4  0.5  0.3  0.9\\n\",\n      \"   0.6  0.9  0.3  0.5  0.   0.4  0.8  1.   0.3  0.5  0.7  0.5  0.8  0.7\\n\",\n      \"   0.6  0.3  0.1  0.2  0.5  1.   0.9  0.5  0.6  0.6  0.2  0.8  0.6  0.   0.5\\n\",\n      \"   0.6  0.8  0.5  0.8  0.8  0.9  0.7  0.9  0.5  0.2  1.   1.   0.1  0.3\\n\",\n      \"   0.3]\\n\",\n      \" [ 0.   0.3  0.4  0.7  0.2  0.9  0.2  0.3  0.6  0.8  0.4  0.7  0.3  0.5\\n\",\n      \"   0.6  0.3  0.7  0.   0.1  0.1  0.9  0.   0.7  0.7  0.1  0.6  0.6  0.   0.3\\n\",\n      \"   0.5  0.9  0.3  0.1  0.3  0.1  0.9  0.6  0.3  0.3  0.4  0.4  0.2  0.3\\n\",\n      \"   0.1  0.5  0.3  0.8  0.   0.8  0.6  0.2  0.7  0.4  0.8  0.2  0.9  1.   1.\\n\",\n      \"   0.7  0.9  0.1  0.2  0.   0.5  0.8  0.7  0.6  0.7  0.7  0.5  0.9  0.2\\n\",\n      \"   0.2  0.1  0.2  0.1  0.7  1.   0.6  0.3  0.9  1.   0.3  0.3  0.7  0.9\\n\",\n      \"   0.5  0.8  0.9  0.7  0.2  0.7  0.3  0.1  0.9  0.2  0.5  0.6  0.3  0.4]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.random.uniform(size=[10,100])\\n\",\n    \"np.set_printoptions(precision=1, threshold=np.nan, suppress=True)\\n\",\n    \"print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Base-n representations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Convert 12 into a binary number in string format.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1100\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"out1 = np.binary_repr(12)\\n\",\n    \"out2 = np.base_repr(12, base=2)\\n\",\n    \"assert out1 == out2 # But out1 is better because it's much faster.\\n\",\n    \"print(out1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Convert 12 into a hexadecimal number in string format.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'44C'\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.base_repr(1100, base=16)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python [conda root]\",\n   \"language\": \"python\",\n   \"name\": \"conda-root-py\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/6_Linear_algebra.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Linear algebra\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Matrix and vector products\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Predict the results of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = [1,2]\\n\",\n    \"y = [[4, 1], [2, 2]]\\n\",\n    \"#print np.dot(x, y)\\n\",\n    \"#print np.dot(y, x)\\n\",\n    \"#print np.matmul(x, y)\\n\",\n    \"#print np.inner(x, y)\\n\",\n    \"#print np.inner(y, x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Predict the results of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = [[1, 0], [0, 1]]\\n\",\n    \"y = [[4, 1], [2, 2], [1, 1]]\\n\",\n    \"#print np.dot(y, x)\\n\",\n    \"#print np.matmul(y, x)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Predict the results of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([[1, 4], [5, 6]])\\n\",\n    \"y = np.array([[4, 1], [2, 2]])\\n\",\n    \"#print np.vdot(x, y)\\n\",\n    \"#print np.vdot(y, x)\\n\",\n    \"#print np.dot(x.flatten(), y.flatten())\\n\",\n    \"#print np.inner(x.flatten(), y.flatten())\\n\",\n    \"#print (x*y).sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Predict the results of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array(['a', 'b'], dtype=object)\\n\",\n    \"y = np.array([1, 2])\\n\",\n    \"#print np.inner(x, y)\\n\",\n    \"#print np.inner(y, x)\\n\",\n    \"#print np.outer(x, y)\\n\",\n    \"#print np.outer(y, x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Decompositions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Get the lower-trianglular `L` in the Cholesky decomposition of x and verify it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 2.  0.  0.]\\n\",\n      \" [ 6.  1.  0.]\\n\",\n      \" [-8.  5.  3.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[4, 12, -16], [12, 37, -43], [-16, -43, 98]], dtype=np.int32)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Compute the qr factorization of x and verify it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"q=\\n\",\n      \"[[-0.85714287  0.39428571  0.33142856]\\n\",\n      \" [-0.42857143 -0.90285712 -0.03428571]\\n\",\n      \" [ 0.2857143  -0.17142858  0.94285715]] \\n\",\n      \"r=\\n\",\n      \"[[ -14.  -21.   14.]\\n\",\n      \" [   0. -175.   70.]\\n\",\n      \" [   0.    0.  -35.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[12, -51, 4], [6, 167, -68], [-4, 24, -41]], dtype=np.float32)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Factor x by Singular Value Decomposition and verify it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 165,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"U=\\n\",\n      \"[[ 0.  1.  0.  0.]\\n\",\n      \" [ 1.  0.  0.  0.]\\n\",\n      \" [ 0.  0.  0. -1.]\\n\",\n      \" [ 0.  0.  1.  0.]] \\n\",\n      \"s=\\n\",\n      \"[ 3.          2.23606801  2.          0.        ] \\n\",\n      \"V=\\n\",\n      \"[[ 1.  0.  0.]\\n\",\n      \" [ 0.  1.  0.]\\n\",\n      \" [ 0.  0.  1.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 0, 0, 0, 2], [0, 0, 3, 0, 0], [0, 0, 0, 0, 0], [0, 2, 0, 0, 0]], dtype=np.float32)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Matrix eigenvalues\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Compute the eigenvalues and right eigenvectors of x. (Name them eigenvals and eigenvecs, respectively)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"eigenvalues are\\n\",\n      \"[ 1.  2.  3.]\\n\",\n      \"eigenvectors are\\n\",\n      \"[[ 1.  0.  0.]\\n\",\n      \" [ 0.  1.  0.]\\n\",\n      \" [ 0.  0.  1.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.diag((1, 2, 3))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Predict the results of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#print np.array_equal(np.dot(x, eigenvecs), eigenvals * eigenvecs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Norms and other numbers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Calculate the Frobenius norm and the condition number of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"16.8819430161\\n\",\n      \"4.56177073661e+17\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1, 10).reshape((3, 3))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Calculate the determinant of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-2.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1, 5).reshape((2, 2))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Calculate the rank of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.eye(4)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q13. Compute the sign and natural logarithm of the determinant of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-1.0 0.69314718056\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1, 5).reshape((2, 2))\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q14. Return the sum along the diagonal of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"4.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.eye(4)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Solving equations and inverting matrices\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"source\": [\n    \"Q15. Compute the inverse of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[-2.   1. ]\\n\",\n      \" [ 1.5 -0.5]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1., 2.], [3., 4.]])\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/6_Linear_algebra_Solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Linear algebra\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Matrix and vector products\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Predict the results of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[8 5]\\n\",\n      \"[6 6]\\n\",\n      \"[8 5]\\n\",\n      \"[6 6]\\n\",\n      \"[6 6]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = [1,2]\\n\",\n    \"y = [[4, 1], [2, 2]]\\n\",\n    \"print np.dot(x, y)\\n\",\n    \"print np.dot(y, x)\\n\",\n    \"print np.matmul(x, y)\\n\",\n    \"print np.inner(x, y)\\n\",\n    \"print np.inner(y, x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Predict the results of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[4 1]\\n\",\n      \" [2 2]\\n\",\n      \" [1 1]]\\n\",\n      \"[[4 1]\\n\",\n      \" [2 2]\\n\",\n      \" [1 1]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = [[1, 0], [0, 1]]\\n\",\n    \"y = [[4, 1], [2, 2], [1, 1]]\\n\",\n    \"print np.dot(y, x)\\n\",\n    \"print np.matmul(y, x)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Predict the results of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"30\\n\",\n      \"30\\n\",\n      \"30\\n\",\n      \"30\\n\",\n      \"30\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 4], [5, 6]])\\n\",\n    \"y = np.array([[4, 1], [2, 2]])\\n\",\n    \"print np.vdot(x, y)\\n\",\n    \"print np.vdot(y, x)\\n\",\n    \"print np.dot(x.flatten(), y.flatten())\\n\",\n    \"print np.inner(x.flatten(), y.flatten())\\n\",\n    \"print (x*y).sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Predict the results of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"abb\\n\",\n      \"abb\\n\",\n      \"[['a' 'aa']\\n\",\n      \" ['b' 'bb']]\\n\",\n      \"[['a' 'b']\\n\",\n      \" ['aa' 'bb']]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(['a', 'b'], dtype=object)\\n\",\n    \"y = np.array([1, 2])\\n\",\n    \"print np.inner(x, y)\\n\",\n    \"print np.inner(y, x)\\n\",\n    \"print np.outer(x, y)\\n\",\n    \"print np.outer(y, x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Decompositions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Get the lower-trianglular `L` in the Cholesky decomposition of x and verify it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 2.  0.  0.]\\n\",\n      \" [ 6.  1.  0.]\\n\",\n      \" [-8.  5.  3.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[4, 12, -16], [12, 37, -43], [-16, -43, 98]], dtype=np.int32)\\n\",\n    \"L = np.linalg.cholesky(x)\\n\",\n    \"print L\\n\",\n    \"assert np.array_equal(np.dot(L, L.T.conjugate()), x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Compute the qr factorization of x and verify it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"q=\\n\",\n      \"[[-0.85714287  0.39428571  0.33142856]\\n\",\n      \" [-0.42857143 -0.90285712 -0.03428571]\\n\",\n      \" [ 0.2857143  -0.17142858  0.94285715]] \\n\",\n      \"r=\\n\",\n      \"[[ -14.  -21.   14.]\\n\",\n      \" [   0. -175.   70.]\\n\",\n      \" [   0.    0.  -35.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[12, -51, 4], [6, 167, -68], [-4, 24, -41]], dtype=np.float32)\\n\",\n    \"q, r = np.linalg.qr(x)\\n\",\n    \"print \\\"q=\\\\n\\\", q, \\\"\\\\nr=\\\\n\\\", r\\n\",\n    \"assert np.allclose(np.dot(q, r), x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Factor x by Singular Value Decomposition and verify it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 165,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"U=\\n\",\n      \"[[ 0.  1.  0.  0.]\\n\",\n      \" [ 1.  0.  0.  0.]\\n\",\n      \" [ 0.  0.  0. -1.]\\n\",\n      \" [ 0.  0.  1.  0.]] \\n\",\n      \"s=\\n\",\n      \"[ 3.          2.23606801  2.          0.        ] \\n\",\n      \"V=\\n\",\n      \"[[ 1.  0.  0.]\\n\",\n      \" [ 0.  1.  0.]\\n\",\n      \" [ 0.  0.  1.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 0, 0, 0, 2], [0, 0, 3, 0, 0], [0, 0, 0, 0, 0], [0, 2, 0, 0, 0]], dtype=np.float32)\\n\",\n    \"U, s, V = np.linalg.svd(x, full_matrices=False)\\n\",\n    \"print \\\"U=\\\\n\\\", U, \\\"\\\\ns=\\\\n\\\", s, \\\"\\\\nV=\\\\n\\\", v\\n\",\n    \"assert np.allclose(np.dot(U, np.dot(np.diag(s), V)), x)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Matrix eigenvalues\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Compute the eigenvalues and right eigenvectors of x. (Name them eigenvals and eigenvecs, respectively)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"eigenvalues are\\n\",\n      \"[ 1.  2.  3.]\\n\",\n      \"eigenvectors are\\n\",\n      \"[[ 1.  0.  0.]\\n\",\n      \" [ 0.  1.  0.]\\n\",\n      \" [ 0.  0.  1.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.diag((1, 2, 3))\\n\",\n    \"eigenvals = np.linalg.eig(x)[0]\\n\",\n    \"eigenvals_ = np.linalg.eigvals(x)\\n\",\n    \"assert np.array_equal(eigenvals, eigenvals_)\\n\",\n    \"print \\\"eigenvalues are\\\\n\\\", eigenvals\\n\",\n    \"eigenvecs = np.linalg.eig(x)[1]\\n\",\n    \"print \\\"eigenvectors are\\\\n\\\", eigenvecs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Predict the results of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print np.array_equal(np.dot(x, eigenvecs), eigenvals * eigenvecs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Norms and other numbers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Calculate the Frobenius norm and the condition number of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"16.8819430161\\n\",\n      \"4.56177073661e+17\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1, 10).reshape((3, 3))\\n\",\n    \"print np.linalg.norm(x, 'fro')\\n\",\n    \"print np.linalg.cond(x, 'fro')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Calculate the determinant of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-2.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1, 5).reshape((2, 2))\\n\",\n    \"out1 = np.linalg.det(x)\\n\",\n    \"out2 = x[0, 0] * x[1, 1] - x[0, 1] * x[1, 0]\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print out1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Calculate the rank of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.eye(4)\\n\",\n    \"out1 = np.linalg.matrix_rank(x)\\n\",\n    \"out2 = np.linalg.svd(x)[1].size\\n\",\n    \"assert out1 == out2\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q13. Compute the sign and natural logarithm of the determinant of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-1.0 0.69314718056\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(1, 5).reshape((2, 2))\\n\",\n    \"sign, logdet = np.linalg.slogdet(x)\\n\",\n    \"det = np.linalg.det(x)\\n\",\n    \"assert sign == np.sign(det)\\n\",\n    \"assert logdet == np.log(np.abs(det))\\n\",\n    \"print sign, logdet\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q14. Return the sum along the diagonal of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"4.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.eye(4)\\n\",\n    \"out1 = np.trace(x)\\n\",\n    \"out2 = x.diagonal().sum()\\n\",\n    \"assert out1 == out2\\n\",\n    \"print out1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Solving equations and inverting matrices\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q15. Compute the inverse of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[-2.   1. ]\\n\",\n      \" [ 1.5 -0.5]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1., 2.], [3., 4.]])\\n\",\n    \"out1 = np.linalg.inv(x)\\n\",\n    \"assert np.allclose(np.dot(x, out1), np.eye(2))\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/7_Discrete_Fourier_Transform.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from __future__ import print_function\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"datetime.date(2017, 11, 2)\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from datetime import date\\n\",\n    \"date.today()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"author = \\\"kyubyong. https://github.com/Kyubyong/numpy_exercises\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.13.1'\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Complex Numbers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Return the angle of `a` in radian.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.785398163397\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = 1+1j\\n\",\n    \"output = ...\\n\",\n    \"print(output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Return the real part and imaginary part of `a`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"real part= [ 1.  3.  5.]\\n\",\n      \"imaginary part= [ 2.  4.  6.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1+2j, 3+4j, 5+6j])\\n\",\n    \"real = ...\\n\",\n    \"imag = ...\\n\",\n    \"print(\\\"real part=\\\", real)\\n\",\n    \"print(\\\"imaginary part=\\\", imag)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Replace the real part of a with `9`, the imaginary part with `[5, 7, 9]`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 9.+5.j  9.+7.j  9.+9.j]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1+2j, 3+4j, 5+6j])\\n\",\n    \"...\\n\",\n    \"...\\n\",\n    \"print(a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Return the complex conjugate of `a`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1-2j)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = 1+2j\\n\",\n    \"output = ...\\n\",\n    \"print(output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Discrete Fourier Transform\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Compuete the one-dimensional DFT of `a`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  8.00000000e+00 -6.85802208e-15j   2.36524713e-15 +9.79717439e-16j\\n\",\n      \"   9.79717439e-16 +9.79717439e-16j   4.05812251e-16 +9.79717439e-16j\\n\",\n      \"   0.00000000e+00 +9.79717439e-16j  -4.05812251e-16 +9.79717439e-16j\\n\",\n      \"  -9.79717439e-16 +9.79717439e-16j  -2.36524713e-15 +9.79717439e-16j]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.exp(2j * np.pi * np.arange(8))\\n\",\n    \"output = ...\\n\",\n    \"print(output)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Compute the one-dimensional inverse DFT of the `output` in the above question.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a= [ 1. +0.00000000e+00j  1. -2.44929360e-16j  1. -4.89858720e-16j\\n\",\n      \"  1. -7.34788079e-16j  1. -9.79717439e-16j  1. -1.22464680e-15j\\n\",\n      \"  1. -1.46957616e-15j  1. -1.71450552e-15j]\\n\",\n      \"inversed= [ 1. +0.00000000e+00j  1. -2.44929360e-16j  1. -4.89858720e-16j\\n\",\n      \"  1. -7.34788079e-16j  1. -9.79717439e-16j  1. -1.22464680e-15j\\n\",\n      \"  1. -1.46957616e-15j  1. -1.71450552e-15j]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"a=\\\", a)\\n\",\n    \"inversed = ...\\n\",\n    \"print(\\\"inversed=\\\", a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Compute the one-dimensional discrete Fourier Transform for real input `a`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1.+0.j  0.-1.j -1.+0.j]\\n\",\n      \"[ 1.+0.j  0.-1.j -1.+0.j  0.+1.j]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = [0, 1, 0, 0]\\n\",\n    \"output = ...\\n\",\n    \"print(output)\\n\",\n    \"assert output.size==len(a)//2+1 if len(a)%2==0 else (len(a)+1)//2\\n\",\n    \"\\n\",\n    \"# cf.\\n\",\n    \"output2 = np.fft.fft(a)\\n\",\n    \"print(output2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Compute the one-dimensional inverse DFT of the output in the above question.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"inversed= [0, 1, 0, 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"inversed = ...\\n\",\n    \"print(\\\"inversed=\\\", a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Return the DFT sample frequencies of `a`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.     0.125  0.25   0.375 -0.5   -0.375 -0.25  -0.125]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=np.float32)\\n\",\n    \"fourier = np.fft.fft(signal)\\n\",\n    \"n = signal.size\\n\",\n    \"freq = ...\\n\",\n    \"print(freq)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Window Functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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BN27txJbGwsffv2JTg4+JFzARw7dozTp09TpUoVmjZtyv79+/H39/9b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\\nf9alElAadvRbAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bf30fd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(19, 10))\\n\",\n    \"\\n\",\n    \"# Hamming window\\n\",\n    \"window = np.hamming(51)\\n\",\n    \"plt.plot(np.bartlett(51), label=\\\"Bartlett window\\\")\\n\",\n    \"plt.plot(np.blackman(51), label=\\\"Blackman window\\\")\\n\",\n    \"plt.plot(np.hamming(51), label=\\\"Hamming window\\\")\\n\",\n    \"plt.plot(np.hanning(51), label=\\\"Hanning window\\\")\\n\",\n    \"plt.plot(np.kaiser(51, 14), label=\\\"Kaiser window\\\")\\n\",\n    \"plt.xlabel(\\\"sample\\\")\\n\",\n    \"plt.ylabel(\\\"amplitude\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.grid()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/7_Discrete_Fourier_Transform_solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from __future__ import print_function\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"datetime.date(2017, 11, 2)\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from datetime import date\\n\",\n    \"date.today()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"author = \\\"kyubyong. https://github.com/Kyubyong/numpy_exercises\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.13.1'\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Complex Numbers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Return the angle of `a` in radian.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.785398163397\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = 1+1j\\n\",\n    \"output = np.angle(a, deg=False)\\n\",\n    \"print(output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Return the real part and imaginary part of `a`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"real part= [ 1.  3.  5.]\\n\",\n      \"imaginary part= [ 2.  4.  6.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1+2j, 3+4j, 5+6j])\\n\",\n    \"real = a.real\\n\",\n    \"imag = a.imag\\n\",\n    \"print(\\\"real part=\\\", real)\\n\",\n    \"print(\\\"imaginary part=\\\", imag)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Replace the real part of a with `9`, the imaginary part with `[5, 7, 9]`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 9.+5.j  9.+7.j  9.+9.j]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1+2j, 3+4j, 5+6j])\\n\",\n    \"a.real = 9\\n\",\n    \"a.imag = [5, 7, 9]\\n\",\n    \"print(a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Return the complex conjugate of `a`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1-2j)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = 1+2j\\n\",\n    \"output = np.conjugate(a)\\n\",\n    \"print(output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Discrete Fourier Transform\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Compuete the one-dimensional DFT of `a`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  8.00000000e+00 -6.85802208e-15j   2.36524713e-15 +9.79717439e-16j\\n\",\n      \"   9.79717439e-16 +9.79717439e-16j   4.05812251e-16 +9.79717439e-16j\\n\",\n      \"   0.00000000e+00 +9.79717439e-16j  -4.05812251e-16 +9.79717439e-16j\\n\",\n      \"  -9.79717439e-16 +9.79717439e-16j  -2.36524713e-15 +9.79717439e-16j]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.exp(2j * np.pi * np.arange(8))\\n\",\n    \"output = np.fft.fft(a)\\n\",\n    \"print(output)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Compute the one-dimensional inverse DFT of the `output` in the above question.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a= [ 1. +0.00000000e+00j  1. -2.44929360e-16j  1. -4.89858720e-16j\\n\",\n      \"  1. -7.34788079e-16j  1. -9.79717439e-16j  1. -1.22464680e-15j\\n\",\n      \"  1. -1.46957616e-15j  1. -1.71450552e-15j]\\n\",\n      \"inversed= [ 1. +0.00000000e+00j  1. -2.44929360e-16j  1. -4.89858720e-16j\\n\",\n      \"  1. -7.34788079e-16j  1. -9.79717439e-16j  1. -1.22464680e-15j\\n\",\n      \"  1. -1.46957616e-15j  1. -1.71450552e-15j]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"a=\\\", a)\\n\",\n    \"inversed = np.fft.ifft(output)\\n\",\n    \"print(\\\"inversed=\\\", a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Compute the one-dimensional discrete Fourier Transform for real input `a`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1.+0.j  0.-1.j -1.+0.j]\\n\",\n      \"[ 1.+0.j  0.-1.j -1.+0.j  0.+1.j]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = [0, 1, 0, 0]\\n\",\n    \"output = np.fft.rfft(a)\\n\",\n    \"print(output)\\n\",\n    \"assert output.size==len(a)//2+1 if len(a)%2==0 else (len(a)+1)//2\\n\",\n    \"\\n\",\n    \"# cf.\\n\",\n    \"output2 = np.fft.fft(a)\\n\",\n    \"print(output2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Compute the one-dimensional inverse DFT of the output in the above question.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"inversed= [0, 1, 0, 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"inversed = np.fft.ifft(output)\\n\",\n    \"print(\\\"inversed=\\\", a)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Return the DFT sample frequencies of `a`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.     0.125  0.25   0.375 -0.5   -0.375 -0.25  -0.125]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=np.float32)\\n\",\n    \"fourier = np.fft.fft(signal)\\n\",\n    \"n = signal.size\\n\",\n    \"freq = np.fft.fftfreq(n, d=1)\\n\",\n    \"print(freq)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Window Functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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BN27txJbGwsffv2JTg4+JFzARw7dozTp09TpUoVmjZtyv79+/H39/9b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\\nf9alElAadvRbAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bf30fd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(19, 10))\\n\",\n    \"\\n\",\n    \"# Hamming window\\n\",\n    \"window = np.hamming(51)\\n\",\n    \"plt.plot(np.bartlett(51), label=\\\"Bartlett window\\\")\\n\",\n    \"plt.plot(np.blackman(51), label=\\\"Blackman window\\\")\\n\",\n    \"plt.plot(np.hamming(51), label=\\\"Hamming window\\\")\\n\",\n    \"plt.plot(np.hanning(51), label=\\\"Hanning window\\\")\\n\",\n    \"plt.plot(np.kaiser(51, 14), label=\\\"Kaiser window\\\")\\n\",\n    \"plt.xlabel(\\\"sample\\\")\\n\",\n    \"plt.ylabel(\\\"amplitude\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.grid()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/8_Logic_functions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Logic functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Truth value testing\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Let x be an arbitrary array. Return True if none of the elements of x is zero. Remind that 0 evaluates to False in python.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1,2,3])\\n\",\n    \"#\\n\",\n    \"\\n\",\n    \"x = np.array([1,0,3])\\n\",\n    \"#\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Let x be an arbitrary array. Return True if any of the elements of x is non-zero.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1,0,0])\\n\",\n    \"#\\n\",\n    \"\\n\",\n    \"x = np.array([0,0,0])\\n\",\n    \"#\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Array contents\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([1, 0, np.nan, np.inf])\\n\",\n    \"#print np.isfinite(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([1, 0, np.nan, np.inf])\\n\",\n    \"#print np.isinf(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([1, 0, np.nan, np.inf])\\n\",\n    \"#print np.isnan(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Array type testing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j])\\n\",\n    \"#print np.iscomplex(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j])\\n\",\n    \"#print np.isreal(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#print np.isscalar(3)\\n\",\n    \"#print np.isscalar([3])\\n\",\n    \"#print np.isscalar(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Logical operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#print np.logical_and([True, False], [False, False])\\n\",\n    \"#print np.logical_or([True, False, True], [True, False, False])\\n\",\n    \"#print np.logical_xor([True, False, True], [True, False, False])\\n\",\n    \"#print np.logical_not([True, False, 0, 1])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Comparison\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#print np.allclose([3], [2.999999])\\n\",\n    \"#print np.array_equal([3], [2.999999])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Write numpy comparison functions such that they return the results as you see.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ True False]\\n\",\n      \"[ True  True]\\n\",\n      \"[False False]\\n\",\n      \"[False  True]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([4, 5])\\n\",\n    \"y = np.array([2, 5])\\n\",\n    \"#\\n\",\n    \"#\\n\",\n    \"#\\n\",\n    \"#\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#print np.equal([1, 2], [1, 2.000001])\\n\",\n    \"#print np.isclose([1, 2], [1, 2.000001])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/8_Logic_functions_Solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Logic functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Truth value testing\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Let x be an arbitrary array. Return True if none of the elements of x is zero. Remind that 0 evaluates to False in python.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1,2,3])\\n\",\n    \"print np.all(x)\\n\",\n    \"\\n\",\n    \"x = np.array([1,0,3])\\n\",\n    \"print np.all(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Let x be an arbitrary array. Return True if any of the elements of x is non-zero.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1,0,0])\\n\",\n    \"print np.any(x)\\n\",\n    \"\\n\",\n    \"x = np.array([0,0,0])\\n\",\n    \"print np.any(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Array contents\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([1, 0, np.nan, np.inf])\\n\",\n    \"#print np.isfinite(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([1, 0, np.nan, np.inf])\\n\",\n    \"#print np.isinf(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([1, 0, np.nan, np.inf])\\n\",\n    \"#print np.isnan(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Array type testing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j])\\n\",\n    \"#print np.iscomplex(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j])\\n\",\n    \"#print np.isreal(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#print np.isscalar(3)\\n\",\n    \"#print np.isscalar([3])\\n\",\n    \"#print np.isscalar(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Logical operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#print np.logical_and([True, False], [False, False])\\n\",\n    \"#print np.logical_or([True, False, True], [True, False, False])\\n\",\n    \"#print np.logical_xor([True, False, True], [True, False, False])\\n\",\n    \"#print np.logical_not([True, False, 0, 1])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Comparison\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#print np.allclose([3], [2.999999])\\n\",\n    \"#print np.array_equal([3], [2.999999])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Write numpy comparison functions such that they return the results as you see.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ True False]\\n\",\n      \"[ True  True]\\n\",\n      \"[False False]\\n\",\n      \"[False  True]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([4, 5])\\n\",\n    \"y = np.array([2, 5])\\n\",\n    \"print np.greater(x, y)\\n\",\n    \"print np.greater_equal(x, y)\\n\",\n    \"print np.less(x, y)\\n\",\n    \"print np.less_equal(x, y)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Predict the result of the following code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#print np.equal([1, 2], [1, 2.000001])\\n\",\n    \"#print np.isclose([1, 2], [1, 2.000001])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/9_Mathematical_functions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Mathematical functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"__author__ = \\\"kyubyong. kbpark.linguist@gmail.com. https://github.com/kyubyong\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Trigonometric functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Calculate sine, cosine, and tangent of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sine: [ 0.          0.84147098 -0.98803162  0.89399666]\\n\",\n      \"cosine: [ 1.          0.54030231  0.15425145 -0.44807362]\\n\",\n      \"tangent: [ 0.          1.55740772 -6.4053312  -1.99520041]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0., 1., 30, 90])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Calculate inverse sine, inverse cosine, and inverse tangent of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"inverse sine: [ 1.57079633  0.          1.57079633]\\n\",\n      \"inverse cosine: [ 0.          1.57079633  0.        ]\\n\",\n      \"inverse tangent: [ 0.78539816  0.          0.78539816]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-1., 0, 1.])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Convert angles from radians to degrees.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-180.  -90.   90.  180.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-np.pi, -np.pi/2, np.pi/2, np.pi])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Convert angles from degrees to radians.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-3.14159265 -1.57079633  1.57079633  3.14159265]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-180.,  -90.,   90.,  180.])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Hyperbolic functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Calculate hyperbolic sine, hyperbolic cosine, and hyperbolic tangent of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-1.17520119  0.          1.17520119]\\n\",\n      \"[ 1.54308063  1.          1.54308063]\\n\",\n      \"[-0.76159416  0.          0.76159416]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-1., 0, 1.])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Rounding\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Predict the results of these, paying attention to the difference among the family functions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([2.1, 1.5, 2.5, 2.9, -2.1, -2.5, -2.9])\\n\",\n    \"\\n\",\n    \"out1 = np.around(x)\\n\",\n    \"out2 = np.floor(x)\\n\",\n    \"out3 = np.ceil(x)\\n\",\n    \"out4 = np.trunc(x)\\n\",\n    \"out5 = [round(elem) for elem in x]\\n\",\n    \"\\n\",\n    \"#print out1\\n\",\n    \"#print out2\\n\",\n    \"#print out3\\n\",\n    \"#print out4\\n\",\n    \"#print out5\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Implement out5 in the above question using numpy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2.  2.  3.  3. -2. -3. -3.]\\n\"\n     ]\n    }\n   ],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Sums, products, differences\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Predict the results of these.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:34: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(\\n\",\n    \"    [[1, 2, 3, 4],\\n\",\n    \"     [5, 6, 7, 8]])\\n\",\n    \"\\n\",\n    \"outs = [np.sum(x),\\n\",\n    \"        np.sum(x, axis=0),\\n\",\n    \"        np.sum(x, axis=1, keepdims=True),\\n\",\n    \"        \\\"\\\",\\n\",\n    \"        np.prod(x),\\n\",\n    \"        np.prod(x, axis=0),\\n\",\n    \"        np.prod(x, axis=1, keepdims=True),\\n\",\n    \"        \\\"\\\",\\n\",\n    \"        np.cumsum(x),\\n\",\n    \"        np.cumsum(x, axis=0),\\n\",\n    \"        np.cumsum(x, axis=1),\\n\",\n    \"        \\\"\\\",\\n\",\n    \"        np.cumprod(x),\\n\",\n    \"        np.cumprod(x, axis=0),\\n\",\n    \"        np.cumprod(x, axis=1),\\n\",\n    \"        \\\"\\\",\\n\",\n    \"        np.min(x),\\n\",\n    \"        np.min(x, axis=0),\\n\",\n    \"        np.min(x, axis=1, keepdims=True),\\n\",\n    \"        \\\"\\\",\\n\",\n    \"        np.max(x),\\n\",\n    \"        np.max(x, axis=0),\\n\",\n    \"        np.max(x, axis=1, keepdims=True),\\n\",\n    \"        \\\"\\\",\\n\",\n    \"        np.mean(x),\\n\",\n    \"        np.mean(x, axis=0),\\n\",\n    \"        np.mean(x, axis=1, keepdims=True)]\\n\",\n    \"           \\n\",\n    \"for out in outs:\\n\",\n    \"    if out == \\\"\\\":\\n\",\n    \"        pass\\n\",\n    \"        #print\\n\",\n    \"    else:\\n\",\n    \"        pass\\n\",\n    \"        #print(\\\"->\\\", out)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Calculate the difference between neighboring elements, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1  2  3 -7]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 4, 7, 0])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Calculate the difference between neighboring elements, element-wise, and\\n\",\n    \"prepend [0, 0] and append[100] to it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  0   0   1   2   3  -7 100]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 4, 7, 0])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Return the cross product of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 110,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-3  6 -3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 3])\\n\",\n    \"y = np.array([4, 5, 6])\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Exponents and logarithms\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Compute $e^x$, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  2.71828175   7.38905621  20.08553696]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1., 2., 3.], np.float32)\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q13. Calculate exp(x) - 1 for all elements in x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  1.71828175   6.38905621  19.08553696]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1., 2., 3.], np.float32)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q14. Calculate $2^p$ for all p in x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 124,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2.  4.  8.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1., 2., 3.], np.float32)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q15. Compute natural, base 10, and base 2 logarithms of x element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 128,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"natural log = [ 0.  1.  2.]\\n\",\n      \"common log = [ 0.          0.43429448  0.86858896]\\n\",\n      \"base 2 log = [ 0.          1.44269504  2.88539008]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, np.e, np.e**2])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q16. Compute the natural logarithm of one plus each element in x in floating-point accuracy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 131,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  1.00000000e-099   1.00000000e-100]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1e-99, 1e-100])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Floating point routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q17. Return element-wise True where signbit is set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 135,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ True  True  True False False False False]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-3, -2, -1, 0, 1, 2, 3])\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q18. Change the sign of x to that of y, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 140,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-1. -0. -1.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-1, 0, 1])\\n\",\n    \"y = -1.1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Arithmetic operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q19. Add x and y element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 141,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 0 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 3])\\n\",\n    \"y = np.array([-1, -2, -3])\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q20. Subtract y from x element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 142,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([3, 4, 5])\\n\",\n    \"y = np.array(3)\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q21. Multiply x by y element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 144,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 3  0 -5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([3, 4, 5])\\n\",\n    \"y = np.array([1, 0, -1])\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q22. Divide x by y element-wise in two different ways.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 161,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 3.          2.          1.66666667]\\n\",\n      \"[ 3.  2.  1.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([3., 4., 5.])\\n\",\n    \"y = np.array([1., 2., 3.])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q23. Compute numerical negative value of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 146,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-1  1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, -1])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q24. Compute the reciprocal of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 155,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1.   0.5  5. ]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1., 2., .2])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q25. Compute $x^y$, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 163,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1  4]\\n\",\n      \" [ 3 16]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 2], [3, 4]])\\n\",\n    \"y = np.array([[1, 2], [1, 2]])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q26. Compute the remainder of x / y element-wise in two different ways.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 168,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 0 1 1 0 1]\\n\",\n      \"[-1  0 -1  1  0  1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-3, -2, -1, 1, 2, 3])\\n\",\n    \"y = 2\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Miscellaneous\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q27. If an element of x is smaller than 3, replace it with 3.\\n\",\n    \"And if an element of x is bigger than 7, replace it with 7.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 174,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[3 3 3 3 4 5 6 7 7 7]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q28. Compute the square of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 176,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 4 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, -1])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q29. Compute square root of x element-wise.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 177,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1.  2.  3.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1., 4., 9.])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q30. Compute the absolute value of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 178,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 1]\\n\",\n      \" [3 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, -1], [3, -3]])\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q31. Compute an element-wise indication of the sign of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 181,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1  1  0 -1 -1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 3, 0, -1, -3])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/9_Mathematical_functions_solutions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Mathematical functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.11.2'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"__author__ = \\\"kyubyong. kbpark.linguist@gmail.com. https://github.com/kyubyong\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Trigonometric functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q1. Calculate sine, cosine, and tangent of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sine: [ 0.          0.84147098 -0.98803162  0.89399666]\\n\",\n      \"cosine: [ 1.          0.54030231  0.15425145 -0.44807362]\\n\",\n      \"tangent: [ 0.          1.55740772 -6.4053312  -1.99520041]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([0., 1., 30, 90])\\n\",\n    \"print \\\"sine:\\\", np.sin(x)\\n\",\n    \"print \\\"cosine:\\\", np.cos(x)\\n\",\n    \"print \\\"tangent:\\\", np.tan(x)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q2. Calculate inverse sine, inverse cosine, and inverse tangent of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"inverse sine: [ 1.57079633  0.          1.57079633]\\n\",\n      \"inverse cosine: [ 0.          1.57079633  0.        ]\\n\",\n      \"inverse tangent: [ 0.78539816  0.          0.78539816]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-1., 0, 1.])\\n\",\n    \"print \\\"inverse sine:\\\", np.arcsin(x2)\\n\",\n    \"print \\\"inverse cosine:\\\", np.arccos(x2)\\n\",\n    \"print \\\"inverse tangent:\\\", np.arctan(x2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q3. Convert angles from radians to degrees.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-180.  -90.   90.  180.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-np.pi, -np.pi/2, np.pi/2, np.pi])\\n\",\n    \"\\n\",\n    \"out1 = np.degrees(x)\\n\",\n    \"out2 = np.rad2deg(x)\\n\",\n    \"assert np.array_equiv(out1, out2)\\n\",\n    \"print out1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q4. Convert angles from degrees to radians.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-3.14159265 -1.57079633  1.57079633  3.14159265]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-180.,  -90.,   90.,  180.])\\n\",\n    \"\\n\",\n    \"out1 = np.radians(x)\\n\",\n    \"out2 = np.deg2rad(x)\\n\",\n    \"assert np.array_equiv(out1, out2)\\n\",\n    \"print out1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Hyperbolic functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q5. Calculate hyperbolic sine, hyperbolic cosine, and hyperbolic tangent of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-1.17520119  0.          1.17520119]\\n\",\n      \"[ 1.54308063  1.          1.54308063]\\n\",\n      \"[-0.76159416  0.          0.76159416]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-1., 0, 1.])\\n\",\n    \"print np.sinh(x)\\n\",\n    \"print np.cosh(x)\\n\",\n    \"print np.tanh(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Rounding\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q6. Predict the results of these, paying attention to the difference among the family functions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2.  2.  2.  3. -2. -2. -3.]\\n\",\n      \"[ 2.  1.  2.  2. -3. -3. -3.]\\n\",\n      \"[ 3.  2.  3.  3. -2. -2. -2.]\\n\",\n      \"[ 2.  1.  2.  2. -2. -2. -2.]\\n\",\n      \"[2.0, 2.0, 3.0, 3.0, -2.0, -3.0, -3.0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([2.1, 1.5, 2.5, 2.9, -2.1, -2.5, -2.9])\\n\",\n    \"\\n\",\n    \"out1 = np.around(x)\\n\",\n    \"out2 = np.floor(x)\\n\",\n    \"out3 = np.ceil(x)\\n\",\n    \"out4 = np.trunc(x)\\n\",\n    \"out5 = [round(elem) for elem in x]\\n\",\n    \"\\n\",\n    \"print out1\\n\",\n    \"print out2\\n\",\n    \"print out3\\n\",\n    \"print out4\\n\",\n    \"print out5\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q7. Implement out5 in the above question using numpy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2.  2.  3.  3. -2. -3. -3.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print np.floor(np.abs(x) + 0.5) * np.sign(x) \\n\",\n    \"# Read http://numpy-discussion.10968.n7.nabble.com/why-numpy-round-get-a-different-result-from-python-round-function-td19098.html\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Sums, products, differences\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q8. Predict the results of these.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"('->', 36)\\n\",\n      \"('->', array([ 6,  8, 10, 12]))\\n\",\n      \"('->', array([[10],\\n\",\n      \"       [26]]))\\n\",\n      \"\\n\",\n      \"('->', 40320)\\n\",\n      \"('->', array([ 5, 12, 21, 32]))\\n\",\n      \"('->', array([[  24],\\n\",\n      \"       [1680]]))\\n\",\n      \"\\n\",\n      \"('->', array([ 1,  3,  6, 10, 15, 21, 28, 36]))\\n\",\n      \"('->', array([[ 1,  2,  3,  4],\\n\",\n      \"       [ 6,  8, 10, 12]]))\\n\",\n      \"('->', array([[ 1,  3,  6, 10],\\n\",\n      \"       [ 5, 11, 18, 26]]))\\n\",\n      \"\\n\",\n      \"('->', array([    1,     2,     6,    24,   120,   720,  5040, 40320]))\\n\",\n      \"('->', array([[ 1,  2,  3,  4],\\n\",\n      \"       [ 5, 12, 21, 32]]))\\n\",\n      \"('->', array([[   1,    2,    6,   24],\\n\",\n      \"       [   5,   30,  210, 1680]]))\\n\",\n      \"\\n\",\n      \"('->', 1)\\n\",\n      \"('->', array([1, 2, 3, 4]))\\n\",\n      \"('->', array([[1],\\n\",\n      \"       [5]]))\\n\",\n      \"\\n\",\n      \"('->', 8)\\n\",\n      \"('->', array([5, 6, 7, 8]))\\n\",\n      \"('->', array([[4],\\n\",\n      \"       [8]]))\\n\",\n      \"\\n\",\n      \"('->', 4.5)\\n\",\n      \"('->', array([ 3.,  4.,  5.,  6.]))\\n\",\n      \"('->', array([[ 2.5],\\n\",\n      \"       [ 6.5]]))\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:34: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array(\\n\",\n    \"    [[1, 2, 3, 4],\\n\",\n    \"     [5, 6, 7, 8]])\\n\",\n    \"\\n\",\n    \"outs = [np.sum(x),\\n\",\n    \"        np.sum(x, axis=0),\\n\",\n    \"        np.sum(x, axis=1, keepdims=True),\\n\",\n    \"        \\\"\\\",\\n\",\n    \"        np.prod(x),\\n\",\n    \"        np.prod(x, axis=0),\\n\",\n    \"        np.prod(x, axis=1, keepdims=True),\\n\",\n    \"        \\\"\\\",\\n\",\n    \"        np.cumsum(x),\\n\",\n    \"        np.cumsum(x, axis=0),\\n\",\n    \"        np.cumsum(x, axis=1),\\n\",\n    \"        \\\"\\\",\\n\",\n    \"        np.cumprod(x),\\n\",\n    \"        np.cumprod(x, axis=0),\\n\",\n    \"        np.cumprod(x, axis=1),\\n\",\n    \"        \\\"\\\",\\n\",\n    \"        np.min(x),\\n\",\n    \"        np.min(x, axis=0),\\n\",\n    \"        np.min(x, axis=1, keepdims=True),\\n\",\n    \"        \\\"\\\",\\n\",\n    \"        np.max(x),\\n\",\n    \"        np.max(x, axis=0),\\n\",\n    \"        np.max(x, axis=1, keepdims=True),\\n\",\n    \"        \\\"\\\",\\n\",\n    \"        np.mean(x),\\n\",\n    \"        np.mean(x, axis=0),\\n\",\n    \"        np.mean(x, axis=1, keepdims=True)]\\n\",\n    \"           \\n\",\n    \"for out in outs:\\n\",\n    \"    if out == \\\"\\\":\\n\",\n    \"        print\\n\",\n    \"    else:\\n\",\n    \"        print(\\\"->\\\", out)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q9. Calculate the difference between neighboring elements, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1  2  3 -7]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 4, 7, 0])\\n\",\n    \"print np.diff(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q10. Calculate the difference between neighboring elements, element-wise, and\\n\",\n    \"prepend [0, 0] and append[100] to it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  0   0   1   2   3  -7 100]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 4, 7, 0])\\n\",\n    \"\\n\",\n    \"out1 = np.ediff1d(x, to_begin=[0, 0], to_end=[100])\\n\",\n    \"out2 = np.insert(np.append(np.diff(x), 100), 0, [0, 0])\\n\",\n    \"assert np.array_equiv(out1, out2)\\n\",\n    \"print out2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q11. Return the cross product of x and y.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 110,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-3  6 -3]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 3])\\n\",\n    \"y = np.array([4, 5, 6])\\n\",\n    \"\\n\",\n    \"print np.cross(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Exponents and logarithms\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q12. Compute $e^x$, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  2.71828175   7.38905621  20.08553696]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1., 2., 3.], np.float32)\\n\",\n    \"\\n\",\n    \"out = np.exp(x)\\n\",\n    \"print out\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q13. Calculate exp(x) - 1 for all elements in x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 118,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  1.71828175   6.38905621  19.08553696]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1., 2., 3.], np.float32)\\n\",\n    \"\\n\",\n    \"out1 = np.expm1(x)\\n\",\n    \"out2 = np.exp(x) - 1.\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print out1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q14. Calculate $2^p$ for all p in x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 124,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 2.  4.  8.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1., 2., 3.], np.float32)\\n\",\n    \"\\n\",\n    \"out1 = np.exp2(x)\\n\",\n    \"out2 = 2 ** x\\n\",\n    \"assert np.allclose(out1, out2)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q15. Compute natural, base 10, and base 2 logarithms of x element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 128,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"natural log = [ 0.  1.  2.]\\n\",\n      \"common log = [ 0.          0.43429448  0.86858896]\\n\",\n      \"base 2 log = [ 0.          1.44269504  2.88539008]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, np.e, np.e**2])\\n\",\n    \"\\n\",\n    \"print \\\"natural log =\\\", np.log(x)\\n\",\n    \"print \\\"common log =\\\", np.log10(x)\\n\",\n    \"print \\\"base 2 log =\\\", np.log2(x)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q16. Compute the natural logarithm of one plus each element in x in floating-point accuracy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 131,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  1.00000000e-099   1.00000000e-100]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1e-99, 1e-100])\\n\",\n    \"\\n\",\n    \"print np.log1p(x)\\n\",\n    \"# Compare it with np.log(1 +x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Floating point routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q17. Return element-wise True where signbit is set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 135,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ True  True  True False False False False]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-3, -2, -1, 0, 1, 2, 3])\\n\",\n    \"\\n\",\n    \"out1 = np.signbit(x)\\n\",\n    \"out2 = x < 0\\n\",\n    \"assert np.array_equiv(out1, out2)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q18. Change the sign of x to that of y, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 140,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-1. -0. -1.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-1, 0, 1])\\n\",\n    \"y = -1.1\\n\",\n    \"\\n\",\n    \"print np.copysign(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Arithmetic operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q19. Add x and y element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 141,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 0 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, 3])\\n\",\n    \"y = np.array([-1, -2, -3])\\n\",\n    \"\\n\",\n    \"out1 = np.add(x, y)\\n\",\n    \"out2 = x + y\\n\",\n    \"assert np.array_equal(out1, out2)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q20. Subtract y from x element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 142,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 2]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([3, 4, 5])\\n\",\n    \"y = np.array(3)\\n\",\n    \"\\n\",\n    \"out1 = np.subtract(x, y)\\n\",\n    \"out2 = x - y \\n\",\n    \"assert np.array_equal(out1, out2)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q21. Multiply x by y element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 144,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 3  0 -5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([3, 4, 5])\\n\",\n    \"y = np.array([1, 0, -1])\\n\",\n    \"\\n\",\n    \"out1 = np.multiply(x, y)\\n\",\n    \"out2 = x * y \\n\",\n    \"assert np.array_equal(out1, out2)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q22. Divide x by y element-wise in two different ways.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 161,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 3.          2.          1.66666667]\\n\",\n      \"[ 3.  2.  1.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([3., 4., 5.])\\n\",\n    \"y = np.array([1., 2., 3.])\\n\",\n    \"\\n\",\n    \"out1 = np.true_divide(x, y)\\n\",\n    \"out2 = x / y \\n\",\n    \"assert np.array_equal(out1, out2)\\n\",\n    \"print out1\\n\",\n    \"\\n\",\n    \"out3 = np.floor_divide(x, y)\\n\",\n    \"out4 = x // y\\n\",\n    \"assert np.array_equal(out3, out4)\\n\",\n    \"print out3\\n\",\n    \"\\n\",\n    \"# Note that in Python 2 and 3, the handling of `divide` differs.\\n\",\n    \"# See https://docs.scipy.org/doc/numpy/reference/generated/numpy.divide.html#numpy.divide\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q23. Compute numerical negative value of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 146,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-1  1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, -1])\\n\",\n    \"\\n\",\n    \"out1 = np.negative(x)\\n\",\n    \"out2 = -x\\n\",\n    \"assert np.array_equal(out1, out2)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q24. Compute the reciprocal of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 155,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1.   0.5  5. ]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1., 2., .2])\\n\",\n    \"\\n\",\n    \"out1 = np.reciprocal(x)\\n\",\n    \"out2 = 1/x\\n\",\n    \"assert np.array_equal(out1, out2)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q25. Compute $x^y$, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 163,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 1  4]\\n\",\n      \" [ 3 16]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, 2], [3, 4]])\\n\",\n    \"y = np.array([[1, 2], [1, 2]])\\n\",\n    \"\\n\",\n    \"out = np.power(x, y)\\n\",\n    \"print out\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q26. Compute the remainder of x / y element-wise in two different ways.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 168,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 0 1 1 0 1]\\n\",\n      \"[-1  0 -1  1  0  1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([-3, -2, -1, 1, 2, 3])\\n\",\n    \"y = 2\\n\",\n    \"\\n\",\n    \"out1 = np.mod(x, y)\\n\",\n    \"out2 = x % y\\n\",\n    \"assert np.array_equal(out1, out2)\\n\",\n    \"print out1\\n\",\n    \"\\n\",\n    \"out3 = np.fmod(x, y)\\n\",\n    \"print out3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Miscellaneous\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q27. If an element of x is smaller than 3, replace it with 3.\\n\",\n    \"And if an element of x is bigger than 7, replace it with 7.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 174,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[3 3 3 3 4 5 6 7 7 7]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(10)\\n\",\n    \"\\n\",\n    \"out1 = np.clip(x, 3, 7)\\n\",\n    \"out2 = np.copy(x)\\n\",\n    \"out2[out2 < 3] = 3\\n\",\n    \"out2[out2 > 7] = 7\\n\",\n    \"\\n\",\n    \"assert np.array_equiv(out1, out2)\\n\",\n    \"print out1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q28. Compute the square of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 176,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1 4 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 2, -1])\\n\",\n    \"\\n\",\n    \"out1 = np.square(x)\\n\",\n    \"out2 = x * x\\n\",\n    \"assert np.array_equal(out1, out2)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q29. Compute square root of x element-wise.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 177,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1.  2.  3.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1., 4., 9.])\\n\",\n    \"\\n\",\n    \"out = np.sqrt(x)\\n\",\n    \"print out\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q30. Compute the absolute value of x.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 178,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 1]\\n\",\n      \" [3 3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([[1, -1], [3, -3]])\\n\",\n    \"\\n\",\n    \"out = np.abs(x)\\n\",\n    \"print out\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Q31. Compute an element-wise indication of the sign of x, element-wise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 181,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1  1  0 -1 -1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([1, 3, 0, -1, -3])\\n\",\n    \"\\n\",\n    \"out1 = np.sign(x)\\n\",\n    \"out2 = np.copy(x)\\n\",\n    \"out2[out2 > 0] = 1\\n\",\n    \"out2[out2 < 0] = -1\\n\",\n    \"assert np.array_equal(out1, out2)\\n\",\n    \"print out1\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "2.numpy/numpy_exercises/README.md",
    "content": "# NumPy 练习题\n\nNumpy是一个用python实现的科学计算的扩展程序库，包括：\n\n1、一个强大的N维数组对象Array；\n\n2、比较成熟的（广播）函数库；\n\n3、用于整合C/C++和Fortran代码的工具包；\n\n4、实用的线性代数、傅里叶变换和随机数生成函数。numpy和稀疏矩阵运算包scipy配合使用更加方便。\n\nNumPy（Numeric Python）提供了许多高级的数值编程工具，如：矩阵数据类型、矢量处理，以及精密的运算库。专为进行严格的数字处理而产生。多为很多大型金融公司使用，以及核心的科学计算组织如：Lawrence Livermore，NASA用其处理一些本来使用C++，Fortran或Matlab等所做的任务。\n\n本文整理了一个Numpy的练习题，总结了Numpy的常用操作，可以测试下自己对Numpy的掌握程度，有答案哦。\n\n## 试题目录\n  * Array creation routines（数组创建）\n  * Array manipulation routines（数组操作）\n  * String operations（字符串操作）\n  * Numpy-specific help functions（Numpy特定帮助函数）\n  * Input and output（输入和输出）\n  * Linear algebra（线性代数）\n  * Discrete Fourier Transform（离散傅里叶变换）\n  * Logic functions（逻辑函数）\n  * Mathematical functions（数学函数）\n  * Random sampling (numpy.random)（随机抽样）\n  * Set routines（集合操作）\n  * Sorting, searching, and counting（排序、搜索和计数）\n  * Statistics（统计）\n## 试题内容\n试题分为13个练习，每个练习分为两个ipynb文件，文件名带`_Solutions` 的是带答案的文件，建议初学者先练习下不带答案的文件,做不出来再看看答案。\n\n## 作者及来源\n- 作者： Kyubyong\n- 来源：https://github.com/Kyubyong/numpy_exercises \n"
  },
  {
    "path": "3.pandas/1.10-Minutes-to-pandas/10 Minutes to pandas.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 十分钟入门pandas\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#coding:utf8\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这个一篇针对pandas新手的简短入门，想要了解更多复杂的内容，参阅[*Cookbook*](http://pandas.pydata.org/pandas-docs/stable/cookbook.html#cookbook)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通常，我们首先要导入以下几个库：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 创建对象\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过传递一个list来创建**Series**，pandas会默认创建整型索引：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0    1.0\\n\",\n       \"1    3.0\\n\",\n       \"2    5.0\\n\",\n       \"3    NaN\\n\",\n       \"4    6.0\\n\",\n       \"5    8.0\\n\",\n       \"dtype: float64\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s = pd.Series([1,3,5,np.nan,6,8])\\n\",\n    \"s\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过传递一个numpy array，日期索引以及列标签来创建一个**DataFrame**：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\\n\",\n       \"               '2013-01-05', '2013-01-06'],\\n\",\n       \"              dtype='datetime64[ns]', freq='D')\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dates = pd.date_range('20130101', periods=6)\\n\",\n    \"dates\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>-0.900524</td>\\n\",\n       \"      <td>-0.302515</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>1.562916</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>-0.389713</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>-0.240850</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"      <td>-0.604326</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D\\n\",\n       \"2013-01-01 -0.900524 -0.302515 -0.541762  1.562916\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  0.268915\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801 -0.389713\\n\",\n       \"2013-01-05  1.679381  1.468609  0.360648 -0.240850\\n\",\n       \"2013-01-06  0.567867  0.235352  1.117395 -0.604326\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过传递一个能够被转换为类似series的dict对象来创建一个**DataFrame**:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>E</th>\\n\",\n       \"      <th>F</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>2013-01-02</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>test</td>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>2013-01-02</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>train</td>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>2013-01-02</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>test</td>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>2013-01-02</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>train</td>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"     A          B    C  D      E    F\\n\",\n       \"0  1.0 2013-01-02  1.0  3   test  foo\\n\",\n       \"1  1.0 2013-01-02  1.0  3  train  foo\\n\",\n       \"2  1.0 2013-01-02  1.0  3   test  foo\\n\",\n       \"3  1.0 2013-01-02  1.0  3  train  foo\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df2 = pd.DataFrame({ 'A' : 1.,\\n\",\n    \"                     'B' : pd.Timestamp('20130102'),\\n\",\n    \"                     'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\\n\",\n    \"                     'D' : np.array([3]*4,dtype='int32'),\\n\",\n    \"                     'E' : pd.Categorical([\\\"test\\\",\\\"train\\\",\\\"test\\\",\\\"train\\\"]),\\n\",\n    \"                     'F' : 'foo' })\\n\",\n    \"df2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"可以看到各列的数据类型为：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"A           float64\\n\",\n       \"B    datetime64[ns]\\n\",\n       \"C           float32\\n\",\n       \"D             int32\\n\",\n       \"E          category\\n\",\n       \"F            object\\n\",\n       \"dtype: object\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df2.dtypes\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 查看数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"查看frame中头部和尾部的几行：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>-0.900524</td>\\n\",\n       \"      <td>-0.302515</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>1.562916</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>-0.389713</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>-0.240850</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D\\n\",\n       \"2013-01-01 -0.900524 -0.302515 -0.541762  1.562916\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  0.268915\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801 -0.389713\\n\",\n       \"2013-01-05  1.679381  1.468609  0.360648 -0.240850\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>-0.389713</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>-0.240850</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"      <td>-0.604326</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801 -0.389713\\n\",\n       \"2013-01-05  1.679381  1.468609  0.360648 -0.240850\\n\",\n       \"2013-01-06  0.567867  0.235352  1.117395 -0.604326\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.tail(3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"显示索引、列名以及底层的numpy数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\\n\",\n       \"               '2013-01-05', '2013-01-06'],\\n\",\n       \"              dtype='datetime64[ns]', freq='D')\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.index\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Index(['A', 'B', 'C', 'D'], dtype='object')\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.columns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-0.90052384, -0.30251543, -0.54176245,  1.56291588],\\n\",\n       \"       [-0.8841172 , -0.65074073,  0.21734508,  0.26891483],\\n\",\n       \"       [ 0.22082238,  0.79052719,  0.69217223,  0.72344092],\\n\",\n       \"       [ 1.2602764 ,  1.0002968 ,  0.80980141, -0.38971272],\\n\",\n       \"       [ 1.67938067,  1.46860938,  0.36064787, -0.24084994],\\n\",\n       \"       [ 0.56786654,  0.23535244,  1.117395  , -0.60432593]])\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.values\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"describe()能对数据做一个快速统计汇总\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <td>6.000000</td>\\n\",\n       \"      <td>6.000000</td>\\n\",\n       \"      <td>6.000000</td>\\n\",\n       \"      <td>6.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <td>0.323951</td>\\n\",\n       \"      <td>0.423588</td>\\n\",\n       \"      <td>0.442600</td>\\n\",\n       \"      <td>0.220064</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>std</th>\\n\",\n       \"      <td>1.071708</td>\\n\",\n       \"      <td>0.809463</td>\\n\",\n       \"      <td>0.579465</td>\\n\",\n       \"      <td>0.815219</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <td>-0.900524</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>-0.604326</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25%</th>\\n\",\n       \"      <td>-0.607882</td>\\n\",\n       \"      <td>-0.168048</td>\\n\",\n       \"      <td>0.253171</td>\\n\",\n       \"      <td>-0.352497</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>50%</th>\\n\",\n       \"      <td>0.394344</td>\\n\",\n       \"      <td>0.512940</td>\\n\",\n       \"      <td>0.526410</td>\\n\",\n       \"      <td>0.014032</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>75%</th>\\n\",\n       \"      <td>1.087174</td>\\n\",\n       \"      <td>0.947854</td>\\n\",\n       \"      <td>0.780394</td>\\n\",\n       \"      <td>0.609809</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"      <td>1.562916</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"              A         B         C         D\\n\",\n       \"count  6.000000  6.000000  6.000000  6.000000\\n\",\n       \"mean   0.323951  0.423588  0.442600  0.220064\\n\",\n       \"std    1.071708  0.809463  0.579465  0.815219\\n\",\n       \"min   -0.900524 -0.650741 -0.541762 -0.604326\\n\",\n       \"25%   -0.607882 -0.168048  0.253171 -0.352497\\n\",\n       \"50%    0.394344  0.512940  0.526410  0.014032\\n\",\n       \"75%    1.087174  0.947854  0.780394  0.609809\\n\",\n       \"max    1.679381  1.468609  1.117395  1.562916\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"对数据做转置：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>2013-01-01 00:00:00</th>\\n\",\n       \"      <th>2013-01-02 00:00:00</th>\\n\",\n       \"      <th>2013-01-03 00:00:00</th>\\n\",\n       \"      <th>2013-01-04 00:00:00</th>\\n\",\n       \"      <th>2013-01-05 00:00:00</th>\\n\",\n       \"      <th>2013-01-06 00:00:00</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <td>-0.900524</td>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <td>-0.302515</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <td>1.562916</td>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"      <td>-0.389713</td>\\n\",\n       \"      <td>-0.240850</td>\\n\",\n       \"      <td>-0.604326</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06\\n\",\n       \"A   -0.900524   -0.884117    0.220822    1.260276    1.679381    0.567867\\n\",\n       \"B   -0.302515   -0.650741    0.790527    1.000297    1.468609    0.235352\\n\",\n       \"C   -0.541762    0.217345    0.692172    0.809801    0.360648    1.117395\\n\",\n       \"D    1.562916    0.268915    0.723441   -0.389713   -0.240850   -0.604326\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.T\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"按轴进行排序：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>1.562916</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>-0.302515</td>\\n\",\n       \"      <td>-0.900524</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>-0.389713</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>-0.240850</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>-0.604326</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   D         C         B         A\\n\",\n       \"2013-01-01  1.562916 -0.541762 -0.302515 -0.900524\\n\",\n       \"2013-01-02  0.268915  0.217345 -0.650741 -0.884117\\n\",\n       \"2013-01-03  0.723441  0.692172  0.790527  0.220822\\n\",\n       \"2013-01-04 -0.389713  0.809801  1.000297  1.260276\\n\",\n       \"2013-01-05 -0.240850  0.360648  1.468609  1.679381\\n\",\n       \"2013-01-06 -0.604326  1.117395  0.235352  0.567867\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.sort_index(axis=1, ascending=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"按值进行排序 :\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>-0.900524</td>\\n\",\n       \"      <td>-0.302515</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>1.562916</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"      <td>-0.604326</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>-0.389713</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>-0.240850</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  0.268915\\n\",\n       \"2013-01-01 -0.900524 -0.302515 -0.541762  1.562916\\n\",\n       \"2013-01-06  0.567867  0.235352  1.117395 -0.604326\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801 -0.389713\\n\",\n       \"2013-01-05  1.679381  1.468609  0.360648 -0.240850\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.sort_values(by='B')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 数据选择\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"注意：虽然标准的Python/Numpy的表达式能完成选择与赋值等功能，但我们仍推荐使用优化过的pandas数据访问方法：.at，.iat，.loc，.iloc和.ix\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 选取\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"选择某一列数据，它会返回一个**Series**，等同于**df.A**：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2013-01-01   -0.900524\\n\",\n       \"2013-01-02   -0.884117\\n\",\n       \"2013-01-03    0.220822\\n\",\n       \"2013-01-04    1.260276\\n\",\n       \"2013-01-05    1.679381\\n\",\n       \"2013-01-06    0.567867\\n\",\n       \"Freq: D, Name: A, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['A']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过使用**[ ]**进行切片选取：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>-0.900524</td>\\n\",\n       \"      <td>-0.302515</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>1.562916</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D\\n\",\n       \"2013-01-01 -0.900524 -0.302515 -0.541762  1.562916\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  0.268915\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[0:3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>-0.389713</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  0.268915\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801 -0.389713\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['20130102':'20130104']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 通过标签选取\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过标签进行交叉选取：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"A   -0.900524\\n\",\n       \"B   -0.302515\\n\",\n       \"C   -0.541762\\n\",\n       \"D    1.562916\\n\",\n       \"Name: 2013-01-01 00:00:00, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.loc[dates[0]]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"使用标签对多个轴进行选取\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>-0.900524</td>\\n\",\n       \"      <td>-0.302515</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B\\n\",\n       \"2013-01-01 -0.900524 -0.302515\\n\",\n       \"2013-01-02 -0.884117 -0.650741\\n\",\n       \"2013-01-03  0.220822  0.790527\\n\",\n       \"2013-01-04  1.260276  1.000297\\n\",\n       \"2013-01-05  1.679381  1.468609\\n\",\n       \"2013-01-06  0.567867  0.235352\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.loc[:,['A','B']]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>-0.900524</td>\\n\",\n       \"      <td>-0.302515</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B\\n\",\n       \"2013-01-01 -0.900524 -0.302515\\n\",\n       \"2013-01-02 -0.884117 -0.650741\\n\",\n       \"2013-01-03  0.220822  0.790527\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.loc[:,['A','B']][:3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"进行标签切片，包含两个端点\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B\\n\",\n       \"2013-01-02 -0.884117 -0.650741\\n\",\n       \"2013-01-03  0.220822  0.790527\\n\",\n       \"2013-01-04  1.260276  1.000297\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.loc['20130102':'20130104',['A','B']]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"对于返回的对象进行降维处理\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"A   -0.884117\\n\",\n       \"B   -0.650741\\n\",\n       \"Name: 2013-01-02 00:00:00, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.loc['20130102',['A','B']]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"获取一个标量\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"-0.9005238449408509\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.loc[dates[0],'A']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"快速获取标量（与上面的方法等价）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"-0.9005238449408509\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.at[dates[0],'A']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 通过位置选取\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过传递整型的位置进行选取\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"A    1.260276\\n\",\n       \"B    1.000297\\n\",\n       \"C    0.809801\\n\",\n       \"D   -0.389713\\n\",\n       \"Name: 2013-01-04 00:00:00, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.iloc[3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过整型的位置切片进行选取，与python/numpy形式相同\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B\\n\",\n       \"2013-01-04  1.260276  1.000297\\n\",\n       \"2013-01-05  1.679381  1.468609\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.iloc[3:5,0:2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"只对行进行切片\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  0.268915\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.iloc[1:3,:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"只对列进行切片\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>-0.302515</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   B         C\\n\",\n       \"2013-01-01 -0.302515 -0.541762\\n\",\n       \"2013-01-02 -0.650741  0.217345\\n\",\n       \"2013-01-03  0.790527  0.692172\\n\",\n       \"2013-01-04  1.000297  0.809801\\n\",\n       \"2013-01-05  1.468609  0.360648\\n\",\n       \"2013-01-06  0.235352  1.117395\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.iloc[:,1:3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"只获取某个值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"-0.6507407272837356\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.iloc[1,1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"快速获取某个值（与上面的方法等价）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"-0.6507407272837356\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.iat[1,1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 布尔索引\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"用某列的值来选取数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>-0.389713</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>-0.240850</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"      <td>-0.604326</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801 -0.389713\\n\",\n       \"2013-01-05  1.679381  1.468609  0.360648 -0.240850\\n\",\n       \"2013-01-06  0.567867  0.235352  1.117395 -0.604326\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[df.A > 0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"用**where**操作来选取数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1.562916</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D\\n\",\n       \"2013-01-01       NaN       NaN       NaN  1.562916\\n\",\n       \"2013-01-02       NaN       NaN  0.217345  0.268915\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801       NaN\\n\",\n       \"2013-01-05  1.679381  1.468609  0.360648       NaN\\n\",\n       \"2013-01-06  0.567867  0.235352  1.117395       NaN\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[df > 0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"用**isin()**方法来过滤数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df2 = df.copy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>E</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>-0.900524</td>\\n\",\n       \"      <td>-0.302515</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>1.562916</td>\\n\",\n       \"      <td>one</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"      <td>one</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"      <td>two</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>-0.389713</td>\\n\",\n       \"      <td>three</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>-0.240850</td>\\n\",\n       \"      <td>four</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"      <td>-0.604326</td>\\n\",\n       \"      <td>three</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D      E\\n\",\n       \"2013-01-01 -0.900524 -0.302515 -0.541762  1.562916    one\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  0.268915    one\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441    two\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801 -0.389713  three\\n\",\n       \"2013-01-05  1.679381  1.468609  0.360648 -0.240850   four\\n\",\n       \"2013-01-06  0.567867  0.235352  1.117395 -0.604326  three\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']\\n\",\n    \"df2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>E</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"      <td>two</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>-0.240850</td>\\n\",\n       \"      <td>four</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D     E\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441   two\\n\",\n       \"2013-01-05  1.679381  1.468609  0.360648 -0.240850  four\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df2[df2['E'].isin(['two', 'four'])]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 赋值\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"赋值一个新的列，通过索引来自动对齐数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2013-01-02    1\\n\",\n       \"2013-01-03    2\\n\",\n       \"2013-01-04    3\\n\",\n       \"2013-01-05    4\\n\",\n       \"2013-01-06    5\\n\",\n       \"2013-01-07    6\\n\",\n       \"Freq: D, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102',periods=6))\\n\",\n    \"s1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>F</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>-0.900524</td>\\n\",\n       \"      <td>-0.302515</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>1.562916</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>-0.389713</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>-0.240850</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"      <td>-0.604326</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D    F\\n\",\n       \"2013-01-01 -0.900524 -0.302515 -0.541762  1.562916  NaN\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  0.268915  1.0\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441  2.0\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801 -0.389713  3.0\\n\",\n       \"2013-01-05  1.679381  1.468609  0.360648 -0.240850  4.0\\n\",\n       \"2013-01-06  0.567867  0.235352  1.117395 -0.604326  5.0\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['F'] = s1\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过标签赋值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>F</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>-0.302515</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>1.562916</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>-0.389713</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>-0.240850</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"      <td>-0.604326</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D    F\\n\",\n       \"2013-01-01  0.000000 -0.302515 -0.541762  1.562916  NaN\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  0.268915  1.0\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441  2.0\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801 -0.389713  3.0\\n\",\n       \"2013-01-05  1.679381  1.468609  0.360648 -0.240850  4.0\\n\",\n       \"2013-01-06  0.567867  0.235352  1.117395 -0.604326  5.0\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.at[dates[0], 'A'] = 0\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过位置赋值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>F</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>1.562916</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>0.268915</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>0.723441</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>-0.389713</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>-0.240850</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"      <td>-0.604326</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C         D    F\\n\",\n       \"2013-01-01  0.000000  0.000000 -0.541762  1.562916  NaN\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  0.268915  1.0\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  0.723441  2.0\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801 -0.389713  3.0\\n\",\n       \"2013-01-05  1.679381  1.468609  0.360648 -0.240850  4.0\\n\",\n       \"2013-01-06  0.567867  0.235352  1.117395 -0.604326  5.0\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.iat[0,1] = 0\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过传递numpy array赋值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>F</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>1.679381</td>\\n\",\n       \"      <td>1.468609</td>\\n\",\n       \"      <td>0.360648</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>0.567867</td>\\n\",\n       \"      <td>0.235352</td>\\n\",\n       \"      <td>1.117395</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C  D    F\\n\",\n       \"2013-01-01  0.000000  0.000000 -0.541762  5  NaN\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  5  1.0\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  5  2.0\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801  5  3.0\\n\",\n       \"2013-01-05  1.679381  1.468609  0.360648  5  4.0\\n\",\n       \"2013-01-06  0.567867  0.235352  1.117395  5  5.0\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.loc[:,'D'] = np.array([5] * len(df))\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过**where**操作来赋值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>F</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>-5</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>-0.217345</td>\\n\",\n       \"      <td>-5</td>\\n\",\n       \"      <td>-1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>-0.220822</td>\\n\",\n       \"      <td>-0.790527</td>\\n\",\n       \"      <td>-0.692172</td>\\n\",\n       \"      <td>-5</td>\\n\",\n       \"      <td>-2.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>-1.260276</td>\\n\",\n       \"      <td>-1.000297</td>\\n\",\n       \"      <td>-0.809801</td>\\n\",\n       \"      <td>-5</td>\\n\",\n       \"      <td>-3.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>-1.679381</td>\\n\",\n       \"      <td>-1.468609</td>\\n\",\n       \"      <td>-0.360648</td>\\n\",\n       \"      <td>-5</td>\\n\",\n       \"      <td>-4.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>-0.567867</td>\\n\",\n       \"      <td>-0.235352</td>\\n\",\n       \"      <td>-1.117395</td>\\n\",\n       \"      <td>-5</td>\\n\",\n       \"      <td>-5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C  D    F\\n\",\n       \"2013-01-01  0.000000  0.000000 -0.541762 -5  NaN\\n\",\n       \"2013-01-02 -0.884117 -0.650741 -0.217345 -5 -1.0\\n\",\n       \"2013-01-03 -0.220822 -0.790527 -0.692172 -5 -2.0\\n\",\n       \"2013-01-04 -1.260276 -1.000297 -0.809801 -5 -3.0\\n\",\n       \"2013-01-05 -1.679381 -1.468609 -0.360648 -5 -4.0\\n\",\n       \"2013-01-06 -0.567867 -0.235352 -1.117395 -5 -5.0\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df2 = df.copy()\\n\",\n    \"df2[df2 > 0] = -df2\\n\",\n    \"df2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 缺失值处理\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在pandas中，用**np.nan**来代表缺失值，这些值默认不会参与运算。\\n\",\n    \"\\n\",\n    \"reindex()允许你修改、增加、删除指定轴上的索引，并返回一个数据副本。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>F</th>\\n\",\n       \"      <th>E</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C  D    F    E\\n\",\n       \"2013-01-01  0.000000  0.000000 -0.541762  5  NaN  1.0\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  5  1.0  1.0\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  5  2.0  NaN\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801  5  3.0  NaN\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df1 = df.reindex(index=dates[0:4], columns=list(df.columns)+['E'])\\n\",\n    \"df1.loc[dates[0]:dates[1],'E'] = 1\\n\",\n    \"df1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"剔除所有包含缺失值的行数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>F</th>\\n\",\n       \"      <th>E</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C  D    F    E\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  5  1.0  1.0\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df1.dropna(how='any')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"填充缺失值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>F</th>\\n\",\n       \"      <th>E</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>0.217345</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>0.220822</td>\\n\",\n       \"      <td>0.790527</td>\\n\",\n       \"      <td>0.692172</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>1.260276</td>\\n\",\n       \"      <td>1.000297</td>\\n\",\n       \"      <td>0.809801</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C  D    F    E\\n\",\n       \"2013-01-01  0.000000  0.000000 -0.541762  5  5.0  1.0\\n\",\n       \"2013-01-02 -0.884117 -0.650741  0.217345  5  1.0  1.0\\n\",\n       \"2013-01-03  0.220822  0.790527  0.692172  5  2.0  5.0\\n\",\n       \"2013-01-04  1.260276  1.000297  0.809801  5  3.0  5.0\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df1.fillna(value=5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"获取值是否为**nan**的布尔标记\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>F</th>\\n\",\n       \"      <th>E</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                A      B      C      D      F      E\\n\",\n       \"2013-01-01  False  False  False  False   True  False\\n\",\n       \"2013-01-02  False  False  False  False  False  False\\n\",\n       \"2013-01-03  False  False  False  False  False   True\\n\",\n       \"2013-01-04  False  False  False  False  False   True\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pd.isnull(df1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 统计\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"运算过程中，通常不包含缺失值。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"进行描述性统计\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"A    0.474038\\n\",\n       \"B    0.474008\\n\",\n       \"C    0.442600\\n\",\n       \"D    5.000000\\n\",\n       \"F    3.000000\\n\",\n       \"dtype: float64\"\n      ]\n     },\n     \"execution_count\": 48,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"对其他轴进行同样的运算\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2013-01-01    1.114559\\n\",\n       \"2013-01-02    0.936497\\n\",\n       \"2013-01-03    1.740704\\n\",\n       \"2013-01-04    2.214075\\n\",\n       \"2013-01-05    2.501728\\n\",\n       \"2013-01-06    2.384123\\n\",\n       \"Freq: D, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.mean(1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"对于拥有不同维度的对象进行运算时需要对齐。除此之外，pandas会自动沿着指定维度计算。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2013-01-01    NaN\\n\",\n       \"2013-01-02    NaN\\n\",\n       \"2013-01-03    1.0\\n\",\n       \"2013-01-04    3.0\\n\",\n       \"2013-01-05    5.0\\n\",\n       \"2013-01-06    NaN\\n\",\n       \"Freq: D, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)\\n\",\n    \"s\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>F</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>-0.779178</td>\\n\",\n       \"      <td>-0.209473</td>\\n\",\n       \"      <td>-0.307828</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>-1.739724</td>\\n\",\n       \"      <td>-1.999703</td>\\n\",\n       \"      <td>-2.190199</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>-3.320619</td>\\n\",\n       \"      <td>-3.531391</td>\\n\",\n       \"      <td>-4.639352</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>-1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C    D    F\\n\",\n       \"2013-01-01       NaN       NaN       NaN  NaN  NaN\\n\",\n       \"2013-01-02       NaN       NaN       NaN  NaN  NaN\\n\",\n       \"2013-01-03 -0.779178 -0.209473 -0.307828  4.0  1.0\\n\",\n       \"2013-01-04 -1.739724 -1.999703 -2.190199  2.0  0.0\\n\",\n       \"2013-01-05 -3.320619 -3.531391 -4.639352  0.0 -1.0\\n\",\n       \"2013-01-06       NaN       NaN       NaN  NaN  NaN\"\n      ]\n     },\n     \"execution_count\": 51,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.sub(s, axis='index')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Apply 函数作用\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过apply()对函数作用\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>F</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-01</th>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>-0.541762</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-02</th>\\n\",\n       \"      <td>-0.884117</td>\\n\",\n       \"      <td>-0.650741</td>\\n\",\n       \"      <td>-0.324417</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-03</th>\\n\",\n       \"      <td>-0.663295</td>\\n\",\n       \"      <td>0.139786</td>\\n\",\n       \"      <td>0.367755</td>\\n\",\n       \"      <td>15</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-04</th>\\n\",\n       \"      <td>0.596982</td>\\n\",\n       \"      <td>1.140083</td>\\n\",\n       \"      <td>1.177556</td>\\n\",\n       \"      <td>20</td>\\n\",\n       \"      <td>6.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-05</th>\\n\",\n       \"      <td>2.276362</td>\\n\",\n       \"      <td>2.608693</td>\\n\",\n       \"      <td>1.538204</td>\\n\",\n       \"      <td>25</td>\\n\",\n       \"      <td>10.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013-01-06</th>\\n\",\n       \"      <td>2.844229</td>\\n\",\n       \"      <td>2.844045</td>\\n\",\n       \"      <td>2.655599</td>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>15.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A         B         C   D     F\\n\",\n       \"2013-01-01  0.000000  0.000000 -0.541762   5   NaN\\n\",\n       \"2013-01-02 -0.884117 -0.650741 -0.324417  10   1.0\\n\",\n       \"2013-01-03 -0.663295  0.139786  0.367755  15   3.0\\n\",\n       \"2013-01-04  0.596982  1.140083  1.177556  20   6.0\\n\",\n       \"2013-01-05  2.276362  2.608693  1.538204  25  10.0\\n\",\n       \"2013-01-06  2.844229  2.844045  2.655599  30  15.0\"\n      ]\n     },\n     \"execution_count\": 52,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.apply(np.cumsum)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"A    2.563498\\n\",\n       \"B    2.119350\\n\",\n       \"C    1.659157\\n\",\n       \"D    0.000000\\n\",\n       \"F    4.000000\\n\",\n       \"dtype: float64\"\n      ]\n     },\n     \"execution_count\": 53,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.apply(lambda x:x.max()-x.min())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 频数统计\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0    2\\n\",\n       \"1    0\\n\",\n       \"2    5\\n\",\n       \"3    1\\n\",\n       \"4    6\\n\",\n       \"5    1\\n\",\n       \"6    1\\n\",\n       \"7    1\\n\",\n       \"8    5\\n\",\n       \"9    4\\n\",\n       \"dtype: int32\"\n      ]\n     },\n     \"execution_count\": 54,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s = pd.Series(np.random.randint(0, 7, size=10))\\n\",\n    \"s\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1    4\\n\",\n       \"5    2\\n\",\n       \"6    1\\n\",\n       \"4    1\\n\",\n       \"2    1\\n\",\n       \"0    1\\n\",\n       \"dtype: int64\"\n      ]\n     },\n     \"execution_count\": 55,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 字符串方法\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"对于Series对象，在其str属性中有着一系列的字符串处理方法。就如同下段代码一样，能很方便的对array中各个元素进行运算。值得注意的是，在str属性中的模式匹配默认使用正则表达式。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0       a\\n\",\n       \"1       b\\n\",\n       \"2       c\\n\",\n       \"3    aaba\\n\",\n       \"4    baca\\n\",\n       \"5     NaN\\n\",\n       \"6    caba\\n\",\n       \"7     dog\\n\",\n       \"8     cat\\n\",\n       \"dtype: object\"\n      ]\n     },\n     \"execution_count\": 56,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])\\n\",\n    \"s.str.lower()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 合并\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Concat 连接\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"pandas中提供了大量的方法能够轻松对Series，DataFrame和Panel对象进行不同满足逻辑关系的合并操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过**concat()**来连接pandas对象\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <th>3</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1.560690</td>\\n\",\n       \"      <td>2.253479</td>\\n\",\n       \"      <td>1.728586</td>\\n\",\n       \"      <td>1.224112</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>-1.237557</td>\\n\",\n       \"      <td>-1.571768</td>\\n\",\n       \"      <td>-1.687004</td>\\n\",\n       \"      <td>-0.226474</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>-0.591146</td>\\n\",\n       \"      <td>-0.054644</td>\\n\",\n       \"      <td>0.600806</td>\\n\",\n       \"      <td>0.076132</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>-0.567678</td>\\n\",\n       \"      <td>0.426496</td>\\n\",\n       \"      <td>-0.972487</td>\\n\",\n       \"      <td>0.200211</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>-2.073311</td>\\n\",\n       \"      <td>-1.566767</td>\\n\",\n       \"      <td>-0.533602</td>\\n\",\n       \"      <td>1.366468</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>2.244767</td>\\n\",\n       \"      <td>1.612232</td>\\n\",\n       \"      <td>1.934717</td>\\n\",\n       \"      <td>-0.403805</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>-2.640917</td>\\n\",\n       \"      <td>0.640549</td>\\n\",\n       \"      <td>1.257238</td>\\n\",\n       \"      <td>0.043773</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>1.545405</td>\\n\",\n       \"      <td>1.771884</td>\\n\",\n       \"      <td>-0.273687</td>\\n\",\n       \"      <td>2.441483</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>-0.440476</td>\\n\",\n       \"      <td>0.567536</td>\\n\",\n       \"      <td>2.379072</td>\\n\",\n       \"      <td>1.152354</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>-0.047853</td>\\n\",\n       \"      <td>-0.440427</td>\\n\",\n       \"      <td>-1.382389</td>\\n\",\n       \"      <td>0.647217</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"          0         1         2         3\\n\",\n       \"0  1.560690  2.253479  1.728586  1.224112\\n\",\n       \"1 -1.237557 -1.571768 -1.687004 -0.226474\\n\",\n       \"2 -0.591146 -0.054644  0.600806  0.076132\\n\",\n       \"3 -0.567678  0.426496 -0.972487  0.200211\\n\",\n       \"4 -2.073311 -1.566767 -0.533602  1.366468\\n\",\n       \"5  2.244767  1.612232  1.934717 -0.403805\\n\",\n       \"6 -2.640917  0.640549  1.257238  0.043773\\n\",\n       \"7  1.545405  1.771884 -0.273687  2.441483\\n\",\n       \"8 -0.440476  0.567536  2.379072  1.152354\\n\",\n       \"9 -0.047853 -0.440427 -1.382389  0.647217\"\n      ]\n     },\n     \"execution_count\": 57,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame(np.random.randn(10,4))\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[          0         1         2         3\\n\",\n       \" 0  1.560690  2.253479  1.728586  1.224112\\n\",\n       \" 1 -1.237557 -1.571768 -1.687004 -0.226474\\n\",\n       \" 2 -0.591146 -0.054644  0.600806  0.076132,\\n\",\n       \"           0         1         2         3\\n\",\n       \" 3 -0.567678  0.426496 -0.972487  0.200211\\n\",\n       \" 4 -2.073311 -1.566767 -0.533602  1.366468\\n\",\n       \" 5  2.244767  1.612232  1.934717 -0.403805\\n\",\n       \" 6 -2.640917  0.640549  1.257238  0.043773,\\n\",\n       \"           0         1         2         3\\n\",\n       \" 7  1.545405  1.771884 -0.273687  2.441483\\n\",\n       \" 8 -0.440476  0.567536  2.379072  1.152354\\n\",\n       \" 9 -0.047853 -0.440427 -1.382389  0.647217]\"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"#break it into pieces\\n\",\n    \"pieces = [df[:3], df[3:7], df[7:]]\\n\",\n    \"pieces\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <th>3</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1.560690</td>\\n\",\n       \"      <td>2.253479</td>\\n\",\n       \"      <td>1.728586</td>\\n\",\n       \"      <td>1.224112</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>-1.237557</td>\\n\",\n       \"      <td>-1.571768</td>\\n\",\n       \"      <td>-1.687004</td>\\n\",\n       \"      <td>-0.226474</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>-0.591146</td>\\n\",\n       \"      <td>-0.054644</td>\\n\",\n       \"      <td>0.600806</td>\\n\",\n       \"      <td>0.076132</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>-0.567678</td>\\n\",\n       \"      <td>0.426496</td>\\n\",\n       \"      <td>-0.972487</td>\\n\",\n       \"      <td>0.200211</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>-2.073311</td>\\n\",\n       \"      <td>-1.566767</td>\\n\",\n       \"      <td>-0.533602</td>\\n\",\n       \"      <td>1.366468</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>2.244767</td>\\n\",\n       \"      <td>1.612232</td>\\n\",\n       \"      <td>1.934717</td>\\n\",\n       \"      <td>-0.403805</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>-2.640917</td>\\n\",\n       \"      <td>0.640549</td>\\n\",\n       \"      <td>1.257238</td>\\n\",\n       \"      <td>0.043773</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>1.545405</td>\\n\",\n       \"      <td>1.771884</td>\\n\",\n       \"      <td>-0.273687</td>\\n\",\n       \"      <td>2.441483</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>-0.440476</td>\\n\",\n       \"      <td>0.567536</td>\\n\",\n       \"      <td>2.379072</td>\\n\",\n       \"      <td>1.152354</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>-0.047853</td>\\n\",\n       \"      <td>-0.440427</td>\\n\",\n       \"      <td>-1.382389</td>\\n\",\n       \"      <td>0.647217</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"          0         1         2         3\\n\",\n       \"0  1.560690  2.253479  1.728586  1.224112\\n\",\n       \"1 -1.237557 -1.571768 -1.687004 -0.226474\\n\",\n       \"2 -0.591146 -0.054644  0.600806  0.076132\\n\",\n       \"3 -0.567678  0.426496 -0.972487  0.200211\\n\",\n       \"4 -2.073311 -1.566767 -0.533602  1.366468\\n\",\n       \"5  2.244767  1.612232  1.934717 -0.403805\\n\",\n       \"6 -2.640917  0.640549  1.257238  0.043773\\n\",\n       \"7  1.545405  1.771884 -0.273687  2.441483\\n\",\n       \"8 -0.440476  0.567536  2.379072  1.152354\\n\",\n       \"9 -0.047853 -0.440427 -1.382389  0.647217\"\n      ]\n     },\n     \"execution_count\": 59,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pd.concat(pieces)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Join 合并\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"类似于SQL中的合并(merge)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>key</th>\\n\",\n       \"      <th>lval</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   key  lval\\n\",\n       \"0  foo     1\\n\",\n       \"1  foo     2\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"left = pd.DataFrame({'key':['foo', 'foo'], 'lval':[1,2]})\\n\",\n    \"left\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>key</th>\\n\",\n       \"      <th>lval</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   key  lval\\n\",\n       \"0  foo     4\\n\",\n       \"1  foo     5\"\n      ]\n     },\n     \"execution_count\": 61,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"right = pd.DataFrame({'key':['foo', 'foo'], 'lval':[4,5]})\\n\",\n    \"right\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>key</th>\\n\",\n       \"      <th>lval_x</th>\\n\",\n       \"      <th>lval_y</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   key  lval_x  lval_y\\n\",\n       \"0  foo       1       4\\n\",\n       \"1  foo       1       5\\n\",\n       \"2  foo       2       4\\n\",\n       \"3  foo       2       5\"\n      ]\n     },\n     \"execution_count\": 62,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pd.merge(left, right, on='key')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Append 添加\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"将若干行添加到dataFrame后面\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0.415810</td>\\n\",\n       \"      <td>-1.106857</td>\\n\",\n       \"      <td>-0.687920</td>\\n\",\n       \"      <td>2.422911</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0.696149</td>\\n\",\n       \"      <td>-1.235975</td>\\n\",\n       \"      <td>0.201409</td>\\n\",\n       \"      <td>1.424596</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>-0.540622</td>\\n\",\n       \"      <td>0.121096</td>\\n\",\n       \"      <td>-0.861667</td>\\n\",\n       \"      <td>-0.171690</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0.163904</td>\\n\",\n       \"      <td>1.324567</td>\\n\",\n       \"      <td>-0.768324</td>\\n\",\n       \"      <td>-0.205520</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>-1.581152</td>\\n\",\n       \"      <td>-0.079061</td>\\n\",\n       \"      <td>0.251810</td>\\n\",\n       \"      <td>-0.195755</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>1.254246</td>\\n\",\n       \"      <td>1.604556</td>\\n\",\n       \"      <td>0.766464</td>\\n\",\n       \"      <td>-1.090743</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>0.608609</td>\\n\",\n       \"      <td>1.000765</td>\\n\",\n       \"      <td>-0.407980</td>\\n\",\n       \"      <td>0.034970</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>-3.111914</td>\\n\",\n       \"      <td>2.163344</td>\\n\",\n       \"      <td>0.619885</td>\\n\",\n       \"      <td>-0.705518</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"          A         B         C         D\\n\",\n       \"0  0.415810 -1.106857 -0.687920  2.422911\\n\",\n       \"1  0.696149 -1.235975  0.201409  1.424596\\n\",\n       \"2 -0.540622  0.121096 -0.861667 -0.171690\\n\",\n       \"3  0.163904  1.324567 -0.768324 -0.205520\\n\",\n       \"4 -1.581152 -0.079061  0.251810 -0.195755\\n\",\n       \"5  1.254246  1.604556  0.766464 -1.090743\\n\",\n       \"6  0.608609  1.000765 -0.407980  0.034970\\n\",\n       \"7 -3.111914  2.163344  0.619885 -0.705518\"\n      ]\n     },\n     \"execution_count\": 63,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"A    0.163904\\n\",\n       \"B    1.324567\\n\",\n       \"C   -0.768324\\n\",\n       \"D   -0.205520\\n\",\n       \"Name: 3, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 64,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s = df.iloc[3]\\n\",\n    \"s\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0.415810</td>\\n\",\n       \"      <td>-1.106857</td>\\n\",\n       \"      <td>-0.687920</td>\\n\",\n       \"      <td>2.422911</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0.696149</td>\\n\",\n       \"      <td>-1.235975</td>\\n\",\n       \"      <td>0.201409</td>\\n\",\n       \"      <td>1.424596</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>-0.540622</td>\\n\",\n       \"      <td>0.121096</td>\\n\",\n       \"      <td>-0.861667</td>\\n\",\n       \"      <td>-0.171690</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0.163904</td>\\n\",\n       \"      <td>1.324567</td>\\n\",\n       \"      <td>-0.768324</td>\\n\",\n       \"      <td>-0.205520</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>-1.581152</td>\\n\",\n       \"      <td>-0.079061</td>\\n\",\n       \"      <td>0.251810</td>\\n\",\n       \"      <td>-0.195755</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>1.254246</td>\\n\",\n       \"      <td>1.604556</td>\\n\",\n       \"      <td>0.766464</td>\\n\",\n       \"      <td>-1.090743</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>0.608609</td>\\n\",\n       \"      <td>1.000765</td>\\n\",\n       \"      <td>-0.407980</td>\\n\",\n       \"      <td>0.034970</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>-3.111914</td>\\n\",\n       \"      <td>2.163344</td>\\n\",\n       \"      <td>0.619885</td>\\n\",\n       \"      <td>-0.705518</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>0.163904</td>\\n\",\n       \"      <td>1.324567</td>\\n\",\n       \"      <td>-0.768324</td>\\n\",\n       \"      <td>-0.205520</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"          A         B         C         D\\n\",\n       \"0  0.415810 -1.106857 -0.687920  2.422911\\n\",\n       \"1  0.696149 -1.235975  0.201409  1.424596\\n\",\n       \"2 -0.540622  0.121096 -0.861667 -0.171690\\n\",\n       \"3  0.163904  1.324567 -0.768324 -0.205520\\n\",\n       \"4 -1.581152 -0.079061  0.251810 -0.195755\\n\",\n       \"5  1.254246  1.604556  0.766464 -1.090743\\n\",\n       \"6  0.608609  1.000765 -0.407980  0.034970\\n\",\n       \"7 -3.111914  2.163344  0.619885 -0.705518\\n\",\n       \"8  0.163904  1.324567 -0.768324 -0.205520\"\n      ]\n     },\n     \"execution_count\": 65,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.append(s, ignore_index=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 分组\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"对于“group by”操作，我们通常是指以下一个或几个步骤：\\n\",\n    \"* **划分** 按照某些标准将数据分为不同的组\\n\",\n    \"* **应用** 对每组数据分别执行一个函数\\n\",\n    \"* **组合** 将结果组合到一个数据结构\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>one</td>\\n\",\n       \"      <td>0.190663</td>\\n\",\n       \"      <td>0.589384</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>bar</td>\\n\",\n       \"      <td>one</td>\\n\",\n       \"      <td>1.056331</td>\\n\",\n       \"      <td>0.035044</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>two</td>\\n\",\n       \"      <td>0.723645</td>\\n\",\n       \"      <td>0.372672</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>bar</td>\\n\",\n       \"      <td>three</td>\\n\",\n       \"      <td>-1.306869</td>\\n\",\n       \"      <td>0.435296</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>two</td>\\n\",\n       \"      <td>0.673661</td>\\n\",\n       \"      <td>-1.292242</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>bar</td>\\n\",\n       \"      <td>two</td>\\n\",\n       \"      <td>-0.823728</td>\\n\",\n       \"      <td>0.837556</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>one</td>\\n\",\n       \"      <td>0.638573</td>\\n\",\n       \"      <td>2.453041</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>bar</td>\\n\",\n       \"      <td>three</td>\\n\",\n       \"      <td>0.508922</td>\\n\",\n       \"      <td>0.578740</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"     A      B         C         D\\n\",\n       \"0  foo    one  0.190663  0.589384\\n\",\n       \"1  bar    one  1.056331  0.035044\\n\",\n       \"2  foo    two  0.723645  0.372672\\n\",\n       \"3  bar  three -1.306869  0.435296\\n\",\n       \"4  foo    two  0.673661 -1.292242\\n\",\n       \"5  bar    two -0.823728  0.837556\\n\",\n       \"6  foo    one  0.638573  2.453041\\n\",\n       \"7  bar  three  0.508922  0.578740\"\n      ]\n     },\n     \"execution_count\": 66,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', \\n\",\n    \"                          'foo', 'bar', 'foo', 'bar'],\\n\",\n    \"                   'B' : ['one', 'one', 'two', 'three', \\n\",\n    \"                          'two', 'two', 'one', 'three'],\\n\",\n    \"                   'C' : np.random.randn(8),\\n\",\n    \"                   'D' : np.random.randn(8)})\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"分组并对每个分组应用sum函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>bar</th>\\n\",\n       \"      <td>-0.565344</td>\\n\",\n       \"      <td>1.886637</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>foo</th>\\n\",\n       \"      <td>2.226542</td>\\n\",\n       \"      <td>2.122855</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"            C         D\\n\",\n       \"A                      \\n\",\n       \"bar -0.565344  1.886637\\n\",\n       \"foo  2.226542  2.122855\"\n      ]\n     },\n     \"execution_count\": 67,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.groupby('A').sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"按多个列分组形成层级索引，然后应用函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">bar</th>\\n\",\n       \"      <th>one</th>\\n\",\n       \"      <td>1.056331</td>\\n\",\n       \"      <td>0.035044</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>three</th>\\n\",\n       \"      <td>-0.797947</td>\\n\",\n       \"      <td>1.014036</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>two</th>\\n\",\n       \"      <td>-0.823728</td>\\n\",\n       \"      <td>0.837556</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">foo</th>\\n\",\n       \"      <th>one</th>\\n\",\n       \"      <td>0.829236</td>\\n\",\n       \"      <td>3.042425</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>two</th>\\n\",\n       \"      <td>1.397306</td>\\n\",\n       \"      <td>-0.919570</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                  C         D\\n\",\n       \"A   B                        \\n\",\n       \"bar one    1.056331  0.035044\\n\",\n       \"    three -0.797947  1.014036\\n\",\n       \"    two   -0.823728  0.837556\\n\",\n       \"foo one    0.829236  3.042425\\n\",\n       \"    two    1.397306 -0.919570\"\n      ]\n     },\n     \"execution_count\": 68,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.groupby(['A','B']).sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 变形\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 堆叠\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',\\n\",\n    \"                     'foo', 'foo', 'qux', 'qux'],\\n\",\n    \"                    ['one', 'two', 'one', 'two',\\n\",\n    \"                     'one', 'two', 'one', 'two']]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>first</th>\\n\",\n       \"      <th>second</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">bar</th>\\n\",\n       \"      <th>one</th>\\n\",\n       \"      <td>-0.551894</td>\\n\",\n       \"      <td>0.358046</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>two</th>\\n\",\n       \"      <td>-0.773283</td>\\n\",\n       \"      <td>-0.489393</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">baz</th>\\n\",\n       \"      <th>one</th>\\n\",\n       \"      <td>-0.461588</td>\\n\",\n       \"      <td>-1.561085</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>two</th>\\n\",\n       \"      <td>2.304456</td>\\n\",\n       \"      <td>-0.241811</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                     A         B\\n\",\n       \"first second                    \\n\",\n       \"bar   one    -0.551894  0.358046\\n\",\n       \"      two    -0.773283 -0.489393\\n\",\n       \"baz   one    -0.461588 -1.561085\\n\",\n       \"      two     2.304456 -0.241811\"\n      ]\n     },\n     \"execution_count\": 72,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df2 = df[:4]\\n\",\n    \"df2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**stack()**方法对DataFrame的列“压缩”一个层级\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"first  second   \\n\",\n       \"bar    one     A   -0.551894\\n\",\n       \"               B    0.358046\\n\",\n       \"       two     A   -0.773283\\n\",\n       \"               B   -0.489393\\n\",\n       \"baz    one     A   -0.461588\\n\",\n       \"               B   -1.561085\\n\",\n       \"       two     A    2.304456\\n\",\n       \"               B   -0.241811\\n\",\n       \"dtype: float64\"\n      ]\n     },\n     \"execution_count\": 73,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stacked = df2.stack()\\n\",\n    \"stacked\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"对于一个“堆叠过的”DataFrame或者Series（拥有MultiIndex作为索引），**stack()**的逆操作是**unstack()**，默认反堆叠到上一个层级\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>first</th>\\n\",\n       \"      <th>second</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">bar</th>\\n\",\n       \"      <th>one</th>\\n\",\n       \"      <td>-0.551894</td>\\n\",\n       \"      <td>0.358046</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>two</th>\\n\",\n       \"      <td>-0.773283</td>\\n\",\n       \"      <td>-0.489393</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">baz</th>\\n\",\n       \"      <th>one</th>\\n\",\n       \"      <td>-0.461588</td>\\n\",\n       \"      <td>-1.561085</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>two</th>\\n\",\n       \"      <td>2.304456</td>\\n\",\n       \"      <td>-0.241811</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                     A         B\\n\",\n       \"first second                    \\n\",\n       \"bar   one    -0.551894  0.358046\\n\",\n       \"      two    -0.773283 -0.489393\\n\",\n       \"baz   one    -0.461588 -1.561085\\n\",\n       \"      two     2.304456 -0.241811\"\n      ]\n     },\n     \"execution_count\": 74,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stacked.unstack()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>second</th>\\n\",\n       \"      <th>one</th>\\n\",\n       \"      <th>two</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>first</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">bar</th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <td>-0.551894</td>\\n\",\n       \"      <td>-0.773283</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <td>0.358046</td>\\n\",\n       \"      <td>-0.489393</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">baz</th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <td>-0.461588</td>\\n\",\n       \"      <td>2.304456</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <td>-1.561085</td>\\n\",\n       \"      <td>-0.241811</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"second        one       two\\n\",\n       \"first                      \\n\",\n       \"bar   A -0.551894 -0.773283\\n\",\n       \"      B  0.358046 -0.489393\\n\",\n       \"baz   A -0.461588  2.304456\\n\",\n       \"      B -1.561085 -0.241811\"\n      ]\n     },\n     \"execution_count\": 75,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stacked.unstack(1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>first</th>\\n\",\n       \"      <th>bar</th>\\n\",\n       \"      <th>baz</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>second</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">one</th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <td>-0.551894</td>\\n\",\n       \"      <td>-0.461588</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <td>0.358046</td>\\n\",\n       \"      <td>-1.561085</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">two</th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <td>-0.773283</td>\\n\",\n       \"      <td>2.304456</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <td>-0.489393</td>\\n\",\n       \"      <td>-0.241811</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"first          bar       baz\\n\",\n       \"second                      \\n\",\n       \"one    A -0.551894 -0.461588\\n\",\n       \"       B  0.358046 -1.561085\\n\",\n       \"two    A -0.773283  2.304456\\n\",\n       \"       B -0.489393 -0.241811\"\n      ]\n     },\n     \"execution_count\": 76,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stacked.unstack(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 数据透视表\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"      <th>E</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>one</td>\\n\",\n       \"      <td>A</td>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>-0.169373</td>\\n\",\n       \"      <td>-1.070179</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>one</td>\\n\",\n       \"      <td>B</td>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>-0.484603</td>\\n\",\n       \"      <td>-1.627113</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>two</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>-1.289207</td>\\n\",\n       \"      <td>0.635274</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>three</td>\\n\",\n       \"      <td>A</td>\\n\",\n       \"      <td>bar</td>\\n\",\n       \"      <td>0.034032</td>\\n\",\n       \"      <td>-0.553259</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>one</td>\\n\",\n       \"      <td>B</td>\\n\",\n       \"      <td>bar</td>\\n\",\n       \"      <td>0.536531</td>\\n\",\n       \"      <td>-0.088784</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>one</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>bar</td>\\n\",\n       \"      <td>-0.977931</td>\\n\",\n       \"      <td>-0.211193</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>two</td>\\n\",\n       \"      <td>A</td>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>1.486196</td>\\n\",\n       \"      <td>-0.377266</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>three</td>\\n\",\n       \"      <td>B</td>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>-1.108725</td>\\n\",\n       \"      <td>0.050407</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>one</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>foo</td>\\n\",\n       \"      <td>0.562988</td>\\n\",\n       \"      <td>-0.195356</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>one</td>\\n\",\n       \"      <td>A</td>\\n\",\n       \"      <td>bar</td>\\n\",\n       \"      <td>0.510381</td>\\n\",\n       \"      <td>-2.050069</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>two</td>\\n\",\n       \"      <td>B</td>\\n\",\n       \"      <td>bar</td>\\n\",\n       \"      <td>0.005697</td>\\n\",\n       \"      <td>0.708523</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>three</td>\\n\",\n       \"      <td>C</td>\\n\",\n       \"      <td>bar</td>\\n\",\n       \"      <td>0.942276</td>\\n\",\n       \"      <td>0.769442</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"        A  B    C         D         E\\n\",\n       \"0     one  A  foo -0.169373 -1.070179\\n\",\n       \"1     one  B  foo -0.484603 -1.627113\\n\",\n       \"2     two  C  foo -1.289207  0.635274\\n\",\n       \"3   three  A  bar  0.034032 -0.553259\\n\",\n       \"4     one  B  bar  0.536531 -0.088784\\n\",\n       \"5     one  C  bar -0.977931 -0.211193\\n\",\n       \"6     two  A  foo  1.486196 -0.377266\\n\",\n       \"7   three  B  foo -1.108725  0.050407\\n\",\n       \"8     one  C  foo  0.562988 -0.195356\\n\",\n       \"9     one  A  bar  0.510381 -2.050069\\n\",\n       \"10    two  B  bar  0.005697  0.708523\\n\",\n       \"11  three  C  bar  0.942276  0.769442\"\n      ]\n     },\n     \"execution_count\": 77,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,\\n\",\n    \"                   'B' : ['A', 'B', 'C'] * 4,\\n\",\n    \"                   'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\\n\",\n    \"                   'D' : np.random.randn(12),\\n\",\n    \"                   'E' : np.random.randn(12)})\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \" 我们可以轻松地从这个数据得到透视表\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>bar</th>\\n\",\n       \"      <th>foo</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">one</th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <td>0.510381</td>\\n\",\n       \"      <td>-0.169373</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <td>0.536531</td>\\n\",\n       \"      <td>-0.484603</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <td>-0.977931</td>\\n\",\n       \"      <td>0.562988</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">three</th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <td>0.034032</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>-1.108725</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <td>0.942276</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">two</th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>1.486196</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <td>0.005697</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>-1.289207</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"C             bar       foo\\n\",\n       \"A     B                    \\n\",\n       \"one   A  0.510381 -0.169373\\n\",\n       \"      B  0.536531 -0.484603\\n\",\n       \"      C -0.977931  0.562988\\n\",\n       \"three A  0.034032       NaN\\n\",\n       \"      B       NaN -1.108725\\n\",\n       \"      C  0.942276       NaN\\n\",\n       \"two   A       NaN  1.486196\\n\",\n       \"      B  0.005697       NaN\\n\",\n       \"      C       NaN -1.289207\"\n      ]\n     },\n     \"execution_count\": 78,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 时间序列\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"pandas在对频率转换进行重新采样时拥有着简单，强大而且高效的功能（例如把按秒采样的数据转换为按5分钟采样的数据）。这在金融领域很常见，但又不限于此。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:00:01',\\n\",\n       \"               '2012-01-01 00:00:02', '2012-01-01 00:00:03',\\n\",\n       \"               '2012-01-01 00:00:04', '2012-01-01 00:00:05',\\n\",\n       \"               '2012-01-01 00:00:06', '2012-01-01 00:00:07',\\n\",\n       \"               '2012-01-01 00:00:08', '2012-01-01 00:00:09',\\n\",\n       \"               '2012-01-01 00:00:10', '2012-01-01 00:00:11',\\n\",\n       \"               '2012-01-01 00:00:12', '2012-01-01 00:00:13',\\n\",\n       \"               '2012-01-01 00:00:14', '2012-01-01 00:00:15',\\n\",\n       \"               '2012-01-01 00:00:16', '2012-01-01 00:00:17',\\n\",\n       \"               '2012-01-01 00:00:18', '2012-01-01 00:00:19',\\n\",\n       \"               '2012-01-01 00:00:20', '2012-01-01 00:00:21',\\n\",\n       \"               '2012-01-01 00:00:22', '2012-01-01 00:00:23',\\n\",\n       \"               '2012-01-01 00:00:24', '2012-01-01 00:00:25',\\n\",\n       \"               '2012-01-01 00:00:26', '2012-01-01 00:00:27',\\n\",\n       \"               '2012-01-01 00:00:28', '2012-01-01 00:00:29',\\n\",\n       \"               '2012-01-01 00:00:30', '2012-01-01 00:00:31',\\n\",\n       \"               '2012-01-01 00:00:32', '2012-01-01 00:00:33',\\n\",\n       \"               '2012-01-01 00:00:34', '2012-01-01 00:00:35',\\n\",\n       \"               '2012-01-01 00:00:36', '2012-01-01 00:00:37',\\n\",\n       \"               '2012-01-01 00:00:38', '2012-01-01 00:00:39',\\n\",\n       \"               '2012-01-01 00:00:40', '2012-01-01 00:00:41',\\n\",\n       \"               '2012-01-01 00:00:42', '2012-01-01 00:00:43',\\n\",\n       \"               '2012-01-01 00:00:44', '2012-01-01 00:00:45',\\n\",\n       \"               '2012-01-01 00:00:46', '2012-01-01 00:00:47',\\n\",\n       \"               '2012-01-01 00:00:48', '2012-01-01 00:00:49',\\n\",\n       \"               '2012-01-01 00:00:50', '2012-01-01 00:00:51',\\n\",\n       \"               '2012-01-01 00:00:52', '2012-01-01 00:00:53',\\n\",\n       \"               '2012-01-01 00:00:54', '2012-01-01 00:00:55',\\n\",\n       \"               '2012-01-01 00:00:56', '2012-01-01 00:00:57',\\n\",\n       \"               '2012-01-01 00:00:58', '2012-01-01 00:00:59',\\n\",\n       \"               '2012-01-01 00:01:00', '2012-01-01 00:01:01',\\n\",\n       \"               '2012-01-01 00:01:02', '2012-01-01 00:01:03',\\n\",\n       \"               '2012-01-01 00:01:04', '2012-01-01 00:01:05',\\n\",\n       \"               '2012-01-01 00:01:06', '2012-01-01 00:01:07',\\n\",\n       \"               '2012-01-01 00:01:08', '2012-01-01 00:01:09',\\n\",\n       \"               '2012-01-01 00:01:10', '2012-01-01 00:01:11',\\n\",\n       \"               '2012-01-01 00:01:12', '2012-01-01 00:01:13',\\n\",\n       \"               '2012-01-01 00:01:14', '2012-01-01 00:01:15',\\n\",\n       \"               '2012-01-01 00:01:16', '2012-01-01 00:01:17',\\n\",\n       \"               '2012-01-01 00:01:18', '2012-01-01 00:01:19',\\n\",\n       \"               '2012-01-01 00:01:20', '2012-01-01 00:01:21',\\n\",\n       \"               '2012-01-01 00:01:22', '2012-01-01 00:01:23',\\n\",\n       \"               '2012-01-01 00:01:24', '2012-01-01 00:01:25',\\n\",\n       \"               '2012-01-01 00:01:26', '2012-01-01 00:01:27',\\n\",\n       \"               '2012-01-01 00:01:28', '2012-01-01 00:01:29',\\n\",\n       \"               '2012-01-01 00:01:30', '2012-01-01 00:01:31',\\n\",\n       \"               '2012-01-01 00:01:32', '2012-01-01 00:01:33',\\n\",\n       \"               '2012-01-01 00:01:34', '2012-01-01 00:01:35',\\n\",\n       \"               '2012-01-01 00:01:36', '2012-01-01 00:01:37',\\n\",\n       \"               '2012-01-01 00:01:38', '2012-01-01 00:01:39'],\\n\",\n       \"              dtype='datetime64[ns]', freq='S')\"\n      ]\n     },\n     \"execution_count\": 79,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rng = pd.date_range('1/1/2012', periods=100, freq='S')\\n\",\n    \"rng\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2012-01-01 00:00:00    100\\n\",\n       \"2012-01-01 00:00:01     47\\n\",\n       \"2012-01-01 00:00:02    473\\n\",\n       \"2012-01-01 00:00:03    136\\n\",\n       \"2012-01-01 00:00:04    192\\n\",\n       \"2012-01-01 00:00:05    372\\n\",\n       \"2012-01-01 00:00:06    459\\n\",\n       \"2012-01-01 00:00:07    450\\n\",\n       \"2012-01-01 00:00:08    244\\n\",\n       \"2012-01-01 00:00:09    383\\n\",\n       \"2012-01-01 00:00:10    250\\n\",\n       \"2012-01-01 00:00:11     53\\n\",\n       \"2012-01-01 00:00:12    364\\n\",\n       \"2012-01-01 00:00:13    139\\n\",\n       \"2012-01-01 00:00:14    239\\n\",\n       \"2012-01-01 00:00:15    494\\n\",\n       \"2012-01-01 00:00:16    370\\n\",\n       \"2012-01-01 00:00:17    329\\n\",\n       \"2012-01-01 00:00:18     48\\n\",\n       \"2012-01-01 00:00:19    278\\n\",\n       \"2012-01-01 00:00:20    100\\n\",\n       \"2012-01-01 00:00:21    443\\n\",\n       \"2012-01-01 00:00:22    348\\n\",\n       \"2012-01-01 00:00:23     25\\n\",\n       \"2012-01-01 00:00:24    257\\n\",\n       \"2012-01-01 00:00:25    245\\n\",\n       \"2012-01-01 00:00:26     60\\n\",\n       \"2012-01-01 00:00:27     12\\n\",\n       \"2012-01-01 00:00:28    312\\n\",\n       \"2012-01-01 00:00:29    154\\n\",\n       \"                      ... \\n\",\n       \"2012-01-01 00:01:10     31\\n\",\n       \"2012-01-01 00:01:11    225\\n\",\n       \"2012-01-01 00:01:12    364\\n\",\n       \"2012-01-01 00:01:13    171\\n\",\n       \"2012-01-01 00:01:14    351\\n\",\n       \"2012-01-01 00:01:15    459\\n\",\n       \"2012-01-01 00:01:16    363\\n\",\n       \"2012-01-01 00:01:17    369\\n\",\n       \"2012-01-01 00:01:18    421\\n\",\n       \"2012-01-01 00:01:19    389\\n\",\n       \"2012-01-01 00:01:20    408\\n\",\n       \"2012-01-01 00:01:21     63\\n\",\n       \"2012-01-01 00:01:22     55\\n\",\n       \"2012-01-01 00:01:23    368\\n\",\n       \"2012-01-01 00:01:24    439\\n\",\n       \"2012-01-01 00:01:25     14\\n\",\n       \"2012-01-01 00:01:26    220\\n\",\n       \"2012-01-01 00:01:27    250\\n\",\n       \"2012-01-01 00:01:28    495\\n\",\n       \"2012-01-01 00:01:29    391\\n\",\n       \"2012-01-01 00:01:30     79\\n\",\n       \"2012-01-01 00:01:31    408\\n\",\n       \"2012-01-01 00:01:32    232\\n\",\n       \"2012-01-01 00:01:33    413\\n\",\n       \"2012-01-01 00:01:34    373\\n\",\n       \"2012-01-01 00:01:35    355\\n\",\n       \"2012-01-01 00:01:36    308\\n\",\n       \"2012-01-01 00:01:37     51\\n\",\n       \"2012-01-01 00:01:38    375\\n\",\n       \"2012-01-01 00:01:39    185\\n\",\n       \"Freq: S, Length: 100, dtype: int32\"\n      ]\n     },\n     \"execution_count\": 80,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ts = pd.Series(np.random.randint(0,500,len(rng)), index=rng)\\n\",\n    \"ts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated\\n\",\n      \"the new syntax is .resample(...).sum()\\n\",\n      \"  \\\"\\\"\\\"Entry point for launching an IPython kernel.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2012-01-01    26203\\n\",\n       \"Freq: 5T, dtype: int32\"\n      ]\n     },\n     \"execution_count\": 81,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ts.resample('5Min', how='sum')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"时区表示\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DatetimeIndex(['2012-03-06', '2012-03-07', '2012-03-08', '2012-03-09',\\n\",\n       \"               '2012-03-10'],\\n\",\n       \"              dtype='datetime64[ns]', freq='D')\"\n      ]\n     },\n     \"execution_count\": 82,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rng = pd.date_range('3/6/2012', periods=5, freq='D')\\n\",\n    \"rng\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2012-03-06    0.523781\\n\",\n       \"2012-03-07   -0.670822\\n\",\n       \"2012-03-08    0.934826\\n\",\n       \"2012-03-09    0.002239\\n\",\n       \"2012-03-10   -0.091952\\n\",\n       \"Freq: D, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 83,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ts = pd.Series(np.random.randn(len(rng)), index=rng)\\n\",\n    \"ts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2012-03-06 00:00:00+00:00    0.523781\\n\",\n       \"2012-03-07 00:00:00+00:00   -0.670822\\n\",\n       \"2012-03-08 00:00:00+00:00    0.934826\\n\",\n       \"2012-03-09 00:00:00+00:00    0.002239\\n\",\n       \"2012-03-10 00:00:00+00:00   -0.091952\\n\",\n       \"Freq: D, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 84,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ts_utc = ts.tz_localize('UTC')\\n\",\n    \"ts_utc\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"时区转换\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2012-03-05 19:00:00-05:00    0.523781\\n\",\n       \"2012-03-06 19:00:00-05:00   -0.670822\\n\",\n       \"2012-03-07 19:00:00-05:00    0.934826\\n\",\n       \"2012-03-08 19:00:00-05:00    0.002239\\n\",\n       \"2012-03-09 19:00:00-05:00   -0.091952\\n\",\n       \"Freq: D, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 85,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ts_utc.tz_convert('US/Eastern')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"时间跨度转换\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DatetimeIndex(['2012-01-31', '2012-02-29', '2012-03-31', '2012-04-30',\\n\",\n       \"               '2012-05-31'],\\n\",\n       \"              dtype='datetime64[ns]', freq='M')\"\n      ]\n     },\n     \"execution_count\": 86,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rng = pd.date_range('1/1/2012', periods=5, freq='M')\\n\",\n    \"rng\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2012-01-31    1.296132\\n\",\n       \"2012-02-29    1.023936\\n\",\n       \"2012-03-31   -0.249774\\n\",\n       \"2012-04-30    1.007810\\n\",\n       \"2012-05-31   -0.051413\\n\",\n       \"Freq: M, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 87,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ts = pd.Series(np.random.randn(len(rng)), index=rng)\\n\",\n    \"ts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2012-01    1.296132\\n\",\n       \"2012-02    1.023936\\n\",\n       \"2012-03   -0.249774\\n\",\n       \"2012-04    1.007810\\n\",\n       \"2012-05   -0.051413\\n\",\n       \"Freq: M, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 88,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ps = ts.to_period()\\n\",\n    \"ps\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2012-01-01    1.296132\\n\",\n       \"2012-02-01    1.023936\\n\",\n       \"2012-03-01   -0.249774\\n\",\n       \"2012-04-01    1.007810\\n\",\n       \"2012-05-01   -0.051413\\n\",\n       \"Freq: MS, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 89,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ps.to_timestamp()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"日期与时间戳之间的转换使得可以使用一些方便的算术函数。例如，我们把以11月为年底的季度数据转换为当前季度末月底为始的数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"PeriodIndex(['1990Q1', '1990Q2', '1990Q3', '1990Q4', '1991Q1', '1991Q2',\\n\",\n       \"             '1991Q3', '1991Q4', '1992Q1', '1992Q2', '1992Q3', '1992Q4',\\n\",\n       \"             '1993Q1', '1993Q2', '1993Q3', '1993Q4', '1994Q1', '1994Q2',\\n\",\n       \"             '1994Q3', '1994Q4', '1995Q1', '1995Q2', '1995Q3', '1995Q4',\\n\",\n       \"             '1996Q1', '1996Q2', '1996Q3', '1996Q4', '1997Q1', '1997Q2',\\n\",\n       \"             '1997Q3', '1997Q4', '1998Q1', '1998Q2', '1998Q3', '1998Q4',\\n\",\n       \"             '1999Q1', '1999Q2', '1999Q3', '1999Q4', '2000Q1', '2000Q2',\\n\",\n       \"             '2000Q3', '2000Q4'],\\n\",\n       \"            dtype='period[Q-NOV]', freq='Q-NOV')\"\n      ]\n     },\n     \"execution_count\": 90,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')\\n\",\n    \"prng\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1990Q1    0.467059\\n\",\n       \"1990Q2   -0.745741\\n\",\n       \"1990Q3    0.106234\\n\",\n       \"1990Q4   -0.306761\\n\",\n       \"1991Q1   -2.012034\\n\",\n       \"1991Q2   -2.033417\\n\",\n       \"1991Q3   -1.116709\\n\",\n       \"1991Q4    0.031495\\n\",\n       \"1992Q1    0.407074\\n\",\n       \"1992Q2    0.988878\\n\",\n       \"1992Q3   -0.195428\\n\",\n       \"1992Q4   -0.144770\\n\",\n       \"1993Q1    0.733104\\n\",\n       \"1993Q2   -0.742351\\n\",\n       \"1993Q3   -0.464713\\n\",\n       \"1993Q4   -1.247317\\n\",\n       \"1994Q1   -0.108473\\n\",\n       \"1994Q2    0.188728\\n\",\n       \"1994Q3    0.052446\\n\",\n       \"1994Q4   -0.004046\\n\",\n       \"1995Q1    1.075490\\n\",\n       \"1995Q2   -0.436009\\n\",\n       \"1995Q3    0.747165\\n\",\n       \"1995Q4    2.433366\\n\",\n       \"1996Q1   -0.471832\\n\",\n       \"1996Q2   -1.337675\\n\",\n       \"1996Q3    0.931348\\n\",\n       \"1996Q4   -0.461312\\n\",\n       \"1997Q1    0.041943\\n\",\n       \"1997Q2   -1.003802\\n\",\n       \"1997Q3   -0.572660\\n\",\n       \"1997Q4   -0.959966\\n\",\n       \"1998Q1   -1.864559\\n\",\n       \"1998Q2   -2.196461\\n\",\n       \"1998Q3   -1.317999\\n\",\n       \"1998Q4   -0.796643\\n\",\n       \"1999Q1    1.183292\\n\",\n       \"1999Q2   -0.046480\\n\",\n       \"1999Q3   -0.727018\\n\",\n       \"1999Q4   -0.972557\\n\",\n       \"2000Q1    0.541060\\n\",\n       \"2000Q2    1.221401\\n\",\n       \"2000Q3   -1.407534\\n\",\n       \"2000Q4   -0.773754\\n\",\n       \"Freq: Q-NOV, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 91,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ts = pd.Series(np.random.randn(len(prng)), index = prng)\\n\",\n    \"ts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1990-02-01 09:00    0.467059\\n\",\n       \"1990-05-01 09:00   -0.745741\\n\",\n       \"1990-08-01 09:00    0.106234\\n\",\n       \"1990-11-01 09:00   -0.306761\\n\",\n       \"1991-02-01 09:00   -2.012034\\n\",\n       \"1991-05-01 09:00   -2.033417\\n\",\n       \"1991-08-01 09:00   -1.116709\\n\",\n       \"1991-11-01 09:00    0.031495\\n\",\n       \"1992-02-01 09:00    0.407074\\n\",\n       \"1992-05-01 09:00    0.988878\\n\",\n       \"1992-08-01 09:00   -0.195428\\n\",\n       \"1992-11-01 09:00   -0.144770\\n\",\n       \"1993-02-01 09:00    0.733104\\n\",\n       \"1993-05-01 09:00   -0.742351\\n\",\n       \"1993-08-01 09:00   -0.464713\\n\",\n       \"1993-11-01 09:00   -1.247317\\n\",\n       \"1994-02-01 09:00   -0.108473\\n\",\n       \"1994-05-01 09:00    0.188728\\n\",\n       \"1994-08-01 09:00    0.052446\\n\",\n       \"1994-11-01 09:00   -0.004046\\n\",\n       \"1995-02-01 09:00    1.075490\\n\",\n       \"1995-05-01 09:00   -0.436009\\n\",\n       \"1995-08-01 09:00    0.747165\\n\",\n       \"1995-11-01 09:00    2.433366\\n\",\n       \"1996-02-01 09:00   -0.471832\\n\",\n       \"1996-05-01 09:00   -1.337675\\n\",\n       \"1996-08-01 09:00    0.931348\\n\",\n       \"1996-11-01 09:00   -0.461312\\n\",\n       \"1997-02-01 09:00    0.041943\\n\",\n       \"1997-05-01 09:00   -1.003802\\n\",\n       \"1997-08-01 09:00   -0.572660\\n\",\n       \"1997-11-01 09:00   -0.959966\\n\",\n       \"1998-02-01 09:00   -1.864559\\n\",\n       \"1998-05-01 09:00   -2.196461\\n\",\n       \"1998-08-01 09:00   -1.317999\\n\",\n       \"1998-11-01 09:00   -0.796643\\n\",\n       \"1999-02-01 09:00    1.183292\\n\",\n       \"1999-05-01 09:00   -0.046480\\n\",\n       \"1999-08-01 09:00   -0.727018\\n\",\n       \"1999-11-01 09:00   -0.972557\\n\",\n       \"2000-02-01 09:00    0.541060\\n\",\n       \"2000-05-01 09:00    1.221401\\n\",\n       \"2000-08-01 09:00   -1.407534\\n\",\n       \"2000-11-01 09:00   -0.773754\\n\",\n       \"Freq: H, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 92,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ts.index = (prng.asfreq('M', 'end') ) .asfreq('H', 'start') +9\\n\",\n    \"ts\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 分类\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"从版本0.15开始，pandas在**DataFrame**中开始包括分类数据。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>id</th>\\n\",\n       \"      <th>raw_grade</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>a</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>b</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>b</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>a</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>e</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>e</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   id raw_grade\\n\",\n       \"0   1         a\\n\",\n       \"1   2         b\\n\",\n       \"2   3         b\\n\",\n       \"3   4         a\\n\",\n       \"4   5         e\\n\",\n       \"5   6         e\"\n      ]\n     },\n     \"execution_count\": 93,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame({\\\"id\\\":[1,2,3,4,5,6], \\\"raw_grade\\\":['a', 'b', 'b', 'a', 'e', 'e']})\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"把raw_grade转换为分类类型\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0    a\\n\",\n       \"1    b\\n\",\n       \"2    b\\n\",\n       \"3    a\\n\",\n       \"4    e\\n\",\n       \"5    e\\n\",\n       \"Name: grade, dtype: category\\n\",\n       \"Categories (3, object): [a, b, e]\"\n      ]\n     },\n     \"execution_count\": 94,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[\\\"grade\\\"] = df[\\\"raw_grade\\\"].astype(\\\"category\\\")\\n\",\n    \"df[\\\"grade\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"重命名类别名为更有意义的名称\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df[\\\"grade\\\"].cat.categories = [\\\"very good\\\", \\\"good\\\", \\\"very bad\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"对分类重新排序，并添加缺失的分类\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0    very good\\n\",\n       \"1         good\\n\",\n       \"2         good\\n\",\n       \"3    very good\\n\",\n       \"4     very bad\\n\",\n       \"5     very bad\\n\",\n       \"Name: grade, dtype: category\\n\",\n       \"Categories (5, object): [very bad, bad, medium, good, very good]\"\n      ]\n     },\n     \"execution_count\": 96,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[\\\"grade\\\"] = df[\\\"grade\\\"].cat.set_categories([\\\"very bad\\\", \\\"bad\\\", \\\"medium\\\", \\\"good\\\", \\\"very good\\\"])\\n\",\n    \"df[\\\"grade\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"排序是按照分类的顺序进行的，而不是字典序\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>id</th>\\n\",\n       \"      <th>raw_grade</th>\\n\",\n       \"      <th>grade</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>e</td>\\n\",\n       \"      <td>very bad</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>e</td>\\n\",\n       \"      <td>very bad</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>b</td>\\n\",\n       \"      <td>good</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>b</td>\\n\",\n       \"      <td>good</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>a</td>\\n\",\n       \"      <td>very good</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>a</td>\\n\",\n       \"      <td>very good</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   id raw_grade      grade\\n\",\n       \"4   5         e   very bad\\n\",\n       \"5   6         e   very bad\\n\",\n       \"1   2         b       good\\n\",\n       \"2   3         b       good\\n\",\n       \"0   1         a  very good\\n\",\n       \"3   4         a  very good\"\n      ]\n     },\n     \"execution_count\": 97,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.sort_values(by=\\\"grade\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"按分类分组时，也会显示空的分类\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"grade\\n\",\n       \"very bad     2\\n\",\n       \"bad          0\\n\",\n       \"medium       0\\n\",\n       \"good         2\\n\",\n       \"very good    2\\n\",\n       \"dtype: int64\"\n      ]\n     },\n     \"execution_count\": 98,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.groupby(\\\"grade\\\").size()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 绘图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x2472e962d68>\"\n      ]\n     },\n     \"execution_count\": 99,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x2472e95eef0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\\n\",\n    \"ts = ts.cumsum()\\n\",\n    \"ts.plot()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"对于DataFrame类型，**plot()**能很方便地画出所有列及其标签\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x247309914e0>\"\n      ]\n     },\n     \"execution_count\": 100,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x24730a068d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x247309911d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=['A', 'B', 'C', 'D'])\\n\",\n    \"df = df.cumsum()\\n\",\n    \"plt.figure(); df.plot(); plt.legend(loc='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 获取数据的I/O\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## CSV\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"写入一个csv文件\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df.to_csv('data/foo.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"从一个csv文件读入\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Unnamed: 0</th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>2000-01-01</td>\\n\",\n       \"      <td>-0.547915</td>\\n\",\n       \"      <td>0.758504</td>\\n\",\n       \"      <td>-0.791553</td>\\n\",\n       \"      <td>-0.014276</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2000-01-02</td>\\n\",\n       \"      <td>-1.478652</td>\\n\",\n       \"      <td>-1.824863</td>\\n\",\n       \"      <td>0.001416</td>\\n\",\n       \"      <td>-1.067864</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2000-01-03</td>\\n\",\n       \"      <td>-3.070727</td>\\n\",\n       \"      <td>-1.655967</td>\\n\",\n       \"      <td>-0.551411</td>\\n\",\n       \"      <td>-2.188238</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2000-01-04</td>\\n\",\n       \"      <td>-3.317105</td>\\n\",\n       \"      <td>-1.810778</td>\\n\",\n       \"      <td>0.354000</td>\\n\",\n       \"      <td>-1.995760</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>2000-01-05</td>\\n\",\n       \"      <td>-2.822308</td>\\n\",\n       \"      <td>-2.757077</td>\\n\",\n       \"      <td>1.210057</td>\\n\",\n       \"      <td>-1.250592</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>2000-01-06</td>\\n\",\n       \"      <td>-2.223865</td>\\n\",\n       \"      <td>-3.256931</td>\\n\",\n       \"      <td>3.658672</td>\\n\",\n       \"      <td>-1.232510</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>2000-01-07</td>\\n\",\n       \"      <td>-0.559411</td>\\n\",\n       \"      <td>-2.842820</td>\\n\",\n       \"      <td>4.860993</td>\\n\",\n       \"      <td>-0.418853</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>2000-01-08</td>\\n\",\n       \"      <td>-0.524812</td>\\n\",\n       \"      <td>-2.748762</td>\\n\",\n       \"      <td>6.280814</td>\\n\",\n       \"      <td>0.170616</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>2000-01-09</td>\\n\",\n       \"      <td>0.204528</td>\\n\",\n       \"      <td>-0.865022</td>\\n\",\n       \"      <td>5.560456</td>\\n\",\n       \"      <td>0.875871</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>2000-01-10</td>\\n\",\n       \"      <td>0.688895</td>\\n\",\n       \"      <td>-1.704024</td>\\n\",\n       \"      <td>5.226062</td>\\n\",\n       \"      <td>0.444483</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>2000-01-11</td>\\n\",\n       \"      <td>1.272138</td>\\n\",\n       \"      <td>-2.478377</td>\\n\",\n       \"      <td>2.763524</td>\\n\",\n       \"      <td>-1.229797</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>2000-01-12</td>\\n\",\n       \"      <td>1.255657</td>\\n\",\n       \"      <td>-2.994073</td>\\n\",\n       \"      <td>2.869688</td>\\n\",\n       \"      <td>0.745504</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>2000-01-13</td>\\n\",\n       \"      <td>-0.001803</td>\\n\",\n       \"      <td>-2.295823</td>\\n\",\n       \"      <td>2.738087</td>\\n\",\n       \"      <td>2.640307</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>2000-01-14</td>\\n\",\n       \"      <td>0.144209</td>\\n\",\n       \"      <td>-0.926810</td>\\n\",\n       \"      <td>4.087308</td>\\n\",\n       \"      <td>0.164138</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>2000-01-15</td>\\n\",\n       \"      <td>1.304811</td>\\n\",\n       \"      <td>0.625530</td>\\n\",\n       \"      <td>3.542700</td>\\n\",\n       \"      <td>-1.499446</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>2000-01-16</td>\\n\",\n       \"      <td>0.682026</td>\\n\",\n       \"      <td>0.552441</td>\\n\",\n       \"      <td>3.201389</td>\\n\",\n       \"      <td>-0.155674</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>2000-01-17</td>\\n\",\n       \"      <td>-1.065321</td>\\n\",\n       \"      <td>-0.339092</td>\\n\",\n       \"      <td>3.792634</td>\\n\",\n       \"      <td>-1.754946</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>2000-01-18</td>\\n\",\n       \"      <td>-1.268418</td>\\n\",\n       \"      <td>-3.512773</td>\\n\",\n       \"      <td>3.656896</td>\\n\",\n       \"      <td>-0.132124</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>2000-01-19</td>\\n\",\n       \"      <td>-1.936390</td>\\n\",\n       \"      <td>-1.069188</td>\\n\",\n       \"      <td>2.554887</td>\\n\",\n       \"      <td>0.413930</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>2000-01-20</td>\\n\",\n       \"      <td>-1.138678</td>\\n\",\n       \"      <td>-2.993870</td>\\n\",\n       \"      <td>3.094198</td>\\n\",\n       \"      <td>-0.956574</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>2000-01-21</td>\\n\",\n       \"      <td>-0.096721</td>\\n\",\n       \"      <td>-4.141108</td>\\n\",\n       \"      <td>3.341248</td>\\n\",\n       \"      <td>-0.436886</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>2000-01-22</td>\\n\",\n       \"      <td>-0.678763</td>\\n\",\n       \"      <td>-5.012663</td>\\n\",\n       \"      <td>0.823576</td>\\n\",\n       \"      <td>1.303377</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>2000-01-23</td>\\n\",\n       \"      <td>-2.498525</td>\\n\",\n       \"      <td>-6.075938</td>\\n\",\n       \"      <td>0.302908</td>\\n\",\n       \"      <td>-0.294204</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>2000-01-24</td>\\n\",\n       \"      <td>-0.937785</td>\\n\",\n       \"      <td>-6.768487</td>\\n\",\n       \"      <td>-0.078521</td>\\n\",\n       \"      <td>-0.165799</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>2000-01-25</td>\\n\",\n       \"      <td>-0.252338</td>\\n\",\n       \"      <td>-6.882860</td>\\n\",\n       \"      <td>0.545684</td>\\n\",\n       \"      <td>-1.108641</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>2000-01-26</td>\\n\",\n       \"      <td>-0.772653</td>\\n\",\n       \"      <td>-7.080129</td>\\n\",\n       \"      <td>0.764647</td>\\n\",\n       \"      <td>-3.041450</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>2000-01-27</td>\\n\",\n       \"      <td>-0.348831</td>\\n\",\n       \"      <td>-6.035375</td>\\n\",\n       \"      <td>0.507673</td>\\n\",\n       \"      <td>-2.522836</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>2000-01-28</td>\\n\",\n       \"      <td>1.751721</td>\\n\",\n       \"      <td>-5.756738</td>\\n\",\n       \"      <td>-0.469014</td>\\n\",\n       \"      <td>-2.825118</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>2000-01-29</td>\\n\",\n       \"      <td>0.799938</td>\\n\",\n       \"      <td>-4.559172</td>\\n\",\n       \"      <td>-0.086763</td>\\n\",\n       \"      <td>-3.243101</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>2000-01-30</td>\\n\",\n       \"      <td>1.149650</td>\\n\",\n       \"      <td>-4.433331</td>\\n\",\n       \"      <td>1.476689</td>\\n\",\n       \"      <td>-2.479956</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>970</th>\\n\",\n       \"      <td>2002-08-28</td>\\n\",\n       \"      <td>0.466756</td>\\n\",\n       \"      <td>38.780786</td>\\n\",\n       \"      <td>-6.743120</td>\\n\",\n       \"      <td>-15.206051</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>971</th>\\n\",\n       \"      <td>2002-08-29</td>\\n\",\n       \"      <td>1.903199</td>\\n\",\n       \"      <td>36.687258</td>\\n\",\n       \"      <td>-5.720742</td>\\n\",\n       \"      <td>-14.335689</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>972</th>\\n\",\n       \"      <td>2002-08-30</td>\\n\",\n       \"      <td>1.432801</td>\\n\",\n       \"      <td>37.186065</td>\\n\",\n       \"      <td>-6.274877</td>\\n\",\n       \"      <td>-14.879621</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>973</th>\\n\",\n       \"      <td>2002-08-31</td>\\n\",\n       \"      <td>2.218021</td>\\n\",\n       \"      <td>36.200395</td>\\n\",\n       \"      <td>-5.638178</td>\\n\",\n       \"      <td>-15.625389</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>974</th>\\n\",\n       \"      <td>2002-09-01</td>\\n\",\n       \"      <td>3.405318</td>\\n\",\n       \"      <td>36.987365</td>\\n\",\n       \"      <td>-4.441912</td>\\n\",\n       \"      <td>-14.162031</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>975</th>\\n\",\n       \"      <td>2002-09-02</td>\\n\",\n       \"      <td>3.854252</td>\\n\",\n       \"      <td>38.283545</td>\\n\",\n       \"      <td>-4.845949</td>\\n\",\n       \"      <td>-15.449302</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>976</th>\\n\",\n       \"      <td>2002-09-03</td>\\n\",\n       \"      <td>4.328033</td>\\n\",\n       \"      <td>37.123804</td>\\n\",\n       \"      <td>-6.006344</td>\\n\",\n       \"      <td>-14.158929</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>977</th>\\n\",\n       \"      <td>2002-09-04</td>\\n\",\n       \"      <td>5.219103</td>\\n\",\n       \"      <td>37.203486</td>\\n\",\n       \"      <td>-6.035182</td>\\n\",\n       \"      <td>-14.461389</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>978</th>\\n\",\n       \"      <td>2002-09-05</td>\\n\",\n       \"      <td>4.884547</td>\\n\",\n       \"      <td>38.192836</td>\\n\",\n       \"      <td>-6.447570</td>\\n\",\n       \"      <td>-13.541204</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>979</th>\\n\",\n       \"      <td>2002-09-06</td>\\n\",\n       \"      <td>5.817435</td>\\n\",\n       \"      <td>37.669998</td>\\n\",\n       \"      <td>-5.983732</td>\\n\",\n       \"      <td>-12.238303</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>980</th>\\n\",\n       \"      <td>2002-09-07</td>\\n\",\n       \"      <td>7.953334</td>\\n\",\n       \"      <td>37.503576</td>\\n\",\n       \"      <td>-4.783659</td>\\n\",\n       \"      <td>-12.092902</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>981</th>\\n\",\n       \"      <td>2002-09-08</td>\\n\",\n       \"      <td>8.575181</td>\\n\",\n       \"      <td>37.377622</td>\\n\",\n       \"      <td>-3.154240</td>\\n\",\n       \"      <td>-12.895208</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>982</th>\\n\",\n       \"      <td>2002-09-09</td>\\n\",\n       \"      <td>8.242240</td>\\n\",\n       \"      <td>38.155500</td>\\n\",\n       \"      <td>-3.285132</td>\\n\",\n       \"      <td>-13.483775</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>983</th>\\n\",\n       \"      <td>2002-09-10</td>\\n\",\n       \"      <td>9.699693</td>\\n\",\n       \"      <td>37.421024</td>\\n\",\n       \"      <td>-1.763105</td>\\n\",\n       \"      <td>-13.397276</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>984</th>\\n\",\n       \"      <td>2002-09-11</td>\\n\",\n       \"      <td>7.228027</td>\\n\",\n       \"      <td>38.070563</td>\\n\",\n       \"      <td>-1.250151</td>\\n\",\n       \"      <td>-14.455654</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>985</th>\\n\",\n       \"      <td>2002-09-12</td>\\n\",\n       \"      <td>7.276336</td>\\n\",\n       \"      <td>38.503057</td>\\n\",\n       \"      <td>-1.110047</td>\\n\",\n       \"      <td>-15.362900</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>986</th>\\n\",\n       \"      <td>2002-09-13</td>\\n\",\n       \"      <td>6.955453</td>\\n\",\n       \"      <td>37.038613</td>\\n\",\n       \"      <td>-0.571942</td>\\n\",\n       \"      <td>-14.527590</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>987</th>\\n\",\n       \"      <td>2002-09-14</td>\\n\",\n       \"      <td>6.232800</td>\\n\",\n       \"      <td>37.254276</td>\\n\",\n       \"      <td>-1.893050</td>\\n\",\n       \"      <td>-14.385401</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>988</th>\\n\",\n       \"      <td>2002-09-15</td>\\n\",\n       \"      <td>5.331802</td>\\n\",\n       \"      <td>36.671504</td>\\n\",\n       \"      <td>-1.782282</td>\\n\",\n       \"      <td>-13.645305</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>989</th>\\n\",\n       \"      <td>2002-09-16</td>\\n\",\n       \"      <td>5.825638</td>\\n\",\n       \"      <td>36.920733</td>\\n\",\n       \"      <td>-2.718137</td>\\n\",\n       \"      <td>-13.875984</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>990</th>\\n\",\n       \"      <td>2002-09-17</td>\\n\",\n       \"      <td>5.972137</td>\\n\",\n       \"      <td>36.785577</td>\\n\",\n       \"      <td>-3.923763</td>\\n\",\n       \"      <td>-13.281418</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>991</th>\\n\",\n       \"      <td>2002-09-18</td>\\n\",\n       \"      <td>5.154685</td>\\n\",\n       \"      <td>34.789858</td>\\n\",\n       \"      <td>-4.426825</td>\\n\",\n       \"      <td>-13.805227</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>992</th>\\n\",\n       \"      <td>2002-09-19</td>\\n\",\n       \"      <td>4.228035</td>\\n\",\n       \"      <td>34.400350</td>\\n\",\n       \"      <td>-4.431980</td>\\n\",\n       \"      <td>-14.520423</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>993</th>\\n\",\n       \"      <td>2002-09-20</td>\\n\",\n       \"      <td>3.047640</td>\\n\",\n       \"      <td>36.244469</td>\\n\",\n       \"      <td>-5.093584</td>\\n\",\n       \"      <td>-15.106412</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>994</th>\\n\",\n       \"      <td>2002-09-21</td>\\n\",\n       \"      <td>2.984361</td>\\n\",\n       \"      <td>38.095349</td>\\n\",\n       \"      <td>-5.560222</td>\\n\",\n       \"      <td>-16.584216</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>995</th>\\n\",\n       \"      <td>2002-09-22</td>\\n\",\n       \"      <td>3.187098</td>\\n\",\n       \"      <td>37.169742</td>\\n\",\n       \"      <td>-6.132250</td>\\n\",\n       \"      <td>-14.910771</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>996</th>\\n\",\n       \"      <td>2002-09-23</td>\\n\",\n       \"      <td>1.772470</td>\\n\",\n       \"      <td>36.399488</td>\\n\",\n       \"      <td>-6.257600</td>\\n\",\n       \"      <td>-13.668261</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>997</th>\\n\",\n       \"      <td>2002-09-24</td>\\n\",\n       \"      <td>1.886272</td>\\n\",\n       \"      <td>34.543307</td>\\n\",\n       \"      <td>-6.644915</td>\\n\",\n       \"      <td>-15.213739</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>998</th>\\n\",\n       \"      <td>2002-09-25</td>\\n\",\n       \"      <td>2.918438</td>\\n\",\n       \"      <td>33.722114</td>\\n\",\n       \"      <td>-5.736130</td>\\n\",\n       \"      <td>-14.919711</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>999</th>\\n\",\n       \"      <td>2002-09-26</td>\\n\",\n       \"      <td>2.608268</td>\\n\",\n       \"      <td>33.473087</td>\\n\",\n       \"      <td>-5.111581</td>\\n\",\n       \"      <td>-15.969800</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>1000 rows × 5 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"     Unnamed: 0         A          B         C          D\\n\",\n       \"0    2000-01-01 -0.547915   0.758504 -0.791553  -0.014276\\n\",\n       \"1    2000-01-02 -1.478652  -1.824863  0.001416  -1.067864\\n\",\n       \"2    2000-01-03 -3.070727  -1.655967 -0.551411  -2.188238\\n\",\n       \"3    2000-01-04 -3.317105  -1.810778  0.354000  -1.995760\\n\",\n       \"4    2000-01-05 -2.822308  -2.757077  1.210057  -1.250592\\n\",\n       \"5    2000-01-06 -2.223865  -3.256931  3.658672  -1.232510\\n\",\n       \"6    2000-01-07 -0.559411  -2.842820  4.860993  -0.418853\\n\",\n       \"7    2000-01-08 -0.524812  -2.748762  6.280814   0.170616\\n\",\n       \"8    2000-01-09  0.204528  -0.865022  5.560456   0.875871\\n\",\n       \"9    2000-01-10  0.688895  -1.704024  5.226062   0.444483\\n\",\n       \"10   2000-01-11  1.272138  -2.478377  2.763524  -1.229797\\n\",\n       \"11   2000-01-12  1.255657  -2.994073  2.869688   0.745504\\n\",\n       \"12   2000-01-13 -0.001803  -2.295823  2.738087   2.640307\\n\",\n       \"13   2000-01-14  0.144209  -0.926810  4.087308   0.164138\\n\",\n       \"14   2000-01-15  1.304811   0.625530  3.542700  -1.499446\\n\",\n       \"15   2000-01-16  0.682026   0.552441  3.201389  -0.155674\\n\",\n       \"16   2000-01-17 -1.065321  -0.339092  3.792634  -1.754946\\n\",\n       \"17   2000-01-18 -1.268418  -3.512773  3.656896  -0.132124\\n\",\n       \"18   2000-01-19 -1.936390  -1.069188  2.554887   0.413930\\n\",\n       \"19   2000-01-20 -1.138678  -2.993870  3.094198  -0.956574\\n\",\n       \"20   2000-01-21 -0.096721  -4.141108  3.341248  -0.436886\\n\",\n       \"21   2000-01-22 -0.678763  -5.012663  0.823576   1.303377\\n\",\n       \"22   2000-01-23 -2.498525  -6.075938  0.302908  -0.294204\\n\",\n       \"23   2000-01-24 -0.937785  -6.768487 -0.078521  -0.165799\\n\",\n       \"24   2000-01-25 -0.252338  -6.882860  0.545684  -1.108641\\n\",\n       \"25   2000-01-26 -0.772653  -7.080129  0.764647  -3.041450\\n\",\n       \"26   2000-01-27 -0.348831  -6.035375  0.507673  -2.522836\\n\",\n       \"27   2000-01-28  1.751721  -5.756738 -0.469014  -2.825118\\n\",\n       \"28   2000-01-29  0.799938  -4.559172 -0.086763  -3.243101\\n\",\n       \"29   2000-01-30  1.149650  -4.433331  1.476689  -2.479956\\n\",\n       \"..          ...       ...        ...       ...        ...\\n\",\n       \"970  2002-08-28  0.466756  38.780786 -6.743120 -15.206051\\n\",\n       \"971  2002-08-29  1.903199  36.687258 -5.720742 -14.335689\\n\",\n       \"972  2002-08-30  1.432801  37.186065 -6.274877 -14.879621\\n\",\n       \"973  2002-08-31  2.218021  36.200395 -5.638178 -15.625389\\n\",\n       \"974  2002-09-01  3.405318  36.987365 -4.441912 -14.162031\\n\",\n       \"975  2002-09-02  3.854252  38.283545 -4.845949 -15.449302\\n\",\n       \"976  2002-09-03  4.328033  37.123804 -6.006344 -14.158929\\n\",\n       \"977  2002-09-04  5.219103  37.203486 -6.035182 -14.461389\\n\",\n       \"978  2002-09-05  4.884547  38.192836 -6.447570 -13.541204\\n\",\n       \"979  2002-09-06  5.817435  37.669998 -5.983732 -12.238303\\n\",\n       \"980  2002-09-07  7.953334  37.503576 -4.783659 -12.092902\\n\",\n       \"981  2002-09-08  8.575181  37.377622 -3.154240 -12.895208\\n\",\n       \"982  2002-09-09  8.242240  38.155500 -3.285132 -13.483775\\n\",\n       \"983  2002-09-10  9.699693  37.421024 -1.763105 -13.397276\\n\",\n       \"984  2002-09-11  7.228027  38.070563 -1.250151 -14.455654\\n\",\n       \"985  2002-09-12  7.276336  38.503057 -1.110047 -15.362900\\n\",\n       \"986  2002-09-13  6.955453  37.038613 -0.571942 -14.527590\\n\",\n       \"987  2002-09-14  6.232800  37.254276 -1.893050 -14.385401\\n\",\n       \"988  2002-09-15  5.331802  36.671504 -1.782282 -13.645305\\n\",\n       \"989  2002-09-16  5.825638  36.920733 -2.718137 -13.875984\\n\",\n       \"990  2002-09-17  5.972137  36.785577 -3.923763 -13.281418\\n\",\n       \"991  2002-09-18  5.154685  34.789858 -4.426825 -13.805227\\n\",\n       \"992  2002-09-19  4.228035  34.400350 -4.431980 -14.520423\\n\",\n       \"993  2002-09-20  3.047640  36.244469 -5.093584 -15.106412\\n\",\n       \"994  2002-09-21  2.984361  38.095349 -5.560222 -16.584216\\n\",\n       \"995  2002-09-22  3.187098  37.169742 -6.132250 -14.910771\\n\",\n       \"996  2002-09-23  1.772470  36.399488 -6.257600 -13.668261\\n\",\n       \"997  2002-09-24  1.886272  34.543307 -6.644915 -15.213739\\n\",\n       \"998  2002-09-25  2.918438  33.722114 -5.736130 -14.919711\\n\",\n       \"999  2002-09-26  2.608268  33.473087 -5.111581 -15.969800\\n\",\n       \"\\n\",\n       \"[1000 rows x 5 columns]\"\n      ]\n     },\n     \"execution_count\": 102,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pd.read_csv('data/foo.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## HDF5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"HDFStores的读写\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"写入一个HDF5 Store\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df.to_hdf('data/foo.h5', 'df')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"从一个HDF5 Store读入\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 104,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-01</th>\\n\",\n       \"      <td>-0.547915</td>\\n\",\n       \"      <td>0.758504</td>\\n\",\n       \"      <td>-0.791553</td>\\n\",\n       \"      <td>-0.014276</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-02</th>\\n\",\n       \"      <td>-1.478652</td>\\n\",\n       \"      <td>-1.824863</td>\\n\",\n       \"      <td>0.001416</td>\\n\",\n       \"      <td>-1.067864</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-03</th>\\n\",\n       \"      <td>-3.070727</td>\\n\",\n       \"      <td>-1.655967</td>\\n\",\n       \"      <td>-0.551411</td>\\n\",\n       \"      <td>-2.188238</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-04</th>\\n\",\n       \"      <td>-3.317105</td>\\n\",\n       \"      <td>-1.810778</td>\\n\",\n       \"      <td>0.354000</td>\\n\",\n       \"      <td>-1.995760</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-05</th>\\n\",\n       \"      <td>-2.822308</td>\\n\",\n       \"      <td>-2.757077</td>\\n\",\n       \"      <td>1.210057</td>\\n\",\n       \"      <td>-1.250592</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-06</th>\\n\",\n       \"      <td>-2.223865</td>\\n\",\n       \"      <td>-3.256931</td>\\n\",\n       \"      <td>3.658672</td>\\n\",\n       \"      <td>-1.232510</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-07</th>\\n\",\n       \"      <td>-0.559411</td>\\n\",\n       \"      <td>-2.842820</td>\\n\",\n       \"      <td>4.860993</td>\\n\",\n       \"      <td>-0.418853</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-08</th>\\n\",\n       \"      <td>-0.524812</td>\\n\",\n       \"      <td>-2.748762</td>\\n\",\n       \"      <td>6.280814</td>\\n\",\n       \"      <td>0.170616</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-09</th>\\n\",\n       \"      <td>0.204528</td>\\n\",\n       \"      <td>-0.865022</td>\\n\",\n       \"      <td>5.560456</td>\\n\",\n       \"      <td>0.875871</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-10</th>\\n\",\n       \"      <td>0.688895</td>\\n\",\n       \"      <td>-1.704024</td>\\n\",\n       \"      <td>5.226062</td>\\n\",\n       \"      <td>0.444483</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-11</th>\\n\",\n       \"      <td>1.272138</td>\\n\",\n       \"      <td>-2.478377</td>\\n\",\n       \"      <td>2.763524</td>\\n\",\n       \"      <td>-1.229797</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-12</th>\\n\",\n       \"      <td>1.255657</td>\\n\",\n       \"      <td>-2.994073</td>\\n\",\n       \"      <td>2.869688</td>\\n\",\n       \"      <td>0.745504</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-13</th>\\n\",\n       \"      <td>-0.001803</td>\\n\",\n       \"      <td>-2.295823</td>\\n\",\n       \"      <td>2.738087</td>\\n\",\n       \"      <td>2.640307</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-14</th>\\n\",\n       \"      <td>0.144209</td>\\n\",\n       \"      <td>-0.926810</td>\\n\",\n       \"      <td>4.087308</td>\\n\",\n       \"      <td>0.164138</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-15</th>\\n\",\n       \"      <td>1.304811</td>\\n\",\n       \"      <td>0.625530</td>\\n\",\n       \"      <td>3.542700</td>\\n\",\n       \"      <td>-1.499446</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-16</th>\\n\",\n       \"      <td>0.682026</td>\\n\",\n       \"      <td>0.552441</td>\\n\",\n       \"      <td>3.201389</td>\\n\",\n       \"      <td>-0.155674</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-17</th>\\n\",\n       \"      <td>-1.065321</td>\\n\",\n       \"      <td>-0.339092</td>\\n\",\n       \"      <td>3.792634</td>\\n\",\n       \"      <td>-1.754946</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-18</th>\\n\",\n       \"      <td>-1.268418</td>\\n\",\n       \"      <td>-3.512773</td>\\n\",\n       \"      <td>3.656896</td>\\n\",\n       \"      <td>-0.132124</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-19</th>\\n\",\n       \"      <td>-1.936390</td>\\n\",\n       \"      <td>-1.069188</td>\\n\",\n       \"      <td>2.554887</td>\\n\",\n       \"      <td>0.413930</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-20</th>\\n\",\n       \"      <td>-1.138678</td>\\n\",\n       \"      <td>-2.993870</td>\\n\",\n       \"      <td>3.094198</td>\\n\",\n       \"      <td>-0.956574</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-21</th>\\n\",\n       \"      <td>-0.096721</td>\\n\",\n       \"      <td>-4.141108</td>\\n\",\n       \"      <td>3.341248</td>\\n\",\n       \"      <td>-0.436886</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-22</th>\\n\",\n       \"      <td>-0.678763</td>\\n\",\n       \"      <td>-5.012663</td>\\n\",\n       \"      <td>0.823576</td>\\n\",\n       \"      <td>1.303377</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-23</th>\\n\",\n       \"      <td>-2.498525</td>\\n\",\n       \"      <td>-6.075938</td>\\n\",\n       \"      <td>0.302908</td>\\n\",\n       \"      <td>-0.294204</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-24</th>\\n\",\n       \"      <td>-0.937785</td>\\n\",\n       \"      <td>-6.768487</td>\\n\",\n       \"      <td>-0.078521</td>\\n\",\n       \"      <td>-0.165799</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-25</th>\\n\",\n       \"      <td>-0.252338</td>\\n\",\n       \"      <td>-6.882860</td>\\n\",\n       \"      <td>0.545684</td>\\n\",\n       \"      <td>-1.108641</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-26</th>\\n\",\n       \"      <td>-0.772653</td>\\n\",\n       \"      <td>-7.080129</td>\\n\",\n       \"      <td>0.764647</td>\\n\",\n       \"      <td>-3.041450</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-27</th>\\n\",\n       \"      <td>-0.348831</td>\\n\",\n       \"      <td>-6.035375</td>\\n\",\n       \"      <td>0.507673</td>\\n\",\n       \"      <td>-2.522836</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-28</th>\\n\",\n       \"      <td>1.751721</td>\\n\",\n       \"      <td>-5.756738</td>\\n\",\n       \"      <td>-0.469014</td>\\n\",\n       \"      <td>-2.825118</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-29</th>\\n\",\n       \"      <td>0.799938</td>\\n\",\n       \"      <td>-4.559172</td>\\n\",\n       \"      <td>-0.086763</td>\\n\",\n       \"      <td>-3.243101</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-30</th>\\n\",\n       \"      <td>1.149650</td>\\n\",\n       \"      <td>-4.433331</td>\\n\",\n       \"      <td>1.476689</td>\\n\",\n       \"      <td>-2.479956</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-08-28</th>\\n\",\n       \"      <td>0.466756</td>\\n\",\n       \"      <td>38.780786</td>\\n\",\n       \"      <td>-6.743120</td>\\n\",\n       \"      <td>-15.206051</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-08-29</th>\\n\",\n       \"      <td>1.903199</td>\\n\",\n       \"      <td>36.687258</td>\\n\",\n       \"      <td>-5.720742</td>\\n\",\n       \"      <td>-14.335689</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-08-30</th>\\n\",\n       \"      <td>1.432801</td>\\n\",\n       \"      <td>37.186065</td>\\n\",\n       \"      <td>-6.274877</td>\\n\",\n       \"      <td>-14.879621</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-08-31</th>\\n\",\n       \"      <td>2.218021</td>\\n\",\n       \"      <td>36.200395</td>\\n\",\n       \"      <td>-5.638178</td>\\n\",\n       \"      <td>-15.625389</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-01</th>\\n\",\n       \"      <td>3.405318</td>\\n\",\n       \"      <td>36.987365</td>\\n\",\n       \"      <td>-4.441912</td>\\n\",\n       \"      <td>-14.162031</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-02</th>\\n\",\n       \"      <td>3.854252</td>\\n\",\n       \"      <td>38.283545</td>\\n\",\n       \"      <td>-4.845949</td>\\n\",\n       \"      <td>-15.449302</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-03</th>\\n\",\n       \"      <td>4.328033</td>\\n\",\n       \"      <td>37.123804</td>\\n\",\n       \"      <td>-6.006344</td>\\n\",\n       \"      <td>-14.158929</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-04</th>\\n\",\n       \"      <td>5.219103</td>\\n\",\n       \"      <td>37.203486</td>\\n\",\n       \"      <td>-6.035182</td>\\n\",\n       \"      <td>-14.461389</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-05</th>\\n\",\n       \"      <td>4.884547</td>\\n\",\n       \"      <td>38.192836</td>\\n\",\n       \"      <td>-6.447570</td>\\n\",\n       \"      <td>-13.541204</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-06</th>\\n\",\n       \"      <td>5.817435</td>\\n\",\n       \"      <td>37.669998</td>\\n\",\n       \"      <td>-5.983732</td>\\n\",\n       \"      <td>-12.238303</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-07</th>\\n\",\n       \"      <td>7.953334</td>\\n\",\n       \"      <td>37.503576</td>\\n\",\n       \"      <td>-4.783659</td>\\n\",\n       \"      <td>-12.092902</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-08</th>\\n\",\n       \"      <td>8.575181</td>\\n\",\n       \"      <td>37.377622</td>\\n\",\n       \"      <td>-3.154240</td>\\n\",\n       \"      <td>-12.895208</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-09</th>\\n\",\n       \"      <td>8.242240</td>\\n\",\n       \"      <td>38.155500</td>\\n\",\n       \"      <td>-3.285132</td>\\n\",\n       \"      <td>-13.483775</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-10</th>\\n\",\n       \"      <td>9.699693</td>\\n\",\n       \"      <td>37.421024</td>\\n\",\n       \"      <td>-1.763105</td>\\n\",\n       \"      <td>-13.397276</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-11</th>\\n\",\n       \"      <td>7.228027</td>\\n\",\n       \"      <td>38.070563</td>\\n\",\n       \"      <td>-1.250151</td>\\n\",\n       \"      <td>-14.455654</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-12</th>\\n\",\n       \"      <td>7.276336</td>\\n\",\n       \"      <td>38.503057</td>\\n\",\n       \"      <td>-1.110047</td>\\n\",\n       \"      <td>-15.362900</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-13</th>\\n\",\n       \"      <td>6.955453</td>\\n\",\n       \"      <td>37.038613</td>\\n\",\n       \"      <td>-0.571942</td>\\n\",\n       \"      <td>-14.527590</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-14</th>\\n\",\n       \"      <td>6.232800</td>\\n\",\n       \"      <td>37.254276</td>\\n\",\n       \"      <td>-1.893050</td>\\n\",\n       \"      <td>-14.385401</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-15</th>\\n\",\n       \"      <td>5.331802</td>\\n\",\n       \"      <td>36.671504</td>\\n\",\n       \"      <td>-1.782282</td>\\n\",\n       \"      <td>-13.645305</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-16</th>\\n\",\n       \"      <td>5.825638</td>\\n\",\n       \"      <td>36.920733</td>\\n\",\n       \"      <td>-2.718137</td>\\n\",\n       \"      <td>-13.875984</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-17</th>\\n\",\n       \"      <td>5.972137</td>\\n\",\n       \"      <td>36.785577</td>\\n\",\n       \"      <td>-3.923763</td>\\n\",\n       \"      <td>-13.281418</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-18</th>\\n\",\n       \"      <td>5.154685</td>\\n\",\n       \"      <td>34.789858</td>\\n\",\n       \"      <td>-4.426825</td>\\n\",\n       \"      <td>-13.805227</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-19</th>\\n\",\n       \"      <td>4.228035</td>\\n\",\n       \"      <td>34.400350</td>\\n\",\n       \"      <td>-4.431980</td>\\n\",\n       \"      <td>-14.520423</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-20</th>\\n\",\n       \"      <td>3.047640</td>\\n\",\n       \"      <td>36.244469</td>\\n\",\n       \"      <td>-5.093584</td>\\n\",\n       \"      <td>-15.106412</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-21</th>\\n\",\n       \"      <td>2.984361</td>\\n\",\n       \"      <td>38.095349</td>\\n\",\n       \"      <td>-5.560222</td>\\n\",\n       \"      <td>-16.584216</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-22</th>\\n\",\n       \"      <td>3.187098</td>\\n\",\n       \"      <td>37.169742</td>\\n\",\n       \"      <td>-6.132250</td>\\n\",\n       \"      <td>-14.910771</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-23</th>\\n\",\n       \"      <td>1.772470</td>\\n\",\n       \"      <td>36.399488</td>\\n\",\n       \"      <td>-6.257600</td>\\n\",\n       \"      <td>-13.668261</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-24</th>\\n\",\n       \"      <td>1.886272</td>\\n\",\n       \"      <td>34.543307</td>\\n\",\n       \"      <td>-6.644915</td>\\n\",\n       \"      <td>-15.213739</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-25</th>\\n\",\n       \"      <td>2.918438</td>\\n\",\n       \"      <td>33.722114</td>\\n\",\n       \"      <td>-5.736130</td>\\n\",\n       \"      <td>-14.919711</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-26</th>\\n\",\n       \"      <td>2.608268</td>\\n\",\n       \"      <td>33.473087</td>\\n\",\n       \"      <td>-5.111581</td>\\n\",\n       \"      <td>-15.969800</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>1000 rows × 4 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A          B         C          D\\n\",\n       \"2000-01-01 -0.547915   0.758504 -0.791553  -0.014276\\n\",\n       \"2000-01-02 -1.478652  -1.824863  0.001416  -1.067864\\n\",\n       \"2000-01-03 -3.070727  -1.655967 -0.551411  -2.188238\\n\",\n       \"2000-01-04 -3.317105  -1.810778  0.354000  -1.995760\\n\",\n       \"2000-01-05 -2.822308  -2.757077  1.210057  -1.250592\\n\",\n       \"2000-01-06 -2.223865  -3.256931  3.658672  -1.232510\\n\",\n       \"2000-01-07 -0.559411  -2.842820  4.860993  -0.418853\\n\",\n       \"2000-01-08 -0.524812  -2.748762  6.280814   0.170616\\n\",\n       \"2000-01-09  0.204528  -0.865022  5.560456   0.875871\\n\",\n       \"2000-01-10  0.688895  -1.704024  5.226062   0.444483\\n\",\n       \"2000-01-11  1.272138  -2.478377  2.763524  -1.229797\\n\",\n       \"2000-01-12  1.255657  -2.994073  2.869688   0.745504\\n\",\n       \"2000-01-13 -0.001803  -2.295823  2.738087   2.640307\\n\",\n       \"2000-01-14  0.144209  -0.926810  4.087308   0.164138\\n\",\n       \"2000-01-15  1.304811   0.625530  3.542700  -1.499446\\n\",\n       \"2000-01-16  0.682026   0.552441  3.201389  -0.155674\\n\",\n       \"2000-01-17 -1.065321  -0.339092  3.792634  -1.754946\\n\",\n       \"2000-01-18 -1.268418  -3.512773  3.656896  -0.132124\\n\",\n       \"2000-01-19 -1.936390  -1.069188  2.554887   0.413930\\n\",\n       \"2000-01-20 -1.138678  -2.993870  3.094198  -0.956574\\n\",\n       \"2000-01-21 -0.096721  -4.141108  3.341248  -0.436886\\n\",\n       \"2000-01-22 -0.678763  -5.012663  0.823576   1.303377\\n\",\n       \"2000-01-23 -2.498525  -6.075938  0.302908  -0.294204\\n\",\n       \"2000-01-24 -0.937785  -6.768487 -0.078521  -0.165799\\n\",\n       \"2000-01-25 -0.252338  -6.882860  0.545684  -1.108641\\n\",\n       \"2000-01-26 -0.772653  -7.080129  0.764647  -3.041450\\n\",\n       \"2000-01-27 -0.348831  -6.035375  0.507673  -2.522836\\n\",\n       \"2000-01-28  1.751721  -5.756738 -0.469014  -2.825118\\n\",\n       \"2000-01-29  0.799938  -4.559172 -0.086763  -3.243101\\n\",\n       \"2000-01-30  1.149650  -4.433331  1.476689  -2.479956\\n\",\n       \"...              ...        ...       ...        ...\\n\",\n       \"2002-08-28  0.466756  38.780786 -6.743120 -15.206051\\n\",\n       \"2002-08-29  1.903199  36.687258 -5.720742 -14.335689\\n\",\n       \"2002-08-30  1.432801  37.186065 -6.274877 -14.879621\\n\",\n       \"2002-08-31  2.218021  36.200395 -5.638178 -15.625389\\n\",\n       \"2002-09-01  3.405318  36.987365 -4.441912 -14.162031\\n\",\n       \"2002-09-02  3.854252  38.283545 -4.845949 -15.449302\\n\",\n       \"2002-09-03  4.328033  37.123804 -6.006344 -14.158929\\n\",\n       \"2002-09-04  5.219103  37.203486 -6.035182 -14.461389\\n\",\n       \"2002-09-05  4.884547  38.192836 -6.447570 -13.541204\\n\",\n       \"2002-09-06  5.817435  37.669998 -5.983732 -12.238303\\n\",\n       \"2002-09-07  7.953334  37.503576 -4.783659 -12.092902\\n\",\n       \"2002-09-08  8.575181  37.377622 -3.154240 -12.895208\\n\",\n       \"2002-09-09  8.242240  38.155500 -3.285132 -13.483775\\n\",\n       \"2002-09-10  9.699693  37.421024 -1.763105 -13.397276\\n\",\n       \"2002-09-11  7.228027  38.070563 -1.250151 -14.455654\\n\",\n       \"2002-09-12  7.276336  38.503057 -1.110047 -15.362900\\n\",\n       \"2002-09-13  6.955453  37.038613 -0.571942 -14.527590\\n\",\n       \"2002-09-14  6.232800  37.254276 -1.893050 -14.385401\\n\",\n       \"2002-09-15  5.331802  36.671504 -1.782282 -13.645305\\n\",\n       \"2002-09-16  5.825638  36.920733 -2.718137 -13.875984\\n\",\n       \"2002-09-17  5.972137  36.785577 -3.923763 -13.281418\\n\",\n       \"2002-09-18  5.154685  34.789858 -4.426825 -13.805227\\n\",\n       \"2002-09-19  4.228035  34.400350 -4.431980 -14.520423\\n\",\n       \"2002-09-20  3.047640  36.244469 -5.093584 -15.106412\\n\",\n       \"2002-09-21  2.984361  38.095349 -5.560222 -16.584216\\n\",\n       \"2002-09-22  3.187098  37.169742 -6.132250 -14.910771\\n\",\n       \"2002-09-23  1.772470  36.399488 -6.257600 -13.668261\\n\",\n       \"2002-09-24  1.886272  34.543307 -6.644915 -15.213739\\n\",\n       \"2002-09-25  2.918438  33.722114 -5.736130 -14.919711\\n\",\n       \"2002-09-26  2.608268  33.473087 -5.111581 -15.969800\\n\",\n       \"\\n\",\n       \"[1000 rows x 4 columns]\"\n      ]\n     },\n     \"execution_count\": 104,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pd.read_hdf('data/foo.h5', 'df')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Excel\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"MS Excel的读写\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"写入一个Excel文件\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df.to_excel('data/foo.xlsx', sheet_name='Sheet1')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"从一个excel文件读入\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-01</th>\\n\",\n       \"      <td>-0.547915</td>\\n\",\n       \"      <td>0.758504</td>\\n\",\n       \"      <td>-0.791553</td>\\n\",\n       \"      <td>-0.014276</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-02</th>\\n\",\n       \"      <td>-1.478652</td>\\n\",\n       \"      <td>-1.824863</td>\\n\",\n       \"      <td>0.001416</td>\\n\",\n       \"      <td>-1.067864</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-03</th>\\n\",\n       \"      <td>-3.070727</td>\\n\",\n       \"      <td>-1.655967</td>\\n\",\n       \"      <td>-0.551411</td>\\n\",\n       \"      <td>-2.188238</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-04</th>\\n\",\n       \"      <td>-3.317105</td>\\n\",\n       \"      <td>-1.810778</td>\\n\",\n       \"      <td>0.354000</td>\\n\",\n       \"      <td>-1.995760</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-05</th>\\n\",\n       \"      <td>-2.822308</td>\\n\",\n       \"      <td>-2.757077</td>\\n\",\n       \"      <td>1.210057</td>\\n\",\n       \"      <td>-1.250592</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-06</th>\\n\",\n       \"      <td>-2.223865</td>\\n\",\n       \"      <td>-3.256931</td>\\n\",\n       \"      <td>3.658672</td>\\n\",\n       \"      <td>-1.232510</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-07</th>\\n\",\n       \"      <td>-0.559411</td>\\n\",\n       \"      <td>-2.842820</td>\\n\",\n       \"      <td>4.860993</td>\\n\",\n       \"      <td>-0.418853</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-08</th>\\n\",\n       \"      <td>-0.524812</td>\\n\",\n       \"      <td>-2.748762</td>\\n\",\n       \"      <td>6.280814</td>\\n\",\n       \"      <td>0.170616</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-09</th>\\n\",\n       \"      <td>0.204528</td>\\n\",\n       \"      <td>-0.865022</td>\\n\",\n       \"      <td>5.560456</td>\\n\",\n       \"      <td>0.875871</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-10</th>\\n\",\n       \"      <td>0.688895</td>\\n\",\n       \"      <td>-1.704024</td>\\n\",\n       \"      <td>5.226062</td>\\n\",\n       \"      <td>0.444483</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-11</th>\\n\",\n       \"      <td>1.272138</td>\\n\",\n       \"      <td>-2.478377</td>\\n\",\n       \"      <td>2.763524</td>\\n\",\n       \"      <td>-1.229797</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-12</th>\\n\",\n       \"      <td>1.255657</td>\\n\",\n       \"      <td>-2.994073</td>\\n\",\n       \"      <td>2.869688</td>\\n\",\n       \"      <td>0.745504</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-13</th>\\n\",\n       \"      <td>-0.001803</td>\\n\",\n       \"      <td>-2.295823</td>\\n\",\n       \"      <td>2.738087</td>\\n\",\n       \"      <td>2.640307</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-14</th>\\n\",\n       \"      <td>0.144209</td>\\n\",\n       \"      <td>-0.926810</td>\\n\",\n       \"      <td>4.087308</td>\\n\",\n       \"      <td>0.164138</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-15</th>\\n\",\n       \"      <td>1.304811</td>\\n\",\n       \"      <td>0.625530</td>\\n\",\n       \"      <td>3.542700</td>\\n\",\n       \"      <td>-1.499446</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-16</th>\\n\",\n       \"      <td>0.682026</td>\\n\",\n       \"      <td>0.552441</td>\\n\",\n       \"      <td>3.201389</td>\\n\",\n       \"      <td>-0.155674</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-17</th>\\n\",\n       \"      <td>-1.065321</td>\\n\",\n       \"      <td>-0.339092</td>\\n\",\n       \"      <td>3.792634</td>\\n\",\n       \"      <td>-1.754946</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-18</th>\\n\",\n       \"      <td>-1.268418</td>\\n\",\n       \"      <td>-3.512773</td>\\n\",\n       \"      <td>3.656896</td>\\n\",\n       \"      <td>-0.132124</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-19</th>\\n\",\n       \"      <td>-1.936390</td>\\n\",\n       \"      <td>-1.069188</td>\\n\",\n       \"      <td>2.554887</td>\\n\",\n       \"      <td>0.413930</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-20</th>\\n\",\n       \"      <td>-1.138678</td>\\n\",\n       \"      <td>-2.993870</td>\\n\",\n       \"      <td>3.094198</td>\\n\",\n       \"      <td>-0.956574</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-21</th>\\n\",\n       \"      <td>-0.096721</td>\\n\",\n       \"      <td>-4.141108</td>\\n\",\n       \"      <td>3.341248</td>\\n\",\n       \"      <td>-0.436886</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-22</th>\\n\",\n       \"      <td>-0.678763</td>\\n\",\n       \"      <td>-5.012663</td>\\n\",\n       \"      <td>0.823576</td>\\n\",\n       \"      <td>1.303377</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-23</th>\\n\",\n       \"      <td>-2.498525</td>\\n\",\n       \"      <td>-6.075938</td>\\n\",\n       \"      <td>0.302908</td>\\n\",\n       \"      <td>-0.294204</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-24</th>\\n\",\n       \"      <td>-0.937785</td>\\n\",\n       \"      <td>-6.768487</td>\\n\",\n       \"      <td>-0.078521</td>\\n\",\n       \"      <td>-0.165799</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-25</th>\\n\",\n       \"      <td>-0.252338</td>\\n\",\n       \"      <td>-6.882860</td>\\n\",\n       \"      <td>0.545684</td>\\n\",\n       \"      <td>-1.108641</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-26</th>\\n\",\n       \"      <td>-0.772653</td>\\n\",\n       \"      <td>-7.080129</td>\\n\",\n       \"      <td>0.764647</td>\\n\",\n       \"      <td>-3.041450</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-27</th>\\n\",\n       \"      <td>-0.348831</td>\\n\",\n       \"      <td>-6.035375</td>\\n\",\n       \"      <td>0.507673</td>\\n\",\n       \"      <td>-2.522836</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-28</th>\\n\",\n       \"      <td>1.751721</td>\\n\",\n       \"      <td>-5.756738</td>\\n\",\n       \"      <td>-0.469014</td>\\n\",\n       \"      <td>-2.825118</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-29</th>\\n\",\n       \"      <td>0.799938</td>\\n\",\n       \"      <td>-4.559172</td>\\n\",\n       \"      <td>-0.086763</td>\\n\",\n       \"      <td>-3.243101</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2000-01-30</th>\\n\",\n       \"      <td>1.149650</td>\\n\",\n       \"      <td>-4.433331</td>\\n\",\n       \"      <td>1.476689</td>\\n\",\n       \"      <td>-2.479956</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-08-28</th>\\n\",\n       \"      <td>0.466756</td>\\n\",\n       \"      <td>38.780786</td>\\n\",\n       \"      <td>-6.743120</td>\\n\",\n       \"      <td>-15.206051</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-08-29</th>\\n\",\n       \"      <td>1.903199</td>\\n\",\n       \"      <td>36.687258</td>\\n\",\n       \"      <td>-5.720742</td>\\n\",\n       \"      <td>-14.335689</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-08-30</th>\\n\",\n       \"      <td>1.432801</td>\\n\",\n       \"      <td>37.186065</td>\\n\",\n       \"      <td>-6.274877</td>\\n\",\n       \"      <td>-14.879621</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-08-31</th>\\n\",\n       \"      <td>2.218021</td>\\n\",\n       \"      <td>36.200395</td>\\n\",\n       \"      <td>-5.638178</td>\\n\",\n       \"      <td>-15.625389</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-01</th>\\n\",\n       \"      <td>3.405318</td>\\n\",\n       \"      <td>36.987365</td>\\n\",\n       \"      <td>-4.441912</td>\\n\",\n       \"      <td>-14.162031</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-02</th>\\n\",\n       \"      <td>3.854252</td>\\n\",\n       \"      <td>38.283545</td>\\n\",\n       \"      <td>-4.845949</td>\\n\",\n       \"      <td>-15.449302</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-03</th>\\n\",\n       \"      <td>4.328033</td>\\n\",\n       \"      <td>37.123804</td>\\n\",\n       \"      <td>-6.006344</td>\\n\",\n       \"      <td>-14.158929</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-04</th>\\n\",\n       \"      <td>5.219103</td>\\n\",\n       \"      <td>37.203486</td>\\n\",\n       \"      <td>-6.035182</td>\\n\",\n       \"      <td>-14.461389</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-05</th>\\n\",\n       \"      <td>4.884547</td>\\n\",\n       \"      <td>38.192836</td>\\n\",\n       \"      <td>-6.447570</td>\\n\",\n       \"      <td>-13.541204</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-06</th>\\n\",\n       \"      <td>5.817435</td>\\n\",\n       \"      <td>37.669998</td>\\n\",\n       \"      <td>-5.983732</td>\\n\",\n       \"      <td>-12.238303</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-07</th>\\n\",\n       \"      <td>7.953334</td>\\n\",\n       \"      <td>37.503576</td>\\n\",\n       \"      <td>-4.783659</td>\\n\",\n       \"      <td>-12.092902</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-08</th>\\n\",\n       \"      <td>8.575181</td>\\n\",\n       \"      <td>37.377622</td>\\n\",\n       \"      <td>-3.154240</td>\\n\",\n       \"      <td>-12.895208</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-09</th>\\n\",\n       \"      <td>8.242240</td>\\n\",\n       \"      <td>38.155500</td>\\n\",\n       \"      <td>-3.285132</td>\\n\",\n       \"      <td>-13.483775</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-10</th>\\n\",\n       \"      <td>9.699693</td>\\n\",\n       \"      <td>37.421024</td>\\n\",\n       \"      <td>-1.763105</td>\\n\",\n       \"      <td>-13.397276</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-11</th>\\n\",\n       \"      <td>7.228027</td>\\n\",\n       \"      <td>38.070563</td>\\n\",\n       \"      <td>-1.250151</td>\\n\",\n       \"      <td>-14.455654</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-12</th>\\n\",\n       \"      <td>7.276336</td>\\n\",\n       \"      <td>38.503057</td>\\n\",\n       \"      <td>-1.110047</td>\\n\",\n       \"      <td>-15.362900</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-13</th>\\n\",\n       \"      <td>6.955453</td>\\n\",\n       \"      <td>37.038613</td>\\n\",\n       \"      <td>-0.571942</td>\\n\",\n       \"      <td>-14.527590</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-14</th>\\n\",\n       \"      <td>6.232800</td>\\n\",\n       \"      <td>37.254276</td>\\n\",\n       \"      <td>-1.893050</td>\\n\",\n       \"      <td>-14.385401</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-15</th>\\n\",\n       \"      <td>5.331802</td>\\n\",\n       \"      <td>36.671504</td>\\n\",\n       \"      <td>-1.782282</td>\\n\",\n       \"      <td>-13.645305</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-16</th>\\n\",\n       \"      <td>5.825638</td>\\n\",\n       \"      <td>36.920733</td>\\n\",\n       \"      <td>-2.718137</td>\\n\",\n       \"      <td>-13.875984</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-17</th>\\n\",\n       \"      <td>5.972137</td>\\n\",\n       \"      <td>36.785577</td>\\n\",\n       \"      <td>-3.923763</td>\\n\",\n       \"      <td>-13.281418</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-18</th>\\n\",\n       \"      <td>5.154685</td>\\n\",\n       \"      <td>34.789858</td>\\n\",\n       \"      <td>-4.426825</td>\\n\",\n       \"      <td>-13.805227</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-19</th>\\n\",\n       \"      <td>4.228035</td>\\n\",\n       \"      <td>34.400350</td>\\n\",\n       \"      <td>-4.431980</td>\\n\",\n       \"      <td>-14.520423</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-20</th>\\n\",\n       \"      <td>3.047640</td>\\n\",\n       \"      <td>36.244469</td>\\n\",\n       \"      <td>-5.093584</td>\\n\",\n       \"      <td>-15.106412</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-21</th>\\n\",\n       \"      <td>2.984361</td>\\n\",\n       \"      <td>38.095349</td>\\n\",\n       \"      <td>-5.560222</td>\\n\",\n       \"      <td>-16.584216</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-22</th>\\n\",\n       \"      <td>3.187098</td>\\n\",\n       \"      <td>37.169742</td>\\n\",\n       \"      <td>-6.132250</td>\\n\",\n       \"      <td>-14.910771</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-23</th>\\n\",\n       \"      <td>1.772470</td>\\n\",\n       \"      <td>36.399488</td>\\n\",\n       \"      <td>-6.257600</td>\\n\",\n       \"      <td>-13.668261</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-24</th>\\n\",\n       \"      <td>1.886272</td>\\n\",\n       \"      <td>34.543307</td>\\n\",\n       \"      <td>-6.644915</td>\\n\",\n       \"      <td>-15.213739</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-25</th>\\n\",\n       \"      <td>2.918438</td>\\n\",\n       \"      <td>33.722114</td>\\n\",\n       \"      <td>-5.736130</td>\\n\",\n       \"      <td>-14.919711</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2002-09-26</th>\\n\",\n       \"      <td>2.608268</td>\\n\",\n       \"      <td>33.473087</td>\\n\",\n       \"      <td>-5.111581</td>\\n\",\n       \"      <td>-15.969800</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>1000 rows × 4 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   A          B         C          D\\n\",\n       \"2000-01-01 -0.547915   0.758504 -0.791553  -0.014276\\n\",\n       \"2000-01-02 -1.478652  -1.824863  0.001416  -1.067864\\n\",\n       \"2000-01-03 -3.070727  -1.655967 -0.551411  -2.188238\\n\",\n       \"2000-01-04 -3.317105  -1.810778  0.354000  -1.995760\\n\",\n       \"2000-01-05 -2.822308  -2.757077  1.210057  -1.250592\\n\",\n       \"2000-01-06 -2.223865  -3.256931  3.658672  -1.232510\\n\",\n       \"2000-01-07 -0.559411  -2.842820  4.860993  -0.418853\\n\",\n       \"2000-01-08 -0.524812  -2.748762  6.280814   0.170616\\n\",\n       \"2000-01-09  0.204528  -0.865022  5.560456   0.875871\\n\",\n       \"2000-01-10  0.688895  -1.704024  5.226062   0.444483\\n\",\n       \"2000-01-11  1.272138  -2.478377  2.763524  -1.229797\\n\",\n       \"2000-01-12  1.255657  -2.994073  2.869688   0.745504\\n\",\n       \"2000-01-13 -0.001803  -2.295823  2.738087   2.640307\\n\",\n       \"2000-01-14  0.144209  -0.926810  4.087308   0.164138\\n\",\n       \"2000-01-15  1.304811   0.625530  3.542700  -1.499446\\n\",\n       \"2000-01-16  0.682026   0.552441  3.201389  -0.155674\\n\",\n       \"2000-01-17 -1.065321  -0.339092  3.792634  -1.754946\\n\",\n       \"2000-01-18 -1.268418  -3.512773  3.656896  -0.132124\\n\",\n       \"2000-01-19 -1.936390  -1.069188  2.554887   0.413930\\n\",\n       \"2000-01-20 -1.138678  -2.993870  3.094198  -0.956574\\n\",\n       \"2000-01-21 -0.096721  -4.141108  3.341248  -0.436886\\n\",\n       \"2000-01-22 -0.678763  -5.012663  0.823576   1.303377\\n\",\n       \"2000-01-23 -2.498525  -6.075938  0.302908  -0.294204\\n\",\n       \"2000-01-24 -0.937785  -6.768487 -0.078521  -0.165799\\n\",\n       \"2000-01-25 -0.252338  -6.882860  0.545684  -1.108641\\n\",\n       \"2000-01-26 -0.772653  -7.080129  0.764647  -3.041450\\n\",\n       \"2000-01-27 -0.348831  -6.035375  0.507673  -2.522836\\n\",\n       \"2000-01-28  1.751721  -5.756738 -0.469014  -2.825118\\n\",\n       \"2000-01-29  0.799938  -4.559172 -0.086763  -3.243101\\n\",\n       \"2000-01-30  1.149650  -4.433331  1.476689  -2.479956\\n\",\n       \"...              ...        ...       ...        ...\\n\",\n       \"2002-08-28  0.466756  38.780786 -6.743120 -15.206051\\n\",\n       \"2002-08-29  1.903199  36.687258 -5.720742 -14.335689\\n\",\n       \"2002-08-30  1.432801  37.186065 -6.274877 -14.879621\\n\",\n       \"2002-08-31  2.218021  36.200395 -5.638178 -15.625389\\n\",\n       \"2002-09-01  3.405318  36.987365 -4.441912 -14.162031\\n\",\n       \"2002-09-02  3.854252  38.283545 -4.845949 -15.449302\\n\",\n       \"2002-09-03  4.328033  37.123804 -6.006344 -14.158929\\n\",\n       \"2002-09-04  5.219103  37.203486 -6.035182 -14.461389\\n\",\n       \"2002-09-05  4.884547  38.192836 -6.447570 -13.541204\\n\",\n       \"2002-09-06  5.817435  37.669998 -5.983732 -12.238303\\n\",\n       \"2002-09-07  7.953334  37.503576 -4.783659 -12.092902\\n\",\n       \"2002-09-08  8.575181  37.377622 -3.154240 -12.895208\\n\",\n       \"2002-09-09  8.242240  38.155500 -3.285132 -13.483775\\n\",\n       \"2002-09-10  9.699693  37.421024 -1.763105 -13.397276\\n\",\n       \"2002-09-11  7.228027  38.070563 -1.250151 -14.455654\\n\",\n       \"2002-09-12  7.276336  38.503057 -1.110047 -15.362900\\n\",\n       \"2002-09-13  6.955453  37.038613 -0.571942 -14.527590\\n\",\n       \"2002-09-14  6.232800  37.254276 -1.893050 -14.385401\\n\",\n       \"2002-09-15  5.331802  36.671504 -1.782282 -13.645305\\n\",\n       \"2002-09-16  5.825638  36.920733 -2.718137 -13.875984\\n\",\n       \"2002-09-17  5.972137  36.785577 -3.923763 -13.281418\\n\",\n       \"2002-09-18  5.154685  34.789858 -4.426825 -13.805227\\n\",\n       \"2002-09-19  4.228035  34.400350 -4.431980 -14.520423\\n\",\n       \"2002-09-20  3.047640  36.244469 -5.093584 -15.106412\\n\",\n       \"2002-09-21  2.984361  38.095349 -5.560222 -16.584216\\n\",\n       \"2002-09-22  3.187098  37.169742 -6.132250 -14.910771\\n\",\n       \"2002-09-23  1.772470  36.399488 -6.257600 -13.668261\\n\",\n       \"2002-09-24  1.886272  34.543307 -6.644915 -15.213739\\n\",\n       \"2002-09-25  2.918438  33.722114 -5.736130 -14.919711\\n\",\n       \"2002-09-26  2.608268  33.473087 -5.111581 -15.969800\\n\",\n       \"\\n\",\n       \"[1000 rows x 4 columns]\"\n      ]\n     },\n     \"execution_count\": 106,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pd.read_excel('data/foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "3.pandas/1.10-Minutes-to-pandas/README.md",
    "content": "#《十分钟搞定pandas》\n\nThis is the official document [*10 minutes in pandas*](http://pandas.pydata.org/pandas-docs/stable/10min.html) in Chinese.\n\n**在线阅读**：[十分钟搞定pandas](http://nbviewer.ipython.org/github/Wenice/10-Minutes-to-pandas/blob/master/10%20Minutes%20to%20pandas.ipynb)"
  },
  {
    "path": "3.pandas/1.10-Minutes-to-pandas/data/foo.csv",
    "content": ",A,B,C,D\n2000-01-01,-0.5479151810928263,0.7585035671650282,-0.7915527339166057,-0.014276215096031601\n2000-01-02,-1.4786520159926413,-1.8248625282371542,0.0014156168611411113,-1.0678637707676857\n2000-01-03,-3.070726803294386,-1.6559670104838138,-0.5514114277617373,-2.188238071682052\n2000-01-04,-3.317105151330431,-1.8107775637061068,0.35399957681797334,-1.9957602625575013\n2000-01-05,-2.8223080422684053,-2.7570770510658162,1.2100570973141975,-1.250591800103325\n2000-01-06,-2.2238654140791843,-3.2569312430910142,3.658672125514629,-1.2325098503514262\n2000-01-07,-0.5594111460103863,-2.842820321805858,4.860993002850001,-0.41885310344935245\n2000-01-08,-0.5248123941035769,-2.748761819012095,6.280814318672206,0.17061580567436863\n2000-01-09,0.20452797637738251,-0.8650219289606849,5.5604564931096245,0.8758712574726347\n2000-01-10,0.6888948427676997,-1.704024084076032,5.226061715267047,0.44448340249010054\n2000-01-11,1.2721382276801598,-2.478376889434483,2.7635236568855355,-1.2297973063009824\n2000-01-12,1.2556573280476269,-2.9940727284671547,2.8696882264270824,0.7455039549957143\n2000-01-13,-0.001803096363478307,-2.2958225295690524,2.7380872706189385,2.6403070811495812\n2000-01-14,0.14420855917496228,-0.9268104788688967,4.087307513149795,0.16413797136337038\n2000-01-15,1.3048112345082097,0.625529819245046,3.542700248753867,-1.499446048805507\n2000-01-16,0.6820255215869164,0.552441168813194,3.2013890987505933,-0.1556737759579232\n2000-01-17,-1.0653206021467818,-0.3390922219549005,3.792633720572308,-1.7549456837096764\n2000-01-18,-1.268417991846499,-3.5127728937899807,3.656896352448523,-0.13212374936468096\n2000-01-19,-1.9363895722461735,-1.0691882087955191,2.5548874547692533,0.4139300024774504\n2000-01-20,-1.1386783549322619,-2.9938698722214783,3.0941980471639163,-0.9565739071330779\n2000-01-21,-0.0967209592721705,-4.141107976672696,3.341247787033567,-0.4368863889001323\n2000-01-22,-0.6787631029612109,-5.012663362996001,0.8235755214448042,1.303376542224544\n2000-01-23,-2.498525351314982,-6.075937935347145,0.3029075372147785,-0.29420367882053644\n2000-01-24,-0.9377849549144852,-6.768486878935357,-0.07852080930445127,-0.16579929271120988\n2000-01-25,-0.2523381344594503,-6.882859842870638,0.5456844362733024,-1.10864064302118\n2000-01-26,-0.7726525436485714,-7.080129393914403,0.7646473830398788,-3.0414499870053096\n2000-01-27,-0.34883114214948274,-6.035375352760035,0.5076734756100604,-2.5228356312436824\n2000-01-28,1.7517208606760517,-5.756738453776143,-0.469013586616583,-2.825118245978936\n2000-01-29,0.7999382193098763,-4.5591723741285275,-0.08676292365066768,-3.2431010367343807\n2000-01-30,1.1496498779660085,-4.433330666070112,1.4766887503403954,-2.4799560486189054\n2000-01-31,2.015157420590959,-3.914373037341744,1.8003438320421326,-1.7593074887356686\n2000-02-01,2.1446861376492614,-2.493985075771966,1.0528526799065323,-3.370994454742795\n2000-02-02,3.5825237909751344,-1.6883529623660896,0.9555215105407281,-4.506622803727957\n2000-02-03,3.77858456048812,-1.8695906178146002,1.7328568266584483,-6.366590493475355\n2000-02-04,4.911224138464443,-2.792802742526688,2.959023100485572,-6.41543910887446\n2000-02-05,4.919579700872011,-3.6134645422606426,4.106508110010683,-5.132982804055395\n2000-02-06,4.9912248366103045,-3.0800670418111364,3.360152490466963,-4.257640834946202\n2000-02-07,5.84301351347017,-2.274814937876478,3.874482014073722,-3.483566859684065\n2000-02-08,6.282913269927594,-3.3212621807173726,5.406769627108887,-2.487107562447327\n2000-02-09,5.606514188552685,-4.2972780304888625,5.24867711705864,-3.441072678568298\n2000-02-10,4.23969940114971,-3.5294198042534726,6.61626959355184,-4.033283847046932\n2000-02-11,3.810936270661771,-6.209501923923973,7.2146097641512235,-4.675556604570748\n2000-02-12,3.7548349229976044,-5.387459097519608,5.591537353506659,-5.266877990099926\n2000-02-13,4.398418877233283,-4.092690594333276,5.83673928417155,-5.732427205869718\n2000-02-14,4.208439302300944,-3.939930465301921,5.745024490941508,-5.745265088590907\n2000-02-15,4.618456266628262,-4.336032528111899,5.120647699675505,-5.724330331210495\n2000-02-16,4.608565987901993,-3.6825790947879606,5.421018461543752,-6.28194901295681\n2000-02-17,4.0298056115015966,-3.2786845885897034,5.444264565675136,-5.98588569819204\n2000-02-18,4.023146898326493,-2.6424844119702193,5.095117815888905,-6.931500085605662\n2000-02-19,3.9539230087607127,-1.4416559080827118,4.42066814722992,-7.522024117383445\n2000-02-20,2.68300908162072,-1.722367774434868,5.274642325817944,-7.9776930383243965\n2000-02-21,2.7025426914699673,-1.101631128955392,2.4076127311250524,-6.925695073478119\n2000-02-22,3.2464434572643515,-2.0175473264229407,2.3755364896158904,-7.372567213164693\n2000-02-23,3.110297259102999,-1.9732585163924015,4.431850856650208,-5.988990396157977\n2000-02-24,4.020013389337046,-1.6065159460310385,5.451286456877222,-5.436563849532217\n2000-02-25,4.270457043581444,-1.6633765024641105,4.929513873714631,-5.514751638493159\n2000-02-26,4.430837140670408,-1.7978288826924314,5.877764475202645,-5.122168618439229\n2000-02-27,4.78399574274304,-1.7328331187008823,5.996985896543003,-5.424989104361209\n2000-02-28,4.462661708406604,0.5779633496782859,5.866711058719864,-3.9522009007685215\n2000-02-29,3.2167893854841436,1.98206387719617,5.1947265324765475,-5.190891993965762\n2000-03-01,3.8854317027604903,3.2905483296668487,4.428339265231501,-6.609873681067576\n2000-03-02,2.5132138486763402,4.112288030655646,3.9144438847080254,-7.202577217818801\n2000-03-03,2.045303065575,5.674057936091805,4.962552232824457,-6.936759601562905\n2000-03-04,3.1055435636790527,5.303293078839912,5.7178887593991865,-7.533933249489456\n2000-03-05,2.9303282471827683,6.020237430192089,4.7170060058564465,-5.841456207964156\n2000-03-06,3.2086284435204715,6.008074371542074,2.414537360628778,-6.126722057688056\n2000-03-07,3.0640018944765965,4.500526647692864,1.4866874341678717,-6.720315297150439\n2000-03-08,4.417327323439372,4.222542885466145,2.94607008178067,-6.571754118376534\n2000-03-09,5.339925791571573,5.542359474522746,2.801976910296672,-6.972999576994983\n2000-03-10,5.958880014408018,5.498780704401638,4.102043030387295,-7.806555675529262\n2000-03-11,4.867572710104769,5.888326919858121,4.342796901735356,-6.933968040292356\n2000-03-12,4.273108057007724,6.30220610547509,5.123091170488652,-7.599270157425516\n2000-03-13,1.5963877530091803,6.62298926203196,4.6227407012779365,-6.817792328268104\n2000-03-14,1.3459588767646857,6.693547808325552,3.4414036090578035,-5.116991125084802\n2000-03-15,3.146293195248088,5.52075842301444,3.517817745095587,-3.1338073719676114\n2000-03-16,3.1491047500576297,6.446197157923078,4.741201214916111,-3.194735310926937\n2000-03-17,3.8125754837708317,6.544710835692392,4.2959594626002895,-3.265448019946603\n2000-03-18,5.742513263079161,8.649881493581065,3.8983610137449243,-2.425349342122163\n2000-03-19,5.296151210240883,8.596990021030965,4.331231815447542,-1.9003542184733924\n2000-03-20,5.361463662485451,6.995616304326393,4.888053052849155,-3.4877052708034784\n2000-03-21,5.157036208792603,7.888277531994388,5.80034661772927,-2.10355817216402\n2000-03-22,3.9808744143221757,8.734096917732264,7.197354676585626,-3.3067652919937327\n2000-03-23,3.530293921633306,7.3197060561034935,6.843090124091032,-3.584511221994211\n2000-03-24,3.810823772959755,6.01251968558164,8.116529458454334,-5.731550644808493\n2000-03-25,3.7371222414550203,6.735012490417306,8.806076679928053,-4.51320029433043\n2000-03-26,5.464777297358747,7.627008098847979,9.258728238496584,-3.747198978286944\n2000-03-27,5.16750601577976,6.125818555808724,9.272041474609296,-1.9402802639339674\n2000-03-28,5.151204875826996,5.570595563885197,8.752317118669714,-2.9201357939799992\n2000-03-29,5.247080902780622,5.572041050863495,8.038896493448219,-3.2864034928941166\n2000-03-30,4.361738319403585,4.6363574311481495,7.866261940381432,-3.794696285213521\n2000-03-31,5.6454872022309806,6.221476965590354,8.802836763888475,-2.9832443534070605\n2000-04-01,5.625531556234311,8.017764791445074,9.944153926810843,-2.6514204353563953\n2000-04-02,6.331722352177701,7.496392532884768,11.043773381353493,-1.4973999120653196\n2000-04-03,7.892119183701923,7.382798379732132,10.732047860613083,-0.4910283567582858\n2000-04-04,7.2619596129663355,7.692306742870892,10.720233538589643,-0.45035480175257\n2000-04-05,8.114276186599021,7.657963988876193,11.871149624799544,-2.187334410368915\n2000-04-06,9.131771316558963,8.333309823816181,11.11306588946877,-3.7879886335948134\n2000-04-07,8.2884995380164,8.5456091569644,11.912837443397175,-2.7910588531463\n2000-04-08,8.616652794180293,8.178857628533331,12.252474935297595,-2.9866330533034837\n2000-04-09,9.16290052428061,9.092252846521326,13.132969041259996,-2.6060841618206605\n2000-04-10,9.248868345290468,8.733541863700921,13.95647827756978,0.9029024304121447\n2000-04-11,8.410094560704168,8.975362429019112,13.279423521037781,1.8857638556987473\n2000-04-12,8.718676510471601,9.040313793787956,14.613591193095626,0.5942612358116333\n2000-04-13,6.762694168114433,10.127060931833348,15.742276475860102,1.2625066168942203\n2000-04-14,5.759763325809027,9.822077968256034,15.996908424180553,0.18594531116921975\n2000-04-15,6.706595323488484,10.219598786877887,16.638478085589217,0.541211092715733\n2000-04-16,5.712025484436679,10.483109077180357,17.93537728258346,0.41865859742688827\n2000-04-17,5.995856531038643,10.875476386167511,19.040645908911113,1.4986190784973226\n2000-04-18,7.3803503117950955,10.790168674337382,18.885045579736694,1.7110746348758903\n2000-04-19,8.564428671337197,10.62712874420876,18.040924371190147,2.657062778215155\n2000-04-20,8.703841540741315,9.383982000710741,16.3009892953631,2.4486672254020654\n2000-04-21,10.351170020689565,7.680309439105316,18.432954649430865,1.7683901181446153\n2000-04-22,10.718581220110226,7.046721808822742,18.816924519346347,1.2577187075459921\n2000-04-23,12.849079460253824,7.016956859499061,18.373212106492105,2.1444000387829965\n2000-04-24,13.624620215471795,8.8469830252395,19.00714374375892,2.084238396552183\n2000-04-25,14.214705390696208,9.329588155475216,19.530564019361695,1.8730726977840624\n2000-04-26,11.746165517499229,8.176736325378227,19.625605394772514,2.9382780675858955\n2000-04-27,12.01364788481901,8.918385911234497,19.933849539942027,1.2218532778391686\n2000-04-28,9.727036015052295,8.322585438809071,19.965998301571165,2.3907384321479714\n2000-04-29,9.031119711357228,7.993438239454833,20.355646435660194,2.100872104933938\n2000-04-30,9.039117303709464,6.243952828054143,20.88666338957767,3.0056797536014574\n2000-05-01,9.850542129538681,7.643519197165121,21.23806689850051,3.0173866009049224\n2000-05-02,10.769375576740293,7.7305628718936985,22.428660713752237,1.9130097980383411\n2000-05-03,12.172912070165825,8.108562335633113,22.00618596543359,2.3730524336698924\n2000-05-04,13.210968740475845,7.120078196016681,22.03819335197078,0.5831183484574134\n2000-05-05,14.173708572467032,8.07759001106508,21.708481034849257,0.9287982037019358\n2000-05-06,13.528422043996242,8.00484465700068,20.821159198753072,1.505679663286333\n2000-05-07,14.71683612710396,6.819391567459963,22.46077204561903,0.7279244611435826\n2000-05-08,15.064600557086408,7.7890589133566985,24.657723196401857,0.8431295164112514\n2000-05-09,15.975467765871658,8.428284974874,25.447035796315706,0.14777984348138962\n2000-05-10,13.973676371951184,9.40023159709841,25.249738304543,0.13869605980803432\n2000-05-11,12.994261902677692,11.757186561411862,25.581848220497008,0.7861775196035735\n2000-05-12,12.680791994538552,11.052609192285571,24.729766800127592,1.5281643761706873\n2000-05-13,11.496778211603814,12.011417191039786,23.231318352178466,0.6068347299069217\n2000-05-14,11.286389414020897,13.77994174395219,23.246312063306917,-0.25735393978760246\n2000-05-15,10.606467117191698,13.195480819902631,22.262931438236347,1.0489031708663532\n2000-05-16,10.914098844892036,12.136175019095894,22.02079974059002,1.8975606389374142\n2000-05-17,11.377139061776353,12.373669162959452,24.60566277546434,1.4149329054074034\n2000-05-18,12.08264096478579,12.721652544424494,24.17724909390381,1.627612638073566\n2000-05-19,11.93107165086129,12.642703779146162,24.050576049112134,0.6573843459986358\n2000-05-20,9.390346525867637,11.556196818704816,23.24607357768769,0.07028682216897153\n2000-05-21,8.974734387068608,13.747320081868855,21.127975643758838,-0.3418124791690146\n2000-05-22,9.641950486673394,15.961947716999626,19.626483076634003,0.03901106099899887\n2000-05-23,9.286503453250107,16.023056169414154,19.593884390067995,0.5134908068434137\n2000-05-24,9.27707385295611,16.473647129390894,19.698587965051356,0.9795336272395481\n2000-05-25,9.412317353755386,17.215459530327514,21.41800034380297,1.1276154550160697\n2000-05-26,8.05683655591479,15.081028971403384,21.644838530426842,1.0197773705619808\n2000-05-27,7.530415849186657,16.31476509001918,23.37435641691825,2.4996660413210394\n2000-05-28,7.273579674945432,16.540202057768777,23.078010836390554,2.3641254277694754\n2000-05-29,7.569432004448269,16.155542323317878,22.503744077643287,0.3180457103642027\n2000-05-30,7.891127907806678,15.28354932808477,21.7533599714689,-0.17049563935188233\n2000-05-31,8.811296825399914,15.889274146395426,22.085187206771945,0.30653243909714617\n2000-06-01,10.103506464584862,15.325322618470585,21.919676197892958,1.3962601807865256\n2000-06-02,9.883455926915287,15.68223990680751,23.55051142784166,3.096874061491484\n2000-06-03,9.620210682084195,15.403670378611023,23.90096819125178,1.5169271006214604\n2000-06-04,8.801530122544081,16.645087323789323,24.1987589966488,1.547312767864966\n2000-06-05,9.392526720012809,18.077558063499293,24.4356923368315,0.7233747377536697\n2000-06-06,10.004604031513445,18.627746658272926,25.17458151059728,0.46069170905895596\n2000-06-07,7.831780853218504,18.8812285869467,25.034859291714234,0.8265345643482445\n2000-06-08,8.407159727379833,19.148622445170403,24.964092882274457,-0.6090231331994169\n2000-06-09,8.832025623328205,19.2999845857807,25.457526718459004,-2.099332127313692\n2000-06-10,7.809902666843925,21.603606220867576,25.738825748455625,-1.8768475506059459\n2000-06-11,8.641115785987296,22.669017216056957,24.65650640352117,-2.1348451611808015\n2000-06-12,8.224962545881224,23.742197955449956,25.592069019397766,-1.9168465185880759\n2000-06-13,7.127999771398554,21.593235643420993,25.780156154047,-0.9816098356148008\n2000-06-14,6.948601428369489,23.61713228925721,25.232101314920396,-0.9665356368981436\n2000-06-15,7.179798797563664,24.112484947981873,25.960950350276715,-0.7098338579882738\n2000-06-16,7.830854136363416,24.268147951876166,26.693042133639967,0.8543621754769323\n2000-06-17,7.16942557820811,24.283511069714866,27.84563070777805,-0.04385806986290541\n2000-06-18,5.833189070627421,24.54761694202242,25.490612633145354,1.3714890818858605\n2000-06-19,5.221038181116551,25.96716446665566,26.023140650222174,2.4423950508779635\n2000-06-20,2.399678760444473,25.561175367362324,25.36834559988582,3.745926784015115\n2000-06-21,2.789530651743388,26.690624542090163,22.73665300115234,4.1049855060824765\n2000-06-22,2.7894067574409966,27.497562498634245,23.032477897815987,3.8377930335737447\n2000-06-23,2.3526852037623716,28.186427250670825,22.437502109065175,3.593745668443482\n2000-06-24,2.3311992293589663,27.96727171123694,21.102074212310793,3.0110904939362753\n2000-06-25,1.7086289746141758,28.236294049683842,21.811370719867746,2.8430238262086394\n2000-06-26,2.073369161586631,27.840676881716735,20.31268290458233,3.141576768902143\n2000-06-27,2.370706594681833,27.820349288608504,21.141336110056045,2.1350927559919928\n2000-06-28,2.869761695875994,28.37612119823523,21.311436952949666,2.66601966673135\n2000-06-29,2.5552348884986773,27.35605884659218,21.565830200635574,2.2468375983112665\n2000-06-30,3.523177381492901,27.36728022882054,23.205409219782727,1.1708787655507367\n2000-07-01,2.801525178565796,28.281353392274006,24.64712491179292,1.085958161905498\n2000-07-02,4.400879271324683,28.10303831892765,24.03633119633691,1.0309654111091573\n2000-07-03,2.7659920381840344,27.43600166970747,24.065686814324874,0.287548618192656\n2000-07-04,1.8322097623720381,27.952738568786508,25.624814521355045,0.14752476220055608\n2000-07-05,2.295497801428439,28.582961498330867,24.699466516005156,1.0557103234760539\n2000-07-06,1.6658803010032985,28.38546005960531,24.45383769550146,0.40560520177752235\n2000-07-07,2.998144552635987,27.73026101688902,24.725112109510942,1.9846369415988088\n2000-07-08,3.9632909165821646,27.642001317634058,24.381952672072806,0.5574601267247323\n2000-07-09,4.134759050274844,27.595850856462686,25.085812457963176,0.41363059153047543\n2000-07-10,4.446264352583322,27.788856617056663,23.264845387128837,0.6527139796082968\n2000-07-11,4.820931512811497,28.35273113915803,21.30204523475498,1.7294713879695405\n2000-07-12,2.892334555992507,28.881401591612182,23.205129829440523,1.3758157814900343\n2000-07-13,2.672926021541038,29.86677107566537,23.97507411406382,-0.024994231475178097\n2000-07-14,2.006061608120694,29.308120575992195,25.129666860349683,-1.0183533759195336\n2000-07-15,1.0202480875764233,29.58644936518391,26.039903887467762,-0.17004577941968035\n2000-07-16,0.21936764440011225,29.114852710950785,25.600804003071577,-0.6372749077866224\n2000-07-17,-0.5061559018576326,29.927706242443612,25.498021837922945,-1.9146316192701727\n2000-07-18,-0.5341380107818693,29.790541490131183,26.981886804637007,-0.8201056183818443\n2000-07-19,-1.9595906256869196,30.741077778854372,25.93175805028763,-1.3937115337985775\n2000-07-20,-1.8769480540289099,30.758014503331754,25.339767156374393,-1.4181836402175902\n2000-07-21,-1.5850925217351888,31.006052787798648,24.89853495789603,-2.285711287708623\n2000-07-22,0.21320755494892674,30.766810174475083,23.21068705373322,-1.9572175758465562\n2000-07-23,0.3267060558026272,31.011763231512628,22.42621939778488,-0.683080664960575\n2000-07-24,-0.34000255124895645,30.440068221736396,21.63218656224683,-0.5436139018664645\n2000-07-25,0.7597286125744731,30.39827098971702,21.659300177291428,-0.15303628987808415\n2000-07-26,1.3388022518909684,30.14505600376375,20.875104995105183,0.018975896605463227\n2000-07-27,0.01978435473113782,29.009398622000198,23.03131837691802,1.7215691583229125\n2000-07-28,-1.0486067742067922,27.07174048228657,22.533245563043298,1.4190536336297508\n2000-07-29,-1.0427260317352698,27.386781848195955,22.50559454156711,1.5538770517935412\n2000-07-30,0.10631761686902186,27.510440170723253,22.315168815038533,0.7202305375241836\n2000-07-31,-1.5306659737326738,29.607732426372316,21.815629130365274,0.05223654951929202\n2000-08-01,-1.343576317730149,29.931649400946775,22.013732572064278,1.6064329764030632\n2000-08-02,-0.7256977633699521,30.04036202294028,21.951833186402173,2.1113898145155154\n2000-08-03,1.627270211206819,29.814276366238477,22.2874929203075,1.7376360128722492\n2000-08-04,3.0980324860381483,29.45822124884931,21.03847884188657,1.2089247958935645\n2000-08-05,3.5492510951147507,28.856347125726487,23.026772675531667,2.1325061395509186\n2000-08-06,2.7644626282313802,30.194916919808474,23.184191989869777,1.3443634525320158\n2000-08-07,3.102231761320587,29.841701304794487,23.445798224686826,1.9349335252181317\n2000-08-08,1.3226122240034146,29.335951840028695,23.54843984272162,1.2830058581289192\n2000-08-09,1.1686398452072704,28.930482700033476,23.114609999366895,0.9121967448522725\n2000-08-10,1.825851418989115,30.229435749730175,24.930880757393115,1.876758563379624\n2000-08-11,2.2930566298212827,29.97822072662784,25.37464106458355,0.23838215520962414\n2000-08-12,2.486691913126857,30.002318251722198,24.372893205857462,-2.0640175846613236\n2000-08-13,3.5529452766109335,30.827285222933185,26.25583490661103,-1.9328541245208488\n2000-08-14,3.839550380871044,30.10895056746847,24.736816590278195,0.101799209921835\n2000-08-15,2.020742153216613,29.270300586202303,25.70331219314334,-0.4300593565570169\n2000-08-16,0.43027672100375614,29.754357860946975,25.6651853430416,1.2571523859980007\n2000-08-17,-0.45661011823856956,30.652404822996107,25.329336965882252,2.8049792270210614\n2000-08-18,0.5232576759084343,30.209893008968276,27.621246571795613,2.3882946677016688\n2000-08-19,-0.605878366953413,30.035476792850563,26.60523915369789,0.8511317070779694\n2000-08-20,-1.5040868804914953,31.044012283098013,24.274127123299444,1.8413474269388612\n2000-08-21,-2.309078735506744,28.555349924960822,24.798705933264834,3.035632347848898\n2000-08-22,-2.9889780740289456,28.724707730785468,24.253193501941645,5.911239026158834\n2000-08-23,-1.1416749021392618,29.536247255308258,24.965909148231706,6.185883510447938\n2000-08-24,-0.6507409432605924,30.590522448965608,25.686707028235556,6.543494567980154\n2000-08-25,-0.7305966518473951,29.31540694388016,24.609675301812988,7.567526534233766\n2000-08-26,0.636054452477329,28.628345269172325,24.677882745072004,7.597610760319354\n2000-08-27,0.3878817612275197,27.923175008839323,23.973389394469127,6.1042023186361325\n2000-08-28,1.8153413726606127,28.137890814433185,22.312449666548883,6.237468043941574\n2000-08-29,2.443936033663786,27.580256962916817,21.790543229605216,6.305675056817472\n2000-08-30,2.1093446672116354,28.050787270042516,19.66459384218875,7.060788553322078\n2000-08-31,0.01706525200896758,28.68093060502336,18.212371306928425,7.351832783130473\n2000-09-01,-1.5107167381453157,30.21832031734569,18.391907906302396,7.847340046506086\n2000-09-02,-2.3641966628128857,31.831680421780995,18.535490793230323,7.002701393905596\n2000-09-03,-2.7215747652719964,31.478040034502587,16.51106674490523,7.282699742649361\n2000-09-04,-2.2644419414262456,30.177247940057942,16.088741929794107,4.853377479015617\n2000-09-05,-2.427146545939115,31.036039797500873,14.326194217666165,4.565185770745056\n2000-09-06,-3.638230361883371,31.452011707483745,13.94801679886225,3.757978140681942\n2000-09-07,-4.36615344514553,32.56028932496935,13.826210156495454,3.7343926025984415\n2000-09-08,-2.4750558418623676,32.40297577432034,12.722901830442948,5.0315071324849185\n2000-09-09,-2.3446281092705434,33.01521788059914,12.415689496321479,4.45389287656483\n2000-09-10,-2.207495747507607,33.805082739751256,12.407200513943673,6.940973084753254\n2000-09-11,-2.5377333094040306,33.689902716527264,13.916305937582313,5.654035629397407\n2000-09-12,-3.7566090818046414,33.437133009510006,13.618161354603462,6.023946176921353\n2000-09-13,-4.243875068878356,33.08827299269769,11.383153035897127,5.273621597675394\n2000-09-14,-3.195819771362531,32.4297158896498,10.600465244250358,5.1868570234902975\n2000-09-15,-2.6071887586682165,30.26295166083127,10.964965912069554,6.451070658028057\n2000-09-16,-2.663723188288643,30.924739315555023,10.353149785813528,5.407035075804448\n2000-09-17,-0.9481423570728393,29.733297963367782,10.109849434777361,4.296319760862568\n2000-09-18,0.6650310167318914,30.112314901768617,12.167214326290145,3.959374398072914\n2000-09-19,0.9554397293851269,29.705389477938674,11.638490977102508,2.3350169965920595\n2000-09-20,1.3940909170719036,30.310607521854827,13.204743984775758,2.1087443888865125\n2000-09-21,-0.11523770340613426,30.774431561589264,13.786630700345995,4.950186779145337\n2000-09-22,0.005607281270906789,30.288643732573043,13.140966320501786,3.543031059553729\n2000-09-23,-0.2898761383644297,29.904879854691576,11.948811633248555,2.793670186332765\n2000-09-24,0.2098319992100054,29.38018992482331,11.524764626327707,1.9402845156175716\n2000-09-25,0.2212391650193721,29.242747460476323,13.005042834412045,1.217047702147644\n2000-09-26,0.6517848345560792,29.82571726764486,12.883534189999706,2.0142263505820406\n2000-09-27,1.0146619459616577,28.603514915893438,13.542998896359311,0.5115239956201252\n2000-09-28,0.04716450232743519,28.95221169463816,12.864015149586177,1.0473714414087074\n2000-09-29,0.281544099965402,28.117088040946125,11.93204198444298,-0.62792469598587\n2000-09-30,0.03918537387600096,30.085594993213565,13.156151815200667,-0.9300149303281362\n2000-10-01,0.32314242208090854,30.124642672280142,13.732717299831663,-2.5367740074623315\n2000-10-02,0.41372760036661593,29.380998540058105,14.610526197003978,-1.0421888651178415\n2000-10-03,-0.23037470940107507,29.867983970362243,13.61294768022897,-0.5539925296873014\n2000-10-04,1.40614626891553,27.99020428880305,13.911671054016837,-1.7780163149811075\n2000-10-05,1.4604050361584158,28.13452714053033,13.657072823604993,-1.3101227860184093\n2000-10-06,1.9423693908370048,27.930350695690237,14.878773440047201,-1.7887428421086753\n2000-10-07,2.477733020139749,28.072784972499928,16.71301239120095,-1.3961347958234347\n2000-10-08,4.053863705312072,30.06932368399505,16.540611474468,-0.30125121641111763\n2000-10-09,5.7091376206270095,32.39387457370714,16.12034471220906,1.046938802628549\n2000-10-10,5.233666617986685,33.937793271906926,18.236535100490574,1.6333738738602788\n2000-10-11,4.442991988906787,33.79366802443033,19.4951946076699,0.9006012122077734\n2000-10-12,6.916316253477124,33.13251343968089,20.577883326434808,2.472773425173729\n2000-10-13,8.302273277847124,34.57608076950681,21.428197590393072,2.114817381270857\n2000-10-14,9.622279052978287,35.24489788193609,20.941516416207616,0.9567965878554645\n2000-10-15,10.37518654352972,36.324152211375214,21.48853000554736,1.7430477301167677\n2000-10-16,9.505521012214846,35.58572572732208,20.94170176062655,3.5102632195364207\n2000-10-17,8.637696847404687,35.46263775707059,19.237829852569536,3.0959723668068704\n2000-10-18,9.631320370410078,35.12271792844478,20.766051010238456,3.8155397520424312\n2000-10-19,10.762039910082265,34.88911090042456,20.807759766623516,3.499487718027089\n2000-10-20,10.28735093234845,35.56747487677994,20.15604441718233,3.2470082204676958\n2000-10-21,8.328558974370203,34.902597623022785,19.529846667946188,1.7825007960433024\n2000-10-22,9.422037874046259,34.13024501698661,19.644145583333824,0.8508667694452217\n2000-10-23,8.35916246184058,35.03814048437553,18.91134295176759,-0.39919890322325946\n2000-10-24,7.85695213385609,34.656124334941325,18.19311665806326,-0.21204742166169777\n2000-10-25,6.9016648029051835,32.61811342949467,18.7212564502174,-0.2266455733618595\n2000-10-26,6.4229100220600435,33.56846976628876,18.488436888337485,-1.7509184234865736\n2000-10-27,6.598909106707833,33.70512753177329,18.590111316377556,-1.6071948905262454\n2000-10-28,5.04836756828696,34.9186305746746,17.800318707634332,-3.2740081387995605\n2000-10-29,5.635269717140604,34.29746015225312,18.85626820124721,-1.1371878998933718\n2000-10-30,7.131205662676704,33.48974417664093,18.01982503365355,-3.0823391531288724\n2000-10-31,5.903048093024534,33.72465230570068,17.070955479736437,-3.643464247833706\n2000-11-01,6.952020594670534,32.624522328129046,17.1453707076917,-0.8087911801318408\n2000-11-02,6.8322542694170005,32.73953741649748,16.976774546660288,-0.7386045548816833\n2000-11-03,6.414867059386957,33.13881634949875,16.315036725716606,-1.670319502391392\n2000-11-04,9.648522424807556,33.20883923815546,15.710458181219849,-0.20029969850728468\n2000-11-05,8.100666994778846,34.107377325574134,16.786709817023098,-0.5671780615937969\n2000-11-06,7.466284263484471,33.934406828382194,17.509046046983975,0.5419384614275347\n2000-11-07,6.872295757698674,34.03370383602555,17.899916677232675,-0.5975973165513215\n2000-11-08,6.190853284790896,33.44773694298671,18.004914015228742,-0.9104722816523558\n2000-11-09,6.559697261375895,32.5094809993407,17.632642625310048,-1.37387125836737\n2000-11-10,5.768562623867928,32.435861271417124,18.206472732523906,-1.401550855075286\n2000-11-11,5.694716270439078,31.61035141173944,18.5340924396973,-0.9967285971686273\n2000-11-12,6.404737652937556,32.08225776224555,18.330880138440605,-1.4103470790736483\n2000-11-13,5.530091039113547,32.501992098994855,17.150062486965584,-2.0464554919988065\n2000-11-14,6.291021074946932,33.147967517984405,16.782948192844454,-2.6642744074486133\n2000-11-15,5.65926628802209,34.682444560272636,17.650711325955335,-2.2229976623812457\n2000-11-16,6.836504314569369,34.02715066942684,17.285550344758434,-1.9100968614772105\n2000-11-17,6.49082178877653,34.175158531570574,16.4062410344927,-2.891262667455245\n2000-11-18,6.51898516838026,33.13220760534176,15.78417098558323,-0.865389284582887\n2000-11-19,4.720253051768519,33.47628151823707,15.208082801657978,-0.45543097044132447\n2000-11-20,4.6140501050909455,32.93534890147391,15.418052801974238,-1.4173152142835697\n2000-11-21,4.975070534964408,33.06774034469862,15.838299795495823,-3.7139936599031085\n2000-11-22,4.5995349529921565,34.100045669074326,15.282758862444979,-2.4201656617683325\n2000-11-23,2.9647965452972063,33.489841149352245,14.292096467712833,-1.5770554390296798\n2000-11-24,1.4488087887632668,32.60487738882424,13.72562467398418,-0.7760389893552174\n2000-11-25,0.7857304097873958,34.159021216163396,12.192361513449347,-1.852669688998052\n2000-11-26,1.8227428185458476,34.98722565369984,11.610574029215455,-3.691701014016226\n2000-11-27,1.2095960857059744,34.31905010702731,11.679664938432694,-4.497899320155682\n2000-11-28,1.6252753833276978,33.50826615571004,13.342972760747209,-6.409068952508173\n2000-11-29,1.8973800742956177,33.8017829108419,13.315068796238666,-7.237557531542729\n2000-11-30,2.8122925950081594,31.73757779969329,13.697981831310923,-6.259158360925601\n2000-12-01,2.109196351628598,31.98731704791361,14.099446258863901,-5.284865832765974\n2000-12-02,1.8797076691427381,32.48579600254712,14.11967234963866,-4.388469583160566\n2000-12-03,2.898903743009314,32.4398676118068,16.063858754975655,-4.51423622308419\n2000-12-04,1.8971151982416654,31.892581734803123,15.095562057584319,-4.749383252697157\n2000-12-05,3.4086519916769964,33.773598151799476,14.332677122819684,-3.5619576252291476\n2000-12-06,3.695024533357371,35.456276037501546,13.230376238471115,-2.9401304088434967\n2000-12-07,2.5447826539894116,36.04304525824002,14.17946377286752,-3.8190518843227945\n2000-12-08,2.4870352634712916,36.531393822851626,13.72110943043385,-3.8835714739684315\n2000-12-09,2.1763121366233253,36.91002916166499,13.351255195942626,-3.0347784448201356\n2000-12-10,1.526602996123768,37.60924637233787,13.620559822848227,-3.9634583641383188\n2000-12-11,1.8705708644400159,35.66154487480452,13.686997769479891,-5.2043920800500825\n2000-12-12,2.082853481880052,34.52463879783045,13.308583559856903,-4.0241592166014275\n2000-12-13,3.5535688537199803,35.120836937645194,12.731567799005958,-3.4622335970692353\n2000-12-14,5.0032951236190915,37.33404749147878,13.029118211867944,-3.3149606720476754\n2000-12-15,4.719246524896127,37.54769468561018,13.888566234246424,-3.6610814682537547\n2000-12-16,4.848509131744804,37.80784247651826,14.10662738300523,-3.274269549904657\n2000-12-17,6.812081444424473,37.24936995213407,16.141116576285164,-4.3041891601464295\n2000-12-18,7.411197177250875,38.79639143569782,14.582673961599161,-3.8918303475247322\n2000-12-19,6.867133120406276,40.280945177517,14.60939398811954,-3.975515274228677\n2000-12-20,6.447908381513658,40.821501852874405,13.875240808636264,-2.434757858855649\n2000-12-21,6.261628291291947,40.363736747902955,12.59327212784628,-3.80261026115985\n2000-12-22,6.318450496202979,39.54253478942314,13.428694833203817,-4.833787312833712\n2000-12-23,5.6998811142158985,38.636632934354694,14.264728511691004,-6.791656548194617\n2000-12-24,5.79138499661303,39.59527105619955,14.868899097567816,-6.1469905706015435\n2000-12-25,5.8227706025913815,39.52404025000739,13.725143266207116,-4.812609271586917\n2000-12-26,5.522277411306711,39.58715139265423,13.026614186196776,-5.95429243504954\n2000-12-27,5.1252791106221744,39.48162100584306,13.221426963837095,-5.617170197814992\n2000-12-28,5.281168750409122,37.93673789128844,13.880637383230905,-5.780156073693906\n2000-12-29,6.155794805756146,37.823932596626435,12.730532506591276,-5.4062506979029745\n2000-12-30,6.905760961285683,39.217744640578026,13.414755563331608,-5.620324186579249\n2000-12-31,5.829716748002931,39.98228767720026,14.885742811331173,-4.617363934885923\n2001-01-01,5.503759241033415,38.837057608132625,15.177865866242735,-5.913056473595754\n2001-01-02,4.749099421721323,39.91428171310104,16.532487067384967,-5.478569841422457\n2001-01-03,4.868060312736191,38.09794224325348,16.916700607303508,-6.343231088657781\n2001-01-04,5.729959754590219,37.06516549591992,17.212200296514368,-6.958278870025686\n2001-01-05,6.10544165063909,35.547060307368845,16.37956740902512,-7.277361718965547\n2001-01-06,5.968553227306868,36.02560102574818,15.909601903697968,-7.238978301044576\n2001-01-07,6.228308656801743,37.28483543895409,15.711064029754182,-5.073778866802961\n2001-01-08,6.009931490914014,38.3406145704957,14.532559380534632,-3.7120989601163457\n2001-01-09,5.001906794635207,37.83614977796834,14.663646515663967,-5.178665760442101\n2001-01-10,4.234086551116151,38.79961106498149,15.223315917786763,-4.549970878358602\n2001-01-11,4.824591184542143,40.16600842328058,15.832533301595355,-5.327490283981912\n2001-01-12,5.121036306149883,40.600711943412456,13.732940720659045,-3.7593360594683385\n2001-01-13,6.086490347263575,39.290150752257496,15.123380269667344,-3.4127096999275146\n2001-01-14,6.381686885983614,39.37028501118378,15.04928722254484,-2.339245684928096\n2001-01-15,5.9453042951686825,40.44526904687712,15.533281772316103,-3.032588734511112\n2001-01-16,7.094112382760026,39.80620826867413,16.371965879219022,-3.2309535548945085\n2001-01-17,7.699328774271717,38.877851604440465,17.566494915960096,-4.967141224306974\n2001-01-18,6.015528289556153,39.197792353843894,16.30777498380241,-4.097480517381565\n2001-01-19,7.2590165459288745,37.15708062709677,16.77593386242204,-3.6399664279227566\n2001-01-20,8.017673910375056,37.38989234944849,14.43824421469447,-2.589396960476566\n2001-01-21,9.343944324470346,37.333384658679925,15.803354723655652,-2.0623416708055267\n2001-01-22,8.724534976824865,37.45737864724276,14.144641912326056,-1.7774125145099076\n2001-01-23,8.535467750176183,38.01950844664745,15.140865577011002,-3.3632736728484627\n2001-01-24,9.57416262716742,38.83955723024873,15.116220096691823,-2.6265929891824142\n2001-01-25,8.191171468845342,39.01997181405083,14.648585694059179,-1.8328456882217434\n2001-01-26,8.522745324999962,39.89247033151156,13.461449708288079,-0.6159791234988099\n2001-01-27,9.139735372315334,39.70777527557298,13.812120579453557,-0.8274296297414019\n2001-01-28,8.050153859106077,42.37686005605794,12.803572814924047,0.12847298304555133\n2001-01-29,9.054822449464972,42.10207171901224,10.477316219804955,-0.04215232046648515\n2001-01-30,9.81680934049161,42.019990749002275,10.38911612652563,-1.4906654514810573\n2001-01-31,10.613210591617001,40.91717016318322,10.762225553079572,-0.9219500415872485\n2001-02-01,9.970409532094344,39.27424166304476,11.826460150961811,-1.7723785505102314\n2001-02-02,9.383097560504423,39.890021999226676,10.570829643838184,-2.7382360154105942\n2001-02-03,10.979944010865815,39.7496723561291,9.032692519513137,-2.9904608630857217\n2001-02-04,10.094434655148,39.51441401562851,8.135254317899985,-2.314627111405503\n2001-02-05,9.558828967412577,39.885506998248665,10.8736088285529,-2.472037100853699\n2001-02-06,8.759627790068018,40.841535107720695,10.746531785405862,-2.398140639961323\n2001-02-07,9.974858659257224,41.866326937657945,9.303851673083935,-2.789817056255607\n2001-02-08,9.564408031411563,43.23383043292772,8.433757229755376,-2.142652594093805\n2001-02-09,7.785265948283871,44.30987953352701,8.740921820075988,-2.349720400832668\n2001-02-10,7.917715302437031,44.42927817328894,9.003736078920669,-2.488111921340998\n2001-02-11,8.845630850237582,44.615399539643036,7.135804841044616,-3.10149513613374\n2001-02-12,7.826062662332,44.680277277511195,6.661381990866838,-1.7408945264697353\n2001-02-13,7.741823163421331,44.582602130583645,8.565384946277106,-1.6578941805120164\n2001-02-14,9.454276881616645,45.34354394932076,9.866479809689208,-1.6507994030497963\n2001-02-15,9.67718913209644,45.37882305929622,10.258057599834483,-0.9897655236424074\n2001-02-16,9.521890672587546,45.98822488519646,9.334558934222262,-2.2739334726705955\n2001-02-17,9.553173067762343,45.78765996902022,9.02673543324078,-1.6927450278337233\n2001-02-18,10.322412157036895,45.2654098766201,9.730422519159188,-1.756770731870258\n2001-02-19,9.929881852023957,45.20977454386577,9.041031590997544,-1.4088854885073885\n2001-02-20,10.645925258287829,46.71665589010275,10.206129503746208,-1.3318761851917964\n2001-02-21,8.09267471368495,45.77869373928036,11.21233717586592,-1.3334302587773128\n2001-02-22,8.529098667468299,45.856406204369264,11.824016229046656,-0.37013300776765756\n2001-02-23,8.489682218339077,45.79495833436979,13.23140292336489,-0.7844702476696286\n2001-02-24,7.529914136705415,46.227323684932834,10.72795194083732,-0.5287273080077751\n2001-02-25,7.04525560564469,46.28186291301922,9.672421213893964,-0.8397824442322461\n2001-02-26,8.280298743369022,47.94284637556163,8.656166667392558,-1.688198892336031\n2001-02-27,8.556647466059124,47.44160643691543,7.1454138199158725,-1.4598199638550964\n2001-02-28,8.578617911181269,49.062322236389576,7.132490481075853,-2.9752499043040053\n2001-03-01,11.187826322880614,48.56929684649752,6.402998844469964,-1.7036304902179271\n2001-03-02,10.914051098325006,49.77404499521681,6.968478233778134,-1.362882059225329\n2001-03-03,9.918668865213371,48.456979153437096,5.358971479124497,-0.15312136327253367\n2001-03-04,9.353798610652667,47.006631118365306,3.0310849474207644,0.9450575303721889\n2001-03-05,9.819002706153224,46.73592905933224,5.339065022661875,0.8478186297207565\n2001-03-06,7.790153375814229,45.79889524774911,5.5447273810591975,0.8173729692857029\n2001-03-07,7.98147462086624,45.451268848738664,7.020654325530547,2.2212110884108953\n2001-03-08,6.6844336912126066,46.04186478672286,6.283586938942015,2.244967304932588\n2001-03-09,6.336198270608848,44.472221479796815,6.296791403867259,2.5104489483688157\n2001-03-10,6.63097251741894,43.775164620979034,6.7886675930865055,2.05468778803315\n2001-03-11,7.606529197277977,44.480121570558886,6.91884457315605,1.272852637502114\n2001-03-12,7.47766339424638,43.79254539367369,8.195929065619083,0.07493416094515304\n2001-03-13,6.586928247364026,44.3571737941997,9.093204040728109,-0.5028599963329772\n2001-03-14,5.826192078434472,43.5499394259071,9.99354146478374,-0.7621939732357105\n2001-03-15,7.4018180811932455,43.502708857733346,9.352389699530363,-1.10520371601202\n2001-03-16,6.479902386438396,42.21596157318356,9.590661044388806,-1.5919095101167033\n2001-03-17,7.337014582144868,41.88132845913287,11.87255757147825,-1.716683319541024\n2001-03-18,8.160968250740204,40.585577193385454,12.519304792512001,-0.379628160292083\n2001-03-19,8.869861485331036,40.18858776963871,12.165578595262954,-0.555790521124762\n2001-03-20,9.859730345143332,42.16335110007273,11.639383941877536,1.3692076776609905\n2001-03-21,8.897643572659891,42.51724111284834,10.279935983863105,2.211650498128079\n2001-03-22,8.519252826154739,43.59460452983374,11.30579403329277,1.0048379978724031\n2001-03-23,6.461676587892454,44.25872715084234,10.36190423887616,-0.0027527022142526114\n2001-03-24,7.1893241361547515,44.28530741643795,11.165134814685551,0.6482504847886178\n2001-03-25,3.795394874324143,44.36730922352451,10.179542782892211,2.8517771702960015\n2001-03-26,4.059857770819444,44.29951074387277,8.927749875362114,1.9599737166633546\n2001-03-27,5.018494593856667,43.04646307487682,9.88580732656835,1.6474539467274267\n2001-03-28,5.710835252869395,42.651535851838666,9.056781326106547,3.033687553498492\n2001-03-29,4.701561241064543,40.74385630764689,7.785115911651568,2.4548281135190546\n2001-03-30,4.695560058165707,39.68131039682319,5.835108964170706,1.9386437199274469\n2001-03-31,3.6612450350423007,40.265878308986174,5.818453952050944,0.1245182468420043\n2001-04-01,3.289318853104147,38.725824382485676,6.0126479578171015,-0.6836352510303799\n2001-04-02,3.437104627678082,38.833754270875986,5.397662148394684,-0.7699881437961346\n2001-04-03,5.894475272491338,38.64739991587407,6.332634955686869,-0.33686946737775453\n2001-04-04,5.6342728145458985,37.19469404719815,7.48225814430536,-1.285296047958924\n2001-04-05,4.1747401953983285,35.805595387002576,8.85751110178433,-4.048387274732211\n2001-04-06,4.78570516980969,37.474937144610614,8.884990387483631,-4.337643101986636\n2001-04-07,4.025904049757645,36.37689436344344,9.982437123453153,-5.069924892708605\n2001-04-08,3.0857427339695596,36.46256363314862,10.69853853468743,-5.316152948106565\n2001-04-09,4.3871572592287595,37.34425306472257,11.496088675156775,-4.3278988259467255\n2001-04-10,4.17709031539995,36.7113015775191,11.410575180944242,-5.927386162295745\n2001-04-11,3.533894881103011,36.49829823899763,9.87727642292794,-4.985409705786665\n2001-04-12,5.26789740792387,36.09755713356517,9.915516298184237,-3.2711758669244473\n2001-04-13,7.272622158107572,35.452595779517345,9.973569938639521,-3.540063916081792\n2001-04-14,6.818334437067443,35.07098987286739,9.161810839650542,-4.2983994081281605\n2001-04-15,5.639857847266934,35.312370443702,8.086262000790798,-4.366262160356343\n2001-04-16,6.622572365067461,35.725490159504226,8.847982814438616,-3.4894975196325584\n2001-04-17,6.267391818318139,37.37768399992513,7.950733301609649,-4.130423401840357\n2001-04-18,7.684369874411013,36.70402167778688,8.424367397648535,-4.400959973479237\n2001-04-19,6.431940236828805,37.07790811343778,8.632955596796311,-5.417966081520497\n2001-04-20,5.7481576799402445,37.21020081783609,7.479939099738951,-5.480390832316415\n2001-04-21,6.359701001966415,37.37702330292177,6.617562000426278,-4.506493559193894\n2001-04-22,6.390319425942261,36.661064990080305,4.9392906962243295,-3.8409464185933047\n2001-04-23,6.30585643567213,34.80117989833214,4.122876950163264,-3.4021189546546213\n2001-04-24,5.261589054587135,34.53764634419672,3.495238182963558,-3.3839655187979854\n2001-04-25,4.9631902067821985,36.153393781970905,2.925729909534172,-3.5430576826240894\n2001-04-26,7.24408705041926,36.75131835465713,2.936941214326243,-4.144060775581072\n2001-04-27,6.494259749788092,35.42872502538154,3.6867503103912393,-4.047052534528475\n2001-04-28,5.3749663378684565,35.00553194176307,4.159364539140776,-5.728521696388672\n2001-04-29,6.166688170970863,35.99351557155161,5.113498145423908,-5.427566521698036\n2001-04-30,5.570884202869149,36.00570483190867,3.750537971644571,-5.146700471833744\n2001-05-01,4.886048540892621,37.15469318528586,4.154631275643921,-5.038653456043438\n2001-05-02,6.004045239438712,34.79333669283303,4.49720044430367,-6.986512551261249\n2001-05-03,7.070822242731794,34.54864307464261,4.443866286148105,-6.410770946151397\n2001-05-04,7.411081230457838,35.189416376545914,4.573013241568688,-5.832139625954165\n2001-05-05,8.712435137295554,35.95726856030186,2.239807091396822,-5.915201408462183\n2001-05-06,7.308815429974141,36.510458435568296,1.726369203437974,-5.194228577075178\n2001-05-07,6.070402229119733,33.92389647080908,2.534298878316303,-5.15343285385748\n2001-05-08,4.651959877501636,33.698282812786786,1.4365790834200494,-5.601152237596341\n2001-05-09,6.018666575196271,34.09463006065813,0.2855904509278897,-4.4957609075474805\n2001-05-10,5.619889494987087,34.6697235090068,0.20448404974065548,-2.0000342113861773\n2001-05-11,4.016039797105803,35.086085935247496,0.34202571969496265,-1.4025521958322869\n2001-05-12,5.784417757074931,36.93663632156356,0.6429133264008617,-1.265742570614966\n2001-05-13,4.870431671566766,36.66883742228078,0.5287310162574627,-3.3346377392373894\n2001-05-14,6.414323066027149,36.680893212813395,1.0481626131599744,-3.6603171268856998\n2001-05-15,4.811579314517346,37.13694059960904,0.24682079440809745,-4.140189664084867\n2001-05-16,6.329847378141981,37.320333072788564,0.15877011006685443,-3.940946479842947\n2001-05-17,6.650437841338878,38.3037367191814,-0.3581531272692858,-4.138363746466476\n2001-05-18,6.434138934466488,39.96764379959397,-0.1865880203408835,-1.708027735170937\n2001-05-19,7.121354697491109,40.7033581412744,0.2005274087230241,-2.296103700969136\n2001-05-20,7.052233735404124,41.85733951752085,-0.03969029616157255,-1.9121717103562397\n2001-05-21,5.7280052388672456,41.30160189566895,0.7529231735674311,-1.408152806621656\n2001-05-22,6.198222682761453,41.53743497537792,-0.023386246458385385,-2.641057993237446\n2001-05-23,6.032326980585774,42.67681167982284,-0.6586148848644982,-2.7806014091142592\n2001-05-24,5.272543514235733,42.958416120509355,-0.1688237691888298,-2.3590796608542886\n2001-05-25,6.200457329304364,42.608333572325556,-1.491406813718549,-2.0223485556795184\n2001-05-26,5.485286971604739,41.857639331771786,-0.928411388525098,-2.612198311413846\n2001-05-27,5.065737633463403,40.81706362525795,-2.720978452849484,-2.0939853526647143\n2001-05-28,3.8949012262880904,40.80464055795699,-1.157146831364241,-1.0372399316361116\n2001-05-29,3.4019128216165377,41.5915843325676,0.9039854772931055,0.5653547108154782\n2001-05-30,4.296999696142689,43.64331420845798,1.350298976048592,1.8430632599976462\n2001-05-31,5.227858380624231,44.876903572273385,1.4796030818908916,2.415660087667727\n2001-06-01,7.241739757513007,45.07054159410103,0.3925648965875894,1.965701029987127\n2001-06-02,6.516440511274152,45.74732714007582,-0.06748830738275224,2.475512088576453\n2001-06-03,7.475543344020313,46.81306507630798,-1.1989424155196802,1.3119262959979554\n2001-06-04,7.756711403697438,47.377107488996536,-2.153612988929183,-0.2537477139888984\n2001-06-05,7.876012797416326,47.45377395057983,-3.1485249679675493,0.06786235201777246\n2001-06-06,6.395389575241579,46.45684146412582,-3.8525434538482823,0.5982910691239244\n2001-06-07,4.948380707446779,47.04840642136096,-5.034554675583366,1.91591381765768\n2001-06-08,6.516036828812734,46.25995677505605,-5.659497309753919,1.834781191382645\n2001-06-09,8.011770658588155,45.698130079852916,-6.366843278364281,3.28758866656937\n2001-06-10,8.536492915161,45.411732427833186,-5.837096595496406,2.9052361502399537\n2001-06-11,9.788544749734298,45.392557060947524,-5.859295663317769,2.596906268821889\n2001-06-12,10.108224414111833,45.187687841977144,-6.10728135706201,3.357074576027342\n2001-06-13,11.521725629655736,46.421193011979206,-4.179453550711494,1.2369899961222615\n2001-06-14,12.313890670421582,45.58314161179958,-3.326634824449587,1.6643316609829184\n2001-06-15,11.743920165235254,46.2419907890654,-3.554419186277347,0.02681028830151022\n2001-06-16,12.113727370943968,46.87080668466663,-4.155527790883372,-1.4606514339845647\n2001-06-17,13.283632160327851,47.084229812138446,-4.79882458798544,-1.6879079346703887\n2001-06-18,14.699230741170608,47.108593784038796,-3.732385206003809,-2.745451979511262\n2001-06-19,17.051816367508824,47.58517212148085,-3.139805332609535,-1.762930913632614\n2001-06-20,16.379183953411495,48.726248769111486,-2.861358968776531,-0.5635729319340519\n2001-06-21,17.36786287277621,49.06522683694911,-3.4629443087781464,-0.8412134935190458\n2001-06-22,18.2456058562286,50.21790605822649,-2.2017005850612015,0.38588578867300716\n2001-06-23,17.809522029305192,49.05690111634313,-2.183208466991686,0.3575287031306816\n2001-06-24,20.12971648440929,47.13076502871244,-3.7050570509964813,0.7053795417806439\n2001-06-25,19.232835539303696,48.237362213880154,-3.8758451599027883,1.682394878117429\n2001-06-26,19.81523612294692,48.24135112139976,-4.277840652209914,0.397924627131905\n2001-06-27,18.747145620105186,46.67180824256726,-3.8402108126540546,1.392811011037913\n2001-06-28,19.772971948198823,46.15218647956734,-4.105977292826318,3.7389018580130964\n2001-06-29,20.21542676822067,45.90023804923704,-4.370249832840907,3.264091711001159\n2001-06-30,20.441173632737883,44.77046234771771,-5.260659541951448,4.679498590916183\n2001-07-01,19.50599110060455,45.15071494786337,-3.364552571528444,5.101736948346838\n2001-07-02,19.92210068628597,46.371798377979836,-4.999820232918162,6.002929329048421\n2001-07-03,20.865315544501186,47.094489949091894,-6.454073483249345,7.284713512379213\n2001-07-04,22.241544427229886,46.09257522572897,-6.134848294762016,6.626684794257475\n2001-07-05,21.64225973161916,47.69006773488919,-6.492959958529439,7.533049010562264\n2001-07-06,24.022492022859733,45.38274263675027,-7.84982825378053,7.877804717675486\n2001-07-07,23.218268459310078,46.42927623846366,-7.072038738849694,8.538848917794882\n2001-07-08,21.687690647532076,45.81441877829073,-6.328952702676829,8.145315706118462\n2001-07-09,21.900608197961873,45.40454003399529,-6.496799246871805,7.184393296281362\n2001-07-10,21.42524868333816,45.740808994891985,-7.025231812243674,7.09282260721037\n2001-07-11,21.461451783762758,46.54951727441347,-6.7360628853893925,8.57563596908803\n2001-07-12,20.895365504204694,46.76461914083249,-7.041937890759022,9.233049725576086\n2001-07-13,21.149772382153714,46.32789058759868,-7.807794977646855,9.91356212904093\n2001-07-14,22.881664721883972,45.54246332923202,-7.127408280953732,9.081352545052653\n2001-07-15,23.74376337458775,45.58123425833129,-6.172618040850636,9.660653681890139\n2001-07-16,24.398925543250392,45.49846061765798,-6.163515393450108,11.282754198246534\n2001-07-17,24.308746634342032,45.00035983938308,-7.633508044106203,10.605061773565588\n2001-07-18,26.025916788052427,46.27420951174518,-8.506791203262754,9.195047374399753\n2001-07-19,24.65834242218863,46.933074778767136,-8.923403307687872,9.414702279660565\n2001-07-20,25.1664990097308,47.9994789873878,-8.671163300784135,8.81583660235834\n2001-07-21,24.250050939577644,47.85533900863291,-7.192541962090887,7.09847287156915\n2001-07-22,22.93227974046609,47.14874169290698,-6.158253248451907,7.047057847937398\n2001-07-23,21.63856379820883,48.84590516060836,-4.682301793176009,6.55985151962216\n2001-07-24,21.280039018881137,47.67025631830912,-5.184956724387783,7.515261598237492\n2001-07-25,21.23011082105756,46.643571475004926,-5.12022163972382,6.282171055305274\n2001-07-26,20.716918063658635,46.55905353173742,-5.269448102881972,5.606688943145051\n2001-07-27,22.50006532332666,45.04177065674524,-5.449999986108431,6.502627788939263\n2001-07-28,21.817315277182736,45.79138537000093,-5.121289394279367,5.533145217562909\n2001-07-29,20.985247948859904,45.95511012109547,-4.09760381090152,5.198543082771671\n2001-07-30,21.3071123829849,45.99196230014856,-3.9884855687094243,3.292534020091904\n2001-07-31,21.348821141401036,45.17880602020121,-4.618542616834704,2.1894013012062983\n2001-08-01,20.01678347707029,46.525766682360526,-4.732608684853011,3.6932547472998136\n2001-08-02,20.60453198312471,45.42813477796693,-2.3872356405416757,1.7977922416423802\n2001-08-03,20.186997740249936,45.64217900918244,-1.9197086202830007,1.8156057582396883\n2001-08-04,20.096346937180535,44.24697697378764,-0.17571271497977525,1.004883258762427\n2001-08-05,21.45483784238316,44.6252593891575,1.4599794892586726,1.9353661101364699\n2001-08-06,22.050879323517737,44.163398842874145,0.5138891180581563,0.6767868068257494\n2001-08-07,22.39580192342688,43.50430107385971,0.9780741092882546,2.4909986966187176\n2001-08-08,21.88162538580478,41.19195613580194,1.2901151352886289,2.018817774914541\n2001-08-09,21.403815961572036,40.88590368753567,0.026357617751237683,1.3651260325960464\n2001-08-10,19.357708279000477,41.05840524906586,0.09000677358246024,2.9877539026366025\n2001-08-11,17.68196318733851,38.983989852938414,-1.040288885422477,4.1839742533442585\n2001-08-12,18.451828203050965,40.78838840216401,-0.8108939221532018,5.105038447777475\n2001-08-13,18.842254888722067,40.60404335728947,-0.46955840382660297,3.5401474167970033\n2001-08-14,19.67445377216017,39.68833968057459,0.9837843436371037,3.89911318920948\n2001-08-15,20.929846115015714,40.5755510987299,0.3672028456165646,4.264076754432711\n2001-08-16,20.563453369142348,39.62573786377112,1.513699638630786,3.1445035698460115\n2001-08-17,20.791395344107535,40.0652590931714,4.155810550562947,4.317098643863133\n2001-08-18,23.025416221782898,39.8969282078241,5.899518789108212,2.347438407009628\n2001-08-19,22.11025973395954,40.97238795114011,5.501799711037425,1.3510793030321748\n2001-08-20,20.30296259688794,39.51812772281622,4.035162076177294,0.4652138062088844\n2001-08-21,21.159248974907374,39.212473915984624,3.907905467238036,1.7348903090364318\n2001-08-22,22.41271722419443,38.16965495069512,4.2269923615194305,1.8029957415954694\n2001-08-23,23.370762386816583,38.128987664596096,4.817887537942197,3.4476334261427475\n2001-08-24,23.39865245157923,37.112715315272055,5.789281534035388,4.6208132153853665\n2001-08-25,22.587501718344054,36.017128188933604,4.084071970433094,5.367200785109356\n2001-08-26,22.04615893373234,36.41224094162227,3.459526586055366,4.106597900293616\n2001-08-27,20.622250438191966,36.80652894019266,4.3623699769423325,4.77653953835057\n2001-08-28,17.93698457461201,35.75945862800698,5.680785761478584,4.036970294277948\n2001-08-29,20.004777718921243,36.52982316522839,6.688139565602747,3.9219149257388435\n2001-08-30,18.6453425502581,36.756031519945836,7.519895846862774,2.704345933448382\n2001-08-31,19.947482067008558,34.901216321602874,6.008466932690479,2.237933931363923\n2001-09-01,20.119260211740304,34.820367421327504,4.88205752544923,2.8301081178840137\n2001-09-02,17.60187925136328,35.88061327298548,2.2162615729876918,2.209776403071261\n2001-09-03,16.610610475923977,35.93619209114575,1.9089617498096954,2.7802122077474305\n2001-09-04,16.06563810380965,36.734470322630294,3.6496980500560454,3.240850157631392\n2001-09-05,16.119821587200278,37.49451303129608,3.4692473438351246,2.2757379622112914\n2001-09-06,15.211687551814192,36.007960047705886,5.4519200414830165,3.943664341233613\n2001-09-07,15.52656456590484,36.2396550326748,6.371518489515928,4.405808869105248\n2001-09-08,15.961693382842913,36.7900474450611,6.401726040965582,2.8455463024103986\n2001-09-09,15.523253433495318,36.18853821637606,8.124530284506095,0.706105018092916\n2001-09-10,16.15647167569631,36.384159608278,7.829616663510789,1.8684551457465841\n2001-09-11,16.247609434258536,34.88907001629428,6.472135027737518,2.8496795214930475\n2001-09-12,14.7640600136661,36.77444096966613,7.191712300760361,4.327583636977895\n2001-09-13,14.647611810359285,37.08691800017282,7.843156939197244,5.011175319525444\n2001-09-14,13.820882289300906,36.76362877954573,6.888026155412114,6.1903910765301315\n2001-09-15,15.554074598239085,36.567712894799286,6.483908625624182,6.141066742470061\n2001-09-16,17.1246702155433,36.14426812391266,6.010345774814487,6.675127594955661\n2001-09-17,15.031040780836754,36.04794927471214,4.872753245981915,6.9509490568026\n2001-09-18,13.135695670949072,35.200289847980294,4.574720921303576,5.660480336177954\n2001-09-19,13.095259708304514,36.67846317775179,2.143850481650452,5.657418857070584\n2001-09-20,12.572986615355811,34.39929869757946,0.012006418488002879,4.8999810023096755\n2001-09-21,14.73747222375775,33.99306009535053,0.7613611984126266,4.038668087920326\n2001-09-22,14.70560642188863,34.36018196787965,1.6376867513253615,2.9757057790881114\n2001-09-23,16.7948011843013,33.45319584628983,2.2778461611501695,3.6614953542252597\n2001-09-24,14.282746323062895,34.14415277603675,3.0251591797321327,3.9810939121619326\n2001-09-25,12.192386501104234,33.6043382530136,2.5005799630917735,4.180583233368246\n2001-09-26,11.581088511562495,34.48753596020976,2.3268468572088237,5.590657593860188\n2001-09-27,10.290955361791374,35.37385157075069,1.095094703464643,6.108270582999626\n2001-09-28,11.190330286046546,34.69704864152425,-0.16003199380815158,7.3312787569316065\n2001-09-29,10.120957679221396,34.421746730880436,-0.3188848018165986,6.480690084683598\n2001-09-30,9.926648588102807,34.174168072854926,-0.161212212870652,6.278168400193447\n2001-10-01,9.090564692681966,34.18793562235047,0.924337668896479,5.800324962555544\n2001-10-02,8.177699790532774,32.32660767299672,1.0328092439311174,5.629548867910504\n2001-10-03,7.20485473197292,31.37108517048915,2.75955343182485,5.834141727262782\n2001-10-04,7.333230672584123,30.718787118392946,2.999065913114088,6.330372168384675\n2001-10-05,7.272435125000071,31.505114317306564,3.3962588436530883,5.949595796120247\n2001-10-06,5.388572438893376,30.910176632069575,2.132818240110166,5.34552823863928\n2001-10-07,4.979547116826041,28.626733093259396,2.6718877425965055,5.614893186068739\n2001-10-08,5.469281381943203,28.657600195607507,3.860021257544177,5.275719751914943\n2001-10-09,7.139019134846588,27.693310620469965,4.166048393872201,4.907706003576247\n2001-10-10,6.955831923333832,28.4688044889625,5.086768382252009,4.919464254384534\n2001-10-11,7.724979927613988,28.594546984918146,5.384887070994737,5.238666756900844\n2001-10-12,8.38989248693278,28.84110668863768,5.721419896663913,5.912988520900096\n2001-10-13,9.485649648816233,31.134714291370965,5.2162308353141364,5.2422000384360645\n2001-10-14,9.313799079611528,31.352237204811964,5.640223464319817,6.153005438212147\n2001-10-15,10.025224525774586,32.05319756603353,6.339991401615621,6.174268077615479\n2001-10-16,10.167213171238682,33.278719719129825,5.246845996794679,5.960132996993025\n2001-10-17,10.666265911250743,33.31411389616121,5.405342844230312,5.7505754013307975\n2001-10-18,9.283598186074402,31.927487672864206,5.516717215922956,4.880573880159396\n2001-10-19,9.681523071874086,32.04771256970985,5.058255254144909,4.634965202540383\n2001-10-20,10.572953195107623,32.165696670401374,6.095538235322544,5.936679165143395\n2001-10-21,10.115922901386925,29.977096333960546,6.096645604535091,6.766254824374065\n2001-10-22,10.373639278184417,30.439737697776973,5.134233439657428,7.663586326736239\n2001-10-23,9.614212101742554,30.649015777194187,4.38612690405915,7.398579474124356\n2001-10-24,9.128096824297524,30.11356884187269,4.917964139019769,7.447493424605999\n2001-10-25,7.014246930570265,30.104553796469407,5.2034909230914455,8.738485059585354\n2001-10-26,6.896531681053483,30.706778411445907,4.254512539962511,7.868569905991297\n2001-10-27,6.4909015347367225,31.713520385117086,4.7275642970982625,7.744201681354446\n2001-10-28,5.525015287169337,33.566595729494836,4.57688958095721,9.08990678232884\n2001-10-29,7.259720970780395,34.50933313257515,4.661102425877593,7.829850040179414\n2001-10-30,8.95676689429814,33.910711077933954,4.334181472841326,7.350474704315262\n2001-10-31,7.958955375534687,33.51407228604175,4.7546183655294385,6.110134502655738\n2001-11-01,7.775763604899158,32.85619107315497,5.126600897997341,6.386568173650255\n2001-11-02,5.354087451178011,32.758783917540974,8.009557972004105,7.146008348684198\n2001-11-03,5.715224089165379,30.818672215162692,7.81014963430976,7.669121316990013\n2001-11-04,6.165000916881531,31.626593548994496,9.147362329356257,7.383649932303651\n2001-11-05,7.691193317095612,32.310354371011606,9.098295765412017,8.59152960655298\n2001-11-06,7.124394703287769,33.16944856092169,9.310022052299677,7.727128677380049\n2001-11-07,7.462365647272176,34.142828114422954,10.698111427154062,7.813592106107745\n2001-11-08,8.601863615298344,35.797284815962975,11.622889886853866,8.423878043668202\n2001-11-09,7.034853781100065,35.47743942707177,11.795925824853516,8.364388972647289\n2001-11-10,5.007850089770884,36.18517025176995,11.800663273701641,8.606567268453619\n2001-11-11,4.67778407456656,36.437734539915006,11.47543003880114,6.981837235969836\n2001-11-12,3.9522283611136784,37.21625131123837,8.901768721059176,6.242085889733706\n2001-11-13,5.233270630550338,37.242561011899255,10.042395177655061,6.836226798636365\n2001-11-14,3.1954464436329557,36.88055278114155,10.578093639747197,7.628366778812605\n2001-11-15,5.133025156050282,36.115588435121246,9.764194620097268,9.031801699893997\n2001-11-16,3.2424052932235985,35.821487095129136,9.009090578394622,9.982648838056573\n2001-11-17,2.54437207149554,36.48040378035027,9.731958610867233,10.395194579941188\n2001-11-18,3.8425491966883945,38.298173476108566,9.844921259994962,10.135368723477375\n2001-11-19,3.463809029910597,38.38701900960285,9.943574086767649,8.374158658502902\n2001-11-20,3.003493404205702,38.450757392333244,9.194506131238223,8.032654561606783\n2001-11-21,0.49997556524864706,39.06552771472796,8.413976750182034,7.488254534404179\n2001-11-22,1.8158109558175592,37.94771302748708,7.662215294395902,6.929888580848436\n2001-11-23,2.015776402322595,37.81981584860048,7.645994737831494,5.598514384492379\n2001-11-24,2.7314676339524686,37.930244972751545,7.756601952251432,5.67984968558005\n2001-11-25,3.1793213731299184,36.33795917386623,6.879282729447501,3.1956460227679364\n2001-11-26,1.9691852239551177,36.542082532412785,6.403533107248284,4.283995612185605\n2001-11-27,2.5570208935345478,36.84874627676283,6.454158156472656,4.537733610818396\n2001-11-28,1.4474560345136513,36.03453821309129,4.908828884016277,4.487446469644824\n2001-11-29,1.0076397689718746,36.15202517475637,4.420684943098645,4.556813414447545\n2001-11-30,1.7744858931398975,36.345317068092875,4.799490463832136,4.768689547169646\n2001-12-01,1.9665052947852013,37.44543020664677,3.138287679859522,4.052899726794386\n2001-12-02,2.9708380367448237,38.76489161899656,3.724012028132707,4.601591694872982\n2001-12-03,2.7773562403456538,39.141449318843016,2.7264423965197158,1.7435974544655473\n2001-12-04,2.9002842127283595,38.237781143441744,3.6709972563204407,2.356448858126527\n2001-12-05,3.3515666865963127,39.07559111507514,4.12277005615349,1.573125110828986\n2001-12-06,3.792505092361262,39.90795151924473,5.173556920082682,0.6743804848813841\n2001-12-07,6.418381053136979,40.28692702703778,5.353708100534751,0.06090705863093282\n2001-12-08,5.773557793595471,38.20966300477103,5.055460625165453,-0.39715016025802163\n2001-12-09,4.92888868571851,37.861353551440985,3.5837303461218193,-2.77302621456779\n2001-12-10,6.58352125456431,40.00006694205309,3.2794150474983845,-0.6812886222779171\n2001-12-11,6.9554963936971435,40.45560449826505,2.5705480987905815,1.0247728996252317\n2001-12-12,6.456295664449397,38.4719720865536,2.2815173497109082,0.983828322572829\n2001-12-13,4.502536594951016,37.13252024816027,1.784959089282794,0.5486071920032354\n2001-12-14,3.9133532089626697,36.36344674262464,2.317579380753239,0.3182188031299043\n2001-12-15,4.795946138619068,35.595725483195835,1.1939904557737826,-0.07390355490003964\n2001-12-16,3.8646775968863185,33.878257783946566,0.6946971420381081,-0.1363240148225787\n2001-12-17,4.35251071351477,33.84938881631695,0.361762129092673,-1.9861839424896588\n2001-12-18,5.336101124906457,33.16889128349531,0.060439061993427745,-3.721129559996364\n2001-12-19,5.790273656735723,30.798640647941006,1.4426649550766992,-3.3529952711873694\n2001-12-20,6.787094353556743,30.344102145071925,0.38749249436540456,-4.806376307657324\n2001-12-21,7.279483018451908,29.319227315477143,0.32857846293200565,-4.0126952050950875\n2001-12-22,6.979069293438342,28.742614442933995,0.19222428564386654,-3.024760288974022\n2001-12-23,6.520835934934696,26.857952544564466,-0.22987497789166658,-2.2733848995930037\n2001-12-24,6.350892052303508,27.20386567139706,0.5597612528517497,-2.807635933992452\n2001-12-25,8.137900517509772,25.808535868897817,1.731797941821206,-2.5842194058344377\n2001-12-26,10.001860490455707,25.70973275781972,2.6242290729781983,-3.04895346278716\n2001-12-27,10.808576642189355,25.2109844889432,2.4795768863446055,-2.1791978327736885\n2001-12-28,10.48451284907984,24.20512019213274,3.2910611290695386,-1.4213432475120804\n2001-12-29,10.077406048460626,24.176986664155642,5.628902730293397,-1.763848883189749\n2001-12-30,10.083099195641207,25.931795163548195,5.127126871710569,-1.1557476434614666\n2001-12-31,10.196701928507858,25.045674600210074,4.338069373509712,-1.8450757904530484\n2002-01-01,10.689919121805428,25.41473599197034,3.208511622898907,-0.7646420442656194\n2002-01-02,9.594098927833956,26.61957284324243,4.536014543528835,-1.001068787798294\n2002-01-03,7.920381754642914,25.598045605287787,3.754068880958656,-0.16748986987130732\n2002-01-04,8.986087367784773,25.38244160592386,2.8256188982458137,-1.6630552513706323\n2002-01-05,9.591771545419267,27.198761878077985,4.099647112640453,-1.581195326602402\n2002-01-06,8.553303712201847,29.082828155319312,1.6004801730561917,-0.7293760000407856\n2002-01-07,10.154103629177968,27.93689794311821,2.859310077223836,-0.4691147202276215\n2002-01-08,9.84370639117641,27.705784259144156,1.3496078462245293,0.5771350013195786\n2002-01-09,9.649059621094738,26.661915133372673,2.041404766903204,1.6845607769528905\n2002-01-10,10.477283428998144,26.327471997797478,2.5250237685456147,3.248569789841186\n2002-01-11,11.321021570913143,26.48593448776489,2.792654885557721,1.5600144007901415\n2002-01-12,11.365138169505988,27.50619315164108,3.9925868367806596,1.6754713459940438\n2002-01-13,11.182982355751683,27.972600056176347,4.083567060319916,1.875435844554914\n2002-01-14,11.605304702691456,27.22459537038456,3.1570895006560162,1.4306405738538053\n2002-01-15,11.886070965146832,28.047443635987985,4.959477047365752,0.9611986012238944\n2002-01-16,11.344889396189414,27.910550290882725,4.7462984194751225,1.177765566796905\n2002-01-17,11.817902849915049,26.900885606396297,5.114516350287483,2.108592388396763\n2002-01-18,12.033074950408423,26.30615364874709,3.0138095925134607,2.4653129543859347\n2002-01-19,11.471603209993084,27.4472862678701,2.057392755798387,0.9917070450544387\n2002-01-20,11.803166035561677,26.740809019814062,1.0610426351570963,0.25070757095191964\n2002-01-21,11.00680343895195,25.631066005950878,1.1417631822855003,1.6369549129928171\n2002-01-22,11.168829537103628,23.946430157825723,2.224700950190509,2.6642312047931807\n2002-01-23,11.752096380840724,24.860308039390528,1.6333651630117694,1.5057723840145951\n2002-01-24,13.825033792000404,24.883735481021155,2.283760174253172,1.7171933268546526\n2002-01-25,12.717499209141229,24.84831789175078,2.302885038095152,1.7749144040408376\n2002-01-26,12.007070541037791,26.116826045022492,2.6783831017289095,1.7759664597262848\n2002-01-27,10.555882775486218,27.931256542853987,1.4030633893214015,2.2018581553987486\n2002-01-28,9.83964570690923,25.899690129956696,1.8011486624683264,3.0525747931785547\n2002-01-29,11.817029492759874,23.83555725027891,2.986397256468427,2.2855841950879245\n2002-01-30,13.051911006658028,24.733486826639076,2.2543240695920517,4.264499874158733\n2002-01-31,14.515619793013853,26.16901986661303,0.7863571762239832,3.6317766165430814\n2002-02-01,14.408794136562705,27.037381001865583,0.7966814520523282,4.762060168381849\n2002-02-02,16.209018270653413,27.648317835712902,1.1666464717740983,4.7317021891337685\n2002-02-03,15.619200470397415,26.8041279246084,1.3136682970452722,4.30649341986076\n2002-02-04,13.919737203778118,29.58295170657461,1.591445776816705,3.9801178549485705\n2002-02-05,13.71022611701179,29.609714287010576,1.2602534853897156,3.265645956963732\n2002-02-06,15.95268179673183,29.808616268393965,2.998457920977617,4.722723442926996\n2002-02-07,15.498285614865635,30.814533386422205,4.9059681103581205,3.619342640314645\n2002-02-08,17.504453921175376,30.756211039107992,5.527312505969254,3.6505056562486367\n2002-02-09,15.992528353456201,29.643659863128423,5.251539242356751,3.6877125561470043\n2002-02-10,16.75325677321116,28.438811630625718,5.848664519213557,2.618055241294819\n2002-02-11,17.597672455746153,28.07648689823622,7.216089063314505,2.5832634738845086\n2002-02-12,16.613104751170244,28.27334078842121,8.544573120601457,1.429952387904851\n2002-02-13,18.51927709085085,29.357348527416825,9.526713604741241,2.621487211749681\n2002-02-14,17.89163369924528,30.33273766199842,8.808478557029451,3.917756202668185\n2002-02-15,17.19310385229297,29.729901739424395,9.404345335527928,2.858085203280367\n2002-02-16,17.735318180791896,30.79225336112216,8.277346857285885,3.1079088924713747\n2002-02-17,17.550435514592756,30.18814091270696,7.797482203664662,3.5025109880927876\n2002-02-18,19.261254793349835,29.88434550333386,6.6747640743856,3.7007836132616125\n2002-02-19,19.269368731051067,30.49582079236815,6.911211501298462,1.789365957925391\n2002-02-20,19.170733475205747,30.44135659151587,7.283488976504449,0.025290215143180195\n2002-02-21,18.807469138617808,30.822833043495734,6.7598341685693235,-1.485725726364241\n2002-02-22,18.00157636016067,32.0640193820913,6.619647082577677,-0.4347485410189438\n2002-02-23,17.592438682083017,32.0585688575236,7.605497444545715,0.6946626307081651\n2002-02-24,17.56655403990463,30.957309110137015,8.101580401468567,2.088576134725284\n2002-02-25,16.3508592574412,29.725923117882157,9.707684274249821,3.5725994255801474\n2002-02-26,17.446538075348204,28.198926378178278,11.416184399681054,4.103912608507612\n2002-02-27,16.79072183684228,27.802353166231434,11.19963286581051,4.38830501170036\n2002-02-28,17.127139747276612,26.736405917350236,12.044642530161608,4.0402118618106595\n2002-03-01,14.739470014716254,26.336281536678403,12.396514845602569,3.0648716740446793\n2002-03-02,15.123598227658546,27.316126629218957,12.702859063894334,2.604189305563034\n2002-03-03,14.384815875591913,27.154577735819732,11.92042876491194,2.6152406388201186\n2002-03-04,13.904490539943499,26.78602272267983,13.293275884385183,3.3467256142947237\n2002-03-05,14.321352702179388,27.106235746792198,12.278435065119172,2.6700307559236944\n2002-03-06,14.19505368939179,28.277110992548433,11.165647770945897,1.9866165700479868\n2002-03-07,13.461231190438657,28.856344775831953,11.854601467456353,2.6392460644994333\n2002-03-08,13.793070898258534,28.09866545737333,12.83010688402014,3.8734527359510444\n2002-03-09,14.684924914181366,27.77417491614185,12.678285219298525,3.7660900055223583\n2002-03-10,13.876013085260876,28.394363311641655,11.212252732527489,3.8347639115435554\n2002-03-11,13.082032123404636,28.402960944394607,11.138805015894338,2.632912704741756\n2002-03-12,13.74558361188228,28.16617619485997,10.90675742160813,4.065479607292243\n2002-03-13,12.984688187050914,27.054652929135987,11.573950405944512,3.923352128475163\n2002-03-14,13.06323775085511,26.2343899445405,12.841773895922739,3.7520911998612583\n2002-03-15,13.466322299366809,26.465554926195633,11.722720908314344,4.490403169082323\n2002-03-16,13.2155673964832,26.687412112344568,12.880846875224123,4.010009412867779\n2002-03-17,11.428149939997933,27.83873599200323,12.895633929735467,4.158552247002115\n2002-03-18,11.45319031729109,26.342492398251984,12.955679304772996,2.9686776271088915\n2002-03-19,12.579711190560085,26.357776503325656,11.615214905310093,3.025081380427287\n2002-03-20,13.587209849767195,25.944692059041067,10.825359046961303,3.7338626107286004\n2002-03-21,12.972056088504601,26.18850849678151,12.01676127042493,4.513200846937141\n2002-03-22,13.24671561511398,25.295200901869624,12.022807404278907,3.4517805953095673\n2002-03-23,12.509050390128522,25.61774741261698,12.945302797521041,5.432146899876614\n2002-03-24,14.037258835480136,26.26948285199573,13.073500345140685,4.775338129738009\n2002-03-25,12.666308517923007,26.84566307311888,14.117939470532246,3.0133910662674976\n2002-03-26,13.733835846849214,27.65681582678881,13.491433107825694,1.589899508528846\n2002-03-27,14.505090081370776,27.32723121680477,13.916010418828211,0.9880396299957898\n2002-03-28,14.362280470120792,25.34023944801838,14.125040785972258,-0.7589766904181171\n2002-03-29,13.655201069940619,26.11401141372248,13.52798236403561,-1.4767580228425818\n2002-03-30,15.719704354648371,26.413927744874677,11.21810130768026,-1.4332706601337704\n2002-03-31,16.286990070691285,26.155626359211016,11.593136407217171,-0.6933188969414769\n2002-04-01,16.454233437615112,27.130208504380384,12.214884937550895,-2.1365996184016463\n2002-04-02,15.560937841801671,27.068420828826838,13.529101461721785,-0.4018667344574205\n2002-04-03,14.905382657388849,28.4479799159111,13.071488122692857,-0.4769883564960634\n2002-04-04,15.950384769627545,27.44687746812985,13.022607081893103,0.6839740583933734\n2002-04-05,14.671488513494133,28.905938025405224,12.828013147879563,0.6789671805939411\n2002-04-06,13.966780031730826,28.70737270079127,12.86894654332806,1.0588878517004554\n2002-04-07,12.594950666265452,27.735879538583763,11.499126715065978,-0.3765757641543317\n2002-04-08,13.228017617798992,26.248904362406723,13.031937915154803,0.29287392425806624\n2002-04-09,12.125310650985936,26.32995205592232,12.16053516272806,-2.058434992677148\n2002-04-10,11.484714153480095,26.096095568367314,12.60282230894352,-2.4505961127484603\n2002-04-11,10.35621331876515,25.416527388468353,12.074359291819592,-2.0980631289569738\n2002-04-12,8.544720545568236,25.799605080737606,14.935345553090364,-2.9113824199966847\n2002-04-13,8.80114758352938,24.86790349883602,15.680345484931152,-3.7050444795330777\n2002-04-14,9.002973216524854,23.162325692998962,16.652626018234994,-3.6062697446180225\n2002-04-15,10.861481085187354,22.37884732308538,18.408671934502657,-2.970103579969206\n2002-04-16,11.66259704069192,22.21354299490792,17.738942723016343,-3.5112381877582215\n2002-04-17,13.226468386181205,22.531476960130302,17.454944971715953,-4.105744455074736\n2002-04-18,12.81060008472868,21.751106500165147,16.42949269852332,-3.702953725306473\n2002-04-19,13.120856554963687,21.95817360935196,16.585716830016192,-1.962439167788851\n2002-04-20,14.335417458085772,24.037149439520682,15.074854453613902,-3.6010444345677244\n2002-04-21,13.803817477659859,22.200198875537115,14.974844382379937,-4.939567632834501\n2002-04-22,12.918492513209467,23.577325758611618,14.957711787908552,-4.750253631056477\n2002-04-23,13.927166320266743,25.34851121964118,15.416924324995987,-5.593316350617263\n2002-04-24,13.481626676659856,26.526161840367195,15.894788711564217,-5.330446676611626\n2002-04-25,12.789173783589384,25.689254007832538,16.684387445427195,-5.695881088186041\n2002-04-26,13.143154918088355,26.398382975344024,15.966448627505713,-5.148092507767889\n2002-04-27,10.858242256361695,27.086114133301262,14.657376300849954,-4.997160888447258\n2002-04-28,10.574950585462906,27.945672865912442,12.765156316190534,-5.815988159096735\n2002-04-29,12.714528729065602,28.004439150704652,14.008740263381346,-4.961481939482251\n2002-04-30,12.770695694880656,27.365722474420767,13.966451954123785,-3.5597916358986414\n2002-05-01,11.731599525940894,28.9322607165133,15.799266236184513,-4.1414203555307605\n2002-05-02,9.669709800542748,29.08903244269035,16.46375297260826,-5.096430665079807\n2002-05-03,9.327151795389685,28.043479288787438,16.26072177728403,-5.310974190432107\n2002-05-04,9.52156721480844,27.668996386088534,15.563818025311388,-6.897324130460747\n2002-05-05,9.79308643514307,27.38728831770673,14.510374314380842,-6.437694474620948\n2002-05-06,10.184332380334032,26.97369551452054,14.54010920571053,-6.038183895042181\n2002-05-07,10.699168515424507,26.47664185747897,12.807456938183936,-6.99623334230132\n2002-05-08,10.007212294297283,27.55742736439412,11.422298894460827,-8.822065909280923\n2002-05-09,9.26174029032253,28.036768694998965,11.562035284173263,-9.932658841996707\n2002-05-10,9.436959792110368,26.557153978968476,10.950119747425907,-9.229265727323403\n2002-05-11,9.31810551209856,26.111808672097013,9.638679122406781,-8.81024186011419\n2002-05-12,10.750526974657664,25.486214400296447,10.70876083218376,-7.5176843512564195\n2002-05-13,11.321230488588014,23.648490643487133,9.485408000406519,-6.655247652836403\n2002-05-14,11.275875704100967,23.140124458804394,10.92855348362898,-6.576682765332052\n2002-05-15,12.95712459353934,22.38353339665763,11.941382232450605,-4.967616877222564\n2002-05-16,12.248430001666142,23.08109933742369,9.779346445418867,-5.854865734347349\n2002-05-17,11.324922664029282,23.875941529585532,10.415709788072984,-5.606904259904223\n2002-05-18,12.122108599483465,24.912429165588808,10.88943587948652,-4.535983560894385\n2002-05-19,11.69902597430822,25.432699212451432,9.721246630458278,-4.306624686424766\n2002-05-20,11.943583147752586,27.466853010695843,8.556792949375048,-5.491997272009451\n2002-05-21,12.334404490936313,27.65275674587585,8.234127377257918,-3.973073641420297\n2002-05-22,11.95040569918204,28.491723557363976,8.199534153589676,-2.5338040118355236\n2002-05-23,11.62783998607484,28.5128928018195,8.993423345353676,-4.069758110565735\n2002-05-24,11.872101151048936,28.07260665832784,8.812303662940465,-5.052279071252547\n2002-05-25,11.773706875946234,28.440010672505903,9.70589340652998,-4.633435226979444\n2002-05-26,13.108499711558697,27.556461761035855,10.855589120145043,-5.941853586912831\n2002-05-27,12.0481664957758,26.385007840972804,11.306624351727667,-5.9507499305388825\n2002-05-28,12.509317127037148,27.18840584792781,10.987911655602089,-4.892036503246576\n2002-05-29,12.440362246080163,27.734944739343145,9.016870773459686,-5.693549435679297\n2002-05-30,10.433390638204324,28.977192429894945,9.189227356537156,-4.704866559834118\n2002-05-31,10.718927999059517,28.107149790233876,8.168235219856758,-7.028384738464581\n2002-06-01,10.085341305497199,28.842424689144643,8.020840818866137,-6.893638556327868\n2002-06-02,9.206725227119438,30.48380196765894,7.5039439984414384,-9.174397574081443\n2002-06-03,8.846559165589301,30.981962369633237,7.248750877251425,-9.104509962655387\n2002-06-04,8.888680473913597,33.34656625590518,7.735011033162237,-10.238317507031354\n2002-06-05,9.566085883073804,34.55505856730992,7.513690972671664,-10.94935343914638\n2002-06-06,9.190860001584868,32.812065324250824,6.721009501931841,-10.728663420541686\n2002-06-07,9.658962465171763,34.21945420028826,6.430334004642834,-10.953286262922742\n2002-06-08,11.295490650543456,34.248064650478995,4.879097398133197,-9.989312303784917\n2002-06-09,10.42326436284071,34.14781727168654,4.95019703533814,-10.133056305244713\n2002-06-10,10.647765382249757,32.94846341182883,4.956664854936064,-10.793823298358387\n2002-06-11,11.78470788847655,32.01605863026069,4.189287953041459,-11.430786537887592\n2002-06-12,12.943698163599894,32.55406151090813,3.354332205213647,-11.689974814737639\n2002-06-13,11.580850133854055,31.989630784154066,1.801398246006962,-12.501886908467899\n2002-06-14,12.048561706946161,30.726189126451025,2.0894838568338154,-12.178817303331435\n2002-06-15,12.922722323795506,31.075733345796788,1.3388961261219579,-12.455841721597755\n2002-06-16,12.52431941401089,30.75152180364699,0.7718647942812498,-12.05331743843885\n2002-06-17,12.518104898695523,30.50988353137339,0.3225671561040728,-10.739613534232857\n2002-06-18,14.412395257925871,30.9405266434762,0.25524718802247487,-11.325342454995996\n2002-06-19,14.74502773590649,31.829009998288424,-1.1668597777996166,-10.535606405569817\n2002-06-20,14.447490337126334,34.12628709461956,-2.659546936186314,-10.282883971455774\n2002-06-21,14.519829896104769,34.388191867781266,-2.53457107738429,-10.059157769380164\n2002-06-22,14.769606564124159,33.77885481762409,-4.35362531124184,-10.059941477019535\n2002-06-23,14.750896321347,35.02279264006916,-4.593316416142507,-11.977694377548302\n2002-06-24,15.620122074557317,34.152552072177855,-5.123494112733322,-11.504261814895683\n2002-06-25,15.110189306866802,34.270910725422986,-6.315753016144846,-11.43654014277263\n2002-06-26,15.572644217365465,34.86542327360858,-6.138160115997585,-10.549386081816513\n2002-06-27,13.817049810010605,35.779483881911624,-7.538563395914298,-8.729817633253129\n2002-06-28,13.855331749765847,36.36225666285497,-6.397645862459288,-6.421098519319523\n2002-06-29,15.526814951461668,36.367227423118095,-7.129256240579685,-5.99123455814731\n2002-06-30,16.55044417010993,36.91690779193136,-7.882067718138321,-6.79491772959829\n2002-07-01,16.29313950036167,38.53028107390204,-8.394676915495785,-6.841597211505544\n2002-07-02,16.71744050205574,38.04216082084981,-7.971067485786059,-7.565735591578952\n2002-07-03,17.06339277288392,38.07391109927163,-9.089494823633862,-7.768585298371256\n2002-07-04,17.126488233053966,39.75621973923622,-8.600867239714665,-7.121565882513259\n2002-07-05,18.4165042852927,40.985689181579176,-11.224292990419032,-7.591857260168251\n2002-07-06,15.678335722718924,39.82781060470543,-11.412249236224755,-7.28934321286441\n2002-07-07,15.485694643471085,41.15225101065243,-11.738356057877045,-7.36040959957595\n2002-07-08,17.58976214553273,41.23026682389081,-12.056891682878446,-8.225514184085066\n2002-07-09,14.901497551491783,42.5308935354459,-12.823346019394009,-8.740668038506401\n2002-07-10,15.601482664689224,40.05967870214433,-12.327190759781859,-7.47782657829943\n2002-07-11,14.429365677798643,41.16276252428984,-11.370333175438093,-6.674118760668368\n2002-07-12,13.013982140546934,40.9685525322976,-11.138047443538696,-8.249293577275509\n2002-07-13,12.234528402076585,41.99896850980836,-12.234172216853727,-7.6898335615731215\n2002-07-14,13.491918221915382,41.637639355127746,-11.887670393216263,-7.191110717646542\n2002-07-15,12.810221794558789,40.312317716967854,-12.698383239786313,-7.470711388043519\n2002-07-16,10.68430168258815,40.29622613365533,-11.439005518755927,-7.71783690846085\n2002-07-17,10.706703845464045,38.53806694851405,-11.402237304992,-7.536038708320455\n2002-07-18,9.96884792397063,39.05941545371626,-11.007715572290168,-6.315572357670579\n2002-07-19,8.384497308643468,39.00410333276353,-10.176254949525807,-5.7655179473328\n2002-07-20,7.635155879746959,39.62203958055446,-10.68203645743873,-4.876403143824458\n2002-07-21,7.044269548901373,39.96745559908217,-8.963261556387462,-4.478585405175618\n2002-07-22,5.440403198597859,40.069312273033475,-10.036552412097038,-3.16749870932824\n2002-07-23,5.193792480028816,39.95123832700727,-10.722226938863702,-3.680682875052285\n2002-07-24,5.391805554509913,37.93326763965943,-9.31206942834531,-4.557036028904821\n2002-07-25,7.244183136450595,37.12345447970758,-9.948271031493793,-5.136550820981748\n2002-07-26,5.941221425721547,38.684472357238796,-10.516456230551142,-5.089875757621646\n2002-07-27,5.8035612437885575,38.94759170332479,-8.67355211861242,-5.463898339311037\n2002-07-28,4.9353128486824245,36.99514283267967,-7.841840631121893,-4.408786658940581\n2002-07-29,4.599145290593492,36.58009194946813,-6.940737815128477,-5.265388256832939\n2002-07-30,5.684265210536131,34.81401280297974,-8.222975084630166,-7.241342526825129\n2002-07-31,5.389854996638964,34.298514497964746,-8.289803572648644,-8.023878560476035\n2002-08-01,4.871408685419849,33.873230354809365,-9.019943474828336,-7.309372805572734\n2002-08-02,5.254181242622788,33.840439651698084,-9.771536727530998,-9.969860945616436\n2002-08-03,5.0208448821851075,32.03754717423421,-9.95092594002853,-11.500686608265262\n2002-08-04,5.2477397478492485,32.29465959939723,-11.113548548063944,-12.0042349821777\n2002-08-05,5.084995696067859,32.0989393499066,-11.902875858983554,-12.527471183479735\n2002-08-06,4.134466073803749,33.398995443972,-12.844725306114908,-13.383119021601267\n2002-08-07,4.63098141739135,33.26886228410422,-12.426265239652592,-13.38947138879173\n2002-08-08,2.3544527703361977,31.621973933840373,-9.8501665104472,-15.712814865728404\n2002-08-09,2.476136066452763,32.313506728658155,-9.575573848569352,-14.521308894683987\n2002-08-10,1.7470208954616067,32.668219013673855,-11.881517129199898,-13.195898740655345\n2002-08-11,1.3473246134147396,32.88235099581321,-11.527136903935741,-14.105424632145395\n2002-08-12,0.004038859167979636,35.3839008492724,-11.019736568821536,-13.490448530602986\n2002-08-13,-0.4084972188642606,35.80448149977188,-11.807074838962235,-12.529605787188325\n2002-08-14,-0.12495307239637626,34.56515273419395,-10.906779639212761,-13.128378116950811\n2002-08-15,1.4270408684702627,33.91257708697841,-11.070600344121555,-13.848145953447434\n2002-08-16,1.7791910323769193,34.1682116063228,-11.55247804265041,-13.207706136825347\n2002-08-17,0.09685105547829087,33.8090157031248,-13.019434572084243,-15.456041387996406\n2002-08-18,-1.2548498624655975,34.24354958946149,-14.092918128778546,-17.279872289329404\n2002-08-19,-0.8667997509007747,34.639961269684555,-14.693659389322494,-18.300650940157627\n2002-08-20,-1.3434442613325408,32.937457087137126,-12.04321308923043,-17.708716432121605\n2002-08-21,-1.4509145701782944,32.984399004390696,-10.6651070625981,-17.338146904815464\n2002-08-22,0.458424839281409,33.960720664272664,-9.823459519546983,-16.381091156013298\n2002-08-23,1.6623004794220022,34.01403177853265,-7.446201009346926,-16.649441324138223\n2002-08-24,0.6687199234313426,36.782634355362035,-7.542978857839898,-16.542588144529336\n2002-08-25,-0.22122413162609134,38.08426440598867,-7.353930412505883,-16.905011699272308\n2002-08-26,-0.4681551821706163,38.9088771380681,-7.691241647884874,-15.623527893672527\n2002-08-27,-0.489397502423242,38.78446666250478,-7.357399166665481,-14.543498111227883\n2002-08-28,0.46675556076997904,38.7807855963795,-6.743119660586832,-15.206051298049971\n2002-08-29,1.9031991218099655,36.687258179808,-5.720742148862145,-14.335688622183373\n2002-08-30,1.4328010474168904,37.18606471242063,-6.27487680108552,-14.879621255581322\n2002-08-31,2.2180208837685647,36.20039467390441,-5.63817829284806,-15.62538893161285\n2002-09-01,3.4053176475969504,36.98736461672818,-4.441911743584758,-14.162031077685075\n2002-09-02,3.8542524162219887,38.283544752453494,-4.845949106416309,-15.4493020795598\n2002-09-03,4.328033158762817,37.12380353842243,-6.006343902592015,-14.158929174577716\n2002-09-04,5.2191031716508425,37.20348620991238,-6.035182340000086,-14.4613889427918\n2002-09-05,4.8845468509151155,38.19283597715084,-6.447570207705912,-13.54120424399233\n2002-09-06,5.81743464162358,37.66999842176996,-5.983732164175501,-12.238303380307187\n2002-09-07,7.953334388201149,37.503575896087554,-4.7836587918877145,-12.092902499439742\n2002-09-08,8.575180907400478,37.37762203416459,-3.1542398520143102,-12.895207580556056\n2002-09-09,8.24224022995892,38.15549965099275,-3.2851322804937793,-13.483775480729937\n2002-09-10,9.699693062523206,37.421024341551174,-1.7631051506763755,-13.397275957099017\n2002-09-11,7.228026542775897,38.07056283595789,-1.2501513945311247,-14.455653926436211\n2002-09-12,7.276335645108946,38.5030573135807,-1.1100472529767071,-15.362900320893575\n2002-09-13,6.955453039028913,37.03861332956329,-0.5719424712179676,-14.527590060588107\n2002-09-14,6.232799896953183,37.254276267561934,-1.8930498650077556,-14.385401161163287\n2002-09-15,5.3318017762543874,36.671503947824135,-1.78228172609876,-13.645304881042035\n2002-09-16,5.825637585926799,36.92073265723552,-2.7181372166004576,-13.875984224300861\n2002-09-17,5.972137341600076,36.785577111714744,-3.92376306884486,-13.281417679849099\n2002-09-18,5.154684958122226,34.78985759081997,-4.426825410894858,-13.805227134602035\n2002-09-19,4.228035193769872,34.40034961479363,-4.431979923206513,-14.5204231636696\n2002-09-20,3.047639648273046,36.244469117001294,-5.093583882000251,-15.106411641767655\n2002-09-21,2.984360546700751,38.09534886971238,-5.560222337696929,-16.58421622164466\n2002-09-22,3.1870980335787036,37.16974169460996,-6.13224951982493,-14.910771026955588\n2002-09-23,1.7724699661446806,36.39948824425265,-6.25759958937042,-13.668260646294812\n2002-09-24,1.886271767383478,34.54330693974665,-6.6449145870510264,-15.213738811598251\n2002-09-25,2.9184384404811228,33.722114167520076,-5.736130352472307,-14.91971126031068\n2002-09-26,2.608268140109477,33.47308684630478,-5.111580637462243,-15.969799563712865\n"
  },
  {
    "path": "3.pandas/2.Pandas_Exercises/README.md",
    "content": "# Pandas Exercises(Pandas 练习题)\n\n网上可以搜到大量的pandas教程和官方文档，但没有简单的方法来练习。教程是很好的资源，但要付诸实践。 只有实践，才能更好的加深学习。\n\n我从github搜索到了一些pandas的练习题，含完整数据集，并进行整理：\n\n原代码作者：Guilherme Samora\n\n**本练习代码可以在百度云下载：**\n\n链接：https://pan.baidu.com/s/1qzIZAsirJSZLyVm5okcicA \n提取码：umgg \n\n**Pandas练习题目录**\n\n1.Getting and knowing  \n\n- Chipotle  \n- Occupation  \n- World Food Facts\n\n2.Filtering and Sorting\n\n- Chipotle  \n- Euro12  \n- Fictional Army\n\n3.Grouping\n\n- Alcohol Consumption  \n- Occupation  \n- Regiment\n\n4.Apply\n\n- Students \n- Alcohol Consumption  \n- US_Crime_Rates     \n\n5.Merge\n\n- Auto_MPG  \n- Fictitious Names  \n- House Market  \n\n6.Stats\n\n- US_Baby_Names  \n- Wind_Stats\n\n7.Visualization\n\n- Chipotle  \n- Titanic Disaster  \n- Scores  \n- Online Retail  \n- Tips  \n\n8.Creating Series and DataFrames  \n\n- Pokemon  \n\n9.Time Series  \n\n- Apple_Stock  \n- Getting_Financial_Data  \n- Investor_Flow_of_Funds_US  \n\n10.Deleting  \n\n- Iris  \n- Wine  \n\n\n\n**使用方法**\n\n每个练习文件夹有三个不同类型的文件: \n\n1.Exercises.ipynb\n\n没有答案代码的文件，这个是你做的练习\n\n2.Solutions.ipynb\n\n运行代码后的结果（不要改动）\n\n3.Exercise_with_Solutions.ipynb\n\n有答案代码和注释的文件\n\n你可以在Exercises.ipynb里输入代码，看看运行结果是否和Solutions.ipynb里面的内容一致，如果真的完成不了再看下Exercise_with_Solutions.ipynb的答案。\n\n"
  },
  {
    "path": "3.pandas/3.pandas_beginner/README.md",
    "content": "# 《pandas入门教程-2天学会pandas》\n\n**pandas_beginner**\n\n- ## 目录\n  ### 0.导语\n  ### 1.Series\n  ### 2.DataFrame\n  #### 2.1 DataFrame的简单运用\n\n  ### 3.pandas选择数据\n  #### 3.1 实战筛选\n  #### 3.2 筛选总结\n\n  ### 4.Pandas设置值\n  #### 4.1 创建数据\n  #### 4.2 根据位置设置loc和iloc\n  #### 4.3 根据条件设置\n  #### 4.4 按行或列设置\n  #### 4.5 添加Series序列(长度必须对齐)\n  #### 4.6 设定某行某列为特定值\n  #### 4.7 修改一整行数据\n\n  ### 5.Pandas处理丢失数据\n  #### 5.1 创建含NaN的矩阵\n  #### 5.2 删除掉有NaN的行或列\n  #### 5.3 替换NaN值为0或者其他\n  #### 5.4 是否有缺失数据NaN\n\n  ### 6.Pandas导入导出\n  #### 6.1 导入数据\n  #### 6.2 导出数据\n\n  ### 7.Pandas合并操作\n  #### 7.1 Pandas合并concat\n  #### 7.2.Pandas 合并 merge\n##### 7.2.1 定义资料集并打印出\n##### 7.2.2 依据key column合并,并打印\n##### 7.2.3 两列合并\n##### 7.2.4 Indicator设置合并列名称\n##### 7.2.5 依据index合并\n##### 7.2.6 解决overlapping的问题\n\n  ### 8.Pandas plot出图\n\n  ### 9.学习来源\n"
  },
  {
    "path": "3.pandas/3.pandas_beginner/pandas_beginner.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 2天学会Pandas\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 目录\\n\",\n    \"## 0.导语\\n\",\n    \"## 1.Series\\n\",\n    \"## 2.DataFrame\\n\",\n    \"### 2.1 DataFrame的简单运用\\n\",\n    \"\\n\",\n    \"## 3.pandas选择数据\\n\",\n    \"### 3.1 实战筛选\\n\",\n    \"### 3.2 筛选总结\\n\",\n    \"\\n\",\n    \"## 4.Pandas设置值\\n\",\n    \"### 4.1 创建数据\\n\",\n    \"### 4.2 根据位置设置loc和iloc\\n\",\n    \"### 4.3 根据条件设置\\n\",\n    \"### 4.4 按行或列设置\\n\",\n    \"### 4.5 添加Series序列(长度必须对齐)\\n\",\n    \"### 4.6 设定某行某列为特定值\\n\",\n    \"### 4.7 修改一整行数据\\n\",\n    \"\\n\",\n    \"## 5.Pandas处理丢失数据\\n\",\n    \"### 5.1 创建含NaN的矩阵\\n\",\n    \"### 5.2 删除掉有NaN的行或列\\n\",\n    \"### 5.3 替换NaN值为0或者其他\\n\",\n    \"### 5.4 是否有缺失数据NaN\\n\",\n    \"\\n\",\n    \"## 6.Pandas导入导出\\n\",\n    \"### 6.1 导入数据\\n\",\n    \"### 6.2 导出数据\\n\",\n    \"\\n\",\n    \"## 7.Pandas合并操作\\n\",\n    \"### 7.1 Pandas合并concat\\n\",\n    \"### 7.2.Pandas 合并 merge\\n\",\n    \"    *  7.2.1 定义资料集并打印出\\n\",\n    \"    *  7.2.2 依据key column合并,并打印\\n\",\n    \"    *  7.2.3 两列合并\\n\",\n    \"    *  7.2.4 Indicator设置合并列名称\\n\",\n    \"    *  7.2.5 依据index合并\\n\",\n    \"    *  7.2.6 解决overlapping的问题\\n\",\n    \"\\n\",\n    \"## 8.Pandas plot出图\\n\",\n    \"\\n\",\n    \"## 9.学习来源\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 0.导语\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Pandas是基于Numpy构建的，让Numpy为中心的应用变得更加简单。\\n\",\n    \"\\n\",\n    \"本文作者：光城\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.Series\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0     1.0\\n\",\n      \"1     3.0\\n\",\n      \"2     6.0\\n\",\n      \"3     NaN\\n\",\n      \"4    44.0\\n\",\n      \"5     1.0\\n\",\n      \"dtype: float64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"# Series\\n\",\n    \"s = pd.Series([1,3,6,np.nan,44,1])\\n\",\n    \"print(s)\\n\",\n    \"# 默认index从0开始,如果想要按照自己的索引设置，则修改index参数,如:index=[3,4,3,7,8,9]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2.DataFrame\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.1 DataFrame的简单运用\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"                   a         b         c         d\\n\",\n      \"2018-08-19 -0.493739 -1.308738  0.261489  1.322140\\n\",\n      \"2018-08-20 -1.738107  0.699740  1.483715  1.103715\\n\",\n      \"2018-08-21  1.843806  0.636613 -0.605184 -0.809692\\n\",\n      \"2018-08-22 -0.044920  0.275286  1.536055  1.219944\\n\",\n      \"2018-08-23  0.688810 -0.870828 -0.424904 -1.369430\\n\",\n      \"2018-08-24 -0.670488  0.175906  0.214264 -1.099845\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# DataFrame\\n\",\n    \"dates = pd.date_range('2018-08-19',periods=6)\\n\",\n    \"# dates = pd.date_range('2018-08-19','2018-08-24') # 起始、结束  与上述等价\\n\",\n    \"'''\\n\",\n    \"numpy.random.randn(d0, d1, …, dn)是从标准正态分布中返回一个或多个样本值。\\n\",\n    \"numpy.random.rand(d0, d1, …, dn)的随机样本位于[0, 1)中。\\n\",\n    \"(6,4)表示6行4列数据\\n\",\n    \"'''\\n\",\n    \"df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])\\n\",\n    \"print(df)\\n\",\n    \"# DataFrame既有行索引也有列索引， 它可以被看做由Series组成的大字典。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2018-08-19   -1.308738\\n\",\n      \"2018-08-20    0.699740\\n\",\n      \"2018-08-21    0.636613\\n\",\n      \"2018-08-22    0.275286\\n\",\n      \"2018-08-23   -0.870828\\n\",\n      \"2018-08-24    0.175906\\n\",\n      \"Freq: D, Name: b, dtype: float64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df['b'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   0  1   2   3\\n\",\n      \"0  0  1   2   3\\n\",\n      \"1  4  5   6   7\\n\",\n      \"2  8  9  10  11\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 未指定行标签和列标签的数据\\n\",\n    \"df1 = pd.DataFrame(np.arange(12).reshape(3,4))\\n\",\n    \"print(df1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   A          B     C  D      E    F\\n\",\n      \"0  1 2018-08-19   1.0  3   test  foo\\n\",\n      \"1  2 2018-08-19   6.0  3  train  foo\\n\",\n      \"2  3 2018-08-19   9.0  3   test  foo\\n\",\n      \"3  4 2018-08-19  10.0  3  train  foo\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 另一种方式\\n\",\n    \"df2 = pd.DataFrame({\\n\",\n    \"    'A': [1,2,3,4],\\n\",\n    \"    'B': pd.Timestamp('20180819'),\\n\",\n    \"    'C': pd.Series([1,6,9,10],dtype='float32'),\\n\",\n    \"    'D': np.array([3] * 4,dtype='int32'),\\n\",\n    \"    'E': pd.Categorical(['test','train','test','train']),\\n\",\n    \"    'F': 'foo'\\n\",\n    \"})\\n\",\n    \"print(df2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"RangeIndex(start=0, stop=4, step=1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df2.index)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df2.columns)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1 Timestamp('2018-08-19 00:00:00') 1.0 3 'test' 'foo']\\n\",\n      \" [2 Timestamp('2018-08-19 00:00:00') 6.0 3 'train' 'foo']\\n\",\n      \" [3 Timestamp('2018-08-19 00:00:00') 9.0 3 'test' 'foo']\\n\",\n      \" [4 Timestamp('2018-08-19 00:00:00') 10.0 3 'train' 'foo']]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df2.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"              A          C    D\\n\",\n      \"count  4.000000   4.000000  4.0\\n\",\n      \"mean   2.500000   6.500000  3.0\\n\",\n      \"std    1.290994   4.041452  0.0\\n\",\n      \"min    1.000000   1.000000  3.0\\n\",\n      \"25%    1.750000   4.750000  3.0\\n\",\n      \"50%    2.500000   7.500000  3.0\\n\",\n      \"75%    3.250000   9.250000  3.0\\n\",\n      \"max    4.000000  10.000000  3.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 数据总结\\n\",\n    \"print(df2.describe())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"                     0                    1                    2  \\\\\\n\",\n      \"A                    1                    2                    3   \\n\",\n      \"B  2018-08-19 00:00:00  2018-08-19 00:00:00  2018-08-19 00:00:00   \\n\",\n      \"C                    1                    6                    9   \\n\",\n      \"D                    3                    3                    3   \\n\",\n      \"E                 test                train                 test   \\n\",\n      \"F                  foo                  foo                  foo   \\n\",\n      \"\\n\",\n      \"                     3  \\n\",\n      \"A                    4  \\n\",\n      \"B  2018-08-19 00:00:00  \\n\",\n      \"C                   10  \\n\",\n      \"D                    3  \\n\",\n      \"E                train  \\n\",\n      \"F                  foo  \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 翻转数据\\n\",\n    \"print(df2.T)\\n\",\n    \"# print(np.transpose(df2))等价于上述操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   A          B     C  D      E    F\\n\",\n      \"0  1 2018-08-19   1.0  3   test  foo\\n\",\n      \"1  2 2018-08-19   6.0  3  train  foo\\n\",\n      \"2  3 2018-08-19   9.0  3   test  foo\\n\",\n      \"3  4 2018-08-19  10.0  3  train  foo\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"'''\\n\",\n    \"axis=1表示行\\n\",\n    \"axis=0表示列\\n\",\n    \"默认ascending(升序)为True\\n\",\n    \"ascending=True表示升序,ascending=False表示降序\\n\",\n    \"下面两行分别表示按行升序与按行降序\\n\",\n    \"'''\\n\",\n    \"print(df2.sort_index(axis=1,ascending=True))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     F      E  D     C          B  A\\n\",\n      \"0  foo   test  3   1.0 2018-08-19  1\\n\",\n      \"1  foo  train  3   6.0 2018-08-19  2\\n\",\n      \"2  foo   test  3   9.0 2018-08-19  3\\n\",\n      \"3  foo  train  3  10.0 2018-08-19  4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df2.sort_index(axis=1,ascending=False))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   A          B     C  D      E    F\\n\",\n      \"3  4 2018-08-19  10.0  3  train  foo\\n\",\n      \"2  3 2018-08-19   9.0  3   test  foo\\n\",\n      \"1  2 2018-08-19   6.0  3  train  foo\\n\",\n      \"0  1 2018-08-19   1.0  3   test  foo\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 表示按列降序与按列升序\\n\",\n    \"print(df2.sort_index(axis=0,ascending=False))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   A          B     C  D      E    F\\n\",\n      \"0  1 2018-08-19   1.0  3   test  foo\\n\",\n      \"1  2 2018-08-19   6.0  3  train  foo\\n\",\n      \"2  3 2018-08-19   9.0  3   test  foo\\n\",\n      \"3  4 2018-08-19  10.0  3  train  foo\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df2.sort_index(axis=0,ascending=True))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   A          B     C  D      E    F\\n\",\n      \"3  4 2018-08-19  10.0  3  train  foo\\n\",\n      \"2  3 2018-08-19   9.0  3   test  foo\\n\",\n      \"1  2 2018-08-19   6.0  3  train  foo\\n\",\n      \"0  1 2018-08-19   1.0  3   test  foo\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 对特定列数值排列\\n\",\n    \"# 表示对C列降序排列\\n\",\n    \"print(df2.sort_values(by='C',ascending=False))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 3.pandas选择数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.1 实战筛选\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A   B   C   D\\n\",\n      \"2018-08-19   0   1   2   3\\n\",\n      \"2018-08-20   4   5   6   7\\n\",\n      \"2018-08-21   8   9  10  11\\n\",\n      \"2018-08-22  12  13  14  15\\n\",\n      \"2018-08-23  16  17  18  19\\n\",\n      \"2018-08-24  20  21  22  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"dates = pd.date_range('20180819', periods=6)\\n\",\n    \"df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D'])\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2018-08-19     0\\n\",\n      \"2018-08-20     4\\n\",\n      \"2018-08-21     8\\n\",\n      \"2018-08-22    12\\n\",\n      \"2018-08-23    16\\n\",\n      \"2018-08-24    20\\n\",\n      \"Freq: D, Name: A, dtype: int32\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 检索A列\\n\",\n    \"print(df['A'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2018-08-19     0\\n\",\n      \"2018-08-20     4\\n\",\n      \"2018-08-21     8\\n\",\n      \"2018-08-22    12\\n\",\n      \"2018-08-23    16\\n\",\n      \"2018-08-24    20\\n\",\n      \"Freq: D, Name: A, dtype: int32\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df.A)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"            A  B   C   D\\n\",\n      \"2018-08-19  0  1   2   3\\n\",\n      \"2018-08-20  4  5   6   7\\n\",\n      \"2018-08-21  8  9  10  11\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 选择跨越多行或多列\\n\",\n    \"# 选取前3行\\n\",\n    \"print(df[0:3])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"            A  B   C   D\\n\",\n      \"2018-08-19  0  1   2   3\\n\",\n      \"2018-08-20  4  5   6   7\\n\",\n      \"2018-08-21  8  9  10  11\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df['2018-08-19':'2018-08-21'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"A    0\\n\",\n      \"B    1\\n\",\n      \"C    2\\n\",\n      \"D    3\\n\",\n      \"Name: 2018-08-19 00:00:00, dtype: int32\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 根据标签选择数据\\n\",\n    \"# 获取特定行或列\\n\",\n    \"# 指定行数据\\n\",\n    \"print(df.loc['20180819'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A   B\\n\",\n      \"2018-08-19   0   1\\n\",\n      \"2018-08-20   4   5\\n\",\n      \"2018-08-21   8   9\\n\",\n      \"2018-08-22  12  13\\n\",\n      \"2018-08-23  16  17\\n\",\n      \"2018-08-24  20  21\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 指定列\\n\",\n    \"# 两种方式\\n\",\n    \"print(df.loc[:,'A':'B'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A   B\\n\",\n      \"2018-08-19   0   1\\n\",\n      \"2018-08-20   4   5\\n\",\n      \"2018-08-21   8   9\\n\",\n      \"2018-08-22  12  13\\n\",\n      \"2018-08-23  16  17\\n\",\n      \"2018-08-24  20  21\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df.loc[:,['A','B']])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"A    0\\n\",\n      \"B    1\\n\",\n      \"Name: 2018-08-19 00:00:00, dtype: int32\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 行与列同时检索\\n\",\n    \"print(df.loc['20180819',['A','B']])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 根据序列iloc\\n\",\n    \"# 获取特定位置的值\\n\",\n    \"print(df.iloc[3,1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             B   C\\n\",\n      \"2018-08-22  13  14\\n\",\n      \"2018-08-23  17  18\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df.iloc[3:5,1:3]) # 不包含末尾5或3，同列表切片\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             B   C\\n\",\n      \"2018-08-20   5   6\\n\",\n      \"2018-08-22  13  14\\n\",\n      \"2018-08-24  21  22\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 跨行操作\\n\",\n    \"print(df.iloc[[1,3,5],1:3])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"            A   C\\n\",\n      \"2018-08-19  0   2\\n\",\n      \"2018-08-20  4   6\\n\",\n      \"2018-08-21  8  10\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\ipykernel_launcher.py:2: DeprecationWarning: \\n\",\n      \".ix is deprecated. Please use\\n\",\n      \".loc for label based indexing or\\n\",\n      \".iloc for positional indexing\\n\",\n      \"\\n\",\n      \"See the documentation here:\\n\",\n      \"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\\n\",\n      \"  \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 混合选择\\n\",\n    \"print(df.ix[:3,['A','C']])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"            A   C\\n\",\n      \"2018-08-19  0   2\\n\",\n      \"2018-08-20  4   6\\n\",\n      \"2018-08-21  8  10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df.iloc[:3,[0,2]]) # 结果同上\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A   B   C   D\\n\",\n      \"2018-08-22  12  13  14  15\\n\",\n      \"2018-08-23  16  17  18  19\\n\",\n      \"2018-08-24  20  21  22  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 通过判断的筛选\\n\",\n    \"print(df[df.A>8])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A   B   C   D\\n\",\n      \"2018-08-22  12  13  14  15\\n\",\n      \"2018-08-23  16  17  18  19\\n\",\n      \"2018-08-24  20  21  22  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 通过判断的筛选\\n\",\n    \"print(df.loc[df.A>8])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* 3.2 筛选总结\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"1.iloc与ix区别\\n\",\n    \"  > 总结:相同点：iloc可以取相应的值，操作方便,与ix操作类似。\\n\",\n    \"  \\n\",\n    \"  > 不同点：ix可以混合选择，可以填入column对应的字符选择，而iloc只能采用index索引，对于列数较多情况下，ix要方便操作许多。\\n\",\n    \"  \\n\",\n    \"2.loc与iloc区别\\n\",\n    \"  > 总结：相同点：都可以索引处块数据\\n\",\n    \"  \\n\",\n    \"  > 不同点：iloc可以检索对应值,两者操作不同。\\n\",\n    \"  \\n\",\n    \"3.ix与loc、iloc三者的区别\\n\",\n    \"  > n总结：ix是混合loc与iloc操作\\n\",\n    \"  \\n\",\n    \"如下:对比三者操作,输出结果相同\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"A    0\\n\",\n      \"B    1\\n\",\n      \"Name: 2018-08-19 00:00:00, dtype: int32\\n\",\n      \"A    0\\n\",\n      \"B    1\\n\",\n      \"Name: 2018-08-19 00:00:00, dtype: int32\\n\",\n      \"A    0\\n\",\n      \"B    1\\n\",\n      \"Name: 2018-08-19 00:00:00, dtype: int32\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df.loc['20180819','A':'B'])\\n\",\n    \"print(df.iloc[0,0:2])\\n\",\n    \"print(df.ix[0,'A':'B'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4.Pandas设置值\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.1 创建数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A   B   C   D\\n\",\n      \"2018-08-20   0   1   2   3\\n\",\n      \"2018-08-21   4   5   6   7\\n\",\n      \"2018-08-22   8   9  10  11\\n\",\n      \"2018-08-23  12  13  14  15\\n\",\n      \"2018-08-24  16  17  18  19\\n\",\n      \"2018-08-25  20  21  22  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"# 创建数据\\n\",\n    \"dates = pd.date_range('20180820',periods=6)\\n\",\n    \"df = pd.DataFrame(np.arange(24).reshape(6,4), index=dates, columns=['A','B','C','D'])\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.2 根据位置设置loc和iloc\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A     B    C   D\\n\",\n      \"2018-08-20   0  2222    2   3\\n\",\n      \"2018-08-21   4     5    6   7\\n\",\n      \"2018-08-22   8     9  111  11\\n\",\n      \"2018-08-23  12    13   14  15\\n\",\n      \"2018-08-24  16    17   18  19\\n\",\n      \"2018-08-25  20    21   22  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 根据位置设置loc和iloc\\n\",\n    \"df.iloc[2,2] = 111\\n\",\n    \"df.loc['20180820','B'] = 2222\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.3 根据条件设置\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A     B    C   D\\n\",\n      \"2018-08-20   0  2222    2   3\\n\",\n      \"2018-08-21   4     5    6   7\\n\",\n      \"2018-08-22   8     0  111  11\\n\",\n      \"2018-08-23  12     0   14  15\\n\",\n      \"2018-08-24  16     0   18  19\\n\",\n      \"2018-08-25  20     0   22  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 根据条件设置\\n\",\n    \"# 更改B中的数，而更改的位置取决于4的位置，并设相应位置的数为0\\n\",\n    \"df.B[df.A>4] = 0\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A     B    C   D\\n\",\n      \"2018-08-20   0  2222    2   3\\n\",\n      \"2018-08-21   4     5    6   7\\n\",\n      \"2018-08-22   8     0  111  11\\n\",\n      \"2018-08-23  12     0   14  15\\n\",\n      \"2018-08-24  16     0   18  19\\n\",\n      \"2018-08-25  20     0   22  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df.B.loc[df.A>4] = 0\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.4 按行或列设置\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A     B    C   D   F\\n\",\n      \"2018-08-20   0  2222    2   3 NaN\\n\",\n      \"2018-08-21   4     5    6   7 NaN\\n\",\n      \"2018-08-22   8     0  111  11 NaN\\n\",\n      \"2018-08-23  12     0   14  15 NaN\\n\",\n      \"2018-08-24  16     0   18  19 NaN\\n\",\n      \"2018-08-25  20     0   22  23 NaN\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 按行或列设置\\n\",\n    \"# 列批处理，F列全改为NaN\\n\",\n    \"df['F'] = np.nan\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.5 添加Series序列(长度必须对齐)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A     B    C   D   F  E\\n\",\n      \"2018-08-20   0  2222    2   3 NaN  1\\n\",\n      \"2018-08-21   4     5    6   7 NaN  2\\n\",\n      \"2018-08-22   8     0  111  11 NaN  3\\n\",\n      \"2018-08-23  12     0   14  15 NaN  4\\n\",\n      \"2018-08-24  16     0   18  19 NaN  5\\n\",\n      \"2018-08-25  20     0   22  23 NaN  6\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df['E'] = pd.Series([1,2,3,4,5,6], index=pd.date_range('20180820',periods=6))\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.6 设定某行某列为特定值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A     B    C   D   F  E\\n\",\n      \"2018-08-20  56  2222    2   3 NaN  1\\n\",\n      \"2018-08-21   4     5    6   7 NaN  2\\n\",\n      \"2018-08-22   8     0  111  11 NaN  3\\n\",\n      \"2018-08-23  12     0   14  15 NaN  4\\n\",\n      \"2018-08-24  16     0   18  19 NaN  5\\n\",\n      \"2018-08-25  20     0   22  23 NaN  6\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\ipykernel_launcher.py:2: DeprecationWarning: \\n\",\n      \".ix is deprecated. Please use\\n\",\n      \".loc for label based indexing or\\n\",\n      \".iloc for positional indexing\\n\",\n      \"\\n\",\n      \"See the documentation here:\\n\",\n      \"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\\n\",\n      \"  \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 设定某行某列为特定值\\n\",\n    \"df.ix['20180820','A'] = 56\\n\",\n    \"print(df)\\n\",\n    \"#ix 以后要剥离了，尽量不要用了\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A     B    C   D   F  E\\n\",\n      \"2018-08-20  67  2222    2   3 NaN  1\\n\",\n      \"2018-08-21   4     5    6   7 NaN  2\\n\",\n      \"2018-08-22   8     0  111  11 NaN  3\\n\",\n      \"2018-08-23  12     0   14  15 NaN  4\\n\",\n      \"2018-08-24  16     0   18  19 NaN  5\\n\",\n      \"2018-08-25  20     0   22  23 NaN  6\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df.loc['20180820','A'] = 67\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A     B    C   D   F  E\\n\",\n      \"2018-08-20  76  2222    2   3 NaN  1\\n\",\n      \"2018-08-21   4     5    6   7 NaN  2\\n\",\n      \"2018-08-22   8     0  111  11 NaN  3\\n\",\n      \"2018-08-23  12     0   14  15 NaN  4\\n\",\n      \"2018-08-24  16     0   18  19 NaN  5\\n\",\n      \"2018-08-25  20     0   22  23 NaN  6\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df.iloc[0,0] = 76\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.7 修改一整行数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"               A       B      C     D   F    E\\n\",\n      \"2018-08-20  76.0  2222.0    2.0   3.0 NaN  1.0\\n\",\n      \"2018-08-21   NaN     NaN    NaN   NaN NaN  NaN\\n\",\n      \"2018-08-22   8.0     0.0  111.0  11.0 NaN  3.0\\n\",\n      \"2018-08-23  12.0     0.0   14.0  15.0 NaN  4.0\\n\",\n      \"2018-08-24  16.0     0.0   18.0  19.0 NaN  5.0\\n\",\n      \"2018-08-25  20.0     0.0   22.0  23.0 NaN  6.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 修改一整行数据\\n\",\n    \"df.iloc[1] = np.nan # df.iloc[1,:]=np.nan\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"               A    B      C     D   F    E\\n\",\n      \"2018-08-20   NaN  NaN    NaN   NaN NaN  NaN\\n\",\n      \"2018-08-21   NaN  NaN    NaN   NaN NaN  NaN\\n\",\n      \"2018-08-22   8.0  0.0  111.0  11.0 NaN  3.0\\n\",\n      \"2018-08-23  12.0  0.0   14.0  15.0 NaN  4.0\\n\",\n      \"2018-08-24  16.0  0.0   18.0  19.0 NaN  5.0\\n\",\n      \"2018-08-25  20.0  0.0   22.0  23.0 NaN  6.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df.loc['20180820'] = np.nan # df.loc['20180820,:']=np.nan\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"               A    B     C     D   F    E\\n\",\n      \"2018-08-20   NaN  NaN   NaN   NaN NaN  NaN\\n\",\n      \"2018-08-21   NaN  NaN   NaN   NaN NaN  NaN\\n\",\n      \"2018-08-22   NaN  NaN   NaN   NaN NaN  NaN\\n\",\n      \"2018-08-23  12.0  0.0  14.0  15.0 NaN  4.0\\n\",\n      \"2018-08-24  16.0  0.0  18.0  19.0 NaN  5.0\\n\",\n      \"2018-08-25  20.0  0.0  22.0  23.0 NaN  6.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df.ix[2] = np.nan # df.ix[2,:]=np.nan\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"               A    B     C     D   F    E\\n\",\n      \"2018-08-20   NaN  NaN   NaN   NaN NaN  NaN\\n\",\n      \"2018-08-21   NaN  NaN   NaN   NaN NaN  NaN\\n\",\n      \"2018-08-22   NaN  NaN   NaN   NaN NaN  NaN\\n\",\n      \"2018-08-23   NaN  NaN   NaN   NaN NaN  NaN\\n\",\n      \"2018-08-24  16.0  0.0  18.0  19.0 NaN  5.0\\n\",\n      \"2018-08-25  20.0  0.0  22.0  23.0 NaN  6.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df.ix['20180823'] = np.nan\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5.Pandas处理丢失数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 5.1 创建含NaN的矩阵\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A   B   C   D\\n\",\n      \"2018-08-20   0   1   2   3\\n\",\n      \"2018-08-21   4   5   6   7\\n\",\n      \"2018-08-22   8   9  10  11\\n\",\n      \"2018-08-23  12  13  14  15\\n\",\n      \"2018-08-24  16  17  18  19\\n\",\n      \"2018-08-25  20  21  22  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Pandas处理丢失数据\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"# 创建含NaN的矩阵\\n\",\n    \"# 如何填充和删除NaN数据?\\n\",\n    \"dates = pd.date_range('20180820',periods=6)\\n\",\n    \"df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns=['A','B','C','D']) \\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A     B     C   D\\n\",\n      \"2018-08-20   0   NaN   2.0   3\\n\",\n      \"2018-08-21   4   5.0   NaN   7\\n\",\n      \"2018-08-22   8   9.0  10.0  11\\n\",\n      \"2018-08-23  12  13.0  14.0  15\\n\",\n      \"2018-08-24  16  17.0  18.0  19\\n\",\n      \"2018-08-25  20  21.0  22.0  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# a.reshape(6,4)等价于a.reshape((6,4))\\n\",\n    \"df.iloc[0,1] = np.nan\\n\",\n    \"df.iloc[1,2] = np.nan\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 5.2 删除掉有NaN的行或列\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A     B     C   D\\n\",\n      \"2018-08-22   8   9.0  10.0  11\\n\",\n      \"2018-08-23  12  13.0  14.0  15\\n\",\n      \"2018-08-24  16  17.0  18.0  19\\n\",\n      \"2018-08-25  20  21.0  22.0  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 删除掉有NaN的行或列\\n\",\n    \"print(df.dropna()) # 默认是删除掉含有NaN的行\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A     B     C   D\\n\",\n      \"2018-08-22   8   9.0  10.0  11\\n\",\n      \"2018-08-23  12  13.0  14.0  15\\n\",\n      \"2018-08-24  16  17.0  18.0  19\\n\",\n      \"2018-08-25  20  21.0  22.0  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df.dropna(\\n\",\n    \"    axis=0, # 0对行进行操作;1对列进行操作\\n\",\n    \"    how='any' # 'any':只要存在NaN就drop掉；'all':必须全部是NaN才drop\\n\",\n    \"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A   D\\n\",\n      \"2018-08-20   0   3\\n\",\n      \"2018-08-21   4   7\\n\",\n      \"2018-08-22   8  11\\n\",\n      \"2018-08-23  12  15\\n\",\n      \"2018-08-24  16  19\\n\",\n      \"2018-08-25  20  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 删除掉所有含有NaN的列\\n\",\n    \"print(df.dropna(\\n\",\n    \"    axis=1,\\n\",\n    \"    how='any'\\n\",\n    \"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 5.3 替换NaN值为0或者其他\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"             A     B     C   D\\n\",\n      \"2018-08-20   0   0.0   2.0   3\\n\",\n      \"2018-08-21   4   5.0   0.0   7\\n\",\n      \"2018-08-22   8   9.0  10.0  11\\n\",\n      \"2018-08-23  12  13.0  14.0  15\\n\",\n      \"2018-08-24  16  17.0  18.0  19\\n\",\n      \"2018-08-25  20  21.0  22.0  23\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 替换NaN值为0或者其他\\n\",\n    \"print(df.fillna(value=0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 5.4 是否有缺失数据NaN\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"                A      B      C      D\\n\",\n      \"2018-08-20  False   True  False  False\\n\",\n      \"2018-08-21  False  False   True  False\\n\",\n      \"2018-08-22  False  False  False  False\\n\",\n      \"2018-08-23  False  False  False  False\\n\",\n      \"2018-08-24  False  False  False  False\\n\",\n      \"2018-08-25  False  False  False  False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 是否有缺失数据NaN\\n\",\n    \"# 是否为空\\n\",\n    \"print(df.isnull())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"                A      B      C      D\\n\",\n      \"2018-08-20  False   True  False  False\\n\",\n      \"2018-08-21  False  False   True  False\\n\",\n      \"2018-08-22  False  False  False  False\\n\",\n      \"2018-08-23  False  False  False  False\\n\",\n      \"2018-08-24  False  False  False  False\\n\",\n      \"2018-08-25  False  False  False  False\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 是否为NaN\\n\",\n    \"print(df.isna())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"A    False\\n\",\n      \"B     True\\n\",\n      \"C     True\\n\",\n      \"D    False\\n\",\n      \"dtype: bool\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 检测某列是否有缺失数据NaN\\n\",\n    \"print(df.isnull().any())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 检测数据中是否存在NaN,如果存在就返回True\\n\",\n    \"print(np.any(df.isnull())==True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 6.Pandas导入导出\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.1 导入数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    Student ID  name   age  gender\\n\",\n      \"0         1100  Kelly   22  Female\\n\",\n      \"1         1101    Clo   21  Female\\n\",\n      \"2         1102  Tilly   22  Female\\n\",\n      \"3         1103   Tony   24    Male\\n\",\n      \"4         1104  David   20    Male\\n\",\n      \"5         1105  Catty   22  Female\\n\",\n      \"6         1106      M    3  Female\\n\",\n      \"7         1107      N   43    Male\\n\",\n      \"8         1108      A   13    Male\\n\",\n      \"9         1109      S   12    Male\\n\",\n      \"10        1110  David   33    Male\\n\",\n      \"11        1111     Dw    3  Female\\n\",\n      \"12        1112      Q   23    Male\\n\",\n      \"13        1113      W   21  Female\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd # 加载模块\\n\",\n    \"# 读取csv\\n\",\n    \"data = pd.read_csv('student.csv')\\n\",\n    \"# 打印出data\\n\",\n    \"print(data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   Student ID  name   age  gender\\n\",\n      \"0        1100  Kelly   22  Female\\n\",\n      \"1        1101    Clo   21  Female\\n\",\n      \"2        1102  Tilly   22  Female\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 前三行\\n\",\n    \"print(data.head(3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    Student ID name   age  gender\\n\",\n      \"11        1111    Dw    3  Female\\n\",\n      \"12        1112     Q   23    Male\\n\",\n      \"13        1113     W   21  Female\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 后三行\\n\",\n    \"print(data.tail(3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.2 导出数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 将资料存取成pickle\\n\",\n    \"data.to_pickle('student.pickle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    Student ID  name   age  gender\\n\",\n      \"0         1100  Kelly   22  Female\\n\",\n      \"1         1101    Clo   21  Female\\n\",\n      \"2         1102  Tilly   22  Female\\n\",\n      \"3         1103   Tony   24    Male\\n\",\n      \"4         1104  David   20    Male\\n\",\n      \"5         1105  Catty   22  Female\\n\",\n      \"6         1106      M    3  Female\\n\",\n      \"7         1107      N   43    Male\\n\",\n      \"8         1108      A   13    Male\\n\",\n      \"9         1109      S   12    Male\\n\",\n      \"10        1110  David   33    Male\\n\",\n      \"11        1111     Dw    3  Female\\n\",\n      \"12        1112      Q   23    Male\\n\",\n      \"13        1113      W   21  Female\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 读取pickle文件并打印\\n\",\n    \"print(pd.read_pickle('student.pickle'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 7.Pandas合并操作\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 7.1 Pandas合并concat\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d\\n\",\n      \"0  0.0  0.0  0.0  0.0\\n\",\n      \"1  0.0  0.0  0.0  0.0\\n\",\n      \"2  0.0  0.0  0.0  0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"# 定义资料集\\n\",\n    \"df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'])\\n\",\n    \"df2 = pd.DataFrame(np.ones((3,4))*1, columns=['a','b','c','d'])\\n\",\n    \"df3 = pd.DataFrame(np.ones((3,4))*2, columns=['a','b','c','d'])\\n\",\n    \"print(df1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d\\n\",\n      \"0  1.0  1.0  1.0  1.0\\n\",\n      \"1  1.0  1.0  1.0  1.0\\n\",\n      \"2  1.0  1.0  1.0  1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d\\n\",\n      \"0  2.0  2.0  2.0  2.0\\n\",\n      \"1  2.0  2.0  2.0  2.0\\n\",\n      \"2  2.0  2.0  2.0  2.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d\\n\",\n      \"0  0.0  0.0  0.0  0.0\\n\",\n      \"1  0.0  0.0  0.0  0.0\\n\",\n      \"2  0.0  0.0  0.0  0.0\\n\",\n      \"0  1.0  1.0  1.0  1.0\\n\",\n      \"1  1.0  1.0  1.0  1.0\\n\",\n      \"2  1.0  1.0  1.0  1.0\\n\",\n      \"0  2.0  2.0  2.0  2.0\\n\",\n      \"1  2.0  2.0  2.0  2.0\\n\",\n      \"2  2.0  2.0  2.0  2.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# concat纵向合并\\n\",\n    \"res = pd.concat([df1,df2,df3],axis=0)\\n\",\n    \"\\n\",\n    \"# 打印结果\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d\\n\",\n      \"0  0.0  0.0  0.0  0.0\\n\",\n      \"1  0.0  0.0  0.0  0.0\\n\",\n      \"2  0.0  0.0  0.0  0.0\\n\",\n      \"3  1.0  1.0  1.0  1.0\\n\",\n      \"4  1.0  1.0  1.0  1.0\\n\",\n      \"5  1.0  1.0  1.0  1.0\\n\",\n      \"6  2.0  2.0  2.0  2.0\\n\",\n      \"7  2.0  2.0  2.0  2.0\\n\",\n      \"8  2.0  2.0  2.0  2.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 上述合并过程中，index重复，下面给出重置index方法\\n\",\n    \"# 只需要将index_ignore设定为True即可\\n\",\n    \"res = pd.concat([df1,df2,df3],axis=0,ignore_index=True)\\n\",\n    \"\\n\",\n    \"# 打印结果\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d\\n\",\n      \"1  0.0  0.0  0.0  0.0\\n\",\n      \"2  0.0  0.0  0.0  0.0\\n\",\n      \"3  0.0  0.0  0.0  0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# join 合并方式\\n\",\n    \"#定义资料集\\n\",\n    \"df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'], index=[1,2,3])\\n\",\n    \"df2 = pd.DataFrame(np.ones((3,4))*1, columns=['b','c','d','e'], index=[2,3,4])\\n\",\n    \"print(df1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     b    c    d    e\\n\",\n      \"2  1.0  1.0  1.0  1.0\\n\",\n      \"3  1.0  1.0  1.0  1.0\\n\",\n      \"4  1.0  1.0  1.0  1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d    e\\n\",\n      \"1  0.0  0.0  0.0  0.0  NaN\\n\",\n      \"2  0.0  0.0  0.0  0.0  NaN\\n\",\n      \"3  0.0  0.0  0.0  0.0  NaN\\n\",\n      \"2  NaN  1.0  1.0  1.0  1.0\\n\",\n      \"3  NaN  1.0  1.0  1.0  1.0\\n\",\n      \"4  NaN  1.0  1.0  1.0  1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"'''\\n\",\n    \"join='outer',函数默认为join='outer'。此方法是依照column来做纵向合并，有相同的column上下合并在一起，\\n\",\n    \"其他独自的column各自成列，原来没有值的位置皆为NaN填充。\\n\",\n    \"'''\\n\",\n    \"# 纵向\\\"外\\\"合并df1与df2\\n\",\n    \"res = pd.concat([df1,df2],axis=0,join='outer')\\n\",\n    \"\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d    e\\n\",\n      \"0  0.0  0.0  0.0  0.0  NaN\\n\",\n      \"1  0.0  0.0  0.0  0.0  NaN\\n\",\n      \"2  0.0  0.0  0.0  0.0  NaN\\n\",\n      \"3  NaN  1.0  1.0  1.0  1.0\\n\",\n      \"4  NaN  1.0  1.0  1.0  1.0\\n\",\n      \"5  NaN  1.0  1.0  1.0  1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 修改index\\n\",\n    \"res = pd.concat([df1,df2],axis=0,join='outer',ignore_index=True)\\n\",\n    \"\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     b    c    d\\n\",\n      \"1  0.0  0.0  0.0\\n\",\n      \"2  0.0  0.0  0.0\\n\",\n      \"3  0.0  0.0  0.0\\n\",\n      \"2  1.0  1.0  1.0\\n\",\n      \"3  1.0  1.0  1.0\\n\",\n      \"4  1.0  1.0  1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# join='inner'合并相同的字段\\n\",\n    \"# 纵向\\\"内\\\"合并df1与df2\\n\",\n    \"res = pd.concat([df1,df2],axis=0,join='inner')\\n\",\n    \"# 打印结果\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d\\n\",\n      \"1  0.0  0.0  0.0  0.0\\n\",\n      \"2  0.0  0.0  0.0  0.0\\n\",\n      \"3  0.0  0.0  0.0  0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# join_axes(依照axes合并)\\n\",\n    \"#定义资料集\\n\",\n    \"df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'], index=[1,2,3])\\n\",\n    \"df2 = pd.DataFrame(np.ones((3,4))*1, columns=['b','c','d','e'], index=[2,3,4])\\n\",\n    \"print(df1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     b    c    d    e\\n\",\n      \"2  1.0  1.0  1.0  1.0\\n\",\n      \"3  1.0  1.0  1.0  1.0\\n\",\n      \"4  1.0  1.0  1.0  1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d    b    c    d    e\\n\",\n      \"1  0.0  0.0  0.0  0.0  NaN  NaN  NaN  NaN\\n\",\n      \"2  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0\\n\",\n      \"3  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 依照df1.index进行横向合并\\n\",\n    \"res = pd.concat([df1,df2],axis=1,join_axes=[df1.index])\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d    b    c    d    e\\n\",\n      \"1  0.0  0.0  0.0  0.0  NaN  NaN  NaN  NaN\\n\",\n      \"2  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0\\n\",\n      \"3  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0\\n\",\n      \"4  NaN  NaN  NaN  NaN  1.0  1.0  1.0  1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 移除join_axes参数,打印结果\\n\",\n    \"res = pd.concat([df1,df2],axis=1)\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d\\n\",\n      \"0  0.0  0.0  0.0  0.0\\n\",\n      \"1  0.0  0.0  0.0  0.0\\n\",\n      \"2  0.0  0.0  0.0  0.0\\n\",\n      \"3  1.0  1.0  1.0  1.0\\n\",\n      \"4  1.0  1.0  1.0  1.0\\n\",\n      \"5  1.0  1.0  1.0  1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# append(添加数据)\\n\",\n    \"# append只有纵向合并，没有横向合并\\n\",\n    \"#定义资料集\\n\",\n    \"df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'])\\n\",\n    \"df2 = pd.DataFrame(np.ones((3,4))*1, columns=['a','b','c','d'])\\n\",\n    \"df3 = pd.DataFrame(np.ones((3,4))*2, columns=['a','b','c','d'])\\n\",\n    \"s1 = pd.Series([1,2,3,4], index=['a','b','c','d'])\\n\",\n    \"# 将df2合并到df1下面,以及重置index,并打印出结果\\n\",\n    \"res = df1.append(df2,ignore_index=True)\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d\\n\",\n      \"0  0.0  0.0  0.0  0.0\\n\",\n      \"1  0.0  0.0  0.0  0.0\\n\",\n      \"2  0.0  0.0  0.0  0.0\\n\",\n      \"3  1.0  1.0  1.0  1.0\\n\",\n      \"4  1.0  1.0  1.0  1.0\\n\",\n      \"5  1.0  1.0  1.0  1.0\\n\",\n      \"6  2.0  2.0  2.0  2.0\\n\",\n      \"7  2.0  2.0  2.0  2.0\\n\",\n      \"8  2.0  2.0  2.0  2.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 合并多个df,将df2与df3合并至df1的下面,以及重置index,并打印出结果\\n\",\n    \"res = df1.append([df2,df3], ignore_index=True)\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d\\n\",\n      \"0  0.0  0.0  0.0  0.0\\n\",\n      \"1  0.0  0.0  0.0  0.0\\n\",\n      \"2  0.0  0.0  0.0  0.0\\n\",\n      \"3  1.0  2.0  3.0  4.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 合并series,将s1合并至df1，以及重置index，并打印结果\\n\",\n    \"res = df1.append(s1,ignore_index=True)\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     a    b    c    d\\n\",\n      \"0  0.0  0.0  0.0  0.0\\n\",\n      \"1  0.0  0.0  0.0  0.0\\n\",\n      \"2  0.0  0.0  0.0  0.0\\n\",\n      \"3  1.0  1.0  1.0  1.0\\n\",\n      \"4  1.0  1.0  1.0  1.0\\n\",\n      \"5  1.0  1.0  1.0  1.0\\n\",\n      \"6  2.0  2.0  2.0  2.0\\n\",\n      \"7  2.0  2.0  2.0  2.0\\n\",\n      \"8  2.0  2.0  2.0  2.0\\n\",\n      \"     a    b    c    d\\n\",\n      \"0  0.0  0.0  0.0  0.0\\n\",\n      \"1  0.0  0.0  0.0  0.0\\n\",\n      \"2  0.0  0.0  0.0  0.0\\n\",\n      \"3  1.0  1.0  1.0  1.0\\n\",\n      \"4  1.0  1.0  1.0  1.0\\n\",\n      \"5  1.0  1.0  1.0  1.0\\n\",\n      \"6  2.0  2.0  2.0  2.0\\n\",\n      \"7  2.0  2.0  2.0  2.0\\n\",\n      \"8  2.0  2.0  2.0  2.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 总结:两种常用合并方式\\n\",\n    \"res = pd.concat([df1, df2, df3], axis=0, ignore_index=True)\\n\",\n    \"res1 = df1.append([df2, df3], ignore_index=True)\\n\",\n    \"print(res)\\n\",\n    \"print(res1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 7.2.Pandas 合并 merge\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 7.2.1 定义资料集并打印出\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    A   B key\\n\",\n      \"0  A0  B0  K0\\n\",\n      \"1  A1  B1  K1\\n\",\n      \"2  A2  B2  K2\\n\",\n      \"3  A3  B3  K3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"# 依据一组key合并\\n\",\n    \"# 定义资料集并打印出\\n\",\n    \"left = pd.DataFrame({'key' : ['K0','K1','K2','K3'],\\n\",\n    \"                     'A' : ['A0','A1','A2','A3'],\\n\",\n    \"                     'B' : ['B0','B1','B2','B3']})\\n\",\n    \"\\n\",\n    \"right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\\n\",\n    \"                      'C' : ['C0', 'C1', 'C2', 'C3'],\\n\",\n    \"                      'D' : ['D0', 'D1', 'D2', 'D3']})\\n\",\n    \"print(left)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    C   D key\\n\",\n      \"0  C0  D0  K0\\n\",\n      \"1  C1  D1  K1\\n\",\n      \"2  C2  D2  K2\\n\",\n      \"3  C3  D3  K3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(right)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 7.2.2 依据key column合并,并打印\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    A   B key   C   D\\n\",\n      \"0  A0  B0  K0  C0  D0\\n\",\n      \"1  A1  B1  K1  C1  D1\\n\",\n      \"2  A2  B2  K2  C2  D2\\n\",\n      \"3  A3  B3  K3  C3  D3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 依据key column合并,并打印\\n\",\n    \"res = pd.merge(left,right,on='key')\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    A   B key1 key2\\n\",\n      \"0  A0  B0   K0   K0\\n\",\n      \"1  A1  B1   K0   K1\\n\",\n      \"2  A2  B2   K1   K0\\n\",\n      \"3  A3  B3   K2   K1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#  依据两组key合并\\n\",\n    \"#定义资料集并打印出\\n\",\n    \"left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],\\n\",\n    \"                      'key2': ['K0', 'K1', 'K0', 'K1'],\\n\",\n    \"                      'A': ['A0', 'A1', 'A2', 'A3'],\\n\",\n    \"                      'B': ['B0', 'B1', 'B2', 'B3']})\\n\",\n    \"right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],\\n\",\n    \"                       'key2': ['K0', 'K0', 'K0', 'K0'],\\n\",\n    \"                       'C': ['C0', 'C1', 'C2', 'C3'],\\n\",\n    \"                       'D': ['D0', 'D1', 'D2', 'D3']})\\n\",\n    \"print(left)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    C   D key1 key2\\n\",\n      \"0  C0  D0   K0   K0\\n\",\n      \"1  C1  D1   K1   K0\\n\",\n      \"2  C2  D2   K1   K0\\n\",\n      \"3  C3  D3   K2   K0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(right)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 7.2.3 两列合并\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    A   B key1 key2   C   D\\n\",\n      \"0  A0  B0   K0   K0  C0  D0\\n\",\n      \"1  A2  B2   K1   K0  C1  D1\\n\",\n      \"2  A2  B2   K1   K0  C2  D2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 依据key1与key2 columns进行合并，并打印出四种结果['left', 'right', 'outer', 'inner']\\n\",\n    \"res = pd.merge(left, right, on=['key1', 'key2'], how='inner')\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     A    B key1 key2    C    D\\n\",\n      \"0   A0   B0   K0   K0   C0   D0\\n\",\n      \"1   A1   B1   K0   K1  NaN  NaN\\n\",\n      \"2   A2   B2   K1   K0   C1   D1\\n\",\n      \"3   A2   B2   K1   K0   C2   D2\\n\",\n      \"4   A3   B3   K2   K1  NaN  NaN\\n\",\n      \"5  NaN  NaN   K2   K0   C3   D3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"res = pd.merge(left, right, on=['key1', 'key2'], how='outer')\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    A   B key1 key2    C    D\\n\",\n      \"0  A0  B0   K0   K0   C0   D0\\n\",\n      \"1  A1  B1   K0   K1  NaN  NaN\\n\",\n      \"2  A2  B2   K1   K0   C1   D1\\n\",\n      \"3  A2  B2   K1   K0   C2   D2\\n\",\n      \"4  A3  B3   K2   K1  NaN  NaN\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"res = pd.merge(left, right, on=['key1', 'key2'], how='left')\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     A    B key1 key2   C   D\\n\",\n      \"0   A0   B0   K0   K0  C0  D0\\n\",\n      \"1   A2   B2   K1   K0  C1  D1\\n\",\n      \"2   A2   B2   K1   K0  C2  D2\\n\",\n      \"3  NaN  NaN   K2   K0  C3  D3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"res = pd.merge(left, right, on=['key1', 'key2'], how='right')\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 7.2.4 Indicator设置合并列名称\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   col1 col_left\\n\",\n      \"0     0        a\\n\",\n      \"1     1        b\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Indicator\\n\",\n    \"df1 = pd.DataFrame({'col1':[0,1],'col_left':['a','b']})\\n\",\n    \"df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})\\n\",\n    \"print(df1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   col1  col_right\\n\",\n      \"0     1          2\\n\",\n      \"1     2          2\\n\",\n      \"2     2          2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(df2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   col1 col_left  col_right      _merge\\n\",\n      \"0     0        a        NaN   left_only\\n\",\n      \"1     1        b        2.0        both\\n\",\n      \"2     2      NaN        2.0  right_only\\n\",\n      \"3     2      NaN        2.0  right_only\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 依据col1进行合并,并启用indicator=True,最后打印\\n\",\n    \"res = pd.merge(df1,df2,on='col1',how='outer',indicator=True)\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   col1 col_left  col_right indicator_column\\n\",\n      \"0     0        a        NaN        left_only\\n\",\n      \"1     1        b        2.0             both\\n\",\n      \"2     2      NaN        2.0       right_only\\n\",\n      \"3     2      NaN        2.0       right_only\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 自定义indicator column的名称,并打印出\\n\",\n    \"res = pd.merge(df1,df2,on='col1',how='outer',indicator='indicator_column')\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 7.2.5 依据index合并\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     A   B\\n\",\n      \"K0  A0  B0\\n\",\n      \"K1  A1  B1\\n\",\n      \"K2  A2  B2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 依据index合并\\n\",\n    \"#定义资料集并打印出\\n\",\n    \"left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],\\n\",\n    \"                     'B': ['B0', 'B1', 'B2']},\\n\",\n    \"                     index=['K0', 'K1', 'K2'])\\n\",\n    \"right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],\\n\",\n    \"                      'D': ['D0', 'D2', 'D3']},\\n\",\n    \"                     index=['K0', 'K2', 'K3'])\\n\",\n    \"print(left)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     C   D\\n\",\n      \"K0  C0  D0\\n\",\n      \"K2  C2  D2\\n\",\n      \"K3  C3  D3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(right)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"      A    B    C    D\\n\",\n      \"K0   A0   B0   C0   D0\\n\",\n      \"K1   A1   B1  NaN  NaN\\n\",\n      \"K2   A2   B2   C2   D2\\n\",\n      \"K3  NaN  NaN   C3   D3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 依据左右资料集的index进行合并,how='outer',并打印\\n\",\n    \"res = pd.merge(left,right,left_index=True,right_index=True,how='outer')\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"     A   B   C   D\\n\",\n      \"K0  A0  B0  C0  D0\\n\",\n      \"K2  A2  B2  C2  D2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 依据左右资料集的index进行合并,how='inner',并打印\\n\",\n    \"res = pd.merge(left,right,left_index=True,right_index=True,how='inner')\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 7.2.6 解决overlapping的问题\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 96,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   age   k\\n\",\n      \"0    1  K0\\n\",\n      \"1    2  K1\\n\",\n      \"2    3  K2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 解决overlapping的问题\\n\",\n    \"#定义资料集\\n\",\n    \"boys = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'age': [1, 2, 3]})\\n\",\n    \"girls = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'age': [4, 5, 6]})\\n\",\n    \"print(boys)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   age   k\\n\",\n      \"0    4  K0\\n\",\n      \"1    5  K0\\n\",\n      \"2    6  K3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(girls)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 98,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   age_boy   k  age_girl\\n\",\n      \"0        1  K0         4\\n\",\n      \"1        1  K0         5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 使用suffixes解决overlapping的问题\\n\",\n    \"# 比如将上面两个合并时,age重复了,则可通过suffixes设置,以此保证不重复,不同名\\n\",\n    \"res = pd.merge(boys,girls,on='k',suffixes=['_boy','_girl'],how='inner')\\n\",\n    \"print(res)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 8.Pandas plot出图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0      0.143408\\n\",\n      \"1     -1.936116\\n\",\n      \"2     -1.488609\\n\",\n      \"3      1.372508\\n\",\n      \"4     -0.907693\\n\",\n      \"5      0.665021\\n\",\n      \"6     -0.629525\\n\",\n      \"7     -0.470715\\n\",\n      \"8     -1.059255\\n\",\n      \"9     -1.688154\\n\",\n      \"10    -0.254145\\n\",\n      \"11    -0.070213\\n\",\n      \"12     0.057894\\n\",\n      \"13    -0.805895\\n\",\n      \"14     1.688675\\n\",\n      \"15     0.241852\\n\",\n      \"16    -0.967129\\n\",\n      \"17    -1.422592\\n\",\n      \"18     0.873210\\n\",\n      \"19     1.629350\\n\",\n      \"20    -0.248667\\n\",\n      \"21    -0.871348\\n\",\n      \"22     0.088175\\n\",\n      \"23     0.796791\\n\",\n      \"24    -0.457708\\n\",\n      \"25    -1.453474\\n\",\n      \"26     1.997726\\n\",\n      \"27    -1.877039\\n\",\n      \"28    -0.601735\\n\",\n      \"29    -0.659616\\n\",\n      \"         ...   \\n\",\n      \"970   -1.093692\\n\",\n      \"971    0.059065\\n\",\n      \"972   -0.657221\\n\",\n      \"973    0.452148\\n\",\n      \"974   -0.487552\\n\",\n      \"975    0.375511\\n\",\n      \"976    0.287918\\n\",\n      \"977    1.351101\\n\",\n      \"978    1.799478\\n\",\n      \"979   -0.372078\\n\",\n      \"980   -1.320401\\n\",\n      \"981   -2.115900\\n\",\n      \"982   -1.617493\\n\",\n      \"983   -2.343383\\n\",\n      \"984    2.155813\\n\",\n      \"985    0.031667\\n\",\n      \"986   -1.021759\\n\",\n      \"987    0.948910\\n\",\n      \"988    1.464946\\n\",\n      \"989   -0.296323\\n\",\n      \"990    0.071010\\n\",\n      \"991   -0.087358\\n\",\n      \"992   -0.580351\\n\",\n      \"993   -1.497217\\n\",\n      \"994   -0.459714\\n\",\n      \"995    0.125293\\n\",\n      \"996    0.803825\\n\",\n      \"997    0.327007\\n\",\n      \"998   -1.617468\\n\",\n      \"999   -0.115447\\n\",\n      \"Length: 1000, dtype: float64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"data = pd.Series(np.random.randn(1000), index=np.arange(1000))\\n\",\n    \"print(data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0       0.143408\\n\",\n      \"1      -1.792708\\n\",\n      \"2      -3.281317\\n\",\n      \"3      -1.908809\\n\",\n      \"4      -2.816503\\n\",\n      \"5      -2.151481\\n\",\n      \"6      -2.781007\\n\",\n      \"7      -3.251721\\n\",\n      \"8      -4.310977\\n\",\n      \"9      -5.999131\\n\",\n      \"10     -6.253275\\n\",\n      \"11     -6.323489\\n\",\n      \"12     -6.265594\\n\",\n      \"13     -7.071490\\n\",\n      \"14     -5.382814\\n\",\n      \"15     -5.140962\\n\",\n      \"16     -6.108091\\n\",\n      \"17     -7.530683\\n\",\n      \"18     -6.657472\\n\",\n      \"19     -5.028122\\n\",\n      \"20     -5.276789\\n\",\n      \"21     -6.148137\\n\",\n      \"22     -6.059962\\n\",\n      \"23     -5.263171\\n\",\n      \"24     -5.720880\\n\",\n      \"25     -7.174354\\n\",\n      \"26     -5.176628\\n\",\n      \"27     -7.053667\\n\",\n      \"28     -7.655402\\n\",\n      \"29     -8.315018\\n\",\n      \"         ...    \\n\",\n      \"970    22.363987\\n\",\n      \"971    22.423053\\n\",\n      \"972    21.765832\\n\",\n      \"973    22.217979\\n\",\n      \"974    21.730427\\n\",\n      \"975    22.105938\\n\",\n      \"976    22.393856\\n\",\n      \"977    23.744957\\n\",\n      \"978    25.544435\\n\",\n      \"979    25.172357\\n\",\n      \"980    23.851955\\n\",\n      \"981    21.736056\\n\",\n      \"982    20.118563\\n\",\n      \"983    17.775180\\n\",\n      \"984    19.930993\\n\",\n      \"985    19.962660\\n\",\n      \"986    18.940901\\n\",\n      \"987    19.889810\\n\",\n      \"988    21.354757\\n\",\n      \"989    21.058433\\n\",\n      \"990    21.129443\\n\",\n      \"991    21.042085\\n\",\n      \"992    20.461734\\n\",\n      \"993    18.964517\\n\",\n      \"994    18.504803\\n\",\n      \"995    18.630096\\n\",\n      \"996    19.433921\\n\",\n      \"997    19.760929\\n\",\n      \"998    18.143460\\n\",\n      \"999    18.028013\\n\",\n      \"Length: 1000, dtype: float64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(data.cumsum())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 101,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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lVO23YtscTi9vfT2SkV+fkSkMYaSuz7qMJez/4Fa+y5/nb595Hp5oMLjpupFbZBcFeISp7RQv2KN4jvDAJ0zhzDMiGZO0Kfv82X5FeTC5tjkW7ds6SJjy/pAnXnjFeUr8I9QghDY2deT604nSuMNRZQHXKbXMwoIdPsEs+VKZk5+xF+aG+6EPV6ntIYdvBrdi4Eyf833O4+vQDpXmGobqkKOxKH6SOiGKbxTxpNUe0hQ8ASNoHbnkyP0LXFezuCLi9ut76qYxxhYQoHYxv3jB7p4491C3aPykWxf7s6XSMeTpkGF+6+zXcpdh2y6Tm5xfsMqEze+F6rN8WHqfFP6qQTp625c88vUhv5ekGn5+0VJgYVot1cgvT2N9u3AKgGG8lnikm5SFKgvKkaeOYYnRffIXGrsu6rXvwu+ffLwj2StHYK1uwR0jrdqyNO5pRf8NsZdq2HMMPH12gXIwgN8V4/w/Ug1uE5NFafXkvXLMNe1u9fthR0VkZqZOr319bpnx+9d4GnPPblzVyjDaR+Mhba/D26i1a13hKkZnDIucUVk54Gn2vGNeUF6Me0S+Jhey9izOnoZo8veelD4yPXKLyzb++gZufWIyFzmJC3sa+ccdeLW+Zf7y+2lyFNKlswa5jY4frlaCf7/Kmnbjn5RX46p/zE0ANKz7CZ373smdCsmC792fsTp6GlOfXyn/1zLLCNR9u2Y3T73gBP+FstHE6K7/SU8dLhl+l+McXlmPD9rz2HdDYhdfmj6pCJ/vL8V/rhx/97GwOr78uzLjnWnjjhI3kCqY9J9nuljZ8629vCtPKQk4rTTGhNTSXVzSFS37uh/9ZiNe5OaBC/mE2doOTp+57s8vxX3c19ta2HOpvmK0VJvmKB0sfSrmyBbukC724NOifHcVEUtSa8v9f+dC7eH3FZqzcVAzm5Jbdxhjmf7i1oN27nWrjjr2hy49lNVrnhJ1dwPnn69berbP/fq99WD+q35L1O3DDzEX41l/zgiXj6yVxh/yiEcTKTbtC/dgBIBvDNsw8v5nwd1TCbOUy/Br7rAXrhKF5+Wf7n7eDYRYA4Ft/e0N4XHVfun7sUR7zn19ZKdyxLOY0k5DdntXD+f9D68iZbJL6sbs2dderriZLeL9pB9Zsyb+jj72j9qkvFxUn2H/0nwWhmxB88a5XsXHHXpz3+1cKHUPX3QwIuiyK3NoKppgcwxm/ehEn/+J5z7WNm3fj5F88H1jApIO7uq17pxrkciwQE0QHf3dep2H7dml1Hta2PfmX1v8BFX0k47itufV6c5XYzNLGpasJm80OKUdnslaHsHufvXA9Xnl/UyANP2LfvLMZ/3PfPEwXuATqKCCyaJcliijg6Q2izbz9H36i+GYizyK+giYe5u7olIvkGrs7Wt3T4trYMzj5F8/jYz9/1lOn9kbFCfY/vbSiEKdF1Ul3N7fhtQ+KuwXFWVTjv0bkShicPPXmcfE9r0vzCJgknE6yy9GCutZl8dvn38cpt82R7uiiMmPE3X/Vn6U7X7Tb+eCIioykpTGmVSf+3rL+YYNOObyWLpnPiIpoEpQ/8tV7G3D+H+YKwtMWUzU7Ul60Y5XIFv/vNxsT70mb1sQqX1/3lqMMasJdj8W/XU78v+fwmd9553UK3jMJPigubt9357v8XjElnq/WpuIEO496Zxb13yoKZhXnGtHelq5Z4Rf/9a5Q9HfUdyQCOd/pxB8O157XrVNNYSPgs379kjAf6X2x5MHE3Kvde327MX8vcawR2/a0FEwPupcnNsV4hHnxjyQrZ4UfNZ3JU0EiEvz2J1u1aRf+9+9v41KJ+cVTj/BqGAsw59LaJipV//mGpeTbqrgSuXj+g407A3b4gsZOeh+0N1Zt9iiBPJmQkBAmwpOkQUULdpXF0P/AdReIAMB3/v4WgKBNjzfnuMc+9AU3CpQSo913NRc19jBk9+Xvh0lWFPo9vGS7HanYsK3oiqjTFIwxz8ukeoH+Na9RnIcnv+LvthzD1NvnFGLAR0FoitFoZP5eVPfvz3+Poymu3xYe8lbWBh/tbMb1j6qX4Qfy0uy4nvty/t/gq6vup+TWp5bg0r96P2Bejd3VxPVyzPuxh3P2b17Geb9/RXjOVSjcdQn+Z9w+xXqFC3ZV+wYEeww109+Ip9/xQsHtTha/RGZe0Uubx61rTSYTKgRlAk9mhlDhT+b+HfRjD79WlaI1l8Pn/6CeJ2FMrK2J+N4/3xYel8UH2rq7BYvXbZdep0LUj3Ser6guoi7kzz7SughJ0htmLsTidcF5mlWbduEJX0CtqF5k/oVYAAJxdpQLlJzja7bsxh1PLw1MRnr6QM6tY5BcjhUmtk3NpwBBjT3wjrRTyV7Zgl1xLuCFGMcf1i2HewMfeF0d2CjwRY9mfPaU95+31+CNVUF3Lx7ZpHCO6e2GIzrnFzi6C5R02aUR+vSOZ5Z6hti5HMMLS5vwJ4143gW4auYMvewigaczmcy3k0rJ8OeVUwgzXTyePFxGp/1yDr5xv8/EU+gz8jry70OboAM2rPT2WbUfe37Ue8zNzwjP5zxt6A6hg+nO+d3LGPWDxyUlyAlT+FyNveCv3k4FuZ/KFuxKG7vvBYnRIEUbe/GY+1snwl1o/iHnN+1sxuaQ3YoCE7zucc0bbm4LF7L+exVlzVdjT0sbNvkiRn60M5p30O2zvWFz23IMF9z1Gn70n4We46r75M/oaswqmltzBTOZpw4aX4o2j+bJCv//+tll3gB1kpXMWqY0yXFeEPPZmIgt3uLY2B94bZV0w5QwNip3VhKPus6680XMmPN+4W/es8prY1eXHbbAKGrY5fZCRQt2VTjXJENa/zWizXhlk1Ay80rxvJOnIHGcrjPHCV2sUxcRf5m7KlA3P/7n3JZjuPeVFR4h99vnii/Zl+5+DUdwq3vbcsxjw4yj8ctMMds1Fl75yyy4s2pU45L752HKrXlX1nN++zKOvimoWYpGTaqsXfPFxh3N+Pms93DnM8uEwer4OupMesr6uOjKb/xlXuDYorXbMNMxheg2kSvwrnzoXWU6qUmS894S4dHYudHL241b8dPHxRFZvS6SasLkgmuKcT9glSLejQh2IrqbiDYQkf4qGBPlKs4Fh7QJTDFcSVtcDVqqsftNMdHLjaJJBobSDjnGPCFgk3hD+Osza8E6XPfIAoy/bhYOvPZJAMBts4t2Vb+HgT/ee5w9C2Qmp22KdQKywG9R3D8ff3cdlm3Ie0m9K/FwEpti5GX4tT9+kU+g30boP1tkozuBYvLE/OD2hvwWirrPSGRjF5Yvk+sIU9BEIy1NP3aJbd+NEw+Ef8Dc5RPupi2idt0eYxe3tDGlsd8DYKqhvLRRNXASrxgXv1cMkF9pNm/lR1IxKYkwIM5fUp6JCRnGivnrflzWb9vj02zFZoDte4qCiF8ZKMNdSevSGmW1mIOs/VSCxe8JIzpugqgukP77z0k+QPy5sI/9boVZJeMxxei9M7rPyMSmKyrBzmfvluV+aEVs3dWCBc4HOL/dZLB+/48LA8DXX2RmyxY0dkewC8rkPeNKHYhNhhHBzhibA0DsCFom/M/3gRiBeGRhQt9t3Cq3sQsa9q4XPwiEORBdHxbLPQo5SadWce0jC3D3SysCL7/fVzmqvXHTTq/GHsssFsOPWGZjL3g4GHoJdZQG/omqIoIGNXZvPrI4Mbplq9D1JuH7rtiP3V++QnAjzBQjfzYiPveHuVjh2Pp55Uaef/G3aAI3Qz5TjCDDrGcyWVxiqQV+RdvYVR3C/4BnxojpUMjBV9DMd9cKO+sfX1heiAJXyIMBP3lsIb4oCIMQ5gKWBIZ4E8Yim73/WWoNvz3Xq//WyyP6CyMTVHFGbypcgXP1v4t2ZlUR/ntpy/FuhuqP6HWPRLd2hm9MEbQf6z4hkVdMVJQfZ4/GHp7XIu79yys3/vzkplKRKcsV7DMV+6zylgNZHUutyJdsow0img5gOgAMGzbMTJ4KTcDEy+s2un+BzusrNuOY0X0D6W+YuUiZ3/WPzMcmbmm4zDUy0fC2YM5h0H89i4iEgF+QR32Zg8IqmSnmwXmNOOeIIU5eeuV6zQzB53LGr15A19oa/OPrR0eum/t47n+Vm4hWPHu/sPbMBfg/os4NusLjVckKSVWfCZtfcTeW8X4IzdnYn1q4rhAp1A9j6jhOnhXDERWKHAu2g2xyWkYghICgXfk0snbIMYZMCbcSL5lgZ4zNADADAOrr6418v5RDOAPuSW4Wpprjz6+sLPxWrYqLW/O3V28peIkwZk5LCGjsGsNvHn894k2eFjO5/J9v49AhvTBmYI8Im0Sryw/b91WFSAiqbexyrdx/nZu2EN5BVgdF/fgwO0ITIGMAyPvxU+THo2OW27antRDfKVi2Wgnz+P9H7NA5gS3G31bz14gnxF10HBmyFC7YS215r+it8VSY8DtVLWEO8y/XQTbRGldjv2Fm0cc7p2FflOG/Xb8gimqK8b9McUZT/mvcaHvqYbxYY48zeatCpET4j/DP1N83/zmvESeMHZDPK8QU48/3/2a9h217WqQhfp3ShfVwETanysbO/W6J+JEXFaMawcWZ0C2mZ4LJaO/fF9z1WqQ8V38U3G9A1bbFcksr2o0IdiL6G4ATAPQjokYA1zPG7jKRt7JcxTkjgl1RzqK18TW8Qv5SU0y8/HiBm2PiJdZhfLh5N2Yt8LrCTR7Zx3O/UZ+tP3lbDJU9KODC781jMxYICFOv2l7BhKbKlKH6MPo/YAWNXdLZ73x2WWj9dDemEIXIFacvktwrhilHcCKvGF1yDGjxfTTC3DiP+unThd9H3jgbTcrFU+IyRZTaxm7KK+Z8xtggxlgtY2xIKYQ6EDJ5asTGnv9f5Y6VBKmAjFl1/5De/SuK3/bSDTsCqz799x9V4w26nka6XJiHznwE/4GSLelftWkXFq4J/0ivFoTYdRGF31XdokjDly1QKtrYQ6sohTy/gxmJPnSlFEQqRYHvu1EVirYcK+ybC+SdGMZe86TyGn7fAl2h7pnLkU7yAwvWbC20Z9pUtClG5cdu0hSTxpwHkfzrHqaZzJZEJfRHQ+SzSSIYwlzwIl8fo23870PRbCW/ho+IKHJ3BFDYMCGM5Rt3Ss+tEiylj6ux+69rKdjYzS0wC5aZ/1/Xxm7ydWAsLHaON220vJlHCVFt/p4EnVHFe+u341O/fgmXnDAaV0wdl0o9eCrb3VFxzqQpJi2CcV6Y8Lifbz/g3QvzM797Ge80bpGaK8qNbEIwCv6PgWwLwGDZbjouL8PqqGgE4y/CFczPLF7viQ3jJ2C2MjAf4FmgJHhpPtrVjBFXzvSFlwime+2DjzDyqpkez6582vjPs40x9eSp5IOsQ44V5wBq/K5tBuFrJbuX9c5I4K0Ym7LHoaIFu0qymxDsbqdKq09IN8IOuc5/b6+v2IxrHp4fcO8rDLETPoqkiyuCLmcxNHbJqCGsbu72fkkEBADlEFpUBVEJzy9pwkX3NOCOp5cKzubxPxvXA8mUKUbEcsHevAwM7zZuxfptewrP664Xl4MxYJ4vemOS7sHPBYlIZmMvxvTPpCjYc4zh5F88h7/MXRn6LEpl4qpYwd7cmlMOT1ULCnhuPvsQ6bmiJSadThF3okV0/p3GrR5NmHd3TNqXkl7vVzrjCFb/NY+/uxYtbblQs9Dmnc3488srPPFrpt8XDIAVhtJ8AuCFpd6FXaI2Wrc171EhioLo9rCga2j+wDuNW2N/YMNiq4g8QxgDzrzzRUz+6dMYe80T2NXcippsXlzwk8X5bQ7jk8sxbdNUdBNgMRRAqho7A95v2olrHp6vWEjn1qk0kr1ibezXPTIf+3Srk55/etEGrXxG9e8uPec2QUpzp8GAYZrXyTpHwMZuyBSTtC8GtNA4phhfHne9+AE612bwsTH9lde15ph89yBBNf7RIA49ofTdZ0HhKHr27n03R5hAa+Ge1c7mtpItTd/GBbZqzTGMv25WYeTqH70kNsVo2tijCsW8jT1/jX+hkUlkbrXCtKnVwkvFauxzljQp9WjdZswqnkBx5an5TkEICswVigk6Hplg9G9TltQaxVh+E9/75q4MTxySD0+cydN7XwnWYe2WPaH3GFXoXMEFiOLp1b93AAAgAElEQVRReQKJtwr0/j1v1WZc/e/o4QB4Idq4OV688zjd92dPvhc45j7rVl8/S6axx1uLoJU3J9jT1Ng9bsZh32xriglHudGGZh46keXS09i9/DfGHpw8W7kQtnxkuyQalTQUrAbb97Rg0o2zMXf5Js/xOK6oooh+DMAv/hsUQP40JvDblT1liGzsvmNxgncB3o/11NtfKAS4igJ5Figl78z+RUlJBhGtuVwEjT1a3o2bdxc+jNlMeqKOH82FfXxKZYqpWMHOoLZ96/Zf1RAt7SYwPazmw+nyfuzlYsGabdiwfS8e4uJ8A+Z2o1mzZXdgGzY/pl4k0YjBRVSCKTNYHLOVH89GMYlz844i8v0sgSkmp79VYNSR3r2vrCzMZ6hG5knhF0HJw0vLw/6mQcUKdkAtvHXNJzpB/k1oOQEUG/yaIJd0jByBL90tXpYt+2iaEuw6tupSKEiizT6ilivrYkkXtDBmfhmGfzu5pF4xuv0hzkfajZVeUyKNXaasuc/MauwaKG3smr1ZyxSjX6VIpOln7t9BKU1k2/PJBkOmBHvYfpWAfrz2JCxVbPyQlKQa+4dbdgu3dkwCb4pJml9bTt+PPc6jaCmYYtKzsfP9UNYlW1pds2hq1fBQ0YJd1at0NXadBk/Lxm5IvglhjNstPmY5ST88spGOMcHeGp5PkhcpianMlGYWNZKmCP+CoqR4dwxKqLHnInjFxOg3pXB35AX7qbfPEaexphg9woaYuu2oY3tLo0uIvGJMsnNvq2eD6bgkqaPs4/qXV5N52bjoaOxJSPZRMFMHEzZ2njT6ciIbe4gpJskCJaA0m1DrfHxbWuV7pqZBxQr2cJLb2KOkiUOaphh+g2Qg/jxBEs1T9nE11bd1bOxJ6v/6ivi7Pa5SBA0TIxvdlCZoVFwYkrVnW44p28iUKcbUKFGETlA81dZ6aVDRgl0lq0Qb04oopykmTTViOyfYkxST5IVI09QEiMPl+knyIn12xtzY137rb2+GJ9IgabxzwLy7o58kH8+2HFNqvLyGG8dN1hXsHxk2R3nLCK+Xq4SUKn5TxQp2BqZ0d9yl2LWdR0cbT+srm+YM+fY9Xp/2uCTTxtLVNvcqgmm5LFm/PdU6pI0JTfPBNxoN1ERM0pACbSxMYy96BsXpx+7OWP4RrEl0TIKtVmMvLToau6jjDejRKXHZaTYy79MeF8aSxbX/+Sz14qGk6Gjs/0+ykhQonb0zCaZ3e0rFxp5wLkLVjn94YTn2v/oJbN3Vkqo5JQlaNvYSeOfwVLRgNzGs6VKbVZ7fsbcVTTuCAfeT2t2TupmP6tcN3/3EAdLzvMYOxH+hk4wq5i6Pb6PWQUewVzomvGJ4UjErJqzi/YqQFa6CsqO5NXXTXlz8uzQJ0ziCPa35Oj8VK9iTulm5dO2kFuwn/+I54UbHST+8C9dsw44EWvW1Z4xHv+7yUQNvilqxaReel/iaq1i6YQdeWibehLga2KlprisF0gVK7VWaOazdugf/nCcOnKZLs8bHK0PxR1jnTxoW6zpdWjQUjOYSa+wVFd0xsOLNQJ51If6O/C48PEknoWYvShYXJpMh5cfF1LD1ukckkREtAIAh+3RB4+bgBsemaO+Cfd7Kzco4Ojr06lKDjYJRMU+Oxe/TnWvT1V912sgdeZVIrleWxv6j/xSFDAOMqOxxBXSKK5S1yBIph3Xt1R5ZbQzu1SXV/M1PQJdIskRA5x3M5VhsU0znEHNrUhavC5+gr0hTDBFNJaL3iGgZEV1pIk9hOb5OWU7RVaoGAoBTDxqIvr7Y85mMelcYK9hLw57WdM05Jtwd2zuqrQJ54s73dKpJVwv717xwr6NSm2IS3zERZQH8GsBpAMYDOJ+IxifNV1xW8TdjwGzNzTTSoJSCvVNNNmCDzWvs8mva+xC+WogbjlcX0x/oEnZbbXQmwcO20FORtsauw0Nv5COcVoxgBzAJwDLG2HLGWDOABwCcZSDfALwwZYxh0drgpGapKOULUpvNwD+EzmasKaY9YGotgqwl0w6b0B7Q0dhzLP6Ct84pa+xRqCRTzH4A+GnxRudYqkTZXkzG/B+dGvvaUmrsdTUU+JBkMqQ0xViNvbKQ7cNqXGM3mlt8eM11b0v4uzxnSROWbIi32KxTO9DYXSopCJiorwTqT0TTiaiBiBqamqK73uXzKP42sQCne6f4TkGlmt0G8i+Bv7gsEWqVNvbq1/QqlfPqh2infadxa4o1KR9819VR0q5/dAFmvqO3Qb2ftG3sUaikIGCNAIZyfw8BsMafiDE2gzFWzxir799fvQGxjFJqye0JQtDsks0QOtfJNRHTC1ss5vjZuYcpF5fxmF4KTwSMufpxo3nGq4e3P/fuWot9utamUlZ7sLG7VFJIgdcBjCGikURUB+BzAB41kG+A9iTWl6xPb3MFP0RBm36GSLlq1trYS0PUF/VX508EULpJND8EKrunTYbyI06e2mwGX//46FTKS9uPPQovLtuIlZv0Nq1PQuI7Zoy1ArgUwCwAiwD8gzGWyqoWkwr7cfv3M5dZGchmSKmJVOuk25mHDS53FWJz8rgBhfqXS7Anif1jChJ4dPkFvUk617QfjR1ArA3Jo2LkU8YYe5wxdgBjbDRj7EYTeYowGXL07i8faSyvupRteITgaCVD6jg3Osu0S83rV09JnMdhQ3oZqIk5ojxlXpinKchUtIeRHCFoVl23bU9qE4udEmrsPz/3UEM1yVOKb3r7GaNoYPJdMJlXTYYwuFdncxn6IKLARy2TIeUQM+kmyGlg4pmXS9MFgMOH9U50fU2WhL9LSalK/fgB8nk0kWkRSM/+3Cmhxm46hn0p5gorS7Ab7JbtyV4fhyyRssO2R1OMiWdeTsG+r+DjHcXLIcvFoajV2ZMxBUo1UFCVQyBhO6a1CUV7mjwFSiN7KkuwJ3wio/p1K/w2+dUshdkysPI0Q6hVaH3lniATYULzKadnVE3CAEH8hsqqtkuTcq7WLkDAredNwCH7ec1qab1HSZSBu79cb1wQp7GLlZ+KEuxJlbUunHtg1GdbTk0RCNa3e6ca7NO1DqcdvC8+NSE4oWg1dvN0E4R4jmtjL5fGXipUrUQAThw3AP/51nElqUufrnXYf0D3WNf2727exGpt7D6SmGL8DzPqVzMstT+/CUOT2WO9eXs11S9MHoZ9utUhkyH89otH4MiRfQLXtEvBzj2igwb3jJVHuSYdAaBbXbIo1zUVJNj92rQOkwT9UISpJhykOa9VkyVcd4Y4fFXYZHwa3U21YtxYGamXYJAkDznpED7q9cP7dk1Unh++dJ28VQuUDhvaG5NG6L2EJuE/zFdMHRcvjzIOnLqKVipHUNnbm8YetntYVD41oRhJRKU4yRS0qKsydc2N2YzYpg8A5x05FHd/uV56LZH5MADWxu4jyQNJKtjLKVAIXq8Y/72I3gd3mfYNnzrYc3x436545JvH4gfTDjRf0TC4aqvCIagopymmu88Uc/6koZKUYtqDjZ3HdJ/uqlgJrVNuVBu7btiMumxGWmaWCCeNG4gVN08TnjfpsFHI09rYvSR5IDIXqyjX83z5mBHxM4tRNnn+Dr+RUgf2D6NHpxrPM4zr+5+2YO/ZWW5u6SowxUSzsZffK4bH/KSgXt6yc1E14827WsITIW/6kJnwwt6PVEwx1sbuxZRgHrdvj+jX+7rjxIQ+zTEqEIkcy3cg2TMrtcZ4wrgBnluI61uc9odK9eHwT55G1TB53/V2IdhTfJZKd0fu5EOXHFP47X+eSYL0+ZG1a9gjMPGI/EVbP3YfSYZF/LUXHzcyeV3CvvQJ8x/dv+iaSb78dPPOKvZFHT+oJ64poTmG4H1mcVcDpq2xq/JPujSdz7uuJv2X+3+nHICjR/WVni+XGx+fTDUBevRoed2jIpuwDBOycYTw0D7e7RL9brJWsPtI8k7z15oQDnwWabRTry61+MHp8SYYXYhI+jEkInz1+FGJ8o9WF/g09nhdL+2XQpV/0tARvI09qU+8DpdNGYMh+8j3ZM2mOmpTTZ4W4Z+Df4HS96eOxSUnmAkMJjPFhGrsMcr63iljPX/7VxmXwjpaUYI9yQOpq8kUhnpmBHu6rcPH9MjPD5DnbxkHDiq6EapMMaUmr7EX/+ZNMcP66HsQpW2fVA3/ReajaCtPS2+KUfXTL04eHsssKSNOrPE67jn4L6/JZHDygQOSVguA/J0PkwVx3h9RiO2keUauQ/pFmCOJTXBY326FjhNHKPu1ibTbJse8HV23vK8eN7LgxpYRxJgpJ/zogY9zc9eFcnczP2mbYmqzGamHhEmNPYkp5vgx/XDz2YdopVUNDLrUZXGTZj466G4TyPdJV5vNZigweVqK/tu/e6eQFNHLDxPs1hRjkK8eN7LQcUwIhygdbkoMrcMfhU9XF/rUxP0K9xe24bUu1585Hv2618W6dtqhgwC4gcyKx2NPnvpu6PJPHIDLNTetSIpIsMf1ikliivnKsSPQN1Qg5VH6k1PyAFk8uRww7ZB8e6u0d75K7sjlpHEDAiq733wn4+RxA/LXK5BVp38P9XMkij4SCZsstYLdR5IHcuZhgwsNZEaw66edcuBAHDF8n0j589oPEXlNM5JrPj95mGcxhsjFM048joMG94q9JHvsQPFQnxeSUZ5lYIOGmkwkTfoM50MjQ1WXOp/5JGp35DX2JH2wLadvklIlI1DikLY8DMDtn5uAN6/9hPKDx7/HdTUZPPu9E/Cr8ycGrvGvuJbxvVPHoofCTRWQx6Ef3Fs+B+Gvqy4DenonhP05WFOMD1MPxMSy9LAG57tRXU1Ge/GGS1vOa/xpVSzG8HdZV2hkMsEt9eJwwMDuhQ/LwJ6dcMr4gdrXFj4y8LafV7CJ6/j36UcFjvXT1FRl3Pn5w5XnVRquyHwS5UPJ33OSZeXdO9UYade8xi4WAXGyZ4yhNpvBPt3q1Bq77++R/boJIzDmN+QIr0iNxrMUmYn6dqtDtxCXSt3H8H1nJfXwvl0FCoBfY9fMNAGVJdgN5RNHW/L3i2DsGfm1cSbKeA2DALS1eTV4Ee5R92UQfcD8cwVhMVtuOecQ9O5aVxDsPzzzIMz4kr5NnH8x5R46wWPTDhmEcfsG6zain9lQDYG6KM75zSdJ/NiTKBdHj+5rJN7Ic+9tMGqK4Z+Haj8P2ebV/ufpVwZkZDMkbQt3MjwnqFCfbuHmRd1mOnJEcUTut7IljVMVh4oS7KZsU3FeCtEwUReRYD/1ILXWm8sxT2dtjbDzjavBbNrZHNqJ/vn1o6Xnhvbpgs8eOQxA0eZfE/EjVUgewUPnwW8cg1s/e5hQyopWf5pEVceaLOGuC+tx/JjitopRYoh7NHaNZ6EKxKVtilGka9y826gphteKVU/lY5JNOAIOCpqmGJWi1qtLbaA+7nPVaTmRMiIyL7ojDsbCvWCsjd1H0udRcHdUZPS3rwWH//lr/Z1OvzKiVZ6XnjhGeU2bzyWm1ef+yOPGMOnpdOIoAkQlKP9y8eTC79aCYI/WCF6NXYz/+LA+XdGpJhu4z0+WYL/TMBv7yQcOxOmHqO30MmoimmIuO1neR3SFg2pR38/PPSxgNkgCr3uoTDG3nKO31Vw2Q0qvHj6djB+fdRAA4PBh+6CHo727yXUmRUWP2c2TxzVpMbCAfAlOnoYWm5iKEuxJcTUCVUeQnQqaYvRbR1Re2OWMeTUY1VZ3nzxsP1x/5viCIOBfBlOrdQsau3Mv9108qeDxoqJoY5e7rslskDo1N/2OqJ6XyB4rkw38ymEX3itGp//UKiaFdbufKt3B+/WMvVBMBN9fVTJTtlaAv+agwT0xoEdnrf5bl81Ite+TD8yPjLMZwrVn5kP3RlHKwpK+fOVJWPyTqZ6RrP+j7W/rNAKL+UnUqkT0GSJaQEQ5ItI3vMYvTyudLBa6zgIlXTMNAfjH/xTNGMohvEDt0BLszC1L7RWTzRC+cuzIwnCwa23xxUmiHfB1LAr2/L0cP6Y/9u8f7inDP2tdjV3WPqKXN0lIVZGAkbXL2RP3C51o4zlh7ICAP7zHK0bRAc6aMBjfnzoOIxThmU0M5+tqMiCiUI8SXXiNXdenXXb9V44dCSC8/04a0Qf9e3TS0r7dNAWNXaNOREH/ep7Bvbugc2220J6Mhc+fVIJXzHwAZwOYY6Auoaga+Z6vHFn4PVSykrHox64uQ+SaKFo8wb94qn7ll+uXnDA60otJBLREsLGPG1S0AZrqRG0CU4xOjdz7FLleyqDCNd4LZMIi7j1+++T9g3lJ0o6WuHsWPr6+C/3rEAC/V4y8Xvv17oJvnDBaOZ+hb4qR45phTMkZXrgm3eauMGoLuc9bP3tYqPD11ymSxq6brmDeESxI8k+mtveNNhhjixhj75mqTBju43AnRHh4rTjMbqh6KWSN7hcqGYJ2q2eJMNEZRTz5neNxxdRx4e6SvvI8giLk2oGOH23n2ox2JxYt1PBo7MxrihHVUURBsEPhzeM7zLtIpkmUjytvshDd9nH79/P8LXJP9djYE7rxibq4aDWqeoFS/pwpQcN7nsTZmJq/xq12WNWiCGn/AkUdj6A4SkOYKcba2H24jdi7a1CweyPniW9LZ4GS7IXzv8yTRvbx2MpUHSCbIVw25QA8+Z3jCy58YY3LF0cQa4Ay3HuY/rHRAeEoEkoPTD8Kj3/7+MBx/qVxd2SqVcT2ENfFzUuexm9zlF6TUAsMlCuqlKSiIs8m1T2JdrDi+52qDxaFWjQFZELMUNJuTnFWSPN4TTExMhBcox1FVaM8t7+O6tcN/zvlAMy44AjP+Teu/UTgmjgmL78pxp9Du7CxE9FsIpov+HdWlIKIaDoRNRBRQ1NTU7zKOs9D9FJ4BHuI54byhYGepsgPk8PSZ5zVoLxfdliHzdvYJaaHsPI4bUdHozlqVF+hxs5rYO6HhX/OceyoInTdwVyNzpR5SSRbZVmHrUXwP+fhfYOTp7wZS+ceVBq76BmJBIZOOW7dL3eiEn7uSP2dof705aIJ1LMoL6G3jXsvYaPCKIK32H8Il00ZEzDZivzaRbmLalQ0xbCA6WWgbyVqu9DYGWNTGGMHC/49EqUgxtgMxlg9Y6y+f3+xH2soztMTdXgtjd35X+WyF9X2rYNoMkXUuPtyHcA/LI2zpD/pCtu9rUVzgmuK4V03o2hlIqHz3PdOwCtXnRQ4XjTfeK8x9B0pIOpHspdOGCfG65HqYfrHgiGRea8YnbYRhdV11z8IP0oxm5sfIa24eVqkwGAnjhuAi5yJTl4I/+K8w/CNiCF3PaNUp05hykNBoGqo7LL5EHUBmsm4TPm2HdG3K66YOlaaNi0qyxTj/M/b091npBMSVcePPcoz100q1K60NPbi33+ffhQmO7vA63oxEAVfjC9MHq51LeA1//i9YgDNl0lxbkS/bhjUKxirQ2aKMS3YP1M/FF+YPMxzTNYuYbFt+GMTh/UWjiqjxooRfXh+ds5hAGQau6BeGr2UfB/SqIKn6BdePDawZ2d8f+o43HKO/kdCpJ0rvHwBRJuH8XvF6KBrNnHbqlfXOk/bfnrikEC4hHahsasgok8TUSOAowHMJKJZZqolKy//P69xuw+0RkOwu6gmi6Jp7MW0qvCfos0OdBrX7eYEQt/unXDvxZNw09mH4MxD1Qt1+Fvwvys6WtQnDxuM/3fqWBwwsDhKEJlioghapY3dd07u7mhWsneuzeLGT3sFj6yaMvOeSBjJnovH9VNpDqRAen8F3ct7hMU60THFaKa9/6uThSGW3fdJpF0frFg968czAnIqEza35KbT6YvuAr4o8YZEz0RU1sCenfGTsw7C3V+u97WzaCVq+pI9kQMrY+zfAP5tqC6hFGKgZHjBnkFLW1ukyVPVY43klsf9vvSk/XHRPQ2BNLd99jDsI7Dd8R+QrnVZ7Gpu89bVVycgP4t//iSvhqkif7vRheHAnp3wzRO9roDuC8Z/NEXxN8R1UOPv6LKOL8pL1VQ/PusgvL16Kx58ozG8EoWyxcfDIkjqdBmdYFXe9PIJ2ywnTP/19aNDA2/xHLt/X/zwzOLqSd5zScWwPl3RuHl3sE7O/6LuEGWiUFR7XVOMDmceOhh7W3L41MT9tK/JEGm/QhccPQIAsKu5tVg/BJXFdq+xl5qiKYYT7NmgsA+buFF9MTNEOPWgffXqw2Vz0jhx7BedwFePXnocAP/KPZbY9MAQ0ztBgOu+VxPXxq7S2DWvKY5g9PjS0SOwn2JrOHFdJKaYrNg1LmzRFB+hUjf4nN/VT7TIixcW9SP6YHT/7tB9Mvv27IIxXLwTHc8lf928xxytWfA0ktr93bC6stWqbvZaHloZwnlHDo0U5pkQvK+wkWNY/HVrY/ehsqfzD082OaojGDKU3+y6s0ZwpCRuS54YKprDPV34epmyS7tu2fxHVccrJon5RHfyNKn7mEdoCLLq2bkGE0NcCWUv6+RRfQs7WkXdXIOI8I//OdqzwtlFd/WkzpMp1l2dmkhsqhTZ2PlrdBnAeWa5bdqnWx0+uOl0XHiMeG4o7YBacbIPM8VYjd2H29j8C5KNYGPXmRXP7/RDyr0vuQrl8/UdPn9SuLsY3yFNd06Pjd2QXbqosQefbTetWPMKm3KC2zch2Ob+4GT87ouHS9M/ftnxnnACvAArhn1A8KCDaNWuLpNG9vEKPNdsIrAtR3DLF6bh0+qOooBi/xWZ5qL07YuPGyksJ2wXKMD8/Esh/xhKg985IywoWBpUlmB3h6i8EHd+e3ZlkQl2jcaPNGMuSXvT2eHR6/hyRGX6FyjFJYrG/oPTx0nPue+sSGNXmRi0bOwa5iont/DMQvMI0r1TTWFiTZxHeP34337Fwo0/3kWwmYQwX9/fHv9353+3v3vcYoV1zB8d1Kuz4KybxpunPJ24paYenDddThFswBKl79ZkMwV3Tt3rUl/sI8m+Nkv4Kvch4sl4NPZghMoSyPVKE+xB7dzVIL1byYmv54NqiRjVr1skm2yS9unRuShI+Pqc7UzsMMYSayGMRVtEpJPUG1LAOaaxGEXXK4ZfDei/xNR8gRDFaE5WdX8ETiDvinrH+ROF6UW7BOkgcu8VmT9Uj+fCY0ZIz0WJOyNKevB+vbDi5mk4cFBwY5S4QkzbgSFlCSbTWZbeeDquOWO8Vh52M+sQ3MfhEezObz5euezBuXG0RbFmAOCZ751QiB8Rx5sjCl048wWfzdcdd8Qc78cesZyk3UZ1X6KVp0qNPUK5vbvW4hRu4lo3zK/L+EE9AzHbdTU63rXUT3DyS1grAMAVp46V7qOpq7H7qfOscvaOUPnnK/KKocI5ef7FNMFE07jY8/kio/auaOmjzgnp3F8ShH0tch11+o9ZKkqwu4oL/2DcYSofdEn24L4/dRzevPYT6CWINeNHp+1U7eNuYBHmU9+3W11h6MZYtGXuOpjq8O6erXxHF5lneKZ/bFShAqp7IN//MsYIVt/y1xwzui++f5rXnKT7EvnnX/76teImI6o8ojzfznXxXjdRKIKMYPZUWBcNG7ToI+FyCrfTVz6mvk6N+byjpZcZlqQT52lPnirKVl7HzVv4q1gKjT3dfcYMI9Kmph48CEvWL/XEeZA9t2yGhD7lcVG19w9OH4fBvToX7I8i3r7+FGQzhG27WwrHipNiyaeDGFgkU8wXjxqO99ZvxyWCRUyPfes4vLFqizf/EI29S20WWoOOwktAosMA8m6DhwvCKQNAzy75bty7a23sj6A/Ds0xo4vRGiPp/Iob1Y6f4stDNOmaEQhssVwPr31hAlKk8fu8tyLr6xGFWNRl/1Fiq8chrsKeJUJrzFhPJqgswS54It85eQwuPHo4+nKryUx8Ed1OPu+aKTjihtla15xx6CAsXrcdQN6G/i3F1mZA0SS0Y09xQYNIe4p8O9wFUbSNbp1qcOt5E4TnRvXvjlG+jTXcj4ZsVNKtE2du0ujO/u8Df9+TR/WVXnfuEUPR0sZwXv1QbNq515tHaKl53Ock7DsqjV0/aWztslZoYw/mpfqIK00xrldLyAIwQvR7iH7H+UpE3fqPj9y4fOPOyKWG5R+VfP1ZfpQjPJcuFSXYRWQyVBDq0w4dhJnvrPWcH9W/W6QlxH50OrKb4s7PHx6zjOLvgnBjSGxHyU/upYcrCEQa+/mThuHLx4zE4nXbAMg3MAZ4LS1eh89mCF88Ku/nrPPSPPe9E7B5V7PnmEoo6uQZK8CUhIAg8CxQIk85fLUFIeA99TlxbH88+14wsqrKD91fMd3VraLydRC6j0Lej4v551Ps26uzWcEesz0zGQDOYnJ/3HdrY/dR9IrRX1n6zOUnCBd4tFeEGntErcH7bUhPtBe8YgSC/ZsnjkZdTQaHDumNxT+Zik8IXOH8+fhzkU6eKuoUzCOYZkS/bpg4zGvWKZqMghdEefqitlLde1T8Gjv/QRKu/HTPMYbTJJtwi/Lyl5fPK7pEiqqdRu2taQtJPv/xg3pi3L49pFtv8vA7h/XqWouHLjmGy9Nq7B7aBMvaPRSG03mbbJIvd5qaroriRGp8G7tngZKTyWkH7xsIeJUU96Mh8jLiO2+Ymx+LOPy+/pMH4dqH54tPBsw5yU0HOnmo2uoPX4q2HbDSNdT5PytQAJKu/PQIdlcwcU+EKP33ohDPSbPeflOMafj7P2S/Xrjl3PA1KkAw/s7hw8TzQ2lRURp7i7MrjUxjdzsmgTB5VN9IAbP8yLRIHne7tOkfixZ3WlomvJNBJob3rtDs0blGuJFAEtx36dwjhgTORfGGyHEfZB0uOGq4MN55Po+YD0vDHbCQVOGJkr4GmS+gMLLjTTEJbexhH4Y4txbZFBPxOn+f6RQhDowOcdtzx97W8EQpUlGCvRhhUPy0Tdo5XVR51WYzWHHzNFw2RT1JGgXdONu65AofKPMSh588nXKg19wQpTx3KXqcIWqUyUt1PnJNUVM1jEgAAA9mSURBVKdaOpFDdVE9O/eMaGGOOPpleI3c+P5hfY+IImvGkU0xkv4a5u74JWcB1g2fPgTfPim4SXlckrbne44zBQD8/NxDcfB+wUVcaVBRpphWZ2l2WLxuEy9XmrZpFfykWGKHR8Zw1oTBePn9Tbj81AOSV85HjvuQ+t/fKO+z0YlHrcVEijpEzpMpzplH5RXD1+SFK06UnvNz5/kT8c95jThwUI/AuaDGnvLkqbew8Pyd/z9+QH+suHkaAOC7p4zFHc8si1awhAxRorewjZMjn6kfis/U6287mISK0thbuA2VJw7rjQscTwiXoqBJ/nad5zRA3GXgcXFtpzohEmTw2k7Xuhr86vyJGNBDHiskLkV7aFAvjFJlN5Lm2IFBwRJGknJ5VJ45qud/0OD8RhJRQsG6vHDFiYGRThhFU0zwnNtnJgztXdjPU6fvDOjZGd88cX/Je+OzsXNS7pFvHhte38grT6MpZ6WcPI1TVtLtKeNSWRq7O3maIfz7kmCnirP1lYwfnH4gLj9lrEewHzF8H8xbuTl55j6Kngter5jz6ofi/ldX4awJ+hsDlJIc97yDGrt+Iwzo2Rl/uXgyJoSExtUhsPw/ckiBIKocfv2Fw7F47TY88PrqSOUBwNA+XdE35ryHSGMf7awz+PrHg3M+YVEgZfi9YnjtVWcfXv76umwGozX37tXtP6mvPE2Yfyk8YERUmGB3Jk9Dw/Imf5iZDHniuQDAg984BiOunJk4bz/9e3TCRceOxHlHDvG8sMP7dsNb150SO9+0jUmu33SGgsswojbBcWP6hSfSIaZJSOWNoepP3TvVoH5EH/zttdXC8uOgU2eRYO/VpbZgjijk5fwf1axHvv+Df+jVk0+z5MbTQtPrOC2UmmHO6GfsvtFHlKWIvS6isgR7wStGZmPPE/VhvnDFidjKLesPI86wWwUR4boz85Hitu1x6pFAKl9w9HA0rPxIGdHPBEU3xeA5k/15VL9u2mlVwkYvWFm8yeu48ztjBuppsH60+7jC40Xvcvkkpla4gohP5AuTh+HFZRsxLoYQTYtj9++HRy89FodE2L+13FSYYFf7secUWpeKoX26QndK46efPgSTRvaJVkAERHG2o9KnWx3uu3hyeMKEFJegB4NDmVo2/dAlx2BEX33BHjTF5Ln4uJG4+vQDpdcVQwqE56ki6mjxomNHYuKw3vjCH1/FnhbB0lEJUULtJsHrFZP+ytPTDhkUGHUABhwJEnLokHhmwrDNuNOisgS7YENlHjdcas/O4dEb4/L5yfF943XIJtSwSgkvDJN4xaiIurAjMJlKxeMZlcauNMVoFByzvTIZwhHD+2D/Ad0x/8N8+AWd4mJ7m0TE7xXDJOek18cst1qIEoTPJIlsCkT0cyJaTETvENG/iSj57JcCV7DLhtTXnTEed35+IupHpKdRp03B3bG81dDCHZoO7t0laGNP+ZXu1z0/6eiP1hn4wGg66bnzNjpeUKJ3VTX5qsO9F02ONNTXn1x0fsQUMF6vkPT92KuNtjK9yEk19qcAXMUYayWiWwBcBeD7yaslxtXeDpMMizrXZnHGoYOF5yqF4orC9i/aLz1pf0wZPyDv8ucXqCk70l507Ej079EJZx3m9RiKO4l70rgBuPTE/T37bkbJI+pSeD99utXhYwf0w7sfbtXO4ztTxuDkcWp3Sd0PW9j1+d8xrjcl19v/6yCkTRSZrQQkEuyMsf9yf84FcG6y6qiZevC+eP3qKejfI360xvZO2vGlTZLNUMGP25Q/uS412Qw+PTEYykAmSMK+k9kM4XunjhWe09tBKfxcGBccNQLPLm4qrKEI4ztTwhedJRasPj9uzx6rWqaYDq6xV4GN/SIAf5edJKLpAKYDwLBh8e3U1SzUgaIQMR3zotSUy39XKtgTfCp17uQAx4tjUC/9PXP97NurMx6/7PjY16uIOgB0bcM1gpDBkejYch1t5VHYwwU7Ec0GINoG6GrG2CNOmqsBtAK4X5YPY2wGgBkAUF9fXwkKaVnIZAjfnzoOJ40bUO6qRKJcgtxP0BSTvF46eXz9Y6MxeWRfHCHZ5alcxPVj3+t46PBzDuSbPY2zeUpHo1yTp6GCnTE2RXWeiC4EcAaAk1klGIYrgG8ItqZr77SX91cmSJL0TJ17y3u3tC+hDnCT8RHvf29rfpeIzr5NIqJuLWn6gz9mQHcs3bAjNN1LV56EvS1tscs5bEgvvN24Nfb1LhVpiiGiqchPln6cMbbLTJUslUg7UdgDguSY0fkt9fhNmaPnmahK7YbjndW97m5TKva2uhq7d1u+Awf19PwdhulHd84RQ/D1j48OXQG+X+/4JjEA+OvXjsJHO5vDE4ZQke6OAO4E0APAU0T0FhH9zkCdLBVIe5F9/nocOKgnVtw8zbM5deQ8q0SyD+rVBStunqa1A1BRsMv3rU3D315GqcVjt041hUBqcfjxWQcBqFCNnTFmLvCxpSqYduggNG7eja4ljorpUiUy2Bg9nd2tuneO9qq7ZoxOtcGNtKPQUf3YxwzIT6a3VqJgt1hcXK32pLEDcI5gRyWeUf31QwTErUe1cepBAzFrwfrI131+0jC0tLFAiOswRBq7PwRttT5rExQ3CLeC3VLBFL0v1Mz/0anSIG4WOb/5whFoieE7V5PNCBddheFqmvzkqSokgwxTsv/Y/fthxpzlOHJE+5ugFuHGs6pIU4zFUkDzBe7eqfK73IljByBDepOQpshmCNlM6cxbGcoHeZNtQwlo2tgNzb58/ID+WPyTqSXf+CYurgnKCnZLRVPcLb76PV737dUZy28KRiCsJh699Di8/P5GpblFL268uTpVilAHivGs2qwpxlLJVFLwMks4B+/XCwcbiD/eUe3wQ/bJe9Sce7h6viktrGDXoOGaKWXzR6047GPqMOgI7Y4p1vNB3T646XS7NV57pl/36o5PY4K4S9ct1U0HVdgBlHe0UtmRpiztho78AlvkpCXc4m4CHsanJgzGQYN7hids51iN3WIUa7GylIKnL/84tu1uNZ7v7Z+baDzPcmAFu8UISTd0sFii0LtrHXp3TUdrrwasKcZihEtOHI2DBvfE1INEEZ4tFkspsRq7xQjD+3bDzG+ns0mExWKJhtXYLRaLpcqwgt1isViqDCvYLRZLJPjNNiztE2tjt1gskXjga0dh9Wa7YVp7xgp2i8USiV5da9Gra/I4Mpb0sILdYpHw4DeOweJ128pdjbLzmy8cji4VFFnRYgW7xSLliOH74IjhlbGxQ5qcfsigRNd3qom32YclPlawWyyWVHnvhtPKXYUORyKvGCL6CRG9Q0RvEdF/iWiwqYpZLBaLJR5J3R1/zhg7lDE2AcBjAK4zUCeLxWKxJCCRYGeM8TNL3WBjQFksFkvZSWxjJ6IbAXwJwFYAJyaukcVisVgSEaqxE9FsIpov+HcWADDGrmaMDQVwP4BLFflMJ6IGImpoamoydwcWi8Vi8UCmdpUnouEAZjLGDg5LW19fzxoaGoyUa7H4+UfDaozo2w2TRvYpd1UsFqMQ0TzGWH1YukSmGCIawxhb6vz5SQCLk+RnsZjgvPqh5a6CxVJWktrYbyaisQByAFYC+HryKlksFoslCYkEO2PsHFMVsVgsFosZbNhei8ViqTKsYLdYLJYqwwp2i8ViqTKsYLdYLJYqwwp2i8ViqTKsYLdYLJYqw9jK00iFEjUh7/ceh34ANhqsTiVg77ljYO+5Y5DknoczxvqHJSqLYE8CETXoLKmtJuw9dwzsPXcMSnHP1hRjsVgsVYYV7BaLxVJlVKJgn1HuCpQBe88dA3vPHYPU77nibOwWi8ViUVOJGrvFYrFYFFSUYCeiqUT0HhEtI6Iry10fExDRUCJ6logWEdECIrrMOd6HiJ4ioqXO//s4x4mI7nCewTtEdHh57yA+RJQlojeJ6DHn75FE9Kpzz38nojrneCfn72XO+RHlrHdciKg3Ef2LiBY77X10tbczEf2v06/nE9HfiKhztbUzEd1NRBuIaD53LHK7EtGFTvqlRHRhkjpVjGAnoiyAXwM4DcB4AOcT0fjy1soIrQAuZ4wdCOAoAN907utKAE8zxsYAeNr5G8jf/xjn33QAvy19lY1xGYBF3N+3ALjNuefNAC52jl8MYDNjbH8AtznpKpFfAniSMTYOwGHI33vVtjMR7Qfg2wDqnZ3VsgA+h+pr53sATPUdi9SuRNQHwPUAJgOYBOB692MQC8ZYRfwDcDSAWdzfVwG4qtz1SuE+HwHwCQDvARjkHBsE4D3n9+8BnM+lL6SrpH8Ahjgd/iQAjwEg5Bdt1PjbG8AsAEc7v2ucdFTue4h4vz0BfOCvdzW3M4D9AKwG0Mdpt8cAnFqN7QxgBID5cdsVwPkAfs8d96SL+q9iNHYUO4lLo3OsanCGnhMBvApgIGNsLQA4/w9wklXLc7gdwBXI774FAH0BbGGMtTp/8/dVuGfn/FYnfSUxCkATgD855qc/ElE3VHE7M8Y+BPB/AFYBWIt8u81DdbezS9R2NdrelSTYSXCsalx6iKg7gAcBfIcxtk2VVHCsop4DEZ0BYANjbB5/WJCUaZyrFGoAHA7gt4yxiQB2ojg8F1Hx9+yYEs4CMBLAYADdkDdF+Kmmdg5Ddo9G772SBHsjAH6X4iEA1pSpLkYholrkhfr9jLGHnMPriWiQc34QgA3O8Wp4DscC+CQRrQDwAPLmmNsB9CYid7tG/r4K9+yc7wXgo1JW2ACNABoZY686f/8LeUFfze08BcAHjLEmxlgLgIcAHIPqbmeXqO1qtL0rSbC/DmCMM6Neh/wkzKNlrlNiiIgA3AVgEWPsVu7UowDcmfELkbe9u8e/5MyuHwVgqzvkqxQYY1cxxoYwxkYg347PMMa+AOBZAOc6yfz37D6Lc530FaXJMcbWAVhN+c3fAeBkAAtRxe2MvAnmKCLq6vRz956rtp05orbrLACnENE+zkjnFOdYPMo96RBxguJ0AEsAvA/g6nLXx9A9HYf8kOsdAG85/05H3rb4NIClzv99nPSEvHfQ+wDeRd7joOz3keD+TwDwmPN7FIDXACwD8E8AnZzjnZ2/lznnR5W73jHvdQKABqetHwawT7W3M4AfAVgMYD6A+wB0qrZ2BvA35OcQWpDXvC+O064ALnLufRmArySpk115arFYLFVGJZliLBaLxaKBFewWi8VSZVjBbrFYLFWGFewWi8VSZVjBbrFYLFWGFewWi8VSZVjBbrFYLFWGFewWi8VSZfx/RYzyzxBIEAMAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1f5de979fd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# data本来就是一个数据，所以我们可以直接plot\\n\",\n    \"data.plot()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 102,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1f5de9112b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# np.random.randn(1000,4) 随机生成1000行4列数据\\n\",\n    \"# list(\\\"ABCD\\\")会变为['A','B','C','D']\\n\",\n    \"data = pd.DataFrame(\\n\",\n    \"    np.random.randn(1000,4),\\n\",\n    \"    index=np.arange(1000),\\n\",\n    \"    columns=list(\\\"ABCD\\\")\\n\",\n    \")\\n\",\n    \"data.cumsum()\\n\",\n    \"data.plot()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 103,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1f5deb69b00>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = data.plot.scatter(x='A',y='B',color='DarkBlue',label='Class1')\\n\",\n    \"# 将之下这个 data 画在上一个 ax 上面\\n\",\n    \"data.plot.scatter(x='A',y='C',color='LightGreen',label='Class2',ax=ax)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 9.学习来源\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"https://morvanzhou.github.io/tutorials/data-manipulation/np-pd/\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "3.pandas/3.pandas_beginner/student.csv",
    "content": "Student ID,name ,age,gender\n1100,Kelly,22,Female\n1101,Clo,21,Female\n1102,Tilly,22,Female\n1103,Tony,24,Male\n1104,David,20,Male\n1105,Catty,22,Female\n1106,M,3,Female\n1107,N,43,Male\n1108,A,13,Male\n1109,S,12,Male\n1110,David,33,Male\n1111,Dw,3,Female\n1112,Q,23,Male\n1113,W,21,Female"
  },
  {
    "path": "3.pandas/4.Pandas50/Pandas50.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Pandas练习题50题\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \">作者：王大毛，和鲸社区\\n\",\n    \">\\n\",\n    \">出处：https://www.kesci.com/home/project/5ddc974ef41512002cec1dca\\n\",\n    \">\\n\",\n    \">修改：黄海广\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Pandas 是基于 NumPy 的一种数据处理工具，该工具为了解决数据分析任务而创建。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的函数和方法。 这些练习着重DataFrame和Series对象的基本操作，包括数据的索引、分组、统计和清洗。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 基本操作\\n\",\n    \"### 1.导入 Pandas 库并简写为 pd，并输出版本号\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'0.22.0'\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"pd.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2. 从列表创建 Series\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0    0\\n\",\n       \"1    1\\n\",\n       \"2    2\\n\",\n       \"3    3\\n\",\n       \"4    4\\n\",\n       \"dtype: int64\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr = [0, 1, 2, 3, 4]\\n\",\n    \"df = pd.Series(arr) # 如果不指定索引，则默认从 0 开始\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3. 从字典创建 Series\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"a    1\\n\",\n       \"b    2\\n\",\n       \"c    3\\n\",\n       \"d    4\\n\",\n       \"e    5\\n\",\n       \"dtype: int64\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"d = {'a':1,'b':2,'c':3,'d':4,'e':5}\\n\",\n    \"df = pd.Series(d)\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4. 从 NumPy 数组创建 DataFrame\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>A</th>\\n\",\n       \"      <th>B</th>\\n\",\n       \"      <th>C</th>\\n\",\n       \"      <th>D</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2020-01-10 22:46:01.642021</th>\\n\",\n       \"      <td>0.277099</td>\\n\",\n       \"      <td>0.665053</td>\\n\",\n       \"      <td>0.882637</td>\\n\",\n       \"      <td>-0.598895</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2020-01-11 22:46:01.642021</th>\\n\",\n       \"      <td>0.365233</td>\\n\",\n       \"      <td>-2.529804</td>\\n\",\n       \"      <td>-0.699849</td>\\n\",\n       \"      <td>0.159623</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2020-01-12 22:46:01.642021</th>\\n\",\n       \"      <td>-0.831850</td>\\n\",\n       \"      <td>-2.099049</td>\\n\",\n       \"      <td>-0.976407</td>\\n\",\n       \"      <td>-0.342800</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2020-01-13 22:46:01.642021</th>\\n\",\n       \"      <td>0.680800</td>\\n\",\n       \"      <td>1.682999</td>\\n\",\n       \"      <td>0.144469</td>\\n\",\n       \"      <td>-2.503013</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2020-01-14 22:46:01.642021</th>\\n\",\n       \"      <td>-0.413880</td>\\n\",\n       \"      <td>0.876169</td>\\n\",\n       \"      <td>-1.047877</td>\\n\",\n       \"      <td>0.996865</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2020-01-15 22:46:01.642021</th>\\n\",\n       \"      <td>1.373956</td>\\n\",\n       \"      <td>0.029732</td>\\n\",\n       \"      <td>-0.549268</td>\\n\",\n       \"      <td>-0.287584</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                   A         B         C         D\\n\",\n       \"2020-01-10 22:46:01.642021  0.277099  0.665053  0.882637 -0.598895\\n\",\n       \"2020-01-11 22:46:01.642021  0.365233 -2.529804 -0.699849  0.159623\\n\",\n       \"2020-01-12 22:46:01.642021 -0.831850 -2.099049 -0.976407 -0.342800\\n\",\n       \"2020-01-13 22:46:01.642021  0.680800  1.682999  0.144469 -2.503013\\n\",\n       \"2020-01-14 22:46:01.642021 -0.413880  0.876169 -1.047877  0.996865\\n\",\n       \"2020-01-15 22:46:01.642021  1.373956  0.029732 -0.549268 -0.287584\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"dates = pd.date_range('today', periods=6)  # 定义时间序列作为 index\\n\",\n    \"num_arr = np.random.randn(6, 4)  # 传入 numpy 随机数组\\n\",\n    \"columns = ['A', 'B', 'C', 'D']  # 将列表作为列名\\n\",\n    \"df = pd.DataFrame(num_arr, index=dates, columns=columns)\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 5. 从CSV中创建 DataFrame，分隔符为“；”，编码格式为gbk\"\n   ]\n  },\n  {\n   \"cell_type\": \"raw\",\n   \"metadata\": {},\n   \"source\": [\n    \"df = pd.read_csv('test.csv', encoding='gbk', sep=';')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6. 从字典对象创建DataFrame，并设置索引\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>animal</th>\\n\",\n       \"      <th>priority</th>\\n\",\n       \"      <th>visits</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>a</th>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>b</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>c</th>\\n\",\n       \"      <td>0.5</td>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>d</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>e</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>f</th>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>g</th>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>h</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>i</th>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>j</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age animal priority  visits\\n\",\n       \"a  2.5    cat      yes       1\\n\",\n       \"b  3.0    cat      yes       3\\n\",\n       \"c  0.5  snake       no       2\\n\",\n       \"d  NaN    dog      yes       3\\n\",\n       \"e  5.0    dog       no       2\\n\",\n       \"f  2.0    cat       no       3\\n\",\n       \"g  4.5  snake       no       1\\n\",\n       \"h  NaN    cat      yes       1\\n\",\n       \"i  7.0    dog       no       2\\n\",\n       \"j  3.0    dog       no       1\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"data = {\\n\",\n    \"    'animal':\\n\",\n    \"    ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\\n\",\n    \"    'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\\n\",\n    \"    'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\\n\",\n    \"    'priority':\\n\",\n    \"    ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\\n\",\n    \"df = pd.DataFrame(data, index=labels)\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 7. 显示df的基础信息，包括行的数量；列名；每一列值的数量、类型\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'pandas.core.frame.DataFrame'>\\n\",\n      \"Index: 10 entries, a to j\\n\",\n      \"Data columns (total 4 columns):\\n\",\n      \"age         8 non-null float64\\n\",\n      \"animal      10 non-null object\\n\",\n      \"priority    10 non-null object\\n\",\n      \"visits      10 non-null int64\\n\",\n      \"dtypes: float64(1), int64(1), object(2)\\n\",\n      \"memory usage: 400.0+ bytes\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df.info()\\n\",\n    \"# 方法二\\n\",\n    \"# df.describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 8. 展示df的前3行\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>animal</th>\\n\",\n       \"      <th>priority</th>\\n\",\n       \"      <th>visits</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>a</th>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>b</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>c</th>\\n\",\n       \"      <td>0.5</td>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age animal priority  visits\\n\",\n       \"a  2.5    cat      yes       1\\n\",\n       \"b  3.0    cat      yes       3\\n\",\n       \"c  0.5  snake       no       2\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.iloc[:3]\\n\",\n    \"# 方法二\\n\",\n    \"#df.head(3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 9. 取出df的animal和age列\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>animal</th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>a</th>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>b</th>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>c</th>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>0.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>d</th>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>e</th>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>f</th>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>g</th>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>h</th>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>i</th>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>j</th>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"  animal  age\\n\",\n       \"a    cat  2.5\\n\",\n       \"b    cat  3.0\\n\",\n       \"c  snake  0.5\\n\",\n       \"d    dog  NaN\\n\",\n       \"e    dog  5.0\\n\",\n       \"f    cat  2.0\\n\",\n       \"g  snake  4.5\\n\",\n       \"h    cat  NaN\\n\",\n       \"i    dog  7.0\\n\",\n       \"j    dog  3.0\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.loc[:, ['animal', 'age']]\\n\",\n    \"# 方法二\\n\",\n    \"# df[['animal', 'age']]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 10. 取出索引为[3, 4, 8]行的animal和age列\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>animal</th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>d</th>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>e</th>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>i</th>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"  animal  age\\n\",\n       \"d    dog  NaN\\n\",\n       \"e    dog  5.0\\n\",\n       \"i    dog  7.0\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.loc[df.index[[3, 4, 8]], ['animal', 'age']]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 11. 取出age值大于3的行\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>animal</th>\\n\",\n       \"      <th>priority</th>\\n\",\n       \"      <th>visits</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>e</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>g</th>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>i</th>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age animal priority  visits\\n\",\n       \"e  5.0    dog       no       2\\n\",\n       \"g  4.5  snake       no       1\\n\",\n       \"i  7.0    dog       no       2\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[df['age'] > 3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 12. 取出age值缺失的行\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>animal</th>\\n\",\n       \"      <th>priority</th>\\n\",\n       \"      <th>visits</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>d</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>h</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age animal priority  visits\\n\",\n       \"d  NaN    dog      yes       3\\n\",\n       \"h  NaN    cat      yes       1\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[df['age'].isnull()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 13.取出age在2,4间的行（不含）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>animal</th>\\n\",\n       \"      <th>priority</th>\\n\",\n       \"      <th>visits</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>e</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>g</th>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>i</th>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age animal priority  visits\\n\",\n       \"e  5.0    dog       no       2\\n\",\n       \"g  4.5  snake       no       1\\n\",\n       \"i  7.0    dog       no       2\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df[(df['age']>2) & (df['age']>4)]\\n\",\n    \"# 方法二\\n\",\n    \"# df[df['age'].between(2, 4)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 14. f行的age改为1.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df.loc['f', 'age'] = 1.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 15. 计算visits的总和\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"19\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['visits'].sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 16. 计算每个不同种类animal的age的平均数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"animal\\n\",\n       \"cat      2.333333\\n\",\n       \"dog      5.000000\\n\",\n       \"snake    2.500000\\n\",\n       \"Name: age, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.groupby('animal')['age'].mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 17. 在df中插入新行k，然后删除该行\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>animal</th>\\n\",\n       \"      <th>priority</th>\\n\",\n       \"      <th>visits</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>a</th>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>b</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>c</th>\\n\",\n       \"      <td>0.5</td>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>d</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>e</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>f</th>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>g</th>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>h</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>i</th>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>j</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age animal priority  visits\\n\",\n       \"a  2.5    cat      yes       1\\n\",\n       \"b  3.0    cat      yes       3\\n\",\n       \"c  0.5  snake       no       2\\n\",\n       \"d  NaN    dog      yes       3\\n\",\n       \"e  5.0    dog       no       2\\n\",\n       \"f  1.5    cat       no       3\\n\",\n       \"g  4.5  snake       no       1\\n\",\n       \"h  NaN    cat      yes       1\\n\",\n       \"i  7.0    dog       no       2\\n\",\n       \"j  3.0    dog       no       1\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"#插入\\n\",\n    \"df.loc['k'] = [5.5, 'dog', 'no', 2]\\n\",\n    \"# 删除\\n\",\n    \"df = df.drop('k')\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 18. 计算df中每个种类animal的数量\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dog      4\\n\",\n       \"cat      4\\n\",\n       \"snake    2\\n\",\n       \"Name: animal, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['animal'].value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 19. 先按age降序排列，后按visits升序排列\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>animal</th>\\n\",\n       \"      <th>priority</th>\\n\",\n       \"      <th>visits</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>i</th>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>e</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>g</th>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>j</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>b</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>a</th>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>f</th>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>c</th>\\n\",\n       \"      <td>0.5</td>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>no</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>h</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>d</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>yes</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age animal priority  visits\\n\",\n       \"i  7.0    dog       no       2\\n\",\n       \"e  5.0    dog       no       2\\n\",\n       \"g  4.5  snake       no       1\\n\",\n       \"j  3.0    dog       no       1\\n\",\n       \"b  3.0    cat      yes       3\\n\",\n       \"a  2.5    cat      yes       1\\n\",\n       \"f  1.5    cat       no       3\\n\",\n       \"c  0.5  snake       no       2\\n\",\n       \"h  NaN    cat      yes       1\\n\",\n       \"d  NaN    dog      yes       3\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.sort_values(by=['age', 'visits'], ascending=[False, True])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 20. 将priority列中的yes, no替换为布尔值True, False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>animal</th>\\n\",\n       \"      <th>priority</th>\\n\",\n       \"      <th>visits</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>a</th>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>b</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>c</th>\\n\",\n       \"      <td>0.5</td>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>d</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>e</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>f</th>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>g</th>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>h</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>i</th>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>j</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age animal  priority  visits\\n\",\n       \"a  2.5    cat      True       1\\n\",\n       \"b  3.0    cat      True       3\\n\",\n       \"c  0.5  snake     False       2\\n\",\n       \"d  NaN    dog      True       3\\n\",\n       \"e  5.0    dog     False       2\\n\",\n       \"f  1.5    cat     False       3\\n\",\n       \"g  4.5  snake     False       1\\n\",\n       \"h  NaN    cat      True       1\\n\",\n       \"i  7.0    dog     False       2\\n\",\n       \"j  3.0    dog     False       1\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['priority'] = df['priority'].map({'yes': True, 'no': False})\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 21. 将animal列中的snake替换为python\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>animal</th>\\n\",\n       \"      <th>priority</th>\\n\",\n       \"      <th>visits</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>a</th>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>b</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>c</th>\\n\",\n       \"      <td>0.5</td>\\n\",\n       \"      <td>python</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>d</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>e</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>f</th>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>g</th>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>python</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>h</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>cat</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>i</th>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>j</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>dog</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   age  animal  priority  visits\\n\",\n       \"a  2.5     cat      True       1\\n\",\n       \"b  3.0     cat      True       3\\n\",\n       \"c  0.5  python     False       2\\n\",\n       \"d  NaN     dog      True       3\\n\",\n       \"e  5.0     dog     False       2\\n\",\n       \"f  1.5     cat     False       3\\n\",\n       \"g  4.5  python     False       1\\n\",\n       \"h  NaN     cat      True       1\\n\",\n       \"i  7.0     dog     False       2\\n\",\n       \"j  3.0     dog     False       1\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['animal'] = df['animal'].replace('snake', 'python')\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 22. 对每种animal的每种不同数量visits，计算平均age，即，返回一个表格，行是aniaml种类，列是visits数量，表格值是行动物种类列访客数量的平均年龄\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th>visits</th>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <th>3</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>animal</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>cat</th>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>2.25</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>dog</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>6.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>python</th>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>0.5</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"visits    1    2     3\\n\",\n       \"animal                \\n\",\n       \"cat     2.5  NaN  2.25\\n\",\n       \"dog     3.0  6.0   NaN\\n\",\n       \"python  4.5  0.5   NaN\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.pivot_table(index='animal', columns='visits', values='age', aggfunc='mean')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 进阶操作\\n\",\n    \"### 23. 有一列整数列A的DatraFrame，删除数值重复的行\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    A\\n\",\n      \"0   1\\n\",\n      \"1   2\\n\",\n      \"2   2\\n\",\n      \"3   3\\n\",\n      \"4   4\\n\",\n      \"5   5\\n\",\n      \"6   5\\n\",\n      \"7   5\\n\",\n      \"8   6\\n\",\n      \"9   7\\n\",\n      \"10  7\\n\",\n      \"   A\\n\",\n      \"0  1\\n\",\n      \"1  2\\n\",\n      \"3  3\\n\",\n      \"4  4\\n\",\n      \"5  5\\n\",\n      \"8  6\\n\",\n      \"9  7\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})\\n\",\n    \"print(df)\\n\",\n    \"df1 = df.loc[df['A'].shift() != df['A']]\\n\",\n    \"# 方法二\\n\",\n    \"# df1 = df.drop_duplicates(subset='A')\\n\",\n    \"print(df1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 24. 一个全数值DatraFrame，每个数字减去该行的平均数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"          0         1         2\\n\",\n      \"0  0.465407  0.152497  0.861174\\n\",\n      \"1  0.623682  0.627339  0.495652\\n\",\n      \"2  0.835176  0.862376  0.693047\\n\",\n      \"3  0.319698  0.306709  0.654063\\n\",\n      \"4  0.234855  0.194232  0.438597\\n\",\n      \"          0         1         2\\n\",\n      \"0 -0.027619 -0.340529  0.368148\\n\",\n      \"1  0.041457  0.045115 -0.086572\\n\",\n      \"2  0.038310  0.065509 -0.103819\\n\",\n      \"3 -0.107125 -0.120114  0.227239\\n\",\n      \"4 -0.054373 -0.094996  0.149368\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame(np.random.random(size=(5, 3)))\\n\",\n    \"print(df)\\n\",\n    \"df1 = df.sub(df.mean(axis=1), axis=0)\\n\",\n    \"print(df1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 25. 一个有5列的DataFrame，求哪一列的和最小\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"          a         b         c         d         e\\n\",\n      \"0  0.653658  0.730994  0.223025  0.456730  0.288283\\n\",\n      \"1  0.937546  0.640995  0.197359  0.671524  0.006035\\n\",\n      \"2  0.392762  0.174955  0.053928  0.318634  0.464534\\n\",\n      \"3  0.741499  0.197861  0.988105  0.633780  0.914250\\n\",\n      \"4  0.469285  0.309043  0.162127  0.032480  0.863017\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'c'\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame(np.random.random(size=(5, 5)), columns=list('abcde'))\\n\",\n    \"print(df)\\n\",\n    \"df.sum().idxmin()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 26. 给定DataFrame，求A列每个值的前3大的B的值的和\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    A    B\\n\",\n      \"0   a   12\\n\",\n      \"1   a  345\\n\",\n      \"2   a    3\\n\",\n      \"3   b    1\\n\",\n      \"4   b   45\\n\",\n      \"5   c   14\\n\",\n      \"6   a    4\\n\",\n      \"7   a   52\\n\",\n      \"8   b   54\\n\",\n      \"9   c   23\\n\",\n      \"10  c  235\\n\",\n      \"11  c   21\\n\",\n      \"12  b   57\\n\",\n      \"13  b    3\\n\",\n      \"14  c   87\\n\",\n      \"A\\n\",\n      \"a    409\\n\",\n      \"b    156\\n\",\n      \"c    345\\n\",\n      \"Name: B, dtype: int64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame({'A': list('aaabbcaabcccbbc'), \\n\",\n    \"                   'B': [12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})\\n\",\n    \"print(df)\\n\",\n    \"df1 = df.groupby('A')['B'].nlargest(3).sum(level=0)\\n\",\n    \"print(df1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 27. 给定DataFrame，有列A, B，A的值在1-100（含），对A列每10步长，求对应的B的和\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    A   B\\n\",\n      \"0   1   1\\n\",\n      \"1   2   2\\n\",\n      \"2  11  11\\n\",\n      \"3  11  11\\n\",\n      \"4  33  33\\n\",\n      \"5  34  34\\n\",\n      \"6  35  35\\n\",\n      \"7  40  40\\n\",\n      \"8  79  79\\n\",\n      \"9  99  99\\n\",\n      \"A\\n\",\n      \"(0, 10]        3\\n\",\n      \"(10, 20]      22\\n\",\n      \"(20, 30]       0\\n\",\n      \"(30, 40]     142\\n\",\n      \"(40, 50]       0\\n\",\n      \"(50, 60]       0\\n\",\n      \"(60, 70]       0\\n\",\n      \"(70, 80]      79\\n\",\n      \"(80, 90]       0\\n\",\n      \"(90, 100]     99\\n\",\n      \"Name: B, dtype: int64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame({\\n\",\n    \"    'A': [1, 2, 11, 11, 33, 34, 35, 40, 79, 99],\\n\",\n    \"    'B': [1, 2, 11, 11, 33, 34, 35, 40, 79, 99]\\n\",\n    \"})\\n\",\n    \"print(df)\\n\",\n    \"df1 = df.groupby(pd.cut(df['A'], np.arange(0, 101, 10)))['B'].sum()\\n\",\n    \"print(df1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 28. 给定DataFrame，计算每个元素至左边最近的0（或者至开头）的距离，生成新列y\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   X    Y\\n\",\n      \"0  7  1.0\\n\",\n      \"1  2  2.0\\n\",\n      \"2  0  0.0\\n\",\n      \"3  3  1.0\\n\",\n      \"4  4  2.0\\n\",\n      \"5  2  3.0\\n\",\n      \"6  5  4.0\\n\",\n      \"7  0  0.0\\n\",\n      \"8  3  1.0\\n\",\n      \"9  4  2.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame({'X': [7, 2, 0, 3, 4, 2, 5, 0, 3, 4]})\\n\",\n    \"# 方法一\\n\",\n    \"x = (df['X'] != 0).cumsum()\\n\",\n    \"y = x != x.shift()\\n\",\n    \"df['Y'] = y.groupby((y != y.shift()).cumsum()).cumsum()\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   X  Y\\n\",\n      \"0  7  1\\n\",\n      \"1  2  2\\n\",\n      \"2  0  0\\n\",\n      \"3  3  1\\n\",\n      \"4  4  2\\n\",\n      \"5  2  3\\n\",\n      \"6  5  4\\n\",\n      \"7  0  0\\n\",\n      \"8  3  1\\n\",\n      \"9  4  2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 方法二\\n\",\n    \"df['Y'] = df.groupby((df['X'] == 0).cumsum()).cumcount()\\n\",\n    \"first_zero_idx = (df['X'] == 0).idxmax()\\n\",\n    \"df['Y'].iloc[0:first_zero_idx] += 1\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 29. 一个全数值的DataFrame，返回最大3个值的坐标\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"          0         1         2\\n\",\n      \"0  0.974321  0.454025  0.018815\\n\",\n      \"1  0.323491  0.468609  0.834424\\n\",\n      \"2  0.340960  0.826835  0.503252\\n\",\n      \"3  0.812414  0.202745  0.965168\\n\",\n      \"4  0.633172  0.270281  0.915212\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(2, 4), (2, 3), (0, 0)]\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame(np.random.random(size=(5, 3)))\\n\",\n    \"print(df)\\n\",\n    \"df.unstack().sort_values()[-3:].index.tolist()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 30. 给定DataFrame，将负值代替为同组的平均值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   grps  vals\\n\",\n      \"0     a   -12\\n\",\n      \"1     a   345\\n\",\n      \"2     a     3\\n\",\n      \"3     b     1\\n\",\n      \"4     b    45\\n\",\n      \"5     c    14\\n\",\n      \"6     a     4\\n\",\n      \"7     a   -52\\n\",\n      \"8     b    54\\n\",\n      \"9     c    23\\n\",\n      \"10    c  -235\\n\",\n      \"11    c    21\\n\",\n      \"12    b    57\\n\",\n      \"13    b     3\\n\",\n      \"14    c    87\\n\",\n      \"   grps        vals\\n\",\n      \"0     a  117.333333\\n\",\n      \"1     a  345.000000\\n\",\n      \"2     a    3.000000\\n\",\n      \"3     b    1.000000\\n\",\n      \"4     b   45.000000\\n\",\n      \"5     c   14.000000\\n\",\n      \"6     a    4.000000\\n\",\n      \"7     a  117.333333\\n\",\n      \"8     b   54.000000\\n\",\n      \"9     c   23.000000\\n\",\n      \"10    c   36.250000\\n\",\n      \"11    c   21.000000\\n\",\n      \"12    b   57.000000\\n\",\n      \"13    b    3.000000\\n\",\n      \"14    c   87.000000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame({\\n\",\n    \"    'grps':\\n\",\n    \"    list('aaabbcaabcccbbc'),\\n\",\n    \"    'vals': [-12, 345, 3, 1, 45, 14, 4, -52, 54, 23, -235, 21, 57, 3, 87]\\n\",\n    \"})\\n\",\n    \"print(df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def replace(group):\\n\",\n    \"    mask = group < 0\\n\",\n    \"    group[mask] = group[~mask].mean()\\n\",\n    \"    return group\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"df['vals'] = df.groupby(['grps'])['vals'].transform(replace)\\n\",\n    \"print(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 31. 计算3位滑动窗口的平均值，忽略NAN\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   group  value\\n\",\n      \"0      a    1.0\\n\",\n      \"1      a    2.0\\n\",\n      \"2      b    3.0\\n\",\n      \"3      b    NaN\\n\",\n      \"4      a    2.0\\n\",\n      \"5      b    3.0\\n\",\n      \"6      b    NaN\\n\",\n      \"7      b    1.0\\n\",\n      \"8      a    7.0\\n\",\n      \"9      b    3.0\\n\",\n      \"10     a    NaN\\n\",\n      \"11     b    8.0\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0     1.000000\\n\",\n       \"1     1.500000\\n\",\n       \"2     3.000000\\n\",\n       \"3     3.000000\\n\",\n       \"4     1.666667\\n\",\n       \"5     3.000000\\n\",\n       \"6     3.000000\\n\",\n       \"7     2.000000\\n\",\n       \"8     3.666667\\n\",\n       \"9     2.000000\\n\",\n       \"10    4.500000\\n\",\n       \"11    4.000000\\n\",\n       \"Name: value, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame({\\n\",\n    \"    'group': list('aabbabbbabab'),\\n\",\n    \"    'value': [1, 2, 3, np.nan, 2, 3, np.nan, 1, 7, 3, np.nan, 8]\\n\",\n    \"})\\n\",\n    \"print(df)\\n\",\n    \"\\n\",\n    \"g1 = df.groupby(['group'])['value']\\n\",\n    \"g2 = df.fillna(0).groupby(['group'])['value']\\n\",\n    \"\\n\",\n    \"s = g2.rolling(3, min_periods=1).sum() / g1.rolling(3, min_periods=1).count()\\n\",\n    \"s.reset_index(level=0, drop=True).sort_index()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Series 和 Datetime索引\\n\",\n    \"### 32. 创建Series s，将2015所有工作日作为随机值的索引\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2015-01-01    0.503458\\n\",\n       \"2015-01-02    0.194185\\n\",\n       \"2015-01-05    0.550930\\n\",\n       \"2015-01-06    0.174309\\n\",\n       \"2015-01-07    0.316911\\n\",\n       \"2015-01-08    0.288385\\n\",\n       \"2015-01-09    0.293285\\n\",\n       \"2015-01-12    0.340436\\n\",\n       \"2015-01-13    0.630009\\n\",\n       \"2015-01-14    0.076130\\n\",\n       \"Freq: B, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dti = pd.date_range(start='2015-01-01', end='2015-12-31', freq='B') \\n\",\n    \"s = pd.Series(np.random.rand(len(dti)), index=dti)\\n\",\n    \"\\n\",\n    \"s.head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 33. 所有礼拜三的值求和\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"27.272318047689705\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s[s.index.weekday == 2].sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 34. 求每个自然月的平均数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2015-01-31    0.375417\\n\",\n       \"2015-02-28    0.551560\\n\",\n       \"2015-03-31    0.540772\\n\",\n       \"2015-04-30    0.450957\\n\",\n       \"2015-05-31    0.369119\\n\",\n       \"2015-06-30    0.588625\\n\",\n       \"2015-07-31    0.584358\\n\",\n       \"2015-08-31    0.609751\\n\",\n       \"2015-09-30    0.511285\\n\",\n       \"2015-10-31    0.555546\\n\",\n       \"2015-11-30    0.528777\\n\",\n       \"2015-12-31    0.574317\\n\",\n       \"Freq: M, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s.resample('M').mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 35. 每连续4个月为一组，求最大值所在的日期\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2015-01-31   2015-01-15\\n\",\n       \"2015-05-31   2015-02-04\\n\",\n       \"2015-09-30   2015-06-02\\n\",\n       \"2016-01-31   2015-12-08\\n\",\n       \"dtype: datetime64[ns]\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s.groupby(pd.Grouper(freq='4M')).idxmax()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 36. 创建2015-2016每月第三个星期四的序列\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Airline</th>\\n\",\n       \"      <th>FlightNumber</th>\\n\",\n       \"      <th>From_To</th>\\n\",\n       \"      <th>RecentDelays</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>KLM(!)</td>\\n\",\n       \"      <td>10045.0</td>\\n\",\n       \"      <td>LoNDon_paris</td>\\n\",\n       \"      <td>[23, 47]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>&lt;Air France&gt; (12)</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>MAdrid_miLAN</td>\\n\",\n       \"      <td>[]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>(British Airways. )</td>\\n\",\n       \"      <td>10065.0</td>\\n\",\n       \"      <td>londON_StockhOlm</td>\\n\",\n       \"      <td>[24, 43, 87]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>12. Air France</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Budapest_PaRis</td>\\n\",\n       \"      <td>[13]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>\\\"Swiss Air\\\"</td>\\n\",\n       \"      <td>10085.0</td>\\n\",\n       \"      <td>Brussels_londOn</td>\\n\",\n       \"      <td>[67, 32]</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"               Airline  FlightNumber           From_To  RecentDelays\\n\",\n       \"0               KLM(!)       10045.0      LoNDon_paris      [23, 47]\\n\",\n       \"1    <Air France> (12)           NaN      MAdrid_miLAN            []\\n\",\n       \"2  (British Airways. )       10065.0  londON_StockhOlm  [24, 43, 87]\\n\",\n       \"3       12. Air France           NaN    Budapest_PaRis          [13]\\n\",\n       \"4          \\\"Swiss Air\\\"       10085.0   Brussels_londOn      [67, 32]\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pd.date_range('2015-01-01', '2016-12-31', freq='WOM-3THU')\\n\",\n    \"#数据清洗\\n\",\n    \"df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN', 'londON_StockhOlm', \\n\",\n    \"                               'Budapest_PaRis', 'Brussels_londOn'],\\n\",\n    \"              'FlightNumber': [10045, np.nan, 10065, np.nan, 10085],\\n\",\n    \"              'RecentDelays': [[23, 47], [], [24, 43, 87], [13], [67, 32]],\\n\",\n    \"                   'Airline': ['KLM(!)', '<Air France> (12)', '(British Airways. )', \\n\",\n    \"                               '12. Air France', '\\\"Swiss Air\\\"']})\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 37. FlightNumber列中有些值缺失了，他们本来应该是每一行增加10，填充缺失的数值，并且令数据类型为整数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Airline</th>\\n\",\n       \"      <th>FlightNumber</th>\\n\",\n       \"      <th>From_To</th>\\n\",\n       \"      <th>RecentDelays</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>KLM(!)</td>\\n\",\n       \"      <td>10045</td>\\n\",\n       \"      <td>LoNDon_paris</td>\\n\",\n       \"      <td>[23, 47]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>&lt;Air France&gt; (12)</td>\\n\",\n       \"      <td>10055</td>\\n\",\n       \"      <td>MAdrid_miLAN</td>\\n\",\n       \"      <td>[]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>(British Airways. )</td>\\n\",\n       \"      <td>10065</td>\\n\",\n       \"      <td>londON_StockhOlm</td>\\n\",\n       \"      <td>[24, 43, 87]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>12. Air France</td>\\n\",\n       \"      <td>10075</td>\\n\",\n       \"      <td>Budapest_PaRis</td>\\n\",\n       \"      <td>[13]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>\\\"Swiss Air\\\"</td>\\n\",\n       \"      <td>10085</td>\\n\",\n       \"      <td>Brussels_londOn</td>\\n\",\n       \"      <td>[67, 32]</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"               Airline  FlightNumber           From_To  RecentDelays\\n\",\n       \"0               KLM(!)         10045      LoNDon_paris      [23, 47]\\n\",\n       \"1    <Air France> (12)         10055      MAdrid_miLAN            []\\n\",\n       \"2  (British Airways. )         10065  londON_StockhOlm  [24, 43, 87]\\n\",\n       \"3       12. Air France         10075    Budapest_PaRis          [13]\\n\",\n       \"4          \\\"Swiss Air\\\"         10085   Brussels_londOn      [67, 32]\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['FlightNumber'] = df['FlightNumber'].interpolate().astype(int)\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 38. 将From_To列从_分开，分成From, To两列，并删除原始列\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Airline</th>\\n\",\n       \"      <th>FlightNumber</th>\\n\",\n       \"      <th>RecentDelays</th>\\n\",\n       \"      <th>From</th>\\n\",\n       \"      <th>To</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>KLM(!)</td>\\n\",\n       \"      <td>10045</td>\\n\",\n       \"      <td>[23, 47]</td>\\n\",\n       \"      <td>LoNDon</td>\\n\",\n       \"      <td>paris</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>&lt;Air France&gt; (12)</td>\\n\",\n       \"      <td>10055</td>\\n\",\n       \"      <td>[]</td>\\n\",\n       \"      <td>MAdrid</td>\\n\",\n       \"      <td>miLAN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>(British Airways. )</td>\\n\",\n       \"      <td>10065</td>\\n\",\n       \"      <td>[24, 43, 87]</td>\\n\",\n       \"      <td>londON</td>\\n\",\n       \"      <td>StockhOlm</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>12. Air France</td>\\n\",\n       \"      <td>10075</td>\\n\",\n       \"      <td>[13]</td>\\n\",\n       \"      <td>Budapest</td>\\n\",\n       \"      <td>PaRis</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>\\\"Swiss Air\\\"</td>\\n\",\n       \"      <td>10085</td>\\n\",\n       \"      <td>[67, 32]</td>\\n\",\n       \"      <td>Brussels</td>\\n\",\n       \"      <td>londOn</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"               Airline  FlightNumber  RecentDelays      From         To\\n\",\n       \"0               KLM(!)         10045      [23, 47]    LoNDon      paris\\n\",\n       \"1    <Air France> (12)         10055            []    MAdrid      miLAN\\n\",\n       \"2  (British Airways. )         10065  [24, 43, 87]    londON  StockhOlm\\n\",\n       \"3       12. Air France         10075          [13]  Budapest      PaRis\\n\",\n       \"4          \\\"Swiss Air\\\"         10085      [67, 32]  Brussels     londOn\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"temp = df.From_To.str.split('_', expand=True)\\n\",\n    \"temp.columns = ['From', 'To']\\n\",\n    \"df = df.join(temp)\\n\",\n    \"df = df.drop('From_To', axis=1)\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 39. 将From, To大小写统一首字母大写其余小写\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Airline</th>\\n\",\n       \"      <th>FlightNumber</th>\\n\",\n       \"      <th>RecentDelays</th>\\n\",\n       \"      <th>From</th>\\n\",\n       \"      <th>To</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>KLM(!)</td>\\n\",\n       \"      <td>10045</td>\\n\",\n       \"      <td>[23, 47]</td>\\n\",\n       \"      <td>London</td>\\n\",\n       \"      <td>Paris</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>&lt;Air France&gt; (12)</td>\\n\",\n       \"      <td>10055</td>\\n\",\n       \"      <td>[]</td>\\n\",\n       \"      <td>Madrid</td>\\n\",\n       \"      <td>Milan</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>(British Airways. )</td>\\n\",\n       \"      <td>10065</td>\\n\",\n       \"      <td>[24, 43, 87]</td>\\n\",\n       \"      <td>London</td>\\n\",\n       \"      <td>Stockholm</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>12. Air France</td>\\n\",\n       \"      <td>10075</td>\\n\",\n       \"      <td>[13]</td>\\n\",\n       \"      <td>Budapest</td>\\n\",\n       \"      <td>Paris</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>\\\"Swiss Air\\\"</td>\\n\",\n       \"      <td>10085</td>\\n\",\n       \"      <td>[67, 32]</td>\\n\",\n       \"      <td>Brussels</td>\\n\",\n       \"      <td>London</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"               Airline  FlightNumber  RecentDelays      From         To\\n\",\n       \"0               KLM(!)         10045      [23, 47]    London      Paris\\n\",\n       \"1    <Air France> (12)         10055            []    Madrid      Milan\\n\",\n       \"2  (British Airways. )         10065  [24, 43, 87]    London  Stockholm\\n\",\n       \"3       12. Air France         10075          [13]  Budapest      Paris\\n\",\n       \"4          \\\"Swiss Air\\\"         10085      [67, 32]  Brussels     London\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['From'] = df['From'].str.capitalize()\\n\",\n    \"df['To'] = df['To'].str.capitalize()\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 40. Airline列，有一些多余的标点符号，需要提取出正确的航司名称。举例：'(British Airways. )' 应该改为 'British Airways'.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Airline</th>\\n\",\n       \"      <th>FlightNumber</th>\\n\",\n       \"      <th>RecentDelays</th>\\n\",\n       \"      <th>From</th>\\n\",\n       \"      <th>To</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>KLM</td>\\n\",\n       \"      <td>10045</td>\\n\",\n       \"      <td>[23, 47]</td>\\n\",\n       \"      <td>London</td>\\n\",\n       \"      <td>Paris</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>Air France</td>\\n\",\n       \"      <td>10055</td>\\n\",\n       \"      <td>[]</td>\\n\",\n       \"      <td>Madrid</td>\\n\",\n       \"      <td>Milan</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>British Airways</td>\\n\",\n       \"      <td>10065</td>\\n\",\n       \"      <td>[24, 43, 87]</td>\\n\",\n       \"      <td>London</td>\\n\",\n       \"      <td>Stockholm</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>Air France</td>\\n\",\n       \"      <td>10075</td>\\n\",\n       \"      <td>[13]</td>\\n\",\n       \"      <td>Budapest</td>\\n\",\n       \"      <td>Paris</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>Swiss Air</td>\\n\",\n       \"      <td>10085</td>\\n\",\n       \"      <td>[67, 32]</td>\\n\",\n       \"      <td>Brussels</td>\\n\",\n       \"      <td>London</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"           Airline  FlightNumber  RecentDelays      From         To\\n\",\n       \"0              KLM         10045      [23, 47]    London      Paris\\n\",\n       \"1       Air France         10055            []    Madrid      Milan\\n\",\n       \"2  British Airways         10065  [24, 43, 87]    London  Stockholm\\n\",\n       \"3       Air France         10075          [13]  Budapest      Paris\\n\",\n       \"4        Swiss Air         10085      [67, 32]  Brussels     London\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df['Airline'] = df['Airline'].str.extract(\\n\",\n    \"    '([a-zA-Z\\\\s]+)', expand=False).str.strip()\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 41. Airline列，数据被以列表的形式录入，但是我们希望每个数字被录入成单独一列，delay_1, delay_2, ...没有的用NAN替代。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Airline</th>\\n\",\n       \"      <th>FlightNumber</th>\\n\",\n       \"      <th>From</th>\\n\",\n       \"      <th>To</th>\\n\",\n       \"      <th>delay_1</th>\\n\",\n       \"      <th>delay_2</th>\\n\",\n       \"      <th>delay_3</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>KLM</td>\\n\",\n       \"      <td>10045</td>\\n\",\n       \"      <td>London</td>\\n\",\n       \"      <td>Paris</td>\\n\",\n       \"      <td>23.0</td>\\n\",\n       \"      <td>47.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>Air France</td>\\n\",\n       \"      <td>10055</td>\\n\",\n       \"      <td>Madrid</td>\\n\",\n       \"      <td>Milan</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>British Airways</td>\\n\",\n       \"      <td>10065</td>\\n\",\n       \"      <td>London</td>\\n\",\n       \"      <td>Stockholm</td>\\n\",\n       \"      <td>24.0</td>\\n\",\n       \"      <td>43.0</td>\\n\",\n       \"      <td>87.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>Air France</td>\\n\",\n       \"      <td>10075</td>\\n\",\n       \"      <td>Budapest</td>\\n\",\n       \"      <td>Paris</td>\\n\",\n       \"      <td>13.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>Swiss Air</td>\\n\",\n       \"      <td>10085</td>\\n\",\n       \"      <td>Brussels</td>\\n\",\n       \"      <td>London</td>\\n\",\n       \"      <td>67.0</td>\\n\",\n       \"      <td>32.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"           Airline  FlightNumber      From         To  delay_1  delay_2  \\\\\\n\",\n       \"0              KLM         10045    London      Paris     23.0     47.0   \\n\",\n       \"1       Air France         10055    Madrid      Milan      NaN      NaN   \\n\",\n       \"2  British Airways         10065    London  Stockholm     24.0     43.0   \\n\",\n       \"3       Air France         10075  Budapest      Paris     13.0      NaN   \\n\",\n       \"4        Swiss Air         10085  Brussels     London     67.0     32.0   \\n\",\n       \"\\n\",\n       \"   delay_3  \\n\",\n       \"0      NaN  \\n\",\n       \"1      NaN  \\n\",\n       \"2     87.0  \\n\",\n       \"3      NaN  \\n\",\n       \"4      NaN  \"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"delays = df['RecentDelays'].apply(pd.Series)\\n\",\n    \"delays.columns = ['delay_{}'.format(n) for n in range(1, len(delays.columns)+1)]\\n\",\n    \"df = df.drop('RecentDelays', axis=1).join(delays)\\n\",\n    \"df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 层次化索引\\n\",\n    \"### 42. 用 letters = ['A', 'B', 'C']和 numbers = list(range(10))的组合作为系列随机值的层次化索引\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"A  0    0.250785\\n\",\n       \"   1    0.146978\\n\",\n       \"   2    0.596062\\n\",\n       \"   3    0.064608\\n\",\n       \"B  0    0.709660\\n\",\n       \"   1    0.515778\\n\",\n       \"   2    0.483163\\n\",\n       \"   3    0.524490\\n\",\n       \"C  0    0.360434\\n\",\n       \"   1    0.987620\\n\",\n       \"   2    0.527151\\n\",\n       \"   3    0.636960\\n\",\n       \"dtype: float64\"\n      ]\n     },\n     \"execution_count\": 48,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"letters = ['A', 'B', 'C']\\n\",\n    \"numbers = list(range(4))\\n\",\n    \"\\n\",\n    \"mi = pd.MultiIndex.from_product([letters, numbers])\\n\",\n    \"s = pd.Series(np.random.rand(12), index=mi)\\n\",\n    \"s\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 43. 检查s是否是字典顺序排序的\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s.index.is_lexsorted()\\n\",\n    \"# 方法二\\n\",\n    \"# s.index.lexsort_depth == s.index.nlevels\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 44. 选择二级索引为1, 3的行\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"A  1    0.146978\\n\",\n       \"   3    0.064608\\n\",\n       \"B  1    0.515778\\n\",\n       \"   3    0.524490\\n\",\n       \"C  1    0.987620\\n\",\n       \"   3    0.636960\\n\",\n       \"dtype: float64\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s.loc[:, [1, 3]]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 45. 对s进行切片操作，取一级索引至B，二级索引从2开始到最后\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"A  2    0.596062\\n\",\n       \"   3    0.064608\\n\",\n       \"B  2    0.483163\\n\",\n       \"   3    0.524490\\n\",\n       \"dtype: float64\"\n      ]\n     },\n     \"execution_count\": 51,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s.loc[pd.IndexSlice[:'B', 2:]]\\n\",\n    \"# 方法二\\n\",\n    \"# s.loc[slice(None, 'B'), slice(2, None)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 46. 计算每个一级索引的和（A, B, C每一个的和）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"A    1.058433\\n\",\n       \"B    2.233091\\n\",\n       \"C    2.512164\\n\",\n       \"dtype: float64\"\n      ]\n     },\n     \"execution_count\": 52,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"s.sum(level=0)\\n\",\n    \"#方法二\\n\",\n    \"#s.unstack().sum(axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 47. 交换索引等级，新的Series是字典顺序吗？不是的话请排序\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0  A    0.250785\\n\",\n      \"1  A    0.146978\\n\",\n      \"2  A    0.596062\\n\",\n      \"3  A    0.064608\\n\",\n      \"0  B    0.709660\\n\",\n      \"1  B    0.515778\\n\",\n      \"2  B    0.483163\\n\",\n      \"3  B    0.524490\\n\",\n      \"0  C    0.360434\\n\",\n      \"1  C    0.987620\\n\",\n      \"2  C    0.527151\\n\",\n      \"3  C    0.636960\\n\",\n      \"dtype: float64\\n\",\n      \"False\\n\",\n      \"0  A    0.250785\\n\",\n      \"   B    0.709660\\n\",\n      \"   C    0.360434\\n\",\n      \"1  A    0.146978\\n\",\n      \"   B    0.515778\\n\",\n      \"   C    0.987620\\n\",\n      \"2  A    0.596062\\n\",\n      \"   B    0.483163\\n\",\n      \"   C    0.527151\\n\",\n      \"3  A    0.064608\\n\",\n      \"   B    0.524490\\n\",\n      \"   C    0.636960\\n\",\n      \"dtype: float64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"new_s = s.swaplevel(0, 1)\\n\",\n    \"print(new_s)\\n\",\n    \"print(new_s.index.is_lexsorted())\\n\",\n    \"new_s = new_s.sort_index()\\n\",\n    \"print(new_s)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"## 可视化\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"df = pd.DataFrame({\\\"xs\\\": [1, 5, 2, 8, 1], \\\"ys\\\": [4, 2, 1, 9, 6]})\\n\",\n    \"plt.style.use('ggplot')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 48. 画出df的散点图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1f188ddacc0>\"\n      ]\n     },\n     \"execution_count\": 55,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1f18862bf60>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"df.plot.scatter(\\\"xs\\\", \\\"ys\\\", color = \\\"black\\\", marker = \\\"x\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 49. 可视化指定4维DataFrame\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1f18aea4c18>\"\n      ]\n     },\n     \"execution_count\": 56,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1f18aebb6a0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame({\\n\",\n    \"    \\\"productivity\\\": [5, 2, 3, 1, 4, 5, 6, 7, 8, 3, 4, 8, 9],\\n\",\n    \"    \\\"hours_in\\\": [1, 9, 6, 5, 3, 9, 2, 9, 1, 7, 4, 2, 2],\\n\",\n    \"    \\\"happiness\\\": [2, 1, 3, 2, 3, 1, 2, 3, 1, 2, 2, 1, 3],\\n\",\n    \"    \\\"caffienated\\\": [0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0]\\n\",\n    \"})\\n\",\n    \"\\n\",\n    \"df.plot.scatter(\\n\",\n    \"    \\\"hours_in\\\", \\\"productivity\\\", s=df.happiness * 100, c=df.caffienated)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 50. 在同一个图中可视化2组数据，共用X轴，但y轴不同\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(-1, 12)\"\n      ]\n     },\n     \"execution_count\": 57,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1f18af1bb70>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame({\\n\",\n    \"    \\\"revenue\\\": [57, 68, 63, 71, 72, 90, 80, 62, 59, 51, 47, 52],\\n\",\n    \"    \\\"advertising\\\":\\n\",\n    \"    [2.1, 1.9, 2.7, 3.0, 3.6, 3.2, 2.7, 2.4, 1.8, 1.6, 1.3, 1.9],\\n\",\n    \"    \\\"month\\\":\\n\",\n    \"    range(12)\\n\",\n    \"})\\n\",\n    \"\\n\",\n    \"ax = df.plot.bar(\\\"month\\\", \\\"revenue\\\", color=\\\"green\\\")\\n\",\n    \"df.plot.line(\\\"month\\\", \\\"advertising\\\", secondary_y=True, ax=ax)\\n\",\n    \"ax.set_xlim((-1, 12))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "3.pandas/4.Pandas50/README.md",
    "content": "# Pandas练习题50题\n\n>作者：王大毛，和鲸社区\n>\n>出处：https://www.kesci.com/home/project/5ddc974ef41512002cec1dca\n>\n>修改：黄海广\n\nPandas 是基于 NumPy 的一种数据处理工具，该工具为了解决数据分析任务而创建。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的函数和方法。 这些练习着重DataFrame和Series对象的基本操作，包括数据的索引、分组、统计和清洗。"
  },
  {
    "path": "3.pandas/README.md",
    "content": "# Pandas简介\n\nPandas 是基于NumPy 的一种工具，该工具是为了解决数据分析任务而创建的。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的工具。pandas提供了大量能使我们快速便捷地处理数据的函数和方法。你很快就会发现，它是使Python成为强大而高效的数据分析环境的重要因素之一。\n\n\n\n## 一、《十分钟搞定pandas》\n\n**[10-Minutes-to-pandas](1.10-Minutes-to-pandas)**，这是十分钟搞定pandas [*10 minutes in pandas*](http://pandas.pydata.org/pandas-docs/stable/10min.html)的中文翻译。\n\n## 二、《pandas练习题》\n\n**[Pandas_Exercises](2.Pandas_Exercises)**，这个是pandas的练习题。\n\n## 三、《pandas入门教程-2天学会pandas》\n\n**[pandas_beginner](3.pandas_beginner)**\n\n- - ## 目录\n    - 0.导语\n    - 1.Series\n    - 2.DataFrame\n    2.1 DataFrame的简单运用\n\n    - 3.pandas选择数据\n    3.1 实战筛选\n    3.2 筛选总结\n\n    - 4.Pandas设置值\n    4.1 创建数据\n    4.2 根据位置设置loc和iloc\n    4.3 根据条件设置\n    4.4 按行或列设置\n    4.5 添加Series序列(长度必须对齐)\n    4.6 设定某行某列为特定值\n    4.7 修改一整行数据\n\n    - 5.Pandas处理丢失数据\n    5.1 创建含NaN的矩阵\n    5.2 删除掉有NaN的行或列\n    5.3 替换NaN值为0或者其他\n    5.4 是否有缺失数据NaN\n\n    - 6.Pandas导入导出\n    6.1 导入数据\n    6.2 导出数据\n\n    - 7.Pandas合并操作\n    7.1 Pandas合并concat\n    7.2.Pandas 合并 merge\n      * 7.2.1 定义资料集并打印出\n  \n      * 7.2.2 依据key column合并,并打印\n  \n      * 7.2.3 两列合并\n  \n      * 7.2.4 Indicator设置合并列名称\n  \n      * 7.2.5 依据index合并\n  \n      * 7.2.6 解决overlapping的问题\n    - 8.Pandas plot出图\n    - 9.学习来源\n  \n    ## 四、《Pandas50题》\n  \n    **[Pandas50](4.Pandas50)**，这个是pandas的练习题50题。\n    \n    \n  \n\n"
  },
  {
    "path": "4.scipy/1.scipy-intro.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pylab as pl\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib as mpl\\n\",\n    \"mpl.rcParams['font.sans-serif'] = ['SimHei']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# SciPy-数值计算库\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.0.0'\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import scipy\\n\",\n    \"scipy.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 常数和特殊函数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"299792458.0\\n\",\n      \"6.62607004e-34\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scipy import constants as C\\n\",\n    \"print (C.c) # 真空中的光速\\n\",\n    \"print (C.h) # 普朗克常数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(9.10938356e-31, 'kg', 1.1e-38)\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"C.physical_constants[\\\"electron mass\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1609.3439999999998\\n\",\n      \"0.0254\\n\",\n      \"0.001\\n\",\n      \"0.45359236999999997\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 1英里等于多少米, 1英寸等于多少米, 1克等于多少千克, 1磅等于多少千克\\n\",\n    \"print(C.mile)\\n\",\n    \"print(C.inch)\\n\",\n    \"print(C.gram)\\n\",\n    \"print(C.pound)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import scipy.special as S\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1.0\\n\",\n      \"0.0\\n\",\n      \"1e-20\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print (1 + 1e-20)\\n\",\n    \"print (np.log(1+1e-20))\\n\",\n    \"print (S.log1p(1e-20))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[(200, 4), (200, 4), (200, 4), (200, 4)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"m = np.linspace(0.1, 0.9, 4)\\n\",\n    \"u = np.linspace(-10, 10, 200)\\n\",\n    \"results = S.ellipj(u[:, None], m[None, :])\\n\",\n    \"\\n\",\n    \"print([y.shape for y in results])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1531b6bde80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=使用广播计算得到的`ellipj()`返回值\\n\",\n    \"fig, axes = pl.subplots(2, 2, figsize=(12, 4))\\n\",\n    \"labels = [\\\"$sn$\\\", \\\"$cn$\\\", \\\"$dn$\\\", \\\"$\\\\phi$\\\"]\\n\",\n    \"for ax, y, label in zip(axes.ravel(), results, labels):\\n\",\n    \"    ax.plot(u, y)\\n\",\n    \"    ax.set_ylabel(label)\\n\",\n    \"    ax.margins(0, 0.1)\\n\",\n    \"    \\n\",\n    \"axes[1, 1].legend([\\\"$m={:g}$\\\".format(m_) for m_ in m], loc=\\\"best\\\", ncol=2);\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "4.scipy/10.scipy-spatial.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pylab as pl\\n\",\n    \"from scipy import spatial\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib as mpl\\n\",\n    \"mpl.rcParams['font.sans-serif'] = ['SimHei']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 空间算法库-spatial\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 计算最近旁点\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.5244435681885733 0.4982156075770372\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.sort(np.random.rand(100))\\n\",\n    \"idx = np.searchsorted(x, 0.5)\\n\",\n    \"print (x[idx], x[idx - 1]) #距离0.5最近的数是这两个数中的一个\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy import spatial\\n\",\n    \"np.random.seed(42)\\n\",\n    \"N = 100\\n\",\n    \"points = np.random.uniform(-1, 1, (N, 2))\\n\",\n    \"kd = spatial.cKDTree(points)\\n\",\n    \"\\n\",\n    \"targets = np.array([(0, 0), (0.5, 0.5), (-0.5, 0.5), (0.5, -0.5), (-0.5, -0.5)])\\n\",\n    \"dist, idx = kd.query(targets, 3)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([list([48]), list([37, 78]), list([22, 79, 92]), list([6, 35, 58]),\\n\",\n       \"       list([7, 42, 55, 83])], dtype=object)\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"r = 0.2\\n\",\n    \"idx2 = kd.query_ball_point(targets, r)\\n\",\n    \"idx2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{(1, 46),\\n\",\n       \" (3, 21),\\n\",\n       \" (3, 82),\\n\",\n       \" (3, 95),\\n\",\n       \" (5, 16),\\n\",\n       \" (9, 30),\\n\",\n       \" (10, 87),\\n\",\n       \" (11, 42),\\n\",\n       \" (11, 97),\\n\",\n       \" (18, 41),\\n\",\n       \" (29, 74),\\n\",\n       \" (32, 51),\\n\",\n       \" (37, 78),\\n\",\n       \" (39, 61),\\n\",\n       \" (41, 61),\\n\",\n       \" (50, 84),\\n\",\n       \" (55, 83),\\n\",\n       \" (73, 81)}\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"idx3 = kd.query_pairs(0.1) - kd.query_pairs(0.08)\\n\",\n    \"idx3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10390803b38>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=用cKDTree寻找近旁点\\n\",\n    \"x, y = points.T\\n\",\n    \"colors = \\\"r\\\", \\\"b\\\", \\\"g\\\", \\\"y\\\", \\\"k\\\"\\n\",\n    \"\\n\",\n    \"fig, (ax1, ax2, ax3) = pl.subplots(1, 3, figsize=(12, 4))\\n\",\n    \"\\n\",\n    \"for ax in ax1, ax2, ax3:\\n\",\n    \"    ax.set_aspect(\\\"equal\\\")\\n\",\n    \"    ax.plot(x, y, \\\"o\\\", markersize=4)\\n\",\n    \"    \\n\",\n    \"for ax in ax1, ax2:\\n\",\n    \"    for i in range(len(targets)):\\n\",\n    \"        c = colors[i]\\n\",\n    \"        tx, ty = targets[i]\\n\",\n    \"        ax.plot([tx], [ty], \\\"*\\\", markersize=10, color=c)\\n\",\n    \"\\n\",\n    \"for i in range(len(targets)):\\n\",\n    \"    nx, ny = points[idx[i]].T\\n\",\n    \"    ax1.plot(nx, ny, \\\"o\\\", markersize=10, markerfacecolor=\\\"None\\\", \\n\",\n    \"             markeredgecolor=colors[i], markeredgewidth=1)\\n\",\n    \"    \\n\",\n    \"    nx, ny = points[idx2[i]].T\\n\",\n    \"    ax2.plot(nx, ny, \\\"o\\\", markersize=10, markerfacecolor=\\\"None\\\", \\n\",\n    \"             markeredgecolor=colors[i], markeredgewidth=1)\\n\",\n    \"    \\n\",\n    \"    ax2.add_artist(pl.Circle(targets[i], r, fill=None, linestyle=\\\"dashed\\\"))\\n\",\n    \"    \\n\",\n    \"for pidx1, pidx2 in idx3:\\n\",\n    \"    sx, sy = points[pidx1]\\n\",\n    \"    ex, ey = points[pidx2]\\n\",\n    \"    ax3.plot([sx, ex], [sy, ey], \\\"r\\\", linewidth=2, alpha=0.6)\\n\",\n    \"    \\n\",\n    \"ax1.set_xlabel(u\\\"搜索最近的3个近旁点\\\")\\n\",\n    \"ax2.set_xlabel(u\\\"搜索距离在0.2之内的所有近旁点\\\")\\n\",\n    \"ax3.set_xlabel(u\\\"搜索所有距离在0.08到0.1之间的点对\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(100, 100)\\n\",\n      \"(100, 5)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scipy.spatial import distance\\n\",\n    \"dist1 = distance.squareform(distance.pdist(points))\\n\",\n    \"dist2 = distance.cdist(points, targets)\\n\",\n    \"print(dist1.shape)\\n\",\n    \"print(dist2.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0.15188266 0.09595807 0.05009422 0.11180181 0.19015485]\\n\",\n      \"[0.15188266 0.09595807 0.05009422 0.11180181 0.19015485]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print (dist[:, 0]) # cKDTree.query()返回的与targets最近的距离\\n\",\n    \"print (np.min(dist2, axis=0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(22, 92) 0.005346210248158245\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dist1[np.diag_indices(len(points))] = np.inf\\n\",\n    \"nearest_pair = np.unravel_index(np.argmin(dist1), dist1.shape)\\n\",\n    \"print (nearest_pair, dist1[nearest_pair])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[22 92] 0.005346210248158245\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dist, idx = kd.query(points, 2)\\n\",\n    \"print (idx[np.argmin(dist[:, 1])], np.min(dist[:, 1]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"N = 1000000\\n\",\n    \"start = np.random.uniform(0, 100, N)\\n\",\n    \"span = np.random.uniform(0.01, 1, N)\\n\",\n    \"span = np.clip(span, 2, 100)\\n\",\n    \"end = start + span\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_count_at(start, end, time):\\n\",\n    \"    mask = (start < time) & (end > time)\\n\",\n    \"    return np.sum(mask)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x103929e0f28>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=使用二维K-d树搜索指定区间的在线用户\\n\",\n    \"def _():\\n\",\n    \"    N = 100\\n\",\n    \"    start = np.random.uniform(0, 100, N)\\n\",\n    \"    span = np.random.normal(40, 10, N)\\n\",\n    \"    span = np.clip(span, 2, 100)\\n\",\n    \"    end = start + span\\n\",\n    \"\\n\",\n    \"    time = 40\\n\",\n    \"    \\n\",\n    \"    fig, ax = pl.subplots(figsize=(8, 6))\\n\",\n    \"    ax.scatter(start, end)\\n\",\n    \"    mask = (start < time) & (end > time)\\n\",\n    \"    start2, end2 = start[mask], end[mask]\\n\",\n    \"    ax.scatter(start2, end2, marker=\\\"x\\\", color=\\\"red\\\")\\n\",\n    \"    rect = pl.Rectangle((-20, 40), 60, 120, alpha=0.3)\\n\",\n    \"    ax.add_patch(rect)\\n\",\n    \"    ax.axhline(time, color=\\\"k\\\", ls=\\\"--\\\")\\n\",\n    \"    ax.axvline(time, color=\\\"k\\\", ls=\\\"--\\\")\\n\",\n    \"    ax.set_xlabel(\\\"Start\\\")\\n\",\n    \"    ax.set_ylabel(\\\"End\\\")\\n\",\n    \"    ax.set_xlim(-20, 120)\\n\",\n    \"    ax.set_ylim(-20, 160)\\n\",\n    \"    ax.plot([0, 120], [0, 120])\\n\",\n    \"\\n\",\n    \"_()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"class KdSearch(object):\\n\",\n    \"    def __init__(self, start, end, leafsize=10):\\n\",\n    \"        self.tree = spatial.cKDTree(np.c_[start, end], leafsize=leafsize)\\n\",\n    \"        self.max_time = np.max(end)\\n\",\n    \"      \\n\",\n    \"    def count_at(self, time):\\n\",\n    \"        max_time = self.max_time\\n\",\n    \"        to_search = spatial.cKDTree([[time - max_time, time + max_time]])\\n\",\n    \"        return self.tree.count_neighbors(to_search, max_time, p=np.inf)\\n\",\n    \"    \\n\",\n    \"naive_count_at(start, end, 40) == KdSearch(start, end).count_at(40)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> **QUESTION**\\n\",\n    \"\\n\",\n    \"> 请读者研究点数`N`和`leafsize`参数与创建K-d树和搜索时间之间的关系。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 凸包\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[2 5]\\n\",\n      \" [2 6]\\n\",\n      \" [0 5]\\n\",\n      \" [1 6]\\n\",\n      \" [1 0]]\\n\",\n      \"[5 2 6 1 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(42)\\n\",\n    \"points2d = np.random.rand(10, 2)\\n\",\n    \"ch2d = spatial.ConvexHull(points2d)\\n\",\n    \"print(ch2d.simplices)\\n\",\n    \"print(ch2d.vertices)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x103929e0ef0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=二维平面上的凸包\\n\",\n    \"poly = pl.Polygon(points2d[ch2d.vertices], fill=None, lw=2, color=\\\"r\\\", alpha=0.5)\\n\",\n    \"ax = pl.subplot(aspect=\\\"equal\\\")\\n\",\n    \"pl.plot(points2d[:, 0], points2d[:, 1], \\\"go\\\")\\n\",\n    \"for i, pos in enumerate(points2d):\\n\",\n    \"    pl.text(pos[0], pos[1], str(i), color=\\\"blue\\\")\\n\",\n    \"ax.add_artist(poly);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(38, 3)\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(42)\\n\",\n    \"points3d = np.random.rand(40, 3)\\n\",\n    \"ch3d = spatial.ConvexHull(points3d)\\n\",\n    \"ch3d.simplices.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 沃罗诺伊图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"points2d = np.array([[0.2, 0.1], [0.5, 0.5], [0.8, 0.1],\\n\",\n    \"                     [0.5, 0.8], [0.3, 0.6], [0.7, 0.6], [0.5, 0.35]])\\n\",\n    \"vo = spatial.Voronoi(points2d)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0.5    0.045 ]\\n\",\n      \" [0.245  0.351 ]\\n\",\n      \" [0.755  0.351 ]\\n\",\n      \" [0.3375 0.425 ]\\n\",\n      \" [0.6625 0.425 ]\\n\",\n      \" [0.45   0.65  ]\\n\",\n      \" [0.55   0.65  ]]\\n\",\n      \"[[-1, 0, 1], [-1, 0, 2], [], [6, 4, 3, 5], [5, -1, 1, 3], [4, 2, 0, 1, 3], [6, -1, 2, 4], [6, -1, 5]]\\n\",\n      \"[[-1, 0], [0, 1], [-1, 1], [0, 2], [-1, 2], [3, 5], [3, 4], [4, 6], [5, 6], [1, 3], [-1, 5], [2, 4], [-1, 6]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(vo.vertices); print(vo.regions); print(vo.ridge_vertices)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"bound = np.array([[-100, -100], [-100,  100], \\n\",\n    \"                  [ 100,  100], [ 100, -100]])\\n\",\n    \"vo2 = spatial.Voronoi(np.vstack((points2d, bound)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\spatial\\\\_plotutils.py:20: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0.\\n\",\n      \"  was_held = ax.ishold()\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1039421e588>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=沃罗诺伊图将空间分割为多个区域\\n\",\n    \"fig, (ax1, ax2) = pl.subplots(1, 2, figsize=(9, 4.5))\\n\",\n    \"ax1.set_aspect(\\\"equal\\\")\\n\",\n    \"ax2.set_aspect(\\\"equal\\\")\\n\",\n    \"spatial.voronoi_plot_2d(vo, ax=ax1)\\n\",\n    \"for i, v in enumerate(vo.vertices):\\n\",\n    \"    ax1.text(v[0], v[1], str(i), color=\\\"red\\\")\\n\",\n    \"    \\n\",\n    \"for i, p in enumerate(points2d):\\n\",\n    \"    ax1.text(p[0], p[1], str(i), color=\\\"blue\\\")\\n\",\n    \"\\n\",\n    \"n = len(points2d)\\n\",\n    \"color = pl.cm.rainbow(np.linspace(0, 1, n))\\n\",\n    \"for i in range(n):\\n\",\n    \"    idx = vo2.point_region[i]\\n\",\n    \"    region = vo2.regions[idx]\\n\",\n    \"    poly = pl.Polygon(vo2.vertices[region], facecolor=color[i], alpha=0.5, zorder=0)\\n\",\n    \"    ax2.add_artist(poly)\\n\",\n    \"ax2.scatter(points2d[:, 0], points2d[:, 1], s=40, c=color, linewidths=2, edgecolors=\\\"k\\\")\\n\",\n    \"ax2.plot(vo2.vertices[:, 0], vo2.vertices[:, 1], \\\"ro\\\", ms=6)\\n\",\n    \"\\n\",\n    \"for ax in ax1, ax2:\\n\",\n    \"    ax.set_xlim(0, 1)\\n\",\n    \"    ax.set_ylim(0, 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 3 1 7 4 6 5]\\n\",\n      \"[6, -1, 2, 4]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print (vo.point_region)\\n\",\n    \"print (vo.regions[6])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 德劳内三角化\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[8 5 7]\\n\",\n      \" [1 5 3]\\n\",\n      \" [5 6 7]\\n\",\n      \" [6 0 7]\\n\",\n      \" [0 6 2]\\n\",\n      \" [6 1 2]\\n\",\n      \" [1 6 5]\\n\",\n      \" [9 5 8]\\n\",\n      \" [4 9 8]\\n\",\n      \" [5 9 3]\\n\",\n      \" [9 4 3]]\\n\",\n      \"[[104.58977484 127.03566055]\\n\",\n      \" [235.1285     198.68143374]\\n\",\n      \" [107.83960707 155.53682482]\\n\",\n      \" [ 71.22104881 228.39479887]\\n\",\n      \" [110.3105     291.17642838]\\n\",\n      \" [201.40695449 227.68436282]\\n\",\n      \" [201.61895891 226.21958623]\\n\",\n      \" [152.96231864  93.25060083]\\n\",\n      \" [205.40381294 -90.5480267 ]\\n\",\n      \" [235.1285     127.45701644]\\n\",\n      \" [267.91709907 107.6135    ]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = np.array([46.445, 263.251, 174.176, 280.899, 280.899, \\n\",\n    \"              189.358, 135.521, 29.638, 101.907, 226.665])\\n\",\n    \"y = np.array([287.865, 250.891, 287.865, 160.975, 54.252,\\n\",\n    \"              160.975, 232.404, 179.187, 35.765, 71.361])\\n\",\n    \"points2d = np.c_[x, y]\\n\",\n    \"dy = spatial.Delaunay(points2d)\\n\",\n    \"vo = spatial.Voronoi(points2d) \\n\",\n    \"print(dy.simplices)\\n\",\n    \"print(vo.vertices)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\spatial\\\\_plotutils.py:20: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0.\\n\",\n      \"  was_held = ax.ishold()\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1039296a630>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=德劳内三角形的外接圆与圆心\\n\",\n    \"cx, cy = vo.vertices.T\\n\",\n    \"\\n\",\n    \"ax = pl.subplot(aspect=\\\"equal\\\")\\n\",\n    \"spatial.delaunay_plot_2d(dy, ax=ax)\\n\",\n    \"ax.plot(cx, cy, \\\"r.\\\")\\n\",\n    \"for i, (cx, cy) in enumerate(vo.vertices):\\n\",\n    \"    px, py = points2d[dy.simplices[i, 0]]\\n\",\n    \"    radius = np.hypot(cx - px, cy - py)\\n\",\n    \"    circle = pl.Circle((cx, cy), radius, fill=False, ls=\\\"dotted\\\")\\n\",\n    \"    ax.add_artist(circle)\\n\",\n    \"ax.set_xlim(0, 300)\\n\",\n    \"ax.set_ylim(0, 300);\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "4.scipy/2.scipy-optimize.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pylab as pl\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib as mpl\\n\",\n    \"mpl.rcParams['font.sans-serif'] = ['SimHei']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 拟合与优化-optimize\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 非线性方程组求解\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-0.70622057 -0.6        -2.5       ]\\n\",\n      \"[0.0, -9.126033262418787e-14, 5.329070518200751e-15]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from math import sin, cos\\n\",\n    \"from scipy import optimize\\n\",\n    \"\\n\",\n    \"def f(x): #❶\\n\",\n    \"    x0, x1, x2 = x.tolist() #❷\\n\",\n    \"    return [\\n\",\n    \"        5*x1+3,\\n\",\n    \"        4*x0*x0 - 2*sin(x1*x2),\\n\",\n    \"        x1*x2 - 1.5\\n\",\n    \"    ]\\n\",\n    \"\\n\",\n    \"# f计算方程组的误差，[1,1,1]是未知数的初始值\\n\",\n    \"result = optimize.fsolve(f, [1,1,1]) #❸\\n\",\n    \"print (result)\\n\",\n    \"print (f(result))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-0.70622057 -0.6        -2.5       ]\\n\",\n      \"[0.0, -9.126033262418787e-14, 5.329070518200751e-15]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def j(x):  #❶\\n\",\n    \"    x0, x1, x2 = x.tolist()\\n\",\n    \"    return [[0, 5, 0],\\n\",\n    \"            [8 * x0, -2 * x2 * cos(x1 * x2), -2 * x1 * cos(x1 * x2)],\\n\",\n    \"            [0, x2, x1]]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"result = optimize.fsolve(f, [1, 1, 1], fprime=j)  #❷\\n\",\n    \"print(result)\\n\",\n    \"print(f(result))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 最小二乘拟合\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"k = 0.6134953491930442 b = 1.794092543259387\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from scipy import optimize\\n\",\n    \"\\n\",\n    \"X = np.array([ 8.19,  2.72,  6.39,  8.71,  4.7 ,  2.66,  3.78])\\n\",\n    \"Y = np.array([ 7.01,  2.78,  6.47,  6.71,  4.1 ,  4.23,  4.05])\\n\",\n    \"\\n\",\n    \"def residuals(p): #❶\\n\",\n    \"    \\\"计算以p为参数的直线和原始数据之间的误差\\\"\\n\",\n    \"    k, b = p\\n\",\n    \"    return Y - (k*X + b)\\n\",\n    \"\\n\",\n    \"# leastsq使得residuals()的输出数组的平方和最小，参数的初始值为[1,0]\\n\",\n    \"r = optimize.leastsq(residuals, [1, 0]) #❷\\n\",\n    \"k, b = r[0]\\n\",\n    \"print (\\\"k =\\\",k, \\\"b =\\\",b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1a350dee748>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=最小化正方形面积之和（左），误差曲面（右）\\n\",\n    \"scale_k = 1.0\\n\",\n    \"scale_b = 10.0\\n\",\n    \"scale_error = 1000.0\\n\",\n    \"\\n\",\n    \"def S(k, b):\\n\",\n    \"    \\\"计算直线y=k*x+b和原始数据X、Y的误差的平方和\\\"\\n\",\n    \"    error = np.zeros(k.shape)\\n\",\n    \"    for x, y in zip(X, Y):\\n\",\n    \"        error += (y - (k * x + b)) ** 2\\n\",\n    \"    return error\\n\",\n    \"\\n\",\n    \"ks, bs = np.mgrid[k - scale_k:k + scale_k:40j, b - scale_b:b + scale_b:40j]\\n\",\n    \"error = S(ks, bs) / scale_error\\n\",\n    \"\\n\",\n    \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n    \"from matplotlib.patches import Rectangle\\n\",\n    \"\\n\",\n    \"fig = pl.figure(figsize=(12, 5))\\n\",\n    \"\\n\",\n    \"ax1 = pl.subplot(121)\\n\",\n    \"\\n\",\n    \"ax1.plot(X, Y, \\\"o\\\")\\n\",\n    \"X0 = np.linspace(2, 10, 3)\\n\",\n    \"Y0 = k*X0 + b\\n\",\n    \"ax1.plot(X0, Y0)\\n\",\n    \"\\n\",\n    \"for x, y in zip(X, Y):\\n\",\n    \"    y2 = k*x+b\\n\",\n    \"    rect = Rectangle((x,y), abs(y-y2), y2-y, facecolor=\\\"red\\\", alpha=0.2)\\n\",\n    \"    ax1.add_patch(rect)\\n\",\n    \"\\n\",\n    \"ax1.set_aspect(\\\"equal\\\")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"ax2 = fig.add_subplot(122, projection='3d')\\n\",\n    \"\\n\",\n    \"ax2.plot_surface(\\n\",\n    \"    ks, bs / scale_b, error, rstride=3, cstride=3, cmap=\\\"jet\\\", alpha=0.5)\\n\",\n    \"ax2.scatter([k], [b / scale_b], [S(k, b) / scale_error], c=\\\"r\\\", s=20)\\n\",\n    \"ax2.set_xlabel(\\\"$k$\\\")\\n\",\n    \"ax2.set_ylabel(\\\"$b$\\\")\\n\",\n    \"ax2.set_zlabel(\\\"$error$\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"真实参数: [10, 0.34, 0.5235987755982988]\\n\",\n      \"拟合参数 [10.25218748  0.3423992   0.50817423]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x1a3531df940>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1a3530f69e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=带噪声的正弦波拟合\\n\",\n    \"def func(x, p):  #❶\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    数据拟合所用的函数: A*sin(2*pi*k*x + theta)\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    A, k, theta = p\\n\",\n    \"    return A * np.sin(2 * np.pi * k * x + theta)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def residuals(p, y, x):  #❷\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    实验数据x, y和拟合函数之间的差，p为拟合需要找到的系数\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    return y - func(x, p)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"x = np.linspace(0, 2 * np.pi, 100)\\n\",\n    \"A, k, theta = 10, 0.34, np.pi / 6  # 真实数据的函数参数\\n\",\n    \"y0 = func(x, [A, k, theta])  # 真实数据\\n\",\n    \"# 加入噪声之后的实验数据\\n\",\n    \"np.random.seed(0)\\n\",\n    \"y1 = y0 + 2 * np.random.randn(len(x))  #❸\\n\",\n    \"\\n\",\n    \"p0 = [7, 0.40, 0]  # 第一次猜测的函数拟合参数\\n\",\n    \"\\n\",\n    \"# 调用leastsq进行数据拟合\\n\",\n    \"# residuals为计算误差的函数\\n\",\n    \"# p0为拟合参数的初始值\\n\",\n    \"# args为需要拟合的实验数据\\n\",\n    \"plsq = optimize.leastsq(residuals, p0, args=(y1, x))  #❹\\n\",\n    \"\\n\",\n    \"print(u\\\"真实参数:\\\", [A, k, theta])\\n\",\n    \"print(u\\\"拟合参数\\\", plsq[0])  # 实验数据拟合后的参数\\n\",\n    \"\\n\",\n    \"pl.plot(x, y1, \\\"o\\\", label=u\\\"带噪声的实验数据\\\")\\n\",\n    \"pl.plot(x, y0, label=u\\\"真实数据\\\")\\n\",\n    \"pl.plot(x, func(x, plsq[0]), label=u\\\"拟合数据\\\")\\n\",\n    \"pl.legend(loc=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[10.25218748  0.3423992   0.50817425]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def func2(x, A, k, theta):\\n\",\n    \"    return A*np.sin(2*np.pi*k*x+theta)   \\n\",\n    \"\\n\",\n    \"popt, _ = optimize.curve_fit(func2, x, y1, p0=p0)\\n\",\n    \"print (popt)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"真实参数: [10, 0.34, 0.5235987755982988]\\n\",\n      \"拟合参数 [ 0.71093469  1.02074585 -0.12776742]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"popt, _ = optimize.curve_fit(func2, x, y1, p0=[10, 1, 0])\\n\",\n    \"\\n\",\n    \"print(u\\\"真实参数:\\\", [A, k, theta])\\n\",\n    \"\\n\",\n    \"print(u\\\"拟合参数\\\", popt)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 计算函数局域最小值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Nelder-Mead : min= 5.30934e-10, f count=125, fprime count=  0, fhess count=  0\\n\",\n      \"Powell      : min=           0, f count= 52, fprime count=  0, fhess count=  0\\n\",\n      \"CG          : min= 9.63056e-21, f count= 39, fprime count= 39, fhess count=  0\\n\",\n      \"BFGS        : min= 1.84992e-16, f count= 40, fprime count= 40, fhess count=  0\\n\",\n      \"Newton-CG   : min= 5.22666e-10, f count= 60, fprime count= 97, fhess count= 38\\n\",\n      \"L-BFGS-B    : min=  6.5215e-15, f count= 33, fprime count= 33, fhess count=  0\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:415: RuntimeWarning: Method Nelder-Mead does not use gradient information (jac).\\n\",\n      \"  RuntimeWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:420: RuntimeWarning: Method Nelder-Mead does not use Hessian information (hess).\\n\",\n      \"  RuntimeWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:415: RuntimeWarning: Method Powell does not use gradient information (jac).\\n\",\n      \"  RuntimeWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:420: RuntimeWarning: Method Powell does not use Hessian information (hess).\\n\",\n      \"  RuntimeWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:420: RuntimeWarning: Method CG does not use Hessian information (hess).\\n\",\n      \"  RuntimeWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:420: RuntimeWarning: Method BFGS does not use Hessian information (hess).\\n\",\n      \"  RuntimeWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:420: RuntimeWarning: Method L-BFGS-B does not use Hessian information (hess).\\n\",\n      \"  RuntimeWarning)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def target_function(x, y):\\n\",\n    \"    return (1 - x)**2 + 100 * (y - x**2)**2\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class TargetFunction(object):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        self.f_points = []\\n\",\n    \"        self.fprime_points = []\\n\",\n    \"        self.fhess_points = []\\n\",\n    \"\\n\",\n    \"    def f(self, p):\\n\",\n    \"        x, y = p.tolist()\\n\",\n    \"        z = target_function(x, y)\\n\",\n    \"        self.f_points.append((x, y))\\n\",\n    \"        return z\\n\",\n    \"\\n\",\n    \"    def fprime(self, p):\\n\",\n    \"        x, y = p.tolist()\\n\",\n    \"        self.fprime_points.append((x, y))\\n\",\n    \"        dx = -2 + 2 * x - 400 * x * (y - x**2)\\n\",\n    \"        dy = 200 * y - 200 * x**2\\n\",\n    \"        return np.array([dx, dy])\\n\",\n    \"\\n\",\n    \"    def fhess(self, p):\\n\",\n    \"        x, y = p.tolist()\\n\",\n    \"        self.fhess_points.append((x, y))\\n\",\n    \"        return np.array([[2 * (600 * x**2 - 200 * y + 1), -400 * x],\\n\",\n    \"                         [-400 * x, 200]])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def fmin_demo(method):\\n\",\n    \"    target = TargetFunction()\\n\",\n    \"    init_point = (-1, -1)\\n\",\n    \"    res = optimize.minimize(\\n\",\n    \"        target.f,\\n\",\n    \"        init_point,\\n\",\n    \"        method=method,\\n\",\n    \"        jac=target.fprime,\\n\",\n    \"        hess=target.fhess)\\n\",\n    \"    return res, [\\n\",\n    \"        np.array(points) for points in (target.f_points, target.fprime_points,\\n\",\n    \"                                        target.fhess_points)\\n\",\n    \"    ]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"methods = (\\\"Nelder-Mead\\\", \\\"Powell\\\", \\\"CG\\\", \\\"BFGS\\\", \\\"Newton-CG\\\", \\\"L-BFGS-B\\\")\\n\",\n    \"for method in methods:\\n\",\n    \"    res, (f_points, fprime_points, fhess_points) = fmin_demo(method)\\n\",\n    \"    print(\\n\",\n    \"        \\\"{:12s}: min={:12g}, f count={:3d}, fprime count={:3d}, fhess count={:3d}\\\"\\n\",\n    \"        .format(method, float(res[\\\"fun\\\"]), len(f_points), len(fprime_points),\\n\",\n    \"                len(fhess_points)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:415: RuntimeWarning: Method Nelder-Mead does not use gradient information (jac).\\n\",\n      \"  RuntimeWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:420: RuntimeWarning: Method Nelder-Mead does not use Hessian information (hess).\\n\",\n      \"  RuntimeWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:415: RuntimeWarning: Method Powell does not use gradient information (jac).\\n\",\n      \"  RuntimeWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:420: RuntimeWarning: Method Powell does not use Hessian information (hess).\\n\",\n      \"  RuntimeWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:420: RuntimeWarning: Method CG does not use Hessian information (hess).\\n\",\n      \"  RuntimeWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:420: RuntimeWarning: Method BFGS does not use Hessian information (hess).\\n\",\n      \"  RuntimeWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_minimize.py:420: RuntimeWarning: Method L-BFGS-B does not use Hessian information (hess).\\n\",\n      \"  RuntimeWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1a35316a0f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=各种优化算法的搜索路径\\n\",\n    \"def draw_fmin_demo(f_points, fprime_points, ax):\\n\",\n    \"    xmin, xmax = -3, 3\\n\",\n    \"    ymin, ymax = -3, 3\\n\",\n    \"    Y, X = np.ogrid[ymin:ymax:500j,xmin:xmax:500j]\\n\",\n    \"    Z = np.log10(target_function(X, Y))\\n\",\n    \"    zmin, zmax = np.min(Z), np.max(Z)\\n\",\n    \"    ax.imshow(Z, extent=(xmin,xmax,ymin,ymax), origin=\\\"bottom\\\", aspect=\\\"auto\\\", cmap=\\\"gray\\\")\\n\",\n    \"    ax.plot(f_points[:,0], f_points[:,1], lw=1)\\n\",\n    \"    ax.scatter(f_points[:,0], f_points[:,1], c=range(len(f_points)), s=50, linewidths=0)\\n\",\n    \"    if len(fprime_points):\\n\",\n    \"        ax.scatter(fprime_points[:, 0], fprime_points[:, 1], marker=\\\"x\\\", color=\\\"w\\\", alpha=0.5)\\n\",\n    \"    ax.set_xlim(xmin, xmax)\\n\",\n    \"    ax.set_ylim(ymin, ymax)\\n\",\n    \"\\n\",\n    \"fig, axes = pl.subplots(2, 3, figsize=(9, 6))\\n\",\n    \"methods = (\\\"Nelder-Mead\\\", \\\"Powell\\\", \\\"CG\\\", \\\"BFGS\\\", \\\"Newton-CG\\\", \\\"L-BFGS-B\\\")\\n\",\n    \"for ax, method in zip(axes.ravel(), methods):\\n\",\n    \"    res, (f_points, fprime_points, fhess_points) = fmin_demo(method)\\n\",\n    \"    draw_fmin_demo(f_points, fprime_points, ax)\\n\",\n    \"    ax.set_aspect(\\\"equal\\\")\\n\",\n    \"    ax.set_title(method)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 计算全域最小值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def func(x, p):\\n\",\n    \"    A, k, theta = p\\n\",\n    \"    return A*np.sin(2*np.pi*k*x+theta)\\n\",\n    \"\\n\",\n    \"def func_error(p, y, x):\\n\",\n    \"    return np.sum((y - func(x, p))**2)   \\n\",\n    \"\\n\",\n    \"x = np.linspace(0, 2*np.pi, 100)\\n\",\n    \"A, k, theta = 10, 0.34, np.pi/6 \\n\",\n    \"y0 = func(x, [A, k, theta])\\n\",\n    \"np.random.seed(0)\\n\",\n    \"y1 = y0 + 2 * np.random.randn(len(x))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[10.25218676 -0.34239909  2.63341581]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"result = optimize.basinhopping(func_error, (1, 1, 1),\\n\",\n    \"                      niter = 10,\\n\",\n    \"                      minimizer_kwargs={\\\"method\\\":\\\"L-BFGS-B\\\",\\n\",\n    \"                                        \\\"args\\\":(y1, x)})\\n\",\n    \"print (result.x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1a352e40cc0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=用`basinhopping()`拟合正弦波\\n\",\n    \"pl.plot(x, y1, \\\"o\\\", label=u\\\"带噪声的实验数据\\\")\\n\",\n    \"pl.plot(x, y0, label=u\\\"真实数据\\\")\\n\",\n    \"pl.plot(x, func(x, result.x), label=u\\\"拟合数据\\\")\\n\",\n    \"pl.legend(loc=\\\"best\\\");\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "4.scipy/3.scipy-linalg.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pylab as pl\\n\",\n    \"import numpy as np\\n\",\n    \"from scipy import linalg\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib as mpl\\n\",\n    \"mpl.rcParams['font.sans-serif'] = ['SimHei']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 线性代数-linalg\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 解线性方程组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"5.38 ms ± 120 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\\n\",\n      \"8.14 ms ± 994 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from scipy import linalg\\n\",\n    \"\\n\",\n    \"m, n = 500, 50\\n\",\n    \"A = np.random.rand(m, m)\\n\",\n    \"B = np.random.rand(m, n)\\n\",\n    \"X1 = linalg.solve(A, B)\\n\",\n    \"X2 = np.dot(linalg.inv(A), B)\\n\",\n    \"print (np.allclose(X1, X2))\\n\",\n    \"%timeit linalg.solve(A, B)\\n\",\n    \"%timeit np.dot(linalg.inv(A), B)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"luf = linalg.lu_factor(A)\\n\",\n    \"X3 = linalg.lu_solve(luf, B)\\n\",\n    \"np.allclose(X1, X3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"50.6 ms ± 1.94 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\\n\",\n      \"3.49 ms ± 306 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\\n\",\n      \"20.1 ms ± 1.42 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\\n\",\n      \"4.49 ms ± 65 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"M, N = 1000, 100\\n\",\n    \"np.random.seed(0)\\n\",\n    \"A = np.random.rand(M, M)\\n\",\n    \"B = np.random.rand(M, N)\\n\",\n    \"Ai = linalg.inv(A)\\n\",\n    \"luf = linalg.lu_factor(A)   \\n\",\n    \"%timeit linalg.inv(A)\\n\",\n    \"%timeit np.dot(Ai, B)\\n\",\n    \"%timeit linalg.lu_factor(A)    \\n\",\n    \"%timeit linalg.lu_solve(luf, B) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 最小二乘解\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from numpy.lib.stride_tricks import as_strided\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def make_data(m, n, noise_scale):  #❶\\n\",\n    \"    np.random.seed(42)\\n\",\n    \"    x = np.random.standard_normal(m)\\n\",\n    \"    h = np.random.standard_normal(n)\\n\",\n    \"    y = np.convolve(x, h)\\n\",\n    \"    yn = y + np.random.standard_normal(len(y)) * noise_scale * np.max(y)\\n\",\n    \"    return x, yn, h\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def solve_h(x, y, n):  #❷\\n\",\n    \"    X = as_strided(\\n\",\n    \"        x, shape=(len(x) - n + 1, n), strides=(x.itemsize, x.itemsize))  #❸\\n\",\n    \"    Y = y[n - 1:len(x)]  #❹\\n\",\n    \"    h = linalg.lstsq(X, Y)  #❺\\n\",\n    \"    return h[0][::-1]  #❻\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Average error of H1: 0.0\\n\",\n      \"Average error of H2: 0.2958422158342371\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x, yn, h = make_data(1000, 100, 0.4)\\n\",\n    \"H1 = solve_h(x, yn, 120)\\n\",\n    \"H2 = solve_h(x, yn, 80)\\n\",\n    \"\\n\",\n    \"print(\\\"Average error of H1:\\\", np.mean(np.abs(h[:100] - h)))\\n\",\n    \"print(\\\"Average error of H2:\\\", np.mean(np.abs(h[:80] - H2)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x16bf16073c8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=实际的系统参数与最小二乘解的比较\\n\",\n    \"fig, (ax1, ax2) = pl.subplots(2, 1, figsize=(6, 4))\\n\",\n    \"ax1.plot(h, linewidth=2, label=u\\\"实际的系统参数\\\")\\n\",\n    \"ax1.plot(H1, linewidth=2, label=u\\\"最小二乘解H1\\\", alpha=0.7)\\n\",\n    \"ax1.legend(loc=\\\"best\\\", ncol=2)\\n\",\n    \"ax1.set_xlim(0, len(H1))\\n\",\n    \"\\n\",\n    \"ax2.plot(h, linewidth=2, label=u\\\"实际的系统参数\\\")\\n\",\n    \"ax2.plot(H2, linewidth=2, label=u\\\"最小二乘解H2\\\", alpha=0.7)\\n\",\n    \"ax2.legend(loc=\\\"best\\\", ncol=2)\\n\",\n    \"ax2.set_xlim(0, len(H1));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 特征值和特征向量\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[1.13027756+0.j 0.76972244+0.j]\\n\",\n      \"[[ 0.91724574  0.79325185]\\n\",\n      \" [-0.3983218   0.60889368]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"A = np.array([[1, -0.3], [-0.1, 0.9]])\\n\",\n    \"evalues, evectors = linalg.eig(A)\\n\",\n    \"\\n\",\n    \"print(evalues)\\n\",\n    \"print(evectors)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x16bf1480198>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=线性变换将蓝色箭头变换为红色箭头\\n\",\n    \"points = np.array([[0, 1.0], [1.0, 0], [1, 1]])\\n\",\n    \"\\n\",\n    \"def draw_arrows(points, **kw):\\n\",\n    \"    props = dict(color=\\\"blue\\\", arrowstyle=\\\"->\\\")\\n\",\n    \"    props.update(kw)\\n\",\n    \"    for x, y in points:\\n\",\n    \"        pl.annotate(\\\"\\\",\\n\",\n    \"                    xy=(x, y), xycoords='data',\\n\",\n    \"                    xytext=(0, 0), textcoords='data',\\n\",\n    \"                    arrowprops=props)\\n\",\n    \"\\n\",\n    \"draw_arrows(points)\\n\",\n    \"draw_arrows(np.dot(A, points.T).T, color=\\\"red\\\")    \\n\",\n    \"draw_arrows(evectors.T, alpha=0.7, linewidth=2)\\n\",\n    \"draw_arrows(np.dot(A, evectors).T, color=\\\"red\\\", alpha=0.7, linewidth=2)    \\n\",\n    \"\\n\",\n    \"ax = pl.gca()\\n\",\n    \"ax.set_aspect(\\\"equal\\\")\\n\",\n    \"ax.set_xlim(-0.5, 1.1)\\n\",\n    \"ax.set_ylim(-0.5, 1.1);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(42)\\n\",\n    \"t = np.random.uniform(0, 2*np.pi, 60)\\n\",\n    \"\\n\",\n    \"alpha = 0.4\\n\",\n    \"a = 0.5\\n\",\n    \"b = 1.0\\n\",\n    \"x = 1.0 + a*np.cos(t)*np.cos(alpha) - b*np.sin(t)*np.sin(alpha)\\n\",\n    \"y = 1.0 + a*np.cos(t)*np.sin(alpha) - b*np.sin(t)*np.cos(alpha)\\n\",\n    \"x += np.random.normal(0, 0.05, size=len(x))\\n\",\n    \"y += np.random.normal(0, 0.05, size=len(y))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-0.55214278  0.5580915  -0.23809922  0.54584559 -0.08350449 -0.14852803]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"D = np.c_[x**2, x*y, y**2, x, y, np.ones_like(x)]\\n\",\n    \"A = np.dot(D.T, D)\\n\",\n    \"C = np.zeros((6, 6))\\n\",\n    \"C[[0, 1, 2], [2, 1, 0]] = 2, -1, 2\\n\",\n    \"evalues, evectors = linalg.eig(A, C)     #❶\\n\",\n    \"evectors = np.real(evectors)\\n\",\n    \"err = np.mean(np.dot(D, evectors)**2, 0) #❷\\n\",\n    \"p = evectors[:, np.argmin(err) ]         #❸\\n\",\n    \"print (p)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x16bf142c3c8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=用广义特征向量计算的拟合椭圆\\n\",\n    \"def ellipse(p, x, y):\\n\",\n    \"    a, b, c, d, e, f = p\\n\",\n    \"    return a*x**2 + b*x*y + c*y**2 + d*x + e*y + f\\n\",\n    \"\\n\",\n    \"X, Y = np.mgrid[0:2:100j, 0:2:100j]\\n\",\n    \"Z = ellipse(p, X, Y)\\n\",\n    \"pl.plot(x, y, \\\"ro\\\", alpha=0.5)\\n\",\n    \"pl.contour(X, Y, Z, levels=[0]);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 奇异值分解-SVD\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(505, 375)\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"r, g, b = np.rollaxis(pl.imread(\\\"vinci_target.png\\\"), 2).astype(float)\\n\",\n    \"img = 0.2989 * r + 0.5870 * g + 0.1140 * b\\n\",\n    \"img.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(505, 505)\\n\",\n      \"(375,)\\n\",\n      \"(375, 375)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"U, s, Vh = linalg.svd(img)\\n\",\n    \"print(U.shape)\\n\",\n    \"print(s.shape) \\n\",\n    \"print(Vh.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\mathtext.py:854: MathTextWarning: Font 'default' does not have a glyph for '-' [U+2212]\\n\",\n      \"  MathTextWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\mathtext.py:855: MathTextWarning: Substituting with a dummy symbol.\\n\",\n      \"  warn(\\\"Substituting with a dummy symbol.\\\", MathTextWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x16bf142c080>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=按从大到小排列的奇异值\\n\",\n    \"pl.semilogy(s, lw=3);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def composite(U, s, Vh, n):\\n\",\n    \"    return np.dot(U[:, :n], s[:n, np.newaxis] * Vh[:n, :])\\n\",\n    \"\\n\",\n    \"print (np.allclose(img, composite(U, s, Vh, len(s))))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#%fig=原始图像、使用10、20、50个向量合成的图像（从左到右）\\n\",\n    \"img10 = composite(U, s, Vh, 10)\\n\",\n    \"img20 = composite(U, s, Vh, 20)\\n\",\n    \"img50 = composite(U, s, Vh, 50)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"UsageError: Line magic function `%array_image` not found.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%array_image img; img10; img20; img50\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x16bf1557b00>\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x16bf16bcf28>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pl.imshow(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x16bf186fc88>\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x16bf157a6d8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pl.imshow(img10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x16bf1d99cc0>\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x16bf18b9390>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pl.imshow(img20)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x16bf1de9da0>\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x16bf1db30f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pl.imshow(img50)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "4.scipy/4.scipy-stats.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pylab as pl\\n\",\n    \"from scipy import stats\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import matplotlib as mpl\\n\",\n    \"mpl.rcParams['font.sans-serif'] = ['SimHei']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 统计-stats\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 连续概率分布\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['ksone',\\n\",\n       \" 'kstwobign',\\n\",\n       \" 'norm',\\n\",\n       \" 'alpha',\\n\",\n       \" 'anglit',\\n\",\n       \" 'arcsine',\\n\",\n       \" 'beta',\\n\",\n       \" 'betaprime',\\n\",\n       \" 'bradford',\\n\",\n       \" 'burr',\\n\",\n       \" 'burr12',\\n\",\n       \" 'fisk',\\n\",\n       \" 'cauchy',\\n\",\n       \" 'chi',\\n\",\n       \" 'chi2',\\n\",\n       \" 'cosine',\\n\",\n       \" 'dgamma',\\n\",\n       \" 'dweibull',\\n\",\n       \" 'expon',\\n\",\n       \" 'exponnorm',\\n\",\n       \" 'exponweib',\\n\",\n       \" 'exponpow',\\n\",\n       \" 'fatiguelife',\\n\",\n       \" 'foldcauchy',\\n\",\n       \" 'f',\\n\",\n       \" 'foldnorm',\\n\",\n       \" 'weibull_min',\\n\",\n       \" 'weibull_max',\\n\",\n       \" 'frechet_r',\\n\",\n       \" 'frechet_l',\\n\",\n       \" 'genlogistic',\\n\",\n       \" 'genpareto',\\n\",\n       \" 'genexpon',\\n\",\n       \" 'genextreme',\\n\",\n       \" 'gamma',\\n\",\n       \" 'erlang',\\n\",\n       \" 'gengamma',\\n\",\n       \" 'genhalflogistic',\\n\",\n       \" 'gompertz',\\n\",\n       \" 'gumbel_r',\\n\",\n       \" 'gumbel_l',\\n\",\n       \" 'halfcauchy',\\n\",\n       \" 'halflogistic',\\n\",\n       \" 'halfnorm',\\n\",\n       \" 'hypsecant',\\n\",\n       \" 'gausshyper',\\n\",\n       \" 'invgamma',\\n\",\n       \" 'invgauss',\\n\",\n       \" 'invweibull',\\n\",\n       \" 'johnsonsb',\\n\",\n       \" 'johnsonsu',\\n\",\n       \" 'laplace',\\n\",\n       \" 'levy',\\n\",\n       \" 'levy_l',\\n\",\n       \" 'levy_stable',\\n\",\n       \" 'logistic',\\n\",\n       \" 'loggamma',\\n\",\n       \" 'loglaplace',\\n\",\n       \" 'lognorm',\\n\",\n       \" 'gilbrat',\\n\",\n       \" 'maxwell',\\n\",\n       \" 'mielke',\\n\",\n       \" 'kappa4',\\n\",\n       \" 'kappa3',\\n\",\n       \" 'nakagami',\\n\",\n       \" 'ncx2',\\n\",\n       \" 'ncf',\\n\",\n       \" 't',\\n\",\n       \" 'nct',\\n\",\n       \" 'pareto',\\n\",\n       \" 'lomax',\\n\",\n       \" 'pearson3',\\n\",\n       \" 'powerlaw',\\n\",\n       \" 'powerlognorm',\\n\",\n       \" 'powernorm',\\n\",\n       \" 'rdist',\\n\",\n       \" 'rayleigh',\\n\",\n       \" 'reciprocal',\\n\",\n       \" 'rice',\\n\",\n       \" 'recipinvgauss',\\n\",\n       \" 'semicircular',\\n\",\n       \" 'skewnorm',\\n\",\n       \" 'trapz',\\n\",\n       \" 'triang',\\n\",\n       \" 'truncexpon',\\n\",\n       \" 'truncnorm',\\n\",\n       \" 'tukeylambda',\\n\",\n       \" 'uniform',\\n\",\n       \" 'vonmises',\\n\",\n       \" 'vonmises_line',\\n\",\n       \" 'wald',\\n\",\n       \" 'wrapcauchy',\\n\",\n       \" 'gennorm',\\n\",\n       \" 'halfgennorm',\\n\",\n       \" 'crystalball',\\n\",\n       \" 'argus']\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from scipy import stats\\n\",\n    \"[k for k, v in stats.__dict__.items() if isinstance(v, stats.rv_continuous)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(array(0.), array(1.))\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stats.norm.stats()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(array(1.), array(4.))\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X = stats.norm(loc=1.0, scale=2.0)\\n\",\n    \"X.stats()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(1.0048352738823323, 3.9372117720073554)\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = X.rvs(size=10000) # 对随机变量取10000个值\\n\",\n    \"np.mean(x), np.var(x) # 期望值和方差\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(1.0048352738823323, 1.984240855341749)\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stats.norm.fit(x) # 得到随机序列期望值和标准差\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"max pdf error: 0.018998755595167102, max cdf error: 0.018503342378306975\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\ipykernel_launcher.py:1: VisibleDeprecationWarning: Passing `normed=True` on non-uniform bins has always been broken, and computes neither the probability density function nor the probability mass function. The result is only correct if the bins are uniform, when density=True will produce the same result anyway. The argument will be removed in a future version of numpy.\\n\",\n      \"  \\\"\\\"\\\"Entry point for launching an IPython kernel.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"pdf, t = np.histogram(x, bins=100, normed=True)  #❶\\n\",\n    \"t = (t[:-1] + t[1:]) * 0.5  #❷\\n\",\n    \"cdf = np.cumsum(pdf) * (t[1] - t[0])  #❸\\n\",\n    \"p_error = pdf - X.pdf(t)\\n\",\n    \"c_error = cdf - X.cdf(t)\\n\",\n    \"print (\\\"max pdf error: {}, max cdf error: {}\\\".format(\\n\",\n    \"    np.abs(p_error).max(),\\n\",\n    \"    np.abs(c_error).max()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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DT+0kOD0xxuNc35uBXuY8fxtXhlXQbDBqqdUhuTT187eQKTjdyre4AYyKDCJ/+AGNiHbMQ8VlDBnkyOTaYzflj+M+QA0SOFry9TbL6QCk/mhXX8w4OfgG1xT236w2/CBiz8JIfp6Wl8e2339LS0kJSUhJhYWFs2LCBlJQUNm3aRGpqKi+//DLvvvsuixYtIj4+ni+++IIXX3wRT09P3nzzTe68805eeuklTp8+3e+lZ+wpvNt56TkhxGvAe8BY4GMp5ZMOD0rpE8dO1fHxpkxeHpzFkLiJ6j5aP1BXak6u8FQ543S5GGKnMzFhKB4Gx//IFoyPIMOWSGRoEENO78DPbKCgqrHnDTUSFRVFYmIi8fHxDBkyhMLCQlJTU9m/fz+pqans27ePRx99lFdeeQWbzYbZbOa3v/0t69ato6qqiscff5zf//73xMXFMXr0aP7617+e2/fZ0jM5OTl9WXomhYsL6HZLCBEBhEkp02lfFPwWIcTujis9dVLqxKSUPPNlFotNaSTHBML4e9R9tH6gOoWTs57cjgErAaOv77N/487kSGKCvAhvFZC7kRH+gymqtnP48TJXVH0lMjKS2NhY6uvrCQ8P5/jx40B7rbT169df1H7RokVs27aNNWvWkJWVxRNPPMGcOXPOfa5B6Rm7C+8Cj3J+sfA9QKqUslQI8T4wH/iqc2NVeNd5fLG3GFmwk4UJDXiOu18V+ewn6krNmdms+JTupMQjBq9Axw47dmbQ65gxLBgRMwuAFPNxiqqd90rtrOPHj/POO+8wd+7cC67UZs2aRV5eHgCtra089NBDNDY2MnXqVL788ssLElpXZ0vPLFiwgIkTL5drroq9hXd1wBxgU8dbWVLK0o7X6cBFpRmklG9JKZOllMkhIY69/6rYr6bJwqur0/nhoP3EDh8L0Y6d4KVcmkpqzqw0E0vjGUoDpvTPv+cVCIPHMEEe5VR1ndM+r7ZixQpefvllmpqamDp1KuvWrbtg9uPWrVuJiYmhurqa1157jenTp+Pl5YWfn9+5ffzxj3+ktLQ9P3RXembmzJkkJiYC7aVnHOxsAV1oL6Cbd4l2s4Bd8vwP4gMhxDghhB5YQJcSUIrzePGbo8xo3sqMWH904+5Sw479yK7hRyHEO8Ao4Gsp5QuXaBMGLJNSzur4PgLYBeR0NLlDSllx9SEPIPlpFLV4oovtx1UHYq4lIDONeEsO1Y0WAr09+u/fttPYsWNZs2YNvr6+vPfee/ziF79ACHHuSk1KycKFC0lOTuazzz7jpptuorCwkE8++YRt27ade7D6iSeeADQpPWNP4V2AG4Etnb5/HvgIEMBXUsqLx1oVze08cZr0PWn8KbKSoPFLVLHPftZjUrNnppYQIoD22Vnend6eCvy+YxaXYqfy2mbqW9qIM9djKc9mY3MCU0N8+i+A4ASMfmFM0R2lqLrRKZNa54Kf9957Lz/4wQ8Qnc6Ez155dS7sOXToUHJycuhOf5eesafwbke7p7t8f5D2GZCKk2pps/Kbz/dxn/cexiQOg/i+uxeudM+e4ccUep6pZQUWA7Wd3psG/EgIsVcI8YerCXIgeWb5Qb73t+1UHtpEfauNDFsi0UFe/ReAEBhiZxItyqgoybtkM2cZmtTr9RckNAAhRJ9Xqr7a45dSVkspP+tIaIqbeGvzCSKrdzI3Ro9x/J29Lg2lXD17klrXmVphXRtIKWullDVd3v6G9oQ4GZguhLjoDFMI8bAQIl0IkV5RoUYmpZSk51fT1NLMri1rSG+NogFPYoK8e97YgQYNn0EbeshP6/Zzs9nM6dOnnSax9TcpJadPn8ZsVqtCKOcVVjXywcZMfhhynPARUyFkuNYhDUj2nEbYNVOrG2lSyhYAIcQ+2mdqXVDjREr5FvAWQHJy8sD8C9lJYVUTVQ2t3BtZTc2pGt6uDiA6yIv4/hx+BPz9A8g1xJNQvhfaWsFw4RBkZGQkRUVFDOQTEbPZTGRkpNZhKE7kj98cYZ5ud3sl+VG9q0ivOI49Se3sTK2dtM/UOmbnvr8VQtwN1AA3AP+4oggHkH2F7atXPBpfQW1gArFTbmdqXBA6Xf/PnCrxm0BL01dQuh+GTr7gM6PReMF9LUUZ6NLzqjh8cB+vRVfgPeou9Uyahuy56loO3CeE+AtwJ3BICNHtDMgungM20p4M35RS2psMB6x9BWeIMNYTZilk+OS5TB8WrElCAxBB8ZRYvKFwJwAbj5Zjsdo0iUVRnJmUkhdWHWaxVzojYoeqpbA01mNSk1LW0n5vbCcwR0q5v5upx2fbpnR6vVFKOUJKOVZK+TcHxevWMgvPsCC4EJ3QQeTknjfoQ5GB3mxuikNW5nA0N5cH3t3Dl/scvMajoriBlVmliOJ0bohowzj6excN1yv9y677Y2qmVt9rabNypOQMs0wnIGw0eA7SNJ6hgZ7saI2nuc1GzbH2Ui17nbzOmqL0t2aLlf9ZfYAlflnEDhsJEZO0DmnAUyuKOInDJbXE2AqI8LTA0H5aQeQyEkJ9qcWbElMsomgPAhuZhWe0DktRnMo7204SW7eHa6M80I1eoFYOcQIqqTmJ7LJ6Jumy8fcPgNDRWofD8MHtNcQOiBG01FeTIIo5XlZHfYuqAq0oANUNrby/6SD3BucQlpgMQfFah6SgkprTKCqvYLQuH++4qU7xwGaIr4kgbw92NoRR3qznGo9cbBKyitTVmqIAvLk5lylte0iO9IGRt2kdjtJBJTUnIYr34mfSo4+epnUo5wwf7MvBU41sa4xmblAFnjSrIUhFAcpqm1mRlsVdIQUMGnGtWt/Riaik5iQCq/Zh8RkC/n1XYqa3hg/25WBxLTvbEgjzMTBvUDGZBSqpKcqrG7K5jj2MjQqGxBu1DkfpRCU1JyBrijA3llIf6lwzp0Z03FcrJQhz0FBSffPVlZoy4OWfbmDLnkxuDyvHd1QqmP21DknpRCU1J1BzbDstNh3Goc6W1M7XH/NPvIYYQxXUnaJBTRZRBrC/rDvOPMMeRkcPVg9aOyGV1LRms9KYt5ujMorIwc5VqTgxzBchINjHA5+4qXiZjEzUZVNa06R1aIqiieyyOg5kZXDz4Bq8Rs8Do2fPGyn9SiU1rZUfprHuDBm2hH5fjb8nnh56YoK8iQvxAZMvYvAYJuiyKa5q0Do0RdHE3zfmcLMhg+ExQyFmltbhKN1QSU1rBTupbDORp4tiyCDnO+v748Iknrm5vfK2Z/wMfGmiseiAxlEpSv/LP93AsawdXBfWiOfo+aA3ah2S0g2V1LTUUgflh8m0DSMy0Bu9RosXX860uCDGRrYv2RUUM44GPDGU7NE4KtcnhHhHCLFDCNHtOqpCCIMQokAIsanjK6nj/eeEEHuEEK/3b8TKG99lM0+fQWJ8HAx1nkdvlAuppKalonSQNjY3xhIb7FxDj90xGI3kmUfiVX0UWuq1DsdlCSEWAnop5XQgTgiR0E2zscDHUsqUjq8DQohJtJeBmgKUCyFS+zHsAS2vsoHjmVuYEWbBO+lW0Kk/nc5K/WS0IiUU7qbOcwi7T5sYGe7X8zZOoGLQOJpaWqE4Q+tQXFkK8FnH67W0J6qupgG3CCF2d1zVGYDZwOeyveT4t4C6qdNP/rb+CDfoMxgxfAQMmaB1OMplqKSmlZpCqCthbW0MVptk0UTXqKJsDhrKCUsgFO5qT8zKlfAGztbxqQLCummzB0iVUk4BjMB8e7YTQjwshEgXQqQP5MrkjpRTXkdR1iamhoHPWLVosbOzK6n1NP7f0SZMCLG10/dGIcRKIcR2IcSDjgjWrRTswqYz8voxP64ZFkSMCww/AgwZ5NleZ62mGGqKtA7HVdUDZ2cF+dB9P8ySUpZ2vE4HEuzZTkr5lpQyWUqZHBLiXI+IuKpX1h7iBmMmiaPGQehIrcNRetBjUrNn/F8IEQC8R/uZ5Fk/BzKklNcA3xdC+DooZtdntSCLM8hoGcqJGhtLpkRrHZHdIgaZyWiLo8mqg8Jd/GnNUX70npo40ksZnB9yHAfkddPmAyHEOCGEHlgA7LdzO8WBDhTVUHP4O5IHG/Ae+z11leYC7LlSS6Hn8X8rsBiovcR2W4DkK4rQDdWdTOfrvSf41R5PooO8mDuqu9En5zRkkCfNmKjwHY61KJ3Pdp1ge85ppBqK7I3lwH1CiL8AdwKHhBAvdGnzPPABkAnskFKuB7YBE4QQrwC/Aj7ux5gHpFfW7OcGj4MkJk1RpWVchD01TrqO40/s2kBKWQsgLjyLsWv8H3gYICoqyt6YXV7R/u/IazJzz/xUFk+JwsPgOrc2zz5Ld9IzCY9T+4hsziZTDqOqoZUgH5PG0bkGKWWtECIFmAv8qaOi/P4ubQ7SPgOy83u2jhmPNwOvSClP9lPIA9KO3NPoTnzHxBgT5qTvaR2OYid7/praM/5/RdsNyPH/+gqaTx0nkxHcMz0ab5P2tdN642xSy24bzNFaI8m6YwAUVauls3pDSlktpfysI6H1ZrsmKeUyKeWJvopNASklr67O4AbzURLGX+tU1TOUy7MnQV3pOL4a/+9OwQ4qGyw0hk7EZNBrHU2v+ZkN+JoMrMgq4fOKoUzwriSYGpXUFLfy7aEyQks3Mj7SF+PoW7UOR+kFe5KaPeP/3XkPeK5j/H8UsOvKw3QT1jZshbvZ2TCY+CjXmMLflRCC3902msKqJjY3xTEifBDJumMUVTdqHZqiOESb1cb/frOT671PEDtpLngHax2S0gs9jn3ZM/7fqW1Kp9f5Qoi5tF+t/VZKaXVIxK6s7AB1NVVsbR3Lw0MHaR3NFfv+pEjmjgpj98kq4uprmJG/kdyqOq3DUhSHWJZRROKZLYwbGYJ++Dytw1F6ya77Y1cx/l/SsV3NlYXnZvLTKG31IltGMC7StQsL+nsamTsqDBFzDSGmNvRlWVqHpChXrdli5dN120nxLSEqeT6YXWOlH+U815l25+Kaqkopzc1ic/MwvDyM7eVc3EHICKRnEGHVe7WORFGu2jtbTzCpcStJcREIVQDUJamk1k++XPEZG45W8FpOEEmR/k65Iv8VEYLqkMkMaipA1pZoHY2iXLHT9S1s2LyRWYFnGDJ5ARjNWoekXAGV1PrB8eJKWnLTMEZO5Onbp/LsbaO1DsmhbJFTaLHpqD+2SetQFOWKvbb+KHNsO0gaPgyiZ2gdjnKFVFLrB0tXLMfX0MZNt97BkqlRjBjsXuP0g0OC2W+Lp+XkTmhVsyAV13OysoHsPeuYGtJGYPIdoHO9x22Udiqp9bHC0w14FacxNCYBvyGJWofTJyIDPEmzjaahsal99X5FcTGvfLOfuYa9jEyaCGFjtA5HuQoqqfWxE0f3ESaqCUpKddvFUCMDPCkliONtoZC3FWw2rUNSFLvtK6im9cg3TBxswnfiHW7bTwcKldT6mOX4BhqFF0OT3Leeo6/ZyMIJEbxyIpzikiIoO6B1SIpiFyklf/sqjetMR0mcnAr+rrkognKeSmp9qbYUXeVRigZNwuTh3ov9/v72JGRoEl/ntlKe+Y3W4SiKXdYcKGVo6RqSokMwj1HLYbkDldT6kDyxkfIGKzLK/WdSeXroeev+Kew3jmfb7nQKcw9pHZKiXFZLm5Wlq79hsncZw2beqR60dhMqqfWBtJxK7v3bGk4d3kZaawKJUeFah9QvIgZ58p8P3kcjJlZ98YGqsaY4tTc3HGVS/SbGDB+OPu5arcNRHEQlNQeramjlF59m4l+ynW3Hy9liS2JshOuu89hbw8KDiJhwA/41x8g6fFjrcBSlWznl9RzfuozxwZLo2T8EnfpT6C7UT9LBfrP8IJbGGu4NL2KXJY4GvR+Jg91kSSw7Tb3+dqTBxMHNn2sdiqJcRErJ/3y+idmGAyRNmwvBw7QOSXEgldQcqLqhla8PlPK7UWVMjfGnYvAsxg8d5JJ1066Gl7cfpoQURGkmp4rztQ7HKQkh3hFC7BBCPHOJz/2FEN8IIdYKIb4UQngIIQxCiAIhxKaOr6T+jtsdrNhXRFTRKpKiB+M3YZHW4SgOppKaA+0rrMaHRqbrDqOLmMQbj9zEP++frHVYmpieupBWaWDfhk+0DsXpCCEWAnop5XQgTgiR0E2ze4C/SClvAE4B84ANcufxAAAO4klEQVSxwMdSypSOL/XsRC/VNltYs2oZE3yrSbz+B2AaWKMoA4FdSa2ns8ru2gzEs8q9+We4zpBFkJceEufh5WHA12zUOixNRISF0hQ5k4YTu2k+ra7WukgBPut4vZbzFeLPkVL+XUq5ruPbEKAcmAbcIoTY3dHfLqqHKIR4WAiRLoRIr6io6JvoXdhLX2xnuiWNpAnT0A8dmCec7q7HpGbPWeUl2gy4s8pjJ/O5wTsXY/Q08AnROhzNTbpuITVtRg5t+EjrUJyNN1Dc8boKCLtUQyHEdCBASrkT2AOkSimnAEZgftf2Usq3pJTJUsrkkBD1O9jZV5lFmA99SlJkABHX3q9WDnFT9lyppdDDWeUl2gyos8o2q43BJesI8TWBqpYLwOSECE4MmsapnH3IimNah+NM6gHPjtc+XKIfCiECgdeABzveypJSlna8Tge6G7ZUulFU3ciq5R8x2beasfMeAs8ArUNS+og9Sc2es8ru2gyIs8rT9S385N8ZfLV5ByNtORA/R3WYDkIIxlxzK3mNJsp3fqrWhDwvg/Mnh+OAvK4NhBAewFLgKSnl2fHbD4QQ44QQemABsL8fYnV5bVYbL3y4luvkHpKnz8YQNUXrkJQ+ZE9Ss+essrs2A+KsckVmCd8cLCV7w/vU4cmQSbdoHZJTmT8uinVyKsX5OVC4U+twnMVy4D4hxF+AO4FDQogXurR5CJgI/LrjnvRi4HngAyAT2CGlXN+fQbuqNzYcYtSpL5mUOJTA6T9Qw45uzp6k1uNZ5SXaDIizym8PneK2gHzG+9ZwwC+FyBB1ldaZv5eRkMSpbKv2x3Z4JbTUaR2S5qSUtbQP2e8E5kgp90spn+nS5g0pZUCne9KfSikPSinHSimTpJS/1iJ2V5NZUE3xlvdJDraRcONPwcNb65CUPnbRfa5uLAe2CiGGADcBdwkhXujSCbu2mQZkAR8BAvjKHc8qqxpaOZxXzH/FZDFxzAxmT34Qoc4CL/K9CRE8d2Qqcwo2kv/Jm1zz/V8Q4O2hdViaklJWc/4+tNIHGlvbeO+jD5htymP83AfUQ9YDRI9XanaeVXZtUzMQzio3HD7Frbo0YgZ5IMbeidnDnnOEgef6EWE0egTzan4UtdlpvPXFaq1DUtyclJL/+Xg1kxo2MXbSNXiNuknrkJR+YtdfYXvOKgfimefRfVuZYiogcOIj4DtY63CclqeHnnfun0xT8xiM21+m9tgydh6dxLQRQ7UOTXFTH2/cS3j2RwyPjyMu9WF1H20AUSuKXKGCoiIiClfiHx6PiL9e63Cc3rS4IOaMiiT59p8Tbraw/cs3sFnVbEjF8b5Oz+b0d68SHeTFpO8/CR5eWoek9COV1K6Ezcr+Fa9iEJIxt/1MrfDdC+bgGMImLyC84QiZ29UwpOJYa/fnc2T5SyT4WZm15Gl0vq75qJBy5dRf4ytwauen1J3Kpm3MYkLC1BBab026/k5KTTFU7f4EqtUSWopjrMw4ScbS/2aMbx3X3v1/MYepiSEDkUpqvZWfRs6ur0kXY/ne/Ju1jsYlGQ16fKbdz/EzgqrNf4emaq1DUlzcpzuyyfryJZL9qpm9+Am8ho7TOiRFIyqp9UbZIap3fcj6ykDCZywmyMekdUQu6/vTR/Ax89h5vBRL2ptkF5WTXaaeYVN6R0rJ2+uzyF/1J6YPquHaxU/gGaMWKh7I1Bx0e1Vm07b7HTaWerDScB0brlVDG1cjyMfEk3fO4S+fNNCyYwPZ3z3Dp4Zb+fTROQwLVeVAlJ41tLTxwqebCD/+AVODYcadT2KMGKt1WIrGVFKzw6HMnRSv+xtHaoz8o+0WHkkdwSCvgf3wsCPcMnYIDS3z+GQ1/NeQNDwrv+Gx94188rPr8RugJXsU+5yoqOfFd5cxvWY1E2JDGbfgCURQnNZhKU5AJbXLaGxt471PP8X32OfUeoQiZ/yYl6MjmDvykpVClF5aPDmKxZMfhtJpRG76B/LQJ/z7u0B+Ol8tOqt076u9eWxa8S436g6SPGkM0Tc+Bl6BWoelOAmV1LohpWRNVhG7V/+LhMZ9hMaN5fY7f4m3j6/Wobmv8LGE3fAEicX/j5qMV7FNfArd4NFaR6U4kcKqRv73q40E5ixjnm8TybMXEjj5DtCrq3rlPJXUuvHyF5uQe/9NsncdCSm3kzjnXvUsWn8IikfO+j8UffVXyje8yuCxc2HkrWBQE3IGsmaLlbfXZVKQtoxJumPEx0Qy/uZfYghXJz3KxVRS66y1kbqDXxO+/xPCwgKZfcdv0IcnaR3VgDJn4iieXr2QwPqj3HpwPQ2Hd1MbN59RE69Fr1cnFgOJlJK1e7PZ8u1SEhv3cVuwJ6Nn3k3AuFvBaNY6PMVJqaQG0FJH24mt6PM2U1BQzq62RB5d+Dj6cHXvrL+ZjXrmjYviud3wnvDnVt0Owg+8QnbaCmKmLeD/brWSOiqcp+aP1DpUpY80tlhYu303uXu+JaTuMDO89AyfeT3DZt0B3sFah6c4uQGV1JotVoqqGxkW6gs2K1QepzlvN0f3buF4aTXFpnjWWW8kMC6WhEiV0LTyeGoCMUFexAZPItjndgr3b6Jw91c0r/wrd+l92bEtgRV+t/C9mRO0DlVxEJvVyuGjhzi8dztncnfj03aGBG9vQifPZfJ1t6P3DdU6RMVFuH1Sa7PayC6vJ/1kJcs27cFcV8jd8c3MDTlDfX09m0/Ws60xGlPCYg7UmDl6qo5/zYrVOuwBLczPzCOz4899PzF6ERuGzaDg8G7uDc0nbtdOyr/dx6fbQzBFjGHOjOn4hw8Dsz8HS2rR6wQjw/263beUEkDVvdPYmZoz5OZmcyr/GLUlx7FV5iAsTSB0BIQlkjTlboZPmIUwemodquJixNlOftlGQrwDjAK+llJ2LTt/yTb2bHdWcnKyTE9P7/Yzm00ixGX+EEkJ1lawNEJLPbTUUXemgoM5+WzZfwR9QwUh4gxhPnoCvTzIKJfk2CI4LKNp8EvgpcUTmRoXhNUmyTvdQHyIevjXmdVUVfL12m+wlBxEVOfiqZOE+ZtokJ6kV5molP7ER0dz3YRETD4B/Cu9EuHhzcioMP6ZVsjphlZ+khLPfdOi8Tb17rzOZpPodJdOiEKIDCllck/70bpP9RVps9Lc2EB9Qx21NTU0N5yhqa6amupKaqsqaK4pw1ZXhuhUAd1iCsAYlkj4sHFMTJ6Bv59/v8asODd7+9RZPfZoIcRCQC+lnC6E+KcQIkFKmd1TGyCpp+3slbYrjV1rP2GwjxGDsHKmvglsFjx1NkK9BL4GK4L25NzSZuNMYytNrVYkgmifIEZOGEbokGgGRyUiAqIxVOrwKa1jql7HvKTB5x701euESmguwD8wmCV33QdAdmk1n67fwY7iHHxby5iXCB4tVeQWHyOr8FsAJhh0IMFy0MbjnmaMPp6cWGfh3Q1GhgT6YTAaqG2RVDRY8DF74O/tiRAg0UHHb1ZDq42y2maiR0/l7oWLrip+Z+hTx44dZt/GzwHae44Em5QI5Lm+BCCwoZMSpA0pbWCzIm1WrNY2pLUNm7UVW1sbOlsrepsFIS1wifPkZr03eAVhCh2JX0gk4UPjiB02ksAA9YyZ4jj2nKamcL7451pgJtC1I3XXZkJP2wkhHgYeBoiKirpkAME+Howc7MOpOgsN0khIWCAGowcNVh0Hats406SjBRPNwgRGb8JjQ4gIH0xCVCTTh4VedGY90RsmRquO5A4SwgN45r75F70/srGeLVk51Faf5qbhPnjYmikpP81Qfx16ayv55dXszaukoPwMAiveRkF8kCcNzRbOVJ8G2tOZDglITHrBhAATwf4OqQGXgsZ9qqG+jpbT5yskCASI9ldnUxuARGBDgNCB0CGFHp1ej05nRudhRG/wwNNoBIMJYTChM5owmLzwMHtj9vbDy8cfs88gBoeFEuLnrYZ9lT5nT1LzBoo7XlcBE+1s0+N2Usq3gLegfajkUgGMSJrMiCS1SKliPy8vH+ZNG3/BezGdluuM7vjSiOZ9auKkqUycNPVKYlcUp2bPgz/1wNm7tT6X2Ka7NvZspygDkepTitJH7OkUGbQPcwCMA/LsbGPPdooyEKk+pSh9xJ7hx+XAViHEEOAm4C4hxAtSymcu02Ya7beLu76nKIrqU4rSZ+yd0h8AzAW2SClP2dvGnu06bV8B5F+uzVUIBir7aN/9zZ2OBdzveIZLKXtc+Vr1KafjTsfjTscCdvaps+xKaq5OCJHem+ccnJk7HQuo43FV7nac7nQ87nQs0PvjUTeaFUVRFLehkpqiKIriNgZKUntL6wAcyJ2OBdTxuCp3O053Oh53Ohbo5fEMiHtqiqIoysAwUK7UFEVRlAFAJTVFE0IIgxCiQAixqeNLlRhXlKug+lQ7t6mnJoRYz6WPp0hKeW9/xtMXelN2xAWMBT6WUj6pdSBXSwgRBiyTUs4SQhiBL4BA4B0p5T+1je7KqT7lclSfwo2SGvCilHJ9dx8IIRYIIQzAiY4vgJ9LKQ/0W3RXyZ5yJS5mGnCLEGIOcAB4RErZpnFMvdbxMPR7tC82DPBzIENK+awQYrUQYqmUsu7Se3Bqqk+5FtWnGFjDj2fPYlI6vlym83VI4eKyI65sD5AqpZwCGIGL68e4BiuwGKjt+D6F8z+nLYDbPATbDdWnnIvqUwyspHb2LGa3EOKdjrNMV9K17EiYhrE4QpaUsrTjdTqQoGUwV0pKWSulrOn0lrv9nC5H9SnnovoUAyupufpZjLuVHflACDFOCKEHFgD7tQ7IQdzt53Q5qk85F9WnevrQzbj6WYy7lR15HvgAyAR2XOrejQtyt5/T5ag+5VxUn8K9Jor05AMhxO+Bg7SfxfxB43h6q7tSJC5LSnmQ9nsy7uY9YLUQYhbts+p2aRxPX1J9yomoPtXObVYUEUKkXm6mFpADfAQI4Csp5a/7Mz5H6E3ZEUU7HX8kZwLfdrk34FJUn1KcRW/6lDsltaVAyCU+zpRSPt6f8SiKq1N9SnFFbpPUFEVRFGUgTRRRFEVR3JxKaoqiKIrbUElNURRFcRsqqSmKoihuQyU1RVEUxW38fw+MzG0ZZC8fAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x20554bb0f60>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=正态分布的概率密度函数（左）和累积分布函数（右）\\n\",\n    \"fig, (ax1, ax2) = pl.subplots(1, 2, figsize=(7, 2))\\n\",\n    \"ax1.plot(t, pdf, label=u\\\"统计值\\\")\\n\",\n    \"ax1.plot(t, X.pdf(t), label=u\\\"理论值\\\", alpha=0.6)\\n\",\n    \"ax1.legend(loc=\\\"best\\\")\\n\",\n    \"ax2.plot(t, cdf)\\n\",\n    \"ax2.plot(t, X.cdf(t), alpha=0.6); \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(array(1.), array(1.))\\n\",\n      \"(array(2.), array(2.))\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(stats.gamma.stats(1.0))\\n\",\n    \"print(stats.gamma.stats(2.0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(array(4.), array(8.))\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stats.gamma.stats(2.0, scale=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([4.40563983, 6.17699951, 3.65503843, 3.28052152])\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = stats.gamma.rvs(2, scale=2, size=4)\\n\",\n    \"x   \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.12169605, 0.07037188, 0.14694352, 0.15904745])\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stats.gamma.pdf(x, 2, scale=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.12169605, 0.07037188, 0.14694352, 0.15904745])\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X = stats.gamma(2, scale=2) \\n\",\n    \"X.pdf(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 离散概率分布\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x = range(1, 7)    \\n\",\n    \"p = (0.4, 0.2, 0.1, 0.1, 0.1, 0.1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([2, 5, 2, 6, 1, 6, 6, 5, 3, 1, 5, 2, 1, 1, 1, 1, 1, 2, 1, 6])\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dice = stats.rv_discrete(values=(x, p))\\n\",\n    \"dice.rvs(size=20)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(42)\\n\",\n    \"samples = dice.rvs(size=(20000, 50))\\n\",\n    \"samples_mean = np.mean(samples, axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 核密度估计\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x20556d5f5c0>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x20556d0fe80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=核密度估计能更准确地表示随机变量的概率密度函数\\n\",\n    \"_, bins, step = pl.hist(\\n\",\n    \"    samples_mean, bins=100, normed=True, histtype=\\\"step\\\", label=u\\\"直方图统计\\\")\\n\",\n    \"kde = stats.kde.gaussian_kde(samples_mean)\\n\",\n    \"x = np.linspace(bins[0], bins[-1], 100)\\n\",\n    \"pl.plot(x, kde(x), label=u\\\"核密度估计\\\")\\n\",\n    \"mean, std = stats.norm.fit(samples_mean)\\n\",\n    \"pl.plot(x, stats.norm(mean, std).pdf(x), alpha=0.8, label=u\\\"正态分布拟合\\\")\\n\",\n    \"pl.legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x20556d330b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=`bw_method`参数越大核密度估计曲线越平滑\\n\",\n    \"for bw in [0.2, 0.3, 0.6, 1.0]:\\n\",\n    \"    kde = stats.gaussian_kde([-1, 0, 1], bw_method=bw)\\n\",\n    \"    x = np.linspace(-5, 5, 1000)\\n\",\n    \"    y = kde(x)\\n\",\n    \"    pl.plot(x, y, lw=2, label=\\\"bw={}\\\".format(bw), alpha=0.6)\\n\",\n    \"pl.legend(loc=\\\"best\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 二项、泊松、伽玛分布\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([4.01877572e-01, 4.01877572e-01, 1.60751029e-01, 3.21502058e-02,\\n\",\n       \"       3.21502058e-03, 1.28600823e-04])\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stats.binom.pmf(range(6), 5, 1/6.0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.00675531110335309\\n\",\n      \"0.0006301754049777564\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x2055839ec18>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=当n足够大时二项分布和泊松分布近似相等\\n\",\n    \"lambda_ = 10.0\\n\",\n    \"x = np.arange(20)\\n\",\n    \"\\n\",\n    \"n1, n2 = 100, 1000\\n\",\n    \"\\n\",\n    \"y_binom_n1 = stats.binom.pmf(x, n1, lambda_ / n1)\\n\",\n    \"y_binom_n2 = stats.binom.pmf(x, n2, lambda_ / n2)\\n\",\n    \"y_poisson = stats.poisson.pmf(x, lambda_)\\n\",\n    \"print(np.max(np.abs(y_binom_n1 - y_poisson)))\\n\",\n    \"print(np.max(np.abs(y_binom_n2 - y_poisson)))\\n\",\n    \"#%hide\\n\",\n    \"fig, (ax1, ax2) = pl.subplots(1, 2, figsize=(7.5, 2.5))\\n\",\n    \"\\n\",\n    \"ax1.plot(x, y_binom_n1, label=u\\\"binom\\\", lw=2)\\n\",\n    \"ax1.plot(x, y_poisson, label=u\\\"poisson\\\", lw=2, color=\\\"red\\\")\\n\",\n    \"ax2.plot(x, y_binom_n2, label=u\\\"binom\\\", lw=2)\\n\",\n    \"ax2.plot(x, y_poisson, label=u\\\"poisson\\\", lw=2, color=\\\"red\\\")\\n\",\n    \"for n, ax in zip((n1, n2), (ax1, ax2)):\\n\",\n    \"    ax.set_xlabel(u\\\"次数\\\")\\n\",\n    \"    ax.set_ylabel(u\\\"概率\\\")\\n\",\n    \"    ax.set_title(\\\"n={}\\\".format(n))\\n\",\n    \"    ax.legend()\\n\",\n    \"fig.subplots_adjust(0.1, 0.15, 0.95, 0.90, 0.2, 0.1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"time=1000, max_error=0.01964230201602718\\n\",\n      \"time=50000, max_error=0.001798012894964722\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x2055848d828>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=模拟泊松分布\\n\",\n    \"np.random.seed(42)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def sim_poisson(lambda_, time):\\n\",\n    \"    t = np.random.uniform(0, time, size=lambda_ * time)  #❶\\n\",\n    \"    count, time_edges = np.histogram(t, bins=time, range=(0, time))  #❷\\n\",\n    \"    dist, count_edges = np.histogram(\\n\",\n    \"        count, bins=20, range=(0, 20), density=True)  #❸\\n\",\n    \"    x = count_edges[:-1]\\n\",\n    \"    poisson = stats.poisson.pmf(x, lambda_)\\n\",\n    \"    return x, poisson, dist\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lambda_ = 10\\n\",\n    \"times = 1000, 50000\\n\",\n    \"x1, poisson1, dist1 = sim_poisson(lambda_, times[0])\\n\",\n    \"x2, poisson2, dist2 = sim_poisson(lambda_, times[1])\\n\",\n    \"max_error1 = np.max(np.abs(dist1 - poisson1))\\n\",\n    \"max_error2 = np.max(np.abs(dist2 - poisson2))\\n\",\n    \"print(\\\"time={}, max_error={}\\\".format(times[0], max_error1))\\n\",\n    \"print(\\\"time={}, max_error={}\\\".format(times[1], max_error2))\\n\",\n    \"#%hide\\n\",\n    \"fig, (ax1, ax2) = pl.subplots(1, 2, figsize=(7.5, 2.5))\\n\",\n    \"\\n\",\n    \"ax1.plot(x1, dist1, \\\"-o\\\", lw=2, label=u\\\"统计结果\\\")\\n\",\n    \"ax1.plot(x1, poisson1, \\\"->\\\", lw=2, label=u\\\"泊松分布\\\", color=\\\"red\\\", alpha=0.6)\\n\",\n    \"ax2.plot(x2, dist2, \\\"-o\\\", lw=2, label=u\\\"统计结果\\\")\\n\",\n    \"ax2.plot(x2, poisson2, \\\"->\\\", lw=2, label=u\\\"泊松分布\\\", color=\\\"red\\\", alpha=0.6)\\n\",\n    \"\\n\",\n    \"for ax, time in zip((ax1, ax2), times):\\n\",\n    \"    ax.set_xlabel(u\\\"次数\\\")\\n\",\n    \"    ax.set_ylabel(u\\\"概率\\\")\\n\",\n    \"    ax.set_title(u\\\"time = {}\\\".format(time))\\n\",\n    \"    ax.legend(loc=\\\"lower center\\\")\\n\",\n    \"\\n\",\n    \"fig.subplots_adjust(0.1, 0.15, 0.95, 0.90, 0.2, 0.1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x20558482710>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=模拟伽玛分布\\n\",\n    \"def sim_gamma(lambda_, time, k):\\n\",\n    \"    t = np.random.uniform(0, time, size=lambda_ * time) #❶\\n\",\n    \"    t.sort()  #❷\\n\",\n    \"    interval = t[k:] - t[:-k] #❸\\n\",\n    \"    dist, interval_edges = np.histogram(interval, bins=100, density=True) #❹\\n\",\n    \"    x = (interval_edges[1:] + interval_edges[:-1])/2  #❺\\n\",\n    \"    gamma = stats.gamma.pdf(x, k, scale=1.0/lambda_) #❺\\n\",\n    \"    return x, gamma, dist\\n\",\n    \"\\n\",\n    \"lambda_ = 10\\n\",\n    \"time = 1000\\n\",\n    \"ks = 1, 2\\n\",\n    \"x1, gamma1, dist1 = sim_gamma(lambda_, time, ks[0])\\n\",\n    \"x2, gamma2, dist2 = sim_gamma(lambda_, time, ks[1])\\n\",\n    \"#%hide\\n\",\n    \"fig, (ax1, ax2) = pl.subplots(1, 2, figsize=(7.5, 2.5))\\n\",\n    \"\\n\",\n    \"ax1.plot(x1, dist1,  lw=2, label=u\\\"统计结果\\\")\\n\",\n    \"ax1.plot(x1, gamma1, lw=2, label=u\\\"伽玛分布\\\", color=\\\"red\\\", alpha=0.6)\\n\",\n    \"ax2.plot(x2, dist2,  lw=2, label=u\\\"统计结果\\\")\\n\",\n    \"ax2.plot(x2, gamma2, lw=2, label=u\\\"伽玛分布\\\", color=\\\"red\\\", alpha=0.6)\\n\",\n    \"\\n\",\n    \"for ax, k in zip((ax1, ax2), ks):\\n\",\n    \"    ax.set_xlabel(u\\\"时间间隔\\\")\\n\",\n    \"    ax.set_ylabel(u\\\"概率密度\\\")\\n\",\n    \"    ax.set_title(u\\\"k = {}\\\".format(k))\\n\",\n    \"    ax.legend(loc=\\\"upper right\\\")\\n\",\n    \"    \\n\",\n    \"fig.subplots_adjust(0.1, 0.15, 0.95, 0.90, 0.2, 0.1);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"202.3388747879705\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"T = 100000\\n\",\n    \"A_count = int(T / 5)\\n\",\n    \"B_count = int(T / 10)\\n\",\n    \"\\n\",\n    \"A_time = np.random.uniform(0, T, A_count) #❶\\n\",\n    \"B_time = np.random.uniform(0, T, B_count)\\n\",\n    \"\\n\",\n    \"bus_time = np.concatenate((A_time, B_time)) #❷\\n\",\n    \"bus_time.sort()\\n\",\n    \"\\n\",\n    \"N = 200000\\n\",\n    \"passenger_time = np.random.uniform(bus_time[0], bus_time[-1], N) #❸\\n\",\n    \"\\n\",\n    \"idx = np.searchsorted(bus_time, passenger_time) #❹\\n\",\n    \"np.mean(bus_time[idx] - passenger_time) * 60    #❺\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"199.99833251643057\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.mean(np.diff(bus_time)) * 60\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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b34MI4IKUcrB7vZQnrd64stMr7e+ZQa9kgVa3gn4A/kFKeQK3dghFC/EchxEeqf7YAX0at4UJ4F/BxIcQzwC7gl1DrtxD+hxDiKiGEDvwyoWa/qPU7J0JjhBBNwHPAD6nm/Z1mulTMgxDiGSnlLUKIrcD3gacItYTrpZSLLml0LiOEuA/4ErC/+tL/B9yPug8bpuoA9y0gShgw/weEe/VqDRdIVaDeiXoWNowQ4nLgm4AAvkNoMn+OUEu9HbhdStlQufFzQpjC8uf9PZ+oFiO4CXhc/SMuDHUfLh21hktDrd/SEELEgd3AS1LKYw23O1eEqUKhUCgUq8W5smeqUCgUCsWqoYSpQqFQKBRLZM0muu/o6JA9PT2rPQ2FQqFQnMe8+OKLY1LKzvnOW7PCtKenh7179672NBQKhUJxHiOEONHIecrMq1AoFArFElHCVKFQKBRrnpzlcnysRM5yV3sqM7JmzbwKhUKhUEAoSB97eQBfSnQh2H1FN80Jc7WnNQWlmSoUCoViTZOxHHwp6WqK40tJxnJWe0qnoYSpQqFQKNY0bYkIuhAM5svoQtCWiKz2lE5DmXkVCoVCsWhylkvGcmhLRJbN9NqcMNl9Rfeyj7MUlDBVKBQKxaJYyb3M5oS5JoVoDWXmVSgU5y1r3UN0sUy/ruW6zozlULQ9NARF21uTe5krhdJMFQrFecnZ4CG6GKZf1807O3nuyOiyXKchBAcHcngBGBrccVnXGen3bERppgqF4rzkbPAQXQzTr6s3U1q26/Sk5NKuZm7Y0c6lXc1MWM45qek3wrIKUyHEXwkhfql6/HUhxE+EEJ9bzjEVCoViLmomT0OINe8huhime772tCWX7TrbEhFSMYNASjRNsKc3w/NHRnns5YHzTqAum5lXCHEzsEFK+V0hxD2ALqW8QQjxt0KIC6SUh5drbIVCoZiJmUygnpRr1kN0Mczk+bo7vjyesJPHKlRc9p/K0tUUZzBfJmM558yaNsKyaKZCCBP4b0CvEOIu4BbgW9W3nwBumqXdvUKIvUKIvaOjo8sxNYVCcR4z3QTqScm2juQ599BvTphTrmv638sx1tZl1IDPBpbLzPsR4CDwAHAd8HGgv/peBlg/UyMp5YNSymuklNd0ds5b8UahUCgWxNkQ/H82MJN3cE1LvWln5znjzLUQlsvM+wvAg1LKISHE3wE3AvHqeymU45NCoVgFzobg/7XOXF7Qaz0WdDlZLqF2BNhePb4G6OFN0+5VQO8yjatQKBRzspwmz/OBc9ULeqksl2b6deBvhRAfAEzCPdPvCCG6gTuA65dpXIVCoTinWIl0fQthrZvKV2u9lkWYSikLwPsmvyaEuAV4N/CAlDK3HOMqFArFucRaTCyxlk3lq7leK7Z3KaWckFJ+S0o5tFJjKhQKxWSWklZvNVIPTjepnsiUTpvDasxrrZrKV9MErdIJKhSKZWGtmSdrWkux4lHxAu7etZHN7Yl522QsB0OIJafkW8x6TDapOm7Az45niJpafQ4AD7/YR8F2SUdN3nv15hn7XmufxXKxmiZoJUwVCsUZZy2aJzOWQ7Hi0TdhMWG5PLLvFL92w7ZZ5zX5GiZKDjFDZ3tnalEJCRa7HlOSIpRd9vdPTYpQqLjsO5WlKWZydLTEtdvauDLRckbGPhtZTRO0ClFRKBRnnLXo8dmWiFDxAiYsl9ZEhJipzzmvydcgJQzmyxwbLS5K41nKetSTIrRPTYpgCMFAtozrBQAIAHlmxz4bWS0TtNJMFQrFGWctenw2J0zu3rWRR/adImbqpKLGnPOqXcOx0SLHx4ps60xR8Xzu2Nm14Af1mViPyVpXzexctD00IUhHjbrAXY6xFfOjhKlCoTjjrJS5baF7gZvbE/zaDdsaalO7hgP9WRCwvSM08XpyqvrXyByWsh7T+29OmBwfCyvBbO9IAXBJVxNXbmyZsd+17H17LqGEqUKhWBaWOxvOUvYhG51Xc8Lkyo0t9GWsGTW7ulOT7VFxfe7etWlWp6bFrMds1zhZ20xFjVkF6VLGViwMJUwVCsVZyeS9wOWsUjKXZpexHIq2R1+mzITl8Mi+fn7thp4zNo/ZrlFpm2sPJUwVijXO+RLWUKNv3KI3U6KnLTln6MpK7QXOtf5tiQgV12fCcmhNmMQMbdFCfaZx5rpGpW2uLZQwVSjWMGcyrOFsEMp94xb/5clDeAEYGvyHd19cF6gz7R3evLOzLnjnCnFp5LpnOm++9Q+dmjbxyL5+YoZGKja3U9NcY880jtJAzx6UMFUo1jBnypR5tsQa9mZKeAH0tCfpHS/RmymxuT0x4/yBeiKFvozF7vjp19Todc92XiPrHzo19SxJ4M01jtJAzw5UnKlCsYY5U6bMlYg1PBNp7doTESquxxvDBQwNetrCUI8TmRIjhQqGEJzMWPzk2BgnxkvzXlOj1z3beY2u/1JjG+cap7aufePWiqcNVDSO0kwVijXMfGa+Rk2YbYkIthvwcn+O9CJNkXMxlwa4EDPrvlNZrtzYwkTZ4X1v2VzXSn92PMOhwQJPvDqMAHrHSly+sZmYqc8p6BoVhrOdt1Jm1tnGqa3raNHmwKksV21soSMdnVHDPhvM+OcySpgqFGuc2cx8CzXdCkAIGWbKOcPMZqZcyBxrfVza3cxgvkw8atRfj5oaV29tqzr6RGhPRfGCgOu2dZKOmbMKkEaF4VznrZSZdaZxat7Cx0ZKDOdsjkZL9cxNk889W8z45zLKzKtQnKUsxHSbsRwipsbl3S1EzNDjdD6z7ELMtvVsQWNFJkphhp6FznGy9my7QV07rPUtkbQnowDkK2Fi961tyXnNq9NNsLNd11qshFLzFi67PsmoTsXxqXjBaRp2bZ2boiYjhQonMqVVmvH5i9JMFYpVYLpJbqEmupzlUii75CyX8aJNOmo2lBpvcl7XuTSZ+TSdmTxrd21q4Z9e6qM1HuG5I6PsjnfPOO7xsdKs1ykA2/Vx/YB82T3No/WOy7qYsBwQsHUOD9651u2fXuyjUPFIxwzeN0uVlUb6WQmTas1bGPqRUiIE3L1r44whOo4b8NTx0AyeimYWtT6KxaOEqUKxwkwXVDfv7FxQea/JpcQODuTZ3JYgFZ17zOlmzPm8VOd6fzbP2qcODTNacPB8WTdFbutInpZPdrbrzFgOfiApOd5pVV0mm0DnK5s2FyfGSxw4lSUdMzk+VuS6ntOrrMzHSptUG/EWbk6YXNvTRr7isb0jSd52ly2JhWJmlJlXoVhhpps+ezPze6XO1D4ZM9A0wZa2BNGq6XYuJpsxZ9MYa6bP6e+XbY/nDo/SN27NaLrNWA4xU6c1EWHCcqeYImvjelLOeZ2NVHXJWS4H+rIcOJVdnHlavFlYRVb/XqgXcqOm6zNZtLsRE/TW9iTrm6LkbVcltF8FlGaqUKww0wVVT1ty1tyvc7UvVjwMDUqON28FlOnMVIFkuqZVe79sezz43NF6IoV7b94xo+drKmqwuS1OZzoyqylyLs/a0KQ5e1WXmon2wKksEti1qeW0YtjzaY1b25Ls2tRCwXbZ3pGkNR5ZsJbZiIfwajgEqQQPq8u8wlQIIYCrpZR7Z3hvm5Ty+LLMTKE4R5npobc73vhDcMoe4uVdeFI21G6mfU6AA/1ZRos2HckoRdubkv8V4IneDLmKx8bmOKNFm3HLmTHz0HwP8rke9rUUgu2JCLdcuG7GPdGwGLZHOha+VpjBlDmf+bo5YfLeqzc3bO6eb/1nu9aVyhs809yUEF0dGtVM/5cQ4h+BY8BPpZR7hBBXA38J3LBss1MozlEmP/RylsuJ8RIIGtYuF/rQzFkuD7/YR8EOvWDfc+kGJsoOPzueIVO0+eHrI3S3xGmOGdxxWVe9zWMvD9CXsTg8lGcoVyGiCyKadlrmIaDhsmbT36+lELScgOF8mXdevJ6OdJSt1YQNtfUp2h6mLihUXCSwvSN52nrNpzXWvlAYQtR/LyYpxnzrr2qInn/MKUyFEEJKKYUQR4C/AbYCdwgh/gawgfetwBwVinOWRkyXZ4ITmRL7TmVpipkcGiowXnKIGIJDgwU0AZoQaIQ1O2v1Omva1abWBBeub2JLe4LWRAQnCKZoXSfGS7wykFu0SbOWQnBdOspAtowUTNmLnLw+F61P84Frt5CKGTN6q86lNU5x3BrMcWl3M6mowc07OxvW7htFmVzPP+bTTP9FCFEEmoGdwNXANcB3gMuATuDUss5QoTgHqWlIhbI7r+nyjCCpJ2twvIAgkGxrT3OgL4cT+DTFTCQwYbmUbY/jY6W61la0PZriBt3NcTQtfC1nuYwVHNIxg6F8hdeH82xtTTJuu5zIlLgy0TJr4viaFt4aj+BJSXsigqHBSMFG10BI6trcdNOuFwR0tcTZ1pE8bS0nm6/nMr0mYwZeAMmIgS8lnpRs60jWHYbOlPBbislVZTM6+5hPmL6XUBv9HeALwCBwl5TSF0L0AH8rhHinlNNKz1cRQqwHfiCl/AUhxNeBS4HHpJRfPFMXoFCsNEt90E12TnHcYFbT5VzjTBZKkzW06eXLan20JiJcuamFQsWjqzlG3NTJ2y6XdDcxUXKImRonMhYC+K9Pvc4Vm1roTEXZtamFccvhph0deIFkT2+GPb0ZXjw5wY6OJH4Q8NNj4/Rnyzx1cJiLNzSRipr1WNNixSNbdrlpZwctcZPvHRjk+FgRxw/w/IC39LTRmYpy7807GLcc2hMRvOBNyd+WiJCOGRwfK85o2l2Io89kx60gkJzMWHSmo7QlIvSNW1Mcn1Yzg5DKZnR2Mp8w/W3gAmAMOAD8OXBcCPE/gcuB/zibIK3yVSAuhLgH0KWUNwgh/lYIcYGU8vAZmL9CsaKciQfddOeUa7e18fYLOqcIxvly3c5kGs6X3Snly+69eQf7TmXrffzipRvq5kwITb+2m6E5ZjKYq3DRujT9uTJDOZtkpAjAU4dGaE2a9GUsLt/YjB9Ijo0WyRTD+p1lN8ALJJd3N/PzvgkuWp8mamr0ZkoUKx5HRou8Npjn5f4cmiCMI7U9bC/A9QNaRorETZ141ODmjc1TrvuV/hy7r+jmfVdv5rqetlmdkhp19KmZXk+Ml0JhHgQIIF92eWRfP28MF2lNRNjcFl/VGM3Vcl5SLI354kybCMOxbgQihMJ3P7AP6AFena2hEOI2oAQMAbcA36q+9QRw0yxt7hVC7BVC7B0dHW34IhSKlWK+GMNGYgsnO6fYbgCEMYJXbmqpPzTnGmey6bMpZtZNw5PLl3kBvDqYr6eYG87bTFSTKNTMj+mYSdTU2N6ZoqslRtkNqDgByahO3vY4PFxgomRjCo3hvE2x4lHxAvIVD10TFCoeugZBIBkt2iQiOjFTr4f7VLyAgWwZgIgu8H2ImRoTlkPZ8UlEdMZLDtmq9g1hUoXhvE1T1Kxfd3PC5MrNLVPWZ6a1bMTRpzlhko6H119LrdibKREzNFoTJhOWQ8X1V9VhSDkvnZ3Mp5k+Reit+wHgh8DdwFWEmuoDwB8Cn5veSAgRAT5fPf9RIAn0V9/OAG+ZaTAp5YPAgwDXXHPNXBqvQrEkFmuqna9UVk2rst2A67a1TdE0p4TCVDWkPb0Z9p/K1rWw2lwMIZgouZRt/7SC07OZPsu2R9n1ODxSIAgkE0WbkxNlBnNlXE9SqITJEJriZt071nYDBvNlUlGD2y/bwA8ODrKpNc6RkSL5ssux0RI/PZ5hS1sCUxdsaY3z4okMbakIfiBpTURYn44xUXb4rbfvIB7RQUBT3ORdF6/jxRPjeL6kf6JMU9xEeBobmmI4XoCuaQgEriv5ybExtrQm2NOb4dhYkeNjRa7c1DJv+sHZHH3m+nxni/Pd3JqgMx3MGCO7kijnpbOT+YTprUCeMATmIsJ90xuBf5NS/g8hxN8LITQpZTCt3WeAv5JSZsMwVYpAvPpeCpV5SbGKLMVUO9eDbnKy8R8eH6Zou6xLx2ZNF5i2TCKmNmOlleeOjBIzNSquzx07u06LlZxu+gTq5ctOZiyGcmW++/IgxYqHHwS0JCLs6c0AkrZklDeGC3Xv2Ot62mhNhHucXc1xBrNltnckcQJJImKQsRwu29jM68MFRgo2cVPnmp42xgo2hi4e4mRQAAAgAElEQVTerPIS0eteva/057i8u5m37ejE9gP2HMvQ0RQhW3K59dL1PP36MI4f0BQ3eOHYGIfHihgaXLmxhXddvJ7j40Uu3dDUUJrF6Y4+832+S43zXQlUvOjZx5zCVEr5R0KIJKE3r189/3NSyheqp9w3gyAFeBdwmxDi48AuYAvQB/wboWb7+hmav0KxYKbvSZ3IlEhbs5fxapQ3K6eUkMC29hR52+XVgRwnxy060lGC4E2T7WC2zGC2TNnxQ01pokyh7FKseAznbTY0RcmUbF4dzAFMCd9oTphTcsoeHwtTEl7W3UzGsvEmJOmYSaHsUXJ8TMMjGTU4lSkzkreJR3Qiho4XBKTjJp6UFG0PUfX8cYOAk5kynh/QHDMoVTwEcMmGNBNVM3NHOkrWcnj+8CipmMFho8iJcYtt7UkG8xU6khUqno/leCSjOtvaUxy0c/RmSiSiBl0xkxOZEhXXpzMVZaxoM1F2aLUjrEvHSMWNRe0dNrLnOF1YzSe8lHetYj7mTdogpSwR7n3W6AcQQnQCHUB2hjZvrx0LIZ4B7gSeE0J0A3cA1y9p1grFEphs5nPcgJ8dzxA1tQUlmZ9J65lsvm3qNcjbblhSbCDHC8fGkBI2tya4aWcH//TaMAdOZXG8gO6WgHhE5+/3nsT1AvwgzGH75GsVkHB4uMh3jAF2bWmZ1dN08jV1pmKcMC0ODeUp2R5BAONSki+HZmNNExiaYHN7om4izpdd9p3M0jdhEQSS5oRJEEhMQ+Pi9U3csLODg4N5PCnZtamFa3vaMDTBXz59mFzFI1O0WZfOMVq0ee7wKF3NMQ4PF7h4QxrXk3iB5PXhAkIIbtrRQd+EhS8lEUOQt1xeG8yja/Bbb9/BuuZY3az9Sn9uwXuHZ3rPUXnXKhphKbl5vwocB/54rpOklLcACCFuAd4NPCClzC1hXIViSUw28xXKLvv7s1MSEKQtE0MIPClP+10ou/Omq7sy0UJrIkJvpoQuBM++MUpHMorrBaxvijA+yYHIcX2yJQfL0WmKmYwVbPK2y86OJONFm45UlGTUYCRfoXesBFXt9IYdHTOaLk9kSiDhwvVpvrPvFONFl3HLQVbDTXZ0pkjFTAxNcMtFndywPewnYzlsaUsQSEnJ9rFsn+62GFFDJ2d7pKrlymr9b21PcqA/i6ZpbGtPMpKvoOuwpS1BxnK4aEMTJzMW7ekovoQd61Jsbk1Qcjwu2JDmkq4mejMlLu9uZl06hhRhfOm65tiUGNLF7B0udM9xPq1zJb1rlQZ89rIoYSqE+ARhEoePNdpGSjnBmx69CsWqUjPr5SyXVwZyHBstki27ZK1RorrGwcEc2zpTHB8tsr0jxaGhPFvak6SiBjFTn1JN5QcvD9IUN9ncmmDCcijaHgcH80RNjZzlcmgoz6GhAoEM0HTB7ZqG6wX0ZSyG8hU2NEeJ6hqBhMFchYrr05+xMHUd15e0JEx6x0ocHStRcTx+3pfj+SOj/Obbd9IUN+sP33zZ5ZnXR4kZGrYfsL4pznjJYThXJmLq6IDleJiGxiWbWrisqzl0RBrwQIKpCQayZbxAIgPJWAHGSy4bmmI8+lI/V25q5kB/joiu0ZGOcuP29nqyhageOhMhIGFqDOUq2K5PqVo3tAmDAEkqakxJrO+4AUITBEFAaoaarIvdO2y0XSNa50p51yoN+OxmQcJUCNEB/CnQBtwupZy75pNCscZpTpjcvLOTR/adIpABh4ctrtzUghcAErwAyp5P30QZXdNAwK9eu4Wuljhl2+Mvnz4SmiwDybp0lJipY/sBMV3j9su7GCs4tCUjtCUjSMLsQz8+PErU1Ci7Pm1Jgw3pOBvb4rTETd4YKvLiyQwF2yUVNbhqczOdqXCvdcJy6C079GVKjJVsQLCtI0nE1LDdgEzJ4WTGIhHRyZddetqTtKei7FyfYkdnmtGizS0XdPILW1tpjUd4/OAQe3szDOTKbGyO09US5y1bW9nUkmDcsunLWEgJubLD80fHeOq1ERzfp7s5xrqmGNf1tPEf3n1xPUG9F0iG8hX+aW8fE5aDoQku39jMZd3NwJu5eydresfGipQdH1PXWA33/Ub3V1fCu1bFl57dzOpVK4TQhBAfFUL8OyHErwshHgQeAv6XlPJuKWVh5aapOFdpJC5zpnOWWitycntPSlqTEba2JZkoObw2kCeomkWDIGA4V8EPJDFTQxCWGtvWkeTkhMV4ySYdM9A1wYTlYBoaTRGD0WKFH78xwnixgueHiQ0MLXTu8QNJRzXzTmsiymjRxnYDrt3aRsX1qXgButAo2j4Cwe2XddGWjFJ2fKQE09CJGjpZy6Fgu3Q1xSlUPMqOhy5gIFum5Ph0pKNsaU3QkohgOT5xQ+eC9Wla46EJerRgIwEZhMHkEV2jLRkhGtGImzoXrEuzvilGoeLj+gERQ6IJcH2J4wUgwsLVN1/QycbWBOm4SSAlUVPnsu5mNCHor8aZNidmrqVacX1a4iZXbGxuqCbrmfr8azSqdU6e/3Kh4kvPbubSTCPADsAjDGfZDLQAG2sJ8FdgfopzmEbMWtOrnbz36s0AM7abbb9pcqWQ2v7nZDPj5tYEhwbzHBspMWHZCCG4clMzl6xvIm+5BIFkY0ucVNSgM2XQmoiQs1xePZVjvBiadROmXvWWLTGSd5BIRgsuMVOjOWZQLLvoCZOK62FVXMYKNkXbZbzkkI7qaAJeODaOpsFwNtQIdV3H9yVZy+Xdl6xnS1uCZ4+MkrccTF3Q1RQnHTU5Nlqkd6zEG8N5muMmmoCIobOvLxtmQrppB6eyZV4dyLLnRIZv7T1Jd3OCl/uzjOQrFG2PsuuzY12Ku3dtmrJGFc/n4EAOCZTsMKFDS9Ksa7e1HL619cxZLkEQcHi4wFC+zECuzGMvD5zmqFUr4XZhZ5qf9mY4Nlqsx9POt29YywBVqJqQ37eEwgBrKaZzLc1FsXBmFaZSygrw2cmvCSGagE8Qeub+ipRyeJnnpziHacSsNbnaydHREtduayMdM09rB7ML2OmVQipuQMzUWJ+O8f3Dgzzz+iiDuTKOJ2lJGqxPR/ECyU+OjTNatGlNmFywLk3Z9WhJmjx3ZJSe9iQnJiwu7W7i+FgJQUAiotf3RqUUJCKSmGkybrlUvACnaKMJQdQ0qPiSUxNlLMfDcgzGSw6uLxnJ2wihoWuQiOq8MVLgr398hGzFpTMVpTVuEtE04hGNpqTJDdvb+Z8/O8G/Hh3FdgOylsuNO9rpSMfY0hY6/MQjOv3ZMiMFh/GiS972kFhoQpCKGmxsTeD7oYbaFH9zr3F3vJsD/Vnihk4iatA/EaYUvGBduh6X6ksZ5vY1dLZ3pgD40PU99GdDQXpZV/Npn20tjrb2mWzrTFHxwnja2T7HKffEeIkDp7KkYybHx4pc19M2JUxooaylmM61NBfFwlhQ8gQpZb6apP73CSvKLP4OVpwTLMXc1kjtyYGJMq4X4Hg+JdujWPHq2YGOjRbRhcAQggP92bC6SdTk5LjFT46N1ZPBD+dtbC9grOCStVwqjsdgtsxrQzlcPyAR1YlHdHQtrJpyKmsxVrCx3VDjnLBcshWbqKmzPh2jaIdZhhwvIB0zSUUM0vEo65piaAJMXUdKScULqLgBvi8xdA2kQBOCRDQUboGE1kSEsuvRN15iMFdmvOig6xoIgeX6lBwPRCjoHC80F29uT7C9M02maPPovgFeG8hhaIJERMdyfY6PlzB1UXf4QVBPl5evuGSLTqgRxwxiER1NCLZ1JmipevXWaE6YXLmxhY50lKipccH6NNf1tJOOm/RNWPW0fzFTp+IF9c/xsu5m3nPpBjpT0Rk/2+nVWzRgrOjQl7HmTdcIhOtRPZTVvxWK1WZR3rxSyheEEA8AfwV88MxOSXG2sFTvw8nmvp5pCcxrpryxok2h4jKUqxA3dX54aJiorhM1NSqez02bOnjuyCinMhZ7eicwdMiVPV4ZzPGvR0Yp2wEDuTJD+Qol22MoX0YguW57B7bn0d0S4/XBAgPZMgJIRg1yZY9ipYDrB3S3xulpT5I0dQ4O5dnbO0EqqvMLW1rxgzAm9LKNTZwYtxjIlLFdH19KggB6mmPcdEEnBwfz9GUsSp6P7cEbgwXakhHGC2U0Xcf3fUaLNkdHilT8AIIwD4qmQcUNeKU/h+MFJE2dns4krhcwnCtzfKxExnKxHA/Pk+gCpIDhXIVX+nPcuL2DzW0JJsoOmiboSEUZyFXYsS7FnuMZOtMRUjGTbe1JNjTHpqQtnGxqrcXOFm2Pxw8OEQSSfX0T2H7Ay/1Z3rKllbt3bZySRD9jObPWCZ1cvaXi+vzLq0PoQjBWsPmdW3eGiS9Gi1S8AEOcLim3tiXZtamFgu2yvSNZzwClUKwmi44zlVL+gxDi52dyMoqzi6V6H9bMfb6U9GUsdsffFMY1U17U0BjIhU48rckoP359hA1NCbZ1JtjcmmDcchgt2Lx4coJMycFyPFpTEfKWy2MDQzRFDWwvQNME6WrNTjeAl/tztKci2K6PE8hq6S2dtmSU14cKRE0NQxO0xCNc19PGG8MFspbLWMkhawnefkEnuq4RMzRaEhFips7xsSKncib5soduwHDR4alDI1i2hxtIdKER4FO0vdAZSdOQgSQABrMVio6PkICAjnQEUw/3W0dyZXwJti9Z70l+/Rc2cWy0yKHBArbrEwShY5BhaGgCoqbOQK7MYL5CbybMjCQIUwcCHBoqUPED8rZPcyLCuy5dT1dLvC70pn9JunlnJ68M5BgpVDg2Wqp7O0c0jbIXYDl+3TzcyBesyXuD7akI33t5gJ0daQbzFcarQrhWDu25I6NT7ota+/devVntLSrWFA2beYUQvyRCrqm9JqVUaQHPY5bqfTinSa9qyqu4ATIIHWpCr1jQdRjJ21S8gIim8Wp/nozl0NUcI2rqWLaH7fkgJYEE2/MRQlC0XYq2S0QXlF2PihMKog3pGC3x0AR6MmPh+j6BHyYv6Bu3eOb1Efb2TpCvuLTETbxA8uzhUcYKFQxN4/hoiYGJMi2JCFIKdE1gVufregExQwfA8XzS8dAsWnI8dCFIRg0QAi8IkBIMQyAk+L6kWPHIlFwkgoiuhfO2PQIp2b4uhRdIHD8gCEDTIW5qBDKg4voYmgaEKQI1BL6UdLfGEQJGC2WQEg2J5XgA9UQJx8dKnMiU6u2Ktlcvp5aKmjhewFjBJpABAbC5NQzpOTFeCtuOl+Y1007Weq/raQ9LwOUrGBr0tCXr3tXbO1Kz9rES3rUKxUKYVzMVQvyelPLPgd8Fvg98mTD3ruI8Z6neh9NLkRUq7pt7rzLUpPqzFiBxvQDb89ERBFLiy4DLupr4788f41SmxFA2TL3XmjTZ0pqgUAn3Wyf8CpYjEQLKtk80olN2fIQAmQgFli8lg/kKrudTdgICCSXbQdNg78kJ9pycAMAUEI9oSAmjeQuJ4NX+HKYeVkoxNIHrV9MBBhJBQMZyEMh6SEvZ8UnHTGKmwUjBxgkC4rpA0wW25+N4EkODihfm6624LgaSctWM60vJU6+FSeKdIEAX4AKeB0XpYeoaF21IcfXWdja1JPj+K4MYmkbc1Ljjsi4u62rmW3v7KDs+uUqYwOHFkxO0xE2eOjRCzNAolF0ODGSJGgaJiMZVG1s4OJgL9zcFvP3CTu68qrt+vqYJ9vRmiJgajhsgYcoXrMnCE053MKrFqk4uaK5CRBRnG42Yed9NWBS8CfgBsEsI8QQQA74vpfzyMs5PscZZivfhTKXI9hzPIIGoGRpNHE/Wj0u2R2dzaI7cvi7F4ZECL1f3E4u2T/+ExYamOLmYSzJq0Bw3yZZdNCSFik9LwmB9U5xMyaE9GWF9c4xLuyIcGy8R0zUc18everZMjr+s4Upw7cl1HWR1jj4RU+B6EiEEri8JAkkypuE4ARFdQwpJRzJCKmpgGoLtnSleOpGlIxVluFAhpgvKtoXngy8hkKHZyHIChKYRJSAeMdnSluBAf45SxQ2ditqSDEyUwvNFqBGnoia3XbSOpw4NYwiNmKmzvSPJhOXww0PDOF6ALyV+dd32nZxgMGuRr/gkIzoD2QpSQjyhsa0jhRMEXNrdTDJiUHI8ulribOtIsrE1Ua2t6rL/1JspGa/a1EI6Zs4oPC/vbj5ta2BbRyhEp98XyoyrOJuY08wrhPjPwA4hxH8CIlLKdwN7pJTvAW4Dbl6BOSrOcXIVFz8IH7CjRZtTGYumqInrSwxdkIwYeH5YhDqiQ8H2ODJc4KUTGSzHxw18dAHxqE6AZLTkEDN13CDUMiOmFmp7bkDWckiYOptaE/SPl3llME9HMkprMqycMll46g16iUpASonrhQkNdAEIcNwAv2qWDgi/DLSlo3Sm46xPx0nFDOIRvW6GlQhMXaABnh+acCWAlCSqhbyH8mX8agKIouNTrDiYho6haQghiBka6ViEVwfzlB2fmKmTKdq8MVJgf98ExYqLF0iQEt0QRDSNku0TMXRaExFGihWEgO7WOH4AQoSmV10IRosVHC+gUA4tCLUEDMjwWmua5Na2ZN0EO92Uj2DNJElQKM4k82mmzxBqpj8E3ll7UQhxE3CRlHL38k1Nca5TjwG1w3jDsutzcjzUsn54aJgL16fpSEXZ72TJlh1sz2cwZ+MFYXym7QXYXkBU14joYLsBQnqMF21cNyCqG1S0ANMIvzPu2txCUzxCpmQzkq9waCRPzNDpG7d42852vABOZkr4vsStphNshACImwb5IKzQUgsS8v3wd75S9c7FpWy7pCI60UhYJDtrOVQ8H02EWnhN701qgmRcR0gYLbp4lbCEmrBcHF9SqHi0JSNoAnZ0xBkvOgQCtrYmyFoOx8aKvHB0jLhpcHS0SFPc4NBgnnzZRchwzuuSEdY1xdi1qZmWZARfStJVAR/VNSpeWCi7KW7W969PjheJGBqvDOSm1GmVwFWbWurF0GtM31ff2hZ63yqtU3GuMV8906eFEBUp5b9WnY8EoAMfAf5sRWaoWNPMlXWoVmGkNRE5LWyiLRHh1YEcrw/nWZeK4flhGrwNTTG6WxKcylp0NcWwXJ+u5jiSMBNQqAH6eF6AoevoQrCpNcHFG9KcmrCImgb9WYuu5ih9mTI9HclqKj6PmGmwrSNMRn8yY+H7kkCXuH5AvuzR3RyjWPFoT0foy5TRgOa4yURVu2qOR8haNqmYyWjeBsDUwAlAIklFwixIw3mb0P0HpAwLAccN6EjFSUVNrtzUyvbOJJuaE+w5OU5HKsrJiTIl24fq/uqW9gQ97ckwu1LGAsKImZaEQVPc5LWBPD0dSUbyNts601y12eCKjc2A4OX+LEhBWypKazxCf7aMrolq+TWNqzY1U7BdbrloHTfu6GBre3LK51I7rmWMOjFeImpqbG5LcGqiTDIS1hmteQrXTLbp2Okm/9lMtkqIKs415hSmQoiLgP9TCHEP8DShWfgJKeVXV2JyitWlkfR8Nc2k5kwCYdaiH78xyqv9OUpOqHVt70jg+pKIrtOSMOmbKPPGUI4Jy2WsUCEg9IIVhB6iti852J9jKF9homRTcN5UE00tDG+hqseJCYvhQhnQQoccXzI4YVHxwjN0wrh+ocOP39Cw7ICq0kjZC71Zn3xtpK6I9ucqaNXe8xU31HxNHRkElOwA27Xxqid71Y6y5fCg7IT7rrX+a5Q9ODVRZqxYIWuF2Y4u7mri6FiR0bxNvuJSqvhoWrhfWnJ8bC9gc1ucQ0MFLNvD0MPMSOlqCbVTGYtcOSw+vqMzySVdm/nR6yP87EQG2w3CMJkOjaxlM1EC1w+TTPTnymxsiXPlxha2tidnFXC1vc6aU1EQhM5RJccjFTXoaUvSl7EaMtkCda9cJUgV5yLzmXnvB14H3gDeA1wAIIT4JqFgjUgp71nWGSpWhdniBSe/PjmNXK0WaC0e8aXeCYqOR9kNGC2UGcpVmLBsEhGTZDR0crFdH01IKl6o2dV2LI+NlYibZmjadQPKzlR7aypqUHR8dCGxPchXQtGViulsaI6hS3hjxK2bTOuCzYfAD04TdHC6RbfWNgjAjIYVTbSqh4ETMCtRU6M9alCwPUp2MKXfALBcyRvDBWKmQcULmLAcbM/D0HTikTBm1QskN2xvp6cjyZbWBNmSx8GhPAlTJxXTuWlnBxuaYxweKZBOhN7LW9qSjFsORdslpocexxXXp+z5rGuKsT4dYyBX4W07OwgCSTyic3S8SG+mNGMs6PQY4ppT0R2Xd01JxLA7Pr+jkCotpjgfmNMBSUr5m0ASuAa4nTDh/fOEYTIfr/4ozkEylkOx4qFVM9Mc6M/WNdLaQ3Z6GjmqoRvr07EwLV/GIlO0cVxJseLieKEDUP9EGc8PkFJSdsJ9wilesz6UXRfL9vD9gOmyq+x4yECiCRGaUqs/FcdnohTObyaBCZzW13xIwNAFvh/uz05mJgelWESve/Oas/x3VVyJ7VUzJUkZlnYj1EhNQ9CeitASj5C1XDQhWNcUJREJUx42xyP0dCS5cH2ai9Y1oQtByfZxfJ/xgs1owabkeARBQEQXXNCZQtc0AhmaoSOaoDURYUNz7LRY0MmpIWfa69zWkaQpfroZdz5HoYZSBCoUZznzmXl/D/gqcKGUMieE+BlgAxlVNebcJWe5HB4u8MzrIzh+QM5yKHs+bwwV2NKWYDBbpuyEcZAXr09zYrxEyfF4RcKhwRwv9+U4OlLA8qrmVaBo+w0LsgCwZ5GGovqeBDw3vAVrN6ITQKHsUnZnH2mhN20AZIoupg7WtGuQMjQh16ZqCrBdHyklXtWJScwwpk+oXY8XHRzXp+JLojokIjq3XrCOKza38MLRMY6PFXl9KM+2jiQXrU+TjhpsbEtwWVczmZLDzvUpIoYgZ3kcGSnxb8cyGCJcg/XNcSquz/FMic2tcVqrmmM6ZmBoGto0j9qZsh5d3t0MgrpT0WI1TFVaTHE+MJ+Z912Ez4OMEKIPeDvwvwFPohI3nJPUSp49/cYIR0eLBIFEaILDQwXeEAX+9WhY97K7JSAe0Xn2jVH2nZzARyKQJKORUButSp2a1ngm0Ai1QXeODis+CH82vXRx+ISeuaI6fjXCg4gh0ISkXHPfFRDV9VDgSp/ADRAiXC9DE+RsH0ML41ctxwOhhZ0RfkEIbJ8nXhsmb7s8d3gUU9MZzjtYjs9Vm5tJRAx+8dINbG5PsDvezYlMiWzJ5VS2wljRJms5eEGYESkdN7mkK01ED/MIHxsrIYTk8u6W02JBmxMmx8fedCY6NlbkkX39tCbNulYKi08fqeJGFecDswpTIUTNIVEAm4Cu6vkfBNYLIT4CHJFSvrASE1WcWaZnpakdZyyH0aINVQ9V2/fxHTgyXKQ1FSEdMyjbPoWKi+eHlVhcGaAhKHuSIHDxF2pLbZCAMO5xPum8XCaTmneupoWp+3RNq6fjA/AD8IJw59eTAYYeOlUBRCM6mu0jCIWzqDpbTbYcS0LnqXzFwwvA0CSW7VOsuHQkYwRIvKrwbU6YpC2TloRJS9zk8EiBou2RjOjEIhqBL2lJRGlLRhjOV3C9sDpOLYF8azwyJVHC9ILdMUM/TWguRcNUpcUU5zpzaab/Hngb8EnCBA0eoWBtJVQS2oC/EUJcI6W0l3uiiqXRN27VU7Y1xc26uc52g1DLqqaC29KW4OhIgaF8hUzBpSYqrIly6CwTBLjum3uSNa/XmgjzgmWSpFWWS1A3gkb4D9AU0/ClIAiCesxmDUOHHR0pxooOEUND0zWCQOIFAXnLwauaqB0vIAhkvd8AEBJKjstY3kYTYXKKqBkK7D29GeKRMCVgjbZEBF0TFCouEU3QFDcxdY0trQku29jEr167BYBH9p2iJRnm1c2WHVqqtUgnJ5CfrD3WvLSnC02lYSoUszNXcfCvCyHywP8FWITCMyGl/L+FELdLKf9cCGEBccJ9VMUapW/c4r88eaiq7cC/u2ZL3Vz3cn8OISTbUimeOj5MX8YKswYZGt6kPiSQs8Mwl8nCYxVl27KjQf16pYSoKYiZOlvbkuQrLmNFG00TEEh0QkeliivJlD00XSNqalyyoZneTJGS7ZOKh1VqAilpTZiMFh3ScR2vmtHo4q4mHD+gKx0jHtV5bahAKmqi6xo71iVpiUfqmimEwu3anjb6MhZtyQgSgaEJbrmokxu2d9TNt63JSP2zjhga2ztSM5ppJ2uPs3npKg1ToZiZ+fZMPw68nzBBQ4Rp3r9SygeXaV6KBpgeBzo5/nPCcijaYTzgQK6MF0BPe5Le8RKD2TKD2Qr7T2YZyVcwdI0jw0XGSzbrU1HGSw7jRXtG55nzze1MEHrYSl/i+xJfl4wWbTw/QGiiHs5TM9GaekDE0BCApgnytovjBiQjOoYQ6BH9/2/vzKPkuuo7//m9pfau6k3qbm0tecFajGS8YWFDPIxNBgg5YclMMuAwk3AIc8LMhJBhYGAyhCXDSSZkTkhC4kycEyCTACEmGIaw2sYYjG0Z4w3Ltqx9672ru9ZX7/3mj/u6VWp1Sy31JlXfzzl9VMut9+7t10/fur/7u9+fsUBsmGzbtO9RqgekfAdXHDoyHmsLKYZKNdK+8dWtNyJGSwGd2eQZodX+riwbOjM8cWQMBbZtaJ8WUjg9fNuW8hCYV5jWiqbFcn6cS0wV+DLwTeDzwE3x676I/AnwdVX92swPiUgB+HtMsmMJI8ifBrYDX1PVjy1O91cvs2VfPvDCIJO1Bo8fGqPWMAWn1xfSXLEmRxRFHBguEUXK/uESPz48whOHxqg3TEWVtO/iew4vDpbMdg0RXGHanGCKVtPS5mzcmUSAJxAGp7bvaC1kshbiAgikPEE9U+ot6Zp9om1Jc1u9ODjJoZEK9SDkJT1tXLY2y5auHIJxOPrOT0+wb6iE5zgkPYe1+SSXrclyQ38n+wYmySU9giCit96OifsAABpASURBVJCkkPaZzSq4kPH5xes2cuPmztMyb5vfbw7NAjZMa7EsAWdLQHobJvHoBkzSkdv8NnDfbEIa81bgk6r6LRH5NPBLgKuqu0XkLhG5UlWfX5whrE4ODpc4WazRl09xvFjl4f0jnCzWyCU9Ko0GQUOnq544rsNrtvfy4lCJXNJjpFyjWAmphxo720A5CElEEWnfo5BJ0NeRoj8IORqvlVYDPW1P56VO2jcZur4rNBpKMuFSD03d00pwai3TcwVcIe27VOM6p46Ywtig7NrQwfBkjXqo9OSThJFyw+YOMgmPKK6nWg1CNnZkeMt1G+nrOFWEuyuX5AuPHqIrm+T5gQl68ykKGZ96FHHzld0IwouDExTSCbb15Xkxrhe6M9N+2lgKGf+M12a+P1NgLRbL4nK2mel3gF3Al4DfAY4Cjoj4wDFV/Ye5Pqiqf9b0dA3wNkwZNzCz3FsAK6YXyHg54JEDIzx+aJSvjBoP22eOjeM4QiOMODle48R4lXoQMlSqkfJd7nv2BCeLNRKOQz7j89zJCZq3Y4YRBJFSCoLY4q+CI8JENTxt7bRVmNrOUo+TgIKasfILo1NfFaYci0CpBKZ2qGBm6w0JSfkOve1p6mHE4ESN4VId3xEaoTIwUSPtuwyX6ojAlrU5dqwvTIfj9w+V2NbbRlc2yf7hEuV6g+dOFunMJaZt+iarDToyScQxxv8K5A94p1kAWiyWi4OzJSAdB/6LiNwM7FHVqog8pKoBcMd8Di4iuzHZvwcwYgwwAlw7R/t3Au8E2LRp03zHsOoYKdcp1RoMlmpMVgMGRNiUcHnpunYeeG6AQyNlyrXQrOOV6tz/3AAT5QYqZmvJcLV+1qzYEBivtXJq0Sma14V9xxT4DiM97YuGKyYRKeW7NMIQDU2R8EI6Qb3RoC3l4TqQTyVY355mohYyWq5z+Zocb9iVpy+fPk1Im8Pzv3DNeh58cZD+jizFWsCNWzrZ2JXhlayZzsIdKwesa0+xrbdAsRbMe3+nxWJZPs5ZHFxVH2x6/OH5HlhEOoFPAW/GePym47dyzGFjGCc03Qlw/fXXt0I08YKZuQ/0R/uHOT5e4Yb+TgCeOlakUgtJuC6TtYDBiRoHhiYYKdWBUyHZKFTKtYaZXU69WDvTom+1MvVHJkCoghvvIW00+epGar6EuI7Ehg1CIZ3Ec4UohN6C+dNuS7kUKw2OFyuszaXoyae4tr+TLd3Z6fPNND7obU9xVU+eUJW1idS0QUJDdToL1+wNDSnWAusgZLFcpJxTTC8EEUkAXwQ+oKoHRWQPJrT7ECZ0vHcpznupc3i4zNPHx3EQDo2WiSLlZLHK8dEqDx8axnMcPpM4QDbpMllpcGSsQhQZE4FK0ODEWIXxckCzL3ygnJFh04ph27Nxai/sKXzHzC4nqkY0jcuR4gOCUki7iAiRQtCIUJTJqvnNKUq5HnDF2hztWZ+hyRq+59BXSOE5DvuHSuw9USThCJ6cnjbkiTBaqlOph+SS3pz1PZuzcHMpj9decbrBvMViubhYEjEFfg0Tyv2giHwQ+GvgDhFZB7yWU1nBlpjDw2X+59d/yuHRMrVGRG8+SVc2yRNHxxkoVmlESltSOFGs0ggVgelQZBjBeCWitXd9XjgpX6g3dDozOeVAIZfg5su7eeC5QUZKwSl/3dh0vpBKkE15nCxWwXWINCJyoDefwnOFvkKaq3rb6MmnyKcS3HJlN7mkx317ByjXGxwbqzJZa9DTnuaOmzZPh3gfeGGQlOdSDSJeu2PNnOXPrEGCxXJpsSRiqqqfxmyFmUZEvgLcDvy+qo4vxXkvZQ6MlChWGkYkw4ijo1WOjFQoBQ1UQ2ohNEp1NHbcsbI5P6Yrt4hxGFIgnfJIey5rc0l8zzFryfF7tUaE60A9DOnPZiiWAkaD+rSxZhiZUm9X9bbRU0hNGyDkkh4InChWKddD2tM+2ZTP0ERteo1zKsQ7VbJuyoBhrrqxdq+nxXLpsFQz0zNQ1VHgC8t1vouVZmOFqbDds8eL3L3nCE8dG6NUM6HEKN7WslrchpaCKQejzkyCk5O16X2zxUrDVHVR5R23XMYffvNZKvHWn1AhCmG80qA945NJuZQDh4ZCISFsX59nY0eaN1+7gR+8OMyTR8fxXeGRAyMkfIfObII1uSSj5YAoiuhuO2W0MJu3ra31abG0BssmppZTRguTtQbPHBtne1+BwWKVf/rJMUYrdYI41ug7cxsJWOZHYmpXtBhnovaUTxAp2YRLJYh4SW8bbSmPQsanO5dkaLJOKYhMxXtPKKR91ubTdOWSHBouc3CkjKpZZ+3Jp2hEJtQuopTrxvWoL28Skd796ispVgPyKZ8d6wqz+t/OVq3lfCqxWCyWiwsrpkvITHP5H+4b4tBIGVfg8HAVzxUePzjG8GT9NCOEaFXnMS8OUXiqwlktatCWTOLHpgwiRgRdxzFexCJkko6pgypmr2nSdbh9Ww9PHhvn4EgZ33XIJMxaZ7Vh2iV8h6u72k22bRBOzzibBXQmM0O3ttanxdIaWDFdALOtdR0eLvPw/mGOjFW477kBgkCpNhq0p32qYcTAeJXRUkCgsOfw6KzHDa2YnpXZsnNn0uBUo2I5IumGlGoNHFdwRMj6PgeHJ3lhYJKxyRqe59KWdunMpEj7ws2Xd7O1L8/Wvjzb+/J8/4VBkr6LAG+8Zj35tM9TR8cXnG1rE40sltbAiukFMttaV7ES8JGvPs2TR8cZK9epN4z13FQW6XxEwDI3vkDSE7IJj9FqQBCeaW3oAL5rzBcqgeLGs9OE5+A5vmmvwjPHx3EdhzCKyKZ81ren6Suk6GpLcO3GzmlzhC3dWV5xRTc71hXOELzFEkGbaGSxXPrMap5gOTfNm+9DVUbKdZORWzUb6yM15uhTQipYIV0oDTUVXLrb06DMavye8gTPdZB4f6fjCAnPoS3pGkeoyIRoXcchmzAFvkNVMgmXrX15NsVORPMJuRYyPlu6s6dZBI6Xg8UfuMViueixM9MLZLa1Lk9MPcnBiSr1GcppI7cLR4GxSsTaICSX8pmsmaxcRyCb9FjTlmDXhnZSCZf+jiw/PjzG4dESGd8lnXS5tj/D3hOT9HelGa8ERAr1UOnvzPC6q/vYsb4AnFlV5VwZtzYj12KxWDE9T5q3tmzuyrJvcJJQI54+No4rgu8ICU9wVXEwxaKnyndZ5kaafkIg4Qj1SEl7EIRmVpp0ARF68ymu6+9ishawb3CSaza2oyibOrLs3Ng+nQA0Xg44OFzi2HiFQ8NlLluTY1Nnlm3r8mzuzDJaMftHZxrHzxTCmRaAMzNuz/W+xWJpfayYzpMpq7+njo4zMlnj8cOj1IKIk5M1NFRcRyjXoxlWfVZC50vsiUDKh1IAjamUZj0VKq+F4IvSmU0wUQ8IwsgIYdrnwX1DDE0GPHuyyHtv3zq9Drkz005/V5aR0rHpZKGd69spZHzyZWOkcC7OlXFrM3ItFosV03lweLjMH37rWYYmAp4bKNIIlbFyMGMN1Arn+TAzGSufdOgtpLn1qjUkPZfnByZZ357ivr2DHB2rEKniiHBVT45N3Tku686yf3iSG/u7ODpe4YXBEi/paePAcIkDIyU2dmWmjz1bxuz5hGbPlXFrM3ItFosV0xlMhQYnqw1yaY+OdILv7j3J8wOTVOshxXL9DGciy8JpS3r05FPcelUPr7iiG4AfPD/EE0fGGSrVqcXrpNdv6aIt5VGsBaxtS7FjfYGNnRkefGGQA8MlPAc2d2bPOP7MjNnzDc2eK+PWZuRaLKsbK6Yx4+WAp4+N8+1nTvLUsTEOjpTJeC5BqAxPVqlYS6LzprlWaDOeQFvGpVINCRUSDqiY/Z/PHC+yY51JBHrmRJGuXJKXAG0pnzfsXMert/UApycJFTI+771967RBRvOsdC5saNZisSwmVkwxQvrFPYf5wb4h9p2c4Nh4lUZkA7cXSlvSJVIl6TkkXIdQIzxxyCY9HEdoS/vctKWL7/705LQtX6iQTXhEkU6vYyZ9h9de3ceLQyX+5da17NzYPn2OmbPAjV2ZeYlo8+dtaNZisSwWVkyBg8Ml9g1MEDYiJuvhdGkzy/njAEnPIVLIJlyCSEl7Pt1tSToyPr7r4AiU6wHdbUmSnsvRsQopz+z3rDai6VmiK0KxFtCTT9LfdWbodqHY0KzFYlksVq2YTmXnHh0p89UnjvHsiSKV1VY1ewE4DmzpzDBcrjFRDYkiyCdd1rQlaUv7ZBIuuaRPEEZUggaZhEdvIcW1GzvZ0JHm/ucHySY9gihibT7Jjr48qYTLG69Zv+gOQxaLxbLUrEoxnSrEvffkBAeHStNbLyzzI+0JHRmfq/raODHuc3S0Cqr0tKe4Yk0bCc8hUmVwokY5aLCpPYOKcHysyqFcmaPjFdoz/nQI9+WbO+nrSNt6nhaL5ZJl1Ynp4eEy9zxxjIFihclqYIX0HDiYteOpX5PrQITiuA5bewsUyyHduYjJWkg1CGnPeKR8j0MjZdbkkgyXYKRcQzHVWtqSHuV6g2oQTodwd6yfu8qKxWKxXAqsGjE9PFzm608d50uPHaFYqXO8eO7N+qudjA/t6ST1MCRSY6TgAJmkx6bODKpKPu2R9IWgoVzRk+Pfv2ILAHc/foSU7/ISaWN7X57JaoMvP36Ux4+M4TnwzldeTjrp2RCuxWJpCVaFmB4eLvORrz7Nj/YPU6zaPS5z4QL5jEcjjLhiTQ7PNUYK3dkEjiP05JO8OFRm14YCE7UG2/ryvOllGxgt10Ggv/OULd/bd285owj2NZvaySY8SvUG6aTHlu7FTyqyWCyWlWBViOmBkRLHx8pWSGfgYApoh1FcusyDfMqn1ohYW0hTDyPWd6S5vr+T48UKu9a3k0uN01AllzxlyzfblpTZimDnkt6049BEJWC8HNhZqcViaQlWhZjWg4inj0+udDeWjZnrnAKkfQdPBMeF9kwS3xFSCQdXHFM2TJQoMiXLLl+TZUt3hlzSJ+W708YG/V1Z43N7ARm2U/s6D46UeHj/CD85OsZTx8ZthRWLxdIStLyYjpcD7n7sSMsbMHhAe9anvzPN1RsK7Dkwxki5TrESICJs7MiwNp9kXSHNjg0FStUGV68vEKoyUKxx794BevMpXhic5A0v7ePazZ3T+z1niueFil8h49NW9kn6jq2wYrFYWoqWFtPxcsDPf+p7HBytrnRXlpxIoNqIKAcRr7qyh2NjNU5OVKkEEY6YUHc26TJWqVOqNsilvOlSZYeHyzzw/AAnilXa0x43buk6wyh+sbA2fhaLpRVpaTF9/5cebwkhbUs4bOhIM1yuU603KNcUcSDlC5mEyYitR1BI+2R8j4Tv8Ds/t4PP/vAg33thgDBSRkt10gmXq/rybFuXn17vBGPFd77etheKtfGzWCytyLKKqYj8FbAd+Jqqfmypz/fjw6NLfYolJ59y2LG+nZu2dLH3xASTtYCfHi9SDUI6Mkm2dGfZ0JHmyWPjBGFEPu9NC+Idu/vZNzzBsbEquZSP5zgInCakU5yvt+1CsGYMFoul1Vg2MRWRNwGuqu4WkbtE5EpVfX4pz3miGCzl4RedjAs9hTRBpJRqAR3ZBLsv7+atN/aTTnp4IoyW6+wbmOTBfUO0pTw2dGR4zfZeDo+UKdYCdvQVpkVxY1eG9952FV/cc4S075BKeKfZ9VksFotlcVjOmemtwBfix98EbgFOE1MReSfwToBNmzYtY9eWDw8jcqrKtf3tbO015ca6cglu3NxFPu2fVk+1ee8m8Wd3bmzn1dt6TguVzjWr3L6+wG92ZGxY1WKxWJaQ5RTTLHA0fjwCXDuzgareCdwJcP3111+yCbgC+C70FVIIwnCpjiPQnUuysTMTh1zhPbdtnVUEd2bazzzoDM4nVGrDqhaLxbK0LKeYTgLp+HEOsx1ySTnwidez+f1fW5JjO0B3zmdrbxvDkzVUHK7bVCCb9KkHEb0Fs0WlPe2z5+AIpXrEq67spi3lL0uij8VisViWj+UU0z2Y0O5DwC5g73Kc9MAnXr8cpzkr29cXTntuRdRisVhai+UU0y8DD4jIOuC1wE3LeG6LxWKxWJYMUV2+pUkR6QBuB76nqifO0XYQOLhIp+4GhhbpWBczq2Gcq2GMYMfZSqyGMULrjrNfVdecq9GyiulKISKPqur1K92PpWY1jHM1jBHsOFuJ1TBGWD3jnIslTwKyWCwWi6XVsWJqsVgsFssCWS1ieudKd2CZWA3jXA1jBDvOVmI1jBFWzzhnZVWsmVosFovFspSslpmpxWKxWCxLhhVTi8VisZw3ItIpIreLSPdK9+VioOXFVET+SkR+KCIfWum+LAUi4onIIRG5L/556Ur3abERkR4ReSB+7IvIPSLyoIj86kr3bTGZMc71InKk6bqec5/bxY6IFETk6yLyTRG5W0QSrXh/zjHOlrpHY8+ArwI3AveKyJpWvJbnQ0uLaXPZN+AyEblypfu0BOwE/k5Vb41/nlzpDi0m8U37N5hCCQD/EdijqjcDbxGRthXr3CIyyzhfDny86boOrlzvFo23Ap9U1dcAJ4BfojXvz5njfD+td4/uBH5LVT8OfAN4Na15LedNS4sps5d9azVuAn5ORB6Ovxkua8H3ZSAE/g1QjJ/fyqlr+j2gVTaJzxznTcA7ROQxEfm9levW4qGqf6aq34qfrgHeRgven7OMs0GL3aOqer+qPiQir8LMTn+WFryW50Ori+nMsm89K9iXpeIR4DZVvRHwgdetcH8WFVUtqup400steU1nGefXMV8cbgB2i8jOFenYEiAiu4EO4DAteC2naBrnt2jBe1REBPMFcBRQWvhazodWF9NlL/u2Ajyhqsfjx48CrR5eWQ3XFOAHqjqhqiHwY1rkuopIJ/Ap4Fdp4Ws5Y5wteY+q4TeAJ4BX0KLXcr60+oCnyr6BKft2YOW6smR8VkR2iYgL/ALwk5Xu0BKzGq4pwDdEpE9EMsBrgKdWukMLRUQSwBeBD6jqQVr0Ws4yzpa7R0Xkv4rIr8RP24FP0ILX8ny45GP352A1lH37CPB/AQG+oqrfXuH+LDV/A/w/EXklsB340Qr3Z6n4XeBeoA78uaouS/3fJebXgGuBD4rIB4G/Bu5owftz5jjvBT5La92jdwJfEJF3YL7ofRn4Xgtey3nT8g5I51P2zXJpEN+wtwDfmLHOaLnEsPdn67Dar2XLi6nFYrFYLEtNq6+ZWiwWi8Wy5FgxtVgsFotlgVgxtVgsFotlgVgxtViWgHh7xFzv/dv5mIOLyObF7NM5ztUlIr8cP/bjDfkX3G7GZ5LN7UTEjbeJWCwtgxVTi2WREJEPi0i3iLwe+IOzNL0F+JWzvI+IpIGviUj+LG0+LiJb4scJEfnSefT1P4vIu5pemgQ+ETstfQb4joh8O/4ZFZHCfNuJyB0i8pyIPCsiH8BsX3pERB4VkUeBh2kRFyCLZYpW32dqsSwL8Szylar6YRH5Z+CjItKpqiNxlZA/Bkpx8yywWUReHT8vAO/BOB09BFQwG+HbgK/EkzoXuF9VmytyXAf89/jx7UBZRLbGz18EdmP2ID8PbFXV3qbPNoAg7rsLdAK/BZxQ1V+eMbb7gPp82wE14JMYp59/xAjwm5uafkdV75n1F2mxXKJYMbVYFoc/xlQHQVVDEfko8EfA2+MqIf9CRN4M7FbV3wYQkT8HHlTVz4qIqNmndoOIbAL+D8Y8/HZV/UzziWJRG8I46TwmIvcAL8OY5L8fYzz+Joxg3q2q7xaRR8SUrLs6fn0XEIlIDbgH+AdVvVlEvjEjBPuv4n8VYxM3n3ZR/Pg6Vd0rIr8OfBp4GuPZ+vbz//VaLBc3NsxrsSwQEflt4AVVfSReD82q6t1AQ0Q+JIb3Ae/DzBSn+EfgbSLyp7GQEq+l3gW8C0hi7Oem1hk9MGINPKaqtwK/CVwFHAfepar/DuO4U8NUonljPGNcq6p3AX+kqu8D7gY+r6qfi9sGcZ88Vb1NVW+LHzea+jvfdmD+b2nEG/m/jKmeciuwDfjb8/sNWywXP3ZmarEsgDhU+wbgdhHZiAnXfj5++z8Afw+8CiNCrwO+LyIvx8wOP4nx3U03HfLNwGbgcxhBujwWQxdjSXdn3G5X/Ho7Rqw+G3/m5zEiXMKI6dTM9KF4HfYeEfmZswxpq4hM2d3tWkC7CLNu/CHMrHWqHqsAORFJqupXznJ8i+WSwoqpxbIw7gV+pKp1EflvwPviMO8bMT6sb4rb3Q8gIv8L+H3MeuiHVPVY88FU9S+Av4jbbgD+t6q+ZZbzPq6qt4nIrcCtqrpPRMI4MSiNEdPmyJOoakVE/pSz14D9aTzbnFoDvdB2SYzQXwk8iVm3/TvgY/G/c2Y7WyyXIlZMLZYFEIdnS7GIrVHVe+O3rga2YGafzXwX+D1gH/DPzW/Ea5rvBsrxSwngMhH5fvw8A9ylqn8yR3d+AziJEdMy5v5+o4hcDayP+/uX8bnmKgN2TfOMU+YuZH2udjnMLPsP4n69DrOOq8Aw8I05jmuxXJJYMbVYFoiIXIZZg3xARO7EJNkIRnC+CFQxyUS3YkK4P4tJGHpIRB7CbB35p3hN866m424CPjnHzPRlM8K8TM1yRaRLVTVOEJoK8/6Pmd1ueuxMPVfVM/a/xntmI8z/F/NtN6yq/1pE+uL214nI/Zis4/dgRNViaRmsmFosC2ccUxruMeA5YL+qBiLyJsx66SOYdcW/VNWpknGPi8jnMGumtwATsxzXZ5Z7NBbJPar6GhHZDbwyfv3tmDXKh+KmjwJ7AVT1d5s+/4vAfwLeEb+Uw4Rlz0BE/haTYFQXkfZ5tvOb38JkGQN8FHg58OuYLxQWS8tgq8ZYLC1CnDnrqergOdqlgUhVa/M4Zpuqzib0F9TOYmlVrJhaLBaLxbJA7D5Ti8VisVgWiBVTi8VisVgWiBVTi8VisVgWiBVTi8VisVgWyP8HWzqflcK4W1MAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x2055839ef60>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=观察者偏差\\n\",\n    \"import matplotlib.gridspec as gridspec\\n\",\n    \"pl.figure(figsize=(7.5, 3))\\n\",\n    \"\\n\",\n    \"G = gridspec.GridSpec(10, 1)\\n\",\n    \"ax1 = pl.subplot(G[:2,  0])\\n\",\n    \"ax2 = pl.subplot(G[3:, 0])\\n\",\n    \"\\n\",\n    \"ax1.vlines(bus_time[:10], 0, 1, lw=2, color=\\\"blue\\\", label=u\\\"公交车\\\")\\n\",\n    \"ptime = np.random.uniform(bus_time[0], bus_time[9], 100)\\n\",\n    \"ax1.vlines(ptime, 0, 1, lw=1, color=\\\"red\\\", alpha=0.2, label=u\\\"乘客\\\")\\n\",\n    \"ax1.legend()\\n\",\n    \"count, bins = np.histogram(passenger_time, bins=bus_time)\\n\",\n    \"ax2.plot(np.diff(bins), count, \\\".\\\", alpha=0.3, rasterized=True)\\n\",\n    \"ax2.set_xlabel(u\\\"公交车的时间间隔\\\")\\n\",\n    \"ax2.set_ylabel(u\\\"等待人数\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"200.0\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from scipy import integrate\\n\",\n    \"t = 10.0 / 3  # 两辆公交车之间的平均时间间隔\\n\",\n    \"bus_interval = stats.gamma(1, scale=t)\\n\",\n    \"n, _ = integrate.quad(lambda x: 0.5 * x * x * bus_interval.pdf(x), 0, 1000)\\n\",\n    \"d, _ = integrate.quad(lambda x: x * bus_interval.pdf(x), 0, 1000)\\n\",\n    \"n / d * 60\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 学生t-分布与t检验\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"max error: 0.006832108369761447\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x20556d89e48>\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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getCoqPbX5MJ+WE0zKSYc8a98sS4hzkjJhPv2H17Fl4zqmTB3ZO3gjSX5+PjU1NTQ2NlodJey53W7y8y9+pVYt9irq7DvZga+2gvg4Q+GkGRCfZnUkFlxRzobD6+iu2kZHr5eUeJfVkcKC0+lk4sSJVseICTqMo6LOkx8cZ5btKBMzE3EWzLc6DgDjJ88iJTWdZH87r2/cbnUcFYO02Kuo0uv18+r2oxRLLZNykyFvjtWRBthsjJs28N7B7g/e0TFqNeq02Kuo8tKukxR6D5OT5CS9YDq4U6yOdNrMsitxu+xkdFay+ViL1XFUjNFir6LKn7YcZ47t6MAes2OHXLPPMs6cKYzPzSKHNl58T4dy1OjSYq+ixv66Dg4er6PEUU9hZiLkzbY60kfZ7BTNWggCjQc309Cpe9Sq0aPFXkWN1VtPMEOqKMpKwDlmOrjC707I9EmXkZ8Wz1RzjNUfnLA6joohWuxV1HjnQCOzbUeZkJkQdkM4p2WVMCkvizxp4ZVNu/EH9I1aNTq02KuocKqjj6amBoodp8hISYAxM62ONDS7g7yS+SS5HWR1VfJWZYPViVSM0GKvosLGI83MsFWRkxyHPWcaOOOH72QR29i5FOckM1OqeGJTtdVxVIzQYq+iwsYjzcyQKnJT4sJ3COdD2dOYmJtGob2JHQePUd3cbXUiFQO02KuosP3ISSZIPbkpCZATXuujf4zDhXvcTMZnJDDTVsUfNx+3OpGKASEr9iKSISIrRCQrVOdUKhi1bb242w4R5xDSC0rAlWB1pOHlzaEoO4kZUsU7B3URMDXygir2IvKYiGwUkQfP8/104C/AAuBtEckOpp9SobDxSDNTbcfJSY7DljvD6jjByZlBZkoCRbZT1J46RZfHZ3UiFeWGLfYicitgN8YsAopEpHiIZrOBbxlj/hl4HZgXZD+lLtmmw42UyAlykt2QG6azcM7ldOPInUZ6ootpVFNxos3qRCrKBXNlXw6sHjxeAyw5t4Ex5h1jzCYRWcrA1f3GYPopdamMMVQf2U8CHrJy8iApx+pIwRszm6wkFzNsVew43mp1GhXlgin2iUDt4HELkDtUIxER4E6gFegPpp+I3C8iW0Vkq25eoC7GiZZe0rsO4nLYyCwqBRGrIwUvdwaZSXFMlHp2V+t8ezWygin2XcCHk5aTztfHDPhbYBdwUzD9jDGPGmPKjDFl2dnZF5pdKTYebRoYwkmJwxauN1KdT1wS6WOLceCn7cQ+XfZYjahgiv02zgzBzAGqzm0gIt8VkS8OfpoGtAXTT6lLVXGomjxpISs1BTInWx3ngqWOn43baWdM31Gqm3usjqOiWDDbEj4PrBeRscB1wOdE5CFjzNkzbB4FVovIV4E9DIzRJ5/Tz9pdn1XUMcbQcnQHAJkTZoE98nbZlNzpZCa5mNp/gh3HW5iQFX6Lt6noMOxPhzGmQ0TKgRXAz4wx9UDFOW1aB79/tnP7tYcksVKDqpp7yO49QpzLTs7kUqvjXJzUAlLSMkhtreXw0cMwr8DqRCpKBTXP3hjTaoxZPVjog3ax/ZQKxqZD9UyWWnKT45Cc6VbHuTgiJBcOrLvfWV0xTGOlLp4ul6Ai1rEDO3HiJzF3AsSnWR3noo2bMg8E3K0H6fX6rY6jopQWexWRjDF0H98FQO7kMF/4bBjxedNJjY+jkHr2VJ+yOo6KUlrsVUQ60tDJWO9R3C47eVPmWx3n0rgSsGVOxIah+oDuTatGhhZ7FZF27qsknS5SU9ORtPFWx7lkyYVzAB23VyNHi72KSHWHBq6AE/JnR9Zds+dRMK0MAEdzJSYQsDiNikZa7FXECQQM/ro9ABQOFslIV1hYRJ8jGbu3i1M1h62Oo6KQFnsVcQ7VniK7/yRul5OxxXOtjhMSYrPRnzmw6Ur1/q0Wp1HRSIu9ijgHd29BMNiyJiNhvNfshTo93/74bouTqGikxV5FnJajOwFImxgdV/UfKiyZgw87/uZj4OmyOo6KMlrsVUTx+/3YmioBKJ51ucVpQmv2hFyqGENrtxdv/V6r46goo8VeRZR9+3Zj9/Xic2cyLj/yp1yeLcXtpCt1CgFjqDu0w+o4KsposVcR5dDuzQAkFETY2vVBSi44a9xep2CqENJiryJK++Cbl0XTL7M4ycgoLiqiiRTa2tugrcrqOCqKaLFXEaP2VANxXbWI3cHM2RG+RMJ5lBamczBQQFOXFxr2Wx1HRREt9ipiVGzfBIAjuxhXnNviNCOjOCeJGucEejw+uo7r0gkqdLTYq4hRP7hEQqSvcvlJbDYhNX8aXhy01lVBn+75o0JDi72KCN19XqTxAAjMKI2uKZfnmj0+i6Mmj6ZuDzRUWh1HRYmgir2IPCYiG0XkwfN8P1VEXhWRNSLynIi4RMQhIsdFZN3gx6zQRlexZPuuCuJMH67kbDJz8q2OM6LmFaZz0BTQ1OmBhn1Wx1FRYthiLyK3AnZjzCKgSESKh2h2F/CwMWYlUA9cC8wG/mSMKR/80HvA1UU7smdgvZiU8dGxyuUnmVuQxoFAPi3dXnwN+3UKpgqJYK7sy4HVg8drgCXnNjDGPGKMeWPw02ygAVgI3CgiWwb/MvjY5uYicr+IbBWRrY2NjRf1BFT0CwQM3TUD1wqTZ0TnlMuzpSe6GJOXT30ghbrGNmg9ZnUkFQWCKfaJQO3gcQuQe76GIrIISDfGbAI+AJYbYxYATuD6c9sbYx41xpQZY8qys7MvOLyKDfuqaknz1hPncjGxZI7VcUbFraXjOBgo4GhTl07BVCERTLHvAj5cWjDpfH1EJANYBXxl8Eu7jDF1g8dbgaGGf5Qa1q6dA6tcusdMQZzROeXyXJ8pHcsRyedkWx89NToCqi5dMMV+G2eGbuYAVec2EBEX8BTwfWNM9eCX/yAic0TEDtwM6KRhdVEajwysEzOuJHqnXJ4rJ9lN/qRZeI2NE1WHdAqmumTBFPvngXtE5GHgDmCviDx0Tpv7gHnADwZn3twJ/Bj4A7AT2GiMWRvC3CpG1Lf1Et9+BLtNmDprgdVxRtXNZRM4asZytKlbp2CqS/axN03PZYzpEJFyYAXwM2NMPedcpRtjfgX8aojus0MRUsWuzTt2kkQvyem5uNPHWh1nVC2flsuzzvGUdJ+g/vB2xhRG9/0FamQFNc/eGNNqjFk9WOiVGjXVlQNTLtMnRP+Uy3O5nXYmTh/4a+b4gZ06BVNdEr2DVoWtvn4/3rqBTTymzI7Nq9rrL59BI6nUNDbjaz5qdRwVwbTYq7C1+cAJ8gINpCW6ySycYXUcS8wrTKctcTJ9Xj+VuzZbHUdFMC32KmztrRiYcpmQVwIxMuXyXCLC5BllAFTt32pxGhXJtNirsGSMoeXYwMbi46dG59r1wVq6aBH92OlqqKajvcXqOCpCabFXYWn/yQ5y+qpwu+yMnxbbxb4gO5VAxmQCAcPmTe9ZHUdFKC32KixtqdhFEr2kZ+RgS8mzOo7lJg5uw3hk7wcWJ1GRSou9Cks1BwbGpzOL5sTclMuhlF2+GLtNcLYcorqp0+o4KgJpsVdhp7nLg6t5PzYRimfG5pTLcyWljyE9exzxeHjz/S1Wx1ERSIu9Cjsb9h4jn0ayUhOIHzvN6jhho3DwjepDe7YQCBiL06hIo8VehZ1DewauXJPGTQdHnMVpwkfxrAUkuOxkdh/hgyqdlaMujBZ7FVZ8/gDdJ3YBMGF69G9UciHs2cXkZ6UwVpp5f59uaKIujBZ7FVa2HWukwHec5HgnY4pje8rlx9idpORPB+DU4LLPSgVLi70KKxUV24ijn5TsAkjIsDpO2MkvmQ8CzsZ99Hh9VsdREUSLvQorpw4PXLHm6lX9kBIK5pCR6KKIWrYfa7A6joogWuxV2DjR3E165wEcdhtFM3TK5ZASMnBnFhJHP4f27bQ6jYogWuxV2Ni4ez8ZdJKZnoYra6LVccJW2sSB7Rnbj+m4vQpeUMVeRB4TkY0i8uB5vp8qIq+KyBoReW5wT9ph+yl1tup9A1MuU8bPAZteh5xP0czLQSC+tZJej47bq+AM+xMlIrcCdmPMIqBIRIqHaHYX8LAxZiVQD1wbZD+lAOjx+vAPblQyWe+a/UQpuRNxJaaTbLrYW7nP6jgqQgRz+VQOrB48XgMsObeBMeYRY8wbg59mAw3B9FPqQ5sqa8g3daQnuUkfP8vqOOFNBPuYmQCcqNSF0VRwgin2iUDt4HELkHu+hiKyCEg3xmwKpp+I3C8iW0Vka2Nj4wUFV9Fl/+4t2DAk5E0BV4LVccJezpSB2Up9NbssTqIiRTDFvguIHzxOOl8fEckAVgFfCbafMeZRY0yZMaYsOzv7QnKrKGKMoW1wo5KCqXrXbDBmzCrDJ3ak/QR9Hc1Wx1ERIJhiv40zQzBzgKpzGwy+IfsU8H1jTHWw/ZQCqKxrJ7fvKG6XnQnTtNgHIy05kY6kSQQChiN7dRVMNbxgiv3zwD0i8jBwB7BXRB46p819wDzgByKyTkTuHKLfyyHMraLItp07SMBDSmYetpTzjhKqc8QXzAWg8ZDuTauGN2yxN8Z0MPBm6ybgamNMhTHmwXPa/MoYk26MKR/8+PMQ/dpDH19Fg5MfblQyaZ7FSSJL4dQyDIK3/gD4PFbHUWEuqMnMxphWY8xqY0z9hZz8Yvup2NHa7T29UcmU2Trl8kLMm1LICZNNS1cP3vr9VsdRYU7vXFGW2rj7IDm0kpaaTGLuFKvjRJTMpDg6U0sIBAw1OgVTDUOLvbLUh28uJubPBLvD4jSRJ3ViKQDtVTshELA4jQpnWuyVZfr9AXpODEy5nDxjgcVpItOMKSW0kkRrawu0VQ/fQcUsLfbKMlsP1TDOV0NyvIu8kjKr40SkBUWZ7A+Mp7HTg69ut9VxVBjTYq8ss2/7BmyYgU3FXYlWx4lI2clxdKeV4A8YGg5tszqOCmNa7JUljDF0HB2YclkwY5HFaSLbuMkz8eCk9dRx6Na7adXQtNgrS1SeaCCrrwqX00HxLC32l2LBpFwOmAIaOj1wSody1NC02CtL7Nr6HnYCuMdMwRafYnWciLZwYgaVgUIaO/vwn9SF0dTQtNgrS7QcGZgXnjdVr+ovVU6Km76MqfT5bbTWVEKf3qyuPk6LvRp1dc0tJLYfxmazMWP+YqvjRIW5RXkcNPk0dPRCXYXVcVQY0mKvRt3ODzbgwI89axLupHSr40SFhUWZ7A5MpL69D2q3Wx1HhSEt9mrUNRzYDEDWFF0LJ1SuLM7ikIynpsNH76nD0NtqdSQVZrTYq1HV2dWJvakSI8KcsiutjhM1MpPiWDI1n8pAAceau+HkDqsjqTCjxV6NqoqtG7EZP/7UCWRk5VgdJ6rcPj+fikARRxu7MbVa7NVHabFXo+rk/vcBSJ+sO1KF2tVTc2iKL6KxF1pqD+sNVuojtNirUdPv6cV/ah8As+brEE6oOe02bpw3gUpTyNEmHcpRH6XFXo2afRWbML5+uhLymVCQb3WcqHT7/Hx2BYqobu6m/4RuV6jOCKrYi8hjIrJRRB78hDa5IrL+rM/HiUjN4J6060QkOxSBVeQ6vmdgCCe5SFe4HCnT8lJwjJlOh8/ByeNHoKvB6kgqTAxb7EXkVsBujFkEFIlI8RBt0oHfAWcvXXg58M9n7UvbGKrQKvIYnwfvyT0ATC3VIZyRdFvZePaZ8QNDOTrnXg0K5sq+HFg9eLwGWDJEGz9wJ9Bx1tcWAl8Vke0i8i+XElJFvqrKbXj6+mhxjmF28USr40S1m+aOYy+TqGvvpevYB2CM1ZFUGAim2CcCtYPHLUDuuQ2MMR3GmHMX5HiVgV8UlwGLRGT2uf1E5H4R2SoiWxsb9cI/mh2r2ABAXOE87DaxOE10y0h0MaGklB4Tx/HjR6GzzupIKgwEU+y7gPjB46Qg+wC8b4zpNMb4gR3Ax4Z/jDGPGmPKjDFl2dk6pB+1fF56Tgys1zJ5jq6FMxpuKxvPnsDEwTn3OpSjgivc2zgzdDMHqAry3K+LSJ6IJAArgT0XHk9Fg/oDm+no6qbOlsvCmSVWx4kJV5VkcyK+hI7efk4d2KRDOSqoYv88cI+IPAzcAewVkYeC6Pcj4G1gE/B/jTEHLj6mimT7t6wBwDVxEfGbfEPdAAAWYUlEQVQuu8VpYoPTbmNeaRmdxHPiRDW011gdSVls2GJvjOlgYOx9E3C1MabCGDPkFExjTPlZx28bY6YaY2YbY34RorwqwvR3NNB6Yj/92Fl45Uqr48SU28oK2RuYSFVzD94Tuj9trAtq/N0Y02qMWW2MqR/pQCq67N60hj6vn1NJUymbNMbqODFl6pgU+nJm0+8LUL17gw7lxDi9g1aNnECAuj3vAjBh7jJEdBbOaFt82QLaSaTmZC00H7Y6jrKQFns1YhqOVdDe2kirpLJssW4/aIWb5o5jJ1Opa++led86q+MoC2mxVyNm18bXwYAUXk5mstvqODEpPdFF9oxyjBEOV7wHnk6rIymLaLFXI8Lf10nH0e0YhHlX6BuzVrp32RwOmAKONXTSdmCD1XGURbTYqxGxe9Ob9Hn7aYyfyIJpRVbHiWmTc5JxFS0mYAx7N72mb9TGKC32KvSMobriLQDyZpVj0+URLPeZlStoJ5Ha2uN01OyzOo6ygBZ7FXJNtYfpaaqhV9xcfdXVVsdRwKyCdHrzFuDzG7a887LVcZQFtNirkNv+3msYY/CPnU9OapLVcdSgpdfciEFoOfwB3R2tVsdRo0yLvQqpQL+XtsObAJixUN+YDSfzp06kN70En8/Hu2/p1X2s0WKvQmrX1vX4+nrodOexcM4sq+Oos4gIsxZfB0Ddrrfw9PssTqRGkxZ7FVKHt68FIGPaVbpufRhasGAx9sR03N5W3nj3PavjqFGkxV6FTEvDSXz1B/CJnSVXLbc6jhqC2OxMKF0GQOWm1/D5AxYnUqNFi70KmS3rXiRgDL6c2eRlZVodR51H2dLrSXS7GNN7kNe3H7I6jholWuxVSHh7u2ivfAeA6UtusjiN+iT2hHTyS+Zjw/D+268QCOhNVrFAi70KiS1vv4Tf20dH0kQunzvH6jhqGLMXX0+Cy05exw7e2n/K6jhqFGixV5fM+LzUV7wGwITLb9KljCOAc8x0JhTkk0Enb7z7jtVx1CgIqtiLyGMislFEhtyharBNroisP+tzp4i8JCIbROQroQirwtOuTW/g7e6g3ZXL1UuWDN9BWc9mo/iyT2G3Cck173C4ocvqRGqEDVvsReRWwG6MWQQUiUjxEG3Sgd8BiWd9+RvANmPMYuB2EUkOUWYVTgIBqra8BEDm3OuJczosDqSClVhSTn5OBkVSxyvrdBpmtAvmyr4cWD14vAYY6tLND9wJdJyn37tA2UUlVGGtas8GultO0WFLZeUyvWM2ojjjKZw/cJNVz95X6PLoTVbRLJhinwjUDh63ALnnNjDGdBhj2i+0n4jcLyJbRWRrY2Nj8KlVeDCG/e89D4CrZDlpibpBSaQpmH8daSnJFPpP8MaGzVbHUSMomGLfBcQPHicF2SeofsaYR40xZcaYsuzs7CBPq8JFU9Vu2uuO0U08y1bcaHUcdTFcCWTPHrgBrnbL8xhd6z5qBVO4t3Fm6GYOUBXkuS+2n4oQFeueIWAMnoIljM9JszqOukhzr7oZu8tNRtdhtu/aZXUcNUKCKfbPA/eIyMPAHcBeEXkoiH6/A34kIj8HpgP6N2IU6Wk4Rmv1Hjw4uWL5zVbHUZfAGZ9C4pSrADi4/hmL06iRMmyxN8Z0MPBm6ybgamNMhTFmyCmYxpjys46rgRXABmC5McYfisAqPOx4+xm8vgBNmfOZN2ms1XHUJbp82W34xQGn9lB/4rDVcdQICGr83RjTaoxZbYypv5CTG2NODvY7981bFcH8nY00HdqCHxulV+lVfTTIzs7Gm78QYwy73n7K6jhqBOgdtOqC7Xn3Wbr7+jmRMJ2r506xOo4KkdLy2/Bhp/PYNjytNVbHUSGmxV5dkL7m4xzf+RYBhMlXfEbXrI8ipVPGczJ1Lp5+P3vfedrqOCrEtNir4BnD+ucepbPXy9GkUm5aXGp1IhVCIgO/wP3YaKzcBJ26QFo00WKvgrZ76zs0Vu+lV9zcfOdXcTvtVkdSIXb9ZdPY65hGc5eHms3PWh1HhZAWexWUrp4edr7+OzCQNOczzJ6YZ3UkNQISXA6y595AP3aO79kATbq5SbTQYq+C8vSff4e9rw1/8lhuvfl2q+OoEfTZK+ewnlKqmrqpffd34Nc1c6KBFns1rHcrKpEjb2KzCUtuvh+nQ1e2jGaFmQnMuPJmGkljx/6D9B98w+pIKgS02KtP1NLtZf0Lv8WJn3HTr2Biie5CFQu+ds1UtqUsp6O3n/3vPgNdulBhpNNir87LGMOq1a8y0XuIjJREFn/6PqsjqVHidtr52m3Xst0Us6+mhaZNT4AukhbRtNir83pxZw1Jh1/EYRfmL/sstsR0qyOpUbRoUibuWbfQZVzs3L4ZU7vd6kjqEmixV0M61dHH8y88TZ60MHPyRLLnXm91JGWBb396Pu+7FtPQ4eHAW78Hb4/VkdRF0mKvPsYYw0+e2shi32bGpsUzbdndYHdaHUtZID3RxU033kK1yWXP0Ro6Kl6wOpK6SFrs1cc8t/0EY44+Raqjn3nzFyJ5c62OpCz0mdJxnBx/E30+w+4NL0NrtdWR1EXQYq8+4lRHH+te+gNFUsfsSQWkLLoXRNe/iWUiwnduK2ezzKG6qZuqt36rc+8jkBZ7dZoxhlVP/oXLfdvIS4tnyrVfA3eK1bFUGCjMTGDWNXfQShIV+/fRvUOXQY40WuzVaS9tqaTw+LO4HMLcZZ9DskusjqTCyJeXlrA18zO09xnef/MFPEc3WB1JXQAt9gqAhvYeDrz6CEn0Ujx9HulzdANx9VFOu41//vINvBd/NY2dHt5//v/iazpmdSwVpKCKvYg8JiIbRWTI7QiHaiMiDhE5LiLrBj9mhSq0Ci1jDE/+8THG+WrIyMhgzk1fB5teB6iPG5sWz3e++kUqHLOoa+liw59/hunTjegiwbA/0SJyK2A3xiwCikSkOMg2s4E/GWPKBz92hzq8Co2169eTWfs2DoeN+Td/E3GnWh1JhbHi3GQ++8WvUyN51NbVs/5P/65v2EaAYC7fyoHVg8drgCVBtlkI3CgiWwav+nX1rDB0qqGB2rceRTCMLfsM2UWzrY6kIkBZUQ5lt3+bThKpObKH9174tdWR1DCCKfaJQO3gcQuQG2SbD4DlxpgFgBP42C2YInK/iGwVka2NjbrQ0mjrbG9h7W9/iMvXjS17Mouvv8vqSCqCXDNnEmOX/w0+7BzfsYZN6162OpL6BMEU+y4gfvA46Tx9hmqzyxhTN/i1rcDHhn+MMY8aY8qMMWXZ2dkXFFxdGm9PB68/9kOk6xSehFxW3PM9xKY7T6kL8+nyK4ifdycYOPLW42zYvNnqSOo8gin22zgzdDMHqAqyzR9EZI6I2IGbgYpLSqpCJtDXxRu//V94W2rpcmay4ss/IjMj0+pYKkLdfsvtOCYtRQI+Dr/0v/ndC68TCOgKmeEmmGL/PHCPiDwM3AHsFZGHhmnzMvBj4A/ATmCjMWZt6GKri+bt4d0nfkJ7fRUd9jSWfPF/kZ831MicUsEREW7/4v8gZ8ZSnOJHPvg1P/rNn2nv7bc6mjqLmCDWqBaRdGAF8K4xpv5i23ySsrIys3Xr1gvtpi5Efx8f/PmfOVS5hzZJofTOf2TRzMlWp1LRwhj2vfE4+97/C30+w/qka/nWvZ+nZEyy1cmimohsM8aUDdcuqMnUxphWY8zqTyriwbRRFvJ52Pf8zzhUuYdWkii88Tta6FVoiTB9xb2U3/AFMhKcLO16jR//8jH+suuk1ckUegdtbOhp4dhL/8quXTtpJ5HEq77JjZfPtDqVikYiZF12G8tu+TITsxK4wbzDH5/8A//6aqWO41tM575HOU/tbna+uIojJxtpNUn0lN7PXy+fb3UsFeXipl3Lojg3mW/9HqnezNvre/l60w38nzvnEe/SWV9W0GIfrQIBjm14ir3vPkNHbz8HGE/m4i/z7U/NRnTJYjUKpKicEoeb1PWP4zi0myOVdfzVf3+G/7h3GdnJcVbHizla7KOQt7uN9596mLojewgY2JV8JV/4/JeZU6h7yKpRVriQMSszWJb4W1y7j5B76nF+sKqa73zlLorH6PLZoymo2TijQWfjhMaRygp2PPefeLvb6CIe39wv8pXPrMDt1D+dlYW83XRu+X9seO8tWro8HHAU86k7v8EV0wqsThbxgp2No8U+SrQ0NbDuxcfpO7YJjKE1voDLb/uflE2daHU0pQYYg+fo+7z/4q+pa26nQ5IYU/5XfPrqK7HbdGjxYmmxjxF9fX2s/cuTtO9+FfF7CYgNmbycm++8j0S3y+p4Sn1MoLOBd//8MCerDgBwMr6EcQtv48bFc0l268b2F0qLfZQL+AOsW/c6Ne+vxuFpA6A/ZyZLPv0VJk3Uq3kV5gJ+3n/tj9RtfxlPnweDsNc+lbS5n+bzV82hICPB6oQRQ4t9lDIBP9s+eJ9d657G3XkcAF/SWGasvJf58y6zOJ1SF8bf08redU9Rv2cdTR29+LCzxUzDNmUF/+O6Uibn6N23w9FiH208XRzatpY9G16ht31gOWgTl8y4hbez9JobsNn1DVgVwboaqd38DMd3v0d1cw+9xsEeipm56FPctXIxLp1gcF5a7KNFazUnd66hcvu71Ld2AdDjSCVvznKWXXsL7vhEiwMqFULttXRWvMC+HRs50jjw792XlMfiq29gUmk5uPTf+7m02EcqY6C1Ck/tLpoOb6O6+ihVzd0YI1TZChhX+iluvXY5KfH65quKYh11VH6whoqNa/H3dYHAlLw0Zs+/Etf4yyCzGJxuq1OGBS32EcTf7+HEwZ2cOvgB3pO76Wxvo723H2MMvcSxkxIKSlfw5ZWX6Z2HKqb09nn444svU7PrbSZTS7LbzpTcZBLiXDizJ5OQP4OUwtlIyliI0TvDtdiHmS6Pj101bdQ099DUdApv01GktYr4ruMk9p1CjP9021aSqDTj6cucTv6kmdy3dDL56To7QcWuihNt/OSpDSQ17WCqnKBAGhEGapfNJgTiUuhJmUTe+BKmT5vJhKJixB4b0zi12FustdvL1qMN7Dt0mOrqY3Q0nSCHNgqlgRS6P9LWIHS5x+DPmUnahFImT57CzPxUEly6moVSH/L6Ajy17QT76zpobmnF0XKI1K4jFPiqSKb3I23jXE4SsgoZU1jM5CnTSc7Kh6ScqBzz12I/Goyht7uDEydPcqr+JM2N9bS3NtDRVI/pqieTztNXHyJCeqKTFLcTV3wipE0gLqeIlLxisguKSUjUKWZKXYzuvn4aa49y4sgeao4doLP+KIn9Lad/9gCcDhtupx1xJtAfn4VJzMaenEtKZi55uWPIHzuWpJRMsEfeBVawxT6oZyYijwHTgZeNMeduSXjeNsH0Cwd9/X46+vrp6emjt7eLvt5uPD1dePu68fR24e3uxNfbgb+3A7+nC/F0It4uTG8bXq/nI+dyApkM/GmZmRRHcsYYsvLGk18wAXf6OEgrgKTcmB1fVCrUEt1OEieVMGFSCQCBgGH/iQYq9u6l6vB+uhuqSO1vI9vXTlxvO3S0A0cA6AbqgO1AvMuOKyEFd3IG8UnpEJeMLS4RmzsZhzsJuzsZZ0Iyrrh44uITiXMn4k5IJN7lwGkP/61Bhi32InIrYDfGLBKR34pIsTHm0HBtgFnD9Ttbv89HXX0dJuDHBAL4AwFMwE8gECAQCOD39+P3+fH7ffj9fgKD//X5+vEPfvh8Pvy+fgL+gc+Nz0vA7yPg84J/4Os+bx9eTy/9Xg8+bx+B/j7w9+PGiwP/+eKdl0fiID4dV3ImiWnZpGXkkJObx6SiScSljoEYGTdUKlzYbMKM8bnMGJ8LXEMgYGjr7ae5s4+W1ma6mk/S11qPt+MUPe1N9HY04+9pJeDtotfbSntb6wU9ngcnHonD2FwYhwub3QWOOGwOF3anC7szDqczDofTRZzLhcvlwhUXh8vlAnEQEDsBsRHAPnCMDbvDSZzTQZzLQZzTSZzLhdtlx+lwDSxRLgI2GzYJ/v6DYK7sy4HVg8drgCXAuUV7qDalQfQ7rfNUNW//4m+CyXzJBHANfsDAEIvLYcNmd+G3uzGOeIzDDY54cMVji0vGHp+CK2Hgw52YSnxSGtnZ2YzLSscRAb/VlYpVNpuQkegiI9EFY1KAjy8n4g8Yalt6OFZXz8mTdbS1NYO3G/F2I/1d2Pq7sfV34/D1YPN7sPv7sAU82P1eJOAjzvSDHz7petEP9Ax+WCGYYp8I1A4etwDzgmwzbD8RuR+4H2B8Xjr+uBTAhhHBYAOxYcQ28FtMHGCzgc0OYkdk4FhsDsTuwGZ3gM2J3W4f+NzhROxObA4XNocTuyMOu8NBnDuepMREUhKTSElKJDU5kcSEBMThHrgK1+EVpWKO3SYUZiVSmDUJZk0KvmMgAH4P/Z4e+vp66OvppdfTS1+vB4+nB4+nF0+fB4/Xg9fjweP10u/14u334u/3ICbAwDW9H7vxYyOAzfgxJoDf7yPg9+Hz+fEH/AT8fkzAj5gAAgiBj7wvMZxgin0XED94nMTQ+9YO1WbYfsaYR4FHYeAN2nv+8XdBB1dKKcvZbGCLx+mMx5mUiRXTLO754e+DahfM+MM2BoZgAOYAVUG2CaafUkqpURDMlf3zwHoRGQtcB3xORB4yxjz4CW0WAmaIrymllLLAsFf2xpgOBt6A3QRcbYypOKfQD9WmfaivhTa6UkqpYAU1z94Y08qZmTVBtwmmn1JKqZGncwaVUioGaLFXSqkYoMVeKaVigBZ7pZSKAWGz6qWINALVVucAsoAmq0OECX0tztDX4gx9Lc4Ih9divDEme7hGYVPsw4WIbA1mudBYoK/FGfpanKGvxRmR9FroMI5SSsUALfZKKRUDtNh/3KNWBwgj+lqcoa/FGfpanBExr4WO2SulVAzQK3ullIoBWuyVUkERkVQReVVE1ojIcyLiGr5X9BKRXBHZYXWOYEXeVuohICJrOf9zrzHG3D2aeaykr8XwROQxYDrwsjHmIavzWOgu4GFjzBsi8ivgWuBFizNZ6d85s0FT2IvJYg/8qzFm7VDfEJGbzzrOBV4zxpSOWrLR94mvhYikAk8CdqAbuNMY4x3NgFYSkVsBuzFmkYj8VkSKjTHn3Us5mhljHjnr02ygwaosVhORaxj4eai3OkuwdBjnk0XUb+4R8uHV3EoG/mFfa3Ge0VbOmWW613Bm97WYJSKLgHRjzCars1hhcPjqH4HvWZ3lQsTqlf2wIvE390jQqzkSgdrB4xZgnoVZLCciGcAq4Dars1joe8Ajxpg2EbE6S9D0yn4IkfqbeyTF8NVcF2f+uksihn9mBn8ungK+b4wJh3WsrLIc+FsRWQfMFZHfWJwnKDH7D3cYp39zWx0kHJx1NfcVq7NYYBtnhm7mAFXWRbHcfQz8ZfMDEVknIndaHcgKxpilxphyY0w5sNMY81WrMwVDh3GGthy4RkT+lsHf3JHyPzTU9GqO54H1IjIWuA5YaHEeyxhjfgX8yuoc4WSw4EcEvbIfQqT+5h4hMX01Z4zpYOBN2k3A1caYdmsTKXVxYnK5BBF5ioE3G4ey0xjzd6OZx0r6WigVG2Ky2CulVKzRYRyllIoBWuyVUioGaLFXSqkYoMVeKaVigBZ7pZSKAf8f9KcPyV75TDMAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x20558441e48>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=模拟学生t-分布\\n\",\n    \"mu = 0.0\\n\",\n    \"n = 10\\n\",\n    \"samples = stats.norm(mu).rvs(size=(100000, n))  #❶\\n\",\n    \"t_samples = (np.mean(samples, axis=1) - mu) / np.std(\\n\",\n    \"    samples, ddof=1, axis=1) * n**0.5  #❷\\n\",\n    \"sample_dist, x = np.histogram(t_samples, bins=100, density=True)  #❸\\n\",\n    \"x = 0.5 * (x[:-1] + x[1:])\\n\",\n    \"t_dist = stats.t(n - 1).pdf(x)\\n\",\n    \"print(\\\"max error:\\\", np.max(np.abs(sample_dist - t_dist)))\\n\",\n    \"#%hide\\n\",\n    \"pl.plot(x, sample_dist, lw=2, label=u\\\"样本分布\\\")\\n\",\n    \"pl.plot(x, t_dist, lw=2, alpha=0.6, label=u\\\"t分布\\\")\\n\",\n    \"pl.xlim(-5, 5)\\n\",\n    \"pl.legend(loc=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x20558a2eb38>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=当`df`增大，学生t-分布趋向于正态分布\\n\",\n    \"fig, (ax1, ax2) = pl.subplots(1, 2, figsize=(7.5, 2.5))\\n\",\n    \"ax1.plot(x, stats.t(6-1).pdf(x), label=u\\\"df=5\\\", lw=2)\\n\",\n    \"ax1.plot(x, stats.t(40-1).pdf(x), label=u\\\"df=39\\\", lw=2, alpha=0.6)\\n\",\n    \"ax1.plot(x, stats.norm.pdf(x), \\\"k--\\\", label=u\\\"norm\\\")\\n\",\n    \"ax1.legend()\\n\",\n    \"\\n\",\n    \"ax2.plot(x, stats.t(6-1).sf(x), label=u\\\"df=5\\\", lw=2)\\n\",\n    \"ax2.plot(x, stats.t(40-1).sf(x), label=u\\\"df=39\\\", lw=2, alpha=0.6)\\n\",\n    \"ax2.plot(x, stats.norm.sf(x), \\\"k--\\\", label=u\\\"norm\\\")\\n\",\n    \"ax2.legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n = 30\\n\",\n    \"np.random.seed(42)\\n\",\n    \"s = stats.norm.rvs(loc=1, scale=0.8, size=n)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2.658584340882224 Ttest_1sampResult(statistic=2.658584340882224, pvalue=0.01263770225709123)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"t = (np.mean(s) - 0.5) / (np.std(s, ddof=1) / np.sqrt(n))\\n\",\n    \"print (t, stats.ttest_1samp(s, 0.5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-1.1450173670383303\\n\",\n      \"Ttest_1sampResult(statistic=-1.1450173670383303, pvalue=0.26156414618801477) Ttest_1sampResult(statistic=-0.3842970254542196, pvalue=0.7035619103425202)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ((np.mean(s) - 1) / (np.std(s, ddof=1) / np.sqrt(n)))\\n\",\n    \"print (stats.ttest_1samp(s, 1), stats.ttest_1samp(s, 0.9))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x20558a28208>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=红色部分为`ttest_1samp()`计算的p值\\n\",\n    \"x = np.linspace(-5, 5, 500)\\n\",\n    \"y = stats.t(n-1).pdf(x)\\n\",\n    \"plt.plot(x, y, lw=2)\\n\",\n    \"t, p = stats.ttest_1samp(s, 0.5)\\n\",\n    \"mask = x > np.abs(t)\\n\",\n    \"plt.fill_between(x[mask], y[mask], color=\\\"red\\\", alpha=0.5)\\n\",\n    \"mask = x < -np.abs(t)\\n\",\n    \"plt.fill_between(x[mask], y[mask], color=\\\"red\\\", alpha=0.5)\\n\",\n    \"plt.axhline(color=\\\"k\\\", lw=0.5)\\n\",\n    \"plt.xlim(-5, 5);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.012633433707685974\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from scipy import integrate\\n\",\n    \"x = np.linspace(-10, 10, 100000)\\n\",\n    \"y = stats.t(n-1).pdf(x)\\n\",\n    \"mask = x >= np.abs(t)\\n\",\n    \"integrate.trapz(y[mask], x[mask])*2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.012695\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"m = 200000\\n\",\n    \"mean = 0.5\\n\",\n    \"r = stats.norm.rvs(loc=mean, scale=0.8, size=(m, n))\\n\",\n    \"ts = (np.mean(s) - mean) / (np.std(s, ddof=1) / np.sqrt(n))\\n\",\n    \"tr = (np.mean(r, axis=1) - mean) / (np.std(r, ddof=1, axis=1) / np.sqrt(n))\\n\",\n    \"np.mean(np.abs(tr) > np.abs(ts))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Ttest_indResult(statistic=-2.2391470627176755, pvalue=0.033250866086743665)\\n\",\n      \"Ttest_indResult(statistic=-0.5946698521856172, pvalue=0.5551805875810539)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(42)\\n\",\n    \"\\n\",\n    \"s1 = stats.norm.rvs(loc=1, scale=1.0, size=20)\\n\",\n    \"s2 = stats.norm.rvs(loc=1.5, scale=0.5, size=20)\\n\",\n    \"s3 = stats.norm.rvs(loc=1.5, scale=0.5, size=25)\\n\",\n    \"\\n\",\n    \"print (stats.ttest_ind(s1, s2, equal_var=False)) #❶\\n\",\n    \"print (stats.ttest_ind(s2, s3, equal_var=True))  #❷\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 卡方分布和卡方检验\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"max error: 0.0030732520533635066\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x20556ede198>\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x20558a279b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=使用随机数验证卡方分布\\n\",\n    \"a = np.random.normal(size=(300000, 4))\\n\",\n    \"cs = np.sum(a**2, axis=1)\\n\",\n    \"\\n\",\n    \"sample_dist, bins = np.histogram(cs, bins=100, range=(0, 20), density=True)\\n\",\n    \"x = 0.5 * (bins[:-1] + bins[1:])\\n\",\n    \"chi2_dist = stats.chi2.pdf(x, 4)\\n\",\n    \"print(\\\"max error:\\\", np.max(np.abs(sample_dist - chi2_dist)))\\n\",\n    \"#%hide\\n\",\n    \"pl.plot(x, sample_dist, lw=2, label=u\\\"样本分布\\\")\\n\",\n    \"pl.plot(x, chi2_dist, lw=2, alpha=0.6, label=u\\\"$\\\\chi ^{2}$分布\\\")\\n\",\n    \"pl.legend(loc=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x20556ef7278>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=模拟卡方分布\\n\",\n    \"repeat_count = 60000\\n\",\n    \"n, k = 100, 5\\n\",\n    \"\\n\",\n    \"np.random.seed(42)\\n\",\n    \"ball_ids = np.random.randint(0, k, size=(repeat_count, n)) #❶\\n\",\n    \"counts = np.apply_along_axis(np.bincount, 1, ball_ids, minlength=k) #❷\\n\",\n    \"cs2 = np.sum((counts - n/k)**2.0/(n/k), axis=1) #❸\\n\",\n    \"k = stats.kde.gaussian_kde(cs2) #❹\\n\",\n    \"x = np.linspace(0, 10, 200)\\n\",\n    \"pl.plot(x, stats.chi2.pdf(x, 4), lw=2, label=u\\\"$\\\\chi ^{2}$分布\\\")\\n\",\n    \"pl.plot(x, k(x), lw=2, color=\\\"red\\\", alpha=0.6, label=u\\\"样本分布\\\")\\n\",\n    \"pl.legend(loc=\\\"best\\\")\\n\",\n    \"pl.xlim(0, 10);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[80 93 97 64 66]\\n\",\n      \"[89 76 79 71 85]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def choose_balls(probabilities, size):\\n\",\n    \"    r = stats.rv_discrete(values=(range(len(probabilities)), probabilities))\\n\",\n    \"    s = r.rvs(size=size)\\n\",\n    \"    counts = np.bincount(s)    \\n\",\n    \"    return counts\\n\",\n    \"\\n\",\n    \"np.random.seed(42)\\n\",\n    \"counts1 = choose_balls([0.18, 0.24, 0.25, 0.16, 0.17], 400)\\n\",\n    \"counts2 = choose_balls([0.2]*5, 400)\\n\",\n    \"\\n\",\n    \"print(counts1)\\n\",\n    \"print(counts2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"chi1 = 11.375 p1 = 0.022657601239769634\\n\",\n      \"chi2 = 2.55 p2 = 0.6357054527037017\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"chi1, p1 = stats.chisquare(counts1)\\n\",\n    \"chi2, p2 = stats.chisquare(counts2)\\n\",\n    \"\\n\",\n    \"print (\\\"chi1 =\\\", chi1, \\\"p1 =\\\", p1)\\n\",\n    \"print (\\\"chi2 =\\\", chi2, \\\"p2 =\\\", p2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x20558515c18>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=卡方检验计算的概率为阴影部分的面积\\n\",\n    \"x = np.linspace(0, 30, 200)\\n\",\n    \"CHI2 = stats.chi2(4)\\n\",\n    \"pl.plot(x, CHI2.pdf(x), \\\"k\\\", lw=2)\\n\",\n    \"pl.vlines(chi1, 0, CHI2.pdf(chi1))\\n\",\n    \"pl.vlines(chi2, 0, CHI2.pdf(chi2))\\n\",\n    \"pl.fill_between(x[x>chi1], 0, CHI2.pdf(x[x>chi1]), color=\\\"red\\\", alpha=1.0)\\n\",\n    \"pl.fill_between(x[x>chi2], 0, CHI2.pdf(x[x>chi2]), color=\\\"green\\\", alpha=0.5)\\n\",\n    \"pl.text(chi1, 0.015, r\\\"$\\\\chi^2_1$\\\", fontsize=14)\\n\",\n    \"pl.text(chi2, 0.015, r\\\"$\\\\chi^2_2$\\\", fontsize=14)\\n\",\n    \"pl.ylim(0, 0.2)\\n\",\n    \"pl.xlim(0, 20);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1.0724852071005921\\n\",\n      \"0.300384770390566\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"table = [[43, 9], [44, 4]]\\n\",\n    \"chi2, p, dof, expected = stats.chi2_contingency(table)\\n\",\n    \"print(chi2)\\n\",\n    \"print(p)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(0.43434343434343436, 0.23915695682224306)\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stats.fisher_exact(table)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "4.scipy/5.scipy-integrate.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pylab as pl\\n\",\n    \"import numpy as np\\n\",\n    \"from scipy import integrate\\n\",\n    \"from scipy.integrate import odeint\\n\",\n    \"import matplotlib as mpl\\n\",\n    \"mpl.rcParams['font.sans-serif'] = ['SimHei']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 数值积分-integrate\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 球的体积\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def half_circle(x):\\n\",\n    \"    return (1-x**2)**0.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3.1415893269307373\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"N = 10000\\n\",\n    \"x = np.linspace(-1, 1, N)\\n\",\n    \"dx = x[1] - x[0]\\n\",\n    \"y = half_circle(x)\\n\",\n    \"2 * dx * np.sum(y) # 面积的两倍 \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3.1415893269315975\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.trapz(y, x) * 2 # 面积的两倍\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3.141592653589797\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from scipy import integrate\\n\",\n    \"pi_half, err = integrate.quad(half_circle, -1, 1)\\n\",\n    \"pi_half * 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def half_sphere(x, y):\\n\",\n    \"    return (1-x**2-y**2)**0.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2.094395102393199 1.0002356720661965e-09 2.0943951023931953\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"volume, error = integrate.dblquad(half_sphere, -1, 1, \\n\",\n    \"        lambda x:-half_circle(x), \\n\",\n    \"        lambda x:half_circle(x))\\n\",\n    \"\\n\",\n    \"print (volume, error, np.pi*4/3/2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 解常微分方程组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x18b3f0fc2b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=洛伦茨吸引子：微小的初值差别也会显著地影响运动轨迹\\n\",\n    \"from scipy.integrate import odeint \\n\",\n    \"import numpy as np \\n\",\n    \"\\n\",\n    \"def lorenz(w, t, p, r, b): #❶\\n\",\n    \"    # 给出位置矢量w，和三个参数p, r, b计算出\\n\",\n    \"    # dx/dt, dy/dt, dz/dt的值\\n\",\n    \"    x, y, z = w.tolist()\\n\",\n    \"    # 直接与lorenz的计算公式对应 \\n\",\n    \"    return p*(y-x), x*(r-z)-y, x*y-b*z\\n\",\n    \"\\n\",\n    \"t = np.arange(0, 30, 0.02) # 创建时间点 \\n\",\n    \"# 调用ode对lorenz进行求解, 用两个不同的初始值 \\n\",\n    \"track1 = odeint(lorenz, (0.0, 1.00, 0.0), t, args=(10.0, 28.0, 3.0)) #❷\\n\",\n    \"track2 = odeint(lorenz, (0.0, 1.01, 0.0), t, args=(10.0, 28.0, 3.0)) #❸\\n\",\n    \"#%hide\\n\",\n    \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n    \"fig = pl.figure()\\n\",\n    \"ax = Axes3D(fig)\\n\",\n    \"ax.plot(track1[:,0], track1[:,1], track1[:,2], lw=1)\\n\",\n    \"ax.plot(track2[:,0], track2[:,1], track2[:,2], lw=1);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### ode类\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def mass_spring_damper(xu, t, m, k, b, F):\\n\",\n    \"    x, u = xu.tolist()\\n\",\n    \"    dx = u\\n\",\n    \"    du = (F - k*x - b*u)/m\\n\",\n    \"    return dx, du \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x18b412006a0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=滑块的速度和位移曲线\\n\",\n    \"m, b, k, F = 1.0, 10.0, 20.0, 1.0\\n\",\n    \"init_status = 0.0, 0.0\\n\",\n    \"args = m, k, b, F\\n\",\n    \"t = np.arange(0, 2, 0.01)\\n\",\n    \"result = odeint(mass_spring_damper, init_status, t, args)\\n\",\n    \"#%hide\\n\",\n    \"fig, (ax1, ax2) = pl.subplots(2, 1)\\n\",\n    \"ax1.plot(t, result[:, 0], label=u\\\"位移\\\")\\n\",\n    \"ax1.legend()\\n\",\n    \"ax2.plot(t, result[:, 1], label=u\\\"速度\\\")\\n\",\n    \"ax2.legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from scipy.integrate import ode\\n\",\n    \"\\n\",\n    \"class MassSpringDamper(object): #❶\\n\",\n    \"    \\n\",\n    \"    def __init__(self, m, k, b, F):\\n\",\n    \"        self.m, self.k, self.b, self.F = m, k, b, F\\n\",\n    \"        \\n\",\n    \"    def f(self, t, xu):\\n\",\n    \"        x, u = xu.tolist()\\n\",\n    \"        dx = u\\n\",\n    \"        du = (self.F - self.k*x - self.b*u)/self.m\\n\",\n    \"        return [dx, du] \\n\",\n    \"\\n\",\n    \"system = MassSpringDamper(m=m, k=k, b=b, F=F)\\n\",\n    \"init_status = 0.0, 0.0\\n\",\n    \"dt = 0.01\\n\",\n    \"\\n\",\n    \"r = ode(system.f) #❷\\n\",\n    \"r.set_integrator('vode', method='bdf')\\n\",\n    \"r.set_initial_value(init_status, 0)\\n\",\n    \"\\n\",\n    \"t = []\\n\",\n    \"result2 = [init_status]\\n\",\n    \"while r.successful() and r.t + dt < 2: #❸\\n\",\n    \"    r.integrate(r.t + dt)\\n\",\n    \"    t.append(r.t)\\n\",\n    \"    result2.append(r.y)\\n\",\n    \"    \\n\",\n    \"result2 = np.array(result2)\\n\",\n    \"np.allclose(result, result2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class PID(object):\\n\",\n    \"    \\n\",\n    \"    def __init__(self, kp, ki, kd, dt):\\n\",\n    \"        self.kp, self.ki, self.kd, self.dt = kp, ki, kd, dt\\n\",\n    \"        self.last_error = None\\n\",\n    \"        self.status = 0.0\\n\",\n    \"        \\n\",\n    \"    def update(self, error):\\n\",\n    \"        p = self.kp * error\\n\",\n    \"        i = self.ki * self.status\\n\",\n    \"        if self.last_error is None:\\n\",\n    \"            d = 0.0\\n\",\n    \"        else:\\n\",\n    \"            d = self.kd * (error - self.last_error) / self.dt\\n\",\n    \"        self.status += error * self.dt\\n\",\n    \"        self.last_error = error\\n\",\n    \"        return p + i + d\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"控制力的终值: 19.943404699515057\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x18b42591cc0>\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x18b4150b9e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=使用PID控制器让滑块停在位移为1.0处\\n\",\n    \"def pid_control_system(kp, ki, kd, dt, target=1.0):\\n\",\n    \"    system = MassSpringDamper(m=m, k=k, b=b, F=0.0)\\n\",\n    \"    pid = PID(kp, ki, kd, dt)\\n\",\n    \"    init_status = 0.0, 0.0\\n\",\n    \"\\n\",\n    \"    r = ode(system.f)\\n\",\n    \"    r.set_integrator('vode', method='bdf')\\n\",\n    \"    r.set_initial_value(init_status, 0)\\n\",\n    \"\\n\",\n    \"    t = [0]\\n\",\n    \"    result = [init_status]\\n\",\n    \"    F_arr = [0]\\n\",\n    \"\\n\",\n    \"    while r.successful() and r.t + dt < 2.0:\\n\",\n    \"        r.integrate(r.t + dt)\\n\",\n    \"        err = target - r.y[0]  #❶\\n\",\n    \"        F = pid.update(err)  #❷\\n\",\n    \"        system.F = F  #❸\\n\",\n    \"        t.append(r.t)\\n\",\n    \"        result.append(r.y)\\n\",\n    \"        F_arr.append(F)\\n\",\n    \"\\n\",\n    \"    result = np.array(result)\\n\",\n    \"    t = np.array(t)\\n\",\n    \"    F_arr = np.array(F_arr)\\n\",\n    \"    return t, F_arr, result\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"t, F_arr, result = pid_control_system(50.0, 100.0, 10.0, 0.001)\\n\",\n    \"print(u\\\"控制力的终值:\\\", F_arr[-1])\\n\",\n    \"#%hide\\n\",\n    \"fig, (ax1, ax2, ax3) = pl.subplots(3, 1, figsize=(6, 6))\\n\",\n    \"ax1.plot(t, result[:, 0], label=u\\\"位移\\\")\\n\",\n    \"ax1.legend(loc=\\\"best\\\")\\n\",\n    \"ax2.plot(t, result[:, 1], label=u\\\"速度\\\")\\n\",\n    \"ax2.legend(loc=\\\"best\\\")\\n\",\n    \"ax3.plot(t, F_arr, label=u\\\"控制力\\\")\\n\",\n    \"ax3.legend(loc=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\optimize\\\\_basinhopping.py:282: OptimizeWarning: Unknown solver options: approx_grad\\n\",\n      \"  return self.minimizer(self.func, x0, **self.kwargs)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[56.67106149 99.97434757  1.33963577]\\n\",\n      \"Wall time: 55.1 s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%time\\n\",\n    \"from scipy import optimize\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def eval_func(k):\\n\",\n    \"    kp, ki, kd = k\\n\",\n    \"    t, F_arr, result = pid_control_system(kp, ki, kd, 0.01)\\n\",\n    \"    return np.sum(np.abs(result[:, 0] - 1.0))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"kwargs = {\\n\",\n    \"    \\\"method\\\": \\\"L-BFGS-B\\\",\\n\",\n    \"    \\\"bounds\\\": [(10, 200), (10, 100), (1, 100)],\\n\",\n    \"    \\\"options\\\": {\\n\",\n    \"        \\\"approx_grad\\\": True\\n\",\n    \"    }\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"opt_k = optimize.basinhopping(\\n\",\n    \"    eval_func, (10, 10, 10), niter=10, minimizer_kwargs=kwargs)\\n\",\n    \"print(opt_k.x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"t=0.5000000000000002, x=1.07098, u=0.315352\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x18b2fb52400>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=优化PID的参数降低控制响应时间\\n\",\n    \"kp, ki, kd = opt_k.x\\n\",\n    \"t, F_arr, result = pid_control_system(kp, ki, kd, 0.01)\\n\",\n    \"idx = np.argmin(np.abs(t - 0.5))\\n\",\n    \"x, u = result[idx]\\n\",\n    \"print (\\\"t={}, x={:g}, u={:g}\\\".format(t[idx], x, u))\\n\",\n    \"#%hide\\n\",\n    \"fig, (ax1, ax2, ax3) = pl.subplots(3, 1, figsize=(6, 6))\\n\",\n    \"ax1.plot(t, result[:, 0], label=u\\\"位移\\\")\\n\",\n    \"ax1.legend(loc=\\\"best\\\")\\n\",\n    \"ax2.plot(t, result[:, 1], label=u\\\"速度\\\")\\n\",\n    \"ax2.legend(loc=\\\"best\\\")\\n\",\n    \"ax3.plot(t, F_arr, label=u\\\"控制力\\\")\\n\",\n    \"ax3.legend(loc=\\\"best\\\");\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "4.scipy/6.scipy-signal.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pylab as pl\\n\",\n    \"import numpy as np\\n\",\n    \"from scipy import signal\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib as mpl\\n\",\n    \"mpl.rcParams['font.sans-serif'] = ['SimHei']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 信号处理-signal\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 中值滤波\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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gBUrJoSpnBCv1ZzDp1cnrsHReEDDkGbVdLETUZ/KSYbvH3K8wwk2rSB9fYNbTMj8m+a+1vE3OSy66K6YzYiBl2RVGygUeBMKcnTACqKrzmzDJRQn0FXGjtpyzHyqwCsQUNK4AKQuNNyRYplhJwiTGCg6ropJli1GMy6CZGY/fjnHUJ6To7C/XVkzPdsXqnGApesizsH9EegCIrRhgjTYrJSTWRZTUFz4rRsOaIXWvtnskdRK3ZAyhQtiqmy4hk1rwb2Aw8H8HXHBudl8Qcx8Wbx/Rj1e39PH2gjrfPtnG6qYerp+dSlmMFEIN9w0GnE17L6ZfEdjRGQZO4wSUMw6BH3GImgw6zQRfQY2/usfHUvlq2n2mle9BJqtnAull53LeizPd3CJcGQxmpugyWc4pa9yQbvuwYEPpu+dVjak094BB/E/+WAudb+rAYdVhNBrKsxsv7xQSi/GrY/xsRRB3rIJtkwOOBur3iAefXvygWRMxjV1W1R1XVoKWaiqJ8WlGU/Yqi7G9tnaB2n7V7xSCMML0Vl9vDz14/y/ofvsXP3zyHWa/j5vlFfGrddCxGPUUZFo43dIcXQIWh1DLZq11IMYoOm0cPgNmgw2LUY/dLd1RVld/vvMiN39/OD147g9ujMqcwHaNO4edvnmP9D9/ip6+fxT2G7BaHGy7pp1GqtOCKdGplvNNwCDwuIQOEyTMH6/jY7/ehU2B6XiplOVY2LChk0Omm3PtQzbIaQ2fFaGhB1Mkqx7SeEtlBY7j+E0XU6lxVVf0V8CuAFStWTMweufOSGOEVRrdFm9PNF/58kNdPtXBHZQkPbZxHUebwn9uwoJBHd1fzr88e5Vt3LsSgH+U5aM2BnBmiEm/m+phqbDHHZQNDCnavHGI26rAYdb4CJZfbw78+e5Sn9tdx3ex8Hn7PAiryhlS8hq5B/vOlk/zwtTOcaOjhx/cvwWLUj/q2DrcHhyEfs+sYZkfXxPxu8UrdPkgrCkuG9HhUvv/qaX6x/TwrK7L5xh2rmFWYDsD/fmiF6A/j/ZRmpZg42d0z+vvrdFC6QrQYsPeJwTSTiZrdoqalcGGsV5JEWTFulwgaZUwZ9VSn28On/rCf10+18K27FvKzB5ZeZtQBvnHHAr5wwwwe31vL1547Fp7nXrpCZIR0117Jb5E8OAfBaPFlwZgNeiwGPTaXG1VVefAvh3lqfx1/f9Msfv+xlcOMOkBJVgr/8/5lfP32+bxyoonP/elAWE29HC433YY8dIpCqmMSNafqbxf1G6UrwnIovvuKMOrvv6qcxz+1moVTMocdz7KayEkVck6m1Uhrr51BRxg7oCkrRDfVxkk2F9XeC83HxPWPcl+YQCSPYe9vETdUGL2h/2PLCXacbeO/37uID62eGvQ8nU7hyxvm8sUbZvLEvlp+/mbwIco+ipeIxkiTvTGY5rF75RBNirE5Pfz8zXM8V9XAP908m3+6eTZKCEP0ibXT+NadC3nzdCv/sfXkqG/rdKv0GHNRdArpjkk04ad+v/g+Zfmopz72bg2/fOs8H7iqnG/fNfpOdOPCYvrsLr789OHRnZuMEkgvhrr94a48OWg8IuxPGJW+0SDihl1V1esj/Zph0dMgvqeXhDzt2UN1PLq7mk9dO43NK8PL8/3nW2Zz55ISfvjaGfZeHCVP12SFwgVevXOSabz+OAfBYPZJL2Zv8PRwbRfff/UMdy0p4e9uDC/P94Orp/KJtdP4/a5LbD3SGPJch8uDzmCmT59FprNl3L9GQqCqQobJnTlq0P5kYw/ffOE467zyV6iHqsbaWXn8vw1z2XKkkd+8czH0yYoivPbOi1HpOx43NFZBasGoikHY8bpxkjwee0+D8JRDTElq7rHxjeePs7Iim6/cOjfsl1YUhW/fvYiyHCtferIqdJ92gNJV4OgTBRuTFZcNjCk4vCPXFEXBYtTT3u9gen4q/3XP4rCMisa/3DaXyrIsHnruKC3eVLxAaJOAugx5ZLraIvGbxD9dNaIR3Sjeos3p5ktPVpGRYuRH91WOHjPy47PXTWf9vEK+98ppXxFfUKZ4kxcmSxKBrQfazkLJkpAymMej8vHf7+PpA3UTvqTkMey9jZBWKCapB0BVVR569hgOt4fv3Tu2mxogzWzgx5uX0Ng9yA9fPRP65IJ5IohSP8m2o/44B8Fgwe70YPYWvViMenQK/OB9laSYRg+E+mPQ6/jhfZXYnG6+9tyxoOdpQ5m7jYWkubpE++Zkp26fcGpCjL4D+OVb5znV1Mv37l1MbtrYqrIVReE/71lIiknPl/9y+PLBG/5Yc8TuoW7f5Ghn3XgYUEdtYfKXA7W8eTo68mDyGPaeeqHvBeHN0y1sO9nMP98857JAXbgsLc/mA1dN5Q+7L3G8IcQQDp1e/JEbDwvtbTLi9djtLjdmo7jNPnPddP7n/ctYWp59RS85Iz+Nv79pFq+eaGbH2cAfEIdLPEh6Tfl4VHVIoktWPG4h+xUuDNnwq7ZjgEe2n+f2xcXcMPfKZv8WpFv4+qb5HKzp4plD9aFPnrJcxL0mQxJBY5VwKtODx/c6+x1852+nWFWRw3uXjZ7gMV6Sw7A7+kX+aJAL63R7+I+tJ5mel8pHr6kY11s9eMscsqwmHn7hRGi9bM5GUQF74Hei8+NkQlW9wVORFWM2CO98ZUUOGxeNb/DxJ9ZOY2qulYdfPBGwZYDDLaSYXlORMOwd58f1fnFP21kh+40SNP321pPoFIWHNoUekTcady+dwpKyLL778in67QGmKmloSQTJHkS1dUP7eeHIhZBhfrztDD02F/9+V3hxjfGSHIa9wxvQySwLePjxvTVcaO3nXzfO8zU5ulIyrUa+dPNs9l7qYPuZENsqkxVWf148yU88N7nKrN0OkSFg1Ax75G4zs0HPQxvnca6lL6BW6fRKMQ5TBg0UiIZwyUxjlSjKCzHT9FBNJy8fb+Jz18+gOHP0Nr6h0OkU/u2O+bT02vnV2xeCn2iyQs500bsmmdFkmBBTqmo7Bnhsbw2bV5YxtygjKstKDsPeWAVG7400ApvTzf+8cY5V03K4ad6VbUFHsnlFGWU5KXz/ldOhtUajBeZuEoGtun0Ree+EwCn6xGBIweFy+xpLRYqb5xeypCyLn71+dlglK3iDp97S+BNMF1JAsmZneNxC6itaCHpj0NN++NoZclJNfHzt2IfOBGJZeTa3Lijit+9cDN1qIL1Y1HQks87eUCV+zxBp1j967Qw6ReHvb5wVtWUlvmF3u0RHxaJFAQsDnthbQ0uvnX9cPytiWyCTQceX1s/meEMPrxxvCn1y4UKxkzj7yuTx2r19YibCYwcRyPunm2fT0G3jyX3DNVwtK8ao13Fc9T7oGw9F9P3jhraz4OwP6S2+e6GdHWfb+Pz1M0gzR65w5h/Wz6LX7uI3O0OkP6YXid1bsrbyHewSO5IQ1/98ax/PVtXzkTUVAYsgJ4rEN+ytp4QhCRCRtrvcPPLWeVZNy+Hq6bkRfds7l0yhItfKI2+dD621a+OxBtrFV7JSf3Bo2+3nsducbp/GHkmunZXHiqnZ/HL7yAHWQooxGhTaPamiJ3myyjFhyDC/2H6evDQTHwxRiHclzCvO4LaFRfzunYt0B+sjk14kvveGrj1IWBqrENkwwQ37/751HpNex6fXXa4mTCSJb9gbDgkZJm/2ZYeeP9RAc4+dv7txZsQDFnqdwqfXzeBIXTe7z49isLVtWt8o3n0ic+I5OPua+G8txdBoYdDhxmqOvGFXFIXPXT+Dhm4bW44MZb5oWTFGvU5MBipekpxyjCbDFC4IKsOcaOjhrTOtfOyaaWH12RkrX7hhJr12F0/srQl8QprXsCfrfd9QJQoitQfYCBq7B3n2UD2bV5aRN8b00vGS2Ibd7RT9GYoWX5a/rqoqv37nAnOL0lk7M29C3v6eZVPISzPzyFujZF5oN3hvkt7gqiqaPmm/n0vz2C0MONxYx5izHi43zClgVkEa//vWBVRVRVVVX1aMSa8Tnry2k0s2Oab9nJBhQuRO/+rt86Sa9Hzwqsh66xoLp2SyZkYuv9t5yTesYxgmK1gyk/O+H+wU1bUhrv9v37mIR4VPXRtdbx0S3bCHkGHePtvGmeY+PnXt9AlLL7IY9Xx0zVR2nG0LXY1ntIAlKzlvcBB/A9UtbnaXwzc9aciwT0xTJJ1O4VPrpnOqqZed59pxe1RUVfQVN+gVnG5VFMtkTRU7u2SiIbQM09JjY8uRRjavLCfTGjywOl4+tW46TT3Dd03DSC9OTilGk/eCyDD9dhdP7KvltoVFY54pEAkS27A3VIExFfIujzb/fudF8tPN3FEZunfMeNm8shyjXuHPe4JsRzXSi5PXsNu1h5oqilI0j92YQr/DReoEeewA76ksIdtq5LG91b6hzFrw1O1RRdZSyRLorhNjy5IBj1uk2YWQYZ7aX4vLo/KhqyfGW9e4fnY+MwvS+MPu6sAnpBdBbxJmxjRWQUZp0BYmzx6qp9fm4mPjrJu5UhLTsNt6oO2ckGGKL5dh6rsG2X6mlftXlkU81W4k+elmNiwo4ukDtaHbmqYXitSvZMyMcfjtVnqbvR67Ijx2u5uUCfLYQeya7l1eyqvHm6nvFA8Uk17nq1dwejxDWQvJEkT1yTCBvUW3R+XxvbWsnZnnG0Y9USiKwvtXlVNV28WJhgA929OLxQziZEocGOgQsx+CXH9VVXl01yUWTclk2RVWWY+XxDPsqgp7fgG7f+aVYS6flqSlwN23InDBUqT54Oqp9NhcvBhsOwrJeYNr+Bv2vib6B/pwYMTpEZr3RHrsAA+sKsflUfnzu2LXZDQMjXhLSjnGJ8PMD3h4++kW6rsG+cBV4XUvHS/3LJuCyaDj8UBBVK1osO1sVNYSFbT7KEia496LHZxt6ePDV0+NSpVpIBLPsPc2Cc1uxk2w9kuQPzwbxuX28NS+WtbNyo+atnXVtBxmFaTx5z1BtqMgKlAhOTMEHP3iu94Mfc38ccdp3r7Y65ulaY1g/nQgpuensWZGLn/ZLx7oZr+hzE4tqFeyFHrqoC/Be7R7PNAUOhvmz+/WUJBuZv38wqgsKctq4vZFxTx3qJ4Bx4g2AxklYM3zVmgmCY1VYkpVWn7Aw0/uryXNbGDT4vG1zxgPiWfYGw8DCky/HrIrLju8/XQrTT02HlgVHW8FxHb0A1eVc7ium6N1QZqDpSdxZoymsWdXQG8zgwN9dDj0vg/5RGXF+POBq6bS732QmAw6jAbNY/cadq3zYaJP9mn39oYJIgPUdgzw5ukW7l9ZNu72GWPhgavK6bW72HJ4RKBUUcS1bzs95AAkMv3tok1ykGyYHpuTl442ckdlyYQlDYRDAhr2KtE6wBK458Lje2vITzdHrH1AuNyzvJQUo54/vxvEazemQGq+aBiU6DhtorlT7T4R73D0gs4ovJj+FoyufnpchiGPPQqG/eb5hb5cYaNeh1Enbm0toIo1Rzx4El1nH0WGeWJfDQpwfxQdG4AVU7PFrjWQHFOyRPQOSoZmeFrabHFgw77lcCM2p4fNK6MjAwcjsQy7JsME8VYaugZ583QL960ojaq3ApBhMXLnkhKer2oI3vWuaHFyeC7nXoNDf4SqP8Hpv4nfx5wm4giqhyJ3I90uIwN2YdhTo+C5mAw67ltR6vtvo0FIMS63XzZG8ZLElmNGkWGcbg9P7qvjxrmFlGSNr9nXWFEUhQdWlXO4tuvyltaZZZCSA0eegJe+LL72/y6q64sYDVWimjk1cCX7k/trmV2YRmVpZsDj0SKxDHtfMxgswkAG4Kn9tXhUuD/MkXeR5r3LSxl0unn5WBC5xee5HI3uwiKJqoqbO3cWZE+D3gYhxZjSxO+3+H6ed13NFtdK+jUpZgIqTwPxoaunsnp6DvOK04eyYvxb+2oOQaLKMaPIMO+cbaOtz+57wEWbe5ZNwWzQ8cTeET3YFQUqHxDyacVacd80VoleK4lEf5uoYg4SND3d1Mvh2i7uW1EWs6CpRmIZ9uJKuOXbkJIV8HBVbRfzijNiUhAAYjtalpPCs8GGEGSWgTU3seWAnnoYaBPjzzKmQF+LMDamNNAbcZVexQ7XPKrtaX4ae3S0xuLMFJ749NWUZlt9ht07ozC/AAAgAElEQVThb9hTshNbjhlFhnnmUD3ZViPXz4muDKmRZTVxy4IithxpuLwSNX82zL9TfC18r/i3pgQbQqNlwwTR15/aX4tRr3D30okfpDEaiWXYIWAHR41em4vsCayyGw1FUbh7aSk7z7fR1B1gLqeiiKd9IssxDVWg6MSuKb0QnAPCuJvTABhwCvnFo0JbrwNgwtMdAzEs3dEfTY5JtGKlUWSYXpuTV483cfvikgmv3QjF3UtL6Bxw8naoWQXp3mlDiZYp01AlHIMAA8MdLg/PHqpn/bzCMY8dnAgSz7CHoM/mIt0Su0g0iAkzqgrPVwXx2kuWJq4co6piC507UxhyrQeO2w4mUQjjX6TV7B06PdHpjoEIKMXAUHZMonnt7edCyjAvH2vC7vJwV4y9xWtn5ZOTauLZYPe/RnGlSCSwBShqikf6WoRDEESG2XG2lY5+B++LkQw2kqQy7L02J2nm2HnsANPyUllanhVCjilNXDmmt1EMDdFubv9yalM6gC8TBqBJM+wT0FlwNAwj89g1rDki+JVoTcEaQ8swzx6qpyLXyrLywDJltDDqddy+uJhtJ5rptYUYwlG0GFCh5WTU1jYuRukNs+VII5kpRtbODJzbHm2Sy7DbY++xA9yzdAqnmnoDl1grivDaE1GOaagCFDHUBETnPoM3+0KTYvwKVIY89ugb9qGWAgF6lBRrvWMSpArY4/H2hpkfUIZp7B5k94V27lo6JeZBO4C7lk7B7vIETyIAIcXozYkz7LqxSgR9Uy5vEWBzunntRDMbFhTGVAbzJz5WEQE8HpW+ODHsty8uwahXePbQ5TM5AWFYVI/op51INB6G3BlDNQSKIvRSCCLF2DHoFJ/eHU18GnugdrKJ1srXJ8MEDto9X9WAqsJdS2IftANYWpbF1Fwrz1eFaLGh04mq1J5RJJt4oLdZrDOIt/72mVb67C42LZ7YhoNjIWkMe7/DhaoSF4Y9O9XE9XMKeL6qAXcgjzGz1FtmnSByjMsupiP1NV2uMWqtEkyaxz5cikkx6WPiRWp57Jdp7DAkxySKHNZwKKgMo6oqzx6sZ1l5FhUT3PArXBRF4c4lU9h5vs23awtIZil018d/58eGQ4ASVF/ferSRLKuRNTMiO6VtPCS8YdfG0vV5i4LSLbHV2DXuWjKFll47715sp2vAwesnm4cO+uSYM34tb+MU5yBs+ybs/Ani5h5eQ1DjSGfH2TYcemFU/A17W589KsVJgbB4x/F1BRvbliiTlVwOaDjonel7+b19prmP0829MQ+ajuSuJSWoKrwQymvPLBWB93jPUNKq3QOkWducbradaObWBUVRL4oMRfys5Ar5+yeq+OenDtNrE4Y9kgN7x8ONcwuwmvS8eLiRb75wnE88up+OfsfQCb5ipTiXY5qOiZTG2bfCVZ8Vurofv7yQxy/aKql3iNqBQeeQxq6qsdHXAcpzrBRlWHj9ZEvgEzTvK953TY2HRRfT8qsDHt56tBFFgVsXBh7PFium56cxuzCN3ReCPzibyRXOejzr7D2NIavdt59upd/h5vY4kmEgwQ17Z7+Dl442cqy+22fY40GKAUgx6Vk/r5AXDzfw/GHhtdR2DAydkDFF9I6Jdzmg4ZAIGM2+FQrmDjtkd7l58VQPuz0L6PO2DxgY0ZM+Gn1iAqHTKWxcVMzbZ1rpCZSdkZorCsbi3bDX7Bb3Se6MgIdfOtrIVdNyKEi3RHlho5OVYrq826OXhq5Brv3lKep7HPGtszd6Ewa0NNkRbDnSQE6qidXTL89tjyUJbdhfPdGE26PS3m/3pVbFixQDcEdliU8iAqjzDoIAhoqV2s/GrxzjGBDjB4uXiPWOYMeZNt8Dtdcurr8WPNXrxPmx7HC3aXERDrdnuAzmT8kS0alvoCO6CwuXvlboOA/lqwNe/zPNvZxr6WPToti1hw2F2ajD5gw8WKa2YwCHR0cb2UJnj0dUVTg2OdMv26mCuNdfP9nCrQuLMMSRDAMJbti3HhXpVB39DroHNcMeHx47wLrZeZRkWvi0d5htXefA8BPiXY5pOipmmQbJxvCfc9nnNfCax56XZgJiU3WqsbQsm+JMC1uPBJm5qXXoi9cBHNp9EWCYDIjcaUWBDXEmw2iYDXpszsBTxdq9smSbvgC6quNzslhPvehPNWV5wMNvnm5h0Onm9jh8sCasYe/sd7DzXBtZViMedcgbjifDbjboeecrN/LV2+aSmWIc7rFD/MsxDQdFMVVW4KZq75xrZ/lUkder7UwGHG5Meh3ZVmHYY9qT2ifHtAWXY7LKof5g9BcXDs3HxT0SoIQdhAyzqiI+ZRgAi1F3ec8YL219dgDqDGUihhOPOnv9AdE+I4gMs/VII3lpJlZNiy8ZBhLYsGsyzGbv+LvqdlHsE09SDAjjoigKpdkpl3vsw7JjemOzwGDYeqD1tPBWgqQrDjhcTPU2XNMM+6DDRYpJ73vAxkpj19i4qBiH28O2E0HkmCnLRal4vA1AcfSLFNPCBQEP+2SYGE7pGQ2LMbjH3tYrDPslxVuC33o6WssKD1UVD/z8ub7iO38GHC5eP9UclzIMJLBh33q0ifIcK+tmixLeS+0DKEpsytfDQRj2wcsPFC8B1PjrHdNwCFCDbkNBND7K9UouvX5SjNWk92UnpcY4S2lpWRYloeSYkmWAIryzeKLlJKBC4cKAh7ceic9sGH8sRh22IB57a59XinF4B7S0norm0kan4wLYuoLe/2+casHm9MRdNoxGQhp2TYbZuKjYZ1iq2/tJMxvQ6WJfUh2I0mwrdZ2Dvrx7HxklkFoQf3JA/X7IKB0a6TcCl9uDy6OSZjZi0uuGDLvT7fXYxc4pJcYeuybH7Djb5ovDDMOSAXmzhWGPp0KZpiNgTg8qg8W7DANCirQH09i9UkyvzQX586DzopjMFS/UHxRTwbT2GSPYcriR/HQzKyviT4aBBDXsmgyzaVExuamiRWZzj530OMlhD0RpdgqDTvfwXHYQMkfpClE2Hi/ZGX2tIltkSuCgHQz1OTcbdaRZDPT5ZcVY/aSYWAZPNTYuHkWOKV0BA+3QeSmq6wqK0wbNJ4JmI51p7uVsnMswMNxjb+m10eJXhapp7D02p5A7VI+QJOMBj1ukORYtBMPlLXj77C7ePN3CxoVFvuyveCMhDfvWo02U5aSwcErGsP7r8aav+1OaLbTogHLMlBWAGj9yQP0BQBlVhgEwG3SkmQ1+WTEurEYDaT6NPfYP26VlWUzJSmHr0SByTNFi0Bni5/o3HQGPM+j1TwQZBkT1r9uj4nR7+NdnjvLR3+3zHWvzSjG9NpfocW5MFcH6eKD1tOjNE+T6v36yGbvLE1e9YUaScIZdk2E2LSpBURQMep3PuMdTRsxISrNFF8SAhj01F3JmQN2+2MsBqvcBkzsz6KQqALvXsJs0w+4LngopJsP7kI118BRE75KNi4rYcbY1sBxjtIggZcOh+Ei7q9svspGyKwIefuloIyvjXIYBsZsDUXbf2ufgRGMP51tFzYYmxfQMOsXwnCnLRJzJMRD09aJGw0EwWoVEFICtRxopSDezYurlnR7jhYQz7P4yjEZOqtDZ0+LYsE/xGfYgN27pSpEz2xVgyns06a6F/paQ3jr4e+xCdhkZPPVlxcSJPLZxUTFOt8prQbNjVggvrS3G2RmDXUKSKF0ZUIY565Vhbo9zGQZEVgwIJ2DQW4H68rEmBh1u+h1udMpQ0J2yVeBxxb6mwGUXXVeLKwNOa+u1Odl+ppWNi4rjNp4HETbsiqL8RlGU3YqifC2Sr+uPvwyjoY2iimcpJsNiJNtq5ExzkCrT4kohB9TtC3w8WtTtF+sIkrurBX/tLhEUMxl0pFsMw/LYU+JMYwdY4pVjXgomxxTMF15a7d7oLmwkdfsR2UgrAh6O194wgdAasdmcbl/h2ktHG336emm2lUGnW3TgzCwTPdrrYnz9Gw6JxmRlqwIefv1kCw6Xhzsq4/vBGjHDrijKPYBeVdWrgemKosyK1Gtr+GfD+LeCzfV67PEsxQBcP6eA1081B24la7KKCHz9QXAH7q8x4bhdwrAULhTrGYHD5eH672/nsXdrfKXi5pFSjFNLd4yPrBiNYXLMQAA5Rm8Qu5SmI7EbgKKqojdMzgxICzyJJ1FkGPCXYjwMegvXjjf0cLCmExDTxsDrtSuK2KV0XhJj6GJFzR7Rijp7WsDDW440UJxpYWlZ/MowEFmP/XrgKe9/vwqs9T+oKMqnFUXZryjK/tbWEINuQ/DaiWbcHpXbFw0PWmgpj/GcFQNCDugacLLrfJCOd6UrwdkPrTEaF9Z0WLz/1DUBD+8810Z1+wDnW/uGsmIM3qwY75a63+7CajKwqiKHzSvKqCyN7ag2f3xyTLDeMeWrhRwQqyBq+3kYaAvayfFscy9nmuO3N8xIzAZNihEe+/r5YpTiH3dXA0OGvUeLe5SuEJWesdo19TaLtMuyqwLKYN2DTt4+0xb3MgxE1rCnAlo3nw6g0P+gqqq/UlV1haqqK/Lzr2wu4F1Lp/CHj68aJsMA5KRqUkx8G/ZrZ+WRbjaw9UiQHtX5c8XAiljd2DV7ICVH5HUHQMsqsTnd2J3+wVMjvTYXbo+K3eUhxagn02rkv+9dHPMCJX9GlWMyS4UkUL07NkHsmt1i1GCwEnavDHNbAsgwINIdQdwvg043M/PTqCzLYn+18Nhn5HsNu9buwZIpPgN1+2ITxK7dIx4spSsDHt52ohmH2xP3aaYQWcPeB3gHYJIW4dcGhBFZNzv/sok8WsOpeNbYQQSTbp5fyCvHg8gxOr3Q9pqPiSBaNOlvE0G78qsDeisOl4dXj4uye5vT4+exCz3d4fb4Mk7iIRMmEIqicNvCENkxIH7/3oboB7EdA6L3eukKMJgCnuKTYTLiX4aBoeBpl1f6SjEZ2Oj3UKrwl2I0ylaJis/2s9FbKIjc9bp9IjvKkhHwlK1HG5mSlcLSsvjZhQYjksb3AEPySyVwKYKvHRJfVkwceYfB2LiomO5BJzvPBZkaM/WaIa01mtTsAZSgQaOd59vo8X4AhccugmGaxg6iCAXi17CDKFZyutXQvWP0Ju/1iCL1B0TuetlVAQ+fa0ksGQbEvQHQOTD0wL9toVh/usXgKy7s8X/IFi4UOe3Vu6K72JYTol9T2eqAh7sHnOw428rGRUVxMTB8NCJp2J8DPqQoyg+B+4CtEXztkOQmiBQDcO1sTY4JIgek5kHBPHFjewKXY0ccj1tsQwsXBM1d33qkkXSzgdmFacKw+xUoade9pUdkO6TEQVFSMLTeMUHlGKNFNGarPyBS36KB9iDPKIWssoCnbD3SlFAyDAx57J3eausUk57yXCsLSjIoSDeTkSLuk2Eeu94IZStFENvWHb3F1uwRLRwCzJUFeOVEE063Gre9YUYSMcOuqmoPIoC6B7hBVdWo/VWWTc3iizfMZM3MvGi95RVjNui5eUEhr55oDtrSlKnXgL0nen3am48Lb6U8sLeiyTA3zy8k3WLE5nIPy2Mf8tiFIYxnj11RFG7z9o4J2MoXxHVw26OXU91dK3p/B7n+4JVhpiaODAN+hn1AGHbtvvjuvYv59t2LfNLpZX+HqWtFi4Gad6OzUJdDeOxTVoAusEnceqSR0uwUFpdePnAjHomoDq6qaqeqqk+pqhrVHqhmg54HN8xJCCkGYJMmx5wPIscUzBdBzEvvRGdB1bvAnAEFgVvEajLMxkXFpBj12Jye4ZWnluFSTLykOAZDa+UbdLJS9jSRU31pR3SCqBd3gN4ctCjsXEsvp5t72bgocbx1GAqejjTsC0oyWT09l3SzAUXBJ/H5SMuHvDlQvTM6QdSuGvEgyQucoe2rdl9cnBAyDCRg5WkysHZWHumWEHKMTgcV14jGYD1BzokUvU0ivbJibVBv5SWvDHPt7DwsRh2DDjcO15DGnu7NWdekmHhtnayxtCzLO1kpiP+hKDBtHXTXifatE4mtR5Swl60KWDsAfjJMAunrMJTuqDW+SzEOd7x0OoU0k2G4xq4x7VoRRI3GTFqt+VuQFg6vnmjCFSDNOp6Rhj0GmA0iO+bV403B5Ziy1aICtHrnxC7mwluiPWmQ3HWn28OrJ5pZP78Qs0GP2ajH5nIH9NhbfVJMfO+cdDqF2xYW8/bZVt+s3MuYskIE8S5sn9jFVO8SufPT1gU8rKoqz1XVs6oih8IEkmHA32MPni2VkWIcrrFrFCwQ08UuvDnxu6bOi6J1tik14OEtRxopz7FelmYdz0jDHiNuX1xMj80VPDvGnCaGQNS+O3GVkPY+keJVulIEjgKw85zoY77R6y1aDHrsTs9l3R0B9l3qQFGgKDP+DdCmxUU4XB5ePxmkytFgEg+7pqMiFXQicDuF3FMwH9IKAp6y92IHF9v6uW9F4KBqPKN57FrwNJBhT7cYAsc6dDqYdp2QSTovTtwiVVV47DmBK007+h3sOt+eUDIMSMMeM9bOzCfdYmBLMDkGYMaN4HbAxbcnZhEX3xLe4vTrgp7y0tFG0swGrp0lAtMpJh2D3qwYvU501/RlxfTaWVWRQ3765T2s442lZdkUZViCt/IFIQfo9HDu9YlZRM1u0Xhsxk1BT3lyXy3pZoPvwZpI6HUKRr3i09gDxV4yLMbgu6ayVaJ/z0RdfxAPbUdfUBnm5WOi6WAiNF3zRxr2GGEy6LhlfhGvngghx2QUi/4xF9+O/HQZx4B43eLFQackaTLMzfMLfRkOFu/kebtL9P4A4bUb9cKbub0yMXRInU7htkVFvHUmhBxjyRSSWO27MNgZ2QW4XcJgZU+D3BkBT+kedLL1aCN3Li2J+4B0MCwGvZ8Uc7lEl5FioGcwSG8kgxmmXy8K9rrrJmaBPn09sMe+9WgD0/JSmV+cODIMSMMeU25fXEyvzcU750L0zpl5s5jifmlHZN/84tvgssGsDUFP2XW+na4B5zBvURtQbHd5fE2eFEURYwkTLM9606JiHC4Pb5wK0XRq5k2AGnmvsW6vCA7OuiXosPA3TomBDvcsK43se0cRs1EM24AgGrvF6PPoA1JxrWizcOaVyC9OVaH6HfEAD+DctPTa2H2+nU2LEkuGAWnYY8o1M/PIsBiCZ2cAZE8V1XjntkVOa7f3iqBU0SLInBL0tK1HGobJMCACYh5VjAcz+U1nz041cc3MPPLS4l+G0VhWnk1xpoUXDwfp3QNgzRFee/Uu6A/SvG2suBxw5mWx/S8IPMwBYNvJFvLTzSyJo0ZqY8Xie/gPVaL6s3BKJo3dNmo7gswpMFmFVNh0JPJtHpqPC4999q0BH65bjzTiUeHOJYmxC/VHGvYYYjLouGWBkGO0/uYBmXu7qII8+1pk3vjMK0K7n3tH0FNsTjd/O9rEhgVFPhkGhopOegadPo8d4OfvX8Z3710cmfVFCZ1O4Y7KErafbvUF+AIye4NoDnVqS2Te+OLboqpy3nuCeusOl4e3Trdy09yCuO8kGArNmKcY9QG93nWzhdOw42yIAPX0G0RzvOPPRS5DxuOBU1tF5k2QNg7PVzUwrziDWYWBEwviGWnYY8wmTY4JdWNnFItA0sW3RWvR8dDXIlIoy6+G9MKgp20/3UKv3cVdS4d7K5ph7x50+rIeAOYVZ1CcmUKiceeSElwelZeOhQiipmTBjBtEvnnHODM0bN1w7jWxCwuirQO8e7GdPruL9fOC/40SAe1+CVaNPCM/jZJMCzvOhpAjjRaYcxt0nI9cNXb1TtHsbe4mESAfQU37AFW1XQnprYM07DHnmhmaHDNKIdLc20Uw6ehfrtxrUVU4/AQYLGL7GYLnDjWQl2ZmzYzhbRr8Dbu/FJOozC/OYGZBGs8fCiHHgMhcsWTBkSfH18Pn+LMiE2n+XSFPe/1kC2aDjmsSoE1GKLT7JVjwV1EUrp2Vzzvn2nAF6niqUb5GVAMfe2Z8iQT2XuisFt563hwoXhLwtBcOiw7kdyRIMsBIEv+TmeCYDDo2LS7m5eNNDDhCTE6yZAjj3n5WZGlcCdW7hNcz/86grUlBGO03TrVwR2Ux+hEyQIq/x25M/NtHURTuWlLC3ksd1HcFGDSuYbTAovdBb+PYA6luF1Q9Bjt+IPrPzNoQdEISgNuj8tLRRtbNzk/YbBgNTYqxGoMXrV07O49em4vDdSHaS+l0sHiz2PGcfPHKFtNxEbZ9E975oeikuejegFKYqqo8X9XAyopspmQl3i4UpGGPC+5eWsqAw80rx0dpsTN1DeTOhGN/Hbsk09MIJ54TQzSCaIoaLx9rxOH2cNeSywOrWjCsZ9CVFB47wHsqxe/5QtUoXnvRQtH58czfxtZq4OQL4mFssIgGbzNuDHn6ngvttPTaA17/RGM0jx1g7cw8DDpl9Ps/Z5oIpFa/A03HxrYQ5yAc/IPoibTsw3Dtg0GLwk41iYHh70lQbx2kYY8LVkzNpiwnhWcO1oc+UVFg6YdEv/ADvxM3azg4bbD/t0LKWfKBoAE7jeerRO5uoE522gd10OnGHOc9YcKlPNfKsvIsnq8a5foDLLoPUrLhwO9Fn5fRaKgShWAV18LVX4DF94n5qiF49lA96WYDN80LbHgSCc0RCNXxM8tq4sa5BTx7qD60HANi15pZBof+GP5sVI8HDv1JpJcu+7BotpYRvODohcMN6HVKQhaFaUjDHgfodAp3Ly3lnXNtNHWPoh+mZImbs68F9v6fSJ0LhdMG7/5SzNJc9tGg/dY1mrpt7L7Qzp1LSgJmMVj85JdA6WuJyp1LpnCqqZfTTb2hTzRZYcXHxUN1zy9EW4ZgtJwSBii7YlRNXWPQ4eblY03ctmh4NlKiYjGEDp5qvHd5Ka299tDZMSD6ta/4OCh62PMIDHSEPl9VRVyq+RgsuDto6wANj0flhaoG1s7MIzeBUndHkjyfzATnnqVTUFV4LhyvMX8OLP2gkAN2/yx4VeRAB+z5OXRVw7KPQN7MUV/6xcMNqKowdIHwNzamJDLsmxaLeMKzh8K4/pmlsPJT0N8Ku34mOmSOpLueU1t+RLc+G1Z9ZlQvXePVE0302V1JIcMAvjjMaMNXbphTQLbVyNMHw6gwtebA6s+KArtdPxXB0EC47GJnW7NLFPoFabTmz67z7dR3DfLe5YlbFAYQ3234JhEVeaksn5rNMwfr+My66aNXuk1ZJtK0qh6Dt74rSq+1fheqR8wvrd4NqLD8Y6J1wCioqspfDtSypCzLN0F+JP6GPZk89rw0M9fPzuevB+t48JbZGEaLH+TPFgb74KMiKDp1DRQuEkE+jwf7gT+y/UI/askmPhekHW8gntxXS1lOCqun547zN4oPtJTY0Vo5mww67lwyhcferaF7wEmmdZT5xVnlQtra+3+w88eikV3ebCFTgpBdzr8hZgfPv0t8PsLgqf21ZKYYuWV+YqeZSsMeR9y9dApfe+4Yxxt6WDgljEktxZUiBezE83D6peHHFJ3oGrjgHkgNz0gcrOniTHMf37lnUdBzktWwA9y/qpzX/7CfN0+3cnM4H+z82XDd/xOFMxffHtbi1+mEP7nXc4sz/O18dXs/u8638+AtsxO6KMmfcIKnGvcuL+X3uy7x4pEGPrh66ugvnlkK131FZMk0HLw8WyyrXMSkQtQL+NM94OTl4008sLIs4WUwadjjiNsXF/PvL57grwfrwjPsICL7qz4Ffa1inJ5GelHQ/tLBeGJvDakmfcjcXYvBX2NP7Jt/JDfMyacg3cyT+2rCM+wg+ows/wjY7ho2FKW+z0Ld3uN0BxoiEYSn9teiU+De5YnXojcYvnTHMAz7gpIM5hal89eDdeEZdhAxj8rNIhW1rxlUb42BzgBphaMmCvjzXFU9DpeH+1Ym/vVPLpcrwcmymrh5fiHPHqrH5hxjEUxavvBMtK8xGvVem5MtRxq5o7KE1BAjBv09r2TS2AEMeh33Li/ljVMtowexR2LJhIK5vq8uRBl69+AowW0vLreHv+yv4/o5BQnRzz5cRqs89UdRFN67rJRDNV2cbw0RlA6ETicyXTJLxVd60ZiMOogH64KSDBaUJMZc01Ak1yczCfjA6nK6Bpyh+7RPAC8cbmDQ6eb+VeUhz7MYkleKAdi8sgyPCk8fqB3X62hzPMP12N8600pLr53NSeAt+mMJM3iqcefSEvQ6haf2je/6j5Vj9d0cb+hJmuuffJ/MBOfq6bnMLEjjj7svRfV9n9hby9yidCpHmcKu0ynD+rAnG1NzU1kzI5cn99fi8Vx5wyltjme4hv2JfbXkpZm5cW7i5677MxaPHaAg3cLN8wr5y4G6se9ax8FT+2tFALcyObKRku+TmeAoisKHVk/lcF03h2u7ovKex+q7OVrfzf0ry8LqO615YckmxWhsXllGbccguy9ceZtebdxbOIa9pcfGG6dauHd5KcYkqebVGIvGrvHB1VPp6Hfwt1CN2SLMliON3DK/cPRsnAQhue6iJOHuZVOwmvT8cU+Q/NwI8/jeGswGHXcvDS93V/PCki14qrFhQRFZViN/fvfKr782FSgcw/7EvlrcHpX7ViR27nQgfFkxY8gyWTMjl2l5qfxpT4T7rwdBVVU6BxxMD5Lim4hIwx6HZFiM3L10Ci8ebgjdJzwCdA04eOZgPXdUloTtrWgf1mT12C1GPZtXlvHysSbqOoMMgBgFzWO3OT0hJQW7y80f91Rz3ex8puenXdF7xTNDLQXCT8DT6RQ+cFU5B6o7ORqqMViEcHlUVJWk2i0lz2+SZHzo6qnYXR6ePjBBsx69PL63lkGnm49fE7rU2h/tw5qMGrvGh6+uQFEU/rD7yrz2Hj9PvSeE1771SCOtvXY+vjb8659IzCpIZ2ZBGjMLxvbQum9lGWlmA79+ZwzN1q4Qp7c/jTGJ7ufk+U2SjLlFGayqyOHR3ZdGb4x0hTjdHh7ddYk1M3KZXxL+sN6UJJdiAKZkpXDrwjgEuUIAAB3RSURBVCIe31tDnz1EO+Ug9NqGfiaYHKOqKr/deZEZ+amsm5XYfdeDUZZjZds/XTfmFM4Mi5HNK8vYeqSRhlDtlCOA0yWC5MnSrRSkYY9rPrVuOnWdgxOW+vhCVQNNPTY+MUZv0ZzkUozGJ9dOo9fm4rEr0No1KQaCG/a3z7ZxrL6HT14bRguJSchH11TgUVV+t3OcU6tGwe4WUpn02CVR4aa5BcwpTOcX28+NK/UuEG6Pys/fPMe84owxp9gNBU+T+/ZZWp7NNTNz+b8dF8ecetdjc5KXJvqWBDPsP3/jHEUZFu5ZlhwpdpGmLMfKeypL+NOeGtr77BF//Zr2AWxON063+GyZpccuiQY6ncLnrp/BmeY+Xj0xzlmnI9h6tJELbf383Y0zx+wtam0Fkt1jB/jiDbNo7bXz5BgLZnoGXZRmi+ZfXQOXG/a9FzvYe6mDT6+bntSS1nj54o2zsLnc/OadyHrt51r6uPEH23libw0Ol6axJ8+uKfk/mQnO7YuLmZGfyvdfPR0xrd3l9vCTbWeYVZDGrQuKxvzzWluBZPfYAVZPz2FlRTY/f/Nc6NGFI+ixOSnLEYZ9pMeuqirfe+UU+elmHhil0neyM7MgjdsXl/Dorku0RdBr/+Frp3F5VDoHnEPBU+mxS6KFQa/jyxvmcK6lj2fC6RUeBk/sq+V8az8PbphzRV0EtbYCk8HTVBSFr9w6l5ZeO7/ZEZ7XqKoqvTaXb17mSMO+7WQL+y518o/rZyX8TNNo8I/rZ2FzefjJtrMReb2jdd28dFT00Le7PD6PXQZPJVFlw4IiKsuy+OGrZ64oQ8OfPruLH287w8qK7CvuOZ3slacjWVGRw4YFhfzyrfO09o7uNQ443Lg9KtlWI+kWwzDD7nB5+O+XTzE9L5X7ViRHX5KJZkZ+Gh+4qpzH9tZwrmWUCVejoKoq/77lODmpJswGHQ6XB4dMd5TEAkVR+MYd82nutfHj186M67V+8Opp2vsdPLRp/hVnYlgmkRSj8ZVb5+J0q/zH1hOjnqtlxGSkGMlMMQ7LY//1Oxc419LHQ5vmJdXWf6L5h5tmYTXq+cYLx1HVK08k+OvBevZd6uQrt87BatLjcLtxej12GTyVRJ1l5dk8sKqc3+26xLH6K6vGO1TTye93XeJDq6eypCz07NNQDEkxk+f2mZ6fxudvmMHzVQ1sPx16iLLWTiDDIgx7l9ew17QP8JNtZ7l1QRE3zUvsCT3RJjfNzFdum8vOc+08tf/KOj+299n5r5dOsqw8i/ctL8Ns0ONweXxZMdJjl8SEr2yYS26qiX944tCYAnkA/XYXD/7lMEUZFr68Yc641jEUPJ1c+vDnrp/BjPxUvvrXoyHT74Y8dgOZKUa6B0WA7h+ePIRJr+Mb75kfrSUnFe9fVc5V03L4j60nqe0YW6sHVVX58tNH6LW5+Pbdi0SXUp8U481jlx67JBZkWo38ePMSLrT187XnjoW9JVVVla/89QgX2/r5wfsqSbeMr4PdbQuLePCW2WSkTK4BXGaDnp8+sJSOAQf/+GQV7iC1BZr0onns3YNOvvvyKQ7VdPGd9y6mODMlmstOGnQ6he/dW4kCfOaPBxh0hF9b8Jt3LvLGqRb+ZeNc5hWLKmuTQYfD7cEhK08lsWbNzDz+/sZZPHOwnh+FoberqsoPXj3DliONPLhhDmtmjr90fWpuKl+8cdakrJZcUJLJw+9ZwI6zbXz9+cAP15Ea+4XWPv5vx0U+fPVUNi0ujvaSk4ryXCs/uX8pJ5t6+KenqnypiqHYeqSRb790kg0LCvnomgrfv5v0OuzOoeCpKYny2Ed1uRRF2RbivDpVVT8Y2SVJRuMf18+isXuQn75xDo8K/3Rz4OHHbo/K9189zSPbz7N5RRmfXRfeUF9JaO5fWUZtxwC/2H4egG/esWBYhpCmsadbDGRajXhU2LCgkH+7XUowkeCGuQV8bdN8vrXlBF987CA/3rw0aNro0wfq+JdnjrBiajY/uX/pMGdE89idvnTH5JEWw9lLf0dV1W2BDiiKcpf3eyHwtKqq10ZycZLAKIrCf969CID/efMch+u6+Jfb5g1r5HW8oZtvbTnBngsdPLCqjG/fteiKctYll6MoCl/eMAcVeGT7eY7Xd/Nvd8xnWXk2iqLQ6/XY0y0GNi0qxuNReXDDHAxJtNWPNZ9YOw2dAg+/eIJNP93Bzz+wzCexADR0DfKj187wlwN1rJmRyyMfWO5rhaFhMuiwuzx+3R2T5/MxbpFUUZRs4FEgebrUJwAGvY7/fu9iFpdm8d9/O8XGn+5gZkEaU7JSaOga5GxLH6kmPd+7dzH3Li+dlLLJRKIVLlWWZvLVZ47y3kd2c+/yUr7/vkq6B52YDTrMBj2LS7NYXHrlGUiS4HzsmmnMKUznC48d5HuvnOa3H13Jc4fq+fU7FzjZ2ItOgc9cN50Hb5kTMDBqNujotbmG8tiT6MEbieiXG9gMPB/qJEVRPg18GqC8XJZRRwJFUfjg6qncUVnC43tr2H+pg6YeG+U5VjavLON9y8uSZtRXvHLrwmLWzc7nS09W8fKxJr7/vkra+xzkpZljvbRJwZqZeSyfmk19lw2AR3dfoqXHzueum8H9q8p8/XoCYTboaPevPE2idMdxG3ZVVXuAUT1CVVV/BfwKYMWKFZFtVTjJyUwx8tnrZsB1UkOPBVaTgcqyLF453syAw0VLr538dGnYo0VempnD3klLrb121s7M48EwUnqFFOMeCp4mkceePL+JRBJDCtPFIImWHjstvTYKM6RhjxZ5aWY6+h14PCqtY3iomvRa8NRboCQNu0Qi8afAa8hbeu209NopSB/bxCDJlZObZsLtUanpGMDu8oQtg2kFSk63B71OQZ9EyQXSsEskEUAz5LUdA3QNOCmQUkzU0Az5ycYegLA9dq2lgMPtwahPHqMO4Wnsn1EU5WtBjlUBzwGoqnp9pBYlkSQamiE/3iCMS4GUYqKGz7A3ic6PYUsxWksBlyep9HUIw7Crqvq+aCxEIklksqxGTHqdr0GblGKiR366GEF4aoweu6+lgNuTVBkxIKUYiSQiKIpCfrqZ4w3CsMusmOgx5LF7DXu4Grteh9OtYncmn8eeXL+NRBJDCjLM9HsbUxVmSI89WmSmGDHoFGo7BjHqFTJTwqvd0Lz0frsrqVr2gjTsEknE0HR2vU4hN9UU49VMHhRFITdNXO/cVHPYrTO0eQL9DldSpTqCNOwSScTQvPS8NJPsyxNlNDlmLBKYZth7bS4pxUgkksBoHrsMnEafKzHsmhTTJ6UYiUQSDM2gyxz26OMz7GPo0eMz7DZXUs07BWnYJZKIke/NXZc57NEnz5vyOCaP3dt/XQRPk0s6k4ZdIokQUoqJHXmpV66x9yVh8DRuhlY6nU7q6uqw2WyxXookSlgsFkpLSzEak6O1cFmOlVSTnrlF6bFeyqRD89jH0i5Zk2JUNbk6O0IcGfa6ujrS09OpqKiQQyEmAaqq0t7eTl1dHdOmTYv1ciJChsXI3ofWYw0ypk0ycUzLS/N+D3/ej3+1qQyeThA2m43c3Fxp1CcJiqKQm5ubdDu0VLNB3sMxYElZFru+euOw8ZCj4W/YZfB0ApEfiMmF/HtLIklJVsqYzveXX5JNY0+u32YS8NJLL9HZ2QlATU0Ne/fujfGKJJLExGL0l2KSy8mIG409HvB4PCiKwje+8Q02bNhAbW0tzc3NrFu3jhdeeIGvf/3r/PnPf+Y73/kOaWlpvPvuu9xzzz10dHQAcPbsWbZt28a8efNwu91cf/31WK1WFEWhvb2dtrY25swRI7sGBgb42c9+RmVlJQAPPfQQn/zkJ5k2bRoOh4MHHniAv/71r8PWp6oqDz30EI899hjV1dV0d3fz6KOPYjKJwFFRURFFRUUArFu3DqPROMwrrq2t5fTp0xN+HSWSREBLdxz538mANOx+vPHGG3zzm9+kvr6eLVu2YLfbcTgc/OEPf6C9vZ1XXnmFW2+9lYcffpjHH3/c93MGg7iMOt2QB6DX63nttdew2+3o9Xp27NjBtm3bePjhh/F4xIzFjIwhPfDAgQN861vfAuC1117DarVy6tQpAKZPn47JZGLr1q2sXLmS48ePU11djU6nY/Hixbz55pu43W5Wr17tM+xGo5FXXnnFtzaA6667boKunESSeAwPnkqPPWlZv34969ev56c//SnXXHMNnZ2ddHR0UFlZyfbt2/nMZz7D008/DQh9eOvWrdx4440UFhYyb948tm/fzuuvv05RURHZ2dns37+fxx57bJjB/+pXv4rH42HhwoV8/vOfx+12k5eXR2VlJcuWLeOOO+7g0KFDZGRk8J3vfIe9e/fyzDPPMHfuXP7rv/6LpUuXsnbtWn75y19itVrR6XR4PB4MBgMPPvig731cLhcf+chHhnnsra2t0buYEkmck8zB07g07A+/eJwT3kk0kWJ+SQbfuGNByHPe/v/tnXtUVGW7wH8vM1wUUik5ZhJ5WZJXPrx8mmbG9wHqsvqO6adpkUc7rsSjp+MfVqifZhcpU9OWBhimZGin9Ku1TPKGp7Es5ICKll1QkyNeRg0T0bjN8J4/ZmbLwAADzjDj+P7WYrFnv8/e+9nP7P3Ms5/3fZ/99de88MILdOjQgc8++6xee2pqKhMmTKB3794AZGRksHDhQl5++WUmTZqEEIKjR48yduxYQkNDCQ4O5scff3R4rL59LbrodDoGDhzIvn37MBgMpKSk0LlzZ1auXEm7du2YPXs2gYGBbNq0SRsW2KFDB5KSkuyicRs1NTVIKdm1axdms9muzfYjYFtWKO5k7CJ25dh9l4qKChISErTINyUlhYCAAGbMmAHAihUr6Nq1qyYfFhZGYmIiERERBAcHk56eTlVVlRYll5WV0bVrVzIyMjAYDOzatYu33npLW7Zx9OhRYmJiuHr1KuPGjePZZ58lISGB7du3U1lZSXBwMGPGjGHQoEGkpqZiMpkwGo3odDqEEEgpNSe/d+9eVq5cSUBAAN9++y0PP/yw3TmazWamT5/OpEmT3GlKhcLrsRsV42Pj2L3SsTcVWbuLUaNGER4ezv3330+PHj04f/48fn5+ZGZmcvLkSfbv38/hw4dJT0+nvLycjz/+GJ1Ox+XLl3n77bc5ePAgNTU1WlR89erVBo8lpdSWo6Ojyc7OxmAwYDAY6NGjBzqdjmPHjlFeXk5wcDDBwcH88ccfAAQEBLB+/Xq7/XXv3p2JEycyevRoRo8ezZkzZ0hKSmLLli1usJRCcfvjrxMIoWae3hH4+fkRGxtLRkYGaWlpBAUFMW3aNCZPnoxOp6OyspLnn3+e/v37a9F3bm4ufn5+7N69my5dumgRd35+PgcPHiQuLk7bf1xcHIGBgTzzzDON6vHee+/RqVMnysvLadu2rV2bXq9Hr9eTnZ2trXvsscfsZE6cOMH58+eJiYnR1h06dAij0UhwsPOz8xQKX0UIQYDOj0pTjYrYfR1btF2bnJwcCgsLCQkJYfLkyQghqKqqolevXuTm5rJp0yays7Oprq6mbdu2dOvWjeLiYtLT09m3bx99+vRp9JhHjhyxS8UA3HfffQCUlJRoqR2TyQRYLsgjR47Y/WDYonkbsbGxxMbGap+rqqoYNmyYcuoKRS0C9BbH7mudp751Ni7ggQceICkpCbAUJqusrKSsrIxFixbRsWNH9Ho9Op1OS5WkpqYydOhQqqurSU9PZ9iwYbRr145z586xdOnSJp262Wxm0KBBGAwGUlNTtej8ww8/pGfPnkRERGiy169fp6qqiurqagYMGEB2drb2FxIS4nD/xcXF9OrVi6FDh5KQkOAiKykUvoGtwqOvDXcUtXO9rcXgwYNlfn6+3bqffvpJG22igN9//x2TyURYWNgt78tsNqPz0gkY6ntXeJLhb+7jfGkFa58ewONR93lanSYRQhySUg5uSk6lYryU0NBQl+3LW526QuFpAv0t94avdZ761tkoFApFM7A5dF/rPPWts1EoFIpmYJukpDpPFXY1xKurq6murm7V45eVlbXq8VRFSYWvEqBXEfsdxWuvvVZvEpCNcePGsX//foqKiti4cSPPPfccRUVFnDp1CpPJhJSSbdu22Q2dnDJlCjt27CA7O5vc3FxtfVpaGsXFxXb7rz2MsS62semNTX4qLS3l119/JS8vjy+//JK1a9cyY8YM9u/fbye3cOFCTp8+DViGQ06YMKHevmwVJY1GIwUFBZw+fZq0tDQKCgooKCjAaDRqsiNHjiQ2NlaruRMXF6dVs1QovBEtFeNjEbvqPG2AwMBArRxubU6dOkVgYCCVlZVs3bqVvLw8Kisr2bZtGyaTidmzZxMSEoLRaGTKlCl88sknANoMVX9/f7sIPzIykgkTJrB69WoWLFgAwPfff69NLNqyZYs2ph0s49uTkpIoLCxkyJAh9fT79NNPWbx4MX369CEsLIzw8HC6du3K9OnT6zlZVVFScacTaK3J7mudp8qx16J379506dIFsKQcAgIC2LRpExUVFYSEhLBr1y4WLFhA7969iYuL48033+Ts2bP4+flRWlrKokWLuOsuy4uM58yZw9SpUxkzZgwVFRWcPHmSoqIi9Ho9x48f1yLlyMhIDh48SE5ODjExMSxZsoTHH3+cHTt20LdvX81pjh49moqKCrtqjVeuXKGiooLCwkJtXU1NDY8++igTJ060O7fy8nIOHz5MVFQUYWFhqqKkQsFNhx7gY+PYlWOvhb+/vzZNf8WKFdx7770kJCRQVFTEnDlz2Lp1K0ePHqVbt274+flx48YNPvroIwCysrK0PPSJEyd48cUXeeedd+jUqRNpaWkkJiYSFRXF9evXmTZtGiEhIfzwww88+eSTrFixgnvuuaeePtnZ2VoVxt27d9u15eTkMGvWLK2MsI3KykqHTxpws/KjqiipUFiw5djVizZagx8+g2vnXLvPdl2g3/hGRZpyPH379mX16tUYDAbAEgWfPHkSgEuXLmlyPXv2ZPny5Zw4cQKTycTy5cs5ffo0M2bMIDo6mszMTAD69evHV199RXFxMdevX2fDhg2sW7eOBx98kKioKEaOHMnatWvr6WEwGJg/fz47d+6kc+fOdm1Xr15l+PDhbNy4kXPn7G349NNPM2bMGEBVlFQooHbnqYrYfRaTyaR1XNpSMRkZGVRUVNCuXTv69OljV5PlwoULWger0WgkPj5eawsNDeXMmTPodDoWL17MgAEDmD9/PoGBgcycOVOTCw8P591336V///68/vrrZGZmsnfvXl566SWeeOKJejpmZWWRnJzMF198QceOHeu1nzlzhuHDh3PhwgXtBwjgwIED7NmzR/usKkoqFLVKCqgceyvQRGTtLjZs2KB1SNZOxdTNY9vo2LGjVrSr7hDAlJQUoqOjMZvNbN68mYsXL9KrVy/mzZtHRkYG48ePR6/X88EHHwCWVMv8+fMpLi4mOTmZ3Nxcli1bZrfP9PR0MjIyyMrKokOHDg7PITc3l+TkZIdPH009kaiKkoo7jUC9deapjw139E7H7iFqjzKx5Y0BgoKCiIqKAizD/2pqajCbzbRv354RI0YAN2uvm81miouLycrKYuDAgej1en7++WeKiopYtWoVFy5coEePHnz++edERESQkpJCZmYmM2fOpF+/frRp04bIyEjWrl1rV9Vx2rRp3LhxAykle/fuJT4+3s65SykxGAwEBwcTGBhISUmJ3bDJ0tJSxo4dq31WFSUVito5duXY7whslRTrUllZyW+//cbYsWMJCwtjyZIlWlteXh4mk4l+/fqxdOlS1q1bR2JiIt27d2fu3LlMmjSJixcvsnnzZqKjozl+/DgpKSlkZWWRmJjIK6+8wrFjx/juu+9Yv349I0aMYN68eTzyyCMMHjyYuXPncvHiRVatWsWyZcu4cuUKANeuXWPNmjUMGTKE5ORkAEJCQuyi6wMHDrB9+3bgZkXJPXv2kJOTwzfffANYKkq+8cYbPPTQQ/XsULuipI1Ro0Y5tF1xcTHx8fG0adOGqVOntvAbUCjcj6+mYlR1RzdSXV2Nv7+/U7KFhYVcvnzZruPRaDQSEBDA3Xff3exjl5aW0r59+2Zt44mKkr74vStuHwovlmH45RLPj+zhaVWcwmXVHYUQ2Y3InZVSqiLfDeCsUwfLePbIyEi7dbYx7C2huU4dVEVJxZ1HZKe7iOx0l6fVcDnOpGLeklJmO2oQQowTQrQH/hvQATeAp6SU9XMYCoVCoWgVXJFYegZ4R0o5CjACY1q6I0+khRSeQ33fCoV7uGXHLqVMkVLutX4MAy45khNCPC+EyBdC5DuaZh4UFERJSYm62e8QpJSUlJQQFBTkaVUUCp/DZaNihBDDgFAp5UFH7VLK94H3wdJ5Wrc9PDycs2fPqtoidxBBQUGEh4d7Wg2FwudwiWMXQtwNrAHq1311En9/f60uiUKhUChazi2nYoQQAcBWYL6U8v9uXSWFQqFQ3Aqu6Dz9d2AgsFAIYRBCPOWCfSoUCoWihTiTipkphPhHA20FUsq5QKoLdVIoFArFLeCRmadCiMtAS9M2HYHfXKiOK/FW3ZRezcNb9QLv1U3p1TxaqtcDUsomp4Z7xLHfCkKIfGem1HoCb9VN6dU8vFUv8F7dlF7Nw916+VblG4VCoVAox65QKBS+xu3o2N/3tAKN4K26Kb2ah7fqBd6rm9KrebhVr9sux65QKBSKxrkdI3aFQqFQNIJy7M1ACNFeCLFTCLFHCPG5ddZtXRm9EOKMdbKWQQjR3xO6egtCiFm1bFEghFjnQEbZDBBCdBJCfGNdjrDa4n+EEO8L27sK62/TRQhxtpbtbv0tKbcRdWz2ai07/CyEmN/ANr5vMymlV/4BHwA5wD9uRcbFOv0HEG9dTgX+5kBmILDMA/bSA2cAg/WvfwNyrwJ5wHse0HENMNjTNgM6Ad9Yl/2BL4Bvgeca2cYpuVvQKRTYBRy2fl4K9LYu7wSiGthuPDCrlW3WBThb61oLa2Q7t92jdW1Wp20b0MUTNgPaW7+zPcDnQICzdnCVvbwyYhdCjAd0UsphQHchRM+WyLga6VyJ4oeAx4UQ/yuE+EAI0VrvlY0CPpZSxlj/vq8rIIQYBIwAhgCXhBBxdWXchRCiC9BJSpnvoLnVbCaECAU+BGxv2P5P4JCU8mHg70KIhl6n46xcSzEDTwHXAKSUC6WUP1nb7qHhySwPATOEEIeFEMku1glwaLOhwNJa15rDkqytcI/a2azWcf+M5e1u5xrYzt02q/uOisk4YQdX2ssrHTsQA3xqXd6DxRm1RMYtNFGiOA+Ik1IOwRLljW0ltZxxjo8C/5SW0GA38Egr6QYwm4ZLT7Smzeo6gxhuXkdfAw1NGnFWrkVIKa9JKUvrrrfWXjoupTzfwKY7rbr9GRgmhIhypV5W6trMWccYgxvv0YZsBvwXlqfDhnCrzRwEgAk4Z4cYJ+WaxFsdezBg+7W9guUxsCUyLqdWieLnGhA5JqW8YF3OB9z+JGHFGefoKZv5AX/B8tjuiFazmQNn4KxNWt12QojuwDxgbiNi30kpy6SUZuAIbrCdA5s56xg9YbMOwL9IKU81IuZ2m1l1GYYlXVRMK19j3urYrwNtrMshONbTGRmX4mSJ4o+EEH8SQuiAccBRd+tlxRnn2Oo2s/IIkGt9UnCEp2wGztukVW1nTX98jCWf7ygqtbFbCNFZCNEWGAX84E69rDjrGD1xvf0r8GUTMm63WZ0AsNWvMW917Ie4+RjyJ6CohTKupm6J4leEEG/UkXkN+AgoAHJkAy8CdwPOOEdP2AxgNJb0BUKIPl5kM3DeJq1tuyQgAlhjvdYeFUL8VQgxp47cq8BXwEEgTUr5i5v1AucdoyeuN+1aA/CEzRwEgK1/jbmrZ/gWe5XbYXFM7wA/WU/yjSZk2ntabw/brB9wDPgey4iKu4H1dWT8sIzqeBf4Bejmab09aC+D9f8DwHGrTfIAHfBXYE4d+Xpynj4HD9rsL8DP1uttjnVdH3WPauc9C/idm6OG/q2uHdxtL6+deWp9FI0HvpZSGlsqo7BHCNEGeAzLELFfPa2PNyCEuA9LpLRbNpL2cFZOcRN1j1pw1g6uspfXOnaFQqFQtAxvzbErFAqFooUox65QKBQ+hnLsCoVC4WMox65QKBQ+hnLsCoVC4WP8P5NJ8vSh57mYAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1ff6f587dd8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=使用中值滤波剔除瞬间噪声\\n\",\n    \"t = np.arange(0, 20, 0.1)\\n\",\n    \"x = np.sin(t)\\n\",\n    \"x[np.random.randint(0, len(t), 20)] += np.random.standard_normal(20)*0.6 #❶\\n\",\n    \"x2 = signal.medfilt(x, 5) #❷\\n\",\n    \"x3 = signal.order_filter(x, np.ones(5), 2)\\n\",\n    \"print (np.all(x2 == x3))\\n\",\n    \"pl.plot(t, x, label=u\\\"带噪声的信号\\\")\\n\",\n    \"pl.plot(t, x2 + 0.5, alpha=0.6, label=u\\\"中值滤波之后的信号\\\")\\n\",\n    \"pl.legend(loc=\\\"best\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 滤波器设计\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sampling_rate = 8000.0\\n\",\n    \"\\n\",\n    \"# 设计一个带通滤波器：\\n\",\n    \"# 通带为0.2*4000 - 0.5*4000\\n\",\n    \"# 阻带为<0.1*4000, >0.6*4000\\n\",\n    \"# 通带增益的最大衰减值为2dB\\n\",\n    \"# 阻带的最小衰减值为40dB\\n\",\n    \"b, a = signal.iirdesign([0.2, 0.5], [0.1, 0.6], 2, 40) #❶\\n\",\n    \"\\n\",\n    \"# 使用freq计算滤波器的频率响应\\n\",\n    \"w, h = signal.freqz(b, a) #❷\\n\",\n    \"\\n\",\n    \"# 计算增益\\n\",\n    \"power = 20*np.log10(np.clip(np.abs(h), 1e-8, 1e100)) #❸\\n\",\n    \"freq = w / np.pi * sampling_rate / 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1ff6f59a518>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=用频率扫描波测量的频率响应\\n\",\n    \"# 产生2秒钟的取样频率为sampling_rate Hz的频率扫描信号\\n\",\n    \"# 开始频率为0， 结束频率为sampling_rate/2\\n\",\n    \"t = np.arange(0, 2, 1/sampling_rate) #❶\\n\",\n    \"sweep = signal.chirp(t, f0=0, t1=2, f1=sampling_rate/2) #❷\\n\",\n    \"# 对频率扫描信号进行滤波\\n\",\n    \"out = signal.lfilter(b, a, sweep) #❸\\n\",\n    \"# 将波形转换为能量\\n\",\n    \"out = 20*np.log10(np.abs(out)) #❹\\n\",\n    \"# 找到所有局部最大值的下标\\n\",\n    \"index = signal.argrelmax(out, order=3)  #❺\\n\",\n    \"# 绘制滤波之后的波形的增益\\n\",\n    \"pl.figure(figsize=(8, 2.5))\\n\",\n    \"pl.plot(freq, power, label=u\\\"带通IIR滤波器的频率响应\\\") \\n\",\n    \"pl.plot(t[index]/2.0*4000, out[index], label=u\\\"频率扫描波测量的频谱\\\", alpha=0.6) #❻\\n\",\n    \"pl.legend(loc=\\\"best\\\")\\n\",\n    \"#%hide\\n\",\n    \"pl.title(u\\\"频率扫描波测量的滤波器频谱\\\")\\n\",\n    \"pl.ylim(-100,20)\\n\",\n    \"pl.ylabel(u\\\"增益(dB)\\\")\\n\",\n    \"pl.xlabel(u\\\"频率(Hz)\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 连续时间线性系统\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0,0.5,'位移（米）')\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1ff6f5cc470>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=系统的阶跃响应和正弦波响应\\n\",\n    \"m, b, k = 1.0, 10, 20\\n\",\n    \"\\n\",\n    \"numerator = [1]\\n\",\n    \"denominator = [m, b, k]\\n\",\n    \"\\n\",\n    \"plant = signal.lti(numerator, denominator)  #❶\\n\",\n    \"\\n\",\n    \"t = np.arange(0, 2, 0.01)\\n\",\n    \"_, x_step = plant.step(T=t)  #❷\\n\",\n    \"_, x_sin, _ = signal.lsim(plant, U=np.sin(np.pi * t), T=t)  #❸\\n\",\n    \"#%hide\\n\",\n    \"pl.plot(t, x_step, label=u\\\"阶跃响应\\\")\\n\",\n    \"pl.plot(t, x_sin, label=u\\\"正弦波响应\\\")\\n\",\n    \"pl.legend(loc=\\\"best\\\")\\n\",\n    \"pl.xlabel(u\\\"时间（秒）\\\")\\n\",\n    \"pl.ylabel(u\\\"位移（米）\\\")\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "4.scipy/7.scipy-interpolate.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pylab as pl\\n\",\n    \"from scipy import interpolate\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib as mpl\\n\",\n    \"mpl.rcParams['font.sans-serif'] = ['SimHei']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 插值-interpolate\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 一维插值\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> **WARNING**\\n\",\n    \"\\n\",\n    \"> 高次`interp1d()`插值的运算量很大，因此对于点数较多的数据，建议使用后面介绍的`UnivariateSpline()`。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x274ccb5f6a0>\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x274caae26d8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=`interp1d`的各阶插值\\n\",\n    \"from scipy import interpolate\\n\",\n    \"\\n\",\n    \"x = np.linspace(0, 10, 11)\\n\",\n    \"y = np.sin(x)\\n\",\n    \"\\n\",\n    \"xnew = np.linspace(0, 10, 101)\\n\",\n    \"pl.plot(x, y, 'ro')\\n\",\n    \"for kind in ['nearest', 'zero', 'slinear', 'quadratic']:\\n\",\n    \"    f = interpolate.interp1d(x, y, kind=kind)  #❶\\n\",\n    \"    ynew = f(xnew)  #❷\\n\",\n    \"    pl.plot(xnew, ynew, label=str(kind))\\n\",\n    \"\\n\",\n    \"pl.legend(loc='lower right')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 外推和Spline拟合\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x274bb27f588>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x274caae2588>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=使用UnivariateSpline进行插值：外推（上），数据拟合（下）\\n\",\n    \"x1 = np.linspace(0, 10, 20)\\n\",\n    \"y1 = np.sin(x1)\\n\",\n    \"sx1 = np.linspace(0, 12, 100)\\n\",\n    \"sy1 = interpolate.UnivariateSpline(x1, y1, s=0)(sx1)  #❶\\n\",\n    \"\\n\",\n    \"x2 = np.linspace(0, 20, 200)\\n\",\n    \"y2 = np.sin(x2) + np.random.standard_normal(len(x2)) * 0.2\\n\",\n    \"sx2 = np.linspace(0, 20, 2000)\\n\",\n    \"spline2 = interpolate.UnivariateSpline(x2, y2, s=8)  #❷\\n\",\n    \"sy2 = spline2(sx2)\\n\",\n    \"\\n\",\n    \"pl.figure(figsize=(8, 5))\\n\",\n    \"pl.subplot(211)\\n\",\n    \"pl.plot(x1, y1, \\\".\\\", label=u\\\"数据点\\\")\\n\",\n    \"pl.plot(sx1, sy1, label=u\\\"spline曲线\\\")\\n\",\n    \"pl.legend()\\n\",\n    \"\\n\",\n    \"pl.subplot(212)\\n\",\n    \"pl.plot(x2, y2, \\\".\\\", label=u\\\"数据点\\\")\\n\",\n    \"pl.plot(sx2, sy2, linewidth=2, label=u\\\"spline曲线\\\")\\n\",\n    \"pl.plot(x2, np.sin(x2), label=u\\\"无噪声曲线\\\")\\n\",\n    \"pl.legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.053  3.151  6.36   9.386 12.603 15.619 18.929]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.array_str(spline2.roots(), precision=3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x274ccb1c358>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=计算Spline与水平线的交点\\n\",\n    \"def roots_at(self, v):  #❶\\n\",\n    \"    coeff = self.get_coeffs()\\n\",\n    \"    coeff -= v\\n\",\n    \"    try:\\n\",\n    \"        root = self.roots()\\n\",\n    \"        return root\\n\",\n    \"    finally:\\n\",\n    \"        coeff += v\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"interpolate.UnivariateSpline.roots_at = roots_at  #❷\\n\",\n    \"\\n\",\n    \"pl.plot(sx2, sy2, linewidth=2, label=u\\\"spline曲线\\\")\\n\",\n    \"\\n\",\n    \"ax = pl.gca()\\n\",\n    \"for level in [0.5, 0.75, -0.5, -0.75]:\\n\",\n    \"    ax.axhline(level, ls=\\\":\\\", color=\\\"k\\\")\\n\",\n    \"    xr = spline2.roots_at(level)  #❸\\n\",\n    \"    pl.plot(xr, spline2(xr), \\\"ro\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 参数插值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x274ccd64780>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x274ccd00f28>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=使用参数插值连接二维平面上的点\\n\",\n    \"x = [\\n\",\n    \"    4.913, 4.913, 4.918, 4.938, 4.955, 4.949, 4.911, 4.848, 4.864, 4.893,\\n\",\n    \"    4.935, 4.981, 5.01, 5.021\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"y = [\\n\",\n    \"    5.2785, 5.2875, 5.291, 5.289, 5.28, 5.26, 5.245, 5.245, 5.2615, 5.278,\\n\",\n    \"    5.2775, 5.261, 5.245, 5.241\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"pl.plot(x, y, \\\"o\\\")\\n\",\n    \"\\n\",\n    \"for s in (0, 1e-4):\\n\",\n    \"    tck, t = interpolate.splprep([x, y], s=s)  #❶\\n\",\n    \"    xi, yi = interpolate.splev(np.linspace(t[0], t[-1], 200), tck)  #❷\\n\",\n    \"    pl.plot(xi, yi, lw=2, label=u\\\"s=%g\\\" % s)\\n\",\n    \"\\n\",\n    \"pl.legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 单调插值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x274ccd11780>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from scipy import interpolate\\n\",\n    \"\\n\",\n    \"x = np.arange(0, 2 * np.pi + np.pi / 4, 2 * np.pi / 8)\\n\",\n    \"y = np.sin(x)\\n\",\n    \"tck = interpolate.splrep(x, y, s=0)\\n\",\n    \"xnew = np.arange(0, 2 * np.pi, np.pi / 50)\\n\",\n    \"ynew = interpolate.splev(xnew, tck, der=0)\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"plt.plot(x, y, 'x', xnew, ynew, xnew, np.sin(xnew), x, y, 'b')\\n\",\n    \"plt.legend(['Linear', 'Cubic Spline', 'True'])\\n\",\n    \"plt.axis([-0.05, 6.33, -1.05, 1.05])\\n\",\n    \"plt.title('三次样条插值')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 多维插值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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GPWWvv7vD5irIKdF4rx4bOtrfW2vPGmLPAq4BftdY+AnzG6/N58ogMRbksWLd57e8EYzvDZUsoS22h2rGOkalVzlQ0LVzM/vNCJhNvcNVO3ncOh7SFc17bf98vYxEyGflOZVm6wmkG7tkzr50QHrBRftV9pqcL85HQCqbMm5J6haHIb7s/N9EWcWSi7zbTqb3DWvs4VUSGolwRHOa8XsQc1GSjXvY8hVCU0UQY2jPqh9JXkTO98tU/xD5IyGTkm4FkxJETBCGBMAZbCJXpDPbFV3AF63pZXvYamDcZOYEUMhPF8DOfs+p5WxfzG822e8Gzm+W1KnFP1iyqigwedsSR1jJSFEVRAC1doShrQ4p24Pepm2rm/zm3aRYS6ZycibyBJvNFdbJYnocgyz/PAmaSRqR5xo9E8vMQzoEt2ntj2Ct293uzsrgEUJ2YtkGuJUC+i98sIo5qpTNCZiJfUwjlNXhlMQZFezIcM6udwlb/PftMar+3zFPoIuJoGVQgKMoRs4wggLgwiH2ubQGR7/eED6EpvNQtWC6qK6M6BnPMgM3i3IMpGTO3Ive8SLppoC3DUX3fArkwuFC0z49hr3h7j7pAcGxAeQznxgw2i/udGsHJQpiYNiGQf8HqWi/QHlIm3fUH+0y33RkQfbH4V6ajriOODoqajBRFURRANQRFWUuaTCwhzaBNK4hpEPHn101GMs8glNQmX522MCnOScvbg/IQe9vbrxy8cnc9/0Vz/EgkcjPR+aJ9HrhQvN2oIYi2yzTcH1dO6FNOg2gyEw1EWzqpXdLbDqUWMRjCrFeY0gaTWg2j/BZV3adYxFG95EX9h3LX/Sq165KYpijKgqQtzvUFvs085Pdx7yVF/wjkIt9nPpQy9hnpV3Dmoy122SuSCrcGI4ZyYZW2+BieOWdvVJmJLlC1zxMWCFD3J5QCgTwyCWBaRDBdQxWdVBuf35bZzoHMZzOCbJCbx7JB/TeEXEj613zkAu9HgK2i/PVaCwRL2pdOzUD+VrAMTZhvLtC3S179zMS04q+m3/NdacdDs+mXbIvwpv+U1m/4PWn9zrz24bSOwMOcSeqXmmmuKErFWgsERblSiW12FnEI+7t+aR7yo3tStQxH/TS0aWkG6jPxYuv9UhVTqlPCxmXE0S5bpYN5d3vIsDjEvmZu8R21AVwN2amIJton7FSOWX42qH92rt8OXFN8LRM7TEceouNHKgnzlivzPRmOmWSV4x3y36eeCLh8TkJXqFNZURRFAVRDUJS1IaUMRWj37/pIB3Oo6Fzoff/+IaTzc8yAQVmhc6/RpNtjxqDwIfS9M5X3t/Nt9MY2YVt8k7O5HHf16oqJS21hSjgnoUddQ0Bchzw81Z3zfEpqBUOqMhc73rjd9RG1cxVcCGq2PaOfuXDcfvG8+pnUMg8hlpMQ89/IrOWDoAJBUdaQ+ZpF89FEEDcTjctFe14INAkH+fzMW7AgN3M489GUbK6q7Hwuw7xjeos9LpzOl/BrLo6qxXSb3BQD847aYqUygRVLmn32iAsEd32DOR91ed29bhSmns2LsOHG4VdGjZiJarkUxXfrjy4xHlTnS7vX0G/sI8tSSFOSo8dsLtLIsNzJAWoyUhRFUQDVEBTlyFm0YmnbTn9Mf06j8K9JbSKkZUjqu9jN0gw0ZsBWYaxpOw8hz0ioTEaltnB6xNDtus9T1xakhiAdzxRlKFzeQPBXqmsO+6JfzNl8vnjdRDipR5W2UKuM6ldJDR34I4rk9WaUBwT1snkNId/598u2zFpu+zvpMvxUBYKirBFh802vdQH3zURSELhrk7KdMS5W25gpSY7FjzjaE4u8u+dYmJHkPXreZ939Sn/C9oQbTxcJAPIgnG3gdNE+T104kNci2rzonhFfyPYjbRnM5BZ/l5sgzU57Y1Hawj9rWbZDJiNRRttMKQ8IyrL530Sa12JknmloFajJSFEURQFUQ1CUI+UgB9z4ph9pEnI79onQBOS1SoMYxLWPmXBUil2tM/3IMhXy3vKZElmmwWUwD5iwdW1udjp1cb9e1VQeinNaXAe2LlYawiaUB6vuUXcwOzORn70sncq+g3kfL09BlrSQ5TRkWx7QEzAZMYVsWmUt57/HNFjN1D8sp6uzDlJYc4Fgkn6E1KzUB7kp+cmf+OaLk/pd+vh2eyfgj5796rQHPz+t29t/6fa0jsCL35DW7xOJ9zv/tbR+px5K63c1jyc+uSqz3Eab+r2ONEUWtZl1ZI2hmA/BLdR+2/WdjPvMpsX9phmzaSTEsZevcoPhhN1BUY6i8CXssVszTfkVPufuxZR+VlQEffIjDEMnpvnt4vUaV/76YmXi2aW+sLvr7s9NyIS2mnAo/rAhT3rzJUnoTGfprAhMRz/CKDRn/VDTWF2j6j4Hq3yqJiNFURQFWHsNQVGuVMLab5upKM3B3AuacmR7tzCyTMZ9xqNiRz/tsV+0mWbgNIRp/YTe/V4e4z7qTblqmGtsF4Z5AsHJ0zulw3rMoFbmIvQdZUJWtj3j6U/+dv7ryGidHYIawlbRvmaUH4yT3zvMlPBiJx3SsWiluRs5YtqA1BbE9axoh44plRyldqsCQVGOmEUii2R/6TeY0K8tvr4PYUy/rDY6ZsBknL+/u7NVCYHRAEbF4i8jZ+bWr6JPb4NLw3wZHQ1z0+loZ4u9q/N4nd3tTVEKOywcJBkzBtfkAubG0blwKGcgmufaKUy/FbxlucDJyCHJBvUMZvcaXRiXXTEjMmDZxT8WjnpQ1GSkKIqiAKohKMplxXz9osqpXB1GUzmK6yaj4v1xn92dXFvYH/Vhp8j6GlHtwGU7FIoDc0dHAnBiyE5xv92rt5hcV2go2UA4m+sJcDI/oUzUOjPj+tlONRYZuQP1KJ8pVbF6oSn0gMeKtjwDwf8KTkNwkUqb3rXa2QiSDjfostqpS1A7ClQgKMqaEPMfxCKLfL9BXTjUaxmN6dfNRDvF8jjayG30kL+ORFvW5Gk7b3goXp/Im5eu3uaxQvDsXrfJ5LRMXgtHTVXCYUp204MAXDMd1aN78i8mf6CSG4Ded/L25qxa2M+LbrtV95rJaFO8unYvE7WM/ANy5G/QtpJ2sNI2nYYWOj1tGdRkpCiKogCqISjKWpFShbTtsJwZvbJP6dSdDcpoov1RP9cMINcEpIYQ0hZ8DcG3ucjXIVXF0h1gJ3dAj3au4aEbC/PRDfHkNYnb9fae/iCnpvvV8/MPRrWWa4uxbHwHeq5qKZVT+WTkKzit4FTxH+QlMoJnJ2fielM7kbajSQ+Lox+BohxjUiNF2oSDbEsfQnlt1GcyKmwfo0FYCDxBXCCEhIMkJBCeAK6u7ndplEcifXvUZ3JjYdIa1ENn67cs7EIZPOPpDwCwPcuzfaOmK2G+OTWAzcKJcH4HzovQ1FCSmhMIW8DJIt90a5uqiN2QapGX5yTEzl2W4wJmgdV2kUihpr5d1Thaa4FgEzOVJ+XfTDOLnJN86ffSMpB5Z+IN/zyt2zve/NakftntzbHMktt5R1K/17w78YbPTOyXdvxxGQ6ZwmGk7yvKcWWtBYKiKPWds1+zqP5epRVIUxHkpSgulfkGpu48jpmMUsxHDrlDLyOOqGkI1f02ODe6MR/z0zKm2y0aAlX9n5uemTuat7mE17l6FZVRnUP42vNwypW6GFXlKKazopQ2VTTR5tA7FGe7ul+tHTsLOtQW+5jYYUfV+0e36VGBoChrSigZzaeepDbvQ3C1iSajQZV57JuAUsxHO8x/NmY6kgKh5R47o+t58BnFWE/XlyOZ0VsmcBUL9U3PfLASCtJMk1E363ynGstGUVp7Q4axhpCLvRQI29SjqULnNUgfwoCaKWnWy2N4uljwV+Vv6DTKyBhzlzHmY8aYtxykj6KsEzqvleNCZ2LGGPN6ILPWvsQY815jzHOstV9ZtI+irBOHMa9jUUOxvqHTyaY1DaEyJbnIokvTrCpLEUtA87WFJ0Q7ZD7yI46gbiY5Ie5xNVFtYTS9BoAHn9GDa4Nfe56BMB9ll8JmGrmLlzv9izRrOTKySJ7cdpqwtiDPf/adzcV9bA9mvfrfaU2Ti0SVHTZdaghngQ8W7Y8AL1uyj6KsE2fRea0cE7o0RG0D3yjajwE/sEwfY8xtwG0AJ59+tf+2ohw2ncxr8Of26bn3Y2caN+0cQ+cNzMjKw23KMw2mWT3zuM2HkBqO6juY/R26DEFt0TL2p6eqM0tSNIViJ37mWQ9xygWSZtRt+25H/x3q+RHOhxDyJfh+AOlPOC3a7t4nvGfK7y9//qxe3E9qcjFtb+ppDinVcA9ClwJhhyqU9wRh7aO1j7X2TuBOgBtueZrtcHyKsgydzGuYn9sHNRH4//hrTuhp3anMaCNs6kkVDiHzUUrkc8zBHKmZtF+khD0wfQZtUeLlwppl3PSs3Hx0argfFkjbVOc1n6MuEJryKnxzkDQZhdrSZCSE02R41Vx9Kd9MdKWZjO6nUpVfCDywZB9FWSd0XivHhi41hA8DHzXGnAFeA/y0MeYOa+1bGvrc2uHzFWUVrMW8DhWxk9fde9BgVpDmnZDZJ1VbKHfU8pDKUO7vBowCRfQSMp8vsc2DvXnzUexwGWeOOXPTw1yfFQ+Su/ttqhDU01QH7YzFWELI7GTpSI6Fo25TaSVD2C+eP+vNFxycen93saKFVZ/L6Exla+15Y8xZ4FXAr1prHwE+09Ln3NyNFGWNuFzm9dx5zIWp6JIzGcXOAPaFg0xYCyWvTaGqF3pBtOUpxA5ZdHoTRkWFoK9HzFceznz0IDe1+hRqi+uZvAb2DcNzmNACfo5KIIRKa0ukT6QpP0EmrAl/wmSYG2HG2fwBQf4hSDFifqVVCIhOsxustY9TRVss3WdRUn+Y3XJyJvD1xH5/dj6x47vSut3x4rRuvD3xuTC7Pe2v+e0/dnvaDb+a1u3iLWkWyUVKiowTa8V3+Y/lqOa1ohw2mqmsKFcYwUgT/8zfmLbQZlZin1wzgPyUAWcykqYjGXIkTxtwm6dr4dFT88+MsE8VfZRdW52XUP961QagrKR6Tca1g7y63VDu4k9TOZilhhDLXpaZzynmI1dDcBvGA3dAUX+uwmt+1Gl1LVSocP5ApPm/264K24EKBEW5IpmWpiKxWDSdQO9eQwKh9rk9KjPRHtUiLwWDMxv5Jxa7PqLe6BPXxsclks32e4X5yPkV5qN2S2StoPF2viBf9/TvcGq7eOZjVPWtL1KZj0KlteUKmVFPRpNtIRD2i/ZkuFE709ot7lIIhEJQ52tUtS/4fn/rzr1eED0gR1EURQFUQ1CU40fsjGQIawsjAJcStEddG3DmI6k1hMqgblA3Kwnn807hMX6EcAkK0R718jIXDz97Rm+7OECndp5mRe2MiGzA7pPzZIqrt88xdCYjqSFI5/IiDmbhSN7fhr0TuVY0zgYi56BXyz/IHzcIJqClFDVcFSoQFOUKpNcLL5L5m5Hrvp8h+Kb0EUjzkBQSbRFH8h6CJ64Nl9GWtYWKazvD63noGfl3dKWx849VbT980y3I4+0+J7dzQbZ1ccTQFwgj5s9wdmOSB+EUQsAOYVwmoG0wzvI/7LJZmoykDyGUmNYUTjwN9ImdnHdQ1GSkKIqiAKohKMqRYLBkzBY2CeTRNc2ht7UInFJT2Kj/aw/9y09eDfbFq2tLW1MoWS12cKWMRNqooo+G1DUE2S5ez/XycOX+d03K8xKyiPmodmgQg/KUvpPbF9jczs1dg/Ekv9/oUvmzmYjD2/Yqf/1keJWIJqrMRBMG5TPH3nVoiixKy09wdJm8pgJBUS4D3EI3Jastem0LYUnsX3qPsKBYeGWICQnZDt3Uj0QqzEqPbrQKBIZ5JM23h08me2q+cg8YB3+TWklw+uwWAmGXLU4WfpD+ILcZDQYT+oX9qDebkU3FfUQJa5cdPRG+gLFnGnLPlOdch/r6UUblM+aij1brU1CTkaIoigKsuYbg1Oo2+kyS7veksmRjO488LbFj71R7H4Bpapb0J9K6vfM1ifeDd/z0W9M6Pj+t2187c2/ys1NYJFP5AieT+q1D5chFkaYeOf6Yack3H7nP95iRFTaPq4rXSynn/vqagrzuymjXdvQb4gOuvSeu7Ufa0qwFnr2PAAAT6UlEQVQkcxlE8tro2qrC6gngUdF2r2XNoCHfGV4HwODaynwktQXJjMxz9ua/oVtHttgtNYQsm9HL4mYodz+pCZSRTdSjjCrzUXUtFGWUogn4DuWu5vtaCwRFOQ64yJipJwQWiSLJmNYPpS8EgXu9lD/IPTDeHkbaUxP5gBQKPhuEhYK8hx/G6oTDKdgp7v0E9XMV3KsQEvsn8o3ZEyd22RrkPoE+k6hAqGcL5wv0VjGOCf1SOMjfNbY59e3/buGfCsEjnxMSHr4fIMWfsIpzldVkpCiKogCqISjK2uJ2pjN65e60x4yZaId2rRkzssxpCLkZaX+4D70Nd+N4jH+sXZaI3qRWwbTWhvkcAz8XoQkvx2FU9PdLccO81lBoDucevZrNp1YawsCZfop9t4+sJyTNSE5D6DMOagi+Ga9u+qmcwFJbkJpBqG+K2Sfl/O28dMVyqEBQlDUnYxpdLLKAcJCLX5mg1ptVAkEu9r5wkG155KTrP92EIkInN/G0CQSHjDKSfgiffe+V+bLc7jV0otuJITtX536mre29UiDEzEfTQCG5Mf3SfDSg7/2ucbORfHX3CS34MXNQTKiEy1+vZulWk5GiKIoCqIagKEfKoslpcvefeU7oaicrHKEi2uhSTBOIaQXyPGS3Q3/CQBnpJbWBWPGfvcB19558bWCurhLzWoPQFnaeyMe3ub3HVlFj6QInxe9Wd8BXu/H8deDlD7jfVZqP4kMN5xP4jmJ3LVSKIuZIToks0sQ0RbmCCNm5ZTKa/w9eCoda2KknEPrDMaNhEaLaa0j6CgkB/8jLkTMZycSzyBGapWnI9yW4pUf6IRpMSb7caTrpbSf/EnsXN7mwnQuHfpErDPnv07Sw14riCX/CmH5rxJH7fDXs5kU+Ziaqf/X5Y1JXhZqMFEVRFEA1BEVZG3qR3IM8sij/p5qJ3e0s0pbmo36W74onvX5V12i4ETYTzWkCkXa5Sz9JvD4R5KWxQ85m/yS1pqilCL4ZSR7mU7THo0F5QE5uPMrNVxkzkWcQzlPYCuzs+0zKCK/8s+FCR6HaQjGtQH4mpY//fuzPy7LmAiEtU3mrrMXezFN4KPnJX3zlzWkd3zBs7wPwnjel9ZsmntH87LRuAPx5Wrf//fyXLnDTdq4rM4eaWSQBy4UGdnlPRVFy1lwgKIoC1W50JvwJWeFdqNqBsFOnKQwnbAzzXfH+cFAWhqv5Cpr8BiHf8aMGuKb4gyxncV60/fMS8PrHNAThS/AL8IWQ2kLR3h/1mcyKQnPZgN3i3n3GZTtUKLB+23o4aF0LC2862py9sexk2V7Eb9BlmRYVCIpyJFTlr0MO49gh8jIqKZakNmAsTBVFhc9swGCYm4/2h30YFpqtbzKS7dhZy5InCsEydTW9fHNQ4BxlIFjhlC1qAiGUPNcWnFRzNg+YjIqS09t9kXg2YFKYjPZqUVtb/t1qzMhqJS0cTQ7qpoW97UCcts+sAnUqK4qiKIBqCIqyVoQK3cnrsozFTISaZjVTUj0nAQqtYZjfc2M4Yf9Ecf/pRtDcMqcVRA6KKXF5ACO5y/cL14WQR2tuUmoOTRnU7v2UFIbpfDXR/LSDXHOQDuae95v51B3KWfB6E6mmpPz6/JdblSNZogJBUS4j5GIlK6L6CWsu5l6ajtyBLlsnMi4UC+UlGXF0grgQaDt03t1jB9hxZqQtqjIXftVTaTIy1VvufrK8da3UtXiNle0ux2yYTaukM1ezaJPd0nyUG48q4SBf/XatmqxIbpNmvyZCC3i4NlG7mWhVpiM1GSmKoiiAagiKcqRIJ7FfxqItzl3mJEC+23Xvy3IL+TVRJmHQo184mEcnMpgW2+42sxDEz1IImXdq+QHCNBWLIOpR1wRCGoJ8P/TM2v3CNT/9rGG/uJ1/4E3FGEptIitNRRnT8h6pDubQmMLXFzcT5c8xrf1CqEBQlDUklqQG82Yjh1ugnLkov1YlVdVO+DpR3XvkTouXC3WT3yBWKdW9ynpIMmGszezk369JIPhmpJCQoir/LWk6sxhgzKD8XaeFdwZcDsyYJnJh3CxZlw0jXbY89iKoyUhRFEUBEjQEY8zdDf2+bq39mW6HtDipmcpneDj5ni96atrZxp/85Ren3fD7EjOav554RnPqmc/AVbdeTO+cwBNcndSvbafkaKsgKekyA/nXX/kBgOcZY+4JvH1oc7seNdSek+CQpiHZTxa6cxE0U7KaSck5mGfDjNmJPAJoH3LTjk/TWcshDSGU6BbLZWi6n3R2hzSEWBRSeb+0+ddUXmJWZHtU11djVGna/R+GZuBI+XbvtNbeHXrDGPO64vUu4Gbgd6y1d0T69oC/KP4DeKO19nOLD1lRuuGlb76VB37/gS9Za8/6763D3I6FoDqkoJhRZdH6NY6g8i+4viWiEsgusD91S0Ig8se1pd/AFwgnECGohENafWJnOsf8Ce411Bbju2o4KQ8IipvZeiB+m/xa5VfIau32UuWLljNv4zCFAXRgMjLGvB7IrLUvAZ5ljHlOpOsLgA9Ya88W/6kwUNYandvKcaML/ecs8MGi/RHgZcBXAv1uBX7UGPPDwOeAX7TWzu0ZjDG3AbcBnHr66Q6GpyhLc5ZDnNvLRBxV79f79hv6+jV0ZoPqz874ut/brJ/BHNMKfA3BL38RcyrLwqeI9jDQlhpCzIwkrw/z6KKsN6M/rJLOQr9hilmzcjqnmzar+y/+mVQNYxUFHLsQCNvAN4r2Y8APRPp9CniltfZhY8x/AV4L/JbfyVp7J3AnwI23nFn2rGhF6YKVze2n3HLGppoX/IijcMnmevjpuOwri99lxfuRSBnffFT+qSFMNCQQQkXxUkxG8j5tmcpNiWtFSO1gOCbLqmSztkzkENKvIw8qqvepIotkATz3+VWwqmq+XQiEHarc8xPEzVCftda6mXgfEFO/FWVd0LmtHCu6EAj3k6vSHwdeCHwp0u/9xpi3AZ8HXge8vYNnK8oqOfS5HYs4Cp0BXP9cfddb9W+Om5+7z6C6z7iI0hn1tqBXbM1H1M1HO6Lt3o+VzY5VTA2VnhjS7Lz2Hc3l9X2GJ3LDV384KbWhPuPy95SVYSGmcfXKvvXr8zvzDHmA0bS2e18kgi6FVZ/z0YVA+DDwUWPMGeA1wK3GmJuBv2etfYvo91bgv5KHL/xWLHJJUdaIlc/tZSJXUkwe/iI3Y1aLNGplUJ3HDDAp2pdG/cq3MKIuCNxrm0CA+dBTv93kT3CvNb9BbuDaOLHHoPAb9LO6EHBZ27L4X2zBXtas5CemdSEcDvOwpxSB8IvGmLdE3vu0tfbDxpizwKuAX7XWngPOAbXPWGs/Tx6NoShrwf/5j5+GeB6Czm3l2NEqEKy1P5HQ53GqaAxFuSz4O7/xY9xh3hHMQ3AcxtwOJ6OFNYcUB3N+vf7ZAeOy2meKppAxo+ccsqdnTIpd9+7OFvtOcxgNYFTkK7Q5kpfRENoS4IpoIobj8jS4rRO79AeT8nv2S5PRhPppZ9NgexFkRFj9emU+ksTOy/Y5yuNf17qWkSHtL6mfqApfzePJz34uX07qd+KpF5L6Pfz3zyT1ezwxC3grWl9+nhv4Zqf9UtXp1POPF7VzHxcW9SeE6hpJnD19zCAoFJoWSudbyHqz8hSy8WjCftHG1UMabcSzkxcRCLIdKnU93OcqZxoajisz0WBSVi7oMy6/p3/EaC/Qlr/DooSEQ0pdo3U7+1trGSmKoijAmmsIinKcOKiDOdXxHDMf+dFLoaicXjYj2y5Kaw8npbbgTiabjAZcKrWFAUzdYTnixk2b5tChN8N9KMxUV/Xcs8dlWYr+cEI/G5ffx5mJ5tvOlDQW2kK4pIXUII4TKhAUZc1J8ScsEhUDlfmofmxjPRSzOklsWltA3cI6yfr0t/P7TGaFgBlOmLljK6e71Yll06wSFFCZmGpfqHr+VaKd9WZlGWtnGsp6szLpbMC4HFPGtBRy0mTUZ+KZw+aFXUhIHJSYP2FdUZORoiiKAqiGoChrRSjiyL8ecjDnZRWW3YW6U9Wq5UCWaZjQr5lQZkIrcZ8ZZEVp7SxjPMi1hdksK7UFoNaeBjSEnqcVyHZVgiL/jnJn32dcjk9qAtJMJDWHjFmpIUltYZWkOJjXAdUQFEVRFEA1BEVZS2J+g1hF1BR/gtQm5LGRLmehOjG4fmaw69f0fPc6JSt35bMsY5ZV12UUcuygGTl2GQ4asvNLrcU5j/OM5GZNwA9B9X0Hvl9BXr/SUYGgKGtKzHwU63NQ89GASSkEpNDImDEuRIU8oF4KhOq85voB9g55vxRSBENsAZfCQV4PC4f5BX9VZqTLwcGsJiNFURQFUA1BUdae+dyD5lIXMfNRrNRC/Jnz5wDMiv2zuy5NT6H3q2fXD+VJeX79z/O7eNlXmn2k8zhuBpo3PYXHMfX+3Fwl9XLnihAIqX8xi5R7SLUXbpVnTDVzhoeT+k0az7qqWGQy9hNLQ5wkrQxHatXM1DEua0o47jQJCv885rQKqdO5Bb6pj/QzSJORQwoHmCQJgvqz4ouvXOwhbO7x/QxtfoHQ+36fKx01GSmKoijAFaIhKMqVTkp+grwmzUdQdzTnMfHzO+Pm3IfKJBSKVqqqq9Y1gZjZaBFSI49c35hzOGYmCt2n6Xmxvqmss3NZBYKiHAEGWzO/pJISjur+DNUiv6jZI1ZmW5qBfD+Do16BVY51UhtTG6FF11/85Xjn358PKXV9m81RYYGx6FgvR9RkpCiKogCqISjKkbKslgBxE498L3TNj0KS+QYhmrSFagceLpIXq/ff9p3bkuv88cZMQymfi59HHRrXwTWBdTQVOVQgKMoRI237i30uXi475ltw15aNnGkTJlJI5Nfnx5f6/KbFNy4M0oRAW/+2z6WO83JDTUaKoigKoBqColzWhExCofdDmoK7vmjJi9R8Bz+aaZHEuNgz69fmx7qoVuB/JvSs46AZOFQgKMqasIw/of75eO2jmOAIhajC4vWQQs+e9z10ZztPOQd5ESGQco+Dss6+A8exEgiLZfemZeOm9kvNAl40mzOF1O/ddb9VkLpgXg7/+BRl3ThWAkFR1p1lHcz1ezSfzRzLWcife3BtwX+WvO9BWcR8s6xW0HQ99f3LFRUIirKGrNJ8JN/3+7QJB1isPpK870GEQvsC3R6m2navVQqBy0Vj1SgjRVEUBVANQVHWlq7MR44utAUI77qX0RoWIfW+sfyG1JyGZd5v/uzloRk4VCAoyprjh3Iuf59uhEPo/dhCfFhRRiEOIgQW7XeloCYjRVEUBUjUEIwxdzf0/bq19me6G5KiHA6//soPADzPGHNP4O21nNcHdTZX92mP/klNevNJ0Ry6ZF3Dqi83cxGkm4zeaa29O/SGMeZ1xesNwIestS+P3cQYswH8d+Aa4C5r7XsXHK+idMZL33wrD/z+A1+y1p7131vned2Fb6G6V7sZye/X1j9lQV3mGM1FWfTzx808FKITk5Ex5knA+4Dtlq5vBO631v4Q8OPGmJNdPF9RVoHOa+W40ZVTeQb8FPCbLf3OAm8u2vcCtwB/KDsYY24Dbiv+OH6Huf3zHY2xS64DHj3qQQTQcS3GSeB5De93Nq9hfm7fYd6hczsdHddiNM3rKJ0IBGvteQBjTFvXbeAbRfsx4IbAve4E7izud5+19pYuxtglOq7FWONxvRJ4Z+z9Lud1cT+d20ui41oMY8x9y3zusKOMdoDNon3iCJ6vKKtA57VyRXDYE/d+4GVF+4XAA4f8fEVZBTqvlSuClSWmGWNeAdxsrX2PuPw+4HeNMS8HbgY+0XKbO1c1vgOi41qMdR0XLDi2jub1ws89RHRci3FFjctYa9s7GfMbwPWRtz9trf1nyQ805gz5bup/WmvPpX5OUbpG57Wi1EkSCIqiKMqVjzq/FEVRFOAIitutaxmMdR1XCGPMXeS26t+x1t6xbJ/DHpcxpgf8RfEfwButtZ87pLE1Zhx3kW28rnNoXccVQuf2UmPrbm5baw/1P+CVDe+9rni9Afhoy302gN8G/gT4+UMa113Ax4C3NPTtAV8D7in++96Of7/XA79WtN8LPGeZPiv4e00Z1w8A/+YI5tyTgN8D/rShzz8HfqVo/y5wckVzSOf2weaQzu36czud22tnMlrXcgHGmNcDmbX2JcCzjDHPiXR9AfABa+3Z4r+udwlngQ8W7Y9QhTsu2qdrUp55K/CjxphPGmPuKnZVh4HLOD7f0Ocs1fhdtnGn6Nxu5Sw6txel07m9dgKBtC8Ih/APuOF5TRNx1RMjJSs2KXP2CMb1KfLd6ovId8GvPYRxYa09b9sjfw7jN9O53YzO7QXpem6vnUBI/IJw+BMj9XmrnhgpWbFHkTmb8szPWmsfLtr3AbGd6FGw8t9M53YrOrdXQ/JvtnYCYQEOe2KkPm/VEyMlK/YoMmdTnvl+Y8wLjTEZ8DrgM4cwrlTWKdtY57bO7S5J/s0uZ4Fw2BMj9XmrnhgfBn7WGPMu4CeBLxhj/KgHv8/vdDyGZcf1VuD9wKeBj9nIGRurxhjzCmPMG7zL7wP+tTHm3aRnG68Knds6t5fiwHP7CLzirREPRfse0X4F8Aav718BvgC8m1yVzVY5LuAU+T+AdwFfBE4XP+4dXt/vAT4LfA542wojC34SuPEgfY5iXOv8H3CmGP/pVcwh0b5HtHVuLziHdG4vNf6kuX3omcrrWi4gZVxFlMirgHuttY8c5HnKlYfObeVyR0tXKIqiKMDl7UNQFEVROkQFgqIoigKoQFAURVEKVCAoiqIogAoERVEUpeD/AxCwU3SrLPcQAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x274ccc9cf98>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=使用interp2d类进行二维插值\\n\",\n    \"def func(x, y):  #❶\\n\",\n    \"    return (x + y) * np.exp(-5.0 * (x**2 + y**2))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# X-Y轴分为15*15的网格\\n\",\n    \"y, x = np.mgrid[-1:1:15j, -1:1:15j]  #❷\\n\",\n    \"fvals = func(x, y)  # 计算每个网格点上的函数值\\n\",\n    \"\\n\",\n    \"# 二维插值\\n\",\n    \"newfunc = interpolate.interp2d(x, y, fvals, kind='cubic')  #❸\\n\",\n    \"\\n\",\n    \"# 计算100*100的网格上的插值\\n\",\n    \"xnew = np.linspace(-1, 1, 100)\\n\",\n    \"ynew = np.linspace(-1, 1, 100)\\n\",\n    \"fnew = newfunc(xnew, ynew)  #❹\\n\",\n    \"#%hide\\n\",\n    \"pl.subplot(121)\\n\",\n    \"pl.imshow(\\n\",\n    \"    fvals,\\n\",\n    \"    extent=[-1, 1, -1, 1],\\n\",\n    \"    cmap=pl.cm.jet,\\n\",\n    \"    interpolation='nearest',\\n\",\n    \"    origin=\\\"lower\\\")\\n\",\n    \"pl.title(\\\"fvals\\\")\\n\",\n    \"pl.subplot(122)\\n\",\n    \"pl.imshow(\\n\",\n    \"    fnew,\\n\",\n    \"    extent=[-1, 1, -1, 1],\\n\",\n    \"    cmap=pl.cm.jet,\\n\",\n    \"    interpolation='nearest',\\n\",\n    \"    origin=\\\"lower\\\")\\n\",\n    \"pl.title(\\\"fnew\\\")\\n\",\n    \"pl.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### griddata\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> **WARNING**\\n\",\n    \"\\n\",\n    \"> `griddata()`使用欧几里得距离计算插值。如果K维空间中每个维度的取值范围相差较大，则应先将数据正规化，然后使用`griddata()`进行插值运算。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x274cde53160>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=使用gridata进行二维插值\\n\",\n    \"# 计算随机N个点的坐标，以及这些点对应的函数值\\n\",\n    \"N = 200\\n\",\n    \"np.random.seed(42)\\n\",\n    \"x = np.random.uniform(-1, 1, N)\\n\",\n    \"y = np.random.uniform(-1, 1, N)\\n\",\n    \"z = func(x, y)\\n\",\n    \"\\n\",\n    \"yg, xg = np.mgrid[-1:1:100j, -1:1:100j]\\n\",\n    \"xi = np.c_[xg.ravel(), yg.ravel()]\\n\",\n    \"\\n\",\n    \"methods = 'nearest', 'linear', 'cubic'\\n\",\n    \"\\n\",\n    \"zgs = [\\n\",\n    \"    interpolate.griddata((x, y), z, xi, method=method).reshape(100, 100)\\n\",\n    \"    for method in methods\\n\",\n    \"]\\n\",\n    \"#%hide\\n\",\n    \"fig, axes = pl.subplots(1, 3, figsize=(11.5, 3.5))\\n\",\n    \"\\n\",\n    \"for ax, method, zg in zip(axes, methods, zgs):\\n\",\n    \"    ax.imshow(\\n\",\n    \"        zg,\\n\",\n    \"        extent=[-1, 1, -1, 1],\\n\",\n    \"        cmap=pl.cm.jet,\\n\",\n    \"        interpolation='nearest',\\n\",\n    \"        origin=\\\"lower\\\")\\n\",\n    \"    ax.set_xlabel(method)\\n\",\n    \"    ax.scatter(x, y, c=z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 径向基函数插值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x274caacec88>\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x274caacee80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=一维RBF插值\\n\",\n    \"from scipy.interpolate import Rbf\\n\",\n    \"\\n\",\n    \"x1 = np.array([-1, 0, 2.0, 1.0])\\n\",\n    \"y1 = np.array([1.0, 0.3, -0.5, 0.8])\\n\",\n    \"\\n\",\n    \"funcs = ['multiquadric', 'gaussian', 'linear']\\n\",\n    \"nx = np.linspace(-3, 4, 100)\\n\",\n    \"rbfs = [Rbf(x1, y1, function=fname) for fname in funcs]  #❶\\n\",\n    \"rbf_ys = [rbf(nx) for rbf in rbfs]  #❷\\n\",\n    \"#%hide\\n\",\n    \"pl.plot(x1, y1, \\\"o\\\")\\n\",\n    \"for fname, ny in zip(funcs, rbf_ys):\\n\",\n    \"    pl.plot(nx, ny, label=fname, lw=2)\\n\",\n    \"\\n\",\n    \"pl.ylim(-1.0, 1.5)\\n\",\n    \"pl.legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"multiquadric [-0.88822885  2.17654513  1.42877511 -2.67919021]\\n\",\n      \"gaussian [ 1.00321945 -0.02345964 -0.65441716  0.91375159]\\n\",\n      \"linear [-0.26666667  0.6         0.73333333 -0.9       ]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for fname, rbf in zip(funcs, rbfs):\\n\",\n    \"    print (fname, rbf.nodes)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x274cdfd9a58>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=二维径向基函数插值\\n\",\n    \"rbfs = [Rbf(x, y, z, function=fname) for fname in funcs]\\n\",\n    \"rbf_zg = [rbf(xg, yg).reshape(xg.shape) for rbf in rbfs]\\n\",\n    \"#%hide\\n\",\n    \"fig, axes = pl.subplots(1, 3, figsize=(11.5, 3.5))\\n\",\n    \"for ax, fname, zg in zip(axes, funcs, rbf_zg):\\n\",\n    \"    ax.imshow(\\n\",\n    \"        zg,\\n\",\n    \"        extent=[-1, 1, -1, 1],\\n\",\n    \"        cmap=pl.cm.jet,\\n\",\n    \"        interpolation='nearest',\\n\",\n    \"        origin=\\\"lower\\\")\\n\",\n    \"    ax.set_xlabel(fname)\\n\",\n    \"    ax.scatter(x, y, c=z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"group_control\": {\n     \"group\": 0\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x274ce08e898>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=`epsilon`参数指定径向基函数中数据点的作用范围\\n\",\n    \"epsilons = 0.1, 0.15, 0.3\\n\",\n    \"rbfs = [Rbf(x, y, z, function=\\\"gaussian\\\", epsilon=eps) for eps in epsilons]\\n\",\n    \"zgs = [rbf(xg, yg).reshape(xg.shape) for rbf in rbfs]\\n\",\n    \"#%hide\\n\",\n    \"fig, axes = pl.subplots(1, 3, figsize=(11.5, 3.5))\\n\",\n    \"for ax, eps, zg in zip(axes, epsilons, zgs):\\n\",\n    \"    ax.imshow(\\n\",\n    \"        zg,\\n\",\n    \"        extent=[-1, 1, -1, 1],\\n\",\n    \"        cmap=pl.cm.jet,\\n\",\n    \"        interpolation='nearest',\\n\",\n    \"        origin=\\\"lower\\\")\\n\",\n    \"    ax.set_xlabel(\\\"eps=%g\\\" % eps)\\n\",\n    \"    ax.scatter(x, y, c=z)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "4.scipy/8.scipy-sparse.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import numpy as np\\n\",\n    \"import pylab as pl\\n\",\n    \"from scipy import sparse\\n\",\n    \"from scipy.sparse import csgraph\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 稀疏矩阵-sparse\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 稀疏矩阵的储存形式\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dict_keys([(2, 3), (3, 3), (4, 3)])\\n\",\n      \"dict_values([1.0, 2.0, 3.0])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scipy import sparse\\n\",\n    \"a = sparse.dok_matrix((10, 5))\\n\",\n    \"\\n\",\n    \"a[2, 3] = 1.0\\n\",\n    \"a[3, 3] = 2.0\\n\",\n    \"a[4, 3] = 3.0\\n\",\n    \"print(a.keys())\\n\",\n    \"print(a.values())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[list([]) list([]) list([1.0]) list([3.0, 2.0]) list([]) list([]) list([])\\n\",\n      \" list([]) list([]) list([])]\\n\",\n      \"[list([]) list([]) list([3]) list([2, 4]) list([]) list([]) list([])\\n\",\n      \" list([]) list([]) list([])]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"b = sparse.lil_matrix((10, 5))\\n\",\n    \"b[2, 3] = 1.0\\n\",\n    \"b[3, 4] = 2.0\\n\",\n    \"b[3, 2] = 3.0\\n\",\n    \"print(b.data)\\n\",\n    \"print(b.rows)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[3 4 2 3] [2 3 3 2] [ 1  2  3 10]\\n\",\n      \"[[ 0  0  0  0  0  0]\\n\",\n      \" [ 0  0  0  0  0  0]\\n\",\n      \" [ 0  0  0 11  0  0]\\n\",\n      \" [ 0  0  3  0  2  0]\\n\",\n      \" [ 0  0  0  0  0  0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"row = [2, 3, 3, 2]\\n\",\n    \"col = [3, 4, 2, 3]\\n\",\n    \"data = [1, 2, 3, 10]\\n\",\n    \"c = sparse.coo_matrix((data, (row, col)), shape=(5, 6))\\n\",\n    \"print (c.col, c.row, c.data)\\n\",\n    \"print (c.toarray())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 矩阵向量相乘\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1, -3, -1], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \" import numpy as np\\n\",\n    \"from scipy.sparse import csr_matrix\\n\",\n    \"A = csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]])\\n\",\n    \"v = np.array([1, 0, -1])\\n\",\n    \"A.dot(v)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 示例1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"构造一个1000x1000 lil_matrix并添加值：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy.sparse import lil_matrix\\n\",\n    \"from scipy.sparse.linalg import spsolve\\n\",\n    \"from numpy.linalg import solve, norm\\n\",\n    \"from numpy.random import rand\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"A = lil_matrix((1000, 1000))\\n\",\n    \"A[0, :100] = rand(100)\\n\",\n    \"A[1, 100:200] = A[0, :100]\\n\",\n    \"A.setdiag(rand(1000))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在将其转换为CSR格式，并求解$A x = b$的$x$：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"A = A.tocsr()\\n\",\n    \"b = rand(1000)\\n\",\n    \"x = spsolve(A, b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"将其转换为密集矩阵并求解，并检查结果是否相同：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x_ = solve(A.toarray(), b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在我们可以使用以下公式计算错误的范数：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"err = norm(x-x_)\\n\",\n    \"err < 1e-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 示例2\\n\",\n    \"构造COO格式的矩阵：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy import sparse\\n\",\n    \"from numpy import array\\n\",\n    \"I = array([0,3,1,0])\\n\",\n    \"J = array([0,3,1,2])\\n\",\n    \"V = array([4,5,7,9])\\n\",\n    \"A = sparse.coo_matrix((V,(I,J)),shape=(4,4))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"注意，索引不需要排序。\\n\",\n    \"\\n\",\n    \"转换为CSR或CSC时，将对重复的（i，j）条目进行求和。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"I = array([0,0,1,3,1,0,0])\\n\",\n    \"J = array([0,2,1,3,1,0,0])\\n\",\n    \"V = array([1,1,1,1,1,1,1])\\n\",\n    \"B = sparse.coo_matrix((V,(I,J)),shape=(4,4)).tocsr()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这对于构造有限元刚度矩阵和质量矩阵是有用的。\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "4.scipy/9.scipy-ndimage.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pylab as pl\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 图像处理-ndimage\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 形态学图像处理\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def expand_image(img, value, out=None, size = 10):\\n\",\n    \"    if out is None:\\n\",\n    \"        w, h = img.shape\\n\",\n    \"        out = np.zeros((w*size, h*size),dtype=np.uint8)\\n\",\n    \"    \\n\",\n    \"    tmp = np.repeat(np.repeat(img,size,0),size,1)\\n\",\n    \"    out[:,:] = np.where(tmp, value, out)\\n\",\n    \"    out[::size,:] = 0\\n\",\n    \"    out[:,::size] = 0\\n\",\n    \"    return out\\n\",\n    \"    \\n\",\n    \"def show_image(*imgs): \\n\",\n    \"    for idx, img in enumerate(imgs, 1):\\n\",\n    \"        ax = pl.subplot(1, len(imgs), idx)\\n\",\n    \"        pl.imshow(img, cmap=\\\"gray\\\")   \\n\",\n    \"        ax.set_axis_off()    \\n\",\n    \"    pl.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0)        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 膨胀和腐蚀\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1a649fb9b38>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=四连通和八连通的膨胀运算\\n\",\n    \"from scipy.ndimage import morphology\\n\",\n    \"\\n\",\n    \"def dilation_demo(a, structure=None):\\n\",\n    \"    b = morphology.binary_dilation(a, structure)\\n\",\n    \"    img = expand_image(a, 255)\\n\",\n    \"    return expand_image(np.logical_xor(a,b), 150, out=img)\\n\",\n    \"\\n\",\n    \"a = pl.imread(\\\"scipy_morphology_demo.png\\\")[:,:,0].astype(np.uint8)\\n\",\n    \"img1 = expand_image(a, 255)\\n\",\n    \"\\n\",\n    \"img2 = dilation_demo(a)\\n\",\n    \"img3 = dilation_demo(a, [[1,1,1],[1,1,1],[1,1,1]])\\n\",\n    \"show_image(img1, img2, img3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1a649fb9278>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=不同结构元素的膨胀效果\\n\",\n    \"img4 = dilation_demo(a, [[0,0,0],[1,1,1],[0,0,0]])\\n\",\n    \"img5 = dilation_demo(a, [[0,1,0],[0,1,0],[0,1,0]])\\n\",\n    \"img6 = dilation_demo(a, [[0,1,0],[0,1,0],[0,0,0]])\\n\",\n    \"show_image(img4, img5, img6)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1a64c92bda0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=四连通和八连通的腐蚀运算\\n\",\n    \"def erosion_demo(a, structure=None):\\n\",\n    \"    b = morphology.binary_erosion(a, structure)\\n\",\n    \"    img = expand_image(a, 255)\\n\",\n    \"    return expand_image(np.logical_xor(a,b), 100, out=img)\\n\",\n    \"\\n\",\n    \"img1 = expand_image(a, 255)\\n\",\n    \"img2 = erosion_demo(a)\\n\",\n    \"img3 = erosion_demo(a, [[1,1,1],[1,1,1],[1,1,1]])\\n\",\n    \"show_image(img1, img2, img3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Hit和Miss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1a64c9f8da0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=Hit和Miss运算\\n\",\n    \"def hitmiss_demo(a, structure1, structure2):\\n\",\n    \"    b = morphology.binary_hit_or_miss(a, structure1, structure2)\\n\",\n    \"    img = expand_image(a, 100)\\n\",\n    \"    return expand_image(b, 255, out=img)\\n\",\n    \"\\n\",\n    \"img1 = expand_image(a, 255)\\n\",\n    \"\\n\",\n    \"img2 = hitmiss_demo(a, [[0,0,0],[0,1,0],[1,1,1]], [[1,0,0],[0,0,0],[0,0,0]])\\n\",\n    \"img3 = hitmiss_demo(a, [[0,0,0],[0,0,0],[1,1,1]], [[1,0,0],[0,1,0],[0,0,0]])\\n\",\n    \"\\n\",\n    \"show_image(img1, img2, img3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1a64c932fd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%fig=使用Hit和Miss进行细线化运算\\n\",\n    \"def skeletonize(img):\\n\",\n    \"    h1 = np.array([[0, 0, 0],[0, 1, 0],[1, 1, 1]]) #❶\\n\",\n    \"    m1 = np.array([[1, 1, 1],[0, 0, 0],[0, 0, 0]]) \\n\",\n    \"    h2 = np.array([[0, 0, 0],[1, 1, 0],[0, 1, 0]]) \\n\",\n    \"    m2 = np.array([[0, 1, 1],[0, 0, 1],[0, 0, 0]])    \\n\",\n    \"    hit_list = [] \\n\",\n    \"    miss_list = []\\n\",\n    \"    for k in range(4): #❷\\n\",\n    \"        hit_list.append(np.rot90(h1, k))\\n\",\n    \"        hit_list.append(np.rot90(h2, k))\\n\",\n    \"        miss_list.append(np.rot90(m1, k))\\n\",\n    \"        miss_list.append(np.rot90(m2, k))    \\n\",\n    \"    img = img.copy()\\n\",\n    \"    while True:\\n\",\n    \"        last = img\\n\",\n    \"        for hit, miss in zip(hit_list, miss_list): \\n\",\n    \"            hm = morphology.binary_hit_or_miss(img, hit, miss) #❸\\n\",\n    \"            # 从图像中删除hit_or_miss所得到的白色点\\n\",\n    \"            img = np.logical_and(img, np.logical_not(hm)) #❹\\n\",\n    \"        # 如果处理之后的图像和处理前的图像相同，则结束处理\\n\",\n    \"        if np.all(img == last): #❺\\n\",\n    \"            break\\n\",\n    \"    return img\\n\",\n    \"\\n\",\n    \"a = pl.imread(\\\"scipy_morphology_demo2.png\\\")[:,:,0].astype(np.uint8)\\n\",\n    \"b = skeletonize(a)\\n\",\n    \"#%hide\\n\",\n    \"_, (ax1, ax2) = pl.subplots(1, 2, figsize=(9, 3))\\n\",\n    \"ax1.imshow(a, cmap=\\\"gray\\\", interpolation=\\\"nearest\\\")\\n\",\n    \"ax2.imshow(b, cmap=\\\"gray\\\", interpolation=\\\"nearest\\\")\\n\",\n    \"ax1.set_axis_off()\\n\",\n    \"ax2.set_axis_off()\\n\",\n    \"pl.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 图像分割\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"squares = pl.imread(\\\"suqares.jpg\\\")\\n\",\n    \"squares = (squares[:,:,0] < 200).astype(np.uint8) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"各种距离值 [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\\n\",\n      \" 24 25 26 27]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scipy.ndimage import morphology\\n\",\n    \"squares_dt = morphology.distance_transform_cdt(squares)\\n\",\n    \"print (\\\"各种距离值\\\", np.unique(squares_dt))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"squares_core = (squares_dt > 8).astype(np.uint8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy.ndimage.measurements import label, center_of_mass\\n\",\n    \"\\n\",\n    \"def random_palette(labels, count, seed=1):\\n\",\n    \"    np.random.seed(seed)\\n\",\n    \"    palette = np.random.rand(count+1, 3)\\n\",\n    \"    palette[0,:] = 0\\n\",\n    \"    return palette[labels]\\n\",\n    \"\\n\",\n    \"labels, count = label(squares_core)\\n\",\n    \"h, w = labels.shape\\n\",\n    \"centers = np.array(center_of_mass(labels, labels, index=range(1, count+1)), np.int)\\n\",\n    \"cores = random_palette(labels, count)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"index = morphology.distance_transform_cdt(1-squares_core, \\n\",\n    \"                                          return_distances=False, \\n\",\n    \"                                          return_indices=True) #❶\\n\",\n    \"near_labels = labels[index[0], index[1]] #❷\\n\",\n    \"\\n\",\n    \"mask = (squares - squares_core).astype(bool)\\n\",\n    \"labels2 = labels.copy()\\n\",\n    \"labels2[mask] = near_labels[mask] #❸\\n\",\n    \"separated = random_palette(labels2, count)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1a64c972860>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#%figonly=矩形区域分割算法各个步骤的输出图像\\n\",\n    \"fig, axes = pl.subplots(2, 3, figsize=(7.5, 5.0), )\\n\",\n    \"fig.delaxes(axes[1, 2])\\n\",\n    \"axes[0, 0].imshow(squares, cmap=\\\"gray\\\");\\n\",\n    \"axes[0, 1].imshow(squares_dt)\\n\",\n    \"axes[0, 2].imshow(squares_core, cmap=\\\"gray\\\")\\n\",\n    \"ax = axes[1, 0]\\n\",\n    \"ax.imshow(cores)\\n\",\n    \"center_y, center_x = centers.T\\n\",\n    \"ax.plot(center_x, center_y, \\\"o\\\", color=\\\"white\\\")\\n\",\n    \"ax.set_xlim(0, w)\\n\",\n    \"ax.set_ylim(h, 0)\\n\",\n    \"\\n\",\n    \"axes[1, 1].imshow(separated)\\n\",\n    \"\\n\",\n    \"for ax in axes.ravel():\\n\",\n    \"    ax.axis(\\\"off\\\")\\n\",\n    \"    \\n\",\n    \"fig.subplots_adjust(wspace=0.01, hspace=0.01)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "4.scipy/README.md",
    "content": "## Scipy简介\n\nScipy是一个用于数学、科学、工程领域的常用软件包，可以处理插值、积分、优化、图像处理、常微分方程数值解的求解、信号处理等问题。它用于有效计算Numpy矩阵，使Numpy和Scipy协同工作，高效解决问题。\n\nScipy是由针对特定任务的子模块组成：\n\n| 模块名            | 应用领域           |\n| :---------------- | :----------------- |\n| scipy.cluster     | 向量计算/Kmeans    |\n| scipy.constants   | 物理和数学常量     |\n| scipy.fftpack     | 傅立叶变换         |\n| scipy.integrate   | 积分程序           |\n| scipy.interpolate | 插值               |\n| scipy.io          | 数据输入输出       |\n| scipy.linalg      | 线性代数程序       |\n| scipy.ndimage     | n维图像包          |\n| scipy.odr         | 正交距离回归       |\n| scipy.optimize    | 优化               |\n| scipy.signal      | 信号处理           |\n| scipy.sparse      | 稀疏矩阵           |\n| scipy.spatial     | 空间数据结构和算法 |\n| scipy.special     | 一些特殊的数学函数 |\n| scipy.stats       | 统计               |\n\n"
  },
  {
    "path": "5.data-visualization/1.matplotlib/examples/spx.csv",
    "content": ",SPX\n1990-02-01 00:00:00,328.79\n1990-02-02 00:00:00,330.92\n1990-02-05 00:00:00,331.85\n1990-02-06 00:00:00,329.66\n1990-02-07 00:00:00,333.75\n1990-02-08 00:00:00,332.96\n1990-02-09 00:00:00,333.62\n1990-02-12 00:00:00,330.08\n1990-02-13 00:00:00,331.02\n1990-02-14 00:00:00,332.01\n1990-02-15 00:00:00,334.89\n1990-02-16 00:00:00,332.72\n1990-02-20 00:00:00,327.99\n1990-02-21 00:00:00,327.67\n1990-02-22 00:00:00,325.7\n1990-02-23 00:00:00,324.15\n1990-02-26 00:00:00,328.67\n1990-02-27 00:00:00,330.26\n1990-02-28 00:00:00,331.89\n1990-03-01 00:00:00,332.74\n1990-03-02 00:00:00,335.54\n1990-03-05 00:00:00,333.74\n1990-03-06 00:00:00,337.93\n1990-03-07 00:00:00,336.95\n1990-03-08 00:00:00,340.27\n1990-03-09 00:00:00,337.93\n1990-03-12 00:00:00,338.67\n1990-03-13 00:00:00,336.0\n1990-03-14 00:00:00,336.87\n1990-03-15 00:00:00,338.07\n1990-03-16 00:00:00,341.91\n1990-03-19 00:00:00,343.53\n1990-03-20 00:00:00,341.57\n1990-03-21 00:00:00,339.74\n1990-03-22 00:00:00,335.69\n1990-03-23 00:00:00,337.22\n1990-03-26 00:00:00,337.63\n1990-03-27 00:00:00,341.5\n1990-03-28 00:00:00,342.0\n1990-03-29 00:00:00,340.79\n1990-03-30 00:00:00,339.94\n1990-04-02 00:00:00,338.7\n1990-04-03 00:00:00,343.64\n1990-04-04 00:00:00,341.09\n1990-04-05 00:00:00,340.73\n1990-04-06 00:00:00,340.08\n1990-04-09 00:00:00,341.37\n1990-04-10 00:00:00,342.07\n1990-04-11 00:00:00,341.92\n1990-04-12 00:00:00,344.34\n1990-04-16 00:00:00,344.74\n1990-04-17 00:00:00,344.68\n1990-04-18 00:00:00,340.72\n1990-04-19 00:00:00,338.09\n1990-04-20 00:00:00,335.12\n1990-04-23 00:00:00,331.05\n1990-04-24 00:00:00,330.36\n1990-04-25 00:00:00,332.03\n1990-04-26 00:00:00,332.92\n1990-04-27 00:00:00,329.11\n1990-04-30 00:00:00,330.8\n1990-05-01 00:00:00,332.25\n1990-05-02 00:00:00,334.48\n1990-05-03 00:00:00,335.57\n1990-05-04 00:00:00,338.39\n1990-05-07 00:00:00,340.53\n1990-05-08 00:00:00,342.01\n1990-05-09 00:00:00,342.86\n1990-05-10 00:00:00,343.82\n1990-05-11 00:00:00,352.0\n1990-05-14 00:00:00,354.75\n1990-05-15 00:00:00,354.28\n1990-05-16 00:00:00,354.0\n1990-05-17 00:00:00,354.47\n1990-05-18 00:00:00,354.64\n1990-05-21 00:00:00,358.0\n1990-05-22 00:00:00,358.43\n1990-05-23 00:00:00,359.29\n1990-05-24 00:00:00,358.41\n1990-05-25 00:00:00,354.58\n1990-05-29 00:00:00,360.65\n1990-05-30 00:00:00,360.86\n1990-05-31 00:00:00,361.23\n1990-06-01 00:00:00,363.16\n1990-06-04 00:00:00,367.4\n1990-06-05 00:00:00,366.64\n1990-06-06 00:00:00,364.96\n1990-06-07 00:00:00,363.15\n1990-06-08 00:00:00,358.71\n1990-06-11 00:00:00,361.63\n1990-06-12 00:00:00,366.25\n1990-06-13 00:00:00,364.9\n1990-06-14 00:00:00,362.9\n1990-06-15 00:00:00,362.91\n1990-06-18 00:00:00,356.88\n1990-06-19 00:00:00,358.47\n1990-06-20 00:00:00,359.1\n1990-06-21 00:00:00,360.47\n1990-06-22 00:00:00,355.43\n1990-06-25 00:00:00,352.31\n1990-06-26 00:00:00,352.06\n1990-06-27 00:00:00,355.14\n1990-06-28 00:00:00,357.63\n1990-06-29 00:00:00,358.02\n1990-07-02 00:00:00,359.54\n1990-07-03 00:00:00,360.16\n1990-07-05 00:00:00,355.68\n1990-07-06 00:00:00,358.42\n1990-07-09 00:00:00,359.52\n1990-07-10 00:00:00,356.49\n1990-07-11 00:00:00,361.23\n1990-07-12 00:00:00,365.44\n1990-07-13 00:00:00,367.31\n1990-07-16 00:00:00,368.95\n1990-07-17 00:00:00,367.52\n1990-07-18 00:00:00,364.22\n1990-07-19 00:00:00,365.32\n1990-07-20 00:00:00,361.61\n1990-07-23 00:00:00,355.31\n1990-07-24 00:00:00,355.79\n1990-07-25 00:00:00,357.09\n1990-07-26 00:00:00,355.91\n1990-07-27 00:00:00,353.44\n1990-07-30 00:00:00,355.55\n1990-07-31 00:00:00,356.15\n1990-08-01 00:00:00,355.52\n1990-08-02 00:00:00,351.48\n1990-08-03 00:00:00,344.86\n1990-08-06 00:00:00,334.43\n1990-08-07 00:00:00,334.83\n1990-08-08 00:00:00,338.35\n1990-08-09 00:00:00,339.94\n1990-08-10 00:00:00,335.52\n1990-08-13 00:00:00,338.84\n1990-08-14 00:00:00,339.39\n1990-08-15 00:00:00,340.06\n1990-08-16 00:00:00,332.39\n1990-08-17 00:00:00,327.83\n1990-08-20 00:00:00,328.51\n1990-08-21 00:00:00,321.86\n1990-08-22 00:00:00,316.55\n1990-08-23 00:00:00,307.06\n1990-08-24 00:00:00,311.51\n1990-08-27 00:00:00,321.44\n1990-08-28 00:00:00,321.34\n1990-08-29 00:00:00,324.19\n1990-08-30 00:00:00,318.71\n1990-08-31 00:00:00,322.56\n1990-09-04 00:00:00,323.09\n1990-09-05 00:00:00,324.39\n1990-09-06 00:00:00,320.46\n1990-09-07 00:00:00,323.4\n1990-09-10 00:00:00,321.63\n1990-09-11 00:00:00,321.04\n1990-09-12 00:00:00,322.54\n1990-09-13 00:00:00,318.65\n1990-09-14 00:00:00,316.83\n1990-09-17 00:00:00,317.77\n1990-09-18 00:00:00,318.6\n1990-09-19 00:00:00,316.6\n1990-09-20 00:00:00,311.48\n1990-09-21 00:00:00,311.32\n1990-09-24 00:00:00,304.59\n1990-09-25 00:00:00,308.26\n1990-09-26 00:00:00,305.06\n1990-09-27 00:00:00,300.97\n1990-09-28 00:00:00,306.05\n1990-10-01 00:00:00,314.94\n1990-10-02 00:00:00,315.21\n1990-10-03 00:00:00,311.4\n1990-10-04 00:00:00,312.69\n1990-10-05 00:00:00,311.5\n1990-10-08 00:00:00,313.48\n1990-10-09 00:00:00,305.1\n1990-10-10 00:00:00,300.39\n1990-10-11 00:00:00,295.46\n1990-10-12 00:00:00,300.03\n1990-10-15 00:00:00,303.23\n1990-10-16 00:00:00,298.92\n1990-10-17 00:00:00,298.76\n1990-10-18 00:00:00,305.74\n1990-10-19 00:00:00,312.48\n1990-10-22 00:00:00,314.76\n1990-10-23 00:00:00,312.36\n1990-10-24 00:00:00,312.6\n1990-10-25 00:00:00,310.17\n1990-10-26 00:00:00,304.71\n1990-10-29 00:00:00,301.88\n1990-10-30 00:00:00,304.06\n1990-10-31 00:00:00,304.0\n1990-11-01 00:00:00,307.02\n1990-11-02 00:00:00,311.85\n1990-11-05 00:00:00,314.59\n1990-11-06 00:00:00,311.62\n1990-11-07 00:00:00,306.01\n1990-11-08 00:00:00,307.61\n1990-11-09 00:00:00,313.74\n1990-11-12 00:00:00,319.48\n1990-11-13 00:00:00,317.67\n1990-11-14 00:00:00,320.4\n1990-11-15 00:00:00,317.02\n1990-11-16 00:00:00,317.12\n1990-11-19 00:00:00,319.34\n1990-11-20 00:00:00,315.31\n1990-11-21 00:00:00,316.03\n1990-11-23 00:00:00,315.1\n1990-11-26 00:00:00,316.51\n1990-11-27 00:00:00,318.1\n1990-11-28 00:00:00,317.95\n1990-11-29 00:00:00,316.42\n1990-11-30 00:00:00,322.22\n1990-12-03 00:00:00,324.1\n1990-12-04 00:00:00,326.35\n1990-12-05 00:00:00,329.92\n1990-12-06 00:00:00,329.07\n1990-12-07 00:00:00,327.75\n1990-12-10 00:00:00,328.89\n1990-12-11 00:00:00,326.44\n1990-12-12 00:00:00,330.19\n1990-12-13 00:00:00,329.34\n1990-12-14 00:00:00,326.82\n1990-12-17 00:00:00,326.02\n1990-12-18 00:00:00,330.05\n1990-12-19 00:00:00,330.2\n1990-12-20 00:00:00,330.12\n1990-12-21 00:00:00,331.75\n1990-12-24 00:00:00,329.9\n1990-12-26 00:00:00,330.85\n1990-12-27 00:00:00,328.29\n1990-12-28 00:00:00,328.72\n1990-12-31 00:00:00,330.22\n1991-01-02 00:00:00,326.45\n1991-01-03 00:00:00,321.91\n1991-01-04 00:00:00,321.0\n1991-01-07 00:00:00,315.44\n1991-01-08 00:00:00,314.9\n1991-01-09 00:00:00,311.49\n1991-01-10 00:00:00,314.53\n1991-01-11 00:00:00,315.23\n1991-01-14 00:00:00,312.49\n1991-01-15 00:00:00,313.73\n1991-01-16 00:00:00,316.17\n1991-01-17 00:00:00,327.97\n1991-01-18 00:00:00,332.23\n1991-01-21 00:00:00,331.06\n1991-01-22 00:00:00,328.31\n1991-01-23 00:00:00,330.21\n1991-01-24 00:00:00,334.78\n1991-01-25 00:00:00,336.07\n1991-01-28 00:00:00,336.03\n1991-01-29 00:00:00,335.84\n1991-01-30 00:00:00,340.91\n1991-01-31 00:00:00,343.93\n1991-02-01 00:00:00,343.05\n1991-02-04 00:00:00,348.34\n1991-02-05 00:00:00,351.26\n1991-02-06 00:00:00,358.07\n1991-02-07 00:00:00,356.52\n1991-02-08 00:00:00,359.35\n1991-02-11 00:00:00,368.58\n1991-02-12 00:00:00,365.5\n1991-02-13 00:00:00,369.02\n1991-02-14 00:00:00,364.22\n1991-02-15 00:00:00,369.06\n1991-02-19 00:00:00,369.39\n1991-02-20 00:00:00,365.14\n1991-02-21 00:00:00,364.97\n1991-02-22 00:00:00,365.65\n1991-02-25 00:00:00,367.26\n1991-02-26 00:00:00,362.81\n1991-02-27 00:00:00,367.74\n1991-02-28 00:00:00,367.07\n1991-03-01 00:00:00,370.47\n1991-03-04 00:00:00,369.33\n1991-03-05 00:00:00,376.72\n1991-03-06 00:00:00,376.17\n1991-03-07 00:00:00,375.91\n1991-03-08 00:00:00,374.95\n1991-03-11 00:00:00,372.96\n1991-03-12 00:00:00,370.03\n1991-03-13 00:00:00,374.57\n1991-03-14 00:00:00,373.5\n1991-03-15 00:00:00,373.59\n1991-03-18 00:00:00,372.11\n1991-03-19 00:00:00,366.59\n1991-03-20 00:00:00,367.92\n1991-03-21 00:00:00,366.58\n1991-03-22 00:00:00,367.48\n1991-03-25 00:00:00,369.83\n1991-03-26 00:00:00,376.3\n1991-03-27 00:00:00,375.35\n1991-03-28 00:00:00,375.22\n1991-04-01 00:00:00,371.3\n1991-04-02 00:00:00,379.5\n1991-04-03 00:00:00,378.94\n1991-04-04 00:00:00,379.77\n1991-04-05 00:00:00,375.36\n1991-04-08 00:00:00,378.66\n1991-04-09 00:00:00,373.56\n1991-04-10 00:00:00,373.15\n1991-04-11 00:00:00,377.63\n1991-04-12 00:00:00,380.4\n1991-04-15 00:00:00,381.19\n1991-04-16 00:00:00,387.62\n1991-04-17 00:00:00,390.45\n1991-04-18 00:00:00,388.46\n1991-04-19 00:00:00,384.2\n1991-04-22 00:00:00,380.95\n1991-04-23 00:00:00,381.76\n1991-04-24 00:00:00,382.76\n1991-04-25 00:00:00,379.25\n1991-04-26 00:00:00,379.02\n1991-04-29 00:00:00,373.66\n1991-04-30 00:00:00,375.34\n1991-05-01 00:00:00,380.29\n1991-05-02 00:00:00,380.52\n1991-05-03 00:00:00,380.8\n1991-05-06 00:00:00,380.08\n1991-05-07 00:00:00,377.32\n1991-05-08 00:00:00,378.51\n1991-05-09 00:00:00,383.25\n1991-05-10 00:00:00,375.74\n1991-05-13 00:00:00,376.76\n1991-05-14 00:00:00,371.62\n1991-05-15 00:00:00,368.57\n1991-05-16 00:00:00,372.19\n1991-05-17 00:00:00,372.39\n1991-05-20 00:00:00,372.28\n1991-05-21 00:00:00,375.35\n1991-05-22 00:00:00,376.19\n1991-05-23 00:00:00,374.96\n1991-05-24 00:00:00,377.49\n1991-05-28 00:00:00,381.94\n1991-05-29 00:00:00,382.79\n1991-05-30 00:00:00,386.96\n1991-05-31 00:00:00,389.83\n1991-06-03 00:00:00,388.06\n1991-06-04 00:00:00,387.74\n1991-06-05 00:00:00,385.09\n1991-06-06 00:00:00,383.63\n1991-06-07 00:00:00,379.43\n1991-06-10 00:00:00,378.57\n1991-06-11 00:00:00,381.05\n1991-06-12 00:00:00,376.65\n1991-06-13 00:00:00,377.63\n1991-06-14 00:00:00,382.29\n1991-06-17 00:00:00,380.13\n1991-06-18 00:00:00,378.59\n1991-06-19 00:00:00,375.09\n1991-06-20 00:00:00,375.42\n1991-06-21 00:00:00,377.75\n1991-06-24 00:00:00,370.94\n1991-06-25 00:00:00,370.65\n1991-06-26 00:00:00,371.59\n1991-06-27 00:00:00,374.4\n1991-06-28 00:00:00,371.16\n1991-07-01 00:00:00,377.92\n1991-07-02 00:00:00,377.47\n1991-07-03 00:00:00,373.33\n1991-07-05 00:00:00,374.08\n1991-07-08 00:00:00,377.94\n1991-07-09 00:00:00,376.11\n1991-07-10 00:00:00,375.74\n1991-07-11 00:00:00,376.97\n1991-07-12 00:00:00,380.25\n1991-07-15 00:00:00,382.39\n1991-07-16 00:00:00,381.54\n1991-07-17 00:00:00,381.18\n1991-07-18 00:00:00,385.37\n1991-07-19 00:00:00,384.22\n1991-07-22 00:00:00,382.88\n1991-07-23 00:00:00,379.42\n1991-07-24 00:00:00,378.64\n1991-07-25 00:00:00,380.96\n1991-07-26 00:00:00,380.93\n1991-07-29 00:00:00,383.15\n1991-07-30 00:00:00,386.69\n1991-07-31 00:00:00,387.81\n1991-08-01 00:00:00,387.12\n1991-08-02 00:00:00,387.18\n1991-08-05 00:00:00,385.06\n1991-08-06 00:00:00,390.62\n1991-08-07 00:00:00,390.56\n1991-08-08 00:00:00,389.32\n1991-08-09 00:00:00,387.12\n1991-08-12 00:00:00,388.02\n1991-08-13 00:00:00,389.62\n1991-08-14 00:00:00,389.9\n1991-08-15 00:00:00,389.33\n1991-08-16 00:00:00,385.58\n1991-08-19 00:00:00,376.47\n1991-08-20 00:00:00,379.43\n1991-08-21 00:00:00,390.59\n1991-08-22 00:00:00,391.33\n1991-08-23 00:00:00,394.17\n1991-08-26 00:00:00,393.85\n1991-08-27 00:00:00,393.06\n1991-08-28 00:00:00,396.64\n1991-08-29 00:00:00,396.47\n1991-08-30 00:00:00,395.43\n1991-09-03 00:00:00,392.15\n1991-09-04 00:00:00,389.97\n1991-09-05 00:00:00,389.14\n1991-09-06 00:00:00,389.1\n1991-09-09 00:00:00,388.57\n1991-09-10 00:00:00,384.56\n1991-09-11 00:00:00,385.09\n1991-09-12 00:00:00,387.34\n1991-09-13 00:00:00,383.59\n1991-09-16 00:00:00,385.78\n1991-09-17 00:00:00,385.5\n1991-09-18 00:00:00,386.94\n1991-09-19 00:00:00,387.56\n1991-09-20 00:00:00,387.92\n1991-09-23 00:00:00,385.92\n1991-09-24 00:00:00,387.71\n1991-09-25 00:00:00,386.88\n1991-09-26 00:00:00,386.49\n1991-09-27 00:00:00,385.9\n1991-09-30 00:00:00,387.86\n1991-10-01 00:00:00,389.2\n1991-10-02 00:00:00,388.26\n1991-10-03 00:00:00,384.47\n1991-10-04 00:00:00,381.25\n1991-10-07 00:00:00,379.5\n1991-10-08 00:00:00,380.67\n1991-10-09 00:00:00,376.8\n1991-10-10 00:00:00,380.55\n1991-10-11 00:00:00,381.45\n1991-10-14 00:00:00,386.47\n1991-10-15 00:00:00,391.01\n1991-10-16 00:00:00,392.8\n1991-10-17 00:00:00,391.92\n1991-10-18 00:00:00,392.5\n1991-10-21 00:00:00,390.02\n1991-10-22 00:00:00,387.83\n1991-10-23 00:00:00,387.94\n1991-10-24 00:00:00,385.07\n1991-10-25 00:00:00,384.2\n1991-10-28 00:00:00,389.52\n1991-10-29 00:00:00,391.48\n1991-10-30 00:00:00,392.96\n1991-10-31 00:00:00,392.45\n1991-11-01 00:00:00,391.32\n1991-11-04 00:00:00,390.28\n1991-11-05 00:00:00,388.71\n1991-11-06 00:00:00,389.97\n1991-11-07 00:00:00,393.72\n1991-11-08 00:00:00,392.89\n1991-11-11 00:00:00,393.12\n1991-11-12 00:00:00,396.74\n1991-11-13 00:00:00,397.41\n1991-11-14 00:00:00,397.15\n1991-11-15 00:00:00,382.62\n1991-11-18 00:00:00,385.24\n1991-11-19 00:00:00,379.42\n1991-11-20 00:00:00,378.53\n1991-11-21 00:00:00,380.06\n1991-11-22 00:00:00,376.14\n1991-11-25 00:00:00,375.34\n1991-11-26 00:00:00,377.96\n1991-11-27 00:00:00,376.55\n1991-11-29 00:00:00,375.22\n1991-12-02 00:00:00,381.4\n1991-12-03 00:00:00,380.96\n1991-12-04 00:00:00,380.07\n1991-12-05 00:00:00,377.39\n1991-12-06 00:00:00,379.1\n1991-12-09 00:00:00,378.26\n1991-12-10 00:00:00,377.9\n1991-12-11 00:00:00,377.7\n1991-12-12 00:00:00,381.55\n1991-12-13 00:00:00,384.47\n1991-12-16 00:00:00,384.46\n1991-12-17 00:00:00,382.74\n1991-12-18 00:00:00,383.48\n1991-12-19 00:00:00,382.52\n1991-12-20 00:00:00,387.04\n1991-12-23 00:00:00,396.82\n1991-12-24 00:00:00,399.33\n1991-12-26 00:00:00,404.84\n1991-12-27 00:00:00,406.46\n1991-12-30 00:00:00,415.14\n1991-12-31 00:00:00,417.09\n1992-01-02 00:00:00,417.26\n1992-01-03 00:00:00,419.34\n1992-01-06 00:00:00,417.96\n1992-01-07 00:00:00,417.4\n1992-01-08 00:00:00,418.1\n1992-01-09 00:00:00,417.61\n1992-01-10 00:00:00,415.1\n1992-01-13 00:00:00,414.34\n1992-01-14 00:00:00,420.44\n1992-01-15 00:00:00,420.77\n1992-01-16 00:00:00,418.21\n1992-01-17 00:00:00,418.86\n1992-01-20 00:00:00,416.36\n1992-01-21 00:00:00,412.64\n1992-01-22 00:00:00,418.13\n1992-01-23 00:00:00,414.96\n1992-01-24 00:00:00,415.48\n1992-01-27 00:00:00,414.99\n1992-01-28 00:00:00,414.96\n1992-01-29 00:00:00,410.34\n1992-01-30 00:00:00,411.62\n1992-01-31 00:00:00,408.78\n1992-02-03 00:00:00,409.53\n1992-02-04 00:00:00,413.85\n1992-02-05 00:00:00,413.84\n1992-02-06 00:00:00,413.82\n1992-02-07 00:00:00,411.09\n1992-02-10 00:00:00,413.77\n1992-02-11 00:00:00,413.76\n1992-02-12 00:00:00,417.13\n1992-02-13 00:00:00,413.69\n1992-02-14 00:00:00,412.48\n1992-02-18 00:00:00,407.38\n1992-02-19 00:00:00,408.26\n1992-02-20 00:00:00,413.9\n1992-02-21 00:00:00,411.43\n1992-02-24 00:00:00,412.27\n1992-02-25 00:00:00,410.45\n1992-02-26 00:00:00,415.35\n1992-02-27 00:00:00,413.86\n1992-02-28 00:00:00,412.7\n1992-03-02 00:00:00,412.45\n1992-03-03 00:00:00,412.85\n1992-03-04 00:00:00,409.33\n1992-03-05 00:00:00,406.51\n1992-03-06 00:00:00,404.44\n1992-03-09 00:00:00,405.21\n1992-03-10 00:00:00,406.89\n1992-03-11 00:00:00,404.03\n1992-03-12 00:00:00,403.89\n1992-03-13 00:00:00,405.84\n1992-03-16 00:00:00,406.39\n1992-03-17 00:00:00,409.58\n1992-03-18 00:00:00,409.15\n1992-03-19 00:00:00,409.8\n1992-03-20 00:00:00,411.3\n1992-03-23 00:00:00,409.91\n1992-03-24 00:00:00,408.88\n1992-03-25 00:00:00,407.52\n1992-03-26 00:00:00,407.86\n1992-03-27 00:00:00,403.5\n1992-03-30 00:00:00,403.0\n1992-03-31 00:00:00,403.69\n1992-04-01 00:00:00,404.23\n1992-04-02 00:00:00,400.5\n1992-04-03 00:00:00,401.55\n1992-04-06 00:00:00,405.59\n1992-04-07 00:00:00,398.06\n1992-04-08 00:00:00,394.5\n1992-04-09 00:00:00,400.64\n1992-04-10 00:00:00,404.29\n1992-04-13 00:00:00,406.08\n1992-04-14 00:00:00,412.39\n1992-04-15 00:00:00,416.28\n1992-04-16 00:00:00,416.04\n1992-04-20 00:00:00,410.18\n1992-04-21 00:00:00,410.26\n1992-04-22 00:00:00,409.81\n1992-04-23 00:00:00,411.6\n1992-04-24 00:00:00,409.02\n1992-04-27 00:00:00,408.45\n1992-04-28 00:00:00,409.11\n1992-04-29 00:00:00,412.02\n1992-04-30 00:00:00,414.95\n1992-05-01 00:00:00,412.53\n1992-05-04 00:00:00,416.91\n1992-05-05 00:00:00,416.84\n1992-05-06 00:00:00,416.79\n1992-05-07 00:00:00,415.85\n1992-05-08 00:00:00,416.05\n1992-05-11 00:00:00,418.49\n1992-05-12 00:00:00,416.29\n1992-05-13 00:00:00,416.45\n1992-05-14 00:00:00,413.14\n1992-05-15 00:00:00,410.09\n1992-05-18 00:00:00,412.81\n1992-05-19 00:00:00,416.37\n1992-05-20 00:00:00,415.39\n1992-05-21 00:00:00,412.6\n1992-05-22 00:00:00,414.02\n1992-05-26 00:00:00,411.41\n1992-05-27 00:00:00,412.17\n1992-05-28 00:00:00,416.74\n1992-05-29 00:00:00,415.35\n1992-06-01 00:00:00,417.3\n1992-06-02 00:00:00,413.5\n1992-06-03 00:00:00,414.59\n1992-06-04 00:00:00,413.26\n1992-06-05 00:00:00,413.48\n1992-06-08 00:00:00,413.36\n1992-06-09 00:00:00,410.06\n1992-06-10 00:00:00,407.25\n1992-06-11 00:00:00,409.05\n1992-06-12 00:00:00,409.76\n1992-06-15 00:00:00,410.29\n1992-06-16 00:00:00,408.32\n1992-06-17 00:00:00,402.26\n1992-06-18 00:00:00,400.96\n1992-06-19 00:00:00,403.67\n1992-06-22 00:00:00,403.4\n1992-06-23 00:00:00,404.04\n1992-06-24 00:00:00,403.84\n1992-06-25 00:00:00,403.12\n1992-06-26 00:00:00,403.45\n1992-06-29 00:00:00,408.94\n1992-06-30 00:00:00,408.14\n1992-07-01 00:00:00,412.88\n1992-07-02 00:00:00,411.77\n1992-07-06 00:00:00,413.84\n1992-07-07 00:00:00,409.16\n1992-07-08 00:00:00,410.28\n1992-07-09 00:00:00,414.23\n1992-07-10 00:00:00,414.62\n1992-07-13 00:00:00,414.87\n1992-07-14 00:00:00,417.68\n1992-07-15 00:00:00,417.1\n1992-07-16 00:00:00,417.54\n1992-07-17 00:00:00,415.62\n1992-07-20 00:00:00,413.75\n1992-07-21 00:00:00,413.76\n1992-07-22 00:00:00,410.93\n1992-07-23 00:00:00,412.08\n1992-07-24 00:00:00,411.6\n1992-07-27 00:00:00,411.54\n1992-07-28 00:00:00,417.52\n1992-07-29 00:00:00,422.23\n1992-07-30 00:00:00,423.92\n1992-07-31 00:00:00,424.21\n1992-08-03 00:00:00,425.09\n1992-08-04 00:00:00,424.36\n1992-08-05 00:00:00,422.19\n1992-08-06 00:00:00,420.59\n1992-08-07 00:00:00,418.88\n1992-08-10 00:00:00,419.42\n1992-08-11 00:00:00,418.9\n1992-08-12 00:00:00,417.78\n1992-08-13 00:00:00,417.73\n1992-08-14 00:00:00,419.91\n1992-08-17 00:00:00,420.74\n1992-08-18 00:00:00,421.34\n1992-08-19 00:00:00,418.19\n1992-08-20 00:00:00,418.26\n1992-08-21 00:00:00,414.85\n1992-08-24 00:00:00,410.72\n1992-08-25 00:00:00,411.61\n1992-08-26 00:00:00,413.51\n1992-08-27 00:00:00,413.53\n1992-08-28 00:00:00,414.84\n1992-08-31 00:00:00,414.03\n1992-09-01 00:00:00,416.07\n1992-09-02 00:00:00,417.98\n1992-09-03 00:00:00,417.98\n1992-09-04 00:00:00,417.08\n1992-09-08 00:00:00,414.44\n1992-09-09 00:00:00,416.36\n1992-09-10 00:00:00,419.95\n1992-09-11 00:00:00,419.58\n1992-09-14 00:00:00,425.27\n1992-09-15 00:00:00,419.77\n1992-09-16 00:00:00,419.92\n1992-09-17 00:00:00,419.93\n1992-09-18 00:00:00,422.93\n1992-09-21 00:00:00,422.14\n1992-09-22 00:00:00,417.14\n1992-09-23 00:00:00,417.44\n1992-09-24 00:00:00,418.47\n1992-09-25 00:00:00,414.35\n1992-09-28 00:00:00,416.62\n1992-09-29 00:00:00,416.8\n1992-09-30 00:00:00,417.8\n1992-10-01 00:00:00,416.29\n1992-10-02 00:00:00,410.47\n1992-10-05 00:00:00,407.57\n1992-10-06 00:00:00,407.18\n1992-10-07 00:00:00,404.25\n1992-10-08 00:00:00,407.75\n1992-10-09 00:00:00,402.66\n1992-10-12 00:00:00,407.44\n1992-10-13 00:00:00,409.3\n1992-10-14 00:00:00,409.37\n1992-10-15 00:00:00,409.6\n1992-10-16 00:00:00,411.73\n1992-10-19 00:00:00,414.98\n1992-10-20 00:00:00,415.48\n1992-10-21 00:00:00,415.67\n1992-10-22 00:00:00,414.9\n1992-10-23 00:00:00,414.1\n1992-10-26 00:00:00,418.16\n1992-10-27 00:00:00,418.49\n1992-10-28 00:00:00,420.13\n1992-10-29 00:00:00,420.86\n1992-10-30 00:00:00,418.68\n1992-11-02 00:00:00,422.75\n1992-11-03 00:00:00,419.92\n1992-11-04 00:00:00,417.11\n1992-11-05 00:00:00,418.34\n1992-11-06 00:00:00,417.58\n1992-11-09 00:00:00,418.59\n1992-11-10 00:00:00,418.62\n1992-11-11 00:00:00,422.2\n1992-11-12 00:00:00,422.87\n1992-11-13 00:00:00,422.43\n1992-11-16 00:00:00,420.68\n1992-11-17 00:00:00,419.27\n1992-11-18 00:00:00,422.85\n1992-11-19 00:00:00,423.61\n1992-11-20 00:00:00,426.65\n1992-11-23 00:00:00,425.12\n1992-11-24 00:00:00,427.59\n1992-11-25 00:00:00,429.19\n1992-11-27 00:00:00,430.16\n1992-11-30 00:00:00,431.35\n1992-12-01 00:00:00,430.78\n1992-12-02 00:00:00,429.89\n1992-12-03 00:00:00,429.91\n1992-12-04 00:00:00,432.06\n1992-12-07 00:00:00,435.31\n1992-12-08 00:00:00,436.99\n1992-12-09 00:00:00,435.65\n1992-12-10 00:00:00,434.64\n1992-12-11 00:00:00,433.73\n1992-12-14 00:00:00,432.84\n1992-12-15 00:00:00,432.57\n1992-12-16 00:00:00,431.52\n1992-12-17 00:00:00,435.43\n1992-12-18 00:00:00,441.28\n1992-12-21 00:00:00,440.7\n1992-12-22 00:00:00,440.31\n1992-12-23 00:00:00,439.03\n1992-12-24 00:00:00,439.77\n1992-12-28 00:00:00,439.15\n1992-12-29 00:00:00,437.98\n1992-12-30 00:00:00,438.82\n1992-12-31 00:00:00,435.71\n1993-01-04 00:00:00,435.38\n1993-01-05 00:00:00,434.34\n1993-01-06 00:00:00,434.52\n1993-01-07 00:00:00,430.73\n1993-01-08 00:00:00,429.05\n1993-01-11 00:00:00,430.95\n1993-01-12 00:00:00,431.04\n1993-01-13 00:00:00,433.03\n1993-01-14 00:00:00,435.94\n1993-01-15 00:00:00,437.15\n1993-01-18 00:00:00,436.84\n1993-01-19 00:00:00,435.13\n1993-01-20 00:00:00,433.37\n1993-01-21 00:00:00,435.49\n1993-01-22 00:00:00,436.11\n1993-01-25 00:00:00,440.01\n1993-01-26 00:00:00,439.95\n1993-01-27 00:00:00,438.11\n1993-01-28 00:00:00,438.66\n1993-01-29 00:00:00,438.78\n1993-02-01 00:00:00,442.52\n1993-02-02 00:00:00,442.55\n1993-02-03 00:00:00,447.2\n1993-02-04 00:00:00,449.56\n1993-02-05 00:00:00,448.93\n1993-02-08 00:00:00,447.85\n1993-02-09 00:00:00,445.33\n1993-02-10 00:00:00,446.23\n1993-02-11 00:00:00,447.66\n1993-02-12 00:00:00,444.58\n1993-02-16 00:00:00,433.91\n1993-02-17 00:00:00,433.3\n1993-02-18 00:00:00,431.9\n1993-02-19 00:00:00,434.22\n1993-02-22 00:00:00,435.24\n1993-02-23 00:00:00,434.8\n1993-02-24 00:00:00,440.87\n1993-02-25 00:00:00,442.34\n1993-02-26 00:00:00,443.38\n1993-03-01 00:00:00,442.01\n1993-03-02 00:00:00,447.9\n1993-03-03 00:00:00,449.26\n1993-03-04 00:00:00,447.34\n1993-03-05 00:00:00,446.11\n1993-03-08 00:00:00,454.71\n1993-03-09 00:00:00,454.4\n1993-03-10 00:00:00,456.33\n1993-03-11 00:00:00,453.72\n1993-03-12 00:00:00,449.83\n1993-03-15 00:00:00,451.43\n1993-03-16 00:00:00,451.37\n1993-03-17 00:00:00,448.31\n1993-03-18 00:00:00,451.89\n1993-03-19 00:00:00,450.18\n1993-03-22 00:00:00,448.88\n1993-03-23 00:00:00,448.76\n1993-03-24 00:00:00,448.07\n1993-03-25 00:00:00,450.88\n1993-03-26 00:00:00,447.78\n1993-03-29 00:00:00,450.77\n1993-03-30 00:00:00,451.97\n1993-03-31 00:00:00,451.67\n1993-04-01 00:00:00,450.3\n1993-04-02 00:00:00,441.39\n1993-04-05 00:00:00,442.29\n1993-04-06 00:00:00,441.16\n1993-04-07 00:00:00,442.73\n1993-04-08 00:00:00,441.84\n1993-04-12 00:00:00,448.37\n1993-04-13 00:00:00,449.22\n1993-04-14 00:00:00,448.66\n1993-04-15 00:00:00,448.4\n1993-04-16 00:00:00,448.94\n1993-04-19 00:00:00,447.46\n1993-04-20 00:00:00,445.1\n1993-04-21 00:00:00,443.63\n1993-04-22 00:00:00,439.46\n1993-04-23 00:00:00,437.03\n1993-04-26 00:00:00,433.54\n1993-04-27 00:00:00,438.01\n1993-04-28 00:00:00,438.02\n1993-04-29 00:00:00,438.89\n1993-04-30 00:00:00,440.19\n1993-05-03 00:00:00,442.46\n1993-05-04 00:00:00,444.05\n1993-05-05 00:00:00,444.52\n1993-05-06 00:00:00,443.26\n1993-05-07 00:00:00,442.31\n1993-05-10 00:00:00,442.8\n1993-05-11 00:00:00,444.36\n1993-05-12 00:00:00,444.8\n1993-05-13 00:00:00,439.23\n1993-05-14 00:00:00,439.56\n1993-05-17 00:00:00,440.37\n1993-05-18 00:00:00,440.32\n1993-05-19 00:00:00,447.57\n1993-05-20 00:00:00,450.59\n1993-05-21 00:00:00,445.84\n1993-05-24 00:00:00,448.0\n1993-05-25 00:00:00,448.85\n1993-05-26 00:00:00,453.44\n1993-05-27 00:00:00,452.41\n1993-05-28 00:00:00,450.19\n1993-06-01 00:00:00,453.83\n1993-06-02 00:00:00,453.85\n1993-06-03 00:00:00,452.49\n1993-06-04 00:00:00,450.06\n1993-06-07 00:00:00,447.69\n1993-06-08 00:00:00,444.71\n1993-06-09 00:00:00,445.78\n1993-06-10 00:00:00,445.38\n1993-06-11 00:00:00,447.26\n1993-06-14 00:00:00,447.71\n1993-06-15 00:00:00,446.27\n1993-06-16 00:00:00,447.43\n1993-06-17 00:00:00,448.54\n1993-06-18 00:00:00,443.68\n1993-06-21 00:00:00,446.22\n1993-06-22 00:00:00,445.93\n1993-06-23 00:00:00,443.19\n1993-06-24 00:00:00,446.62\n1993-06-25 00:00:00,447.6\n1993-06-28 00:00:00,451.85\n1993-06-29 00:00:00,450.69\n1993-06-30 00:00:00,450.53\n1993-07-01 00:00:00,449.02\n1993-07-02 00:00:00,445.84\n1993-07-06 00:00:00,441.43\n1993-07-07 00:00:00,442.83\n1993-07-08 00:00:00,448.64\n1993-07-09 00:00:00,448.11\n1993-07-12 00:00:00,448.98\n1993-07-13 00:00:00,448.09\n1993-07-14 00:00:00,450.08\n1993-07-15 00:00:00,449.22\n1993-07-16 00:00:00,445.75\n1993-07-19 00:00:00,446.03\n1993-07-20 00:00:00,447.31\n1993-07-21 00:00:00,447.18\n1993-07-22 00:00:00,444.51\n1993-07-23 00:00:00,447.1\n1993-07-26 00:00:00,449.09\n1993-07-27 00:00:00,448.24\n1993-07-28 00:00:00,447.19\n1993-07-29 00:00:00,450.24\n1993-07-30 00:00:00,448.13\n1993-08-02 00:00:00,450.15\n1993-08-03 00:00:00,449.27\n1993-08-04 00:00:00,448.54\n1993-08-05 00:00:00,448.13\n1993-08-06 00:00:00,448.68\n1993-08-09 00:00:00,450.72\n1993-08-10 00:00:00,449.45\n1993-08-11 00:00:00,450.46\n1993-08-12 00:00:00,448.96\n1993-08-13 00:00:00,450.14\n1993-08-16 00:00:00,452.38\n1993-08-17 00:00:00,453.13\n1993-08-18 00:00:00,456.04\n1993-08-19 00:00:00,456.43\n1993-08-20 00:00:00,456.16\n1993-08-23 00:00:00,455.23\n1993-08-24 00:00:00,459.77\n1993-08-25 00:00:00,460.13\n1993-08-26 00:00:00,461.04\n1993-08-27 00:00:00,460.54\n1993-08-30 00:00:00,461.9\n1993-08-31 00:00:00,463.56\n1993-09-01 00:00:00,463.15\n1993-09-02 00:00:00,461.3\n1993-09-03 00:00:00,461.34\n1993-09-07 00:00:00,458.52\n1993-09-08 00:00:00,456.65\n1993-09-09 00:00:00,457.5\n1993-09-10 00:00:00,461.72\n1993-09-13 00:00:00,462.06\n1993-09-14 00:00:00,459.9\n1993-09-15 00:00:00,461.6\n1993-09-16 00:00:00,459.43\n1993-09-17 00:00:00,458.83\n1993-09-20 00:00:00,455.05\n1993-09-21 00:00:00,452.95\n1993-09-22 00:00:00,456.2\n1993-09-23 00:00:00,457.74\n1993-09-24 00:00:00,457.63\n1993-09-27 00:00:00,461.8\n1993-09-28 00:00:00,461.53\n1993-09-29 00:00:00,460.11\n1993-09-30 00:00:00,458.93\n1993-10-01 00:00:00,461.28\n1993-10-04 00:00:00,461.34\n1993-10-05 00:00:00,461.2\n1993-10-06 00:00:00,460.74\n1993-10-07 00:00:00,459.18\n1993-10-08 00:00:00,460.31\n1993-10-11 00:00:00,460.88\n1993-10-12 00:00:00,461.12\n1993-10-13 00:00:00,461.49\n1993-10-14 00:00:00,466.83\n1993-10-15 00:00:00,469.5\n1993-10-18 00:00:00,468.45\n1993-10-19 00:00:00,466.21\n1993-10-20 00:00:00,466.07\n1993-10-21 00:00:00,465.36\n1993-10-22 00:00:00,463.27\n1993-10-25 00:00:00,464.2\n1993-10-26 00:00:00,464.3\n1993-10-27 00:00:00,464.61\n1993-10-28 00:00:00,467.73\n1993-10-29 00:00:00,467.83\n1993-11-01 00:00:00,469.1\n1993-11-02 00:00:00,468.44\n1993-11-03 00:00:00,463.02\n1993-11-04 00:00:00,457.49\n1993-11-05 00:00:00,459.57\n1993-11-08 00:00:00,460.21\n1993-11-09 00:00:00,460.33\n1993-11-10 00:00:00,463.72\n1993-11-11 00:00:00,462.64\n1993-11-12 00:00:00,465.39\n1993-11-15 00:00:00,463.75\n1993-11-16 00:00:00,466.74\n1993-11-17 00:00:00,464.81\n1993-11-18 00:00:00,463.62\n1993-11-19 00:00:00,462.6\n1993-11-22 00:00:00,459.13\n1993-11-23 00:00:00,461.03\n1993-11-24 00:00:00,462.36\n1993-11-26 00:00:00,463.06\n1993-11-29 00:00:00,461.9\n1993-11-30 00:00:00,461.79\n1993-12-01 00:00:00,461.89\n1993-12-02 00:00:00,463.11\n1993-12-03 00:00:00,464.89\n1993-12-06 00:00:00,466.43\n1993-12-07 00:00:00,466.76\n1993-12-08 00:00:00,466.29\n1993-12-09 00:00:00,464.18\n1993-12-10 00:00:00,463.93\n1993-12-13 00:00:00,465.7\n1993-12-14 00:00:00,463.06\n1993-12-15 00:00:00,461.84\n1993-12-16 00:00:00,463.34\n1993-12-17 00:00:00,466.38\n1993-12-20 00:00:00,465.85\n1993-12-21 00:00:00,465.3\n1993-12-22 00:00:00,467.32\n1993-12-23 00:00:00,467.38\n1993-12-27 00:00:00,470.54\n1993-12-28 00:00:00,470.94\n1993-12-29 00:00:00,470.58\n1993-12-30 00:00:00,468.64\n1993-12-31 00:00:00,466.45\n1994-01-03 00:00:00,465.44\n1994-01-04 00:00:00,466.89\n1994-01-05 00:00:00,467.55\n1994-01-06 00:00:00,467.12\n1994-01-07 00:00:00,469.9\n1994-01-10 00:00:00,475.27\n1994-01-11 00:00:00,474.13\n1994-01-12 00:00:00,474.17\n1994-01-13 00:00:00,472.47\n1994-01-14 00:00:00,474.91\n1994-01-17 00:00:00,473.3\n1994-01-18 00:00:00,474.25\n1994-01-19 00:00:00,474.3\n1994-01-20 00:00:00,474.98\n1994-01-21 00:00:00,474.72\n1994-01-24 00:00:00,471.97\n1994-01-25 00:00:00,470.92\n1994-01-26 00:00:00,473.2\n1994-01-27 00:00:00,477.05\n1994-01-28 00:00:00,478.7\n1994-01-31 00:00:00,481.61\n1994-02-01 00:00:00,479.62\n1994-02-02 00:00:00,482.0\n1994-02-03 00:00:00,480.71\n1994-02-04 00:00:00,469.81\n1994-02-07 00:00:00,471.76\n1994-02-08 00:00:00,471.05\n1994-02-09 00:00:00,472.77\n1994-02-10 00:00:00,468.93\n1994-02-11 00:00:00,470.18\n1994-02-14 00:00:00,470.23\n1994-02-15 00:00:00,472.52\n1994-02-16 00:00:00,472.79\n1994-02-17 00:00:00,470.34\n1994-02-18 00:00:00,467.69\n1994-02-22 00:00:00,471.46\n1994-02-23 00:00:00,470.69\n1994-02-24 00:00:00,464.26\n1994-02-25 00:00:00,466.07\n1994-02-28 00:00:00,467.14\n1994-03-01 00:00:00,464.44\n1994-03-02 00:00:00,464.81\n1994-03-03 00:00:00,463.01\n1994-03-04 00:00:00,464.74\n1994-03-07 00:00:00,466.91\n1994-03-08 00:00:00,465.88\n1994-03-09 00:00:00,467.06\n1994-03-10 00:00:00,463.9\n1994-03-11 00:00:00,466.44\n1994-03-14 00:00:00,467.39\n1994-03-15 00:00:00,467.01\n1994-03-16 00:00:00,469.42\n1994-03-17 00:00:00,470.9\n1994-03-18 00:00:00,471.06\n1994-03-21 00:00:00,468.54\n1994-03-22 00:00:00,468.8\n1994-03-23 00:00:00,468.54\n1994-03-24 00:00:00,464.35\n1994-03-25 00:00:00,460.58\n1994-03-28 00:00:00,460.0\n1994-03-29 00:00:00,452.48\n1994-03-30 00:00:00,445.55\n1994-03-31 00:00:00,445.77\n1994-04-04 00:00:00,438.92\n1994-04-05 00:00:00,448.29\n1994-04-06 00:00:00,448.05\n1994-04-07 00:00:00,450.88\n1994-04-08 00:00:00,447.1\n1994-04-11 00:00:00,449.87\n1994-04-12 00:00:00,447.57\n1994-04-13 00:00:00,446.26\n1994-04-14 00:00:00,446.38\n1994-04-15 00:00:00,446.18\n1994-04-18 00:00:00,442.46\n1994-04-19 00:00:00,442.54\n1994-04-20 00:00:00,441.96\n1994-04-21 00:00:00,448.73\n1994-04-22 00:00:00,447.63\n1994-04-25 00:00:00,452.71\n1994-04-26 00:00:00,451.87\n1994-04-28 00:00:00,449.1\n1994-04-29 00:00:00,450.91\n1994-05-02 00:00:00,453.02\n1994-05-03 00:00:00,453.03\n1994-05-04 00:00:00,451.72\n1994-05-05 00:00:00,451.38\n1994-05-06 00:00:00,447.82\n1994-05-09 00:00:00,442.32\n1994-05-10 00:00:00,446.01\n1994-05-11 00:00:00,441.49\n1994-05-12 00:00:00,443.75\n1994-05-13 00:00:00,444.14\n1994-05-16 00:00:00,444.49\n1994-05-17 00:00:00,449.37\n1994-05-18 00:00:00,453.69\n1994-05-19 00:00:00,456.48\n1994-05-20 00:00:00,454.92\n1994-05-23 00:00:00,453.2\n1994-05-24 00:00:00,454.81\n1994-05-25 00:00:00,456.34\n1994-05-26 00:00:00,457.06\n1994-05-27 00:00:00,457.33\n1994-05-31 00:00:00,456.5\n1994-06-01 00:00:00,457.63\n1994-06-02 00:00:00,457.65\n1994-06-03 00:00:00,460.13\n1994-06-06 00:00:00,458.88\n1994-06-07 00:00:00,458.21\n1994-06-08 00:00:00,457.06\n1994-06-09 00:00:00,457.86\n1994-06-10 00:00:00,458.67\n1994-06-13 00:00:00,459.1\n1994-06-14 00:00:00,462.37\n1994-06-15 00:00:00,460.61\n1994-06-16 00:00:00,461.93\n1994-06-17 00:00:00,458.45\n1994-06-20 00:00:00,455.48\n1994-06-21 00:00:00,451.34\n1994-06-22 00:00:00,453.09\n1994-06-23 00:00:00,449.63\n1994-06-24 00:00:00,442.8\n1994-06-27 00:00:00,447.31\n1994-06-28 00:00:00,446.07\n1994-06-29 00:00:00,447.63\n1994-06-30 00:00:00,444.27\n1994-07-01 00:00:00,446.2\n1994-07-05 00:00:00,446.37\n1994-07-06 00:00:00,446.13\n1994-07-07 00:00:00,448.38\n1994-07-08 00:00:00,449.55\n1994-07-11 00:00:00,448.06\n1994-07-12 00:00:00,447.95\n1994-07-13 00:00:00,448.73\n1994-07-14 00:00:00,453.41\n1994-07-15 00:00:00,454.16\n1994-07-18 00:00:00,455.22\n1994-07-19 00:00:00,453.86\n1994-07-20 00:00:00,451.6\n1994-07-21 00:00:00,452.61\n1994-07-22 00:00:00,453.11\n1994-07-25 00:00:00,454.25\n1994-07-26 00:00:00,453.36\n1994-07-27 00:00:00,452.57\n1994-07-28 00:00:00,454.24\n1994-07-29 00:00:00,458.26\n1994-08-01 00:00:00,461.01\n1994-08-02 00:00:00,460.56\n1994-08-03 00:00:00,461.45\n1994-08-04 00:00:00,458.4\n1994-08-05 00:00:00,457.09\n1994-08-08 00:00:00,457.89\n1994-08-09 00:00:00,457.92\n1994-08-10 00:00:00,460.3\n1994-08-11 00:00:00,458.88\n1994-08-12 00:00:00,461.94\n1994-08-15 00:00:00,461.23\n1994-08-16 00:00:00,465.01\n1994-08-17 00:00:00,465.17\n1994-08-18 00:00:00,463.17\n1994-08-19 00:00:00,463.68\n1994-08-22 00:00:00,462.32\n1994-08-23 00:00:00,464.51\n1994-08-24 00:00:00,469.03\n1994-08-25 00:00:00,468.08\n1994-08-26 00:00:00,473.8\n1994-08-29 00:00:00,474.59\n1994-08-30 00:00:00,476.07\n1994-08-31 00:00:00,475.49\n1994-09-01 00:00:00,473.17\n1994-09-02 00:00:00,470.99\n1994-09-06 00:00:00,471.86\n1994-09-07 00:00:00,470.99\n1994-09-08 00:00:00,473.14\n1994-09-09 00:00:00,468.18\n1994-09-12 00:00:00,466.21\n1994-09-13 00:00:00,467.51\n1994-09-14 00:00:00,468.8\n1994-09-15 00:00:00,474.81\n1994-09-16 00:00:00,471.19\n1994-09-19 00:00:00,470.85\n1994-09-20 00:00:00,463.36\n1994-09-21 00:00:00,461.46\n1994-09-22 00:00:00,461.27\n1994-09-23 00:00:00,459.67\n1994-09-26 00:00:00,460.82\n1994-09-27 00:00:00,462.05\n1994-09-28 00:00:00,464.84\n1994-09-29 00:00:00,462.24\n1994-09-30 00:00:00,462.71\n1994-10-03 00:00:00,461.74\n1994-10-04 00:00:00,454.59\n1994-10-05 00:00:00,453.52\n1994-10-06 00:00:00,452.36\n1994-10-07 00:00:00,455.1\n1994-10-10 00:00:00,459.04\n1994-10-11 00:00:00,465.79\n1994-10-12 00:00:00,465.47\n1994-10-13 00:00:00,467.79\n1994-10-14 00:00:00,469.1\n1994-10-17 00:00:00,468.96\n1994-10-18 00:00:00,467.66\n1994-10-19 00:00:00,470.28\n1994-10-20 00:00:00,466.85\n1994-10-21 00:00:00,464.89\n1994-10-24 00:00:00,460.83\n1994-10-25 00:00:00,461.53\n1994-10-26 00:00:00,462.62\n1994-10-27 00:00:00,465.85\n1994-10-28 00:00:00,473.77\n1994-10-31 00:00:00,472.35\n1994-11-01 00:00:00,468.42\n1994-11-02 00:00:00,466.51\n1994-11-03 00:00:00,467.91\n1994-11-04 00:00:00,462.28\n1994-11-07 00:00:00,463.07\n1994-11-08 00:00:00,465.65\n1994-11-09 00:00:00,465.4\n1994-11-10 00:00:00,464.37\n1994-11-11 00:00:00,462.35\n1994-11-14 00:00:00,466.04\n1994-11-15 00:00:00,465.03\n1994-11-16 00:00:00,465.62\n1994-11-17 00:00:00,463.57\n1994-11-18 00:00:00,461.47\n1994-11-21 00:00:00,458.3\n1994-11-22 00:00:00,450.09\n1994-11-23 00:00:00,449.93\n1994-11-25 00:00:00,452.29\n1994-11-28 00:00:00,454.16\n1994-11-29 00:00:00,455.17\n1994-11-30 00:00:00,453.69\n1994-12-01 00:00:00,448.92\n1994-12-02 00:00:00,453.3\n1994-12-05 00:00:00,453.32\n1994-12-06 00:00:00,453.11\n1994-12-07 00:00:00,451.23\n1994-12-08 00:00:00,445.45\n1994-12-09 00:00:00,446.96\n1994-12-12 00:00:00,449.47\n1994-12-13 00:00:00,450.15\n1994-12-14 00:00:00,454.97\n1994-12-15 00:00:00,455.34\n1994-12-16 00:00:00,458.8\n1994-12-19 00:00:00,457.91\n1994-12-20 00:00:00,457.1\n1994-12-21 00:00:00,459.61\n1994-12-22 00:00:00,459.68\n1994-12-23 00:00:00,459.83\n1994-12-27 00:00:00,462.47\n1994-12-28 00:00:00,460.86\n1994-12-29 00:00:00,461.17\n1994-12-30 00:00:00,459.27\n1995-01-03 00:00:00,459.11\n1995-01-04 00:00:00,460.71\n1995-01-05 00:00:00,460.34\n1995-01-06 00:00:00,460.68\n1995-01-09 00:00:00,460.83\n1995-01-10 00:00:00,461.68\n1995-01-11 00:00:00,461.66\n1995-01-12 00:00:00,461.64\n1995-01-13 00:00:00,465.97\n1995-01-16 00:00:00,469.38\n1995-01-17 00:00:00,470.05\n1995-01-18 00:00:00,469.71\n1995-01-19 00:00:00,466.95\n1995-01-20 00:00:00,464.78\n1995-01-23 00:00:00,465.82\n1995-01-24 00:00:00,465.86\n1995-01-25 00:00:00,467.44\n1995-01-26 00:00:00,468.32\n1995-01-27 00:00:00,470.39\n1995-01-30 00:00:00,468.51\n1995-01-31 00:00:00,470.42\n1995-02-01 00:00:00,470.4\n1995-02-02 00:00:00,472.79\n1995-02-03 00:00:00,478.65\n1995-02-06 00:00:00,481.14\n1995-02-07 00:00:00,480.81\n1995-02-08 00:00:00,481.19\n1995-02-09 00:00:00,480.19\n1995-02-10 00:00:00,481.46\n1995-02-13 00:00:00,481.65\n1995-02-14 00:00:00,482.55\n1995-02-15 00:00:00,484.54\n1995-02-16 00:00:00,485.22\n1995-02-17 00:00:00,481.97\n1995-02-21 00:00:00,482.72\n1995-02-22 00:00:00,485.07\n1995-02-23 00:00:00,486.91\n1995-02-24 00:00:00,488.11\n1995-02-27 00:00:00,483.81\n1995-02-28 00:00:00,487.39\n1995-03-01 00:00:00,485.65\n1995-03-02 00:00:00,485.13\n1995-03-03 00:00:00,485.42\n1995-03-06 00:00:00,485.63\n1995-03-07 00:00:00,482.12\n1995-03-08 00:00:00,483.14\n1995-03-09 00:00:00,483.16\n1995-03-10 00:00:00,489.57\n1995-03-13 00:00:00,490.05\n1995-03-14 00:00:00,492.89\n1995-03-15 00:00:00,491.88\n1995-03-16 00:00:00,495.41\n1995-03-17 00:00:00,495.52\n1995-03-20 00:00:00,496.14\n1995-03-21 00:00:00,495.07\n1995-03-22 00:00:00,495.67\n1995-03-23 00:00:00,495.95\n1995-03-24 00:00:00,500.97\n1995-03-27 00:00:00,503.2\n1995-03-28 00:00:00,503.9\n1995-03-29 00:00:00,503.12\n1995-03-30 00:00:00,502.22\n1995-03-31 00:00:00,500.71\n1995-04-03 00:00:00,501.85\n1995-04-04 00:00:00,505.24\n1995-04-05 00:00:00,505.57\n1995-04-06 00:00:00,506.08\n1995-04-07 00:00:00,506.42\n1995-04-10 00:00:00,507.01\n1995-04-11 00:00:00,505.53\n1995-04-12 00:00:00,507.17\n1995-04-13 00:00:00,509.23\n1995-04-17 00:00:00,506.13\n1995-04-18 00:00:00,505.37\n1995-04-19 00:00:00,504.92\n1995-04-20 00:00:00,505.29\n1995-04-21 00:00:00,508.49\n1995-04-24 00:00:00,512.89\n1995-04-25 00:00:00,512.1\n1995-04-26 00:00:00,512.66\n1995-04-27 00:00:00,513.55\n1995-04-28 00:00:00,514.71\n1995-05-01 00:00:00,514.26\n1995-05-02 00:00:00,514.86\n1995-05-03 00:00:00,520.48\n1995-05-04 00:00:00,520.54\n1995-05-05 00:00:00,520.12\n1995-05-08 00:00:00,523.96\n1995-05-09 00:00:00,523.56\n1995-05-10 00:00:00,524.36\n1995-05-11 00:00:00,524.37\n1995-05-12 00:00:00,525.55\n1995-05-15 00:00:00,527.74\n1995-05-16 00:00:00,528.19\n1995-05-17 00:00:00,527.07\n1995-05-18 00:00:00,519.58\n1995-05-19 00:00:00,519.19\n1995-05-22 00:00:00,523.65\n1995-05-23 00:00:00,528.59\n1995-05-24 00:00:00,528.61\n1995-05-25 00:00:00,528.59\n1995-05-26 00:00:00,523.65\n1995-05-30 00:00:00,523.58\n1995-05-31 00:00:00,533.4\n1995-06-01 00:00:00,533.49\n1995-06-02 00:00:00,532.51\n1995-06-05 00:00:00,535.6\n1995-06-06 00:00:00,535.55\n1995-06-07 00:00:00,533.13\n1995-06-08 00:00:00,532.35\n1995-06-09 00:00:00,527.94\n1995-06-12 00:00:00,530.88\n1995-06-13 00:00:00,536.05\n1995-06-14 00:00:00,536.47\n1995-06-15 00:00:00,537.12\n1995-06-16 00:00:00,539.83\n1995-06-19 00:00:00,545.22\n1995-06-20 00:00:00,544.98\n1995-06-21 00:00:00,543.98\n1995-06-22 00:00:00,551.07\n1995-06-23 00:00:00,549.71\n1995-06-26 00:00:00,544.13\n1995-06-27 00:00:00,542.43\n1995-06-28 00:00:00,544.73\n1995-06-29 00:00:00,543.87\n1995-06-30 00:00:00,544.75\n1995-07-03 00:00:00,547.09\n1995-07-05 00:00:00,547.26\n1995-07-06 00:00:00,553.99\n1995-07-07 00:00:00,556.37\n1995-07-10 00:00:00,557.19\n1995-07-11 00:00:00,554.78\n1995-07-12 00:00:00,560.89\n1995-07-13 00:00:00,561.0\n1995-07-14 00:00:00,559.89\n1995-07-17 00:00:00,562.72\n1995-07-18 00:00:00,558.46\n1995-07-19 00:00:00,550.98\n1995-07-20 00:00:00,553.54\n1995-07-21 00:00:00,553.62\n1995-07-24 00:00:00,556.63\n1995-07-25 00:00:00,561.1\n1995-07-26 00:00:00,561.61\n1995-07-27 00:00:00,565.22\n1995-07-28 00:00:00,562.93\n1995-07-31 00:00:00,562.06\n1995-08-01 00:00:00,559.64\n1995-08-02 00:00:00,558.8\n1995-08-03 00:00:00,558.75\n1995-08-04 00:00:00,558.94\n1995-08-07 00:00:00,560.03\n1995-08-08 00:00:00,560.39\n1995-08-09 00:00:00,559.71\n1995-08-10 00:00:00,557.45\n1995-08-11 00:00:00,555.11\n1995-08-14 00:00:00,559.74\n1995-08-15 00:00:00,558.57\n1995-08-16 00:00:00,559.97\n1995-08-17 00:00:00,559.04\n1995-08-18 00:00:00,559.21\n1995-08-21 00:00:00,558.11\n1995-08-22 00:00:00,559.52\n1995-08-23 00:00:00,557.14\n1995-08-24 00:00:00,557.46\n1995-08-25 00:00:00,560.1\n1995-08-28 00:00:00,559.05\n1995-08-29 00:00:00,560.0\n1995-08-30 00:00:00,560.92\n1995-08-31 00:00:00,561.88\n1995-09-01 00:00:00,563.84\n1995-09-05 00:00:00,569.17\n1995-09-06 00:00:00,570.17\n1995-09-07 00:00:00,570.29\n1995-09-08 00:00:00,572.68\n1995-09-11 00:00:00,573.91\n1995-09-12 00:00:00,576.51\n1995-09-13 00:00:00,578.77\n1995-09-14 00:00:00,583.61\n1995-09-15 00:00:00,583.35\n1995-09-18 00:00:00,582.77\n1995-09-19 00:00:00,584.2\n1995-09-20 00:00:00,586.77\n1995-09-21 00:00:00,583.0\n1995-09-22 00:00:00,581.73\n1995-09-25 00:00:00,581.81\n1995-09-26 00:00:00,581.41\n1995-09-27 00:00:00,581.04\n1995-09-28 00:00:00,585.87\n1995-09-29 00:00:00,584.41\n1995-10-02 00:00:00,581.72\n1995-10-03 00:00:00,582.34\n1995-10-04 00:00:00,581.47\n1995-10-05 00:00:00,582.63\n1995-10-06 00:00:00,582.49\n1995-10-09 00:00:00,578.37\n1995-10-10 00:00:00,577.52\n1995-10-11 00:00:00,579.46\n1995-10-12 00:00:00,583.1\n1995-10-13 00:00:00,584.5\n1995-10-16 00:00:00,583.03\n1995-10-17 00:00:00,586.78\n1995-10-18 00:00:00,587.44\n1995-10-19 00:00:00,590.65\n1995-10-20 00:00:00,587.46\n1995-10-23 00:00:00,585.06\n1995-10-24 00:00:00,586.54\n1995-10-25 00:00:00,582.47\n1995-10-26 00:00:00,576.72\n1995-10-27 00:00:00,579.7\n1995-10-30 00:00:00,583.25\n1995-10-31 00:00:00,581.5\n1995-11-01 00:00:00,584.22\n1995-11-02 00:00:00,589.72\n1995-11-03 00:00:00,590.57\n1995-11-06 00:00:00,588.46\n1995-11-07 00:00:00,586.32\n1995-11-08 00:00:00,591.71\n1995-11-09 00:00:00,593.26\n1995-11-10 00:00:00,592.72\n1995-11-13 00:00:00,592.3\n1995-11-14 00:00:00,589.29\n1995-11-15 00:00:00,593.96\n1995-11-16 00:00:00,597.34\n1995-11-17 00:00:00,600.07\n1995-11-20 00:00:00,596.85\n1995-11-21 00:00:00,600.24\n1995-11-22 00:00:00,598.4\n1995-11-24 00:00:00,599.97\n1995-11-27 00:00:00,601.32\n1995-11-28 00:00:00,606.45\n1995-11-29 00:00:00,607.64\n1995-11-30 00:00:00,605.37\n1995-12-01 00:00:00,606.98\n1995-12-04 00:00:00,613.68\n1995-12-05 00:00:00,617.68\n1995-12-06 00:00:00,620.18\n1995-12-07 00:00:00,616.17\n1995-12-08 00:00:00,617.48\n1995-12-11 00:00:00,619.52\n1995-12-12 00:00:00,618.78\n1995-12-13 00:00:00,621.69\n1995-12-14 00:00:00,616.92\n1995-12-15 00:00:00,616.34\n1995-12-18 00:00:00,606.81\n1995-12-19 00:00:00,611.93\n1995-12-20 00:00:00,605.94\n1995-12-21 00:00:00,610.49\n1995-12-22 00:00:00,611.95\n1995-12-26 00:00:00,614.3\n1995-12-27 00:00:00,614.53\n1995-12-28 00:00:00,614.12\n1995-12-29 00:00:00,615.93\n1996-01-02 00:00:00,620.73\n1996-01-03 00:00:00,621.32\n1996-01-04 00:00:00,617.7\n1996-01-05 00:00:00,616.71\n1996-01-08 00:00:00,618.46\n1996-01-09 00:00:00,609.45\n1996-01-10 00:00:00,598.48\n1996-01-11 00:00:00,602.69\n1996-01-12 00:00:00,601.81\n1996-01-15 00:00:00,599.82\n1996-01-16 00:00:00,608.44\n1996-01-17 00:00:00,606.37\n1996-01-18 00:00:00,608.24\n1996-01-19 00:00:00,611.83\n1996-01-22 00:00:00,613.4\n1996-01-23 00:00:00,612.79\n1996-01-24 00:00:00,619.96\n1996-01-25 00:00:00,617.03\n1996-01-26 00:00:00,621.62\n1996-01-29 00:00:00,624.22\n1996-01-30 00:00:00,630.15\n1996-01-31 00:00:00,636.02\n1996-02-01 00:00:00,638.46\n1996-02-02 00:00:00,635.84\n1996-02-05 00:00:00,641.43\n1996-02-06 00:00:00,646.33\n1996-02-07 00:00:00,649.93\n1996-02-08 00:00:00,656.07\n1996-02-09 00:00:00,656.37\n1996-02-12 00:00:00,661.45\n1996-02-13 00:00:00,660.51\n1996-02-14 00:00:00,655.58\n1996-02-15 00:00:00,651.32\n1996-02-16 00:00:00,647.98\n1996-02-20 00:00:00,640.65\n1996-02-21 00:00:00,648.1\n1996-02-22 00:00:00,658.86\n1996-02-23 00:00:00,659.08\n1996-02-26 00:00:00,650.46\n1996-02-27 00:00:00,647.24\n1996-02-28 00:00:00,644.75\n1996-02-29 00:00:00,640.43\n1996-03-01 00:00:00,644.37\n1996-03-04 00:00:00,650.81\n1996-03-05 00:00:00,655.79\n1996-03-06 00:00:00,652.0\n1996-03-07 00:00:00,653.65\n1996-03-08 00:00:00,633.5\n1996-03-11 00:00:00,640.02\n1996-03-12 00:00:00,637.09\n1996-03-13 00:00:00,638.55\n1996-03-14 00:00:00,640.87\n1996-03-15 00:00:00,641.43\n1996-03-18 00:00:00,652.65\n1996-03-19 00:00:00,651.69\n1996-03-20 00:00:00,649.98\n1996-03-21 00:00:00,649.19\n1996-03-22 00:00:00,650.62\n1996-03-25 00:00:00,650.04\n1996-03-26 00:00:00,652.97\n1996-03-27 00:00:00,648.91\n1996-03-28 00:00:00,648.94\n1996-03-29 00:00:00,645.5\n1996-04-01 00:00:00,653.73\n1996-04-02 00:00:00,655.26\n1996-04-03 00:00:00,655.88\n1996-04-04 00:00:00,655.86\n1996-04-08 00:00:00,644.24\n1996-04-09 00:00:00,642.19\n1996-04-10 00:00:00,633.5\n1996-04-11 00:00:00,631.18\n1996-04-12 00:00:00,636.71\n1996-04-15 00:00:00,642.49\n1996-04-16 00:00:00,645.0\n1996-04-17 00:00:00,641.61\n1996-04-18 00:00:00,643.61\n1996-04-19 00:00:00,645.07\n1996-04-22 00:00:00,647.89\n1996-04-23 00:00:00,651.58\n1996-04-24 00:00:00,650.17\n1996-04-25 00:00:00,652.87\n1996-04-26 00:00:00,653.46\n1996-04-29 00:00:00,654.16\n1996-04-30 00:00:00,654.17\n1996-05-01 00:00:00,654.58\n1996-05-02 00:00:00,643.38\n1996-05-03 00:00:00,641.63\n1996-05-06 00:00:00,640.81\n1996-05-07 00:00:00,638.26\n1996-05-08 00:00:00,644.77\n1996-05-09 00:00:00,645.44\n1996-05-10 00:00:00,652.09\n1996-05-13 00:00:00,661.51\n1996-05-14 00:00:00,665.6\n1996-05-15 00:00:00,665.42\n1996-05-16 00:00:00,664.85\n1996-05-17 00:00:00,668.91\n1996-05-20 00:00:00,673.15\n1996-05-21 00:00:00,672.76\n1996-05-22 00:00:00,678.42\n1996-05-23 00:00:00,676.0\n1996-05-24 00:00:00,678.51\n1996-05-28 00:00:00,672.23\n1996-05-29 00:00:00,667.93\n1996-05-30 00:00:00,671.7\n1996-05-31 00:00:00,669.12\n1996-06-03 00:00:00,667.68\n1996-06-04 00:00:00,672.56\n1996-06-05 00:00:00,678.44\n1996-06-06 00:00:00,673.03\n1996-06-07 00:00:00,673.31\n1996-06-10 00:00:00,672.16\n1996-06-11 00:00:00,670.97\n1996-06-12 00:00:00,669.04\n1996-06-13 00:00:00,667.92\n1996-06-14 00:00:00,665.85\n1996-06-17 00:00:00,665.16\n1996-06-18 00:00:00,662.06\n1996-06-19 00:00:00,661.96\n1996-06-20 00:00:00,662.1\n1996-06-21 00:00:00,666.84\n1996-06-24 00:00:00,668.85\n1996-06-25 00:00:00,668.48\n1996-06-26 00:00:00,664.39\n1996-06-27 00:00:00,668.55\n1996-06-28 00:00:00,670.63\n1996-07-01 00:00:00,675.88\n1996-07-02 00:00:00,673.61\n1996-07-03 00:00:00,672.4\n1996-07-05 00:00:00,657.44\n1996-07-08 00:00:00,652.54\n1996-07-09 00:00:00,654.75\n1996-07-10 00:00:00,656.06\n1996-07-11 00:00:00,645.67\n1996-07-12 00:00:00,646.19\n1996-07-15 00:00:00,629.8\n1996-07-16 00:00:00,628.37\n1996-07-17 00:00:00,634.07\n1996-07-18 00:00:00,643.56\n1996-07-19 00:00:00,638.73\n1996-07-22 00:00:00,633.77\n1996-07-23 00:00:00,626.87\n1996-07-24 00:00:00,626.65\n1996-07-25 00:00:00,631.17\n1996-07-26 00:00:00,635.9\n1996-07-29 00:00:00,630.91\n1996-07-30 00:00:00,635.26\n1996-07-31 00:00:00,639.95\n1996-08-01 00:00:00,650.02\n1996-08-02 00:00:00,662.49\n1996-08-05 00:00:00,660.23\n1996-08-06 00:00:00,662.38\n1996-08-07 00:00:00,664.16\n1996-08-08 00:00:00,662.59\n1996-08-09 00:00:00,662.1\n1996-08-12 00:00:00,665.77\n1996-08-13 00:00:00,660.2\n1996-08-14 00:00:00,662.05\n1996-08-15 00:00:00,662.28\n1996-08-16 00:00:00,665.21\n1996-08-19 00:00:00,666.58\n1996-08-20 00:00:00,665.69\n1996-08-21 00:00:00,665.07\n1996-08-22 00:00:00,670.68\n1996-08-23 00:00:00,667.03\n1996-08-26 00:00:00,663.88\n1996-08-27 00:00:00,666.4\n1996-08-28 00:00:00,664.81\n1996-08-29 00:00:00,657.4\n1996-08-30 00:00:00,651.99\n1996-09-03 00:00:00,654.72\n1996-09-04 00:00:00,655.61\n1996-09-05 00:00:00,649.44\n1996-09-06 00:00:00,655.68\n1996-09-09 00:00:00,663.76\n1996-09-10 00:00:00,663.81\n1996-09-11 00:00:00,667.28\n1996-09-12 00:00:00,671.15\n1996-09-13 00:00:00,680.54\n1996-09-16 00:00:00,683.98\n1996-09-17 00:00:00,682.94\n1996-09-18 00:00:00,681.47\n1996-09-19 00:00:00,683.0\n1996-09-20 00:00:00,687.03\n1996-09-23 00:00:00,686.48\n1996-09-24 00:00:00,685.61\n1996-09-25 00:00:00,685.83\n1996-09-26 00:00:00,685.86\n1996-09-27 00:00:00,686.19\n1996-09-30 00:00:00,687.33\n1996-10-01 00:00:00,689.08\n1996-10-02 00:00:00,694.01\n1996-10-03 00:00:00,692.78\n1996-10-04 00:00:00,701.46\n1996-10-07 00:00:00,703.34\n1996-10-08 00:00:00,700.64\n1996-10-09 00:00:00,696.74\n1996-10-10 00:00:00,694.61\n1996-10-11 00:00:00,700.66\n1996-10-14 00:00:00,703.54\n1996-10-15 00:00:00,702.57\n1996-10-16 00:00:00,704.41\n1996-10-17 00:00:00,706.99\n1996-10-18 00:00:00,710.82\n1996-10-21 00:00:00,709.85\n1996-10-22 00:00:00,706.57\n1996-10-23 00:00:00,707.27\n1996-10-24 00:00:00,702.29\n1996-10-25 00:00:00,700.92\n1996-10-28 00:00:00,697.26\n1996-10-29 00:00:00,701.5\n1996-10-30 00:00:00,700.9\n1996-10-31 00:00:00,705.27\n1996-11-01 00:00:00,703.77\n1996-11-04 00:00:00,706.73\n1996-11-05 00:00:00,714.14\n1996-11-06 00:00:00,724.59\n1996-11-07 00:00:00,727.65\n1996-11-08 00:00:00,730.82\n1996-11-11 00:00:00,731.87\n1996-11-12 00:00:00,729.56\n1996-11-13 00:00:00,731.13\n1996-11-14 00:00:00,735.88\n1996-11-15 00:00:00,737.62\n1996-11-18 00:00:00,737.02\n1996-11-19 00:00:00,742.16\n1996-11-20 00:00:00,743.95\n1996-11-21 00:00:00,742.75\n1996-11-22 00:00:00,748.73\n1996-11-25 00:00:00,757.03\n1996-11-26 00:00:00,755.96\n1996-11-27 00:00:00,755.0\n1996-11-29 00:00:00,757.02\n1996-12-02 00:00:00,756.56\n1996-12-03 00:00:00,748.28\n1996-12-04 00:00:00,745.1\n1996-12-05 00:00:00,744.38\n1996-12-06 00:00:00,739.6\n1996-12-09 00:00:00,749.76\n1996-12-10 00:00:00,747.54\n1996-12-11 00:00:00,740.73\n1996-12-12 00:00:00,729.3\n1996-12-13 00:00:00,728.64\n1996-12-16 00:00:00,720.98\n1996-12-17 00:00:00,726.04\n1996-12-18 00:00:00,731.54\n1996-12-19 00:00:00,745.76\n1996-12-20 00:00:00,748.87\n1996-12-23 00:00:00,746.92\n1996-12-24 00:00:00,751.03\n1996-12-26 00:00:00,755.82\n1996-12-27 00:00:00,756.79\n1996-12-30 00:00:00,753.85\n1996-12-31 00:00:00,740.74\n1997-01-02 00:00:00,737.01\n1997-01-03 00:00:00,748.03\n1997-01-06 00:00:00,747.65\n1997-01-07 00:00:00,753.23\n1997-01-08 00:00:00,748.41\n1997-01-09 00:00:00,754.85\n1997-01-10 00:00:00,759.5\n1997-01-13 00:00:00,759.51\n1997-01-14 00:00:00,768.86\n1997-01-15 00:00:00,767.2\n1997-01-16 00:00:00,769.75\n1997-01-17 00:00:00,776.17\n1997-01-20 00:00:00,776.7\n1997-01-21 00:00:00,782.72\n1997-01-22 00:00:00,786.23\n1997-01-23 00:00:00,777.56\n1997-01-24 00:00:00,770.52\n1997-01-27 00:00:00,765.02\n1997-01-28 00:00:00,765.02\n1997-01-29 00:00:00,772.5\n1997-01-30 00:00:00,784.17\n1997-01-31 00:00:00,786.16\n1997-02-03 00:00:00,786.73\n1997-02-04 00:00:00,789.26\n1997-02-05 00:00:00,778.28\n1997-02-06 00:00:00,780.15\n1997-02-07 00:00:00,789.56\n1997-02-10 00:00:00,785.43\n1997-02-11 00:00:00,789.59\n1997-02-12 00:00:00,802.77\n1997-02-13 00:00:00,811.82\n1997-02-14 00:00:00,808.48\n1997-02-18 00:00:00,816.29\n1997-02-19 00:00:00,812.49\n1997-02-20 00:00:00,802.8\n1997-02-21 00:00:00,801.77\n1997-02-24 00:00:00,810.28\n1997-02-25 00:00:00,812.03\n1997-02-26 00:00:00,805.68\n1997-02-27 00:00:00,795.07\n1997-02-28 00:00:00,790.82\n1997-03-03 00:00:00,795.31\n1997-03-04 00:00:00,790.95\n1997-03-05 00:00:00,801.99\n1997-03-06 00:00:00,798.56\n1997-03-07 00:00:00,804.97\n1997-03-10 00:00:00,813.65\n1997-03-11 00:00:00,811.34\n1997-03-12 00:00:00,804.26\n1997-03-13 00:00:00,789.56\n1997-03-14 00:00:00,793.17\n1997-03-17 00:00:00,795.71\n1997-03-18 00:00:00,789.66\n1997-03-19 00:00:00,785.77\n1997-03-20 00:00:00,782.65\n1997-03-21 00:00:00,784.1\n1997-03-24 00:00:00,790.89\n1997-03-25 00:00:00,789.07\n1997-03-26 00:00:00,790.5\n1997-03-27 00:00:00,773.88\n1997-03-31 00:00:00,757.12\n1997-04-01 00:00:00,759.64\n1997-04-02 00:00:00,750.11\n1997-04-03 00:00:00,750.32\n1997-04-04 00:00:00,757.9\n1997-04-07 00:00:00,762.13\n1997-04-08 00:00:00,766.12\n1997-04-09 00:00:00,760.6\n1997-04-10 00:00:00,758.34\n1997-04-11 00:00:00,737.65\n1997-04-14 00:00:00,743.73\n1997-04-15 00:00:00,754.72\n1997-04-16 00:00:00,763.53\n1997-04-17 00:00:00,761.77\n1997-04-18 00:00:00,766.34\n1997-04-21 00:00:00,760.37\n1997-04-22 00:00:00,774.61\n1997-04-23 00:00:00,773.64\n1997-04-24 00:00:00,771.18\n1997-04-25 00:00:00,765.37\n1997-04-28 00:00:00,772.96\n1997-04-29 00:00:00,794.05\n1997-04-30 00:00:00,801.34\n1997-05-01 00:00:00,798.53\n1997-05-02 00:00:00,812.97\n1997-05-05 00:00:00,830.29\n1997-05-06 00:00:00,827.76\n1997-05-07 00:00:00,815.62\n1997-05-08 00:00:00,820.26\n1997-05-09 00:00:00,824.78\n1997-05-12 00:00:00,837.66\n1997-05-13 00:00:00,833.13\n1997-05-14 00:00:00,836.04\n1997-05-15 00:00:00,841.88\n1997-05-16 00:00:00,829.75\n1997-05-19 00:00:00,833.27\n1997-05-20 00:00:00,841.66\n1997-05-21 00:00:00,839.35\n1997-05-22 00:00:00,835.66\n1997-05-23 00:00:00,847.03\n1997-05-27 00:00:00,849.71\n1997-05-28 00:00:00,847.21\n1997-05-29 00:00:00,844.08\n1997-05-30 00:00:00,848.28\n1997-06-02 00:00:00,846.36\n1997-06-03 00:00:00,845.48\n1997-06-04 00:00:00,840.11\n1997-06-05 00:00:00,843.43\n1997-06-06 00:00:00,858.01\n1997-06-09 00:00:00,862.91\n1997-06-10 00:00:00,865.27\n1997-06-11 00:00:00,869.57\n1997-06-12 00:00:00,883.46\n1997-06-13 00:00:00,893.27\n1997-06-16 00:00:00,893.9\n1997-06-17 00:00:00,894.42\n1997-06-18 00:00:00,889.06\n1997-06-19 00:00:00,897.99\n1997-06-20 00:00:00,898.7\n1997-06-23 00:00:00,878.62\n1997-06-24 00:00:00,896.34\n1997-06-25 00:00:00,888.99\n1997-06-26 00:00:00,883.68\n1997-06-27 00:00:00,887.3\n1997-06-30 00:00:00,885.14\n1997-07-01 00:00:00,891.03\n1997-07-02 00:00:00,904.03\n1997-07-03 00:00:00,916.92\n1997-07-07 00:00:00,912.2\n1997-07-08 00:00:00,918.75\n1997-07-09 00:00:00,907.54\n1997-07-10 00:00:00,913.78\n1997-07-11 00:00:00,916.68\n1997-07-14 00:00:00,918.38\n1997-07-15 00:00:00,925.76\n1997-07-16 00:00:00,936.59\n1997-07-17 00:00:00,931.61\n1997-07-18 00:00:00,915.3\n1997-07-21 00:00:00,912.94\n1997-07-22 00:00:00,933.98\n1997-07-23 00:00:00,936.56\n1997-07-24 00:00:00,940.3\n1997-07-25 00:00:00,938.79\n1997-07-28 00:00:00,936.45\n1997-07-29 00:00:00,942.29\n1997-07-30 00:00:00,952.29\n1997-07-31 00:00:00,954.31\n1997-08-01 00:00:00,947.14\n1997-08-04 00:00:00,950.3\n1997-08-05 00:00:00,952.37\n1997-08-06 00:00:00,960.32\n1997-08-07 00:00:00,951.19\n1997-08-08 00:00:00,933.54\n1997-08-11 00:00:00,937.0\n1997-08-12 00:00:00,926.53\n1997-08-13 00:00:00,922.02\n1997-08-14 00:00:00,924.77\n1997-08-15 00:00:00,900.81\n1997-08-18 00:00:00,912.49\n1997-08-19 00:00:00,926.01\n1997-08-20 00:00:00,939.35\n1997-08-21 00:00:00,925.05\n1997-08-22 00:00:00,923.54\n1997-08-25 00:00:00,920.16\n1997-08-26 00:00:00,913.02\n1997-08-27 00:00:00,913.7\n1997-08-28 00:00:00,903.67\n1997-08-29 00:00:00,899.47\n1997-09-02 00:00:00,927.58\n1997-09-03 00:00:00,927.86\n1997-09-04 00:00:00,930.87\n1997-09-05 00:00:00,929.05\n1997-09-08 00:00:00,931.2\n1997-09-09 00:00:00,933.62\n1997-09-10 00:00:00,919.03\n1997-09-11 00:00:00,912.59\n1997-09-12 00:00:00,923.91\n1997-09-15 00:00:00,919.77\n1997-09-16 00:00:00,945.64\n1997-09-17 00:00:00,943.0\n1997-09-18 00:00:00,947.29\n1997-09-19 00:00:00,950.51\n1997-09-22 00:00:00,955.43\n1997-09-23 00:00:00,951.93\n1997-09-24 00:00:00,944.48\n1997-09-25 00:00:00,937.91\n1997-09-26 00:00:00,945.22\n1997-09-29 00:00:00,953.34\n1997-09-30 00:00:00,947.28\n1997-10-01 00:00:00,955.41\n1997-10-02 00:00:00,960.46\n1997-10-03 00:00:00,965.03\n1997-10-06 00:00:00,972.69\n1997-10-07 00:00:00,983.12\n1997-10-08 00:00:00,973.84\n1997-10-09 00:00:00,970.62\n1997-10-10 00:00:00,966.98\n1997-10-13 00:00:00,968.1\n1997-10-14 00:00:00,970.28\n1997-10-15 00:00:00,965.72\n1997-10-16 00:00:00,955.25\n1997-10-17 00:00:00,944.16\n1997-10-20 00:00:00,955.61\n1997-10-21 00:00:00,972.28\n1997-10-22 00:00:00,968.49\n1997-10-23 00:00:00,950.69\n1997-10-24 00:00:00,941.64\n1997-10-27 00:00:00,876.99\n1997-10-28 00:00:00,921.85\n1997-10-29 00:00:00,919.16\n1997-10-30 00:00:00,903.68\n1997-10-31 00:00:00,914.62\n1997-11-03 00:00:00,938.99\n1997-11-04 00:00:00,940.76\n1997-11-05 00:00:00,942.76\n1997-11-06 00:00:00,938.03\n1997-11-07 00:00:00,927.51\n1997-11-10 00:00:00,921.13\n1997-11-11 00:00:00,923.78\n1997-11-12 00:00:00,905.96\n1997-11-13 00:00:00,916.66\n1997-11-14 00:00:00,928.35\n1997-11-17 00:00:00,946.2\n1997-11-18 00:00:00,938.23\n1997-11-19 00:00:00,944.59\n1997-11-20 00:00:00,958.98\n1997-11-21 00:00:00,963.09\n1997-11-24 00:00:00,946.67\n1997-11-25 00:00:00,950.82\n1997-11-26 00:00:00,951.64\n1997-11-28 00:00:00,955.4\n1997-12-01 00:00:00,974.77\n1997-12-02 00:00:00,971.68\n1997-12-03 00:00:00,976.77\n1997-12-04 00:00:00,973.1\n1997-12-05 00:00:00,983.79\n1997-12-08 00:00:00,982.37\n1997-12-09 00:00:00,975.78\n1997-12-10 00:00:00,969.79\n1997-12-11 00:00:00,954.94\n1997-12-12 00:00:00,953.39\n1997-12-15 00:00:00,963.39\n1997-12-16 00:00:00,968.04\n1997-12-17 00:00:00,965.54\n1997-12-18 00:00:00,955.3\n1997-12-19 00:00:00,946.78\n1997-12-22 00:00:00,953.7\n1997-12-23 00:00:00,939.13\n1997-12-24 00:00:00,932.7\n1997-12-26 00:00:00,936.46\n1997-12-29 00:00:00,953.35\n1997-12-30 00:00:00,970.84\n1997-12-31 00:00:00,970.43\n1998-01-02 00:00:00,975.04\n1998-01-05 00:00:00,977.07\n1998-01-06 00:00:00,966.58\n1998-01-07 00:00:00,964.0\n1998-01-08 00:00:00,956.05\n1998-01-09 00:00:00,927.69\n1998-01-12 00:00:00,939.21\n1998-01-13 00:00:00,952.12\n1998-01-14 00:00:00,957.94\n1998-01-15 00:00:00,950.73\n1998-01-16 00:00:00,961.51\n1998-01-20 00:00:00,978.6\n1998-01-21 00:00:00,970.81\n1998-01-22 00:00:00,963.04\n1998-01-23 00:00:00,957.59\n1998-01-26 00:00:00,956.95\n1998-01-27 00:00:00,969.02\n1998-01-28 00:00:00,977.46\n1998-01-29 00:00:00,985.49\n1998-01-30 00:00:00,980.28\n1998-02-02 00:00:00,1001.27\n1998-02-03 00:00:00,1006.0\n1998-02-04 00:00:00,1006.9\n1998-02-05 00:00:00,1003.54\n1998-02-06 00:00:00,1012.46\n1998-02-09 00:00:00,1010.74\n1998-02-10 00:00:00,1019.01\n1998-02-11 00:00:00,1020.01\n1998-02-12 00:00:00,1024.14\n1998-02-13 00:00:00,1020.09\n1998-02-17 00:00:00,1022.76\n1998-02-18 00:00:00,1032.08\n1998-02-19 00:00:00,1028.28\n1998-02-20 00:00:00,1034.21\n1998-02-23 00:00:00,1038.14\n1998-02-24 00:00:00,1030.56\n1998-02-25 00:00:00,1042.9\n1998-02-26 00:00:00,1048.67\n1998-02-27 00:00:00,1049.34\n1998-03-02 00:00:00,1047.7\n1998-03-03 00:00:00,1052.02\n1998-03-04 00:00:00,1047.33\n1998-03-05 00:00:00,1035.05\n1998-03-06 00:00:00,1055.69\n1998-03-09 00:00:00,1052.31\n1998-03-10 00:00:00,1064.25\n1998-03-11 00:00:00,1068.47\n1998-03-12 00:00:00,1069.92\n1998-03-13 00:00:00,1068.61\n1998-03-16 00:00:00,1079.27\n1998-03-17 00:00:00,1080.45\n1998-03-18 00:00:00,1085.52\n1998-03-19 00:00:00,1089.74\n1998-03-20 00:00:00,1099.16\n1998-03-23 00:00:00,1095.55\n1998-03-24 00:00:00,1105.65\n1998-03-25 00:00:00,1101.93\n1998-03-26 00:00:00,1100.8\n1998-03-27 00:00:00,1095.44\n1998-03-30 00:00:00,1093.6\n1998-03-31 00:00:00,1101.75\n1998-04-01 00:00:00,1108.15\n1998-04-02 00:00:00,1120.01\n1998-04-03 00:00:00,1122.7\n1998-04-06 00:00:00,1121.38\n1998-04-07 00:00:00,1109.55\n1998-04-08 00:00:00,1101.65\n1998-04-09 00:00:00,1110.67\n1998-04-13 00:00:00,1109.69\n1998-04-14 00:00:00,1115.75\n1998-04-15 00:00:00,1119.32\n1998-04-16 00:00:00,1108.17\n1998-04-17 00:00:00,1122.72\n1998-04-20 00:00:00,1123.65\n1998-04-21 00:00:00,1126.67\n1998-04-22 00:00:00,1130.54\n1998-04-23 00:00:00,1119.58\n1998-04-24 00:00:00,1107.9\n1998-04-27 00:00:00,1086.54\n1998-04-28 00:00:00,1085.11\n1998-04-29 00:00:00,1094.62\n1998-04-30 00:00:00,1111.75\n1998-05-01 00:00:00,1121.0\n1998-05-04 00:00:00,1122.07\n1998-05-05 00:00:00,1115.5\n1998-05-06 00:00:00,1104.92\n1998-05-07 00:00:00,1095.14\n1998-05-08 00:00:00,1108.14\n1998-05-11 00:00:00,1106.64\n1998-05-12 00:00:00,1115.79\n1998-05-13 00:00:00,1118.86\n1998-05-14 00:00:00,1117.37\n1998-05-15 00:00:00,1108.73\n1998-05-18 00:00:00,1105.82\n1998-05-19 00:00:00,1109.52\n1998-05-20 00:00:00,1119.06\n1998-05-21 00:00:00,1114.64\n1998-05-22 00:00:00,1110.47\n1998-05-26 00:00:00,1094.02\n1998-05-27 00:00:00,1092.23\n1998-05-28 00:00:00,1097.6\n1998-05-29 00:00:00,1090.82\n1998-06-01 00:00:00,1090.98\n1998-06-02 00:00:00,1093.22\n1998-06-03 00:00:00,1082.73\n1998-06-04 00:00:00,1094.83\n1998-06-05 00:00:00,1113.86\n1998-06-08 00:00:00,1115.72\n1998-06-09 00:00:00,1118.41\n1998-06-10 00:00:00,1112.28\n1998-06-11 00:00:00,1094.58\n1998-06-12 00:00:00,1098.84\n1998-06-15 00:00:00,1077.01\n1998-06-16 00:00:00,1087.59\n1998-06-17 00:00:00,1107.11\n1998-06-18 00:00:00,1106.37\n1998-06-19 00:00:00,1100.65\n1998-06-22 00:00:00,1103.21\n1998-06-23 00:00:00,1119.49\n1998-06-24 00:00:00,1132.88\n1998-06-25 00:00:00,1129.28\n1998-06-26 00:00:00,1133.2\n1998-06-29 00:00:00,1138.49\n1998-06-30 00:00:00,1133.84\n1998-07-01 00:00:00,1148.56\n1998-07-02 00:00:00,1146.42\n1998-07-06 00:00:00,1157.33\n1998-07-07 00:00:00,1154.66\n1998-07-08 00:00:00,1166.38\n1998-07-09 00:00:00,1158.56\n1998-07-10 00:00:00,1164.33\n1998-07-13 00:00:00,1165.19\n1998-07-14 00:00:00,1177.58\n1998-07-15 00:00:00,1174.81\n1998-07-16 00:00:00,1183.99\n1998-07-17 00:00:00,1186.75\n1998-07-20 00:00:00,1184.1\n1998-07-21 00:00:00,1165.07\n1998-07-22 00:00:00,1164.08\n1998-07-23 00:00:00,1139.75\n1998-07-24 00:00:00,1140.8\n1998-07-27 00:00:00,1147.27\n1998-07-28 00:00:00,1130.24\n1998-07-29 00:00:00,1125.21\n1998-07-30 00:00:00,1142.95\n1998-07-31 00:00:00,1120.67\n1998-08-03 00:00:00,1112.44\n1998-08-04 00:00:00,1072.12\n1998-08-05 00:00:00,1081.43\n1998-08-06 00:00:00,1089.63\n1998-08-07 00:00:00,1089.45\n1998-08-10 00:00:00,1083.14\n1998-08-11 00:00:00,1068.98\n1998-08-12 00:00:00,1084.22\n1998-08-13 00:00:00,1074.91\n1998-08-14 00:00:00,1062.75\n1998-08-17 00:00:00,1083.67\n1998-08-18 00:00:00,1101.2\n1998-08-19 00:00:00,1098.06\n1998-08-20 00:00:00,1091.6\n1998-08-21 00:00:00,1081.24\n1998-08-24 00:00:00,1088.14\n1998-08-25 00:00:00,1092.85\n1998-08-26 00:00:00,1084.19\n1998-08-27 00:00:00,1042.59\n1998-08-28 00:00:00,1027.14\n1998-08-31 00:00:00,957.28\n1998-09-01 00:00:00,994.26\n1998-09-02 00:00:00,990.48\n1998-09-03 00:00:00,982.26\n1998-09-04 00:00:00,973.89\n1998-09-08 00:00:00,1023.46\n1998-09-09 00:00:00,1006.2\n1998-09-10 00:00:00,980.19\n1998-09-11 00:00:00,1009.06\n1998-09-14 00:00:00,1029.72\n1998-09-15 00:00:00,1037.68\n1998-09-16 00:00:00,1045.48\n1998-09-17 00:00:00,1018.87\n1998-09-18 00:00:00,1020.09\n1998-09-21 00:00:00,1023.89\n1998-09-22 00:00:00,1029.63\n1998-09-23 00:00:00,1066.09\n1998-09-24 00:00:00,1042.72\n1998-09-25 00:00:00,1044.75\n1998-09-28 00:00:00,1048.69\n1998-09-29 00:00:00,1049.02\n1998-09-30 00:00:00,1017.01\n1998-10-01 00:00:00,986.39\n1998-10-02 00:00:00,1002.6\n1998-10-05 00:00:00,988.56\n1998-10-06 00:00:00,984.59\n1998-10-07 00:00:00,970.68\n1998-10-08 00:00:00,959.44\n1998-10-09 00:00:00,984.39\n1998-10-12 00:00:00,997.71\n1998-10-13 00:00:00,994.8\n1998-10-14 00:00:00,1005.53\n1998-10-15 00:00:00,1047.49\n1998-10-16 00:00:00,1056.42\n1998-10-19 00:00:00,1062.39\n1998-10-20 00:00:00,1063.93\n1998-10-21 00:00:00,1069.92\n1998-10-22 00:00:00,1078.48\n1998-10-23 00:00:00,1070.67\n1998-10-26 00:00:00,1072.32\n1998-10-27 00:00:00,1065.34\n1998-10-28 00:00:00,1068.09\n1998-10-29 00:00:00,1085.93\n1998-10-30 00:00:00,1098.67\n1998-11-02 00:00:00,1111.6\n1998-11-03 00:00:00,1110.84\n1998-11-04 00:00:00,1118.67\n1998-11-05 00:00:00,1133.85\n1998-11-06 00:00:00,1141.01\n1998-11-09 00:00:00,1130.2\n1998-11-10 00:00:00,1128.26\n1998-11-11 00:00:00,1120.97\n1998-11-12 00:00:00,1117.69\n1998-11-13 00:00:00,1125.72\n1998-11-16 00:00:00,1135.87\n1998-11-17 00:00:00,1139.32\n1998-11-18 00:00:00,1144.48\n1998-11-19 00:00:00,1152.61\n1998-11-20 00:00:00,1163.55\n1998-11-23 00:00:00,1188.21\n1998-11-24 00:00:00,1182.99\n1998-11-25 00:00:00,1186.87\n1998-11-27 00:00:00,1192.33\n1998-11-30 00:00:00,1163.63\n1998-12-01 00:00:00,1175.28\n1998-12-02 00:00:00,1171.25\n1998-12-03 00:00:00,1150.14\n1998-12-04 00:00:00,1176.74\n1998-12-07 00:00:00,1187.7\n1998-12-08 00:00:00,1181.38\n1998-12-09 00:00:00,1183.49\n1998-12-10 00:00:00,1165.02\n1998-12-11 00:00:00,1166.46\n1998-12-14 00:00:00,1141.2\n1998-12-15 00:00:00,1162.83\n1998-12-16 00:00:00,1161.94\n1998-12-17 00:00:00,1179.98\n1998-12-18 00:00:00,1188.03\n1998-12-21 00:00:00,1202.84\n1998-12-22 00:00:00,1203.57\n1998-12-23 00:00:00,1228.54\n1998-12-24 00:00:00,1226.27\n1998-12-28 00:00:00,1225.49\n1998-12-29 00:00:00,1241.81\n1998-12-30 00:00:00,1231.93\n1998-12-31 00:00:00,1229.23\n1999-01-04 00:00:00,1228.1\n1999-01-05 00:00:00,1244.78\n1999-01-06 00:00:00,1272.34\n1999-01-07 00:00:00,1269.73\n1999-01-08 00:00:00,1275.09\n1999-01-11 00:00:00,1263.88\n1999-01-12 00:00:00,1239.51\n1999-01-13 00:00:00,1234.4\n1999-01-14 00:00:00,1212.19\n1999-01-15 00:00:00,1243.26\n1999-01-19 00:00:00,1252.0\n1999-01-20 00:00:00,1256.62\n1999-01-21 00:00:00,1235.16\n1999-01-22 00:00:00,1225.19\n1999-01-25 00:00:00,1233.98\n1999-01-26 00:00:00,1252.31\n1999-01-27 00:00:00,1243.17\n1999-01-28 00:00:00,1265.37\n1999-01-29 00:00:00,1279.64\n1999-02-01 00:00:00,1273.0\n1999-02-02 00:00:00,1261.99\n1999-02-03 00:00:00,1272.07\n1999-02-04 00:00:00,1248.49\n1999-02-05 00:00:00,1239.4\n1999-02-08 00:00:00,1243.77\n1999-02-09 00:00:00,1216.14\n1999-02-10 00:00:00,1223.55\n1999-02-11 00:00:00,1254.04\n1999-02-12 00:00:00,1230.13\n1999-02-16 00:00:00,1241.87\n1999-02-17 00:00:00,1224.03\n1999-02-18 00:00:00,1237.28\n1999-02-19 00:00:00,1239.22\n1999-02-22 00:00:00,1272.14\n1999-02-23 00:00:00,1271.18\n1999-02-24 00:00:00,1253.41\n1999-02-25 00:00:00,1245.02\n1999-02-26 00:00:00,1238.33\n1999-03-01 00:00:00,1236.16\n1999-03-02 00:00:00,1225.5\n1999-03-03 00:00:00,1227.7\n1999-03-04 00:00:00,1246.64\n1999-03-05 00:00:00,1275.47\n1999-03-08 00:00:00,1282.73\n1999-03-09 00:00:00,1279.84\n1999-03-10 00:00:00,1286.84\n1999-03-11 00:00:00,1297.68\n1999-03-12 00:00:00,1294.59\n1999-03-15 00:00:00,1307.26\n1999-03-16 00:00:00,1306.38\n1999-03-17 00:00:00,1297.82\n1999-03-18 00:00:00,1316.55\n1999-03-19 00:00:00,1299.29\n1999-03-22 00:00:00,1297.01\n1999-03-23 00:00:00,1262.14\n1999-03-24 00:00:00,1268.59\n1999-03-25 00:00:00,1289.99\n1999-03-26 00:00:00,1282.8\n1999-03-29 00:00:00,1310.17\n1999-03-30 00:00:00,1300.75\n1999-03-31 00:00:00,1286.37\n1999-04-01 00:00:00,1293.72\n1999-04-05 00:00:00,1321.12\n1999-04-06 00:00:00,1317.89\n1999-04-07 00:00:00,1326.89\n1999-04-08 00:00:00,1343.98\n1999-04-09 00:00:00,1348.35\n1999-04-12 00:00:00,1358.63\n1999-04-13 00:00:00,1349.82\n1999-04-14 00:00:00,1328.44\n1999-04-15 00:00:00,1322.85\n1999-04-16 00:00:00,1319.0\n1999-04-19 00:00:00,1289.48\n1999-04-20 00:00:00,1306.17\n1999-04-21 00:00:00,1336.12\n1999-04-22 00:00:00,1358.82\n1999-04-23 00:00:00,1356.85\n1999-04-26 00:00:00,1360.04\n1999-04-27 00:00:00,1362.8\n1999-04-28 00:00:00,1350.91\n1999-04-29 00:00:00,1342.83\n1999-04-30 00:00:00,1335.18\n1999-05-03 00:00:00,1354.63\n1999-05-04 00:00:00,1332.0\n1999-05-05 00:00:00,1347.31\n1999-05-06 00:00:00,1332.05\n1999-05-07 00:00:00,1345.0\n1999-05-10 00:00:00,1340.3\n1999-05-11 00:00:00,1355.61\n1999-05-12 00:00:00,1364.0\n1999-05-13 00:00:00,1367.56\n1999-05-14 00:00:00,1337.8\n1999-05-17 00:00:00,1339.49\n1999-05-18 00:00:00,1333.32\n1999-05-19 00:00:00,1344.23\n1999-05-20 00:00:00,1338.83\n1999-05-21 00:00:00,1330.29\n1999-05-24 00:00:00,1306.65\n1999-05-25 00:00:00,1284.4\n1999-05-26 00:00:00,1304.76\n1999-05-27 00:00:00,1281.41\n1999-05-28 00:00:00,1301.84\n1999-06-01 00:00:00,1294.26\n1999-06-02 00:00:00,1294.81\n1999-06-03 00:00:00,1299.54\n1999-06-04 00:00:00,1327.75\n1999-06-07 00:00:00,1334.52\n1999-06-08 00:00:00,1317.33\n1999-06-09 00:00:00,1318.64\n1999-06-10 00:00:00,1302.82\n1999-06-11 00:00:00,1293.64\n1999-06-14 00:00:00,1294.0\n1999-06-15 00:00:00,1301.16\n1999-06-16 00:00:00,1330.41\n1999-06-17 00:00:00,1339.9\n1999-06-18 00:00:00,1342.84\n1999-06-21 00:00:00,1349.0\n1999-06-22 00:00:00,1335.88\n1999-06-23 00:00:00,1333.06\n1999-06-24 00:00:00,1315.78\n1999-06-25 00:00:00,1315.31\n1999-06-28 00:00:00,1331.35\n1999-06-29 00:00:00,1351.45\n1999-06-30 00:00:00,1372.71\n1999-07-01 00:00:00,1380.96\n1999-07-02 00:00:00,1391.22\n1999-07-06 00:00:00,1388.12\n1999-07-07 00:00:00,1395.86\n1999-07-08 00:00:00,1394.42\n1999-07-09 00:00:00,1403.28\n1999-07-12 00:00:00,1399.1\n1999-07-13 00:00:00,1393.56\n1999-07-14 00:00:00,1398.17\n1999-07-15 00:00:00,1409.62\n1999-07-16 00:00:00,1418.78\n1999-07-19 00:00:00,1407.65\n1999-07-20 00:00:00,1377.1\n1999-07-21 00:00:00,1379.29\n1999-07-22 00:00:00,1360.97\n1999-07-23 00:00:00,1356.94\n1999-07-26 00:00:00,1347.76\n1999-07-27 00:00:00,1362.84\n1999-07-28 00:00:00,1365.4\n1999-07-29 00:00:00,1341.03\n1999-07-30 00:00:00,1328.72\n1999-08-02 00:00:00,1328.05\n1999-08-03 00:00:00,1322.18\n1999-08-04 00:00:00,1305.33\n1999-08-05 00:00:00,1313.71\n1999-08-06 00:00:00,1300.29\n1999-08-09 00:00:00,1297.8\n1999-08-10 00:00:00,1281.43\n1999-08-11 00:00:00,1301.93\n1999-08-12 00:00:00,1298.16\n1999-08-13 00:00:00,1327.68\n1999-08-16 00:00:00,1330.77\n1999-08-17 00:00:00,1344.16\n1999-08-18 00:00:00,1332.84\n1999-08-19 00:00:00,1323.59\n1999-08-20 00:00:00,1336.61\n1999-08-23 00:00:00,1360.22\n1999-08-24 00:00:00,1363.5\n1999-08-25 00:00:00,1381.79\n1999-08-26 00:00:00,1362.01\n1999-08-27 00:00:00,1348.27\n1999-08-30 00:00:00,1324.02\n1999-08-31 00:00:00,1320.41\n1999-09-01 00:00:00,1331.07\n1999-09-02 00:00:00,1319.11\n1999-09-03 00:00:00,1357.24\n1999-09-07 00:00:00,1350.45\n1999-09-08 00:00:00,1344.15\n1999-09-09 00:00:00,1347.66\n1999-09-10 00:00:00,1351.66\n1999-09-13 00:00:00,1344.13\n1999-09-14 00:00:00,1336.29\n1999-09-15 00:00:00,1317.97\n1999-09-16 00:00:00,1318.48\n1999-09-17 00:00:00,1335.42\n1999-09-20 00:00:00,1335.53\n1999-09-21 00:00:00,1307.58\n1999-09-22 00:00:00,1310.51\n1999-09-23 00:00:00,1280.41\n1999-09-24 00:00:00,1277.36\n1999-09-27 00:00:00,1283.31\n1999-09-28 00:00:00,1282.2\n1999-09-29 00:00:00,1268.37\n1999-09-30 00:00:00,1282.71\n1999-10-01 00:00:00,1282.81\n1999-10-04 00:00:00,1304.6\n1999-10-05 00:00:00,1301.35\n1999-10-06 00:00:00,1325.4\n1999-10-07 00:00:00,1317.64\n1999-10-08 00:00:00,1336.02\n1999-10-11 00:00:00,1335.21\n1999-10-12 00:00:00,1313.04\n1999-10-13 00:00:00,1285.55\n1999-10-14 00:00:00,1283.42\n1999-10-15 00:00:00,1247.41\n1999-10-18 00:00:00,1254.13\n1999-10-19 00:00:00,1261.32\n1999-10-20 00:00:00,1289.43\n1999-10-21 00:00:00,1283.61\n1999-10-22 00:00:00,1301.65\n1999-10-25 00:00:00,1293.63\n1999-10-26 00:00:00,1281.91\n1999-10-27 00:00:00,1296.71\n1999-10-28 00:00:00,1342.44\n1999-10-29 00:00:00,1362.93\n1999-11-01 00:00:00,1354.12\n1999-11-02 00:00:00,1347.74\n1999-11-03 00:00:00,1354.93\n1999-11-04 00:00:00,1362.64\n1999-11-05 00:00:00,1370.23\n1999-11-08 00:00:00,1377.01\n1999-11-09 00:00:00,1365.28\n1999-11-10 00:00:00,1373.46\n1999-11-11 00:00:00,1381.46\n1999-11-12 00:00:00,1396.06\n1999-11-15 00:00:00,1394.39\n1999-11-16 00:00:00,1420.07\n1999-11-17 00:00:00,1410.71\n1999-11-18 00:00:00,1424.94\n1999-11-19 00:00:00,1422.0\n1999-11-22 00:00:00,1420.94\n1999-11-23 00:00:00,1404.64\n1999-11-24 00:00:00,1417.08\n1999-11-26 00:00:00,1416.62\n1999-11-29 00:00:00,1407.83\n1999-11-30 00:00:00,1388.91\n1999-12-01 00:00:00,1397.72\n1999-12-02 00:00:00,1409.04\n1999-12-03 00:00:00,1433.3\n1999-12-06 00:00:00,1423.33\n1999-12-07 00:00:00,1409.17\n1999-12-08 00:00:00,1403.88\n1999-12-09 00:00:00,1408.11\n1999-12-10 00:00:00,1417.04\n1999-12-13 00:00:00,1415.22\n1999-12-14 00:00:00,1403.17\n1999-12-15 00:00:00,1413.33\n1999-12-16 00:00:00,1418.78\n1999-12-17 00:00:00,1421.03\n1999-12-20 00:00:00,1418.09\n1999-12-21 00:00:00,1433.43\n1999-12-22 00:00:00,1436.13\n1999-12-23 00:00:00,1458.34\n1999-12-27 00:00:00,1457.1\n1999-12-28 00:00:00,1457.66\n1999-12-29 00:00:00,1463.46\n1999-12-30 00:00:00,1464.47\n1999-12-31 00:00:00,1469.25\n2000-01-03 00:00:00,1455.22\n2000-01-04 00:00:00,1399.42\n2000-01-05 00:00:00,1402.11\n2000-01-06 00:00:00,1403.45\n2000-01-07 00:00:00,1441.47\n2000-01-10 00:00:00,1457.6\n2000-01-11 00:00:00,1438.56\n2000-01-12 00:00:00,1432.25\n2000-01-13 00:00:00,1449.68\n2000-01-14 00:00:00,1465.15\n2000-01-18 00:00:00,1455.14\n2000-01-19 00:00:00,1455.9\n2000-01-20 00:00:00,1445.57\n2000-01-21 00:00:00,1441.36\n2000-01-24 00:00:00,1401.53\n2000-01-25 00:00:00,1410.03\n2000-01-26 00:00:00,1404.09\n2000-01-27 00:00:00,1398.56\n2000-01-28 00:00:00,1360.16\n2000-01-31 00:00:00,1394.46\n2000-02-01 00:00:00,1409.28\n2000-02-02 00:00:00,1409.12\n2000-02-03 00:00:00,1424.97\n2000-02-04 00:00:00,1424.37\n2000-02-07 00:00:00,1424.24\n2000-02-08 00:00:00,1441.72\n2000-02-09 00:00:00,1411.71\n2000-02-10 00:00:00,1416.83\n2000-02-11 00:00:00,1387.12\n2000-02-14 00:00:00,1389.94\n2000-02-15 00:00:00,1402.05\n2000-02-16 00:00:00,1387.67\n2000-02-17 00:00:00,1388.26\n2000-02-18 00:00:00,1346.09\n2000-02-22 00:00:00,1352.17\n2000-02-23 00:00:00,1360.69\n2000-02-24 00:00:00,1353.43\n2000-02-25 00:00:00,1333.36\n2000-02-28 00:00:00,1348.05\n2000-02-29 00:00:00,1366.42\n2000-03-01 00:00:00,1379.19\n2000-03-02 00:00:00,1381.76\n2000-03-03 00:00:00,1409.17\n2000-03-06 00:00:00,1391.28\n2000-03-07 00:00:00,1355.62\n2000-03-08 00:00:00,1366.7\n2000-03-09 00:00:00,1401.69\n2000-03-10 00:00:00,1395.07\n2000-03-13 00:00:00,1383.62\n2000-03-14 00:00:00,1359.15\n2000-03-15 00:00:00,1392.14\n2000-03-16 00:00:00,1458.47\n2000-03-17 00:00:00,1464.47\n2000-03-20 00:00:00,1456.63\n2000-03-21 00:00:00,1493.87\n2000-03-22 00:00:00,1500.64\n2000-03-23 00:00:00,1527.35\n2000-03-24 00:00:00,1527.46\n2000-03-27 00:00:00,1523.86\n2000-03-28 00:00:00,1507.73\n2000-03-29 00:00:00,1508.52\n2000-03-30 00:00:00,1487.92\n2000-03-31 00:00:00,1498.58\n2000-04-03 00:00:00,1505.97\n2000-04-04 00:00:00,1494.73\n2000-04-05 00:00:00,1487.37\n2000-04-06 00:00:00,1501.34\n2000-04-07 00:00:00,1516.35\n2000-04-10 00:00:00,1504.46\n2000-04-11 00:00:00,1500.59\n2000-04-12 00:00:00,1467.17\n2000-04-13 00:00:00,1440.51\n2000-04-14 00:00:00,1356.56\n2000-04-17 00:00:00,1401.44\n2000-04-18 00:00:00,1441.61\n2000-04-19 00:00:00,1427.47\n2000-04-20 00:00:00,1434.54\n2000-04-24 00:00:00,1429.86\n2000-04-25 00:00:00,1477.44\n2000-04-26 00:00:00,1460.99\n2000-04-27 00:00:00,1464.92\n2000-04-28 00:00:00,1452.43\n2000-05-01 00:00:00,1468.25\n2000-05-02 00:00:00,1446.29\n2000-05-03 00:00:00,1415.1\n2000-05-04 00:00:00,1409.57\n2000-05-05 00:00:00,1432.63\n2000-05-08 00:00:00,1424.17\n2000-05-09 00:00:00,1412.14\n2000-05-10 00:00:00,1383.05\n2000-05-11 00:00:00,1407.81\n2000-05-12 00:00:00,1420.96\n2000-05-15 00:00:00,1452.36\n2000-05-16 00:00:00,1466.04\n2000-05-17 00:00:00,1447.8\n2000-05-18 00:00:00,1437.21\n2000-05-19 00:00:00,1406.95\n2000-05-22 00:00:00,1400.72\n2000-05-23 00:00:00,1373.86\n2000-05-24 00:00:00,1399.05\n2000-05-25 00:00:00,1381.52\n2000-05-26 00:00:00,1378.02\n2000-05-30 00:00:00,1422.45\n2000-05-31 00:00:00,1420.6\n2000-06-01 00:00:00,1448.81\n2000-06-02 00:00:00,1477.26\n2000-06-05 00:00:00,1467.63\n2000-06-06 00:00:00,1457.84\n2000-06-07 00:00:00,1471.36\n2000-06-08 00:00:00,1461.67\n2000-06-09 00:00:00,1456.95\n2000-06-12 00:00:00,1446.0\n2000-06-13 00:00:00,1469.44\n2000-06-14 00:00:00,1470.54\n2000-06-15 00:00:00,1478.73\n2000-06-16 00:00:00,1464.46\n2000-06-19 00:00:00,1486.0\n2000-06-20 00:00:00,1475.95\n2000-06-21 00:00:00,1479.13\n2000-06-22 00:00:00,1452.18\n2000-06-23 00:00:00,1441.48\n2000-06-26 00:00:00,1455.31\n2000-06-27 00:00:00,1450.55\n2000-06-28 00:00:00,1454.82\n2000-06-29 00:00:00,1442.39\n2000-06-30 00:00:00,1454.6\n2000-07-03 00:00:00,1469.54\n2000-07-05 00:00:00,1446.23\n2000-07-06 00:00:00,1456.67\n2000-07-07 00:00:00,1478.9\n2000-07-10 00:00:00,1475.62\n2000-07-11 00:00:00,1480.88\n2000-07-12 00:00:00,1492.92\n2000-07-13 00:00:00,1495.84\n2000-07-14 00:00:00,1509.98\n2000-07-17 00:00:00,1510.49\n2000-07-18 00:00:00,1493.74\n2000-07-19 00:00:00,1481.96\n2000-07-20 00:00:00,1495.57\n2000-07-21 00:00:00,1480.19\n2000-07-24 00:00:00,1464.29\n2000-07-25 00:00:00,1474.47\n2000-07-26 00:00:00,1452.42\n2000-07-27 00:00:00,1449.62\n2000-07-28 00:00:00,1419.89\n2000-07-31 00:00:00,1430.83\n2000-08-01 00:00:00,1438.1\n2000-08-02 00:00:00,1438.7\n2000-08-03 00:00:00,1452.56\n2000-08-04 00:00:00,1462.93\n2000-08-07 00:00:00,1479.32\n2000-08-08 00:00:00,1482.8\n2000-08-09 00:00:00,1472.87\n2000-08-10 00:00:00,1460.25\n2000-08-11 00:00:00,1471.84\n2000-08-14 00:00:00,1491.56\n2000-08-15 00:00:00,1484.43\n2000-08-16 00:00:00,1479.85\n2000-08-17 00:00:00,1496.07\n2000-08-18 00:00:00,1491.72\n2000-08-21 00:00:00,1499.48\n2000-08-22 00:00:00,1498.13\n2000-08-23 00:00:00,1505.97\n2000-08-24 00:00:00,1508.31\n2000-08-25 00:00:00,1506.45\n2000-08-28 00:00:00,1514.09\n2000-08-29 00:00:00,1509.84\n2000-08-30 00:00:00,1502.59\n2000-08-31 00:00:00,1517.68\n2000-09-01 00:00:00,1520.77\n2000-09-05 00:00:00,1507.08\n2000-09-06 00:00:00,1492.25\n2000-09-07 00:00:00,1502.51\n2000-09-08 00:00:00,1494.5\n2000-09-11 00:00:00,1489.26\n2000-09-12 00:00:00,1481.99\n2000-09-13 00:00:00,1484.91\n2000-09-14 00:00:00,1480.87\n2000-09-15 00:00:00,1465.81\n2000-09-18 00:00:00,1444.51\n2000-09-19 00:00:00,1459.9\n2000-09-20 00:00:00,1451.34\n2000-09-21 00:00:00,1449.05\n2000-09-22 00:00:00,1448.72\n2000-09-25 00:00:00,1439.03\n2000-09-26 00:00:00,1427.21\n2000-09-27 00:00:00,1426.57\n2000-09-28 00:00:00,1458.29\n2000-09-29 00:00:00,1436.51\n2000-10-02 00:00:00,1436.23\n2000-10-03 00:00:00,1426.46\n2000-10-04 00:00:00,1434.32\n2000-10-05 00:00:00,1436.28\n2000-10-06 00:00:00,1408.99\n2000-10-09 00:00:00,1402.03\n2000-10-10 00:00:00,1387.02\n2000-10-11 00:00:00,1364.59\n2000-10-12 00:00:00,1329.78\n2000-10-13 00:00:00,1374.17\n2000-10-16 00:00:00,1374.62\n2000-10-17 00:00:00,1349.97\n2000-10-18 00:00:00,1342.13\n2000-10-19 00:00:00,1388.76\n2000-10-20 00:00:00,1396.93\n2000-10-23 00:00:00,1395.78\n2000-10-24 00:00:00,1398.13\n2000-10-25 00:00:00,1364.9\n2000-10-26 00:00:00,1364.44\n2000-10-27 00:00:00,1379.58\n2000-10-30 00:00:00,1398.66\n2000-10-31 00:00:00,1429.4\n2000-11-01 00:00:00,1421.22\n2000-11-02 00:00:00,1428.32\n2000-11-03 00:00:00,1426.69\n2000-11-06 00:00:00,1432.19\n2000-11-07 00:00:00,1431.87\n2000-11-08 00:00:00,1409.28\n2000-11-09 00:00:00,1400.14\n2000-11-10 00:00:00,1365.98\n2000-11-13 00:00:00,1351.26\n2000-11-14 00:00:00,1382.95\n2000-11-15 00:00:00,1389.81\n2000-11-16 00:00:00,1372.32\n2000-11-17 00:00:00,1367.72\n2000-11-20 00:00:00,1342.62\n2000-11-21 00:00:00,1347.35\n2000-11-22 00:00:00,1322.36\n2000-11-24 00:00:00,1341.77\n2000-11-27 00:00:00,1348.97\n2000-11-28 00:00:00,1336.09\n2000-11-29 00:00:00,1341.93\n2000-11-30 00:00:00,1314.95\n2000-12-01 00:00:00,1315.23\n2000-12-04 00:00:00,1324.97\n2000-12-05 00:00:00,1376.54\n2000-12-06 00:00:00,1351.46\n2000-12-07 00:00:00,1343.55\n2000-12-08 00:00:00,1369.89\n2000-12-11 00:00:00,1380.2\n2000-12-12 00:00:00,1371.18\n2000-12-13 00:00:00,1359.99\n2000-12-14 00:00:00,1340.93\n2000-12-15 00:00:00,1312.15\n2000-12-18 00:00:00,1322.74\n2000-12-19 00:00:00,1305.6\n2000-12-20 00:00:00,1264.74\n2000-12-21 00:00:00,1274.86\n2000-12-22 00:00:00,1305.95\n2000-12-26 00:00:00,1315.19\n2000-12-27 00:00:00,1328.92\n2000-12-28 00:00:00,1334.22\n2000-12-29 00:00:00,1320.28\n2001-01-02 00:00:00,1283.27\n2001-01-03 00:00:00,1347.56\n2001-01-04 00:00:00,1333.34\n2001-01-05 00:00:00,1298.35\n2001-01-08 00:00:00,1295.86\n2001-01-09 00:00:00,1300.8\n2001-01-10 00:00:00,1313.27\n2001-01-11 00:00:00,1326.82\n2001-01-12 00:00:00,1318.55\n2001-01-16 00:00:00,1326.65\n2001-01-17 00:00:00,1329.47\n2001-01-18 00:00:00,1347.97\n2001-01-19 00:00:00,1342.54\n2001-01-22 00:00:00,1342.9\n2001-01-23 00:00:00,1360.4\n2001-01-24 00:00:00,1364.3\n2001-01-25 00:00:00,1357.51\n2001-01-26 00:00:00,1354.95\n2001-01-29 00:00:00,1364.17\n2001-01-30 00:00:00,1373.73\n2001-01-31 00:00:00,1366.01\n2001-02-01 00:00:00,1373.47\n2001-02-02 00:00:00,1349.47\n2001-02-05 00:00:00,1354.31\n2001-02-06 00:00:00,1352.26\n2001-02-07 00:00:00,1340.89\n2001-02-08 00:00:00,1332.53\n2001-02-09 00:00:00,1314.76\n2001-02-12 00:00:00,1330.31\n2001-02-13 00:00:00,1318.8\n2001-02-14 00:00:00,1315.92\n2001-02-15 00:00:00,1326.61\n2001-02-16 00:00:00,1301.53\n2001-02-20 00:00:00,1278.94\n2001-02-21 00:00:00,1255.27\n2001-02-22 00:00:00,1252.82\n2001-02-23 00:00:00,1245.86\n2001-02-26 00:00:00,1267.65\n2001-02-27 00:00:00,1257.94\n2001-02-28 00:00:00,1239.94\n2001-03-01 00:00:00,1241.23\n2001-03-02 00:00:00,1234.18\n2001-03-05 00:00:00,1241.41\n2001-03-06 00:00:00,1253.8\n2001-03-07 00:00:00,1261.89\n2001-03-08 00:00:00,1264.74\n2001-03-09 00:00:00,1233.42\n2001-03-12 00:00:00,1180.16\n2001-03-13 00:00:00,1197.66\n2001-03-14 00:00:00,1166.71\n2001-03-15 00:00:00,1173.56\n2001-03-16 00:00:00,1150.53\n2001-03-19 00:00:00,1170.81\n2001-03-20 00:00:00,1142.62\n2001-03-21 00:00:00,1122.14\n2001-03-22 00:00:00,1117.58\n2001-03-23 00:00:00,1139.83\n2001-03-26 00:00:00,1152.69\n2001-03-27 00:00:00,1182.17\n2001-03-28 00:00:00,1153.29\n2001-03-29 00:00:00,1147.95\n2001-03-30 00:00:00,1160.33\n2001-04-02 00:00:00,1145.87\n2001-04-03 00:00:00,1106.46\n2001-04-04 00:00:00,1103.25\n2001-04-05 00:00:00,1151.44\n2001-04-06 00:00:00,1128.43\n2001-04-09 00:00:00,1137.59\n2001-04-10 00:00:00,1168.38\n2001-04-11 00:00:00,1165.89\n2001-04-12 00:00:00,1183.5\n2001-04-16 00:00:00,1179.68\n2001-04-17 00:00:00,1191.81\n2001-04-18 00:00:00,1238.16\n2001-04-19 00:00:00,1253.69\n2001-04-20 00:00:00,1242.98\n2001-04-23 00:00:00,1224.36\n2001-04-24 00:00:00,1209.47\n2001-04-25 00:00:00,1228.75\n2001-04-26 00:00:00,1234.52\n2001-04-27 00:00:00,1253.05\n2001-04-30 00:00:00,1249.46\n2001-05-01 00:00:00,1266.44\n2001-05-02 00:00:00,1267.43\n2001-05-03 00:00:00,1248.58\n2001-05-04 00:00:00,1266.61\n2001-05-07 00:00:00,1263.51\n2001-05-08 00:00:00,1261.2\n2001-05-09 00:00:00,1255.54\n2001-05-10 00:00:00,1255.18\n2001-05-11 00:00:00,1245.67\n2001-05-14 00:00:00,1248.92\n2001-05-15 00:00:00,1249.44\n2001-05-16 00:00:00,1284.99\n2001-05-17 00:00:00,1288.49\n2001-05-18 00:00:00,1291.96\n2001-05-21 00:00:00,1312.83\n2001-05-22 00:00:00,1309.38\n2001-05-23 00:00:00,1289.05\n2001-05-24 00:00:00,1293.17\n2001-05-25 00:00:00,1277.89\n2001-05-29 00:00:00,1267.93\n2001-05-30 00:00:00,1248.08\n2001-05-31 00:00:00,1255.82\n2001-06-01 00:00:00,1260.67\n2001-06-04 00:00:00,1267.11\n2001-06-05 00:00:00,1283.57\n2001-06-06 00:00:00,1270.03\n2001-06-07 00:00:00,1276.96\n2001-06-08 00:00:00,1264.96\n2001-06-11 00:00:00,1254.39\n2001-06-12 00:00:00,1255.85\n2001-06-13 00:00:00,1241.6\n2001-06-14 00:00:00,1219.87\n2001-06-15 00:00:00,1214.36\n2001-06-18 00:00:00,1208.43\n2001-06-19 00:00:00,1212.58\n2001-06-20 00:00:00,1223.14\n2001-06-21 00:00:00,1237.04\n2001-06-22 00:00:00,1225.35\n2001-06-25 00:00:00,1218.6\n2001-06-26 00:00:00,1216.76\n2001-06-27 00:00:00,1211.07\n2001-06-28 00:00:00,1226.2\n2001-06-29 00:00:00,1224.38\n2001-07-02 00:00:00,1236.72\n2001-07-03 00:00:00,1234.45\n2001-07-05 00:00:00,1219.24\n2001-07-06 00:00:00,1190.59\n2001-07-09 00:00:00,1198.78\n2001-07-10 00:00:00,1181.52\n2001-07-11 00:00:00,1180.18\n2001-07-12 00:00:00,1208.14\n2001-07-13 00:00:00,1215.68\n2001-07-16 00:00:00,1202.45\n2001-07-17 00:00:00,1214.44\n2001-07-18 00:00:00,1207.71\n2001-07-19 00:00:00,1215.02\n2001-07-20 00:00:00,1210.85\n2001-07-23 00:00:00,1191.03\n2001-07-24 00:00:00,1171.65\n2001-07-25 00:00:00,1190.49\n2001-07-26 00:00:00,1202.93\n2001-07-27 00:00:00,1205.82\n2001-07-30 00:00:00,1204.52\n2001-07-31 00:00:00,1211.23\n2001-08-01 00:00:00,1215.93\n2001-08-02 00:00:00,1220.75\n2001-08-03 00:00:00,1214.35\n2001-08-06 00:00:00,1200.48\n2001-08-07 00:00:00,1204.4\n2001-08-08 00:00:00,1183.53\n2001-08-09 00:00:00,1183.43\n2001-08-10 00:00:00,1190.16\n2001-08-13 00:00:00,1191.29\n2001-08-14 00:00:00,1186.73\n2001-08-15 00:00:00,1178.02\n2001-08-16 00:00:00,1181.66\n2001-08-17 00:00:00,1161.97\n2001-08-20 00:00:00,1171.41\n2001-08-21 00:00:00,1157.26\n2001-08-22 00:00:00,1165.31\n2001-08-23 00:00:00,1162.09\n2001-08-24 00:00:00,1184.93\n2001-08-27 00:00:00,1179.21\n2001-08-28 00:00:00,1161.51\n2001-08-29 00:00:00,1148.56\n2001-08-30 00:00:00,1129.03\n2001-08-31 00:00:00,1133.58\n2001-09-04 00:00:00,1132.94\n2001-09-05 00:00:00,1131.74\n2001-09-06 00:00:00,1106.4\n2001-09-07 00:00:00,1085.78\n2001-09-10 00:00:00,1092.54\n2001-09-17 00:00:00,1038.77\n2001-09-18 00:00:00,1032.74\n2001-09-19 00:00:00,1016.1\n2001-09-20 00:00:00,984.54\n2001-09-21 00:00:00,965.8\n2001-09-24 00:00:00,1003.45\n2001-09-25 00:00:00,1012.27\n2001-09-26 00:00:00,1007.04\n2001-09-27 00:00:00,1018.61\n2001-09-28 00:00:00,1040.94\n2001-10-01 00:00:00,1038.55\n2001-10-02 00:00:00,1051.33\n2001-10-03 00:00:00,1072.28\n2001-10-04 00:00:00,1069.63\n2001-10-05 00:00:00,1071.38\n2001-10-08 00:00:00,1062.44\n2001-10-09 00:00:00,1056.75\n2001-10-10 00:00:00,1080.99\n2001-10-11 00:00:00,1097.43\n2001-10-12 00:00:00,1091.65\n2001-10-15 00:00:00,1089.98\n2001-10-16 00:00:00,1097.54\n2001-10-17 00:00:00,1077.09\n2001-10-18 00:00:00,1068.61\n2001-10-19 00:00:00,1073.48\n2001-10-22 00:00:00,1089.9\n2001-10-23 00:00:00,1084.78\n2001-10-24 00:00:00,1085.2\n2001-10-25 00:00:00,1100.09\n2001-10-26 00:00:00,1104.61\n2001-10-29 00:00:00,1078.3\n2001-10-30 00:00:00,1059.79\n2001-10-31 00:00:00,1059.78\n2001-11-01 00:00:00,1084.1\n2001-11-02 00:00:00,1087.2\n2001-11-05 00:00:00,1102.84\n2001-11-06 00:00:00,1118.86\n2001-11-07 00:00:00,1115.8\n2001-11-08 00:00:00,1118.54\n2001-11-09 00:00:00,1120.31\n2001-11-12 00:00:00,1118.33\n2001-11-13 00:00:00,1139.09\n2001-11-14 00:00:00,1141.21\n2001-11-15 00:00:00,1142.24\n2001-11-16 00:00:00,1138.65\n2001-11-19 00:00:00,1151.06\n2001-11-20 00:00:00,1142.66\n2001-11-21 00:00:00,1137.03\n2001-11-23 00:00:00,1150.34\n2001-11-26 00:00:00,1157.42\n2001-11-27 00:00:00,1149.5\n2001-11-28 00:00:00,1128.52\n2001-11-29 00:00:00,1140.2\n2001-11-30 00:00:00,1139.45\n2001-12-03 00:00:00,1129.9\n2001-12-04 00:00:00,1144.8\n2001-12-05 00:00:00,1170.35\n2001-12-06 00:00:00,1167.1\n2001-12-07 00:00:00,1158.31\n2001-12-10 00:00:00,1139.93\n2001-12-11 00:00:00,1136.76\n2001-12-12 00:00:00,1137.07\n2001-12-13 00:00:00,1119.38\n2001-12-14 00:00:00,1123.09\n2001-12-17 00:00:00,1134.36\n2001-12-18 00:00:00,1142.92\n2001-12-19 00:00:00,1149.56\n2001-12-20 00:00:00,1139.93\n2001-12-21 00:00:00,1144.89\n2001-12-24 00:00:00,1144.65\n2001-12-26 00:00:00,1149.37\n2001-12-27 00:00:00,1157.13\n2001-12-28 00:00:00,1161.02\n2001-12-31 00:00:00,1148.08\n2002-01-02 00:00:00,1154.67\n2002-01-03 00:00:00,1165.27\n2002-01-04 00:00:00,1172.51\n2002-01-07 00:00:00,1164.89\n2002-01-08 00:00:00,1160.71\n2002-01-09 00:00:00,1155.14\n2002-01-10 00:00:00,1156.55\n2002-01-11 00:00:00,1145.6\n2002-01-14 00:00:00,1138.41\n2002-01-15 00:00:00,1146.19\n2002-01-16 00:00:00,1127.57\n2002-01-17 00:00:00,1138.88\n2002-01-18 00:00:00,1127.58\n2002-01-22 00:00:00,1119.31\n2002-01-23 00:00:00,1128.18\n2002-01-24 00:00:00,1132.15\n2002-01-25 00:00:00,1133.28\n2002-01-28 00:00:00,1133.06\n2002-01-29 00:00:00,1100.64\n2002-01-30 00:00:00,1113.57\n2002-01-31 00:00:00,1130.2\n2002-02-01 00:00:00,1122.2\n2002-02-04 00:00:00,1094.44\n2002-02-05 00:00:00,1090.02\n2002-02-06 00:00:00,1083.51\n2002-02-07 00:00:00,1080.17\n2002-02-08 00:00:00,1096.22\n2002-02-11 00:00:00,1111.94\n2002-02-12 00:00:00,1107.5\n2002-02-13 00:00:00,1118.51\n2002-02-14 00:00:00,1116.48\n2002-02-15 00:00:00,1104.18\n2002-02-19 00:00:00,1083.34\n2002-02-20 00:00:00,1097.98\n2002-02-21 00:00:00,1080.95\n2002-02-22 00:00:00,1089.84\n2002-02-25 00:00:00,1109.43\n2002-02-26 00:00:00,1109.38\n2002-02-27 00:00:00,1109.89\n2002-02-28 00:00:00,1106.73\n2002-03-01 00:00:00,1131.78\n2002-03-04 00:00:00,1153.84\n2002-03-05 00:00:00,1146.14\n2002-03-06 00:00:00,1162.77\n2002-03-07 00:00:00,1157.54\n2002-03-08 00:00:00,1164.31\n2002-03-11 00:00:00,1168.26\n2002-03-12 00:00:00,1165.58\n2002-03-13 00:00:00,1154.09\n2002-03-14 00:00:00,1153.04\n2002-03-15 00:00:00,1166.16\n2002-03-18 00:00:00,1165.55\n2002-03-19 00:00:00,1170.29\n2002-03-20 00:00:00,1151.85\n2002-03-21 00:00:00,1153.59\n2002-03-22 00:00:00,1148.7\n2002-03-25 00:00:00,1131.87\n2002-03-26 00:00:00,1138.49\n2002-03-27 00:00:00,1144.58\n2002-03-28 00:00:00,1147.39\n2002-04-01 00:00:00,1146.54\n2002-04-02 00:00:00,1136.76\n2002-04-03 00:00:00,1125.4\n2002-04-04 00:00:00,1126.34\n2002-04-05 00:00:00,1122.73\n2002-04-08 00:00:00,1125.29\n2002-04-09 00:00:00,1117.8\n2002-04-10 00:00:00,1130.47\n2002-04-11 00:00:00,1103.69\n2002-04-12 00:00:00,1111.01\n2002-04-15 00:00:00,1102.55\n2002-04-16 00:00:00,1128.37\n2002-04-17 00:00:00,1126.07\n2002-04-18 00:00:00,1124.47\n2002-04-19 00:00:00,1125.17\n2002-04-22 00:00:00,1107.83\n2002-04-23 00:00:00,1100.96\n2002-04-24 00:00:00,1093.14\n2002-04-25 00:00:00,1091.48\n2002-04-26 00:00:00,1076.32\n2002-04-29 00:00:00,1065.45\n2002-04-30 00:00:00,1076.92\n2002-05-01 00:00:00,1086.46\n2002-05-02 00:00:00,1084.56\n2002-05-03 00:00:00,1073.43\n2002-05-06 00:00:00,1052.67\n2002-05-07 00:00:00,1049.49\n2002-05-08 00:00:00,1088.85\n2002-05-09 00:00:00,1073.01\n2002-05-10 00:00:00,1054.99\n2002-05-13 00:00:00,1074.56\n2002-05-14 00:00:00,1097.28\n2002-05-15 00:00:00,1091.07\n2002-05-16 00:00:00,1098.23\n2002-05-17 00:00:00,1106.59\n2002-05-20 00:00:00,1091.88\n2002-05-21 00:00:00,1079.88\n2002-05-22 00:00:00,1086.02\n2002-05-23 00:00:00,1097.08\n2002-05-24 00:00:00,1083.82\n2002-05-28 00:00:00,1074.55\n2002-05-29 00:00:00,1067.66\n2002-05-30 00:00:00,1064.66\n2002-05-31 00:00:00,1067.14\n2002-06-03 00:00:00,1040.68\n2002-06-04 00:00:00,1040.69\n2002-06-05 00:00:00,1049.9\n2002-06-06 00:00:00,1029.15\n2002-06-07 00:00:00,1027.53\n2002-06-10 00:00:00,1030.74\n2002-06-11 00:00:00,1013.6\n2002-06-12 00:00:00,1020.26\n2002-06-13 00:00:00,1009.56\n2002-06-14 00:00:00,1007.27\n2002-06-17 00:00:00,1036.17\n2002-06-18 00:00:00,1037.14\n2002-06-19 00:00:00,1019.99\n2002-06-20 00:00:00,1006.29\n2002-06-21 00:00:00,989.14\n2002-06-24 00:00:00,992.72\n2002-06-25 00:00:00,976.14\n2002-06-26 00:00:00,973.53\n2002-06-27 00:00:00,990.64\n2002-06-28 00:00:00,989.82\n2002-07-01 00:00:00,968.65\n2002-07-02 00:00:00,948.09\n2002-07-03 00:00:00,953.99\n2002-07-05 00:00:00,989.03\n2002-07-08 00:00:00,976.98\n2002-07-09 00:00:00,952.83\n2002-07-10 00:00:00,920.47\n2002-07-11 00:00:00,927.37\n2002-07-12 00:00:00,921.39\n2002-07-15 00:00:00,917.93\n2002-07-16 00:00:00,900.94\n2002-07-17 00:00:00,906.04\n2002-07-18 00:00:00,881.56\n2002-07-19 00:00:00,847.75\n2002-07-22 00:00:00,819.85\n2002-07-23 00:00:00,797.7\n2002-07-24 00:00:00,843.43\n2002-07-25 00:00:00,838.68\n2002-07-26 00:00:00,852.84\n2002-07-29 00:00:00,898.96\n2002-07-30 00:00:00,902.78\n2002-07-31 00:00:00,911.62\n2002-08-01 00:00:00,884.66\n2002-08-02 00:00:00,864.24\n2002-08-05 00:00:00,834.6\n2002-08-06 00:00:00,859.57\n2002-08-07 00:00:00,876.77\n2002-08-08 00:00:00,905.46\n2002-08-09 00:00:00,908.64\n2002-08-12 00:00:00,903.8\n2002-08-13 00:00:00,884.21\n2002-08-14 00:00:00,919.62\n2002-08-15 00:00:00,930.25\n2002-08-16 00:00:00,928.77\n2002-08-19 00:00:00,950.7\n2002-08-20 00:00:00,937.43\n2002-08-21 00:00:00,949.36\n2002-08-22 00:00:00,962.7\n2002-08-23 00:00:00,940.86\n2002-08-26 00:00:00,947.95\n2002-08-27 00:00:00,934.82\n2002-08-28 00:00:00,917.87\n2002-08-29 00:00:00,917.8\n2002-08-30 00:00:00,916.07\n2002-09-03 00:00:00,878.02\n2002-09-04 00:00:00,893.4\n2002-09-05 00:00:00,879.15\n2002-09-06 00:00:00,893.92\n2002-09-09 00:00:00,902.96\n2002-09-10 00:00:00,909.58\n2002-09-11 00:00:00,909.45\n2002-09-12 00:00:00,886.91\n2002-09-13 00:00:00,889.81\n2002-09-16 00:00:00,891.1\n2002-09-17 00:00:00,873.52\n2002-09-18 00:00:00,869.46\n2002-09-19 00:00:00,843.32\n2002-09-20 00:00:00,845.39\n2002-09-23 00:00:00,833.7\n2002-09-24 00:00:00,819.29\n2002-09-25 00:00:00,839.66\n2002-09-26 00:00:00,854.95\n2002-09-27 00:00:00,827.37\n2002-09-30 00:00:00,815.28\n2002-10-01 00:00:00,847.91\n2002-10-02 00:00:00,827.91\n2002-10-03 00:00:00,818.95\n2002-10-04 00:00:00,800.58\n2002-10-07 00:00:00,785.28\n2002-10-08 00:00:00,798.55\n2002-10-09 00:00:00,776.76\n2002-10-10 00:00:00,803.92\n2002-10-11 00:00:00,835.32\n2002-10-14 00:00:00,841.44\n2002-10-15 00:00:00,881.27\n2002-10-16 00:00:00,860.02\n2002-10-17 00:00:00,879.2\n2002-10-18 00:00:00,884.39\n2002-10-21 00:00:00,899.72\n2002-10-22 00:00:00,890.16\n2002-10-23 00:00:00,896.14\n2002-10-24 00:00:00,882.5\n2002-10-25 00:00:00,897.65\n2002-10-28 00:00:00,890.23\n2002-10-29 00:00:00,882.15\n2002-10-30 00:00:00,890.71\n2002-10-31 00:00:00,885.76\n2002-11-01 00:00:00,900.96\n2002-11-04 00:00:00,908.35\n2002-11-05 00:00:00,915.39\n2002-11-06 00:00:00,923.76\n2002-11-07 00:00:00,902.65\n2002-11-08 00:00:00,894.74\n2002-11-11 00:00:00,876.19\n2002-11-12 00:00:00,882.95\n2002-11-13 00:00:00,882.53\n2002-11-14 00:00:00,904.27\n2002-11-15 00:00:00,909.83\n2002-11-18 00:00:00,900.36\n2002-11-19 00:00:00,896.74\n2002-11-20 00:00:00,914.15\n2002-11-21 00:00:00,933.76\n2002-11-22 00:00:00,930.55\n2002-11-25 00:00:00,932.87\n2002-11-26 00:00:00,913.31\n2002-11-27 00:00:00,938.87\n2002-11-29 00:00:00,936.31\n2002-12-02 00:00:00,934.53\n2002-12-03 00:00:00,920.75\n2002-12-04 00:00:00,917.58\n2002-12-05 00:00:00,906.55\n2002-12-06 00:00:00,912.23\n2002-12-09 00:00:00,892.0\n2002-12-10 00:00:00,904.45\n2002-12-11 00:00:00,904.96\n2002-12-12 00:00:00,901.58\n2002-12-13 00:00:00,889.48\n2002-12-16 00:00:00,910.4\n2002-12-17 00:00:00,902.99\n2002-12-18 00:00:00,891.12\n2002-12-19 00:00:00,884.25\n2002-12-20 00:00:00,895.76\n2002-12-23 00:00:00,897.38\n2002-12-24 00:00:00,892.47\n2002-12-26 00:00:00,889.66\n2002-12-27 00:00:00,875.4\n2002-12-30 00:00:00,879.39\n2002-12-31 00:00:00,879.82\n2003-01-02 00:00:00,909.03\n2003-01-03 00:00:00,908.59\n2003-01-06 00:00:00,929.01\n2003-01-07 00:00:00,922.93\n2003-01-08 00:00:00,909.93\n2003-01-09 00:00:00,927.57\n2003-01-10 00:00:00,927.57\n2003-01-13 00:00:00,926.26\n2003-01-14 00:00:00,931.66\n2003-01-15 00:00:00,918.22\n2003-01-16 00:00:00,914.6\n2003-01-17 00:00:00,901.78\n2003-01-21 00:00:00,887.62\n2003-01-22 00:00:00,878.36\n2003-01-23 00:00:00,887.34\n2003-01-24 00:00:00,861.4\n2003-01-27 00:00:00,847.48\n2003-01-28 00:00:00,858.54\n2003-01-29 00:00:00,864.36\n2003-01-30 00:00:00,844.61\n2003-01-31 00:00:00,855.7\n2003-02-03 00:00:00,860.32\n2003-02-04 00:00:00,848.2\n2003-02-05 00:00:00,843.59\n2003-02-06 00:00:00,838.15\n2003-02-07 00:00:00,829.69\n2003-02-10 00:00:00,835.97\n2003-02-11 00:00:00,829.2\n2003-02-12 00:00:00,818.68\n2003-02-13 00:00:00,817.37\n2003-02-14 00:00:00,834.89\n2003-02-18 00:00:00,851.17\n2003-02-19 00:00:00,845.13\n2003-02-20 00:00:00,837.1\n2003-02-21 00:00:00,848.17\n2003-02-24 00:00:00,832.58\n2003-02-25 00:00:00,838.57\n2003-02-26 00:00:00,827.55\n2003-02-27 00:00:00,837.28\n2003-02-28 00:00:00,841.15\n2003-03-03 00:00:00,834.81\n2003-03-04 00:00:00,821.99\n2003-03-05 00:00:00,829.85\n2003-03-06 00:00:00,822.1\n2003-03-07 00:00:00,828.89\n2003-03-10 00:00:00,807.48\n2003-03-11 00:00:00,800.73\n2003-03-12 00:00:00,804.19\n2003-03-13 00:00:00,831.9\n2003-03-14 00:00:00,833.27\n2003-03-17 00:00:00,862.79\n2003-03-18 00:00:00,866.45\n2003-03-19 00:00:00,874.02\n2003-03-20 00:00:00,875.67\n2003-03-21 00:00:00,895.79\n2003-03-24 00:00:00,864.23\n2003-03-25 00:00:00,874.74\n2003-03-26 00:00:00,869.95\n2003-03-27 00:00:00,868.52\n2003-03-28 00:00:00,863.5\n2003-03-31 00:00:00,848.18\n2003-04-01 00:00:00,858.48\n2003-04-02 00:00:00,880.9\n2003-04-03 00:00:00,876.45\n2003-04-04 00:00:00,878.85\n2003-04-07 00:00:00,879.93\n2003-04-08 00:00:00,878.29\n2003-04-09 00:00:00,865.99\n2003-04-10 00:00:00,871.58\n2003-04-11 00:00:00,868.3\n2003-04-14 00:00:00,885.23\n2003-04-15 00:00:00,890.81\n2003-04-16 00:00:00,879.91\n2003-04-17 00:00:00,893.58\n2003-04-21 00:00:00,892.01\n2003-04-22 00:00:00,911.37\n2003-04-23 00:00:00,919.02\n2003-04-24 00:00:00,911.43\n2003-04-25 00:00:00,898.81\n2003-04-28 00:00:00,914.84\n2003-04-29 00:00:00,917.84\n2003-04-30 00:00:00,916.92\n2003-05-01 00:00:00,916.3\n2003-05-02 00:00:00,930.08\n2003-05-05 00:00:00,926.55\n2003-05-06 00:00:00,934.39\n2003-05-07 00:00:00,929.62\n2003-05-08 00:00:00,920.27\n2003-05-09 00:00:00,933.41\n2003-05-12 00:00:00,945.11\n2003-05-13 00:00:00,942.3\n2003-05-14 00:00:00,939.28\n2003-05-15 00:00:00,946.67\n2003-05-16 00:00:00,944.3\n2003-05-19 00:00:00,920.77\n2003-05-20 00:00:00,919.73\n2003-05-21 00:00:00,923.42\n2003-05-22 00:00:00,931.87\n2003-05-23 00:00:00,933.22\n2003-05-27 00:00:00,951.48\n2003-05-28 00:00:00,953.22\n2003-05-29 00:00:00,949.64\n2003-05-30 00:00:00,963.59\n2003-06-02 00:00:00,967.0\n2003-06-03 00:00:00,971.56\n2003-06-04 00:00:00,986.24\n2003-06-05 00:00:00,990.14\n2003-06-06 00:00:00,987.76\n2003-06-09 00:00:00,975.93\n2003-06-10 00:00:00,984.84\n2003-06-11 00:00:00,997.48\n2003-06-12 00:00:00,998.51\n2003-06-13 00:00:00,988.61\n2003-06-16 00:00:00,1010.74\n2003-06-17 00:00:00,1011.66\n2003-06-18 00:00:00,1010.09\n2003-06-19 00:00:00,994.7\n2003-06-20 00:00:00,995.69\n2003-06-23 00:00:00,981.64\n2003-06-24 00:00:00,983.45\n2003-06-25 00:00:00,975.32\n2003-06-26 00:00:00,985.82\n2003-06-27 00:00:00,976.22\n2003-06-30 00:00:00,974.5\n2003-07-01 00:00:00,982.32\n2003-07-02 00:00:00,993.75\n2003-07-03 00:00:00,985.7\n2003-07-07 00:00:00,1004.42\n2003-07-08 00:00:00,1007.84\n2003-07-09 00:00:00,1002.21\n2003-07-10 00:00:00,988.7\n2003-07-11 00:00:00,998.14\n2003-07-14 00:00:00,1003.86\n2003-07-15 00:00:00,1000.42\n2003-07-16 00:00:00,994.09\n2003-07-17 00:00:00,981.73\n2003-07-18 00:00:00,993.32\n2003-07-21 00:00:00,978.8\n2003-07-22 00:00:00,988.11\n2003-07-23 00:00:00,988.61\n2003-07-24 00:00:00,981.6\n2003-07-25 00:00:00,998.68\n2003-07-28 00:00:00,996.52\n2003-07-29 00:00:00,989.28\n2003-07-30 00:00:00,987.49\n2003-07-31 00:00:00,990.31\n2003-08-01 00:00:00,980.15\n2003-08-04 00:00:00,982.82\n2003-08-05 00:00:00,965.46\n2003-08-06 00:00:00,967.08\n2003-08-07 00:00:00,974.12\n2003-08-08 00:00:00,977.59\n2003-08-11 00:00:00,980.59\n2003-08-12 00:00:00,990.35\n2003-08-13 00:00:00,984.03\n2003-08-14 00:00:00,990.51\n2003-08-15 00:00:00,990.67\n2003-08-18 00:00:00,999.74\n2003-08-19 00:00:00,1002.35\n2003-08-20 00:00:00,1000.3\n2003-08-21 00:00:00,1003.27\n2003-08-22 00:00:00,993.06\n2003-08-25 00:00:00,993.71\n2003-08-26 00:00:00,996.73\n2003-08-27 00:00:00,996.79\n2003-08-28 00:00:00,1002.84\n2003-08-29 00:00:00,1008.01\n2003-09-02 00:00:00,1021.99\n2003-09-03 00:00:00,1026.27\n2003-09-04 00:00:00,1027.97\n2003-09-05 00:00:00,1021.39\n2003-09-08 00:00:00,1031.64\n2003-09-09 00:00:00,1023.17\n2003-09-10 00:00:00,1010.92\n2003-09-11 00:00:00,1016.42\n2003-09-12 00:00:00,1018.63\n2003-09-15 00:00:00,1014.81\n2003-09-16 00:00:00,1029.32\n2003-09-17 00:00:00,1025.97\n2003-09-18 00:00:00,1039.58\n2003-09-19 00:00:00,1036.3\n2003-09-22 00:00:00,1022.82\n2003-09-23 00:00:00,1029.03\n2003-09-24 00:00:00,1009.38\n2003-09-25 00:00:00,1003.27\n2003-09-26 00:00:00,996.85\n2003-09-29 00:00:00,1006.58\n2003-09-30 00:00:00,995.97\n2003-10-01 00:00:00,1018.22\n2003-10-02 00:00:00,1020.24\n2003-10-03 00:00:00,1029.85\n2003-10-06 00:00:00,1034.35\n2003-10-07 00:00:00,1039.25\n2003-10-08 00:00:00,1033.78\n2003-10-09 00:00:00,1038.73\n2003-10-10 00:00:00,1038.06\n2003-10-13 00:00:00,1045.35\n2003-10-14 00:00:00,1049.48\n2003-10-15 00:00:00,1046.76\n2003-10-16 00:00:00,1050.07\n2003-10-17 00:00:00,1039.32\n2003-10-20 00:00:00,1044.68\n2003-10-21 00:00:00,1046.03\n2003-10-22 00:00:00,1030.36\n2003-10-23 00:00:00,1033.77\n2003-10-24 00:00:00,1028.91\n2003-10-27 00:00:00,1031.13\n2003-10-28 00:00:00,1046.79\n2003-10-29 00:00:00,1048.11\n2003-10-30 00:00:00,1046.94\n2003-10-31 00:00:00,1050.71\n2003-11-03 00:00:00,1059.02\n2003-11-04 00:00:00,1053.25\n2003-11-05 00:00:00,1051.81\n2003-11-06 00:00:00,1058.05\n2003-11-07 00:00:00,1053.21\n2003-11-10 00:00:00,1047.11\n2003-11-11 00:00:00,1046.57\n2003-11-12 00:00:00,1058.53\n2003-11-13 00:00:00,1058.41\n2003-11-14 00:00:00,1050.35\n2003-11-17 00:00:00,1043.63\n2003-11-18 00:00:00,1034.15\n2003-11-19 00:00:00,1042.44\n2003-11-20 00:00:00,1033.65\n2003-11-21 00:00:00,1035.28\n2003-11-24 00:00:00,1052.08\n2003-11-25 00:00:00,1053.89\n2003-11-26 00:00:00,1058.45\n2003-11-28 00:00:00,1058.2\n2003-12-01 00:00:00,1070.12\n2003-12-02 00:00:00,1066.62\n2003-12-03 00:00:00,1064.73\n2003-12-04 00:00:00,1069.72\n2003-12-05 00:00:00,1061.5\n2003-12-08 00:00:00,1069.3\n2003-12-09 00:00:00,1060.18\n2003-12-10 00:00:00,1059.05\n2003-12-11 00:00:00,1071.21\n2003-12-12 00:00:00,1074.14\n2003-12-15 00:00:00,1068.04\n2003-12-16 00:00:00,1075.13\n2003-12-17 00:00:00,1076.48\n2003-12-18 00:00:00,1089.18\n2003-12-19 00:00:00,1088.66\n2003-12-22 00:00:00,1092.94\n2003-12-23 00:00:00,1096.02\n2003-12-24 00:00:00,1094.04\n2003-12-26 00:00:00,1095.89\n2003-12-29 00:00:00,1109.48\n2003-12-30 00:00:00,1109.64\n2003-12-31 00:00:00,1111.92\n2004-01-02 00:00:00,1108.48\n2004-01-05 00:00:00,1122.22\n2004-01-06 00:00:00,1123.67\n2004-01-07 00:00:00,1126.33\n2004-01-08 00:00:00,1131.92\n2004-01-09 00:00:00,1121.86\n2004-01-12 00:00:00,1127.23\n2004-01-13 00:00:00,1121.22\n2004-01-14 00:00:00,1130.52\n2004-01-15 00:00:00,1132.05\n2004-01-16 00:00:00,1139.83\n2004-01-20 00:00:00,1138.77\n2004-01-21 00:00:00,1147.62\n2004-01-22 00:00:00,1143.94\n2004-01-23 00:00:00,1141.55\n2004-01-26 00:00:00,1155.37\n2004-01-27 00:00:00,1144.05\n2004-01-28 00:00:00,1128.48\n2004-01-29 00:00:00,1134.11\n2004-01-30 00:00:00,1131.13\n2004-02-02 00:00:00,1135.26\n2004-02-03 00:00:00,1136.03\n2004-02-04 00:00:00,1126.52\n2004-02-05 00:00:00,1128.59\n2004-02-06 00:00:00,1142.76\n2004-02-09 00:00:00,1139.81\n2004-02-10 00:00:00,1145.54\n2004-02-11 00:00:00,1157.76\n2004-02-12 00:00:00,1152.11\n2004-02-13 00:00:00,1145.81\n2004-02-17 00:00:00,1156.99\n2004-02-18 00:00:00,1151.82\n2004-02-19 00:00:00,1147.06\n2004-02-20 00:00:00,1144.11\n2004-02-23 00:00:00,1140.99\n2004-02-24 00:00:00,1139.09\n2004-02-25 00:00:00,1143.67\n2004-02-26 00:00:00,1144.91\n2004-02-27 00:00:00,1144.94\n2004-03-01 00:00:00,1155.97\n2004-03-02 00:00:00,1149.1\n2004-03-03 00:00:00,1151.03\n2004-03-04 00:00:00,1154.87\n2004-03-05 00:00:00,1156.86\n2004-03-08 00:00:00,1147.2\n2004-03-09 00:00:00,1140.58\n2004-03-10 00:00:00,1123.89\n2004-03-11 00:00:00,1106.78\n2004-03-12 00:00:00,1120.57\n2004-03-15 00:00:00,1104.49\n2004-03-16 00:00:00,1110.7\n2004-03-17 00:00:00,1123.75\n2004-03-18 00:00:00,1122.32\n2004-03-19 00:00:00,1109.78\n2004-03-22 00:00:00,1095.4\n2004-03-23 00:00:00,1093.95\n2004-03-24 00:00:00,1091.33\n2004-03-25 00:00:00,1109.19\n2004-03-26 00:00:00,1108.06\n2004-03-29 00:00:00,1122.47\n2004-03-30 00:00:00,1127.0\n2004-03-31 00:00:00,1126.21\n2004-04-01 00:00:00,1132.17\n2004-04-02 00:00:00,1141.81\n2004-04-05 00:00:00,1150.57\n2004-04-06 00:00:00,1148.16\n2004-04-07 00:00:00,1140.53\n2004-04-08 00:00:00,1139.32\n2004-04-12 00:00:00,1145.2\n2004-04-13 00:00:00,1129.44\n2004-04-14 00:00:00,1128.17\n2004-04-15 00:00:00,1128.84\n2004-04-16 00:00:00,1134.61\n2004-04-19 00:00:00,1135.82\n2004-04-20 00:00:00,1118.15\n2004-04-21 00:00:00,1124.09\n2004-04-22 00:00:00,1139.93\n2004-04-23 00:00:00,1140.6\n2004-04-26 00:00:00,1135.53\n2004-04-27 00:00:00,1138.11\n2004-04-28 00:00:00,1122.41\n2004-04-29 00:00:00,1113.89\n2004-04-30 00:00:00,1107.3\n2004-05-03 00:00:00,1117.49\n2004-05-04 00:00:00,1119.55\n2004-05-05 00:00:00,1121.53\n2004-05-06 00:00:00,1113.99\n2004-05-07 00:00:00,1098.7\n2004-05-10 00:00:00,1087.12\n2004-05-11 00:00:00,1095.45\n2004-05-12 00:00:00,1097.28\n2004-05-13 00:00:00,1096.44\n2004-05-14 00:00:00,1095.7\n2004-05-17 00:00:00,1084.1\n2004-05-18 00:00:00,1091.49\n2004-05-19 00:00:00,1088.68\n2004-05-20 00:00:00,1089.19\n2004-05-21 00:00:00,1093.56\n2004-05-24 00:00:00,1095.41\n2004-05-25 00:00:00,1113.05\n2004-05-26 00:00:00,1114.94\n2004-05-27 00:00:00,1121.28\n2004-05-28 00:00:00,1120.68\n2004-06-01 00:00:00,1121.2\n2004-06-02 00:00:00,1124.99\n2004-06-03 00:00:00,1116.64\n2004-06-04 00:00:00,1122.5\n2004-06-07 00:00:00,1140.42\n2004-06-08 00:00:00,1142.18\n2004-06-09 00:00:00,1131.33\n2004-06-10 00:00:00,1136.47\n2004-06-14 00:00:00,1125.29\n2004-06-15 00:00:00,1132.01\n2004-06-16 00:00:00,1133.56\n2004-06-17 00:00:00,1132.05\n2004-06-18 00:00:00,1135.02\n2004-06-21 00:00:00,1130.3\n2004-06-22 00:00:00,1134.41\n2004-06-23 00:00:00,1144.06\n2004-06-24 00:00:00,1140.65\n2004-06-25 00:00:00,1134.43\n2004-06-28 00:00:00,1133.35\n2004-06-29 00:00:00,1136.2\n2004-06-30 00:00:00,1140.84\n2004-07-01 00:00:00,1128.94\n2004-07-02 00:00:00,1125.38\n2004-07-06 00:00:00,1116.21\n2004-07-07 00:00:00,1118.33\n2004-07-08 00:00:00,1109.11\n2004-07-09 00:00:00,1112.81\n2004-07-12 00:00:00,1114.35\n2004-07-13 00:00:00,1115.14\n2004-07-14 00:00:00,1111.47\n2004-07-15 00:00:00,1106.69\n2004-07-16 00:00:00,1101.39\n2004-07-19 00:00:00,1100.9\n2004-07-20 00:00:00,1108.67\n2004-07-21 00:00:00,1093.88\n2004-07-22 00:00:00,1096.84\n2004-07-23 00:00:00,1086.2\n2004-07-26 00:00:00,1084.07\n2004-07-27 00:00:00,1094.83\n2004-07-28 00:00:00,1095.42\n2004-07-29 00:00:00,1100.43\n2004-07-30 00:00:00,1101.72\n2004-08-02 00:00:00,1106.62\n2004-08-03 00:00:00,1099.69\n2004-08-04 00:00:00,1098.63\n2004-08-05 00:00:00,1080.7\n2004-08-06 00:00:00,1063.97\n2004-08-09 00:00:00,1065.22\n2004-08-10 00:00:00,1079.04\n2004-08-11 00:00:00,1075.79\n2004-08-12 00:00:00,1063.23\n2004-08-13 00:00:00,1064.8\n2004-08-16 00:00:00,1079.34\n2004-08-17 00:00:00,1081.71\n2004-08-18 00:00:00,1095.17\n2004-08-19 00:00:00,1091.23\n2004-08-20 00:00:00,1098.35\n2004-08-23 00:00:00,1095.68\n2004-08-24 00:00:00,1096.19\n2004-08-25 00:00:00,1104.96\n2004-08-26 00:00:00,1105.09\n2004-08-27 00:00:00,1107.77\n2004-08-30 00:00:00,1099.15\n2004-08-31 00:00:00,1104.24\n2004-09-01 00:00:00,1105.91\n2004-09-02 00:00:00,1118.31\n2004-09-03 00:00:00,1113.63\n2004-09-07 00:00:00,1121.3\n2004-09-08 00:00:00,1116.27\n2004-09-09 00:00:00,1118.38\n2004-09-10 00:00:00,1123.92\n2004-09-13 00:00:00,1125.82\n2004-09-14 00:00:00,1128.33\n2004-09-15 00:00:00,1120.37\n2004-09-16 00:00:00,1123.5\n2004-09-17 00:00:00,1128.55\n2004-09-20 00:00:00,1122.2\n2004-09-21 00:00:00,1129.3\n2004-09-22 00:00:00,1113.56\n2004-09-23 00:00:00,1108.36\n2004-09-24 00:00:00,1110.11\n2004-09-27 00:00:00,1103.52\n2004-09-28 00:00:00,1110.06\n2004-09-29 00:00:00,1114.8\n2004-09-30 00:00:00,1114.58\n2004-10-01 00:00:00,1131.5\n2004-10-04 00:00:00,1135.17\n2004-10-05 00:00:00,1134.48\n2004-10-06 00:00:00,1142.05\n2004-10-07 00:00:00,1130.65\n2004-10-08 00:00:00,1122.14\n2004-10-11 00:00:00,1124.39\n2004-10-12 00:00:00,1121.84\n2004-10-13 00:00:00,1113.65\n2004-10-14 00:00:00,1103.29\n2004-10-15 00:00:00,1108.2\n2004-10-18 00:00:00,1114.02\n2004-10-19 00:00:00,1103.23\n2004-10-20 00:00:00,1103.66\n2004-10-21 00:00:00,1106.49\n2004-10-22 00:00:00,1095.74\n2004-10-25 00:00:00,1094.8\n2004-10-26 00:00:00,1111.09\n2004-10-27 00:00:00,1125.4\n2004-10-28 00:00:00,1127.44\n2004-10-29 00:00:00,1130.2\n2004-11-01 00:00:00,1130.51\n2004-11-02 00:00:00,1130.56\n2004-11-03 00:00:00,1143.2\n2004-11-04 00:00:00,1161.67\n2004-11-05 00:00:00,1166.17\n2004-11-08 00:00:00,1164.89\n2004-11-09 00:00:00,1164.08\n2004-11-10 00:00:00,1162.91\n2004-11-11 00:00:00,1173.48\n2004-11-12 00:00:00,1184.17\n2004-11-15 00:00:00,1183.81\n2004-11-16 00:00:00,1175.43\n2004-11-17 00:00:00,1181.94\n2004-11-18 00:00:00,1183.55\n2004-11-19 00:00:00,1170.34\n2004-11-22 00:00:00,1177.24\n2004-11-23 00:00:00,1176.94\n2004-11-24 00:00:00,1181.76\n2004-11-26 00:00:00,1182.65\n2004-11-29 00:00:00,1178.57\n2004-11-30 00:00:00,1173.82\n2004-12-01 00:00:00,1191.37\n2004-12-02 00:00:00,1190.33\n2004-12-03 00:00:00,1191.17\n2004-12-06 00:00:00,1190.25\n2004-12-07 00:00:00,1177.07\n2004-12-08 00:00:00,1182.81\n2004-12-09 00:00:00,1189.24\n2004-12-10 00:00:00,1188.0\n2004-12-13 00:00:00,1198.68\n2004-12-14 00:00:00,1203.38\n2004-12-15 00:00:00,1205.72\n2004-12-16 00:00:00,1203.21\n2004-12-17 00:00:00,1194.2\n2004-12-20 00:00:00,1194.65\n2004-12-21 00:00:00,1205.45\n2004-12-22 00:00:00,1209.57\n2004-12-23 00:00:00,1210.13\n2004-12-27 00:00:00,1204.92\n2004-12-28 00:00:00,1213.54\n2004-12-29 00:00:00,1213.45\n2004-12-30 00:00:00,1213.55\n2004-12-31 00:00:00,1211.92\n2005-01-03 00:00:00,1202.08\n2005-01-04 00:00:00,1188.05\n2005-01-05 00:00:00,1183.74\n2005-01-06 00:00:00,1187.89\n2005-01-07 00:00:00,1186.19\n2005-01-10 00:00:00,1190.25\n2005-01-11 00:00:00,1182.99\n2005-01-12 00:00:00,1187.7\n2005-01-13 00:00:00,1177.45\n2005-01-14 00:00:00,1184.52\n2005-01-18 00:00:00,1195.98\n2005-01-19 00:00:00,1184.63\n2005-01-20 00:00:00,1175.41\n2005-01-21 00:00:00,1167.87\n2005-01-24 00:00:00,1163.75\n2005-01-25 00:00:00,1168.41\n2005-01-26 00:00:00,1174.07\n2005-01-27 00:00:00,1174.55\n2005-01-28 00:00:00,1171.36\n2005-01-31 00:00:00,1181.27\n2005-02-01 00:00:00,1189.41\n2005-02-02 00:00:00,1193.19\n2005-02-03 00:00:00,1189.89\n2005-02-04 00:00:00,1203.03\n2005-02-07 00:00:00,1201.72\n2005-02-08 00:00:00,1202.3\n2005-02-09 00:00:00,1191.99\n2005-02-10 00:00:00,1197.01\n2005-02-11 00:00:00,1205.3\n2005-02-14 00:00:00,1206.14\n2005-02-15 00:00:00,1210.12\n2005-02-16 00:00:00,1210.34\n2005-02-17 00:00:00,1200.75\n2005-02-18 00:00:00,1201.59\n2005-02-22 00:00:00,1184.16\n2005-02-23 00:00:00,1190.8\n2005-02-24 00:00:00,1200.2\n2005-02-25 00:00:00,1211.37\n2005-02-28 00:00:00,1203.6\n2005-03-01 00:00:00,1210.41\n2005-03-02 00:00:00,1210.08\n2005-03-03 00:00:00,1210.47\n2005-03-04 00:00:00,1222.12\n2005-03-07 00:00:00,1225.31\n2005-03-08 00:00:00,1219.43\n2005-03-09 00:00:00,1207.01\n2005-03-10 00:00:00,1209.25\n2005-03-11 00:00:00,1200.08\n2005-03-14 00:00:00,1206.83\n2005-03-15 00:00:00,1197.75\n2005-03-16 00:00:00,1188.07\n2005-03-17 00:00:00,1190.21\n2005-03-18 00:00:00,1189.65\n2005-03-21 00:00:00,1183.78\n2005-03-22 00:00:00,1171.71\n2005-03-23 00:00:00,1172.53\n2005-03-24 00:00:00,1171.42\n2005-03-28 00:00:00,1174.28\n2005-03-29 00:00:00,1165.36\n2005-03-30 00:00:00,1181.41\n2005-03-31 00:00:00,1180.59\n2005-04-01 00:00:00,1172.92\n2005-04-04 00:00:00,1176.12\n2005-04-05 00:00:00,1181.39\n2005-04-06 00:00:00,1184.07\n2005-04-07 00:00:00,1191.14\n2005-04-08 00:00:00,1181.2\n2005-04-11 00:00:00,1181.21\n2005-04-12 00:00:00,1187.76\n2005-04-13 00:00:00,1173.79\n2005-04-14 00:00:00,1162.05\n2005-04-15 00:00:00,1142.62\n2005-04-18 00:00:00,1145.98\n2005-04-19 00:00:00,1152.78\n2005-04-20 00:00:00,1137.5\n2005-04-21 00:00:00,1159.95\n2005-04-22 00:00:00,1152.12\n2005-04-25 00:00:00,1162.1\n2005-04-26 00:00:00,1151.83\n2005-04-27 00:00:00,1156.38\n2005-04-28 00:00:00,1143.22\n2005-04-29 00:00:00,1156.85\n2005-05-02 00:00:00,1162.16\n2005-05-03 00:00:00,1161.17\n2005-05-04 00:00:00,1175.65\n2005-05-05 00:00:00,1172.63\n2005-05-06 00:00:00,1171.35\n2005-05-09 00:00:00,1178.84\n2005-05-10 00:00:00,1166.22\n2005-05-11 00:00:00,1171.11\n2005-05-12 00:00:00,1159.36\n2005-05-13 00:00:00,1154.05\n2005-05-16 00:00:00,1165.69\n2005-05-17 00:00:00,1173.8\n2005-05-18 00:00:00,1185.56\n2005-05-19 00:00:00,1191.08\n2005-05-20 00:00:00,1189.28\n2005-05-23 00:00:00,1193.86\n2005-05-24 00:00:00,1194.07\n2005-05-25 00:00:00,1190.01\n2005-05-26 00:00:00,1197.62\n2005-05-27 00:00:00,1198.78\n2005-05-31 00:00:00,1191.5\n2005-06-01 00:00:00,1202.22\n2005-06-02 00:00:00,1204.29\n2005-06-03 00:00:00,1196.02\n2005-06-06 00:00:00,1197.51\n2005-06-07 00:00:00,1197.26\n2005-06-08 00:00:00,1194.67\n2005-06-09 00:00:00,1200.93\n2005-06-10 00:00:00,1198.11\n2005-06-13 00:00:00,1200.82\n2005-06-14 00:00:00,1203.91\n2005-06-15 00:00:00,1206.58\n2005-06-16 00:00:00,1210.96\n2005-06-17 00:00:00,1216.96\n2005-06-20 00:00:00,1216.1\n2005-06-21 00:00:00,1213.61\n2005-06-22 00:00:00,1213.88\n2005-06-23 00:00:00,1200.73\n2005-06-24 00:00:00,1191.57\n2005-06-27 00:00:00,1190.69\n2005-06-28 00:00:00,1201.57\n2005-06-29 00:00:00,1199.85\n2005-06-30 00:00:00,1191.33\n2005-07-01 00:00:00,1194.44\n2005-07-05 00:00:00,1204.99\n2005-07-06 00:00:00,1194.94\n2005-07-07 00:00:00,1197.87\n2005-07-08 00:00:00,1211.86\n2005-07-11 00:00:00,1219.44\n2005-07-12 00:00:00,1222.21\n2005-07-13 00:00:00,1223.29\n2005-07-14 00:00:00,1226.5\n2005-07-15 00:00:00,1227.92\n2005-07-18 00:00:00,1221.13\n2005-07-19 00:00:00,1229.35\n2005-07-20 00:00:00,1235.2\n2005-07-21 00:00:00,1227.04\n2005-07-22 00:00:00,1233.68\n2005-07-25 00:00:00,1229.03\n2005-07-26 00:00:00,1231.16\n2005-07-27 00:00:00,1236.79\n2005-07-28 00:00:00,1243.72\n2005-07-29 00:00:00,1234.18\n2005-08-01 00:00:00,1235.35\n2005-08-02 00:00:00,1244.12\n2005-08-03 00:00:00,1245.04\n2005-08-04 00:00:00,1235.86\n2005-08-05 00:00:00,1226.42\n2005-08-08 00:00:00,1223.13\n2005-08-09 00:00:00,1231.38\n2005-08-10 00:00:00,1229.13\n2005-08-11 00:00:00,1237.81\n2005-08-12 00:00:00,1230.39\n2005-08-15 00:00:00,1233.87\n2005-08-16 00:00:00,1219.34\n2005-08-17 00:00:00,1220.24\n2005-08-18 00:00:00,1219.02\n2005-08-19 00:00:00,1219.71\n2005-08-22 00:00:00,1221.73\n2005-08-23 00:00:00,1217.59\n2005-08-24 00:00:00,1209.59\n2005-08-25 00:00:00,1212.37\n2005-08-26 00:00:00,1205.1\n2005-08-29 00:00:00,1212.28\n2005-08-30 00:00:00,1208.41\n2005-08-31 00:00:00,1220.33\n2005-09-01 00:00:00,1221.59\n2005-09-02 00:00:00,1218.02\n2005-09-06 00:00:00,1233.39\n2005-09-07 00:00:00,1236.36\n2005-09-08 00:00:00,1231.67\n2005-09-09 00:00:00,1241.48\n2005-09-12 00:00:00,1240.56\n2005-09-13 00:00:00,1231.2\n2005-09-14 00:00:00,1227.16\n2005-09-15 00:00:00,1227.73\n2005-09-16 00:00:00,1237.91\n2005-09-19 00:00:00,1231.02\n2005-09-20 00:00:00,1221.34\n2005-09-21 00:00:00,1210.2\n2005-09-22 00:00:00,1214.62\n2005-09-23 00:00:00,1215.29\n2005-09-26 00:00:00,1215.63\n2005-09-27 00:00:00,1215.66\n2005-09-28 00:00:00,1216.89\n2005-09-29 00:00:00,1227.68\n2005-09-30 00:00:00,1228.81\n2005-10-03 00:00:00,1226.7\n2005-10-04 00:00:00,1214.47\n2005-10-05 00:00:00,1196.39\n2005-10-06 00:00:00,1191.49\n2005-10-07 00:00:00,1195.9\n2005-10-10 00:00:00,1187.33\n2005-10-11 00:00:00,1184.87\n2005-10-12 00:00:00,1177.68\n2005-10-13 00:00:00,1176.84\n2005-10-14 00:00:00,1186.57\n2005-10-17 00:00:00,1190.1\n2005-10-18 00:00:00,1178.14\n2005-10-19 00:00:00,1195.76\n2005-10-20 00:00:00,1177.8\n2005-10-21 00:00:00,1179.59\n2005-10-24 00:00:00,1199.38\n2005-10-25 00:00:00,1196.54\n2005-10-26 00:00:00,1191.38\n2005-10-27 00:00:00,1178.9\n2005-10-28 00:00:00,1198.41\n2005-10-31 00:00:00,1207.01\n2005-11-01 00:00:00,1202.76\n2005-11-02 00:00:00,1214.76\n2005-11-03 00:00:00,1219.94\n2005-11-04 00:00:00,1220.14\n2005-11-07 00:00:00,1222.81\n2005-11-08 00:00:00,1218.59\n2005-11-09 00:00:00,1220.65\n2005-11-10 00:00:00,1230.96\n2005-11-11 00:00:00,1234.72\n2005-11-14 00:00:00,1233.76\n2005-11-15 00:00:00,1229.01\n2005-11-16 00:00:00,1231.21\n2005-11-17 00:00:00,1242.8\n2005-11-18 00:00:00,1248.27\n2005-11-21 00:00:00,1254.85\n2005-11-22 00:00:00,1261.23\n2005-11-23 00:00:00,1265.61\n2005-11-25 00:00:00,1268.25\n2005-11-28 00:00:00,1257.46\n2005-11-29 00:00:00,1257.48\n2005-11-30 00:00:00,1249.48\n2005-12-01 00:00:00,1264.67\n2005-12-02 00:00:00,1265.08\n2005-12-05 00:00:00,1262.09\n2005-12-06 00:00:00,1263.7\n2005-12-07 00:00:00,1257.37\n2005-12-08 00:00:00,1255.84\n2005-12-09 00:00:00,1259.37\n2005-12-12 00:00:00,1260.43\n2005-12-13 00:00:00,1267.43\n2005-12-14 00:00:00,1272.74\n2005-12-15 00:00:00,1270.94\n2005-12-16 00:00:00,1267.32\n2005-12-19 00:00:00,1259.92\n2005-12-20 00:00:00,1259.62\n2005-12-21 00:00:00,1262.79\n2005-12-22 00:00:00,1268.12\n2005-12-23 00:00:00,1268.66\n2005-12-27 00:00:00,1256.54\n2005-12-28 00:00:00,1258.17\n2005-12-29 00:00:00,1254.42\n2005-12-30 00:00:00,1248.29\n2006-01-03 00:00:00,1268.8\n2006-01-04 00:00:00,1273.46\n2006-01-05 00:00:00,1273.48\n2006-01-06 00:00:00,1285.45\n2006-01-09 00:00:00,1290.15\n2006-01-10 00:00:00,1289.69\n2006-01-11 00:00:00,1294.18\n2006-01-12 00:00:00,1286.06\n2006-01-13 00:00:00,1287.61\n2006-01-17 00:00:00,1283.03\n2006-01-18 00:00:00,1277.93\n2006-01-19 00:00:00,1285.04\n2006-01-20 00:00:00,1261.49\n2006-01-23 00:00:00,1263.82\n2006-01-24 00:00:00,1266.86\n2006-01-25 00:00:00,1264.68\n2006-01-26 00:00:00,1273.83\n2006-01-27 00:00:00,1283.72\n2006-01-30 00:00:00,1285.19\n2006-01-31 00:00:00,1280.08\n2006-02-01 00:00:00,1282.46\n2006-02-02 00:00:00,1270.84\n2006-02-03 00:00:00,1264.03\n2006-02-06 00:00:00,1265.02\n2006-02-07 00:00:00,1254.78\n2006-02-08 00:00:00,1265.65\n2006-02-09 00:00:00,1263.78\n2006-02-10 00:00:00,1266.99\n2006-02-13 00:00:00,1262.86\n2006-02-14 00:00:00,1275.53\n2006-02-15 00:00:00,1280.0\n2006-02-16 00:00:00,1289.38\n2006-02-17 00:00:00,1287.24\n2006-02-21 00:00:00,1283.03\n2006-02-22 00:00:00,1292.67\n2006-02-23 00:00:00,1287.79\n2006-02-24 00:00:00,1289.43\n2006-02-27 00:00:00,1294.12\n2006-02-28 00:00:00,1280.66\n2006-03-01 00:00:00,1291.24\n2006-03-02 00:00:00,1289.14\n2006-03-03 00:00:00,1287.23\n2006-03-06 00:00:00,1278.26\n2006-03-07 00:00:00,1275.88\n2006-03-08 00:00:00,1278.47\n2006-03-09 00:00:00,1272.23\n2006-03-10 00:00:00,1281.42\n2006-03-13 00:00:00,1284.13\n2006-03-14 00:00:00,1297.48\n2006-03-15 00:00:00,1303.02\n2006-03-16 00:00:00,1305.33\n2006-03-17 00:00:00,1307.25\n2006-03-20 00:00:00,1305.08\n2006-03-21 00:00:00,1297.23\n2006-03-22 00:00:00,1305.04\n2006-03-23 00:00:00,1301.67\n2006-03-24 00:00:00,1302.95\n2006-03-27 00:00:00,1301.61\n2006-03-28 00:00:00,1293.23\n2006-03-29 00:00:00,1302.89\n2006-03-30 00:00:00,1300.25\n2006-03-31 00:00:00,1294.87\n2006-04-03 00:00:00,1297.81\n2006-04-04 00:00:00,1305.93\n2006-04-05 00:00:00,1311.56\n2006-04-06 00:00:00,1309.04\n2006-04-07 00:00:00,1295.5\n2006-04-10 00:00:00,1296.62\n2006-04-11 00:00:00,1286.57\n2006-04-12 00:00:00,1288.12\n2006-04-13 00:00:00,1289.12\n2006-04-17 00:00:00,1285.33\n2006-04-18 00:00:00,1307.28\n2006-04-19 00:00:00,1309.93\n2006-04-20 00:00:00,1311.46\n2006-04-21 00:00:00,1311.28\n2006-04-24 00:00:00,1308.11\n2006-04-25 00:00:00,1301.74\n2006-04-26 00:00:00,1305.41\n2006-04-27 00:00:00,1309.72\n2006-04-28 00:00:00,1310.61\n2006-05-01 00:00:00,1305.19\n2006-05-02 00:00:00,1313.21\n2006-05-03 00:00:00,1308.12\n2006-05-04 00:00:00,1312.25\n2006-05-05 00:00:00,1325.76\n2006-05-08 00:00:00,1324.66\n2006-05-09 00:00:00,1325.14\n2006-05-10 00:00:00,1322.85\n2006-05-11 00:00:00,1305.92\n2006-05-12 00:00:00,1291.24\n2006-05-15 00:00:00,1294.5\n2006-05-16 00:00:00,1292.08\n2006-05-17 00:00:00,1270.32\n2006-05-18 00:00:00,1261.81\n2006-05-19 00:00:00,1267.03\n2006-05-22 00:00:00,1262.07\n2006-05-23 00:00:00,1256.58\n2006-05-24 00:00:00,1258.57\n2006-05-25 00:00:00,1272.88\n2006-05-26 00:00:00,1280.16\n2006-05-30 00:00:00,1259.87\n2006-05-31 00:00:00,1270.09\n2006-06-01 00:00:00,1285.71\n2006-06-02 00:00:00,1288.22\n2006-06-05 00:00:00,1265.29\n2006-06-06 00:00:00,1263.85\n2006-06-07 00:00:00,1256.15\n2006-06-08 00:00:00,1257.93\n2006-06-09 00:00:00,1252.3\n2006-06-12 00:00:00,1237.44\n2006-06-13 00:00:00,1223.69\n2006-06-14 00:00:00,1230.04\n2006-06-15 00:00:00,1256.16\n2006-06-16 00:00:00,1251.54\n2006-06-19 00:00:00,1240.13\n2006-06-20 00:00:00,1240.12\n2006-06-21 00:00:00,1252.2\n2006-06-22 00:00:00,1245.6\n2006-06-23 00:00:00,1244.5\n2006-06-26 00:00:00,1250.56\n2006-06-27 00:00:00,1239.2\n2006-06-28 00:00:00,1246.0\n2006-06-29 00:00:00,1272.87\n2006-06-30 00:00:00,1270.2\n2006-07-03 00:00:00,1280.19\n2006-07-05 00:00:00,1270.91\n2006-07-06 00:00:00,1274.08\n2006-07-07 00:00:00,1265.48\n2006-07-10 00:00:00,1267.34\n2006-07-11 00:00:00,1272.43\n2006-07-12 00:00:00,1258.6\n2006-07-13 00:00:00,1242.28\n2006-07-14 00:00:00,1236.2\n2006-07-17 00:00:00,1234.49\n2006-07-18 00:00:00,1236.86\n2006-07-19 00:00:00,1259.81\n2006-07-20 00:00:00,1249.13\n2006-07-21 00:00:00,1240.29\n2006-07-24 00:00:00,1260.91\n2006-07-25 00:00:00,1268.88\n2006-07-26 00:00:00,1268.4\n2006-07-27 00:00:00,1263.2\n2006-07-28 00:00:00,1278.55\n2006-07-31 00:00:00,1276.66\n2006-08-01 00:00:00,1270.92\n2006-08-02 00:00:00,1277.41\n2006-08-03 00:00:00,1280.27\n2006-08-04 00:00:00,1279.36\n2006-08-07 00:00:00,1275.77\n2006-08-08 00:00:00,1271.48\n2006-08-09 00:00:00,1265.95\n2006-08-10 00:00:00,1271.81\n2006-08-11 00:00:00,1266.74\n2006-08-14 00:00:00,1268.21\n2006-08-15 00:00:00,1285.58\n2006-08-16 00:00:00,1295.43\n2006-08-17 00:00:00,1297.48\n2006-08-18 00:00:00,1302.3\n2006-08-21 00:00:00,1297.52\n2006-08-22 00:00:00,1298.82\n2006-08-23 00:00:00,1292.99\n2006-08-24 00:00:00,1296.06\n2006-08-25 00:00:00,1295.09\n2006-08-28 00:00:00,1301.78\n2006-08-29 00:00:00,1304.28\n2006-08-30 00:00:00,1305.37\n2006-08-31 00:00:00,1303.82\n2006-09-01 00:00:00,1311.01\n2006-09-05 00:00:00,1313.25\n2006-09-06 00:00:00,1300.26\n2006-09-07 00:00:00,1294.02\n2006-09-08 00:00:00,1298.92\n2006-09-11 00:00:00,1299.54\n2006-09-12 00:00:00,1313.0\n2006-09-13 00:00:00,1318.07\n2006-09-14 00:00:00,1316.28\n2006-09-15 00:00:00,1319.66\n2006-09-18 00:00:00,1321.18\n2006-09-19 00:00:00,1317.64\n2006-09-20 00:00:00,1325.18\n2006-09-21 00:00:00,1318.03\n2006-09-22 00:00:00,1314.78\n2006-09-25 00:00:00,1326.37\n2006-09-26 00:00:00,1336.35\n2006-09-27 00:00:00,1336.59\n2006-09-28 00:00:00,1338.88\n2006-09-29 00:00:00,1335.85\n2006-10-02 00:00:00,1331.32\n2006-10-03 00:00:00,1334.11\n2006-10-04 00:00:00,1350.2\n2006-10-05 00:00:00,1353.22\n2006-10-06 00:00:00,1349.59\n2006-10-09 00:00:00,1350.66\n2006-10-10 00:00:00,1353.42\n2006-10-11 00:00:00,1349.95\n2006-10-12 00:00:00,1362.83\n2006-10-13 00:00:00,1365.62\n2006-10-16 00:00:00,1369.06\n2006-10-17 00:00:00,1364.05\n2006-10-18 00:00:00,1365.8\n2006-10-19 00:00:00,1366.96\n2006-10-20 00:00:00,1368.6\n2006-10-23 00:00:00,1377.02\n2006-10-24 00:00:00,1377.38\n2006-10-25 00:00:00,1382.22\n2006-10-26 00:00:00,1389.08\n2006-10-27 00:00:00,1377.34\n2006-10-30 00:00:00,1377.93\n2006-10-31 00:00:00,1377.94\n2006-11-01 00:00:00,1367.81\n2006-11-02 00:00:00,1367.34\n2006-11-03 00:00:00,1364.3\n2006-11-06 00:00:00,1379.78\n2006-11-07 00:00:00,1382.84\n2006-11-08 00:00:00,1385.72\n2006-11-09 00:00:00,1378.33\n2006-11-10 00:00:00,1380.9\n2006-11-13 00:00:00,1384.42\n2006-11-14 00:00:00,1393.22\n2006-11-15 00:00:00,1396.57\n2006-11-16 00:00:00,1399.76\n2006-11-17 00:00:00,1401.2\n2006-11-20 00:00:00,1400.5\n2006-11-21 00:00:00,1402.81\n2006-11-22 00:00:00,1406.09\n2006-11-24 00:00:00,1400.95\n2006-11-27 00:00:00,1381.96\n2006-11-28 00:00:00,1386.72\n2006-11-29 00:00:00,1399.48\n2006-11-30 00:00:00,1400.63\n2006-12-01 00:00:00,1396.71\n2006-12-04 00:00:00,1409.12\n2006-12-05 00:00:00,1414.76\n2006-12-06 00:00:00,1412.9\n2006-12-07 00:00:00,1407.29\n2006-12-08 00:00:00,1409.84\n2006-12-11 00:00:00,1413.04\n2006-12-12 00:00:00,1411.56\n2006-12-13 00:00:00,1413.21\n2006-12-14 00:00:00,1425.49\n2006-12-15 00:00:00,1427.09\n2006-12-18 00:00:00,1422.48\n2006-12-19 00:00:00,1425.55\n2006-12-20 00:00:00,1423.53\n2006-12-21 00:00:00,1418.3\n2006-12-22 00:00:00,1410.76\n2006-12-26 00:00:00,1416.9\n2006-12-27 00:00:00,1426.84\n2006-12-28 00:00:00,1424.73\n2006-12-29 00:00:00,1418.3\n2007-01-03 00:00:00,1416.6\n2007-01-04 00:00:00,1418.34\n2007-01-05 00:00:00,1409.71\n2007-01-08 00:00:00,1412.84\n2007-01-09 00:00:00,1412.11\n2007-01-10 00:00:00,1414.85\n2007-01-11 00:00:00,1423.82\n2007-01-12 00:00:00,1430.73\n2007-01-16 00:00:00,1431.9\n2007-01-17 00:00:00,1430.62\n2007-01-18 00:00:00,1426.37\n2007-01-19 00:00:00,1430.5\n2007-01-22 00:00:00,1422.95\n2007-01-23 00:00:00,1427.99\n2007-01-24 00:00:00,1440.13\n2007-01-25 00:00:00,1423.9\n2007-01-26 00:00:00,1422.18\n2007-01-29 00:00:00,1420.62\n2007-01-30 00:00:00,1428.82\n2007-01-31 00:00:00,1438.24\n2007-02-01 00:00:00,1445.94\n2007-02-02 00:00:00,1448.39\n2007-02-05 00:00:00,1446.99\n2007-02-06 00:00:00,1448.0\n2007-02-07 00:00:00,1450.02\n2007-02-08 00:00:00,1448.31\n2007-02-09 00:00:00,1438.06\n2007-02-12 00:00:00,1433.37\n2007-02-13 00:00:00,1444.26\n2007-02-14 00:00:00,1455.3\n2007-02-15 00:00:00,1456.81\n2007-02-16 00:00:00,1455.54\n2007-02-20 00:00:00,1459.68\n2007-02-21 00:00:00,1457.63\n2007-02-22 00:00:00,1456.38\n2007-02-23 00:00:00,1451.19\n2007-02-26 00:00:00,1449.37\n2007-02-27 00:00:00,1399.04\n2007-02-28 00:00:00,1406.82\n2007-03-01 00:00:00,1403.17\n2007-03-02 00:00:00,1387.17\n2007-03-05 00:00:00,1374.12\n2007-03-06 00:00:00,1395.41\n2007-03-07 00:00:00,1391.97\n2007-03-08 00:00:00,1401.89\n2007-03-09 00:00:00,1402.84\n2007-03-12 00:00:00,1406.6\n2007-03-13 00:00:00,1377.95\n2007-03-14 00:00:00,1387.17\n2007-03-15 00:00:00,1392.28\n2007-03-16 00:00:00,1386.95\n2007-03-19 00:00:00,1402.06\n2007-03-20 00:00:00,1410.94\n2007-03-21 00:00:00,1435.04\n2007-03-22 00:00:00,1434.54\n2007-03-23 00:00:00,1436.11\n2007-03-26 00:00:00,1437.5\n2007-03-27 00:00:00,1428.61\n2007-03-28 00:00:00,1417.23\n2007-03-29 00:00:00,1422.53\n2007-03-30 00:00:00,1420.86\n2007-04-02 00:00:00,1424.55\n2007-04-03 00:00:00,1437.77\n2007-04-04 00:00:00,1439.37\n2007-04-05 00:00:00,1443.76\n2007-04-09 00:00:00,1444.61\n2007-04-10 00:00:00,1448.39\n2007-04-11 00:00:00,1438.87\n2007-04-12 00:00:00,1447.8\n2007-04-13 00:00:00,1452.85\n2007-04-16 00:00:00,1468.33\n2007-04-17 00:00:00,1471.48\n2007-04-18 00:00:00,1472.5\n2007-04-19 00:00:00,1470.73\n2007-04-20 00:00:00,1484.35\n2007-04-23 00:00:00,1480.93\n2007-04-24 00:00:00,1480.41\n2007-04-25 00:00:00,1495.42\n2007-04-26 00:00:00,1494.25\n2007-04-27 00:00:00,1494.07\n2007-04-30 00:00:00,1482.37\n2007-05-01 00:00:00,1486.3\n2007-05-02 00:00:00,1495.92\n2007-05-03 00:00:00,1502.39\n2007-05-04 00:00:00,1505.62\n2007-05-07 00:00:00,1509.48\n2007-05-08 00:00:00,1507.72\n2007-05-09 00:00:00,1512.58\n2007-05-10 00:00:00,1491.47\n2007-05-11 00:00:00,1505.85\n2007-05-14 00:00:00,1503.15\n2007-05-15 00:00:00,1501.19\n2007-05-16 00:00:00,1514.14\n2007-05-17 00:00:00,1512.75\n2007-05-18 00:00:00,1522.75\n2007-05-21 00:00:00,1525.1\n2007-05-22 00:00:00,1524.12\n2007-05-23 00:00:00,1522.28\n2007-05-24 00:00:00,1507.51\n2007-05-25 00:00:00,1515.73\n2007-05-29 00:00:00,1518.11\n2007-05-30 00:00:00,1530.23\n2007-05-31 00:00:00,1530.62\n2007-06-01 00:00:00,1536.34\n2007-06-04 00:00:00,1539.18\n2007-06-05 00:00:00,1530.95\n2007-06-06 00:00:00,1517.38\n2007-06-07 00:00:00,1490.72\n2007-06-08 00:00:00,1507.67\n2007-06-11 00:00:00,1509.12\n2007-06-12 00:00:00,1493.0\n2007-06-13 00:00:00,1515.67\n2007-06-14 00:00:00,1522.97\n2007-06-15 00:00:00,1532.91\n2007-06-18 00:00:00,1531.05\n2007-06-19 00:00:00,1533.7\n2007-06-20 00:00:00,1512.84\n2007-06-21 00:00:00,1522.19\n2007-06-22 00:00:00,1502.56\n2007-06-25 00:00:00,1497.74\n2007-06-26 00:00:00,1492.89\n2007-06-27 00:00:00,1506.34\n2007-06-28 00:00:00,1505.71\n2007-06-29 00:00:00,1503.35\n2007-07-02 00:00:00,1519.43\n2007-07-03 00:00:00,1524.87\n2007-07-05 00:00:00,1525.4\n2007-07-06 00:00:00,1530.44\n2007-07-09 00:00:00,1531.85\n2007-07-10 00:00:00,1510.12\n2007-07-11 00:00:00,1518.76\n2007-07-12 00:00:00,1547.7\n2007-07-13 00:00:00,1552.5\n2007-07-16 00:00:00,1549.52\n2007-07-17 00:00:00,1549.37\n2007-07-18 00:00:00,1546.17\n2007-07-19 00:00:00,1553.08\n2007-07-20 00:00:00,1534.1\n2007-07-23 00:00:00,1541.57\n2007-07-24 00:00:00,1511.04\n2007-07-25 00:00:00,1518.09\n2007-07-26 00:00:00,1482.66\n2007-07-27 00:00:00,1458.95\n2007-07-30 00:00:00,1473.91\n2007-07-31 00:00:00,1455.27\n2007-08-01 00:00:00,1465.81\n2007-08-02 00:00:00,1472.2\n2007-08-03 00:00:00,1433.06\n2007-08-06 00:00:00,1467.67\n2007-08-07 00:00:00,1476.71\n2007-08-08 00:00:00,1497.49\n2007-08-09 00:00:00,1453.09\n2007-08-10 00:00:00,1453.64\n2007-08-13 00:00:00,1452.92\n2007-08-14 00:00:00,1426.54\n2007-08-15 00:00:00,1406.7\n2007-08-16 00:00:00,1411.27\n2007-08-17 00:00:00,1445.94\n2007-08-20 00:00:00,1445.55\n2007-08-21 00:00:00,1447.12\n2007-08-22 00:00:00,1464.07\n2007-08-23 00:00:00,1462.5\n2007-08-24 00:00:00,1479.37\n2007-08-27 00:00:00,1466.79\n2007-08-28 00:00:00,1432.36\n2007-08-29 00:00:00,1463.76\n2007-08-30 00:00:00,1457.64\n2007-08-31 00:00:00,1473.99\n2007-09-04 00:00:00,1489.42\n2007-09-05 00:00:00,1472.29\n2007-09-06 00:00:00,1478.55\n2007-09-07 00:00:00,1453.55\n2007-09-10 00:00:00,1451.7\n2007-09-11 00:00:00,1471.49\n2007-09-12 00:00:00,1471.56\n2007-09-13 00:00:00,1483.95\n2007-09-14 00:00:00,1484.25\n2007-09-17 00:00:00,1476.65\n2007-09-18 00:00:00,1519.78\n2007-09-19 00:00:00,1529.03\n2007-09-20 00:00:00,1518.75\n2007-09-21 00:00:00,1525.75\n2007-09-24 00:00:00,1517.73\n2007-09-25 00:00:00,1517.21\n2007-09-26 00:00:00,1525.42\n2007-09-27 00:00:00,1531.38\n2007-09-28 00:00:00,1526.75\n2007-10-01 00:00:00,1547.04\n2007-10-02 00:00:00,1546.63\n2007-10-03 00:00:00,1539.59\n2007-10-04 00:00:00,1542.84\n2007-10-05 00:00:00,1557.59\n2007-10-08 00:00:00,1552.58\n2007-10-09 00:00:00,1565.15\n2007-10-10 00:00:00,1562.47\n2007-10-11 00:00:00,1554.41\n2007-10-12 00:00:00,1561.8\n2007-10-15 00:00:00,1548.71\n2007-10-16 00:00:00,1538.53\n2007-10-17 00:00:00,1541.24\n2007-10-18 00:00:00,1540.08\n2007-10-19 00:00:00,1500.63\n2007-10-22 00:00:00,1506.33\n2007-10-23 00:00:00,1519.59\n2007-10-24 00:00:00,1515.88\n2007-10-25 00:00:00,1514.4\n2007-10-26 00:00:00,1535.28\n2007-10-29 00:00:00,1540.98\n2007-10-30 00:00:00,1531.02\n2007-10-31 00:00:00,1549.38\n2007-11-01 00:00:00,1508.44\n2007-11-02 00:00:00,1509.65\n2007-11-05 00:00:00,1502.17\n2007-11-06 00:00:00,1520.27\n2007-11-07 00:00:00,1475.62\n2007-11-08 00:00:00,1474.77\n2007-11-09 00:00:00,1453.7\n2007-11-12 00:00:00,1439.18\n2007-11-13 00:00:00,1481.05\n2007-11-14 00:00:00,1470.58\n2007-11-15 00:00:00,1451.15\n2007-11-16 00:00:00,1458.74\n2007-11-19 00:00:00,1433.27\n2007-11-20 00:00:00,1439.7\n2007-11-21 00:00:00,1416.77\n2007-11-23 00:00:00,1440.7\n2007-11-26 00:00:00,1407.22\n2007-11-27 00:00:00,1428.23\n2007-11-28 00:00:00,1469.02\n2007-11-29 00:00:00,1469.72\n2007-11-30 00:00:00,1481.14\n2007-12-03 00:00:00,1472.42\n2007-12-04 00:00:00,1462.79\n2007-12-05 00:00:00,1485.01\n2007-12-06 00:00:00,1507.34\n2007-12-07 00:00:00,1504.66\n2007-12-10 00:00:00,1515.96\n2007-12-11 00:00:00,1477.65\n2007-12-12 00:00:00,1486.59\n2007-12-13 00:00:00,1488.41\n2007-12-14 00:00:00,1467.95\n2007-12-17 00:00:00,1445.9\n2007-12-18 00:00:00,1454.98\n2007-12-19 00:00:00,1453.0\n2007-12-20 00:00:00,1460.12\n2007-12-21 00:00:00,1484.46\n2007-12-24 00:00:00,1496.45\n2007-12-26 00:00:00,1497.66\n2007-12-27 00:00:00,1476.27\n2007-12-28 00:00:00,1478.49\n2007-12-31 00:00:00,1468.36\n2008-01-02 00:00:00,1447.16\n2008-01-03 00:00:00,1447.16\n2008-01-04 00:00:00,1411.63\n2008-01-07 00:00:00,1416.18\n2008-01-08 00:00:00,1390.19\n2008-01-09 00:00:00,1409.13\n2008-01-10 00:00:00,1420.33\n2008-01-11 00:00:00,1401.02\n2008-01-14 00:00:00,1416.25\n2008-01-15 00:00:00,1380.95\n2008-01-16 00:00:00,1373.2\n2008-01-17 00:00:00,1333.25\n2008-01-18 00:00:00,1325.19\n2008-01-22 00:00:00,1310.5\n2008-01-23 00:00:00,1338.6\n2008-01-24 00:00:00,1352.07\n2008-01-25 00:00:00,1330.61\n2008-01-28 00:00:00,1353.96\n2008-01-29 00:00:00,1362.3\n2008-01-30 00:00:00,1355.81\n2008-01-31 00:00:00,1378.55\n2008-02-01 00:00:00,1395.42\n2008-02-04 00:00:00,1380.82\n2008-02-05 00:00:00,1336.64\n2008-02-06 00:00:00,1326.45\n2008-02-07 00:00:00,1336.91\n2008-02-08 00:00:00,1331.29\n2008-02-11 00:00:00,1339.13\n2008-02-12 00:00:00,1348.86\n2008-02-13 00:00:00,1367.21\n2008-02-14 00:00:00,1348.86\n2008-02-15 00:00:00,1349.99\n2008-02-19 00:00:00,1348.78\n2008-02-20 00:00:00,1360.03\n2008-02-21 00:00:00,1342.53\n2008-02-22 00:00:00,1353.11\n2008-02-25 00:00:00,1371.8\n2008-02-26 00:00:00,1381.29\n2008-02-27 00:00:00,1380.02\n2008-02-28 00:00:00,1367.68\n2008-02-29 00:00:00,1330.63\n2008-03-03 00:00:00,1331.34\n2008-03-04 00:00:00,1326.75\n2008-03-05 00:00:00,1333.7\n2008-03-06 00:00:00,1304.34\n2008-03-07 00:00:00,1293.37\n2008-03-10 00:00:00,1273.37\n2008-03-11 00:00:00,1320.65\n2008-03-12 00:00:00,1308.77\n2008-03-13 00:00:00,1315.48\n2008-03-14 00:00:00,1288.14\n2008-03-17 00:00:00,1276.6\n2008-03-18 00:00:00,1330.74\n2008-03-19 00:00:00,1298.42\n2008-03-20 00:00:00,1329.51\n2008-03-24 00:00:00,1349.88\n2008-03-25 00:00:00,1352.99\n2008-03-26 00:00:00,1341.13\n2008-03-27 00:00:00,1325.76\n2008-03-28 00:00:00,1315.22\n2008-03-31 00:00:00,1322.7\n2008-04-01 00:00:00,1370.18\n2008-04-02 00:00:00,1367.53\n2008-04-03 00:00:00,1369.31\n2008-04-04 00:00:00,1370.4\n2008-04-07 00:00:00,1372.54\n2008-04-08 00:00:00,1365.54\n2008-04-09 00:00:00,1354.49\n2008-04-10 00:00:00,1360.55\n2008-04-11 00:00:00,1332.83\n2008-04-14 00:00:00,1328.32\n2008-04-15 00:00:00,1334.43\n2008-04-16 00:00:00,1364.71\n2008-04-17 00:00:00,1365.56\n2008-04-18 00:00:00,1390.33\n2008-04-21 00:00:00,1388.17\n2008-04-22 00:00:00,1375.94\n2008-04-23 00:00:00,1379.93\n2008-04-24 00:00:00,1388.82\n2008-04-25 00:00:00,1397.84\n2008-04-28 00:00:00,1396.37\n2008-04-29 00:00:00,1390.94\n2008-04-30 00:00:00,1385.59\n2008-05-01 00:00:00,1409.34\n2008-05-02 00:00:00,1413.9\n2008-05-05 00:00:00,1407.49\n2008-05-06 00:00:00,1418.26\n2008-05-07 00:00:00,1392.57\n2008-05-08 00:00:00,1397.68\n2008-05-09 00:00:00,1388.28\n2008-05-12 00:00:00,1403.58\n2008-05-13 00:00:00,1403.04\n2008-05-14 00:00:00,1408.66\n2008-05-15 00:00:00,1423.57\n2008-05-16 00:00:00,1425.35\n2008-05-19 00:00:00,1426.63\n2008-05-20 00:00:00,1413.4\n2008-05-21 00:00:00,1390.71\n2008-05-22 00:00:00,1394.35\n2008-05-23 00:00:00,1375.93\n2008-05-27 00:00:00,1385.35\n2008-05-28 00:00:00,1390.84\n2008-05-29 00:00:00,1398.26\n2008-05-30 00:00:00,1400.38\n2008-06-02 00:00:00,1385.67\n2008-06-03 00:00:00,1377.65\n2008-06-04 00:00:00,1377.2\n2008-06-05 00:00:00,1404.05\n2008-06-06 00:00:00,1360.68\n2008-06-09 00:00:00,1361.76\n2008-06-10 00:00:00,1358.44\n2008-06-11 00:00:00,1335.49\n2008-06-12 00:00:00,1339.87\n2008-06-13 00:00:00,1360.03\n2008-06-16 00:00:00,1360.14\n2008-06-17 00:00:00,1350.93\n2008-06-18 00:00:00,1337.81\n2008-06-19 00:00:00,1342.83\n2008-06-20 00:00:00,1317.93\n2008-06-23 00:00:00,1318.0\n2008-06-24 00:00:00,1314.29\n2008-06-25 00:00:00,1321.97\n2008-06-26 00:00:00,1283.15\n2008-06-27 00:00:00,1278.38\n2008-06-30 00:00:00,1280.0\n2008-07-01 00:00:00,1284.91\n2008-07-02 00:00:00,1261.52\n2008-07-03 00:00:00,1262.9\n2008-07-07 00:00:00,1252.31\n2008-07-08 00:00:00,1273.7\n2008-07-09 00:00:00,1244.69\n2008-07-10 00:00:00,1253.39\n2008-07-11 00:00:00,1239.49\n2008-07-14 00:00:00,1228.3\n2008-07-15 00:00:00,1214.91\n2008-07-16 00:00:00,1245.36\n2008-07-17 00:00:00,1260.32\n2008-07-18 00:00:00,1260.68\n2008-07-21 00:00:00,1260.0\n2008-07-22 00:00:00,1277.0\n2008-07-23 00:00:00,1282.19\n2008-07-24 00:00:00,1252.54\n2008-07-25 00:00:00,1257.76\n2008-07-28 00:00:00,1234.37\n2008-07-29 00:00:00,1263.2\n2008-07-30 00:00:00,1284.26\n2008-07-31 00:00:00,1267.38\n2008-08-01 00:00:00,1260.31\n2008-08-04 00:00:00,1249.01\n2008-08-05 00:00:00,1284.88\n2008-08-06 00:00:00,1289.19\n2008-08-07 00:00:00,1266.07\n2008-08-08 00:00:00,1296.32\n2008-08-11 00:00:00,1305.32\n2008-08-12 00:00:00,1289.59\n2008-08-13 00:00:00,1285.83\n2008-08-14 00:00:00,1292.93\n2008-08-15 00:00:00,1298.2\n2008-08-18 00:00:00,1278.6\n2008-08-19 00:00:00,1266.69\n2008-08-20 00:00:00,1274.54\n2008-08-21 00:00:00,1277.72\n2008-08-22 00:00:00,1292.2\n2008-08-25 00:00:00,1266.84\n2008-08-26 00:00:00,1271.51\n2008-08-27 00:00:00,1281.66\n2008-08-28 00:00:00,1300.68\n2008-08-29 00:00:00,1282.83\n2008-09-02 00:00:00,1277.58\n2008-09-03 00:00:00,1274.98\n2008-09-04 00:00:00,1236.83\n2008-09-05 00:00:00,1242.31\n2008-09-08 00:00:00,1267.79\n2008-09-09 00:00:00,1224.51\n2008-09-10 00:00:00,1232.04\n2008-09-11 00:00:00,1249.05\n2008-09-12 00:00:00,1251.7\n2008-09-15 00:00:00,1192.7\n2008-09-16 00:00:00,1213.6\n2008-09-17 00:00:00,1156.39\n2008-09-18 00:00:00,1206.51\n2008-09-19 00:00:00,1255.08\n2008-09-22 00:00:00,1207.09\n2008-09-23 00:00:00,1188.22\n2008-09-24 00:00:00,1185.87\n2008-09-25 00:00:00,1209.18\n2008-09-26 00:00:00,1213.27\n2008-09-29 00:00:00,1106.42\n2008-09-30 00:00:00,1166.36\n2008-10-01 00:00:00,1161.06\n2008-10-02 00:00:00,1114.28\n2008-10-03 00:00:00,1099.23\n2008-10-06 00:00:00,1056.89\n2008-10-07 00:00:00,996.23\n2008-10-08 00:00:00,984.94\n2008-10-09 00:00:00,909.92\n2008-10-10 00:00:00,899.22\n2008-10-13 00:00:00,1003.35\n2008-10-14 00:00:00,998.01\n2008-10-15 00:00:00,907.84\n2008-10-16 00:00:00,946.43\n2008-10-17 00:00:00,940.55\n2008-10-20 00:00:00,985.4\n2008-10-21 00:00:00,955.05\n2008-10-22 00:00:00,896.78\n2008-10-23 00:00:00,908.11\n2008-10-24 00:00:00,876.77\n2008-10-27 00:00:00,848.92\n2008-10-28 00:00:00,940.51\n2008-10-29 00:00:00,930.09\n2008-10-30 00:00:00,954.09\n2008-10-31 00:00:00,968.75\n2008-11-03 00:00:00,966.3\n2008-11-04 00:00:00,1005.75\n2008-11-05 00:00:00,952.77\n2008-11-06 00:00:00,904.88\n2008-11-07 00:00:00,930.99\n2008-11-10 00:00:00,919.21\n2008-11-11 00:00:00,898.95\n2008-11-12 00:00:00,852.3\n2008-11-13 00:00:00,911.29\n2008-11-14 00:00:00,873.29\n2008-11-17 00:00:00,850.75\n2008-11-18 00:00:00,859.12\n2008-11-19 00:00:00,806.58\n2008-11-20 00:00:00,752.44\n2008-11-21 00:00:00,800.03\n2008-11-24 00:00:00,851.81\n2008-11-25 00:00:00,857.39\n2008-11-26 00:00:00,887.68\n2008-11-28 00:00:00,896.24\n2008-12-01 00:00:00,816.21\n2008-12-02 00:00:00,848.81\n2008-12-03 00:00:00,870.74\n2008-12-04 00:00:00,845.22\n2008-12-05 00:00:00,876.07\n2008-12-08 00:00:00,909.7\n2008-12-09 00:00:00,888.67\n2008-12-10 00:00:00,899.24\n2008-12-11 00:00:00,873.59\n2008-12-12 00:00:00,879.73\n2008-12-15 00:00:00,868.57\n2008-12-16 00:00:00,913.18\n2008-12-17 00:00:00,904.42\n2008-12-18 00:00:00,885.28\n2008-12-19 00:00:00,887.88\n2008-12-22 00:00:00,871.63\n2008-12-23 00:00:00,863.16\n2008-12-24 00:00:00,868.15\n2008-12-26 00:00:00,872.8\n2008-12-29 00:00:00,869.42\n2008-12-30 00:00:00,890.64\n2008-12-31 00:00:00,903.25\n2009-01-02 00:00:00,931.8\n2009-01-05 00:00:00,927.45\n2009-01-06 00:00:00,934.7\n2009-01-07 00:00:00,906.65\n2009-01-08 00:00:00,909.73\n2009-01-09 00:00:00,890.35\n2009-01-12 00:00:00,870.26\n2009-01-13 00:00:00,871.79\n2009-01-14 00:00:00,842.62\n2009-01-15 00:00:00,843.74\n2009-01-16 00:00:00,850.12\n2009-01-20 00:00:00,805.22\n2009-01-21 00:00:00,840.24\n2009-01-22 00:00:00,827.5\n2009-01-23 00:00:00,831.95\n2009-01-26 00:00:00,836.57\n2009-01-27 00:00:00,845.71\n2009-01-28 00:00:00,874.09\n2009-01-29 00:00:00,845.14\n2009-01-30 00:00:00,825.88\n2009-02-02 00:00:00,825.44\n2009-02-03 00:00:00,838.51\n2009-02-04 00:00:00,832.23\n2009-02-05 00:00:00,845.85\n2009-02-06 00:00:00,868.6\n2009-02-09 00:00:00,869.89\n2009-02-10 00:00:00,827.16\n2009-02-11 00:00:00,833.74\n2009-02-12 00:00:00,835.19\n2009-02-13 00:00:00,826.84\n2009-02-17 00:00:00,789.17\n2009-02-18 00:00:00,788.42\n2009-02-19 00:00:00,778.94\n2009-02-20 00:00:00,770.05\n2009-02-23 00:00:00,743.33\n2009-02-24 00:00:00,773.14\n2009-02-25 00:00:00,764.9\n2009-02-26 00:00:00,752.83\n2009-02-27 00:00:00,735.09\n2009-03-02 00:00:00,700.82\n2009-03-03 00:00:00,696.33\n2009-03-04 00:00:00,712.87\n2009-03-05 00:00:00,682.55\n2009-03-06 00:00:00,683.38\n2009-03-09 00:00:00,676.53\n2009-03-10 00:00:00,719.6\n2009-03-11 00:00:00,721.36\n2009-03-12 00:00:00,750.74\n2009-03-13 00:00:00,756.55\n2009-03-16 00:00:00,753.89\n2009-03-17 00:00:00,778.12\n2009-03-18 00:00:00,794.35\n2009-03-19 00:00:00,784.04\n2009-03-20 00:00:00,768.54\n2009-03-23 00:00:00,822.92\n2009-03-24 00:00:00,806.12\n2009-03-25 00:00:00,813.88\n2009-03-26 00:00:00,832.86\n2009-03-27 00:00:00,815.94\n2009-03-30 00:00:00,787.53\n2009-03-31 00:00:00,797.87\n2009-04-01 00:00:00,811.08\n2009-04-02 00:00:00,834.38\n2009-04-03 00:00:00,842.5\n2009-04-06 00:00:00,835.48\n2009-04-07 00:00:00,815.55\n2009-04-08 00:00:00,825.16\n2009-04-09 00:00:00,856.56\n2009-04-13 00:00:00,858.73\n2009-04-14 00:00:00,841.5\n2009-04-15 00:00:00,852.06\n2009-04-16 00:00:00,865.3\n2009-04-17 00:00:00,869.6\n2009-04-20 00:00:00,832.39\n2009-04-21 00:00:00,850.08\n2009-04-22 00:00:00,843.55\n2009-04-23 00:00:00,851.92\n2009-04-24 00:00:00,866.23\n2009-04-27 00:00:00,857.51\n2009-04-28 00:00:00,855.16\n2009-04-29 00:00:00,873.64\n2009-04-30 00:00:00,872.81\n2009-05-01 00:00:00,877.52\n2009-05-04 00:00:00,907.24\n2009-05-05 00:00:00,903.8\n2009-05-06 00:00:00,919.53\n2009-05-07 00:00:00,907.39\n2009-05-08 00:00:00,929.23\n2009-05-11 00:00:00,909.24\n2009-05-12 00:00:00,908.35\n2009-05-13 00:00:00,883.92\n2009-05-14 00:00:00,893.07\n2009-05-15 00:00:00,882.88\n2009-05-18 00:00:00,909.71\n2009-05-19 00:00:00,908.13\n2009-05-20 00:00:00,903.47\n2009-05-21 00:00:00,888.33\n2009-05-22 00:00:00,887.0\n2009-05-26 00:00:00,910.33\n2009-05-27 00:00:00,893.06\n2009-05-28 00:00:00,906.83\n2009-05-29 00:00:00,919.14\n2009-06-01 00:00:00,942.87\n2009-06-02 00:00:00,944.74\n2009-06-03 00:00:00,931.76\n2009-06-04 00:00:00,942.46\n2009-06-05 00:00:00,940.09\n2009-06-08 00:00:00,939.14\n2009-06-09 00:00:00,942.43\n2009-06-10 00:00:00,939.15\n2009-06-11 00:00:00,944.89\n2009-06-12 00:00:00,946.21\n2009-06-15 00:00:00,923.72\n2009-06-16 00:00:00,911.97\n2009-06-17 00:00:00,910.71\n2009-06-18 00:00:00,918.37\n2009-06-19 00:00:00,921.23\n2009-06-22 00:00:00,893.04\n2009-06-23 00:00:00,895.1\n2009-06-24 00:00:00,900.94\n2009-06-25 00:00:00,920.26\n2009-06-26 00:00:00,918.9\n2009-06-29 00:00:00,927.23\n2009-06-30 00:00:00,919.32\n2009-07-01 00:00:00,923.33\n2009-07-02 00:00:00,896.42\n2009-07-06 00:00:00,898.72\n2009-07-07 00:00:00,881.03\n2009-07-08 00:00:00,879.56\n2009-07-09 00:00:00,882.68\n2009-07-10 00:00:00,879.13\n2009-07-13 00:00:00,901.05\n2009-07-14 00:00:00,905.84\n2009-07-15 00:00:00,932.68\n2009-07-16 00:00:00,940.74\n2009-07-17 00:00:00,940.38\n2009-07-20 00:00:00,951.13\n2009-07-21 00:00:00,954.58\n2009-07-22 00:00:00,954.07\n2009-07-23 00:00:00,976.29\n2009-07-24 00:00:00,979.26\n2009-07-27 00:00:00,982.18\n2009-07-28 00:00:00,979.62\n2009-07-29 00:00:00,975.15\n2009-07-30 00:00:00,986.75\n2009-07-31 00:00:00,987.48\n2009-08-03 00:00:00,1002.63\n2009-08-04 00:00:00,1005.65\n2009-08-05 00:00:00,1002.72\n2009-08-06 00:00:00,997.08\n2009-08-07 00:00:00,1010.48\n2009-08-10 00:00:00,1007.1\n2009-08-11 00:00:00,994.35\n2009-08-12 00:00:00,1005.81\n2009-08-13 00:00:00,1012.73\n2009-08-14 00:00:00,1004.09\n2009-08-17 00:00:00,979.73\n2009-08-18 00:00:00,989.67\n2009-08-19 00:00:00,996.46\n2009-08-20 00:00:00,1007.37\n2009-08-21 00:00:00,1026.13\n2009-08-24 00:00:00,1025.57\n2009-08-25 00:00:00,1028.0\n2009-08-26 00:00:00,1028.12\n2009-08-27 00:00:00,1030.98\n2009-08-28 00:00:00,1028.93\n2009-08-31 00:00:00,1020.62\n2009-09-01 00:00:00,998.04\n2009-09-02 00:00:00,994.75\n2009-09-03 00:00:00,1003.24\n2009-09-04 00:00:00,1016.4\n2009-09-08 00:00:00,1025.39\n2009-09-09 00:00:00,1033.37\n2009-09-10 00:00:00,1044.14\n2009-09-11 00:00:00,1042.73\n2009-09-14 00:00:00,1049.34\n2009-09-15 00:00:00,1052.63\n2009-09-16 00:00:00,1068.76\n2009-09-17 00:00:00,1065.49\n2009-09-18 00:00:00,1068.3\n2009-09-21 00:00:00,1064.66\n2009-09-22 00:00:00,1071.66\n2009-09-23 00:00:00,1060.87\n2009-09-24 00:00:00,1050.78\n2009-09-25 00:00:00,1044.38\n2009-09-28 00:00:00,1062.98\n2009-09-29 00:00:00,1060.61\n2009-09-30 00:00:00,1057.08\n2009-10-01 00:00:00,1029.85\n2009-10-02 00:00:00,1025.21\n2009-10-05 00:00:00,1040.46\n2009-10-06 00:00:00,1054.72\n2009-10-07 00:00:00,1057.58\n2009-10-08 00:00:00,1065.48\n2009-10-09 00:00:00,1071.49\n2009-10-12 00:00:00,1076.19\n2009-10-13 00:00:00,1073.19\n2009-10-14 00:00:00,1092.02\n2009-10-15 00:00:00,1096.56\n2009-10-16 00:00:00,1087.68\n2009-10-19 00:00:00,1097.91\n2009-10-20 00:00:00,1091.06\n2009-10-21 00:00:00,1081.4\n2009-10-22 00:00:00,1092.91\n2009-10-23 00:00:00,1079.6\n2009-10-26 00:00:00,1066.95\n2009-10-27 00:00:00,1063.41\n2009-10-28 00:00:00,1042.63\n2009-10-29 00:00:00,1066.11\n2009-10-30 00:00:00,1036.19\n2009-11-02 00:00:00,1042.88\n2009-11-03 00:00:00,1045.41\n2009-11-04 00:00:00,1046.5\n2009-11-05 00:00:00,1066.63\n2009-11-06 00:00:00,1069.3\n2009-11-09 00:00:00,1093.08\n2009-11-10 00:00:00,1093.01\n2009-11-11 00:00:00,1098.51\n2009-11-12 00:00:00,1087.24\n2009-11-13 00:00:00,1093.48\n2009-11-16 00:00:00,1109.3\n2009-11-17 00:00:00,1110.32\n2009-11-18 00:00:00,1109.8\n2009-11-19 00:00:00,1094.9\n2009-11-20 00:00:00,1091.38\n2009-11-23 00:00:00,1106.24\n2009-11-24 00:00:00,1105.65\n2009-11-25 00:00:00,1110.63\n2009-11-27 00:00:00,1091.49\n2009-11-30 00:00:00,1095.63\n2009-12-01 00:00:00,1108.86\n2009-12-02 00:00:00,1109.24\n2009-12-03 00:00:00,1099.92\n2009-12-04 00:00:00,1105.98\n2009-12-07 00:00:00,1103.25\n2009-12-08 00:00:00,1091.94\n2009-12-09 00:00:00,1095.95\n2009-12-10 00:00:00,1102.35\n2009-12-11 00:00:00,1106.41\n2009-12-14 00:00:00,1114.11\n2009-12-15 00:00:00,1107.93\n2009-12-16 00:00:00,1109.18\n2009-12-17 00:00:00,1096.08\n2009-12-18 00:00:00,1102.47\n2009-12-21 00:00:00,1114.05\n2009-12-22 00:00:00,1118.02\n2009-12-23 00:00:00,1120.59\n2009-12-24 00:00:00,1126.48\n2009-12-28 00:00:00,1127.78\n2009-12-29 00:00:00,1126.2\n2009-12-30 00:00:00,1126.42\n2009-12-31 00:00:00,1115.1\n2010-01-04 00:00:00,1132.99\n2010-01-05 00:00:00,1136.52\n2010-01-06 00:00:00,1137.14\n2010-01-07 00:00:00,1141.69\n2010-01-08 00:00:00,1144.98\n2010-01-11 00:00:00,1146.98\n2010-01-12 00:00:00,1136.22\n2010-01-13 00:00:00,1145.68\n2010-01-14 00:00:00,1148.46\n2010-01-15 00:00:00,1136.03\n2010-01-19 00:00:00,1150.23\n2010-01-20 00:00:00,1138.04\n2010-01-21 00:00:00,1116.48\n2010-01-22 00:00:00,1091.76\n2010-01-25 00:00:00,1096.78\n2010-01-26 00:00:00,1092.17\n2010-01-27 00:00:00,1097.5\n2010-01-28 00:00:00,1084.53\n2010-01-29 00:00:00,1073.87\n2010-02-01 00:00:00,1089.19\n2010-02-02 00:00:00,1103.32\n2010-02-03 00:00:00,1097.28\n2010-02-04 00:00:00,1063.11\n2010-02-05 00:00:00,1066.19\n2010-02-08 00:00:00,1056.74\n2010-02-09 00:00:00,1070.52\n2010-02-10 00:00:00,1068.13\n2010-02-11 00:00:00,1078.47\n2010-02-12 00:00:00,1075.51\n2010-02-16 00:00:00,1094.87\n2010-02-17 00:00:00,1099.51\n2010-02-18 00:00:00,1106.75\n2010-02-19 00:00:00,1109.17\n2010-02-22 00:00:00,1108.01\n2010-02-23 00:00:00,1094.6\n2010-02-24 00:00:00,1105.24\n2010-02-25 00:00:00,1102.94\n2010-02-26 00:00:00,1104.49\n2010-03-01 00:00:00,1115.71\n2010-03-02 00:00:00,1118.31\n2010-03-03 00:00:00,1118.79\n2010-03-04 00:00:00,1122.97\n2010-03-05 00:00:00,1138.7\n2010-03-08 00:00:00,1138.5\n2010-03-09 00:00:00,1140.45\n2010-03-10 00:00:00,1145.61\n2010-03-11 00:00:00,1150.24\n2010-03-12 00:00:00,1149.99\n2010-03-15 00:00:00,1150.51\n2010-03-16 00:00:00,1159.46\n2010-03-17 00:00:00,1166.21\n2010-03-18 00:00:00,1165.83\n2010-03-19 00:00:00,1159.9\n2010-03-22 00:00:00,1165.81\n2010-03-23 00:00:00,1174.17\n2010-03-24 00:00:00,1167.72\n2010-03-25 00:00:00,1165.73\n2010-03-26 00:00:00,1166.59\n2010-03-29 00:00:00,1173.22\n2010-03-30 00:00:00,1173.27\n2010-03-31 00:00:00,1169.43\n2010-04-01 00:00:00,1178.1\n2010-04-05 00:00:00,1187.44\n2010-04-06 00:00:00,1189.44\n2010-04-07 00:00:00,1182.45\n2010-04-08 00:00:00,1186.44\n2010-04-09 00:00:00,1194.37\n2010-04-12 00:00:00,1196.48\n2010-04-13 00:00:00,1197.3\n2010-04-14 00:00:00,1210.65\n2010-04-15 00:00:00,1211.67\n2010-04-16 00:00:00,1192.13\n2010-04-19 00:00:00,1197.52\n2010-04-20 00:00:00,1207.17\n2010-04-21 00:00:00,1205.94\n2010-04-22 00:00:00,1208.67\n2010-04-23 00:00:00,1217.28\n2010-04-26 00:00:00,1212.05\n2010-04-27 00:00:00,1183.71\n2010-04-28 00:00:00,1191.36\n2010-04-29 00:00:00,1206.78\n2010-04-30 00:00:00,1186.69\n2010-05-03 00:00:00,1202.26\n2010-05-04 00:00:00,1173.6\n2010-05-05 00:00:00,1165.87\n2010-05-06 00:00:00,1128.15\n2010-05-07 00:00:00,1110.88\n2010-05-10 00:00:00,1159.73\n2010-05-11 00:00:00,1155.79\n2010-05-12 00:00:00,1171.67\n2010-05-13 00:00:00,1157.44\n2010-05-14 00:00:00,1135.68\n2010-05-17 00:00:00,1136.94\n2010-05-18 00:00:00,1120.8\n2010-05-19 00:00:00,1115.05\n2010-05-20 00:00:00,1071.59\n2010-05-21 00:00:00,1087.69\n2010-05-24 00:00:00,1073.65\n2010-05-25 00:00:00,1074.03\n2010-05-26 00:00:00,1067.95\n2010-05-27 00:00:00,1103.06\n2010-05-28 00:00:00,1089.41\n2010-06-01 00:00:00,1070.71\n2010-06-02 00:00:00,1098.38\n2010-06-03 00:00:00,1102.83\n2010-06-04 00:00:00,1064.88\n2010-06-07 00:00:00,1050.47\n2010-06-08 00:00:00,1062.0\n2010-06-09 00:00:00,1055.69\n2010-06-10 00:00:00,1086.84\n2010-06-11 00:00:00,1091.6\n2010-06-14 00:00:00,1089.63\n2010-06-15 00:00:00,1115.23\n2010-06-16 00:00:00,1114.61\n2010-06-17 00:00:00,1116.04\n2010-06-18 00:00:00,1117.51\n2010-06-21 00:00:00,1113.2\n2010-06-22 00:00:00,1095.31\n2010-06-23 00:00:00,1092.04\n2010-06-24 00:00:00,1073.69\n2010-06-25 00:00:00,1076.76\n2010-06-28 00:00:00,1074.57\n2010-06-29 00:00:00,1041.24\n2010-06-30 00:00:00,1030.71\n2010-07-01 00:00:00,1027.37\n2010-07-02 00:00:00,1022.58\n2010-07-06 00:00:00,1028.06\n2010-07-07 00:00:00,1060.27\n2010-07-08 00:00:00,1070.25\n2010-07-09 00:00:00,1077.96\n2010-07-12 00:00:00,1078.75\n2010-07-13 00:00:00,1095.34\n2010-07-14 00:00:00,1095.17\n2010-07-15 00:00:00,1096.48\n2010-07-16 00:00:00,1064.88\n2010-07-19 00:00:00,1071.25\n2010-07-20 00:00:00,1083.48\n2010-07-21 00:00:00,1069.59\n2010-07-22 00:00:00,1093.67\n2010-07-23 00:00:00,1102.66\n2010-07-26 00:00:00,1115.01\n2010-07-27 00:00:00,1113.84\n2010-07-28 00:00:00,1106.13\n2010-07-29 00:00:00,1101.53\n2010-07-30 00:00:00,1101.6\n2010-08-02 00:00:00,1125.86\n2010-08-03 00:00:00,1120.46\n2010-08-04 00:00:00,1127.24\n2010-08-05 00:00:00,1125.81\n2010-08-06 00:00:00,1121.64\n2010-08-09 00:00:00,1127.79\n2010-08-10 00:00:00,1121.06\n2010-08-11 00:00:00,1089.47\n2010-08-12 00:00:00,1083.61\n2010-08-13 00:00:00,1079.25\n2010-08-16 00:00:00,1079.38\n2010-08-17 00:00:00,1092.54\n2010-08-18 00:00:00,1094.16\n2010-08-19 00:00:00,1075.63\n2010-08-20 00:00:00,1071.69\n2010-08-23 00:00:00,1067.36\n2010-08-24 00:00:00,1051.87\n2010-08-25 00:00:00,1055.33\n2010-08-26 00:00:00,1047.22\n2010-08-27 00:00:00,1064.59\n2010-08-30 00:00:00,1048.92\n2010-08-31 00:00:00,1049.33\n2010-09-01 00:00:00,1080.29\n2010-09-02 00:00:00,1090.1\n2010-09-03 00:00:00,1104.51\n2010-09-07 00:00:00,1091.84\n2010-09-08 00:00:00,1098.87\n2010-09-09 00:00:00,1104.18\n2010-09-10 00:00:00,1109.55\n2010-09-13 00:00:00,1121.9\n2010-09-14 00:00:00,1121.1\n2010-09-15 00:00:00,1125.07\n2010-09-16 00:00:00,1124.66\n2010-09-17 00:00:00,1125.59\n2010-09-20 00:00:00,1142.71\n2010-09-21 00:00:00,1139.78\n2010-09-22 00:00:00,1134.28\n2010-09-23 00:00:00,1124.83\n2010-09-24 00:00:00,1148.67\n2010-09-27 00:00:00,1142.16\n2010-09-28 00:00:00,1147.7\n2010-09-29 00:00:00,1144.73\n2010-09-30 00:00:00,1141.2\n2010-10-01 00:00:00,1146.24\n2010-10-04 00:00:00,1137.03\n2010-10-05 00:00:00,1160.75\n2010-10-06 00:00:00,1159.97\n2010-10-07 00:00:00,1158.06\n2010-10-08 00:00:00,1165.15\n2010-10-11 00:00:00,1165.32\n2010-10-12 00:00:00,1169.77\n2010-10-13 00:00:00,1178.1\n2010-10-14 00:00:00,1173.81\n2010-10-15 00:00:00,1176.19\n2010-10-18 00:00:00,1184.71\n2010-10-19 00:00:00,1165.9\n2010-10-20 00:00:00,1178.17\n2010-10-21 00:00:00,1180.26\n2010-10-22 00:00:00,1183.08\n2010-10-25 00:00:00,1185.62\n2010-10-26 00:00:00,1185.64\n2010-10-27 00:00:00,1182.45\n2010-10-28 00:00:00,1183.78\n2010-10-29 00:00:00,1183.26\n2010-11-01 00:00:00,1184.38\n2010-11-02 00:00:00,1193.57\n2010-11-03 00:00:00,1197.96\n2010-11-04 00:00:00,1221.06\n2010-11-05 00:00:00,1225.85\n2010-11-08 00:00:00,1223.25\n2010-11-09 00:00:00,1213.4\n2010-11-10 00:00:00,1218.71\n2010-11-11 00:00:00,1213.54\n2010-11-12 00:00:00,1199.21\n2010-11-15 00:00:00,1197.75\n2010-11-16 00:00:00,1178.34\n2010-11-17 00:00:00,1178.59\n2010-11-18 00:00:00,1196.69\n2010-11-19 00:00:00,1199.73\n2010-11-22 00:00:00,1197.84\n2010-11-23 00:00:00,1180.73\n2010-11-24 00:00:00,1198.35\n2010-11-26 00:00:00,1189.4\n2010-11-29 00:00:00,1187.76\n2010-11-30 00:00:00,1180.55\n2010-12-01 00:00:00,1206.07\n2010-12-02 00:00:00,1221.53\n2010-12-03 00:00:00,1224.71\n2010-12-06 00:00:00,1223.12\n2010-12-07 00:00:00,1223.75\n2010-12-08 00:00:00,1228.28\n2010-12-09 00:00:00,1233.0\n2010-12-10 00:00:00,1240.4\n2010-12-13 00:00:00,1240.46\n2010-12-14 00:00:00,1241.59\n2010-12-15 00:00:00,1235.23\n2010-12-16 00:00:00,1242.87\n2010-12-17 00:00:00,1243.91\n2010-12-20 00:00:00,1247.08\n2010-12-21 00:00:00,1254.6\n2010-12-22 00:00:00,1258.84\n2010-12-23 00:00:00,1256.77\n2010-12-27 00:00:00,1257.54\n2010-12-28 00:00:00,1258.51\n2010-12-29 00:00:00,1259.78\n2010-12-30 00:00:00,1257.88\n2010-12-31 00:00:00,1257.64\n2011-01-03 00:00:00,1271.87\n2011-01-04 00:00:00,1270.2\n2011-01-05 00:00:00,1276.56\n2011-01-06 00:00:00,1273.85\n2011-01-07 00:00:00,1271.5\n2011-01-10 00:00:00,1269.75\n2011-01-11 00:00:00,1274.48\n2011-01-12 00:00:00,1285.96\n2011-01-13 00:00:00,1283.76\n2011-01-14 00:00:00,1293.24\n2011-01-18 00:00:00,1295.02\n2011-01-19 00:00:00,1281.92\n2011-01-20 00:00:00,1280.26\n2011-01-21 00:00:00,1283.35\n2011-01-24 00:00:00,1290.84\n2011-01-25 00:00:00,1291.18\n2011-01-26 00:00:00,1296.63\n2011-01-27 00:00:00,1299.54\n2011-01-28 00:00:00,1276.34\n2011-01-31 00:00:00,1286.12\n2011-02-01 00:00:00,1307.59\n2011-02-02 00:00:00,1304.03\n2011-02-03 00:00:00,1307.1\n2011-02-04 00:00:00,1310.87\n2011-02-07 00:00:00,1319.05\n2011-02-08 00:00:00,1324.57\n2011-02-09 00:00:00,1320.88\n2011-02-10 00:00:00,1321.87\n2011-02-11 00:00:00,1329.15\n2011-02-14 00:00:00,1332.32\n2011-02-15 00:00:00,1328.01\n2011-02-16 00:00:00,1336.32\n2011-02-17 00:00:00,1340.43\n2011-02-18 00:00:00,1343.01\n2011-02-22 00:00:00,1315.44\n2011-02-23 00:00:00,1307.4\n2011-02-24 00:00:00,1306.1\n2011-02-25 00:00:00,1319.88\n2011-02-28 00:00:00,1327.22\n2011-03-01 00:00:00,1306.33\n2011-03-02 00:00:00,1308.44\n2011-03-03 00:00:00,1330.97\n2011-03-04 00:00:00,1321.15\n2011-03-07 00:00:00,1310.13\n2011-03-08 00:00:00,1321.82\n2011-03-09 00:00:00,1320.02\n2011-03-10 00:00:00,1295.11\n2011-03-11 00:00:00,1304.28\n2011-03-14 00:00:00,1296.39\n2011-03-15 00:00:00,1281.87\n2011-03-16 00:00:00,1256.88\n2011-03-17 00:00:00,1273.72\n2011-03-18 00:00:00,1279.21\n2011-03-21 00:00:00,1298.38\n2011-03-22 00:00:00,1293.77\n2011-03-23 00:00:00,1297.54\n2011-03-24 00:00:00,1309.66\n2011-03-25 00:00:00,1313.8\n2011-03-28 00:00:00,1310.19\n2011-03-29 00:00:00,1319.44\n2011-03-30 00:00:00,1328.26\n2011-03-31 00:00:00,1325.83\n2011-04-01 00:00:00,1332.41\n2011-04-04 00:00:00,1332.87\n2011-04-05 00:00:00,1332.63\n2011-04-06 00:00:00,1335.54\n2011-04-07 00:00:00,1333.51\n2011-04-08 00:00:00,1328.17\n2011-04-11 00:00:00,1324.46\n2011-04-12 00:00:00,1314.16\n2011-04-13 00:00:00,1314.41\n2011-04-14 00:00:00,1314.52\n2011-04-15 00:00:00,1319.68\n2011-04-18 00:00:00,1305.14\n2011-04-19 00:00:00,1312.62\n2011-04-20 00:00:00,1330.36\n2011-04-21 00:00:00,1337.38\n2011-04-25 00:00:00,1335.25\n2011-04-26 00:00:00,1347.24\n2011-04-27 00:00:00,1355.66\n2011-04-28 00:00:00,1360.48\n2011-04-29 00:00:00,1363.61\n2011-05-02 00:00:00,1361.22\n2011-05-03 00:00:00,1356.62\n2011-05-04 00:00:00,1347.32\n2011-05-05 00:00:00,1335.1\n2011-05-06 00:00:00,1340.2\n2011-05-09 00:00:00,1346.29\n2011-05-10 00:00:00,1357.16\n2011-05-11 00:00:00,1342.08\n2011-05-12 00:00:00,1348.65\n2011-05-13 00:00:00,1337.77\n2011-05-16 00:00:00,1329.47\n2011-05-17 00:00:00,1328.98\n2011-05-18 00:00:00,1340.68\n2011-05-19 00:00:00,1343.6\n2011-05-20 00:00:00,1333.27\n2011-05-23 00:00:00,1317.37\n2011-05-24 00:00:00,1316.28\n2011-05-25 00:00:00,1320.47\n2011-05-26 00:00:00,1325.69\n2011-05-27 00:00:00,1331.1\n2011-05-31 00:00:00,1345.2\n2011-06-01 00:00:00,1314.55\n2011-06-02 00:00:00,1312.94\n2011-06-03 00:00:00,1300.16\n2011-06-06 00:00:00,1286.17\n2011-06-07 00:00:00,1284.94\n2011-06-08 00:00:00,1279.56\n2011-06-09 00:00:00,1289.0\n2011-06-10 00:00:00,1270.98\n2011-06-13 00:00:00,1271.83\n2011-06-14 00:00:00,1287.87\n2011-06-15 00:00:00,1265.42\n2011-06-16 00:00:00,1267.64\n2011-06-17 00:00:00,1271.5\n2011-06-20 00:00:00,1278.36\n2011-06-21 00:00:00,1295.52\n2011-06-22 00:00:00,1287.14\n2011-06-23 00:00:00,1283.5\n2011-06-24 00:00:00,1268.45\n2011-06-27 00:00:00,1280.1\n2011-06-28 00:00:00,1296.67\n2011-06-29 00:00:00,1307.41\n2011-06-30 00:00:00,1320.64\n2011-07-01 00:00:00,1339.67\n2011-07-05 00:00:00,1337.88\n2011-07-06 00:00:00,1339.22\n2011-07-07 00:00:00,1353.22\n2011-07-08 00:00:00,1343.8\n2011-07-11 00:00:00,1319.49\n2011-07-12 00:00:00,1313.64\n2011-07-13 00:00:00,1317.72\n2011-07-14 00:00:00,1308.87\n2011-07-15 00:00:00,1316.14\n2011-07-18 00:00:00,1305.44\n2011-07-19 00:00:00,1326.73\n2011-07-20 00:00:00,1325.84\n2011-07-21 00:00:00,1343.8\n2011-07-22 00:00:00,1345.02\n2011-07-25 00:00:00,1337.43\n2011-07-26 00:00:00,1331.94\n2011-07-27 00:00:00,1304.89\n2011-07-28 00:00:00,1300.67\n2011-07-29 00:00:00,1292.28\n2011-08-01 00:00:00,1286.94\n2011-08-02 00:00:00,1254.05\n2011-08-03 00:00:00,1260.34\n2011-08-04 00:00:00,1200.07\n2011-08-05 00:00:00,1199.38\n2011-08-08 00:00:00,1119.46\n2011-08-09 00:00:00,1172.53\n2011-08-10 00:00:00,1120.76\n2011-08-11 00:00:00,1172.64\n2011-08-12 00:00:00,1178.81\n2011-08-15 00:00:00,1204.49\n2011-08-16 00:00:00,1192.76\n2011-08-17 00:00:00,1193.89\n2011-08-18 00:00:00,1140.65\n2011-08-19 00:00:00,1123.53\n2011-08-22 00:00:00,1123.82\n2011-08-23 00:00:00,1162.35\n2011-08-24 00:00:00,1177.6\n2011-08-25 00:00:00,1159.27\n2011-08-26 00:00:00,1176.8\n2011-08-29 00:00:00,1210.08\n2011-08-30 00:00:00,1212.92\n2011-08-31 00:00:00,1218.89\n2011-09-01 00:00:00,1204.42\n2011-09-02 00:00:00,1173.97\n2011-09-06 00:00:00,1165.24\n2011-09-07 00:00:00,1198.62\n2011-09-08 00:00:00,1185.9\n2011-09-09 00:00:00,1154.23\n2011-09-12 00:00:00,1162.27\n2011-09-13 00:00:00,1172.87\n2011-09-14 00:00:00,1188.68\n2011-09-15 00:00:00,1209.11\n2011-09-16 00:00:00,1216.01\n2011-09-19 00:00:00,1204.09\n2011-09-20 00:00:00,1202.09\n2011-09-21 00:00:00,1166.76\n2011-09-22 00:00:00,1129.56\n2011-09-23 00:00:00,1136.43\n2011-09-26 00:00:00,1162.95\n2011-09-27 00:00:00,1175.38\n2011-09-28 00:00:00,1151.06\n2011-09-29 00:00:00,1160.4\n2011-09-30 00:00:00,1131.42\n2011-10-03 00:00:00,1099.23\n2011-10-04 00:00:00,1123.95\n2011-10-05 00:00:00,1144.03\n2011-10-06 00:00:00,1164.97\n2011-10-07 00:00:00,1155.46\n2011-10-10 00:00:00,1194.89\n2011-10-11 00:00:00,1195.54\n2011-10-12 00:00:00,1207.25\n2011-10-13 00:00:00,1203.66\n2011-10-14 00:00:00,1224.58\n"
  },
  {
    "path": "5.data-visualization/1.matplotlib/matplotlib-beginner.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# matplotlib学习之基本使用\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 目录\\n\",\n    \"  ### 1.figure学习\\n\",\n    \"  ### 2.设置坐标轴\\n\",\n    \"  ### 3.Legend 图例\\n\",\n    \"  ### 4.Annotation 标注\\n\",\n    \"  ### 5.tick能见度\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.figure学习\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import  matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"#导入包\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x=np.linspace(-3,3,50)#产生-3到3之间50个点\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y1=2*x+1#定义函数\\n\",\n    \"y2=x**2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x217d02584a8>]\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x217cff762e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 绘制直线\\n\",\n    \"plt.figure()\\n\",\n    \"plt.plot(x,y1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"曲线与直线绘制一块\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x217cff89898>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# num=3表示图片上方标题 变为figure3，figsize=(长，宽)设置figure大小\\n\",\n    \"plt.figure(num=3,figsize=(8,5))\\n\",\n    \"plt.plot(x,y2)\\n\",\n    \"# 红色虚线直线宽度默认1.0\\n\",\n    \"plt.plot(x,y1,color='red',linewidth=1.0,linestyle='--')\\n\",\n    \"plt.show()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.设置坐标轴\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-1.   -0.25  0.5   1.25  2.  ]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x217d019dba8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# num=3表示图片上方标题 变为figure3，figsize=(长，宽)设置figure大小\\n\",\n    \"plt.figure(num=3,figsize=(8,5))\\n\",\n    \"plt.plot(x,y2)\\n\",\n    \"# 红色虚线直线宽度默认1.0\\n\",\n    \"plt.plot(x,y1,color='red',linewidth=1.0,linestyle='--')\\n\",\n    \"\\n\",\n    \"plt.xlim((-1,2))#设置x轴范围\\n\",\n    \"plt.ylim((-2,3))#设置轴y范围\\n\",\n    \"\\n\",\n    \"#设置坐标轴含义， 注：英文直接写，中文需要后面加上fontproperties属性\\n\",\n    \"plt.xlabel(u'价格',fontproperties='SimHei')\\n\",\n    \"plt.ylabel(u'利润',fontproperties='SimHei')\\n\",\n    \"\\n\",\n    \"# 设置x轴刻度\\n\",\n    \"# -1到2区间，5个点，4个区间，平均分：[-1.,-0.25,0.5,1.25,2.]\\n\",\n    \"new_ticks=np.linspace(-1,2,5)\\n\",\n    \"print(new_ticks)\\n\",\n    \"plt.xticks(new_ticks)\\n\",\n    \"\\n\",\n    \"# 设置y轴刻度\\n\",\n    \"'''\\n\",\n    \"设置对应坐标用汉字或英文表示，后面的属性fontproperties表示中文可见，不乱码，\\n\",\n    \"内部英文$$表示将英文括起来，r表示正则匹配，通过这个方式将其变为好看的字体\\n\",\n    \"如果要显示特殊字符，比如阿尔法，则用转意符\\\\alpha,前面的\\\\ 表示空格转意\\n\",\n    \"'''\\n\",\n    \"plt.yticks([-2,-1.8,-1,1.22,3.],\\n\",\n    \"           ['非常糟糕','糟糕',r'$good\\\\ \\\\alpha$',r'$really\\\\ good$','超级好'],fontproperties='SimHei')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x217d0452128>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# num=3表示图片上方标题 变为figure3，figsize=(长，宽)设置figure大小\\n\",\n    \"plt.figure(num=3,figsize=(8,5))\\n\",\n    \"plt.plot(x,y2)\\n\",\n    \"# 红色虚线直线宽度默认1.0\\n\",\n    \"plt.plot(x,y1,color='red',linewidth=1.0,linestyle='--')\\n\",\n    \"# 设置边框/坐标轴\\n\",\n    \"gca='get current axis/获取当前轴线'\\n\",\n    \"ax=plt.gca()\\n\",\n    \"# spines就是脊梁，即四个边框\\n\",\n    \"# 取消右边与上边轴\\n\",\n    \"ax.spines['right'].set_color('none')\\n\",\n    \"ax.spines['top'].set_color('none')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# matlibplot并没有设置默认的x轴与y轴方向，下面就开始设置默认轴\\n\",\n    \"ax.xaxis.set_ticks_position('bottom')\\n\",\n    \"ax.yaxis.set_ticks_position('left')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# 设置坐标原点\\n\",\n    \"# 实现将(0,-1)设为坐标原点\\n\",\n    \"# 设置y轴上-1为坐标原点的y点,把x轴放置再-1处\\n\",\n    \"ax.spines['bottom'].set_position(('data',-1)) # 也可以是('axes',0.1)后面是百分比，相当于定位到10%处\\n\",\n    \"# 设置x轴上0为坐标原点的x点，将y轴移置0处\\n\",\n    \"ax.spines['left'].set_position(('data',0))\\n\",\n    \"\\n\",\n    \"# 再写一遍以下代码，因为以上使用set_position后，中文会显示不出来\\n\",\n    \"\\n\",\n    \"plt.yticks([-2,-1.8,-1,1.22,3.],\\n\",\n    \"           ['非常糟糕','糟糕',r'$good\\\\ \\\\alpha$',r'$really\\\\ good$','超级好'],fontproperties='SimHei')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.Legend 图例\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x217d04ae748>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x217d04ae0b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import  matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"x=np.linspace(-3,3,50)\\n\",\n    \"y1=2*x+1\\n\",\n    \"y2=x**2\\n\",\n    \"# 绘制直线\\n\",\n    \"plt.figure()\\n\",\n    \"# plt.plot(x,y1)\\n\",\n    \"# 曲线与直线绘制一块\\n\",\n    \"# num=3表示图片上方标题 变为figure3，figsize=(长，宽)设置figure大小\\n\",\n    \"plt.figure(num=3,figsize=(8,5))\\n\",\n    \"\\n\",\n    \"# 设置x轴范围\\n\",\n    \"plt.xlim((-1,2))\\n\",\n    \"# 设置轴y范围\\n\",\n    \"plt.ylim((-2,3))\\n\",\n    \"# 设置坐标轴含义\\n\",\n    \"# 注：英文直接写，中文需要后面加上fontproperties属性\\n\",\n    \"plt.xlabel(u'价格',fontproperties='SimHei')\\n\",\n    \"plt.ylabel(u'利润',fontproperties='SimHei')\\n\",\n    \"\\n\",\n    \"# -1到2区间，5个点，4个区间，平均分：[-1.,-0.25,0.5,1.25,2.]\\n\",\n    \"new_ticks=np.linspace(-1,2,5)\\n\",\n    \"# print(new_ticks)\\n\",\n    \"plt.xticks(new_ticks)\\n\",\n    \"\\n\",\n    \"plt.yticks([-2,-1.8,-1,1.22,3.],\\n\",\n    \"           ['非常糟糕','糟糕',r'$good\\\\ \\\\alpha$',r'$really\\\\ good$','超级好'],fontproperties='SimHei')\\n\",\n    \"\\n\",\n    \"# 设置legend图例\\n\",\n    \"\\n\",\n    \"l1,=plt.plot(x,y2) # 可添加label属性，只不过如果这里添加了，下面legend再添加，下面的就会覆盖此处的！\\n\",\n    \"# 红色虚线直线宽度默认1.0\\n\",\n    \"l2,=plt.plot(x,y1,color='red',linewidth=1.0,linestyle='--')\\n\",\n    \"'''\\n\",\n    \"prop={'family':'SimHei','size':15}显示中文\\n\",\n    \"legend(hadles=[,,],labels=[,,],loc='best/upper right/upper left/.../lower right')\\n\",\n    \"handles就是你给他添加legend的线,如果要用handles，则前面的plt.plot，必须用l1,形式(不要忘记逗号)\\n\",\n    \"此处labels会覆盖上述的plt.plot()的label\\n\",\n    \"loc默认是best,给你放在一个合适的位置上，如果你拉伸弹框，位置会跟着变，自动放置合适位置\\n\",\n    \"'''\\n\",\n    \"plt.legend(handles=[l1,l2],prop={'family':'SimHei','size':15},loc='lower right',labels=['直线','曲线'])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.Annotation 标注\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x217d0a11e80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import  matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"x=np.linspace(-3,3,20)\\n\",\n    \"y=2*x+1\\n\",\n    \"# 绘制直线\\n\",\n    \"plt.figure(num=1,figsize=(8,5),)\\n\",\n    \"plt.plot(x,y)\\n\",\n    \"# gca='get current axis/获取当前轴线'\\n\",\n    \"ax=plt.gca()\\n\",\n    \"# spines就是脊梁，即四个边框\\n\",\n    \"# 取消右边与上边轴\\n\",\n    \"ax.spines['right'].set_color('none')\\n\",\n    \"ax.spines['top'].set_color('none')\\n\",\n    \"ax.xaxis.set_ticks_position('bottom')\\n\",\n    \"ax.spines['bottom'].set_position(('data',-0)) # 也可以是('axes',0.1)后面是百分比，相当于定位到10%处\\n\",\n    \"ax.yaxis.set_ticks_position('left')\\n\",\n    \"ax.spines['left'].set_position(('data',0))\\n\",\n    \"\\n\",\n    \"# 绘制特定散点\\n\",\n    \"x0=1\\n\",\n    \"y0=2*x0+1\\n\",\n    \"# plot散点图，上述plt.plot(x,y)变为plt.scatter(x,y)绘制出来就是散点图\\n\",\n    \"# s代表大小，b代表blue\\n\",\n    \"plt.scatter(x0,y0,s=50,color='b')\\n\",\n    \"# 把两个点放进去plot一下，画出垂直于x轴的一条线，[x0,x0]表示两个点的x,[0,y0]表示两个点的y\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# 绘制(x0,y0)垂直于x轴的线\\n\",\n    \"\\n\",\n    \"# k--表示黑色虚线，k代表黑色，--表示虚线,lw表示线宽\\n\",\n    \"plt.plot([x0,x0],[0,y0],'k--',lw=2.5)\\n\",\n    \"# 添加注释 annotate\\n\",\n    \"\\n\",\n    \"'''\\n\",\n    \"其中参数xycoords='data' 是说基于数据的值来选位置, xytext=(+30, -30) 和 textcoords='offset points'\\n\",\n    \"对于标注位置的描述 和 xy 偏差值, arrowprops是对图中箭头类型的一些设置.\\n\",\n    \"'''\\n\",\n    \"\\n\",\n    \"plt.annotate(r'$2x+1=%s$'%y0,xy=(x0,y0),xycoords='data',xytext=(+30,-30),textcoords='offset points',fontsize=16,arrowprops=dict(arrowstyle='->',connectionstyle='arc3,rad=.2'))\\n\",\n    \"# 添加注释 text\\n\",\n    \"\\n\",\n    \"# 其中-3.7, 3,是选取text的位置, 空格需要用到转字符\\\\ ,fontdict设置文本字体.\\n\",\n    \"plt.text(-3.7,3,r'$This\\\\ is\\\\ the\\\\ some\\\\ text.\\\\mu\\\\ \\\\sigma_i\\\\ \\\\alpha_t$',\\n\",\n    \"         fontdict={'size':'16','color':'red'})\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 5.tick能见度\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x217d05d9630>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"x = np.linspace(-3, 3, 50)\\n\",\n    \"y = 0.1*x\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"# 设置 zorder 给 plot 在 z 轴方向排序\\n\",\n    \"plt.plot(x, y, linewidth=10, zorder=1)\\n\",\n    \"plt.ylim(-2, 2)\\n\",\n    \"ax = plt.gca()\\n\",\n    \"ax.spines['right'].set_color('none')\\n\",\n    \"ax.spines['top'].set_color('none')\\n\",\n    \"ax.spines['top'].set_color('none')\\n\",\n    \"ax.xaxis.set_ticks_position('bottom')\\n\",\n    \"ax.spines['bottom'].set_position(('data', 0))\\n\",\n    \"ax.yaxis.set_ticks_position('left')\\n\",\n    \"ax.spines['left'].set_position(('data', 0))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# 调整坐标\\n\",\n    \"\\n\",\n    \"# 对被遮挡的图像调节相关透明度，本例中设置 x轴 和 y轴 的刻度数字进行透明度设置\\n\",\n    \"for label in ax.get_xticklabels()+ax.get_yticklabels():\\n\",\n    \"    label.set_fontsize(12)\\n\",\n    \"    '''\\n\",\n    \"    其中label.set_fontsize(12)重新调节字体大小，bbox设置目的内容的透明度相关参，\\n\",\n    \"    facecolor调节 box 前景色，edgecolor 设置边框， 本处设置边框为无，alpha设置透明度.\\n\",\n    \"    '''\\n\",\n    \"    # 其中label.set_fontsize(12)重新调节字体大小，bbox设置目的内容的透明度相关参，\\n\",\n    \"#     facecolor调节 box 前景色，edgecolor 设置边框， 本处设置边框为无，alpha设置透明度.\\n\",\n    \"    label.set_bbox(dict(facecolor='white',edgecolor='none',alpha=0.7))\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 重要参考资料-A Brief matplotlib API Primer（一个简单的matplotlib API入门）\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 这个内容来自《利用python进行数据分析第二版》，主要内容：\\n\",\n    \"\\n\",\n    \"### 1 Figures and Subplots（图和子图）\\n\",\n    \"### 2 Colors, Markers, and Line Styles（颜色，标记物，线样式）\\n\",\n    \"### 3 Ticks, Labels, and Legends（标记，标签，图例）\\n\",\n    \"### 4 Annotations and Drawing on a Subplot（注释和在subplot上画图）\\n\",\n    \"### 5 Saving Plots to File（把图保存为文件）\\n\",\n    \"### 6 matplotlib Configuration（matplotlib设置）\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"要用Jupyter notebook进行可交互式的绘图，需要执行下面的语句，这样就可以直接在Notebook里绘图了。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib notebook\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pandas as pd\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data = np.arange(10)#生成数据\\n\",\n    \"data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n  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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x217d26acb38>]\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"seaborn和pandas内建的一些绘图函数能帮我们省去很多画图的繁杂工作，但如果我们想要定制化地做出一些函数无法绘出的图，就需要了解一些matplotlib的API了。\\n\",\n    \"\\n\",\n    \"# 1 Figures and Subplots（图和子图）\\n\",\n    \"\\n\",\n    \"在matplotlib中画的图，都是在Figure对象中的。可以用plt.figure创建一个：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"如果实在ipython里执行，可以看到一个空白的绘图窗口出现，但是在jupyter中没有任何显示，除非我们输入一些命令。plt.figure有一些选择，其中figsize保证figure有固定的大小和长宽比，这样也方便保存到磁盘中。\\n\",\n    \"\\n\",\n    \"我们不能在一个空白的figure上绘图，必须要创建一个或更多的subplots（子图），用add_subplot:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ax1 = fig.add_subplot(2, 2, 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这行代码的意思是，figure是2x2（这样一共有4幅图），而且我们选中4个subplots（数字从1到4）中的第1个。如果要创建另外两个子图，可以输入：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ax2 = fig.add_subplot(2, 2, 2)\\n\",\n    \"ax3 = fig.add_subplot(2, 2, 3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"有一个注意点，在使用Jupyter notebook的时候，绘图可能在一个cell之后被重置，所以对于一些复杂的绘图，必须把绘图命令全部放在一个notebook cell中。\\n\",\n    \"\\n\",\n    \"这里我们在一个cell中执行这些命令：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure() \\n\",\n    \"ax1 = fig.add_subplot(2, 2, 1) \\n\",\n    \"ax2 = fig.add_subplot(2, 2, 2) \\n\",\n    \"ax3 = fig.add_subplot(2, 2, 3)\\n\",\n    \"\\n\",\n    \"# 下面出现交互式界面后，不要关闭，运行之后的命令，可以看到最后一副图中出现了线\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"如果输入`plt.plot([1.5, 3.5, -2, 1.6])`这样的命令，matplotlib会把图画在最后一个figure的最后一个子图上。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x217d332b2e8>]\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(np.random.randn(50).cumsum(), 'k--')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"'k--'是一个style（样式）选项，它表示使用黑色的虚线。在这里，fig_add_subplot返回的是一个AxesSubplot对象，我们可以直接在空白的subplot上绘图，直接在对应的AxesSubplot对象上调用方法即可：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"_ = ax1.hist(np.random.randn(100), bins=20, color='k', alpha=0.3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.collections.PathCollection at 0x217d3918320>\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax2.scatter(np.arange(30), np.arange(30) + 3 * np.random.randn(30))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"因为创建一个带有多个subplot的figure是很常见的操作，所以matplotlib添加了一个方法，plt.subplots，来简化这个过程。这个方法会创建一个新的figure，并返回一个numpy数组，其中包含创建的subplot对象：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"f, axes = plt.subplots(2, 3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000020253447E48>,\\n\",\n       \"        <matplotlib.axes._subplots.AxesSubplot object at 0x000002025349A550>,\\n\",\n       \"        <matplotlib.axes._subplots.AxesSubplot object at 0x00000202534C6470>],\\n\",\n       \"       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000202535084E0>,\\n\",\n       \"        <matplotlib.axes._subplots.AxesSubplot object at 0x0000020253542588>,\\n\",\n       \"        <matplotlib.axes._subplots.AxesSubplot object at 0x000002025357A550>]],\\n\",\n       \"      dtype=object)\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"axes\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这个操作是很有用的。axes能用一个二位数据来索引，例如，axes[0, 1]。我们可以使用sharex和sharey来指定不同subplot有相同的x-或y-axis（其实就是令坐标轴的范围相同），这能让我们在同一范围内进行数据之间的比较。不然的话，matplotlib会自动绘图的范围不一定是一样的。\\n\",\n    \"\\n\",\n    \"### Adjusting the spacing around subplots（调整subplot直接的间隔）\\n\",\n    \"\\n\",\n    \"默认情况下，matplotlib会在subplot之间留下一定间隔的边距，这取决于绘图的高度和跨度。所以如果我们调整绘图的大小，它会自动调整。我们可以用Figure对象下的subplots_adjust方法来更改间隔，当然，也可以用第一层级的函数：\\n\",\n    \"\\n\",\n    \"    subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)\\n\",\n    \"    \\n\",\n    \"wspace和hspace控制figure宽度和长度的百分比，可以用来控制subplot之间的间隔。这里有一个例子，我们让间隔为0：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)\\n\",\n    \"for i in range(2):\\n\",\n    \"    for j in range(2):\\n\",\n    \"        axes[i, j].hist(np.random.randn(500), bins=50, color='k', alpha=0.5)\\n\",\n    \"plt.subplots_adjust(wspace=0, hspace=0)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们注意到轴上有些标签重叠了。matplotlib不会检查标签是否重叠，所以我们需要直接规定明确的tick location（记号位置）和tick labels（记号标签），这部分会在之后介绍。\\n\",\n    \"\\n\",\n    \"# 2 Colors, Markers, and Line Styles（颜色，标记物，线样式）\\n\",\n    \"\\n\",\n    \"matplotlib的plot主函数能接受x和y坐标，在可选项中，字符串能指定颜色和线样式。例如，画出x和y，用绿色的点线：\\n\",\n    \"\\n\",\n    \"`ax.plot(x, y, 'g--')`\\n\",\n    \"\\n\",\n    \"这种方法可以很方便的同时指定颜色和线样式；不过有些用户可能不喜欢直接把规定颜色和样式的字符串写在一起，当然，我们也可以写得更明确一些：\\n\",\n    \"\\n\",\n    \"`ax.plot(x, y, linestyle='--', color='g')`\\n\",\n    \"\\n\",\n    \"有很多可供选择的颜色缩写，当然，我们也可以使用任意的颜色，通过制定hex code(十六进制码，比如'#CECECE')。通过查看plot的字符串文档，我们可以看到可供选择的所有线样式（直接输入`plot?`）。\\n\",\n    \"\\n\",\n    \"另外还可以用markers（标记物）来高亮实际的数据点。因为matplotlib创建一个continuous line plot（连续线条图）的话，如果想要插入，可能看不清楚哪里可以插入数据点。而marker可以作为样式的一部分，字符串必须按颜色，标记物类型，样式这样的顺序：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x217d488d2b0>]\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(np.random.randn(30).cumsum(), 'ko--')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"当然，我们也快成写得更准确一些：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x217d48b4588>]\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(np.random.randn(30).cumsum(), color='k', linestyle='dashed', marker='o')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"对于点线图，我们注意到，默认情况下，后续点是通过线性添加上的。这个可以通过drawstyle来更改：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data = np.random.randn(30).cumsum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x217d48b49b0>]\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(data, 'k--', label='Default') \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x217d48be710>]\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x217d4896240>\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"plt.legend(loc='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"当运行上面命令时，我们注意到，输出有`<matplotlib.legend.Legend at 0x10906fda0>`这样的字样。这是matplotlib返回我们添加的那些子图的索引。大部分时候我们可以无视这种输出。这里，我们把label传递给了plot，这样通过plt.legend显示出每条线的意义。\\n\",\n    \"\\n\",\n    \"注意：我们必须调用plt.legend（或ax.legend，如果有axes的话）来创建一个legend（图例），不论是非传入label。（译者：经测试，如果不调用plt.legend的话，是看不到label的）\\n\",\n    \"\\n\",\n    \"# 3 Ticks, Labels, and Legends（标记，标签，图例）\\n\",\n    \"\\n\",\n    \"对于大部分绘图的装饰，有两种主要的方法：使用pyplot（matplotlib.pyplot）和用更对象导向的简单的matplotlib API。\\n\",\n    \"\\n\",\n    \"pyplot界面是为交互式使用而设计的，它包含很多方法，比如xlim, xticks, xticklabels。这些方法控制绘图的范围，标记位置，标记标签。有两种使用方法：\\n\",\n    \"\\n\",\n    \"- 调用的时候不传入参数，使用当前的参数设置（例如，plt.xlim()返回当前X轴的范围）\\n\",\n    \"- 调用的时候传入参数，使用传入的参数设置（例如，plt.xlim([0, 10]), 令X轴的范围从0到10）\\n\",\n    \"\\n\",\n    \"所有这些方法，作用于激活的或最新创建的AxesSubplot对象上。每一个都在subplot有对应的两个方法；比如对于xlim，就有对应的ax.get_xlim和ax.set_xlim。这里作者使用subplot的方法，这样会更清晰。\\n\",\n    \"\\n\",\n    \"### Setting the title, axis labels, ticks, and ticklabels（设定标题，轴标签，标记，标记标签）\\n\",\n    \"\\n\",\n    \"这里创建一个简单的图，画一个随机漫步：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n  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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure() # 直到下一个黑体标题出现前，不要关闭这个fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ax = fig.add_subplot(1, 1, 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x217d48beb00>]\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.plot(np.random.randn(1000).cumsum())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"为了改变x-axis tick（x轴标记），使用set_xticks和set_xticklabels。前者告诉matplotlib沿着x轴的范围，把标记放在哪里；默认会把所在位置作为标签，但我们可以用set_xticklabels来设置任意值作为标签：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ticks = ax.set_xticks([0, 250, 500, 750, 1000])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'],\\n\",\n    \"                            rotation=30, fontsize='small')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"rotation选项让x轴上的标记标签有一个30度的旋转。set_xlabel给x轴一个名字，而set_title给subplot一个标题：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5,1,'My first matplotlib plot')\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.set_title('My first matplotlib plot')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5,99.92,'Stages')\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.set_xlabel('Stages')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"用相同的流程来更改y轴，把上面代码里的x变为y。axes类有一个set方法，能让我们一次设置很多绘图特性。对于上面的例子，我们可以写成下面这样：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[Text(0.5,99.92,'Stage'), Text(0.5,1,'My first matplotlib plot')]\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"props = {\\n\",\n    \"    'title': 'My first matplotlib plot',\\n\",\n    \"    'xlabel': 'Stage'\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"ax.set(**props)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Adding legends （添加图例）\\n\",\n    \"\\n\",\n    \"图例对于绘图很重要。有很多方式可以添加图例。最简单的方法是用label参数：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from numpy.random import randn\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x20255aad588>]\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.plot(randn(1000).cumsum(), 'k', label='one')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x217d4ead748>]\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.plot(randn(1000).cumsum(), 'k--', label='two')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x217d4ec30f0>]\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.plot(randn(1000).cumsum(), 'k.', label='three')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"做完了上面的部分，调用ax.legend()或plt.legend()，来自动创建图例：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x217d4ecb278>\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.legend(loc='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"legend方法有一些选项，比如用loc参数设定位置。更多信息，可以参考字符串文档（ax.legend?）\\n\",\n    \"\\n\",\n    \"loc告诉matplotlib把图例放在哪里。如果不挑剔的话，直接设定'best'就可以了，它会自动选择一个合适的位置。如果想要从图例中排除一个或更多的元素，那就不要传入label，或设置`label='_nolegen_'`。\\n\",\n    \"\\n\",\n    \"# 4 Annotations and Drawing on a Subplot（注释和在subplot上画图）\\n\",\n    \"\\n\",\n    \"除了标准的绘图类型，我们可能希望画出自己的绘图注释，包括文本，箭头或其他形状。我们可以添加注释和文本，通过text，arrow，和annotate函数。text能在指定的坐标(x, y)上写出文本，还可以自己设定样式：\\n\",\n    \"\\n\",\n    \"`ax.test(x, y, 'Hello world!', family='monospace', fontsize=10)`\\n\",\n    \"\\n\",\n    \"注释可以画出文本和箭头。这里我们做一个例子，画出S&P 500指数自2007年后的价格，并用注释指出在2008~2009年经融危机期间一些重要的日期。\\n\",\n    \"\\n\",\n    \"下面的内容可以在一个cell里直接执行：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5,1,'Important dates in the 2008-2009 financial crisis')\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from datetime import datetime\\n\",\n    \"\\n\",\n    \"fig = plt.figure()\\n\",\n    \"ax = fig.add_subplot(1, 1, 1)\\n\",\n    \"\\n\",\n    \"data = pd.read_csv('examples/spx.csv', index_col=0, parse_dates=True)\\n\",\n    \"spx = data['SPX']\\n\",\n    \"\\n\",\n    \"spx.plot(ax=ax, style='k-')\\n\",\n    \"\\n\",\n    \"crisis_data = [\\n\",\n    \"    (datetime(2007, 10, 11), 'Peak of bull market'),\\n\",\n    \"    (datetime(2008, 3, 12), 'Bear Stearns Fails'),\\n\",\n    \"    (datetime(2008, 9, 15), 'Lehman Bankruptcy')\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"for date, label in crisis_data:\\n\",\n    \"    ax.annotate(label, xy=(date, spx.asof(date) + 75),\\n\",\n    \"                xytext=(date, spx.asof(date) + 225),\\n\",\n    \"                arrowprops=dict(facecolor='black', headwidth=4, \\n\",\n    \"                                width=2, headlength=4),\\n\",\n    \"                horizontalalignment='left', verticalalignment='top')\\n\",\n    \"    \\n\",\n    \"# Zoom in on 2007-2010\\n\",\n    \"ax.set_xlim(['1/1/2007', '1/1/2011'])\\n\",\n    \"ax.set_ylim([600, 1800])\\n\",\n    \"\\n\",\n    \"ax.set_title('Important dates in the 2008-2009 financial crisis')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在这幅图里，有一些点是值得强调的：ax.annotate方法能在x和y坐标指示的位置画出标签。我们可以用set_xlim和set_ylim方法来手动地设置开始和结束的边界，而不是用matplotlib的默认边界。最后，ax.set_title添加一个主标题。\\n\",\n    \"\\n\",\n    \"可以联网看matplotlib gallery上查看更多关于标注的例子。\\n\",\n    \"\\n\",\n    \"要想画出图形的话，更需要细心一些。matplotlib有一些对象可以用来表示一些常见的图形，被称之为patches。其中一些，比如Rectangle和Circle，在matplotlib.pyplot也有，但是全套画图形的方法还是在matplotlib.patches里。\\n\",\n    \"\\n\",\n    \"给图中添加一个图形，必须先添加一个patch对象，shp，然后通过调用ax.add_patch(shp)把它添加到subplot中：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img 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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.patches.Polygon at 0x217d55dfc88>\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"ax = fig.add_subplot(1, 1, 1)\\n\",\n    \"\\n\",\n    \"rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3)\\n\",\n    \"circ = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3)\\n\",\n    \"pgon = plt.Polygon([[0.15, 0.15], [0.35, 0.4], [0.2, 0.6]],\\n\",\n    \"                   color='g', alpha=0.5)\\n\",\n    \"\\n\",\n    \"ax.add_patch(rect)\\n\",\n    \"ax.add_patch(circ)\\n\",\n    \"ax.add_patch(pgon)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 5 Saving Plots to File（把图保存为文件）\\n\",\n    \"\\n\",\n    \"我们可以用plt.savefig来保存图。这个方法等同于直接在figure对象上调用savefig方法。例如，想要保存一个SVG版本的图片，键入：\\n\",\n    \"\\n\",\n    \"    `plt.savefig('figpath.svg)`\\n\",\n    \"\\n\",\n    \"保存的文件类型通过文件名后缀来指定。即如果使用 .pdf做为后缀，就会得到一个PDF文件。这里有一些重要的设置，作者经常用来刊印图片：\\n\",\n    \"\\n\",\n    \"- dpi，控制每英寸长度上的分辨率\\n\",\n    \"- bbox_inches, 能删除figure周围的空白部分\\n\",\n    \"\\n\",\n    \"比如我们想要得到一幅PNG图，有最小的空白，400 DPI，键入：\\n\",\n    \"\\n\",\n    \"    plt.savefig('figpath.png', dpi=400, bbox_inches='tight')\\n\",\n    \"    \\n\",\n    \"savefig不仅可以写入磁盘，还可以导出为任意像是文件一样的对象，比如BytesIO：\\n\",\n    \"\\n\",\n    \"    from io import BytesIO\\n\",\n    \"    buffer = BytesIO()\\n\",\n    \"    plt.savefig(buffer)\\n\",\n    \"    plot_data = buffer.getvalue()\\n\",\n    \"\\n\",\n    \"看下图关于savefig更多的选项：\\n\",\n    \"\\n\",\n    \"![](http://oydgk2hgw.bkt.clouddn.com/pydata-book/xc0am.png)\\n\",\n    \"\\n\",\n    \"# 6 matplotlib Configuration（matplotlib设置）\\n\",\n    \"\\n\",\n    \"matplotlib很多默认的设置是可以自己定义的，通过修改一些全局设定，比如图大小，subplot间隔，颜色，字体大小，网格样式等等。一种更累设定的方式是用rc方法，例如，想要设置全局的图大小为10 x 10，键入：\\n\",\n    \"\\n\",\n    \"    plt.rc('figure', figsize=(10, 10))\\n\",\n    \"    \\n\",\n    \"rc中的第一个参数是我们想要自定义的组件，比如'figure', 'axes', 'xtick', 'ytick', 'grid', 'legend'，或其他。然后添加一个关键字来设定新的参数。一个比较方便的写法是把所有的设定写成一个dict：\\n\",\n    \"\\n\",\n    \"    font_options = {'family': 'monospace',\\n\",\n    \"                    'weight': 'bold',\\n\",\n    \"                    'size'  : 'small'}\\n\",\n    \"    plt.rc('font', **font_options)\\n\",\n    \"    \\n\",\n    \"更详细的设定可以去看一下文档，matplotlib影城而设置文件*matplotlibrc*，位于*matplotlib/mlp-data*文件夹下。如果按自己的方式修改这个文件，并把这个文件放在主目录下，更名为*.matplotlibrc*的话，在每次启动matplotlib的时候，会自动加载这个文件。\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "5.data-visualization/1.matplotlib/matplotlib50/Pokemon.csv",
    "content": "Name,Type 1,Type 2,Total,HP,Attack,Defense,Sp. Atk,Sp. Def,Speed,Generation,Legendary\nBulbasaur,Grass,Poison,318,45,49,49,65,65,45,1,FALSE\nIvysaur,Grass,Poison,405,60,62,63,80,80,60,1,FALSE\nVenusaur,Grass,Poison,525,80,82,83,100,100,80,1,FALSE\nVenusaurMega Venusaur,Grass,Poison,625,80,100,123,122,120,80,1,FALSE\nCharmander,Fire,,309,39,52,43,60,50,65,1,FALSE\nCharmeleon,Fire,,405,58,64,58,80,65,80,1,FALSE\nCharizard,Fire,Flying,534,78,84,78,109,85,100,1,FALSE\nCharizardMega Charizard X,Fire,Dragon,634,78,130,111,130,85,100,1,FALSE\nCharizardMega Charizard Y,Fire,Flying,634,78,104,78,159,115,100,1,FALSE\nSquirtle,Water,,314,44,48,65,50,64,43,1,FALSE\nWartortle,Water,,405,59,63,80,65,80,58,1,FALSE\nBlastoise,Water,,530,79,83,100,85,105,78,1,FALSE\nBlastoiseMega Blastoise,Water,,630,79,103,120,135,115,78,1,FALSE\nCaterpie,Bug,,195,45,30,35,20,20,45,1,FALSE\nMetapod,Bug,,205,50,20,55,25,25,30,1,FALSE\nButterfree,Bug,Flying,395,60,45,50,90,80,70,1,FALSE\nWeedle,Bug,Poison,195,40,35,30,20,20,50,1,FALSE\nKakuna,Bug,Poison,205,45,25,50,25,25,35,1,FALSE\nBeedrill,Bug,Poison,395,65,90,40,45,80,75,1,FALSE\nBeedrillMega Beedrill,Bug,Poison,495,65,150,40,15,80,145,1,FALSE\nPidgey,Normal,Flying,251,40,45,40,35,35,56,1,FALSE\nPidgeotto,Normal,Flying,349,63,60,55,50,50,71,1,FALSE\nPidgeot,Normal,Flying,479,83,80,75,70,70,101,1,FALSE\nPidgeotMega Pidgeot,Normal,Flying,579,83,80,80,135,80,121,1,FALSE\nRattata,Normal,,253,30,56,35,25,35,72,1,FALSE\nRaticate,Normal,,413,55,81,60,50,70,97,1,FALSE\nSpearow,Normal,Flying,262,40,60,30,31,31,70,1,FALSE\nFearow,Normal,Flying,442,65,90,65,61,61,100,1,FALSE\nEkans,Poison,,288,35,60,44,40,54,55,1,FALSE\nArbok,Poison,,438,60,85,69,65,79,80,1,FALSE\nPikachu,Electric,,320,35,55,40,50,50,90,1,FALSE\nRaichu,Electric,,485,60,90,55,90,80,110,1,FALSE\nSandshrew,Ground,,300,50,75,85,20,30,40,1,FALSE\nSandslash,Ground,,450,75,100,110,45,55,65,1,FALSE\nNidoran_,Poison,,275,55,47,52,40,40,41,1,FALSE\nNidorina,Poison,,365,70,62,67,55,55,56,1,FALSE\nNidoqueen,Poison,Ground,505,90,92,87,75,85,76,1,FALSE\nNidoran_,Poison,,273,46,57,40,40,40,50,1,FALSE\nNidorino,Poison,,365,61,72,57,55,55,65,1,FALSE\nNidoking,Poison,Ground,505,81,102,77,85,75,85,1,FALSE\nClefairy,Fairy,,323,70,45,48,60,65,35,1,FALSE\nClefable,Fairy,,483,95,70,73,95,90,60,1,FALSE\nVulpix,Fire,,299,38,41,40,50,65,65,1,FALSE\nNinetales,Fire,,505,73,76,75,81,100,100,1,FALSE\nJigglypuff,Normal,Fairy,270,115,45,20,45,25,20,1,FALSE\nWigglytuff,Normal,Fairy,435,140,70,45,85,50,45,1,FALSE\nZubat,Poison,Flying,245,40,45,35,30,40,55,1,FALSE\nGolbat,Poison,Flying,455,75,80,70,65,75,90,1,FALSE\nOddish,Grass,Poison,320,45,50,55,75,65,30,1,FALSE\nGloom,Grass,Poison,395,60,65,70,85,75,40,1,FALSE\nVileplume,Grass,Poison,490,75,80,85,110,90,50,1,FALSE\nParas,Bug,Grass,285,35,70,55,45,55,25,1,FALSE\nParasect,Bug,Grass,405,60,95,80,60,80,30,1,FALSE\nVenonat,Bug,Poison,305,60,55,50,40,55,45,1,FALSE\nVenomoth,Bug,Poison,450,70,65,60,90,75,90,1,FALSE\nDiglett,Ground,,265,10,55,25,35,45,95,1,FALSE\nDugtrio,Ground,,405,35,80,50,50,70,120,1,FALSE\nMeowth,Normal,,290,40,45,35,40,40,90,1,FALSE\nPersian,Normal,,440,65,70,60,65,65,115,1,FALSE\nPsyduck,Water,,320,50,52,48,65,50,55,1,FALSE\nGolduck,Water,,500,80,82,78,95,80,85,1,FALSE\nMankey,Fighting,,305,40,80,35,35,45,70,1,FALSE\nPrimeape,Fighting,,455,65,105,60,60,70,95,1,FALSE\nGrowlithe,Fire,,350,55,70,45,70,50,60,1,FALSE\nArcanine,Fire,,555,90,110,80,100,80,95,1,FALSE\nPoliwag,Water,,300,40,50,40,40,40,90,1,FALSE\nPoliwhirl,Water,,385,65,65,65,50,50,90,1,FALSE\nPoliwrath,Water,Fighting,510,90,95,95,70,90,70,1,FALSE\nAbra,Psychic,,310,25,20,15,105,55,90,1,FALSE\nKadabra,Psychic,,400,40,35,30,120,70,105,1,FALSE\nAlakazam,Psychic,,500,55,50,45,135,95,120,1,FALSE\nAlakazamMega Alakazam,Psychic,,590,55,50,65,175,95,150,1,FALSE\nMachop,Fighting,,305,70,80,50,35,35,35,1,FALSE\nMachoke,Fighting,,405,80,100,70,50,60,45,1,FALSE\nMachamp,Fighting,,505,90,130,80,65,85,55,1,FALSE\nBellsprout,Grass,Poison,300,50,75,35,70,30,40,1,FALSE\nWeepinbell,Grass,Poison,390,65,90,50,85,45,55,1,FALSE\nVictreebel,Grass,Poison,490,80,105,65,100,70,70,1,FALSE\nTentacool,Water,Poison,335,40,40,35,50,100,70,1,FALSE\nTentacruel,Water,Poison,515,80,70,65,80,120,100,1,FALSE\nGeodude,Rock,Ground,300,40,80,100,30,30,20,1,FALSE\nGraveler,Rock,Ground,390,55,95,115,45,45,35,1,FALSE\nGolem,Rock,Ground,495,80,120,130,55,65,45,1,FALSE\nPonyta,Fire,,410,50,85,55,65,65,90,1,FALSE\nRapidash,Fire,,500,65,100,70,80,80,105,1,FALSE\nSlowpoke,Water,Psychic,315,90,65,65,40,40,15,1,FALSE\nSlowbro,Water,Psychic,490,95,75,110,100,80,30,1,FALSE\nSlowbroMega Slowbro,Water,Psychic,590,95,75,180,130,80,30,1,FALSE\nMagnemite,Electric,Steel,325,25,35,70,95,55,45,1,FALSE\nMagneton,Electric,Steel,465,50,60,95,120,70,70,1,FALSE\nFarfetch'd,Normal,Flying,352,52,65,55,58,62,60,1,FALSE\nDoduo,Normal,Flying,310,35,85,45,35,35,75,1,FALSE\nDodrio,Normal,Flying,460,60,110,70,60,60,100,1,FALSE\nSeel,Water,,325,65,45,55,45,70,45,1,FALSE\nDewgong,Water,Ice,475,90,70,80,70,95,70,1,FALSE\nGrimer,Poison,,325,80,80,50,40,50,25,1,FALSE\nMuk,Poison,,500,105,105,75,65,100,50,1,FALSE\nShellder,Water,,305,30,65,100,45,25,40,1,FALSE\nCloyster,Water,Ice,525,50,95,180,85,45,70,1,FALSE\nGastly,Ghost,Poison,310,30,35,30,100,35,80,1,FALSE\nHaunter,Ghost,Poison,405,45,50,45,115,55,95,1,FALSE\nGengar,Ghost,Poison,500,60,65,60,130,75,110,1,FALSE\nGengarMega Gengar,Ghost,Poison,600,60,65,80,170,95,130,1,FALSE\nOnix,Rock,Ground,385,35,45,160,30,45,70,1,FALSE\nDrowzee,Psychic,,328,60,48,45,43,90,42,1,FALSE\nHypno,Psychic,,483,85,73,70,73,115,67,1,FALSE\nKrabby,Water,,325,30,105,90,25,25,50,1,FALSE\nKingler,Water,,475,55,130,115,50,50,75,1,FALSE\nVoltorb,Electric,,330,40,30,50,55,55,100,1,FALSE\nElectrode,Electric,,480,60,50,70,80,80,140,1,FALSE\nExeggcute,Grass,Psychic,325,60,40,80,60,45,40,1,FALSE\nExeggutor,Grass,Psychic,520,95,95,85,125,65,55,1,FALSE\nCubone,Ground,,320,50,50,95,40,50,35,1,FALSE\nMarowak,Ground,,425,60,80,110,50,80,45,1,FALSE\nHitmonlee,Fighting,,455,50,120,53,35,110,87,1,FALSE\nHitmonchan,Fighting,,455,50,105,79,35,110,76,1,FALSE\nLickitung,Normal,,385,90,55,75,60,75,30,1,FALSE\nKoffing,Poison,,340,40,65,95,60,45,35,1,FALSE\nWeezing,Poison,,490,65,90,120,85,70,60,1,FALSE\nRhyhorn,Ground,Rock,345,80,85,95,30,30,25,1,FALSE\nRhydon,Ground,Rock,485,105,130,120,45,45,40,1,FALSE\nChansey,Normal,,450,250,5,5,35,105,50,1,FALSE\nTangela,Grass,,435,65,55,115,100,40,60,1,FALSE\nKangaskhan,Normal,,490,105,95,80,40,80,90,1,FALSE\nKangaskhanMega Kangaskhan,Normal,,590,105,125,100,60,100,100,1,FALSE\nHorsea,Water,,295,30,40,70,70,25,60,1,FALSE\nSeadra,Water,,440,55,65,95,95,45,85,1,FALSE\nGoldeen,Water,,320,45,67,60,35,50,63,1,FALSE\nSeaking,Water,,450,80,92,65,65,80,68,1,FALSE\nStaryu,Water,,340,30,45,55,70,55,85,1,FALSE\nStarmie,Water,Psychic,520,60,75,85,100,85,115,1,FALSE\nMr. Mime,Psychic,Fairy,460,40,45,65,100,120,90,1,FALSE\nScyther,Bug,Flying,500,70,110,80,55,80,105,1,FALSE\nJynx,Ice,Psychic,455,65,50,35,115,95,95,1,FALSE\nElectabuzz,Electric,,490,65,83,57,95,85,105,1,FALSE\nMagmar,Fire,,495,65,95,57,100,85,93,1,FALSE\nPinsir,Bug,,500,65,125,100,55,70,85,1,FALSE\nPinsirMega Pinsir,Bug,Flying,600,65,155,120,65,90,105,1,FALSE\nTauros,Normal,,490,75,100,95,40,70,110,1,FALSE\nMagikarp,Water,,200,20,10,55,15,20,80,1,FALSE\nGyarados,Water,Flying,540,95,125,79,60,100,81,1,FALSE\nGyaradosMega Gyarados,Water,Dark,640,95,155,109,70,130,81,1,FALSE\nLapras,Water,Ice,535,130,85,80,85,95,60,1,FALSE\nDitto,Normal,,288,48,48,48,48,48,48,1,FALSE\nEevee,Normal,,325,55,55,50,45,65,55,1,FALSE\nVaporeon,Water,,525,130,65,60,110,95,65,1,FALSE\nJolteon,Electric,,525,65,65,60,110,95,130,1,FALSE\nFlareon,Fire,,525,65,130,60,95,110,65,1,FALSE\nPorygon,Normal,,395,65,60,70,85,75,40,1,FALSE\nOmanyte,Rock,Water,355,35,40,100,90,55,35,1,FALSE\nOmastar,Rock,Water,495,70,60,125,115,70,55,1,FALSE\nKabuto,Rock,Water,355,30,80,90,55,45,55,1,FALSE\nKabutops,Rock,Water,495,60,115,105,65,70,80,1,FALSE\nAerodactyl,Rock,Flying,515,80,105,65,60,75,130,1,FALSE\nAerodactylMega Aerodactyl,Rock,Flying,615,80,135,85,70,95,150,1,FALSE\nSnorlax,Normal,,540,160,110,65,65,110,30,1,FALSE\nArticuno,Ice,Flying,580,90,85,100,95,125,85,1,TRUE\nZapdos,Electric,Flying,580,90,90,85,125,90,100,1,TRUE\nMoltres,Fire,Flying,580,90,100,90,125,85,90,1,TRUE\nDratini,Dragon,,300,41,64,45,50,50,50,1,FALSE\nDragonair,Dragon,,420,61,84,65,70,70,70,1,FALSE\nDragonite,Dragon,Flying,600,91,134,95,100,100,80,1,FALSE\nMewtwo,Psychic,,680,106,110,90,154,90,130,1,TRUE\nMewtwoMega Mewtwo X,Psychic,Fighting,780,106,190,100,154,100,130,1,TRUE\nMewtwoMega Mewtwo Y,Psychic,,780,106,150,70,194,120,140,1,TRUE\nMew,Psychic,,600,100,100,100,100,100,100,1,FALSE\nChikorita,Grass,,318,45,49,65,49,65,45,2,FALSE\nBayleef,Grass,,405,60,62,80,63,80,60,2,FALSE\nMeganium,Grass,,525,80,82,100,83,100,80,2,FALSE\nCyndaquil,Fire,,309,39,52,43,60,50,65,2,FALSE\nQuilava,Fire,,405,58,64,58,80,65,80,2,FALSE\nTyphlosion,Fire,,534,78,84,78,109,85,100,2,FALSE\nTotodile,Water,,314,50,65,64,44,48,43,2,FALSE\nCroconaw,Water,,405,65,80,80,59,63,58,2,FALSE\nFeraligatr,Water,,530,85,105,100,79,83,78,2,FALSE\nSentret,Normal,,215,35,46,34,35,45,20,2,FALSE\nFurret,Normal,,415,85,76,64,45,55,90,2,FALSE\nHoothoot,Normal,Flying,262,60,30,30,36,56,50,2,FALSE\nNoctowl,Normal,Flying,442,100,50,50,76,96,70,2,FALSE\nLedyba,Bug,Flying,265,40,20,30,40,80,55,2,FALSE\nLedian,Bug,Flying,390,55,35,50,55,110,85,2,FALSE\nSpinarak,Bug,Poison,250,40,60,40,40,40,30,2,FALSE\nAriados,Bug,Poison,390,70,90,70,60,60,40,2,FALSE\nCrobat,Poison,Flying,535,85,90,80,70,80,130,2,FALSE\nChinchou,Water,Electric,330,75,38,38,56,56,67,2,FALSE\nLanturn,Water,Electric,460,125,58,58,76,76,67,2,FALSE\nPichu,Electric,,205,20,40,15,35,35,60,2,FALSE\nCleffa,Fairy,,218,50,25,28,45,55,15,2,FALSE\nIgglybuff,Normal,Fairy,210,90,30,15,40,20,15,2,FALSE\nTogepi,Fairy,,245,35,20,65,40,65,20,2,FALSE\nTogetic,Fairy,Flying,405,55,40,85,80,105,40,2,FALSE\nNatu,Psychic,Flying,320,40,50,45,70,45,70,2,FALSE\nXatu,Psychic,Flying,470,65,75,70,95,70,95,2,FALSE\nMareep,Electric,,280,55,40,40,65,45,35,2,FALSE\nFlaaffy,Electric,,365,70,55,55,80,60,45,2,FALSE\nAmpharos,Electric,,510,90,75,85,115,90,55,2,FALSE\nAmpharosMega Ampharos,Electric,Dragon,610,90,95,105,165,110,45,2,FALSE\nBellossom,Grass,,490,75,80,95,90,100,50,2,FALSE\nMarill,Water,Fairy,250,70,20,50,20,50,40,2,FALSE\nAzumarill,Water,Fairy,420,100,50,80,60,80,50,2,FALSE\nSudowoodo,Rock,,410,70,100,115,30,65,30,2,FALSE\nPolitoed,Water,,500,90,75,75,90,100,70,2,FALSE\nHoppip,Grass,Flying,250,35,35,40,35,55,50,2,FALSE\nSkiploom,Grass,Flying,340,55,45,50,45,65,80,2,FALSE\nJumpluff,Grass,Flying,460,75,55,70,55,95,110,2,FALSE\nAipom,Normal,,360,55,70,55,40,55,85,2,FALSE\nSunkern,Grass,,180,30,30,30,30,30,30,2,FALSE\nSunflora,Grass,,425,75,75,55,105,85,30,2,FALSE\nYanma,Bug,Flying,390,65,65,45,75,45,95,2,FALSE\nWooper,Water,Ground,210,55,45,45,25,25,15,2,FALSE\nQuagsire,Water,Ground,430,95,85,85,65,65,35,2,FALSE\nEspeon,Psychic,,525,65,65,60,130,95,110,2,FALSE\nUmbreon,Dark,,525,95,65,110,60,130,65,2,FALSE\nMurkrow,Dark,Flying,405,60,85,42,85,42,91,2,FALSE\nSlowking,Water,Psychic,490,95,75,80,100,110,30,2,FALSE\nMisdreavus,Ghost,,435,60,60,60,85,85,85,2,FALSE\nUnown,Psychic,,336,48,72,48,72,48,48,2,FALSE\nWobbuffet,Psychic,,405,190,33,58,33,58,33,2,FALSE\nGirafarig,Normal,Psychic,455,70,80,65,90,65,85,2,FALSE\nPineco,Bug,,290,50,65,90,35,35,15,2,FALSE\nForretress,Bug,Steel,465,75,90,140,60,60,40,2,FALSE\nDunsparce,Normal,,415,100,70,70,65,65,45,2,FALSE\nGligar,Ground,Flying,430,65,75,105,35,65,85,2,FALSE\nSteelix,Steel,Ground,510,75,85,200,55,65,30,2,FALSE\nSteelixMega Steelix,Steel,Ground,610,75,125,230,55,95,30,2,FALSE\nSnubbull,Fairy,,300,60,80,50,40,40,30,2,FALSE\nGranbull,Fairy,,450,90,120,75,60,60,45,2,FALSE\nQwilfish,Water,Poison,430,65,95,75,55,55,85,2,FALSE\nScizor,Bug,Steel,500,70,130,100,55,80,65,2,FALSE\nScizorMega Scizor,Bug,Steel,600,70,150,140,65,100,75,2,FALSE\nShuckle,Bug,Rock,505,20,10,230,10,230,5,2,FALSE\nHeracross,Bug,Fighting,500,80,125,75,40,95,85,2,FALSE\nHeracrossMega Heracross,Bug,Fighting,600,80,185,115,40,105,75,2,FALSE\nSneasel,Dark,Ice,430,55,95,55,35,75,115,2,FALSE\nTeddiursa,Normal,,330,60,80,50,50,50,40,2,FALSE\nUrsaring,Normal,,500,90,130,75,75,75,55,2,FALSE\nSlugma,Fire,,250,40,40,40,70,40,20,2,FALSE\nMagcargo,Fire,Rock,410,50,50,120,80,80,30,2,FALSE\nSwinub,Ice,Ground,250,50,50,40,30,30,50,2,FALSE\nPiloswine,Ice,Ground,450,100,100,80,60,60,50,2,FALSE\nCorsola,Water,Rock,380,55,55,85,65,85,35,2,FALSE\nRemoraid,Water,,300,35,65,35,65,35,65,2,FALSE\nOctillery,Water,,480,75,105,75,105,75,45,2,FALSE\nDelibird,Ice,Flying,330,45,55,45,65,45,75,2,FALSE\nMantine,Water,Flying,465,65,40,70,80,140,70,2,FALSE\nSkarmory,Steel,Flying,465,65,80,140,40,70,70,2,FALSE\nHoundour,Dark,Fire,330,45,60,30,80,50,65,2,FALSE\nHoundoom,Dark,Fire,500,75,90,50,110,80,95,2,FALSE\nHoundoomMega Houndoom,Dark,Fire,600,75,90,90,140,90,115,2,FALSE\nKingdra,Water,Dragon,540,75,95,95,95,95,85,2,FALSE\nPhanpy,Ground,,330,90,60,60,40,40,40,2,FALSE\nDonphan,Ground,,500,90,120,120,60,60,50,2,FALSE\nPorygon2,Normal,,515,85,80,90,105,95,60,2,FALSE\nStantler,Normal,,465,73,95,62,85,65,85,2,FALSE\nSmeargle,Normal,,250,55,20,35,20,45,75,2,FALSE\nTyrogue,Fighting,,210,35,35,35,35,35,35,2,FALSE\nHitmontop,Fighting,,455,50,95,95,35,110,70,2,FALSE\nSmoochum,Ice,Psychic,305,45,30,15,85,65,65,2,FALSE\nElekid,Electric,,360,45,63,37,65,55,95,2,FALSE\nMagby,Fire,,365,45,75,37,70,55,83,2,FALSE\nMiltank,Normal,,490,95,80,105,40,70,100,2,FALSE\nBlissey,Normal,,540,255,10,10,75,135,55,2,FALSE\nRaikou,Electric,,580,90,85,75,115,100,115,2,TRUE\nEntei,Fire,,580,115,115,85,90,75,100,2,TRUE\nSuicune,Water,,580,100,75,115,90,115,85,2,TRUE\nLarvitar,Rock,Ground,300,50,64,50,45,50,41,2,FALSE\nPupitar,Rock,Ground,410,70,84,70,65,70,51,2,FALSE\nTyranitar,Rock,Dark,600,100,134,110,95,100,61,2,FALSE\nTyranitarMega Tyranitar,Rock,Dark,700,100,164,150,95,120,71,2,FALSE\nLugia,Psychic,Flying,680,106,90,130,90,154,110,2,TRUE\nHo-oh,Fire,Flying,680,106,130,90,110,154,90,2,TRUE\nCelebi,Psychic,Grass,600,100,100,100,100,100,100,2,FALSE\nTreecko,Grass,,310,40,45,35,65,55,70,3,FALSE\nGrovyle,Grass,,405,50,65,45,85,65,95,3,FALSE\nSceptile,Grass,,530,70,85,65,105,85,120,3,FALSE\nSceptileMega Sceptile,Grass,Dragon,630,70,110,75,145,85,145,3,FALSE\nTorchic,Fire,,310,45,60,40,70,50,45,3,FALSE\nCombusken,Fire,Fighting,405,60,85,60,85,60,55,3,FALSE\nBlaziken,Fire,Fighting,530,80,120,70,110,70,80,3,FALSE\nBlazikenMega Blaziken,Fire,Fighting,630,80,160,80,130,80,100,3,FALSE\nMudkip,Water,,310,50,70,50,50,50,40,3,FALSE\nMarshtomp,Water,Ground,405,70,85,70,60,70,50,3,FALSE\nSwampert,Water,Ground,535,100,110,90,85,90,60,3,FALSE\nSwampertMega Swampert,Water,Ground,635,100,150,110,95,110,70,3,FALSE\nPoochyena,Dark,,220,35,55,35,30,30,35,3,FALSE\nMightyena,Dark,,420,70,90,70,60,60,70,3,FALSE\nZigzagoon,Normal,,240,38,30,41,30,41,60,3,FALSE\nLinoone,Normal,,420,78,70,61,50,61,100,3,FALSE\nWurmple,Bug,,195,45,45,35,20,30,20,3,FALSE\nSilcoon,Bug,,205,50,35,55,25,25,15,3,FALSE\nBeautifly,Bug,Flying,395,60,70,50,100,50,65,3,FALSE\nCascoon,Bug,,205,50,35,55,25,25,15,3,FALSE\nDustox,Bug,Poison,385,60,50,70,50,90,65,3,FALSE\nLotad,Water,Grass,220,40,30,30,40,50,30,3,FALSE\nLombre,Water,Grass,340,60,50,50,60,70,50,3,FALSE\nLudicolo,Water,Grass,480,80,70,70,90,100,70,3,FALSE\nSeedot,Grass,,220,40,40,50,30,30,30,3,FALSE\nNuzleaf,Grass,Dark,340,70,70,40,60,40,60,3,FALSE\nShiftry,Grass,Dark,480,90,100,60,90,60,80,3,FALSE\nTaillow,Normal,Flying,270,40,55,30,30,30,85,3,FALSE\nSwellow,Normal,Flying,430,60,85,60,50,50,125,3,FALSE\nWingull,Water,Flying,270,40,30,30,55,30,85,3,FALSE\nPelipper,Water,Flying,430,60,50,100,85,70,65,3,FALSE\nRalts,Psychic,Fairy,198,28,25,25,45,35,40,3,FALSE\nKirlia,Psychic,Fairy,278,38,35,35,65,55,50,3,FALSE\nGardevoir,Psychic,Fairy,518,68,65,65,125,115,80,3,FALSE\nGardevoirMega Gardevoir,Psychic,Fairy,618,68,85,65,165,135,100,3,FALSE\nSurskit,Bug,Water,269,40,30,32,50,52,65,3,FALSE\nMasquerain,Bug,Flying,414,70,60,62,80,82,60,3,FALSE\nShroomish,Grass,,295,60,40,60,40,60,35,3,FALSE\nBreloom,Grass,Fighting,460,60,130,80,60,60,70,3,FALSE\nSlakoth,Normal,,280,60,60,60,35,35,30,3,FALSE\nVigoroth,Normal,,440,80,80,80,55,55,90,3,FALSE\nSlaking,Normal,,670,150,160,100,95,65,100,3,FALSE\nNincada,Bug,Ground,266,31,45,90,30,30,40,3,FALSE\nNinjask,Bug,Flying,456,61,90,45,50,50,160,3,FALSE\nShedinja,Bug,Ghost,236,1,90,45,30,30,40,3,FALSE\nWhismur,Normal,,240,64,51,23,51,23,28,3,FALSE\nLoudred,Normal,,360,84,71,43,71,43,48,3,FALSE\nExploud,Normal,,490,104,91,63,91,73,68,3,FALSE\nMakuhita,Fighting,,237,72,60,30,20,30,25,3,FALSE\nHariyama,Fighting,,474,144,120,60,40,60,50,3,FALSE\nAzurill,Normal,Fairy,190,50,20,40,20,40,20,3,FALSE\nNosepass,Rock,,375,30,45,135,45,90,30,3,FALSE\nSkitty,Normal,,260,50,45,45,35,35,50,3,FALSE\nDelcatty,Normal,,380,70,65,65,55,55,70,3,FALSE\nSableye,Dark,Ghost,380,50,75,75,65,65,50,3,FALSE\nSableyeMega Sableye,Dark,Ghost,480,50,85,125,85,115,20,3,FALSE\nMawile,Steel,Fairy,380,50,85,85,55,55,50,3,FALSE\nMawileMega Mawile,Steel,Fairy,480,50,105,125,55,95,50,3,FALSE\nAron,Steel,Rock,330,50,70,100,40,40,30,3,FALSE\nLairon,Steel,Rock,430,60,90,140,50,50,40,3,FALSE\nAggron,Steel,Rock,530,70,110,180,60,60,50,3,FALSE\nAggronMega Aggron,Steel,,630,70,140,230,60,80,50,3,FALSE\nMeditite,Fighting,Psychic,280,30,40,55,40,55,60,3,FALSE\nMedicham,Fighting,Psychic,410,60,60,75,60,75,80,3,FALSE\nMedichamMega Medicham,Fighting,Psychic,510,60,100,85,80,85,100,3,FALSE\nElectrike,Electric,,295,40,45,40,65,40,65,3,FALSE\nManectric,Electric,,475,70,75,60,105,60,105,3,FALSE\nManectricMega Manectric,Electric,,575,70,75,80,135,80,135,3,FALSE\nPlusle,Electric,,405,60,50,40,85,75,95,3,FALSE\nMinun,Electric,,405,60,40,50,75,85,95,3,FALSE\nVolbeat,Bug,,400,65,73,55,47,75,85,3,FALSE\nIllumise,Bug,,400,65,47,55,73,75,85,3,FALSE\nRoselia,Grass,Poison,400,50,60,45,100,80,65,3,FALSE\nGulpin,Poison,,302,70,43,53,43,53,40,3,FALSE\nSwalot,Poison,,467,100,73,83,73,83,55,3,FALSE\nCarvanha,Water,Dark,305,45,90,20,65,20,65,3,FALSE\nSharpedo,Water,Dark,460,70,120,40,95,40,95,3,FALSE\nSharpedoMega Sharpedo,Water,Dark,560,70,140,70,110,65,105,3,FALSE\nWailmer,Water,,400,130,70,35,70,35,60,3,FALSE\nWailord,Water,,500,170,90,45,90,45,60,3,FALSE\nNumel,Fire,Ground,305,60,60,40,65,45,35,3,FALSE\nCamerupt,Fire,Ground,460,70,100,70,105,75,40,3,FALSE\nCameruptMega Camerupt,Fire,Ground,560,70,120,100,145,105,20,3,FALSE\nTorkoal,Fire,,470,70,85,140,85,70,20,3,FALSE\nSpoink,Psychic,,330,60,25,35,70,80,60,3,FALSE\nGrumpig,Psychic,,470,80,45,65,90,110,80,3,FALSE\nSpinda,Normal,,360,60,60,60,60,60,60,3,FALSE\nTrapinch,Ground,,290,45,100,45,45,45,10,3,FALSE\nVibrava,Ground,Dragon,340,50,70,50,50,50,70,3,FALSE\nFlygon,Ground,Dragon,520,80,100,80,80,80,100,3,FALSE\nCacnea,Grass,,335,50,85,40,85,40,35,3,FALSE\nCacturne,Grass,Dark,475,70,115,60,115,60,55,3,FALSE\nSwablu,Normal,Flying,310,45,40,60,40,75,50,3,FALSE\nAltaria,Dragon,Flying,490,75,70,90,70,105,80,3,FALSE\nAltariaMega Altaria,Dragon,Fairy,590,75,110,110,110,105,80,3,FALSE\nZangoose,Normal,,458,73,115,60,60,60,90,3,FALSE\nSeviper,Poison,,458,73,100,60,100,60,65,3,FALSE\nLunatone,Rock,Psychic,440,70,55,65,95,85,70,3,FALSE\nSolrock,Rock,Psychic,440,70,95,85,55,65,70,3,FALSE\nBarboach,Water,Ground,288,50,48,43,46,41,60,3,FALSE\nWhiscash,Water,Ground,468,110,78,73,76,71,60,3,FALSE\nCorphish,Water,,308,43,80,65,50,35,35,3,FALSE\nCrawdaunt,Water,Dark,468,63,120,85,90,55,55,3,FALSE\nBaltoy,Ground,Psychic,300,40,40,55,40,70,55,3,FALSE\nClaydol,Ground,Psychic,500,60,70,105,70,120,75,3,FALSE\nLileep,Rock,Grass,355,66,41,77,61,87,23,3,FALSE\nCradily,Rock,Grass,495,86,81,97,81,107,43,3,FALSE\nAnorith,Rock,Bug,355,45,95,50,40,50,75,3,FALSE\nArmaldo,Rock,Bug,495,75,125,100,70,80,45,3,FALSE\nFeebas,Water,,200,20,15,20,10,55,80,3,FALSE\nMilotic,Water,,540,95,60,79,100,125,81,3,FALSE\nCastform,Normal,,420,70,70,70,70,70,70,3,FALSE\nKecleon,Normal,,440,60,90,70,60,120,40,3,FALSE\nShuppet,Ghost,,295,44,75,35,63,33,45,3,FALSE\nBanette,Ghost,,455,64,115,65,83,63,65,3,FALSE\nBanetteMega Banette,Ghost,,555,64,165,75,93,83,75,3,FALSE\nDuskull,Ghost,,295,20,40,90,30,90,25,3,FALSE\nDusclops,Ghost,,455,40,70,130,60,130,25,3,FALSE\nTropius,Grass,Flying,460,99,68,83,72,87,51,3,FALSE\nChimecho,Psychic,,425,65,50,70,95,80,65,3,FALSE\nAbsol,Dark,,465,65,130,60,75,60,75,3,FALSE\nAbsolMega Absol,Dark,,565,65,150,60,115,60,115,3,FALSE\nWynaut,Psychic,,260,95,23,48,23,48,23,3,FALSE\nSnorunt,Ice,,300,50,50,50,50,50,50,3,FALSE\nGlalie,Ice,,480,80,80,80,80,80,80,3,FALSE\nGlalieMega Glalie,Ice,,580,80,120,80,120,80,100,3,FALSE\nSpheal,Ice,Water,290,70,40,50,55,50,25,3,FALSE\nSealeo,Ice,Water,410,90,60,70,75,70,45,3,FALSE\nWalrein,Ice,Water,530,110,80,90,95,90,65,3,FALSE\nClamperl,Water,,345,35,64,85,74,55,32,3,FALSE\nHuntail,Water,,485,55,104,105,94,75,52,3,FALSE\nGorebyss,Water,,485,55,84,105,114,75,52,3,FALSE\nRelicanth,Water,Rock,485,100,90,130,45,65,55,3,FALSE\nLuvdisc,Water,,330,43,30,55,40,65,97,3,FALSE\nBagon,Dragon,,300,45,75,60,40,30,50,3,FALSE\nShelgon,Dragon,,420,65,95,100,60,50,50,3,FALSE\nSalamence,Dragon,Flying,600,95,135,80,110,80,100,3,FALSE\nSalamenceMega Salamence,Dragon,Flying,700,95,145,130,120,90,120,3,FALSE\nBeldum,Steel,Psychic,300,40,55,80,35,60,30,3,FALSE\nMetang,Steel,Psychic,420,60,75,100,55,80,50,3,FALSE\nMetagross,Steel,Psychic,600,80,135,130,95,90,70,3,FALSE\nMetagrossMega Metagross,Steel,Psychic,700,80,145,150,105,110,110,3,FALSE\nRegirock,Rock,,580,80,100,200,50,100,50,3,TRUE\nRegice,Ice,,580,80,50,100,100,200,50,3,TRUE\nRegisteel,Steel,,580,80,75,150,75,150,50,3,TRUE\nLatias,Dragon,Psychic,600,80,80,90,110,130,110,3,TRUE\nLatiasMega Latias,Dragon,Psychic,700,80,100,120,140,150,110,3,TRUE\nLatios,Dragon,Psychic,600,80,90,80,130,110,110,3,TRUE\nLatiosMega Latios,Dragon,Psychic,700,80,130,100,160,120,110,3,TRUE\nKyogre,Water,,670,100,100,90,150,140,90,3,TRUE\nKyogrePrimal Kyogre,Water,,770,100,150,90,180,160,90,3,TRUE\nGroudon,Ground,,670,100,150,140,100,90,90,3,TRUE\nGroudonPrimal Groudon,Ground,Fire,770,100,180,160,150,90,90,3,TRUE\nRayquaza,Dragon,Flying,680,105,150,90,150,90,95,3,TRUE\nRayquazaMega Rayquaza,Dragon,Flying,780,105,180,100,180,100,115,3,TRUE\nJirachi,Steel,Psychic,600,100,100,100,100,100,100,3,TRUE\nDeoxysNormal Forme,Psychic,,600,50,150,50,150,50,150,3,TRUE\nDeoxysAttack Forme,Psychic,,600,50,180,20,180,20,150,3,TRUE\nDeoxysDefense Forme,Psychic,,600,50,70,160,70,160,90,3,TRUE\nDeoxysSpeed Forme,Psychic,,600,50,95,90,95,90,180,3,TRUE\nTurtwig,Grass,,318,55,68,64,45,55,31,4,FALSE\nGrotle,Grass,,405,75,89,85,55,65,36,4,FALSE\nTorterra,Grass,Ground,525,95,109,105,75,85,56,4,FALSE\nChimchar,Fire,,309,44,58,44,58,44,61,4,FALSE\nMonferno,Fire,Fighting,405,64,78,52,78,52,81,4,FALSE\nInfernape,Fire,Fighting,534,76,104,71,104,71,108,4,FALSE\nPiplup,Water,,314,53,51,53,61,56,40,4,FALSE\nPrinplup,Water,,405,64,66,68,81,76,50,4,FALSE\nEmpoleon,Water,Steel,530,84,86,88,111,101,60,4,FALSE\nStarly,Normal,Flying,245,40,55,30,30,30,60,4,FALSE\nStaravia,Normal,Flying,340,55,75,50,40,40,80,4,FALSE\nStaraptor,Normal,Flying,485,85,120,70,50,60,100,4,FALSE\nBidoof,Normal,,250,59,45,40,35,40,31,4,FALSE\nBibarel,Normal,Water,410,79,85,60,55,60,71,4,FALSE\nKricketot,Bug,,194,37,25,41,25,41,25,4,FALSE\nKricketune,Bug,,384,77,85,51,55,51,65,4,FALSE\nShinx,Electric,,263,45,65,34,40,34,45,4,FALSE\nLuxio,Electric,,363,60,85,49,60,49,60,4,FALSE\nLuxray,Electric,,523,80,120,79,95,79,70,4,FALSE\nBudew,Grass,Poison,280,40,30,35,50,70,55,4,FALSE\nRoserade,Grass,Poison,515,60,70,65,125,105,90,4,FALSE\nCranidos,Rock,,350,67,125,40,30,30,58,4,FALSE\nRampardos,Rock,,495,97,165,60,65,50,58,4,FALSE\nShieldon,Rock,Steel,350,30,42,118,42,88,30,4,FALSE\nBastiodon,Rock,Steel,495,60,52,168,47,138,30,4,FALSE\nBurmy,Bug,,224,40,29,45,29,45,36,4,FALSE\nWormadamPlant Cloak,Bug,Grass,424,60,59,85,79,105,36,4,FALSE\nWormadamSandy Cloak,Bug,Ground,424,60,79,105,59,85,36,4,FALSE\nWormadamTrash Cloak,Bug,Steel,424,60,69,95,69,95,36,4,FALSE\nMothim,Bug,Flying,424,70,94,50,94,50,66,4,FALSE\nCombee,Bug,Flying,244,30,30,42,30,42,70,4,FALSE\nVespiquen,Bug,Flying,474,70,80,102,80,102,40,4,FALSE\nPachirisu,Electric,,405,60,45,70,45,90,95,4,FALSE\nBuizel,Water,,330,55,65,35,60,30,85,4,FALSE\nFloatzel,Water,,495,85,105,55,85,50,115,4,FALSE\nCherubi,Grass,,275,45,35,45,62,53,35,4,FALSE\nCherrim,Grass,,450,70,60,70,87,78,85,4,FALSE\nShellos,Water,,325,76,48,48,57,62,34,4,FALSE\nGastrodon,Water,Ground,475,111,83,68,92,82,39,4,FALSE\nAmbipom,Normal,,482,75,100,66,60,66,115,4,FALSE\nDrifloon,Ghost,Flying,348,90,50,34,60,44,70,4,FALSE\nDrifblim,Ghost,Flying,498,150,80,44,90,54,80,4,FALSE\nBuneary,Normal,,350,55,66,44,44,56,85,4,FALSE\nLopunny,Normal,,480,65,76,84,54,96,105,4,FALSE\nLopunnyMega Lopunny,Normal,Fighting,580,65,136,94,54,96,135,4,FALSE\nMismagius,Ghost,,495,60,60,60,105,105,105,4,FALSE\nHonchkrow,Dark,Flying,505,100,125,52,105,52,71,4,FALSE\nGlameow,Normal,,310,49,55,42,42,37,85,4,FALSE\nPurugly,Normal,,452,71,82,64,64,59,112,4,FALSE\nChingling,Psychic,,285,45,30,50,65,50,45,4,FALSE\nStunky,Poison,Dark,329,63,63,47,41,41,74,4,FALSE\nSkuntank,Poison,Dark,479,103,93,67,71,61,84,4,FALSE\nBronzor,Steel,Psychic,300,57,24,86,24,86,23,4,FALSE\nBronzong,Steel,Psychic,500,67,89,116,79,116,33,4,FALSE\nBonsly,Rock,,290,50,80,95,10,45,10,4,FALSE\nMime Jr.,Psychic,Fairy,310,20,25,45,70,90,60,4,FALSE\nHappiny,Normal,,220,100,5,5,15,65,30,4,FALSE\nChatot,Normal,Flying,411,76,65,45,92,42,91,4,FALSE\nSpiritomb,Ghost,Dark,485,50,92,108,92,108,35,4,FALSE\nGible,Dragon,Ground,300,58,70,45,40,45,42,4,FALSE\nGabite,Dragon,Ground,410,68,90,65,50,55,82,4,FALSE\nGarchomp,Dragon,Ground,600,108,130,95,80,85,102,4,FALSE\nGarchompMega Garchomp,Dragon,Ground,700,108,170,115,120,95,92,4,FALSE\nMunchlax,Normal,,390,135,85,40,40,85,5,4,FALSE\nRiolu,Fighting,,285,40,70,40,35,40,60,4,FALSE\nLucario,Fighting,Steel,525,70,110,70,115,70,90,4,FALSE\nLucarioMega Lucario,Fighting,Steel,625,70,145,88,140,70,112,4,FALSE\nHippopotas,Ground,,330,68,72,78,38,42,32,4,FALSE\nHippowdon,Ground,,525,108,112,118,68,72,47,4,FALSE\nSkorupi,Poison,Bug,330,40,50,90,30,55,65,4,FALSE\nDrapion,Poison,Dark,500,70,90,110,60,75,95,4,FALSE\nCroagunk,Poison,Fighting,300,48,61,40,61,40,50,4,FALSE\nToxicroak,Poison,Fighting,490,83,106,65,86,65,85,4,FALSE\nCarnivine,Grass,,454,74,100,72,90,72,46,4,FALSE\nFinneon,Water,,330,49,49,56,49,61,66,4,FALSE\nLumineon,Water,,460,69,69,76,69,86,91,4,FALSE\nMantyke,Water,Flying,345,45,20,50,60,120,50,4,FALSE\nSnover,Grass,Ice,334,60,62,50,62,60,40,4,FALSE\nAbomasnow,Grass,Ice,494,90,92,75,92,85,60,4,FALSE\nAbomasnowMega Abomasnow,Grass,Ice,594,90,132,105,132,105,30,4,FALSE\nWeavile,Dark,Ice,510,70,120,65,45,85,125,4,FALSE\nMagnezone,Electric,Steel,535,70,70,115,130,90,60,4,FALSE\nLickilicky,Normal,,515,110,85,95,80,95,50,4,FALSE\nRhyperior,Ground,Rock,535,115,140,130,55,55,40,4,FALSE\nTangrowth,Grass,,535,100,100,125,110,50,50,4,FALSE\nElectivire,Electric,,540,75,123,67,95,85,95,4,FALSE\nMagmortar,Fire,,540,75,95,67,125,95,83,4,FALSE\nTogekiss,Fairy,Flying,545,85,50,95,120,115,80,4,FALSE\nYanmega,Bug,Flying,515,86,76,86,116,56,95,4,FALSE\nLeafeon,Grass,,525,65,110,130,60,65,95,4,FALSE\nGlaceon,Ice,,525,65,60,110,130,95,65,4,FALSE\nGliscor,Ground,Flying,510,75,95,125,45,75,95,4,FALSE\nMamoswine,Ice,Ground,530,110,130,80,70,60,80,4,FALSE\nPorygon-Z,Normal,,535,85,80,70,135,75,90,4,FALSE\nGallade,Psychic,Fighting,518,68,125,65,65,115,80,4,FALSE\nGalladeMega Gallade,Psychic,Fighting,618,68,165,95,65,115,110,4,FALSE\nProbopass,Rock,Steel,525,60,55,145,75,150,40,4,FALSE\nDusknoir,Ghost,,525,45,100,135,65,135,45,4,FALSE\nFroslass,Ice,Ghost,480,70,80,70,80,70,110,4,FALSE\nRotom,Electric,Ghost,440,50,50,77,95,77,91,4,FALSE\nRotomHeat Rotom,Electric,Fire,520,50,65,107,105,107,86,4,FALSE\nRotomWash Rotom,Electric,Water,520,50,65,107,105,107,86,4,FALSE\nRotomFrost Rotom,Electric,Ice,520,50,65,107,105,107,86,4,FALSE\nRotomFan Rotom,Electric,Flying,520,50,65,107,105,107,86,4,FALSE\nRotomMow Rotom,Electric,Grass,520,50,65,107,105,107,86,4,FALSE\nUxie,Psychic,,580,75,75,130,75,130,95,4,TRUE\nMesprit,Psychic,,580,80,105,105,105,105,80,4,TRUE\nAzelf,Psychic,,580,75,125,70,125,70,115,4,TRUE\nDialga,Steel,Dragon,680,100,120,120,150,100,90,4,TRUE\nPalkia,Water,Dragon,680,90,120,100,150,120,100,4,TRUE\nHeatran,Fire,Steel,600,91,90,106,130,106,77,4,TRUE\nRegigigas,Normal,,670,110,160,110,80,110,100,4,TRUE\nGiratinaAltered Forme,Ghost,Dragon,680,150,100,120,100,120,90,4,TRUE\nGiratinaOrigin Forme,Ghost,Dragon,680,150,120,100,120,100,90,4,TRUE\nCresselia,Psychic,,600,120,70,120,75,130,85,4,FALSE\nPhione,Water,,480,80,80,80,80,80,80,4,FALSE\nManaphy,Water,,600,100,100,100,100,100,100,4,FALSE\nDarkrai,Dark,,600,70,90,90,135,90,125,4,TRUE\nShayminLand Forme,Grass,,600,100,100,100,100,100,100,4,TRUE\nShayminSky Forme,Grass,Flying,600,100,103,75,120,75,127,4,TRUE\nArceus,Normal,,720,120,120,120,120,120,120,4,TRUE\nVictini,Psychic,Fire,600,100,100,100,100,100,100,5,TRUE\nSnivy,Grass,,308,45,45,55,45,55,63,5,FALSE\nServine,Grass,,413,60,60,75,60,75,83,5,FALSE\nSerperior,Grass,,528,75,75,95,75,95,113,5,FALSE\nTepig,Fire,,308,65,63,45,45,45,45,5,FALSE\nPignite,Fire,Fighting,418,90,93,55,70,55,55,5,FALSE\nEmboar,Fire,Fighting,528,110,123,65,100,65,65,5,FALSE\nOshawott,Water,,308,55,55,45,63,45,45,5,FALSE\nDewott,Water,,413,75,75,60,83,60,60,5,FALSE\nSamurott,Water,,528,95,100,85,108,70,70,5,FALSE\nPatrat,Normal,,255,45,55,39,35,39,42,5,FALSE\nWatchog,Normal,,420,60,85,69,60,69,77,5,FALSE\nLillipup,Normal,,275,45,60,45,25,45,55,5,FALSE\nHerdier,Normal,,370,65,80,65,35,65,60,5,FALSE\nStoutland,Normal,,500,85,110,90,45,90,80,5,FALSE\nPurrloin,Dark,,281,41,50,37,50,37,66,5,FALSE\nLiepard,Dark,,446,64,88,50,88,50,106,5,FALSE\nPansage,Grass,,316,50,53,48,53,48,64,5,FALSE\nSimisage,Grass,,498,75,98,63,98,63,101,5,FALSE\nPansear,Fire,,316,50,53,48,53,48,64,5,FALSE\nSimisear,Fire,,498,75,98,63,98,63,101,5,FALSE\nPanpour,Water,,316,50,53,48,53,48,64,5,FALSE\nSimipour,Water,,498,75,98,63,98,63,101,5,FALSE\nMunna,Psychic,,292,76,25,45,67,55,24,5,FALSE\nMusharna,Psychic,,487,116,55,85,107,95,29,5,FALSE\nPidove,Normal,Flying,264,50,55,50,36,30,43,5,FALSE\nTranquill,Normal,Flying,358,62,77,62,50,42,65,5,FALSE\nUnfezant,Normal,Flying,488,80,115,80,65,55,93,5,FALSE\nBlitzle,Electric,,295,45,60,32,50,32,76,5,FALSE\nZebstrika,Electric,,497,75,100,63,80,63,116,5,FALSE\nRoggenrola,Rock,,280,55,75,85,25,25,15,5,FALSE\nBoldore,Rock,,390,70,105,105,50,40,20,5,FALSE\nGigalith,Rock,,515,85,135,130,60,80,25,5,FALSE\nWoobat,Psychic,Flying,313,55,45,43,55,43,72,5,FALSE\nSwoobat,Psychic,Flying,425,67,57,55,77,55,114,5,FALSE\nDrilbur,Ground,,328,60,85,40,30,45,68,5,FALSE\nExcadrill,Ground,Steel,508,110,135,60,50,65,88,5,FALSE\nAudino,Normal,,445,103,60,86,60,86,50,5,FALSE\nAudinoMega Audino,Normal,Fairy,545,103,60,126,80,126,50,5,FALSE\nTimburr,Fighting,,305,75,80,55,25,35,35,5,FALSE\nGurdurr,Fighting,,405,85,105,85,40,50,40,5,FALSE\nConkeldurr,Fighting,,505,105,140,95,55,65,45,5,FALSE\nTympole,Water,,294,50,50,40,50,40,64,5,FALSE\nPalpitoad,Water,Ground,384,75,65,55,65,55,69,5,FALSE\nSeismitoad,Water,Ground,509,105,95,75,85,75,74,5,FALSE\nThroh,Fighting,,465,120,100,85,30,85,45,5,FALSE\nSawk,Fighting,,465,75,125,75,30,75,85,5,FALSE\nSewaddle,Bug,Grass,310,45,53,70,40,60,42,5,FALSE\nSwadloon,Bug,Grass,380,55,63,90,50,80,42,5,FALSE\nLeavanny,Bug,Grass,500,75,103,80,70,80,92,5,FALSE\nVenipede,Bug,Poison,260,30,45,59,30,39,57,5,FALSE\nWhirlipede,Bug,Poison,360,40,55,99,40,79,47,5,FALSE\nScolipede,Bug,Poison,485,60,100,89,55,69,112,5,FALSE\nCottonee,Grass,Fairy,280,40,27,60,37,50,66,5,FALSE\nWhimsicott,Grass,Fairy,480,60,67,85,77,75,116,5,FALSE\nPetilil,Grass,,280,45,35,50,70,50,30,5,FALSE\nLilligant,Grass,,480,70,60,75,110,75,90,5,FALSE\nBasculin,Water,,460,70,92,65,80,55,98,5,FALSE\nSandile,Ground,Dark,292,50,72,35,35,35,65,5,FALSE\nKrokorok,Ground,Dark,351,60,82,45,45,45,74,5,FALSE\nKrookodile,Ground,Dark,519,95,117,80,65,70,92,5,FALSE\nDarumaka,Fire,,315,70,90,45,15,45,50,5,FALSE\nDarmanitanStandard Mode,Fire,,480,105,140,55,30,55,95,5,FALSE\nDarmanitanZen Mode,Fire,Psychic,540,105,30,105,140,105,55,5,FALSE\nMaractus,Grass,,461,75,86,67,106,67,60,5,FALSE\nDwebble,Bug,Rock,325,50,65,85,35,35,55,5,FALSE\nCrustle,Bug,Rock,475,70,95,125,65,75,45,5,FALSE\nScraggy,Dark,Fighting,348,50,75,70,35,70,48,5,FALSE\nScrafty,Dark,Fighting,488,65,90,115,45,115,58,5,FALSE\nSigilyph,Psychic,Flying,490,72,58,80,103,80,97,5,FALSE\nYamask,Ghost,,303,38,30,85,55,65,30,5,FALSE\nCofagrigus,Ghost,,483,58,50,145,95,105,30,5,FALSE\nTirtouga,Water,Rock,355,54,78,103,53,45,22,5,FALSE\nCarracosta,Water,Rock,495,74,108,133,83,65,32,5,FALSE\nArchen,Rock,Flying,401,55,112,45,74,45,70,5,FALSE\nArcheops,Rock,Flying,567,75,140,65,112,65,110,5,FALSE\nTrubbish,Poison,,329,50,50,62,40,62,65,5,FALSE\nGarbodor,Poison,,474,80,95,82,60,82,75,5,FALSE\nZorua,Dark,,330,40,65,40,80,40,65,5,FALSE\nZoroark,Dark,,510,60,105,60,120,60,105,5,FALSE\nMinccino,Normal,,300,55,50,40,40,40,75,5,FALSE\nCinccino,Normal,,470,75,95,60,65,60,115,5,FALSE\nGothita,Psychic,,290,45,30,50,55,65,45,5,FALSE\nGothorita,Psychic,,390,60,45,70,75,85,55,5,FALSE\nGothitelle,Psychic,,490,70,55,95,95,110,65,5,FALSE\nSolosis,Psychic,,290,45,30,40,105,50,20,5,FALSE\nDuosion,Psychic,,370,65,40,50,125,60,30,5,FALSE\nReuniclus,Psychic,,490,110,65,75,125,85,30,5,FALSE\nDucklett,Water,Flying,305,62,44,50,44,50,55,5,FALSE\nSwanna,Water,Flying,473,75,87,63,87,63,98,5,FALSE\nVanillite,Ice,,305,36,50,50,65,60,44,5,FALSE\nVanillish,Ice,,395,51,65,65,80,75,59,5,FALSE\nVanilluxe,Ice,,535,71,95,85,110,95,79,5,FALSE\nDeerling,Normal,Grass,335,60,60,50,40,50,75,5,FALSE\nSawsbuck,Normal,Grass,475,80,100,70,60,70,95,5,FALSE\nEmolga,Electric,Flying,428,55,75,60,75,60,103,5,FALSE\nKarrablast,Bug,,315,50,75,45,40,45,60,5,FALSE\nEscavalier,Bug,Steel,495,70,135,105,60,105,20,5,FALSE\nFoongus,Grass,Poison,294,69,55,45,55,55,15,5,FALSE\nAmoonguss,Grass,Poison,464,114,85,70,85,80,30,5,FALSE\nFrillish,Water,Ghost,335,55,40,50,65,85,40,5,FALSE\nJellicent,Water,Ghost,480,100,60,70,85,105,60,5,FALSE\nAlomomola,Water,,470,165,75,80,40,45,65,5,FALSE\nJoltik,Bug,Electric,319,50,47,50,57,50,65,5,FALSE\nGalvantula,Bug,Electric,472,70,77,60,97,60,108,5,FALSE\nFerroseed,Grass,Steel,305,44,50,91,24,86,10,5,FALSE\nFerrothorn,Grass,Steel,489,74,94,131,54,116,20,5,FALSE\nKlink,Steel,,300,40,55,70,45,60,30,5,FALSE\nKlang,Steel,,440,60,80,95,70,85,50,5,FALSE\nKlinklang,Steel,,520,60,100,115,70,85,90,5,FALSE\nTynamo,Electric,,275,35,55,40,45,40,60,5,FALSE\nEelektrik,Electric,,405,65,85,70,75,70,40,5,FALSE\nEelektross,Electric,,515,85,115,80,105,80,50,5,FALSE\nElgyem,Psychic,,335,55,55,55,85,55,30,5,FALSE\nBeheeyem,Psychic,,485,75,75,75,125,95,40,5,FALSE\nLitwick,Ghost,Fire,275,50,30,55,65,55,20,5,FALSE\nLampent,Ghost,Fire,370,60,40,60,95,60,55,5,FALSE\nChandelure,Ghost,Fire,520,60,55,90,145,90,80,5,FALSE\nAxew,Dragon,,320,46,87,60,30,40,57,5,FALSE\nFraxure,Dragon,,410,66,117,70,40,50,67,5,FALSE\nHaxorus,Dragon,,540,76,147,90,60,70,97,5,FALSE\nCubchoo,Ice,,305,55,70,40,60,40,40,5,FALSE\nBeartic,Ice,,485,95,110,80,70,80,50,5,FALSE\nCryogonal,Ice,,485,70,50,30,95,135,105,5,FALSE\nShelmet,Bug,,305,50,40,85,40,65,25,5,FALSE\nAccelgor,Bug,,495,80,70,40,100,60,145,5,FALSE\nStunfisk,Ground,Electric,471,109,66,84,81,99,32,5,FALSE\nMienfoo,Fighting,,350,45,85,50,55,50,65,5,FALSE\nMienshao,Fighting,,510,65,125,60,95,60,105,5,FALSE\nDruddigon,Dragon,,485,77,120,90,60,90,48,5,FALSE\nGolett,Ground,Ghost,303,59,74,50,35,50,35,5,FALSE\nGolurk,Ground,Ghost,483,89,124,80,55,80,55,5,FALSE\nPawniard,Dark,Steel,340,45,85,70,40,40,60,5,FALSE\nBisharp,Dark,Steel,490,65,125,100,60,70,70,5,FALSE\nBouffalant,Normal,,490,95,110,95,40,95,55,5,FALSE\nRufflet,Normal,Flying,350,70,83,50,37,50,60,5,FALSE\nBraviary,Normal,Flying,510,100,123,75,57,75,80,5,FALSE\nVullaby,Dark,Flying,370,70,55,75,45,65,60,5,FALSE\nMandibuzz,Dark,Flying,510,110,65,105,55,95,80,5,FALSE\nHeatmor,Fire,,484,85,97,66,105,66,65,5,FALSE\nDurant,Bug,Steel,484,58,109,112,48,48,109,5,FALSE\nDeino,Dark,Dragon,300,52,65,50,45,50,38,5,FALSE\nZweilous,Dark,Dragon,420,72,85,70,65,70,58,5,FALSE\nHydreigon,Dark,Dragon,600,92,105,90,125,90,98,5,FALSE\nLarvesta,Bug,Fire,360,55,85,55,50,55,60,5,FALSE\nVolcarona,Bug,Fire,550,85,60,65,135,105,100,5,FALSE\nCobalion,Steel,Fighting,580,91,90,129,90,72,108,5,TRUE\nTerrakion,Rock,Fighting,580,91,129,90,72,90,108,5,TRUE\nVirizion,Grass,Fighting,580,91,90,72,90,129,108,5,TRUE\nTornadusIncarnate Forme,Flying,,580,79,115,70,125,80,111,5,TRUE\nTornadusTherian Forme,Flying,,580,79,100,80,110,90,121,5,TRUE\nThundurusIncarnate Forme,Electric,Flying,580,79,115,70,125,80,111,5,TRUE\nThundurusTherian Forme,Electric,Flying,580,79,105,70,145,80,101,5,TRUE\nReshiram,Dragon,Fire,680,100,120,100,150,120,90,5,TRUE\nZekrom,Dragon,Electric,680,100,150,120,120,100,90,5,TRUE\nLandorusIncarnate Forme,Ground,Flying,600,89,125,90,115,80,101,5,TRUE\nLandorusTherian Forme,Ground,Flying,600,89,145,90,105,80,91,5,TRUE\nKyurem,Dragon,Ice,660,125,130,90,130,90,95,5,TRUE\nKyuremBlack Kyurem,Dragon,Ice,700,125,170,100,120,90,95,5,TRUE\nKyuremWhite Kyurem,Dragon,Ice,700,125,120,90,170,100,95,5,TRUE\nKeldeoOrdinary Forme,Water,Fighting,580,91,72,90,129,90,108,5,FALSE\nKeldeoResolute Forme,Water,Fighting,580,91,72,90,129,90,108,5,FALSE\nMeloettaAria Forme,Normal,Psychic,600,100,77,77,128,128,90,5,FALSE\nMeloettaPirouette Forme,Normal,Fighting,600,100,128,90,77,77,128,5,FALSE\nGenesect,Bug,Steel,600,71,120,95,120,95,99,5,FALSE\nChespin,Grass,,313,56,61,65,48,45,38,6,FALSE\nQuilladin,Grass,,405,61,78,95,56,58,57,6,FALSE\nChesnaught,Grass,Fighting,530,88,107,122,74,75,64,6,FALSE\nFennekin,Fire,,307,40,45,40,62,60,60,6,FALSE\nBraixen,Fire,,409,59,59,58,90,70,73,6,FALSE\nDelphox,Fire,Psychic,534,75,69,72,114,100,104,6,FALSE\nFroakie,Water,,314,41,56,40,62,44,71,6,FALSE\nFrogadier,Water,,405,54,63,52,83,56,97,6,FALSE\nGreninja,Water,Dark,530,72,95,67,103,71,122,6,FALSE\nBunnelby,Normal,,237,38,36,38,32,36,57,6,FALSE\nDiggersby,Normal,Ground,423,85,56,77,50,77,78,6,FALSE\nFletchling,Normal,Flying,278,45,50,43,40,38,62,6,FALSE\nFletchinder,Fire,Flying,382,62,73,55,56,52,84,6,FALSE\nTalonflame,Fire,Flying,499,78,81,71,74,69,126,6,FALSE\nScatterbug,Bug,,200,38,35,40,27,25,35,6,FALSE\nSpewpa,Bug,,213,45,22,60,27,30,29,6,FALSE\nVivillon,Bug,Flying,411,80,52,50,90,50,89,6,FALSE\nLitleo,Fire,Normal,369,62,50,58,73,54,72,6,FALSE\nPyroar,Fire,Normal,507,86,68,72,109,66,106,6,FALSE\nFlabébé,Fairy,,303,44,38,39,61,79,42,6,FALSE\nFloette,Fairy,,371,54,45,47,75,98,52,6,FALSE\nFlorges,Fairy,,552,78,65,68,112,154,75,6,FALSE\nSkiddo,Grass,,350,66,65,48,62,57,52,6,FALSE\nGogoat,Grass,,531,123,100,62,97,81,68,6,FALSE\nPancham,Fighting,,348,67,82,62,46,48,43,6,FALSE\nPangoro,Fighting,Dark,495,95,124,78,69,71,58,6,FALSE\nFurfrou,Normal,,472,75,80,60,65,90,102,6,FALSE\nEspurr,Psychic,,355,62,48,54,63,60,68,6,FALSE\nMeowsticMale,Psychic,,466,74,48,76,83,81,104,6,FALSE\nMeowsticFemale,Psychic,,466,74,48,76,83,81,104,6,FALSE\nHonedge,Steel,Ghost,325,45,80,100,35,37,28,6,FALSE\nDoublade,Steel,Ghost,448,59,110,150,45,49,35,6,FALSE\nAegislashBlade Forme,Steel,Ghost,520,60,150,50,150,50,60,6,FALSE\nAegislashShield Forme,Steel,Ghost,520,60,50,150,50,150,60,6,FALSE\nSpritzee,Fairy,,341,78,52,60,63,65,23,6,FALSE\nAromatisse,Fairy,,462,101,72,72,99,89,29,6,FALSE\nSwirlix,Fairy,,341,62,48,66,59,57,49,6,FALSE\nSlurpuff,Fairy,,480,82,80,86,85,75,72,6,FALSE\nInkay,Dark,Psychic,288,53,54,53,37,46,45,6,FALSE\nMalamar,Dark,Psychic,482,86,92,88,68,75,73,6,FALSE\nBinacle,Rock,Water,306,42,52,67,39,56,50,6,FALSE\nBarbaracle,Rock,Water,500,72,105,115,54,86,68,6,FALSE\nSkrelp,Poison,Water,320,50,60,60,60,60,30,6,FALSE\nDragalge,Poison,Dragon,494,65,75,90,97,123,44,6,FALSE\nClauncher,Water,,330,50,53,62,58,63,44,6,FALSE\nClawitzer,Water,,500,71,73,88,120,89,59,6,FALSE\nHelioptile,Electric,Normal,289,44,38,33,61,43,70,6,FALSE\nHeliolisk,Electric,Normal,481,62,55,52,109,94,109,6,FALSE\nTyrunt,Rock,Dragon,362,58,89,77,45,45,48,6,FALSE\nTyrantrum,Rock,Dragon,521,82,121,119,69,59,71,6,FALSE\nAmaura,Rock,Ice,362,77,59,50,67,63,46,6,FALSE\nAurorus,Rock,Ice,521,123,77,72,99,92,58,6,FALSE\nSylveon,Fairy,,525,95,65,65,110,130,60,6,FALSE\nHawlucha,Fighting,Flying,500,78,92,75,74,63,118,6,FALSE\nDedenne,Electric,Fairy,431,67,58,57,81,67,101,6,FALSE\nCarbink,Rock,Fairy,500,50,50,150,50,150,50,6,FALSE\nGoomy,Dragon,,300,45,50,35,55,75,40,6,FALSE\nSliggoo,Dragon,,452,68,75,53,83,113,60,6,FALSE\nGoodra,Dragon,,600,90,100,70,110,150,80,6,FALSE\nKlefki,Steel,Fairy,470,57,80,91,80,87,75,6,FALSE\nPhantump,Ghost,Grass,309,43,70,48,50,60,38,6,FALSE\nTrevenant,Ghost,Grass,474,85,110,76,65,82,56,6,FALSE\nPumpkabooAverage Size,Ghost,Grass,335,49,66,70,44,55,51,6,FALSE\nPumpkabooSmall Size,Ghost,Grass,335,44,66,70,44,55,56,6,FALSE\nPumpkabooLarge Size,Ghost,Grass,335,54,66,70,44,55,46,6,FALSE\nPumpkabooSuper Size,Ghost,Grass,335,59,66,70,44,55,41,6,FALSE\nGourgeistAverage Size,Ghost,Grass,494,65,90,122,58,75,84,6,FALSE\nGourgeistSmall Size,Ghost,Grass,494,55,85,122,58,75,99,6,FALSE\nGourgeistLarge Size,Ghost,Grass,494,75,95,122,58,75,69,6,FALSE\nGourgeistSuper Size,Ghost,Grass,494,85,100,122,58,75,54,6,FALSE\nBergmite,Ice,,304,55,69,85,32,35,28,6,FALSE\nAvalugg,Ice,,514,95,117,184,44,46,28,6,FALSE\nNoibat,Flying,Dragon,245,40,30,35,45,40,55,6,FALSE\nNoivern,Flying,Dragon,535,85,70,80,97,80,123,6,FALSE\nXerneas,Fairy,,680,126,131,95,131,98,99,6,TRUE\nYveltal,Dark,Flying,680,126,131,95,131,98,99,6,TRUE\nZygarde50% Forme,Dragon,Ground,600,108,100,121,81,95,95,6,TRUE\nDiancie,Rock,Fairy,600,50,100,150,100,150,50,6,TRUE\nDiancieMega Diancie,Rock,Fairy,700,50,160,110,160,110,110,6,TRUE\nHoopaHoopa Confined,Psychic,Ghost,600,80,110,60,150,130,70,6,TRUE\nHoopaHoopa Unbound,Psychic,Dark,680,80,160,60,170,130,80,6,TRUE\nVolcanion,Fire,Water,600,80,110,120,130,90,70,6,TRUE"
  },
  {
    "path": "5.data-visualization/1.matplotlib/matplotlib50/matplotlib50.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 50题matplotlib从入门到精通\\n\",\n    \">作者：王大毛(和鲸社区)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Matplotlib 是 Python 的绘图库。它可与 NumPy 一起使用，提供了一种有效的 MatLab 开源替代方案，也可以和图形工具包一起使用。\\n\",\n    \"\\n\",\n    \"虽然相比其他图形库（Seaborn | pyecharts | plotly | bokeh | pandas_profiling ）这个库丑丑呆呆的，甚至有点难用，但人家毕竟是开山始祖，方法全，能够支持你各类骚操作的需求。可以说是现在python数据分析中，用的人最多的图形库了。\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 一、导入\\n\",\n    \"### 1.导入matplotlib库简写为plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 二、基本图表\\n\",\n    \"### 2.用plot方法画出x=(0,10)间sin的图像\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8b022ef0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0, 10, 30)\\n\",\n    \"plt.plot(x, np.sin(x));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.用点加线的方式画出x=(0,10)间sin的图像\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d1046d8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x, np.sin(x), '-o');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.用scatter方法画出x=(0,10)间sin的点图像\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d104f98>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.scatter(x, np.sin(x));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 5.用饼图的面积及颜色展示一组4维数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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6WDZ0IWkNBHI8E2JYgJCTRSNy0MSQKor5GGygGglcuuVSLFtoGzN6fSQA6Hb9nsNMP+eT2EmsbXRzbolIMDrW601Rx80Gd6/dWOTdd4TtXzz51fecRZsy/SsEoUvW0kmXWZTwHHPsqYfwbJOMRYm3a+M7+ac5SSEp2gUjHI1HeYr+opuP0nHu8zoSVbDUpjYtYQRNMQNmHkmcIU+jHECaCWEFioDuENBXkPEm5aFErWuSDkegZOUCk5VFs8R6Me+9Ja8PXSxt8emcJ17GYmTi6f9a2LSYqeYwxrDW7/PPf3eT7r57jwuzeGYyH4zBe95WCMV1rJ0FWBjPjueA4F4ji32JM+kyZdcBoDCSOc+HA484GM9zo3NkhxI+L6I752RaWJdDKIK2DhURagsSkyNRGpiPrVYXlUTLHY+cJnNEDDElsEBacmdM4ts2lud0uFSMG2NEBGXQCrDF0ioaRRfv+9XkW6i0mK3nsA8p0HoQQgmopR5yk/O7L+2y0J/nuq3NY8rgxwAHgYUwytugaaZ3u+hPGvLyOlZd3ZhkHIoWP5/4ApdeeeSyl63juj5D7+IcfMu1NIRDox8xgy97nEhJQLeWJ4sOrejm+S2LFBK3Ctq/XaA8T5xFWvOd7jAbPE6QqZaKy28drSAEXmU7s+X6tNFIK/H1qYByHNFX89ssHLG90mJ0sPbUIP47r2ExPFPl6eYOPby6h9fHtOWlfxOhdkaTHxphRBMxp3ah7yMscvpZZxKcY13mDNF1AqcaRCvbshVIb2HIG1zm89oJnuVzInWVhsELVHdUfcD0HwWiZ/+QKulzwaWz29n7xMYQvEV2JO9wpqOlwCqdyH7QNT1gzSWKo5sCSYtfmlsFg7E2swVsIRqIYDWNW7qywsbyJ49nUztaYuzqLPKal+STGGD6+tcRqs8t0dbxpwFIIpqoFvl7ewPds3r585njvt6+i0pu7/PrHxegmln1t343c00CqE5rR0klPY18yi/gUI4RF4P8dpMyj1PqxAu6NMSi1jpQ5Av/vHLl62+XCRRxpE6pRWrElJfmCR7JHAofjWFRLAcNwf6vYYDC5lGC1iEx3ioVOcqSds0hnAGJ3XLKSKVPVAuIxMTVojN3Aii5hJecASBPFzd/epLG4gV/wEELw1e9uEz+RGv00LDfa3FvZYKryfPynQgimqwVu3Ftlo328TEAha0jrAkZvPPX5R3WKNZbzxlOP8TJgSZeye+HQx1EQQvxSCHFTCHFHCPGf7vH6BSHEvxRCfCyE+EwI8aeHjZkJ8SlHyhy54I+xrTMovbRvZtzjaDNE6SVs6wy54I+RMnfk8znS4TuVN+mlA2I9EtjaZIFwuLfYTlULWJYkTXYLqcEQWQMqqkbFnyTeQ7BVVCHunEPYEcIaAgatDUppKmWPSim3PZaWXYzdxAqvYoWvbrs52ustwn5EoZpHCUMiDXbRpb7cID1GBuCThFHCBzcWqRZzY+8D+DhSCgo5n/evz5MeI1FGCIHt/gSQGH38dG5jNEatYbs/+UbUJx5HrQkxslj+O+DvAW8Cfy6EePOJw/5z4B8bY74P/LvAf3/YuJkQfwOQIiDwf0Hg/QJMSqqWSVUdrXsYE2NMjNY9UlUnVctgUgLvFwT+Lw7s5rEfVbfM96tv0Ul6hCqiuhUdsJdBLi3J3HSFOFHox9KQDZrIHpCPS1SGk1SmSnuKNYCOysTNy6iwirBDwqhP5UzI3JyFsbtou4mxmsh0Aqf3U+zoKuKxS7vZ6lKPelxvrnGrtc7NtVWauZTPWyv8468+4qP1RVrR4TewJ7m71EAphXfEwkbPQj5w6Q4iFuvHy24TMofj/yHG9I/lLzYmwagVLPc7SPvacaf7UmK2Ojkf9DgCPwHuGGO+NsbEwP8G/Du7TgUP71xl4OAiLmQ+4m8MQghc5xUc++KoRKaqk6qVbUtIyjyudRXLmsGSU88c6D/tT/KTie/xefsGkRhSm8jTbg3JF3YnIwS+w7nZCourLVzHwngKLVLKwwlKUW3UOqngkSsFRP0YL797A81oj3QwQ79VIYxb/OB7U/g4EFmgA6x0EmF2+jATlXK3s8H9tE17OKRaLIOBUGtmZqvYnkW1VOBWe50vN9d4ozLNOxNn8KzDfxZpqriz0KDynJJB9qJc8PnqwRoXz+zdGWU/pDWFE/wpafg3mHQVrOq+9SdG7ZM2gRTL+xmW/epztfZfFIaxbcbNAY+n6C0CP33imP8S+OdCiP+IUczf3z1s0EyIv2EIIbGtWWxrFo/n24mi6pb52cQPudebJzpzn8XNdWwFruXuqkvh5yym53xWmm3kwOWsPoenH4mYkIKpS5PMf76ITjXyiWgMow2DMAEFb117jZn8HIT7z22QRHzWXCFWitnZGtSHDBo90kRTmimgE8W5n1/GtR2mbAdtDDfb66wMOvxi7ioF5+DsttVml0SpsURIHBXfc6g3u2x0BkyWj+eTlrKKE/wpKrmDTr5AmyYIuRXaJrZCGBUGgWW9guW+/Y0oCP+QVCc0wpWjHDophPjgsb//yhjzq8f+vtdd6cm14J8Df2mM+a+FED8D/mchxNvmgFqimRBnPBOOdHi1dIWL+fNMDG7w4Z2vSKspRhiEEds1IFxczrozvD55jfpqyGqzS2yF5Hx3O+vM8R0mL05Qv7dOvhSAFCilieKRH7daCCjmPa5e3btD9UPCNOGTjWWkEFS8kdjPvnOO9TtrxL2Q8z+5xNTrM+QnH0U5SCGYzRVpRgP+v6U7/Mn51w60jJfX29vF6F8kliWpN7vHFmIYdWOx3dcxzjWMbmJ0e8v6NSBySKuGENWtGORvFrZwqbrnjnJowxjzowNeXwTOP/b3c+x2Pfx94JcAxph3xSjcZBLYt9xfJsQZY8GzXH76+juIhk9jvU1x0t8WYXvrz9ZfKJ4rMjdVYX2zx0qjTbqdZjzK2nArOTbW2vgFH8e2maoVyfsu4TDm6rXZXU1Id2AMt9vraGN2WLVRL6JyrsbVX1zFPSB2uOblqA97fNxY4vdm9q9N0Gj3T6QJaeA5rLeerY6yENaoGNC4CgKdAkauibG4WN4HrgkhXgGWGG3G/XtPHDMP/BHwl0KINwAfOLAIeybEGWiTkug+se5tBe8LbBngygK2PHrsqJSSH/zeFd779S06jQG1yf1TfH3P4fxsdWsjL2UYJcRJitEgzk/SWm6xsdikVMkjLEGvF3Hl6gyl8sHW2kbYpxH2mfBHFqPRhl6jj1fwuPJvXj5QhB8y6ee51V7nSmmCqWB3bHCUpAzChELuxTfW9F2HZruP1mas1dq+DRxxM+7gMYxJhRD/IfBXgAX8I2PMl0KI/wr4wBjzfwD/EPgfhBD/CaN7wH9gDoktzYT4G4w2mqGK6aVDOnGfngpHxdSFIG8FBJYm1WtEagGxXUrykXfXADl7ipp3jZw9faQNPte1+cnPX+Xj392lvtKiMlE80IKVUuB7zq5l/sxEidZshesf3iNJFG9/5zwTU4fXbpjvtQjskdhG/ZioEzL1+jSzb85gHzG6QQpBznb5qlXfW4jjlJPSQCnFVpEjhbdHkf6M/RlXbZGtypL/7Inn/ovH/v86o6qURyb7Jr+BRCphebjB7d4iYTpKEZZCYEsbiUCZlFa8TC9eRgiLglPklXyNSS+HKx+JpjGGWPVY6P+avD3DTPB9XOvw7DHXtfnx719j4X6DLz56gLQl5UruWFlsYZgQK8OPfvEmgS1Zn2+wsdKiUM7hBs6eO/lhmrA5HJCLJJ04JFfLce0Pr1GYPn7GW9n1Wei3iJXCtXbeSLR5svrxi8c8Rcrzt51xWMTPi0yIv0GkWnG7t8TtzhJgKDo5Cv7OpbzWCc3oa2zaTAWTSCSRSrnRWUcIyeV8hfO5EpaQo5A4q4BLgVC1eND7f5nL/5ycfXg6tZSSi5enmZwqcffWCvP3G2AgyLn4gburLKQxhjhOGQ5ikiglX/T5wU8vc/Z8DSnlKEX5QYMHXy3TXH28s8ao8LEQ0ApDkkFM5fIsE5cnCKrBU4deSTFqYtpNQias/K7XTprDOmNn7CQxCfVo9aSnsS+ZEH9DaMU9Ptq8TTcZUPNGQvok2iia0S0i3cWzHoUmeZaNZ9koo7nTa7IW9nmzNEXReeRP9a0KqR6y2PsbLhT+AN8+Wiv6fNHnOz98hdfeOsfK0iZry5s0Gz2U0oitfnPCjNwghaLP3IUac+cnqE4UdoioF7hcev0sl14/S5oo+p0h4SBCK72VQWZRZ4jurnAmP64sMMEgTXiybJDzAkPWnkRv1Y04Tr3njFHURM09e9LT2JdMiL8BLA0afNC8Sc72mPL3r5DVS5aJVHuHCD+OJSQTbkA/TXivucR3y9NM+o+sQVsGaDQrw/e4WPgj5DHKb3q+w6Ur01y6Mo0xhnAYkyRqFDklBX7g4ByxhrHtWJQnCpQndrocNtsK2R+npbi7BROMIhdcxyZV+oULYhSnVArBsctiZnCiXZoPI/s2TzlLgwbvN29Sdgvk7f0jChLVpxsv4FiHb3jlbYei7fBxa41GuDNUypV5YtVjM7rz1HMWQhDkPErlHKVKjmIpOLIIH4RnWUcqfKT36vi8BwKBu8+G2GQ5xzA6vMTnuBlGCVPV01ug/aQwR3ycFJlFfIrZjLu837xJ1S3s2eb+cQZpHYGFPOK915EWZcfl03adn1hzT7gpajSjW1S9q8eyip83xQOy4bTSbCy0qd9tkgwTHN9m+kqNiQuVXf32HmIOGPPMZJml9Q7FFxzClqaKqcqzd2b+NvIyb9ZlFvEpJdWKjzZvk7f9Q0VYG0UvXcO2jl5lDUZi7EmLG9317eaXAFLYaJMwSA+MUX/hFB0fV9okemfxIKMN85+usvDpKpYlKUzksRybhc/XuPfh0p4W8jBNKDguOXvvpI0zkyWkECj94sqJx0mK7zqZRfyUjKnoz3Ph5TFnMo7F7e4SvSRkyj+8HkCqQzDmyNbw4+Rth2Y8ZH7Q5lL+kf9Z4hCmGxSc4xUrf57YUvJ6ZYbPmsvM5h5Zjb2NAZuLbYpT+e0NQNu1KE0VaK/26NT7VM7stDJb8ZCfTV/cFXWRKk2nM6TbCxGp5vqtVSYqefKBQxC4uM+xElu7O+Sdq8dvm5QBiU5ZHj57N5vnRSbEp5ChirjdXaTmHW2JqswB1XGOQNnx+Lrf4mxQ3I4ztqTPIH36guPPi6vlCW601ghVir9VK6K52Mb29o499nIuzYXWDiHuJzF52+V84VFkSBQl3J/f4M69OokaJctqrWk0u/S6Q6SUGGOYqBY4M1OiUBhvN4swTnAcmwuzp7td0UlhC4dp7+UxGp4kE+JTyMpgY9T48oilLLVRPL4VoZQhjDRxrEm3luVSClxH4nkSx5E7FmmWkBijaUQDzgYjwZJYKPOon1yaKLQehZJZtnzmFkRPS2A7/Gz6Av9y5S4zQRFbSpJI7dtbT1qj1x+SaEU7HvIn51/fTuRYWWvx8WcLJKmiXA52hK8ZCXdWNigVfIwxdLpDGhtd5s5WmDtT3RUv/TQYY2h1Q37+nVfw3Rdf3+I49Lsh/c6QVrNHe6NHEisE4PoO1akCpWqBYiWH57/4z/Ey+4gzIT5laKO53Vui5Bzd3ysQpAp6nZh6I2QwTEHorVZycpSg8Fhoj21JJiZcJqounjsSkrzl8GDQ4ow/iu8d9mNaaxGt7k2a9TbDfrSdHC0klGoFJmbKTMyWmZgpYz1D7G2qFY2ow/ygzjBNRn3q7BwXcpNU3cIuS/d8scoPk3N8sL7AdFCkMBGwujHAze3+8SfDhMlXRlZmqFI2wj6/P3OJ6a3U5tt31/j8xhKVckDZ2x2VMlMrstEd0B1GFAOPXM4l8B1WVjt0exGvXpk5uEjREVjf7PPKmRpnp17OspRKaTZW29z5conN9Q4YkLbE853tDt6DfsjaYnMrqkUwd2mSC6/OUJ0svrB6xy9zLmImxKeMfhoSqoTiEYVYKc3Caoebi020EUgnwXJHIWQPr0wFWMLGkR6u9BFGUF+PWF0LqZQd5mYDPM+mGYesrfXYuNNn5UEDzyoxXWnhBe6obOUWRhuiMOHBrRXufLGI69lceesc565M4x8jykAbzb3eGl91Fon7pfqCAAAgAElEQVR1im85ONImVoZW3Ofr3gplJ8c75UtMBzuX7G/VZsnZLr+rP0DXLAyGJEpxvEeXfBqPrPjSXIn6sIclJH909ipzhdFYDxY2+PzGEpMThX0tWykEr56b4ot7q/SGEYXAQ0hBqeTT64XcuVfntSuz24J0XBqbPaaqeb7/6txTvf95s7ne5ZN379DvDAkKHrWZ0r7C+vAaMdpQX95k4es603NV3v7xZfLFF9CY9CWOI86E+JTRS4/e0me90+Czu/foDjtY3pDAyiGFt7u0tRmJXqQHhKqHJRz8II8rAnr9lOu3O5yd8mlsdhjMD5lwAkpTFhV/lvweNwSx1ab+Yav6NEm5+ckD7nyxwDu/d5Wzl6YOtYK00XyyeY97/TVqboGy3BkpEFgjQR+kEb9uXOfHtWucz+8s6/hKqcZUkOfLzVXC78QsfbQGqcZ2bLTSGAEz351i6KS8VpnijcoMwVaURK8f8ckXC9Rq+ZHLoR8yCGN6w2iUFSgknmNRyHkEnsMbF6e5uVCn3Q8p5UYNSgsFn3Z7yFqjw5mZ41mzSms2Wn1makV++tbFF1qA/iioVHH780Vuf7FIvuQzeebon09IQak2+j5bjR7/6v/8mHd+epnzl2ee13SJdcricN9ywCdOJsSnjFbcw5YH/yiVTvlq+Q6351cIfJdaqUyoYoxJR36DJxEghdyOqtBa009axHJIzi8T9uDD39aRgWZiokZOusR6uG+G3pPYjs3EbJk4Svjwr2+wenmDd37vKu4B9Xxvdpa411tj2i8fKNo528ORFu9t3MK33F1RJAXH46fTF/nexBxrr3f5emGNZruPF9icn5tkqlSk5uV2FYH/4sYSUZoyv7pJfbPL1ooax7K256OURq23MRh812VmokjgxtRbXfK+i+vYFIo+84tNapU8nne0n1tvENEfxrz5ygyvXZzZN3tPG0M/jlFGj5JPLIvAef6+1zRRfPKb26wsbDAxU9o3DvsolCfyJHHKx7++zbAXce2d88/FVeFIh1n/4IYCJ0kmxKeMXhriHCDEoerzxcJ1Fpf7VApFrK0lsWuVGKZ15BG+ciklEo9UJ9Q3V+kv2eQDn74QPNgMmSgLfGcCWx4vmcH1HKbmqqzObzDsR/zkD9/C3WPTZqhivuosMuXvv8x9HEfaFJyAL9oP+APvnb2jIyybC5UqFyqH18hoNHu8++k9YpXi2DaFwD+09m+SKuZXmlhScrZWptnr0+oN8RwbjKG52ePMAREP2hi6/ZAoTinnff7ox69SK+1ebQyThAftNg82N2kOh2hjkFteJmPAs21m83kuT0wwk8+PPdRNKc2n795hdaHJ5OzBN8mj4rg2k7NlvvpkHiklV98+UieNYzOuMpjPg0yITxkavasf3ENC1eP68pcsLydUi4Ud4iHxsISP1glSHs1qUkOL9mKCdCOMZRPYNr1eynw94oeXnu7HIoRgYrbMZr3Lh//6K378izexn9jMWuw3EEIgj9HgNG/71MMWraRP1T1+2cuHrDY6/F//8gu6g5CZyaPdCGBUCKhcCEhTxUqjTbWU4+JMgbXNLoMo5ta9Oo7v4DnWdkSJ0pooHkW0GANz0yWuzk0xUc7vEv5hkvDZ2hp3mhtgoOi6TOZyuyrBJUpR7/e5324T2Dbfm53llVptbBXj7n+1wvL9BpNnxyPCD5GWZHK2zI2P7lOZLDI5O/6NyZdYhzMhPm1I5J6FaGIVcr99i7XVlFJhtwUnhMCzqgzSVTAKKQ5xbySGzcUI25NYlkWo+lgyIHAF/c1pun3BxDMUOatOF2kst7jzxQKvf//Sjtfu9leOFRXyEFtaLA4aTy3EC6ub/O6zB4RxSqWYeyqhsW2LSiGg0wtJEsWbr8xggMXVFtfOTTKIYpJ0dDN1bEmtnKec9ynmvX1D0xbbbX6zsIDSmulc/kBRdSyLsmVRBqI05W8X5nnQbvOTc+couId3JzmIzmafGx8/OHBD7lmQlqRYzfPpb27zb/zZ93CP6Mo5CqNaEtlmXcaYyNs+9ai14zltNKvDr1lbjZDY2+6IJ5HCIbCnGKbro0y7A1Kju+sJRoNtj8aSxmJg2tTMRSbzk9xZaVLMebjPsIlUmylx+9N5Zs7VqE6NVF0ZTTMc4IuAdtSlHUakWqMxOFJSdF0qvk/edSg4O5M0POnQTY6+mfk4q40Ov/vsAbVyjsXFDbxn6EcnhKCU9+kNQm7cr/P25VlKOZ+5iTK1Y6YnX6/X+WBpiYlcDt/e+/saDmJ63ZA00aN4cM+mVA7wbJu5YonGYMD/ffsW/9aVK1T8p2sMaozhs9/exc+7+8ZkjwM/57Kx1uHOl4u8+YNLYx07E+KMsVF1C9zupjuea8d1Nvtd+h1BoXCwMFrCJ7BniNIGqY6whLvLuokGmn5T4eUlGDCkgMESAVbq4NgWgzhldbPHhWeIbZWWJF8K+PzdO/zsz77L6qDH5+srfLhSp2jnsOVoA8oSAgtBqgxrvQFL3R7GGPKuy8VyialcMNpEQ6AfWy0Yk2BMD0y09YyNkAVGTXUfEUYJ733xgHLJx3Es1Jj6wRVyPu3ekIV6i4Lroo/ZVeOrRoP3l5aYLRSw5e5C+p3WkNXFFt12CILt71FrjSUlM2fLTM4WmQgCulHEv7h7lz+5eo2Sd/xCRa1Gj9ZG71jREU9LZaLA/ZurXH1r7sAN3eMQ64SFQRY1kTEmRqUuH4lEqmM2oiX6mza2kx5pyWgJl8CeJdFdEt3F6FGxcSEsQNDfiJGOwjCqFyyli23lwTKoNCQSQwq+x3Kzy9xE8Zk2hHJFn7tLdeY/+RjjWxRclwk/ILDdPTMHvccs8ChNudHY4LaUvDFZw3UMkzJAJXdR6U2M2gDxWA8+AwiDII90rmHZlxCyyGe3lzHabLsGrK2+cNYYlt/FvM9ivcW5ifKxxH1jMOD9pcW9RVgblh40WV5o4QUOhbK/63tXSrO63KK+2ubaW2coFn1a4ZC/nZ/nj69cOfZ39uD2Ko73YkLoLFtitGZtscn5K+MJaXOEw1n/+YXHPStjWWMIIf6REKIuhPhiHONl7E/e9vEsh1iPrOJu0iRJDe12in+MH4oQEtcqk7PPEjiT2LIARqJixbCrcF0PWxZw7SqOLKOFxNIS13j0khaWlChl2Ow+nSsARm6IW+Emn1stGistzhSKFF2PmaDKII0Ofb9n21R9H8+y+GS1zmcrtyjrd1HRb8AkCGsGac0gth7SnkFasyBdVPIZyfCfsr7+N8wv16mWH/mk83l/VLR+DEgh8F2H5UabXHA0H22qNb9ZmKfouLtEGGBpocnyYotiJcDfp3+fZUkKpQDbsbn5+QqDfkTFD2j0+9xsNI71GdJEsXy/QbFyfL/905Iv+dy/Nd7WRi9z9bVxOXv+EvjlmMbKOABLSK4WztKJB2ij2UxWUaHDqG/b8S8kISSWCPCsCjlnBhFP4soyjl3CkgFia9GUWimlMMAWLpHuk5oE15Y0n1KIU6P5dNDgXtThbL5EuD4gTUfiN+WXGHmFj7aUd2RK3l6kE67xacNjaKYQcv/NNiE8pDUNcoavF+8g9ecYvbn9ernkk8Tpnu99GhxbEiYpUfpoTGMMm5t9vvh8gd/95jbv/voWH39wj9WVFveam7SGIcU9XAi9TsjKfItiOTiShe16NrZjce/WOsYYpvN5Pl1d3TGXw+h3Q4zhhdYP8XyX7uYAlY7nhgijFdFhj5NiLK4JY8y/FkJcGsdYGYczF0xyvfOAUPXRWhFF9lh8mgBxP8Gyd45lRo5icomH2PoTqQGeXaQ9iPcZaX+U0Xw2aLChQiadrc0jAdEwxi4G5CyPCa/EZtSh6Ozc3IrjlOEwZjiIGYYxaRKh0nVCS3HeLXN/kPBP2xv826/WKOUOvryTFBbqAZWiQscfg/MO0p6mUsrxwIzqIowjOmA4iJmZLDG/ssk71wLWVtt8dX2JdnuAY1u4noMAut2QhQcNbmxuMHm+gs7nd4lffbWD7VpPfN8GW0Z41hDbCpFCYYxAGYc4zSGCgPZmRL8XUSj6aGNY7HS4UqsR65RQxWhjsKUkZ3m7wgb73SHGvLi6yzDKvjPGMOgdvjI64ojZZh2AEOIvgL8AuHDhwos67TeSwPa4kj/Lp63rIKDXT3CcMVT5wtAfxuBrUlthMAgEiVRUB3nkVq6+FBaxDsm7ZXphQrxltWz0htQ7fRKlsaRgopBjqpQjeKIN0t2oTUMNmXistZMxMOxH5Iuj5y4XZrihErrJgILtMxwmbDb79PsRCLAtC8s2OO4GqQuzlCgoh3Az5fp6yOr9kF9eq3LxYkChsPdl3h2oUWSI5WOMhU4+R8gfEgQVquUc3X5I/hk7cCg92j6cm62y2uhQkDafffKAYjFgamp3/J/wJEnX0Ly/ieonXHprdjtKIY5Smus9CqXRZqMlhgTuGoGzhCFBM9o9cKQgsA2eZbZriqw7FXrrikLxGiXX5YPVB3TFJvP9OltJgxgMOcvjYn6KomOR6AHtZJMHrSXWJjbouTkc4xPoAp7J4+s8Fs83k2/YH5cQZ3HEABhjfgX8CuBHP/rRy/xvcip4tXSOz1qfkiiII43jPr0QKxQDInpmSCvoYm/5mgWgpEYYwUBLIjemEAb4iUOsHrokDPONNqudUW8737GxpEQbw0KzzYONNmfKBV6ZKmNJSSuNuBd3qFk7IxcsRzJ8zPqxhcUb5XPcai5za2llJNKOR5B3wYzmHKoGhpRpUaImPJAC14GcsVhPUj74usu9e0PeeCPPpUu5XWF93f6j8qBCOBhZQMXXsbwfc+Fcjc+uL6KUfqZSlr1uxMXzNYoFj7u31+gudJieKe07Zi+OsW1JsRDQbvR5cGONV96aRUjBcBCDgUiHaHGP0DRIYkmqPbTxt8SU7f/aAqYDwUwAlVIH0t9QkyssqFlu9br4hfNM+EWkkBhGWZlr4TJftD8ZFTMqTDMTVJHKxjU+Ng5KxLTstdFGLoKSmqSopvDN8+gaIo7Ug/AoRDrlQe94vvEXSRY1cUpxpM2lQoGPmy1So3Cfwt1vMPQIadNDAJa2sFN7u96uESMvbWWYx9E2Wmg6uT4dIOhZTKJpDkIGWjFd2p0N5tqjZp4r7S6JVlybqfFFuEFeOruSEoQQu8K7eu2IwdcJM6JCkle0TZ/hViiaYxKmSSiISewdS+kEiChbIQsmYsZP+ORTmJ+X/OCHRXK5KpYsIkSBdi/FfWwlIYSHNi20ekAQXOXShUm+vr9OqeQfK8vvId1eSKnkMzNdQivN0vwGZ75z6UBh70Thdi2RQjVgc63H5NyQUi1HZxixkq5QUHVcmWKLHDlLwj57tMoY6kPDUh9Kbp5Z26M5aBOpZWadaVxxGSkkiUmoh8u0kyaWsJnxJ1DGcG/QJe8UsYSNMBKJhcTCNqNNR4OmZzVp2+uU0ikm0nNY45aUMXkTXGlzPjc9nsGeA5kQn2JKtsvrpWnuift4ZvQjOSoKxQZdYmJcHCSCx72ARhhSqSiFAY4eXSbSSLzUxWDo5vt8rRZohjZvV2f29VELISgHPvXOAOPBwE52uCQendDwuDZvbPS4fWuVfMEn74ys5ynKoxrKgE7vgCww2m82GBOiTQetB6ObChKNxYZxOD8Fnbbht++2+NGPV8kFIGWRMJpFiJ2WnBAldLqItC4wM1VCpYoHC03yBW9HQfiDGBWIDynmPa5dmUFKycZGF63Mvsk2D+nHCc6WX1gIgevbrDxosmEivt64zUThPoGbQ+nDIxgsIShseQ4Gqea9KGG6ZXOlXMFlFZPeoJO8wkq4iMGQtx7VBpYCirbPjc4qF+wSWu22TAUSz4yq0/WsDfpWi5nkFXJ6XLHGZizdvU8D4wpf+1+Bd4HXhBCLQoi/P45xMw5GCMG0X+QVZ5K+iYjN0XbCUxTrtElJ8XF52I9DSECAEnpbhD21O+RKIHATh3YaMpxpgXvwzrYQgoLn8mm7gbdParVWBmcrjrfTGXLn9hr5go/9hO9bCEAPQQ8Ad/Rp9DpKrWB0jBA+QvoI6VKwLRYSiUFSrlikyuPjjysoXcWgUPoeaXofbYaPjS8RBrQaNUY9e6bKa9dmieOUbneI2qPR6EOMMQyGMe3OkDPTJV6/dmZbvFcXN/F8G3kEE+/xI6Rv8fnKGvPr93ilvAQmQOnjpyo7QiMLKZ2B5O5GSj8t0k+WWey9iyNsctbuAvuOtMAYem5yYASLQOCbApaxWXZv0ZPNY89v74EFuXHVKTagj/A4KcYVNfHn4xgn43g4wmeoe1ysVkhXBJ1ch74JyeHtu9uvMWzQQWNwn/j6DWACjUkllSi/bQnvxqCNIR6O2ivVCw3ODc5gmf0tRmPBZhQxlxb2XEprY8gVPdJEcefOGkHO3SXC22OZAQiJYYhK66OZi4AnP7IjoKuhraBqQ6kkaDYNt2/Dm2/65AOflUZMktzCsuawrcnRG6UPag3sUTH2WjVPMe+xtt5htd7ZqkfMdvlHrc22W6VWyXHmyjTFwiOrX2vNsBfhuM6hJSOlENuSl2rNvUGbhIgZawPhlHCcIUopLOt4yRU9neD5NmXPop9objcjUi9l0gPLtIC9kx3yts9G1Mc9wlaXjYPQglX3LmdiSV4/fX+9NFG4no1/xNjrI/ES70x9O+z+bygFp0on3aBU8nEWHC7LGdZNhw3TBSPwcXZlp3Xok5Dg8+gCV0aTbG2+VGUe3TY4wf6XhkYhjYNWmqLrk4qUprvJVDS573tCUoQBtY/ZIQx4gcv8YhOVKoLcAT9A00ObBKU2EcLZygjcGymguyXEANUq3PsaZmYMlaLAGBchbJRaABS2NQO4aNNBmlEBeBiVajw3V+PMbIXBMGI4TBgMR6F7rmORCzyCwN2z5rBWZis8TOAd0uW54Ll0owjXsrg/6BBpRUW2UVqCsCkUPZrNAcfRYa0NQ0eRc0YRIL5tWO8PaXTyTE07oOsgSyB2u4xsKTEWkBO7OpzshYWNp3OsOXc5H7+NY54u6qTXHnDu8vh8upFOud8fk6X+HMiE+BSTs0pooykWHIwxSASzssKEKdI2fRqmR2gSwCCRKBQterjYRKTby00LSVXkCfBI/ZR13T3wvFprbBmgjMJ3bXzt0Ha7FNICgdp7Kdk3yb5Vw9JEYXs2yhjW1zoUSwcXptG6hdJNhPC2hXI/XAktBQ8DJoUQFIqGr27Ad74/yrYajVFAqRUENpY1AZpRjYonxMmyRhENj1u8hyEtQZIqarXCoY6JkuuxaDpsxCHdNKbmxGgRYuTI7+rlHNy+RRynuIeIOow2ZOM4wZtwtprAGnp6QODBIBI0+4bJvItRSwj7CnvtjgkEE2fLbN5pHyrEMBLjxEga9jyzydV9y7YeRJqosaU3w9ZmXX5/Q+GkOZlWuxljIbCLCAG+bzE16TMYjHy1jrCYlCVek2e4Kmc4LyepiTxKKDzh4AuXogiYFCXOiCpnRZWiCLCFxMs7SFuiD/KFYnCkO7K6HRuBwDYWLbe973t6pFiGPbtNDHshZy5M0Gz0sCyxZxORRyfXKN0AnENFGMAV0HnChR0EgnYH0hjyPoTxlm9Y5EnVEtpEW+3NxpPEIKXEci1yR+hcXHA9Qp2yNOySty1cNtA4ON6WH18IahN5HNsijtIDfbdKG6IwpVzJ4W+dOzIJkU7wpUvRk8y3NJFywAxHvvd9KFVG9a1VerR/E58cfWuTvtw8/OAnGHRDKhNFyrUxh8SZIzxOiEyITzGu9Km5ZxiqLufP5RmGOzfrhBB4wqEkAmqigIvFWapMiiIVkSMnXBxh7fAnCyEoTQQk4d4bcAY9CuXSFlPlHIke/TAd7TCwByQi2fN9kVb4to3/xC641hoMlCcKrK21CQ5JoNCqjjEJQhxtMScNDFLYiA3zA839vmZpqBmguTuvuXYeuoOHn12CsFDpIowxkyxVmpmzFbxDWlwBBI6NEpAqhSsSkiiiUHZxH4sTl5akNp0nl3dJIkUcpSilt33VaTJ6DqOZmMhTKPk4SFKjCU0ISPKOgyVHAlDvacDC6L2X7gZD4Lqcf2WKXvvoKe2O9mlZq0dOVYetNl3dIW/+6NKR33M0Dq8z8U2oNZFxQkx7F0lNTLnsUqt69Hp7C2HIyJ95lJTdoOIi9rF+lE5wGYVOXZwskSo9SgXeuoiHdrjrPcYYojihlvd3uSf6rSEzF2qkemTBHdjt2CRotb4VcnawUBoDncRwv69ZDP9/9t4kRrIrS9P77n3zezYPPsfgMTAiGGSSmSSzsjKzurOqO6sLvWloI7R6L616JUCAtBEaAgRoq0VtCkJDy9KuVYtCValR3TXkUEkyk0wyGPPk4bO7udtsb7xXi+fhER4+ezgzIpnxA75wt+fPzK7Z+9+55/znP4o7/YzlULEWa+ZHinmt+Nu7GY9lSqI18daySeGidB9NCOJ0usY2u0O+/a2zuJ5NfIiHRawylJGnAyRD0gQqjd03HSkl5ZrP+FSJUsVDGmK7Ddl2TGrNAs2JMm6Q59rL0iEmIdEpppDbw0g9S7DeV6TKAd3hxXWNVYpv2BRMh+Z0lWLFZ9Q/Wlu7hUMoB8RieKTjATbX+ly6PkNtj67Dl8Xr7DXxhoh/yxGYFUpWnVD1uHqlQpJq0j3SCkMdYRzx4zZNSW06IBnlPsRPobfGNGWZxUSlQNlzGS8HDKIEpTSGNgjlTiLWWtMeRdR8j4K3M9qNRzGWazJ5vkk4inapHl6EyrpoNFL6cIBUL85gfqRZGGkUEJiCsiUpWJLAFBQtScWVuFqwEWq69ZSv1hTPlk2gVB8hXr5iPxzFeI7F1QsTfPuDc7Q3hwc6u62HQ1zLpOK69DodSnUHP9j/c5OGJCg6NMaKjE2WaU4UqTYCHNfcsZ4eJkorYqUp2c72N0HmltN0wq29ud55Ix+kMTN+bcsmVTJ7dZIsy0iio0klJZK+bB9+INDdGFAseV/bzDq0OPznFeENEf+WQwjBueAdlE5xPLh8qUS7He/qUguJj0zEAF7Rwq86RIOnF5wmVTFSFSh5LqVCXpRrFDwmKwWiJCUMM3piSJJlRGlKZxjSHUXMVIpcrddJn4u2sjRjNIy58PYMpmnQ60eYh0gBlF5DSBeEw34RcZzB3EgRZprAFIRaMAzhbltye9PgfluyPhIkKpeKOZlgdhwiX/HZsiLvW5AoUrQ+vqHR80jTjN4w5KPrZ7FMg/GJCh989wKbmwP6vXDP9t1WFGIqCJRJrZpQm9ht3H8SCCGwVIZpGLvy9KYUdMOt16KftZkP0gjfsKg7z3K1rudw5b2zRMOEODycjE1tE8ruocd1WgMs2+SjP7yGdYQi5HERZSkP+61Df14V3qgmvgFwjYCzwds87P+aqckGYZjx6HGfWtXe7njLUEdqJngKIQS1yYD1VBENEgxPQeZQcH2m6sVnTSAI6oFH2XPohiHrUe45YUnJRLNMsxjgmAaryZDHSa7GyNKMXnvIhesz2x63aZId7CCnE1AxQhbQQgJylzuaUjA/ygk605KFgWCgoIYGEwyhyTQsDyRLA3ATxUyoKZQF713WfHJDcWMF3h3LELKC1sMTR8VpmrG+OeCDd87QqD6boTdzpobv29y5tcjqShfDkFi2iSDPJT9ptSl5Lucv1/H9JzzsaKRSe/oSHwf9OCaQJk3XY11HeBjbxve2Ab1t/s3Xr5eEGEJwvTy1a2p4oeRz9dtnufPFPHGYEJR3a7ifwsAilIOt3dTu95Clis31HuVqwEc/uor7kiZL+8GWJueC+qmcSwjxJ8D/Sa6I/7+01v/HHsf8t8B/IN9wfK61/ncHnfMNEX9D0HDOEGYDlsL7nD+ff+EeP+5TKlnbzmzHTYFJQ9A4U2Tp8Tr9DkzWysw0y3uSgiklZd/F8w3er0zsejyQudVjvGVheeH6DM3JZ4L/w16bVvG2oY1AImURpbpb0XGO9ViTaIhTyWYscKTGNqAocwMcyK8cy8inJrdHgpvLko+qGa4t+M51zadfJTxcdznX9FBqgJRHa0pQSm3ly2EUxYzChA+vn+XC9G7JVK1e4Hs/eIt+L2R5qU2/H6IyjbAlZ8sx58dqZDpBxjbnKz6P25ukUu47s+7AddOaQZJgW5K65VMwXexUspqFhCqfA2gKQZJqRqkm1REZQyq2x1vFcVxj7zx5UPJ556NZntxfZX2pgxvYON7uYwUChCYVyQ5NsVaaXmdIPEp56/0zXLw2hfES8w+PhlPZWRjAnwI/BuaBj4UQf6G1/uq5Yy4D/wvwA631phDiUEH0a03EicroJyH9NCLVCgF4hk3RcvHNU+y4+QZACMGMfwWBYCG8x7nzFUoli1u32oxGArNgEHM8s/P8Ih5QqJe5ONFEdUKyKMHy9o5aMjIszLydVKkdXWSeMBC9lNAxuPbhLMXyTq8E0zRQB1ZLkp2XkSyB6qG3mi7iDDYTTZoJNmOBa+S1egnspWwWAjwj99S4sSB594zCtuD6lQHt1hk22pLA36RamdozNaC1pj+M2OgO6fZDBqOYLMsYRDG2YXJ+ps7jpU2GYcJks0SttNuovlB0uVR8dtPqhiG//HyOL1dvM0xjRNphomBzsV5jqdunF8d4hrmnBHD368u344lSjBcCiq7FXJjn78umQ9GwGaqUjSwk1IpIKMJMMRMUmfDPEpiHR6aWbXHh2jS1sRKLj9bpbgyQhsRxLUzLfCZD1LnyQilFNEoY9iO00kyerXPpnZnTl6nth9Mpxn0XuKe1fgAghPhz4N8AXz13zH8P/KnWehNAa33osLzXkog78ZD7vTXu99bQW+IXsfVhiq0W0JodcLU8wZRf2XO22W8TtBrlVXpA4CLkySbtCiGZ9q/gmUUeD24QVOG7HzW5/7DH+qqkZ0VUXAPjEBN5rTX9UUScJDQKdb515SKlwGfQG/Ho1hLdjT6GZeAVnju0G9oAACAASURBVJmIJ3HKaq+NWIBRK8xJUAhsxyIouhQrPt+/dIG5UrSLhAEKBYf1tYNyiTuvIoGBlA2UWkHj0Us1SsNmJLd8eCHSUBV5d91+pywVYBALVjuC6WqfitckmQh4/5zJ4gKsbfYxpCTw7LwrTsN6Z8DCaodhGG1FfLniwzINrk2OMzlWQUpBFKfcn1/n1uMVyoHHlfNjzGw99mytQ1Q6j8qeMBo8xlE3mS242FKSZSuM0lu4VHi3WWd1WGS+p+jFKRKBZUgsKZ8NDdWKJFPbksKy49AMAjzLIlTJjjWUQlAwLApbEe9GpvigPKIQjIM8XnqgUi9SqRcZ9kM21rp0NwYMOqNtP+vICtlsd7GFR6VW4OylcSbP1vELp+QjcUSckihiGnjy3O/zwO+9cMxbAEKIn5Bvwv6D1vqvDjrpa0XEqcq43V3mi80FLGlQs4N9hxwOkoifrN6j4RT4qHGesv2bm6d1GtBao9UqKrlNls3teMwwziKttxBy/NiFGiEEdWeaolljbniTTb3EuUsmlakxPl6MidqKLMsr5EKKbVJWOs/dZqQoFPWKz5Wpt2kUK9vStKDo8fZHswy6I1YX2mysdIijmM21PuEoIvZTpvvN7Run0ppwGGO7FsKQjAdFWpZmmCT41s5trHeop4DYvbOUHpIKSrXpJg5ZJrZuAFsGLjpPS+wFrUAaYJgQSM18K2aiEmAYExhZQuZk/MG3G0TZVZ4sbbK03mVhtc2T5TbdQYhrW1iWkXfaBQ7NaoFKyd8RrXqOhbc1hXgU5pOiH9c2+PbVGQI3IUu+IkvvIDQgfdZDg15awdUBaZZv37XuszrMaHjrnC8uMVPwaEUTrI1KDOKMYZKAzoMVS0oC2yawbQq2g/3ca5FH0MlKka/pSeEX3JxcZ0GpXMusMk1ftXm/9B3KxeJvdNzS84izlIfdI7U4N4QQnzz3+59teak/xV6L+CLHm8Bl4EfADPAPQoh3tNb7ykdeGyKOsoSfrt5nNezSdA6fDBxYDoHl0IlH/M3iDX44dplJ/+QmI79JaJ2RRZ+QpTfz2Wqyud0lprVCqWWy0QMM8yqG89GBXgr7wTY8LhW/wyjr0wrnUTyiXFM4DQOdmiQxjEbZViOAQsi8Q69WGGcsmCCwi3u2pgoEhZJPoeRTHSty89NHlOsFSiIgNCLOeRM4po1fcHE8OydhIUiTjIc35vHqDiuTEWcqlR2aYs+zQOS+DHtrieUz1/PnXouWFTQQZR16iY0l82siAprP5YZfRBKBVwRQGDIiVT79eIK6LXGEwVo05IoMKLku1y9NUqv4/PSzIbMzdYp+bqpkGBLbMo50s/RcC8+1aPeG/OOn/5nvXF6mXPQQsrH9+WYq3G5DFkIgcJGixyi1iJWPFCCJmXAfULMr9LJLhFmFXpSS6S3bS9vA3mP9TGFukbHe9bkqrRE6xbECOKWJG1IauJ5BpjN0FlAtn5Y15slgGybnC0dqcV7XWn94wOPzwJnnfp8BFvc45uda6wR4KIS4TU7MH+930teCiFOV8dPV+6xHfca9431gZdsjykz+fuUO/2LyGg23+DW9ytOB1pos+gSV3kIYk7vadPPuriqIMiq9A2gM53snljB5RoGZ4CpT/mVceZebvQcEnkGqUqiCFBJH+tiGiyVcjCOSfr874t6X85SqAZZt0mPIOT3J2ebuQh2AaRk0JqpsrnUhjlgxDCZLz0T7pmnQbJZotfoEhd1bYyHcF3k4/zsCqKClIlURrpESKwNPCIoHLFmWaIq1GNBIUcM0A/oh1Au5nGuUpkiZX7jzK21+/sUjKiUf56X8cVNm6jeQzHHrccBb5+rUK8/Wu+F5mIYgVQrLMEC6hAkUbLGdXlHYRKqG1j2y6BfcaJ2jHdVz1cLWAk0WLGZKDv5z7nVSgCdtUp1ivtCVGKVQ8hKkeeEl3tveiFVIyfrtCJCOiI+By0KIWWAB+LfAi4qI/wT8d8D/LYRokKcqHhx00teCiG93l1kNu8cm4adwDIui5fGztQf8q6nr2MZr8bb2hFYreSS8Bwk/DyEk2hgnS28jrVmEsTfBHRVSGFwuXWAxbONKB8c+eeSTphl3v3iC69lYtklK3qAwTu3Q/602S8TLGywv9Vi3DBres0LN2FiJ1ZXursgXAJF7S6QqoxdBN8pIsnyzLSSMUhOESSIShI4YI0NoAVo+O5cGUKSpwnIEXhAgjBICCykUafpshykESFmi1R7wsy8eUSt52C9JwkX7F1iyRaomCXzF7UervHtpiuLWjafuB0wVSzzc3KTg2HmUS8CEv1PPHKWKh20TjeZa5T6LI0k3aeZvUWtWBwnLg4T3xgLK7jOiLxgeq3Eb84XrI0wUk0WBME5H3vU8YhVRsV4Ts51TSBJrrVMhxL8H/po8//sftdY3hBD/G/CJ1vovth77YyHEV0AG/E9a6wNFyq+8ytWJR3y5uUDDeblI1jdtRlnCzc7SKb2yrwdZchMhgyMZ1gghEbJAltw8led2DZtvlS/RSfqHKBQOxuZ6jzRR2I61NW5pxDk9icPRlCzNsQpjqwJfmKwO+tuNDb7vUKsH+YDQF5Aqzcog4PbagMVeQpSqbX5NM01nqFgbZYxik0mjimVUELKAkCbbTCwMEB5JVKY+PYE06oitrbjWGnMrjzHKRlTsMmnm84sbjykFzkuSsKZg/wpLrpOqnOxMKfFciztzq9tm81IKPpqeYapQpOZ5TBUKXK6fwTWBrZtdqjSPOnkXomc6hFmJ6eAevpmnH4UQFB0Tz5B8vjpglDxrfCmaPnu522RZyHj1DKeVlngKpXPf5rrzeowoOi3PH631X2qt39JaX9Ra/+9bf/tft0gYneN/1Fq/rbV+V2v954ed85UT8cP+GqYwDs0JHwV1O+Bud4U4O55M62WglKLfHbG+0mFtuc3acoeN9R5RuNvzQashOlsAcYzIX5TQ2TxaDU7l9U54dWYLU6zHnRMNZlRas/R4HS/IRyZ1GdCgQp2jvycpJbYwuJqVOF+usjToEaYJQsC5cw3QkD5HIHGa8XBjk/bIxjM1BVvimBLTEM9+bIknBFYo2OwpMm3nFpaijJA1hKwjZIU49CjWXILyTtJJFRS3oscoGzIRXOfmwxVGUYLvvpxU0jaeYMt5UrUzMnQskzjNmFt+5lBW8hwmikUqjkfN8zFNF4xJ0Pnn34kyEqVxnprSYxCmPtP+PYznDJdsM2+5Wew9i6YtaVA0AiL17LhhFFEJXMrB6U9WH2Y9xuzp3KnvVeMoLPzbPqHjpEhVxr3eKlX7dHSEhpRkWrESdjkTHL5NPiniOGVlYZP5h2tsbgzQW+3EzxdCtNa4nkVzssKZ2SbVegGtR2jNvr68e0EIgdISrUMEL79OQgiul2dRKB4PlqlZpe1hlUfBoDsiHCUUqx5dBtQoc15PHqtrD6BYCZi7ucwf/TcfcKZY5udLT9iMQiqOy4WLTe7cXqZY8lBoHm620VoTuAEq89A6RgibVGtGW/ryS5bDahwz1IJUaVZ7KeMlc4d0LYk0Ugoak/aOV5tmGssQlH1JpiOEsCiZ5/jVkyc0Ki+35lIMKVi/JlN7fx+LvsPSepepRnnbVP5spcyXy6s4Txs4ZBVUH617rA0l7gvFuEzbWCJizJ1jaXRx++8Fy2ChH3O24mBtLUTdKdEdbk3c0CmDOOPazBX2nUB6QmQ6JdMpU/65Uz3vSRGpjAdHU028ErxSIu6nUW4Wc4qSFtuwWP2aiDgcxTy4vcSjeyuoTOMXHCo1f19JTpJkLM9vMHd/jWLJ5dK1gMaJ0mWne7uWQvJu+SIl0+fL7kMcYVE0dzcc7IXhICKSeUQ1o8eZoIY8wcbKdix67SFxmHCmVKHpF5jvd7ixtkLsaAqTASsLbYY6I0kzPMtkpCISEZCmQ6SUmEgmpUXVMDGBYZwySDWmIYgzTXuUUfNzgknCvOtt+pKDYe18n/1Ica5hISW0wg4XS7/HxnqylSd+uW4s17i3ZbG497ZfCoEUgla7z9R4XtRqBAGNwKcTRhQcGxBgTpNEj0mzLo69W38bpgUq9hob0QSRym8eUgq0hmGstnPFrrRo2hXWohZJDJPVWcZKp58b7qWbzAbX8IzfULPGIXCkyWzx9N/naeHVEnESnfpuwDMs1sP+qZ5Ta83Kwiaf/+IBmVKUq8H2SHStE+J0jVStk2UdFDEgMaSPKaoUyk2kKBGFMb/82TyNZo/r36njB0db+txiUiHE6YrfpZDMFqZpOFVudB6wFrcxMAgMB1taO0hZa02sUoYqZC1s4wmLa/oCwZ49a8dDkmS4gGuaXKrUuVCu0RoNaU+O+NSb4//95VdIJ6OlExAKBwPXsqjqAdNGHes5lce05zFIBvQTcA3oh4qyI0lGCsOWTM/aWM7Om8YwUniWZKJiEaWbCGOMb9Xe57/+030qxZNragEEMa75mPSQ2W2+Z7Ow1mWyWdke4Hq5UefjJwskWZYrKDBQchpEBDpEC+cF7wZBqg3K9hqrYfDcXyF7LgWlNVQNRUsJIhpcm54+FVOh59FPO5StGhPumcMPfgPgFRNxtpXMP00YQhJme3vyngRZprjx6SMe3VuhXA9wnKfFHUWczRMlj9BkCByEcDDIL16tEiIWiLI5DFHAc64yNjVOa+UJ//DXc3zn+1M0J45AZLqHMKYQsnD4sSdA0fL5XuMdesmQhdEqK+EGrSTvcBNbulPQBKbPOX+CSbfBUtgiOKXpui+maaQQNP2Aph+QTitWNts8bD+B0MGzHQzTyKcl62XWsw3GRX07Ii9aJtO+y9wwYpRAEis22ynT0zbVcZvnMzBaa/qhwjQk1884aNFhM/H4w6k/glQQxynll+z8sowlchOdg7f9ppQMlCKME7ytSRqOZfL2+Bi/Xlqm6NiYhoEhTZA1kAnoVu6fK+xtQo4zn6qzyno0g9L5pa3J5XhaA3oIhKS6iW9d5eLUACUHwOl5/w7SDrZ0uFJ8f7vr8g0OxyslYsHpmzFr9LFysAchSzM++6cHLD5p0ZwsP2slVRHD5EuyrIshS3s2XAhhI7dUBEqN6Icf41qXqDYuMOz+kn/6+3W++wd1xib3j7ry7rs+pvf7p/J+DkLR8rlqnedq6TypyghVhNL5WjrSxpL5V+VJsMKCWjulZ9UY1v4kdWdxjdAZcnlmikE/YaPVYziM8mKf0WAg1whVF08WERhoBWVM6lnKapIgHElWkLj1fB6eyvKOu1Gc31waJZPzTYNYbzBMAn44+cecKYyztN49lZ2aLZdR+mgdn1pDGKfbRAxQCzyuT4zx1coavqWxTRPPskiVgSWn0HoAurtlCC/RwkCjcOWQQVYkVRmWSAiMbu61K2uE6hLtxOGPLp1nvOjyVfdTuskGRbP6UpGx0ope2iYwClwrf+f1KNC9iFdYjDsMr5SIfdM+DUOkHYiylNoptDtrrfni00cszm/QGC/tIOFB9BmaBNOoHulcUnoIbROmd9HGLF4whZRLfPyP8P0/bFJt7G5g0FpDtoJhziLky2mIjwtTGhTk3mtYbZZQSu+yoDwuhv2QaqO477h0rTWtsIeQGlOalEsmpZKbT08eRAxHEYOwSCsTlOI+Go1teRSLFh9MloldxeNwSHeoCDxBEkOmwDLgfNOiXpSkYsBmOqLqXuRH4/+MupsrP3r90aF+HIdDYxktMnU0WaYUMBxFVF8YnNosBLxnGNxcWSUMQ+qez5NOG8txEKIMugSEuXeyHiF0gi1aDLRBP5JcqNWR9jhaFGmNNLZh8uO3zjJezHdY75a/y5PhfRZGj/CNAo5x/HRMmI0YZX1m/AvMeBcw5Wuq43+Fxu+H4ZWuWMFy8z75l7yon8cojWmWX564Fp+0mLu/uiMS1hpG6S00EYY83nZOCAODKlH2EGl9C8cDP13kVz9P+OGPz2I7xtZzaNB9tOohzfMYzu8fSXP8m0Kh5NGcLDPohgSHTFs+CIPeiLc/OH/gMVrpXS3NvmfjezZQZJAGVMwiM26FLH2MkMtAjBAeApe6bzNvRUxPCIZZBhoECUoP6GSKptvkw+aPmSqc26EcCZNsuwZwUkhCBClHVSMYUpLsM7C14rl8eGaGh61NFrodTCHpR3FeyBMC8BDCA8pkskjglZmPLhN4kvHyBK0wJlWKy406701NPFNjAKa0mC1cpeaM8aB/k3ayjiUcPCM4MLWgtGKY9cl0jG+U+Fble691B12UpTzsvFFN7AnXsBhzS/TTiKL18jlHrTUKzbh7spyXUopeZ8TGep+//+tf47g2a8sdvMDB9x0y1kizFqY8mSJDCInURaLkDoHzXYqVcdaW73L3y0dcey+PUAQaYYxhut/Nc8OvEQk/xYVr0/zT3944MREncUIap2RJxsKDVeSWOXq1Wdz2pBVCUHA8VkYabe32RwBItaLsFLBtH+xrKH0Bla2h1ApKdRBKMx3Aj8cc4gwGmUKIKrY1Q9l9C8fc+3M8jZhAiORUd3uWIXlrrM5UuciTdodfLS6x0uvjbqUrTJl3EA4T6Ecd4jTlbLlGJ4y4VK9xsVGn5u//eZWtGu9Xvk8v7bASPqEVr2w1/eRtjs/XCyAv9tbtCSbcMxTM0qkX/E4bjmEyW/r6JK0vi1e+h7hanuC/rtw+FSLuJSHjbpGyfTyCSJKU+Ydr3Lu1TBQmLDxep7s5xC84rKmnbbSaxsxjStWAIzaQ7QkpbVI1IFUb2OY4jckac49XOXdllnLVQwgXccxo+zeN+kSZarNEu9WjUj96R2SaZGyudrnz+SOakxV+9Y+38yh16xq2HIuL12eYmm3iBQ6XxhosPFqjlw4pmTtlUJGKsaVJ2X5WxJTCQZozwEzebBK1uDZdwfPq+MKgKgqIIwwEdUxju9vt5DheQjJTaodb2n4oODbXxptcqFVZ6HZ5sLHBSn/AMEnQWlOwNRfrVT668A5jxQI139sRAR8EIQQlq0LJqnBRXydSI0bZkCSLUSgkEsuw8YwAR7q/XcW4V9ywcRheORGPeyUmvTKb8ZDqS+R2M6UYZjE/HL98rP/bWO/xq5/fZzSMKZV9XNfi0Z2Uxnhph4ZU0afb26C9kdCcgHL15K9V4hNnT7DNcaQUWE7AwmNNtfH1tIIqrQiziGE2ItnqqjKEgW96+IZ37AvKMCQf/vOr/OxvvqCz0adcO1zR0Wn1uPfrJ3Q3B5y9PM7F69O8GDImccrtzx5z69OHXP1wltlzNW4u1ugZbTbiHqY20Do3oHdMkyulc5j7vHatQeMyO34ewziev26p4JGpl7tqNcaxLnylwd/HcH8vOJbJhXqNC/UaSZqRaoUhJJYIEdLH8iZP8KqfQQqJZwSvjQ74VPAmR7w/pJB8WD/PXy18ySiN8U4weUNpzWrU473qNDXn6F+cxSctPv3pPYKCS2Msj0JXlza3JgW/IKsyRziWTSYtVhY3iaNkRxHvOBDCJlNttE4RwqRU8Xl8f4W33pnGdk6v33+QDlkcrfBkuECqd04Ozs268maCaXeCaX+SknV0iZzr2fz+j9/hk7+7xfpSm2LFx9mn8NZaanPj4/tYtsGV988yea7BXvt2yzapjZXIUsWNXzzg8iihZLu0N1wGvZh+PEKj8AyHqufRkwl2wcLaY8ROZxgyVS1RcI8/A63gOy+dVdDa4VkYdvjZBOA6J7scLdPA2spFqyxCyukTnecNXh1eORFDXrT75xNX+C/Lt1BaE1hHv3gypViNelwqNrlaPnoU0Frr8elP7lGpBTumxq4udfY0KReiD1gYhsQPXDZbfaQhqTePb1YkRD66W+kIQ5gYhkRpzcZaj4mZl89jJSrhfu8xc6N5pDAomYV97S2VViyFq8wNF5lwm1wtXcIxjnYzdH2H7/3Ld1iaW+fejXnWl9pYtolpG0gpyTLFxmqH+188Yep8k+nzDQp7TOd4EYYpqYwV+ev/7zPUhM+qragXPMYLz1I2cZpxd2md+0JwrllhuvasqDqMEpTWvHf+ZFGh7+bTOOIkPbHZj8Yi00UEMZqDv89JlmGaEsd6+Zuw0AnSaL70eb5piLKMh+3Nww98RXgtiBig6Rb5l5Nv87O1+6yEXep2cKAHgtaabhIyymLeq85wtTxx5C12HKf86uf3KZTcHSScZYrRIKJY3iPHLLLtrY0Q4AUOrdUuQcHF3WNo4tHwLEq1LIP2Rv+libib9Pm8fYMwi6hZh2tDpZCUrSJaa9bjDX6y/jHvVd6m7hxNmmdaBmcujjNzYYzN9R5Lcy3CfphbTdomrcVNvvMHV46UvniKOM348skKqScxVgZ85/dnubOeV7zdLftO2zSwTY9Ixfxy9TE3QkGt5BMmCVIb/Osr13FPOJZdSsHlc2PcuLe4YwLzcZFmdWxjgUzvT8RKazq9EWPVIovLbeIkBa0RUuK7Fp5n47r29gDYw6CFBvlqTdhfRziGwWzpaN/pV4HXhogBak7AH09d5053mdudZWKVYUsTz7DyyQXkkzxClUc8U16Zf1a9TPUY6QiAufurhKN4Ox3xFOEozicj7EVe2ng6OA/Ymsdmm6wttTlz4XgRSJYpomHEaL1N1O+C0mQKkmHM1EyVQiU40UiZTtLl49ZnONKhZh9PSiSEoGKViFTMJxu/5jvVd2i6R+/NF0JQa5aoNZ+t6cZql8WHq8ci4Uwpbs6vECUplYJPL+pBN+K9c5PcXVqnPQgxpcR0oCU69MQQ5UJrM2GYpJytVzg/VuFuuMSDhWUuFsa5Vp7C2Wca8X44M17hi7uLZEqd2AslUjO41iOybPdjSZrR64VsbAwYDCOMBHrWKE+JbXU6raTPCoblksfkRIVi0d3X/0LrEUIUEeL1lZG9wd54rYgYcqu+65Vp3ipNsBp2WQ/7rIU9YpUihaThFhh3SzS9IiXr+PKpLFM8uL1MubJ7i5zE6ZZEZze0LiBYB549p2WbuQlOmOC4h1/o8Simvd6ju9ZBWAOiVgPDsEAIVKaYu7OE7vUJyj6Xvj3L5IXxI+eMwyzk040vcA0Xzzi5AsWRNsIS/Kr9Jb9f/4DiMfLGL+Lx7cVj57zXu0O6o4hqkK+zV/RYvbfG2+fqfHt2ml4YcX9jjS+Hc6DBxsrz3OUA05S8OzOxvcXPtOJef4WVsMMPx64caTLxU3iOxdXzY9x+tErzGDeS55GqKpkqIAjRW74cmVJstAZsbA4QQKo1U+MVatX9gwmNZhgm3LyzhG2ZXDjfoLLH95ess6U7f32LUq8Sr/OqvHZE/BSWNJj2q0z7p7ud6LaHxHFCaY8vslJ6/0q32ju3KQQM+tGBRKyUZmO5zcbiBtI0cIoGQlSwqzu3kEpIGtM14lHM5393g7uf3Of9P3qHxvTBkanWmlvd+2j0S5HwU9jSwpUOX3Zu8936+0cen/Q8kjhl8eE6tbHjSPE08xsd/OfI27RNRr2QYWdEoRZg2BCWBrxdGscSJoh8MKYQ0BlGrHUHzNTziNAQkqZToh0P+IfV2/xo/BruMSLjK+fGmF9tMxjFBIcON90LkmF6maL9KUnmMhrFLC21STOF51qkSmHCoVabArE98y5OMm7dWWJsrMSZ6fp2ykLrCC3sLfneG/y24VSEgEKIPxFC3BZC3BNC/M+ncc6vC73OkP3ujQfeMZUP2gGx01DItExGe0yUeIo0yZi/vcjG8iZe0cMLHKQRQbRbqiZEvsV3fIfmdB1pGvzkP33MnU/vH2ji3oo3WQ5XKZunN68vMH26aY/F0cqJ/j+J03zw8jFahbujiGEU76l7VWmG1po7owUsYeAaFoYUGEJs65B9x2K+1UGpnRrgih0wSCO+2HxCkmVs9kcstrrMr7dZbHVY7wyIkt3DBEzT4KPr5xiM4j0fPwribIYkazIcrDA310KI3G1NoUmSjOlm+VhWm7ZlUCp7tFp9vrq1QBTl+mGdrWPav3fqLn3fKHyTjeFF7njzp8CPyaeXfiyE+Aut9Vcve+6vA/3uCGsfoxl5oKBeoJNphH0P9LMcnGEIomhvt7c0yZi/s0iWZARPLRVFgtYmOtuZx1NK7Wqr9Qoutmdz82d30Fpz5cNLez7Pw8EcgXE0P+HjoGQWeTCYY9o7eiH0KVSmjr0V7I+ifbvaVKbpqRFDFVEx944gLUMyCBVhnO6YqpFlimwEf/X4K+7qNhbGtnwvR56QKrg2l6YanGlW8Lai8lrJ5wfvzfKPnz2gXHS3i4VHh+Tx0kVkfJvAr4IwSTNFFCfMjFdxTyBXFAgKBZfhMObm7SWuvmXjurNI8/UwYX8dEaUZjzb3nWb/ynEaqYnvAve01g8AhBB/Dvwb4LUk4tw6cO+r3XGtg/tbsxpkFTAGoJ4jg33upKtza6RJihe4zw6UQ/Tocl78ew5JnBEUd+cwDUNSn6lz8+d3qTTLjJ/bWRjspwM24jZ16/Qrwra06EU92nGH2hFVFE9hWsaxA4wky5sS9oI0JMtxe4f/8J4Qeesz5Cmb5Y0ej1c3yJQmMzLwU8acvVUFUZLy+cNFPn+wxOXpBm+fHcO2TCYaJf7g2xf5+RePGI0SKiXvyDe9XnfE7bsDJhrvM938gnYvINMmZ8ZrBP7LOZT5vk0crXH/YcD1dz96kxs+AI5hMFt+fVUTp5GamAaePPf7/NbfXks4rkWW7lHGJp8aIYXIc8V7QqCTWdAmyCEASuWk8yJ6G316mwNc/ym5ajC66GgK0t1EEMcZpX2MyA1DUm6W+Oy/fEkc7pzo208GuRPA13QRGsKkveVPfBxYjoVpGaS7tvQJKlsmSb4kiT8nTW+hVAtQSJH7GSRpRmulw8Nbizz4ap61xTZhkrCedPHl4QU3KQSjOOGLR8vcX1rHdyzKgUvZ8VlJO/v+n2OZjJULNEo+9xbX+Ztf3mGtkw8ZGK8X+fH3rtCsFVjZ6DEY7Z+Oeoo0Vdy/v4rnWrQHTW4/uUy1NOTSyvdaVgAAIABJREFUTPDSJAwaU27iuEWeLL3Lo8e9lzzfG7xKnAYR78UAu5hMCPE/CCE+EUJ8srZ2Wn62x0e5GuxLtFIKihWfeJ9UAwDaRkdXQFsgu2RpgufvJAelNGtPWriBkxOkiMDooaNpdLx3k4HWGj/Yn2Rc3yGOEh7fnGeQxKyN+iwPe9zrLZOkO6cwnCYcw2YjPv6WzjAks9em6W4+HXqqydJHxNFPSJObaNVB6yEqWyOJPyeOfoZl9IjCmLk7S2ysdvNUjQIlNHdvL9DrDA+84TxdgihO+fz+IqMoplLwtqWAJgaRPnxogJSCZjnAkIK//ewec6v5+/ddm++9e54fvHcB17ZYafVotQcMRvEub4pMKe4/WGWjM2QUp1imwcz4e/j+v8K2Ukxjg9w0/vgQxFjmKnE2Ri/8AeVKkxs3F+h0Ryc63xu8epxGamIeeH4mygyw+OJBWus/A/4M4MMPP3xlafFi2QORk+VeRZKxyQp3b8zv65MLgHbQ4dsIcxktHuIEEqUshLARwmDUH5FmIY5tgsjQykUPr0K2d24zTTMsy6Cwz9QLrTUdHbNQTfj4i19w2d9AytwR6+HgCZnOsGWXsm1zplCk6fm5G9cpwJYW/fRkE6SnLzS589ljtFJk2V2y7AlSVuCFRh0BoGN88zarCxauLOAF+fpnScbE1QlEyeTx8jpjhQr2Pq3AYZLgmCZ3F9awDCNf/xeeRx0jYeJvTcb4yVcPWRuN0dUR3SjKt7nnq1w1xul2RqxuDmh1+mRqy59Ma0ZJzNL6JlNnSlRKLkXfwzUsUhXQGf4LPOcGrjmH1iapKnEUu0whQgzZR2PSG31EkuV+HYYBtmNy78EKH7x//sjv73cO33DTn4+By0KIWWAB+LfAvzuF834tcD2b6TN1Vpc7e2qJSxUPwzJI0wxzDw+DZ5Ak0QRpWKZaKqL0Bqlqo3VCd7OFaZvopIZOK5AVOEiTMRrGnDlb2/PG0FYRN7NN+jrGNiV2BIVIUtpyPdtMHbRWmMIkzDJ+3VrHlILLpSozxeJLTysRiBNH20HRY/Jcg5UndwjKT5Cytn8OXtioyMdOl1GeB1ikUYppm7gll8zQCAW9zQH1ib1zvKM4BQWeZe4iYchJ+NAc84v/IzSPojaf/HKRjy6doeg5xErx6fIiQsA/OzPLD8+Pk2Qp870WDwcrbCRDhgt9dCWm63bpxj2I8zW0pEnDKdFMrlF0LmCbc7jW422TSaUc9NZlKdAIESFEnt5ROmAQvU+STqBfsAAsFj3mFzZ5++oUnvsaTsd4HfBNJmKtdSqE+PfAX5Pf1v+j1vrGS7+yrxEXr04y/3idLNutVJBSMnWmztyDFcqH6DsH3ZBzl8Zx7QowBYBWivtzX+IELjo7PCpNU4UUklp9Z9NAqhUPsg6PVI9AmNRlHi33ZMKwN9omYkNIEp0hhMAzTTzTJFWKr9otlkYD3qk1CF7Cw0Bpva/D2VHw9ncv0Fr+OwYdh2Lt4JtCHEmqlmQ1G2Bqk2E/pHy5wfxGh1Gc0PEi+sMVmiolcCx818azLRzLIExShmFCYJnbiocXMVQRTfPouuZUKT5vLZOiqToeq+s96ud8hCHwLYswTfnPj+5xdaLEarxJrBMKpkvDLbK42qVZKe2qH2Q6YzVsszjaoGh5nA8uU7SuYMguhuxiGhtIESJQKAzSbJwsq5LpApkqsd8NXYq8I29hqc2l2a/Hxe+3GXGa8WjzG+41obX+S+AvT+NcvwmUKj5X3p3h9q/n93RQG5sss7HaZTSMduV/n2K45Ukx9kJ0liQZaZrhH3HCw6AXcvHy2A7Pi1Qrfp2t01IhNeHsiGpNx6S/OYDz+e+B4bGejbCfi5BMKWm4Pr045ucrS3w0NkHJPlmUFKuYonny7jrH6fHBj2w+/Xtor0eUa/a+2mLDzFMh5qjNw3ZM6WwdncSYysAxTSqRz6CUICX0woj2IERrjWObufeENCgH+3dbZloxcQx1SSsc0k9jak5+zlZvSLsfUt0qqqYkPB6ssDC3zPfPnMGU+Y07HCYkcbadXtnxHoVBsNURGqYRX7QfMuM3mPLqGLJBlF448ut7Eb7vsLrafUPEe8A2Dc5Xvtmqid9KXLwyydTZGuur3V3FOykl59+aIImzPRUWw0GENCQXrkzsIhWVHl0/O+iHVOsBtcYzolNa80XWYlNF1KW7e8qxIUnTZ0oE3/TJ9N5Fn6JtY0uDj1eX6SfxnscchkjH1J2TexdoPcAvmHzvxxOMz/hsrIZsrIQk8QvFrVSx0Ul5MBeRxpoLb09hVj0M08AyJEII7IHEL7hICa5l4tsW0pAMo5jhKKHTD8nU3msRqoSC4RIcQXXxFHODDv5znXiubbLYylUXa2GbL9qPcCxJkkjS7Nl3aDQ82hRx13QoWj4LwxY3u3Mk6mRNI0/hOCYbm4MDVD+/w9C5VcxhP68Kv7NEbBiS9797gfOXxllf6TAa7iQqP3CYfWuSXjfcrohnmaLTHuK4Flffndm7rVkcbTL1cBhjWSbnZhs7IvI51WNdhVT3I4wXTu6b7taf935SzzQxhOSLVutEuV6lFRX7+G5eaZrR7g5ZWO5xby5hfjWhdqHI+380yYV3yoSDjI2VnJTXl0Ju3O2zZkq+9a+nGZ+2ma4EXCqWcAyDXpLQavexSgENo0I7GdKPYoZpQjVwuDLZRIo84r2/1KL/grQs1Rmhjpl1xo8l8xukCbbxLLXg2RabgxFz3XXu9pYomN6Wf7Ymes7ZZ9CLj2zaJIWkZAeM0pivOk9eiowNKcmUZhSe7Kb7Bq8Or63XxG8Chmnw7gfnmZip8sWnj1hf6WBaJq5vY9sGtWaRKGpw7+YStm3guBZnLzQZm6zs25b6NMVw0EDUQT/EskzeujaB/VxKoqdj7mVtKmL/qC1LM5znJjnY0qZilemnfbx9JvAWLItWOGKu12W2dHRSDbOQolmgdIzW6W4/5PF8iwdP1remPXfJkgRhDPM1QaCBmStlpsdtAs/g14+H9NsJ4xUbIRTlWszafSAKGROSCjbyfBV/oogGXGnSM4dM+mVs0yRKMlKlKbg2qVLMrW0y3ahQ9l0SnTJQEVecacrG8aaqGEKgtH62KxEQZjG3NxaZqJSeaz4RO3YuSZwhjeMVSQPLY5CMuNtd5Fr5zIl14YK87vAGu/EqI97D8DtNxE/RHC/zoz95l831PssLm7TWe3Q2h2gN1XqBD39wibXlDqWyT7VROPAiMUwDN3DIkgzzhcp9lin6vYhKxeP8xeYOEgZ4kHWwhMQ44PxpnFF6obDXdOpsxG1cqfYdNlpxHO522kwHhR1R3n7QWtNPh7xfuX4kUkjTjDsPV7j1YBXTkFRKPoYh0TogiR6B0AiR50yV0ixvpDxZiYmlJjMFUzUbIQRKDWicucD4+UuE/RClNI5v71hLrcdZSjZ5GK8ySmOy5wJAU0o82+bReouxRoGi6/C2c4baCVzkxr2ApWGfkp3f+DKtacVdKrG7TcJpprCk3FEQfXH69FERWB7teMBKuMmEdzJfar01Ff0NdiLOMh5tfMOLdd8ESCmpj5Wo7+MWFg5jbvzqMYtzLVzfonBAm2ulWWL1yQaFLfLIMsVwECMEzF5s0mgUduWWhzplTY2oHhANQ36heYWdkW/B8hn3GqyFrX0La4aQaK1ZHQ2ZKRwe4XbTHuNukzG3ceixw1HMz375kO5gRL0a7PDvFUJimOfIkltg5C5yUgoqRZPNfsKvbg2pFg2qnonnAzpFGpMIKfD2mRIthGDKrtEwS7TSHp/3nxCJCPlUJSzBtyxky+L92VncE6pGpvwSTwZdsq15cBthF6QmS54RXSeKeatR3bFDMgxxYqlUyXJ51F+jYhVwTzA2DMGxTIR+V2AbBuerr2+x7g0RHxGub/Od71/i3KUxHt5ZYXlxEwE4ronj2Tui29pkjSd3l6EvUZnGNCXTM1XqjcK+zQhranhoq3I0igkqPv4erdCTzhi9pM8wG+LvswUvWDaPet1DiXiYjpBCcrV08dBoeBTG/OMn90mSlGZt7/NKYxKlNtDZGsgqWmnCKOP24xHNionWgq/u9Lh2KaVQuoKUR4tebWkyaVcZakWQtXExEQgkAssw6YYRC+sdLk4efjPZCwXL5q1SnVuddXzDopX08U07961Qik4Y0wg8Zso7X68b2KTJyVqOpTCQQrAStjlXOIH6QYNzinMP3+A3gzdEfAwIIWiMl2mMlxn0QzbWemys9Witdum2h9sDPKQUVBoFTEMwPlXLpyocImfb0BHOIc0Go37I5Q8u7vmYIQ0uFs5xr/eIQTYk2IOMHcOgFY6Is2zf9EQ/HaC04sPae7iHeBsrpfn0izmiKKF6gOZaCAPTfJtu9wYrTx6zsZLQGUhavQzPFhQbBlbZ4f7jad771vFtSoQUWFh4LzQ5lHyHpc0eU/Uy3rFd03KcKZSxDYNfrs8zTBMS8kaLQZIyWytzrlraNcHDC16OCH3TYSXcZNqvHzgu7EUkSYbn2Xs2s7wB3+yGjt9VBAWXoOByZjZ3Q8syhVIKgcAwJb3Na/zd//NTCgXnUBLWWtPWEQX2v4AH3RGVsRLVA4zWbWlzqTjL3HCBTtwjMH3MF8hdCBim6S4iznTGZtyhbJV4t3KVwDy8sDW3sMFqq8dY4+AmiSRKePDlPJ2WwLTOElSGLI/WKVclUpoM2w5qw2bTG1Etr3PhwvEiQdcy95StCSGQQrLW7nN27OTb0jGvQNM3qXpNoijD90w+PD+1bxu55+ef40EF24MghSTTmk48oO4evQFlFMaMN49jxP/1I1WKRGX5ZHQhsKX50t2e30S8IeJTgmHIHV16pVqRt7//Fl/8wy2aZxoH5u0SFJnWGPscMwpHJGJE8+oEw6yLyCSWYWMKe5dPsC0tLgRn2bQ6LIyWGGmFLSxsaW0X8p5KrbTWRCpikOWpiCvFi5wNpo/kPZymGV/eWaRaPrj7MIkSbn36kDhKKG91Aw4iF4XE2opSja2MRtgN+fyn9xhvlAhKRzc4911732AncC0WN7rMNPZXuhyGOEtJdEbZ9ulEETO10oFeHpZtUKq6hMMU1z9ZdGxJg04yPBYRh2HC9OSrzYOmSrESdlkadlkLe7Tj0Y66pRSChltgzC0x5Zeo2qfvo/3biDdE/DXiwrfOM+qH3P/sEfXp+q526oOQiZRIDuiqdWI7YuKtJi09D6NnlCOQFK0qZauJ+5wxvBSSulOlYpXpJj3W4w0GWS4fG6QR7aSDGefFw5JZ5HpwljG3jiX/f/be7EeyLL/v+5xz7n5jz4zIvfa1t+mZHvbMcIYckiJNyqBpmJDgJwOGYQjwix8MvRkwDPtF0IshGH4wof9ANmQRlgyJlDgSNSSHs3TP9F5de+VSucZ+467n+CGysjIrl8qsyuqu6akvUEB3VcSJGzfu/d7f+S3f72PSKHRMqsfyl0r4OGpv/nd1o0+aF9QOEdmHMdHf/nCRNMkoVR5H2KMk5yAe8yoeo60BH/71Tb71+28c+1wdlXZQUpJrwyjNCJ9RgyHWj/uStTbHkrCcOVPls/dXn5mIHWnRy6Jjvz7NcjzXZnLi2acgnwejPONWf51Pu6ukRY6rLHzLpuXt7TIqjGaYJXwcL/OL9iINJ+S12jTzYf2FRsppXnBv/VXXxK8khBC89p2rOJ7DJz/6nLASEFb3b/nFrphBo4lUh6HdJhkmuNLjwsULeOH+CFEbzSDv0s028WTIhLOAqzyEEFhSoqSk7lapu1W00WQ6Yznq8nb9LGdKdQLl74t+Mz2kk3xKP73N4x4sjWc1qbtv4FvjVMy95S2CIxTqcl2w1R6wudljYmJvVBclKfYhEWWlFrC0tEXUHRFUj2cO69qKsu8SpzneQflRY4ifg4hHRYYQ4/STpSTlA0aXn0Sl7mG7iiwtsJ2Te/5ZQtHLo2O7SHe7I964Pnuih/1pwBjD/WGbH6/fIzcFdTfAdg9PaykhCW2X0B53B0V5yl+u3mTKq/Bu8xwV58VYPblKcW7idHYLQog/AP4JY22df2qM+UeHvO7vAf8M+DVjzE+OWvMVEb9gSCm58s5FWmcmef/ffcj64iau7xDWwp2bxkJgC0lCTFetEGcjVGTTbLVozNRRB6jAjaUWNZ1Y0I+hk2xQ6FVKVo1AlXEsi6rrUvd9JoIAz7JwlYuvPKa8OqUD7Iayos/y8C/QJsdTkzupDGMMuR6yNPh3TAe/TmjPs9keUikdfNOMsoz3N5ZZWdwgthJC4+OJx5HhKMlw7YMvPSUlGli9s875t88c+zzPTVT4dHH9QCJWSjJMUiZ4/J2TLCfJc7Qe53EtJQkc+8BtcqELJIJhlDE7VcKynk52UgrOX5vg0/dXqTaO7+ixAyFgR5PtaESjlDBwOLvwbN0hz4pUF/x4/S53+ptMeCGuOjmdBJZDYDl00hH/avFD3m2e40L5xXyP0xjoOK41nBCiDPz3wI+Os+4rIv6CUGtW+c2//x3aq13ufnif1XvrFPlYNc0Axu1zP1ihLDymW7OUG+FODnU3jDG0RykrvYRhmiOlwFGCqusDhlT3kJbCUXV6acp6FMHmJq0wZKEyVu8q2/sJ1BjDavQ3AHjWXtdoIQQWIbkU3B/8Bybt75PmKeqQNrnlYY9RlpB3R0hfspINOOdUEdsT9dqYIx2pHN/m4e11zr21cGzz0VrZx7UUSZbvI3khBFmu0cbQHyWstHus94fb537sXGcMeI7NwkSViVKAvauYKRi7tmhjmJo8/ta/PhnQmi2zuTqkXHsxkZ7Whn4/5vvfvXKoF+OLQFrk/OXqTdbjATPBfuGsk6Lm+GS64K9Wb5MUOddr06d0pKeO41rD/a/APwb+4XEWfUXEXyCklEzM1JmYqWOMIeqPSKKEuIgY9jowmme2fPgIcpIX3GtHtEcZvi2p+k8StcCVAf28jRSSst0gsO0xeccxD7pdLtRrBw59JcUmadHBtx93LBhjiIqIzbRNL+9R6IJMD7mT/Rl3hx5Dt0TNblCxG1jSwRhDqod0sxW24ocMsiHGligh2TQdQquOK58+TCKkQBdjwaUnpxN3wxhDYQwGgxKSy/NNfnF7BdtSe/ONxpDkGe/dWWaYpjiWohp4+8gjzQs+X9nglhCca9aZrY8JRklJt5/w1oUZgn3n/GicuVRn0I2JBilB6QSpETOezjuqcKqNYX29x/Vrs0w0vrjccK41P1y7zUY8pOWfnnO4LRVTfpmfbtzHEorL1ebT33QSnE772kHWcN/a/QIhxNeBBWPM/yuEeEXELzOEEISVAL/s8WnvBmedOksP+3u1DXahF2fc2BggBdSOIAMhBK706WVbeCrAlmPCKTkOSZGRZoZ/c+cmv3X2PKH9mBgG2SJyV7EuKRIWR0v08wGWtHHluA3PGI9hPsSXDQSCtWSFtWSFCadFbnrERZeKoyhZAZsiJpQO08pFoOlna/RZRyibQlsoKTDGoLctgwRy+7sLEAcPt4zyjI1RxFYyopvGO0QMgtCyyQPDcr/P7C5R/GGSshlF1H0Pegmbm0PWtncjtmtTbZUIqsFYStPyKbTm5uomSVZwvlUnGWlKJZvZqZOTju0orr49xSfvrZ6IjDNT4Cv30AKW1mMSvnyxxbXLX2z0+Fl3leWoy0xw+q1ySkqafomfbN6j6ZeoOcerExwLxyPiSSHE7nzun2y7Cz3CkdZwYpzP+9+A//okh/aKiL9krCeL9PMt6u4kC6FmJYqouXvHnDujlBsbAwJ7LAn5NEghUcKinazR9OYRQpJrja0UVxtN+knCn92+ye9duLRDxoVJkNuXQzftcm/0ACkUZXsv+QgUys5BGBSKUJXITcat/vtYEma8syjH4s2WRt4YEAh3W1heIoWFMQXG3qCbDBDYaPbKjEoUeWoxWSojd+Viozzjdm+LtWicUvCUIrTtHc0HYwyZ1ihPsjmMWNnsc75cIxQOD9bblDPJ2qiLkALHs1G+A2YsorR6ZxMpt6hNV6lNjwc06qHP4laXOMuZbZS5OP/s7W+eb/Pa16f5/IM1elsjStWn95anOqd5iNt0NErp92OuX5vl2uXpL7T9q51E/HxrkZb34iJwWyo8ZfGjtTv87ty1U1kzzQvubRyra2LDGPPNI/79adZwZeAN4Afbv8s08KdCiD86qmD3ioi/RGhTsDT6nFCNb7hL1QpLw4hM652ugijN+fwEJPwItnSIi4ikiPGsgG4y4nKtQVIMkVbGxijmX9zs8XcvXqVslVDCRZPTy3rcie7hKx9L7r88DAW2ZVMKHLJMYzuSfr623VcrWE8e0vJmUErRmq3QXhlgVdyd7xvlfXJSEvp4oonNE8arRjOKhpirsDy6zaQ7z8Yo4UZ3E0sI6u7+lAKMdwKOUjhKUZpucX+jw6edDYpejl6KqFTK+GVvr1WTAMuxxrZW2rC1uEUap7TOTo7jHgMPu33+82+9xodpRlJkuOrZ2tFc3+L6O9Os3O+xeLuDZSuC0sHFQRh3nVTtvdFglhV0uxFB4PD97175QtMRj/CTjXv4ln2sTo7nQdXxeRj1uNffOpX1HEtxbvJUuiaOtIYzxnSBnWqjEOIHwD981TXxEqOXbZHplMAeb/EC2+KNiTrvrW/S8j0McHtziMCgtSbRGiHG27fj3AhKWPTzLbppBCJiUPQZjsBsN8wtdzP+5Z0VLk2EVKyAJGuzno0OJWGATEeU7AUmWx737w3AzkiLIY4cF+5iHdNJt2i4TWozARsPxpoLuUnpZ20MhsD1iWIDagh6LxHnmSGwLFpnZxjmfT5t/4ykqNDyasc2RFVScHayjqsFP/3gFsqA8uuH++XBjsjQYGNApg3hdJmFVo3Qs3nY7XOl2eLTzs+oC43DJooRggKDRU6ZlAlyUSWjdujnKCWZP1+jPhmwcr/L5uoQIcDxbGxH7UTc2hRYQlGxQ7KsYBSnxHGG59q8fn2WswuTX2hh7hHaScR6MmDa/2Km92quz0edfT7Ez45TyBEfZg0nhPhfgJ8YY/70WdZ9RcRfIjbTh9hPqK3NhQHrwxGfbrXpDGIWezHBdvuaMaALjS40UoBrWYSuS+DZWLbaF11pY1iJlqnYVV5vNHGVwzBOx9KNlmQmDFjqppyvBgxEyo3+XQSGheDsgcerTQ4YPKvJ1JTFnTt9RlkbucuB2BUe/bxHaJXwSj7Vlk97vU8e9JHCQgkFChxlk5sEKXKEeXwZjvo556+UcUo29wcZm4nBV1vkxsfi+PlCKSHajCgpm1LV40G/S8st4dkWltr7INPakBfFeEzdVWTdEdffPs/kZJm8iLm59AF/UO/QErfwTYARPhp3+4GmsejhsI4wmoKQoblILGbgEO2QsOxw6fUmCxfrbK0N6WyMGPSSHWeNYR7T8uq0t4b4vkNzssLCbJ3JidIX3ie8G7f669gn0L54XnjK5mHSO53FDKemNXGQNZwx5n865LW/dZw1XxHxl4h+tokjH7c1JXnOg60O/U6fbJRwpzui7jooII1z4mGyozVrNEQmYYsBUghKvkujXiIIx7q+w3zAZtLHEoKr9Tp5Cvfa69t9s6ANY93gcsidzpA3pkq4qkWUL3Mvus2sPx4OGX+YoTAxuY6pudexhIflQ7Pl8Plyn9quIRUhQAmbXtajqXymr1RY66+Q9SGoPCaR0Fe0B6BVjKIExjDsppRqDme+MUU7SVkZxtQcF4NmK31Iy104NFJ/EllWsLrcIQhsZutVEp2TZwUlxyZKc5JdLhZKSQLPJnRdSr5DMkyJOxGmnqDyz9B5ynq/yWTpCg+idaq7CkgGRYFNsd2jLE1ClZ8TmLv0eItcHB49up7FzJkqM2eqaG1Ik5w4SRnkCb89/Sa1MHxpBHwKo7nV26DhnUxc/3nhPENv8i8jfjW+5UuIXGckOsazQ4wxrPcjbq5tYLSh6rvMG8HasKAXJ8SDBBuwlMVB3UwaGCQJ0XJKJfRw6tDXI5qeT8PXZHnKw80BnmvtcpUYi5pvdvtoYLqSoaRL079GP1thcXSPaXc89myMxrVqVN2rOPIxsZy94HNzeazPa9mPo3Fb2IyKiELnDE2b1us+vdua/lqKVOCGFq49NgRN0wQZO5jCYFUc3vj+HMYS3N0YEtoWQggEisLkdLMNJtzjdQhEW0PSOGNqdgIlBYG06WuD7dtcmqhhMDuiPOKJQrgqOTx88BnT0znSrmDbPoNE8HqzzmbaIy5SPHVw94MWLglNlBnQMD+ka94ikU9XlJNS4HoWXYZ8b/YaU+HJ7aleJAZZstMm+EUisE5H0jPNC+6tvRpxfoUnUJgcwbg4dXNti+V2j4rv7Gz9VqOUMNXQK5CWYiQNBRprOxGwR0gFsGyLuChYGfXwSLnSrNOquKRFTGc4RCl3301kKUlaCAZRzP3+gFbJRgmbmnOGuGgw0jmT/gVs4WPJ/WkB1xWcu6y4+6mmXJVjQXR2hsIYFRHDoo/v+vjXBfWzBf3VhN5KjM7BKgTDuKDW9JGBy9z5MmHFYWU4ojAQ7Eof2NJlVAxJdYojn94C1m2PcFyLcJevYGjbrEUDGr6PEvKQQplBmBVM3ibN5/EdGyVyktwgheRSaYYPOnexhDwyOi9ECW08arxHR3MsMt5I+iwEk8wFE0997ReNfpZ8KSqS7jF3QE+DYynONl8Jw7/CExBCYAzceLjJWm9APfR3nFwLY1jfGlJ0RgSBQygEuTEMTMHIaBLMuN9f7MyFYSGpKIVVypCZGUeEng02DJOEsnOwSpprKaIsZ20QMVN+HO16qsQw7zHMEybdg217BIrapOTiVYdbN1KCQOK42+RmYJB1tqPN8d85gWLifEDjvI8pDLlOGUWK+/cczs4HTLa8cV57GOMfMNYthWKY93Cco0dgoyRDYKj63p5oVwqB0dBPEmrewflmozfAbIEIeKSsqQ1pjJd0AAAgAElEQVRY24W00PK4Vpnn494iJYsjydgIi9Q0qPJztkxILg53w95IejS9Ct9oPF2M/8vAIEu+FPnK0zwXL7Nn3a+si/OLRJLmbLaHLK60ub+8xeJKm/WtAXHy2GZdolhqd3nY61ELvD0/RG+YMOjEuIGzcyFaQlCTFjPK4Yx0mFcOs9JlXjqckS7zyqHmFHhS41o2UZGzvt6n0BohrMOtpQ04StJJin3FDF+V2Egfkpv9zsKagmE+IMpjys2c6296FLmh39XoYrzlj3WEdUD0KrYHNobDHE9V+I3vTGGHFlmuGWY5udEHdkhYwiYuhkee+8EoBQFX51sIsd/d2lGSrXh0yLtj0KvwKN+7Tb5ZoSm5j4+n5pR4rbLAqMiIsuSghXZghEVBQMX8AnHAecx0wVrcYcqt8a2JKycSgv8ikW/rbfxSwxzjz5eEVxHxKSHPCx5u9LhxZ51uP2KnEXX7xx1HwIYwcLlyfgo7UDzspNS9YN/lvbY0Hjw4LBoYaz88CUNhRQhj7YjY9KIYbwg1v8Ygyg+Ui0zygla1xFreI9OGXXwzHq81hkHWobYrCtUYHkR3GOQ9EgPt0T3OVC/yxjs+K4sZq8s5oywHleOWBKjxSdAa0sSQZeOrvtFUvHP1LGHgMdeO+fB2h7VhfOjtLoSk0PmBimR5oelHCfVywKXZSfJujCcUqda4u3QjbKWI8mxbY2LvJ5liGbDQBUglcN2xz582MF3b+0CpOSFv185za/iQTjqkZLmHRseFCHDMBr65TyQujM+F0XSziMJovl6/yNmweSwd6C8N4ngCRK/wbHhFxKeAlbUuP/voAUmaUwrdQ73bAOIk4ycf3uWTlXWaFwO0n8IuZ44kzoiG6YmlE43QGFEg9ZgwlBQUStDuRMyfKzOIumRFsUfMJslylJRUA5etnk1uclz2fq4jXXp5ew8RpzomyvuUrAqBChjlWwzyHnVngjPnHWbnbZbWYwZdhYkgGo33+LYjqNYllaokqEXUgxlCd9yZMVn3+PW3mvyrny8Tb2iUzvFcdcB2WKDJUdu2SGleECUZUgguzTWZqpXGKYhGyEy1xGIc4fp7BXyMgazIcfYMZ4zADEGUiIYZM2cCpJL0RjmzNYeSt//3cJXN9co8G0mPxWiTYTrEEgpX2fsi24wqvrnFZj7NIB+3AS4ETa5V5ijZpzjG+4LgKYvil9wd+mWO518R8XMgzwve/2SJe4ub1Co+1QNMPZ+E59rIWCEsweZ9xSDfYm7Sx9ruD416CVKKE8ceRuQ8eanZtiYeSopEc65ZZ3mrxzBOdvLTgWsz26hgK4WrPHIdwxOTbkrYjIohGrOzNRVsK8YZkMIisBqkeoRBI5AoC+pNSbNpU3b3n5NMj5C4VOzZPX/v2IqZKR8vtBiNcjY6j3trlRQIMXYX6ekUKcbk7tsWl2YnmSg/oZgmBeeuz3Dvrz4jd/enOoonTrDRHUBitEFrw8SUNxbSTzTvXjxcOU0gaLpVJt0K/WzEetKjlw6J8vjx77Et7+aLPhWrz/nqVebCSfxDOi9eRlQOUOz7InCQBdazIM0K7j181TXxlUOeF/zo5/dY2+jRmiwfu6hgjOFBu0vJc3Esn63hEovpGvOzLSwlGfVjHEtBUTx9sV3Q8oDXC4PIfNrdiLPzE1yYbhCnOYU2WEribctFamPwVYCU0T6ftfF22aBNjtzWFHalx4TTYjNdA6Bqt6jaId1sEUeGaGMo2WVyPdhWERPb312T6QESh0n/OuqQ/LHnSuqlgKmGR5Jp0lQTJTm6MMS64Ey5TjUI8B0bz7H2pRgeYWKhzrlWjdsbXerV4Ohik+5gjEuvkzF3LsT1LNZ7KWcmXJrlp7dQCQQVO6Bij/tsh1FKtxMxHGRkqcYSAt8JCMqCcnni0Pa3lxUVx/tSIspRkT39RceAYynOPodv4YvGKyJ+BhhjeO/jRdY2ejQnTqbINUxShklCPRhHihXO0NM3WV7pMD/XIBll+I6C0cmIeN8xyhyhHRzhsd4ZcHZ+AoE4JE+smfADynaVuIjwntAZNma76LXrTmx5s1ScsZynpzwkCk+VaCcP6GWbnAnO09cRqY6RQlDoBIQgtFpU7LkDi3gAniXpJdtRsJIEShJ4UMNBm4JcS86XjiccrmzF137jCslffMKDdo96JdzZeag9pJxRZBmDvmRqwWd6wWe526Psp5ybG7G6XWS1hIMrqzgqxJFl5BOTc8koZ+Venzsfton6KTCuCzzSVDY6Q4j3EVaBGzice22BuUtTBMfYSX3Z8JRN2fYY5Rn+KfX2HgejPH36i74CeEXEz4DltS73l7donZCEAQZJuifitPIaod1kkG7S6XqAwRICR0Buxv99LJjdYjbjYpiVltFSkBXFHiGhJxHnBTNljylvgTuDTyhMMR5FfrQc7Lh1PP47gS/3EnZo1dFGMu1foeGUWY4Em9kKoapRsWfxrQZKHH0TN3yHB90RZXf/v6U6pmIf3Ep3GBzf5tt/5zUqP73Lx7cfojONtAS5n4E97vBI4j62Mpy5EqBLI25sPmSmkXH9rMYIB7GdN0/1kFHRxmQGJSzK1hwle5oikdx4b4P7n3UwQKnqMDFzQLugMUAH4dTIU82Nn93m0x/fZPbCFNfevURYebkJ+Xp1mh9t3PnCiFgbw+kkJrbxEqe4XxHxCZFmOe999IBa5dncZ3tRvGdeXyBw4jk8f8D61gY2YyWwhm3xMM0pqeN9hjSKRz04RmSotIIwFpocKSVpXmA7BxNxbgyt0MeRDlPeAsvxXUJV3rZ1L1DSwhJPv1SifIAlLa5VvomjPCa9S/xs618TqhJCaLQegrCR4vBzV3IPvsmNMRS6oGqf3EanyApa9ZDY9VnstGnHKZ9spSglmJiqMXcxJKhadHnIVJjy1gWfybK77xiVAHtb70KbnG52j7v37rL4owqi8Km1/KOlMoUALYAC27WZmKmPdYWXt1j9v/6GN793lblL08gXrGz2rJgLa8gNeWwfvedFNx1xrnSyB+9heNzN/nLiFRGfEMurPbJMU6s826nrJck4B7wL0tgE8WUS+QmZHSNzn5ozJuInc7aHQWwTsZYpVlZBFWPCMIXBLimyPIcD0hJxXlBxbGrbE2g1ZwKDZjV+gCNdtNH41tFyi8YYenkHT3pcr76DJS0G6RLd9DOUvsdG2se3/J1WZiVLeNYZHNUY9zjvQsWz8S1Fmhd7zlNSRJTsGp48ntZBHGeMopT1G6tsfLaG5SiqrTKiavO7k00cZTNIEjrtAcniIra8zbe/PUU1OOYuxyiWfm5x6/0efr1PvTaDFMctaO3KwUtBbbJCluT87C8+Yn1xi6/95vUDfQq/CGRpTjyMMWacGvJK3o7QkKssXq/P8EF7iakXrMBWaE1S5FyrnqLo/auI+KsBYww37qxSLh2wbz4m8kLjHjQ1pl0q+TXWkxtgdympOjVL0c8L/KdGxQYj8rHDRR6i8r1kZTmHtx71koxvzDT2kH3daeKpgJXRXXp5h4bbOvCBoI1mmPcpyJj2zjDvXyTKNrg/+lsMKZ4qMRu+yd3BJ4DC3i5QaRMzzD5mlFn49hVc67E9kxSCixMlPlztMrl9nnI9ztFOuQscBa0N/d6I1eUenfaQ3t1N+vfbuDUfYkEvyfAqLhPzPo5lMeF7NEsR3VGHwX3BSpBTftc8VQDeGMPNnwy5/1HExKyPkDDMVwEIrCMidqMZi4Xs//0tRzE5V2Px5jJ5lvPO33nzCyPjQWfI4ucrLN9aZdgd7il+CiGotSqcuT7H1LkW16rT3Bts0c/iA70PTwvr8YC3GvPUj3CEPile5sm65yJiIcTfB/5n4Drw7tPEj3/ZEcUZwyilOfFiBLlt6eHFF9G9VYr5Hs0wp9uX5MbadrnYDQNCo+WYpFQWYicTJN7GziuKXKMchTokJdGNU1qhx8wBxSJfhcz5FyhZNcpWnV7RZtuRCGEe/afAZgJlqtzpaP5y8T/STm5jCR8pLZQYUHdTqvYMsbnPpGewlYsUHlJ4aJMxTD/EmKt49mMthtmKz63NAaOswFaapBgxH1zGPkJjoig0d2+us7HWx/Vs5CgjXx9Qn6vtFMvawxFOR/HZzVWuXpxCqBGD9HNcq4k7t8mDz2KCmsWZa0cTzL2PRtz/KKIxY++sbcuQYb6KLQPsQ6P2BER1zwMt0wn9bJN2torWGirw4ONPabPEd3/nu4RW9ZlSYMfBaBjz8V/fYOnzh1iWIqwFTDzxUNbakEQJ7/3FRyj1CdfevcS7l8/yZyuf4krrhaijdZIRDTfkWnXq1Nd+WfG8Z/FD4I+B//MUjuWlx2AYH+k+fBxYSqGPeDJbliKw50nvRlSbmnlrlbvZgJLaP3cvtI2d1pBZiNQ2BoO0hmiZILRLnhXUp0rkiH2CP2mhKQy8OVU7sK1rbBw64M3at5lwp8h1RqwjCpOTac16lHCj06GXJSjRQ4kIzF1mwvqOPnFhDFFWsBUXjIoJPutsslCOOROWsZVEChsha0TZZwhh4VrjG89WkrdmqvzgziITgc18cJmSdfhWWGvDrRurdLciqvUxCS79fBGn7O0Q5SjLqIceZ2oNhlHCJzeWmTmzhrQ8pLQxwqfWKrj3wYi5Sy7KOviH7m/l3PrpgNqUvcdhWiBQwqWXL9OwzyMO0iI2Cahx77Q2ms1kkU66BmJs+iq3I2B3LuTmR7ehNeDcxXOcL31tj1zqaWD1/jo/+/MPwMDkXONQspdS4Jc8/JJHnhV89MPPqN+u8Y3vLPDT4SITbniqZNxJRygp+N7UxWMbARwbX9WI2BjzCZyuMMfLjGH0/K00Fc9laxDhHCLwrZQgqPkwyil6glnvEroY8HDUp6zk+EFg5HiU2ezvZHCTBqNghTxL8EIPN3TJt52LHyEtNJ045d25CQL74Eugn3doenM0nHHawJI2JVllIx7y1w/v0UlHVB2PmaCCMQXro4/wrNIekXglBKGtCG0FOAxzjzvdDe72urzesGn6HkpYWLJKlH2GrRpoA3ERYds535ydZaXt48uDdyB5kpMMEhZvrbOy1KFSC8iGCXlakEUpfiPEYIjSjCLXNCyP1Y0eBsPDuMtWssmVCy2wC5RoYFlLpLFLezVjcm5/9F3kho9/2MML5YFErYRNqgckuo+nnhD4MWbc1y0baKNZjW8zyDr4qrzP0UNKSaPVYOVvR1RbW6T6R1ytfOvUyHj51kN+8q9/TmWygusfv5/ZshXNhUl6m30W//wm3/jdi7w3XKZku5TsZ0/XwfjBvxEPCS2H35q58tzr7f+Ar3Bq4lcNeVHsiYKeBTXfZbXXZ/dY8248cjG+8LU5PvvxfQQw75UQCFbyISVhH5Cm2PV+bSPbdXRlg9Lk44v5EREP05xRXvDu3ARTpf0pCWMM/bxDya5yIbw+7ntOMwqtuR+1udHfoGy7exx8k6KNJsMWByu8PUJoOVyuzNJNR3y82Wc6TDhTiVBCUOgBenSLknuGljdP053Dnyjxgb/K+w9XmPB9vO22qeHmkPWba7QfdDBas3hvC8tWJIudMfGuDzBCkEpopwmmgKYb0MnG0RYIjNzk4cMcy97CsiW+I2hVc1zfYenz+EAiXrsXM9jKacwcTl6W9IiKDTxZfYJgR+O0hCyxET8Yk/ARUb7tWYy6CZ0bGe7XMm71f8a1yrf3tRGeFO21Lj/5Nz+n2qriHNKh8jRUJsr0tvqs/OAuv/OHr/OTrQesRX0m/fCZ9DLiImMribhYbvL1iXm8Z/QF/GXGU4lYCPHnjJ1In8T/aIz5F8f9ICHEPwD+AcCZM2eOfYAvE4QQz729CT13nyLYbjwaRAsqHle+ucDt95cY9mJmywG+UNzPBwhTEAhrf/FMa5Iow/VDZpqTRGqVkR7iWT4CwXqUUHYsvnemSdXbTyaZThnkXSbdGeriAj/8fJHl7jiCvDva4mHS40y5QTjhwK63D/Ml1LE7BsbGkGXbYz1O6cUub00EYGKEEJwr/8YesnlrapoJ3+evHtxnqx+R3u7Qvr2B5dqUJkOGgwSr4hOGzvb502wtd+is99EPt2jMNZidmtwZ5AAwZGiV4+YuRaqolW2youDhlo9lNknX6xizd1rSGMPdD0aE1aMLaBKLVA/JSbB45HCix9/Pvk6uM7rpGp56endGOOGz8ukmC282GZg2/bxNxX52reI8y3nv335IUAmemYQfodIos7G0RfvDNX7/3df4qL3CJ92HCKDm+E9NVxhjGOQJwyzFUza/NX2Z+fDFTb6lWcH9lV/iEWdjzO+exgcZY/4E+BOAb37zmy/xJuFwhL67o33wzGu4DmXfI87ynRHj3cgLjb+9XQyrPte+c46lz9fZWu7iKckVv8qqGNE2CVKDJxTkkCVjicX6dJlaq4yUAkf7LMYPkEHG6qjL1Uady43aHk0GbTSpjon1CFvYXCm/TTz0+fObtwkcm1Yp4Fa0SSQyzpbqJHnO+w+WuTrdZLpaJtcRmR7gypM5SkghaHkODwYJtlB8fbJKnK+TFB08a2/v6Fylyn+ycIH/5//+IYtL6zgNH2FJMq3pdiIsS1IYTZzkbPQHDKMEN3Qpey5iK2FIh8pM/TGxinGKyXElnW7BxIQ9PieqiSqGdAc9HqzZzExUsLd3Er2NnKh7dDS8G4VOsR5ZTdEDNY+QNQbp6rjQeYx0nrIkRaZpL/UpnfFYHd15LiJ+8Okyg86A5vzJe7EPQmO6xs337jJ/dZa3J+a5Um3xYNDm4+5DOskIxPh3tqTaltswpLoYxzIGWn6JX5s8R8srn34++Ak4tuLM1KsR568ESoFzKl3hC/UqH6+sHUjExhiC4PHNbjsW516fYfpsg62VHu21Pq3YoWokHVLaJOApgimXoOwjbEk3SwBDoQ0VOc3fu3aNkj+iX6wTFV3QZke4RwpJxapzNrxKzZlkEGf829s3mAjHLV5r8YD7UYeGPR7C8KXEVpIbq+uEroNrP7sWgNgm49u9iEnPpulDYeJ9r8uznA9+8ClTts/Fb7xGZzSik8R0RjH9OAUM8cgwGCRULJdKvUwxyLFtGyxDtBUhpKQyPc7bGpEBAilBF2aXJKZklExRqy6TJhF3lzMWpht4jsXmUnJoAW/f90KSmxEuFTB9IEBY5wDopusnyvW6JZu1Wx0mz52lm6+T6QRbnjx/WhSaz9+7Q3Xy9Pp/pZIoS7L42TLXv32FwHK4WpvicrXFME/oZwlbyZAoTzGAEpKq7e3siL7IUWn4Cg90CCH+C+B/B5rAvxRCvG+M+f1TObKXEGHgYitJnhdYT+nxzLOcXndEmmQUhcZSCi9wKFd96qFP6DiMsgzffnwxPtJz8A8ooHgll9nLTWYvNynygjzTCDEuoOQYRnlGth1tCCFwpaIbxbx7boGrU83tVa5QmJxUj01IpVA40t2T17u9voqSAseySIqczwZrlC1v71i2VDhKsdTpcr75fDeTFIKGa/PeZo/vTRsM+8XTb3+4SHu9R3NuHClPhiGT4Tgf7fZgECWsbPSohGUspUi0YquzAdjjjoTQZbjRxy37uKELFBx2W8ZDRfDaRQLvHkkmuLeyxbmZBp3VDDc4XtQ2VrfToHsU2ibRV9BZjq0MmU7x1NG59N1wA5veejT+HwO5ybA5ORF31rokUUK5frqtl5XJCnc+esCVX7u0M/ghhaBse5Rtj9ngJfLee4llPJ+3a+KfA//8lI7lpYdSkgtnJ/n8zjoT9YNvpmgQs/6wy/pqF63HwwGPROFNYbBsxcxCg/ONGh+urOFa1k77WDRKmWiUsO2jSV5Zak+zvwM4au97ulHMdKXMpam9W1klLPxD8ndZUXBzbZN6MI7Ybg830RicA1wjfMdhvR8x33j+CMtREpnDR1sx55+YWIz6MTfeu0tj+nCboQerbULfwdo+B07goiyFLjRSSRAC27XpL7dxLj3uTdUalPXYPFTnYx2KcmuSfhYSWjdRYsC9FU17TVI7xkMnLwyjRDPK26yPfNbbNYTYHK+PIS21aVYl5ZJ/rMKvtCRFWpBG2Z68/EnR3eg/dVDlWaAsRZEVRL3o1En+VwmvUhMnxNnZBp/dWt03b2+M4eFimwd31lGWJCx5B174eV6weHcDIQTNmRJr0Yha6COMIcs0U63nJ7ZRmpFpzbsXFk6kCZDmBdoYlJQkRc7DuE/tENFyOfYhIi9O5+auOTZLUY9RDuEuvlu6vYqQYifa2g2jDYMkQWu9Q8Iw1iIutUp0l7s44VgzQtoWySAmHaXYJRvQpImm0Xh8CwzaKdNXyihbonVIP30DVy1j0vsM+ob61AQHTcVluaHdhfXNnDjJidOE4WACowNcZ0TgaxqVgJLnsGksltfaBL0Rs9O1p+6sxl9IkEQZwhnbRT0Lth62cfxTbgnbhWH35Sfir2xq4lcRYeBy9eIUn91a25mwM8aweHedlQdtytWjhV8sS1Gu+mRZQX+xR206oDMcoQpBs1mmXH6+XtEozRgkCb9z7SJV/2RrCSHQ29u31aQ/tpp/SlHJliG59ilMghLPfqMbcmzhsDyEyV3c/+DGCuXawbuP1XafXBt810FrvUcsx6+XSKOMuBNhPyJjJUn6I5zQRTPeqZYralzB30yptFymr+zuZlAkxQKprtONbtBKRniu2N7hjg1cNzqSxeWxZrPv2ShVJtMWldIUknEfc5rlPHjYxnVsGlNVRLBFGhcsrbRZmGscS+QnzoZM2a1nyg8DJFGCdUjP+GkgT/enlF4mZGnBg6Vf4q6JV9iPK+daLK926Q1iKiWPjYddVu5vUakfX5HNthVhySVajahNhTyMh1ybOrms5iMYY9gajhBC8DvXL9EsHz8P+QieZRE6NnGWszjqEh4hXp7rAiXH4vJSz9NNP38uIs50xEx4js+667w5MYMQgjTOiPoxk7P7q92F1txfaVMte6AN6xt9wuDx5wshqM7WEEIQbQ2QtoW0JOkgAVMmTiAsCXRc0BvlNOYCzny9jrQOIEXhYWSdh12PczNlBCl5kXNvMaPTKwhDB6Vs8hw6/S62ZyHy8bEIxvl2x7LI8pzlpZzSbErFC4iilE43ovHUSNKQkzLlnX/m8/vC8ZIPddm2YmHm5e2aeDn19l5yWJbi22+fxxhDtxfx4O46YdU/8YShZSsKDLqX8l/9wa8hlGC1NyA7oTvHIElZ6Q6YqZX5u29dfSYShvE46/WZFuvDIXGR75HrPOgz5+vVMRmrCQQSfUCh7TjQFCCg5DRJdcFwWww8idND7+/eICYvxg+Dei2kXPKIomSPwaWQkspsjYmLLdySS57kjDoR3bUBeuDgyxF+zebK91qc+2YDZR8y7WhJLCXHaYdMYihxb9GhO3CpVipYlocQimiUImSONGUOsne1LYvA9uht2AySDr5ns9WJME9piUyLhNCrUD6hFvNuBOWALD0dt4uD8Lx9yV8IXrk4f/VQCl2+/+5l/r9/+wGdfsxseLJo0BjDYJRiORZTgc9Co8K56Tqfr27y2co6SVHgWRaBM46mdkMbQ5zlRElKrjUT5ZDfOjPLbL3y3OPmZydq/HhxkdHw8OJQlKY4lsVUdRzBC6GoOpfopJ9iyyryBM93gyErelScyyjhgBm3PZVs98gbY3m9i+tY258P01NVVqWg1xthWQrHUQjGqRUncMd/6gH99T4Lb5/h3IUAb/J9PH/iUKulR1C2xA0tkkLT7Y8Q2qXTy6mWH/8uWhsGUYxlG6Q+vFNASkmoW/TaS1jNAbpwiOJ0TzS/G0kWIaXgjZlv7XMEOQkaMzWWPl+BFxQUhtXTU0l7YXh5myZeEfHzoBS6tFyX/lSVrUGCY0kCzz6SDI0xjNKcJM1p1kPOTzfotyOW7m1y6fosr89NcXW6yWqvz+JWl7X+kHY0GFOFeEwZ9dDnzEST+XqNenh6zg6OZfH22Rk+6C3TjiJ828bbbrFL85wozXBtizfmpvbIeXpWk7JJ6We3sWUZeYxLS1OQFT1K9hkC+/HwZj9NmAnGPdQHdRwVWtMbxJRLj3PgUgqmp6pUKz6dTkR/kGxH02OdOGMMjq24cG2Gb3znMkpKctWn4B6Kpw9JlCY9suWIdjcmGhrK4V5SNMZgiJHUEObo30NJhRpNMex2cYIRo6yPb+zHbYTGkOjR2Ccwc7ky+yaB++xpK4DqZOXIic5nRZZkOJ6D/5y1jReNV8LwX2FkWc6gE/HaxWn6UcLKZo+NbjR2N5ZiW2FsXADLCz3WBDbQqARcmZ+ksl1ECkouq0ttLl0fK3NZSjJXrzJXH0dWaZ6T5sX2AIbAs60X6pBgWZLrs1PYWvFgq0MnigFD4DpcmZpkohTs6VJ4hNCeQwqbbnoT0NgiQIr9YbU2GZkZ98ZWnMt7SFgKSabHqRk3cHBce1/fdpJkYznOJ9YVQOA7BL5Dlhekab4zMm5ZCh1n2K2QB/0e7XhEL3VJpUSJNmXbo+YpJgJF2ZH7Hqbllsfm3QHdUYGrnH0FWU0GSGRxvMk3z3GItqqUTJNS0ybTQ7R5bAxUthtU7SbRMGf23OwRKx0P1ckypXrIaBDjl06PNHubA6596+JL6yryCGma82Bp68s+jEPxioifA0Wmd9igHLiUgybnpguGccpwlDIYJWgDUkDg2ZR8j8AbOw/vhrIk6RH5u0fFni8KY0lHQbMc0iyHO5HUcdIevtXCVTXifIthvkhWdJ4o5BgUDhX7PK41OU5H7MGuHK8QzJxrsnR7lVrzcVvfKMmfus20LbUzngwQ6ZwbWY/AUpR6HTxlUXYChLhKJj8hLWIW+w73OhmhK7nccKh5j99fmRpLana3cuZaTwrkp0COyGYwjjxW3epR2iSKDJPePNWShzaPHrYKKeQ4fVVsMXfx+XV5hRBc/sYFfvpnvzg1Is6zHATMXZo5lfVeJBzH4sxLXKx7RcTPgwNuOMdWOPGFZbcAABUgSURBVLZP/YTOvOIlqptaUu7huZPmnaVwCOxpAmuKTA/QpBhTIIRCYOHIyqFVdm0Mjnx8WS5cneHuJ4t7HEIKrfcU5Y6CMYaHRcxSFiEswWyzPh7y2IGHra8jrVu4dgSmRJIb3luJma/aXKjZKCmwHEXtTMiN2xuIabOzdm6isZGoc5G0ktLpjfDd4/2WUkqyLKdc9hBC7tOMHvZGTM41KB3SvndSzF6c4sGnE3Q3+lQnny/VAbD1sMPXfvP6qUbYv6p4ee7+X0JYlnpsNf8cyLMC13t5nokl+zlGuHZDCGxVxlUTeFYLV03gqCflIZ98D5Sdx4Wr6kSJ2YtTdDb6O393XB8/Ywz3i4jlYoQ1zJhdaD5Bwo8+0kMV1xB6GsQA14qo+YalXsbHGwnFdldDaS4EYyiynFwPyc0QVzYI7Yso6VIt+2itj3095HmB6zoHppm0NkS9iCvfOL2WNSklb/3maxitGQ32a3qcBFurHVoLk5x5bf6Uju4F4zgdE19iMe8VET8HLFsxNVtn0H++izoaJMyde3ZVrdNGxfkSIxwDFWdvP/Dr715CSUm0TR7jbfvTl1opRmwUCU6UE1ZDKkdEgQKF0nOo/A2EnkbIlFoQsTHq83lnAy03sUoDKudthp0BjmpSti/h29M73QyuazFRLxFF6VPJOE4yXMc61P+ws9rh/BsLTJzydjqsBnz7P3uH0XDEoDM88fu1Nmwsb1GdKPPO77350ueG9+AVEX91cf7KFMno2fszi0IjpGB67nRsw08DrrIoOy5x/uL6Tg9CUuR4ysZ/QhjcC13e/f23SIYJg26EY48dq4/CUOesFCOsKMfxXaYutY6l7SBwUWYWlX8Nlb9Bw7rISmeObu86TvE13MZ1pmfeQg9D1AFTbhONkEYjJBqlJGm+L4WSFwXRKMG2FbPTNRxn/05o2BvhBC5Xv3nxqcf7LKi3qvzGH38b27XYWNokz47Xtz4axGwsbrBwdZZ3/9Ov4xygaf0yQ5in//my8PLsh39J0WiW8QOHJM5wvZM3tffaEWcvtE6tId6YDEwMwt9nVX8SXK+1+PHagx1XjC8C3WTE2825A9MOtcky3/ujd3jv33/C5mpnXCg9AnejHjpOKU9WaZ6bPLEj8rhR0EUJl4qdc3MTvjbRRMg+V791js//6gaj3gi/srcWIIWgOVGiFLi0exGDJ1IAtq2YalYplzzSPKf0RP/waBCTRAnf/aNvvtAhiUqjxG/88be4/cF9bv7sNlla4IUuXuBib3+uMYZklBIPE7Iko1QL+fYfvsPU2eZTVn/58Kpr4isOKSVvvHOOH/3gUxqt8vFEXLYRDROkFJy/epAByslhig108gOMSRAiQHq/jZCHq5YdhTOlGj9ee4A25kBz0dOGNgYNnC0dvhUv10O++4df5/5ny9z8Z39Nd62LUgrLsUCMRYDytGBkckYlzbmrcwS144+dHwZXWWylYw3kejlAOBZXvnuVG391g8HWkPCJ0XYhBEHgEAQOWVaQFwVGj/V7XUftvDaNcmZbj4c/+u0BeVbw63/4zqkU054GZSkuf/08599YYO3+Bqt319lcadPd6G+Hh4JyI2Th2iyzF6aoT704R+kXDcexWDhgVP5ZIIT4A+CfMFaA+qfGmH/0xL//D8B/C+TAOvDfGGPuHbXmKyI+BUzN1nnr187zi5/coT5Rwj5gu/kkhv2YNMn5zm9fJzylqrNO/iMGB6HqGN1FJ3+D8v/gmdbyLJtrtSafddZpBS+eFDbjiIuVCcKnFAqVpTj/+gK/b0k++Og+HoJhe4jWBttRhPWQVZGR6xGhd4qDLkpxt93h199YYHNryGSjxNXvXeX+L+6ztbRFqR6OHwhPwLbVgbKm2oyTFo1qQJ4VbK12aLSqfO37r1E+RGL1RcGyLWYvTjN7cRwQFHkx1qtW8pcrB/wFQIztuf8P4PeAReDHQog/NcZ8vOtl7wHfNMZEQoj/DvjHwH951LqviPiUcO7yNLZr8f6PboMxlKo+7gFby+EgJhqkhCWH7/3e65RPaTTUGI0xEcix6zIiBN17rjXfmJjh7qBNlGcELzBFEecZSgjenjz+4ML5+Qk+f7BOs1GidX7vVvne6jJecbrH60hFOx3xtavz/Pu//Zw0zXF8h4vvXqS+WOfez++heyP8sreztT8Kg2HKZCVgsDnEGM3r377MudcXDpT7/KJx0jTO/9/encW4dV4HHP8f8vJyJ2c4nEXD2S3ZlizLsi3bcVPXae0AbpE6SNECaZsiaAIEBRqgBfqQBkb70FcDfUqB1kCDvhhNW7RF0iy1ncJAkKhKbcuOYVnWYlsazaIZzcrherl8fZjRWLFnJTlzuZwfMIAoXnw8VyLPfPy20xKMQRqzs/BR4Kox5gMAEfkO8HlgMxEbY1694/pzwJd2a1QTcQOlRpIk++LMTi3x/sUZ1lbybBzbu16ayFTpTka57/QovQPxhr7hRTyIdxhTuY6ROGJWwHe8rjb9XotfGRjj5RuX8Xujn1jn2ghVY1gs5nkqdde+SudEwwH6k1FW1vLEP1aNes1xfmkJXCMUiiXi8QDBoI/Tx4f4+S+u0ZeM4hGhZ7iHWF+M5Zllbl65ST6dRkSw/BY+v29zyVy1WqVcLJPLFigVKyQG4tzz8DiDE/2E9rnu/CBlVnJcuzDF3I0F/CGbsRMpBif6W7933JjJuBRw447HU8BjO1z/VeBHuzWqibjB/AEfY0f7GZnoZWUpS8kpr5dKsrwEQ3bDesBb8fgfo1oKQ2UBrAfw+E7U3eaRUIwHkyneWphmIBRr6Hhx1Rhu5tLcnzhCKrz/kjqnjqV45dylX9oCbYxp+Li2Uy7jEQ/9iRiVqmFooIvl1V6uXLtFb896oVaf30ffeB+9o71kV7IU0nnSC2tkl7M4eQcMeH1erLCfeDLKr//aCcaPDjRd73N1cY2z/3UeRIh0hSgVy7zxPxdYnFnh1BP3tuwY8T4kReT1Ox6/sFH4+Lat/gG2TPEi8iXgDPDkbi+qifiAeDweEocw4XInERuv/VDD270/MUDVVPnF4ix9wciOx2PuVblaZS6/xsnEAKeTgzV9wGPhAA/cneKt96boT94+CU42vn3sbdPHbqrGkMk5nBjvp+T5qPTVybtTeDwe3vtgjlg0QGhjKZd4hEgiQiQRITn20ZCJMYal1RweER5/cJxkk1azePfcFSy/tXkYv8+2CIT9TF6aYfieQRL9TVSDbh9KToXpyT2tmlgwxpzZ4fkpYPiOx0PAzMcvEpGngeeAJ40xxd1eVBOx2pWI8EDPICHL5rX5G9hei25/7V+lV50ChXKJR/uGuberr66EOZHqYX4xzdzS2mZyi/hsnGoF/za1+fbKGEM6U2AwGScaDpArlTZrA3o8wsm7B+lPxjh/YZL5xTX8tkUoaG+ecVE1Bscpk807VCpVho90c/LYIMEaljkeBqdQYnF2hZ6PrS4QESzb4tbUYssmYp/tZWioIasmXgOOicg4MA18EfiDOy8QkQeBfwCeMcbM76VRTcRqT0SEu7t6GQhFOTc3yWwuTdiyifr8e95unCk5ZEpFksEwT6WO0lVHMr/N4xEeOTnKubevMb+Uobc7TFcgwExmra5EXDWG1UyBvu4IY6kEa8Ui/VscuN+biPDZTx9nYTnD5MwS80sZVoq5jS+wQjwa4NhoL8NHuomGW/dMBrNeHtztMOrTgDFiY0xZRL4OvMT68rVvG2MuiMjfAK8bY74HPA9EgH/b+GxMGmOe3aldTcRqX2J2gKeHjnEzt8a7S3PM5tMIQsBrEbR82B7vZtVqp1ohXy5RrJSpYugPRnmkb5gjoWhDj/H0WV4ePzXGG+/dYHJ2mVjAz6RZrbk9xymTLTikersYHUwgsl6QdSyx9e5Hj0fo64nS17M+PFK+XYTV6znQ40obzQ746BtKsLKYIZb4aOikWjWUnTL9w82zDX/fGriF2RjzQ+CHH/u7v77jz0/vt01NxGrfPCIMhmMMhmOknQIL+SzzhQzz+Qy3CoXNybKQZTMYjtEfjNITCDWkB7wdy/Ly6H2jDCbjvHHxBlWnStZTImzvfRigXKmSyTn4fR5O3nWE+MZKhmK5jN+y6I/ubVx3P5t6ms3xR49y9vvnWZpbJRwLUt44c/uu+0cOZZNJp9JErOoSswPE7AAT8Y96S42aKNsvEWF4oJtkd4Tuy2F+cOESTsHGb3uxfRbWFmt0S+UKTqlCqVzBZ3kZO9JFX09s81pjDIvZHE9MjGG1UO+2VtFEhCe+8AiT780yf2OBSDzEfZ+6m/6RnpZeMVFyykxPLrodxrY0EauGc/sDG/T7ePL+u/BHfLxxfRpvRUhnC2RzzicWH/l9FolYiJ54mFg08IllbwvZPKPd3Yx217ZVvBWFokHufWSCex+ZcDuUhrFti6FhPRheqUP38EiKfLnM7FqGo8NJKpX1klW3e+zWRnXmrRhjWMjm6Q4GeGx0yPVfLqoBdj4nylXt/11LdSyf18uvTowynuhiNp2hZCoE/BbBgI+Af+uhCljfwHEznWEwFuUzR8fxH2KZKnVwZA8/btF3mGprPq+Xx8dGGOmK8383plnNrxH0+QjZPnx3FEAtVSrkSyVyThm/tZ7AR7u7tCesDoUmYtURUl1xno1Fmc9k+XBxmblMhqVcfvN5v2XRFw4zPtzNQCzaERNzHacxh/4cCE3EqmN4PR6OxKIcia0vwyqWy5tL7XT4ob05xTJT1xbcDmNb+u5THUuTb+ewbS9DI827IUXfiQesUqmytlYgkymwvJQlX1gvLGlZXrq6QsRiQSKRAMFga9X/Uqrl6NBE58nnHaanlrh6dQ7HqYCA7bPwWh5E1reNzsysQHW9UsPAQJzxiT56eiJ49lDkUim1T82bh+tLxCLyPPDbgAO8D/yxMWalEYG1qkqlyocfzPPuxRkEiMVDxOJbb3mNbOyYrVYNy8tZZn92mURPhNOnR4g20UHhSqmDVe/U8CvASWPMKeAy8M36Q2pdmUyBn/70MhcuTNPdHaYnGd2yXtnHeTxCNBakty9GNlvk1Vcv8uGHtzBN/FVKqdZj9vDjjrp6xMaYl+94eA743frCaV2rqznOnr2CiNDbF6u5nVgsSLlc4a03r5PPOxw/Xtuh6UqpjzjFMtMf3HI7jG01coz4K8C/NLC9lpHJFDh79gqW5SUcrr9WmmV56e2LcenSLF6vh3vuOdKAKJXqXLZtkRpNuh3GtnZNxCLyY2Bgi6eeM8Z8d+Oa54Ay8OIO7XwN+BrAyMhITcE2o2rV8Oab1xGkIUn4No9HSCajXHx3mmQySk9Pc5bWUUrVb9dEvNshxyLyZeBzwFNmh0HNjQJ8LwCcOXOmbQY/r1+7xeJihr46hiO24/V6iESDvHn+Gk9+5viexpuVUtto4jmXuibrROQZ4BvAs8aYXGNCah3FYol3LkyTSHyyhE6jhEI2ubzD5GTz7gpSqvmZ9US8249L6h0j/hbgB17ZmFA6Z4z5k7qjahEzMyubmzMOUiwW5OqVOcbGevFuc2KYUmp765N1e6rj6Yp6V00cbVQgraZaNbx/de5Q1vvatsXqSu7AhkCUane2bZEaa97JOu1e1SiXK5LLO/j9h7M50bYt5ufTh/JaSrWnNl1H3Mmy2eKhvp4/4GNhYe1QX1OpttK8c3XaI65VOp0/1DMh/H6LtbUClUoT13tRStVEe8Q1KhRKWN7DW04mIhhjqFSqOmGn1L4ZMM3bidFE3EJ0o7NStXGKZabfb9NVE53MZ3mpVg/5N6xBj8hUqgbrqyZ63Q5jW5qIaxSLrx/Oc1gcp0wwZB/4mmWl2lfzztZpIq5ROOw/1FPRioWSriFWqh7tusW5k0UiATxeD+Xy4QxPFAqluo7XVKrjNe8yYk3EtfJ6PUxM9LKWzu9+cZ1ur5TQHrFS9WjeTKxDE3UYHu7hyuWbVKvmQCfR0ukco2NJbFv/u5SqhVMsMX3lptthbEs/2XWIRAKMjCaZurFE4oDOCy6VKpgqTEz0HUj7SnUC27ZINfFnSIcm6nTiRArL56VQKB1I+8tLGe4/NdzQQ+eV6kzNOzShibhOtm3x8ENjpNN5SqXGLmdbXMwwONjN8HBPQ9tVqhMZY3b9cYsm4gbo7Ytx5sw4y4sZHKdcd3vGGBYXMyS6wzz40Jhu4lCqEZq3Q6xjxI0yNJTA4xHOv3ENr+UhHg/V1I7jlFlZyZJKJTh9elTLIynVAE7BYerStNthbEsTcQMNDnYTj4d4++1J5m6mCUf8ex7bLZcrpFfziEd4+OEJhoa6D3XDiFLtLNYT5ak/fGLX6/7KpTr04sa4iIjcAq4f+gvvTxJoh0Jxeh/Nox3uAQ7/PkaNMXUdFCEi/8163LtZMMY8U89r1cKVRNwKROR1Y8wZt+Ool95H82iHe4D2uY9mopN1SinlMk3ESinlMk3E23vB7QAaRO+jebTDPUD73EfT0DFipZRymfaIlVLKZZqIdyAiz4vIeyLytoj8p4h0uR1TLUTk90TkgohURaSlZrtF5BkRuSQiV0XkL92OpxYi8m0RmReRd9yOpVYiMiwir4rIxY330p+5HVM70US8s1eAk8aYU8Bl4Jsux1Ord4DfAX7idiD7ISJe4O+A3wROAL8vIifcjaom/wQc+trUBisDf2GMOQ58CvjTFv2/aEqaiHdgjHnZGHP78IhzwJCb8dTKGHPRGHPJ7Thq8Chw1RjzgTHGAb4DfN7lmPbNGPMTYMntOOphjJk1xpzf+PMacBFIuRtV+9BEvHdfAX7kdhAdJgXcuOPxFPrhd52IjAEPAj93N5L20fFnTYjIj4GBLZ56zhjz3Y1rnmP9q9mLhxnbfuzlPlrQVodt6DIfF4lIBPh34M+NMWm342kXHZ+IjTFP7/S8iHwZ+BzwlGnitX673UeLmgKG73g8BMy4FEvHExEf60n4RWPMf7gdTzvRoYkdiMgzwDeAZ40xObfj6UCvAcdEZFxEbOCLwPdcjqkjyfpRgP8IXDTG/K3b8bQbTcQ7+xYQBV4RkbdE5O/dDqgWIvIFEZkCHgd+ICIvuR3TXmxMlH4deIn1yaF/NcZccDeq/RORfwb+F7hHRKZE5Ktux1SDTwN/BPzGxmfhLRH5LbeDahe6s04ppVymPWKllHKZJmKllHKZJmKllHKZJmKllHKZJmKllHKZJmKllHKZJmKllHKZJmKllHLZ/wP6NgJwRjapVwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d1e8780>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"rng = np.random.RandomState(0)\\n\",\n    \"x = rng.randn(100)\\n\",\n    \"y = rng.randn(100)\\n\",\n    \"colors = rng.rand(100)\\n\",\n    \"sizes = 1000 * rng.rand(100)\\n\",\n    \"\\n\",\n    \"plt.scatter(x, y, c=colors, s=sizes, alpha=0.3,\\n\",\n    \"            cmap='viridis')\\n\",\n    \"plt.colorbar(); # 展示色阶\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.绘制一组误差为±0.8的数据的误差条图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<Container object of 3 artists>\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d26a208>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0, 10, 50)\\n\",\n    \"dy = 0.8\\n\",\n    \"y = np.sin(x) + dy * np.random.randn(50)\\n\",\n    \"\\n\",\n    \"plt.errorbar(x, y, yerr=dy, fmt='.k')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 7.绘制一个柱状图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d2d55c0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = [1,2,3,4,5,6,7,8]\\n\",\n    \"y = [3,1,4,5,8,9,7,2]\\n\",\n    \"label=['A','B','C','D','E','F','G','H']\\n\",\n    \"\\n\",\n    \"plt.bar(x,y,tick_label = label);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 8.绘制一个水平方向柱状图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d1bd0f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.barh(x,y,tick_label = label);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 9.绘制1000个随机值的直方图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d39d400>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"data = np.random.randn(1000)\\n\",\n    \"plt.hist(data);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 10.设置直方图分30个bins，并设置为频率分布\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d3e2b38>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.hist(data, bins=30,histtype='stepfilled', density=True)\\n\",\n    \"plt.show();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 11.在一张图中绘制3组不同的直方图，并设置透明度\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAEVNJREFUeJzt3X2QXXddx/H3h9TyjEUSKiQpKRqqoVQelgIyQoXgJIIJM+IQHJyiOBkYCigy0oJWrf/U4gidoX8YoYqCVKggkQmWZx1nBBOglD5YyVQkSxBCIVTloWb4+sfebS+bu7tnk7t77v72/ZrJ9J5zT+/9TLP93N/+zjm/m6pCktSW+/QdQJI0fpa7JDXIcpekBlnuktQgy12SGmS5S1KDLHdJapDlLkkNstwlqUFn9PXG69evry1btvT19pK0Kn3605/+elVtWOy43sp9y5YtHDp0qK+3l6RVKcl/djnOaRlJapDlLkkNstwlqUGWuyQ1yHKXpAZZ7pLUIMtdkhpkuUtSgyx3SWpQb3eoau34yK1fvefx9m1n95hEWjscuUtSgyx3SWqQ5S5JDbLcJalBlrskNchyl6QGWe6S1CDLXZIaZLlLUoMsd0lqkOUuSQ3qVO5JdiS5PcnhJJeOeP4lSY4luXHw59fHH1WS1NWiC4clWQdcAzwHmAYOJtlfVbfOOfRvquqSZcgoSVqiLqtCXggcrqo7AJJcB+wG5pa7dI/hlSAlrbwu0zIbgSND29ODfXP9YpKbklyfZPNY0kmSTkmXcs+IfTVn+++BLVV1AfAR4O0jXyjZm+RQkkPHjh1bWlJJUmddyn0aGB6JbwKODh9QVXdW1fcGm38GPGnUC1XVvqqaqqqpDRs2nEpeSVIHXcr9ILA1yblJzgT2APuHD0jyiKHNXcBt44soSVqqRU+oVtWJJJcANwDrgGur6pYkVwCHqmo/8Koku4ATwDeAlyxjZknSIjp9h2pVHQAOzNl3+dDjy4DLxhtNknSqvENVkhrUaeQuLafha+K3bzu7xyRSOxy5S1KDLHdJapDlLkkNstwlqUGeUNXYuFiYNDkcuUtSgyx3SWqQ5S5JDbLcJalBlrskNchyl6QGWe6S1CDLXZIaZLlLUoMsd0lqkOUuSQ1ybZlJd/sH73183s7+XkPSquLIXZIaZLlLUoMsd0lqkOUuSQ2y3CWpQZa7JDXIcpekBnmdu1aU37MqrQxH7pLUIMtdkhpkuUtSgzqVe5IdSW5PcjjJpQsc94IklWRqfBElSUu1aLknWQdcA+wEtgEvSrJtxHEPBl4FfGrcISVJS9Nl5H4hcLiq7qiqu4HrgN0jjvtD4Crgu2PMJ0k6BV3KfSNwZGh7erDvHkmeAGyuqg8s9EJJ9iY5lOTQsWPHlhxWktRNl+vcM2Jf3fNkch/gTcBLFnuhqtoH7AOYmpqqRQ5fm4bXXp9gs9erb992ds9JJI3SZeQ+DWwe2t4EHB3afjBwPvCJJF8Engrs96SqJPWnS7kfBLYmOTfJmcAeYP/sk1X1rapaX1VbqmoL8ElgV1UdWpbEkqRFLVruVXUCuAS4AbgNeHdV3ZLkiiS7ljugJGnpOq0tU1UHgANz9l0+z7EXnX4sSdLp8A5VSWqQq0KuJnOvpDlvZz85JE08R+6S1CDLXZIaZLlLUoMsd0lqkCdU15pVdFLWJQ6kU+fIXZIa5Mi9FcMj8gkejUtaGY7cJalBlrskNchyl6QGOeeu0zJ7Rcukvp60Vjlyl6QGWe6S1CDLXZIaZLlLUoMsd0lqkOUuSQ2y3CWpQZa7JDXIcpekBnmHaovmrtkuac1x5C5JDbLcJalBlrskNcg599XMuXVJ83DkLkkNstwlqUGdyj3JjiS3Jzmc5NIRz78syeeT3Jjkn5NsG39USVJXi5Z7knXANcBOYBvwohHl/ddV9biqejxwFfAnY08qSeqsywnVC4HDVXUHQJLrgN3ArbMHVNVdQ8c/EKhxhtTaNvzVe9u3nd1jEmn16FLuG4EjQ9vTwFPmHpTkFcBrgDOBZ416oSR7gb0A55xzzlKzSpI66jLnnhH7ThqZV9U1VfVjwOuA3xn1QlW1r6qmqmpqw4YNS0sqSeqsS7lPA5uHtjcBRxc4/jrg+acTSpJ0erqU+0Fga5Jzk5wJ7AH2Dx+QZOvQ5nOBL4wvoiRpqRadc6+qE0kuAW4A1gHXVtUtSa4ADlXVfuCSJNuB/wO+CVy8nKElSQvrtPxAVR0ADszZd/nQ41ePOdfa4jICksbMO1QlqUEuHKYFeY25tDo5cpekBlnuktQgp2XWuuGTueft7C+HpLFy5C5JDbLcJalBTsvoJMNXyEhanRy5S1KDLHdJapDlLkkNstwlqUGWuyQ1yHKXpAZZ7pLUIMtdkhpkuUtSg7xDdaW4QJekFeTIXZIa5Mhd9xr8drH+6HEAvv7IZ/WZRtJpsNzVmQuKSauH5S519Ikjn5j3uYs2X7RiOaQuLPc+DJ9c1Zrlh4WWkydU17gbjxznxiPH+44hacwsd0lqkOUuSQ1yzl3zWn/0Y/c89rJIaXVx5C5JDbLcJalBnco9yY4ktyc5nOTSEc+/JsmtSW5K8tEkjxp/VGnmRqrZP5Lmt2i5J1kHXAPsBLYBL0qybc5hnwWmquoC4HrgqnEHlSR112XkfiFwuKruqKq7geuA3cMHVNXHq+rbg81PApvGG1OStBRdyn0jcGRoe3qwbz4vBbwFU5J61OVSyIzYVyMPTF4MTAHPnOf5vcBegHPOOadjREnSUnUp92lg89D2JuDo3IOSbAfeADyzqr436oWqah+wD2BqamrkB4T64RIEUlu6TMscBLYmOTfJmcAeYP/wAUmeAPwpsKuqvjb+mJKkpVi03KvqBHAJcANwG/DuqrolyRVJdg0OeyPwIOA9SW5Msn+el5MkrYBOyw9U1QHgwJx9lw893j7mXKvT3KV8/a7UieVyu2qdd6hKUoMsd0lqkKtCqlkLTb1IrXPkLkkNcuQujYEnaDVpLPc1yBuWFuZ0jlpguasTv5Xp1PlhoT445y5JDXLkvpzm3tQkSSvEcteq57SHdDLLfY1o8STq7Fft3Xz8OI/ffFbPaaTJYrlLE2i+30a8rFJdeUJVkhpkuUtSgyx3SWqQ5S5JDfKEqpowfDWQV85Ilru0qrhAmbpyWkaSGuTI/XS5xICkCWS5a2LcfPxf5n3u/LOetoJJpNXPaRlJapAjdy3Z8Nru4Pru0iRy5C5JDbLcJalBlrskNchyl6QGWe5qzo1Hjjf55STSUljuktSgTpdCJtkBXA2sA95aVVfOef4ZwJuBC4A9VXX9uINqbVvoBifNcN0ZDVu03JOsA64BngNMAweT7K+qW4cO+xLwEuC1yxFS0vLwA6FdXUbuFwKHq+oOgCTXAbuBe8q9qr44eO77y5BRkrREXebcNwJHhranB/skSROqS7lnxL46lTdLsjfJoSSHjh07diovIUnqoEu5TwObh7Y3AUdP5c2qal9VTVXV1IYNG07lJSRJHXSZcz8IbE1yLvBlYA/wy8uaStJYLXTiVG1atNyr6kSSS4AbmLkU8tqquiXJFcChqtqf5MnA+4CHAr+Q5A+q6rHLmlyrlpc1Ssuv03XuVXUAODBn3+VDjw8yM10jSZoArucuaSSvgV/dLHctq+Ev9vBLPaSVY7mrWcOLhz1+81k9JpFWnguHSVKDHLkv1e0f7DvBxPjMd74AwF3H73/Sc+ef9bSVjiNpiOWuZTF7ueNDBh8AMPpDQNLysNw1Vg+586a+I0jCcpc0Rl4+OTks9wbNXiXiFSL38soZrTVeLSNJDXLkLmnJXIhs8lnuDRueipC0tjgtI0kNcuTehTcuSVplHLlLUoMcuWvFzL3B6a6HXdBTEql9jtwlqUGO3Edxjn1JXHJAmjyO3CWpQZa7JDXIaRlJK8JFxVaW5S7g3i/eGOWJ99+6gkkkjYPlPmsVnkRdqZUOFyp+SZPJOXdJatDaHbmvwpH66XIELq0da7fcV6n5Vnp0BcjuRv23Gp7W8os9Vt58J1s90Xrq1la5r5HR+moZoc9385PLEkinb22VuzQPf/NRa1Znuc8dgZ+3s58cWhHDI3xH9WuL18afuk7lnmQHcDWwDnhrVV055/n7An8JPAm4E3hhVX1xvFEn3ye+eeu8z1300G1jeb07vvO/S34daa1xDr9DuSdZB1wDPAeYBg4m2V9Vw83zUuCbVfXjSfYAfwS8cDkCt2jeKYEHrWwOnWy+k6uz+z3hqknVZeR+IXC4qu4ASHIdsBsYLvfdwO8PHl8PvCVJqqrGmLUbp2xWvVNdZdL14qV7dSn3jcCRoe1p4CnzHVNVJ5J8C3gY8PVxhGzBQlM2Z/HIFUwitWGh+fhx/jtwatM5fZ8v6FLuGbFv7oi8yzEk2QvsHWz+T5I7mfwPgPVMdkbznZ5JzweTn9F8p28pGR/V5aAu5T4NbB7a3gQcneeY6SRnAD8MfGPuC1XVPmDf7HaSQ1U11SVoXyY9o/lOz6Tng8nPaL7TtxwZu6wtcxDYmuTcJGcCe4D9c47ZD1w8ePwC4GO9zLdLkoAOI/fBHPolwA3MXAp5bVXdkuQK4FBV7QfeBvxVksPMjNj3LGdoSdLCOl3nXlUHgANz9l0+9Pi7wC+dwvvvW/yQ3k16RvOdnknPB5Of0Xynb+wZ4+yJJLXH9dwlqUETUe5JXpnk9iS3JLmq7zyjJHltkkqyvu8scyV5Y5J/S3JTkvclmYjbJpPsGPy9Hk5yad95hiXZnOTjSW4b/Ny9uu9MoyRZl+SzST7Qd5ZRkpyV5PrBz99tSZ7Wd6ZhSX5z8Pd7c5J3Jblfz3muTfK1JDcP7fuRJB9O8oXBPx86jvfqvdyT/Cwzd7heUFWPBf6450gnSbKZmeUXvtR3lnl8GDi/qi4A/h24rOc8w8tW7AS2AS9KsvQFdpbPCeC3quongacCr5iwfLNeDdzWd4gFXA38Q1X9BPBTTFDWJBuBVwFTVXU+MxeE9H2xx18AO+bsuxT4aFVtBT462D5tvZc78HLgyqr6HkBVfa3nPKO8CfhtRtyYNQmq6kNVdWKw+Ulm7kXo2z3LVlTV3cDsshUToaq+UlWfGTz+b2ZKaWO/qX5Qkk3Ac4G39p1llCQPAZ7BzNVyVNXdVTVpayefAdx/cP/NAzj5Hp0VVVX/xMn3AO0G3j54/Hbg+eN4r0ko98cAP5PkU0n+McmT+w40LMku4MtV9bm+s3T0a8AkfCvJqGUrJqo8ZyXZAjwB+FS/SU7yZmYGFd/vO8g8Hg0cA/58MHX01iQP7DvUrKr6MjMzAV8CvgJ8q6o+1G+qkc6uqq/AzKADePg4XnRF1nNP8hHgR0c89YZBhocy86vxk4F3J3n0St4EtUi+1wM/t1JZ5rNQxqp6/+CYNzAz3fDOlcw2j05LUvQtyYOAvwV+o6ru6jvPrCTPA75WVZ9OclHfeeZxBvBE4JVV9akkVzMzpfC7/caaMZi73g2cCxwH3pPkxVX1jn6TrYwVKfeq2j7fc0leDrx3UOb/muT7zKyzcGwlsi2UL8njmPnB+FwSmJnu+EySC6vqv1YqHyz83xAgycXA84BnT8jdwV2WrehVkh9iptjfWVXv7TvPHE8HdiX5eeB+wEOSvKOqXtxzrmHTwHRVzf7Gcz1jmi8ek+3Af1TVMYAk7wV+Gpi0cv9qkkdU1VeSPAIYy9T0JEzL/B3wLIAkjwHOZEIW+amqz1fVw6tqS1VtYeaH+YkrXeyLGXyZyuuAXVX17b7zDHRZtqI3mfm0fhtwW1X9Sd955qqqy6pq0+Dnbg8zS3pMUrEz+P/gSJLzBruezQ8uBd63LwFPTfKAwd/3s5mgE75DhpdvuRh4/zhedBK+Zu9a4NrBpUF3AxdPyMhzNXkLcF/gw4PfMD5ZVS/rM9B8y1b0mWmOpwO/Anw+yY2Dfa8f3I2t7l4JvHPwAX4H8Ks957nHYKroeuAzzExXfpae71ZN8i7gImB9kmng94ArmZmOfikzH0incrf/ye9lj0pSeyZhWkaSNGaWuyQ1yHKXpAZZ7pLUIMtdkhpkuUtSgyx3SWqQ5S5JDfp/rqjcBCYwHUkAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d2255c0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x1 = np.random.normal(0, 0.8, 1000)\\n\",\n    \"x2 = np.random.normal(-2, 1, 1000)\\n\",\n    \"x3 = np.random.normal(3, 2, 1000)\\n\",\n    \"\\n\",\n    \"kwargs = dict(alpha=0.3, bins=40, density = True)\\n\",\n    \"\\n\",\n    \"plt.hist(x1, **kwargs);\\n\",\n    \"plt.hist(x2, **kwargs);\\n\",\n    \"plt.hist(x3, **kwargs);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 12.绘制一张二维直方图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d1e0e80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mean = [0, 0]\\n\",\n    \"cov = [[1, 1], [1, 2]]\\n\",\n    \"x, y = np.random.multivariate_normal(mean, cov, 10000).T\\n\",\n    \"plt.hist2d(x, y, bins=30);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 13.绘制一张设置网格大小为30的六角形直方图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d3e29e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.hexbin(x, y, gridsize=30);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 三、自定义图表元素\\n\",\n    \"### 14.绘制x=(0,10)间sin的图像，设置线性为虚线\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d3e2cc0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0,10,100)\\n\",\n    \"plt.plot(x,np.sin(x),'--');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 15.设置y轴显示范围为(-1.5,1.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8e45e710>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0,10,100)\\n\",\n    \"plt.plot(x, np.sin(x))\\n\",\n    \"plt.ylim(-1.5, 1.5);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 16.设置x,y轴标签variable x，value y\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8e4c1358>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0.05, 10, 100)\\n\",\n    \"y = np.sin(x)\\n\",\n    \"plt.plot(x, y, label='sin(x)')\\n\",\n    \"plt.xlabel('variable x');\\n\",\n    \"plt.ylabel('value y');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 17.设置图表标题“三角函数”\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8e55eac8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0.05, 10, 100)\\n\",\n    \"y = np.sin(x)\\n\",\n    \"plt.plot(x, y, label='sin(x)')\\n\",\n    \"plt.title('三角函数');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 18.显示网格\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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D6Xl3IRxODG17afYZOp+GBOaOsDsUj7p41kiERNv70vtYa1NVtPlzF2bqLPGjx6myeMm1kAjnpQ3l+5+mQ77qqiWGQHE7DCx+c4YYJIxjrJwvxuCthSAR3zkzn9f1nqW/R6rS6sud3niZ1aLTfLMTjLhHhgTmjOFLZyP6y0K41a2IYpLePVnO27iKfui44agvdPj13NK0dTl7+sMzqUJQfK6+7yDvHargvdyThtuD5GFk+I50hETZe2Hna6lAsFTyvqI89v+M0yfFRLAiSb0vdctITmDlqGKs+0Oq06ttfdpdhDAHbRbUv8dERLJuezur95TSG8EVoTQyDcLbuIluKq7k/N5OIIPq21O2+3EyOVTeFfHVa9c7pNLy0u6sZNTMxxupwPO6B60ZxscPB6/vKrQ7FMsH3qeYDL7u+Ld1/bXB9W+q2dFoa0RFhvLT7zNV3ViFn2/FznK27GLTv/+kjE5icNpQ/h/BFaE0MA+R0Gl7+8AzzxycF5bcl6KpO3zY1jTf2lXOxXUdCq0ut2nWGYTER5OcEVzNqNxHhk3MyKapo4GCIdt3WxDBAu07Wcqb2IvfMHml1KF51X24mjW2drCvUkdDq/9Q2t7OhsJK7ZmYQFR7YY3euZNmMDCLDw/jrntDshKGJYYBe3lNGbKSNxVMCaxbJgbpubCKjk2J4aVdo/mOo3r269ywdDhO0zUjdEoZEsDDbzur95bR3Oq0Ox+c0MQxAc1snbx6s4PZpacREemLxO/8lItw7eyTvl57n9PkWq8NRfuLlPWVMG5kQFCOdr+aeWSO50NLBliPVVofic5oYBmDdoUpa2h3cMzu4vy11+/jskYjAy3v0IrSCI5UNHK5o4O6ZvS7pHnRuzBpBcnwUr4TgmB5NDAPw8p4yRifFcO2Y4VaH4hNpCUO4YcIIXtl7NmR7Z6j/8+qHZwkPE+6Ynm51KD4RbgvjzhnpbC2upra53epwfMojiUFEFotIsYiUiMijvTz+ORGpEZF9rp+Hezz2oIgcc/086Il4vOFMbQvvl57nnlkjA3p67YG6a2YGZRcusufUBatDURZyOA2v7TvLTdckkxQXZXU4PvPx2SPpcBhW7wutifXcTgwiYgOeAJYA2cADIpLdy64vGmNmuH6ech2bCPwAuA6YA/xARPzy6/jrrjfGXbNCoxrdbVFOKkMibLyqM06GtPePn6eqoS3k3v+TUoeSkz6Uv34YWu9/T9QY5gAlxphSY0w7sApY3s9jFwEbjTG1xpgLwEZgsQdi8ihjDK/tK2fOmERGDg/OsQt9iY0KJz/HzpsHK0Kyd4bq8sreMuKjw1kwOTjHLlzJ3bNGcvBsPUerGq0OxWc8kRgygJ5XJ8tc2y73cRE5ICIvi0j31dv+HmupoooGSqqbWDYjNNpWL3fnjAzqWjp4+2iN1aEoC7S0d7LuUCW3T00L+HVHBmP5jHRsYfK3VoNQ4Ik+l701uF9+pfIN4AVjTJuIfBlYCdzSz2O7TiKyAlgBYLfbKSgoGHCgTU1Ngzpu1ZF2bALDGkopKAisBcMHW+aeHE5DfCQ8uWEvEdXRngnMizxR5kDjzTJvL++kpd3BWKnxq7+rL1/nyYlhvLSjlNzICkuvMfqqzJ5IDGVAz/6bI4FLZp8yxpzvcfd/gZ/3ODbvsmMLejuJMeZJ4EmA3Nxck5eX19tuV1RQUMBAj3M6DY9u30LexOEszb92wOe02mDK3JuPNxXy5w9OM2vu9QyN9u+1fT1V5kDizTI/+8wHZAxr4ot33kxYmP90vPDl63wuvoz/95f9JIyfwaxR1l0G9VWZPdGUtAvIEpGxIhIJfAJY3XMHEem5IPIy4LDr9nogX0SGuy4657u2+Y0PTtZS2dDK8hDpu92XO2dm0N7pZN3BSqtDUT5U29zOeyXnWDYj3a+Sgq8tyrETGR7G6hCZcdXtxGCM6QS+RtcH+mHgJWNMoYg8JiLLXLt9Q0QKRWQ/8A3gc65ja4Ef0ZVcdgGPubb5jdf3nSUm0saCySlWh2Kp6SMTGDsiltdCqJ1VwVuHKnA4DXdMC83ra93ioyO4dVIKaw5U0OkI/k4YHpnXwRizFlh72bbv97j9XeC7fRz7DPCMJ+LwtLZOB2sPVpKfbQ/6KTCuRkS4Y1oav9laQk1jG8nxodOXPZS9sb+c8cmxTE6LtzoUyy2bns5bhyrZUVrLDVkjrA7Hq3Tk8xW8c/Qc9Rc7Qr4ZqdvS6ek4Tde3SBX8qhpa2Xmiljump4fUoM6+3DwphbiocFbvD/5asyaGK1hzoJzhMRHcMCG4vx301zX2eK6xx7FmvyaGUPDmgQqMgaUh3ozULTrCxqKcVN46VElbZ3CvU6KJoQ+tHQ42FVWxeEpqUC7fOVhLp6Wz61QtlfWtVoeivOyNA+Vkpw1lQkqc1aH4jWUz0mls7aSgOLjH9OgnXh8Kimtobndw+1T9ttTT0mlpGANvHtRaQzA7U9vC3tN1ITNhXn9dPz6JxNhI3jwQ3O9/TQx9ePNgBYmxkcwdl2h1KH5lXHIc2WlDeWN/aHTbC1VrXB98S6elXWXP0BJuC2NRjp3Nh6to7Qje5iRNDL242O5g8+GuZqRwbUb6iKXT09h3po4ztbqAT7B6Y385MzKHBe265u64bWoaze2OoG5O0k+9Xmwtrqal3cHSqfptqTdLXc1r2pwUnE6ea6aookFrC32YNy6J4TERrA3i978mhl68eaCCEXGRzBmrzUi9GZUUw/SRCaw5oM1JwWitqzvyEv1i1KtwWxiLp6QGdXOSJobLtLR3svmINiNdze3T0jh0tkGbk4LQWwcrmZ45jIxhQ6wOxW8Fe3OSfvJdZsuRalo7nNob6SqWTOn6NqmD3YLLmdoWDp6t5/apqVaH4teCvTlJE8Nl3jpYqc1I/ZCZGMOUjKG8dUgn1Qsm3R903Ylf9S7Ym5M0MfTQ2uFga3E1+Tmp2EJ4Jsn+WjIljb2n6yivu2h1KMpD1h6qZNrIBO2N1A/B3JykiaGHt4/W0NLuYMkUrUb3R/ffaZ3WGoJC2YUW9p+p09pCP3U3J60LwuZUTQw9rDtUScKQCOaOS7I6lIAwLjmOSanxmhiCRPfreJteX+iXcFsYCybb2Xy4OujWQ9fE4NLW2TU30sJsu86NNACLp6Sy61Qt1Q06d1KgW3uwgpz0oYxOirU6lICxZGoqjW2dbDt+zupQPEo/AV22l5ynsa1Tm5EG6LapXXMnrS/UWkMgq2po5cPTdfr+H6DrJ4wgLiqc9UFWa/ZIYhCRxSJSLCIlIvJoL49/S0SKROSAiGwWkdE9HnOIyD7Xz+rLj/WVtw5VEBcVHvQLcHhaVkoc45NjWatLfga0Da7EvlgTw4BEhdu4ZVIKG4qqgmplN7cTg4jYgCeAJUA28ICIZF+2214g1xgzDXgZ+EWPxy4aY2a4fpZhgU6Hk41FVdw6OYWocJsVIQQsEWHJlDR2njhPbXO71eGoQVpXWMn45FgmpOhKbQO1eEoqtc3t7Dp5wepQPMYTNYY5QIkxptQY0w6sApb33MEYs9UY0z1Edgcw0gPn9ZidJ2q50NKh1ehBWpSTitPApsNVVoeiBqGupZ0dpbVaWxikvInJRIWHBVXvJE8sZJwBnOlxvwy47gr7PwS81eN+tIjsBjqBnxljXuvtIBFZAawAsNvtFBQUDDjQpqamXo/7Y1EbkWEgVUcoOFc84Of1Z32V2ZOMMSRFC39+u5CUpuNePVd/+KLM/sadMr93tgOH0zCi9SwFBYHTJOhPr3NOorD6w9PcNLSGMC8ug+qrMnsiMfT2VzC97ijyaSAXuKnH5lHGmHIRGQdsEZGDxpiPfLoYY54EngTIzc01eXl5Aw60oKCAy49zOg2PbN/MzZOTWHRr7oCf09/1VmZvWNZUyPM7T5M77wbiojzxtho8X5XZn7hT5udW7iY9oZ7PLbsloNZ29qfX+UJCGf/w4n6GjZ/BrFHDvXYeX5XZE01JZUBmj/sjgY9MuykiC4DvAcuMMW3d240x5a7fpUABMNMDMfXb/rI6qhraWJSj1Wh3LMpJpb3TydtBOAo0mDW3dfLOsRoWTUkNqKTgb26ZZCfCJkHTO8kTiWEXkCUiY0UkEvgEcEnvIhGZCfwPXUmhusf24SIS5bo9ArgeKPJATP22vrCK8DDh1kl2X5426Fw7JpHE2Ejtthpg3j5aQ3unU78Yual7YOz6wkqM6bXBJKC4nRiMMZ3A14D1wGHgJWNMoYg8JiLdvYx+CcQBf7msW+pkYLeI7Ae20nWNwaeJYUNRJXPHJZEQE+HL0wYdW5iwcLKdrUeqaesMvknFgtW6Q5UkxUZy7RidNNJdi3JSOXm+hWPVTVaH4jaPjGMwxqw1xlxjjBlvjPmJa9v3jTGrXbcXGGPsl3dLNcZsN8ZMNcZMd/1+2hPx9FdJdSOlNc0sytHagicsmmKnsa2T7cfPWx2K6of2Tidbj1SzYLJdJ430gPzsrs+RYGhOCumRz+sLu7pXLszWarQnzB/fNQp0gzYnBYTtx8/R2NbJoin6xcgTUoZGM3PUMDYUBX637RBPDF0rVaUmRFsdSlCIjrCRNzGZjUVVOJyB384a7DYUVREbaWP+eB3t7ymLclI5eLY+4KeiD9nEUF53kQNl9dqM5GGLclI519TO3tPBMwo0GDmdho1FVeRNTCE6Qkf7e0p3c1Kg15pDNjFsdFX3tDeGZ+VNTCbCJkFRnQ5m+8rqqGlsI1+/GHnUuOQ4slLiAv79H7KJYUNRJRNS4hifHGd1KEElPjqCeeNHBE23vWC1wdVNO29iitWhBJ38HHvXNDsBPHdYSCaG+pYOdpTW/q3apzwrP9vOqSDptheMjDFsKKxk3vgkEoZoN21PW5STisNp2HKk+uo7+6mQTAxbirsujuZrM5JXLAySdtZgdbymidJzzfrFyEumZiSQlhAd0IM9QzIxbCyqIiU+imkZCVaHEpTsQ6OZkRkc3faCUXc37QWaGLxCRFiYbeedYzVcbA/MwZ4hlxhaOxwUFNewMNtOmA7q8Zr8HDsHyuqpqA/sbnvBaENRFdNHJpCWMMTqUIJWfnYqrR1O3isJzCU/Qy4xbD9+jpZ2hzYjeVm+a9DgJq01+JXK+lb2n6n7W3Of8o7rxiUSHx3OxqLAbE4KucSwsaiKuKhw5o7TuWG8aUJKHOOSY7U5yc9sdC2mpF+MvCvCFsYtk1LYfLg6IAd7hlRicBrDxqJq14pLOqjH2/KzU3n/+HnqL3ZYHYpy2VhUxZikGLJStJu2ty3MtnO+uZ0PA3CwZ0glhtI6J+ea2vTbko8szLbT6TQUFAdut71g0tjawfvHz7Ew265rL/jATdckE2kL+9tg2kASUonhw2oHETYhb2Ky1aGEhJmZwxgRF6XNSX6ioLiGDod20/aVrsGeSWwIwMGeIZUYzl90Mm/8CIZG66AeXwgLExZMTuHt4hpdo8EPbCyqIik20qtLT6pLLcy2c/J8CyUBNtjTI4lBRBaLSLGIlIjIo708HiUiL7oe3ykiY3o89l3X9mIRWeSJePrylRnRPPXZ4FvX2Z/l59hpauvkfV2jwVIdDidbi6u5ZVKKrr3gQ38b7BlgtWa3E4OI2IAngCVANvCAiGRftttDwAVjzATgceDnrmOz6VoKNAdYDPzW9XxeExkeUpUky80fP4KYSFtAtrMGk52ltTS2dmozko/Zh0YzPXNYwM0C4IlPyTlAiTGm1BjTDqwCll+2z3Jgpev2y8Ct0nX1azmwyhjTZow5AZS4nk8FiegIGx/LSmbT4SqcAdhtL1hsKKokOiKMGybo2gu+lp9tZ39ZPVUNrVaH0m+eSAwZwJke98tc23rdx7VGdD2Q1M9jVYBbmG2nqqGNg2frrQ4lJBlj2FRUxY1ZyQyJ1G7avtY9J1Ug1ZrDPfAcvTVYXv7VsK99+nNs1xOIrABWANjtdgoKCgYQYpempqZBHRfI/KHMUe2GMIH/fesD7rkm0uvn84cy+9qVynyy3kF5fStLMp1B9XcJlNfZGIM9RnjxvcOMbD3h1nP5qsyeSAxlQGaP+yOB8j72KRORcCABqO3nsQAYY54EngTIzc01eXl5Aw60oKCAwRwXyPylzM+dfJ+jze3k5d0kkUdBAAAZnUlEQVTk9XP5S5l96Upl/s8NxYRJCV+96yYSY72fmH0lkF7nZS1FrNx+itx5NxAXNfiPXV+V2RNNSbuALBEZKyKRdF1MXn3ZPquBB1237wG2mK6OvauBT7h6LY0FsoAPPBCT8jMLs1M5WtXEyXPNVocScjYUVZE7OjGokkKgWZidSrvDydvFNVaH0i9uJwbXNYOvAeuBw8BLxphCEXlMRJa5dnsaSBKREuBbwKOuYwuBl4AiYB3wVWOMdngPQoHYzhoMztS2cKSyUZfwtNjs0cNJjI1kQ4BMqueJpiSMMWuBtZdt+36P263AvX0c+xPgJ56IQ/mvzMQYJqXGs7Goii9+bJzV4YSM7v7zOpuqtWxhwq2TUlhfWEmHw0mEzb+7zft3dCqo5Gfb2X2qlvNNbVaHEjI2FlVyjT2O0UmxVocS8hZm22lo7eSDE7VWh3JVmhiUz+TnpOI0sDmA18INJBea29l18sLf1sZQ1roxK5noiMCYVE8Tg/KZnPShpCdEB8Q/RjDYcqRrLQBtRvIPQyJt3JiVHBCT6mliUD7TvRbuuwG8Fm4g2VhUhX1oFFN1bXO/kZ9tp7y+lcLyBqtDuSJNDMqnFrrWwn33WGB02wtUrR0O3jlWw4LJura5P7l1sp0w8f9J9TQxKJ/qXgvX3/8xAt22kq61zbUZyb8kxkaSOybR7yfV08SgfOr/1sKtotPhtDqcoLWhsIr4qHDmj9dJ8/xNfradI5WNnD7fYnUofdLEoHwuPzuVCy0d7DkVeGvhBgKH07DpcBV5k1J0mnk/1N1LzJ8Hu+m7RvncTRO71sLV5iTv2HPqAueb21mko5390qikrsGe/vz+18SgfC4uKpzrJySxPgC67QWiDYWVRNrCuOkaXdvcX+XnpLL7ZC21ze1Wh9IrTQzKEotyUim7cJHDFY1WhxJUjDFsKKpi/oQk4nVtc7+Vn23vGux52D9rDZoYlCVunWxHxL/bWQNRcVUjp2tbdLSzn+se7Lm+UBODUn+THB9F7ujhfvuPEag2FFYhAguyU6wORV2BiJCfk8q7x2poae+0OpyP0MSgLJOfncrhigbO1Ppvt71As6GokpmZw0iJj7Y6FHUV+Tl22jr9c40GTQzKMt1rBPhz74xAcrbuIofONpCfo81IgWDOmESGx0Sw3g8Hu2liUJYZnRTLpNR4v/zHCETrD3X9HfN1tHNACLeFsWCync1Hqmnv9K/Bnm4lBhFJFJGNInLM9Xt4L/vMEJH3RaRQRA6IyP09HvuDiJwQkX2unxnuxKMCT362nd0ndY0GT1hXWMlEezzjkuOsDkX106KcVBpbO9lRet7qUC7hbo3hUWCzMSYL2Oy6f7kW4LPGmBxgMfArERnW4/HvGGNmuH72uRmPCjDdazRs8tNue4Gioc2w+2Qti6ZoM1IguSFrBDGRNtb5Wa3Z3cSwHFjpur0SuPPyHYwxR40xx1y3y4FqQEfeKKCr297I4UNYd8i//jECzd7qTpwGFuv1hYASHWEjb2IyG4uqcDr9Z7Cnu4nBboypAHD9vmIfORGZA0QCx3ts/omrielxEYlyMx4VYESEJVNS2VZynobWDqvDCVh7qhxkJg5hclq81aGoAVqUk0pNYxt7z/jP3GFytSkJRGQT0NvXkO8BK40xw3rse8EY85HrDK7H0oAC4EFjzI4e2yrpShZPAseNMY/1cfwKYAWA3W6fvWrVqiuXrBdNTU3ExYVW+2sglPnYBQc/2dnKl6ZFMS893O3nC4Qye1JLh+HrW5pZODqCT0wKne9WwfI6d71+LSwcHX7V18/dMt988817jDG5V93RGDPoH6AYSHPdTgOK+9hvKPAhcO8VnisPWNOf886ePdsMxtatWwd1XCALhDI7HE5z7Y83mi//abdHni8QyuxJr+0tM6MfWWN2nzxvdSg+FUyv82ef3mlu/PkW43Q6r7ifu2UGdpt+fMa625S0GnjQdftB4PXLdxCRSOBV4I/GmL9c9lia67fQdX3ikJvxqAAUFiYsykmloFiX/ByM9YWVJEQJMzN7rayrALB4Siqna1soqvCPJT/dTQw/AxaKyDFgoes+IpIrIk+59rkP+BjwuV66pT4vIgeBg8AI4MduxqMC1OIpqVzscPD2Uf8bBerPWjscbD1Sw+wUmy7hGcDys7uW/HzroH90wnCrQdcYcx64tZftu4GHXbefA57r4/hb3Dm/Ch7XjU1kmGsU6GLtctlv7xyt4WKHg1l2nQIjkCXFRXHd2CTWHqrg2/nX0NWIYh0d+az8QrgtjIWT7Ww6XOV3o0D92dqDFQyLiWBSov4rB7rbpqZSWtPMseomq0PRxKD8x+IpXaNAtx0/Z3UoAaGt08Gmw9Usyk4lXJuRAt6inFTET5qTNDEov3FD1gjio8J562CF1aEEhHePnqOprZMlU7XpLRikDI0md/Rw3jpk/ftfE4PyG1HhNhZk21lfWEWHQ5uTrmbtoQoShkRw/YQRVoeiPGTJlDSOVDZSWmNtc5ImBuVXbpuaRv3FDraVaHPSlbR3OtlYVMXCbDsRNv03DhbdHS/esniKGH1HKb9yo6s5aa02J13RtpJzNLZ2cvvUNKtDUR6UPmwIMzKHWT53mCYG5VeiI7qakzYUaXPSlaw9WEF8dLg2IwWh26amcvBsPafON1sWgyYG5Xdum5pGXUsH24/71xz1/qLD4WSDqxkpMlz/hYPNba5a4JoD1tWa9V2l/M6NWSOIiwpnrYX/GP5s+/Hz1F/s4LYp2owUjEYOj2HmqGGaGJTqKTrCxoLJKawvqtTmpF6s2V9OfFQ4N2RpM1KwWjotncMVDRy3qHeSJgbll26flq7NSb1o63SwrrCSRVNSiY6wWR2O8pLbp6YhAm9aVGvQxKD8UnfvpDf2l1sdil95u7iGxtZO7piebnUoyotSE6K5dnQiaw5Y8/7XxKD8UnSEjUVTUll/qJLWDp2Ku9sbBypIjI1k/vgkq0NRXrZ0ehpHq5o4WtXo83NrYlB+a9n0dBrbOiko1qm4AVraO9lUVMVtU1N1UFsIWDIljTDpuqbka/ruUn5r/vgkRsRFanOSy6bD1VzscHDHNG1GCgXJ8VHMHZfEmgMV3atc+owmBuW3wm1h3DY1jU2Hq2hq67Q6HMu9sb8c+9Aorh2TaHUoykfumJ5O6blmCst9u7KbW4lBRBJFZKOIHHP97nVtQRFx9Fi9bXWP7WNFZKfr+Bddy4Aq9TfLZ6TT1ulkQ6H1UxFbqf5iB28X17B0Wrqu1BZCbpuSRqQtjNf2nvXped2tMTwKbDbGZAGbXfd7c9EYM8P1s6zH9p8Dj7uOvwA85GY8KsjMGjWcjGFDWB3izUnrCytpdzi1N1KISYiJIG9iMq/vL8fh9F1zkruJYTmw0nV7JXBnfw+UrrXrbgFeHszxKjSICHdMT+e9Y+eobW63OhzLvLb3LKOTYpg+MsHqUJSP3TUzg5rGNrb7cAErdxOD3RhTAeD6ndLHftEisltEdohI94d/ElBnjOluPC4DMtyMRwWhZdPT6XQa3gzRGVcr6i/yful57pqZYflawMr3bp6UQnx0OK/t9V2tOfxqO4jIJqC3JaK+N4DzjDLGlIvIOGCLiBwEerua0mddSURWACsA7HY7BQUFAzh9l6ampkEdF8iCoczGGEbGCSsLishsPXHV/YOhzD29WdqOMZDeXkZBQe8fDsFW5v4IpTLPHAFv7i/j+jnGN2U2xgz6BygG0ly304DifhzzB+AeQIBzQLhr+zxgfX/OO3v2bDMYW7duHdRxgSxYyvz7ghIz+pE15nh141X3DZYyG2OM0+k0C/6jwNz9221X3C+YytxfoVTmbSU1ZvQja8xP/7zRrecBdpt+fMa625S0GnjQdftB4PXLdxCR4SIS5bo9ArgeKHIFudWVJPo8XimAO2dmECbwqo97Z1itsLyBY9VN3DVTW1lD2dyxSaQlRPN+uW+6bbubGH4GLBSRY8BC131EJFdEnnLtMxnYLSL76UoEPzPGFLkeewT4loiU0HXN4Wk341FByj40mhuyknnlw7M4fdg7w2qvfHiWSFsYS6fpFNuhLCxMWDY9nUPnHJxvavP6+a56jeFKjDHngVt72b4beNh1ezswtY/jS4E57sSgQsfHZ2Xw96v2sfNELfNCYK6gToeT1fvLuWVSCsNidIhPqLtrVgZ7ik/R3OYgKc6759KRzypg5GenEhcVzl8/LLM6FJ94t+Qc55rauGuWNiMpmJQ6lK/NjGZUUozXz6WJQQWMIZE2bp+axlsHK2hpD/4pMl7eU8awmAhunthXL3ClvEMTgwood8/KoLndwbpDwT1FRm1zOxsKK7lrZoau66x8Tt9xKqBcOyaRUYkxvLT7jNWheNWre8/S4TDcf22m1aGoEKSJQQWUsDDh/msz2VFaS6lF6+F6mzGGF3edZkbmMCalDrU6HBWCNDGogHNv7kjCw4QXdwVnrWHvmTqOVjXxCa0tKItoYlABJyU+mlsnp/CXPWW0dQbfsp8vfnCGmEgbS3UmVWURTQwqID0wZxS1ze1sLKqyOhSPamrr5I0D5dwxLZ24KLeGGSk1aJoYVEC6MSuZjGFDeOGD01aH4lFr9pfT0u7g/jnajKSso4lBBSSb6yL0tpLznDrfbHU4HmGM4bmdp5hoj2dm5jCrw1EhTBODClj35WYSJvDCB8FxEfrD0xc4dLaBz8wbresuKEtpYlABKzUhmoXZdlbtOs3F9sC/CP2H7aeIjw7XmVSV5TQxqID2+evHUtfSwWv7Ans67qqGVt46WMF9uZnE6kVnZTFNDCqgXTc2kclpQ3l224nuhaAC0vM7T+Mwhs/OG211KEppYlCBTUT4/PVjOFrVxPbj560OZ1DaO538eedpbp6YwuikWKvDUUoTgwp8y6ankxQbybPbrr4etD9ae7CCc01tWltQfsOtxCAiiSKyUUSOuX4P72Wfm0VkX4+fVhG50/XYH0TkRI/HZrgTjwpN0RE2PnXdKDYfqQ64rqvGGJ5+7wRjR8Tysaxkq8NRCnC/xvAosNkYkwVsdt2/hDFmqzFmhjFmBnAL0AJs6LHLd7ofN8bsczMeFaI+PXc04WHCs9tOWh3KgLxXco6DZ+tZ8bFxhIVpF1XlH9xNDMuBla7bK4E7r7L/PcBbxpgWN8+r1CVShkazbHoGq3adpqE9cC5C/3brcexDo7hbV2lTfkTc6ckhInXGmGE97l8wxnykOanH41uA/zTGrHHd/wMwD2jDVeMwxvS60rWIrABWANjt9tmrVq0acLxNTU3ExXl5sVQ/E0plrmhy8k/vXWThSMMnp/h/mY/XOfjRjlbunxjJkrERbj1XKL3O3bTMA3fzzTfvMcbkXnVHY8wVf4BNwKFefpYDdZfte+EKz5MG1AARl20TIIquGsf3rxaPMYbZs2ebwdi6deugjgtkoVbmrz6/x0z8pzWmrrnd6lCu6uGVu8y0H643ja0dbj9XqL3OxmiZBwPYbfrxGXvVpiRjzAJjzJRefl4HqkQkDcD1u/oKT3Uf8KoxpqPHc1e44m0DngXmXDWTKXUFX715Aq0OeHa7f/dQOlbVyMaiKh6cP0ZnUVV+x91rDKuBB123HwRev8K+DwAv9NzQI6kIXdcnDrkZjwpxk9OGMjPFxrPbTtLY2nH1Ayzyu4LjDImw8fn5Y6wORamPcDcx/AxYKCLHgIWu+4hIrog81b2TiIwBMoG3Lzv+eRE5CBwERgA/djMepbhjfAT1Fzv4045TVofSq+LKRl7dd5ZPzx3F8NhIq8NR6iPcqsMaY84Dt/ayfTfwcI/7J4GPdLswxtzizvmV6s24BBs3XZPMk++U8qk5o0mIce/Crqf9cv0R4qLC+bu8CVaHolSvdOSzCkqPLJ5E/cUOnigosTqUS+w6Wcumw9V8+abxWltQfksTgwpK2elD+fiskfxh20nO1PrHsBljDD976wgp8VF84fqxVoejVJ80Maig9e38awgLg1+sL7Y6FAA2FlWx59QFvrngGoZE2qwOR6k+aWJQQSstYQhfvHEcb+wvZ+/pC5bG0tbp4GfrjjBuRCz35Y60NBalrkYTgwpqX7ppPCPiIvnRmiKcTuumyvh9QSmlNc38yx3ZhNv03075N32HqqAWFxXOo0sm8+HpOsu6rx6vaeKJrSXcMT2dmyemWBKDUgOhiUEFvY/PyuBj1yTz83VHfH4h2hjD9149SHREGN9fmu3Tcys1WJoYVNATEX5691QEePSVAz5dAvQve8rYUVrLd2+bTHJ8lM/Oq5Q7NDGokJAxbAjfvW0y20rO8+KuMz4555naFn68pohrxwzn/txMn5xTKU/QxKBCxifnjGLuuER+tKaIo1WNXj1Xa4eDLz+3B4D/uHeGLsKjAoomBhUywsKEX90/k5iocL74x93UtbR77Vw/XF1IYXkDj98/g1FJMV47j1LeoIlBhZTUhGh+/+nZVNS18vUX9tLpcHr8HC/tPsOqXWf46s3juXWy3ePPr5S3aWJQIWf26OH8+M4pvHvsHD9964hHL0ZvLa7mn187xPzxSXxr4USPPa9SvqQrhKiQdN+1mRRVNPD0eycIE/in2ybTtSzI4G09Us2X/rSHLHscv/3ULGx6XUEFKE0MKmR9f2k2xhj+990T1LV08NO7pw56VPLmw1V85bkPmZgaz58emsOwGJ05VQUut5qSROReESkUEaeI9LnAtIgsFpFiESkRkUd7bB8rIjtF5JiIvCgi+t+kfCYsTPjhshy+uSCLv+wp48vPfUht88AuSHc4nPzXpmN86U97mJQWz3MPXadJQQU8d68xHALuBt7pawcRsQFPAEuAbOABEekeAvpz4HFjTBZwAXjIzXiUGhAR4ZsLruFfl+VQUFzNLf9RwKoPTvdrXqVjVY3c/dvtPL7pKLdPS+NPD13nd4sCKTUY7q7gdhi4WtvsHKDEGFPq2ncVsFxEDgO3AJ907bcS+CHwO3diUmowHpw/hnnjk/jn1w7x6CsH+fMHp7lzRgY3T0ph7IjYv+3X2uFgY1EVr+09y9tHaxg6JILffWoWS6amWRi9Up7li2sMGUDPoaZlwHVAElBnjOnssf0jy38q5SvX2ON5ccVcXt17lie2lvDYmiIeW1NEWkI0YSK0dTpoaO2kvdNJ6tBoHrphLA/fOE6nulBB56qJQUQ2Aam9PPQ9Y8zr/ThHb9UJc4XtfcWxAlgBYLfbKSgo6MepL9XU1DSo4wKZlnngEoF/mQ3VLUM4UOPgeF0HYSJE2GBIuI1pIyKZmBhGmFRRuKfKY3G7Q1/n0OCrMl81MRhjFrh5jjKg50QxI4Fy4BwwTETCXbWG7u19xfEk8CRAbm6uycvLG3AgBQUFDOa4QKZlds99HnkW79PXOTT4qsy+GOC2C8hy9UCKBD4BrDZdo4q2Ave49nsQ6E8NRCmllBe52131LhEpA+YBb4rIetf2dBFZC+CqDXwNWA8cBl4yxhS6nuIR4FsiUkLXNYen3YlHKaWU+9ztlfQq8Gov28uB23rcXwus7WW/Urp6LSmllPITOleSUkqpS2hiUEopdQlNDEoppS6hiUEppdQlNDEopZS6hHhykRJfEZEa4NQgDh1B18C6UKJlDg1a5tDgbplHG2OSr7ZTQCaGwRKR3caYPqcHD0Za5tCgZQ4NviqzNiUppZS6hCYGpZRSlwi1xPCk1QFYQMscGrTMocEnZQ6pawxKKaWuLtRqDEoppa4iJBKDiCwWkWIRKRGRR62Ox9tEJFNEtorIYREpFJG/tzomXxERm4jsFZE1VsfiKyIyTEReFpEjrtd8ntUxeZuI/IPrvX1IRF4QkWirY/I0EXlGRKpF5FCPbYkislFEjrl+D/fGuYM+MYiIDXgCWAJkAw+ISLa1UXldJ/BtY8xkYC7w1RAoc7e/p2t691DyX8A6Y8wkYDpBXn4RyQC+AeQaY6YANrrWeQk2fwAWX7btUWCzMSYL2Oy673FBnxjomta7xBhTaoxpB1YByy2OyauMMRXGmA9dtxvp+qAI+vW0RWQkcDvwlNWx+IqIDAU+hmstE2NMuzGmztqofCIcGCIi4UAMV1j9MVAZY94Bai/bvBxY6bq9ErjTG+cOhcSQAZzpcb+MEPiQ7CYiY4CZwE5rI/GJXwH/CDitDsSHxgE1wLOuJrSnRCTW6qC8yRhzFvh34DRQAdQbYzZYG5XP2I0xFdD1BRBI8cZJQiExSC/bQqIrlojEAX8FvmmMabA6Hm8SkaVAtTFmj9Wx+Fg4MAv4nTFmJtCMl5oX/IWrXX05MBZIB2JF5NPWRhVcQiExlAGZPe6PJAirnZcTkQi6ksLzxphXrI7HB64HlonISbqaC28RkeesDcknyoAyY0x3jfBluhJFMFsAnDDG1BhjOoBXgPkWx+QrVSKSBuD6Xe2Nk4RCYtgFZInIWBGJpOsi1WqLY/IqERG62pwPG2P+0+p4fMEY811jzEhjzBi6XuMtxpig/xZpjKkEzojIRNemW4EiC0PyhdPAXBGJcb3XbyXIL7j3sBp40HX7QeB1b5zErTWfA4ExplNEvgasp6v3wjPGmEKLw/K264HPAAdFZJ9r2z+51t5WwefrwPOuLz6lwOctjserjDE7ReRl4EO6euDtJQhHQYvIC0AeMEJEyoAfAD8DXhKRh+hKkPd65dw68lkppVRPodCUpJRSagA0MSillLqEJgallFKX0MSglFLqEpoYlFJKXUITg1JKqUtoYlBKKXUJTQxKKaUu8f8BeRPdqUzIBwkAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8e5974a8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0.05, 10, 100)\\n\",\n    \"y = np.sin(x)\\n\",\n    \"plt.plot(x, y)\\n\",\n    \"plt.grid()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 19.绘制平行于x轴y=0.8的水平参考线\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.lines.Line2D at 0x21e8e614eb8>\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8e5f02b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0.05, 10, 100)\\n\",\n    \"y = np.sin(x)\\n\",\n    \"plt.plot(x, y)\\n\",\n    \"plt.axhline(y=0.8, ls='--', c='r')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 20.绘制垂直于x轴x<4 and x>6的参考区域，以及y轴y<0.2 and y>-0.2的参考区域\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8e5f9780>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0.05, 10, 100)\\n\",\n    \"y = np.sin(x)\\n\",\n    \"plt.plot(x, y)\\n\",\n    \"plt.axvspan(xmin=4, xmax=6, facecolor='r', alpha=0.3) # 垂直x轴\\n\",\n    \"plt.axhspan(ymin=-0.2, ymax=0.2, facecolor='y', alpha=0.3);  # 垂直y轴\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 21.添加注释文字sin(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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6fXjCets4e1uwvszoU5cYqGs7z7vFa7spOIcDfe35GVs9OYlSgPy/uKbE6FEt5z/+oi72wu4TYiGAWe8nZUq/MpEjmjBvDSx9qdVoN7m85ZRiDx3ZRHUxESCCrZiWx7lAFzT58EVoTw2UobzjP24U13J2dSqAXnS31uis7leM153y+Oq0G1tNjeCXH1oyaGhVqdThOd+/8cZzv7GbtwcGmfPN+3ver5gJr7GdLd1/pXWdLvVbOTCQk0I9XckqHLqx8zs4TdZQ3nPfa7/+slEimJ47mrz58EVoTwyXq6TGs2V/K1ZOivfJsCWzV6RVZibxxsILzHToSWvX30t5SxoQGsjTTu5pRe4kIn5qXSn5lE0d8tOu2JoZLtPdUPaX157njihSrQxlRd2Wn0tzexcY8HQmt/qG+pYPNeVXcNieZ4ADPHrtzMatmJxMU4Mff9/lmJwxNDJdozb4ywoL8WT7Ds2aRvFTzJ0QxPjqUV/b65h+GGthrB8rp7DZe24zUK3JUIEsy4ll3qIKOrh6rw3E5TQyXoKW9izePVHLzzERCgwKsDmdEiQh3XpHCB8VnKDnTanU4yk2s2VfGzJRIrxjpPJQ75qZwtrWTt4/WWB2Ky2liuAQbc6to7ejmjiu8+2yp1yevSEEE1uzTi9AKjlY1UVDZxO1zBlzS3etcmx5DbEQwr/rgmB5NDJdgzb4yxkeHcmXaWKtDcYnEyFEsnBzDqwfKfbZ3hvqH1/aXE+An3DIryepQXCLA349bZyexvbCG+pYOq8NxKackBhFZLiKFIlIkIo8O8PrnRKRWRA7abw/2ee1+ETluv93vjHhGQml9Kx8Un+GOuSkePb32pbptTjJlZ8+z7/RZq0NRFuruMbx+sJzrpsR6zJrmzvDJK1Lo7DasO+hbE+s5nBhExB/4HXATkAHcKyIZAxR92Rgz23572r5vFPADYD4wD/iBiLjl6fha+xfjtrm+UY3utSwzgVGB/rymM076tA9OnKG6qd3nvv/TEkaTmTSav+/3re+/M2oM84AiY0yxMaYDeAlYPcx9lwFbjDH1xpizwBZguRNicipjDK8frGBeWhQpY71z7MJgwoIDWJoZz5tHKn2yd4ayefVAGREhASye7p1jFy7m9rkpHClv5Fh1s9WhuIwzEkMy0PfqZJl924U+KSKHRWSNiPRevR3uvpbKr2yiqOYcq2b7RtvqhW6dnUxDayfvHKu1OhRlgdaOLjbmVnFzVqLHrztyOVbPTsLfTz5qNfAFzkgMAzW4X3il8g0gzRgzE9gKPHcJ+9oKijwkIjkiklNb69ofqLUHKwjwE27O8uxV2i7XwvQYosOCeF2bk3zS5rxqWju6uc1HeiNdKCY8mGsmx7DuUIXPdMJwRmIoA/r230wB+s0+ZYw5Y4xptz/9P+CK4e7b5z2eMsZkG2OyY2NjnRD28PT0GNYdrOC6KbGMDQty2XHdSaC/H7fMSmJLQbUue+iDXjtQTvKYUVyZFmV1KJZZNSuJ0vrzHChtsDoUl3BGYtgLpIvIBBEJAu4B1vUtICJ9T7VXAQX2x5uApSIy1n7Real9m9v48FQ9VU1trPbRs6Vet85JpqOrh41HqqwORblQfUsH7xfVsWp2En5+vtMb70LLMuMJCvBjnY/MuOpwYjDGdAEPY/tBLwBeMcbkichjIrLKXuxrIpInIoeArwGfs+9bD/wIW3LZCzxm3+Y21h4sJzTIn8XT46wOxVKzUiKZEBPG6z7UzqrgrdxKunsMt8z0zetrvSJCArlxWhzrD1fS1e39nTCcMq+DMWYDsOGCbd/v8/g7wHcG2fePwB+dEYeztXd1s+FIFUsz4r1+CoyhiAi3zEzkt9uLqG1uJzbCd/qy+7I3DlUwKTaM6YkRVodiuVWzkngrt4rdxfUsTI+xOpwRpSOfL+LdY3U0nu/0+WakXitnJdFjbGeRyvtVN7Wx52Q9t8xK8qlBnYO5floc4cEBrDvk/bVmTQwXsf5wBWNDA1k42bvPDoZrSnwEU+LDWX9IE4MvePNwJcbASh9vRuoVEujPsswE3sqtor3Lu9cp0cQwiLbObrbmV7N8RoJXLt95uVbOTGLv6XqqGtusDkWNsDcOV5CROJrJceFWh+I2Vs1Oormtix2F3j2mR3/xBrGjsJaWjm5uztKzpb5WzkzEGHjziNYavFlpfSsHShp8ZsK84bpmUjRRYUG8edi7v/+aGAbx5pFKosKCWDDRd/tuD2RibDgZiaN545BvdNvzVevtP3wrZ/rmoM7BBPj7sSwznm0F1bR1em9zkiaGAZzv6GZbga0ZKUCbkT5m5axEDpY2UFqvC/h4qzcOVTA7dYzXrmvuiBVZibR0dHt1c5L+6g1ge2ENrR3drPTRKTCGstLevKbNSd7pVF0L+ZVNWlsYxFUToxkbGsgGL/7+a2IYwJuHK4kJD2LeBG1GGsi46FBmpUSy/rA2J3mjDfbuyDfpidGAAvz9WD4jwaubkzQxXKC1o4ttR7UZaSg3z0wkt7xJm5O80FtHqpiVOobkMaOsDsVteXtzkv7yXeDtozW0dfZob6Qh3DTDdjapg928S2l9K0fKG7k5K8HqUNyatzcnaWK4wFtHqrQZaRhSo0KZkTyat3J1Uj1v0vtD15v41cC8vTlJE0MfbZ3dbC+sYWlmAv4+PJPkcN00I5EDJQ1UNJy3OhTlJBtyq5iZEqm9kYbBm5uTNDH08c6xWlo7urlphlajh6P332mj1hq8QtnZVg6VNmhtYZh6m5M2emFzqiaGPjbmVhE5KpAFE6OtDsUjTIwNZ1pChCYGL9H7/7hCry8MS4C/H4unx7OtoMbr1kPXxGDX3mWbG2lJRrzOjXQJls9IYO/pemqadO4kT7fhSCWZSaMZHx1mdSge46asBJrbu9h5os7qUJxKfwHtdhWdobm9S5uRLtGKLNvcSZvytNbgyaqb2thf0qDf/0t0zeQYwoMD2ORltWanJAYRWS4ihSJSJCKPDvD6N0UkX0QOi8g2ERnf57VuETlov627cF9XeSu3kvDgAK9fgMPZ0uPCmRQbxgZd8tOjbbYn9uWaGC5JcIA/N0yLY3N+tVet7OZwYhARf+B3wE1ABnCviGRcUOwAkG2MmQmsAX7e57XzxpjZ9tsqLNDV3cOW/GpunB5HcIC/FSF4LBHhphmJ7Dl5hvqWDqvDUZdpY14Vk2LDmBynK7VdquUzEqhv6WDvqbNWh+I0zqgxzAOKjDHFxpgO4CVgdd8CxpjtxpjeIbK7gRQnHNdp9pys52xrp1ajL9OyzAR6DGwtqLY6FHUZGlo72F1cr7WFy7RoaizBAX5e1TvJGYkhGSjt87zMvm0wDwBv9XkeIiI5IrJbRG4dbCcRecheLqe21rn9hjfmVhES6Md1U+Kc+r6+YkbyaJLHjPqoOUJ5lq0FNXT3GJZlamK4HKFBAVw3JZZNedX09Birw3EKZySGgUaCDfivIyKfBrKBX/TZPM4Ykw18CviViEwaaF9jzFPGmGxjTHZsbKyjMX+kp8ewOb+K66bEMipIm5Euh4iwNDOed4/Xca69y+pw1CXamFtFUmQIWcmRVofisW7KSqCqqY2DZQ1Wh+IUzkgMZUBqn+cpwMem3RSRxcB3gVXGmPbe7caYCvt9MbADmOOEmIbtUFkD1U3terbkoGWZCXR09fCOF44C9WYt7V28e7yWZTMSENHR/pfrhmnxBPqL1/ROckZi2Auki8gEEQkC7gH69S4SkTnA/2JLCjV9to8VkWD74xjgGiDfCTEN26a8agL8hBunxbvysF7nyrQoosKCtNuqh3nnWC0dXT16YuSg3oGxm/KqMMbzm5McTgzGmC7gYWATUAC8YozJE5HHRKS3l9EvgHDgbxd0S50O5IjIIWA78LgxxqWJYXN+FQsmRhMZGujKw3odfz9hyfR4th+tob3L+yYV81Ybc6uIDgviyjSdNNJRyzITOHWmleM156wOxWFOGcdgjNlgjJlijJlkjPmJfdv3jTHr7I8XG2PiL+yWaozZZYzJMsbMst8/44x4hquoppni2haWZWptwRmWzYinub2LXSfOWB2KGoaOrh62H61h8fR4nTTSCZZm2H5HvKE5yadHPm/Ks3WvXJKh1WhnuHqSbRSo9k7yDLtO1NHc3sWyGXpi5Axxo0OYM24Mm/M9v9u2jycG20pVCZEhVofiFUIC/Vk0NZYt+dV0e0m3PW+2Ob+asCB/rp6ko/2dZVlmAkfKGz1+KnqfTQwVDec5XNaozUhOtiwzgbpzHRwo8Z5RoN6op8ewJb+aRVPjCAnUbtrO0tuc5Om1Zp9NDFvs1T3tjeFci6bGEugvXlGd9mYHyxqobW5nqZ4YOdXE2HDS48I9/vvvs4lhc34Vk+PCmRQbbnUoXiUiJJCrJsV4Tbc9b7XZ3k170VQd7e9sSzPjbdPsePDcYT6ZGBpbO9ldXP9RtU8519KMeE57Sbc9b2SMYXNeFVdNiiZylHbTdrZlmQl09xjePlozdGE35ZOJ4e1C28XRpdqMNCKWeEk7q7c6UXuO4roWPTEaIVnJkSRGhnj0YE+fTAxb8quJiwhmps4NMyLiR4cwO9U7uu15o95u2os1MYwIEWFJRjzvHq/lfIdnDvb0ucTQ1tnNjsJalmTE46eDekbM0sx4Dpc1Utno2d32vNHm/GpmpUSSGDnK6lC81tKMBNo6e3i/yDOX/PS5xLDrRB2tHd3ajDTCltoHDW7VWoNbqWps41Bpw0fNfWpkzJ8YRURIAFvyPbM5yecSw5b8asKDA1gwUeeGGUmT48KZGBumzUluZot9MSU9MRpZgf5+3DAtjm32tS48jU8lBtugnhr7iks6qGekLc1I4IMTZ2g83zl04R07QAQefnh4b/7rX0NoKNTXX7zct78NiYnQ1ja89/VyW/KrSYsOJT1Ou2mPtCUZ8Zxp6WC/Bw729KnEcKC0gbpz7Xq25CJLMuLp6jHsKBxGt72MDHjxRfjCF4Yuawz893/D7bdD1BA1v4cegqoq+Otfhxe0F2tu6+SDE3UsyYjXtRdc4LopsQT5+300mNaT+FRi2JxfRaC/sGiq81aAU4ObkzqGmPDgjzcn1dTAjTdCeDiMHg3z58M778C998If/2grs2iRrQbx3e9CXBxMmwYFBbbX3n8fSkpglX1W9yeftJX9wx+gsRGSkiAzE9rbYfJk274vv+yyz+2udhTW0tmt3bRdxTbYM5rNHjjY06cSQ0VDG1dNimF0iA7qcQU/P2Hx9DjeKaztv0bDCy/A22/D178Ov/wlzJ4N3YN06zt8GO67DwoL4b/+y7bt/fdt91deabv/yldsiebRR+HBB22J509/guDgf5TbuRN6ekbkc3qKLfnVRIcFMXfcWKtD8RlLMuI5daaVIg8b7OmUxCAiy0WkUESKROTRAV4PFpGX7a/vEZG0Pq99x769UESWOSOewfzPvXN4+rPZI3kIdYGlmfGca+/ig75rNKSn2+7feQdOnIB77oGEQc5if/lLeOQR2+NTp2z3JSW2+6Qk270IPPOMLbmsWWMr35s0esu1tECdZ3YddIbO7h62F9Zww7Q4XXvBhT4a7OlhzUkOJwYR8Qd+B9wEZAD3ikjGBcUeAM4aYyYDTwA/s++bgW0p0ExgOfCk/f1GTFCAT1WSLHf1pBhCg/z7t7OuXAm7d8Py5baz/xtugK6ugd8gKgoCAmyPL6xV9K2enz1razoCqKwcuJwPt6vvKa6nua1Lm5FcLH50CLNSx3jcLADO+JWcBxQZY4qNMR3AS8DqC8qsBp6zP14D3Ci2q1+rgZeMMe3GmJNAkf39lJcICfTnE+mxbC2opqe3296aNbB+PaSm2q4FAFRUDP9Nx43rv09nJ3zucxATA1/9Kjz7LGzY8I/yFRUQFgbR0Q5/Hk+1Ob+KkEA/Fk7WtRdcbWlGPIfKGqlu8pyecc5IDMlAaZ/nZfZtA5axrxHdCEQPc1/l4ZZkxFPd1M6R8kbbhtBQW3L48pfhlVfg7rttP+rDtXCh7T4nx3b/ox/BoUPw29/amp6mTYMvfhEaGv5R7pprwM83a4vGGLbmV3NteiyjgrSbtqv1zknlSb2TApzwHgPVzy+8BD9YmeHsa3sDkYeAhwDG9Z7Vsf/+AAAZaUlEQVQxKo/Q2669Od+2Yh4rVthuF+rbNLRjx+CvLVwI48fDunVw113w2GO2W6/e3ksARUVw9KhtPIOPyqtooqKxjW8smWJ1KD5pclw4adGhbMmv5tMLxlsdzrA44xSqDEjt8zwFuLBd4KMyIhIARAL1w9wXAGPMU8aYbGNMdmysdjf1JGPDgrgybazzzphE4JvfhFdfHXqA21NPQXy8rSusj9qcV4WfwOLpOg2GFXon1fvgxBnOtQ9yLc3NOCMx7AXSRWSCiARhu5i87oIy64D77Y/vAN42to6964B77L2WJgDpwIdOiEm5mSUZCRyrPsepuhbnvOHXvgatrUMPcPv5z20D3EJ8d13vzfnVZI+PIiosyOpQfNaSjAQ6unt4p7DW6lCGxeHEYL9m8DCwCSgAXjHG5InIYyJiH4HEM0C0iBQB3wQete+bB7wC5AMbgX8yxnjmPLXqojyxndUblNa3crSqWZfwtNgV48cSFRbEZg+ZVM8Z1xgwxmwANlyw7ft9HrcBdw6y70+AnzgjDuW+UqNCmZYQwZb8ar74iYlWh+MzevvP62yq1vL3E26cFsemvCo6u3sI9HfvjhDuHZ3yKksz4sk5Xc+Zc+1Wh+IztuRXMSU+nPHRYVaH4vOWZMTT1NbFhyeHuC7mBjQxKJdZmplAj4FtHrwWric529LB3lNnP1obQ1nr2vRYQgI9Y1I9TQzKZTKTRpMUGeIRfxje4O2jtrUAtBnJPYwK8ufa9FiPmFRPE4Nymd5ue+958Fq4nmRLfjXxo4PJ0rXN3cbSjHgqGtvIq2iyOpSL0sSgXGqJfS3c9457Rrc9T9XW2c27x2tZPF3XNncnN06Px0/cf1I9TQzKpXrXwnX3PwxPt7PItra5NiO5l6iwILLTotx+Uj1NDMql/rEWbjVd3b69PsJI2pxXTURwAFdP0knz3M3SjHiOVjVTcqbV6lAGpYlBudzSjATOtnay77TnrYXrCbp7DFsLqlk0LU6nmXdDvb3E3Hmwm35rlMtdN9W2Fq42J42MfafPcqalg2U62tktjYu2DfZ05++/JgblcuHBAVwzOZpNHtBtzxNtzqsiyN+P66boZJPuamlmAjmn6qlv6bA6lAFpYlCWWJaZQNnZ8xRUNlsdilcxxrA5v5qrJ0cToWubu62lGfG2wZ4F7llr0MSgLHHj9HhE3Lud1RMVVjdTUt+qo53dXO9gz015mhiU+khsRDDZ48e67R+Gp9qcV40ILM6IszoUdREiwtLMBN47Xktrh/ut0aCJQVlmaUYCBZVNlNa7b7c9T7M5v4o5qWOIi/Dd9Sc8xdLMeNq73HONBk0MyjK9awS4c+8MT1LecJ7c8iaWZmozkieYlxbF2NBANrnhYDdNDMoy46PDmJYQ4ZZ/GJ5oU67t33Gpjnb2CAH+fiyeHs+2ozV0dLnXYE+HEoOIRInIFhE5br8fO0CZ2SLygYjkichhEbm7z2t/EpGTInLQfpvtSDzK8yzNiCfnlK7R4Awb86qYGh/BxNhwq0NRw7QsM4Hmti52F5+xOpR+HK0xPApsM8akA9vszy/UCnzWGJMJLAd+JSJj+rz+LWPMbPvtoIPxKA/Tu0bDVjfttucp6s61k3OqnmUztBnJkyxMjyE0yJ+NblZrdjQxrAaesz9+Drj1wgLGmGPGmOP2xxVADaAjbxRg67aXMnYUG3Pd6w/D02zNr6bHwHK9vuBRQgL9WTQ1li351fT0uM9gT0cTQ7wxphLAfn/RPnIiMg8IAk702fwTexPTEyIS7GA8ysOICDfNSGBn0Rma2jqtDsdjbcyrIjVqFNMTI6wORV2iZZkJ1Da3c6DUfeYOGzIxiMhWEckd4Lb6Ug4kIonAX4DPG2N6r7R8B5gGXAlEAY9cZP+HRCRHRHJqa92ve5e6fMtnJNDR3cN2XfLzsjS1dbKzqI7lmQmI6NoLnub6aXEE+otb1ZqHTAzGmMXGmBkD3NYC1fYf/N4f/gH/skVkNPAm8D1jzO4+711pbNqBZ4F5F4njKWNMtjEmOzZWW6K8yZzUscRFBLvVH4Yn2X60hs5uw3K9vuCRRocEcvWkGDblVbvN3GGONiWtA+63P74fWHthAREJAl4D/myM+dsFr/UmFcF2fSLXwXiUB/LzE5ZlJrCjUJf8vByb8qqIjQhmTurHOgUqD7F8RgIl9a3kV7rHkp+OJobHgSUichxYYn+OiGSLyNP2MncBnwA+N0C31BdE5AhwBIgBfuxgPMpDLZ+RwPnObt45ps2El6Kts5vtR2tZlqlLeHqypRm2JT/fOuIeteYAR3Y2xpwBbhxgew7woP3x88Dzg+x/gyPHV95j/oQoxthHgWqTyPC9e6yW853dLNPeSB4tOjyY+ROi2ZBbyb8unWL5tSId+azcQoC/H0umx7O1oNrtRoG6sw1HKhkTGsiCidFWh6IctCIrgeLaFo7XnLM6FE0Myn0sn2EbBbrzRJ3VoXiE9q5uthbUsCwjgUB//VP2dMsyExA3aU7Sb5NyGwvTY4gIDuCtI5VWh+IR3jtWx7n2Lm7K0mYkbxA3OoTs8WN5K9f6778mBuU2ggP8WZwRz6a8ajq7tTlpKBtyK4kcFcg1k2OsDkU5yU0zEjla1UxxrbXNSZoYlFtZkZVI43nbgC01uI6uHrbkV7MkI16bkbxIb8eLtywe06PfKOVWrrU3J23Q5qSL2llUR3NbFzdnJVodinKipDGjmJ06xvLBnpoYlFsJCbQ1J23O1+aki9lwpJKIkABtRvJCK7ISOFLeyOkzLZbFoIlBuZ0VWYk0tHay64R7zVHvLjq7e9hsb0YKCtA/YW+zwl4LXH/YulqzfquU27k2PYbw4AA2WPiH4c52nThD4/lOVszQZiRvlDI2lDnjxmhiUKqvkEB/Fk+PY1N+lTYnDWD9oQoiggNYmK7NSN5q5cwkCiqbOGFR7yRNDMot3TwzSZuTBtDe1c3GvCqWzUggJNDf6nDUCLk5KxEReNOiWoMmBuWWensnvXGowupQ3Mo7hbU0t3Vxy6wkq0NRIyghMoQrx0ex/rA1339NDMothQT6s2xGAptyq2jr1Km4e71xuJKosCCunqRzI3m7lbMSOVZ9jmPVzS4/tiYG5bZWzUqiub2LHYU6FTdAa0cXW/OrWZGlcyP5gptmJOIntmtKrqbfLuW2rp4UTUx4kDYn2W0tqOF8Zze3zNRmJF8QGxHMgonRrD9c6fKV3TQxKLcV4O/HiqxEthZUc669y+pwLPfGoQriRwdzZVqU1aEoF7llVhLFdS3kVbh2ZTeHEoOIRInIFhE5br8fcG1BEenus3rbuj7bJ4jIHvv+L9uXAVXqI6tnJ9He1cPmPOunIrZS4/lO3imsZeXMJF2pzYesmJFIkL8frx8od+lxHa0xPApsM8akA9vszwdy3hgz235b1Wf7z4An7PufBR5wMB7lZeaOG0vymFGs8/HmpE15VXR092hvJB8TGRrIoqmxrD1UQXeP65qTHE0Mq4Hn7I+fA24d7o5iW7vuBmDN5eyvfIOIcMusJN4/Xkd9S4fV4Vjm9QPljI8OZVZKpNWhKBe7bU4ytc3t7HLhAlaOJoZ4Y0wlgP0+bpByISKSIyK7RaT3xz8aaDDG9DYelwHJDsajvNCqWUl09Rje9NEZVysbz/NB8Rlum5Ns+VrAyvWunxZHREgArx9wXa15yMQgIltFJHeA2+pLOM44Y0w28CngVyIyCRjoGz5oXUlEHrInl5zaWu2+6EumJ0YwNT6C1/aXWR2KJV4/UIExtjNH5XtCAv1ZMSORjbmVnO9wzZieIRODMWaxMWbGALe1QLWIJALY72sGeY8K+30xsAOYA9QBY0QkwF4sBRg0JRpjnjLGZBtjsmNjYy/hIypPJyLcPjeZ/SUNlq9s5WrGGF7dX8YV48cyPjrM6nCURVbPSaKlo5stBdUuOZ6jTUnrgPvtj+8H1l5YQETGikiw/XEMcA2Qb2wdc7cDd1xsf6UAbp2TjJ/Aay7unWG1vIomjtec09qCj1swIZrEyBDWuuj772hieBxYIiLHgSX254hItog8bS8zHcgRkUPYEsHjxph8+2uPAN8UkSJs1xyecTAe5aXiR4ewMD2WV/eX0+PC3hlWe3V/OUH+fqycqVNs+zI/P2HVrCTeOVbLmXPtI368gKGLDM4Ycwa4cYDtOcCD9se7gKxB9i8G5jkSg/Idn5ybzNdfOsiek/Vc5QNzBXV197DuUAU3TItjTKgO8fF1t81NpvRsKy3t3USHj+yxdOSz8hhLMxIIDw7g7z5yEfq9ojrqzrVz21xtRlIwLWE0T953BeOiQ0f8WJoYlMcYFeTPzVmJvHWkktYO758iY82+MsaEBnL91MF6gSs1MjQxKI9y+9xkWjq62Zjr3VNk1Ld0sDmvitvmJOu6zsrl9BunPMqVaVGMiwrllZxSq0MZUa8dKKez23D3lalWh6J8kCYG5VH8/IS7r0xld3G9145pMMbw8t4SZqeOYVrCaKvDUT5IE4PyOHdmpxDgJ7y81ztrDQdKGzhWfY57tLagLKKJQXmcuIgQbpwex9/2ldHe5X3Lfr78YSmhQf6s1JlUlUU0MSiPdO+8cdS3dLAl3zVTBLjKufYu3jhcwS0zkwgPdmiYkVKXTROD8kjXpseSPGYUL35YYnUoTrX+UAWtHd3cPU+bkZR1NDEoj+Rvvwi9s+gMp8+0WB2OUxhjeH7PaabGRzAndYzV4SgfpolBeay7slPxE3jxQ++4CL2/5Cy55U185qrxuu6CspQmBuWxEiJDWJIRz0t7S1w2T/1I+tOu00SEBOhMqspymhiUR/v8NRNoaO3k9YOePR13dVMbbx2p5K7sVML0orOymCYG5dHmT4hieuJont15EtsSH57phT0ldBvDZ68ab3UoSmliUJ5NRPj8NWkcqz7HrhNnrA7nsnR09fDXPSVcPzVOV2lTbkETg/J4q2YlER0WxLM7T1odymXZcKSSunPtWltQbsOhxCAiUSKyRUSO2+/HDlDmehE52OfWJiK32l/7k4ic7PPabEfiUb4pJNCf++aPY9vRGo/rumqM4Zn3TzIhJoxPpOta5so9OFpjeBTYZoxJB7bZn/djjNlujJltjJkN3AC0Apv7FPlW7+vGmIMOxqN81KcXjCfAT3h25ymrQ7kk7xfVcaS8kYc+MRE/P+2iqtyDo4lhNfCc/fFzwK1DlL8DeMsY0+rgcZXqJ250CKtmJfPS3hKXrInrLE9uP0H86GBu11XalBtxNDHEG2MqAez3Qy01dQ/w4gXbfiIih0XkCREJHmxHEXlIRHJEJKe2ttaxqJVX+ur1k2jv6uGZ9z3jWsOBkrN8UHyGBxdOJDjA3+pwlPrIkIlBRLaKSO4At9WXciARSQSygE19Nn8HmAZcCUQBjwy2vzHmKWNMtjEmOzZW22LVx02KDefmrET+/MFpGls7rQ5nSE/uOEHkqEDunT/O6lCU6mfIxGCMWWyMmTHAbS1Qbf/B7/3hr7nIW90FvGaM+egv1hhTaWzagWeBeY59HOXr/un6yZxr7+LZXe5dazhe3cyW/GruvzpNZ1FVbsfRpqR1wP32x/cDay9S9l4uaEbqk1QE2/WJXAfjUT5ueuJolmTE8+zOUzS3uW+t4fc7TjAq0J/PX51mdShKfYyjieFxYImIHAeW2J8jItki8nRvIRFJA1KBdy7Y/wUROQIcAWKAHzsYj1I8fP1kGs938pfdp60OZUCFVc28drCcTy8Yx9iwIKvDUepjHKrDGmPOADcOsD0HeLDP81PAx7pdGGNucOT4Sg1kVuoYrpsSy1PvFnPfvPFEhgZaHVI/v9h0lPDgAL66aLLVoSg1IB35rLzSI8un0Xi+k9/tKLI6lH72nqpna0ENX75uktYWlNvSxKC8UkbSaD45N4U/7TxFab17DJsxxvD4W0eJiwjmC9dMsDocpQaliUF5rX9dOgU/P/j5pkKrQwFgS341+06f5RuLpzAqSMctKPeliUF5rcTIUXzx2om8caiCAyVnLY2lvaubxzceZWJMGHdlp1gai1JD0cSgvNqXrptETHgQP1qfT0+Pdes1/GFHMcW1Lfz7LRkE+OufnXJv+g1VXi08OIBHb5rO/pIGy7qvnqg9x++2F3HLrCSunzrUrDFKWU8Tg/J6n5ybzCemxPKzjUddfiHaGMN3XztCSKAf31+Z4dJjK3W5NDEoryci/OftWQjw6KuHXboE6N/2lbG7uJ7vrJhObMSgc0Qq5VY0MSifkDxmFN9ZMZ2dRWd4eW+pS45ZWt/Kj9fnc2XaWO7OTnXJMZVyBk0Mymd8at44FkyM4kfr8zlW3Tyix2rr7ObLz+8D4Jd3ztZFeJRH0cSgfIafn/Cru+cQGhzAF/+cQ0Nrx4gd64fr8siraOKJu2czLjp0xI6j1EjQxKB8SkJkCH/49BVUNrTxzy8eoKu7x+nHeCWnlJf2lvJP10/ixunxTn9/pUaaJgblc64YP5Yf3zqD947X8Z9vHXXqxejthTV87/Vcrp4UzTeXTHXa+yrlSrpCiPJJd12ZSn5lE8+8fxI/gX9bMR3bsiCXb/vRGr70l32kx4fz5H1z8dfrCspDaWJQPuv7KzMwxvB/752kobWT/7w967JHJW8rqOYrz+9nakIEf3lgHmNCdeZU5bkcakoSkTtFJE9EekQk+yLllotIoYgUicijfbZPEJE9InJcRF4WEf1rUi7j5yf8cFUm31iczt/2lfHl5/dT33JpF6Q7u3v49dbjfOkv+5iWGMHzD8zXpKA8nqPXGHKB24F3BysgIv7A74CbgAzgXhHpHQL6M+AJY0w6cBZ4wMF4lLokIsI3Fk/hP1ZlsqOwhht+uYOXPiwZ1rxKx6ubuf3JXTyx9Rg3z0zkLw/Md7tFgZS6HI6u4FYADNU2Ow8oMsYU28u+BKwWkQLgBuBT9nLPAT8Efu9ITEpdjvuvTuOqSdF87/VcHn31CH/9sIRbZydz/bQ4JsSEfVSurbObLfnVvH6gnHeO1TJ6VCC/v28uN2UlWhi9Us7limsMyUDfoaZlwHwgGmgwxnT12f6x5T+VcpUp8RG8/NACXjtQzu+2F/HY+nweW59PYmQIfiK0d3XT1NZFR1cPCaNDeGDhBB68dqJOdaG8zpCJQUS2AgkDvPRdY8zaYRxjoOqEucj2weJ4CHgIYNy4ccM4rFKXTkS4fW4Kt89NoeRMKzuO1bD/9Fn8/ISQQH8iggO4bmos8ydEa68j5bWGTAzGmMUOHqMM6DtRTApQAdQBY0QkwF5r6N0+WBxPAU8BZGdnWzexvvIZ46JD+exVaXz2qjSrQ1HKpVwxwG0vkG7vgRQE3AOsM7ZRRduBO+zl7geGUwNRSik1ghztrnqbiJQBVwFvisgm+/YkEdkAYK8NPAxsAgqAV4wxefa3eAT4pogUYbvm8Iwj8SillHKcuHJuemfJzs42OTk5VoehlFIeRUT2GWMGHXPWS+dKUkop1Y8mBqWUUv1oYlBKKdWPJgallFL9aGJQSinVj0f2ShKRWuD0Zewag21gnS/Rz+wb9DP7Bkc/83hjTOxQhTwyMVwuEckZTlctb6Kf2TfoZ/YNrvrM2pSklFKqH00MSiml+vG1xPCU1QFYQD+zb9DP7Btc8pl96hqDUkqpoflajUEppdQQfCIxiMhyESkUkSIRedTqeEaaiKSKyHYRKRCRPBH5utUxuYqI+IvIARFZb3UsriIiY0RkjYgctf+fX2V1TCNNRP7F/t3OFZEXRSTE6picTUT+KCI1IpLbZ1uUiGwRkeP2+7EjcWyvTwwi4g/8DrgJyADuFZEMa6MacV3AvxpjpgMLgH/ygc/c6+vYpnf3Jb8GNhpjpgGz8PLPLyLJwNeAbGPMDMAf2zov3uZPwPILtj0KbDPGpAPb7M+dzusTAzAPKDLGFBtjOoCXgNUWxzSijDGVxpj99sfN2H4ovH49bRFJAW4GnrY6FlcRkdHAJ7CvZWKM6TDGNFgblUsEAKNEJAAI5SKrP3oqY8y7QP0Fm1cDz9kfPwfcOhLH9oXEkAyU9nlehg/8SPYSkTRgDrDH2khc4lfAt4EeqwNxoYlALfCsvQntaREJszqokWSMKQf+CygBKoFGY8xma6NymXhjTCXYTgCBuJE4iC8khoFWbPeJrlgiEg78HfiGMabJ6nhGkoisBGqMMfusjsXFAoC5wO+NMXOAFkaoecFd2NvVVwMTgCQgTEQ+bW1U3sUXEkMZkNrneQpeWO28kIgEYksKLxhjXrU6Hhe4BlglIqewNRfeICLPWxuSS5QBZcaY3hrhGmyJwpstBk4aY2qNMZ3Aq8DVFsfkKtUikghgv68ZiYP4QmLYC6SLyAQRCcJ2kWqdxTGNKBERbG3OBcaY/7Y6HlcwxnzHGJNijEnD9n/8tjHG688ijTFVQKmITLVvuhHItzAkVygBFohIqP27fiNefsG9j3XA/fbH9wNrR+IgASPxpu7EGNMlIg8Dm7D1XvijMSbP4rBG2jXAZ4AjInLQvu3fjDEbLIxJjZx/Bl6wn/gUA5+3OJ4RZYzZIyJrgP3YeuAdwAtHQYvIi8AiIEZEyoAfAI8Dr4jIA9gS5J0jcmwd+ayUUqovX2hKUkopdQk0MSillOpHE4NSSql+NDEopZTqRxODUkqpfjQxKKWU6kcTg1JKqX40MSillOrn/wPH2wnVH9F9dQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d423cf8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0.05, 10, 100)\\n\",\n    \"y = np.sin(x)\\n\",\n    \"plt.plot(x, y)\\n\",\n    \"plt.text(3.2, 0, 'sin(x)', weight='bold', color='r');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 22.用箭头标出第一个峰值\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8e59c518>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0.05, 10, 100)\\n\",\n    \"y = np.sin(x)\\n\",\n    \"plt.plot(x, y)\\n\",\n    \"plt.annotate('maximum',xy=(np.pi/2, 1),xytext=(np.pi/2+1, 1),\\n\",\n    \"             weight='bold',\\n\",\n    \"             color='r',\\n\",\n    \"             arrowprops=dict(arrowstyle='->', connectionstyle='arc3', color='r'));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 四、自定义图例\\n\",\n    \"### 23.在一张图里绘制sin,cos的图形，并展示图例\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d22eb00>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0, 10, 1000)\\n\",\n    \"fig, ax = plt.subplots()\\n\",\n    \"\\n\",\n    \"ax.plot(x, np.sin(x), label='sin')\\n\",\n    \"ax.plot(x, np.cos(x), '--', label='cos')\\n\",\n    \"ax.legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 24.调整图例在左上角展示，且不显示边框\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d22eb00>\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.legend(loc='upper left', frameon=False);\\n\",\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 25.调整图例在画面下方居中展示，且分成2列\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d22eb00>\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ax.legend(frameon=False, loc='lower center', ncol=2)\\n\",\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 26.绘制的图像，并只显示前2者的图例\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8e5d6da0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"y = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 2, 0.5))\\n\",\n    \"lines = plt.plot(x, y)\\n\",\n    \"\\n\",\n    \"# lines 是 plt.Line2D 类型的实例的列表\\n\",\n    \"\\n\",\n    \"plt.legend(lines[:2], ['first', 'second']);\\n\",\n    \"\\n\",\n    \"# 第二个方法\\n\",\n    \"#plt.plot(x, y[:, 0], label='first')\\n\",\n    \"#plt.plot(x, y[:, 1], label='second')\\n\",\n    \"#plt.plot(x, y[:, 2:])\\n\",\n    \"#plt.legend(framealpha=1, frameon=True);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 27.将图例分不同的区域展示\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(-0.5, 10.5, -1.1, 1.1)\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d0c6048>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots()\\n\",\n    \"\\n\",\n    \"lines = []\\n\",\n    \"styles = ['-', '--', '-.', ':']\\n\",\n    \"x = np.linspace(0, 10, 1000)\\n\",\n    \"\\n\",\n    \"for i in range(4):\\n\",\n    \"    lines += ax.plot(x, np.sin(x - i * np.pi / 2),styles[i], color='black')\\n\",\n    \"ax.axis('equal')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 设置第一组标签\\n\",\n    \"ax.legend(lines[:2], ['line A', 'line B'],\\n\",\n    \"          loc='upper right', frameon=False)\\n\",\n    \"\\n\",\n    \"# 创建第二组标签\\n\",\n    \"from matplotlib.legend import Legend\\n\",\n    \"leg = Legend(ax, lines[2:], ['line C', 'line D'],\\n\",\n    \"             loc='lower right', frameon=False)\\n\",\n    \"ax.add_artist(leg);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 五、自定义色阶\\n\",\n    \"### 28.展示色阶\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d1cc9b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0, 10, 1000)\\n\",\n    \"I = np.sin(x) * np.cos(x[:, np.newaxis])\\n\",\n    \"\\n\",\n    \"plt.imshow(I)\\n\",\n    \"plt.colorbar();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 29.改变配色为'gray'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d34a208>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(I, cmap='gray');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 30.将色阶分成6个离散值显示\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8e533160>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(I, cmap=plt.cm.get_cmap('Blues', 6))\\n\",\n    \"plt.colorbar()\\n\",\n    \"plt.clim(-1, 1);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 六、多子图\\n\",\n    \"### 31.在一个1010的画布中，(0.65,0.65)的位置创建一个0.20.2的子图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d416518>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax1 = plt.axes()\\n\",\n    \"ax2 = plt.axes([0.65, 0.65, 0.2, 0.2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 32.在2个子图中，显示sin(x)和cos(x)的图像\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8d3cd198>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4], ylim=(-1.2, 1.2))\\n\",\n    \"ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4], ylim=(-1.2, 1.2))\\n\",\n    \"\\n\",\n    \"x = np.linspace(0, 10)\\n\",\n    \"ax1.plot(np.sin(x));\\n\",\n    \"ax2.plot(np.cos(x));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 33.用for创建6个子图，并且在图中标识出对应的子图坐标\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8e4cd240>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for i in range(1, 7):\\n\",\n    \"    plt.subplot(2, 3, i)\\n\",\n    \"    plt.text(0.5, 0.5, str((2, 3, i)),fontsize=18, ha='center')\\n\",\n    \"    \\n\",\n    \"# 方法二\\n\",\n    \"# fig = plt.figure()\\n\",\n    \"# fig.subplots_adjust(hspace=0.4, wspace=0.4)\\n\",\n    \"# for i in range(1, 7):\\n\",\n    \"#     ax = fig.add_subplot(2, 3, i)\\n\",\n    \"#     ax.text(0.5, 0.5, str((2, 3, i)),fontsize=18, ha='center')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 34.设置相同行和列共享x,y轴\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8e4692b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 35.用[]的方式取出每个子图，并添加子图座标文字\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8e4692b0>\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for i in range(2):\\n\",\n    \"    for j in range(3):\\n\",\n    \"        ax[i, j].text(0.5, 0.5, str((i, j)), fontsize=18, ha='center')\\n\",\n    \"fig\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 36.组合绘制大小不同的子图，样式如下\\n\",\n    \"Image Name\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8ee619b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)\\n\",\n    \"plt.subplot(grid[0, 0])\\n\",\n    \"plt.subplot(grid[0, 1:])\\n\",\n    \"plt.subplot(grid[1, :2])\\n\",\n    \"plt.subplot(grid[1, 2]);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 37.显示一组二维数据的频度分布，并分别在x,y轴上，显示该维度的数据的频度分布\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8ee34cf8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mean = [0, 0]\\n\",\n    \"cov = [[1, 1], [1, 2]]\\n\",\n    \"x, y = np.random.multivariate_normal(mean, cov, 3000).T\\n\",\n    \"\\n\",\n    \"# Set up the axes with gridspec\\n\",\n    \"fig = plt.figure(figsize=(6, 6))\\n\",\n    \"grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2)\\n\",\n    \"main_ax = fig.add_subplot(grid[:-1, 1:])\\n\",\n    \"y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)\\n\",\n    \"x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)\\n\",\n    \"\\n\",\n    \"# scatter points on the main axes\\n\",\n    \"main_ax.scatter(x, y,s=3,alpha=0.2)\\n\",\n    \"\\n\",\n    \"# histogram on the attached axes\\n\",\n    \"x_hist.hist(x, 40, histtype='stepfilled',\\n\",\n    \"            orientation='vertical')\\n\",\n    \"x_hist.invert_yaxis()\\n\",\n    \"\\n\",\n    \"y_hist.hist(y, 40, histtype='stepfilled',\\n\",\n    \"            orientation='horizontal')\\n\",\n    \"y_hist.invert_xaxis()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 七、三维图像\\n\",\n    \"### 38.创建一个三维画布\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e90543da0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from mpl_toolkits import mplot3d\\n\",\n    \"fig = plt.figure()\\n\",\n    \"ax = plt.axes(projection='3d')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 39.绘制一个三维螺旋线\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8f117d30>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = plt.axes(projection='3d')\\n\",\n    \"\\n\",\n    \"# Data for a three-dimensional line\\n\",\n    \"zline = np.linspace(0, 15, 1000)\\n\",\n    \"xline = np.sin(zline)\\n\",\n    \"yline = np.cos(zline)\\n\",\n    \"ax.plot3D(xline, yline, zline);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 40.绘制一组三维点\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8f1734a8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = plt.axes(projection='3d')\\n\",\n    \"zdata = 15 * np.random.random(100)\\n\",\n    \"xdata = np.sin(zdata) + 0.1 * np.random.randn(100)\\n\",\n    \"ydata = np.cos(zdata) + 0.1 * np.random.randn(100)\\n\",\n    \"ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap='Greens');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 八、宝可梦数据集可视化\\n\",\n    \"### 41.展示前5个宝可梦的Defense,Attack,HP的堆积条形图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"df = pd.read_csv('Pokemon.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8f9e5c18>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pokemon = df['Name'][:5]\\n\",\n    \"hp = df['HP'][:5]\\n\",\n    \"attack = df['Attack'][:5]\\n\",\n    \"defense = df['Defense'][:5]\\n\",\n    \"ind = [x for x, _ in enumerate(pokemon)]\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10,10))\\n\",\n    \"plt.bar(ind, defense, width=0.8, label='Defense', color='blue', bottom=attack+hp)\\n\",\n    \"plt.bar(ind, attack, width=0.8, label='Attack', color='gold', bottom=hp)\\n\",\n    \"plt.bar(ind, hp, width=0.8, label='Hp', color='red')\\n\",\n    \"\\n\",\n    \"plt.xticks(ind, pokemon)\\n\",\n    \"plt.ylabel(\\\"Value\\\")\\n\",\n    \"plt.xlabel(\\\"Pokemon\\\")\\n\",\n    \"plt.legend(loc=\\\"upper right\\\")\\n\",\n    \"plt.title(\\\"5 Pokemon Defense & Attack & Hp\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 42.展示前5个宝可梦的Attack,HP的簇状条形图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8f9e5278>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"N = 5\\n\",\n    \"pokemon_hp = df['HP'][:5]\\n\",\n    \"pokemon_attack = df['Attack'][:5]\\n\",\n    \"\\n\",\n    \"ind = np.arange(N)\\n\",\n    \"width = 0.35       \\n\",\n    \"plt.bar(ind, pokemon_hp, width, label='HP')\\n\",\n    \"plt.bar(ind + width, pokemon_attack, width,label='Attack')\\n\",\n    \"\\n\",\n    \"plt.ylabel('Values')\\n\",\n    \"plt.title('Pokemon Hp & Attack')\\n\",\n    \"\\n\",\n    \"plt.xticks(ind + width / 2, (df['Name'][:5]),rotation=45)\\n\",\n    \"plt.legend(loc='best')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 43.展示前5个宝可梦的Defense,Attack,HP的堆积图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8fcfb4e0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = df['Name'][:4]\\n\",\n    \"y1 = df['HP'][:4]\\n\",\n    \"y2 = df['Attack'][:4]\\n\",\n    \"y3 = df['Defense'][:4]\\n\",\n    \"\\n\",\n    \"labels = [\\\"HP \\\", \\\"Attack\\\", \\\"Defense\\\"]\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots()\\n\",\n    \"ax.stackplot(x, y1, y2, y3)\\n\",\n    \"ax.legend(loc='upper left', labels=labels)\\n\",\n    \"plt.xticks(rotation=90)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 44.公用x轴，展示前5个宝可梦的Defense,Attack,HP的折线图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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07Nyio1dTUcPDgwWabgTf0XPc/+rrqVuE1dT8p8DkaqvDamqpSWFjYaOkqNze3Xt8wXwm1oU7DvoBlTLSL1o7VER8pRERuB4qBa4EpXulsILBQVUc1tW1LA1pj/YS6du3K+PHjgzrSBk5ZUVdCQgK9e/dutsqublq0V+G1tZqaGgoKChotXeXl5VFWVha0TZ8+fRocksnXmbhnz54ROhtjOo5oDWjt/usqIklAjKoe8l6fCdwBvAJcCdznPb/c1sfOz89vML2srIw+ffpwzDHHhBSkjtYqvLZWU1PDrl27Gi1d5eXlUV5eHrRNamoqmZmZjB8/nnPPPbde0EpOtin8jDlaRaK40B940as+iwOeUtU3RWQZ8JyIXAPkAxe39YHT09MbLKFlZGTwn//8p60PFxWO5J5jdXU1O3fubHQcwfz8/Hr3Dfv27UtmZibZ2dnMnj07qFowIyOD7t27h+M0jTFRoN0DmqpuBrIbSN8LTA/nsVszksPRrO49x7y8PH+/qm9961tUV1ezY8eORhtd5Ofn1xtuqn///v6O0t/4xjeCSljp6ekkJSW1+3kaY6JDxO+hHYnWjLYf7laO0aSxe44JCQmkpaWRn59PVVVV0HsDBgxotNFFeno6iYmJ7ZV9Y0wjovUe2lEX0EzoYmJiGu3fdvnll9dreJGenu4fqNcY03FFa0CzJnemUU3dc3zqqacikCNjjGmcDcttGnX33XfXqyK0e47GmI6qU1c5ishuoH4RIjSpwJ5m1zK9gTQgHqgAtgP7IpqjzsG+Xy1n16xljuR6Zahq37bMTEfQqQPakRCR5dFYhxwudr1axq5Xy9k1axm7XvVZlaMxxpioYAHNGGNMVDiaA9rcSGegk7Hr1TJ2vVrOrlnL2PWq46i9h2aMMSa6HM0lNGOMMVHEApoxxpio0GEDmohUi8inIrJaRFaKyMkhbLNQRHK8103PtNmJicgAEXlGRL4SkS9E5A0RmSMir0U6b6EQkVwRSY10PhoTzd+dI+H9fc2sk/YjEXnI8tQ6InKViKiITA9Iu8BLuyiMx+3U160xHTagAaWqOkFVs4FbgHsjnaGWEpHYMOxTgBdxE6AOV9UxwK24aXmOZL8ddhi0cFzHzqKDfS5PA5fVSbvMS4+UZvPUwa6hX0C+1gKXB7x1GbA6zIfviJ9lSJr6PDtyQAvUA9gPICJTAksiIvKgiFzV0EYi8gevdLdARPp6adeKyDKv5PcvEUn00i8Wkc+89EVeWqaIfODtw19KbCoPXunj1yLyIWGY0w2YClSq6l99Car6KfAB0F1EXhCR9SIyzwt+ePlZ5p3f3ID0hSJyj4i8D/xQRB4TkYdF5D0R2SwiZ4jIoyKyTkQeCzjfh0VkuYh8LiK/CUjPFZHfeNdqrYiM9tL7iMh8EVklIo8AErDNFSKy1CuNP+ILXiJSLCJ3iMgnwOQwXMdmicizInJ2wPJjInKh952YEJD+kYhkedfrU++xSkSSRaS79/3zXZPZ3jaZIvJZwD5uFDd7e73Ppf3OuFkvAOeKSAK4cwAGAR+KyM+879ga33fCO8d1IvI377syX0S6ee8F1qakikiu93pswPdhjYiM8NJfEpEV3n7mBOTpD748ichFIvKCl6drROR+EdkEbGvvfElAKd/L12Pe68e8fL0H/NZb5QPgBBHpIiLdgWOATwO2nyQi73vHeUtEBnrpx3t5WSwiv/N9n6SR361QP0tvOSKfZwuvWz0dOaB18y7CeuDvwJ0t3D4JWKmqxwHvA7d56f9W1eO9kt864Bov/dfATC/9PC+tEJjh7eNS4IEQj12mqqeq6jMtzHMoxgErGnlvIvAjYAwwDDjFS3/QO+dxQDfg3IBtUlT1DFX9g7fcC5gG/Bh4FfgjMBYYH/Aj/gtvhIIs4AwRyQrY3x7vej0M3Oil3QZ8qKoTcTOTpwOIyLG463qKqk4AqgHfXD5JwGeqeqKqfhjitWlrz3j5Q0TicfP1vYH7Pl7lpY8EElR1De58r/fO5TSgFCgDLvCuyVTgDyIiNK/u5xJx3pyFS4FZXtJlwLPADGAEcAIwAZgkIqd764wA/qKqY4EDwIXNHOY64E/eNcwBtnnpV6vqJC/tBhHpE7BNYJ6GenkCOBl4DxgYoXw1ZiTwNVX9qbeswDvATGA27m8EABHpAvwZuMg7zqOAbzDVfwDXqepk3N+OT7O/W419lqqqInImkf08G1P3utXTkQOar8pxNO6iPxHiD4FPDbVf7CeBU73X47z/XtbifjzHeukfAY+JyLWAr4qrC/A3b93ncYEiFM82v0pYLFXVbapag/sPL9NLnyoin3jnMY3ac4b6eX1VXV+OtUCBqq719vd5wP4uEZGVwCpvX4HX5d/e84qA9U/HfQao6ut4pW1cgJgELBORT73lYd571cC/WnT2be8/wDTvv9izgEWqWor7Lpzr/dhcDTzmrf8RcL+I3IALSFW40ug9IrIG96OVRmjVw5H6DjUnsKrKV0V1pvdYBawERuN++AC2eDUIEPydaMxi4FYRuQk33mCpl36DiKwGlgBDAvZfN09Dqa02248LtpHKV2OeV9XqOmnPeOdQt9pvFO6f2Le9v5FfAoNFJAVIVtWPvfUCp78I9Xeroc8SIv95Nqah6xakw/ZDE5FiVe0esFwAjMdF6VtV9ezU1FTNzMyMVBaNMaZTWrFixR5c8N+MKzA8raqjwN2qATao6iOB23jVkq95NT2IyI1Ad1W9XUTewf0uLxWRwbgamUxvveHAObjao+/hCht3AWeqaomILARuV9WFInJIVZO97a7Alciu8qoeX1PVF5o6rw55s7QucfdiYoG9uNH1x4hIwqRJk7AJPo0xpmVEJE9Vi71g8ijBpcK3gDtFZJ63ThpQ2cwuc3G1LUsBf+tMERkGbFbVB7zXWcAWYL8XzEYDJwXsp8C7FfElcAFwqCXn1ZEDWjeviA2u2uZKr7i5VUSeA9ZELmvGGBMVnsbdJvC3eFTV+V5QWezd5SkGriD4Pl1dvweeE5FvA+8GpF8KXCEilcAu4A7gMHCdVw3/Ja7a0edm4DVgK/AZ0J0W6LBVjqHIyclRK6EZY0zLiMiKaJx6piM3CjHGGGNCZgHNGGNMVLCAZowxJipYQDPGGBMVLKAZY4yJChbQjDHGRAULaMYYY6KCBTRjjDFRwQKaMcaYqGABzRhjTFSwgGaMMSYqWEAzxhgTFcIW0ETkUREplOBp5nuLyNsistF77uWli4g8ICKbvGm6jwtXvowxxkSncJbQHqN2em+fm4EFqjoCWOAtg5sNeIT3mAM8HMZ8GWNM57Z4Mdx7r3s2fmGbD01VF3kznAaaDUzxXj8OLARu8tKfUDeXzRIRSRGRgaq6M1z5M8aYDk8Vdu2CN9+El192D5+YGEhIgAULYPLkyOWxA2m2hCYi1zSQdl8rj9ffF6S8535eehpuQjefbV5aQ/mZIyLLRWT57t27W5kNY4zpQA4cgA8/hFtugTFjQMQ9YmJg0CC4+urgYAZQUwMVFbBwYUSy3BGFUkK7SETKVHUegIg8BCS0cT6kgbQGZx5V1bnAXHATfLZxPowxJjyKi2HTJli9Gl57zQWoysrmt0tIgNmz4fzzYdYsWL8epk93wSw+HqZMCXvWO4tQAto3gFdEpAZ3r2ufqv6glccr8FUlishAoNBL3wYMCVhvMLCjlccwxpjIKC2Fr76CjRth1Sp45RUXwEIxbpwLWueeC1lZ0K1bw+tNnuyqGRcudMHMqhv9Gg1oItI7YPF7wEvAR8AdItJbVfe14nivAFcC93nPLwek/7eIPAOcCBTZ/TNjTIdUUQFbtrigtXEjLFvmSlslJc1vGxPjSlvnnQfHHw8jRrhSVktNnmyBrAFNldBW4Kr9JOD5HO+hwLCmdiwiT+MagKSKyDbgNlwge867L5cPXOyt/gZwNrAJKAG+27rTMcaYNlBdDXl5LmBt2OCeFy+G5ctD237UKBe4Jk9298SGDYO4sLXBM55Gr7CqDj2SHavq5Y28Nb2BdRW4/kiOZ4wxLVJTA9u31wYsX/D64AMoKgptH+eeC6eeCmPHusCVkQGxseHNt2lUs/8yiMj1wDxVPeAt9wIuV9WHwp05Y4w5IqpQUBActDZuhC+/hM8/D20fw4bB1Km1QWvMGBg82LVCNB1KKGXga1X1L74FVd0vItcCFtCMMZGnCnv3BgcsXwDbsAEOHw5tP6edFhy0xoyBAQMscHUioQS0GBERr1oQEYkFWnEX0xhjjkBRUf2A5Xvs3x/aPvr2dS0IA4PWsce6dNPphRLQ3sI15PgrrjHIdcCbYc2VMebodPiw66vVUNAqLGx+e5/09NpgFRi4evUKX95NxIUS0G4Cvg/8F66l43zg7+HMlDEmipWVwebNwQFr+XIXyA4dCn0/IjB0aHBpa8wYGD0akpPDl3/TYTUb0FS1BjdYsA0YbIwJTWVlcF+twBJXfr677xWq2Fg45pjgktaYMa5pfGJi+M7BdDqhtHIcAdwLjAG6+tJVtcl+aMaYKFdd7YJTQ/e1tmxx7/v07OkeJSUuCJWWumbzDRk3Di68sDaAjRjhhn8yphmhVDn+A9cp+o/AVFynZ2v2Y8zRoKYGduxoOGh99ZUbNcMnKckFn7FjXbVfaal7lJS4/l75+bXrDh7sGmdkZ7shnu65x5Xq4uNh7lwbBcO0SigBrZuqLvBaOuYBt4vIB7ggZ4zp7FRdg4uGGmJs3OiCkk9Cgqv+GzUKvv516NGjNmgVFsKaNfD661BVVbv+uHFuXV8AGz8e+vQJzsPXvmZjE5ojFkpAKxORGGCjiPw3sJ3aaV+MMZ3Fvn31A5ZvObAxRlyc60w8cqQb1X3ECDcCRnW128fatS5wPfZYcMtDX6nLN7hudrbbNpQhn2xsQtMGQgloPwISgRuAO3HVjt8JZ6aMMa108GDDAWvjRheMfGJiXJAaORJOPtkFnpEj3XPXrm4UjTVr3EjxCxbAunW1U534Sl3nnOOCVlaWe9QtdRnTzkIJaJmqugwoxhs0WEQuBj4JZ8aMMY0oKantq1U3aBUUBK87ZIgLUhdfHBy0hg51zd7Xr3dBa80aN0fX6tXBpa60NBe0zj7bPbek1GVMOwvlW3kL8HwIacaYtlJe7vpqNXRfa9u24HUHDHBB5pxzagPWiBEwfHhts/aCgtoS13PPudd1S11jx7p9+KoLrdRlOpmm5kM7CzelS5qIPBDwVg+gKtwZMybqVVVBbm79oLVhg2sRGNisvU8fF6SmTasNWL5HYCfiioraUtcTT9SWvgJLbmlpLlj5Sl1ZWS4QWqnLdHJNfYN3AMuB83Bzo/kcAn4czkwZEzVqamDr1oYbY2zZUtsaEFyLwZEjXeOI73wnuIqwoSGbCgthyZLaoLV6dcOlrrPPDm5hmJraPuduTDtraj601cBqEemvqo8HviciPwT+FO7MGdMpqAb31QoscX31las+9ElMdAEqOxsuuig4aPXt2/DI7hUVLmD5gpbvubFSly94WanLHGVC+bZfBvy/OmlXYQHNHE1UYffuhhtibNoUPEVJQoK7fzVihAswgfe1Bg1qejqSwsLgoNVYqeuss4JbGFqpy5gm76FdDnwTGCoirwS81QPYE+6MGRMR+/c33BBjwwbXJN4nLs61FBwxwk3+GHhPa8iQ5mctrqhwk0z6glZDpa5Bg1zQCgxeo0ZZqcuYRjT1l/ExsBNIBf4QkK7ApeHMlDFhdeiQK1XVbYixcaObKNJHxPXVGjECvv3t2oA1cqRL79Kl4f0vXhw86oVvBI3A4PXFF7Wlrvj42lJXYAtDK3UZ0yJN3UPLA/KAySIyAVdauwTYAvyrfbJnTCuVlgb31QoMWrt2Ba+bluaC1IUXBgetYcMaHhS3pgaKi91+ioqCH6tWwf/+r2vsIQIpKcEdmgcNcsFq1qzge12NBUdjTMiaqnIcibt/djmwF3gWEFWd2k55M6ZpFRW1fbXqVhNu3Rq8bv/+LlCddZYLKv36uUdqqispBQalpUth/vz6wcr3OHiw8ZHiA6lCZib88pe197psZmRjwqapKsf1wAfA11V1E4CIWHN9076qqiAvrzZgrV7tJoNcsya07bt3d0FE1Q13u5+xAAAgAElEQVTntHhx8LQmDYmNrZ3uxPfIzKx9nZJS/33fY9MmuPRSF2zj4+HBB22MQmPaSVMB7UJcCe09EXkTeAabNsa0lqqrBmyoxLN/vws2y5a5YOW7t9QaMTGuP1dTQae5oJSU1HRLxKYce6wb+9BGjjem3TV1D+1F4EURSQLOx3Wm7i8iDwMvqur8dsqj6QjKyuDAgcar4YqKmn7/wIHgTsQtlZUFOTnuflNTAal799YHo7ZiI8cbExHNtv9V1cPAPGCeiPQGLgZuBiygdRbl5S0LPA2lB07k2JgePVzpKnD+rOYMH+4CVU4OTJzomqUPGuRKWsYY0wIt6tCiqvuAR7xHq4lILm4IrWqgSlVzvGD5LJAJ5AKXqOr+IzlOVKioaF1pKHA5cKSKxiQnB5d0+vVzjShSUlyLvsJCN2zSxIlu/cLC4MfOnW5UjMBgFhtb21crcESMESMgPb35vlrGGNMCkeyhOVVVAzto3wwsUNX7RORmb/mmyGStjdRtPdeaoFRW1vxxuncPDkapqa7kE8r9opQUF8waCy6LF7uOw+Xl8M47rhR24EDt+yIuOI0YAd/8ZnAH46FDrTm6MabddKQhB2YDU7zXjwMLCVdAq9vxtSFVVa55dmur6IqK3LxVzUlMDA46vXoFt6hrLiD16BHekSMWLqxtpFFTEzy3lm+Kkq5dw3d8Y4wJUaQCmgLzRUSBR1R1LtBfVXcCqOpOEekXliP7ShwVFe4+zZQprhRRNzAFjs3XmG7d6ged9PTQW9X16NHxSzBTprjOxb5m6H/6kzV4MMZ0SJEKaKeo6g4vaL0tIutD3VBE5gBzANLT01t+5IUL3Y+zquuPtHq1qxrr2RMGDw69qXePHu4HPtpNnmzN0I0xnUJEApqq7vCeC0XkReAEoEBEBnqls4FAYSPbzgXmAuTk5GiLDz5liqsi85U4XnnFfqSbY83QjTGdQLu3jRaRJBFJ9r0GzgQ+A14BrvRWuxJ4OSwZ8JU47rzTPdsPtTHGRIVIlND64zps+47/lKq+KSLLgOdE5BogH9ffLTysxGGMMVGn3QOaqm4GshtI3wtMb+/8GGOMiQ42HIMxxpioYAHNGGNMVLCAZowxJipYQDPGGBMVLKAZY4yJCqLa8r7JHYWI7AbyWrl5KrCn2bWMj12vlrHr1XJ2zVrmSK5Xhqr2bcvMdASdOqAdCRFZrqo5kc5HZ2HXq2XserWcXbOWsetVn1U5GmOMiQoW0IwxxkSFozmgzY10BjoZu14tY9er5eyatYxdrzqO2ntoxhhjosvRXEIzxhgTRSygGWOMiQodNqCJSLWIfCoiq0VkpYicHMI2C0Ukx3tdHP5cRoaIDBCRZ0TkKxH5QkTeEJE5IvJapPMWChHJFZHUSOejMdH83TkS3t/XzDppPxKRhyxPrSMiV4mIisj0gLQLvLSLwnjcTn3dGtNhAxpQqqoTVDUbuAW4N9IZaikRiQ3DPgV4EVioqsNVdQxwK26euSPZb0RmLw9FOK5jZ9HBPpengcvqpF3mpUdKs3nqYNfQLyBfa4HLA966DFgd5sN3xM8yJE19nh05oAXqAewHEJEpgSUREXlQRK5qaCMR+YNXulsgIn29tGtFZJlX8vuXiCR66ReLyGde+iIvLVNEPvD24S8lNpUHr/TxaxH5kPBMUjoVqFTVv/oSVPVT4AOgu4i8ICLrRWSeF/zw8rPMO7+5AekLReQeEXkf+KGIPCYiD4vIeyKyWUTOEJFHRWSdiDwWcL4Pi8hyEflcRH4TkJ4rIr/xrtVaERntpfcRkfkiskpEHgEkYJsrRGSpVxp/xBe8RKRYRO4QkU+AiMzGKiLPisjZAcuPiciF3ndiQkD6RyKS5V2vT73HKhFJFpHu3vfPd01me9tkishnAfu4UURu914HfS7td8bNegE4V0QSwJ0DMAj4UER+5n3H1vi+E945rhORv3nflfki0s17L7A2JVVEcr3XYwO+D2tEZISX/pKIrPD2MycgT3/w5UlELhKRF7w8XSMi94vIJmBbe+dLAkr5Xr4e814/5uXrPeC33iofACeISBcR6Q4cA3wasP0kEXnfO85bIjLQSz/ey8tiEfmd7/skjfxuhfpZessR+TxbeN3q6cgBrZt3EdYDfwfubOH2ScBKVT0OeB+4zUv/t6oe75X81gHXeOm/BmZ66ed5aYXADG8flwIPhHjsMlU9VVWfaWGeQzEOWNHIexOBHwFjgGHAKV76g945jwO6AecGbJOiqmeo6h+85V7ANODHwKvAH4GxwPiAH/FfeCMUZAFniEhWwP72eNfrYeBGL+024ENVnQi8AqQDiMixuOt6iqpOAKqBb3nbJAGfqeqJqvphiNemrT3j5Q8RicdNQPsG7vt4lZc+EkhQ1TW4873eO5fTgFKgDLjAuyZTgT+IiNC8up9LxHmT8C4FZnlJlwHPAjOAEcAJwARgkoic7q0zAviLqo4FDgAXNnOY64A/edcwB9jmpV+tqpO8tBtEpE/ANoF5GurlCeBk4D1gYITy1ZiRwNdU9afesgLvADOB2bi/EQBEpAvwZ+Ai7ziPAnd7b/8DuE5VJ+P+dnya/d1q7LNUVRWRM4ns59mYutetno4c0HxVjqNxF/2JEH8IfGqo/WI/CZzqvR7n/feyFvfjOdZL/wh4TESuBXxVXF2Av3nrPo8LFKF4tvlVwmKpqm5T1Rrcf3iZXvpUEfnEO49p1J4z1M/rq+r6cqwFClR1rbe/zwP2d4mIrARWefsKvC7/9p5XBKx/Ou4zQFVfxytt4wLEJGCZiHzqLQ/z3qsG/tWis297/wGmef/FngUsUtVS3HfhXO/H5mrgMW/9j4D7ReQGXECqwpVG7xGRNbgfrTRCqx6O1HeoOYFVVb4qqjO9xypgJTAa98MHsMWrQYDg70RjFgO3ishNuPEGS730G0RkNbAEGBKw/7p5Gkpttdl+XLCNVL4a87yqVtdJe8Y7h7rVfqNw/8S+7f2N/BIYLCIpQLKqfuyt91TANqH+bjX0WULkP8/GNHTdgnTYfmgiUqyq3QOWC4DxuCh9q6qenZqaqpmZmZHKojHGdEorVqzYgwv+m3EFhqdVdRS4WzXABlV9JHAbr1ryNa+mBxG5EeiuqreLyDu43+WlIjIYVyOT6a03HDgHV3v0PVxh4y7gTFUtEZGFwO2qulBEDqlqsrfdFbgS2VVe1eNrqvpCU+fVIW+W1iXuXkwssBc3uv4YEUmYNGkSy5cvj2zmjDGmkxGRPFUt9oLJowSXCt8C7hSRed46aUBlM7vMxdW2LAX8rTNFZBiwWVUf8F5nAVuA/V4wGw2cFLCfAu9WxJfABcChlpxXR65y9N1D+xRX/XKlqlar6lbgOWBNZLNnjDHto7SimqISF1MWbdhN5s2vsyx3X1vs+mkgG1flCYCqzsdVYS72qi1fAJKb2c/vgf8SkY9x09r4XAp85v2OjwaeAN4E4rxq+Dtx1Y4+NwOvAe8CO1t6Mh22yjEUOTk5aiU0Y0y0OVhWyaGyKtJSulFWWc3oX71Jctc41t4+k+oa5dhfv8nyX36NHl27tGr/IrIiGqee6cglNGOMOSrsO1zBup0H/csn3bOAGfe/D0DXLq6NWkafRABiY4QNd53V6mAWzTrFPTRjjIkmhQfL2FRYzMnHuNq5Hz37Kdv2lfDujVMAiI+L4UBJJWWV1XTtEstX95xNbExLGnkfnayEZowxYVZwsIyXP92O7xbPQwu/4prHl1NZXQPAuEE92LznMFv3lQDw8vWn8M5PzvCXziyYhcYCmjHGtLHCg2U8uSSP4vIqAN76fBc/fOZTtu5z3bDOn5jGpIxerMo/AMC3TsrgkpzB+Jo0ZPRJ4ph+3Rvct2mcBTRjjDlC+w9X8OiHW8jdcxiADQXF/PKlz1iZ58YQOGf8QOZ970QOV7gAN3pAMp/tKGLNNhfQ0lK68f8uyibdu09mWsfuoRljTAuVVlTz+OJcJg5J4cRhfSitrOaO176gW3wsmalJ5GT2YuGNU0jv7QJUn+4J3PHqJ/Ts1oXnrptM1y6xLL55Ot3ij9pxt8PCSmjGGNMMVeXBdzfy4io3FGGXWOHBdzfx0Vd7ARiU0o0lt0zn8hPSAdcy8b0vC5n1p0XU1Lh6xNvPG8s93xjn36cFs7ZnAc0YYxowd9FXPLBgIwAiwpuf72LpFleFGBcbw+JbpvGTGSP961er8ts311NU6jpAp6V0Y8KQFIq9asbJw/twTL/m+iebI2FVjsYYAzy5JI+Vefu5/1I3qcS6nYc47DXqAHjxB6fQJba2DJDctQuFh8oQhL7JCew+VM7cRZs5cWhvpozqx5ljB3Dm2AHtfh5HMyuhGWOOSi9/up0LHvqIaq9KsKi0kl0Hy/zL91+Szdzv1A6mERjMwI3mcepv3+PRj7YAkD24J0tvnc6UUf3a6QxMXSEFNBHpLyL/JyL/8ZbHiMg1zW1njDGRpKr+vl+LNuxm6u8XsquoDICEuBiSu3bhQEkFANdPPYanrj3J3+erodmq7n79C27+lxtGtkfXLtx1/jgumjTYv36f7glhPyfTuFBLaI/hRmAe5C1vwE0FYIwxHYaq+jsrf7HjIKfc9y6LvYYbqd0TGN63u79v2KxxA3ni6hOaDEKbdxfzD68EBq6UFlhSuyRnCMP7Wn+xjiLUgJaqqs/h5rHBm7iw6YnWRB4VkUIJnma+t4i8LSIbvedeXrqIyAMissmbpvu4Vp6PMeYoUl2j/gC1t7ic4+9+h2eWbQVgcO9uTMzoRWKCayowZlAP/n5lTrMdlncfKqfKC4rvri/krtfXsbPIdYj++azR3Hn+uKY2NxEUakA77E2RrQAichJQ1Mw2j1E7vbfPzcACVR0BLPCWwc0GPMJ7zAEeDjFfxpijSGV1DYWHXJVhTY1y8n0LuH/+BgB6J8Vz9viBDO+bBLgqwb988zgmDEkJef8r8vZz0r0L+GDjHgAuzhnCklumM7BntzY+ExMOoQa0nwCvAMNF5CPcnDb/09QGqroIqDthz2zgce/148D5AelPqLMESBGRgSHmzRgTpcoqq9m8u9i/fOHDH/PzF9w9rJgY4ZpTh3LaSDfAr4hwx+xxnDw8tcF9NaS8qpqfPb+aZ5flAzA+rSfXTz3GX4rr2a0LfZPtvlhnEVKzfVVdKSJnAKMAAb5U1eZmMG1If1Xd6e1zp4j4mgOlAVsD1tvmpdWb4E1E5uBKcaSnp7ciC8aYjqq4vIoNBYc4Lr0XAD9+9lM+21HEBz+fBsC1pw2jW5faDslzTh/e4mNsLDjE1v0lTBvdn4S4WPL2lpCZ6kp18XExQX3LTOcSaivHi4Fuqvo5rlT1bBvf52poKOkGZx5V1bmqmqOqOX379m3DLBhj2ltRaSXvfFHgH03jL+9t4pK/LqbE64z83VOGctu5Y/0tFb+ePYivjenf4uMUB/Qn+8P8Ddzy77X+Yz77/ZO4fuoxR3oqpgMItcrxV6p6SEROBWbiqgtbc5+rwFeV6D0XeunbgCEB6w0GdrRi/8aYDqyotJLX1+z0N5Wf//kuvvfEcjYWumrFiyYN5h/fPZ64GPfTdMLQ3nxtTP8Gm9CH6pml+eTc9Tb7Drtj3nzWaF77n9OIaaJ5vumcQg1ovhaN5wAPq+rLQHwrjvcKcKX3+krg5YD073itHU8CinxVk8aYzutweRX/WrGNr7z7YJsKi7n+qZX+pvTTj+3Pc9+fTGaqG8R3eN/unDaiL/FxrR/zYWdRKT97fjVf7HAzQB+X0YvvnjKUGq+Ul5maZPfFolSo35rtIvIIcAnwhogkNLetiDwNLAZGicg2ryP2fcAMEdkIzPCWAd4ANgObgL8BP2jxmRhjIq6yuoanl+azZLMLWOVVNfz0+dXM/7wAgKzBPXnp+lP81Ya9k+I5YWhvEuKObKDeTYWHWL/LBbCucbG8s66AjYWHABjZP5mbZo0m1To9Rz3x1U03uZJIIq4J/lpV3ehVF45X1fnhzmBTcnJydPny5ZHMgjFHvac+yScxPpbzJ6ahqky66x3OGT/Q319rU2Exw1KT/FV8baWyuoYusTHU1Cgn3buACUNS/ENV+d4zDRORFaqa0/yanUuorRxLRORloL+I+JoWrg9ftowxHdXzy7dSeKjc35Di3yu3kZIYz/kT09yo9D88LahKLxwzL9/7n3V8tGmP/17Yny+fyFCv/xnUH3fRHB1CCmgi8j/AbUAB3mghuFaIWWHKlzGmg3htzQ4WbdjN/7soG4BlufvYWFjsD2iPXX0C3RNqf0r69eja5nn4ctch/rkkl1+dO4aEuFjGDOyBIFRU1RAfF8OJw/q0+TFN5xPq9DE/BEap6t5wZsYYE3kLvyxk7qLN/OO7x5MQF8v2/aWs2VZEWWU1XbvEcvcF44NKQIHBrC1tKiymT1I8vZLi2VlUyosrt3NpTjrjB/dk9oQ0Zk8Iy2FNJxZquXwrzQ91ZYzphFbl72f2Xz5iy57DgBsf8XB5FYUHywGYc/ow3vzR6XT1OjSHszrPd09/674Svnb/+zy/wo23cNqIviz/5QzGD+4ZtmObzi/Uf602AwtF5HWg3JeoqveHJVfGmDZXU6PExAh5ew9z3ZMr+dnMkUwb3Z8e3bqQEBtDcZnrfDz92P5MP7a283J79NNSVf77qVX0TU7g9vPGMqR3In+8NJtTjnHDWMXGCN3ij6wlpIl+of6rlQ+8jet7lhzwMMZ0UOVVrvtoSUUV036/kP/70E2D0r9HV/omJxAf6wLE8L7dee66yWEv/Rz2hrXyuePVL/jm35YALmgO6Nk1qDHJBRMH0y+57e/HmegVaivH3wCISJKqHg5vlowxLaWqHCyrome3LgCc/acPyBrck/suzCIxPo4Th/VmSG/Xeblrl1ieuPqEsOdp7bYiPv5qD98/w423eM8b63h19Q5W33YmIsLQvknExdaW/n517piw58lEt1DHcpwsIl8A67zlbBF5KKw5M8Y0qqZG2XGg1L981T+Wce0TtX0yz80eyOThtS3/7v1GFrPGDWjzfBSXV1HtjYn43vpCLnr4Yw574yYu3ryH+95cT1GJG8f80uOH8IdLJuCtzrdPyuDWs49t8zyZo1eoVY7/ixvDcS+Aqq4GTg9Xpowxwaqqa/wjYQDc+uJaznvwI38jim8cl8bFkwb73//BlGOYPSGtTfNQVFrJ/M93sbfY3UZ/6/NdjLvtLX81ooh77PfGabz8hHQ+/81Meia6UmPW4BRmjOlPbBt3sDbGJ+TmSqq6tU5SkzNWG2Nar7yqmmW5+6j0Zk5+ZNFmzvrTB/5BfS+YmMYtZ432l45mT0jj4pwhje6vJXxBsvBQGb966TNW5e8HIHfPYeb8cwXLct3y2EE9+MmMkf5qzimj+vH8dSczuJer2kzu2oXE+PA06TemISE32xeRkwEVkXgRuRGv+tEYc+TKKqv5cOMe/4jw73xRyMV/Xczn3gC7Z40bwAOXTfSPeXjisD5cOGkwcUfQhF5V2bqvhO1e1WVRaSVTfvceT37iJruMj43hpVXbyd3rbpuPHpjMiz84mSmj3LRNg3slcsP0EQxKsdmcTccQ6l/DdcD1uEk3twETvGVjTCuUV1WzYF0Bm7xpU7bsOcwV//cJC790Myqdckwf5n57kn/YqGF9u/P17EFH3HT9uWVbefsLN1BwjcKZf1zE3z/YDECPrnEcl9GLtBTXsjAlMZ41t5/JBRNdVWZCXCwT03v5+6MZ09E0N2L+b72XU1X1W6raX1X7qeoVNmqIMaGrqVH+s3anfxT6qmplzj9X8NKq7QCM6p/M41efwJljXcONlMR4zhw7oMWjcKiqv1oS4PZXPueOV7/wL//fh1v414ptgOvb9cdLs7n8BDc8q4hw/yUTmDa6ffugGdNWmvtrOVtEfgncAjzfDvkxJmr8Z+1OKqprmD0hjZgY4a7X13FcRi9OGtaHpIQ4XvzByYzs77pzxsQIZ4xs+QzsGwoOsWXPYWZ6gXDOP1ew40Apr99wWoPrPzPnJFK8RhoAs8YNbMWZGdMxNRfQ3gT2AEkichAQ3KDEAqiq9ghz/ozpNN7+ooCvdhdzndfv6qml+Rwur/K3Nnzq2hMZ2LP2flPW4JSQ9quq/pLS218UMP/zXfzuYjdQ8JNL8vj3yu2cebub1fmiSYM5WFrp3/b288YG7atXUmvm5TWmc2iyylFVf6aqPYHXVbWHqiYHPrdTHo3pkBZt2M2tL671twr8aNMenlySR43X8vB/L53A89ed7F8/o09SszMxl1ZUsyp/PxVVrnXjCyu2kfWb+Rwqc0Fq2/4SluXuo9jr6zXn9GH854e1pbGZYwe0WWtHYzqbkBqFqOpsEckQka8BiEg3EbGhr8xRZUXePuY8sdwfXHL3HubddYUUeSWin88axQc/n+qfyLJP94Rm+1xtP1DKAws2+jtJL1hfwAUPfezv2zU0NZHzJ6RR7gW4q07OZOHPpvrvrQ3ulciQ3ol2r8sYQh8p5FrgBeARL2kw8FK4MmVMR7Ch4BBXPrrU36G5tKKGLwsOsbOoDIBvnpDO4lumkZLoqvES4+MaDCwlFVX+EtXWfSVc/NeP+XDjHgD2H67g/rc3sG6nO8aJQ/vwyLcnkd7H9eWalNGbO88fR2p3N8ahBS5jGhdqs/3rgVOAgwCquhHoF65MGRMJhYfKuGzuYt78bBcASQlx7Cwq9fcNO+WYPrz/s6n+hhxxsTGICF/tLmZnkSthqSr/XJLHmm0HANhbXM7Y297iuWVuXIKUxC6oQrVXTTl6QDKf/2amf3T7vskJzBw7gB5daxtuGGNCE2pAK1dVf1tgEYnDNQ4xptPx3eOqqq7hooc/5hcvrmXrvhJ6J8ZTXaO8sno7C78sJC2lG/N/fAZvfraLxz/O9ZeOZtz/flBT+PMf/IhH3nd9uUSEe99Y528a3zspnp/NHMUJQ3sDbvSMF/7rZH+LxrjYGJLCNEGmMUebUP+S3heRW4FuIjID+AHwaviyZUzodh8qp6Siiow+SYCbcbmkopqzx7sm6ffP/xJE+MmMkVz1j6Us/HI3Z48fwEPfmkRmahLzPsmn4GAZf7/yeJ6/7mRm/nERTy/NZ8ooVwmxZc/hoCGcZozpz6gBtbeQf39JNkO84Z4AFv289h6XiPCDKceE/RoYY0IPaDcD1wBrge8DbwB/D1emzNGlqKSSvYfLGdbXjYqxIm8/W/Yc5iJvsN1nlubz+Y6D3Hn+OABue/kzVm8r4qXrTwHgVy99xuY9xcz/8RkA/OW9TRSXu4D20+dW86+V2zgvexAAORm92FhQTE6GKzH9/uJsvp49iKSAETjmXXsiiQHL/7zmxKD8/nzW6KBlXx8wH9/9LmNM+wp1PrQaEXkJeElVd4c5T6aTOVxeReGhctJ7JxIbI2woOMTy3P1ckuPGGnz7iwJeWb2DP106gZgY4W+LNvPoR1v4+OZpiAgPvreRfy7JY/2dZwHw2podPL98mz+gbT9Qyhc7a0eaHzuoJ927uq+uqnLm2P70SnRN1X/75nqW5e5n1a9mAHD6yFRGDejOtacNA+C/p43gv6eNCMp/3Q7NFpCM6ZyaG/pKROR2EdkDrAe+FJHdIvLr9smeaQ/lVdVs3VdCWaWbQGH7gVKeW77VP4/Vyvz9/PjZTyk85Fr3vbF2J5PvXUDBQbf875XbmPr7hf7GEx9t2sOtL671t+wrOFjGZ9uLKPNmUM5MTWLq6H5BI8X/7qJsf3+uG6aN4L0bp/jz99MzR/Gv/3L9uWpqlAnpKfyPF5SeXJLHT55bzZhBrlvklJF9+dW5Y/xjHs6ekMac04db60BjjgLNNQr5Ea514/Gq2kdVewMnAqeIyI/DnjsTkqrqGnYVlfknVjxQUsG/V25j2/4SAPL2HuZnz6/2Nz9flb+fU3/7Livy3DQgi7/ay2n/7z3/yO7rdx7k5y+sIW+fG2W9qKSSZbn7OFjq9t+/RwKnjUjFFyNOOSaVP16a7a+m+8Zxg1lyy3R/S70rTsrgvRun+O9DzRjTn3suGO8fKX5cWk++nj3IH3R6JcXTNznBf25rtxWxx5uD6/2Nuznzj4tY6U1pctqIvtx1/ji6BoxCf82pQ20AXWOORqra6ANYBaQ2kN4XWNXUts3sNxd3P+5TYLmX1ht4G9joPfdqbj+TJk3SaFBTU6O7D5XpgZIKVVUtr6zWlz/drl/uOqiqqkWlFXrzv9boRxt3q6rqzgOlOuV37+mrq7erquqmwkOacdNr+uLKbaqq+uWug5px02v+99fvPKgn3fOOLtpQqKqqW3YX64+fWaXrd7r97yoq1WeX5WvBwVJVVT1cXqlb9x3W8srqdroCtaqqa3R57l7dVHjIn9eMm17Tfy7OVVXVg6UV+uyyfN1zqKzd82ZMtPD97kbbo7kSWhdV3dNAENwNHGlHmamqOkFVc7zlm4EFqjoCWOAtdxpFJZVBo5zP/3wXq7ce8C//5tXPeW3NDsC77/PH9/3TdlRWKzl3vcMTH+cCUKPKDU+v4p11bpoPwY3ht9UrcSUlxDIurSe9vQ69A3p05Z4LxjNhiBsbMLNPEu/dOIXp3qjpowYks/iW6Zw2wt0rykxN4v5LJ/hb6vXv0ZVLcobQL9lNG5IYH8fgXonNDtPUVhZ/tZdlufsAqKqp4Zt/+4R5S9ycXBl9EvnLN4/zN7xI7tqFS3KG0Mfucxlj6miuUUhFK99rjdnAFO/148BC4KY2PkajDpdXUVFV4x+89eOv9hAjwknD+gCu5VzPbl244qQMAL77j6UM79udX547BoBZf1rEqcek+geNvfXFz5gxph/ZXpB5b30hyQlxkOWaco8d1NNfrRYfF8Nd54/zB6SEuBje/vHp9O/pAkxy1y4s/+XX/HlN7tqFP18+0b+clBDHN09M9y/Hx8UwNDWp7S9SG/n4qz3sP1zJOfsJxaoAAA4jSURBVFmuWf2vX/6MtF7deOy7J5AQF8sTV5/ACK/zsoj41zPGmKaIauP9o0WkGjjc0FtAV1VtVSlNRLYA+3Gdsx9R1bkickBVUwLW2a+qvRrYdg4wByA9PX1SXl5ea7LAL15cS1FpJQ9+8zgALvnrYkTg2e9PBuAbD31EYnwcT37PNdm+fO4SBvbsyv2XTgBciWtwr0SuOXUo4BpGDOjZlZOHpwLw1e5ienbrYi3mgE8272Xt9iK+57U0/P4/l7OxsJh3fzoFcENMDejZ1UbHMKadiMiKgNqxqNFkQAvbQUUGqeoOEemHu1/2P8AroQS0QDk5Obp8+fJW5eHBdzdSXF7NzWe5PkW+4Y5mjXNVW1v2HCYhLsaml2+FVfn7eXX1Tn55zrHExAj3/Wc985bkseJXM4iPi2FnUSkp3eKPePZlY0zrWEALVwZEbgeKgWuBKaq6U0QGAgtVdVRT2x5JQDNtZ/2ug/zfB1u4+azR9OmewHPLt3LHq1/w1o9PJy2lG0WllXTrEttu9+SMMU2L1oDW7r8wIpLkm3pGRJKAM4HPgFeAK73VrgRebu+8mdBs3VfCDU+v8g/Ae7i8infWFZC71zVamT1hEJ/+egZpXum2Z7cuFsyMMWEXiV+Z/sCHIrIaWIqbPPRN4D5ghohsBGZ4y6aD+P1bX/pbaSbGx/LJlr3sOOA6Vk8c0osVv5zBpAxXQ5wQF+vvY2aMMe2l3Yf5VtXNQHYD6XuB6e2dHxOalMQuFJe5jtV9uiew5Jbp/o7QMc1MYmmMMe3B5q0wIfG1UPSxoaSMMR2N1QsZY4yJChbQjDHGRAULaMYYY6KCBTRjjDFRIeIdq4+EiOwGWjf2FaQC9QZeNo2y69Uydr1azq5ZyxzJ9cpQ1b7Nr9a5dOqAdiREZHk09pQPF7teLWPXq+XsmrWMXa/6rMrRGGNMVLCAZowxJioczQFtbqQz0MnY9WoZu14tZ9esZex61XHU3kMzxhgTXY7mEpoxxpgoYgHNGGNMVOiwAU1EqkXkUxFZLSIrReTkELZZKCI53uvi8OcyMkRkgIg8IyJficgXIvKGiMwRkdcinbdQiEiuiKRGOh+NiebvzpHw/r5m1kn7kYg8ZHlqHRG5SkRURKYHpF3gpV0UxuN26uvWmA4b0IBSVZ2gqtnALcC9kc5QS4lIbBj2KcCLuBm9h6vqGOBW3DxzR7LfDjvzQjiuY2fRwT6Xp4HL6qRd5qVHSrN56mDX0C8gX2uBywPeugxYHebDd8TPMiRNfZ4dOaAF6gHs///tnXuw1VUVxz9flQCF0EwbNAs0EQXxgWikCTimqUzq+BxB85FF40haOmmappXpGGZkmWl2s1R8Zpo5YCYPFXk/LviqlElnDLOQkkAQVn+s9eP+7uEe7uF5Ltf1mblzz9ln7/1b+/Hba6/1O2dtAEmDy5aIpFsknd1SIUmjwrp7StJOkXa+pGlh+T0kadtIP0XSvEifGGk9JE2KOlZbiWuTIayPqyQ9A5yyCfpiCLDCzH5eJJjZbGAS0EXSg5JeknR3KD9CnmnRvl+U0sdLuk7SBOBrkhok3SrpaUmvShok6U5JL0pqKLX3VknTJc2XdE0pfYGka6KvGiX1jvQdJY2TNEvSbYBKZYZLmhrW+G2F8pL0rqRrJU0BBm6CfmwVSfdJOrb0vkHSSTEn9i+lPyupX/TX7PibJamrpC4x/4o+OT7K9JA0r1THJZK+E6+bjcvma3GrPAgMldQRvA3ALviBvZfGHJtbzIlo44uSbo+5Mk5S5/is7E35qKQF8bpPaT7MlbRnpD8iaUbU8+WSTKMKmSSdLOnBkOk8STdJ+ivwxuaWSyUrP+RqiNcNIdfTwA2RZRJwsKQOkroAnwJml8r3lzQhrjNWUvdIHxCyTJZ0YzGfVGXdqnUs431dxnMd+20N2rJC6xyd8BJwB/DddSy/HTDTzA4EJgBXR/rDZjYgLL8XgfMi/Srg6Ej/QqS9BXwu6jgNGF3jtZeZ2WFmNmYdZa6FvsCMKp8dAFwE7APsDhwa6bdEm/sCnYGhpTLbm9kgMxsV73cAjgAuBh4DfgT0AfYtLeJXRISCfsAgSf1K9b0d/XUrcEmkXQ08Y2YHAI8CnwCQtDfer4ea2f7ASmBYlNkOmGdmh5jZMzX2zcZmTMiHpA/hB9D+EZ+PZ0d6L6Cjmc3F23tBtOWzwFJgGXBi9MkQYJRU02FyleNSd+IQ3qnA5yPpdOA+/IT5PYGDgf2B/pIOjzx7Aj81sz7AO8BJrVxmBPDj6MODgDci/Vwz6x9pIyXtWCpTlqlnyATwGeBpoHud5KpGL+BIM/tGvDfgT8DRwPH4PQKApA7AT4CT4zp3At+Pj38FjDCzgfi9U9DqulVtLM3MJB1FfcezGpX9tgZtWaEVLsfeeKffVeNCULCKpon9W+CweN03di+N+OLZJ9KfBRoknQ8ULq4OwO2R9wFcUdTCfa1n2SRMNbM3zGwVvsPrEelDJE2JdhxBU5thTVkfM/8tRyOw0Mwao775pfpOlTQTmBV1lfvl4fg/o5T/cHwMMLPHCWsbVxD9gWmSZsf74iTRlcBD69T6jc8TwBGxiz0GmGhmS/G5MDQWm3OBhsj/LHCTpJG4Qnoft0avkzQXX7R2pTb3cL3mUGuUXVWFi+qo+JsFzAR64wsfwGvhQYDmc6Iak4FvSfomHm9waaSPlDQHeB7YrVR/pUw9aXKbLcKVbb3kqsYDZrayIm1MtKHS7bcXvol9Mu6RK4GPS9oe6Gpmz0W+e0plal23WhpLqP94VqOlfmtGm/QtV2Jmk+VfItgJeJ/mirhTrdXE/wbgBDObI3cTDo5rjJB0CHAcMDuskQuBhcB+cc1lUUdrMiypUab1YT5Q7WHxe6XXK4FtJHUCfgYcZGavy91aZXkrZS3qWFVR36qoryduiQwws0XhEujUQvmVNJ9fLf3gUcCvzezyFj5b1trk3dSY2TJJ4/Gd82nEDW9m/5P0JL6bPhXfZWJm10t6HDgWeF7SkcCn8Xnb38xWhCumE/WdQxvCI7jSPhDobGYzJQ0DfmBmt5Uzhhurck52jtfl9q9uu5ndI3czHweMlfQlfO4dCQyMvh9fKmOFTPi9sXXIBLCiznJRWU+wxtia2VRJffGN/CulvbuA+WGFlduwQ2UdJS6m5XWrkjXGsnTNLaLfKmnLFtpq5M9itgb+hUfX30fuM++G7+pbYiuaFv4zCN8w0BV4M3bXhXsLSXuY2RQzuwqPYL0b0A14MyyUM2my3GqVYVPwZ6BjWJKF7AOAQVXyF5Pibbl/fkO/OfVhfGItlvQx3HJpjYlEX0s6BndrAjwFnCxp5/jsI5I+uYHybWzGAOfgLsSxpfQ7cFfONDP7N6yeQ41mdgMwHd/ZdgPeCmU2BCjatxDYWf58sSPN3cBtFjN7FxiPu76KHf1Y4NyYX0jatRjTtbAAt86hNCcl7Q68amajcddbP7wPF8Xi1xvfJBQsxO/V8fhG67XSZ3PqKZekvSVtBZzYyjULLse/4FXmZWAnSQNDjg6S+pjZIuC/koprlr/gUW3dakaVsYQ6j+d69Ntq2rKF1jlMbPAdwxdjx/66pPuBucBfcLO4JZYAfSTNABYTz0KAbwNTcKXUiCs4gBvlDyyFL7RzcMvmIUmn4L74JQBh6dQiw0YnfNwnAjdLugzffS3Ad1st5X9H0u14WxcA0zbw+nMkzcJ3w6/ibrbWuAa4N9yUE4C/R10vSLoSGBcTeAVwAet/JNCmYBxwF/ComS0vEs1shqT/4M8xCi4KpbUSeAF3WXYFHpM0HXcDvxTlV0i6Fp+LrxXpWwj34q7l0wHMbFw8D50clsW7wHCaP9ep5IfA/ZLOxDdpBacBwyWtAP4BXIvfdyPCbfsy7qYquAz4A/68shtrKrS36yzX68A8oMtargmAmT3RQtpy+df3R8fmeRvgZvz+Ow93LS7BFdPiKNbiulWFZmMZ12wL41lzv5XJ0FdJsh5I2gVfRHrHTjhJNiuSuoSVRWxuu5tZW/pW7GZni3A5JklbQtJZuGV1RSqzpI4cJ/8m+DzcJf69egtUb9JCS5IkSdoFaaElSZIk7YJUaEmSJEm7IBVakiRJ0i5IhZZ84FHTyQ7zJD2giO9ZJW+zGIxJkrQdUqElSVOYtb7Acjz+XJIkWxip0JKkOZPwaOdI+npYbfMkXVSZUdLu8qj6AyRtLY94XkQo/0rkGSyPlH6/pFckXS9pmDwCeaOkPSLfJ+VR+efG/yKAc4Ok0ZKek5+AsMnOyEqSLZ1UaEkSyM9ZOgZolNQfD3l1CB6a53xJB5Ty7oUHTz7HzKbhURsWm9kAYEDk7xnZ98OPgdkXD0XUy8wOxsNnXRh5bgHuMrN+wN00j5DeHQ+uPRS4fqM3PEnaCanQkqQpzNp0PCzXL3EF8jszWxLRGB7Gf7wKHmz498DwUuTxo4Czop4pwI40RRCfZmZvmtl7wN/wcFrg4ch6xOuBNEVM/w1Np0MAPGJmq8zsBTbwINckac+05ViOSbK5WBrnNa1GWutRRYvxWHOH4jH1wGOAXmhm5QDGSBrMmqcWlE80qHYPliMelMuvyxFKSfKBIi20JGmZicAJkraVtB0e+XtSfLYcOAG3yM6ItLHAV+WnOCCpV5SrledoChA7jKbTIZIkqZG00JKkBeJMrQb8VF+AO8xslvxMKMxsiaSh+MGLS/DnYT2AmWHd/RNXerUyErhT0qVR9pyN0Y4k+SCRsRyTJEmSdkG6HJMkSZJ2QSq0JEmSpF2QCi1JkiRpF6RCS5IkSdoFqdCSJEmSdkEqtCRJkqRdkAotSZIkaRf8H5ruUNRJtrIrAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8ee34630>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = df['Name'][:5]\\n\",\n    \"y1 = df['HP'][:5]\\n\",\n    \"y2 = df['Attack'][:5]\\n\",\n    \"y3 = df['Defense'][:5]\\n\",\n    \"\\n\",\n    \"# Create two subplots sharing y axis\\n\",\n    \"fig, (ax1, ax2,ax3) = plt.subplots(3, sharey=True)\\n\",\n    \"\\n\",\n    \"ax1.plot(x, y1, 'ko-')\\n\",\n    \"ax1.set(title='3 subplots', ylabel='HP')\\n\",\n    \"\\n\",\n    \"ax2.plot(x, y2, 'r.-')\\n\",\n    \"ax2.set(xlabel='Pokemon', ylabel='Attack')\\n\",\n    \"\\n\",\n    \"ax3.plot(x, y3, ':')\\n\",\n    \"ax3.set(xlabel='Pokemon', ylabel='Defense')\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 45.展示前15个宝可梦的Attack,HP的折线图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8f9d68d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(df['HP'][:15], '-r',label='HP')\\n\",\n    \"plt.plot(df['Attack'][:15], ':g',label='Attack')\\n\",\n    \"plt.legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 46.用scatter的x,y,c属性，展示所有宝可梦的Defense,Attack,HP数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8fa0f358>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = df['Attack']\\n\",\n    \"y = df['Defense']\\n\",\n    \"colors = df['HP']\\n\",\n    \"\\n\",\n    \"plt.scatter(x, y, c=colors, alpha=0.5)\\n\",\n    \"plt.title('Scatter plot')\\n\",\n    \"plt.xlabel('HP')\\n\",\n    \"plt.ylabel('Attack')\\n\",\n    \"plt.colorbar();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 47.展示所有宝可梦的攻击力的分布直方图，bins=10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8eee0e80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = df['Attack']\\n\",\n    \"num_bins = 10\\n\",\n    \"n, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5)\\n\",\n    \"plt.title('Histogram')\\n\",\n    \"plt.xlabel('Attack')\\n\",\n    \"plt.ylabel('Value')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 48.展示所有宝可梦Type 1的饼图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x21e8f131c88>\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8f9c7d68>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(1, figsize=(8,8))\\n\",\n    \"df['Type 1'].value_counts().plot.pie(autopct=\\\"%1.1f%%\\\")\\n\",\n    \"plt.legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 49.展示所有宝可梦Type 1的柱状图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8fcf6a58>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = df['Type 1'].value_counts().plot.bar(figsize = (12,6),fontsize = 14)\\n\",\n    \"ax.set_title(\\\"Pokemon Type 1 Count\\\", fontsize = 20)\\n\",\n    \"ax.set_xlabel(\\\"Pokemon Type 1\\\", fontsize = 20)\\n\",\n    \"ax.set_ylabel(\\\"Value\\\", fontsize = 20)\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 50.展示综合评分最高的10只宝可梦的系数间的相关系数矩阵\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21e8fc14710>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import seaborn as sns\\n\",\n    \"\\n\",\n    \"top_10_pokemon=df.sort_values(by='Total',ascending=False).head(10)\\n\",\n    \"corr=top_10_pokemon.corr()\\n\",\n    \"\\n\",\n    \"fig, ax=plt.subplots(figsize=(10, 6))\\n\",\n    \"sns.heatmap(corr,annot=True)\\n\",\n    \"ax.set_ylim(9, 0)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "5.data-visualization/1.matplotlib/plot_distribution/README.md",
    "content": "# 常见概率分布的Matplotlib实现\n>文章来源于公众号：Python与算法社区，作者zglg\n\n本次实验所用的4种常见分布，连续分布的代表：beta分布、正态分布，均匀分布，离散分布的代表：二项分布。"
  },
  {
    "path": "5.data-visualization/1.matplotlib/plot_distribution/plot_distribution.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 常见概率分布的Matplotlib实现\\n\",\n    \">文章来源于公众号：Python与算法社区，作者zglg\\n\",\n    \"\\n\",\n    \"本次实验所用的4种常见分布，连续分布的代表：beta分布、正态分布，均匀分布，离散分布的代表：二项分布。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from scipy.stats import beta, norm, uniform, binom\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from functools import wraps\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 定义带四个参数的画图装饰器\\n\",\n    \"def my_plot(label0=None,\\n\",\n    \"            label1=None,\\n\",\n    \"            ylabel='probability density function',\\n\",\n    \"            fn=None):\\n\",\n    \"    def decorate(f):\\n\",\n    \"        @wraps(f)\\n\",\n    \"        def myplot():\\n\",\n    \"            fig = plt.figure(figsize=(16, 9))\\n\",\n    \"            ax = fig.add_subplot(111)\\n\",\n    \"            x, y, y1 = f()\\n\",\n    \"            ax.plot(x, y, linewidth=2, c='r', label=label0)\\n\",\n    \"            ax.plot(x, y1, linewidth=2, c='b', label=label1)\\n\",\n    \"            ax.legend()\\n\",\n    \"            plt.ylabel(ylabel)\\n\",\n    \"            plt.show()\\n\",\n    \"            plt.savefig('img/%s' % (fn, ))\\n\",\n    \"            plt.close()\\n\",\n    \"\\n\",\n    \"        return myplot\\n\",\n    \"\\n\",\n    \"    return decorate\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 均匀分布(uniform)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"@my_plot(label0='b-a=1.0', label1='b-a=2.0', fn='uniform.png')\\n\",\n    \"def unif():\\n\",\n    \"    x = np.arange(-0.01, 2.01, 0.01)\\n\",\n    \"    y = uniform.pdf(x, loc=0.0, scale=1.0)\\n\",\n    \"    y1 = uniform.pdf(x, loc=0.0, scale=2.0)\\n\",\n    \"    return x, y, y1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 二项分布\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"@my_plot(\\n\",\n    \"    label0='n=50,p=0.3',\\n\",\n    \"    label1='n=50,p=0.7',\\n\",\n    \"    fn='binom.png',\\n\",\n    \"    ylabel='probability mass function')\\n\",\n    \"def bino():\\n\",\n    \"    x = np.arange(50)\\n\",\n    \"    n, p, p1 = 50, 0.3, 0.7\\n\",\n    \"    y = binom.pmf(x, n=n, p=p)\\n\",\n    \"    y1 = binom.pmf(x, n=n, p=p1)\\n\",\n    \"    return x, y, y1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 高斯分布\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"@my_plot(label0='u=0.,sigma=1.0', label1='u=0.,sigma=2.0', fn='guass.png')\\n\",\n    \"def guass():\\n\",\n    \"    x = np.arange(-5, 5, 0.1)\\n\",\n    \"    y = norm.pdf(x, loc=0.0, scale=1.0)\\n\",\n    \"    y1 = norm.pdf(x, loc=0., scale=2.0)\\n\",\n    \"    return x, y, y1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## beta分布\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"@my_plot(label0='a=10., b=30.', label1='a=4., b=4.', fn='beta.png')\\n\",\n    \"def bet():\\n\",\n    \"    x = np.arange(-0.1, 1, 0.001)\\n\",\n    \"    y = beta.pdf(x, a=10., b=30.)\\n\",\n    \"    y1 = beta.pdf(x, a=4., b=4.)\\n\",\n    \"    return x, y, y1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 绘制所有四种分布\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"【均匀分布、二项分布、高斯分布、beta分布】\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21f4c04aac8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Clq6HhxDulOPedfPGL0rnn+ikq3/tefD8HAAJ57TXpO9/xuz5aO2wqH+97n3TGGX6m3/Ll8fwMIJKVjtc0auu/e4pcAKXrlVekDRukAQOkKVPi+zmdO5+YvvJ3fydt3RrfzwKAInPOn5Z27Jj0qU9JVTFOUWnf3p+dK7Gai3h17NhR27dvp9ANwDmn7du35z3RuSlxnpMLAMkWvUO67rr4lh0i11wjzZghPf209Ld/K33/+/H+PAAokp//XHr+ealPH+kf/iH+n3f99X7P74MP+uuGQBwqKytVX1+vrVyYDqJjx46qrKxs9dczeApAaXJOGjRIWr++eAOhVqyQxo3zRxbNnStNnRr/zwSAGO3ZI40cKW3cKP3gB9LHPhb/zzx0yB8htGePtGqVH30AoDQweAoAmjN/vi9w+/WTLrigOD9zxAjpc5/zBfanP+33BANAiv3DP/gCd+pU6aMfLc7PrKigZRlA8yhyAZSmBx/099de6yelFMudd0rnnOPPwPjP/yzezwWAAlu2zI8bMJPuvbe4L6XXXefvo5dyAMhFkQug9DhXnKnKTenaVfrmN/3jL35Revvt4v58ACgA56Tbb/e7Lz7xCan6tM2DhTVzptSlizRvnm/KAYBcFLkASs/ixX4jV9++0rRpxf/5N9wgXXqptH27X9kFgJR56CHp2WelXr2kf/zH4v/8Tp2kD33IP3744eL/fADJRpELoPRE/W3XXCOVlRX/55v5AyXLy6XZs6UFC4qfAQBaad8+P15A8gVunz5hctCyDOBUKHIBlJ7oHVGxW5VzjRnje/2ck267jSFUAFLjn/5JevNNadIk36ocygc/KHXsKP3xj/7IcwCIUOQCKC2vvy4tX+577C65JGyWu+6Szj5bevll6Sc/CZsFAPJQVyf967/6x/fcE6YZJtK1qzRrln9MyzKAXBS5AEpLtIp79dVS+/Zhs3TvLt19t3/8+c9LO3eGzQMAzXBO+t//Wzp8WLrlluKdvtacqCGHo4QA5KLIBVBaoiI32swV2p/9mXTRRdKWLdJXvhI6DQCc0uOPS7/5jdSjh/T1r4dO411xhdShgzRnjrR5c+g0AJKCIhdA6Vi5UnrtNf8O7bLLQqfxzHzPX7t2/n7bttCJAKBJX/3qifuzzgqbJdK9uzRjhl9lfuSR0GkAJAVFLoDSEfWzXXmlVFERNkuuCRP8kULHjvkzOQAgYTZvlubP94OeQg6bakrUssyUZQARilwApSMJU5VP5QMf8PdPPRU2BwA04emn/f306f6M2iS58kp/Ittzz9EMA8CjyAVQGtas8efRdu3qe9uSZuZMf//0077vDgAS5Le/9ffRS1WSnHGG34Fy7Jj0q1+FTgMgCShyAZSG6HyJK67w/XZJM3as1L+/tGmT3zcMAAnR0HBiJTdqOkkaWpYB5KLIBVAaktyqLPkBVNEKc7RkAgAJUFsrbd0qDRggjRwZOk3Trr7an9n77LPSjh2h0wAIjSIXQPa98Yb0yitS587SrFmh05xatERCkQsgQaKXpA98wF+PS6I+faRLLpGOHvVHHQEobRS5ALIvalWeNcsXukl1+eX+HeQLL0j79oVOAwCSTszDS2qrciQ6/pyWZQAUuQCyLzo6KKmtypHevaUpU6TDh/2YUAAIbPdu6Y9/9K3A739/6DTNu+Yaf53wt7/1uQGULopcANm2caP00kv+XNwPfSh0mtOjZRlAgvzhD74F+LzzpJ49Q6dp3tlnS9Om+euETzwROg2AkChyAWTbI4/4I3lmzpS6dQud5vQocgEkSO5+3DSIGnaiBh4ApYkiF0C2Re90os1aSXfeeVKPHtLKldK6daHTAChhzqVnP27k2mv9/ZNPSnv3hs0CIByKXADZtWWL9PzzUvv20oc/HDpNfsrLpcsu849ZzQUQ0KpV0tq1Uq9eUnV16DT5Oecc6YILpIMHpd/8JnQaAKFQ5ALIrkcflRoa/NTipG8myxUtmURLKAAQQHSd7f3v94On0iJqWWbKMlC6KHIBZFf0DictrcqRqMj93e+kI0fCZgFQsqIid+bMsDlaKmpZ/vWvpQMHwmYBEAZFLoBs2r5d+v3v/fLDVVeFTtMy73mPNHKktGePNHdu6DQAStDhw36ysiTNmBE2S0sNHOjbq/ftY9cHUKoocgFk02OPSceOSe97nz9/Nm2YsgwgoJde8kXi2LF+n2va0LIMlDaKXADZFL2zid7ppA1FLoCA0jZV+WTRLpXHH5cOHQqbBUDxUeQCyJ6dO6VnnpHatZOuvjp0mtaZPl2qqJDmz5e2bg2dBkCJSdv5uCcbOlSaMEHavVt69tnQaQAUG0UugOx54gk/sOnii6UzzwydpnU6d/b5nfMFOwAUyVtvSYsWSZ06SdOmhU7TerQsA6WLIhdA9qS9VTlCyzKAAJ5+2t9fconUsWPQKG0S/Qr41a8YVA+UGopcANmyZ4/fTGYmXXNN6DRtExW5Tz/tV3QBoAjS3qocGTlSGjNG2rHjxKRoAKWBIhdAtjz5pJ8y8t73Sv37h07TNmPG+LGmb70lLV4cOg2AEtDQcGIlN+1FrnRiABUty0BpocgFkC1ZaVWW/Gp0dEAlLcsAimDBAmnbNuncc6URI0KnabvoV8Ejj0hHj4bNAqB4KHIBZMe+fX4lV5KuvTZslkKJllKi8zwAIEa5rcpmYbMUwtix0vDhvnCfMyd0GgDFQpELIDueekrav1867zy/DJEF73+/PwrpxRelvXtDpwGQcVGRO3Nm2ByFYnZiNfehh8JmAVA8FLkAsiN6BxNtwsqC3r2lKVP8aNDnngudBkCG7dolvfyyVFYmXXZZ6DSFE/1KePhh6dixsFkAFAdFLoBsOHhQevxx/zhLRa7EUUIAiuL3v/f7Vs8/X+rRI3Sawpk4URo0yM/w++MfQ6cBUAwUuQCy4emnfTvvpEnS4MGh0xQWRS6AIsjK0UEny21ZZsoyUBoocgFkQ5amKp9s6lS/rFJXJ61ZEzoNgAxyLrtFrnTiV8PDD/tjkgBkG0UugPQ7fFh67DH/OGutypJUXi5dfrl/zGougBjU1Unr1vkxAJMnh05TeFOmSAMGSPX10quvhk4DIG4UuQDS73e/8xNTxo3zZ0VkES3LAGIUvbRcfrkfPJU1ZieugdKyDGQfRS6A9Mtyq3IkKnJ//3s/aRkACijLrcqR3H25zoXNAiBeFLkA0u3IEenRR/3jLLYqRwYMkEaNkvbs8Wd8AECBHDok/eEP/vGMGWGzxOmCC6R+/aT166UFC0KnARAnilwA6fb889Lbb0sjR0qjR4dOEy9algHE4MUXpf37/Y6P/v1Dp4lPu3bStdf6x7QsA9lGkQsg3R55xN9ff73fdJVlUZH71FNhcwDIlFJoVY5ELcvRrw4A2USRCyDdXnrJ32e5xy4yfbrUsaPvs9uyJXQaABkRFbkzZ4bNUQwXXSR17iytWCFt2xY6DYC4UOQCSK99+6TXXvOjQLN45sXJOnWSLr7YP37mmbBZAGTCxo3S4sW+8LvootBp4ldefuLXBUcJAdlFkQsgvebPlxoapPHj/Tu0UsC+XAAF9PTT/v6SS6SKiqBRiua88/z9K6+EzQEgPhS5ANIreocSvWMpBVGR+/TTvsAHgDYopf24EYpcIPsocgGk19y5/r6UitzRo6VzzpE2b/Y9hgDQSseOndj5UKpFLtcKgWyiyAWQXqW4kmt2YjoMU5YBtMGCBdL27dLAgdLw4aHTFE9lpT8vd+dOqa4udBoAcaDIBZBOGzb4W48e0ogRodMUF/tyARRAbqty1k9gy2VGyzKQdRS5ANIpemcyZYrUrsReyt7/fv/P/NJL0t69odMASKmoGaSUWpUjFLlAtpXYO0MAmVGKrcqRM86Qpk6VjhyR/vCH0GkApNCuXX6sQVmZ9L73hU5TfBS5QLZR5AJIp1IuciValgG0ye9+5wdPXXCB3/VRaqqrfdvyokXSgQOh0wAoNIpcAOlz7JhUU+Mfl3qRy/ApAK1QikcH5erWTRozRjp6VFq4MHQaAIVGkQsgfZYulfbt8yNBzzwzdJowpkzxbcurV/sbAOTJuRNFbjSsvRTRsgxkF0UugPQp9VZlSSov9wOoJFqWAbTIihXS+vVSnz7SpEmh04RDkQtkF0UugPShyPXYlwugFaKXjMsvL73h9LkocoHsivWlzcxmmtkKM1tlZl9o4s8vNrMFZnbUzK4/6c9uNrO6xtvNceYEkDIUuV5U5P7+99Lhw2GzAEiNUt+PGxkzRurSRVq3TtqyJXQaAIUUW5FrZmWS7pU0S9JoSTea2eiTnvaGpFsk/fdJX9tL0l2SzpM0VdJdZnZGXFkBpMiePX5Pbnm5NHFi6DRhVVZKo0f7s3Jffjl0GgApcPCg9Nxz/vGMGUGjBFdW5qcsS6zmAlkT50ruVEmrnHNrnHOHJf1c0lW5T3DOrXPOLZbUcNLXfkDSM865t51zOyQ9I6mERyMAOK6mxk9NmTBB6tQpdJrwoqkxTFkGkIcXX/RH5kyYIPXrFzpNeLQsA9kUZ5F7jqQ3cz6ub/xc3F8LIMtoVX4n9uUCaIHoeliptypHKHKBbIqzyLUmPucK+bVm9kkzqzGzmq1bt7YoHICUosh9p2nTpI4d/UGPmzeHTgMg4diP+07Rr5JXX5UaTu4rBJBacRa59ZIG5HxcKWljIb/WOXefc67aOVfdt2/fVgcFkBLOnShyzz8/bJak6NRJmj7dP37mmbBZACTahg3SkiVS587ShReGTpMM55zjxxvs3u2PVgKQDXEWufMkDTOzQWbWQdKfSnosz6/9raQZZnZG48CpGY2fA1DK6uulTZukM86Qhg0LnSY5aFkGkIenn/b3l14qVVSEzZIktCwD2RNbkeucOyrpNvnidJmk/3HOLTWzr5rZlZJkZlPMrF7Sn0j6npktbfzatyX9g3yhPE/SVxs/B6CURe9Apk6VrKldDSUqt8il3w7AKdCq3DSKXCB7yuP85s65JyU9edLnvpzzeJ58K3JTX/tDST+MMx+AlJk719+zH/edRo2SBgyQ3nxTqq2VJk0KnQhAwhw7dmIldybnVbxD9Csl+hUDIP3ibFcGgMJi6FTTzGhZBtCsmhppxw5p0CBp6NDQaZJl8mR/Zu5rr0n794dOA6AQKHIBpMORI9L8+f7x1KlhsyTR5Zf7+zlzwuYAkEjRS8P7389uj5N16SKNHetXu6NfMwDSjSIXQDosWSIdOCANGSL16RM6TfJE06ZfecVPoQaAHFEjzAUXhM2RVOzLBbKFIhdAOtCq3LwBA6Szz/b9iHV1odMASBhGGjSPIhfIFopcAOlAkds8s3eu5gJAow0b/K17d2nkyNBpkokiF8gWilwA6UCRe3q8SwPQhNzT19rxzq9JI0dK3br5IfWbNoVOA6CteKkDkHy7dknLl0sdOkhVVaHTJBfnYABoAq3Kp1dWJk2Z4h9znRBIP4pcAMk3b54fplRVJVVUhE6TXNXVfplm0SI/pAsAdKJoi3Y0oGk0wwDZQZELIPloVc5Pt27SmDHS0aPSwoWh0wBIgKNH/Rm5Ei+hp0ORC2QHRS6A5KPIzV+0VEPLMgD509f275cGD5b69g2dJtmiXzHz5vkzcwGkF0UugGRzjiK3JViKAJCDl8/8nX22dO650t690rJlodMAaAuKXADJtn69tGWL1Lu3NGRI6DTJR5ELIAdFbsvwEgpkA0UugGTLPfvCLGyWNBg1yu/NXb9eeuut0GkABBbtXGDoVH4ocoFsoMgFkGwsQ7QM52AAaLRzp2+75fS1/FHkAtlAkQsg2ShyW47zcgHID1CSOH2tJSZN8tcKlyzxe3MBpBNFLoDkOnJEWrDAP546NWyWNIn6ElmKAEoa5+O2XOfO0vjxUkODNH9+6DQAWosiF0ByLV4sHTwoDR8u9eoVOk16cA4GANEI01pcJwTSjyIXQHJF7ba8Q2uZs86SBg70vXavvx46DYAAnGPoVGux4wNIP4pcAMnFMkTrMT0FKGlr10rbtkl9+kiDBoVOky68fALpR5ELILkocluPd2lASct9+eT0tZYZPlzq0UPauFGqrw+dBkBrUOQCSKYdO6SVK/1I0PHjQ6dJn6g/kX47oCS3CXOgAAAgAElEQVTRqtx67dqdmHXIdUIgnShyASTTq6/6+0mT/CGPaJmJE6X27aWlS6U9e0KnAVBkNMK0Dc0wQLpR5AJIJt6htU3HjtKECX76THRYJoCScOiQtHChfzxlStgsaUWRC6QbRS6AZKLIbTvOwQBKUm2tdPiwNGqU1LNn6DTpFP3qqamRjh4NmwVAy1HkAkge5yhyC4GlCKAk8fLZdn37+qnU+/f7XR8A0oUiF0DyrFkjbd/u32UMHBg6TXrlDp9yLmwWAEXD0KnC4DohkF4UuQCSh7MvCmPIEKl3b2nzZumNN0KnAVAkrOQWBkUukF4UuQCSh3dohWHGORhAidm61TfDdO4sjR0bOk26UeQC6UWRCyB5KHILh/NygZISvXxWV0vl5WGzpF10Etvrr0u7d4dOA6AlKHIBJAtnXxQWSxFASeEaYeHknsRWUxM6DYCWoMgFkCyLFvmzL0aO5OyLQojalRcs8P9eAWQaRW5hcZ0QSCeKXADJwju0wjrjDGnECOngQWnx4tBpAMSooeHESyiTlQuDIhdIJ4pcAMlCkVt4vEsDSsKKFX7v6Dnn+BvaLvflk5PYgPSgyAWQLBS5hcfwKaAkcD5u4Q0b5hti3npLevPN0GkA5IsiF0BybN8urVrlp32MGxc6TXawkguUBK4RFh4nsQHpRJELIDlyz75o3z5sliwZN85fOKir8xcSAGQSRW48aIYB0ociF0By8A4tHu3b+wsHkvTqq2GzAIjFvn1+tlxZmTR5cug02UIzDJA+FLkAkoMiNz68SwMybf58P1153DipS5fQabIlaleeP186ciRsFgD5ocgFkAzOnVhlpMgtPIpcINO4Rhif3r2loUP9SWyvvRY6DYB8UOQCSIa6OmnHDunss6UBA0KnyZ5oUxnnYACZxGTleHGdEEgXilwAyZC7DGEWNksWVVZK/fr5Cwl1daHTACgwVnLjRZELpAtFLoBk4B1avMx4lwZkVH29tGGD1KOHNGJE6DTZxMsnkC4UuQCSgSI3fpyDAWRS9PI5darUjnd2sZgwQerQQVq+XNq5M3QaAKfDSyGA8A4elBYt8quN0VE3KDyWIoBM4hph/CoqpIkT/eN588JmAXB6FLkAwlu40J/LMHq01L176DTZVV3tl3kWLZIOHAidBkCBMHSqOLhOCKQHRS6A8FiGKI6uXaWxY6WjR6UFC0KnAVAAR4/681ulE+e5Ih4UuUB6UOQCCI8it3h4lwZkypIl0v790uDBUt++odNkW+7LJyexAclGkQsgPIrc4mH4FJAptCoXz+DBUp8+0tat0rp1odMAaA5FLoCwtm6V1q6VOneWxowJnSb7WMkFMoVrhMVjdqIlnJdQINkocgGEFb1TqK6WysvDZikFI0dK3bpJb7whbdoUOg2ANqLILS6uEwLpQJELICzeoRVXWRlLEUBG7NwpLVvmz2+tqgqdpjRQ5ALpQJELICyK3OLjXRqQCdF5rRMn+nNcEb/oGuGCBdLhw2GzADg1ilwA4TQ0SK++6h9T5BYPw6eATIiuUzF0qnjOOEMaPlw6dEhavDh0GgCnQpELIJyVK6Vdu6T+/aXKytBpSkd0QaGmRjp2LGwWAK0WXafiGmFx0QwDJB9FLoBwFizw91OmhM1Ras48Uxo4UNq7V3r99dBpALSCc+z2CCX6lRX9CgOQPBS5AMKJ3iFMnBg2RymiZRlItTVrpG3bpL59pUGDQqcpLdGvLIpcILkocgGEs3Chv580KWyOUkS/HZBquau4ZmGzlJoJE/y/86VLGT4FJBVFLoAwnDtR5LKSW3wUuUCq0aocTrdu0rBh0pEjvtAFkDwUuQDCWL9e2rFD6tNHOuec0GlKz8SJUvv2/h3a7t2h0wBooWinAZOVw6BlGUg2ilwAYeS2KtNrV3wdO0pVVX5FvaYmdBoALXDokFRb6186mdsXRrTLJvpVBiBZKHIBhEGrcnjREhAty0Cq1Nb6vaCjRkk9eoROU5qiX10UuUAyUeQCCIPJyuFFm/mYsAykCufjhhf96qqt5bhxIIkocgGEwWTl8HKHTzkXNguAvDF0Krw+faQBA6T9+6W6utBpAJyMIhdA8W3eLG3c6EdUDhkSOk3pGjJE6t3b//dYvz50GgB5YuhUMtCyDCRXrEWumc00sxVmtsrMvtDEn1eY2S8a//wVMxvY+Pn2Zna/mb1mZsvM7Itx5gRQZNE7ggkTpHZcawvGjKOEgJTZulVau1bq3FkaMyZ0mtLGhGUguWJ7d2lmZZLulTRL0mhJN5rZ6JOe9jFJO5xzQyV9S9K/NH7+TyRVOOfGSZos6a+iAhhABtCqnBwUuUCqRH9Vq6ul8vKwWUodE5aB5IpzCWWqpFXOuTXOucOSfi7pqpOec5Wk+xsfPyjpMjMzSU5SFzMrl9RJ0mFJHOQIZAWTlZMj6ndk+BSQCrQqJ0duuzJjDYBkibPIPUfSmzkf1zd+rsnnOOeOStolqbd8wbtP0iZJb0j6V+fc2zFmBVBMTFZOjqlT/f2CBf5MEgCJxtCp5Kis9GMN3n5beuON0GkA5IqzyLUmPnfyda5TPWeqpGOS+ksaJOn/M7PB7/oBZp80sxozq9m6dWtb8wIohl27pNWrpQ4dpNEn72BA0fXsKY0cKR06JC1eHDoNgGY0NEivvuofs5Ibnhkty0BSxVnk1ksakPNxpaSNp3pOY2tyD0lvS/pfkp5yzh1xzm2R9JKk6pN/gHPuPudctXOuum/fvjH8IwAouEWL/P24cVL79mGzwOO8XCAVli+Xdu/2K4j9+4dOA4kJy0BSxVnkzpM0zMwGmVkHSX8q6bGTnvOYpJsbH18v6ffOOSffovw+87pIOl/S8hizAigWWpWTh+FTQCrQqpw8TFgGkim2Irdxj+1tkn4raZmk/3HOLTWzr5rZlY1P+09Jvc1slaTPSYqOGbpXUldJS+SL5R855+ijA7KAycrJE/U9UuQCiRb9FaVVOTloVwaSKdbh8865JyU9edLnvpzz+KD8cUEnf93epj4PIAOYrJw848ZJnTpJdXXS9u1+kgqAxIl2FLCSmxxDh0pdu0obNkhbtkhnnhk6EQAp3nZlAHinAwek11+X2rWTxo8PnQaR8nJp8mT/OJpqAyBR9u2TXntNKis78dcV4bVrJ02Y4B+zmgskB0UugOJZskQ6dsxP8+3cOXQa5OK8XCDRamr8dOXx43n5TBqGTwHJc9oi18yuNbM6M9tlZrvNbI+Z7S5GOAAZQ6tycjF8Ckg0hk4lF/tygeTJZ0/uNyR92Dm3LO4wADKOycrJFa3kvvqqXy5qR6MPkCQMnUouJiwDyZPPu5jNFLgACoLJyskVHby5Y4cfQAUgURg6lVyjR/tj31et8ucYAwgvnyK3xsx+YWY3NrYuX2tm18aeDEC2HD0qLW48CayqKmwWNC1698zwKSBRNmyQNm6UevSQhg8PnQYn69DBD6mXpEWLwmYB4OVT5HaXtF/SDEkfbrxdEWcoABm0fLl08KA0cKB0xhmh06ApU6f6e4pcIFGiv5JTprCTIKloWQaS5bR7cp1zHy1GEAAZR6ty8lHkAokU/ZWM/ooieZiwDCRLPtOVK83sETPbYmabzewhM6ssRjgAGcLQqeSrrpbMpNpa6dCh0GkANGKycvIxYRlIlnyaXn4k6TFJ/SWdI+nxxs8BQP44Pij5uneXRo2SDh9mYxmQEMeO+TNyJd+ujGQaP95fI1y61O/MARBWPkVuX+fcj5xzRxtvP5bUN+ZcALKkoYF25bSgZRlIlBUrpD17pAEDpH79QqfBqXTpIo0Y4S9KLFkSOg2AfIrcbWb252ZW1nj7c0nb4w4GIEPWrvXnKpx1Fu/Ski7qh4z6IwEERatyetCyDCRHPkXuX0q6QdJbkjZJur7xcwCQH1Zx04OVXCBRGDqVHkxYBpIjn+nKb0i6sghZAGQV+3HTY9w4qaJCWrlS2rGD456AwChy04MJy0BynLLINbPPO+e+YWbfkeRO/nPn3O2xJgOQHUxWTo/27f2K+8sv+2k3l18eOhFQsg4ckBYv9mfjTp4cOg1OJ/oVt3ixdPSoVH7apSQAcWmuXXlZ432NpPlN3AAgP7Qrpwv7coFEWLjQF0tjxkhdu4ZOg9Pp1Ut6z3v8xYkVK0KnAUrbKa8xOeceb3y43zn3y9w/M7M/iTUVgOzYtEnavFnq0UMaNCh0GuSDfblAItCqnD4TJ0rr1/sLFGPGhE4DlK58Bk99Mc/PAcC7Ra3KVVX+EEEkX26R6961WwVAkVDkpg8TloFkaG5P7ixJH5R0jpl9O+ePuks6GncwABlBq3L6DB4s9e7tV+DffFM699zQiYCSRJGbPgyfApKhuZXcjfL7cQ/qnXtxH5P0gfijAcgEJiunj9mJd9XsywWC2LZNWr1a6tRJGjs2dBrkK7fIpREGCOeURa5zbpFz7n5J4yT91Dl3f+PHv5J0qFgBAaQck5XTiX25QFDz5vn7yZOZ0psm/ftLZ54p7dwprVsXOg1QuvLZk/u0pE45H3eS9Gw8cQBkyo4d/rd8x47SyJGh06AlKHKBoGhVTiczWpaBJMinyO3onNsbfdD4uHN8kQBkRm2tvx8/nqWItIneWdfU+DNMABRVtFMgOtEL6REVuVEjE4Diy6fI3WdmxyfGmNlkSQfiiwQgM2hVTq8+ffwAqv37pddfD50GKCnOsZKbZkxYBsLLZ2nls5J+aWYbGz/uJ+kj8UUCkBlMVk63qVOlNWv8u+3x40OnAUrG2rXS9u1S377Se94TOg1ainZlILzTruQ65+ZJGinpU5L+WtIo59z8uIMByAAmK6cb+3KBIHJXcTlePH0GD5a6dZM2bZLeeit0GqA05dOuLElTJI2XNFHSjWZ2U3yRAGTC/v3S8uVSWZk0blzoNGiNaDMgxwgBRcV+3HRr147VXCC00xa5ZvYTSf8q6SL5YneKpOqYcwFIu8WLpYYGafRoP10Z6TNxor9IsWSJtG9f6DRAyWA/bvpR5AJh5bMnt1rSaOc40hpAC9CqnH6dOvm9uAsX+iFi06aFTgRk3pEjJ2b2TZkSNgtajwnLQFj5tCsvkXR23EEAZAyTlbOBlmWgqF57TTp4UBo2TOrVK3QatBYTloGw8ily+0h63cx+a2aPRbe4gwFIOSYrZwPDp4CiolU5G0aOlCoq/ID6nTtDpwFKTz7tyl+JOwSAjDlyxC9HSFJVVdgsaBuKXKCoKHKzoX17P3OxpkaqrZUuuSR0IqC0nLbIdc49X4wgADLk9delw4elIUOk7t1Dp0FbjBwpde0qrV8vbd4snXVW6ERAplHkZsekSb7IXbiQIhcotnymK+8xs92Nt4NmdszMdhcjHICUolU5O8rKTky/YTUXiNXu3f4aYfv2NMFkAROWgXBOW+Q657o557o33jpKuk7SPfFHA5BaTFbOFlqWgaKYP19yTpowgZPXsoAJy0A4+Qyeegfn3KOS3hdDFgBZwWTlbKHIBYqCVuVsGT/eN8MsXy4dOBA6DVBaTrsn18yuzfmwnfy5uZyZC6BpDQ1+yoZEkZsVuUVuQ4PUrsXXRwHkITqpiyI3Gzp18mMNli71sxj57woUTz7vVD6cc/uApD2SroozFIAUW71a2rtX6t+fIUVZUVnp/3vu3CmtWhU6DZBZ0UpudDw10o+WZSCMUxa5ZvYvjQ9/45z7aOPtE865f3TObSlSPgBpQ6tyNtGyDMRqwwZ/695dGj48dBoUSjR/keFTQHE1t5L7QTNrL+kLxQoDIAOYrJxNFLlArObN8/dTprAjIEuYsAyE0dye3KckbZPU5aQjg0ySc85x+CWAd2OycjZF/ZPRpkEABRX91aJVOVuio6AWL5aOHPHHQwGI3ymvFTrn7nDO9ZD065wjhLpHRwoVMSOAtHCOduWsmjxZMvNDxQ4dCp0GyBwmK2dTz57S4MH+ZXP58tBpgNKRzzm5DJkCkJ8NG6Rt26QzzpDe857QaVBIPXr4MaGHD/slCQAF09Bwol2ZIjd7aFkGio9dHwAKJ7dV2SxsFhRe9O6blmWgoJYvl/bs8YPM+/ULnQaFxoRloPgocgEUDq3K2RZtFmT4FFBQHB2UbUxYBoqvRUWumZ1hZuPjCgMg5ZisnG1MWAZiwX7cbIuu+9bW+tZ0APE7bZFrZs+ZWXcz6yVpkaQfmdm/xR8NQOowWTnbxo2TKiqkFSuknTtDpwEygyI3284+299275bWrAmdBigN+azk9nDO7ZZ0raQfOecmS3p/vLEApM727dIbb0idO0vDh4dOgzh06HBilT6akgOgTQ4ckBYt8mfjVleHToO40LIMFFc+RW65mfWTdIOkJ2LOAyCtot/cEyZIZWVhsyA+tCwDBVVbKx09Ko0eLXXtGjoN4sKEZaC48ilyvyrpt5JWOefmmdlgSXXxxgKQOrQqlwaKXKCgaFUuDUxYBoqr/HRPcM79UtIvcz5eI+m6OEMBSKHoNzdDp7It9xgh5zgqCmij6EQuitxsy21X5qUTiF8+g6e+0Th4qr2Z/c7MtpnZnxcjHIAUYSW3NAwZIvXqJW3eLL35Zug0QOpxfFBpGDhQ6tlT2rJF2rQpdBog+/JpV57ROHjqCkn1koZLuiPWVADSZe9eaeVKqbxcGjMmdBrEyYyWZaBAtm+XVq+WOnXipTPrzKSqKv+YlmUgfvkUue0b7z8o6WfOubdjzAMgjRYt8v1XY8f6I2aQbRS5QEFEQ8onTZLat2/+uUg/JiwDxXPaPbmSHjez5ZIOSPprM+sr6WC8sQCkCq3KpSXqq4w2EwJoleivEK3KpYEJy0DxnHYl1zn3BUkXSKp2zh2RtE/SVXEHA5AiFLmlZcoUf19T488+AdAqTFYuLUxYBoonn3ZlSTpH0nVmdpOk6yXNiC8SgNRhsnJp6dtXGjRI2r9fWrYsdBoglZyjyC01I0b4/dfr10tvs/kPiFU+05XvkvSdxtulkr4h6cqYcwFIi8OHpaVL/VSNCRNCp0Gx5B4lBKDF1q6Vtm2T+vTxk3eRfeXl0vjx/nFtbdgsQNbls5J7vaTLJL3lnPuopAmSmCwDwFu6VDpyRBo2TOraNXQaFEu0iZDhU0Cr5B4dxJmppYOWZaA48ilyDzjnGiQdNbPukrZIGhxvLACpQatyaWLCMtAmtCqXJiYsA8WRz3TlGjPrKen7kuZL2iuJdzUAPIZOlaaJE6WyMmnJEmnfPqlLl9CJgFShyC1NTFgGiiOf6cp/7Zzb6ZybLelySTc3ti0DAEVuqercWRo3Tjp2jL47oIWOHJHmz/ePo2HlKA1jx/rrg8uX++uDAOKR13RlMxtvZldKmiRpqJldG28sAKlw7NiJ6RkUuaWHfblAqyxZIh08KA0dKvXuHToNiqljR2nMGD9de/Hi0GmA7MpnuvIPJf1Q0nWSPtx4uyLmXADSoK7OHyMzYIAfEYrSwr5coFVoVS5ttCwD8ctnT+75zrnRrfnmZjZT0n9IKpP0A+fc10/68wpJD0iaLGm7pI8459Y1/tl4Sd+T1F1Sg6QpzrmDrckBICa0Kpc2ilygVShyS9vEidL997PTA4hTPu3KL5tZi4tcMyuTdK+kWZJGS7qxie/zMUk7nHNDJX1L0r80fm25pJ9KutU5N0bSJZKOtDQDgJgxWbm0jRrlj41at07asiV0GiA1ouOlo45/lBYmLAPxy6fIvV++0F1hZovN7DUzy2cXwVRJq5xza5xzhyX9XNJVJz3nqsbvL0kPSrrMzEzSDEmLnXOLJMk5t905dyyffyAARRQVuazklqayMqm62j9mNRfIy5490uuvS+XlUlVV6DQIYcIEf79kiXT4cNgsQFblU+T+UNJfSJqpE/txP5zH150j6c2cj+sbP9fkc5xzRyXtktRb0nBJzsx+a2YLzOzzTf0AM/ukmdWYWc3WrVvziASgYBoapJoa/zgqdFB6aFkGWmT+fD90aMIEP4QIpad7d2nECF/gMnwKiEc+Re4bzrnHnHNrnXPro1seX2dNfM7l+ZxySRdJ+rPG+2vM7LJ3PdG5+5xz1c656r59++YRCUDBrFwp7d4tnXOO1L9/6DQIJSpyo/5LAM2K/qqwH7e0cX0QiFc+Re5yM/tvM7vRzK6Nbnl8Xb2kATkfV0raeKrnNO7D7SHp7cbPP++c2+ac2y/pSfnjiwAkBZNTIL3zGCF38nVMACeLXjrZj1vaKHKBeOVT5HaSdEh+n2xLjhCaJ2mYmQ0ysw6S/lTSYyc95zFJNzc+vl7S751zTtJvJY03s86Nxe90Sa/n8TMBFAtFLiS/kt+vn7Rzp7RqVeg0QOLx0gmJIheI22mPEHLOfbQ139g5d9TMbpMvWMsk/dA5t9TMviqpxjn3mKT/lPQTM1slv4L7p41fu8PM/k2+UHaSnnTO/bo1OQDEhOUISJKZf7f2q1/5/yeGDQudCEisjRul+voTezJRuiZMkDp0kJYvl3btknr0CJ0IyJZ8VnJbzTn3pHNuuHNuiHPuHxs/9+XGAlfOuYPOuT9xzg11zk11zq3J+dqfOufGOOfGOueaHDwFIJBDh6TaWl/gTJ4cOg1Ciy50sC8XaFZ0bXDKFKldrO/AkHQVFX66tnN+GBmAwuIlFkDLLVokHTniz0nt3j10GoRG3x2QF1qVkYuXTiA+py1yzaysGEEApAjv1JArOkJq4UIOfQSawUsnclHkAvHJZyV3lZndbWajY08DIB14p4ZcPXpII0f6AnfRotBpgERqaJDmzfOPeemERJELxCmfIne8pJWSfmBmc83sk2ZGfyJQyihycbLco4QAvMuKFf5o8cpKjhaHN2yYv0a4YYO/ASic0xa5zrk9zrnvO+feK+nzku6StMnM7jezobEnBJAsO3f6d2sVFdK4caHTIClYkgCaxbVBnKxdOz+ETDqxyg+gMPLak2tmV5rZI5L+Q9I3JQ2W9LikJ2POByBpot/EEyf68w8AiSIXOA2KXDQl+v+B4fRAYZ32nFxJdZL+IOlu59wfcz7/oJldHE8sAInFOzU0Zfx4v7q/fLlf7e/ZM3QiIFGiIoajxZGL64NAPPLZk3uTc+5juQWumV0oSc6522NLBiCZKHLRlA4d/Oq+JNXUhM0CJMzBg34mG0eL42TRr9J58/xwMgCFkU+R++0mPvedQgcBkALOnViOoMjFyViSAJpUWysdPSqNHi116xY6DZKkXz9pwABpzx4/7gJAYZyyXdnMLpD0Xkl9zexzOX/UXRJn5wKlqL5e2rzZt6IOZe4cThIVuXPnhs0BJAzXBtGcqVOlN9/01wdHjQqdBsiG5lZyO0jqKl8Id8u57ZZ0ffzRACRObquyWdgsSJ73vtffv/QSfXdAjhde8PcXXBA2B5KJJhig8E65kuuce17S82b2Y+fc+iJmApBU0W9gJqegKQMH+kNA6+ul11+Xxo4NnQgIzjlpzhz/ePr0sFmQTBS5QOGdciXXzP698eE9ZvbYybci5QOQJAydQnPMTryLj97VAyVuxQpp61bp7LOlYcNCp0ESTZ7sXz4XLfJDygC0XXPtyj9pvP9X+bNxT74BKCXHjp2YmhudXg+c7OLGk+Wefz5sDiAhor8KF1/MLg80rVs3P5TsyBFf6AJou+balec33vNOBYA//3TvXuk975HOOit0GiRVVOTOmeP7NHlXjxIXNTVEfzWApkydKi1d6hum2BEEtF1z05Vfk+RO9efOufGxJAKQTLQqIx8jRkhnnim99Za0ahX9mShpzp1YyWU/Lpozdar0ox+xLxcolFMWuZKuKFoKAMlHkYt8mPklqwcf9O/uKXJRwtaulTZskHr18u2owKkwfAoorFPuyXXOrW/uVsyQABKAIhf5ym1ZBkpY9Fdg2jSpXXNTUFDyxo2TKiqklSulHTtCpwHSr7npyi823u8xs90n3xcvIoDgDhyQFi/279ImTQqdBklHkQtIYj8u8te+/Ylfr/Pmhc0CZEFzK7kXNd53c851P/m+eBEBBLdwoXT0qDRmjNS1a+g0SLpx46SePaX16/0NKFHsx0VLRAOnaFkG2i6v5hkzm2Rmt5vZZ8xsYtyhACQMrcpoiXbtfH+mxGouSlZ9vbRmjT8eZsKE0GmQBuzLBQrntEWumX1Z0v2SekvqI+nHZnZn3MEAJAhFLlqKlmWUuBde8PcXXiiVNzfmE2iUW+S6U55vAiAf+bzs3ihponPuoCSZ2dclLZD0tTiDAUgQily0VNSf+TxHraM0Rdd3aFVGvgYP9pO4N2+W3nxTOvfc0ImA9MqnXXmdpI45H1dIWh1LGgDJs327tHq11KmTNHZs6DRIi4kTpS5dpLo6adOm0GmAoouu7zB0Cvkyo2UZKJTmpit/x8y+LemQpKVm9mMz+5GkJZL2FisggMCiMY+TJ9Nzh/yVl/s+TelE3yZQIrZskZYt89cGq6tDp0GaUOQChdHcO9aaxvv5kh7J+fxzsaUBkDy0KqO1pk+Xnn7aL2ndcEPoNEDRvPiiv7/gAqlDh7BZkC4UuUBhnLLIdc7dX8wgABKKIhetxfAplChaldFaU6b4+5oa6dgxqawsbB4grfKZrjzMzB40s9fNbE10K0Y4AIE5R5GL1psyRaqokJYs8Xu7gRIRXdehyEVLnXmmNHCgtG+fb3kH0Dr5DJ76kaTvSjoq6VJJD0j6SZyhACTE+vXS1q1Snz7+ty7QEhUV0vnn+8fsy0WJ2LlTWrRIat/+xP/+QEvQsgy0XT5Fbifn3O8kmXNuvXPuK5LeF28sAImQu4prFjYL0ik6P4WWZZSIF1/0TTBTp/rBU0BLUeQCbZfPqNSDZtZOUp2Z3SZpg6Qz440FIBFeecXf06qM1mJfLkoMrcpoq/PO8/fRr2AALZfPSu5nJXWWdLukyZL+QtLNcYYCkBDsx0VbnX++P05o4UJp167QaYDYRUVu1MQAtNTEiX7g1GuvSfv3h7T5LbYAACAASURBVE4DpNNpi1zn3Dzn3F5JuyXd7py71jk3N/5oAII6elSaP98/jsY9Ai3VpYv//6ehQfrjH0OnAWK1d6+fituunfTe94ZOg7Tq0kUaO9ZPV164MHQaIJ3yma5cbWavSVos6TUzW2Rmk+OPBiCopUulAwekwYP94CmgtWhZRol4+WVfmEyaJHXrFjoN0ox9uUDb5NOu/ENJf+2cG+icGyjp0/ITlwFkGa3KKJSoyI0ODwUyilZlFApFLtA2+RS5e5xzx89+cM69KGlPfJEAJEL0mzWagAG01oUX+v7NefPYYIZMi67jMHQKbUWRC7TNKYtcM5tkZpMkvWpm3zOzS8xsupn9/5KeK1pCAGGwkotC6dFDqqry+7znMtIB2XTwoJ+GayZddFHoNEi70aOlzp2lNWukbdtCpwHSp7mV3G823qokDZd0l6SvSBol6YLYkwEIZ98+ackSP95x4sTQaZAFtCwj4159VTp8WBo3TurVK3QapF15uTS5cQLOvHlhswBpdMoi1zl3aTO39xUzJIAiW7DAT8MdP17q1Cl0GmRBtEmR4VPIKM7HRaHRsgy0Xj7TlXuY2b+ZWU3j7Ztm1qMY4QAEQqsyCi3q35w7Vzp0KGwWIAbsx0WhUeQCrZfvdOU9km5ovO0W05WBbKPIRaH16SONGeM3LtJ7h4w5cuTEMdAUuSiUaO7jq69KzoXNAqRNPkXuEOfcXc65NY23v5c0OO5gAAKiyEUcOC8XGbVggR8cPmKEdNZZodMgK849VzrzTD94at260GmAdMmnyD1gZsfnBJrZhZIOxBcJQFBbtvjfpl26SKNGhU6DLGFfLjKKVmXEwezEteZXXgmbBUibfIrcWyXda2brzGydpHsk/VWsqQCEE63iVlf76cpAoUyb5u9feskfJwRkRHTdJrqOAxQK+3KB1ilv7g/NrJ2kEc65CWbWXZKcc7uLkgxAGLQqIy79+0tDh0qrVkkLF0pTpoROBLTZsWPSiy/6x6zkotAocoHWaXYl1znXIOm2xse7KXCBEkCRizjRsoyMWbxY2rVLGjhQGjAgdBpkTXQtcMECP+AMQH7yaVd+xsz+xswGmFmv6BZ7MgDF5xxFLuIVLXVFmxiBlKNVGXHq1cs3wBw4IC1dGjoNkB7Ntis3+svG+0/nfM6JCctA9qxeLe3Y4ceDsiSBOERF7gsvSA0NUrt8rrUCyRUVubQqIy5Tp/pdHq++KlVVhU4DpMNp31045wY1caPABbIoWsU97zw/1hEotIED/bkYO3dKS5aETgO0iXMUuYgf+3KBljttkWtmHc3sc2b2sJk9ZGafNbOOxQgHoMhoVUYxcF4uMmLZMn+Gab9+0pAhodMgqyhygZbLp0/sAUljJH1H/vig0ZJ+EmcoAIFQ5KIY2JeLjMjdj0vzC+JSVSWVl/s9uXv3hk4DpEM+Re4I59zHnHN/aLx9UtLwuIMBKLIjR/z4RsmfkQvEJXfCsnNhswBtEF2noVUZcerUSZowwY8xiH5NA2hePkXuQjM7//+1d+dRUlXn+sef3d00M8isMggOoKiAyqCIE4PiiBqNGmdRHO/Vn8aoSa6JJjeJ0URdXo0iagxxntFEEQQVmQQRRRGUSUVmGRXoprv374+3TqppuqFpumqfqvp+1qpVp6oLeNBD1Xlrv3vv6IFzro+kiamLBCCIWbOkoiKpc2epWbPQaZDN9tvPFjdbsUL68svQaYAaYT4u0omWZWDnVKfI7SNpknNukXNukaTJko5xzs1yzn2a0nQA0odWZaSLc7QsI+MtWCAtWSK1bCl17Ro6DbIdRS6wc6qzhdDglKcAEB5FLtLp6KOlF16wobBhw0KnAXZaNIp71FHMx0XqUeQCO2eHRa73/ut0BAEQ2NSpdk+Ri3SI5uW+9571fVIlIMMwHxfp1KWL1Lix9PXX0vLlNuMDQNWq064MINutX297YdSpY6tbAKl24IE293vxYmnRotBpgJ1WfmVlINXy85NrQjKaC+wYRS4A6aOPbDSte3epHttgIw3y8qzPU2K/XGScb7+VFi6UmjSRunULnQa5gpZloPoocgEwHxdhlN9KCMgg0Snbr5+NsAHpQJELVB9FLgCKXITBCsvIULQqI4TyRS5bjAPbR5ELIFnk9ukTNgdyS48etpLK/PnSd9+FTgNUG/vjIoS2baU99pDWrpXmzQudBog3ilwg1y1ZYov/NGkide4cOg1ySUGBdOSRdjxhQtgsQDUtXy7NmSM1aCAddljoNMglztGyDFQXRS6Q66ZNs/tevWwxICCdaFlGhom+j+nb1xakB9IpariiyAW2L6VXtM65wc65uc65ec65Wyv5eV3n3HOJn091znWs8PMOzrkfnHM/T2VOIKcxHxchRUUui08hQ9CqjJAYyQWqJ2VFrnMuX9KDkk6U1FXSec65rhVeNlTSGu/9vpLulXRXhZ/fK+nNVGUEIIpchNWrl21bNXu2tHJl6DTADkVNBxS5CCHaK/fjj6Xi4rBZgDhL5Uhub0nzvPcLvPfFkp6VNKTCa4ZIejJx/KKkAc45J0nOudMlLZD0eQozArmtrCzZrkyRixAKC6UjjrBj5uUi5lavlmbNstOWdfoQQtOm0v77S0VFdi4CqFwqi9y2kr4t93hx4rlKX+O9L5G0TlIL51xDSbdIuiOF+QB89ZW0bp0t2bjnnqHTIFfRsowMMXGibd3Sp481IAAh0LIM7Fgqi1xXyXMVd/Wq6jV3SLrXe//Ddv8A54Y556Y756avpM0N2HlTp9o9o7gIiSIXGYJWZcRB9JEdfYQD2FZBCn/vxZLal3vcTtKSKl6z2DlXIKmppNWS+kg6yzn3Z0m7SSpzzm323v9f+V/svR8uabgk9ezZk22xgZ3FfFzEweGH2zK1M2faBpC77RY6EVCp6HuYY44JmwO5jZFcYMdSOZI7TdJ+zrlOzrlCSedKGlXhNaMkXZw4PkvSOG+O8t539N53lHSfpD9ULHAB1AKKXMRBgwa2AJX31g8KxNCGDdKMGVJ+fnIaORBCt242L3zOHJtxBGBbKStyE3Nsr5M0WtIXkp733n/unLvTOXda4mWPyebgzpN0o6RtthkCkCJFRTZy5px02GGh0yDX0bKMmJs8WSottbfLRo1Cp0Euq1tX6tHDvhf86KPQaYB4Suk+ud77f3vvO3vv9/He/2/iudu996MSx5u992d77/f13vf23i+o5Pf4rff+nlTmBHLSJ59IW7bYMo1Nm4ZOg1wX9X9Gkx6BmIlOTVqVEQfMywW2L6VFLoAYi67Y6LtDHPTtK+Xl2bDED9tdcxAIImoyYNEpxEH00U3zC1A5ilwgV739tt0PGhQ2ByBJTZpIhxwilZRIU6aETgNsZdMmW8LAOalfv9BpAGnAALt/7z1p8+awWYA4osgFctHGjdKECXbFNnBg6DSAoWUZMTV1qlRcLHXvzuLfiIc2bWxe7qZNrNcHVIYiF8hFEybYwlOHHiq1bBk6DWBYfAoxRasy4uj44+0+aswCkESRC+Si6BMx+oQE4iDqA506lf47xApFLuKIIheoGkUukIsochFHLVpIBx9sXQYsGYqYKCqSJk2y46OOCpsFKO/II6X69W03wOXLQ6cB4oUiF8g1330nffaZ1LChrWgLxEk0R/z118PmABLGjbN5j926Sa1bh04DJNWrJx17rB2PGRM0ChA7FLlArok+CY87TiosDJsFqOiMM+z+5Zcl78NmAWSnoiSdeWbYHEBlaFkGKkeRC+QaWpURZ337Sq1aSQsXSp9+GjoNclxpqfTaa3Ycff8CxEn5IpfvBYEkilwgl5SVJUdyKXIRR/n50pAhdvzKK2GzIOdNmiStXCntvbdNFwfi5oADpLZtbU7urFmh0wDxQZEL5JKZM6VVq6QOHaTOnUOnASoXDZlR5CKw6BQ84wzbVhyIG+doWQYqQ5EL5JLyrcpcsSGuBgyQGje2duX580OnQY7yPlnkMh8XcUaRC2yLIhfIJczHRSaoW1c6+WQ7ZjQXgcycKS1aJO2+u3T44aHTAFUbONC+t37/fVsJHABFLpA7fvxR+uAD+yQcMCB0GmD7aFlGYNGpN2SIlMfVEmKsZUvp0ENtT+cJE0KnAeKBt20gV7z3nrRli9Srl9S8eeg0wPadeKKN6E6eLC1bFjoNclD5+bhA3EUNWqNHh80BxAVFLpAraFVGJmnc2HrwvE/u4QKkyVdfSZ99JjVtaluKA3HHvFxgaxS5QK6Ivt6lyEWmiFb7oWUZaRadcqecIhUWhs0CVEffvlLDhvblzJIlodMA4VHkArngm2+kOXNsdIwVVJApTj3VJkO+8460dm3oNMghtCoj0xQWJrsOxowJmwWIA4pcIBdEn3j9+0t16oTNAlRXq1bSUUdJJSXSv/4VOg1yxJIl0pQpUr160uDBodMA1UfLMpBEkQvkAubjIlOxyjLSLJoCfvzx1v4JZIroI37MGKmsLGwWIDSKXCDblZZKY8faMUUuMk1U5L75JhtAIi2i71OiKeFApujcWerQQVq5Uvrkk9BpgLAocoFsN2OGtHq11KmTtM8+odMAO6dDB+mww6SNG5lohpRbs0YaP17Kz7cp4UAmcY6WZSBCkQtku/Ktys6FzQLURDSa+/LLYXMg673xhk0BP+YYthNHZqLIBQxFLpDtmI+LTBcVua+/bhUIkCKsqoxMN2CAfZ/9wQfSjz+GTgOEQ5ELZLMNG6RJk2wblv79Q6cBauaAA2yy2erV0vvvh06DLLVxo/TWW3Z8+ulhswA11by51KuXVFwsvfde6DRAOBS5QDZ7910b+erTR9ptt9BpgJpxLrkKEKssI0VGj7a1zXr3ltq1C50GqDlalgGKXCC70aqMbFF+KyH2xkAK0KqMbEGRC1DkAtlt9Gi7p8hFpuvZU2rbVvruO2n69NBpkGW2bLEp3xJFLjLf4YdLjRtLX3whfftt6DRAGBS5QLZauFD66iupaVPrvwMyWV5ecqIkLcuoZe+9J61da9O/u3QJnQbYNXXqJJfhYOc15CqKXCBbRZ9sAwZIBQVhswC1gXm5SJHolIpOMSDT0bKMXEeRC2Qr5uMi2xx9tC0dOneu9eEBtaCsjPm4yD7RR/+YMVJpadgsQAgUuUA2KimR3nnHjilykS0KCqRTT7VjRnNRSz78UFq6VOrQQTr00NBpgNqxzz5Sp06289qMGaHTAOlHkQtko+nTbYLZvvvapxyQLaKhtpdfDpsDWSP6vuT00223KiAbOEfLMnIbRS6QjWhVRrY6/nipQQPpo4+kb74JnQYZzvvk9yW0KiPbUOQil1HkAtmIIhfZqn596cQT7fjVV8NmQcb7/HNp3jypZUupX7/QaYDa1b+/LUw/aZK0YUPoNEB6UeQC2WbdOmnKFCk/XzruuNBpgNoXDbkxLxe7KDqFTjuNReiRfXbbTerTx5bpePfd0GmA9KLIBbLN+PG2lOIRR0hNmoROA9S+k0+2iuT996VVq0KnQQZjVWVkO1qWkasocoFsM3q03dOqjGy1227Wh1dWJo0aFToNMtSiRdLHH0uNGkkDB4ZOA6QGRS5yFUUukG2Yj4tcQMsydlE0pfvEE6V69cJmAVKld29r6vryS/tiB8gVFLlANpk/X1qwQGrWTOrZM3QaIHWGDLE9MsaMYUUV1Ei0qvKZZ4bNAaRSQYE0YIAdjxkTNguQThS5QDaJRnEHDrSFp4BstcceNu+8qEh6663QaZBhVqyQPvhAKiyUTjopdBogtU44we6j2UxALqDIBbIJrcrIJbQso4ZGjbI9cgcMYH0+ZL/okuCdd2ylZSAXUOQC2WLLFmncODseNChsFiAdoiL3jTdsRBeoJlZVRi7p1Enad19p7Vpp+vTQaYD0oMgFssWHH0rr10tdukh77RU6DZB6++wjdetmc3KjL3iAHVi/Xho71qZ0DxkSOg2QHqyyjFxDkQtkC1qVkYtoWcZO+ve/peJiqV8/qXXr0GmA9KDIRa6hyAWyBUUuclFU5L72mlRaGjYLMgKtyshFxx1n61FOmSKtWxc6DZB6FLlANlizxtqV69SRjj02dBogfbp1swlnK1ZIkyeHToOY27zZRnIlilzkliZNbEH60lJp/PjQaYDUo8gFssE770hlZVLfvlKjRqHTAOnjXLJaiTY+BarwzjvSDz9IPXpIHTuGTgOkFy3LyCUUuUA2oFUZuezMM+3+lVdsXxigCtH3INEpA+QSilzkEopcINN5T5GL3HbEEVKbNtKiRdInn4ROg5gqKbH9cSValZGbevaUdttNmj/fbkA2o8gFMt1XX0lffy21aCEdckjoNED65eUl94JhlWVUYeJEadUq2y/0wANDpwHSLz9fGjjQjhnNRbajyAUyXfRJNWiQfYIBuYithLAD5VdVdi5sFiCUE06we4pcZDuKXCDT0aoMSP372/Khs2ZJ8+aFToOY8T5Z5DIfF7ls0CC7HzdO2rIlbBYglShygUxWXJzcCyD65AJyUWGhdMopdsxoLiqYMUP65htpjz2k3r1DpwHC2WsvqUsXaf1623kQyFYUuUAmmzLF9sPo2lVq1y50GiAsWpZRheiUOP10m8IN5DJWWUYu4K0eyGS0KgNJgwdLdetKkydLS5eGToMYKT8fF8h1FLnIBRS5QCajyAWSGjVK/lt47bWwWRAbc+dKs2fb1inHHhs6DRDescdKdepYu/KaNaHTAKlBkQtkqu+/l6ZPt7mIRx8dOg0QD9GqQi+9FDYHYuPll+3+1FPtwh7IdY0aSX37SmVltgAVkI0ocoFMNXasLRnar5/UsGHoNEA8nHaatSyPHSvNmRM6DQLbskV6+GE7PvvssFmAOImaXkaPDpsDSBWKXCBT0aoMbKt5c+nSS+34nnvCZkFwL7xgqyp37iydfHLoNEB8lJ+X633YLEAqUOQCmWjLFunf/7ZjilxgazfdJDknjRwpLVkSOg0C8V7685/t+OabWVUZKO+QQ6QWLaSvv5Y+/TR0GqD28ZYPZKJRo6Rly6T995d69AidBoiXffeVfvIT20f6/vtDp0Egb78tffKJtPvu0gUXhE4DxEt+vnTOOXb8yCNhswCpQJELZKKHHrL7a66xESsAW/vFL+z+4YeldevCZkEQ0Sju9ddL9eqFzQLE0dVX2/3IkdL69WGzALWNIhfINF98YcshNmggXXRR6DRAPPXqZftkrF8vDR8eOg3SbPp0e5ts3Fi66qrQaYB4Ougg25zhhx+kf/4zdBqgdqW0yHXODXbOzXXOzXPO3VrJz+s6555L/Hyqc65j4vlBzrmPnHOzEvf9U5kTyCjRUqEXXCA1bRo2CxBnt9xi9/fdJxUVhc2CtLr7bru/8krbHxdA5a65xu4feogFqJBdUlbkOufyJT0o6URJXSWd55zrWuFlQyWt8d7vK+leSXclnl8l6VTv/cGSLpY0MlU5gYzy44/S3/9ux1GfEYDKnXCCdPDBtvjUU0+FToM0mT9fevFF2xP3+utDpwHi7YwzpDZtpM8/lyZMCJ0GqD2pHMntLWme936B975Y0rOShlR4zRBJTyaOX5Q0wDnnvPcfe++jJTE/l1TPOVc3hVmBzPD009Z+2bcvC04BO+Jccm7u3XdLZWVh8yAt/vIX+199/vlSu3ah0wDxVlgoXXGFHUfLfQDZIJVFbltJ35Z7vDjxXKWv8d6XSFonqUWF1/xE0sfee3rNkNu833rBKQA7ds45Uvv20pw50htvhE6DFFuxQnriCTu++eawWYBMMWyYbbH10ku2cQOQDVJZ5Fa25GvFbv/tvsY5d6CshfnKSv8A54Y556Y756avXLmyxkGBjDB5sjRzptSqlXTWWaHTAJmhTh3pxhvtOFpuF1nr//5P2rxZOvVUqWvFCVIAKtW+vTRkiFRSIj36aOg0QO1IZZG7WFL7co/bSVpS1WuccwWSmkpanXjcTtIrki7y3s+v7A/w3g/33vf03vds1apVLccHYiYaxb38cqku3ftAtV1+udSsmTRxot2QlX74wYpcKdmlDqB6ogaxRx6xYhfIdKkscqdJ2s8518k5VyjpXEmjKrxmlGxhKUk6S9I47713zu0m6V+SbvPec0UCrFghvfCCzTG8stLGBgBVadRIuvZaO2Y0N2s9/ri0Zo10xBHSkUeGTgNklv79pc6dpe++k15/PXQaYNelrMhNzLG9TtJoSV9Iet57/7lz7k7n3GmJlz0mqYVzbp6kGyVF2wxdJ2lfSf/jnJuZuLVOVVYg9h5/XCoulk45Rdprr9BpgMzzX/9lHRCjRtle08gqW7bYglOSjeK6yiZDAahSXl5y0wYWoEI2cD5LNsXq2bOnnz59eugYQO0rLZX22Uf6+mvpzTelwYNDJwIy09VX2z7Tl10mPfZY6DSoRU89ZVuHd+kizZ5tF+wAds6aNVLbttKmTbZWX5cuoRMB23LOfeS977mj1/ExAMTdm29agbv33tLxx4dOA2Sum26y6mfkSOvJQ1bwPtmFfvPNFLhATTVrJv3sZ3b88MNhswC7io8CIO6ivqGrr+bqDdgV++4r/eQn1tt6//2h06CWvP229Omn0u6722gugJqLFqB64gnpxx/DZgF2BVfMQJzNny+99ZZUr5506aWh0wCZL9o89eGHpXXrwmZBrbjrLru/4QYWngd21aGHSocfbm+PzzwTOg1QcxS5QJw9/LD14p17rtSiReg0QObr1Us67jhpwwbbKwMZbdo0afx4qXFj6aqrQqcBskM0mvvgg3YJAmQiilwgrjZtslWVpeQnDoBdF22iet99UlFR2CzYJXffbfdXXSU1bRo2C5Atzj7bvlefOVOaOjV0GqBmKHKBuHr+eWn1aqlnTxt9AlA7TjhB6tZNWrpU+uc/Q6dBDc2bJ730klSnjnT99aHTANmjXj1p6FA7ZjshZCqKXCCuok8WRnGB2uVccjT37rulsrKweVAjf/2r/a+74ALb9gRA7bnqKnurfO45adWq0GmAnUeRC8TR9OnShx/aev7nnBM6DZB9fvpTqUMHae5c6fXXQ6fBTlqxwlZ/lZJriQGoPZ06SSedJBUXJ2dOAZmEIheIo7/9ze4vvVRq0CBsFiAb1akj3XijHUebrCJjPPCAtHmzdNpp0gEHhE4DZKeokezhh6XS0rBZgJ1FkQvEzZo10tNP2zHLhQKpM3SodUtMmiR98EHoNKimH36wVV+lZNc5gNp3wgk2ortwoTR6dOg0wM6hyAXi5oknbIji+OOl/fYLnQbIXo0aSdddZ8eM5maMxx6z7wL79pWOPDJ0GiB75ecnv2uPvlgCMgVFLhAnZWXJVuVrrw2bBcgF111nS4m+/ro0e3boNNiBLVtswSlJuuWWsFmAXHDZZVLdutKbb0oLFoROA1QfRS4QJ2PH2r4YHTpIJ58cOg2Q/Vq3trnvknTPPWGzYIeee0765htp//2lU04JnQbIfi1b2jp93kuPPBI6DVB9FLlAnETbBl15pfUJAUi9m26S8vJsz9zFi0OnQRW8T3aV33yz/S8DkHpRY9ljj9lsKiAT8BEBxMU331jLZJ06yV3YAaTePvtIZ51lvbD33x86DaowerQ0a5a0557S+eeHTgPkjt69pUMPlb7/XnrhhdBpgOqhyAXiYvhwm5N71llSmzah0wC5Jdps9ZFHpLVrw2ZBpaJR3BtusDmCANLDueR2QlHDGRB3FLlAHBQXS48+asfRJwmA9OnZU+rfX9qwgYlnMTRtmjR+vNSkiTRsWOg0QO457zypaVNpyhRpxozQaYAdo8gF4uDll6UVK6SDD2ZPDCCUaNPV++5j4lnMRKO4V11lF9oA0qtBg+QafdEmEECcUeQCcRBtQHfttdYXBCD9jj9e6t5dWrbMFqFCLMybJ730klRYKF1/feg0QO6K9sx96inbqxqIM4pcILRPP5U++EBq3JjVVICQnEuO5t59t82RR3B/+YutrHzhhbboFIAwunSRBg6UNm2SnnwydBpg+yhygdCivp+LL5YaNQqbBch1Z58t7bWX9OWX0jPPhE6T8xYtkp54wo5//vOgUQAouZ3QQw/xPSDijSIXCGn9emnkSDu++uqwWQDYFl6/+pUdX3uttGBB2Dw5rLhYOuccqahIOvdcaf/9QycCcMopUrt20ldfSePGhU4DVI0iFwhp5Ejpxx+lY4+VunYNnQaAJF1+uXT66dK6dVZdFReHTpSTfvlL6cMPpQ4dkssWAAiroEC68ko7ZjshxBlFLhCK98lPCLYNAuLDOenxx61tedo06bbbQifKOW+8YXNxCwqk556TmjcPnQhA5PLL7d/ma69JixeHTgNUjiIXCOX996XZs6U99rBRIwDx0ayZ9OyzdiX3179Kr78eOlHO+PZbW6JAkv7wB+nww8PmAbC13XeXfvITm5M7fHjoNEDlKHKBUKL+u2HDbB4ggHg5/HDpj3+044svlr75JmyeHFBSIp13nrR6tXTiidJNN4VOBKAyUQPa8OHM6EA8UeQCISxZIr3yipSfL11xReg0AKpy443SSSfZppDnnSdt2RI6UVb7zW+kiRNtq6B//EPK4yoFiKWjjpIOOkhavtwuZ4C44eMDCOF//seGLIYMkdq2DZ0GQFXy8mxDyLZtpUmTpNtvD50oa739tg2c5+XZ7k0tW4ZOBKAqziVHc3/7W1tDE4gTilwg3Z55xha1qVdPuvPO0GkA7EjLlvbvNi9P+tOfpNGjQyfKOkuXShdcYOvx3XGHdPTRoRMB2JFLLrGNIebMkW64IXQaYGsUuUA6zZ+fXHv//vulAw8MmwdA9Rx1VPJLqQsvtCkHqBWlpdL550srV0oDB7KYNZAp6te39fnq1ZNGjLBjIC4ocoF0KS62PTc3bJDOPpu5uECmue02adAgq8bOP9+qM+yy3/9eGj9eatNG+uc/bakCAJnh4IOle++142HDpAULwuYBIhS5GLCmAwAAFOxJREFUQLr86lfS9Om29+bw4TahBUDmyMuTRo60auzdd6Xf/S50oow3fry1JzsnPfWU/acFkFmuvNK2FNqwwdbnY7VlxAFFLpAOb74p3XOPDVE884y0226hEwGoiTZtrBpzztqXx40LnShjrVhhA+LeS7/+tTRgQOhEAGrCOenRR6UOHaQPP7R/z0BoFLlAqi1dantsStaXd8QRYfMA2DUDBtgK6d5blbZ8eehEGaeszKY2L11qi0yxaDWQ2Zo1s+/w8/Olu+9mfT6ER5ELpFJ0JRetqPKLX4ROBKA23H67dMwx0rJl9m+8rCx0oozy5z/blkEtW0pPPy0VFIROBGBX9e2bXJ/voovs7REIhSIXSKW77pLeeUdq3drm8uXxTw7ICvn5Vp21bCmNGWNbC6FaPvgg2c74j3+wVTiQTW65xZpdVqzg+z+ExRU3kCqTJllLo2RXcrvvHjYPgNq155725ZVk/9YnTAibJwN8/70tTFNaao0tJ54YOhGA2pSfb2+LrVpJY8da1wYQAkUukApr1iSv5G6+WTrhhNCJAKTC4ME2dFFWZv/mV60KnSi2vJcuuURavNiWJvj970MnApAKe+whPfmkHf/619LkyWHzIDdR5AK1zXvbA/ebb6TevbmSA7Ld735nk9G++86qOPrzKnXvvdIbbyQXqKlTJ3QiAKly4onSTTfZd/3nnSetXRs6EXINRS5Q2x55RHrpJalJE7uSKywMnQhAKtWpY//WmzWT/vUvq+awlQ8/tAFvSXriCdsuHEB2+8MfpJ49pa+/tu/+vQ+dCLmEIheoTbNmSf/v/9nx8OHS3nuHzQMgPTp0SPbn3XqrNGVK2DwxsnatdM45UkmJdMMN0pAhoRMBSIfCQunZZ6XGjaUXX7S9dIF0ocgFasuPP9qV3ObN0tChdgwgd5x6qn3JVVIinXuuzc3Pcd7b2+GiRTaic9ddoRMBSKd99rEGN0m6/nrps8/C5kHuoMgFassNN0hffCEdcIB0//2h0wAI4U9/knr1sv68Cy+UNm4MnSgY76U//lF6+WWbvfHcc8zeAHLReedJl15qYwDnnJPTb4tII4pcoDY895w0YoRUt64dN2wYOhGAEAoL7T2gaVObn3v44dLcuaFTpd2GDXZh+6tf2eMRI5i9AeSyBx6QunSRZs9OzuoCUokiF9hVCxdKw4bZ8b33SgcfHDYPgLA6dbI9czt3tnn6PXta4ZsjPv00+Vdu3Njuzz47dCoAITVsaO8FdevakiUvvBA6EbIdRS6wK7Zssbl369dLZ54pXXVV6EQA4uDgg6Xp060374cf7H3iuuukoqLQyVLqiSekPn2kL7+UunWz/wQ//WnoVADioHt36S9/seMrrrAxAiBVKHKBXfHrX9veGB06WD+ec6ETAYiLxo1ta6EHH7Q25gcflPr1y8oru40bbc7dZZfZvLvLLrMFpjt3Dp0MQJxcc410+unSunU2pWHLltCJkK0ocoGaevtt6c9/lvLzpaeftj0yAaA85+yqbuJEqWNHG9o89FBp1KjQyWrN3Lk2evv3v0v169to7mOP2TEAlOecvT+0by9NnSrdfnvoRMhWFLlATSxbZiunStKdd0pHHhk2D4B469lTmjFDOu002zh2yBDpF7/I+GGM556zv9pnn9miMlOnSpdcEjoVgDhr3tzGBvLybEH6t98OnQjZiCIX2FkrVtg8uxUrpP79pVtuCZ0IQCZo1kx69VXp7rutA+Tuu+095LvvQifbaUVF0rXX2lTjaMrxtGmsuwegevr1k377Wzu+8EL7DhCoTRS5QHV5b/PrunaV3n9fat1aGjnSLlYBoDqck37+c+ndd6U995Q++EDq0UMaMyZ0smpbuNCaVx56yKYaP/SQjco0bhw6GYBM8stf2vd8K1ZIvXvblmNZvjYf0ogiF6iOpUulM86QfvYz6fvvpYEDrS9vzz1DJwOQifr1kz7+2N5LVq2STjjBhjVKS0Mn265Ro2xK8Ucf2RTjiROlq69mzT0AOy8/X3r9demGG6SyMukPf5AOOcQur4BdRZELbI/3tppK167Sa69JTZpIjz5qE0g6dgydDkAma91aeuutZM/eHXdIgwfbsEbMbNki3XyzTSWOphTPmGHzcQGgpho0kO69N7m1+BdfSH37WsPLpk2h0yGTUeQCVfn2W+mkk2xfjLVr7fjzz6XLL2fYAkDtyM+XfvMb++KsVStp7FhrX54wIXSy/1i8WDruOOmeeyzuPfdIr7zCgvIAas+RR0ozZ9p6fJLtp9u9e6zeCpFhKHKBiryXhg+XDjzQRlmaNZP+8Q/pjTekdu1CpwOQjQYOtPblfv1sesRxx0l33WU9fAG9/ba1D06cKLVtK733nnTTTXzPB6D21a9vb3tTptgl2FdfScccI/33f9sCd8DOoMgFylu40C42r7xS2rDBdiz//HNb+o+rOgCp1LatNH68rdheWirdeqv17/3xj1b4psnGjdKTT0pHHWVThVetkgYNshqc3dIApFqvXjbv/9e/tm2GHnhA6tZNGjcudDJkEopcQLLRkgcekA46yN5FW7aUnn1WevllaY89QqcDkCsKCmzjyFGjpPbtpfnzbQnS9u3tS7c33pBKSlLyR8+YIV1zjb3lXXKJLfzcsKH0u99Jb75p3dQAkA5169p7z7RpNoNj4UJpwADpqquk9etDp0MmcN770BlqRc+ePf306dNDx0Am+uor6bLL7IpOsj1wH3iAKzoAYZWWWr/wiBFW9EbFbdu2tlbAZZdJnTrt0h+xbp1t/zNixNb7VPbpI11xhfTTn7I1EICwtmyxNuY777Tj9u1tVtngwaGTIQTn3Efe+x0ue0iRi9xVWirdd5/1w2zeLLVpI/3tb7ZVEADEybJltjbAiBH2xVxk0CBbDG/IEBv6qAbvbY7tiBHS888nVzBt1ky66CJp6FDp4INT8HcAgF3w2Wf2/V50uX/JJdJf/8oieLmGIhfYntmzbRQk2oztwgut4G3ePGwuANge76X337cK9cUX7Qs6yaZYXHSRFbwHHFDpL125Mlknz5mTfL5/f/tlZ5wh1auXhr8DANRQSYkVtrffLhUV2fSKhx+WTjstdDKkC0UuUNGCBdKYMbZFx6hRUnGxtf098oh08smh0wHAzlmzRnrqKdu7+9NPk88feaRVrWefrbL6DTV2rBW2r75qrX6StPvuNiIydKi0zz5h4gNATc2ZY2MVkyfb46OPtvblQYNsRfj8/LD5kDoUucD339siUmPHWnG7cOHWPx861DZia9o0TD4AqA3eW//eiBEqeupFzf6xg2aqhz4p7KVX65ytr3+09QXy8rxOOsnpiits2++CgsC5AWAXlJbaEiq//GVy2oVkTXn9+9tmGYMGSXvvHS4jah9FLnLP5s020SwqamfMsIu/SLNm9q43aBDvegAy3sqV0ief2G3mTLv/4guvkpKttzvrqIUaqsd0qZ5Q246FtlRp9+7J+44d2SINQMZavVp65x279BszRlq0aOuf7713suDt35+ZaZmOIhfZr6zMWvSid7UJE5Lz0ySpsNDa9gYNsne3Qw+lfwVAxiktlebNSxay0f2SJdu+1jnbWrd7d6nHHst1RNlEHf3jm8r75GNbtaWoaNtf1LSpbUIZFb3du9t2akzQBZCB5s9PjneMG2czOyLOSYcdlix6+/blrS7TxKLIdc4NlnS/pHxJI7z3f6rw87qS/iHpMEnfSzrHe78o8bPbJA2VVCrpv733o7f3Z1HkZinvpQ0bbGXR5cvttnSpNGmSfW23cuXWr+/ePVnUHnWU1KBBmNwAUA1FRfY2tmrV1vcrV9rb3qxZdtu4cdtf27BhsiaN6tODDrLnK1VSIs2du3WlPHOmtGLFtq/Nz5e6dLHftEMH21KtZctt7xs1YhQYQGyVllpjX1T0TpxoS7JE6te3y8Vjj5X22ss22mjTxtYtaN5cyssLFh1VCF7kOufyJX0paZCkxZKmSTrPez+73GuukdTNe3+Vc+5cSWd4789xznWV9Iyk3pL2lDRWUmfvfWlVfx5Fbgbx3jZnXL586+K1qlv50dmK2rdPFrUDBkitW6fv7wEgZ3lvNeOmTfYWVf4WPbduXdUFbHT8ww/V+/Pat9+6mO3Rw1rwauUCbNmybQvfuXOtW2ZH6tatugCO7ps3tyvJevWS99Gtfn3ruqFQBpAGGzda41+0Duknn1T92vx8u6yMCt/KbrvvbvctWtAsmC5xKHKPkPRb7/0Jice3SZL3/o/lXjM68ZrJzrkCScsktZJ0a/nXln9dVX9e3IvcB856T6Ulu/rfuopfX+nT23mt98m5qr6skueiY1/560vL7OqutHT799FxxcfFxVJpSfX/2nUKpcaNpSaNpUaN7XjPPaTOXewiiosjQNLWU9BD/R4Vf335x9U5jh7vzK2srPLnorefmtxKSrYtXssXsJs3V68G3JGCgmQ9WL42jG4HHGBFbdrnkG3aZO3Ns2ZZEVxVtV5+tZddUbHwrfi4oMCuIHfl5lzNbnl5yePyyj+u6nhHP6uJXf09+MwE/mP5hgYa91V7TVm0u5ZtaKjlGxpo+YYGWrahgdZuqn4fc54rU+O6W1S3oESFBWUqzC9VYX6Z6haUqrCgVIX5pXacX6bCglLVzY+eL/vPz+rklynPeeU5n3jLSRwrOpac7Gf2GvvZf16v5K8rr+K/+Io/r/ia/bo30Cl39Kr23z3dqlvkpnJtxbaSvi33eLGkPlW9xntf4pxbJ6lF4vkpFX5t29RFTb2bX+qjItH0XyNbJK1O3AAgJvLzt63HytdojRtvW7xWLGibNo1pzVG/vtSrl922Z+PGbYenKw5Zr1lT+bcE0a24OHkMAGnURtJ5iVtFRSrUCrXWcrXZ6rZMu2/z3GrfQus215VUN71/gRQ4a/pknXJH6BS7LpVFbmUf2xW/OqjqNdX5tXLODZM0TJI6dOiws/nS6rrDpqikNM3f4Fb1Uufsh9Hv5co/pwr3FY4l+1b7P9+O50l5+Tv3OL9AqlOnJn97ADsQx4Gimg541WSgrfxzuzLoV1Cw/cHFevXYgkeSrXuw1152q6nSUpucXNWQ+aZNW3cG1fS2sy0CFVsFytuZtoSqflYTtd1qAaBKdSW1T9ySNklamLglFZfm68cthSoqLVBxab6Ky+y+qLTgP8fbPC7LV1HJ1o/LfJ697XgnLycv/ec5L5d4XvLeJV/jpTI5+cTPyvN+6w9aX0lxUPFtofuh2dF3ncqP6MXa+rxoJ6niWpDRaxYn2pWbysbrqvNr5b0fLmm4ZO3KtZY8Be6ZfmzoCAAAxE9+vhXLLBQIIEMVJm6Ij1SuGTZN0n7OuU7OuUJJ50oaVeE1oyRdnDg+S9I4b5OER0k61zlX1znXSdJ+kj5MYVYAAAAAQBZI2UhuYo7tdZJGy7YQetx7/7lz7k5J0733oyQ9Jmmkc26ebAT33MSv/dw597yk2ZJKJF27vZWVAQAAAACQUrxPbjrFfXVlAAAAAEDNVXd1ZbY4BgAAAABkDYpcAAAAAEDWoMgFAAAAAGQNilwAAAAAQNagyAUAAAAAZA2KXAAAAABA1qDIBQAAAABkDYpcAAAAAEDWoMgFAAAAAGQNilwAAAAAQNagyAUAAAAAZA2KXAAAAABA1qDIBQAAAABkDYpcAAAAAEDWoMgFAAAAAGQNilwAAAAAQNagyAUAAAAAZA3nvQ+doVY451ZK+jp0jh1oKWlV6BBABZyXiBvOScQR5yXiiPMScZPqc3Iv732rHb0oa4rcTOCcm+697xk6B1Ae5yXihnMSccR5iTjivETcxOWcpF0ZAAAAAJA1KHIBAAAAAFmDIje9hocOAFSC8xJxwzmJOOK8RBxxXiJuYnFOMicXAAAAAJA1GMkFAAAAAGQNitw0cc4Nds7Ndc7Nc87dGjoPco9z7nHn3Arn3GflnmvunBvjnPsqcd8sZEbkHudce+fceOfcF865z51z1yee59xEEM65es65D51znyTOyTsSz3dyzk1NnJPPOecKQ2dF7nHO5TvnPnbOvZF4zHmJoJxzi5xzs5xzM51z0xPPBf8Mp8hNA+dcvqQHJZ0oqauk85xzXcOmQg76u6TBFZ67VdI73vv9JL2TeAykU4mkm7z3B0g6XNK1ifdHzk2EUiSpv/e+u6QekgY75w6XdJekexPn5BpJQwNmRO66XtIX5R5zXiIOjvPe9yi3dVDwz3CK3PToLWme936B975Y0rOShgTOhBzjvX9f0uoKTw+R9GTi+ElJp6c1FHKe936p935G4niD7OKtrTg3EYg3PyQe1kncvKT+kl5MPM85ibRzzrWTdLKkEYnHTpyXiKfgn+EUuenRVtK35R4vTjwHhNbGe79UsmJDUuvAeZDDnHMdJR0iaao4NxFQoiV0pqQVksZImi9prfe+JPESPscRwn2SfiGpLPG4hTgvEZ6X9LZz7iPn3LDEc8E/wwvS/QfmKFfJcyxrDQAJzrlGkl6SdIP3fr0NUABheO9LJfVwzu0m6RVJB1T2svSmQi5zzp0iaYX3/iPn3LHR05W8lPMS6Xak936Jc661pDHOuTmhA0mM5KbLYkntyz1uJ2lJoCxAecudc3tIUuJ+ReA8yEHOuTqyAvcp7/3Liac5NxGc936tpHdl88V3c85FgwN8jiPdjpR0mnNukWzaW3/ZyC7nJYLy3i9J3K+QfSnYWzH4DKfITY9pkvZLrIBXKOlcSaMCZwIkOw8vThxfLOm1gFmQgxJzyh6T9IX3/q/lfsS5iSCcc60SI7hyztWXNFA2V3y8pLMSL+OcRFp572/z3rfz3neUXUeO896fL85LBOSca+icaxwdSzpe0meKwWe4856uhnRwzp0k+8YtX9Lj3vv/DRwJOcY594ykYyW1lLRc0m8kvSrpeUkdJH0j6WzvfcXFqYCUcc71kzRB0iwl55n9UjYvl3MTaeec6yZbKCVfNhjwvPf+Tufc3rIRtOaSPpZ0gfe+KFxS5KpEu/LPvfencF4ipMT590riYYGkp733/+uca6HAn+EUuQAAAACArEG7MgAAAAAga1DkAgAAAACyBkUuAAAAACBrUOQCAAAAALIGRS4AAAAAIGtQ5AIAAAAAsgZFLgAAAAAga1DkAgAAAACyxv8H0bXbOvlyMIoAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21f4e30a1d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21f4e6686d8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21f4e8e0080>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"distrs = [unif, bino, guass, bet]\\n\",\n    \"for distri in distrs:\\n\",\n    \"    distri()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "5.data-visualization/2.seaborn/README.md",
    "content": "# 数据可视化的利器-Seaborn简易入门\n\nMatplotlib试着让简单的事情更加简单，困难的事情变得可能，而Seaborn就是让困难的东西更加简单。 \n\nSeaborn是针对统计绘图的，一般来说，seaborn能满足数据分析90%的绘图需求。\n\nSeaborn其实是在matplotlib的基础上进行了更高级的API封装，从而使得作图更加容易，在大多数情况下使用seaborn就能做出很具有吸引力的图，应该把Seaborn视为matplotlib的补充，而不是替代物。\n\n用matplotlib最大的困难是其默认的各种参数，而Seaborn则完全避免了这一问题。\n\n### seaborn一共有5个大类21种图，分别是：\n\n- Relational plots 关系类图表\n\n  1. relplot() 关系类图表的接口，其实是下面两种图的集成，通过指定kind参数可以画出下面的两种图\n  2. scatterplot() 散点图\n  3. lineplot() 折线图\n\n- Categorical plots 分类图表\n\n  1. catplot() 分类图表的接口，其实是下面八种图表的集成，通过指定kind参数可以画出下面的八种图\n  2. stripplot() 分类散点图\n  3. swarmplot() 能够显示分布密度的分类散点图\n  4. boxplot() 箱图\n  5. violinplot() 小提琴图\n  6. boxenplot() 增强箱图\n  7. pointplot() 点图\n  8. barplot() 条形图\n  9. countplot() 计数图\n\n- Distribution plot 分布图\n\n  1. jointplot() 双变量关系图\n  2. pairplot() 变量关系组图\n  3. distplot() 直方图，质量估计图\n  4. kdeplot() 核函数密度估计图\n  5. rugplot() 将数组中的数据点绘制为轴上的数据\n\n- Regression plots 回归图\n\n  1. lmplot() 回归模型图\n  2. regplot() 线性回归图\n  3. residplot() 线性回归残差图\n\n- Matrix plots 矩阵图\n\n  1. heatmap() 热力图\n  2. clustermap() 聚集图\n\n\n\n![公众号](../images/gongzhong.jpg)\n\n机器学习qq群：554839127（我们有6个群，加过一个就不需要加了）\n"
  },
  {
    "path": "5.data-visualization/2.seaborn/Searborn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 数据可视化-Seaborn简易入门\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Matplotlib试着让简单的事情更加简单，困难的事情变得可能，而Seaborn就是让困难的东西更加简单。\\n\",\n    \"\\n\",\n    \"seaborn是针对统计绘图的，一般来说，seaborn能满足数据分析90%的绘图需求。\\n\",\n    \"\\n\",\n    \"Seaborn其实是在matplotlib的基础上进行了更高级的API封装，从而使得作图更加容易，在大多数情况下使用seaborn就能做出很具有吸引力的图，应该把Seaborn视为matplotlib的补充，而不是替代物。\\n\",\n    \"\\n\",\n    \"用matplotlib最大的困难是其默认的各种参数，而Seaborn则完全避免了这一问题。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### seaborn一共有5个大类21种图，分别是：\\n\",\n    \"\\n\",\n    \"* Relational plots 关系类图表\\n\",\n    \"    1. relplot() 关系类图表的接口，其实是下面两种图的集成，通过指定kind参数可以画出下面的两种图\\n\",\n    \"    2. scatterplot() 散点图\\n\",\n    \"    3. lineplot() 折线图\\n\",\n    \"\\n\",\n    \"* Categorical plots 分类图表\\n\",\n    \"    1. catplot() 分类图表的接口，其实是下面八种图表的集成，，通过指定kind参数可以画出下面的八种图\\n\",\n    \"    2. stripplot() 分类散点图\\n\",\n    \"    3. swarmplot() 能够显示分布密度的分类散点图\\n\",\n    \"    4. boxplot() 箱图\\n\",\n    \"    5. violinplot() 小提琴图\\n\",\n    \"    6. boxenplot() 增强箱图\\n\",\n    \"    7. pointplot() 点图\\n\",\n    \"    8. barplot() 条形图\\n\",\n    \"    9. countplot() 计数图\\n\",\n    \"\\n\",\n    \"* Distribution plot 分布图\\n\",\n    \"    1. jointplot() 双变量关系图\\n\",\n    \"    2. pairplot() 变量关系组图\\n\",\n    \"    3. distplot() 直方图，质量估计图\\n\",\n    \"    4. kdeplot() 核函数密度估计图\\n\",\n    \"    5. rugplot() 将数组中的数据点绘制为轴上的数据\\n\",\n    \"\\n\",\n    \"* Regression plots 回归图\\n\",\n    \"    1. lmplot() 回归模型图\\n\",\n    \"    2. regplot() 线性回归图\\n\",\n    \"    3. residplot() 线性回归残差图\\n\",\n    \"\\n\",\n    \"* Matrix plots 矩阵图\\n\",\n    \"    1. heatmap() 热力图\\n\",\n    \"    2. clustermap() 聚集图\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"# 如果不添加这句，是无法直接在jupyter里看到图的\\n\",\n    \"import seaborn as sns\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"有一套的参数可以控制绘图元素的比例。\\n\",\n    \"\\n\",\n    \"首先，让我们通过`set()`重置默认的参数：\\n\",\n    \"\\n\",\n    \"有五种seaborn的风格，它们分别是：`darkgrid`, `whitegrid`, `dark`, `white`, `ticks`。它们各自适合不同的应用和个人喜好。默认的主题是`darkgrid`。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sns.set(style=\\\"ticks\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>dataset</th>\\n\",\n       \"      <th>x</th>\\n\",\n       \"      <th>y</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>I</td>\\n\",\n       \"      <td>10.0</td>\\n\",\n       \"      <td>8.04</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>I</td>\\n\",\n       \"      <td>8.0</td>\\n\",\n       \"      <td>6.95</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>I</td>\\n\",\n       \"      <td>13.0</td>\\n\",\n       \"      <td>7.58</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>I</td>\\n\",\n       \"      <td>9.0</td>\\n\",\n       \"      <td>8.81</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>I</td>\\n\",\n       \"      <td>11.0</td>\\n\",\n       \"      <td>8.33</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"  dataset     x     y\\n\",\n       \"0       I  10.0  8.04\\n\",\n       \"1       I   8.0  6.95\\n\",\n       \"2       I  13.0  7.58\\n\",\n       \"3       I   9.0  8.81\\n\",\n       \"4       I  11.0  8.33\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Load the example dataset for Anscombe's quartet\\n\",\n    \"df = sns.load_dataset(\\\"anscombe\\\")\\n\",\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"seaborn内置了不少样例数据，为dataframe类型，\\n\",\n    \"`df = sns.load_dataset(\\\"anscombe\\\")`即读取“`anscombe`”样例数据，如果要查看数据，可以使用类似`df.head()`命令查看\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##  lmplot(回归图)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"lmplot是用来绘制回归图的，通过lmplot我们可以直观地总览数据的内在关系。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0x1acf7dc5208>\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf7dc54a8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Anscombe's quartet\\n\",\n    \"==================\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"# Show the results of a linear regression within each dataset\\n\",\n    \"sns.lmplot(x=\\\"x\\\", y=\\\"y\\\", col=\\\"dataset\\\", hue=\\\"dataset\\\", data=df,\\n\",\n    \"           col_wrap=2, ci=None, palette=\\\"muted\\\", height=4,\\n\",\n    \"           scatter_kws={\\\"s\\\": 50, \\\"alpha\\\": 1})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0x1acf7dfe550>\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf7d96b70>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Show the results of a linear regression within each dataset\\n\",\n    \"sns.lmplot(x=\\\"x\\\", y=\\\"y\\\", col=\\\"dataset\\\", hue=\\\"dataset\\\", data=df,\\n\",\n    \"           col_wrap=2, ci=None, palette=\\\"muted\\\", height=4,\\n\",\n    \"           scatter_kws={\\\"s\\\": 50, \\\"alpha\\\": 1})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0x1acf85667b8>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf85665c0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Multiple linear regression\\n\",\n    \"==========================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set()\\n\",\n    \"\\n\",\n    \"# Load the iris dataset\\n\",\n    \"iris = sns.load_dataset(\\\"iris\\\")\\n\",\n    \"\\n\",\n    \"# Plot sepal with as a function of sepal_length across days\\n\",\n    \"g = sns.lmplot(x=\\\"sepal_length\\\", y=\\\"sepal_width\\\", hue=\\\"species\\\",\\n\",\n    \"               truncate=True, height=5, data=iris)\\n\",\n    \"\\n\",\n    \"# Use more informative axis labels than are provided by default\\n\",\n    \"g.set_axis_labels(\\\"Sepal length (mm)\\\", \\\"Sepal width (mm)\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0x1acf88a1438>\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf88a1da0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Faceted logistic regression\\n\",\n    \"===========================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"# import statsmodels.api as sm \\n\",\n    \"sns.set(style=\\\"darkgrid\\\")\\n\",\n    \"\\n\",\n    \"# Load the example titanic dataset\\n\",\n    \"df = sns.load_dataset(\\\"titanic\\\")\\n\",\n    \"\\n\",\n    \"# Make a custom palette with gendered colors\\n\",\n    \"pal = dict(male=\\\"#6495ED\\\", female=\\\"#F08080\\\")\\n\",\n    \"\\n\",\n    \"# Show the survival proability as a function of age and sex\\n\",\n    \"g = sns.lmplot(x=\\\"age\\\", y=\\\"survived\\\", col=\\\"sex\\\", hue=\\\"sex\\\", data=df,\\n\",\n    \"               palette=pal, y_jitter=.02, logistic=True)\\n\",\n    \"g.set(xlim=(0, 80), ylim=(-.05, 1.05))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##  kdeplot(核密度估计图)\\n\",\n    \"核密度估计(kernel density estimation)是在概率论中用来估计未知的密度函数，属于非参数检验方法之一。通过核密度估计图可以比较直观的看出数据样本本身的分布特征。具体用法如下：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf8704a20>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Different cubehelix palettes\\n\",\n    \"============================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"sns.set(style=\\\"dark\\\")\\n\",\n    \"rs = np.random.RandomState(50)\\n\",\n    \"\\n\",\n    \"# Set up the matplotlib figure\\n\",\n    \"f, axes = plt.subplots(3, 3, figsize=(9, 9), sharex=True, sharey=True)\\n\",\n    \"\\n\",\n    \"# Rotate the starting point around the cubehelix hue circle\\n\",\n    \"for ax, s in zip(axes.flat, np.linspace(0, 3, 10)):\\n\",\n    \"\\n\",\n    \"    # Create a cubehelix colormap to use with kdeplot\\n\",\n    \"    cmap = sns.cubehelix_palette(start=s, light=1, as_cmap=True)\\n\",\n    \"\\n\",\n    \"    # Generate and plot a random bivariate dataset\\n\",\n    \"    x, y = rs.randn(2, 50)\\n\",\n    \"    sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=ax)\\n\",\n    \"    ax.set(xlim=(-3, 3), ylim=(-3, 3))\\n\",\n    \"\\n\",\n    \"f.tight_layout()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(3.8,4.5,'setosa')\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf89b6860>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Multiple bivariate KDE plots\\n\",\n    \"============================\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"darkgrid\\\")\\n\",\n    \"iris = sns.load_dataset(\\\"iris\\\")\\n\",\n    \"\\n\",\n    \"# Subset the iris dataset by species\\n\",\n    \"setosa = iris.query(\\\"species == 'setosa'\\\")\\n\",\n    \"virginica = iris.query(\\\"species == 'virginica'\\\")\\n\",\n    \"\\n\",\n    \"# Set up the figure\\n\",\n    \"f, ax = plt.subplots(figsize=(8, 8))\\n\",\n    \"ax.set_aspect(\\\"equal\\\")\\n\",\n    \"\\n\",\n    \"# Draw the two density plots\\n\",\n    \"ax = sns.kdeplot(setosa.sepal_width, setosa.sepal_length,\\n\",\n    \"                 cmap=\\\"Reds\\\", shade=True, shade_lowest=False)\\n\",\n    \"ax = sns.kdeplot(virginica.sepal_width, virginica.sepal_length,\\n\",\n    \"                 cmap=\\\"Blues\\\", shade=True, shade_lowest=False)\\n\",\n    \"\\n\",\n    \"# Add labels to the plot\\n\",\n    \"red = sns.color_palette(\\\"Reds\\\")[-2]\\n\",\n    \"blue = sns.color_palette(\\\"Blues\\\")[-2]\\n\",\n    \"ax.text(2.5, 8.2, \\\"virginica\\\", size=16, color=blue)\\n\",\n    \"ax.text(3.8, 4.5, \\\"setosa\\\", size=16, color=red)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## FacetGrid \\n\",\n    \"是一个绘制多个图表（以网格形式显示）的接口。\\n\",\n    \"\\n\",\n    \"步骤：\\n\",\n    \"\\n\",\n    \"1、实例化对象\\n\",\n    \"\\n\",\n    \"2、map，映射到具体的 seaborn 图表类型\\n\",\n    \"\\n\",\n    \"3、添加图例\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0x1acf88ab128>\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf92ec7f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Overlapping densities ('ridge plot')\\n\",\n    \"====================================\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"white\\\", rc={\\\"axes.facecolor\\\": (0, 0, 0, 0)})\\n\",\n    \"\\n\",\n    \"# Create the data\\n\",\n    \"rs = np.random.RandomState(1979)\\n\",\n    \"x = rs.randn(500)\\n\",\n    \"g = np.tile(list(\\\"ABCDEFGHIJ\\\"), 50)\\n\",\n    \"df = pd.DataFrame(dict(x=x, g=g))\\n\",\n    \"m = df.g.map(ord)\\n\",\n    \"df[\\\"x\\\"] += m\\n\",\n    \"\\n\",\n    \"# Initialize the FacetGrid object\\n\",\n    \"pal = sns.cubehelix_palette(10, rot=-.25, light=.7)\\n\",\n    \"g = sns.FacetGrid(df, row=\\\"g\\\", hue=\\\"g\\\", aspect=15, height=.5, palette=pal)\\n\",\n    \"\\n\",\n    \"# Draw the densities in a few steps\\n\",\n    \"g.map(sns.kdeplot, \\\"x\\\", clip_on=False, shade=True, alpha=1, lw=1.5, bw=.2)\\n\",\n    \"g.map(sns.kdeplot, \\\"x\\\", clip_on=False, color=\\\"w\\\", lw=2, bw=.2)\\n\",\n    \"g.map(plt.axhline, y=0, lw=2, clip_on=False)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Define and use a simple function to label the plot in axes coordinates\\n\",\n    \"def label(x, color, label):\\n\",\n    \"    ax = plt.gca()\\n\",\n    \"    ax.text(0, .2, label, fontweight=\\\"bold\\\", color=color,\\n\",\n    \"            ha=\\\"left\\\", va=\\\"center\\\", transform=ax.transAxes)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"g.map(label, \\\"x\\\")\\n\",\n    \"\\n\",\n    \"# Set the subplots to overlap\\n\",\n    \"g.fig.subplots_adjust(hspace=-.25)\\n\",\n    \"\\n\",\n    \"# Remove axes details that don't play well with overlap\\n\",\n    \"g.set_titles(\\\"\\\")\\n\",\n    \"g.set(yticks=[])\\n\",\n    \"g.despine(bottom=True, left=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0x1acf948f0f0>\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf94a2e48>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"FacetGrid with custom projection\\n\",\n    \"================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set()\\n\",\n    \"\\n\",\n    \"# Generate an example radial datast\\n\",\n    \"r = np.linspace(0, 10, num=100)\\n\",\n    \"df = pd.DataFrame({'r': r, 'slow': r, 'medium': 2 * r, 'fast': 4 * r})\\n\",\n    \"\\n\",\n    \"# Convert the dataframe to long-form or \\\"tidy\\\" format\\n\",\n    \"df = pd.melt(df, id_vars=['r'], var_name='speed', value_name='theta')\\n\",\n    \"\\n\",\n    \"# Set up a grid of axes with a polar projection\\n\",\n    \"g = sns.FacetGrid(df, col=\\\"speed\\\", hue=\\\"speed\\\",\\n\",\n    \"                  subplot_kws=dict(projection='polar'), height=4.5,\\n\",\n    \"                  sharex=False, sharey=False, despine=False)\\n\",\n    \"\\n\",\n    \"# Draw a scatterplot onto each axes in the grid\\n\",\n    \"g.map(sns.scatterplot, \\\"theta\\\", \\\"r\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0x1acf832c898>\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfa91bcf8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Facetting histograms by subsets of data\\n\",\n    \"=======================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"darkgrid\\\")\\n\",\n    \"\\n\",\n    \"tips = sns.load_dataset(\\\"tips\\\")\\n\",\n    \"g = sns.FacetGrid(tips, row=\\\"sex\\\", col=\\\"time\\\", margin_titles=True)\\n\",\n    \"bins = np.linspace(0, 60, 13)\\n\",\n    \"g.map(plt.hist, \\\"total_bill\\\", color=\\\"steelblue\\\", bins=bins)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfaa9c780>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Plotting on a large number of facets\\n\",\n    \"====================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"ticks\\\")\\n\",\n    \"\\n\",\n    \"# Create a dataset with many short random walks\\n\",\n    \"rs = np.random.RandomState(4)\\n\",\n    \"pos = rs.randint(-1, 2, (20, 5)).cumsum(axis=1)\\n\",\n    \"pos -= pos[:, 0, np.newaxis]\\n\",\n    \"step = np.tile(range(5), 20)\\n\",\n    \"walk = np.repeat(range(20), 5)\\n\",\n    \"df = pd.DataFrame(np.c_[pos.flat, step, walk],\\n\",\n    \"                  columns=[\\\"position\\\", \\\"step\\\", \\\"walk\\\"])\\n\",\n    \"\\n\",\n    \"# Initialize a grid of plots with an Axes for each walk\\n\",\n    \"grid = sns.FacetGrid(df, col=\\\"walk\\\", hue=\\\"walk\\\", palette=\\\"tab20c\\\",\\n\",\n    \"                     col_wrap=4, height=1.5)\\n\",\n    \"\\n\",\n    \"# Draw a horizontal line to show the starting point\\n\",\n    \"grid.map(plt.axhline, y=0, ls=\\\":\\\", c=\\\".5\\\")\\n\",\n    \"\\n\",\n    \"# Draw a line plot to show the trajectory of each random walk\\n\",\n    \"grid.map(plt.plot, \\\"step\\\", \\\"position\\\", marker=\\\"o\\\")\\n\",\n    \"\\n\",\n    \"# Adjust the tick positions and labels\\n\",\n    \"grid.set(xticks=np.arange(5), yticks=[-3, 3],\\n\",\n    \"         xlim=(-.5, 4.5), ylim=(-3.5, 3.5))\\n\",\n    \"\\n\",\n    \"# Adjust the arrangement of the plots\\n\",\n    \"grid.fig.tight_layout(w_pad=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##  distplot(单变量分布直方图)\\n\",\n    \"在seaborn中想要对单变量分布进行快速了解最方便的就是使用`distplot()`函数，默认情况下它将绘制一个直方图，并且可以同时画出核密度估计(KDE)。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfb1ecba8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Distribution plot options\\n\",\n    \"=========================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"white\\\", palette=\\\"muted\\\", color_codes=True)\\n\",\n    \"rs = np.random.RandomState(10)\\n\",\n    \"\\n\",\n    \"# Set up the matplotlib figure\\n\",\n    \"f, axes = plt.subplots(2, 2, figsize=(7, 7), sharex=True)\\n\",\n    \"sns.despine(left=True)\\n\",\n    \"\\n\",\n    \"# Generate a random univariate dataset\\n\",\n    \"d = rs.normal(size=100)\\n\",\n    \"\\n\",\n    \"# Plot a simple histogram with binsize determined automatically\\n\",\n    \"sns.distplot(d, kde=False, color=\\\"b\\\", ax=axes[0, 0])\\n\",\n    \"\\n\",\n    \"# Plot a kernel density estimate and rug plot\\n\",\n    \"sns.distplot(d, hist=False, rug=True, color=\\\"r\\\", ax=axes[0, 1])\\n\",\n    \"\\n\",\n    \"# Plot a filled kernel density estimate\\n\",\n    \"sns.distplot(d, hist=False, color=\\\"g\\\", kde_kws={\\\"shade\\\": True}, ax=axes[1, 0])\\n\",\n    \"\\n\",\n    \"# Plot a historgram and kernel density estimate\\n\",\n    \"sns.distplot(d, color=\\\"m\\\", ax=axes[1, 1])\\n\",\n    \"\\n\",\n    \"plt.setp(axes, yticks=[])\\n\",\n    \"plt.tight_layout()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## lineplot\\n\",\n    \"绘制线段\\n\",\n    \"\\n\",\n    \"seaborn里的lineplot函数所传数据必须为一个pandas数组，这一点跟matplotlib里有较大区别，并且一开始使用较为复杂，`sns.lineplot`里有几个参数值得注意。\\n\",\n    \"\\n\",\n    \"x: plot图的x轴label\\n\",\n    \"\\n\",\n    \"y: plot图的y轴label\\n\",\n    \"\\n\",\n    \"ci: 与估计器聚合时绘制的置信区间的大小\\n\",\n    \"\\n\",\n    \"data: 所传入的pandas数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1acfc3db588>\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfb3e2390>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Timeseries plot with error bands\\n\",\n    \"================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"darkgrid\\\")\\n\",\n    \"\\n\",\n    \"# Load an example dataset with long-form data\\n\",\n    \"fmri = sns.load_dataset(\\\"fmri\\\")\\n\",\n    \"\\n\",\n    \"# Plot the responses for different events and regions\\n\",\n    \"sns.lineplot(x=\\\"timepoint\\\", y=\\\"signal\\\",\\n\",\n    \"             hue=\\\"region\\\", style=\\\"event\\\",\\n\",\n    \"             data=fmri)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1acfc4ad438>\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfc447c50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Lineplot from a wide-form dataset\\n\",\n    \"=================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"\\n\",\n    \"rs = np.random.RandomState(365)\\n\",\n    \"values = rs.randn(365, 4).cumsum(axis=0)\\n\",\n    \"dates = pd.date_range(\\\"1 1 2016\\\", periods=365, freq=\\\"D\\\")\\n\",\n    \"data = pd.DataFrame(values, dates, columns=[\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\"])\\n\",\n    \"data = data.rolling(7).mean()\\n\",\n    \"\\n\",\n    \"sns.lineplot(data=data, palette=\\\"tab10\\\", linewidth=2.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## relplot\\n\",\n    \"这是一个图形级别的函数，它用散点图和线图两种常用的手段来表现统计关系。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0x1acfc465470>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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oHH/uAw5szKHKayC7xCVUglxifdlu3K7pucLuG+uv8AgMtLW78JC1Yc7vdbPl9+1XVWxAqkwjUBEEQhCqnqioHp77LycX79W25BhUAl6qQ4Xbo23vc1vWKrm0JDcC3fg3tPopELclNmGzV92/8dfPVVF0QKpUI1ARBEIQqpbhdHJj8D04tPaxvC7DasQVrH1E5qgNFi9lo3qYpNWqFX/E9Qkp0f/q7ZWqWGKe2bf0ObEX2C50mCFVOBGqCIAhClVEK8tg/5m+c/i1e3xZktdNq2kAy87SsadklZnv26Hdl3Z7nBbdvpL+2KSaamxSsGLRykZ1df+wp13UFobKJQE0QBEGoEorDxv5xTxO7LUvfFmS10/mf95GQo21TVJUc2dPt2atf93LdK6Sjp0Wt0GmkocXl1f25dZ0YpyZUTyJQEwRBEKpE9FtziN1TpJeDrTZufv8efG9uwKk/E1BUlROuLIqcLgACgwNo06lVue5VckJBod1IPaODINmTTy3uVEL5HkIQKpkI1ARBEIRrLnndfo7O8bRiBVttdJp+F4F3DsRxbAvRx7M54cois0S35z1Tx2MwGMp1P3OwP/5NagOgqhJGl4FgWdL3J51JKueTCELlEoGaIAiCcE3lHk9k1yOfQvEEAT+zg7Z3tSH4gQk4Y3aSmpjJzrQU7yDt0fFMeWLiVd23Vv+O+uscm4XmZkUvp6dmoCjKhU4ThColAjVBEAThmrFn5LJt8oe48m0AmA1umoblEDhxDKrTTuHpg7z6xW6v5aImPz6BJ6c/iiRJZV32stS+42b9dXaRhUZmJ8bidUTdikJ6SsZVXV8QKoMI1ARBEIRrQnG42PHAvyiMTwNAlhSahmVjCTRjad8Gd+Ix5izcz/Ekz+SC227pxuMvPXzVQRpAWJfmmEP8AXAqBnwUsEiertTkpJSrvocgVDQRqAmCIAjXxKE355Ox43hxSaVRaC6+ZhfWblEgy2Qc3smyTbH68ZGyP4+//EiFBGkAstFArf5RellxGLFQIlBLFIGaUP2IQE0QBEGodPmxKZz+v9/0cmRYASE+Wvem7/ChKOnxLFu9H7tTW3jdTzLSMrQmdds0rNB61B54k/7aZrfgU6JFLeF0YoXeSxAqggjUBEEQhEp3/KPFqC4tCAtpHERNawEAksWIT5ebKDq5jyUbTuvH15H9aNajLbJcsR9TNW9tj2zRFnS3OU0ElLh+/Mn4sk4ThCpTqYHa+vXrGTVqFIMGDeKtt94CYNu2bQwbNowBAwbw8ccfV+btBUEQhGogLyaJ+EVb9HK9iFzO92ZaOrZCdRayftUW0rO1CQYmZMJlH5r1aFPhdTH6WYno3VYvB5X4GDwbe7bC7ycIV6vSArWEhARee+01Zs+ezbJlyzh69CibNm3ipZdeYvbs2axatYrDhw+zadOmyqqCIAiCUA0cm7mI84t1RvRuhU9Wqr7Pb8hgXLEH+GltjL6ttsEXq6+VtgM6V0p9SnZ/BqpG/XVqclql3E8QrkalBWq//fYbgwcPplatWphMJj7++GN8fHxo0KAB9erVw2g0MmzYMFavXl1ZVRAEQRCqWM7ReBJ/2aGXm/SugbugOF+ZQcZ6Sw/2/LaBmIQcACSgttGPSZ8+SXCt0EqpU+0BnfTX/m5PoJaZmVMp9xOEq2G89CHlExcXh8lk4tFHH+XcuXPceuutNGvWjBo1aujHREREkJJSepZNbm4uubm5XtuSk5Mrq6qCIFQR8V6/8R398Gf9dcTtUez/4w9CXCp1jBKWVg1RMmOZ9fVezzGyD+PeeJC2t998octVCGtEMMGtI8k+mkgQBmRAAewOB3k5eQQEBVTavQXhSlVaoOZ2u9mzZw9z587F19eXqVOnYrVavaZZq6p6wWnX3377LbNmzaqsqgmCUE2I9/qNLevAac79ugcAFZWfC5LYtkNbqqmVyUhfRxgFz/6Hk2melqyR44fQa9KASq9bnUGdyT6aiBUJCwaK0CY6JCelikBNqFYqLVALDw+ne/fuhIZqTdf9+/dn9erVXuu0paWlERERUercKVOmcOedd3ptS05OZuLEq1s+RBCE6kW8129sR99fCGhB2vGmwWzb8ae+75jTxbGN27GWyGNWLyKC+z54/JrUrc7w7hz9aAkWFSyygSK1OFBLTKZZ6ybXpA6CcDkqLVDr27cvL774Irm5ufj5+bF582YGDhzIl19+SVxcHJGRkaxYsYLRo0eXOjcwMJDAwMDKqpogCNWEeK/fuOJ++oOU9QfIk1RijAqbTp+44HG24pYsgGc+eLrC03GUJaB5Xay+Mkqh2yvpbVLcuWtyf0G4XJUWqHXo0IEHH3yQCRMm4HQ66dmzJ3fffTeNGzfmySefxG6306dPHwYOHFhZVRD+Ylx2JwdW7yTuz5O06X8TLXq1q+oqCcJfiuJWyEhI5cAXKzg8dx35JpUMGeLd+diLAzITEgN8fTkiu4ktXu8ToEGTevS8vcc1q6skSYQ3CibxSCa+kkFfID4uOu6a1UEQLkelBWoAd911F3fddZfXtu7du7Ns2bLKvK3wF5N25hzbf1jHrp83UZCZB8COH9bxwYm5VVyzK6O63CgFBahFNlSbDbXIhmKzYW7WBNnfr6qrJwgXtenrVfz60QLsBcXBl5ZTliLVRaKSrx83OVBmUICd0Gf7cyytkPmbUjmXaePF956tsKWiLldYh3okHskkoESLWuIZsTqBUL1UaqAmCJUlIyGVE5sPsn/lDmK2Hi61v0aj2lVQq/Ir/GMbGa++h5pfUGqfZLVSe9E3GGvWuMCZglD1ko7G8ctbc1GLc6WVdMaVe76xiiYmGOAHlhbhGIL8iOo1nM5PVXxS28sV1K4xcIDAEoFaSlJq2ScIQhUQgZpwXSjKLeTkjqOc2HyQE5sPkh574RQOwXXC6H53P3pNrvxZYxVFVRSyP5p9wSANQLXZcJ6JE4GaUC2pqsqiV7/WgzSjCv4KhNUMxdS/FVu/09JzSMCDQQYMsoTvoCjMXUZiCK9fhTUHvw7NMUgKQaonUMtIz6zCGglCaSJQE6olW34Rp3cf5+T2I5zccZTEw2cu+G0dQJIlWt/Wie4T+tGqT0dkQ8UMRnbkFBA3fyNn5q7DVWgn6oP7qT3gpkued6Vs23bjSioewGyQMYSGIPlYkXx8kH2sWLvehPXmqAq/ryBUhD2LN3NmjzZRQFKhg0Oi0a0d6Tj7Me4Z/LB+3G2+Es3MEpZmIfjeMaHKgzQAc5NGWE0uQlyej8K8gkIcdgdmi7kKayYIHiJQE6oVl93J98/+m0Ord6G4lTKPM1nNNOnaiha929NhcFdC6oRf0X0Slm7jwD++AUkivFtL7V/3VgS1rk9+bAqn/ruauJ/+wF1o18/Zcd/H3PTpo9Qf3euy76MqCoUJ6fjWr1Hm+Ju8BUv11wF3jybk6Uev6FkEoaoU5hSw+OX/6uW6bmgysDNdvniSZQt/JSlOWzvTX4KJATJIEPjQxGoRpAEYggLx9ZPxzZEwI+NA+5uTcjaVeo0iq7h2gqARgZpQrWz870oOrNxRarskS9Rt04gWvdrRonc7GnZqjsl65d94VUXh2MzFHP/nYn3b2VW7ObtqNwBGfx9c+UUXPtetsOeJz3HlFdH43tsvea+sA6fZ+9QX5J5IJHJkd7p88WSpY5wJSdi27y5+SAn/u4Zf8TMJQmVzxidi274Hn55dMEbWAbQuz+/HvoHN5gDArEKXfp3p+uVTyCYjy3/8VT//zgCZQIOEtVk41ltHVskzlMW/biiW7BwssgGHqgVqyUkiUBOqDxGoCdVG9rkMfpu1RC/XblGP5r3a0bR7G5p0bolP0NXNfHQV2tn7ty9IWr6z7GP+J0gLbFmPRlP6c+bb38k9ngCqyv5p/4czr4gWT144qFIcLo5/vIQTn/6CWtwqmL7zwjmk8n9eBqrWpWvt0QVT8YfglVILcnHHR6PmZaPmZaHmZaPkZSFZfDD3HY1co3zXFQTFZiP14Wdxp2eQ9YmJoAcmETBpDBuf+oJjx+O0wWfAze2b0fO/f0M2GTkdHcvhfUcB7UOmr4+ktaY989Q1n9l5KYGt6mE9nINFMpCnOgE4lyByqQnVhwjUhGpj2TvzcBRpXY21W9bnuRXvYjAaLnHW5Sk6l8n2KR+RffCMvi2ib3vaTBtL1p+nSN9xnPTtx7ClZIMkUev2KJo+PIgaPVsjSRL1RnZn64T3yfrzFABH3v4RZ3YBTR4aiCUsANmkvZVyjsaz56nPyTnsycVk8LHQ8Z0ppeqk2GzkL1utlwPGjijXsymZKRT+6zmwF174AKcD68TnynVtQSjasAV3eoZWcDjJnv01h2f9yqYMFxQPB40IDWTYP+/Hvnc/qs3Gku9W6uffbJU8rWmde1bBE1xcUKfmGBYewafEzM/4mPgqrJEgeBOBmlBuqqqye/NedmzaTVTX9vQeUP4/wid3HOXP5dv08qgZ91ZYkJZzLIGtd7+HLTlL39bkwTtoN+MeZKOBkA6NaXzv7aiqSlFiOgZfC5Yw72z55hB/ei18iR1TPiJtq9ZSEP3v5UT/ezkApmA/LOGBFMSlojo9mdbDurXkpn89gn/DmqXqVbh6HWqell/KGFkHa/fO5Xo+57ZVZQdpgBwplsMRNK7Es+T9vAxzy2b4Dex3WefkL1+jv1ZVSMr153CBm9zikQcSMFCNJ/XuB7V7qCqrUzzvgdt8tda0gAcmVdhzVCT/qFYYpEX4lwzUTiZUYY0EwZsI1IQr5nQ4WfvLOub/ZyExR08CMP8/C1hz6BeCQq58OSC3y82SGd/o5aih3WnarXWF1FV1K+yeOksP0iSDTId37qXxlP6ljlVycimc/QWupHNIFjOS1YpktSBZLRhr1cT/ruH0mPd3dj78Kclr93md68wuwJntSa8hW020+cc4mj40EOkCS+Koqkregl/0sv9dwy943CWfz+XEuW+TXja06Ypcow6SfzBSQAhyRF0MdRpd8XWFG0/B2g1kvv1P1AItqDfWqomlY9uLnuM6l4J9d/H6nJJEZrMenNsQQ4LZMwO7k7WQMINTL++zqeQUzwMKlaGDRcLSMBDrrYMq9oEqiLlpQ6wmF4GKJ1ATXZ9CdSICNeGy2Yrs/PTVIhZ8vYi05HSvfRG1a2Ayl+/Xafv83zl7XOtqMPtYGP7yPVdd1/Nif9iojS1D64Ls/t1zRPQu/eGkqioZr72PbWvZ49fy5v9MwITRdPnkQaLn/M7Zlbuwp+diz8qDEqlDQqKacPOnjxLQrG6Z13IcOIIzWutGlSwW/IeXbyk197E9UJCrXScoHOs9zyPJFdMSKdwYFJud7I/+Tf6SlV7b835acslArWDFGn0MZXpwM2I3nCRbhoLi7xRGVDr7FoIkYahZA2Pd2mzaFQ1oK4TcFmHEv4mFwAfvRjJUz99L2d8fXz+JoFxP/dJT0i9yhiBcWyJQEy7b9MffYNPqLV7brD5Who0bxOTHJ+Dr53vF18zPzOXXjxbo5f5PjCS4dthV1xXAmV/E0fcX6uUWTw2/YJAGkP/T0osGaQCq3UHu//1A/pJV1HtwEq1+fxvJZEJ1Kziy8rGn54IsEdCsziUHTJdMyeE7uD9yYMAVPJmHc/c6/bXx5r4iSBO8OM/EkT7tTZynzpTaV7h+M+70DE7/vIPEZTtxF9hwFdpxF9lxFzmQjDJSUSGyEoYsqRQm5gCQaPB8KYnqEESdcZ3x6TkEY4NWpJ2KYU+fh/T9w7v64xNiwdzjjsp/2KvgXzOA0BxPi3h2bj6KolyzBeIF4WJEoCZclpijp7yCtLCIUMbeP4pRk0aUq7vzvFUf/kRh8R/I8AY16fvg0Kuu63nR/16OPU37cLHWDqXpI4MveJzj5GmyPv2PXva/azi+t9+qrbdps6PkF5C38Becx2MAULJzyJo5i7yfllDj47cwNayPJTwQS/jl/Rzc6RkUrvtDLweMKeckgux03NH79bLp5tvKdR3hxuSMTyT53idRCzwBiO/tt+I6l4Lj8DFwu4l9/xsOzd17iSt5PibyJZXs4u8CkgR9RjXGGGTEeXgNSsZpVv60GXdx63K7CCORgQaM7boj+QdV9ONVqIDmdfGPPokRCRcqbkUhMy08K00/AAAgAElEQVSL8JoV86VREK6GCNSEy/LL/BX66179u/PenDeuOnP3kfX72PHjer088tXJGC2mq7rmeYVJGcR87unqafOPsRh9LaWOU2x2Ml56GxzaGBtT86aEPDsVyez9bH7D7qBw7Qay//0V7nMpALgSksiY/i41v5t9RSkH8pesBLc22NrSsS3m5uUb7O/auwGK8z4ZmrZHDi09YUH4a1IVhcy3PtKDNMliJuS5x/G7cwiFazeQ8fLbKCqcWLAHPb/GZUiL8IccrVuzbVQooeFWfZ/rbDSrfvesuzuwifZ+M3Wt/su5BXVti2X5SSyyAZfqAiA5KUUEakK1IAI14ZJsRXZ+XbRWL49/8K6rDtIOrt7Fd09+glo8/qVV3yja9Ku45ZmOvvsTik0LvoLbN6T+XRdeTSD70y9xno4FtLFi4e+8XCpIA5BkGb+B/fDt25u8hUvJnv01OJw4jkXjOHQUS/vLW1haVVWvWXT+Y8uX/FNVFJx7PEGusfPlzeAT/hryl6zEvu+gVjDIRPznn1jatiInJZP98YWYrKEY02zY7FqQZgzwoddP/8ASFoDBx4KkuDl75xRcRXbcikzQS89R4BPA1mc/0+/Ru38dzJ1H4E4+iTvhCMfOZBF7TgvirEa4tYEFKSISuVHFTAyqTAFd2uPLEiwYKEAL1M4lJNO2U/Wvu3DjE4GacEkbf/2DvBwtjUSd+rW5uVenq7revmXbmPfMLH2JqNB6EYx5+4Grrud5WQdOE/+zp5u23Wv3XHBGZdGWHeSXGCsW/OxUTA0vvrSNZDETeM9YnKfjKCjOgZa34JfLDtScJ0/jPqstKC/5++Hb9/KXoyrJffoIaqbWsoePP8Y2Xct1HeHG40pNI/vTOXo5cNI4bBG1WDnjG7bPX4fL4QQMNHL5URcVCYlWz40itFNT/Zz8paswOIswGMHUuD61xt3O0je/09+zjZsH0qBbFIaIRhgiGuGu3ZxV89/Uz+/bwIKPScLU7Y5ql+D2QsxNGhJgcmFVDFA8BC/+pMilJlQPIlATLmnpPE+354gJQ65qgO2uhRv58e//0VvSajSuzWPzXqmwCQSqqnJoxjy9XPuOm6jRs/S3Ynd6Jhmvf6iXffr0wH/U5Y+PCxg7Ug/UCn/fhPvpRzGEh17yvKJN2z337NEFyVS+rl7X7t/116aoW5BMYgHpv7q8mCROffMb1piD+BZ3eRbVqsveNJntvZ/CaXd6HX/GCDZU2uCmft8WXvsKSrT6+g2/g6LcAnb84Jm40qtvTVz79uD+cx+WofdiqNmAI0me6w9sagGTGVNUn8p41Aon+/rg5wt++SVyqZ04XYU1EgQPEagJFxV3KoF927UB6waDgaFjy5cLye10sf2HdSx69f/0bbWaRzL1+1cIjAiukLoCnFuzl/TtxwCQjAbaTr+71DGqqpLxxgcoWdkAGMLDCJ3+/BV98ze3bIalQ1vsBw6Dy0X+khUEPTT5kucV/eFJ6utzS4/Lvl9JamE+rsOe9VBFt6eQfSSOLXe9jT0rj1wJ8A8kWZFJTXfA4bVex/oE+FCUpy2Vds4IZoObzj+vIOLlpwFwno7Vfq8BDDJ+g/qzfu5aHEXamp41a/vQ3L8QJUabSVr42VEsdz5M975dOR0dS696ZtrWMGLs0AvJ1//a/AAqgF+4lYB8T7B5NjaxCmsjCB4iUBMuatkPngH5Pft1o0at8Ms6Ly89h9i90cT+GUPcvhjiD57CWbx4M0Dd1g159PuX8A8t/4zR/+W2OTj0uqc1rdHkfgQ0Lb3GZf5PS7Bt262Xw15/EUPwlc9K8x87Qv9Ay1+0gsB7775oC5krLR3H0eI1Pw0GfHqUcyWC/X+AS/tAkes2EQlt/+JyjiWwZcw7JOfkEW1RcUqAs/TEmXrtG3PH30aT/NFSdh6KIa248SjObeK777Zyb8f2sGUrhRu36uf49OpG9KF41s/2DBHo1SMENbFEug+nHfuCz3i44y2MmVgTP5xIktbteT0JaBRG0BnPCh+pKZlVWBtB8BCBmlAmp8PJygWetShHTLx012DC4TOs//wXDvy6E7VEEtiS6ndowiPf/QPfoIr9tn3i02UUnNHGbZkCfWn13KhSx2ipOL7UywGTxmLtWr5JDL639SY7PAx3eoaWcmPDFvwG9PU6xp2ZhDvhKIaajbFtO6Zvt3RqX+7caa4SudNMojXtLy0vJoktY97GkZlHoqk4SCtBNsg07tKKPg8MplWfDkR/uoycfadogYRZkkiStTFnSXYDHz3xBT18C2hndSJLWp7bXVI46+57Xx+qEBxipq1/OtiKb2A0g0v7Aube/weBMoCEXKcRcmRTrifBHZsQvM6zIkFmdl4V1kYQPESgJpRp82/byEzXll6KqF2D7n27XPA4VVWJ2XaYdV8sI3rzoTKvF1I3nDb9OjHkhfFYA648Oe7F5J08S/SsZXq5zcvjS63XqdjsZLz8jlcqjuCp95X7npLJhP+ooeR8+S2gJc0tGaipjiIcu38BlwN34hEKlntSF/jc0r1c93QnnUI5W9yaYTRj7Ni73PUXrm/5p8+x+a63tUTLQE0FCgwqsioR4oZQRaJh24b0+/pFsg6cZuMdr5BzVBsgLyEx4IEhJKXEs3bVAQBsqsz6ggAO2nzo0zKYI3Iox3/2jKkMDDYzYVggRttZ7Rr+wfg8+QGONfNwlVjGDLhuJhGU5Ne5A6FsRUKbT2B3usjPK8A/wK+qqyZchWnTptGlSxdGjSr9xf16IQI1oUwlc6cNHTcIo7H0r0tyTCLzn5tNwsHSA28bRDWjcecWNOzUnAZRTQmqeenB9uWhqir7X/waxaFNqw/p1JRGk0onf83+bI6eoV2yWAh/+6ULpuK4Ev6jhpLz9TxwubAfOIzjeAzmls0AcJ3ao7c2qA4X9mNx+nm+vcsXqLkOeLqljO26I/mID5EbkTO/CMXhwhJautU151gCiUu3ETtvgx6kyZLCLaHZDDY7cd5yO4cWHEZVFPIPxrKu3zTyTyd7XSOoXUNaPXsnbSUVv+iprDtdRG7xWpfpbiOLjuQD+frxjZoFMn5sLXxPer5smIffjxwcjmXsUxgat8X+yxxwOsAv8Lr8AmFp344goxuLy4ANLc9hcmIyTVuVL8+hIFQUEagJF3QuMZkdG7VxXJIkMfzu0ln97QU25tz3PpmJafo2SZboOKQ7tz0yjMi212bsVMKiraRtPard3yAT9eEDpdJxFG3ZSf5PS/Ry8DNTMTVqcNX3NoSH4tvvFgrXaDnN8hYsJezVF1Bt+bhiPasGOE6lgas4OW1EAEraAdSIECSzzxXdz3Vkl/7a2LF8qT2E6u3c2n3sevQz3IV2LGGBBDSvS0DzuphD/Dm3eq++du15sqTSLDybAB83IS88RcCYEUjt1nLgpW8AvII0g4+Fls/eSdOHB2EoTi7da+lsbopLYutvB1n3nxU4iuxe1+91eyQDhtRGOuj5fTa06ISxfU9A+/tg6twPQ8NWuA7vwNDyJiTLlf1eVweG4FBCrAqWAgM2VQvUzsUmiECtGlJVlZkzZ/L7779jMBgYN24ct9xyC6+++irZ2dn4+vry8ssv0759ewA2btzI/PnzycjI4NFHH2XcuHEUFBTwxhtvEBMTg9vt5qGHHmLo0KEsXryYJUuWkJ2dTd++fZk8eTKvvvoqycnJSJLEc889R48ePfjss89ISUkhLi6OpKQkxowZw9SpU7Hb7bz++uvs3bsXk8nEY489xuDBgzl48CDvvvsuNpuNkJAQXn/9derVq3dZzysCNeGClv+wSh+X0uWWm6lTr3apY1Z88IMepBnNJrqOvZVbHxpCeINa16yejqx8Dr32vV5u8uBAgtt4B2DujEwy3vhAL/vc0gP/0RW3VFXAuJF6oFa4Zj3BTz2MO2EPKNofeykoAleGZ2CypUVN3GdP4M5IxBw1EEPY5b1ZlbQk1HSt2wmTBUOTdhX2DEL1kLE7mp0Pf6Ina7Zn5GLfnqvPZP5fRlmhcWg2gcEGwt+dgU+vbgA0uX8AedFJnP7mN/3YukO70G7GPfhGek8Ikv398WvTggFtWtBlfD9WvD+fvUu2YPH3YcxLd9I66DRKfLxnKSqTBcvIh0t1bco16mDue/12LwGEhRiwFhjIKS7HH4+BQbdWZZWEC1i9ejX79u1j+fLlOJ1OJkyYwPz583nuuecYMGAA+/fv529/+xtr1mhpZhwOBwsXLiQmJobJkyczbtw4Pv/8c9q0acP7779Pfn4+48ePp0OHDgCkpKSwatUqjEYjzzzzDKNHj6Zfv36kpqYyYcIEli7VJtecOHGCefPmkZeXR//+/Zk4cSILFiygsLCQX3/9lYyMDO6991769+/PK6+8whdffEGdOnXYvHkz06dP55tvvrms5xWBmlCK2+1m2Y+r9PLIC0wiOLXrGFu+9eRaGvveQ3Qedcs1qV9Jh9/5EXuG1v3jUyeU1n+/y2u/qqpkvDkTJVNLxSGHhRI6/bkKHT9jbtcaU8tmOI/HoNod5C9cgrlurr7f2LQ7tj2LPMe3LA5k7QU4dizG2LwrxqZdkKSL56dzHfXMVDU073jR3GmqquI6vQ8lLVZfZoriwBvZgLHxTRgiGl7RcwoVw5Wcihzgj+znPU4z93gi2yZ9qAdpZZFlCLLYCPW1EWS1Y6oRRo1/vYW5ZXOv49q/OQlTiB950WdpNLkfNfu0Q8lOp2jhv5AkA3JILTCZwWhGDg7H0KITwbVCuefjJxjx8iRMVjPymW24juShxnm67c0DxiOHRlTcD6QaCYoMwC+pUE96G3s8tkrrI1zY7t27GTRoEGazGbPZzPz58+nbty8DBmjLlXXs2JGgoCBOn9aG5PTr1w9JkmjWrBlZWdq4623btmGz2Vi0SPvbXFhYSEyMtp5z69at9aE+27Zt4/Tp03z66acAuFwuEhK0Vu2uXbtiNpsJCwsjODiYvLw8du/ezdixY5FlmRo1arBy5Uqio6NJSEhg6tSp+jPk53uGFlyKCNSEUnZv3kfqOa2lLDg0iFsG9PTa7yiy8+PfPYuYt74tipvvvPZjUjJ2RxM717OMUoe3p2D0s3odU7BkJbYtO/Vy2OsvYgipuLxtoHX9BIwbSWZxAt3cb38i+N5uGGsHIYdG4kou0HO2yWEh+A6fhPPwOnAUASqu6B0oGUmYOw5EspY95qxkoGZsXXZqD1VVcR3bjOvMvjKPcWQmYe4yEkP4xVdiECpW3s/LyPrwM+SAAGrN+w/GmjUAbW3arRPew5mttVpZwgPps+w1JIOB3Ogkck8kkLdlH4aj+wk25mOQtUjC3K4V4e9Ox1ir9DqvsslImxfH6mV3djJF/5kOmRkXrJtcvwU+D0xHsvoSEB6EqqoUJRxDOXpUD/LlOo0w9ay41ujqJrBlLQJ2eH4+ibFnq7A2QlmMRqPXl+2EhAS9B+g8VVVxF6+pbDBo4y9LnqMoCh9++CFt2miryqSnpxMUFMTy5cuxWq1ex3377bcEB2ufG6mpqYSFhfH7779jsXjS4EiShKqqpeoWFxeHoihERkbyyy+/AFpjSHp6+mU/b/lTzAs3rJULPSk5Bo4egMnsnRvs138uID1WG/diDfBhzNsPXrMZXqqikLb1KHue/JwtY9/Vt9ca0InaA2/2OtaZkETWPz/XywF3j8Knm/cxFcVvwG0YamsflqrNQc73O3FnFWJs2QPb5hKrEfTujrF2U6y9JyKH1tW3KxkJ2LbMwxV/CHdGAkpBFqrb07Ki5uegxBXnYJNkjC3LTiniitlx0SBNu6CCY89ylJyUcjytUB72w8fJ+nAWuBWU7BwKlmvvM0dWPlvHv0fRWa173Ohnpcf8F/FvXBu/BhHUvj2KGjkniTi5mTBznhakGQwETb2fmnM+uWCQ9r/cySexLfy4zCANQIk/QdHXb6LatWS47owklIN/gr14zJrVF+vE55EMhjKvcb3za9+MYMnzfMlny/55CVWnc+fOrF27FqfTSVFREU8//TSSJLF2rZbcef/+/aSnp9OsWbMyr9GtWzd++OEHQAu+hg8fzrlz5y543Pz58wE4efIkw4YNo6io6KJ1W7VKGzqUkZHBPffcQ926dcnJyWHPnj0ALFq0iOeff/6yn1e0qAle8nPz2fjrH3p56BjvpJWx+2LY9JWnW3TEK5MqbPmni7GlZnPmu3XELfiDwvg0r30GHwsd357iFSyqLjcZ099FtWkJn4yN6hP0+IOVVj/JYqbGx2+Rct/jqEUOlHw7OfP34jPkMQq9ViPQZntKVn/MXUdrQdXJ4gkC9kKch9Z5X9hkQQ6sgZpbpHdhyg1aIPlfOEGv89RuXDGeFkS5ZhOMDTvC+Z+N4sZ58DdUWz64ndh3LcXSYyyyX0gF/SSE81RV5eh7C0jZcBBrRCDSkQNYbCYsRhmXInPuq/U4VseTtf80rnztD79kMtDt/54hpL1nIo794BHyl3gST5saNyDsjX/os4svVQfX6T04965FPXXSsyM8HMnHB2QDclAd3Ae131ElTgvWfO6fjnPN91DcTQRgHfc35PDSY1VvJJYWLYiQluvljJyCKqyNUJbbb7+dw4cPM2rUKBRFYfLkyXTt2pUZM2bw2WefYTKZ+OyzzzBfZFb/E088wYwZMxg6dChut5sXXniB+vXr68HUea+88gqvvvoqw4YNA+CDDz7A37/sHKATJkzgrbfeYvjw4QBMnz6dgIAAPvnkE95++23sdjv+/v68//77l/28kvq/7YXVVGJiIv369WPdunVERkZWdXVuWEvnreCdF7QuvGatmzLv96/0fU6bg4+G/oOUk0kANO/djke/e6nSW9Nyjsaz+S4tqef/Cmhel47v3EuNXt6Louf8dy45X3yjFQwGan07q9QYnormzjxL/g+zyZm7U5/haWxUH9eZ4txVFgt11y1Gtnp3z7rT43H8uRochaWuqR9z5AhkaN/ujbeOxDJwUqmfu+vMfpxHN+pluUZDzDcNRTJ4fx9T8jKwb18ATq2lRPIJxNJj3EW7Xa+lG+W9fnbNXnZM+ejyT5Akunz+BJEjPalbVFUl9dHnsO/Vcp1Ze3ejxnuvAW7sK75BLczDcufDyP6lu/NVVcV5aB2uuIMof/4JxZMBpIi6SK1bIinFq1uE1EFy++BY4VneTYqIRE31LKFk7NYf652PXcnjX5fc5+KY3+NZZtnTzw9TY0P0r/j5V2zeR0G4EqJFTfBSsttzyFjv1rTfZi3RgzSLn5Vx75ae+VXRco4llArSTEG+1LuzJ/XH3UJIx8al6mA/cpycOd/p5aBH7630IE1VVVwntmJuGE7g6ChyF+wFFT1IA7B2valUkAZgCK+PtfdEXPEHUfMzUW35xf8KQFVQFcWrZUPJT8S2bg6STyCSxQ/Jqn27c8cd0I+RwyIvGKQByAFhmG8egWPnYlBcqEW52HcvwdJtDJKp9NJDwpVTVZXoT3+57OOttUNp+/J4ryANwLZzrx6kYTAQ8uxjSBYztsVf4Nqlzei0Az6T/l7qmkryKdwJh7WJAOdnbBpN+Nzzd1TJrSVjRkXJOouhfjvMQ+/Tg7WSQRqhoZiHVl5rdHUih9QgxEfFYvfkUjuXcE6k6BCqlAjUBF3CmUQO7NJWFjAYDQwcdbu+z15g8+ryHDptAqGRNSq1PrnRnuVxAIwBPkS9fz91BnfGYL1wk7Zis5Hx6nvg1lq0zO3bEDh5XKXWE8B1Zh9KZnEQ2yaS4Kc7kf3xHK9jLrYagWT1w9Tce7+qqqhFeTh2rkJRimdu+vgg+fqCvRDVXsiFmsPl4NqYbx5+wSDtPENoHcydhuDYuwxUFTU3HcfB37DcdOMOFL+W0rcdI3Ov1tUoodIgJBe7y4CrVj1sRSqkpeJnchLSsTENPnkFnzqhSJKEUpiDkhaHkpmIajCTPetn/Zr+IwZhqlcX18lDuHZ6Flp3H9mJkpKAXNM7zYsr9k/UnBzUBE/eNfPgyfpxxpY9cR3fol0j/hDGJjdjHjwZxyrPlxwsFkx9hiBfZIbxjUSy+hIaJGPJKZFL7XSsCNSEKiUmEwi6VQs96TZ69O1KaLhn3NLB1bv0RJg1m9alx8T+ZV5HdSskrdjF2TV7ceaW3Z13MXknz3otj2P096HXj9OoN6pnmUEaQPas/+KK0z6YJF8fwt+YVumDn92ZSfoHHoCxYUcCJ44n8IF7PAdJkp7jSsnNxL5mPs6dv6EWlT0GRpIkZN9AKJ4xCiA3bKGtr1jWOUERmLuMRLrIMecZajbC1N4TjCvppWdOCRfmzs4h9bEXSBpxD/ZDR0vtP1FiObMwvyLC/Ww07BRBz9/+ya1LXqZFjSwig/PxP3cCi8WB88hGbBu/xb7h/3AeXo/7bDRFa37DeVxLFyBZzAQ+eA+qw4Z90Wzvm6kqjg2LvDYpOam4U+NQjh/XtxmatsfUfZBeNja+CUOdFnrZdWoPijsD0y1DtTGNRiNy69YYG3gPK7jRhdb2wwfP34zYYycvcrQgVD7RoiYA2hTklSUCtaHjBnnt37Nks/668+hbkOULx/iqorD7idkkLtEGJ0sGmZCoJkTc0o6IPm0J7dQU2XTxX7v80+fYPPot7KlagGL0s9LzhxcJvenig6cdJ8+Qv8DT3RTy3OMYI+tc9JyrpdoLcexbpacvkIJrYWyppTMJevReUFXyl64kYOydGMJDUd0uiubM0LuW7Mu+wtimC8ab+mJo1h5J9g4qVUXBXSIth6XPGOQGzb27R+3F/zUYMTaKuqLuS2Nka0DCHX8QQ7221936jFVBdblJf+kt7Lu0mbWZb86k1g9z9C8EWQfPkLrh4PmjqRVQiGSxEPbOK8hWC3K9uhgb1scVG49qt5M3fzaWJt4TclRFpWC9J8jyG9oXY0QN7Mu+Qs0snqlrtoJDmyzj2r8Fpf9Y5HDt990V+yfqqVOeGZs+fljGPOG1YockSZja90e15eutwWpRHiogd+kCBgOS2YJco2EF/vSqP/8mEfjtStdzqZ05Vnp5PEG4lio1UJs0aRKZmZl64rg33niD+Ph4Pv/8c1wuF1OmTGHixImVWQXhMu3bvp/kJO0DIDAkkJ79uun7ss9lELNVW+NPkiRuGln20kVHP/xZD9JAa13L3BND5p4Yjv9zMZbwQFo+O4pGk24rFbCpikL8z1s49Op3OLK1ljiDxUS3z6cSevPFgzRVVcn+1xdQ3EVo7dIJv+EDr+AncOVUVcGxfzXYz2dst2LuNFgPtiRJIvix+wl+7H79HNeu373H/7gcuA5swXVgC1JgKKZud2DqMxLJqKVEUZJOoeYVj0/zDUBu0BxJNiD5BoHvhWd+XiljZCuMka0q5Fp/Bdmz5uhBGoDzdByFv2/C7w5tfdnozzytaSE+dqxGN0GPPIC5SUN9u0/vbuTFauMXHUcSPYGabEQOq4ttfxLuNC0hpmQxYmkCju3LcG7zDD+wjHwI15+bccfs19KtbFiCdczjqPYCXAe2oqaklDj2YeRg7xUJACSDCXO30bgTjuA8vhWctuJ7asG+XKPBZbXO3kj82jcjcH60Xk6KLZ2yQRCupUoL1FRVJTY2lg0bNuiBWkpKCs888wyLFy/GbDYzfvx4unbtStOmTSurGsJlWrHAM4ngjpH9MFs8f5z3/rJV7xJr2qNNmek44n76gxMfL9XLPnXDKTqb4cmID9jTcznw0jec/r+1tJ0+gVq3RyFJEhm7ozk4/Tuy9nu+vcqSSpOAFGwvTSPxXX/MLZoSPPV+LB1Kd8XYtu3CtqN4WrUsE/zs1CtqHVJVFSXuOK7DO8HtRG7YCkPjNsgBZaetcMXsREn3TBYwd7wD2Sew7HvYCnH89qNelgJCPEEYoOZm4lj7A65D27SFrus08k5y2+rmUi1uwrVVsHoded8vLLU9579z8e3fh/zYVJJWeNZjrR1QgBwajP+Y4V7H+9zSnby5CwBwRKeiqirmdrdhiGyD+2wseS94Ekpb29ZAUh041vykv5cMzaMwdroVObQmRTHaGpyufRtR+o/BFX8QJfqEfr6xY29MF1kkXZJkjPXbYajVDGf0dtxxBznfnGSodekUIDca3xZNCS7R9ZlyLvMiRwtC5au0QO380g33338/2dnZjB07Fj8/P7p166Zn+L3jjjtYvXo1TzzxRGVVQ7gMhQWFbFjpyZ02ZKynJUpVVfYs9uwrawWCtK1H2fe8Z/B8zds60P2753HmFJC25Sipmw+R/Nuf2FK07sy8mLNsnzyTGr3aYAkLIPGXHV7XM8luGoXmEGjVUgioefnY9+wn9fG/E/HlP7G09oytUV0usv71hV72HzEIc9PGl/XsSmoizj//wLV/s6dLCWDbr4CWpsDQuA3GZh20xaaLW7rcaXFe+cqMTbtgiLj4IvSOTUtRC7Qxd1JQOL4vfIaSdhbX3o249v+Bmq+tMKici6No1ouY+4/FfbTEIuwXWY1AqDiu1DRSp76AOyMT37698B8xGHOHNjijT5H5pifdhrV7Z+wHj6IWFOA6E0/hb5uIXntSD6YCrXZ8zS4CJ49D9vFepNzSrg2SnxW1wIaSZ0PJN2Go3x4K88j6xzSUAu33XjJJWP0LUPaVSGBsMGIafA+SJGFo1Bq5UWuUM0dBcePYsBjXqT/BWZws2T8Iy4iHLuu5JbMVc9u+KPXb4jrzJ5LFD0Pdllfxk7w+GWtFEi4bKJ70SWZe+cbZCkJFqbRALTc3l+7duzN9+nScTieTJ09m0KBB1KjhmSkYERHBwYMHL3hubm6u17bk5OTKqupf3vqVf1BUqCXcbNS8Ia3ae4KgpCOxJEdrXXVmHwvtB3YpdX7eybPseOBjVKf2ly2wVT26/OdJZKMBS1ggkSO6ETmiG26bg1P/XcPxT5biytPul7bliNe1JFRqBhRQO6CQgDv64Eo6hzM2HrW4fqrNRtrTL1Hr6+JT0z0AACAASURBVM/08Wf5S1Z6cpX5+Wpjwy5ByUjG9tMnnmz/ZVBTE3GlJuLasQZ8AzB1uhVD+244oz3BqxxWD2Pzbhe5Cig5GTg3e7rEzAMnIJksGOo0wlCnEebBk3DuWIPj17ngdIDbhWPNfM8FjCYMzTpc8rmuN9XxvW4/cESfkFKwfA0Fy9dgbFgftagItXjMl7FBPcLfnU7u9wvI/e/3AKT++zvi97v169QOKEAOCcZ/9LBS91ALMjA3CcN+UBsb5kxWQXGT/drfKTpeIslsQwuS0btlWGrUEOehNUjGgRhqNMTcbwy2/74O4DUbFLQktZJv2ck5L0QOrIG5w4ArOudGIofXppYRJLfWrmhzuyksKMTXT+RSq06WL19+0WFUx44d4+WXX6agoICbb76Z119/Xe/du95UWq2joqKIiorSy3fddRfvvvuu16KkqqpesHvq22+/ZdasWZVVNeF/rFzwq/566NiBXv9PdpdoTWt3R2es/t4tA/b0XLZN/EBfo9BaM5ge3/8dU0DpP2oGq5nmTwyjwfg+HJu5iDNz16H+P3vnHV5Fmf3xz7Tb0itJCIQQQggdQhelKFWQZsVeWF2767prW1dd66rrqvvbVbHvWlAsYAMUbEjvJZQQIBBKCqQn995pvz8m3JtLKko19/M8eZ7cmfd9551J5s6Z857zPbUyGgBxnaJIqNyOXTZwnj2Y2Cf/AtQKd27bQeEt92CUVWAcLqXw9ntp88ZLCJLkF7YFwq+djhQT3eT5mpqK+79/xziwO3CHMwS5xxCEsCj0nZsw9mwHXfPvr65AXfw56uLPITwcITERoV0qtj5jmy2o7l3wvmWAYdVLlHsHFrAXJBnbWecjd+6D+8MXrWPXvXadeiDYA6/9b4HT8V53DuqHvW8vPGv8unTabv8StxDiIu65RxFDQwi7bBoV73+KWVXFga0lmKolGhxq8xJmVxv0pgGoWxdj75LgM9TcqzZT+a+nqfghx9fGnplK+PUTMfZsRd+VDdUVCHFxCImJoLrxrvgMOX0gUrseiO3SMfbmBBxD6t4PuXPv43ptWgOCK4yoMClQS23PAdKCEh2nDQUFBTz//PNNhlHdc889PPbYY/Tu3Zv777+fDz/8kOnTp5/CWf9yTpihtmrVKlRVZfBgSxvKNE3atm1LUZG//E9RURHx8fH1+l599dVMmTIlYNvBgweDiQcngIL9haxeYsW4iKLIuGn+N2ld01k7158Y0G9q/WXP9Q+8RVVeIWCVchr8zh9xtW26pJQ9NpzeT11Lx+tGs+2Fz1DLqul05TDcDz8EsmW4hV3pLyYtCAK2LunEPfcYBTf/Ebwq2p59FN31ALYunTHKLI+MlNiG8MumNXvO3kUf+400UULq2h+lzzkBS5sApupBz9uOvmODtTRZUqd0VXk5Znk5gmEHueksS/3AbrTV3/k+286/OiD7ri5iXBLO3z+O+uMcvAs+8BmKcreBzZ7XmcjpeK+LYaHEv/Ic3s1bqZzzNdXzF/k8ugAxj96H0sEqZi9FhBN22RTKX/sfXs0f15RwxJt2YX1vml68B6N4D0paHEgC6Cbq1hxKc3J8mYZyQjRx/3kBMTQEmGSJHntq0KtLUdd+5Utg0XKWW0vw8TFQ11BzubC3gkoCJ4qYeBf2Q34ttfwdu4KG2mnEkiVLmgyj2rdvH263m969rReVqVOn8uKLLwYNtaOpqKjgxRdf5IMPPkBVVT799FOeeeYZ7rnnHg4fPozT6WTBggX87W9/q9c3PDyc8PDGg7KDHD/WLvN7DfoM7kVsG7+Rtf2nDVQUW3FT4fFRdD6rR0Bfz6Fy9n3pD3bv/++bierVstgwgPDOben/f7cAUPrvN3DXxtXYunXB3qdHvfb23t2JfewBiv/8iKUdtXEL3o1bfPsjb5uBYG86Q03Pz0X9zi8iaht/Fbaz6z9MAQTFjtypB3KnHiijLsbz9evoW9ZYpZxq45CMnA14PnwJ+yV3NGp8eb96xx8EntEXuVPPJucoiBK24VORMrJQF81GCI1AzhrZZJ8zldP1XhcEAXv3TOzdM4m66/dUf/sDnjXrcZ4zBNewIQFtw6dfSMX7n5KkVmICITaVCIeX8CuvQd+2CjVvK8qwKYiRsZZ3uFZzT3Qo2DJS8GbvBqDWJkAMsRM381+1RlrtfETR8vg6Q5CGTse79iufpAYALgXCwqCiAgQB+ZzxDZaVCtIywjvE4dqyn7Laz7lrNzFsYuPaka0NLT8bfe/m5hv+AqR23WplgxqnsLCwyTCqo/fHxcVRUCcL+kzjhBlqI0aMYP369UyePBnDMJg+fTpZWVncddddXHXVVaiqyoUXXkjPnk0/tIKcWNav3Oj7vc/AwBiolZ/4tdOyJp+FKAUaIvmfLcXUrKdLdL90ksb9smB3o7qGytn++K3wKy9uNGPTNfJsou6+hZJnA5fLbD274ho1vMnjmJqK58MXfRIeYmpXlLPOb3Z+pmmibVkMghupa1dMrxehXEXPXg2Atu4nsDuxT7mxfv3N7evQt1seSwQR2/grmz3eEaTEFKTL725x+yAnBtHlJPSCsYQ2IvcihocRNn0q5sz/khZjPdrFyAjsaRF43n8eAH3nZpy3P4tekItZVljbUcI57GyfoQaAJBD30tMoiW0anY/gCME2cBp63nr0gzswSg4gYCB264ZZUIAQGYmtb9Co+DW4OiQQKmzyeTh3r89pukOQk4phGAHftUeHUTW3/0zjhEbW3Xnnndx5550B2yZOnOirQh/k1LN+5Sbf7736d/f97q6oZtMCv7esoWzPPbP9avztL2xcW605qubOwyivLRPVNhHniKbHCrt0ClpBoU/eALBqIDZzI3q/mYVRUFtOR7HjOEoAtDG0nWsC6mgqGYOQM8/G89mrVpIBVhC34HBhG2cVSzfKD6P+/CXqUr+IsNxvBFJCSrPHC3LmYXnVPsGstJYkQyeORP3yDd9+4+Ae1MVfYNpU3zY5tQ/GviUB48Q8dDf2nvW9yUcjiCJyah/k1D6YuopxeD/Gob0Y7Q4jtUlDjKgfUhKk5bhS2xFh+h+P+/acud6YE4Gc3LVZr9eJJCEhgVWrVvk+Hx1GlZCQEBBmVVxc3GCY1ZnCmZkCEeS4UFleSW6t6rYoinTP8uuTrf96OarHeqgkZaaQlBloYFTs2E/J2lwABEUieVLjdSybwtR0yt/zL0WGXX5hi0o+Rd42AzSdys/nET79QuzdmxZs1fO2of7g13izjb8KMSah6bmZJtr2ZWg7/DIcUmI6cubZ1tLYpBngcaOt/QHAN75ZU2XFpNVNRFDs2EZf1ux5BTk9MQ0ds7wIITS6QQFYMSyU6D/dxqGH/46ta2fkklWBf3/A+80HiH16IzgcgIBg2jBzluFKd+A5qBJ++YWEnD+u3tjNIUgKUlwKUlzwJeB44eycFqClVlRScQpnE+RohgwZwksvvdRoGFXbtm2x2+2sXr2arKws5syZwznnnNPEiKc3QUOtFbNxTbZfyDazIyGhVqamaZqsnF1HO62BJIK63rTEUX2xRfklALT9B/Gs34ScnIS9R9NvXdXf/YS+35JjECPCCblgLKamgupFcIY02k8QRaLuvpmou5sPmDZVD+6P/gVm7ZJnx+6I6V0tJXabA7ltJoLddVQfN9518zEKd/m2idFtUXqN8XnuBFHEftGtmJ4an95ZXWPQN9e4ttin3IgY3nQ2apDTF3Xjt+j5WxBCorCfdWmDZbpCxo/Ced7Z1Lz8F8x9tWr2rlCE0EirGoXqxcjNRerWDSEyEc/nbwHgaG8nZPwwnFf+7iSeUZCmcHVJJ7qOR63U7T2FswlyNG3atGkwjGrGjBncfvvt9OjRg2effZYHH3yQyspKunXrxlVXXXWqp/2LCRpqrZgNK/zxab36+5dbvv33Z+SusIL0BVGg76SzAvqZhsHeOoZa8oR+VC/6CfeKNbiXr0bb6w9yDr/uciJuuqbBJUbTNAOWL0MvugBB81D9rwcxDx3APmkGyqAxv/j8THc16pofUJd+jVlUOydZgaRo1BWf+tpp25YiteuKnNoXMSQSo+IQ3tWfY1bVKYYe294qDyUF3jKCJOGY/gfcbz2BviNQE1Bsl45t+BSkrgNatMQa5PTEqCpFz7fuB7OqBHXbEmzdR9RrZ5om3k9ewdxneZoRRRyX34Og2Kj5933WtkOHMIuLodLAPFSrF+dwYZ90w8k4lSAtRA4LJU4REdRaLTXDCGqpnWY0FEY1c6ZfdL1Lly7Mnj376G5nJEFDrRWzflWd+LQBlqG2/MPv+OqZWb7tAy4aTkR8YBml4mXbqM4vBkAJsaE98zjFqkpDlL/xLmrOTmL+dl9AFpt+6DClL83Em20Jzgp2G2EXT8bzzQc+o8oz93XElC5Iice2pGMU7EVd+jXq6u99RauPIKR2oJ7kmaGh521Az9uIGJ+KcWgv6HViidL6IWcMaVQrTVBsOK6+F/d7/0DfugYpvRe2EVMRU7ue0QGsQSz0PRsDP+etx2jbBTEq0bfNKMzH+80stA0/+7bZJlyL3Mm6r+Q+56CttbzURk4OaP5lUfv5Vwe9rachURF27MV+LbV9OXmk9w7WxA1y8gkaaq0UTdPYvMYvbdGrfw82L1zNh/f530jSh3Tjwkevq9d3z2x/NmikUIZ4tJEmgmgXMWqspcaan5Zy8JpbiXvuUeS2iVR8NIeyl9/GrKrydQmZMAZBrURb/o1/HF3D8+GLOG95KkDfrCm8i2YHKvofQZIQkpMREmrj0uwupDZpGKUHMcuPBJ2aGIU76/SRUXqORk7q3OxxBZsD5zX3Y+p6i2LsgpwZmLqG1oAMgXfjt9iHTsco3Ie6aLZloNWpaSv3PxdlyHjfZ7FrT9i01CrtVOd+kdK6I/cPZmiejkS1CcNRR0tt1+LlQUMtyCkhaKi1UnKyc31lo9okxVNzsJS3b3kBo7ZSQFJmCte9cjeyPdBA0mu87PvcH1wf47TGECNc2CJ1lGgZOdIyVGpy3bj3WLEd2u49HLz6FuT4WNSdeQFjOkcMJfKOG/HMes4XR3YEY/8uvItmY29BIL6et82qAFAHITIWYiMR4uMR7E6ktl2QEjsjxrRFEESrGPuhvWg7V2MU+ecluCKwZU1EDI9t9rgBxwsaab8p9IM7QK31ytpDQPOArmGUFVHzxiMYOZvq9ZF6DMY++Xc+b6r1P5aH0LEj5rY6JctkG/apNwW9rqcprqQYXNkSRwIgclZupPUW1gpyKgkaaq2UuvFpGZlpzLz+76i1AbPRyXH87u17cTRQBurA/NW+Op12WSPEpiLYJcK7S4gOy6gTk1Kxjb8K4X/PIIWVUrWlBgwwK6tQK/1eNLl9MlH33IpzcH+0nPXoW2sLTwsCcv/z0FZY3jX1u48RYuNA1JFT+zQoPWDqGp5PX/Z5NcTkTthGXYI3bwWCZtVnVDLOQk4NLKkjCAJSbHuk2PYY5UVoezaBKKJ0Gohgc/yiaxvkt0PdZU+5Qy8QZdTsHzC3b8csLAxoK3Xpi23kRUgpGQHbzbICzOoyhPh4zIICKLUe/bZRFyPGJp34kwjyi3C2a0NYnczPvTv3n8LZBGnNBA21Vkpd/bTD6/cQUW55skKiw7jxnfvqxaUBGBWl7H7lI9/nGJcbQYDQrg5EhxW/JfcbiX3yDATFjuOS2+Htp5BcEpUbqjA8lhElOB1E3HAlYdOnISgKpqHj/fIt37hy1kjsU27EKNqHsSsbDAPv3DcR+/ZFP7gD+8CpAfFBAOriLzAO1HrEFDuOy/+IXpjjM9IEZzhSStP6VGJ4XINB4kFaJ0ZFsV/9XxCRk7uBzYn6w2cBRprUJQvbqEuQkjs1OI62vzYOUxBQRl6AuXc/YlQcyjmTTvg5BPnluDq2I7yOoXagqKyJ1kGCnDiCqWitENM02VAnkUAqtYwZm8vO7968l/iO9d/yta2rKXn8DgrXHfBti3HV4OxoR4mWQVawT/u9JSJbK10gdx2AMnwKcrhE+IBQ7Mk2QsadQ+LHbxF+9aUIiuWB01Z/H2Bk2cZchiCKOC6+DY54tWpqMHfvBl3Fs+IzjDL/g9I4XIj3mw98n22jLkFwudB2rfVtkzOGIIjBZckgLUfL83vTpIQ0BEcIevYKjK3+e0dISEA+b0qjRpppGuj7t/s+yx374LjwZmznXhT8fzzNcaWlEFVHouNQtecUziZIayZoqLVCDu4roPCAFUCvSBIhgvVldM4Vo2nfK7DwsKmpeL54C/ebj7N/XSWYVjxNqM1LaJKNkHFnYRt3Ba67/okyYFS9Y9lGT0fq2B3RJhKS4cQu52Js+Rmjwlr+MT01eOe/628/bLIvA06MboNtrL84t7lvH+bhw6B58Kz4FKPiEKZp4pnzKqjWsq2YmIIydAJqznIwrMw6ITwOKSlwOSpIkKYwyg+j7Vzr0xmU2vdE37cT9wcv+BtFRCB06oS+a42vXb1xDu3zFVDH5kKMaXeipx7kOOFMjiOmzqJTmaZjeoJ6aqcLL7zwAuPHj+f888/nzTffrLf/22+/ZdKkSVxwwQXcfPPNlJWduR7R4NJnK2R9nfi0EFPyBTNXvvYN2yMj6DRjLN4Nm6le8C32kAOoO3PZvcIkb5c/ZisuQSbuvfeQIyOaPJYgSdin30XNC3/ErCiB6kq8X72Dd97/kDL6ItidmLVGmxAejTLsqOWgNnEQFQUlJQAYmzZBbCxihw54ln+CFJEaENtmn3ITZk0F+l6/10PpclYwYDtIkxgVJeg7N/t+zMJ8a4ckQXgEqseBvnU1qLVL6dFtEDI7I4gCZkUxZlkhQmT9+pz6fn/ygJSYHtTTO4NwJkQRZYoIgqWl5sGkbEsOkb27Nds3yIllxYoVLFu2jLlz56JpGuPHj2fYsGF07NgRgMrKSh5++GE+/vhj2rRpwwsvvMBLL73Egw8+eIpn/ssIGmqtkA114tPCsMrhhBuguL1sevQ9dr7wEZ2yIggp30FBucSuXU7cqv9fRRIN0v91X7NG2hHEsCgcV/6JmreegOraUiyGgb5lVUA72+jLAgL4TcPAyNuI2Lkzxpo1flmD4mKM4mKExET0w4t87eWBo5FSMvCs/tKfVBCTjBgbLK3TGjBN08rQVBxNGuZ6yX5LzNjQMXUdbfE89E0rGmmsQ8lhtOUL/NscLpzXPoC2fzP6PkviRtu7CdtRhpqpa+gH/MW8g17dMws5xEG4Q8Hu8Wup7VmxPmionQYMGDCAd955B1mWKSgoQNd1XC5/8puqqvz1r3+lTRvrnszIyODzzz8/VdP91QQNtVZI3USCcNEy1Nro/gdbdZnKhkXFOJUQatRAeQ67A/o8PJ3QAb1820zVA7KtyYejlJJByL0vo21YgrpqEcbuLQH7xcQOyFnDA7YZBbmY7goEux1xwCCEcg1941L/cQ/44+Ww2TAjHXjXz8c46H84Kl2GBr1pv1FMTzX6gRxrCbzyEEZFMageBFcE9iGX1CsLBqDlZ6Ou9xtdRn4+5s6d9dohCCCKlqFWF1HEcfkfEeOTkSR8hpq+fxtm13MQJP/9ou/dbMl5AIIzrF4CTJDTn7D4MBz5fi213JUb6Pm76ad4VqcedfV3qKsWNd/wF6D0G4mS1XxSl6IovPjii7zxxhuMHTvWZ5QBREVFMWqUFYrjdrt59dVXufLKK0/IfE8GQUOtlVFZUUXuVv+DKVRQEEzoE1uDKymE3ZtMnyh/XSNNkiBtWn+6PPV7ZJff66XuWIm27Wewu5BTs5BTejRYtBpAsDtR+p+L0v9cjOL9qKu+Q1u/2FqyvPi2esHVdZMBlIyBKBlD0Pfm4P36v+i5gfpVQseOUF2CXl3i2yYmpCNGNl14PciZiamruH94x69xVndfdRnaztUomWdjGgb61tVImf3ANNG2L/O3Ky0NNNJCQxGioxEiIiA8HEQRKbodUnRHjIN5mKXFSJn9kDtbEi9idFsEVyRmdSloXvQDO5CTM33zU3f49QallN7BF4YzEGdiNCH5fi21ndv3nNL5BAnk9ttvZ8aMGdx00018+OGHXHLJJQH7KyoquOWWW+jSpQtTpkw5RbP89QQNtVbGptXZGEatFIcgIwsiCRJkTE5AK/QQcjCf/NJQDtc4a3uYxCc7yJr7LM6kmICx9IJdlpEG4KlG2/oTWu5K5NTeyB16IyiN65CJsUnYx16OvU6yQF2MskKMklrdIkFETukJgNQuHceMR9C3r8XzxZuYhfsQktsjxMUFDiCIKBlDju3iBDljML1un7eqIbS8Dcid+qNvWYP3xzkIKxciDzkPs6bc6q+bGNv9nlchNgH53ImgezG91eCpQXCGYeszFsERCl36cvjJfxJ5Xrq/jyAgtevmuwf0vZt8hpq2ez14qq2G9hBLgy3IGYezfQJhK/0vkPuKSpto3XpQska0yOt1osjNzcXr9ZKZmYnT6WT06NFsqysmDRQWFnL99dczaNAg7r///lM00+ND0FBrZWxY6U8kCBMsz1dWbDVVP5ZjVniwSdAxppw4bw2e1AySL+iJ+cMyFEELGMeoKce7fn79A6hutO3L0HauQe7UH7ljv1/kSdB2+71pUmK69bCsRRAE5Iy+yBl9MT01VkKCt8Yy7soKMGvKkdp0QgytrwUX5LeB6AzD1ud89EN7EJzhiGExCGExeFfMwaw8BLqKunMt6pdvgSsUAfDMegmhQweEiAjMHbvAbQk3CyHhOGc8ihjZeBUK79bt1Py4BLldW8KvuMi3XU7uirZ9CZgmxuF9GFUlCDYnWu5KXxslfSCCFPyqPRNxtk8goo6WWmGVF9Mwgkkhp5j8/HxefPFF3n/fqkSzcOFCpk2b5tuv6zo33XQT48aN4+abbz5V0zxuBL89Whkr5i3x/R4u2LCLJh21KswKfxslWqbd+d1QOrVDbNOGmh0S5W+/T/S9dwJgGjrqmq/9y06OUJSO/dB2rfF5LNC8aFt/RrCH+rwMLcX0VAVqT3Xo3WhbwW55/gSbEykuBSkumDjQWpASOyElBuqXyWn9UGtfINTl8zBLi6G0GH3/bgDM7GxMxeYz0hBE7NPvbtJIA6j4aC4xT/6F0n++QtilUxFk6+EtOEIQ41J9NWL1vZtBEP3Zoa4IpHbB4PMzFVdSDNF1tdR0A73oEHKbuCZ6BTnRDBs2jA0bNjB58mQkSWL06NGcf/75zJgxg9tvv52DBw+SnZ2NruvMn299H3Tv3p3HH3/8FM/8lxE01FoR1YfK2bJlh+9zuGiji92NVOvwEmwCrnQH9kFdkNonA2AU7EDp3pbyj5eil16LFBmBtvVnjNLaQH5BwNZnPFJ0ElJKT/T929ByV2JWHgZA274UKanzMYl7ans2gmEF7wqRCcEg7CCNYppmgMdWSspA274Uo7oMc28eQko6Zp5/iRNdB73G99E2djpypx6Ymu4zvo7GKK9A27sfR+8eOIcOpOb7xbjOG+bbL7fvhrfWUNP2ZuML8gTkzoPPSGFbU/WApLR6z5EzMZq4Oo/JUkPHuyc/aKidBtx2223cdtttAdtmzpwJQI8ePdi6deupmNYJoXXfha2MFW9/jYolW2FDxC5IZNosr5ggC4T3D8U5sBvOax9Bauv3ggkRodgSoeyF59AP7kDbtca3T844C0GSMXUNQRSRkzOxD7kEauPTzJryAE2z5jANHS1vg3/8JrxpQVo3pq7hXflZgNisIIrIHbMwi4oQoqIQO3fCdtntCG3aWFmcdZC6DkAZZgUYF9/7CNqBggaPUzl3HiEXjAEgdNpEKmbPDdgvxqXCkQxTb7XPUBPCYs9YSQ7v+vkBy7etFWdiDDGGhFz7qNSB/PVbmu4UJMhxpllDzTAMXnvtNf785z9TWVnJK6+8gn50ynqQM4LspX4DKEywESlqJMhW7FlIVydKp3Tk4eej71yF0ms0Sp9xPtkNx5CuuJeswrP0M98YQnQyRsl+1B0r8K6ai1kbPC0oduS0/r52as4KzDpehqY4OghbSkxvukOQVosgyYihMRiFuwK2i0ldMA8cQGjbFjxV6EXbETMyEAcNQuo/FCGhLUJkLESFYVZZWcIR119ByTP/qldhwDQMqhf+QMgoK3BaiopEadcWz4bN/nmIInJy13rzUzKGnJGZnkblYUDAKMrz3dOtFWdSNDbwVW8B2Lx8Q+MdggQ5ATRrqP39739n+/btbNhg/XP+9NNPPPnkkyd8YkGOP3k5eb7fnYJMpsODIIA92YZjQC+c1/0FJfNsBMmGuuVHpMTO2M++HDEyETEyAnuHcGqW1i4jSQqCAErnwdizJqB0Hoxn1VyMcqs0ldyhF9hDrLaeKssAawbTXYWW45dPkDv2PSOXjYKcPORO/dFyVwUYWPqmpUjpvRDk2oert/YFQpYRU9NxXPUnHJf9AcEL6saFaPnZ2DI7I7dLovqb7wPGdy9bhb1PTwS7X3ImbPo0Kt77OKDd0XFoQmQCYnzqcTzTk4eWuxolrR9ypwFWKbZWjBzmRHbZCa+z/Llla14TPYIEOf40a6gtXbqUp556CrvdTmhoKG+88QY///zzyZhbkOOI7lHZV1Ts++wUJDLtbqRQkdCx/XDe8FeEkHAro7LzIARJQV2/AKMgFzGxM2JcCo4RPXCv3YNWVoPcaQC2gdMQw61YDTEqEVvf81E3f49+cAeCJKOkD/QdT8tdacW9NIG65SfQrFp6QkhUcNnzBKDt3Yz7+7cxjiR9nOEINidibHtfqSZT11GXzsM+9mo4Ss9PiEoE00SKaYeYkoFRkI/S53yMskK8a74k5Pz+lL/zPlrRQZ/hV/nx54RNmxgwjpKagunxou3zCy6LIVGIse39bTKOrWyZfngf2r6taLvXo+YsR83+sdazdXIx3ZWY7grEqETEuBTM6lKMqtYrSSEIAs6kGKLqGGq7Clvv9QhyamjWUJNlM6atogAAIABJREFUGbFObIfNZkOWgzkIZxplm/MoNv3Lj8kyRCgGYeMycFx2G4LsF7e1jLXBSMldLekDZwhScleUviMJG5FBxSebMY3oeg8i0RmGbcBk9P3bUXOWIyZ3RXDVlplSPWg719AY+qF89P3+4E+l+4igN+04Yho63s3fY5QexNZnLOraeZjamV1g2tR11BXfYhYdwvvdbLxLvsL7xZvIXfsjhjVk6AsonS1tPUEQkLsNQN+6Blu34cidBiC6XIROHMLhx57Es2QW7hXfASC3rZ/MEnbZVCpmfRqwzdZ7DFJKb5Q+45BiW1583SgvQtu2xHpJsTkRIxMQIuLRdq87tgtyHNB2rUVO7QNY10jpPMSaWyvGlRRDfJ3Mz4PV3npL5EGCnEiaNdQ6d+7Mu+++i67r7Ny5k4ceeoiMjDMzQLY1c3j1DsrwxxZ2tqmEjkjDNrCfzytWF0EQkGLbISV0QkrsjJyUga3zYEJvfYjwIW05/PcXqVlcf1lEkBSUPuMwPVWYhbuQOw/27dN2rWkw5sU0DNTN3/s+S4npSHW8EycK0zDOyC9cddUi1DU/tLi96anGu3IOYlgMth7nIka0Qe7UH++6eZimcQJneuIRHC6EkAik+BTM6hKk1K4oQycAtYkotlr5lvA4xJBIxHC/DIecNRxtzfcAiOFxyMldCbvkGsTwNhhVkVT8bxYhk8c3eFx7/z54s7dhVFb652IPwaw8hJ6f7QsBaAna7vUoXYYip/RETupsycwkZWCUFZ7U/09TdWOUHECM6+DbJka2AQGM0oMnbR6nG47EaBJN/4vsYV1HLy07hTMK0tpo1lB74IEH2Lx5M4cOHeKyyy6jurr6jK1A35rZvWwTem3GpwAUukwcZ2ciJaQf0xKNGB6NktaJ6NunU/nJF1TM+qxeG+NwCVqRiHf7GqSkDISw2oejrqI2kEmm563HrKhdlpVk5Mxzjvn8fgneNV+irvsaU9eab3yaYJQWoy5fgLZqYcvalxXiWTUXOX0Qcvsevu1SfCpSbHvU7B9P1FRPOIIkIfccgtJ7KLZzL8N0Skjd+iModmu/3YV9yMXIPUchyDaUOi8NAGJYFMgyRklhwPaI6y6h9Pl/o+bsQ6jZQUMIgkDIpHFUfvaVb5tRVQqKA6XrcNSc5XjXL8B0VzbY/wim141ZVYJwVKkzQRAQIxP8MjgnAS1vI1L7HvW+D+TOQ1C3LTkjX2qOB87EaOJMERHruqiYFAQzP4OcRJo11Hbu3MkTTzzBkiVLWL58Oc8++yzZ2dknY25BjiNbVviz1FxIZCPi9uqYMR1Y8t63zHv+oxaPpQybgr78a2L//jDqnnwOP/lPyv/7IUV/foSD193O4adexJu9g7KZX6OXFKFk+B+Qet4G1O3LMMqLME0T012Fut1faF1OH4joDDs+J90EesFOBEcIUkInS+LhDMhuM00Tz5yZ2C+4HpyhGIcblpM4glFWiLr5e+xZE5Cik+rtP7I02JJEj9MdQZKR23Wvt1wohkQhyjbEqESEBv6vlKyRaKu+8302PTWo898k7NorCJsxA2PzcrxL5zV4zJAxI6n+9kdMr7WErO/bgpyciRgaZV3z5Ey8a79u8vpqezcjtevmM45Mrxtt83Lcs/8PfcMatFULMbWWZUz/GkxdQy/IRUrqXG+fGBKJEBqNUbT7hM/jdMSZGI0NMSDzc92C1r0cfDqwaNEipk6dyrhx43jssccabff9998zcuTIkziz40+jwWbZ2dmYpsmf//xnnnvuOd/blKZpPPzwwyxYsOCkTTLIr8NbUsmuQ4d8n2MkifOHd+Sfb24m6p3/0HfiEHKXb6GqpIKQqOaNJDEyFiE2EWPPVqLvuZXqhT+CKOIaMwI53r+MWtk2mkP3PkLc//0TITIBs/QgGDpazjK0nGVW/JriCEwgSO17/C/AUZi6hrpjBfYBkxEUB4IjDM+qudh6jkIMi2l+gGM5lrsKbf9Wq/bpr4y509YvRoxJQGqXjtJrKNr6xdhGTGuwramrqJu+Q+k7PqD81tEomefgXfOFpaAf3+FXze9UIyV3xbv0IzRRwTR0SzTZ0NGLdmMf2HBBZqlLX7zffYxy7kVgmrg/+Ce24VOQu2QBoO5KQV34CXhqsA0PHEOw2XCdN4yqr74lZNI49KI85E4D/GPHtEMclIx31VzEyARrGbEOpmmgH8zB1n8y6rL5aFtWgepBSu+FctYETF3FM/8t9JkPI4RGoPQaitzzxNSv1fdtQU7KaPR/VOk0AO/qLxDjOpyRkiO/BmdiNADhyFRgGc2b12zm/FM5qVbO3r17+etf/8pHH31ETEwMV199NT/88APDhg0LaFdcXMzTTz99imZ5/GjUUHv//ff5+eefKSws5NZbb/V3kGVGjRp1UiYX5PhQsi6XIvzLe+GKTP6aGrZUljHzq+fQ9xZTtmYHX9/5b7qkt0ercqNVedDdXjiy3CEIlhxHRAi9n7wW2/ApeD55GWfHbrjObXipMmTSZNTcjRx++Gmi7pmBd9Ucv0YaYFaXAf5YD6Xb8JOSQKDtXI3crruvaLwYlYitzzjUdfOQ0wcdlzJUpmmg521A378NMbot6ubvULqf+4sfcmZVOeriL3D+7hEApMwsvK890qihpm5ZjNShV7PeSUEUsfUei5az/Iw31ARRROk1GrOi2Po/qv2R2nX1/a3r9ZFkpNSu6Lkb0TcvR+7cx2ekAcgp3dG6ZGMcKsbz1TvYxl0Z8DcMnTqBwlvuwTGkG1JUUr3/X0EQULqPQF07D9ugaQH7jcJdiM4oPG8/hZTRB8dFtyKERgT0N/oPR4xJw71iA1X/eYPIu8KQO/fgeGLqGtrezdgHTm20jWB3IUa3xSjIRUro1Gi73yIh7eMBiEZmX+22nXtbHoMY5PjzzTffMH78eBISrJCB559/HrvdXq/dgw8+yK233spzzz13sqd4XGnUUPvb3/4GWBfgrrvuOmkTCnL8ObRkAyWCTm2IGpEuJ+cPiEdZ5+L5CXczbdxIegzvw2evf87I26ciuRzILjuS0wZHHkq1BlvOf77k4MK1JI7OQoiIQd+1BSm14VqegiASesEYqr5dS9krHxD5h5swivIwCnagF+4GzYtpWONKSeknpU6nUV2GcWgvtoGBBo7oisDWfzLetV9b8/kVczHKClCzf0SM74Bt0IUIooSa/SP67vXIqccmOWKUFWJUFKP+8BW2MdMRbJbBISh2xOgE9IN5SAmBc9WLdoPqRm7bpWUHESWM7Vsh8+xjmtvpiBgaBaFRx9RH6XcuNa8/gtzzLJTBYwP2CaKE0r4HRoIbM2cL6vefYhvhN2hElxPHoP5Uz/2M0Iun1xvbNE3MKg2pbRe03JUo6YN8+7wr5mHu3o19yk1IyWkB/fTiw1TO/Rr3z0sAHefI81B6D+Hw408R9+Y7x+2Fxqg4jLrxW5S0fghHyZkcjdyxr+VVa5PW4hcOo6wQU/MixSQfj+meEkI7JSLaFeJU/+PyQKX7FM7o1FP5xQKq5jYcEvBrCblgLKETRjfZJi8vD0VRuOmmmzhw4ADDhw/nzjvvDGjzzjvv0LVrV3r16nVC5nkyaTZG7a677iI7O5tVq1axcuVKli1bxocffngy5hbkOHHgu1VU1JHmSMGGbHdy7cwH2ZvgoOM9U0m96Gziu6bgCXUQkdmOkJR4HPGROOIirJ/4SBzxkaTdMJZd7ywCwDZiGt7vP27ssADIyd1w9ktCDA2l/PX3kOI7YkopePdHUP5FHmXvraP8o03IaWcdt/PVi/dgqg1/kapbfkLpcnaDDxpBsaP0GBlQwupYMHUN7+bvUXOWW5Ud0vr7Hqhy5lD04j3oRccmlqlu+RE9Nxuzogi9cCveDd+i7duKqbqRew9FW7c4cA7eGtTtS1G6j2jxMfTslXAS4gJPV8Q2ydjGX4Vt/FUN7peSMzEKclDOvRjjYB7qUdc89MLzqfpmpT9ppg5lr75N4R33UfbOAtSdORjlxZiGjufLNzF25+K8/q/1jDRT0yn+yxMo7ZOJfeFpIq8fSfjlFxJx43UIzggqZv7nuJy3lp+NumkhSq/RLfKSCbXSIUdXgmgI0zTRdq5B3boYdetiTG9Ns31OV0RZIrxLu4DMzxLtzElA+i2i6zpLly7liSeeYNasWWzYsIFPP/XL5Wzfvp0FCxZw8803n8JZHj+aFUR78MEHWbhwIR6Ph/j4ePbs2UNWVhYXX3zxyZhfkF+JaZrszinEXUeaY8KNXWh37jBsHdpw0TVTmPX6x1x/51UMuHAYKz/+gQvuv6LR8VxtYxBtMpW7CwjtkIAQEoGetw0ppWHJFsEZBoJA2DXTKH/5PQp//0dsXTOw9+lJ6JQJSJER1Py8nEMPPEnss48gOp2/6nz1g7loeesxNS9y20wri61WB1Av3F37sGnTaH/RGW55+lR3o8tlDWF6qvGu/RopuStS12H1DEFBELH1HoN35WegOPB+/Cr2KTciRsc3OqZRVgiyHX3VYpzX/QWclvyDUbwX78o5CDHt0HesxzSnIwgCpmmiblqEkjG0xXM3TRP1569wXH53i8/1t4jSu3FvoiBKyO16oOdvwn7RrbjffBwxIhoptbZsVE0hti7puJetokJzEtI+npCUeCq/WIB+sJCE/72MZ/U6Sv/zBjWLN+LMbIsgqtin/wHBVT9+sPztD3CeM9hX+F13hmNUliCGRhH9zJMUXHIVjpFjsKUHlldzr1rH4SeeJ/7lZ5FiojAO52MU7cGoLkMMjUYIjUYMi0ZwhFni0ood24ApCFLLdTHljn3xrvkaMT61Ua+a6anGu/FbxIh4bAMmYxzKR83+EVvvMS0+zulGZPcUEtbvQBCshQm3aXB4z36i29dP0mkNhE4Y3azX60QSGxvL4MGDiY624gfPO+88NmzYwNSplrd73rx5FBUVMW3aNFRVpbCwkOnTp/Pee++dsjn/Gpr1qC1ZsoSFCxcyatQoXn31Vd58800cjpY/wIKcWio3b2Wfph1Z9URBIKljJFKC9SU/evK5LFm4jMryStIGdWXniq3oWtO1XFOvPo9db38L1HrVvmvGq9auO0Z+NlF330ybmf8k6q7f4xp+FlKkFY/jPGsgoZdMpvjPj2C4f/mSglFVirZzFbasCdgHXWh5uJbNtjxsuoaaswwlo/lgbCmhE/rB3JYft+IQnlVzUTKGICdnNvoAExQ7Sq8xuGf9AyE6Hu/8d5scV8tbj7lrN7ZzJiG4Qi3JhrBY5NQ+2AZfhBgShSmZeJd9bmXt7duC4Ag7NrHVXdmIbZLrxUYFCURKzkQ/kIPprsAx/W48X/0Xo8iKWNL3bSX8hmup+N9HOOIiWXXHy2z96+tUzVtI9P13WbVy+/WhzcwXkCLCKP3fItRqESm6bb3jeLdux7N+I2GX+BMXpIRO6AWWTIgUFkH0nddz6M8PYbitSh+mrlP6yttUvPcRIWMGUP7yP/Gu+MzSREvohK3HSMT4DmBoaPlb8K5fgJSQhq3b8GMy0sDSihPCYzGKG/YM68V7LTmYjlko6YMQBNHSRBQEK9zhDCWiWwoKIq46vo2VH3x+CmfUuhkxYgSLFy+mvLwcXdf56aef6NbNX8bt9ttvZ/78+cyZM4dXX32V+Pj4M9ZIgxYYanFxcbhcLjp27Mj27dsZOHAgBw+2XPzw6aef5t577wVgy5YtTJ06lTFjxvDAAw+gBd3HJ5xDX3xLgeC/zlEOGzhCEaMstXVJkrj4uql88NpsRFEk45yebP2xabmG+LO7cWjFNvQaL2JcEmLbjlS//CDVLz9IzVtP4PnsVbw/fIZZUwWAGNcBvTgP02hcXNV1zhBCp0yg+M+PYnqOXTHf1FXU9QtQajWzBElGScvCljUB/cB2PIvfQ07uimBr3mMnJaajH9jeouPqRXmoGxdi6zPOd02bwsjfiegIh6Q4TENH35vT8PmobvT8XMyaKqQeg+vtFwQROTkT+5irMXZuxbNsNvqeTcgtMETr4v1xDsrZFxxTn9aIIErYeo9FXf8NRk0JjkvvwP3hS+hFe0FSUNq1R2oTh91bxqBnr6Tyu6Vs3xdC5R5/2TbcVdgclUTcPpGa5bvqxfgYbg+H//4vou//g88LDCDGpWAU+g0j+6iJODrHUPLY31ELiij8/R8wy/cQdnFfXKOH4s0txTZoGkrnwUjRSQj2EKTotsjte2DrNhz7gMm/KiFASeuHlrs6QFfNNE3U3NVou9di7z+5nhGqZJ6DmrPsjK2GEdHNEuCOqFuc/adVp2o6rZ5evXpxww03MH36dMaPH09SUhLTpk1jxowZbNy48VRP77jTrKGmKAorV64kLS2NH3/8kYqKCqqrW6Y5tXTp0oB143vuuYeHHnqI+fPnY5rmby7WzagoQc/bdqqnEcCBRWs5VCfjMz0tEimhU4DX57wLRrBk0XJUr0r/aeew8uOmRVAFUaTtBYPIn2sVULePuhTXTY/huukxHJfcgTxwDILdieeLN33tpbgOzca2uEYMJXTiGApvvJWa/7U8S8da8vsOObU3Ymh04FztLmw9zsPWfxJS+5Zlywl2FyBguquabKflrUfbvQ5b/0mIruY9UkZFKd5vZuG4+E6r6kOPLLzz3m1QSFTN24SRsx37xOubDNyWOnTFLDmEbcBUbAMmH5OHRD+QZyUlxCQ03zgIYkgktgGT0HauwagsxD7hWtz/exYp1krmCL/qUspefouSx5+j93uP0uvp61n3p9fZ9a6l0+aZ+wa2MdNxjbqauH/9A8/6TZT+503f37/s/14n7KILkBMCl8MFWQGbA6OmwvosCITfehd6XjZFN95K6AWDibjpFhyDpqGk9sLesyueNb8szrIlCI5QhNAojEP5QK3czfr5oLqxZU3wJbwE9LE5UDr2RT1Dy1FFdLUMtag6paRy85rWMQxyYrnwwgv54osvmD9/Pg899BCiKDJz5kx69Aj8nk9OTmbRokWnaJbHh2YNtXvuuYcPPviAYcOGsXXrVgYNGsQFFzT/Bl5aWsrzzz/PTTfdBMC+fftwu9307m1lvU2dOpV5805M1sipwjPnNTyfv3HaKHgbJUXs2lVJuel/i+2cFoWUGBjbIkkSQ0cN4ccFPxObkkBNWRVVJRVNjp1yyTnkzapv0AnOEKTEFJRBY0DX0HI3Wcdo1w1t7+Z67Y9GCVextwulfP46jIK9LTlN9L2bQbEjJdYX6zyC6Io4JmkMKakz+oGGvV0AesEujMP7sWVN9CnhN4Vpmng++Q/2CdciOEOQU/tiVhYhxrdF37y8XlttyZcovc9BjKpf3qsugigideyGkbe12ay9o1F/nIMybNIx9WntCIoDW78LMCoOoVceQOyYhnfeh5g1VSgd2qF0TiPqrt8jJycR3rktQz+8n/zPllC2YB7YHcjpvRAkBVFRiP7LHxEUmUN/fZqaJSvQiw/hGnsuQL3vEKlNGsbBXExDR9uzETVvJaEjuxH70K2ETLgUMcz/ghIyaRxVc78+oddBTuuHlrsSo6YC74pPkdp0ROlyFoLQ+CNFTEjHrKnAKDl51RaOF0qYi5AObYjDn1BwoLx1Z34GOXk0a6ht2rSJ5557DkVR+PDDD1m2bBl33HFHswM/9NBD3HXXXYSHhwNQWFhIXJz/oRMXF0dBQcNvJOXl5eTn5wf8HMty66lAy16JEBKO1L4z+s5Np3o6AHjXLeFgjUhV3WLsbWMQo+oHwE68ZByff2CVw+l7wRDWft70m68tMpSQlHhK1u1svM2EayyPkeq1gvQBo7q80fbq6u/Qtq8l4uG/I8a3o/J/rzU5B7CkMPT921C6DG2ynbZpGeraH9EP5rWoZJTUJs0XF3Q0pqGj7VhRq/vW7C0EgLrkK6SkjkgdLMkMQZRQMs/GjI+xlonrqM9rueswi4tRhk5s0dhy77PR1v3UorZHMEqLMStLkZJPrSbWmXivC6KIrfsIxJAo5K6DsI26BPc7T2PWVBF1503Ye/pjZQRJpPs9k9j4+EfYj8oqFQSBiBuuxDkwi5Jn/oXrhmvY+/HPrLzl/1g06n62POuP/ZTapKLt2Yhn6UeYmhf7oGk4L70Lbfk89PzAeEpbp45oBwoD6pAeb0RnOIIrHO+quSjdhjf5knQEQRCwdR+BN/sHtN3r0fZuRj+wHb1wl1V+6zQnolsKiXU8aodU7bR5KQ/y26bZp8z7778f8DksrPk0/o8++ojExEQGD/bH1hiGEeDRME2zUQ/H22+/zbnnnhvwc/nllzd73FOF6anBu2g29rFXoAwZh7rkxL7NtpSSBd9TJoIHf2xYSreuDV73uIRY7A47+bv30Wv8INZ9uazZ8Ttecx47a5MKGkIMjUTpfy7e7z8BQE7LQt38XYOFwNV1P6FtXo7j0jsRJJno++6m8rsN6MWNP7RNTcW76Ttsvcc0qitlairuT19B37EB3NWoS+fhfu1Rql9+EPesFzAK8xvsJyh2UBwY1fWLL+t7NiIlprco3g1AP7AbPXslysgLA7aLUYmIYZFIaZmoy+Zb8zVNvF++jW3idQhSy7SyxMQOGAX5x1RqSF38+WkRm3am3et1kTv0QkkfgJzWA+XcC3H/92lMd2BYiKmpOHPm4+zShYKfG457FHr3JVfKYN0js1DLqsi8eyojv3mCw6t3UJa9x2qjOFC6DcM+cCpKxywESUFwhuC4/G48c2b6EhuO4Bo9guoF31s6bk3Ehv4alC5nYx80DTG8aa9vXQRHKLYe50HtvWN6qi2dwI0LUXeuPq0Nn8jugYZajalTurN5qZIgQX4tzQa0pKam8uCDD9KvXz9cLpdv++jRjafmfvXVVxQVFTFp0iTKysqorq5GEASKivxqzsXFxcTHNyxNcPXVVzNlSmC5loMHD562X+Deb2ahnD0RwRmC4AwBXcMoKUSMalx64URjlBZTsCKPElPGwP/ll9J3QKN9Jl8+gc/e/YJbH7iRqLaxHNi2l8SMxjMIo3p1pGp3Ad7SSmyRDZcpkvufi/v1RzEK85HikzHLCtG2/oxSR1xV27AEbd1POK64x4rHAaTYGFyjRlD+j+eIeuKZBsfWdiy3SjM1UiLJqCzF88ELyL2GovS3lpSOLFyYhoFRsAf3hy/huPJPiBH1S0fJSRno+7dhRqdjlJWjpKZgqm60fVuxD76o0etSF1PX8Xw2E8cldzRoeCkZZ+EumY257ieUvsNRt6xAcIWgpPdp0fhgeSrkjD7o29Yid2v87+ubU3Ul+t4d2M6/psXHOFGcafd6Y8ideoJhUPP2k0hJqRjF+zE9bqvyQWY/uj85jKVXPUv80G6INv/XrudwBStufJE+z1xPRGb7gDF7P3UNq+98lbNnP4Ag1WZPHoUYHo3j0jtxv/88jivuQYy0tNxCxo6g8NY/IR1eg+mpxnntgw1KgfwaWrLk3xBieCxieKDmnNkxCy13Nd6Vn6H0OO+k1Ps9ViK6pWBDxIlETa3c0erPvua8u285xTML8lunWUOttLSU0tJS8vL8WUeCIDRpqL355pu+3z/55BNWrFjBk08+yYQJE1i9ejVZWVnMmTOHc85puPRQeHi4b8n0dEffvwujeD+286/2bVMGjUVdOq/eUsdJndeWVezcL3IYj29bZJid8JTGl7oGDuvPzOfeQvWqLdJUA3+sWvqN4xvcLwgC9guuxzP3dRzX/QWpQ2/UDd+g7duKFN0O9ecv0fdux3HFn3xG2hHCb/49BydfRFjebuSUDgH7jIpDGOVF2DIaFsrV9+Xi+Wwm9gnXNqjxJogiUmIH7FNvwv3eP3Bec79lZNdBjE/Fu3AJ5U++i2katHn1n2g5KywV9xYqw2urFiFn9mtUL01Q7Ng6DUCt8uD5+r/oOzdiv/T2Fo1dFzlrBO73/4HUqQeCvWlPn7p8PsqgMadFzcYz6V5vDrlzb4TwaNC8iLFJCA5XwP5204ayY+bXdL7FWtLWqtwsv+EFuv/lsnpGGkBIShsSR/dlx8yvSb+p8cqSYkwC9mm/x/3+P3BedR+m5sU7/z0EvQKx9zVIUS7c7z6L45r7EZRji2M8WQiCiNKpP0ZZCuqar5BSeyMnNazNeKqI6F6bNILsM9Q2/biG81q3BGGQk0CzS5///e9/6/288847ADz11FPHdLBnn32WJ598krFjx1JdXc1VV506Q+Z4YBo63s/fxH5BYGae1Lk3+q5sTNXTRO8Ti7ZnO/urRA4b/oDX5HZxTQf7iqIvqSBtUFf2rM8lZ0nT8XbJkwaTP2cZpt748ooYn4zYPgNt1SLL+9OhH95571Lz2l8RYhNxXHVfgw8QUVGIuO4SDv/tyYDtpmmiZv+A0oCwLIC6bjHeL9/GceWfGhXiPYKUlIrtvItxv/dcvaVDs6qG0jcXEX7DxbiGnUXFRx9jVBQjtklrZLRATK8bdeW3KGc1bMT6zjOhE8TEYBTsQUjtiJR87A8oMSoO28gLcc96scmlLuNwAdrmFSesuHdrR0poj5TcqZ6RBpB65UgOLFhDTUEJhldjxY0vkX7TOGIHNl7qK23GWA7MW01VMxmGUmIH7GOvpOa1h3G//zxKv5FE3HE31T+uQk7rjjxgFJ5ZL1rF6k8x1d8vblQvUYyIxzZoKkZRHmru6pM8s6ZxJkZjiw4luk5CQe7uYM3PICeelkVCN8Ly5cubbTN16lSfQdelSxdmz57NvHnzeO6557DZTs+3u+YwVQ/edfNxz34eMa07YnSg0r0gilaA99pjC/A+nrg3ZFNsilTWSSRol9ah2X5HkgpEUeT6V//It/+ew4Z5KxptLzltdJg+nOW/ewGtqvEsKNuIqagrF+KZ+zru9/+B0vc8hD5ZyJlZviVB0zTRD+/Du34+ntVfYKpuXFMuwSwvwb18pW8sPT/biu86SorDNE2833+CvmUljmsfRAwP3N8Ycnov5D7D8Hx72OTiAAAgAElEQVT0L5+RY1RVU/Snhwm/ahrCoWxsIaVUvvM/tDXrcL/2CNWv/AX3Jy83aRSpi7+wPFfNLBEJgoCt2whIT0XpO/IXe7rkLllIKRl4v/mgwf1GYT7u9/6B4+LbjlnoNMivR5Qlut17MZv+9j6r73yZthMHkjg6q9k+PR+/mnX3vdVs/JaUmonj2gdw/u5RpI7dsGf1wrMxG1NVUXqdhdihC95TnJWu7txN6T9foezfbzTaRpAUlJ6jMMsL0PZtPYmzaxpBEIjomkJ8nYWoA+Xu0zqu7rdOZWUlEyZMID/fijWeNWsWEyZMYOLEidx33314vfV1+/Lz87n88suZNGkSV155Jfv27avX5nTjVxlqrfUfVN36E9rOdeg5myCq4bgPJWsE6urvqFr0EyUvvopefPikzc/0uilcfYASoW50GrTv0nxmVt2kAmdECNfP/CMrZ//A8g+/a7RP6hUjaX/hUJZc8QzuwoaztwTFhn3K75Ay+uK88W8ofYZh6zoM77p5GDUVqLmr8CyZhX4gBzm1L3JKL7wr52C6K4iYMZ2SZ1/A1HRMbw3ano3Iaf0Dz9nQ8c59HbOqAvsldzS5xGPqOjWLl1P0p4c59Ogz1CxdidzzLMTEDni//i96WSmFv78DR7KEsHcVRskBpG69cY0dhKq2xXHtA7hu/BtiZCxqbaJEvWNUlqFtW4Pcd1iz1xxAdIVj7z0GuV235hs3gXLOJMzSItT1Pwds1/ftxP3Rv3BM/wNi/JlbIPtMJ3ZwJoIkEtkjlZRLGg79OJrIbilE9uhA3gc/NNtWDI/2ZSILgoBz6GBqflwKgG3ohP9n7zyjq6i6MPzM3JbeKyRAgIQSWui99y5NqlQpgiiIdEQRpEsTUBBQEaQ3ld57R3poISGBVNLbbTPfj4sJMY2SAPrlWStrkZk55+yZMDN7ztn73aDSoD+6Pacu8g1ZlolZ8D3OC2dgCItA+1f2s/WCIKCq0BxjyC1Tjdyop8T/nPUHyJvE1rcobs9nfuoMyLFRObQoIL+4evUqPXr0IDAwEICHDx+yatUqNmzYwK5du5AkKctqBIsWLaJNmzbs3LmT5s2bs2DBgjds+cvzWo7auxDj8qaREqIwPrqJHBiI6OWFFHbXVJPxHwhmFiQG69jy0VxWLt7HX8MmvzHHVnrykIDHIrHoeP4vVKT4i5UW+jupAEBtrqHf8lHcO3OTIysyl0zRpWgJuxeCe8uqlJ/aizP9viXudtb6Z4pCXihL+aX9v1E4FEJRuDT66wcRzKzQ1OyC2rchoo0zCidPVOWbobuyB5VfFdROChLWb0bvfxKVT80MM0KyXof2twUIDq5o2vTNVjLDEBlF3I+/Ej7wE7RXr2M/cjDWPTqhvXKd8A8/JeHEA7T3HhPRdyAWNcpgPfxzLAZPQ+lXF2O4PzYfjkB74w7GpyZnVNWwE8bHARjuXMl8XQ5tRt2oywvHsgEonIu+coD23wiCgKbTUPRn92J8bJJOMQb6o9250pQ04ZB9ndMC3gxVFw3Fe1j2MWdZUXrUewRvPcXteVvRJ2QvOJ4SHpPhOWPVoSUJG3ekzfyqW/bG+PgBxoe3X834bJCinqA7+Qepm79D1mU9s5584Cgq7+KoihXBYexIYhb/kFYG63mMATdJXjga48PbqCu3Rn/zFFETviJp90GkhPyTHHkR/pn5mSwbibmecyWXAvKHTZs2MXXq1LSkRLVazdSpU7GyMpXb8/Hx4cmTJ5naSZJE4jPpmpSUlH9FScyC9Y8XQDYYQKFAEAT0t08gJ8SD0Yhga1Kj198+gbpGpwyO65Ote1i9MYDTWi3xUgJnziQy8offqDeoG0pl/l52Q9A9ghMVxEpJyM/NqXl6vdhMyvNJBSq1CoVKSc/5H7Fj2i9smbIaaydbnvgHkRAZh8rcNHNVsVUNavdsSo0fPubC8KWUHt0J14YVch1L6VEWpUfZLPeJ1g5oqrZHd2U3lq3qEb/zMNrr1jh+nZ4xKqckkbpuHspqTVBVzFpLTTYaiZn7HYYnYVi1b4lN3/cRVOlxJmrvEshyf/R3H5B88Ci2oz7DokF6DJeySHkkG2dECxtsh/Uj7vs1OE4diyCKmHUdQcqqr02ltJ45QdLTMFOCSfuBuV/sV0CWZZDlbB1SQaUxZQKunYOqblv0Z/dh3ncCguV/I2j//xGFmZq6GycQsvMMp7rPolDrahTv3xylhYbkJ08J2X6a0P2XUZirUVqaUWnWAMycbVE4OWJW3Y+kP/Zj1b4lgiiiadUH7a4fMfeanGkc2WhE/yAQ7bWbmFWthKqYKcnBcOcyur3rQGOOoFIj2LsgOrggJ8RifHQP0dEVRekqKIr4oNu/AU3bfhn6lZKSSVi/FZdlpgxuhZMD1l07EPf9Guw/NYmiy0nxaP/8GSQJTdfh6HavRRz4BUlHH6Eu64TC2YfkIyexbNsMjHowGkBj+UYnDGzLFcUMBRpEtM/y6a8evUTj+k3fmA3vAkGbjhP0W+4zvK9C0R4NKNot99nmGTNmZPi9cOHCFC5sKl0WHR3NunXrmDlzZqZ2n3zyCd27d2ft2rXo9Xo2btyYN4bnIwWOWi7IOh2RoyaDQsRu2PsYIwKRAgMRS3qDIIAsIz0NRhd0h+vzj5Bw/wmWtYuzY/VuruqTiHoWzP9YSmXctB9wWLaJJu0a0uK9ppSv4psvDxndpUuEGxUkyvoMGmqexV/MUfs7qeDYvpM0bdcobdt7U/tyfd8FNJZm1OrRBGtnk9q/QWdgee/p+NQtj1MRV2r/OpZzQxajMFPjVDP7QOkXQdBYoK7WEd2lP7CsoEEXkkRYp/exrOWFytnKVD+x7QCUJbMuDyVLEtHT56Mu7YPD+OyFmgVBQF2qJOpSmbNiRRvnNK0oM78KJPy2Dd2d+6hLmYLGzbp8ROqmJZgPmIKg1qDbtx51i5759gKJX70edRlvzGtnL8Uh2jqiaTcA3dHtJiftHxmtBfz7EBQinp3qULhdDYI2neBE5+morM1RWJrh2aEWdX4bj9JCQ9Q5f872m0/JIa3xaF8T697diBj6GRaN6yFaWSI6uiFY2GAMvofC0xtZpyNhw3ZSr1xDTkhEVcILdbnSRE+fj02/nijNkzFcPYX50OkIGnNkXSpyTCRSdDiCly/qNn3TZo5lWSb1l1kYQ+5nEFOO+3Et1j07I1qkZyRbtGxC8qHjpP51A4U+HP35A6ib90DpXREA0bkwCT/8gGhlje2gQaSe/oP4dRtRuSaDQglGA6KDByrv3CVp8grrkoUQ1UpsDEoiMcU/3br2gMZvzIICciM8PJxBgwbRuXNnatSokWn/uHHjmDZtGk2bNmXfvn2MGDGCXbt2vdsrhPJr0KFDh9dp/lIEBwfLPj4+cnBw8BsbU5IkOerL2XLC7/vklOu35Med3pejvxgmJy4aIWuvHpC11w7KyX8skMO/nyEfqjdMDvnznHx0zq/yqKLd5C4ebeRq7vVz/Jn/xZJ8sftRj/flEZ5d5KaFmqaN1aJCx5fqIyI0Uh7Y7iM5JirmhY5/fDtIXtZ7umw0GmVZluXkJ0/lYx2nydKz318XSZJkY6LJFn1EpBz+6UQ55vs1slGny7HN0+nz5bi1G/PEhr/RBT6Swz8eL0uSlLZNf+20nLJhoWx4dFdOWTc/T8d7HmNCghzaf4QsGQz5Nsbb5m3c6/9GDClaWRubmOU+fWKKfGXCGvnckMVy6tN4OenAUTl64fdp+40RIXLyzzNlyWiUIyd8Lcf/tk02xMSm7dfFJ8m6p7FyWL9BctSo4bJRp31hu4zREXLSsomyZNCb+goIlMNHjMtwv6SdQ2SU/KRLHzn5tyWy9I8xko8fl5906Cwbdfq0beEjxqXZKUlGOfX0JllKyfoa5BeHmk2Qe7u3Snu2jqr65t6DBWSmUaNGac+K+/fvyw0bNpRXrVqV5bFPnz6Vq1evnmFbjRo15KdPn+a7na9DrjFqT548yfATGhpKTEwMABMmTMh3R/JtoU3VcmDCbBafu0PPycsZNnwaSc1Lo70eTNKtZBRFK6PyqUnI6WhurQ+iQv8inNlzmB1Lf+eppCXImF4r01XUoEJESUaPfcPKzZw/kbcp6HJqMvfvJBMj6zAX0idMPb0Kv1Q/zm5OfPhZP0b3m8DvG3Yj5aJuXqh0EXxql+P4alNVBnN3BxxrliJkZ+4VDv5J1Dl/DtQbQ/Tl9BJOgiAgWtoBoHR2wvnb6SicHIkcOobkg8eQDRllB2RZJmb+UhRurtj07vbSNuSEqqgnSs/CpJ5Kz4ZVlq+FYONA6tq5qFv0zNPxnidhw3asu3V84aoFBfx3UZipUdtmPVOqtDSj0jf9KNarEWc+mIdQoSL6+w/RB5niR0XnwghKNdEz5qAu44N19/dQ2JlCOcIOX+VElxmc6jgBVT0/FCX8iJn+LbI2cwZdVoj2zigr1kF/fNezBILl2I8amuWMhaAW0HhoiD8WSPzPG0nacxDt9Vvo7j0gbtVG7Lo1QApKj6ezaFyPlMOmbHpBEFF610R/98xLXbfXxda3WIaan49jUt7o+AVkTWJiIgMHDuSTTz5hwIABWR5jb2+PRqPh4sWLAFy6dAlLS0scHF5MIeBtkauj1qNHD5o0aUL79u3p2LEjjRs3pl69etSrVy/fY63eNJIksX/HIcYNmkKzMm2Z/Mse9vx1n7CIaG7ee8KIb44S4eOOWc0ahH08lQtDl5EYb0XVUT5cO/OYU7//RZKk544hPfPRp1AhOjgXprjCBlfRgioqeypVTNfJmj95EXrdi5f+yQ1j4B0exiqJk7UZHMMiLxif9jw1GlRj+eaFhIaEM7LHGB74Z1/XE6DR4HbcOnyZ8PumdGef4e24v3IvRu3LnV/A6v1UnNmf2/O34b9ge5YabYIgYN25HU7zvkL/MIjwQZ8Qv34rUlIysiwTu3gFopUltgPzR+Heun9PYlb8kvbiA1A374nm/ZGIjm75MqaUmEjK6fNYNGuYL/0X8O8iKiiM7dN+xmjIXhvNpV45Knz9AecHLcSiby9iF69I25caa4n06B7WvU1VNiSdgetfruPRxqNUe9+O8kNqcm1bOCnefpjVrk7Ex+PQ3c+9ZJIsSch2xUnY/AcRH36CplxZVMWLZXmsbtdqbD8ZjePkz1CXK4OUnELy4RPEr16Pw8RRaFr3QH90W1qChHmjuiQfOZnWXuHkiaxLQYp/c3pmdv/I/IxM1SMb374+3f87W7ZsISoqijVr1tChQwc6dOjAokWLAJg0aRKHDh1CEAS+++47Zs+eTbt27Zg7dy5Llix5y5bnTq6eVu3atalRowYdO3YEYN++fZw6dYru3bszdepUNm/enO9Gvizavb+ibtQ5R4X2xIdhBG8/jUu9cjhW80GWZb4ePZs/N+3Ntk2MXubTX+8wsaUziieWuJoH4FahPOFrjnLgGuhlmduGmLSSTe6ebizZvZzkY5e4P2oFlwzJyFjQwGjgrrmK5BQ9D+8FsXHVVnoP654n555y4hShRiXJsgHL5776XjQ+7Z9ozDQMHtOfoAfBfPvFYkqWKcHAUR9gYZlZ0FNUiHSdMYiN41cw7NdJqKzMKdqtHgGr979wlltqRCy6uCRc6vriXLsMD37cx8nuM6k8fzCWRTLXFFTY22H74QfYfNCdpD0HiPh4PKK1FariRbEd0u+VzvmfGFN0PNl3kYQ7j0m495jUiFgEhQJRcEWcOhunbyajLOSGoFCgLFEuQ1vtrTvELvoBy1ZNsWzX4rVmwgpm0wp4Hqeibrh4ufPTsAX0XjQCjUXW2WsOfiXwndSdyzM2U9rPjpRT55ASkjCERGJZzwc5IoSkZBWXR6/E872alCgaiKpCbayrNKJOhxSufL4KC08nvCeMInbBctQ+JbAZ1BvxuWw5WZJIPX+ZpD/3Y3gcirpUSTTN22P2+BIWg7KubmK4cRbByhZFsTIAKAu7Z3mc4OiGMeAGyhLlTbN+SgXGqKconExl31Sl66K/dRR1tY5vJM7I1reIKfPz2VCJkpHk0BAsPYrm+9gFZObw4cMA9OvXj379+mV5zPOJBxUqVHgn/ZacyHVGzd/fP81JA2jRogU3btygbNmy6PV5NxOUlyi8ypKyZgZSfGbtMtkocXfZHxxsNI7bc7ZwovN04v1D2LHu90xOmrsCOlgKfGQnYpZ2U8KXu0+RmOiPnbWRxHVbOeifSohRyy1DDNpnpUXMLcz49ueZ2DvaUbhTExpUtqaYwoYgQwI37iXSs36xtHF+/PYnIkLz5osw8fwlnkgSGkGRZgu8eMZndhQt4cnCX+dQqpw3w7uNZs/W/VnKjTh7uVOxdQ0OLd8JQLFejXj853l0MS+WVh+47gjFepoSGARRpOTgVlT4qg8Xhi/lzpJd3Fv+Jzdnb+KviT9xccQyLn+2EkOyFkGjxqpjG1x/XIjd8IHYjRyc60P78e4LJD3K/brfXf4HsVcf4lSnLBVm9KX+zqnU3z4FtbMd6g8+IGrSdAyRmbWUEnftJXbJShymjMEYG0f4h6NIPn46y+tmjI4h+egppMSkLG0omE0rICvq9GlOjW4NWdl/NgmRWWsYAjjVKE3p0e/hfzmRpwtXkvTHPuwnjSbOohKXhszj8qgVVPyqO27aU6irNUFVxXQPqqzMqbZsOOZuDlz6YiOO879GVbwoEUPHkHL6PIYnYcT+8BPh/T8m9fxlbAf2xm31EhzGfYJVx44ovX3R7f8tc9WPlCR0R7ejbpn7jLe6UWf0R9L1Ci2a1Cf54PG030UrewRLe6SIN1Mg3da3KBbPan7+zc2jp9/I2AX8f5Kro2YwGLh7927a73fv3kWSJLRaLQaDIV+Ne1WUpSqjaTeA1F9mYwx7lLY93j+Eo22ncmPaeqRU04ND0hn4Y9i3zJ+yOO24SnaOfGFtyRx7Nb1tFDQr5cj0uk5YPCu/pENm5qMYlp0KZEqkxOqEOO4YYjNUAfh66RRKlC6e9nvDUe9R2FyFQhAIM+qxf5SKVyFT4eHkpBQWT1uWJ+cecD2SGFmHraAhVU7/+7yuowbParx2bMKyzQsIehDM8PdHc+tqZuXwOn2a8/DSXULvBiOqlPiMaIf/wh259i8ZjIQeuEKhVlUzbLctW4R6myZi6emMZXE3XOuXx6t3Y8qO74ZL/fJc/HhZ2vKoIIqofUrk6qQFrj/Cg5V7uTUr59RsQ1Iq4Yf+otzkHrjU9cXc1T6t72J9mhBy7A4O4z8latw0jDGmF6Ws1xM9axG623dwWTwTlUchbPv1wPnbr9Feukrk8LGkXrhC8uETRM9ZQvjAT4ie/i06/7tEfjoxS6evYDatgOwo16wq7cb3ZNWH84gIyKwb9Tcu9crhPbw9AUmuBItFOfH+bKLvxVKkhi21F3RFdfpnVI06oSxXM0M7QRAoMaA5ro0q4v/tNixbN8N54QxSjpwkZsFy1KV9cF21CPuRg9PkPP5G3aQroq0DKd9PxnAt/SNFu/sX1E26Zllq65+Idk4Ijm4YHpgEci0a1iH5WEZBZ5V3TQz3L7yRElkqGwssPZywEdJXLK6dzqylWEABeUWuS59jxoyhT58+eHt7I0kSQUFBzJs3j8WLF9O06burHaMoXByzXp+R+tsClE268+DPB6Z4J33GG1mLxM/+f6ETTNs9bW341EpLaoJAWKIVD2U1t6MFFDJ0svFkR2wwiYKEAdifTS3PzrWL4xzziNA7wagEAY1GjWXlujSs8BuR563xN8Rw7r6KD/v4MHG1KZlg/87DdOzdjqp1Kr/yORuePOJBtJI4OQVP0YqADDNqL5dMkBPmFuYMHTuQx49CWTJ9OVbWVnw+4xM0ZiaxVlEUaf15dw4t30nvBSNwb1GFgNX7SQqKwLJo1sXJAcIOXsG1YQVEVeb/lgpzNR4da2XabuHhhDYqjquTfqbizH4vtPTxcO0hIk7cpO6mCZwfuoS428HYlslaDPjhL4co1qsRgiLzN41znbLcmrUJxfj3sR89jKixX+Iw4VOi536HZeumWLVrmfEc7GyxHzUMQ2g4Ceu3oHBxxrJtc9SffZTmgOn87xL1+Zc4fjk27aX392ya68qFuZ5bAf+fFPXzptfCEfz6yRI+XD0OK8esNfPcm/mhcbBCNFNjW7YIgiBgDLpD6i+zMOs7AUWR7KuXFO/fjHMDFxJ56hbOdcriMGl0rnYJCgWqWq1QVqqP7ug29Gf3ofCtDrpUlGWq5tr+b9QNO5G6bh5ij1GIjm6IlhYYwiJQupmeJ4LaDEUhb4yPrqMsVumF+31VbMt7YRcawN8VWP1vBOb7mAX8/yLIWa3D/IPY2FguXryIQqGgcuXK2NrakpiYiJVV1uWT8oOQkBCaNGnCoUOH8PB48dkhOTmR0+1GE34zfelNVCsp/VknDEmpzFnyE/6CKWtHI4rM97LGLSUJWYZzRmvOxD4X9yGDi97IEfkpsUJGh88CBbII7xd2xjZJJlFQk6RTkJKsRStAMd/CNPaLYe3WJK4lJ6FAoL2XHdfsJQ5fMNUp8/IpxroDq1Bm4ai8CHHL5/Pd7DMc1sdSUmHLFYNpZsbZzYk/L299pT5fhB3rfifofjCfTP0ow/YV/WfTeVp/HD1diL58n/sr91J9+Yhs+znVaw5+cwdiUcjxpW24Pm09GgdrfEa0y/G4B6v3E33xLlUWD0NUKoi7/Qj/+duo8eOnmY41puo43mk6DXZ8gajO+m9yb/mfaJxsKNK1HqmXrhIzfykOkz9DU/blC6v/jeFxKFFTvsF+5BA0lcoR9+NalB6FsGzZ5JX7/Dfxqvd6AXDj4CUCzt+m/cSs48KyQ9ZrX6gqhjY6gdO951J77Rg02TiDOSFFR6A/sRNVk66IVnYv1zY8mNRty1HVao0uTIcxKipDRrcsGdGd24ayZHUUzvkbL3ZrzhZ+XPQrJ2WTAkJRazM239mXr2MW8P9LrkufKSkpHDlyhJCQEAIDA9m2bRtr1qx5o07a6xB7NyKDk2ZbSE3tQW4UtbrGX6Hn0pw0gPcES1yTk9BLMpsSLNnxVOKeIZar+igijMkgQIRaQT2HwlT2KISDqMFLYUMVlTMughk1JFvcghSYRylxjpQoFqenjF6kok5AvvqEP05KmFkqKCRaEiGlcDlIy6AWJTF/pu7/8G4gG1e/mkMlyzKJZy4TLMmYC8o8jU/LjQ492xL2JJyLpy5n2N7ow7YcXWkqReVQuSTIMpGnsy5dkxgQisJM/UpOGkC5yd2JvRlE8PbsY0Xur9xDzOX7VF3yEaLSNINlW6YIgkpJzNXMGa1BG45RpFOdbJ00gCLd6hG0yRQvY1alIu7rV7yWkwamoGrnb6cT+8NPJP6xryA2rYAXxrdJZR5dfUB8NjV3s+NFS5dpHKwpN7kHl8f8+Eol8UQHFzQdPnxpJw1AdPXEfNBUpMBbCE+vkXIs470uiArUVdtjeHgFw6Ps64jmBTalPXCR05c+w5O0/7e1rwvIf3J11MaOHcvatWvx9/fn7t27aT/vKru/38HnLT7mzC6T1s69bzel7XN0kyjlEgOP47l818Cy7elxBX6yJcX0NvwcLTIiQuBosg4ZKKywpK7gyFNRR8qzmK/oJD3WcUrKKB0opLBErTBVAaguZf2wExCwlwS840Xea6jCTCHgIJoRLml5uCeIfm3TX+wr560hNCTspc9big0l6E4ssc/i07TS8/FpebfsmRWCIDDum1Esm7mSxPh0p7hkrbKE3Q1JC3KuOL0vt2ZvIupMZmct4OdDFO/76jNGgihSZeEQgjYcI/LULWSjRHJIFJGnbxO04Rh/jV9D3M1HVFk0NNMyZpnPOnF7fsbi6pLeQNCmExTr1SjHcTWONpg52xLvH/LKtmeFws4W5wXTST15DpueXQpi0wp4IQRBoMnQ9hz6fme+jeFcuwy2ZYoQsGp/vo2RHYJKg6bjYNQVayLHPCZl9xak54qiCyoN6qrtkGLD0Pufyjfnyaa0J04oEJ+lfqZKMk8jnubLWAUUkOsa2507d9i9e/e/QjNNm6JlxrRF6JE4PvQarX6qRJmz4aie+aPOxljiInRsuB/HgeTbacWVCgsKmki2PBZ0nNUJlFaly0C4GaDV0Pb0al+VSf2n4BElm8omPacN9kSjZ/TEEdhcCCIpOAobbzesrZ5iU0hEoZQ4Pd0fo14mKSyVhCBr6tZ3J/HIA+4ZYrl4Q8NHvUqz50IEgYHhJCel8M2YuSz+bd5LpZrrrp0hME5BrJRKCaUtAfqYtH2+flnX0sxL7BztGDS6L3MnL+KrxZMA00ujfv9WHP9pL20+747GyYZaP33G2YELKfVJB1wbmMo+GVN0RF+8S/mprycUq9CoqP7Dx5wfuoTb87dh7m6PhaczlkWcKdyuBk61SmdZH9O6ZCHUdlY8vXgXx6qmGJ3graco1KoqimeznTnh9UFTHq49RMUZfV/L/n8impnhNOuLPO2zgP8+pRtW4sjKP4gNi8bOLX+EPEuPfo/TPWfjWKs0dr5vXpZCWa4mdpMmEjNjPlZ3rqFQ6xFdCqMsWx2lb3VU5ZtgeHAB/V97UVVohqDI2/eXlZcrlgollpKShGdJZPcuX8OpVUExqQLynlxn1Nzc8ke8Mz8QFSIK1bOac8Dus3+xTA7nL5JQK3Wc0Gv5ONzIvmQ5zUkzE6CdvQXBZjK7xXg8ldZp/RXVQ4exPagwuSelKvjw3nuVsa/nicos/eVttFDi4luEpoM6UO37j2nw6wgq9PWlSAVrrKL8MQ+4gnfl9KSDh2cSKeVlhUZQoBJEHhqNhB54wNi+ldMcs3PHL7Lrt90vfN6yPpWUk6cJ0qrQPzuzp5geHqIo0qBl1sXK85rajWtiaWnBwd+PpG3zbVaF+2dukpqQDIDa3opaP4/m3tLfCd1vSqQI2XkGjw61si0y/jKo7ayou2EC9bdNplsViowAACAASURBVNrS4fiO70axno1wrlM2x/7LfPYe/vO3AyYJl4drD1G834slyzjWKEXMtYcYkrNOLimggDeJIAg0+agjh5blnmn9qohKBX5zB3L9y1/f2pKfmZ8fzksXkHwrHrlsG9T1O6I/9SfS0zAEQUBVsjqiUxH0t0/k2pesS32psUWVEsfi7lg+l/l560SBRMeb5LvvvqNNmza0adOGOXPmAKZqSc2bN08TvD1w4ECmdgEBAfTp04f27dszcOBA4uLi3rTpL02ub0YfHx8++OADli9fzpo1a9J+3kVUahUzJ32YIW06RZDYK8YyW4pkVbxE4nPPFDelisHFbSnfwA3J1w6VoMBCUIEM9kZIcbfgTkwUj28Goo8KoVPzMsSgoPU3/SlWxQdLB2tSi1gzcupHGB8HkLJ4DMmzh6LduAjD5eOQbNLE8iwlYe/6LGZMkgncco+SpQvhJloSJiVz6GgMZZwUdO/VPM22hV8tJfxJxAudtyH4Fkk3HxNglLESVDyV0h86frUqYu/48vEgr8rHU4ayfsWmNF04URSp1bMpp9YdTDtGZW1BzTWjCfjpICG7zhK08ThFutV7YzZmhWVRVyw8nYg8eZPHf5zHpUEFVNa5SweA6cXo0b4GITvfbCmbAgrIDp865YgICCXmcWaplzzD0Rqb8sV4/Pu5/BsjF5SuzjgvnknKvsMk7DmBsnEX9Cd2pe/39EVOiUeKz/46GG6cJWXpBJK/n0zyD1NI3bAQ3eEtyMac5afsfYti+9yi1K3zt17/hAp4IU6fPs3JkyfZvn07O3bs4ObNmxw4cIAbN27w66+/snPnTnbu3EmzZs0ytJNlmWHDhvHhhx+ya9cuypQpw4oVK7IZ5d0h1/ngpKQkihYtyqNHj3I79K0jSxJFfl3LNEeR5TG2PDQmoHs2w/T8XIelQkmnEm70q56MIAhoValM2fOUEgpTgkRhSaDbguEUalaGO5t+4/j3G4m6H4xg5UghvYK5ExfQpGwFijaugJwUjVf0LVJ+Ww/Z3NiCAL51DJzepUQyyCRG6vEoY85dQUWQnMAdvUD00fsM6NaEk6duEPzwMUkJScwaN59vf5mV4xKobNChv3+eoLvxxMoitqKGcGO6aGrj1g1e/8K+BOYW5nw2bSQzx83n259nIggCVTrUYUnXL6nfr2XabKTS0oyaq0dxbvBiLL1cUdu9/eSUUp925NLI75H0BmquyV164HmKdK3H2QELKdajYf4YV0ABL4EgCDQb3pED322n28wP86TPzWu2c/Ov24SFhGM0GjG3MMeg1dHm5A3cm1dGYZZ7mEB+IJqb4/jNZOJW/Ez8+r2Yu8QjJcamJSyoytRDf+tYtpUL9BcOYTZgMqKtI7JkRI57im73L0gRISjci2U7rk1pTxx2pU8KBAQXxKi9KZydnRk/fjxqten/XIkSJdLqkU+cOJHw8HCaNWvGiBEjEJ9bSbl58yYWFhbUr18fgKFDhxIfH/9WzuFlyNVRmzlz5puwI28QBBT2dngkh9JCqeG8YM4TOZEQYxISMgpBoEWVUtRLlKn5aWnkC+dBp2P54Ris9QrUCgVmFmb0/mkCbtVNAf5l+w/B+9YxFC6dUbiVBKDKH0e5eOoyNx8GMLyiEt3uX9JtUKpReJVFUaw0oldZkCVSV36JhbWMd2Udd86bbmz9iQDMbdW4JJkTLqfy2+5oRjR6xKQZwxnacyIApw6dZc/W/bTu0iLbUzYEXMIYGsWjJCXxUiqOCjNi5fTiyW9q2fN5fP3KULtRDQa2/4hK1SrQqE19KrWrzYWtx6ndK305UWGmpubqUZkKqr8tLAo5YlPGE1GlRONgnXuD51DbWWHh4UTsjUDsyhXLHwMLKOAlKFnLl0PLd/E0OAJHz+z1C1+UClV9qd2kJq6FnNNili+euszCMd/iMG8TtSa/nCRIXiKIInZD+xO3ah3acAOKU7vRtDDFvIpWDghWDkjhASjcSmRoJ8U9BUFAtHV81o8Cwd4FsVgZ5MgnkKOj5oHzczU/wxJTMegNryyv9G/iwtbjnNt0JPcDX4Ea3RpRrXP9HI/x9vZO+3dgYCB79uxh3bp1nD9/nqlTp2Jtbc2QIUPYsmUL3bqlS7g8evQIJycnJk6cyO3btylevDhTpkzJl/PIS7Jd+vzkk08AaNeuXZY/7yKCIOC6ajGWUyZikaqmpFHEU2FNeaUD1oKKNtUrUjfCSJVB3ogKEbFkeaISJY4+0lJItASg5ZhuaU4agKBUoa7QNM1JA2jStiEpT0JwiLiLe+yDtO2iR0ksRn2L+aAvUDfthrJEOZQlK6B4pvRdpLQRe09TZqggC7gajDiJ5kRJKcTqBc5suE15V4mu/d9L6/PbL5YQFZ71l5qsTcIQcJmkg7cJ1CoxIhMv6/h7dbd8FV9c3DPXx3wTdO3/Hiu2L6FO05rs3XaAn7buZNm8VUT+o1SWqFS8tS/xrCj/RU98J3TL/cAs8PqgMbfmbOHR1pOE7DrLk32XCD9yldibQXlsZQEFvBjNRnZi/6K80VAsVd6HwkXcMySWVa1TmSkrvmDezxu5feZanozzOtj074EhNpWUo8eRtenSSyrvGugfXMi0nGm4fBRV5YaZ+hGd3JGisq/yAGBTyhMbFGielZIyyhAUEPz6J1HAC3Pv3j0GDBjA2LFjKV68OEuXLsXFxQVzc3P69OnDsWPHMhxvMBg4f/48PXr0YPv27Xh6ejJr1qy3ZP2Lk63r/+GHpunycePGpU0v/hsQHe15cvUJyDKFjRCrMXBNH08RhTVnLtzhlo0KH2MJXJ29UZQqwbz5J3EXLREFAaeirtTp3TzH/mVZRndgI5+6PERyfnZdBAFVo86om3bLMrtI07IXybfOI0gSZavGcybMAklvxCFJJkgjYC2oCUPHmUvxlL98no8+68upQ2d58iiU+NgEZk/4ljmrpmeattffO4/uYTiJdyN5YLDE+h/xaY3bvNllz3+iVCqpUtuPKrX9kCSJNROWcevwFRr0yvkav02yqorwojhU8aZot/roYhPRxyYh6QxIegMaJ5u3khlXQAHFq5biyA+/E3onGPdSWVffeF1Klffhy9lj+HLwV0xa8xUVqpbLl3FeBEEUcfxiLOH9h6HevQWL9/qYtqvMUHqUxRB4BVWJaoDpWW64dRHzIdMy9SM6F8bw18kcx7LwdMJCpcBCr0Qrm1YF7t+8R4lSXnl8Vu8e1TrXz3XWK7+5dOkSI0eOZOLEibRp04Y7d+4QGBhIixamFShZljOpVTg7O1O0aFHKlzcpDrRt25aRI0e+cdtflmxn1MqVM91sc+fOpXr16pl+3kVkWSbl1DYCfzFlesRh4IwhlhoOLtiJGnyUdmgSlQz98hjbLiRxK8zA3WgDjoKp+kCb/vVQ5iBuKhsNaLcsRX9oE0pRQK0QEOycMB88DU2LntmmgIvOhVFWNwU1WtrIlGtpmr2zkgWsJHBTWBBqTMFCDwd/uY4y4DQT54xJa39s70mO7D6eoU8pOQ79ncsk7viLJyki4VIKtoKaWDk9Gq9h67d7Iz2PKIr0nDSAK1tOYHxHljrzGkEQKNy2Ol69G1NiYAu8h7Wh1MgOaUXmCyjgbdB6zPvsnpdzTdvXpXynBgyqWJ2lU5exfsUmJEnKvVE+IVpZ4jjzS2JXbsGYkJC2XVGkHFL4Q+RUUwyvFOSPwqM4glKVqQ/Bzgk5NudEDEEUcfJyx1JIf+7fPXcpj84i/zA+end1UF+U0NBQhg8fzrx582jTpg1gev9/8803xMXFodfr2bhxY6ZkAj8/P6Kjo/H3N9WoPnz4ML6+vm/c/pcl16xPc3NzwsJeXoD1rSBLPNl7BX2igVgMbFHE8L6PFePHl6FscVPMkZ2owdtgy57vdzGi11iKiNYIgkBRJ4nSttHZd61NIfXnWRguHk7bpvCphMWnC1AUz/0PrW76PqhNDqGbQwTeXUwevatRwFxQIiETLkg8vp9I2N6TVC5uQetWDdPaz5u8KIOYrN7/FAm7riAbJE4ni0jI6JHTlj1LlfOmcBH3F71ybwRzGwvKNavKpe25p8sXUEABeYN7KU8sbC0JuOCfr+PUmt6P7goXoq8/ZPh7IwkJSBeB1kbFE7zjNJdGreBw84k8vZi/zoK6ZHGsWtfn6dhJafIhgiCiLFULvf8JZL0W/YXDKKtmrXsmiApkWcpVesStvJdJKeAZdy/lXBHhzJFzDOv6KROGTGXVgp85uucEwQ9D3phjawx5gP5cZsmKfxurVq1Cq9Uya9asNCmOK1euMHjwYHr06EGbNm0oU6YMbdu2BUwrhNevX8fMzIylS5cyefJk2rRpw7lz5xg/fvxbPpvcyXWdJyUlhSZNmuDm5oaFRbpcwe+//56vhr0Kgqgg+EwKMRjYIURTxdqWTvUsUNlZ02t0WU4eCmXvjkeICDgmgB0OKETTcmLLSkaMN88idxiUSW9LSogldc0MpMfp8WjKKo3QdB72wkKKorUdqvrt0R80VUrwcn9AfGUP9JeDCVCaZtXuGVMphTl/rLlPqc2z8QnXckwQSRIkosKfsnTmCsbNHI0UF0HSrv1glEmNSeZQioiP0p4HhnQ9mEZvedkzO+r1a8HyntOp3KFujrOXBRRQQN7RclRXfvt8OcPWTX4pIe2XwaqYKxW/7I3HuTsUSZD5rNVw/BxdqelkEpR2qeeL95DWqB2tOdN3PjVXj8LczT5fbAGwHjQM7UeDSdz+J9adTC9shaMnUmw42ou/Y3x4DQo7YwjzR+lRBoVr8QztBQtrSE4Ay+xrmjqV88J2R/pz7H5g1pJK4Y8jWPDVd9ja2TDzh6/Q6/Q88H/IA/8ADv95DKVSwRcLJ+TBWeeM/tx+VDWa5X7gO87kyZOZPHlylvt69eqVadvKlSvT/l2xYkW2bNmSb7blB7m+KSdNmvQm7MgTZEki5mYoB4RYSint6F9XQunjC0o1gkFHg171cG3hwrpPl6JP1aF49sCqUFzEw1FGTog1TYd7pSv5S0/DSPnxK+To8LRtqiZdUTfr/tIPPHX9DhjO7kdOjIX4aCoNrkvyhDCcEnVIooYQOZFQgyWSpCQsQocFIs1kO3YIppm+rT/vpFWn5hQPPoP25hOMiVrWxws4iuYoEDIsezZ+h5Y9n0djYYZfu9qc23iYOn3e3Vi1Agr4L2Ff2InCvsW4ceAS5ZtXzbdxHCqXxKFySbyHtaG13sDqhb/wy7ELlCvmSLWiNrgVtsfM2pJKs/pz4aOl1Fk/Nt+SiQQzC6w7NiXml41Ytm6KaGZa0VCVqAqxCYi126Gq1Qa0yegu/YFo746gNk9rLzoXQop8jCIHR82mtAcOshIBk8j602QdCXEJWNuaVnD0Oj0bftzCiQOnGTllGOUqp79bnN2cqNmwGoab55k1Zx3H9p7M1yx9WZuCFBGM6Omd+8EFvFNk66g9ePCAEiVKYGlp+SbteT0EAfcm5ah87Bqlikq4OAuYtRuKYGkD2hTQWFBRELDf6MSPg+aSEBmL2lxDy56V4J5pOc5w7XSaoyY9DSPlhy+Q457FKggimo4foqqZvVxGjuZpzFE37YZ2h0lgTz6zi0rtPAjbGkOkUY+HworzUgIdDbY8UsqU0UNZ0YzrsoYHgskJm/Hxl8xxkbH28+DBzuucTRXxUVjwVE5Nq7bg5VOMYt7vbvB67V5N+e79r6jWtSHqdyjjs4AC/ss0/agjPw6aS9nGfiiUr1c7NvjGQ/Z+uwltYioIpngtCztLnIq60WZsd0RRRKlSMvjzAQwY9QH+1+5y4eQlNq3ZjixJTF/2BV4fNObK2NVUWTQk32b5NC27Y3b2KrFfTcF++sy0FRDDleOY9RhlGtfMEqVPDfR3TqMun15vWHQqhBT5BEWxMtn2b1PaEwsELAQlSc9qQW9as53kxGRuX7uDQW+gabtGLN+yEEUW9XplWUZ3fCdDK6r5fMmvVKxWDrt8Eig3/HUCVaV6+XatC8g/snXUZs+ezYoVK+jatSvu7u4Z1uoFQeDQoUNvxMCXQRAE/Pp6odNepkoJCUWpqmmih5ilO5xFKpZgzJ+zuLzrFCVrlsXZLIHUvx21G2dRtxuAHBtJyornnDSlGrNeo1GWfb1ECmX1pugvHUEKvgeAuRBCLU8dtx5a4SCaESml8EDUYa8UKWSXiKUMzaNs+ZFI9ILMw0eR7PYqSrvzgSyPlfEUbRAEgafG57I939HZtL9Rmamp3rUhp389QMNBbd62OQUU8H+Bpb01vk2qcHHbcWp0e7UEl6hH4eyZvwnZKNFxygc4e5niYCWjRHJsIvuXbOPW4SuUa1olrY1SqaRc5bKUq1yW/iP7cOLAaVYvWsvoaR8Td/MRD1bupeTgVnlyjv9EUJth+9UcwnsMIHn5l5j3GQUGPWjMTR/wz1A4F8Pw6AZSXDiirStgmlEz3L6YY/8aZ1ssVEosdKo0R83/r9v0GtaDD0f3w8zCLMf2xrtXUHiWxNpoYFhRZ+ZOWsSM76e+5llnjf7Kccz75v/yagF5T7bJBAEBAYSHh1OiRAm2bt3K1q1b2bZtG9u2bWPr1rzR5clrZFnGeOU4VUqY5pay0sf5GxsXOxoOaoNHOS8UXr5pcQhyfDSGqydNTlrsc05avwmv7aQBCAol5oO/QlW3ralkAeBcTkENR5Pmj5fChvOKJMp6WnNBa46NmR5f21TqyekPlV9PPGLbg3gSDWZYiGokWSZGSl/2bPSOO2pgEjX8648zaJNersZeAQUU8OrU79+S0+sOcvvoX0QFhb1wBvbT4Ai2ffkTmyf9SP3+rfjgu0/SnDQw1Vm2crShyUcdOL5mT4591W1ai4d3A3n8KBTf8d2IPHWLwN+OEnc7GF1sYp7XDhVVKmwG9UeX4kjq2jlo/1iDKoskAlWZeuhvn0wbX3QujBSZs5aaIAg4uNlnyPy0UQlUqlEhVycNQH/iD1R126Gs2pgK8mPsnew4sPNwru1eFmPIA0RHdwTzf9EKWQFpZDujVqdOHRo2bAhArVq10rbLsowgCNy+fTvfjXtpZCk9lszMAkWZF4vFEBQKlL41MJw3ZcNoNyxM36lUYdZ3PErvinlmpqA2Q9NuAMoKdUjdshRFRAjVPFO4lKghUafAUdLwe3gC1Yo5EBqnw80qmaZaG25pVYQJevSSzPpEGTv0ROojMcoyxmf5noWLuOPtWzIXC94+SrWS2r2acuKnvTQd3vFtm1NAAf8XqM01dJk+EP9jV7ny+2liHkchSRKW9taUqF6GkrV8cS/tiSiKSJLEnePXOP3rAQRRpE6fZrw3tW+OS2e2LvY4errw8NIdvKqUyvIYQRAYMnYgP8xZxbTvJlN1yTAerNlPwE8HSA2PQR+bhCxJ2JQpQrEeDbGr6PXay3UWzRuSuHkn1l+Pw3jtMAofv7R9SQeOor16A7vBfREdCmF8fBulR1mwsEZOTsihVxOupYpgEZKe4Xrl9PUXssn46C6CjT2inROyrSNS5BM+GvM5I/pMwK9WRZxcHF/+RLNBf24/qpoFMcH/VrJ11L766iu++uorevXqxbp1696kTa+MICpQNeyE4eY5k66Z6sXjn5Tla6U5aukbVZh9MB6lT6U8ttSEomgpLD6Zj+7QZqyNW6kanMLRaEtcRXPuJEXTwt6F81IhqqhT0KfGUN1gzS7JlFggAdHo4R8fn43aNPjXxCBUea8eS7p+SZ3ezTC3LfjSK6CAN4Fn+eJ4ls+Y4ZgQGcv9c7c5ve4AoXeCsbS3JiEqjhI1yvDe1L44vEQJqkYftuXPuRuyddQAylUuy/ofNuF/7S6lK/hQ+pOMH2uyLBNz+T4Pfz1EwtQnFG5bHc/OdV+6tNvfCKKIdd/3Sdi0C/tPh5rGMBqJW74GY9RTzBvVI2LkBKx7vIdoGYjCtQSCSgOCgCwZEcTsY/oKVS2FcOh02u+hsQmE3A7Co0zOccK6YzvQNDeVuRIEAWW5mggPrvDpF8OZNe5b5q7OLHL+KpiSCEIQPd79D/gCsiZXHbV/i5P2N5pWvbEcswRl+Vq5H/wcihLlwOK5wuAKJWZ9xqEs5Zd9ozxAUKrQtOiJ5eh51O9dHgulhCAIFFHY8POpe1Swjycs1oiybTNSLFKwyuFP5uLuzPsDOuervXmJQqmg8ZB2fP/BN+z4+hf++vMssaEFhY0LKOBNY+1sh1/bWnSdMYiRW76i2zeDGP7bFNqN7/lSThqAS4lCAITff5zjcUPHDeL7OT9muU8QBByqeFN53ofUWT8Ola0lp3vPRdIZsjz+RTCvVwud/z0M4ZFICYlEjZmK6GCPw9SxWDSojcuyuehu3iFhwwVSz+4DQLRzRo6JzLFf1yrexEpaTLmfYJRlfvp8aY7LylLkY5BlRFePtG0qv/oYrhynQrVylK9clm+/WPJSy8CRYVGsX7GJ65duYjCkXyfDleP/ySSCPn360KZNmzQdtatXrwKQmJhI27ZtCXlulvN5Dh48SIcOHWjfvj0fffQRcXFxWR73LiHIeR0QkE+EhITQpEkTDh06hIeHR+4NXgHtvvXoD28xOWkfjENZukrujfKY/dOWs2e1qT5ZuJRAXWs9JUspWR8k07JVLSpcuMqR6xIPRZloUUBEoFLzanT5cgDO7k5ZZha96xi0ekJuBhJ05R6Bl+8SFx6DYxFXqndpQImaZRDFXL8nCvgP8Sbu9QLyl8BLdzm3+Sjvzxqc43FzJiygfou61GxYLdc+7/+4F0EQKDHw1bLuAVIvXiHht20YY2KxG9IPsxqZn/GpN25ztd80fFdPQR11C7FwcZSlKmfbZ8rTONqV74yIQCw6AHp0aUu5UiVpNvy9LNukblmKqlpTFEUzzjqmrJ2DpvUHiI5uLJu1EmSZjybkfA3BJKS7cv5PdOrTnru3HnD7qj82ttZUrVsZ+eweIgpXJPRJFNFRMVSt7ceQsQNz7fNdRpZl6tevz5EjRzKUibp69SqTJ0/m4cOH7N27N9PzIzExkZYtW7J161ZcXV1ZtGgRCQkJ2WqyvSvkq+LookWL2LdvH4Ig0KVLF/r378/p06eZOXMmWq2WVq1aMWrUqPw04aVQN30fRXFfRKdCiPZvp5h5/VF9Obr5PCkJKTgLVuyOj8LvscBIT0vECzeIw4WStjFEyDLGVNMXUpNeLXHzcH0r9uYFSo2KYpW9KVbZmwYDWwMQejeY85uO8ufcDZRt7Ef1Lg2wc8+7mI0CCigg/yhWxYc9CzYTFxGDrUv2orYDR/dlwuCpVK9fJdcPMq8+TTjR6WuKdm+A0jL3QP2sMKvqR+qlq9i1aY7Ko1CWx2gVlkTEqLFcuIHSn7Y0JRTk4KhdvX4XB4UGowyxkslRM5jJ3D97C98mVShUukjasbIsk/DbZpRx4ZmcNABV1cboLx1B07wHw8YNYsGXS1m9aC0DPumT5dgGvYFls1YS8SSCxevnYmWTvioUHRXDhd/3YnBxpkLHZhQq4o69o91/YmYtICAAgAEDBhAbG0u3bt3o3bs3mzZtYurUqYwdOzbLdnq9nqlTp+LqanpflipV6p0U7/8n+eaonT9/nrNnz7Jr1y4MBgOtW7emVq1aTJw4kbVr1+Lu7s6QIUM4duwYDRq8Gyr6gkKRp0kDr4KZtQX1+rdm/+KtiIJANTsPWlvpMMeAhauEh004kR4Cew+ZypaYW5vjXaf8W7U5P3D38aTD5D4YdAZuHb7MlimrsXNzoMv0f/eXYAEF/L9Qv39LTqzZS9txPbI9xtHZgWp1q7Bv+0Fadc452F2hUVG8f3Pufb+bMp91emW77Ib0y3G///xtVJvXnZuT1lHGuTByQM5loXZv2Ye7ypJYg46/xSxvHznBnC0r2fD5DwxbNwmFyvSqTdy4nZTdu5EVGjQ94tCJKg4u3U5ceAz1+rWkSHk/dIc2Izd9H0EUGfXlcGaNm8+6HzbSa8j7aWMaDAaC7j9i3pTFtOjYhI8nD83kgDk42dPQIRHl0AFZOoWvw5+b9/L7hpyze1+Vdt1b0aZryxyPiY+Pp1atWkyZMgW9Xs8HH3yAl5cXM2bMyLGdvb19Wv3P1NRUVqxYQZ8+WTvB7xL55qhVr16dX375BaVSSXh4OEajkfj4eIoWLYqnpycA7dq1Y+/evZkctfj4eOLj4zNs+9fUG80D6vdvybFVf6JNSiU2QYtydHuKJF5CDnkIwM176V+e5VvW+E+XYlKqlVRoWZ0KLauz9pMlBN94iGc5r7dtVgF5xP/7vf5fpkwjPw4u20lqQjJm1hbZHtdzSDc+7TWWlp2a5Trb49mpNsc7TqN436ZonLKvGPCqxN4IBFHEpUVt/L/eQOKDSNRRodken5SYTOijUDytrNDHpWeI3g9PRHHuAPZPk/im0od42NviYaZCHfYYuwoulBjxAQfe/4zbCgcaf9wZZy93jq/Zw59zIqhW1oEKd6+iKe2HIAiMmzWarz6dyfjBX5AYl4hOp0ehEHEp5MKYr0dSonTxLG2TZRlj8H3U7f57H7d+fn74+aXHj3fp0oVjx45Rp06dF2qfkJDA8OHDKV26NO+9l/Xy9LtEvr7hVSoVixcvZvXq1bRs2ZKIiAicndOXFF1cXAgPD8/U7ueff+a7777LT9PeaSztranTuxmHfzBNyf7x/SGajuxOaT8FqmPruRmc/kCo1LrG2zLzjdN0eEf2LthM/+WjX7ptXHg01s52BfFu7xj/7/f6fxlRFKnVowmHvt9Fm8+7Z3ucpZUFJcp4cf3STSpULZdjn4Io4vNxB+4s3kmFaXk/E+K/YDtlP+/KvYAIXGsVJmjFH5SsnX0Cw5Hdxynp6YGLKomYS/dRIaJHItUgsX/2QVpO6YJlwwZc/HY557dcxN63JC5uRfhj6HKqdq1H1zvncbBKQZlyg86DKpFsMOPM5jN8N+g7PvpzMea2loiiyBcLxvM4KBQXa/W4twAAIABJREFUd2fMzDUApP51Hf21a8SdOYucmISUmITKuzjWXdoDID15iOheLFPt6rygTdeWuc565ScXL15Er9enSYfJspwhVi0nIiIiGDhwIDVr1mTixIn5aWaeke9vrZEjR3LmzBlCQ0MJDAzM8MX0tybbP+nbty+HDh3K8PNvyz59XRoOaoNSY1rejA5PYtOkVXzdZyXfn3IgNtl0zSxsLf+Ty57Z4e7jiSCKPPF/9FLtUuKSWD14PpJByv3gAt4oBff6f5uqneoRGxrNmd9yrmTTqXd7tv/6YrFCbk0rEX8nhOTgnLMxX5aYqwGIahU6B3M+7v4Zd1zURFwIAKUKWZe1MPf+HYewSpKpUMMUh2YlqNL2WY7qjEX4BaRfZ1A6PpIhC4bRYtpQzHwK02FoR+xDEnD9dhrxy1eRejsOwdoZC42Bxh28qOkrcvTjMaSsmErKr3PR71iB650jiOd2oz3xJ1GfTyDh53WItjaoS5XEvEFtrN/vSMrhExjCTIXhjbcuoPR9fZH2d5GEhATmzJmDVqslMTGR7du3py1p5oTRaGTo0KG0atWKSZMm/Wvi9fLNUXvw4EGaKK65uTnNmzfn3LlzREam31yRkZG4uGRO/baxscHDwyPDj5ubW36Z+k5i7WxHy0+7ZNgmyzKPb6U7KeWaV/tPL3tmRbOPOnJw6Y6XavP7rPU0H9np/+5a/RsouNf/24iiSPfZg7l1+ArX9p7P9jifct6EhYQRH5u7wKwgCJQZ05lbc7bkpan8r707j4/p3v84/jqzZV9EEgmSIIkg1L4EN7FvEbH1ooretpa2l+rt1Vp66eJWqkpbbv266NVS7a3WUoq2KKqoWCOWWIMgsolIIslk5vz+iA6ppbQmMxOf5+Ph8cicWc77nHFmPnO+5/v9HpmznPr/6Mfc6f/HlFkvsGrrcfQ+bly5oGK+RfPnxXMZ6PU61EIjdYOMaNTyhdrRQyk4/20KakQPFJ9quHXvRPX6IXQe04eGY2Kp2rIu+yZ+SJVJo1GvmsmeOhdTngFD4+60fGUKhy/q0T76Ik59R2KI7oOuQStK8yFn7v8w1PLHvbEnTkEeuLRthdNDkejr1MJj+CDyFn8JQOmJA2hDK+cP+Y4dOxITE0Pfvn0ZMGAAAwYMKNcU+lsjR47kwIEDbNy4kUOHDvHdd99ZhvWYMmVKBSb/Y6z2zZWWlsa7777L559/DsCGDRsYPHgwM2fO5PTp09SsWZPVq1czYIDjjPtV0To/FU+T3lEkf7+LA98ncjLxCKr5+mgqzfveXXt8ZVIjshalxUYuHj9HtbAav/v44zsOUZR/lcjOFT/UihACtHodw+aOY8GTb+Li6Up421s3b/Yc0I21X3/PoCd+/zuhasu6HJv/LScWfEdAlya4hfy5Xu85e46jc3fhWFYmqsmMmnaZyIZhZBaa8Nh6garxF6B6+WtjV/1vDQ/Vr4u/wR3tpT24KQpuGr2lQ8GRPQcxXcrjyufL8J8386Z11hnRkeKURA5+cpimbz6JW1w3cud+RP7Sb/AeO5LGUdX5ZdFaovpGY843UvjDNkpSjuP/3hy0vlUxX8qkeOWH6BpcH9rEuXVz8j5egvHUCRQ3z3sa9N3RjB8/nvHjx9/yvo0by0/D9eGHHwLQqFEjjhw5YvVs95vVCrWYmBiSkpLo27cvWq2Wbt26ERsbi4+PD2PHjqW4uJiYmBh69LBdO7cjqBrkT8wTvYh5ohf5OXkc2rCHU7uPEtSoDnUfoGbPG3V5pi/r31vB0NnP3PFxJUUlrH7jc5744PkKSiaEuBWDs4HH/jOeD5+cibOH600zIwB0je/E2MH/5K+P97+rJqlms0dyfu0uDs38ioLTGXhG1CSwezMCu937j7Ijb6+gwUuDGD/uNRq6+XNs+0EaPBTKinUb6H+qBFP6WXQPXX98Wuo5dv28l071GlG/Zx0Kd6XgodPibrzhjNqRM2S88DK+U/6BxtXlpnWWntxL3b/35cTy4+z422wiJw7C97VJFCcdJOf1OYRfvsz/lh0g4vQRdB5uGCLr4TX6Mcu+0VTxA40Gc3Y6ik81KLmKaizCY3Bf8t7/CM+HZcqoysKqbUFjx45l7Nix5ZZFRUXxzTffWHO1lZa7jyetHu5Aq4c72DqKTQU3DqUwN5+s0+n4hty+meyHd7+m7dAuePh5V2A6IcStuHi58dh/nuPjUbMIrB9MjfohBNYLpnq9EFw8XXFxdaFuZBhJick0bvX7P0IN3u7UGtKBWkM6lI1PlnKOo++t5sqx89R9Ju6uc+XsPYHBy42P/u9/uOaVMmT2SLyrV+WTp2cTFOjBxdxCsn7YRY1uZcNjmM1m3pg0h39OH8fqyQsJjK/Nni0FhDQJ5lziCUuHgsISE8X/GIuhbuhN61SLCzFnpmKIGkjEuEguHzrDoZlL0boYqP98f/zfnQFA/QnTOde8NU0HdCn/fFXFdDoJqnhStHwu2sgmmHXOXC1ScXUxUrz/AMaYMNS9RWi8qqHxDULx8HWYa7JEedIFTjikzk/3ZcP82xf85w6d5tyh07QcEF2BqYQQd+Lp783Yr14h5vFeuHp7cGTTPhaPn8vHo97CZCyl37A+LFt87z/kFUXBs15Nmr89ivyT6Rz7v2/v+rnHF3zHvuIrbN2wnZlr3qVGgxDcvN1x9/Gie6tQtrvC+Z+uT0e07NOVNGnVCFd0BEYEkb1+O7qqvtTr3x5FUcpdp5aScuKW6zQe+wVdWCsUpewr2KtBMG0+fo7QJ7qTNHUxe/75IaaiEqKfHsxPC1aVm0pKVVVKj+7AXHAJp26PQVEpWR7N+O/b+1nw761o6nfCHFSDlEWn2T51Oz+PXcqWgTPY3GUcm7qO5+DUD+5x7wpbk0JNOKQ6LSLIPZ/NpXNZN91nKjWx/OWF9H/1b/ILUgg7o9VpCQivSbM+bYl9YQgjP36RBp2bsuzlhYTVr0PGhUwuX8r73ddRVfWmuTAVjYambz5B3pE0jn/4+wOyllzK51jySbamHOHVBa/g7n19ZP9WD3fg3JHLVAmrwaGLxZQWFnL+7AW+W7GBEX8fStJ3idTv0JijK0/QYNoIqpVeAsDthkLt8K59N63TnH8JtfAyGr+bJ233aRZG28UT8GvbgF1j5+MRXIfAEG9S1m+zbHPp0e2ophL0DWIwlmr47qALq6e+T78pw6jlW4VFnV/k/OWqOF1Op+2n/yTm2xl0+H42HTbMJfqbV6n/jz6/u1+EfZFCTTisTmP68O2sLzix8zDHdxzi2LaDHP35AGve/IKG3VrgG+y402oJ8SBpM6gTemcDWz/9jtiB3Vnz1Xfl7i8tLSXjwvURA/Iycnnvkem8038qa2cvJTP1+iDJikZDs7dGknvgNCcWlH+d3zr9v80cpYgaEcE3jeFWt30jTh7JYeiwbmzXFXHx4694c/Lb/PO1cRRdKSTlpyQ8z6XjU88XDiah/vgjLlpw19xQqB04Wu41VWMxxsNb0Ee0veOPyKD+7fBpEU7StEV0GDuYzfOXWYo0TKWYarZg1/KfmD90OiGd/8IjnQycev1LmvdozWUXEw1nPknwyHiuflt++zUunmi8pVe1o5HxCoTDCotqwPnDp0nZkoSiUVAUBUWjwcPPm788Jp1UhHAkcZOG8vHot2g9pBPvLF5Fq780J3HrHhJ/2k3e5StUqepNdkYO4XVrc2XfWZ6cNZbghrU58lMSa978gvycK7QZ1JHmfdujaDU0nzOKXc/+H/qvtxI8oP1N61PNZlJXbON8UT6PPzL8pvs1Wg1hrepiPpeB4uvKu7O/JKBJGCHV/Fg95yu6jO7Dydc+pXH/OhSs/R7v0b3xS15JbobJ8hpHU85hNptRjEWUpu7DnHUWbe2maLxuHpbqt8JH9yJp2mIubTqBi7uBpAUfkZtdwpHDVzCX/kS9mMaM/nQyTs4GtscuI+iRlgQNiab19mVsXbmXrk/14eKT43GJjio36K3i7obW2+sPvkvCFhT1t+eO7VRaWhqdO3dmw4YN1KxZ09ZxhBBWIsf6g+tqXiEf/C0B15YhZGRl07J9c1q0b4aPb9nE7skb97BkxkI8HgriWMpJoru349Exg9HpdVy9XMCn496l39QR+IeWTbhuKirhp4H/pvWC8bhUKz85fMZPyfz0f6v4+vRhPv7+A5xdb57o/WLyYdYkfErDrnV5460VvFCvIRcPn+VgqYaWNaoT4JWFf7AvnsM7oAuJZNnf3+Xnn1NJNGZgvDZOx5KP/06QF+hqN0VTLfSeLsdQzWYSn/4P+voBJO1Lpn5sRxp0aoa7T9n0WeZSE4lj5uLfvDqB1XLQPdQOY8o+PvzvcR7/4Hm0Bw9SuHFrudfU162D5xAZFsuRSNOnEEIIu+Di6cojbz1NwS+pdGreHH+9GxmHznB633F++mQd2z9bz9RlCUx6awIfffMf3NzdeGrgeJL3HMLFy40e4wey/r3rA2JrnQ00/NcQkl769KZ1nfpkPWnFV/EO8LllkQbg36AuhbkF1GvWhE9f70fL/03nQuO69B/QkHoBuVRVLlH135Og6DKaKtWp1bYBiqKUu07t2NkrGNoMRBsQds/XzCoaDc3fGUPB9uPEdO1ARGQdnJ3KxkZTTWZ2j5uPf0wjao15GPPFs5Tu2YShYSs6jY5jw/yVuPwliqrTJpT7V1mLtGHDhvHLL7/c9eM///xzyziv9k6aPoUQQtgNv1oBDHlzDGcPnORKVi6Zpy5wNa8AFy83Hn//ebQ6LVA268HDf+tHdPd2zJ46F78AX56aOJL87Dyyz2ZQNaisedG3dT3Slm/n3JpEavQqGxz2avolCnILSM+/TOvet58vWdFoadIuiL2/pNFSm8b+KROp4uFO+EtPU3ryKKaUnZjOH0Yb3AhFUQiOagysxV3Rk6sWA5By5Aw9/0SnJq2Tnlbvj+P0F5s59ckG8k9fxHS1hNIrV6k1pAO1h3UGQN8shpIfvsBp4DNEBits+e9aLp3Pokp1XwBKS0rZtXwLl9Mv0f3Zylms3YshQ4bYOsJdk0JNCCGEXakWVuOuZh4BqFbdnzc+eo0f125h7ODneerpYWyY/w1/ff1Jy2Mipwxi66AE/No1wODlRupnP1IcXo2io9m0iWlxx9d/qEN9Fs7aQbsvXmbTwKmMeCqSok8TMOddwtD9EUwXT+AU9VcAfB+KRKuoZR0Kfp2h4OCth+i4FwYvN8JH9yq3TDWby117pmsajeLqYVnW/dmBfP/uMvq9/Bi//G8ju5dvpXFsGzo8Gfun89iaqqrMmjWL9evXo9VqGTSobIy7r776ioSEBPLy8pgyZQqdOnUiKyuLKVOmcP78eXQ6Hc899xzR0dHMnTsXKBvvddWqVcyfPx9FUWjUqBGvvfYaJSUlvPrqqxw7dgyTycTIkSPp3bu3TbZXCjUhhBAOr2PPaGoEBfLmlHdo6OpL7oVsvAOrAqD3cKXec/1Ifm0JTRIeJ339Xs74uZB7tZDwyLA7vq5ztUB8Ajz46l8f07RPe6r06AX0x5ybhSnvAlrXcBRN2Vk+rV5PVQ8tBZevf7WmHLuA2WxGo7m/Vxopv3k9xeCMrlGU5XZo6/ps/GAV7w1+lZYDonnmi6nonSvHlFLr1q1jz549rFq1CqPRyCOPPEJxcTHh4eEsX76cH3/8kXnz5tGpUydee+012rRpw9/+9jfOnj3LkCFDWLHievP4xYsXmTFjBsuWLSMgIIAJEyawefNm9u3bR2RkJG+88Qb5+fkMHjyYxo0bExQUVOHbK9eoCSGEqBTqNgxn5POPkZSbzg/zV5a7L7BrU0yFxSS/ugSP1hHkFxdTIyTwdwsojYcvLTuHcWLnYdoNuz4tk+JVFXPaYXTB5Yf1qFmrCk5o0VHW3Flw1cjZlPLDdFSU4e+OY+zSl2k3rFulKdIAEhMT6dmzJwaDATc3N1auXImfnx9dupTN4BAWFsalS2Xj2u3YsYOBAwcCEBQUROPGjdm/f7/ltfbu3UuzZs0ICCgbtuTNN9+kS5cubNu2jS+++IL4+HiGDh1KYWEhx44dq+AtLSNn1IQQQlQaraJbkJWRzSevLaDbM33xquZjue+hV4ezocsknJ/oghLoTus2kb/7ehoPX0JCnHh+1Qx0hutfmebsNBQvfxR9+Y4IQY2C2JOUfe06tRIADu9IJKR+vfu0hXfPye3WnSQcnU6nK9cxIy0tjcLCQrTasjObN97324EtVFXFZLo+hMpvXysnJwcomyrszTffJDKy7P9IVlYWXl62GdZEzqgJIYSoVHoN7E7zji2Y+sS0csudfD3pkfgOKb8c4nzuJVpF3/n6tLInuUJxYbmiRy0qoDRlG7raTW96ePVG4QDlppJK3n3wD26JuJWWLVvy/fffYzQauXr1Kk8++SQXL1685WPbtGnDV199BcDZs2fZs2cPTZo0sdzfqFEj9u3bR2Zm2YDKr7/+Ohs2bKBNmzaWXqEZGRn06dOHCxcuWHnLbk0KNSGEEJXOP2f/k5wL2Xz50dflll/JyUPrpOfypTyqVf/9gWcVRQGtDtVkBEAtuUrJnm/RN+yAxvXmMyw1Wj0EgIfmelPjgaQ/36FAXNe1a1eaNWtG//79GThwIMOHD6d27dq3fOyUKVPYsWMHcXFxPP3000yfPh1//+vve7Vq1ZgyZQpPPPEEvXv3xtnZmf79+/P3v/+doqIievfuzYgRI5gwYQLBwcEVtYnlyIC3Qgi7Ise6uF8OrN/NxGdeJq5zB1p1b0N420j2rNpGRm4uZy5l8fxr4+7qdUoObUZXPQLFzZuSXavQRbRD61P9lo9VTSZebvAI2UUqO41lZ3m0WoWNKWtwcXW9b9smHhxyRk0IIUSl1KhLc95e/jZ70k6Sl3WZLyd9yLbF68kxXr27Zs9rNB6+mC+dp2T3anThbW5bpAEoWi3VA5zQKxpclLJr2kwmleRtu/709ogHkxRqQgghKq3wBqG069qWC+YCnvhoApM2zubAnoM0b9vk9598jcbTF2PKdnR1WqD1/f3hGarXKRsWxPOG69T2b0u89/BCIIWaEEKISm7oU4PY8v3PnD5+hoL8QgBc3e6+GVLx9MOp7V/R+te6q8dXjygr5jyV69epJe0+fPeBhbiBFGpCCCEqNZ1Ox8QZ/yBh4lvs2rqHplF3fzYNQFE0aDz97vrxNZqUDcVxY4eC5IOnMZvN97ReIUAKNSGEEA+AkLBgYnr8hYSJb9EmpqVV1+XbOBK9RsUZLfprX7P5hSWcOHLSqusVlZMUakIIIR4If328P73/2pN6D9W16nq0ftWp5qWiKEq55s/9W3dadb2icpJCTQghxANBo9HwzORRlhHsrUXR6QmsUXYNnIfmhg4Fv+y16nofFK+88grx8fH06tWLhg0bEh8fT3x8PF9/XTZmXkpKCrGxjj/5/K9kCikhhBDiPgsK8ycx+Uz5DgV7bTNXZGUzbVrZjBNpaWkMHz6clSuvz+u6YsUK3nrrLfR6/e2e7nDkjJoQQghxn9VuVR8AN0Vv+aK9kH6JrIvZtgtVyV25coUNGzYwe/ZsW0e5r+SMmhBCCHGfBXbvhcu0dVw1KrgrBvKuTdC+75ckuvTpaON0f87ixYv54IMPKCwsvO+v7erqyqhRo3j00Ufv+bkeHh7MnTuXtLS0+57LluSMmhBCCHGfaatWIzjYHSg/8G3SdsefoWDx4sVWKdIACgsLWbx4sVVe21FJoSaEEEJYQe2oRkD58dT2Jx6wVZz75tFHH8XVSvOWurq6/qGzaZWZNH0KIYQQVhDasyMs3lGuQ8HRlLNcLbyKi6uLDZP9OY8++qgUUxVIzqgJIYQQVhDcrB4aBXSKBlfLBO1mDu6V6aTE3ZNCTQghhLACg4sTNcIDAfC4ceDbnUm2ilSp1KxZk40bN971ckclhZoQQghhJbXbl80remOHgv07ZOBbcfekUBNCCCGspHbzCOA3E7TvTcFkMtkqknAwUqgJIYQQVlK7Rdm8os5oMfw6QXv+VY4kHbVlLOFArFqozZs3j9jYWGJjY5k5cyYA27ZtIy4ujm7dujFnzhxrrl4IIYSwKa9qPnj7eaEoCl4aJ8vyX7Y4/nhqomJYrVDbtm0bW7duZfny5axYsYKDBw+yevVqJk+ezHvvvceaNWtITk5m8+bN1ooghBBC2FztNg0A8L6hQ8HOLYm2iiMcjNXGUfPz82PixIkYDGX/MUNDQ0lNTSUkJISgoCAA4uLiWLduHTExMeWem5eXR15eXrll6enp1ooqhLAROdbFg6BOy3rsXbUdb40TXLs0LSkx2eHHUxMVw2qFWnh4uOXv1NRU1q5dy6OPPoqfn59lub+/PxcvXrzpuZ988gnz5s2zVjQhhJ2QY108CGo1K7tOzaBocUNHAaWUlprYu2M/bTu1sXE6Ye+sPjPBsWPHGD16NC+88AJarZbU1FTLfaqqoijKTc8ZMWIE/fr1K7csPT2doUOHWjuuEKICybEuHgSB9YIxGHSUlJTipXGiwFwKlF2nJoXavXvllVfYs2cPRqORM2fOEBoaCsDAgQPZtWsXJ0+eBGDMmDHExsbaMup9YdVCbffu3YwbN47JkycTGxvLzp07yczMtNyfmZmJv7//Tc/z9PTE09PTmtGEEHZAjnXxINDqtAQ3DuV4YgreGgPnzQUA7Nws16n9EdOmTQMgLS2N4cOHs3LlSgDmzJlD9erVeeedd8jOziY+Pp7WrVvj6+try7h/mtU6E1y4cIFnnnmGWbNmWSraxo0bc+rUKU6fPo3JZGL16tVER0dbK4IQQghhF+pERQLgqRgsX7wnUlLJuphtu1CVTKtWrRg2bBgAVatWxdvbm6ysLBun+vOsdkZtwYIFFBcXk5CQYFk2ePBgEhISGDt2LMXFxcTExNCjRw9rRRBCCCHswq8D32oVDV6KgUtqCQCJW3fTc0A3W0arNNq1a2f5e82aNZSUlBAWFmbDRPeH1Qq1l156iZdeeumW933zzTfWWq0QQghhd0KahoMCqOChOFkKtV+27HLIQu2tt94iJSXlvr9uREQEzz///J96jbVr1/L666/z0UcfodNZ/VJ8q3P8LRBCCCHsnIunKzUb1CLtYCreGgNnzGXLd25OvG3HOnv2Z4spa1m0aBELFixgwYIFRERE2DrOfSFTSAkhhBAVoG77RgC4K3r0lBVmWRk5nDyaasNUlcf69etZuHAhn3/+eaUp0kAKNSGEEKJC/FqoKYpCFeX6dFI7ZTqp++Ldd9+luLiYMWPGEB8fT3x8PAcOHLB1rD9Nmj6FEEKIClC7ZT20Oi2mUhMeGgMZpiKgrFAbMvJhG6dzPDVr1mTjxo2W25X1+nc5oyaEEEJUAIOzgdrNy2Yp8L5hgvY9P+/BWGK0VSxh56RQE0IIISpIRHRjAJwVHW5oAbhaVMKBPYdsGUvYMSnUhBBCiAoS3q6h5W+vG86qbf9hmy3iCAcghZoQQghRQYIa1cHJ2QCA5w2F2paV620VSdg5KdSEEEKICqLRaghrUx8Ab8VwrfETTp3P4uypNNsFE3ZLCjUhhBCiAtXr3Bwom07Kl+tn1TatkLNq4mZSqAkhhBAVqO6N16lpnS1/b1r+gy3iCDsnhZoQQghRgfxqB+LuUVageWuc+HXyqOTjaWRn5tgumLBLUqgJIYQQFUhRFMJbhQOgV7T4Uda5QAW2rPrRhsmEPZJCTQghhKhgDXq0sfzto7ne/Pnjl2tsEUfYMSnUhBBCiApWN6aZ5W/3G4bp2J18goL8QltEEnZKCjUhhBCignn6V8GnigsAThodVa9NvW00q2z/bqstowk7I4WaEEIIYQP124Va/q7G9ebPjUtW2SKOsFNSqAkhhBA2ENm1ueVvV931Qm37roMySbuwkEJNCCGEsIHwrn/BxaVsbgKDosNDLfu7wGhi1+ZEW0YTdkQKNSGEEMIGdK7uRLauCZQN2RGgXO9U8OPiFbaKJeyMFGpCCCGEjTTudkPvzxtmKfhp615MJpMtIgk7I4WaEEIIYSMRXdvj4lrW5OmiMeCsln0tZ18tYeWib2wZTdgJKdSEEEIIG9H5BtKgiR9Q1vwZqnG13Pfe6x+Qm51rq2jCTkihJoQQQtiIomh4qFOk5bav1h2va50K8vILeS/hQ1tFE3ZCCjUhhBDChup2boPztd6fpRqF9nhZ7lu55FuS9xyyVTRhB6RQE0IIIWzIKbAODR6qYrntrnUiTC3rWKCqKjMnvy0dCx5gUqgJIYQQNqQ4u9GofR3L7SwNdFY9r00qBUeSUljx2WrbhBM2J4WaEEIIYWMRMc1xci5r/izSgE7R0cbsYbl/fsKHXJKOBQ8kKdSEEEIIGzPUCC3X/JmlVWmDB1W41rEg9wrzpWPBA0kKNSGEEMLGNFUCadjC33I7SwtmoIvZ27Lsm8/XcOLISRukE7YkhZoQQghhY4pGS932ja43fyqw20nFQ+NEqGIAwGw2886r820ZU9iAFGpCCCGEHXCuEUq3PkGW26UKpBhUaiqeKNeW7di0k+2bdtomoLAJqxdq+fn59O7dm7S0NAC2bdtGXFwc3bp1Y86cOdZevRBCCOEQNH4hRMUE8PjY+lSpen2C9lKdnmoaF8vtua/Nl+E6HiBWLdT279/PkCFDSE1NBaCoqIjJkyfz3nvvsWbNGpKTk9m8ebM1IwghhBAOQePqheLuQ1g9L8ZNfoimwdeLsyCtB5pr59WOHz7J3BfeoaSoxFZRRQWyaqH25ZdfMm3aNPz9yy6QTEpKIiQkhKCgIHQ6HXFxcaxbt+6m5+Xl5ZGWllbuX3p6ujWjCiFsQI51IcrT120LgJOzlj5xfkRrinExg0HRUlPrZnnc0s9X8WbsixRcumKrqKKC6H7/IX/cv//973K3MzIy8PPzs9z29/fn4sWLNz3vk08+Yd68edaMJoSwA3KsC1GeNjA+6u9DAAAQEklEQVQMTbVQzBdPoK/jR8Mq++G8E9mKgpfGnXQKKcGMETO7U46y+o3PGZQwytaxhRVZtVD7LbPZjKIoltuqqpa7/asRI0bQr1+/csvS09MZOnSo1TMKISqOHOtC3MzQsCNF2WfROIN7qDdBBfmouZ74mLWY8OQ7TdnAt+fN+axf8h0tB8ZQp0WEjVMLa6nQQi0gIIDMzEzL7czMTEuz6I08PT3x9PSsyGhCCBuQY12ImynO7ujr/wXjgQ0Ywv3xO3GIy0VOXC5yogmu7FfySVdLMQOHSnNYOOE/TPt+Nlp9hX6liwpSocNzNG7cmFOnTnH69GlMJhOrV68mOjq6IiMIIYQQdk8b1BCNTw2cIquj6DXUqpKHTmNGQaGXyQfna61RRsxsSknm23e/snFiYS0VWqg5OTmRkJDA2LFj6dWrF3Xq1KFHjx4VGUEIIYSwe4qioG/UGW0Vd1zbhaHXmgmpkgeAP3oGmKqiu1asFWHi7TkLSEs5bcvIwkoq5Dzpxo0bLX9HRUXxzTffVMRqhRBCCIelcfdBF94a16JiivadpQpX8XW9SlahC0E40VepwtdqDiqQby7hmQHP8f66+VSr4X/L67+FY5IGbSGEEMJO6eo0Ry25iseAUi5//ANB3le4Uqyn2KQjrNSFDoo3PyplnQsu5GTTp9Vfcfd0IzSiDqH1a9O4ZSP+0q0d7h5uv7MmYa+kUBNCCCHslKLRYmgQg75+NEUHsihO3Euo72WO53hTYtTSWnUjU2MiWb0+nlp+XgH7Ew+wP/EAyz79BidnA+26RNG9bxfadmqNk7PTHdYo7I3M9SmEEELYOUVRqPLPv4NWg6u+lPq+2XhXLQWgu8mD+hovPBUDWm5u8iwuKmHj6s28+OS/6NmkPz+u3VLR8cWfIIWaEEII4QAMobXw+GvZuIN6rUqoczYBngXoUehV6kY3pSptdNVoofenga4KwVp33DX6cq+Rn5fPx3M+tUV88QdJ06cQQgjhILxGD6fguw2Yc3JRFKjpmY+b3sipHE/CSjUEKipnDJClcaYKzgThQaFqxKWKyqlSIxmX8+nUO8bWmyHugZxRE0IIIRyExt2dqi+/iLaaP1zr2VnFpZiwqpcBFTdVoX6xltYY8dCYAHBV9Ci5BjoVaVnoq6FPrsyn60jkjJoQQgjhQFzatqLGt5+jqirmK1co/OAVvFKPQ0oxRw84A2AocqKTdyF7TCbOGg0ApJY68/llPQMTk6hqyw0Q90TOqAkhhBAOSFEUtJ6euI2agi64BrWaQXCT69ek5eS60rl9EA9Vu/5Vf9ms5fs0oy3iij9IzqgJIYQQDkzj6YPr+DmoeTk09fSl6JEZZGw9AsCJtZl4AfU0Ckf1KmYFSgq1tg0s7omcURNCCCEcnGJwQuMbiNagp9WC53Gv7Vfufj+zQotihXolCtHtm9sopfgj5IyaEEIIUYkYvNxo97+XSH71M4xXruLs742TnxfO/l64hfgT0LWZrSOKeyCFmhBCCFHJuAX70fqj8baOIe4DafoUQgghhLBTUqgJIYQQQtgpKdSEEEIIIeyUFGpCCCGEEHZKCjUhhBBCCDslhZoQQgghhJ2SQk0IIYQQwk5JoSaEEEIIYaekUBNCCCGEsFMOMzOByWQCID093cZJhBABAQHodNb5+JBjXQj7Yc1jXdwdh9n7mZmZAAwdOtTGSYQQGzZsoGbNmlZ5bTnWhbAf1jzWxd1RVFVVbR3ibhQVFZGcnIyfnx9arbZC152ens7QoUP57LPPCAgIqNB132+yLfbLkbbHmr+y7/ZYd6T9dSNHzO2ImUFy3w9yRs32HGbvOzs706JFC5tmCAgIqDS/LGRb7Fdl2557da/HuqPuL0fM7YiZQXILxyadCYQQQggh7JQUakIIIYQQdkoKNSGEEEIIO6V9+eWXX7Z1CEfg5ORE69atcXJysnWUP022xX5Vtu2xNkfdX46Y2xEzg+QWjs9hen0KIYQQQjxopOlTCCGEEMJOSaEmhBBCCGGnpFD7HatWraJXr15069aNzz77zNZx7sq8efOIjY0lNjaWmTNnArBt2zbi4uLo1q0bc+bMsTz28OHD9O/fn+7duzNlyhRKS0ttFfuO3njjDSZOnAjcPvP58+cZOnQoPXr04KmnnqKgoMCWkW9p48aN9O/fn549ezJ9+nTA8d+bipKRkcGoUaPo27cvgwcPJi0tDYC8vDxGjRpFz549GTp0qGVmg5KSEiZMmEDPnj3p168fJ06csFn2Q4cO0bBhQ8vt22VTVZU33niDHj160KtXL3bv3m2TvLt372bgwIHEx8czYsQIzp07BzjGvv6VvX92V8bPaWElqrit9PR0tWPHjuqlS5fUgoICNS4uTj127JitY93Rzz//rA4aNEgtLi5WS0pK1OHDh6urVq1SY2Ji1DNnzqhGo1F9/PHH1U2bNqmqqqqxsbHq3r17VVVV1UmTJqmfffaZLePf0rZt29TWrVurL774oqqqt888atQodfXq1aqqquq8efPUmTNn2ibwbZw5c0Zt3769euHCBbWkpEQdMmSIumnTJod+byrSiBEj1CVLlqiqqqpLlixRn332WVVVVfWVV15R33//fVVVVXX58uWW5R999JH6r3/9S1VVVd25c6f68MMP2yC1qhYWFqqDBw9W69ata1l2u2xr165VR44cqZpMJvXkyZNq165dVaPRWOGZO3bsqB4+fFhVVVVdunSpOmbMGFVV7X9f/8reP7sr4+e0sB45o3YH27Zto02bNnh7e+Pq6kr37t1Zt26drWPdkZ+fHxMnTsRgMKDX6wkNDSU1NZWQkBCCgoLQ6XTExcWxbt06zp07R1FREU2aNAGgf//+drd9ubm5zJkzhzFjxgDcNrPRaCQxMZHu3buXW25PfvjhB3r16kVAQAB6vZ45c+bg4uLisO9NRcrJyeHIkSMMHjwYgAEDBjB+/HgANm3aRFxcHAC9e/dmy5YtGI1GNm3aRJ8+fQBo2bIlOTk5nD9/vsKzJyQkMGLEiHLLbpdt8+bN9OrVC41GQ+3atQkMDGTv3r0VmrekpIRnn32WevXqARAREcGFCxcsue15X//K3j+7K9vntLAuKdTuICMjAz8/P8ttf39/Ll68aMNEvy88PNxyQKemprJ27VoURbnldvx2+/z8/Oxu+6ZOncpzzz2Hp6cncPN78mvmS5cu4e7ubpmTzh635fTp05hMJsaMGUN8fDxLliy57f8xR3hvKtLZs2epXr06CQkJDBgwgHHjxqHX64Hy/yd0Oh3u7u7k5OTcch+mp6dXaO4NGzZQVFREjx49yi2/XbaMjAz8/f1vWl6RDAYD8fHxAJjNZubNm0eXLl1uym1v+/pG9v7ZXdk+p4V1Ocxcn7ZgNptRFMVyW1XVcrft2bFjxxg9ejQvvPACWq2W1NRUy32/boe9b9/SpUsJDAwkKiqKZcuWAbd/T26V3Z62BcBkMrFr1y4WLVqEq6srTz31FM7OzrfcHnt/b6xp7dq1zJgxo9yykJAQDh06xNixY5k0aRJLly5l4sSJLFq06Kbnq6qKRqO5aZ/9uryiMtepU4f8/HwWLlx4y4y3ynar991ame+Ue+HChZSUlDBx4kRKS0sZPXr0LZ9vi319Nxzl+KkMn9PC+qRQu4OAgAB27dpluZ2ZmVnu16692r17N+PGjWPy5MnExsayc+dOy0W/cH07AgICyi3Pysqyq+1bs2YNmZmZxMfHc/nyZQoLC1EU5ZaZfXx8uHLlCiaTCa1Wa5fvla+vL1FRUfj4+ADQpUsX1q1bh1artTzGUd4ba+rZsyc9e/Yst+zMmTP069ePjh07AmXNbr92xvD39ycrK4uAgABKS0spKCjA29ubatWqkZGRQXBwMGDdfXirzEuXLuX9999n6NChlmXx8fF89tlnt80WEBBARkaG5fHWft9vlRugoKCAp556Cm9vb+bPn285e2kP+/puOMJnd2X5nBbWJ02fd9C2bVu2b99OTk4OV69e5fvvvyc6OtrWse7owoULPPPMM8yaNYvY2FgAGjduzKlTpyxNb6tXryY6OpoaNWrg5ORk6Vm2cuVKu9q+//73v6xevZqVK1cybtw4OnXqxIwZM26ZWa/X06JFC9asWQPAihUr7GpbADp27MjWrVvJy8vDZDLx008/0aNHD4d8bypacHAwAQEBbN68GYAff/yRyMhIAGJiYlixYgVQVty3aNECvV5PTEwMK1euBGDXrl04OTlRvXr1Csv88MMPs379elauXGnJsXLlStzd3W+bLTo6mlWrVmEymTh9+jSpqak0atSowjL/asKECYSEhPD2229jMBgsy+11X/+WvX92V6bPaWF9MjPB71i1ahXvv/8+RqORgQMHMnLkSFtHuqPp06fz9ddfW37ZAgwePJhatWoxY8YMiouLiYmJYdKkSSiKwpEjR3jppZfIz88nMjKSGTNmlPtgthfLli1j586dJCQk3DbzuXPnmDhxItnZ2QQGBjJ79my8vLxsHb2cr776ioULF2I0GmnXrh0vvfQSv/zyi0O/NxXl5MmTTJs2zXI9YkJCArVq1SI3N5eJEydy9uxZPDw8mDVrFjVr1qS4uJipU6eSnJyMwWBg+vTpluLOFiIiIkhJSQG4bTZVVZk5cyZbtmwBYNKkSbRv375Ccx46dIh+/foRFhZmuebT39+fDz/80GH2Ndj3Z3dl/ZwW1iGFmhBCCCGEnZKmTyGEEEIIOyWFmhBCCCGEnZJCTQghhBDCTkmhJoQQQghhp6RQE0IIIYSwU1Koifvi8ccfJycnh5EjR3L8+HFbxxFCWIkc60JULBmeQ9wXERERbN++3TLqvhCicpJjXYiKJVNIiT9t0qRJAIwYMYLjx4/z5ZdfUlhYyOzZswkMDOTUqVO4uLgwatQoFi1axKlTp+jWrRuTJ08GYOPGjcyfPx+j0YizszMvvvgiTZs2teUmCSFuQY51IWxAFeI+qFu3rpqdna127NhRTUpKUnfs2KHWr19fPXjwoKqqqvrEE0+ogwYNUouLi9Xs7Gw1MjJSTU9PV0+dOqX27t1bzcnJUVVVVY8ePaq2a9dOLSgosOXmCCFuQ451ISqWnFETVlOzZk0aNGgAlM3V6OHhgcFgwMfHBzc3Ny5fvkxiYiIZGRk89thjlucpisKZM2eoV6+ejZILIe6FHOtCWI8UasJqfjsX3a/zBt7IbDYTFRXF22+/bVl24cIF/P39rZ5PCHF/yLEuhPVIr09xX2i1WkpLS+/5eVFRUfz888+cOHECgM2bN9OnTx+Kiorud0QhxH0gx7oQFUvOqIn7okePHgwbNoyCgoJ7el5YWBivvvoq//jHP1BVFZ1Ox/z583Fzc7NSUiHEnyHHuhAVS4bnEEIIIYSwU9L0KYQQQghhp6RQE0IIIYSwU1KoCSGEEELYKSnUhBBCCCHslBRqQgghhBB2Sgo1IYQQQgg7JYWaEEIIIYSdkkJNCCGEEMJO/T/N2ShHjCPKHwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfc46e4e0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Line plots on multiple facets\\n\",\n    \"=============================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"sns.set(style=\\\"ticks\\\")\\n\",\n    \"\\n\",\n    \"dots = sns.load_dataset(\\\"dots\\\")\\n\",\n    \"\\n\",\n    \"# Define a palette to ensure that colors will be\\n\",\n    \"# shared across the facets\\n\",\n    \"palette = dict(zip(dots.coherence.unique(),\\n\",\n    \"                   sns.color_palette(\\\"rocket_r\\\", 6)))\\n\",\n    \"\\n\",\n    \"# Plot the lines on two facets\\n\",\n    \"sns.relplot(x=\\\"time\\\", y=\\\"firing_rate\\\",\\n\",\n    \"            hue=\\\"coherence\\\", size=\\\"choice\\\", col=\\\"align\\\",\\n\",\n    \"            size_order=[\\\"T1\\\", \\\"T2\\\"], palette=palette,\\n\",\n    \"            height=5, aspect=.75, facet_kws=dict(sharex=False),\\n\",\n    \"            kind=\\\"line\\\", legend=\\\"full\\\", data=dots)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## boxplot\\n\",\n    \"箱形图（Box-plot）又称为盒须图、盒式图或箱线图，是一种用作显示一组数据分散情况资料的统计图。它能显示出一组数据的最大值、最小值、中位数及上下四分位数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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JSYYgQEUlMo9FYbUWkp6cDgCyDmddEiIhIMIYIEREJxhAhIiLBGCJERCQYQ4SI7I5LwbsOhggR2R2XgncdDBEisiverdK1MESIyK54t0rXwhBxYux3Jjni3SpdC0PEibHfmeSIS8G7FoaIk9Lr9Thx4gTMZjMKCgrYGiHZ0Gq1lltfcyl45WOIOKm8vDxLl0FdXR1bIyQbXAretTBEnNQ//vEPq4+JpMS7VboOhoiTCgwMtPqYSEqNS8GzFaJ8DBEndevWLauPiYgcgSHipMLDwy0XL1UqFUfAEJEkGCJOSqvVNhlGyb5nIpICQ0QAOUzyu3sETEREBPueiUgSDBEB5DLJjyNgiEhqDJEOktPichwBQ0RSY4h0EBeXIyL6Px5SF+BsWlpcbuLEiRJXRUQtyczMRHFxseDtG7dNT0+3qY7Q0FDEx8fb9BlyxRDpoPDwcJw4cQImk4mLyxHJXHFxMa5cLkZglxBB23t7dgYAVBnqBddws/yq4G2dAUOkg7RaLQoKCgBwcTmi1uj1emzbtg1JSUmSX7ML7BKCscNmSbb/nCPvS7ZvR+A1kQ7i4nJEbZPLCEYSH0NEAA6tJWqdnEYwkvisdmc9/PDDlqU17mY2m6FSqXDq1CnRCpOzxqG1RNRcSyMYOfhEuayGSE5OjqPqICKF4AhG12I1RL7//nurG4eGhlp9fe3atThw4ABUKhUmTJiA6dOnIz8/H6mpqTAajRg1ahTmz5/f8aqJSLY4gtG1WA2R7du3t/qaSqWCVqtt9fXCwkKcOHEC+/btQ11dHUaPHo3IyEgsWrQI27dvR0hICGbNmoWjR48iOjpa+DcgIlnhCEbXIjhE2qLRaLBt2zZ4eHjg2rVrMJlMMBgM6NWrF3r27AkAiI2NRW5uLkOESEEaRzDm5+dzBKMLsBoib775Jl599VXMnj27xdc3btxo9cM9PT2xbt06bNmyBTExMSgtLUX37t0trwcFBeHatWvNtjMYDM1GdJSUlFjdFxHJh1arRUlJCVshLsBqiERGRgKATf8Q5s2bh5kzZ2L27NkoKipqMtqrcZTXr23duhXr168XvE8lKCwstHQJtKaiogIA4O/v3+p7IiIioNFo7FobUVs4gtF1WA2R4cOHAwDi4uJw69Yt/Otf/4KHhwcGDhzYZhP1559/xp07dzBgwAB06tQJWq0Wubm5lhspAUBZWRmCgoKabZuUlIS4uLgmz5WUlGDy5Mnt/mKuoLG1Zi1EiIjE1K5lT44cOYKFCxeiX79+MJlMuHLlCtasWYNBgwa1uo1Op8O6devwySefAAAOHTqExMRErFq1CpcuXUJYWBhycnKQkJDQbFu1Wu3y/agajabNFkTjonA84yMiqbQrRNauXYsdO3agX79+ABqG/i5ZsgSZmZmtbhMdHY0zZ85g/PjxcHd3h1arxZgxYxAYGIjk5GQYjUZER0cjJibGPt+EZKutrjl2yzkf/p1So3aFiEqlsgQIANx3332WGanWJCcnNztLjoyMxL59+zpYJikZu+WUh3+nrsNqiJSXlwMA7r//fnz44YdITEyEm5sbMjMzMXjwYIcUKAWeZdlXW11z7JZzPvw7pUZWQ2Tw4MFQqVSWVsfbb79teaxSqbBw4UKHFCk3PMsiImpgNUR++OGHNj8gJycHY8eOtVtBcsCzLCKi9rF5KfgPP/zQHnUQEZETsjlE2nOBnYiIlMnmEGlpxjkREbkG3tmQiIgEa9c8ESJyjMzMTBQXF9v0GY3bNw4AESI0NBTx8fE21UGuweYQ4TURIvspLi7GlcvFCOwSIvgzvD07AwCqDPWCtr9ZflXwvsn12BwisbGx9qiDiP4rsEsIxg6bJdn+c468L9m+yflYDZG2AuLzzz/HjBkz7FoQERE5D6shsmTJEkfVQURETshqiNw9a7u8vBy3b9+G2WyGyWTC5cuXRS+OiIjkrd1LwW/atAkA4O7ujtraWvTt2xeff/65qMUREZG8tWueyN69e/HVV19h5MiRyMvLQ2pqKvr27St2bUREJHPtaokEBgYiKCgIffr0wQ8//IDx48dj8+bNYtdGROT0DJU3UF5xx6Z5O3Ke+9OuEPHw8MDly5fRp08fnDx5ElFRUTAajXYvhohIaWrr7sCkqsXV26WCP6Pep+H/Qj+j5nq14H23pV0hMmvWLCxZsgTvvfce1q5di+zsbAwbNky0ooiIlMSnmy96xd0n2f4vZX0v2me3K0R+97vfYevWrQCA7OxsXLp0CW5uXHaLiMjVWU2C8vJylJeXY+bMmdDr9SgvL4fRaES3bt0wb948R9VIREQyZbUl8tJLL+Hbb78F0HDPcMtGHh4YOXKkuJUREZHsWQ2RxrsWvvLKK0hNTXVIQURE5DzadU0kNTUVp0+fxjfffIPa2lpERUVh0KBBYtdGRGSTiooK3Co3SLqoZF2dESrxBkdJrl1Xx7OzszFv3jzo9XpUVVXhxRdfxK5du8SujYiIZK5dLZGPPvoIGRkZCAoKAgDMnDkTM2bMwKRJk0QtjsheCgsLUVBQ0OrrFRUVAAB/f3+rnxMREdFkTTmSN39/f7iZ/SRdWn9r9jJ4+HpKtn+xtStE6uvrLQECAD169OAQX5KVtu4IWFFRAYPB0OrrjZNnrb0HAL788kurYcQ7ApKraVeIdOnSBV9++SUef/xxAA0/SAEBAaIWRtQRxcXFuHilCD7dfFt+gwfgFtj62aDnf/us3do4Y6zEbVTevt3ia2LOCiaSq3aFSHJyMhYtWoTly5cDADw9PbFhwwZRCyPqKCXPCiaSK6shUl5eDgBYvnw5MjIycP78eahUKoSGhuJPf/oTcnNzHVIkERHJU7snG0ZGRgIAzGYzJxsSEREATjYkIiIbtGuIFQOEiIhawnG6REQkWLtGZwm1fv167N+/HwAQHR2NBQsWID8/H6mpqTAajRg1ahTmz58vZglEJMD169cVeyc+si/RQiQ/Px/Hjh1DVlYWVCoVnnnmGeTk5GD16tXYvn07QkJCMGvWLBw9ehTR0dFilSFbbU2Oaw/+oCqPHNZ6ulF+FYDJ+rybNsj5TnxkX6KFSPfu3ZGSkgIvLy8AwL333ouioiL06tULPXv2BADExsYiNze3WYgYDIZmM4dLSkrEKlUSbU6Oawf+oJKYpJx3wzk3zkO0EOnXr5/lz0VFRdi/fz+mTJmC7t27W54PCgrCtWvXmm27detWrF+/XqzSbG4F2KMFUFxczMlx1Iwc1nrKOfI+yiuuSrZ/ci6iXhMBgJ9++gmzZs3CggUL4O7ujqKiIstrZrMZKpWq2TZJSUmIi4tr8lxJSQkmT55sl5qKi4tx5XIxAruECNre27MzAKDKUC+4BqPRiE5Q7qJsROQaRA2R7777DvPmzcOiRYswZswYFBYWoqyszPJ6WVlZk4UdG6nVaqjVajFLQ2CXEMlX9iT7qaioQE15taStq5rr1ajoUiHZ/omkINoQ36tXr2Lu3LlYvXo1xowZAwAYOHAgLl68iEuXLsFkMiEnJwdDhw4VqwQiIhKZaC2RDz/8EEajEWlpaZbnEhMTkZaWhuTkZBiNRkRHRyMmJkasEsiF+Pv7o9LjtuTXmPw7Wb8fCZHSiBYiixcvxuLFi1t8bd++fWLtlhxMLkOVr1+/DvjZVAYRCSD6hXVSNlsHKQC2D1S4WX4Vbu6Amx8HKpD81NebUHO9VrHX6xgiZDOpBylwSCqRdBgiRNSMyWTCHQnPnpU00s3NzR2e3XwUe72OCzASEZFgbIkQUTPu7u7wDJTu7Jkj3ZwHQ4SIFO1m+VXBC1rermnoUuvkIzzQ6uqM8FTw6hQMESJSrNDQUJu2L6+oBAB0CwoQ/BneFd421SB3DBEiUixbb3PQOHcpOTnZps8QutK2M3DJEJHDPRvq6oxQcSV2InJyHJ1FRESCuWRLRA73bPhb5hLUVSt3FisRuQa2RIiISDCXbInIgdJnsRKRa2BLhIiIBGNLhBSj5rrwOxvWVdcCADx8hU8Kq7leDfQUvDmRU2KIkCJ4e3sjtJvwiWXFNxvuaRLyP81v19xuPW2f3CYnUoay0gLZlmMJyPt4MkRIEbp162bzhDDAtkllSiJ5KCsokO3xPeR8PBkiRNQMQ9l+bJ01D8j7ePLCOhERCcYQISIiwVy2O4vLQxMR2c4lQ4TLQ9uPHBazvFF+FfUqtWT7tzdbTnAA209ybpZfhZ9aGRe1SXwuGSJcHprkyh4jaGw9yfFThypmZBSJzyVDhOxHDotZ5hx5H37+yri8p/SRPKQ8yvjJIyIiSbAlQi6hsLAQBQUFrb5eXNwwmavxLL41ERER0Gg0dq2NyJkxRIgAqNXKuTBP5EgMEXIJGo2GLQgiETBEJKSURdk4JJXIdTFEJKKURdk4JJXItTFEJKKUoZxK+R5EJAyH+BIRkWCih0hlZSXGjh0LnU4HAMjPz0dsbCy0Wi3WrFkj9u6JiEhEonZnnT59GosXL0ZRUREAoKamBosWLcL27dsREhKCWbNm4ejRo4iOjhazjA6zx5wCzicgIlcgaktk165deO211xAU1HDh98yZM+jVqxd69uwJDw8PxMbGIjc3V8wSRKFWqzmvgIgIIrdE3nzzzSaPS0tL0b17d8vjoKAgXLt2rdl2BoMBBoOhyXMlJSXiFNkCzikgImofh47Oqq+vh0qlsjw2m81NHjfaunUr1q9f78jSiIhIAIeGSHBwMMrKyiyPy8rKLF1dd0tKSkJcXFyT50wmE27fvo3g4GDR6yQiovZxaIgMHDgQFy9exKVLlxAWFoacnBwkJCQ0ex+vORAROQeHhoi3tzfS0tKQnJwMo9GI6OhoxMTEOLIEIiKyI4eEyOHDhy1/joyMxL59+xyxWyIip+DM0wq47AkRkczJuXufIUJEJDFnnlbAtbOIiEgwhggREQnG7iyZautCGyDvi21E5BoYIk5MzhfbSNmceTTR3ZTyPaTEEJEpZ77QRqSUExylfA8xMUSIqMOUcpKjlO8hJV5YJyIiwRgiREQkGEOEiIgE4zUREh1HwBApF0OEJMcRMETOiyFCouMIGCLl4jURIiISjCFCRESCMUSIiEgwhggREQnGECEiIsEYIkREJBhDhIiIBGOIEBGRYCqz2WyWuggiap+O3PEyNDS01fdwGRmyF85YJ1IYLiNDjsSWCBERCcZrIkREJBhDhIiIBGOIEBGRYAwRIiISjCFCRESCMUSIiEgwhggREQnGECEiIsEYIkREJBhDhIiIBGOIEBGRYAwRIiISjCFCRESCMUSIiEgwhggREQnGECEiIsEYIkREJBhvjyuRuro6lJSUSF0GETlIcHAwPDyU9ytXed/ISZw/fx7jxo2TugwicpC9e/eif//+UpdhdwwRiXTq1AkAsHPnTgQHB0tcjfMrKSnB5MmTeTztgMfSvhqPZ+PPvNIwRCTi7u4OoKGJGxYWJnE1ysHjaT88lvbV+DOvNLywTkREgjFEiIhIMIYIEREJ5r5s2bJlUhfhqry9vREREQFvb2+pS1EEHk/74bG0LyUfT5XZbDZLXQQRETkndmcREZFgDBEiIhKM80Ts7PXXX8epU6dQW1uLy5cv49577wUAGAwGxMfHIzk5WeIKnZ9Op0NMTIzl2DbauHEjQkJCLI9nzpyJFStWoEePHo4u0Snk5uZi06ZNqKurg9lsxrhx4/DMM8+0+v5du3bB19cXY8eOdWCVzqGjx1JJGCJ29tprrwFo+EU3bdo07N27FwCQnp4uZVmKExQUZDm2rdm8ebODqnE+165dw8qVK5GZmYmuXbuiqqoKU6dORe/evTFixIgWtzl16hQ0Go2DK5U/IcdSSdid5UBnzpxBYmIiHnvsMUuoZGZmIiUlxfKeqVOnoqCgAAUFBZgwYQLi4+OxcOFCqUp2KikpKZg9ezZGjRqFw4cPY/jw4dDpdFKXJUu3bt1CbW0tampqAAB+fn5IS0tD3759sX//fkyaNAlPPvkkYmJicOrUKeTn5+Pw4cNYt24dvvnmG4mrlxdrx/Luf4MFBQWYOnUqgIaf81WrVuEPf/gDnnjiCRw9elSy+m3FlogD3bhxA59++ikqKysxfPhwTJ8+3er7i4qK8NVXX8Hf399BFTqP0tLJLG8tAAAEL0lEQVTSJgtYxsbGAgC6dOmCjRs3AgBWrFghSW3OoH///hgxYgQef/xxDBgwABEREYiNjUXPnj2xdOlSbNy4EYGBgdi9ezc2bdqEjRs3Yvjw4dBoNHj00UelLl9WWjuWvXr1srpdbW0tPvvsMxw+fBhr165FdHS0gyq2L4aIAz366KPw8vJCYGAgunbtCr1eb/X9vXv3ZoC0oqXurJSUFDz44IMSVeR8Xn/9dcyZMwfHjh3DsWPHMGnSJKxevRobNmzA4cOHcfHiRRQWFsLNjR0WbWntWFrTGMb9+vVDeXm5I8oUBUPEge6+l4BKpYLZbLb8v1Ftba3lzz4+Pg6tTwl4zNrnyJEjqK6uxujRo5GQkICEhATs2rULO3fuxDvvvIMnn3wSgwYNwm9/+1vs3LlT6nJlrbVjuXv3bgCw/HzX1dU12a5x4qFKpXJswXbGUwyJde3aFT///DPMZjOuXLmCc+fOSV0SuQAfHx/89a9/tfTXm81mnD17Fl5eXlCpVJg9ezYiIiJw8OBBmEwmAA2r0Db+mf5Pa8dywIAB6Nq1K86fPw8AOHTokJRlioYtEYkNGTIEe/bsQUxMDHr37o1HHnlE6pLIBQwePBh//vOfMXv2bEvr99FHH8WGDRuQkpKCUaNGQaVSISoqCt999x2Ahn+r77zzDvz9/RETEyNl+bLS2rGcO3cufv/732P58uVYv349oqKiJK5UHFz2hIiIBGN3FhERCcYQISIiwRgiREQkGEOEiIgEY4gQEZFgDBEikeTm5lrWSiJSKoYIEREJxhAhsqO1a9fi8ccfx4QJE3Dw4EEAwMWLFzF9+nRMmjQJjz32GJ577jkYjUbs27cPiYmJlm1/+eUXREVF4c6dO1KVT9RhDBEiO/nyyy+Rl5eH7Oxsy2rNQMPNnMaPH49du3YhLy8POp0OR44cQUxMDC5fvoyffvoJAJCRkYG4uDh4eXlJ+TWIOoQhQmQnx48fxxNPPIHOnTvDw8MDCQkJAICXX34ZgYGB2Lx5M5YtW4bS0lJUV1fDy8sLEydOREZGBkwmE7KysjBp0iSJvwVRx3DtLCI7unsVIXd3dwDAiy++CJPJhFGjRmHYsGG4evWq5X2JiYmYMGECNBoN+vXrh549e0pSN5FQbIkQ2cnQoUORm5sLg8GA+vp6y/1Ojh07hrlz52L06NEAgNOnT1tWww0JCcFDDz2Et956C0899ZRktRMJxZYIkZ1ER0fj3LlzSEhIgFqtRv/+/XHr1i3Mnz8fc+fOha+vLzp37oxBgwbh8uXLlu3i4+OxfPlyp72zHbk2ruJLJKH6+nq88cYbuOeee/Dss89KXQ5Rh7E7i0gilZWViIiIwNWrVzFt2jSpyyEShC0RIiISjC0RIiISjCFCRESCMUSIiEgwhggREQnGECEiIsEYIkREJNj/B5g4ofhTxqwOAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfc6278d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Grouped boxplots\\n\",\n    \"================\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"ticks\\\", palette=\\\"pastel\\\")\\n\",\n    \"\\n\",\n    \"# Load the example tips dataset\\n\",\n    \"tips = sns.load_dataset(\\\"tips\\\")\\n\",\n    \"\\n\",\n    \"# Draw a nested boxplot to show bills by day and time\\n\",\n    \"sns.boxplot(x=\\\"day\\\", y=\\\"total_bill\\\",\\n\",\n    \"            hue=\\\"smoker\\\", palette=[\\\"m\\\", \\\"g\\\"],\\n\",\n    \"            data=tips)\\n\",\n    \"sns.despine(offset=10, trim=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## violinplot\\n\",\n    \"violinplot与boxplot扮演类似的角色，它显示了定量数据在一个（或多个）分类变量的多个层次上的分布，这些分布可以进行比较。不像箱形图中所有绘图组件都对应于实际数据点，小提琴绘图以基础分布的核密度估计为特征。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1acfc6aba20>\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfc5daf98>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Violinplots with observations\\n\",\n    \"=============================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"sns.set()\\n\",\n    \"\\n\",\n    \"# Create a random dataset across several variables\\n\",\n    \"rs = np.random.RandomState(0)\\n\",\n    \"n, p = 40, 8\\n\",\n    \"d = rs.normal(0, 2, (n, p))\\n\",\n    \"d += np.log(np.arange(1, p + 1)) * -5 + 10\\n\",\n    \"\\n\",\n    \"# Use cubehelix to get a custom sequential palette\\n\",\n    \"pal = sns.cubehelix_palette(p, rot=-.5, dark=.3)\\n\",\n    \"\\n\",\n    \"# Show each distribution with both violins and points\\n\",\n    \"sns.violinplot(data=d, palette=pal, inner=\\\"points\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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tA0w/zHCgKHK4xz8EcAgMDe5TrnjNnDnJzcwEAL730El566SWL7GMaIg4MYRIHsdh4SYk4DB1S3oEZTFUFuq4c6B/92sNVHMxJVOsKTdOorq6GSMSFu7txD6yjQ4eWFuP3XCKRYMSIEYzbaUuG553BAgqFAi4ugs6aK/Zc5sMR0Gq1UCrJNWSC3txKtOHHWTC3p6uJ0BOtVguapnuUzTDhjPtiRBwYQqFQwN2d3/kzEYehQVYNzGG6F7u6lQx6Y/QLn8/v9RhCd0yegN72bQAiDoR+aG5WwM3tYcglcSsNDVO2uVRKwliHSm/iQBt+rM46jPpkDGXzXa1Wg8ulelRjtSeYDi4g4sAQLS0t3cSBMDRM4kCu6dAxuZV6WzkMF3EQiURoaGgY1AOUpmmo1eo+Vw2A7VcONE2joaEBIhFzCaPD485gGbVaDY1GCzc3PtrbH+Y36HS6YfPlYxpTaLBUykd7u/PVyrcmSqUSFEV185cb9DpwebxhsyEdGBiIioqKboU/zUWv16O1tRViMRd8/kOB0Gj0UKmMezcikYjRB/NgEIlEg67m2hvkycUApmQtiaS7OLS3t8Pd3d1WZjk0zc3NEIv54PE4AIg4DAWlUgmRiNdtdmvQ64bVfgOfz8fo0aMHdWxSUhJOnz6Nl1+O6ubmzMioRWJiGQBgyZIl3VoUOAPDY9rAMqYyD6bsaBMkiWvwNDc3QyodPg8vNlGpVBCJurtEDHod+Dxyfc2hsLAQnp6iYbf/RcSBAUzVQ005DiaIOAyeRzf4CYOno6OjhzjQBh14fOI4GAidTofi4iKEhEj7/ZwzZpoTcWAAUzTIo19AEo45eFpaWobdTI0tlMqOHhMX2mAgOQ5mUFpaCq1Wh9DQ/sXBGXtIE3FggIfRIGTlwAQajQZKpQpubsTtwQS9rxz04A6TzeihkJ+fDw6HwqhR/YuDMxbaJHcHA5hyGvj87peTiMPgMF03V1eycmCCjo4OSCTdhZamaXA4ZOUwEHl5uQgKcu0WBtwbpgmiM0HEgQF6FQeKQ9xKg8R03cjKYejodDqo1ZoewRIA4IRJvYzS3NyM2lo5QkMHjjh0xpa2RBwYQKfTgculwOE8/LbxRS5k5TBITNfNVOGWMHj6iqQDjB0LCX1jqpAaHu4x4Ged8btOxIEB9Hp9j2QivlCCFie8YayBKW/E1BuDMHj6EgeKomCgnW8TlUkePHgADw8hvLz6T27j8ARQKJqtZJX1IOLAAAaDoUfNFb7IBR3t7U4ZxcA27e3toKieocEEyzEJ7aOrMIriQK8jLUL7QqPRoLCwEOHh7gOWxhBKpFAompzuu866OLz33nt4/fXXAQD379/HmjVrsHjxYrzxxhs26YvKBsaVQ/cbiCeUgKbpzhwIgvm0t7dDLObbvF6NM9CnOHC5pH90PxQUFECn0yEiYuAeDULXEdDr9Z09XZwFVsUhJSUFx44d6/z5//2//4ff/OY3SExMBE3TOHjwIJvDWw2DwdDjQcYXuQBwTl8k2xija8iqgQn62r/hcHjQ6pwv/JIp7t27B5GIh6Ag1wE/K5Z6AwBqamrYNsuqsCYOCoUCH3zwAXbt2gUAqKyshEqlwmOPPQYAWLNmDc6cOcPW8FaFpukekR88obFdoGnmRjCf3uLyCYOjtbUVAgG3R0VRDpfvlLH5TKDX6/HgwX2EhbmDyx34ESmSeoGiKFRWVlrBOuvBmjj85je/wauvvgqp1Jg8IpfL4ePj0/m+j48Pamtr2Rreqhhjxh9ZOQjJymGwKJUdEInIyoEJ+iolz+HxoVGrnbLsw1ApKSmBUqnC2LEDRykBRqEVu/ugvLycZcusCyvfwEOHDsHPzw+xsbE4etTYxPtR14txtm2ZTzknJ8cuO6zV19f3eI0vMq4ccnNzyRfQQlpaWuDt/bBRu0ajQXp6ug0tclyqq6t6LWDI4fFB0zRu3bpFymg8wp07d8DjcTB6tPkVlV08/VFamoVbt27ZZRn06Ohoi49hRRwSEhJQV1eHlStXorm5GR0dHaAoqlst9fr6eshkMovOGxkZybSpjFBQUIDWVnm31zg8Abg8PqRS6aD+MMOZhIQfumWkCgQCcg0HyblzZyGV9gzF5PKEAIAJEybA1XVgv/pwwWAw4Pz5cwgNlfbb3OdRXL0CUVd0B97e3oMuDW5vsCIO//nPfzr/f/ToUdy8eRP79u3DihUrkJ6ejujoaHz//feIi4tjY3iro9fre20fSBLhLMfYdUszYLkCwsDodDq0tbV1una7wuUbXU1qtZqIQxcqKyvR0tKKuXMte8C7+QSBoijk5+c7jThYdf3zpz/9Cfv27cOSJUvQ0dGBbdu2WXN41uhLHHhCF1JCw0K0Wi1omibiwAAKhQI0Dbi7C3u8x+UZxcEe3bS2JCcnBxwOZVZWdFe4fCFcPP3x4MesameA9V2/NWvWYM2aNQCAcePG4fDhw2wPaXW0Wu2PHcu6wxe7orlZ3ssRhL4wFTCzZElP6J2GhgYAwIgRvYgD3/gaEYeH0DSNe/dyEBzsNqiACOnIUFTlXENTUxNGjBg4P8Lesb+dEwdEo9H0qMgKAHyRK1pbW8iGtAU8FAdyaw4Vkzh4evbcc+AQceiBXC5HQ0MjxowZ3IPdY2QYAGOOhDNAvoEMoNGoe32YCcRu0Ov1nfVtCAPzsDcGWTkMFblcDpGI12tCIe9HcXDGaqKDxfRQHzPGMpeSCaGrB8TuPsjMzGTSLJtBxIEBTA3cH4UvNm70NTc7X1EutjDNZIlbaejI5bXw9hb1GjJucis5Yx+CwXL//j34+7sMqY/IiIAxqKioQGNjI4OW2QYiDgxgFIeeDzOB2A2AcWOQYB4mcSArh6FhMBhQU1MDmUzS6/ucHzekycrBSEtLCyorqxARMbhVg4kRAWMBGHMlHB0iDkNEo9FAq9X1WkFUIDaGEJKVg/mYZrIkQ3poNDY2Qq3WYOTI3sWBoihw+UKy5/AjeXl5AMzr3dAfAokUrl4BuH37jsPvNRJxGCL99R7gCkTgcHlk5WABppksWTkMDVMpBz8/lz4/wyPi0Elubi7c3ATw8REP+VyeQRPQ2Njg8OU0iDgMkYdVL3v6KSmKgkAiJeJgAcZsepDCe0OkpKQEQiGv34cdhy8kbiUY85QKCwsQGiplpEy8h38EOFyew5d8IeIwREwuo97q1wDGcNamJiIO5kJ6OTBDUVEhgoJc+r2OXJ6ArBxgzIpWqzUW1VLqDy5fAA//MbibmenQG/5EHIaIaVUglfZMNAKMPkiy52A+7e3tpJfDEGlsbERjYxNCQvp/2HH5InSQlQOKiooAAMHBboyd0yt4IrQaDbKyshg7p7Uh4jBEmpqaIBLx+vSRC8RuaG9vI7XzzaS1tYX0jh4iuT+WcAgNHUgciFsJAIqLi+HjI4ZEwtx95+LpB7GbJ27dusXYOa0NEYch0tTUBHf3vuOiTbkOpMaSebS0EHEYKvfu3YOXlxheXj0zo7vCFYigGubiYDAYUF5ehsBAZosPUhQFz+CJqKioQFVVFaPnthZEHIZIY2NDr7VrTJhyHYhraWAMBgNaW9t6bU5DMI/W1lYUFxeb1aiGxxdBq9UO61WtXC6HWq1BQADzlWk9gyaAw+U57OqBiMMQ0Ov1UCgU8PDoWxz4RBzMpqWlBQaDod+VGKF/srKyQNM0IiO9BvwsT2hcWQxn11JFRQUAwN+/75DfwcITiODhH4Hbd+445MY/EYch0NzcDL3egBEj+l6+C0SkhIa5NDU1Aei9xDRhYGiaRlraLYwc6QJv74Hj9XkC42eGc+2vqqoqCATcXosTMoH36ChoNRqHzJgm4jAEHla97PthxuHxwROIiDiYQX8lpgkDU1FRgdpaOR57zNusz/OExuzp4SwO1dXVkMnErIVOSzx8IfHwRUpqqsNlTPcbMzhlypReL5qp/3NGRgZrhjkCDx9m/c86+GI3kghnBg0NDeBwKLJyGCQpKSkQCLiYMGFglxLwUByGa7dCmqYhl9di/PienfKYgqIoeI+OQtntsygsLER4eDhrYzFNv+Jw6tQpa9nhkDQ0NIDP5w4YXSMQSztdJoS+qaurw4gRInA4JAHOUlpaWpCdnYUpU7zNLj3CFw3vSLrW1laoVGqUlLTg668fICbGD2FhxvDf2toOnD9f1vnZlSvDOr/nmZn1yMqqR2urxqxxRgSMQfW9a0hOTnYeccjJyen34ICAAEaNcTTq6+sxYoRwwCWpQCJFU3lZ54qL0Dt1dXJ4eZFVw2BISUmBwWBAdLSv2cdweXzw+MJh6/Ksr68HgF4bdTEJh8uDV8hk5ObeQH19Pby9zXP72Zp+xeGrr77q8z2KorBo0SLGDXIkGhrq4e098MNM6OIOnVaL1tbWXpu9EwCdTofGxkaMGTPS1qY4HCqVCjdupGLcOE+LN1aHs8vTJA7r1kX0cGX6+kqwefO4Xo+bPNkbkyd7IyOjFomJZb1+5lG8QyajNj8N169fx8qVK4dmuJUYtDgMd0xhrGPGyAb8rNDVGHPe0NBAxKEP6urqYDDQjFTFHG6kpKRArdYgJsZyYRVI3NHQ4PiNaQZDY2MjuFwKUin7odN8kQs8A8chPSMDTzzxBFxcmA+dZZp+xeGdd97BG2+8gV27dvX6/ieffMKKUY6AOWGsJkSungCMCTejR49m2zSHpLa2FgDg49N7/wFC76jValy/noTwcA+MHGn5A0fo6oGG4hLo9XpwucOrEm5jYyPc3Qd2CzOFLDwaDWU5SE5OxpNPPmmVMYdCv+IQGxsLAFi8ePGgTv6Xv/wFiYmJoCgK69atw3PPPYfk5GTs27cParUaS5cuxauvvjqoc9sacyOVAOPSncsXdD4ACT2pqakBl0sNWPKB0J3k5GQolSrMnj24SYfIzRN6vR5NTU0O4wtniqampn4TWJlG5OYJd79wpKSm4vHHH4dIZN/3er87MQsWLAAArF69GvPmzYOHhwe8vb2xcOFCrF69ut8T37x5E6mpqThx4gSOHDmCr776Cg8ePMCePXvw0UcfISEhAdnZ2bhy5Qpzv40VMfkrzXmYURQFsdQHlQ5aY8UaVFVVwdtbTCKVLEClUv24anCHv//gyj+IpUZBqK6uZtI0h0Ch6L8uGhuMHDMdapUKKSkpVh13MJhVG/ny5cv41a9+hYiICOj1epSXl+ODDz7A9OnT+zxmxowZ+PLLL8Hj8VBbWwu9Xo+WlhYEBwcjKCgIABAfH48zZ85g7ty5zPw2VqS+vh5CIdfs8tJiD19Ul9yFTqcDj0dKUneFpmnU1FQjNLSnS6m+XgmFogNvv/02AIDicLB40aLOVe1wxrRqmDMndNDnELl5geJwUFlZiUmTJjFonX2jVqvR0aGEu7t5OSFMIfHwhdR3NJKSriM2NtauVw9mxXD95S9/wf79+7F//3588803+Mc//oF9+/YNeByfz8df//pXLF++HLGxsZDL5fDx8el8XyaTOayrRS6Xw9NTZLa/0tXTD3q93mErNLJJa2sr2ts74OvbUxyUSj0AwDMkCp4hURC4+SAhIQFlZeZFiTgrSqUS168nISLCo99WoAPB4fIgcZehtLSUQevsn4elWqxfx8tvXCxUKiWSkpKsPrYlmDWFpSgKERERnT9HRkaanQr+yiuvYOfOndi1axdKSkq6PUwtjfvPycmxmwJWVVWViIgwvzmIi6c/ACApKQljx45lyyyHpKamBgDg69t7pBJf5IqAyMcBADqNCrlXDuDzL77A/HnzIJEMzw3s+/fvQ6VSY86csCGfy8UrAOWFt3Hjxo1hs6o1TdKsuedgQuIhg4d/BK5evQaxWGyV1UN0dLTFx/R7J5jinydOnIhPP/0UGzZsAIfDwdGjRxETE9PviQsLC6HRaDB+/HiIxWIsWrQIZ86c6RYRUVdXB5ls4FBQE5GRkWZ/lk3a2tpw9OhRi2zni1wglnpBqVQO6g/lzJj2nWSygR/0PIEIoTOfQn7SQaSlpWHnzp1wdWW+3LI9o1KpkJDwAyIiBheh9ChSWQjkBemQSqUYP348AxbaP6ZJpoeHbdw6fuNnobm6EPX19Xab99CvWykmJgaxsbE4ePAg/vjHPyI6OhpTp07F3r1K3TASAAAgAElEQVR78fnnn/d74oqKCrz55pvQaDTQaDS4cOECNmzYgOLiYpSWlkKv1+PUqVOIi4tj8vexCqbNu97cIP3hJgtBSUmJQ/eVZYPq6mp4eIggEpm5fyP1RuiMp9DYpMC//v3vYZfhe/PmTahUasye7c/I+Vy8/MHlCwesiOBMNDQ0QCjkQSy2TfiuyHUEvEIm4datW3brWu/32/jgwYMBT3Dq1CmsWLGix+tz585FZmYmVq1aBS6Xi0WLFmH58uXw9PTE7t27oVarMXfuXCxZsmTw1tsIk7/b0lmb1Hc05AXpyM/Px8SJE9kwzSGpqqqETGbZDM7VOxBhMatQdOMEPv7kE2zftg1+fn4sWWg/6HQ6JCdfR0iIdEh7DV3hcLhw9wtDTs49PPWUBgKB8/fTqKurg6en9XIcesNvXAwUlbk4deoUnn/+ebsrrTPkoiKffvppn+/t3r0bCQkJOHnyJHbv3g3AmDtx4sQJJCYmYs+ePXZ3QcyhpKQEMpnE7AJnJly9/MEXipGdnc2SZY6HWq1GY2MjfH0tf9C5egcifM56qLUGfPKPfyAzM5MFC+2LrKwstLa2YeZMZsuMeAZNgEajHhbXEDDV8bJtpBBPIMbIsbEoKiqyy1XbkMXB0WqUDxWtVouysjIEB5u/GW2CojiQ+oXj/oMH0GjMq+jo7FRXV4OmLXfRmZC4+2BM3EYI3Xzw3Xff4fvvv3fqtpcpKcnw8hJj9Ghmy7C4egVALPXC9eRkGAwGRs9tbyiVSrS2ttlFqRbvkEkQu/vg1A8/2J27ecji4Igz/6FQUFAAnU6H0FD3QR3vGTAWOq0W9+7dY9gyx8S0fzNy5OCjjvgiF0TMXgtZeDRu3ryJDz/8EJWVlUyZaDdUVVWhsrIKU6f6MP69oygKsvBpkNfWOv29aYqOswdxoDgcBE6ej9aWFly8eNHW5nSDdIKzkKysLAiFvEGtHABj2KBQIkV6ejrDljkmlZWVkEj4A/bEGAiKw0VA5OMIi12N5tYOfPzxxzh//jx0Oh1DltqejIwMcLkcs/pDD4YRgWMhcvPE2bNnneq6PYpp4sBEpBcTuHr6w2tUJK5fv94pXPYAEQcLUKlUuHcvB+PHjwCXO7hLR1EUPEdFoqioqLM+03CmqqoSI0dKGJsJS2XBGDt/KzwCxuHSpUv4+9//7hQJXgaDATdu3IBAwMHRowUoLHwYoVVb24Gvv37Q+a+t7aFbLTOzvvP148cL+x2DojgIiIxDQ0MDrl+/ztrvYmsqKyvh5iaAi8vQJiRM4h/5OHgCEY4dO243bj2y52ABGRkZ0Gp1iIoaWoEyz1GRoCgKt27dYsgyx0StVkMur4O/P7MzOJ5AhJDoxQiNWYnmdhX++c9/4vjx41AqlYyOY01KS0thMBjMLtcyWKS+IXD3C8eFCxfsNsRyqJSVlSIgwD5WDSZ4AhH8JjyOiopypKWl2docAGZmSPdHfHw8E3bYPTqdDtevX0dAgOugi5yZEIhd4T4yDLfS0rBgwYJhETrYG5WVlaBpmrGQzEdx9x0N1/lbUf0gFbfS0pBz7x6WL1uGqKgoh9sry83NBYdD4dlnJ0Ag6B4lZ05jGhPvvjvwgycoagFyL+3Ht999hxd37XKq+1OhUEChaEZ0dJCtTemBZ9B4NJbfw5nEREyYMMHmyZ39isNAD/6TJ09ix44djBpkr6Snp0OhUGDRooiBP2wGPmFTkJ9UgNu3b2PmzJmMnNPRKC8vB4Ahi21/cHkCBE6Mg2fQOFTcvYhDhw4hLS0NK1eu7Fbny94pKMhHYKBrD2FgA75QglFTFqEw9TiOHTuGp59+2uHEtC9KSkoAAKNGDW7PkE0oikLQ5AV4cHk/EhIS8PTTT9vUnn7F4a233rKWHXaNUqnE+fPnEBTkNugopUdx8fSHZIQvkpKSMH36dHA4w2/7p7i4GN7eYtZdJQAgcZch4vFn0FCahfJ71/G3v/0NcXFxmDdvnt3XE1KpVKipqcGcOcxkRJuD1DcEfuNnITMzGT4+Pp3l+x2dwsJCiEQ8s0q12AKRmydk4dNw9+5NTJ8+3abNwfr9VsyYMaPz/wqFAkqlEjRNQ6/XD6uqmGfOnIFSqcSTT0YyNoOiKAq+4dNQfOsHZGdnY/LkyYyc11HQ6/UoLS1BZKSH1cakKAreIZPhPjIMlTnXcOnSJWRmZWHtmjUIDg62mh2WYnS/sbvC6g3fiOlQtzXhwoULcHNz67dEvyNA0zQKCwsQHOzGaN+QypxrqM27Cd+I6ZD6hgAAOprlqMx62KsmZNoy8EVG92lDWQ4ay4zhwjyRC0ZPW9b5uYqsy+hQyEFxuDhx8iR+9vLLNuvQZ3bJ7tmzZ+OJJ57AkiVLsGjRIrz77rts22YX5ObmIi0tDTNnjhx0olZfuPuFQ+zmiYsXL9pNhIK1KC8vh0ajZTyZyxz4IheERC9BWOwqtHZo8M9//hMJCQl2mzzHRC7IYKAoCqMeewJSWQiOHz+OjIwMq47PNHV1dWhubrHJPWcJFEVB6OIOeW2tTYNWzFpPf//997h06RLeffddvPbaa0hNTXXYDm6W0NzcjCNHDsPHR4zHHw9g/PwURcF3bAxK0hKQmZmJxx57jPEx7JXc3Fzjw2eU7b6oUlkIxs3fgqqcJFy/fh15eXnYsGEDRo5ktjTFUJHL5ZBI+JBIrB96SXG4GD1jBYpuGDs6GgwGTJs2zep2MEFeXh4AMOYaNhEQ+Tiksu4rT4m7DBFz1vf6ea9RkfAa1XuF6cBJ8wAYVzkFyUdw/sIFREVFQSy2fsKeWSsHT09PyGQyhIaG4sGDB1i1alXnhXZWtFotvvnmG2g0aqxeHQYej509AQ//CIjdfXDunHMlbA3Egwf3ERTkCrHYtv5+Lk+AoKgFCItdDUVrBz76+GO7myE3NDTA09P6fQdMcLg8hM58ClJZMI4dO2b3TWr6Ijc3Fz4+Yri72+5amgtFUQiIjIOyo8NmE3Gznng8Hg9lZWUIDQ1FWloadDqd3dUBYRKapnHs2DGUl5djxYoQeHmxp9oURcF/whwoFE1ITU1lbRx7ora2FnJ5HcaOHWFrUzqRyoIxdu4mSDz8cOTIEZw+fdpuXH226HX8KBwuD6NnxMPDPwKnT5+2q+tjDiqVCqWlJQgLY3bVwCYSDxlGBI5HckqKTcrSmyUOL7zwAt566y3MmzcP586dw7x58wZs9uPIJCYm4u7du4iLC8C4cZ6sjyeVBUPqG4ILFy+ira2N9fFszd27d0FRsMq1tQS+yAVhsavhHTIZSUlJOHr0qM0fgDRNo7W1DW5uts814HB5CJm2FN6jo5CUlITvvvvObvdpHqWgoAB6vQFhYdYLgGACv/ExMBgMNqm7ZNaafsKECfjiiy8AAMePH0dpaanThl5euXIF165dw9SpMsyaZb3+AAET5+LBJfuIb2YTvV6P27czEBrqPuR6SmxgKoTGE0pw+3YqhEKhTRM9VSoV9Hq93ZR6oCgOAifNg0AiRXb2NSgUzdi6dYvNE7YG4uTJk6Ao4OrVim4RhzKZBE8+OarXYzIz65GVVd/re0880fsxTCOUuMMreBLS09MRFxcHLy926mr1Rr9PeGM2oQI7d+5Ec3MzFAoF1Go1vL298corr1jLRquRlJSEs2fPYsIETzz55CirJv6IXEfAN3wa7t69i/z8fKuNa20ePHiAlpZWPPaY/SagURQFv3ExkIVNRWpqqk2LJHZ0dACAzfdmumIMw47G6OkrUFVdjQ8/+siuCsY9Ck3TUCo7IBLxHDKZb+SYGaA4XFy6dMmq4/Z7x/3yl7/sLMDVNYuXx+Nh8eLF7FpmZa5evYrExESMHz8C8fGhjMZBm4vvmOlQVOXh2LHj+PnPX4FQaP8bZ5aSnJwMqVSI8HDzl/c6dQfykw4BAGOx5MrmOgDG1q0jxzyM3zeNAwCyiGh0NMtx6tQPGDt2rE1mx/YoDiY8/MMhkDyN4psn8Mknn+CZZ56xyx7Ucrkcer0B8+ePQlSU+ZOSR0uPPEplZSsT5g0IX+QCr+BJuHPnDubPn2+11UO/K4dPP/0UDx48wOrVq/HgwYPOf9nZ2fjf//1fqxjINjRN4/z580hMTMSECZ546qkwmwgDYPTpBk15Es0tzUhISLCJDWxSWlqKkpISTJ8us9k1tgQKHARFLYRWq8HVq1dtYoNKpQIAiES2SYQaCImHDGMe3wC+qyf279+Pq1ev2l0xzoKCAgBASIh95zf0h2/ENFAUx6qRS2ZNR/bt24e7d+/i2rVr0Gq1mDNnjsNnSwLGMsgJCQlISUnB5MneWLo0xOYPLVdPf8jCpiItLQ3jxo2zy5nYYLl48SLEYr7FLiWeUNJrzPhQY8l7o7fzufuFI+P2bSxZssTqe20mcbC0Ja014YtdETF7PUozEpGYmIj6+nqsXLnSZpm9j1JcXIwRI0QOEcLaF3yRCzyDJ+L27dtYsGABPDzY31g3604/fvw4XnnlFTQ3N6O9vR3/9V//hYMHD7JtG6vo9XocPnwYKSkpmD7dF8uW2V4YTPiNi4XE3QdHjhy1SQgbGxQVFaGgoACzZo20SvE4JnEfGQZlR4dNSlg7gjgApkimZfAdMwPp6en44osv7CLcnaZplJaWICjIvjfMzcE3YhpowGqrWLPE4fPPP8ehQ4fwxhtv4M0338Thw4fx5ZdfDnjc3//+dyxfvhzLly/H+++/D8Doc46Pj8eiRYvwwQcfDM36QaLVavH111/j7t27mDs3AAsXBtnVRhWHy0Nw9FJotFp8d/Ag9Hq9rU0aEsYV2g+QSoWYMkVma3MsRuRmDLltbGy0+tgP3Ur2t+fwKBRFwX/8LIya8iSKiorwz3/9C62t1vHL90VTUxM6OpSM9wyxBQKxGzyDJiAtLQ0tLS2sj2eWOBgMBshkD7/Uvr6+Ay6vk5OTkZSUhGPHjuH48ePIycnBqVOnsGfPHnz00UdISEhAdna21bP/VCoV/vOf/yA3NxeLFwdj1ix/uxIGEyI3TwROXoDSkhJcuHDB1uYMibS0NFRX12D+/ADw+Y4XAs3hGh/Mtshg7+joAIdDMXbdaJqGoroA+UmH0FJb8nCcZjnykw51/tOq2jvfayjL6Xy9OK37XlhF1uXO92ryjHWAvEZFInTmStRU1+BPf/qTTVe/9tYSdKj4hk+D3mBAcnIy62OZNR3x8PDA+fPn8cQTTwAAzp8/D3f3/jMNfXx88Prrr3c2CgkLC0NJSQmCg4MRFGRstBEfH48zZ85g7ty5Q/kdzKa9vR3/+c9nqK2txapVYRg/nr0krNLbZ8Hl8ocUXeMZNB5tDZW4cuUKRo0ahXHjem/oYs+0t7fj7NlEjBrlxur1ZhOdxjR7F1l9bFO00oEDuT3eG2yMvjWQ+oZA7O4NZXM9/vWvf2Hnzp0DPjPYoK7OGJXm7W39vx0bCF094OEfgdQbNzB37lxWay6ZJQ67d+/Gnj17sHfvXgAAn8/Hhx9+2O8xEREPm+KUlJTg9OnT2LJlS7cGKzKZzGp+3JaWFnz22adoamrEunURDpNGHzhpHpqrC3Hw0CG8/NJLVk2CYYJTp05BrVZj8eJwu1yhmYO6zehO8vYeWnvYwdDW1gYul7nrRlEUPPzCETR5frfX2djcHzdvM9qbalCYfBT/+fxzvPDTn1q9gFxDQwOkUiH4fPves7EE34jpyK3Mw40bNzBv3jzWxulXHBQKBQBg7969OHToEAoKCowFoQIC8Oyzz+LMmTMDDpCfn48XXngBr732GrhcbmcnJsC4xLXkgZGTk9Ppg7UEpVKJxMRE0LQBPj7db87a2g6cP/+wN8XKlWGdmbtdZ18uLnysWhXW+blz58oglxtndaGh7oiN7Z5NHTxlEcTS7g+TwXwBOVwexsRtQO6VA/j3vz/FvHlz7b45jYnq6mpkZmZizhx/eHtbv6okU7Q31YDP56O4uLjb/WsNamtrERjoig0bxlp0XF8x+ua0CWUSlxEjMXpGPApTjuGTTz7BnDlzrBrxVVFRAanUPrLLmULi7gM3WTCuXr0KiURiVlRYdHS0xeOYnQQXGxsLwPhANzcJLj09Ha+88gr27NmD5cuX4+bNm53LPMC45Ou6lzEQkZG9z176o729Hf/85z9A0wbIZGIIhY7xYO2K0MUdIdFLUZhqLF2yfv16u5+Fd3R04OzZRPj4SIZchqRrElxXxO4+fc5cu7rpHiVg0lxI3Hu/7x4dhwagU7UhLCzMJqWqz58/B1dXxw3BBAA3nyAEPfYEym6fhUKhwJNPPmm1sZOSrlml06C1kYVNRWHKMfB4PEydOpWVMfq9ap9++ikA4Ne//jX27dtn0Ymrq6vx8ssv44MPPugUlqioKBQXF6O0tBSBgYE4deoU1q5dO0jTB8a4+fwZFIombN48DkFBPfvGWtKcvSt9+XrZQuobAr9xsbh7NwVBQUGd19QeoWkaJ06cQEdHB55+egK4XMfbhDZB63VQtzcjIiLO6mPr9Xq0trZCKrW/fseW4jVqAtrqK3D58mWMHz8egYGBVhlXo9FAIHA+cXDzGQWx1AspKSm2EQcTlgoDYBQWtVrdrWPchg0b8O6772L37t1Qq9WYO3culixZYvG5zcFgMODgwe9QU1OL9evDexUGR8N3zAy0N9UgISEBAQEBGDXKugJlLpmZmcjKyoK7uwDnz5chJsavc49nMG68vpLg+qM/P3l/PDqOvDADHYpajB1rmVuHCZqbm0HTsHm5bqYInDQXrfJiJCQkYOfOnVZZ/RoMBrvJX2ISiqLgFTwJFVmXUVlZiYAA5puRsSapb775Jt58881e3ztx4gRbw3Zy/vx55ObmYfHiYIcr09sXFEUheOpi5F39Bge++Qa7f/YzuLjYV4ieQqHAiRPfQyDgQCp1/IdaS20JvH18MGKE9XtPNDU1AYBDZ/Z2hcsXwndsDEozL6GsrMwqfbu5XC70enbLrpt6SHfFKi5P2gCKw8WdO3ccSxxsSWFhIa5evYKoKG9Mnep4SVf9wROIEDJtOfKufYfDhw9j69atdlM+3WAw4PDhwzAYdHj++UiMGNEzfHCwbjxbYNDr0N5QicmxtuldYkq68/BwDnEAAM/A8ajKuYbbt29bRRxEIhFUKiXr49gCiuJAKgtBVnY2li1bxvhKzOnEQafT4fvvj8PDQ2S1muvWRuIhQ0BkHPKyLiElJQWzZ8+2tUkAgJSUFBQXF2PZspBehcHRaG+sgsGgR2hoqE3Gb2xsBIdDMboCMyXBqVq650BYY6Zrws1nFIqKis03egi4urqxnqXdWw/p/mDK5QkADaXZKLtTiNraWsZ7n9vHlJNBUlNT0dDQiEWLRjlcDR9L8B49Ge4jQ5GYmGgXtfTlcjnOnk1ERISHXc3+h0JbQxUAICQkxCbjNzY2wt1d6HQ+c5HUG42NDVbJOPf09ERTk9ruKsUyhau3MaG4rKxsgE9ajlOtHPR6Pa5fT0JwsBShoY6R5DZYKIpC0GNPIPfSfhw6dAgvvfSSzapgGgwGHDlyBHw+hSVLQuw+zNZcOppq4COT2SQzGgCamhrh4cHsvk1fSXD9weRMFwBq82+BpmmrtGD18fGBWq3Dl1/eB4/HGXJwBPAwx6m1VcO6/QMhkEjB4wtRXV3N+LmdauVQWFiIlpZWTJvmXPsMfcEXShA4eQFqampw7do1m9mRmpqKiooKPPnkKLts/TkYaJqGsrkWQVYKuewNhULhNJvRXdFrNaAoyiqTGVPIrEbj2MUr+4KiKPAlbqwU4nOqlYOpqceNGzW4dat7WY6h9Ir19ZX0+t7XXz/o8VpTk+UZ3EPBwz8cHn7huHjpEqKioqweVdPc3Ixz584iNNQdEyY4Zu2k3tCq2qBVK+Hv72+b8bVatLd3QCq1fpQU23Q0y+Ejk1lFHPz8/MDn8xAcLMWiRd33BYaa45SRUYvERObdOZbC4QlZKY/uVOJQV1cHPp9jFz5aU+G9rrC16Wcw6KHX6XDmzBls3LhxSHZbytmzZ6HX67FokXV7brONUiEHAJuJQ3u7sSqqs6zETOi1arQ3VGJ8NDuJW4/C4/EwenQoCgvLLC7X4yjQBh24XObL0ziVOLS3tzNah2Ygept1XLhQhps3rdsUhsPhgi92Q3Z2NmsJMb1RVVWFO3fuIDZ2pFNEJ3WlXVELiqLg5ze00h+DxZ57Rw+FpopcGPQ61rJ6e2PcuHHIy8tDXZ0SMlnvXgBHRqdsg1RK8hz6RSAQQKtlf5PLHHorvNcfQ93002vVuHfuM1y+fBmbN2+2+DyD4eLFixAKeYiJsc0DlE3aGyrh5+fXWXLe2mg0xs1OR+x/0RcGvQ61+TcREBBgtfIZgLEm26lTJ3HvXqPTiYNW1Q6Nqt2iGnXm4lTi4OXlhezscqddPvYHly+E1+go3Lt3E01NTazvPdTX1+P+/fuYPduf9S5lXQvvDaU/homKrMtQNhsLQLrJQjByzMN+6PlJh0DTNDoUNZhiw/wRUyQPk+W6bU1tQRo0yjYsWfKMVb+frq6uCA+PQFZWCeLiAuzC7cwULfJSAGAlF8epxCEkJARpaWmoqmpHQIDj94y1FO/giajNu9nZhJxNbt26BQ6HwtSpPgN/2MHQaZSgDQabNlcyPTz1embj87smwVlDaE0ETJqH2rybmDRpkk2SCqdPn46vv85DQYECY8YwO3Eylc+w5vU0jdVYlgMPDw9W3J9OJQ7jx4+HQMDH7dvyYSkOAokULp7+uHfvPqviQNM0srIyERrqDldX9t0ufRXeY6NBTcSc9chPOgQxDzYtbCgUGkNYNRr7cJMOBZqmUZp+GhKJBE899ZRNbBg7dizc3aVIS6tlXBxsRUdTLdoaKrF48WJWSug4lTiIRCJER09DamoqYmL8HLrBzGCRyoJR/SAFKpWKteStmpoaNDe3YM6cEFbOb0vaGqvQ1lCJpUuX2rRmlaurcXLT1qZl9LzW7AQXMWc9aJpG8a1T6Giqxo4dOyCR2Mbnz+VyERs7C2fOnEF1dTv8/JgrWNlb+Qy2rqcJmqZRkHwEEokLZsyYYbnRZuBU4gAA8+fPx+3bGUhMLO2298BkZiTQs/ubKefB2nkOjyL2MG5M1dTUsFb2oby8HACcogx6V2iaRlXONbi4uGL69OkDH8Airq6u4PF4UChsez8Nlap7SWiuLsSyZcswevRom9oyffp0XL58CSkp1VizJtymtgyVxrJ7aKuvwFNPPcXaJNDpxMHFxQXLli3H0aNH4e4ucMoM0/4QiI0P7La2NtbGkMvloCjghx+Ke2wsspFsaC3qiu6gvbEaa9eu7XTr2AoOhwOZzAd378pRU2OckDAxwbEm8sLbkBekY+bMmZg1a5ZNbOiKSCRCbOwsXLp0CXJ5h8NGLqnbm1GZcwUhISGsTmKcThwAYOrUqcjPz0dWVhaefDIYERHd+zmw0f3NdD5b5Dl0heIYs07ZLGrW2toKLpfjVBFh7U01qLqXhDFjx2LKlCm2NgcAMGpUMKqrqx0y+q6hLAeV2VcwYcIErFixwm7snzVrFpKTr+PatUqsXRtha3MsxqDTovjmSfC5XKxbt45V16dTigNFUVizZg0aGhrw/fdF2LBhDAIDh8cGtV5jdEOwWSxOp9PBy0vUp8D2hb31azCh6WhByc2TkLq5Yd3atXbzIAsNDUVqairmzAlASIi023v23BejqSIXZXfOIywsDM8884zd9BsBAIlEgtmz5+DixYuoqmqDv7/jPBdo2oCS9NNQttRj+/btrIer289fjWEEAgG2bt0KqdQdBw/mo6am3dYmWQXlj3X62UiKMcHj8aDVOkcJZK2qHYUpx0DRemzbttWuOutFRESAz+chN7fJ1qaYTVNlHkoyziA4OBhbtmwBj2d/88/Zs2dDIhHj8uVKhynlTdM0yu9eRHNNEVasWIExY8awPqbTigMASKVSPP/8DojFLjhwIA8VFew2/bAHWmqL4eYmZXVW4e7ujpYWx6+Rr1G2oSD5CHTqNmzbtpXxZilDRSAQYPz4Cbh3rxFarf1XFW2qyEVp+mmMGjUK27dts1l2+UCIRCLMn78ApaUtKCpivpop09A0jcrsK2gozUZcXBxiY2OtMq79yTrDeHh4YOfOn+Kzzz7Ft9/mYfXqMKv0lDYV3rNmYkxw9FK0yEsxKzaGVdeIj48PdDoDvvjiHvh8LuuRYGygam1EYepxQKfGc88+a7OGPgMxc+ZMZGZmIienEY89Zr8Jh43l91F6+yxCgoOxbds2m2/oD8SMGTOQnJyMS5cqMHq01G6zpmmaRmXWFdQV38GsWbOwaNEiq43t1CsHEyaB8PaW4dChAty9W2drk1ihNu8mKNCszyxMIYkqlf3PZnujta4c+UkHwaP0+MlPdtitMABAcHAw/PxGIiWlBgaDfa7U6kuzUZqRiNDRo7F9+3a7FwbA6BpdvHgx6uo6kJ3dewSdraENBpTdPoe64juYPXs2K32i+4P1lUNbWxs2bNiATz75BIGBgUhOTsa+ffugVquxdOlSvPrqq2ybAABwc3PDzp0/xYEDB5CQUACFQo24uADWLnZvhffYTIxpb6xG3rXvEBMTw/pGlZeXF7y9vSCRaHpsirIRCcYk9SWZqMi8DC9vL2zftg2envbdg4KiKCxYsBBff/01srPrMXmyfa0e6oruoiLrEiLGjMHmTZvA5ztOifGJEyciMDAQV65UYdw4T7tqK2zQaVGcloCW2mIsWLAACxYssHqgBKsrh7t372Ljxo0oKSkBAKhUKuzZswcfffQREhISkJ2djStXrvR/EgYRCoXYtm0bpk2bhuTkahw/XugQvtyB0GlUKE0/DanUHU8++STr41EUhSlTpqKsrBUNDUrWx2MCg16HsjvnUX73IsIjwvHirl12Lwwmxo8fj4CAAFy9WmVXHc1qC9JRkXUJ48ePx5/F830AABlWSURBVJbNmx1KGADjfbxs2TK0tWlsGn7+KFp1BwqSj6Clthjx8fFYuHChTSLoWBWHgwcP4u233+6MnMnMzERwcDCCgoLA4/EQHx+PM2fOsGlCD7hcLlatWoWlS5ciN7cJ+/fnoqXF9r1gB4tBr0NJ2g/QqduxadNGq/U7njZtGng8HlJTa6wy3lDQKFtRcP1w54betq1bbdYXejBQFIXly5ejtVVjN9e7Ju8mqnKuYeLEidi4caNdRiWZQ3BwMCIjI5GaWsN4qZLBoGptRP6176BqrcemTZsQExNjM1tYFYd33nkH06ZN6/xZLpfDx+fhslgmk6G21vqKTVEU5syZgy1btqKpSYvPP7+Pykr2MorZgjboUZqRiNa6cqxevRpBQUFWG9vV1RUzZsxAVlYD6uvtd/XQWleOvCsHoGlvxMaNG1krUsY2wcHBmDx5MlJTq22+WqvOTUX1/WRMnjwZTz/9tFXafbLJokWLoNfTSEqqtKkdrXVlyL/2Hbi0Djt/8hNERlre34VJrCr3BoOh2/LI0szPnJwcqFTM1pp5/PE4pKQk4+uvH2DZshBMnGh/SVq9YVwxJKC5pgiTJ0+GwWBAenq6VW0YMWIEeDwuzp8vwzPPjLGb5DHAeG/JCzNQlZMEV1dXxMTMhlqttvo1YpLAwEDcv38fp0+XYvPmsTa53tUPUlGTm4pRo0YhLCwMd+7csboNbBASEoI7d4owfbovvLysX7CzviQLFZmX4OrqglmzZkEul0MulzN2/ujoaIuPsao4jBw5EnV1DyOF6urqLErWYktJZ86ciQMHDuDkyWLU16swdy57G9VMoNOoUHzrFNrqK7BixQqrxT33BkVROHXqFHJyGuxGWPU6Lcpun4WiKh+RkZF2USuJKYRCIY4dO4aMjDpER7OX6NgbNbk3UJObiilTpmDNmjUOuQLri7Fjx+J///dPuHy5wqplNWjagMrsa6gruo3w8Ahs3LjBblyeVv3rRkVFobi4GKWlpdDr9Th16hTi4uKsaUKvSCQSPPfcc5g2bRpSUqpx7Jj9blSr2xXITzqIjsYqrF+/3qbCABiFNSgoCOfOldvF3o26oxn5175Dc3UBFi9ejI0bNzqNMADGGWB4eDguXaqwagXg2oJ0VD9IwWOPPeZ0wgAY3aSPPx6HvDyF1VzMeq0GRTdOoq7oNmJjY7Ftm33thVn1LywUCvHuu+9i9+7dWLZsGUJDQ7FkyRJrmtAnXTeq8/Ka8PXXuWhvt/0GVVda5KXIu/INoFPiueeew2OPPWZrk8DhcLBu3ToYDBROniyyaSx+e2M18q9+C4O6Ddu3b0dcXJxdrwAHA0VRWL16NTgcLn74ocQq17u+OBNVOdcwadIkrF271umEwcTs2bPh4iLBlSvs7z1oOlqQn3QQrfISPPXUU1ixYoXd7d1Y5a988eLFzobisbGxOHHiBBITE7Fnzx67+vKaNqo3bdqM+no1vvrqgc37MwBG/3lN7g0UphyDp6cHXn7pJZu0WuwLb29vxMc/hbKyVly7ZptNPUV1AQqSj8BVIsaLL+5CRITjVdw0Fw8PD6xYEY/y8lbcusVu9FJTZS7KMy9i7NixWL9+vdMKA2CcvM6dOw+lpS0oLWWvrEaHQo68a99Br27Fs88+i5kzZ7I21lBw3r/0EJgwYQKef34H1GoKX375oLOsgy3QqZUouvE9qh+kICoqym7j86dOnYro6GgkJ1cjL8+6heIaSnNQfOsH+PuNxIsv7uoWEeesTJkyBePHj8eVK5Woq2Mneqm1vhylGYkIDg7Gxo0b7W5mywYzZsyAm5srrl6tYqV2WEttCQquH4JYyMOuF15AeLj9Nh0i4tAHo0aNwk9/+gJ4PBG+/jrXJqGubQ1VyL3yNdrry/HUU09h/fr1dlvMDADi4+MREOCPkyeLWXtgPUp9SRbK7pxDeFgYduzYYVdVVdmEoiisWrUKIpEYJ08WQa9ntte0qrURJTdPwdvLC1u3bnW4BLfBwufzMXfuPFRUtKKsjNlCnY3lD1B04wR8vL3w4q5d8PX1ZfT8TEPEoR98fHzw05++AInEDd99l4eqKusIBE3TqMm7hYLrh+AiEuCFF17AzJkz7coF1xt8Ph+bN2+BQCDC4cMF6Ohgd8+msfw+yu9ewJixY7F161a7Fk42cHV1xcqVq1Bb24GUFObcSzqNCkU3TkAo4GP79u0Qi4dXL/Zp06bB1dUF169XMXbOuuK7KM04g5DRIdi5cyekUumAx9gaIg4DMGLECOzY8RNIJFJ8+20+6y4mnVqJotTjqL5/HZGRkfjZz15GQEAAq2Myibu7OzZv3oK2Ni2OHi2ETsfsjNZEa10Zym6fw+jRo7HJgTN0h0pkZCQmT56M69erGLk3adqA0vTT0KlasXnzJtbrdNkjfD4fjz8eh9LSVkY8BvLCDFRkXsK4cePw7PbtdhWR1B9EHMzAw8MDO3bsgFAoxrff5kOhULMyTnvjj26kBmPj8A0b7Cfm2RJGjRqFNWvWory8FadPlzDuu1W3N6MkLQHePt7YsmXLsHF59EV8fDzEYjESEoYevVSbdwst8lKsWLECwcHBDFnoeEyfPh1isQg3bgxtRVZXfBeV2VcRGRnpcGVGiDiYyYgRI/Dss89Br6dw6FA+1Grm8iBomkZd0R3kJx2Gi4jvMG6k/oiKisLChQuRnd2A5ORqxs5LG4wzWy4FbN2yxSHFk2kkEgmWL1+B6up2pKcPPqu2raES1bmpiIqKYrVxvSMgFAoxY8ZM5OY2DTpisbHiASoyL2HsuHF45plnHEoYACIOFuHr64tNmzajsVGFkyeLGJkRG/Q6lN0+i4qsyxgzJgIvv+xYbqT+mD9/PqKionD1aiXu3Wtg5Jy1Belob6rB6tWr4OXlxcg5nYHJkycjIiIc165VDio/R6/VoCwjESM8PLBy5UqHnpgwRUxMDLhcDtLSLBfctoZKlN0+h+CQEGzcsMEhI72IOFhIWFgYli5dhvx8xZDL/GrVHSi4fhiN5fexYMECbNmyxak2/yiKwpo1axAcHIxTp0r+f3v3GtTUtb4B/AmJAUJAghRRT7io3CziHSwiFFGLVNSCIk4LM/6P9djafrAznfKprdXptJ3Wjm2dYfSznXqtvZxTBlu0LdVDrZ6ReikCophyiSKQhltCkv8HJlEagYBJdrLz/L5UaDI+7Im8e+211rse+/mtoe8vtN+owZw5czB37lwnpRQHiUSCtWvzMThowZkzmnG/v/WPcxjo1WHTpk2i2lH+OEJCQvDkk8n4/feOcbVKN/TpcevCtwhTqbyylbkVi8MEPPXUU0hKSsLZsxrcvTuxScB+fSfqfz6Cgb/uYcuWLcjJyRHlBiOZTIbnn38eISGTceJEA3S6ic/XtNXVQAIL8vLyxn6xDwoPD0d6+jL8/vs9tLc7/rns7WrH3abLSEtL8+l5hkdZunQpBgYGce3afYdePzSh/x/APIgXXngeCoXCxQldR3y/jdzA2sIgMDAQ3347/knA3m4tGqqPQmoZxLZt25CcnOyipJ4hKCgIpaWlGByU4PjxhnH3rTL290B/T4OO21ewePFin1xB46isrCwEBgbg7FnHRg9Dh9f/BIVC4dbzib1FVFQUpk6NwP/+59jRwtqGS9B3tGDDhg3jairqiVgcJigoKAjPPrsWbW09qK11/Aza3q52NP5yAoH+cvzrX9vdegaDkCIiIlBUtBlabS/+/W/HVzANDaYsqP/lOICh/jc0ssDAQGRmZuHmzW6HHuPp2m9B3/EnVubkcHL/ESQSCZYsSUVbW8+YS4UHerrR9sd5JCUlYd68eW5K6DosDo8hJSUF0dFR+OmnPx26G+7TdaDx/JcIUgRi+/YXER7uGS2u3SUxMRErV67C9ev38dtvjs3XREQooFAEYtOmTdi2bRsnoR2QlpYGhSJwzE1cFosF7Tf+i9BQ1bBDuWi4efPmQSr1G/MmsOVaNfykfsjPzxfFhD6Lw2OQSCRYvfoZ9PQYxxx2Ggd6cbPmFPzlMvzzn//ns49GMjMzkZSUhKoqDTSase9sJ03yg1wux/z58xEbG+uGhN7P398fTz2VjsbG7lFPjdN3aNDT2Y6srEyvXE3jLgqFAvHxCbh2rXPER8h9unvoaqnH8owMTJ482c0JXYPF4THFxMQgJiYGFy5oR/zgWMxm3LrwLcyGPpSWlPj03a+fnx8KCwsREjIZX311E319g0JHEqXU1FRIpdJRl2Heu3kZAYGBWLBggRuTead58+ahp8cwYr+l9vrfMEkuR3p6upuTuQ6LgxMsW7YMOt0AGhq6Hvn/W+v+C31HCwoKCmyty31ZYGAgiouLodcbUVl5W+g4oqRUKpGcnIyrVzse+chzcKAP3W03sWjhQq9daulOCQkJmDRJhj/+sF+1ZDL2o7ulHgsXLPDq1Ul/x+LgBAkJCQgKUuDKFfuNXr1dWrTXX8CCBQtEMUnlLGq1GtnZ2bh27b7TNsjRcIsXL8bAgAl1dfY3LZ0tN2CxmD3iwChvIJfLER+fgBs3uu0WU3S1NMBsNk3onGZPxuLgBFKpFCkp89DY2A2T6cEHx2KxQFNbhSBFEJ599lkBE3qmrKwszJgxA6dP33F5B1dfFBMTg8mTQ3D9un3x7W5txJQpUzBt2jQBknmnOXPmoKfHgNbWnmHf7267icmTQzF9+nSBkrkGi4OTJCUlYXDQPGxTXHfbTfR0tmH16lWi2vnsLFKpFAUFBejvN01oVy+Nzs/PD8nJc9HUpEN//4O5HbPJCH3Hn0hKShLFqhp3iY+Ph0QiQUND97DvW8wmJCTEi+5asjg4SXR0NGQyGbTaB6tDtA2/QaUK44TfKCIjI7Fs2TLU1t4T5EAlsZszZw5MJgtu3Xpw7GXP/VZYzCbMmjVLwGTeR6FQYMaMGWhqsj9CNCYmxv2BXIzFwUlkMhnUajX6+4cm/3q7tOi534ply9K5THAM2dnZUCqDUFV1xyVHM/oytVqNgAB/NDY+uNsd0HdCIpEgKipKwGTeafbs2Wht1Q8biQEQTbPMh7E4ONHDK5E6NX/Az8+PE34O8Pf3R07OSmg0ershOz0eqVSKmTNnobl5+KgsPDycO6InYObMmbBYAI1GD4ViqAV3YKDCI891f1yCFIdvvvkGeXl5WL16NQ4fPixEBJd4eHLvr7vNmD17NucaHLRo0SKoVKH45RfXHOzuy2JjY9HV1Q+dzmD7ntgmT90lKioKfn5+0Gj+glI5dCxtUdEmUTbNdPtP1N7ejo8//hiff/45Tp06hSNHjqChocHdMVzC+sGxio+PFzCNd5FKpcjKehqtrT24fdu5B7v7Ouvjo5YWva3wPvHEE0JG8lqTJk3C9OnToNE8WLEktoloK7cXh3PnzmHp0qUIDQ2FQqHAM888g4qKCnfHcAmVSoXNmzfbvmb74/GZP38+goIUuHDh8c7JoOEiIyMhlUqHLcH0tb5ezqRWR6GtrQdiH+C6/dw6rVY77K4lIiICtbW1Dr336tWr6O+f2JF97tLf34+oqCjI5XK0tLSgtdV5R2T6gn/8Q40bN+qg0xkQEjI0bDcYDLh48aLAybxbcLBy2BkPbW1tMBgMo7yDRmIwGGA0mtHRMfS7qL6+Hjqd/QomTzKRDXpuLw5ms3nYMMxisTg8LHvyySddFcup2FZ64mJiYlBXV4crV+4hPX3oubhcLhfd7lN3a2pqQn39VdvXqampomkQ524zZszAxYsXbS284+LiEBcXJ3Aq53P7Y6XIyEjcvfugg+ndu3e9/lAMcp4pU6ZArVbj+vVOoaOISkREBPT6ByOFoKAgAdN4t/DwcEilfsP2NImR24tDeno6zp8/j/v376Ovrw+VlZXIzMx0dwzyYHPnzoVW24vOTs9+hOhN/j7HIJO5/aGBaMhkMoSFhY3aDl0M3F4cpk6dil27dqG0tBQbNmzA2rVrkZKS4u4Y5MESExMBgHsenOjhdfiRkZECJhGHiIip6O0Vd7t5QW4f8vPzkZ+fL8RfTV5gypQpCAsLw+3bOgQEcHe5Mzx8uJQYWz24mxg3vf2d+HZukCjMnDkTd+7oRb9c0F38/f0REOAvdAzRYHEgEkh0dDT6+wdtywXp8YWEhAgdQTRCQ0OFjuByLA7kkayNzP7eO58mTqkMFjqCaPjCMmAWB/JI4eHhkMk43+BMwcEsDs7iC6MwFgfySFKplP1/nIx7G5zHFzrasjiQxwoPZ3FwJmuHYJPJJHAS7yfWZnsPY3Egj2Vdfmk2mwVOIg7W4jAwMCBwEvIGLA7ksawrQvR6Tko7g/VRSF+fuHf2uov1sadYd5uL86ciUbBO+nHk4Bz+/kP7HNiN1TleeOEFtLW1ifa4VRYH8lhqtRrR0dFQq9VCRxEFuXyoBTofKzlHeHi4qM/FYHEgj6VUKrF9+3ahY4iGtThw5ECO4JwDkY+wFgej0ShwEvIGLA5EPsI6cTo4KO5uouQcLA5EPsJaHJRKpcBJyBtwzoHIR4SEhCA9PR3JyclCRyEvILFY2BSZiIiG42MlIiKyw+JARER2WByIiMgOiwMREdlhcSAiIjssDkREZMdr9jlYLBb2hCEimiC5XD6uQ4q8pjgYDAZcuXJF6BhERF4pOTnZ1rbdEV6zCY4jByKiiRvvyMFrigMREbkPJ6SJiMgOiwMREdlhcSAiIjssDkREZIfFgYiI7LA4EBGRHRYHIiKy4zU7pIW2e/duXLp0CUajEc3NzZg1axYAQKfToaCgAK+++qrACb2fRqNBbm6u7dpalZeXY9q0abavX3zxRezduxdTp051d0SvUFFRgYMHD2JwcBAWiwXr16/Htm3bRnz90aNHoVAosHbtWjem9A7jvZZiwuLgoLfeegvA0C+w0tJSfPXVVwCATz/9VMhYohMREWG7tiM5dOiQm9J4n/b2drz//vs4efIkVCoVenp6UFJSgtjYWOTk5DzyPZcuXUJqaqqbk3q+iVxLMeFjJSeora1FcXExsrOzbcXi5MmTKCsrs72mpKQENTU1qKmpwcaNG1FQUIA33nhDqMhepaysDDt27MCaNWtQVVWFFStWQKPRCB3LI3V2dsJoNKK/vx8AEBQUhPfeew+zZ8/Gd999h6KiIqxbtw65ubm4dOkSzp07h6qqKnzyySf4+eefBU7vWUa7lg9/BmtqalBSUgJg6N/5Bx98gM2bN2PVqlX48ccfBcv/uDhycIKOjg588cUX0Ov1WLFiBbZu3Trq62/duoUzZ84gODjYTQm9h1arxfr1621f5+fnAwBCQ0NRXl4OANi7d68g2bxBYmIicnJysHLlSiQlJSEtLQ35+flQq9V48803UV5ejrCwMBw/fhwHDx5EeXk5VqxYgdTUVCxfvlzo+B5lpGsZHR096vuMRiOOHDmCqqoq7N+/H1lZWW5K7FwsDk6wfPlyyOVyhIWFQaVSobu7e9TXx8bGsjCM4FGPlcrKypCSkiJQIu+ze/duvPzyy6iurkZ1dTWKiorw4Ycf4sCBA6iqqkJTUxN+/fVX+PnxwcFYRrqWo7EW2bi4OHR1dbkjpkuwODiBTPbgMkokElgsFtt/rYxGo+3PAQEBbs0nBrxmjjl79ix6e3uRl5eHwsJCFBYW4ujRozh8+DD27duHdevWYcmSJUhISMDhw4eFjuvRRrqWx48fBwDbv+/BwcFh77O2xR5PB1RPxFsHF1GpVGhsbITFYsGdO3dQV1cndCTyAQEBAfjoo49sz8MtFguuX79ua9e8Y8cOpKWl4fTp0zCZTAAAqVRq+zM9MNK1TEpKgkqlQkNDAwDghx9+EDKmy3Dk4CLp6ek4ceIEcnNzERsbi0WLFgkdiXzA0qVL8corr2DHjh220ery5ctx4MABlJWVYc2aNZBIJMjIyMDFixcBDH1W9+3bh+DgYOTm5goZ36OMdC137tyJhQsXYs+ePfjss8+QkZEhcFLX4HkORERkh4+ViIjIDosDERHZYXEgIiI7LA5ERGSHxYGIiOywOBCNU0VFha2XDpFYsTgQEZEdFgciB+zfvx8rV67Exo0bcfr0aQBAU1MTtm7diqKiImRnZ+Oll17CwMAAvv76axQXF9ve29LSgoyMDBgMBqHiE40biwPRGL7//ntUVlbi1KlTtu67wNAhORs2bMDRo0dRWVkJjUaDs2fPIjc3F83NzaivrwcAHDt2DM899xzkcrmQPwbRuLA4EI3h/PnzWLVqFZRKJWQyGQoLCwEAr7/+OsLCwnDo0CG8/fbb0Gq16O3thVwux6ZNm3Ds2DGYTCZ8+eWXKCoqEvinIBof9lYicsDDXWakUikA4LXXXoPJZMKaNWvw9NNPo7W11fa64uJibNy4EampqYiLi4NarRYkN9FEceRANIbMzExUVFRAp9PBbDbbzpuorq7Gzp07kZeXBwC4fPmyrbvptGnTMH/+fLz77rvYsmWLYNmJJoojB6IxZGVloa6uDoWFhQgJCUFiYiI6Ozuxa9cu7Ny5EwqFAkqlEkuWLEFzc7PtfQUFBdizZ4/XngRGvo1dWYlcwGw245133sH06dOxfft2oeMQjRsfKxE5mV6vR1paGlpbW1FaWip0HKIJ4ciBiIjscORARER2WByIiMgOiwMREdlhcSAiIjssDkREZIfFgYiI7Pw/vErmdvQTDYUAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfc5e7198>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Grouped violinplots with split violins\\n\",\n    \"======================================\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"whitegrid\\\", palette=\\\"pastel\\\", color_codes=True)\\n\",\n    \"\\n\",\n    \"# Load the example tips dataset\\n\",\n    \"tips = sns.load_dataset(\\\"tips\\\")\\n\",\n    \"\\n\",\n    \"# Draw a nested violinplot and split the violins for easier comparison\\n\",\n    \"sns.violinplot(x=\\\"day\\\", y=\\\"total_bill\\\", hue=\\\"smoker\\\",\\n\",\n    \"               split=True, inner=\\\"quart\\\",\\n\",\n    \"               palette={\\\"Yes\\\": \\\"y\\\", \\\"No\\\": \\\"b\\\"},\\n\",\n    \"               data=tips)\\n\",\n    \"sns.despine(left=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfc8a3668>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Violinplot from a wide-form dataset\\n\",\n    \"===================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"\\n\",\n    \"# Load the example dataset of brain network correlations\\n\",\n    \"df = sns.load_dataset(\\\"brain_networks\\\", header=[0, 1, 2], index_col=0)\\n\",\n    \"\\n\",\n    \"# Pull out a specific subset of networks\\n\",\n    \"used_networks = [1, 3, 4, 5, 6, 7, 8, 11, 12, 13, 16, 17]\\n\",\n    \"used_columns = (df.columns.get_level_values(\\\"network\\\")\\n\",\n    \"                          .astype(int)\\n\",\n    \"                          .isin(used_networks))\\n\",\n    \"df = df.loc[:, used_columns]\\n\",\n    \"\\n\",\n    \"# Compute the correlation matrix and average over networks\\n\",\n    \"corr_df = df.corr().groupby(level=\\\"network\\\").mean()\\n\",\n    \"corr_df.index = corr_df.index.astype(int)\\n\",\n    \"corr_df = corr_df.sort_index().T\\n\",\n    \"\\n\",\n    \"# Set up the matplotlib figure\\n\",\n    \"f, ax = plt.subplots(figsize=(11, 6))\\n\",\n    \"\\n\",\n    \"# Draw a violinplot with a narrower bandwidth than the default\\n\",\n    \"sns.violinplot(data=corr_df, palette=\\\"Set3\\\", bw=.2, cut=1, linewidth=1)\\n\",\n    \"\\n\",\n    \"# Finalize the figure\\n\",\n    \"ax.set(ylim=(-.7, 1.05))\\n\",\n    \"sns.despine(left=True, bottom=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## heatmap热力图\\n\",\n    \"利用热力图可以看数据表里多个特征两两的相似度。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1acfcaf05f8>\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfc815710>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Annotated heatmaps\\n\",\n    \"==================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set()\\n\",\n    \"\\n\",\n    \"# Load the example flights dataset and conver to long-form\\n\",\n    \"flights_long = sns.load_dataset(\\\"flights\\\")\\n\",\n    \"flights = flights_long.pivot(\\\"month\\\", \\\"year\\\", \\\"passengers\\\")\\n\",\n    \"\\n\",\n    \"# Draw a heatmap with the numeric values in each cell\\n\",\n    \"f, ax = plt.subplots(figsize=(9, 6))\\n\",\n    \"sns.heatmap(flights, annot=True, fmt=\\\"d\\\", linewidths=.5, ax=ax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1acfcc732e8>\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfc9bdd30>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Plotting a diagonal correlation matrix\\n\",\n    \"======================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"from string import ascii_letters\\n\",\n    \"\\n\",\n    \"sns.set(style=\\\"white\\\")\\n\",\n    \"\\n\",\n    \"# Generate a large random dataset\\n\",\n    \"rs = np.random.RandomState(33)\\n\",\n    \"d = pd.DataFrame(data=rs.normal(size=(100, 26)),\\n\",\n    \"                 columns=list(ascii_letters[26:]))\\n\",\n    \"\\n\",\n    \"# Compute the correlation matrix\\n\",\n    \"corr = d.corr()\\n\",\n    \"\\n\",\n    \"# Generate a mask for the upper triangle\\n\",\n    \"mask = np.zeros_like(corr, dtype=np.bool)\\n\",\n    \"mask[np.triu_indices_from(mask)] = True\\n\",\n    \"\\n\",\n    \"# Set up the matplotlib figure\\n\",\n    \"f, ax = plt.subplots(figsize=(11, 9))\\n\",\n    \"\\n\",\n    \"# Generate a custom diverging colormap\\n\",\n    \"cmap = sns.diverging_palette(220, 10, as_cmap=True)\\n\",\n    \"\\n\",\n    \"# Draw the heatmap with the mask and correct aspect ratio\\n\",\n    \"sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\\n\",\n    \"            square=True, linewidths=.5, cbar_kws={\\\"shrink\\\": .5})\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## jointplot\\n\",\n    \"用于2个变量的画图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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ayLDU0d2jrriZt2dWojZWNausISpL65aRp3LA8jSvpq5GDcjWkKIvtcRhDpNGlzkBI5RUNWr2hRis/263de1uU5PPqxDEFOnFMoQbmZyjM7xzEiXDYUl1DqyprmlVZ26wtO5rUuL9DkpTk82hIUZZGDMzR8IE5GtI/U8X5GdzxDDFBpB0mFAqrrSOo1vagguGwPPLI45E8Ho88knw+j/zJPvmTfUr2eW05Y7WlLaCa+lZVVu/Tll1N2rKzSWUV9QoEw0ryeTR2WJ4mjMjTqMG58vt8nBCGuGdZlhr3d2r3nv2qrm/RrtoWVVTvU2t78NDPyUz3a2BBhorzM1TQN1152amf/5WmvOxUZaQlc6wbESPSEbAsS8FQWB2BsDoDIXV0htQZCKm980BkD8a2tSOgto6g2tq7/vrhX+sMhHo0Q5LPq5Rkr5KTffIneZWc5FNKsk/JyV75k3zyJ3sPBD3JqySfV52BkDoDYbW0B9S0v0ONzR3a3xY49HrJSV4NKsjQiEE5GjogS8X9MuVP9nIyGPB5uPc0tqm+uU17m9pV29Cm6r0tatrf+aWf7vN61CctWX3SkpXx+V9pqUlK+fyb7JRkn1L8B/7en3Tg71M+//PqT/Ypyec99Oc2OemLf9/V/3KJWXxydaTb29u1Z8+eXv/7lmVp0bOfqq6pTbIsWZYUCh/4uvX5j8OWpbBlyQpbClsH/lnYshQIhNUZDPUoXkk+r1JTDv7hTFLqwT+sfq/8yUnyJ3nl9x/45/6kA3/oLFmywocmVjgsBcOWQqGwAqGwwmFLwdCBbxaCobBCobCCQUuBz38cCIYUCP7fP/cnHQh2WopPaanJ6pOarOzMFGWlJys7I0V9M1Mkj4coA93k8Ry47Ku1LaDmtk61tAXU0hZUe2dQHZ0Hvmlv7wyprSOojs6wAqGQgoGwOoNhdYZCCofs+cPm9XqU5PPK5/Mq2eeRz+dVks8jz+fnjBxY1Hvk9X7+v56DXzuwUyeP5PV4NG9WiSaO6N2TxkKhkOrq6jR+/Hilpqba8ctKeK6O9AcffKCrrrrK9BgAgMMsW7ZMAwcOND1GXHD1Henz8w98t7dkyRL179/f8DQAkNiqq6tZONnM1ZH2+Q48oq5///581wYAiDtc/AcAgEMRaQAAHIpIAwDgUEQaAACHclykf/nLX2rhwoWmxwAAwDhHRXrFihV6/vnnTY8BAIAjOCbSjY2Nuv/++3XzzTebHgUAAEdwTKR/9rOf6dZbb1VWVpbpUQAAcARHRPqZZ55RUVGRpk+fbnoUAAAcwxF3HPvrX/+quro6zZs3T01NTWptbdU999yj22+/3fRoAAAY44hIP/roo4f+/rnnntOqVasINAAg4TliuxsAAHyZI1bSh7v44ot18cUXmx4DAADjHBfpWNvT1G56BABwjH7ZqaZHwGHY7gYAwKGINAAADkWkAQBwKCINAIBDEWkAAByKSAMA4FBEGgAAhyLSAAA4FJEGAMChiDQAAA5FpAEAcCgiDQCAQxFpAAAcikgDAGxlWZbpEeIGkQYA2CoUNj1B/CDSAABbBam0bYg0AMBWRNo+RBoAYKtgMGR6hLhBpAEAtmrvJNJ2IdIAAFu1dwZNjxA3iDQAwFZtHayk7UKkAQC2autgJW0XIg0AsFVbe8D0CHGDSAMAbMV2t32INADAVm2cOGYbIg0AsFVrG5G2C5EGANhqfxvHpO1CpAEAtmpp6zQ9Qtwg0gAAW+1vZSVtFyINALAV2932IdIAAFu1cJ20bYg0AMBWre0BhcOW6THiApEGANjKsg6EGpEj0gAA2+1r4QxvOxBpAIDtiLQ9iDQAwHZN+ztMjxAXiDQAwHaNRNoWRBoAYDtW0vYg0gAAW/l8HjU2E2k7EGkAgK0yUpNVv49I24FIAwBslZHuV0Nzu+kx4gKRBgDYqk9ashrY7rYFkQYA2CojLVmNrKRtQaQBALbqk5asto6Q2juCpkdxPSINALBVRppfklTPajpiRBoAYKuMtCRJUn0TkY4UkQYA2Cqrz4GVdPXeVsOTuB+RBgDYKisjRR6PtLO22fQorkekAQC2SvJ5lZuZoqo9LaZHcT0iDQCwXV52mqqJdMSINADAdnnZqaqub5VlWaZHcTUiDQCwXUFuuto6gqprbDM9iqsRaQCA7YrzMyRJm3c0Gp7E3Yg0AMB2Rf36yOf1qKyi3vQorkakAQC2S/J51T8vXZsqWUlHgkgDAKKiOD9DW3Y1KRzm5LHeItIAgKgYVJipto6gKqr3mR7FtYg0ACAqRgzMkSR9sKHG8CTuRaQBAFGRnZGi/nnpRDoCRBoAEDWjBueqvKJBre0B06O4EpEGAETNqMG5CoUtfbKpzvQorkSkAQBRM6QwU6l+n5Z/utv0KK5EpAEAUePzeTWuJE8r1+5We2fQ9DiuQ6QBAFE1ZVSB2jtDWrm22vQorpNkegAA5pVXNHTr55UOyY3yJIhHQwdkKSczRctWV+qMqQNNj+MqRBpIUN0Nc1f/DrFGT3g9Hk0ema9/fLRT9fva1Tcr1fRIrsF2N5BgyisaehVou18DiWXyqHxZlvS31ZWmR3EVIg0kiGiElVijuwpy01UyIEt/eW+bQqGw6XFcg0gDcS4WISXU6I4ZEwdob1M7J5D1AJEG4lSsV7msqnE8o4f0Vd+sFL3wj82mR3ENx0T6gQce0Ny5czV37lz96le/Mj0O4FqmY0mocTRer0fTxw9QWUWDNu3g90l3OCLSy5cv17vvvqvnn39eL7zwgtatW6c33njD9FiAKxyMsuk4H84pc8B5ThhToJRkn55/i9V0dzjiEqz8/HwtXLhQfr9fkjR8+HBVVVUZngpwHjfFj8u10JVUf5JOHluodz+p0lXn7VdxfobpkRzNESvpkSNHavLkyZKk7du3a+nSpTr99NMNTwWY58RVck+5dW5Ez2mTi5Xk8+qp18tNj+J4joj0QZs2bdINN9yg2267TUOHDjU9DmCE26PclXj79SAyGel+TRvXX//4aKd21e03PY6jOSbSa9as0XXXXacf/vCHmj9/vulxgJiJh9Vyd8X7rw/dN/PgavoNVtPH4ohI7969W9/97nf161//WnPnzjU9DhB1iRLlriTirxlflpnu1ynj+usfH7KaPhZHnDj2yCOPqKOjQ/fdd9+hry1YsEBXXnmlwakAexGn/8NJZZAOHJtevb5Gj768TnfccIrpcRzJEZG+4447dMcdd5geA4gK4nx05RUNhDqBZab7ddqUYr25qlJrt+zR+OH9TI/kOI7Y7gbiUaJuZ/cUn1FimzlxgLL7+PXwS2sVDlumx3EcIg1EAeHpGT6vxOVP9unsU4Zoy84mvf3xLtPjOI4jtruBeBEPsSnbXn/UfzZ6aN+ovS9b34lr8qh8Lf+0So+9sk7TJxQpJdlneiTHYCUN2MStgS7bXv+Fv7r7c6OBQwSJyevxaM6pw7SnsV1/4pKsLyDSQAKyI7bRjjUSS8mAbE0pzdezf9+siup9psdxDCIN2MDpUenJarm3r203p3+msN/s6cOU4vfpN898wklkn+OYNBAhJ8YkWivc7rxnNI9bI75lpCVr9vShevbvm/XGqkqdO22I6ZGMI9JABJwSaBNRPho7Y83JZIlnammBPiyv1aN/WadTxvVXTmaK6ZGMYrsb6CXTgY72SVyRsmsu058zYsvj8WjerOFq7wzqN3/+WJaV2NveRBroBZPhcHKYj2TXrIQ6sRTkpuuck4do5dpq/e2DHabHMYpIAz1kKhhuivORCDV66tSJAzRsQJZ+//xnqqlvNT2OMUQa6AGTgXY7O77JINSJw+v16NIzRypsWfrPJ9YolKBnexNpoBtM3mQjHgJ9OEKN7srNStX5pw7T+m31evEfW0yPYwSRBo6D7W37EWp019TSAo0rydMfl67XxsrE+/+dSANHweo5ugg1usPj8Wj+6cOVle7XfY+tVnNrp+mRYopIA10wffZ2oiDU6I701GQtOLtU9fvadf8THybU3ciINHAY0w94SKRAH0So0R2DCjM199RhWr2hRs/+fZPpcWKGO44BnzP9H/tYBrr8OMf2SgfH9i5fZdvruZ0ojuuUcf1VUb1P/7t0g0YP6asJI/qZHinqiDQSnuk4S/YG+ngBjsZr2BH1SELN7UMTg8fj0UWzhqtqT4t++cfV+s9/Ol0FfdNNjxVVbHcjoZkOtG135KpsOPSXCXa9fySfhen/LxEbKf4kXXXuaHUGQ/q3R1aqtT1geqSoItJIWKb/o25nnJ0k0pkINY6nIDddC84u1Y6a/fr3x+P7RidEGgnJ9MlhEZ8s5cA4H8kNM8K9Rg7K1QWnDdMHG2r06MvrTI8TNUQaCcXtZ2+7MXy9mZfVNLrjlHFFmjGxSC++vUVLV2w3PU5UcOIYEobb4+xmB+eP9VnjiH+zpw/T3qZ2/e65T5Wfk6YTxxSaHslWrKSBKIp0a9uNK+dj6cmvhdU0usPn9eiKs0apKC9d9z62Suu27jU9kq2INBJCrP+jbddxZwDHl+pP0rVzxiq7T4r+7eGV2rKz0fRItiHSiHuxDHSinBQWiVitppFYMtL9uv78cfL7ffrZgyu0s7bZ9Ei24Jg04lqsAm3X5VR22FxR26t/b8SQAlvevzvKKxs4Pg3b5WSm6Ibzx+nBFz7TT3+3XL/83mkqyHX3zU6INOJWtAPtlLuE9TbKPXmdWAYciES/nDRdN3esHn5prX76u+W677szlZuVanqsXmO7G3En2pdZ2fmc50i2tjdX1NoW6O6+1+F/Raq7v+7eftacPJa4BuRn6Jo5Y7WnqV3/sug9NexrNz1SrxFpxJV4j7OdkYyUk2bpCvfyTmxDi7J07Zwx2tPQptsXvad6l4aaSCNuRCvQToqzU/V2vng+QQ7mDRuQrWvmjFFdQ5tu/+27rgw1kYbrRWt72844S70LktPjfCQ3zYrEcDDUexrbXRlqIg3XiuaxZ7tXzr0NtBu5dW7Er2EDsnXt3LGuDDWRhutEO86mn07lttVzV9w+P+LP0KIsXTt3rOo+D7VbTibjEiy4SrRPDIuU6Uup6qq29PrfzR8wPOL3P9zmilou3YKjHDyZ7LG/btDti97TPd85VbmZzr48i0jDFZx8zbMdJz9FEuhIwnys17E72rHEmd04moPHqP/41/X6yaL3dM+3ZyonM8X0WEdFpOFoxLlrdoW5O+/h5lgDXSkZkK1rZo/VY39dr5/87j3d8+1TlZ3hzFBzTBqO5cRAR3Ii2OF6e9y5rmpLTAJt13tybBpOVVKcrWtmj9HuPS36yaL31LS/w/RIXSLScCSnBjpSkZwUFus4d/X+JmYYPbRvzN8TiWH4wBx9ffYYVe1p0Z0Pr1R7R9D0SF9CpOE4Tgu0yZWzZC6OR+OkWbrC8Wj0xIiBObrirFHavLNR9z62WsFQ2PRIX0CkkVB6EuhI42zHbTOdGkSnzgX0xthheZp/+nB9WF6r/376I1mWZXqkQzhxDI7ilEusIrnG2S5OD2Fd1RZOKkPcOHFMf+1r6dSbq3doaFG2Lj5zhOmRJBFpOIhbA233yVF2x7mhqvxLX8sdUGrrezgFW92IxJknDFJNfZsWv7JOg/tn6sQxhaZHItKIf90NtOk4S/YEuqsoH+/nODXanDSGWPJ4PLrkzBHa29SmXz/+gf7nR19Rfm6a0Zk4Jo24Fo1AR+O2nXacHNZQVd6tQNv97wLxxJ/s05XnlCoQCus/nlijUNjs8WkiDUeI1lOsuvXe3Qx0tO6pHavVczReJ9LZSwfbtz3NVjfskpedpgtmlmjd1r16/q3NRmch0ohLdj5iUorOcWe7Lq2yewXMihqQppYWaFxJnp54rUzVe1uMzUGkEXfsPEnMztWznWE+KJ6DyvFomOTxeHT+qcPk9Xi06LlPjV2WRaSRsLoT6EgcHuVoXE4VzUC7Lf5sdSMasjNS9NWTBunDslqt3lBjZAbO7oZxdh6Ptus4dG/vqx0rbonosR5VaefxaCBapo8v0vvrqvXHV9brxNGF8no9MX1/VtKIGyYCHc2V8tG4JdBAPPD5vPrqiYNVUd2sdz7eFfP3J9LAYbobaFP303ZaoKN1x7GeHI9mqxvRNnFkP/Xvm66+BfHLAAAdYklEQVSn3ihXOMaXZBFpxIVo3bCkK6Zu1+m0QAOJwuvx6LQpxdpZu19rymJ7bJpIw/Viuc1NoO3B8Wi4zcTh/ZSdkaJnlm2K6fsSaUAEOhqOddKYXdjqRqz4fF7NnDhAG7bXq6zC3vswHAuRhqvFapvb1PFntwY6ElwfDac6cWyh0lKS9OcYrqa5BAtGRfPJV911vFU0Z24DkKSUZJ+mje+vt9bsVGX1Pg3unxX19yTScC07VtEmAh0vIe7tmd0cj4abzZgwQO99WqWn3tio275+YtTfj0gjrkWyzR1JoOMlxL3F8WjEqz5pyZo2vkjvfLRLV55TqkGFmVF9P45Jw5XseICG3Q/NOHgMOdEDHQmOR8MNZk4qVnKSV0+/Ef0/60QaxkT7eHQsV9GEGUgcGWnJOmVcf7398S7tqtsf1fdiuxuu46RVtJPiXF9VdtR/1nfA6BhOAsS/mZOLtXJdtR5fukH/75qTovY+rKQRl2JxspgTAl1fVXbor+P9PDsd66QxHqqBRJCZ7tdpk4r17idV+mzLnqi9D5GGEb3d6u7OKjoW29ymAn14lHsaXrtDbTfu1w23mTWlWDmZKfr9c58qFApH5T3Y7kZCsWObO1aBNhnV3AGlxt4bcAt/sk9zpg/VE6+X69UV2zV3Zont70Gk4RpOWUXbKdYhrq8q4/g0YKNxJXkaMTBb//tqmWZOLlZ2Roqtr892N2KuN1vddgTaCavoSLar3Y7j0YhHHo9Hc08tUVt7QP+7dIPtr89KGgnBjpPFehvoeItxb08aswvHo+E0hX3TNX1CkV5fWaHzpg/ViIE5tr02K2nElKlV9LFEK9CJuFruLW5iArf76omD1SctWb977lOFw5Ztr+uYSL/88suaM2eOzjnnHC1ZssT0OIgjdt9Z7HiIM5B4UlOSdM4pQ1Re0aD3Pqmy7XUdEemamhrdf//9euKJJ/TCCy/o6aef1ubNm02PBZuZWEXHepubOHfNruPRbHXDyaaWFqgoL12LX1mnQDBky2s6ItLLly/XtGnTlJOTo/T0dJ177rl69dVXTY8FF3DSNrdbAh2tOXt7PJqtbsQLr9ejc6cNVW1Dm5Yu327LazrixLHa2lrl5+cf+nFBQYE+/fRTgxPBCSK9/Wcst7kjDV/D7u59M5BbFPn1y8e7BOtY10j39vGUQKIYOShHQ4uy9PxbmzX31GHy+SJbCzsi0uFwWB6P59CPLcv6wo/hftF4mIZTtrl7E+juRvlY/54dwY4FtrqRSDwej2ZOGqDHXy3TirW7NXNScUSv54jt7v79+6uuru7Qj+vq6lRQEP1LOeBcdjxE42hMBrphd3mvA93Va8VSNC69Yqsb8Wj0kL7Ky0rVK+9ui/i1HBHpGTNmaMWKFaqvr1dbW5tef/11zZo1y/RYcLBIVtHHE41A2xnnI1/XTtwOFIic1+vRuOF52rC9Xi1tgchey6aZIlJYWKhbb71V11xzjS666CKdf/75mjhxoumxYJOebnWbXEVHK9DxLBZPvWKrG25TOjhXobCljzfWHf8nH4MjjklL0gUXXKALLrjA9Bhwgd6uou26N3d3A+3EOB/rpLFYnzDGVjfi2eDCTHk90sbKep06aUCvX8cRK2nACbqzinZzoKOBVTTQNZ/Pq8w+ftU1tkf0Oj2KdCgU0uuvv64333xTwWDw0NeXLl0a0RDAQZFsdUeyirbz8ZNODTSraCC2stL9qt8Xw0jfdtttWrdunTZs2KCvfe1rqqiokCQ9+eSTEQ0BdFckNy85mmheauUUvX08ZW/P6GYVDUiBUFj+pBheJ11bW6v/+I//kCRddNFFuv3223XLLbdENADiWzSuj+4pO45FO3Wb245rpY+2io5WoFlFI1G0tAWU1ccf0Wv0KPGBQECdnZ2SpEGDBmnRokX6wx/+oE2bNkU0BBCp3l52lQjHoXu7zX00sXgcpcQqGu7W3hHU/raA+uWkRfQ6x4x0TU3NF368cOFCNTU1HfpxRkaGfvvb32rhwoURDQFEy7FW0W4PdHdW0bE+Ds0qGjhga1WTLEuaPCr/+D/5GI4Z6csuu0xr16499OPJkyd/4R7bkuTz+TRv3ryIhgC6IxrHo93KVKBjtc3NKhput2lHo/xJXo0ryYvodY4Z6R/96Ef6xje+0eUTqZYtW6YFCxZE9ObA4Xp7ZvfRtrrjdRXtxkADiaQjENInm+o0pbRAyUm+iF7rmCeOXXjhhRo0aJC+973vadu2bfrmN7+pl156SY888oiCwaCuvfbaiN4ccCqnnsnt1kCzikYi+Xhjrdo7Q5o3K/LLF497dveUKVP06KOP6vLLL9fDDz+sIUOG6JZbbtF5550nr5d7ocB93HpNdCIEGnC7YCisdz6u0uD+mRo/PLKtbuk4kd6zZ48WL16sP/3pT5o6daq8Xq+am5s1bdo0Ao2YOtrx6N5sdR+PE1fRTgx0d/Q00Kyi4Xbvr61W/b523XzxBFseuXzM0p599tmqqqrS4sWL9cgjj+jBBx/UxIkTddlll2nz5s0RvzkQa25cRTs10ByHBr6otT2gv63ZoXElfTV9Qu/v1324Y66kX3rpJQ0aNOjQjz0ej26//XaVlJTo6quv1r//+7/rtNNOs2UQwAmctoo+XqCPdycxkyeJsYpGovnLe9vUEQjpGxeOt+01jxnpwwN9uAULFmjw4MH64Q9/qBUrVtg2DNAT0djqdpJIAh3JNdAmAg24Xdn2en28sU4XnT5cIwfZ9w1nrx9VOWPGDC1ZssS2QYCjsev6aDu3uqPJ7dvbvQk0q2i42f62gF54e4sG9Oujr88eY+trR3T2V0lJiV1zIMFF8vSrw7n9hDECDbiLZVl69m+b1Noe0D9/bar8yZFdF32kXq+kAZN6e69uO+UWldp28likcZbcGWjA7d77tErllQ26du4YlQ6x/88AkUZCcOpWd3efYmXi+LMU/UCzioabbd3VpFdXbNeU0nxdcubIqLwHkYaj9eR4tIkTxnqzmu7p4yVNXf/MCho4usbmDj3xepnyc9N129Un2nJNdFeINFzHCVvdhzsY3WPFujfPfTa1vS3FJtCsouFWHYGQHn91g0JhSz+57mRlpEf2zOhjIdKIe7Ha6u5NiI/G6dvbEoFGYgqHLT39Rrl2723RbVefqGHF2VF9PyIN4+w4sztero2O1upZck6gAbeyLEuvLN+msooGXXf+WM2cXBz19yTScKxYPz+674DRxi7DiiTOUuy2t6XIA80qGm711oc7teKz3Tpv2pConSh2JCINV3Ha8ehIHS/OUnRXzxL34Aa6Y8Xa3XpjVaWmTyjSty+ZFLP3JdLAYWK1mo40zpKZQLOKRiL6sLxWL7+zVZNH9tOPrz5BXm90zuTuCpGG69l9PDqaoe5OnCVnrp45Do1EtKasRs/9fbNGD+2rO244RclJ9t5R7HiINNCFgzG1K9Z2xFlyd6BZRcNtPthQo+ffOhDoO781XSn+2CeTSMORYn3S2NFEEuvuhlmKTZwlVtBAdy3/tEp/eW+bxpXk6ec3TlOqgUBLRBou0tuTxnIHlEZ8rXRXwT083D0J8pGcunqW7As0q2i4hWVZenN1pf6+ZqemlObrJ9edbGQFfRCRRkKwI9RHiiTMkrNXzxIraCSeUCisl97dqtXrazRrSrFuXTBFSTE+Bn0kIg3E2PHiLJm/tIpAI9G0dQT1xGtl2rKrSRfMHKYbL5oQtftx9wSRRsI4GEdTT8SKVZwlZwWarW443Z7GNv3xr+vV0Nyhm+ZP0PkzS0yPdAiRhmuMGFLQ5XHp/AHDe3QZ1uGxjEWw3RJniRU0Es/mnY164rUy+Xxe/es3p2lKaff+rMUKkUZUlQ7JVXmFM87U7ko0g92dOEvmt7YPItBINCvX7tZf3t2qwrw++ukNp2hQYabpkb6ESMORSgfn9ugyrJ6uprtyZFR7E+3uhllKjDiz1Q0nCoXCemX5Nq1cW60Jw/vpX647SZlRfNxkJIg04oYdoT5cT4LbU4kQaMCJ9rV06snXy1VRvU+zpw/Vt+ZPUJLPa3qsoyLScJWjHZc+yO5Q2404A+Zsq2rSU2+Uq6MzpO9dNlnnTBtieqTjItKIOwdD6KRYHy/Okr3Pez6aWMaZrW44hWVZWv7pbi1dsU15OWn6t29N14hB7vj9SaRh3OihfVW2vf5LXz/acenjraYPOjKMpqJtevXMqhmJrCMQ0nNvbdZnm/doyqh8/ejqE5XVx5nHn7tCpOFK3Q314bqKZTTDbXL1bDrMrKLhBHUNrVryWpnqGtt0xVmjdNV5ox1xg5KeINKIukguwzrWWd69CfWRohHu7sRZsj/QpsMMOMmGbXv1p2WblJTk1R3Xn6yTxxWZHqlXiDQc4Whb3sdjR6iP1Jtt8u6GWYr/OLOKhklhy9LfPtihv32wQ4P7Z+qO609WUb8M02P1GpGG4x3vmumD0bM71gf1JMDHY1egnRZmwAnaO4L607KNKqto0KmTBuifFkwx9ohJu7h7eiSM7tzc5PAARivYkUiEQLOKhim1Da16fOkG1Td36Przx2r+GSNcd/y5K0QajnG8Le+e3IWsqyCaCrddZ287Oc4SgYY567ft1Z+WbVRKsk8/d+D9tyNBpOEqPb1d6OFMhJtAA9FjWZb+8eFOvb6qUkOLsvST609W/7w+pseyFZFGTHT3DO/unEAWSaiPFK0tcrueWuX0OEsEGmaEQmG98PYWrSmr1bTx/fWjq05QisuPP3cl/n5FSAgH42ZXrCX7gk2ggehq7wzqidfKtHlnky6aNVw3XDguLo4/d4VIw3F6cjlWNGItfTm0x4t2d8N8EIEGeqelLaDFr6zT7r2tuvniCZp7aonpkaKKSMORenrd9JHRi3a0e4vjz0DvNe3v0B/+sk4N+zp029Un6tRJA0yPFHVEGo7V2xucSNGPdm/EKtCHR7S3d3o73usCsda4v0MPvfCZWjuCuuP6k3TCmP6mR4oJIg1HiyTUhzMd7WgG+ljxPF5YjxVxogynaG7t1CMvrVVbR1B3fnOaxg3vZ3qkmCHScDy7Qn24w6MZ7WBHK9B2RJQQw+la2gJ65KV1am7t1M++cUpCBVoi0nCJgxGzO9ZS9E4+O/y1bX1NwooEEQiG9b+vblD9vnbdft1JmjQyfm5S0l1e0wMgcdgRl2ieVFU6ONe2qPbktXryayLQSBSWZenZv29SZXWzbrl8kk4amxjHoI9EpOE6o4f2dXSse/LvEmiga3//cKc+3bxHl311pL564mDT4xjDdjdc6/DARXMbXDr+Vnhvok6gga5t2dWoZasqNW1cf3199hjT4xhFpBEXjgxeNE80swOBBrq2v7VTf3pzo/Jz0/VPV06N2zuJdReRRlyK9io7Ek6/WQlgimVZeuHtLWrrCOrn35yuPmnJpkcyjkgj7jkp2D0NNKtoJJJ1W/dq/bZ6LTh7lEbxe18SJ44hwUT7pDM7EWgkkraOoF5+d6sGFWZqwdmlpsdxDCKNhGQi1m755gAw4a01O7S/NaDvXTZJPh9pOojtbiS0aN4k5cj36AlW0Ugk9fvatfyz3Tp10gCNGZZnehxH4dsVQNFb5bJ6Bo7vzdWV8no9um7uWNOjOA6RRkw5eYVo9xZ4NB6YAcSbPY1t+mRTnc46abAK8/qYHsdx2O4GjhDp2eCRhJ5AI9H846OdSvJ6dflZo0yP4khEGjFXOiTX1uccR1N3g822NtBz+1o69fHGOp15wiD1y0kzPY4jEWmgm6IdYlbRSDSr1lUrHLZ08ZkjTI/iWByThhEECUhswVBY76+v1sSR/TSoMNP0OI5FpGFM6ZBcYv05PgckmrKKBrW0BTRnxjDTozga290w7niBcsvx694i0EhEH5bVKLuPX6eMS8znRHeXIyK9Zs0a3XvvvQoEAsrJydE999yj4uJi02PBIboTsXgPORBPmls7tbGyQXNPHcbdxY7DEZH+8Y9/rN/+9rcaPXq0/vznP+vuu+/WokWLTI8FFzky5G6JNqtoJKKPN9YpbEnnThtiehTHM/4tTGdnp37wgx9o9OjRkqTS0lLt3r3b8FRwu4PHu50cQSfPBkSLZVlaU1arkuJsDSnKNj2O4xlfSfv9fs2bN0+SFA6H9cADD+iss84yPBXiyeExdMoKm0AjUVXXt6q2oVXnzxxnehRXiGmkly5dqnvvvfcLXyspKdHixYvV2dmphQsXKhgM6qabborlWEggB+NoMtYEGols7ZY98nikM6YOND2KK8Q00rNnz9bs2bO/9PWWlhZ9+9vfVk5OjhYtWqTk5ORYjoUEZOquZwQaicyyLH22Za9KB+cqNyvV9DiuYPyYtHTgxLEhQ4bov/7rv+T3+02PgwQR62PWBBqJrqa+VXsa23TqpAGmR3EN48ek169fr2XLlmnEiBGaP3++JKmgoEAPPfSQ4cmQKKK9BU6cgQM++3yre9YULrHtLuORHjt2rMrLy02PAUQl1gQa+D9rtx7Y6u6bxcM0ust4pAGniTTWhBn4spr6VtU1tOmCmSWmR3EVIg0cRU8u3SLMwLGt3bJHHkmns9XdI0Qa6AYiDERmw/Z6DR+Yrb7ZbHX3hCPO7gYAxK+m/R2q2tOik8YUmh7FdYg0ACCqyirqJUkzJrHV3VNEGgAQVWUVDeqXk6Yh/TNNj+I6RBoAEDWdgZC27GzS1NICeTwe0+O4DpEGAETNlp2NCobCmj6hv+lRXIlIAwCipqyiQal+nyaNLDA9iisRaQBAVFiWpfLKBo0f3k/JSeSmN/jUAABRUdfYpn0tnZpamm96FNci0gCAqNiyq0mSNLWU66N7i0gDAKJi684m9c1K1YD8PqZHcS0iDQCwXdiytLWqSeNK8rj0KgJEGgBgu+o9LWrrCGrSyH6mR3E1Ig0AsF1lTbMkadJIThqLBJEGANhuV91+ZaYnq7BvuulRXI1IAwBst6Nmv4YNyOZ4dISINADAVp2BoOoaWjVyUI7pUVyPSAMAbFVd3yZL0uihuaZHcT0iDQCw1d7GNknSyEFEOlJEGgBgq/p97UpJ9qlvVqrpUVyPSAMAbNXQ3KHCvHROGrMBkQYA2Kp+X7uK8rgVqB2INADAVvtaOlScn2F6jLhApAEAtrIsqagfNzGxA5EGANguLzvN9AhxgUgDAGzHmd32INIAANvlEmlbEGkAgK08krL7+E2PEReINADAVqkpyfL5yIsd+BQBALZK8ZMWu/BJAgBslZKcZHqEuEGkAQC2YiVtHz5JAICtUv2spO1CpAEAtvInkxa78EkCAGyVxJndtuGTBADYyuflEZV2IdIAAFtxjbR9+CQBALbyeUmLXfgkAQC2YiFtHz5KAICtfD6OSduFSAMAbMV2t334JAEAtuLsbvsQaQCArTi72z58kgAAW7GStg+RBgDYikjbh0gDAGzFdrd9+CQBALZiJW0fIg0AsBWRtg+RBgDYiu1u+/BJAgBsxUraPkQaAGCrJG4LahsiDQCwlZftbtvwSQIAbJXEvbttwycJALAVT8GyD5EGANiK3W778FECAGyV5POZHiFuEGkAgK3Y7rYPkQYA2IoTx+zDJwkAsFVSEitpuxBpAICtuC2offgkAQC2YrvbPnySAABbceKYfYg0AMBWPlbStuGTBADYikPS9uGjBADYihPH7MMnCQCwFdvd9uGTBADYyuflxDG7EGkAgK3Y7rYPnyQAwFY02j6O+ijXr1+v8ePHmx4DABABj4ftbrs4JtJtbW266667FAgETI8CAIAjOCbS9913n6699lrTYwAA4BiOiPSyZcvU3t6u8847z/QoAAA4RlIs32zp0qW69957v/C1kpIS7d+/X4sXL47lKAAAOF5MIz179mzNnj37C1975pln9Pvf/15XXXXVoa/NmzdPS5YsUUZGRizHAwDAUWIa6a5cdtlluuyyyw79uLS0VC+++KLBiQAAcAZHHJMGAABf5rhIl5eXmx4BAABHcFykAQDAAUQaAACHItIAADgUkQYAwKGINAAADkWkAQBwKCINAIBDEWkAAByKSAMA4FBEGgAAhzL+gA3T+mWnmh4BAIAusZIGAMChiDQAAA5FpAEAcCgiDQCAQxFpAAAcikgDAOBQRBoAAIci0gAAOBSRBgDAoYg0AAAORaQBAHAoIg0AgEMRaQAAHIpIAwDgUK5+VGUoFJIkVVdXG54EAMB/i+3n6kjX1dVJkq666irDkwAADqqurtbAgQNNjxEXPJZlWaaH6K329natXbtW+fn58vl8pscBgIQWCoVUV1en8ePHKzU11fQ4ccHVkQYAIJ5x4hgAAA5FpAEAcCgiDQCAQxFpAAAcikgDAOBQRBoAAIci0gAAOBSRjsD69es1fvx402O4wpo1a3TppZdq3rx5uvbaa7Vr1y7TIznSyy+/rDlz5uicc87RkiVLTI/jCg888IDmzp2ruXPn6le/+pXpcVzjl7/8pRYuXGh6DBwHke6ltrY23XXXXQoEAqZHcYUf//jHuvvuu/Xiiy/qggsu0N133216JMepqanR/fffryeeeEIvvPCCnn76aW3evNn0WI62fPlyvfvuu3r++ef1wgsvaN26dXrjjTdMj+V4K1as0PPPP296DHQDke6l++67T9dee63pMVyhs7NTP/jBDzR69GhJUmlpqXbv3m14KudZvny5pk2bppycHKWnp+vcc8/Vq6++anosR8vPz9fChQvl9/uVnJys4cOHq6qqyvRYjtbY2Kj7779fN998s+lR0A1EuheWLVum9vZ2nXfeeaZHcQW/36958+ZJksLhsB544AGdddZZhqdyntraWuXn5x/6cUFBgWpqagxO5HwjR47U5MmTJUnbt2/X0qVLdfrppxueytl+9rOf6dZbb1VWVpbpUdANrn4KVrQtXbpU99577xe+VlJSov3792vx4sVmhnK4o31mixcvVmdnpxYuXKhgMKibbrrJ0ITOFQ6H5fF4Dv3Ysqwv/BhHt2nTJt1000267bbbNHToUNPjONYzzzyjoqIiTZ8+Xc8995zpcdANPGCjh5555hn9/ve/V58+fSRJZWVlGj16tJYsWaKMjAzD0zlXS0uLvv3tbysnJ0e//vWv5ff7TY/kOM8//7w++OAD/eIXv5Ak/eY3v5FlWbrlllsMT+Zsa9as0fe//33dfvvtmjt3rulxHO36669XXV2dfD6fmpqa1Nraqosuuki333676dFwFEQ6QqWlpSovLzc9huN95zvfUV5enu688055vRxl6UpNTY2uvPJK/fnPf1ZaWpoWLFigu+66SxMnTjQ9mmPt3r1b8+fP1/3336/p06ebHsdVnnvuOa1atUr33Xef6VFwDGx3I+rWr1+vZcuWacSIEZo/f76kA8dbH3roIcOTOUthYaFuvfVWXXPNNQoEArr00ksJ9HE88sgj6ujo+EJoFixYoCuvvNLgVIB9WEkDAOBQ7DsCAOBQRBoAAIci0gAAOBSRBgDAoYg0AAAORaQBAHAoIg3EWGVlpSZPnqza2tpDX3vppZc0c+ZMHjwC4AuINBBjgwcP1hlnnKHHHntMkvTRRx/prrvu0m9/+1sVFRUZng6AkxBpwIAbb7xRTz/9tDZu3Kjvfe97uvPOO7m7GIAvIdKAAePGjdPEiRN1+eWXa8GCBZozZ47pkQA4EJEGDAiHw/J6vfJ4PPrWt7516OvNzc269NJLNWXKFG3cuNHghACcgEgDBtx3331qbm7W0KFD9dJLLx36empqqh588EGde+65BqcD4BREGoixp556Sm+++aZ+85vf6MYbb9Qjjzyig8+5SU5OVt++fQ1PCMApiDQQQ8uXL9f999+vRYsWqV+/fjr33HMVCAS0bNky06MBcCAiDcTIli1bdOutt+pXv/qVSktLJUk+n0/XX389z9YG0CWeJw040MKFC3XDDTdo1KhRpkcBYBCRBhzmxhtv1IYNG1RcXKwrrrhCF198semRABhCpAEAcCiOSQMA4FBEGgAAhyLSAAA4FJEGAMChiDQAAA5FpAEAcCgiDQCAQxFpAAAcikgDAOBQ/z9QwVH9nVVkMwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfcc663c8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Joint kernel density estimate\\n\",\n    \"=============================\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"white\\\")\\n\",\n    \"\\n\",\n    \"# Generate a random correlated bivariate dataset\\n\",\n    \"rs = np.random.RandomState(5)\\n\",\n    \"mean = [0, 0]\\n\",\n    \"cov = [(1, .5), (.5, 1)]\\n\",\n    \"x1, x2 = rs.multivariate_normal(mean, cov, 500).T\\n\",\n    \"x1 = pd.Series(x1, name=\\\"$X_1$\\\")\\n\",\n    \"x2 = pd.Series(x2, name=\\\"$X_2$\\\")\\n\",\n    \"\\n\",\n    \"# Show the joint distribution using kernel density estimation\\n\",\n    \"g = sns.jointplot(x1, x2, kind=\\\"kde\\\", height=7, space=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"HexBin图\\n\",\n    \"\\n\",\n    \"直方图的双变量类似物被称为“hexbin”图，因为它显示了落在六边形仓内的观测数。该图适用于较大的数据集。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.JointGrid at 0x1acf8cc2160>\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf8cc2668>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Hexbin plot with marginal distributions\\n\",\n    \"=======================================\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"ticks\\\")\\n\",\n    \"\\n\",\n    \"rs = np.random.RandomState(11)\\n\",\n    \"x = rs.gamma(2, size=1000)\\n\",\n    \"y = -.5 * x + rs.normal(size=1000)\\n\",\n    \"\\n\",\n    \"sns.jointplot(x, y, kind=\\\"hex\\\", color=\\\"#4CB391\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf8cc2128>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Linear regression with marginal distributions\\n\",\n    \"=============================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"sns.set(style=\\\"darkgrid\\\")\\n\",\n    \"\\n\",\n    \"tips = sns.load_dataset(\\\"tips\\\")\\n\",\n    \"g = sns.jointplot(\\\"total_bill\\\", \\\"tip\\\", data=tips, kind=\\\"reg\\\",\\n\",\n    \"                  xlim=(0, 60), ylim=(0, 12), color=\\\"m\\\", height=7)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## barplot(条形图)\\n\",\n    \"条形图表示数值变量与每个矩形高度的中心趋势的估计值，并使用误差线提供关于该估计值附近的不确定性的一些指示。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf8cc2a20>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Horizontal bar plots\\n\",\n    \"====================\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"\\n\",\n    \"# Load the example car crash dataset\\n\",\n    \"crashes = sns.load_dataset(\\\"car_crashes\\\").sort_values(\\\"total\\\", ascending=False)\\n\",\n    \"\\n\",\n    \"# Initialize the matplotlib figure\\n\",\n    \"f, ax = plt.subplots(figsize=(6, 15))\\n\",\n    \"# Plot the total crashes\\n\",\n    \"sns.set_color_codes(\\\"pastel\\\")\\n\",\n    \"sns.barplot(x=\\\"total\\\", y=\\\"abbrev\\\", data=crashes,\\n\",\n    \"            label=\\\"Total\\\", color=\\\"b\\\")\\n\",\n    \"\\n\",\n    \"# Plot the crashes where alcohol was involved\\n\",\n    \"sns.set_color_codes(\\\"muted\\\")\\n\",\n    \"sns.barplot(x=\\\"alcohol\\\", y=\\\"abbrev\\\", data=crashes,\\n\",\n    \"            label=\\\"Alcohol-involved\\\", color=\\\"b\\\")\\n\",\n    \"\\n\",\n    \"# Add a legend and informative axis label\\n\",\n    \"ax.legend(ncol=2, loc=\\\"lower right\\\", frameon=True)\\n\",\n    \"ax.set(xlim=(0, 24), ylabel=\\\"\\\",\\n\",\n    \"       xlabel=\\\"Automobile collisions per billion miles\\\")\\n\",\n    \"sns.despine(left=True, bottom=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## catplot\\n\",\n    \"分类图表的接口，通过指定kind参数可以画出下面的八种图\\n\",\n    \"\\n\",\n    \"stripplot() 分类散点图\\n\",\n    \"\\n\",\n    \"swarmplot() 能够显示分布密度的分类散点图\\n\",\n    \"\\n\",\n    \"boxplot() 箱图\\n\",\n    \"\\n\",\n    \"violinplot() 小提琴图\\n\",\n    \"\\n\",\n    \"boxenplot() 增强箱图\\n\",\n    \"\\n\",\n    \"pointplot() 点图\\n\",\n    \"\\n\",\n    \"barplot() 条形图\\n\",\n    \"\\n\",\n    \"countplot() 计数图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0x1acfc915c88>\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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roI1WvXr1MlbVUjC3sevXq6ZVXXtF//dd/qUqVKrb7hw8f7tBgAACUhNatW2vChAn64osv1K1bN6WkpOjw4cPq06ePJOnatWs6deqUOnXqpD59+mjZsmVKTEx0cuo72S1sX19fST/vUgAAwDRt2rRRXFycdu7cqWXLlqlTp04aMGCAxo4dK0nKzMxU1apVJUlnz56Vl5eXzp07pxo1ajgz9h3sFnZkZKQk6cqVK3J1dZWPj4/DQwEAUFIWLlyoxo0ba9CgQfLx8dG//vUvZWZmavjw4bJarerfv78++OADpaam6vz583r//fcVERGh2NjYMnVWT7tJTp8+rcmTJ9vOLd6yZUstXLhQderUcXg4AAD+qIEDB2rSpEmKjo6Wj4+PIiMjlZiYqP79+ys/P1/Dhw9XnTp1NGbMGM2fP1+BgYFq2rSpoqOjNWLECGfHt7Fb2NOmTVO/fv0UGhoqq9WqjRs3avr06Vq9enVp5AMA4A+pX7++Nm7cWOi+V155Ra+88kqh+7Zt22b7+dbe5bLE7lHi2dnZCgsLk7u7uzw8PDR48GBlZGSURjYAAPALu4X9yCOP6IsvvrBNnzhxQvXq1XNoKAAAUJjdXeLfffedBg8erMaNG8vNzU3ffPON/P391atXL0lSfHy8w0MCAFDR2S3sSZMmlUYOAABwD3YLu3Xr1qWRAwAA3IPdz7ABAIDzUdgAAKe6mZtv1Li369ixoy5cuODw55GKsUscAABH8nB31aCZe0t83PVzO5T4mM5UZGHfOgq8KBwdDgAoD/bv368VK1bI3d1dFy5cUMeOHeXt7a2dO3dKkqKiopSQkKAtW7YoOztb7u7uWrJkiR555BHbGPn5+Vq4cKEOHDig/Px8hYSEaNiwYSWas8jCnjFjRok+EQAAZdXhw4e1bds2+fr6ql27dpoyZYo2bdqkadOmadu2bdqzZ48++ugjeXl5afny5Vq3bl2hnoyJiZEkbd68WTdv3tSrr76q5s2b68knnyyxjEUW9u1Hh1++fFnZ2dmyWq3Kz8/XuXPnSiwAAADO9thjj6l27dqSJD8/P7Vt21aSVKdOHV29elVLlizRtm3bdObMGX366adq0qRJoccnJSUpJSVFycnJkqSsrCz93//9X+kU9i3Lly9XVFSUJMnV1VW5ublq2LAhu8QBAOWGu7t7oWlXV1fbz99//70GDBigl19+WcHBwapRo4ZSUlIKLZ+fn6/JkyerS5cukn6+ZGeVKlVKNKPdo8S3bNmiPXv2qGvXrkpMTFRkZKQaNmxYoiEAACirjh49qgYNGmjYsGEKDAzUzp07lZ9f+Aj0oKAgxcTEKDc3V9evX9egQYP01VdflWgOu1vYDz74oCwWix555BEdP35cL774olauXFmiIQAAKKvat2+v48ePq3v37rJarXrqqad08uTJQsuEhYXp7Nmz6tOnj/Ly8hQSEqI2bdqUaA67he3m5qZz587pkUce0cGDB9W+fXvduHGjREMAACqum7n5DvkK1s3cfHm4u9pdrk2bNoXKdffu3bafx40bd8/H3r5sRETE70hZfHZ3ib/22muaMWOGOnTooB07dqhDhw4KCgpyaCgAQMVRnFItS+M6i90t7CeffFLPPfecJCkuLk5nz55V48aNHR4MAAD8yu4WdqdOnfSXv/xFBw8eVOXKlRUQECAXF5fSyAYAAH5ht7B37dqlli1basGCBerWrZtWrVqlzMzM0sgGAAB+Ybewq1atqoEDB+pf//qXli1bpu3bt+vZZ58tjWwAAOAXxbr4x7Fjx7R582YlJCSoefPmWr58uaNzAQCA29gt7F69eik7O1shISGKjY1VzZo1SyMXAAC4jd1d4lOnTtXOnTs1evRoyhoAUOIK8m46ddxp06apU6dO2rp1a4lnmDp1qjZt2lQiYxW5hb1y5UqFh4dr9+7d2rNnzx3zHf0FcQBAxVDJzUMnFg8r8XEfmxRdrOU2b96sI0eOyMPDo8QzlKQiC7tq1aqSfr5qCQAA5dGoUaNktVrVr18/DR8+XGvWrFFBQYGaNWumWbNmydPTU08//bQ6deqkI0eOqEaNGgoNDdVHH32kS5cuaf78+WrdurUOHDigpUuXKicnR1evXtW0adPUuXPnQs8VFxd31/GLq8hd4mFhYZKkGjVqaNiwYRo7dmyhPwAAmG7FihWSpMWLFysmJkYbNmzQli1bVL16da1atUqSlJGRoeDgYMXFxenGjRvauXOn1q9fr3HjxmnNmjWSpLVr1+rtt9/W5s2b9fbbb99xcPbJkyeLHL+47B50tn//fi1btkwdO3ZUv3791LJly/t6AgBA6fN0cyl0i3vbv3+/zp49q/79+0uScnNz1bRpU9v84OBgSVLdunXVqlUrSb9eK1uSFi1apD179ighIUGHDx/W9evX72v84rBb2EuXLtWVK1e0detWzZs3Tzk5OerXr5+GDh16X08EACg9vRo9oJ3f/qTOD1d1dhQj5Ofn6/nnn7cdn3X9+vVCl9C8/fPt26+VfcugQYNsFxFp27atJk2adF/jF4fdo8Ql6YEHHtCAAQP02muvydvbm8trAkAZF2iprAltLAq0VHZ2FCO0adNGO3bs0A8//CCr1arZs2fbdnfbc/nyZZ05c0ZvvPGGgoODtWvXrjvK+I+Mf4vdLexvvvlGsbGxSkhIUNOmTTVixAh17Njxvp4EAICiFOTdLPYR3fc7biW34h35HRAQoLFjx2ro0KEqKChQkyZNNHLkyGI91tfXV3379lWPHj3k5uamoKAg5eTkKCsrq0TGv8XFarVa77VAhw4d1LdvX4WEhKhOnTr3NThKniO++lCaHPGmLI9YzxUD6xn3w+4WdqtWrTgqHAAAJ7P7GfbJkydlZyMcAAA4mN0tbH9/f/Xo0UOPP/64qlSpYrufM50BAFB67BZ2y5Yt+e41AABOZrew+fwaAADnK9blNe8mPj6+xMMAAIC7s1vYM2bMsP2cm5urbdu2qX79+sUaPD4+Xv/4xz+Ul5enoUOH6qWXXrrrcnv37tXcuXO1e/fuYsYGAKBisVvYrVu3LjTdrl07hYWF6fXXX7/n41JTU7V06VJt2rRJHh4eCgsLU5s2bdSwYcNCy2VkZGjBggW/IzoAABVHsU5Nersff/xRaWlpdpfbt2+fgoKC5OvrK29vb3Xt2lUJCQl3LBcREcHn5AAA2HHfn2F/9913GjBggN2B09LS5O/vb5u2WCw6cuRIoWU+/PBDNW3aVI8//nhx8wIAUCHd12fYLi4uevDBB/Xoo4/aHbigoEAuLr9e1s1qtRaaPnHihBITExUdHa1Lly7db24dO3ZMOTk59/04k926pJvpDh065OwIZRrruWKoiOu5vLxmZynWZ9jnz59X/fr1tXfvXh04cEBDhgxR1ar3vmRbrVq1dPDgQdt0enq6LBaLbTohIUHp6ekKDQ1Vbm6u0tLSNGjQIK1fv75YwZs1a1as5VD28KatGFjPFQPrufTY/Qx75syZWrlypf7zn/8oIiJCFy5c0F//+le7A7dr105JSUnKzMxUdna2EhMTbRcAl6Tx48dr+/bt2rJli6KiomSxWIpd1gAAVDR2C/vrr7/W7NmztWPHDvXp00eRkZG6ePGi3YFr1qypCRMmaMiQIXrxxRfVs2dPtWjRQuHh4Tp69GiJhC9LkpOTNXHiRCUnJzs7CgCgHLK7S9xqtapSpUr6/PPPNWrUKEkq9mfHvXr1uuOgtZUrV96xXL169Yz/DnZ0dLROnjyprKwsBQUFOTsOAKCcsbuF/dBDDyk8PFwXLlxQ69at9eabbyogIKA0shnl1oXKb79gOQAAJcXuFnZkZKR27NihVq1ayd3dXU8++aRefPHF0sgGAAB+Ybewvb291bt3b9v0wIEDHRoIAADc6b7PdAYAAEofhQ0AgAEobAAADEBhAwBgAAobAAADUNgAABiAwgYAwAAUNgAABqCwAQAwAIUNAIABKGwAAAxAYQMAYAAKGwAAA1DYAAAYgMIGAMAAFDYAAAagsAEAMACFDQCAAShsAAAMQGEDAGAAChsAAANQ2AAAGIDCBgDAABQ2AAAGoLABADAAhQ0AgAEobAAADEBhAwBgAAobAAADUNgAABiAwgYAwAAUNgAABqgwhX0zN9/ZEQAA+N3cnB2gtHi4u2rQzL0OGz/jh2xJ0qUfsh32POvndnDIuACAsq/CbGEDAGAyChsAAANQ2AAAGIDCBgDAABQ2AAAGoLABADAAhQ0AgAEobAAADEBhAwBgAAobAAADUNjAfUhOTtbEiROVnJzs7CgAKpgKcy5xoCRER0fr5MmTysrKUlBQkLPjAKhA2MIG7kNWVlahWwAoLRQ2AAAGoLABADAAhQ0AgAEobAAADEBhAwBgAAobAAADUNgAABiAwgYAwAAUNgAABqCwAQAwAIUNAIABHFrY8fHx6t69u7p06aJ169bdMX/nzp3q3bu3XnjhBY0ePVpXrlxxZBwAAIzlsMJOTU3V0qVLtX79esXFxWnjxo06deqUbf61a9c0e/ZsRUVF6ZNPPlHjxo317rvvOioOAABGc1hh79u3T0FBQfL19ZW3t7e6du2qhIQE2/zc3FzNmjVLNWvWlCQ1btxY33//vaPiAECxcd1zlEUOux52Wlqa/P39bdMWi0VHjhyxTfv5+elPf/qTJCknJ0dRUVEaPHhwscc/duyYcnJyir18q1atir0sHOvQoUPOjvC73bhxw3brqNdRXv6tmrye33vvPV28eFEZGRlyd3d3yHNUxPVcXl6zszissAsKCuTi4mKbtlqthaZv+emnnzRmzBgFBASoT58+xR6/WbNmJZITpc/kN62np6ft1uTXURrKy99PeXkdjsLfT+lx2C7xWrVqKT093Tadnp4ui8VSaJm0tDQNGjRIjRs31rx58xwVBQAA4zmssNu1a6ekpCRlZmYqOztbiYmJCg4Ots3Pz8/XqFGj9Pzzz2v69Ol33foGAAA/c9gu8Zo1a2rChAkaMmSIcnNz1bdvX7Vo0ULh4eEaP368Ll26pG+++Ub5+fnavn27JKl58+ZsaQMAcBcOK2xJ6tWrl3r16lXovpUrV0qSAgMDdfz4cUc+PQAA5QZnOgMAwAAUNgAABqCwS4iLq0ehWwAAShKFXUJ8Huog92oN5PNQB2dHAQCUQw496Kwi8XywkTwfbOTsGACAcootbAAADEBhAwBgAAobAAADUNgAABiAwgYAwAAUNgAABqCwAQAwAIUNwEg3c/OdHQEoVZw4BeXOzdx8ebi7OjsGHMzD3VWDZu51yNgZP2RLki79kO2w51g/t4NDxkX5RWGj3OE/cgDlEbvEAQAwAIUNAIABKGwAAAxAYQMAYAAKGwAAA1DYAAAYgMIGAMAAFDYAAAagsAEAMACFDQCAAShsAAAMQGEDAGAAChsAAANQ2AAAGIDCBgDAABQ2APyGi6tHoVugLKCwAeA3fB7qIPdqDeTzUAdnRwFs3JwdAADKGs8HG8nzwUbOjgEUwhY2AAAGoLABADAAhQ0AgAEobAAADEBhAwBgAAobAAADUNgAABiAwgYAwAAUNnAfOGUlAGehsIH7wCkrATgLpyYF7gOnrATgLGxhAwBgAAobAAADUNgAABiAwgYAwAAUNgAABqCwAQAwAIUNAIABKGwAAAxAYQMAYAAKGwAAA1DYAAAYgMIGAMAAFDYAAAagsAEAMACFDQCAAShsAAAMQGEDAGAAhxZ2fHy8unfvri5dumjdunV3zE9JSVFISIi6du2q6dOnKy8vz5FxAAAwlsMKOzU1VUuXLtX69esVFxenjRs36tSpU4WWmTx5smbOnKnt27fLarUqJibGUXEAADCam6MG3rdvn4KCguTr6ytJ6tq1qxISEjR27FhJ0sWLF5WTk6MnnnhCkhQSEqK//e1vGjRokN2xrVarbt68ed+ZqlV2ue/HlCU3btxQgVdVZ8f4Q27cuFEqz2PyumY9Fx/r2bl+z3r28PCQi4u5682ZHFbYaWlp8vf3t01bLBYdOXKkyPn+/v5KTU0t1tg3b97U119/fd+Zwrv43PdjypKvv/5aajvM2TH+kN+z3n4Pk9c167n4WM/O9XvWc/PmzeXp6emANOWfwwpztK33AAAIkElEQVS7oKCg0G9RVqu10LS9+ffi4eGh5s2bl1xYAECp8PDwcHYEYzmssGvVqqWDBw/aptPT02WxWArNT09Pt01nZGQUmn8vLi4u/IYGAKhQHHbQWbt27ZSUlKTMzExlZ2crMTFRwcHBtvl169aVp6enDh06JEnasmVLofkAAOBXLlar1eqowePj4/X+++8rNzdXffv2VXh4uMLDwzV+/HgFBgbq+PHjioiI0LVr19SsWTNFRkayuwQAgLtwaGEDAICSwZnOAAAwAIUNAIABKGwAAAxAYQMAYACHfQ8bxXfhwgV169ZNjz76aKH7mzZtqs6dO6tTp07FGmfatGkaO3as6tat64iYKIaEhARFRUUpLy9PVqtVvXv31ogRI0o1w6ZNm3TgwAHNnz+/VJ+3opszZ46++OIL5ebm6ty5c7b389WrVxUSEqJx48YVWn7Xrl36+uuv9cYbb9xz3HfffVeS7ng8Kh4Ku4ywWCzasmXLHxpj//79GjNmTAklwv1KTU3VggULtGnTJvn5+en69esaPHiwHn744WL/0gVzzZo1S9LPv4APGTLE9n6+Vbi/1alTJ/5d4L5Q2GXY1KlT1bp1a7Vu3VojRoyQn5+fvLy8NGXKFM2cOVN5eXny9PRUZGSkEhMTlZaWppEjR2rdunXy8/NzdvwK58cff1Rubq5ycnIkSVWqVNH8+fPl6empI0eOKDIyUjk5OfLz89OcOXNUv359paSkaObMmcrJydEDDzygxYsXq1atWlqxYoU++eQTubq66umnn9bkyZP1/fffa+zYsWrUqJFSUlJUvXp1LV++XL6+voqLi9M//vEP+fj4qG7duvL29nby3wZud+TIEYWFhSk1NdW2tX37npCOHTuqRYsWSklJ0fr167V582bFxMTIz89P1apVU4sWLZz9ElAG8Bl2GZGWlqbevXvb/nzwwQeF5n/77bdatGiRVq9erTVr1mj48OHatGmT+vfvr6+++kojR46UxWJRVFQUZe0kAQEB6tSpkzp37qy+fftq0aJFKigoUO3atRUREaElS5Zo8+bNGj58uGbMmCFJmjRpkkaPHm27dvyaNWv0v//7v9q9e7diY2O1efNmnT17Vhs2bJAkHT9+XMOHD9fWrVtVrVo1xcfHKzU1VYsXL9a6deu0ceNGXb9+3Zl/DbiLH374QR9++KFiY2O1atUqXbt27Y5lgoODtX37dn333Xe2db969WpdunTJCYlRFrGFXUbcbZf41KlTbT9Xr15d9erVkyQ9++yzmjt3rj799FN17NhRzz33XKlmRdHmzJmj0aNH67PPPtNnn32m/v37a+TIkTp//rxef/1123LXrl1TZmam0tPTbevv1qVlFyxYoB49eqhy5cqSpNDQUMXFxenZZ59V9erV1bRpU0lSo0aNdOXKFX355Zdq2bKlatSoIUnq1auXkpOTS/Nlw45nnnlGHh4eevDBB+Xn56crV67csczjjz8uSTpw4ICeffZZValSRZLUrVs3FRQUlGpelE0UtiG8vLxsP3fr1k0tW7bUnj17FB0drb179+rtt992YjpI0t69e5WVlaXu3bsrNDRUoaGhiomJUXx8vOrVq2f7hSw/P18ZGRlyd3cvdIW6GzduKC0t7a7/Oefl5UlSoYveuLi42K5yd/sJC93ceFuXNbevk9+ur1turdu7rc+bN286PiTKPHaJG+jPf/6zjh49qrCwML3xxhv65ptvJEmurq7Kz893crqKy8vLS0uWLNGFCxck/XzJ2JSUFD3xxBO6cuWK7ep1sbGxmjRpkqpWraqaNWvqs88+k/TzBXCWL1+uoKAgbdu2TTk5OcrLy1NsbKyCgoKKfN5WrVrpq6++UmpqqgoKCvQ///M/jn+xcJi2bdtqz549+umnn3Tjxg3t2LHD2ZFQRvCruIFGjRql6dOn6+9//7vc3d01e/ZsSVKHDh00cuRIffDBB6pfv75zQ1ZAQUFBGjt2rEaNGqXc3FxJP+8KHTdunDp27Kh58+bpxo0b8vHx0YIFCyRJixYt0uzZs7Vo0SL5+flp4cKFslgsSklJUWhoqPLy8tS+fXu9/PLLRX6WWaNGDUVERGjYsGGqXLmyGjZsWGqvGSWvSZMmGjp0qPr27atq1aqpTp06zo6EMoKLfwAAYAB2iQMAYAAKGwAAA1DYAAAYgMIGAMAAFDYAAAagsIFStn//fvXs2dPZMQAYhsIGAMAAnDgFcLCPP/5Yq1evVqVKleTn56eQkBDbvG+//VZz587V9evXlZ6eroCAAC1btkyenp7629/+ph07dsjd3V1+fn6KjIyUxWIp8n4A5RuFDTjQ8ePHtXjxYm3evFm1a9dWdHS0VqxYYTu3dExMjF588UX17t1bubm5CgkJ0d69e9WiRQutWbNGSUlJ8vDw0D//+U8dOXJEzZo1u+v9nTt3dvIrBeBoFDbgQElJSWrfvr1q164tSRo2bJiaNGmit956S5I0efJkff7551q5cqXOnDmjtLQ0ZWVlqWbNmgoICFCfPn0UHBys4OBgtW3bVgUFBXe9H0D5R2EDDuTq6lroilw5OTk6ffq0bXrixInKz8/X888/rw4dOuj777+X1WpVpUqVtHbtWh09elRJSUl655139Mwzz+gvf/lLkfcDKN846AxwoDZt2igpKUlpaWmSpA0bNmjRokW2+Z999pnGjBmj7t27S5IOHz6s/Px8HT9+XD179tSjjz6q1157TcOGDdPRo0eLvB9A+ccWNuBAjRs31uTJkzVixAhJkr+/v+bMmaP3339fkjRhwgSNGTNG3t7e8vHx0VNPPaVz586pX79+ev755xUaGipvb295eXkpIiJCAQEBd70fQPnH1boAADAAu8QBADAAhQ0AgAEobAAADEBhAwBgAAobAAADUNgAABiAwgYAwAAUNgAABvh/mYPsT9Lkdi8AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfcbab2e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Grouped barplots\\n\",\n    \"================\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"\\n\",\n    \"# Load the example Titanic dataset\\n\",\n    \"titanic = sns.load_dataset(\\\"titanic\\\")\\n\",\n    \"\\n\",\n    \"# Draw a nested barplot to show survival for class and sex\\n\",\n    \"g = sns.catplot(x=\\\"class\\\", y=\\\"survived\\\", hue=\\\"sex\\\", data=titanic,\\n\",\n    \"                height=6, kind=\\\"bar\\\", palette=\\\"muted\\\")\\n\",\n    \"g.despine(left=True)\\n\",\n    \"g.set_ylabels(\\\"survival probability\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0x1acfc8c3a20>\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfdefda90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Plotting a three-way ANOVA\\n\",\n    \"==========================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"\\n\",\n    \"# Load the example exercise dataset\\n\",\n    \"df = sns.load_dataset(\\\"exercise\\\")\\n\",\n    \"\\n\",\n    \"# Draw a pointplot to show pulse as a function of three categorical factors\\n\",\n    \"g = sns.catplot(x=\\\"time\\\", y=\\\"pulse\\\", hue=\\\"kind\\\", col=\\\"diet\\\",\\n\",\n    \"                capsize=.2, palette=\\\"YlGnBu_d\\\", height=6, aspect=.75,\\n\",\n    \"                kind=\\\"point\\\", data=df)\\n\",\n    \"g.despine(left=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## pointplot\\n\",\n    \"点图代表散点图位置的数值变量的中心趋势估计，并使用误差线提供关于该估计的不确定性的一些指示。点图可能比条形图更有用于聚焦一个或多个分类变量的不同级别之间的比较。他们尤其善于表现交互作用：一个分类变量的层次之间的关系如何在第二个分类变量的层次之间变化。连接来自相同色调等级的每个点的线允许交互作用通过斜率的差异进行判断，这比对几组点或条的高度比较容易。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x1acfe1aa438>\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfddc81d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Conditional means with observations\\n\",\n    \"===================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"iris = sns.load_dataset(\\\"iris\\\")\\n\",\n    \"\\n\",\n    \"# \\\"Melt\\\" the dataset to \\\"long-form\\\" or \\\"tidy\\\" representation\\n\",\n    \"iris = pd.melt(iris, \\\"species\\\", var_name=\\\"measurement\\\")\\n\",\n    \"\\n\",\n    \"# Initialize the figure\\n\",\n    \"f, ax = plt.subplots()\\n\",\n    \"sns.despine(bottom=True, left=True)\\n\",\n    \"\\n\",\n    \"# Show each observation with a scatterplot\\n\",\n    \"sns.stripplot(x=\\\"value\\\", y=\\\"measurement\\\", hue=\\\"species\\\",\\n\",\n    \"              data=iris, dodge=True, jitter=True,\\n\",\n    \"              alpha=.25, zorder=1)\\n\",\n    \"\\n\",\n    \"# Show the conditional means\\n\",\n    \"sns.pointplot(x=\\\"value\\\", y=\\\"measurement\\\", hue=\\\"species\\\",\\n\",\n    \"              data=iris, dodge=.532, join=False, palette=\\\"dark\\\",\\n\",\n    \"              markers=\\\"d\\\", scale=.75, ci=None)\\n\",\n    \"\\n\",\n    \"# Improve the legend \\n\",\n    \"handles, labels = ax.get_legend_handles_labels()\\n\",\n    \"ax.legend(handles[3:], labels[3:], title=\\\"species\\\",\\n\",\n    \"          handletextpad=0, columnspacing=1,\\n\",\n    \"          loc=\\\"lower right\\\", ncol=3, frameon=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## scatterplot(散点图)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1acfe1d2940>\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfe1c1400>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Scatterplot with categorical and numerical semantics\\n\",\n    \"====================================================\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"\\n\",\n    \"# Load the example iris dataset\\n\",\n    \"diamonds = sns.load_dataset(\\\"diamonds\\\")\\n\",\n    \"\\n\",\n    \"# Draw a scatter plot while assigning point colors and sizes to different\\n\",\n    \"# variables in the dataset\\n\",\n    \"f, ax = plt.subplots(figsize=(6.5, 6.5))\\n\",\n    \"sns.despine(f, left=True, bottom=True)\\n\",\n    \"clarity_ranking = [\\\"I1\\\", \\\"SI2\\\", \\\"SI1\\\", \\\"VS2\\\", \\\"VS1\\\", \\\"VVS2\\\", \\\"VVS1\\\", \\\"IF\\\"]\\n\",\n    \"sns.scatterplot(x=\\\"carat\\\", y=\\\"price\\\",\\n\",\n    \"                hue=\\\"clarity\\\", size=\\\"depth\\\",\\n\",\n    \"                palette=\\\"ch:r=-.2,d=.3_r\\\",\\n\",\n    \"                hue_order=clarity_ranking,\\n\",\n    \"                sizes=(1, 8), linewidth=0,\\n\",\n    \"                data=diamonds, ax=ax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## boxenplot（增强箱图）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1acfe1e1c18>\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfe1cb5f8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Plotting large distributions\\n\",\n    \"============================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"\\n\",\n    \"diamonds = sns.load_dataset(\\\"diamonds\\\")\\n\",\n    \"clarity_ranking = [\\\"I1\\\", \\\"SI2\\\", \\\"SI1\\\", \\\"VS2\\\", \\\"VS1\\\", \\\"VVS2\\\", \\\"VVS1\\\", \\\"IF\\\"]\\n\",\n    \"\\n\",\n    \"sns.boxenplot(x=\\\"clarity\\\", y=\\\"carat\\\",\\n\",\n    \"              color=\\\"b\\\", order=clarity_ranking,\\n\",\n    \"              scale=\\\"linear\\\", data=diamonds)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Scatterplot（散点图）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfcbdb780>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Scatterplot with continuous hues and sizes\\n\",\n    \"==========================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"sns.set()\\n\",\n    \"\\n\",\n    \"# Load the example iris dataset\\n\",\n    \"planets = sns.load_dataset(\\\"planets\\\")\\n\",\n    \"\\n\",\n    \"cmap = sns.cubehelix_palette(rot=-.2, as_cmap=True)\\n\",\n    \"ax = sns.scatterplot(x=\\\"distance\\\", y=\\\"orbital_period\\\",\\n\",\n    \"                     hue=\\\"year\\\", size=\\\"mass\\\",\\n\",\n    \"                     palette=cmap, sizes=(10, 200),\\n\",\n    \"                     data=planets)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.JointGrid at 0x1acf8bb1908>\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf9237d68>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Scatterplot with marginal ticks\\n\",\n    \"===============================\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"white\\\", color_codes=True)\\n\",\n    \"\\n\",\n    \"# Generate a random bivariate dataset\\n\",\n    \"rs = np.random.RandomState(9)\\n\",\n    \"mean = [0, 0]\\n\",\n    \"cov = [(1, 0), (0, 2)]\\n\",\n    \"x, y = rs.multivariate_normal(mean, cov, 100).T\\n\",\n    \"\\n\",\n    \"# Use JointGrid directly to draw a custom plot\\n\",\n    \"grid = sns.JointGrid(x, y, space=0, height=6, ratio=50)\\n\",\n    \"grid.plot_joint(plt.scatter, color=\\\"g\\\")\\n\",\n    \"grid.plot_marginals(sns.rugplot, height=1, color=\\\"g\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## PairGrid\\n\",\n    \"用于绘制数据集中成对关系的子图网格。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.PairGrid at 0x1acfc830978>\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acf8a96668>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Paired density and scatterplot matrix\\n\",\n    \"=====================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"sns.set(style=\\\"white\\\")\\n\",\n    \"\\n\",\n    \"df = sns.load_dataset(\\\"iris\\\")\\n\",\n    \"\\n\",\n    \"g = sns.PairGrid(df, diag_sharey=False)\\n\",\n    \"g.map_lower(sns.kdeplot)\\n\",\n    \"g.map_upper(sns.scatterplot)\\n\",\n    \"g.map_diag(sns.kdeplot, lw=3)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1ac8125c3c8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Paired categorical plots\\n\",\n    \"========================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"\\n\",\n    \"# Load the example Titanic dataset\\n\",\n    \"titanic = sns.load_dataset(\\\"titanic\\\")\\n\",\n    \"\\n\",\n    \"# Set up a grid to plot survival probability against several variables\\n\",\n    \"g = sns.PairGrid(titanic, y_vars=\\\"survived\\\",\\n\",\n    \"                 x_vars=[\\\"class\\\", \\\"sex\\\", \\\"who\\\", \\\"alone\\\"],\\n\",\n    \"                 height=5, aspect=.5)\\n\",\n    \"\\n\",\n    \"# Draw a seaborn pointplot onto each Axes\\n\",\n    \"g.map(sns.pointplot, scale=1.3, errwidth=4, color=\\\"xkcd:plum\\\")\\n\",\n    \"g.set(ylim=(0, 1))\\n\",\n    \"sns.despine(fig=g.fig, left=True)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## residplot\\n\",\n    \"线性回归残差图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1ac8166acc0>\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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fR6evJqFSLw+FAdXW10sWQzQO/fxS55hwIwnDOPBAOotvfG214HNE4bjaasax0ERYVz8fCaXPhaXbj7679m1HvN/LOYGQFINlG27AYxrnuj/Fhey1q2mtxvqfxiouO1WTFomnzsKxsMapnXIN5hbNG/V2ppLfjIhlK7otEY2fCNfn29nZs3boVe/fuhcViwV133YVrr70WCxcuTPQtZZNjzsayssVYVrY4ts4b9OHj3iY09DajsbcZH7uaozV+SUK3rxfdvl4cbzsd2z7PYsPcwlmYWzgL7oF+NPY2Y2b+dJiNZiX+JErAUOrNYrTAF/KjL+CBPzx855ZlsuK62Z/CZ2Z+AsvLrxrVEOpwOq54v1SNfzAZTVhWtgjLyhbhzmtugS/kx9mO8/jAeQYfttfC6elAIBxATUcdajrq8ErN6yjMysfSkoVYXFKJJSWVmF84Gyaj5m7MKQ0SPiqOHj2K6667DoWFhQCAL37xi/jjH/+I73znO7IVTk42Sw6qypeiqnxpbN1AOIBD77+BrPKcWPBvcrchHAmjP+iNnVRDjIIBM/KnY27hLMwpmDH4NRPFOUVpq1XpUaoGDq2cU42dp17FQDgwqmZcmJWPdUtX48b5K6fUOD+VRvlk/qYcczY+PXM5Pj1zOQCgy9eDuq56fNR5AaecZ9Hu7YJroA/vtZyIdT4wG81YUDQHi0sWYElJJRYXz0dBGhpy1UKNg8/UIuF+8h0dHSgtLY0tl5WVob29Pe7XHzlyBAAQDodht9tx4MABAMDAwADsdjsOHToEAPB4PLDb7XjjjTcAAC6XC3a7HW+//TYAoKurC3a7HUePHgUAXLp0CXa7He+//z4AoKWlBXa7HQ5HtGbW2NgIu92OU6dOIctkBbpD+O0Pd+Jvc1Zg8+qH8eg130be+yJum7MaNy/+PObbZkEIRjNaohRBs7sN71w8ht+cfhWb//yfuP/3/w93/ubbeOjAk/j5X3fi+cMv4f9ssqP24jlIkoSjR4/Cbrejq6sLAPD222/DbrfD5XIBAN544w3Y7XZ4PB4AwKFDh2C32zEwEK1xHjhwAHa7HeFwGEA0RWa322P7cd++fbj//vtjy3v27MGGDRtiy7t27cLGjRtjyy+//DIefPDB2PKOHTvw8MMPx5ZffPFFfP/7348tv/DCC3j88cdjy9u2bcNTTz0VW37uueewZcuW2PKzzz6LZ599Nra8ZcsWPPfcc7Hlp556Ctu2bYstb/jX/4t/+/cnYuMS3nv5T3j8P56KjUt4+OGHsWPHjtj2Dz74IF5++eXY8saNG7Fr167Y8nf++bt45pWtePjwZvzi+C74Qv5YgM+15MBw3Iebhc9h7ZLPw2IwT+nYK4ANx3/xJ3R+1AYACPQP4Pgv/gQ0BwAMH3s7D+7GdsduOFvbUPdfx9F4tgHbHbtx8K+HY8ceAFy4cAF2ux1nzpwBANTV1cFut6OuLlqxOHPmTPRYaevB5+Z8Bp8xLcPA/nb8y/Jv4BvVd+HqvIUw+KLHZkgMobarHvtrD+Hf33kB33ztX2H/3UO4+7lvYd8HB9HkasWf3vpTQseeKEZ7Fqn12BtKozl+dxRth87HGtj/5QcPTXjsPf7443jhhRdiy9///vfx4osvxpbHOvaGjg3gymNvw4YN2LNnT2z5/vvvx759+2LLdrsdr7/+OoDE4t7IfTUVCdfkI5HIqNqrJElTqs22tLTA4XBAFEV4PB58/PHHcDgcCAaD8Hg8aGhogMPhgN/vh8fjQX19PQoKCuDxeODxeHDhwgXYbDa43W54PB6cP38eVqsVPT09sWWTyYTOzk54PB6cO3cOQPRE9Hg8qKuriwVOj8eD2tpaDAwMoLm5Gf5LHmS1C1g0ez5sAQFNB89g3Z1fhrHIgo+c53GmpRaFc4rhjngRQQQwCWjsb0Vjf2v0j1thxA/e2wrrMQuywxa4F4nY8d5uzMybjs6GS+j3enDq1Cnk5uaivr4eHo8HH3zwAbKzs9HQ0ACPx4OTJ0/CYrHg448/hsfjwYkTJ2A0GtHY2AiPxxO7aF28eBF9fX2x5aamJrjd7thyc3PzqOWWlha4XK7YcmtrK3p7e2PLbW1t6O7uji07nU709PTElof239Bye3s7gsHgqGUAseWOjg5YLJbYcmdnJ/x+f2y5oesiDLkmiEERfvghRSRIooidf92LyMwAent70draGtve5XLFjh0AcLvdaGpuwitvv4oP+86h4zMhdEjngJ7BA80bwaxIKdZWfR6F5nw8u+dZXLx4cdJjr7i4GB988MGoY2+JYS6ORiLwDfjg9Xrh93ohRiKYY5gOh8MRO/aO1L0D6xwbIqEIImIEkbAIMRjC6x8eHnXstba2XnHseTwefPTRR/B4PLH/9dmzZ+F2u1FfXw+vx4POhkuYPn06FrtnoO6wA//4T/+IgE3E6dazqHc1wVBsQQQRuML9QIUBu+r2Y1fdfpgkI8RrIvjFX3ZiTt4MNDU2ol/w4c/H/4L8nDzUN9SPeewN/T/Veuzt/OteiOEQpEgE4UgEYlCEGAnhXEcDzCHjuMdeV1cXIpFIbLm7uxtmszm2PNaxN23atFHHXnNz8+hjsakpttzX1xc71obiTGNjY8Jxz+fzIREJN7zu27cPx48fj11Zf/azn0GSpEnTNeloeJ2KZBpSwmIYbf3taOlzosndhmZ3G1r6nLjk6cREu9UgGFBuK8GM/HJU5JVjRl4ZpueWYUZeOYqyCxRL/SjRqDRW46gkSfCEfPjZzU9O+NoevwtvNPwFRxr+gm5fb2x9tikL183+FG6Ydx2Wli6AQZj6Det4+yKetEAyf5McgmIIH/c2oa6rAee6GlDX3QD3QF9cr7UaLcg2ZyHbnIUcUzayzVkIeAdQVlwKg2CIDvwaeXhKI38cXhAgwCAYYBCGvhtiy8I460cuGw3R7yaDEUbBCJPBBKPBCNPglzG23ojn3t2OHFM2BEGIfgGQJMAX9mPzqk0wGIwwCAKMgnHw94PD14ThYWzRvyu6LEAYfNxkBJGIiIgkQZRERKQITp76AFXXVCEohhAMhxAUg4NfIQTEIILhwe/i8O8Cg9sFxCDyLLm44+q1CY3hSXvD68qVK/HTn/4UPT09yM7OxqFDh/DEE08k+naaZDKaMKdwJuYUzsTKEetDYgjO/g4097Whra8drf3tcPZFB3AFxCAiUgROT8dgj58PR72n1WjB9NxSlOeVYnpuKcpsJdHl3BKU5EzTXTe6RMYlXOhuxB/OHcF7zSdG9We/umwxbpy/EtfOWpGy0aTxjH9Qeo4Yi9GMJSULsKRkAYDoBabD2zUq6LeMM8ArMBiMXJddFM57L6al7IlyYeyLmH3/Jvk/rDG5ly8umY/rZ6evMpVwkC8vL8fGjRvxta99DaFQCLfffjuWL18uZ9k0y2w0x4L/SBEpgh6fC2397Wjtu4TW/ktw9nfgUn8Huny9kBDt1nfR3YqL7tYr3tcgGFCaMw3luaUoyy3B9NySEReBUk0Onol3XIIkSTjhrMFrH/0ParvqY+vzrLm4cf5KfKHyc5ieV5bu4o9JbWMtBEFA+eAx8nfzro2tD4kh+EMD8IUH4A8NwB/ywxcawEB4AL7Q4LqwHxdbm1BQXARJkqJfkEZP5TDqx+iCJA3WhGNfI5YhQRrrd5HhZVESIUZEhAe/i5HIiJ9FhCNhhCVxwjvmdLIYzbAaLbAYLbCYzNHvsXVmWEzR72W2YqyYfnVay5ZUn6tbbrkFt9xyi1xl0T2DYECJbRpKbNOwfPpVo34XFENo93TGBnBd8nSiw9uJS54udPl6YidNu7cL7d4uYIw27jxrLqbbSmIXgTJbCcpsxSjLLUFxdqEq7wImmwIiKIbwfvNJ7K87jIuultjr5hXOwk2L/wEr53waFpV1a413WotEeoTI2YvEbDTDbDQjH3kTbucIqreffGQw+J9o+xB/OPcGurw9KM4pxOcX/A2WlCy47OIRvYBIkEZdHKLLsZ8gSRiVPjKOSPecPXsWy6uugXlEADcbzaruXceOtSphMZoxu2AGZhfMuOJ34YiILm93NMB7ooG/wzP4s7cLgXC0d0d/wIP+gAfnexqveA+DYEBJTtGowF9mK0ZFXjlm5k+P9jRSyOUpkHBERH3PRfy58X28dfF9eIPDDU6fmH4V1i1djavLlqj6xJosrZPIdMRqncJYSQaDARYYcN3sT+G62Z8CMHwh/O2ZA7J3p2y3tKnmjjFeDPIaYDIYMT2vbMyDS5Ik9AX60e7pin55O3HJ04lObzc6PN3o8bsgDTYidXi7x51ordRWjHwpBx+ZmjA7vwKzCipSHvzDERFdvp7BC1YXmtytaOi5iEZXC0KRcGw7o2DAZ2Z+EuuuWo0F0+amrDzplMjAKj6MZnK8EF6JQV7jBEFAQVY+CrLysbik8orfh8QQuny96PBGA2k00Heh0xO9M/AEvQCATm83OtGN+trm4feGgFLbNMwqmIFZ+RXIteTAbDDBbDQP3qaaYDaYYTVZUGDNQ0FWPqwmCwbCgcEc7wB8IX9stKlroA89flc0qHuH01DjmZ5bin+o/Bz+fv71E87QqMWBMInMdsqH0UyOF8IrMcjrnNloRkVeGSrGucXsG+hHS58TzW4nTtSfQsASRkufE30BDyRIsdr/0EyJqSAIAkqyizAjvxwLps1FZdFcLJw2L67upFqtuSXSA0fpXjvpwKeyyY9BPsPlZ+VhWVYelpUtRklfbqyBrW+gH819zljff2d/OwZCAQQj4WhvETGMkBhCMBJCIBwccxbHIQKEWOOVAAErKq7GNeVXoTy3BGWDXUNNCTYKx1NzU2NNP5EeOGrrtSM3OS7YmXAhnCoGeRpTflYers7Kw9UjJnkbT0SKwBP0oW+gH0ExiCyTFdnmbDz37otw+/uQNaJrZyAcQH/Qi1UL/1aWck5Wc1NrTT+RB8vo/WE0fCpbajDIq4waa52TMQgG5FtzkW/NHbW+y9eb8lvnyWpuas7RJvJgGT0/jEYLT2Vr8Dbj92++ranzk0F+ilIZhNVa60xUOm6dJ6u5MUerHWp/KttJZw0OdR5FbnaOps7PhGehzERDQXhoxsShf/LQjInJGlnrFAQBVpMVZoMR+2sPy/L+6Xbr0lUIRUQEwoHoQzrCAdlvnaM1tztRmF0AT8iHwuyCUQ/xKLMVIyiOfkRfpudo1Sodx0sy9tcehkkwaO78ZE1+ClJ966+3Wme6csgT1dyYo9UOtbc5dHi7YRJGh0wtnJ8ZE+TlSLOkOgjrsWeA0jlktQeOeGmxrSYRSh8vEymzFeNS7+j5RLRwfmZEkJ8o1z0VqQ7CrHWmhpoDRzz01lajVbcuXYX/PPorBDR2fmZETl6uXHeqc4aT5ZcpNU46a/D4m1vxwO8fxeNvbpWtjUUuemur0aoVFVVYXbpSc+dnRtTkJ0yzTOExmOm49dd6rVNrxqsl35BfjWqoY+ZFvbXVaFmlbTbuqP6y0sWYkowI8nKmWfQShMfK8Wai8RrT33edxh1Q7mQe+f/xBX0QxTAKswtiv9dCLlgOmdIWkUoZka5Re9esdBuvK2iDt3nyF+tMh7cbFuPop0hZjBa4Q/0KlejK/0+WyQpXoB8uvzujjt9Ud1nOFBlRk58ozeJocyhdvLRLRe1VqzWu8e7yCswTP0gjlS7//xQMzsA5EA7AE/Jpav8mQ82jlbUkI4I8oJ80ixzGy/H2+HvHecXE4un9odaLwHg9mlYWflKxMo31/8m35sFgMKblQeBqwbYIeWRMkKdhctdeJ6txpaoLoBwXjvHu8iJtgYTLlSw9jpdIBPeDPDIiJ0+jjddGcW1hYg9iHy+vPVTjSkUXQDnztSsqqvDYjRvxs5ufxGM3blT8DoNtSFHcD/JgkM9A4/XHr7TNTuj9ymzFcA/0oa2/HU3uVrT1t8M90BercU12EUiEnvuOc7xEFPeDPJiuUYjSOeqx2igSbYS+umwxPuq8AAECDIKAkBiGS+zHF8qic8an4rZb7/latiFFcT8kjzV5Beita9iZjnMoysqH2WiCBAlmowlFWfk403EOQGpuuzm7JFF8WJNXgNa6hk1219Hh7Ub+4IO8h0iSFKtVp2KkMOf5IYoPg7xlCokyAAAJp0lEQVQCtJRqiKdnTDzpGLlvu/UyuyRRqjHIK0BLXcPiuetQqlbNfC3R5JiTV4CWuobF0zOGvSCI1Is1eQVoKdUQ710Ha9VE6sQgrxCtBEU2cA5TutsrUSIY5GlCWrrrSCW5p2bgBYPShUFeRdR64mvlriOV5Oz2ysf5UTqx4VUl9DZASm/knJpBz1MykPowyKsET3x1k3OEbSrm8iEaD4O8SvDEVzc5u71qaUqGk84a7Gr9g2ofck6TY5BXCS2d+JlIzrEAWhknMZRC9IZ9TCFqGBteVYJdFdVPrgZorfRYGkohGgyIpRDVPMcSjY1BXiW0cuKTPLTQY2lojiU//LF1TCFqD4O8imjhxKfMMTTaeSSmELUn4Zy8w+HA7bffjnXr1uGee+5Ba2urnOUiIoUNtR2EIiFVtx3QxBIO8g8++CCefPJJvPbaa7jlllvw5JOZ8xR5okww1NhsM+Vw4jkNSyhdEwwG8b3vfQ9Lly4FACxZsgS//vWvZS0YESlvRUUVIjMDqK6ujmt7tY7azmQJ1eQtFgvWrVsHAIhEIti2bRu+8IUvyFowItIWjtpWJ0GSJGmiDQ4ePIhnnnlm1LrKykrs2LEDwWAQmzZtgtvtxgsvvACz2TzpBwYCAdTU8J9O8mjwNuN912m4Q/0oMOfh2sLlqLTNVrpYGWlX6x/gDftgNgzHgVAkBJspB+tnrlWwZPpSVVUFq9U6+YaDJk3XrFmzBmvWrLlivdfrxbe//W0UFhbi+eefjyvAJ1PQVHE4HHHfiuqd1vbFSWcN3nI4YDYZMc1ahKAYxFt9DixctDDpFIHW9kUqxbsvfunch3xbPgRBiK2TJAmekE83+1LJ4yLRCnJSDa9z587Fc889B4vFMvkLiGTG+X7UhaO21SmhIH/27FkcOXIEJ06cwFe+8hWsW7cO3/zmN+UuG9GEON+PumhluoZMk1DvmmXLlqGurk7ushBNiZYeiJ4JOGpbnTjiVYcyoRvbSWcN+gMeOD2dMBmMKMoqgMlgZM1RYRy1rT6chVJnMqEb29DfKEZElGZPAwB0+npgMpg4WIfoMgzyOpMJjZEj/0abNQez8itQkVuKXKuNAZ7oMgzyOpMJjZGZ8DcSyYVBXmcyoRtbJvyNRHJhkNeZTOjGluzfeNJZg8ff3MpH2lFGYJDXGTkfU6dWyfyNmdAwTTQSu1DqUCZ0Y0v0bxzZaAuAj7Qj3WNNnjIKG20p0zDIU0Zhoy1lGqZrKKPcunQVtjt2A+EALEYLgmJQdw3TqZAJo6j1ijV5yiiZ0DAttwZvMxurNYw1eco4mdAwLaf3XadhNrGxWqtYkyeiCblD/Wys1jAGeSKaUIE5j43VGsZ0DRFN6NrC5Xirz6FYYzUbfZPDmjwRTajSNluxxmqOUE4ea/JENCmlGqs5Qjl5rMkTkWpxhHLyGOSJSLU4Qjl5DPJEpFqZMHV2qjHIE5FqcYRy8tjwSkSqxhHKyWFNnohIxxjkiYh0jOkaShhHIhKpH2vylBCORCTSBgZ5SsjIkYiCIMBqssJsMGJ/7WGli0ZEIzBdQwnp8HYj15wzal2qRyIyPUQ0dazJU0LSPRKR6SGixDDIU0LSPRKR6SGixDDIU0LSPRKRE1URJYY5eUpYOkciltmK4fK7Y1POApyoiigerMmTJnCiKqLEsCZPmhBND4G9aygu7Ik1jEGeNIMTVVE8hnpimQ3GUT2x7q1GRh4/TNcQka6wJ9ZoDPJEpCvsiTVa0kH+7NmzqKrKvFsgIlInPjJwtKSCvN/vxxNPPIFQKCRXeYiIksKeWKMlFeQ3b96Me+65R66yEBEljY8MHE2QJElK5IVHjhzBoUOHsGXLFixZsgR1dXVxvS4QCKCmhvONEBEloqqqClardfINB03ahfLgwYN45plnRq2rrKyEx+PBjh07plzAIVMtaKo4HA5UV1crXQxV4L4Yxn0xjPtimJL7ItEK8qRBfs2aNVizZs2odXv27MHPf/5z3H333bF169atw86dO5GbmzvlQhARUWokNBjqjjvuwB133BFbXrJkCV577TXZCkVERPJgP3kiIh2TZVqDeBtdAWConTcYDE6yZfoEAgGli6Aa3BfDuC+GcV8MU2pfDMXMqfaVSbh3TaL6+/tx7ty5dH4kEZFuLF68GHl5eXFvn/YgH4lE4PV6YTabIQhCOj+aiEizJElCKBSCzWaDwRB/pj3tQZ6IiNKHDa9ERDrGIE9EpGMM8kREOsYgT0SkYwzyREQ6xiBPRKRjDPJERDqW0UH+9ddfx0033YTVq1dj586dShdHUR6PBzfffDNaWlqULoqitm3bhrVr12Lt2rX44Q9/qHRxFPXjH/8YN910E9auXYuXXnpJ6eKowpYtW7Bp0yalizElGRvk29vbsXXrVvzmN7/Bq6++it27d+PChQtKF0sRp06dwvr169HY2Kh0URR19OhRvPPOO9i3bx9effVVnDlzBocPH1a6WIo4duwY3nvvPezfvx+/+93v8PLLL6OhoUHpYinq3Xffxb59+5QuxpRlbJA/evQorrvuOhQWFiInJwdf/OIX8cc//lHpYinilVdewWOPPYaysjKli6Ko0tJSbNq0CRaLBWazGQsWLEBbW5vSxVLEZz/7WfzqV7+CyWRCd3c3RFFETk6O0sVSjMvlwtatW3HfffcpXZQpk2UWSi3q6OhAaWlpbLmsrAynT59WsETKeeqpp5QugiosWrQo9nNjYyMOHjyIXbt2KVgiZZnNZvzkJz/BL3/5S3zpS19CeXm50kVSzA9+8ANs3LgRTqdT6aJMWcbW5CORyKgJ0iRJ4oRpBAA4f/48vv71r+Ohhx7CvHnzlC6OojZs2IB3330XTqcTr7zyitLFUcSePXtQUVGB66+/XumiJCRja/LTp0/H8ePHY8udnZ0Zn66g6DM8N2zYgEceeQRr165VujiKqa+vRzAYxFVXXYXs7GysXr16Ss+N0JMDBw6gs7MT69atg9vths/nw9NPP41HHnlE6aLFJWOD/MqVK/HTn/4UPT09yM7OxqFDh/DEE08oXSxSkNPpxAMPPICtW7dqttYml5aWFvzkJz+JpauOHDmCr371qwqXShkjexbt3bsXx44d00yABzI4yJeXl2Pjxo342te+hlAohNtvvx3Lly9XulikoO3btyMQCGDz5s2xdXfddRfWr1+vYKmUccMNN+D06dP48pe/DKPRiNWrV2f0nY2WcT55IiIdy9iGVyKiTMAgT0SkYwzyREQ6xiBPRKRjDPJERDrGIE9EpGMM8kREOsYgT0SkY/8f8xH1m/20zPUAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1ac811dcdd8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Plotting model residuals\\n\",\n    \"========================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"\\n\",\n    \"# Make an example dataset with y ~ x\\n\",\n    \"rs = np.random.RandomState(7)\\n\",\n    \"x = rs.normal(2, 1, 75)\\n\",\n    \"y = 2 + 1.5 * x + rs.normal(0, 2, 75)\\n\",\n    \"\\n\",\n    \"# Plot the residuals after fitting a linear model\\n\",\n    \"sns.residplot(x, y, lowess=True, color=\\\"g\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.FacetGrid at 0x1acfc915860>\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1ac815ff978>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Scatterplot with varying point sizes and hues\\n\",\n    \"==============================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"sns.set(style=\\\"white\\\")\\n\",\n    \"\\n\",\n    \"# Load the example mpg dataset\\n\",\n    \"mpg = sns.load_dataset(\\\"mpg\\\")\\n\",\n    \"\\n\",\n    \"# Plot miles per gallon against horsepower with other semantics\\n\",\n    \"sns.relplot(x=\\\"horsepower\\\", y=\\\"mpg\\\", hue=\\\"origin\\\", size=\\\"weight\\\",\\n\",\n    \"            sizes=(40, 400), alpha=.5, palette=\\\"muted\\\",\\n\",\n    \"            height=6, data=mpg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## swarmplot\\n\",\n    \"能够显示分布密度的分类散点图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x1ac816b4fd0>\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1ac816236a0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Scatterplot with categorical variables\\n\",\n    \"======================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"sns.set(style=\\\"whitegrid\\\", palette=\\\"muted\\\")\\n\",\n    \"\\n\",\n    \"# Load the example iris dataset\\n\",\n    \"iris = sns.load_dataset(\\\"iris\\\")\\n\",\n    \"\\n\",\n    \"# \\\"Melt\\\" the dataset to \\\"long-form\\\" or \\\"tidy\\\" representation\\n\",\n    \"iris = pd.melt(iris, \\\"species\\\", var_name=\\\"measurement\\\")\\n\",\n    \"\\n\",\n    \"# Draw a categorical scatterplot to show each observation\\n\",\n    \"sns.swarmplot(x=\\\"measurement\\\", y=\\\"value\\\", hue=\\\"species\\\",\\n\",\n    \"              palette=[\\\"r\\\", \\\"c\\\", \\\"y\\\"], data=iris)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## pairplot\\n\",\n    \"变量关系组图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.axisgrid.PairGrid at 0x1acfc6e1fd0>\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1acfa85a668>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Scatterplot Matrix\\n\",\n    \"==================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"sns.set(style=\\\"ticks\\\")\\n\",\n    \"\\n\",\n    \"df = sns.load_dataset(\\\"iris\\\")\\n\",\n    \"sns.pairplot(df, hue=\\\"species\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## clustermap\\n\",\n    \"聚集图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<seaborn.matrix.ClusterGrid at 0x1acfaf6e7b8>\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1ac82c81f60>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\\"\\\"\\\"\\n\",\n    \"Discovering structure in heatmap data\\n\",\n    \"=====================================\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"sns.set()\\n\",\n    \"\\n\",\n    \"# Load the brain networks example dataset\\n\",\n    \"df = sns.load_dataset(\\\"brain_networks\\\", header=[0, 1, 2], index_col=0)\\n\",\n    \"\\n\",\n    \"# Select a subset of the networks\\n\",\n    \"used_networks = [1, 5, 6, 7, 8, 12, 13, 17]\\n\",\n    \"used_columns = (df.columns.get_level_values(\\\"network\\\")\\n\",\n    \"                          .astype(int)\\n\",\n    \"                          .isin(used_networks))\\n\",\n    \"df = df.loc[:, used_columns]\\n\",\n    \"\\n\",\n    \"# Create a categorical palette to identify the networks\\n\",\n    \"network_pal = sns.husl_palette(8, s=.45)\\n\",\n    \"network_lut = dict(zip(map(str, used_networks), network_pal))\\n\",\n    \"\\n\",\n    \"# Convert the palette to vectors that will be drawn on the side of the matrix\\n\",\n    \"networks = df.columns.get_level_values(\\\"network\\\")\\n\",\n    \"network_colors = pd.Series(networks, index=df.columns).map(network_lut)\\n\",\n    \"\\n\",\n    \"# Draw the full plot\\n\",\n    \"sns.clustermap(df.corr(), center=0, cmap=\\\"vlag\\\",\\n\",\n    \"               row_colors=network_colors, col_colors=network_colors,\\n\",\n    \"               linewidths=.75, figsize=(13, 13))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "5.data-visualization/README.md",
    "content": "# 数据可视化\n## 一、matplotlib学习之基本使用\n**代码目录：**[1.matplotlib](1.matplotlib)\n\n### 目录\n\n - 1.figure学习\n - 2.设置坐标轴\n - 3.Legend 图例\n - 4.Annotation 标注\n - 5.tick能见度\n\n\n\n### 重要参考资料-A Brief matplotlib API Primer（一个简单的matplotlib API入门）\n**（这个内容来自《利用python进行数据分析第二版》，主要内容）：**\n\n1. Figures and Subplots（图和子图）\n2. Colors, Markers, and Line Styles（颜色，标记物，线样式）\n3. Ticks, Labels, and Legends（标记，标签，图例）\n4. Annotations and Drawing on a Subplot（注释和在subplot上画图）\n5. Saving Plots to File（把图保存为文件）\n6. matplotlib Configuration（matplotlib设置）\n\n\n\n## 二、数据可视化的利器-Seaborn简易入门\n\n**代码目录：**[2.seaborn](2.seaborn)\n\nMatplotlib试着让简单的事情更加简单，困难的事情变得可能，而Seaborn就是让困难的东西更加简单。 \n\nSeaborn是针对统计绘图的，一般来说，seaborn能满足数据分析90%的绘图需求。\n\nSeaborn其实是在matplotlib的基础上进行了更高级的API封装，从而使得作图更加容易，在大多数情况下使用seaborn就能做出很具有吸引力的图，应该把Seaborn视为matplotlib的补充，而不是替代物。\n\n用matplotlib最大的困难是其默认的各种参数，而Seaborn则完全避免了这一问题。\n\n### seaborn一共有5个大类21种图，分别是：\n\n- Relational plots 关系类图表\n  1. relplot() 关系类图表的接口，其实是下面两种图的集成，通过指定kind参数可以画出下面的两种图\n  2. scatterplot() 散点图\n  3. lineplot() 折线图\n- Categorical plots 分类图表\n  1. catplot() 分类图表的接口，其实是下面八种图表的集成，通过指定kind参数可以画出下面的八种图\n  2. stripplot() 分类散点图\n  3. swarmplot() 能够显示分布密度的分类散点图\n  4. boxplot() 箱图\n  5. violinplot() 小提琴图\n  6. boxenplot() 增强箱图\n  7. pointplot() 点图\n  8. barplot() 条形图\n  9. countplot() 计数图\n- Distribution plot 分布图\n  1. jointplot() 双变量关系图\n  2. pairplot() 变量关系组图\n  3. distplot() 直方图，质量估计图\n  4. kdeplot() 核函数密度估计图\n  5. rugplot() 将数组中的数据点绘制为轴上的数据\n- Regression plots 回归图\n  1. lmplot() 回归模型图\n  2. regplot() 线性回归图\n  3. residplot() 线性回归残差图\n- Matrix plots 矩阵图\n  1. heatmap() 热力图\n  2. clustermap() 聚集图\n\n"
  },
  {
    "path": "6.scikit-learn/.gitignore",
    "content": ""
  },
  {
    "path": "6.scikit-learn/LICENSE",
    "content": "CC0 1.0 Universal\n\nStatement of Purpose\n\nThe laws of most jurisdictions throughout the world automatically confer\nexclusive Copyright and Related Rights (defined below) upon the creator and\nsubsequent owner(s) (each and all, an \"owner\") of an original work of\nauthorship and/or a database (each, a \"Work\").\n\nCertain owners wish to permanently relinquish those rights to a Work for the\npurpose of contributing to a commons of creative, cultural and scientific\nworks (\"Commons\") that the public can reliably and without fear of later\nclaims of infringement build upon, modify, incorporate in other works, reuse\nand redistribute as freely as possible in any form whatsoever and for any\npurposes, including without limitation commercial purposes. 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  },
  {
    "path": "6.scikit-learn/README.md",
    "content": "# PyParis 2018: Machine learning using scikit-learn\n\n### Instructors\n\n* Guillaume Lemaitre - Inria, Paris-Saclay Center for Data Science\n* Jeremie du Boisberranger - scikit-learn @ fondation Inria\n* Joris Van den Bossche - Inria, Paris-Saclay Center for Data Science\n* and others to come ...\n\n### Obtaining the Tutorial Material\n\nIf you have a GitHub account, it is probably most convenient if you clone or\nfork the GitHub repository. You can clone the repository by running:\n\n```bash\ngit clone https://github.com/glemaitre/pyparis-2018-sklearn.git\n```\n\nIf you are not familiar with git or don't have an GitHub account, you can\ndownload the repository as a `.zip` file by heading over to the GitHub\nrepository (https://github.com/glemaitre/pyparis-2018-sklearn) in your browser\nand click the green “Download” button in the upper right.\n\nPlease note that we may add and improve the material until shortly before the\ntutorial session, and we recommend you to update your copy of the materials one\nday before the tutorials. If you have an GitHub account and cloned the\nrepository via GitHub, you can sync your existing local repository with:\n\n```bash\ngit pull origin master\n```\n\nIf you don't have a GitHub account, you may have to re-download the `.zip`\narchive from GitHub.\n\n### Requirements\n\nTo check if your system have the required libraries, you can execute the\nfollowing script:\n\n```bash\npython check_environment.py\n```\n\nIf you are using `conda`, you can create a specific environment for this\ntutorial with the following commands:\n\n```bash\nconda env create environment.yml\nconda activate pyparis_sklearn  # or source activate pyparis_sklearn\n```\n\n### References\n\nThis material is based on the fruitful work of the `scikit-learn` community and\nmore broadly to the Pythonista of the whole data science community."
  },
  {
    "path": "6.scikit-learn/check_environment.py",
    "content": "# This script is adapted from Andreas Mueller:\n# https://github.com/amueller/scipy-2018-sklearn/blob/master/check_env.ipynb\n\nfrom __future__ import print_function\nfrom distutils.version import LooseVersion as Version\nimport sys\n\n\ntry:\n    import curses\n    curses.setupterm()\n    assert curses.tigetnum(\"colors\") > 2\n    OK = \"\\x1b[1;%dm[ OK ]\\x1b[0m\" % (30 + curses.COLOR_GREEN)\n    FAIL = \"\\x1b[1;%dm[FAIL]\\x1b[0m\" % (30 + curses.COLOR_RED)\nexcept:\n    OK = '[ OK ]'\n    FAIL = '[FAIL]'\n\ntry:\n    import importlib\nexcept ImportError:\n    print(FAIL, \"Python version 3.4 (or 2.7) is required,\"\n                \" but %s is installed.\" % sys.version)\n\n\ndef import_version(pkg, min_ver, fail_msg=\"\"):\n    mod = None\n    try:\n        mod = importlib.import_module(pkg)\n        if pkg in {'PIL'}:\n            ver = mod.VERSION\n        else:\n            ver = mod.__version__\n        if Version(ver) < min_ver:\n            print(FAIL, \"%s version %s or higher required, but %s installed.\"\n                  % (lib, min_ver, ver))\n        else:\n            print(OK, '%s version %s' % (pkg, ver))\n    except ImportError:\n        print(FAIL, '%s not installed. %s' % (pkg, fail_msg))\n    return mod\n\n\n# first check the python version\nprint('Using python in', sys.prefix)\nprint(sys.version)\npyversion = Version(sys.version)\nif pyversion >= \"3\":\n    if pyversion < \"3.5\":\n        print(FAIL, \"Python version 3.5 (or 2.7) is required,\"\n                    \" but %s is installed.\" % sys.version)\nelif pyversion >= \"2\":\n    if pyversion < \"2.7\":\n        print(FAIL, \"Python version 2.7 is required,\"\n                    \" but %s is installed.\" % sys.version)\nelse:\n    print(FAIL, \"Unknown Python version: %s\" % sys.version)\n\nprint()\nrequirements = {'numpy': \"1.9\", 'scipy': \"0.13.3\", 'matplotlib': \"2.0\",\n                'sklearn': \"0.20\", 'pandas': \"0.21\", 'notebook': \"5\"}\n\n# now the dependencies\nfor lib, required_version in list(requirements.items()):\n    import_version(lib, required_version)\n"
  },
  {
    "path": "6.scikit-learn/data/README.md",
    "content": "### About the titanic dataset\n\nThe dataset was downloaded from OpenML. License and other information are\navailable at: https://www.openml.org/d/40945\n\nAuthor: Frank E. Harrell Jr., Thomas Cason\n\nSource: [Vanderbilt Biostatistics](http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.html)\n\nPlease cite: The original Titanic dataset,describing the survival status of\nindividual passengers on the Titanic. The titanic data does not contain\ninformation from the crew, but it does contain actual ages of half of the\npassengers. The principal source for data about Titanic passengers is the\nEncyclopedia Titanica. The datasets used here were begun by a variety of\nresearchers. One of the original sources is Eaton & Haas (1994) Titanic:\nTriumph and Tragedy, Patrick Stephens Ltd, which includes a passenger list\ncreated by many researchers and edited by Michael A. Findlay.\n\n### About the adult dataset\n\nThe dataset was downloaded from OpenML. License and other information are\navailable at: https://www.openml.org/d/179\n\nAuthor: Ronny Kohavi and Barry Becker\n\nSource: UCI - 1996-05-01\n\nPlease cite: Ron Kohavi, \"Scaling Up the Accuracy of Naive-Bayes Classifiers: a\nDecision-Tree Hybrid\", Proceedings of the Second International Conference on\nKnowledge Discovery and Data Mining, 1996\n\nNote: This dataset is not the original UCI dataset. It has some discretized\nfeatures. See version 2 for the original.\n\nPrediction task is to determine whether a person makes over 50K a\nyear. Extraction was done by Barry Becker from the 1994 Census database. A set\nof reasonably clean records was extracted using the following conditions:\n((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))\n\nRonny Kohavi and Barry Becker. Data Mining and Visualization, Silicon Graphics.\ne-mail: ronnyk '@' live.com for questions.\n"
  },
  {
    "path": "6.scikit-learn/data/adult_openml.csv",
    "content": "\"age\",\"workclass\",\"fnlwgt\",\"education\",\"education-num\",\"marital-status\",\"occupation\",\"relationship\",\"race\",\"sex\",\"capitalgain\",\"capitalloss\",\"hoursperweek\",\"native-country\",\"class\"\n2,State-gov,77516,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,Self-emp-not-inc,83311,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,215646,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,234721,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,338409,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,Cuba,<=50K\n2,Private,284582,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,160187,9th,5,Married-spouse-absent,Other-service,Not-in-family,Black,Female,0,0,0,Jamaica,<=50K\n3,Self-emp-not-inc,209642,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,45781,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,4,0,3,United-States,>50K\n2,Private,159449,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,280464,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,4,United-States,>50K\n1,State-gov,141297,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n0,Private,122272,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,205019,Assoc-acdm,12,Never-married,Sales,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Private,121772,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,?,>50K\n1,Private,245487,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,2,Mexico,<=50K\n0,Self-emp-not-inc,176756,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,186824,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,28887,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,292175,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n2,Private,193524,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,302146,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Federal-gov,76845,9th,5,Married-civ-spouse,Farming-fishing,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,117037,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,<=50K\n4,Private,109015,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,216851,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,168294,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,?,180211,Some-college,10,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,0,3,South,>50K\n2,Private,367260,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,193366,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,190709,Assoc-acdm,12,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,266015,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,386940,Bachelors,13,Divorced,Exec-managerial,Own-child,White,Male,0,1,2,United-States,<=50K\n1,Federal-gov,59951,Some-college,10,Married-civ-spouse,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,State-gov,311512,Some-college,10,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,0,United-States,<=50K\n3,Private,242406,11th,7,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,Puerto-Rico,<=50K\n0,Private,197200,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,544091,HS-grad,9,Married-AF-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,84154,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,?,>50K\n3,Self-emp-not-inc,265477,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,507875,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,88506,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,172987,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,94638,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,289980,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Federal-gov,337895,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,144361,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,128354,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,101603,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,271466,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,32275,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,Other,Female,0,0,2,United-States,<=50K\n0,Private,226956,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,?,<=50K\n3,Private,51835,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,3,Honduras,>50K\n3,Federal-gov,251585,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,109832,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,237993,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,216666,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,56352,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n2,Private,147372,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,188146,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,59496,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,<=50K\n1,?,293936,7th-8th,4,Married-spouse-absent,?,Not-in-family,White,Male,0,0,2,?,<=50K\n3,Private,149640,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,116632,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,105598,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,155537,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,183175,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,169846,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Self-emp-inc,191681,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,?,200681,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,101509,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,309974,Bachelors,13,Separated,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,162298,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,211678,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,124744,Some-college,10,Married-civ-spouse,Prof-specialty,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,Private,213921,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,Mexico,<=50K\n2,Private,32214,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,212759,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,309634,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,125927,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,446839,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,276515,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Cuba,<=50K\n3,Private,51618,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,159937,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,343591,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,4,0,2,United-States,>50K\n3,Private,346253,HS-grad,9,Divorced,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,268234,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,202051,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,54334,9th,5,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,410867,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,249977,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,286730,Some-college,10,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,212563,Some-college,10,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,117747,HS-grad,9,Married-civ-spouse,Sales,Wife,Asian-Pac-Islander,Female,0,1,1,?,<=50K\n1,Local-gov,226296,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,115585,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,191277,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,202683,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,171095,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,England,<=50K\n1,Federal-gov,249409,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,124191,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,198282,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Self-emp-not-inc,149116,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,188300,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,103432,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,317660,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n0,?,304873,10th,6,Never-married,?,Own-child,White,Female,4,0,1,United-States,<=50K\n1,Private,194901,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,189265,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,124692,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,432376,Bachelors,13,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,65324,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,335605,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,2,3,Canada,>50K\n1,Private,377869,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,2,0,1,United-States,<=50K\n2,Private,102864,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,95647,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,303090,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,197371,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,247552,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,102632,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,199915,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,118853,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,77143,Bachelors,13,Never-married,Exec-managerial,Own-child,Black,Male,0,0,2,Germany,<=50K\n1,State-gov,267989,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,301606,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Private,287828,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,111697,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,2,1,United-States,<=50K\n1,Private,114937,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,?,129305,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,365739,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,69621,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,43323,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,2,2,United-States,<=50K\n2,Self-emp-not-inc,120985,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,1,United-States,<=50K\n2,Private,254202,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,146195,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,125933,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Iran,>50K\n2,Self-emp-not-inc,56920,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,163127,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,34310,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,81973,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,66614,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,232782,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,316868,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,Mexico,<=50K\n2,Private,196584,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,>50K\n4,Private,105376,Some-college,10,Never-married,Tech-support,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,185814,HS-grad,9,Never-married,Transport-moving,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,175374,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,108293,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,181232,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n2,?,174662,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,186009,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,Private,198183,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,163003,Bachelors,13,Never-married,Exec-managerial,Other-relative,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n0,Private,296158,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n3,?,252903,HS-grad,9,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,187715,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,214542,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,494223,Some-college,10,Separated,Sales,Unmarried,Black,Male,0,2,0,United-States,<=50K\n1,Private,191535,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,228456,Bachelors,13,Separated,Other-service,Other-relative,Black,Male,0,0,3,United-States,<=50K\n4,?,38317,1st-4th,2,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,252752,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,78374,Masters,14,Divorced,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,88419,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,2,England,<=50K\n2,Self-emp-not-inc,201080,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,207157,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n2,Federal-gov,235485,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,102628,Masters,14,Widowed,Protective-serv,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,25828,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Local-gov,54826,Assoc-voc,11,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,124953,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,3,2,United-States,<=50K\n1,State-gov,175325,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,96062,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,428030,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,149624,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,253814,HS-grad,9,Married-spouse-absent,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,312956,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,483777,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,183930,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,37274,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n2,Local-gov,181344,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,114580,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,633742,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,286370,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,>50K\n2,Federal-gov,29054,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,304030,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,143129,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,?,135105,Bachelors,13,Divorced,?,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,99928,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,<=50K\n4,State-gov,109567,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,155222,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,159567,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,523910,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,120939,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,130760,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,197387,5th-6th,3,Married-civ-spouse,Transport-moving,Other-relative,White,Male,0,0,2,Mexico,<=50K\n2,Private,99374,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,56795,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n1,Private,138992,Masters,14,Married-civ-spouse,Prof-specialty,Other-relative,White,Male,2,0,2,United-States,>50K\n0,Self-emp-not-inc,32921,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,397317,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n0,?,170653,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,Italy,<=50K\n3,Private,259323,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,254817,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,<=50K\n2,State-gov,48211,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,140164,11th,7,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,128757,Bachelors,13,Married-civ-spouse,Other-service,Husband,Black,Male,2,0,2,United-States,>50K\n1,Private,36270,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,210563,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,4,0,1,United-States,>50K\n0,Private,65368,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,160943,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,208358,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,153790,Some-college,10,Never-married,Sales,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Private,85815,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,125417,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,635913,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,>50K\n3,Private,313321,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,182609,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Poland,<=50K\n2,Private,109434,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,255004,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,197860,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,187656,1st-4th,2,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,51744,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,4,2,United-States,<=50K\n3,Private,176681,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,0,United-States,<=50K\n3,Local-gov,140359,Preschool,1,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,243313,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,24215,10th,6,Divorced,?,Not-in-family,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,167687,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,3,United-States,<=50K\n4,Private,314209,Assoc-voc,11,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,Columbia,<=50K\n4,Private,176796,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,538583,11th,7,Separated,Transport-moving,Not-in-family,Black,Male,2,0,2,United-States,<=50K\n2,Private,130408,HS-grad,9,Divorced,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,159732,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,110978,Some-college,10,Divorced,Craft-repair,Other-relative,Other,Female,0,0,2,United-States,<=50K\n1,Private,76714,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,State-gov,268700,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,170525,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,180138,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Iran,>50K\n2,Local-gov,115076,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,115458,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,347890,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,196001,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n0,State-gov,273905,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,119156,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,179488,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,2,2,United-States,<=50K\n4,Private,203580,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,?,<=50K\n4,Private,236596,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,183916,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,207578,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,153141,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,?,<=50K\n2,Private,112763,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,390781,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n4,Local-gov,171328,10th,6,Widowed,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Local-gov,27382,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,259014,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,303044,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,0,0,2,Cambodia,>50K\n0,Private,117789,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,172579,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,187666,Assoc-voc,11,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,204518,7th-8th,4,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,150042,Bachelors,13,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,98092,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,245918,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,146013,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,378322,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,257295,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,4,Thailand,>50K\n0,?,218956,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,Canada,<=50K\n4,Private,21174,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,185480,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,222205,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,69867,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,191260,9th,5,Never-married,Other-service,Own-child,White,Male,1,0,1,United-States,<=50K\n3,Self-emp-not-inc,30653,Masters,14,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,4,United-States,<=50K\n1,Local-gov,209109,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,70377,HS-grad,9,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,477983,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,170924,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,190174,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,193787,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,279472,Some-college,10,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,2,0,3,United-States,>50K\n0,Private,34918,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,Germany,<=50K\n2,Local-gov,97688,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,175413,Assoc-acdm,12,Divorced,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,173960,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,205759,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,425161,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,220531,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,176609,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,371987,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,193884,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Ecuador,<=50K\n2,Private,200352,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,127595,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,220419,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,231931,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,248402,Bachelors,13,Never-married,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,111095,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-inc,57424,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,?,157443,Masters,14,Married-civ-spouse,?,Wife,Asian-Pac-Islander,Female,1,0,2,?,<=50K\n0,Private,278130,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,169469,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,146268,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,153718,Some-college,10,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n1,Private,217460,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,238638,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,303296,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,Laos,<=50K\n2,Private,173321,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,193945,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,83082,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,193815,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,34987,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,59306,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,142897,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,2,0,1,Taiwan,>50K\n0,?,860348,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,205607,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,>50K\n0,Private,199698,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,191954,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,138714,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,399087,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Other-relative,White,Female,0,0,2,Mexico,<=50K\n1,Private,423158,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,159841,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,174308,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,50356,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,3,United-States,<=50K\n1,Private,186110,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,200381,11th,7,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,174309,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,78383,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,211601,Assoc-voc,11,Never-married,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n2,Private,187728,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,3,United-States,>50K\n4,Self-emp-not-inc,321171,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,127921,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,1,0,3,United-States,<=50K\n2,Private,206565,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,224563,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,178686,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,98545,10th,6,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,242606,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,270942,5th-6th,3,Never-married,Other-service,Other-relative,White,Male,0,0,3,Mexico,<=50K\n1,Private,94235,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,71195,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,104112,HS-grad,9,Never-married,Sales,Unmarried,Black,Male,0,0,1,Haiti,<=50K\n2,Private,261192,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,94936,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,296478,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,State-gov,119272,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,85043,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,State-gov,293364,Some-college,10,Never-married,Protective-serv,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,241895,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,36135,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,?,151989,Assoc-voc,11,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,101128,Assoc-acdm,12,Married-spouse-absent,Other-service,Not-in-family,White,Male,0,0,1,Iran,<=50K\n1,Private,156464,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,117963,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,192262,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,111363,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,329752,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n4,?,372020,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,95432,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,161400,11th,7,Widowed,Other-service,Unmarried,Other,Male,0,0,2,United-States,<=50K\n2,Private,96129,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,111949,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,117125,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Portugal,<=50K\n2,Private,348022,10th,6,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n4,Private,270092,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,180609,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,174575,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,1,2,United-States,>50K\n0,Private,410439,HS-grad,9,Married-spouse-absent,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,92262,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,183081,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,362589,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,212448,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,481060,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,185885,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,89821,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,State-gov,184018,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,256649,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,160323,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Local-gov,350845,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,267404,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,35633,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,80914,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,172927,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,174319,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,214955,5th-6th,3,Divorced,Craft-repair,Not-in-family,White,Female,0,4,2,United-States,<=50K\n0,Private,344991,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,108699,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,117312,Some-college,10,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,396099,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,134152,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,162028,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,4,0,United-States,>50K\n0,Private,25429,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,232392,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,220098,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,301302,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,277946,Assoc-acdm,12,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,98101,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,?,>50K\n1,Private,196164,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,115562,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,96975,Some-college,10,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,137300,HS-grad,9,Never-married,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,86872,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,132178,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,416103,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,108574,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,288353,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,227689,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,166481,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,Other,Male,0,3,2,Puerto-Rico,<=50K\n2,Private,445382,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,4,United-States,>50K\n1,Private,110145,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,317253,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,?,123147,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,2,2,United-States,>50K\n1,Private,364657,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,42346,Some-college,10,Divorced,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,241951,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,118500,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,188386,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,State-gov,1033222,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,92440,12th,8,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,190762,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,426017,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,243867,11th,7,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,240283,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,61777,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,175024,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,1,0,0,United-States,<=50K\n1,State-gov,92003,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,188401,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,228528,10th,6,Never-married,Craft-repair,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,133373,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,255191,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,1,2,United-States,<=50K\n0,Private,204653,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,4,Dominican-Republic,<=50K\n4,Self-emp-inc,222289,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,287480,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,107762,HS-grad,9,Widowed,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,?,202521,11th,7,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,204116,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,29662,Assoc-acdm,12,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,116358,Some-college,10,Never-married,Craft-repair,Own-child,Asian-Pac-Islander,Male,0,3,2,Philippines,<=50K\n1,Private,208405,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,284843,HS-grad,9,Never-married,Farming-fishing,Not-in-family,Black,Male,1,0,3,United-States,<=50K\n1,Local-gov,117018,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,81281,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,340148,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,363425,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,45857,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Federal-gov,191073,HS-grad,9,Never-married,Armed-Forces,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,116632,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,405855,9th,5,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,Mexico,<=50K\n0,Private,298227,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,290521,HS-grad,9,Widowed,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,56915,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,146538,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,258872,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,206399,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,197332,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,245062,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,197583,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,?,>50K\n2,Self-emp-not-inc,234885,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,72887,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Private,180374,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,351299,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,54012,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,115745,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,116632,Assoc-acdm,12,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,288825,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,132601,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,193374,1st-4th,2,Married-spouse-absent,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,170070,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,126708,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,<=50K\n3,Private,35598,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,33983,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,192776,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,118551,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,0,United-States,>50K\n4,Private,201965,Some-college,10,Never-married,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,>50K\n0,?,139883,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,285020,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,303990,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,49401,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,279196,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,211870,9th,5,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,281432,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,161155,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,197904,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,111746,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Portugal,<=50K\n2,Self-emp-not-inc,170721,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,State-gov,70100,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,193626,HS-grad,9,Married-spouse-absent,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,?,271749,12th,8,Never-married,?,Other-relative,Black,Male,1,0,2,United-States,<=50K\n0,Private,189775,Some-college,10,Married-spouse-absent,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n4,?,401531,1st-4th,2,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n4,Local-gov,286967,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,164427,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,91039,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,347934,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,371373,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,32220,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,187251,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,178107,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,343121,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,262749,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,403107,5th-6th,3,Never-married,Other-service,Own-child,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,64293,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,303588,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Local-gov,324960,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Poland,<=50K\n4,Local-gov,114060,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,48925,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,180980,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,France,<=50K\n0,Private,181054,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,388093,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,249609,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,112131,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,543162,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,91996,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,141944,Assoc-voc,11,Married-spouse-absent,Handlers-cleaners,Unmarried,White,Male,0,1,2,United-States,<=50K\n3,?,251804,5th-6th,3,Widowed,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,37070,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,337587,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,189346,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,222216,Assoc-voc,11,Widowed,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,267044,Some-college,10,Never-married,Adm-clerical,Not-in-family,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n0,?,214635,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n0,?,204226,Some-college,10,Never-married,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,108116,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,99146,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n3,Private,196232,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n0,Local-gov,248344,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Local-gov,186035,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,177905,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,3,United-States,>50K\n1,Private,85812,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,221172,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,99183,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,190387,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,202692,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,109339,11th,7,Divorced,Machine-op-inspct,Unmarried,Other,Female,0,0,3,Puerto-Rico,<=50K\n1,Private,108658,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,197202,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,101739,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n4,Private,231559,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Local-gov,207853,12th,8,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,190942,1st-4th,2,Widowed,Priv-house-serv,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,102345,Assoc-voc,11,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,41493,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,190027,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,210525,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,133937,Doctorate,16,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,237903,Some-college,10,Never-married,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,163862,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,201872,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,84179,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,51662,10th,6,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,233327,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,259510,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,184831,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,245724,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,27053,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,205343,11th,7,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,229328,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Federal-gov,319560,Assoc-voc,11,Divorced,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,>50K\n4,Private,136218,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,54576,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,323069,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,0,?,<=50K\n1,Private,148291,HS-grad,9,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,152453,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,114053,Bachelors,13,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,212960,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,264052,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,82804,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,334273,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,27337,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,188436,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,433665,7th-8th,4,Separated,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,Self-emp-not-inc,110663,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,87490,Masters,14,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,354351,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,95469,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,242718,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,22463,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,158156,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,350162,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Male,0,0,2,United-States,>50K\n0,?,165532,12th,8,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,28738,Assoc-acdm,12,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,283635,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,86646,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,195733,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,69884,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,199713,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,181659,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,340939,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,197747,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,34292,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,156764,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,25826,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n4,Self-emp-inc,103948,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,?,137390,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,105138,HS-grad,9,Married-civ-spouse,?,Wife,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Private,39352,7th-8th,4,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,168387,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,Canada,>50K\n0,Private,117789,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,267147,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,99399,Some-college,10,Never-married,?,Unmarried,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,214242,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,200408,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,Private,136455,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,239824,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,217039,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,51290,7th-8th,4,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,175674,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,194404,Assoc-acdm,12,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,45612,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,410114,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,182521,HS-grad,9,Never-married,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Local-gov,339772,HS-grad,9,Separated,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,169658,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,200853,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,2,0,3,United-States,<=50K\n0,Private,247564,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,249909,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,208122,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,1,0,2,United-States,<=50K\n1,Private,109881,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,207824,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,369027,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,114117,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,51048,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,102388,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,190483,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,462440,11th,7,Widowed,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n4,Private,109351,9th,5,Widowed,Priv-house-serv,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,34383,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,241832,9th,5,Married-spouse-absent,Handlers-cleaners,Unmarried,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,124187,HS-grad,9,Never-married,Farming-fishing,Own-child,Black,Male,0,0,3,United-States,<=50K\n1,Private,153614,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,267556,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,205469,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,268090,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n3,Self-emp-not-inc,165039,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,120451,10th,6,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,154374,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,103649,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,>50K\n4,Self-emp-not-inc,35723,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,262601,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,226181,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,175697,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Self-emp-inc,248145,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,Cuba,<=50K\n3,Self-emp-not-inc,289436,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,75654,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,199378,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,160968,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,188563,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,55849,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,195322,Doctorate,16,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Local-gov,402089,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,78277,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n4,?,158611,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,169496,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,130959,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,556660,HS-grad,9,Never-married,Exec-managerial,Other-relative,White,Male,2,0,3,United-States,<=50K\n1,Private,292472,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,>50K\n2,State-gov,143774,Some-college,10,Separated,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,288341,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n1,State-gov,71592,Some-college,10,Never-married,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n4,?,167358,9th,5,Widowed,?,Unmarried,White,Female,1,0,0,United-States,<=50K\n1,Private,106742,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,219288,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,174524,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,335183,12th,8,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,261293,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,111900,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,194360,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,81145,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,341204,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,3,0,2,United-States,>50K\n1,State-gov,249362,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,247019,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n0,?,114746,11th,7,Married-spouse-absent,?,Own-child,Asian-Pac-Islander,Female,0,2,2,South,<=50K\n0,Private,172146,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,2,2,United-States,<=50K\n3,Federal-gov,110457,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,80077,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,368700,11th,7,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,182556,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,219420,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,240817,HS-grad,9,Never-married,Sales,Own-child,White,Female,1,0,2,United-States,<=50K\n0,Private,102726,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,226267,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,125457,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,204021,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,92262,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,161141,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Portugal,>50K\n1,Self-emp-not-inc,190290,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,430828,Some-college,10,Separated,Exec-managerial,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,State-gov,59342,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,136721,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,149422,7th-8th,4,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,86644,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,195124,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,Dominican-Republic,<=50K\n1,Private,167350,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,113000,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,140027,Some-college,10,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,262425,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,316702,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n0,State-gov,335453,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,?,202480,Assoc-acdm,12,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,203628,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,118710,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,189620,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,Poland,<=50K\n0,Private,475028,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,110866,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,243605,Bachelors,13,Widowed,Sales,Unmarried,White,Female,0,1,2,Cuba,<=50K\n0,Private,163870,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,80145,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,295566,Doctorate,16,Divorced,Prof-specialty,Unmarried,White,Female,4,0,4,United-States,>50K\n2,Private,63042,Bachelors,13,Divorced,Exec-managerial,Own-child,White,Female,0,0,3,United-States,>50K\n2,Private,229148,12th,8,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,Jamaica,<=50K\n2,Private,242552,Some-college,10,Never-married,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,177665,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,208103,11th,7,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,Private,296450,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,70282,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,271767,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,?,<=50K\n2,Private,144995,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,<=50K\n2,Local-gov,382635,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,Honduras,<=50K\n1,Private,295697,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,194141,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,378418,HS-grad,9,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,214399,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,217460,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,182556,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,125831,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,<=50K\n1,Private,271328,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,2,0,2,United-States,<=50K\n3,Local-gov,50459,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,162140,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,177937,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,?,>50K\n2,Private,111502,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,299047,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,223212,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Self-emp-not-inc,118474,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,?,>50K\n0,Private,352139,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,173093,Some-college,10,Divorced,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,181655,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,4,2,United-States,<=50K\n0,Private,332702,Assoc-voc,11,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,?,51164,Some-college,10,Married-civ-spouse,?,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Private,234901,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,131414,Some-college,10,Never-married,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,State-gov,260960,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,156052,HS-grad,9,Widowed,Other-service,Unmarried,Black,Female,1,0,0,United-States,<=50K\n2,Private,279914,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,192453,Some-college,10,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,200939,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,151408,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n1,Private,112847,Assoc-voc,11,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,316929,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,126319,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,197422,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,267736,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,267034,11th,7,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,Haiti,<=50K\n3,State-gov,193047,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,356089,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,223515,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,87510,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,145111,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,48093,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,31757,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,285854,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,120064,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,167381,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,103408,HS-grad,9,Never-married,Farming-fishing,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,101460,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,420537,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Local-gov,119411,HS-grad,9,Divorced,Protective-serv,Unmarried,White,Male,0,0,2,Portugal,<=50K\n3,Self-emp-inc,128272,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,386773,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,283268,10th,6,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,State-gov,301526,Some-college,10,Married-spouse-absent,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,151790,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,Germany,<=50K\n3,Self-emp-not-inc,106252,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,188557,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,171114,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,327323,5th-6th,3,Separated,Farming-fishing,Not-in-family,White,Male,0,0,1,Guatemala,<=50K\n1,Private,244147,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,280282,Assoc-voc,11,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,116442,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,282579,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,51838,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,73585,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,<=50K\n2,Private,226902,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,279129,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,146908,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n1,Private,196690,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,2,2,United-States,<=50K\n2,Private,130760,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,49572,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,237601,Bachelors,13,Never-married,Sales,Not-in-family,Other,Female,0,0,3,United-States,>50K\n2,Private,169628,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,36671,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,4,3,United-States,<=50K\n0,Private,231193,12th,8,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n4,?,192130,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,?,149704,HS-grad,9,Never-married,?,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,Private,102102,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,32185,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,196061,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,211046,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,1,0,2,United-States,<=50K\n4,Private,31577,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,162343,Some-college,10,Never-married,Other-service,Other-relative,Black,Male,0,0,0,United-States,<=50K\n4,Private,128831,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,316688,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,90758,Masters,14,Never-married,Tech-support,Not-in-family,White,Male,0,0,1,United-States,>50K\n2,Private,274363,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,England,>50K\n2,Private,154538,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,106085,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,2,1,United-States,<=50K\n4,Self-emp-not-inc,315859,11th,7,Never-married,Farming-fishing,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Private,51471,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,193830,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,231043,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n3,?,23780,Masters,14,Married-spouse-absent,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,169879,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,3,United-States,>50K\n4,Private,270333,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,138768,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,191571,HS-grad,9,Separated,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,219941,Some-college,10,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,94113,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,137510,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,32607,10th,6,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,93208,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,4,Italy,<=50K\n2,Private,254440,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,186556,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,169871,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,191277,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,167159,Assoc-voc,11,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,171871,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,154411,Assoc-voc,11,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,129227,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,110331,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,<=50K\n4,Private,34269,HS-grad,9,Widowed,Transport-moving,Unmarried,White,Male,0,1,2,United-States,>50K\n4,Private,174355,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,680390,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,233130,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Self-emp-inc,165474,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,?,257780,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,194259,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,2,United-States,>50K\n1,Private,280093,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,177387,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,28929,11th,7,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,105304,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,499233,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,180572,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,Private,321435,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,86108,HS-grad,9,Widowed,Farming-fishing,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,198124,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,135162,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,146813,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,291175,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,387569,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,102895,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,33274,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,86551,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,138192,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,118966,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,99784,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,90980,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,177407,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,96467,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,327886,Doctorate,16,Divorced,Prof-specialty,Own-child,White,Male,0,0,3,United-States,>50K\n1,Private,111567,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,166545,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,142182,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,188798,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,38563,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,Private,216284,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,191547,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,285335,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,142712,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,80945,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,309055,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,62339,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,368700,11th,7,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,176186,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,266855,Bachelors,13,Separated,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,48087,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,121313,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,143437,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n3,Self-emp-not-inc,160724,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,4,2,China,>50K\n3,Private,282753,5th-6th,3,Divorced,Other-service,Unmarried,Black,Male,0,0,1,United-States,<=50K\n2,Private,194636,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,153044,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Black,Female,0,0,0,United-States,<=50K\n2,Private,411797,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,117683,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,376540,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,72393,9th,5,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,270335,Bachelors,13,Married-civ-spouse,Adm-clerical,Other-relative,White,Male,0,0,2,Philippines,>50K\n1,Private,96226,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,95336,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,258498,Some-college,10,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,3,United-States,<=50K\n4,?,149698,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,205865,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-inc,155781,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,?,<=50K\n3,Self-emp-not-inc,406468,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,177119,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,?,144397,Some-college,10,Divorced,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,372525,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,164170,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,India,<=50K\n2,Private,183800,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n2,Self-emp-not-inc,177307,Prof-school,15,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,170108,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,341995,Some-college,10,Divorced,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,226508,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,87418,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,109165,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,28856,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,175897,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,99697,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n1,?,90270,Assoc-acdm,12,Married-civ-spouse,?,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,152375,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,171550,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,211154,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,202570,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,168496,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,68898,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,93235,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,278924,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,311020,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,175878,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,543028,HS-grad,9,Never-married,Sales,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,202027,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,158926,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,3,South,<=50K\n4,Self-emp-inc,76860,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,136063,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,186648,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,257509,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,98155,Some-college,10,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,274198,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Mexico,<=50K\n2,Private,97083,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n4,?,29825,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,262153,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,214738,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,138022,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,91842,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,373662,1st-4th,2,Married-spouse-absent,Priv-house-serv,Not-in-family,White,Female,0,0,2,Guatemala,<=50K\n2,Private,162003,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,52114,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,241843,Preschool,1,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,375871,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Mexico,<=50K\n2,Private,186934,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,176900,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,21906,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,132222,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,2,United-States,>50K\n1,Private,143653,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,111567,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,78602,Assoc-acdm,12,Divorced,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,465507,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,196373,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,293227,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,241752,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,166398,Some-college,10,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,184682,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,108293,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,2,United-States,>50K\n2,Private,250802,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,325159,Some-college,10,Divorced,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,State-gov,174675,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,227065,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,269080,7th-8th,4,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,177722,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,133461,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,239683,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,?,<=50K\n2,Self-emp-inc,398473,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Local-gov,298785,10th,6,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,123424,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,176286,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,150062,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,169240,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,288273,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,Mexico,<=50K\n2,Private,526968,10th,6,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,57066,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,323573,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-inc,368825,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,189721,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,164966,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n2,?,94954,Assoc-voc,11,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,202046,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,161538,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,105252,Bachelors,13,Widowed,Exec-managerial,Not-in-family,White,Male,0,4,2,United-States,>50K\n2,Private,200153,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,32185,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Male,0,0,4,United-States,<=50K\n0,Private,178326,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,255957,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,2,0,2,United-States,<=50K\n2,State-gov,188693,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,182977,HS-grad,9,Widowed,Other-service,Not-in-family,Black,Female,1,0,2,United-States,<=50K\n1,Private,159929,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,123207,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,284317,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,184699,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,154474,HS-grad,9,Never-married,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,318280,HS-grad,9,Widowed,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,254907,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,349221,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Female,0,0,1,United-States,<=50K\n3,Private,335973,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,126701,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,122159,Some-college,10,Widowed,Prof-specialty,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,Private,187370,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Private,194636,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,124793,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,192835,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,290226,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,112840,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,89325,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,33109,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,>50K\n2,Private,82465,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n2,Self-emp-inc,329980,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,148294,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,168212,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,4,United-States,>50K\n2,State-gov,343642,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Local-gov,115244,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,162572,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,356067,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,271567,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,180804,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,123011,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,109186,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Germany,<=50K\n3,Private,220537,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,124827,Assoc-voc,11,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,767403,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,118494,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,173208,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,107373,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,26973,Assoc-voc,11,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,191965,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,122346,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,117201,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,198316,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,Japan,<=50K\n3,Local-gov,123075,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,209370,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,33117,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,129042,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,169133,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Yugoslavia,<=50K\n1,Private,201624,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,?,<=50K\n2,Private,368561,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,207848,10th,6,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,138370,Masters,14,Married-spouse-absent,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n1,Private,93106,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,223515,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Male,0,2,0,United-States,<=50K\n1,Private,389713,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,206365,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,?,431192,7th-8th,4,Widowed,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,?,241616,HS-grad,9,Never-married,?,Unmarried,White,Male,0,3,2,United-States,<=50K\n4,Self-emp-inc,150726,9th,5,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,0,?,<=50K\n2,Private,123785,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,289984,HS-grad,9,Divorced,Priv-house-serv,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,?,164309,11th,7,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n4,Private,137018,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,137994,Some-college,10,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,341204,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,167005,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,34446,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,187160,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,?,196288,Assoc-acdm,12,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,217961,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,74631,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,156667,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n4,Private,125155,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,263925,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Canada,>50K\n1,Private,296453,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n3,Self-emp-not-inc,44728,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,193026,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,Iran,<=50K\n1,Private,87643,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,106742,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,302122,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,193960,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,185385,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,277647,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,128848,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,377701,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,Mexico,<=50K\n1,Private,157886,Assoc-acdm,12,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,175958,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,223004,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,199352,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,4,United-States,>50K\n2,Private,29984,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,181651,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,117312,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Local-gov,34029,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,132879,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,215310,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,55863,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,3,United-States,>50K\n0,Private,220384,11th,7,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,36012,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,137645,Bachelors,13,Never-married,Sales,Not-in-family,Black,Female,0,1,2,United-States,<=50K\n0,Private,191342,Bachelors,13,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,3,Taiwan,<=50K\n3,Private,31339,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,227910,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,173728,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,167816,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,81642,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,195258,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,232475,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,241259,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,118161,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,201954,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,150533,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,412296,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,133060,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,120539,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,196025,Doctorate,16,Married-spouse-absent,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,3,China,<=50K\n1,Private,107793,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,163870,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,361280,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Male,0,0,0,India,<=50K\n4,Private,92178,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,80710,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,260729,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,3,1,United-States,>50K\n2,Private,182254,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,140282,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-inc,149865,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,218184,9th,5,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,Mexico,<=50K\n2,Private,118619,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,196791,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n1,Local-gov,167999,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,51259,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,131088,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,118212,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,293791,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,289430,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Mexico,>50K\n1,Private,35378,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n2,State-gov,60227,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,168139,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,290763,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,226355,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,4,4,?,>50K\n2,Private,51100,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,227644,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,205267,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,288020,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Japan,<=50K\n1,Private,140863,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,170915,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,State-gov,50178,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,112497,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,95244,Some-college,10,Divorced,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,117606,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,89508,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n4,Federal-gov,124244,HS-grad,9,Widowed,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,154374,Some-college,10,Divorced,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,294936,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,347132,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,?,181934,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,316672,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n2,Private,189382,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,?,184018,Some-college,10,Divorced,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,184307,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Jamaica,>50K\n3,Self-emp-not-inc,246212,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,250504,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,3,United-States,>50K\n1,Private,138705,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,328447,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Mexico,<=50K\n0,Private,194608,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,230891,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,212448,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,2,Germany,<=50K\n2,Private,214010,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,200235,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,354573,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n1,Self-emp-inc,205733,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,185041,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,84409,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n3,Self-emp-inc,293196,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,241626,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,520586,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,35633,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,?,<=50K\n3,Private,302847,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,165309,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,117529,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,Mexico,<=50K\n3,Private,106092,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,445824,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,227332,Bachelors,13,Never-married,Transport-moving,Unmarried,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n0,Private,275691,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,193459,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,284329,HS-grad,9,Widowed,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,114691,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,96062,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,133963,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,2,United-States,>50K\n1,Private,178506,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,350498,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,4,0,0,United-States,>50K\n0,?,131573,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,206291,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,182302,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,241346,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,157043,11th,7,Divorced,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,404616,Masters,14,Married-civ-spouse,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,>50K\n0,Private,411862,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,183013,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,169982,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,188544,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,State-gov,356619,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,45857,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,289886,11th,7,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,?,146015,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,216237,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,416745,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,202952,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,167725,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,165637,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,43280,Some-college,10,Never-married,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,118779,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,State-gov,191269,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Local-gov,247507,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,239155,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,182862,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,33886,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,444304,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,187161,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,116892,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,176813,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,151616,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,240747,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,Dominican-Republic,<=50K\n3,Private,75472,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,?,<=50K\n2,Federal-gov,320818,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,4,United-States,>50K\n1,Local-gov,235271,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,166497,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,344060,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,221196,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,113544,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,321117,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,79619,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,?,42004,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,135289,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,320984,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,203070,Some-college,10,Separated,Adm-clerical,Own-child,White,Male,0,0,4,United-States,<=50K\n1,Private,32406,Some-college,10,Divorced,Craft-repair,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,99185,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,205839,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,?,150389,Bachelors,13,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Self-emp-not-inc,243631,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,3,0,2,United-States,>50K\n1,?,163003,HS-grad,9,Divorced,?,Not-in-family,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n1,Private,231263,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Private,200818,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,247379,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,349151,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,22154,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,176317,HS-grad,9,Widowed,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,22245,Masters,14,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,?,>50K\n1,Private,236436,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,354078,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,166813,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,358740,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,England,<=50K\n4,Self-emp-not-inc,208426,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,265266,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n3,Federal-gov,31838,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,175034,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,413297,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,106347,11th,7,Separated,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,174754,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,441454,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,209344,HS-grad,9,Married-civ-spouse,Sales,Other-relative,White,Female,0,0,2,Cuba,<=50K\n1,Private,185732,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,65372,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,33975,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,326297,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,State-gov,194630,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,167414,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,165799,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,192866,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,166459,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,148995,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,190040,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,209432,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,229465,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Self-emp-not-inc,397466,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,283767,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,?,<=50K\n3,Federal-gov,202452,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,218555,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Private,128604,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,65466,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,141326,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Federal-gov,369468,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,136137,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,236770,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,89534,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n4,?,195779,Assoc-voc,11,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,29778,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,153516,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,163594,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,189623,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n3,Self-emp-not-inc,343748,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,387430,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,409505,Bachelors,13,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,200734,Bachelors,13,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,115831,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,150296,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n0,Private,323545,HS-grad,9,Never-married,Tech-support,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,232577,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,152754,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,129007,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,171584,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,0,United-States,>50K\n3,Private,386136,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,342865,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,186785,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,2,3,United-States,<=50K\n2,Federal-gov,158926,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n4,?,36039,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,164019,Some-college,10,Never-married,Farming-fishing,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Private,88926,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,2,United-States,>50K\n3,Private,188861,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,370119,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,182062,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,37238,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,421132,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,178660,12th,8,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,795830,1st-4th,2,Widowed,Other-service,Unmarried,White,Female,0,0,1,El-Salvador,<=50K\n2,Private,278403,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,279661,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,1,United-States,<=50K\n2,Private,113397,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,280093,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,<=50K\n0,Private,236696,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,265266,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,34935,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,58222,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,301010,Some-college,10,Never-married,Armed-Forces,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,419721,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,Japan,<=50K\n4,Self-emp-inc,186791,Some-college,10,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,180686,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,209103,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,32668,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,256956,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,202203,5th-6th,3,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Mexico,<=50K\n2,Private,85995,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,125421,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,>50K\n2,Federal-gov,283037,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,192932,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,244689,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,179646,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,509350,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,Canada,>50K\n0,Private,96279,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,119098,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n1,?,327120,Assoc-acdm,12,Never-married,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,State-gov,144928,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,55237,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,101265,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,1,0,1,United-States,<=50K\n0,Private,114874,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,190525,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,121912,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,83893,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,?,138507,10th,6,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,256522,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,?,<=50K\n3,Private,168381,HS-grad,9,Widowed,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,2,India,>50K\n0,Private,293579,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,285290,11th,7,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,188488,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,324469,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,275244,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n4,Private,265099,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,146767,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,40681,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,0,United-States,<=50K\n2,Private,174938,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,240124,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,269708,Bachelors,13,Divorced,Tech-support,Own-child,White,Female,1,0,0,United-States,<=50K\n2,State-gov,34180,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,225904,Prof-school,15,Never-married,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,89392,Masters,14,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,46857,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,105363,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,195105,HS-grad,9,Never-married,Sales,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n1,Private,184117,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,134768,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Germany,>50K\n0,?,145886,11th,7,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,153078,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,?,>50K\n4,?,225652,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,1,0,3,United-States,<=50K\n1,Private,467108,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,199765,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,173938,HS-grad,9,Separated,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,191161,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,132606,5th-6th,3,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,30073,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,2,3,United-States,>50K\n2,Private,155190,10th,6,Never-married,Craft-repair,Other-relative,Black,Male,0,0,3,United-States,<=50K\n1,Private,42900,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,191161,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,181820,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,105974,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,146378,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,103440,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,203435,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,Italy,<=50K\n1,Federal-gov,168312,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,257764,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,171301,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Federal-gov,225339,Some-college,10,Widowed,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,152234,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,4,0,2,Japan,>50K\n0,Private,444554,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,403788,Assoc-acdm,12,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,?,190997,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,221550,Masters,14,Never-married,Other-service,Not-in-family,White,Female,0,0,1,Poland,<=50K\n3,Self-emp-inc,98929,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,169203,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,102332,HS-grad,9,Divorced,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,230684,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,449257,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,198766,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,97429,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,Canada,<=50K\n0,Private,208999,Some-college,10,Separated,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,37072,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,163101,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,119075,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,137314,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,127303,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,349116,HS-grad,9,Never-married,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,266324,Some-college,10,Divorced,Exec-managerial,Other-relative,White,Male,0,1,4,Iran,>50K\n0,?,194095,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,46496,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,29904,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,289403,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,2,2,?,>50K\n4,Private,226922,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,2,1,United-States,<=50K\n0,Federal-gov,234151,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,238287,10th,6,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,230624,10th,6,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,United-States,>50K\n3,Private,398212,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,2,0,2,United-States,<=50K\n3,Self-emp-not-inc,114758,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,246519,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,137815,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,260696,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,325007,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,113176,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,66815,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,51795,HS-grad,9,Divorced,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,241523,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,30226,11th,7,Divorced,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,352628,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,143912,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,130021,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,329778,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,196945,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,4,Thailand,<=50K\n2,Private,24342,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,34368,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,173839,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,73211,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n1,Private,86723,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,179186,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,0,0,4,United-States,>50K\n1,Private,127610,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,115070,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,172582,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,256202,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,202872,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,184102,11th,7,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,130703,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,134727,11th,7,Divorced,Machine-op-inspct,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,Germany,<=50K\n2,Self-emp-inc,36228,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,1,United-States,>50K\n2,Private,297847,9th,5,Married-civ-spouse,Other-service,Wife,Black,Female,1,0,1,United-States,<=50K\n0,Private,213644,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,173796,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,147322,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Peru,<=50K\n4,Private,296253,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,180871,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,?,169882,Some-college,10,Never-married,?,Own-child,White,Female,1,0,0,United-States,<=50K\n1,State-gov,211115,Some-college,10,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,183870,10th,6,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,441620,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Federal-gov,218542,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,141327,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,67716,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,175339,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,<=50K\n4,?,347089,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,336595,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,27997,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,145574,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,30447,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,256866,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n2,Self-emp-not-inc,120837,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,185283,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,229466,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,298225,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,185749,11th,7,Widowed,Transport-moving,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,?,333100,10th,6,Never-married,?,Own-child,White,Male,1,0,1,United-States,<=50K\n3,Self-emp-inc,125892,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,563883,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,>50K\n4,Private,311249,HS-grad,9,Widowed,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,221757,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Private,310152,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,211453,HS-grad,9,Widowed,?,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,94113,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,192945,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,161508,10th,6,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,177675,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,51100,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,100584,10th,6,Divorced,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Federal-gov,163003,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,67728,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n3,Private,101320,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n0,Private,42706,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,228535,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,120939,Prof-school,15,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,>50K\n0,Private,98283,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Local-gov,216481,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,State-gov,208869,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,207940,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,34248,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,83727,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,183077,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,197850,11th,7,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,235271,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,35236,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,255822,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,263925,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,256263,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,293535,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,209448,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,2,Mexico,<=50K\n1,Private,57651,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,3,2,United-States,<=50K\n0,Private,174592,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Federal-gov,278763,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,175232,Masters,14,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,>50K\n1,Private,402812,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,101150,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,103538,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,156877,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,4,0,1,United-States,>50K\n1,Private,23940,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,210295,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,80058,11th,7,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,187119,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,3,4,United-States,<=50K\n2,Self-emp-not-inc,105021,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,225775,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,395831,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,50282,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Private,32732,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,179436,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,?,290593,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,123253,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,48433,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,245317,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,431745,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n2,State-gov,436006,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,224943,Some-college,10,Married-spouse-absent,Prof-specialty,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,167990,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n2,Self-emp-inc,217054,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n4,Self-emp-not-inc,298834,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,125000,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,England,>50K\n2,Private,123983,Bachelors,13,Divorced,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n3,Private,155489,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,284834,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,1,0,1,United-States,<=50K\n0,Private,212495,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,1,2,United-States,<=50K\n0,Local-gov,32124,9th,5,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Local-gov,246891,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,141483,9th,5,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,31985,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,170800,Some-college,10,Never-married,Farming-fishing,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,166295,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,4,3,United-States,<=50K\n0,Private,231286,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n1,Private,159322,HS-grad,9,Divorced,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,176026,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,118025,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,26898,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,232628,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,85995,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,125421,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n3,Private,245305,10th,6,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,73493,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,197058,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,122116,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,75742,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,214731,10th,6,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,265954,HS-grad,9,Separated,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,197156,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,162245,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,4,United-States,<=50K\n2,Local-gov,203070,HS-grad,9,Separated,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,165695,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,473040,5th-6th,3,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,168107,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,163494,10th,6,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,180342,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,122381,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,148069,10th,6,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,200973,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,130806,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,117148,7th-8th,4,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,213977,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,134768,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,?,>50K\n2,Private,139338,12th,8,Divorced,Transport-moving,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,315877,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,195124,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,?,<=50K\n0,Private,352057,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,236684,Some-college,10,Never-married,Other-service,Other-relative,Black,Female,0,0,0,United-States,<=50K\n0,Private,208447,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,149640,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,111177,Bachelors,13,Widowed,?,Not-in-family,White,Female,4,0,0,United-States,>50K\n3,Private,154342,7th-8th,4,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,141459,HS-grad,9,Separated,Other-service,Other-relative,Black,Female,0,0,2,United-States,<=50K\n3,Private,111797,Some-college,10,Never-married,Other-service,Not-in-family,Black,Female,0,0,1,Outlying-US(Guam-USVI-etc),<=50K\n1,Private,111900,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,78707,11th,7,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,160574,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,174714,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,62534,Bachelors,13,Never-married,?,Own-child,Black,Female,0,0,2,Jamaica,<=50K\n2,Private,216907,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,198148,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,124265,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,?,261059,10th,6,Separated,?,Own-child,White,Male,1,0,2,United-States,<=50K\n3,Private,208137,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,257250,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,State-gov,147253,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,244268,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,213255,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,266912,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,169104,Bachelors,13,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n1,Private,200511,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,128715,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,4,0,2,United-States,>50K\n3,Self-emp-not-inc,65535,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,103395,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,71046,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,Scotland,<=50K\n1,Self-emp-not-inc,125442,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,169188,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,121471,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,207281,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,46097,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,206671,Some-college,10,Never-married,?,Own-child,White,Male,1,0,3,United-States,<=50K\n3,Private,98361,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,?,>50K\n2,Self-emp-not-inc,322143,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,149184,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Local-gov,119829,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,910398,Bachelors,13,Never-married,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,176570,11th,7,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,216129,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,27207,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,68830,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,State-gov,178818,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,236944,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n3,State-gov,273771,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,318533,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n1,?,451940,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,102318,HS-grad,9,Separated,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,379350,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,21095,Some-college,10,Divorced,Other-service,Unmarried,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n4,Self-emp-not-inc,211547,12th,8,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,85272,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,46406,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,England,>50K\n3,Private,53833,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,161007,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,53707,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,370119,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,310907,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,375833,11th,7,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,107513,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,58683,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Self-emp-not-inc,179557,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,70240,HS-grad,9,Never-married,Other-service,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Private,147206,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,175548,HS-grad,9,Never-married,Other-service,Not-in-family,Other,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,163174,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,126010,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,147876,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,4,0,3,United-States,>50K\n2,Private,428350,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,2,2,United-States,<=50K\n2,?,200904,Assoc-acdm,12,Married-civ-spouse,?,Wife,Black,Female,0,0,1,Haiti,<=50K\n2,Private,328466,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,1,0,4,Mexico,<=50K\n4,Local-gov,258973,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,State-gov,345969,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,127796,5th-6th,3,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n2,Private,405723,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,175942,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,284196,10th,6,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,89718,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,1,0,3,United-States,<=50K\n1,Self-emp-inc,175761,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,206369,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,158993,HS-grad,9,Divorced,Other-service,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Private,285066,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,126754,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n4,State-gov,209280,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,1,United-States,>50K\n3,Self-emp-not-inc,52888,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,133821,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,240763,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,39054,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,119272,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,143372,10th,6,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,323421,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,136028,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,163189,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,202729,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,421871,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,120277,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,Italy,>50K\n1,?,211798,HS-grad,9,Separated,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,198901,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,214617,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,179715,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,107231,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,110355,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,184378,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,273454,7th-8th,4,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,Cuba,<=50K\n2,Private,443040,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n2,?,71701,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,160151,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,107991,11th,7,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,94391,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,99835,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,43711,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,83756,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,120914,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,180052,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,170846,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Italy,>50K\n2,Private,37937,Masters,14,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,?,168340,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,?,>50K\n0,Private,38455,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,128059,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,420895,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,166744,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,238768,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,176270,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,140592,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,211466,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,4,United-States,<=50K\n2,Private,188540,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,39581,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,171150,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n3,Private,117496,9th,5,Divorced,Other-service,Not-in-family,White,Female,0,0,2,Canada,<=50K\n2,Private,145160,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,28520,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,103851,11th,7,Never-married,Adm-clerical,Own-child,White,Female,1,0,0,United-States,<=50K\n0,Private,375077,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n3,State-gov,281590,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,2,United-States,>50K\n2,Private,151504,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,415287,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,3,2,United-States,>50K\n3,Private,32212,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,123606,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,202565,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,177927,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,256723,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,46247,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,266926,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,112031,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,?,376277,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,168817,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,187487,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,?,158784,7th-8th,4,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,67222,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n2,Private,201723,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,267408,HS-grad,9,Widowed,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n3,Federal-gov,168191,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n3,Private,105444,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,156728,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,148600,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,19914,Some-college,10,Divorced,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,190767,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,233955,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,>50K\n1,Private,30381,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,187069,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,367314,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,101119,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,86551,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Local-gov,218995,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,57711,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,303521,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,199067,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,247445,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,186078,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,77634,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,180060,Masters,14,Never-married,Exec-managerial,Own-child,White,Male,2,0,4,United-States,<=50K\n3,Private,56482,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,314177,HS-grad,9,Never-married,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,239755,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,377680,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,134960,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,1,United-States,>50K\n1,Private,294493,Bachelors,13,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,32616,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,2,0,United-States,<=50K\n2,Private,182655,Bachelors,13,Divorced,Other-service,Not-in-family,White,Male,0,0,2,?,>50K\n4,Local-gov,52267,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,117963,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,98881,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,196963,7th-8th,4,Divorced,Craft-repair,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,166988,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,193459,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,182342,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,496743,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,154781,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,219371,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,99179,11th,7,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,224910,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,304651,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,349689,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,106850,10th,6,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,196328,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,169323,Bachelors,13,Married-civ-spouse,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,162924,Bachelors,13,Divorced,Exec-managerial,Not-in-family,Asian-Pac-Islander,Male,0,0,3,Japan,<=50K\n2,Self-emp-not-inc,34037,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,?,167651,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,197384,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,251795,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n4,?,266081,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,165309,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,215873,10th,6,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,133938,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n3,Private,159816,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,0,United-States,>50K\n0,Private,228424,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,195576,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,105200,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,0,United-States,<=50K\n1,Private,167350,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,52199,HS-grad,9,Married-spouse-absent,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,171338,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,120173,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,3,United-States,>50K\n0,?,158762,10th,6,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,169818,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,>50K\n1,Private,288419,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,207546,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,147707,HS-grad,9,Widowed,Farming-fishing,Unmarried,White,Male,0,4,2,United-States,<=50K\n0,?,228373,10th,6,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,193882,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,31033,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,272950,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,183523,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,238415,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,19302,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Local-gov,339671,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,3,0,2,United-States,>50K\n1,Local-gov,103260,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,79331,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,4,0,2,United-States,>50K\n2,Private,135056,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,142723,5th-6th,3,Married-spouse-absent,Handlers-cleaners,Unmarried,White,Female,0,0,2,Puerto-Rico,<=50K\n1,Federal-gov,188569,9th,5,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,57322,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,178309,9th,5,Never-married,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,166107,Masters,14,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,53042,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,Trinadad&Tobago,<=50K\n1,Private,155343,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,35595,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,429507,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Federal-gov,159670,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,151210,7th-8th,4,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,186792,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,204640,Some-college,10,Widowed,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,87205,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,112847,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n2,Private,107306,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,State-gov,211319,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,183606,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,205390,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,232871,7th-8th,4,Married-civ-spouse,Protective-serv,Husband,White,Male,1,0,0,United-States,<=50K\n3,Self-emp-inc,101017,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,114495,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,183898,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,163921,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,311764,11th,7,Widowed,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n3,Private,188330,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,267174,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,36228,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n3,Private,199739,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,185407,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n2,State-gov,206139,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,282063,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,332379,7th-8th,4,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,418324,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,263338,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,158948,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,221532,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,202920,HS-grad,9,Never-married,Prof-specialty,Unmarried,White,Female,4,0,2,Dominican-Republic,>50K\n2,Local-gov,118909,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,286469,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,191914,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,State-gov,142766,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,198744,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,272780,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,State-gov,219553,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,261232,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,64292,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,312131,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,30713,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,246439,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,338105,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n0,Private,228243,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,62463,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,1,2,United-States,<=50K\n2,Private,31603,Bachelors,13,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,165054,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,121618,7th-8th,4,Never-married,Transport-moving,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Federal-gov,273194,HS-grad,9,Never-married,Transport-moving,Not-in-family,Black,Male,1,0,2,United-States,<=50K\n0,?,163665,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,538319,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,Puerto-Rico,<=50K\n1,Private,238246,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,244665,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,131811,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,231777,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,156807,9th,5,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,236861,Bachelors,13,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,229842,HS-grad,9,Never-married,Transport-moving,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Local-gov,190057,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,State-gov,55076,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,152545,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,153434,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,171095,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,239322,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,138999,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,95450,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,176520,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,72338,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n4,?,386261,Bachelors,13,Married-spouse-absent,?,Unmarried,Black,Female,0,0,0,United-States,<=50K\n0,Private,235722,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,128884,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,187226,9th,5,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,298332,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,173607,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,226756,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,157887,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,State-gov,171111,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,126314,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,174018,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,144778,Some-college,10,Separated,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,201522,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,22966,Bachelors,13,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,399088,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,282202,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,0,2,El-Salvador,<=50K\n2,Private,102606,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,246862,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,Italy,>50K\n1,Federal-gov,508336,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Local-gov,263431,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,235733,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,107910,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,184425,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n0,Self-emp-not-inc,143062,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,Greece,<=50K\n0,Private,199545,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,197015,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,149617,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,33610,HS-grad,9,Divorced,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,192002,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,67791,Some-college,10,Widowed,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,445382,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,112283,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,157249,11th,7,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,109872,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,119838,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,149943,Some-college,10,Never-married,Other-service,Not-in-family,Other,Male,0,1,2,?,<=50K\n4,Without-pay,27012,7th-8th,4,Widowed,Farming-fishing,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,91666,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,270276,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,179271,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,161819,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,339681,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,1,0,2,United-States,<=50K\n1,Self-emp-not-inc,219897,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,91683,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,188834,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,187046,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,191807,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,179951,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,324420,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-not-inc,66632,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,121718,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,162034,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,218990,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Local-gov,125863,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,225330,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,120426,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,119741,Masters,14,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,32000,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,>50K\n0,?,124242,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,278581,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,230224,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,204374,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,2,3,United-States,<=50K\n2,Private,188386,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,164922,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,195176,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,166740,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,?,156008,11th,7,Married-civ-spouse,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,162551,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Other-relative,Asian-Pac-Islander,Female,0,0,3,China,<=50K\n0,Private,211231,HS-grad,9,Married-civ-spouse,Tech-support,Other-relative,White,Female,0,0,3,United-States,>50K\n0,Private,169990,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,221832,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,255454,Bachelors,13,Separated,Prof-specialty,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,28160,Bachelors,13,Married-spouse-absent,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,State-gov,159219,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Canada,>50K\n1,Local-gov,103148,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,165186,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,31782,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,249101,HS-grad,9,Divorced,Protective-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,243190,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,2,United-States,>50K\n0,Local-gov,153405,11th,7,Never-married,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,329980,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n4,Private,176079,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,218542,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,State-gov,303446,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,Nicaragua,<=50K\n2,Private,102606,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,483201,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,144608,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,226013,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,165475,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,263637,10th,6,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,201495,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,213720,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,170483,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,214303,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,190511,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,242150,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,159755,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,147629,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,268022,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,188711,Bachelors,13,Never-married,Transport-moving,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Private,452205,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,260847,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,291374,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,189933,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,133969,HS-grad,9,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,South,>50K\n1,Private,330664,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,672412,11th,7,Separated,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,122999,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,3,0,2,United-States,>50K\n1,Private,111415,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Germany,<=50K\n1,Private,217235,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,121956,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,4,0,2,Cambodia,>50K\n0,Private,120172,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,343403,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,104790,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,473547,10th,6,Divorced,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,260106,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Federal-gov,168232,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,348491,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,24106,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n4,Self-emp-inc,197553,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,421065,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,138852,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,?,169631,Assoc-acdm,12,Married-AF-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,379412,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,181992,Some-college,10,Never-married,Sales,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,365640,HS-grad,9,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,?,<=50K\n1,Private,236564,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,363418,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,>50K\n3,Private,112351,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,204704,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n2,Private,54611,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,128132,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,30599,Masters,14,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,379522,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,196504,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,82552,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,Private,104024,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,293114,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,2,United-States,<=50K\n4,Private,74141,9th,5,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,0,0,3,United-States,>50K\n2,Private,192337,Bachelors,13,Separated,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,262478,HS-grad,9,Never-married,Farming-fishing,Own-child,Black,Male,0,0,1,United-States,<=50K\n4,Private,185072,Some-college,10,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,2,Jamaica,<=50K\n0,Private,296045,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,246595,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,4,United-States,<=50K\n0,Private,54472,Some-college,10,Married-spouse-absent,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,331065,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,1,2,United-States,<=50K\n0,Private,161708,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,264936,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,113545,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,212237,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,<=50K\n1,Private,170430,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,?,<=50K\n1,Private,173806,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,2,0,3,United-States,<=50K\n4,Federal-gov,370890,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,4,2,United-States,<=50K\n2,Private,505119,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Cuba,>50K\n0,Private,193089,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,33432,Assoc-acdm,12,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,103110,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,England,<=50K\n1,Private,160362,Some-college,10,Divorced,Other-service,Other-relative,White,Male,0,0,2,Nicaragua,<=50K\n1,Private,204621,Assoc-acdm,12,Divorced,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,35309,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,154373,Bachelors,13,Never-married,?,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,194772,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,154410,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,220563,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,253354,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,211699,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,1,2,United-States,>50K\n4,Self-emp-not-inc,167501,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,0,United-States,>50K\n1,Private,229732,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,185465,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,335764,11th,7,Married-civ-spouse,Sales,Own-child,Black,Male,0,0,1,United-States,<=50K\n0,Private,460046,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,33487,Some-college,10,Never-married,?,Other-relative,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Private,176924,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,213307,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,83893,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,194102,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,238611,7th-8th,4,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,113597,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,208406,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,274528,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Self-emp-not-inc,60116,10th,6,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,196816,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,166368,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,303954,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,99386,Bachelors,13,Married-spouse-absent,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,188569,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,302868,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,283342,11th,7,Never-married,Other-service,Other-relative,Black,Male,0,0,0,United-States,<=50K\n0,Private,233777,Some-college,10,Never-married,Sales,Unmarried,White,Male,0,0,3,Mexico,<=50K\n0,Private,170038,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,261319,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,367237,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Male,3,0,2,United-States,>50K\n1,Private,126838,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,354104,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,176321,12th,8,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Mexico,<=50K\n3,Private,85129,HS-grad,9,Divorced,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n0,?,376474,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,62507,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,111252,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,156889,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,549430,HS-grad,9,Never-married,Priv-house-serv,Unmarried,White,Female,0,0,2,Mexico,<=50K\n3,Private,29696,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,98837,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,86150,Bachelors,13,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,1,United-States,>50K\n1,Private,204991,Some-college,10,Divorced,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,371886,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,103605,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,54851,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,133050,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n2,Local-gov,126569,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Federal-gov,144259,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,161482,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,305449,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,125010,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,304133,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,120617,HS-grad,9,Separated,Protective-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,157747,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,297396,Some-college,10,Separated,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,121287,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,308493,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,Honduras,<=50K\n2,Private,49115,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,208302,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,304032,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,207301,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,123211,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,33521,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,?,410351,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,410034,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,175339,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,?,27937,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,168211,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,>50K\n1,Private,125680,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,0,Japan,<=50K\n4,Local-gov,160829,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,266529,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,115023,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,?,<=50K\n3,State-gov,224149,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Private,150930,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,343699,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,172826,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,163392,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n0,?,103810,12th,8,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,213821,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,211265,Some-college,10,Married-spouse-absent,Craft-repair,Other-relative,Black,Female,0,0,1,Dominican-Republic,<=50K\n4,Local-gov,160586,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,146454,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,203277,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,309895,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,26522,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,3,1,United-States,>50K\n4,Private,103809,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,90291,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,State-gov,181761,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,35330,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,2,3,United-States,<=50K\n2,Local-gov,135776,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,188172,Doctorate,16,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,179579,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,193626,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,108887,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,199070,HS-grad,9,Never-married,Protective-serv,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,441591,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,185254,5th-6th,3,Never-married,Priv-house-serv,Own-child,White,Female,0,0,2,El-Salvador,<=50K\n0,Private,109307,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,81853,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n1,Private,23621,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Local-gov,145178,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,Jamaica,>50K\n3,State-gov,30575,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,130620,11th,7,Separated,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,India,<=50K\n2,Local-gov,22155,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,106437,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,79787,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,326857,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,81853,HS-grad,9,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Private,120933,Some-college,10,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,153143,Some-college,10,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,Puerto-Rico,<=50K\n3,Private,27669,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,105444,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,169785,Masters,14,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,122493,HS-grad,9,Widowed,Tech-support,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,242670,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,54933,Masters,14,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,209317,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n0,Self-emp-not-inc,282631,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,98044,11th,7,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,187487,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,60186,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,75648,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,201175,11th,7,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,19302,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,300812,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,146659,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,1,United-States,>50K\n4,Private,101887,10th,6,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,4,United-States,<=50K\n4,?,117778,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,60726,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,201763,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,119253,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,4,United-States,>50K\n3,Self-emp-not-inc,121124,5th-6th,3,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Italy,>50K\n2,Private,220132,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,60639,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,195262,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n4,?,113544,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n3,?,331650,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,100587,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,Private,298130,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,242391,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,197867,Assoc-voc,11,Divorced,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Private,151977,10th,6,Separated,Priv-house-serv,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n2,Private,277347,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,125249,HS-grad,9,Separated,Protective-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,222142,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,270194,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,169995,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,359155,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,123992,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,266080,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,201531,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,179704,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,393673,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,244147,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n2,Self-emp-not-inc,438696,Masters,14,Divorced,Sales,Unmarried,White,Male,0,0,0,United-States,>50K\n1,Self-emp-not-inc,207568,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-inc,54052,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,187581,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,77102,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,353010,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,54131,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Federal-gov,39890,Some-college,10,Widowed,Transport-moving,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,156877,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,355686,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,300168,12th,8,Separated,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,488720,9th,5,Married-civ-spouse,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,157287,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,184659,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,214169,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,192149,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,137253,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,373050,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,90377,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,3,United-States,<=50K\n1,Federal-gov,183151,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,227158,Bachelors,13,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,34021,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,165148,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,211668,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,2,United-States,>50K\n2,Private,358886,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,47707,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,306982,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,South,<=50K\n3,Local-gov,52590,HS-grad,9,Widowed,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,?,179352,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,158156,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,70055,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,131852,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n4,Self-emp-not-inc,177825,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,127215,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,175183,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,142287,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,221324,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,227602,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n0,Private,228452,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,State-gov,39380,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,96862,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,336360,7th-8th,4,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,257644,11th,7,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,State-gov,235853,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,270577,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,222900,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,99254,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,>50K\n3,Private,224763,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Cuba,<=50K\n4,Self-emp-not-inc,174056,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,127306,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,339506,HS-grad,9,Never-married,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,178322,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Germany,>50K\n1,Private,189843,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,160815,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,207665,HS-grad,9,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n2,State-gov,160402,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,170263,Some-college,10,Never-married,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,184659,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,338320,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,101017,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,204322,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,241350,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Federal-gov,217994,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,128143,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,164065,Masters,14,Divorced,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Local-gov,78866,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,236769,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,239539,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n2,Private,34028,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,State-gov,207847,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,175935,Doctorate,16,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n0,Federal-gov,218445,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,215833,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,156976,Assoc-voc,11,Separated,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,220647,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,218343,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,241431,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n2,Local-gov,123983,Bachelors,13,Never-married,Exec-managerial,Unmarried,Asian-Pac-Islander,Male,0,2,2,Vietnam,<=50K\n0,Private,73289,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,408623,Bachelors,13,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n3,Private,169180,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,54929,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,306779,Assoc-voc,11,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,159549,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,482082,12th,8,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,Mexico,<=50K\n1,Local-gov,286101,HS-grad,9,Never-married,Transport-moving,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,167955,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Poland,<=50K\n2,Self-emp-not-inc,209040,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,105017,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,27776,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,242941,Some-college,10,Never-married,Sales,Own-child,White,Female,0,2,0,United-States,<=50K\n2,Private,118853,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,119565,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,196827,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,<=50K\n3,Private,275361,Assoc-acdm,12,Widowed,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,225193,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,329783,10th,6,Never-married,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,107411,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n0,State-gov,258490,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,?,120243,11th,7,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,219509,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,>50K\n1,Local-gov,29174,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,40083,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,Canada,<=50K\n0,Private,87528,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,116379,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,Taiwan,>50K\n3,Local-gov,216214,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,268051,Some-college,10,Married-civ-spouse,Protective-serv,Other-relative,Black,Female,0,0,1,Haiti,<=50K\n2,Self-emp-not-inc,121718,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,201901,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,2,0,United-States,<=50K\n3,Private,109089,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,346382,11th,7,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,284129,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,143030,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,212619,Assoc-voc,11,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,199011,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,Private,118901,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,129865,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,157900,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,349341,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,158685,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,386585,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,52386,Some-college,10,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n2,Private,246891,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,190385,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,37869,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,217807,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,149784,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n4,State-gov,201293,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,128764,7th-8th,4,Widowed,Transport-moving,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,27444,Some-college,10,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,62438,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n1,Local-gov,151726,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,29841,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,131608,Some-college,10,Widowed,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,110562,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,190541,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,State-gov,33142,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,139272,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,234633,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,238386,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,460835,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n0,?,243190,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,0,China,<=50K\n4,Federal-gov,97855,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,77146,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n2,Private,200863,Some-college,10,Widowed,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,?,41107,Bachelors,13,Married-spouse-absent,?,Not-in-family,White,Male,0,0,2,Canada,<=50K\n4,Private,77415,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,236770,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,173093,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,0,2,2,Philippines,>50K\n1,Private,235124,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,282604,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,199288,11th,7,Separated,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,191659,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,4,United-States,>50K\n0,Private,43285,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,160837,11th,7,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Guatemala,<=50K\n0,Private,230574,10th,6,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,176178,HS-grad,9,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,116358,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,>50K\n1,?,253873,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,107787,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Canada,<=50K\n0,Self-emp-not-inc,519627,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n0,Private,191460,11th,7,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,198282,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,214858,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,64875,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,675421,9th,5,Never-married,Handlers-cleaners,Own-child,White,Male,1,0,2,United-States,<=50K\n4,Self-emp-not-inc,134768,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Federal-gov,207342,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,64830,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,220066,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n2,Private,82521,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,176711,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,England,<=50K\n0,?,217421,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,111900,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,196943,Some-college,10,Separated,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,481987,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n4,?,184506,11th,7,Married-civ-spouse,?,Husband,White,Male,0,1,0,United-States,<=50K\n0,?,121313,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,158420,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,256000,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,183892,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,42734,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,181773,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,184945,Some-college,10,Separated,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,107248,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,215382,Masters,14,Separated,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,>50K\n0,Private,122999,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,758700,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,3,Mexico,<=50K\n2,State-gov,166606,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,192060,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Male,0,0,1,?,<=50K\n4,?,340939,9th,5,Married-civ-spouse,?,Husband,White,Male,1,0,2,United-States,<=50K\n4,Private,205708,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Poland,<=50K\n3,Private,67450,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,England,<=50K\n0,Private,242077,HS-grad,9,Divorced,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,129573,HS-grad,9,Never-married,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,181132,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,England,>50K\n0,Private,212302,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,83411,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,1,2,United-States,<=50K\n0,?,148751,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,317681,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,?,103986,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,1,2,United-States,<=50K\n4,Private,30602,7th-8th,4,Married-spouse-absent,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,Private,172893,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,211804,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n1,Self-emp-not-inc,312055,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,65390,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,200500,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,241962,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,78530,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,Canada,>50K\n0,Private,189950,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,111387,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,1,2,United-States,<=50K\n0,Private,241951,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,343059,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,302465,12th,8,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,2,2,United-States,<=50K\n3,Private,156843,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,3,United-States,>50K\n0,?,79728,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,55284,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,509364,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,State-gov,117927,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,137651,Some-college,10,Never-married,Machine-op-inspct,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Private,131060,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,346963,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,183611,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,1,0,3,United-States,<=50K\n1,Private,134737,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,36503,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,250121,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,330535,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,387776,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,41474,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Local-gov,318972,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,4,United-States,<=50K\n1,Private,86143,Some-college,10,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Private,181139,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,326232,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,4,3,United-States,>50K\n2,Local-gov,153976,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,59469,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,127139,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,136343,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,350624,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,177351,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,166149,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,4,1,United-States,<=50K\n1,Private,121523,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-not-inc,267396,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,83045,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,160449,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,124137,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,1,Greece,>50K\n0,?,287681,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,154194,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,295127,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,240521,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n4,Self-emp-not-inc,244087,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,356250,Prof-school,15,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,1,China,<=50K\n2,State-gov,293791,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,44308,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,210527,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,State-gov,151763,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,State-gov,267581,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,100188,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,111746,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,171091,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,355645,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,0,Trinadad&Tobago,<=50K\n3,Local-gov,137678,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,70894,Assoc-acdm,12,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,171306,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,100997,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,63921,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,32897,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,251854,HS-grad,9,Never-married,Protective-serv,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,345121,10th,6,Separated,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,86220,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,172845,Assoc-voc,11,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,171398,10th,6,Never-married,Sales,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,174391,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,207058,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,291251,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,224377,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,105813,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,180916,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,122749,Assoc-voc,11,Divorced,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,31069,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Self-emp-not-inc,284343,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,319371,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,174224,Assoc-voc,11,Divorced,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,?,183958,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,127772,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,>50K\n3,Private,80651,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,62793,HS-grad,9,Divorced,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,191712,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Self-emp-not-inc,237532,HS-grad,9,Married-civ-spouse,Sales,Wife,Black,Female,0,0,3,Dominican-Republic,>50K\n3,Federal-gov,20179,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,311376,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,432565,Assoc-voc,11,Married-civ-spouse,Tech-support,Other-relative,White,Female,0,0,2,Canada,>50K\n2,Self-emp-inc,329980,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,4,3,United-States,>50K\n1,Self-emp-not-inc,125190,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,342946,11th,7,Never-married,Transport-moving,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,219835,Assoc-voc,11,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,123429,10th,6,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,69209,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,1,United-States,<=50K\n3,Private,66356,HS-grad,9,Separated,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,195897,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,153132,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,230875,11th,7,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,92298,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,185145,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,297296,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,164849,9th,5,Married-civ-spouse,?,Husband,Black,Male,1,0,0,United-States,<=50K\n3,Private,145214,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,242341,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Private,240542,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,104772,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,?,152802,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,181666,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,415520,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,258761,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,88842,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n0,?,356717,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,158438,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,206206,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,51816,HS-grad,9,Never-married,Protective-serv,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,253814,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,161745,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,3,3,United-States,<=50K\n4,Private,162947,5th-6th,3,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Puerto-Rico,<=50K\n3,Private,163027,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,146788,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,73309,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,?,143867,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,104216,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,345705,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,133770,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n2,Private,209392,HS-grad,9,Divorced,Protective-serv,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n4,Private,262345,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,277545,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,?,>50K\n3,?,174525,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,2,0,2,?,<=50K\n1,Private,490332,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,211570,11th,7,Never-married,Handlers-cleaners,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,374918,12th,8,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,106728,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,173649,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,?,<=50K\n1,Private,174597,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,233533,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,?,169785,Masters,14,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,133169,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,198824,Assoc-voc,11,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,174056,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,188696,Assoc-voc,11,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,90692,HS-grad,9,Divorced,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,271933,Bachelors,13,Never-married,Exec-managerial,Other-relative,White,Female,0,2,2,United-States,<=50K\n3,Self-emp-not-inc,102359,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,213668,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,294789,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,157599,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Local-gov,134935,12th,8,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,466224,Some-college,10,Never-married,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,111985,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,264627,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,213427,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,279015,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,165937,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n1,Federal-gov,188343,HS-grad,9,Separated,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,158609,Assoc-voc,11,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,193036,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,198632,Some-college,10,Married-spouse-absent,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,175912,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,White,Male,1,0,2,United-States,<=50K\n0,?,192773,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,101387,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,60783,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,183224,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n4,Local-gov,100776,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,57600,Doctorate,16,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,0,0,2,?,<=50K\n0,Private,174063,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,306495,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,249741,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,93021,HS-grad,9,Never-married,Adm-clerical,Unmarried,Other,Female,0,0,2,United-States,<=50K\n2,Private,49626,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,63062,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,320835,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Local-gov,123727,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,State-gov,110517,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,2,0,2,India,<=50K\n2,Private,149670,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,0,United-States,<=50K\n2,Private,172425,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,216116,9th,5,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,Haiti,<=50K\n3,Private,174209,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,175083,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,129059,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,1,United-States,<=50K\n0,Private,121313,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,181317,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,166851,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,29616,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-inc,105582,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n3,?,124993,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n0,?,148509,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,230246,9th,5,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,?,<=50K\n4,Private,117881,11th,7,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,203408,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,446219,10th,6,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,110331,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,207946,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,?,45537,Masters,14,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,188330,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,147629,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,153799,1st-4th,2,Married-spouse-absent,Machine-op-inspct,Unmarried,White,Female,0,0,2,Dominican-Republic,<=50K\n1,Private,203776,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,168071,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,348430,1st-4th,2,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,Portugal,<=50K\n3,Private,103407,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,152046,11th,7,Never-married,?,Not-in-family,White,Female,0,0,1,Germany,<=50K\n2,Private,153205,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n1,Private,326104,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,238162,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,221336,HS-grad,9,Divorced,Adm-clerical,Other-relative,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,180656,Some-college,10,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,?,<=50K\n4,Self-emp-not-inc,145329,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,1,0,0,United-States,<=50K\n2,Private,315776,Masters,14,Never-married,Exec-managerial,Not-in-family,Black,Male,3,0,3,United-States,>50K\n4,?,150516,HS-grad,9,Widowed,?,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Private,325802,Assoc-acdm,12,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,133985,10th,6,Never-married,Craft-repair,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,269329,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,3,0,2,United-States,>50K\n2,Private,183203,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,76127,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,195891,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,162137,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,State-gov,37672,Assoc-voc,11,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,161708,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,Private,80616,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,209276,HS-grad,9,Married-civ-spouse,Other-service,Husband,Other,Male,0,0,2,United-States,<=50K\n0,?,34443,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,192835,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,203240,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,102308,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,40829,11th,7,Never-married,Sales,Other-relative,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n0,Private,60726,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,1,United-States,<=50K\n1,State-gov,116677,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,57067,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,304906,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,101590,Prof-school,15,Widowed,Adm-clerical,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n1,Private,258102,5th-6th,3,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,241185,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,124827,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,76625,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,263339,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,135645,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,245626,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n0,Private,210781,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,235786,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,160167,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n3,Federal-gov,30731,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,314375,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,81528,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,3,United-States,<=50K\n3,Private,182854,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,296798,11th,7,Never-married,Tech-support,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,Private,194426,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,4,0,2,United-States,>50K\n2,?,70645,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,141807,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,?,112871,11th,7,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,State-gov,71344,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,341410,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,118941,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,?,159755,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,128509,5th-6th,3,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,?,<=50K\n1,Self-emp-not-inc,229125,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,142756,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-inc,243871,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,213140,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,196857,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,138626,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,161334,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,1,Nicaragua,<=50K\n3,Private,273536,7th-8th,4,Married-civ-spouse,Sales,Husband,Other,Male,0,0,3,Dominican-Republic,<=50K\n1,Private,115631,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,2,0,3,United-States,<=50K\n1,Private,185957,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,334357,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,96102,Masters,14,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,213226,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Iran,>50K\n0,Private,115248,Some-college,10,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n2,Private,185061,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n1,Private,147638,Bachelors,13,Never-married,Adm-clerical,Other-relative,Asian-Pac-Islander,Female,0,0,2,Hong,<=50K\n0,Private,280298,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,163516,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,277434,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,206983,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Columbia,<=50K\n3,Private,108993,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,288551,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,176069,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,State-gov,183486,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,>50K\n2,Private,163215,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,4,0,2,United-States,>50K\n4,Private,94692,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,118462,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,407068,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,Mexico,<=50K\n2,Self-emp-not-inc,243587,Some-college,10,Separated,Other-service,Own-child,White,Female,0,0,2,Cuba,<=50K\n3,Private,23074,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Private,237735,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,188291,1st-4th,2,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,284166,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,423460,11th,7,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,287681,7th-8th,4,Never-married,Other-service,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n1,Private,509364,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,139391,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,91964,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,117526,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,91343,Some-college,10,Widowed,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,336969,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,El-Salvador,<=50K\n3,Private,255364,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,167670,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,211494,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,136198,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Federal-gov,409815,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,188823,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,146326,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,154374,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,216563,HS-grad,9,Never-married,?,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,197286,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,100722,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,377622,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,145964,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,358636,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,4,United-States,<=50K\n3,Private,155489,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,57413,Some-college,10,Divorced,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,320421,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,174752,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,229364,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,157486,10th,6,Divorced,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,92682,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n4,Federal-gov,101338,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,132652,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,34616,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,218903,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,204098,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Other-relative,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,64045,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,189763,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,26248,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,92079,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,280071,Some-college,10,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,3,United-States,<=50K\n0,Private,224059,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,185520,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,3,0,2,United-States,>50K\n0,Private,265567,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,106890,Assoc-voc,11,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,39586,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,153132,Bachelors,13,Divorced,Sales,Unmarried,White,Male,0,0,2,?,<=50K\n3,Private,209912,Bachelors,13,Divorced,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n2,Private,144169,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,50442,Some-college,10,Never-married,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Female,1,0,1,United-States,<=50K\n1,Private,89644,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,275889,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,Mexico,<=50K\n1,Private,231638,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,224474,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,2,0,3,United-States,>50K\n1,Private,355259,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Federal-gov,68330,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,185410,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,87653,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,286853,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,96710,HS-grad,9,Married-civ-spouse,Priv-house-serv,Other-relative,Black,Female,0,0,0,United-States,<=50K\n4,Private,160143,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,186925,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,1,0,3,United-States,<=50K\n3,Self-emp-inc,109705,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,94235,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,225279,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,2,2,?,<=50K\n2,Local-gov,297449,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,205896,HS-grad,9,Divorced,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,93717,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,2,United-States,>50K\n2,Private,194710,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,236391,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,State-gov,189123,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,358677,HS-grad,9,Divorced,Other-service,Unmarried,Black,Male,0,0,1,United-States,<=50K\n1,State-gov,199539,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,128170,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,231238,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,296152,Some-college,10,Divorced,Exec-managerial,Other-relative,White,Female,1,0,0,United-States,<=50K\n3,Private,166003,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,281437,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,190231,9th,5,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,0,Nicaragua,<=50K\n3,Private,122026,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,?,205527,HS-grad,9,Divorced,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,174102,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,Greece,>50K\n2,Private,125461,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n4,Self-emp-not-inc,184335,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,211345,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,Mexico,<=50K\n2,Local-gov,147328,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,222993,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,225978,Some-college,10,Separated,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,121124,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,?,656036,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,?,346762,11th,7,Divorced,?,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Private,234057,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Federal-gov,306515,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,116562,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,171159,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,199011,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,443508,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,Canada,>50K\n0,Private,29810,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,238831,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,566117,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,255044,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,436253,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,300687,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,144071,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,>50K\n3,State-gov,133917,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,3,3,?,>50K\n1,Private,188767,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,300777,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,26987,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,174395,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,3,Greece,<=50K\n4,Private,90290,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,183735,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,123273,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,186916,Masters,14,Divorced,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Private,43554,5th-6th,3,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,4,2,United-States,<=50K\n3,Private,178251,Assoc-acdm,12,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,255885,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,64292,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,State-gov,194773,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,Germany,<=50K\n2,Self-emp-inc,133060,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,258006,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,Cuba,<=50K\n3,Private,92215,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,33945,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,2,0,3,United-States,<=50K\n4,Private,153048,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,192200,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,<=50K\n1,Private,355571,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,139268,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,34402,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,25955,11th,7,Never-married,Other-service,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,209609,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,168283,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,295488,11th,7,Never-married,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,190895,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,164190,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,216010,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,387568,10th,6,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,State-gov,188386,Masters,14,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,174491,HS-grad,9,Widowed,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,31221,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,272451,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,152652,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,104413,HS-grad,9,Widowed,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n2,Private,105936,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,2,0,0,United-States,<=50K\n0,Private,379066,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,4,1,United-States,<=50K\n1,Private,214858,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,237735,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,158592,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,237321,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,>50K\n2,Private,23646,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,169240,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,454508,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,130356,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,427686,10th,6,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,36411,12th,8,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,548510,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,1,United-States,<=50K\n2,Private,187264,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n1,State-gov,140752,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,325596,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,175804,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,107302,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,41161,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,401832,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,353808,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,349910,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,161478,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Japan,<=50K\n0,Private,400225,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,367533,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,69306,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,270366,10th,6,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,103751,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,75227,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Local-gov,132563,Prof-school,15,Divorced,Prof-specialty,Unmarried,Black,Female,0,2,2,United-States,<=50K\n1,State-gov,79580,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,344624,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,1,2,United-States,>50K\n2,Self-emp-inc,186359,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n3,Private,121685,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,75104,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,188343,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,246449,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,85088,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,545483,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,243986,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,32778,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,369114,HS-grad,9,Separated,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,217200,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,149220,Assoc-voc,11,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,?,162034,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,157813,11th,7,Divorced,?,Unmarried,White,Female,0,0,3,Canada,<=50K\n0,?,179715,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,335549,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,4,2,United-States,>50K\n3,Private,102308,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,367749,1st-4th,2,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,El-Salvador,<=50K\n0,Private,98281,12th,8,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,115792,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,277788,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,103435,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,37646,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,385632,7th-8th,4,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,210278,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,335357,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,272165,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,148995,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,113434,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,132551,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,115433,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,1,United-States,>50K\n1,Private,227890,HS-grad,9,Never-married,Protective-serv,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,503012,5th-6th,3,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,Private,250873,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,407930,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,148187,11th,7,Never-married,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,159322,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,334368,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,196328,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,270842,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,235079,Preschool,1,Widowed,Craft-repair,Unmarried,Black,Male,0,0,0,United-States,<=50K\n4,?,327154,HS-grad,9,Widowed,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,188391,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n0,Federal-gov,30559,HS-grad,9,Married-AF-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,255098,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,248010,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,174515,HS-grad,9,Married-spouse-absent,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,171956,Some-college,10,Separated,Adm-clerical,Own-child,White,Female,0,0,2,Puerto-Rico,<=50K\n4,Private,193130,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,108670,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,186172,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,348854,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,271828,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,148606,10th,6,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,123983,Masters,14,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Male,0,0,2,Taiwan,<=50K\n0,Private,24896,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,1,Germany,<=50K\n3,Private,573583,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,Italy,>50K\n4,Self-emp-inc,106175,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,4,United-States,>50K\n2,Private,307767,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,200574,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,59083,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,2,3,United-States,<=50K\n3,Private,358056,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,114670,9th,5,Widowed,Priv-house-serv,Not-in-family,Black,Female,1,0,0,United-States,<=50K\n1,Local-gov,262042,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,1,2,United-States,<=50K\n0,Private,206010,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,183869,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,?,>50K\n1,Private,159001,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,155818,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,96055,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,131776,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,228613,11th,7,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,198163,Masters,14,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,37028,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,177304,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,144064,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,146659,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,26904,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,238917,7th-8th,4,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,170148,HS-grad,9,Divorced,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,27821,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,220460,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Canada,<=50K\n3,Private,101320,Assoc-acdm,12,Married-civ-spouse,Sales,Wife,White,Female,0,3,2,United-States,>50K\n1,Private,173858,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n3,Private,91048,HS-grad,9,Divorced,Machine-op-inspct,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,298696,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,207202,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,?,230397,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,180599,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,?,199046,Assoc-voc,11,Never-married,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,132686,Prof-school,15,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,Italy,>50K\n0,Private,240063,Bachelors,13,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,177705,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,3,United-States,<=50K\n1,Private,511361,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,89397,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,239439,11th,7,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,36989,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,76978,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Private,200068,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,454941,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,107218,Bachelors,13,Never-married,Tech-support,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n0,Local-gov,182070,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,176360,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,452405,Preschool,1,Never-married,Other-service,Other-relative,White,Female,0,0,1,Mexico,<=50K\n0,?,297396,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,84790,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,186787,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,169662,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,125933,Some-college,10,Widowed,Exec-managerial,Unmarried,Black,Female,0,2,2,United-States,<=50K\n0,?,35448,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,225548,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,1,United-States,<=50K\n1,Private,240842,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,103931,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,232618,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n3,Local-gov,288548,Masters,14,Separated,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,220609,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,26145,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,268525,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,133758,7th-8th,4,Widowed,?,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n2,Private,121264,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,29814,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n1,Private,193701,HS-grad,9,Never-married,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,183279,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,163942,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,Ireland,<=50K\n4,Private,188612,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,102771,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,85625,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,245090,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n2,Private,131239,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,182074,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,187046,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,90624,11th,7,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,37933,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,182177,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,1,0,1,United-States,<=50K\n4,Private,716416,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,190562,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,State-gov,141583,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,98941,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,201729,9th,5,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,175485,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,149168,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,115971,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,161708,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,244903,11th,7,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,155664,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,112754,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,178385,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n0,Private,44064,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,120939,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,165134,Assoc-voc,11,Never-married,Exec-managerial,Unmarried,White,Female,0,0,1,Columbia,<=50K\n1,Private,100405,10th,6,Married-civ-spouse,Farming-fishing,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,361888,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Japan,<=50K\n2,Local-gov,167864,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,202950,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,218188,HS-grad,9,Divorced,Machine-op-inspct,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,234962,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,1,Mexico,<=50K\n4,?,177226,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,259931,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,189528,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,34996,Some-college,10,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,112584,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,117589,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,145234,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,267086,Assoc-voc,11,Divorced,Tech-support,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,44434,Some-college,10,Divorced,Tech-support,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,Private,96130,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,181382,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,168845,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,271767,Masters,14,Separated,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,194636,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,State-gov,194894,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,>50K\n1,Private,132686,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,185848,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,2,0,3,United-States,<=50K\n2,State-gov,184378,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,270859,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,231866,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Private,36032,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,State-gov,172962,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,98350,Prof-school,15,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,3,2,Philippines,>50K\n3,Private,24185,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,53930,10th,6,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,?,<=50K\n0,Private,85088,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,2,1,United-States,<=50K\n2,Self-emp-not-inc,94962,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,England,<=50K\n1,Private,480861,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,187702,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n0,Private,52262,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,52636,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,175273,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,327825,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Female,0,4,2,United-States,<=50K\n3,Private,125892,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n2,?,78255,HS-grad,9,Divorced,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,398827,HS-grad,9,Married-AF-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,208919,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,365996,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,307638,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,33068,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,254291,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,125417,Prof-school,15,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,3,United-States,>50K\n1,State-gov,28848,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,?,273425,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,194723,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,Mexico,<=50K\n0,Private,195118,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,Private,123273,5th-6th,3,Divorced,Transport-moving,Not-in-family,White,Male,0,2,3,United-States,<=50K\n3,Private,220115,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,265706,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,279129,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,122742,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Self-emp-inc,172654,Prof-school,15,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,119199,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,107793,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,237943,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,64632,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,96245,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,361494,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,122850,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,173652,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,164663,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,98678,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,245529,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,55294,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,140583,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,79797,HS-grad,9,Married-spouse-absent,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Japan,>50K\n4,?,113044,HS-grad,9,Widowed,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,283499,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,51111,Bachelors,13,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,232475,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,176140,11th,7,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,301654,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,376455,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,?,192569,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,229803,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,337639,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,130849,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,296282,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,266645,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,State-gov,110128,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,90196,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,40024,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,144322,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,162340,Some-college,10,Widowed,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,169069,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,113601,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,157145,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,4,0,United-States,<=50K\n2,Private,111275,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,3,United-States,<=50K\n3,Local-gov,102076,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,?,182117,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,145409,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,190122,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,331482,Prof-school,15,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,United-States,>50K\n4,Self-emp-not-inc,170114,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,2,4,United-States,<=50K\n3,Self-emp-inc,193188,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,267588,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,4,United-States,<=50K\n3,Self-emp-inc,200471,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,?,175586,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Local-gov,322658,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,263982,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,266287,12th,8,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,278187,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,81413,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,4,4,United-States,<=50K\n0,Private,221745,Some-college,10,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,140764,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,206351,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,176814,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Local-gov,245307,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n4,State-gov,124971,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n1,Private,119545,Some-college,10,Married-civ-spouse,Exec-managerial,Own-child,White,Male,3,0,3,United-States,>50K\n0,Private,179203,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Federal-gov,44075,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,178319,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,219754,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,198316,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,168165,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,356838,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,1,0,3,Poland,<=50K\n3,Self-emp-inc,210736,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,173212,Assoc-acdm,12,Never-married,Farming-fishing,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Private,130431,5th-6th,3,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,?,169809,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,197481,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,155066,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,31290,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,54102,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,181546,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,153484,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,State-gov,351228,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,131976,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,200639,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,267546,Assoc-acdm,12,Separated,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,179875,11th,7,Divorced,Other-service,Unmarried,Other,Female,0,0,2,United-States,<=50K\n0,?,237865,Some-college,10,Never-married,?,Own-child,Black,Male,0,0,2,?,<=50K\n2,Private,300528,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,67716,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,4,0,3,United-States,>50K\n3,Federal-gov,326048,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,191188,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,32172,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,252903,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n2,Federal-gov,334314,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,83704,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,160574,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,203776,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,328610,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,295589,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,3,2,United-States,>50K\n2,Private,174373,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,247752,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,199244,10th,6,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,139992,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,95680,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,189933,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,498785,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,177526,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,150121,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n4,Federal-gov,130454,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,119079,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,220939,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,94235,Prof-school,15,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,>50K\n0,Private,305874,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,62020,HS-grad,9,Widowed,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,235624,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Germany,>50K\n2,Local-gov,247514,Masters,14,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,275726,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,72896,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,110510,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,173938,Prof-school,15,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,?,>50K\n1,Private,200641,10th,6,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,Mexico,<=50K\n3,Private,211654,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,?,>50K\n2,Private,242720,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,111567,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,179533,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n0,State-gov,334693,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,198096,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,355756,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,19395,Some-college,10,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,242586,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,208358,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n3,Private,160647,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,227943,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,197665,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,216129,12th,8,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,Trinadad&Tobago,<=50K\n1,Local-gov,326104,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,57211,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,100219,Assoc-acdm,12,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,291192,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,93415,Bachelors,13,Never-married,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,>50K\n1,Private,191502,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,261382,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,170230,Bachelors,13,Married-spouse-absent,Other-service,Not-in-family,White,Female,0,0,2,?,<=50K\n4,Private,374924,HS-grad,9,Separated,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,320984,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,338320,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,135190,7th-8th,4,Separated,Machine-op-inspct,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n4,Private,157909,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,1,0,3,United-States,<=50K\n1,Private,637222,12th,8,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,430084,HS-grad,9,Divorced,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,125279,HS-grad,9,Married-spouse-absent,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,221955,5th-6th,3,Married-spouse-absent,Farming-fishing,Other-relative,White,Male,0,0,2,Mexico,<=50K\n3,Self-emp-inc,180195,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,208778,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,81534,Some-college,10,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n2,Private,325538,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,?,<=50K\n1,Private,142264,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,Dominican-Republic,<=50K\n0,Private,128604,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,3,South,<=50K\n2,Private,277886,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,100029,Bachelors,13,Widowed,Sales,Unmarried,White,Male,0,0,4,United-States,>50K\n1,Private,169269,7th-8th,4,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,160472,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,?,123983,Bachelors,13,Never-married,?,Own-child,Other,Male,0,0,2,United-States,<=50K\n3,Private,297884,10th,6,Widowed,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,99131,HS-grad,9,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,44392,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,29441,7th-8th,4,Widowed,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,199029,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,4,3,United-States,>50K\n4,Federal-gov,181508,HS-grad,9,Widowed,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,190625,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,194740,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Greece,<=50K\n1,Private,27380,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,160631,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,224531,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,283005,11th,7,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,101926,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n3,Local-gov,135102,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,3,2,United-States,<=50K\n0,Self-emp-not-inc,113436,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n2,Private,248973,Bachelors,13,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,225334,Prof-school,15,Married-civ-spouse,Sales,Wife,White,Female,4,0,1,United-States,>50K\n2,Self-emp-not-inc,157562,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,3,4,United-States,>50K\n4,Local-gov,310085,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,129597,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,<=50K\n1,?,53042,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,204205,7th-8th,4,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,169324,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,1,United-States,>50K\n3,?,134447,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,236731,1st-4th,2,Separated,Exec-managerial,Not-in-family,White,Male,0,0,1,?,<=50K\n3,Private,141301,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,235124,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,367020,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,149102,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,Poland,<=50K\n1,Private,423770,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,Mexico,<=50K\n2,Private,211759,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Other,Male,0,0,2,Puerto-Rico,<=50K\n0,?,110998,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,56883,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,223062,Some-college,10,Separated,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,406662,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,206600,9th,5,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,3,Mexico,<=50K\n2,Local-gov,147510,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,235646,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,187577,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,132832,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,4,0,2,?,>50K\n3,Self-emp-inc,278322,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,278924,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n3,State-gov,203039,11th,7,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,145651,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,Local-gov,144531,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,91145,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,211762,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,?,111563,Assoc-voc,11,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,180985,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,?,>50K\n1,Private,207537,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,2,3,United-States,<=50K\n0,Private,417657,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,189890,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,2,0,2,United-States,<=50K\n1,Private,223212,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,Peru,>50K\n1,Private,108658,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,190023,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,222130,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,164866,Assoc-acdm,12,Separated,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,170983,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,186269,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,286026,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,403433,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,3,United-States,>50K\n0,?,224209,HS-grad,9,Married-civ-spouse,?,Wife,Black,Female,0,0,1,United-States,<=50K\n4,Private,123160,10th,6,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,99527,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,123178,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,231043,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,317733,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n4,Private,241056,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,220066,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,180342,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,31840,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,183168,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,386036,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,446358,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,Mexico,>50K\n2,Private,28035,Some-college,10,Never-married,Farming-fishing,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Private,282155,HS-grad,9,Separated,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,192384,Prof-school,15,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,383637,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,457402,5th-6th,3,Never-married,Other-service,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n1,Self-emp-inc,80249,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,State-gov,159537,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,240859,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Cuba,<=50K\n1,Private,83446,11th,7,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n4,?,29866,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,185503,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,68781,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,220589,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,51136,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,54560,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,28221,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,Canada,>50K\n0,Private,201413,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,40425,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,189461,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,200576,11th,7,Divorced,Craft-repair,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,92691,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,664821,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,El-Salvador,<=50K\n2,Private,175130,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,391016,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,249315,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,111169,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,334946,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,352248,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,173804,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,155449,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,73689,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,227594,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,161676,11th,7,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,75913,12th,8,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,242552,Some-college,10,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Federal-gov,352094,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,3,0,2,Guatemala,>50K\n1,Private,159732,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,131230,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,1,2,United-States,<=50K\n3,Private,180695,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,189922,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,409189,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,111252,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,294395,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,172718,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,43403,Some-college,10,Divorced,Farming-fishing,Not-in-family,White,Female,0,1,3,United-States,<=50K\n4,Private,111963,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,247869,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,114032,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,?,356838,12th,8,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,179633,HS-grad,9,Never-married,Tech-support,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,19847,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,231689,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,209942,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,197492,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,262439,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,283037,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,?,144533,HS-grad,9,Widowed,?,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,83446,HS-grad,9,Widowed,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,215443,HS-grad,9,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,268252,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,4,United-States,<=50K\n2,Private,181015,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-inc,139916,Assoc-voc,11,Married-civ-spouse,Sales,Husband,Other,Male,0,3,4,Mexico,<=50K\n0,Private,195770,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,125194,11th,7,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,58654,Assoc-voc,11,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,252327,5th-6th,3,Married-spouse-absent,Craft-repair,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,116508,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Germany,<=50K\n2,Private,166988,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,374163,HS-grad,9,Married-spouse-absent,Farming-fishing,Not-in-family,Other,Male,0,0,2,Mexico,<=50K\n1,?,96851,Some-college,10,Never-married,?,Not-in-family,White,Female,0,2,1,United-States,<=50K\n1,Private,196788,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,186172,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,245628,11th,7,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,0,United-States,<=50K\n0,Private,159732,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,129856,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,182812,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,1,0,3,Dominican-Republic,<=50K\n2,Private,314322,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,102976,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,42959,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,256356,11th,7,Never-married,Priv-house-serv,Other-relative,White,Female,0,0,2,Mexico,<=50K\n1,Private,136277,10th,6,Never-married,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Private,284616,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,185554,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,138847,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,33487,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,84306,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,<=50K\n2,Self-emp-not-inc,223881,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n4,Private,149653,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,348739,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,235442,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,34506,HS-grad,9,Separated,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,346964,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,192208,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,305874,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,462890,10th,6,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Private,89508,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,200153,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,179446,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,208965,9th,5,Never-married,Machine-op-inspct,Unmarried,Other,Male,0,0,2,Mexico,<=50K\n1,Private,40142,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,57452,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,327573,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,151267,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,4,0,2,United-States,>50K\n2,Private,265266,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,203836,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,Columbia,<=50K\n3,?,163998,HS-grad,9,Married-spouse-absent,?,Not-in-family,White,Male,0,0,0,United-States,>50K\n3,Self-emp-not-inc,28281,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,293196,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,Iran,>50K\n2,Private,214627,Doctorate,16,Widowed,Prof-specialty,Unmarried,White,Male,4,0,2,Iran,>50K\n0,Private,368852,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,353396,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,161745,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,97963,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,156542,Prof-school,15,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,State-gov,198103,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,55377,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,173730,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,374588,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,174330,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,78141,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,?,190324,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,0,United-States,<=50K\n1,Private,31350,11th,7,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,243607,5th-6th,3,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Local-gov,134671,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,197023,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Private,117674,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,169815,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,598606,9th,5,Separated,Handlers-cleaners,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Federal-gov,122861,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,166235,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n2,Private,187821,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,340940,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n3,Self-emp-not-inc,194791,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,231323,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,305597,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,25429,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,State-gov,192779,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,346478,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,341368,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,295612,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,168936,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,218558,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Private,336598,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,308205,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,357173,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,457237,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,284799,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,179423,Some-college,10,Never-married,Transport-moving,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,363405,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,139183,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,203482,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,112554,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,99476,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,93690,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,220585,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,194638,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,154785,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,?,162108,Bachelors,13,Divorced,?,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Self-emp-inc,214542,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,161922,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,207940,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,259351,10th,6,Never-married,Other-service,Other-relative,Amer-Indian-Eskimo,Male,0,0,2,Mexico,<=50K\n4,Private,208395,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,116391,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,239781,Preschool,1,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,174351,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Italy,<=50K\n3,Self-emp-not-inc,44368,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,El-Salvador,>50K\n1,Local-gov,188798,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,50122,Assoc-voc,11,Divorced,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,111398,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,2,United-States,>50K\n0,State-gov,152035,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,139003,HS-grad,9,Never-married,?,Other-relative,Other,Female,0,0,0,United-States,<=50K\n3,Local-gov,249289,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,257726,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,113175,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,151158,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,465326,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,356772,HS-grad,9,Never-married,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,364782,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,198385,7th-8th,4,Widowed,Other-service,Unmarried,White,Female,0,0,0,?,<=50K\n1,Private,329301,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-inc,254859,11th,7,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,203488,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,>50K\n0,Local-gov,222800,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,96452,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,170050,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,116580,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n3,Private,400004,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,183608,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,194055,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,210443,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,43272,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,108945,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,114691,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,304169,11th,7,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,503923,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,2,0,2,United-States,>50K\n1,Private,340428,Bachelors,13,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,>50K\n3,State-gov,106705,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,146391,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,235389,7th-8th,4,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,1,Portugal,<=50K\n1,Private,39665,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,113823,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,England,<=50K\n2,Private,217826,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,?,<=50K\n3,Private,349304,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,?,197688,HS-grad,9,Never-married,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,54507,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,117833,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,2,3,United-States,<=50K\n2,Private,163396,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,88566,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,1,0,1,United-States,<=50K\n1,Private,323619,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,75755,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,148903,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,40915,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,Private,182606,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,2,?,<=50K\n0,Private,131033,11th,7,Never-married,Other-service,Other-relative,Black,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,168475,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,121568,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,139098,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,357338,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,283268,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,572751,Prof-school,15,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,Mexico,>50K\n2,Private,315321,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,1,3,United-States,<=50K\n1,Private,120461,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,65278,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,208503,Some-college,10,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,112835,Masters,14,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,265038,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,89478,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,276229,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,366232,9th,5,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,Cuba,<=50K\n1,Private,152035,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,205339,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,75995,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,192236,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,?,188618,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,229737,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,199688,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,52953,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,221043,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,Federal-gov,115389,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,204205,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,338816,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,197387,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,42485,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,367706,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,102493,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,263746,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,115358,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,189680,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,?,282622,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,127651,10th,6,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,?,<=50K\n4,Private,230823,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Cuba,<=50K\n0,Private,300812,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,174732,HS-grad,9,Never-married,Other-service,Other-relative,Black,Male,0,0,2,United-States,<=50K\n3,State-gov,183710,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,137018,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,213008,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,357848,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,165799,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,188571,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,97883,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,105862,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,3,2,United-States,>50K\n2,Local-gov,57424,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,151476,Some-college,10,Separated,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,129583,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Female,0,0,0,United-States,<=50K\n4,Private,180920,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,182416,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,251915,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,187127,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,69045,Some-college,10,Never-married,Sales,Not-in-family,Black,Male,0,0,2,Jamaica,<=50K\n4,Private,192869,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,74163,12th,8,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,60847,Assoc-voc,11,Never-married,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,?,213055,11th,7,Never-married,?,Not-in-family,Other,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,116057,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,1,0,0,United-States,<=50K\n2,Private,82393,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n0,Local-gov,134181,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n3,Private,159910,Bachelors,13,Divorced,Exec-managerial,Not-in-family,Black,Male,4,0,2,United-States,>50K\n1,Self-emp-inc,117570,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-inc,214169,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,56331,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,35576,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,149168,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,157165,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,278130,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,257200,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,283122,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,580248,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,230054,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,519006,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,37332,HS-grad,9,Never-married,?,Own-child,White,Female,1,0,0,United-States,<=50K\n0,?,365871,7th-8th,4,Never-married,?,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,State-gov,235882,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n2,Private,336513,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,115551,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,State-gov,50048,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n2,Self-emp-inc,382802,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,4,United-States,>50K\n0,?,180303,Bachelors,13,Never-married,?,Not-in-family,Asian-Pac-Islander,Male,0,0,1,?,<=50K\n4,Private,106023,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,332379,Some-college,10,Married-spouse-absent,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,95465,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,96102,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,36440,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,>50K\n0,Self-emp-not-inc,209384,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,Private,50814,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,143865,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,?,104661,Some-college,10,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,50442,Some-college,10,Never-married,Exec-managerial,Own-child,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n0,Private,236601,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,100999,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n2,?,362685,Preschool,1,Widowed,?,Not-in-family,White,Female,0,0,0,El-Salvador,<=50K\n4,Self-emp-not-inc,32423,HS-grad,9,Married-civ-spouse,Farming-fishing,Wife,White,Female,4,0,2,United-States,<=50K\n4,?,154236,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,3,0,2,United-States,>50K\n1,Self-emp-inc,153546,Assoc-voc,11,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,182355,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,?,191444,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,44216,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n2,Private,97688,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,209022,11th,7,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,96016,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,52138,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,1,United-States,>50K\n4,Private,159046,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,138634,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,130125,10th,6,Never-married,Other-service,Own-child,Amer-Indian-Eskimo,Female,1,0,0,United-States,<=50K\n4,Private,247355,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,Canada,<=50K\n2,Self-emp-not-inc,227065,Some-college,10,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,244771,Some-college,10,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,0,Jamaica,<=50K\n0,Private,215616,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Canada,<=50K\n4,Private,386672,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-inc,177543,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n3,Federal-gov,617021,Bachelors,13,Married-civ-spouse,Tech-support,Husband,Black,Male,3,0,2,United-States,>50K\n0,Local-gov,117109,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Private,373550,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,19847,Some-college,10,Divorced,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,189590,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,58343,HS-grad,9,Divorced,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,354201,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,119422,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,363405,HS-grad,9,Separated,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,181863,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,194472,HS-grad,9,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,247328,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,Mexico,<=50K\n4,Self-emp-not-inc,130731,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,236910,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,378251,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,120760,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n0,Private,203182,Bachelors,13,Never-married,Exec-managerial,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Private,130304,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,1,3,United-States,<=50K\n1,Local-gov,352542,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,191024,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,197728,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,316185,7th-8th,4,Widowed,Protective-serv,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,89226,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,292353,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,2,United-States,<=50K\n2,Private,304570,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n1,Private,180296,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,361487,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,218490,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n4,Self-emp-not-inc,231777,Bachelors,13,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,189832,Assoc-acdm,12,Never-married,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,232308,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,33308,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,333677,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,170651,HS-grad,9,Never-married,Other-service,Own-child,White,Female,1,0,2,United-States,<=50K\n2,Private,343403,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,166386,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Wife,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n1,Federal-gov,48099,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,143062,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,104704,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,30497,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,State-gov,174325,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,3,0,2,United-States,>50K\n1,Private,286675,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,59474,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,378384,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,245842,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,274222,Bachelors,13,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,3,0,2,United-States,>50K\n0,Private,342575,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,206051,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,234213,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,145189,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,233490,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,344129,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,171315,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,181485,Bachelors,13,Never-married,Sales,Not-in-family,Black,Male,0,0,2,United-States,>50K\n3,Private,255412,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,France,>50K\n2,Private,262409,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,1,2,United-States,<=50K\n2,Private,199590,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,84726,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,226883,HS-grad,9,Divorced,?,Own-child,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,184335,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,102025,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,0,3,United-States,<=50K\n2,Private,183898,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,3,Germany,>50K\n1,Private,55291,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,150025,5th-6th,3,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Guatemala,<=50K\n2,Private,100584,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Local-gov,181755,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,>50K\n2,Private,150528,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,107277,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,247205,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,England,<=50K\n0,Private,291979,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,270985,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,<=50K\n3,Private,62605,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,176863,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,53197,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,267776,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,308205,7th-8th,4,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,306383,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,35494,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,291968,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,80933,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n3,Private,271828,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,121993,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,31023,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,36425,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,407684,9th,5,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,2,Mexico,<=50K\n1,Private,241895,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,<=50K\n2,Self-emp-not-inc,158555,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,140363,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,1,0,1,United-States,<=50K\n3,Private,123429,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,40060,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,290286,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,?,249271,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,106169,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,76487,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,437994,Some-college,10,Never-married,Other-service,Other-relative,Black,Male,0,0,0,United-States,<=50K\n2,Private,113555,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,2,0,3,United-States,>50K\n2,Private,160120,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n2,Local-gov,343079,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,0,United-States,<=50K\n1,Private,406662,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Self-emp-not-inc,37618,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,114158,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,115562,HS-grad,9,Divorced,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,353994,Bachelors,13,Married-civ-spouse,Exec-managerial,Other-relative,Asian-Pac-Islander,Female,0,0,2,China,>50K\n0,Private,344891,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n2,Private,286750,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,>50K\n1,Private,194197,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,206599,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Local-gov,596776,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,Guatemala,<=50K\n3,Private,56841,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,112561,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,147110,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,175339,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,234901,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,?,298133,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,217083,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,97757,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,151868,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,25864,HS-grad,9,Never-married,Exec-managerial,Unmarried,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n1,Private,109419,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,203070,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,107843,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,United-States,>50K\n4,State-gov,264544,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,148644,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,125762,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n2,?,53606,Assoc-voc,11,Married-civ-spouse,?,Wife,White,Female,2,0,0,United-States,<=50K\n0,Private,193741,11th,7,Never-married,Other-service,Other-relative,Black,Male,0,0,1,United-States,<=50K\n1,Private,588905,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,115613,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,222374,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,185359,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,173647,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,31166,HS-grad,9,Divorced,Prof-specialty,Not-in-family,Other,Female,0,0,1,Germany,<=50K\n0,?,517995,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,Mexico,<=50K\n0,Self-emp-not-inc,189027,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,296125,HS-grad,9,Separated,Priv-house-serv,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,?,640383,Bachelors,13,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,334291,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,318450,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,174163,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,119721,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,142719,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,162593,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,236852,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,154863,HS-grad,9,Never-married,Protective-serv,Other-relative,Black,Male,0,2,2,United-States,<=50K\n2,Private,168894,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,344920,Some-college,10,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,33355,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n4,?,196782,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,291518,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,170244,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,369549,Some-college,10,Never-married,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,23438,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,>50K\n0,Private,202673,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,171780,Assoc-acdm,12,Divorced,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Local-gov,264503,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,244341,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,209109,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,187392,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,119578,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,195105,HS-grad,9,Divorced,Priv-house-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,101752,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,95825,Some-college,10,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,362654,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,?,29810,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,77332,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,87518,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,2,3,United-States,<=50K\n4,Private,113324,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,96299,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,>50K\n3,Private,237729,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,200973,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,212456,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,131568,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,185859,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,231981,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-inc,117963,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,78172,Some-college,10,Married-AF-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,164135,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,171216,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,140664,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,249277,HS-grad,9,Never-married,Exec-managerial,Own-child,Black,Male,0,0,4,United-States,<=50K\n3,Federal-gov,117847,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,52372,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Federal-gov,95806,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,Private,137428,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,169047,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,339168,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,504725,10th,6,Never-married,Sales,Other-relative,White,Male,0,0,0,Guatemala,<=50K\n1,Private,132870,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,135840,10th,6,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,35644,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,198148,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,220098,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,262515,11th,7,Never-married,Other-service,Other-relative,White,Male,0,0,0,United-States,<=50K\n0,?,423863,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Federal-gov,111567,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,194096,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,420917,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,197871,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,253116,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,206535,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,State-gov,70447,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n3,Private,201217,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,209970,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,196745,Some-college,10,Never-married,Other-service,Own-child,White,Female,1,0,0,United-States,<=50K\n1,Local-gov,175262,Masters,14,Married-civ-spouse,Prof-specialty,Other-relative,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,304955,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,181265,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,200973,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,37440,Bachelors,13,Never-married,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,395170,Assoc-voc,11,Married-civ-spouse,Other-service,Wife,Amer-Indian-Eskimo,Female,0,0,1,Mexico,<=50K\n3,?,32385,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,353213,Assoc-acdm,12,Separated,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,38619,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n0,Private,177711,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,190761,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,27776,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,470663,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,71738,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,74156,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,202467,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,1,2,United-States,>50K\n0,Private,123983,11th,7,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n2,Private,193494,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,169886,Bachelors,13,Never-married,?,Not-in-family,White,Female,0,0,0,?,<=50K\n2,Private,130571,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,90363,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n3,Private,83444,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,239093,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,1,0,2,United-States,<=50K\n4,Local-gov,151369,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,56630,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,117095,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,189985,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,34862,Some-college,10,Never-married,?,Own-child,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n2,Self-emp-inc,126675,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,199806,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,57596,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,103459,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,282398,Some-college,10,Separated,Tech-support,Unmarried,White,Male,0,0,2,United-States,>50K\n2,Private,298841,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,33300,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n0,?,306031,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,306467,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,189888,12th,8,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,83861,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,117393,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,129934,Some-college,10,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n3,Private,179010,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,375680,Bachelors,13,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,2,?,<=50K\n3,Private,316101,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,293305,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n3,Local-gov,175750,HS-grad,9,Divorced,Transport-moving,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Private,121718,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,2,3,United-States,>50K\n4,?,94931,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,1,0,2,United-States,<=50K\n3,State-gov,229272,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,142828,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,22743,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,76371,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,216129,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,107425,Masters,14,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,611029,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,363032,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,170020,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,137900,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,322674,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,23778,7th-8th,4,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,147845,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,175759,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,166459,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,128212,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Wife,Asian-Pac-Islander,Female,0,0,2,Vietnam,>50K\n3,Federal-gov,127455,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,134699,HS-grad,9,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,254230,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,159715,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,116286,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,146719,HS-grad,9,Divorced,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,361888,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,?,26553,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,>50K\n3,Self-emp-not-inc,32825,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,225768,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n1,Federal-gov,393728,Some-college,10,Divorced,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,160369,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,191807,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n3,Federal-gov,176969,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,1,2,United-States,<=50K\n3,Federal-gov,33863,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,?,182687,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,>50K\n4,State-gov,141459,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,174233,Some-college,10,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Local-gov,95393,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,221095,HS-grad,9,Never-married,Craft-repair,Other-relative,Black,Male,0,0,2,United-States,<=50K\n3,Private,104501,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,?,437851,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,131230,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,495888,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,El-Salvador,<=50K\n4,Private,185691,11th,7,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,201822,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,3,2,United-States,<=50K\n3,Local-gov,549341,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,1,United-States,<=50K\n1,Private,247445,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,199566,Bachelors,13,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,139057,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,4,Taiwan,>50K\n3,Private,185039,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,166124,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,82649,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,109275,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,408328,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,186338,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,?,130856,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,251579,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,76612,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,22546,Bachelors,13,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,53684,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,183627,11th,7,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,73203,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,108426,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,3,England,<=50K\n3,Private,116287,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,Columbia,<=50K\n2,Self-emp-inc,145697,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,326156,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,201127,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,250791,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,328216,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,400443,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,95985,5th-6th,3,Widowed,Other-service,Unmarried,Black,Male,0,0,0,United-States,<=50K\n1,Local-gov,127651,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,250679,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,103950,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,200199,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,State-gov,295791,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,191841,Assoc-acdm,12,Separated,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,82622,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,160728,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,109849,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,339897,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,?,37215,Bachelors,13,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,371299,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,421837,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,29702,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,117381,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,4,England,<=50K\n2,?,240027,HS-grad,9,Divorced,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,338740,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,28359,HS-grad,9,Separated,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,?,315026,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,314525,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Private,173005,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,286750,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,163985,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,219318,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,1,Puerto-Rico,<=50K\n2,Private,44121,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n3,Self-emp-not-inc,103794,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,310632,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,153976,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,174575,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,82388,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,<=50K\n1,Private,207253,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,England,<=50K\n4,?,251951,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,746786,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,308296,HS-grad,9,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,101825,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,2,United-States,>50K\n0,Private,109009,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,413363,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,<=50K\n4,?,117751,Assoc-acdm,12,Divorced,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,State-gov,296326,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,208358,9th,5,Divorced,Handlers-cleaners,Not-in-family,White,Male,2,0,3,United-States,<=50K\n2,Private,120277,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,Ireland,<=50K\n0,Private,193219,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,Jamaica,<=50K\n2,Private,86399,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,215251,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,124470,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,228649,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,386397,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,96798,Masters,14,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,?,106707,Assoc-acdm,12,Married-civ-spouse,?,Husband,Black,Male,0,0,0,United-States,>50K\n1,Private,159768,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,1,0,2,Ecuador,<=50K\n3,Private,139464,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,Ireland,<=50K\n4,State-gov,550848,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,68505,9th,5,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,122215,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,3,United-States,<=50K\n1,Private,159442,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,80638,Bachelors,13,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,1,China,<=50K\n3,Private,192390,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,191324,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,1,United-States,<=50K\n4,?,147284,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,State-gov,73009,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,177858,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Male,0,0,3,United-States,>50K\n2,Private,163003,Bachelors,13,Married-spouse-absent,Tech-support,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,95551,HS-grad,9,Separated,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,125298,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,State-gov,198186,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,295949,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n2,Private,182668,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,124905,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,171635,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,376240,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,157391,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,114357,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,178134,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,207201,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,124483,Bachelors,13,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,3,?,>50K\n4,Private,102103,HS-grad,9,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,92036,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,236426,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,400966,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,404573,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,227571,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,145917,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,190226,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,356555,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,66473,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,?,172256,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,?,118902,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,0,4,0,United-States,>50K\n0,Self-emp-inc,163039,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,89559,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,35507,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,163303,Assoc-voc,11,Divorced,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,192712,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,381153,10th,6,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,222434,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,34706,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,47857,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,195216,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,103643,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,Greece,<=50K\n1,Local-gov,329426,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,183612,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,184105,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,211385,Assoc-acdm,12,Never-married,Other-service,Not-in-family,Black,Male,0,0,1,Jamaica,<=50K\n0,Private,61777,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,320194,Prof-school,15,Separated,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,>50K\n0,Private,199444,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,312588,10th,6,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,168675,HS-grad,9,Separated,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,87556,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,220421,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,404599,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,99065,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,2,Poland,>50K\n4,Local-gov,109973,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,246652,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,57423,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,291248,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,Black,Male,0,0,2,United-States,<=50K\n3,Private,163708,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,240358,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,25955,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,101593,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,227890,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,225053,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,228472,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,245378,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,156623,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,3,0,3,Philippines,>50K\n1,Private,35032,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,258849,Assoc-voc,11,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,190115,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,63910,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Private,510072,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,210867,11th,7,Divorced,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,263024,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,306785,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,104333,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,340734,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,288585,HS-grad,9,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,0,South,<=50K\n2,Private,241765,11th,7,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,111058,Assoc-acdm,12,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,104662,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,313986,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,52037,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,146589,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,131776,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,1,0,2,Germany,<=50K\n1,Private,254221,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,152909,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n2,Self-emp-not-inc,211785,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Female,0,0,0,United-States,<=50K\n4,Private,160362,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,387215,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,2,0,United-States,<=50K\n2,Private,187046,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n0,?,208874,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,169631,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,202956,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,80467,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,407672,Some-college,10,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,243425,HS-grad,9,Divorced,Other-service,Other-relative,White,Female,0,0,3,Peru,<=50K\n3,?,174964,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,347491,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,146161,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,449432,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,175499,11th,7,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,288825,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,4,4,United-States,<=50K\n1,Local-gov,134813,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,190401,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,260617,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,45604,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,67841,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,244522,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,430471,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,194698,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,94235,Bachelors,13,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,188330,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,United-States,<=50K\n3,Local-gov,146181,9th,5,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,177125,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-inc,68330,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,95636,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,238329,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,416129,Preschool,1,Married-civ-spouse,Other-service,Not-in-family,White,Male,0,0,2,El-Salvador,<=50K\n0,Private,285004,Bachelors,13,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,3,Taiwan,<=50K\n4,?,256514,Bachelors,13,Widowed,?,Other-relative,White,Female,1,0,0,United-States,<=50K\n0,Private,186294,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,188786,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,31352,Some-college,10,Divorced,Protective-serv,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,>50K\n0,Private,197613,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,161942,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,275438,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,361721,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,144968,HS-grad,9,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,190539,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,2,0,3,United-States,<=50K\n0,Private,178037,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,306985,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,87928,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,242619,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,154165,9th,5,Divorced,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,511331,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,221026,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,222182,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,126569,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,202344,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,190423,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,238917,5th-6th,3,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,El-Salvador,<=50K\n2,Private,221947,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n2,Self-emp-inc,37997,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,147098,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,278253,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,195411,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,76196,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,120131,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n0,Self-emp-not-inc,186014,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,Germany,<=50K\n1,Private,205903,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,125405,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,219838,12th,8,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,19395,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,223327,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,114062,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,95654,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,Iran,>50K\n2,Private,177305,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,299616,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,117681,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,237651,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,State-gov,150570,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,106705,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,1,0,3,United-States,<=50K\n0,?,174714,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,175958,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,1,0,3,United-States,<=50K\n1,Private,144064,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,?,107112,7th-8th,4,Never-married,?,Other-relative,Black,Male,0,0,1,United-States,<=50K\n0,Private,54152,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,?,<=50K\n1,Private,152951,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,190487,HS-grad,9,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,1,Ecuador,<=50K\n0,Private,306666,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,195148,HS-grad,9,Married-civ-spouse,Craft-repair,Own-child,White,Male,1,0,2,United-States,<=50K\n1,Self-emp-not-inc,226624,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,157569,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,22966,Some-college,10,Married-spouse-absent,Tech-support,Unmarried,White,Male,0,0,0,United-States,<=50K\n3,Private,379682,Assoc-voc,11,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,446559,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,41794,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,90409,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,125491,Some-college,10,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Vietnam,<=50K\n1,?,129661,Assoc-voc,11,Married-civ-spouse,?,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,>50K\n3,Self-emp-not-inc,104748,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n3,Local-gov,169182,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,3,Dominican-Republic,<=50K\n3,Private,324655,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,?,>50K\n0,Private,122272,Bachelors,13,Never-married,Farming-fishing,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,114798,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,289707,HS-grad,9,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,137691,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,84610,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,166789,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,348728,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,348092,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,Haiti,<=50K\n4,Private,154526,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,288371,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Canada,>50K\n0,Private,182342,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,244366,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,102423,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,259688,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,98733,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,174856,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,United-States,<=50K\n4,Self-emp-not-inc,141797,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,327202,12th,8,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,76996,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,260560,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,370990,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,129010,12th,8,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,452640,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,120796,9th,5,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,45334,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Asian-Pac-Islander,Male,0,0,4,?,<=50K\n1,Private,229523,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,127388,12th,8,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,395567,11th,7,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,119422,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,3,United-States,<=50K\n4,Private,193895,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,163083,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Self-emp-not-inc,155343,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,73895,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,107682,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,321166,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,154940,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,103700,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,63509,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,243842,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,187221,7th-8th,4,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,58597,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,190290,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,158352,Masters,14,Never-married,?,Not-in-family,White,Female,3,0,1,United-States,>50K\n1,Private,62165,Some-college,10,Never-married,Sales,Other-relative,Black,Male,0,0,1,United-States,<=50K\n0,?,307149,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,280134,10th,6,Never-married,Sales,Not-in-family,White,Male,0,0,3,El-Salvador,<=50K\n1,Private,118736,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,171114,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,169638,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,125461,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,145434,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,152182,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,233724,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,153963,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,88120,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,96330,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,66118,Some-college,10,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,182178,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n2,Self-emp-not-inc,53628,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Private,174865,9th,5,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,66194,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,Outlying-US(Guam-USVI-etc),<=50K\n1,Private,73796,Some-college,10,Widowed,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,State-gov,28366,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,231741,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,0,United-States,<=50K\n1,Private,237865,Masters,14,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,195453,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,116934,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,87867,12th,8,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,456399,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,263608,Some-college,10,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,263498,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,183765,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,?,<=50K\n1,Federal-gov,469705,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,3,2,United-States,<=50K\n2,Local-gov,113253,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,138768,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,302146,11th,7,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,253866,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,214858,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,243476,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,169104,Some-college,10,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,103218,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,57233,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,228320,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,217421,HS-grad,9,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,185041,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n1,Self-emp-not-inc,37302,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,3,0,4,United-States,>50K\n1,Private,261059,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,59767,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,333541,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,133352,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n2,Private,99270,HS-grad,9,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,204629,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,34104,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,>50K\n1,Private,312667,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,329603,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,281021,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,275385,Some-college,10,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,129177,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,385591,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,?,201179,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,38360,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,73796,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,67671,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,257621,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,180052,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,656036,Bachelors,13,Separated,Adm-clerical,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,215943,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,488720,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Federal-gov,199298,7th-8th,4,Widowed,Other-service,Unmarried,White,Female,0,0,1,Puerto-Rico,<=50K\n1,Private,305692,Some-college,10,Married-civ-spouse,Sales,Wife,Black,Female,0,0,2,United-States,<=50K\n4,Private,114994,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,88265,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,168569,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,175413,HS-grad,9,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,2,Jamaica,<=50K\n2,Private,161226,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,?,160995,10th,6,Divorced,?,Not-in-family,White,Female,1,0,0,United-States,<=50K\n0,Private,208598,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,200471,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,256609,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,176684,Assoc-voc,11,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,206512,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,212640,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,148724,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,266510,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,240252,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,358975,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,124242,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,434710,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,204338,HS-grad,9,Never-married,Farming-fishing,Unmarried,White,Male,0,0,1,?,<=50K\n3,Private,241844,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,191342,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Cambodia,<=50K\n2,Private,221947,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,111483,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,1,3,United-States,<=50K\n1,Private,65278,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,133403,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,166416,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,?,142158,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,221480,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,1,Ecuador,<=50K\n1,Self-emp-not-inc,189878,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,278403,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,184710,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,177775,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,275943,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,Nicaragua,<=50K\n4,Self-emp-not-inc,225473,Some-college,10,Widowed,Craft-repair,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,289403,Bachelors,13,Separated,Adm-clerical,Unmarried,Black,Male,0,0,1,United-States,<=50K\n1,Private,269060,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,449354,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,214413,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,80058,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,202027,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Self-emp-not-inc,123440,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,191524,Assoc-voc,11,Separated,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,308144,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,Private,164204,1st-4th,2,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,?,<=50K\n3,Private,205100,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,195750,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,149756,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Local-gov,240358,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,241174,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,356838,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Canada,<=50K\n1,Self-emp-inc,115705,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,137142,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,296066,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,401335,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,?,182771,Bachelors,13,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,4,Philippines,<=50K\n1,Self-emp-inc,186824,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Federal-gov,162187,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,98010,Some-college,10,Married-spouse-absent,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,172538,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,80163,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,43959,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n3,Private,162632,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n4,Self-emp-not-inc,115422,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,100933,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,270379,HS-grad,9,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,20109,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,Amer-Indian-Eskimo,Female,0,0,4,United-States,<=50K\n3,Private,114758,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n0,Private,100345,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,184901,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,87239,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,127363,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n3,Federal-gov,199720,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,Germany,>50K\n2,Private,143058,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,36489,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,141698,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,26358,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,195532,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,3,0,2,United-States,>50K\n0,Private,30039,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,125159,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,Jamaica,<=50K\n0,Private,246250,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,77370,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,355569,Assoc-voc,11,Never-married,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,180603,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,201785,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,256211,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n1,Private,146764,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n0,?,211968,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,Iran,<=50K\n1,Private,200515,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,52636,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,27049,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,111128,10th,6,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,348038,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,Puerto-Rico,>50K\n1,Private,93930,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,397831,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,2,United-States,<=50K\n3,Private,33794,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,178215,9th,5,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,>50K\n0,Local-gov,191910,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,340110,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,4,United-States,>50K\n3,Self-emp-not-inc,133694,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,Black,Male,0,0,2,Jamaica,>50K\n3,Private,148398,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,133515,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,181667,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,Canada,<=50K\n4,Private,159715,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n3,Federal-gov,174040,Some-college,10,Separated,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,117700,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,37215,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,46807,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n3,Self-emp-not-inc,317360,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,425627,Some-college,10,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,82623,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n0,?,63574,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,140854,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,185061,11th,7,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,160118,12th,8,Never-married,Sales,Not-in-family,White,Female,0,0,0,?,<=50K\n3,Private,282680,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,137591,Some-college,10,Never-married,Sales,Own-child,White,Male,0,2,2,United-States,<=50K\n0,Private,198163,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,132749,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,31264,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,2,0,2,United-States,>50K\n0,Private,399449,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,27494,Some-college,10,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,Taiwan,<=50K\n3,Private,368561,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,102096,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,>50K\n0,Private,406078,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,100506,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,29658,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,20469,HS-grad,9,Never-married,?,Other-relative,Asian-Pac-Islander,Female,0,0,0,South,<=50K\n4,Private,181953,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,304175,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,170070,Assoc-acdm,12,Divorced,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,?,193416,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,194908,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,357962,9th,5,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,214716,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,207578,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,146409,Some-college,10,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,341643,Bachelors,13,Never-married,Other-service,Other-relative,White,Male,0,0,3,United-States,<=50K\n3,Private,131631,11th,7,Separated,Machine-op-inspct,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Private,88842,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n4,?,128900,Some-college,10,Widowed,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,417136,HS-grad,9,Divorced,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,336763,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,1,2,United-States,<=50K\n1,Private,209301,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Canada,<=50K\n1,Private,120986,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n1,Private,51025,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,218281,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,114994,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,335481,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,1,United-States,<=50K\n0,Private,174503,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,230478,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,State-gov,149650,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Iran,>50K\n2,Private,149419,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,?,341539,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,185099,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,132930,Masters,14,Never-married,?,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,128472,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,124971,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,344060,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,286750,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,4,United-States,>50K\n2,Private,296999,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Private,123681,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,232024,11th,7,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,52267,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,119182,HS-grad,9,Separated,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,191230,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,Yugoslavia,<=50K\n3,Federal-gov,23780,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,184553,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,242651,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,246226,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,86745,Bachelors,13,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n0,Private,106889,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,460835,HS-grad,9,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,213140,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Italy,<=50K\n1,State-gov,37070,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Canada,<=50K\n1,State-gov,93589,HS-grad,9,Divorced,Protective-serv,Own-child,Other,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,213258,HS-grad,9,Divorced,Farming-fishing,Unmarried,White,Male,0,0,4,United-States,<=50K\n2,State-gov,46814,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,168873,Some-college,10,Divorced,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,284737,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,309620,Some-college,10,Married-civ-spouse,Sales,Husband,Other,Male,0,0,3,?,<=50K\n3,Private,197418,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,?,132737,10th,6,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,185041,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,159604,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,123557,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,275421,Assoc-voc,11,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,167147,12th,8,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,197583,10th,6,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,175109,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,1,2,United-States,>50K\n3,Private,117502,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,180401,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,146603,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,143822,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,51985,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,48121,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,234807,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,2,0,2,United-States,>50K\n2,Federal-gov,65324,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,302149,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n0,Private,168403,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Private,159897,Some-college,10,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,416338,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,370615,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,219371,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,Jamaica,<=50K\n3,Private,120970,10th,6,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,22966,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,Canada,<=50K\n0,Private,34541,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Canada,<=50K\n1,Private,191027,Assoc-acdm,12,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,107458,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,121832,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,233825,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,3,United-States,>50K\n0,Private,73839,11th,7,Divorced,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,109165,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,State-gov,103063,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,29762,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,4,United-States,<=50K\n3,Private,111979,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,150125,Assoc-voc,11,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,301853,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,296738,11th,7,Separated,?,Not-in-family,White,Female,2,0,3,United-States,<=50K\n2,Private,118001,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,149337,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,36601,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,118600,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,1,2,United-States,<=50K\n2,Private,279272,Assoc-acdm,12,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,181020,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,165998,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,218136,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,Outlying-US(Guam-USVI-etc),<=50K\n0,Self-emp-inc,182200,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,39363,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,0,?,<=50K\n0,Private,140001,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,193260,Bachelors,13,Married-civ-spouse,Craft-repair,Other-relative,Asian-Pac-Islander,Male,0,0,1,India,<=50K\n0,Private,191243,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,207887,Bachelors,13,Divorced,Exec-managerial,Other-relative,White,Female,0,0,3,United-States,<=50K\n2,Federal-gov,211450,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,184759,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,197836,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,232308,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,?,189888,Assoc-acdm,12,Never-married,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,301614,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,146674,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,225291,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,148509,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,1,India,<=50K\n4,Private,136413,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,126060,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,73064,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,1,United-States,<=50K\n0,Private,39026,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,33035,12th,8,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,193494,10th,6,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,147440,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,153131,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,64671,HS-grad,9,Divorced,Handlers-cleaners,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Self-emp-not-inc,225399,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,3,0,2,United-States,>50K\n0,Private,174391,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,377757,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,364310,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,Germany,<=50K\n1,Private,110643,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,70240,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,1,Philippines,<=50K\n4,State-gov,32694,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,95047,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,2,United-States,>50K\n1,Private,264936,HS-grad,9,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,367329,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,56026,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,186452,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,125417,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,242082,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,31023,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,397346,Assoc-acdm,12,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,424079,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Self-emp-not-inc,108947,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,3,0,2,United-States,>50K\n0,State-gov,261979,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,55507,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n0,?,291407,12th,8,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,353358,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,67339,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,235109,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,208180,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,State-gov,423561,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,145290,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n0,Private,403671,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,49325,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,370494,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n0,Private,267012,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,191856,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,80445,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,379798,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,168387,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,301948,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,4,0,0,United-States,<=50K\n2,Private,274809,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,233193,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,299635,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,236396,11th,7,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,688355,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,37019,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,148015,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,4,0,2,United-States,>50K\n2,Private,122975,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,1,Trinadad&Tobago,<=50K\n3,State-gov,349795,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,229846,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Female,0,0,2,?,<=50K\n2,Private,108945,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,237498,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,188872,5th-6th,3,Divorced,Transport-moving,Unmarried,White,Male,2,0,2,United-States,<=50K\n2,Private,324019,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,82488,Some-college,10,Divorced,Sales,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n3,Private,206964,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,37088,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,152540,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,143554,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,126242,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,127185,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,164018,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,<=50K\n0,Private,210184,11th,7,Separated,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,117528,Assoc-voc,11,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,124973,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,182117,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,220049,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,247975,Some-college,10,Never-married,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n3,Private,50164,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,State-gov,123160,Masters,14,Married-spouse-absent,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,0,China,<=50K\n3,Self-emp-inc,219962,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,?,>50K\n3,Private,79324,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,129100,11th,7,Separated,Other-service,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Private,210275,HS-grad,9,Separated,Transport-moving,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,189462,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,171114,Assoc-voc,11,Separated,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,201799,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,?,200426,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n0,?,24395,Some-college,10,Never-married,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,191149,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,34173,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,350979,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Laos,<=50K\n2,Private,147314,HS-grad,9,Married-civ-spouse,Sales,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n2,Private,136081,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,?,232894,9th,5,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,373403,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,120601,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,130926,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Federal-gov,72338,Assoc-voc,11,Never-married,Prof-specialty,Other-relative,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,129624,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,328697,Some-college,10,Divorced,Protective-serv,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,191196,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,191117,11th,7,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,110243,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,181580,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,89030,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,345493,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,3,Taiwan,>50K\n0,Self-emp-not-inc,277700,Some-college,10,Separated,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,198478,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,250679,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,168837,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,Canada,>50K\n1,Private,142675,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,299050,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,107833,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,1,2,United-States,>50K\n3,Private,121958,7th-8th,4,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,282948,Some-college,10,Married-civ-spouse,Tech-support,Husband,Black,Male,1,0,2,United-States,<=50K\n1,Private,176683,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,France,<=50K\n3,Private,34377,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,209833,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,State-gov,41506,10th,6,Divorced,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Local-gov,125159,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,4,0,2,?,>50K\n2,Self-emp-not-inc,147206,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,93664,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,315065,7th-8th,4,Never-married,Other-service,Other-relative,White,Male,0,0,3,Mexico,<=50K\n4,Private,381851,9th,5,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Local-gov,185769,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,186272,9th,5,Married-civ-spouse,Adm-clerical,Husband,Black,Male,2,0,2,United-States,>50K\n1,Private,312667,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,343925,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,Jamaica,<=50K\n1,Private,195994,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,398843,Some-college,10,Separated,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,73514,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Private,288049,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,54759,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,155343,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,401104,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n0,?,124884,9th,5,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,287306,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,Black,Female,4,0,2,?,>50K\n3,Private,113995,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,146378,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,?,<=50K\n2,Private,111499,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,34374,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,162187,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,147654,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,182467,Assoc-voc,11,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,183970,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,332588,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,26781,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n0,Private,48610,11th,7,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,162632,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,91711,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,198003,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,179048,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,?,<=50K\n0,Private,262778,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,2,0,3,United-States,<=50K\n4,Private,102470,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,123170,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,164243,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,262511,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,51170,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,91949,Doctorate,16,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,123727,HS-grad,9,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,173175,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Self-emp-not-inc,120301,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,250967,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,285432,Assoc-acdm,12,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,36235,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,317219,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n3,Local-gov,110965,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,123283,HS-grad,9,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,0,United-States,<=50K\n0,?,249087,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,152940,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,376680,HS-grad,9,Never-married,Tech-support,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,231232,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,Canada,<=50K\n3,Self-emp-not-inc,168625,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,0,United-States,>50K\n1,Private,33939,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,155659,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n1,Local-gov,190228,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,216178,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,587310,7th-8th,4,Never-married,Other-service,Other-relative,White,Male,0,0,1,Guatemala,<=50K\n0,Private,155919,9th,5,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,Private,227386,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,138152,12th,8,Never-married,Craft-repair,Other-relative,Other,Male,0,0,3,Guatemala,<=50K\n2,Private,167482,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,57957,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,157747,9th,5,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,88570,Assoc-voc,11,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,0,Germany,>50K\n2,Private,273308,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,3,Mexico,<=50K\n3,Private,216292,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,131298,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,386378,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,179668,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,210812,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,311671,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,215247,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Federal-gov,125856,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,74631,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,24008,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,354591,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,1,0,2,United-States,<=50K\n1,Private,155343,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n3,Private,308334,1st-4th,2,Widowed,Other-service,Unmarried,Other,Female,0,0,1,Mexico,<=50K\n2,Private,245361,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,158319,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,204486,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n0,Private,314823,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,Dominican-Republic,<=50K\n1,Private,211334,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,4,United-States,<=50K\n2,Self-emp-not-inc,73199,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,1,0,4,Vietnam,<=50K\n0,Private,126550,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,260782,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,1,2,El-Salvador,<=50K\n1,Private,114224,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,64292,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,628797,Some-college,10,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,219775,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,212894,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,260019,7th-8th,4,Never-married,Farming-fishing,Unmarried,Other,Male,0,0,2,Mexico,<=50K\n1,Private,228075,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,0,1,Mexico,<=50K\n0,Private,239806,Assoc-voc,11,Never-married,Other-service,Other-relative,White,Female,0,0,2,Mexico,<=50K\n0,Private,324637,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,163620,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,4,United-States,>50K\n1,Private,194200,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,State-gov,129200,Some-college,10,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Federal-gov,207172,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,135312,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,100734,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,226443,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,110871,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,192704,12th,8,Never-married,Exec-managerial,Not-in-family,White,Male,2,0,3,United-States,<=50K\n3,?,224108,HS-grad,9,Widowed,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,78870,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,3,0,2,United-States,>50K\n2,Private,107762,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,183611,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,Germany,<=50K\n4,Local-gov,249078,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,208452,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,302195,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,199947,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,379118,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,174855,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,173736,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,39369,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,196348,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,340917,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,4,0,2,United-States,>50K\n4,Private,97077,10th,6,Widowed,Sales,Unmarried,Black,Female,0,0,0,United-States,<=50K\n3,Private,200098,Bachelors,13,Divorced,Sales,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n1,Federal-gov,127651,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,315128,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Federal-gov,206823,Bachelors,13,Divorced,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,316093,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Private,112115,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,Ireland,>50K\n4,?,203821,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,250051,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,298635,Masters,14,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,3,2,Philippines,>50K\n1,State-gov,109193,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,130849,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,43959,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,222810,Some-college,10,Divorced,Other-service,Other-relative,White,Female,3,0,2,?,>50K\n2,Self-emp-not-inc,27242,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,53158,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,206520,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,164190,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,287988,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,?,200819,7th-8th,4,Divorced,?,Own-child,White,Male,0,0,4,United-States,<=50K\n0,Private,83891,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,65087,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Self-emp-not-inc,363418,Bachelors,13,Separated,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n4,?,182378,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,278870,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,174789,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,228608,Some-college,10,Never-married,Craft-repair,Other-relative,Asian-Pac-Islander,Female,0,0,2,Cambodia,<=50K\n0,Private,184400,HS-grad,9,Never-married,Transport-moving,Own-child,Asian-Pac-Islander,Male,0,0,1,?,<=50K\n3,Private,263568,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,117381,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,83411,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,49156,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,421449,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,238944,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,188982,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,0,United-States,>50K\n3,Private,175925,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,164190,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,232914,Assoc-voc,11,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,120121,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,180805,HS-grad,9,Never-married,Transport-moving,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n4,Local-gov,161944,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,319149,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Mexico,<=50K\n3,?,22428,Masters,14,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,290528,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,123984,Assoc-acdm,12,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Philippines,<=50K\n3,Private,34186,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Federal-gov,282680,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,4,United-States,>50K\n2,Private,183892,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,3,0,2,United-States,>50K\n2,Local-gov,195124,11th,7,Divorced,Sales,Unmarried,White,Male,2,0,3,Puerto-Rico,>50K\n3,State-gov,55938,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,209900,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,179717,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,1,3,United-States,>50K\n1,Private,150361,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,164102,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,Private,252714,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,Italy,<=50K\n1,Private,205204,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,168906,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,112115,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,116531,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,202994,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,191917,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,2,0,2,United-States,<=50K\n0,Private,341294,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,216734,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,182187,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,424988,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,379118,HS-grad,9,Divorced,Other-service,Unmarried,Black,Male,0,0,0,United-States,<=50K\n3,Private,168232,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,147171,Some-college,10,Never-married,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n1,Self-emp-inc,207668,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,?,>50K\n1,Private,193650,11th,7,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,200187,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,188644,5th-6th,3,Married-spouse-absent,Craft-repair,Other-relative,White,Male,0,0,2,Mexico,<=50K\n4,Private,398067,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,29658,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,154966,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,364099,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,?,291374,10th,6,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Federal-gov,97837,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,117983,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,345497,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,64167,Assoc-voc,11,Never-married,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,315877,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,3,2,United-States,<=50K\n4,Federal-gov,232151,Some-college,10,Divorced,Adm-clerical,Other-relative,Black,Female,1,0,2,United-States,<=50K\n4,Private,225526,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,289653,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,179462,7th-8th,4,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,67317,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,77764,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,253438,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,150309,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,83064,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,376973,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,311184,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,159449,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,168288,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,74883,Bachelors,13,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Female,0,1,2,Philippines,<=50K\n0,Private,275190,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,189838,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-inc,101338,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,331894,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Self-emp-not-inc,40293,HS-grad,9,Never-married,Farming-fishing,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,88904,Bachelors,13,Separated,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,145041,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,Dominican-Republic,<=50K\n1,Private,46385,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,4,United-States,>50K\n2,State-gov,363591,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,183327,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Female,0,1,0,United-States,<=50K\n1,State-gov,182556,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,267859,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,El-Salvador,<=50K\n4,Private,190747,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,162869,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,4,United-States,<=50K\n1,Private,141229,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,174216,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,366416,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,172538,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,193026,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,184424,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,3,2,United-States,>50K\n3,Local-gov,337768,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Local-gov,179059,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,99549,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,72619,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,State-gov,55764,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,30267,11th,7,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,308144,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,206351,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,282304,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,176077,Some-college,10,Never-married,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,142719,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,114973,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Federal-gov,159548,Assoc-acdm,12,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,91209,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,196564,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,149220,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,169699,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,218215,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,156718,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,55720,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,257250,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,194630,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,398931,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,1,3,United-States,>50K\n2,Self-emp-not-inc,362062,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Local-gov,101593,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Private,196266,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,197332,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,97842,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,86837,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,3,2,United-States,>50K\n0,Private,57324,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,116852,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,Portugal,>50K\n2,Private,154430,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,4,0,3,United-States,>50K\n2,Private,38468,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,188808,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,177163,Masters,14,Widowed,Prof-specialty,Unmarried,White,Female,1,0,3,United-States,<=50K\n2,Private,187322,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,107578,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,1,0,2,United-States,<=50K\n2,Private,168680,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,256755,Bachelors,13,Never-married,Handlers-cleaners,Other-relative,White,Female,0,0,2,Cuba,<=50K\n1,Private,360799,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,188476,11th,7,Never-married,Exec-managerial,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,30457,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,252752,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,443508,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,244408,Some-college,10,Never-married,Adm-clerical,Other-relative,Asian-Pac-Islander,Female,0,0,1,Vietnam,<=50K\n2,Private,178983,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,143068,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,<=50K\n1,Local-gov,247328,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,201732,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,246829,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,290267,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,119170,Some-college,10,Separated,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,207923,Some-college,10,Married-spouse-absent,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,State-gov,170142,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,187164,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,303867,9th,5,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,291429,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,213179,Some-college,10,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,>50K\n1,State-gov,111843,Assoc-acdm,12,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,297154,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,2,United-States,<=50K\n3,Federal-gov,68493,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n3,Federal-gov,340718,11th,7,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,194059,12th,8,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,47296,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,2,0,United-States,<=50K\n1,State-gov,286310,HS-grad,9,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,207202,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,132601,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,?,139183,10th,6,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,160785,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,117849,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n2,Local-gov,225605,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,190290,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,164799,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Federal-gov,21876,Some-college,10,Divorced,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,160785,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,272425,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,168538,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,204205,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,142287,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,169926,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,205024,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,374764,Bachelors,13,Widowed,Exec-managerial,Unmarried,White,Male,0,0,0,United-States,<=50K\n0,Private,108779,Masters,14,Separated,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,293136,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,227332,Assoc-voc,11,Widowed,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,246308,11th,7,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,Puerto-Rico,<=50K\n1,Private,51331,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,153078,Assoc-acdm,12,Never-married,Craft-repair,Own-child,Other,Male,0,0,3,United-States,<=50K\n3,Private,169180,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,193451,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,305147,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,138892,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,402397,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,3,United-States,>50K\n1,Private,223267,HS-grad,9,Never-married,Exec-managerial,Other-relative,White,Male,0,0,3,United-States,<=50K\n0,Private,29250,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n3,?,203953,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,29696,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,315640,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,3,2,China,>50K\n2,Private,632613,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,Mexico,<=50K\n4,Private,282023,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,77760,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,148599,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,414994,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,339863,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,3,0,3,United-States,>50K\n1,Private,499249,HS-grad,9,Married-spouse-absent,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Guatemala,<=50K\n2,?,144354,9th,5,Separated,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,252058,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,99543,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,117963,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,194652,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,299705,Some-college,10,Never-married,Handlers-cleaners,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Federal-gov,27433,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,39986,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,135342,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,270142,Assoc-voc,11,Separated,Exec-managerial,Unmarried,Black,Female,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,118267,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,266043,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,35633,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,74568,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,214816,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,222971,5th-6th,3,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,Private,259425,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,212120,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,245880,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,54947,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-inc,79627,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n3,Private,151474,Bachelors,13,Never-married,Tech-support,Other-relative,White,Female,0,1,2,United-States,<=50K\n1,Private,132661,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,2,0,2,United-States,<=50K\n1,Private,161674,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,62346,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,227236,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,283033,11th,7,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,298249,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,251229,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,199949,9th,5,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n0,State-gov,305498,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,203836,5th-6th,3,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,79440,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,1,Japan,<=50K\n3,Local-gov,142719,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,119859,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,141410,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,202872,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,198813,HS-grad,9,Divorced,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Federal-gov,129707,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,445758,5th-6th,3,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,?,30246,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,173981,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,108506,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n1,Private,134886,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,181970,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,2,United-States,<=50K\n4,Self-emp-inc,282913,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Cuba,<=50K\n4,Local-gov,196013,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,348491,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,United-States,>50K\n3,Private,416164,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Other,Male,0,0,3,Mexico,<=50K\n0,Private,121037,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,103111,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,Canada,<=50K\n4,Self-emp-not-inc,147589,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,24008,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,123838,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n3,Self-emp-not-inc,175456,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,84774,HS-grad,9,Married-civ-spouse,Priv-house-serv,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,194590,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,1,United-States,<=50K\n1,Private,134566,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,211678,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,44822,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,144586,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,119156,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,371987,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,144125,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,31905,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n3,Self-emp-not-inc,121124,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,58126,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,318518,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,296509,7th-8th,4,Separated,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,473133,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,155434,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,99185,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,56648,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Local-gov,118481,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,321666,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,1,0,2,United-States,<=50K\n0,State-gov,119838,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,330695,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,58039,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,313022,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,178134,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,165309,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,216181,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,178745,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,111067,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,163788,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,295591,1st-4th,2,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Private,123075,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,78045,11th,7,Married-civ-spouse,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,255004,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,254221,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,Private,174714,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,450580,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,128230,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,192894,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,325390,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,20333,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,3,0,2,United-States,>50K\n1,Federal-gov,128714,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,170797,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,269186,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,127671,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,211840,Some-college,10,Separated,Sales,Unmarried,Black,Female,0,0,0,United-States,<=50K\n2,Private,163392,HS-grad,9,Never-married,Transport-moving,Other-relative,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n2,Private,201495,Bachelors,13,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,251854,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,Jamaica,<=50K\n2,Private,279297,HS-grad,9,Never-married,Sales,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,195462,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,170769,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,142443,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,182809,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,121441,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,275094,1st-4th,2,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,170263,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,172571,Some-college,10,Divorced,Craft-repair,Own-child,White,Male,0,0,3,Poland,<=50K\n1,Private,178615,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,279524,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,165201,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,323006,HS-grad,9,Widowed,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,235168,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,114844,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n3,Local-gov,216414,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,34378,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,3,United-States,<=50K\n3,State-gov,80914,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,73292,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,212165,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,52386,Some-college,10,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n1,Private,205649,Assoc-acdm,12,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,109638,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,200408,Assoc-acdm,12,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,187720,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,236180,Bachelors,13,Married-spouse-absent,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,118693,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,363130,HS-grad,9,Never-married,Other-service,Unmarried,Black,Male,0,0,0,United-States,<=50K\n2,Private,225544,Masters,14,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Poland,<=50K\n4,Federal-gov,243612,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,160786,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,234320,7th-8th,4,Never-married,Prof-specialty,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,314646,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,124971,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,209184,Bachelors,13,Married-civ-spouse,Sales,Husband,Other,Male,0,0,2,Puerto-Rico,<=50K\n2,State-gov,121838,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,265275,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,71417,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,45522,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,250135,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,3,United-States,<=50K\n0,Private,120283,12th,8,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,216972,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,116791,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,State-gov,26290,Assoc-voc,11,Widowed,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,Private,216134,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,143932,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,217120,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,State-gov,223944,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,185452,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,Canada,<=50K\n4,Local-gov,44273,HS-grad,9,Widowed,Transport-moving,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,178983,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,219288,7th-8th,4,Widowed,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,349190,Assoc-acdm,12,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,158685,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,4,2,United-States,>50K\n2,Federal-gov,57924,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,270324,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,1,United-States,<=50K\n2,Private,33001,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,204021,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,Canada,<=50K\n1,Private,192506,Bachelors,13,Never-married,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n4,Private,372967,10th,6,Divorced,Adm-clerical,Other-relative,White,Female,0,0,4,Germany,<=50K\n1,Private,273929,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,<=50K\n2,Private,195821,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,56179,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,1,0,3,United-States,<=50K\n0,?,127003,9th,5,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,124090,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,199600,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,255847,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,United-States,>50K\n3,Self-emp-not-inc,218311,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,167336,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,59938,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,263728,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,278230,Some-college,10,Divorced,Farming-fishing,Unmarried,White,Female,4,0,1,United-States,>50K\n4,?,180603,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,43910,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,190139,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,109001,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,159931,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,194987,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Local-gov,87310,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,133937,Masters,14,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,207064,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,36011,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,168294,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n3,Local-gov,194895,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,>50K\n4,Self-emp-not-inc,49884,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,27305,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,229977,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,64520,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,?,134886,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,305379,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,202284,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,99185,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,159662,HS-grad,9,Married-civ-spouse,Sales,Own-child,White,Male,0,0,1,United-States,>50K\n4,Private,197865,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,175149,HS-grad,9,Divorced,Transport-moving,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,349633,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,135293,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,1,0,2,?,<=50K\n0,Private,242893,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,218667,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,144811,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,146091,Doctorate,16,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,2,United-States,>50K\n0,Private,206861,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,?,<=50K\n4,Self-emp-not-inc,226215,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,114447,Assoc-voc,11,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,124187,11th,7,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n3,Private,147954,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,1,0,2,United-States,<=50K\n1,Self-emp-inc,64379,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,156501,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,207668,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n4,?,161279,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,225707,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Cuba,>50K\n2,Local-gov,115603,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,506329,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,>50K\n4,Private,275034,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,1,United-States,<=50K\n4,?,172637,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,56483,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,144778,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,33213,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,?,>50K\n2,Local-gov,297248,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,2,United-States,>50K\n0,Private,137042,10th,6,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,33308,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,158420,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,Iran,<=50K\n0,Private,41763,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,?,220640,Bachelors,13,Divorced,?,Other-relative,Other,Female,0,0,0,United-States,<=50K\n1,Private,149734,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,3,United-States,<=50K\n0,?,262245,Assoc-voc,11,Never-married,?,Own-child,White,Female,1,0,2,United-States,<=50K\n0,Private,349691,Some-college,10,Never-married,Sales,Other-relative,Black,Female,0,0,2,United-States,<=50K\n3,Private,185385,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,174463,Assoc-voc,11,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,236068,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n4,?,445168,Bachelors,13,Widowed,?,Not-in-family,Amer-Indian-Eskimo,Female,0,0,3,United-States,<=50K\n0,Private,91334,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,33895,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,214816,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,229773,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,166386,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,0,0,1,Taiwan,<=50K\n2,Private,266135,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,300379,12th,8,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Federal-gov,392502,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,73809,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,193720,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,316183,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,162944,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,186888,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,United-States,>50K\n1,?,330132,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,192017,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n0,State-gov,161978,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,202930,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Local-gov,323309,7th-8th,4,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,197332,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,204033,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,?,<=50K\n0,Private,271274,11th,7,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,174242,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,209483,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,99146,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,2,3,United-States,>50K\n3,Self-emp-not-inc,102346,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,181666,Assoc-acdm,12,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,207367,Some-college,10,Married-spouse-absent,Other-service,Not-in-family,White,Female,0,0,2,Cuba,<=50K\n1,State-gov,82622,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,202296,Assoc-voc,11,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,142182,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,94342,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,41493,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,Canada,<=50K\n0,Private,181712,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,164607,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,41496,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,143098,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n2,Local-gov,196529,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,157332,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,154935,Assoc-acdm,12,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,223231,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,2,Mexico,<=50K\n1,?,253860,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,362589,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,94880,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,309580,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,130389,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,Scotland,<=50K\n0,Private,349365,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,376936,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,179557,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,105577,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,224207,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,47907,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,191283,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,20953,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n0,State-gov,186569,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,43221,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,161141,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,203003,HS-grad,9,Never-married,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,141758,9th,5,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,113322,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,343847,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,214068,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,116632,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,240160,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,516337,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Self-emp-inc,284651,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,141420,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,42750,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,165278,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,167265,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,139907,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,236415,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,312966,9th,5,Separated,Handlers-cleaners,Other-relative,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,118941,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,198068,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,373952,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,236111,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,0,3,United-States,>50K\n4,Private,157778,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,143604,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,319831,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,132728,Masters,14,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,137606,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,<=50K\n1,?,61343,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,268234,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,100135,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,2,1,United-States,<=50K\n3,Self-emp-not-inc,34973,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,323790,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Private,319733,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Poland,>50K\n0,?,180339,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,125591,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,60772,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,74680,Masters,14,Divorced,Adm-clerical,Not-in-family,White,Male,0,3,3,United-States,<=50K\n1,Self-emp-not-inc,141185,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,204668,Assoc-voc,11,Separated,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,273792,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,70037,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,4,3,?,>50K\n2,Private,343068,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,177907,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,144063,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,257574,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,67065,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,183356,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,152940,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,227128,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,45607,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,155489,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,230704,HS-grad,9,Never-married,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,?,267955,9th,5,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,165115,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,49923,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,272240,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,255476,7th-8th,4,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,Mexico,<=50K\n4,Private,194290,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,145548,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,175262,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,37306,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,137547,Bachelors,13,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n3,Private,276515,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,Cuba,<=50K\n0,Private,174626,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,215310,11th,7,Divorced,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,332355,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,204057,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,391591,12th,8,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,169092,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,>50K\n1,Private,230743,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,190963,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,204840,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,3,Mexico,<=50K\n0,Private,169853,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,212091,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,202822,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,226989,Some-college,10,Married-spouse-absent,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,140011,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,432376,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,Germany,<=50K\n1,Private,90273,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,?,>50K\n0,Private,224424,Bachelors,13,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,168943,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,571853,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,156464,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,108542,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,194325,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,114797,Bachelors,13,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,40135,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,204756,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,228190,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,0,United-States,<=50K\n1,Private,163392,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,>50K\n3,Private,138845,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,169853,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Never-worked,206359,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,224097,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,160786,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,190044,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,145290,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,120268,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,4,United-States,<=50K\n0,Private,327434,10th,6,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-inc,218302,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,1184622,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,1,United-States,<=50K\n4,Local-gov,227796,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,206343,HS-grad,9,Never-married,Protective-serv,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,36851,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,148550,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,157079,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,?,>50K\n1,Federal-gov,142470,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,86750,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,361631,Masters,14,Separated,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Private,163229,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,179594,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,254773,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,3,United-States,>50K\n1,Private,58065,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,205428,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,41183,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,?,308064,HS-grad,9,Never-married,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,173924,9th,5,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Puerto-Rico,>50K\n0,State-gov,142547,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,119704,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,275364,Bachelors,13,Divorced,Tech-support,Unmarried,White,Male,2,0,2,Germany,>50K\n2,Self-emp-not-inc,207392,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,147215,12th,8,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,101562,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,3,United-States,<=50K\n4,Private,216413,Bachelors,13,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,State-gov,188986,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Female,0,1,4,United-States,<=50K\n2,State-gov,52849,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,304710,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,0,Vietnam,<=50K\n0,Private,265657,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,258298,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,4,2,United-States,>50K\n1,Private,360814,9th,5,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,53260,HS-grad,9,Divorced,Other-service,Unmarried,Other,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,127315,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,233777,HS-grad,9,Never-married,Transport-moving,Other-relative,White,Male,0,0,2,?,<=50K\n1,Local-gov,197530,Masters,14,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,340940,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,88432,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,183810,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,51744,Masters,14,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,>50K\n1,Private,175614,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n1,Self-emp-not-inc,235237,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,>50K\n4,Private,227266,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,146499,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,1,2,United-States,<=50K\n4,Local-gov,337064,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,141003,Assoc-voc,11,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,117791,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,172846,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,73514,HS-grad,9,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n4,Private,211075,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,197816,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,4,United-States,<=50K\n4,Private,43221,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,183780,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,<=50K\n2,Private,26781,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,271550,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,250157,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,State-gov,913447,Some-college,10,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,153078,Bachelors,13,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n1,Private,181091,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,231491,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,State-gov,95423,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,234663,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,283602,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,4,0,2,United-States,>50K\n3,Private,328669,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,143741,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,83508,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Female,1,0,4,United-States,<=50K\n4,State-gov,81954,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,261375,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,310045,9th,5,Married-spouse-absent,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Female,0,0,1,China,<=50K\n2,Private,316211,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,88564,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,61299,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,113364,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,476573,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,267107,5th-6th,3,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,Italy,<=50K\n1,Private,48123,12th,8,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,3,United-States,<=50K\n1,Private,214635,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,<=50K\n3,Private,115585,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,194141,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n0,?,23233,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,89991,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,101709,HS-grad,9,Never-married,Transport-moving,Own-child,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n0,Private,237455,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,206492,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,?,<=50K\n4,Private,28729,11th,7,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,153475,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,El-Salvador,<=50K\n2,Private,275517,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,128002,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,175485,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,189664,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,209808,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,176992,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,154669,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,191271,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,375482,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,102953,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,169182,10th,6,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Columbia,<=50K\n3,Private,184005,HS-grad,9,Divorced,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Female,1,0,2,United-States,<=50K\n3,Self-emp-inc,30751,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,145477,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,91964,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,49249,Some-college,10,Divorced,Other-service,Unmarried,White,Male,0,0,4,United-States,<=50K\n0,Private,218956,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,241306,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,251572,HS-grad,9,Widowed,?,Not-in-family,White,Male,0,0,1,Poland,<=50K\n0,Private,319842,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,332401,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n3,Local-gov,182388,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,205939,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,203914,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,State-gov,156294,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,254211,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,151504,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n4,Private,85548,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,4,0,0,United-States,>50K\n0,Self-emp-not-inc,30800,10th,6,Married-spouse-absent,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,Private,131230,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,61850,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,227800,7th-8th,4,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,133454,10th,6,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,104094,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,105422,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,142182,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,336643,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n4,Self-emp-inc,200577,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,208703,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,0,2,Japan,<=50K\n3,?,193895,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,2,England,<=50K\n0,Private,272428,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,2,0,2,United-States,<=50K\n1,Private,56701,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n1,Private,288592,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,266439,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,276868,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,131435,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,175127,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n0,Private,277444,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,63296,Masters,14,Divorced,Prof-specialty,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,96337,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,221955,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,Mexico,<=50K\n2,Private,197923,Bachelors,13,Never-married,Adm-clerical,Unmarried,Black,Female,1,0,2,United-States,<=50K\n1,Private,632593,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,205970,Some-college,10,Never-married,Craft-repair,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,139730,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,201901,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,230224,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,113464,1st-4th,2,Never-married,Other-service,Own-child,Other,Male,0,0,1,Dominican-Republic,<=50K\n3,Private,94461,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,271379,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,231738,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,England,<=50K\n1,Local-gov,198183,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,State-gov,140764,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,183479,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,165767,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,139364,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,227491,HS-grad,9,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n0,Private,222254,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,193494,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,29261,Assoc-acdm,12,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,174368,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,108196,10th,6,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,110622,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n0,?,201680,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,130277,5th-6th,3,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,98130,Bachelors,13,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,235521,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,595000,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,>50K\n1,Self-emp-not-inc,349148,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,117583,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,3,0,3,United-States,>50K\n1,Private,164583,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,340091,Some-college,10,Separated,Other-service,Unmarried,White,Female,0,0,4,United-States,<=50K\n0,Private,49092,Bachelors,13,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,186884,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,1,United-States,<=50K\n2,State-gov,167265,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,34104,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n0,Self-emp-inc,265116,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,128378,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,?,<=50K\n1,Private,158416,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,169878,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,296728,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,342458,Assoc-acdm,12,Divorced,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,38771,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,269300,Bachelors,13,Never-married,Other-service,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n2,Private,111483,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n4,?,199114,10th,6,Separated,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,33863,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,132874,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,277024,HS-grad,9,Separated,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,112160,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,703067,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,127264,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,257200,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,57206,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,201319,Some-college,10,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,114079,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,230979,Some-college,10,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,292472,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Cambodia,>50K\n4,?,286732,7th-8th,4,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,134444,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,172403,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,191357,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,279288,10th,6,Never-married,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n4,Private,389254,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,303867,HS-grad,9,Separated,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,164113,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,3,0,2,United-States,>50K\n2,Private,111499,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,266084,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,61580,Some-college,10,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,231348,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,164748,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,205337,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,54566,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,34419,Bachelors,13,Never-married,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,116442,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,290740,Assoc-acdm,12,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,255582,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,112517,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,169397,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,172664,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,329005,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,123253,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,81865,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,173314,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,0,3,United-States,<=50K\n1,Private,34572,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,159028,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,149184,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,363134,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,308709,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,257295,Some-college,10,Never-married,Sales,Other-relative,Asian-Pac-Islander,Male,0,4,2,South,<=50K\n1,Private,168479,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,142501,HS-grad,9,Never-married,Other-service,Other-relative,Black,Female,0,0,0,United-States,<=50K\n4,Private,338345,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,177675,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,262617,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,1,0,2,United-States,<=50K\n0,Private,200997,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,176683,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,376072,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,177675,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,348430,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,320451,Bachelors,13,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n0,Private,38151,11th,7,Never-married,Other-service,Other-relative,White,Male,0,0,2,Philippines,<=50K\n3,Local-gov,123382,Assoc-voc,11,Separated,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,151029,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,484475,11th,7,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,329792,7th-8th,4,Divorced,Transport-moving,Unmarried,White,Male,0,0,4,United-States,<=50K\n1,Private,148903,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,301614,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,176319,HS-grad,9,Married-civ-spouse,Sales,Own-child,White,Female,0,0,2,United-States,>50K\n3,State-gov,53197,Doctorate,16,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,291407,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,204527,Masters,14,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,476391,Some-college,10,Divorced,Farming-fishing,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,224964,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,306225,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Poland,<=50K\n0,Private,292023,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,94041,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,Ireland,<=50K\n3,Self-emp-inc,187563,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,176101,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,1,0,3,United-States,<=50K\n2,Private,749105,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,?,230020,5th-6th,3,Married-civ-spouse,?,Husband,Other,Male,0,0,2,United-States,<=50K\n0,Private,216070,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,Amer-Indian-Eskimo,Female,0,0,3,United-States,>50K\n3,Self-emp-not-inc,105010,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,198203,Some-college,10,Married-spouse-absent,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Local-gov,215419,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,120460,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,199316,Some-college,10,Married-civ-spouse,Craft-repair,Other-relative,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n3,Private,146919,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,174744,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,189564,Masters,14,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,249957,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,146574,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,156417,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Male,0,0,0,United-States,<=50K\n2,Private,236110,5th-6th,3,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n0,Private,63363,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,190107,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,126569,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,176756,12th,8,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,115161,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,138892,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,256864,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,265083,10th,6,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,249948,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,31141,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,164190,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,?,<=50K\n2,State-gov,67544,Masters,14,Divorced,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,174789,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,199753,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,122246,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Female,3,0,2,United-States,>50K\n4,?,188166,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,96586,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,189590,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,140590,Some-college,10,Never-married,Sales,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,255702,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,260782,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,169926,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,2,United-States,>50K\n2,State-gov,151322,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,192869,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,93604,7th-8th,4,Never-married,Craft-repair,Own-child,White,Male,0,2,1,United-States,<=50K\n1,Private,86958,9th,5,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,228723,HS-grad,9,Divorced,Craft-repair,Not-in-family,Other,Male,0,0,2,?,>50K\n1,Private,192644,HS-grad,9,Separated,Handlers-cleaners,Unmarried,White,Male,0,0,1,Puerto-Rico,<=50K\n4,Private,284080,1st-4th,2,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,43269,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,190040,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,306108,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,220148,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,381645,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,216361,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,0,United-States,<=50K\n1,Private,213722,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,112271,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,208277,Some-college,10,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,>50K\n2,State-gov,352628,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,129620,10th,6,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,249550,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,178749,Masters,14,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,173542,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,167670,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,81578,9th,5,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,160662,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,163322,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Female,0,0,1,?,<=50K\n0,Private,152189,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,106176,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n4,State-gov,159191,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,1,2,United-States,<=50K\n4,?,250263,Some-college,10,Married-civ-spouse,?,Husband,White,Male,1,0,1,United-States,<=50K\n2,Private,78410,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,131379,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,166929,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,380357,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,79190,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,342164,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,182616,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,339473,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,381153,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,300816,Bachelors,13,Never-married,Adm-clerical,Unmarried,White,Male,0,0,0,United-States,<=50K\n3,Private,240988,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n0,Private,149224,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,168216,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,286487,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,305597,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,109766,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Self-emp-not-inc,188798,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,240170,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Germany,<=50K\n1,Private,459465,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,162506,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,145441,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Federal-gov,129573,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,?,>50K\n2,Private,27444,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,195258,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,55272,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,164526,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,4,2,United-States,>50K\n3,Private,27802,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,165289,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,274657,5th-6th,3,Never-married,Other-service,Not-in-family,White,Male,0,0,3,Guatemala,<=50K\n0,Private,317175,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,163237,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,170408,Assoc-voc,11,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,?,55950,Bachelors,13,Never-married,?,Own-child,Black,Female,0,0,2,Germany,<=50K\n2,Private,76625,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,366066,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,349368,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,286824,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,373263,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,161978,HS-grad,9,Separated,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,543922,Masters,14,Divorced,Transport-moving,Not-in-family,White,Male,4,0,3,United-States,>50K\n3,Local-gov,109089,Prof-school,15,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,110151,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,34110,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,118506,Bachelors,13,Married-civ-spouse,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,117789,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,353881,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,200471,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Portugal,<=50K\n0,Private,258517,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,190367,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,174704,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,179413,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,329530,9th,5,Never-married,Priv-house-serv,Own-child,White,Male,0,0,2,Mexico,<=50K\n1,Private,273818,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Mexico,<=50K\n3,Private,256522,1st-4th,2,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,Puerto-Rico,<=50K\n2,Private,196001,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,282660,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,72630,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,50295,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,203240,9th,5,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,172618,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,202168,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,176839,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,176140,HS-grad,9,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,>50K\n4,Private,39952,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,292465,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,161285,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,355320,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Canada,>50K\n4,Private,182460,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,69345,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,3,United-States,>50K\n4,Self-emp-not-inc,102058,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,165804,Some-college,10,Never-married,Adm-clerical,Own-child,Other,Female,0,0,2,United-States,<=50K\n3,Private,318259,Assoc-voc,11,Divorced,Tech-support,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,117606,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,170718,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,413297,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,190457,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,88278,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Local-gov,217296,HS-grad,9,Married-civ-spouse,Transport-moving,Wife,White,Female,2,0,1,United-States,<=50K\n4,?,97231,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,123429,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,420282,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,498325,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,248533,Some-college,10,Never-married,Sales,Other-relative,Black,Female,0,0,2,United-States,<=50K\n3,Private,137354,Masters,14,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n2,Private,272910,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,206054,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,92141,Assoc-acdm,12,Widowed,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,163199,Some-college,10,Divorced,Tech-support,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,195860,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,115717,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,2,United-States,<=50K\n0,Private,120029,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Private,221762,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,342164,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,176356,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,133239,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,169101,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,159442,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,174461,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,361280,10th,6,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n3,State-gov,447579,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,England,<=50K\n1,?,308995,Some-college,10,Divorced,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,248448,7th-8th,4,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,161141,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,212465,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,170871,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Local-gov,233865,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,163052,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,348690,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,34845,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Germany,>50K\n0,Private,206861,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,349230,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,130840,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,415354,10th,6,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,132191,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,202466,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,224421,Some-college,10,Divorced,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,236804,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,107658,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,102771,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,221403,12th,8,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n4,?,211574,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,52645,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,276310,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,134613,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,215479,HS-grad,9,Divorced,Transport-moving,Not-in-family,Black,Male,0,0,0,Haiti,<=50K\n3,Private,266529,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,265807,Some-college,10,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,67716,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,178951,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,241126,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,176544,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,169180,Some-college,10,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,282461,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,157069,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,99357,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,3,United-States,>50K\n2,Self-emp-not-inc,414991,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,?,<=50K\n4,Self-emp-inc,338316,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,59612,10th,6,Divorced,Farming-fishing,Unmarried,White,Male,0,0,4,United-States,<=50K\n0,Private,220426,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,115912,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,27032,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,170720,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,183162,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,192360,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n4,?,165694,Masters,14,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,128553,Some-college,10,Never-married,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,209423,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Cuba,<=50K\n2,Self-emp-not-inc,121510,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,93793,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,133602,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,391329,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,96359,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Greece,>50K\n0,Private,203894,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,196193,Masters,14,Married-spouse-absent,Prof-specialty,Other-relative,White,Male,0,0,3,?,<=50K\n0,Private,195994,1st-4th,2,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,Guatemala,<=50K\n0,Private,50879,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,186849,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,201127,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,110998,HS-grad,9,Never-married,Other-service,Other-relative,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,190466,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,1,0,2,United-States,<=50K\n4,Self-emp-not-inc,173935,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,>50K\n0,Private,167140,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,2,1,United-States,<=50K\n0,Private,110230,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,287658,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,224954,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n0,?,394820,Some-college,10,Separated,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,37618,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n4,Self-emp-not-inc,29306,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,37314,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,420749,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,482732,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,206215,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,101364,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Self-emp-inc,185369,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,216856,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,256019,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,348144,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,1,0,3,United-States,<=50K\n0,Private,190293,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,25932,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,176729,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,166961,11th,7,Separated,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,86373,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,320513,7th-8th,4,Married-spouse-absent,Craft-repair,Not-in-family,Black,Male,0,0,3,Dominican-Republic,<=50K\n1,State-gov,190290,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,111891,7th-8th,4,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,45796,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,108496,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Self-emp-not-inc,120539,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,>50K\n2,Self-emp-not-inc,164526,Masters,14,Never-married,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,323155,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,Mexico,<=50K\n1,Private,65389,HS-grad,9,Never-married,Other-service,Not-in-family,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n0,Private,414871,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,161607,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,224953,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,261382,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n4,Self-emp-not-inc,231818,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Greece,<=50K\n2,Self-emp-inc,184018,HS-grad,9,Divorced,Sales,Unmarried,White,Male,1,0,3,United-States,<=50K\n2,Self-emp-inc,133060,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,35032,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,304212,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,50442,9th,5,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,146091,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,267431,Bachelors,13,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,121240,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,192572,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,211028,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,346122,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,202203,Bachelors,13,Never-married,Adm-clerical,Other-relative,White,Female,0,0,3,United-States,<=50K\n0,Private,159297,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n0,Private,310158,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,193246,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,200089,Some-college,10,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,38353,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,76280,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,243665,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,68872,HS-grad,9,Married-civ-spouse,Transport-moving,Wife,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n1,Private,103596,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,88055,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,186203,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,257910,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,200227,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,124975,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,4,0,3,United-States,>50K\n1,Private,227669,Some-college,10,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,117210,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,Greece,<=50K\n0,Private,76144,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,98667,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Local-gov,155818,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,283760,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,281907,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,186183,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,202153,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,365683,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,Private,187538,10th,6,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,209432,HS-grad,9,Separated,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,126950,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,110028,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,104660,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,437281,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,259643,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,2,0,2,United-States,<=50K\n0,Private,217961,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,2,1,United-States,<=50K\n0,?,134746,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,120539,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,25803,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,63596,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n0,Local-gov,325493,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,211239,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,206686,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,427965,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,218550,Some-college,10,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,4,0,0,United-States,>50K\n4,Private,163385,Some-college,10,Widowed,Sales,Not-in-family,White,Male,0,0,1,United-States,>50K\n3,Private,124993,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,107410,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,152373,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,161226,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,213799,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,204461,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,377798,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,116375,9th,5,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,210164,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n4,Self-emp-not-inc,258752,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,327435,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,301199,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,186221,11th,7,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,203924,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,192236,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,152035,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,201454,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,156580,Some-college,10,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,115851,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,106753,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n4,Private,359292,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,83003,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,78817,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,200967,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,<=50K\n2,State-gov,107164,Some-college,10,Separated,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,189674,HS-grad,9,Never-married,Priv-house-serv,Unmarried,Black,Female,0,0,1,?,<=50K\n1,Self-emp-not-inc,90614,HS-grad,9,Separated,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,323790,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,4,United-States,>50K\n2,Self-emp-not-inc,242552,12th,8,Divorced,Craft-repair,Other-relative,Black,Male,0,0,1,United-States,<=50K\n0,Private,90935,Assoc-voc,11,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,165667,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,Canada,>50K\n1,Private,162604,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,Black,Male,0,0,2,United-States,<=50K\n2,Private,205424,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,97411,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,Laos,<=50K\n2,Private,184857,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,165226,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,115784,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,368476,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,Mexico,<=50K\n1,Private,53063,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,134566,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,153471,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,107164,10th,6,Never-married,Transport-moving,Not-in-family,White,Male,0,4,3,United-States,>50K\n2,Private,180303,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,Japan,>50K\n2,Local-gov,236321,HS-grad,9,Divorced,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,141868,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,367655,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,203518,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,119558,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,108276,Bachelors,13,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,385452,10th,6,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,162003,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,349028,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,45114,Bachelors,13,Never-married,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,112797,9th,5,Divorced,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,183639,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,177121,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,239755,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,150361,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,293091,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,200089,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Mexico,>50K\n2,Private,91836,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,324960,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,84616,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,252930,10th,6,Divorced,Adm-clerical,Unmarried,Other,Female,0,0,2,United-States,<=50K\n3,Private,44000,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,3,United-States,>50K\n1,Private,154843,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,99307,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,1,0,3,United-States,>50K\n2,Private,182567,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,>50K\n1,Private,93206,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Private,100109,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,>50K\n3,Private,114927,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,121287,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,189916,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,157747,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,39232,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,133861,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,505980,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,183374,HS-grad,9,Widowed,?,Not-in-family,White,Female,1,0,0,United-States,<=50K\n4,Private,193216,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n2,Self-emp-not-inc,140752,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,549349,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,179008,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,190554,10th,6,Divorced,Exec-managerial,Own-child,White,Male,0,0,3,United-States,>50K\n3,Private,80924,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,319054,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,297094,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,170562,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,240738,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,297544,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,169905,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,149637,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,182526,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,158315,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,227232,Bachelors,13,Separated,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,96483,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,3,0,3,United-States,>50K\n2,Private,286970,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,223529,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,316261,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,0,United-States,>50K\n2,Private,170214,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,224361,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,124919,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,3,Japan,<=50K\n3,?,103654,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,306352,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Self-emp-not-inc,227858,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-inc,150533,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,144478,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,Poland,<=50K\n0,Private,254547,Some-college,10,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,1,Jamaica,<=50K\n3,Self-emp-not-inc,313243,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,149981,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,1,0,0,United-States,<=50K\n2,Private,125461,Bachelors,13,Never-married,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,306967,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,192976,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Private,192133,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,Greece,<=50K\n4,?,131608,HS-grad,9,Divorced,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Federal-gov,339388,Assoc-acdm,12,Divorced,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,203240,10th,6,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,83827,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,160440,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,108502,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,410913,HS-grad,9,Married-spouse-absent,Farming-fishing,Unmarried,Other,Male,0,0,2,Mexico,<=50K\n4,Private,193818,9th,5,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,163582,10th,6,Divorced,?,Unmarried,White,Female,0,0,0,?,<=50K\n2,Private,103789,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,34572,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,43408,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,105787,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,90693,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,285575,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n3,Local-gov,56482,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,3,0,3,United-States,>50K\n0,Private,496025,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,382764,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,259284,HS-grad,9,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,185385,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,286836,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,139145,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Local-gov,44246,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,169611,11th,7,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,133403,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,187327,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,180032,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,46561,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,86065,12th,8,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,256014,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,188403,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,396758,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,4,United-States,>50K\n0,Private,60485,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,271276,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n4,Private,229525,9th,5,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,34574,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,112432,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,105312,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,221396,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,304872,9th,5,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,319733,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,176012,9th,5,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,213750,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,248384,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,351187,HS-grad,9,Divorced,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,138179,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,0,2,2,United-States,<=50K\n4,Private,50223,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,117477,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,194360,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,118108,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Local-gov,90730,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n0,Self-emp-inc,38307,11th,7,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,116391,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,210496,10th,6,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,168475,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,174386,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,166744,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,375114,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,373469,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,339667,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,91711,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,82049,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,236242,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,140319,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,34080,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,204816,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,187124,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,72310,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Private,175127,12th,8,Married-civ-spouse,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,205707,Masters,14,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n2,Local-gov,236586,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,71792,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Private,87584,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,136878,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,287983,Bachelors,13,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Female,0,4,3,Philippines,<=50K\n2,Private,110607,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,109015,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,235071,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,88653,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,<=50K\n3,Private,332243,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,291547,5th-6th,3,Married-civ-spouse,?,Wife,Other,Female,0,0,2,Mexico,<=50K\n2,Private,45093,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Federal-gov,161337,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,211222,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,295117,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,England,>50K\n1,Private,206541,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,238415,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,29810,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,108023,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,114324,Assoc-voc,11,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,172281,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,3,United-States,<=50K\n4,Local-gov,197290,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,191177,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n4,Private,562558,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,79531,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,157881,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,204816,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,185695,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,167482,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,83748,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,0,0,4,South,<=50K\n1,Private,39232,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,236827,9th,5,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,154410,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,135308,Bachelors,13,Never-married,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,204042,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,308239,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,183884,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,98948,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,141642,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,162623,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,186934,Bachelors,13,Married-spouse-absent,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,179512,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,391192,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,87054,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,30008,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,113466,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,642830,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,182117,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,162432,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,242184,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,170850,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,3,United-States,<=50K\n4,Private,435022,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,120707,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,El-Salvador,>50K\n0,Private,170800,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,268575,HS-grad,9,Never-married,Craft-repair,Unmarried,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n1,Private,269354,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,?,<=50K\n2,Private,224232,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,?,153072,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,177368,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,163293,Prof-school,15,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,178530,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,183523,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,Iran,<=50K\n1,Private,207267,10th,6,Separated,Other-service,Unmarried,White,Female,1,0,1,United-States,<=50K\n4,State-gov,27037,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n1,Private,176711,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,163215,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,?,>50K\n1,Private,394727,10th,6,Never-married,Handlers-cleaners,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,195488,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,<=50K\n1,State-gov,443546,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,121023,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,51838,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,258888,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n2,State-gov,189385,Some-college,10,Separated,Exec-managerial,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,198146,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,337766,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,210525,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,185602,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,173804,11th,7,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,251243,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Self-emp-not-inc,415847,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,119793,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,181705,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,182360,HS-grad,9,Separated,Prof-specialty,Unmarried,Other,Female,0,0,3,Puerto-Rico,<=50K\n3,Private,61885,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,146520,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,323790,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,146268,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,287031,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,3,0,2,United-States,>50K\n1,Local-gov,292217,HS-grad,9,Divorced,Protective-serv,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,88126,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,143046,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,401623,Some-college,10,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,2,Jamaica,>50K\n2,Self-emp-not-inc,283122,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,3,3,United-States,>50K\n4,Self-emp-not-inc,155057,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,260254,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,152292,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,138594,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n1,Self-emp-not-inc,523095,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,175262,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n3,Private,323706,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,316470,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,163815,Masters,14,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,72208,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,74784,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,383518,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,4,0,2,United-States,>50K\n0,Self-emp-not-inc,266668,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,347519,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,336088,HS-grad,9,Divorced,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Female,0,0,3,United-States,<=50K\n2,Private,190350,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,204052,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,31362,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,155981,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,<=50K\n4,Private,195161,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Self-emp-inc,269583,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,26994,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,116539,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n3,Self-emp-not-inc,189933,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,101283,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,113598,Some-college,10,Separated,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n0,Private,188793,HS-grad,9,Married-civ-spouse,Sales,Husband,Other,Male,0,0,1,United-States,<=50K\n1,Private,109996,Assoc-acdm,12,Married-spouse-absent,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,195681,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,?,<=50K\n3,Private,436770,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,84253,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,383493,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,216867,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,Mexico,<=50K\n0,Private,401051,10th,6,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,83196,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,325596,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,187322,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,193949,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,133373,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,113324,HS-grad,9,Widowed,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,178818,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,152810,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,335997,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,436493,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,704108,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,150084,Some-college,10,Separated,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,341204,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,187336,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,204209,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,206066,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,63509,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,391121,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,56026,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,60981,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,228255,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,86745,Bachelors,13,Married-civ-spouse,Prof-specialty,Other-relative,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,Private,234327,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,59948,9th,5,Never-married,Adm-clerical,Unmarried,Black,Female,1,0,0,United-States,<=50K\n1,Private,137814,Some-college,10,Divorced,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,167692,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,245090,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Self-emp-not-inc,256963,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,160033,Some-college,10,Never-married,Protective-serv,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,289430,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,305053,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n4,Self-emp-not-inc,172370,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,320510,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,171355,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,65027,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,215190,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,?,149385,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,169324,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,138938,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,557082,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,273287,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,Jamaica,<=50K\n1,Self-emp-not-inc,77209,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,317153,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,95469,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,302859,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,333651,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,177596,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,157240,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,1,Iran,>50K\n0,Private,184779,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,138358,Some-college,10,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Private,176285,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,102180,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,209507,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,229741,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,324546,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,337195,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n4,State-gov,194068,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,250647,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,477106,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,104329,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,224566,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,169841,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,42563,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,31368,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,132755,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,279129,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,?,86143,HS-grad,9,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,State-gov,44172,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,State-gov,93076,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,146653,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,221366,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,Germany,<=50K\n2,Private,189404,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,White,Male,0,0,1,?,<=50K\n1,Private,172304,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,116666,Some-college,10,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,0,India,<=50K\n2,Self-emp-not-inc,64112,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,55718,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,126675,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,102112,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,226505,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,211527,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,175069,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,Yugoslavia,<=50K\n0,Private,25249,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,73411,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,207185,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,Puerto-Rico,>50K\n4,Private,127139,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,41809,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,297449,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n3,Private,141483,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,117227,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,377401,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,4,Canada,>50K\n1,Local-gov,167063,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,253759,Some-college,10,Married-civ-spouse,Tech-support,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,183096,Some-college,10,Divorced,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,269654,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,293076,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,34104,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,80057,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Germany,>50K\n2,Self-emp-inc,369781,7th-8th,4,Divorced,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n0,Private,223811,Assoc-voc,11,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,163053,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,189461,HS-grad,9,Never-married,Sales,Other-relative,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,145166,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,86310,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,263224,11th,7,Never-married,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,280362,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,301031,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,74966,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,254493,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,204241,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,225024,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,148222,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,State-gov,113868,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,0,United-States,>50K\n2,Private,132633,HS-grad,9,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,?,<=50K\n2,Private,44780,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,86373,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n4,Local-gov,176753,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,164707,Assoc-acdm,12,Never-married,Exec-managerial,Unmarried,White,Female,1,0,3,?,<=50K\n3,Local-gov,370733,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,216851,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,137951,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,185279,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,159724,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,103233,Bachelors,13,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,63509,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,174353,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,168109,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,159724,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,105010,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,3,0,United-States,<=50K\n1,Private,179112,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Male,0,0,2,?,<=50K\n3,Private,364913,11th,7,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,155664,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,230568,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,86492,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,71305,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,189933,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n3,Self-emp-inc,191978,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n1,Private,38948,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,139127,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,301568,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,149044,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,3,3,China,<=50K\n2,Private,197344,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,32244,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,1,0,1,United-States,<=50K\n2,Self-emp-not-inc,315406,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,State-gov,47170,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Amer-Indian-Eskimo,Female,0,0,3,United-States,>50K\n1,State-gov,208785,Some-college,10,Separated,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,196338,9th,5,Separated,Priv-house-serv,Unmarried,White,Female,0,0,0,Mexico,<=50K\n1,Private,269243,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,215115,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,2,?,<=50K\n0,Private,117767,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,176101,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,138283,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,132320,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,471452,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,147653,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,4,United-States,<=50K\n0,Private,49179,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,174921,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,95997,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n2,Private,247245,9th,5,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,67072,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,?,95329,Some-college,10,Divorced,?,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,107882,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,241825,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,79443,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,233059,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,226980,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,181087,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,305597,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,311671,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,129879,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,4,0,2,United-States,>50K\n2,Private,83375,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,115824,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,1,2,United-States,<=50K\n2,Private,141657,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,50276,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,177216,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,2,2,Haiti,<=50K\n2,Private,228057,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,Puerto-Rico,<=50K\n2,Private,222848,10th,6,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,<=50K\n4,Private,121111,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,Greece,<=50K\n2,Private,298885,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,149909,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,387430,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,121972,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,280167,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n1,State-gov,191355,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,112115,Some-college,10,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,?,104094,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,211032,Preschool,1,Married-civ-spouse,Farming-fishing,Other-relative,White,Male,4,0,1,Mexico,<=50K\n3,Private,199307,Some-college,10,Divorced,Craft-repair,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,205175,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,257750,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,191260,11th,7,Never-married,Other-service,Own-child,White,Male,1,0,0,United-States,<=50K\n1,Private,342730,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,249983,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,161508,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,338376,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,334308,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,133471,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,129177,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,178811,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,178537,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,235535,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,298155,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,145114,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,194096,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,State-gov,191779,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,159732,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,170230,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n2,Private,104719,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,163083,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,403552,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n4,Private,218009,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,179313,10th,6,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,51961,12th,8,Never-married,Sales,Other-relative,Black,Male,0,0,3,United-States,<=50K\n4,Private,426001,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,0,Puerto-Rico,<=50K\n4,Local-gov,176493,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,124068,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,108510,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,181528,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,173754,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,169699,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,126849,10th,6,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n1,Private,204470,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,116367,Some-college,10,Divorced,Adm-clerical,Other-relative,White,Female,2,0,2,United-States,<=50K\n0,Private,117363,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,106297,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,108933,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,190143,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,246677,HS-grad,9,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,175360,10th,6,Never-married,Prof-specialty,Not-in-family,White,Male,0,4,4,United-States,>50K\n2,Local-gov,210259,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,166304,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,303051,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,49308,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,192262,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,192349,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n2,Self-emp-not-inc,48063,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,170214,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,51048,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,246562,5th-6th,3,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Mexico,>50K\n4,Local-gov,215175,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,114967,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,464536,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,451996,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,138852,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,353012,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,321822,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n3,Self-emp-not-inc,324506,HS-grad,9,Widowed,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,3,South,<=50K\n2,Private,162256,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,356689,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,260199,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,103605,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,316211,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,308691,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,194404,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,334427,10th,6,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,213226,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,342824,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,1,0,2,United-States,<=50K\n0,Private,33105,Some-college,10,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,147638,Bachelors,13,Separated,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n0,Private,315643,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,106257,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,342768,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,108960,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,168071,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,136935,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,188774,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,280344,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,202496,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,134768,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,175686,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,194748,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,61307,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,165001,Masters,14,Divorced,Exec-managerial,Unmarried,White,Male,4,0,3,United-States,>50K\n1,Private,325658,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,201844,HS-grad,9,Separated,?,Unmarried,White,Female,0,0,2,Mexico,<=50K\n0,Private,505980,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,185336,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,362795,Masters,14,Divorced,Prof-specialty,Unmarried,White,Male,4,0,4,Mexico,>50K\n1,Private,126829,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,264600,10th,6,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,82743,Assoc-acdm,12,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,Iran,<=50K\n4,Self-emp-not-inc,125178,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,128487,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,321758,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,128220,7th-8th,4,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,176814,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Canada,<=50K\n1,Private,188069,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,3,?,>50K\n0,State-gov,156423,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,169905,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,?,157289,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,176972,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,171424,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,4,1,United-States,<=50K\n1,Private,91811,HS-grad,9,Separated,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,203924,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,Private,177484,11th,7,Married-civ-spouse,Other-service,Husband,Black,Male,0,2,2,United-States,<=50K\n0,?,454614,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,242108,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,1,0,0,United-States,<=50K\n4,Private,132972,9th,5,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,157947,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,177482,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,246891,Some-college,10,Widowed,Sales,Unmarried,White,Male,0,0,3,United-States,>50K\n1,State-gov,158834,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,?,203834,Bachelors,13,Never-married,?,Not-in-family,Asian-Pac-Islander,Female,0,0,3,Taiwan,<=50K\n1,Private,110442,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,240676,Some-college,10,Divorced,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,192939,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,260696,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,55363,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,144949,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,116878,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n1,Local-gov,357954,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,?,170038,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,190290,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Italy,<=50K\n1,State-gov,203279,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,1,0,3,India,<=50K\n1,Private,167761,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,138845,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,144844,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,?,161930,HS-grad,9,Never-married,?,Own-child,Black,Female,0,1,1,United-States,<=50K\n1,Private,55743,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,117721,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-not-inc,116385,11th,7,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,301867,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,238913,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,123983,Some-college,10,Married-civ-spouse,Sales,Own-child,Asian-Pac-Islander,Male,0,0,4,South,<=50K\n1,Private,165510,Bachelors,13,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,183513,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,119281,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,152629,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,110171,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,211440,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,359259,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,125796,11th,7,Separated,Other-service,Not-in-family,Black,Female,0,0,2,Jamaica,<=50K\n1,Private,39609,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,111567,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,Germany,>50K\n0,Private,44064,Some-college,10,Separated,Other-service,Not-in-family,White,Male,0,4,2,United-States,>50K\n1,Self-emp-not-inc,120066,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n2,Private,132633,11th,7,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,1,Guatemala,<=50K\n2,Private,192702,Masters,14,Never-married,Craft-repair,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,166813,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,40444,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,290504,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,178505,Some-college,10,Never-married,Exec-managerial,Other-relative,White,Female,0,1,2,United-States,<=50K\n0,Private,175370,Some-college,10,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,72931,7th-8th,4,Married-spouse-absent,Adm-clerical,Not-in-family,White,Male,0,0,0,Italy,>50K\n1,?,234542,Assoc-voc,11,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,284021,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,277974,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,111275,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,191776,Masters,14,Divorced,Sales,Unmarried,White,Female,4,0,2,United-States,>50K\n1,Private,125527,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,38294,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,1,0,2,United-States,<=50K\n2,Private,313022,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,2,0,2,United-States,>50K\n2,Private,179668,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,198660,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,216116,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,Black,Female,0,0,2,Jamaica,<=50K\n4,Private,200922,7th-8th,4,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,153372,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,406603,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,0,Iran,<=50K\n0,Local-gov,248344,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n3,Private,240629,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Italy,>50K\n2,Private,314310,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,259785,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,127111,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,178272,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,75134,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,195985,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,221955,9th,5,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,177675,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,182828,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,270889,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,183096,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,336951,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,State-gov,295589,Some-college,10,Separated,Adm-clerical,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,289980,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,Mexico,<=50K\n4,Self-emp-inc,70720,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n3,Private,163352,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,190776,Assoc-acdm,12,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,313986,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,473748,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,163003,HS-grad,9,Never-married,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n1,Private,183061,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n3,Private,123584,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,120910,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,227554,Some-college,10,Married-spouse-absent,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n4,Private,182677,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,2,0,2,South,<=50K\n3,Private,214955,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,209768,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,258120,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,Jamaica,<=50K\n3,Private,110015,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Greece,<=50K\n3,Private,152652,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,Federal-gov,43206,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,3,United-States,>50K\n1,Self-emp-not-inc,114639,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,221172,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n0,?,128538,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,131615,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,353824,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,178417,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,178644,HS-grad,9,Widowed,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,271665,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,223732,Some-college,10,Separated,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,169003,12th,8,Never-married,Adm-clerical,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,State-gov,338816,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,4,United-States,>50K\n1,Private,506858,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,265628,Assoc-voc,11,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,173495,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,177413,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,31670,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,154451,11th,7,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,265535,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,Jamaica,>50K\n1,Private,118941,Some-college,10,Divorced,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,214617,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,265097,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,276087,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,<=50K\n2,Private,124692,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,306784,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,434102,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,387641,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,181824,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,1,United-States,>50K\n2,Local-gov,177907,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,2,2,United-States,>50K\n4,Private,87329,11th,7,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,263130,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,262882,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,37546,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,1,United-States,>50K\n0,Private,27433,11th,7,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,393945,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,173927,Assoc-voc,11,Never-married,Prof-specialty,Own-child,Other,Female,0,0,3,Jamaica,<=50K\n2,Private,343403,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,111128,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,193882,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,310864,Bachelors,13,Never-married,Tech-support,Not-in-family,Black,Male,0,0,2,?,<=50K\n2,Private,128354,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,113364,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,198559,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,136913,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,115488,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,154227,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,279667,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,281030,HS-grad,9,Never-married,Sales,Unmarried,White,Male,0,0,4,United-States,<=50K\n0,Private,283945,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,454989,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,391349,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,166704,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,151835,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,199085,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,61487,HS-grad,9,Never-married,Prof-specialty,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,120251,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,273230,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,358373,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,267891,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,234880,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,48358,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,96452,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,204751,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,375868,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,413373,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,537222,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,33975,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,162327,11th,7,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,182691,HS-grad,9,Divorced,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,300829,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,114508,9th,5,Separated,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,214627,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,129684,HS-grad,9,Divorced,Exec-managerial,Not-in-family,Black,Female,2,0,3,United-States,<=50K\n0,State-gov,120041,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,361138,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Private,76893,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,205424,Bachelors,13,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,>50K\n4,Private,176839,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,229148,12th,8,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,Jamaica,<=50K\n4,Self-emp-inc,154537,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,>50K\n3,Private,181901,HS-grad,9,Married-spouse-absent,Farming-fishing,Other-relative,White,Male,0,0,0,Mexico,<=50K\n0,Private,152004,11th,7,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,205188,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,30840,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n4,Private,66634,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,180220,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,291052,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,2,United-States,<=50K\n2,Self-emp-not-inc,99651,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,327723,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,32291,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,345122,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,127384,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,363296,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Black,Male,0,0,4,United-States,<=50K\n2,Local-gov,86551,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Private,30070,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,595000,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Black,Female,0,0,1,United-States,<=50K\n0,?,152328,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,?,177824,HS-grad,9,Separated,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,111483,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,199555,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,50018,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n2,Private,218490,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,49020,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,3,2,United-States,<=50K\n4,Private,213321,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,<=50K\n1,Private,159187,HS-grad,9,Divorced,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,83033,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,Germany,<=50K\n2,Self-emp-not-inc,31848,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,4,United-States,<=50K\n1,Self-emp-not-inc,24961,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,182117,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,146576,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,>50K\n0,Private,176690,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,122651,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,149650,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,Canada,<=50K\n1,Private,454508,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,Iran,<=50K\n3,Self-emp-not-inc,269068,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,3,Philippines,>50K\n2,Private,266530,HS-grad,9,Married-civ-spouse,Other-service,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,?,198542,Bachelors,13,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,133144,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,1,0,0,United-States,<=50K\n0,Private,217961,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,221661,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Mexico,<=50K\n2,Local-gov,60735,Bachelors,13,Divorced,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,121124,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,48588,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,48087,7th-8th,4,Divorced,Craft-repair,Not-in-family,White,Male,0,1,2,United-States,<=50K\n3,Self-emp-not-inc,240138,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,273010,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,1,0,2,United-States,<=50K\n2,Private,104196,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,230035,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,38918,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Germany,>50K\n4,?,205011,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,176079,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,180052,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,173005,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,378723,Some-college,10,Divorced,Adm-clerical,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,233624,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,192591,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,249860,11th,7,Divorced,Priv-house-serv,Unmarried,Black,Female,0,0,0,United-States,<=50K\n0,Private,247564,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,238912,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,190227,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,293287,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,180807,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,250217,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,4,United-States,<=50K\n0,Private,217418,Some-college,10,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n0,Local-gov,137510,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n4,State-gov,163047,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,577521,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,221533,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,255675,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,114079,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,155781,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,243762,11th,7,Separated,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,113062,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,217028,Masters,14,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,110723,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,191858,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,179423,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,339588,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,Peru,<=50K\n0,Private,206815,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,Peru,<=50K\n3,State-gov,103743,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,235683,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n4,?,207321,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,State-gov,197495,Some-college,10,Divorced,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Federal-gov,424012,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,178469,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,92886,10th,6,Widowed,Sales,Unmarried,White,Female,0,0,2,Canada,<=50K\n2,Self-emp-not-inc,214008,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,325732,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,28572,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,1,United-States,<=50K\n0,Private,118376,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,51799,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Local-gov,115488,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,190621,Some-college,10,Divorced,Exec-managerial,Other-relative,Black,Female,0,0,3,United-States,<=50K\n3,Private,193568,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,192878,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,264663,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,234731,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,308373,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,205644,HS-grad,9,Separated,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,321851,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,206399,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,124563,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,198211,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,130795,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,71269,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,319280,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,125933,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,107236,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,32732,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,284763,11th,7,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,112668,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,376483,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,402778,9th,5,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,36177,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,125489,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,304791,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,209205,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,?,112821,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n2,Local-gov,178100,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,70261,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,State-gov,186634,12th,8,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,32958,Some-college,10,Separated,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,254746,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,158746,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,140854,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,51506,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,189564,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n2,Federal-gov,325538,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n4,Private,213975,Assoc-voc,11,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,431426,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,199763,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n4,Private,161563,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,252024,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,43945,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,178487,HS-grad,9,Divorced,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,604506,HS-grad,9,Married-civ-spouse,Transport-moving,Own-child,White,Male,0,0,4,Mexico,<=50K\n2,Private,228157,Some-college,10,Never-married,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Laos,<=50K\n2,Private,199191,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,189775,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,171080,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,117310,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-inc,82049,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,126094,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,202516,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,246392,Assoc-acdm,12,Separated,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,?,69328,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,292803,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,286989,Preschool,1,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,190483,Some-college,10,Divorced,Sales,Own-child,White,Female,0,0,3,Iran,<=50K\n0,Private,235849,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,359766,7th-8th,4,Divorced,Handlers-cleaners,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,128016,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,360096,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,170154,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,337286,Masters,14,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,204322,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,<=50K\n4,Self-emp-not-inc,143833,12th,8,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,365613,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,Canada,<=50K\n1,Private,100135,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,180096,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,371827,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,Portugal,<=50K\n1,Private,61270,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Other,Female,0,0,2,Columbia,<=50K\n2,Federal-gov,564135,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,198759,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n3,State-gov,303462,Some-college,10,Separated,Protective-serv,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,193106,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,250201,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,200426,Assoc-voc,11,Married-spouse-absent,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,222654,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,53366,7th-8th,4,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,132222,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,100828,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,31264,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,202027,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,168906,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,255454,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,245524,12th,8,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,386040,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,35424,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,?,93655,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,152629,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,>50K\n3,Self-emp-not-inc,151159,10th,6,Married-spouse-absent,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,410240,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,138970,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,269722,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,223678,HS-grad,9,Never-married,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n3,State-gov,197184,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,143486,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,160625,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,140516,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,85341,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,108293,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,4,2,United-States,<=50K\n2,Self-emp-not-inc,192507,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,186932,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,223580,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,236861,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,327886,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,407618,9th,5,Divorced,?,Not-in-family,White,Female,1,0,2,United-States,<=50K\n4,Self-emp-inc,197060,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,229180,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,Cuba,<=50K\n0,Private,284317,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,73514,Some-college,10,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Female,0,0,3,Philippines,<=50K\n1,Private,47907,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,State-gov,134782,Assoc-acdm,12,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,118831,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n2,Private,299505,HS-grad,9,Separated,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,267161,Some-college,10,Married-civ-spouse,Tech-support,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,119177,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,327886,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,187730,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,109015,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,110015,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,Greece,<=50K\n0,Private,104146,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Local-gov,50442,Some-college,10,Never-married,Adm-clerical,Own-child,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n1,Private,57640,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,333664,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,224858,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,290641,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,?,191118,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,34402,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,1,3,United-States,<=50K\n1,Private,245378,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,179136,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,116788,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,129699,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,39606,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,England,>50K\n2,Self-emp-inc,95150,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,102479,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,199191,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,229636,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,53833,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,27997,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,124487,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,111363,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,107630,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,134287,Assoc-voc,11,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,283004,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,4,Thailand,<=50K\n0,Private,33616,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,121124,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,188189,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,106255,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,282830,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,243904,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,Honduras,<=50K\n4,Private,165017,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,White,Male,1,0,2,United-States,<=50K\n1,Private,131584,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,427781,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,334291,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,173224,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,87507,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n1,Private,187560,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,204497,10th,6,Divorced,Transport-moving,Not-in-family,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n4,Private,230545,7th-8th,4,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,Cuba,<=50K\n1,Private,118161,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,150499,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,96554,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,288551,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,104003,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,124963,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,198388,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,126204,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,91709,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,152109,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-not-inc,191954,7th-8th,4,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,108097,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,<=50K\n1,Local-gov,289991,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,92115,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,320277,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,33610,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Private,168276,10th,6,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,175127,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,254973,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,95336,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Private,346975,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,2,United-States,>50K\n1,Private,227282,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,138153,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,174132,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,3,2,United-States,>50K\n1,Private,182237,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n0,?,111252,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,217775,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,?,168863,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,394503,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,141657,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,125441,11th,7,Never-married,Other-service,Own-child,White,Male,1,0,0,United-States,<=50K\n1,Private,172230,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,282944,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,55377,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,49352,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,213887,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,24046,HS-grad,9,Widowed,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,State-gov,208122,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,176118,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n0,Private,227994,Some-college,10,Married-spouse-absent,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n3,Private,215389,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Private,99434,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,190964,HS-grad,9,Separated,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,113700,Bachelors,13,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,259840,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,168827,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,28984,Assoc-voc,11,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,182211,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,82393,Some-college,10,Never-married,Craft-repair,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,183639,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,342448,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,469907,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,2,2,United-States,<=50K\n1,Local-gov,211920,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,33658,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n2,Federal-gov,34178,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,400630,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,161251,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,255685,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,2,Outlying-US(Guam-USVI-etc),<=50K\n2,Private,199256,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,143716,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,221666,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,145409,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,3,Canada,>50K\n0,Private,39615,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,104440,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,151382,7th-8th,4,Divorced,Machine-op-inspct,Unmarried,White,Male,0,1,2,United-States,<=50K\n4,Self-emp-not-inc,503675,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,>50K\n4,Private,306233,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,216475,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,1,2,United-States,>50K\n3,Private,50748,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,3,England,<=50K\n0,Private,107190,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,206874,Assoc-voc,11,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,83141,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,444089,11th,7,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,141896,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,33487,Some-college,10,Divorced,Tech-support,Unmarried,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n2,Private,65372,Doctorate,16,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,>50K\n1,Private,341346,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,343403,Doctorate,16,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,0,?,<=50K\n3,Private,287480,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,107287,10th,6,Widowed,Exec-managerial,Unmarried,White,Female,0,4,3,United-States,>50K\n3,Private,199067,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,182771,Assoc-voc,11,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n1,Private,159737,10th,6,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,110643,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,117583,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,43479,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,203003,7th-8th,4,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,Germany,<=50K\n3,Private,133963,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,227794,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,112137,Some-college,10,Never-married,Prof-specialty,Other-relative,Asian-Pac-Islander,Female,0,0,0,South,<=50K\n3,Self-emp-not-inc,110457,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,281565,HS-grad,9,Widowed,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,3,South,<=50K\n3,Federal-gov,297906,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n0,Private,151506,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Federal-gov,139455,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,Cuba,<=50K\n2,Private,26987,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,233312,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,161092,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,98361,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,188928,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,164922,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,185673,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,193598,Preschool,1,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,Private,274111,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,245482,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n4,Private,160932,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,44368,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,291374,HS-grad,9,Separated,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,280927,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,222993,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,25240,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,204052,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,74054,11th,7,Never-married,Sales,Own-child,Other,Female,0,0,0,?,<=50K\n3,Private,169042,10th,6,Never-married,Other-service,Not-in-family,White,Female,0,0,1,Ecuador,<=50K\n1,Private,104509,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,4,United-States,>50K\n2,Local-gov,185394,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,2,United-States,>50K\n2,Local-gov,254146,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,227856,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n0,Private,183041,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,107682,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Self-emp-inc,287598,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,182186,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Dominican-Republic,<=50K\n2,Self-emp-inc,194636,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,4,United-States,>50K\n2,Private,112305,Some-college,10,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,212661,10th,6,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,32709,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n2,Federal-gov,46366,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,24106,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,170850,Bachelors,13,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,1,2,?,<=50K\n2,Self-emp-not-inc,40666,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,182975,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,345122,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,208311,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,120045,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,?,201299,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,152940,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,243580,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,182128,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,2,0,3,United-States,<=50K\n2,?,176458,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,101562,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,108699,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,175878,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,177675,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,213887,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,357619,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,Germany,<=50K\n0,Private,435835,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,2,3,United-States,<=50K\n2,Private,165799,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,71469,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,229745,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,284916,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,28419,Assoc-voc,11,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,26950,Masters,14,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,107231,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n3,Local-gov,512103,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,245090,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,314153,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,243988,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Self-emp-not-inc,82551,Assoc-voc,11,Married-civ-spouse,Tech-support,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,Private,42706,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,235795,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-not-inc,108001,9th,5,Never-married,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,State-gov,112497,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,2,2,United-States,<=50K\n4,Self-emp-not-inc,128206,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,224634,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,362999,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,346693,7th-8th,4,Never-married,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,175759,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,99199,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n0,?,219987,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,143445,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,Private,118710,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,224185,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,118972,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,165360,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,38950,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-inc,277256,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,247151,11th,7,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,213722,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,209955,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,174395,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,138626,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,2,3,United-States,<=50K\n0,?,179973,Assoc-voc,11,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,200207,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,156587,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,33016,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,197496,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,?,<=50K\n1,Private,153588,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,99736,Masters,14,Divorced,Prof-specialty,Unmarried,White,Male,4,0,3,United-States,>50K\n2,Private,284166,HS-grad,9,Never-married,Sales,Unmarried,White,Male,0,0,3,United-States,>50K\n0,Private,716066,10th,6,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,188519,HS-grad,9,Divorced,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,109080,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,174421,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,259351,Some-college,10,Never-married,Craft-repair,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,Mexico,<=50K\n2,Federal-gov,284403,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,85319,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,3,United-States,>50K\n0,?,201766,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,State-gov,340475,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,487486,HS-grad,9,Widowed,Handlers-cleaners,Unmarried,White,Male,0,0,2,?,<=50K\n4,?,484298,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,170617,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,3,United-States,<=50K\n3,Private,94055,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,117779,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,209770,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,317443,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n4,?,140237,Preschool,1,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,107411,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,122493,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,195124,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,?,<=50K\n2,Private,101978,Some-college,10,Separated,Machine-op-inspct,Not-in-family,White,Male,0,4,3,United-States,>50K\n0,Private,335453,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,318329,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,100321,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,81145,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n0,Private,62865,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,176262,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,168103,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,208174,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,188815,HS-grad,9,Never-married,Other-service,Own-child,White,Female,4,0,0,United-States,<=50K\n4,Self-emp-not-inc,226092,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,212668,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,381583,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,United-States,<=50K\n3,Private,239439,HS-grad,9,Separated,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,172493,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,239876,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Male,0,0,2,United-States,<=50K\n4,?,221881,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Local-gov,218184,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n1,Self-emp-not-inc,206889,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,110668,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,1,United-States,<=50K\n1,Private,211028,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,202984,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,20296,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,194690,7th-8th,4,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,204984,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,35021,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,3,1,China,>50K\n2,Self-emp-not-inc,238574,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,345360,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,192381,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,479765,7th-8th,4,Never-married,Sales,Other-relative,White,Male,0,0,2,Guatemala,<=50K\n2,Self-emp-inc,34091,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,151773,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,299080,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,135339,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,1,0,2,Vietnam,<=50K\n1,Local-gov,52156,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,318647,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,80145,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,343646,Bachelors,13,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,Mexico,>50K\n2,Self-emp-not-inc,198692,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,266635,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,197672,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,185846,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,315110,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,220754,Doctorate,16,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,64292,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,126060,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,78012,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,2,2,United-States,<=50K\n1,Private,210562,Assoc-voc,11,Divorced,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,350181,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,233421,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,167170,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,260801,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,173370,Bachelors,13,Separated,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,135520,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Dominican-Republic,<=50K\n1,Private,121308,Some-college,10,Divorced,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,444743,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,65225,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,State-gov,136982,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,Black,Female,0,0,2,Honduras,<=50K\n2,State-gov,271962,Bachelors,13,Divorced,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,204046,10th,6,Divorced,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,225823,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,183009,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Other,Female,0,1,2,United-States,<=50K\n3,Private,121038,Assoc-voc,11,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Private,49092,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,148709,HS-grad,9,Separated,Handlers-cleaners,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,209205,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,285865,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Federal-gov,216129,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,40955,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,Japan,<=50K\n3,Private,197189,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,33001,HS-grad,9,Divorced,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,227399,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,164050,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,259087,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,236262,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,177929,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,166929,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,?,>50K\n1,Private,199963,11th,7,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n1,State-gov,98776,HS-grad,9,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,135056,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,55363,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n2,State-gov,102343,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,4,India,>50K\n1,Private,231263,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,226913,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,129573,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,191001,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,69345,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,204556,HS-grad,9,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,192626,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,202812,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,405177,10th,6,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,227890,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,101352,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Private,82572,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,132686,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,149210,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,4,0,2,United-States,>50K\n1,Private,245661,HS-grad,9,Separated,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,483596,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,1,United-States,<=50K\n2,State-gov,104663,Doctorate,16,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,Italy,>50K\n1,Private,347166,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,108540,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,333305,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,155408,HS-grad,9,Married-spouse-absent,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Federal-gov,246372,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,30290,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,347321,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,205852,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,163215,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,?,<=50K\n3,State-gov,93449,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n3,Self-emp-inc,116927,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,164526,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Yugoslavia,>50K\n1,Private,31573,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,125159,Some-college,10,Never-married,Adm-clerical,Other-relative,Black,Male,0,0,2,Haiti,<=50K\n2,State-gov,201105,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n0,?,122745,HS-grad,9,Never-married,?,Own-child,White,Male,0,2,2,United-States,<=50K\n1,Private,150570,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,118941,11th,7,Never-married,Other-service,Not-in-family,White,Female,0,0,2,Ireland,<=50K\n3,Private,141388,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,174714,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,State-gov,75755,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,133144,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,318865,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,109638,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,92969,1st-4th,2,Separated,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,376028,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,144161,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,183778,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,398904,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,170846,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Local-gov,204277,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,205152,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,225395,7th-8th,4,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,3,Mexico,<=50K\n2,Private,33975,HS-grad,9,Married-civ-spouse,Exec-managerial,Other-relative,White,Male,0,0,2,United-States,>50K\n3,Private,147032,HS-grad,9,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,0,Philippines,<=50K\n4,Private,174826,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,232769,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,36984,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,292264,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,303973,HS-grad,9,Never-married,Priv-house-serv,Other-relative,White,Female,0,2,0,Mexico,<=50K\n0,Private,287988,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-inc,330144,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,191948,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,324601,1st-4th,2,Separated,Machine-op-inspct,Own-child,White,Female,0,0,2,Guatemala,<=50K\n2,State-gov,200289,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,<=50K\n0,Private,113307,7th-8th,4,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,?,194087,Some-college,10,Never-married,?,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,155213,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,175127,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,358461,Some-college,10,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,State-gov,354929,Assoc-acdm,12,Divorced,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,State-gov,104501,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n2,Private,112929,7th-8th,4,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,132832,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,State-gov,357691,Masters,14,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,114605,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,525878,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,68358,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,174571,10th,6,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,42703,Assoc-voc,11,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,220589,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,197558,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,423250,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,29254,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,?,308924,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,276247,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,213841,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,181677,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,160061,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,285295,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n2,Private,265266,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,222115,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,4,0,2,United-States,>50K\n0,State-gov,194954,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,156926,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,217414,Some-college,10,Divorced,Protective-serv,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,538443,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,?,192399,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,383493,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,193235,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,99452,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,254134,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,90446,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,116613,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Portugal,<=50K\n2,Local-gov,238188,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,95909,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,82319,12th,8,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,182274,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,2,2,United-States,>50K\n4,Private,179625,10th,6,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,119793,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,254989,11th,7,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,104830,7th-8th,4,Never-married,Transport-moving,Unmarried,White,Male,0,0,1,Guatemala,<=50K\n3,Federal-gov,110373,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,135416,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,298225,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,166740,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,213668,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,276624,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,226789,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,31023,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,116666,HS-grad,9,Never-married,Protective-serv,Own-child,Amer-Indian-Eskimo,Male,2,0,3,United-States,<=50K\n2,Private,136986,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,179580,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,Private,103277,Some-college,10,Divorced,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Federal-gov,351141,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,191161,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,148709,Some-college,10,Never-married,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,128382,Some-college,10,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,144361,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,172538,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,102476,Bachelors,13,Never-married,Farming-fishing,Own-child,White,Male,4,0,3,United-States,>50K\n2,Private,46028,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,198452,HS-grad,9,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,193895,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,233421,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,378747,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,31251,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,71540,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,194772,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,34568,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,1,United-States,<=50K\n3,Self-emp-not-inc,36480,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,116528,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,52152,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,216690,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,<=50K\n2,Local-gov,227065,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,84013,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,82051,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,176185,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Iran,<=50K\n4,Private,115414,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-inc,493034,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,4,0,3,United-States,>50K\n3,Private,354923,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,393712,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,98941,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,141483,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,172479,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,226145,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,394612,Bachelors,13,Never-married,Tech-support,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,231085,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,183810,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,186159,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,162282,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,219021,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,273206,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,332355,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,102729,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,198096,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,292933,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,135924,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,99146,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,27409,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,299507,Assoc-acdm,12,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,102631,Some-college,10,Widowed,Farming-fishing,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,153486,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,434292,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,240172,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,219426,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,295791,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,114032,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,2,2,United-States,>50K\n0,Local-gov,496382,Some-college,10,Married-spouse-absent,Adm-clerical,Own-child,White,Female,0,0,2,Guatemala,<=50K\n1,Private,376483,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,1,United-States,<=50K\n1,Private,107218,HS-grad,9,Never-married,Other-service,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Private,246207,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,?,80564,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,83089,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,Mexico,>50K\n2,Local-gov,328301,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,301614,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,199739,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,180060,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,121040,Assoc-acdm,12,Never-married,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,125550,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,170772,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,180551,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,48189,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,432154,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,Mexico,<=50K\n1,Private,263200,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,123207,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,110798,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,238481,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,2,United-States,<=50K\n1,Private,185528,Some-college,10,Divorced,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,181311,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,528616,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,272950,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,?,195532,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,197583,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,48612,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Local-gov,31533,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n1,Federal-gov,148138,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,3,2,Iran,<=50K\n1,Local-gov,30069,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,1,0,2,United-States,<=50K\n4,?,170182,Some-college,10,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Local-gov,230885,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,174102,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,352606,HS-grad,9,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,241153,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,155433,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,109414,Some-college,10,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Male,0,3,2,United-States,<=50K\n2,Private,125461,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,331556,10th,6,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,138575,HS-grad,9,Never-married,?,Other-relative,White,Male,0,0,3,United-States,<=50K\n1,Private,223514,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,115418,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,1,0,2,United-States,<=50K\n2,Private,193026,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,1,2,?,<=50K\n2,Private,147206,12th,8,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,174592,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,268620,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,150886,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,112362,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,195507,HS-grad,9,Widowed,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,192983,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,120544,9th,5,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,59083,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,208277,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Local-gov,184678,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,278736,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,39464,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,162343,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Dominican-Republic,<=50K\n2,Private,204046,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,255647,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,Mexico,<=50K\n3,Private,123011,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,291362,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,159187,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,126414,Bachelors,13,Married-spouse-absent,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,227626,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,173783,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,211075,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,176756,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,1,4,United-States,>50K\n1,Self-emp-not-inc,31095,Some-college,10,Separated,Farming-fishing,Not-in-family,White,Male,2,0,3,United-States,<=50K\n3,Self-emp-not-inc,32372,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,4,United-States,<=50K\n2,Private,331651,Some-college,10,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,146325,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,515025,10th,6,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,394474,Assoc-acdm,12,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,400535,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n1,Self-emp-not-inc,337505,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n2,Private,211860,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,102684,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,225657,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,121966,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,396790,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Local-gov,149949,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,252187,11th,7,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,209934,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Federal-gov,229300,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,170769,Doctorate,16,Divorced,Sales,Not-in-family,White,Male,4,0,3,United-States,>50K\n3,Private,200618,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,216984,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,212760,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,150309,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Private,174655,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,109621,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,225124,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,172695,11th,7,Widowed,Other-service,Not-in-family,White,Female,0,0,1,El-Salvador,<=50K\n4,Self-emp-not-inc,238479,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,37754,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,85018,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,256466,HS-grad,9,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,3,Philippines,>50K\n0,Private,169188,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,210945,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,287031,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,224361,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,108464,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,75826,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,120277,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,104439,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,56870,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,200819,12th,8,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,170562,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,80933,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,33088,11th,7,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,112763,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,2,0,2,United-States,>50K\n1,Private,177651,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,261943,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,169785,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,Italy,<=50K\n0,Private,141481,11th,7,Married-civ-spouse,Sales,Other-relative,White,Female,0,0,3,United-States,<=50K\n2,Private,433491,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,86615,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,125550,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,State-gov,421223,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,26999,Bachelors,13,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,241998,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,0,United-States,>50K\n1,?,133861,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,115323,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,23778,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,190836,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,159179,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,?,205479,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n0,?,47713,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,163237,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,202202,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,168837,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,112271,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,52537,HS-grad,9,Never-married,Transport-moving,Unmarried,Black,Male,0,0,1,United-States,<=50K\n1,Private,38353,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,141698,10th,6,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,28856,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,175652,11th,7,Never-married,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,213008,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,196501,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n4,Private,118798,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,92463,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,125165,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,103980,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,180362,Bachelors,13,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,53903,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,179735,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,?,277390,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,122177,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Private,188161,HS-grad,9,Separated,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,170108,HS-grad,9,Separated,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,175262,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,Mexico,<=50K\n0,?,204441,HS-grad,9,Never-married,?,Other-relative,Black,Male,0,0,0,United-States,<=50K\n0,Private,164395,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,115630,11th,7,Never-married,Adm-clerical,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Private,178815,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,168223,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,202560,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,<=50K\n2,Private,100295,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,3,Canada,>50K\n2,Private,172256,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,51664,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,358893,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,4,2,United-States,<=50K\n1,Private,115963,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,333910,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,148948,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,130561,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n3,Private,428350,HS-grad,9,Married-civ-spouse,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,188808,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,112847,HS-grad,9,Married-civ-spouse,Transport-moving,Own-child,Other,Male,0,0,2,United-States,<=50K\n3,Private,110748,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,156653,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,196491,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,254413,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,91262,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,154785,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Wife,Asian-Pac-Islander,Female,0,0,4,Thailand,<=50K\n3,Private,84231,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,226327,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,248406,Some-college,10,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,54317,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,2,3,United-States,<=50K\n0,?,32732,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,95918,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,375675,12th,8,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,244172,HS-grad,9,Separated,Transport-moving,Unmarried,White,Male,0,0,2,Mexico,<=50K\n3,Federal-gov,233555,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,?,<=50K\n2,Private,326342,11th,7,Married-civ-spouse,Other-service,Husband,Black,Male,1,0,2,United-States,<=50K\n1,Private,77271,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Female,0,0,0,England,<=50K\n1,Private,33397,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,446358,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,151810,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Private,125461,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,133906,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,155106,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,203637,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,232766,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,305319,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,121023,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,198997,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,167140,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n0,Private,38772,10th,6,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,253759,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,130067,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Private,203828,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,221558,Masters,14,Separated,Prof-specialty,Unmarried,White,Female,0,0,1,?,<=50K\n1,Private,156464,10th,6,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,72333,Some-college,10,Divorced,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Local-gov,83671,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n1,Private,339482,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,91928,Some-college,10,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,99203,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,455995,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n4,Private,192515,HS-grad,9,Widowed,Farming-fishing,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,111483,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,0,United-States,>50K\n0,Private,221129,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,85413,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,196125,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,265638,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,177727,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,205822,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,112607,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,177595,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,<=50K\n0,Private,183315,11th,7,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,Private,116279,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,122493,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,215419,Assoc-acdm,12,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,310101,Some-college,10,Separated,Sales,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,61885,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,United-States,>50K\n2,Private,59107,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Private,227214,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,0,2,Ecuador,<=50K\n4,Private,239450,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,118847,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,95226,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,659273,11th,7,Never-married,?,Own-child,Black,Female,0,0,2,Trinadad&Tobago,<=50K\n0,Private,215395,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,170600,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,91044,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,318639,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n0,Private,160398,Some-college,10,Married-spouse-absent,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,216824,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n1,Private,308945,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,4,United-States,<=50K\n3,Private,30840,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,99309,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,188576,Bachelors,13,Separated,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n3,Private,83064,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,403865,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,235786,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,191893,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,149184,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,152909,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,435604,Assoc-voc,11,Separated,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,109282,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,248178,Some-college,10,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,1,United-States,<=50K\n0,?,112683,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,209103,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,183639,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,107233,HS-grad,9,Never-married,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n1,Private,175387,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Self-emp-not-inc,178255,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,?,<=50K\n1,Self-emp-not-inc,38223,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n1,Private,228873,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,202182,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,425092,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Self-emp-not-inc,152587,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,39089,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,>50K\n3,Private,204304,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,116103,Some-college,10,Separated,Craft-repair,Unmarried,White,Male,2,0,3,United-States,>50K\n3,Private,290640,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n4,Federal-gov,81973,Some-college,10,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,1,2,United-States,>50K\n1,Private,134890,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,452924,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,2,Mexico,<=50K\n4,Private,245193,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,State-gov,34339,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,184756,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,392160,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,1,Mexico,<=50K\n3,Private,168337,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,309513,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,77219,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,212888,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,361888,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n4,Local-gov,237879,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,93099,Some-college,10,Married-civ-spouse,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,225193,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,50814,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,123681,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,249351,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,222311,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,301762,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,195298,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,541737,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,1,0,1,United-States,<=50K\n4,Private,241065,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,129513,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,374262,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,382146,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,?,185291,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,30447,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,49893,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,197387,Some-college,10,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,Mexico,<=50K\n2,Self-emp-not-inc,111957,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,340458,12th,8,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,185670,1st-4th,2,Widowed,Prof-specialty,Unmarried,White,Female,0,0,1,Mexico,<=50K\n2,Private,210945,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,350661,Prof-school,15,Separated,Tech-support,Not-in-family,White,Male,0,0,3,Columbia,>50K\n2,Private,190543,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,70261,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,179048,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Greece,<=50K\n1,Private,242094,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,117634,Some-college,10,Widowed,Craft-repair,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,82531,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,193374,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n1,?,186420,Bachelors,13,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,323605,7th-8th,4,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Private,371064,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,39927,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,64292,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,198660,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,4,0,3,United-States,>50K\n3,?,196975,HS-grad,9,Divorced,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,210165,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,144137,Some-college,10,Divorced,Priv-house-serv,Other-relative,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,155657,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,72953,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,107548,9th,5,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,163258,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,221324,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,444822,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,Mexico,<=50K\n0,Private,154398,11th,7,Never-married,Other-service,Own-child,Black,Male,0,0,0,Haiti,<=50K\n1,Private,120672,11th,7,Divorced,Handlers-cleaners,Other-relative,Black,Male,0,2,2,United-States,<=50K\n3,Private,159650,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,290754,10th,6,Widowed,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,49654,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Federal-gov,147352,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,227943,Assoc-acdm,12,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,423024,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,?,64322,7th-8th,4,Separated,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,445940,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,230824,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,48882,HS-grad,9,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,168195,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,188644,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,136077,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,119793,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,336513,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,186991,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,218948,7th-8th,4,Never-married,?,Not-in-family,White,Female,0,0,1,Mexico,<=50K\n1,Private,211435,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,280169,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,0,United-States,<=50K\n1,Private,109997,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,286789,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,102460,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,287160,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,198097,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,119111,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,174461,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,281678,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,377725,Bachelors,13,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,151053,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,186539,Masters,14,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,?,149478,Some-college,10,Never-married,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,198452,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,198863,Prof-school,15,Divorced,Exec-managerial,Not-in-family,White,Male,0,4,3,United-States,>50K\n1,Private,176711,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,165310,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Private,213008,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Japan,<=50K\n0,State-gov,38251,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,125761,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,148645,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,273435,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,<=50K\n2,Private,208613,Bachelors,13,Separated,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,192565,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,183885,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,243631,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n2,Private,191754,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,261278,Some-college,10,Separated,Sales,Other-relative,Black,Male,0,0,1,United-States,<=50K\n3,Private,127014,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,197919,Assoc-acdm,12,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,217460,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,86551,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,98051,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,215917,Some-college,10,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Self-emp-not-inc,192982,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,334132,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Private,136986,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,116812,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,189123,11th,7,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,1,3,United-States,<=50K\n1,Private,89648,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,?,190027,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,0,United-States,<=50K\n4,Private,99248,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,57600,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,199224,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,140363,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,308812,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,275421,Some-college,10,Never-married,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,213321,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,157747,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,182314,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,220589,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,?,208640,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n1,Self-emp-not-inc,189346,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,1,0,3,United-States,<=50K\n3,Private,124071,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,20469,Some-college,10,Divorced,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,154227,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,105044,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,35910,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,189203,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,116493,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Local-gov,19700,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,48718,10th,6,Never-married,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Private,106113,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,256263,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,202498,7th-8th,4,Separated,?,Not-in-family,White,Male,0,0,2,Guatemala,<=50K\n2,Private,120074,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,122922,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,116903,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,<=50K\n2,Local-gov,222596,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,107302,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,India,<=50K\n2,Private,156400,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,53373,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,58916,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,167159,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,283806,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,140426,1st-4th,2,Married-spouse-absent,Other-service,Not-in-family,White,Male,0,0,1,?,<=50K\n2,Local-gov,61778,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,33310,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,202560,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,60828,Some-college,10,Never-married,Farming-fishing,Own-child,White,Female,0,0,3,United-States,<=50K\n3,State-gov,153486,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,167536,Assoc-acdm,12,Widowed,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,370990,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,198867,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,174924,Some-college,10,Divorced,Protective-serv,Unmarried,White,Male,0,0,3,Germany,<=50K\n1,Private,175856,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,169628,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,2,?,<=50K\n1,?,125159,Some-college,10,Never-married,?,Not-in-family,Black,Male,0,0,2,?,<=50K\n1,Private,220690,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,160035,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,<=50K\n4,Self-emp-not-inc,116878,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Greece,<=50K\n1,Self-emp-not-inc,134737,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,81648,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n3,State-gov,122177,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,69614,10th,6,Separated,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,112115,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,299422,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n4,?,162882,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,112854,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,33417,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,224559,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n2,?,468706,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,357028,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,51158,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,2,0,2,United-States,>50K\n3,Private,186303,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,127749,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,291386,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,138054,Assoc-acdm,12,Never-married,Other-service,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,174533,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,200835,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,108658,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,180985,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,34803,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,75867,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,156819,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,61272,9th,5,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Portugal,<=50K\n0,Private,39827,Some-college,10,Married-civ-spouse,Machine-op-inspct,Wife,Other,Female,0,0,2,Puerto-Rico,<=50K\n2,Private,130007,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,80324,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,322614,Preschool,1,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Male,0,2,2,Mexico,<=50K\n1,Private,140869,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,181902,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,Poland,>50K\n1,Private,287908,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,309630,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,28225,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,428584,HS-grad,9,Married-civ-spouse,?,Wife,Black,Female,1,0,0,United-States,<=50K\n0,Private,39222,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,359131,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,0,?,>50K\n0,Private,122272,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,198400,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,<=50K\n4,?,73091,7th-8th,4,Widowed,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,283338,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,208946,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,348416,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,379046,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n1,Private,183887,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,127961,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,211129,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,187649,HS-grad,9,Separated,Protective-serv,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,94754,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,231826,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,142764,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,126822,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Private,188069,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,284395,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,31267,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,161444,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Columbia,<=50K\n0,Private,144483,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,133655,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,112074,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,249727,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,165754,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,172822,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,288433,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,33331,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,168071,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,207277,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,130620,Some-college,10,Married-spouse-absent,Sales,Own-child,Asian-Pac-Islander,Female,0,0,1,India,<=50K\n2,Private,136244,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,972354,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,245297,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,State-gov,71151,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,118352,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,117210,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,120068,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,48343,11th,7,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,84451,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,?,76437,Some-college,10,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,281704,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,123011,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,104729,HS-grad,9,Divorced,Machine-op-inspct,Other-relative,White,Female,0,0,3,United-States,<=50K\n1,Private,110134,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,186067,10th,6,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,214702,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,Puerto-Rico,<=50K\n3,Private,384795,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,175931,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,366324,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,118717,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,219835,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,Mexico,<=50K\n0,Private,176486,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,273435,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,182661,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,212304,7th-8th,4,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,133963,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,>50K\n3,Private,165152,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,274724,Some-college,10,Never-married,Other-service,Other-relative,White,Male,0,0,2,Nicaragua,<=50K\n3,Private,196707,Prof-school,15,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,213002,12th,8,Never-married,Sales,Not-in-family,White,Male,2,0,3,United-States,<=50K\n0,?,26620,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,361481,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n1,Private,148581,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n3,Private,459189,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,>50K\n1,Self-emp-not-inc,214689,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,289364,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,174907,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,348099,10th,6,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,?,104965,9th,5,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,31600,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,286282,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,181705,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,238912,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,134737,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n4,?,157403,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,2,0,0,United-States,>50K\n2,Private,197429,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,47343,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,67083,Bachelors,13,Never-married,Exec-managerial,Unmarried,Asian-Pac-Islander,Male,1,0,2,Cambodia,<=50K\n0,Private,249957,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,175942,HS-grad,9,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,2,France,<=50K\n3,Private,192982,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,209547,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,142675,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,190333,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,196396,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,166740,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,174533,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,210867,7th-8th,4,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,?,<=50K\n2,Private,118486,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,2,0,1,United-States,>50K\n2,Private,144067,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,106964,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,178136,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,196554,Prof-school,15,Separated,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,>50K\n2,Self-emp-not-inc,403550,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,498216,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,192755,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,0,United-States,>50K\n0,?,53738,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,156192,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,189802,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,213149,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,2,2,United-States,>50K\n1,Self-emp-not-inc,179171,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,2,Germany,<=50K\n1,Private,77634,11th,7,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,189830,Some-college,10,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,127190,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,?,174147,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,138107,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,269733,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,State-gov,227734,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,318822,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,48885,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,205424,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,173858,7th-8th,4,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,Cambodia,<=50K\n1,Private,202450,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,154779,Some-college,10,Never-married,Sales,Other-relative,Other,Female,0,0,2,United-States,<=50K\n1,Private,180551,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,177522,HS-grad,9,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,277328,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,Cuba,<=50K\n1,Private,112584,10th,6,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,State-gov,85384,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,?,123971,11th,7,Divorced,?,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,69019,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,112847,HS-grad,9,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,52900,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,37937,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,59380,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,114770,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,216481,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,176469,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,176831,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n2,Federal-gov,410034,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,93662,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,144236,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,240917,11th,7,Separated,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n3,Private,608184,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,3,2,United-States,>50K\n3,Private,243361,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,35166,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-inc,182655,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,142717,Doctorate,16,Divorced,Craft-repair,Not-in-family,White,Female,2,0,3,United-States,>50K\n1,Private,272944,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,219233,HS-grad,9,Never-married,?,Own-child,Black,Male,0,2,1,United-States,<=50K\n0,Private,228686,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,236818,Assoc-voc,11,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,117865,HS-grad,9,Married-AF-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,106538,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,153891,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,190909,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,191002,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,Poland,<=50K\n2,Private,89073,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,238342,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,259532,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,189282,HS-grad,9,Married-civ-spouse,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,132481,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,205659,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,Thailand,>50K\n1,Private,182323,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,216256,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,1,United-States,<=50K\n3,Federal-gov,166419,11th,7,Never-married,Sales,Not-in-family,Black,Female,2,0,2,United-States,<=50K\n1,Private,152246,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Private,155659,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,155198,9th,5,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,100931,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,162945,7th-8th,4,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,334346,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,181597,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,133969,HS-grad,9,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,4,South,<=50K\n3,Private,210217,Bachelors,13,Divorced,Sales,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Private,169711,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Germany,>50K\n4,?,300104,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,2,0,4,United-States,>50K\n0,Private,271521,HS-grad,9,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n0,Private,51255,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,26669,Assoc-acdm,12,Married-civ-spouse,Other-service,Wife,White,Female,0,0,4,United-States,<=50K\n3,Private,194580,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,177974,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,315640,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,0,China,<=50K\n3,Self-emp-inc,136913,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,230961,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,167062,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,120131,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,243368,Preschool,1,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n1,Private,171876,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,136866,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,316820,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,1,2,United-States,<=50K\n3,Private,185459,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,?,81761,HS-grad,9,Divorced,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,43716,Assoc-voc,11,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,220939,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,148657,Preschool,1,Married-civ-spouse,?,Wife,White,Female,0,0,2,Mexico,<=50K\n3,Federal-gov,40808,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,183473,HS-grad,9,Divorced,Transport-moving,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,108496,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,204838,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,132686,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,State-gov,117906,10th,6,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,304386,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,?,248113,Preschool,1,Married-spouse-absent,?,Other-relative,White,Male,0,0,2,Mexico,<=50K\n2,Private,165215,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,0,United-States,>50K\n0,?,215463,12th,8,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,259719,Some-college,10,Divorced,Handlers-cleaners,Unmarried,Black,Male,0,0,2,Nicaragua,<=50K\n0,?,35829,Some-college,10,Divorced,?,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,248795,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,124692,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Local-gov,128054,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,179731,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,4,4,United-States,>50K\n1,Self-emp-inc,113543,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,252153,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,45891,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,112263,11th,7,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,47791,12th,8,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,202980,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,Peru,<=50K\n0,Private,34918,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,91251,7th-8th,4,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,1,China,<=50K\n1,Private,132996,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,306215,Assoc-voc,11,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,203570,HS-grad,9,Separated,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,355918,Bachelors,13,Separated,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,198841,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,282964,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,312197,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,Mexico,>50K\n2,Private,98779,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,United-States,<=50K\n1,Private,200246,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,182771,Some-college,10,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Private,199908,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,172104,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Other,Male,0,0,2,India,>50K\n3,Self-emp-not-inc,35295,Bachelors,13,Never-married,Sales,Unmarried,White,Male,0,0,3,United-States,>50K\n1,Private,216858,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,332187,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n4,Private,255109,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,111332,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,238431,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,131552,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,110239,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,255830,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,?,175648,11th,7,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,82998,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,164320,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,263498,Assoc-voc,11,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,162381,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,229651,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,357348,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,269657,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,82880,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,389755,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,195136,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,2,United-States,>50K\n2,Private,207685,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,?,<=50K\n0,Private,222925,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Own-child,White,Female,1,0,2,United-States,<=50K\n0,?,196388,Assoc-acdm,12,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,50341,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,214134,10th,6,Never-married,Transport-moving,Not-in-family,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n2,Private,114032,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,192053,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,240231,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Japan,>50K\n2,Private,44402,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,191503,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,163530,HS-grad,9,Divorced,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Local-gov,136823,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n4,Private,121912,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,58624,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,74056,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,144259,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,2,0,4,?,>50K\n4,Private,182028,Assoc-acdm,12,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,209040,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,206046,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,182494,7th-8th,4,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,185057,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,1,Scotland,<=50K\n4,Private,147473,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,221722,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n0,?,388811,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,221912,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,48189,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,382272,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,48347,Bachelors,13,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,143046,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,>50K\n4,Self-emp-inc,137940,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,249571,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,121318,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,224531,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,185019,12th,8,Never-married,Other-service,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n4,Private,27886,7th-8th,4,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,94741,12th,8,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,107801,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Female,0,4,0,United-States,<=50K\n2,Private,191256,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,256866,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,197148,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,312271,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,118657,HS-grad,9,Separated,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,224338,Assoc-voc,11,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,242488,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,<=50K\n0,?,234970,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,227915,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,105717,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,2,1,United-States,<=50K\n2,Self-emp-not-inc,160962,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,?,353881,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,1,0,3,United-States,>50K\n0,Private,188950,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,201328,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,218678,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,184255,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,200968,Some-college,10,Married-civ-spouse,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,>50K\n1,Private,102264,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,300584,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,208946,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,105021,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,124751,Some-college,10,Never-married,Priv-house-serv,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,274057,11th,7,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Private,132879,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,260960,Bachelors,13,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,208415,HS-grad,9,Divorced,Exec-managerial,Not-in-family,Black,Male,0,0,2,?,<=50K\n2,Private,356934,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,154410,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,35378,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,301210,1st-4th,2,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,0,United-States,<=50K\n1,Private,73621,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,108140,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,217198,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,157332,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,202956,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,173495,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,149811,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,4,3,Canada,<=50K\n2,Private,444219,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Private,125120,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,190429,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,190303,Assoc-acdm,12,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,248164,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n1,Federal-gov,208534,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,343721,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,?,>50K\n1,Self-emp-inc,196373,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,433788,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,122086,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,137314,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,33068,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,210688,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,117833,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,1,United-States,<=50K\n2,State-gov,103474,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,115880,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,233933,10th,6,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,52781,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,586657,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Japan,>50K\n4,Private,113080,7th-8th,4,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,251905,Assoc-voc,11,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,225964,Some-college,10,Widowed,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,?,194096,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,263831,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,133136,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,121634,10th,6,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,Mexico,<=50K\n0,Self-emp-inc,40767,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,355789,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n2,Local-gov,311914,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,91189,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,344060,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,113823,Bachelors,13,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,185800,Masters,14,Divorced,Prof-specialty,Unmarried,Black,Female,2,0,2,United-States,>50K\n1,Private,76107,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,117618,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,238008,HS-grad,9,Widowed,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,136480,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,285200,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,1,United-States,>50K\n0,Private,351040,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Puerto-Rico,<=50K\n1,Private,1226583,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,195767,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,187540,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,79372,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,226665,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,213209,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,211005,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,96178,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,328216,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,110713,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,225456,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,159908,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,1,2,United-States,<=50K\n2,Private,118308,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,180309,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,39630,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,273828,5th-6th,3,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,Private,172071,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,2,Jamaica,<=50K\n1,Private,218887,HS-grad,9,Never-married,Farming-fishing,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,664670,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,209149,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,84619,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,447346,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,37869,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,State-gov,99086,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,143582,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Wife,Asian-Pac-Islander,Female,0,3,4,?,<=50K\n2,Private,326886,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,181755,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,249368,HS-grad,9,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,326400,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,504725,5th-6th,3,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n2,Private,88967,11th,7,Never-married,Transport-moving,Unmarried,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,170721,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n3,Private,148953,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,342752,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,220871,7th-8th,4,Widowed,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,29675,HS-grad,9,Widowed,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n3,Federal-gov,183611,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,115215,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,152231,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,41356,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,225142,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,121313,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,134821,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,311350,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,102106,10th,6,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,427055,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,117860,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,285885,9th,5,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,212800,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,194864,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,31438,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,148254,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,113035,1st-4th,2,Widowed,Priv-house-serv,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n4,Private,106595,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,144521,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,172232,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n3,State-gov,123592,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,2,0,1,United-States,<=50K\n0,Private,191921,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,237379,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,208463,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Federal-gov,68985,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,22418,9th,5,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,163047,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,153870,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,4,2,United-States,<=50K\n0,?,124954,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,197702,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,166415,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,State-gov,116211,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,33644,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,State-gov,33331,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,4,United-States,>50K\n3,Private,73019,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,169182,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,Puerto-Rico,<=50K\n3,Private,20438,Some-college,10,Separated,Exec-managerial,Unmarried,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n0,Private,109869,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,316849,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,208043,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,153790,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n4,State-gov,153451,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,96840,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,192732,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,209101,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,146919,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Local-gov,192323,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,217019,HS-grad,9,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,198211,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,222490,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,106758,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,561334,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,203710,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,203322,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,123703,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n3,State-gov,312015,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,209428,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,El-Salvador,<=50K\n4,Private,230292,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,114420,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,120238,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,100375,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,42485,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,130620,12th,8,Married-civ-spouse,Sales,Wife,Asian-Pac-Islander,Female,0,0,1,?,<=50K\n2,Local-gov,134367,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,147099,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,36214,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,119904,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Self-emp-inc,105779,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,>50K\n4,Private,165020,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,187098,Prof-school,15,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,3,United-States,>50K\n2,?,142030,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,241360,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,<=50K\n4,Private,121319,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,>50K\n3,Private,151580,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,162572,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,35917,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,35723,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,194773,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,62155,Some-college,10,Never-married,Sales,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,192203,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,>50K\n3,Private,174370,Some-college,10,Separated,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,161007,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,270517,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,Mexico,<=50K\n2,Private,163847,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,193882,Assoc-voc,11,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,160037,7th-8th,4,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,189944,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,4,United-States,<=50K\n4,Private,115364,HS-grad,9,Widowed,Sales,Unmarried,White,Male,0,0,1,United-States,<=50K\n2,Private,163174,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,188900,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,1,0,1,United-States,<=50K\n0,Private,214399,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,156616,HS-grad,9,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,204862,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,55921,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,172475,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,1,0,2,United-States,<=50K\n0,Private,153082,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,195418,Masters,14,Divorced,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Local-gov,276840,12th,8,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,97933,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Wife,White,Female,0,1,2,United-States,>50K\n3,Self-emp-inc,119099,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Self-emp-not-inc,83411,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,198992,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,337825,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,192002,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,189346,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,231962,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,164488,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n3,Private,200471,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,228921,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Male,0,4,2,United-States,>50K\n2,Private,184846,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,233851,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,499001,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,Mexico,<=50K\n4,Local-gov,125768,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,255004,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Private,157624,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,146767,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,118291,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Private,313181,HS-grad,9,Divorced,Adm-clerical,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,87891,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,226443,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,81132,Some-college,10,Married-civ-spouse,Craft-repair,Other-relative,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n0,Private,216436,Bachelors,13,Never-married,Sales,Other-relative,Black,Female,0,0,1,United-States,<=50K\n0,Private,213412,Bachelors,13,Never-married,Tech-support,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,179358,HS-grad,9,Widowed,Handlers-cleaners,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,369825,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,2,0,3,United-States,<=50K\n4,Private,199763,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,239390,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,173613,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Self-emp-inc,37869,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,302845,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n1,State-gov,85218,Masters,14,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,48268,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,173968,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,70982,Assoc-voc,11,Never-married,Other-service,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n3,Private,166857,9th,5,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,256191,HS-grad,9,Never-married,?,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,162872,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,152148,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,139193,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,791084,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,137214,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,183258,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,154035,HS-grad,9,Widowed,Handlers-cleaners,Other-relative,Black,Male,0,0,1,United-States,<=50K\n2,Private,115323,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,213055,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Other,Female,0,0,3,United-States,<=50K\n2,Private,155064,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,33551,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,169995,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,168262,Masters,14,Separated,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n2,Private,104196,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,114055,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,274398,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n4,?,27979,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,1,United-States,<=50K\n4,?,244122,Assoc-voc,11,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,196571,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,101607,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,122109,HS-grad,9,Never-married,Prof-specialty,Unmarried,White,Female,0,1,2,United-States,<=50K\n4,Self-emp-inc,255822,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,195184,HS-grad,9,Widowed,Priv-house-serv,Unmarried,White,Female,0,0,0,Cuba,<=50K\n1,Federal-gov,245372,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,169583,Bachelors,13,Married-AF-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,224531,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,186151,HS-grad,9,Separated,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,118693,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,297449,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,125206,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,393264,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,108140,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,264968,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,318106,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,156025,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,149455,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,359985,5th-6th,3,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,Mexico,<=50K\n2,State-gov,165108,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,115178,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,149224,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,352056,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,174717,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,?,173064,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,147755,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n3,Self-emp-not-inc,135716,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,44216,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,37359,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,178255,Some-college,10,Married-civ-spouse,Priv-house-serv,Wife,White,Female,0,0,2,?,<=50K\n1,State-gov,70617,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,0,China,<=50K\n1,Private,154950,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,356934,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,271714,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,247025,HS-grad,9,Never-married,Protective-serv,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,107417,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,116554,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,917220,12th,8,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,430084,Some-college,10,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,202937,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,Poland,<=50K\n1,Private,62737,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,508548,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,275223,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n1,Self-emp-not-inc,381931,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,246974,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,105431,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,146311,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,159869,Doctorate,16,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,204641,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,66297,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n2,Private,227615,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n4,?,107744,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,360393,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,263340,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,141918,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,294292,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,128736,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,511289,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,302406,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,101517,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,State-gov,161334,Masters,14,Married-spouse-absent,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n0,Self-emp-inc,189148,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,103111,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,51620,Bachelors,13,Separated,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,31606,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,34292,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,107882,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,39529,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,135315,9th,5,Never-married,Sales,Own-child,Other,Female,0,0,1,United-States,<=50K\n1,Private,107812,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,229729,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,111891,HS-grad,9,Separated,Machine-op-inspct,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,Private,340917,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,202952,10th,6,Divorced,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n4,Private,333230,HS-grad,9,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,114955,Assoc-acdm,12,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,159869,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,57758,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,207064,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,193090,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n4,Private,151364,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,88638,Masters,14,Never-married,Prof-specialty,Unmarried,White,Female,3,0,3,United-States,>50K\n1,Local-gov,304960,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,3,2,United-States,<=50K\n3,Private,102828,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Greece,<=50K\n0,?,210029,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,State-gov,154246,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,2,0,3,United-States,<=50K\n1,Private,142519,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,104455,Bachelors,13,Married-spouse-absent,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n4,Self-emp-inc,192230,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,292592,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,330132,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,51111,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,258037,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Cuba,>50K\n2,Local-gov,188291,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,2,United-States,>50K\n1,State-gov,349066,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,191188,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,133503,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,1,0,0,United-States,<=50K\n2,Private,146497,Some-college,10,Widowed,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,240468,Some-college,10,Married-spouse-absent,Sales,Own-child,White,Female,0,2,2,United-States,<=50K\n2,Private,175120,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,416577,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,253814,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,159247,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,102471,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,4,Puerto-Rico,<=50K\n2,Private,213464,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,211968,Assoc-voc,11,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,32016,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,512992,11th,7,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,135020,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,109133,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Portugal,<=50K\n1,Private,142712,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,76900,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,112176,Some-college,10,Divorced,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,262233,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,122066,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,Hungary,<=50K\n1,Private,194690,7th-8th,4,Separated,Other-service,Own-child,White,Male,0,0,3,Mexico,<=50K\n1,Private,306678,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n0,?,217769,Some-college,10,Never-married,?,Own-child,White,Female,1,0,0,United-States,<=50K\n1,Local-gov,308945,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,46699,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,377757,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,220993,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,1,3,United-States,<=50K\n2,Private,102147,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,113770,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,139012,Bachelors,13,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n2,Private,148900,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Federal-gov,329426,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,181408,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,101950,Prof-school,15,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,32537,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,209547,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,202373,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,151476,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,Self-emp-not-inc,174824,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,3,0,2,United-States,>50K\n0,Private,138768,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,143482,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,200190,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,168407,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,2,0,2,United-States,<=50K\n0,Private,148315,Some-college,10,Separated,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,270517,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,Mexico,<=50K\n2,Private,53506,Bachelors,13,Divorced,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,105693,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,189589,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,164574,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,185744,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,33155,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,215955,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,>50K\n2,Self-emp-not-inc,233571,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,211253,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,191385,Assoc-acdm,12,Divorced,Protective-serv,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Private,137895,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,159699,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,295922,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,175856,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,216129,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,407669,7th-8th,4,Widowed,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n2,Local-gov,214242,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,285457,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,124420,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,2,0,2,United-States,<=50K\n0,?,246386,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,142751,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,283635,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,322931,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,76482,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,431745,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,141944,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,193042,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,67006,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,240398,Bachelors,13,Never-married,Sales,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n1,Federal-gov,182714,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,>50K\n3,Federal-gov,172046,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,185177,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,102858,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,84954,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,1,0,4,United-States,<=50K\n0,Private,115895,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,184589,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,282611,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,218649,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,157541,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,145419,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,122616,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,204584,Masters,14,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,117210,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,69481,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,148492,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,3,United-States,>50K\n0,Private,106957,11th,7,Never-married,Craft-repair,Own-child,Asian-Pac-Islander,Male,4,0,2,Vietnam,>50K\n1,Private,29312,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n4,Private,120302,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,111916,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,182227,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,219110,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,200192,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,Germany,<=50K\n0,Private,427862,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,State-gov,33551,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,164043,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,116632,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,175133,Some-college,10,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,289731,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,256362,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,282612,Assoc-voc,11,Never-married,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,73679,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,237824,HS-grad,9,Married-spouse-absent,Priv-house-serv,Other-relative,Black,Female,0,0,3,Jamaica,<=50K\n2,Local-gov,357720,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,155489,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,Poland,<=50K\n2,Private,138077,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,183479,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,103596,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,172304,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,313853,Bachelors,13,Divorced,Other-service,Unmarried,Black,Male,0,0,2,United-States,>50K\n0,Private,294485,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,637080,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,385959,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,116539,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,129263,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,141253,10th,6,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,35626,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,94937,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,220269,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,169544,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,214604,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,81540,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,24013,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,124940,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,State-gov,313729,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,192237,10th,6,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,168524,Assoc-voc,11,Married-civ-spouse,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,113324,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,215477,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,199903,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,431861,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,105938,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,2,0,United-States,<=50K\n1,Private,274679,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,177499,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,1,1,United-States,<=50K\n1,Private,206125,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,221740,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n4,Private,202652,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,348960,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,171876,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,157932,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,201344,Bachelors,13,Divorced,Craft-repair,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,354739,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n1,Private,40067,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,326862,Some-college,10,Divorced,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,189762,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,?,149049,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,226246,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,29020,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,4,0,0,United-States,>50K\n0,Private,38251,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,196385,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,217054,Some-college,10,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,104973,Masters,14,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,238959,Masters,14,Divorced,Exec-managerial,Unmarried,Black,Female,3,0,2,United-States,>50K\n2,State-gov,34218,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,292962,HS-grad,9,Never-married,Craft-repair,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Private,235924,Bachelors,13,Divorced,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,98656,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,102610,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,296466,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,323069,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,184756,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,233993,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,130724,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,181855,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Other,Male,4,0,4,United-States,>50K\n4,Self-emp-not-inc,127543,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,4,United-States,<=50K\n2,Private,187164,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,<=50K\n3,Private,113912,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,216479,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,135480,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,204160,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n4,State-gov,114650,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,240172,Bachelors,13,Never-married,Exec-managerial,Other-relative,White,Male,0,0,3,United-States,<=50K\n1,Private,184831,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,124590,HS-grad,9,Never-married,Exec-managerial,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,State-gov,120429,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,202033,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,156874,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,177727,10th,6,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n3,Local-gov,334409,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,311255,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,Haiti,<=50K\n0,Private,214227,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,115849,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,671292,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,31460,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,141824,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,310152,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Private,179953,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,1,0,1,United-States,<=50K\n1,Private,137952,Some-college,10,Married-civ-spouse,Other-service,Husband,Other,Male,0,0,2,Puerto-Rico,<=50K\n2,Private,103323,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,174426,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,192779,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Male,0,4,2,United-States,>50K\n1,Private,169955,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,Puerto-Rico,<=50K\n2,Self-emp-not-inc,48087,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,132601,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Self-emp-inc,253060,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,108435,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,State-gov,210452,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,134181,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,45487,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,183522,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,United-States,>50K\n2,Private,199303,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,83064,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,134997,Some-college,10,Separated,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,44419,Some-college,10,Never-married,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,442612,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Local-gov,158092,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,374833,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,112650,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,183390,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,207418,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,?,335453,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,243660,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,54243,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,50385,Bachelors,13,Divorced,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,>50K\n3,State-gov,187581,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,37380,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,247025,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,29231,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,State-gov,101094,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,176716,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,118429,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,221532,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,?,120572,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,124680,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,153160,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,114678,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,2,0,2,United-States,<=50K\n3,State-gov,142856,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,29702,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,277700,Preschool,1,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,67433,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,121124,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,394447,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,79649,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,203763,Doctorate,16,Divorced,Prof-specialty,Unmarried,White,Female,0,0,4,United-States,<=50K\n3,Private,229029,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,?,494638,Assoc-acdm,12,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,162816,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,109117,Assoc-voc,11,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,32732,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,217692,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,34590,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n0,?,276864,Some-college,10,Never-married,?,Own-child,White,Female,0,2,0,United-States,<=50K\n4,Private,168625,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n2,Private,91037,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,171484,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,200453,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,36990,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,198211,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,30475,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,70995,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,4,United-States,>50K\n1,Private,245790,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,273324,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,2,0,United-States,<=50K\n4,Private,182687,Assoc-acdm,12,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,247807,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,163113,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,>50K\n3,Private,180522,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,203353,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,87469,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,216563,11th,7,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,87372,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n3,Local-gov,173584,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Local-gov,80282,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,319854,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,Taiwan,>50K\n2,Federal-gov,408229,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,431307,10th,6,Married-civ-spouse,Protective-serv,Wife,Black,Female,0,0,3,United-States,<=50K\n2,Private,134088,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,246396,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,159255,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,106014,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,186934,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,120130,Some-college,10,Separated,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,203849,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,207940,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,302406,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,144594,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n4,?,171050,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,459007,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,372181,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,>50K\n3,Self-emp-not-inc,172034,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,156566,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Self-emp-inc,338320,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,353696,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,Canada,<=50K\n3,Self-emp-not-inc,342907,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,169717,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,103762,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,State-gov,47570,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,119432,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,144165,Bachelors,13,Never-married,Prof-specialty,Own-child,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n1,Private,180647,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,312232,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,>50K\n1,State-gov,150488,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,200876,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,188199,9th,5,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,State-gov,118793,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,204325,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,256671,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,231515,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,Cuba,<=50K\n0,Private,100669,Some-college,10,Never-married,Handlers-cleaners,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n1,Private,88913,Some-college,10,Separated,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n0,Private,363219,Some-college,10,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,?,291547,Bachelors,13,Married-civ-spouse,?,Not-in-family,Other,Female,0,0,0,Mexico,<=50K\n2,Private,308945,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,100316,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,296453,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,298834,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,Canada,<=50K\n2,Self-emp-not-inc,188694,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,29240,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,186934,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,154908,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,22201,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n3,Private,216999,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,186916,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,116677,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,95763,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,266710,Some-college,10,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,117849,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,242460,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,202729,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,181652,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,174760,Assoc-acdm,12,Married-spouse-absent,Farming-fishing,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,56121,11th,7,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,390369,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,149726,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,51262,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,190350,12th,8,Never-married,Other-service,Unmarried,Black,Female,0,0,1,?,<=50K\n3,Federal-gov,205288,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,1,United-States,>50K\n2,Private,154835,HS-grad,9,Separated,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Private,89028,HS-grad,9,Divorced,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,4,0,2,United-States,>50K\n2,Private,194630,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,212207,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,204788,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,158688,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,97723,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,193026,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,257250,7th-8th,4,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Private,355978,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,200574,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,376929,5th-6th,3,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n3,State-gov,123219,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,82778,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Self-emp-not-inc,115882,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,103021,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,297767,Some-college,10,Separated,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,259479,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,167787,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Local-gov,40021,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,4,United-States,<=50K\n3,Private,245275,10th,6,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,37402,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,103608,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n4,Private,137192,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n1,Private,137618,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Self-emp-inc,96509,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,Taiwan,<=50K\n4,Private,196174,10th,6,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,172612,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,141186,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,228190,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,190290,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,?,>50K\n2,Federal-gov,307404,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,152436,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,182541,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,2,3,United-States,<=50K\n2,Private,282153,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,?,41281,Bachelors,13,Married-spouse-absent,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,162003,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,190759,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,208122,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,173832,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,129173,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,287548,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,216116,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,?,<=50K\n0,Private,146706,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,285200,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,314375,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,203943,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,1,United-States,>50K\n0,?,274746,HS-grad,9,Never-married,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,517000,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,66173,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,182823,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,159479,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Other,Male,0,0,3,United-States,<=50K\n0,Private,135568,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,333676,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,201699,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,96020,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,176138,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,47496,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,187158,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,249727,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,237624,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,175254,Some-college,10,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,42924,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,205950,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,111985,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,167476,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,221172,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,?,188711,Some-college,10,Divorced,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,199448,Assoc-voc,11,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,313038,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,148431,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Other,Female,0,0,2,United-States,<=50K\n0,Private,112432,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,57914,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,145166,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,247119,7th-8th,4,Widowed,Machine-op-inspct,Unmarried,Other,Female,0,0,2,Dominican-Republic,<=50K\n3,Private,196278,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,366531,Assoc-voc,11,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,216481,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,188027,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,66686,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,74775,Bachelors,13,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,1,Vietnam,<=50K\n4,?,325537,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,250499,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,>50K\n4,Self-emp-not-inc,192869,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-inc,121352,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,70985,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,2,United-States,<=50K\n1,Self-emp-not-inc,123116,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,339163,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,Mexico,<=50K\n4,Self-emp-not-inc,124771,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,167531,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,4,0,3,United-States,>50K\n4,?,77053,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,4,2,United-States,<=50K\n0,Private,199266,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,190728,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,99212,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,>50K\n2,Local-gov,421446,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,>50K\n4,Private,215944,9th,5,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,72310,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,57512,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,89413,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,28151,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,Private,46786,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,0,United-States,>50K\n1,Private,226943,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,182402,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,305352,10th,6,Divorced,Craft-repair,Other-relative,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,189253,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,296485,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,204375,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Self-emp-not-inc,249585,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,148995,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Self-emp-inc,168071,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,194995,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Italy,<=50K\n0,Private,211049,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,?,196630,Assoc-voc,11,Separated,?,Unmarried,White,Female,0,0,2,Mexico,<=50K\n0,Private,50397,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,1,United-States,<=50K\n2,Private,177905,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,204374,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,60001,Bachelors,13,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,>50K\n1,Private,223046,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,44921,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,154571,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n2,Private,67136,Assoc-voc,11,Separated,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,188675,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,Jamaica,>50K\n0,Private,390817,5th-6th,3,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n0,?,145964,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,30424,11th,7,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,548361,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,189148,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,266707,1st-4th,2,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,0,United-States,<=50K\n3,Self-emp-not-inc,311569,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,187653,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,235379,Assoc-acdm,12,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,188615,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,322691,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,184698,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Dominican-Republic,<=50K\n3,Private,144361,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,130057,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,117963,Doctorate,16,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,123876,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,248445,HS-grad,9,Divorced,Handlers-cleaners,Other-relative,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,207172,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,State-gov,119904,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,3,United-States,>50K\n4,Private,134768,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,269168,HS-grad,9,Married-civ-spouse,Other-service,Husband,Other,Male,0,0,2,?,<=50K\n4,Private,132026,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,3,0,2,United-States,>50K\n2,Private,60722,Some-college,10,Divorced,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Japan,>50K\n2,Private,648223,1st-4th,2,Married-spouse-absent,Farming-fishing,Unmarried,White,Male,0,0,2,Mexico,<=50K\n4,Private,298695,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,219835,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,313729,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,140644,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,203488,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,132341,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,161683,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,312771,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,258102,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,24127,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,254367,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,?,185426,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,152629,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,141058,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,233130,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,406641,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,State-gov,119422,10th,6,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,255486,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,1,United-States,<=50K\n0,Private,161532,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,75759,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,163332,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,103802,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,1,2,?,<=50K\n3,Private,34832,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,37933,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n0,Private,165107,Some-college,10,Never-married,Priv-house-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,126011,Assoc-voc,11,Divorced,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,56651,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,522881,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,Mexico,<=50K\n1,Private,191777,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,1,England,<=50K\n1,Private,132686,12th,8,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,201112,HS-grad,9,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,174283,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,208591,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,126399,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,142073,HS-grad,9,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,395567,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,180455,Bachelors,13,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,235853,9th,5,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,160731,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,31935,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,4,United-States,<=50K\n2,Private,35166,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,161092,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,223019,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,179673,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n3,State-gov,248895,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,200323,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,230020,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,United-States,<=50K\n1,Private,134890,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,162096,9th,5,Married-civ-spouse,Machine-op-inspct,Other-relative,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n3,Private,103824,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,Haiti,<=50K\n1,State-gov,61431,12th,8,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,197319,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,183618,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,268598,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Other,Male,2,0,3,Puerto-Rico,>50K\n3,Private,263729,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,39493,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,185360,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,132661,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,266400,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,433669,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,216473,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Self-emp-not-inc,217404,10th,6,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,227778,Assoc-voc,11,Never-married,Other-service,Other-relative,Black,Male,0,0,2,United-States,<=50K\n4,State-gov,96262,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,247566,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,139616,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,73585,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,37869,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,Private,165814,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,108913,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,34975,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,157078,10th,6,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,232672,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,294295,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,130454,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Local-gov,461678,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,252284,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,256737,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,96480,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Germany,<=50K\n0,Private,234263,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,109952,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,262570,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,65716,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,201732,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,174788,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,278924,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,101593,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,?,193863,7th-8th,4,Widowed,?,Other-relative,White,Female,0,0,0,Poland,<=50K\n2,Private,342768,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,242606,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n1,State-gov,176727,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,99179,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,354104,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,61956,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,137917,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,224658,Some-college,10,Married-civ-spouse,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,51100,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Private,224361,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,362912,Some-college,10,Never-married,Craft-repair,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,218782,10th,6,Never-married,Handlers-cleaners,Other-relative,Other,Male,0,0,2,United-States,<=50K\n1,Private,103389,Masters,14,Divorced,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,308944,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,140092,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,202210,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,416059,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,281030,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,169758,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,193666,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,139907,10th,6,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-inc,119422,HS-grad,9,Never-married,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,1,India,<=50K\n1,Private,149324,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,>50K\n2,Private,259307,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,74160,Masters,14,Divorced,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,>50K\n3,Private,134797,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,41103,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,193026,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,303986,5th-6th,3,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Cuba,<=50K\n1,Private,126569,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n4,Private,166461,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,?,61387,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,254746,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,28678,Masters,14,Married-civ-spouse,?,Husband,White,Male,3,0,0,United-States,>50K\n0,?,180976,10th,6,Never-married,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,282642,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,3,2,United-States,>50K\n4,Self-emp-not-inc,136413,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,131463,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,177240,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,218490,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,El-Salvador,>50K\n4,?,260543,10th,6,Widowed,?,Other-relative,Asian-Pac-Islander,Female,0,0,0,China,<=50K\n0,?,80680,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,20728,HS-grad,9,Never-married,Sales,Own-child,White,Female,2,0,2,United-States,<=50K\n3,Federal-gov,117628,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,91939,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,2,1,United-States,<=50K\n1,State-gov,175931,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,309566,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,123703,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,369678,HS-grad,9,Never-married,?,Not-in-family,Other,Male,0,0,1,United-States,<=50K\n4,Private,29928,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,167868,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,235894,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,189888,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,1,0,3,United-States,<=50K\n2,Private,111545,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,175972,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,254270,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,185057,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,157593,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,1,0,0,United-States,<=50K\n1,Private,101345,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,>50K\n3,Local-gov,176751,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n1,Self-emp-not-inc,97723,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,127601,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,227597,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,143995,Some-college,10,Never-married,?,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,250051,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,284078,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,207668,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,163787,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,119170,11th,7,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,188612,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Nicaragua,<=50K\n2,Private,114605,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,?,317761,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,164197,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,329266,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,207383,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,123598,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,259931,11th,7,Separated,Machine-op-inspct,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,Private,134737,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,106900,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,87054,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,82622,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,181659,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,321205,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,2,0,1,United-States,<=50K\n2,Self-emp-not-inc,231348,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,276096,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,290560,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,307315,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,State-gov,99156,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,237928,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,153501,HS-grad,9,Never-married,Transport-moving,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,?,149700,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,189680,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,374524,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n4,Self-emp-not-inc,127805,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,150217,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,Poland,<=50K\n1,Private,295649,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,China,<=50K\n0,Private,197182,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,241998,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,156410,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,3,United-States,>50K\n4,Private,473836,7th-8th,4,Widowed,Farming-fishing,Other-relative,White,Female,0,0,2,Guatemala,<=50K\n0,Private,198431,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,113936,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,318915,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,175406,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n1,?,193172,Assoc-voc,11,Married-civ-spouse,?,Own-child,White,Female,3,0,3,United-States,>50K\n0,Federal-gov,320294,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,400285,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n0,?,283731,Bachelors,13,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,227154,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Private,298659,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,Mexico,<=50K\n3,Private,212120,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,320510,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,175800,HS-grad,9,Never-married,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,170169,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,344157,11th,7,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,199441,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,225456,HS-grad,9,Never-married,Tech-support,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Private,61178,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,175262,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,2,England,<=50K\n2,Private,152568,HS-grad,9,Widowed,Sales,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,182108,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,4,0,1,United-States,>50K\n3,Private,273771,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,208291,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,224358,10th,6,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,55176,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,152711,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,68684,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,185452,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Federal-gov,175232,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,173851,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,162327,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,3,?,>50K\n2,Local-gov,51424,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,123416,12th,8,Separated,Prof-specialty,Own-child,White,Female,1,0,2,United-States,<=50K\n1,Private,262656,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,233194,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,290660,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,151105,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,179117,Assoc-acdm,12,Never-married,Machine-op-inspct,Not-in-family,Black,Female,4,0,3,United-States,>50K\n4,?,33608,Some-college,10,Married-civ-spouse,?,Husband,White,Male,3,0,1,United-States,>50K\n2,Private,317434,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,126569,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Local-gov,745768,Some-college,10,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,69927,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,0,United-States,<=50K\n1,Private,302603,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,46788,Bachelors,13,Divorced,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n2,Private,289886,5th-6th,3,Married-civ-spouse,Other-service,Husband,Other,Male,0,1,2,Nicaragua,<=50K\n2,Private,179135,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,175873,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,57426,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,312206,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Without-pay,344858,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n1,State-gov,177035,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,88055,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,111095,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,192251,10th,6,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,29807,HS-grad,9,Separated,Handlers-cleaners,Unmarried,White,Female,0,0,2,Japan,<=50K\n1,Federal-gov,211596,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,268276,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,181070,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,England,>50K\n3,Local-gov,20676,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n1,Private,115803,11th,7,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,124827,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,95336,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,257942,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,72593,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,147340,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,185325,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,357943,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,215395,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,2,0,United-States,<=50K\n3,Local-gov,30682,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Federal-gov,29591,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Other,Female,0,0,2,United-States,<=50K\n2,Private,215392,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,110554,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,2,0,2,United-States,>50K\n2,Self-emp-not-inc,133584,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,El-Salvador,<=50K\n2,Private,210438,7th-8th,4,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,256916,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,73541,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,109952,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,197975,5th-6th,3,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,401723,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,179524,Bachelors,13,Separated,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,State-gov,296282,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,145844,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,191965,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,2,0,1,United-States,<=50K\n3,Private,96792,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,185041,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,3,United-States,<=50K\n0,?,233779,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,347834,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,215373,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,169426,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,202856,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,50276,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,187454,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,126098,HS-grad,9,Separated,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,250639,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-inc,195366,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,186845,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n0,Federal-gov,119156,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,Private,162343,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n3,Private,108435,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,Greece,>50K\n1,Self-emp-not-inc,394927,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,172281,Bachelors,13,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,370767,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,<=50K\n2,Private,352005,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,165681,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,258819,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,130793,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,118909,Assoc-acdm,12,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,Jamaica,<=50K\n2,Private,202466,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n3,Private,161558,10th,6,Married-spouse-absent,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,188246,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,160120,Masters,14,Never-married,Prof-specialty,Unmarried,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n2,Private,144594,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n1,Self-emp-not-inc,123429,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,340110,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,523067,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,El-Salvador,<=50K\n3,Self-emp-not-inc,113513,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,186809,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n3,Self-emp-not-inc,320421,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,295589,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,370548,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,120572,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,110977,Doctorate,16,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,55860,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,158800,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,131568,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,173613,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,216867,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,104089,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,208106,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Ecuador,<=50K\n1,State-gov,340269,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,236246,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,213408,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,Cuba,<=50K\n2,?,84232,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,302945,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,Thailand,<=50K\n4,?,28197,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,262749,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,198265,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,170871,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,177761,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,Other,Male,0,0,3,United-States,<=50K\n4,Private,175689,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,Cuba,>50K\n2,Private,205100,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,77759,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,State-gov,77905,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,193575,11th,7,Never-married,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,State-gov,116520,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,85154,12th,8,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,0,1,Germany,<=50K\n3,Private,180532,Masters,14,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,508891,HS-grad,9,Divorced,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,211345,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,170877,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,97318,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,184105,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,150941,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,303942,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,273929,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,197077,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,162825,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,159869,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,158343,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,2,?,<=50K\n0,?,406920,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,227986,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,137527,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,180150,12th,8,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,239539,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n4,Private,281792,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,224799,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,292639,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,1,United-States,<=50K\n4,Private,22313,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,194636,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,156089,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,193720,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,218667,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,358837,Some-college,10,Never-married,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,174685,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,168854,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,133696,Bachelors,13,Never-married,Sales,Unmarried,White,Male,0,0,4,United-States,<=50K\n0,Federal-gov,350680,Assoc-acdm,12,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,Poland,<=50K\n0,Private,115215,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,152958,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,217200,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,235124,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,Dominican-Republic,<=50K\n1,Local-gov,144949,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,135470,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,281209,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,155489,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,290306,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,182042,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,210008,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,234938,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,<=50K\n3,Private,315423,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,<=50K\n1,Self-emp-not-inc,30244,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Local-gov,30008,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,201328,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,96468,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,486332,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,46162,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Local-gov,98350,Some-college,10,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,3,Philippines,<=50K\n2,Local-gov,175958,9th,5,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,119309,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,2,0,United-States,<=50K\n2,Private,175935,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,3,3,United-States,<=50K\n2,Private,204527,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,57827,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,418176,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n0,Private,262744,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,177287,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,255004,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,183735,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,318644,Prof-school,15,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,132125,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,206051,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,99185,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,?,>50K\n1,Private,225750,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,245777,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,169092,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,211035,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,285432,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,154779,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,37237,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,417419,7th-8th,4,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,33975,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,42485,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,170017,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,152683,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,1,United-States,<=50K\n0,Private,41721,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,66634,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,257216,Masters,14,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,167882,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,179428,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,57512,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,301614,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,193820,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,2,2,United-States,<=50K\n4,Private,222247,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,>50K\n2,Self-emp-inc,189092,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,217509,HS-grad,9,Widowed,Priv-house-serv,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Thailand,<=50K\n1,Private,308691,Masters,14,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,169672,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,120914,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,370156,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,398220,5th-6th,3,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-not-inc,208277,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,337456,HS-grad,9,Divorced,Protective-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,172666,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,32280,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,194901,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,57329,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,Japan,<=50K\n1,Private,173730,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n2,Local-gov,153312,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,0,United-States,>50K\n0,Private,274797,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,359249,Assoc-voc,11,Never-married,Protective-serv,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,152744,Some-college,10,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n4,Private,188041,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,97723,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,354529,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,249727,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,189590,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,State-gov,298871,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n3,Self-emp-not-inc,205296,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n3,Private,303637,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,242861,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,37599,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,State-gov,199381,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,2,United-States,>50K\n1,Self-emp-not-inc,56328,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,256211,Some-college,10,Never-married,Machine-op-inspct,Other-relative,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n4,Local-gov,163685,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,266084,Some-college,10,Divorced,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Private,161111,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,199031,Some-college,10,Divorced,Transport-moving,Own-child,White,Male,0,1,2,United-States,<=50K\n3,Private,166634,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,Germany,<=50K\n4,Self-emp-not-inc,204085,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,369527,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,464945,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,174684,HS-grad,9,Divorced,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,166295,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,220511,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,246936,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,104509,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,?,266337,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,252168,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,92093,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,88055,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,129591,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,142719,HS-grad,9,Married-spouse-absent,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,?,264924,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,128796,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,115336,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,4,United-States,<=50K\n3,Private,190333,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,179444,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,218676,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,148194,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,184833,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,280639,HS-grad,9,Widowed,Other-service,Other-relative,White,Female,1,0,0,United-States,<=50K\n0,Private,217769,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,?,180553,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,56009,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,255334,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n3,Self-emp-inc,328216,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,?,>50K\n1,Private,349154,10th,6,Separated,Farming-fishing,Unmarried,White,Female,0,0,2,Guatemala,<=50K\n2,Local-gov,24763,Some-college,10,Divorced,Transport-moving,Unmarried,White,Male,2,0,2,United-States,<=50K\n2,State-gov,41834,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,113466,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,130856,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,268797,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,0,United-States,<=50K\n3,Private,202117,11th,7,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,280146,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,70377,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,236696,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,222572,Masters,14,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,110702,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,1,0,3,United-States,<=50K\n2,Private,96129,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Local-gov,200492,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,193820,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,454508,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,3,2,United-States,<=50K\n4,Private,220789,Bachelors,13,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,101345,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,Canada,>50K\n2,Private,140559,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,64885,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,402361,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,143582,HS-grad,9,Separated,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,3,China,<=50K\n3,Private,185385,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,112706,Assoc-voc,11,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,130364,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,147428,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,205895,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,273569,HS-grad,9,Widowed,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,153160,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,167918,Masters,14,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n2,Private,195661,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,146243,Some-college,10,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,?,105428,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,149943,HS-grad,9,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Male,0,0,3,?,<=50K\n3,Local-gov,246197,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,192563,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,244115,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Local-gov,98587,Some-college,10,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,145886,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,244315,HS-grad,9,Divorced,Craft-repair,Other-relative,Other,Male,0,0,2,United-States,<=50K\n3,Private,192779,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,209464,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,25141,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,405793,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,53498,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,476653,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,162312,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,4,South,<=50K\n2,Private,277022,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Female,2,0,2,Nicaragua,<=50K\n2,State-gov,109762,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,123031,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,3,Trinadad&Tobago,<=50K\n3,Federal-gov,119890,Assoc-voc,11,Separated,Tech-support,Not-in-family,Other,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,409230,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,223308,Masters,14,Separated,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,?,129150,10th,6,Separated,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,119199,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,46221,Doctorate,16,Married-spouse-absent,Other-service,Not-in-family,White,Male,4,0,3,?,>50K\n2,Local-gov,351161,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,174533,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,324386,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,126568,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,275703,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,219611,Bachelors,13,Never-married,Sales,Not-in-family,Black,Female,1,0,3,United-States,<=50K\n3,Private,200471,11th,7,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,155261,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,State-gov,74040,7th-8th,4,Divorced,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,226296,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,211968,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,126446,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,262885,11th,7,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,188069,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,113546,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,227070,10th,6,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,136997,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,?,119006,HS-grad,9,Widowed,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,212407,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,197810,Masters,14,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,35309,Bachelors,13,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Male,0,0,1,?,<=50K\n2,Private,141802,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,?,184513,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,>50K\n1,Self-emp-not-inc,124187,Assoc-acdm,12,Never-married,Other-service,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n0,Private,201743,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,156736,10th,6,Never-married,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,47261,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,150693,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,233734,Masters,14,Divorced,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,>50K\n2,State-gov,35969,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,159550,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,190823,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,213378,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,257500,HS-grad,9,Separated,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,488706,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,239405,5th-6th,3,Divorced,Other-service,Other-relative,Black,Female,0,0,2,Haiti,<=50K\n1,Federal-gov,105189,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,2,0,3,United-States,<=50K\n4,State-gov,109735,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,172942,Some-college,10,Divorced,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,209899,Masters,14,Never-married,Tech-support,Not-in-family,Black,Female,3,0,3,United-States,>50K\n1,Self-emp-inc,87745,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,187881,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,234125,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,272944,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,129232,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,100345,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,4,0,3,United-States,>50K\n4,Self-emp-not-inc,195835,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,251854,11th,7,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,103474,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,22042,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,343721,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,232368,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,174478,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,282023,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,274690,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,251675,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,El-Salvador,<=50K\n1,?,647882,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,?,<=50K\n4,Private,128367,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Male,1,0,2,United-States,<=50K\n1,Private,37380,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,173730,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,353824,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,225890,Some-college,10,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,State-gov,147147,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n3,Private,233780,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,Black,Female,1,0,2,United-States,<=50K\n1,Private,394927,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n1,Local-gov,188682,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,?,115209,Prof-school,15,Married-spouse-absent,?,Unmarried,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n2,Private,277192,5th-6th,3,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,2,Mexico,<=50K\n0,Private,314182,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,220776,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n1,Local-gov,189269,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,35429,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,154374,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n4,Private,161460,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,251487,7th-8th,4,Widowed,Machine-op-inspct,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,177531,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,53942,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,113481,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,361324,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,330087,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,276221,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,121055,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,118696,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,289741,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,238401,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,262038,5th-6th,3,Married-spouse-absent,Farming-fishing,Unmarried,White,Male,0,0,1,Mexico,<=50K\n4,Self-emp-not-inc,26911,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,161155,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,252519,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,Haiti,>50K\n2,Private,43712,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n4,?,167826,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,188900,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,120057,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,134113,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,165822,Some-college,10,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,99161,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,74581,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Male,0,0,4,United-States,<=50K\n0,Private,304643,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,121821,1st-4th,2,Married-civ-spouse,Other-service,Husband,Other,Male,0,0,2,Dominican-Republic,<=50K\n0,Private,154863,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Male,0,0,1,United-States,<=50K\n2,Local-gov,365430,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Canada,>50K\n1,Private,183111,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,50178,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,186845,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,159908,12th,8,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,162189,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,1,0,2,Peru,<=50K\n1,Private,128509,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Female,0,0,2,El-Salvador,<=50K\n0,Private,143032,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,382368,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,210013,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,293928,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,208503,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n2,State-gov,191841,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,3,0,2,United-States,>50K\n3,Self-emp-not-inc,355978,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n4,Local-gov,202738,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,144322,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,155141,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,4,0,United-States,>50K\n0,Private,160120,10th,6,Never-married,Transport-moving,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n1,Self-emp-inc,190450,HS-grad,9,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,Germany,<=50K\n2,Private,212900,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,115677,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,252250,11th,7,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,212041,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,State-gov,198145,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,>50K\n4,Local-gov,113658,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,32426,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,98791,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,203828,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,State-gov,186634,12th,8,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,125147,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,247455,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,2,0,2,United-States,>50K\n0,Private,97215,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,330826,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,200802,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,156266,HS-grad,9,Never-married,Sales,Own-child,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,72257,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,363087,HS-grad,9,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,25955,Some-college,10,Never-married,Craft-repair,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,334633,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,109162,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,569761,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,209900,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,272986,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,?,52267,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,82946,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,104651,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,58441,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,269733,HS-grad,9,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,128453,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,179468,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,183081,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,102938,Bachelors,13,Never-married,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n1,?,157289,11th,7,Never-married,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,359828,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,155659,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,585203,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,3,0,2,United-States,>50K\n4,Private,173601,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,214541,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,1,2,United-States,<=50K\n3,Self-emp-not-inc,163352,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n2,Self-emp-not-inc,153976,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,247676,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,2,0,2,United-States,<=50K\n3,State-gov,155372,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,329733,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,162576,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,176520,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,State-gov,226885,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,120781,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,375827,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,205504,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,198813,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,254291,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,159908,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,40000,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n4,Private,102874,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,117381,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,3,0,2,United-States,>50K\n4,Private,180239,Masters,14,Widowed,Craft-repair,Unmarried,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n4,Private,539563,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,261561,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,81057,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,160120,Bachelors,13,Married-civ-spouse,Sales,Husband,Other,Male,0,0,2,?,<=50K\n0,Private,41979,10th,6,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,275110,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,4,United-States,>50K\n4,Private,265661,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,193246,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,France,<=50K\n1,Private,236543,12th,8,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,2,Mexico,<=50K\n0,Private,29510,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,State-gov,105804,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,194604,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,1038553,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,209320,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,193231,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,1,0,3,United-States,<=50K\n2,Private,307468,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,255941,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n2,Local-gov,107845,Assoc-acdm,12,Divorced,Protective-serv,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Self-emp-not-inc,567788,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,91857,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,732569,9th,5,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,86613,1st-4th,2,Never-married,Other-service,Not-in-family,White,Male,0,0,0,El-Salvador,<=50K\n3,Private,35961,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Female,0,0,1,Germany,<=50K\n3,Private,114754,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,235124,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,2,United-States,>50K\n2,Local-gov,218490,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,3,0,1,United-States,>50K\n1,Private,329426,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,181015,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,264740,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,381153,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,189759,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,230467,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,1,2,Germany,<=50K\n2,Private,218542,Some-college,10,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,298507,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,>50K\n4,Private,111189,7th-8th,4,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,1,Dominican-Republic,<=50K\n0,Private,168997,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,168894,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,149809,Assoc-acdm,12,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,344073,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,416165,Some-college,10,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,41490,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,40269,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,?,243256,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,250536,Some-college,10,Separated,Other-service,Unmarried,Black,Female,0,0,1,Haiti,<=50K\n3,Federal-gov,105586,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n4,Private,51499,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,189878,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,179481,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,2,0,2,United-States,<=50K\n0,Private,299765,Some-college,10,Separated,Adm-clerical,Other-relative,Black,Female,0,0,2,Jamaica,<=50K\n2,Self-emp-inc,155664,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,>50K\n1,Private,54608,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,174702,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,285020,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,201145,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,125796,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,1,Jamaica,<=50K\n3,Private,249072,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,99156,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,State-gov,94754,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,India,<=50K\n2,Private,111128,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,157887,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,74194,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,168191,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,28334,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,84278,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,?,>50K\n2,Private,721161,Some-college,10,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,188069,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,145178,Some-college,10,Divorced,Craft-repair,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,52967,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,177578,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,185384,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n4,Private,66008,HS-grad,9,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,3,England,<=50K\n4,Private,329059,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,348802,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,34233,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,509629,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,27956,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,4,Philippines,<=50K\n2,Local-gov,83286,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,309098,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,188950,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,224217,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,222899,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,123306,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,279337,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,347166,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Local-gov,251396,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Canada,>50K\n0,Self-emp-inc,143034,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,57635,Assoc-voc,11,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n1,Local-gov,162651,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n4,Private,28334,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Local-gov,84570,Some-college,10,Never-married,Adm-clerical,Own-child,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,181091,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,Iran,>50K\n3,Local-gov,117496,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n4,State-gov,216160,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Columbia,>50K\n3,Self-emp-inc,204447,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,374969,10th,6,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,35015,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,179869,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,137733,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,193125,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,103649,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,State-gov,54260,Doctorate,16,Married-civ-spouse,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,1,0,2,China,<=50K\n1,Private,197932,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,2,Mexico,>50K\n2,Private,249720,Bachelors,13,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,223613,1st-4th,2,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,1,Cuba,<=50K\n0,Private,259865,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,301694,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n3,Self-emp-inc,276934,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,395512,12th,8,Married-civ-spouse,Machine-op-inspct,Other-relative,Other,Male,0,0,2,Mexico,<=50K\n2,Private,168071,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,45317,Some-college,10,Separated,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,311177,Some-college,10,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,190636,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,1,3,United-States,>50K\n4,Private,221336,10th,6,Widowed,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n0,Private,120691,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,1,?,<=50K\n1,Private,107389,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,293440,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,145409,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,213902,5th-6th,3,Never-married,Priv-house-serv,Other-relative,White,Female,0,0,2,El-Salvador,<=50K\n4,Private,100099,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,191856,Masters,14,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n2,Local-gov,233891,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,96073,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,England,>50K\n1,Private,474136,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Self-emp-not-inc,355856,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,Philippines,<=50K\n0,?,144685,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,2,2,Taiwan,<=50K\n3,Self-emp-not-inc,139212,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,143931,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,160703,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,191291,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,68729,Some-college,10,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,3,2,United-States,>50K\n4,Private,119986,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n2,Private,227545,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,32776,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,228881,Some-college,10,Separated,Machine-op-inspct,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n0,Private,84648,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Federal-gov,101996,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,?,68954,HS-grad,9,Widowed,?,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n3,Local-gov,285060,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n3,Self-emp-inc,209569,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,>50K\n1,Local-gov,331126,Bachelors,13,Never-married,Protective-serv,Own-child,Black,Male,0,0,3,United-States,<=50K\n1,Private,279872,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,150560,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,4,0,2,United-States,>50K\n1,Local-gov,185647,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,128871,7th-8th,4,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Federal-gov,386331,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,3,United-States,<=50K\n3,Private,117814,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,220609,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,117022,HS-grad,9,Married-spouse-absent,Farming-fishing,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,176751,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n4,?,76371,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,80410,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,127202,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,121471,11th,7,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,219086,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,271571,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,241583,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,374253,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,214993,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,199995,Bachelors,13,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,450924,12th,8,Married-civ-spouse,Other-service,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,120359,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,93125,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,1,United-States,<=50K\n0,Private,187513,Assoc-voc,11,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,243569,Some-college,10,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,295510,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,29732,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,211743,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,251396,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n4,Private,477697,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,151584,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,193882,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,117542,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,0,United-States,<=50K\n1,Private,242460,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,411395,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,191025,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,154571,Assoc-voc,11,Never-married,Sales,Unmarried,Asian-Pac-Islander,Male,0,0,3,South,<=50K\n1,Private,208657,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,29599,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,423711,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,122000,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,148581,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,222978,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,149118,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,218407,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,4,Cuba,<=50K\n3,Self-emp-not-inc,112200,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Male,4,0,2,United-States,>50K\n2,Private,85604,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,111232,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,99199,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,199995,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,122850,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n4,?,90557,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,?,271935,11th,7,Never-married,?,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,361497,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,399020,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,345277,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,Federal-gov,55233,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,200515,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,188119,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,176683,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,309178,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,40021,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-inc,49923,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,36635,Some-college,10,Never-married,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,325706,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,India,>50K\n1,Private,124407,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,301568,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,339956,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Private,176335,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,198452,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,213945,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,Iran,>50K\n3,Private,171807,Bachelors,13,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,>50K\n0,Private,362826,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,344329,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,137678,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,175424,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,State-gov,73296,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,1,0,2,United-States,<=50K\n1,State-gov,137613,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,0,Taiwan,<=50K\n4,Self-emp-not-inc,354405,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,130057,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,362883,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,49017,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,149943,Masters,14,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n2,Self-emp-inc,99185,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,294708,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,228238,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,156819,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,332727,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,289944,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,116103,HS-grad,9,Widowed,Exec-managerial,Other-relative,White,Male,1,0,2,United-States,<=50K\n1,Private,24153,Some-college,10,Married-civ-spouse,Other-service,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,273425,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,231183,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,313930,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,114483,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,162108,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,168807,7th-8th,4,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,143828,Masters,14,Divorced,Prof-specialty,Unmarried,Black,Female,3,0,2,United-States,>50K\n4,Private,242769,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,England,<=50K\n3,Local-gov,111558,Some-college,10,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,69770,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,291981,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,102460,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,151584,HS-grad,9,Divorced,Sales,Own-child,White,Male,0,2,2,United-States,<=50K\n3,Local-gov,287320,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,115677,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,239632,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,409172,Bachelors,13,Married-civ-spouse,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,186849,HS-grad,9,Never-married,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,118861,10th,6,Married-civ-spouse,Craft-repair,Wife,Other,Female,0,0,3,Guatemala,<=50K\n1,Private,142689,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,?,<=50K\n2,State-gov,170924,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,274451,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,153489,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,186489,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,192409,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,State-gov,337599,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,195545,HS-grad,9,Divorced,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,61892,HS-grad,9,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,175697,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,80303,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,419658,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,319163,Some-college,10,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,126743,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Mexico,<=50K\n2,Private,301568,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,120461,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,268145,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,257337,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,213354,Masters,14,Separated,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n0,Private,303431,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,124963,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,158218,HS-grad,9,Never-married,Farming-fishing,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,State-gov,553473,Bachelors,13,Married-civ-spouse,Protective-serv,Wife,Black,Female,0,0,3,United-States,<=50K\n3,Private,46155,HS-grad,9,Married-civ-spouse,Priv-house-serv,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,138714,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,231781,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,496414,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,?,<=50K\n0,Private,19410,HS-grad,9,Divorced,Sales,Unmarried,Amer-Indian-Eskimo,Female,0,0,3,United-States,<=50K\n4,?,28471,9th,5,Widowed,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,185821,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,272667,Assoc-acdm,12,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,?,194031,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,144995,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,162494,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,171968,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,232569,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,161819,11th,7,Separated,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,123343,11th,7,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,105449,Bachelors,13,Never-married,Priv-house-serv,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,181717,Assoc-voc,11,Separated,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,102359,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,72887,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,154571,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Private,255191,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,174789,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,110402,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,208513,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,121904,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,34335,HS-grad,9,Divorced,Sales,Not-in-family,Amer-Indian-Eskimo,Male,4,0,2,United-States,>50K\n3,Private,59380,Some-college,10,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,135285,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,4,1,United-States,<=50K\n2,Self-emp-inc,126675,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,217363,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,91836,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,184813,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,178142,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,102359,9th,5,Widowed,Handlers-cleaners,Unmarried,White,Male,0,4,2,United-States,>50K\n1,Self-emp-inc,281832,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Cuba,>50K\n1,Private,96226,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,195124,7th-8th,4,Married-spouse-absent,Prof-specialty,Other-relative,White,Male,0,0,1,Puerto-Rico,<=50K\n0,Private,56322,Some-college,10,Never-married,Other-service,Own-child,White,Male,1,0,1,United-States,<=50K\n3,Local-gov,97449,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,339773,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,210926,HS-grad,9,Separated,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,199499,Assoc-voc,11,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Federal-gov,190729,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,191385,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Private,193479,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,225165,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,346766,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,152307,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,79990,11th,7,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,170649,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,197207,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,229732,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,204402,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,181065,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,179579,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,?,>50K\n3,Private,237729,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,4,4,United-States,>50K\n0,?,164574,Assoc-acdm,12,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,179574,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,191782,HS-grad,9,Never-married,Other-service,Other-relative,Black,Female,0,0,1,United-States,<=50K\n4,Private,146660,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,115945,Some-college,10,Never-married,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,210875,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,137898,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,216965,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,201554,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,57970,7th-8th,4,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,208378,12th,8,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,61343,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,283872,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n4,Private,225603,9th,5,Divorced,Farming-fishing,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,401333,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,278228,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,145377,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,120238,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,187215,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,2,United-States,>50K\n1,Self-emp-not-inc,144063,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,238721,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,164920,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,152493,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,92968,Bachelors,13,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,136836,HS-grad,9,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Federal-gov,216453,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,349148,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,State-gov,309620,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,0,Taiwan,<=50K\n0,State-gov,347803,Some-college,10,Never-married,Adm-clerical,Not-in-family,Other,Male,0,0,0,United-States,<=50K\n2,Private,85995,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,167428,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,164569,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,308279,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,56322,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,?,203015,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,211654,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,120126,9th,5,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,239043,11th,7,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,179761,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,312017,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,Germany,<=50K\n3,Private,257485,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,49243,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,229716,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,341672,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n0,Private,32311,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,275236,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,400356,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,184596,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,<=50K\n0,Private,186909,HS-grad,9,Never-married,Sales,Other-relative,White,Female,1,0,1,United-States,<=50K\n2,Private,152420,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,United-States,<=50K\n2,State-gov,261929,Doctorate,16,Married-spouse-absent,Prof-specialty,Unmarried,White,Male,4,0,4,United-States,>50K\n0,Private,235442,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,161691,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,?,173945,11th,7,Married-civ-spouse,?,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,355918,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n2,State-gov,198660,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,122649,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,421967,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n3,Local-gov,259377,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,2,United-States,>50K\n3,Private,74305,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,34340,7th-8th,4,Widowed,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,182752,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,Iran,<=50K\n0,?,48393,Some-college,10,Never-married,?,Own-child,White,Male,0,0,4,United-States,<=50K\n2,Private,34248,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,186677,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,167851,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,146460,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,209650,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,132986,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,94429,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,252406,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,174592,Masters,14,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,151322,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,37237,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,101192,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,152900,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,94081,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,329408,11th,7,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,106028,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,?,164866,10th,6,Divorced,?,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-inc,167793,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,138692,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,173968,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,228320,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,96585,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,156580,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,Puerto-Rico,<=50K\n4,Private,210673,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Local-gov,137753,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,0,United-States,<=50K\n1,Private,29865,HS-grad,9,Divorced,Sales,Not-in-family,Amer-Indian-Eskimo,Female,0,0,3,United-States,<=50K\n1,Private,196044,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,308995,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,Jamaica,<=50K\n4,Private,159008,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,0,United-States,>50K\n1,Private,362491,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,94395,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,320047,10th,6,Married-spouse-absent,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,98535,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,183170,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,?,331511,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,195686,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,178244,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,127833,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,269722,Masters,14,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,136819,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,205604,5th-6th,3,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n1,Private,132078,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,234880,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,196816,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,237943,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Self-emp-inc,140852,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,105614,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,83492,7th-8th,4,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,225772,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,242713,12th,8,Separated,Priv-house-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,355865,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,173316,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,35662,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,4,United-States,>50K\n0,Private,297246,11th,7,Never-married,Priv-house-serv,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,108945,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,112158,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,?,<=50K\n0,Self-emp-not-inc,57298,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,115323,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,?,<=50K\n3,Self-emp-not-inc,164582,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,295067,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,Private,177265,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,336543,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,Asian-Pac-Islander,Male,0,0,2,Hong,>50K\n2,Self-emp-not-inc,52870,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,316820,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n2,Local-gov,200153,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,453067,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,27166,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,299598,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,122048,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,345277,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,113147,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,34007,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,255014,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,152667,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,231053,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,103651,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,124137,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,198183,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,183627,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,3,Ireland,<=50K\n0,Private,466458,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,114396,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,186376,Bachelors,13,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,4,Philippines,>50K\n1,Private,290964,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,1,2,United-States,<=50K\n4,Self-emp-not-inc,282095,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,244974,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,114691,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,107160,12th,8,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,142573,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,203833,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,47791,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,133729,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,135339,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,?,>50K\n3,Private,135803,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,4,0,3,South,>50K\n1,Private,128591,9th,5,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,133853,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,?,137363,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,243569,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,119156,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,391114,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,252506,Some-college,10,Divorced,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,State-gov,117503,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,Italy,<=50K\n0,State-gov,117833,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,294183,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,394927,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,259323,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n0,?,207988,HS-grad,9,Married-civ-spouse,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,96635,Some-college,10,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,1,South,<=50K\n1,Private,192283,Assoc-voc,11,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,214881,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,167474,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,110713,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,201204,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,197666,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,162002,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,263561,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,2,United-States,>50K\n2,Private,224799,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,89942,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,238685,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,38795,9th,5,Separated,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,90414,Bachelors,13,Married-spouse-absent,Craft-repair,Unmarried,White,Female,0,0,3,Ireland,<=50K\n0,Private,190805,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,23780,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,285263,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,177331,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,347530,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Private,230039,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Female,0,1,2,United-States,<=50K\n0,?,210547,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,204752,12th,8,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,104001,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,253116,10th,6,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,169037,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,202027,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,170099,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,212847,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n3,State-gov,307392,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,233428,HS-grad,9,Divorced,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,355728,Some-college,10,Separated,Exec-managerial,Not-in-family,White,Male,0,3,2,England,<=50K\n3,Private,177995,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,Mexico,>50K\n0,Private,283613,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,184598,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,185647,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,192894,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n2,Self-emp-not-inc,284706,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,3,3,United-States,>50K\n2,Private,179579,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,131679,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,132973,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,154713,HS-grad,9,Divorced,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,121718,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Italy,<=50K\n1,Private,255279,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,202559,Bachelors,13,Married-civ-spouse,Other-service,Other-relative,Asian-Pac-Islander,Male,0,0,1,Philippines,<=50K\n0,Private,123095,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,1,2,United-States,<=50K\n1,Private,153326,Bachelors,13,Married-civ-spouse,Prof-specialty,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,75695,Some-college,10,Separated,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-inc,206609,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,234780,HS-grad,9,Never-married,Farming-fishing,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,178778,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,171355,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,0,United-States,<=50K\n4,Federal-gov,95680,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,196673,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,2,0,2,United-States,<=50K\n3,Federal-gov,73670,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n4,Self-emp-not-inc,139960,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,397280,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,?,<=50K\n1,Private,60374,HS-grad,9,Widowed,Craft-repair,Unmarried,White,Female,0,1,1,United-States,<=50K\n3,Private,421561,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,245196,10th,6,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,27620,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,187570,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,102884,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,228399,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,340234,HS-grad,9,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,4,0,2,United-States,>50K\n2,Private,176293,Some-college,10,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,108435,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,161187,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n0,Private,278391,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,157941,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,182866,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,370888,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,1,0,0,Germany,<=50K\n1,Private,206512,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,357954,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,India,<=50K\n1,Private,189346,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,234652,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,113436,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,204145,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,157305,Preschool,1,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Dominican-Republic,<=50K\n1,Private,104045,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,280422,Some-college,10,Separated,Other-service,Not-in-family,White,Female,0,0,1,Peru,<=50K\n4,Federal-gov,173754,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,211154,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,321435,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,177083,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,178829,Masters,14,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n1,Federal-gov,287658,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,209894,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,334744,HS-grad,9,Separated,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,306967,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,52187,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,101978,HS-grad,9,Separated,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,483530,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,77357,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,149770,Masters,14,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,328606,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Male,4,0,4,United-States,>50K\n4,?,172652,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,188293,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,116608,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,348960,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,329530,9th,5,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Local-gov,93476,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,195744,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,125833,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n0,State-gov,191117,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,311020,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,210464,HS-grad,9,Never-married,Other-service,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Private,135289,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,156266,9th,5,Married-civ-spouse,Farming-fishing,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,154210,Some-college,10,Never-married,Adm-clerical,Other-relative,Asian-Pac-Islander,Male,0,0,0,Puerto-Rico,<=50K\n4,?,160625,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,2,0,0,United-States,>50K\n2,Self-emp-not-inc,331481,Bachelors,13,Divorced,Craft-repair,Not-in-family,Black,Male,0,2,3,?,<=50K\n1,Self-emp-not-inc,249249,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,261725,1st-4th,2,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n0,Private,239612,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,226696,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,190330,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,193755,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Private,192740,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,201924,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,77146,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,126414,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,?,<=50K\n1,Private,43652,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,227244,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,3,United-States,>50K\n1,Private,160731,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,287878,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,166758,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,183811,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,2,United-States,<=50K\n2,Self-emp-not-inc,254818,Masters,14,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,2,Peru,<=50K\n0,?,220517,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,295046,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,190568,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,1,2,United-States,<=50K\n2,State-gov,211915,Some-college,10,Separated,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,295621,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,1,United-States,>50K\n1,Private,204567,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,204235,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,186982,Some-college,10,Separated,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,133586,HS-grad,9,Married-civ-spouse,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,165930,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,164898,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,278155,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,115705,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,150553,9th,5,Married-spouse-absent,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n1,Private,185127,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,201595,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-inc,165815,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,102420,Bachelors,13,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n3,Local-gov,344172,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,222450,Some-college,10,Separated,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,212245,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,190625,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,203488,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,304260,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Local-gov,243665,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,189238,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,Mexico,<=50K\n2,Private,77373,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,410351,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,36385,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,110150,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,198316,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,127772,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,199058,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,285730,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Local-gov,334133,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n4,State-gov,97030,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,67090,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,397963,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,1,0,0,United-States,<=50K\n3,Private,182533,Bachelors,13,Never-married,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n0,Private,560804,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,365050,7th-8th,4,Never-married,Farming-fishing,Unmarried,Black,Female,0,0,0,United-States,<=50K\n0,Private,110200,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,150025,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,?,<=50K\n2,Private,299828,5th-6th,3,Separated,Sales,Unmarried,Black,Female,0,0,1,Puerto-Rico,<=50K\n1,Private,109282,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,103435,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,34747,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,137522,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,?,>50K\n2,Private,286789,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,211032,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,?,192916,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,2,0,0,United-States,<=50K\n1,Private,219318,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,Puerto-Rico,<=50K\n3,Private,112873,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,36069,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,<=50K\n3,Private,73434,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Germany,>50K\n3,Private,200576,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,172962,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,44006,Assoc-voc,11,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,234474,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n2,Private,212826,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,234901,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,200700,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,41258,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,249644,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,?,230165,Bachelors,13,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,351731,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,114765,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,349884,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-inc,204247,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,143392,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,37913,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Italy,>50K\n0,Self-emp-inc,150683,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,207611,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,State-gov,319666,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,155961,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Local-gov,117833,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,447079,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Self-emp-inc,142404,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,155752,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,252292,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,111450,12th,8,Never-married,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,528616,5th-6th,3,Never-married,Other-service,Other-relative,White,Male,0,0,2,Mexico,<=50K\n0,Self-emp-not-inc,228786,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,80572,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,180271,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,United-States,>50K\n3,Federal-gov,237819,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,157612,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,1,0,2,United-States,<=50K\n4,Private,379062,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,191910,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,326064,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,2,0,1,United-States,<=50K\n0,Private,312353,12th,8,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Local-gov,213307,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,209057,Bachelors,13,Married-spouse-absent,Sales,Own-child,White,Male,0,0,3,United-States,>50K\n2,Private,340148,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,154171,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,94064,Assoc-voc,11,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,119098,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,388496,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,0,Puerto-Rico,>50K\n3,Private,181363,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,210031,HS-grad,9,Divorced,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,206951,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,485496,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,210259,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,118551,9th,5,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,180911,11th,7,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,>50K\n3,State-gov,242517,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,298113,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,277783,Masters,14,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Private,155862,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,171924,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,243900,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,231160,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,356882,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,49020,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,105460,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,England,<=50K\n4,Private,157749,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,131568,7th-8th,4,Divorced,Transport-moving,Unmarried,White,Male,0,0,0,United-States,<=50K\n3,Private,332355,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,204501,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,305767,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n1,Private,129761,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,130126,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,102828,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,160984,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,255282,11th,7,Never-married,?,Own-child,Black,Male,0,2,3,United-States,<=50K\n0,?,346341,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,285897,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,356689,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,192386,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,394860,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,113129,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,55929,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,177018,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,161141,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,309463,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,165468,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,49218,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,119129,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,0,United-States,<=50K\n4,Self-emp-not-inc,162130,5th-6th,3,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,4,United-States,>50K\n2,Federal-gov,129573,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,2,2,United-States,<=50K\n0,Private,306850,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,135296,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,4,2,United-States,>50K\n2,Self-emp-not-inc,187322,HS-grad,9,Divorced,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,55674,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,148298,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,34845,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,200733,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,191858,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,425528,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,0,4,United-States,<=50K\n1,Private,44780,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,0,United-States,>50K\n1,Private,125856,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,100508,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,148294,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,39324,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,147397,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,24728,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,177616,5th-6th,3,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,163826,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,199947,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,386949,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,116133,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,196307,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,177181,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,324854,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,Private,188505,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,State-gov,502316,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,26892,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,102058,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,167728,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,233681,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,26756,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,101890,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,192337,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,England,>50K\n3,Private,340982,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,1,0,2,Philippines,>50K\n3,State-gov,102308,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,84747,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,197752,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,185336,HS-grad,9,Widowed,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,?,289984,Some-college,10,Never-married,?,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,125417,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,278480,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,146412,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,193042,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,53956,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,3,3,United-States,<=50K\n4,Private,175491,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,3,Ecuador,>50K\n4,?,33186,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,144154,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,194901,Prof-school,15,Divorced,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,335777,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n3,Private,139268,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,33887,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,283613,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,141245,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,Puerto-Rico,<=50K\n3,Private,298130,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,186096,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n4,Private,187656,Some-college,10,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,102308,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,124639,Some-college,10,Separated,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,388112,1st-4th,2,Never-married,Farming-fishing,Unmarried,White,Male,0,0,4,Mexico,<=50K\n0,Private,109952,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,164529,12th,8,Never-married,Farming-fishing,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,247750,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n0,State-gov,103588,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,248919,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,<=50K\n1,Self-emp-not-inc,178551,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,136137,Some-college,10,Married-civ-spouse,Exec-managerial,Other-relative,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,55377,Bachelors,13,Never-married,Adm-clerical,Unmarried,Black,Male,0,0,2,United-States,>50K\n2,Local-gov,177728,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,243580,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,188535,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,63910,HS-grad,9,Divorced,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n0,Private,219535,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,180609,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,59313,Some-college,10,Separated,Other-service,Not-in-family,Black,Male,0,0,2,?,<=50K\n4,Private,170428,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Female,0,0,0,Puerto-Rico,<=50K\n3,Private,102615,Masters,14,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,193132,9th,5,Separated,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,124137,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,136629,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,148995,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,?,203076,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,63424,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,241895,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,266973,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,188048,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,366929,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,214129,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,250818,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,240979,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,98283,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,India,>50K\n1,Private,104746,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,<=50K\n2,Private,103710,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,159580,Bachelors,13,Never-married,Other-service,Own-child,Black,Female,0,0,4,United-States,<=50K\n2,Private,117409,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,140001,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,31650,Bachelors,13,Married-civ-spouse,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,State-gov,80771,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,66278,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,107801,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,2,1,United-States,<=50K\n1,Private,206609,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,282461,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,188246,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,279763,11th,7,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Private,467799,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,137674,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,158284,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,204219,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,State-gov,210498,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,63526,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n2,Federal-gov,216924,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,372559,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Federal-gov,199114,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,4,2,United-States,<=50K\n3,Private,168539,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,189911,11th,7,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,61958,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,0,United-States,<=50K\n3,State-gov,68898,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Male,0,4,2,United-States,>50K\n2,Private,204450,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,311350,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,113750,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,359591,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,132879,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,301199,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,State-gov,267540,Some-college,10,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,185407,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Poland,>50K\n3,Self-emp-inc,191277,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,78980,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,241463,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,4,United-States,>50K\n3,Private,216999,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,120508,Bachelors,13,Divorced,Protective-serv,Unmarried,White,Female,0,0,3,Germany,<=50K\n1,Private,122612,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Thailand,<=50K\n0,Private,94057,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,State-gov,197558,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,351869,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,1,2,United-States,>50K\n3,Self-emp-not-inc,121761,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,?,<=50K\n2,Federal-gov,184556,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,268281,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,235646,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,186909,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,35783,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,188861,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,363591,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,469921,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,51150,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,174325,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,347530,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,72351,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,185129,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,>50K\n2,Private,188571,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,255252,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,291951,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,223046,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,37937,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,295127,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,183801,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,116218,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,143069,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,235951,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,112840,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,146454,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,0,Greece,<=50K\n3,Federal-gov,43705,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,122283,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,2,India,>50K\n0,Private,376647,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,1,0,1,United-States,<=50K\n3,Private,101299,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,96798,5th-6th,3,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,194654,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,206889,Assoc-acdm,12,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,226902,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,State-gov,150755,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,200679,HS-grad,9,Never-married,Farming-fishing,Own-child,Black,Male,0,0,3,United-States,<=50K\n4,Private,183678,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,33138,12th,8,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,57071,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n4,?,35303,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,3,0,1,United-States,>50K\n2,Private,188576,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n1,Private,169496,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,58124,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,356344,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,444134,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n0,?,340117,11th,7,Never-married,?,Unmarried,Black,Female,0,0,3,United-States,<=50K\n1,Private,219619,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,334585,10th,6,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,331046,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,?,443179,Bachelors,13,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,?,239529,11th,7,Widowed,?,Not-in-family,White,Female,2,0,1,United-States,<=50K\n0,Private,100345,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,205653,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,112383,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,21626,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,1,0,3,United-States,<=50K\n0,Private,135568,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,190532,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Federal-gov,266598,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,116608,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,353263,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,State-gov,157617,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,21698,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,77143,12th,8,Separated,Transport-moving,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,State-gov,342852,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,176602,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,146343,Some-college,10,Married-civ-spouse,Sales,Wife,Black,Female,0,0,2,United-States,<=50K\n4,?,146645,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,221966,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,1,0,3,United-States,<=50K\n0,Private,215546,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,State-gov,173020,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,247734,Bachelors,13,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,252202,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,497300,HS-grad,9,Never-married,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,426431,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,162410,Some-college,10,Widowed,Tech-support,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,?,143516,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n0,Private,190350,10th,6,Married-civ-spouse,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,194504,Some-college,10,Separated,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Federal-gov,110884,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,187652,Assoc-acdm,12,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,81400,1st-4th,2,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,El-Salvador,<=50K\n4,?,97831,HS-grad,9,Widowed,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,180920,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,189186,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,144172,Assoc-acdm,12,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,607848,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,207301,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,293073,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,210452,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,41400,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,164170,Bachelors,13,Never-married,Tech-support,Unmarried,Asian-Pac-Islander,Female,0,0,0,Philippines,<=50K\n3,Private,112906,Masters,14,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,126268,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,208311,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,0,United-States,>50K\n4,Private,28291,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Local-gov,121998,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Federal-gov,31621,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,108386,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,134727,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,208391,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,112271,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,173350,Assoc-voc,11,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,243190,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,0,India,>50K\n3,Private,185436,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,290409,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,80058,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,370045,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,36936,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,231180,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,119793,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,102178,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,135039,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,?,317780,Some-college,10,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,232840,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,33975,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,256997,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,298301,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,310380,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,182100,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,501172,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Mexico,<=50K\n2,State-gov,143939,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,>50K\n0,Private,85088,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,282313,10th,6,Never-married,Handlers-cleaners,Own-child,Black,Male,0,2,2,United-States,<=50K\n2,Private,230054,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,236338,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,321943,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,218782,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Other,Male,0,0,2,United-States,<=50K\n1,Private,191385,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,Canada,<=50K\n2,Self-emp-inc,185497,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,0,0,4,?,<=50K\n1,Private,126129,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,199268,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,255693,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,203488,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,203233,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,203836,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,187847,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,116358,Some-college,10,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Self-emp-inc,198660,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,4,United-States,>50K\n2,Self-emp-not-inc,89636,Bachelors,13,Married-civ-spouse,Sales,Wife,Asian-Pac-Islander,Female,0,0,3,South,<=50K\n3,Private,120629,Some-college,10,Widowed,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,150226,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,137898,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,146574,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,88725,HS-grad,9,Never-married,Craft-repair,Not-in-family,Other,Female,0,0,2,?,<=50K\n0,Private,142022,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,284898,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,212448,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,203039,9th,5,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,227489,HS-grad,9,Never-married,Tech-support,Other-relative,Black,Male,0,0,2,?,<=50K\n0,Private,105289,10th,6,Never-married,Other-service,Other-relative,Black,Female,0,0,0,United-States,<=50K\n1,?,223745,Some-college,10,Never-married,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,242994,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,196385,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,116202,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,140045,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,133503,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Private,226585,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,85041,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,162442,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,3,United-States,>50K\n4,Private,279980,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,0,United-States,>50K\n0,Private,216563,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,231964,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,263855,12th,8,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,124915,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,178312,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,215862,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n0,State-gov,39236,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,349910,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,75839,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,176711,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,266525,Some-college,10,Never-married,Prof-specialty,Other-relative,Black,Female,1,0,0,United-States,<=50K\n0,?,34307,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,331776,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,111469,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,198965,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,288185,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,198050,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,242580,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n2,Private,173128,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,87905,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,173704,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,93225,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,323269,Some-college,10,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Private,158046,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,4,United-States,<=50K\n1,Private,133503,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,172296,Some-college,10,Separated,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,?,201105,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,176486,Some-college,10,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-inc,182750,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,82497,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,208872,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,145269,11th,7,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,19214,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,149347,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,53850,7th-8th,4,Married-civ-spouse,?,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Private,158294,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,1,0,2,United-States,>50K\n3,Private,152073,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,189623,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,341368,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,201603,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,1,0,2,United-States,<=50K\n1,Private,270572,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,285295,Bachelors,13,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n0,Private,126779,11th,7,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,?,202874,HS-grad,9,Separated,?,Unmarried,White,Female,0,0,2,Columbia,<=50K\n1,Private,373499,5th-6th,3,Never-married,Other-service,Not-in-family,White,Male,0,0,3,El-Salvador,<=50K\n0,Private,244773,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,State-gov,96862,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,162632,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,159755,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,37088,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,335421,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Never-worked,188535,7th-8th,4,Divorced,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,State-gov,349365,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,33002,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,330715,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,146857,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,275522,7th-8th,4,Widowed,Other-service,Unmarried,White,Female,0,0,4,United-States,<=50K\n0,Private,43646,HS-grad,9,Married-civ-spouse,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,154548,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,47907,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,238397,Bachelors,13,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,195949,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,?,354351,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,349169,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,158662,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,>50K\n0,Local-gov,23438,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,107302,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,174196,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,226871,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,124971,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,214061,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,441700,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,104892,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,234386,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,188278,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,244395,11th,7,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,30916,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,219565,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,377486,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,137232,HS-grad,9,Divorced,Protective-serv,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,233369,Some-college,10,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,71067,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,195176,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,98639,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,183778,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,123011,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,4,3,United-States,>50K\n0,Private,164938,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,2,0,2,United-States,<=50K\n1,?,147471,HS-grad,9,Divorced,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,206046,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,81497,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,189225,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,141264,Some-college,10,Never-married,Exec-managerial,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,Private,97939,Assoc-acdm,12,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,160829,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,483822,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,El-Salvador,<=50K\n3,State-gov,148738,Some-college,10,Divorced,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,289982,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,58702,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,1,0,3,United-States,>50K\n0,Private,146706,Some-college,10,Married-civ-spouse,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,420973,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,124959,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,121471,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,198237,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,280758,11th,7,Never-married,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Private,191544,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,261023,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,State-gov,231043,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,340917,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,167140,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,370795,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,209609,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,209454,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,78530,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,88922,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,86972,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,165468,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,134367,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,199058,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,183612,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,191982,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n0,Private,514033,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,4,United-States,<=50K\n4,Private,172364,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,190105,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-inc,119422,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,236592,12th,8,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,Italy,<=50K\n3,State-gov,43952,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,194636,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,235853,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,150528,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,213722,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,41432,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,285775,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,470663,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,114520,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,113466,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,224559,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,186385,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,167094,10th,6,Divorced,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,216508,12th,8,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,384236,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,181265,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,190997,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,98287,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,103456,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,184723,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,3,1,United-States,<=50K\n0,Private,165622,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,101597,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,146378,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,152163,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,106812,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,148211,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,2,0,3,United-States,<=50K\n2,Private,187581,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,135296,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,144515,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,2,United-States,<=50K\n3,Private,210736,10th,6,Married-spouse-absent,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,210165,9th,5,Married-spouse-absent,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,224584,Some-college,10,Divorced,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,80771,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,164733,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,119411,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,>50K\n4,Local-gov,177596,10th,6,Separated,Other-service,Not-in-family,Black,Female,0,0,4,United-States,<=50K\n2,?,396116,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,185251,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,173590,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n4,Federal-gov,196307,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,293091,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,175232,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,51047,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,334618,Some-college,10,Never-married,Protective-serv,Not-in-family,Black,Female,4,0,2,United-States,>50K\n3,Local-gov,152795,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,205601,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n3,Private,129177,Bachelors,13,Widowed,Other-service,Not-in-family,White,Female,0,4,0,United-States,>50K\n3,Self-emp-not-inc,121548,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,244566,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,75073,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,179008,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,170800,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,373344,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,127961,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,99392,Some-college,10,Divorced,Craft-repair,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,392812,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,Germany,<=50K\n1,Private,262478,HS-grad,9,Never-married,Farming-fishing,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,32825,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,167380,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,203204,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n1,Federal-gov,105138,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,145714,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,3,0,1,United-States,>50K\n0,Private,182276,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,275385,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,292472,Some-college,10,Married-civ-spouse,Sales,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,>50K\n0,Self-emp-not-inc,73514,HS-grad,9,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n1,Private,199600,HS-grad,9,Never-married,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,111499,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,4,United-States,>50K\n0,Private,202560,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,99309,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,124987,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,287986,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,119411,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,198668,7th-8th,4,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,117583,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,234664,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,114357,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,176949,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,189710,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,205309,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,195576,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,216825,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,Mexico,<=50K\n0,?,329174,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,197036,10th,6,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,181291,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,1,3,United-States,>50K\n1,Private,206512,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,State-gov,38309,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,2,0,2,United-States,<=50K\n2,Private,312766,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,139671,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n4,Federal-gov,38621,Assoc-voc,11,Widowed,Other-service,Unmarried,Black,Female,1,0,2,United-States,<=50K\n1,Private,124827,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,77820,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Federal-gov,56904,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,190115,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,106682,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,42596,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,143058,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,102615,11th,7,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,Canada,<=50K\n3,Private,139703,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,Germany,>50K\n2,Private,240124,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,132565,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,323798,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,1,0,3,United-States,<=50K\n3,Private,96359,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,165201,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Federal-gov,165630,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,264526,Assoc-acdm,12,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,102359,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,37359,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,?,232618,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,115497,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,157747,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,41099,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,472604,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,Mexico,<=50K\n1,Private,348618,5th-6th,3,Married-spouse-absent,Transport-moving,Unmarried,Other,Male,0,0,0,El-Salvador,<=50K\n2,Private,135606,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,248445,HS-grad,9,Separated,Transport-moving,Other-relative,White,Male,0,0,3,Mexico,<=50K\n2,Private,112093,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,197552,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,303822,10th,6,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,288566,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,487411,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,43348,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,State-gov,239409,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,337606,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,1,2,United-States,<=50K\n1,Private,32528,Assoc-voc,11,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,1,2,United-States,<=50K\n3,State-gov,118447,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,234690,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,141003,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,216042,Some-college,10,Divorced,Tech-support,Own-child,White,Female,0,2,4,United-States,<=50K\n2,Private,190482,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,381965,Bachelors,13,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,186943,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,142707,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,53447,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,127772,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,344414,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,194138,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n3,?,558183,Assoc-voc,11,Married-spouse-absent,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,150154,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,306114,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,177121,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Local-gov,368797,Masters,14,Widowed,Prof-specialty,Unmarried,White,Male,0,0,1,United-States,>50K\n2,Self-emp-inc,175715,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n4,Private,416829,11th,7,Separated,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,350001,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,339952,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,114967,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,164190,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n3,Local-gov,166039,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,250135,HS-grad,9,Never-married,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,234960,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,2,3,United-States,>50K\n1,Private,103628,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,430005,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,106517,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,162236,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,92430,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,40641,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,169388,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,410034,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,237525,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,4,United-States,>50K\n1,Private,150057,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,148549,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,75742,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,177675,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Germany,>50K\n3,Local-gov,193249,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,266072,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,0,El-Salvador,<=50K\n1,?,80165,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,339324,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,111238,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,284086,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,206051,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,426263,Masters,14,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,>50K\n3,Private,102583,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,277647,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,124808,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,Germany,>50K\n3,Private,193061,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,121411,12th,8,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,89202,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,232900,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,319280,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,?,165209,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,193494,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,195066,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,99146,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,92028,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,174419,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,57916,7th-8th,4,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,383384,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,198223,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,109813,11th,7,Never-married,Tech-support,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,174298,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,45687,Some-college,10,Divorced,Other-service,Not-in-family,Black,Male,2,0,3,United-States,>50K\n1,Private,263614,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,96128,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,220262,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,35340,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,280483,HS-grad,9,Separated,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,254211,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,351324,Some-college,10,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,58602,5th-6th,3,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,64922,Bachelors,13,Separated,Other-service,Not-in-family,White,Male,0,0,4,England,<=50K\n2,Federal-gov,185616,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,3,2,United-States,<=50K\n2,Private,185832,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,254767,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,3,United-States,<=50K\n2,Federal-gov,32312,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,109421,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,183205,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,156897,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,4,2,United-States,>50K\n3,Local-gov,145886,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Local-gov,29819,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,3,3,United-States,>50K\n1,Private,244566,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,253801,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Ecuador,<=50K\n0,Private,181313,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,State-gov,150566,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,237713,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,112137,Preschool,1,Married-civ-spouse,Machine-op-inspct,Wife,Asian-Pac-Islander,Female,2,0,2,Cambodia,<=50K\n3,Local-gov,187969,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,224108,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n3,Private,174754,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,219705,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,<=50K\n1,Private,167062,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,190325,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,108859,HS-grad,9,Separated,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,344351,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,153127,Some-college,10,Widowed,Priv-house-serv,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,180881,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,183066,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,339002,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,185480,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,?,>50K\n0,Private,172047,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,94600,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,302604,Some-college,10,Separated,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,248094,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,36467,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,53181,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,181032,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,248990,11th,7,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,40512,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,2,0,1,United-States,<=50K\n2,Private,117381,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,>50K\n0,?,173125,12th,8,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,?,316663,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,3,United-States,<=50K\n1,Private,154966,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n0,Private,198259,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,167939,HS-grad,9,Married-civ-spouse,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,131275,HS-grad,9,Never-married,Craft-repair,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,?,236523,10th,6,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,272950,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,174503,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,116800,Assoc-voc,11,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,110713,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,202044,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,300528,11th,7,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,54985,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,>50K\n4,Private,133126,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,74593,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,302424,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,344492,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,349148,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,222221,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,234699,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,Black,Female,0,0,3,United-States,>50K\n0,Local-gov,243178,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,189728,HS-grad,9,Separated,Priv-house-serv,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,318593,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,108681,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,187376,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,75409,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,172581,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,266150,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,271092,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,?,<=50K\n3,Private,135643,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Other-relative,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n4,Private,46466,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,130652,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,114459,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n3,?,109832,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,2,0,1,Canada,>50K\n2,Private,195554,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,244589,11th,7,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,271901,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,139978,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,180446,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n4,?,178724,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n2,State-gov,341643,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,289653,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,118725,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,4,0,4,United-States,>50K\n1,Private,187891,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,116338,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n3,Private,102771,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,89652,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,2,0,1,United-States,>50K\n3,Federal-gov,439608,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,330144,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,251905,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,218955,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,188972,Doctorate,16,Separated,Prof-specialty,Unmarried,White,Female,0,0,0,Canada,<=50K\n4,Self-emp-not-inc,25825,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,202046,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,3,2,United-States,<=50K\n4,Private,116104,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,Germany,<=50K\n0,Private,194891,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,125550,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,4,0,1,United-States,>50K\n4,Private,116468,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,0,United-States,<=50K\n1,?,285131,Assoc-acdm,12,Never-married,?,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,State-gov,409201,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,379819,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,<=50K\n4,Private,97167,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,244803,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n3,Self-emp-not-inc,115851,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,118058,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,258589,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,158810,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,92431,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,4,2,United-States,>50K\n4,Self-emp-not-inc,165695,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,?,97281,Some-college,10,Separated,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,244147,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,2,3,United-States,<=50K\n4,Self-emp-inc,253741,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,0,United-States,>50K\n0,Private,170482,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,241001,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,165001,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,297117,11th,7,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,340260,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,96480,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,185177,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,172907,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n1,Self-emp-not-inc,308874,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,54098,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,288608,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,254148,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,111128,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,171116,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,96062,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Federal-gov,276776,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,152878,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,149211,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,58343,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,127601,Some-college,10,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,357781,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,137367,Some-college,10,Never-married,Handlers-cleaners,Other-relative,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Private,110978,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,34503,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,84119,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n0,Private,223515,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,372525,Masters,14,Divorced,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,116365,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,111268,Assoc-acdm,12,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,225599,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,2,0,2,India,>50K\n4,?,83511,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,Portugal,<=50K\n3,Self-emp-not-inc,199596,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,301867,HS-grad,9,Never-married,Sales,Own-child,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n4,Private,191983,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,105803,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,456236,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,116255,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,235109,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,91716,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,121102,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Female,0,3,1,United-States,<=50K\n4,Private,235781,Some-college,10,Divorced,Farming-fishing,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n2,Private,136986,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,33658,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,53878,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,200928,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,173736,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,214385,Assoc-voc,11,Never-married,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,102509,10th,6,Divorced,Transport-moving,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Private,173047,Bachelors,13,Divorced,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,1,2,Philippines,<=50K\n4,Self-emp-not-inc,241297,Some-college,10,Widowed,Farming-fishing,Not-in-family,White,Female,2,0,2,United-States,<=50K\n0,Private,329054,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,274158,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,241153,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,200117,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,2,3,?,>50K\n2,Private,229516,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,4,Mexico,<=50K\n4,?,250091,Bachelors,13,Divorced,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,State-gov,247075,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,0,United-States,<=50K\n0,Private,315524,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,1,Dominican-Republic,<=50K\n0,Private,126945,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,29874,Some-college,10,Separated,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,115579,Assoc-voc,11,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,29580,11th,7,Married-civ-spouse,Sales,Husband,White,Male,2,0,1,United-States,>50K\n2,Private,56483,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,89852,1st-4th,2,Married-civ-spouse,?,Husband,White,Male,0,0,2,Portugal,<=50K\n0,Private,420779,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Black,Male,0,0,1,United-States,<=50K\n0,Private,255474,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,241444,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,3,Puerto-Rico,<=50K\n2,Private,85995,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,116986,12th,8,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,217962,12th,8,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,?,<=50K\n2,Private,20507,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,184099,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,117816,7th-8th,4,Divorced,Handlers-cleaners,Other-relative,White,Male,0,0,4,United-States,<=50K\n0,Private,263899,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,0,Haiti,<=50K\n1,Private,45869,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,186539,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,326310,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,84564,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,247294,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,72793,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,261375,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,3,United-States,<=50K\n3,Private,77905,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,66838,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,State-gov,194682,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n4,Private,180211,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,1,Philippines,<=50K\n4,?,79272,Some-college,10,Widowed,?,Not-in-family,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n4,Private,101198,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,80574,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,198663,HS-grad,9,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,160340,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,205860,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,2,2,United-States,<=50K\n4,State-gov,69579,Some-college,10,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,379242,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,113323,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n3,Self-emp-not-inc,312477,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,259505,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,171335,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,?,541282,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,155970,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,99682,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,Canada,>50K\n0,Private,117789,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,296158,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,78859,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,188070,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,189811,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,518030,Bachelors,13,Never-married,Protective-serv,Not-in-family,Black,Male,0,1,2,Puerto-Rico,<=50K\n1,Private,360593,HS-grad,9,Divorced,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,145504,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,459248,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,?,288419,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,2,Mexico,<=50K\n2,State-gov,126094,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,209483,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,32239,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,Private,210355,11th,7,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,84547,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,?,260579,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,105585,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,132320,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,129172,Some-college,10,Never-married,Other-service,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,222374,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,201498,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,251675,Some-college,10,Divorced,Sales,Not-in-family,White,Male,3,0,3,Cuba,>50K\n2,Private,114157,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,148121,Bachelors,13,Married-spouse-absent,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n4,?,84053,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,96480,Some-college,10,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,179423,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,State-gov,123329,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,134130,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,188644,Preschool,1,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,226388,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,209205,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,209808,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,<=50K\n2,Self-emp-inc,56236,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,1,0,2,United-States,<=50K\n0,Private,28648,11th,7,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,State-gov,34996,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,281422,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,214716,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,314177,10th,6,Never-married,Handlers-cleaners,Other-relative,Black,Male,0,0,2,United-States,<=50K\n3,Private,112310,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,203783,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,205499,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,145441,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,155701,7th-8th,4,Separated,Other-service,Unmarried,White,Female,0,0,2,Peru,<=50K\n2,State-gov,186934,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,209433,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,80933,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,102607,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,254809,10th,6,Divorced,Machine-op-inspct,Unmarried,White,Female,0,1,1,United-States,<=50K\n0,Self-emp-not-inc,102942,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,State-gov,175057,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,68781,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,108594,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,98283,Prof-school,15,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Male,0,1,2,India,>50K\n2,Private,56269,Some-college,10,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,152503,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,206951,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,82393,9th,5,Never-married,Other-service,Own-child,Asian-Pac-Islander,Male,0,0,0,Philippines,<=50K\n2,Private,167396,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Guatemala,<=50K\n1,Self-emp-not-inc,123397,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,?,147653,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,118652,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,114401,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,1,0,United-States,<=50K\n2,Private,186272,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,2,0,2,United-States,>50K\n3,Private,182689,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,231016,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n2,Self-emp-inc,60949,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,129513,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,84306,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,117507,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,88050,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n0,Private,305498,HS-grad,9,Divorced,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,295308,11th,7,Never-married,Priv-house-serv,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,114459,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,176017,10th,6,Never-married,Other-service,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Private,248445,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,214542,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,384508,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,403489,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,143953,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,254904,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,98995,10th,6,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,237078,11th,7,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,193995,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,205829,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,205852,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,37072,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,275338,Bachelors,13,Divorced,Sales,Unmarried,White,Female,1,0,2,United-States,<=50K\n2,State-gov,122353,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,100009,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,?,37030,Assoc-acdm,12,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,135056,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,135162,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,?,<=50K\n1,Private,280618,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,226717,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,173938,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,291355,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Federal-gov,160155,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,29762,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,82473,9th,5,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,172071,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,Jamaica,<=50K\n1,Private,166210,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,330263,HS-grad,9,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,247043,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Federal-gov,155238,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,130557,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,56986,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,220692,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,121650,5th-6th,3,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,1,United-States,<=50K\n4,Private,174603,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,341846,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,99339,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,1,2,United-States,<=50K\n1,Private,34437,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,141058,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,Mexico,<=50K\n3,Private,192323,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,117674,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,?,<=50K\n2,Private,28572,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,120277,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,164309,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,102771,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,147951,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,0,United-States,<=50K\n0,Private,188409,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,1,United-States,<=50K\n2,Private,173888,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,247006,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,82889,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,259363,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Federal-gov,159165,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,112062,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,299050,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,186452,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,548580,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Guatemala,<=50K\n0,Private,234057,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,241350,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,108196,9th,5,Never-married,Craft-repair,Other-relative,White,Male,1,0,2,United-States,<=50K\n3,Private,278322,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,157443,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,1,Taiwan,>50K\n2,Self-emp-not-inc,37618,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,238582,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,28887,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,77820,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,110946,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,230420,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,206521,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,156877,HS-grad,9,Divorced,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,283227,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,141957,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,58337,10th,6,Never-married,Sales,Unmarried,White,Female,0,0,1,?,<=50K\n4,Local-gov,161027,5th-6th,3,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,31670,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,205844,Bachelors,13,Never-married,Exec-managerial,Own-child,Black,Female,0,0,4,United-States,<=50K\n1,State-gov,46144,HS-grad,9,Married-AF-spouse,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,168055,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,98350,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n4,?,182668,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,208613,Prof-school,15,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,334522,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n3,State-gov,187686,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,365916,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,190719,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,218184,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,Jamaica,<=50K\n1,Private,222162,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,148524,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,267085,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,307555,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,229180,Some-college,10,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,Cuba,<=50K\n0,Private,279041,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,312017,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,54782,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,1,2,United-States,<=50K\n4,Private,70697,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,263970,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,188774,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,302770,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,183639,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,178551,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,175343,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,190078,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,117627,Some-college,10,Divorced,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,108419,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,183701,10th,6,Widowed,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n1,State-gov,208406,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,148884,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,87285,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,199058,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,173628,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,370837,Bachelors,13,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,179484,12th,8,Never-married,?,Own-child,Other,Male,0,0,2,United-States,<=50K\n0,Private,342769,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,65145,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,150533,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,<=50K\n3,Local-gov,272182,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,403467,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,3,0,2,United-States,>50K\n1,Private,252168,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,80430,11th,7,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,189623,Bachelors,13,Divorced,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,115806,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,4,2,United-States,>50K\n0,?,28357,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,226084,HS-grad,9,Widowed,Priv-house-serv,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,150817,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-inc,190911,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,120126,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n2,Local-gov,255559,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,142370,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,173679,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,35854,Some-college,10,Married-spouse-absent,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,82161,10th,6,Widowed,Transport-moving,Unmarried,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,129845,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,226505,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,>50K\n3,Private,151584,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,136419,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,66460,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,379940,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,102936,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,205309,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n1,?,156890,10th,6,Divorced,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,208711,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,3,United-States,>50K\n3,Private,137547,HS-grad,9,Divorced,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,220168,HS-grad,9,Never-married,Sales,Other-relative,Black,Female,0,0,1,Jamaica,<=50K\n3,Local-gov,37672,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,196643,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,355686,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,197484,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,115023,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,234824,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,4,United-States,<=50K\n1,State-gov,361497,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,351871,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,324231,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,123490,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,188245,11th,7,Never-married,Priv-house-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,50349,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,47176,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,State-gov,290661,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,221172,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,188950,Assoc-voc,11,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,356882,Doctorate,16,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-inc,150533,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,167149,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,301835,5th-6th,3,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,313729,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,130957,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,197732,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,250541,10th,6,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,218785,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,4,United-States,<=50K\n0,?,232512,HS-grad,9,Separated,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,194630,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,38721,HS-grad,9,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,201519,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,279337,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,?,27187,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,4,0,United-States,>50K\n1,Private,87560,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,?,100820,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,4,0,United-States,<=50K\n4,Private,208431,Some-college,10,Widowed,Exec-managerial,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n3,Private,143822,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,163205,Some-college,10,Separated,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,171924,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,State-gov,137616,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,156516,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,4,0,United-States,<=50K\n2,Private,119101,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,117556,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,147863,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,2,0,2,Vietnam,<=50K\n1,Self-emp-not-inc,24504,HS-grad,9,Separated,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n1,?,157624,HS-grad,9,Separated,?,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,181721,10th,6,Never-married,Farming-fishing,Own-child,Black,Male,0,0,3,United-States,<=50K\n2,Local-gov,55363,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,92865,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,258633,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,?,<=50K\n3,Federal-gov,221532,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,183224,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,2,Taiwan,>50K\n1,Private,381153,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,300871,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,33710,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,>50K\n1,Private,158333,5th-6th,3,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,Columbia,<=50K\n2,Private,288103,11th,7,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,108907,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,358533,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,126613,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,164190,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,199816,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,98228,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,129060,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,22245,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,226918,Bachelors,13,Never-married,Sales,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n3,Private,398652,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,268840,Some-college,10,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,>50K\n1,?,103710,Bachelors,13,Divorced,?,Unmarried,White,Female,0,0,0,?,<=50K\n4,Private,91384,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,174767,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,126675,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,82285,Bachelors,13,Married-spouse-absent,Other-service,Other-relative,Black,Female,0,0,2,Haiti,<=50K\n3,Private,177727,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,345236,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,347692,11th,7,Divorced,?,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n4,Private,156000,10th,6,Widowed,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n4,Private,228806,9th,5,Divorced,Priv-house-serv,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n3,Local-gov,184428,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,102938,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,161063,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,253752,10th,6,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,274800,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,129804,9th,5,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n0,Federal-gov,65547,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,107658,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,161097,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,118376,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,131224,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,120985,HS-grad,9,Divorced,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,215392,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,63685,HS-grad,9,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,Cambodia,<=50K\n3,Private,131826,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,211440,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,31023,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,145439,5th-6th,3,Married-civ-spouse,Other-service,Husband,Other,Male,2,0,2,Mexico,<=50K\n0,Private,255161,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,411950,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,275818,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,3,2,United-States,<=50K\n0,Private,318082,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Local-gov,287988,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,138342,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,El-Salvador,<=50K\n2,Federal-gov,115932,Bachelors,13,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,60358,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,140117,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,158040,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,321990,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,?,>50K\n1,Private,232784,Assoc-acdm,12,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,349368,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,325573,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,140176,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,128478,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,?,318264,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,147989,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,?,<=50K\n2,Federal-gov,155659,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,288433,Masters,14,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Federal-gov,329205,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,171373,11th,7,Widowed,Farming-fishing,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,228860,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,196116,Prof-school,15,Divorced,Prof-specialty,Own-child,White,Female,1,0,4,United-States,<=50K\n0,Private,47771,11th,7,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,201680,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,337378,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,246449,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,1,0,3,United-States,<=50K\n3,Private,227714,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,177285,Assoc-voc,11,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,71701,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,Portugal,<=50K\n3,Private,30219,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,2,2,United-States,<=50K\n2,Private,280167,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,27408,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,167031,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Columbia,<=50K\n2,Private,173682,Masters,14,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,278557,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,113688,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,252986,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,33669,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,100776,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,177457,Some-college,10,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,312767,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,43354,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-inc,375422,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,South,<=50K\n3,Self-emp-not-inc,263568,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,?,74335,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,Germany,<=50K\n1,Private,302097,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,3,United-States,<=50K\n1,Private,248010,Bachelors,13,Married-spouse-absent,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,?,87369,9th,5,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,405577,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,167065,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,102476,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,526528,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,2,0,2,United-States,<=50K\n1,Private,175878,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,213894,11th,7,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,150262,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,75363,Some-college,10,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,272671,Bachelors,13,Divorced,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,411007,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,4,0,2,United-States,>50K\n2,Private,222434,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,180246,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,171236,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,367037,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,304651,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,97017,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,146879,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,3,2,United-States,<=50K\n2,State-gov,320818,Some-college,10,Married-spouse-absent,Other-service,Other-relative,Black,Male,0,0,2,Haiti,<=50K\n3,Self-emp-not-inc,84735,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n3,Private,184428,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,326886,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,?,169624,HS-grad,9,Divorced,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,212102,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,175837,11th,7,Never-married,Farming-fishing,Other-relative,White,Female,0,0,2,Puerto-Rico,<=50K\n3,Private,177487,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,286750,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,3,2,United-States,>50K\n2,Private,171424,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,194981,HS-grad,9,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,199362,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,204226,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,72506,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,61040,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n2,Federal-gov,194630,Masters,14,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,391867,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,94080,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,289405,11th,7,Never-married,Machine-op-inspct,Own-child,Other,Male,0,0,0,United-States,<=50K\n1,Private,170130,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,158118,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,2,2,United-States,<=50K\n1,Private,447739,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,39824,HS-grad,9,Widowed,?,Not-in-family,White,Male,1,0,0,United-States,<=50K\n4,?,312500,5th-6th,3,Widowed,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,223342,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,1,1,United-States,<=50K\n4,?,293385,Preschool,1,Married-civ-spouse,?,Husband,Black,Male,0,0,1,United-States,<=50K\n0,Private,106377,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,66118,Bachelors,13,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,274883,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,123773,Assoc-acdm,12,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,70655,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,177426,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,200374,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n0,State-gov,159269,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,235894,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,97723,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,167309,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,98106,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,22201,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,2,0,2,Philippines,>50K\n2,Private,108993,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,265954,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,100960,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,170092,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,326156,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,216932,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,36956,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,214014,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,99872,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n4,State-gov,151459,10th,6,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,161662,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n4,Private,367200,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,86648,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n3,Local-gov,168539,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,140741,11th,7,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,197651,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,123053,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,Japan,>50K\n0,Private,330571,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,204235,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,346766,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,257250,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,163396,Some-college,10,Never-married,Tech-support,Not-in-family,Other,Female,0,0,2,United-States,<=50K\n4,?,135839,HS-grad,9,Widowed,?,Not-in-family,White,Female,1,0,0,United-States,<=50K\n0,Private,36251,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,149102,HS-grad,9,Married-spouse-absent,Handlers-cleaners,Not-in-family,White,Male,1,0,3,Poland,<=50K\n4,?,222395,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,29152,12th,8,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,79303,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,272338,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,State-gov,200497,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,148392,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,164243,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,<=50K\n2,State-gov,129298,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,174981,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,>50K\n3,Local-gov,328610,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,77774,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n2,State-gov,134069,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,209214,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,1,United-States,>50K\n1,Private,153805,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,Other,Male,0,0,2,Ecuador,<=50K\n1,Private,168827,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,373432,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,57600,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,302847,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,227594,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Federal-gov,44777,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n3,?,133963,HS-grad,9,Widowed,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,279615,Bachelors,13,Divorced,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,276133,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,136314,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,204410,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,>50K\n4,Self-emp-inc,223215,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,184625,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Self-emp-inc,265917,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,158647,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,22055,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n2,Local-gov,176716,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,270721,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,100321,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,79050,HS-grad,9,Never-married,Transport-moving,Unmarried,Black,Male,0,0,4,United-States,<=50K\n2,Local-gov,42703,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,116952,7th-8th,4,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,331643,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,207937,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,1,2,United-States,<=50K\n4,Private,223486,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,0,England,<=50K\n1,Private,340332,Bachelors,13,Separated,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,184813,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,32185,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,197886,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,State-gov,248374,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,382499,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,State-gov,108320,Masters,14,Divorced,Prof-specialty,Unmarried,White,Male,2,0,1,United-States,<=50K\n3,Self-emp-inc,161386,9th,5,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,<=50K\n3,Local-gov,110172,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,144032,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,224426,Masters,14,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,230408,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n3,Local-gov,20795,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,174714,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,State-gov,398626,Doctorate,16,Divorced,Prof-specialty,Unmarried,White,Male,4,0,2,United-States,>50K\n1,Private,149531,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,34113,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,323790,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,331381,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,160647,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,Ireland,>50K\n1,Private,339142,HS-grad,9,Separated,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,164857,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,267859,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,167725,Bachelors,13,Married-spouse-absent,Transport-moving,Not-in-family,Other,Male,0,0,4,India,<=50K\n3,Federal-gov,586657,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n4,Self-emp-not-inc,105907,1st-4th,2,Widowed,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n0,Private,200677,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,193882,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,138026,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,122385,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,49020,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,283715,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,286406,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Private,166416,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,156334,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,45607,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Local-gov,112362,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,200419,Assoc-acdm,12,Separated,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,State-gov,341638,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,34161,12th,8,Separated,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,127151,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,Canada,>50K\n3,Private,321959,Some-college,10,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,2,United-States,>50K\n3,Local-gov,35211,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,214935,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,132130,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,222247,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,165799,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,257874,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,357173,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,305739,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,172047,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,110677,Some-college,10,Separated,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,405684,HS-grad,9,Never-married,?,Other-relative,White,Male,0,0,1,Mexico,<=50K\n4,Private,82388,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,289230,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,>50K\n1,Private,101812,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,2,0,2,United-States,<=50K\n3,State-gov,336509,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,383402,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,328216,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,280362,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,212064,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,2,0,1,United-States,<=50K\n2,Private,173704,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,433375,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Mexico,<=50K\n4,Self-emp-not-inc,106551,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,22418,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,54816,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,358199,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,190044,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,97698,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,53366,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,236136,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,326232,7th-8th,4,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,581071,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,220589,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,161463,Some-college,10,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,95255,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,223267,Some-college,10,Divorced,Protective-serv,Own-child,White,Male,0,0,4,United-States,<=50K\n0,Private,236769,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,England,<=50K\n4,Self-emp-inc,229498,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,0,United-States,>50K\n2,Private,177083,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,287681,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,Columbia,<=50K\n2,Private,49797,Some-college,10,Separated,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,174051,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,194901,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,252250,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,191277,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,174907,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,167140,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,236543,12th,8,Divorced,Protective-serv,Own-child,White,Male,0,0,3,Mexico,<=50K\n2,Private,214242,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,216864,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,4,2,United-States,<=50K\n1,Private,245211,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,1,0,1,United-States,<=50K\n4,Private,437727,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,200418,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,167334,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,146834,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,78424,Assoc-voc,11,Never-married,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,182675,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Self-emp-not-inc,38079,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,115178,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,195949,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,167415,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,223214,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,22245,Bachelors,13,Married-civ-spouse,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,State-gov,81853,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,United-States,>50K\n1,Private,147921,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,29261,HS-grad,9,Married-AF-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,257758,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,136546,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,205493,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,71650,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,150217,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,258648,10th,6,Widowed,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,114798,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,186188,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,175255,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,249935,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,120277,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,193165,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,185027,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,Ireland,>50K\n0,Private,221418,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,56063,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,153927,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,163110,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-inc,175696,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,143189,5th-6th,3,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,Dominican-Republic,<=50K\n0,?,114969,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,32778,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,150683,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,78104,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,335005,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,1,0,2,United-States,<=50K\n3,Local-gov,311551,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n2,Self-emp-not-inc,201520,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,124111,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,166386,11th,7,Married-civ-spouse,Machine-op-inspct,Wife,Asian-Pac-Islander,Female,0,0,1,Hong,<=50K\n2,State-gov,117471,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,361307,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,142038,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,276552,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,50402,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,174090,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,277760,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,24243,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Self-emp-inc,151089,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,4,United-States,>50K\n3,Self-emp-not-inc,165278,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,182752,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,173002,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,261232,11th,7,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,164607,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,129573,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,36186,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,325744,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,329793,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,133616,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,83401,5th-6th,3,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,239880,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,201737,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,192182,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,143540,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,28334,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,245873,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,199095,Assoc-voc,11,Widowed,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,104461,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,3,0,3,Italy,>50K\n1,Local-gov,183923,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,1,United-States,>50K\n1,Private,129707,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n2,Local-gov,575442,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,184682,Assoc-acdm,12,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,69251,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Private,225507,Assoc-voc,11,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,167515,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,407068,1st-4th,2,Married-spouse-absent,Other-service,Not-in-family,White,Male,0,0,2,Guatemala,<=50K\n2,Private,170019,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,?,<=50K\n3,Local-gov,125892,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,35824,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,67083,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n0,Private,107801,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,95577,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,?,<=50K\n2,Private,118536,HS-grad,9,Divorced,Machine-op-inspct,Other-relative,Black,Male,0,0,2,United-States,<=50K\n4,Private,198078,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,78261,Prof-school,15,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,234108,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,241998,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,3,United-States,<=50K\n2,Private,92717,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,257683,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,40388,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,55424,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,169600,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,1,0,0,United-States,<=50K\n2,Local-gov,319271,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,75050,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,182896,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,188274,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,211497,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,113806,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,?,>50K\n3,Local-gov,172246,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,219962,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,186815,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,132749,Bachelors,13,Never-married,?,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,209801,9th,5,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n0,State-gov,178517,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,169364,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,Ireland,<=50K\n1,Federal-gov,164707,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,144084,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,133692,Bachelors,13,Divorced,Protective-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,184169,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,1,United-States,>50K\n2,Self-emp-inc,145290,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,24824,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,178319,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,235829,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,196280,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,54202,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,220237,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,59146,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,64148,Some-college,10,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,196621,HS-grad,9,Married-spouse-absent,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,195668,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,Cuba,>50K\n1,State-gov,263000,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,554986,Some-college,10,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,?,108211,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,217654,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Germany,>50K\n3,Private,139671,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,102771,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Portugal,<=50K\n2,Private,213019,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,228493,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,22907,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,24364,Some-college,10,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,<=50K\n0,Federal-gov,41432,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,235259,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,343476,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,326886,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,248313,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,30290,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,188540,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,237943,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,198870,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,233980,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,171090,9th,5,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,3,United-States,<=50K\n0,Private,353039,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Female,0,0,2,Mexico,<=50K\n3,Federal-gov,213140,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,188136,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,1,2,United-States,<=50K\n1,Private,130057,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,345339,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,182074,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,176557,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,State-gov,71630,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,2,2,United-States,<=50K\n0,Private,159849,11th,7,Never-married,Protective-serv,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,183425,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,125933,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Local-gov,180123,HS-grad,9,Married-spouse-absent,Farming-fishing,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,592930,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,>50K\n1,Private,183802,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,77005,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,80914,5th-6th,3,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,165667,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,123991,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,181307,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,124137,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Poland,<=50K\n0,?,137363,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,291979,HS-grad,9,Married-civ-spouse,Sales,Other-relative,White,Male,0,0,0,United-States,<=50K\n3,Private,91251,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n1,Federal-gov,148153,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,131463,10th,6,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,State-gov,127651,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,239018,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,276087,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,386877,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,210464,HS-grad,9,Divorced,Adm-clerical,Other-relative,Black,Female,0,0,1,United-States,<=50K\n0,Private,632834,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,245465,Assoc-acdm,12,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,198087,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,27408,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,242713,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,?,<=50K\n4,Private,314727,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,>50K\n2,State-gov,269733,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,177287,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,167711,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,112181,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,339002,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n2,State-gov,24721,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,37092,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,216563,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,204447,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n0,?,151153,Some-college,10,Never-married,?,Not-in-family,Asian-Pac-Islander,Male,4,0,3,South,>50K\n2,Private,187089,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,423052,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,169180,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Hong,<=50K\n0,Private,104981,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n1,?,120074,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,269323,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,141549,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,214858,10th,6,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n1,Private,173524,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,365049,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n2,Private,60355,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,86808,HS-grad,9,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,State-gov,174171,Some-college,10,Separated,Tech-support,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Federal-gov,504951,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,294064,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,France,<=50K\n3,Private,120131,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,199058,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,152328,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,88564,7th-8th,4,Married-spouse-absent,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,95113,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,247558,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,?,>50K\n0,Private,178421,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,484861,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n1,Local-gov,225291,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,205735,1st-4th,2,Separated,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,58898,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,3,United-States,<=50K\n2,Private,355468,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n4,Self-emp-not-inc,184362,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,347513,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,138768,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,29810,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,126501,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,60783,10th,6,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,179772,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,281911,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,70447,HS-grad,9,Never-married,Transport-moving,Other-relative,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,?,449576,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,3,Mexico,<=50K\n1,Private,327964,9th,5,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,496538,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Self-emp-not-inc,153066,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,77651,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,119493,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,256240,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,177374,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,1,0,0,United-States,<=50K\n2,Local-gov,37848,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,129336,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,183511,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,120131,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,190508,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,1,United-States,<=50K\n1,Private,363130,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,240356,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,133166,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,32916,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,117477,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,34748,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,2,0,United-States,>50K\n0,Private,459463,12th,8,Married-spouse-absent,Other-service,Unmarried,Black,Female,0,0,3,United-States,<=50K\n0,Private,95989,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,118088,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,150570,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,>50K\n1,?,505438,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,Mexico,<=50K\n2,Private,179731,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,122109,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,2,2,United-States,<=50K\n1,Local-gov,163942,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,106670,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,123403,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,119986,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,66622,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,?,40060,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,260578,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n4,Local-gov,96076,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,70604,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,230329,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,1,0,United-States,>50K\n3,Private,49715,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,116531,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,214542,Some-college,10,Divorced,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Local-gov,335005,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,Italy,<=50K\n0,Private,258633,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,203240,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,104457,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n3,Local-gov,99131,HS-grad,9,Married-civ-spouse,Prof-specialty,Other-relative,White,Female,0,4,2,United-States,>50K\n3,State-gov,125796,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,2,2,United-States,>50K\n0,?,479482,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,167790,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,133758,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,3,2,United-States,<=50K\n0,Private,106843,10th,6,Never-married,Craft-repair,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,Private,117959,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,174921,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,134152,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,99364,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n0,Local-gov,155905,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,467108,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,304622,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,198692,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,178050,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,162687,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,113151,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,158924,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Self-emp-not-inc,141795,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,33404,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,>50K\n4,Self-emp-inc,178771,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,168553,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,110648,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,151053,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,142871,Some-college,10,Separated,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,?,343161,11th,7,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,183523,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,222216,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,121874,Some-college,10,Divorced,Sales,Unmarried,White,Male,0,0,3,United-States,>50K\n1,Private,467108,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,34393,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,42003,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,180418,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,199590,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Mexico,<=50K\n1,Private,144949,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,155594,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,162576,7th-8th,4,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,232475,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,269474,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,140644,Bachelors,13,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,?,39640,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,?,346014,7th-8th,4,Separated,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,159726,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,290856,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,217886,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,199915,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,106546,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,1,0,2,United-States,<=50K\n3,Local-gov,220640,Masters,14,Divorced,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Female,0,0,3,United-States,>50K\n1,Federal-gov,88913,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,288486,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,227411,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,99935,Masters,14,Married-civ-spouse,Protective-serv,Husband,White,Male,3,0,3,United-States,>50K\n4,Private,201112,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,123778,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,204596,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,190290,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,196674,Some-college,10,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,108435,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,186359,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,137076,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,State-gov,262819,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,171655,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,183319,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,El-Salvador,<=50K\n2,Private,127306,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,68678,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,140108,9th,5,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,263444,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,265554,HS-grad,9,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,410216,11th,7,Married-civ-spouse,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,State-gov,20534,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,188917,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,98695,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,411950,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,237819,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,187424,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,198316,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,139703,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,152596,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,194726,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,82601,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,229843,Some-college,10,Never-married,?,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n4,Private,122276,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Italy,<=50K\n3,State-gov,188386,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,92298,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,390657,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,89041,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,314897,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n1,Private,166343,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,3,?,<=50K\n2,Private,88781,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Germany,>50K\n4,Private,41762,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,South,>50K\n1,Private,849857,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Nicaragua,<=50K\n0,Private,307496,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,324372,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,99270,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,Germany,>50K\n1,Private,160731,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Poland,>50K\n3,State-gov,148306,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,259019,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,224894,5th-6th,3,Married-civ-spouse,Priv-house-serv,Wife,Black,Female,0,0,0,Haiti,<=50K\n0,Private,258470,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,197919,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,213719,Assoc-acdm,12,Never-married,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,226535,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,146042,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,180339,Assoc-voc,11,Never-married,Farming-fishing,Not-in-family,White,Female,0,2,1,United-States,<=50K\n0,Private,99970,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,300687,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,219906,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,122234,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,0,?,<=50K\n3,Private,158641,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,239539,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Local-gov,102308,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,186934,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,234447,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,125933,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,142760,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,309056,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,48859,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,110594,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,426562,11th,7,Divorced,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,169037,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,123075,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,195744,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,81896,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,172047,Assoc-acdm,12,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,253814,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,66473,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,56248,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,1,4,United-States,>50K\n2,Local-gov,271521,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Other,Male,0,0,2,United-States,>50K\n3,Private,265295,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,174308,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,196342,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,223594,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,3,0,2,Puerto-Rico,>50K\n1,Private,149787,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,124686,7th-8th,4,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,50163,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,175789,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,218215,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,166371,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,169469,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,145081,7th-8th,4,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,214521,Prof-school,15,Widowed,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,287233,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,>50K\n3,Private,201310,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,?,<=50K\n3,Self-emp-not-inc,197836,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,2,3,United-States,<=50K\n3,Private,158294,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,127366,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,203697,Bachelors,13,Married-civ-spouse,Prof-specialty,Own-child,White,Male,0,0,4,United-States,<=50K\n2,Private,168730,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,165232,Some-college,10,Divorced,Tech-support,Not-in-family,Black,Female,0,0,2,Trinadad&Tobago,<=50K\n4,Private,175942,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Federal-gov,356689,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,Japan,<=50K\n3,Private,132912,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,187226,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,254765,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,202565,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,103925,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,1,0,0,United-States,<=50K\n0,Private,112164,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,?,<=50K\n4,Self-emp-not-inc,70623,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,102729,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,558944,7th-8th,4,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,256967,10th,6,Never-married,Sales,Other-relative,Black,Female,0,0,2,United-States,<=50K\n4,?,144583,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,102412,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,159788,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,55743,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,United-States,>50K\n3,State-gov,148171,Doctorate,16,Divorced,Prof-specialty,Unmarried,White,Male,0,0,3,France,>50K\n0,Local-gov,271354,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,98524,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,272913,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,Mexico,<=50K\n0,Private,324445,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,155469,Assoc-acdm,12,Widowed,Tech-support,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,102945,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,291904,10th,6,Divorced,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,186601,HS-grad,9,Separated,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,172401,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,193285,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,176244,7th-8th,4,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,117779,HS-grad,9,Never-married,Transport-moving,Own-child,White,Female,0,0,1,United-States,<=50K\n0,?,34616,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,169182,9th,5,Widowed,Other-service,Not-in-family,White,Female,0,0,1,Puerto-Rico,<=50K\n1,Private,180758,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,141637,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,169023,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,2,United-States,>50K\n1,Self-emp-not-inc,101266,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,164190,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,142282,Some-college,10,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,103984,Bachelors,13,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,187601,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,36218,11th,7,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n1,State-gov,106334,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,?,<=50K\n2,Local-gov,249392,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,110355,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,117944,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,163836,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,145592,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Guatemala,<=50K\n0,Private,108495,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,India,<=50K\n1,Self-emp-not-inc,212041,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,182451,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,124020,HS-grad,9,Married-spouse-absent,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,199116,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n0,?,144114,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,107438,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,2,2,United-States,<=50K\n4,Private,405362,7th-8th,4,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,175856,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,United-States,>50K\n0,?,262241,HS-grad,9,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,420054,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,3,0,3,United-States,>50K\n1,Private,86681,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,187161,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,State-gov,691903,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,United-States,>50K\n2,Private,219483,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,199058,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,192010,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,Poland,<=50K\n1,Federal-gov,419691,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,3,United-States,>50K\n1,Local-gov,356089,Bachelors,13,Never-married,Prof-specialty,Other-relative,White,Male,0,0,3,United-States,<=50K\n1,Private,684015,5th-6th,3,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,El-Salvador,<=50K\n0,Private,36882,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,203180,Some-college,10,Divorced,Farming-fishing,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,183811,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,103966,Masters,14,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,304602,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,57233,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n3,State-gov,289207,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,224019,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,267966,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,214800,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,241528,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,197365,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,296724,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,136226,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,40623,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,264874,HS-grad,9,Never-married,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,112847,HS-grad,9,Never-married,Farming-fishing,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,?,236090,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,89028,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n4,State-gov,210673,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,60193,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,216137,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,139743,Some-college,10,Widowed,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,32276,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,423605,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,Nicaragua,>50K\n1,Private,298871,Bachelors,13,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,318255,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,347867,HS-grad,9,Married-spouse-absent,Sales,Not-in-family,White,Male,0,2,2,United-States,<=50K\n4,Private,279636,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,405386,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,297188,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,182342,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,229148,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,3,Jamaica,<=50K\n1,Private,189620,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,2,0,2,England,<=50K\n0,Private,413557,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,246025,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,0,Honduras,<=50K\n1,Private,390997,1st-4th,2,Never-married,Farming-fishing,Not-in-family,Other,Male,0,0,3,Mexico,<=50K\n3,Private,102058,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,247298,12th,8,Married-spouse-absent,Other-service,Own-child,Other,Female,0,0,0,United-States,<=50K\n1,Private,140108,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,?,216941,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,1,0,2,United-States,<=50K\n3,Private,81654,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,177526,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,64631,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,110028,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,203761,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,163870,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,117299,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,50648,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,166517,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,173800,Bachelors,13,Married-spouse-absent,?,Not-in-family,Asian-Pac-Islander,Female,0,0,0,Taiwan,<=50K\n2,Self-emp-inc,181762,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,340880,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,114758,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,2,0,2,United-States,<=50K\n3,Private,138847,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,215014,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,183778,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,273629,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,113870,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,213955,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Male,0,3,2,United-States,<=50K\n1,Private,114982,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,205338,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,57924,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,3,0,3,United-States,>50K\n4,?,225063,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,0,South,<=50K\n1,Self-emp-not-inc,202027,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,281356,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Other,Male,0,0,2,United-States,<=50K\n2,Private,30824,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,1,0,0,United-States,<=50K\n4,Private,98809,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,2,0,2,United-States,<=50K\n1,Private,38223,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,172232,HS-grad,9,Never-married,Tech-support,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,140544,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,221366,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,180799,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,111499,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,2,United-States,>50K\n2,Self-emp-not-inc,155930,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,201122,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,101709,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,140121,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,172709,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,120131,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,117444,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,256764,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,176185,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,?,<=50K\n0,Private,223811,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,201603,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,138765,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,133974,Assoc-voc,11,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,137953,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,103403,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,461678,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,70884,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,466498,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,>50K\n0,Private,148644,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,190739,HS-grad,9,Never-married,Other-service,Other-relative,Black,Male,0,0,1,United-States,<=50K\n1,Private,299507,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,211424,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,106721,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,192017,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,530099,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,119153,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,202450,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,50341,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,140001,Some-college,10,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Italy,<=50K\n0,?,220517,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,52921,Some-college,10,Widowed,?,Not-in-family,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n1,Private,31964,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,148207,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,151627,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,402539,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,188278,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,96219,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,340534,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,160339,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Columbia,<=50K\n1,Private,120135,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,303817,Some-college,10,Divorced,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,181091,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,200515,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,160893,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,183096,9th,5,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,Yugoslavia,>50K\n0,Private,241367,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,342084,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,193855,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,80410,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,554317,9th,5,Married-spouse-absent,Other-service,Other-relative,White,Male,0,0,1,Mexico,<=50K\n3,Private,85109,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,<=50K\n1,Private,108569,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,120959,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,222011,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,238530,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,48404,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,88055,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,0,United-States,<=50K\n1,Private,238381,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,3,0,2,United-States,>50K\n0,Private,243923,HS-grad,9,Married-civ-spouse,Transport-moving,Other-relative,White,Male,0,0,4,United-States,<=50K\n2,Private,305597,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,141841,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,2,0,2,United-States,>50K\n2,Private,129764,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,150993,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,147140,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,30219,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,167967,HS-grad,9,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,133278,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,172510,Some-college,10,Widowed,Prof-specialty,Not-in-family,White,Female,1,0,0,Hungary,<=50K\n2,Private,192251,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,>50K\n2,Private,210844,Bachelors,13,Divorced,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,>50K\n1,Private,263015,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,155118,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,4,0,1,United-States,>50K\n0,State-gov,232918,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,143542,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,45607,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,29828,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,104118,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,191446,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,27484,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,205987,Prof-school,15,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,Cuba,<=50K\n2,Local-gov,143385,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,200508,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,186995,HS-grad,9,Divorced,Protective-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,54159,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,113481,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,235271,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,349365,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,4,United-States,<=50K\n0,Private,283637,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,70282,Assoc-acdm,12,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,166051,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,193720,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,124836,Some-college,10,Divorced,?,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,236379,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,122026,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,114537,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,191834,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,420054,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,160045,Some-college,10,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,303187,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,?,>50K\n2,Private,190088,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,126977,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,63004,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n4,Private,391121,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,211450,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,156413,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,116797,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n3,Local-gov,204447,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,66935,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,344278,11th,7,Separated,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,108574,Assoc-voc,11,Never-married,Priv-house-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,244605,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,363677,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,1,United-States,>50K\n4,Private,219762,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,269318,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,77884,HS-grad,9,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Self-emp-not-inc,70100,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,213643,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,69640,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,170012,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,329924,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,193285,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,261241,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,2,3,United-States,<=50K\n2,Federal-gov,108183,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,3,2,South,>50K\n0,Private,296618,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,257796,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,155320,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,151888,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,143118,HS-grad,9,Widowed,?,Unmarried,White,Female,0,4,0,United-States,<=50K\n1,Private,66278,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,2,United-States,<=50K\n4,Private,92444,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,>50K\n3,Private,229272,HS-grad,9,Divorced,Other-service,Other-relative,Black,Male,0,0,1,Haiti,<=50K\n2,Self-emp-not-inc,207202,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,154176,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,1,2,United-States,<=50K\n3,Private,180899,Masters,14,Divorced,Exec-managerial,Unmarried,White,Male,0,2,2,United-States,>50K\n1,Private,205337,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,180779,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n1,Self-emp-not-inc,343021,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,176814,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n4,State-gov,88638,Doctorate,16,Never-married,Prof-specialty,Other-relative,White,Female,0,4,0,United-States,>50K\n3,Private,248059,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,409604,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,185053,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,332884,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,4,United-States,>50K\n4,Private,212864,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,66473,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,285169,11th,7,Never-married,Priv-house-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,175431,9th,5,Divorced,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,?,152641,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,339346,Masters,14,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,287306,Some-college,10,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,70604,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,88926,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,91275,Some-college,10,Never-married,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Private,244554,10th,6,Widowed,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,232586,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,127678,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,162184,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,408229,1st-4th,2,Never-married,Other-service,Not-in-family,White,Male,0,0,2,El-Salvador,<=50K\n2,State-gov,139734,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,197286,12th,8,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,Germany,<=50K\n3,Private,229983,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,1,0,1,United-States,>50K\n0,Private,252803,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,110890,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,>50K\n3,Private,160724,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,4,South,<=50K\n0,Private,89625,HS-grad,9,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,?,266037,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,126730,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,96854,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,186788,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,28996,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,347166,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,110311,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,310850,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,220694,Bachelors,13,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,149405,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,131699,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,49996,11th,7,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,187112,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,180859,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,185647,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,316606,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,274657,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n2,Federal-gov,193583,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,2,0,3,United-States,<=50K\n0,Private,338836,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,216814,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,106935,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,223433,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,174789,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,135603,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,?,344719,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,372484,11th,7,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,181820,Some-college,10,Never-married,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,235371,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,216711,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,?,>50K\n0,Private,299399,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,202508,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,172025,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,246891,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,450920,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,53598,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,103757,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,76017,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,80158,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,90881,Some-college,10,Separated,Exec-managerial,Not-in-family,White,Male,3,0,3,United-States,>50K\n2,Private,427952,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,230955,12th,8,Never-married,?,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n3,Private,177916,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,342642,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,253642,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,1,United-States,<=50K\n0,Private,219086,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,162593,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,87561,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,142411,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,154422,Some-college,10,Divorced,Sales,Own-child,Asian-Pac-Islander,Female,0,0,1,Philippines,<=50K\n0,Private,169104,Some-college,10,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n3,Private,193047,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,151141,12th,8,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,267912,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,3,Mexico,>50K\n2,Private,137126,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,152453,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Guatemala,<=50K\n0,Private,357059,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,State-gov,202011,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,98283,Bachelors,13,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,176965,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,187919,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,274916,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,105813,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n2,Local-gov,193524,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,152734,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,?,<=50K\n0,Private,263641,HS-grad,9,Divorced,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,102076,Bachelors,13,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,State-gov,155594,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,33331,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,State-gov,156773,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,?,<=50K\n4,Self-emp-not-inc,115439,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,181652,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,120268,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,196308,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,40690,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Private,228583,HS-grad,9,Divorced,Other-service,Unmarried,White,Male,0,0,2,Columbia,<=50K\n0,Private,695136,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,209236,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,214838,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,188436,HS-grad,9,Separated,Exec-managerial,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,177625,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,124591,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,230856,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Female,1,0,3,United-States,<=50K\n3,Federal-gov,221532,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,232577,Assoc-voc,11,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,168216,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,214702,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,237620,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,State-gov,54887,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,164526,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,224506,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,?,<=50K\n4,Private,183870,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,208330,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,168370,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,320376,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n1,Private,192384,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,167350,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,103538,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,58522,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,191342,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n0,Private,193820,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,258490,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,56520,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,102476,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,311357,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,166497,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,160724,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,2,0,2,Philippines,>50K\n1,Private,338270,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,282394,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,383269,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,119386,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,196975,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,334221,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,27385,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,133846,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,361888,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,230429,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,328776,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,243829,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,306646,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,1,0,3,United-States,>50K\n3,Private,138179,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,280069,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,305759,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,?,<=50K\n4,Local-gov,164876,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-inc,138597,Assoc-acdm,12,Never-married,Prof-specialty,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Private,111483,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,144778,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,171015,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,112494,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,408473,12th,8,Never-married,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,State-gov,27802,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,236318,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,121836,Masters,14,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,2,United-States,>50K\n2,Self-emp-not-inc,315971,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,698418,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Private,329530,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,194456,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,England,>50K\n0,Private,282579,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,26401,Masters,14,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,State-gov,364958,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,83998,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,94364,Some-college,10,Never-married,Prof-specialty,Not-in-family,Other,Female,0,0,0,United-States,<=50K\n2,Private,174189,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,101967,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,146908,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,126675,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,4,2,United-States,<=50K\n0,Private,31606,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,Germany,<=50K\n0,Private,132327,Some-college,10,Married-spouse-absent,Sales,Unmarried,Other,Female,0,0,1,Ecuador,<=50K\n0,Private,112459,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,48894,HS-grad,9,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,181943,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,247685,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Local-gov,195808,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,172052,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,1,South,>50K\n3,Local-gov,50178,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,<=50K\n4,Private,351711,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,190305,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,464103,1st-4th,2,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Guatemala,<=50K\n0,?,36348,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,120238,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Poland,<=50K\n1,Private,354095,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,308901,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,State-gov,208826,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,England,<=50K\n0,Private,369677,10th,6,Separated,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,98524,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,239723,Some-college,10,Married-spouse-absent,Craft-repair,Unmarried,White,Female,1,0,2,United-States,<=50K\n4,Private,231232,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,236396,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n0,?,119156,Bachelors,13,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,320451,Some-college,10,Never-married,Protective-serv,Own-child,Asian-Pac-Islander,Male,0,0,1,India,<=50K\n2,Private,38397,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,189183,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,199281,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,286342,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,152810,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,176981,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,117549,10th,6,Never-married,Sales,Other-relative,Black,Female,0,0,0,United-States,<=50K\n4,Private,254797,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,133336,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,182826,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,136224,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,134475,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,0,2,2,United-States,<=50K\n3,Private,272778,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,279183,Some-college,10,Married-civ-spouse,Other-service,Own-child,White,Female,0,0,2,United-States,>50K\n3,Private,110243,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,202071,HS-grad,9,Widowed,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,197642,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,125591,11th,7,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,197462,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,238831,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,182177,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Yugoslavia,<=50K\n2,Local-gov,240504,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,125892,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,154430,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Private,207685,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,Black,Female,2,0,2,United-States,<=50K\n3,Private,222020,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,243240,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,158734,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,257691,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,144483,Assoc-voc,11,Divorced,Sales,Own-child,White,Female,1,0,1,United-States,<=50K\n0,Private,209826,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,30244,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,133050,Some-college,10,Never-married,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,138332,Some-college,10,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,201398,Masters,14,Widowed,Prof-specialty,Unmarried,White,Male,0,0,3,?,<=50K\n2,Private,526968,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,79036,Assoc-voc,11,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,240323,Some-college,10,Widowed,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,270544,12th,8,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,State-gov,199551,11th,7,Separated,Tech-support,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,231052,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,State-gov,203072,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,126771,12th,8,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,31848,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,328981,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Private,159670,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,450695,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,182028,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,349620,10th,6,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,161066,HS-grad,9,Divorced,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n3,Private,213611,7th-8th,4,Married-spouse-absent,Priv-house-serv,Unmarried,White,Female,0,1,1,Guatemala,<=50K\n0,Private,548303,HS-grad,9,Married-civ-spouse,Prof-specialty,Own-child,White,Male,0,0,2,Mexico,>50K\n1,Private,150861,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,Japan,<=50K\n1,?,335625,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,133766,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,200511,HS-grad,9,Separated,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,50103,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,?,148266,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,0,0,Mexico,<=50K\n3,Private,177211,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,132686,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,21626,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,52900,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,150084,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,248886,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,118259,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,3,United-States,<=50K\n4,Private,145493,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,219546,Bachelors,13,Married-civ-spouse,Exec-managerial,Other-relative,White,Male,1,0,3,United-States,<=50K\n2,Federal-gov,399155,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,227310,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,333270,Masters,14,Married-civ-spouse,Craft-repair,Wife,Asian-Pac-Islander,Female,0,0,1,Philippines,<=50K\n3,Private,231495,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,133935,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,55237,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,183034,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,245487,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,2,Mexico,<=50K\n1,Private,185480,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,114251,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,181814,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,340917,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,241998,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,United-States,>50K\n2,Self-emp-inc,125324,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,34744,Assoc-acdm,12,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,131608,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,226916,Bachelors,13,Widowed,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,124137,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,96282,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,337050,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,229335,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,State-gov,199495,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,111675,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,139209,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,32372,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,203784,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,164190,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,64875,Some-college,10,Never-married,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,41806,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,208725,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,79019,Masters,14,Widowed,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,136951,HS-grad,9,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,203554,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,252947,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,2,1,United-States,<=50K\n2,Private,170861,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,199590,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,>50K\n2,Private,529216,Bachelors,13,Divorced,Tech-support,Unmarried,Black,Male,2,0,2,?,>50K\n1,Private,195576,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,<=50K\n1,Private,182177,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Ireland,<=50K\n0,State-gov,183678,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,209320,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,206862,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,168941,11th,7,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,201263,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,75333,10th,6,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n4,?,299494,11th,7,Married-civ-spouse,?,Husband,White,Male,1,0,2,United-States,<=50K\n4,Self-emp-not-inc,163212,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,4,0,2,United-States,>50K\n4,Private,139290,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,400416,10th,6,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,223763,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,77927,Bachelors,13,Widowed,Other-service,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,Private,175804,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,91525,HS-grad,9,Never-married,Sales,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,Private,279968,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,77698,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,?,198686,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n4,?,190340,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,113491,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,202878,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,108431,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,194490,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,48093,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,>50K\n0,Private,136824,11th,7,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,143280,10th,6,Never-married,Priv-house-serv,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,150062,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,298510,HS-grad,9,Divorced,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,177147,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,2,0,4,United-States,<=50K\n3,Private,115025,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,350440,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n4,Private,83850,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,62669,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,229773,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,196234,HS-grad,9,Divorced,Craft-repair,Own-child,White,Female,0,0,2,Puerto-Rico,<=50K\n4,?,163595,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,157249,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,3,3,United-States,>50K\n4,Private,80174,HS-grad,9,Divorced,Exec-managerial,Other-relative,White,Female,1,0,3,United-States,<=50K\n3,Self-emp-inc,49069,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,122952,HS-grad,9,Separated,Craft-repair,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,123856,11th,7,Never-married,Sales,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,216181,Assoc-voc,11,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,180062,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,188535,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,106143,Doctorate,16,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n4,Self-emp-not-inc,170421,Some-college,10,Widowed,Craft-repair,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,283087,Some-college,10,Never-married,Exec-managerial,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Federal-gov,341051,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,34378,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,380674,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,304469,10th,6,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,99146,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,205109,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,99156,HS-grad,9,Separated,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,97842,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,100875,11th,7,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,200576,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Private,355053,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,118376,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,?,<=50K\n2,Local-gov,117760,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Private,117567,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Federal-gov,189632,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,170108,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,27243,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,192663,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,526164,Bachelors,13,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,146579,HS-grad,9,Divorced,Sales,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,60288,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,State-gov,241951,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,213140,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,218124,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,279802,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,153078,HS-grad,9,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Male,0,0,4,?,>50K\n2,Private,167919,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,250832,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,193158,HS-grad,9,Divorced,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,172032,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,39484,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,84298,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,269015,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,Germany,>50K\n0,?,262196,10th,6,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Federal-gov,125892,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,134890,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,261119,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,119409,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Other,Female,0,0,2,Columbia,<=50K\n3,Self-emp-not-inc,118793,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,184207,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,191027,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,207782,Assoc-acdm,12,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,209146,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,79445,10th,6,Married-civ-spouse,?,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,187724,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,66777,Assoc-voc,11,Married-civ-spouse,Other-service,Other-relative,White,Female,1,0,2,United-States,<=50K\n4,Private,158002,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,305834,Some-college,10,Never-married,Craft-repair,Own-child,White,Female,0,0,1,United-States,<=50K\n2,?,122265,HS-grad,9,Divorced,?,Not-in-family,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n0,Private,211798,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,123011,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,36302,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,176867,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n4,Private,169204,HS-grad,9,Widowed,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,38232,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,State-gov,277657,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,32271,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,116825,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n1,Self-emp-not-inc,226198,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,28145,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Private,140477,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,165050,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,202937,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,316298,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,203070,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,103995,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,176137,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,103948,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Local-gov,39581,Prof-school,15,Separated,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,506436,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,Peru,<=50K\n1,Private,226975,Some-college,10,Never-married,Sales,Own-child,White,Male,0,2,3,United-States,<=50K\n3,State-gov,154493,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,137223,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,102323,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,257765,7th-8th,4,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Guatemala,<=50K\n3,Private,42924,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,167599,11th,7,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,1,United-States,<=50K\n4,?,368925,5th-6th,3,Widowed,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,?,100881,Assoc-acdm,12,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,52738,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,98418,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,381153,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,103700,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,298635,Bachelors,13,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n1,Private,127895,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,212760,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,281384,HS-grad,9,Married-AF-spouse,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n4,Private,181200,12th,8,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,257364,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,283281,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,214502,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,69333,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,190060,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,95864,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,?,144872,Some-college,10,Married-civ-spouse,?,Husband,White,Male,2,0,2,United-States,>50K\n0,?,275778,9th,5,Never-married,?,Own-child,White,Female,0,0,1,Mexico,<=50K\n2,Private,27332,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,24395,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,330695,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,171615,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,>50K\n1,Private,116372,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,38599,12th,8,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,202184,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,315303,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,103456,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,State-gov,163480,Masters,14,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,317425,11th,7,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n4,Private,216941,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,116541,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,>50K\n2,Private,186396,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,273194,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,1,0,1,United-States,<=50K\n0,Private,385540,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,201631,9th,5,Married-civ-spouse,Farming-fishing,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,439919,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,182117,Bachelors,13,Never-married,Other-service,Other-relative,White,Male,0,0,0,United-States,<=50K\n0,State-gov,334113,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,184837,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,2,0,2,United-States,>50K\n3,?,228372,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n3,Federal-gov,211123,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,38819,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,162391,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,<=50K\n0,?,302836,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,2,El-Salvador,<=50K\n1,State-gov,89040,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,264210,Some-college,10,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,87157,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,398918,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,?,123612,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,155818,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,243660,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,134195,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,238638,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,159929,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,198668,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,215504,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,158002,Some-college,10,Never-married,Craft-repair,Other-relative,White,Male,0,0,3,Ecuador,<=50K\n3,Local-gov,35305,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,195994,1st-4th,2,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,Guatemala,<=50K\n2,State-gov,321824,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,180449,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,201764,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,250038,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,?,<=50K\n1,Self-emp-not-inc,226535,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,136121,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,47199,11th,7,Never-married,Priv-house-serv,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,215895,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,24647,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,734193,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,?,321086,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,192589,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,326283,Bachelors,13,Never-married,Other-service,Unmarried,Other,Male,0,0,2,United-States,<=50K\n1,Private,207284,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,109089,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,274528,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,142646,7th-8th,4,Widowed,Priv-house-serv,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,180859,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,188610,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,169604,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,260560,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,188245,HS-grad,9,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Local-gov,103315,Masters,14,Never-married,Exec-managerial,Other-relative,White,Female,4,0,4,United-States,>50K\n2,Local-gov,52465,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,737315,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,195143,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,219420,Doctorate,16,Divorced,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Private,198170,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,183168,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,196545,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,168412,HS-grad,9,Married-civ-spouse,Sales,Other-relative,White,Female,0,0,2,Poland,<=50K\n3,Private,174386,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,El-Salvador,>50K\n2,Private,544686,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,1,0,2,Nicaragua,<=50K\n3,Private,95661,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,468713,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,169112,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,74024,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,110622,5th-6th,3,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,0,Vietnam,<=50K\n2,Local-gov,33331,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,181557,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n4,Private,142624,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,Yugoslavia,>50K\n2,Self-emp-not-inc,192251,10th,6,Married-civ-spouse,Other-service,Wife,White,Female,1,0,2,United-States,<=50K\n1,Private,146091,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,174575,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,200949,10th,6,Never-married,Other-service,Unmarried,White,Female,0,0,2,Peru,<=50K\n3,Local-gov,201560,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n4,Federal-gov,149386,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,168672,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,2,United-States,>50K\n4,Private,38352,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,180272,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n0,State-gov,275421,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,173051,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,167474,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,267138,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,135138,Bachelors,13,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,218357,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,107236,12th,8,Married-civ-spouse,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,138416,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,Mexico,<=50K\n1,Private,154863,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,194004,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,339123,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,548361,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,101812,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,127111,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,171807,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,40666,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,340682,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,175052,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,321629,HS-grad,9,Never-married,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,154405,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,108402,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,346275,11th,7,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,42476,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,161708,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,70447,Some-college,10,Never-married,Prof-specialty,Unmarried,Asian-Pac-Islander,Male,2,0,0,United-States,<=50K\n1,Private,189759,Bachelors,13,Never-married,Transport-moving,Not-in-family,White,Male,2,0,2,United-States,<=50K\n4,?,137354,Some-college,10,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n1,Private,250724,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Jamaica,<=50K\n1,Federal-gov,149368,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,154641,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,38309,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n3,Local-gov,202733,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,4,United-States,>50K\n1,Private,56150,11th,7,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,260254,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,108083,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,71344,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,174215,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,114366,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,158962,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,179498,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Germany,<=50K\n1,Private,31935,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,149909,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,?,58740,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,216552,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,255348,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,176050,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,125101,Assoc-voc,11,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,197286,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,337469,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,1,0,0,Mexico,<=50K\n1,Private,159737,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Private,316211,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,127610,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,1,United-States,>50K\n2,Local-gov,556652,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,265576,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,347653,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,62374,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,170230,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,203051,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-inc,115880,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,167735,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n3,Self-emp-inc,181413,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,185554,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,350387,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,225102,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,?,<=50K\n3,Private,105582,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,2,United-States,>50K\n1,Self-emp-not-inc,350247,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,150025,9th,5,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,?,>50K\n2,Private,107737,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,?,334741,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,115562,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,131584,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,95855,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n3,Private,391016,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,51089,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,188044,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,2,United-States,>50K\n4,Private,117898,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,70240,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Self-emp-not-inc,187693,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,341672,Bachelors,13,Separated,Tech-support,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Private,208043,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n0,Local-gov,289982,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,76344,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,200973,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,111377,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,136684,HS-grad,9,Widowed,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,176716,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,166894,Some-college,10,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,243872,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,155621,5th-6th,3,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Columbia,<=50K\n3,Private,102597,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,60331,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,75024,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,Canada,<=50K\n4,Private,174474,10th,6,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,1,Peru,<=50K\n2,Private,145441,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,83434,Bachelors,13,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Japan,>50K\n0,Private,691830,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Private,189203,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,115784,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,280167,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,?,407338,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,52978,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,2,3,United-States,<=50K\n4,Private,169329,HS-grad,9,Married-civ-spouse,Tech-support,Husband,Black,Male,0,2,2,Trinadad&Tobago,>50K\n0,Private,315065,10th,6,Never-married,Other-service,Unmarried,White,Male,0,0,3,Mexico,<=50K\n0,Local-gov,167835,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,63105,HS-grad,9,Never-married,Prof-specialty,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,520775,12th,8,Never-married,Priv-house-serv,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,47902,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,145434,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,56392,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,162312,HS-grad,9,Divorced,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Japan,<=50K\n1,Private,204074,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,99246,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,102085,Some-college,10,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,168794,Preschool,1,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,State-gov,332379,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,233419,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,57233,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,192337,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,442429,HS-grad,9,Separated,Craft-repair,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,Private,369114,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,261334,9th,5,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,160303,HS-grad,9,Widowed,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,50474,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,321577,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,29591,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,334744,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Self-emp-not-inc,269474,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,287306,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,33619,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,0,United-States,<=50K\n2,Private,149347,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,96249,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,370502,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,188246,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,167558,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,1,Mexico,<=50K\n1,Private,292185,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,101593,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Local-gov,70164,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n2,Private,269722,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,175502,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,233165,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,177351,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,212114,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,288959,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,231619,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,146919,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,388811,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,243560,Some-college,10,Never-married,Sales,Unmarried,White,Female,0,0,2,?,<=50K\n1,Self-emp-not-inc,98360,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,369538,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,31740,Some-college,10,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,223660,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Male,2,0,2,United-States,<=50K\n0,Private,333611,5th-6th,3,Never-married,Other-service,Other-relative,White,Male,0,0,3,Mexico,<=50K\n1,Self-emp-not-inc,108247,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,76129,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,Guatemala,<=50K\n2,Private,91711,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,?,166855,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,182062,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,252752,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n1,Private,43953,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n0,Local-gov,84224,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,100675,1st-4th,2,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,Poland,<=50K\n3,Private,155509,HS-grad,9,Separated,Other-service,Other-relative,Black,Female,0,0,1,United-States,<=50K\n2,Private,29814,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,241805,Some-college,10,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,1,United-States,<=50K\n2,Private,214838,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,240810,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,154076,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n1,?,175552,5th-6th,3,Married-civ-spouse,?,Wife,White,Female,0,0,2,Mexico,<=50K\n3,Private,170287,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Poland,>50K\n4,Private,145995,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,433669,Assoc-acdm,12,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,?,<=50K\n0,Private,233626,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,607799,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,88500,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,127809,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,243743,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,177211,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,231180,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,253856,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,177075,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,152855,HS-grad,9,Never-married,Exec-managerial,Own-child,Other,Female,0,0,2,Mexico,<=50K\n2,Private,191137,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Private,255559,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,169815,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,221215,10th,6,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,270059,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,?,31588,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,345403,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,194897,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,388741,Some-college,10,Never-married,Adm-clerical,Unmarried,Other,Female,0,0,2,United-States,<=50K\n1,Private,355856,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n3,Private,122109,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,75673,HS-grad,9,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,141058,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,4,3,United-States,<=50K\n2,Private,47902,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,221343,1st-4th,2,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,255675,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,203505,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,125106,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,139890,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,76878,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n3,Self-emp-not-inc,28035,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,43953,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,3,2,United-States,<=50K\n2,Private,163237,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,55890,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,255934,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,168654,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,Canada,<=50K\n3,Self-emp-not-inc,39986,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,208451,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,206681,12th,8,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,117779,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,3,United-States,>50K\n2,Self-emp-not-inc,129150,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n2,?,177273,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,226443,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,146326,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,187901,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,97153,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,188694,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,187493,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,212468,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,84726,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,137907,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,34361,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,254114,Some-college,10,Married-spouse-absent,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,170174,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,190895,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Local-gov,317443,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,375603,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,203076,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,53893,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,171748,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,1,United-States,<=50K\n3,Private,167770,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,204584,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,660870,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,105686,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,?,70282,Masters,14,Married-civ-spouse,?,Wife,Black,Female,4,0,0,United-States,>50K\n1,Private,148607,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,255849,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,255921,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,England,<=50K\n1,Private,113326,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,440456,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,105493,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,259757,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Male,0,1,3,United-States,>50K\n2,Local-gov,89491,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,171818,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,51151,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,188957,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,97933,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,195447,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,46907,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n3,Self-emp-inc,383365,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Self-emp-not-inc,203408,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,148182,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,211497,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,48063,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,211804,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,3,United-States,>50K\n3,Private,185407,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,225927,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,314525,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,208577,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,222884,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,209538,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,177114,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,173754,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,121370,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,67125,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,67240,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,198346,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,141003,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Self-emp-inc,60668,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,104256,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,131002,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,155151,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,<=50K\n1,Private,177720,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,39615,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,203871,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,2,United-States,>50K\n4,State-gov,25045,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Male,1,0,2,United-States,<=50K\n2,Private,112264,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,169100,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,155659,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Germany,>50K\n2,Private,291665,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,1,United-States,<=50K\n1,Private,224215,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,270502,11th,7,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,125487,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,51385,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,112763,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,108926,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,366957,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,4,0,3,India,>50K\n2,Local-gov,109766,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,226106,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,92792,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,186950,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,230478,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,231638,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,120461,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,33673,12th,8,Never-married,Transport-moving,Not-in-family,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n1,Private,191385,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,229946,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Columbia,<=50K\n3,Self-emp-not-inc,160131,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,190895,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,126021,HS-grad,9,Never-married,Craft-repair,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,27815,9th,5,Divorced,Other-service,Not-in-family,White,Female,0,2,1,United-States,<=50K\n2,Private,203542,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,144592,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,223004,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,183257,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,172714,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,131611,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,253060,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,471990,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,>50K\n2,Private,138966,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,385412,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,184101,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,103344,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n2,Local-gov,135786,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,227359,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,State-gov,86912,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,83033,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,1,0,0,United-States,<=50K\n0,Private,172581,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,State-gov,274111,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,2,2,United-States,<=50K\n2,Private,187795,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,483822,7th-8th,4,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,Guatemala,<=50K\n4,Self-emp-inc,220543,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,152953,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Dominican-Republic,<=50K\n1,Private,239755,Some-college,10,Never-married,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,177905,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,200136,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,111625,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,336513,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,162915,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,116662,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,24763,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,225580,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,169104,Assoc-acdm,12,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n2,Private,212894,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,93997,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Italy,<=50K\n0,Private,189924,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,274424,11th,7,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,188246,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,284211,HS-grad,9,Widowed,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,198259,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,368517,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,168768,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Federal-gov,122220,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,United-States,>50K\n1,Private,136204,Masters,14,Separated,Exec-managerial,Not-in-family,White,Male,0,4,3,United-States,>50K\n2,Private,175641,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,173324,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n4,Local-gov,31195,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Federal-gov,88876,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,176069,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,215297,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,198425,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,180957,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,206129,Assoc-voc,11,Never-married,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,65950,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,197618,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,185357,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,134890,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,193043,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,153633,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,115890,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,394447,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,1,0,3,France,<=50K\n4,Private,343957,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,247986,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,238959,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,?,>50K\n4,Private,159048,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,423222,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,89735,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,31778,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,?,157327,5th-6th,3,Married-civ-spouse,?,Husband,Black,Male,0,0,0,United-States,<=50K\n3,Private,233511,Masters,14,Divorced,Sales,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,327112,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,1,2,United-States,>50K\n1,Private,236543,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,194475,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,303510,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,171242,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,39388,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,197218,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,State-gov,151991,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,374524,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,?,267352,11th,7,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,364563,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,186035,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,47541,HS-grad,9,Divorced,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,151107,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,500509,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,138107,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,4,2,United-States,>50K\n0,Federal-gov,225515,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,153291,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,169885,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,112780,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,175778,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,55213,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,38950,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Self-emp-not-inc,65991,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,174330,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,35224,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,175622,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,164678,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n3,?,87263,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,163671,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,4,United-States,>50K\n0,Self-emp-not-inc,181317,10th,6,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,177945,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,47168,10th,6,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,190023,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,168782,Assoc-voc,11,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,175290,7th-8th,4,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,Private,145463,1st-4th,2,Widowed,Priv-house-serv,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n3,Private,159755,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,113364,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,487742,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,304710,Some-college,10,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n3,Local-gov,185846,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,212894,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n4,Self-emp-not-inc,315460,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,135643,HS-grad,9,Widowed,Craft-repair,Unmarried,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n2,Private,220977,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,1,0,2,India,>50K\n0,?,117444,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,202683,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,164866,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,191814,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n1,?,227160,Some-college,10,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,158077,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,191103,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,193701,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,143046,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,206297,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,188563,HS-grad,9,Divorced,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,35102,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,203055,Some-college,10,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,309932,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,243432,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,177107,Assoc-voc,11,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,113929,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,291509,12th,8,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,222011,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,186824,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,192768,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,234962,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,83253,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,248990,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,346159,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,272656,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,60552,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,State-gov,33798,Some-college,10,Divorced,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,112158,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,200992,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,98155,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,79586,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Other,Male,0,0,3,United-States,<=50K\n0,State-gov,143062,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,101146,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n0,?,284450,11th,7,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n4,State-gov,159021,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,353270,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,162312,Some-college,10,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n3,State-gov,231961,Doctorate,16,Divorced,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,>50K\n2,Private,181943,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,163595,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,130856,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,208875,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,El-Salvador,>50K\n1,Self-emp-not-inc,58744,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,116641,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,69333,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,320811,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,197886,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,253914,1st-4th,2,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,Mexico,<=50K\n0,Private,89154,9th,5,Never-married,Other-service,Not-in-family,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,372317,9th,5,Separated,Other-service,Unmarried,White,Female,0,0,1,Mexico,<=50K\n0,Self-emp-not-inc,296090,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,3,?,<=50K\n2,Private,192614,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,403489,11th,7,Divorced,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,169652,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,217467,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,162104,9th,5,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,175912,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,179533,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,149624,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,289147,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,347720,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,406978,Bachelors,13,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,193199,11th,7,Never-married,Sales,Unmarried,White,Female,0,0,0,Poland,<=50K\n2,Self-emp-inc,163998,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,173115,10th,6,Separated,Exec-managerial,Not-in-family,Black,Male,2,0,4,United-States,<=50K\n1,Private,333701,Assoc-voc,11,Never-married,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,State-gov,48121,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,2,0,United-States,<=50K\n2,Private,186256,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,104525,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,104097,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,3,United-States,<=50K\n4,Private,212806,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,203353,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,130126,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n0,?,270043,10th,6,Never-married,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,218435,HS-grad,9,Married-spouse-absent,Sales,Unmarried,White,Female,0,0,0,Cuba,<=50K\n1,Private,154120,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,193537,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Dominican-Republic,<=50K\n2,Private,84535,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,150999,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n1,State-gov,157673,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,217424,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,358886,12th,8,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,3,United-States,<=50K\n2,Private,186191,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,212660,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-inc,31740,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,498785,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,35945,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n3,Local-gov,162566,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,Canada,<=50K\n1,Private,118861,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n1,Private,206609,Some-college,10,Never-married,Sales,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,423064,HS-grad,9,Separated,Adm-clerical,Other-relative,Black,Male,0,0,1,United-States,<=50K\n3,Private,191957,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,223934,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n4,?,129246,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,195486,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,4,Jamaica,<=50K\n2,Private,114580,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Female,0,0,2,Vietnam,<=50K\n0,Private,119215,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,240554,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,199067,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,144084,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,358682,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,59612,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,391585,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Local-gov,101345,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,117618,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,231238,9th,5,Separated,Farming-fishing,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,143046,HS-grad,9,Widowed,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,326857,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,4,4,United-States,>50K\n2,Private,203642,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,Private,88579,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,240517,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,4,United-States,<=50K\n4,Local-gov,156649,1st-4th,2,Widowed,Handlers-cleaners,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,143392,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,365465,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,4,Philippines,<=50K\n0,State-gov,264710,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n4,State-gov,223830,9th,5,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,154374,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,242521,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,124569,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,209230,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,162228,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,60267,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,76901,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,137876,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,347910,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,138917,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,532379,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,31532,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,30973,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,117295,1st-4th,2,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,295282,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,190786,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,246207,Bachelors,13,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,130780,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,186212,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,175526,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,207025,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n2,Federal-gov,82622,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,199688,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,?,>50K\n2,State-gov,318886,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,256005,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,217715,5th-6th,3,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,205803,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n4,Self-emp-not-inc,240491,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Cuba,<=50K\n1,Private,154120,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,69251,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Private,333505,HS-grad,9,Married-spouse-absent,Transport-moving,Own-child,White,Male,0,0,2,Peru,<=50K\n1,Private,168521,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,193568,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,426895,12th,8,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,131826,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,79646,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,167031,Bachelors,13,Never-married,Prof-specialty,Unmarried,Other,Female,0,0,1,United-States,<=50K\n1,Private,73199,11th,7,Never-married,Other-service,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Private,114056,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,110417,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,33266,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,154410,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,?,154537,Some-college,10,Divorced,?,Unmarried,White,Female,0,0,3,United-States,>50K\n0,Private,27780,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,142914,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,4,United-States,<=50K\n2,Private,190987,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,2,0,2,United-States,>50K\n0,Private,314422,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,273771,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,175083,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,63665,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Local-gov,193416,Some-college,10,Never-married,Protective-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,74275,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,122609,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,225456,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Local-gov,116892,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,196971,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,4,United-States,<=50K\n0,Private,105312,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,108699,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,171615,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,388023,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,181553,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,170850,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,187479,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,277720,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,493862,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,2,0,2,United-States,>50K\n1,Private,220754,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,209768,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,93225,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,341709,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,236242,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,121889,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,318190,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,111306,7th-8th,4,Widowed,Farming-fishing,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,198614,11th,7,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,193231,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,104614,11th,7,Never-married,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,172368,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,60331,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,154568,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n2,Private,192939,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,138184,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,2,1,United-States,<=50K\n2,Private,238567,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,England,>50K\n1,Private,208068,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Mexico,<=50K\n3,Private,181810,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,2,0,2,United-States,<=50K\n0,Federal-gov,283918,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,107276,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,4,2,United-States,>50K\n0,Private,37783,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,263552,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,255439,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,344275,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,70568,1st-4th,2,Never-married,Other-service,Other-relative,White,Female,0,0,1,El-Salvador,<=50K\n0,Private,127827,12th,8,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,185203,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,123436,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n3,Self-emp-not-inc,136322,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,1,2,United-States,<=50K\n0,Private,187052,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,177769,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,68268,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,424855,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,<=50K\n2,Federal-gov,81853,HS-grad,9,Divorced,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n1,Self-emp-inc,153549,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,271393,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,198148,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,469602,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,163290,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,295949,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,125279,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,182866,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,111563,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,34173,Bachelors,13,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,183627,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,197757,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Private,98941,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,205474,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,206659,Some-college,10,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,?,191394,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,244661,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Private,47396,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,270721,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,State-gov,32694,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,171256,Assoc-acdm,12,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,169982,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,<=50K\n3,Self-emp-not-inc,217210,HS-grad,9,Widowed,Other-service,Other-relative,Black,Female,0,0,2,United-States,<=50K\n3,Private,218329,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,386643,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,125933,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,155767,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,432555,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,2,2,United-States,<=50K\n1,Private,54929,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,162136,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,256504,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,162098,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,103110,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,227610,10th,6,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,176696,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,220019,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,242984,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,187847,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,132636,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,108887,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,195897,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,112181,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,>50K\n4,Local-gov,391926,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,195505,10th,6,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,43819,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,2,United-States,>50K\n0,Private,145389,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,?,186824,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,101833,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,82283,5th-6th,3,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n3,Private,99602,HS-grad,9,Separated,Craft-repair,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,213276,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,424468,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n1,Private,176123,10th,6,Never-married,Machine-op-inspct,Other-relative,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n1,Private,38797,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,101859,7th-8th,4,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,87158,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,205066,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,56929,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,3,?,<=50K\n1,Private,25322,Bachelors,13,Married-spouse-absent,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Male,0,4,2,?,<=50K\n1,Private,87950,Assoc-voc,11,Divorced,Sales,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,150154,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,142076,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Male,2,0,2,United-States,>50K\n1,State-gov,112139,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,149217,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n1,Private,189974,Some-college,10,Divorced,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,109199,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,190290,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,189404,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,1,United-States,>50K\n1,Federal-gov,428271,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,State-gov,134192,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,168211,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,277314,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,Black,Male,0,3,3,United-States,>50K\n2,Federal-gov,316120,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,>50K\n2,Private,107276,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,?,112453,HS-grad,9,Separated,?,Not-in-family,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n0,Private,346909,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n4,?,105017,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,317360,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,189017,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,138179,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,299813,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Dominican-Republic,<=50K\n2,Private,265083,5th-6th,3,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,1,Mexico,<=50K\n3,Private,185846,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,184655,Assoc-acdm,12,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,Private,200295,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,117319,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n3,Private,63000,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,106942,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,52795,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,51264,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,France,>50K\n2,Self-emp-not-inc,410919,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,105592,Assoc-acdm,12,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,183151,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,209912,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n3,Self-emp-not-inc,275845,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,241851,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,89299,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,106648,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,58426,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,121912,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,170730,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,257555,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,51499,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,195000,Bachelors,13,Never-married,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,108741,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,184964,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,156815,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,49325,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,121718,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Germany,<=50K\n0,Private,172076,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,327901,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,215990,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,210866,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,322873,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,265698,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,26990,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,177896,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,189107,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,306830,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Nicaragua,<=50K\n4,Federal-gov,39110,11th,7,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,Canada,<=50K\n1,Private,155475,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,135803,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,1,Philippines,<=50K\n3,Private,117849,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,339321,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,318822,11th,7,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,174794,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,204277,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,3,United-States,>50K\n3,Private,182460,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,1,United-States,>50K\n0,Private,193920,Masters,14,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,?,<=50K\n2,Federal-gov,91468,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,106760,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,Canada,>50K\n1,Private,375680,Assoc-acdm,12,Never-married,Craft-repair,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,222615,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,190968,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,76767,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,203098,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,162187,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,242729,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,253784,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,206051,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,181553,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,80986,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,200783,7th-8th,4,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,42596,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,464502,Assoc-acdm,12,Never-married,Sales,Not-in-family,Black,Male,0,0,2,?,<=50K\n4,Private,205724,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,446140,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,32287,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,56774,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,308118,Bachelors,13,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,?,<=50K\n1,Private,176279,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,103277,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,225780,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,>50K\n3,Private,154728,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,149943,HS-grad,9,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Japan,<=50K\n2,State-gov,22245,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,93056,7th-8th,4,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,270522,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,123218,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,123959,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,2,0,Hungary,<=50K\n1,Self-emp-not-inc,103642,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,157747,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,154083,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,State-gov,23037,Some-college,10,Never-married,Other-service,Own-child,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n0,?,226891,HS-grad,9,Never-married,?,Other-relative,Asian-Pac-Islander,Female,0,0,0,South,<=50K\n1,Private,50028,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,138251,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,369825,7th-8th,4,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,44364,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,230704,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,42044,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,56340,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,156015,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,163434,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,85251,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,187411,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,155124,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n0,Private,396633,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,3,United-States,>50K\n2,Private,182313,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,52596,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,?,260111,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,143570,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,160634,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,>50K\n3,Private,29909,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,94215,12th,8,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,151990,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n3,Federal-gov,188081,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,218445,5th-6th,3,Never-married,Priv-house-serv,Unmarried,White,Female,0,0,0,Mexico,<=50K\n4,Private,235775,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,Cuba,<=50K\n0,Private,98605,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,188398,HS-grad,9,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,140365,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,202950,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Iran,>50K\n0,Private,218215,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,197816,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,147002,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Female,0,0,2,Puerto-Rico,<=50K\n3,Private,138497,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,57711,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,169925,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,72310,11th,7,Never-married,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,170800,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,215095,11th,7,Never-married,Prof-specialty,Unmarried,White,Female,0,0,1,Puerto-Rico,<=50K\n2,Private,480717,Bachelors,13,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n4,Local-gov,34632,Bachelors,13,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,140664,Assoc-acdm,12,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,177858,Bachelors,13,Married-civ-spouse,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,160369,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,2,United-States,>50K\n2,Private,129102,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,278522,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Federal-gov,124953,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,63184,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,165815,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,248584,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,226871,Bachelors,13,Divorced,Protective-serv,Not-in-family,Black,Male,0,0,3,United-States,>50K\n2,Private,267717,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,60367,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,134120,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,95639,HS-grad,9,Never-married,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,132053,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,138768,Assoc-acdm,12,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,203910,HS-grad,9,Widowed,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,109952,HS-grad,9,Married-civ-spouse,Tech-support,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,155781,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,49398,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,159303,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,248339,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,190539,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,1,3,United-States,<=50K\n1,Private,183620,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,25468,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n2,Private,201495,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,52221,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,96460,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,325353,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,2,United-States,>50K\n1,Self-emp-not-inc,176027,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,266135,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n4,State-gov,194252,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,1,0,2,United-States,>50K\n4,?,164835,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,363192,Assoc-voc,11,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,31360,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,63503,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,157614,HS-grad,9,Divorced,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,160647,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,2,0,1,United-States,>50K\n2,Private,363395,Some-college,10,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,338376,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,87523,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,280714,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,119565,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,171482,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,49249,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,331552,12th,8,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,174426,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,184105,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,37933,Bachelors,13,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,291529,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,0,United-States,>50K\n0,Private,376416,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,263612,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,Haiti,<=50K\n0,Private,227471,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,191103,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,35644,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,227298,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,187508,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,184378,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Puerto-Rico,<=50K\n3,Self-emp-not-inc,190333,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,155372,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,259882,Assoc-voc,11,Never-married,Sales,Unmarried,Black,Female,0,0,0,United-States,<=50K\n2,Private,217077,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,103596,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,188236,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,353010,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,70655,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,64874,Assoc-acdm,12,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,219240,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,104849,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n2,Private,173590,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,412316,HS-grad,9,Never-married,Sales,Other-relative,Black,Male,0,0,2,?,<=50K\n4,Self-emp-inc,195835,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,170579,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Federal-gov,230545,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,Puerto-Rico,<=50K\n4,Private,162297,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,169549,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,117528,Bachelors,13,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,273876,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,529104,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,456110,11th,7,Divorced,Transport-moving,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,?,180868,11th,7,Never-married,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,170301,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,1,0,2,United-States,<=50K\n1,Private,55717,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,166181,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,52242,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,224629,Masters,14,Never-married,Exec-managerial,Not-in-family,Other,Male,0,0,1,Cuba,<=50K\n0,Private,197997,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,46144,Some-college,10,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,State-gov,180871,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,212311,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,232874,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,175999,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,177121,Some-college,10,Separated,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,299358,HS-grad,9,Widowed,Other-service,Other-relative,White,Female,0,2,1,United-States,<=50K\n0,?,326624,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,129836,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,225515,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,145664,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,151764,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,183523,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,257869,Some-college,10,Separated,Other-service,Not-in-family,White,Male,0,0,1,Columbia,<=50K\n2,Private,73025,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,1,China,<=50K\n0,Private,165532,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Federal-gov,140035,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,325159,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n4,Federal-gov,161926,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,163665,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,106938,HS-grad,9,Married-civ-spouse,Tech-support,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Private,97453,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,242464,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,1,0,2,United-States,>50K\n3,Private,155233,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,4,0,2,United-States,>50K\n1,Private,248653,1st-4th,2,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Private,59313,12th,8,Married-spouse-absent,Transport-moving,Not-in-family,Black,Male,0,0,2,?,<=50K\n0,Private,141297,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,227325,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,123653,5th-6th,3,Separated,Other-service,Not-in-family,White,Male,0,0,0,Italy,<=50K\n4,Federal-gov,176317,10th,6,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,77146,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,169124,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,179413,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,180137,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,State-gov,179319,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,45766,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,152810,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n4,Private,214052,5th-6th,3,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,201141,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,43599,HS-grad,9,Widowed,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,292536,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,82161,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,180656,Some-college,10,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,?,<=50K\n0,Private,181370,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,148623,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,84399,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,143331,10th,6,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,48779,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,175495,HS-grad,9,Never-married,?,Own-child,Black,Female,0,0,1,United-States,<=50K\n4,Private,83542,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,214619,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,160035,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,39603,Some-college,10,Never-married,Craft-repair,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,181589,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,Columbia,<=50K\n1,Private,261511,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,29522,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,36340,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,320984,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n4,?,403625,Some-college,10,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n0,Private,122346,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,105794,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,4,0,3,United-States,>50K\n3,Private,152883,HS-grad,9,Widowed,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,State-gov,123037,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,?,339682,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,182074,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,248588,12th,8,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,187584,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,Canada,<=50K\n2,Private,46706,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,190290,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,247294,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Peru,<=50K\n0,Private,117779,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,121602,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,451744,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,107793,HS-grad,9,Divorced,Other-service,Own-child,White,Male,1,0,2,United-States,<=50K\n1,Private,339772,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,185582,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,260614,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,53220,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,213844,HS-grad,9,Married-AF-spouse,Craft-repair,Wife,Black,Female,0,0,2,United-States,>50K\n1,Private,213226,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,58582,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,193116,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,201410,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,190525,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,138285,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Iran,<=50K\n3,Private,111939,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,109277,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,331539,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,China,>50K\n1,Private,396745,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n2,Private,126675,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,349022,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,?,98145,Some-college,10,Divorced,?,Unmarried,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n2,Private,234901,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Germany,>50K\n2,Private,100681,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,Self-emp-not-inc,265097,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,237379,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,44793,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,270942,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,1,Mexico,<=50K\n4,Private,193622,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,187749,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,0,Philippines,<=50K\n1,Private,160178,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,680390,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,96245,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,34803,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,170091,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,231813,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,23789,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,438711,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,<=50K\n4,Private,169804,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n4,Local-gov,376506,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,Private,28791,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,162814,HS-grad,9,Divorced,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,58108,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,102226,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,209131,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,157117,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,172865,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,29798,12th,8,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n4,?,229424,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,80680,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,1,0,1,United-States,<=50K\n3,Local-gov,238959,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,>50K\n1,Private,189462,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,139347,HS-grad,9,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,188108,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,2,0,2,United-States,<=50K\n2,Self-emp-inc,111128,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,81540,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,257562,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,59496,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,29974,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,102597,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,41419,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,118565,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n3,State-gov,312897,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,England,>50K\n0,Private,166290,9th,5,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,160261,HS-grad,9,Never-married,Tech-support,Own-child,Asian-Pac-Islander,Male,4,0,1,China,>50K\n1,Self-emp-not-inc,116834,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,?,<=50K\n0,Private,203076,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,201197,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,273803,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,156797,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,283896,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,149368,HS-grad,9,Divorced,Sales,Unmarried,White,Male,1,0,1,United-States,<=50K\n3,Private,156926,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,?,163911,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,165881,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,86872,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,167523,Bachelors,13,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,154950,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,171231,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n4,Private,244933,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,256908,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,>50K\n1,Self-emp-not-inc,33442,Assoc-voc,11,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,126142,10th,6,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,?,268222,11th,7,Never-married,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,167106,HS-grad,9,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,Hong,<=50K\n0,Local-gov,50065,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,State-gov,252529,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,?,199665,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,343579,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,190817,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,151089,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n3,Private,186820,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,2,0,2,United-States,<=50K\n4,Self-emp-not-inc,210731,7th-8th,4,Divorced,Sales,Other-relative,White,Male,0,0,0,Mexico,<=50K\n2,Private,123816,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,77071,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,4,1,United-States,<=50K\n2,Private,115085,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,170525,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,4,0,2,United-States,>50K\n0,Private,209949,11th,7,Never-married,Sales,Own-child,White,Female,0,2,0,United-States,<=50K\n4,Self-emp-not-inc,34297,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,180985,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,33365,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,2,Canada,<=50K\n0,Private,197752,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,180551,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,77975,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,159297,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,0,0,2,?,>50K\n3,Private,94342,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,34180,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,367251,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,172407,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,303462,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,220269,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,45093,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,Canada,<=50K\n1,Private,101709,HS-grad,9,Separated,Transport-moving,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,219591,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,76625,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,342599,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,125846,1st-4th,2,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,?,<=50K\n3,Local-gov,238257,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,206253,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,172571,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,95165,Doctorate,16,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,141181,5th-6th,3,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,267843,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Private,181382,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,>50K\n0,?,207782,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n4,?,103161,HS-grad,9,Widowed,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,132320,Some-college,10,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,201138,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,239058,12th,8,Widowed,Handlers-cleaners,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-inc,239755,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,176262,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,264738,HS-grad,9,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,Germany,<=50K\n1,Private,182218,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,318982,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,216666,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Guatemala,<=50K\n3,Private,274200,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,150095,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,192978,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,68021,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,28568,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,115057,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,139568,11th,7,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,138497,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,182460,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,2,China,>50K\n0,Private,253310,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-inc,130856,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,389765,7th-8th,4,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,52781,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,146178,HS-grad,9,Never-married,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,231053,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,>50K\n0,?,145964,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,483450,9th,5,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-inc,198316,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,160614,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-inc,325171,10th,6,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,172898,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,186473,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,286967,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,111939,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,>50K\n4,Federal-gov,325089,10th,6,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,143582,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Private,308027,HS-grad,9,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,105060,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Federal-gov,39643,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,186191,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,3,United-States,>50K\n4,Local-gov,267763,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,124293,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,36271,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,143459,9th,5,Separated,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,186376,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,52822,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,104509,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,184456,Prof-school,15,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,192302,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,156822,10th,6,Never-married,Sales,Not-in-family,White,Female,0,2,1,United-States,<=50K\n0,Private,214413,Masters,14,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,108574,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,223934,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,200559,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,137722,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,261677,9th,5,Never-married,Handlers-cleaners,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,136331,HS-grad,9,Married-spouse-absent,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,329993,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,91819,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,201122,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,315423,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,103277,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,236805,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,74883,Bachelors,13,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n0,Private,115443,11th,7,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,150528,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,43701,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,419053,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,183594,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,390348,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,36989,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,2,0,4,United-States,<=50K\n3,Private,247895,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n4,Private,191446,1st-4th,2,Married-civ-spouse,Other-service,Other-relative,Black,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,33521,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,>50K\n4,Private,46087,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,?,129188,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,4,0,0,United-States,>50K\n2,Private,356824,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,158746,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,153323,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,130391,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,173613,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,362883,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,182757,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,50397,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Federal-gov,101709,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Private,202570,12th,8,Never-married,Adm-clerical,Other-relative,Black,Male,0,0,3,?,<=50K\n2,Private,145649,HS-grad,9,Separated,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,136343,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,142166,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,242001,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,127089,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n3,Local-gov,124071,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,4,United-States,>50K\n2,Local-gov,190368,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,?,19793,Some-college,10,Divorced,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,67661,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,62278,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,295010,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,203897,Bachelors,13,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,2,Cuba,<=50K\n1,Private,265314,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,159603,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,134331,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,123011,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Poland,>50K\n1,Private,274964,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,66309,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,73471,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,26671,HS-grad,9,Never-married,?,Other-relative,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Private,357118,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Self-emp-inc,184655,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n0,?,55492,Assoc-voc,11,Never-married,?,Not-in-family,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n0,Private,175266,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,188008,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n2,Private,87284,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,1,United-States,>50K\n3,Private,330087,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Self-emp-inc,56975,HS-grad,9,Divorced,Sales,Unmarried,Asian-Pac-Islander,Female,0,0,4,?,<=50K\n1,Private,150025,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n0,?,189203,Assoc-acdm,12,Never-married,?,Other-relative,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,330874,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,136824,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,201179,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,324654,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n0,Federal-gov,366207,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,103860,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,106700,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,163557,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,286261,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,123083,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,125197,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,278073,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,Black,Male,0,0,1,United-States,<=50K\n3,Private,133963,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,71467,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,76487,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,215245,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,127185,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,179720,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,88909,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,341995,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,173938,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,344275,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,150463,HS-grad,9,Never-married,Priv-house-serv,Unmarried,Other,Female,0,0,2,Guatemala,<=50K\n2,Local-gov,209544,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n2,Local-gov,201723,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,343476,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,Japan,>50K\n3,Self-emp-inc,77392,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,171156,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,357118,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Federal-gov,167749,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,352882,HS-grad,9,Divorced,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,4,South,>50K\n0,Private,51201,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,365986,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,>50K\n1,Private,400416,11th,7,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,31533,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,106900,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,United-States,>50K\n2,Local-gov,192337,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,118712,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,1,2,United-States,<=50K\n1,Private,301654,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,145162,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,?,>50K\n0,Private,88126,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,England,<=50K\n4,Private,165017,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Italy,>50K\n1,Private,238342,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,857532,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,134378,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,260797,10th,6,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,138765,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,?,256674,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,247444,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Columbia,<=50K\n3,State-gov,454063,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,180539,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,397346,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,107160,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,262024,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,131230,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,274451,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n2,State-gov,365986,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,204515,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,99316,12th,8,Divorced,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,?,206681,11th,7,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,268726,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,275395,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,383322,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,126822,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,168355,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,162667,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,Columbia,<=50K\n2,Private,373403,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,274562,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,249362,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,111567,9th,5,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,?,216508,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,145784,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,State-gov,209317,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,259505,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,345360,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,England,<=50K\n2,Local-gov,198096,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n2,Self-emp-inc,33126,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,206354,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,1484705,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,26410,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,220901,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,44671,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,38620,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,89040,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,370160,Some-college,10,Separated,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,208946,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,131230,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,60358,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,350853,5th-6th,3,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,?,<=50K\n0,Private,209782,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,351952,Some-college,10,Never-married,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,142081,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,Mexico,<=50K\n0,Private,164775,9th,5,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,Guatemala,<=50K\n2,Local-gov,47858,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,404085,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,218678,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,184655,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,<=50K\n2,Private,321760,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,185399,Masters,14,Divorced,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,409200,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,40077,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,31740,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,233722,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,192039,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,222618,11th,7,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,State-gov,213646,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Local-gov,194141,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n3,State-gov,80282,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,166350,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,60641,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,124827,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,105438,HS-grad,9,Separated,Machine-op-inspct,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,85244,Bachelors,13,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,120535,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,269604,5th-6th,3,Never-married,Other-service,Unmarried,Other,Female,0,0,2,El-Salvador,<=50K\n1,Private,247711,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,380922,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,281221,Bachelors,13,Never-married,Adm-clerical,Other-relative,Asian-Pac-Islander,Female,0,0,2,Taiwan,<=50K\n0,Private,269687,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,181758,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,136787,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,107882,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,172579,Assoc-voc,11,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,29933,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n1,Federal-gov,38905,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,168826,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,424034,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,117509,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,196971,Bachelors,13,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,69525,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,0,United-States,<=50K\n0,Private,374116,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,283913,5th-6th,3,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,4,England,<=50K\n2,State-gov,147258,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,139903,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,112959,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,264148,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,256211,Some-college,10,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,1,Vietnam,<=50K\n1,Self-emp-not-inc,142519,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,281852,HS-grad,9,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,4,United-States,<=50K\n2,Private,380543,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,204402,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,192203,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,199005,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n0,Self-emp-inc,61838,10th,6,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,210095,11th,7,Married-spouse-absent,Handlers-cleaners,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n0,Private,187352,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,32451,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,140569,Some-college,10,Separated,Sales,Not-in-family,White,Male,4,0,3,United-States,>50K\n2,Private,87556,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,2,0,2,United-States,<=50K\n0,Private,79443,9th,5,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,Mexico,<=50K\n1,Private,212622,Masters,14,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,32650,Assoc-voc,11,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,125461,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,219867,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,206609,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,101299,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,29437,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,87164,11th,7,Widowed,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,146103,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,169324,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,Haiti,<=50K\n3,Private,138370,7th-8th,4,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,2,2,China,<=50K\n1,Private,29523,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,383745,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,1,2,United-States,>50K\n0,?,247075,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,?,200967,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,?,175985,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,189404,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,<=50K\n1,Self-emp-not-inc,267661,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,182926,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,243858,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,?,43587,HS-grad,9,Married-spouse-absent,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,31339,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,204682,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,1,0,2,Japan,<=50K\n0,Private,73145,9th,5,Never-married,Craft-repair,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,218184,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n2,Local-gov,223237,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,93319,HS-grad,9,Never-married,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,?,212300,HS-grad,9,Separated,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,187356,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,220832,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,211361,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,134195,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,218249,11th,7,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Private,70720,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Self-emp-not-inc,342384,11th,7,Married-civ-spouse,Craft-repair,Own-child,White,Male,0,3,3,United-States,<=50K\n1,Private,237317,9th,5,Never-married,Craft-repair,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n0,Private,359759,Some-college,10,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,0,Philippines,<=50K\n3,Self-emp-not-inc,181758,Doctorate,16,Never-married,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,>50K\n4,Self-emp-inc,267101,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,222221,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,55139,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,220237,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,>50K\n2,Private,101073,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,69884,Prof-school,15,Married-spouse-absent,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,201127,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,164733,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,State-gov,129447,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,32837,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,200117,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,219183,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,?,188842,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,272669,Bachelors,13,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,0,South,<=50K\n4,Self-emp-inc,336188,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,4,United-States,>50K\n4,?,191288,7th-8th,4,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,176185,Some-college,10,Divorced,Exec-managerial,Other-relative,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,197728,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,144778,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,<=50K\n1,?,133373,Bachelors,13,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,197399,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,86010,10th,6,Widowed,Transport-moving,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,228873,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,187415,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,3,?,<=50K\n4,Self-emp-inc,112945,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n4,Private,98361,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n0,Private,129172,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,316205,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,226629,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n1,State-gov,180886,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,69333,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,213620,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,197397,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,Other,Female,0,0,0,Puerto-Rico,<=50K\n0,Private,223648,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,?,<=50K\n1,Private,179915,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,4,United-States,<=50K\n3,Private,339905,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,112956,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,421837,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,Mexico,>50K\n2,Private,187999,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,77313,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,231948,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,37109,HS-grad,9,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,3,Philippines,<=50K\n1,Private,79387,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,?,133963,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,177937,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,0,2,Poland,<=50K\n4,Private,173488,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,183355,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,147989,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,<=50K\n0,Private,289944,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,62278,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,110457,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,295763,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,3,United-States,<=50K\n4,State-gov,100063,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,194962,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,227597,HS-grad,9,Never-married,Armed-Forces,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,117606,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Federal-gov,44774,Bachelors,13,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,177648,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,172571,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n2,?,203482,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,153931,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,84774,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,157127,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,170786,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,281030,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,203761,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,4,0,2,United-States,>50K\n1,Private,167405,HS-grad,9,Married-spouse-absent,Farming-fishing,Own-child,White,Female,0,0,2,Mexico,<=50K\n2,Local-gov,188436,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,388849,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,176998,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,200316,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,160300,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,236684,Assoc-voc,11,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Local-gov,247794,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,267325,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,1,0,2,United-States,<=50K\n2,Private,279490,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,State-gov,280618,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,248406,HS-grad,9,Separated,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,226494,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,220460,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,108317,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,147256,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,>50K\n0,Private,110371,HS-grad,9,Married-civ-spouse,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,114060,7th-8th,4,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n1,Federal-gov,31161,HS-grad,9,Divorced,Exec-managerial,Not-in-family,Other,Female,0,0,2,United-States,<=50K\n2,Private,105862,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,4,United-States,>50K\n1,Private,402089,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n0,?,425447,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,137300,Assoc-voc,11,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,State-gov,326691,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,275093,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,112497,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,174491,HS-grad,9,Divorced,Tech-support,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,114835,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,137898,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,153151,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,2,0,2,United-States,<=50K\n1,Private,134886,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,193815,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,237833,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,101593,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,164924,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,174201,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,36169,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,144071,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,180859,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,221915,Some-college,10,Widowed,Craft-repair,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,26892,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,351084,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,97306,Bachelors,13,Divorced,Craft-repair,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,185027,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,182539,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,215395,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,186434,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,>50K\n2,?,217921,9th,5,Married-civ-spouse,?,Wife,Asian-Pac-Islander,Female,0,0,2,Hong,<=50K\n3,Local-gov,346668,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,412952,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,167009,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,316000,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,216256,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,341835,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,169841,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,200681,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Outlying-US(Guam-USVI-etc),<=50K\n3,Self-emp-not-inc,456956,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,276075,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Federal-gov,96657,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,374313,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,110998,Masters,14,Widowed,Tech-support,Unmarried,Asian-Pac-Islander,Female,0,0,2,India,<=50K\n1,Private,53285,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,104613,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,?,303317,11th,7,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,298070,Assoc-voc,11,Separated,Other-service,Unmarried,White,Female,2,0,1,United-States,<=50K\n0,Private,318822,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,375078,7th-8th,4,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,Mexico,<=50K\n0,?,232799,HS-grad,9,Never-married,?,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,210851,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,213745,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,204447,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,318934,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,237386,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,182629,Masters,14,Divorced,Sales,Not-in-family,White,Male,0,0,1,Iran,<=50K\n2,Private,144778,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,117166,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,237630,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,171550,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,165302,Some-college,10,Divorced,Adm-clerical,Unmarried,Other,Female,0,0,2,United-States,<=50K\n2,State-gov,42186,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,1,0,0,United-States,<=50K\n3,Private,284952,10th,6,Separated,Sales,Unmarried,White,Female,0,0,2,Italy,<=50K\n4,Private,96099,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,198759,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,227886,HS-grad,9,Never-married,Exec-managerial,Own-child,Black,Female,0,0,1,Jamaica,<=50K\n1,Private,391874,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,184370,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,135839,Assoc-voc,11,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,194698,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,342175,Masters,14,Divorced,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,67218,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,205152,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,434467,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,110150,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n3,?,123382,HS-grad,9,Separated,?,Not-in-family,Black,Female,0,3,2,United-States,<=50K\n2,State-gov,404573,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,99462,11th,7,Never-married,Other-service,Own-child,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n4,Private,170310,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,199883,12th,8,Divorced,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,70034,7th-8th,4,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,Portugal,<=50K\n1,Private,393357,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,3,United-States,<=50K\n4,?,249043,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,72630,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n4,Private,223133,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,State-gov,345969,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,195520,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Private,257942,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,269300,Some-college,10,Married-spouse-absent,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,137354,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Federal-gov,232997,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,77266,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,164190,Prof-school,15,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,153536,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n3,Local-gov,26832,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,188096,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,>50K\n3,Self-emp-inc,369522,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,110998,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n1,Private,205152,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,>50K\n1,?,163890,Some-college,10,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,358631,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,185354,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,336061,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,47011,Bachelors,13,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,149949,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Private,59496,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,32950,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,109912,Doctorate,16,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,1,United-States,>50K\n0,Private,199555,Assoc-voc,11,Never-married,Sales,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Private,91299,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Private,99359,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,2,2,United-States,<=50K\n2,Private,242559,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,286391,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,1,0,0,United-States,<=50K\n4,Private,132870,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,4,0,United-States,<=50K\n3,Federal-gov,22428,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,239150,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,170563,Assoc-voc,11,Separated,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,173542,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,286026,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,72887,HS-grad,9,Married-civ-spouse,Craft-repair,Own-child,Asian-Pac-Islander,Male,1,0,2,United-States,<=50K\n3,Local-gov,163229,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,165726,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,70055,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,184655,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,139906,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,4,United-States,<=50K\n1,Local-gov,198211,Assoc-voc,11,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,146540,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,132304,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,190916,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Never-worked,237272,10th,6,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,755858,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,127315,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,304302,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,184942,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,267989,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,188377,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,221059,Masters,14,Married-civ-spouse,Prof-specialty,Other-relative,Other,Female,3,0,2,United-States,>50K\n1,Private,340787,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,140782,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,2,United-States,>50K\n4,Private,169071,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,151094,Assoc-voc,11,Separated,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,122922,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,151141,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,136651,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,177285,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,3,United-States,>50K\n1,Local-gov,128016,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,200318,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,250354,10th,6,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,191069,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,27856,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,523484,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n2,Federal-gov,257175,Bachelors,13,Divorced,Tech-support,Unmarried,Black,Female,0,1,2,United-States,<=50K\n4,Private,174864,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,>50K\n2,Private,196029,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,200471,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,353195,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,222868,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,221791,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,197114,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,160220,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,274917,Masters,14,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,348460,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,112683,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,345831,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,105370,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,345006,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n3,Private,195329,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,1,0,1,Italy,<=50K\n2,Local-gov,108765,Assoc-voc,11,Never-married,Exec-managerial,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Private,138022,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,175029,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,189574,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,141409,10th,6,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n2,Self-emp-not-inc,186035,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,165235,Bachelors,13,Separated,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n0,Private,105043,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,230684,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,345705,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,Private,248584,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,436861,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,4,0,2,United-States,>50K\n1,Private,200153,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,398625,11th,7,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,114043,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,169544,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,343849,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,162572,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,291578,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,136162,Assoc-voc,11,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,376133,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,4,0,0,United-States,>50K\n3,Self-emp-inc,302612,Masters,14,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,240166,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,193152,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Private,248094,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,<=50K\n2,Private,119281,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,300404,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,>50K\n0,Private,82847,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-inc,161153,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,3,3,United-States,>50K\n2,Federal-gov,287008,Masters,14,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,654141,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,252646,Some-college,10,Separated,Transport-moving,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,171924,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,219742,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,State-gov,153788,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,60639,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,96062,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Greece,<=50K\n3,Private,165614,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,159888,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,110586,Some-college,10,Widowed,Priv-house-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,143062,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Self-emp-inc,413557,9th,5,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,137658,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,398931,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,311764,10th,6,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n4,Private,98725,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,140854,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,97304,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Unmarried,White,Male,1,0,2,?,<=50K\n1,Federal-gov,352768,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,?,27184,HS-grad,9,Widowed,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,237229,Assoc-voc,11,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,142494,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,210313,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Guatemala,<=50K\n2,Private,194538,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,26698,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,1,2,United-States,>50K\n1,Private,211032,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,107909,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,136077,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,184737,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,2,2,United-States,<=50K\n1,Private,214689,Bachelors,13,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,?,147558,Bachelors,13,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,93793,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,247025,Assoc-voc,11,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,284403,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,221977,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Federal-gov,339956,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,161097,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n4,Private,223696,1st-4th,2,Divorced,Craft-repair,Not-in-family,Other,Male,0,0,2,Dominican-Republic,<=50K\n1,Private,234500,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,97005,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,242615,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,174938,Bachelors,13,Divorced,Tech-support,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Private,160120,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n3,Private,193775,Bachelors,13,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,59583,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,157913,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,308205,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,158506,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,201769,11th,7,Never-married,Protective-serv,Not-in-family,Black,Male,4,0,2,United-States,>50K\n3,Private,330470,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,184078,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,123384,Masters,14,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,330132,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,274720,5th-6th,3,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,Jamaica,<=50K\n3,Private,129673,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Federal-gov,205584,5th-6th,3,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,327127,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,225892,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,224886,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,27763,HS-grad,9,Married-civ-spouse,Other-service,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Private,73684,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Portugal,<=50K\n0,Private,107452,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,23871,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,309272,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,469864,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,286230,11th,7,Divorced,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,State-gov,186308,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,113062,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,86150,11th,7,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,0,Philippines,<=50K\n2,Private,262038,5th-6th,3,Married-spouse-absent,Farming-fishing,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n1,Private,279231,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Italy,<=50K\n4,?,188903,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,183786,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,339358,5th-6th,3,Married-civ-spouse,Farming-fishing,Other-relative,White,Female,0,0,2,Mexico,<=50K\n1,Private,287737,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,99203,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,297449,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,113481,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,204042,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,43387,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,England,>50K\n2,Private,99233,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,313729,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,99679,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,169745,7th-8th,4,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,19914,Some-college,10,Widowed,Exec-managerial,Unmarried,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n1,Private,113543,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,224241,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,137367,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,China,<=50K\n1,Private,263908,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,280798,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,203849,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,62546,Doctorate,16,Separated,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,197344,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,93225,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,187560,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,61743,5th-6th,3,Never-married,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,186648,10th,6,Separated,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,173321,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,<=50K\n3,State-gov,246820,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,?,424034,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,291755,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,104945,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,3,United-States,<=50K\n3,Private,85423,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,214235,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,278632,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,27415,11th,7,Never-married,?,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Local-gov,143392,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,277408,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,336793,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,184112,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,2,United-States,>50K\n3,Private,74660,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,395026,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,171215,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,121362,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,409200,Assoc-acdm,12,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,268965,12th,8,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,136262,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,141323,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,108083,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,82210,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,400943,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,308489,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,187053,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,75826,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,413345,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,356567,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,223811,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,159313,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,250170,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n4,Private,135617,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,187346,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,108103,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,255476,5th-6th,3,Never-married,Other-service,Other-relative,White,Male,0,0,2,Mexico,<=50K\n0,Private,68577,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,155961,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,1,Jamaica,<=50K\n0,State-gov,264102,Some-college,10,Never-married,Other-service,Other-relative,Black,Male,0,0,2,Haiti,<=50K\n2,Private,167777,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,225399,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,199998,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,199856,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n1,?,189765,5th-6th,3,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,193042,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,222810,Some-college,10,Divorced,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,162595,Some-college,10,Married-spouse-absent,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,208826,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,120190,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,27242,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,348099,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,1,2,United-States,<=50K\n1,Private,185041,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n1,Private,309196,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,254285,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,4,Germany,>50K\n2,Self-emp-inc,336226,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,>50K\n2,Private,240698,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,411797,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,>50K\n0,Private,178843,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,136177,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,243409,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Germany,<=50K\n2,Private,258049,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,164748,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,24185,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,167476,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,106900,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,53497,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,335704,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,211022,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,163003,Bachelors,13,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Female,0,0,3,Taiwan,<=50K\n2,Self-emp-inc,77146,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,67433,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,458549,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,Mexico,<=50K\n1,Private,190469,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,195411,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,216889,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,336007,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,167350,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,241857,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n3,Private,125892,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,272209,HS-grad,9,Divorced,Priv-house-serv,Unmarried,Black,Female,0,0,4,United-States,<=50K\n3,Private,175221,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,180195,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,38090,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,310085,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,118686,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,112963,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,120131,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,?,<=50K\n0,Private,43937,Some-college,10,Never-married,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,210438,11th,7,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,176724,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n1,Self-emp-not-inc,113364,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,73986,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,197932,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,193285,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,223342,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,49749,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,?,211553,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,201865,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,322143,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,4,United-States,>50K\n3,Private,158702,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,4,2,?,<=50K\n3,Self-emp-not-inc,275625,Bachelors,13,Divorced,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,3,South,>50K\n0,Private,206599,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,89813,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Scotland,<=50K\n0,State-gov,156848,HS-grad,9,Married-civ-spouse,Protective-serv,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,162494,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,205407,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,375313,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Federal-gov,930948,Some-college,10,Separated,Adm-clerical,Unmarried,Black,Female,2,0,3,United-States,<=50K\n1,Private,127895,Some-college,10,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,248754,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,188096,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,216811,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,113870,Masters,14,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,343052,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,280966,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,42044,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,309513,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,163604,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,224198,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,338283,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,242375,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,81286,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,243368,Preschool,1,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n1,Private,217803,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,323020,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,34278,Assoc-voc,11,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,184579,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,210781,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,142673,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,131714,10th,6,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n3,Local-gov,74784,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,181372,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n0,?,62507,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,155664,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,174924,HS-grad,9,Separated,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n4,Private,113440,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,147227,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,207022,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Local-gov,123011,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,1,United-States,>50K\n0,Private,184678,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,182437,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,98639,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,174201,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,123780,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,374116,HS-grad,9,Never-married,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,212005,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,123965,Bachelors,13,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,242619,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,2,0,2,United-States,<=50K\n4,Local-gov,138502,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,113635,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,Ireland,<=50K\n4,Private,664366,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,218311,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,278557,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,314773,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,194861,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,400616,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,208117,Bachelors,13,Never-married,Prof-specialty,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,184498,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,117674,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,162621,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,368739,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,196994,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,420629,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,245491,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n3,Self-emp-not-inc,276456,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,1,0,1,United-States,>50K\n4,Local-gov,169133,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,99307,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n2,Self-emp-inc,120131,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,456236,Some-college,10,Divorced,Sales,Own-child,White,Male,0,0,2,United-States,>50K\n3,Private,107123,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,125461,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,2,0,1,United-States,<=50K\n2,Local-gov,36924,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,167065,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,53642,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,154668,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,102238,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,54595,10th,6,Widowed,Other-service,Not-in-family,Black,Female,0,3,2,United-States,<=50K\n1,Private,152951,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,257042,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,74243,Assoc-voc,11,Widowed,Craft-repair,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,149049,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,117186,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,178322,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,286911,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,203635,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,>50K\n4,Self-emp-not-inc,177271,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,149427,9th,5,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,101656,10th,6,Never-married,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n2,Private,274363,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,241025,Bachelors,13,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,338836,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,210534,5th-6th,3,Separated,Adm-clerical,Other-relative,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,95725,Assoc-voc,11,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,178013,10th,6,Married-civ-spouse,?,Wife,White,Female,0,0,0,Cuba,<=50K\n3,Federal-gov,167410,Bachelors,13,Divorced,Tech-support,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,158162,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n3,Private,241935,11th,7,Married-civ-spouse,Other-service,Husband,Black,Male,3,0,2,United-States,>50K\n0,Federal-gov,406955,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,341762,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,239303,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,?,<=50K\n1,Private,38848,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,54744,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,332194,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,154950,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,196342,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,201292,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,339767,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,England,>50K\n1,Private,250066,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,318886,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,124076,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,State-gov,242122,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,34019,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,230754,Masters,14,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,213842,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,196386,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,El-Salvador,<=50K\n1,Self-emp-not-inc,62165,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,?,<=50K\n1,Private,134737,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,>50K\n1,Private,515629,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,119199,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,90222,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,28443,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,159442,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,Ireland,<=50K\n3,Private,315804,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,135840,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,81232,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,>50K\n2,Private,118001,7th-8th,4,Separated,Farming-fishing,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,207875,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,164898,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,170066,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,111994,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,166636,HS-grad,9,Divorced,Other-service,Other-relative,Black,Female,0,0,1,United-States,<=50K\n0,State-gov,61737,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,241885,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,234190,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,230899,5th-6th,3,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,114158,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,1,United-States,>50K\n1,Private,222442,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Cuba,<=50K\n1,Private,157612,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,199903,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,?,292627,1st-4th,2,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,156687,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,0,0,2,Japan,<=50K\n1,Private,369522,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,226297,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,356017,11th,7,Never-married,Other-service,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,189257,9th,5,Never-married,Handlers-cleaners,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Private,157541,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,69251,Assoc-voc,11,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,State-gov,272944,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,113667,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,222011,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,191196,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,169104,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,146679,Some-college,10,Never-married,Exec-managerial,Own-child,Black,Male,0,0,1,United-States,<=50K\n4,Private,226985,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,153066,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,?,159303,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,200109,HS-grad,9,Married-civ-spouse,Priv-house-serv,Wife,White,Female,2,0,2,United-States,<=50K\n0,State-gov,109445,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,99491,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,172571,Assoc-voc,11,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,143582,7th-8th,4,Married-civ-spouse,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,3,?,<=50K\n1,Private,207113,10th,6,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,192712,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,154297,10th,6,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,238913,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,1,0,1,United-States,<=50K\n2,Private,110402,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,207213,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,606111,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,Germany,>50K\n1,Private,34112,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,119156,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,249787,HS-grad,9,Never-married,Other-service,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,153516,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n0,State-gov,260754,Bachelors,13,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,155621,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,3,Columbia,<=50K\n2,Private,33983,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,306601,Bachelors,13,Never-married,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,Mexico,<=50K\n0,Private,270075,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,109430,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,187115,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,463667,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,52262,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,144064,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,147821,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,?,<=50K\n4,?,232719,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,268620,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,81132,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Private,323069,Assoc-acdm,12,Divorced,Sales,Unmarried,White,Female,0,1,2,United-States,<=50K\n1,Private,242984,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,172684,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Mexico,>50K\n2,Private,103932,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,State-gov,431637,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,188942,Some-college,10,Married-civ-spouse,Sales,Wife,Black,Female,0,0,2,Puerto-Rico,<=50K\n3,Federal-gov,170354,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,28518,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,193380,Bachelors,13,Never-married,Prof-specialty,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,Private,175942,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,53956,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,120773,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,96219,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,104164,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,190429,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,?,243030,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,228660,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,368757,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,220563,12th,8,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,233571,Assoc-voc,11,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,187847,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,84298,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n2,Self-emp-not-inc,254303,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,109611,9th,5,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Portugal,<=50K\n3,Private,189183,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,206951,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,282882,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,377061,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,209906,1st-4th,2,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,1,Puerto-Rico,<=50K\n3,Local-gov,176059,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,279015,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,4,Taiwan,>50K\n0,Private,347292,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,277314,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,?,29887,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,341439,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Private,209460,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,3,United-States,<=50K\n4,Private,114263,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,Hungary,>50K\n4,Private,230899,9th,5,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,2,Mexico,<=50K\n2,Private,271767,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,20956,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n3,Private,39986,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,45784,Some-college,10,Never-married,Prof-specialty,Other-relative,White,Female,0,0,0,United-States,<=50K\n4,Private,126991,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n0,?,234648,11th,7,Never-married,?,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,207676,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,State-gov,413345,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,<=50K\n4,Private,122033,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,169611,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,90363,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,372636,HS-grad,9,Never-married,Sales,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,340917,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,34273,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,2,Canada,<=50K\n0,Private,161027,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,99844,HS-grad,9,Never-married,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,207685,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,74680,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n3,Self-emp-inc,334273,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n1,Private,36069,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,100563,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,174308,11th,7,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,109413,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,212600,Some-college,10,Separated,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,271710,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,230816,Assoc-voc,11,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,103277,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,318947,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,187167,Assoc-acdm,12,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,204742,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,282062,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,283510,HS-grad,9,Never-married,?,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,280093,11th,7,Married-spouse-absent,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,202729,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,205950,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,392286,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,119207,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,195554,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,173005,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,192862,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,164712,Some-college,10,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,195808,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,199444,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,126346,9th,5,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,177675,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,50341,Masters,14,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,237943,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,2,2,United-States,<=50K\n0,Private,126945,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,92061,HS-grad,9,Widowed,?,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,?,109938,11th,7,Married-civ-spouse,?,Wife,Asian-Pac-Islander,Female,0,0,2,Laos,<=50K\n2,Private,267252,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,3,2,United-States,>50K\n1,Private,174704,11th,7,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,124771,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,200603,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n4,State-gov,165827,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,301199,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,215790,Some-college,10,Widowed,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,87556,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,111467,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,82646,Doctorate,16,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,Private,162282,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,239074,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,214925,Masters,14,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n0,Private,194247,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,211531,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,223267,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,201635,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,188738,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n0,Private,133055,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,61761,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n4,Private,103344,Bachelors,13,Widowed,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,109814,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,225294,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,97277,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,146711,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,286452,10th,6,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,20308,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,224203,Some-college,10,Widowed,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,225978,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,237720,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,156743,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,509364,5th-6th,3,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n3,Private,144351,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,375515,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,103529,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,199472,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,348152,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,221166,9th,5,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,341762,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n0,?,634226,10th,6,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,State-gov,159449,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,110238,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,Private,458558,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,340217,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,155106,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,90523,HS-grad,9,Widowed,Transport-moving,Unmarried,White,Male,0,0,4,United-States,<=50K\n0,Private,122756,11th,7,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,293828,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,Jamaica,<=50K\n3,Private,299291,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,483261,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,122038,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,160647,Bachelors,13,Widowed,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,106541,5th-6th,3,Married-civ-spouse,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,126945,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,188505,Bachelors,13,Married-AF-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,377850,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,193586,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,315417,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Self-emp-inc,57233,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,195253,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,172991,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,223215,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,95799,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,213385,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Local-gov,202467,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,145574,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,<=50K\n2,Private,147548,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,105216,Some-college,10,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,77760,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,167990,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Canada,<=50K\n2,Private,167005,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,108435,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,56645,Bachelors,13,Widowed,Farming-fishing,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Local-gov,304973,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,42596,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,220641,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,101452,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,England,>50K\n1,Private,188888,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Local-gov,168790,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,98361,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,401762,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Local-gov,160187,Masters,14,Widowed,Exec-managerial,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,203715,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,144351,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,420749,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Germany,<=50K\n3,Private,106151,11th,7,Divorced,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,362482,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,State-gov,38151,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,42706,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,126199,Some-college,10,Divorced,Transport-moving,Unmarried,White,Male,1,0,3,United-States,<=50K\n1,Private,165510,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,216068,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,215624,Some-college,10,Never-married,Machine-op-inspct,Unmarried,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n2,Private,239708,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,199378,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,230420,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,395022,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,338620,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,210142,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,446358,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Local-gov,352614,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,293528,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,State-gov,55395,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,128538,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,428405,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,126838,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,136836,Assoc-acdm,12,Divorced,Transport-moving,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,105838,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,139903,Bachelors,13,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,106103,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,186824,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,350387,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,142912,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,321403,9th,5,Separated,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,114937,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,?,286689,Masters,14,Never-married,?,Not-in-family,White,Male,2,0,1,United-States,<=50K\n1,Private,147258,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,3,2,United-States,<=50K\n0,Private,451996,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,149833,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,211968,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,287908,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,>50K\n2,Private,166549,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,25216,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,286452,Assoc-acdm,12,Divorced,Sales,Unmarried,White,Female,1,0,2,United-States,<=50K\n3,Private,162034,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,186932,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,4,United-States,>50K\n1,Private,82938,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,122048,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,118710,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,243226,10th,6,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,268514,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,365289,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,165365,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,1,0,2,Laos,<=50K\n0,Private,219266,HS-grad,9,Married-civ-spouse,Prof-specialty,Own-child,White,Female,0,0,2,?,<=50K\n0,Private,283757,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,206553,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,113364,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,328949,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,83930,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,325355,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,2,United-States,>50K\n0,Private,131852,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,119506,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,State-gov,100818,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,162302,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,182211,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,194205,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,Mexico,<=50K\n0,Private,141040,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n4,Private,346033,9th,5,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,177125,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,241174,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n4,Local-gov,130532,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,168496,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,362787,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,244771,11th,7,Separated,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,48123,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,173858,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,3,2,South,>50K\n1,Private,207201,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,37933,12th,8,Married-spouse-absent,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,33323,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,175943,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,2,0,1,United-States,>50K\n4,?,306178,10th,6,Divorced,?,Not-in-family,White,Male,1,0,2,United-States,<=50K\n4,Local-gov,229110,HS-grad,9,Widowed,Exec-managerial,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,113511,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,333677,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,236021,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,>50K\n0,?,371089,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,115023,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,State-gov,133586,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,91137,9th,5,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,105598,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,352812,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,2,United-States,>50K\n1,Private,204829,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,247733,HS-grad,9,Divorced,Priv-house-serv,Unmarried,Black,Female,0,0,0,United-States,<=50K\n2,?,370585,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,103257,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,178915,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,54260,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,55395,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,233511,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,318331,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,195985,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,38876,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,81413,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,172618,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,174717,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n4,Private,224984,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,4,0,0,Germany,>50K\n4,Private,423297,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,88856,7th-8th,4,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,169104,Assoc-acdm,12,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,0,Philippines,<=50K\n1,Federal-gov,39207,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,340018,10th,6,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,State-gov,30796,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,155403,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n0,Private,238092,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,225605,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,289148,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,339863,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,178778,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,>50K\n1,Private,568490,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,129345,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,447882,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,314165,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,382859,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,82504,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,149700,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,209844,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,62546,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,228686,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,326587,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,202091,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,310774,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,450246,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,84375,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,142444,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,>50K\n1,Private,82246,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,2,2,United-States,<=50K\n0,Private,192766,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,53109,11th,7,Never-married,Other-service,Own-child,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n2,Self-emp-inc,121836,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,?,>50K\n2,Self-emp-not-inc,298130,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,135645,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,265275,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,?,410114,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Without-pay,232719,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,167716,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,4,United-States,<=50K\n4,Private,107627,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,129674,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,3,Mexico,<=50K\n1,Self-emp-inc,114053,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,202560,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,219902,HS-grad,9,Separated,Transport-moving,Unmarried,Black,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,192654,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,238966,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,112942,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,153484,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,161874,Some-college,10,Never-married,Exec-managerial,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,260106,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,240374,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,?,251612,5th-6th,3,Never-married,?,Unmarried,White,Female,0,0,2,Mexico,<=50K\n3,Private,223696,12th,8,Married-spouse-absent,Handlers-cleaners,Not-in-family,Other,Male,0,0,3,Dominican-Republic,<=50K\n3,Private,176134,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Private,186959,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,456236,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,98948,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,166662,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,448626,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,167482,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,189792,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,399052,9th,5,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,104196,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n3,Self-emp-not-inc,152752,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,268545,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,Jamaica,<=50K\n3,Self-emp-inc,148532,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,281784,Bachelors,13,Never-married,Tech-support,Not-in-family,Black,Male,0,1,3,United-States,>50K\n0,Private,225724,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,200192,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,170850,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Federal-gov,224858,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n4,State-gov,159908,11th,7,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,>50K\n1,Private,115488,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,1268339,HS-grad,9,Married-spouse-absent,Tech-support,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,195755,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,186272,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,181388,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,177181,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,91488,1st-4th,2,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,0,United-States,<=50K\n2,Private,230961,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,309955,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Local-gov,63042,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,29814,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,116230,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,?,167678,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,1,Ecuador,<=50K\n1,Private,191088,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,63814,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,285865,Assoc-acdm,12,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,?,160776,Assoc-voc,11,Divorced,?,Not-in-family,White,Female,0,0,2,France,<=50K\n3,Federal-gov,299831,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,1,2,United-States,<=50K\n3,Private,162741,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,Black,Female,4,0,2,United-States,>50K\n3,Private,204990,HS-grad,9,Never-married,Tech-support,Unmarried,Black,Female,0,0,1,Jamaica,<=50K\n4,Self-emp-inc,171315,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,296462,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,103860,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,159816,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,1,United-States,>50K\n3,Private,96586,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,202720,9th,5,Married-spouse-absent,Machine-op-inspct,Unmarried,Black,Male,0,0,4,Haiti,<=50K\n1,Private,202822,Masters,14,Never-married,Tech-support,Unmarried,Black,Female,0,0,2,?,<=50K\n3,Self-emp-not-inc,379883,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Mexico,>50K\n4,?,123464,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,294121,Assoc-acdm,12,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,?,179981,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,234387,HS-grad,9,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,154537,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,125856,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,156015,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,116632,Bachelors,13,Divorced,Sales,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Private,124963,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Self-emp-not-inc,115215,10th,6,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,254905,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,195532,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,190067,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,1,2,United-States,>50K\n4,Private,181828,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,?,<=50K\n1,Private,203674,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,1,2,United-States,<=50K\n0,Private,322585,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,246262,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,211129,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,?,<=50K\n3,Private,139268,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,188540,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,251167,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,1,Mexico,<=50K\n3,Private,94809,Some-college,10,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,265038,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,182566,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,220109,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,2,2,United-States,<=50K\n2,Private,208470,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,28683,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,233571,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,24562,Bachelors,13,Divorced,Other-service,Unmarried,Other,Female,0,0,2,United-States,<=50K\n4,Local-gov,36364,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,4,2,United-States,<=50K\n4,Private,168569,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,167098,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,271579,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,191355,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,31659,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,3,United-States,>50K\n2,State-gov,83411,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,40856,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,115605,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,132326,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,220213,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,172511,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,156745,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,218916,Prof-school,15,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,306114,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,196675,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,73411,Prof-school,15,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,184659,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,?,75890,Some-college,10,Widowed,?,Unmarried,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n1,Private,320451,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,4,Hong,>50K\n1,Private,172498,Some-college,10,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,131588,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,124520,Assoc-voc,11,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,93806,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,173192,Assoc-voc,11,Separated,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,198554,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,26502,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n4,Private,225267,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,150042,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,211319,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,208358,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,58115,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,219267,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,129573,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,27834,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,415037,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n3,Private,191529,Bachelors,13,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,132806,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Federal-gov,137059,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Federal-gov,102308,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,164309,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,40955,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,England,>50K\n4,Private,141085,HS-grad,9,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Federal-gov,258124,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Italy,>50K\n1,Private,467579,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,2,2,United-States,>50K\n1,Private,145139,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,231141,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n4,Self-emp-not-inc,146674,HS-grad,9,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,3,?,<=50K\n1,Private,242207,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n2,?,102541,Assoc-voc,11,Married-civ-spouse,?,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,135416,Some-college,10,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,267284,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,130812,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,183765,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,?,<=50K\n2,Local-gov,188823,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,200593,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,124094,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Poland,<=50K\n0,Private,50411,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,101689,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,73091,HS-grad,9,Separated,Other-service,Not-in-family,Black,Male,0,2,3,United-States,<=50K\n0,?,107801,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,176969,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,342709,HS-grad,9,Married-spouse-absent,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,368561,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,26915,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,157974,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n3,Private,109832,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Self-emp-inc,116358,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,3,0,2,?,>50K\n4,Self-emp-not-inc,195881,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,183000,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Without-pay,302347,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,2,0,2,United-States,<=50K\n0,?,151463,11th,7,Never-married,?,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,Private,217200,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,31740,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n4,Private,35520,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,369843,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,199227,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,144711,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,2,2,United-States,<=50K\n2,Private,382802,10th,6,Widowed,Machine-op-inspct,Not-in-family,Black,Male,0,1,2,United-States,<=50K\n0,Private,254781,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,70657,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,50791,Masters,14,Divorced,Sales,Not-in-family,White,Male,0,2,3,United-States,<=50K\n1,Self-emp-not-inc,222162,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,94606,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,104196,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,455995,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,166210,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,198986,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,292465,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,99388,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,El-Salvador,<=50K\n2,Private,698363,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,154940,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,401998,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,162825,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,271795,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,134671,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,87583,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,248619,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,130200,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,178922,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,51985,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,125933,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n2,State-gov,104280,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,617860,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,122112,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,181758,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,223671,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n2,Self-emp-not-inc,140117,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,107458,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,215948,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Other,Male,0,0,2,?,<=50K\n2,Federal-gov,306440,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,615893,Masters,14,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,Nicaragua,<=50K\n1,Self-emp-inc,201186,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,4,0,2,United-States,>50K\n1,Private,37210,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,196084,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,166181,HS-grad,9,Divorced,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Federal-gov,291096,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,232841,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,?,131982,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,408788,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,42924,Doctorate,16,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,181091,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,200246,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,282023,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,128990,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,106838,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,144750,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,108140,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,103323,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,268022,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,197114,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,191628,HS-grad,9,Never-married,Transport-moving,Not-in-family,Black,Male,1,0,2,United-States,<=50K\n4,Local-gov,176118,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,42401,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,322385,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n3,State-gov,123011,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,210945,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,130620,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,China,>50K\n1,Private,248990,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,132705,9th,5,Separated,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,94892,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,141858,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,81232,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,114561,Bachelors,13,Married-spouse-absent,Prof-specialty,Other-relative,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n2,Local-gov,191776,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,128354,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,37088,9th,5,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,414812,7th-8th,4,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,156799,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,33983,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,4,0,2,United-States,>50K\n3,Self-emp-not-inc,194995,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n2,Self-emp-inc,73431,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,155664,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,4,United-States,>50K\n1,?,182386,11th,7,Divorced,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,State-gov,281074,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,Local-gov,248346,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,167482,10th,6,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n0,?,171088,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,211763,Doctorate,16,Separated,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,>50K\n0,Private,122166,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,370119,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,138940,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,174575,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,>50K\n4,Private,101132,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,292307,Bachelors,13,Married-spouse-absent,Craft-repair,Not-in-family,Black,Male,0,0,2,Dominican-Republic,<=50K\n3,Self-emp-not-inc,248776,Masters,14,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Private,314007,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,213226,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,1,?,<=50K\n2,Private,76845,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n0,Private,148320,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,54261,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,223352,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,1,0,1,United-States,<=50K\n0,Private,211013,9th,5,Never-married,Other-service,Own-child,White,Female,0,0,3,Mexico,<=50K\n2,Private,209833,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,356272,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,143538,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,242960,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,263871,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,151105,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,207685,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,1,3,England,>50K\n3,Local-gov,46537,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,1,0,0,United-States,<=50K\n2,Self-emp-inc,84324,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,224716,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,186269,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,143731,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,236391,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,54560,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,266325,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n1,Federal-gov,42900,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,State-gov,183710,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,278254,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,119992,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,284329,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,368727,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,353696,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,31387,Bachelors,13,Married-civ-spouse,Adm-clerical,Own-child,Amer-Indian-Eskimo,Female,1,0,1,United-States,<=50K\n1,Private,110931,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,169532,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,285522,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,198774,Bachelors,13,Divorced,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,123291,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,146110,Some-college,10,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,29814,HS-grad,9,Never-married,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Private,195595,7th-8th,4,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Guatemala,<=50K\n2,Private,92649,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,290688,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,427382,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,State-gov,234854,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,276568,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,250038,Masters,14,Married-civ-spouse,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,150861,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,87205,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,England,<=50K\n3,Private,343579,1st-4th,2,Married-spouse-absent,Farming-fishing,Not-in-family,White,Male,0,0,0,Mexico,<=50K\n0,Private,94401,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,120238,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,Poland,>50K\n1,Private,205440,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,198996,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,294253,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,256628,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,223131,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,207301,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,270460,7th-8th,4,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,125457,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n2,Local-gov,212856,11th,7,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,197389,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,73338,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,68037,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,185027,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,107123,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,109482,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,4,United-States,<=50K\n1,Private,174543,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,208407,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,1,United-States,<=50K\n4,Self-emp-not-inc,211584,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,108540,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,202416,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,160155,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,176178,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,265148,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Jamaica,<=50K\n1,Private,220631,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,3,?,<=50K\n1,Self-emp-not-inc,303692,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,135845,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,State-gov,199915,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,State-gov,150533,Bachelors,13,Married-civ-spouse,Prof-specialty,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,85482,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,24473,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,272944,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,?,82077,Some-college,10,Divorced,?,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n3,State-gov,194895,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,314153,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,176253,Some-college,10,Divorced,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,113959,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,167581,Bachelors,13,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,79586,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Iran,<=50K\n2,Private,166662,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,72896,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,345730,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,302473,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,42346,HS-grad,9,Widowed,Exec-managerial,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,243921,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,131620,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Own-child,White,Female,0,0,2,Dominican-Republic,<=50K\n3,Private,158924,HS-grad,9,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,32921,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,252897,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,4,0,2,United-States,>50K\n2,Private,155657,11th,7,Never-married,Handlers-cleaners,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,155106,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,82775,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,26248,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,88991,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,England,>50K\n4,Federal-gov,125155,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,218039,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,53524,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,259352,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,296453,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,278915,12th,8,Never-married,Handlers-cleaners,Own-child,Black,Female,0,0,3,United-States,<=50K\n0,Private,565313,Some-college,10,Never-married,Other-service,Own-child,Black,Male,1,0,4,United-States,<=50K\n0,Federal-gov,274103,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,271118,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,207107,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,Asian-Pac-Islander,Male,0,3,2,Philippines,<=50K\n1,Local-gov,138597,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Local-gov,180985,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,4,0,2,United-States,>50K\n4,Self-emp-not-inc,159939,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,110920,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,34862,Bachelors,13,Divorced,Sales,Not-in-family,Amer-Indian-Eskimo,Male,0,1,3,United-States,>50K\n0,Local-gov,163205,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n4,Private,110003,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,229051,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,144898,Some-college,10,Never-married,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,211596,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,48458,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,2,2,United-States,<=50K\n4,Private,201393,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Male,0,2,2,United-States,<=50K\n0,Self-emp-not-inc,136450,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,193586,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,91189,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,227832,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,271936,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,61343,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,157778,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,201680,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,228320,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,33404,10th,6,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,103205,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,279029,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,213092,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,126104,Masters,14,Divorced,Adm-clerical,Not-in-family,White,Female,0,3,2,United-States,<=50K\n1,Private,119124,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,31924,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,253799,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,?,<=50K\n3,Private,266138,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,>50K\n4,Private,185001,10th,6,Widowed,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,34102,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,115214,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,2,0,4,United-States,<=50K\n1,Private,289484,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,State-gov,287908,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,158284,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,60668,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Portugal,<=50K\n2,State-gov,222978,Doctorate,16,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n4,Private,244605,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,150601,10th,6,Separated,Adm-clerical,Unmarried,White,Male,0,4,2,United-States,<=50K\n1,Private,199143,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,131681,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,346406,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,3,United-States,<=50K\n1,Federal-gov,391122,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,280344,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,188809,Doctorate,16,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,277488,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,181561,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,158545,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,313573,Bachelors,13,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,591711,Some-college,10,Married-spouse-absent,Transport-moving,Not-in-family,Black,Male,0,0,2,?,<=50K\n2,Private,268183,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,392286,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,233312,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,520231,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,186831,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,141085,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,?,198019,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,198660,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,409230,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Guatemala,<=50K\n2,Private,376025,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,80167,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,99357,Masters,14,Divorced,Prof-specialty,Own-child,White,Female,1,0,2,United-States,<=50K\n0,Private,82847,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,3,Portugal,>50K\n0,Private,22201,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Asian-Pac-Islander,Male,0,0,2,Thailand,<=50K\n2,Private,275223,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,117595,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,207668,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,179981,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,192583,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,66304,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,200671,Bachelors,13,Divorced,Transport-moving,Own-child,Black,Male,2,0,2,United-States,<=50K\n4,Private,32365,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,28497,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,222978,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,1,2,United-States,<=50K\n0,Private,160261,Some-college,10,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n3,Private,120724,12th,8,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,91733,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,146929,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,205706,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,181666,Some-college,10,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,279452,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,207568,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,22494,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n0,Private,210026,10th,6,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,190889,Masters,14,Never-married,Prof-specialty,Not-in-family,Other,Female,0,0,2,?,<=50K\n0,Private,109869,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,285263,9th,5,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n1,Private,192588,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,232945,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,Other,Male,0,0,1,United-States,<=50K\n3,Local-gov,31339,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,305147,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,188914,HS-grad,9,Widowed,Machine-op-inspct,Other-relative,Black,Female,0,0,2,Haiti,<=50K\n4,Self-emp-not-inc,141165,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,136218,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,371382,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n0,?,199177,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,221366,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,3,United-States,>50K\n0,Private,403671,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,193871,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,306183,Some-college,10,Divorced,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,159938,HS-grad,9,Divorced,?,Not-in-family,White,Male,3,0,2,United-States,>50K\n3,Private,124194,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,69847,Bachelors,13,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n1,State-gov,169323,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,172327,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,118889,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,0,United-States,<=50K\n3,Private,166220,Assoc-acdm,12,Married-civ-spouse,Sales,Wife,White,Female,2,0,2,United-States,<=50K\n2,Private,186420,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,192779,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,105616,Some-college,10,Widowed,Adm-clerical,Unmarried,Black,Female,0,0,3,United-States,<=50K\n0,Private,141113,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,<=50K\n4,Private,160275,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,164507,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Columbia,<=50K\n2,Private,207578,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,India,>50K\n3,Private,314592,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,?,254630,Assoc-voc,11,Divorced,?,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Private,159522,7th-8th,4,Divorced,Machine-op-inspct,Unmarried,Black,Female,1,0,2,United-States,<=50K\n0,Private,112130,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,192835,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,206280,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,308861,Some-college,10,Separated,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,93206,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,3,4,United-States,>50K\n2,Private,206066,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,309895,Some-college,10,Divorced,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,216129,Some-college,10,Married-spouse-absent,Exec-managerial,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,State-gov,287420,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,163595,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,170092,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n2,Private,287031,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,4,United-States,>50K\n2,Private,59474,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,99151,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,206888,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,177119,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,4,?,<=50K\n0,Private,173736,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,182163,11th,7,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,Germany,<=50K\n2,Local-gov,311080,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,389857,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,297152,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Federal-gov,130534,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,137301,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,316235,HS-grad,9,Divorced,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,32922,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,118303,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,>50K\n0,Private,188241,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,236731,7th-8th,4,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n2,Private,209397,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,290640,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,221915,Prof-school,15,Never-married,Prof-specialty,Other-relative,White,Female,0,0,4,United-States,<=50K\n3,Private,175246,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,159724,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n2,State-gov,160369,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,461337,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,187311,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,29312,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,197365,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,301747,HS-grad,9,Separated,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,135439,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,340917,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,155057,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,?,200749,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,323627,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,?,154921,5th-6th,3,Never-married,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,131425,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,184242,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,149769,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Cambodia,<=50K\n2,Private,124924,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,253003,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,State-gov,250976,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,104196,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,250182,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,188331,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,187322,Bachelors,13,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,130714,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,40955,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,107125,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n3,Private,145714,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,?,>50K\n1,Private,133937,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,293485,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,1,0,2,United-States,>50K\n1,?,203260,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,143368,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,51789,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,State-gov,211049,7th-8th,4,Never-married,Tech-support,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,81794,12th,8,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,139193,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,3,3,United-States,<=50K\n3,Private,150999,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,332657,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,240043,10th,6,Married-spouse-absent,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,186188,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,Iran,<=50K\n4,State-gov,223400,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n4,Local-gov,102442,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,236599,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,283237,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,150106,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,102076,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,374764,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,32528,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,50053,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,212864,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,30673,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,?,248248,1st-4th,2,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,0,1,Philippines,<=50K\n0,Private,419554,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,177216,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,118158,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,116391,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Germany,<=50K\n4,Private,194312,9th,5,Widowed,Craft-repair,Not-in-family,White,Male,0,0,0,?,<=50K\n2,Private,111895,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,193290,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,2,0,United-States,<=50K\n0,Federal-gov,287988,Bachelors,13,Never-married,Armed-Forces,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,147653,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,117674,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,187458,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,410351,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,207578,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,?,55621,Some-college,10,Married-civ-spouse,?,Husband,Black,Male,0,0,1,United-States,>50K\n1,State-gov,271243,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Male,0,0,2,Jamaica,<=50K\n1,Private,188798,Some-college,10,Divorced,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,168656,Bachelors,13,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,1,Outlying-US(Guam-USVI-etc),<=50K\n1,Private,460408,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,1,0,3,United-States,<=50K\n1,Private,241885,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,133061,9th,5,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,194097,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,219137,10th,6,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Private,31621,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,207685,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,57052,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,109854,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,369678,HS-grad,9,Never-married,?,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n0,Private,53611,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,344916,Assoc-acdm,12,Divorced,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Local-gov,198813,Bachelors,13,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n4,Private,180733,Masters,14,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,188073,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,?,159077,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,174829,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,142791,7th-8th,4,Widowed,Sales,Other-relative,White,Female,0,2,0,United-States,<=50K\n4,Self-emp-not-inc,43221,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,4,2,United-States,>50K\n1,Private,188736,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Other-relative,Other,Female,0,0,0,Columbia,<=50K\n1,Local-gov,222654,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,?,<=50K\n4,Private,251836,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n2,Federal-gov,112388,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,209641,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,313945,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Ecuador,<=50K\n0,?,134974,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,152742,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Self-emp-inc,153291,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,353432,10th,6,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,96635,Some-college,10,Never-married,Machine-op-inspct,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n3,?,202560,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,150057,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,114844,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,2,3,United-States,<=50K\n2,Private,132847,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,41356,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,93705,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,309350,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,123084,11th,7,Married-civ-spouse,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,174662,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n4,Federal-gov,177295,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,211880,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,454915,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,232475,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,244605,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,150876,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,2,3,United-States,>50K\n3,Private,257337,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,329144,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,116960,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,267663,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,47871,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,>50K\n1,Private,295922,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,England,>50K\n2,Private,175625,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,129586,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,190179,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,168071,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Self-emp-not-inc,202027,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,202662,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,101436,HS-grad,9,Divorced,Adm-clerical,Other-relative,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n0,?,119234,Some-college,10,Never-married,?,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,360743,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,>50K\n4,Local-gov,93272,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,145574,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,101722,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,2,0,3,United-States,<=50K\n1,Private,135785,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,218415,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,127709,HS-grad,9,Never-married,Farming-fishing,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Federal-gov,448337,HS-grad,9,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,310320,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,251521,11th,7,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,255503,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,116608,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Private,71009,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n0,Private,174975,Assoc-voc,11,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,108023,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,204018,11th,7,Never-married,Sales,Unmarried,White,Male,0,0,0,United-States,<=50K\n4,?,366563,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,121846,7th-8th,4,Widowed,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n4,Private,278139,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,114691,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,State-gov,536725,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,0,Japan,<=50K\n3,Private,94432,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,286002,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Male,0,0,1,Nicaragua,<=50K\n3,Private,101684,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,352834,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,99146,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,231413,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,158846,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,190786,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,306513,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,152148,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,309580,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,130832,Bachelors,13,Never-married,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,194897,HS-grad,9,Never-married,Sales,Own-child,Amer-Indian-Eskimo,Male,2,0,2,United-States,<=50K\n1,Private,130078,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,39986,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,379198,HS-grad,9,Never-married,Other-service,Other-relative,Other,Male,0,0,2,Mexico,<=50K\n3,Private,189762,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,178147,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,332379,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,175759,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n0,?,262062,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,275446,HS-grad,9,Never-married,Sales,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,278522,11th,7,Never-married,Farming-fishing,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,54683,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,1,2,United-States,<=50K\n4,Private,136107,9th,5,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,205894,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,210736,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,166634,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,185283,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,180553,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,199058,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,?,<=50K\n0,Private,145005,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,184655,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,358554,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n4,Private,307423,9th,5,Never-married,Other-service,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,472070,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,115562,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,32446,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,33121,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,183345,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,119793,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n3,Self-emp-not-inc,97883,HS-grad,9,Separated,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,31732,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,206250,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,103323,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,135436,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,376455,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,160703,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,131699,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,243842,9th,5,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,349910,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,170262,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,2,United-States,>50K\n1,Private,184306,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Private,224202,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,151540,11th,7,Widowed,Tech-support,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,231197,10th,6,Married-spouse-absent,Craft-repair,Unmarried,White,Male,0,0,2,Mexico,<=50K\n0,Private,279968,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,162651,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Male,0,0,2,Columbia,<=50K\n2,Self-emp-not-inc,130126,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,160120,Doctorate,16,Divorced,Adm-clerical,Other-relative,Other,Male,0,0,2,?,<=50K\n4,Private,161662,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,201664,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,137142,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,122206,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,183169,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,126513,HS-grad,9,Separated,Craft-repair,Unmarried,Black,Female,0,0,2,?,<=50K\n1,Federal-gov,185053,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,408427,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,198581,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,199198,7th-8th,4,Widowed,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,184306,Assoc-voc,11,Never-married,Transport-moving,Own-child,White,Male,0,3,3,United-States,<=50K\n4,Private,172740,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,205153,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,164964,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,162606,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,179627,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,103408,Some-college,10,Divorced,Prof-specialty,Not-in-family,Black,Male,0,0,2,Germany,>50K\n1,Private,36440,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,57512,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,187981,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,393768,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,108726,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,180551,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Self-emp-not-inc,176240,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,70720,12th,8,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,35890,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,283676,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,105540,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,408717,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,2,0,3,United-States,<=50K\n0,Private,57916,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,177974,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,<=50K\n1,?,177304,10th,6,Divorced,?,Not-in-family,White,Male,0,0,2,Columbia,<=50K\n0,Private,115839,12th,8,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,?,205256,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,4,United-States,<=50K\n2,Private,117802,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,211355,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,173243,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,343200,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,401690,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,Mexico,<=50K\n2,Private,196123,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,168981,Masters,14,Divorced,Exec-managerial,Own-child,White,Female,4,0,3,United-States,>50K\n4,Self-emp-not-inc,213866,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,55176,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,153976,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,119176,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,169117,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,2,2,United-States,>50K\n2,Private,156550,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,109609,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,26698,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,236497,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,306309,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,242773,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,124680,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,2,0,3,United-States,<=50K\n3,Local-gov,43909,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,112820,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Private,148300,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,133449,9th,5,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n0,Private,263670,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,4,United-States,<=50K\n0,Private,276494,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,190115,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n4,Private,317479,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,151248,Some-college,10,Divorced,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,130532,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,0,0,2,Poland,<=50K\n4,Private,160062,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,299635,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,171225,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n3,Private,33304,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,95634,Bachelors,13,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n0,Private,243878,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,181721,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Federal-gov,201435,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,334032,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,220019,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,71772,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,27661,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,191411,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n2,Private,123945,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,37778,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,171216,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,93955,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,163809,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,346754,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,188436,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,72443,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,2,3,United-States,<=50K\n4,Private,186350,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,>50K\n0,?,214238,7th-8th,4,Never-married,?,Unmarried,White,Female,0,0,2,Mexico,<=50K\n3,State-gov,394860,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,262642,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,125550,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,192504,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,131310,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,249322,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,172755,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,209993,11th,7,Separated,Priv-house-serv,Unmarried,White,Female,0,0,0,Mexico,<=50K\n1,Private,166961,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,315291,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,284703,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,166565,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,173854,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,189219,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,210781,Bachelors,13,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,France,<=50K\n4,Local-gov,171328,HS-grad,9,Separated,Protective-serv,Other-relative,Black,Female,0,4,2,United-States,<=50K\n2,Private,199832,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,251292,5th-6th,3,Separated,Other-service,Other-relative,White,Female,0,0,0,Cuba,<=50K\n4,Private,122246,12th,8,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,190767,Assoc-voc,11,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,278736,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,124963,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,167476,11th,7,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,246104,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,171615,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,264095,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,3,0,1,Cuba,>50K\n3,Private,177114,Assoc-acdm,12,Widowed,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,146154,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,198196,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,79712,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,154785,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,182423,HS-grad,9,Divorced,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,?,347292,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,118584,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,219835,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,?,<=50K\n0,?,148769,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,197418,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,253190,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,192273,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,129573,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,173807,11th,7,Never-married,Craft-repair,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,217893,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,102938,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,407495,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,50053,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,Japan,<=50K\n4,Private,233382,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,Cuba,<=50K\n1,Private,270968,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Local-gov,272166,Bachelors,13,Separated,Prof-specialty,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n0,Private,199915,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,305781,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,107682,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,188507,7th-8th,4,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,2,Dominican-Republic,<=50K\n0,?,28311,11th,7,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,197069,Some-college,10,Married-spouse-absent,Adm-clerical,Not-in-family,Black,Male,2,0,2,United-States,<=50K\n0,Private,177839,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,77665,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,127728,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,?,172175,Doctorate,16,Never-married,?,Not-in-family,White,Male,0,4,2,United-States,>50K\n1,Private,106742,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,192838,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,79531,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,State-gov,337766,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,33234,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,?,34088,12th,8,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,176904,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,172148,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,199058,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,48093,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,143664,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,168337,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,195212,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,?,<=50K\n2,Private,230329,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Canada,>50K\n2,Private,376072,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,430175,HS-grad,9,Divorced,Craft-repair,Other-relative,Black,Female,0,0,3,United-States,<=50K\n2,Federal-gov,240628,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,Self-emp-inc,158294,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n3,Private,28735,HS-grad,9,Divorced,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,167482,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,113203,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,103948,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,310525,12th,8,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,105138,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,153489,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,State-gov,254949,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,118149,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,267965,11th,7,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,50646,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,147700,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,United-States,<=50K\n0,Private,446771,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,168262,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,117058,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,140957,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,>50K\n1,Private,186126,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,<=50K\n3,Private,268234,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,485117,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,31350,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,England,<=50K\n2,State-gov,210830,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,196420,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,172165,10th,6,Divorced,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,186565,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,119359,Bachelors,13,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,109684,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,169589,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,125421,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,500002,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,224141,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,113290,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n4,?,123992,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,58098,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,3,2,United-States,<=50K\n3,?,37672,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,198145,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n3,Federal-gov,35406,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,199419,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,145441,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,238438,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,212954,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,56582,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,176931,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,188571,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,312500,Assoc-voc,11,Divorced,Farming-fishing,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,278404,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,114225,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,184016,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,183009,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n4,Private,205759,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,462294,Assoc-acdm,12,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,102085,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,83311,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,248694,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,190747,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,162988,10th,6,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,156890,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,310380,Some-college,10,Married-spouse-absent,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,172186,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,311497,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,443508,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,152156,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,155890,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,312528,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,282744,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Canada,<=50K\n1,Private,205145,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,119918,Bachelors,13,Never-married,?,Not-in-family,Black,Male,0,0,2,?,<=50K\n0,Private,401451,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n4,?,173427,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,Cuba,<=50K\n0,Private,189027,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,35551,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,42706,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,106910,5th-6th,3,Widowed,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,0,Philippines,<=50K\n0,Private,53245,9th,5,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,221672,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,71898,Preschool,1,Never-married,Priv-house-serv,Not-in-family,Asian-Pac-Islander,Female,0,0,3,Philippines,<=50K\n3,Private,222107,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,277588,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,178983,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n2,Federal-gov,391744,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,418020,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,State-gov,39236,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,86808,Bachelors,13,Never-married,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,147640,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,3,2,United-States,<=50K\n0,Private,184756,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,191256,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,105943,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,2,United-States,<=50K\n2,Private,101272,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,State-gov,175023,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,357612,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,82777,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,218521,Some-college,10,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,179534,11th,7,Widowed,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,33339,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,148549,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,198069,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,236586,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,167261,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,160942,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,1,0,3,United-States,<=50K\n2,Private,107584,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,<=50K\n1,Local-gov,251854,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,163140,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,302579,HS-grad,9,Divorced,Other-service,Other-relative,Black,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,64632,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,83141,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,326048,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,83471,HS-grad,9,Widowed,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n0,Private,170070,12th,8,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,207875,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,119722,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,0,United-States,<=50K\n0,Private,335665,11th,7,Never-married,Other-service,Other-relative,Black,Female,0,0,1,United-States,<=50K\n0,Private,212522,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,42069,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,1,0,2,United-States,<=50K\n1,?,131777,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,3,2,United-States,<=50K\n1,Private,236396,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,159911,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,133833,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,226947,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,174201,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,49707,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,201988,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,162347,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,182833,Some-college,10,Never-married,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,383603,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,70466,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,184846,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,176756,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,112512,HS-grad,9,Widowed,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,137296,Assoc-acdm,12,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,37821,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,295108,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,408717,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,255635,9th,5,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Mexico,<=50K\n3,Self-emp-not-inc,177783,7th-8th,4,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,179400,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,0,United-States,<=50K\n1,Private,240283,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,410034,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,180667,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,196332,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Local-gov,159187,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,225065,Preschool,1,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Mexico,<=50K\n0,Private,178147,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,272669,Some-college,10,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,347491,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,146399,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,75167,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,133373,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,84737,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,1,United-States,>50K\n0,Private,96483,HS-grad,9,Never-married,Other-service,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n4,Private,368005,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,36032,HS-grad,9,Divorced,Protective-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,174215,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,228772,5th-6th,3,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,2,Mexico,<=50K\n0,Private,242912,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,86701,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n1,Private,166549,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,121622,Masters,14,Never-married,Prof-specialty,Unmarried,White,Female,0,1,2,United-States,<=50K\n0,Private,201613,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,29874,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,168138,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,162404,Bachelors,13,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n0,?,162160,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,2,Taiwan,<=50K\n1,Private,139116,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,3,United-States,<=50K\n2,Private,192381,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,370585,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,151038,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,36311,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n1,Private,271933,Masters,14,Never-married,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,182401,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,234743,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,182140,HS-grad,9,Separated,Transport-moving,Unmarried,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,215591,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n4,Self-emp-not-inc,96459,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,205562,Masters,14,Never-married,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,188081,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,121245,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,127273,11th,7,Never-married,Other-service,Other-relative,White,Male,0,0,0,United-States,<=50K\n0,Private,114345,9th,5,Never-married,Craft-repair,Unmarried,White,Male,1,0,2,United-States,<=50K\n0,Private,341227,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,166893,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,>50K\n4,?,65730,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,145231,Assoc-acdm,12,Divorced,Adm-clerical,Own-child,White,Female,0,2,2,United-States,<=50K\n4,Self-emp-not-inc,102510,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,4,United-States,>50K\n2,Self-emp-not-inc,285335,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,177087,11th,7,Never-married,Adm-clerical,Unmarried,Black,Male,0,0,1,United-States,<=50K\n2,Private,240504,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,218490,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,384651,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,189551,HS-grad,9,Divorced,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,194791,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,194630,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,177647,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,51620,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,251421,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,180477,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,391736,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,170091,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,175360,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,276153,Bachelors,13,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Female,2,0,2,United-States,<=50K\n3,Federal-gov,105788,Bachelors,13,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,3,United-States,>50K\n2,Local-gov,248476,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,>50K\n1,Private,168443,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,120201,HS-grad,9,Divorced,Adm-clerical,Own-child,Other,Female,0,0,4,United-States,<=50K\n4,Private,114678,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Private,167440,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,265266,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Cuba,>50K\n1,Private,212235,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,44671,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,87282,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,112754,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,3,United-States,>50K\n1,Self-emp-not-inc,322238,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,65382,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,115176,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,162236,Masters,14,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,?,>50K\n2,Private,409902,HS-grad,9,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Local-gov,204062,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,283305,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,435638,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,114733,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,162343,Some-college,10,Never-married,Adm-clerical,Other-relative,Black,Male,0,0,1,United-States,<=50K\n0,?,195981,HS-grad,9,Widowed,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,79531,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,395078,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,159641,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,1,2,United-States,<=50K\n0,Private,159567,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,133917,Assoc-voc,11,Never-married,Sales,Other-relative,Black,Male,0,0,2,?,<=50K\n3,Private,196894,11th,7,Separated,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,132879,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,190290,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,102828,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,128493,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,State-gov,290677,Masters,14,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n0,Private,283757,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,169104,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n3,Private,171409,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,319165,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,203182,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,?,211968,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,215384,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,3,3,United-States,<=50K\n1,Private,166666,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,156566,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,140564,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,322208,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,420277,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,123430,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,Mexico,<=50K\n2,Self-emp-inc,151584,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Self-emp-not-inc,348960,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,168232,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,>50K\n3,Self-emp-inc,201699,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,511517,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,118001,10th,6,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,193961,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n0,Private,32732,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,223548,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,389932,HS-grad,9,Divorced,Transport-moving,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,102345,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,107584,Some-college,10,Separated,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,?,34321,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,State-gov,39478,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,276221,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,385242,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,235646,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,123306,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,38573,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,216889,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,386705,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,249585,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,47276,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,162758,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,146497,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,190765,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,186314,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,213615,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,162322,Assoc-voc,11,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n2,State-gov,115932,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,392694,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,State-gov,143517,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,123429,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Italy,>50K\n3,Private,254285,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,238311,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,281647,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,75167,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,252862,Assoc-voc,11,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,199240,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,0,England,<=50K\n2,Private,145762,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,142443,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,99361,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,105138,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,183171,11th,7,Never-married,Other-service,Own-child,Black,Male,1,0,1,United-States,<=50K\n0,Private,151866,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,297261,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,148998,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,143046,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,183850,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,248841,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,198452,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,161092,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,112497,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,155963,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,147560,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,376393,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,151790,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,438139,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,163911,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,214896,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,102821,Some-college,10,Married-civ-spouse,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-not-inc,90021,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,77085,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Japan,>50K\n2,Private,158555,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,28160,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,462255,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,144949,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,116207,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,187308,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,189890,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,185267,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,63434,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,1366120,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,495061,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n1,Local-gov,134886,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,2,1,United-States,<=50K\n1,Private,129707,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,?,181337,10th,6,Never-married,?,Own-child,Other,Female,0,0,0,United-States,<=50K\n3,Private,74784,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,44392,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,406641,Some-college,10,Never-married,Handlers-cleaners,Other-relative,White,Female,0,0,0,United-States,<=50K\n3,Private,89041,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,>50K\n2,?,139770,Some-college,10,Divorced,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,180212,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,?,338212,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,178472,9th,5,Separated,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,384236,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,168470,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,80485,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,?,181705,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,216867,10th,6,Never-married,Other-service,Other-relative,White,Male,0,0,1,Mexico,<=50K\n2,Federal-gov,214541,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,383239,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,70034,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,?,266287,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,128485,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n4,?,89015,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,106740,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,167527,11th,7,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,19302,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,210150,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,179824,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,420351,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,State-gov,215443,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,116044,11th,7,Separated,Craft-repair,Other-relative,White,Male,1,0,3,United-States,<=50K\n1,Private,215306,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Cuba,<=50K\n2,Private,108069,Some-college,10,Never-married,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,260046,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,31053,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,362302,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,87205,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,191703,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,242968,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n0,Local-gov,185575,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,177858,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Self-emp-not-inc,73585,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,301802,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,108467,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,431245,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,157217,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,204935,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,277112,11th,7,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-inc,59145,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Local-gov,159773,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,118793,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n1,State-gov,152457,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,187901,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,1,2,United-States,<=50K\n3,Private,266529,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,256179,Some-college,10,Never-married,?,Own-child,White,Male,1,0,0,United-States,<=50K\n4,Private,113756,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,83444,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,30529,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,4,3,United-States,>50K\n3,?,146325,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,198825,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,71489,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,111218,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,221626,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,203482,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n2,Self-emp-not-inc,352196,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,355918,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,168262,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,182615,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,211482,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,386370,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,85077,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,3,0,United-States,>50K\n3,Local-gov,180010,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Without-pay,142210,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,415706,5th-6th,3,Separated,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n3,Private,237731,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,343506,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,116163,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,France,<=50K\n4,?,206560,HS-grad,9,Widowed,?,Not-in-family,Other,Female,0,0,1,Puerto-Rico,<=50K\n3,State-gov,153451,HS-grad,9,Married-civ-spouse,Tech-support,Wife,White,Female,0,2,2,United-States,>50K\n1,Private,301862,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,33429,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,169583,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,146497,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,0,Germany,<=50K\n3,Self-emp-not-inc,383384,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,240809,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,203763,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,218785,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,?,381741,Assoc-acdm,12,Never-married,?,Own-child,White,Male,0,2,0,United-States,<=50K\n0,Private,244602,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,State-gov,175696,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,101027,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,99270,HS-grad,9,Never-married,Transport-moving,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,224393,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,192381,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,131686,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,84390,Assoc-voc,11,Married-spouse-absent,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,277533,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,72880,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,149646,Some-college,10,Divorced,?,Own-child,White,Female,0,0,0,?,<=50K\n3,Private,209057,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,108909,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,74949,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,235639,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,137421,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,Hong,<=50K\n3,Private,122412,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,434894,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,379959,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,95885,11th,7,Never-married,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,4,0,3,United-States,>50K\n2,Private,225330,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Female,0,0,3,Poland,>50K\n2,Private,32627,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,65171,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,193380,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,184823,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,81259,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,301369,12th,8,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,190968,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,196610,7th-8th,4,Widowed,Exec-managerial,Not-in-family,White,Male,2,0,2,United-States,>50K\n1,Private,330715,HS-grad,9,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,77698,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,139770,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,2,0,2,United-States,<=50K\n0,Private,109053,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,Private,312653,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,193260,Masters,14,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,?,>50K\n1,Private,331831,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,54202,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,4,0,3,United-States,>50K\n3,Private,163948,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,36228,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,160167,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,178356,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,4,United-States,<=50K\n2,Private,104196,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,288353,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,187693,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,114988,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,117392,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,121124,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,195638,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,245053,Some-college,10,Divorced,Handlers-cleaners,Own-child,White,Male,0,1,2,United-States,<=50K\n3,State-gov,216734,Prof-school,15,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,197827,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,49156,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,126133,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,304463,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,214288,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,274969,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,189072,Bachelors,13,Never-married,Tech-support,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,128047,Some-college,10,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,210338,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,122442,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,167081,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,1,0,3,United-States,<=50K\n1,Private,251421,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Federal-gov,219519,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,33355,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,441210,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,178356,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,231196,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,State-gov,40925,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,270587,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,England,<=50K\n2,Private,219266,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,114967,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,344492,HS-grad,9,Separated,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,369387,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,101771,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,137428,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,121012,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,139290,10th,6,Separated,Machine-op-inspct,Own-child,White,Female,0,0,3,United-States,<=50K\n4,Private,199193,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,286689,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,?,123727,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Federal-gov,208640,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,120130,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,241431,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,120450,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,152240,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,200960,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,314310,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,44566,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,21792,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,182074,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,221850,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Ecuador,>50K\n2,Private,240628,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,318641,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,140863,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,129150,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,143003,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,2,0,3,India,>50K\n1,Self-emp-not-inc,198664,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,4,0,4,South,>50K\n2,Private,244945,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,138514,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,92008,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,207415,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,188626,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,257250,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,133696,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,195919,10th,6,Never-married,Handlers-cleaners,Not-in-family,Other,Male,0,0,2,Dominican-Republic,<=50K\n2,Private,119266,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,140474,Assoc-acdm,12,Divorced,Craft-repair,Own-child,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n0,Private,69739,10th,6,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,293176,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,217961,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,163725,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,419394,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,220836,11th,7,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,334291,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,298601,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,2,0,2,United-States,<=50K\n2,Private,200360,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,203482,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,99126,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,2,United-States,>50K\n4,Private,109190,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,34848,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,29732,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n0,Private,87867,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,123515,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,175935,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,229456,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,184105,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n2,Local-gov,99554,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,190227,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,29020,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,306459,1st-4th,2,Separated,Handlers-cleaners,Unmarried,White,Male,0,0,1,Honduras,<=50K\n2,Private,193995,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,105059,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,71751,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,4,United-States,>50K\n1,Private,176683,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,342709,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,53838,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,209482,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,214242,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n3,?,34458,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,100375,Some-college,10,Married-spouse-absent,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,149949,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,189762,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,79874,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,104576,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,>50K\n1,State-gov,355700,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,213625,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,204984,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,144593,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,?,<=50K\n0,Private,217169,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,184883,9th,5,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n2,?,136419,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,57758,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n3,Self-emp-not-inc,30908,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,217971,9th,5,Widowed,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,160703,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,142675,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,248833,HS-grad,9,Married-AF-spouse,?,Wife,White,Female,1,0,0,United-States,<=50K\n4,Private,171242,11th,7,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,Canada,<=50K\n1,Private,376979,9th,5,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,175935,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,Private,277530,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,104501,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,94041,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Ireland,<=50K\n2,Local-gov,593246,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,121074,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,192596,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,142457,11th,7,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Private,136028,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,216145,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,2,0,2,United-States,<=50K\n0,Private,157894,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,164593,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,2,0,2,United-States,>50K\n0,Private,252993,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,Columbia,<=50K\n2,Private,145711,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,358199,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,219591,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,205005,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n3,Private,221936,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,120914,10th,6,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,155761,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,195914,Some-college,10,Never-married,Sales,Own-child,Black,Female,1,0,1,United-States,<=50K\n2,Local-gov,236687,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,318036,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,53306,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,174645,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,321817,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,206948,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,402975,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n4,?,289930,Bachelors,13,Separated,?,Not-in-family,White,Female,1,0,0,United-States,<=50K\n2,Private,367049,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,2,0,2,United-States,<=50K\n2,Private,143486,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,27187,Masters,14,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,187717,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,378104,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,113870,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n2,Private,252518,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,326334,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,279914,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,320451,HS-grad,9,Never-married,Protective-serv,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,207853,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,237294,HS-grad,9,Widowed,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,112181,Assoc-voc,11,Married-civ-spouse,Tech-support,Wife,White,Female,0,3,1,United-States,>50K\n1,State-gov,259705,Some-college,10,Separated,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,117789,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,449432,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,89083,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,59612,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,129980,9th,5,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,108233,Assoc-acdm,12,Separated,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,342709,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,126675,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,141118,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,273701,Some-college,10,Never-married,?,Other-relative,Black,Male,4,0,0,United-States,<=50K\n3,Private,173243,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,161092,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,209691,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,89508,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,399522,11th,7,Married-spouse-absent,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,State-gov,136939,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,264436,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,199572,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,28291,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,215990,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,179594,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,139391,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,1,United-States,>50K\n2,Private,187370,Masters,14,Divorced,Exec-managerial,Unmarried,White,Male,2,0,4,United-States,>50K\n1,Private,473133,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n4,Self-emp-not-inc,205246,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Male,0,4,3,United-States,>50K\n1,Private,182308,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,51662,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,289468,11th,7,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,201954,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n2,Self-emp-not-inc,26781,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Private,100960,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,203761,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Female,1,0,2,United-States,<=50K\n0,Private,213811,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,124672,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,219300,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,270436,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,212619,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,193586,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,2,United-States,<=50K\n2,Private,84136,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,264834,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,98995,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,278254,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,167987,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,72887,Bachelors,13,Married-spouse-absent,Tech-support,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Private,176467,9th,5,Never-married,Transport-moving,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,85902,10th,6,Widowed,Transport-moving,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,223433,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,108435,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,172496,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,241998,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,245948,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Female,1,0,2,United-States,<=50K\n0,Private,187513,Assoc-voc,11,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,440138,HS-grad,9,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,England,<=50K\n0,Private,218215,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,158948,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,94413,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,183598,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,192664,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,392812,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,155818,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,195000,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,308205,5th-6th,3,Never-married,Farming-fishing,Other-relative,White,Male,0,0,2,Mexico,<=50K\n3,Private,104879,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,152307,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,145964,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,97419,HS-grad,9,Married-civ-spouse,Protective-serv,Wife,Black,Female,0,0,2,United-States,<=50K\n0,?,12285,Some-college,10,Never-married,?,Not-in-family,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n1,Private,263150,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,?,189885,HS-grad,9,Widowed,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,151888,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,254167,10th,6,Separated,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,331482,Assoc-acdm,12,Divorced,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,177189,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,186886,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,33221,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,188171,10th,6,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,209770,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,164488,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,<=50K\n4,Local-gov,180869,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,190350,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,137192,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,3,3,South,>50K\n2,Private,204057,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,Germany,<=50K\n3,Private,198774,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,1,2,United-States,<=50K\n4,Private,134906,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,174515,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,259363,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,201742,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n1,Private,209609,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,185127,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,462838,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,176967,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,284129,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,37546,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,116666,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,120724,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,314240,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,Private,423222,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,201127,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,202239,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,209629,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,165922,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,133520,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,99888,Assoc-voc,11,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,176410,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,103214,Doctorate,16,Never-married,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Female,0,0,3,United-States,>50K\n1,Private,122612,Bachelors,13,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,3,0,3,Philippines,>50K\n3,Private,226735,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,4,United-States,>50K\n2,Self-emp-inc,151089,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,244312,9th,5,Never-married,Craft-repair,Own-child,White,Male,0,0,1,El-Salvador,<=50K\n1,Private,209317,9th,5,Never-married,Other-service,Not-in-family,White,Male,0,0,2,El-Salvador,<=50K\n3,Private,99096,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,1,2,United-States,<=50K\n0,Private,374116,HS-grad,9,Never-married,Priv-house-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,205249,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Japan,<=50K\n2,Self-emp-not-inc,326083,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,183523,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Hungary,<=50K\n2,Private,350783,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,140849,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,175943,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,125933,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,225124,HS-grad,9,Divorced,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,272090,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,El-Salvador,<=50K\n3,Private,40666,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,35245,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,167482,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,204662,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,291147,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,179869,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,205100,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,352139,Some-college,10,Divorced,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,111268,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,247111,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,271446,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,132412,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,74712,HS-grad,9,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n0,Private,94662,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,33126,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,133584,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,103759,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,<=50K\n4,?,64448,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,374367,Assoc-voc,11,Separated,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,179666,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,Canada,<=50K\n0,Private,99219,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,180211,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,Taiwan,>50K\n3,Local-gov,219276,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n2,Private,150011,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,231231,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,182217,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,Scotland,<=50K\n1,Private,277342,Some-college,10,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,140001,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,99651,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,223319,Some-college,10,Divorced,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,235307,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,206343,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,1,0,2,Cuba,<=50K\n3,Local-gov,156003,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,529223,Bachelors,13,Never-married,Sales,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,202871,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,58337,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,Federal-gov,298643,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,191188,10th,6,Widowed,Farming-fishing,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Private,96287,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,104443,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,323054,10th,6,Divorced,Other-service,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,95917,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,Canada,<=50K\n1,Private,238305,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,2,0,?,<=50K\n0,Private,49296,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,50953,Some-college,10,Never-married,Priv-house-serv,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,124507,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,239523,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,309124,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,240172,Bachelors,13,Married-spouse-absent,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,105010,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,135056,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,0,?,<=50K\n0,Private,178478,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,23871,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,362309,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,257781,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,2,1,United-States,<=50K\n2,Private,175669,11th,7,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,2,United-States,>50K\n3,Private,297906,Some-college,10,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n2,Private,230684,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,123011,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,170866,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,182543,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,Mexico,<=50K\n4,Self-emp-not-inc,236470,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,33725,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,188941,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,206878,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,173806,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,190709,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,149102,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,Poland,<=50K\n0,Private,25265,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,100669,Some-college,10,Married-civ-spouse,Craft-repair,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,114158,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,228057,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,54012,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,219967,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,239865,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,119421,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n4,Self-emp-not-inc,220187,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,33068,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,3,2,United-States,<=50K\n2,Self-emp-not-inc,277783,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,3,3,United-States,<=50K\n2,Private,175515,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,271795,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,70055,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,352806,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n4,Private,266189,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,102945,7th-8th,4,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,173851,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,144092,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,198681,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,351810,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n3,Private,180142,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,175360,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,224498,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,154641,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,152540,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,217663,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,138575,HS-grad,9,Never-married,Protective-serv,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,?,32477,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,101104,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,3,0,0,United-States,>50K\n1,Private,44677,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,456618,7th-8th,4,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,227282,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,27624,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,281403,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n4,Federal-gov,39181,Doctorate,16,Divorced,Exec-managerial,Not-in-family,White,Female,0,4,3,United-States,>50K\n3,Private,377140,5th-6th,3,Never-married,Priv-house-serv,Unmarried,White,Female,0,0,1,Nicaragua,<=50K\n1,Private,299810,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,181916,Some-college,10,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,237044,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n4,Self-emp-inc,123053,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,3,India,>50K\n4,State-gov,269512,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,44767,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,United-States,>50K\n1,Private,67218,7th-8th,4,Married-civ-spouse,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,176992,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,43712,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,379919,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,104509,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,1,0,0,United-States,<=50K\n0,Private,212370,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,179666,12th,8,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,233882,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,0,4,2,Vietnam,<=50K\n0,Private,197387,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,Mexico,<=50K\n1,Local-gov,220656,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,181091,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,135028,HS-grad,9,Separated,Adm-clerical,Other-relative,Black,Female,0,0,1,United-States,<=50K\n2,Private,185057,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,?,<=50K\n3,Private,106498,10th,6,Widowed,Transport-moving,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,203003,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,223789,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,184026,Some-college,10,Never-married,Prof-specialty,Not-in-family,Other,Male,0,0,3,United-States,<=50K\n1,?,335427,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,65866,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,1,2,United-States,<=50K\n1,Private,372692,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,45607,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,State-gov,303176,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,<=50K\n1,Private,138190,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,1,2,United-States,<=50K\n1,Self-emp-not-inc,212895,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,31359,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n4,Private,147989,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,145290,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,262684,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,1,2,United-States,<=50K\n1,Private,132601,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,30759,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,319889,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,29431,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,111483,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,184756,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,651396,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,1,1,United-States,<=50K\n1,Private,187560,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,84848,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,?,36243,Doctorate,16,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,88913,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n0,Private,73190,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,132529,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,214542,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,217006,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,169785,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,75573,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,Germany,<=50K\n2,Private,239171,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,53566,Doctorate,16,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,117109,Some-college,10,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,1,United-States,<=50K\n1,Private,398019,7th-8th,4,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,0,Mexico,<=50K\n0,Private,114008,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,204653,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,254935,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,84755,Some-college,10,Widowed,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,198145,Masters,14,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,>50K\n3,Private,174020,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,2,2,United-States,<=50K\n0,Private,451951,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,172175,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,209472,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,336707,Assoc-voc,11,Separated,Craft-repair,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,?,431861,10th,6,Separated,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,156728,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,290321,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,State-gov,206577,Some-college,10,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,149324,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n1,?,49593,Some-college,10,Married-civ-spouse,?,Wife,Black,Female,0,0,1,United-States,<=50K\n3,Private,98975,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,181659,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,174789,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,102308,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,184801,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,176014,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,256861,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,239397,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,233777,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,236520,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,70754,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,245378,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,176729,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,154120,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,88913,Some-college,10,Never-married,Handlers-cleaners,Own-child,Asian-Pac-Islander,Female,1,0,2,United-States,<=50K\n0,Private,517036,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,El-Salvador,<=50K\n2,Private,436361,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Private,231037,5th-6th,3,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,209831,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,143833,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,2,United-States,>50K\n3,?,167381,HS-grad,9,Widowed,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,215468,Bachelors,13,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,200700,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,191777,HS-grad,9,Never-married,Protective-serv,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Federal-gov,195437,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,149396,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,104746,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,108147,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,238859,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,23157,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,497788,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,141558,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,117963,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,232356,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,157941,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,103642,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,169727,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,274731,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,161572,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,48779,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,141511,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,314153,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,168334,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,267252,Masters,14,Separated,Exec-managerial,Unmarried,Black,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,312055,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,207937,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,232653,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,246841,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,154087,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,199011,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,205100,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,177907,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,1,0,0,?,<=50K\n0,Private,50400,Some-college,10,Married-civ-spouse,Sales,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Local-gov,97064,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,65038,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,292472,Some-college,10,Never-married,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,2,2,Cambodia,<=50K\n0,Private,225211,9th,5,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,320192,1st-4th,2,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,State-gov,119421,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,1,1,United-States,<=50K\n0,Private,83580,Some-college,10,Never-married,Prof-specialty,Own-child,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n1,Private,133696,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,3,0,2,United-States,>50K\n2,Private,141584,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,4,2,United-States,>50K\n2,Private,529216,HS-grad,9,Separated,Transport-moving,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,390817,5th-6th,3,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,2,Mexico,<=50K\n0,?,85733,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,155976,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,221172,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,270842,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,82622,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,371064,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,54744,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,22641,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,218957,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,441637,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,143699,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,183096,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,97176,11th,7,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,122493,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,311376,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,78928,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,1,0,2,United-States,<=50K\n4,Private,123582,10th,6,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,174215,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,183902,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,247880,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,256636,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,152875,Bachelors,13,Married-civ-spouse,?,Wife,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n1,Private,22422,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,?,178215,Some-college,10,Widowed,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,194360,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,>50K\n4,Private,247187,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,63921,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,224889,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,178564,Bachelors,13,Never-married,Prof-specialty,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,47619,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,92775,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,50837,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,235894,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,244974,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,526734,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,243484,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,201664,HS-grad,9,Married-civ-spouse,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,234640,HS-grad,9,Married-spouse-absent,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,268022,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,223267,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,99199,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,137076,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,115411,Some-college,10,Divorced,Sales,Own-child,White,Male,1,0,2,United-States,<=50K\n3,Private,313146,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,29980,7th-8th,4,Never-married,Farming-fishing,Other-relative,White,Male,1,0,0,United-States,<=50K\n2,Self-emp-inc,543042,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,271807,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,97934,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,191196,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,264627,11th,7,Divorced,Exec-managerial,Unmarried,White,Female,0,0,4,United-States,<=50K\n1,Private,183801,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,209227,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,216208,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,377095,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,317535,1st-4th,2,Married-civ-spouse,Protective-serv,Other-relative,White,Male,0,0,2,Mexico,<=50K\n2,Private,247880,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,152246,Some-college,10,Never-married,Handlers-cleaners,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n0,Private,428299,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,161708,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,167859,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,85194,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,119471,7th-8th,4,Never-married,Craft-repair,Not-in-family,Other,Male,0,0,2,?,<=50K\n2,Private,117683,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,139347,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,2,United-States,>50K\n0,Private,427744,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,122116,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,227931,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,226497,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,83783,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,197113,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Other,Male,0,0,3,Puerto-Rico,<=50K\n1,Private,204742,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,331527,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,213179,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,188260,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,298161,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Nicaragua,<=50K\n2,Private,143774,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n3,Local-gov,139296,11th,7,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,152389,Some-college,10,Never-married,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,309974,Some-college,10,Separated,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,37085,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,270059,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,130045,7th-8th,4,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,188038,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,168203,7th-8th,4,Never-married,Farming-fishing,Other-relative,Other,Male,0,0,2,Mexico,<=50K\n3,Private,171807,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,186696,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,177531,10th,6,Divorced,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,115464,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,501144,Some-college,10,Never-married,Sales,Other-relative,Black,Female,0,0,2,United-States,<=50K\n4,Local-gov,180079,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,205894,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,?,<=50K\n2,Self-emp-not-inc,218490,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,?,>50K\n0,Local-gov,203924,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,91857,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,229700,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,158704,10th,6,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,190911,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,139176,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,119684,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,0,United-States,>50K\n4,Private,124930,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,4,2,United-States,<=50K\n0,Private,168693,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,250038,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,353927,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,216390,9th,5,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,1,0,2,United-States,<=50K\n0,Private,230248,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,117728,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,115851,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,193335,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,203894,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,100109,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n3,State-gov,157639,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,235320,Masters,14,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,127686,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,28572,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n4,?,91534,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,184687,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,267945,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,131899,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,192614,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,186808,Bachelors,13,Married-civ-spouse,Craft-repair,Own-child,White,Male,0,0,2,United-States,>50K\n3,Private,44116,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,46442,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,78022,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,417668,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,223763,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,223851,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,115634,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,114459,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,197093,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,357145,Doctorate,16,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,59231,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,292303,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,122288,Some-college,10,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,52322,Bachelors,13,Never-married,Tech-support,Not-in-family,Other,Male,0,0,3,United-States,<=50K\n1,Local-gov,105830,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,107125,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,281860,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,283320,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,26598,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,220783,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,?,121694,7th-8th,4,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,208302,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,172664,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,54611,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,631947,10th,6,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,394484,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,239120,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,37683,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,4,0,3,Canada,>50K\n3,Local-gov,193012,Masters,14,Divorced,Protective-serv,Not-in-family,Black,Male,0,0,3,United-States,>50K\n3,Private,143098,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,3,2,China,>50K\n4,Private,84888,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,188503,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,337778,11th,7,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,94432,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,168906,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,116143,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,128272,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n4,Federal-gov,301383,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,174995,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,State-gov,289909,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,154641,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,209034,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,2,0,2,United-States,<=50K\n1,Private,203488,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,141118,Masters,14,Divorced,Prof-specialty,Own-child,White,Female,0,0,3,United-States,>50K\n1,Private,169589,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,137645,Bachelors,13,Never-married,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Local-gov,489085,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,36302,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,253420,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,269300,HS-grad,9,Separated,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,282609,5th-6th,3,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,Honduras,<=50K\n3,Private,346978,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,182395,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,205051,10th,6,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,128736,10th,6,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,236110,12th,8,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Cuba,>50K\n2,Private,312271,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,126978,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n3,Private,204692,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,195956,Bachelors,13,Divorced,Tech-support,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,State-gov,202682,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,231912,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,24982,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,278938,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,36489,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,154874,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,74581,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,311446,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,2,United-States,>50K\n2,Self-emp-inc,162164,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,239708,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,162856,Some-college,10,Divorced,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,85109,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,169042,HS-grad,9,Separated,Prof-specialty,Unmarried,White,Female,0,1,2,Puerto-Rico,<=50K\n0,Private,436798,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,345363,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,England,<=50K\n2,Private,49837,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,296516,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,180283,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,95639,HS-grad,9,Never-married,Craft-repair,Other-relative,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,33155,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,329059,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Italy,>50K\n3,Private,24694,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,443855,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n3,?,294691,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,301867,Some-college,10,Never-married,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n3,Private,226875,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,362835,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,180339,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,4,United-States,<=50K\n3,Self-emp-inc,207489,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,3,Germany,<=50K\n2,Private,336643,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,143653,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,State-gov,101475,Assoc-acdm,12,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,263871,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,77820,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,95465,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,257910,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,244372,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,>50K\n2,Self-emp-not-inc,126738,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-inc,97082,12th,8,Widowed,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n4,Private,133164,7th-8th,4,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,104617,7th-8th,4,Never-married,Other-service,Other-relative,White,Female,0,0,4,Mexico,<=50K\n4,Self-emp-inc,105339,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n3,Self-emp-inc,258735,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,182926,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,166193,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,206125,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,346594,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,108301,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,73498,7th-8th,4,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,129150,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,181280,Masters,14,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,1,United-States,<=50K\n2,Private,146908,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,183765,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,?,>50K\n0,Private,164488,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,307468,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,93884,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,279833,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,4,2,United-States,>50K\n3,Private,137658,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,2,Dominican-Republic,<=50K\n1,Private,101562,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,136331,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,259846,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,98719,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,168682,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,198953,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,Black,Female,0,0,0,United-States,<=50K\n2,?,29115,Some-college,10,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,173673,5th-6th,3,Never-married,Other-service,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n0,Private,67958,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,98980,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n3,State-gov,94174,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,122442,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,154675,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,116632,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,?,238685,11th,7,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,139391,Some-college,10,Married-civ-spouse,?,Husband,White,Male,4,0,1,United-States,>50K\n2,Private,169031,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,237452,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,Cuba,>50K\n2,Private,216968,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,?,<=50K\n1,?,216479,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,>50K\n0,State-gov,126822,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,51461,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,54953,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,222654,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,37676,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,159319,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,125321,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,209609,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,224947,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,438427,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,384276,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,196805,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,4,United-States,<=50K\n1,Private,242097,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,184306,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,161954,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Germany,<=50K\n4,Private,258561,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,95280,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,212783,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,205004,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,387844,12th,8,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,83880,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,161155,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,265698,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n4,Self-emp-inc,146477,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,97261,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,437890,HS-grad,9,Never-married,Exec-managerial,Unmarried,Black,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,133736,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,169983,11th,7,Widowed,Sales,Not-in-family,White,Female,1,0,1,United-States,<=50K\n2,Private,126675,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,175754,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,2,3,United-States,<=50K\n1,Private,121768,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,Poland,<=50K\n0,Private,180052,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,124454,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,190115,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,<=50K\n2,Private,222584,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,22245,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,114160,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,228960,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,132572,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,103020,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Wife,Other,Female,0,0,2,Puerto-Rico,<=50K\n2,Private,187802,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,2,2,United-States,>50K\n1,Local-gov,50649,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,137698,5th-6th,3,Married-spouse-absent,Farming-fishing,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n3,Self-emp-inc,30575,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n4,Private,202220,Some-college,10,Separated,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,50178,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,207791,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,540712,HS-grad,9,Never-married,Other-service,Other-relative,Black,Male,0,2,1,United-States,<=50K\n3,Private,321770,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,202053,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,143699,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,115066,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,223751,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,354075,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,32732,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,State-gov,390867,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,101697,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,279721,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,223400,Assoc-acdm,12,Married-civ-spouse,Priv-house-serv,Other-relative,White,Female,0,0,1,Poland,<=50K\n3,?,206357,5th-6th,3,Married-civ-spouse,?,Wife,White,Female,0,0,2,Mexico,<=50K\n2,Private,76417,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,?,184682,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,78170,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,201410,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,189013,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,119913,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,549174,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,214706,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,33811,Bachelors,13,Married-civ-spouse,?,Wife,Other,Female,0,0,2,Taiwan,>50K\n2,Private,234220,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,Cuba,<=50K\n0,Private,237720,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,185942,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,>50K\n4,Local-gov,286983,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,140027,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,?,115258,11th,7,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,155408,HS-grad,9,Widowed,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,117963,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,158737,12th,8,Married-civ-spouse,Machine-op-inspct,Other-relative,Other,Male,0,0,2,Ecuador,<=50K\n1,Local-gov,199471,Assoc-voc,11,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,287701,Assoc-acdm,12,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,>50K\n2,Private,137707,Assoc-voc,11,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n1,State-gov,108116,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,366900,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,187355,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,Canada,>50K\n2,Private,33105,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,4,United-States,>50K\n3,Self-emp-not-inc,268639,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,3,Canada,<=50K\n1,Private,358975,Some-college,10,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,3,Hungary,<=50K\n1,Private,199227,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,248249,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,460437,9th,5,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,187294,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,115932,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,181762,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,27049,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,806552,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,150755,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,Canada,>50K\n4,Private,69867,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,160786,11th,7,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,Germany,<=50K\n2,Private,219546,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,24872,Some-college,10,Separated,Transport-moving,Not-in-family,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n0,Private,110371,12th,8,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,Mexico,<=50K\n0,?,376474,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,304602,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,143699,Some-college,10,Never-married,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,238917,1st-4th,2,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n3,Private,200618,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,183043,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Local-gov,209752,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,?,175653,Assoc-acdm,12,Divorced,?,Not-in-family,White,Female,4,0,2,United-States,>50K\n3,Private,196707,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n2,Local-gov,98725,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,180150,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,151227,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n0,?,118847,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,282538,Assoc-voc,11,Separated,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,89534,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,291011,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,166187,HS-grad,9,Widowed,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,>50K\n0,Private,188669,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,178948,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,188738,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,Italy,>50K\n2,Self-emp-not-inc,160808,Some-college,10,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,93605,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,318331,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,109921,HS-grad,9,Separated,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,87605,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,89477,Some-college,10,Widowed,Farming-fishing,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,48301,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,220748,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Black,Male,0,0,3,United-States,<=50K\n2,Private,387068,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,250743,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,78258,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,31387,Doctorate,16,Married-spouse-absent,Prof-specialty,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,289190,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,604537,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,Mexico,<=50K\n1,Private,328466,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,403187,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,219546,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,220531,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,204648,Assoc-voc,11,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,201908,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,?,109912,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,365683,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,175674,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,203488,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,106406,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,172756,1st-4th,2,Widowed,Machine-op-inspct,Not-in-family,White,Female,1,0,1,Ecuador,<=50K\n2,Private,125167,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,249339,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,94652,Some-college,10,Never-married,Craft-repair,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,195394,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,130302,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,66686,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,336042,HS-grad,9,Separated,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,193586,Some-college,10,Separated,Farming-fishing,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,325461,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n4,Local-gov,313852,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,30509,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,3,United-States,<=50K\n0,Local-gov,32639,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,234953,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,120629,Bachelors,13,Divorced,Exec-managerial,Not-in-family,Black,Female,4,0,3,United-States,>50K\n2,Private,350379,5th-6th,3,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,?,176967,11th,7,Never-married,?,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Private,36423,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,123397,HS-grad,9,Married-civ-spouse,Transport-moving,Wife,White,Female,2,0,1,United-States,>50K\n2,Private,130813,HS-grad,9,Divorced,Machine-op-inspct,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,35236,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,33350,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,177380,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,216129,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,1,Jamaica,<=50K\n2,Private,335104,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,199741,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,0,3,1,United-States,<=50K\n4,Self-emp-inc,165881,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,387777,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,149943,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,Taiwan,>50K\n2,Private,188834,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,290661,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,155603,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,4,2,United-States,<=50K\n0,Private,114838,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,Italy,<=50K\n3,Local-gov,168553,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,103064,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,123833,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,55621,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Local-gov,189834,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,217926,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,341672,HS-grad,9,Married-spouse-absent,Transport-moving,Other-relative,Asian-Pac-Islander,Male,0,1,3,India,>50K\n1,Private,163003,Bachelors,13,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,1,0,2,Taiwan,<=50K\n0,Private,194352,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,54878,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,393248,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,279315,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,392812,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,34998,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,51016,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,132717,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,186078,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,196123,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Self-emp-inc,304906,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,41521,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,346847,Assoc-voc,11,Separated,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,107233,HS-grad,9,Never-married,Craft-repair,Other-relative,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,150125,Assoc-acdm,12,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,400535,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,409622,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,2,Mexico,<=50K\n1,Private,136448,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,202950,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Iran,<=50K\n2,Local-gov,197012,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Female,3,0,2,England,>50K\n4,Private,237691,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,170277,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,160784,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,33798,12th,8,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,197838,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,223212,7th-8th,4,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,125762,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,283969,Some-college,10,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,0,United-States,<=50K\n0,Private,374163,12th,8,Married-civ-spouse,Farming-fishing,Husband,Other,Male,0,0,3,Mexico,<=50K\n3,State-gov,118567,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,147655,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,82797,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,142573,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,235167,5th-6th,3,Married-spouse-absent,Priv-house-serv,Not-in-family,White,Female,0,0,1,Mexico,<=50K\n0,Private,53245,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,2,0,United-States,<=50K\n3,Private,28035,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,247082,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,123397,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,133327,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,102270,7th-8th,4,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,45817,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,240988,9th,5,Married-civ-spouse,Machine-op-inspct,Other-relative,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n0,Private,386378,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,State-gov,350651,12th,8,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n0,State-gov,76142,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,73773,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,?,281504,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,293358,Some-college,10,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Private,146906,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,331474,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,213719,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,101795,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,228265,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,130206,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,324254,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,223019,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,189666,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,139086,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,359327,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,?,<=50K\n2,Self-emp-not-inc,75065,12th,8,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,Vietnam,<=50K\n3,Private,139843,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,34310,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,4,2,United-States,<=50K\n3,Private,346014,Some-college,10,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,163278,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,Private,31460,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,33725,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,?,63552,7th-8th,4,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,State-gov,300623,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,177072,Some-college,10,Never-married,Prof-specialty,Other-relative,White,Male,0,0,0,United-States,<=50K\n4,?,37331,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,167725,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,131180,11th,7,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,275859,HS-grad,9,Widowed,Craft-repair,Unmarried,White,Male,3,0,3,Mexico,>50K\n3,Private,275181,5th-6th,3,Divorced,Other-service,Not-in-family,White,Male,0,0,2,Cuba,<=50K\n1,Private,398988,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,222654,10th,6,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,111129,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,137795,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,242150,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,237873,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,367749,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n1,Private,206600,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,2,Mexico,<=50K\n3,Federal-gov,247043,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,187702,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,41718,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,151835,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,118938,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,224870,HS-grad,9,Divorced,Machine-op-inspct,Other-relative,Other,Female,0,0,2,Ecuador,<=50K\n2,Private,178341,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,61343,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,36989,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,226296,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,186624,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,Cuba,<=50K\n0,Private,172582,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,State-gov,227392,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,187563,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,137499,HS-grad,9,Widowed,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,239397,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Local-gov,327164,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,140798,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,187450,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,194580,5th-6th,3,Divorced,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,372682,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,235442,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,128065,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,91545,10th,6,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,154604,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,192150,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,216522,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,156040,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,2,2,United-States,>50K\n0,Private,206861,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,97632,Some-college,10,Divorced,Sales,Not-in-family,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n1,Private,189530,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,State-gov,381789,Some-college,10,Separated,Exec-managerial,Own-child,White,Male,0,4,2,United-States,<=50K\n4,Self-emp-inc,368797,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,41183,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,191062,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,132963,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,153551,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,66473,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,240323,HS-grad,9,Separated,Sales,Unmarried,Black,Female,0,0,0,United-States,<=50K\n4,Local-gov,242095,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Self-emp-inc,128016,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,29526,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,342953,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,215476,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,231919,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,52537,Some-college,10,Never-married,Tech-support,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,27920,11th,7,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,153052,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,199303,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,233369,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,345789,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,3,United-States,>50K\n4,Private,238913,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,195607,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,245173,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,2,2,United-States,<=50K\n2,Private,138441,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,67467,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,102569,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n0,Private,213341,11th,7,Married-spouse-absent,Handlers-cleaners,Own-child,White,Male,0,2,2,Dominican-Republic,<=50K\n1,Private,37202,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,140219,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,298860,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,51362,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,199947,Some-college,10,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,32552,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,183845,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,El-Salvador,<=50K\n1,Private,181091,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,1,England,<=50K\n3,Self-emp-inc,135643,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,3,South,<=50K\n2,State-gov,96249,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,181220,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,133025,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,124865,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,45599,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,194293,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Private,102180,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,121130,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,138768,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n2,State-gov,98989,HS-grad,9,Married-civ-spouse,Other-service,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,State-gov,126327,Assoc-acdm,12,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,113364,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,326199,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,376789,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,137063,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,279145,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,178815,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,3,0,2,United-States,>50K\n0,Self-emp-not-inc,245369,HS-grad,9,Separated,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,49593,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,State-gov,238648,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,2,0,2,United-States,>50K\n3,Private,166181,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,3,United-States,>50K\n4,Self-emp-inc,249043,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,1,United-States,>50K\n2,Private,156403,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,128529,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,186934,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,3,United-States,>50K\n3,?,148489,HS-grad,9,Married-spouse-absent,?,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Local-gov,387770,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,115511,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,201410,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,Philippines,>50K\n2,Private,220585,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,282066,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,280966,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,291586,Bachelors,13,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,142227,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,104025,11th,7,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,148254,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,170562,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,222490,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,57674,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,233624,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,42734,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,233107,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Mexico,<=50K\n4,Private,143110,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,195844,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,115896,Assoc-voc,11,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,303851,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,172475,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n3,Self-emp-not-inc,30008,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,147921,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,172716,12th,8,Married-civ-spouse,Armed-Forces,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,155057,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,?,152569,Assoc-voc,11,Widowed,?,Not-in-family,White,Female,0,4,2,United-States,<=50K\n4,Self-emp-not-inc,132728,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,195136,Assoc-acdm,12,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,377322,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n3,Local-gov,293941,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,182123,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,32528,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,140206,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,378221,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Mexico,>50K\n0,Private,211601,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,119411,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,240013,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,95552,HS-grad,9,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,183710,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,189382,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,380633,5th-6th,3,Widowed,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n3,Private,53407,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,150480,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,175674,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,375313,HS-grad,9,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n0,?,278391,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,212888,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,487085,7th-8th,4,Never-married,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,174461,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,133201,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,77253,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,141511,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Self-emp-inc,181608,10th,6,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,127610,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,154571,Some-college,10,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n3,Private,33842,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,150080,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,30916,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,151294,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,48829,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,2,1,United-States,<=50K\n0,Private,193769,9th,5,Never-married,Other-service,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Private,277455,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,225780,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,436341,Some-college,10,Married-AF-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,255386,HS-grad,9,Never-married,Craft-repair,Other-relative,Asian-Pac-Islander,Male,0,0,2,Cambodia,<=50K\n2,Private,174938,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,174789,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,245628,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,Mexico,<=50K\n0,Private,228752,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,354148,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,192900,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,190391,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,353263,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,Italy,>50K\n1,Private,113198,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,207578,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,93206,Some-college,10,Never-married,Handlers-cleaners,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Local-gov,163998,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,111961,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,<=50K\n0,Private,219122,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,111445,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,309778,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,223020,Assoc-voc,11,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,303155,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,41035,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,159191,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,244408,Some-college,10,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n4,Self-emp-not-inc,473748,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,71823,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,0,United-States,<=50K\n1,Local-gov,83066,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,150154,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,190786,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,178033,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Male,2,0,3,United-States,<=50K\n0,Self-emp-not-inc,159909,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,190885,HS-grad,9,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,2,Guatemala,<=50K\n0,Private,243786,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,124020,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,159016,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,183800,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,193434,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,245029,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,98746,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,Canada,>50K\n3,Federal-gov,140664,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,344920,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,2,0,United-States,<=50K\n2,Private,169980,11th,7,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,3,United-States,<=50K\n1,State-gov,155397,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,245317,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,74182,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,280570,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,30664,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,109952,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,192793,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,243442,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,106297,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,328060,9th,5,Separated,Other-service,Unmarried,Other,Female,0,0,2,Mexico,<=50K\n1,Self-emp-not-inc,48702,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,111283,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,4,0,1,United-States,>50K\n2,Private,484024,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,208470,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,172032,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,29927,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,England,<=50K\n3,Private,98012,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,108468,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,207301,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,3,2,United-States,<=50K\n1,Private,168403,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,66935,Bachelors,13,Never-married,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,42044,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,184806,Prof-school,15,Never-married,Prof-specialty,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Private,1455435,Assoc-acdm,12,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,445382,Some-college,10,Divorced,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,278576,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,84979,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,659504,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,136986,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,278107,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,1,1,United-States,<=50K\n1,Private,96219,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,131091,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,205410,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,416745,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,180667,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,72119,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,108945,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Female,4,0,2,United-States,>50K\n3,Federal-gov,195949,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,101345,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,439263,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,Peru,<=50K\n4,Private,213095,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Federal-gov,59932,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,172815,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,40915,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,139012,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,>50K\n2,Private,121781,Some-college,10,Divorced,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n3,?,130667,HS-grad,9,Separated,?,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,147110,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n0,Local-gov,237811,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,Haiti,<=50K\n2,?,128640,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,111476,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,289716,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,141944,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,323773,11th,7,Married-civ-spouse,Priv-house-serv,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,State-gov,176663,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,155233,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,143327,Some-college,10,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,177212,Some-college,10,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,123088,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,47085,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,102106,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,235894,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,172046,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-not-inc,197207,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,152452,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,172928,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,?,214896,9th,5,Divorced,?,Unmarried,White,Female,0,0,2,Mexico,<=50K\n3,Private,116338,HS-grad,9,Separated,Prof-specialty,Unmarried,White,Female,0,1,3,United-States,<=50K\n3,Private,276664,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,59924,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,194141,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,2,2,United-States,<=50K\n3,Private,95128,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,292504,Some-college,10,Married-spouse-absent,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,45796,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,119359,Prof-school,15,Married-civ-spouse,Sales,Wife,Amer-Indian-Eskimo,Female,4,0,2,South,>50K\n3,State-gov,104280,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,172291,HS-grad,9,Divorced,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,Private,180988,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,110748,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,?,556688,9th,5,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,22494,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,267859,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Cuba,>50K\n4,Local-gov,256821,HS-grad,9,Divorced,Protective-serv,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,117346,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,62374,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,314659,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,114761,7th-8th,4,Widowed,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,93225,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,165315,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,124771,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,27408,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,198841,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,271792,Bachelors,13,Married-spouse-absent,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,64289,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,183390,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,240771,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,234919,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,El-Salvador,<=50K\n0,Private,88231,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,154422,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Private,119098,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,151580,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,118793,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,30499,Bachelors,13,Divorced,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,?,166545,Some-college,10,Married-civ-spouse,?,Wife,White,Female,3,0,0,United-States,>50K\n1,Private,271710,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n2,State-gov,308498,HS-grad,9,Married-spouse-absent,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,172695,Assoc-voc,11,Divorced,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,29962,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,200332,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,291702,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,67234,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,168038,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,137814,Some-college,10,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,126233,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,79036,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,327474,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,145160,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n4,?,37092,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,129387,Assoc-acdm,12,Divorced,Tech-support,Unmarried,White,Female,0,0,2,?,<=50K\n3,Self-emp-not-inc,33304,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,359001,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,3,United-States,>50K\n1,?,143162,10th,6,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,133515,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,168901,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,<=50K\n3,Private,750972,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,142924,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,>50K\n4,Self-emp-inc,228075,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,1,United-States,>50K\n1,Private,91189,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,290609,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,31102,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,0,0,South,<=50K\n2,Self-emp-not-inc,216921,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,120046,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,324629,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Yugoslavia,<=50K\n2,Private,81132,Some-college,10,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n1,Private,160279,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,229732,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Local-gov,144723,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,148431,Assoc-acdm,12,Married-civ-spouse,Sales,Wife,Other,Female,3,0,2,United-States,>50K\n0,Private,160398,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,129460,9th,5,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,252752,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,58222,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,?,424884,10th,6,Separated,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,114459,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,?,46400,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,223934,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,84119,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,159123,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,195532,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,191299,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,198316,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,162301,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,143152,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,1,United-States,<=50K\n0,Private,92609,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,247819,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,229223,Some-college,10,Never-married,Protective-serv,Own-child,White,Female,0,0,2,United-States,>50K\n2,Self-emp-inc,142719,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,86111,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n0,State-gov,35633,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,164749,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,607848,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,173630,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,311184,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,?,<=50K\n3,Private,49737,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,183616,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,England,<=50K\n4,Private,129426,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,454915,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,55568,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,29874,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,393715,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,143953,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,90363,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,53727,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,130021,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,173630,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,410351,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,399386,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,157932,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,133061,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,46400,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,107895,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,63021,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,186144,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,27959,HS-grad,9,Never-married,Other-service,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,179569,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,101299,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,113129,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,4,United-States,<=50K\n1,Private,316470,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Mexico,<=50K\n4,Self-emp-not-inc,89884,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,32121,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,315303,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,254500,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,419895,5th-6th,3,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,2,Mexico,<=50K\n2,Private,159549,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,160786,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n0,Self-emp-not-inc,258474,10th,6,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,370119,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,50837,7th-8th,4,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,137506,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,548256,12th,8,Married-civ-spouse,Transport-moving,Husband,Black,Male,3,0,2,United-States,>50K\n2,Local-gov,175642,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,4,0,2,United-States,>50K\n0,Private,183594,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,341353,Bachelors,13,Never-married,Other-service,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-inc,247981,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,2,0,3,United-States,<=50K\n1,Private,193565,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,39606,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,127149,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n1,?,233371,HS-grad,9,Married-civ-spouse,?,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,182752,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,269060,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,179949,HS-grad,9,Divorced,Transport-moving,Unmarried,Black,Female,0,0,0,United-States,<=50K\n0,Federal-gov,32950,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,<=50K\n1,Private,160445,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,223999,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,81487,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,1,2,United-States,<=50K\n0,Private,314539,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,337721,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,100793,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Federal-gov,255407,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,92775,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,33308,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,State-gov,493363,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,?,159589,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,107218,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Private,123586,Some-college,10,Never-married,Adm-clerical,Unmarried,Other,Male,0,0,2,United-States,<=50K\n3,Private,158352,5th-6th,3,Married-civ-spouse,Other-service,Other-relative,White,Female,0,0,1,Italy,<=50K\n2,Private,76317,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,?,176753,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,122346,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,463194,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,162228,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,115005,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,183285,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,169605,10th,6,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,450695,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,124692,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,63918,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,102569,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,289309,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,101825,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,206833,HS-grad,9,Separated,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,77873,9th,5,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,145333,Doctorate,16,Divorced,Prof-specialty,Other-relative,White,Male,4,0,3,United-States,>50K\n4,?,194548,Some-college,10,Married-spouse-absent,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,206351,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,198200,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,140001,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,1,El-Salvador,<=50K\n0,?,287988,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,143604,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,146161,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n2,Private,196529,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,1,0,2,?,<=50K\n4,Self-emp-not-inc,192413,Prof-school,15,Divorced,Prof-specialty,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,139889,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,4,United-States,<=50K\n1,Private,104917,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,161478,Bachelors,13,Divorced,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,3,United-States,<=50K\n1,Private,35644,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,116751,Assoc-voc,11,Divorced,Protective-serv,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,238867,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,2,2,United-States,<=50K\n1,Private,265706,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,State-gov,179668,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,57951,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,176711,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,368675,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,216149,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,>50K\n1,Private,173851,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,90705,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,1,2,United-States,<=50K\n3,State-gov,216342,Bachelors,13,Widowed,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,140752,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,116508,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,224361,9th,5,Divorced,?,Unmarried,White,Female,0,0,0,Cuba,<=50K\n2,Private,180303,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n4,?,196736,1st-4th,2,Never-married,?,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n3,Local-gov,110327,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,185607,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Local-gov,244856,11th,7,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,198068,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,97136,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,164658,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,235693,11th,7,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,197038,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Local-gov,97419,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,208872,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,2,4,United-States,<=50K\n1,Private,205528,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,146042,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,222641,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,376936,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,138077,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,155913,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,36006,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,214678,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,369538,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,166565,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,257043,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,181130,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n4,?,254834,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,4,0,0,United-States,>50K\n2,Self-emp-not-inc,38876,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,187073,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,156996,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,4,3,?,>50K\n4,Private,313749,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,331651,Prof-school,15,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,Japan,>50K\n0,Private,243368,Preschool,1,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,32921,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,117167,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,401930,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,2,2,United-States,>50K\n1,Private,114691,Bachelors,13,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,99385,Bachelors,13,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,210308,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,2,1,United-States,<=50K\n2,Private,252327,9th,5,Separated,Craft-repair,Own-child,White,Male,0,0,1,Mexico,<=50K\n2,Private,90582,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,190194,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,264188,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,243776,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,67065,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,204209,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,226668,HS-grad,9,Never-married,Other-service,Not-in-family,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n1,Self-emp-inc,174215,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,2,0,2,France,>50K\n1,Private,315143,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Cuba,>50K\n2,Private,118681,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n2,Self-emp-not-inc,208109,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,116901,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,405644,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Mexico,<=50K\n1,Federal-gov,293550,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,2,0,2,United-States,<=50K\n2,Local-gov,328581,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,217962,Some-college,10,Never-married,Protective-serv,Other-relative,Black,Male,0,0,2,?,<=50K\n4,Private,158827,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,65475,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,159709,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,140474,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,144778,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Italy,>50K\n2,Self-emp-not-inc,83242,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,143385,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,167544,Assoc-acdm,12,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,122175,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,378747,10th,6,Separated,Transport-moving,Unmarried,Black,Male,0,0,2,United-States,>50K\n0,Private,230475,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,120781,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,South,>50K\n4,Private,206232,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,298400,Bachelors,13,Divorced,Sales,Not-in-family,Black,Male,2,0,3,United-States,>50K\n3,Federal-gov,163671,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,<=50K\n2,Self-emp-not-inc,140583,Masters,14,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,137253,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n1,Private,246974,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,182470,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n4,Self-emp-inc,107617,HS-grad,9,Separated,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,116358,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,?,>50K\n1,Private,250819,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,196508,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,367533,10th,6,Married-civ-spouse,Craft-repair,Own-child,Other,Male,0,0,2,United-States,>50K\n4,Private,188709,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,271160,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,173674,HS-grad,9,Divorced,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n4,?,257790,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,322391,11th,7,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,209691,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,104232,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,86786,10th,6,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,88233,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,240888,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,169719,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,>50K\n0,Private,129240,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,160968,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,236861,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,109282,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,215047,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,115932,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Ireland,>50K\n1,Private,55360,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,224658,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,376302,Assoc-voc,11,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,Nicaragua,>50K\n1,Private,183597,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,1,0,3,Germany,<=50K\n2,Private,115289,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,258883,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,69132,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,207301,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,179671,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,140456,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,327397,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,200235,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,108435,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,1,United-States,<=50K\n3,Private,195978,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,329144,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,250674,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,176897,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,132716,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Germany,>50K\n4,Private,174201,9th,5,Widowed,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,167617,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,254949,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,319582,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,248990,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Guatemala,<=50K\n3,Private,144396,11th,7,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,200469,Some-college,10,Never-married,Protective-serv,Unmarried,Black,Female,2,0,2,United-States,<=50K\n0,Federal-gov,55636,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,185624,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,125442,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,160943,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,243841,HS-grad,9,Divorced,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n0,Private,34616,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,235847,Prof-school,15,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,174789,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,280111,11th,7,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,236055,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,237865,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,194612,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,173851,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,372483,Some-college,10,Never-married,Other-service,Other-relative,Black,Male,0,0,1,United-States,<=50K\n4,Federal-gov,422149,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,174201,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,272618,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,74660,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,201481,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,175232,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,336440,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,46645,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,State-gov,31141,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,281425,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,31510,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,310255,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Federal-gov,82393,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,190514,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,165513,Some-college,10,Divorced,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,178931,HS-grad,9,Married-civ-spouse,?,Husband,Amer-Indian-Eskimo,Male,2,0,2,United-States,<=50K\n1,Private,226696,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,195813,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,Other,Male,2,0,2,Puerto-Rico,>50K\n2,Private,165815,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,123983,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,Japan,>50K\n2,Private,235371,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,147258,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n4,?,222289,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,3,0,3,United-States,>50K\n4,Self-emp-inc,171564,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,1,England,>50K\n1,Private,255949,Bachelors,13,Never-married,Sales,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Private,186272,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,282872,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,111676,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,199501,Some-college,10,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,151443,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,Black,Female,0,0,1,United-States,<=50K\n1,Private,145935,HS-grad,9,Never-married,Exec-managerial,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Federal-gov,230387,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,127592,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,210828,Some-college,10,Never-married,Handlers-cleaners,Own-child,Other,Male,0,0,1,United-States,<=50K\n2,Private,297186,HS-grad,9,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,116554,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,144593,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,?,<=50K\n1,State-gov,147719,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,0,India,<=50K\n4,Self-emp-not-inc,89011,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Canada,<=50K\n1,Private,38158,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,178686,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,172826,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,155752,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,100099,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,231688,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,0,United-States,<=50K\n1,?,147215,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,50122,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,86837,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Private,113364,Bachelors,13,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,289390,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,77884,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Private,390157,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,89587,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,234328,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,365430,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,410439,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,129525,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,166527,Some-college,10,Never-married,Exec-managerial,Own-child,Other,Female,0,0,2,United-States,<=50K\n2,?,109912,Assoc-acdm,12,Never-married,?,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,210906,HS-grad,9,Married-civ-spouse,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,405284,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,138269,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,25429,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,231672,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,258550,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,268147,9th,5,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,54411,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,?,<=50K\n3,Private,37289,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,157951,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-inc,225165,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,238049,9th,5,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,1,El-Salvador,<=50K\n1,Private,197252,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Self-emp-inc,216636,12th,8,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,183575,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,19752,11th,7,Never-married,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Private,103925,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,4,United-States,<=50K\n4,Private,31577,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,61298,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,190541,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,366089,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n1,?,389857,HS-grad,9,Married-civ-spouse,?,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,?,192644,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,216129,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,1,3,United-States,<=50K\n1,Private,51944,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,2,0,2,United-States,>50K\n1,Self-emp-not-inc,67482,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,4,United-States,<=50K\n1,?,108775,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,Dominican-Republic,<=50K\n0,State-gov,279243,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,278391,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,Nicaragua,<=50K\n4,Private,349898,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,219441,10th,6,Never-married,Sales,Unmarried,Other,Female,0,0,1,Dominican-Republic,<=50K\n0,Private,173255,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,1,0,1,United-States,<=50K\n3,Federal-gov,29623,12th,8,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,217460,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,163604,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,>50K\n1,Private,163110,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,238685,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,?,251854,Bachelors,13,Married-civ-spouse,?,Wife,Black,Female,0,0,1,?,>50K\n1,Private,213308,Assoc-voc,11,Separated,Adm-clerical,Own-child,Black,Female,0,0,3,United-States,<=50K\n0,Private,193773,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n4,Private,114011,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,52144,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,347934,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,293399,11th,7,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,118630,Assoc-voc,11,Widowed,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,127306,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,4,0,2,United-States,>50K\n2,Private,366180,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Local-gov,188950,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,189382,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,24515,9th,5,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,283116,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,1,0,3,United-States,<=50K\n2,Self-emp-not-inc,182217,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,552354,12th,8,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,163021,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,61885,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Self-emp-not-inc,182898,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,183092,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,30289,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,77572,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n3,State-gov,118330,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,469056,HS-grad,9,Divorced,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Private,145574,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,302041,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,32552,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,185413,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,26543,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Federal-gov,163870,Some-college,10,Never-married,Armed-Forces,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,240063,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,208748,5th-6th,3,Divorced,Machine-op-inspct,Unmarried,Other,Female,0,0,2,Dominican-Republic,<=50K\n1,Local-gov,84119,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,84130,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,261062,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,336010,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,389270,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,138293,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,240389,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,190297,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,?,170070,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,149457,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,81534,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,4,Japan,>50K\n0,Private,378322,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,3,3,United-States,<=50K\n1,Federal-gov,196912,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,116143,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n1,Self-emp-not-inc,80933,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,190660,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,120155,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,167159,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Private,58343,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,1,0,2,United-States,>50K\n2,Federal-gov,161240,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,126402,HS-grad,9,Never-married,Farming-fishing,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n0,Private,148709,HS-grad,9,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,318280,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n1,Local-gov,80058,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,4,United-States,<=50K\n2,Private,274689,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,157367,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,1,?,<=50K\n1,Private,217460,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,33727,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,166961,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,146117,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,160216,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,?,<=50K\n4,Self-emp-not-inc,124449,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,0,United-States,>50K\n0,Private,50163,9th,5,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,235271,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,121124,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Self-emp-not-inc,144218,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,94334,7th-8th,4,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,169982,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Self-emp-not-inc,35295,HS-grad,9,Never-married,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,133969,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,1,0,4,Japan,<=50K\n2,Private,35429,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,205580,5th-6th,3,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,177794,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,167474,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,35211,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,117244,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,194850,Some-college,10,Married-civ-spouse,Other-service,Husband,Other,Male,0,0,2,Mexico,<=50K\n0,Private,144911,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,197240,12th,8,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,101338,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,148522,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,97261,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,166606,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,229414,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,209213,Bachelors,13,Never-married,Prof-specialty,Other-relative,Black,Male,0,0,0,United-States,<=50K\n1,Private,291968,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n4,Federal-gov,127858,Some-college,10,Widowed,Tech-support,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,302406,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,29054,Assoc-voc,11,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,336007,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,349230,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,2,2,United-States,>50K\n2,Local-gov,101481,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,46704,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,233639,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,31725,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,54850,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,1,3,United-States,<=50K\n1,Private,293512,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,375655,Bachelors,13,Never-married,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,105817,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Local-gov,203408,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,162302,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,163455,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,100135,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,41517,11th,7,Married-spouse-absent,?,Unmarried,Black,Female,0,0,0,United-States,<=50K\n0,Private,102182,12th,8,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,414683,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,194352,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,194096,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,153602,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,215495,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Mexico,<=50K\n1,Private,164607,Bachelors,13,Separated,Tech-support,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,34878,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,126569,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n4,?,315728,HS-grad,9,Widowed,?,Unmarried,White,Female,1,0,4,United-States,<=50K\n1,Private,22422,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,178222,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,56841,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,300275,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,197288,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,157786,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,110684,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,140729,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,90127,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,37997,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,61308,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,171199,Bachelors,13,Divorced,Machine-op-inspct,Unmarried,Other,Female,0,0,2,Puerto-Rico,<=50K\n3,Private,128432,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,195023,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,122473,9th,5,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,1,2,United-States,<=50K\n2,Private,171888,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,183784,10th,6,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,219262,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,71379,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,234519,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,96824,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,242597,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,127388,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,204536,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,143804,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,80680,Some-college,10,Married-civ-spouse,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,301227,5th-6th,3,Separated,Priv-house-serv,Unmarried,Other,Female,0,0,1,Mexico,<=50K\n1,Self-emp-not-inc,201930,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,176616,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,353219,9th,5,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,126076,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,156493,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,435503,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,561489,Masters,14,Divorced,Exec-managerial,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n0,Federal-gov,100345,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,36275,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,110794,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,143766,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,76313,HS-grad,9,Married-civ-spouse,Armed-Forces,Other-relative,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n1,Private,121308,11th,7,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,216672,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,89942,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,State-gov,103406,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,State-gov,158291,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,455361,9th,5,Never-married,Other-service,Unmarried,White,Male,0,0,1,Mexico,<=50K\n2,Private,225263,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,1,3,United-States,<=50K\n3,Private,225307,11th,7,Divorced,Craft-repair,Own-child,White,Female,0,0,3,United-States,>50K\n2,Private,286115,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,187830,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,142506,Bachelors,13,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Local-gov,148576,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,185325,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,27939,Some-college,10,Married-civ-spouse,Sales,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n0,Private,383603,10th,6,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,140790,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,226629,HS-grad,9,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n3,Private,228516,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,Columbia,<=50K\n3,Self-emp-not-inc,119762,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,299197,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,149297,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n1,Local-gov,202558,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,175232,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,157473,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,409842,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,105787,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,144056,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,45363,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,4,2,United-States,>50K\n0,Private,205838,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,115326,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,186890,10th,6,Married-civ-spouse,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Local-gov,304386,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,24529,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Private,183557,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,342730,Assoc-acdm,12,Separated,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,182191,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,2,0,1,Canada,<=50K\n4,Self-emp-not-inc,67841,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,351381,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,293691,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,1,2,Japan,<=50K\n2,Self-emp-inc,220821,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,190027,10th,6,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,343944,11th,7,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,110457,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,72333,HS-grad,9,Divorced,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,193494,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,334999,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,274363,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,113806,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,1,United-States,>50K\n0,Private,52536,Assoc-acdm,12,Divorced,Tech-support,Own-child,White,Female,0,1,1,United-States,<=50K\n2,Private,187720,Assoc-voc,11,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,104996,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,214555,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,52963,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,190511,7th-8th,4,Divorced,Handlers-cleaners,Not-in-family,White,Male,1,0,1,United-States,<=50K\n0,Private,75821,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,123291,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,>50K\n3,Local-gov,226497,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,282979,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,166549,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,187746,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,157145,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,227551,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,115306,Masters,14,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,169249,HS-grad,9,Separated,Other-service,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,221966,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,224566,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,28119,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,323810,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,210498,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,174995,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,1,Hungary,<=50K\n2,Private,161141,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,210534,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Self-emp-not-inc,112650,7th-8th,4,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,318891,Assoc-acdm,12,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,375655,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,228465,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,?,102130,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,183213,Assoc-voc,11,Widowed,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Local-gov,177305,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,2,United-States,<=50K\n2,Private,34037,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,116613,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,175540,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,150768,Bachelors,13,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,1,3,United-States,>50K\n2,Private,176634,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,209993,1st-4th,2,Widowed,Other-service,Other-relative,White,Female,0,0,0,Mexico,<=50K\n0,Local-gov,206002,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,201259,11th,7,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,202286,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,96062,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,<=50K\n2,Local-gov,578377,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,509500,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,2,0,2,United-States,>50K\n3,Local-gov,324021,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,107737,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,129865,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,103586,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,187513,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,172891,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,207449,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,209103,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,408813,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,209292,HS-grad,9,Never-married,Sales,Other-relative,Black,Female,0,0,1,Dominican-Republic,<=50K\n3,Private,144361,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,209538,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,244402,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,889965,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,1,0,1,United-States,<=50K\n2,Self-emp-not-inc,298444,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,163237,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,311795,12th,8,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n2,Private,155972,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,291783,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,153535,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,249771,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,4,United-States,<=50K\n2,Private,462180,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,308540,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,34701,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,106252,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,138944,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,140713,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,Jamaica,>50K\n3,Local-gov,216931,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,162312,Some-college,10,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,0,Philippines,<=50K\n4,Self-emp-inc,253062,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Federal-gov,359249,Some-college,10,Separated,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,231413,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,197054,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,130931,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,30565,HS-grad,9,Married-AF-spouse,Other-service,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,105138,HS-grad,9,Divorced,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Local-gov,178383,Some-college,10,Separated,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,241998,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n4,Self-emp-not-inc,196403,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,232421,HS-grad,9,Married-spouse-absent,Transport-moving,Not-in-family,Other,Male,0,0,1,Canada,<=50K\n1,Private,130369,Assoc-voc,11,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,336329,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,337867,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Local-gov,104614,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,223548,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n2,State-gov,506329,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,2,?,>50K\n3,Private,64479,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,284095,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,221336,Some-college,10,Divorced,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n2,Private,428499,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,3,United-States,>50K\n3,Private,208302,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,412156,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,2,Mexico,<=50K\n1,Self-emp-not-inc,182177,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n3,Local-gov,129972,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,186420,Masters,14,Separated,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,203488,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,128796,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,55395,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,314770,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,135044,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,319248,10th,6,Never-married,Other-service,Unmarried,White,Female,0,0,1,Mexico,<=50K\n1,Local-gov,236415,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,?,151584,Some-college,10,Never-married,?,Not-in-family,White,Male,3,0,3,United-States,>50K\n0,?,133983,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,81220,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,Canada,<=50K\n3,Private,151087,HS-grad,9,Separated,Prof-specialty,Other-relative,Other,Female,0,0,2,Puerto-Rico,<=50K\n1,Private,322171,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,190628,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Columbia,<=50K\n2,Local-gov,106982,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,227856,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,?,213477,7th-8th,4,Divorced,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,266083,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,257068,Some-college,10,Married-spouse-absent,Transport-moving,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,37591,Bachelors,13,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,150533,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Other-relative,White,Male,0,0,3,United-States,>50K\n1,Private,211184,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,136610,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,244054,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,240698,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,172906,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,238959,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,?,163085,HS-grad,9,Separated,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,State-gov,172022,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,218062,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,201799,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,150717,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,94391,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,153064,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,156771,10th,6,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,216639,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,82161,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,159159,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,310014,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n3,State-gov,133014,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,36214,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,399022,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,179758,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,48947,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,201865,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,319122,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,155151,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,24106,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Philippines,>50K\n1,Private,257863,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,?,28967,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,379393,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,152752,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,154874,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,2,0,1,United-States,<=50K\n1,Private,154210,11th,7,Married-spouse-absent,Sales,Own-child,Asian-Pac-Islander,Male,0,0,1,India,<=50K\n2,Private,335716,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,94744,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,133861,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,240137,1st-4th,2,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n2,Private,80004,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,109702,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,39610,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,90046,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,193855,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,206889,Bachelors,13,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,86298,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,149650,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,4,3,United-States,>50K\n0,Private,323139,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,237993,Prof-school,15,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,?,<=50K\n0,Private,36058,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,163393,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,93535,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,112952,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,182541,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n1,Local-gov,73392,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n2,?,507086,HS-grad,9,Divorced,?,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n4,Private,195868,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,276851,HS-grad,9,Divorced,Protective-serv,Own-child,White,Female,0,2,2,United-States,<=50K\n0,?,39901,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,33124,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,419732,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,171095,Assoc-acdm,12,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,199278,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,235205,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,168232,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,145964,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,>50K\n1,Local-gov,72338,HS-grad,9,Divorced,Farming-fishing,Own-child,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n3,Private,153870,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,323798,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,198830,11th,7,Never-married,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,Private,267040,10th,6,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,167187,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,230684,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,659558,12th,8,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,181661,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,186144,7th-8th,4,Never-married,Machine-op-inspct,Not-in-family,Other,Female,0,0,2,Mexico,<=50K\n0,Federal-gov,178517,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,131417,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,1,United-States,<=50K\n2,Private,57233,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,379798,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,122175,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,107302,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,127651,10th,6,Never-married,Transport-moving,Other-relative,White,Male,0,2,2,United-States,<=50K\n1,Self-emp-not-inc,102884,Bachelors,13,Married-civ-spouse,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,241753,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,173611,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,232666,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,352207,Assoc-voc,11,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,241998,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n3,Private,279129,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,177057,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,155659,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,251603,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,19914,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n4,Private,115023,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,101709,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n0,Private,313702,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,250068,12th,8,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,227359,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,196827,Assoc-acdm,12,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,118550,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,285004,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Male,0,0,1,South,<=50K\n2,Private,280169,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,144608,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,?,>50K\n3,Private,76860,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,0,0,0,Philippines,<=50K\n2,Self-emp-not-inc,167280,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,334783,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n4,?,141580,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,226443,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,366065,Some-college,10,Never-married,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,225724,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n4,State-gov,132204,1st-4th,2,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,258276,Bachelors,13,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,1,0,2,?,<=50K\n2,Private,197711,10th,6,Divorced,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Portugal,<=50K\n0,Private,30619,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,335015,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,61272,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,106544,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,144169,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,40295,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Private,266091,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,1,0,3,Cuba,<=50K\n4,Private,143030,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,1,?,<=50K\n2,State-gov,192397,Some-college,10,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,114351,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,?,63466,HS-grad,9,Married-spouse-absent,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,132304,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Scotland,<=50K\n4,Private,128162,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,125938,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,2,El-Salvador,<=50K\n2,Private,170174,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,203451,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,109917,7th-8th,4,Separated,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,114937,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,231196,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,238474,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,314149,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,31728,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,360131,5th-6th,3,Married-civ-spouse,Craft-repair,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,141308,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,83411,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,?,119835,7th-8th,4,Divorced,?,Not-in-family,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n1,Local-gov,296537,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,193047,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,State-gov,39630,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,213975,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,259803,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Federal-gov,55465,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,181307,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,211301,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,200450,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,176731,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,140985,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,4,0,1,United-States,>50K\n4,Private,125784,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,152176,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,111423,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,130126,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Federal-gov,30111,Some-college,10,Widowed,Prof-specialty,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,?,214989,Some-college,10,Never-married,?,Own-child,White,Female,0,2,1,United-States,<=50K\n0,Private,272800,12th,8,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,195881,Some-college,10,Divorced,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,170924,Some-college,10,Never-married,Prof-specialty,Other-relative,White,Male,0,0,0,United-States,<=50K\n0,Private,131473,Some-college,10,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,0,Vietnam,<=50K\n2,Private,149466,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n0,Private,190418,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,Canada,<=50K\n4,Local-gov,167889,Doctorate,16,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,Iran,<=50K\n2,Private,177989,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,186035,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,195805,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,54800,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,100605,HS-grad,9,Never-married,Sales,Own-child,Other,Male,0,0,2,Puerto-Rico,<=50K\n0,Private,253190,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,203301,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,175696,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,278304,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,93193,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,158688,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,327612,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,210844,Some-college,10,Married-spouse-absent,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,147340,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,130436,1st-4th,2,Divorced,Craft-repair,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,206600,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,El-Salvador,<=50K\n4,Private,284680,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,127738,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,213412,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,287927,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,249332,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Ecuador,<=50K\n2,Local-gov,290403,Assoc-voc,11,Divorced,Protective-serv,Own-child,White,Female,0,0,2,Cuba,<=50K\n3,Private,54772,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n2,Self-emp-inc,56651,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n2,Federal-gov,178470,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,62865,HS-grad,9,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,107196,HS-grad,9,Widowed,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,86860,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,130684,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,164682,Assoc-voc,11,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,198316,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,261816,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,Outlying-US(Guam-USVI-etc),<=50K\n4,Private,280309,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,97176,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,95835,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,323016,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,4,0,2,United-States,>50K\n0,?,280670,10th,6,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,136306,11th,7,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,65171,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,25864,HS-grad,9,Separated,Prof-specialty,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,149531,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,33887,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,172822,11th,7,Divorced,Transport-moving,Not-in-family,White,Male,0,4,4,United-States,>50K\n4,Private,106748,7th-8th,4,Married-civ-spouse,Other-service,Wife,White,Female,0,0,4,United-States,<=50K\n2,Private,131826,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,216691,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,4,0,2,United-States,>50K\n2,Private,133328,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,164737,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,99064,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,59460,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,208725,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,138513,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,121055,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,149784,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,114495,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,133278,12th,8,Separated,?,Unmarried,Black,Female,0,0,3,United-States,<=50K\n1,Private,212276,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,440129,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,98012,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,2,United-States,>50K\n1,Private,145284,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,177147,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,141537,10th,6,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,48093,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n0,Local-gov,314819,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,123572,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,170800,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,332401,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,193038,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,351161,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n2,Federal-gov,106910,HS-grad,9,Never-married,Transport-moving,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,?,163726,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,609935,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,?,<=50K\n3,State-gov,314627,Masters,14,Divorced,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,115945,Doctorate,16,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-inc,272248,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,167878,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,176972,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,31095,Assoc-voc,11,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,130834,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,207415,Assoc-acdm,12,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,264457,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,340588,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Mexico,<=50K\n4,?,42435,10th,6,Widowed,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,107411,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,290640,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Germany,>50K\n1,Private,106179,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,Canada,<=50K\n0,Private,247679,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,171598,Bachelors,13,Married-spouse-absent,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,234460,7th-8th,4,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,Dominican-Republic,<=50K\n4,Private,196674,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,182540,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,172694,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,29571,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,130438,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,213421,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,189956,Bachelors,13,Married-civ-spouse,Protective-serv,Wife,Black,Female,4,0,2,United-States,>50K\n4,Private,133144,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-inc,24050,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,276967,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,184857,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,145160,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,192251,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,190650,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Male,0,0,2,Taiwan,<=50K\n3,Local-gov,255927,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,99086,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,216811,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Private,110563,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,120471,HS-grad,9,Never-married,Transport-moving,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n0,Private,183066,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,State-gov,298786,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,297884,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,253612,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,1,0,1,United-States,<=50K\n0,Self-emp-not-inc,207438,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,148522,11th,7,Never-married,Other-service,Own-child,White,Male,0,2,0,United-States,<=50K\n4,Private,139660,Some-college,10,Divorced,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,165474,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,120277,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,67929,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,229418,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,41356,Assoc-acdm,12,Never-married,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,185127,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,109133,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,148315,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,145692,Some-college,10,Never-married,Protective-serv,Not-in-family,Black,Male,0,3,2,United-States,<=50K\n3,Private,210424,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,1,0,2,United-States,<=50K\n4,Private,198526,HS-grad,9,Widowed,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,521400,5th-6th,3,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,100882,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,124818,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,190836,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n4,Private,71367,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,303032,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,?,98989,9th,5,Divorced,?,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,State-gov,390781,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n1,Private,54782,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,?,202683,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,213081,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,Jamaica,<=50K\n1,Self-emp-inc,89718,Some-college,10,Separated,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,225106,10th,6,Never-married,Other-service,Own-child,White,Female,0,2,0,United-States,<=50K\n1,Private,253262,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,78181,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,158206,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,337720,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,State-gov,391257,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,134756,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,183404,Some-college,10,Separated,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,192793,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,203943,12th,8,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,?,<=50K\n3,Private,89400,Some-college,10,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,237868,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,139187,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,126701,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,172175,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,164210,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,608184,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n0,?,198797,11th,7,Never-married,?,Own-child,White,Male,0,0,0,Peru,<=50K\n3,Local-gov,425804,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,?,117618,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,119164,Bachelors,13,Never-married,Other-service,Unmarried,White,Male,0,0,2,?,<=50K\n2,Self-emp-inc,92036,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,77146,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,191803,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,54932,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,251694,Bachelors,13,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,268145,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,104842,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,3,Haiti,<=50K\n4,Local-gov,227332,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,212512,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,3,United-States,<=50K\n3,Private,133436,7th-8th,4,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,309055,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,59202,HS-grad,9,Never-married,Priv-house-serv,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,32709,Some-college,10,Divorced,Sales,Not-in-family,White,Female,1,0,2,United-States,<=50K\n4,Self-emp-inc,73559,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,117963,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,169121,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,308889,11th,7,Never-married,Adm-clerical,Other-relative,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n2,Local-gov,144940,Masters,14,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,102041,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,335998,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,29557,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,210313,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,Guatemala,<=50K\n1,Private,190784,Some-college,10,Divorced,Machine-op-inspct,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,107597,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,4,0,1,United-States,>50K\n4,Private,97168,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,155930,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n4,Self-emp-not-inc,181033,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,344572,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,State-gov,170165,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,178835,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,118230,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,149640,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,30271,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,154165,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,341797,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,145246,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,280093,HS-grad,9,Separated,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,373469,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,199172,Bachelors,13,Never-married,Protective-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,177199,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,258932,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,139960,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,258037,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,?,<=50K\n1,Private,116677,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,59496,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,34218,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,200246,9th,5,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,316246,Bachelors,13,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,239161,Some-college,10,Separated,Protective-serv,Own-child,Other,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,173411,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,259226,11th,7,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,195516,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,200598,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,<=50K\n2,State-gov,160369,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,?,415913,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,147253,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,199674,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,State-gov,198493,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,377121,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,400635,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,?,<=50K\n2,Private,513660,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,175069,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,82552,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,1,0,3,United-States,<=50K\n1,?,78388,10th,6,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,171705,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,315640,Bachelors,13,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,3,Iran,<=50K\n2,Private,266860,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,192829,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,237317,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Male,2,0,2,United-States,>50K\n2,State-gov,110426,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,2,?,>50K\n2,Private,327606,12th,8,Separated,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,34845,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,58582,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,155659,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,210029,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,381618,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,298449,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n1,State-gov,226789,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,210736,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,>50K\n3,State-gov,111163,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,76860,HS-grad,9,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n0,Private,92112,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,136787,HS-grad,9,Divorced,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,29810,Some-college,10,Never-married,Transport-moving,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,360884,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,2,United-States,>50K\n1,Private,266022,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,142874,Assoc-acdm,12,Married-spouse-absent,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,72338,HS-grad,9,Never-married,Sales,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n3,?,177305,Assoc-voc,11,Married-civ-spouse,?,Wife,Black,Female,0,0,1,United-States,>50K\n2,Private,165106,Bachelors,13,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,1,3,?,>50K\n2,Private,424478,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n4,Private,189721,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Italy,>50K\n2,Private,34180,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,183279,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,35309,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n0,Private,259109,Assoc-acdm,12,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,Puerto-Rico,<=50K\n4,Self-emp-not-inc,148690,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Male,4,0,0,United-States,>50K\n4,Private,125019,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,3,0,3,United-States,>50K\n2,Self-emp-inc,172538,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,410615,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,322547,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,300760,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,232782,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,174645,11th,7,Divorced,Craft-repair,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,164693,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,206861,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,195602,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n1,Self-emp-not-inc,422960,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,116360,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n3,Private,278530,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,188291,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Self-emp-not-inc,163948,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,64544,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,101468,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n0,Private,107882,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,182691,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,203776,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,201268,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,29762,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,186346,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,196690,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,99604,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,1,United-States,>50K\n2,Private,194772,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,95446,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,257126,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,194733,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,98361,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,124924,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,111971,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,130714,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,208358,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,147627,9th,5,Never-married,Priv-house-serv,Not-in-family,Black,Female,1,0,1,United-States,<=50K\n1,Private,149507,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,164870,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,236861,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,2,2,United-States,<=50K\n2,Private,220314,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,Mexico,<=50K\n2,Local-gov,329980,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,2,2,Canada,<=50K\n4,Local-gov,318537,12th,8,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,183284,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,334368,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n3,Private,109227,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,4,United-States,<=50K\n1,Private,118551,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,163057,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-inc,253101,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,20098,Assoc-voc,11,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,196227,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,175374,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,234037,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,341762,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,174714,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,222835,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,251786,1st-4th,2,Separated,Other-service,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n0,Private,164219,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,251120,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,236993,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,105896,Some-college,10,Divorced,Protective-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,211527,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,317809,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,?,>50K\n0,Private,185287,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,31014,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,151985,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,89389,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,406051,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,>50K\n3,Self-emp-not-inc,171986,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,167848,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,213019,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,211424,Bachelors,13,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,168981,Assoc-voc,11,Never-married,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,122348,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,139753,Bachelors,13,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,176178,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,145220,9th,5,Never-married,Priv-house-serv,Unmarried,White,Female,0,0,2,Columbia,<=50K\n2,Local-gov,188612,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,445728,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,318002,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,235722,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,367984,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,212705,Masters,14,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,411273,10th,6,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,103986,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,203761,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,266792,Some-college,10,Married-civ-spouse,?,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,116800,Assoc-acdm,12,Never-married,Protective-serv,Own-child,White,Male,0,0,3,United-States,<=50K\n0,State-gov,99199,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,162327,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,100479,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,32587,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Federal-gov,321990,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,Cuba,>50K\n3,Private,108914,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,61343,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n3,Local-gov,81154,Assoc-voc,11,Never-married,Protective-serv,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,162945,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,4,2,United-States,<=50K\n2,Private,225504,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,191712,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n2,Private,176063,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,198587,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,34965,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-inc,467108,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,?,263899,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,0,England,<=50K\n1,Private,204984,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,217568,HS-grad,9,Widowed,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,48343,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,193130,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,253354,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,258026,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,?,211360,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,191367,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,148995,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,123901,HS-grad,9,Never-married,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,117496,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,1,United-States,>50K\n2,Self-emp-inc,32356,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,206506,10th,6,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,0,El-Salvador,<=50K\n2,Private,218729,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,52498,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n0,Private,136767,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,219540,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,114059,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,247337,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,310969,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,171546,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,217455,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,410489,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,146391,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,165484,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,184271,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,231347,Some-college,10,Separated,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,95469,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,244025,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Amer-Indian-Eskimo,Male,0,0,3,Puerto-Rico,<=50K\n3,Federal-gov,46537,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,205730,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,383745,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,1,United-States,>50K\n1,Private,328199,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,84553,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,221072,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,?,<=50K\n0,Private,123983,Assoc-voc,11,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n4,?,191024,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,167868,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,225879,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Other,Female,0,0,1,Mexico,>50K\n4,Self-emp-inc,247232,10th,6,Married-civ-spouse,Exec-managerial,Wife,White,Female,1,0,1,United-States,<=50K\n0,Private,143791,10th,6,Never-married,Other-service,Own-child,Black,Female,0,0,0,United-States,<=50K\n4,Private,177271,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Federal-gov,129786,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,31339,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,236267,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,130620,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,1,Philippines,>50K\n1,Private,208180,Assoc-voc,11,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,292058,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,142712,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,119665,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,116825,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,201177,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,118337,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,173800,Masters,14,Never-married,?,Unmarried,Asian-Pac-Islander,Male,0,0,0,Taiwan,<=50K\n3,Private,289257,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,190912,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,0,2,2,Vietnam,<=50K\n2,Private,140581,Some-college,10,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,174102,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,Puerto-Rico,<=50K\n0,Private,316509,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,20101,HS-grad,9,Widowed,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n1,Private,187279,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,259496,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,181466,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,178202,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,188976,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,203027,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,142022,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Private,119033,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,216181,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,178341,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n0,Local-gov,244408,Bachelors,13,Never-married,Tech-support,Unmarried,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n1,Private,198953,Some-college,10,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,173110,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,66326,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,181091,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,135500,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,133929,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,?,<=50K\n1,Private,86483,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,167787,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,208577,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,4,3,United-States,<=50K\n2,Private,216697,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Other,Male,0,0,1,United-States,<=50K\n1,Local-gov,118457,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,298635,Some-college,10,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,1,Philippines,<=50K\n0,Local-gov,212780,12th,8,Never-married,Handlers-cleaners,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,159187,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,237995,Assoc-voc,11,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Private,160724,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n2,Self-emp-inc,183800,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n3,?,185936,9th,5,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,161198,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,?,113635,11th,7,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,214542,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,?,172991,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,203761,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,161141,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,180117,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,317396,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,237868,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Private,323069,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,181091,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,309122,10th,6,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,105936,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,4,0,2,United-States,>50K\n2,Private,40024,11th,7,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,192443,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,4,0,2,United-States,>50K\n0,State-gov,184216,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,?,256211,1st-4th,2,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Private,205422,10th,6,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Private,22211,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Local-gov,196308,HS-grad,9,Divorced,Exec-managerial,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,389713,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,82566,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,199058,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,160440,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,76034,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,188503,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,2,0,1,United-States,<=50K\n4,Self-emp-not-inc,92845,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,29083,HS-grad,9,Never-married,Sales,Own-child,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n0,Private,234474,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n3,Local-gov,107308,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,111891,Some-college,10,Separated,Sales,Other-relative,Black,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,145419,1st-4th,2,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,4,Italy,>50K\n2,Local-gov,193425,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,2,United-States,>50K\n1,Federal-gov,188278,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,303485,Some-college,10,Never-married,Transport-moving,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,67187,HS-grad,9,Never-married,Exec-managerial,Own-child,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,State-gov,114508,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,204172,Bachelors,13,Never-married,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,162973,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,192695,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,Canada,<=50K\n2,Local-gov,89172,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,163320,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,128230,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,246440,11th,7,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,50567,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,117476,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,315834,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n1,Local-gov,214881,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,195516,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,218653,Bachelors,13,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,87205,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,0,United-States,>50K\n2,Private,164647,Some-college,10,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,129151,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,319697,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,193374,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,167864,Assoc-voc,11,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,197932,Some-college,10,Separated,Priv-house-serv,Not-in-family,White,Female,0,0,1,Guatemala,<=50K\n3,Private,102904,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,216907,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,331395,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,171424,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,35406,7th-8th,4,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,238964,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,213002,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,Private,27882,Some-college,10,Never-married,Machine-op-inspct,Other-relative,White,Female,0,4,2,Holand-Netherlands,<=50K\n0,Private,340543,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,70240,Some-college,10,Married-civ-spouse,Sales,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n0,Self-emp-not-inc,87169,HS-grad,9,Never-married,Farming-fishing,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,253759,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,194431,HS-grad,9,Never-married,Tech-support,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,137843,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n2,?,170649,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,182460,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,26929,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,399022,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,?,50171,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,218490,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,164423,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,124436,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,60981,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,70868,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,150601,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,?,<=50K\n3,Private,228500,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,76767,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,218178,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,615367,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,150324,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,51264,11th,7,Divorced,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,197642,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,229895,10th,6,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,167415,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,166934,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,305597,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,301591,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,229646,HS-grad,9,Married-spouse-absent,Adm-clerical,Not-in-family,Black,Female,0,0,2,Puerto-Rico,<=50K\n1,Self-emp-not-inc,51461,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,206600,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,1,Nicaragua,<=50K\n0,Private,176836,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,204447,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n3,Private,33304,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,174051,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,38918,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,2,4,United-States,<=50K\n1,Private,170017,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,98466,10th,6,Never-married,Farming-fishing,Unmarried,White,Male,0,0,1,United-States,<=50K\n0,Private,188864,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,137815,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,43475,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,557236,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,32779,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,1,0,United-States,<=50K\n1,Private,161765,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,<=50K\n1,Private,207668,Bachelors,13,Never-married,Exec-managerial,Other-relative,White,Male,0,4,3,United-States,>50K\n1,Private,171215,Masters,14,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,?,52590,HS-grad,9,Never-married,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,183751,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,149507,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,98092,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,123714,11th,7,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,190385,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,334273,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,343440,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,208302,HS-grad,9,Divorced,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,Local-gov,280164,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,174714,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,184655,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,140459,11th,7,Never-married,Craft-repair,Other-relative,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,108815,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,152652,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,269499,HS-grad,9,Widowed,Handlers-cleaners,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,33373,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,243674,HS-grad,9,Separated,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,225432,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,215839,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,?,<=50K\n1,Local-gov,195520,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,70092,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,189888,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,64307,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,94235,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,62333,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,260997,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,146268,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,147258,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,207948,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,180607,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,104996,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,562336,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,334366,Some-college,10,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,0,United-States,<=50K\n3,State-gov,142757,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,>50K\n1,Local-gov,220656,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Male,0,0,2,England,<=50K\n2,Private,96483,HS-grad,9,Divorced,Other-service,Own-child,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n2,Private,51744,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,114967,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,393965,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,41838,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n2,Local-gov,143046,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,209174,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,183248,HS-grad,9,Divorced,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,102942,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,4,2,United-States,>50K\n1,Self-emp-not-inc,427474,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,338632,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,89559,Some-college,10,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,Germany,<=50K\n2,Self-emp-not-inc,32533,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,255969,12th,8,Never-married,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,112376,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,?,346053,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,147653,10th,6,Married-civ-spouse,Craft-repair,Wife,White,Female,0,3,1,?,>50K\n4,Self-emp-not-inc,44915,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Local-gov,111450,10th,6,Never-married,Craft-repair,Unmarried,Black,Male,0,0,4,Haiti,<=50K\n4,Private,171429,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,190964,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,109005,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,404453,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,280169,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n2,Self-emp-not-inc,163204,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,192256,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,181755,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,183105,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,2,Cuba,<=50K\n2,Private,335168,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,86643,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,180262,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,127865,Masters,14,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,146042,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,1,0,3,United-States,>50K\n3,Self-emp-not-inc,102110,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,152237,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,?,>50K\n0,Private,202745,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,199303,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,266467,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,345259,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n0,Private,204935,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,244830,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Male,2,0,2,United-States,>50K\n0,Private,190457,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,180138,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,166585,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,29962,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,191129,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,378707,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,116358,HS-grad,9,Never-married,Craft-repair,Other-relative,Amer-Indian-Eskimo,Male,4,0,3,United-States,>50K\n3,Private,240629,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,233320,7th-8th,4,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,302708,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,3,0,3,Japan,>50K\n4,Private,29375,HS-grad,9,Separated,Sales,Not-in-family,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n2,Local-gov,137314,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,140886,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,226968,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,151793,7th-8th,4,Widowed,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,56460,HS-grad,9,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,3,0,United-States,<=50K\n0,Private,72887,HS-grad,9,Never-married,Craft-repair,Own-child,Asian-Pac-Islander,Male,0,0,0,Vietnam,<=50K\n1,Private,261646,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,178615,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,295589,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,377836,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,56510,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,337696,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,183765,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,107846,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,22641,HS-grad,9,Never-married,Protective-serv,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,204590,Assoc-voc,11,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,>50K\n1,Private,114801,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,190591,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,State-gov,220066,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n0,?,228480,HS-grad,9,Married-civ-spouse,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,128378,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,157595,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,152171,11th,7,Never-married,Protective-serv,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,339755,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,?,>50K\n3,Private,240841,7th-8th,4,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,94345,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,289116,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,176647,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,79627,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,210781,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,161981,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,493443,11th,7,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,86459,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n4,Private,312242,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,185408,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,101077,Assoc-acdm,12,Married-spouse-absent,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n3,Private,147200,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,State-gov,166327,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n3,Private,178644,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,126675,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,?,<=50K\n1,Private,158420,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,?,83046,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,46609,10th,6,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,?,<=50K\n0,?,170320,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,37232,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,141877,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,81654,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,177705,Bachelors,13,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,124792,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,3,0,2,United-States,>50K\n1,Self-emp-not-inc,129497,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,114413,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,189511,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,111625,Bachelors,13,Widowed,Exec-managerial,Unmarried,White,Male,3,0,2,United-States,>50K\n2,Private,246431,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,147654,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,443546,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,281751,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,263128,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,292692,12th,8,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n3,Self-emp-inc,96798,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,430554,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,317078,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,108557,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,4,0,2,United-States,>50K\n1,Private,207400,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,187089,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,Local-gov,398986,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,3,United-States,>50K\n2,Private,238980,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,407495,HS-grad,9,Married-spouse-absent,?,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,183800,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,?,226989,HS-grad,9,Divorced,?,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Private,287190,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,Black,Male,0,0,1,United-States,<=50K\n1,Private,111363,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,260938,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,183594,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n4,?,49194,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,117618,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,172496,Masters,14,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,389713,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,174413,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,189843,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,198546,Masters,14,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,82497,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,193090,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,172220,7th-8th,4,Widowed,Priv-house-serv,Not-in-family,White,Female,1,0,1,United-States,<=50K\n3,Private,208451,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,?,234277,HS-grad,9,Married-spouse-absent,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,163729,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,1,0,2,United-States,<=50K\n2,Private,434097,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,192053,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,1,2,United-States,<=50K\n0,State-gov,178628,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,96827,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Canada,<=50K\n1,Private,154667,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,160246,Some-college,10,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,166036,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,186813,HS-grad,9,Never-married,Protective-serv,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,162312,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n4,Private,183893,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,?,270228,Assoc-acdm,12,Married-civ-spouse,?,Husband,Black,Male,3,0,2,United-States,>50K\n2,Private,111829,Masters,14,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,175669,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,State-gov,104097,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,117618,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-inc,202450,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,109570,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,101096,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,>50K\n2,Private,236391,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,136975,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,167523,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,<=50K\n1,Private,240979,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,248612,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n2,Private,151023,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,1,0,2,United-States,<=50K\n1,Private,236436,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,3,0,2,United-States,>50K\n1,?,153167,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,61735,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,243165,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,388885,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,84979,Doctorate,16,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,2,United-States,>50K\n1,Self-emp-not-inc,87209,Masters,14,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,168539,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,179013,HS-grad,9,Separated,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,196643,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,68898,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,156464,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,35884,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,182714,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,344425,9th,5,Married-civ-spouse,Priv-house-serv,Wife,Black,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,177277,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,70767,HS-grad,9,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,520078,Assoc-acdm,12,Divorced,Sales,Unmarried,Black,Male,0,0,3,United-States,<=50K\n3,Local-gov,321770,HS-grad,9,Married-spouse-absent,Transport-moving,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,158416,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,312667,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,208656,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,1,0,0,United-States,<=50K\n1,Private,31481,Bachelors,13,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,259531,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,186239,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,162954,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,249315,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,308237,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,103064,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,185847,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,168521,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,198170,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,353628,10th,6,Separated,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,?,273285,11th,7,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,272069,Assoc-voc,11,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,22328,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,309212,HS-grad,9,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-inc,148888,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Local-gov,324637,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,55139,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,?,212206,Masters,14,Married-civ-spouse,?,Wife,White,Female,0,2,3,United-States,>50K\n1,Private,119004,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,3,2,United-States,<=50K\n2,Private,252079,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,315868,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,392325,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n2,State-gov,174283,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,126832,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,126071,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,265706,Masters,14,Never-married,Sales,Unmarried,White,Male,0,0,3,United-States,>50K\n2,Private,282964,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,328518,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,283499,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,286675,Some-college,10,Never-married,Exec-managerial,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,136472,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Private,132879,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,314798,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,143943,Bachelors,13,Widowed,Tech-support,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,134367,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,366796,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,195573,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,33616,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,164190,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,380281,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Male,0,0,1,Columbia,<=50K\n4,Self-emp-inc,190763,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,209535,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,156003,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,55699,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,183151,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,198790,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,272359,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,4,United-States,>50K\n1,Private,236481,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,0,India,<=50K\n3,Private,143266,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Private,192386,HS-grad,9,Separated,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,99543,12th,8,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,169435,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,34572,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,119272,10th,6,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,211601,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,154785,Some-college,10,Married-spouse-absent,Adm-clerical,Own-child,Other,Female,0,0,1,United-States,<=50K\n0,Private,213041,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Cuba,<=50K\n4,Private,229939,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,175331,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,226443,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,46561,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,161311,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,98215,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,118363,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,4,0,United-States,<=50K\n4,Local-gov,181242,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,356238,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Female,0,0,4,United-States,>50K\n4,Self-emp-not-inc,39380,Some-college,10,Married-spouse-absent,Farming-fishing,Not-in-family,White,Female,4,0,0,United-States,>50K\n1,Private,315287,HS-grad,9,Never-married,Adm-clerical,Other-relative,Black,Male,0,0,2,?,<=50K\n1,Private,269723,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,1,0,3,United-States,<=50K\n4,Private,34098,10th,6,Widowed,Farming-fishing,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,50880,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Germany,<=50K\n2,Federal-gov,356934,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,276309,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,Private,175925,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,<=50K\n1,Self-emp-not-inc,164607,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,224462,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,92863,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,179565,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,31137,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,199495,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,175262,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n2,Private,220585,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,231793,Doctorate,16,Married-spouse-absent,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,191342,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,186420,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,328242,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Hong,>50K\n4,Private,279340,11th,7,Separated,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,174478,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,151771,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,145636,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,120326,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,246439,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,144133,Bachelors,13,Married-civ-spouse,Exec-managerial,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,145522,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,312055,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,235847,Some-college,10,Never-married,Exec-managerial,Other-relative,White,Female,0,0,3,United-States,<=50K\n2,Private,187748,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,396482,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,261688,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,<=50K\n0,Private,39477,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,143058,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,216867,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Mexico,<=50K\n2,Private,230592,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,40338,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,115457,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,374983,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,285419,12th,8,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,385901,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,State-gov,187581,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,299036,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,68729,Some-college,10,Never-married,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,333990,Assoc-voc,11,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,117767,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,184378,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,232591,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,143851,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,89622,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,202498,12th,8,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Dominican-Republic,<=50K\n4,Private,268861,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,4,?,<=50K\n3,Private,343242,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,460408,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,205246,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,230329,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,197871,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,201375,Assoc-acdm,12,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,194290,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,191814,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,95168,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,137876,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,386136,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,?,108390,Some-college,10,Married-civ-spouse,?,Husband,White,Male,1,0,0,United-States,<=50K\n2,Private,152529,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,214891,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Other,Male,0,0,2,Dominican-Republic,<=50K\n0,Private,133654,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,147548,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,73051,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,60166,1st-4th,2,Never-married,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n0,Self-emp-inc,454934,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,?,338355,Assoc-voc,11,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,185621,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,101500,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,36397,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,276540,12th,8,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,293968,Some-college,10,Married-spouse-absent,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n2,?,35523,Assoc-acdm,12,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,186993,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,232132,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,176917,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,105936,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,105821,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,?,34506,Some-college,10,Separated,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,178074,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,116961,7th-8th,4,Widowed,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,191930,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,130807,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,94100,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,144822,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,102191,Masters,14,Widowed,Exec-managerial,Unmarried,White,Female,0,0,4,United-States,<=50K\n0,Private,90934,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,?,296892,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n3,Private,173243,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,258768,Some-college,10,Never-married,Transport-moving,Not-in-family,Black,Male,1,0,4,United-States,<=50K\n1,Private,189759,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,69249,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,133061,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,175202,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,27051,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,60414,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,317360,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,258298,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,174040,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,177566,Some-college,10,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,3,Germany,<=50K\n3,Private,162238,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,87556,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,144322,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,190015,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,183173,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Self-emp-not-inc,151322,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,47392,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,107125,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,265295,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,189219,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,147989,Some-college,10,Married-spouse-absent,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,185732,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,153516,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,191910,Some-college,10,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,216145,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,202872,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,39630,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,114292,9th,5,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,206721,Bachelors,13,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,358585,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,>50K\n1,Private,377283,Bachelors,13,Separated,Sales,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,?,76043,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n4,Without-pay,172949,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,0,United-States,<=50K\n3,Private,110171,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n2,Local-gov,223861,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,163455,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,183892,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,47022,HS-grad,9,Widowed,Handlers-cleaners,Other-relative,White,Female,0,0,3,United-States,<=50K\n3,Federal-gov,145401,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,387074,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,105363,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n4,Federal-gov,195467,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,170217,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,156807,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,255193,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,273640,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,191177,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,184787,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,239409,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,404547,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,State-gov,23740,HS-grad,9,Never-married,Transport-moving,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n0,Private,382153,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,164488,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n0,?,228424,10th,6,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,168539,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,189530,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,89419,Assoc-voc,11,Divorced,Other-service,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,Columbia,<=50K\n1,Private,224512,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,?,314645,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,85787,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,279881,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,194287,7th-8th,4,Never-married,Other-service,Own-child,White,Male,0,2,1,United-States,<=50K\n0,Private,141040,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,222294,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,?,410980,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n3,Private,38795,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,182979,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,223277,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,67065,Assoc-voc,11,Never-married,Priv-house-serv,Not-in-family,White,Male,1,0,1,United-States,<=50K\n3,Federal-gov,160647,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,45796,12th,8,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,110597,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,166961,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,318975,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,Cuba,<=50K\n3,Private,305657,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,120857,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,158712,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,304530,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,327533,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,<=50K\n4,Local-gov,233954,Masters,14,Widowed,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,>50K\n2,Federal-gov,26880,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,70754,7th-8th,4,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,184665,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,245372,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,252668,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,86551,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,241998,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,2,0,2,United-States,>50K\n2,Private,106900,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,204235,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,127772,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,117217,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,215389,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,198050,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,173476,Prof-school,15,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,217349,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,4,0,2,United-States,>50K\n2,Private,377018,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,99894,10th,6,Married-civ-spouse,Sales,Wife,Asian-Pac-Islander,Female,0,0,1,Japan,>50K\n0,Private,170786,9th,5,Never-married,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,250585,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,198769,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,306513,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,178623,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,4,0,3,United-States,>50K\n0,Private,109307,Assoc-voc,11,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,106982,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n3,Self-emp-not-inc,396878,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,1,United-States,<=50K\n0,Private,344278,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,203653,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,2,0,2,United-States,>50K\n2,Local-gov,227890,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,2,United-States,<=50K\n1,Private,107812,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,185143,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,143068,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,114758,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,266337,Assoc-voc,11,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,321787,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,21306,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,Germany,<=50K\n0,Private,271935,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,148952,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,196626,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,108082,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,199439,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,?,304076,11th,7,Never-married,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,81436,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,352971,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,375134,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,206521,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,330466,Bachelors,13,Never-married,Tech-support,Other-relative,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,208302,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,135285,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,171615,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,149698,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,71351,1st-4th,2,Never-married,Other-service,Other-relative,White,Male,0,0,1,El-Salvador,<=50K\n4,Private,84737,7th-8th,4,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n3,Local-gov,375134,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,207103,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,199314,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,Poland,<=50K\n4,Self-emp-not-inc,289741,Masters,14,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,3,United-States,<=50K\n2,Private,240837,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,283499,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,97778,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,21698,10th,6,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,232618,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,175820,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Local-gov,63996,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,182985,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,380127,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,111483,10th,6,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n0,?,31008,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,96346,HS-grad,9,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,317528,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n2,State-gov,223020,Some-college,10,Divorced,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,?,173998,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,1,2,United-States,<=50K\n2,Private,115076,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,133969,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,0,3,United-States,>50K\n2,Private,173858,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n1,Private,193241,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,50644,Assoc-acdm,12,Divorced,Sales,Unmarried,White,Female,1,0,2,United-States,<=50K\n1,Private,178841,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,177017,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,1,2,United-States,<=50K\n0,Private,253267,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,3,2,United-States,>50K\n2,Private,202027,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,3,United-States,>50K\n3,Self-emp-not-inc,321865,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,321709,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,166371,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,2,?,<=50K\n0,Private,210574,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,?,92968,Masters,14,Married-civ-spouse,?,Wife,White,Female,4,0,2,United-States,>50K\n2,Private,474617,HS-grad,9,Divorced,Sales,Unmarried,Black,Male,2,0,2,United-States,<=50K\n0,Private,264390,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,3,2,United-States,<=50K\n1,Self-emp-inc,144949,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,State-gov,90803,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,126701,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,178417,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,197176,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,182227,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,<=50K\n0,Private,117606,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,349502,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,81487,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Puerto-Rico,>50K\n1,State-gov,169583,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,485117,Bachelors,13,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,35603,Some-college,10,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,175390,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,184986,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,174395,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,187711,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,189878,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,224073,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,159726,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,?,65545,Masters,14,Divorced,?,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,456618,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,1,0,2,United-States,<=50K\n1,Private,202397,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n0,Private,206681,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,222020,10th,6,Divorced,Other-service,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,137304,Bachelors,13,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,141645,Some-college,10,Separated,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,218085,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,3,United-States,<=50K\n0,Private,52596,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,197997,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,191444,11th,7,Never-married,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,40767,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,172577,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,241998,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,State-gov,215908,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,212120,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,109133,Masters,14,Separated,Exec-managerial,Not-in-family,White,Male,4,0,3,Iran,>50K\n0,Private,224424,12th,8,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,214985,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,147098,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,149833,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,253770,Some-college,10,Married-civ-spouse,Transport-moving,Wife,White,Female,2,0,2,United-States,>50K\n4,Private,252466,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,State-gov,132717,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,138944,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,280570,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,>50K\n4,Self-emp-not-inc,144380,Some-college,10,Married-spouse-absent,Prof-specialty,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n4,Local-gov,660461,HS-grad,9,Widowed,Adm-clerical,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n3,Private,177211,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,197424,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n1,Self-emp-inc,31717,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,296849,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,193720,HS-grad,9,Married-spouse-absent,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,106698,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,214469,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,185798,Assoc-voc,11,Separated,Craft-repair,Other-relative,White,Male,0,0,3,United-States,>50K\n1,Private,333108,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,35210,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,140845,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,1,2,United-States,<=50K\n0,?,335376,Bachelors,13,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,170455,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,298215,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,?,93834,HS-grad,9,Separated,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,404416,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,?,206916,Bachelors,13,Married-spouse-absent,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,143175,Some-college,10,Widowed,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,409189,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,285750,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Private,235556,Some-college,10,Married-spouse-absent,Sales,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Local-gov,170382,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,England,>50K\n3,Private,195437,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,191130,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,231160,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,47310,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,214635,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,Haiti,<=50K\n3,Federal-gov,65160,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,423222,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,4,United-States,>50K\n1,Private,263307,Bachelors,13,Never-married,Sales,Unmarried,Black,Male,0,0,2,?,<=50K\n4,Self-emp-inc,272896,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,232854,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,442035,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,127875,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,283724,9th,5,Never-married,Craft-repair,Other-relative,Black,Male,0,0,3,United-States,<=50K\n3,Private,403911,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,3,2,United-States,>50K\n0,?,228649,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,177027,Bachelors,13,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,3,0,3,Japan,>50K\n3,Private,249935,11th,7,Divorced,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,533147,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,137862,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,Private,249543,Some-college,10,Never-married,Protective-serv,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,230979,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,2,0,1,United-States,>50K\n2,Private,137126,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,147339,10th,6,Never-married,Prof-specialty,Own-child,Other,Female,0,0,0,United-States,<=50K\n2,Private,256647,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,111696,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,3,2,United-States,<=50K\n0,?,150084,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,285457,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,303867,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,113597,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,151626,HS-grad,9,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,26145,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,176189,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,497253,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,41090,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,<=50K\n2,Self-emp-not-inc,282461,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,225541,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,203488,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,296613,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,99373,10th,6,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,109705,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,144947,Bachelors,13,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,617898,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,38310,7th-8th,4,Divorced,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,248993,HS-grad,9,Married-spouse-absent,Farming-fishing,Other-relative,Black,Male,0,0,2,United-States,<=50K\n4,?,149131,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,Italy,>50K\n1,Private,69311,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,143766,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,213477,Masters,14,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,275691,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,59367,Bachelors,13,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,35551,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,236784,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,0,Cuba,<=50K\n2,Local-gov,193755,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,315291,Bachelors,13,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n0,Private,290504,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,256240,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,199591,Prof-school,15,Married-civ-spouse,?,Wife,White,Female,0,0,1,?,<=50K\n1,Private,178709,Masters,14,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,449354,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,187937,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Never-worked,157131,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,188772,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,157617,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Poland,<=50K\n4,Private,96099,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,122322,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Private,409189,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,175925,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,236878,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,216647,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,300681,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,Jamaica,>50K\n3,Private,327769,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,194723,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,31251,7th-8th,4,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,212506,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,23037,12th,8,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,29054,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,92733,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,184678,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,32528,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,England,>50K\n4,Private,125954,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,73715,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,209212,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,?,<=50K\n2,Private,287037,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,64667,HS-grad,9,Divorced,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,3,Vietnam,<=50K\n1,Self-emp-inc,366662,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,113337,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,387468,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Scotland,>50K\n3,Private,384248,Some-college,10,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Private,332703,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Other,Female,0,1,2,United-States,<=50K\n2,Private,198873,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,1,United-States,>50K\n1,Private,317809,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,<=50K\n2,Local-gov,160910,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n2,Self-emp-inc,182629,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,267652,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,410186,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,365411,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,205337,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,100999,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,197462,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,199143,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Female,2,0,2,United-States,>50K\n3,Private,191978,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,50178,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,72442,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,248512,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,178140,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,354024,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,143589,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,219902,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Local-gov,419722,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Private,154374,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,132601,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,29430,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,30731,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,210825,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,251091,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,219034,11th,7,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,35723,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,358886,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,248708,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,?,77937,12th,8,Divorced,?,Not-in-family,White,Female,0,0,2,Canada,<=50K\n1,Private,30063,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,253799,12th,8,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,England,<=50K\n4,?,41553,Some-college,10,Widowed,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,59146,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,343609,Some-college,10,Separated,Other-service,Unmarried,Black,Female,0,0,3,United-States,<=50K\n1,Private,216010,HS-grad,9,Separated,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,164526,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,150958,5th-6th,3,Never-married,Farming-fishing,Unmarried,White,Male,0,0,3,Guatemala,<=50K\n1,Private,244495,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,199336,Assoc-voc,11,Never-married,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Private,151369,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,118701,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,219611,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,184568,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,246891,Prof-school,15,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,243436,9th,5,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,68318,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n4,Private,56331,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,190591,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,Black,Female,0,0,2,Jamaica,<=50K\n3,Private,140359,7th-8th,4,Divorced,Machine-op-inspct,Unmarried,White,Female,0,4,2,United-States,<=50K\n2,Self-emp-inc,23510,Masters,14,Divorced,Exec-managerial,Unmarried,Asian-Pac-Islander,Male,0,4,3,India,>50K\n1,Private,122540,10th,6,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,212562,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,112497,HS-grad,9,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,1,Ireland,<=50K\n4,Private,147729,5th-6th,3,Widowed,Other-service,Unmarried,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,296066,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,148138,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,4,0,2,Japan,>50K\n4,Private,50351,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,2,0,0,United-States,<=50K\n2,Private,306496,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,210029,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,3,2,United-States,<=50K\n3,Private,163894,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,113936,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,316820,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,53367,9th,5,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,95256,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,127728,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,66686,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,207627,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,1,United-States,<=50K\n4,Self-emp-inc,199768,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,1,United-States,>50K\n3,?,186805,HS-grad,9,Married-civ-spouse,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,154297,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,103064,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,93235,12th,8,Never-married,Other-service,Own-child,White,Female,0,2,1,United-States,<=50K\n4,Private,440607,Preschool,1,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,0,1,Mexico,<=50K\n2,Private,212894,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,167990,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,378460,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,151089,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,153583,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,114639,Some-college,10,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Private,344480,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,188300,11th,7,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,105938,HS-grad,9,Divorced,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,217826,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,1,Jamaica,<=50K\n0,Private,379525,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,177331,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,2,0,2,United-States,>50K\n2,Private,127918,Some-college,10,Never-married,Transport-moving,Unmarried,White,Female,0,0,0,Puerto-Rico,<=50K\n3,Federal-gov,27067,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,250038,9th,5,Never-married,Farming-fishing,Other-relative,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-not-inc,36270,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,3,4,United-States,>50K\n4,Private,308608,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,213574,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,0,United-States,<=50K\n1,Local-gov,235109,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,State-gov,374905,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,118876,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,223716,Some-college,10,Divorced,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,166027,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,275943,7th-8th,4,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,<=50K\n2,Private,198654,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,4,4,India,>50K\n0,Private,109080,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,104333,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,195876,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,390879,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,197748,11th,7,Divorced,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,442045,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,44216,HS-grad,9,Never-married,Protective-serv,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Federal-gov,114537,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,253370,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,274830,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,321763,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,213226,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,>50K\n0,Private,167787,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,352712,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,?,316027,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,?,<=50K\n1,Private,213412,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,202483,HS-grad,9,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,146244,Doctorate,16,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,450544,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,81243,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Male,0,2,2,United-States,<=50K\n2,Private,195258,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,57929,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,953588,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,99064,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n3,Local-gov,194788,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,2,0,3,United-States,>50K\n2,Self-emp-inc,155293,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,204082,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,216283,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,355856,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Cambodia,>50K\n0,Private,297380,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,425622,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,145628,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,115549,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,245482,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n2,Self-emp-inc,142444,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,134026,11th,7,Never-married,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,177366,HS-grad,9,Separated,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Private,38245,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,215944,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,115784,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,170165,HS-grad,9,Divorced,Machine-op-inspct,Other-relative,White,Female,0,0,3,United-States,<=50K\n3,Private,355320,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,116163,HS-grad,9,Separated,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,405644,1st-4th,2,Married-spouse-absent,Farming-fishing,Other-relative,White,Male,0,0,4,Mexico,<=50K\n2,Local-gov,223433,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,41624,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,Mexico,<=50K\n2,Private,151089,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n3,State-gov,285747,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n0,State-gov,108542,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,212318,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,173090,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,26781,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Private,31782,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,189241,11th,7,Married-civ-spouse,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,164229,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,240467,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,263614,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,74500,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,263502,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,47707,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,231638,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,?,389479,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,111128,HS-grad,9,Separated,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,152307,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,?,280134,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,609789,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,?,<=50K\n2,Private,184466,11th,7,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,216411,Assoc-voc,11,Separated,Prof-specialty,Not-in-family,White,Female,0,0,2,Dominican-Republic,<=50K\n3,Self-emp-not-inc,324173,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,300681,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,2,0,1,United-States,>50K\n2,Local-gov,598995,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Federal-gov,140711,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,262241,HS-grad,9,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,308136,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,148590,10th,6,Widowed,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,195635,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,<=50K\n1,Private,228406,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,136398,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,Thailand,>50K\n0,?,305466,Some-college,10,Never-married,?,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-inc,175070,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,34007,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,121195,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Other,Male,0,0,3,United-States,<=50K\n0,Federal-gov,216853,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,81280,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,Yugoslavia,>50K\n0,Private,212936,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,213055,Some-college,10,Never-married,?,Unmarried,Other,Female,0,0,2,United-States,<=50K\n1,Local-gov,220430,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,4,United-States,>50K\n1,Federal-gov,73514,Bachelors,13,Never-married,Exec-managerial,Other-relative,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n0,Private,307371,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,380614,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,Germany,>50K\n2,Private,119992,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,192002,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,Canada,>50K\n0,Private,327518,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,220323,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,421633,Some-college,10,Divorced,Protective-serv,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,154210,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,1,0,3,China,<=50K\n2,Self-emp-not-inc,35034,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n4,?,378239,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n1,State-gov,270218,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,254933,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,61751,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,137876,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,336007,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,2,United-States,>50K\n1,Private,222539,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,233856,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,198616,12th,8,Never-married,Craft-repair,Own-child,White,Male,1,0,0,United-States,<=50K\n1,Private,202027,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,1,United-States,>50K\n0,Private,203182,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,221317,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,186934,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,351402,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,<=50K\n2,Local-gov,179580,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,26803,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,344624,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,3,United-States,>50K\n1,State-gov,59969,HS-grad,9,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,162930,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Italy,<=50K\n3,Self-emp-not-inc,192654,Bachelors,13,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Private,117681,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,179285,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,217161,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,116517,Bachelors,13,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,170336,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Other,Female,0,0,0,United-States,<=50K\n1,Local-gov,256529,HS-grad,9,Separated,Other-service,Own-child,White,Female,0,0,4,United-States,<=50K\n0,Local-gov,227886,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,141706,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,361888,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,185407,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n1,Self-emp-not-inc,176101,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,216730,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,?,155755,HS-grad,9,Divorced,?,Not-in-family,White,Female,2,0,1,United-States,<=50K\n1,Private,609789,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,Mexico,<=50K\n1,Private,136017,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,58880,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,0,United-States,>50K\n2,Private,285787,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,173243,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,160916,Assoc-acdm,12,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,227397,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Self-emp-not-inc,111066,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,189924,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,31740,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,120837,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,<=50K\n1,Private,172304,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,?,166253,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,86492,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,206667,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Self-emp-not-inc,153546,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,189041,HS-grad,9,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,115932,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Local-gov,151626,HS-grad,9,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,37302,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,109001,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,195488,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,216116,Masters,14,Separated,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,118497,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,101233,Assoc-voc,11,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,349703,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,226883,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Germany,<=50K\n0,Private,214635,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,169672,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,71458,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,142621,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Private,125279,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,197303,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,148995,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,2,0,2,United-States,>50K\n1,Private,69251,Some-college,10,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,160123,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,137310,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,?,<=50K\n0,Private,323229,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,138626,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Male,1,0,3,United-States,<=50K\n3,Private,102359,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,151888,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,2,0,3,Ireland,<=50K\n2,Private,404661,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,99146,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n2,Self-emp-not-inc,185325,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,230268,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,38819,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,380614,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,319637,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,149040,12th,8,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,320984,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,?,117201,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,81965,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,182302,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,53434,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,216214,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,24127,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Federal-gov,115066,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,120277,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,134286,Some-college,10,Separated,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,26716,10th,6,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,?,174533,11th,7,Separated,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,175958,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n2,Private,218948,9th,5,Separated,Other-service,Unmarried,Black,Female,0,0,2,?,<=50K\n4,Private,117746,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,206199,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,89922,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,69867,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,109020,Bachelors,13,Never-married,Prof-specialty,Unmarried,Other,Male,0,0,2,United-States,<=50K\n4,?,158847,Assoc-voc,11,Married-spouse-absent,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,130302,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,156728,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,424719,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,217647,Some-college,10,Divorced,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,33087,Assoc-voc,11,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,241895,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,38455,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,81054,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,163215,12th,8,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,156728,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,127930,HS-grad,9,Married-spouse-absent,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,227310,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,96844,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,245199,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,46385,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,186385,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,252714,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,154897,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,320744,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,138852,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,102092,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,32533,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,278151,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,338290,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,34378,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,91959,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,265881,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,276009,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,1,Philippines,<=50K\n1,Private,193898,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,139364,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,306473,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,37232,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,State-gov,56424,12th,8,Never-married,Transport-moving,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,165235,Bachelors,13,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Thailand,<=50K\n1,Private,153326,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,106976,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,109015,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,154100,Masters,14,Never-married,Sales,Not-in-family,White,Female,4,0,2,United-States,>50K\n2,Private,183739,HS-grad,9,Married-civ-spouse,Craft-repair,Own-child,White,Female,0,3,2,United-States,<=50K\n4,Private,367695,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,156015,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,185132,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,188274,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,50512,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,1,0,3,United-States,<=50K\n0,State-gov,147719,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,0,India,<=50K\n1,Private,414525,12th,8,Never-married,Farming-fishing,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Private,289148,HS-grad,9,Married-spouse-absent,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,176069,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,199713,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,297884,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,United-States,<=50K\n1,Private,204829,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,155433,5th-6th,3,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,?,<=50K\n0,Local-gov,32950,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,233511,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,210781,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,190762,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,83315,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,343872,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,1,Haiti,<=50K\n3,Private,185385,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n4,?,302142,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,1,United-States,<=50K\n2,Private,80324,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,4,United-States,>50K\n1,Private,357933,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,211293,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,199265,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,202872,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,195075,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,317378,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,4,0,2,United-States,>50K\n2,Private,187802,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,97212,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,47902,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,76767,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,172297,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,274475,9th,5,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,105244,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,165695,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,253801,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,305597,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,352448,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,242768,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,201080,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,159032,7th-8th,4,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,149568,9th,5,Never-married,Farming-fishing,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,229553,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,0,?,<=50K\n0,State-gov,155775,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,120074,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,257588,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,177907,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,309311,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,138975,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,187778,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,35865,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,234373,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,1,2,United-States,<=50K\n0,?,151141,10th,6,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,144688,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,3,United-States,<=50K\n2,Private,248094,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,248094,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,213821,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,State-gov,55849,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,121712,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,164552,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,2,2,United-States,<=50K\n3,Private,223127,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,190514,7th-8th,4,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,203797,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,378460,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,105908,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,232356,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,<=50K\n0,Private,210526,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,193530,11th,7,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,4,United-States,<=50K\n0,?,22966,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,43535,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,72486,HS-grad,9,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,0,1,China,<=50K\n0,?,229997,Some-college,10,Married-spouse-absent,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,183013,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,113364,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,197380,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,298635,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Hong,>50K\n1,Private,213385,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,?,108464,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,31007,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,35917,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,99385,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,Canada,<=50K\n3,Private,48358,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,241885,HS-grad,9,Never-married,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,24344,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,149686,9th,5,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,State-gov,154432,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,331875,12th,8,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Dominican-Republic,<=50K\n1,Private,259585,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,104748,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,144949,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,199512,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,302438,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,?,129155,11th,7,Widowed,?,Other-relative,Black,Female,0,0,2,United-States,<=50K\n3,Federal-gov,115784,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,96509,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n4,Private,226733,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,244945,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,243768,5th-6th,3,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,?,351161,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,186934,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,89813,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,129432,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Self-emp-not-inc,184702,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,275291,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,258298,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,139743,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,102476,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,4,0,4,United-States,>50K\n0,Private,103840,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,274579,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,156842,Some-college,10,Separated,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,101020,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,68729,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n3,Private,141326,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,168723,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,347166,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Local-gov,213722,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,196797,HS-grad,9,Never-married,Transport-moving,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,207246,Some-college,10,Separated,Exec-managerial,Unmarried,White,Female,0,0,4,United-States,<=50K\n1,Federal-gov,199934,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,272185,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,?,190650,Bachelors,13,Never-married,?,Unmarried,Asian-Pac-Islander,Male,0,0,1,Philippines,<=50K\n4,?,147097,Bachelors,13,Widowed,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,266281,11th,7,Never-married,Machine-op-inspct,Unmarried,Black,Female,2,0,2,United-States,<=50K\n4,Private,96779,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,117162,Assoc-voc,11,Married-civ-spouse,?,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,188352,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Private,359131,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,198824,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,State-gov,68393,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,115613,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,45363,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,121590,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Male,2,0,2,United-States,>50K\n4,Local-gov,292379,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,482732,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,Private,198663,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,230329,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,29887,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,194259,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,Germany,<=50K\n3,Private,126368,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Private,108446,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,220696,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,32008,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,191777,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,?,<=50K\n3,Private,185846,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,127016,7th-8th,4,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,102308,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,4,2,United-States,>50K\n0,Private,157894,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,345405,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,<=50K\n4,Self-emp-not-inc,94156,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,145409,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,190968,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Local-gov,212803,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,168660,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,234481,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,131461,9th,5,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,Haiti,<=50K\n2,Private,408773,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,126117,HS-grad,9,Widowed,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,155489,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,296749,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,185832,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,43235,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,213152,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,334267,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,253101,Bachelors,13,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,64631,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Local-gov,193882,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,1,2,United-States,<=50K\n4,Private,71800,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,170092,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,198223,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,359796,Some-college,10,Divorced,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,110556,HS-grad,9,Separated,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,196858,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n3,?,112860,10th,6,Married-civ-spouse,?,Wife,Black,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,224784,Assoc-acdm,12,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Federal-gov,271544,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,3,2,United-States,>50K\n4,?,142171,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,1,0,1,United-States,<=50K\n2,Private,221172,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,256916,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,157332,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,192894,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,>50K\n0,Private,240183,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,204338,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,122166,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Iran,<=50K\n2,Local-gov,397877,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,115066,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,4,2,United-States,>50K\n1,Self-emp-not-inc,170174,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,171015,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,91262,Some-college,10,Married-spouse-absent,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Local-gov,127678,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,263338,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,129508,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,192107,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,93930,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,207537,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,138542,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,116207,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,198244,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,90614,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,0,United-States,<=50K\n0,Private,211160,12th,8,Married-civ-spouse,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,194630,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,<=50K\n0,Private,161478,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Private,144071,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,167005,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,<=50K\n3,Private,342121,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,71676,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,3,0,United-States,<=50K\n2,Private,124692,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,147236,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,145175,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,259323,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,154978,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Guatemala,<=50K\n4,?,163946,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,127768,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,98588,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,192894,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,194848,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,34446,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Local-gov,177265,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,142977,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,241350,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,154882,Prof-school,15,Widowed,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,60562,9th,5,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,142566,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,176162,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,186303,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,Canada,>50K\n2,Private,237671,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n0,?,184416,10th,6,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n4,Private,68624,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,229504,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,340591,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,262208,Some-college,10,Never-married,Other-service,Not-in-family,Black,Female,0,0,1,Jamaica,<=50K\n1,Private,236008,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,214284,Bachelors,13,Widowed,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,0,Japan,<=50K\n1,Private,169496,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,?,205940,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,195179,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,469697,Some-college,10,Married-civ-spouse,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,140242,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,214415,Some-college,10,Separated,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,452283,HS-grad,9,Divorced,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,244172,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,231972,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,412296,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Mexico,>50K\n1,Private,30497,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,189216,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,268292,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,69306,Some-college,10,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,State-gov,111224,Bachelors,13,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,State-gov,309348,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,?,174995,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,Canada,<=50K\n0,Private,210781,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,286750,11th,7,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,321274,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,192936,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,72743,HS-grad,9,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,187861,HS-grad,9,Separated,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,179579,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,663394,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,302422,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,154373,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,37353,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,109609,Some-college,10,Separated,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,184402,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,224640,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,405526,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,36385,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,4,3,United-States,<=50K\n0,Private,147884,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,164231,11th,7,Separated,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,383306,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,417668,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,161007,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,State-gov,99823,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,37379,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,148645,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,180477,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,123147,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Private,111415,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,107327,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,146565,Assoc-acdm,12,Divorced,Other-service,Not-in-family,White,Female,2,0,1,United-States,<=50K\n2,Private,267556,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,284871,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,194690,9th,5,Never-married,Other-service,Own-child,White,Male,0,0,3,Mexico,<=50K\n1,Federal-gov,145983,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,163998,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,128478,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,250647,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Male,0,0,1,Nicaragua,<=50K\n4,Private,226949,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,157901,11th,7,Married-civ-spouse,Other-service,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,33863,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,40444,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,54373,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,52753,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,Self-emp-not-inc,104423,Some-college,10,Married-civ-spouse,Exec-managerial,Other-relative,White,Male,2,0,2,United-States,>50K\n2,Local-gov,305714,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,4,United-States,<=50K\n2,Local-gov,167440,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,291529,10th,6,Widowed,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,243380,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,38619,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,230684,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,132601,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,193285,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,2,0,2,United-States,<=50K\n3,Private,279156,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,339372,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,101265,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,117789,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,312667,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,255503,11th,7,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,221955,9th,5,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n0,Private,139190,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,185556,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n3,Federal-gov,84278,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,114580,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,185405,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,199539,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,346480,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Local-gov,349431,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,219619,HS-grad,9,Never-married,Sales,Other-relative,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,127491,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Self-emp-not-inc,253899,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,155232,Bachelors,13,Divorced,Protective-serv,Not-in-family,Black,Male,0,0,3,United-States,>50K\n2,Private,182437,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,530454,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,101430,11th,7,Divorced,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,358668,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,90668,10th,6,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,126141,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,238355,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,194031,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,117833,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n3,Private,249686,Prof-school,15,Separated,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,219591,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,221757,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,80625,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,185407,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,163110,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,?,24504,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,159187,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,100462,Assoc-voc,11,Never-married,Exec-managerial,Own-child,White,Female,1,0,3,United-States,<=50K\n1,Private,192936,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,145011,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,181196,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,37778,Masters,14,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,60288,Masters,14,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,84231,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,52028,1st-4th,2,Married-civ-spouse,Other-service,Own-child,Asian-Pac-Islander,Female,0,0,0,Vietnam,<=50K\n4,Private,318763,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,Private,168138,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,113530,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,321896,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,145791,Assoc-voc,11,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,131425,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,145214,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,2,0,0,United-States,<=50K\n4,Local-gov,142166,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,494784,HS-grad,9,Never-married,Sales,Other-relative,Black,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,172479,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,184655,11th,7,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,26669,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,191479,Some-college,10,Divorced,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,86625,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,?,<=50K\n4,State-gov,111795,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,242564,7th-8th,4,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,4,2,United-States,<=50K\n1,Private,364657,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Germany,>50K\n2,Self-emp-not-inc,436107,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,272476,Assoc-acdm,12,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,>50K\n2,Federal-gov,47310,Some-college,10,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,>50K\n0,Private,283796,12th,8,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,1,Mexico,<=50K\n0,Private,161092,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,265230,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Federal-gov,61885,Bachelors,13,Never-married,Transport-moving,Not-in-family,Black,Male,0,3,4,United-States,<=50K\n2,Private,150471,Assoc-acdm,12,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,183041,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,176673,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,235891,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,Columbia,<=50K\n2,Private,163287,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,164040,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,324561,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,99127,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,334999,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,543477,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,65876,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,105866,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,1,United-States,<=50K\n1,Private,214858,HS-grad,9,Married-civ-spouse,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,154076,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,280307,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,Cuba,<=50K\n1,Private,97723,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,233499,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,259612,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,236977,HS-grad,9,Separated,Craft-repair,Own-child,White,Male,0,0,2,Mexico,<=50K\n2,Private,347814,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,197495,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,227594,12th,8,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,165441,7th-8th,4,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,337488,Some-college,10,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Private,167552,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,Haiti,>50K\n0,Private,396722,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,146538,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,51973,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,144778,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,169672,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,240137,5th-6th,3,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n3,State-gov,103179,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,172050,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,178976,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,176185,12th,8,Divorced,Craft-repair,Not-in-family,White,Male,0,4,2,United-States,<=50K\n1,Private,158200,Prof-school,15,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n2,Federal-gov,172571,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,226735,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,148015,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n1,Private,199529,Some-college,10,Separated,Tech-support,Not-in-family,Amer-Indian-Eskimo,Male,0,3,2,United-States,<=50K\n4,Local-gov,35001,7th-8th,4,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n0,?,67586,Assoc-voc,11,Married-civ-spouse,?,Wife,Black,Female,0,0,1,United-States,<=50K\n0,Private,88126,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,226296,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,452452,10th,6,Never-married,Priv-house-serv,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,378546,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,186087,HS-grad,9,Divorced,Tech-support,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,27856,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,234859,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,71733,Some-college,10,Separated,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,207473,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,El-Salvador,<=50K\n3,Private,179291,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,Haiti,>50K\n0,?,253190,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,92968,Bachelors,13,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,?,<=50K\n0,Private,209286,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,122889,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,India,>50K\n1,Private,112358,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,176341,Bachelors,13,Never-married,Tech-support,Unmarried,Asian-Pac-Islander,Female,0,0,2,India,<=50K\n4,Private,247276,7th-8th,4,Widowed,Other-service,Not-in-family,Other,Female,0,0,1,United-States,<=50K\n2,Private,276087,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,257557,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,Black,Male,4,0,2,United-States,<=50K\n2,Local-gov,177937,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,?,<=50K\n4,Self-emp-inc,106395,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,167138,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,213887,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,185647,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,143360,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,176862,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,97614,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,224680,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,1,0,United-States,<=50K\n3,Private,196763,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,306183,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,343061,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,2,Cuba,<=50K\n3,?,193047,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,348521,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,4,4,United-States,>50K\n4,Private,195835,7th-8th,4,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,106273,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,222756,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,110610,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,191982,Some-college,10,Divorced,?,Unmarried,White,Female,0,0,0,Poland,<=50K\n3,Private,247286,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,219042,10th,6,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,224566,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,2,2,United-States,<=50K\n4,Private,204751,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,113398,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,?,170428,Bachelors,13,Never-married,?,Not-in-family,Asian-Pac-Islander,Male,0,0,1,Taiwan,<=50K\n4,Private,258579,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,1,United-States,>50K\n2,Private,162424,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,263005,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Germany,<=50K\n3,Self-emp-inc,26502,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Male,2,0,2,United-States,<=50K\n2,Private,369131,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,114859,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,405309,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,323627,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,106698,Assoc-acdm,12,Divorced,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,51506,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,117251,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,106705,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,217296,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n4,Private,34788,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,143368,HS-grad,9,Divorced,Farming-fishing,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,86600,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n4,State-gov,117017,Some-college,10,Separated,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,?,104756,Some-college,10,Widowed,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,55720,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,481096,5th-6th,3,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,281668,10th,6,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,186145,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,96524,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,187397,Some-college,10,Never-married,Protective-serv,Unmarried,Other,Male,1,0,2,United-States,<=50K\n4,Private,181153,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,Local-gov,375170,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Private,360743,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,420054,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Italy,<=50K\n1,Private,137681,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,28419,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,101214,Bachelors,13,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,>50K\n2,Local-gov,213019,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,207540,Doctorate,16,Separated,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,>50K\n3,Private,145333,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,107306,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,195327,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,196126,Bachelors,13,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,?,<=50K\n0,Private,175465,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,197905,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,118119,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,172571,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,25051,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,210714,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,183083,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,99185,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,283921,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,396467,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,>50K\n3,Private,158680,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,202091,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,285127,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,218630,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,99309,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,165505,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,122272,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,147707,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Federal-gov,44257,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,194995,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,345969,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,31842,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,143582,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,1,Vietnam,<=50K\n3,Private,161438,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,317019,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,158451,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,225883,Some-college,10,Widowed,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,176319,HS-grad,9,Married-civ-spouse,Sales,Own-child,White,Female,2,0,2,United-States,>50K\n4,Self-emp-inc,258883,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,26966,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,202812,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n4,Private,35411,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,190885,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,Private,182162,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,352640,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,213945,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,135102,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,102583,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,225612,Bachelors,13,Widowed,Sales,Not-in-family,White,Male,0,0,1,United-States,>50K\n1,Private,241802,HS-grad,9,Married-civ-spouse,Other-service,Wife,Other,Female,0,0,2,United-States,<=50K\n2,Private,347434,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,305259,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,140830,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,291568,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Other,Male,0,0,2,United-States,<=50K\n3,Private,203067,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,155106,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,?,252752,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n4,?,404601,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,1,United-States,<=50K\n3,Local-gov,100226,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,63503,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,95929,9th,5,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,187618,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,92178,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,220362,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n1,Local-gov,209900,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,272376,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,173854,Bachelors,13,Divorced,Prof-specialty,Other-relative,White,Male,0,0,1,United-States,>50K\n2,Private,278924,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,324568,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,124963,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,211299,Assoc-voc,11,Never-married,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,192791,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,182862,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,46868,Masters,14,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,31365,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,148171,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,142647,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,116230,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,108907,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,?,<=50K\n0,Private,495982,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,334026,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,268571,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,213813,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,241667,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,160920,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,107265,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,?,41609,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,129460,10th,6,Widowed,Adm-clerical,Unmarried,White,Female,0,4,1,United-States,<=50K\n2,?,109912,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,167424,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,270079,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,325923,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,194905,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,183486,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n2,Federal-gov,153066,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,56248,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,4,3,United-States,>50K\n4,Private,105252,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,168195,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,167735,11th,7,Never-married,Craft-repair,Own-child,White,Male,2,0,2,United-States,<=50K\n3,Private,146310,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,256504,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,121425,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,146440,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n4,?,155259,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,98829,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,239321,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,134768,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,556902,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,47907,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,114357,HS-grad,9,Never-married,Tech-support,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,189462,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,<=50K\n2,Private,90646,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,232914,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n0,Private,192201,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n0,Private,27776,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,137476,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,100734,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,111746,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,Portugal,<=50K\n1,Private,184833,10th,6,Separated,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,414721,11th,7,Never-married,Other-service,Own-child,Black,Male,0,2,1,United-States,<=50K\n0,Private,151780,Assoc-voc,11,Never-married,Sales,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n2,State-gov,203628,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,137363,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,172307,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,273403,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,State-gov,37931,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,97030,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,54608,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,108542,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,253814,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,421412,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,207140,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,138153,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,46987,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-inc,183173,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,Local-gov,229531,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,320744,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,257405,5th-6th,3,Never-married,Farming-fishing,Other-relative,Black,Male,0,0,2,Mexico,<=50K\n0,State-gov,432052,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,397280,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,38001,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,101618,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,332727,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,115215,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,178449,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,185267,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,410439,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,85572,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,83517,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,194726,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,322674,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,34540,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,211073,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,194901,HS-grad,9,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,117059,11th,7,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,78875,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,51461,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,266119,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,92374,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,1,United-States,>50K\n3,Private,175262,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,208249,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,196385,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n0,?,110622,Bachelors,13,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,0,0,Taiwan,<=50K\n1,Private,146980,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,4,United-States,<=50K\n0,Private,112974,11th,7,Never-married,Prof-specialty,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,175943,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,0,United-States,>50K\n1,Private,163265,9th,5,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,210932,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,145290,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,198992,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n4,?,174887,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,36651,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,190072,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,49087,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,126622,11th,7,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,174189,9th,5,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,118605,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,377622,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,157272,HS-grad,9,Separated,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,78530,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,190391,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,162678,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,103980,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,293726,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,98350,Preschool,1,Married-spouse-absent,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n1,Private,207668,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,Hungary,<=50K\n1,Federal-gov,41013,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,188186,Masters,14,Divorced,Sales,Not-in-family,White,Female,0,1,2,United-States,<=50K\n2,Federal-gov,320071,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,306908,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,167652,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,173580,Some-college,10,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,273612,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,195555,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,186446,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,418405,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,41793,Masters,14,Separated,Prof-specialty,Not-in-family,White,Female,0,0,3,?,<=50K\n1,Private,183965,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,354784,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,198096,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,732102,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,97847,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,196678,Preschool,1,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,320014,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,298215,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,295127,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,368140,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,187411,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,?,<=50K\n0,?,121070,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,212163,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,108198,HS-grad,9,Divorced,Craft-repair,Own-child,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n2,Federal-gov,294431,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,202560,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Self-emp-inc,266070,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,346122,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,308686,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n4,Self-emp-inc,236096,HS-grad,9,Divorced,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,187711,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,238959,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,93557,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,329980,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,125010,Assoc-voc,11,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,90915,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,289731,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,33114,10th,6,Married-civ-spouse,?,Husband,Amer-Indian-Eskimo,Male,1,0,1,United-States,<=50K\n4,Private,206052,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,191385,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,268804,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,191429,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n1,Self-emp-not-inc,199753,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,92486,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,171088,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,112820,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,32855,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,142964,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,89146,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n3,?,147015,Some-college,10,Divorced,?,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,291968,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,29235,Some-college,10,Married-civ-spouse,Protective-serv,Wife,White,Female,0,0,2,France,>50K\n3,Private,238216,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,323726,Some-college,10,Never-married,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,141663,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,?,218471,HS-grad,9,Never-married,?,Own-child,White,Female,0,2,1,United-States,<=50K\n1,Private,118551,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Local-gov,35092,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,139703,HS-grad,9,Married-spouse-absent,Sales,Unmarried,Black,Female,0,0,1,Jamaica,<=50K\n2,Federal-gov,206190,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,178353,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,169133,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,103179,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,354464,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,124651,11th,7,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,60426,HS-grad,9,Married-civ-spouse,Adm-clerical,Own-child,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Federal-gov,98726,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,133861,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,180303,Bachelors,13,Divorced,Craft-repair,Unmarried,Asian-Pac-Islander,Male,0,0,3,Iran,<=50K\n1,Private,221324,Assoc-voc,11,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,325658,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,210562,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,152249,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n1,Private,178649,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,White,Female,0,0,0,France,<=50K\n2,State-gov,48997,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,243409,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,162442,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,203078,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,155983,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Self-emp-not-inc,182677,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Thailand,<=50K\n1,?,170276,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n3,Private,105381,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,256240,7th-8th,4,Married-civ-spouse,?,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,210275,Masters,14,Divorced,Tech-support,Unmarried,Black,Female,2,0,1,United-States,>50K\n3,Private,150980,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n2,Self-emp-inc,141584,HS-grad,9,Divorced,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,113571,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,154089,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,50197,10th,6,Separated,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,132572,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n3,Private,238185,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,112754,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n0,?,357029,Some-college,10,Married-civ-spouse,?,Wife,Black,Female,1,0,0,United-States,<=50K\n1,State-gov,213389,Some-college,10,Divorced,Protective-serv,Unmarried,White,Female,0,2,2,United-States,<=50K\n3,Self-emp-inc,287647,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,150061,Masters,14,Divorced,Exec-managerial,Unmarried,Black,Female,4,0,3,United-States,>50K\n4,Self-emp-inc,143266,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,68006,7th-8th,4,Never-married,Other-service,Other-relative,White,Female,0,0,3,United-States,<=50K\n2,Private,287079,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,223212,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,173929,Doctorate,16,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,>50K\n3,Self-emp-not-inc,182211,HS-grad,9,Widowed,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,62539,11th,7,Widowed,Other-service,Unmarried,White,Female,0,0,4,Greece,>50K\n1,Private,157612,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,4,0,2,United-States,>50K\n0,Private,305472,Assoc-acdm,12,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,3,United-States,<=50K\n4,Private,548256,12th,8,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,40295,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,112403,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,1,0,2,United-States,<=50K\n4,Private,31137,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,116138,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,?,127833,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,201743,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,240027,Some-college,10,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,129882,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,?,355890,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,107658,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,0,Canada,<=50K\n4,Private,136841,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,1,United-States,>50K\n0,Private,146679,Some-college,10,Never-married,Exec-managerial,Own-child,Black,Male,0,0,1,United-States,<=50K\n4,?,35724,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Federal-gov,42251,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,113838,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,282398,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,33331,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,41031,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,155489,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,53042,12th,8,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,174789,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,203067,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,177408,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,1,United-States,>50K\n2,Private,216626,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,Other,Male,0,0,2,Columbia,<=50K\n1,Private,93034,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Cambodia,<=50K\n4,Self-emp-not-inc,188003,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Local-gov,65535,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,366757,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,414545,Some-college,10,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,295919,Assoc-acdm,12,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,34378,1st-4th,2,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,58359,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,476334,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,255424,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,175856,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,124692,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,118551,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,292019,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,288566,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,137733,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,39432,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,138537,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Laos,<=50K\n2,Private,709445,HS-grad,9,Separated,Craft-repair,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,194809,11th,7,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,89041,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,?,299090,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,159561,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,236328,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,269045,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n0,?,196627,11th,7,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,323798,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,463072,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,199655,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Other,Female,0,2,2,?,<=50K\n0,Self-emp-inc,98756,Some-college,10,Divorced,Adm-clerical,Own-child,White,Female,0,0,3,United-States,<=50K\n3,State-gov,161075,HS-grad,9,Widowed,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,192485,12th,8,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,201579,9th,5,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,117606,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,?,177487,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,237731,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,4,United-States,<=50K\n2,Private,60313,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,270059,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,4,0,1,United-States,>50K\n1,Private,169958,5th-6th,3,Never-married,Craft-repair,Own-child,White,Male,0,0,2,?,<=50K\n0,Private,240686,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,124793,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,113948,Assoc-voc,11,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n0,?,241021,12th,8,Never-married,?,Own-child,Other,Female,0,0,2,United-States,<=50K\n0,Private,147655,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,38876,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,117299,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n0,?,114813,10th,6,Separated,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,136310,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,153132,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,197552,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,69748,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,175738,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,State-gov,78649,Some-college,10,Married-spouse-absent,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,188774,11th,7,Married-spouse-absent,Sales,Not-in-family,White,Male,0,0,3,?,<=50K\n3,Private,155659,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n0,Federal-gov,215891,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,144928,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,33688,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Female,0,2,4,United-States,<=50K\n4,Private,262446,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,191295,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,279173,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,153031,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,202239,7th-8th,4,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,469454,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,3,United-States,>50K\n2,Local-gov,164156,Assoc-acdm,12,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,196482,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,176185,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,France,>50K\n1,Private,287315,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,117210,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,41610,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,160703,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,80511,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,219155,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,106347,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,68899,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n2,Self-emp-not-inc,163985,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,270887,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,205726,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,218899,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,186183,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,4,United-States,>50K\n0,Private,248749,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,197558,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,176514,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,116820,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,128730,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,4,Greece,>50K\n2,Private,215503,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,226129,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,175856,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,>50K\n2,Private,281138,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,98061,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,260560,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,289909,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,59590,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,236769,Assoc-acdm,12,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,423616,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,291407,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,100029,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,204494,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,201680,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,154308,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,150324,11th,7,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,331609,Some-college,10,Widowed,Transport-moving,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n1,Private,100829,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,203169,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,122075,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,178778,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,276345,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,233511,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,289448,Assoc-voc,11,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,173350,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,130589,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,94318,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,297531,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,129762,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,182614,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,2,Poland,<=50K\n4,Private,120067,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,182370,Assoc-acdm,12,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,60949,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,190511,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,188195,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,89534,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,125831,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,183358,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n2,?,75024,7th-8th,4,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,251120,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,2,England,<=50K\n1,Private,108946,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,93223,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,147393,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n4,?,45801,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,<=50K\n1,State-gov,225385,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,23892,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,179668,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,Scotland,<=50K\n1,Self-emp-not-inc,404998,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,68882,1st-4th,2,Widowed,Other-service,Unmarried,White,Female,0,0,1,Portugal,<=50K\n3,Self-emp-not-inc,194065,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,357540,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,<=50K\n1,Private,185336,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n0,State-gov,152503,Some-college,10,Never-married,Tech-support,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,167794,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,96552,Some-college,10,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,169527,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,2,0,0,United-States,<=50K\n3,State-gov,254285,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,32509,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,125492,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,186035,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,?,168794,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,191856,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,2,0,2,United-States,>50K\n2,Private,215503,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,187560,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Self-emp-not-inc,252752,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,4,2,United-States,>50K\n2,Local-gov,210991,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n4,Local-gov,190748,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,117767,Assoc-acdm,12,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,301070,HS-grad,9,Divorced,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,204645,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,4,United-States,>50K\n2,Private,186183,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,131808,Assoc-voc,11,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,State-gov,156292,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,124589,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,262819,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,95500,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,241306,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,238680,Some-college,10,Never-married,Sales,Not-in-family,Black,Male,0,0,3,Outlying-US(Guam-USVI-etc),<=50K\n0,?,42293,10th,6,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,168071,HS-grad,9,Divorced,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,337629,12th,8,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,?,>50K\n3,Private,168001,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,97759,12th,8,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,107096,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,76860,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n0,Private,70076,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,312017,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,174138,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,125892,Bachelors,13,Divorced,Exec-managerial,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,210474,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,157332,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,30771,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,319768,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,France,>50K\n1,Private,209101,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,324609,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,268234,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,178109,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,25955,9th,5,Never-married,Craft-repair,Own-child,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n4,?,123484,HS-grad,9,Widowed,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,129762,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,108506,Assoc-voc,11,Never-married,Farming-fishing,Not-in-family,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n1,Private,241607,Bachelors,13,Never-married,Tech-support,Other-relative,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,214385,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Local-gov,183000,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,290763,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,171924,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,97189,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,195096,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n2,Federal-gov,329088,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,58371,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,256371,12th,8,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,35824,Some-college,10,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,173271,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,391349,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,86153,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,295855,11th,7,Divorced,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,327902,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,285102,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Taiwan,>50K\n4,Private,178353,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,28119,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,197522,Some-college,10,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,108542,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,179781,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,126974,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,180060,Bachelors,13,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,38948,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,271572,9th,5,Never-married,Other-service,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Private,177305,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,238367,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,172232,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,153805,HS-grad,9,Never-married,Other-service,Unmarried,Other,Male,0,0,0,Puerto-Rico,<=50K\n1,Private,26543,Bachelors,13,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,109067,Bachelors,13,Separated,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,213716,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,149809,Preschool,1,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n1,Private,185670,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Federal-gov,233851,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,?,192052,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,4,2,United-States,<=50K\n2,Private,193524,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,<=50K\n0,Private,213385,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,38238,Bachelors,13,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,104438,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Ireland,>50K\n0,Private,202344,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,43434,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,102147,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,231826,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,247378,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,78765,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,184078,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,102942,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,3,2,United-States,<=50K\n0,Private,258430,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,244554,11th,7,Divorced,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,252565,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,262778,Masters,14,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,162572,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,65706,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,102569,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,350498,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n4,?,159542,5th-6th,3,Widowed,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,142383,Assoc-acdm,12,Never-married,Sales,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n2,Private,229236,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Other,Male,0,0,2,Puerto-Rico,<=50K\n4,Private,56559,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,27049,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,36376,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,194360,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,246965,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,191277,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,268525,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,456604,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,223464,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,341797,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,174461,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,392167,10th,6,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,210064,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,233182,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,177550,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,0,United-States,<=50K\n4,Private,143312,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,326334,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,179088,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,207637,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Federal-gov,37289,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,36069,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Federal-gov,53245,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,399904,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,3,Mexico,<=50K\n2,Self-emp-inc,199346,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,343019,10th,6,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,232742,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,390472,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,290124,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,242912,Some-college,10,Never-married,Other-service,Own-child,White,Female,2,0,2,United-States,<=50K\n2,Private,70240,5th-6th,3,Married-spouse-absent,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Local-gov,286405,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,153841,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,137367,Bachelors,13,Never-married,Sales,Unmarried,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n4,Private,313255,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,100734,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,248584,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,60001,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,335065,7th-8th,4,Never-married,Sales,Own-child,White,Male,0,0,1,Mexico,<=50K\n0,Private,219262,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,186830,HS-grad,9,Never-married,Transport-moving,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,226385,Masters,14,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,609789,Assoc-acdm,12,Married-spouse-absent,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,307767,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,217460,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,104052,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,2,2,United-States,<=50K\n2,Local-gov,160893,Preschool,1,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,68358,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,243636,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,71269,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,71898,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Wife,Asian-Pac-Islander,Female,0,0,1,Philippines,<=50K\n2,?,212048,Prof-school,15,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,115040,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Private,111994,Some-college,10,Divorced,Sales,Not-in-family,White,Male,2,0,2,United-States,<=50K\n0,Private,210794,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,88126,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,570821,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,146196,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,169482,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,63577,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,208946,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,26598,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,189203,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,183892,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,194590,Assoc-voc,11,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,188616,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,116707,11th,7,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,99199,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,183620,Some-college,10,Never-married,Protective-serv,Not-in-family,Black,Female,0,0,2,United-States,>50K\n1,Private,110476,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,157043,Masters,14,Divorced,Prof-specialty,Not-in-family,Black,Female,1,0,1,?,<=50K\n3,Private,150726,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,214695,HS-grad,9,Never-married,Sales,Own-child,Black,Male,0,0,3,United-States,<=50K\n2,Private,172694,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,344804,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Mexico,<=50K\n1,Private,319422,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,Peru,<=50K\n1,State-gov,327902,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,438176,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,197656,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,219838,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,35561,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,?,156848,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,190257,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,156464,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,4,England,>50K\n2,Private,65624,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,201699,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,349910,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,187097,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,264314,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,Columbia,<=50K\n2,Self-emp-not-inc,282678,Masters,14,Separated,Exec-managerial,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,188923,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,3,United-States,<=50K\n3,Private,114797,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,2,United-States,<=50K\n4,Private,245215,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,36270,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,107138,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,77820,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,39477,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,58305,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,<=50K\n0,Private,359759,HS-grad,9,Never-married,Craft-repair,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n0,?,249147,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,44797,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,164488,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,48413,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,261276,Some-college,10,Never-married,?,Own-child,Black,Female,0,2,2,Cambodia,<=50K\n1,Self-emp-not-inc,36592,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,280923,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Federal-gov,29617,Some-college,10,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,208802,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Male,4,0,2,United-States,>50K\n1,Private,189240,Some-college,10,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,37932,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,181705,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,147548,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,306784,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n2,?,260953,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,190406,Prof-school,15,Divorced,Prof-specialty,Unmarried,Black,Male,4,0,2,United-States,>50K\n0,Private,230229,5th-6th,3,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,46987,Assoc-voc,11,Never-married,Tech-support,Own-child,White,Female,1,0,2,United-States,<=50K\n4,Private,301108,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,263081,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,>50K\n0,Self-emp-not-inc,37741,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,115834,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,2,0,3,United-States,>50K\n2,Private,150076,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,148254,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,183611,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,258768,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,287658,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Private,95946,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,31267,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,302149,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,2,0,2,Philippines,>50K\n1,Private,250135,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,176073,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,23580,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,163665,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,43953,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,144860,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,61474,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,141570,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,3,2,United-States,>50K\n2,Private,225660,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,336891,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,210164,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,171080,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,143342,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,281627,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,409922,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n4,?,224472,Prof-school,15,Never-married,?,Not-in-family,White,Male,4,0,4,United-States,>50K\n1,Private,157262,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,144949,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,303860,Masters,14,Widowed,Exec-managerial,Not-in-family,White,Male,1,0,0,United-States,<=50K\n1,Private,104293,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,195481,HS-grad,9,Married-civ-spouse,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,193995,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,105216,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,147206,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,173585,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n2,Private,187870,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,248919,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Guatemala,<=50K\n2,Private,280410,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,Haiti,<=50K\n2,State-gov,170861,HS-grad,9,Separated,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,409230,1st-4th,2,Married-civ-spouse,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,340171,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,41017,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,416356,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,261504,12th,8,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,205555,Prof-school,15,Divorced,Prof-specialty,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Private,245317,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,153685,11th,7,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,3,United-States,<=50K\n0,?,169758,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,99374,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,139452,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,227832,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,213024,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n0,?,24008,Some-college,10,Never-married,?,Own-child,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,33487,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,187934,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,Poland,<=50K\n1,Private,421561,11th,7,Married-civ-spouse,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Private,109969,11th,7,Divorced,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,Private,116830,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,117166,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,106951,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,89625,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,0,United-States,>50K\n2,Private,194537,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,144002,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,202214,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,109762,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,292570,11th,7,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,105252,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,3,0,1,United-States,<=50K\n4,Private,94552,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,46401,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,151150,10th,6,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,197689,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,180477,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,181761,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,381153,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,165474,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,190174,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,295991,10th,6,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Without-pay,198262,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,190385,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,?,411560,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,262116,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,178922,9th,5,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,192963,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,4,1,Philippines,>50K\n1,Self-emp-inc,209538,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Self-emp-not-inc,103277,12th,8,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,1,Portugal,<=50K\n0,Private,216086,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,636017,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,155781,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,136873,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,122066,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,>50K\n1,State-gov,346406,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,117915,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,19914,HS-grad,9,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,3,Philippines,<=50K\n3,Private,255364,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,703107,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,62374,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,96245,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,348796,Bachelors,13,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,136873,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,388252,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,47783,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,194167,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Federal-gov,544792,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,434463,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,317219,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,<=50K\n4,Private,221603,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,233711,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,111567,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,79830,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,192259,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,239663,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Local-gov,34987,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Self-emp-not-inc,409189,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,135525,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,152159,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,141363,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,214816,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,42907,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,161815,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,127314,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,395368,Some-college,10,Divorced,Handlers-cleaners,Other-relative,Black,Male,0,0,2,United-States,<=50K\n4,Private,184176,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,112660,9th,5,Divorced,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,183709,Assoc-voc,11,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,434114,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Self-emp-not-inc,165315,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,190997,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,335533,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,176146,5th-6th,3,Separated,Craft-repair,Not-in-family,Other,Male,0,0,1,Mexico,<=50K\n0,Private,272063,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,169564,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,188856,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,3,0,3,United-States,>50K\n0,Private,69847,Bachelors,13,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,198759,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,4,United-States,>50K\n0,Private,175431,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,228357,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,2,?,<=50K\n4,Self-emp-not-inc,284120,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,109133,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,167336,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n4,?,42209,9th,5,Widowed,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,282951,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,303155,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,261899,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,168030,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,1,United-States,>50K\n3,State-gov,71417,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,239130,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,200560,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,157541,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,255004,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,230136,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,3,United-States,>50K\n3,Local-gov,124963,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,1,United-States,>50K\n0,Private,39615,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,47678,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,281315,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,176123,HS-grad,9,Never-married,Tech-support,Other-relative,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n0,?,165350,HS-grad,9,Separated,?,Not-in-family,Black,Male,0,0,3,Germany,<=50K\n1,Private,235862,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,142579,Bachelors,13,Widowed,Sales,Unmarried,Black,Male,0,0,3,United-States,<=50K\n1,Private,38294,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,111483,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,189850,Some-college,10,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,145874,Doctorate,16,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,0,China,<=50K\n0,Private,139012,Assoc-voc,11,Never-married,Transport-moving,Own-child,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n1,Local-gov,211654,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,173090,Masters,14,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,104834,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n2,?,195124,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,Dominican-Republic,<=50K\n2,Private,32146,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,282674,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n2,Private,190403,Some-college,10,Separated,Exec-managerial,Not-in-family,White,Male,0,0,3,Canada,<=50K\n0,Private,247025,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,1,0,3,United-States,<=50K\n1,Private,198258,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,172748,7th-8th,4,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,State-gov,287988,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n3,Self-emp-not-inc,122307,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n4,?,175017,Bachelors,13,Divorced,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,170183,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,150812,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,241185,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-inc,174864,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,30529,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,301637,Assoc-voc,11,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,423222,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,3,United-States,>50K\n2,Private,214781,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,242912,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n3,Private,191529,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,<=50K\n0,Private,117363,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,333158,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,193260,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,1,Mexico,<=50K\n1,State-gov,278378,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,111394,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,102476,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,26451,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,?,209137,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,210945,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,1,Haiti,<=50K\n4,Local-gov,115023,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,53833,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,150057,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,128086,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,28473,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,155509,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,165315,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,?,<=50K\n1,Private,171889,Prof-school,15,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,185057,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,277034,HS-grad,9,Divorced,Tech-support,Unmarried,White,Male,0,0,3,United-States,>50K\n2,Private,166606,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,97453,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,136094,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,61855,HS-grad,9,Never-married,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,182771,Bachelors,13,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,0,China,<=50K\n3,Private,418961,Assoc-voc,11,Divorced,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,106961,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,81846,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,105936,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,36425,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,595088,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,149018,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,229613,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,33521,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,70539,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,<=50K\n3,State-gov,105728,HS-grad,9,Married-civ-spouse,Other-service,Wife,Amer-Indian-Eskimo,Female,0,0,1,United-States,>50K\n1,Private,193215,Some-college,10,Married-civ-spouse,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,137363,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,104892,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,149427,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n0,State-gov,176634,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,183279,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,225775,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,202091,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Private,123151,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,168187,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Federal-gov,33521,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,243678,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,164898,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,262280,Some-college,10,Married-civ-spouse,?,Wife,White,Female,2,0,2,United-States,<=50K\n1,State-gov,290614,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,199265,HS-grad,9,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,207668,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,State-gov,30687,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,State-gov,27939,Some-college,10,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,1,?,<=50K\n0,Private,438996,10th,6,Never-married,Other-service,Other-relative,White,Male,0,0,2,Mexico,<=50K\n3,Private,152915,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,186030,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,297759,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,171242,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,206088,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,182792,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,167725,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,160674,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,194710,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,255027,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,204641,10th,6,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,State-gov,177787,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,54932,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,1,United-States,>50K\n3,Self-emp-not-inc,91506,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,198634,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,227146,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,135647,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,55508,7th-8th,4,Divorced,Farming-fishing,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,174912,HS-grad,9,Separated,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,175925,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,157486,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n3,Local-gov,329144,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,?,81761,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,102318,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,266463,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Federal-gov,107314,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,114158,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,124052,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,144301,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,176683,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,234663,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,178948,HS-grad,9,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,607848,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,202937,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Federal-gov,83413,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,212798,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,192258,Some-college,10,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,112497,9th,5,Married-civ-spouse,Sales,Own-child,White,Male,0,0,3,United-States,>50K\n1,Private,97521,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,160972,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,322931,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,403519,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,330174,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,278155,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,39054,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n4,Private,170287,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,336643,Assoc-voc,11,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,264166,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,2,Columbia,<=50K\n2,Local-gov,433705,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,27044,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,165599,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,159759,Bachelors,13,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,385092,Some-college,10,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,188808,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,Private,167476,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,State-gov,194096,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,182460,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n0,?,102323,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,232139,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,341741,Preschool,1,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,206008,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,3,United-States,<=50K\n3,Private,344415,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,State-gov,372130,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,27766,Bachelors,13,Separated,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,>50K\n0,Private,140764,Assoc-voc,11,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,161259,10th,6,Never-married,?,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Private,22201,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Japan,>50K\n1,Self-emp-inc,187046,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,137591,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,274276,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,341757,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,218542,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,190020,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,221436,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Cuba,>50K\n2,Self-emp-not-inc,52187,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,158776,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,51543,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,146329,12th,8,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,397467,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,105592,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,78171,Some-college,10,Married-spouse-absent,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,State-gov,55377,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,258932,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,White,Female,0,0,4,Italy,<=50K\n1,Private,38606,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,1,2,United-States,<=50K\n0,Private,219841,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,156926,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,160362,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,192161,Bachelors,13,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,208570,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,182771,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,South,>50K\n2,Private,151089,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,163002,HS-grad,9,Separated,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,155657,7th-8th,4,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,0,Yugoslavia,<=50K\n1,Private,217530,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,244406,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Local-gov,152182,10th,6,Never-married,Protective-serv,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,55717,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n2,Private,201454,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,144371,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,277034,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,462832,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,Black,Female,0,0,2,United-States,>50K\n1,Private,200681,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,119565,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Puerto-Rico,>50K\n0,Private,192017,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,84808,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,100154,10th,6,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,169383,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Without-pay,43887,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,54260,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,159876,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,1,0,4,United-States,<=50K\n3,Private,160474,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,1,2,United-States,<=50K\n0,Private,476334,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,52386,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,83671,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,172960,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,191957,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,>50K\n2,Local-gov,40955,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,98080,Prof-school,15,Never-married,?,Not-in-family,Asian-Pac-Islander,Male,2,0,2,Japan,>50K\n2,Private,175643,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n3,State-gov,197184,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n4,Private,187295,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,40822,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,228729,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,?,<=50K\n3,Private,240496,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,51961,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n2,Private,174887,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,95855,11th,7,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,362259,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,30916,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,153148,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,167915,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,45156,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Private,98776,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,209801,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,?,<=50K\n2,Private,183800,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,54595,12th,8,Never-married,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,79637,Bachelors,13,Never-married,Exec-managerial,Own-child,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Private,126566,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,233796,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,1,United-States,>50K\n4,Local-gov,191800,Bachelors,13,Divorced,Adm-clerical,Unmarried,Black,Female,2,0,1,United-States,<=50K\n1,Self-emp-not-inc,527162,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,139466,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,64520,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,97741,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,160173,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,350995,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,?,182836,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,143267,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,346341,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,172175,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,153035,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,200127,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,204470,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,353012,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,194342,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,57898,12th,8,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,164707,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,2,?,<=50K\n2,Private,269028,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,France,<=50K\n4,Private,83922,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,160647,HS-grad,9,Never-married,Farming-fishing,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,125437,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,246011,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,216937,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Other,Female,0,0,3,Guatemala,<=50K\n4,Self-emp-not-inc,66356,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,154981,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,3,United-States,>50K\n4,Federal-gov,197311,Masters,14,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,301743,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,401118,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,98776,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,32528,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,177119,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,193524,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n4,State-gov,192258,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,145917,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,214838,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,176011,Some-college,10,Separated,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,147239,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,159179,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,155963,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,360457,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,114674,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,95708,Masters,14,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Local-gov,100734,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,188972,HS-grad,9,Widowed,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,162667,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,Portugal,<=50K\n2,Self-emp-not-inc,28497,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,1,4,United-States,>50K\n1,Private,180758,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,346635,Masters,14,Divorced,Sales,Unmarried,White,Female,0,4,3,United-States,<=50K\n0,Private,46645,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,203258,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,134480,11th,7,Never-married,Priv-house-serv,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,85548,Some-college,10,Separated,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,Private,195994,1st-4th,2,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,Guatemala,<=50K\n2,State-gov,148316,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,227466,HS-grad,9,Never-married,Other-service,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,68552,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,252257,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,30126,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,304353,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,171968,Bachelors,13,Widowed,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,3,Thailand,<=50K\n0,Private,205839,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,218640,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,>50K\n2,Private,150568,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,382738,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,138940,11th,7,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,258306,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Local-gov,190107,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,2,0,United-States,<=50K\n3,Private,152373,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,141875,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,79586,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,2,?,>50K\n1,Private,157289,HS-grad,9,Married-spouse-absent,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,184498,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,109684,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,1,United-States,<=50K\n3,Private,199832,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,23545,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,175710,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,52028,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n4,Self-emp-not-inc,315977,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,202322,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,251825,Assoc-acdm,12,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,202115,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,>50K\n4,Local-gov,216824,Prof-school,15,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n4,Private,145656,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,137076,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,152621,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Canada,>50K\n2,Self-emp-not-inc,27242,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,358242,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,184117,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,0,United-States,>50K\n1,Private,300290,11th,7,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,149991,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,189759,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,339482,5th-6th,3,Separated,Farming-fishing,Other-relative,White,Male,0,0,3,Mexico,<=50K\n3,Private,100933,HS-grad,9,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,354558,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,162613,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,4,3,United-States,<=50K\n4,Private,285052,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,State-gov,175044,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,45508,5th-6th,3,Married-spouse-absent,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,173351,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,173611,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,?,182543,1st-4th,2,Separated,?,Unmarried,White,Female,0,0,2,Mexico,<=50K\n0,Private,143062,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,137951,10th,6,Separated,?,Other-relative,White,Female,0,0,2,Puerto-Rico,<=50K\n1,Local-gov,293063,Bachelors,13,Married-spouse-absent,Prof-specialty,Other-relative,Black,Male,0,0,2,?,<=50K\n1,Private,377754,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,152373,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,193477,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,277323,HS-grad,9,Never-married,Protective-serv,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,69182,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,219599,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,129371,9th,5,Separated,Other-service,Unmarried,Other,Female,0,0,2,Trinadad&Tobago,<=50K\n0,Private,470875,HS-grad,9,Married-civ-spouse,Sales,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,201734,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,58447,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Local-gov,91689,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,166546,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,293324,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,219262,9th,5,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,403391,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,367749,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n0,Private,128487,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,111363,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,240869,7th-8th,4,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Private,163278,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,416415,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,280030,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,251243,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,167159,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,161857,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,Other,Female,0,0,2,Columbia,<=50K\n2,Private,160035,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,190205,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,?,161290,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,112403,Bachelors,13,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,238726,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,164530,11th,7,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n0,Private,456572,HS-grad,9,Never-married,Farming-fishing,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,177675,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,246739,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,102953,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,224238,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,155489,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,156802,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,3,United-States,>50K\n3,Private,168212,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,331395,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,4,Portugal,<=50K\n2,Local-gov,261497,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,1,United-States,<=50K\n4,Private,365511,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,Other,Male,0,0,2,Mexico,<=50K\n2,Private,187999,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,190350,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,?,166759,12th,8,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,168262,10th,6,Divorced,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,State-gov,122011,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,2,United-States,>50K\n3,Private,165953,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,375980,HS-grad,9,Separated,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,406463,Masters,14,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,231472,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,78913,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,69107,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n0,?,182387,Some-college,10,Never-married,?,Not-in-family,Asian-Pac-Islander,Female,0,0,0,Thailand,<=50K\n1,Private,169002,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,229967,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,422836,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,Mexico,<=50K\n1,State-gov,230922,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,Scotland,<=50K\n2,Private,195892,Some-college,10,Divorced,Transport-moving,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,163346,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,82566,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,86505,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,178780,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,173945,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,176810,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,23813,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,1,0,1,United-States,<=50K\n3,Self-emp-inc,210736,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,343789,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,United-States,<=50K\n1,Private,113838,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,121055,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n4,?,52171,7th-8th,4,Divorced,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,566049,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,67433,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,39014,12th,8,Married-civ-spouse,Priv-house-serv,Wife,Other,Female,0,0,2,Dominican-Republic,<=50K\n0,Private,51939,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,100669,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n3,Private,155659,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,112847,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n2,Local-gov,32185,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,138370,10th,6,Married-spouse-absent,Protective-serv,Not-in-family,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n3,Self-emp-not-inc,172281,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n3,Private,180505,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,168262,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,85126,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,113838,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,197457,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,1,0,2,United-States,<=50K\n1,Private,197905,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,316589,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,336367,Assoc-acdm,12,Never-married,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,143123,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,4,2,United-States,>50K\n0,Private,209955,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,210013,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,224541,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,275653,7th-8th,4,Married-spouse-absent,Machine-op-inspct,Unmarried,White,Female,1,0,2,Puerto-Rico,<=50K\n2,Private,88061,11th,7,Married-spouse-absent,Machine-op-inspct,Unmarried,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n2,Federal-gov,195897,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,43206,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,202950,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,154093,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,112115,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,355954,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,379418,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,286372,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,48087,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,387270,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,270043,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,65738,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,159888,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,278039,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,265434,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,52052,Assoc-voc,11,Widowed,Sales,Not-in-family,White,Female,4,0,3,United-States,>50K\n0,Private,208882,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,229393,11th,7,Never-married,Farming-fishing,Unmarried,White,Male,1,0,2,United-States,<=50K\n0,Private,53513,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,225193,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,166809,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Self-emp-not-inc,175674,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,368947,Bachelors,13,Never-married,Protective-serv,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,194901,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,203173,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,267431,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,0,0,3,United-States,<=50K\n1,Private,111836,Some-college,10,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,198613,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,?,<=50K\n2,Self-emp-inc,149102,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n4,Local-gov,121111,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,130397,10th,6,Never-married,Farming-fishing,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,212847,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,<=50K\n0,Private,184198,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,121287,9th,5,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,120408,Some-college,10,Widowed,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,164678,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,388812,Some-college,10,Never-married,Sales,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Private,294919,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,101684,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,36209,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,123983,Bachelors,13,Divorced,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n2,Self-emp-not-inc,340001,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,203828,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,183789,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,305619,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,174181,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,131869,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,43479,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,203126,9th,5,Never-married,?,Unmarried,White,Female,0,0,2,Dominican-Republic,<=50K\n0,Private,118792,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,272913,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,Mexico,<=50K\n2,Federal-gov,222011,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,301007,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,197731,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,173736,9th,5,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,182590,10th,6,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,93211,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,24763,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Male,2,0,2,United-States,<=50K\n3,Local-gov,219021,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,>50K\n2,Private,137229,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,281030,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,234108,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,46868,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,?,162667,HS-grad,9,Never-married,?,Other-relative,White,Male,0,0,2,El-Salvador,<=50K\n3,Private,173291,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,305160,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,212954,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,112284,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,164198,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,4,0,2,United-States,>50K\n2,Private,152958,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,145389,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,119570,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,272343,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,187720,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,145409,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,208726,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,203488,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,330416,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,25803,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,171150,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,82576,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,329425,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,185452,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,201179,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,182268,Preschool,1,Married-spouse-absent,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,95763,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,125892,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Poland,<=50K\n0,Private,121407,Assoc-voc,11,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,373367,11th,7,Widowed,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,165982,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,165484,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,156890,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,156763,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,244172,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,?,<=50K\n2,Private,219814,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Guatemala,<=50K\n2,Private,171841,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,168524,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,1,United-States,>50K\n4,Private,205643,Prof-school,15,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,?,174904,HS-grad,9,Separated,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,102559,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Canada,>50K\n3,Private,60267,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,388725,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,215712,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,171722,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,193051,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,305446,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,146949,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,322144,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,75742,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,El-Salvador,>50K\n4,?,380687,Bachelors,13,Married-civ-spouse,?,Wife,Black,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,95149,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,68469,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,27653,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,410439,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,37821,Assoc-voc,11,Never-married,Sales,Unmarried,White,Female,0,0,3,?,<=50K\n2,Private,228570,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,141453,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,88215,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,2,China,>50K\n3,Private,48641,12th,8,Never-married,Other-service,Not-in-family,Other,Female,0,0,1,United-States,<=50K\n2,Private,185385,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,341471,HS-grad,9,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,163322,11th,7,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,99357,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,1,United-States,>50K\n2,Self-emp-inc,602513,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,287192,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,Mexico,<=50K\n1,Private,215047,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,97863,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,308118,Assoc-acdm,12,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Private,137192,Bachelors,13,Divorced,Exec-managerial,Unmarried,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n1,Private,275369,7th-8th,4,Separated,Handlers-cleaners,Not-in-family,Black,Male,0,0,1,Haiti,<=50K\n2,Private,99971,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,103713,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,253770,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,162205,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,4,United-States,>50K\n3,Self-emp-not-inc,31267,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,198146,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,178207,Some-college,10,Never-married,Handlers-cleaners,Unmarried,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n0,Private,317175,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,221791,HS-grad,9,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,187124,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,>50K\n4,State-gov,280519,HS-grad,9,Divorced,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,207568,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,192684,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,103260,Bachelors,13,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,191227,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n3,Self-emp-inc,382242,Doctorate,16,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,106900,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,48520,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,<=50K\n3,Private,55527,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,246820,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,33884,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,29762,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,168109,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,207449,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,189098,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,194259,Bachelors,13,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,194630,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,179681,HS-grad,9,Never-married,Transport-moving,Own-child,White,Female,0,0,2,United-States,<=50K\n2,State-gov,136996,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,143604,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n0,Private,243373,12th,8,Never-married,Sales,Other-relative,White,Male,1,0,2,United-States,<=50K\n1,Private,261799,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,143281,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,185556,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Italy,<=50K\n2,Private,111499,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Self-emp-not-inc,280433,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,37314,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,103408,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,?,<=50K\n1,Private,270151,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,96748,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,164775,5th-6th,3,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Guatemala,<=50K\n3,Private,190319,Bachelors,13,Married-spouse-absent,Adm-clerical,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,Philippines,<=50K\n0,Private,213115,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,156926,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Canada,>50K\n2,Private,112967,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,35373,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,220342,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,163167,HS-grad,9,Divorced,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,404951,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,122032,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,143582,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,Other,Female,2,0,1,United-States,<=50K\n2,Private,108140,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,Private,251508,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,197054,Prof-school,15,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,36960,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,165930,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,178960,11th,7,Never-married,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,214503,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,110458,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,202125,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,284329,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,192924,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,340917,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,?,<=50K\n2,Private,340614,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,196678,12th,8,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,266489,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,61474,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,?,99127,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,215955,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n0,Self-emp-inc,215395,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,183898,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,97176,HS-grad,9,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,145160,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,357949,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,177120,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,288229,Some-college,10,Married-civ-spouse,Sales,Other-relative,Asian-Pac-Islander,Female,0,0,2,Greece,<=50K\n2,Private,509060,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,47932,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,103925,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,State-gov,183829,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,138852,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,188186,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,0,Hungary,<=50K\n0,Private,34616,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,220819,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,281540,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,47396,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,141350,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,331433,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,346532,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,241367,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,216256,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Italy,>50K\n2,Local-gov,153031,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,1,United-States,>50K\n2,Private,116138,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Cambodia,<=50K\n0,Private,193166,9th,5,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,275094,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,Mexico,>50K\n3,Private,81548,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,167979,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,67759,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,200190,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,403112,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,214891,Bachelors,13,Married-spouse-absent,Transport-moving,Own-child,Other,Male,0,0,2,?,<=50K\n1,Private,142675,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,88500,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,145308,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,204377,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,260696,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,231181,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,260052,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,178665,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,226267,7th-8th,4,Never-married,Sales,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,111232,12th,8,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,87928,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,212748,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,110677,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,139268,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,306779,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,318331,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,143385,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,288273,12th,8,Separated,Adm-clerical,Unmarried,White,Female,1,0,2,United-States,<=50K\n1,Private,167725,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,3,Philippines,>50K\n3,Private,94081,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,194723,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,163985,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,189759,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Italy,<=50K\n3,State-gov,195922,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,54159,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,166863,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,104501,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Germany,>50K\n2,Private,210626,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,448026,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,170916,10th,6,Never-married,Protective-serv,Own-child,White,Female,0,2,2,United-States,<=50K\n3,Local-gov,283602,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,2,United-States,>50K\n0,Private,189749,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,90934,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Male,0,0,4,Philippines,>50K\n1,State-gov,253121,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,181776,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,162397,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,70708,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n3,State-gov,103406,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,224658,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,213451,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,0,Jamaica,<=50K\n3,Private,139671,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,36201,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,237713,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,0,4,4,United-States,>50K\n0,Local-gov,173497,11th,7,Never-married,Prof-specialty,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Private,375606,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,203488,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n2,Self-emp-not-inc,107231,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,France,<=50K\n0,Private,216811,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,288679,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,105516,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,282972,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Self-emp-inc,117372,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,112497,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,?,186032,Assoc-voc,11,Widowed,?,Not-in-family,White,Female,1,0,1,United-States,<=50K\n1,Private,192384,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,43348,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,181822,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,216070,Masters,14,Married-civ-spouse,Exec-managerial,Wife,Amer-Indian-Eskimo,Female,0,0,3,United-States,>50K\n1,State-gov,112062,Masters,14,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,218551,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,404616,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,169460,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,240081,HS-grad,9,Never-married,Sales,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,147655,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,90277,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,?,<=50K\n3,Private,60751,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,194636,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,3,United-States,<=50K\n2,Self-emp-not-inc,154641,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,3,United-States,<=50K\n2,Private,491000,Bachelors,13,Never-married,Exec-managerial,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,399088,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,186909,Masters,14,Married-civ-spouse,Sales,Wife,White,Female,0,3,1,United-States,>50K\n4,Private,105491,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,34987,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,?,167835,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,288983,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,266070,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,110380,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,4,3,United-States,<=50K\n0,Local-gov,31873,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,294400,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,184308,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,175769,Prof-school,15,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,182273,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,106541,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,138192,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,196791,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,Private,223019,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,109273,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,95490,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,149131,11th,7,Divorced,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,219155,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,England,>50K\n3,Local-gov,82783,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,214858,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,170230,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,209344,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,?,<=50K\n1,Private,90406,11th,7,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,299813,9th,5,Married-civ-spouse,Sales,Wife,White,Female,0,0,4,Dominican-Republic,<=50K\n1,Private,188064,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Canada,<=50K\n3,Private,246117,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,132749,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,201099,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,97490,Some-college,10,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,221252,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n1,Private,116991,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,161691,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,107793,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Germany,>50K\n3,Self-emp-inc,194514,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,3,Trinadad&Tobago,<=50K\n1,Private,278502,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,4,United-States,<=50K\n3,Private,343742,HS-grad,9,Separated,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,?,204074,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,31965,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,143604,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,?,<=50K\n1,Private,174308,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,162551,12th,8,Married-civ-spouse,Sales,Wife,Asian-Pac-Islander,Female,0,0,3,?,<=50K\n2,Self-emp-inc,372525,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,75167,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,176296,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,2,2,United-States,>50K\n0,Private,93518,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,126797,HS-grad,9,Married-spouse-absent,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,25124,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,112137,Some-college,10,Never-married,Prof-specialty,Other-relative,Asian-Pac-Islander,Female,0,0,0,South,<=50K\n1,?,58798,7th-8th,4,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,21472,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,90969,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,149734,HS-grad,9,Separated,Craft-repair,Unmarried,Black,Female,0,1,2,United-States,<=50K\n2,Private,52849,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,106347,Some-college,10,Divorced,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,199735,Bachelors,13,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,2,Germany,<=50K\n0,Private,488541,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,403911,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,172991,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,210945,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,157446,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,109390,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,4,United-States,<=50K\n1,Private,134886,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,4,0,1,United-States,>50K\n2,Private,144579,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,203488,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,202871,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,175412,9th,5,Divorced,Craft-repair,Unmarried,White,Male,1,0,3,United-States,<=50K\n2,Private,336906,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,177596,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,Puerto-Rico,>50K\n1,Private,79448,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,191731,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,?,233014,HS-grad,9,Divorced,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,133937,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,219211,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,State-gov,94529,HS-grad,9,Divorced,Protective-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,247547,HS-grad,9,Separated,Prof-specialty,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,Private,29361,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,166851,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,197069,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Philippines,>50K\n1,Private,153588,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,151369,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,174112,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,520033,12th,8,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,State-gov,194828,Some-college,10,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,?,216908,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,126613,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,26254,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,54042,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,1,0,1,United-States,<=50K\n0,Private,67804,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Local-gov,53481,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,412379,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,220187,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,?,256141,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,268222,HS-grad,9,Separated,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,99131,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,115498,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,0,United-States,<=50K\n4,Private,317847,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,4,3,United-States,>50K\n2,Private,98389,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,173704,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n0,?,211177,12th,8,Never-married,?,Other-relative,Black,Male,0,0,0,United-States,<=50K\n0,Private,115443,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,65078,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,24896,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,184710,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,410450,Bachelors,13,Divorced,Other-service,Unmarried,White,Female,0,0,3,England,>50K\n2,Private,83893,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,113309,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,160625,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,340043,12th,8,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,48976,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,State-gov,243875,Assoc-voc,11,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,554206,HS-grad,9,Separated,Transport-moving,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n2,Private,361888,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,>50K\n2,Self-emp-not-inc,205359,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,<=50K\n3,State-gov,167281,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,35663,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,357437,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,390856,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Federal-gov,331615,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,202415,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,180032,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n2,Private,77247,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,101795,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,272019,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n1,Private,198068,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,199326,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,178841,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,136951,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Self-emp-inc,109240,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,128876,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,103358,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,India,<=50K\n2,Private,354408,12th,8,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,206051,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,155659,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,143299,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,252210,5th-6th,3,Never-married,Other-service,Own-child,White,Male,0,0,2,Mexico,<=50K\n0,?,129240,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,398918,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,240612,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,429346,HS-grad,9,Never-married,Adm-clerical,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,123718,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,455379,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,376416,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-inc,234663,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,282142,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,State-gov,208049,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,68539,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,126501,11th,7,Never-married,Adm-clerical,Own-child,Amer-Indian-Eskimo,Female,0,0,0,South,<=50K\n0,Private,186452,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,127184,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,165267,10th,6,Married-civ-spouse,Farming-fishing,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,124733,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n1,Self-emp-inc,149726,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,41374,HS-grad,9,Widowed,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Local-gov,329759,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,212433,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,185099,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,126754,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,122497,9th,5,Widowed,Other-service,Unmarried,Black,Male,0,0,3,?,<=50K\n1,Private,118056,Some-college,10,Married-spouse-absent,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,200892,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,200790,12th,8,Married-civ-spouse,?,Other-relative,White,Female,4,0,2,United-States,>50K\n1,Self-emp-inc,84119,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,197918,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,150533,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,443742,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,104423,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,169133,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,185551,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,174486,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,State-gov,50468,Prof-school,15,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,196943,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,120691,HS-grad,9,Never-married,Sales,Own-child,Black,Male,0,0,1,United-States,<=50K\n4,State-gov,198815,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,Mexico,<=50K\n4,Private,22186,Some-college,10,Widowed,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,188069,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,233149,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,138358,10th,6,Divorced,Craft-repair,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,338013,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,332666,10th,6,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,166339,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,392886,HS-grad,9,Widowed,Farming-fishing,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,State-gov,141838,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,520759,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n4,Self-emp-inc,37345,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,387779,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,201531,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,123598,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,380614,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,83859,HS-grad,9,Widowed,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n3,State-gov,24790,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,266820,Preschool,1,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n2,Private,85440,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,421837,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,404062,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,0,United-States,>50K\n2,Private,224566,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,294991,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,189610,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n2,Private,219141,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,3,0,2,United-States,>50K\n3,Federal-gov,20956,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,70995,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,215232,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,178295,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,56201,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,98076,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,351810,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Cuba,<=50K\n4,Self-emp-not-inc,144351,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,State-gov,137613,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,0,Taiwan,<=50K\n0,Private,54257,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,230373,11th,7,Never-married,Other-service,Own-child,White,Female,1,0,0,United-States,<=50K\n1,Private,98389,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,184135,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,140121,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,24504,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,129528,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,415578,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,97142,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,201328,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,256620,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,96854,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,141858,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n3,Federal-gov,20795,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,95519,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,112791,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,291407,11th,7,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,239659,Some-college,10,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,4,United-States,<=50K\n1,Private,183151,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,97634,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,143807,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,186934,Masters,14,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,170065,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,108328,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,2,0,3,United-States,<=50K\n4,State-gov,83696,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Female,0,0,2,?,<=50K\n0,Private,204596,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,32604,Some-college,10,Never-married,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,193453,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,4,United-States,>50K\n2,Private,148995,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,85041,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,140851,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,196280,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,38973,Bachelors,13,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,39182,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,198841,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,694812,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,247444,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Nicaragua,<=50K\n2,Private,294270,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,195820,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,329426,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,174871,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,116103,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,206903,Bachelors,13,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Private,217577,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,337693,5th-6th,3,Never-married,Other-service,Own-child,White,Female,0,0,2,El-Salvador,<=50K\n2,Private,204501,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,169186,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,109421,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,267893,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,Black,Male,2,0,2,United-States,>50K\n2,Private,200479,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,221317,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Self-emp-not-inc,132925,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,?,283531,HS-grad,9,Divorced,?,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,170769,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,186410,Prof-school,15,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,307786,1st-4th,2,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,380560,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Local-gov,147258,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,212894,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,124356,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,98791,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,216473,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n4,?,135339,Bachelors,13,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n2,Private,107303,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,152744,Bachelors,13,Divorced,Sales,Other-relative,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n1,Self-emp-not-inc,100079,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n0,Private,117779,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,Hungary,<=50K\n0,Private,197613,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,411068,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,192984,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,66356,7th-8th,4,Never-married,Farming-fishing,Unmarried,White,Male,2,0,2,United-States,<=50K\n1,Federal-gov,137184,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,231105,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,>50K\n0,Local-gov,146586,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,32406,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,578701,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,?,<=50K\n0,Private,206777,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,133495,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,34722,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,133299,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,24967,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,171968,HS-grad,9,Separated,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,412156,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,51290,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,198265,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,1,0,2,United-States,>50K\n0,Private,293565,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,226288,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,110445,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,160634,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,174242,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,390316,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,298860,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,171584,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,232664,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,63676,10th,6,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,170376,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,175964,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,422013,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,4,2,United-States,<=50K\n1,Private,105813,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,306707,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,0,United-States,<=50K\n2,Private,177543,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,320277,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,129495,Some-college,10,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,257042,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,1,0,2,United-States,<=50K\n2,Private,275995,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n0,?,86318,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,280440,Assoc-acdm,12,Never-married,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,371556,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,408229,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,149337,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,209297,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,3,2,United-States,<=50K\n3,Private,355802,Some-college,10,Widowed,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,165949,Bachelors,13,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,1,2,United-States,<=50K\n2,Self-emp-not-inc,112507,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,462869,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,413648,5th-6th,3,Never-married,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,29235,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,149823,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,39530,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,197387,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,Mexico,<=50K\n4,Local-gov,255406,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,43764,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,168322,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,278322,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,115813,Assoc-acdm,12,Separated,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,184456,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,1,0,4,Italy,<=50K\n2,Private,289636,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,101684,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,133425,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,349405,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,124076,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,2,United-States,>50K\n4,Self-emp-not-inc,165968,Assoc-voc,11,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,185099,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,268281,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,154949,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,176711,HS-grad,9,Divorced,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,165064,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,213750,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,77132,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,109667,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,162164,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,219591,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,327462,10th,6,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,236943,9th,5,Divorced,Farming-fishing,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n2,Private,89226,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,124751,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,144122,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,98769,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,170066,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,162439,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,22900,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,102130,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,215743,11th,7,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,381583,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,United-States,>50K\n4,Local-gov,198277,12th,8,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,243178,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,177305,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,167149,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,270872,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,1,0,2,?,<=50K\n1,Private,382368,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,Germany,<=50K\n2,Local-gov,277144,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n0,State-gov,145651,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,2,0,United-States,<=50K\n2,Private,171351,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,265099,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,105617,9th,5,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,217689,Some-college,10,Married-civ-spouse,Other-service,Husband,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n3,?,81136,Assoc-voc,11,Divorced,?,Unmarried,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,73883,Bachelors,13,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,339482,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,326232,Some-college,10,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,>50K\n1,Private,106316,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,198728,Some-college,10,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,126501,Assoc-voc,11,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,233802,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,204501,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,Canada,>50K\n1,Private,208249,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,188693,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,93272,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,159299,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,303588,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,35136,10th,6,Divorced,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,139576,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,252355,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,83812,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,89718,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,222810,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,456618,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,296158,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,162140,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,4,2,United-States,<=50K\n1,Private,36601,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,195337,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,282721,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n2,Private,206049,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,223392,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,27821,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,131827,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,549413,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,69491,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,193755,Assoc-acdm,12,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,598802,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Local-gov,259762,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,0,United-States,<=50K\n0,Private,266255,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,32954,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,291808,HS-grad,9,Divorced,Protective-serv,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,190728,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,59184,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,196456,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,147989,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,195784,12th,8,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,202214,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,225165,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,54825,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,188905,5th-6th,3,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n0,Private,132636,11th,7,Never-married,Transport-moving,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,228320,7th-8th,4,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,415500,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,254247,12th,8,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,?,<=50K\n2,Private,255635,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,2,Mexico,<=50K\n3,Private,96080,9th,5,Separated,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,78181,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,339547,Prof-school,15,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Laos,>50K\n3,Self-emp-not-inc,126500,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n1,Private,511289,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,1,0,4,United-States,<=50K\n1,Private,159574,7th-8th,4,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,224105,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n4,Self-emp-not-inc,128105,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,89508,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,370242,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,67257,Bachelors,13,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,62952,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,111058,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,3,2,United-States,<=50K\n1,Private,29235,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,<=50K\n3,State-gov,101119,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,140516,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,159888,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,?,45643,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,166371,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,3,United-States,<=50K\n2,State-gov,160910,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,257064,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,181307,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n1,Private,83253,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,128700,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,243010,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,Other,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,37778,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,3,United-States,<=50K\n0,Private,132320,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,234755,HS-grad,9,Separated,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,142616,HS-grad,9,Separated,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Private,148509,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,240738,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,32276,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,163921,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,464103,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,?,271346,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,4,0,3,United-States,>50K\n1,Local-gov,327825,HS-grad,9,Divorced,Protective-serv,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,267085,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,266945,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,1,0,2,El-Salvador,<=50K\n0,Private,234663,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,189123,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,<=50K\n3,Private,104996,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,101265,12th,8,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,Italy,<=50K\n0,Private,184975,HS-grad,9,Married-spouse-absent,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,246965,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,227065,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Private,301867,Bachelors,13,Divorced,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Philippines,<=50K\n0,Private,185948,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,134854,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,281030,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,126701,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Male,3,0,2,United-States,>50K\n3,Self-emp-not-inc,95949,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,88528,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Female,0,0,4,United-States,<=50K\n3,Private,24723,10th,6,Divorced,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,?,171411,9th,5,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,184581,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,100067,Some-college,10,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,182863,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Never-worked,462294,Some-college,10,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,85434,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,158092,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,104844,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,304570,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,3,0,2,?,>50K\n3,?,89806,Some-college,10,Divorced,?,Not-in-family,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n2,Private,106183,HS-grad,9,Divorced,Other-service,Unmarried,Amer-Indian-Eskimo,Female,2,0,2,United-States,<=50K\n0,Private,89347,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,157236,Some-college,10,Married-spouse-absent,Handlers-cleaners,Unmarried,White,Male,0,0,2,Poland,<=50K\n0,Private,261259,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,286166,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,122272,HS-grad,9,Never-married,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,248739,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,224238,12th,8,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,138157,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,148460,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,2,0,2,Puerto-Rico,<=50K\n4,Private,236627,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,191364,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,France,>50K\n2,Private,353524,HS-grad,9,Divorced,Exec-managerial,Own-child,White,Female,1,0,2,United-States,<=50K\n2,Private,391040,Assoc-voc,11,Separated,Tech-support,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,134997,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,4,United-States,<=50K\n1,Private,392487,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,216724,HS-grad,9,Divorced,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,174395,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,383058,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n4,Self-emp-not-inc,96073,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-inc,103435,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,96718,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,178948,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,3,0,2,United-States,>50K\n3,Private,173987,9th,5,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,34419,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,224849,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,249857,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,340458,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,427422,Doctorate,16,Married-civ-spouse,Sales,Husband,White,Male,0,4,1,United-States,>50K\n0,?,440417,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,175643,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,297485,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,232954,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,326330,Some-college,10,Divorced,Exec-managerial,Own-child,White,Female,1,0,2,United-States,<=50K\n0,Private,109419,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,127768,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,>50K\n2,Private,252986,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,380544,Assoc-acdm,12,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,306108,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,232855,Some-college,10,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,130126,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,194231,Masters,14,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Self-emp-inc,197038,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,?,168223,Bachelors,13,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n4,State-gov,26109,Prof-school,15,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,285671,HS-grad,9,Never-married,Other-service,Other-relative,Black,Male,0,0,1,United-States,<=50K\n0,Private,153583,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,?,<=50K\n4,Self-emp-inc,103948,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,439919,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,2,Mexico,<=50K\n2,Private,40319,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,159028,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,98675,9th,5,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,90758,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,75435,HS-grad,9,Divorced,Craft-repair,Unmarried,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n0,Private,219189,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,203463,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,187635,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,154641,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,3,0,3,United-States,>50K\n1,Private,27153,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,150324,Assoc-acdm,12,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,83704,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,176262,Assoc-voc,11,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,179423,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,168038,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Private,108765,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,146477,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,Greece,>50K\n4,Local-gov,188220,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,>50K\n2,Private,292855,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,1,United-States,>50K\n1,Private,114870,Some-college,10,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,77723,Bachelors,13,Divorced,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Private,284166,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n4,Private,133902,HS-grad,9,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,191318,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,67794,HS-grad,9,Married-spouse-absent,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,357679,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,4,United-States,>50K\n4,Private,117872,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,55929,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,?,165065,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,Italy,<=50K\n1,Self-emp-not-inc,34307,Assoc-voc,11,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n1,Private,246038,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,147258,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,329144,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,52953,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n0,Private,216181,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,Iran,<=50K\n0,Private,391171,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Local-gov,223242,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,103925,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,1,United-States,>50K\n2,Private,38240,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,148444,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,State-gov,110257,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,101345,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,268098,12th,8,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,?,369084,Some-college,10,Never-married,?,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,Private,288825,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,162688,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,48751,11th,7,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,184099,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,307496,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,?,176986,HS-grad,9,Widowed,?,Unmarried,White,Male,0,0,1,United-States,<=50K\n0,Private,267955,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,283969,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,Mexico,<=50K\n1,State-gov,204516,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,167771,Some-college,10,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,345073,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,?,380219,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,306156,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n4,Self-emp-not-inc,37203,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,1,United-States,>50K\n0,Private,185097,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,144808,Some-college,10,Married-civ-spouse,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,187203,Assoc-acdm,12,Never-married,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,125089,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,289458,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,144798,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,172152,Bachelors,13,Never-married,?,Not-in-family,Asian-Pac-Islander,Male,0,0,1,Taiwan,<=50K\n1,Private,207513,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n0,?,164574,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,199949,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,213024,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,213140,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Self-emp-not-inc,83374,Some-college,10,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,1,United-States,>50K\n2,Private,192939,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,424494,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,215243,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,30682,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,306639,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Local-gov,218678,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,219130,Some-college,10,Never-married,Other-service,Not-in-family,Other,Female,0,0,2,United-States,<=50K\n4,Private,180624,Assoc-acdm,12,Never-married,Prof-specialty,Other-relative,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,200190,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,194472,Some-college,10,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,205767,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,249870,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,211242,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,149912,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,85389,HS-grad,9,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,806316,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,329980,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,?,236612,11th,7,Divorced,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Local-gov,249214,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,257126,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,204397,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,291979,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,138667,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n4,Federal-gov,42298,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,4,0,2,United-States,>50K\n2,Private,375452,Prof-school,15,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,3,United-States,>50K\n1,Private,94413,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,166626,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,326566,Some-college,10,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,165503,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,4,United-States,<=50K\n3,Private,102597,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,113234,Masters,14,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,177277,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,198103,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,3,2,United-States,<=50K\n2,Private,260490,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,237478,11th,7,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,36885,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,166242,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,158603,10th,6,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n0,Private,274228,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,185145,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,28367,Bachelors,13,Married-civ-spouse,Priv-house-serv,Other-relative,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,28612,HS-grad,9,Widowed,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,191429,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,459548,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,0,Mexico,<=50K\n0,Private,65481,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,186130,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,350759,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,359678,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n1,Private,220595,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,29599,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,299153,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,75256,HS-grad,9,Married-civ-spouse,Priv-house-serv,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,143583,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,207505,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,4,United-States,>50K\n2,Private,308550,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,145717,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,334366,11th,7,Separated,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,?,76198,HS-grad,9,Separated,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,155489,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,197322,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,194259,7th-8th,4,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,346189,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,98361,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,?,178556,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,162943,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,19302,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n4,State-gov,67662,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,126675,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,278228,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,169152,HS-grad,9,Never-married,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,204052,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,215392,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Self-emp-inc,83348,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,196816,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,541343,10th,6,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,55921,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,251701,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,?,<=50K\n1,Federal-gov,119848,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,160572,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,3,United-States,<=50K\n0,Private,25837,11th,7,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,236592,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,State-gov,199326,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,341610,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,?,<=50K\n2,Private,175958,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,198965,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,193537,7th-8th,4,Married-spouse-absent,Other-service,Not-in-family,White,Female,0,0,1,Puerto-Rico,<=50K\n0,Private,438839,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,298227,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,Private,271466,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,335570,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,206891,7th-8th,4,Never-married,Farming-fishing,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,162551,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n2,Private,145637,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,101290,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,229376,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,439592,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,161141,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,304570,Bachelors,13,Widowed,Machine-op-inspct,Other-relative,Asian-Pac-Islander,Male,0,0,1,Philippines,<=50K\n0,Private,103277,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Local-gov,407672,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,73928,Assoc-voc,11,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,240150,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,230417,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,China,>50K\n2,Private,260093,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,96020,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,104421,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,152307,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,2,United-States,>50K\n4,State-gov,93415,HS-grad,9,Widowed,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Local-gov,282664,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Other,Female,0,0,2,?,<=50K\n2,Self-emp-not-inc,269733,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,4,United-States,>50K\n0,Private,202871,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,169683,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,271603,7th-8th,4,Never-married,Other-service,Not-in-family,White,Male,0,0,1,?,<=50K\n1,Private,340917,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,329874,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,43770,Some-college,10,Separated,Other-service,Not-in-family,White,Female,2,0,4,United-States,<=50K\n3,State-gov,120781,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n3,Private,138069,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,33309,HS-grad,9,Widowed,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,76432,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,277635,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Local-gov,123088,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,57698,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,181820,HS-grad,9,Separated,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,98985,HS-grad,9,Divorced,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n4,Private,98350,HS-grad,9,Divorced,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n3,Private,125120,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,243409,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,58972,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Male,1,0,2,United-States,<=50K\n2,Private,62857,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,283174,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,107373,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,201155,9th,5,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,187505,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,61778,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,223648,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,2,0,3,United-States,<=50K\n1,Private,149652,10th,6,Never-married,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n4,Private,170324,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,Trinadad&Tobago,<=50K\n2,Private,165937,HS-grad,9,Divorced,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n4,State-gov,114060,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,58913,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,378916,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,241885,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,224421,Assoc-voc,11,Married-AF-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n1,?,213771,HS-grad,9,Widowed,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,315565,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,Cuba,<=50K\n1,Local-gov,153005,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,98211,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,198606,11th,7,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,260333,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,219510,Bachelors,13,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n4,Private,266624,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,136862,1st-4th,2,Never-married,Other-service,Other-relative,White,Female,0,0,2,Guatemala,<=50K\n3,Self-emp-inc,215620,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,187067,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,Canada,<=50K\n0,Private,325921,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,268127,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,142535,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,177083,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,77009,7th-8th,4,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,306405,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,303918,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,3,0,4,United-States,>50K\n0,Federal-gov,262819,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,49087,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,53833,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,1033222,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,3,0,2,United-States,>50K\n0,Private,81145,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,215479,Some-college,10,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,113464,HS-grad,9,Never-married,Transport-moving,Other-relative,Other,Male,0,0,2,Dominican-Republic,<=50K\n4,Private,109530,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n4,Federal-gov,217864,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,117721,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,199484,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,248851,Bachelors,13,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,116968,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,366618,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,240143,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n4,?,424468,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n4,?,320280,Some-college,10,Never-married,?,Not-in-family,White,Male,1,0,0,United-States,<=50K\n0,Private,120238,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,<=50K\n3,?,194186,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,247053,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,180599,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,190330,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,199450,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,199539,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,?,94366,10th,6,Never-married,?,Other-relative,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,29231,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,33126,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,102085,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,212064,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,State-gov,166774,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,Private,95303,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,?,379768,HS-grad,9,Never-married,?,Own-child,Other,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,247383,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,229465,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,135436,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,180052,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,214387,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,149337,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,208326,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,34374,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,58683,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,403037,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,32365,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,155489,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,289886,HS-grad,9,Never-married,Other-service,Unmarried,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n1,Federal-gov,54684,Prof-school,15,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,?,<=50K\n0,Private,101549,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,51579,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,40151,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,244721,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,>50K\n3,Local-gov,228372,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,236873,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,250249,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,93202,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,176723,Some-college,10,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,175526,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,91842,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,71768,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,181220,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,204516,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,89172,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n2,Federal-gov,143547,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,310889,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,150324,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,216472,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,212838,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,168283,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,187702,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,60661,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,115284,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,98350,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,>50K\n0,Private,195372,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,81578,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,111567,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,244572,HS-grad,9,Separated,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,230919,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,282604,Some-college,10,Married-civ-spouse,Protective-serv,Other-relative,White,Male,0,0,1,United-States,<=50K\n3,Private,320196,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,Germany,<=50K\n2,Private,201466,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,254211,Masters,14,Widowed,Sales,Unmarried,White,Male,0,0,3,El-Salvador,>50K\n2,Private,599629,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,>50K\n3,Local-gov,219632,Assoc-acdm,12,Separated,Exec-managerial,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,State-gov,161631,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,202373,Assoc-voc,11,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,169549,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,127185,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,184277,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,119751,HS-grad,9,Married-civ-spouse,Priv-house-serv,Other-relative,Asian-Pac-Islander,Female,0,0,3,Philippines,<=50K\n0,Private,294701,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,26842,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,State-gov,114537,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,126386,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,163787,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,98211,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,175509,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,159854,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,120920,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,187551,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n2,State-gov,27305,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,216711,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,218596,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,280292,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,200496,Bachelors,13,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,78090,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,118693,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,203488,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,172091,HS-grad,9,Never-married,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,113364,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,139889,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Local-gov,301638,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,1,2,United-States,<=50K\n1,Private,110279,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,242859,Some-college,10,Separated,Adm-clerical,Own-child,White,Male,0,0,2,Cuba,<=50K\n0,Private,132986,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,254439,10th,6,Widowed,Transport-moving,Unmarried,Black,Male,1,0,2,United-States,<=50K\n2,Federal-gov,187462,Assoc-voc,11,Divorced,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,264961,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,148065,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n3,Self-emp-inc,200949,Bachelors,13,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,3,?,<=50K\n3,Private,47247,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,571017,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,416577,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,1,0,2,United-States,<=50K\n3,State-gov,296991,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,State-gov,45961,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,2,0,2,United-States,<=50K\n3,Private,302711,11th,7,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,50356,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,199336,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,341178,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Federal-gov,70240,Some-college,10,Divorced,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n3,Private,229394,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,390368,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,4,0,4,United-States,>50K\n3,Private,82098,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n4,Private,170411,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,109532,12th,8,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,142682,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,Dominican-Republic,<=50K\n1,Self-emp-inc,127651,Bachelors,13,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,236472,Bachelors,13,Divorced,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,176047,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,1,0,2,United-States,<=50K\n2,Private,111499,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,425199,Some-college,10,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,229009,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,232713,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,1,0,1,United-States,<=50K\n4,Private,141742,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,234807,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,738812,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,204816,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,342494,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,226311,Some-college,10,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,143062,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,125155,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,329925,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,?,208994,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Local-gov,212864,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,214242,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n3,Self-emp-not-inc,191175,5th-6th,3,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,Mexico,<=50K\n0,Private,118693,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,253593,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,206051,Some-college,10,Married-spouse-absent,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,497280,9th,5,Widowed,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,240562,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,140985,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,Local-gov,191921,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,204049,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n2,Private,331651,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,3,0,3,United-States,>50K\n4,Private,142158,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,249046,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,213019,Assoc-voc,11,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,199599,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,186191,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n0,Private,28008,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,82488,Bachelors,13,Married-civ-spouse,Sales,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n2,Private,117073,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,325786,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,37546,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,204226,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,133299,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,29702,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,307812,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,174545,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,233472,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,184147,HS-grad,9,Separated,Sales,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,198188,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,447066,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,4,0,3,United-States,>50K\n1,Private,200246,Some-college,10,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,166585,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,335570,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,?,<=50K\n2,Private,53569,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,167065,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,113364,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,219266,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Federal-gov,200042,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,205975,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,234083,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,4,2,United-States,<=50K\n4,Private,65325,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,194740,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,99065,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,212793,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,112941,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,187322,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,283676,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,173682,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,168470,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,186454,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,4,0,2,United-States,>50K\n4,Private,141807,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Italy,<=50K\n0,Private,245628,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,0,Mexico,<=50K\n1,Private,264864,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,262841,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,37438,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,170800,Assoc-voc,11,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,152150,Assoc-acdm,12,Separated,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,?,211873,Assoc-voc,11,Married-civ-spouse,?,Wife,White,Female,0,2,0,?,<=50K\n2,Private,159580,12th,8,Divorced,Transport-moving,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,477209,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,70985,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,241998,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,249541,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,135339,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n1,Private,44675,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n3,State-gov,247992,7th-8th,4,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,221626,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,1,0,United-States,<=50K\n2,Self-emp-inc,48087,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,114045,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,State-gov,69251,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,>50K\n4,Private,192670,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,268392,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,?,170994,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,431513,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n0,State-gov,37332,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,35865,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,183891,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,150309,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,4,United-States,<=50K\n4,Private,93318,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,171814,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,183735,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,353541,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Local-gov,152351,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,<=50K\n4,?,271352,10th,6,Divorced,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,345705,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,223751,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,164570,11th,7,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,?,281363,10th,6,Widowed,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,110747,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,34458,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,254293,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,270147,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Private,195491,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,255454,Bachelors,13,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,126125,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,618191,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,163110,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,145409,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,3,United-States,>50K\n2,State-gov,235379,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,55465,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Local-gov,181220,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n2,Private,26672,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,98361,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,219883,HS-grad,9,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,376683,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,1,0,1,United-States,<=50K\n3,Private,33865,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,168794,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,94245,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,34572,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,348751,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,65382,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,116707,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,178054,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,?,>50K\n0,Private,140001,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,166889,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Female,0,2,1,United-States,<=50K\n0,Private,117789,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,238917,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Local-gov,242923,HS-grad,9,Married-civ-spouse,Tech-support,Wife,White,Female,0,2,2,United-States,>50K\n3,Local-gov,330799,9th,5,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,209460,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,75313,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,4,United-States,>50K\n1,?,339100,11th,7,Divorced,?,Not-in-family,White,Female,1,0,3,United-States,<=50K\n0,Private,184779,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,139000,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,361742,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,260782,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,?,<=50K\n3,Private,203435,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,100579,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,356067,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,87250,Bachelors,13,Separated,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,264663,Some-college,10,Separated,Prof-specialty,Own-child,White,Female,0,4,2,United-States,<=50K\n1,Private,255817,5th-6th,3,Never-married,Other-service,Other-relative,White,Female,0,0,2,El-Salvador,<=50K\n3,Self-emp-not-inc,243631,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,1,South,<=50K\n1,Self-emp-inc,544268,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,98061,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,95691,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,Columbia,<=50K\n3,Private,145868,11th,7,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,65038,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,227734,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n0,Local-gov,176831,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Private,211678,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,157240,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,145441,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Yugoslavia,<=50K\n4,Self-emp-inc,66624,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n2,Private,76487,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,557853,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,262352,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,118253,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,146625,11th,7,Widowed,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,174201,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,?,66695,Some-college,10,Never-married,?,Own-child,Other,Female,1,0,1,United-States,<=50K\n2,Private,121130,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,385847,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,83439,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,114158,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,381789,12th,8,Married-civ-spouse,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,82041,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,Canada,<=50K\n1,Self-emp-not-inc,115618,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,106110,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,4,United-States,<=50K\n2,Private,267521,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,90692,Assoc-voc,11,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,Private,57101,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,236913,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,388625,10th,6,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n3,Self-emp-not-inc,261207,7th-8th,4,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,Cuba,<=50K\n2,Private,245487,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Mexico,<=50K\n1,Private,262153,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n2,Private,225516,Assoc-acdm,12,Never-married,Sales,Not-in-family,Black,Male,4,0,2,United-States,>50K\n1,Self-emp-not-inc,68729,HS-grad,9,Never-married,Sales,Other-relative,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n2,Private,126954,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,85074,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,383306,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,128143,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n3,Private,185041,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,99373,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,157942,HS-grad,9,Widowed,Transport-moving,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,241928,HS-grad,9,Separated,Adm-clerical,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n2,Private,348739,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,95654,10th,6,Divorced,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,367306,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,270421,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,?,221592,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,156951,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,>50K\n2,State-gov,39239,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,72744,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,367292,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,408498,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,361493,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,1,0,2,United-States,<=50K\n4,Self-emp-inc,157403,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,231263,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,244147,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,0,United-States,<=50K\n0,Private,220944,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,314007,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,200862,10th,6,Never-married,?,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,33374,11th,7,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,345489,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,83601,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,162302,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,112847,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,147344,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n4,State-gov,183657,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,1,United-States,>50K\n2,Private,130760,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,163948,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,316797,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Own-child,White,Male,0,0,2,Mexico,<=50K\n3,Federal-gov,332243,12th,8,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,195844,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,387250,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,188303,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,3,0,2,United-States,>50K\n4,?,40956,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,178953,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,398988,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,535978,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,296982,Some-college,10,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,231991,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,295799,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,201569,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,193568,11th,7,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,97128,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,203393,Bachelors,13,Married-civ-spouse,Craft-repair,Wife,Black,Female,0,0,1,United-States,>50K\n3,Private,138370,Masters,14,Married-spouse-absent,Protective-serv,Not-in-family,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n2,Self-emp-inc,120277,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,<=50K\n2,Private,91949,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,228372,Bachelors,13,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,>50K\n1,Private,132191,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,274683,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n3,Local-gov,196307,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n4,Private,195835,Some-college,10,Married-spouse-absent,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,185399,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,103684,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,140559,HS-grad,9,Married-civ-spouse,Priv-house-serv,Wife,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,110188,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n1,Local-gov,668319,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,4,United-States,<=50K\n1,Private,112358,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,151810,10th,6,Never-married,Farming-fishing,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,48120,HS-grad,9,Never-married,Transport-moving,Unmarried,Black,Female,1,0,2,United-States,<=50K\n3,Private,144844,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,205839,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,113760,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,138358,10th,6,Separated,Adm-clerical,Not-in-family,Black,Female,0,0,3,Jamaica,<=50K\n3,Self-emp-not-inc,216657,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,278576,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,174373,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,220019,9th,5,Widowed,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,?,311949,HS-grad,9,Never-married,?,Not-in-family,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n1,Private,303867,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,154210,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Hong,<=50K\n1,?,131310,12th,8,Married-civ-spouse,?,Wife,White,Female,0,0,0,Germany,<=50K\n3,Private,202560,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,?,358783,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,423024,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,206671,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,245310,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,31983,12th,8,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,124956,Bachelors,13,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,4,United-States,>50K\n4,Private,118358,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,491421,5th-6th,3,Never-married,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,151580,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,248990,1st-4th,2,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n2,Private,157425,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,221650,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Japan,<=50K\n4,Private,88055,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,216608,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,682947,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,228124,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,217194,10th,6,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,171540,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,210827,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Self-emp-not-inc,410351,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Poland,<=50K\n1,Private,163747,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,108892,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,180096,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,153890,12th,8,Widowed,Exec-managerial,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Private,117480,10th,6,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,163333,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,306710,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,150553,Some-college,10,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,0,Philippines,<=50K\n4,Private,123959,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,24961,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,327120,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,33798,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,81929,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,2,United-States,>50K\n0,Private,298489,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,101697,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,144064,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,195835,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,184723,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,Private,265086,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,235909,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,42645,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,279878,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,104892,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,137063,HS-grad,9,Never-married,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,58972,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,126675,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,286435,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,1,0,2,United-States,<=50K\n3,Private,191389,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,183241,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,91547,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,52781,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,210959,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,365516,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,112271,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,269455,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,164379,Bachelors,13,Divorced,Sales,Unmarried,Black,Female,0,0,1,United-States,>50K\n1,Private,109621,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,104858,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,99270,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,193524,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,149040,HS-grad,9,Widowed,?,Not-in-family,White,Female,1,0,0,United-States,<=50K\n4,State-gov,313946,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,162358,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,200700,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,>50K\n0,Private,116489,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,118310,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,352465,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,107433,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,296538,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,195897,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,216283,Assoc-acdm,12,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,>50K\n4,Private,345780,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,216685,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,210945,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n2,Private,184321,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,2,2,United-States,>50K\n3,Self-emp-not-inc,322691,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,1,0,3,United-States,>50K\n2,Private,192712,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,178272,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,321333,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,120277,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,294029,11th,7,Never-married,Sales,Own-child,Other,Female,0,0,1,Nicaragua,<=50K\n0,Private,112819,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,152636,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,?,301611,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,134808,HS-grad,9,Separated,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,64216,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,State-gov,214284,Masters,14,Never-married,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,0,Taiwan,<=50K\n0,Private,469572,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,3,0,2,United-States,>50K\n2,Self-emp-not-inc,282722,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,231439,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,120277,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,364685,11th,7,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,18827,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,169129,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,202051,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,353244,Bachelors,13,Widowed,?,Unmarried,White,Female,4,0,3,United-States,>50K\n0,Private,574271,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,State-gov,29276,7th-8th,4,Widowed,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,104501,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,394176,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,85625,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,340723,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,149342,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,73715,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,143083,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,290660,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Male,3,0,3,United-States,>50K\n3,Local-gov,98738,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,149912,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,309033,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,3,United-States,>50K\n2,Self-emp-not-inc,96129,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,216096,Some-college,10,Married-spouse-absent,Exec-managerial,Unmarried,White,Female,0,0,1,Puerto-Rico,<=50K\n1,Private,171091,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,79303,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,182380,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,36271,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,118197,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,269652,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Local-gov,193815,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,141957,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,2,4,United-States,>50K\n1,Private,222637,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,Puerto-Rico,<=50K\n1,Private,118230,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,174040,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,State-gov,105748,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,82628,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,1,0,0,United-States,<=50K\n3,Private,205100,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,107916,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,130620,7th-8th,4,Married-spouse-absent,Machine-op-inspct,Unmarried,Other,Female,0,0,2,Dominican-Republic,<=50K\n1,?,361817,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,235646,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,53277,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,456460,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,293091,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,210935,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n3,?,199763,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n4,?,223447,12th,8,Divorced,?,Not-in-family,White,Male,0,0,2,Canada,<=50K\n1,Self-emp-not-inc,233533,Bachelors,13,Separated,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,95647,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,199763,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,74539,10th,6,Never-married,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,84610,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,96930,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,115602,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,<=50K\n0,Private,237341,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,143800,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,163921,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,68273,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,113163,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,478829,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,345705,Some-college,10,Married-civ-spouse,Exec-managerial,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,385077,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,1,0,2,United-States,<=50K\n1,Private,192286,Some-college,10,Divorced,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,3,United-States,<=50K\n2,Local-gov,236391,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,106679,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,?,308242,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,46094,Bachelors,13,Divorced,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,194940,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,341643,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,210474,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,76313,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n1,Private,115858,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,55191,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,364862,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,334787,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,205733,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,120163,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,333677,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,1,0,1,United-States,<=50K\n0,Private,208591,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,341204,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,1,0,1,United-States,<=50K\n4,Self-emp-not-inc,115422,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,111319,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,816750,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n0,Private,167835,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,92262,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,91964,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,107682,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,State-gov,135388,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n2,Self-emp-not-inc,597843,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,Columbia,<=50K\n0,Private,389942,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,442274,12th,8,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,595461,7th-8th,4,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n3,Private,284329,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n1,Self-emp-not-inc,127894,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,196899,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,Asian-Pac-Islander,Female,0,0,3,Haiti,<=50K\n4,Private,212534,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,71209,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,237943,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n2,Private,190759,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,100313,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,344624,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,?,194024,9th,5,Separated,?,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,87497,11th,7,Never-married,Transport-moving,Other-relative,White,Male,0,0,0,United-States,<=50K\n0,Private,236907,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,169639,Assoc-acdm,12,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,105803,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,149507,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,294387,11th,7,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,161708,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,282389,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,64940,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,195727,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,37931,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,170720,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,152958,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,312372,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,4,0,2,United-States,>50K\n2,Private,39581,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,1,El-Salvador,<=50K\n3,Private,206862,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,216934,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,Portugal,<=50K\n0,Private,143062,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,242391,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,165030,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,199251,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,353012,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n4,Private,174491,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,333305,Some-college,10,Married-civ-spouse,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,203138,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,220220,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,305850,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Local-gov,273402,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,<=50K\n4,Private,201344,Some-college,10,Widowed,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,218676,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,141807,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,222434,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,266860,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,34113,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Male,2,0,2,United-States,<=50K\n2,Private,159549,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,195248,Some-college,10,Never-married,Sales,Own-child,Other,Female,0,0,0,United-States,<=50K\n3,Private,109413,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,195343,Doctorate,16,Divorced,Prof-specialty,Unmarried,White,Male,4,0,3,United-States,>50K\n3,Private,185291,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n0,?,140012,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,114366,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,169631,HS-grad,9,Married-spouse-absent,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,163870,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,312232,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,229737,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,India,>50K\n4,?,306563,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,161637,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,3,2,Taiwan,>50K\n1,Private,106014,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,25265,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,71860,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,94113,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,208003,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,113550,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,83046,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,277488,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n0,Private,205830,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,El-Salvador,<=50K\n3,Private,273575,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,245147,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,274720,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,163047,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,47902,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,128798,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,154205,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,176683,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,104737,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,349340,Preschool,1,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n2,State-gov,218249,Some-college,10,Separated,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,281540,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,112847,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Local-gov,126613,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,145419,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n1,Self-emp-not-inc,34572,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,104045,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,?,57665,Bachelors,13,Divorced,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,359001,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,105273,Bachelors,13,Widowed,Craft-repair,Unmarried,Black,Female,2,0,2,United-States,<=50K\n1,Private,201122,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,160035,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,167886,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,32059,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,200453,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,403072,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,37210,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,199416,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,413227,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,?,188675,Some-college,10,Divorced,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,226902,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,195189,Some-college,10,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,116608,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,99131,Masters,14,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,553405,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n3,Local-gov,186117,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,67053,HS-grad,9,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Thailand,<=50K\n2,Private,347960,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,4,0,1,United-States,>50K\n2,Private,325374,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,130413,Bachelors,13,Widowed,Exec-managerial,Not-in-family,White,Female,1,0,0,United-States,<=50K\n2,Private,111949,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,278557,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,<=50K\n0,Private,194905,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,195453,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,282549,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,1,0,2,United-States,<=50K\n4,Private,316119,Some-college,10,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,State-gov,252939,Assoc-voc,11,Never-married,Prof-specialty,Unmarried,Black,Female,2,0,2,United-States,<=50K\n0,State-gov,506329,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n0,Private,316043,11th,7,Never-married,Other-service,Own-child,Black,Male,1,0,0,United-States,<=50K\n4,Federal-gov,319733,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,State-gov,99199,Masters,14,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,204600,HS-grad,9,Separated,Protective-serv,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,173307,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,34446,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,237293,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,2,United-States,>50K\n2,Private,175642,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,203735,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,171589,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,197967,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,413297,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Private,240841,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,152189,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,85874,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,176814,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Local-gov,133336,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,362623,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,37170,7th-8th,4,Divorced,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,30912,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,35448,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,173248,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,1,United-States,<=50K\n2,Private,49626,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,102723,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,166343,1st-4th,2,Widowed,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,168322,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,131117,7th-8th,4,Divorced,Tech-support,Unmarried,White,Female,0,0,2,Columbia,<=50K\n0,?,210474,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,110138,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,107452,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,160594,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,186784,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,2,United-States,<=50K\n4,Local-gov,334666,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,?,191380,10th,6,Married-civ-spouse,?,Husband,White,Male,3,0,3,United-States,>50K\n4,Private,104272,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,19491,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,128715,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,128063,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,37023,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,68748,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,140576,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,327435,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,202729,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,277471,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,189670,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,3,United-States,<=50K\n4,Private,204908,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,171841,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,78247,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,68895,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n1,Private,56658,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n4,Local-gov,259216,9th,5,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,270278,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,Puerto-Rico,<=50K\n4,Private,238806,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,111128,Some-college,10,Separated,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,119429,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,73037,10th,6,Never-married,Transport-moving,Unmarried,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,84409,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,274451,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,246439,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,124242,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,123393,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,159732,HS-grad,9,Widowed,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,161415,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,157568,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,168030,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,State-gov,349910,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Self-emp-inc,130329,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,56964,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,370509,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,France,>50K\n0,Private,106306,Some-college,10,Divorced,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,56480,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,115932,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,154580,10th,6,Married-civ-spouse,Other-service,Husband,Black,Male,1,0,2,United-States,<=50K\n1,Private,404421,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,194901,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,164790,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,3,United-States,>50K\n4,Federal-gov,94242,Some-college,10,Widowed,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,365020,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,160512,HS-grad,9,Separated,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,170331,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,101266,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,100252,Bachelors,13,Divorced,Other-service,Not-in-family,Asian-Pac-Islander,Male,4,0,4,United-States,>50K\n3,Private,217718,5th-6th,3,Married-spouse-absent,Other-service,Unmarried,Black,Female,0,0,1,Haiti,<=50K\n1,Private,170154,Assoc-acdm,12,Separated,Exec-managerial,Unmarried,White,Female,4,0,3,United-States,>50K\n4,Private,105281,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,3,2,United-States,<=50K\n2,?,361838,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n2,State-gov,283917,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,39530,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,212185,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-inc,90752,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,202450,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,1,2,United-States,<=50K\n1,Private,168138,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Male,1,0,3,United-States,<=50K\n3,Private,159755,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,191765,HS-grad,9,Never-married,Adm-clerical,Other-relative,Black,Female,0,4,2,Trinadad&Tobago,<=50K\n0,?,210802,Some-college,10,Never-married,?,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,340880,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,113211,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,134509,Some-college,10,Never-married,Transport-moving,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,State-gov,147280,HS-grad,9,Never-married,Other-service,Other-relative,Other,Male,0,0,2,United-States,<=50K\n2,Private,145441,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,398001,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,31588,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,189975,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,>50K\n3,State-gov,231495,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n2,?,121135,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,186916,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,213140,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,176893,HS-grad,9,Divorced,Craft-repair,Not-in-family,Black,Male,3,0,2,United-States,>50K\n0,Private,115244,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,313243,Some-college,10,Separated,Craft-repair,Not-in-family,White,Male,0,4,2,United-States,>50K\n2,Local-gov,169995,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,198459,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,3,2,United-States,<=50K\n1,Local-gov,66824,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Female,1,0,2,United-States,<=50K\n3,Self-emp-not-inc,52240,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,35305,7th-8th,4,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,State-gov,186451,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Self-emp-not-inc,160724,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,China,>50K\n1,Private,210464,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,207685,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,233717,Some-college,10,Divorced,Exec-managerial,Unmarried,Black,Male,0,0,3,United-States,<=50K\n1,Private,222205,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,167613,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,148773,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,68268,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,174533,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,273230,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,187502,HS-grad,9,Never-married,Sales,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Private,209320,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,56841,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,254627,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,42703,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,374137,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,196385,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,192930,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,99527,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,185437,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,247162,Assoc-acdm,12,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,131534,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,184693,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,Mexico,<=50K\n1,Private,704108,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,220262,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,95654,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,<=50K\n4,Private,89346,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,94392,11th,7,Separated,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,334113,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,32763,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,136651,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Self-emp-not-inc,240236,Assoc-acdm,12,Separated,Sales,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,53271,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,31493,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,>50K\n1,Private,195891,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,209103,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,211424,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,84657,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,151408,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,106819,7th-8th,4,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,132917,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,0,United-States,<=50K\n3,Private,146834,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,164332,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,30656,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,113501,Masters,14,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,165316,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,233955,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Female,4,0,2,United-States,>50K\n0,Private,126613,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,361280,Some-college,10,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,4,Philippines,>50K\n3,?,123044,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,165472,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,99452,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,84977,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,240458,11th,7,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,230858,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,>50K\n4,Private,123218,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,191118,Some-college,10,Married-civ-spouse,?,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,115289,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,373895,Some-college,10,Separated,Handlers-cleaners,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Private,152617,Some-college,10,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,State-gov,72619,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,41865,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,190228,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,193090,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,138692,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n4,Self-emp-inc,153183,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n0,Private,181896,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,268183,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,3,United-States,<=50K\n3,Local-gov,213668,11th,7,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,99369,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Other,Female,0,0,3,United-States,<=50K\n2,Private,104196,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,176839,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,99502,Assoc-voc,11,Divorced,Protective-serv,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,Private,183410,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,25690,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,201986,11th,7,Widowed,?,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Private,188961,Assoc-acdm,12,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,114971,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,121468,Bachelors,13,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,191540,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,146398,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,193553,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n4,Private,121127,10th,6,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,389856,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,290504,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,State-gov,137065,Doctorate,16,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Local-gov,212685,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,71475,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,111450,Some-college,10,Never-married,Adm-clerical,Other-relative,Black,Male,0,0,1,United-States,<=50K\n1,Private,225860,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,129853,10th,6,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,99925,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,227800,1st-4th,2,Separated,Farming-fishing,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n3,State-gov,111130,Assoc-acdm,12,Divorced,Adm-clerical,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,100764,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,275095,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,147500,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,<=50K\n4,Local-gov,150079,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,140863,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,199198,11th,7,Divorced,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,193372,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,196771,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,231826,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Mexico,<=50K\n2,Federal-gov,196456,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,34037,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,174964,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,91608,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,403468,Some-college,10,Separated,Other-service,Unmarried,White,Female,0,0,3,Mexico,<=50K\n1,Private,112900,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,242670,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,187830,HS-grad,9,Divorced,Tech-support,Unmarried,White,Male,2,0,2,United-States,>50K\n0,Self-emp-not-inc,368115,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,1,United-States,>50K\n3,Private,343242,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,113390,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,3,United-States,<=50K\n1,Private,200733,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,236769,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,22494,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,129379,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,239098,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,167501,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,77146,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,82797,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,134886,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n2,Self-emp-inc,218558,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,207568,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,196899,Assoc-acdm,12,Separated,Craft-repair,Not-in-family,Other,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,200960,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,188069,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,?,<=50K\n4,Private,232337,7th-8th,4,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,98656,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,194260,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,?,481987,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,234976,11th,7,Never-married,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,349116,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,175390,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,187720,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,?,>50K\n1,Private,214637,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,185127,Assoc-voc,11,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,98752,9th,5,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,218382,Some-college,10,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,153486,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,174102,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,137142,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,241013,7th-8th,4,Widowed,Farming-fishing,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,267798,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,?,152880,HS-grad,9,Divorced,?,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,263561,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,113324,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,39764,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,172186,Some-college,10,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,460408,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,<=50K\n2,Self-emp-not-inc,185129,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,61270,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,124685,Masters,14,Divorced,Exec-managerial,Not-in-family,Asian-Pac-Islander,Male,0,0,4,Japan,>50K\n4,Self-emp-not-inc,76968,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n4,?,310396,9th,5,Married-civ-spouse,?,Husband,White,Male,2,0,2,United-States,>50K\n1,Federal-gov,37933,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,1,0,2,United-States,<=50K\n0,Private,38772,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,172496,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,306164,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,33795,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,47686,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,193132,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,400004,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,101283,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,192384,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,113838,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,278322,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n4,Private,199713,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,236021,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,138938,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,126946,Some-college,10,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,44791,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,31964,9th,5,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,352156,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,205860,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,113106,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,89182,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,250782,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,193855,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,<=50K\n3,Self-emp-not-inc,132716,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,218637,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,4,3,United-States,>50K\n1,Private,177955,11th,7,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Mexico,<=50K\n1,Private,198660,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,207937,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,168740,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,199625,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,213902,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,Mexico,<=50K\n2,Private,208379,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,113120,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,57827,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,515712,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,54190,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Self-emp-inc,134793,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,396270,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,231620,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n3,Private,174655,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,?,97823,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,344480,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,176732,9th,5,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,143932,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,551962,HS-grad,9,Separated,Handlers-cleaners,Unmarried,White,Female,0,0,3,Peru,<=50K\n1,?,298577,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,257942,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,253062,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,193748,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,368561,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,192964,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,4,United-States,<=50K\n1,Private,217304,Bachelors,13,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n0,Private,120029,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,62124,HS-grad,9,Separated,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,94885,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,192565,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,220912,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,184120,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,140782,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-inc,170785,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,90705,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,108293,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,168283,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,339372,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Private,193672,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,143865,10th,6,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,209317,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,Dominican-Republic,<=50K\n1,State-gov,204461,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,137088,HS-grad,9,Married-civ-spouse,Craft-repair,Other-relative,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,149102,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,182855,10th,6,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,572751,Preschool,1,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Nicaragua,<=50K\n0,Private,83451,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,98116,Bachelors,13,Widowed,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,119225,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,134888,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,745817,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,88368,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,State-gov,122066,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,363219,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,84402,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,34626,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,3,2,United-States,<=50K\n1,Private,150042,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,48014,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,177398,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,373698,12th,8,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,<=50K\n1,Private,422933,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,131088,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,178255,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,Columbia,<=50K\n3,Self-emp-not-inc,129311,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,473171,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,236985,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n1,?,226379,HS-grad,9,Married-civ-spouse,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,?,277700,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,207568,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,85708,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,98765,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,Canada,<=50K\n1,Private,192283,Some-college,10,Never-married,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,State-gov,271012,10th,6,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,189265,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,321880,10th,6,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Private,177465,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,127647,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,State-gov,119033,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,289748,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,2,0,3,United-States,<=50K\n1,Private,209317,HS-grad,9,Separated,Exec-managerial,Not-in-family,White,Male,0,0,2,?,<=50K\n1,Private,284531,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,251120,7th-8th,4,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,113870,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Without-pay,170114,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,121124,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,328199,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,206307,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,236021,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n4,Federal-gov,170603,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,74275,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n1,Self-emp-not-inc,112271,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,118306,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,126754,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,267205,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,?,>50K\n2,Private,205359,11th,7,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,398662,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,202498,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Columbia,<=50K\n1,Private,105650,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n3,Private,191204,Assoc-voc,11,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,56582,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,51579,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,152030,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,227310,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,55854,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,28996,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,160634,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,222450,11th,7,Married-spouse-absent,Other-service,Other-relative,White,Male,0,0,2,El-Salvador,<=50K\n2,Self-emp-inc,180419,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,116084,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,202521,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,186014,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,88368,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,1,0,2,United-States,<=50K\n2,State-gov,190044,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Self-emp-not-inc,35330,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,84848,Some-college,10,Never-married,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,176280,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,145271,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Local-gov,108320,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,106377,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,258730,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,2,Japan,<=50K\n1,Private,58305,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,341672,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Private,176648,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,?,32616,Bachelors,13,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,481175,Some-college,10,Never-married,Exec-managerial,Own-child,Other,Male,0,0,1,Peru,<=50K\n3,Private,187454,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,4,United-States,>50K\n0,Private,25837,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,385077,12th,8,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,68985,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,181572,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,23698,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,?,268127,12th,8,Separated,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,162298,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,144608,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,250630,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,150441,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,189251,Doctorate,16,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,260082,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Columbia,<=50K\n2,Private,139126,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,50132,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,167691,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,77820,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,156513,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,248059,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,283092,11th,7,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,1,Jamaica,<=50K\n0,Private,175883,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,232308,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,269991,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,Puerto-Rico,<=50K\n0,Private,305446,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,120781,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,4,0,2,?,>50K\n4,Private,78707,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,351802,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,196529,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Self-emp-inc,175769,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,153021,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,331902,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,279461,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,145704,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,1,United-States,<=50K\n1,State-gov,205499,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,293926,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,1,United-States,<=50K\n1,Self-emp-not-inc,69132,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,113099,HS-grad,9,Separated,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,206947,Assoc-acdm,12,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,State-gov,159782,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,410543,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,34446,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,209101,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Federal-gov,95902,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,214323,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,236323,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,201127,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,142886,Bachelors,13,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,77313,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,212125,10th,6,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,187098,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,196857,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,155314,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Self-emp-not-inc,203289,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,117059,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n1,Private,178587,Some-college,10,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,82393,9th,5,Never-married,Handlers-cleaners,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n0,?,145258,11th,7,Never-married,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,185145,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,?,>50K\n3,Private,72896,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,134886,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,223212,Preschool,1,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Self-emp-not-inc,174752,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,230563,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,State-gov,353824,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,117363,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,285367,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,139391,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,198170,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,38948,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,188515,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,177810,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,188432,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,3,United-States,>50K\n1,Private,178506,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,129298,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,165315,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,117236,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,?,172214,HS-grad,9,Never-married,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,63434,12th,8,Never-married,Farming-fishing,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,140854,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,133043,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,113176,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,259301,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,196643,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,364365,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,269318,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,108454,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,171637,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,183589,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,107801,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,179877,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,168981,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,120590,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,310773,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,2,Mexico,<=50K\n0,Private,197050,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,159726,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,210797,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,55291,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,141221,Bachelors,13,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,3,1,South,<=50K\n0,Private,276718,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,336163,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,112840,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,165918,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,Peru,<=50K\n3,Private,165745,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,259299,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,3,United-States,>50K\n0,State-gov,197731,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,197702,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,162238,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n2,Private,213260,HS-grad,9,Separated,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,53833,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,89419,HS-grad,9,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,119704,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,433170,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,182714,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,1,?,<=50K\n2,Private,172538,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,?,220115,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,158956,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,25631,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,476558,7th-8th,4,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n3,Federal-gov,35576,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,203463,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,317647,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,170411,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,174182,11th,7,Married-civ-spouse,?,Wife,Other,Female,0,0,1,United-States,<=50K\n3,Private,220055,Bachelors,13,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,231482,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,335979,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,1,0,0,United-States,<=50K\n1,Private,279173,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,89559,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,161950,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,0,0,1,Germany,<=50K\n3,Private,131068,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,219632,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,175507,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,182062,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,287476,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,1,0,2,United-States,<=50K\n2,Private,206253,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,2,2,United-States,<=50K\n0,?,189203,Assoc-acdm,12,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,21698,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,328051,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,356689,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Male,2,0,2,United-States,<=50K\n4,Private,121865,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,420986,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,?,218558,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,288992,10th,6,Divorced,Prof-specialty,Unmarried,White,Male,4,0,4,United-States,>50K\n0,?,189740,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,188909,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,213081,11th,7,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,Jamaica,<=50K\n0,Self-emp-not-inc,157131,11th,7,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,98010,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,207677,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,361870,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,1,United-States,<=50K\n4,Private,266091,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n2,Private,106627,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,3,United-States,<=50K\n3,Self-emp-inc,167793,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n4,Self-emp-not-inc,206682,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,1,United-States,<=50K\n1,Private,243165,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,201928,HS-grad,9,Widowed,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,128346,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,197288,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n0,?,169184,Some-college,10,Never-married,?,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Private,245521,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,Mexico,<=50K\n2,Private,129591,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,47415,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,2,1,United-States,<=50K\n2,Self-emp-not-inc,188563,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Self-emp-not-inc,184710,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,63734,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,111256,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,111483,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,266639,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,93853,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,184207,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,238002,9th,5,Married-civ-spouse,Transport-moving,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,?,30237,Some-college,10,Never-married,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,196545,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,144844,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,280500,Some-college,10,Never-married,Tech-support,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,?,135601,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,409189,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,Mexico,<=50K\n3,Private,23686,Some-college,10,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,>50K\n0,Private,229756,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,95530,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,73199,Assoc-voc,11,Divorced,Tech-support,Unmarried,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Private,196745,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,79481,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,116934,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,100950,Assoc-voc,11,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,Germany,<=50K\n2,Local-gov,56651,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,186954,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,264874,Some-college,10,Never-married,Tech-support,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,State-gov,183092,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Local-gov,273399,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,Peru,<=50K\n1,?,142443,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,177526,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,31267,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,321666,Assoc-acdm,12,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,331861,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,?,<=50K\n0,Private,283515,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,54608,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,162238,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,175931,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,236804,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,168782,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,227065,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,285335,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,259705,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,24384,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,322013,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,49797,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,52566,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,266275,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,285408,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,1,United-States,>50K\n1,Self-emp-not-inc,177858,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,2,2,United-States,<=50K\n2,Federal-gov,183804,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,107231,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,173679,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,163965,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,173585,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,Peru,<=50K\n1,Private,172009,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,44363,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,246392,HS-grad,9,Never-married,Priv-house-serv,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,167033,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,143822,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,37869,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,447488,9th,5,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,1,Mexico,<=50K\n0,Private,239346,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,245975,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,34632,12th,8,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,121362,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,4,2,United-States,>50K\n0,State-gov,24008,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,165492,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,326048,Assoc-acdm,12,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,250821,Prof-school,15,Divorced,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,154641,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,198202,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,170504,Bachelors,13,Never-married,Transport-moving,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,191342,Some-college,10,Never-married,Sales,Not-in-family,Other,Male,0,0,2,India,<=50K\n0,Private,238969,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,344128,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,?,148694,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,180187,Assoc-acdm,12,Widowed,?,Not-in-family,White,Female,0,0,0,Italy,<=50K\n2,State-gov,168894,Assoc-voc,11,Married-spouse-absent,Protective-serv,Own-child,White,Female,0,0,2,Germany,<=50K\n0,Private,203263,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,State-gov,89564,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,97562,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,336540,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,139647,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,160192,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n3,Local-gov,320386,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,32126,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,275445,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,54953,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,103580,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,>50K\n2,Private,245565,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,England,<=50K\n1,Private,39223,10th,6,Separated,Craft-repair,Unmarried,Black,Female,0,0,2,?,<=50K\n3,State-gov,117357,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,4,?,>50K\n4,Private,207385,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,355287,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n4,?,141218,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n3,Local-gov,207677,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,102114,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,60269,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,278632,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,355551,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Female,0,0,2,Mexico,<=50K\n2,Private,246891,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,Canada,>50K\n0,Private,124486,12th,8,Never-married,Other-service,Own-child,White,Male,0,2,0,United-States,<=50K\n4,?,202106,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,191417,9th,5,Widowed,Exec-managerial,Not-in-family,Black,Male,0,0,4,United-States,<=50K\n0,Private,184543,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,122206,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,229015,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,130067,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Local-gov,306495,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,232855,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,171328,Some-college,10,Married-spouse-absent,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Private,144182,HS-grad,9,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,102858,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,?,199495,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,209438,Some-college,10,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,74895,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,3,United-States,<=50K\n2,Private,184378,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,446512,Some-college,10,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Federal-gov,113688,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,333305,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,118535,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,76142,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,38795,9th,5,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,208478,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,?,<=50K\n4,Private,203313,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,1,0,0,United-States,<=50K\n4,Private,247483,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,State-gov,198686,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,56118,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,359808,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,231554,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,34848,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,199934,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,2,0,2,United-States,>50K\n1,Private,196243,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,66360,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,1,United-States,>50K\n0,Private,189487,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,194848,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,167309,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,192878,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,70209,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Federal-gov,123011,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,135339,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,3,0,0,China,>50K\n3,Federal-gov,497486,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,1,0,2,United-States,<=50K\n0,Private,178478,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,149909,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,103323,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,239404,10th,6,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,165082,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,389725,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,374580,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,?,187983,HS-grad,9,Never-married,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,259300,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,277695,9th,5,Never-married,Farming-fishing,Other-relative,White,Male,0,0,0,Mexico,<=50K\n0,Private,230248,7th-8th,4,Separated,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,196342,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,160968,11th,7,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,115438,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,231043,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,129597,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,387108,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,105936,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,107242,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,?,<=50K\n3,Private,125000,Masters,14,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,>50K\n0,Private,229456,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Private,230113,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,106698,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,133454,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,295520,9th,5,Widowed,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,151551,Some-college,10,Separated,Sales,Own-child,Amer-Indian-Eskimo,Male,1,0,3,United-States,<=50K\n4,Private,100313,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,320294,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,162381,1st-4th,2,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,183898,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,1,0,2,United-States,<=50K\n2,Self-emp-inc,32016,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,117028,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,280278,HS-grad,9,Widowed,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,342906,9th,5,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,>50K\n0,Private,181598,11th,7,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,224059,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,148549,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,97355,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,154571,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n2,Self-emp-inc,140988,Bachelors,13,Married-civ-spouse,Sales,Other-relative,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n0,Private,148409,Some-college,10,Never-married,Sales,Other-relative,White,Male,1,0,0,United-States,<=50K\n2,Local-gov,150755,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n1,Private,87006,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,1,2,United-States,<=50K\n1,Private,112158,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,121488,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,State-gov,283635,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,69758,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,199900,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n3,Private,88019,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,31935,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,323055,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,189498,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,89041,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,112507,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,236940,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,278514,HS-grad,9,Divorced,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,433330,Some-college,10,Never-married,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,258379,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,162028,11th,7,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,197997,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,98350,10th,6,Married-spouse-absent,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n2,Private,165848,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,178615,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,228939,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,210498,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,154891,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,165937,Assoc-voc,11,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,?,138768,Bachelors,13,Never-married,?,Own-child,White,Male,1,0,2,United-States,<=50K\n2,Private,160120,Some-college,10,Never-married,Machine-op-inspct,Other-relative,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Private,382368,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,123011,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,119033,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,496856,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,194049,Some-college,10,Divorced,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,299223,Some-college,10,Divorced,Sales,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Private,174788,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,176101,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,38948,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,271933,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,122041,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,115932,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,265105,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,100828,11th,7,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,121319,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,Poland,<=50K\n4,Private,308028,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,213214,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,348618,9th,5,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Mexico,<=50K\n1,Private,275632,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,239161,Some-college,10,Married-civ-spouse,Sales,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,215495,9th,5,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,Mexico,<=50K\n1,Private,214063,Some-college,10,Never-married,Farming-fishing,Other-relative,Black,Male,0,0,4,United-States,<=50K\n2,Private,122493,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,211699,Some-college,10,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,175622,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,153522,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,258339,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,119793,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,133503,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,162840,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,67671,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,188888,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Private,140644,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,126154,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,245659,Some-college,10,Separated,Other-service,Unmarried,White,Female,0,0,2,El-Salvador,<=50K\n1,Private,129624,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,?,<=50K\n3,Private,104068,HS-grad,9,Divorced,Prof-specialty,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,337908,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,0,United-States,<=50K\n2,Private,161141,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,162228,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,116391,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,314310,HS-grad,9,Married-spouse-absent,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,?,394534,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,0,United-States,<=50K\n1,Private,308136,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,194698,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,?,67793,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,302422,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,White,Male,0,1,3,United-States,>50K\n1,Private,289147,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,229826,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,0,United-States,<=50K\n0,?,154235,Some-college,10,Married-civ-spouse,?,Wife,White,Female,2,0,1,United-States,<=50K\n3,Self-emp-inc,246739,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,188041,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,187440,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Local-gov,105266,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,249208,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,203492,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,71076,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,146477,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,201699,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,205949,HS-grad,9,Separated,Craft-repair,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Private,90245,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Federal-gov,177647,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,France,>50K\n2,Private,126494,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,257735,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,1161363,Some-college,10,Separated,Tech-support,Unmarried,White,Female,0,0,3,Columbia,<=50K\n0,?,257343,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,221452,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,260669,10th,6,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,192344,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,80479,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,108808,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,175674,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,272950,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,160786,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,>50K\n3,Self-emp-not-inc,122206,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,121168,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,209547,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Federal-gov,244473,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,176296,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,91666,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,191025,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,2,0,4,United-States,<=50K\n1,State-gov,63704,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,31659,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,191230,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,56340,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,221157,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n4,Local-gov,143910,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,435836,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,61499,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,209182,Preschool,1,Separated,Other-service,Unmarried,White,Female,0,0,2,El-Salvador,<=50K\n2,Self-emp-inc,107218,Some-college,10,Divorced,Sales,Unmarried,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n3,Private,55500,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,357962,Assoc-acdm,12,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,200355,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,320451,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n3,Local-gov,184542,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,206927,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,54310,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,208165,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,146908,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,318416,10th,6,Separated,Other-service,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,207540,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n0,Private,69911,Preschool,1,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,305304,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,295289,HS-grad,9,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,275110,Some-college,10,Separated,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,339773,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,399246,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,1,2,China,<=50K\n2,Self-emp-inc,51264,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,49020,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,1,0,3,United-States,>50K\n2,Private,178100,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,?,215943,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,176178,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,State-gov,180884,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n4,State-gov,130466,HS-grad,9,Widowed,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Private,328525,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,0,United-States,<=50K\n1,Private,142712,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,176321,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,145041,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Cuba,>50K\n1,Private,95423,HS-grad,9,Married-AF-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,215096,9th,5,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,177599,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,123920,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,?,201490,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,46990,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,0,United-States,>50K\n1,Private,388672,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,149210,Bachelors,13,Divorced,Sales,Not-in-family,Black,Male,0,0,2,United-States,>50K\n0,Private,134787,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,185407,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,86143,HS-grad,9,Never-married,Protective-serv,Other-relative,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Private,41721,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,195744,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,96062,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,215150,9th,5,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,270728,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Cuba,<=50K\n2,Private,75012,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,206139,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,50700,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,224258,7th-8th,4,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,>50K\n1,Private,240441,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,1,2,United-States,>50K\n2,Self-emp-not-inc,406811,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Local-gov,34452,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,361341,12th,8,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Thailand,<=50K\n1,Private,78247,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,106900,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,165108,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,England,<=50K\n0,Private,406641,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,171467,HS-grad,9,Divorced,Craft-repair,Unmarried,Black,Male,0,0,3,United-States,>50K\n1,Private,341187,7th-8th,4,Separated,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,119177,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,104896,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,0,United-States,<=50K\n0,Private,342752,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,?,76641,Masters,14,Married-civ-spouse,?,Husband,White,Male,4,0,2,Poland,>50K\n0,Private,47541,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,233461,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,303954,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,163015,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,75763,Some-college,10,Married-civ-spouse,Sales,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,Private,43003,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,328239,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,130856,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,190072,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Iran,>50K\n4,Private,170148,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,104501,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,213140,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,175509,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,173611,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,148995,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,1,United-States,>50K\n0,Private,64520,7th-8th,4,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,139822,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,258700,5th-6th,3,Never-married,Farming-fishing,Other-relative,Black,Male,0,0,2,Mexico,<=50K\n1,Private,34796,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,124963,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,65743,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,161087,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,Jamaica,<=50K\n4,?,424591,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,203836,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n4,State-gov,110199,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,316059,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,255667,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,193689,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,60722,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Private,187847,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,233275,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,215404,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,4,0,2,United-States,>50K\n2,Private,201865,Bachelors,13,Married-spouse-absent,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,118889,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,368739,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,123833,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Private,171344,11th,7,Married-spouse-absent,Transport-moving,Own-child,White,Male,0,0,2,Mexico,<=50K\n2,Private,153976,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,374883,Assoc-voc,11,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,167658,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,348504,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,258509,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,State-gov,108890,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,1,0,2,United-States,<=50K\n1,Private,188236,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,355571,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,425049,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,142555,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,29320,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,207841,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,187329,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,270973,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,197332,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n0,?,175166,Some-college,10,Never-married,?,Own-child,White,Female,1,0,2,United-States,<=50K\n2,Local-gov,160187,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,197918,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,192290,10th,6,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,241895,HS-grad,9,Married-civ-spouse,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,164515,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,147206,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n0,Self-emp-inc,306868,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,169837,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,?,124648,10th,6,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,185057,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,>50K\n0,Private,240049,Preschool,1,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Laos,<=50K\n0,Private,164441,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,179314,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,319854,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Male,2,0,1,United-States,<=50K\n0,Self-emp-inc,148955,Some-college,10,Never-married,Other-service,Own-child,Asian-Pac-Islander,Female,0,0,1,South,<=50K\n0,Private,32950,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Private,206699,HS-grad,9,Divorced,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,385646,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,31438,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,?,<=50K\n2,Private,168598,12th,8,Married-civ-spouse,Adm-clerical,Wife,Black,Female,1,0,2,United-States,>50K\n1,Private,97306,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,?,106910,11th,7,Divorced,?,Not-in-family,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,29582,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,220284,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,Mexico,<=50K\n1,Private,110226,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,4,?,<=50K\n3,Private,240914,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,115496,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,105817,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,330836,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,36327,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,33423,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,75673,Assoc-voc,11,Widowed,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Private,185744,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,186035,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Local-gov,196456,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n0,Private,111450,HS-grad,9,Never-married,Transport-moving,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Private,115289,Some-college,10,Divorced,Sales,Own-child,White,Male,0,1,4,United-States,<=50K\n3,Private,74879,HS-grad,9,Married-spouse-absent,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,117312,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,2,United-States,>50K\n4,Private,272902,Bachelors,13,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,220230,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,90934,Bachelors,13,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,234286,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,364548,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,3,0,2,United-States,>50K\n3,Self-emp-inc,283676,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,195602,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,70761,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,142717,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,124242,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,53481,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,287797,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,188274,Assoc-acdm,12,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,171968,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,74795,Assoc-acdm,12,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,218490,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Germany,>50K\n2,Local-gov,94937,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n4,Private,109511,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,269527,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n3,Self-emp-inc,201689,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,4,?,>50K\n1,Self-emp-not-inc,120672,7th-8th,4,Never-married,Handlers-cleaners,Unmarried,Black,Male,0,0,0,United-States,<=50K\n3,Private,130779,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,441542,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,114801,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,180284,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,27444,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Private,180382,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,2,United-States,<=50K\n4,Private,143266,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,139268,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,126208,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,186191,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,197163,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,4,3,United-States,>50K\n2,State-gov,193524,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,181388,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,124963,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,>50K\n0,Private,188925,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,149230,Assoc-voc,11,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,388725,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,113543,Masters,14,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,187636,Bachelors,13,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,267763,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,<=50K\n4,Federal-gov,143849,11th,7,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,97277,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,199303,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,124852,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,50053,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,97005,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,?,175444,7th-8th,4,Separated,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,337898,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,124076,Bachelors,13,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,277420,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Puerto-Rico,>50K\n3,Private,280278,10th,6,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,241185,12th,8,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,202188,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,2,3,United-States,<=50K\n2,Private,198422,Some-college,10,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,82242,Prof-school,15,Never-married,Prof-specialty,Unmarried,White,Male,4,0,2,Germany,>50K\n1,Private,178429,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,185866,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,?,>50K\n2,Private,212847,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,219661,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,321856,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,313873,5th-6th,3,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,144064,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,139586,Assoc-voc,11,Widowed,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,>50K\n1,Private,419691,12th,8,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,?,212759,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,2,0,0,United-States,<=50K\n1,Private,195562,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Private,205706,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,131310,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,54440,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,200734,HS-grad,9,Separated,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,81859,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,159589,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,300915,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,185057,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,42044,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,166416,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,212737,9th,5,Separated,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,236069,10th,6,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,216414,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n3,Federal-gov,27432,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,145419,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,<=50K\n4,Private,147202,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,Germany,<=50K\n1,Private,29261,Some-college,10,Never-married,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,359543,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Mexico,<=50K\n2,Local-gov,227644,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,90021,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,?,<=50K\n1,Private,188154,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,110142,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,186415,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,175720,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,172865,5th-6th,3,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,Mexico,<=50K\n3,Private,35969,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,433330,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-inc,160261,Bachelors,13,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Male,0,0,1,Taiwan,<=50K\n3,Private,189528,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,113324,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,118500,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,89681,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Federal-gov,199925,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,102308,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,444607,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,176998,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,94559,Bachelors,13,Married-civ-spouse,?,Wife,Other,Female,3,0,3,?,>50K\n1,State-gov,366198,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Germany,>50K\n1,Private,180686,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,108019,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,1,0,2,United-States,<=50K\n0,Private,153542,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,210364,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,4,United-States,>50K\n2,Private,185394,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,222703,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,2,Nicaragua,<=50K\n0,Private,183945,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,161964,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,375574,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Mexico,>50K\n0,Local-gov,312427,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,1,Puerto-Rico,<=50K\n1,Private,53373,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,166330,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,4,0,2,United-States,>50K\n2,Self-emp-inc,124665,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,146719,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,306593,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,156687,Some-college,10,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,1,India,<=50K\n2,Local-gov,153489,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,White,Male,1,0,2,United-States,<=50K\n4,Private,231377,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n2,State-gov,127089,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,329355,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,178319,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,304246,Masters,14,Separated,Prof-specialty,Unmarried,White,Female,0,0,4,United-States,<=50K\n2,Local-gov,174640,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,Black,Female,0,0,3,United-States,>50K\n0,Private,148294,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,298037,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,98155,HS-grad,9,Married-AF-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,102766,Some-college,10,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,78529,HS-grad,9,Never-married,Transport-moving,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,136309,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,275357,Assoc-voc,11,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,33117,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,England,<=50K\n4,Local-gov,199546,Masters,14,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,184128,11th,7,Divorced,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,337039,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,Black,Male,4,0,2,England,>50K\n4,Private,126511,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,325792,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,91901,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,119474,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,153238,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,1,United-States,>50K\n3,Local-gov,321851,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,108557,Some-college,10,Divorced,Sales,Not-in-family,White,Female,1,0,3,United-States,<=50K\n0,State-gov,67217,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,1,0,1,United-States,<=50K\n2,Private,195508,11th,7,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,102193,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,20323,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,122206,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,200652,9th,5,Divorced,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,173590,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,184121,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,53123,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,175990,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,316101,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,34080,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,England,<=50K\n3,Self-emp-not-inc,219718,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,126954,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,99185,HS-grad,9,Widowed,Craft-repair,Unmarried,White,Male,0,0,2,United-States,>50K\n0,?,40052,Some-college,10,Never-married,?,Not-in-family,White,Male,0,3,2,United-States,<=50K\n2,Private,120074,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,77336,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,60981,Some-college,10,Never-married,Sales,Own-child,White,Female,1,0,1,United-States,<=50K\n4,Private,77884,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,65408,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,173279,Bachelors,13,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n3,?,318351,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,157686,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,277434,Assoc-acdm,12,Widowed,Tech-support,Unmarried,White,Male,0,0,2,United-States,>50K\n3,Local-gov,184620,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,34443,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,268553,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n0,?,41356,Assoc-acdm,12,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,459342,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,148549,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,254293,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,Private,104501,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,238367,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,180439,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,100029,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,215990,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n1,State-gov,111567,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,319163,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n4,?,160155,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,378045,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,177083,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,119891,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,<=50K\n4,Private,127779,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,299353,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,63861,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,112403,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,83610,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,4,United-States,>50K\n1,Private,452808,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,176871,Some-college,10,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,100651,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,3,2,United-States,<=50K\n0,Private,266134,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,196307,10th,6,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,87891,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,182314,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,?,136819,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,181666,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,2,?,<=50K\n2,Private,179671,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,27494,HS-grad,9,Divorced,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,3,United-States,>50K\n2,Private,338320,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Canada,<=50K\n3,Private,199688,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,96635,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n0,Private,165064,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,109856,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,82393,HS-grad,9,Never-married,Craft-repair,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,209538,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,209891,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,56026,Bachelors,13,Married-civ-spouse,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,210844,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n2,Private,117158,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,193144,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,137578,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,234108,Assoc-acdm,12,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,155767,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,110820,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,403276,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,147269,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,?,<=50K\n3,Private,123092,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,165673,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,182131,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,0,United-States,>50K\n2,Private,204415,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,92531,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,State-gov,157028,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,228649,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,147253,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,160784,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,163189,Some-college,10,Married-civ-spouse,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,146343,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,225811,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,State-gov,202699,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,2,?,>50K\n4,Private,374108,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,93930,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,412248,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,427474,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n4,State-gov,160158,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,159603,Assoc-acdm,12,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,101017,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,163862,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Without-pay,212588,Some-college,10,Married-civ-spouse,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n2,State-gov,321943,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,317702,9th,5,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,287480,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,135607,Some-college,10,Widowed,Other-service,Unmarried,Black,Female,0,0,2,?,<=50K\n1,Private,168514,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,88642,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,227104,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,157289,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,213321,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,294907,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,251411,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,183594,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,189565,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,1,0,3,United-States,<=50K\n3,Private,217802,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,1,United-States,<=50K\n0,Private,388156,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,447555,10th,6,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,204098,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,193882,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,89870,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,49595,Masters,14,Divorced,Protective-serv,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,228873,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,108185,9th,5,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,176027,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,?,405374,Some-college,10,Separated,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,39606,Assoc-voc,11,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,178353,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,160662,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,196328,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,Jamaica,<=50K\n2,Private,20534,Some-college,10,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,156815,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,360252,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,245056,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n1,Local-gov,422718,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,118081,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,262978,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,187577,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,259323,Prof-school,15,Divorced,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,160920,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,194247,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,134367,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,1,United-States,>50K\n0,Private,123335,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,332249,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,358124,HS-grad,9,Never-married,Other-service,Other-relative,Black,Female,0,0,2,United-States,<=50K\n3,Private,208019,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,318452,11th,7,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,207779,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,238376,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,673764,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,State-gov,239705,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,0,?,<=50K\n2,Private,133974,Some-college,10,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,138285,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,152140,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,287920,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,289572,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n2,State-gov,78765,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,State-gov,99076,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,1,0,3,United-States,<=50K\n2,Self-emp-not-inc,224886,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,<=50K\n4,Private,206532,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,129529,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,202473,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,162312,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n2,Private,72844,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,206947,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,64112,12th,8,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,20057,Some-college,10,Married-spouse-absent,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,State-gov,222884,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,132683,HS-grad,9,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,?,177773,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,144071,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,1,0,0,El-Salvador,<=50K\n1,Private,148429,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,168601,11th,7,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,State-gov,78291,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,243929,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,215039,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,0,?,<=50K\n3,Self-emp-not-inc,185673,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,121142,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,173858,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n4,?,87247,10th,6,Divorced,?,Not-in-family,White,Female,0,0,2,England,<=50K\n2,Private,334991,Some-college,10,Separated,Transport-moving,Unmarried,White,Male,2,0,3,United-States,>50K\n3,Private,93476,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,3,2,United-States,<=50K\n2,Private,174283,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,128676,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,205844,Bachelors,13,Never-married,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,62535,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,240612,HS-grad,9,Married-spouse-absent,Exec-managerial,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,176992,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,254127,Bachelors,13,Never-married,Prof-specialty,Other-relative,White,Female,0,0,3,United-States,<=50K\n1,?,138744,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,128460,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,56582,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,153751,9th,5,Separated,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,284343,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,312692,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,111520,11th,7,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,Nicaragua,<=50K\n3,Self-emp-inc,304955,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,288598,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,117387,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,230484,7th-8th,4,Separated,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Federal-gov,319280,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,186416,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,147372,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,4,2,United-States,>50K\n2,Private,145933,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,4,4,United-States,<=50K\n1,Private,110164,Some-college,10,Divorced,Other-service,Other-relative,Black,Male,0,0,1,United-States,<=50K\n3,Private,225454,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,220342,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,144002,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,225365,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,187983,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,89991,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,225913,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,229737,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,145574,11th,7,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,274363,Some-college,10,Separated,Sales,Not-in-family,White,Male,0,0,4,United-States,>50K\n4,Private,365390,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,266467,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,183384,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,112797,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Federal-gov,76008,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,156780,HS-grad,9,Never-married,Sales,Other-relative,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n2,Local-gov,186909,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,25497,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,102771,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n4,Self-emp-not-inc,248841,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,30916,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,123270,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,210165,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,222596,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,188067,Some-college,10,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,119592,Assoc-acdm,12,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,4,2,?,>50K\n1,Private,250314,9th,5,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,Guatemala,<=50K\n4,Private,205934,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,186172,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,>50K\n4,Self-emp-inc,98418,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,329980,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n4,Private,147653,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,?,195946,Some-college,10,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n1,Self-emp-inc,168221,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,4,United-States,<=50K\n0,Private,151801,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,177154,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,73883,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,175714,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,43535,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,State-gov,104509,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,118230,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,152046,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,Guatemala,<=50K\n2,Private,52327,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,0,2,Iran,>50K\n0,Private,218886,12th,8,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,84119,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,189674,Bachelors,13,Separated,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,222993,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,47429,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,144995,Preschool,1,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,187969,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,288398,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,114591,Some-college,10,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,167737,12th,8,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,248834,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,165686,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,40200,Some-college,10,Widowed,Craft-repair,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,117158,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n3,Local-gov,216657,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,>50K\n4,Private,124242,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,India,<=50K\n2,Local-gov,239119,Masters,14,Divorced,Prof-specialty,Not-in-family,Black,Male,0,0,2,Dominican-Republic,<=50K\n3,Private,190072,Some-college,10,Divorced,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,378114,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,236990,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,101761,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,37745,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n0,?,424494,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,130438,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,100605,Some-college,10,Never-married,Machine-op-inspct,Own-child,Other,Male,0,0,0,United-States,<=50K\n2,Private,220776,HS-grad,9,Separated,Handlers-cleaners,Unmarried,White,Male,0,0,2,Poland,<=50K\n1,Local-gov,154950,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,192283,Masters,14,Married-spouse-absent,Sales,Not-in-family,White,Female,0,0,4,United-States,>50K\n1,Private,210765,Assoc-voc,11,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,147476,HS-grad,9,Divorced,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,State-gov,193241,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,109053,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,265618,HS-grad,9,Separated,Protective-serv,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,172855,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,2,2,United-States,>50K\n1,Private,68848,Bachelors,13,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,229051,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,106039,Bachelors,13,Divorced,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,112835,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,?,205396,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,283400,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,195739,10th,6,Widowed,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,36480,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,303291,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,293900,11th,7,Married-spouse-absent,Craft-repair,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,165922,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,65738,Masters,14,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,175070,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,339814,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,150132,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,377374,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,Japan,<=50K\n4,Self-emp-not-inc,166153,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,110171,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,94477,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,194243,Prof-school,15,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,106347,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n4,Private,214865,HS-grad,9,Widowed,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,?,185619,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,96445,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,102632,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,209034,Assoc-acdm,12,Married-civ-spouse,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,State-gov,153486,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,144371,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,186213,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,188236,10th,6,Widowed,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,418405,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,125796,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,183304,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,329587,10th,6,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,182570,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,446654,9th,5,Married-spouse-absent,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Self-emp-not-inc,254304,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,4,United-States,<=50K\n3,Local-gov,131258,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,Private,103632,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,241895,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,244945,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,20795,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,347322,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,103995,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,2,3,United-States,<=50K\n1,Private,53206,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,?,387839,HS-grad,9,Never-married,?,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,57108,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,177791,10th,6,Divorced,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,33794,Masters,14,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,249935,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,241121,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,98586,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,181920,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,434467,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,113364,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,Vietnam,<=50K\n3,Private,249706,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,95455,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,209867,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,4,0,2,United-States,>50K\n1,Self-emp-inc,79586,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n2,Private,289669,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,347166,Some-college,10,Divorced,Craft-repair,Own-child,White,Male,2,0,2,United-States,<=50K\n2,Private,53835,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,14878,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n1,Private,266126,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,146659,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Honduras,<=50K\n2,Private,125280,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,173535,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,77665,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,280525,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,479621,Assoc-voc,11,Divorced,Tech-support,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,194630,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Female,2,0,2,United-States,>50K\n2,Private,247600,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,2,Taiwan,<=50K\n1,Private,258406,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,4,Mexico,<=50K\n0,Private,107746,11th,7,Never-married,Transport-moving,Other-relative,White,Male,0,0,2,Guatemala,<=50K\n0,?,47407,11th,7,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,229987,Some-college,10,Never-married,Tech-support,Other-relative,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n0,Private,312338,Assoc-voc,11,Never-married,Craft-repair,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Private,225394,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n0,Private,373718,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,State-gov,120131,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,3,0,2,United-States,>50K\n0,Private,472789,1st-4th,2,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,El-Salvador,<=50K\n4,Self-emp-not-inc,27886,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,138352,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n3,Private,123011,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,306567,HS-grad,9,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,2,United-States,>50K\n3,Local-gov,187749,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n0,Private,260594,11th,7,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,236879,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,186808,HS-grad,9,Never-married,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,373213,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,187629,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,106648,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,305781,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,Canada,<=50K\n1,Self-emp-inc,256362,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,<=50K\n0,Private,239947,11th,7,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,349041,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,105252,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,182715,7th-8th,4,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,166210,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,113200,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,142075,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,454843,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,142219,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,112512,12th,8,Separated,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n2,Private,212894,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,State-gov,265201,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,251905,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,4,2,Canada,<=50K\n0,Private,170627,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,354037,Prof-school,15,Married-civ-spouse,Transport-moving,Husband,Black,Male,4,0,3,United-States,>50K\n2,Private,259089,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,21856,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,207946,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,77009,11th,7,Separated,Sales,Not-in-family,White,Female,0,4,2,United-States,<=50K\n1,Private,36539,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,176811,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,456062,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n1,Private,277746,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,288132,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,344415,Masters,14,Married-civ-spouse,Armed-Forces,Husband,White,Male,0,2,2,United-States,>50K\n3,Self-emp-inc,206964,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,198091,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n4,?,150264,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,0,0,0,Canada,>50K\n4,Private,588484,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,113364,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Poland,<=50K\n0,Private,270551,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,?,31478,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,190525,Assoc-voc,11,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,153066,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,150393,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,99911,12th,8,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Local-gov,343447,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,169482,Some-college,10,Married-spouse-absent,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,32855,HS-grad,9,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,194501,11th,7,Widowed,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n3,Private,177705,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,123983,Some-college,10,Separated,Sales,Unmarried,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n2,Private,138975,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n2,Local-gov,235431,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,83043,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,3,2,United-States,<=50K\n2,State-gov,130206,HS-grad,9,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,210053,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,249392,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,87418,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,190387,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,176240,Masters,14,Married-civ-spouse,Transport-moving,Husband,White,Male,3,0,3,United-States,>50K\n0,?,211013,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,Mexico,<=50K\n2,Local-gov,105862,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,2,0,2,United-States,<=50K\n3,Self-emp-not-inc,185195,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,173495,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,78634,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,147284,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,4,United-States,>50K\n3,Self-emp-not-inc,82572,HS-grad,9,Widowed,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,154641,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,39236,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,1,0,1,United-States,<=50K\n0,?,64785,10th,6,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,179337,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,England,<=50K\n4,Private,173047,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,264012,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,227836,Some-college,10,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,321327,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n2,Self-emp-inc,108100,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,4,0,1,?,>50K\n2,Private,146398,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,324120,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,367329,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,301582,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,222789,Bachelors,13,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,170108,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,82297,7th-8th,4,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,3,United-States,<=50K\n4,Local-gov,180162,9th,5,Divorced,Protective-serv,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Local-gov,348172,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,2,0,2,United-States,>50K\n2,Private,809585,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,67728,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,?,163057,HS-grad,9,Widowed,?,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Self-emp-not-inc,102069,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,149700,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n2,Self-emp-not-inc,109273,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,393354,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,>50K\n2,Private,226947,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,86805,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,2,0,2,United-States,>50K\n1,Private,493689,Bachelors,13,Never-married,Tech-support,Not-in-family,Black,Female,0,0,2,France,<=50K\n3,Private,299324,5th-6th,3,Married-spouse-absent,Machine-op-inspct,Unmarried,White,Male,0,0,2,Mexico,<=50K\n3,Self-emp-not-inc,353012,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,174419,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,209472,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,295127,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,182273,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,228200,HS-grad,9,Married-civ-spouse,Priv-house-serv,Wife,Black,Female,0,0,0,United-States,<=50K\n3,Private,263836,HS-grad,9,Widowed,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,178948,Masters,14,Never-married,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,43945,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n4,Self-emp-not-inc,253296,HS-grad,9,Widowed,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,240137,5th-6th,3,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n3,Private,24712,Bachelors,13,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Philippines,<=50K\n2,Self-emp-not-inc,342635,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,115387,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,182998,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n4,?,133248,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,246891,Masters,14,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,30035,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,175232,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,140516,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,64980,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,3,United-States,>50K\n1,Private,155781,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n3,Federal-gov,192065,Some-college,10,Separated,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,227890,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,162249,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,165949,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,445382,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,211948,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,1,2,United-States,<=50K\n3,Private,163678,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,89413,12th,8,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,289700,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,163826,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,185385,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,169031,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,54611,Some-college,10,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,130620,11th,7,Married-spouse-absent,Sales,Own-child,Asian-Pac-Islander,Female,0,0,2,India,<=50K\n1,Private,328663,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Other,Male,0,0,2,United-States,<=50K\n3,Private,169646,Bachelors,13,Separated,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,186815,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,103925,Some-college,10,Never-married,Tech-support,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,?,150393,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,1,1,United-States,<=50K\n0,Private,82777,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,178449,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,51672,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,380162,HS-grad,9,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,212114,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,100800,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,4,United-States,>50K\n1,Private,162572,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n4,Self-emp-inc,179951,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-inc,190759,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,State-gov,236012,7th-8th,4,Widowed,Handlers-cleaners,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,State-gov,164023,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,4,United-States,>50K\n3,Private,172046,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,182926,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,151001,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,Mexico,<=50K\n3,Self-emp-inc,362835,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,97883,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,91911,HS-grad,9,Divorced,Craft-repair,Unmarried,Black,Female,0,0,3,United-States,<=50K\n0,Private,278130,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,146310,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,379412,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,37987,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,256909,HS-grad,9,Married-spouse-absent,Farming-fishing,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,State-gov,482927,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,State-gov,44434,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,255474,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,303674,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,1,0,0,United-States,<=50K\n2,?,195488,12th,8,Separated,?,Not-in-family,White,Female,0,0,2,Puerto-Rico,<=50K\n4,?,114362,Some-college,10,Married-civ-spouse,?,Husband,Black,Male,0,0,1,United-States,<=50K\n1,Private,341504,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,197080,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Male,0,0,0,United-States,<=50K\n2,Private,102945,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,503454,12th,8,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,87561,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,270693,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,2,0,1,United-States,<=50K\n1,Private,252813,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,574271,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,184016,HS-grad,9,Married-civ-spouse,Priv-house-serv,Not-in-family,White,Female,1,0,2,United-States,<=50K\n0,Private,235071,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,158242,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,299810,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,277695,Preschool,1,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Hong,<=50K\n1,Private,23324,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,316582,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,176657,Some-college,10,Separated,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,3,Japan,<=50K\n2,Private,93770,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,124569,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,117313,9th,5,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Ireland,<=50K\n3,Private,53812,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,170456,Assoc-acdm,12,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,115971,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,31432,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,1,0,3,United-States,>50K\n1,Private,112383,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,283092,HS-grad,9,Never-married,Adm-clerical,Other-relative,Black,Male,0,0,2,Jamaica,<=50K\n1,Private,27207,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,46712,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,State-gov,19520,Doctorate,16,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,98630,7th-8th,4,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,159897,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,136629,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Iran,<=50K\n0,Private,407759,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,221884,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,148475,Assoc-voc,11,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,274964,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,160107,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,167265,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,148226,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,3,United-States,<=50K\n1,Private,153869,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,208881,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,256953,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,100147,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,Local-gov,166461,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,>50K\n1,Private,171327,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,297335,Assoc-acdm,12,Married-spouse-absent,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,1,Laos,<=50K\n4,?,133166,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,169589,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Local-gov,273734,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,158301,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,?,257117,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,196725,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,137444,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,286960,11th,7,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,201435,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,216931,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,212665,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,4,United-States,<=50K\n0,Private,462820,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,198841,Assoc-voc,11,Divorced,Tech-support,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,219886,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,163003,Assoc-acdm,12,Never-married,Prof-specialty,Other-relative,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Private,112262,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,213105,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,302072,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,338105,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n4,Self-emp-not-inc,58213,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,125684,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,215419,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Local-gov,413760,Some-college,10,Separated,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,205339,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,236570,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,247552,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,184007,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,341187,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,220187,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,198258,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,175821,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,92288,Masters,14,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n1,Private,261418,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,203319,11th,7,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,166083,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,109001,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,?,106765,Some-college,10,Widowed,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,197387,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,284834,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,87535,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Local-gov,175587,11th,7,Never-married,Protective-serv,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,242700,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,161478,Some-college,10,Never-married,Other-service,Own-child,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n0,Private,51498,12th,8,Never-married,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,220124,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,188503,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,113324,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,208872,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,34180,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,292023,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,141118,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,348592,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,185203,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,165278,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,116933,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,237608,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,4,2,United-States,>50K\n1,Private,84787,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,217892,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n4,Private,325971,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,206878,HS-grad,9,Never-married,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,127772,HS-grad,9,Divorced,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,208577,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,344152,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,40681,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,95108,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,280603,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,188436,Prof-school,15,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,134220,Assoc-voc,11,Divorced,Exec-managerial,Own-child,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,177989,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,164190,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,90897,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,33126,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,270886,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,216129,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,189368,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n0,?,141418,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,306225,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,330664,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,191765,HS-grad,9,Divorced,Tech-support,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,289353,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,1,United-States,<=50K\n0,Private,53147,Bachelors,13,Never-married,Exec-managerial,Own-child,Black,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,122353,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,188767,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,239576,Masters,14,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,155141,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,64520,12th,8,Never-married,Transport-moving,Unmarried,White,Male,0,0,1,United-States,<=50K\n0,Private,478994,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,155654,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,124052,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,245053,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,183585,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,323639,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,186791,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,284303,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,3,0,2,United-States,>50K\n0,Private,186666,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,200153,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,180931,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,183173,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,120131,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Cuba,>50K\n0,Self-emp-not-inc,263300,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,226443,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,404868,11th,7,Never-married,Sales,Own-child,Black,Female,0,2,0,United-States,<=50K\n0,Private,208506,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,46746,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,246183,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,?,165309,7th-8th,4,Separated,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,122749,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,38822,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,167963,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,273241,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,120238,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,167990,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Ireland,<=50K\n0,Private,225507,11th,7,Never-married,Handlers-cleaners,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,125000,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Self-emp-not-inc,174120,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,230959,Bachelors,13,Never-married,Tech-support,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Local-gov,132125,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,68461,Doctorate,16,Married-civ-spouse,?,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n0,Private,227178,11th,7,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,165798,5th-6th,3,Divorced,Other-service,Unmarried,White,Female,0,0,2,Puerto-Rico,<=50K\n2,Private,129573,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,224377,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,179481,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,434268,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,173716,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,244803,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,1,3,Cuba,>50K\n0,Private,114230,HS-grad,9,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,188661,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,216093,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,124963,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,85341,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,193490,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,80058,Prof-school,15,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,139907,Assoc-voc,11,Separated,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,54342,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,188767,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,117222,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,171831,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,187119,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Local-gov,97277,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,219760,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,0,United-States,<=50K\n3,Private,63299,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,171482,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,?,344742,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,210869,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,4,United-States,<=50K\n2,Private,38312,Some-college,10,Married-spouse-absent,Craft-repair,Unmarried,White,Male,0,0,2,United-States,>50K\n3,Private,119939,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,83953,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,101383,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,204374,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,176831,10th,6,Divorced,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,?,60688,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,251305,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,200947,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,46704,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,119721,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,58930,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,247750,HS-grad,9,Widowed,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Private,67725,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,200775,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,183542,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,?,25139,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,123325,Prof-school,15,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,269786,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,51089,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,136985,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,129350,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,35595,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,61299,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,192321,Assoc-acdm,12,Never-married,?,Own-child,White,Female,0,0,4,United-States,<=50K\n1,Private,257644,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,70884,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,159726,11th,7,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,174395,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,175534,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,>50K\n3,Local-gov,173050,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,32519,Some-college,10,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,South,<=50K\n0,Private,322999,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,148874,9th,5,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,43738,Doctorate,16,Widowed,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,>50K\n2,Private,195385,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,149809,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,51985,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,105384,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,137591,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,Greece,<=50K\n3,State-gov,324791,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,184303,Some-college,10,Separated,Priv-house-serv,Other-relative,White,Female,0,0,1,El-Salvador,<=50K\n4,?,314347,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,274010,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,321031,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Federal-gov,313929,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,394669,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n1,Private,152951,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,247115,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,175958,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,109039,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-inc,141326,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,State-gov,74334,Masters,14,Married-civ-spouse,Adm-clerical,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,47462,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Federal-gov,182344,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,State-gov,295912,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,311495,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,236746,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,Private,187643,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,282923,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,501671,10th,6,Divorced,Machine-op-inspct,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Federal-gov,29591,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Male,0,4,2,United-States,>50K\n0,Private,301556,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,187240,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,219483,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,1,United-States,<=50K\n1,Private,594187,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,200474,1st-4th,2,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,152795,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,230789,9th,5,Never-married,Sales,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,311231,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,1,3,United-States,>50K\n1,Private,114691,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,194591,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,114691,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,42017,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,383384,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,29444,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Federal-gov,53727,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,?,<=50K\n2,Private,277022,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,Columbia,<=50K\n2,Local-gov,113324,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,342709,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,179203,12th,8,Never-married,Sales,Other-relative,White,Male,0,0,3,United-States,<=50K\n3,Private,251474,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,93730,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,37894,HS-grad,9,Separated,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,State-gov,272918,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Private,151411,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,210648,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,347491,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,255885,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,356838,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,216164,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,288781,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,439779,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,161638,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Ecuador,<=50K\n1,Private,190525,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,276249,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,147265,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,245090,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Nicaragua,<=50K\n2,State-gov,219682,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,392100,HS-grad,9,Married-civ-spouse,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,358682,Bachelors,13,Never-married,Exec-managerial,Other-relative,White,Male,0,0,3,?,<=50K\n3,Private,262244,Bachelors,13,Never-married,Sales,Not-in-family,Black,Male,0,0,3,United-States,>50K\n3,Private,171228,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,1,Guatemala,<=50K\n0,Local-gov,218445,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Mexico,<=50K\n0,?,182609,HS-grad,9,Never-married,?,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,509462,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,213258,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,118401,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,45814,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,329733,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,>50K\n1,Private,29957,Masters,14,Never-married,Tech-support,Other-relative,White,Male,0,0,1,United-States,<=50K\n3,Private,215854,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,327766,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,405765,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,>50K\n2,Private,80680,Some-college,10,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,177792,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Private,273514,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,202373,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,332785,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,149640,7th-8th,4,Married-spouse-absent,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,40151,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-inc,183686,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,>50K\n3,Federal-gov,32801,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,>50K\n0,?,195282,HS-grad,9,Never-married,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n2,Federal-gov,134026,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Male,1,0,2,United-States,<=50K\n3,Local-gov,96678,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,174533,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,180807,HS-grad,9,Separated,Protective-serv,Not-in-family,White,Male,1,0,0,United-States,<=50K\n4,Private,186324,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,>50K\n2,Self-emp-not-inc,257250,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,212800,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,55360,Some-college,10,Never-married,Sales,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,195253,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,45156,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,435469,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,Mexico,<=50K\n1,Private,231287,Some-college,10,Divorced,Tech-support,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,168854,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n2,Self-emp-not-inc,185057,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,1,0,2,United-States,<=50K\n0,?,91670,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Private,165517,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,73161,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,178792,HS-grad,9,Widowed,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,32897,11th,7,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,250967,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,41901,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,1,2,United-States,<=50K\n3,Private,379779,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,217838,5th-6th,3,Separated,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n2,Self-emp-not-inc,137527,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,4,3,United-States,>50K\n2,Private,198965,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,70645,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,2,0,3,United-States,<=50K\n2,Private,220644,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,2,?,<=50K\n0,Private,175081,9th,5,Never-married,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n1,Private,180299,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,548664,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,278114,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,394927,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,127482,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,England,>50K\n1,Private,236938,Assoc-acdm,12,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,232991,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,2,Mexico,<=50K\n2,Private,34378,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,81513,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,106780,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,178596,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,1,3,United-States,<=50K\n2,Private,329026,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,26490,Bachelors,13,Widowed,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,338033,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,169303,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,2,0,0,United-States,<=50K\n0,Private,21154,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,209449,Some-college,10,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,389143,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,101260,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,198270,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,45781,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,134566,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,283806,9th,5,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,286869,7th-8th,4,Widowed,?,Not-in-family,White,Female,0,2,2,?,<=50K\n3,Private,422813,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,103277,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,201871,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,167728,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,211517,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,118212,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,259846,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n4,Private,98926,Some-college,10,Widowed,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,207352,Bachelors,13,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n1,Private,206609,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,104509,Masters,14,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,170350,HS-grad,9,Divorced,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,183884,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,110964,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,2,United-States,<=50K\n1,State-gov,154410,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,257659,Masters,14,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,274679,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,252662,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,356689,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,205218,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,241306,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,139127,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,175625,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,206459,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,176123,10th,6,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n2,Private,111483,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,106118,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,77845,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,2,0,United-States,<=50K\n0,Private,162094,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,216469,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,1,3,United-States,<=50K\n4,Local-gov,381965,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,145284,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,242482,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,160192,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,280699,Some-college,10,Never-married,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,175942,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,?,>50K\n0,Private,156950,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,215572,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,173593,Masters,14,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,0,Canada,<=50K\n3,Private,193374,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,334039,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,337664,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,113504,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,177072,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,174503,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,214807,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,222596,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,100345,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,409230,12th,8,Never-married,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,112497,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Male,4,0,2,United-States,>50K\n4,Self-emp-inc,115922,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,375049,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,243560,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,Columbia,<=50K\n1,Local-gov,182971,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,127215,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,276241,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,175109,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,498079,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,344394,Some-college,10,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,99872,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,1,0,2,India,>50K\n0,Private,245302,Some-college,10,Divorced,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,43313,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,188467,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,351278,Bachelors,13,Divorced,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,182246,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,79870,Some-college,10,Married-civ-spouse,Exec-managerial,Own-child,White,Female,1,0,2,Japan,<=50K\n3,?,353824,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,?,>50K\n1,Private,387116,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,Jamaica,<=50K\n3,Private,34248,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,198741,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,32950,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,381153,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,100067,11th,7,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,>50K\n1,Private,208785,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,61559,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,176452,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,Peru,<=50K\n2,?,128700,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,328518,Assoc-voc,11,Never-married,Prof-specialty,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,?,201196,11th,7,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,378546,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,212210,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,?,<=50K\n4,Federal-gov,178660,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,235826,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n1,Self-emp-not-inc,22641,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Private,316027,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Cuba,<=50K\n3,Private,431515,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,149770,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,165916,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,107411,Some-college,10,Married-spouse-absent,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,217961,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,Outlying-US(Guam-USVI-etc),<=50K\n2,Self-emp-not-inc,350387,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,325372,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,156718,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,216472,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,106972,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,131934,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,365908,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,1,0,2,United-States,<=50K\n3,Local-gov,359193,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,261012,Some-college,10,Married-spouse-absent,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,272944,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,113654,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,218955,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,115963,7th-8th,4,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,80638,Some-college,10,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,4,United-States,>50K\n2,Private,147258,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,214635,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,200318,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,138270,HS-grad,9,Married-civ-spouse,Sales,Wife,Black,Female,0,0,2,United-States,<=50K\n4,Federal-gov,388594,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,?,>50K\n1,Private,103435,Assoc-voc,11,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,133201,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Italy,<=50K\n0,Private,175183,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,99870,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,107479,9th,5,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,113440,Bachelors,13,Divorced,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,85690,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,1,United-States,<=50K\n0,Private,45713,Some-college,10,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,376230,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,145576,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,2,Japan,>50K\n0,?,67808,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,113936,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,158291,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,193898,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,191982,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,3,United-States,<=50K\n0,?,72953,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,271160,Assoc-voc,11,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,33087,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,106153,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,29444,12th,8,Never-married,Farming-fishing,Not-in-family,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n2,Private,105021,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,239045,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,94413,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,20534,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,350254,1st-4th,2,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n4,Private,194746,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,Cuba,<=50K\n2,Private,269042,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,Laos,<=50K\n0,Private,447488,9th,5,Never-married,Other-service,Unmarried,White,Male,0,0,1,Mexico,<=50K\n0,Private,267706,Some-college,10,Never-married,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,198216,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,227931,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,252646,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,223342,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,1,0,2,England,<=50K\n1,Private,181776,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,132601,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,205410,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,185912,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n2,Private,292570,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Private,76460,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,295163,12th,8,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,27255,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,?,<=50K\n0,Private,69847,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n0,Private,104993,9th,5,Never-married,Handlers-cleaners,Own-child,Black,Male,1,0,2,United-States,<=50K\n2,Private,322980,HS-grad,9,Separated,Adm-clerical,Not-in-family,Black,Male,1,0,2,United-States,<=50K\n0,?,390608,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,317539,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,195678,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,466502,7th-8th,4,Widowed,Other-service,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,220754,HS-grad,9,Separated,Transport-moving,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,202878,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,343476,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,93227,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,3,3,Taiwan,>50K\n4,Self-emp-not-inc,38622,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,173730,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,178623,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,?,<=50K\n1,Private,300783,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,224644,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,191502,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,61885,12th,8,Divorced,Transport-moving,Other-relative,Black,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,213887,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,Canada,>50K\n2,Private,331395,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,145098,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,123075,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,216804,7th-8th,4,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,188291,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,33610,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,234901,Assoc-acdm,12,Separated,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,349148,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,168443,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,211860,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,193961,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n2,Local-gov,52532,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,3,United-States,>50K\n4,Self-emp-not-inc,75804,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n1,Self-emp-not-inc,176185,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,306779,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,265192,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,139347,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,107682,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,34173,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,128378,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,195638,Some-college,10,Separated,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,59287,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,162442,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,?,350603,10th,6,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,344743,Some-college,10,Married-civ-spouse,Adm-clerical,Own-child,Black,Female,0,0,3,United-States,>50K\n1,Private,112077,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,176795,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,>50K\n3,Private,137815,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,309620,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,0,South,<=50K\n2,Private,336880,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,206600,11th,7,Never-married,Other-service,Other-relative,White,Male,0,0,1,Mexico,<=50K\n0,Private,193051,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,<=50K\n4,Federal-gov,229062,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,62793,HS-grad,9,Divorced,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,264939,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n3,Private,370552,Preschool,1,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,El-Salvador,<=50K\n3,Private,163678,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,89667,Bachelors,13,Widowed,?,Not-in-family,Other,Female,0,0,1,United-States,<=50K\n3,Private,558490,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,124680,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,4,0,1,United-States,>50K\n4,Private,208843,7th-8th,4,Widowed,Protective-serv,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,95078,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,169679,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,101320,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,168906,Assoc-acdm,12,Divorced,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,212582,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,170617,Masters,14,Widowed,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,?,170529,Bachelors,13,Married-civ-spouse,?,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Private,99897,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,104892,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,175224,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Nicaragua,<=50K\n0,Private,149704,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,214542,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,167319,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,43716,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,191935,Assoc-acdm,12,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n4,?,158642,HS-grad,9,Widowed,?,Not-in-family,White,Female,1,0,0,United-States,<=50K\n1,Private,338611,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,136419,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,72321,11th,7,Never-married,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,189956,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,>50K\n2,Private,403782,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,456661,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,279041,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,65716,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,189809,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,Jamaica,<=50K\n4,Local-gov,223637,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Local-gov,199343,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,139344,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,119098,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,195025,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,186720,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,328923,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,State-gov,159472,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,138662,Some-college,10,Separated,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,286342,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,>50K\n2,Private,181705,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,193882,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,216497,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,Germany,<=50K\n1,Self-emp-inc,124919,Bachelors,13,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,3,Iran,>50K\n4,Private,109463,Some-college,10,Separated,Sales,Unmarried,White,Female,0,2,1,United-States,<=50K\n4,Private,256274,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,326379,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,174995,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,4,2,United-States,<=50K\n1,Private,243142,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,155118,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,4,United-States,>50K\n3,Private,189607,Bachelors,13,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,39478,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,206951,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,127647,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,234298,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,2,0,3,United-States,<=50K\n2,Private,182302,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,166597,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,33363,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n4,Self-emp-inc,167537,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,179378,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,United-States,<=50K\n3,State-gov,297551,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,198362,Assoc-voc,11,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,240504,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,169662,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,164940,HS-grad,9,Separated,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Private,210488,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,154835,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n1,Private,333296,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,1,?,<=50K\n3,Private,192793,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Iran,>50K\n2,Private,49436,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,1,2,United-States,<=50K\n1,Private,136331,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,509048,HS-grad,9,Never-married,Sales,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Private,318610,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,104521,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,247695,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,219546,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Germany,<=50K\n0,Private,169699,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,131302,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,171852,Bachelors,13,Separated,Prof-specialty,Own-child,Other,Female,0,0,2,United-States,<=50K\n2,State-gov,340091,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,204641,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,213431,HS-grad,9,Separated,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,State-gov,377018,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,184543,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,188236,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,233022,11th,7,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,177420,Some-college,10,Never-married,Adm-clerical,Not-in-family,Other,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,21101,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n0,Private,52486,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,183273,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,1,United-States,>50K\n3,State-gov,36177,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,124956,Bachelors,13,Separated,Prof-specialty,Not-in-family,Black,Female,4,0,3,United-States,>50K\n2,Private,102350,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,165930,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,297574,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,120277,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,87569,Some-college,10,Separated,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,278220,Some-college,10,Never-married,?,Own-child,White,Female,0,2,2,United-States,<=50K\n2,Private,155972,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,162852,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,64860,Some-college,10,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,226013,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,2,0,2,United-States,>50K\n0,Private,322674,Assoc-acdm,12,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,202242,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,175262,Preschool,1,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n0,Private,201682,Bachelors,13,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,166330,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,147612,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,213154,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,33798,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,State-gov,199198,Assoc-voc,11,Widowed,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,90915,Bachelors,13,Married-spouse-absent,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,337778,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Yugoslavia,>50K\n1,Private,187203,HS-grad,9,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,261497,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,?,<=50K\n1,Self-emp-not-inc,361817,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,226546,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,100168,7th-8th,4,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,272625,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,>50K\n3,Private,254516,9th,5,Never-married,Handlers-cleaners,Other-relative,Black,Male,0,0,2,United-States,<=50K\n2,Private,207375,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,39092,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,<=50K\n2,Private,48271,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Self-emp-not-inc,152102,HS-grad,9,Widowed,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,234665,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,127651,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,180060,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,32477,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,137658,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,228287,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,159442,Prof-school,15,Never-married,Sales,Not-in-family,White,Female,4,0,3,United-States,>50K\n2,Private,33310,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,270546,HS-grad,9,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Federal-gov,290290,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,287037,12th,8,Divorced,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,128516,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Self-emp-not-inc,185195,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Federal-gov,49657,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,98005,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,283635,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,98360,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,202872,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Self-emp-not-inc,118365,10th,6,Divorced,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,184285,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,345831,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,99679,Prof-school,15,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,253354,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,190650,Bachelors,13,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Male,0,0,2,Taiwan,<=50K\n1,Private,287737,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,1,2,United-States,>50K\n0,Private,204389,HS-grad,9,Never-married,Adm-clerical,Own-child,Other,Female,0,0,1,Puerto-Rico,<=50K\n1,Federal-gov,294870,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,159442,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,161662,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,52738,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,252024,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,0,Mexico,<=50K\n1,Private,189702,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,407913,HS-grad,9,Separated,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,166527,Some-college,10,Never-married,Adm-clerical,Own-child,Other,Female,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,34918,Assoc-voc,11,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,142712,Masters,14,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,?,<=50K\n0,Federal-gov,201686,11th,7,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,179759,Some-college,10,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,94954,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,350498,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,1,0,United-States,<=50K\n0,Private,201743,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,119344,10th,6,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,149726,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,419146,7th-8th,4,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,174789,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,3,2,United-States,<=50K\n2,Private,171234,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,206325,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,202682,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,121055,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,160187,HS-grad,9,Separated,Prof-specialty,Other-relative,Black,Female,4,0,2,United-States,>50K\n1,Private,84366,10th,6,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,Mexico,<=50K\n4,Private,139391,Some-college,10,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Local-gov,124094,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,1,United-States,<=50K\n2,Private,30759,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,137875,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,?,139049,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,>50K\n0,Private,238384,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,340755,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,224947,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,State-gov,111994,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,125491,Some-college,10,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n1,?,310525,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,0,United-States,<=50K\n0,?,71592,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Local-gov,99185,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,249935,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,206775,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,230704,Assoc-acdm,12,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,0,Jamaica,<=50K\n1,Private,242361,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,134746,10th,6,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,198613,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,<=50K\n4,Private,174040,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,165953,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,273604,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,192409,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,102476,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,234504,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,468713,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,84560,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,148995,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,34816,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,211184,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,33304,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Federal-gov,179985,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,219815,Some-college,10,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,134766,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,106548,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,89787,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,164857,Some-college,10,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,257124,Bachelors,13,Never-married,Transport-moving,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,Private,227446,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Cuba,>50K\n2,Private,125461,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,189749,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,176321,7th-8th,4,Never-married,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,Private,284250,HS-grad,9,Never-married,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,101885,10th,6,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,134130,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,260938,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,238184,HS-grad,9,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,148626,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,48102,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,3,United-States,>50K\n4,Private,234213,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,4,0,3,United-States,>50K\n4,Private,113323,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,34246,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,175070,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,279680,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,188328,HS-grad,9,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,96609,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,84257,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,275632,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,385540,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,196342,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,Ireland,<=50K\n3,Private,97176,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,197714,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,147099,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,186346,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,73434,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,275074,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,209214,5th-6th,3,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,Mexico,<=50K\n2,Private,210525,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,372020,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,<=50K\n3,Private,176684,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,210474,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,293690,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,149775,Masters,14,Never-married,Prof-specialty,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,Private,323009,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,Germany,<=50K\n1,Private,126950,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,172538,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,115411,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,101709,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,1,0,2,United-States,<=50K\n0,Private,265356,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,192565,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n1,Self-emp-not-inc,348771,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,148959,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,126569,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,105936,HS-grad,9,Married-spouse-absent,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,188076,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,184400,10th,6,Never-married,Transport-moving,Own-child,Asian-Pac-Islander,Male,0,0,1,?,<=50K\n4,Private,124242,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n0,State-gov,200819,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,100480,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,129513,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,297796,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,195488,HS-grad,9,Separated,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,153486,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,126845,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,206974,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,412149,10th,6,Never-married,Farming-fishing,Other-relative,White,Male,0,0,1,Mexico,<=50K\n0,Private,653574,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,El-Salvador,<=50K\n2,Private,70562,1st-4th,2,Never-married,Other-service,Unmarried,White,Female,0,0,3,El-Salvador,<=50K\n4,Private,197514,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n0,?,309284,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,334679,Assoc-voc,11,Widowed,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Private,105116,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,151484,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,78765,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,0,4,United-States,>50K\n2,Private,98427,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,<=50K\n3,Private,230767,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Cuba,<=50K\n0,Private,117606,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,68642,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,341638,11th,7,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,65920,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Federal-gov,188246,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,198727,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,160728,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,1,0,2,United-States,<=50K\n1,Private,706026,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,348148,11th,7,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,77884,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n0,Private,160758,10th,6,Never-married,Sales,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,Private,201112,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,3,United-States,>50K\n4,Self-emp-inc,107850,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,230246,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,203034,Bachelors,13,Separated,Sales,Not-in-family,White,Male,0,4,3,United-States,>50K\n0,Private,373935,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Federal-gov,341695,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,119793,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,?,<=50K\n2,Private,178002,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,233130,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,El-Salvador,<=50K\n3,Local-gov,192982,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,33155,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,187346,5th-6th,3,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,Mexico,<=50K\n3,Private,78529,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,101626,9th,5,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,117567,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,110791,Assoc-acdm,12,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,State-gov,207120,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,43206,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,120238,Bachelors,13,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,189219,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,State-gov,190895,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,83517,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,35557,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n2,Local-gov,59313,Some-college,10,Separated,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,202033,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,55658,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,118186,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,279901,HS-grad,9,Married-civ-spouse,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,110954,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,El-Salvador,>50K\n2,Self-emp-not-inc,90159,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,122489,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,2,3,United-States,<=50K\n3,Self-emp-not-inc,43348,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,4,United-States,>50K\n2,Private,34278,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,37778,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,160623,Assoc-acdm,12,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,342458,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,64322,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,373914,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,205884,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n4,Local-gov,208266,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,222450,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,348420,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,136081,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,<=50K\n2,Federal-gov,197284,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,204773,Assoc-acdm,12,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,206066,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,61885,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,299908,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,United-States,>50K\n1,Private,46028,Assoc-acdm,12,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,239865,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,154587,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n1,Private,244473,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,32334,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,319588,Bachelors,13,Married-spouse-absent,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,226735,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,226443,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,359259,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,Portugal,<=50K\n1,Private,36851,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,393480,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,33109,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Self-emp-not-inc,188246,HS-grad,9,Divorced,Sales,Own-child,White,Male,0,1,4,United-States,<=50K\n1,Private,231569,Bachelors,13,Never-married,Sales,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n0,Private,353010,11th,7,Never-married,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Private,102628,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,262285,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,160300,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,156953,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,136823,11th,7,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,160724,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Japan,<=50K\n2,Self-emp-inc,86459,Assoc-acdm,12,Separated,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,238628,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,339954,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,?,222005,HS-grad,9,Never-married,?,Other-relative,White,Female,0,0,2,Mexico,<=50K\n0,Federal-gov,99893,11th,7,Never-married,Adm-clerical,Not-in-family,Black,Female,0,2,2,United-States,<=50K\n2,Private,214117,Some-college,10,Divorced,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Federal-gov,298661,Bachelors,13,Never-married,Tech-support,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,179488,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,48343,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,>50K\n1,Local-gov,100270,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,227065,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n2,Private,126701,9th,5,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,209131,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,400132,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,278155,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,139012,Bachelors,13,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,1,0,2,Vietnam,<=50K\n2,Private,178431,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,<=50K\n2,Private,511068,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,199039,12th,8,Never-married,Sales,Own-child,White,Male,1,0,0,United-States,<=50K\n1,Local-gov,190525,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,3,Germany,>50K\n2,Private,115700,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,167832,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,218164,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,State-gov,171926,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Self-emp-inc,242080,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n4,Federal-gov,223257,HS-grad,9,Widowed,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,386773,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n3,Self-emp-not-inc,105478,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,2,United-States,>50K\n2,Private,140644,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,205970,10th,6,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,216583,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,162432,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,83671,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,205100,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Germany,<=50K\n1,Private,195750,1st-4th,2,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,220562,9th,5,Never-married,Sales,Other-relative,Other,Female,0,0,1,Mexico,<=50K\n2,Self-emp-inc,312232,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,386337,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,?,<=50K\n2,Private,86185,Some-college,10,Widowed,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Private,105586,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,103345,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,150553,Bachelors,13,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,3,United-States,<=50K\n1,Private,26009,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,149388,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,151626,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,169583,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,174486,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Black,Male,4,0,1,Jamaica,>50K\n0,Private,160951,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,1,0,2,United-States,<=50K\n0,Private,213383,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,103078,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,109526,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,142835,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,43475,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,190916,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,175987,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,214385,11th,7,Divorced,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,192652,Bachelors,13,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,207685,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,143857,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,163392,HS-grad,9,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,1,?,<=50K\n3,Private,310774,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,?,427965,HS-grad,9,Separated,?,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,279608,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,312881,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,175083,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,132057,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,0,United-States,<=50K\n2,Private,32878,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,360527,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,99478,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,113035,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,99199,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,452640,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,4,0,3,United-States,>50K\n3,Private,236858,11th,7,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,201865,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,268661,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,475324,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,117295,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,65704,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,?,<=50K\n2,Private,192835,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,76720,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,180686,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n1,Local-gov,133876,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,123727,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,129956,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,96268,11th,7,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,317320,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,86872,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,100863,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,>50K\n4,Private,164332,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,122584,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,34377,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,162030,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,199170,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,470203,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,266803,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,?,188009,7th-8th,4,Divorced,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,State-gov,513416,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,98211,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,196107,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,108273,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,213290,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,2,United-States,>50K\n4,Private,96660,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,4,0,1,United-States,>50K\n0,Local-gov,412316,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,120068,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,101722,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,120268,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n0,State-gov,144429,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,271122,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,255621,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,90934,Assoc-voc,11,Divorced,Protective-serv,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,162745,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,128460,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,30813,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,164585,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,148003,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,4,0,2,United-States,>50K\n3,Private,215647,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,300975,Masters,14,Married-civ-spouse,Other-service,Husband,Black,Male,0,1,2,?,<=50K\n3,Private,421561,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,149909,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n4,?,240857,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,4,2,United-States,>50K\n2,Self-emp-not-inc,138940,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,66755,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,United-States,<=50K\n2,Private,103323,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n0,?,117222,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,29145,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,184659,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,>50K\n3,Self-emp-not-inc,20795,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,311376,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,122660,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,?,137578,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,193689,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,144556,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,228696,1st-4th,2,Married-civ-spouse,Craft-repair,Not-in-family,White,Male,0,4,1,Mexico,<=50K\n4,Private,184183,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,2,0,2,United-States,<=50K\n0,Private,243178,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,236330,Some-college,10,Never-married,?,Own-child,Black,Male,0,2,0,United-States,<=50K\n4,State-gov,190682,Assoc-voc,11,Widowed,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,233786,11th,7,Separated,Other-service,Unmarried,White,Male,0,0,0,United-States,<=50K\n2,Private,102202,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,95299,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,>50K\n2,Self-emp-inc,240504,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,State-gov,169973,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,144937,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,211751,Assoc-voc,11,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,84587,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n2,State-gov,150874,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,1,0,2,United-States,<=50K\n0,?,187332,10th,6,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,188615,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,119704,Some-college,10,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,275190,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,417941,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,196348,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,221955,Bachelors,13,Married-civ-spouse,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,173938,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,123429,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,143732,HS-grad,9,Widowed,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,203126,Bachelors,13,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,0,?,<=50K\n4,Private,174693,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,1,Nicaragua,<=50K\n3,Private,357540,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n4,?,29859,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,1,2,United-States,>50K\n4,Private,314092,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n4,Private,280088,7th-8th,4,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,257380,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,1,United-States,<=50K\n0,Private,165306,Some-college,10,Never-married,Tech-support,Other-relative,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n1,Self-emp-not-inc,109001,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,266439,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,>50K\n4,Self-emp-not-inc,153356,HS-grad,9,Divorced,Sales,Not-in-family,Black,Male,1,0,3,United-States,<=50K\n0,Private,32950,Some-college,10,Never-married,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,182163,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,188246,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,297335,Bachelors,13,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Female,0,0,3,China,<=50K\n2,Private,108366,Bachelors,13,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,328301,Assoc-acdm,12,Married-AF-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,182158,10th,6,Never-married,Priv-house-serv,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,169426,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,?,330571,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,535978,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,29393,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,258883,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,Hungary,>50K\n1,Private,369166,Some-college,10,Never-married,Farming-fishing,Other-relative,White,Female,0,0,4,United-States,<=50K\n2,Local-gov,257855,11th,7,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,164197,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,109517,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,112137,Some-college,10,Never-married,Prof-specialty,Other-relative,Asian-Pac-Islander,Female,0,0,0,South,<=50K\n2,Private,160035,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,50567,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,140011,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,271328,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,183083,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,159869,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,102542,7th-8th,4,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,297742,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,176917,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,165235,Bachelors,13,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,Thailand,<=50K\n1,Self-emp-not-inc,52647,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,48542,12th,8,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,279232,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n4,State-gov,259929,Doctorate,16,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,221780,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,253408,Some-college,10,Widowed,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,298841,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,321313,Masters,14,Never-married,Sales,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,64875,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,275232,Assoc-acdm,12,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,134854,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Greece,>50K\n2,Private,67339,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,?,<=50K\n1,State-gov,192355,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,208528,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,160120,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n2,Private,250238,1st-4th,2,Never-married,Other-service,Other-relative,Other,Female,0,0,2,El-Salvador,<=50K\n3,Private,25031,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,>50K\n2,Local-gov,255847,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,26892,Bachelors,13,Married-AF-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,111979,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,408537,9th,5,Divorced,Craft-repair,Unmarried,White,Female,4,0,2,United-States,>50K\n2,Private,231037,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,30030,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,292120,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,138253,Masters,14,Never-married,Handlers-cleaners,Not-in-family,White,Male,2,0,2,United-States,<=50K\n1,Private,190777,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,41591,Bachelors,13,Never-married,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n1,Private,186733,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,78567,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n0,?,140590,12th,8,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Local-gov,230912,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Private,176185,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,2,2,United-States,<=50K\n0,Private,182227,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,205704,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,State-gov,24342,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,138192,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,334676,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,177526,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,152696,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,114765,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,265509,Assoc-voc,11,Separated,Tech-support,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,180758,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,127921,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,177906,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n1,Federal-gov,182898,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,422249,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,222450,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Local-gov,190027,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,0,United-States,<=50K\n3,Private,281647,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,117963,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,319121,11th,7,Separated,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,225504,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,104334,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,El-Salvador,<=50K\n1,State-gov,48214,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,145714,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,38240,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,27385,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,204254,10th,6,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,411587,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,Honduras,<=50K\n2,Private,221172,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,54190,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,93997,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Local-gov,24139,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,4,United-States,<=50K\n2,Private,112497,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,138907,HS-grad,9,Divorced,Priv-house-serv,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Private,186325,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,199452,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,126677,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,107814,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,1,0,3,United-States,<=50K\n3,Local-gov,93618,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,353352,Assoc-voc,11,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,143058,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,239663,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,167615,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,442274,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,149210,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n3,Federal-gov,174533,Bachelors,13,Separated,Other-service,Unmarried,White,Female,0,0,4,?,<=50K\n2,State-gov,50093,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,270056,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Japan,<=50K\n4,Self-emp-not-inc,131991,Bachelors,13,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n2,State-gov,126336,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,341117,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,108505,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,106566,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,74791,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Male,0,0,3,?,<=50K\n1,Private,24266,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,267967,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,?,181284,12th,8,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,102533,Some-college,10,Separated,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,69757,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,State-gov,210094,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,389147,HS-grad,9,Never-married,Sales,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n2,Private,210648,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,94809,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Local-gov,298717,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,236879,Preschool,1,Widowed,Priv-house-serv,Other-relative,White,Female,0,0,2,Guatemala,<=50K\n1,Private,170148,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,166497,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,247156,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,204052,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,122246,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,180339,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,155574,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,114912,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,>50K\n2,Private,193882,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,112269,Some-college,10,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,171928,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,3,Japan,<=50K\n3,Private,95435,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,1,4,Canada,<=50K\n2,Federal-gov,179638,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,125892,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,721712,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,197369,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,353795,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,1,0,2,United-States,>50K\n3,Private,334679,Masters,14,Separated,Machine-op-inspct,Unmarried,Asian-Pac-Islander,Female,0,0,2,India,<=50K\n0,Private,235853,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,353281,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,203061,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,62932,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,118551,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,4,United-States,<=50K\n3,Private,99184,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,189674,Some-college,10,Separated,Other-service,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,Private,226883,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,109564,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,66872,12th,8,Married-civ-spouse,Sales,Husband,Other,Male,0,0,4,Dominican-Republic,<=50K\n1,Local-gov,268292,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,139290,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,206541,11th,7,Divorced,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,203139,Some-college,10,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,294398,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,386864,10th,6,Never-married,Other-service,Other-relative,White,Male,0,0,1,Mexico,<=50K\n0,Private,369909,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,89922,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,176008,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,State-gov,241506,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,1,0,2,United-States,<=50K\n2,Self-emp-not-inc,174426,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,167497,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,3,0,3,United-States,>50K\n3,Private,292673,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,Mexico,<=50K\n3,Local-gov,134808,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,95763,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,83622,Assoc-acdm,12,Separated,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n0,Private,222490,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,29115,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,66638,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,53926,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,43739,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,104359,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,124604,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,114797,HS-grad,9,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Federal-gov,67320,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Federal-gov,53147,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,13769,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n2,Private,202872,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,149528,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,132879,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,112362,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,156229,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,131650,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,154568,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,>50K\n0,Private,132300,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,124747,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,1,0,2,United-States,>50K\n2,Private,276559,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,4,United-States,>50K\n1,Private,106014,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n4,Self-emp-not-inc,135134,Masters,14,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,86648,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n3,Self-emp-not-inc,107231,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Local-gov,113838,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n4,Federal-gov,25319,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,190561,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n4,?,150031,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,48343,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,211116,10th,6,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,226311,HS-grad,9,Married-AF-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,283743,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,<=50K\n4,Self-emp-not-inc,64102,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,234663,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,247880,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Male,3,0,2,United-States,>50K\n0,Private,615367,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,163090,Assoc-acdm,12,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,192225,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,370183,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,242482,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,169953,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,144182,Preschool,1,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Private,125933,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,203777,Some-college,10,Never-married,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,210991,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,472580,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,200289,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,0,India,<=50K\n2,Private,289669,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,4,2,United-States,>50K\n1,Private,110622,Bachelors,13,Divorced,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n4,State-gov,139616,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,39212,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,51961,Some-college,10,Never-married,Tech-support,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,117849,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,50748,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Female,1,0,1,United-States,<=50K\n2,Self-emp-not-inc,170214,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,3,2,United-States,<=50K\n0,Private,151790,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,168211,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,117651,Bachelors,13,Never-married,Prof-specialty,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,157131,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,225970,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,177951,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,134130,Bachelors,13,Widowed,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,191581,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Local-gov,199172,HS-grad,9,Married-civ-spouse,Protective-serv,Wife,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,262552,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,66434,10th,6,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,77661,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,230856,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,192835,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,181014,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,200445,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,3,2,United-States,<=50K\n1,Self-emp-not-inc,37918,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,111020,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,244665,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,Honduras,<=50K\n3,Private,312477,HS-grad,9,Widowed,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,243493,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,0,United-States,<=50K\n2,State-gov,152023,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,104193,HS-grad,9,Never-married,Other-service,Own-child,White,Female,1,0,2,United-States,<=50K\n3,Private,170850,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,137088,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,2,Ecuador,<=50K\n0,Private,340557,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,298225,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,114150,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,194668,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,188246,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n3,Federal-gov,330901,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,80165,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,83444,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n1,Self-emp-not-inc,85572,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,116632,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,139989,Bachelors,13,Never-married,Sales,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,135803,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Other,Male,0,1,1,India,<=50K\n4,Private,75785,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,248612,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,28572,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,31143,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,216924,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,549174,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,111296,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,3,Mexico,<=50K\n0,Private,208881,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,243666,HS-grad,9,Divorced,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,327164,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,?,<=50K\n2,Self-emp-inc,131288,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,257416,Assoc-voc,11,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,215288,11th,7,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,58582,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,199378,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,114185,Bachelors,13,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,?,<=50K\n2,Private,137421,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,3,Trinadad&Tobago,<=50K\n1,Private,216481,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,196504,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,357870,12th,8,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n3,State-gov,256335,Bachelors,13,Divorced,Exec-managerial,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,168191,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,Italy,<=50K\n2,Private,215596,Bachelors,13,Married-spouse-absent,Other-service,Not-in-family,Other,Male,0,0,2,Mexico,<=50K\n2,Private,184682,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,171914,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,288229,Bachelors,13,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,3,Laos,<=50K\n1,State-gov,144064,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,?,54849,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,141583,10th,6,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,180985,Bachelors,13,Separated,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n0,Private,148709,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,174626,7th-8th,4,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,184801,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,89054,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,147284,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,169973,Assoc-voc,11,Separated,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,222993,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,41099,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,33117,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,162551,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,2,Hong,>50K\n3,Private,122066,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,4,2,Greece,<=50K\n4,?,42938,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,>50K\n3,Private,389843,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Germany,>50K\n2,Private,138940,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,141877,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,172722,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,118523,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,227886,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,80743,HS-grad,9,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n3,Private,199688,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,225823,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,176486,HS-grad,9,Married-spouse-absent,Exec-managerial,Other-relative,White,Female,0,0,3,United-States,<=50K\n4,Private,175777,10th,6,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,295010,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,437825,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,Peru,<=50K\n3,Private,270194,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,242089,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,117555,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,146499,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,222405,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,4,2,United-States,<=50K\n0,?,216595,11th,7,Never-married,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,Private,157991,Assoc-voc,11,Divorced,Tech-support,Unmarried,Black,Female,0,1,2,United-States,<=50K\n1,Private,373553,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,194827,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,60331,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,State-gov,96483,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n2,Private,211154,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,247750,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,204235,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,197113,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,0,United-States,<=50K\n3,Private,178341,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,<=50K\n0,Private,293297,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,35330,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,202056,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,61898,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,1097453,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,176992,10th,6,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,295289,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,298215,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,209934,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,Mexico,<=50K\n1,Private,164938,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,423222,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,124259,Some-college,10,Never-married,Protective-serv,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,232871,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n2,State-gov,73199,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,State-gov,27661,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,461715,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,?,<=50K\n2,Self-emp-not-inc,89413,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n4,Self-emp-not-inc,31826,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,279679,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,221172,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,222020,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,181265,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,261056,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,1,0,3,?,<=50K\n1,Private,204792,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,384508,11th,7,Divorced,Sales,Unmarried,White,Male,1,0,3,Mexico,<=50K\n2,Private,288568,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,182714,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,England,<=50K\n0,Private,471452,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,State-gov,264052,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,146659,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,203027,Assoc-acdm,12,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,218309,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,133625,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,45937,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,?,389850,HS-grad,9,Married-spouse-absent,?,Unmarried,Black,Male,0,0,3,United-States,<=50K\n2,Federal-gov,201617,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,114733,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,State-gov,97778,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,149507,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,82622,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,48014,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,France,<=50K\n4,State-gov,162678,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,213842,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,221447,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,426836,5th-6th,3,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Local-gov,206609,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,50276,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,180497,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,220585,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,202752,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,75993,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,170544,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,115439,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,24384,HS-grad,9,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,209067,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,65225,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,27466,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,England,<=50K\n3,Federal-gov,179869,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,442131,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,243283,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,316627,5th-6th,3,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,208862,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,38645,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,141272,Bachelors,13,Never-married,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,State-gov,29324,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,348588,12th,8,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,124747,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,2,0,2,United-States,>50K\n3,Self-emp-not-inc,477867,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,218361,10th,6,Never-married,Other-service,Own-child,White,Female,0,2,0,United-States,<=50K\n1,Self-emp-not-inc,156809,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,1,3,United-States,<=50K\n0,Private,267945,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,35724,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,187188,Masters,14,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n3,Private,155983,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Federal-gov,414994,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,103474,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,211128,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,203445,Some-college,10,Widowed,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,38312,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n3,Private,178241,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,260761,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Local-gov,36924,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,292590,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,461929,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,189664,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,190577,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,344200,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,337494,Assoc-acdm,12,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,52634,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,194901,Assoc-voc,11,Separated,Craft-repair,Not-in-family,White,Male,0,4,2,United-States,>50K\n0,Private,170091,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,?,189399,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,205072,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,310290,HS-grad,9,Married-civ-spouse,Transport-moving,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Private,134048,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,91959,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,153942,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,234096,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,185330,Some-college,10,Never-married,Craft-repair,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,163772,HS-grad,9,Married-civ-spouse,Other-service,Husband,Other,Male,0,0,2,United-States,<=50K\n4,Private,83800,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,139391,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,478380,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,186845,Bachelors,13,Married-civ-spouse,Sales,Own-child,White,Male,2,0,3,United-States,>50K\n2,Private,262802,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,152157,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,114483,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,118023,Prof-school,15,Divorced,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,220101,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,219424,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,3,United-States,>50K\n3,Private,186117,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,479611,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,80312,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Private,108386,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,125926,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,177102,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,190762,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,180632,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,88019,HS-grad,9,Divorced,Other-service,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Private,135339,12th,8,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,Cambodia,>50K\n1,Private,100662,9th,5,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,2,Columbia,<=50K\n1,Private,183557,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,160035,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,306790,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,269246,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,308334,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,215190,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,419146,5th-6th,3,Never-married,Other-service,Not-in-family,White,Male,0,0,4,Mexico,<=50K\n4,Private,176839,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,184456,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Local-gov,309348,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,1,0,0,United-States,<=50K\n2,Private,56795,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,England,<=50K\n1,Private,201861,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,179509,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,291755,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,243941,Some-college,10,Never-married,Sales,Own-child,Amer-Indian-Eskimo,Female,0,2,1,United-States,<=50K\n4,Self-emp-not-inc,117169,7th-8th,4,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,?,100903,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,159322,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,262872,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,187052,11th,7,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,277583,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,169071,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,96190,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,61603,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,Other,Male,0,0,2,Mexico,<=50K\n2,Private,43711,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,197883,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,99434,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,177639,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,201723,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,222248,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,86143,5th-6th,3,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,?,228620,11th,7,Widowed,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,346034,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,El-Salvador,<=50K\n4,Private,87510,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,37932,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,185063,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,?,125493,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,2,0,2,Scotland,>50K\n3,Private,159755,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,108837,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,110669,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,?,220115,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,45427,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,154669,HS-grad,9,Separated,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,261278,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,2,0,2,Philippines,>50K\n0,Private,71864,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,173495,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,254293,12th,8,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,111883,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,146429,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,472807,1st-4th,2,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,3,Mexico,<=50K\n1,Private,285294,Bachelors,13,Married-civ-spouse,Sales,Wife,Black,Female,4,0,2,United-States,>50K\n0,Private,184665,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,205852,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,83879,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,178564,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,168796,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,269444,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,47353,10th,6,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,29254,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,155343,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,234271,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,257849,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,228230,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,227615,5th-6th,3,Married-spouse-absent,Craft-repair,Other-relative,White,Male,0,0,1,Mexico,<=50K\n1,Private,406826,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,27539,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,97261,12th,8,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,232022,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,168539,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,515797,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,351381,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,161018,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,26721,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,164123,11th,7,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,98418,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,29814,HS-grad,9,Never-married,Transport-moving,Other-relative,White,Male,0,0,3,United-States,<=50K\n0,Private,254613,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,Cuba,<=50K\n3,Private,207677,7th-8th,4,Divorced,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Self-emp-not-inc,217030,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,171199,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,198270,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,131310,HS-grad,9,Separated,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,79923,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-inc,475322,Bachelors,13,Separated,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,134286,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,73746,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,125525,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,?,155676,HS-grad,9,Divorced,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,304949,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,150516,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,State-gov,249096,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,164127,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,304779,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,157043,11th,7,Widowed,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,396538,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,510072,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,?,200017,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,?,60641,Bachelors,13,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,89326,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,200471,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,<=50K\n4,Self-emp-not-inc,82815,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,>50K\n0,Self-emp-not-inc,117210,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,202206,11th,7,Separated,Farming-fishing,Other-relative,White,Male,0,0,2,Puerto-Rico,<=50K\n3,Private,123429,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,353512,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,26683,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,204641,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,225053,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,98776,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,263932,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,108247,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,369648,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,339324,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,<=50K\n4,?,145574,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,317313,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Local-gov,162919,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,186314,Some-college,10,Separated,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,254202,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,108140,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,287317,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,Black,Male,0,0,1,United-States,<=50K\n4,Self-emp-inc,81534,HS-grad,9,Widowed,Sales,Other-relative,Asian-Pac-Islander,Male,0,0,1,United-States,>50K\n2,Private,35945,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,204928,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,208809,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,133625,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,71683,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,570562,HS-grad,9,Widowed,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,36876,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,253006,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n2,Self-emp-not-inc,50096,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,336880,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,?,135840,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,168048,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,187969,11th,7,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,117363,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,256526,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,2,0,2,United-States,<=50K\n3,Private,304416,11th,7,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,248011,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,229826,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,159796,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,165346,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,25386,Assoc-voc,11,Never-married,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,491000,Assoc-voc,11,Divorced,Prof-specialty,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Local-gov,247731,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,Cuba,<=50K\n3,Private,180532,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,189462,Some-college,10,Divorced,Handlers-cleaners,Own-child,White,Male,1,0,2,United-States,<=50K\n2,Private,419134,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,170166,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,173495,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,423024,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,72119,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,1,0,1,United-States,<=50K\n1,Local-gov,19302,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,England,>50K\n0,State-gov,257621,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,118212,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n1,Private,259840,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,115289,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,France,>50K\n1,Local-gov,159662,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,379798,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,227945,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n2,State-gov,36999,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n4,?,131982,Bachelors,13,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,0,0,Vietnam,<=50K\n1,Self-emp-inc,124052,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,273084,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n4,Private,170104,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,96249,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,140915,Bachelors,13,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,1,2,South,<=50K\n3,Private,230657,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,2,0,2,Columbia,<=50K\n1,Private,195576,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,1,0,3,United-States,<=50K\n0,Private,117767,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,112763,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,3,0,2,United-States,>50K\n4,Private,79827,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,103925,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,161744,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,106679,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n2,Self-emp-not-inc,196514,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,61985,9th,5,Separated,?,Not-in-family,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n0,Private,157605,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,137367,11th,7,Married-spouse-absent,Handlers-cleaners,Not-in-family,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n2,Self-emp-inc,110862,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,4,3,United-States,<=50K\n1,Private,74883,Bachelors,13,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,Self-emp-inc,98642,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Local-gov,144778,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,177787,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,1,England,<=50K\n1,?,103651,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,162108,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,217602,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,473133,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,113301,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,?,<=50K\n4,Private,80896,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n1,Local-gov,168387,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,38950,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,107801,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,191277,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,205359,Assoc-acdm,12,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,?,240226,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,203357,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,153064,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,202959,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,105150,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,238474,11th,7,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,1085515,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,82560,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,55965,7th-8th,4,Widowed,Transport-moving,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,161087,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Private,261278,Assoc-voc,11,Never-married,Tech-support,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,182187,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,Black,Male,4,0,2,Jamaica,>50K\n0,Private,138917,11th,7,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,Private,200198,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,205359,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,250201,HS-grad,9,Widowed,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,67153,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Portugal,>50K\n0,Private,244523,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,236599,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,108713,10th,6,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,177147,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,129246,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,?,222381,Some-college,10,Divorced,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,145111,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,62258,11th,7,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,108293,Masters,14,Never-married,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,?,167284,7th-8th,4,Widowed,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,97789,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,111415,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,374524,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,287244,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,341395,10th,6,Never-married,?,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Private,278039,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,98360,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,317032,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,294395,Assoc-voc,11,Widowed,Prof-specialty,Unmarried,White,Female,2,0,2,United-States,<=50K\n2,Self-emp-not-inc,240900,HS-grad,9,Divorced,Farming-fishing,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Private,32896,5th-6th,3,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,97411,7th-8th,4,Never-married,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Laos,<=50K\n0,Private,72355,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,342448,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,187702,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Private,303388,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,112291,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,208668,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n4,Local-gov,28375,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,207277,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n4,?,88675,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,47857,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,372500,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,190968,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,37997,12th,8,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,>50K\n2,Private,257328,HS-grad,9,Widowed,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,127610,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,139324,9th,5,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,164423,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,104501,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,3,2,United-States,<=50K\n1,Private,56121,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,296212,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,157640,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,222504,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,261023,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n3,Private,146567,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Male,4,0,2,United-States,>50K\n1,Private,116910,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,132601,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,185537,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,500720,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Mexico,<=50K\n2,Private,182108,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,231491,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,239415,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,179262,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Without-pay,121004,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,252392,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,163578,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,143266,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Hungary,>50K\n1,Private,285902,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,113094,Bachelors,13,Separated,Adm-clerical,Unmarried,White,Female,0,1,2,United-States,<=50K\n1,Private,278637,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,174540,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,0,United-States,<=50K\n1,Private,188729,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,72143,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,328216,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,165815,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,317702,10th,6,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,215323,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,192939,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,156352,9th,5,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,155066,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,152621,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,298891,11th,7,Never-married,Sales,Not-in-family,White,Female,0,0,2,Honduras,<=50K\n1,Private,193298,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,150309,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,384308,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,305647,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,?,182378,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n4,Federal-gov,23494,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,3,2,United-States,>50K\n2,Private,421633,Masters,14,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,>50K\n0,Private,57723,11th,7,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,?,307837,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,103540,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,136224,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,231573,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,242804,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,163671,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,287701,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,187560,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,222504,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,41356,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,59335,Bachelors,13,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,<=50K\n4,Private,84756,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,407425,12th,8,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,162424,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,175456,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,52603,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,250630,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,233974,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,376302,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,195638,10th,6,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,225775,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,Private,388384,7th-8th,4,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,219021,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n4,Self-emp-not-inc,168654,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,180609,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,114746,HS-grad,9,Separated,Handlers-cleaners,Unmarried,Asian-Pac-Islander,Female,0,0,3,South,<=50K\n0,Private,178037,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n3,State-gov,160045,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,268524,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,174844,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,82488,HS-grad,9,Divorced,Tech-support,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,221167,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Self-emp-not-inc,48014,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,217226,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,?,177902,Some-college,10,Never-married,?,Not-in-family,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n1,Private,39386,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,4,United-States,<=50K\n4,Private,37394,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,115426,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,114158,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,119101,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,4,United-States,>50K\n1,Private,360527,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,225544,12th,8,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,108438,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,230315,Some-college,10,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Dominican-Republic,<=50K\n1,Private,158002,Some-college,10,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,3,Ecuador,<=50K\n2,Private,179468,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,99894,5th-6th,3,Widowed,Priv-house-serv,Not-in-family,Asian-Pac-Islander,Female,0,0,4,United-States,<=50K\n1,Private,270889,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,42279,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,167380,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,3,United-States,<=50K\n2,Private,274913,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,35910,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,68001,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,27162,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,286146,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,95462,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,50103,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,511668,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n2,Self-emp-inc,189679,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,115064,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,215443,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,174789,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n0,Private,91999,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Federal-gov,100931,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,119069,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,277488,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,265662,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,114591,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,2,United-States,>50K\n0,Private,227594,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,129707,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n4,?,175032,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,133569,1st-4th,2,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Local-gov,308654,Some-college,10,Never-married,Protective-serv,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n2,Private,156084,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,380127,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,210781,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,189759,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,3,2,United-States,<=50K\n1,Private,258675,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,223367,11th,7,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,?,204817,9th,5,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n0,Private,409230,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,308077,Prof-school,15,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,Germany,>50K\n4,Private,159049,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,Germany,>50K\n2,Private,353142,Some-college,10,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,143030,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,304857,Masters,14,Separated,Tech-support,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,30912,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,125000,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,181363,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,338620,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,115989,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,111128,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,201273,Some-college,10,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,137354,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n1,Private,133420,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,192208,HS-grad,9,Never-married,Protective-serv,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,220001,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,352612,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,169426,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,319016,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,119751,Masters,14,Never-married,Prof-specialty,Other-relative,Asian-Pac-Islander,Female,0,0,2,Thailand,<=50K\n3,Private,202220,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,1,0,1,United-States,<=50K\n2,Self-emp-not-inc,99220,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,111275,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,261241,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,261725,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n2,Private,182013,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,40666,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,216461,Some-college,10,Divorced,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,320376,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,282951,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n2,State-gov,166697,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,290856,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,455361,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Guatemala,<=50K\n3,Private,82783,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,56536,11th,7,Never-married,Sales,Own-child,White,Female,1,0,0,India,<=50K\n1,Self-emp-not-inc,109959,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,177927,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,192337,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,236272,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,33610,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,209483,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,247006,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,311913,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,?,204756,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,300681,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,24264,Some-college,10,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,266070,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,226978,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Local-gov,362165,Bachelors,13,Widowed,Prof-specialty,Not-in-family,Black,Female,0,4,1,United-States,<=50K\n1,Private,341672,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n2,Private,179488,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,Canada,<=50K\n2,Federal-gov,243872,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,259583,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,159822,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,Poland,>50K\n1,Private,219863,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,206947,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,245572,9th,5,Never-married,Other-service,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,38488,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,182504,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,193815,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Italy,<=50K\n3,?,521665,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,46442,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,3,United-States,>50K\n2,Private,60267,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,264357,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,191814,HS-grad,9,Married-civ-spouse,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,107882,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,174575,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,143331,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,126132,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,198619,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,211287,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,2,United-States,>50K\n3,Federal-gov,238192,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,257780,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,183355,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,148429,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,71221,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Self-emp-not-inc,236769,7th-8th,4,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,32146,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,347491,11th,7,Divorced,Craft-repair,Not-in-family,White,Male,0,2,3,United-States,<=50K\n1,Private,180714,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,3,2,United-States,<=50K\n4,?,188877,9th,5,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,306747,Bachelors,13,Divorced,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,State-gov,478457,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,248990,5th-6th,3,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n3,Self-emp-inc,46281,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,148015,Bachelors,13,Never-married,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,278115,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,190525,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,176673,Some-college,10,Never-married,Sales,Other-relative,Black,Female,0,0,1,United-States,<=50K\n1,?,202366,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,238415,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,37939,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,35649,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,383493,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Federal-gov,204900,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,20809,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,>50K\n1,Private,148207,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,200153,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,169496,Masters,14,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,>50K\n3,Private,22978,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,366898,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Germany,<=50K\n2,Private,324947,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,321577,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,241360,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,207564,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,220860,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,336571,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,56402,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,180280,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,81282,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,86332,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,2,0,3,United-States,<=50K\n1,Local-gov,27051,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,287647,Masters,14,Divorced,Sales,Not-in-family,White,Male,2,0,2,United-States,>50K\n2,Self-emp-not-inc,183735,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,1,United-States,<=50K\n2,Private,100800,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,155094,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n4,?,102693,HS-grad,9,Widowed,?,Not-in-family,White,Male,1,0,1,United-States,<=50K\n1,Private,151053,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,548361,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,173858,Bachelors,13,Married-civ-spouse,Adm-clerical,Other-relative,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n1,Private,347153,Some-college,10,Never-married,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,319146,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,Mexico,>50K\n1,Private,197719,Some-college,10,Never-married,Machine-op-inspct,Other-relative,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Private,197114,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,>50K\n3,Self-emp-not-inc,109418,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,1,United-States,>50K\n4,Private,182062,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,184543,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,175558,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,0,Germany,<=50K\n3,Private,122026,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,340543,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,101950,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,179508,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,225317,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Local-gov,53304,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,282602,Assoc-voc,11,Separated,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,184016,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,250165,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,196467,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,220783,10th,6,Widowed,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,178780,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,65868,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,35459,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,98986,7th-8th,4,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,282092,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,140764,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,33124,HS-grad,9,Separated,Farming-fishing,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,90042,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,102986,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Wife,Asian-Pac-Islander,Female,0,0,2,Laos,>50K\n0,Private,214387,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,4,United-States,<=50K\n2,Private,180667,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,278329,HS-grad,9,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,184440,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,140462,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,202565,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Italy,<=50K\n4,?,181063,10th,6,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,287268,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,215955,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,82552,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,41745,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,73587,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n3,Private,263925,1st-4th,2,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,196119,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,284741,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,293936,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,3,?,<=50K\n1,Private,340428,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,175891,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,276973,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,161599,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,144064,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,236391,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,224943,Assoc-voc,11,Never-married,Sales,Other-relative,Black,Male,0,0,4,United-States,<=50K\n2,Private,151294,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,68982,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,241885,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,189461,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Self-emp-not-inc,36012,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,85355,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,157595,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,197286,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,362747,Some-college,10,Never-married,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,395297,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,144949,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,163665,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,141490,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,147889,Assoc-acdm,12,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,232808,10th,6,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,70668,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,3,United-States,<=50K\n1,Federal-gov,33315,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,63526,12th,8,Never-married,?,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,591711,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,3,?,<=50K\n0,Private,200318,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,97723,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n2,Private,109231,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,102889,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,167106,HS-grad,9,Never-married,Craft-repair,Other-relative,Asian-Pac-Islander,Male,0,0,2,Hong,<=50K\n1,Private,182898,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,3,0,2,United-States,>50K\n4,Private,197918,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,67386,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,126592,HS-grad,9,Separated,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,49469,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,4,0,3,United-States,>50K\n2,Self-emp-not-inc,119929,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,158199,1st-4th,2,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,Portugal,<=50K\n1,Private,341102,9th,5,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,101524,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,202872,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,195201,HS-grad,9,Married-civ-spouse,Sales,Husband,Other,Male,0,0,3,United-States,<=50K\n3,Private,128272,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,263094,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n3,Self-emp-inc,357596,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n3,Local-gov,102628,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,171114,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,216414,Assoc-voc,11,Married-spouse-absent,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,127753,12th,8,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,282698,7th-8th,4,Never-married,Adm-clerical,Own-child,White,Male,0,0,4,United-States,<=50K\n1,Private,139364,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n2,Local-gov,312785,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Male,0,0,1,United-States,<=50K\n0,Private,92864,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,175428,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,104223,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,144784,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,178934,HS-grad,9,Widowed,Other-service,Unmarried,Black,Female,0,0,0,Jamaica,<=50K\n2,Private,211253,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,133122,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,103540,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,172700,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,282484,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,323055,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,291494,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,214702,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,116055,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,1,0,1,United-States,<=50K\n1,Private,226696,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,216827,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,153132,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,307440,Bachelors,13,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n1,Private,278122,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,122195,HS-grad,9,Widowed,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,156890,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,36877,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,131178,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Self-emp-inc,62396,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,>50K\n1,Private,73054,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,96844,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,324922,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,130684,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,178983,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n4,Private,81038,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,151967,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,278107,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,183146,12th,8,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,183638,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,247892,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,1,United-States,<=50K\n0,Private,221480,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,118551,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,?,>50K\n0,Private,518530,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,193787,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-inc,157466,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,141511,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,158712,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,4,United-States,<=50K\n0,Private,252253,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,200450,7th-8th,4,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,3,United-States,<=50K\n1,State-gov,343789,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,277647,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,291566,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,151382,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,221167,Prof-school,15,Divorced,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,196178,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,302422,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,37379,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,82540,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n1,Self-emp-not-inc,182926,Bachelors,13,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,?,<=50K\n2,Private,159911,7th-8th,4,Married-civ-spouse,Other-service,Wife,White,Female,0,0,3,United-States,<=50K\n1,Private,212781,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,207213,Assoc-acdm,12,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,200192,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,180096,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,192812,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,105908,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,4,United-States,<=50K\n3,Private,373366,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,3,Mexico,<=50K\n1,State-gov,234190,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,260868,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,109097,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,171393,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,209146,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,289046,HS-grad,9,Divorced,?,Not-in-family,Black,Male,0,2,2,United-States,<=50K\n3,Private,172281,Masters,14,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,73023,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,122626,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,113635,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,257269,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,4,1,United-States,>50K\n0,?,191806,Some-college,10,Never-married,?,Own-child,White,Male,0,0,4,United-States,<=50K\n4,?,35723,HS-grad,9,Divorced,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,30759,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,105327,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,376058,9th,5,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,219307,9th,5,Divorced,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,208067,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,78631,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n0,Private,210308,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,190661,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,3,0,3,United-States,>50K\n1,Private,594187,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,228476,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,126613,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,30267,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,216811,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,115763,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Local-gov,199368,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,3,United-States,>50K\n3,Private,159755,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n2,Self-emp-not-inc,188335,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,417668,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n3,?,141807,HS-grad,9,Never-married,?,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,296317,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,164898,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,452406,11th,7,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,42696,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,262994,Some-college,10,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,167298,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,103529,11th,7,Divorced,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,97883,Bachelors,13,Widowed,Priv-house-serv,Unmarried,White,Female,4,0,1,United-States,>50K\n3,State-gov,269417,Doctorate,16,Never-married,Exec-managerial,Not-in-family,White,Female,0,4,3,United-States,>50K\n1,Private,199539,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,?,39460,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,62176,Doctorate,16,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,>50K\n1,State-gov,239130,Some-college,10,Divorced,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,151089,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,331611,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,203463,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,151518,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-inc,39844,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,299635,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Germany,<=50K\n4,Private,123393,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,209538,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,238802,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,499197,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,200220,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,114059,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,434430,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,185385,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,1,United-States,<=50K\n0,Private,225156,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,377931,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,<=50K\n1,?,133359,Bachelors,13,Married-spouse-absent,?,Not-in-family,White,Male,0,0,3,?,<=50K\n1,Private,226891,Some-college,10,Never-married,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,1,?,<=50K\n1,Private,201988,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,?,<=50K\n2,Private,287008,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,Germany,>50K\n0,Private,151910,Bachelors,13,Never-married,Machine-op-inspct,Own-child,White,Female,0,2,2,United-States,<=50K\n0,Private,231714,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,178510,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,4,3,United-States,<=50K\n2,Private,178866,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,110643,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,148261,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,217902,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,77207,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,?,377017,Assoc-acdm,12,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,184759,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,0,United-States,<=50K\n4,Self-emp-inc,80333,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,265086,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,?,102058,12th,8,Widowed,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,333843,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,296478,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,116662,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,353298,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,142424,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,200808,12th,8,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,Puerto-Rico,<=50K\n1,Private,119052,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,168981,1st-4th,2,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,151780,Some-college,10,Widowed,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,237065,5th-6th,3,Widowed,Other-service,Other-relative,White,Female,1,0,2,?,<=50K\n0,Private,509866,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,State-gov,249385,Bachelors,13,Never-married,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,State-gov,109462,Bachelors,13,Divorced,Adm-clerical,Unmarried,Black,Female,1,0,2,United-States,<=50K\n3,Private,250034,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,>50K\n2,Private,249720,Bachelors,13,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,258761,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,64048,9th,5,Never-married,Sales,Own-child,White,Female,0,0,2,Portugal,<=50K\n0,State-gov,153534,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,193815,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,255582,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,204527,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,159938,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,1,Italy,<=50K\n1,Self-emp-not-inc,229341,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,128143,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,175479,5th-6th,3,Never-married,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n0,Private,301814,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,238917,11th,7,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n1,Private,205581,Some-college,10,Separated,Tech-support,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,340341,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,147860,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,Black,Female,0,0,2,United-States,<=50K\n0,?,121023,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,259496,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,190228,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,180599,Bachelors,13,Separated,Exec-managerial,Unmarried,White,Male,3,0,2,United-States,>50K\n2,Private,116358,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,180446,Some-college,10,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,264244,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,197988,1st-4th,2,Never-married,Other-service,Not-in-family,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n0,Private,206599,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,313146,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,99212,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,340599,11th,7,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,62932,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,44861,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,53893,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,152810,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n3,Local-gov,128401,Doctorate,16,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,336951,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,95445,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,3,United-States,<=50K\n2,Private,54611,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,315984,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,210313,10th,6,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n0,Private,181020,11th,7,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,120781,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Other,Male,4,0,4,India,>50K\n0,Private,256979,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,Private,47298,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,125461,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,209955,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,182246,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,76860,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n2,?,91949,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,136986,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n1,Federal-gov,183445,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Female,0,0,4,Puerto-Rico,<=50K\n0,Private,130741,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,191878,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,?,233923,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,48121,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,304302,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Federal-gov,284703,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,401198,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,243357,11th,7,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,32276,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,110538,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,257310,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,411950,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,392668,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,52498,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,223433,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,87076,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,224854,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,193379,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,98436,Masters,14,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,116632,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,210381,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n2,Private,90688,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Laos,<=50K\n4,Private,229744,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,59732,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,192900,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,90046,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,Canada,<=50K\n2,Private,272960,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,152071,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Cuba,>50K\n3,Private,301583,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,171964,HS-grad,9,Never-married,?,Own-child,White,Female,0,2,0,United-States,<=50K\n3,Private,315984,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,241962,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,131591,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,207938,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,53197,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,121023,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,287229,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,163911,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,191834,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,204734,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,220978,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,365739,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,50103,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,283293,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,194534,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,4,0,3,United-States,>50K\n0,Private,263338,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,?,504871,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,348592,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,173944,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,226135,9th,5,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,Jamaica,<=50K\n1,Private,172375,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,127728,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,347025,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,191335,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,247779,11th,7,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,State-gov,262664,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,95855,HS-grad,9,Divorced,Protective-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,74501,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,245317,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,29059,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,4,1,United-States,<=50K\n4,Private,200316,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,198341,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n4,Private,100453,7th-8th,4,Separated,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,343190,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n3,Private,235683,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,83237,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,88470,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,198801,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,168107,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,196193,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n1,?,205418,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,695411,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,139268,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Federal-gov,192771,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,122390,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n4,Self-emp-inc,184965,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,180837,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,159548,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,110554,HS-grad,9,Divorced,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,103474,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,178249,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,138768,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,321824,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,244803,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Peru,<=50K\n4,Local-gov,206063,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,167651,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,State-gov,163689,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,45546,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,420986,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,68015,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,175594,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,148673,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,206322,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,272338,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n4,Private,105886,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,4,United-States,<=50K\n4,Private,312498,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,177675,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,152810,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,319122,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,212304,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,208321,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,2,2,United-States,<=50K\n2,Private,240841,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,208978,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Local-gov,442359,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,<=50K\n1,Private,198197,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,261059,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,72791,Some-college,10,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,275395,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,195767,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,462966,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,El-Salvador,<=50K\n0,?,265434,Bachelors,13,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,31269,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,246291,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Federal-gov,128378,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,231180,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,206297,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,337050,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,193075,HS-grad,9,Divorced,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,169652,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Male,0,2,3,United-States,<=50K\n1,Private,35945,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,?,141453,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,252231,Preschool,1,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,Puerto-Rico,<=50K\n1,Private,128016,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,150057,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,258276,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,188465,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-inc,161007,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,403468,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Mexico,<=50K\n3,Federal-gov,181677,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,120243,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,157025,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,306908,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,28061,7th-8th,4,Widowed,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,95540,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,1,0,2,United-States,<=50K\n1,Private,135001,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,293398,HS-grad,9,Separated,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,185106,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,245790,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,134004,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,205036,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,244495,9th,5,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,159179,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,405155,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,177437,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n1,Federal-gov,402361,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,184553,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,302626,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,99138,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,112731,HS-grad,9,Divorced,Other-service,Not-in-family,Other,Female,0,0,2,Dominican-Republic,<=50K\n1,Private,192923,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,4,2,United-States,<=50K\n0,Private,761006,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,125784,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,182344,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,117012,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Federal-gov,30673,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,484669,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,314052,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,38537,Some-college,10,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,?,<=50K\n1,Private,165412,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,198341,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,3,3,India,>50K\n3,Private,116635,Bachelors,13,Separated,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,185452,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,118686,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,76939,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,160646,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n3,State-gov,126754,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Portugal,<=50K\n0,Private,211049,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,311931,5th-6th,3,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,El-Salvador,<=50K\n1,Private,283602,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Mexico,<=50K\n0,Private,155021,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,100569,HS-grad,9,Separated,Farming-fishing,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,380462,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Federal-gov,221943,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,114544,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,248584,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,227468,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,66173,Assoc-acdm,12,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,107624,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,70387,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,2,0,2,India,>50K\n2,Private,423616,Assoc-voc,11,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,98637,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Local-gov,216013,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,210926,11th,7,Separated,Handlers-cleaners,Unmarried,White,Female,0,0,2,Nicaragua,<=50K\n4,Local-gov,255711,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,>50K\n0,Private,77581,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,152461,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Female,4,0,3,United-States,>50K\n0,Private,203263,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,261519,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,91189,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,195433,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,272471,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,311524,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,151386,HS-grad,9,Married-spouse-absent,Other-service,Own-child,Black,Male,0,0,2,Jamaica,<=50K\n1,Private,187625,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,108933,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,135388,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,169383,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,191129,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n3,Private,467611,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,373185,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,Mexico,<=50K\n4,Private,130060,HS-grad,9,Separated,Transport-moving,Unmarried,Black,Female,1,0,2,United-States,<=50K\n4,Private,199934,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,116165,Some-college,10,Widowed,?,Not-in-family,White,Female,0,0,0,Canada,<=50K\n1,Private,42881,10th,6,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n1,?,174666,10th,6,Separated,?,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,169759,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,181547,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Columbia,<=50K\n3,Private,95704,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,237432,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,?,<=50K\n1,Private,226267,5th-6th,3,Married-spouse-absent,Craft-repair,Other-relative,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,159979,Some-college,10,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Female,0,0,3,United-States,<=50K\n1,Private,203488,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,403671,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,192323,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,Yugoslavia,<=50K\n1,Private,167832,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,145166,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,State-gov,155657,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n3,Private,116789,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,39234,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,124111,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,172828,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Outlying-US(Guam-USVI-etc),<=50K\n3,Private,143372,HS-grad,9,Divorced,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,265807,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,<=50K\n0,State-gov,218184,Bachelors,13,Never-married,Protective-serv,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,154087,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,440647,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,193952,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,0,2,?,<=50K\n3,Private,125932,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,284652,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,214635,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,173316,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,65390,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,?,<=50K\n2,Self-emp-inc,45054,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,185042,1st-4th,2,Separated,Priv-house-serv,Other-relative,White,Female,0,0,2,Mexico,<=50K\n1,Private,117381,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,258666,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Female,0,3,2,United-States,<=50K\n1,Private,179668,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,127277,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,>50K\n1,Private,192022,Bachelors,13,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,99551,Bachelors,13,Widowed,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,208899,Bachelors,13,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,287658,Assoc-acdm,12,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,1,Jamaica,<=50K\n1,Private,196125,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,275051,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,23892,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,376455,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,267989,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,30269,Assoc-voc,11,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,204235,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,209057,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,349347,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,154033,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n1,Private,124680,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,4,United-States,<=50K\n1,Private,132805,10th,6,Never-married,Sales,Other-relative,White,Male,0,3,2,United-States,<=50K\n2,Private,99233,Prof-school,15,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,224849,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Local-gov,101110,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,>50K\n0,Private,184839,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,302847,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,181322,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,192213,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Canada,<=50K\n1,State-gov,37250,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-inc,140854,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,158286,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,269095,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n1,Private,279960,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,176239,Some-college,10,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,115360,10th,6,Married-civ-spouse,Machine-op-inspct,Own-child,White,Female,1,0,2,United-States,<=50K\n3,Private,337666,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,?,255276,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,145212,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,185099,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,142756,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,156300,Masters,14,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,?,186266,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,219137,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,110970,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,203067,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,148844,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,154941,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,124111,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,157303,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,113838,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,165737,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n4,Private,140849,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,200363,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,180247,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,82578,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Canada,>50K\n1,Private,227146,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,348886,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,90907,5th-6th,3,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Private,142766,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,246439,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,184784,10th,6,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,195262,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,167967,Masters,14,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,145636,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,170099,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,228253,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,205570,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,506830,Some-college,10,Divorced,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,412435,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,Outlying-US(Guam-USVI-etc),<=50K\n2,Private,163331,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,222756,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,318918,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,105188,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,Haiti,<=50K\n0,Private,199884,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,96483,HS-grad,9,Never-married,Other-service,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,192203,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Canada,<=50K\n3,Private,203392,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,99646,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,167440,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,<=50K\n0,?,210095,5th-6th,3,Never-married,?,Unmarried,White,Female,0,0,1,El-Salvador,<=50K\n2,Private,219591,Some-college,10,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,30270,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,226020,HS-grad,9,Separated,Other-service,Not-in-family,Black,Female,0,0,3,?,<=50K\n0,Private,314165,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,Columbia,<=50K\n1,Private,330715,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,35448,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,State-gov,172970,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n1,Self-emp-inc,189502,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,61518,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,574005,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,281356,1st-4th,2,Never-married,Farming-fishing,Not-in-family,Other,Male,0,0,4,Mexico,<=50K\n2,Private,138975,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,176969,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,132393,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,Poland,<=50K\n2,Private,194924,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,478205,Bachelors,13,Never-married,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,?,128224,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,30118,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,1,0,2,United-States,<=50K\n3,Self-emp-not-inc,290688,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,85566,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,121874,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n2,Self-emp-not-inc,29036,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,348152,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,73715,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,151382,Assoc-voc,11,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,236359,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,19899,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,138760,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,354962,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,181363,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,393360,Some-college,10,Never-married,Protective-serv,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,210736,Some-college,10,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,?,<=50K\n2,Private,110013,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,193304,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,118551,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,201991,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,157446,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,283217,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,247794,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Private,43712,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,35649,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,342719,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n4,?,71467,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,2,United-States,>50K\n0,Private,271837,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,400061,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,2,United-States,>50K\n0,Private,62972,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,174907,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,176452,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,Peru,<=50K\n3,Private,268358,11th,7,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,176904,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,176683,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,98077,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,266461,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,3,United-States,<=50K\n3,Private,312477,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,604045,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,131568,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,97688,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,373628,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,367984,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Self-emp-not-inc,193459,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,250733,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,199725,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Female,0,0,3,United-States,<=50K\n3,Private,156877,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Greece,<=50K\n2,Private,122076,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n2,Self-emp-not-inc,216402,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,India,>50K\n3,Self-emp-not-inc,42402,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,1,United-States,>50K\n0,Private,315974,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,63437,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Ireland,<=50K\n1,Private,160786,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,85374,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,465974,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,78529,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,98037,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Private,178390,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,210940,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,64506,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,128378,Some-college,10,Widowed,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,234460,9th,5,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,Dominican-Republic,<=50K\n1,Private,176760,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n2,State-gov,59460,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,234428,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,215047,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,140426,Doctorate,16,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,Germany,>50K\n1,Private,191777,Masters,14,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,148995,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,229773,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,174461,Assoc-acdm,12,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,250647,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Guatemala,<=50K\n3,Local-gov,119904,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,1,United-States,>50K\n1,Self-emp-not-inc,151402,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,1,4,United-States,<=50K\n2,Private,184556,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,263561,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,177945,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,306889,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,Local-gov,54377,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,144351,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,1,2,United-States,<=50K\n0,Private,95566,Some-college,10,Married-spouse-absent,Sales,Own-child,Other,Female,0,0,1,Dominican-Republic,<=50K\n0,Private,181675,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,172129,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,350759,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,105592,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,?,200061,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,?,<=50K\n1,Self-emp-inc,200689,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,386726,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,2,United-States,>50K\n1,Local-gov,135567,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,2,0,3,United-States,<=50K\n2,Local-gov,282753,Assoc-voc,11,Divorced,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,137367,11th,7,Never-married,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n1,Self-emp-inc,153976,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,96062,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,152933,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,97870,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,0,Germany,<=50K\n3,Private,254291,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,101432,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,125776,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,165479,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,172307,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,176729,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,174276,Some-college,10,Widowed,Sales,Unmarried,White,Female,0,0,3,United-States,>50K\n4,Federal-gov,48102,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,?,>50K\n2,Self-emp-not-inc,79531,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,306460,HS-grad,9,Never-married,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,55284,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,172063,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,141028,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,37274,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,31389,11th,7,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,415913,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,295591,5th-6th,3,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,?,202903,7th-8th,4,Married-civ-spouse,?,Wife,White,Female,1,0,2,Puerto-Rico,<=50K\n4,Private,159770,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,268832,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,126003,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,225193,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,297735,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,225892,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,605502,10th,6,Never-married,Transport-moving,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,174150,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,165466,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,189728,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,360491,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,115040,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,262688,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,3,0,3,United-States,>50K\n4,Self-emp-inc,158437,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,41108,Some-college,10,Widowed,Farming-fishing,Not-in-family,White,Male,0,4,4,United-States,>50K\n0,Private,149875,Bachelors,13,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,131916,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Italy,>50K\n0,Private,60668,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,153132,Assoc-acdm,12,Separated,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,155256,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,244770,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,312108,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,102828,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,93225,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,231002,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n1,Self-emp-not-inc,256992,5th-6th,3,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,Mexico,<=50K\n2,Private,118721,12th,8,Divorced,Adm-clerical,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,151989,Assoc-voc,11,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,109112,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,589809,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n2,Private,172538,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,State-gov,318982,Masters,14,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,204629,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,99894,5th-6th,3,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n0,Private,369463,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,79324,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,61178,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,204226,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,183110,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,96321,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,167031,Some-college,10,Never-married,Other-service,Other-relative,Other,Female,0,0,1,Ecuador,<=50K\n2,Private,108997,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,176796,Doctorate,16,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,134737,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,>50K\n1,Self-emp-inc,49795,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,131588,Some-college,10,Never-married,Tech-support,Unmarried,Black,Female,0,0,0,United-States,<=50K\n0,Private,307643,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,351350,Some-college,10,Divorced,Protective-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,260761,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,156310,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,1,0,0,United-States,<=50K\n2,Private,207789,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,252842,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,0,United-States,<=50K\n1,Private,294936,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,196269,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,Other,Male,0,0,2,United-States,<=50K\n0,Private,46402,7th-8th,4,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,267161,Bachelors,13,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Private,160456,11th,7,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,123983,Some-college,10,Never-married,?,Other-relative,Asian-Pac-Islander,Male,0,0,0,Vietnam,<=50K\n3,Private,123053,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,2,0,2,India,<=50K\n1,Private,426467,1st-4th,2,Never-married,Craft-repair,Not-in-family,White,Male,2,0,2,Guatemala,<=50K\n2,Private,269323,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,42857,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,183915,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,211391,10th,6,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Local-gov,193130,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,86745,Bachelors,13,Never-married,Adm-clerical,Other-relative,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n1,Private,226525,Assoc-voc,11,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,270339,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,343742,10th,6,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n3,Private,150975,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,207301,Assoc-acdm,12,Divorced,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,135924,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,184277,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,>50K\n0,Private,142233,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,120902,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,>50K\n4,Local-gov,158412,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,126161,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,149347,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,322674,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,55390,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,200904,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,1,United-States,>50K\n2,Private,166056,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,116666,Masters,14,Divorced,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,3,India,>50K\n2,Private,168324,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,121772,HS-grad,9,Never-married,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Hong,<=50K\n2,Private,126889,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n0,?,401690,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,117605,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Federal-gov,410446,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-inc,38472,Some-college,10,Widowed,Sales,Not-in-family,White,Female,4,0,3,United-States,>50K\n1,Self-emp-not-inc,335704,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,70261,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,47577,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,117767,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,179641,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,343553,11th,7,Never-married,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,328466,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,Mexico,>50K\n3,Private,265097,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,414791,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,48055,12th,8,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,341672,Some-college,10,Never-married,Adm-clerical,Other-relative,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n3,Private,266764,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,233571,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n3,Private,126592,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,3,0,2,United-States,>50K\n3,Private,70754,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,138852,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,2,0,1,United-States,<=50K\n1,Private,175856,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,193494,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,104334,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,197332,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,205844,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Local-gov,206459,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,202822,7th-8th,4,Never-married,Other-service,Unmarried,Black,Female,0,0,0,Trinadad&Tobago,<=50K\n4,Without-pay,174695,Some-college,10,Married-spouse-absent,Farming-fishing,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,183342,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,105614,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,329603,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Poland,>50K\n2,Private,77373,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,4,United-States,>50K\n1,Private,207473,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,149161,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,?,<=50K\n0,Private,311974,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,1,Mexico,<=50K\n4,Private,175127,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,111625,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,48895,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,27049,HS-grad,9,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,108907,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,?,<=50K\n3,Private,94988,5th-6th,3,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,218343,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,227626,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,272856,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,3,England,<=50K\n2,Private,30916,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,276229,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,289106,Assoc-acdm,12,Separated,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,?,39100,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,192776,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,147280,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,187770,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,State-gov,213296,Bachelors,13,Widowed,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,107410,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,170272,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,86808,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,149210,HS-grad,9,Separated,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n4,Private,123411,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,306779,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,487347,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,283945,10th,6,Never-married,Handlers-cleaners,Other-relative,White,Male,0,2,2,United-States,<=50K\n0,Private,375698,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,271753,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,251854,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,264052,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,State-gov,28451,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,282604,Some-college,10,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,Private,185908,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,>50K\n3,Federal-gov,198186,Bachelors,13,Widowed,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,242521,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,337940,5th-6th,3,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,212064,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,129263,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,144761,HS-grad,9,Widowed,Protective-serv,Not-in-family,White,Male,0,2,0,United-States,<=50K\n2,Private,109912,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,113324,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,187795,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,173724,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,185129,Bachelors,13,Divorced,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,73134,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,236040,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,74194,HS-grad,9,Never-married,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,102130,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,140915,Some-college,10,Never-married,Sales,Other-relative,Asian-Pac-Islander,Male,0,0,1,Philippines,<=50K\n4,?,107575,HS-grad,9,Divorced,?,Not-in-family,White,Female,1,0,1,United-States,<=50K\n2,State-gov,34364,Masters,14,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,258037,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Cuba,>50K\n0,Private,391585,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,233130,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,101345,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,1,0,3,United-States,>50K\n0,?,32897,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,248612,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,212465,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,198587,Some-college,10,Never-married,Tech-support,Not-in-family,Black,Female,1,0,3,United-States,<=50K\n1,Private,405913,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Peru,>50K\n2,Private,588003,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,46807,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,210498,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,206951,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,237466,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n4,Private,279636,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,Guatemala,<=50K\n2,Private,29320,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,271262,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,?,29361,Assoc-acdm,12,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,76773,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,109004,HS-grad,9,Separated,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Private,226902,Bachelors,13,Divorced,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,176552,11th,7,Divorced,Prof-specialty,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n2,Private,182303,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,296253,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,3,0,3,United-States,>50K\n0,Private,218215,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,165695,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,>50K\n3,Self-emp-not-inc,51271,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,4,United-States,<=50K\n2,Private,96100,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,82393,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Asian-Pac-Islander,Male,0,1,2,United-States,<=50K\n0,Private,248978,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,254367,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,1,3,United-States,<=50K\n3,?,200235,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,94429,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,87282,Assoc-voc,11,Never-married,Exec-managerial,Other-relative,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,119793,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,3,United-States,<=50K\n4,?,85815,HS-grad,9,Divorced,?,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n1,Local-gov,197764,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,306982,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n4,Private,80896,HS-grad,9,Separated,Transport-moving,Unmarried,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Private,197886,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,355728,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,121124,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n3,State-gov,193720,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,347292,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,34506,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,326370,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,?,<=50K\n0,?,269221,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,63509,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,148254,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,190511,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,268022,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,?,>50K\n0,Private,20057,7th-8th,4,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,Private,206862,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,189498,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,166320,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,289886,Some-college,10,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Male,0,0,1,Vietnam,<=50K\n0,?,86337,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,54190,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,147069,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,282023,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,379485,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,129338,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,99829,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n2,State-gov,182254,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,109428,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,2,2,United-States,<=50K\n2,Self-emp-not-inc,351161,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n4,?,210750,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,132716,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,242984,Some-college,10,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,101509,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,509629,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,119957,Bachelors,13,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,69727,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,204590,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,0,1,2,United-States,<=50K\n2,?,50862,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,182907,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,206487,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,168015,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,149396,Some-college,10,Never-married,Other-service,Other-relative,Black,Female,0,0,1,Haiti,<=50K\n2,Federal-gov,184964,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,398988,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,128777,7th-8th,4,Divorced,Craft-repair,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,252413,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,1,United-States,>50K\n1,Private,181372,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n4,Private,216851,9th,5,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,106935,Some-college,10,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,363875,Some-college,10,Divorced,Protective-serv,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Private,287277,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,172342,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,308498,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,122127,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,3,0,2,United-States,>50K\n1,Private,106437,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n3,Self-emp-inc,306289,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,201699,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,282062,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,235108,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,339482,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,181820,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,99335,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,367533,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n4,Private,64960,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,<=50K\n3,Private,269095,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,58683,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Self-emp-not-inc,89508,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,<=50K\n0,Private,100999,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,34125,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,115057,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,139126,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,104632,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,Cambodia,>50K\n2,Federal-gov,178866,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n3,Private,139850,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Private,61435,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,309230,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,45613,Some-college,10,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,272615,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,54318,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,165519,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,48495,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,143123,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,431426,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,0,United-States,>50K\n4,Private,256474,Masters,14,Never-married,Protective-serv,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,191451,Masters,14,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,3,United-States,>50K\n2,Private,99146,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,235986,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,3,Cuba,<=50K\n1,Local-gov,429897,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Mexico,>50K\n0,Private,189897,HS-grad,9,Married-civ-spouse,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,145155,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,192257,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,1,0,2,United-States,<=50K\n1,Private,194960,HS-grad,9,Never-married,Farming-fishing,Not-in-family,Other,Male,0,0,2,Puerto-Rico,<=50K\n2,Local-gov,357814,12th,8,Married-civ-spouse,Other-service,Other-relative,White,Female,0,0,1,Mexico,<=50K\n1,Local-gov,137629,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,156526,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,189238,9th,5,Never-married,Other-service,Own-child,White,Female,0,0,2,El-Salvador,<=50K\n0,Private,202989,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,Canada,<=50K\n1,Private,25684,HS-grad,9,Never-married,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,192939,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,138692,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,222249,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,201318,9th,5,Married-civ-spouse,Exec-managerial,Other-relative,White,Male,1,0,3,Columbia,<=50K\n0,?,190650,Bachelors,13,Never-married,?,Not-in-family,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n1,Private,56004,Some-college,10,Never-married,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,182313,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,138962,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,4,?,<=50K\n2,Private,277248,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Cuba,>50K\n0,Private,125031,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,State-gov,216414,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,171176,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,?,<=50K\n1,Private,356133,Some-college,10,Never-married,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,185397,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,308285,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,56651,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,154863,9th,5,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,Trinadad&Tobago,>50K\n3,Federal-gov,44706,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,>50K\n1,?,222548,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,248754,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,104981,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,315065,Some-college,10,Never-married,Other-service,Unmarried,White,Male,0,0,1,Mexico,<=50K\n3,Private,188325,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,221661,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,81973,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Private,169122,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,216734,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,98101,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,292511,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,122971,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,124953,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,123011,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,76417,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,351576,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,33794,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,79923,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,117983,10th,6,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,186110,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,187589,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,>50K\n2,?,319685,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n4,?,64101,12th,8,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,162923,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,288519,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,33798,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,195734,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,1,0,2,United-States,<=50K\n0,Private,214120,HS-grad,9,Never-married,Priv-house-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,113515,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,261230,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,98515,Assoc-voc,11,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,187715,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,214238,7th-8th,4,Never-married,?,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n1,Private,123964,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,<=50K\n1,Private,68991,HS-grad,9,Never-married,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Private,292110,5th-6th,3,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,198320,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,709798,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,372838,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,160402,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,98475,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,97136,Some-college,10,Married-spouse-absent,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,136985,Assoc-acdm,12,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,187356,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n3,State-gov,107231,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,305874,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,290922,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,248254,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,2,United-States,>50K\n2,Private,160808,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,2,0,3,United-States,<=50K\n2,Private,247321,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,247651,7th-8th,4,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,3,United-States,<=50K\n1,Private,214702,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,3,1,United-States,<=50K\n4,Private,75577,7th-8th,4,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,3,United-States,<=50K\n1,Private,561334,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,?,224886,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,401134,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,258170,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,141181,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,292370,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,0,3,?,>50K\n0,Private,300871,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,136721,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,140399,Some-college,10,Never-married,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,109133,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,186534,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,226891,Assoc-voc,11,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n1,Private,241885,Some-college,10,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,97165,Some-college,10,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,212918,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,211585,HS-grad,9,Married-civ-spouse,Transport-moving,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,178309,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,481987,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,215211,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,194901,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,340885,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,>50K\n1,Local-gov,190290,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,188569,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,162282,Assoc-voc,11,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,287315,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,304212,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,200878,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,256864,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,46401,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,37778,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,191722,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,3,0,3,United-States,>50K\n4,Self-emp-not-inc,103643,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,>50K\n0,Private,143766,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n0,State-gov,204425,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,156257,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,?,113185,11th,7,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,112262,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,28031,9th,5,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,320102,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,334273,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,356015,11th,7,Married-spouse-absent,Handlers-cleaners,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,Mexico,<=50K\n3,Private,278900,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,142528,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Federal-gov,343014,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,201017,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,Scotland,<=50K\n1,Self-emp-not-inc,81030,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,34007,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,29662,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,347446,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,90668,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,190403,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,109015,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,3,0,3,United-States,>50K\n2,Private,234807,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,157131,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,94081,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,135566,HS-grad,9,Widowed,Sales,Unmarried,White,Female,1,0,0,United-States,<=50K\n1,Private,103164,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,570002,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,State-gov,215797,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,289405,Some-college,10,Never-married,Sales,Own-child,White,Male,0,2,0,United-States,<=50K\n0,Private,239461,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,101510,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Self-emp-inc,443546,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,141029,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,207202,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n4,Without-pay,137192,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,0,Philippines,<=50K\n1,Private,222989,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,36325,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,73394,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,249046,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,100653,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,1125613,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,101352,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,340476,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,192711,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,273362,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,100451,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,85399,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,168191,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,153475,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,196773,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,180138,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,48347,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,175071,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,4,0,2,United-States,>50K\n4,?,129476,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,181772,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,284317,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,237305,Some-college,10,Never-married,Machine-op-inspct,Other-relative,Black,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,111321,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,278476,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,39060,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,205262,Some-college,10,Never-married,Adm-clerical,Not-in-family,Other,Male,0,0,2,Ecuador,<=50K\n3,Private,198000,Some-college,10,Never-married,Craft-repair,Unmarried,White,Female,0,0,2,United-States,>50K\n0,Private,397962,HS-grad,9,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,Private,178370,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,4,United-States,>50K\n3,Private,121253,Bachelors,13,Married-spouse-absent,Sales,Unmarried,White,Female,0,4,4,United-States,>50K\n2,Private,56072,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,176756,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,60374,HS-grad,9,Married-civ-spouse,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,165681,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,287037,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,55568,Bachelors,13,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,3,United-States,<=50K\n3,Private,155509,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,0,Trinadad&Tobago,<=50K\n0,Private,201178,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,37250,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,<=50K\n4,Private,314149,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,<=50K\n0,Private,264593,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,159589,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,454915,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,285131,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,150057,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,55390,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,314894,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,184948,Assoc-voc,11,Divorced,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,124483,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,0,India,<=50K\n2,Self-emp-inc,97986,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,210562,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,233280,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,164300,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,Dominican-Republic,<=50K\n1,Private,227489,Some-college,10,Never-married,Handlers-cleaners,Other-relative,Black,Male,0,0,2,?,<=50K\n0,Private,263773,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,96459,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,116608,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,180007,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,305466,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,238917,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,El-Salvador,<=50K\n0,Private,129784,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,367390,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,235691,HS-grad,9,Never-married,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,?,166425,Some-college,10,Widowed,?,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,160369,10th,6,Divorced,Farming-fishing,Unmarried,White,Male,0,0,1,United-States,<=50K\n2,Private,206298,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,183523,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,217342,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,State-gov,141858,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,213296,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Self-emp-inc,201682,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,178312,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,4,United-States,>50K\n1,Private,269723,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,200593,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,32616,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,259510,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,1,United-States,<=50K\n2,Self-emp-not-inc,271828,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-inc,78104,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,113703,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Private,187802,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,440706,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,191834,HS-grad,9,Divorced,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,149184,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n3,Self-emp-inc,315998,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,159589,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,60313,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,32855,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,142326,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,201965,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,172333,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,206541,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,177828,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,303440,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,Private,89991,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,186009,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,170988,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,180239,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,3,0,2,?,>50K\n3,Self-emp-not-inc,213654,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,32316,12th,8,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,150371,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,387871,10th,6,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,314649,Some-college,10,Married-civ-spouse,Sales,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n2,Private,240255,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n4,Private,206339,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,230168,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,171424,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,148581,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,89705,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,248406,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,72594,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,137537,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,225065,5th-6th,3,Separated,Sales,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,Private,217274,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,69151,9th,5,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,81107,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,4,United-States,>50K\n2,Private,205852,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,201117,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,397307,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,115422,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,114994,Some-college,10,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,39815,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,151584,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,164938,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,179896,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,1,0,2,United-States,<=50K\n1,Private,253841,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,177955,5th-6th,3,Never-married,Priv-house-serv,Other-relative,White,Female,1,0,2,El-Salvador,<=50K\n4,Private,113323,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,320305,7th-8th,4,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,229287,Bachelors,13,Never-married,Exec-managerial,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,100790,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,331419,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,2,0,3,United-States,>50K\n0,Private,171419,Assoc-voc,11,Never-married,Exec-managerial,Unmarried,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n4,Private,202226,Some-college,10,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,>50K\n3,Private,308087,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,0,United-States,>50K\n3,Private,220124,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,31703,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,153908,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,180599,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,?,252046,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,160062,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,148443,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,91733,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,1,0,2,United-States,<=50K\n2,Private,176634,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,74949,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,165484,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,44738,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,130040,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,234537,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,179016,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,335421,Masters,14,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,State-gov,312678,Masters,14,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,?,313786,HS-grad,9,Divorced,?,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,Private,198751,Bachelors,13,Never-married,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n4,Private,131519,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,285060,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,189765,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,130905,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,146325,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,102821,12th,8,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,137876,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,388998,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,82910,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,309122,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,532845,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,>50K\n3,Private,195833,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,?,<=50K\n4,?,98882,Masters,14,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,133515,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,France,<=50K\n0,Private,55215,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,176357,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,185836,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,54152,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,212437,Some-college,10,Widowed,Machine-op-inspct,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Private,224566,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,200040,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,41526,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,1,Canada,<=50K\n1,Private,89598,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,323811,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,30824,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,181096,Some-college,10,Never-married,Tech-support,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Private,217953,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Mexico,<=50K\n2,Private,222635,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n3,?,121942,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,346871,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,2,0,3,United-States,>50K\n1,Private,184889,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Federal-gov,101709,11th,7,Never-married,Other-service,Own-child,Asian-Pac-Islander,Male,0,0,0,Philippines,<=50K\n0,Private,125010,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,53135,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,498328,10th,6,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,604380,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,174327,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,357283,HS-grad,9,Never-married,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Federal-gov,280728,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,185039,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,0,United-States,>50K\n3,Self-emp-inc,251240,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,143046,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Greece,<=50K\n1,Private,210541,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,172364,HS-grad,9,Separated,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,138611,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n3,Private,176227,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,>50K\n1,Private,139647,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,?,174461,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,123345,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,164427,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,205235,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,192779,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,163434,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,264055,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,336215,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Federal-gov,78307,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,233059,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,91433,10th,6,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,157525,Some-college,10,Divorced,Protective-serv,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n0,Private,86065,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Female,0,0,2,Mexico,<=50K\n2,Private,22831,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,180181,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,212617,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,4,Ecuador,<=50K\n0,?,125905,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,336793,Bachelors,13,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,314649,HS-grad,9,Married-spouse-absent,Handlers-cleaners,Other-relative,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n0,Private,283969,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,Mexico,<=50K\n1,Self-emp-not-inc,35595,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,410240,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,178120,5th-6th,3,Divorced,Priv-house-serv,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n1,State-gov,294400,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,65353,Some-college,10,Divorced,Transport-moving,Own-child,White,Male,1,0,3,United-States,<=50K\n3,Private,189719,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,23438,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,178037,HS-grad,9,Never-married,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,109815,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,197860,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,271933,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,141663,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,?,199609,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,92215,9th,5,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Private,93449,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,3,Japan,<=50K\n1,Private,235393,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,151864,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,189277,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,?,204577,Bachelors,13,Married-civ-spouse,?,Husband,Black,Male,0,3,3,United-States,>50K\n2,Private,344572,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,Private,265356,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,166880,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n4,Private,188650,5th-6th,3,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,?,>50K\n4,Private,213249,Assoc-voc,11,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,112627,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Private,125120,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,60409,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,243190,Assoc-acdm,12,Separated,Craft-repair,Unmarried,Asian-Pac-Islander,Male,3,0,2,United-States,>50K\n3,Private,583755,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,68089,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,306646,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,186573,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,279580,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,437909,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,420691,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Federal-gov,94193,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,154076,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n3,Private,145879,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,208946,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,231826,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,178587,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,213208,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,2,Jamaica,<=50K\n1,?,139770,Assoc-voc,11,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,153869,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,88676,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,151089,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,138621,Assoc-voc,11,Separated,Priv-house-serv,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n2,Private,30457,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n4,Self-emp-not-inc,213349,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,192776,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,192884,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,103024,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,510072,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,178615,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,249956,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,177705,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,121124,Prof-school,15,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,?,25837,11th,7,Never-married,?,Own-child,White,Male,0,0,4,United-States,<=50K\n2,Private,557349,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Yugoslavia,<=50K\n1,Private,89735,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,1,2,United-States,<=50K\n1,Private,222548,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,47314,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,?,>50K\n4,Private,316359,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,200089,1st-4th,2,Married-civ-spouse,Other-service,Other-relative,White,Male,0,0,2,England,<=50K\n4,Private,271795,11th,7,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,31801,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,3,United-States,<=50K\n0,Private,196508,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,189933,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,?,501172,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,1,0,Mexico,<=50K\n1,Private,361497,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,150175,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,155106,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,62272,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,189916,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,324011,9th,5,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,105803,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,?,53588,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,107998,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,340567,1st-4th,2,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n2,Private,167777,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,228860,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n2,Self-emp-inc,40666,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,277488,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,>50K\n2,Local-gov,195897,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,242984,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,236497,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,312634,11th,7,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,59829,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,France,<=50K\n1,Private,24292,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,180407,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,Germany,<=50K\n3,Self-emp-not-inc,121238,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,281982,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,348739,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,194189,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,329130,11th,7,Separated,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,205939,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,1,0,0,United-States,<=50K\n1,Private,62165,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,224361,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,34722,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,175972,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,359428,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,138504,HS-grad,9,Separated,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,268952,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,257978,Assoc-voc,11,Widowed,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,118799,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,78356,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,Jamaica,<=50K\n1,Self-emp-not-inc,609789,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,123157,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,84197,Masters,14,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,162312,HS-grad,9,Never-married,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,4,South,<=50K\n2,Private,138441,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,239753,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,3,0,United-States,<=50K\n2,Private,262158,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,133373,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,57916,HS-grad,9,Separated,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,142897,Assoc-voc,11,Never-married,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,161016,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,227491,HS-grad,9,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n3,Private,306790,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,33831,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,188972,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,313546,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,220585,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Local-gov,476599,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,163665,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,306646,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,206470,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Germany,<=50K\n1,Private,169583,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,State-gov,127085,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,152044,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,111387,10th,6,Divorced,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,102318,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,213692,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,163665,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,30529,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,290226,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,182136,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,73266,Some-college,10,Never-married,Transport-moving,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,State-gov,60412,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,187891,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,194304,Some-college,10,Divorced,Transport-moving,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,160910,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,148300,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,165743,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,123174,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,?,>50K\n2,Private,184018,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,188069,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Philippines,>50K\n3,Private,138852,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,2,0,2,El-Salvador,>50K\n1,?,78529,10th,6,Separated,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,164441,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,199419,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,181342,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,173382,Assoc-acdm,12,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,184924,9th,5,Never-married,Handlers-cleaners,Own-child,White,Male,0,2,0,United-States,<=50K\n0,Private,215384,11th,7,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,424094,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,212120,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,185764,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,133969,Masters,14,Divorced,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,32616,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,149210,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,161210,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,285621,Masters,14,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,282069,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,97508,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,356823,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,4,0,2,United-States,>50K\n1,Private,171133,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,231638,Some-college,10,Never-married,Tech-support,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,191342,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,China,>50K\n3,Private,226497,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,373606,Some-college,10,Divorced,Sales,Unmarried,White,Male,0,0,4,United-States,>50K\n1,Private,39150,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,288840,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,293703,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,79586,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,82098,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,4,United-States,<=50K\n2,Private,245372,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,78261,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,355996,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,218490,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n2,Private,110908,Assoc-voc,11,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,34218,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,248895,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,363707,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,272411,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,128033,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,177287,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,197344,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,285858,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,193868,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,232082,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,27408,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,247043,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,162404,HS-grad,9,Never-married,Protective-serv,Not-in-family,Black,Male,1,0,2,United-States,<=50K\n4,Private,236341,5th-6th,3,Widowed,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n4,Local-gov,179285,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,0,United-States,<=50K\n1,Private,30433,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,102771,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,221172,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,108116,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,375499,10th,6,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n1,Private,178688,Assoc-voc,11,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,276709,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,?,238087,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,84790,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,37482,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,State-gov,178686,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,?,153926,HS-grad,9,Married-civ-spouse,?,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Private,110748,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,116613,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,108687,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,365739,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,195284,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,125933,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,>50K\n2,Private,140854,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,193237,1st-4th,2,Widowed,Sales,Other-relative,White,Male,0,0,2,Mexico,<=50K\n2,Private,46870,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,351324,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,189265,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,236564,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,557644,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,374454,HS-grad,9,Divorced,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,?,160654,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,122775,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,214413,11th,7,Never-married,Machine-op-inspct,Unmarried,White,Male,2,0,3,United-States,<=50K\n1,Private,329425,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,178312,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,241951,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,130143,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,114580,Prof-school,15,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,4,3,United-States,>50K\n2,Self-emp-inc,130126,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,399387,7th-8th,4,Separated,Priv-house-serv,Unmarried,Black,Female,0,0,0,United-States,<=50K\n3,Private,163814,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,69586,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,237903,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,219897,Masters,14,Never-married,?,Not-in-family,White,Female,0,0,1,Canada,<=50K\n1,Private,243165,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,State-gov,173806,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,65308,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,408531,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,>50K\n2,Private,235786,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,314963,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,81206,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Federal-gov,293196,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,95329,Masters,14,Divorced,Protective-serv,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,45474,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,372728,Bachelors,13,Never-married,Other-service,Not-in-family,Black,Female,0,0,1,Jamaica,<=50K\n1,Federal-gov,116394,Bachelors,13,Married-AF-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,34180,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,>50K\n3,Private,327589,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,706180,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,32550,10th,6,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,173858,Prof-school,15,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n3,Self-emp-inc,230095,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,139012,Assoc-voc,11,Never-married,Adm-clerical,Other-relative,Asian-Pac-Islander,Male,1,0,2,Vietnam,<=50K\n4,Private,174711,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,171150,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n1,Self-emp-inc,77689,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,193898,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,195000,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,303121,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,188540,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,158656,Assoc-acdm,12,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,204196,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,>50K\n1,Private,183802,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,148995,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,190903,11th,7,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n2,State-gov,173780,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,251239,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n2,Private,112761,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,State-gov,425785,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,197731,Assoc-voc,11,Married-spouse-absent,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,119156,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,133819,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,185556,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,>50K\n3,Private,109277,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,36020,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,45857,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,184882,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n2,State-gov,342834,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,234743,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,106179,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,<=50K\n2,Private,177895,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,2,0,2,United-States,<=50K\n4,?,257876,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,86067,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,66634,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,4,0,3,United-States,>50K\n1,Private,138441,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,279802,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,407138,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,3,Mexico,<=50K\n4,Private,31732,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,204172,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,100593,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,162623,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,80933,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,47425,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,107812,Bachelors,13,Married-civ-spouse,Sales,Other-relative,White,Male,0,0,2,United-States,>50K\n0,Self-emp-inc,104443,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,117496,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,2,2,United-States,>50K\n1,Private,209691,7th-8th,4,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,314525,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,190772,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,199298,5th-6th,3,Divorced,Other-service,Not-in-family,White,Female,0,0,2,?,<=50K\n3,Private,187370,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,216129,Bachelors,13,Divorced,Other-service,Not-in-family,Black,Female,0,0,3,?,<=50K\n3,Federal-gov,219293,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,4,United-States,>50K\n0,Private,136363,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,233799,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,207611,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,178344,Assoc-voc,11,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,187652,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n0,Private,188545,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,3,0,United-States,<=50K\n2,Local-gov,58124,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,321733,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Private,206253,9th,5,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,152140,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,76281,Bachelors,13,Married-spouse-absent,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,606752,Masters,14,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,29933,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,114158,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,1,0,0,United-States,<=50K\n3,?,227203,Assoc-acdm,12,Married-spouse-absent,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-inc,65624,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,34146,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,34378,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,4,United-States,<=50K\n1,Private,141490,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,199227,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,224954,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,231357,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,113530,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,22245,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,36383,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Mexico,>50K\n1,Private,320305,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,?,201657,Bachelors,13,Divorced,?,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,48935,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,101455,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,243960,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,90915,Assoc-acdm,12,Never-married,Other-service,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,315287,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,106255,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,215895,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Italy,>50K\n1,Self-emp-not-inc,170979,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,210525,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,195488,HS-grad,9,Never-married,Priv-house-serv,Own-child,White,Female,0,0,2,Guatemala,<=50K\n0,Private,152246,Some-college,10,Never-married,Other-service,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,187794,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,3,United-States,>50K\n2,Private,110396,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n4,?,89391,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n2,State-gov,254817,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,41777,12th,8,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,234841,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,79586,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n2,Private,115331,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,63564,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,132053,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,2,1,United-States,<=50K\n2,Private,370502,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,Mexico,<=50K\n1,Private,59083,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,69413,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,32981,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,176683,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,144116,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,209213,HS-grad,9,Never-married,Sales,Not-in-family,Black,Male,0,0,2,?,<=50K\n1,State-gov,150657,Bachelors,13,Never-married,Prof-specialty,Other-relative,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,124793,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,22211,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n3,Private,270565,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,38251,Assoc-acdm,12,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,State-gov,162945,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,Black,Male,4,0,3,United-States,>50K\n3,Private,195638,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,118806,1st-4th,2,Widowed,Craft-repair,Other-relative,White,Female,0,2,2,Columbia,<=50K\n2,Self-emp-not-inc,44006,Assoc-voc,11,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,119679,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,1,2,United-States,<=50K\n0,Private,333953,12th,8,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,172111,Bachelors,13,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,32372,12th,8,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,United-States,<=50K\n4,?,117525,Assoc-acdm,12,Divorced,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,123681,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,317360,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Federal-gov,119832,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,135056,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,State-gov,135162,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,194004,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,177633,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,212864,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,30509,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,118712,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n2,Private,199018,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,151799,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,181280,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,232024,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,226267,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,240467,Masters,14,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,154374,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,231473,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,158813,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,346478,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,4,2,United-States,>50K\n3,Private,215990,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,177154,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n2,Self-emp-not-inc,238188,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,156800,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,130620,Assoc-acdm,12,Married-spouse-absent,Craft-repair,Other-relative,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n3,Private,175339,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,37937,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,166634,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,2,United-States,>50K\n1,Private,221167,Bachelors,13,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,179641,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,213195,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,157747,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,227840,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,169104,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,?,>50K\n2,Private,186916,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,37646,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,157028,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,188774,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,4,2,United-States,>50K\n4,?,146272,Some-college,10,Married-civ-spouse,?,Husband,White,Male,1,0,0,United-States,<=50K\n0,Private,182656,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,200471,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,358465,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,78602,11th,7,Never-married,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,213416,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Local-gov,345911,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,119522,Bachelors,13,Divorced,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,126320,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,235271,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Private,141745,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,359461,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,109351,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,3,0,2,United-States,>50K\n4,Private,148113,10th,6,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,75478,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,100375,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,?,28455,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,231413,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,119421,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,206998,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,183810,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,187053,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,155781,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,3,United-States,<=50K\n3,?,193895,7th-8th,4,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,48520,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-inc,170125,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,107584,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,196742,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,?,244214,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,127921,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,42617,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,191389,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,187983,Prof-school,15,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,215110,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,230292,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,90159,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,175398,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,53366,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,46155,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n3,Private,61708,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,>50K\n1,Local-gov,112650,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,173682,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,160981,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,72257,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,182332,Assoc-voc,11,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,48788,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,2,0,3,United-States,<=50K\n0,Private,417668,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,107458,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,147551,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Self-emp-inc,33729,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,101977,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,374716,9th,5,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,214378,HS-grad,9,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,>50K\n0,Private,111243,HS-grad,9,Never-married,Sales,Other-relative,White,Female,0,0,3,United-States,<=50K\n2,Private,252947,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,118500,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,195612,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Local-gov,174575,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,190391,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,166715,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,142725,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,73471,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n3,Private,241745,5th-6th,3,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,Private,316141,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,2,0,2,United-States,<=50K\n4,Local-gov,248595,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,90189,7th-8th,4,Divorced,Priv-house-serv,Own-child,Black,Female,0,0,0,United-States,<=50K\n2,Private,205195,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,148940,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,298035,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,154728,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,166809,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n2,State-gov,97136,Bachelors,13,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,347623,Masters,14,Never-married,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,117917,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n2,Private,266860,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,71864,Some-college,10,Never-married,Craft-repair,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,158451,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,229826,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,121788,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,151365,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,360884,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Private,36480,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n2,Self-emp-not-inc,116666,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,0,1,United-States,>50K\n4,Local-gov,214143,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Cuba,<=50K\n0,Private,45316,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,311974,1st-4th,2,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,Mexico,<=50K\n3,Self-emp-not-inc,48495,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,115945,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,170846,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,142922,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,181301,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,286675,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,233168,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,177304,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,336984,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,379412,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,180778,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,141876,Masters,14,Never-married,Prof-specialty,Unmarried,White,Male,0,0,2,?,<=50K\n0,Private,228306,Some-college,10,Married-AF-spouse,Other-service,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,329993,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,247469,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,673764,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,Private,155775,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,81223,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,236021,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,103371,Assoc-voc,11,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,199480,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,152657,10th,6,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Federal-gov,460214,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,91039,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,197372,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,?,267198,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,State-gov,111883,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,66917,11th,7,Married-civ-spouse,Farming-fishing,Own-child,White,Male,0,0,2,Mexico,<=50K\n0,Private,292583,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,391679,HS-grad,9,Never-married,Sales,Other-relative,White,Male,0,0,3,United-States,<=50K\n1,Private,475324,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,218164,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,101534,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,0,United-States,>50K\n2,Federal-gov,65706,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,156606,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,200967,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,164493,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,547886,Bachelors,13,Separated,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,232145,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,96421,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,Outlying-US(Guam-USVI-etc),<=50K\n1,Private,554206,Some-college,10,Never-married,Tech-support,Not-in-family,Black,Male,0,0,2,Philippines,<=50K\n3,Local-gov,234143,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,380544,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,103886,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,State-gov,54709,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,276548,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,176046,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,4,2,United-States,<=50K\n2,Private,114605,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,323713,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,261382,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,223548,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,Mexico,<=50K\n3,Self-emp-not-inc,355978,Doctorate,16,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,107218,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,31717,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,328947,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,148431,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,121602,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,244087,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,83425,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,157308,11th,7,Married-civ-spouse,Handlers-cleaners,Wife,Asian-Pac-Islander,Female,1,0,0,Philippines,<=50K\n0,Private,57898,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,State-gov,175304,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,102663,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,99175,11th,7,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,208358,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,361561,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,215115,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,207066,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,160910,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,64879,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,430035,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,Mexico,<=50K\n2,State-gov,74163,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,98389,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,386019,9th,5,Never-married,Farming-fishing,Unmarried,White,Male,0,0,4,United-States,<=50K\n0,Private,112795,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,332465,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,38611,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,368797,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,24106,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,108683,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n1,Self-emp-not-inc,241998,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,312446,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,69333,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,172538,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,275884,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,43479,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,199864,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,3,2,United-States,<=50K\n4,Private,235197,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,170376,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,325179,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n0,?,351195,9th,5,Never-married,?,Other-relative,White,Male,0,2,1,El-Salvador,<=50K\n1,Private,141841,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,207817,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,Columbia,<=50K\n0,Private,137974,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,161325,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n3,Private,293623,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Dominican-Republic,<=50K\n0,Private,37783,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,308027,Bachelors,13,Divorced,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,149218,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Local-gov,374450,HS-grad,9,Married-civ-spouse,Transport-moving,Wife,White,Female,2,0,2,United-States,>50K\n2,Local-gov,61885,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,291196,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,45366,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,203027,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,223752,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,?,>50K\n0,Private,132680,10th,6,Never-married,Other-service,Own-child,White,Female,0,2,0,United-States,<=50K\n3,Private,155574,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,193565,Masters,14,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,123598,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,456236,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,163229,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,419740,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,118853,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,4,United-States,>50K\n1,Private,31449,Assoc-acdm,12,Divorced,Machine-op-inspct,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,204163,Some-college,10,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,3,United-States,<=50K\n0,Private,177629,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,186370,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,188307,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,55481,Masters,14,Never-married,Tech-support,Unmarried,White,Male,0,0,2,Nicaragua,<=50K\n3,Private,119471,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,3,Philippines,>50K\n4,Local-gov,167347,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,184378,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,348960,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,69640,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,297457,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,279593,11th,7,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,211968,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,194561,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,140414,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,24763,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n2,State-gov,462832,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,Trinadad&Tobago,<=50K\n2,Private,48972,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,35032,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,228583,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n3,Private,392668,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n1,Private,108140,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,112497,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,142581,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,147982,11th,7,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,440129,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,200734,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,1,Trinadad&Tobago,<=50K\n3,Private,31807,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,166153,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,212954,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,52291,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n4,Self-emp-not-inc,303588,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,96176,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,184632,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,137618,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,160029,11th,7,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,178780,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,39756,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,35309,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,117253,HS-grad,9,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,303212,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,214542,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,3,Canada,<=50K\n1,Private,342019,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,126668,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,401508,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,25005,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n1,Self-emp-not-inc,85708,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,115677,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,144259,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,3,United-States,<=50K\n0,Private,197583,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,State-gov,142766,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,132626,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-inc,185621,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Local-gov,29887,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,117381,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,211482,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,90653,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Female,0,1,2,United-States,<=50K\n3,Private,209535,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,187873,Masters,14,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,174732,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,297847,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,3,2,United-States,<=50K\n4,Private,110213,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,162601,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,108438,10th,6,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,132222,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,174394,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,322789,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n3,Federal-gov,72436,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,?,60726,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,190273,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,?,393376,11th,7,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,140571,Assoc-voc,11,Divorced,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,584790,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,197666,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,Greece,<=50K\n2,Private,245090,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,3,El-Salvador,<=50K\n2,Private,192569,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Local-gov,158291,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,3,0,2,United-States,>50K\n0,?,113915,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Local-gov,287658,Masters,14,Divorced,Prof-specialty,Not-in-family,Black,Male,0,0,2,Jamaica,<=50K\n0,Private,192455,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,317040,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Private,218689,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,116626,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,2,0,United-States,<=50K\n1,Federal-gov,48458,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,241469,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,1,United-States,<=50K\n1,Private,167990,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,261929,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,425804,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,33394,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,1,United-States,>50K\n4,Private,72812,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,89040,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,164518,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,182740,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,361875,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,197130,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,340335,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,293984,10th,6,Married-civ-spouse,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,State-gov,261584,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,Outlying-US(Guam-USVI-etc),<=50K\n0,Private,170302,HS-grad,9,Never-married,Farming-fishing,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Private,481987,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,88449,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,261897,10th,6,Widowed,Farming-fishing,Unmarried,White,Male,0,0,0,United-States,<=50K\n4,Private,250552,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,88513,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,168293,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,283921,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,407043,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,63745,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,49893,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,241962,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,338416,10th,6,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n0,?,212888,11th,7,Married-civ-spouse,?,Wife,White,Female,0,0,3,United-States,<=50K\n4,Federal-gov,310320,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,359972,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,64643,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,?,<=50K\n4,Private,125000,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,286675,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,165532,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,349986,Assoc-voc,11,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,213140,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n2,Federal-gov,219155,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,India,>50K\n1,Private,183612,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,391114,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,219632,5th-6th,3,Married-spouse-absent,Machine-op-inspct,Other-relative,White,Male,0,0,2,Mexico,<=50K\n3,Self-emp-inc,320124,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,Amer-Indian-Eskimo,Female,4,0,2,United-States,>50K\n2,Private,799281,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,657397,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,State-gov,373432,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,168660,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,191149,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,356824,HS-grad,9,Separated,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,191782,11th,7,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,29859,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,3,0,3,United-States,>50K\n3,Private,204226,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,246862,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,496526,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,426431,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,84154,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,45937,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,130021,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,63021,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,367306,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,65624,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,144928,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,117747,Some-college,10,Never-married,Craft-repair,Other-relative,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n0,Private,266681,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,152035,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,190023,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,233130,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,149637,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Federal-gov,224277,Some-college,10,Widowed,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,121559,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,230951,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,345285,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,28367,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,320744,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,1,0,3,United-States,<=50K\n1,Private,243773,9th,5,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,151474,9th,5,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,135465,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,210781,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,359001,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,119471,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n1,Private,226396,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,283122,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,326400,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n1,?,169186,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,158752,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n1,?,208406,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,96741,Assoc-acdm,12,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,State-gov,255191,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,177733,9th,5,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,1,Dominican-Republic,<=50K\n3,State-gov,137815,12th,8,Never-married,Other-service,Own-child,White,Male,2,0,2,United-States,<=50K\n2,?,187203,Assoc-voc,11,Divorced,?,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,168515,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,122672,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,195199,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,179813,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,178623,Assoc-acdm,12,Never-married,Sales,Not-in-family,Black,Female,0,0,3,Trinadad&Tobago,<=50K\n3,Private,41890,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,373050,12th,8,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n2,Private,80430,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,198613,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,330571,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,209205,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,132243,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,237670,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,193586,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,197353,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n0,Self-emp-not-inc,74538,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,89718,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,93169,Some-college,10,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,292915,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,0,United-States,>50K\n2,Private,328570,Some-college,10,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,312157,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,193459,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,236804,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,126223,HS-grad,9,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,State-gov,172281,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,153894,Bachelors,13,Never-married,Sales,Unmarried,White,Female,0,0,2,Peru,<=50K\n1,Private,331395,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,92472,10th,6,Married-spouse-absent,Farming-fishing,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,318647,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,332931,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,76212,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,301168,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Italy,<=50K\n0,Private,440969,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,154950,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,218343,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,239577,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,247936,HS-grad,9,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,0,Taiwan,<=50K\n4,Local-gov,203525,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,182342,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,25649,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,243569,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,187870,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,4,United-States,>50K\n0,?,289116,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,487330,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,34019,10th,6,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,250541,11th,7,Never-married,?,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,318987,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,140558,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,303455,Masters,14,Widowed,Prof-specialty,Unmarried,White,Female,2,0,3,United-States,>50K\n2,Self-emp-not-inc,76855,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,308764,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,339905,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,227856,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,156430,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,?,98265,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,116640,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,0,United-States,<=50K\n2,Private,187167,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,184078,12th,8,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,108140,Bachelors,13,Divorced,Tech-support,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,150533,Some-college,10,Separated,Craft-repair,Not-in-family,White,Male,0,2,3,United-States,<=50K\n3,Self-emp-not-inc,313702,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,39803,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,2,2,United-States,<=50K\n0,Private,252752,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,111700,Some-college,10,Divorced,Sales,Other-relative,White,Female,0,0,0,United-States,>50K\n2,Private,361842,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,231438,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,178469,HS-grad,9,Never-married,Other-service,Own-child,Asian-Pac-Islander,Female,0,0,0,?,<=50K\n4,Local-gov,116620,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,112212,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,1,2,United-States,<=50K\n4,Self-emp-not-inc,109101,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n4,Federal-gov,72998,11th,7,Divorced,Craft-repair,Not-in-family,Black,Female,4,0,2,United-States,>50K\n2,Private,147265,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,314645,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,444554,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,129629,Assoc-voc,11,Never-married,Tech-support,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,106761,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,189924,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,311194,11th,7,Never-married,Sales,Unmarried,Black,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,89737,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,49298,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,190333,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,251923,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,298445,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,3,United-States,>50K\n1,Private,180284,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,154342,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n4,State-gov,68658,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,203783,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,250037,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,158688,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,214781,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,404023,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n4,State-gov,109015,12th,8,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,194630,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,239375,Bachelors,13,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,35576,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,4,3,United-States,>50K\n2,Federal-gov,363630,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,3,0,3,United-States,>50K\n1,Self-emp-not-inc,182926,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,117222,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,110643,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,170217,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,193285,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,161075,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,322691,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,229431,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n4,?,106282,9th,5,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,105694,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,199883,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,100800,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,256278,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Other-relative,Other,Female,0,0,1,El-Salvador,<=50K\n1,Private,156464,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n3,Self-emp-inc,129525,HS-grad,9,Never-married,Sales,Other-relative,White,Male,0,0,2,?,<=50K\n0,Private,285013,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,248911,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,?,<=50K\n1,?,369386,Some-college,10,Married-civ-spouse,?,Wife,White,Female,2,0,2,United-States,>50K\n2,Private,219902,HS-grad,9,Separated,Transport-moving,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,375482,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,England,<=50K\n0,Private,169124,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,183000,Prof-school,15,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,28053,Bachelors,13,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,242984,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n4,State-gov,132055,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,212894,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Guatemala,<=50K\n4,Private,223975,7th-8th,4,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,357788,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,406811,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,Canada,<=50K\n0,Private,154422,Bachelors,13,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,Private,140644,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,355477,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,151773,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,341548,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,512771,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,141580,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,48988,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,201022,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,82777,HS-grad,9,Married-civ-spouse,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,152676,7th-8th,4,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,Puerto-Rico,<=50K\n0,Private,115815,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,168009,10th,6,Married-civ-spouse,Machine-op-inspct,Own-child,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n1,Private,213152,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,?,>50K\n3,Private,89690,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,126868,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,95128,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,185567,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,>50K\n0,Private,301408,Some-college,10,Never-married,Sales,Own-child,White,Female,0,2,1,United-States,<=50K\n1,Private,216256,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,182541,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,172855,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,68684,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,364832,7th-8th,4,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,264300,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,349910,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,276218,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,251196,Some-college,10,Never-married,Protective-serv,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,196898,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,58343,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-inc,101061,11th,7,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Private,415051,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,>50K\n0,Private,174043,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,129460,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,1,Ecuador,<=50K\n0,State-gov,110946,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,313873,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,81132,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,2,0,2,Philippines,>50K\n4,Federal-gov,255386,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Laos,<=50K\n0,Private,191497,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,128617,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,368949,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,?,>50K\n1,Local-gov,263600,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,257277,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,339442,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,Black,Male,1,0,2,United-States,<=50K\n1,Local-gov,289442,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,162667,11th,7,Never-married,?,Unmarried,White,Male,0,0,2,El-Salvador,<=50K\n0,Local-gov,466325,11th,7,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,142169,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,252079,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,119628,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,Hong,<=50K\n3,Private,175804,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,70720,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,United-States,<=50K\n3,State-gov,201513,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,257609,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,124692,Some-college,10,Married-civ-spouse,Exec-managerial,Own-child,White,Male,0,0,2,United-States,>50K\n0,Private,268525,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,250630,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,180277,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Hungary,<=50K\n2,Self-emp-not-inc,191342,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,South,<=50K\n1,Private,250967,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,3,United-States,>50K\n3,Private,153254,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,362600,5th-6th,3,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,171933,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,211408,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,48193,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,22463,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,440969,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,State-gov,164922,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,134524,Assoc-voc,11,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,176689,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,220993,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,512828,HS-grad,9,Never-married,Protective-serv,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,422275,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Local-gov,65291,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,197080,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,3,0,3,United-States,>50K\n3,Federal-gov,181657,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,190257,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,238068,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,337046,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,187248,HS-grad,9,Married-civ-spouse,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,?,250037,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,?,<=50K\n3,Private,285750,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,3,United-States,<=50K\n0,Private,260617,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,216999,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,531055,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,>50K\n2,State-gov,121265,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,184466,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,297676,Assoc-acdm,12,Widowed,Sales,Unmarried,White,Female,0,0,2,Cuba,<=50K\n3,Private,114228,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Local-gov,121144,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,26842,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,113054,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,256636,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,152246,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n2,Private,108140,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,?,203353,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,207207,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,115420,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,80058,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,48520,Assoc-acdm,12,Never-married,Protective-serv,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,411652,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,154405,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,104917,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n0,State-gov,261422,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,48915,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,172037,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,144833,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,275116,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,72886,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,103575,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,200783,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,152810,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,Germany,<=50K\n2,Local-gov,44694,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,?,48703,11th,7,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,91905,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,168906,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,State-gov,147215,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,153546,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,35595,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,225507,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,345504,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,137205,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,327779,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n2,?,213416,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,1,Mexico,<=50K\n2,Private,362883,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,131309,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,188331,Some-college,10,Separated,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,194740,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,43711,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,187033,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n0,Private,233923,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,84278,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,437666,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,3,United-States,<=50K\n4,Private,186386,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Male,4,0,2,United-States,>50K\n0,Private,129767,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,2,2,United-States,<=50K\n1,Private,180284,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,108320,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,2,0,2,United-States,<=50K\n4,Self-emp-inc,75214,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,284758,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,188330,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,333651,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n1,Local-gov,115305,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,172962,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Private,198096,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,163265,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,128608,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,107460,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,251841,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,403671,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,2,Mexico,<=50K\n4,Private,159378,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,170070,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n3,State-gov,192323,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,135796,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,232985,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,Private,34532,Bachelors,13,Never-married,Tech-support,Not-in-family,Black,Male,0,0,1,Jamaica,<=50K\n0,?,371316,10th,6,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,236994,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,208366,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,102640,HS-grad,9,Widowed,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,111377,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,472166,Some-college,10,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,?,86551,12th,8,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,70943,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,294919,HS-grad,9,Divorced,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,408383,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,255454,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,193260,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,?,191935,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,125461,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,97005,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,183319,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,167049,12th,8,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,185216,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,161838,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,165848,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,138816,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,99761,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,112139,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,129020,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,365465,Assoc-voc,11,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,259873,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,89622,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,201556,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,176286,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,192894,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,Black,Male,0,0,1,United-States,<=50K\n2,Private,172232,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,163204,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,1,United-States,<=50K\n2,Private,265737,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,2,3,Cuba,>50K\n2,Private,215304,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,185952,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,216845,HS-grad,9,Never-married,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,35683,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,371305,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,102359,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,200089,5th-6th,3,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,1,Guatemala,<=50K\n3,State-gov,207120,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,295334,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,234537,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,142922,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,181641,Some-college,10,Divorced,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,185325,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,167336,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,2,United-States,>50K\n0,Private,379778,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,176117,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,100228,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,150296,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,25005,Masters,14,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,0,United-States,<=50K\n3,Private,134120,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n2,Self-emp-not-inc,251710,10th,6,Married-spouse-absent,Other-service,Not-in-family,White,Female,0,2,0,United-States,<=50K\n0,Private,653574,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,El-Salvador,<=50K\n2,Private,175441,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,333119,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,89154,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,2,El-Salvador,<=50K\n4,Private,198727,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,87284,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,180686,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,227070,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,3,El-Salvador,<=50K\n4,Local-gov,189824,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,2,0,2,United-States,>50K\n0,Local-gov,348986,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Black,Male,0,0,2,United-States,<=50K\n2,Private,96185,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,112693,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,417605,5th-6th,3,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,Self-emp-not-inc,140300,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,340408,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,187539,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,237051,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,175622,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,389725,12th,8,Divorced,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,182812,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,Dominican-Republic,<=50K\n2,Self-emp-not-inc,245372,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,3,United-States,<=50K\n1,Local-gov,93886,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,502837,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,Peru,<=50K\n1,State-gov,212232,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n4,Private,300104,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,156933,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,1,United-States,<=50K\n0,Private,286734,Some-college,10,Never-married,Adm-clerical,Not-in-family,Other,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,143482,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,4,United-States,>50K\n2,Private,226357,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,104892,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,223194,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,1,2,Haiti,<=50K\n2,Self-emp-not-inc,272090,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n4,Private,204816,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,230039,7th-8th,4,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,242619,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,131982,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,3,South,<=50K\n1,Private,87310,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,134566,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n1,Federal-gov,163862,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,239409,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,203717,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,172274,Doctorate,16,Divorced,Prof-specialty,Unmarried,Black,Female,0,4,1,United-States,>50K\n1,Self-emp-not-inc,65278,Assoc-acdm,12,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,135289,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,246974,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,180060,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,Yugoslavia,<=50K\n0,Private,118023,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,102308,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,45564,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,137646,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,237646,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,189843,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,118261,Masters,14,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,288437,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Other,Male,2,0,2,United-States,<=50K\n2,Private,106347,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,316471,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,50058,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,182089,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,186865,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,State-gov,158206,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,229744,1st-4th,2,Married-civ-spouse,?,Husband,White,Male,2,0,0,Mexico,<=50K\n1,Private,141545,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,<=50K\n4,Local-gov,50929,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,132529,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,260696,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,231180,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,223277,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,279538,11th,7,Married-civ-spouse,Handlers-cleaners,Other-relative,White,Male,1,0,1,United-States,<=50K\n3,Private,46044,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,168071,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,79691,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,114204,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,124111,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,104521,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,128516,Assoc-acdm,12,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,>50K\n1,Private,112564,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,32186,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,239663,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,1,0,3,United-States,<=50K\n3,Private,269284,Assoc-acdm,12,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,175537,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,444304,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,27415,11th,7,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n2,Private,174343,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,148143,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,209213,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,?,<=50K\n0,Private,165097,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,51574,HS-grad,9,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,2,2,United-States,<=50K\n3,Private,167651,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,29075,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,Private,396895,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Mexico,<=50K\n4,State-gov,71075,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,129573,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Local-gov,183765,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,164991,HS-grad,9,Divorced,Sales,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Local-gov,154891,HS-grad,9,Divorced,Protective-serv,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,200117,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,176389,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,342567,Bachelors,13,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,178841,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Local-gov,191149,Masters,14,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,29702,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,157893,HS-grad,9,Never-married,Transport-moving,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,31993,7th-8th,4,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n0,Federal-gov,210736,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,3,2,United-States,<=50K\n0,Private,39615,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,200511,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,47818,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,183155,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,374905,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,128876,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,202872,10th,6,Married-spouse-absent,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,153414,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,24790,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,316769,11th,7,Never-married,Other-service,Unmarried,Black,Female,0,0,2,Jamaica,<=50K\n2,Private,126569,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,128538,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,234640,Assoc-voc,11,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,?,65372,Some-college,10,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,343377,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,30731,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,412379,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,112320,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,181929,HS-grad,9,Widowed,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,>50K\n1,Local-gov,100135,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,128061,HS-grad,9,Never-married,Other-service,Own-child,White,Female,1,0,0,United-States,<=50K\n4,?,402306,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,1,Canada,<=50K\n1,?,98389,Some-college,10,Never-married,?,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Private,179565,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,64922,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n4,Private,102610,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n4,?,115513,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,150548,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,133219,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,2,0,1,United-States,>50K\n3,Local-gov,67001,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,162347,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,138557,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,170456,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,Italy,<=50K\n2,Private,66006,10th,6,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,State-gov,176077,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,218322,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-inc,181691,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,?,<=50K\n3,Private,168232,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,161690,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,242736,Assoc-acdm,12,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,67317,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,265807,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,3,United-States,<=50K\n2,Private,99357,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,170070,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,State-gov,231166,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,62339,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,118520,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,155659,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,157331,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,341762,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,164190,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,83064,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,304283,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,436798,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,29302,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,?,<=50K\n2,Private,104660,Masters,14,Widowed,Exec-managerial,Unmarried,White,Male,2,0,2,United-States,>50K\n2,Private,79036,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,165622,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,177287,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,199847,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,22966,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,59068,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,77336,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,96524,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,143868,9th,5,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,121424,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,176279,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,177905,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,4,United-States,>50K\n3,Private,205100,7th-8th,4,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,?,<=50K\n4,Private,353881,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,177937,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,122244,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,125892,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,155472,Assoc-acdm,12,Never-married,Prof-specialty,Unmarried,Black,Female,1,0,3,United-States,<=50K\n2,Private,355728,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,245274,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,240330,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,182944,HS-grad,9,Widowed,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,264498,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,110426,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,2,United-States,>50K\n0,Private,166971,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,347653,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,33975,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,215219,11th,7,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,190772,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,2,2,United-States,<=50K\n4,?,331527,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,162494,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,>50K\n1,Local-gov,85918,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,91367,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,182342,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,129640,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,133536,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,148738,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,2,1,United-States,<=50K\n3,Private,102583,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,111387,9th,5,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,241752,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,334593,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,101950,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,212856,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n3,Private,183973,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,142061,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,158615,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,29145,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,40135,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,224640,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,146651,HS-grad,9,Married-civ-spouse,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,167737,HS-grad,9,Never-married,Transport-moving,Other-relative,White,Male,0,0,3,United-States,<=50K\n0,Private,60331,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,187167,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,157131,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,255237,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,192325,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,163342,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,129775,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,206008,Some-college,10,Never-married,Sales,Unmarried,White,Male,1,0,2,United-States,<=50K\n0,Private,397317,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,745768,Some-college,10,Never-married,Protective-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,141550,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,35576,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,376383,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,0,1,Mexico,<=50K\n3,Self-emp-not-inc,200825,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n1,?,362787,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,116789,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,160300,HS-grad,9,Married-spouse-absent,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,362654,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,107801,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,170939,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,2,0,2,United-States,<=50K\n1,Local-gov,224234,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,478346,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,3,0,2,United-States,>50K\n4,Private,211162,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,147638,Bachelors,13,Never-married,Adm-clerical,Other-relative,Asian-Pac-Islander,Female,0,0,2,Hong,<=50K\n2,Private,104647,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,67365,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,230959,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,2,2,Philippines,>50K\n2,Private,176335,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,4,United-States,>50K\n1,Self-emp-not-inc,268482,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,288731,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,231082,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,State-gov,333530,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,214288,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,118023,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,187088,Some-college,10,Never-married,Adm-clerical,Own-child,Other,Female,0,0,0,Cuba,<=50K\n4,?,174073,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,133833,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,229772,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,210082,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,119287,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,1,United-States,>50K\n2,Self-emp-not-inc,111772,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,122999,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,44767,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,200574,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,236596,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,33124,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Local-gov,308764,HS-grad,9,Widowed,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,103524,HS-grad,9,Separated,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,?,99483,HS-grad,9,Never-married,?,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Private,230951,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,99355,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,857532,12th,8,Never-married,Protective-serv,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,81116,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,3,2,United-States,<=50K\n2,Private,154410,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,Poland,<=50K\n0,Private,198943,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,311696,11th,7,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,<=50K\n2,Private,252897,Some-college,10,Divorced,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,39539,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n3,Self-emp-inc,122066,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,110977,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Local-gov,199590,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,Mexico,>50K\n0,Private,202721,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,197565,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,206827,Some-college,10,Never-married,Sales,Own-child,White,Female,2,0,1,United-States,<=50K\n2,Federal-gov,190895,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n0,Self-emp-inc,158751,Assoc-voc,11,Never-married,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,State-gov,243631,10th,6,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,?,219277,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,45381,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,167482,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,225014,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,405083,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,186061,Some-college,10,Widowed,?,Unmarried,Black,Female,0,4,2,United-States,<=50K\n1,Federal-gov,24153,10th,6,Married-civ-spouse,Other-service,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,126569,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Ecuador,>50K\n4,?,137658,HS-grad,9,Married-civ-spouse,?,Husband,Other,Male,0,0,0,Columbia,<=50K\n0,Private,315476,Assoc-acdm,12,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,248186,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,206903,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,191380,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n0,Private,191910,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,145119,Some-college,10,Never-married,Other-service,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n0,Private,130840,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,33126,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,334105,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Local-gov,354104,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,111985,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,321187,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,138142,Some-college,10,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,296999,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,155106,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,4,4,United-States,>50K\n2,Local-gov,174491,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,State-gov,173266,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,25610,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,Other,Male,0,0,2,Japan,>50K\n3,Private,187563,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,196344,1st-4th,2,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Mexico,<=50K\n2,Private,205047,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,715938,Bachelors,13,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,224520,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,229656,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,97883,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,131298,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,197875,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,172766,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,175796,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,51973,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,28186,Bachelors,13,Divorced,Farming-fishing,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,291979,11th,7,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,180752,Bachelors,13,Never-married,Protective-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,234657,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,39411,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,State-gov,334273,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,192779,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n0,?,105312,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,171676,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,2,2,United-States,<=50K\n1,Self-emp-not-inc,182714,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,Private,231866,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,102102,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,50248,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,195519,Masters,14,Never-married,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,State-gov,34310,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n1,?,314913,11th,7,Divorced,?,Own-child,White,Male,0,0,3,United-States,<=50K\n2,State-gov,747719,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,3,United-States,>50K\n2,Local-gov,188280,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,1,0,2,United-States,<=50K\n0,Private,110978,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,India,>50K\n0,Private,79682,10th,6,Never-married,Priv-house-serv,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Private,294671,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,340899,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,1,4,United-States,<=50K\n2,Private,192259,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,190228,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,118947,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,55861,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,238433,1st-4th,2,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Cuba,<=50K\n2,State-gov,166744,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,144586,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,134367,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,133616,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,203039,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,217460,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,106733,11th,7,Never-married,Craft-repair,Own-child,White,Male,1,0,2,United-States,<=50K\n2,State-gov,212027,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,126569,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,289960,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,174102,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,181716,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,172822,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,293091,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,107443,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Portugal,<=50K\n4,Private,95283,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,65278,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,220943,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,1,2,United-States,<=50K\n3,Private,257940,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,134945,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,105582,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,1,0,3,United-States,<=50K\n3,Private,169324,HS-grad,9,Separated,Other-service,Not-in-family,Black,Female,0,0,2,Jamaica,<=50K\n2,State-gov,98989,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,113838,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,1,0,3,Germany,<=50K\n0,Private,143436,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,143604,10th,6,Married-spouse-absent,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,226311,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,94610,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n4,Self-emp-not-inc,26716,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,160261,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,0,India,<=50K\n3,Private,117310,Assoc-acdm,12,Widowed,Tech-support,Unmarried,White,Female,2,0,2,United-States,<=50K\n3,Private,154342,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,89202,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,174704,HS-grad,9,Divorced,Sales,Unmarried,Black,Male,0,0,3,United-States,<=50K\n3,Private,153486,HS-grad,9,Separated,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,360097,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,230356,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,163870,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,199753,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,333505,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,Nicaragua,<=50K\n4,Local-gov,149281,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,138514,Assoc-voc,11,Divorced,Tech-support,Unmarried,Black,Female,0,0,3,United-States,<=50K\n4,Federal-gov,66504,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,206487,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,170020,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,217605,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,145711,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,169155,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,34127,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,110142,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,222646,12th,8,Separated,Machine-op-inspct,Other-relative,White,Female,0,0,2,Cuba,<=50K\n0,Private,182643,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,303565,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,Germany,<=50K\n1,Private,140092,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,178811,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,?,267399,12th,8,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Local-gov,192387,9th,5,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,127610,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,258862,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,225231,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,3,0,3,United-States,>50K\n0,Private,174926,9th,5,Never-married,Other-service,Own-child,White,Male,0,0,0,?,<=50K\n3,State-gov,238187,Bachelors,13,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,191444,HS-grad,9,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,198822,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,251323,9th,5,Married-civ-spouse,Farming-fishing,Other-relative,White,Male,0,0,2,Cuba,<=50K\n0,Private,168187,Some-college,10,Never-married,Other-service,Other-relative,White,Female,2,0,1,United-States,<=50K\n4,Private,370881,Assoc-acdm,12,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,198183,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,210610,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,269182,Some-college,10,Separated,Tech-support,Unmarried,Black,Female,2,0,2,United-States,<=50K\n3,Private,141727,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,1,0,2,United-States,<=50K\n2,Private,185848,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n1,Private,46746,11th,7,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,120475,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,135845,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,310255,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,379885,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n4,Self-emp-not-inc,31428,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,211013,Assoc-voc,11,Married-civ-spouse,Other-service,Other-relative,White,Female,0,0,3,Mexico,<=50K\n3,Private,175029,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,119539,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,?,>50K\n1,Private,247025,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,252327,7th-8th,4,Never-married,Other-service,Own-child,White,Male,0,0,2,Mexico,<=50K\n0,Self-emp-not-inc,375313,Some-college,10,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n4,Private,107165,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,108470,11th,7,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Private,150057,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,189468,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n1,?,198393,HS-grad,9,Never-married,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,181031,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,569930,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,27411,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,147397,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,242922,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,154949,11th,7,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,>50K\n2,Self-emp-inc,423217,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,195385,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,100009,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,191628,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,340880,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Philippines,>50K\n0,Private,207173,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,48010,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,229051,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,193366,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,57781,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,?,121136,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,433989,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,136687,HS-grad,9,Separated,Machine-op-inspct,Unmarried,Other,Female,0,0,2,United-States,<=50K\n2,State-gov,154117,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n4,Private,294009,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,239038,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,244064,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Male,0,0,2,United-States,<=50K\n4,Private,128348,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,3,0,3,United-States,>50K\n1,Private,66278,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,162643,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,179659,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n0,Private,205218,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,154033,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,158528,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,366219,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,2,3,United-States,>50K\n1,Private,301862,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,228406,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,120131,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,4,United-States,>50K\n3,Local-gov,127943,HS-grad,9,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,301514,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,156980,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,124685,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n3,Private,305673,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Canada,>50K\n1,Local-gov,31391,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,33658,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,211391,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,402998,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,78855,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Private,320451,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,3,2,Hong,>50K\n3,Private,49278,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,?,248876,Bachelors,13,Divorced,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,242586,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,359696,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,4,0,3,United-States,>50K\n3,Local-gov,296085,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n2,Private,233130,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,189511,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Germany,>50K\n1,Private,124420,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,67072,Bachelors,13,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,2,0,3,United-States,<=50K\n3,Private,194908,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,94991,HS-grad,9,Divorced,Other-service,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,194561,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,75726,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,1,2,United-States,<=50K\n1,Private,60722,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,59944,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,220840,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-inc,104235,Masters,14,Never-married,Other-service,Own-child,White,Male,0,0,4,United-States,<=50K\n4,Private,142714,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,110490,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,154076,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,130557,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,107108,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,207172,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Mexico,<=50K\n1,Private,304595,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,475322,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,107620,11th,7,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,301911,Some-college,10,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,1,Laos,<=50K\n1,Private,267866,HS-grad,9,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,2,3,Iran,>50K\n1,Private,269786,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,167474,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,115023,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n4,Local-gov,86590,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n3,State-gov,187087,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,184307,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,225859,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,1,0,1,United-States,<=50K\n1,Private,57889,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,157932,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,187830,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n3,Private,251180,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,3,United-States,<=50K\n4,Private,317083,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,190895,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,328606,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,403860,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,215479,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,157639,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,152129,12th,8,Never-married,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Private,239284,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,234302,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,218724,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,106330,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,35032,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,234641,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,170730,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,218322,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,47929,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,142411,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,219122,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,132887,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,1,0,2,Jamaica,<=50K\n1,State-gov,44464,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,180928,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,3,United-States,<=50K\n0,?,199426,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,139703,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,202642,Bachelors,13,Separated,Prof-specialty,Other-relative,Black,Female,0,0,2,Jamaica,<=50K\n0,Private,160049,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,239755,11th,7,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,152369,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,42900,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,?,117017,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,175017,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Italy,<=50K\n2,Private,342642,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,143730,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,191098,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n2,Private,208106,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,167737,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,315971,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,142717,Some-college,10,Divorced,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,190227,Masters,14,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,79864,Masters,14,Separated,Exec-managerial,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,34067,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,222882,HS-grad,9,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,44464,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,1,3,United-States,>50K\n1,Private,256062,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,Puerto-Rico,<=50K\n0,Private,251073,9th,5,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,149949,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,165235,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n0,?,243190,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n4,?,160662,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,3,United-States,<=50K\n4,Self-emp-not-inc,175942,Some-college,10,Widowed,Exec-managerial,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,Private,212793,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,153312,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,173296,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,120131,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,117444,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,226196,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,202872,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Private,176716,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,82540,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,41643,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,197292,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,76491,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,101094,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,119944,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,141626,Some-college,10,Never-married,Tech-support,Own-child,White,Male,1,0,0,United-States,<=50K\n1,Private,122575,Bachelors,13,Never-married,Exec-managerial,Unmarried,Asian-Pac-Islander,Male,0,0,3,Vietnam,<=50K\n1,Private,194740,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,263200,5th-6th,3,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,1,Mexico,<=50K\n3,Local-gov,140644,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,202115,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n0,Federal-gov,27142,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,318046,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,276369,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,67187,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n0,Private,133582,1st-4th,2,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,216672,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,?,<=50K\n1,Private,45796,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,31778,HS-grad,9,Separated,Prof-specialty,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Private,190044,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,144351,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,172145,10th,6,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,193130,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,140478,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,122390,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,116830,12th,8,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,117683,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,106491,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,39803,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,363053,9th,5,Never-married,Priv-house-serv,Unmarried,White,Female,0,0,1,Mexico,<=50K\n0,Private,54472,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,200471,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,54317,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,113443,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,4,0,1,United-States,>50K\n1,Private,159623,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,161235,Assoc-voc,11,Never-married,?,Own-child,White,Male,0,0,4,United-States,<=50K\n1,Private,247978,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,305846,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Self-emp-not-inc,214014,Some-college,10,Never-married,Sales,Own-child,Black,Male,4,0,3,United-States,>50K\n1,Private,226525,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,247819,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,194940,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,289991,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,585361,9th,5,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,91145,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,231604,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,Germany,<=50K\n1,Private,273269,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,202683,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,159179,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,28952,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,?,214925,10th,6,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,163708,9th,5,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,200235,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,109209,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,166153,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,268213,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,?,>50K\n1,Private,69056,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,State-gov,237141,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,277541,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,289039,Some-college,10,Never-married,Protective-serv,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,134737,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,56613,Some-college,10,Never-married,Protective-serv,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,36699,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Local-gov,333530,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,185366,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,154017,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,215504,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,222539,10th,6,Never-married,Transport-moving,Not-in-family,White,Male,1,0,3,United-States,<=50K\n3,Private,191565,1st-4th,2,Divorced,Other-service,Unmarried,Black,Female,0,0,2,Dominican-Republic,<=50K\n3,Private,111939,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,State-gov,53903,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,146659,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,194200,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n3,State-gov,78529,Masters,14,Separated,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,194829,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,330174,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n3,Self-emp-inc,230767,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,Cuba,>50K\n3,Local-gov,137250,Masters,14,Widowed,Prof-specialty,Unmarried,Black,Female,0,2,1,United-States,<=50K\n2,Private,254478,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,300908,Assoc-acdm,12,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,187830,Assoc-voc,11,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,Poland,<=50K\n0,Private,201138,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,44503,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,381357,9th,5,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,311124,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,96330,Some-college,10,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,228238,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,56964,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,127772,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,386397,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,404998,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,34545,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,157886,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,101299,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,134447,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,191822,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,70919,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,266343,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,87239,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,236487,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Germany,<=50K\n1,Private,224147,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,197200,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,124265,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,79980,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,128814,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,?,208862,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,51262,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-inc,98116,Some-college,10,Widowed,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,82393,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Asian-Pac-Islander,Male,0,0,2,Germany,<=50K\n3,Private,57534,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,218962,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,204752,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,243631,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,>50K\n2,Private,170299,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,60331,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,269318,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,>50K\n4,State-gov,132819,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,119665,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,150057,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,128567,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,230874,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,148526,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,160192,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,74660,Some-college,10,Widowed,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,142494,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,122042,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,37088,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,61778,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,?,176356,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,Germany,<=50K\n1,Private,123302,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Poland,<=50K\n0,Private,89760,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,165304,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,2,United-States,>50K\n4,Private,104945,7th-8th,4,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,192973,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,97863,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Italy,>50K\n1,Private,73585,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,29145,Assoc-voc,11,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,175232,HS-grad,9,Divorced,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,325374,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,107231,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,4,United-States,<=50K\n0,Private,129345,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,228395,Some-college,10,Never-married,Sales,Other-relative,Black,Female,0,0,0,United-States,<=50K\n3,Private,452402,Some-college,10,Separated,Exec-managerial,Unmarried,Black,Female,0,0,3,United-States,<=50K\n3,Self-emp-inc,338260,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,165138,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,109055,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,193122,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,425497,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,191858,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,297155,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Local-gov,181282,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,111700,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,35065,HS-grad,9,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Private,298539,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Self-emp-not-inc,95435,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,162160,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,176037,Assoc-voc,11,Divorced,Tech-support,Not-in-family,Black,Male,4,0,2,United-States,>50K\n2,Private,314007,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n3,Private,197683,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,242521,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,290321,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,44064,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,?,174163,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,374790,9th,5,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,231562,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,376150,Some-college,10,Married-spouse-absent,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,99987,10th,6,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,120126,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,33717,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,132879,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Italy,<=50K\n2,Private,304570,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,China,>50K\n2,Private,100292,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,117473,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,239833,HS-grad,9,Married-spouse-absent,Transport-moving,Unmarried,Black,Male,0,0,3,United-States,<=50K\n3,?,155233,12th,8,Married-civ-spouse,?,Wife,White,Female,0,0,2,Italy,<=50K\n1,Private,130369,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Female,1,0,3,Germany,<=50K\n1,Private,347166,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,502752,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,State-gov,255575,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,277946,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,?,214541,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,143123,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,69132,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,34973,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,236992,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,492263,10th,6,Separated,Machine-op-inspct,Own-child,White,Male,0,0,1,Mexico,<=50K\n2,Private,180019,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,47086,Bachelors,13,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,222853,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,344176,HS-grad,9,Never-married,Sales,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,223212,Bachelors,13,Never-married,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,110981,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,162688,Assoc-voc,11,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,181307,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,148903,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,2,0,3,United-States,>50K\n2,Private,306440,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,France,<=50K\n0,Private,210311,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,127117,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,54732,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,271521,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,Philippines,>50K\n1,?,216908,10th,6,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,543922,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,766115,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n4,?,52728,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,284166,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,1,3,United-States,>50K\n3,Private,122206,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,95989,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,334039,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n3,Self-emp-not-inc,225456,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,112847,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,2,0,1,United-States,>50K\n4,Self-emp-not-inc,171840,HS-grad,9,Widowed,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,180695,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,121012,9th,5,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,126569,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,227791,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,<=50K\n3,Self-emp-not-inc,290290,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,251521,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,41938,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,27678,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,3,United-States,<=50K\n1,Private,133756,HS-grad,9,Divorced,Farming-fishing,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Private,215990,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,461337,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,344855,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,0,United-States,>50K\n0,State-gov,214542,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,258170,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,242147,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n2,Private,235700,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,278130,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,261241,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,85995,Masters,14,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,3,South,>50K\n2,Private,340885,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,152889,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,195023,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Columbia,<=50K\n3,State-gov,109600,Masters,14,Married-spouse-absent,Exec-managerial,Unmarried,White,Female,2,0,2,United-States,>50K\n1,?,249463,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,158177,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,State-gov,47818,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,391468,11th,7,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,199995,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,231043,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,281768,7th-8th,4,Divorced,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,267790,9th,5,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,217379,Some-college,10,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,421561,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,50953,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,138504,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,State-gov,177064,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,103064,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,2,0,2,United-States,<=50K\n4,Private,184493,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,104089,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,149204,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,405284,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,<=50K\n0,Local-gov,137296,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,87771,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,2,2,United-States,<=50K\n2,State-gov,125499,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,3,0,3,India,>50K\n1,Private,59083,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,138332,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,198914,HS-grad,9,Never-married,Sales,Unmarried,Black,Male,0,0,1,United-States,<=50K\n3,Local-gov,238162,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,123677,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Asian-Pac-Islander,Female,0,0,2,Laos,<=50K\n2,Federal-gov,325538,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,251063,Some-college,10,Separated,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,51471,HS-grad,9,Married-civ-spouse,Tech-support,Wife,White,Female,0,3,2,United-States,>50K\n2,Private,175681,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,?,<=50K\n2,Private,165599,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,149640,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,England,>50K\n1,Private,143526,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,211160,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,342989,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,173631,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,141876,HS-grad,9,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,137604,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,129232,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,271550,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,456922,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,232242,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,352188,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,114967,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,201981,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,159247,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,125905,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,186824,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,121012,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,110844,Masters,14,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,143003,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,2,3,India,>50K\n1,Federal-gov,59732,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,178489,Bachelors,13,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,?,<=50K\n2,?,252127,Some-college,10,Widowed,?,Unmarried,Black,Female,0,0,0,United-States,<=50K\n2,Private,109633,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,160811,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,365110,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,113080,9th,5,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,206074,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,173062,Bachelors,13,Never-married,Handlers-cleaners,Unmarried,Black,Male,0,0,2,United-States,<=50K\n4,Private,117273,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,153805,Some-college,10,Married-civ-spouse,Transport-moving,Other-relative,Other,Male,0,0,3,Ecuador,>50K\n3,Private,293802,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Local-gov,153132,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,166809,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,34122,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,231725,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,63210,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,108293,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,>50K\n4,Private,116878,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Italy,<=50K\n2,Private,110622,Prof-school,15,Married-civ-spouse,Adm-clerical,Other-relative,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Local-gov,180318,10th,6,Never-married,Farming-fishing,Unmarried,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-inc,112318,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,252079,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,27153,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,73312,11th,7,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,145409,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,167882,Some-college,10,Widowed,Other-service,Other-relative,Black,Female,0,0,2,Haiti,<=50K\n0,Private,236696,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,28791,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n1,State-gov,118551,Bachelors,13,Married-civ-spouse,Tech-support,Own-child,White,Female,2,0,1,?,>50K\n4,Private,187292,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,189922,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,584259,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,173992,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,253759,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,111243,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,147850,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,171015,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,118023,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,?,<=50K\n1,Self-emp-not-inc,361497,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,137290,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,401886,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Male,0,0,0,United-States,<=50K\n3,Private,201882,Masters,14,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,30793,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n2,Federal-gov,139161,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,Black,Female,0,2,2,United-States,<=50K\n3,Private,210736,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,167781,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,103986,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,144592,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,493034,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,184078,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,191814,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,329852,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,223660,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,177087,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,>50K\n1,Private,143766,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,234271,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,314822,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,195584,Assoc-acdm,12,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,207938,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n2,Private,126850,Prof-school,15,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,279485,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,267717,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,?,175935,HS-grad,9,Separated,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,163665,Some-college,10,Never-married,Transport-moving,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,200468,10th,6,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,91501,HS-grad,9,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,182771,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,56392,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,1,3,United-States,<=50K\n1,Private,20511,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,538822,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,332008,Some-college,10,Never-married,Craft-repair,Unmarried,Asian-Pac-Islander,Male,0,0,2,Taiwan,<=50K\n4,Self-emp-inc,220789,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,114760,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,>50K\n4,?,90338,HS-grad,9,Widowed,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,181576,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,198841,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,53197,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,>50K\n1,State-gov,542265,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,193026,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,25505,Assoc-voc,11,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,375657,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,201599,11th,7,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,181820,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,334224,Some-college,10,Married-civ-spouse,Craft-repair,Wife,White,Female,3,0,2,United-States,>50K\n1,State-gov,54318,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,141388,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,57101,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,168515,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,Germany,<=50K\n4,Private,163665,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,207513,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,293291,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,70088,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,199346,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n3,Local-gov,143949,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,1,0,2,United-States,>50K\n1,Private,207201,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,430283,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,2,United-States,>50K\n2,Local-gov,293809,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,378009,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,226608,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,1,Guatemala,>50K\n0,Private,314182,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,170544,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,94196,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,193047,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,112607,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,146949,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,309513,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,191389,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,213902,7th-8th,4,Never-married,Priv-house-serv,Own-child,White,Female,0,0,1,El-Salvador,<=50K\n4,Self-emp-not-inc,46514,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,75855,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,2,0,2,?,>50K\n0,Private,38707,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,188568,Some-college,10,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,215014,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,Mexico,<=50K\n1,Private,184477,12th,8,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,204235,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,39054,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-inc,272531,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,358701,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,Mexico,<=50K\n3,Private,217750,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,200374,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,Private,498349,Bachelors,13,Never-married,Transport-moving,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,State-gov,170458,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,57233,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,188432,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,33300,Assoc-acdm,12,Never-married,Farming-fishing,Other-relative,White,Male,4,0,2,United-States,>50K\n1,Private,225779,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,46677,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,227968,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,1,Haiti,>50K\n1,Private,85355,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,207120,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,229223,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,2,United-States,>50K\n0,Private,224640,Assoc-acdm,12,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,139012,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n2,Federal-gov,130749,Some-college,10,Divorced,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,204516,10th,6,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,105479,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,197093,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,431245,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,155150,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,216035,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,388247,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,208908,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,259301,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,69333,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,4,United-States,>50K\n1,Private,167893,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n1,Federal-gov,386877,Assoc-voc,11,Never-married,Tech-support,Own-child,Black,Male,2,0,2,United-States,<=50K\n3,Private,146551,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,238360,Bachelors,13,Separated,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Private,187748,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,50748,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,50136,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,El-Salvador,<=50K\n2,Private,111483,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,298871,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n1,Private,147340,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,132704,Masters,14,Separated,Prof-specialty,Not-in-family,White,Male,4,0,1,United-States,>50K\n3,State-gov,327786,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Federal-gov,243636,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,194417,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,236696,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,337130,1st-4th,2,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,273051,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Yugoslavia,>50K\n2,Private,186191,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,268451,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,154600,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,405309,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,99185,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,191765,HS-grad,9,Divorced,Other-service,Other-relative,Black,Female,0,0,1,United-States,<=50K\n0,Private,253583,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,?,297054,HS-grad,9,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,204397,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,288771,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,173987,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,33662,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,2,United-States,>50K\n0,Private,91658,Some-college,10,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,226902,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,232586,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,291755,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,207032,HS-grad,9,Married-spouse-absent,?,Unmarried,Black,Female,0,0,2,Haiti,<=50K\n0,Private,161478,Some-college,10,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,109833,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,229394,11th,7,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,?,69285,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,491862,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,311534,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,420895,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,226374,10th,6,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Federal-gov,101345,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,48779,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,152676,HS-grad,9,Divorced,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,164877,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,97521,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,88564,5th-6th,3,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,188246,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,189185,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,State-gov,163069,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,251905,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,112403,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,?,<=50K\n0,Private,36882,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,195891,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,194905,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,133503,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,31621,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,413373,Doctorate,16,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,196029,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,107302,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,151267,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,>50K\n3,Private,256861,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,82777,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,147430,HS-grad,9,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,60726,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,165754,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,448337,HS-grad,9,Separated,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,185079,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,418702,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,41504,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,387335,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,2,0,United-States,<=50K\n0,Private,261720,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,133963,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,?,357750,11th,7,Widowed,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,State-gov,179488,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,60135,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,308746,Prof-school,15,Widowed,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,278720,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,477505,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,164711,Some-college,10,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,208277,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,39943,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,104542,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,286634,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,142712,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,336404,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,117983,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,108796,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,59469,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,Iran,<=50K\n2,Private,171968,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,119254,10th,6,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,278617,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,72338,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n3,Local-gov,343231,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,63910,HS-grad,9,Married-civ-spouse,Sales,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,190350,9th,5,Married-civ-spouse,Protective-serv,Wife,Black,Female,0,0,2,United-States,<=50K\n0,State-gov,176162,Bachelors,13,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,37720,10th,6,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,421467,Assoc-acdm,12,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,138441,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,146767,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,?,369678,12th,8,Never-married,?,Not-in-family,Other,Male,0,2,2,United-States,<=50K\n0,Private,160445,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,211695,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,102346,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,4,0,United-States,>50K\n3,Private,128796,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,111129,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,44566,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,118497,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,334291,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,237920,Doctorate,16,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,136331,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,187397,HS-grad,9,Never-married,Other-service,Other-relative,Other,Male,0,0,3,Mexico,<=50K\n1,Self-emp-not-inc,119793,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,24982,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n1,Self-emp-not-inc,231714,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,229272,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n4,?,68219,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,268831,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,149640,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,>50K\n1,Private,261725,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,161387,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,260167,HS-grad,9,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,200928,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,155594,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,79539,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,469454,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,331482,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,225193,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,370727,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,3,2,United-States,>50K\n1,Private,82393,HS-grad,9,Married-civ-spouse,Other-service,Own-child,Asian-Pac-Islander,Male,0,0,1,Philippines,<=50K\n4,?,37170,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,58484,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,156464,Bachelors,13,Never-married,Prof-specialty,Other-relative,White,Male,0,0,2,?,<=50K\n3,Private,344621,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,174752,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Self-emp-inc,174202,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,261203,7th-8th,4,Never-married,Other-service,Unmarried,Other,Female,0,0,1,?,<=50K\n4,Private,316000,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,216871,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,2,United-States,<=50K\n1,Private,246933,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Self-emp-not-inc,112115,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,264651,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,99185,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,176186,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,100219,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,46691,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,297735,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,132222,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,189656,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Local-gov,224934,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,149218,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,158508,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,State-gov,261203,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,309504,10th,6,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,State-gov,324637,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,267426,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,229016,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,46401,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,114288,HS-grad,9,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,203849,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,193882,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,311269,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,156117,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,169917,7th-8th,4,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,222615,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,106900,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n2,Federal-gov,78036,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,4,United-States,>50K\n1,Private,380560,HS-grad,9,Never-married,Farming-fishing,Other-relative,White,Male,0,0,2,Mexico,<=50K\n2,Private,167106,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,1,0,1,Philippines,>50K\n3,Private,289436,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,749636,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,154120,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,105119,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,181081,HS-grad,9,Divorced,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,182237,10th,6,Separated,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,102130,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,266324,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,4,United-States,>50K\n3,Private,170562,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,240543,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,187046,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,389254,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,179955,Some-college,10,Widowed,Transport-moving,Unmarried,White,Female,0,0,1,Outlying-US(Guam-USVI-etc),<=50K\n0,Private,197997,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-inc,343789,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,191088,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,2,3,United-States,<=50K\n2,Local-gov,141649,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,433906,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,207982,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,175925,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,85767,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,281030,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,313986,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,396595,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n0,?,189203,Assoc-acdm,12,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,163108,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,141590,11th,7,Never-married,Priv-house-serv,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,137421,12th,8,Never-married,Transport-moving,Not-in-family,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n2,Private,67728,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,Italy,<=50K\n1,Private,345522,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,4,United-States,>50K\n2,Private,330087,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,204322,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,50295,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,147258,Assoc-voc,11,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n0,Private,194260,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,437727,9th,5,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,34100,Some-college,10,Widowed,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,?,186611,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,280960,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,33117,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,169628,Bachelors,13,Never-married,Sales,Unmarried,Black,Female,0,0,1,United-States,>50K\n0,State-gov,124942,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,143368,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Private,255621,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,154227,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,171438,Assoc-voc,11,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,191524,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,377017,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,192806,7th-8th,4,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,?,259120,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,234195,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,147596,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,147251,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,176157,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Local-gov,176162,Assoc-voc,11,Never-married,Protective-serv,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,384150,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,107665,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,?,82635,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,165827,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,287306,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n4,Self-emp-not-inc,78786,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,33310,Prof-school,15,Divorced,Other-service,Not-in-family,White,Female,0,4,1,United-States,<=50K\n0,Private,349368,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,117674,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,310889,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,?,187167,HS-grad,9,Separated,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,379919,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,34862,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,201723,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Local-gov,161463,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,186410,Prof-school,15,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Federal-gov,62020,Prof-school,15,Divorced,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,>50K\n2,Private,42044,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,170230,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,341358,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,199426,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,89172,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,?,148955,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,0,0,South,<=50K\n2,Private,140673,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n0,?,71788,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,326033,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,129305,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,171067,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,143582,Some-college,10,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Japan,<=50K\n0,?,171461,10th,6,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,257980,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,182866,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,69333,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,668362,1st-4th,2,Widowed,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,132879,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,181219,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n0,?,166018,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,120518,HS-grad,9,Widowed,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,183532,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,49298,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,157332,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,213726,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,31143,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,256173,10th,6,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,184872,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n4,Private,202652,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,Dominican-Republic,<=50K\n4,?,101602,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,60940,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,3,0,3,France,>50K\n0,Private,292590,HS-grad,9,Married-civ-spouse,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,141420,Bachelors,13,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,159389,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,254534,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,89508,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,238980,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,178946,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,204752,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,290213,Some-college,10,Separated,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,102615,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,291965,Some-college,10,Never-married,Tech-support,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,Local-gov,175339,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,90547,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,<=50K\n0,?,449101,HS-grad,9,Married-civ-spouse,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,101722,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,<=50K\n1,?,981628,HS-grad,9,Divorced,?,Unmarried,Black,Male,0,0,2,United-States,<=50K\n4,?,147989,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,204470,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,311409,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,3,0,1,United-States,>50K\n1,Private,190027,HS-grad,9,Never-married,Other-service,Other-relative,Black,Female,0,0,2,?,<=50K\n2,Private,218015,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,77634,Preschool,1,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,42984,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,413297,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,4,Mexico,<=50K\n3,Self-emp-not-inc,218835,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,England,<=50K\n1,Private,341051,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,252419,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Federal-gov,347935,Some-college,10,Never-married,Protective-serv,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,237848,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,174826,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,170086,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,470368,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Federal-gov,75235,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,?,35854,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,746432,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,258498,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,176063,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,26865,7th-8th,4,Never-married,Farming-fishing,Unmarried,White,Male,0,0,0,United-States,<=50K\n3,Private,104724,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,346321,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,402462,Bachelors,13,Married-spouse-absent,Transport-moving,Unmarried,White,Male,0,0,1,Columbia,<=50K\n1,Private,153078,Prof-school,15,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,176063,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,451059,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,229533,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,106437,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,294313,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,3,United-States,<=50K\n4,Private,67903,9th,5,Separated,Farming-fishing,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,133669,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,251730,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,72896,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,155664,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,206520,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,72338,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,4,Japan,>50K\n2,Local-gov,34640,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Other,Male,0,2,2,United-States,>50K\n1,Private,236543,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n2,Local-gov,43702,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,335248,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,198197,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,281768,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,160594,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,231826,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,188171,Assoc-acdm,12,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,125000,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,166509,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,402367,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,3,0,2,United-States,>50K\n4,Local-gov,204123,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,220786,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,254146,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n1,Local-gov,152461,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,223669,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,120270,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,304602,Assoc-voc,11,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n3,Private,24108,Some-college,10,Separated,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,32546,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Male,2,0,2,United-States,>50K\n2,Private,93885,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,210765,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,191276,Assoc-voc,11,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,71438,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,330571,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,138634,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,112264,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,205865,HS-grad,9,Never-married,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,224640,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,180758,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n1,?,499935,Assoc-voc,11,Never-married,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,107762,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,214787,12th,8,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,211032,1st-4th,2,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,208353,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,157273,10th,6,Never-married,Other-service,Other-relative,Black,Male,0,0,0,United-States,<=50K\n2,Private,75891,Bachelors,13,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,177675,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,182370,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,200525,11th,7,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,174242,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,95566,1st-4th,2,Married-spouse-absent,Other-service,Own-child,Other,Female,0,0,1,Dominican-Republic,<=50K\n1,Private,30290,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,240951,HS-grad,9,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,183810,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,94342,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,148577,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,103634,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,83542,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,76131,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,0,0,2,United-States,>50K\n2,Federal-gov,262402,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,198286,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,145441,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,?,273558,Some-college,10,Never-married,?,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n3,Local-gov,117496,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,128876,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,199698,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,65390,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,128645,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,53481,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,92215,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,78859,Masters,14,Widowed,Prof-specialty,Unmarried,White,Female,0,1,0,United-States,<=50K\n4,Self-emp-inc,187502,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,242080,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,41837,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,291374,12th,8,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,148995,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n4,Private,159008,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,271013,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,199046,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,164280,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Portugal,<=50K\n1,Local-gov,116960,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,100054,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,183824,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,313925,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,379883,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Cuba,>50K\n4,?,92593,Some-college,10,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,189777,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,198330,Masters,14,Widowed,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,127451,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,?,31577,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,?,90230,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,301024,Bachelors,13,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,175732,HS-grad,9,Never-married,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n0,Private,218889,9th,5,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Private,117605,9th,5,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,154571,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n2,Private,228057,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Dominican-Republic,<=50K\n1,Private,173998,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,90752,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,51008,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,113398,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,74977,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,101593,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,228346,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,180418,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,44489,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,277488,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,103064,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,226872,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,330416,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,186495,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,State-gov,205712,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,217743,11th,7,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,52565,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,1,2,United-States,<=50K\n0,Private,239954,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,349986,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,117585,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,2,United-States,>50K\n4,Self-emp-not-inc,122094,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,26857,7th-8th,4,Widowed,Farming-fishing,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Local-gov,192321,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,88095,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,1,Mexico,<=50K\n2,Private,144067,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,0,?,<=50K\n1,Private,124187,9th,5,Married-civ-spouse,Farming-fishing,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,123681,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,145638,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,130513,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,2,Peru,<=50K\n3,Federal-gov,197038,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,189092,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,198841,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,317969,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,103121,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,2,2,United-States,>50K\n1,Private,111589,10th,6,Never-married,Other-service,Unmarried,Black,Female,0,0,2,Jamaica,<=50K\n3,Local-gov,267952,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,63899,11th,7,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,473625,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,187901,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,4,2,United-States,>50K\n0,Private,24090,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,102729,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,91666,12th,8,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,215873,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,152109,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,175586,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,232614,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,State-gov,229465,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,147110,Some-college,10,Divorced,Adm-clerical,Own-child,White,Male,4,0,2,United-States,>50K\n2,Local-gov,161240,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,358124,HS-grad,9,Never-married,Other-service,Other-relative,Black,Female,0,0,3,United-States,<=50K\n3,Private,222529,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,338320,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,62026,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,263886,Some-college,10,Never-married,Sales,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n3,Private,310774,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,98155,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,259307,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,358753,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,29762,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,202729,HS-grad,9,Married-civ-spouse,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,28790,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,53209,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,169020,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,127195,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,211731,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n2,Self-emp-not-inc,126614,Bachelors,13,Divorced,Exec-managerial,Not-in-family,Other,Male,0,0,1,Iran,<=50K\n2,Private,259463,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,228411,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,117827,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,57216,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,State-gov,250821,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Self-emp-inc,88564,Some-college,10,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,172822,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,251579,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,118399,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,178383,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,170866,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n4,?,268954,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n3,?,89951,12th,8,Married-civ-spouse,?,Wife,Black,Female,0,0,2,United-States,>50K\n0,Private,203894,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,237065,Some-college,10,Divorced,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,108435,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,93213,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-inc,231230,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,386175,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,128392,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,223515,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,208630,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,2,2,United-States,<=50K\n4,?,97969,1st-4th,2,Married-spouse-absent,?,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,174295,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,60229,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,66095,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,130057,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n4,Private,179743,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,0,United-States,<=50K\n1,Private,192022,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,45288,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,178764,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,99476,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,41973,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,162228,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,211968,Some-college,10,Never-married,Sales,Own-child,White,Female,0,2,1,United-States,<=50K\n3,Private,211226,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,Other,Male,0,0,2,United-States,<=50K\n2,Private,33397,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,120839,12th,8,Divorced,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,36327,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,139703,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,107827,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,140219,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,3,0,3,United-States,>50K\n2,Local-gov,203761,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,114719,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,344394,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,195516,7th-8th,4,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,Mexico,<=50K\n2,State-gov,31627,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,<=50K\n4,Private,174032,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,226875,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,566537,Preschool,1,Married-civ-spouse,Other-service,Husband,White,Male,0,2,2,Mexico,<=50K\n0,Private,36162,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,31478,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,1,0,3,United-States,<=50K\n3,Private,294991,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,108495,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,161532,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Local-gov,332249,HS-grad,9,Separated,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,268147,Assoc-voc,11,Never-married,Tech-support,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Federal-gov,317847,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,52028,1st-4th,2,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n0,Private,184045,Some-college,10,Never-married,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,206609,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,152968,Some-college,10,Separated,Adm-clerical,Other-relative,White,Male,1,0,2,United-States,<=50K\n0,Private,213015,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Black,Male,1,0,2,United-States,<=50K\n1,Private,313835,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,66385,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,205940,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,1,0,1,United-States,<=50K\n3,Self-emp-inc,260938,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,60567,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,335067,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,331126,HS-grad,9,Never-married,Other-service,Unmarried,Black,Male,0,0,1,United-States,<=50K\n3,Private,156612,12th,8,Divorced,Transport-moving,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,188436,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,227468,Some-college,10,Widowed,Protective-serv,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,183580,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,50990,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,384246,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,375313,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Private,176410,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Own-child,White,Female,2,0,0,United-States,>50K\n3,Private,93639,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,30289,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,124950,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,126675,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,145964,12th,8,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,345712,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,97474,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,180342,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,167087,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,192825,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,318749,Assoc-voc,11,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,Germany,<=50K\n1,?,147638,Masters,14,Never-married,?,Not-in-family,Other,Female,0,0,2,Japan,<=50K\n4,Federal-gov,293971,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,229566,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,242464,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,111067,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,United-States,>50K\n0,?,155697,9th,5,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,106554,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n3,Private,23776,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,43909,HS-grad,9,Divorced,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,105808,9th,5,Widowed,Transport-moving,Unmarried,White,Male,0,0,2,United-States,>50K\n2,Private,169995,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,141388,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,241431,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,78374,HS-grad,9,Never-married,?,Other-relative,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,158948,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,115498,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,?,>50K\n1,Private,272411,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,30529,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n4,?,263374,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,2,Canada,<=50K\n1,Private,190228,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,126060,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,391192,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,214069,HS-grad,9,Separated,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,170871,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,118993,Some-college,10,Separated,Exec-managerial,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,245880,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,174794,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,0,0,3,Germany,<=50K\n4,Local-gov,153408,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,?,330301,7th-8th,4,Separated,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,385278,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Federal-gov,38434,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,111679,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,168956,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,86143,Some-college,10,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n3,Private,99835,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,263561,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,118536,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,209691,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Canada,<=50K\n3,Private,123374,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,137225,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,119359,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,0,China,>50K\n4,Private,134153,10th,6,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,121124,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,147655,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,165138,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Federal-gov,312017,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,272950,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,259323,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,281739,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,119156,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,165881,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,136075,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,187465,11th,7,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,328561,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,<=50K\n3,Private,350440,Some-college,10,Married-civ-spouse,Craft-repair,Other-relative,Asian-Pac-Islander,Male,0,0,2,Cambodia,>50K\n2,Self-emp-not-inc,109133,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,109814,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,86643,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,154521,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,45912,HS-grad,9,Widowed,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,175070,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,4,2,United-States,>50K\n2,Private,338033,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,158963,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,121441,11th,7,Never-married,Exec-managerial,Other-relative,White,Male,0,4,2,United-States,>50K\n3,Self-emp-not-inc,242391,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,119964,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Private,193344,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,Germany,<=50K\n1,Local-gov,45554,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,249716,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,58985,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,456367,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,117381,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,240922,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Female,0,1,0,United-States,<=50K\n1,Private,226443,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,364342,Assoc-voc,11,Never-married,Sales,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n2,Local-gov,101593,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,267471,12th,8,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,186849,11th,7,Divorced,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,174603,5th-6th,3,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,0,Italy,<=50K\n1,Private,115040,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,142766,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,1,0,0,United-States,<=50K\n2,Private,59660,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n2,Self-emp-not-inc,49595,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,127491,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,155933,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,2,0,United-States,<=50K\n0,Private,122272,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,143771,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,91384,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,135874,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,207066,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n3,Private,172493,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,189956,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,106967,Masters,14,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,200153,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,149943,HS-grad,9,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Male,2,0,3,?,<=50K\n2,Private,151736,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,67852,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,54229,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,154120,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,157217,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,381645,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,160785,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,133584,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,170230,Masters,14,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,250206,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,128363,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,163434,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,195690,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,138991,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,118419,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,185407,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,283079,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,119655,12th,8,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,153416,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,?,204868,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,220362,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,203078,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n4,State-gov,104361,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n4,Private,274096,10th,6,Divorced,Transport-moving,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,State-gov,455553,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,112283,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,64506,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,State-gov,24395,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,182581,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,0,United-States,>50K\n1,Private,100669,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n0,Private,178025,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,113913,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,3,United-States,<=50K\n1,Private,55191,Assoc-acdm,12,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,223206,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,2,Vietnam,>50K\n0,Local-gov,162551,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Female,0,0,1,China,<=50K\n0,Private,693066,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,96867,5th-6th,3,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,256362,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,539864,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,241153,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,284395,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,180039,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,178416,Assoc-voc,11,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,175710,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,?,<=50K\n0,Local-gov,164775,5th-6th,3,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Guatemala,>50K\n3,Private,176897,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,265097,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,193090,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,186009,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,3,United-States,<=50K\n1,Private,175262,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,109928,11th,7,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,162834,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,177896,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,181372,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,70645,Preschool,1,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,128272,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,106723,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,122348,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,177905,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,254547,Some-college,10,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,Jamaica,<=50K\n3,Self-emp-inc,102308,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,2,United-States,>50K\n2,Private,33105,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,215441,Some-college,10,Never-married,Adm-clerical,Not-in-family,Other,Male,0,0,2,?,<=50K\n2,Local-gov,197919,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,206139,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,117849,Assoc-acdm,12,Divorced,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,323044,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Germany,>50K\n1,Private,90415,Assoc-voc,11,Never-married,Tech-support,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,294913,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,127573,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,180190,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,231013,Bachelors,13,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,356015,HS-grad,9,Separated,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,1,Hong,<=50K\n1,Private,198069,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,99141,HS-grad,9,Divorced,Farming-fishing,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,188246,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,116508,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n2,Federal-gov,38434,Bachelors,13,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n0,Private,128477,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,91839,Bachelors,13,Married-civ-spouse,Other-service,Husband,Amer-Indian-Eskimo,Male,3,0,0,United-States,>50K\n2,Private,409922,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,185041,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,103925,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,34037,Bachelors,13,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,251659,Some-college,10,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,1,3,?,>50K\n0,Private,57145,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,182108,Doctorate,16,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Self-emp-inc,213296,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,28765,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,37792,10th,6,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,232036,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,33678,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Without-pay,159908,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,176761,HS-grad,9,Never-married,Craft-repair,Other-relative,Other,Male,0,0,2,Nicaragua,<=50K\n1,Private,260954,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,1,United-States,<=50K\n2,Local-gov,180342,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,324791,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,183801,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,204235,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,249720,Assoc-voc,11,Married-spouse-absent,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Private,127084,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,1,United-States,<=50K\n2,Local-gov,201495,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,447346,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,206008,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,286020,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,99891,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,169544,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,313749,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,89182,12th,8,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Italy,<=50K\n2,Private,258102,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,255466,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,38795,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,311907,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,171924,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,164488,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,297991,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,3,United-States,<=50K\n1,Private,478315,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,34832,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,67804,9th,5,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,34568,Assoc-voc,11,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,47151,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,?,120617,Some-college,10,Never-married,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,318046,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,363963,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,92811,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,33678,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,66118,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,160474,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,159960,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,242987,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Columbia,<=50K\n4,Private,232719,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,103153,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,2,United-States,<=50K\n2,Local-gov,162187,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,207391,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Never-worked,176673,HS-grad,9,Married-civ-spouse,?,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Private,356882,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,427686,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-inc,191196,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,?,>50K\n2,Private,377798,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,43712,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,205939,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,161691,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,4,2,United-States,>50K\n1,Private,346034,12th,8,Married-spouse-absent,Handlers-cleaners,Unmarried,White,Male,0,0,1,Mexico,<=50K\n2,Private,144460,Some-college,10,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,Italy,<=50K\n0,Never-worked,153663,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,262617,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Federal-gov,173851,HS-grad,9,Never-married,Armed-Forces,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,?,126540,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,117963,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,219737,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,328466,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,Mexico,<=50K\n3,State-gov,138852,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,195532,Some-college,10,Never-married,Protective-serv,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,188246,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,138162,Some-college,10,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,110714,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,123075,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,330466,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Hong,<=50K\n1,Private,254304,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,435842,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,118657,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,278188,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,233777,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,Mexico,<=50K\n2,Self-emp-inc,328466,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,176580,5th-6th,3,Married-spouse-absent,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,?,156608,11th,7,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,172415,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,194951,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,3,Ireland,<=50K\n1,Local-gov,318921,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,189462,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,192813,Masters,14,Widowed,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,199136,Bachelors,13,Widowed,Craft-repair,Not-in-family,White,Male,4,0,0,Germany,>50K\n1,Private,156805,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n4,?,93318,HS-grad,9,Widowed,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,121966,Bachelors,13,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,347336,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,205950,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,212143,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,187821,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,250807,11th,7,Never-married,Craft-repair,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,291755,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,36077,7th-8th,4,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,119793,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Portugal,<=50K\n2,Private,184655,10th,6,Divorced,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,162256,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n2,Self-emp-not-inc,204405,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,133355,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,89559,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,115066,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,139514,Preschool,1,Married-civ-spouse,Machine-op-inspct,Other-relative,Black,Male,0,0,4,Dominican-Republic,<=50K\n4,State-gov,200316,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,166502,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,226422,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,251305,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,190482,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,122215,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,248356,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,214594,7th-8th,4,Married-civ-spouse,Protective-serv,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,220460,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,174043,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,137547,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,4,0,2,Philippines,>50K\n3,Self-emp-not-inc,111959,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Scotland,>50K\n3,Private,40641,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,205940,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,265077,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,395736,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,306225,HS-grad,9,Divorced,Craft-repair,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Japan,<=50K\n1,Private,180299,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,214896,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,El-Salvador,<=50K\n0,Private,273792,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,224474,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,271431,9th,5,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,150171,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,381789,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,170984,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,108256,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,23789,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,176321,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,260425,Assoc-acdm,12,Separated,Tech-support,Unmarried,White,Female,1,0,1,United-States,<=50K\n3,Private,248059,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,56248,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,199763,HS-grad,9,Separated,Protective-serv,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,200047,12th,8,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,191712,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Self-emp-not-inc,156033,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,173736,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,135458,HS-grad,9,Divorced,Tech-support,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,185660,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,222005,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Private,161510,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n3,Local-gov,186303,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,2,2,United-States,>50K\n3,Local-gov,143533,7th-8th,4,Never-married,Other-service,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Private,288154,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,325372,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Portugal,<=50K\n1,Private,379959,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,168387,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,234640,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,232475,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,205152,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,112115,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,183854,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,164386,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,149620,Some-college,10,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,199590,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,?,<=50K\n1,Private,83742,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,65080,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,191335,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,227778,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,48280,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,66304,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,45834,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,298995,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,<=50K\n3,Private,161950,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,98776,11th,7,Widowed,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,102268,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,180771,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Wife,Amer-Indian-Eskimo,Female,0,0,1,Mexico,<=50K\n0,?,203992,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,206878,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,110622,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,Local-gov,203334,Doctorate,16,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,Self-emp-not-inc,50483,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,274502,7th-8th,4,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,208068,Preschool,1,Divorced,Other-service,Not-in-family,Other,Male,0,0,4,Mexico,<=50K\n2,Self-emp-not-inc,168098,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,213307,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Female,2,0,1,United-States,<=50K\n0,Private,175128,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,40955,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,60890,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,102686,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,>50K\n0,Private,190273,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,176185,Some-college,10,Married-spouse-absent,Craft-repair,Own-child,White,Male,0,0,3,United-States,>50K\n3,Private,304504,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,390657,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,41381,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,2,0,United-States,<=50K\n3,Private,101432,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,190682,HS-grad,9,Widowed,Craft-repair,Not-in-family,Black,Female,0,2,3,United-States,<=50K\n3,Private,158993,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,117798,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,137554,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,71556,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,?,>50K\n2,Private,257416,9th,5,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,195617,Some-college,10,Separated,Exec-managerial,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,236318,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,42251,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,257933,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,109133,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,261943,11th,7,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,1,Honduras,<=50K\n1,Private,139057,Masters,14,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n2,Private,237943,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,98611,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,Poland,<=50K\n4,Private,128092,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,284317,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,185041,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n4,Local-gov,223214,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,173664,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,269665,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,121521,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,199713,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,193689,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-inc,181974,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,?,<=50K\n3,Private,485710,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,185647,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,30673,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,160467,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,1,0,2,United-States,<=50K\n2,Private,186819,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,67234,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,30673,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n3,?,114648,12th,8,Divorced,?,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,182117,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,State-gov,222966,7th-8th,4,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,201495,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,301229,Assoc-voc,11,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,157747,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,155382,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,268083,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,113987,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,216984,Some-college,10,Married-civ-spouse,Other-service,Own-child,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n3,Private,177669,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,164190,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,355645,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,<=50K\n4,?,134152,9th,5,Divorced,?,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,63079,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,217597,HS-grad,9,Divorced,Sales,Own-child,White,Male,0,0,3,?,<=50K\n0,Private,381895,11th,7,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,403910,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,179010,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,436163,11th,7,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,321709,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,153918,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,403788,HS-grad,9,Never-married,Craft-repair,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,60567,11th,7,Divorced,Transport-moving,Unmarried,White,Male,0,1,3,United-States,<=50K\n4,Private,138145,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,79649,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,312088,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,208630,Masters,14,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Private,182401,10th,6,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,32916,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,302372,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,155093,10th,6,Divorced,Other-service,Not-in-family,Black,Female,0,0,2,Dominican-Republic,<=50K\n1,Private,192965,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,107302,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n0,Local-gov,514716,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,270436,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,42972,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,142657,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Federal-gov,47358,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,176175,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,131459,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,110417,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,364548,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,177398,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,4,United-States,<=50K\n1,Private,273243,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,147707,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,77266,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,191648,Assoc-acdm,12,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,0,United-States,<=50K\n4,?,120478,Assoc-voc,11,Divorced,?,Unmarried,White,Female,0,0,0,?,<=50K\n1,Private,211349,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,203715,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,292592,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,125976,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,?,320084,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,3,United-States,>50K\n1,?,33811,Bachelors,13,Never-married,?,Not-in-family,Asian-Pac-Islander,Female,0,0,4,United-States,<=50K\n1,Private,204461,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,337992,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,Japan,>50K\n2,Private,179137,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,325033,12th,8,Never-married,Protective-serv,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,160216,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Private,345898,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Private,139180,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,4,0,2,United-States,>50K\n4,?,287372,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n2,State-gov,252208,HS-grad,9,Separated,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,?,202822,HS-grad,9,Separated,?,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n4,?,129912,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,119199,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,199655,Masters,14,Divorced,Other-service,Not-in-family,Other,Female,0,0,1,United-States,<=50K\n2,Local-gov,111499,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,198216,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,260761,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Self-emp-not-inc,99359,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,1,0,3,United-States,<=50K\n2,State-gov,255835,Some-college,10,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,27242,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,34066,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,84661,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,116138,Masters,14,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Male,0,0,0,Taiwan,<=50K\n3,Private,321865,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,310152,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,257302,Assoc-acdm,12,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,154374,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,151910,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,201490,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,287927,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,2,United-States,>50K\n0,Private,226802,11th,7,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,89814,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,336951,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,160323,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,3,0,2,United-States,>50K\n0,?,103497,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,198693,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,?,227026,HS-grad,9,Never-married,?,Unmarried,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,104626,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,1,United-States,>50K\n0,Private,369667,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,104996,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,184454,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n2,Federal-gov,212465,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,82091,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,299831,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,279724,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,3,United-States,>50K\n2,Private,346189,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,State-gov,444554,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,128354,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,60548,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,85019,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,?,>50K\n1,Private,107914,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,238588,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n4,?,132015,7th-8th,4,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,220931,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,Peru,<=50K\n0,Private,205947,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,432824,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,4,United-States,>50K\n0,Private,236427,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,134446,HS-grad,9,Separated,Machine-op-inspct,Unmarried,Black,Male,0,0,3,United-States,<=50K\n3,Private,99516,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,109282,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,State-gov,106444,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,3,0,2,United-States,>50K\n4,Self-emp-not-inc,186651,11th,7,Widowed,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Self-emp-not-inc,188274,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,258120,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,43311,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,191846,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,403681,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,248446,5th-6th,3,Never-married,Priv-house-serv,Not-in-family,White,Male,0,0,3,Guatemala,<=50K\n0,Private,269430,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,257509,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,136384,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,120277,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,465326,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,103634,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,138371,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,242832,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,290208,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,186272,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,<=50K\n3,Private,201062,11th,7,Separated,Priv-house-serv,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,131916,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,54440,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,280215,HS-grad,9,Divorced,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,214399,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,2,1,United-States,<=50K\n0,Private,54164,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,4,0,3,United-States,>50K\n2,Private,219446,9th,5,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n0,Private,110677,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,145985,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,382078,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,>50K\n2,Self-emp-inc,170721,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,269705,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,101135,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,118429,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,31208,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,281384,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,171807,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,109912,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,2,?,<=50K\n2,Self-emp-inc,445382,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,105460,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,170338,HS-grad,9,Separated,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,102606,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,323887,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,175622,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,229636,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,388946,Some-college,10,Separated,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,269034,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Dominican-Republic,<=50K\n0,?,165361,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,75012,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,174379,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,312477,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,72055,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,67001,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,213734,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,83141,Some-college,10,Separated,Other-service,Not-in-family,White,Male,0,2,2,United-States,<=50K\n2,Self-emp-inc,223881,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,?,>50K\n1,Self-emp-not-inc,113752,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,170482,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,244689,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,160631,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Federal-gov,228724,Some-college,10,Never-married,Armed-Forces,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,?,38434,Masters,14,Married-civ-spouse,?,Wife,White,Female,3,0,0,United-States,>50K\n4,Private,292946,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,77443,7th-8th,4,Never-married,Other-service,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n1,Private,176410,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,0,United-States,>50K\n4,Federal-gov,98984,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,198751,Masters,14,Never-married,Other-service,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,479296,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,235218,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,164877,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,272087,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,169699,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,?,189762,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,202191,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,212261,Some-college,10,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,301568,9th,5,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,155233,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,75826,10th,6,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,201520,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,154236,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,1,0,2,United-States,<=50K\n0,Private,289227,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,217439,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,179020,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,149624,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,337266,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,?,30796,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,103541,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,206721,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,96773,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,200967,11th,7,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,180019,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,179866,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,>50K\n1,Local-gov,198770,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,219256,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,248730,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,110732,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,181020,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,183791,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,42972,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,134813,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,115438,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Ireland,>50K\n2,Private,239296,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n2,Private,428420,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,247846,HS-grad,9,Never-married,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,334105,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,100793,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,244478,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,142921,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,182863,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,171128,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,145402,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,306309,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,83822,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,262118,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,1,Germany,<=50K\n2,Private,155972,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n2,Self-emp-inc,214503,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,159303,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,174995,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,26669,Assoc-voc,11,Widowed,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,177727,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,124771,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,456736,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,1,0,1,United-States,<=50K\n1,Private,216604,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n1,Private,221561,11th,7,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,<=50K\n3,Private,114564,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,315476,11th,7,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,State-gov,67874,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,126110,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,102264,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,537222,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,113732,Some-college,10,Never-married,Handlers-cleaners,Unmarried,Black,Female,0,1,2,United-States,<=50K\n2,Self-emp-inc,93225,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,43064,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,136921,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,388885,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,142249,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,56841,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,156493,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,159021,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,190910,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,41879,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,1,United-States,<=50K\n4,Local-gov,137249,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,236157,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,189759,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,239877,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,21175,12th,8,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,67229,Masters,14,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,236391,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,325596,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,83411,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,154227,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,248010,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,198613,Masters,14,Never-married,Exec-managerial,Own-child,White,Male,2,0,2,United-States,<=50K\n4,Self-emp-inc,321529,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,?,168524,HS-grad,9,Married-civ-spouse,?,Own-child,White,Female,0,0,2,United-States,>50K\n2,Private,203079,Bachelors,13,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,284652,HS-grad,9,Divorced,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,201368,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,59840,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,52753,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,513100,Bachelors,13,Married-spouse-absent,Farming-fishing,Not-in-family,Black,Male,0,0,2,?,<=50K\n0,Private,199266,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,196385,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,163205,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,411047,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,?,48574,7th-8th,4,Widowed,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,209440,HS-grad,9,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,3,United-States,<=50K\n1,Private,56964,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,299197,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,240628,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,355313,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,132267,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,174861,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,142443,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,26716,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,185588,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,175029,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,34848,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,411273,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Local-gov,143437,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,357145,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,236861,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,53355,11th,7,Never-married,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,29106,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Federal-gov,213274,Assoc-voc,11,Divorced,Craft-repair,Unmarried,White,Female,2,0,2,United-States,<=50K\n2,Private,22463,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,180497,Bachelors,13,Never-married,Tech-support,Own-child,Black,Female,0,0,1,United-States,<=50K\n3,Private,37306,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,137814,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,447488,5th-6th,3,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,220915,Assoc-voc,11,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,42251,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,162312,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n0,Private,77698,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Private,282951,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,311259,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,65479,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,277256,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,312088,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Local-gov,169719,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,270276,9th,5,Separated,?,Not-in-family,White,Female,1,0,2,United-States,<=50K\n4,?,172744,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,96869,12th,8,Never-married,Priv-house-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,237943,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,119751,Masters,14,Never-married,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,3,Thailand,<=50K\n1,Private,236861,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,201092,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,147215,Assoc-voc,11,Divorced,Tech-support,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,152373,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,227968,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,Haiti,<=50K\n1,Private,362617,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,103435,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,281412,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,105239,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,<=50K\n0,Private,230165,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,177493,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n0,Federal-gov,104443,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,?,110342,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,3,United-States,<=50K\n1,Private,143385,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,107189,HS-grad,9,Married-civ-spouse,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,212944,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n2,State-gov,138634,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,99970,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,203717,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,313956,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,177937,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,?,<=50K\n1,Private,193868,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,250939,Some-college,10,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,Federal-gov,57629,Some-college,10,Divorced,Tech-support,Not-in-family,Black,Male,2,0,2,United-States,<=50K\n2,Private,281768,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,State-gov,260782,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,243769,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,1,0,United-States,<=50K\n3,Private,109937,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,134890,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,100293,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,132179,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,116372,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,255412,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,2,United-States,>50K\n4,?,195789,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,342400,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,65481,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,169085,11th,7,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,177221,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,65325,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,118944,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,State-gov,149337,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,237868,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,106183,HS-grad,9,Never-married,Other-service,Own-child,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n2,Private,226388,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n0,Private,220754,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-inc,204021,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,347391,Some-college,10,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Federal-gov,413930,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,174201,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,145917,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,241797,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,265168,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Private,171234,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,178452,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,157857,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,Federal-gov,512864,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,296462,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,171615,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,214071,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,172421,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,195488,10th,6,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,316841,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,236267,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,0,1,2,United-States,<=50K\n1,Private,236543,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,El-Salvador,>50K\n0,Private,318483,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,163756,Assoc-voc,11,Never-married,Farming-fishing,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,238186,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Other-relative,White,Male,0,3,3,United-States,<=50K\n2,Private,329980,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,169269,11th,7,Never-married,Handlers-cleaners,Other-relative,White,Male,0,2,2,Puerto-Rico,<=50K\n2,Local-gov,34744,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,98114,HS-grad,9,Divorced,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,109667,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,263690,Bachelors,13,Never-married,Prof-specialty,Unmarried,Black,Male,0,0,2,?,<=50K\n1,Private,147430,HS-grad,9,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,224238,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,212894,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,136708,HS-grad,9,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,1,0,4,Vietnam,<=50K\n4,Local-gov,38573,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,197200,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,182796,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,184527,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,2,0,2,United-States,>50K\n1,Private,145231,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,1,United-States,<=50K\n3,Private,43354,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,?,318865,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,355712,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,98776,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,257557,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,102258,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,170287,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,243409,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,55608,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,248057,HS-grad,9,Separated,Handlers-cleaners,Own-child,White,Male,0,0,2,Puerto-Rico,<=50K\n1,Private,95530,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,54038,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,161245,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,388725,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,71807,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,?,>50K\n0,Private,228216,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,303121,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,78020,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,249254,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,2,0,2,United-States,<=50K\n1,Private,87218,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,304299,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,196234,9th,5,Divorced,Other-service,Unmarried,White,Female,0,0,3,Dominican-Republic,<=50K\n4,Private,197875,10th,6,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,48063,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,253596,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,39257,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,56150,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,179415,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,252445,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,275918,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,106562,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,198654,HS-grad,9,Divorced,Exec-managerial,Unmarried,Black,Female,4,0,2,United-States,>50K\n4,Private,107318,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,181896,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,106014,Assoc-voc,11,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,?,319993,HS-grad,9,Widowed,?,Unmarried,White,Female,0,0,2,Mexico,<=50K\n0,Private,197997,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,173542,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,207564,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,224462,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,123361,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,United-States,>50K\n1,Private,90409,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,165001,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n1,Federal-gov,149573,Assoc-acdm,12,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,249456,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,149898,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,292985,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,50223,Some-college,10,Divorced,Handlers-cleaners,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,400074,Some-college,10,Married-civ-spouse,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,197399,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,209547,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,52433,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,355978,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,48214,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,190873,10th,6,Divorced,Other-service,Unmarried,White,Female,0,0,2,Germany,<=50K\n0,Private,278390,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,203217,7th-8th,4,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,279175,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,194590,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,198813,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,187294,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,302903,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,1,2,United-States,<=50K\n0,Private,154835,HS-grad,9,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n4,?,73402,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,100345,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,126320,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,142226,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,112076,Doctorate,16,Married-AF-spouse,Exec-managerial,Wife,White,Female,0,1,1,United-States,>50K\n3,Private,225339,HS-grad,9,Widowed,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,211208,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,200808,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,Columbia,<=50K\n3,Private,220618,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,210493,11th,7,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,369734,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,27898,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,138193,Bachelors,13,Divorced,Prof-specialty,Other-relative,White,Female,0,0,3,United-States,>50K\n1,Private,224234,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,188741,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,183772,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,?,155041,HS-grad,9,Never-married,?,Own-child,White,Female,1,0,2,United-States,<=50K\n2,Private,79586,HS-grad,9,Separated,Machine-op-inspct,Own-child,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n2,Private,355781,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Japan,>50K\n4,?,156158,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,116358,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n2,Private,59287,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,138868,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,185885,Assoc-acdm,12,Never-married,Tech-support,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,Private,40299,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,500068,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n2,Private,51494,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,179481,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,365907,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,284343,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,204862,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,272476,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,175130,11th,7,Divorced,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,118941,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,164339,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,1,0,4,United-States,<=50K\n0,?,213291,Assoc-acdm,12,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,55457,10th,6,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,280292,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,446894,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n2,State-gov,67083,Some-college,10,Married-civ-spouse,Prof-specialty,Other-relative,Asian-Pac-Islander,Male,0,0,2,Cambodia,<=50K\n3,Self-emp-inc,159219,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Self-emp-inc,168539,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,88145,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,Other,Male,0,0,2,?,<=50K\n1,Private,150309,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,122999,Some-college,10,Never-married,Tech-support,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,302195,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,210982,Assoc-voc,11,Separated,Adm-clerical,Unmarried,Black,Female,1,0,2,United-States,<=50K\n2,Private,177140,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,113838,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,97165,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,104301,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,192028,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,115931,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,147280,HS-grad,9,Never-married,Other-service,Own-child,Other,Male,0,0,0,United-States,<=50K\n1,Private,185433,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,>50K\n1,Private,599057,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,?,50626,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,71467,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,183977,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,?,26586,10th,6,Married-spouse-absent,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,196858,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,160594,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,65278,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,102258,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,196947,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,233150,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,42857,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,118059,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,169262,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,<=50K\n1,Private,95108,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,161092,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,345253,Some-college,10,Never-married,Adm-clerical,Not-in-family,Other,Male,1,0,2,United-States,<=50K\n2,State-gov,111275,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,178878,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,157237,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,155664,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,322658,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,208503,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,223326,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,2,1,United-States,<=50K\n2,Private,20308,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,124751,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,113364,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Germany,<=50K\n3,State-gov,196900,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,168170,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n2,Private,205338,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,98806,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,226452,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,479179,11th,7,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,471990,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,44728,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,33117,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,264876,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,47235,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,293628,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,0,?,<=50K\n1,Private,193122,HS-grad,9,Divorced,Sales,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,149347,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,Poland,>50K\n0,Private,129172,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,1,0,2,United-States,<=50K\n4,Self-emp-not-inc,151255,Some-college,10,Widowed,Farming-fishing,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Private,98886,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,2,0,2,Mexico,<=50K\n0,Private,238673,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,137991,Some-college,10,Married-AF-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,85942,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,85783,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,174789,Bachelors,13,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,457162,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Private,176026,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n4,Private,88594,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,311101,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,273989,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,370242,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,194630,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,313817,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,195843,Assoc-voc,11,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,203160,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,175856,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,75435,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,State-gov,291676,9th,5,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,192869,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,124464,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,98776,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,107960,5th-6th,3,Never-married,Machine-op-inspct,Other-relative,Asian-Pac-Islander,Male,0,0,2,Laos,<=50K\n4,State-gov,312286,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,48520,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,224541,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,Mexico,<=50K\n2,Local-gov,237713,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,309990,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,37019,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,48976,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,2,0,United-States,>50K\n0,Private,170183,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,142988,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,Federal-gov,163205,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,455379,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,104423,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,104936,10th,6,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,542610,HS-grad,9,Never-married,Transport-moving,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,208117,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,105141,Some-college,10,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,156120,5th-6th,3,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,3,?,<=50K\n0,?,38455,HS-grad,9,Never-married,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,213339,HS-grad,9,Separated,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,177989,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,<=50K\n4,State-gov,107732,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,Other,Male,0,0,2,Columbia,<=50K\n1,Private,312403,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,176992,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,84294,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,143062,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,139671,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,116814,HS-grad,9,Widowed,Adm-clerical,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,37778,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,240900,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,202652,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,52187,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,349751,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Male,1,0,3,United-States,<=50K\n0,Local-gov,238384,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,209844,Some-college,10,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,333301,10th,6,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,214974,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,113453,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,162238,HS-grad,9,Widowed,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,98779,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,165001,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,78528,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,353881,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,251396,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,178100,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,Private,416165,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,177536,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,England,>50K\n2,Private,203717,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,107231,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,106448,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,196816,Assoc-voc,11,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,191342,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,3,Philippines,>50K\n0,Private,170194,11th,7,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,113587,10th,6,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,72425,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,217480,Some-college,10,Separated,Adm-clerical,Not-in-family,Black,Male,3,0,2,United-States,>50K\n3,Private,120914,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,33945,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,199753,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,144284,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,53833,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,151158,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,125577,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,242323,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,State-gov,195181,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n4,?,145748,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,341672,Some-college,10,Never-married,Adm-clerical,Other-relative,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n1,Private,116369,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,113446,5th-6th,3,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Self-emp-not-inc,121285,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,25124,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,4,4,United-States,<=50K\n1,Private,182274,HS-grad,9,Separated,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,103548,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,38434,Masters,14,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,317313,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,177543,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,139834,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,118478,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,3,United-States,>50K\n1,Self-emp-not-inc,147951,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,201734,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,198196,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,141876,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,>50K\n0,Private,325179,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,143774,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,Germany,>50K\n0,Private,152328,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,4,0,3,United-States,>50K\n1,Private,479600,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,180599,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,>50K\n0,Private,448026,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,1,0,1,United-States,<=50K\n2,Private,300333,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Cuba,<=50K\n2,Local-gov,184105,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,202084,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,29515,HS-grad,9,Divorced,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,247328,5th-6th,3,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,188246,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,105788,HS-grad,9,Separated,Exec-managerial,Unmarried,Black,Female,2,0,1,United-States,<=50K\n0,Self-emp-inc,352640,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,132576,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,128829,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,93140,HS-grad,9,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,155965,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,335902,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,<=50K\n0,Private,158522,Some-college,10,Never-married,Machine-op-inspct,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n3,Private,174806,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,32207,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,43607,Bachelors,13,Widowed,Adm-clerical,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,State-gov,168224,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,180318,11th,7,Separated,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,311376,Some-college,10,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,101562,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,267859,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,Cuba,<=50K\n2,Private,86143,Assoc-voc,11,Never-married,Craft-repair,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Local-gov,217304,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,410034,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,293073,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n0,?,39493,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,247425,Some-college,10,Never-married,Sales,Not-in-family,Black,Male,0,0,2,Haiti,<=50K\n3,Private,128338,7th-8th,4,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,189344,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,366154,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,163051,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,<=50K\n1,Private,437200,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,323798,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,182570,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,200318,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,48520,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,130007,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,166248,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,203554,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n4,?,192715,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,2,0,United-States,<=50K\n1,Self-emp-inc,291333,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n3,Self-emp-not-inc,39140,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,266439,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,126840,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,166419,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,287129,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,206827,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,173888,HS-grad,9,Married-spouse-absent,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,State-gov,253250,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,337239,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n4,?,113445,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Federal-gov,201127,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,403107,5th-6th,3,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n4,?,179078,HS-grad,9,Widowed,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,?,126402,11th,7,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Federal-gov,223892,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n4,State-gov,191318,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,394708,HS-grad,9,Never-married,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,119474,HS-grad,9,Never-married,Sales,Own-child,White,Female,1,0,1,United-States,<=50K\n0,Private,419984,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,?,164730,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,190678,HS-grad,9,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,?,<=50K\n1,Local-gov,197897,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,England,<=50K\n1,Private,286675,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,191986,10th,6,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,90525,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,4,0,0,United-States,>50K\n1,Private,56150,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,248327,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,90860,Some-college,10,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n1,Federal-gov,104858,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,?,228686,Some-college,10,Divorced,?,Own-child,White,Male,0,2,1,United-States,<=50K\n3,Private,196707,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,29430,HS-grad,9,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,54038,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,3,0,United-States,>50K\n4,Private,281025,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,258832,HS-grad,9,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Male,0,0,0,Philippines,<=50K\n0,Private,131220,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,?,190454,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,315971,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,?,>50K\n3,Private,265097,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,185057,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,169557,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,2,0,2,United-States,<=50K\n1,Self-emp-inc,333636,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,4,United-States,<=50K\n0,Private,181652,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,34307,Some-college,10,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Federal-gov,198813,Bachelors,13,Never-married,Adm-clerical,Unmarried,Black,Female,0,1,2,United-States,<=50K\n4,Local-gov,240030,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,226089,10th,6,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,190941,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,160261,Some-college,10,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Male,1,0,3,England,<=50K\n1,Private,208458,HS-grad,9,Divorced,Sales,Unmarried,Other,Female,0,0,2,Mexico,<=50K\n3,Private,112761,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,67716,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n4,Self-emp-not-inc,121076,Doctorate,16,Divorced,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,182556,12th,8,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,231348,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,55395,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,344073,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,<=50K\n2,Federal-gov,318912,Assoc-voc,11,Divorced,Adm-clerical,Own-child,Black,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,237546,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,346341,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,?,<=50K\n3,Private,305090,Some-college,10,Separated,Sales,Other-relative,White,Female,0,0,3,United-States,<=50K\n0,Local-gov,198478,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,321435,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,259716,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,Mexico,<=50K\n2,Private,191547,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,?,171482,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,225927,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,256504,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,168526,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,44489,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,159449,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,387540,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,314819,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Private,34722,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,125831,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,4,0,3,United-States,>50K\n0,?,239805,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,264663,11th,7,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,294121,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,83912,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,Mexico,<=50K\n1,Private,241626,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,137815,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,153891,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,83774,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,1,0,2,United-States,<=50K\n4,Private,199067,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n2,Self-emp-inc,57233,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,211517,12th,8,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,132879,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,72776,7th-8th,4,Never-married,Farming-fishing,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,54318,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,?,143582,HS-grad,9,Married-spouse-absent,?,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Taiwan,<=50K\n4,Self-emp-not-inc,174564,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,179579,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,187618,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,2,2,United-States,<=50K\n1,Private,186819,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,60087,Some-college,10,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,28987,9th,5,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n4,Private,187355,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,218555,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,94214,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,2,0,3,Thailand,>50K\n2,Private,204729,Assoc-voc,11,Separated,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,?,281668,Some-college,10,Never-married,?,Other-relative,Black,Female,0,0,2,United-States,<=50K\n0,Private,236696,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,Taiwan,<=50K\n1,Private,179747,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,187322,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,116975,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,205950,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,190968,7th-8th,4,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,160458,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,190911,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,85382,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,129793,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,270181,Assoc-acdm,12,Married-spouse-absent,Other-service,Unmarried,Black,Female,0,0,3,United-States,<=50K\n0,Private,243723,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,168113,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,652784,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,315984,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,311311,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,111336,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,100252,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Own-child,Asian-Pac-Islander,Male,0,0,3,South,<=50K\n2,State-gov,186990,Prof-school,15,Widowed,Prof-specialty,Not-in-family,Other,Female,0,0,3,United-States,>50K\n2,State-gov,241633,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,>50K\n3,Federal-gov,252616,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Federal-gov,46366,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,3,United-States,>50K\n3,Local-gov,266138,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,32732,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,361978,HS-grad,9,Divorced,Craft-repair,Unmarried,Black,Female,1,0,2,United-States,<=50K\n0,State-gov,77661,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,4,2,United-States,>50K\n2,Self-emp-inc,60087,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,197550,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Private,170701,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,159822,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,211494,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,2,3,United-States,<=50K\n1,Federal-gov,340899,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,224752,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,102568,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,220690,11th,7,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,303170,Some-college,10,Never-married,Priv-house-serv,Own-child,White,Female,0,0,1,United-States,<=50K\n0,?,143331,11th,7,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,192162,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,201635,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,55263,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,State-gov,133728,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,347025,7th-8th,4,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,110998,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,122347,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,127875,11th,7,Never-married,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,167534,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,1,United-States,<=50K\n1,State-gov,152560,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,265144,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,302679,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,290763,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,86837,Preschool,1,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Private,147118,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,103575,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,169469,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,189334,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,139571,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,111545,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,72776,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,4,0,0,United-States,<=50K\n3,Private,307973,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,211666,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,143766,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,112812,HS-grad,9,Married-civ-spouse,Protective-serv,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,43290,10th,6,Divorced,Other-service,Not-in-family,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n4,Private,111385,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,145744,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,126754,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,White,Male,0,0,2,Italy,<=50K\n3,State-gov,54260,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n2,State-gov,34895,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,166740,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,143062,Bachelors,13,Never-married,Other-service,Own-child,White,Female,1,0,1,United-States,<=50K\n1,Local-gov,191957,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,109456,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,198183,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,157449,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,53874,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,Cuba,<=50K\n2,Private,191754,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,320071,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,1,3,United-States,<=50K\n0,Private,164574,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,185870,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n2,State-gov,165745,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,40323,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,199378,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,289889,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,152200,Some-college,10,Married-civ-spouse,Protective-serv,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,198231,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,28865,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,26915,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,?,108926,Some-college,10,Married-civ-spouse,?,Husband,Black,Male,0,0,0,United-States,<=50K\n0,State-gov,204034,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,243368,Preschool,1,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n1,Local-gov,191088,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,1,0,3,United-States,<=50K\n1,Local-gov,194515,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,28984,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,3,1,United-States,<=50K\n3,Private,125892,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,37932,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,England,<=50K\n4,Private,249751,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,191948,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,97306,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,176178,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,142764,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,148431,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,State-gov,148380,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,1,0,0,United-States,<=50K\n2,Private,314890,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,177493,12th,8,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,327435,Masters,14,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Private,275967,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,176520,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,186463,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,50380,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,323987,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,192445,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,142851,9th,5,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n0,State-gov,42750,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,199011,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,98644,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,?,>50K\n2,Private,173963,11th,7,Separated,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,State-gov,284763,Some-college,10,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,108775,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,233312,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,Poland,<=50K\n3,Private,197826,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,123007,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Male,0,3,1,United-States,<=50K\n1,Private,264012,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,214247,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n0,Private,200121,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,138069,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,<=50K\n0,Private,33551,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,89083,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,369438,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,1,United-States,>50K\n4,Private,93997,Bachelors,13,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,169699,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,115429,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,State-gov,96652,Assoc-voc,11,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,103759,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,54422,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,107215,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,194630,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,102142,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,134638,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,46920,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,207973,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Canada,<=50K\n0,Private,208946,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,252431,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,251730,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,301251,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,?,137632,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,2,Haiti,<=50K\n2,Private,197274,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,106043,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,195636,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,237956,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,0,2,Cuba,<=50K\n4,Private,31532,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,276157,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,?,207668,Bachelors,13,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,142921,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,217039,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Local-gov,259169,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,409173,HS-grad,9,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,3,Puerto-Rico,>50K\n1,State-gov,73161,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,<=50K\n1,Private,241431,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,151764,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n3,Federal-gov,131726,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Private,334291,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,67243,1st-4th,2,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,Portugal,>50K\n2,Private,370261,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,573446,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n1,Local-gov,189775,12th,8,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,312206,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,86939,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,221757,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,213140,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,308673,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,149069,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n3,Private,69345,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,112158,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,4,United-States,>50K\n1,Private,386299,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,61838,11th,7,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,290286,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,143069,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,145714,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,285570,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,399705,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,186224,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,172918,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,198270,HS-grad,9,Married-civ-spouse,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,307767,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,208630,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,169002,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,?,186369,9th,5,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,99203,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,Private,197836,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,140136,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,1,United-States,>50K\n0,Local-gov,402230,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,167159,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,116409,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,105161,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,263908,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,189565,HS-grad,9,Divorced,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,224059,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,27457,Masters,14,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,240988,Assoc-acdm,12,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,41017,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,214498,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,186361,Assoc-voc,11,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,165799,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,266983,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,165416,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,226497,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,99516,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,177287,Some-college,10,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,27385,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,147452,10th,6,Never-married,Sales,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,144334,HS-grad,9,Never-married,Exec-managerial,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,217926,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,153312,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,>50K\n0,Private,126822,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,51415,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,171886,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,216319,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,>50K\n3,Private,279337,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,115085,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,453233,10th,6,Separated,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,400943,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,226883,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Private,138050,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,204585,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,220558,11th,7,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,Private,198841,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,67244,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,75785,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,85423,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,134724,Assoc-voc,11,Married-civ-spouse,Other-service,Wife,White,Female,1,0,2,United-States,>50K\n4,Private,109567,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,132053,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,157413,1st-4th,2,Divorced,Farming-fishing,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,238567,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,153133,12th,8,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,186256,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,260265,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,131819,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,141584,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,245121,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,<=50K\n4,Private,22502,7th-8th,4,Divorced,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,23778,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,2,0,2,United-States,<=50K\n3,Private,380922,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n2,Self-emp-not-inc,173651,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,191137,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,217006,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,22211,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Local-gov,57711,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Germany,<=50K\n4,Self-emp-not-inc,123190,9th,5,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,4,United-States,>50K\n2,Private,110028,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,4,0,3,United-States,>50K\n3,Self-emp-not-inc,174102,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Male,4,0,3,United-States,>50K\n4,Self-emp-not-inc,183249,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n0,?,240183,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,49665,Assoc-voc,11,Divorced,Machine-op-inspct,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,210438,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,53373,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,295706,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Local-gov,273457,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,165949,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,142152,11th,7,Never-married,Transport-moving,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,189203,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,State-gov,179089,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,53956,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,<=50K\n4,Self-emp-inc,77816,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Local-gov,74593,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,196158,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,3,0,3,United-States,>50K\n0,Private,28544,11th,7,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,662460,10th,6,Widowed,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,152328,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,120277,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,180278,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,?,426589,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,111558,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,3,1,United-States,>50K\n0,?,243203,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,Puerto-Rico,<=50K\n2,Self-emp-not-inc,195686,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Private,226696,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n3,?,118058,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,172237,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,114074,Assoc-voc,11,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,171589,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,?,>50K\n0,?,184271,Assoc-acdm,12,Never-married,?,Own-child,White,Female,1,0,0,United-States,<=50K\n4,Local-gov,218724,HS-grad,9,Widowed,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,134190,10th,6,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,181964,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,3,United-States,>50K\n2,Private,385330,7th-8th,4,Separated,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Private,242406,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Dominican-Republic,<=50K\n2,Federal-gov,107584,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,127892,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,160261,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n3,?,285200,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,1,United-States,<=50K\n3,Private,153254,HS-grad,9,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,294672,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,145924,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,280005,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,66893,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,47039,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,36186,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,159383,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,192010,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,216479,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,274680,Preschool,1,Separated,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,211345,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Nicaragua,<=50K\n0,Private,203561,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,170815,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,200565,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,89655,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,234195,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,98466,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,191437,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,77792,HS-grad,9,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n0,Local-gov,134181,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,State-gov,195690,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Self-emp-inc,94869,Masters,14,Divorced,Prof-specialty,Not-in-family,Black,Male,2,0,2,United-States,>50K\n2,State-gov,267464,Some-college,10,Separated,Tech-support,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,?,257006,11th,7,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,81010,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,54260,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n4,Federal-gov,171995,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,1,0,2,United-States,<=50K\n2,Private,245665,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,35855,11th,7,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,State-gov,189838,Some-college,10,Divorced,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,629900,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,>50K\n1,Private,84119,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,47343,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,103614,10th,6,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,303092,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,124520,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,220857,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,State-gov,247481,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,283281,7th-8th,4,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,25610,Preschool,1,Never-married,Handlers-cleaners,Not-in-family,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n2,Private,13769,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,283969,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,Mexico,<=50K\n0,Private,185522,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,?,173309,7th-8th,4,Widowed,?,Not-in-family,White,Female,1,0,0,United-States,<=50K\n2,Private,144005,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,State-gov,205949,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Private,158948,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,240357,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Private,243929,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,201700,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,208402,Some-college,10,Divorced,Adm-clerical,Unmarried,Other,Female,2,0,2,Mexico,<=50K\n0,Private,120599,11th,7,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,231826,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,499971,11th,7,Widowed,Handlers-cleaners,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,227972,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,Germany,>50K\n4,State-gov,299680,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,231822,10th,6,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,185459,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,90582,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,184615,7th-8th,4,Widowed,Machine-op-inspct,Unmarried,White,Female,1,0,2,United-States,<=50K\n1,Private,173858,HS-grad,9,Never-married,Craft-repair,Other-relative,Asian-Pac-Islander,Male,0,0,1,China,<=50K\n1,Private,132326,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,315037,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,175122,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,239577,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,96975,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,61885,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,185870,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,State-gov,142282,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,334744,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,201600,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,34378,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,180271,HS-grad,9,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,123833,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,213304,5th-6th,3,Separated,Other-service,Unmarried,White,Female,0,0,2,El-Salvador,<=50K\n1,Private,296538,9th,5,Divorced,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,391937,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,175339,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,4,2,United-States,>50K\n1,Private,60726,Masters,14,Never-married,Exec-managerial,Not-in-family,Black,Male,2,0,3,United-States,<=50K\n2,Private,191103,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,32174,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,176219,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n1,Self-emp-not-inc,294434,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,310885,7th-8th,4,Never-married,Other-service,Own-child,White,Male,0,0,2,Mexico,<=50K\n1,Private,171133,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,162951,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,223934,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,23940,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,88073,Bachelors,13,Divorced,Tech-support,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,57371,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,73684,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,4,United-States,>50K\n2,Private,107164,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,120126,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n2,Self-emp-not-inc,54611,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,131117,HS-grad,9,Widowed,Transport-moving,Unmarried,White,Female,0,0,2,Puerto-Rico,<=50K\n4,Federal-gov,101676,10th,6,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,403965,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,177083,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Canada,<=50K\n3,Private,173987,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,352080,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n1,?,102400,HS-grad,9,Married-civ-spouse,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,378426,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,Columbia,<=50K\n2,Private,210857,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n4,Private,165775,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,295896,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,238944,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,149640,Some-college,10,Separated,Craft-repair,Unmarried,White,Male,1,0,2,Honduras,<=50K\n4,?,106175,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n3,Private,191320,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,134771,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n3,?,295538,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,120277,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,82393,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n3,Private,146121,5th-6th,3,Married-spouse-absent,Machine-op-inspct,Unmarried,Asian-Pac-Islander,Female,0,0,0,Vietnam,<=50K\n1,Private,162544,7th-8th,4,Never-married,Priv-house-serv,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,216178,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,248694,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,219140,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,360401,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,2,3,United-States,<=50K\n2,Private,319962,HS-grad,9,Divorced,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,115284,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,29702,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,?,107085,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,State-gov,204361,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,218312,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,182332,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,127876,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,316702,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,292549,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,203178,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,180099,10th,6,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,154541,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,95465,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,171839,Masters,14,Married-civ-spouse,Other-service,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,115562,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,State-gov,42478,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,117054,5th-6th,3,Divorced,?,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-inc,124137,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,State-gov,107503,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,<=50K\n0,Private,70261,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,367037,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n2,Private,258339,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,Iran,<=50K\n2,Self-emp-not-inc,269509,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,State-gov,301302,Doctorate,16,Married-spouse-absent,Tech-support,Not-in-family,White,Male,0,0,0,?,<=50K\n3,Private,369367,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,224582,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,343591,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,?,53540,11th,7,Divorced,?,Unmarried,Black,Female,0,0,0,United-States,<=50K\n3,Private,153536,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,91262,HS-grad,9,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n2,Private,238574,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,247558,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,188278,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,194995,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,168782,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,287229,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,202153,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n4,?,230586,10th,6,Widowed,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,115358,HS-grad,9,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,78283,12th,8,Never-married,Transport-moving,Unmarried,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n4,Federal-gov,168854,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,222011,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,>50K\n4,Private,157749,Bachelors,13,Widowed,Exec-managerial,Unmarried,White,Male,0,4,2,United-States,>50K\n1,Private,203814,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,74660,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,Canada,>50K\n1,Private,292603,Some-college,10,Divorced,Other-service,Not-in-family,Black,Female,0,0,1,Dominican-Republic,<=50K\n2,Private,172364,1st-4th,2,Married-civ-spouse,Transport-moving,Wife,White,Female,2,0,3,United-States,<=50K\n1,Private,305619,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,157990,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,120243,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,296991,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,England,>50K\n0,Private,174845,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,180475,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,152652,11th,7,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,172941,Bachelors,13,Never-married,Prof-specialty,Unmarried,Black,Male,0,0,0,United-States,<=50K\n2,Federal-gov,450770,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,166003,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,204935,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,60949,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,181132,Masters,14,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,408988,Some-college,10,Never-married,Sales,Own-child,White,Female,1,0,1,United-States,<=50K\n3,Private,169515,10th,6,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,250087,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,208613,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,225172,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,?,125905,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,165007,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,165346,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,37655,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,172047,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,178530,12th,8,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,347491,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,208049,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,0,1,2,United-States,<=50K\n0,Private,111019,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,163826,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,117023,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,281982,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,150025,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n3,Private,176215,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,166062,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,28568,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,53833,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,219967,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,309017,Some-college,10,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,353083,Some-college,10,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,257992,Assoc-voc,11,Never-married,Sales,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,283174,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n2,Self-emp-not-inc,185413,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,207513,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,56213,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,100961,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,51700,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,199224,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,70055,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,183683,10th,6,Married-spouse-absent,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,45868,7th-8th,4,Separated,Priv-house-serv,Not-in-family,Other,Female,0,0,1,Mexico,<=50K\n2,Private,94706,Bachelors,13,Never-married,Prof-specialty,Own-child,Amer-Indian-Eskimo,Male,4,0,2,United-States,>50K\n3,Private,322183,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,>50K\n1,Self-emp-not-inc,226976,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,262678,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,135056,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,4,0,2,United-States,>50K\n1,?,148380,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,1,0,3,United-States,>50K\n2,Private,270710,9th,5,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,166220,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,229803,HS-grad,9,Married-spouse-absent,Transport-moving,Not-in-family,Black,Male,0,0,3,Haiti,<=50K\n4,Self-emp-inc,172652,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,204796,10th,6,Separated,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,186634,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,106672,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,135339,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,>50K\n3,State-gov,287547,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n0,?,197583,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,159737,HS-grad,9,Separated,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Local-gov,252217,12th,8,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,202466,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,123031,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,226296,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,232036,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,233779,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n0,?,152328,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,481987,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,107563,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,184806,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,188850,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,127573,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,72443,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,142080,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,>50K\n3,Private,143353,HS-grad,9,Divorced,Priv-house-serv,Unmarried,Black,Female,0,0,0,United-States,<=50K\n4,Private,172433,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,67874,Some-college,10,Divorced,Sales,Unmarried,White,Male,0,1,3,United-States,<=50K\n2,Private,415578,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,370498,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,140513,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,193882,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,289909,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,284078,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Self-emp-not-inc,83953,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,167380,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,112761,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,420040,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Federal-gov,42900,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,32086,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,291181,9th,5,Never-married,Other-service,Own-child,White,Female,0,0,3,Mexico,<=50K\n0,Private,71009,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n4,Federal-gov,200764,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,?,133336,10th,6,Divorced,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,166193,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,240229,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,334032,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,184901,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,132912,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,217290,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,184655,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,242739,Bachelors,13,Divorced,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,279344,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,3,United-States,>50K\n4,Private,166691,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,154374,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,31740,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,129396,11th,7,Never-married,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n3,Private,195015,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,187431,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,259087,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,62428,Some-college,10,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,0,United-States,<=50K\n0,Private,77572,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Asian-Pac-Islander,Female,0,0,1,South,<=50K\n1,Private,245402,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,201117,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,36779,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,177028,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,106306,Some-college,10,Never-married,Craft-repair,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,101582,7th-8th,4,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,158357,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,377850,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,207443,11th,7,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,3,United-States,<=50K\n0,Private,130959,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,112497,Bachelors,13,Married-spouse-absent,Exec-managerial,Unmarried,White,Male,2,0,3,United-States,>50K\n2,Private,190545,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,114292,11th,7,Never-married,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,75171,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,312939,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,52870,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,174947,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,106069,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,298696,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,392812,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,117932,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,135527,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,135158,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,54260,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,28171,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,150324,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,109494,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,204209,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n0,Private,328167,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,157617,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,?,<=50K\n4,Self-emp-inc,180955,5th-6th,3,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,478373,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,245090,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,209900,10th,6,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,228649,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,190297,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,44780,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,>50K\n3,State-gov,32372,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,137184,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,366425,Doctorate,16,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,160307,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,3,2,United-States,<=50K\n4,Private,170480,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,224393,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,173212,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,86646,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,2,0,3,United-States,<=50K\n0,Private,108683,Some-college,10,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,70021,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,192939,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,144086,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,97614,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,260198,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,486194,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,Guatemala,<=50K\n0,Private,112225,Some-college,10,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n3,Private,164200,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,52401,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,195821,Doctorate,16,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,2,United-States,>50K\n1,Private,108140,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,187981,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,108126,9th,5,Separated,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,168212,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Self-emp-not-inc,198613,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,?,>50K\n0,Private,52098,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,509364,Some-college,10,Married-civ-spouse,Adm-clerical,Own-child,White,Male,0,0,2,United-States,>50K\n4,?,128614,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n0,Local-gov,238384,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,317434,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,158688,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,267412,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,391074,10th,6,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,135692,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,78529,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,117700,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,83413,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n2,?,210875,11th,7,Divorced,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,108023,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,103435,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,632733,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,266019,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,0,United-States,<=50K\n1,Private,41210,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,125892,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,Poland,<=50K\n3,Private,135339,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,272372,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,291300,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n2,Local-gov,157473,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,329948,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,264722,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,132853,1st-4th,2,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Local-gov,216586,11th,7,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,504725,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,Mexico,<=50K\n0,State-gov,150083,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,188789,7th-8th,4,Widowed,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,101427,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,1,0,1,United-States,<=50K\n0,Private,103277,Assoc-voc,11,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,191013,Bachelors,13,Widowed,Prof-specialty,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,220667,7th-8th,4,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,188800,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,24361,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,4,0,2,United-States,>50K\n1,Private,82910,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,105586,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,4,0,2,United-States,>50K\n4,Self-emp-not-inc,240294,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Private,21755,Some-college,10,Never-married,Craft-repair,Other-relative,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n4,Private,73522,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,?,<=50K\n2,Private,222450,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,35595,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,239061,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,122473,Masters,14,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,3,3,United-States,>50K\n0,Private,190290,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,Canada,<=50K\n4,Private,193375,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,148576,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,72896,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,180299,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,221550,Bachelors,13,Separated,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,183011,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,0,Haiti,<=50K\n1,?,370209,HS-grad,9,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,298449,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,<=50K\n0,Local-gov,300812,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,173938,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,282172,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,87300,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,64520,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,119567,Masters,14,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,117310,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n1,State-gov,28738,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n0,?,99695,10th,6,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,366089,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,234397,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,132686,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,196963,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,State-gov,46492,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,274245,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,360032,10th,6,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,144778,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,142245,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,178074,Masters,14,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,3,2,United-States,>50K\n0,?,218171,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,0,South,<=50K\n1,Local-gov,130242,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,98980,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,284629,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,114591,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Private,134639,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,3,0,2,United-States,>50K\n1,Private,134890,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,199545,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,227282,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,308241,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,>50K\n2,Private,214781,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,>50K\n3,Local-gov,173630,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,348059,Assoc-acdm,12,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n1,Private,208785,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,151809,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,71592,HS-grad,9,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n4,Private,132704,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,250583,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,114765,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,194924,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,73471,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,1,2,United-States,<=50K\n3,Private,250423,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,145441,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,86681,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,188643,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,1,1,United-States,<=50K\n4,Private,68326,Assoc-voc,11,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,382859,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,?,211968,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,Iran,<=50K\n3,Local-gov,132368,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,196123,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,178066,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,1,United-States,<=50K\n2,State-gov,105936,Prof-school,15,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,306639,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,26502,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,204698,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,118057,HS-grad,9,Widowed,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,State-gov,199266,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,248145,HS-grad,9,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,2,Nicaragua,<=50K\n3,Private,239284,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,188738,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,209101,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,197816,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,?,60726,Bachelors,13,Never-married,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,211678,Masters,14,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,304957,HS-grad,9,Widowed,Priv-house-serv,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,278552,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,79303,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,64632,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,560313,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n2,Local-gov,174597,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,139946,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n0,Private,277695,Preschool,1,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-not-inc,138471,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,320615,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,4,2,United-States,<=50K\n3,Private,164954,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,263728,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,3,United-States,<=50K\n2,Private,103980,Some-college,10,Divorced,Prof-specialty,Own-child,White,Male,1,0,1,United-States,<=50K\n1,Private,159442,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,Ireland,<=50K\n1,Federal-gov,113838,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,28171,Masters,14,Married-civ-spouse,Protective-serv,Husband,White,Male,4,0,4,United-States,>50K\n2,Self-emp-not-inc,227253,Preschool,1,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,Mexico,<=50K\n0,Private,211129,Bachelors,13,Never-married,Exec-managerial,Other-relative,White,Female,0,0,3,United-States,>50K\n0,Private,120003,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,245182,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,188767,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,227890,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,131650,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,258725,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,127678,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,110747,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,409246,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,128829,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,170031,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,150917,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,197372,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,43479,Doctorate,16,Divorced,Prof-specialty,Unmarried,White,Male,0,0,4,Canada,>50K\n2,Private,166549,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,119684,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,1,0,1,United-States,<=50K\n2,Private,651702,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,199829,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,2,United-States,<=50K\n0,Private,86745,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Private,644278,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,31137,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,3,United-States,<=50K\n1,Private,104525,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,?,91278,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,111361,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,?,291692,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,228881,HS-grad,9,Never-married,Craft-repair,Other-relative,Other,Male,0,0,2,Puerto-Rico,<=50K\n4,Private,152731,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,178310,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,116360,Assoc-voc,11,Divorced,Tech-support,Other-relative,Black,Female,0,0,0,United-States,<=50K\n0,Private,535852,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Private,30828,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,39518,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,445758,5th-6th,3,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,Mexico,<=50K\n2,Private,222504,Assoc-voc,11,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,357619,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Germany,<=50K\n0,?,121389,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,228847,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,118598,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Private,49893,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,452353,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,285000,Bachelors,13,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,263300,HS-grad,9,Separated,Priv-house-serv,Unmarried,Black,Female,0,0,3,United-States,<=50K\n1,State-gov,132675,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,Germany,<=50K\n1,Private,175232,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,257421,12th,8,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,196227,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,34965,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,475775,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,196857,11th,7,Never-married,Adm-clerical,Own-child,White,Female,1,0,0,United-States,<=50K\n2,Private,159917,9th,5,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,212803,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,118902,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Scotland,<=50K\n0,Local-gov,166827,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,180060,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,47218,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,73541,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,150688,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,India,>50K\n2,Private,207824,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n2,Self-emp-inc,198282,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,206359,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,125659,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n4,Local-gov,129193,Some-college,10,Widowed,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,167457,7th-8th,4,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,455469,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,206891,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,269323,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,187715,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,71376,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,268707,11th,7,Married-civ-spouse,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,215620,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,158438,HS-grad,9,Divorced,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,209629,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,121076,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,97933,9th,5,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,177423,HS-grad,9,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,2,0,2,Philippines,<=50K\n2,Private,185520,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,38321,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,213307,1st-4th,2,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,Mexico,<=50K\n3,Private,328581,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,177368,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,148903,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,385520,HS-grad,9,Widowed,Farming-fishing,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Self-emp-not-inc,193716,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,238899,Bachelors,13,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Private,209993,5th-6th,3,Married-civ-spouse,Priv-house-serv,Wife,White,Female,0,0,2,El-Salvador,<=50K\n3,State-gov,136060,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,192944,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,29927,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,200593,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,311376,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,206814,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,278391,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,Nicaragua,<=50K\n1,Private,364657,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,?,120781,Doctorate,16,Married-spouse-absent,?,Unmarried,Asian-Pac-Islander,Male,0,0,0,India,<=50K\n4,Private,175080,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,?,522241,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,161240,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,162282,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,199419,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,171114,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,208855,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,381030,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,219337,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,180010,HS-grad,9,Divorced,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,189382,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,121712,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,?,192257,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,?,68620,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,352188,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,398874,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,191930,HS-grad,9,Never-married,Other-service,Other-relative,Black,Male,0,0,3,United-States,<=50K\n1,Private,269168,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,?,123536,Some-college,10,Never-married,?,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Private,173651,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,149337,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,146674,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,?,>50K\n4,Private,173483,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,223669,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,182177,Some-college,10,Divorced,Protective-serv,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,109414,Some-college,10,Never-married,Sales,Other-relative,Asian-Pac-Islander,Male,0,0,0,India,<=50K\n3,Self-emp-inc,150917,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n4,Self-emp-not-inc,39128,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,103540,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,110212,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,222450,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,4,2,El-Salvador,<=50K\n0,?,113760,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,?,253717,11th,7,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,306908,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,263871,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,55294,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,174063,Assoc-voc,11,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,State-gov,258735,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,275867,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,154235,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,210448,Some-college,10,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,337908,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,State-gov,205333,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,187447,Some-college,10,Separated,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,153589,9th,5,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,149988,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,398959,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,194096,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,44041,Assoc-acdm,12,Married-spouse-absent,Adm-clerical,Other-relative,White,Male,0,0,3,United-States,<=50K\n0,Private,208946,Assoc-acdm,12,Never-married,Sales,Own-child,White,Male,0,0,1,?,<=50K\n3,Private,202117,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,303129,HS-grad,9,Divorced,Transport-moving,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,215419,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,175069,Some-college,10,Never-married,Sales,Own-child,White,Male,1,0,1,United-States,<=50K\n2,Federal-gov,20469,HS-grad,9,Divorced,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Private,254680,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,4,United-States,<=50K\n2,Self-emp-inc,46385,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,178463,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,229296,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,179352,Assoc-acdm,12,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,177368,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,156015,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,308945,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,119575,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,332689,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,150817,12th,8,Never-married,Protective-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,50941,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Federal-gov,59664,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,2,0,2,United-States,<=50K\n0,Private,56613,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,162372,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n4,Private,77927,5th-6th,3,Divorced,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,?,157278,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,170214,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,Iran,<=50K\n1,Private,76493,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,130431,5th-6th,3,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,State-gov,23037,Some-college,10,Never-married,Other-service,Own-child,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n0,Self-emp-not-inc,176321,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,105449,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,157217,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,382532,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,250918,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n3,State-gov,139268,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,United-States,>50K\n2,Self-emp-not-inc,200352,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,267915,HS-grad,9,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,376474,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,153047,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,154236,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,United-States,>50K\n0,?,261881,11th,7,Never-married,?,Other-relative,Black,Female,0,0,0,United-States,<=50K\n1,Private,427744,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,100580,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,238179,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,4,2,United-States,<=50K\n2,State-gov,272471,Some-college,10,Widowed,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,259058,Masters,14,Divorced,Prof-specialty,Unmarried,White,Male,0,2,2,United-States,<=50K\n2,Private,112656,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,197286,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,26145,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,314440,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,57691,HS-grad,9,Separated,Exec-managerial,Not-in-family,White,Male,0,4,4,United-States,<=50K\n1,Private,301251,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,243410,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,119008,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,169435,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,200327,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n4,Private,31501,Assoc-voc,11,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,223327,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,2,2,United-States,<=50K\n3,Private,191130,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,?,191561,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,245724,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,194134,Assoc-voc,11,Never-married,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n0,Private,140764,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,1,2,United-States,<=50K\n2,Self-emp-not-inc,189941,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,149368,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,237546,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,211051,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,307468,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,27847,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,39263,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,1,0,1,United-States,<=50K\n3,Local-gov,183610,Masters,14,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,235847,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,32627,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n2,Private,42026,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,72808,11th,7,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,377113,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,176389,Bachelors,13,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,71075,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,110327,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,392167,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,130808,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,351757,10th,6,Never-married,Other-service,Unmarried,White,Male,0,0,1,El-Salvador,<=50K\n0,Self-emp-not-inc,345420,7th-8th,4,Never-married,Farming-fishing,Other-relative,White,Male,0,0,3,United-States,<=50K\n3,Private,220984,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,236834,9th,5,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,1,Mexico,<=50K\n2,Private,153489,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,330850,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,337195,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,214816,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,92863,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,226894,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Male,4,0,2,United-States,>50K\n3,Private,40666,Bachelors,13,Divorced,Exec-managerial,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,?,142043,11th,7,Never-married,?,Other-relative,White,Male,0,0,0,United-States,<=50K\n4,Private,105239,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,112763,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,219220,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,2,United-States,<=50K\n2,State-gov,168223,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,175639,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,167482,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,178417,HS-grad,9,Married-civ-spouse,Protective-serv,Own-child,White,Male,0,0,2,United-States,>50K\n1,Local-gov,33604,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n1,Private,62082,Bachelors,13,Never-married,Sales,Own-child,Other,Male,0,0,2,United-States,<=50K\n1,Private,149902,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,74784,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-inc,54260,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,119986,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,165218,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Local-gov,192853,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Other,Male,0,0,2,Jamaica,>50K\n1,Private,56299,11th,7,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,?,394690,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,208406,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,165827,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,249559,HS-grad,9,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,151382,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,161141,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,57092,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,116927,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,133876,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,229259,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,338162,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,38948,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,169276,HS-grad,9,Divorced,Machine-op-inspct,Other-relative,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,364803,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,4,United-States,<=50K\n2,Private,302677,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,1,3,United-States,<=50K\n1,Private,235485,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,91189,Some-college,10,Never-married,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,149596,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,89211,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,State-gov,241854,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,213351,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,74631,9th,5,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,128696,11th,7,Married-civ-spouse,Tech-support,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Private,141069,HS-grad,9,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,?,347248,10th,6,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,176947,7th-8th,4,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,274200,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,94036,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,?,188431,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,176802,11th,7,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,258973,Some-college,10,Widowed,?,Not-in-family,White,Female,1,0,0,United-States,<=50K\n3,Private,235646,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,175883,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,154430,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,258126,9th,5,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,Cuba,<=50K\n1,Federal-gov,337575,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,308241,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,?,162165,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,298623,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,270935,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,338833,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,?,341631,HS-grad,9,Never-married,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,233786,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,366876,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,183279,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,29606,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,>50K\n0,Local-gov,137300,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,184070,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,188610,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,41356,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,145409,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,?,>50K\n0,?,287413,HS-grad,9,Never-married,?,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Local-gov,100011,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,119673,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,140782,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,4,0,United-States,>50K\n1,Private,193246,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,420526,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,34574,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,400061,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,2,Philippines,>50K\n3,Private,107009,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,Portugal,<=50K\n0,Private,33551,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,121640,Some-college,10,Divorced,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,179524,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,473206,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,54422,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,202416,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,158685,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,76534,HS-grad,9,Married-civ-spouse,Exec-managerial,Other-relative,Asian-Pac-Islander,Female,0,0,1,China,<=50K\n2,State-gov,218948,Doctorate,16,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,Jamaica,<=50K\n2,Self-emp-not-inc,175120,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,100154,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,160510,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,?,>50K\n4,Private,223214,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n2,Private,79488,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,136986,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,202339,11th,7,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,205737,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,80145,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,303579,Some-college,10,Never-married,?,Own-child,White,Male,0,2,0,United-States,<=50K\n3,Private,235108,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,State-gov,201363,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Self-emp-inc,244172,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,>50K\n4,?,99349,Bachelors,13,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Federal-gov,338242,Assoc-voc,11,Widowed,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,29702,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,146352,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,215182,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,?,133456,Assoc-acdm,12,Widowed,?,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,79586,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n1,Private,181822,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,123809,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,1,United-States,>50K\n2,Private,35429,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,1,0,2,United-States,<=50K\n3,Private,151584,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,303725,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,194404,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,224229,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,236396,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,40255,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,State-gov,214881,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Honduras,>50K\n3,Private,332465,Some-college,10,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,165218,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,34506,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,79190,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,79001,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,137876,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Federal-gov,40103,10th,6,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Self-emp-inc,145964,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,268282,7th-8th,4,Married-spouse-absent,Farming-fishing,Other-relative,White,Male,0,0,1,Mexico,<=50K\n4,Local-gov,272587,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,1,0,0,United-States,<=50K\n0,Private,220993,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,88676,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,185386,5th-6th,3,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Private,177420,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,>50K\n0,?,203353,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,100734,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,181119,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,172232,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,243678,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,170174,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,102202,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,249720,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,167835,Assoc-voc,11,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,266780,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Private,173740,10th,6,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,40024,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,193260,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,3,South,>50K\n0,Private,175752,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,202662,10th,6,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,167350,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,412379,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,121568,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,1,2,United-States,<=50K\n2,Private,56651,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,238567,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,144949,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,234633,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,147397,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,247547,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,266645,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,154897,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Private,112507,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,110884,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n0,Private,151588,Some-college,10,Married-spouse-absent,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,217210,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,2,2,United-States,>50K\n0,?,185357,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,139701,5th-6th,3,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,Dominican-Republic,<=50K\n2,Private,50707,Bachelors,13,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,370119,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,4,3,United-States,>50K\n4,Self-emp-not-inc,252842,HS-grad,9,Never-married,Farming-fishing,Other-relative,White,Male,0,0,0,United-States,<=50K\n4,Private,106707,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,149486,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,427541,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Female,4,0,2,United-States,>50K\n3,Self-emp-not-inc,22154,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,144949,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,4,2,United-States,>50K\n0,Private,228686,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,113010,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Federal-gov,361278,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,109509,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,172155,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Ecuador,>50K\n3,Private,204304,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,233362,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,141609,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,179479,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,1,0,2,Yugoslavia,<=50K\n1,Private,193565,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,314539,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,208908,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,375526,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,291494,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,117747,Bachelors,13,Divorced,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n4,Private,331569,HS-grad,9,Married-civ-spouse,Sales,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Private,146786,10th,6,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,147098,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,137076,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,223811,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,172354,Assoc-voc,11,Widowed,Adm-clerical,Not-in-family,White,Female,1,0,3,United-States,<=50K\n1,Private,154410,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n4,Private,277203,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,97295,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,95680,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Private,328981,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,75435,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Private,116288,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,136105,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,2,2,United-States,>50K\n3,Local-gov,134042,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,253003,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,4,2,United-States,>50K\n2,Private,193106,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,117528,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,194537,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,195820,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,265671,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,90636,Some-college,10,Never-married,?,Own-child,Amer-Indian-Eskimo,Female,1,0,2,United-States,<=50K\n4,Private,166107,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n3,Federal-gov,106207,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,187135,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,231793,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,?,228326,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,125933,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n1,Local-gov,211032,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n3,Local-gov,125796,11th,7,Never-married,Farming-fishing,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,29526,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,158993,10th,6,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,116379,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,China,>50K\n2,Private,201343,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,402718,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-inc,98360,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,285580,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n1,?,119851,Some-college,10,Divorced,?,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,325509,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,204745,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,1,0,2,United-States,<=50K\n4,Private,152874,Some-college,10,Widowed,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,139715,HS-grad,9,Never-married,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,141584,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,156848,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,40151,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,50648,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,122920,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,91571,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,227220,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,State-gov,344519,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,133061,Some-college,10,Never-married,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,219054,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Local-gov,194276,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n3,Self-emp-inc,168211,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,<=50K\n3,Local-gov,220054,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Self-emp-inc,405601,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,240979,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,4,United-States,<=50K\n3,Local-gov,202606,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,220896,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,24274,HS-grad,9,Never-married,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n1,Private,263444,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,4,0,2,United-States,>50K\n3,Local-gov,99064,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,203967,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n3,State-gov,94186,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,<=50K\n4,?,110931,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,66934,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,196385,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,47012,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,216013,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,98921,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,320294,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,247102,10th,6,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,155632,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n0,Self-emp-inc,120753,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,213921,Some-college,10,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,Mexico,<=50K\n1,Private,94235,11th,7,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,84141,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,237943,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,225895,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,126569,Bachelors,13,Divorced,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,172307,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,111520,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,283079,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,109969,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,146159,7th-8th,4,Widowed,Priv-house-serv,Not-in-family,Black,Female,0,2,1,United-States,<=50K\n0,State-gov,247319,Some-college,10,Never-married,Other-service,Not-in-family,Amer-Indian-Eskimo,Female,0,0,3,United-States,<=50K\n4,Local-gov,200764,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,>50K\n0,Private,123868,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,137063,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,112137,Bachelors,13,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,3,South,<=50K\n2,Private,188069,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,102828,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,187221,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,0,El-Salvador,<=50K\n4,Private,343982,10th,6,Widowed,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,146949,10th,6,Never-married,Sales,Own-child,White,Male,0,0,4,United-States,<=50K\n2,Private,150011,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,107812,9th,5,Never-married,Transport-moving,Not-in-family,White,Male,2,0,1,United-States,<=50K\n2,Private,207392,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,4,United-States,>50K\n4,State-gov,140851,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,216842,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,112115,Bachelors,13,Never-married,Exec-managerial,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,185279,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,194980,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,189530,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,38158,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,4,United-States,>50K\n3,?,246219,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,143822,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,300851,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Private,184874,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,1,0,2,United-States,<=50K\n2,Private,83827,Some-college,10,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,England,<=50K\n2,Private,112847,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Private,581128,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,202652,Assoc-voc,11,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,171888,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,45427,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,185848,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,282553,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,153614,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,65353,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,244172,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,148995,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,Private,274228,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,156383,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,47403,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,?,226593,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,94041,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,3,1,United-States,<=50K\n1,Private,271710,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,231797,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,188403,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,444725,Prof-school,15,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,3,Hungary,>50K\n0,Private,242605,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,244605,Bachelors,13,Widowed,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,335276,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,284616,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,162151,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,60358,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,151693,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,369907,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,171636,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,118901,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,28419,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,608881,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,112564,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,2,?,<=50K\n0,Private,171472,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,236804,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,212252,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n4,Private,119907,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,352797,HS-grad,9,Married-spouse-absent,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,97281,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,154351,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,1,0,2,United-States,<=50K\n0,Private,117606,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,222089,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,Thailand,<=50K\n2,Private,199668,HS-grad,9,Divorced,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,194869,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,283268,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,170278,5th-6th,3,Widowed,Sales,Not-in-family,White,Female,0,0,2,Italy,<=50K\n1,Federal-gov,90787,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,110749,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,173864,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,278442,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n1,State-gov,162705,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,4,United-States,>50K\n2,Private,326352,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,105854,HS-grad,9,Never-married,Craft-repair,Other-relative,Other,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,116608,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,>50K\n3,Private,182655,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,213834,Assoc-voc,11,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,42881,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n4,Self-emp-not-inc,240414,Bachelors,13,Widowed,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,37688,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,189922,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,?,323309,HS-grad,9,Never-married,?,Own-child,Other,Male,0,0,3,South,<=50K\n2,Federal-gov,341638,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,114758,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,288557,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,?,191817,11th,7,Never-married,?,Own-child,White,Male,0,0,0,Mexico,<=50K\n0,Private,222851,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,93605,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,263984,Some-college,10,Married-spouse-absent,Exec-managerial,Not-in-family,Black,Male,0,0,2,?,<=50K\n0,Private,190916,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,384787,9th,5,Never-married,Sales,Other-relative,White,Female,0,0,2,Mexico,<=50K\n0,Local-gov,43921,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,183739,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,490871,11th,7,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,1,United-States,<=50K\n1,Private,173473,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,24504,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Private,113080,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,197093,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,56924,HS-grad,9,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Federal-gov,207723,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n1,Private,327902,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,197860,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,2,Haiti,<=50K\n3,Private,95647,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,98227,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,430151,11th,7,Never-married,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n4,Private,73069,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,101345,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,2,2,United-States,<=50K\n2,Private,196123,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,24008,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,67433,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,224466,HS-grad,9,Never-married,?,Other-relative,Black,Male,0,0,1,United-States,<=50K\n1,Private,292120,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,198362,Bachelors,13,Never-married,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,231507,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,216178,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,?,91447,Bachelors,13,Widowed,?,Not-in-family,White,Female,0,4,0,United-States,<=50K\n2,Private,232820,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,53956,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,155913,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,104827,HS-grad,9,Widowed,Tech-support,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,197222,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,255406,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,3,2,United-States,<=50K\n3,Federal-gov,278076,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Local-gov,231348,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,196286,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,76417,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,190964,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,<=50K\n4,Federal-gov,239486,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,101709,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n3,Private,120914,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,60722,HS-grad,9,Divorced,Sales,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,257405,5th-6th,3,Never-married,Farming-fishing,Unmarried,Black,Male,0,0,2,Mexico,<=50K\n4,Private,32209,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,431245,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,95082,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,220986,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,87771,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,199233,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,1,United-States,>50K\n0,Private,133515,Bachelors,13,Never-married,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,117310,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,163027,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,169711,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,91317,Assoc-acdm,12,Never-married,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,106159,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,177063,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,175059,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,129573,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,169611,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,247506,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,37085,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,202033,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,179864,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,88020,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n1,?,243190,Bachelors,13,Never-married,?,Not-in-family,Asian-Pac-Islander,Male,0,0,1,?,<=50K\n1,Private,102270,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,81286,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,?,205690,Assoc-voc,11,Never-married,?,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,37314,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,29213,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,451019,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,125892,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,259412,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Federal-gov,207540,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,110167,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,430336,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,3,0,2,United-States,>50K\n2,Self-emp-inc,210610,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,Cuba,>50K\n1,Private,86483,10th,6,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,138507,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Federal-gov,345157,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,161342,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,159109,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,54608,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,433602,HS-grad,9,Never-married,Sales,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,350103,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,166193,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,56236,HS-grad,9,Divorced,Protective-serv,Unmarried,Black,Male,1,0,2,United-States,<=50K\n3,Private,156926,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,26698,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,75993,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,312271,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,70282,HS-grad,9,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,259109,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,192360,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,373432,Some-college,10,Separated,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,176998,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,32950,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,48520,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,4,United-States,<=50K\n3,State-gov,237525,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,202746,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,179255,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,337825,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,191517,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,239130,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,4,2,United-States,>50K\n2,Private,233366,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,Other,Male,1,0,2,Mexico,>50K\n2,?,137492,HS-grad,9,Divorced,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,66893,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,1,3,United-States,>50K\n4,Private,266646,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,Black,Male,1,0,2,United-States,<=50K\n1,Private,238246,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,Germany,<=50K\n0,Private,215616,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,148316,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,172402,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,195918,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,33016,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,267281,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,43608,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,57827,HS-grad,9,Widowed,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,110145,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,162884,HS-grad,9,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,3,Columbia,<=50K\n2,State-gov,145166,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,193720,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,310639,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,196360,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,370675,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,3,Hong,<=50K\n2,Private,398931,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n1,Local-gov,104329,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,103575,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,State-gov,222800,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,176239,9th,5,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,321274,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,192713,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,?,<=50K\n0,Private,407714,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,?,247547,HS-grad,9,Never-married,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,123219,5th-6th,3,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,165950,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,182509,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,27408,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,110592,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,175409,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,172822,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,4,0,3,United-States,>50K\n0,Private,265361,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,106491,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,179557,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,187919,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,196707,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,216129,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,100480,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,69905,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,4,0,2,United-States,>50K\n2,Private,297767,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,214627,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,251908,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,4,2,United-States,>50K\n3,Self-emp-inc,304695,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,48121,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,125228,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,408012,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,161642,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,181212,Some-college,10,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,76482,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,295073,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,?,69596,10th,6,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,262461,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,112680,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,342642,Masters,14,Married-spouse-absent,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,211968,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,153536,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,188923,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,391114,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,45599,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,?,<=50K\n2,Private,119929,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,130442,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,192602,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,328881,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,165034,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,93174,HS-grad,9,Divorced,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,205903,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-inc,197496,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,226941,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,199193,Assoc-acdm,12,Divorced,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,187870,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,364899,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,4,United-States,>50K\n1,Private,437994,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n0,Private,166827,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,207819,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,37939,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,77146,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,?,29075,11th,7,Divorced,?,Unmarried,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n0,Private,167868,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,150132,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,365881,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,105044,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,145636,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,161547,Bachelors,13,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Federal-gov,77218,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,241126,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,155981,Bachelors,13,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,45741,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,1,United-States,>50K\n0,Private,256356,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,State-gov,105803,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,29702,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,107882,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,77572,Some-college,10,Divorced,Sales,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,209768,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,89360,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,2,0,3,United-States,>50K\n1,Self-emp-not-inc,227540,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n2,Private,292570,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,1,0,2,United-States,<=50K\n2,Private,409189,11th,7,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,194901,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,4,United-States,<=50K\n1,Local-gov,219796,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,117166,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,<=50K\n2,Private,228320,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,236391,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,193451,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,223367,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,33001,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,173858,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,3,0,1,China,>50K\n1,Private,240441,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,160264,11th,7,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,230403,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,154536,10th,6,Widowed,Craft-repair,Unmarried,Black,Female,0,3,2,United-States,<=50K\n2,Self-emp-not-inc,247024,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,410199,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,1,United-States,>50K\n0,Private,191878,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,2,?,<=50K\n4,State-gov,54269,10th,6,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,205997,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,47343,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,4,United-States,>50K\n1,Federal-gov,403489,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,232149,Bachelors,13,Divorced,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n3,?,339547,Some-college,10,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,0,3,?,<=50K\n2,Private,186819,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,89991,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,112139,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,244571,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,220696,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,135102,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,209417,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,199689,Bachelors,13,Married-spouse-absent,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,240172,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,94492,10th,6,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,188564,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,264527,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,189922,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,182044,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,271173,Some-college,10,Never-married,Craft-repair,Own-child,Black,Male,2,0,2,United-States,<=50K\n1,Private,203034,Assoc-voc,11,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,100734,Bachelors,13,Married-civ-spouse,Exec-managerial,Other-relative,White,Female,0,0,2,Greece,>50K\n1,Private,169269,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,Puerto-Rico,>50K\n4,Private,24244,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,132222,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,199118,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Private,223212,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Peru,<=50K\n1,Private,284859,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,112854,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,92968,Masters,14,Never-married,Tech-support,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,181553,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n0,Private,266668,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,29144,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,389856,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,111589,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,232938,Some-college,10,Never-married,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,103540,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,>50K\n4,Private,249814,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,30776,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,184779,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,93449,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n1,Local-gov,178107,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,198956,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Other,Male,0,0,1,United-States,<=50K\n3,Private,130143,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,171807,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,436431,Preschool,1,Married-civ-spouse,?,Other-relative,White,Female,0,0,2,Mexico,<=50K\n0,Private,162205,10th,6,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,97470,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,158603,Assoc-voc,11,Never-married,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,348430,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,109385,1st-4th,2,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,188998,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,210591,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,37170,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,169583,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Private,180624,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,186311,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,106471,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,37302,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,91608,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,263896,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,335376,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,186531,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,42706,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,180115,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,191196,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,209109,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,199224,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,State-gov,42488,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,0,United-States,<=50K\n0,Self-emp-not-inc,63574,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,227943,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,297551,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,343579,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,230246,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,1,0,4,United-States,<=50K\n4,Self-emp-not-inc,110199,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,178691,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,165855,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Germany,<=50K\n1,Private,27565,Assoc-voc,11,Married-civ-spouse,Craft-repair,Wife,Amer-Indian-Eskimo,Female,0,0,1,United-States,>50K\n3,Private,220115,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n1,Private,113751,11th,7,Divorced,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,128793,5th-6th,3,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,97472,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,153064,5th-6th,3,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,0,Yugoslavia,>50K\n4,Private,190488,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,326283,Some-college,10,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n2,Private,61287,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,214288,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,198856,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,914061,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,186648,Assoc-acdm,12,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,350759,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,?,197988,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,188571,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,112776,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,100345,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,291783,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,156387,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,295166,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,132849,Masters,14,Never-married,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,300497,Some-college,10,Divorced,Exec-managerial,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Private,338089,Masters,14,Separated,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,104257,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,112247,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Federal-gov,73070,Masters,14,Never-married,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,49298,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n3,Local-gov,289390,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,219546,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,194490,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,358655,Masters,14,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,286026,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,401473,Masters,14,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,197967,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,216647,10th,6,Divorced,Protective-serv,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,355536,HS-grad,9,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,193130,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,67725,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,209629,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,143964,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,249072,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,?,285742,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,130221,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Federal-gov,201815,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Local-gov,67243,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,202263,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,122048,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,231866,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,211440,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,25955,Assoc-voc,11,Never-married,Prof-specialty,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,?,274499,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,305474,10th,6,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,Haiti,<=50K\n4,Local-gov,222702,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n1,State-gov,120460,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,31657,Assoc-voc,11,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,327079,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,234633,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,203776,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n0,Self-emp-inc,242138,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,276937,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,117528,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,171351,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,138471,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,329341,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,62539,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,2,2,United-States,<=50K\n4,Private,265579,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,218215,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,116369,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,403107,Preschool,1,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,State-gov,103389,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,>50K\n1,State-gov,624006,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,344094,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,147377,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,90579,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,2,0,3,United-States,<=50K\n3,Private,91972,HS-grad,9,Married-civ-spouse,Priv-house-serv,Wife,White,Female,0,0,1,United-States,>50K\n4,Self-emp-not-inc,275236,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,340432,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,158592,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,>50K\n3,Private,278303,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,300681,10th,6,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,?,<=50K\n2,Private,160192,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,2,0,4,United-States,<=50K\n1,Private,148429,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,210474,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,510072,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,328906,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,2,0,1,United-States,<=50K\n1,Private,247196,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,178839,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,England,>50K\n4,Private,178764,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,218490,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,44047,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,125268,Bachelors,13,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,76845,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,?,<=50K\n2,Local-gov,484475,Bachelors,13,Never-married,Other-service,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n0,Private,114357,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,219619,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,?,33016,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,319205,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,?,389182,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,3,Germany,<=50K\n1,Private,262118,Assoc-voc,11,Never-married,Exec-managerial,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Private,141340,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,189703,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,?,307589,Bachelors,13,Married-civ-spouse,?,Wife,Asian-Pac-Islander,Female,0,0,0,Philippines,<=50K\n1,Private,116531,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,142621,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,327573,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,24896,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,69251,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,91716,11th,7,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,93717,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,111499,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,?,193537,Assoc-acdm,12,Divorced,?,Unmarried,White,Female,0,0,0,Dominican-Republic,<=50K\n0,Private,307267,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,249196,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,?,201700,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,188644,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,Mexico,<=50K\n1,Private,255004,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,100145,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,183274,11th,7,Never-married,Other-service,Own-child,White,Female,1,0,1,United-States,<=50K\n2,Self-emp-not-inc,44671,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,354351,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,346189,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,304864,Some-college,10,Divorced,Tech-support,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,505365,Bachelors,13,Separated,Sales,Unmarried,White,Male,0,0,4,Canada,<=50K\n0,State-gov,268520,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,State-gov,210295,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,103339,10th,6,Never-married,Sales,Own-child,White,Female,0,2,0,United-States,<=50K\n1,Private,145437,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,56071,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,233729,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,United-States,>50K\n2,Private,265932,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,154347,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,277507,HS-grad,9,Married-spouse-absent,Handlers-cleaners,Not-in-family,White,Male,0,2,2,United-States,<=50K\n3,Federal-gov,172898,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,182655,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n0,?,175431,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,181460,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,149771,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Private,45363,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,180010,Some-college,10,Separated,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,?,103886,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,233198,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,124974,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,29059,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,136331,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,106900,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,314464,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,152810,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,76728,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,55854,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,36801,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,?,243631,HS-grad,9,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,1,0,0,South,<=50K\n3,Local-gov,155654,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,173987,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,115725,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,154556,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,?,<=50K\n1,Self-emp-not-inc,234976,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,122913,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,187411,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,193285,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,96219,HS-grad,9,Separated,?,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,117767,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,81534,HS-grad,9,Never-married,Sales,Unmarried,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Private,202125,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,225660,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,203279,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,0,India,<=50K\n2,Self-emp-not-inc,151960,Some-college,10,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,368561,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,202046,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,197286,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,285570,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,380899,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,325217,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,111058,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n1,State-gov,162312,Some-college,10,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,3,South,<=50K\n2,Self-emp-inc,136986,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,97654,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,229116,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,159549,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,195760,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,277788,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Portugal,>50K\n2,State-gov,120201,Some-college,10,Divorced,Adm-clerical,Own-child,Other,Female,0,0,2,United-States,<=50K\n0,Private,236601,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,4,2,United-States,<=50K\n4,?,94809,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,219757,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,160728,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,308945,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,185859,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,3,United-States,>50K\n3,?,163748,Masters,14,Divorced,?,Unmarried,White,Female,0,0,1,?,<=50K\n3,State-gov,48358,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,3,United-States,>50K\n2,Federal-gov,77792,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,114753,Some-college,10,Widowed,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,234259,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,152617,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,204567,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,145209,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,240698,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,?,195181,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,299536,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,238802,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,150519,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,237903,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,257940,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,383670,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,179666,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,259727,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,277772,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,67198,Assoc-acdm,12,Widowed,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,22419,9th,5,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n2,Private,99373,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,147629,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,1,United-States,>50K\n1,Self-emp-not-inc,145714,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,49358,12th,8,Never-married,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,214956,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,66172,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,136749,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,?,258231,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,1,?,<=50K\n0,Private,43937,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,114639,11th,7,Never-married,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,104090,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Scotland,<=50K\n0,Private,137510,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,Germany,<=50K\n0,Private,123586,Some-college,10,Never-married,Adm-clerical,Own-child,Other,Male,0,0,1,United-States,<=50K\n2,Private,293628,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Philippines,>50K\n2,Federal-gov,239409,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,593246,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,30269,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,2,0,1,United-States,>50K\n1,Private,48456,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,153894,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,1,Puerto-Rico,<=50K\n0,Private,182117,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,231148,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,184176,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,29702,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n2,Private,276759,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,179731,HS-grad,9,Never-married,Priv-house-serv,Other-relative,White,Female,0,0,0,?,<=50K\n4,Private,234570,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,143485,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,143062,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,146516,Some-college,10,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,3,United-States,<=50K\n0,?,180395,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,108256,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,211392,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,271243,12th,8,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,Haiti,<=50K\n0,Local-gov,197822,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,167678,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Dominican-Republic,>50K\n1,Private,30101,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,232220,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,?,212588,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,306156,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,70985,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,185459,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,91141,10th,6,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,347653,Bachelors,13,Divorced,Other-service,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,189759,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,272792,Bachelors,13,Divorced,Craft-repair,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,?,95708,11th,7,Divorced,?,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n4,?,111712,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,132767,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,162245,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,152148,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n3,?,186565,Masters,14,Divorced,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,193385,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,185778,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,162370,Masters,14,Separated,Prof-specialty,Not-in-family,White,Female,0,0,1,Iran,<=50K\n2,Private,340763,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Male,0,4,3,United-States,<=50K\n4,Private,148949,10th,6,Married-civ-spouse,Other-service,Husband,Black,Male,2,0,1,United-States,<=50K\n4,Self-emp-not-inc,147393,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,187203,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,147206,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,228182,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,177426,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n1,?,161288,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,133530,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n2,Private,117849,11th,7,Married-civ-spouse,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,132670,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,3,0,0,United-States,>50K\n2,Self-emp-inc,98360,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,226500,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,35644,Assoc-voc,11,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,49325,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,142738,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,4,0,0,United-States,>50K\n3,Self-emp-not-inc,207841,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,269355,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n1,Self-emp-inc,190290,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,209034,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,174571,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,198583,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,128055,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,319271,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,91857,HS-grad,9,Divorced,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,376416,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,323829,HS-grad,9,Divorced,Protective-serv,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,209646,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,90872,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,287454,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n0,Private,208946,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,130805,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,247090,9th,5,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,3,United-States,<=50K\n0,Private,249150,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,187138,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,166497,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,155433,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,State-gov,164593,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,?,<=50K\n2,Private,211168,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,162688,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,185072,Bachelors,13,Separated,Prof-specialty,Unmarried,Black,Female,0,0,2,Jamaica,>50K\n3,Private,154153,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,166258,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,190650,Masters,14,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n4,State-gov,165792,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n4,Private,313170,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,188279,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,Thailand,<=50K\n1,Self-emp-not-inc,209301,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,194820,HS-grad,9,Separated,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,171393,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,268282,7th-8th,4,Married-civ-spouse,Farming-fishing,Other-relative,White,Male,0,0,1,Mexico,<=50K\n0,Private,219519,Some-college,10,Never-married,Sales,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n2,State-gov,369131,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,195571,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,114686,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,1,0,2,United-States,<=50K\n0,Private,356861,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,156848,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,147766,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,134768,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,156882,Some-college,10,Married-civ-spouse,Sales,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,131404,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-inc,233727,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n0,?,216867,Some-college,10,Never-married,?,Other-relative,White,Male,0,0,2,Mexico,<=50K\n3,Private,168556,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,60459,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,212894,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,41865,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,175413,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,149902,Some-college,10,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,180497,Assoc-acdm,12,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,107302,Bachelors,13,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,1,United-States,>50K\n1,Private,93283,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,264351,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n4,State-gov,136819,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n1,Self-emp-not-inc,295010,Some-college,10,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,291904,HS-grad,9,Divorced,Priv-house-serv,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n0,Self-emp-inc,225442,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n4,State-gov,170108,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,193859,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,1,0,1,Germany,<=50K\n2,Local-gov,326701,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,United-States,>50K\n2,State-gov,196373,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,258730,HS-grad,9,Never-married,Priv-house-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,190293,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,170263,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,>50K\n0,Private,300275,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,255254,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Private,166461,11th,7,Divorced,Machine-op-inspct,Unmarried,Black,Female,2,0,2,United-States,<=50K\n1,Private,96219,HS-grad,9,Divorced,Other-service,Own-child,White,Female,1,0,1,United-States,<=50K\n2,Self-emp-inc,186845,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,99697,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,143069,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,117674,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,261504,HS-grad,9,Married-spouse-absent,Transport-moving,Other-relative,White,Female,0,0,2,Dominican-Republic,<=50K\n2,Private,29660,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,202560,Assoc-acdm,12,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,>50K\n1,Private,178713,11th,7,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,100734,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,112009,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,144056,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,70209,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,143816,Some-college,10,Never-married,Machine-op-inspct,Other-relative,Black,Male,0,0,1,United-States,<=50K\n0,?,164732,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,State-gov,714597,Some-college,10,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,187703,Assoc-voc,11,Widowed,Prof-specialty,Unmarried,White,Female,4,0,2,United-States,>50K\n3,Private,418901,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Mexico,<=50K\n0,Private,169188,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,70554,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,31801,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n0,Self-emp-not-inc,195000,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,215392,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,2,United-States,>50K\n1,Private,97723,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,121012,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,218956,12th,8,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,171003,7th-8th,4,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-inc,154782,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n1,?,132372,HS-grad,9,Never-married,?,Unmarried,White,Female,0,0,2,?,<=50K\n0,?,151404,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,816750,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n4,Private,92943,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,?,104489,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,218456,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,Hungary,<=50K\n2,Private,301614,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,307134,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,106347,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,127961,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,206297,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,171156,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,104729,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,Mexico,<=50K\n3,Private,85109,Some-college,10,Never-married,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,Private,199143,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,110371,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,250782,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,281574,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Private,147889,10th,6,Married-AF-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,298753,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,248841,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,274948,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,41763,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,176683,Some-college,10,Never-married,?,Own-child,White,Male,0,2,2,United-States,<=50K\n2,Private,385251,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,145964,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,33460,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,State-gov,121586,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,112008,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,Germany,<=50K\n0,Private,163053,11th,7,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,34446,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,147201,Bachelors,13,Separated,Prof-specialty,Own-child,Black,Male,0,0,1,United-States,<=50K\n4,Private,491214,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,102729,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,?,70282,HS-grad,9,Never-married,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,216214,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,212206,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,235567,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,356410,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,223558,HS-grad,9,Never-married,Tech-support,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Federal-gov,160473,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,150999,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,230961,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,169114,Some-college,10,Widowed,Prof-specialty,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Private,301070,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,163687,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,180419,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,114828,12th,8,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,208606,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,165977,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,110408,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,266047,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,176124,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,144063,10th,6,Never-married,Craft-repair,Unmarried,White,Male,0,0,4,United-States,<=50K\n1,Self-emp-inc,446724,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,357118,Bachelors,13,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,102388,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,191515,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,94413,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Male,1,0,2,United-States,<=50K\n2,Federal-gov,32627,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,218249,11th,7,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,308798,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,199005,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,State-gov,108432,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,149218,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,552529,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,222596,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Poland,>50K\n1,Private,168961,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,206951,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,386236,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,Mexico,<=50K\n0,Private,196388,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,162675,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,Cuba,<=50K\n2,Private,187847,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,186934,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,27663,7th-8th,4,Separated,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,180271,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,215503,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,Canada,<=50K\n2,Private,110862,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,197905,Some-college,10,Widowed,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,355124,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,3,2,Mexico,<=50K\n1,Self-emp-not-inc,109621,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,194995,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,137136,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n3,Private,67229,11th,7,Divorced,Transport-moving,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,197033,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,187746,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,1,0,1,United-States,<=50K\n2,Private,98211,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,54298,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,49275,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,1,3,United-States,<=50K\n0,Private,237386,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,67243,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,168191,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,0,Italy,<=50K\n0,Private,132327,Some-college,10,Separated,Adm-clerical,Other-relative,Other,Female,0,0,2,Ecuador,<=50K\n0,Private,175109,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,State-gov,224638,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,128487,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,179747,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,195416,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,>50K\n2,Private,176949,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,114691,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,122540,Some-college,10,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,93461,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,54098,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,333838,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,174789,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,227515,10th,6,Widowed,Transport-moving,Unmarried,White,Female,0,0,2,Greece,<=50K\n2,Federal-gov,391585,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,83315,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,213310,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,127303,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,255476,5th-6th,3,Never-married,Other-service,Other-relative,White,Male,0,0,1,Mexico,<=50K\n2,Private,320451,Bachelors,13,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,?,>50K\n1,Private,454717,Some-college,10,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,374474,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,78401,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,168887,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,254711,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,>50K\n0,Private,196678,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,217201,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,160398,12th,8,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,288829,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,185706,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,201615,Assoc-acdm,12,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,157092,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,130561,11th,7,Never-married,Sales,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,202450,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,303942,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,339346,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n0,?,234838,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,38389,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,147548,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,116626,Doctorate,16,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Local-gov,110110,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,4,United-States,>50K\n2,Private,230478,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,398220,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-not-inc,187346,Assoc-acdm,12,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,175827,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,211494,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,3,3,United-States,<=50K\n4,Private,105745,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,237428,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Private,200766,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,4,0,2,United-States,>50K\n0,State-gov,24896,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,107164,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,202083,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Canada,<=50K\n2,State-gov,53768,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,159577,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,209674,7th-8th,4,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,309348,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,206046,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,227531,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,135339,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,2,India,>50K\n0,Private,155503,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,176835,Masters,14,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,3,0,2,United-States,<=50K\n0,Private,163067,Some-college,10,Never-married,Protective-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,212607,Some-college,10,Never-married,Adm-clerical,Unmarried,Other,Female,0,0,2,Puerto-Rico,<=50K\n3,Self-emp-inc,162381,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,195890,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Federal-gov,49358,10th,6,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,136077,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Poland,<=50K\n2,Private,119297,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,48947,Assoc-voc,11,Widowed,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,32825,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,State-gov,82847,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,119684,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,264143,9th,5,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,30690,7th-8th,4,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,113631,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,366889,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,393962,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,165484,Bachelors,13,Separated,Sales,Not-in-family,White,Male,0,0,4,United-States,>50K\n2,Federal-gov,90737,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,379798,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,190911,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,72887,11th,7,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n4,Private,192309,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,98361,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,121253,Doctorate,16,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,State-gov,270859,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Self-emp-not-inc,223705,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,Columbia,<=50K\n2,Private,125892,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Self-emp-not-inc,202683,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,99131,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,2,0,2,United-States,>50K\n4,Private,197577,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Without-pay,43627,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,175185,Assoc-voc,11,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,377405,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,47541,Masters,14,Never-married,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,218729,Some-college,10,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,1,United-States,>50K\n1,Local-gov,197430,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,259010,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,121124,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,334593,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,374764,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,192845,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,144524,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,136402,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,255847,7th-8th,4,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,177955,9th,5,Never-married,Priv-house-serv,Unmarried,White,Female,0,0,1,El-Salvador,<=50K\n1,Private,151835,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,65098,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,328119,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Mexico,<=50K\n3,Private,125147,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,62834,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,0,United-States,>50K\n3,State-gov,230095,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,348059,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n1,Private,425622,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Local-gov,336320,Bachelors,13,Divorced,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,225809,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,216073,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,267912,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Mexico,<=50K\n1,Private,108706,HS-grad,9,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,575172,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,1,United-States,>50K\n0,Private,311489,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,189123,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,95998,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,177487,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,213002,Some-college,10,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,0,United-States,<=50K\n3,Private,272723,7th-8th,4,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,84231,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,475322,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,2,1,United-States,<=50K\n0,Private,120268,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,2,2,United-States,<=50K\n0,?,60331,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,172618,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,State-gov,173273,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,52921,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,210364,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,87310,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,1,0,2,United-States,<=50K\n3,Private,332489,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Germany,>50K\n1,Private,100333,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,216867,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,?,177974,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,292110,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,219137,Assoc-acdm,12,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,159589,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,186815,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,22149,HS-grad,9,Never-married,Other-service,Own-child,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n0,Private,228724,Assoc-voc,11,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,187392,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,105930,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n3,State-gov,182907,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,322025,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,0,United-States,<=50K\n0,Private,263886,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n1,Local-gov,362775,10th,6,Married-civ-spouse,Other-service,Wife,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n3,Private,96062,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,?,191665,Some-college,10,Widowed,?,Not-in-family,White,Female,0,4,2,United-States,<=50K\n1,Self-emp-not-inc,159322,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,3,4,United-States,<=50K\n1,Local-gov,163867,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,136204,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,1,United-States,>50K\n4,Private,160431,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,163324,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n1,?,161309,Prof-school,15,Married-civ-spouse,?,Wife,White,Female,4,0,3,United-States,>50K\n1,Private,208881,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,183127,HS-grad,9,Divorced,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,192225,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,222081,Bachelors,13,Never-married,Prof-specialty,Other-relative,Black,Female,0,0,1,United-States,<=50K\n1,Private,183627,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,187921,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,25497,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Private,353824,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,250804,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,385583,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,84788,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,127704,7th-8th,4,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,99208,Preschool,1,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,347993,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,175958,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,278553,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Black,Male,4,0,4,United-States,>50K\n3,Private,186009,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,55104,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,179411,HS-grad,9,Widowed,Tech-support,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,68452,HS-grad,9,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,202027,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,113401,10th,6,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,186934,Bachelors,13,Married-civ-spouse,Prof-specialty,Other-relative,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,190020,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,198493,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,256448,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,622192,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n4,Private,133728,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,181824,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,286422,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,378585,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,121012,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,164864,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,1,2,United-States,<=50K\n0,Private,74706,11th,7,Never-married,Priv-house-serv,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,185582,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,>50K\n2,Private,132633,Some-college,10,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,2,?,<=50K\n2,Federal-gov,230438,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,175540,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,115304,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,340269,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,171889,12th,8,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,94873,HS-grad,9,Widowed,Other-service,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,?,144194,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,141131,12th,8,Divorced,Machine-op-inspct,Unmarried,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n0,Private,192735,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,238186,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,4,United-States,<=50K\n0,Private,305609,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,312845,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,33884,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,264874,Assoc-voc,11,Never-married,?,Other-relative,White,Female,0,0,2,?,<=50K\n1,State-gov,268832,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,99651,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,257597,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,195904,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,266497,9th,5,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n4,Private,287972,Bachelors,13,Widowed,Other-service,Other-relative,Black,Female,0,0,0,United-States,<=50K\n3,Private,200569,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,117292,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,3,0,2,United-States,>50K\n4,?,223075,Bachelors,13,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,175339,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-inc,333197,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,4,2,United-States,<=50K\n4,Private,53707,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,39212,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,228500,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,234663,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n3,?,88073,Bachelors,13,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,420040,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,126517,Some-college,10,Separated,Sales,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,238002,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,Mexico,<=50K\n3,State-gov,21412,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,147804,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,222445,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,126675,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,301080,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,382532,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,232356,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,167350,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,3,United-States,<=50K\n1,?,375703,HS-grad,9,Divorced,?,Other-relative,Black,Female,0,2,2,United-States,<=50K\n1,Private,252708,12th,8,Never-married,Sales,Other-relative,White,Female,0,0,2,Mexico,<=50K\n1,Private,186824,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,176101,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,175121,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,355850,11th,7,Never-married,Transport-moving,Own-child,White,Male,0,2,0,United-States,<=50K\n2,Private,180931,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,State-gov,30219,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,350387,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,India,>50K\n0,Private,194247,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,137653,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,131762,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,283587,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,218164,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,287581,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,281725,5th-6th,3,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n4,Private,50120,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,1,1,United-States,<=50K\n2,Private,156667,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,566066,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,121352,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,?,>50K\n0,Private,260977,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,90860,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Private,218302,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,170142,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,171889,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,193172,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,164134,HS-grad,9,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,204283,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n3,?,81169,HS-grad,9,Separated,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,92143,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,3,0,3,United-States,>50K\n1,Private,181099,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,102791,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,364548,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n0,Self-emp-inc,153516,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,189528,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n4,Local-gov,154171,Some-college,10,Widowed,Machine-op-inspct,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,90752,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,278763,Assoc-voc,11,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,253581,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,59660,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,170988,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,160984,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,493732,1st-4th,2,Never-married,Farming-fishing,Own-child,White,Female,0,0,2,Mexico,<=50K\n2,Private,325802,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,196344,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Guatemala,<=50K\n1,State-gov,316589,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,365739,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,309990,Some-college,10,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,241852,12th,8,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,184105,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,134680,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,274545,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,207853,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,4,United-States,>50K\n0,Private,284061,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,186446,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,255575,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,302661,Assoc-acdm,12,Widowed,Exec-managerial,Unmarried,White,Male,4,0,2,United-States,>50K\n3,Private,148509,10th,6,Married-spouse-absent,Prof-specialty,Other-relative,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n3,Private,211239,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n4,Private,50468,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,4,0,United-States,<=50K\n2,Private,316820,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,145964,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,185084,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,183111,Assoc-voc,11,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,63042,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,339738,HS-grad,9,Married-civ-spouse,Transport-moving,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,273049,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n3,State-gov,239256,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,110102,HS-grad,9,Widowed,Farming-fishing,Not-in-family,White,Male,0,2,4,United-States,<=50K\n1,State-gov,165764,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,1,United-States,<=50K\n0,Private,152744,Some-college,10,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n4,Local-gov,212303,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,118544,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,269548,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,Mexico,<=50K\n0,State-gov,319303,Some-college,10,Divorced,Other-service,Unmarried,White,Male,0,4,2,United-States,>50K\n4,Without-pay,216001,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,205816,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,427437,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,198259,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,54314,9th,5,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,195744,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,294547,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,77521,11th,7,Never-married,Priv-house-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,288158,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,125010,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,80945,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Nicaragua,>50K\n0,Private,33016,10th,6,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,388725,Masters,14,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Local-gov,347136,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,158294,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,4,United-States,>50K\n1,Private,362787,10th,6,Married-spouse-absent,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,39388,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,322691,Masters,14,Widowed,Exec-managerial,Own-child,White,Male,0,0,4,United-States,>50K\n1,Private,31659,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n4,Private,176940,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,189027,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,98719,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,172238,HS-grad,9,Widowed,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,?,170456,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,129009,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,247580,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,204516,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,192652,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,336543,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,?,143938,HS-grad,9,Separated,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,272591,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,312372,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,172270,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,342414,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,123886,HS-grad,9,Never-married,Sales,Other-relative,Black,Female,0,0,2,United-States,<=50K\n0,Private,398130,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,142989,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,331539,HS-grad,9,Never-married,Craft-repair,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n0,Private,225156,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,311863,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,170682,11th,7,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,1,United-States,<=50K\n0,Private,96178,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,37932,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,198126,7th-8th,4,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,344275,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,?,>50K\n2,Private,112497,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,178487,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,2,2,United-States,<=50K\n2,Private,55395,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,131239,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,104772,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,427320,Bachelors,13,Divorced,Other-service,Not-in-family,Black,Male,1,0,2,United-States,<=50K\n1,?,73296,11th,7,Never-married,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,216853,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,259757,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,200734,HS-grad,9,Separated,Priv-house-serv,Not-in-family,Black,Female,0,0,3,Nicaragua,<=50K\n0,?,87515,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,Germany,<=50K\n0,Private,161245,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,262024,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,287681,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,Mexico,<=50K\n1,Private,303601,12th,8,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,365410,Some-college,10,Separated,?,Other-relative,White,Female,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,394356,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,263150,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,State-gov,86618,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,120277,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,90393,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,79078,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,State-gov,197344,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,120998,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,37522,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,96321,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,217302,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,137109,10th,6,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,227823,Assoc-acdm,12,Divorced,Adm-clerical,Own-child,White,Female,0,0,4,United-States,<=50K\n2,Private,22149,HS-grad,9,Never-married,Other-service,Own-child,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n2,Private,176900,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,154368,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,183445,HS-grad,9,Separated,Priv-house-serv,Own-child,White,Female,0,0,2,Guatemala,<=50K\n0,Private,193537,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,313873,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,34186,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,271807,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,?,46449,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,128065,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,176486,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,191072,Bachelors,13,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,34452,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,123253,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,113461,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,116267,12th,8,Never-married,Craft-repair,Own-child,White,Male,0,0,0,Columbia,<=50K\n1,Private,30433,Bachelors,13,Never-married,Tech-support,Other-relative,White,Female,0,0,4,United-States,<=50K\n0,Private,198512,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,131826,Prof-school,15,Widowed,Prof-specialty,Unmarried,White,Male,4,0,3,United-States,>50K\n1,Private,129764,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,49398,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,Local-gov,292285,11th,7,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Federal-gov,91726,Masters,14,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,178282,HS-grad,9,Widowed,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,227458,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,183470,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,275446,Some-college,10,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,328013,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,4,United-States,<=50K\n0,Private,382688,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,122988,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,175537,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,256278,HS-grad,9,Never-married,Other-service,Other-relative,Other,Female,0,0,1,El-Salvador,<=50K\n1,Private,161153,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,48189,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,186531,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,96866,Some-college,10,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,117555,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,98549,HS-grad,9,Never-married,?,Own-child,White,Female,0,2,1,United-States,<=50K\n2,Private,101782,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,234753,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n4,Private,59469,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,614113,Some-college,10,Separated,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,Private,203505,Doctorate,16,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,128365,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,36990,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,>50K\n0,Private,303240,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,217043,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,176079,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,4,0,1,United-States,>50K\n2,Self-emp-inc,266047,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n2,Self-emp-inc,285890,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,Haiti,>50K\n0,Private,70261,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,214236,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,143385,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,150507,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,292264,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,110861,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,225064,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,154120,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Trinadad&Tobago,<=50K\n0,Private,34465,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,89598,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,1,United-States,<=50K\n3,Private,183668,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,189382,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,1,2,United-States,<=50K\n4,?,165103,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,44216,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,150471,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,32627,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,153109,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,352451,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,176716,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n3,Federal-gov,171850,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,260496,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,154410,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n1,Self-emp-not-inc,23500,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,178312,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,3,4,United-States,>50K\n3,Private,62593,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Private,123291,Some-college,10,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,313817,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,State-gov,229270,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Other,Male,0,1,2,United-States,<=50K\n2,Private,212027,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,259532,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,213258,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,316337,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,179123,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Private,191765,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,Scotland,<=50K\n4,Self-emp-inc,188877,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,55395,Some-college,10,Married-spouse-absent,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,161051,Some-college,10,Never-married,Tech-support,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,241844,HS-grad,9,Separated,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,232142,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,311534,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,128986,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,67019,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,208826,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,256813,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,160919,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,107584,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,159472,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,408318,7th-8th,4,Married-spouse-absent,Other-service,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n4,Private,194956,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,21764,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,277347,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,104455,Some-college,10,Married-spouse-absent,Sales,Own-child,Asian-Pac-Islander,Female,0,0,4,United-States,>50K\n1,Private,117584,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n2,Private,131288,Assoc-voc,11,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,99014,Some-college,10,Divorced,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,141003,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,344014,Some-college,10,Divorced,Tech-support,Unmarried,Black,Female,0,2,2,United-States,<=50K\n2,Private,175600,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,240504,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,3,3,United-States,>50K\n0,Private,174436,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,194869,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,164901,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,72886,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,130126,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,196514,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,103651,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,261419,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,206339,10th,6,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,445365,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,227466,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n3,Private,96854,HS-grad,9,Divorced,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,163595,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,181096,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,95984,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,472517,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,60751,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,107302,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,106501,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,32732,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,174789,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,301628,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,27436,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,93977,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,139127,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,258231,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,136309,11th,7,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,266433,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,140363,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,179715,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,204816,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,35520,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,101320,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n2,Private,210857,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Self-emp-not-inc,60949,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,139095,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,233493,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,1,2,United-States,<=50K\n2,Private,176249,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,Private,187746,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,49593,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,240160,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,76286,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,0,2,India,>50K\n0,Private,65225,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,225330,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,101562,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Self-emp-not-inc,27539,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,3,0,4,United-States,>50K\n4,Local-gov,227311,10th,6,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,51260,HS-grad,9,Never-married,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,256609,HS-grad,9,Married-spouse-absent,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Local-gov,123939,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,203182,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Private,111128,Some-college,10,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,112451,HS-grad,9,Never-married,Other-service,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,?,177461,Some-college,10,Divorced,?,Unmarried,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n0,Private,332155,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,178983,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,199392,5th-6th,3,Divorced,Machine-op-inspct,Other-relative,White,Female,0,0,2,Nicaragua,<=50K\n0,Private,311015,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,126038,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,402124,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,198660,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,228457,11th,7,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,223267,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,4,United-States,<=50K\n0,Self-emp-not-inc,249046,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,127948,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,154785,Some-college,10,Never-married,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n1,Private,248404,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,137978,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,144778,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,133250,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,402771,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,?,97075,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,116234,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,262818,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,Guatemala,<=50K\n3,Private,138342,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,123374,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,202373,HS-grad,9,Never-married,Sales,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,180522,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n0,Local-gov,140647,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,136898,Assoc-voc,11,Widowed,Exec-managerial,Unmarried,White,Female,0,0,3,?,<=50K\n1,Private,140927,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,114055,Assoc-voc,11,Never-married,Prof-specialty,Unmarried,White,Female,1,0,2,United-States,<=50K\n3,Private,114222,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,51089,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,37353,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,379672,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,130727,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,Private,172046,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,228764,Assoc-voc,11,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,376393,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,185283,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,195789,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,243115,HS-grad,9,Married-spouse-absent,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,147558,Some-college,10,Divorced,Sales,Not-in-family,White,Female,2,0,1,United-States,>50K\n0,State-gov,62865,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,275197,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,124015,Masters,14,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,282951,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,105253,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,119164,HS-grad,9,Never-married,Exec-managerial,Other-relative,White,Male,0,0,2,?,<=50K\n1,Self-emp-not-inc,263081,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,4,United-States,<=50K\n3,Private,96062,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,44942,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,3,United-States,>50K\n2,Federal-gov,127879,Some-college,10,Never-married,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,109633,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,109404,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,126677,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,>50K\n3,Private,101113,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,117523,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,Columbia,<=50K\n1,Private,183523,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n3,Self-emp-not-inc,311231,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,459556,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Private,95551,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,126675,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,200246,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,108435,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,Italy,<=50K\n0,Private,141332,11th,7,Never-married,Sales,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Private,117310,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,182380,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,226878,Masters,14,Married-civ-spouse,?,Wife,Black,Female,3,0,3,Jamaica,>50K\n3,Private,123807,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,109539,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,38455,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,294209,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,130215,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,1,?,<=50K\n1,Private,285294,Assoc-acdm,12,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,168221,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,288907,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,2,0,2,United-States,>50K\n1,Private,391349,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,170276,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Private,117963,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,68015,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,285208,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,181091,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,53956,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,198223,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,83601,Prof-school,15,Widowed,Prof-specialty,Not-in-family,White,Male,1,0,3,United-States,<=50K\n1,Self-emp-not-inc,201579,5th-6th,3,Never-married,Prof-specialty,Unmarried,White,Male,0,0,0,Mexico,<=50K\n2,Private,137367,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,Thailand,<=50K\n1,Private,227325,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Male,0,0,3,Scotland,<=50K\n1,Private,129814,Some-college,10,Separated,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,193050,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,204557,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,165743,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,48935,Some-college,10,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,1,United-States,<=50K\n4,Private,177906,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,93985,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,148351,7th-8th,4,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,?,<=50K\n4,Local-gov,172646,9th,5,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,145409,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,548568,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,117849,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,320425,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,158734,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,168416,HS-grad,9,Married-spouse-absent,Priv-house-serv,Not-in-family,White,Female,0,0,2,Poland,<=50K\n4,Self-emp-not-inc,33487,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,205072,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,210525,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,32948,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,196689,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,87032,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,325159,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,52131,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,266439,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,61850,Masters,14,Never-married,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,163015,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,225135,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,109001,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,45796,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,214987,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Private,311974,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,Mexico,<=50K\n3,Private,77404,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,153132,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,64631,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,151364,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,87487,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,200479,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,30740,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,153484,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,214385,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,?,565769,Preschool,1,Never-married,?,Not-in-family,Black,Male,0,0,2,South,<=50K\n2,Self-emp-not-inc,92162,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,210945,11th,7,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,63105,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,185602,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,329980,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n4,Local-gov,111712,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n0,Local-gov,48317,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,84661,Some-college,10,Divorced,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,121622,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,37514,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,174993,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,159472,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,195635,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,108282,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,55946,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,123306,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,284250,Some-college,10,Married-civ-spouse,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,113443,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,309178,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,69236,Some-college,10,Divorced,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Japan,<=50K\n1,Local-gov,182926,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,126675,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,187693,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,41100,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,State-gov,261839,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,97197,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,260645,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,116878,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Portugal,>50K\n3,?,227690,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,199411,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,194813,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,177087,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,4,3,United-States,>50K\n2,Self-emp-not-inc,242434,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,4,0,3,United-States,>50K\n1,Private,399123,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Black,Male,0,2,2,United-States,<=50K\n3,Private,216999,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,47270,12th,8,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,122215,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,37898,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,61343,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,32533,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,296897,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,201101,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,155293,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,101593,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,104908,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,139023,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,429832,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,352542,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,29559,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,205711,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,160706,11th,7,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n4,Federal-gov,101626,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,245226,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Private,118286,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,187703,Assoc-voc,11,Never-married,Other-service,Other-relative,White,Male,0,0,2,Guatemala,<=50K\n3,Private,289707,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,68330,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,<=50K\n1,Private,118786,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Private,203785,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,32732,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,204567,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,131808,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,3,0,4,United-States,>50K\n0,Private,33272,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,117477,11th,7,Married-spouse-absent,Other-service,Unmarried,Black,Female,0,0,2,Jamaica,<=50K\n2,Self-emp-not-inc,240191,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,4,0,4,South,<=50K\n2,Private,93287,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,127651,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,222477,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,0,United-States,>50K\n0,Private,345734,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,111567,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,108293,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,424988,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Local-gov,94529,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,1,0,3,United-States,>50K\n2,Private,163322,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,181132,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,140092,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,131913,Some-college,10,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n4,Private,175945,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,247053,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,226203,12th,8,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,205865,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,3,United-States,<=50K\n0,Local-gov,200109,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,175029,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,55465,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,36989,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,255685,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,180765,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,180607,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,48063,12th,8,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n2,State-gov,159491,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,167789,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,124971,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,61710,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,127895,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,390348,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,Japan,<=50K\n0,Private,205337,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,260954,10th,6,Widowed,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,?,331237,HS-grad,9,Separated,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,177526,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,1,0,1,United-States,<=50K\n1,Private,113882,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,29144,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,124685,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n0,Private,88440,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,265074,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,306985,Masters,14,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,4,3,United-States,>50K\n4,Private,181494,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,138403,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,216473,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,143123,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,132717,HS-grad,9,Married-civ-spouse,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,State-gov,389792,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,359001,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,260052,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,4,0,2,United-States,>50K\n0,Private,63633,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,234192,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Local-gov,237523,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,183778,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,205916,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,131633,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,33121,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,32899,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,152171,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,127441,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,23074,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,England,<=50K\n2,Private,91585,Some-college,10,Widowed,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,83451,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Local-gov,219122,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,176500,12th,8,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,?,246862,Bachelors,13,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,38468,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,?,35633,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,0,?,<=50K\n0,Private,194608,9th,5,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,269723,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,165054,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,127537,9th,5,Married-spouse-absent,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,326931,9th,5,Never-married,Transport-moving,Unmarried,Other,Male,0,0,2,El-Salvador,<=50K\n0,Private,307133,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,?,<=50K\n2,Private,371576,Some-college,10,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,160400,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,426350,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n1,State-gov,121789,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,218183,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,91189,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,232190,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,233033,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,74263,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n1,Private,205950,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,213383,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,345577,Some-college,10,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,322144,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,158825,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,4,0,2,United-States,>50K\n4,Self-emp-inc,51286,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,2,0,4,United-States,>50K\n2,Private,82488,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Federal-gov,40909,Some-college,10,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,114939,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,221534,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,149455,Some-college,10,Separated,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,?,353524,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,328734,10th,6,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,112906,Assoc-voc,11,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,155038,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,100125,Assoc-acdm,12,Divorced,Transport-moving,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,State-gov,177048,Some-college,10,Married-civ-spouse,Protective-serv,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,72338,Masters,14,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n0,Private,254547,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,1,Outlying-US(Guam-USVI-etc),<=50K\n0,Local-gov,186213,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,270557,Masters,14,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Private,41411,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,116445,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,247540,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,3,1,United-States,<=50K\n2,Private,358753,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n2,Self-emp-not-inc,156897,Prof-school,15,Never-married,Prof-specialty,Own-child,White,Male,0,1,3,United-States,>50K\n2,Self-emp-not-inc,360879,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,3,4,United-States,>50K\n3,Private,256051,11th,7,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,2,2,United-States,<=50K\n1,Private,179877,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,266583,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,1,0,2,United-States,<=50K\n2,Private,187711,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,206707,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,80655,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,409464,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,235997,12th,8,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,Mexico,<=50K\n0,Private,59948,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,323833,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,290290,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,1,3,United-States,<=50K\n0,?,291746,12th,8,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,189173,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n3,State-gov,392668,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,132529,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,68080,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,?,>50K\n0,Private,194717,11th,7,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,307767,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,90051,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,1,0,2,Canada,<=50K\n4,State-gov,267799,Doctorate,16,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,>50K\n3,Private,81535,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,Self-emp-not-inc,334267,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,55912,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,172706,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,240467,Some-college,10,Separated,Transport-moving,Not-in-family,Black,Female,0,0,2,United-States,>50K\n0,Private,186006,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,65738,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,192878,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,413870,Some-college,10,Never-married,Exec-managerial,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,176341,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,?,28367,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,117210,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,231286,Some-college,10,Never-married,?,Own-child,Black,Male,0,0,1,Jamaica,<=50K\n2,Private,188465,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,253456,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,140592,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,171335,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,73928,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n0,Private,161415,11th,7,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,Private,395297,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,1,Japan,<=50K\n2,State-gov,385357,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n2,State-gov,160599,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,222450,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,38603,7th-8th,4,Divorced,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,178946,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,106471,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,341643,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,97952,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,?,<=50K\n1,Private,111567,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,3,United-States,>50K\n2,Local-gov,201764,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,153549,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,264016,Bachelors,13,Married-civ-spouse,Prof-specialty,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Private,194636,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,184271,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Local-gov,49296,Some-college,10,Married-spouse-absent,Prof-specialty,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,96099,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,2,0,3,United-States,<=50K\n0,Self-emp-not-inc,304699,10th,6,Never-married,Craft-repair,Own-child,White,Male,0,0,2,England,<=50K\n0,Private,267181,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,154076,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,98209,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,92003,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,103759,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,269681,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,789600,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,152165,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,260560,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,214781,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,4,United-States,>50K\n4,Private,207188,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,246258,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,101563,Masters,14,Divorced,Exec-managerial,Unmarried,White,Male,2,0,2,United-States,>50K\n4,Private,69955,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,124111,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,?,<=50K\n2,Private,237091,Some-college,10,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,2,Peru,<=50K\n1,Private,318644,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,138917,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,97405,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,196674,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,405281,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,186256,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,120277,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,161035,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,176244,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Other-relative,White,Female,0,0,2,Mexico,<=50K\n3,Private,32454,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,346478,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,196158,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,208470,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,215616,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,275357,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-inc,304871,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,99185,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,115085,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,82050,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,123681,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,193152,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,309466,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,100883,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,Canada,<=50K\n2,Private,32528,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,245199,10th,6,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,72375,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,117963,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,160440,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Federal-gov,113570,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n4,?,191830,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,232328,9th,5,Divorced,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,92028,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,138342,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,3,United-States,>50K\n2,Private,197810,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,102142,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,104223,Bachelors,13,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,132835,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,109195,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,203463,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,33114,11th,7,Divorced,Handlers-cleaners,Unmarried,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n1,Private,187450,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,104213,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,257849,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,50490,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,85508,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,60449,Bachelors,13,Widowed,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,131310,Some-college,10,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,158177,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,115922,11th,7,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,403471,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,3,United-States,<=50K\n0,Private,176131,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,149531,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,262243,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,64658,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,127914,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,182211,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,48520,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,403118,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,119344,HS-grad,9,Married-civ-spouse,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,334456,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,263110,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,279015,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n4,Private,195878,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,217652,12th,8,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,44807,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,129777,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,195568,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,?,>50K\n2,Private,227466,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,228457,11th,7,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,247053,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,188669,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,40666,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Italy,<=50K\n4,?,190514,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,404661,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,39986,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,2,3,United-States,<=50K\n2,Private,175133,Some-college,10,Divorced,Tech-support,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Private,101375,10th,6,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,256680,Assoc-acdm,12,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,State-gov,136878,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,106151,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,?,>50K\n2,?,242221,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,101387,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,196828,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,?,195075,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,333910,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,103540,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,36876,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,158651,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n0,Private,196943,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,184583,Some-college,10,Divorced,Other-service,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,244817,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,386726,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,373593,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,Italy,>50K\n1,Private,206199,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,93283,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,103628,Bachelors,13,Married-spouse-absent,?,Not-in-family,White,Female,0,0,0,India,<=50K\n0,Local-gov,391936,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,168740,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,150568,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,201178,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,75813,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Local-gov,398988,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,158363,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,81961,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,170017,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,348092,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,Haiti,<=50K\n1,Private,54861,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,74991,HS-grad,9,Widowed,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,106552,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,27539,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,268913,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Iran,<=50K\n4,Private,199888,HS-grad,9,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,288216,Some-college,10,Married-spouse-absent,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,378036,12th,8,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,127314,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,115963,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,332928,11th,7,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,178510,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,53147,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Private,115880,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n4,Private,375502,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,155659,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,122240,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,112305,Assoc-voc,11,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Federal-gov,35136,11th,7,Never-married,Other-service,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,215423,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,116358,HS-grad,9,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Male,0,4,2,Philippines,<=50K\n0,Private,171461,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,131584,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,29520,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Local-gov,246463,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,32616,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,144144,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,?,222789,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,227594,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,375606,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,180532,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,54342,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,208798,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,377401,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,110861,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,144594,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,129345,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,424340,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,187702,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,293917,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,160143,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,345450,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n3,State-gov,180881,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,102690,11th,7,Never-married,Machine-op-inspct,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Private,265371,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,167333,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,447144,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,280077,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,143920,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,190507,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,59469,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,74501,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,215458,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,281685,Assoc-voc,11,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,78273,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,105475,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Puerto-Rico,<=50K\n3,Private,174260,HS-grad,9,Widowed,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,149102,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,188331,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,4,0,2,United-States,>50K\n0,Private,864960,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,154526,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,60783,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,186269,HS-grad,9,Never-married,Other-service,Own-child,White,Male,1,0,1,United-States,<=50K\n1,Private,398019,1st-4th,2,Separated,Priv-house-serv,Other-relative,White,Female,0,0,1,Mexico,<=50K\n3,Federal-gov,237503,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,93762,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,59916,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,203264,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,299119,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Federal-gov,114072,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n0,?,167875,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,130525,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,71283,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,85440,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,136077,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,222434,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Canada,>50K\n0,Private,138111,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,225746,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Private,240358,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,Jamaica,<=50K\n0,Private,139863,1st-4th,2,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Private,278632,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,71046,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,312985,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,<=50K\n3,Federal-gov,276309,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,199116,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,2,Dominican-Republic,<=50K\n2,Private,52870,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n3,State-gov,79324,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,188882,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,72338,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n0,?,234108,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,113936,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,182521,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Female,4,0,1,United-States,>50K\n4,Private,124198,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,4,United-States,>50K\n0,Private,228960,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,176360,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,178649,Bachelors,13,Divorced,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Private,338740,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,205659,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,258883,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,196638,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,95246,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n0,?,216672,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,61956,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,2,0,2,United-States,<=50K\n1,Private,157216,Masters,14,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,150250,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,1,1,United-States,<=50K\n2,Private,112838,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,158688,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,227864,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,173858,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Other-relative,Asian-Pac-Islander,Male,0,3,2,China,<=50K\n3,Private,30012,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,?,50163,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,State-gov,143822,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,?,497414,7th-8th,4,Married-spouse-absent,?,Not-in-family,White,Female,0,0,1,Mexico,<=50K\n1,Private,235109,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,339196,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,181028,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,59460,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,97212,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,3,1,United-States,<=50K\n1,Private,103642,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,70447,Some-college,10,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,<=50K\n1,Private,321456,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,Germany,<=50K\n0,Private,126613,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,149508,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,332154,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n0,?,471876,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,140669,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,107164,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,225707,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Cuba,<=50K\n4,Self-emp-inc,56588,Some-college,10,Widowed,Exec-managerial,Unmarried,White,Female,0,0,4,United-States,<=50K\n1,Self-emp-inc,183125,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,4,United-States,>50K\n4,Private,177368,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,218653,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,191137,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,181585,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,142566,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,220821,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,280966,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,153155,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,195446,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,77884,1st-4th,2,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,99373,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,118729,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,108414,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,198366,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,0,United-States,<=50K\n2,Private,238384,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,214695,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,120420,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,186934,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,100368,9th,5,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,723746,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,427422,Some-college,10,Married-civ-spouse,?,Husband,White,Male,1,0,0,United-States,<=50K\n2,Private,54271,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,189680,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Italy,>50K\n3,Private,230796,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,195843,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,109912,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,4,0,3,England,>50K\n0,Private,42069,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,335950,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,4,United-States,<=50K\n2,Private,163174,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,81145,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,312052,7th-8th,4,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,209934,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,Mexico,<=50K\n0,?,269221,Assoc-acdm,12,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,322691,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,99849,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,213004,Some-college,10,Never-married,?,Own-child,White,Female,0,2,1,United-States,<=50K\n3,Private,182313,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,201505,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,227119,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,202395,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,170583,11th,7,Never-married,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,State-gov,21838,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n3,Self-emp-inc,68898,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,226702,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,168079,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,173628,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,164529,11th,7,Never-married,Farming-fishing,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,301514,Some-college,10,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n3,Private,194580,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,165611,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,96480,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,224700,Assoc-voc,11,Divorced,Protective-serv,Unmarried,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,141962,10th,6,Divorced,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,377815,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,271379,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,421837,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,77953,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,345122,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,?,172855,11th,7,Divorced,?,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,87131,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Guatemala,<=50K\n0,Private,328906,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,21626,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,143909,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,178835,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Private,94809,Some-college,10,Married-spouse-absent,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,172768,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,204742,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,144949,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,195562,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,56482,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,1,United-States,>50K\n3,Federal-gov,36314,7th-8th,4,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,329980,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,>50K\n4,Local-gov,103344,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,169708,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Local-gov,249949,Some-college,10,Divorced,Exec-managerial,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,186934,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,692831,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n0,Private,154078,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,91733,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,325373,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,160369,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Local-gov,196126,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,120053,HS-grad,9,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,204337,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,128016,HS-grad,9,Never-married,Tech-support,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,?,199301,Assoc-voc,11,Never-married,?,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,49027,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,192022,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,147099,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,334744,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,207621,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,194458,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,184245,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Mexico,>50K\n1,Private,242704,HS-grad,9,Never-married,Tech-support,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,?,278130,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,251073,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,153209,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,360879,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,115066,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,409172,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,223637,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n2,Private,161141,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,535869,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n4,Federal-gov,49921,9th,5,Divorced,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,335067,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,209460,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,355236,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,240374,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,221428,12th,8,Married-civ-spouse,Sales,Own-child,Other,Male,0,0,1,United-States,<=50K\n2,Private,356250,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n0,Private,356347,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,245356,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,247088,HS-grad,9,Separated,Craft-repair,Own-child,Black,Male,0,0,3,United-States,<=50K\n1,?,200381,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,300333,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Dominican-Republic,>50K\n2,Private,109594,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,4,United-States,>50K\n0,Local-gov,221480,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,433624,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,179681,Assoc-voc,11,Married-spouse-absent,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,136208,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,159715,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,4,0,2,United-States,<=50K\n1,Private,164683,HS-grad,9,Never-married,Transport-moving,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,152307,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,256908,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,227943,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,33673,Masters,14,Widowed,?,Not-in-family,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n1,Private,116991,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,96076,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,201314,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,153021,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,334422,Some-college,10,Divorced,Protective-serv,Unmarried,Black,Male,0,0,3,United-States,<=50K\n2,Private,160192,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,?,51216,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,323212,Some-college,10,Separated,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-inc,179030,Bachelors,13,Married-civ-spouse,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,1,South,<=50K\n0,Private,129345,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,31023,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,164616,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n1,Federal-gov,121093,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,300373,10th,6,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,95708,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,3,0,3,United-States,>50K\n2,State-gov,235779,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,114158,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,192226,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,166416,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,211215,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,157617,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,96170,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,224045,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,350550,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,114719,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,124111,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,250224,HS-grad,9,Married-civ-spouse,Craft-repair,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,?,232060,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,195258,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,285775,HS-grad,9,Never-married,Protective-serv,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,146687,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,76128,HS-grad,9,Divorced,Craft-repair,Not-in-family,Other,Male,0,0,3,Ecuador,<=50K\n1,Private,241607,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,273675,HS-grad,9,Married-spouse-absent,Other-service,Other-relative,Black,Female,0,0,1,Puerto-Rico,<=50K\n1,Private,210867,11th,7,Separated,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,144752,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n1,Private,185820,HS-grad,9,Married-civ-spouse,Sales,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,252518,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,?,<=50K\n1,Private,123833,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,291569,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,638116,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n3,Private,269045,11th,7,Widowed,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,102852,7th-8th,4,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,195447,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,173944,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,276728,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Black,Female,0,0,2,United-States,<=50K\n0,State-gov,173534,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,Ecuador,<=50K\n0,Private,198368,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,27620,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,192235,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,467936,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,Mexico,<=50K\n0,Private,264136,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,184009,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,4,3,United-States,>50K\n3,Private,165001,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,123484,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,123384,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,235307,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,238384,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,171351,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n2,State-gov,162424,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,333838,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,100303,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,58447,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n2,Local-gov,317185,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,103323,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,2,2,United-States,<=50K\n0,Private,221694,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,214091,HS-grad,9,Widowed,Other-service,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,?,171062,Bachelors,13,Never-married,?,Not-in-family,Black,Male,0,0,2,England,<=50K\n3,Private,278200,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,187592,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,188382,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,65584,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,117789,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,402089,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,69730,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,34218,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,54566,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,698039,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,76571,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,133201,7th-8th,4,Divorced,Craft-repair,Unmarried,White,Male,0,1,2,France,<=50K\n3,Federal-gov,146786,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,96154,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,State-gov,143880,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,132397,12th,8,Never-married,Other-service,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,?,45613,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,136226,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,334291,Bachelors,13,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,183017,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,207917,7th-8th,4,Married-civ-spouse,Other-service,Husband,Black,Male,1,0,0,United-States,<=50K\n4,?,136431,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,80303,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,210509,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,?,48915,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,91316,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,205670,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,25319,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,264129,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,40165,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,Japan,<=50K\n2,Federal-gov,79529,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,164519,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,184178,Assoc-acdm,12,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,<=50K\n1,State-gov,427812,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,Mexico,<=50K\n4,Self-emp-not-inc,172618,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,472861,11th,7,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,114157,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,104164,Assoc-voc,11,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,180680,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,300915,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,308171,Some-college,10,Separated,Tech-support,Unmarried,Black,Female,0,0,3,United-States,<=50K\n4,Private,320833,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,?,167710,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,228577,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,221464,11th,7,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n1,Self-emp-not-inc,213307,10th,6,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,Mexico,<=50K\n2,Private,165815,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,248919,1st-4th,2,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,4,Mexico,<=50K\n0,?,116934,10th,6,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,285365,Some-college,10,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,3,United-States,<=50K\n4,Private,134960,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Scotland,<=50K\n0,Private,449101,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,46019,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,2,0,3,United-States,>50K\n4,?,161027,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n1,State-gov,19513,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Japan,<=50K\n4,Self-emp-not-inc,258121,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,242782,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,193365,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,182402,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,254767,11th,7,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,112115,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,117299,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,214781,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,197474,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,234791,Some-college,10,Divorced,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,126584,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,28865,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,163729,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,218407,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,Columbia,>50K\n4,Private,95428,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,146103,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,1,United-States,>50K\n0,Private,150312,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,76206,9th,5,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,340543,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,125461,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,218015,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,178160,Assoc-acdm,12,Widowed,Sales,Not-in-family,White,Female,0,0,2,Germany,<=50K\n0,Private,169905,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,226629,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,180313,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,236276,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,124901,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,214275,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,3,United-States,<=50K\n3,Local-gov,371886,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,282779,Masters,14,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,>50K\n0,Private,218415,Some-college,10,Never-married,Tech-support,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,105431,HS-grad,9,Divorced,Farming-fishing,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,?,373231,Some-college,10,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,59792,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Taiwan,<=50K\n2,Private,75742,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,186731,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,310197,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,73413,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,175232,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,338948,HS-grad,9,Divorced,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,95647,11th,7,Never-married,Transport-moving,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Self-emp-inc,677398,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,263300,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Federal-gov,218325,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n2,Local-gov,156261,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,165817,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,304605,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,245361,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,230685,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,184502,11th,7,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Local-gov,116736,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,178952,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,156266,11th,7,Married-civ-spouse,Farming-fishing,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n0,Private,163519,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,296090,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,119742,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,269763,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,287833,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n0,?,190093,12th,8,Never-married,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,148003,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,131414,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,172571,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,184440,12th,8,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,216479,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,293475,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,109982,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,205033,12th,8,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,56658,HS-grad,9,Never-married,Transport-moving,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Private,159008,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,37302,HS-grad,9,Divorced,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,107417,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,236379,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,57637,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,276214,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,113749,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,100837,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,4,United-States,<=50K\n2,Private,239058,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,286419,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n3,?,50934,Assoc-acdm,12,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,283969,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,Mexico,<=50K\n4,Private,152839,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,32290,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,204373,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,126528,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,245408,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,127610,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,132919,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n4,Private,58547,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,<=50K\n2,Private,251091,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,149950,HS-grad,9,Widowed,Priv-house-serv,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,464621,Some-college,10,Never-married,Farming-fishing,Own-child,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,170230,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,100294,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,234108,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,33046,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,84428,Some-college,10,Widowed,Sales,Not-in-family,Asian-Pac-Islander,Female,1,0,2,United-States,<=50K\n1,Self-emp-not-inc,107662,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,0,Canada,<=50K\n0,Private,220874,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,88564,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,144778,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,?,179861,10th,6,Never-married,?,Own-child,White,Male,0,0,0,Poland,<=50K\n1,Private,166671,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,97180,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,194091,11th,7,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,308498,10th,6,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Federal-gov,321865,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,181814,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,374423,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,3,2,United-States,>50K\n3,Private,213668,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,213236,HS-grad,9,Separated,Other-service,Unmarried,White,Male,0,0,2,Dominican-Republic,<=50K\n4,Self-emp-not-inc,115439,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,124111,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,176185,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,2,0,2,United-States,>50K\n1,Private,211231,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,259715,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,248600,10th,6,Never-married,Other-service,Other-relative,White,Female,4,0,1,United-States,<=50K\n2,Private,153997,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,3,2,Puerto-Rico,>50K\n2,Private,67779,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,236861,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,367334,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,213391,9th,5,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,301124,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,>50K\n2,Private,184117,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,233923,Assoc-voc,11,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,348592,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,111817,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,170983,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,121407,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,210275,HS-grad,9,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n0,Private,116358,HS-grad,9,Never-married,Other-service,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Private,189123,Masters,14,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,48087,Bachelors,13,Divorced,Machine-op-inspct,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Private,179488,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,370990,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,169760,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n2,State-gov,34493,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,185584,Bachelors,13,Widowed,Machine-op-inspct,Unmarried,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n2,Private,324311,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,Mexico,<=50K\n4,Self-emp-not-inc,96299,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,147322,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Columbia,<=50K\n1,Private,135289,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,State-gov,128586,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,185590,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,107458,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,151874,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,3,United-States,<=50K\n1,State-gov,413846,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,203836,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,110028,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,57640,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,67874,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,169112,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,154410,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,63234,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,2,0,0,United-States,<=50K\n4,Private,121036,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,408328,Preschool,1,Married-spouse-absent,Handlers-cleaners,Unmarried,White,Male,0,0,2,Mexico,<=50K\n1,Private,269254,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,115438,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,332249,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,State-gov,160261,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n0,Private,167316,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,State-gov,291586,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,184046,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Federal-gov,178025,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,104280,12th,8,Married-civ-spouse,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,302604,11th,7,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,225243,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,327120,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Portugal,<=50K\n3,Self-emp-not-inc,43878,Doctorate,16,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,?,40915,Bachelors,13,Married-spouse-absent,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,83444,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,351416,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,117310,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,2,3,United-States,<=50K\n2,Private,324231,9th,5,Never-married,Farming-fishing,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,161802,1st-4th,2,Married-civ-spouse,Priv-house-serv,Wife,Black,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,184804,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,4,2,United-States,<=50K\n1,Federal-gov,547931,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,46395,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,182313,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,169069,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,203182,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,142924,Bachelors,13,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,180656,5th-6th,3,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,Guatemala,<=50K\n4,Private,187485,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n4,?,157778,Some-college,10,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,149337,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,97054,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,377017,Bachelors,13,Never-married,Sales,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Private,106900,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,378723,10th,6,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,209955,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,312766,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,70857,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,238917,11th,7,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,El-Salvador,<=50K\n3,State-gov,53497,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,283174,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,?,190497,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,104447,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,4,2,United-States,<=50K\n2,Private,73023,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Private,177896,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,349951,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,<=50K\n1,Private,80179,HS-grad,9,Separated,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,308955,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,126896,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,State-gov,116385,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,State-gov,172425,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,106615,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,261929,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,163870,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-inc,242080,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,30796,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,207578,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,140001,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,217942,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,301010,11th,7,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,?,222007,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,72630,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,Local-gov,204377,11th,7,Divorced,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,189614,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,100345,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,163687,10th,6,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,174215,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,1,United-States,>50K\n1,Private,37646,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,84154,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,116493,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,259972,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,185338,Bachelors,13,Widowed,Tech-support,Unmarried,White,Female,0,0,2,United-States,>50K\n2,Private,99212,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,54780,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,393673,Some-college,10,Never-married,Tech-support,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,66687,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,133986,Some-college,10,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,248694,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,212888,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,304955,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,172232,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,32446,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,182701,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,Mexico,<=50K\n0,Private,164920,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,0,Germany,<=50K\n0,Private,274424,HS-grad,9,Never-married,Tech-support,Unmarried,White,Female,1,0,2,United-States,<=50K\n4,Private,176904,10th,6,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,217530,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,318450,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,210945,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,181838,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,91931,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,123681,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,181628,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n4,?,305145,Bachelors,13,Widowed,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,174533,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,94342,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,215624,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n3,Self-emp-not-inc,112485,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,27484,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,186454,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,2,0,2,Vietnam,<=50K\n1,State-gov,187746,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n4,Private,358628,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,295939,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,127198,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Germany,<=50K\n3,Private,81497,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,143078,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,4,3,United-States,>50K\n4,Self-emp-not-inc,177806,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,210926,9th,5,Divorced,Farming-fishing,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,Private,195144,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,252563,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,170785,12th,8,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,111232,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,59584,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,148254,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,3,2,?,>50K\n1,Local-gov,19302,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,4,Germany,>50K\n3,Private,285224,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,172256,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,State-gov,128260,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,156163,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,?,<=50K\n1,Private,155914,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,58471,HS-grad,9,Never-married,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,282389,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,117915,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,163628,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,287436,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n4,State-gov,139736,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Local-gov,136643,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,180572,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,State-gov,148805,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,497039,Some-college,10,Divorced,Tech-support,Unmarried,Black,Female,0,0,3,United-States,<=50K\n0,Private,226956,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,157184,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,United-States,>50K\n0,Private,315470,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,252079,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,138022,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,48520,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,346605,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,139770,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,209899,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,55844,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,215789,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,126913,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,State-gov,101950,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,451742,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,173754,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,350131,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,4,2,United-States,<=50K\n1,Private,185820,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,176837,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,271282,Bachelors,13,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,?,420081,Assoc-acdm,12,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,State-gov,142282,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,266855,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,148143,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,State-gov,21189,Bachelors,13,Divorced,Adm-clerical,Other-relative,Black,Female,0,0,1,United-States,<=50K\n2,Private,110013,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,350400,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,275507,Some-college,10,Divorced,Sales,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Private,169948,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,>50K\n2,Private,298161,Assoc-voc,11,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,Cuba,<=50K\n2,?,120131,HS-grad,9,Never-married,?,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,State-gov,113129,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,201680,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,158609,10th,6,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,22313,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,261012,HS-grad,9,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,3,United-States,<=50K\n3,Private,104501,12th,8,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,50178,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,65624,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,236481,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,0,India,<=50K\n3,Private,213041,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,105127,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,?,127441,Some-college,10,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,210541,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,163512,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,0,Guatemala,<=50K\n2,Private,170376,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,132465,1st-4th,2,Married-spouse-absent,Other-service,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Private,253827,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,186383,HS-grad,9,Married-civ-spouse,Priv-house-serv,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,111985,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,152909,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,279340,7th-8th,4,Widowed,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,154571,Some-college,10,Never-married,Craft-repair,Other-relative,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n1,Private,270889,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,241731,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,?,256649,Bachelors,13,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,176711,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,359155,HS-grad,9,Separated,Transport-moving,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,State-gov,103651,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,138872,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,180195,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,Local-gov,175979,Bachelors,13,Divorced,Prof-specialty,Other-relative,White,Female,0,0,3,United-States,<=50K\n4,Local-gov,53612,Masters,14,Separated,Prof-specialty,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Local-gov,28357,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,460322,Bachelors,13,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,182954,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Dominican-Republic,<=50K\n0,Private,242871,10th,6,Never-married,Sales,Own-child,White,Female,1,0,0,United-States,<=50K\n3,Local-gov,30636,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n3,Local-gov,274657,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,White,Male,2,0,2,United-States,<=50K\n0,?,179807,10th,6,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,230215,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,260549,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,408208,HS-grad,9,Never-married,Craft-repair,Other-relative,Black,Male,0,0,2,United-States,<=50K\n4,Private,143837,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,203784,Preschool,1,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Federal-gov,190020,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,666014,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,50753,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,197515,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,209476,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,192995,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,2,0,2,United-States,<=50K\n0,Private,39640,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,203488,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,125791,Assoc-acdm,12,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,167424,Some-college,10,Never-married,Priv-house-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,169590,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n4,?,174533,Bachelors,13,Never-married,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,474568,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,?,>50K\n2,Private,414910,7th-8th,4,Divorced,Sales,Not-in-family,Other,Female,0,0,1,United-States,<=50K\n0,Self-emp-inc,95997,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,191797,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n4,?,143732,1st-4th,2,Widowed,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,65624,Assoc-acdm,12,Never-married,Machine-op-inspct,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,352614,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,?,>50K\n1,Private,301251,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,98524,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,112512,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Local-gov,170861,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,244668,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Male,3,0,2,Mexico,>50K\n0,Private,148890,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,149898,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,240629,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,19522,Some-college,10,Never-married,Tech-support,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Private,152839,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,105788,Masters,14,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,>50K\n0,Private,314823,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n0,?,287681,5th-6th,3,Never-married,?,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n3,?,313445,HS-grad,9,Separated,?,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,289148,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,166193,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,206857,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,150683,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,155759,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,211459,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,191103,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,88856,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,2,United-States,>50K\n2,Private,193882,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n4,State-gov,222792,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,190137,HS-grad,9,Never-married,Sales,Own-child,Other,Male,0,0,2,United-States,<=50K\n2,Private,174308,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,172787,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Male,0,4,1,United-States,>50K\n4,Private,146391,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,Ireland,<=50K\n1,Private,179708,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,314375,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,120277,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,244906,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,251890,10th,6,Widowed,Other-service,Not-in-family,White,Female,0,0,1,Puerto-Rico,<=50K\n0,Private,220993,HS-grad,9,Married-civ-spouse,Sales,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,309131,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,263200,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,Ecuador,<=50K\n3,Self-emp-not-inc,92469,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,32406,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,235070,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,Black,Female,0,0,1,Haiti,<=50K\n3,Private,196571,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,258819,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,33945,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,452640,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,112772,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,34845,Assoc-voc,11,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,119051,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n0,Private,197767,Some-college,10,Never-married,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,181578,HS-grad,9,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,2,?,>50K\n4,Private,329654,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,3,0,3,United-States,>50K\n4,Federal-gov,47534,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,Private,341294,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,336042,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,56019,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,3,India,>50K\n2,Private,86505,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,274106,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Federal-gov,52765,9th,5,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,136848,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,298227,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-not-inc,215493,HS-grad,9,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,197583,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,265190,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,96921,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,202420,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,252616,7th-8th,4,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n2,Private,102976,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,70439,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,184290,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,72887,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n0,Local-gov,237498,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,228043,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,144056,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n1,Private,187711,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,282489,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,359155,Bachelors,13,Divorced,Tech-support,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,87169,HS-grad,9,Never-married,Farming-fishing,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n1,Private,251091,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n2,Private,130126,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,163265,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,250040,7th-8th,4,Divorced,Prof-specialty,Other-relative,White,Female,0,0,0,?,<=50K\n4,?,218764,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,176773,Preschool,1,Married-civ-spouse,Farming-fishing,Husband,Black,Male,0,0,2,Haiti,<=50K\n2,Self-emp-not-inc,98941,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,217467,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,97176,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n1,Private,230503,Some-college,10,Never-married,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,227321,Some-college,10,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,199698,9th,5,Never-married,Transport-moving,Unmarried,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,347491,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,103925,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Germany,<=50K\n1,Private,124569,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,80680,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,State-gov,119197,Masters,14,Divorced,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n4,Private,147055,9th,5,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,316470,9th,5,Married-spouse-absent,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,Private,260082,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Cuba,<=50K\n0,?,228960,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,256496,10th,6,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,133351,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,151835,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n3,?,224793,Bachelors,13,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,101480,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,138719,11th,7,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,129121,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,401069,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,188758,10th,6,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,191598,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,3,2,United-States,<=50K\n1,Private,330715,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,284317,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,393673,12th,8,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,206609,10th,6,Never-married,Transport-moving,Not-in-family,White,Male,0,4,3,United-States,<=50K\n4,?,88545,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,224632,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,227529,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,210148,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,224174,Assoc-voc,11,Widowed,Craft-repair,Not-in-family,Black,Male,0,0,2,?,<=50K\n0,Private,193787,Some-college,10,Never-married,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,244953,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,223749,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,37650,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n3,Private,358382,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,155275,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,180574,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,101853,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,34161,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,83311,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,217125,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,166368,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,44793,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,123147,Some-college,10,Never-married,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,184529,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,224566,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,195994,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,186376,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n4,Federal-gov,38749,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,3,Philippines,>50K\n4,?,78375,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,148867,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,1,United-States,>50K\n2,Private,207066,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,339423,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,172186,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,?,138564,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,208259,10th,6,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,203376,Masters,14,Widowed,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,243165,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,213008,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,159323,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,4,Canada,<=50K\n0,Private,197050,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,166855,7th-8th,4,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,163072,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,191807,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,State-gov,48634,Bachelors,13,Never-married,Protective-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,287737,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Private,162623,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,104993,HS-grad,9,Never-married,Sales,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,?,256211,Assoc-voc,11,Married-civ-spouse,?,Husband,Asian-Pac-Islander,Male,0,3,2,Poland,<=50K\n0,Private,298605,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,115803,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,183342,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,115971,9th,5,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,254547,HS-grad,9,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,0,United-States,<=50K\n2,Private,211940,Some-college,10,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,State-gov,136819,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,186000,Assoc-voc,11,Widowed,Craft-repair,Unmarried,White,Female,0,0,2,Canada,<=50K\n0,Private,289982,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,El-Salvador,<=50K\n4,Private,137344,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,174413,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,186993,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,176388,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Private,49469,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,83800,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,194809,Some-college,10,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,194397,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,181363,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,?,227243,HS-grad,9,Widowed,?,Unmarried,White,Female,0,0,1,Puerto-Rico,<=50K\n0,Private,176136,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,?,102541,10th,6,Never-married,?,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,166088,Assoc-voc,11,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,95634,Some-college,10,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,Canada,<=50K\n1,Private,66304,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,64292,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,1,0,1,United-States,<=50K\n1,Private,41610,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,198028,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,228652,Some-college,10,Divorced,Machine-op-inspct,Own-child,Other,Male,0,0,2,Mexico,<=50K\n2,Private,165815,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,238255,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,65740,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n3,Private,279543,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Cuba,>50K\n2,Private,114765,10th,6,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,279580,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,73257,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,Germany,<=50K\n4,Private,80621,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,State-gov,193602,Some-college,10,Widowed,Exec-managerial,Not-in-family,Black,Male,4,0,2,United-States,>50K\n0,?,141445,9th,5,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,224512,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,98642,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,India,>50K\n0,?,182288,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,765214,Masters,14,Separated,Exec-managerial,Not-in-family,White,Male,0,4,2,United-States,>50K\n0,Private,224785,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,2,4,United-States,<=50K\n0,?,285177,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,241880,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,201495,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,165215,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n1,Self-emp-inc,99146,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,92795,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,54022,Some-college,10,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,175268,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,123983,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n1,Private,269323,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,32798,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,101077,Prof-school,15,Widowed,Prof-specialty,Not-in-family,White,Female,0,4,2,United-States,>50K\n0,?,157332,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,390746,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,Ireland,<=50K\n1,Private,200318,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,36425,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,221172,5th-6th,3,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,337995,Bachelors,13,Divorced,Exec-managerial,Unmarried,Black,Male,4,0,3,United-States,>50K\n3,Private,64421,Some-college,10,Widowed,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,199915,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,207658,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,124810,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,253060,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,76878,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n0,?,38455,HS-grad,9,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,41294,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,205195,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,162236,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Private,445480,12th,8,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,761800,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,188300,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,138088,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,132304,Some-college,10,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,126173,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,259873,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,122215,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,190015,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,313132,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,103323,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,?,44789,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,192932,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,264025,HS-grad,9,Separated,Transport-moving,Unmarried,Black,Male,1,0,4,United-States,<=50K\n2,Private,30269,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,1,0,3,United-States,<=50K\n0,Private,283092,HS-grad,9,Never-married,Sales,Other-relative,Black,Male,0,0,2,Jamaica,<=50K\n0,Private,125236,10th,6,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,187308,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,150519,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,32587,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,1,2,United-States,>50K\n2,Private,244803,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Columbia,<=50K\n0,Private,316793,HS-grad,9,Married-civ-spouse,Sales,Wife,Black,Female,0,0,1,United-States,<=50K\n2,Private,106068,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,191878,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,?,159008,Bachelors,13,Married-spouse-absent,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,181983,Doctorate,16,Divorced,Exec-managerial,Not-in-family,White,Female,0,4,3,England,>50K\n4,Private,113293,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,224711,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,>50K\n0,Private,460356,12th,8,Never-married,Other-service,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n2,Local-gov,184474,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,1,0,3,United-States,<=50K\n2,Private,289890,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,183148,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,178109,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,351760,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,176967,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,67444,11th,7,Never-married,Other-service,Other-relative,Black,Male,0,0,0,United-States,<=50K\n0,Private,48343,HS-grad,9,Never-married,Sales,Not-in-family,Black,Female,0,0,1,?,<=50K\n0,Private,1047822,11th,7,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,200448,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,34364,Assoc-acdm,12,Separated,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,95725,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,124802,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,196673,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,196943,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,43819,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,173020,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,102690,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,199018,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,201954,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,?,<=50K\n1,Private,168854,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,1,2,United-States,<=50K\n0,Private,53702,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,154043,HS-grad,9,Widowed,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,64112,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n4,?,294420,Bachelors,13,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,325159,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,267706,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,70240,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,213307,7th-8th,4,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,0,Mexico,<=50K\n4,Private,192845,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,273010,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,177775,Assoc-voc,11,Never-married,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,?,393122,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,345577,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,72257,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,113129,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,292380,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,0,United-States,<=50K\n1,Private,121040,Assoc-voc,11,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,142097,9th,5,Married-civ-spouse,Priv-house-serv,Wife,White,Female,0,0,0,United-States,<=50K\n3,Federal-gov,34998,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,41021,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,152889,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,436651,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n0,?,256504,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,215479,HS-grad,9,Never-married,Other-service,Other-relative,Black,Male,0,0,2,United-States,<=50K\n3,State-gov,100285,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Male,4,0,2,United-States,>50K\n4,Private,373099,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,99357,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,67243,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Portugal,<=50K\n1,Private,231263,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,243942,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,194102,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,141748,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,211013,HS-grad,9,Separated,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,102652,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,201127,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,4,United-States,<=50K\n4,?,172667,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,175958,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Italy,<=50K\n1,Private,73928,10th,6,Separated,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n3,Private,212944,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,544319,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,348960,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,280519,10th,6,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,155172,Assoc-acdm,12,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,106856,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,397346,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,253814,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,201490,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,176806,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,107038,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,122194,Some-college,10,Married-civ-spouse,Craft-repair,Wife,White,Female,2,0,2,United-States,>50K\n1,Self-emp-not-inc,180928,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,143746,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,183523,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,179262,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,190759,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,200400,Doctorate,16,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,166320,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,205954,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,251786,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,166371,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,?,135046,Some-college,10,Never-married,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,170423,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,393673,Masters,14,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,115438,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,173944,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,?,292803,Some-college,10,Divorced,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n4,Private,149756,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n2,Private,192251,Some-college,10,Divorced,Craft-repair,Own-child,White,Female,0,0,3,United-States,>50K\n0,Private,163687,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,200421,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,368014,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n3,?,141483,10th,6,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,191480,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,202466,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,?,28765,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,141584,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,143123,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,122194,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,110171,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,2,United-States,>50K\n2,Self-emp-inc,342510,Bachelors,13,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,42279,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,201122,HS-grad,9,Separated,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,254025,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,410114,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,320422,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,2,0,3,United-States,<=50K\n4,Private,67153,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,224406,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,211678,10th,6,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,257017,Assoc-voc,11,Never-married,Other-service,Other-relative,Black,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,106232,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,State-gov,41115,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,161245,12th,8,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,37618,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,321787,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,123011,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,66278,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,181054,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,129786,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,245402,11th,7,Divorced,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n0,?,192711,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,240124,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,370675,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,34066,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,>50K\n1,Private,53553,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,319758,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,43556,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,97952,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,4,United-States,<=50K\n2,Private,244522,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,188108,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,187022,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,173422,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n4,State-gov,103575,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,Germany,<=50K\n0,Private,116830,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,219504,12th,8,Divorced,Transport-moving,Unmarried,Black,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,102102,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,129661,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,State-gov,189346,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,113211,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,256866,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,186408,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,1,0,2,United-States,<=50K\n0,Private,50411,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,118941,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,?,171868,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,99065,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,238917,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,220740,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,192660,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,56962,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n0,?,156780,Some-college,10,Never-married,?,Not-in-family,Asian-Pac-Islander,Female,0,0,0,?,<=50K\n0,Private,122048,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,2,0,2,United-States,<=50K\n3,Private,172511,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,186790,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,196280,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,61885,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,143822,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,315640,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,0,China,<=50K\n1,Private,617917,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,35448,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,124483,Bachelors,13,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Male,0,4,2,India,<=50K\n4,Private,230904,11th,7,Widowed,Machine-op-inspct,Not-in-family,Black,Female,0,2,1,United-States,<=50K\n1,Private,164461,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,450695,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,352156,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,166634,HS-grad,9,Never-married,Adm-clerical,Own-child,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Private,151107,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,3,3,United-States,>50K\n3,Private,219751,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,85604,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,231482,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Portugal,<=50K\n0,Private,138152,9th,5,Never-married,Craft-repair,Other-relative,Other,Male,0,0,3,Guatemala,<=50K\n1,Private,309196,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,91666,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,89734,Some-college,10,Never-married,Other-service,Other-relative,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,79661,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,197150,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,El-Salvador,<=50K\n1,?,41281,Bachelors,13,Married-spouse-absent,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,53448,12th,8,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,3,China,<=50K\n2,Self-emp-not-inc,255543,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n3,State-gov,367209,Doctorate,16,Married-spouse-absent,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,>50K\n2,Private,226500,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,292710,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,235732,11th,7,Never-married,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,301614,Bachelors,13,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,261714,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,?,35751,1st-4th,2,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,266316,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,1,United-States,<=50K\n2,Self-emp-inc,189941,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,3,United-States,>50K\n3,Self-emp-not-inc,143535,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,234537,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,181435,11th,7,Divorced,Other-service,Unmarried,White,Male,2,0,3,United-States,<=50K\n2,Private,94210,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,344060,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,301359,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,184527,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,333070,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,1,United-States,<=50K\n0,Private,149574,Some-college,10,Married-spouse-absent,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,34037,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,123502,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,267661,Assoc-voc,11,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,109920,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,134120,HS-grad,9,Divorced,Sales,Other-relative,White,Female,0,0,3,United-States,<=50K\n0,Private,192254,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,94809,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,1,0,1,United-States,<=50K\n0,Private,183789,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,86643,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,190290,Some-college,10,Never-married,?,Own-child,White,Male,0,0,4,United-States,<=50K\n4,?,228140,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,198349,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,113597,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,280567,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,298967,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,134615,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,89852,12th,8,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,289442,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,159109,11th,7,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,105495,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n4,Private,155093,Assoc-voc,11,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,312923,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,Mexico,<=50K\n4,Private,202435,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,49154,11th,7,Never-married,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,184456,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,95329,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,India,<=50K\n2,Private,173938,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n4,Private,373216,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,204226,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,222745,Doctorate,16,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,106728,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,61287,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,146011,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Private,166744,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,54651,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,?,42894,11th,7,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,131230,Bachelors,13,Never-married,Protective-serv,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,271312,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,163776,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,230126,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,37718,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,245975,9th,5,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,4,United-States,<=50K\n4,State-gov,115439,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,97554,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,109762,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n3,Private,138342,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,429696,Some-college,10,Married-civ-spouse,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n4,?,309955,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,1,0,United-States,<=50K\n3,Private,275154,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,52849,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,?,>50K\n0,State-gov,191165,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,277471,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,171754,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,?,<=50K\n2,Private,117936,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,249956,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,170502,Masters,14,Divorced,Exec-managerial,Not-in-family,Black,Male,0,0,4,United-States,>50K\n0,Private,202951,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,396722,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,93557,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,103805,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,92141,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,171924,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,3,United-States,>50K\n2,Local-gov,173804,Bachelors,13,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,139571,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,142076,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,126514,Some-college,10,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,68729,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n0,Private,37783,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,183580,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,106637,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,411604,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,214635,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,?,<=50K\n1,Private,201663,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,153064,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,212465,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,93604,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,141221,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n2,Local-gov,289653,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,2,3,United-States,<=50K\n0,Private,219835,7th-8th,4,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,Guatemala,<=50K\n2,State-gov,187119,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Private,406518,HS-grad,9,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,0,Yugoslavia,<=50K\n1,Self-emp-not-inc,372793,Bachelors,13,Divorced,Exec-managerial,Not-in-family,Black,Male,0,0,1,?,<=50K\n3,?,229029,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,2,0,0,United-States,>50K\n3,Private,145105,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,171181,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,1,0,0,United-States,<=50K\n4,Private,80927,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,191357,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,153542,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,27802,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,275792,Bachelors,13,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Federal-gov,162876,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,197600,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,134247,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,179597,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,148003,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,3,2,United-States,>50K\n1,Private,185177,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,133069,10th,6,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,177154,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,State-gov,29324,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,54911,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,219611,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,State-gov,200294,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,177063,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,140001,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,237433,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n2,State-gov,99185,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,52291,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,247328,HS-grad,9,Separated,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,388594,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Local-gov,130730,HS-grad,9,Divorced,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,115458,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,113866,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,284710,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,Columbia,>50K\n4,Local-gov,168381,Assoc-voc,11,Widowed,Adm-clerical,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,167063,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,33794,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,263574,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,95552,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,245724,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,<=50K\n4,Private,152731,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,366876,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,203488,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,30529,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,2,United-States,<=50K\n4,Private,201159,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,182158,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,443377,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,101618,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,132576,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n3,?,123429,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,147098,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Male,0,4,3,United-States,>50K\n2,Private,30424,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,0,3,2,United-States,<=50K\n3,Private,68898,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,158864,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Federal-gov,180103,Assoc-voc,11,Never-married,Tech-support,Unmarried,Black,Male,1,0,2,United-States,<=50K\n3,Private,317625,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,80933,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,107373,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n1,Self-emp-not-inc,220148,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,63503,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Greece,>50K\n4,Private,210350,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,1,Mexico,<=50K\n4,Private,194589,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,200453,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,101575,12th,8,Divorced,Transport-moving,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,201232,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,168553,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,166606,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,104756,HS-grad,9,Married-AF-spouse,?,Wife,White,Female,0,2,2,United-States,<=50K\n1,Private,106014,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,100882,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,108836,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,271493,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,204629,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,Canada,>50K\n0,Private,153078,Bachelors,13,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,148316,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,293485,HS-grad,9,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,257105,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,248160,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,104704,11th,7,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Private,209057,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,243233,Some-college,10,Married-civ-spouse,Armed-Forces,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,204314,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,>50K\n4,Private,108969,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,103397,HS-grad,9,Divorced,Handlers-cleaners,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,198452,Bachelors,13,Separated,Tech-support,Unmarried,White,Female,2,0,2,United-States,<=50K\n2,Private,216572,HS-grad,9,Separated,Priv-house-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,311920,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,1,0,United-States,>50K\n2,Self-emp-inc,363298,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,146906,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,339814,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,169408,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Self-emp-not-inc,308296,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,59380,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,195634,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,0,United-States,>50K\n1,Federal-gov,180656,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,144793,11th,7,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,56820,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,41414,9th,5,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,160731,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,175778,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,230238,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n2,State-gov,372130,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,2,0,3,United-States,<=50K\n1,Private,167501,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,141029,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,135525,Masters,14,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,522881,Assoc-voc,11,Never-married,Exec-managerial,Other-relative,White,Male,0,0,2,Mexico,<=50K\n4,Private,162009,10th,6,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,365745,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,68393,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,203576,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,138513,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,?,188686,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,39551,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,127366,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,183747,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,136331,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,81794,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,222596,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,108943,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,189092,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,152109,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,195565,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,255927,Bachelors,13,Widowed,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,100734,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Local-gov,266433,Some-college,10,Widowed,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,158672,11th,7,Separated,Other-service,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n1,Private,102268,12th,8,Divorced,Protective-serv,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,228399,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,298510,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,126225,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,228456,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Self-emp-inc,437161,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,183608,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,174395,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,221366,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,421010,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,245333,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,352277,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,29874,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,77322,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,260560,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,217848,12th,8,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,283731,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,190759,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,109567,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,209826,Some-college,10,Never-married,Farming-fishing,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,232801,10th,6,Divorced,Machine-op-inspct,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,154374,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n1,Self-emp-inc,126738,Assoc-acdm,12,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,202156,HS-grad,9,Married-civ-spouse,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,195447,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,?,113301,11th,7,Separated,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,189203,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,223019,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,195878,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,Cuba,<=50K\n4,Private,163150,HS-grad,9,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,139278,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,196494,Some-college,10,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,303704,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,304082,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,Peru,<=50K\n0,Private,106943,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,220993,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,83984,Some-college,10,Married-civ-spouse,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,State-gov,340605,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,379710,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,145933,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n1,Self-emp-not-inc,208068,9th,5,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Guatemala,<=50K\n2,Private,172718,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,53285,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,139793,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,1,0,2,United-States,<=50K\n4,?,365350,5th-6th,3,Married-spouse-absent,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,144064,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,182676,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,2,Mexico,<=50K\n1,Private,108574,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,163845,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,344804,5th-6th,3,Married-spouse-absent,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,State-gov,252818,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,114231,10th,6,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,111895,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,128814,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,168941,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,212578,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,2,0,United-States,<=50K\n1,?,251120,Some-college,10,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,192773,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,State-gov,180667,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,186172,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,309590,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,Jamaica,<=50K\n2,Private,34178,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,>50K\n2,Private,103759,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,137900,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,223911,Some-college,10,Married-spouse-absent,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,55720,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,123535,11th,7,Married-civ-spouse,Other-service,Husband,Other,Male,0,0,2,Guatemala,<=50K\n0,Private,479296,9th,5,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n4,?,263125,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,1,0,1,United-States,<=50K\n4,?,174817,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,134890,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,183887,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,3,3,United-States,>50K\n1,Private,55360,HS-grad,9,Never-married,Sales,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,113211,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,224203,11th,7,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,United-States,<=50K\n4,?,132112,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,113635,12th,8,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,52262,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,202300,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,307761,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,324655,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,23336,Masters,14,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,206199,11th,7,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Federal-gov,365430,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,42740,Some-college,10,Separated,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,160594,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,202069,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,1,United-States,<=50K\n0,?,142875,10th,6,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,60414,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,340885,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,194096,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,222506,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n2,Private,55191,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,88572,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,216757,Doctorate,16,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,57534,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,96321,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,201908,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,237868,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,285570,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,187625,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,376755,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,137078,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,175943,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,211208,11th,7,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,105821,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,205694,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,Canada,<=50K\n2,Private,148485,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,142264,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,125892,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,250226,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,300679,Some-college,10,Never-married,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,112626,Some-college,10,Never-married,Priv-house-serv,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,153883,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,3,0,2,United-States,>50K\n3,Private,103648,Assoc-voc,11,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,State-gov,162487,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,?,<=50K\n3,Local-gov,331650,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,3,0,1,United-States,>50K\n3,Self-emp-inc,171338,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n3,Self-emp-not-inc,178319,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,217460,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,182653,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,?,152837,HS-grad,9,Widowed,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,459189,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,87858,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,125279,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,169542,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,363418,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,England,>50K\n2,Private,198282,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,104620,Masters,14,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,176137,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,168347,HS-grad,9,Separated,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,191814,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n2,Local-gov,150533,Masters,14,Married-civ-spouse,Protective-serv,Husband,White,Male,3,0,1,United-States,>50K\n1,Private,115677,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,182590,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,239648,Some-college,10,Never-married,Machine-op-inspct,Unmarried,Asian-Pac-Islander,Male,0,0,2,Cambodia,<=50K\n4,Private,139031,HS-grad,9,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,141340,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,170645,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n2,Local-gov,241506,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,163921,Some-college,10,Widowed,Adm-clerical,Unmarried,Black,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,104958,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,144284,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,181139,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,209962,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,87218,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,182189,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,196337,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,238605,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,106501,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,3,United-States,<=50K\n0,Private,172169,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,242922,HS-grad,9,Never-married,Tech-support,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n4,Private,257555,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,192302,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-inc,115487,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,70160,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,410351,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,236421,12th,8,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,196662,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,Puerto-Rico,<=50K\n3,Self-emp-not-inc,203004,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n0,Private,200819,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,222866,10th,6,Never-married,Farming-fishing,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,204160,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,141707,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,123157,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,?,<=50K\n1,Private,219863,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,29841,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,35723,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,163948,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,?,255117,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,100032,HS-grad,9,Married-civ-spouse,Protective-serv,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,33087,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,324469,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,337001,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,151747,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,85057,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,>50K\n0,Private,257910,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,94331,12th,8,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,250261,1st-4th,2,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n1,Private,97359,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,121294,7th-8th,4,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,2,Poland,<=50K\n3,Self-emp-inc,211020,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,?,165295,7th-8th,4,Separated,?,Unmarried,White,Female,0,0,2,Mexico,<=50K\n4,Self-emp-inc,116057,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,2,0,2,United-States,<=50K\n3,Private,469005,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Local-gov,197886,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,379917,Assoc-voc,11,Never-married,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,30912,Assoc-acdm,12,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,206889,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,87490,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Local-gov,241851,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,155899,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,253135,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,?,<=50K\n4,Local-gov,120408,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,77884,Assoc-voc,11,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,162887,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,154843,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,India,<=50K\n2,Local-gov,115511,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,121492,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,103596,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,457070,7th-8th,4,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,73461,HS-grad,9,Never-married,Tech-support,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,153078,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,2,4,South,>50K\n3,Private,194788,10th,6,Divorced,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,203181,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,230279,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,89041,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,92717,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,1,2,United-States,<=50K\n1,Private,257033,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Private,145166,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,20308,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,?,203784,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,38353,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,133373,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,>50K\n4,?,167978,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,166302,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,333304,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,265706,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Male,2,0,2,United-States,<=50K\n4,Self-emp-not-inc,111916,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,>50K\n4,State-gov,213700,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,276559,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,4,2,United-States,>50K\n2,Private,36989,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,181566,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n0,Private,202920,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Germany,<=50K\n1,Self-emp-not-inc,24529,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,137320,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,?,106791,HS-grad,9,Widowed,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,160510,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n1,?,112584,HS-grad,9,Separated,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,233779,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,276005,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,192251,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,3,0,United-States,>50K\n4,?,308689,5th-6th,3,Married-civ-spouse,?,Husband,Black,Male,0,0,2,Cuba,<=50K\n3,Private,274528,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,23856,11th,7,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,175220,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,3,0,3,Taiwan,>50K\n2,Self-emp-not-inc,233150,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,153169,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,298449,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,188949,11th,7,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,157911,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,243330,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,271343,Some-college,10,Separated,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,45564,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Male,2,0,3,United-States,<=50K\n3,Private,262043,Bachelors,13,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,103323,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,96480,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n3,Private,154117,HS-grad,9,Separated,Craft-repair,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,151856,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,132053,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,2,2,United-States,<=50K\n1,Private,199118,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,Guatemala,<=50K\n2,Self-emp-not-inc,119272,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,209792,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,185084,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,230931,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,Puerto-Rico,<=50K\n0,Private,162282,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,185366,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,93557,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,160428,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,159650,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,137678,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,56841,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,33124,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,219117,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,2,0,3,United-States,<=50K\n2,Private,208045,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,128578,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,351731,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,201694,Assoc-acdm,12,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,205152,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,30713,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,190107,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Federal-gov,244480,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,347112,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,106297,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,128516,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,55950,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,324505,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,130760,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,174413,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,3,2,United-States,<=50K\n1,?,20877,Some-college,10,Never-married,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,144238,11th,7,Never-married,Farming-fishing,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,193047,Doctorate,16,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,300099,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,369902,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,42166,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,171924,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Canada,>50K\n3,Private,201984,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,306420,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,46746,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,185325,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,201697,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,181372,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,112077,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,370057,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,72591,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,105803,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,198478,HS-grad,9,Never-married,Farming-fishing,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,119017,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,138872,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n4,Federal-gov,97213,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,36556,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,200904,10th,6,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,186256,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,1,United-States,<=50K\n0,Private,115815,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,England,<=50K\n2,Private,308770,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n0,Local-gov,187792,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,233571,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,3,2,United-States,>50K\n1,Private,131913,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,31558,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,255004,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,1,0,4,United-States,<=50K\n0,Local-gov,315287,Some-college,10,Never-married,Protective-serv,Other-relative,Black,Male,0,0,2,Trinadad&Tobago,<=50K\n0,Private,182545,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,750972,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,505365,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n0,Local-gov,177475,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,203761,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,36104,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,179708,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,77392,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n4,?,86709,Some-college,10,Never-married,?,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n4,Local-gov,173992,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,119665,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,188391,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,326005,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,England,>50K\n0,Private,203203,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,64102,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,169188,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,385793,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Mexico,<=50K\n0,Private,390537,Some-college,10,Never-married,Sales,Unmarried,White,Female,0,0,1,El-Salvador,<=50K\n1,Private,115677,11th,7,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,230248,Assoc-acdm,12,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,?,59056,10th,6,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,108038,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,Cuba,>50K\n2,Local-gov,282461,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,184659,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,182470,Assoc-voc,11,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,458609,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,2,0,1,United-States,<=50K\n4,Private,104476,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Female,0,1,2,United-States,<=50K\n1,Private,200802,Assoc-voc,11,Separated,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,170608,10th,6,Separated,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,197322,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,118358,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,?,520231,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,198017,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,?,<=50K\n1,Private,131045,Assoc-voc,11,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,272166,Some-college,10,Never-married,?,Own-child,White,Male,0,2,1,United-States,<=50K\n1,Private,110083,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,335569,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,167159,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,170326,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,319052,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n4,Private,174662,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,110732,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Federal-gov,409815,Some-college,10,Divorced,Adm-clerical,Other-relative,Black,Female,0,0,3,United-States,<=50K\n1,Private,79874,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,116641,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,1,United-States,>50K\n1,Private,87209,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,152172,10th,6,Married-civ-spouse,Machine-op-inspct,Wife,White,Male,0,0,2,?,<=50K\n3,Self-emp-not-inc,142222,Some-college,10,Separated,Exec-managerial,Unmarried,White,Female,1,0,3,United-States,<=50K\n3,Local-gov,120521,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,247752,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,34161,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,589155,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,149784,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,402522,1st-4th,2,Divorced,Farming-fishing,Unmarried,White,Male,0,0,2,Thailand,<=50K\n1,Private,228346,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,415755,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,289653,Bachelors,13,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,>50K\n0,Private,165018,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,322866,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,244813,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,538193,11th,7,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,3,United-States,<=50K\n2,Private,256367,12th,8,Divorced,Farming-fishing,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,95864,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,291167,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,126569,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Poland,<=50K\n1,Private,128016,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,2,United-States,>50K\n0,?,323584,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n4,?,115431,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,246156,10th,6,Never-married,Craft-repair,Other-relative,White,Male,0,0,1,Honduras,<=50K\n2,Private,346081,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Local-gov,156383,Some-college,10,Never-married,Protective-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,151267,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,249039,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,157454,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,143540,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,120733,7th-8th,4,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,344381,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Private,149787,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,268525,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,396099,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,221442,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,115198,9th,5,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,3,United-States,<=50K\n3,Federal-gov,102359,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,298885,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,93213,Masters,14,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,130760,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,236834,Some-college,10,Divorced,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,31709,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,192053,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,95918,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,Germany,<=50K\n2,Self-emp-inc,132879,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,64940,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,106910,HS-grad,9,Divorced,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n0,Private,210474,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,393965,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Local-gov,117789,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,134498,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,>50K\n1,Private,212068,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,3,2,United-States,<=50K\n1,Private,169544,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,32995,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,261241,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,1,3,United-States,<=50K\n2,Private,145784,HS-grad,9,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n1,Private,252646,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,161944,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,249644,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,195081,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,428251,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,198145,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,348059,Doctorate,16,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,43587,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,318612,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,1,2,United-States,<=50K\n0,?,235661,10th,6,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,129528,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,200427,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,188243,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,306633,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,85019,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,?,>50K\n0,?,356286,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Private,102771,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,34739,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,?,201959,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,126743,5th-6th,3,Never-married,Other-service,Other-relative,White,Male,1,0,3,Mexico,<=50K\n3,Local-gov,85341,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,275943,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,82823,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,1,United-States,<=50K\n1,Private,183388,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,116489,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,215789,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,365871,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Local-gov,199275,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,3,0,2,United-States,>50K\n2,Self-emp-not-inc,34111,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,?,314567,Some-college,10,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,102576,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,Trinadad&Tobago,<=50K\n1,Private,103524,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,114222,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,246933,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,107812,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,109162,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,112798,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,30612,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,105994,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,113090,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,26252,Assoc-acdm,12,Never-married,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,49469,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,172169,Some-college,10,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,1,United-States,<=50K\n2,Private,151029,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,134242,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,87282,Assoc-voc,11,Never-married,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n0,Private,84250,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,76107,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n4,Self-emp-inc,36085,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,220333,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,105363,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Portugal,<=50K\n0,Private,198668,12th,8,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,157473,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,126568,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,220896,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Federal-gov,236048,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,34218,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,4,United-States,>50K\n4,Private,155915,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,139684,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,23778,Bachelors,13,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,236804,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,454321,1st-4th,2,Widowed,Handlers-cleaners,Other-relative,White,Male,0,0,0,Nicaragua,<=50K\n2,Private,229148,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,3,Outlying-US(Guam-USVI-etc),<=50K\n4,Local-gov,119986,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,455399,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,4,0,2,United-States,>50K\n0,Private,301694,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n4,?,155142,HS-grad,9,Widowed,?,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n1,Private,259652,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,156642,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,94208,1st-4th,2,Divorced,Other-service,Unmarried,White,Female,0,0,1,Mexico,<=50K\n1,Private,117719,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Portugal,<=50K\n1,Local-gov,100817,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,144990,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,198841,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,223881,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,264017,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,Canada,<=50K\n0,State-gov,26842,Assoc-voc,11,Married-AF-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n2,Private,477345,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,3,2,Mexico,<=50K\n0,Private,267412,Preschool,1,Never-married,Other-service,Own-child,Black,Female,1,0,0,Jamaica,<=50K\n4,Self-emp-inc,190610,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,281237,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,254593,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,159187,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,200450,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,140854,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,242517,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,2,United-States,>50K\n3,Self-emp-not-inc,294671,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,State-gov,68358,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,107096,Bachelors,13,Never-married,Sales,Unmarried,White,Male,0,2,3,United-States,<=50K\n2,Private,244419,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,195636,10th,6,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n2,Private,368586,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Female,0,0,2,Puerto-Rico,<=50K\n1,Private,215808,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,165822,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,193379,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,120121,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,311603,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,323798,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,253890,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,105252,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,220696,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,194097,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,181291,Some-college,10,Married-civ-spouse,Other-service,Own-child,White,Female,3,0,2,United-States,>50K\n1,Private,258594,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,138976,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n0,Private,81145,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,250853,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,365739,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,257863,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,203697,Masters,14,Never-married,Tech-support,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,87205,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,195161,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,?,<=50K\n2,Private,470486,1st-4th,2,Married-spouse-absent,Handlers-cleaners,Unmarried,White,Male,0,2,2,Mexico,<=50K\n3,Local-gov,93557,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,3,United-States,>50K\n2,Private,107991,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,63081,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,73988,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,136080,HS-grad,9,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,State-gov,49115,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,314649,HS-grad,9,Married-spouse-absent,Farming-fishing,Unmarried,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n0,Private,166224,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,?,118484,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,<=50K\n4,Local-gov,291529,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,252392,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,Mexico,<=50K\n2,Private,86912,Bachelors,13,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,193537,9th,5,Never-married,Priv-house-serv,Unmarried,White,Female,0,0,3,Puerto-Rico,<=50K\n1,Private,83231,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,325461,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,36011,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,274869,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,178322,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,67666,Masters,14,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,153005,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,138269,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,265204,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,437318,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,208109,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,91103,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,State-gov,388225,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,42162,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,417227,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,State-gov,180220,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,187560,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,127573,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,Federal-gov,68898,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,78662,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,2,Mexico,<=50K\n4,Private,158776,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,164575,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,328301,Some-college,10,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,213897,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,Japan,>50K\n2,Private,230684,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,381679,Some-college,10,Never-married,Tech-support,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,360884,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,256992,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,112115,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,113577,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,189382,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,201080,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,344415,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,201232,HS-grad,9,Married-civ-spouse,Priv-house-serv,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,332194,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,216864,9th,5,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,290922,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,223548,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,109419,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,3,0,2,United-States,>50K\n1,Private,135296,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n4,State-gov,100270,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,99497,12th,8,Never-married,Other-service,Own-child,Other,Female,0,0,1,United-States,<=50K\n1,?,223665,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,4,United-States,<=50K\n3,Self-emp-inc,341762,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,236483,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,311570,HS-grad,9,Married-civ-spouse,?,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,588739,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,2,India,<=50K\n2,Self-emp-inc,79521,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,327435,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,229636,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,3,United-States,<=50K\n1,Private,124483,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,1,India,<=50K\n4,Private,218764,Assoc-voc,11,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,178100,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,197057,Some-college,10,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,191161,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Private,266828,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,251526,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,0,United-States,<=50K\n0,?,145964,HS-grad,9,Never-married,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,307149,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,37238,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,129020,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,209432,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,139364,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Federal-gov,169124,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,116391,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,176025,HS-grad,9,Never-married,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,44712,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,190759,Some-college,10,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,185692,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,80576,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,282173,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,187158,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,214468,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,185410,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,87757,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,449578,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,309028,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,155293,12th,8,Divorced,Sales,Not-in-family,White,Female,0,2,2,United-States,<=50K\n3,Private,32825,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,216845,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n2,State-gov,149640,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,140985,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,218188,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,State-gov,187327,HS-grad,9,Separated,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,182511,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,157639,9th,5,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,258498,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,87632,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,228394,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,State-gov,200732,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,Philippines,>50K\n2,Private,49657,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,106179,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,135267,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,486436,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,69757,Bachelors,13,Divorced,Exec-managerial,Other-relative,White,Female,0,0,3,United-States,<=50K\n3,Private,190319,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,1,2,Thailand,>50K\n0,Private,188409,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,181246,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,103573,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,180725,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,State-gov,34862,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n3,Self-emp-inc,275236,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,76487,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,75073,Assoc-acdm,12,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,231929,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,186410,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,344624,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,>50K\n4,Private,97847,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,387521,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,?,193511,Bachelors,13,Never-married,?,Own-child,White,Female,0,0,1,El-Salvador,<=50K\n0,Private,325033,12th,8,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,>50K\n2,Private,285637,HS-grad,9,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n0,Private,186014,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,203160,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,190290,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,219553,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,290882,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,133403,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,1,United-States,<=50K\n1,Private,150154,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,203076,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,158592,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,215115,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,117476,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,159269,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,189924,Assoc-voc,11,Never-married,Exec-managerial,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,226296,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,103886,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,148508,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,79586,Some-college,10,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,1,India,>50K\n2,?,95049,Assoc-voc,11,Separated,?,Own-child,White,Female,0,0,2,?,<=50K\n2,Self-emp-inc,192835,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,316184,HS-grad,9,Never-married,Other-service,Other-relative,Black,Male,0,0,2,United-States,<=50K\n3,Private,132476,Doctorate,16,Divorced,Tech-support,Unmarried,White,Male,2,0,2,United-States,>50K\n2,Private,76487,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,302712,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,225193,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,102092,11th,7,Widowed,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,Private,173754,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,38238,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,212027,11th,7,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,173593,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,132718,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,103588,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,75387,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,38444,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,35603,HS-grad,9,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n0,Private,588484,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,1,0,2,United-States,<=50K\n4,?,191118,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,88638,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n4,Private,27086,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,184319,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,307375,Some-college,10,Never-married,Other-service,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,Private,93511,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,32950,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,313945,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,275517,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,132145,9th,5,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,377798,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,198000,Bachelors,13,Never-married,Sales,Other-relative,White,Female,0,0,2,United-States,>50K\n4,Private,166591,HS-grad,9,Divorced,Priv-house-serv,Unmarried,Black,Female,1,0,4,United-States,<=50K\n4,Self-emp-not-inc,117030,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,275369,Some-college,10,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,300584,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,230997,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,73199,12th,8,Never-married,Farming-fishing,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n4,Private,362068,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,162604,HS-grad,9,Never-married,Craft-repair,Other-relative,Black,Male,0,0,2,United-States,<=50K\n2,Private,86143,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,116477,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,102308,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n4,Self-emp-inc,199067,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,Greece,>50K\n3,Private,205100,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,127493,Assoc-acdm,12,Widowed,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,34761,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,221480,Some-college,10,Never-married,Tech-support,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,216473,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,147206,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,2,United-States,>50K\n3,Private,162868,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,335701,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,250322,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,182856,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,Private,97743,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,227065,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,59840,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,140446,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,86150,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,0,3,2,United-States,>50K\n3,Private,147876,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,219199,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,28497,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,405177,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,320451,Bachelors,13,Married-spouse-absent,Sales,Other-relative,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n4,Self-emp-not-inc,30661,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,2,United-States,>50K\n1,Local-gov,38268,HS-grad,9,Separated,Other-service,Unmarried,White,Male,0,0,2,United-States,>50K\n2,Private,199900,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,172538,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,194517,11th,7,Never-married,Farming-fishing,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,129024,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,203828,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,146659,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,29261,Assoc-acdm,12,Never-married,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,366109,10th,6,Never-married,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,212091,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,202872,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,373903,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,289403,HS-grad,9,Divorced,Tech-support,Not-in-family,Black,Male,0,0,2,?,<=50K\n0,Private,60552,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,188325,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n0,?,398480,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,254202,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n2,Self-emp-inc,277858,Bachelors,13,Widowed,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,102346,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,?,<=50K\n1,Private,226629,12th,8,Separated,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,219632,1st-4th,2,Widowed,Machine-op-inspct,Unmarried,White,Male,0,0,2,Mexico,<=50K\n0,Private,449101,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,330535,Doctorate,16,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,>50K\n2,Private,202937,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Federal-gov,269733,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,355856,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,275100,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,Greece,>50K\n1,State-gov,136997,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,136931,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Thailand,<=50K\n1,?,346736,HS-grad,9,Never-married,?,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,264936,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,269722,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,251905,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,180636,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,116915,Some-college,10,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,182516,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,199862,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,127482,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,142968,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,115258,10th,6,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,190822,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,68898,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Self-emp-inc,151999,12th,8,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,236471,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,29075,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Amer-Indian-Eskimo,Female,2,0,2,United-States,<=50K\n2,State-gov,186990,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,210369,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,179644,Assoc-voc,11,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,119128,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,188386,HS-grad,9,Divorced,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,120645,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Local-gov,303176,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,358434,Bachelors,13,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,36091,HS-grad,9,Separated,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Private,250648,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,131918,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,151504,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,161880,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Private,123681,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,94090,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,129980,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,237258,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n4,Self-emp-not-inc,147377,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,253627,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Puerto-Rico,>50K\n4,?,528618,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,27881,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,79874,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,156981,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,195418,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,175185,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,273796,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n2,State-gov,373699,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,82508,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,162551,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Hong,<=50K\n0,Private,166297,Bachelors,13,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n0,Local-gov,100125,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,175690,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,184441,7th-8th,4,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,167737,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,186121,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,177851,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,106961,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,419712,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,208875,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,373628,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,1,2,United-States,<=50K\n1,Private,331861,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,England,<=50K\n1,Private,249948,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,99316,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,252570,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,89160,12th,8,Never-married,Priv-house-serv,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,49092,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,87757,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,806552,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,70754,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,150437,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,?,46836,7th-8th,4,Separated,?,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n1,State-gov,117186,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,239625,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,128483,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,53367,12th,8,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,358355,9th,5,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,139522,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,1,2,Italy,<=50K\n1,Private,93017,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,101320,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,1,2,Canada,>50K\n4,Self-emp-inc,105582,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,2,United-States,>50K\n2,Private,121718,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,111836,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,96840,HS-grad,9,Widowed,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,176839,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,193553,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,4,United-States,>50K\n3,Private,168232,HS-grad,9,Married-spouse-absent,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,146325,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Yugoslavia,>50K\n1,Private,111567,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,478354,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,209768,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,188909,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,321313,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,?,264228,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,345066,10th,6,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,32855,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,287372,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,214807,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,275110,Some-college,10,Married-civ-spouse,Protective-serv,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,352089,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,110171,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,<=50K\n0,Private,211391,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,91506,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,180949,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-inc,169072,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,264554,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,99065,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Private,201122,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,323636,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,Canada,<=50K\n2,Local-gov,184112,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Private,243367,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,State-gov,149248,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,248748,Bachelors,13,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,242616,Bachelors,13,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,207246,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,3,2,United-States,>50K\n4,Self-emp-not-inc,343631,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n3,Private,403121,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,184435,11th,7,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,181405,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n4,Self-emp-not-inc,75140,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,467936,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,?,<=50K\n1,Self-emp-not-inc,181212,Some-college,10,Separated,Farming-fishing,Unmarried,White,Female,0,0,4,United-States,<=50K\n2,Private,324421,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,344624,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n3,Private,98735,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Local-gov,186172,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,107157,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,353871,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,175958,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,252134,7th-8th,4,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,Cuba,<=50K\n1,Private,95923,Assoc-acdm,12,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,203250,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,296212,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,333838,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,345730,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,128141,Bachelors,13,Separated,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,249347,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,Cuba,>50K\n3,Private,171914,9th,5,Widowed,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,344519,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n1,Self-emp-inc,196385,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,87546,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,85668,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,126613,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,239753,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,162796,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Federal-gov,197189,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,25806,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,0,?,<=50K\n1,Private,89813,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,142167,Masters,14,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,?,<=50K\n2,Private,171589,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,203985,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,394191,12th,8,Never-married,Transport-moving,Own-child,White,Male,0,0,3,Germany,<=50K\n3,Private,155433,Bachelors,13,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,39581,Bachelors,13,Separated,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,305834,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,200220,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,229732,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,190333,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,155983,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,211351,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,2,0,2,United-States,>50K\n0,?,505168,9th,5,Never-married,?,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,256417,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,Mexico,<=50K\n0,?,165069,10th,6,Never-married,?,Own-child,White,Male,0,2,2,United-States,<=50K\n0,Private,249385,Some-college,10,Never-married,Craft-repair,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,168723,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,165866,Assoc-voc,11,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,48553,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,244751,HS-grad,9,Never-married,Adm-clerical,Own-child,Other,Male,0,0,2,United-States,<=50K\n0,Private,228230,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,152951,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,29023,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n3,Self-emp-not-inc,136455,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,3,United-States,<=50K\n2,?,245372,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,>50K\n0,?,155863,Some-college,10,Never-married,?,Own-child,White,Female,0,2,1,United-States,<=50K\n2,Private,126675,Some-college,10,Widowed,Machine-op-inspct,Other-relative,White,Male,0,0,2,?,<=50K\n2,Private,184659,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,<=50K\n2,Federal-gov,33289,HS-grad,9,Widowed,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,111377,HS-grad,9,Separated,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,103651,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,53481,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,238917,5th-6th,3,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,El-Salvador,<=50K\n0,Private,167495,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,114222,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,182323,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,137589,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,181091,Bachelors,13,Never-married,Sales,Own-child,White,Male,4,0,1,United-States,>50K\n2,Private,156580,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,Dominican-Republic,<=50K\n1,Private,210926,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,255503,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,116546,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,34218,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,?,305327,Some-college,10,Never-married,?,Own-child,Other,Female,0,0,1,United-States,<=50K\n0,Private,107882,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,858091,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,79646,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,103089,Some-college,10,Separated,Prof-specialty,Unmarried,White,Female,1,0,2,United-States,<=50K\n2,Self-emp-not-inc,145441,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,0,1,United-States,<=50K\n0,State-gov,117210,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,379246,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,130018,11th,7,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,121466,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,339518,Assoc-acdm,12,Married-spouse-absent,Machine-op-inspct,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,388672,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,190091,Assoc-voc,11,Married-spouse-absent,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,197918,11th,7,Never-married,Craft-repair,Unmarried,Black,Male,0,0,3,United-States,<=50K\n1,Private,361497,7th-8th,4,Never-married,Farming-fishing,Other-relative,White,Male,0,0,3,Portugal,<=50K\n4,?,451327,Bachelors,13,Married-civ-spouse,?,Husband,Other,Male,0,0,1,United-States,>50K\n0,Private,340217,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,63516,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,269786,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,63338,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,179127,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Italy,<=50K\n1,Private,124090,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,215188,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,482082,11th,7,Married-civ-spouse,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n0,Private,234725,12th,8,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,289890,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,232036,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,195416,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,22154,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,103734,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,32587,HS-grad,9,Divorced,Other-service,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,190303,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,270488,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,104509,Some-college,10,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,132589,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,112812,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,126441,Some-college,10,Married-spouse-absent,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,123075,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,207955,5th-6th,3,Married-spouse-absent,Other-service,Not-in-family,White,Male,0,0,2,Ecuador,<=50K\n3,Private,43705,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,116968,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,182142,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,74056,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,132565,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,256796,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,Self-emp-inc,191520,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n2,Self-emp-not-inc,33394,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Local-gov,45501,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,74389,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,201874,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,143804,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,95471,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,267458,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,70668,10th,6,Never-married,Priv-house-serv,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,260782,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,299215,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,99156,HS-grad,9,Divorced,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,53905,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,94210,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,116508,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,176711,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,118058,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,State-gov,89285,Some-college,10,Never-married,Protective-serv,Not-in-family,Other,Female,4,0,2,United-States,>50K\n3,Private,91093,Some-college,10,Divorced,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,204577,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,162041,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,175615,Some-college,10,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,2,Japan,<=50K\n2,Private,99679,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,>50K\n0,Private,263398,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,147653,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,32521,11th,7,Married-spouse-absent,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,198871,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,127651,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,143608,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,50048,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,?,378922,HS-grad,9,Married-spouse-absent,?,Not-in-family,White,Female,0,0,0,Canada,<=50K\n1,Private,292883,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,190491,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n4,State-gov,132145,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,126853,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,>50K\n0,Private,59184,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,663291,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n1,Local-gov,76978,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,196512,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,4,1,United-States,>50K\n0,?,103851,11th,7,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,241126,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,266828,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,204984,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,1,2,United-States,<=50K\n3,Private,188950,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,226528,Doctorate,16,Married-spouse-absent,Prof-specialty,Not-in-family,Other,Male,0,0,3,England,>50K\n2,Private,268893,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,165473,Bachelors,13,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,447554,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Self-emp-inc,304955,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,198265,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,395206,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,312667,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,1,0,3,United-States,<=50K\n0,Private,117767,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,170482,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,4,0,2,United-States,>50K\n1,Private,309778,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,289991,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,255543,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,119079,11th,7,Married-civ-spouse,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,318168,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,1,0,0,United-States,<=50K\n2,Private,67317,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,337953,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,451603,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,455995,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,209768,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,27385,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,226296,HS-grad,9,Never-married,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,285335,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,376700,Bachelors,13,Never-married,Sales,Own-child,Black,Male,2,0,3,United-States,<=50K\n1,Private,150324,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,96460,HS-grad,9,Separated,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,?,188141,Some-college,10,Widowed,?,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,163985,HS-grad,9,Separated,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,85420,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,0,United-States,<=50K\n0,Private,416103,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,224357,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,116062,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,194259,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,3,?,>50K\n1,Private,460408,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Self-emp-not-inc,178878,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,416745,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,292136,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,176731,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,104097,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,203482,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,360224,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,23580,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,195891,HS-grad,9,Married-civ-spouse,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,182862,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n4,Private,148956,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,95862,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,48393,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,132633,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,2,2,United-States,<=50K\n1,Local-gov,182074,HS-grad,9,Separated,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,136848,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,197054,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,243033,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,154174,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,59380,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,122240,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,193945,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,350103,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,32365,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,94345,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,?,166437,Bachelors,13,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,149653,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,157271,11th,7,Divorced,Other-service,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n4,Private,164599,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n4,Self-emp-inc,104443,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,2,?,<=50K\n3,Private,411595,5th-6th,3,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,Mexico,<=50K\n0,Private,198368,11th,7,Never-married,Other-service,Own-child,White,Male,1,0,0,United-States,<=50K\n2,Self-emp-not-inc,115932,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,158397,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,101345,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,48853,Masters,14,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,Cuba,<=50K\n2,Private,38145,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,127651,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,185896,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,Amer-Indian-Eskimo,Male,0,0,3,Mexico,<=50K\n1,State-gov,92531,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,195904,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,153095,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,581025,9th,5,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Local-gov,202384,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,122177,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,405713,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,212185,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,266347,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,49469,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,210830,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,188612,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,104017,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,3,1,United-States,>50K\n0,Private,154785,Some-college,10,Never-married,Adm-clerical,Other-relative,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n0,Private,39477,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,99554,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,0,Poland,<=50K\n4,Private,255978,HS-grad,9,Widowed,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,98823,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,109598,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,266971,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,334593,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,41035,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n1,State-gov,238591,Some-college,10,Separated,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,117012,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,2,United-States,>50K\n1,Private,192002,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,137135,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,150600,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,117464,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,111971,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,290044,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,1,Canada,<=50K\n0,Private,197186,10th,6,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,61127,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,236379,11th,7,Never-married,Transport-moving,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,Private,207100,Bachelors,13,Never-married,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,288630,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,203181,Assoc-voc,11,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,146770,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,191789,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,453983,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,106014,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,218955,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,115771,Assoc-voc,11,Widowed,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,305379,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,53063,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,139466,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,State-gov,152537,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,400535,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,330802,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,117789,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,330836,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,323985,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n3,Local-gov,282701,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,180695,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,1,2,United-States,<=50K\n2,Private,314007,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n3,Without-pay,124963,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,380922,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Local-gov,222381,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,656488,Assoc-voc,11,Divorced,Tech-support,Unmarried,Black,Male,0,0,3,United-States,<=50K\n2,Private,98776,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,143050,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,118792,11th,7,Never-married,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n0,Private,154964,HS-grad,9,Divorced,Machine-op-inspct,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Private,163847,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,282398,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,78954,11th,7,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,203988,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,111130,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n2,Private,149388,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,39464,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,94064,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,342510,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,2,United-States,>50K\n4,Self-emp-not-inc,163726,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,194496,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,298045,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,42100,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,77143,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Private,233197,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,295120,11th,7,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,85021,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n3,?,191659,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,244194,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n1,Local-gov,287229,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,1,Japan,<=50K\n0,Private,324046,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,65018,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,0,China,<=50K\n2,Private,421633,Assoc-voc,11,Divorced,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,93235,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,227232,HS-grad,9,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,121775,Assoc-voc,11,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,65382,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,179422,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,276868,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,87317,10th,6,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,108247,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,197505,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,127493,10th,6,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,75640,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,320811,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,247053,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,119735,9th,5,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,Mexico,<=50K\n1,Private,157950,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,113732,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,224763,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Cuba,<=50K\n2,Self-emp-not-inc,40024,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,296594,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Federal-gov,53956,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,71009,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,2,0,2,?,>50K\n1,Private,191834,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,107236,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,231284,HS-grad,9,Never-married,Farming-fishing,Not-in-family,Other,Male,0,0,2,Puerto-Rico,<=50K\n1,State-gov,203488,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,41721,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,205100,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,4,United-States,>50K\n4,Private,75673,Some-college,10,Widowed,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,?,105598,11th,7,Never-married,?,Not-in-family,White,Male,0,2,2,Outlying-US(Guam-USVI-etc),<=50K\n4,Self-emp-not-inc,177832,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,478457,11th,7,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Local-gov,194759,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,2,4,United-States,<=50K\n4,Self-emp-not-inc,30310,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,130010,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,170302,HS-grad,9,Widowed,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,120080,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,183781,HS-grad,9,Widowed,Craft-repair,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,422836,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,El-Salvador,<=50K\n3,Private,266860,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,393456,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,State-gov,318382,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,354520,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Private,123425,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,123989,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,175778,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,State-gov,73928,Bachelors,13,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,>50K\n1,Private,33731,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,557349,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,255252,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,219164,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,2,0,2,United-States,>50K\n0,Local-gov,129050,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,111797,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,192900,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,56651,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,51961,Some-college,10,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,3,Philippines,<=50K\n2,Private,141584,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,421350,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,24740,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,3,United-States,<=50K\n1,Local-gov,498267,HS-grad,9,Separated,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,117583,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,191455,Some-college,10,Married-civ-spouse,Tech-support,Wife,Other,Female,0,0,0,United-States,<=50K\n0,Private,135716,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,27766,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,323919,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,114561,5th-6th,3,Widowed,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,1,Philippines,<=50K\n0,Private,216137,9th,5,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,165539,HS-grad,9,Widowed,Exec-managerial,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n2,Private,32016,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,89040,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,264514,Bachelors,13,Divorced,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,Private,24790,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,181139,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,168514,10th,6,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,354493,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,206707,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,230684,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,192381,Bachelors,13,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,397752,HS-grad,9,Married-spouse-absent,Farming-fishing,Other-relative,White,Male,0,0,0,Mexico,<=50K\n3,State-gov,120173,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,228394,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,186112,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,272237,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,169711,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,4,0,4,United-States,>50K\n2,Self-emp-not-inc,172560,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,213700,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,181820,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,120361,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,169324,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Black,Female,2,0,1,United-States,>50K\n1,Private,262092,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,143436,Bachelors,13,Never-married,Prof-specialty,Own-child,Other,Female,0,0,0,?,<=50K\n2,Private,147099,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,138594,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,100606,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,350850,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,66432,Some-college,10,Separated,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Local-gov,229148,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,236601,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,144844,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,366088,9th,5,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,162164,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,442478,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,181814,11th,7,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,175109,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n1,Self-emp-inc,152109,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,246891,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,164802,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Other,Female,3,0,2,India,>50K\n0,Private,57711,HS-grad,9,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,117789,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,120900,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,114673,Masters,14,Never-married,Prof-specialty,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,78529,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,282165,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,149337,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,250517,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,76131,HS-grad,9,Never-married,?,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n2,Private,352971,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,?,243981,HS-grad,9,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,?,421228,Masters,14,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,94156,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,306868,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,187164,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,179415,10th,6,Married-civ-spouse,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,45776,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,2,4,United-States,<=50K\n4,Private,256723,Some-college,10,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,31909,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,90526,12th,8,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,?,127306,Some-college,10,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,179423,Bachelors,13,Never-married,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,140169,10th,6,Separated,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,Private,37359,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,125813,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,209415,10th,6,Divorced,Protective-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,206619,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,283737,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,162187,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,191571,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,Private,33725,9th,5,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,236543,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,Federal-gov,73047,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,230574,7th-8th,4,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,Mexico,<=50K\n1,Private,178109,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,282023,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,101761,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,98168,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,287681,11th,7,Never-married,Farming-fishing,Other-relative,White,Male,0,0,2,Mexico,<=50K\n2,?,265685,Some-college,10,Divorced,?,Not-in-family,White,Male,0,0,4,Puerto-Rico,<=50K\n2,State-gov,91670,Some-college,10,Divorced,Prof-specialty,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,State-gov,61989,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,138513,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,95423,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,30917,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n0,?,316304,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,102791,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,416506,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,Self-emp-inc,245611,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Federal-gov,655066,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Other,Male,0,0,2,Peru,>50K\n4,Self-emp-not-inc,87584,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,304223,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,40690,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,>50K\n0,Private,348131,11th,7,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n4,Private,191477,5th-6th,3,Widowed,Priv-house-serv,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,115438,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,176917,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n2,?,104196,HS-grad,9,Separated,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,202182,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,308239,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,163581,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,4,0,2,Puerto-Rico,>50K\n1,Local-gov,211239,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,2,0,2,United-States,<=50K\n1,Private,121321,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,State-gov,120172,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,190916,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,2,0,United-States,<=50K\n0,Private,340288,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,426431,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,226027,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,278736,12th,8,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,Mexico,<=50K\n3,Private,168462,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,379070,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,214541,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,Canada,<=50K\n3,Self-emp-inc,29887,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,138022,11th,7,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,208018,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,126876,HS-grad,9,Divorced,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,182703,Masters,14,Divorced,Adm-clerical,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,161153,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,168443,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,335522,9th,5,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,220104,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,2,3,United-States,<=50K\n1,?,162312,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n2,Private,104772,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,161472,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,91506,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,186717,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,77927,5th-6th,3,Never-married,Handlers-cleaners,Other-relative,Asian-Pac-Islander,Female,0,0,3,Philippines,<=50K\n3,Private,140063,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,317580,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,122533,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,57423,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,103331,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,417543,Doctorate,16,Widowed,Prof-specialty,Not-in-family,Black,Male,3,0,3,United-States,>50K\n4,Private,253854,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,106850,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,314007,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,494371,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,270421,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,203488,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,<=50K\n1,Private,167691,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,198318,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,319831,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,70240,Masters,14,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n4,Private,227113,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,2,United-States,<=50K\n0,Private,168997,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,231929,12th,8,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,207969,Some-college,10,Never-married,Exec-managerial,Own-child,Black,Male,0,0,1,United-States,<=50K\n4,Private,192656,Some-college,10,Widowed,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,187215,HS-grad,9,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,El-Salvador,<=50K\n3,Self-emp-inc,119570,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,188659,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,110013,Bachelors,13,Divorced,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,55860,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,282069,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,26585,HS-grad,9,Never-married,Other-service,Not-in-family,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,218890,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,211154,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,230095,10th,6,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,737315,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,144084,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,48384,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,541755,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,178778,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,Private,436198,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,82521,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,<=50K\n2,Private,367020,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,174461,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,162501,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,193026,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,218172,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n2,Private,110318,Masters,14,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,126675,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,2,United-States,<=50K\n0,Private,116788,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,161092,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,159109,11th,7,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,213191,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,240629,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,227960,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,Puerto-Rico,<=50K\n3,Private,151580,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,France,>50K\n2,Private,160893,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,184117,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,32059,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,361219,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,334984,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,4,2,United-States,>50K\n3,Self-emp-not-inc,33300,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,199713,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,401134,Assoc-acdm,12,Divorced,Other-service,Unmarried,White,Female,0,4,2,United-States,<=50K\n2,Private,132702,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,306693,Some-college,10,Married-civ-spouse,?,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,Private,286166,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,123515,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,132053,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-inc,266400,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,335248,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,36147,Prof-school,15,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,0,United-States,>50K\n0,Private,266467,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,4,2,United-States,<=50K\n2,Private,143809,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,334366,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,>50K\n2,Private,347653,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n1,Private,386806,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Mexico,>50K\n3,Private,202322,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,El-Salvador,<=50K\n3,Private,594521,9th,5,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,174267,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,?,169542,12th,8,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,227392,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,99131,HS-grad,9,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,225860,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,287317,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,46091,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,170050,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,352317,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,225267,Some-college,10,Divorced,Sales,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,217545,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,?,<=50K\n1,Private,183778,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,210013,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,49115,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,310429,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,114691,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,124356,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,51284,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n3,?,294443,Assoc-voc,11,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,200009,10th,6,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,258862,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,37778,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,402771,HS-grad,9,Married-spouse-absent,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,201520,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n3,Local-gov,55237,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,52750,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,197189,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,2,0,3,United-States,<=50K\n2,Private,96564,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,334105,Assoc-acdm,12,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,115323,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,157289,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,320877,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,198186,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,?,<=50K\n4,Private,195543,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,>50K\n3,Private,103406,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,>50K\n0,?,320451,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,1,?,<=50K\n0,Private,23940,Some-college,10,Never-married,Other-service,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Private,45857,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,195557,Assoc-acdm,12,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,229148,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n0,?,152328,Bachelors,13,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,186157,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,127712,Assoc-voc,11,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Poland,<=50K\n0,Private,254351,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,182163,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,442656,11th,7,Never-married,Sales,Unmarried,White,Female,0,0,4,Guatemala,<=50K\n1,Private,111363,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,491000,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,156065,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,243743,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,173938,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,4,0,United-States,<=50K\n2,Private,86308,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n1,Private,216068,Assoc-acdm,12,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,237432,12th,8,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,177216,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,212895,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,122749,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,Germany,<=50K\n2,Private,254303,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,Hungary,>50K\n0,Private,73679,HS-grad,9,Never-married,Transport-moving,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,455995,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,214288,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,228075,5th-6th,3,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Hong,<=50K\n1,Private,412017,10th,6,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,236900,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,289442,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,237298,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,State-gov,47072,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,197036,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,State-gov,175507,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,350131,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,150057,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,United-States,<=50K\n2,Self-emp-inc,190650,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,South,>50K\n1,Private,430672,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,99316,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,?,191776,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,97723,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,3,United-States,>50K\n1,?,197288,11th,7,Never-married,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,239409,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,195554,7th-8th,4,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,76855,Some-college,10,Divorced,Transport-moving,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,281315,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,224058,10th,6,Divorced,Transport-moving,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n0,Private,232799,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,1,0,3,United-States,<=50K\n1,Private,174163,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,47178,5th-6th,3,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,97950,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,342765,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,209818,Bachelors,13,Divorced,Prof-specialty,Other-relative,White,Female,0,0,3,United-States,<=50K\n2,Private,349534,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n2,Self-emp-inc,170214,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,145284,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,124161,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,105357,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,355700,Prof-school,15,Married-AF-spouse,Prof-specialty,Wife,White,Female,4,0,3,United-States,>50K\n1,Private,99928,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,308739,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-inc,179781,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n3,Federal-gov,297906,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,?,>50K\n0,Private,189663,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,339029,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,87076,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,109928,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,218456,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,176756,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,>50K\n4,?,214923,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,191789,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,?,<=50K\n0,Private,238383,10th,6,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,315476,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,195148,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,?,174274,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Private,143208,7th-8th,4,Divorced,Other-service,Unmarried,White,Female,0,0,2,?,<=50K\n2,Private,30201,Assoc-voc,11,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,200352,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,117028,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,1,Poland,<=50K\n2,Private,44489,HS-grad,9,Widowed,Farming-fishing,Unmarried,White,Male,0,0,4,United-States,<=50K\n3,Private,236222,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,496856,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,132675,11th,7,Separated,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,89226,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,?,112660,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,294466,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,201011,7th-8th,4,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,27624,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,4,United-States,>50K\n1,Private,385959,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,214691,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,196253,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,242341,Some-college,10,Divorced,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,195016,Some-college,10,Never-married,Prof-specialty,Not-in-family,Other,Female,0,0,1,United-States,<=50K\n3,Private,174794,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,England,>50K\n4,Self-emp-not-inc,134470,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,3,United-States,<=50K\n0,Private,166360,10th,6,Never-married,Craft-repair,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,26671,Bachelors,13,Divorced,Prof-specialty,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,589838,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,149169,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,287920,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n4,Private,56080,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n0,State-gov,211798,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,415266,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,147110,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,228873,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,305348,9th,5,Never-married,Craft-repair,Other-relative,Black,Male,0,0,2,United-States,<=50K\n3,Federal-gov,189831,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,247379,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,198841,Some-college,10,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,364986,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,<=50K\n1,Private,203488,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,141118,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,155862,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,324550,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,174353,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,181912,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,168191,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,4,0,2,United-States,>50K\n1,?,216068,Assoc-acdm,12,Married-civ-spouse,?,Wife,White,Female,2,0,0,United-States,>50K\n2,Private,125461,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,162688,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,234406,12th,8,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,State-gov,114537,HS-grad,9,Separated,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,68111,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,322799,HS-grad,9,Separated,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,479296,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,323385,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n4,Private,162772,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,27166,HS-grad,9,Married-spouse-absent,Transport-moving,Not-in-family,White,Male,4,0,2,United-States,>50K\n3,?,142642,HS-grad,9,Married-spouse-absent,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,162954,Some-college,10,Married-AF-spouse,Handlers-cleaners,Wife,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,90533,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,234286,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n0,Private,355559,12th,8,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,32528,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,132847,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,279724,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,30827,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,179772,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,112264,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,93690,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,178983,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,194740,10th,6,Widowed,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,283037,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,312485,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,202450,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,272569,10th,6,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,231865,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,195693,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,Jamaica,<=50K\n1,Private,108574,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,605504,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,140985,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,England,<=50K\n0,State-gov,160369,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,303440,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,263871,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,28338,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,298624,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,139126,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n4,Private,197994,HS-grad,9,Never-married,Other-service,Other-relative,Black,Female,0,0,1,United-States,<=50K\n1,Local-gov,241259,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,248568,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,304779,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,143266,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,169719,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,257128,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,78507,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,490332,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,244200,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,Puerto-Rico,<=50K\n2,Self-emp-not-inc,95298,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,329174,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,107142,12th,8,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,State-gov,33975,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,201579,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-not-inc,122852,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,272742,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,4,0,3,United-States,>50K\n3,Private,161691,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,2,0,2,United-States,<=50K\n2,Local-gov,223410,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n4,Private,250832,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,United-States,<=50K\n2,Local-gov,282069,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,369164,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,218521,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,136405,Assoc-voc,11,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,199018,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,299249,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,235567,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,73493,Some-college,10,Separated,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,320012,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,183898,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,190027,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,87891,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,304001,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,171424,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,123807,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,?,210802,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Private,80220,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,216413,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,423453,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,178835,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,304001,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,167482,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,26543,Some-college,10,Separated,Prof-specialty,Not-in-family,White,Male,0,4,2,United-States,>50K\n3,Private,176409,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,87018,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,251603,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,366180,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,186916,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n1,Self-emp-not-inc,164461,11th,7,Divorced,Sales,Unmarried,White,Male,0,1,2,United-States,<=50K\n2,Private,54102,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,199058,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,>50K\n0,Private,293324,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,96798,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,132340,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,175925,Bachelors,13,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,33230,11th,7,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Local-gov,298871,HS-grad,9,Never-married,Other-service,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n1,Private,142760,Assoc-voc,11,Never-married,Sales,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,200700,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,117310,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,?,<=50K\n2,Private,238188,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,354496,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,416541,9th,5,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,42902,9th,5,Separated,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,180317,Assoc-voc,11,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,378581,12th,8,Never-married,Protective-serv,Other-relative,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,213620,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,186905,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,4,0,2,United-States,>50K\n3,Private,182054,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,189634,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,170070,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,445382,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,168211,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,341760,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,152452,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,558752,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Private,153813,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,81859,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,51664,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,334421,Bachelors,13,Divorced,Sales,Unmarried,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n1,Private,239415,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,62701,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Self-emp-inc,347491,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,108739,11th,7,Widowed,Protective-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,340917,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,1,0,2,United-States,<=50K\n3,Federal-gov,160636,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,Mexico,<=50K\n3,Private,116927,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,179423,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,347829,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,56317,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,?,>50K\n2,Self-emp-inc,347189,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,201520,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,43533,5th-6th,3,Separated,Other-service,Other-relative,White,Female,0,0,2,El-Salvador,<=50K\n0,Private,313786,HS-grad,9,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,367216,Some-college,10,Married-spouse-absent,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,408988,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,175662,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,161552,Preschool,1,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,311743,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,323229,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,163204,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,201481,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,154210,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n2,Local-gov,247547,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,254230,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,156464,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,108322,Some-college,10,Married-AF-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,213179,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,160122,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,?,80392,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,254202,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,Germany,<=50K\n1,State-gov,232914,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,206609,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Male,0,2,2,United-States,<=50K\n1,Private,44623,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,199005,Assoc-acdm,12,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,403344,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,118577,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,122889,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,2,0,2,Taiwan,>50K\n0,Private,196508,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,40915,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,143774,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,173004,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,Black,Male,0,0,0,United-States,<=50K\n3,Private,353824,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,171058,Some-college,10,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,335400,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Dominican-Republic,<=50K\n1,Local-gov,263650,Bachelors,13,Never-married,Sales,Unmarried,Black,Female,0,0,0,United-States,<=50K\n4,Private,187025,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,149218,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,190916,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,240989,1st-4th,2,Married-civ-spouse,Farming-fishing,Other-relative,White,Male,0,0,2,Mexico,<=50K\n3,Private,216093,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,111483,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,214810,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,165402,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,2,0,2,United-States,>50K\n3,Federal-gov,36489,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,173923,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,273147,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,113814,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,118768,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,34916,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,73023,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,State-gov,179869,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,259131,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Male,2,0,2,United-States,<=50K\n3,Private,257756,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,Germany,<=50K\n3,Private,448862,HS-grad,9,Never-married,Transport-moving,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,150553,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,205152,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,220499,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,134252,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,175808,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,185621,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,278391,Assoc-acdm,12,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,174907,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,175642,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,Self-emp-not-inc,216889,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,183557,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,196674,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,169188,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,203785,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,196707,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,190297,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,255595,11th,7,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,374983,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,176178,Bachelors,13,Married-civ-spouse,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,181165,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,212490,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,215766,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,261688,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,123417,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,108431,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,4,2,United-States,>50K\n4,Private,32954,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,224752,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,122353,11th,7,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,176159,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,189407,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,181772,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,109133,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,165229,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,315449,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Private,37848,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,54595,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,127914,Some-college,10,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,121596,Some-college,10,Never-married,Other-service,Own-child,White,Female,1,0,1,United-States,<=50K\n2,Private,95336,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,299197,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,299991,11th,7,Divorced,Adm-clerical,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Private,70034,9th,5,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,Portugal,<=50K\n1,Private,256970,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,108706,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,227832,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,272865,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,60070,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,223730,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,22743,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,2,2,United-States,<=50K\n1,Private,195994,Bachelors,13,Separated,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,181242,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,133169,11th,7,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,India,<=50K\n0,Private,99199,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,246949,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,284889,Bachelors,13,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,150309,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Amer-Indian-Eskimo,Male,0,2,2,United-States,>50K\n0,Private,201799,Bachelors,13,Never-married,Transport-moving,Own-child,White,Female,0,0,4,United-States,<=50K\n4,Private,52090,Prof-school,15,Divorced,Tech-support,Unmarried,White,Male,0,0,2,United-States,>50K\n4,Local-gov,107338,Some-college,10,Widowed,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,32356,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,50092,Bachelors,13,Divorced,Exec-managerial,Unmarried,Other,Male,0,1,2,United-States,<=50K\n1,Private,311446,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,128553,Assoc-voc,11,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,203203,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,429281,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,?,<=50K\n1,Private,192660,Assoc-voc,11,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Local-gov,170449,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n4,?,221417,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n4,?,156942,1st-4th,2,Separated,?,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n0,Private,177504,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,378546,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,33001,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,213722,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,152307,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,>50K\n4,Self-emp-not-inc,53777,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,60668,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,132544,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,277772,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,415755,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,136080,HS-grad,9,Divorced,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,241607,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,Private,180190,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,400356,Some-college,10,Married-spouse-absent,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,154274,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,3,0,2,United-States,>50K\n3,Private,146497,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,189498,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,65942,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,151382,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,56651,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,164488,Assoc-acdm,12,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,183061,HS-grad,9,Never-married,Farming-fishing,Own-child,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n1,Private,289228,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,38918,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,194630,HS-grad,9,Separated,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,262848,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,157595,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,102476,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,93663,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,202450,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,72393,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,53147,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,337944,11th,7,Separated,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,37939,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,118993,9th,5,Married-civ-spouse,Transport-moving,Other-relative,White,Female,0,0,2,?,<=50K\n4,Private,772919,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,143062,Some-college,10,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,32593,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,167882,12th,8,Never-married,Other-service,Unmarried,Black,Female,0,0,3,Haiti,<=50K\n3,Local-gov,48413,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,123429,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,244261,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,318891,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,259788,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,248876,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,334418,1st-4th,2,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,Puerto-Rico,<=50K\n2,Self-emp-not-inc,166497,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,260833,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,107477,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,37932,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,216948,10th,6,Separated,Sales,Other-relative,Other,Male,0,0,2,Cuba,<=50K\n2,Private,157473,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,117222,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,186733,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,231472,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Local-gov,187689,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,323985,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,270655,12th,8,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,301614,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,112754,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,35633,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,?,<=50K\n1,Self-emp-not-inc,112358,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,247337,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-inc,115411,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,State-gov,884434,Some-college,10,Separated,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,72812,HS-grad,9,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,192549,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,54310,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,33386,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,233433,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,106373,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,215591,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,184531,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,69495,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,22228,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,55899,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,181498,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,203572,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,120601,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,74706,10th,6,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,259673,Some-college,10,Married-civ-spouse,?,Husband,Other,Male,0,0,2,Puerto-Rico,<=50K\n3,Private,126441,1st-4th,2,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n0,Private,127784,Some-college,10,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,33658,Some-college,10,Married-spouse-absent,Craft-repair,Unmarried,White,Male,0,4,2,United-States,>50K\n2,Private,234901,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,34307,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,124660,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,278637,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,373545,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,172548,9th,5,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,28074,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n4,Self-emp-not-inc,127539,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,1,United-States,<=50K\n0,?,180246,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,State-gov,377017,Masters,14,Never-married,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,144925,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,156877,9th,5,Separated,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,153019,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,32825,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,114120,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,?,92440,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,32016,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,165953,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,96061,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,106422,HS-grad,9,Married-civ-spouse,Sales,Wife,Black,Female,0,1,2,United-States,>50K\n3,Self-emp-not-inc,167281,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,137895,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,177487,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,344696,Some-college,10,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,51415,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,4,United-States,<=50K\n2,Private,134367,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,289636,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,165115,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,206008,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,State-gov,149342,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,90042,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,495288,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,234970,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,2,?,<=50K\n3,Self-emp-not-inc,123011,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,260454,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,39026,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,278021,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,159399,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,340665,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,3,1,United-States,<=50K\n1,State-gov,392518,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,229826,Bachelors,13,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,185503,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,399117,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,168232,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,377322,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,141742,HS-grad,9,Widowed,Farming-fishing,Unmarried,White,Male,1,0,0,United-States,<=50K\n2,Private,31964,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Self-emp-not-inc,29019,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,3,United-States,<=50K\n4,?,183420,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,305319,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,182189,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,257250,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,269485,Preschool,1,Divorced,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n1,Self-emp-not-inc,182177,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,179481,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Poland,<=50K\n1,Private,199118,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Nicaragua,<=50K\n3,Private,33084,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,185407,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,177907,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,145441,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,2,United-States,>50K\n0,Private,104830,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,247507,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,216601,11th,7,Divorced,Machine-op-inspct,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,91670,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Private,106740,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,122562,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,109133,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,2,0,3,United-States,<=50K\n1,Private,196123,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,123436,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,42298,9th,5,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,297248,HS-grad,9,Married-spouse-absent,Craft-repair,Unmarried,White,Male,0,0,2,Columbia,<=50K\n0,Private,117363,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,79864,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,190762,1st-4th,2,Widowed,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n3,Private,155509,Bachelors,13,Separated,Prof-specialty,Unmarried,Black,Female,0,0,1,Jamaica,<=50K\n2,Federal-gov,163434,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,153832,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,147629,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,488720,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n3,Private,169180,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,2,1,United-States,<=50K\n2,Private,188763,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,229647,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,321456,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,199698,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,226010,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,116298,7th-8th,4,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,130057,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,369188,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,4,United-States,>50K\n1,Private,155193,HS-grad,9,Separated,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,159574,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,299353,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Local-gov,99971,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,190160,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,283416,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,224277,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n1,Private,111567,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,151411,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,346736,Assoc-acdm,12,Never-married,Exec-managerial,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,264055,Some-college,10,Never-married,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,309620,HS-grad,9,Never-married,Sales,Not-in-family,Other,Male,0,0,2,?,<=50K\n2,Private,224541,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,235334,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,4,0,3,United-States,>50K\n0,Private,296158,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,153997,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,231482,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,278553,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,91251,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,0,China,<=50K\n3,Self-emp-not-inc,192053,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,207120,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,125892,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,186824,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,200471,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,4,2,United-States,>50K\n0,Private,117779,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,44793,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,37646,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,174127,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,110103,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,2,2,United-States,<=50K\n0,Private,74631,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,211239,Some-college,10,Married-AF-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,?,157008,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,90406,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,199998,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,132350,7th-8th,4,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,233427,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,71489,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,<=50K\n1,Self-emp-inc,119411,Some-college,10,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,351772,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,34254,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,178693,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,168262,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,34169,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,1,0,1,United-States,>50K\n1,Private,328118,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,122353,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n0,Private,37315,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,181916,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,55465,Assoc-acdm,12,Never-married,Other-service,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Private,192203,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,91683,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,39302,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,171356,Assoc-acdm,12,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,197189,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,112362,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,228326,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,307353,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,172160,11th,7,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,234738,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,<=50K\n1,Private,33117,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,217380,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,4,3,United-States,>50K\n2,Private,157954,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,164299,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,4,United-States,>50K\n4,Self-emp-not-inc,224981,10th,6,Widowed,Craft-repair,Other-relative,White,Male,0,0,0,Mexico,<=50K\n0,Private,281209,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,200479,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,132750,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n0,Private,21154,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,1,1,United-States,<=50K\n1,State-gov,189843,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,116657,HS-grad,9,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,113522,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,176185,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,227796,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,2,0,2,United-States,>50K\n0,Private,194891,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,197189,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,182191,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,Private,242559,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,1,2,United-States,<=50K\n4,Self-emp-not-inc,122348,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,40024,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,225978,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,407436,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,119471,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Japan,<=50K\n1,Private,249409,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,1,United-States,<=50K\n2,?,217409,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,148995,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,200046,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,215618,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,280081,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,340651,Bachelors,13,Married-civ-spouse,Other-service,Husband,Black,Male,0,3,3,United-States,>50K\n2,Private,111000,Masters,14,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,135521,Assoc-voc,11,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,194102,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,295127,Some-college,10,Divorced,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,?,102310,Assoc-acdm,12,Divorced,?,Not-in-family,White,Female,0,0,2,Canada,<=50K\n3,Private,240175,11th,7,Separated,Other-service,Unmarried,Black,Male,0,0,1,United-States,<=50K\n2,Private,145441,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,243019,Preschool,1,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,215596,9th,5,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Mexico,<=50K\n0,State-gov,31350,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,246965,Some-college,10,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,99838,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,?,<=50K\n2,Private,340797,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,?,29937,HS-grad,9,Widowed,?,Not-in-family,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n2,Local-gov,30056,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,309903,10th,6,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,State-gov,256984,Some-college,10,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,181723,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,Germany,<=50K\n2,Private,101020,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Male,2,0,3,United-States,>50K\n2,Self-emp-not-inc,106900,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,195770,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,102737,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,191779,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,99479,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,<=50K\n4,Private,196891,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,?,208066,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,54341,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,140001,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,248220,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,172962,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,88215,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n0,Private,110142,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,136986,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,206775,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,53497,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,238534,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,Puerto-Rico,<=50K\n2,Private,143123,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,60269,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,82623,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n2,Local-gov,99666,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,95680,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n2,State-gov,208163,Assoc-voc,11,Separated,Protective-serv,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,369781,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,311288,11th,7,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,152889,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,160086,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,117963,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,186884,HS-grad,9,Married-spouse-absent,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,313830,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,212529,Some-college,10,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,124330,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,104501,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,43501,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,83774,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,216845,Preschool,1,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Local-gov,168191,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,166894,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,110083,HS-grad,9,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,194371,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,Canada,>50K\n2,Federal-gov,125933,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,4,2,United-States,<=50K\n0,Private,444554,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,190228,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,>50K\n1,Private,604045,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,241126,Some-college,10,Divorced,Tech-support,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,168355,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,State-gov,158451,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,1,0,2,United-States,<=50K\n3,Private,141608,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,33157,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,187563,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,26252,Assoc-acdm,12,Never-married,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Local-gov,318647,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,152572,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,Puerto-Rico,<=50K\n1,Private,77634,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,199513,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,1,3,United-States,<=50K\n0,Private,260327,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,437940,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,137069,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n1,Local-gov,32587,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,193960,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,170651,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,2,United-States,>50K\n4,?,186163,1st-4th,2,Widowed,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,114544,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,159724,Bachelors,13,Married-spouse-absent,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,697806,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,140011,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,411700,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n1,State-gov,179633,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,317690,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n2,Local-gov,213334,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,165304,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Greece,<=50K\n4,Private,192325,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,385183,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,232650,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,182474,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,119992,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,376016,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,144638,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Federal-gov,113612,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,?,106161,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,48160,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,55176,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,291232,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,250149,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,?,221064,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,87745,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,97136,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,632271,Some-college,10,Married-spouse-absent,Adm-clerical,Other-relative,White,Female,0,0,2,Peru,<=50K\n0,Private,295607,10th,6,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,163443,7th-8th,4,Widowed,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,213136,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,107882,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,214378,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,236427,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,34292,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,204780,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,4,0,2,United-States,>50K\n0,Private,161508,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,151959,HS-grad,9,Widowed,Other-service,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n2,Private,196001,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,211259,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,35136,Bachelors,13,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,288353,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n3,Local-gov,93449,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,2,Philippines,>50K\n0,Private,40432,10th,6,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,180632,12th,8,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,204113,HS-grad,9,Widowed,Protective-serv,Not-in-family,White,Female,3,0,0,United-States,<=50K\n0,Private,336101,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,235062,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,312552,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,3,4,United-States,<=50K\n2,Private,226947,7th-8th,4,Separated,Other-service,Other-relative,White,Male,0,0,2,El-Salvador,<=50K\n2,Private,29393,HS-grad,9,Never-married,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n2,?,115376,Some-college,10,Married-civ-spouse,?,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,146574,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,175389,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Mexico,<=50K\n1,Self-emp-inc,316688,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,187919,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,?,188436,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,Canada,<=50K\n2,Private,80666,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,381965,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,4,United-States,<=50K\n1,Private,192039,Assoc-acdm,12,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,181091,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,50191,9th,5,Divorced,Craft-repair,Unmarried,White,Male,2,0,2,United-States,<=50K\n1,Private,155256,Bachelors,13,Never-married,Tech-support,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,104973,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,348771,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Canada,<=50K\n3,Private,96415,HS-grad,9,Widowed,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Private,213136,Doctorate,16,Widowed,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,155259,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,95155,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,178787,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,361497,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,254890,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,296478,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,179791,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,110010,HS-grad,9,Divorced,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,89622,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,118614,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,35683,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,163815,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,3,2,United-States,<=50K\n3,Private,175305,7th-8th,4,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,96245,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,201017,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,95388,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,249332,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,194759,Assoc-acdm,12,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,260062,10th,6,Never-married,Other-service,Own-child,White,Female,2,0,2,United-States,<=50K\n2,Self-emp-not-inc,166213,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,46743,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,?,<=50K\n0,Private,112387,10th,6,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,324685,9th,5,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,87943,7th-8th,4,Married-civ-spouse,Craft-repair,Wife,Other,Female,0,0,3,?,<=50K\n2,Federal-gov,187510,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,188703,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,127961,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,200117,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,142030,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,226296,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,239130,Prof-school,15,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,165475,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,328216,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n0,?,118023,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,206066,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,141005,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,104870,Assoc-voc,11,Never-married,Other-service,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,253250,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,497788,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,128354,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,140782,10th,6,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,216129,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,65757,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,264758,Some-college,10,Married-civ-spouse,?,Husband,Black,Male,0,0,2,Haiti,<=50K\n0,Private,245361,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,216689,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,139517,11th,7,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Local-gov,188903,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,170094,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,108836,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,189565,HS-grad,9,Married-civ-spouse,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,347993,1st-4th,2,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n3,Private,187308,Some-college,10,Married-civ-spouse,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,State-gov,136077,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,165457,Bachelors,13,Never-married,Tech-support,Own-child,Asian-Pac-Islander,Male,1,0,2,United-States,<=50K\n3,Federal-gov,175428,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,?,143574,Some-college,10,Widowed,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,349148,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,103990,Masters,14,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,183884,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,464484,HS-grad,9,Married-spouse-absent,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,190786,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,348592,HS-grad,9,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,177839,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,152156,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,141807,HS-grad,9,Married-spouse-absent,Craft-repair,Other-relative,White,Male,0,0,2,Poland,<=50K\n3,Local-gov,188537,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,203233,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,28978,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,116211,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,97683,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,283945,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,115178,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n3,State-gov,77102,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,132220,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,129301,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,187592,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,312667,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,206951,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,587310,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Own-child,White,Male,0,0,2,El-Salvador,<=50K\n4,Private,328227,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,0,United-States,>50K\n1,Private,100634,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,274200,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n0,Self-emp-inc,29582,11th,7,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,174812,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,State-gov,138513,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,219137,7th-8th,4,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,265148,Bachelors,13,Never-married,Sales,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Private,302606,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,197600,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,167415,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,13769,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n1,Private,109390,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,218188,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,167159,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,128529,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,200973,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,118235,HS-grad,9,Never-married,Sales,Not-in-family,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n0,Local-gov,250165,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,1,0,2,United-States,<=50K\n3,Private,269620,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,?,<=50K\n3,Private,212162,5th-6th,3,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,147638,Bachelors,13,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,India,<=50K\n2,Private,304605,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,165267,9th,5,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,122037,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,165611,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,262634,7th-8th,4,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,280766,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,Cuba,<=50K\n0,Private,226668,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n2,Private,130200,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,98005,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,308857,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,108914,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,210464,Masters,14,Widowed,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,172748,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,192140,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,126568,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,179339,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,31621,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,365009,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,344698,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,159911,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,389456,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,167472,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,201412,10th,6,Never-married,Farming-fishing,Own-child,White,Male,1,0,0,United-States,<=50K\n1,Self-emp-not-inc,331861,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Private,97541,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,71865,9th,5,Married-civ-spouse,Machine-op-inspct,Wife,Other,Female,0,0,2,Portugal,<=50K\n1,Private,196564,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,218956,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,37359,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,?,<=50K\n2,Private,184630,Bachelors,13,Divorced,Handlers-cleaners,Not-in-family,White,Male,2,0,2,United-States,<=50K\n0,Local-gov,161955,11th,7,Never-married,Adm-clerical,Own-child,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n0,Private,200089,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Guatemala,<=50K\n2,Self-emp-not-inc,193026,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,336423,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,184529,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,153876,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,269687,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,153805,Some-college,10,Never-married,Craft-repair,Unmarried,Other,Male,0,0,2,Ecuador,<=50K\n2,Private,151504,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,330087,Assoc-acdm,12,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,164970,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,239539,Some-college,10,Never-married,Craft-repair,Own-child,Asian-Pac-Islander,Male,0,0,2,?,>50K\n3,Self-emp-not-inc,50215,Assoc-voc,11,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,80123,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,<=50K\n3,?,55139,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,276096,Some-college,10,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,222813,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,172232,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,386773,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,87556,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,169926,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,104196,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,320027,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,116637,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,307640,Assoc-voc,11,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,138386,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,1,United-States,<=50K\n0,Private,269015,HS-grad,9,Never-married,Other-service,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,Private,90308,Preschool,1,Never-married,Other-service,Unmarried,White,Male,0,0,1,El-Salvador,<=50K\n2,Local-gov,39581,HS-grad,9,Separated,Other-service,Not-in-family,Black,Female,2,0,2,United-States,<=50K\n3,Self-emp-not-inc,241688,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Cuba,<=50K\n0,Local-gov,467759,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,303677,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,47392,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,97925,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,243240,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,472344,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Private,177054,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,212206,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,244147,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,167336,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,<=50K\n2,State-gov,135060,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,?,>50K\n3,Private,52184,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,159187,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,22494,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,219745,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,113323,Masters,14,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,181099,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,216741,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,106365,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,124973,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,73199,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n3,Federal-gov,362679,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,197222,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,33115,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,37997,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,162067,Masters,14,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,Haiti,<=50K\n1,Private,133839,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,398874,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,289224,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,261259,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,438587,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,271162,11th,7,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,115880,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,29810,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,277695,Preschool,1,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n1,Private,277347,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,220445,HS-grad,9,Widowed,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,231507,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,184477,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,174714,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,118792,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,274689,Assoc-acdm,12,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,148315,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,99844,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n2,State-gov,143437,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Other,Female,0,0,2,United-States,<=50K\n0,Private,114357,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,?,427055,Some-college,10,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Local-gov,137518,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,183125,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,269317,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,107682,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,159589,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n3,Private,46401,Bachelors,13,Married-spouse-absent,Sales,Not-in-family,White,Female,0,0,0,Germany,<=50K\n4,Self-emp-not-inc,164754,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n4,?,109446,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,314068,Assoc-voc,11,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,242138,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,203070,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,266960,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,239511,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,?,244749,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,Cuba,<=50K\n0,Private,244698,5th-6th,3,Never-married,Farming-fishing,Other-relative,White,Male,0,0,1,Mexico,<=50K\n0,Private,207258,9th,5,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,Mexico,<=50K\n4,Private,111563,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,233149,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,166789,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n3,Self-emp-inc,22743,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,4,United-States,>50K\n2,Local-gov,180599,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,28738,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,259846,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Self-emp-inc,158950,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,185948,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,?,187553,7th-8th,4,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,169092,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,2,0,2,Canada,>50K\n2,Private,129298,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,120204,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,229337,HS-grad,9,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,183674,12th,8,Married-spouse-absent,Sales,Unmarried,White,Female,0,0,1,?,<=50K\n1,Private,538243,Some-college,10,Separated,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,108947,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,128516,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,147560,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,131808,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,234537,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,165160,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,90275,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,143583,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,210020,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,135603,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,441227,Masters,14,Married-civ-spouse,?,Husband,Black,Male,2,0,3,United-States,>50K\n2,Private,341943,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,38274,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,327435,Some-college,10,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n1,Federal-gov,175262,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,376474,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,171150,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,4,United-States,<=50K\n1,Private,459465,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,El-Salvador,<=50K\n2,Local-gov,188391,Assoc-acdm,12,Divorced,Other-service,Unmarried,White,Male,0,0,3,United-States,>50K\n2,Private,196373,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,122913,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,37314,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,198492,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,20946,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,281608,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,213643,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,1,3,United-States,<=50K\n0,Private,39222,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,208238,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,261207,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Peru,<=50K\n2,Private,131192,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,148214,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,155965,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,269004,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,97883,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,3,Italy,<=50K\n3,Private,177942,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Local-gov,360494,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,45136,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,173483,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,0,United-States,<=50K\n0,Private,205953,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n2,Private,169823,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,99591,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,32627,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,378009,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,233511,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,173987,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,162302,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,192812,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,217661,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,4,2,United-States,>50K\n4,Private,353031,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,155483,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,274158,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,26987,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,2,United-States,<=50K\n3,Private,68493,HS-grad,9,Married-spouse-absent,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,?,257421,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,38257,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Local-gov,175586,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,Private,316323,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,>50K\n2,Private,117802,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,454950,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,284277,11th,7,Never-married,Other-service,Own-child,White,Male,1,0,0,United-States,<=50K\n1,State-gov,90409,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,248094,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,138692,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,173938,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n4,?,146722,12th,8,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,145439,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,324445,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,176410,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,129275,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,399123,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,76044,Masters,14,Divorced,Prof-specialty,Unmarried,Other,Male,2,0,1,Mexico,>50K\n1,Private,87632,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,269605,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,37718,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,3,United-States,>50K\n4,?,162659,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,277434,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,209205,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,34235,HS-grad,9,Widowed,?,Not-in-family,White,Female,1,0,0,United-States,<=50K\n2,Private,141186,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,123681,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,174215,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,96354,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,109108,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,107302,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,250054,Some-college,10,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,50459,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,<=50K\n4,Local-gov,22975,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,97189,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,238859,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,State-gov,239303,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,0,United-States,<=50K\n1,Private,310655,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,276218,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,94235,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,135339,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n0,Private,199703,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,52532,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,186376,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,229124,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,152508,11th,7,Married-civ-spouse,Sales,Wife,Other,Female,0,0,0,United-States,<=50K\n2,Private,54260,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,48520,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,439777,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,191389,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,118714,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,34616,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,?,199074,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,112858,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,199555,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,211013,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,107425,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,106549,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,State-gov,110556,Masters,14,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,265097,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Germany,<=50K\n2,Private,215219,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,142988,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,239450,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,162084,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,83066,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,181705,Some-college,10,Separated,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,138737,HS-grad,9,Divorced,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,Federal-gov,332194,9th,5,Never-married,Adm-clerical,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Private,291979,HS-grad,9,Never-married,Sales,Unmarried,White,Male,0,0,1,United-States,<=50K\n4,Private,162761,Some-college,10,Widowed,Sales,Not-in-family,White,Male,1,0,1,United-States,<=50K\n0,Private,153643,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,4,United-States,<=50K\n3,Private,30908,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,92179,10th,6,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,50408,12th,8,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n3,Federal-gov,191013,HS-grad,9,Separated,Sales,Other-relative,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Private,170969,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n0,?,302229,HS-grad,9,Never-married,?,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Private,80026,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,93056,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,414791,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,37894,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,2,1,United-States,<=50K\n1,Local-gov,164243,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,36651,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,26248,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Private,244522,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,183279,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,177063,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,220220,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,180779,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,238787,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,32086,Some-college,10,Divorced,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,302149,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Cambodia,>50K\n2,Self-emp-not-inc,136986,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n3,State-gov,61062,Doctorate,16,Separated,Exec-managerial,Own-child,Asian-Pac-Islander,Male,1,0,2,United-States,<=50K\n1,Private,260782,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,82061,5th-6th,3,Never-married,Craft-repair,Not-in-family,Other,Male,0,0,1,Mexico,<=50K\n0,Private,254351,HS-grad,9,Married-civ-spouse,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,128699,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,171328,Bachelors,13,Married-spouse-absent,Prof-specialty,Other-relative,Black,Female,1,0,2,United-States,<=50K\n0,?,152719,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,0,Haiti,<=50K\n2,Private,97688,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,199248,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,67240,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,1490400,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,188503,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,180206,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,201872,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,314373,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,107737,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Portugal,<=50K\n2,Private,209093,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,218357,HS-grad,9,Separated,Transport-moving,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,163434,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,326701,5th-6th,3,Separated,Craft-repair,Not-in-family,Other,Male,0,0,2,Mexico,<=50K\n2,Private,164612,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n1,Self-emp-not-inc,37429,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,408208,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n3,Private,105638,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,81259,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,201141,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,394927,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,165998,Prof-school,15,Married-civ-spouse,Armed-Forces,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,41888,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,4,4,United-States,>50K\n0,Private,72887,HS-grad,9,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,56551,9th,5,Divorced,Craft-repair,Unmarried,White,Female,2,0,2,United-States,<=50K\n0,Private,227603,Some-college,10,Never-married,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,203776,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Poland,<=50K\n4,Private,202060,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n4,Private,178282,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,57151,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,399455,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,52630,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,124692,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,278254,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,162098,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,304143,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,287031,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,102478,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,172425,HS-grad,9,Married-spouse-absent,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n3,Private,56664,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,247514,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,>50K\n1,Private,307353,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,111129,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,190539,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,Greece,>50K\n3,Self-emp-inc,224314,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,59987,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,<=50K\n1,Local-gov,111746,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,162958,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,3,3,United-States,<=50K\n4,?,129802,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,303155,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,301634,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,156294,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,145033,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n0,?,768659,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,134679,11th,7,Never-married,Handlers-cleaners,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,188798,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,122033,Some-college,10,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,4,United-States,<=50K\n0,?,223515,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,235124,12th,8,Divorced,Other-service,Not-in-family,White,Male,0,0,1,?,<=50K\n3,Private,341814,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n1,State-gov,764638,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,3,United-States,>50K\n3,Federal-gov,303637,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,307543,10th,6,Never-married,Transport-moving,Own-child,White,Male,0,0,4,Cuba,<=50K\n2,Local-gov,151267,Some-college,10,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,<=50K\n2,Private,157403,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,124483,Masters,14,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,3,India,>50K\n1,Private,26803,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,131899,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,119992,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,4,3,United-States,<=50K\n1,Private,198068,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,264351,12th,8,Separated,Adm-clerical,Own-child,White,Male,0,0,2,Mexico,<=50K\n0,?,352430,11th,7,Never-married,?,Own-child,White,Male,0,2,1,United-States,<=50K\n4,Private,29797,HS-grad,9,Divorced,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,54670,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Female,0,0,2,?,<=50K\n3,Private,192713,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,79662,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,190023,11th,7,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,301251,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,115336,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n4,Self-emp-not-inc,98015,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n1,Self-emp-not-inc,48189,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,248754,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,195602,12th,8,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,185797,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,1,0,3,United-States,<=50K\n3,Private,192588,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Philippines,<=50K\n2,Private,160837,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n3,Local-gov,128378,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,335846,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,179991,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,151763,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,127875,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,132686,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,240900,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,65876,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,165852,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,437458,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,261995,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,342480,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n4,Private,270131,5th-6th,3,Widowed,Other-service,Unmarried,White,Female,0,0,4,Mexico,<=50K\n3,Private,216414,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,4,United-States,>50K\n1,Private,259425,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,144086,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,246124,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,321865,Prof-school,15,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,32080,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,201697,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Local-gov,300687,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,307724,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,?,<=50K\n4,Private,40856,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,2,2,United-States,<=50K\n0,?,115085,HS-grad,9,Married-civ-spouse,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,State-gov,202139,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,190151,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,208277,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,107405,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,194096,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,162029,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,Private,172155,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Peru,<=50K\n3,Self-emp-inc,114674,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,116298,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,320510,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,158144,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,181651,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,51150,12th,8,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,State-gov,118544,Some-college,10,Divorced,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,85423,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,56460,Bachelors,13,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,211208,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,154337,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,125542,11th,7,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,175847,5th-6th,3,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,2,Mexico,>50K\n1,Private,229731,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,White,Female,0,0,1,El-Salvador,<=50K\n2,Self-emp-not-inc,40666,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,3,United-States,<=50K\n4,Federal-gov,215900,HS-grad,9,Never-married,Adm-clerical,Other-relative,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,?,186792,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,?,151552,11th,7,Never-married,?,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,122002,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,32174,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,349148,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,209691,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,163021,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,283342,10th,6,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,?,45186,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,175398,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,175455,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,194946,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,183869,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,1,0,4,United-States,<=50K\n0,?,167428,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,227615,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,196797,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,>50K\n1,Local-gov,273051,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,27085,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,235646,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,168358,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,167725,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,91819,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,105830,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,201159,12th,8,Widowed,Machine-op-inspct,Other-relative,White,Female,0,0,3,United-States,<=50K\n0,Private,137363,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,164725,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,1,0,1,United-States,<=50K\n3,Private,29438,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Private,131656,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,4,1,United-States,>50K\n1,State-gov,35306,9th,5,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,198206,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,103513,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,143078,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,109494,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,1,2,United-States,<=50K\n1,Private,52732,7th-8th,4,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,164495,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,Germany,<=50K\n0,Self-emp-not-inc,105997,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Federal-gov,105959,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,216540,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,159623,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,87207,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,47621,9th,5,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,190297,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,4,United-States,>50K\n4,Private,48034,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,162236,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,>50K\n4,Private,104724,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,129806,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,170174,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,265148,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,192237,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,406491,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,231866,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,292055,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,140612,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n1,Private,191573,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,203635,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,171483,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,68798,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,31752,HS-grad,9,Divorced,Machine-op-inspct,Other-relative,White,Female,0,0,2,?,<=50K\n4,?,291856,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,135848,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,0,Guatemala,<=50K\n0,Private,72887,Some-college,10,Never-married,Other-service,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n3,Private,275163,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,29276,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,1,0,3,United-States,<=50K\n3,Private,224207,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,237356,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,393829,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,193720,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,140729,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,54560,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,214288,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,4,United-States,<=50K\n2,Self-emp-inc,88500,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,287092,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Private,225263,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,Local-gov,140027,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,32000,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,228516,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,0,0,Portugal,<=50K\n1,Private,157612,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,197200,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,89598,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,153799,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,4,?,>50K\n4,?,101761,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,225456,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,348960,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,157111,10th,6,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,85877,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n4,Self-emp-not-inc,32819,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,517995,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,103948,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,96016,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,60668,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,270379,HS-grad,9,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,190756,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,221417,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,158940,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n4,State-gov,121395,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n1,Private,196866,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,302239,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,718736,Some-college,10,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,178615,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,158096,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,317988,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,325596,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,120461,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,?,30680,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,125010,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,268781,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,1,0,United-States,<=50K\n3,Private,36020,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,433682,Bachelors,13,Never-married,Tech-support,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,349148,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,148805,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,285775,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,126524,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,2,United-States,>50K\n3,Private,270221,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,117222,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,118941,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,172667,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,241306,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,<=50K\n1,State-gov,292317,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,182918,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n4,Self-emp-not-inc,106430,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,119207,HS-grad,9,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,377692,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,284907,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,190160,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Male,0,3,0,Poland,<=50K\n4,Self-emp-inc,226215,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,169324,9th,5,Divorced,Other-service,Unmarried,Black,Female,0,0,2,Haiti,<=50K\n0,Private,191460,10th,6,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,219155,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,?,52654,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,198466,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,255965,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n2,?,54953,HS-grad,9,Divorced,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,290441,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,206927,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n4,Self-emp-inc,165609,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,206609,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,2,3,United-States,<=50K\n4,Private,211846,10th,6,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,102446,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,114483,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,199657,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,192878,10th,6,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,346081,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,26668,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Puerto-Rico,<=50K\n4,?,272425,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,2,0,0,United-States,<=50K\n4,Local-gov,159643,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n3,?,22743,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,142875,10th,6,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,256304,HS-grad,9,Never-married,?,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Private,163380,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,162187,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,153353,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,134414,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,39212,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,344060,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,Japan,>50K\n0,Local-gov,140240,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,210722,Prof-school,15,Divorced,?,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,285946,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,216645,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,54065,7th-8th,4,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,44159,12th,8,Never-married,Other-service,Other-relative,Other,Male,0,0,2,Dominican-Republic,<=50K\n3,Private,188729,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,296982,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,?,<=50K\n4,Local-gov,277203,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,153949,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n3,Federal-gov,269890,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Federal-gov,61518,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,176050,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,202700,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,477083,11th,7,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,221532,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,282155,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n2,Private,365307,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,248776,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,166863,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,191149,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,126822,10th,6,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,281743,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,212041,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,264351,7th-8th,4,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,Mexico,<=50K\n3,Private,117198,HS-grad,9,Widowed,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,202937,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,?,201062,HS-grad,9,Married-civ-spouse,?,Wife,Black,Female,0,0,0,United-States,<=50K\n3,Private,96062,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,99902,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Ireland,>50K\n3,Private,76268,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n4,Private,200517,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,222478,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,?,168471,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,403027,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,201292,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,360814,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,155574,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,135525,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,24763,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-inc,184456,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Italy,>50K\n2,Private,30412,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,93225,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,359988,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,122314,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,Federal-gov,51662,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,137991,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,119458,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,208068,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Other,Male,2,0,2,Mexico,>50K\n1,Private,219553,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,308924,HS-grad,9,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,169748,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,164970,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,190987,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,250804,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,England,<=50K\n1,Private,85374,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,39667,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,0,United-States,<=50K\n2,Private,84817,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,2,0,2,United-States,<=50K\n2,Private,227615,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,155737,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,3,0,2,United-States,>50K\n2,Private,133935,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,El-Salvador,>50K\n0,Federal-gov,316438,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,107433,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,28572,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,291414,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,202153,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,324655,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,27207,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,184435,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,122749,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,181146,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n0,Private,225311,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,33474,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,126319,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,247806,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,85815,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n3,Private,204629,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,195540,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,Black,Male,0,1,2,United-States,<=50K\n1,Private,113866,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,114691,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Ireland,<=50K\n0,?,227943,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,310260,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,189922,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,249949,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,?,38455,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,123429,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,99549,5th-6th,3,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,98954,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,49794,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,80451,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,129764,Some-college,10,Divorced,Sales,Unmarried,White,Male,1,0,3,United-States,<=50K\n1,Private,189702,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n4,Self-emp-not-inc,78020,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,182843,HS-grad,9,Divorced,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,53956,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,223376,Bachelors,13,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,151933,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,1,2,United-States,>50K\n3,Private,100931,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,442478,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,153082,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,182414,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,217926,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,176536,Some-college,10,Separated,Adm-clerical,Other-relative,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,237943,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Poland,<=50K\n0,Private,117789,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,113838,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n0,Private,278414,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,122493,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,110820,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,106141,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n2,Self-emp-not-inc,215896,Some-college,10,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,3,United-States,<=50K\n3,Private,547108,Bachelors,13,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,4,0,2,?,>50K\n3,Federal-gov,169078,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n4,Self-emp-not-inc,227906,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,1,Germany,<=50K\n4,Private,61298,5th-6th,3,Separated,Machine-op-inspct,Other-relative,White,Female,0,0,2,Ecuador,<=50K\n3,Private,184285,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,64156,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,56248,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,171275,7th-8th,4,Divorced,Other-service,Not-in-family,Other,Male,0,0,2,Peru,<=50K\n2,Private,123490,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,420842,Assoc-acdm,12,Divorced,Priv-house-serv,Other-relative,White,Female,0,0,2,?,<=50K\n2,Private,51233,Bachelors,13,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,353263,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,262031,12th,8,Never-married,Other-service,Other-relative,White,Male,0,0,0,United-States,<=50K\n3,Private,334421,Prof-school,15,Never-married,Prof-specialty,Other-relative,Asian-Pac-Islander,Female,0,1,1,China,<=50K\n0,Private,200153,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,187972,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,360186,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,368832,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,359131,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,295279,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,378272,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,150817,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,145246,Some-college,10,Divorced,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,185490,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,217424,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,Private,190483,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,391016,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,30509,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,3,United-States,<=50K\n1,Private,267661,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,197369,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,393691,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,168441,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,85109,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,190457,10th,6,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,289430,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,166697,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Local-gov,310355,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,Germany,<=50K\n1,Private,300828,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n0,Private,188923,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,167482,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,114968,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,102988,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Ecuador,>50K\n4,Local-gov,330144,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,362654,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,179481,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,204817,Bachelors,13,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,1,United-States,<=50K\n1,Private,172402,Some-college,10,Never-married,Adm-clerical,Unmarried,Other,Female,0,0,2,United-States,<=50K\n2,Private,54611,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,179151,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,30829,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,474229,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,246981,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Private,271610,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,179138,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,268722,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n3,Private,111410,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,125550,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,2,United-States,>50K\n0,Private,51985,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,302584,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,311184,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Local-gov,133969,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,Thailand,<=50K\n2,Local-gov,214242,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,4,United-States,>50K\n1,Private,372149,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,203967,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,147344,HS-grad,9,Never-married,Transport-moving,Other-relative,White,Male,0,0,3,?,<=50K\n2,Self-emp-inc,139268,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,349198,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,222756,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,<=50K\n3,Local-gov,196395,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,316304,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,347653,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Germany,<=50K\n2,Private,176063,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n4,Private,176835,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,144092,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,148709,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,194141,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,215423,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,128378,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,34431,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,180690,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,142712,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,State-gov,185619,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,1,United-States,>50K\n2,Self-emp-not-inc,358373,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,81648,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,244580,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,184570,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,210150,Masters,14,Never-married,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,212213,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,182148,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,55390,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,66788,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Male,0,0,2,Portugal,<=50K\n2,Federal-gov,265604,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,110677,Some-college,10,Married-civ-spouse,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,320077,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,201817,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,142725,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,2,4,United-States,>50K\n2,Self-emp-not-inc,53956,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,116892,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,34572,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,287008,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,30740,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,162104,7th-8th,4,Never-married,Priv-house-serv,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,237024,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,Mexico,<=50K\n0,Private,228306,HS-grad,9,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,127388,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,72393,Bachelors,13,Married-spouse-absent,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,160813,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,255586,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,342575,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,181466,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,234108,Assoc-acdm,12,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,66414,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,227307,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,157145,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,252457,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,142769,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,49539,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,2,0,2,United-States,<=50K\n1,Private,249438,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,289293,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,Dominican-Republic,<=50K\n4,Self-emp-not-inc,198884,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,229259,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,289223,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,2,2,United-States,>50K\n0,Private,42401,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,295055,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,214781,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Male,0,2,2,United-States,<=50K\n0,Private,95552,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,308764,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,185394,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,358382,Some-college,10,Separated,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,195946,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,Thailand,<=50K\n1,Private,296897,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,230961,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,169022,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,209301,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,252058,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,30012,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,202046,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,52262,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,96660,HS-grad,9,Divorced,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,200618,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,177368,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,0,United-States,<=50K\n0,Private,311311,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,State-gov,142856,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,134890,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,3,3,United-States,<=50K\n2,Self-emp-inc,179579,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,State-gov,173623,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,1,United-States,<=50K\n4,Self-emp-inc,99328,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,2,United-States,>50K\n2,Local-gov,224799,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,231781,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,41763,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,51240,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,206923,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,Other,Female,0,3,2,United-States,>50K\n1,Self-emp-inc,132601,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,357348,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n0,Private,150683,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-inc,188615,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,0,United-States,>50K\n4,Private,159007,Bachelors,13,Divorced,Farming-fishing,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,Private,130959,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,0,Canada,<=50K\n3,Private,158746,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,498833,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Nicaragua,<=50K\n3,Private,193188,Masters,14,Never-married,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,136277,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n1,Private,137991,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,187098,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,176839,Doctorate,16,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,State-gov,185384,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,87867,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,111779,11th,7,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,185556,HS-grad,9,Separated,Protective-serv,Not-in-family,White,Male,0,3,1,United-States,<=50K\n4,Private,59469,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Canada,<=50K\n4,Private,164435,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,259336,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,Peru,<=50K\n2,Self-emp-not-inc,277488,HS-grad,9,Separated,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,104258,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,141427,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,267052,10th,6,Never-married,Farming-fishing,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,114764,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,151143,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,176357,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,190303,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,1,0,0,United-States,<=50K\n1,Private,220692,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,181970,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,263339,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n0,Self-emp-not-inc,83704,9th,5,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,324960,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,96062,9th,5,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,96678,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,33435,Assoc-voc,11,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,399008,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,2,0,2,United-States,<=50K\n4,Private,159722,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,225172,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,135033,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Ecuador,<=50K\n2,Private,179671,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,182460,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,231196,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,181974,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,326104,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,126850,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,33644,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,92649,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,353696,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,238342,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,882849,Bachelors,13,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,318280,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,151322,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Self-emp-inc,111567,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,279996,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,103743,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,4,United-States,<=50K\n3,Private,30846,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,191393,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Male,0,1,2,United-States,<=50K\n1,State-gov,140564,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,<=50K\n2,Federal-gov,243177,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,104996,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,191202,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,247379,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,96129,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,?,58440,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,125031,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,183772,Assoc-acdm,12,Never-married,Adm-clerical,Other-relative,White,Female,0,0,4,United-States,<=50K\n2,Private,78488,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,121058,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,84673,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,172830,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Private,307520,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n0,Private,327797,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,108945,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,164473,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,144778,Bachelors,13,Married-spouse-absent,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,69477,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,137946,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,167737,Bachelors,13,Widowed,Other-service,Own-child,White,Male,0,3,3,United-States,<=50K\n1,Private,195602,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,?,<=50K\n1,Private,140206,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,272551,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Female,0,1,3,United-States,>50K\n0,Private,114939,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,265477,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,France,>50K\n3,Local-gov,252029,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Self-emp-inc,263786,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,397877,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,316438,5th-6th,3,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Local-gov,283921,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,199903,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,339814,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,191140,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,174215,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Private,124420,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,289228,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,2,United-States,>50K\n1,Private,200610,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,140327,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,2,0,1,United-States,>50K\n2,Local-gov,86643,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,226624,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,365516,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,153288,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,235124,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,?,<=50K\n1,Self-emp-inc,160731,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,230999,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,453663,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,250967,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,?,96844,Some-college,10,Never-married,?,Not-in-family,White,Female,0,2,0,United-States,<=50K\n2,Federal-gov,149102,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,3,2,United-States,<=50K\n2,Private,226452,9th,5,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,2,Mexico,<=50K\n2,Private,34378,7th-8th,4,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,177277,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,316470,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,293104,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,380357,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,?,>50K\n2,Private,101318,Some-college,10,Married-spouse-absent,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,China,>50K\n1,?,339099,Some-college,10,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,131662,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,163333,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,71738,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,141276,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,242552,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,Honduras,<=50K\n1,Private,246439,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,81232,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,157568,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,117476,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,198201,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,167483,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Self-emp-inc,150384,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,96244,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,33678,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,1,United-States,<=50K\n2,Private,180985,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,101192,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,207561,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,105749,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,443508,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,249087,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,279231,Assoc-voc,11,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,180477,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,144522,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,El-Salvador,<=50K\n2,Local-gov,248263,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,?,498411,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,102442,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,262877,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,325537,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,161638,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,2,Columbia,<=50K\n3,Self-emp-not-inc,24367,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,108140,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n4,?,205110,10th,6,Widowed,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,504423,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,Japan,>50K\n2,Private,264700,HS-grad,9,Married-civ-spouse,Tech-support,Wife,Black,Female,0,0,1,United-States,<=50K\n0,Private,335067,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,153551,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,186077,HS-grad,9,Widowed,Transport-moving,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,85001,Masters,14,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,216999,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,107231,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,52870,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n4,Self-emp-not-inc,228899,7th-8th,4,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Local-gov,90956,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,186934,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-inc,37394,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,?,30840,Some-college,10,Divorced,?,Unmarried,White,Female,0,0,2,Germany,<=50K\n1,Private,185177,Assoc-voc,11,Separated,Tech-support,Own-child,White,Male,0,1,2,United-States,<=50K\n1,Private,312055,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,176262,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,161482,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,373448,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,<=50K\n2,Self-emp-not-inc,277630,Some-college,10,Divorced,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,150904,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,187334,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,328937,7th-8th,4,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Local-gov,132879,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,49159,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,133299,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n4,?,268315,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,176430,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,211344,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,162302,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,229225,Masters,14,Divorced,Other-service,Not-in-family,Black,Female,0,0,0,United-States,>50K\n3,Self-emp-not-inc,77404,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,?,>50K\n3,Local-gov,202044,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,94128,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,189888,Assoc-acdm,12,Never-married,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,94041,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,State-gov,322840,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,Poland,>50K\n3,Federal-gov,746660,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,2,2,United-States,>50K\n3,Private,84587,HS-grad,9,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,33126,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,247445,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,210377,10th,6,Married-civ-spouse,Exec-managerial,Wife,Black,Female,0,0,2,United-States,<=50K\n0,?,239862,Some-college,10,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,327112,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,188563,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,189098,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,26326,Assoc-voc,11,Divorced,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Private,145636,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,3,United-States,>50K\n2,State-gov,255456,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,India,>50K\n1,Private,196373,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,2,2,United-States,<=50K\n1,Private,167476,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,59083,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,186934,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,188650,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Federal-gov,373043,HS-grad,9,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,1,Germany,<=50K\n3,Private,250423,Some-college,10,Married-spouse-absent,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,334032,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,89487,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,230205,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,3,1,United-States,<=50K\n1,Private,212980,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,351770,Some-college,10,Divorced,Farming-fishing,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,State-gov,167482,HS-grad,9,Never-married,Protective-serv,Own-child,White,Male,0,3,2,United-States,<=50K\n0,Private,42251,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,52822,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,229472,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,93034,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,Laos,<=50K\n1,Private,415167,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,161182,Some-college,10,Widowed,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,166549,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,36296,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,272442,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,366139,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,127651,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n4,Private,158077,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,154950,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,197886,Assoc-acdm,12,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,211518,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,214303,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,154120,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,186303,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,488459,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,423883,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,117963,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,38430,7th-8th,4,Widowed,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,176969,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,116839,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,212407,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,110075,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,3,United-States,>50K\n2,Private,183096,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,126754,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,216178,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,188391,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,?,27251,11th,7,Widowed,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,40230,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n3,Private,100009,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,?,222442,Some-college,10,Divorced,?,Own-child,White,Male,0,0,1,El-Salvador,<=50K\n0,Local-gov,403471,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,3,United-States,<=50K\n3,Private,161482,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,83141,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,2,0,3,United-States,<=50K\n4,Self-emp-inc,31661,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,101073,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,99682,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,1,0,2,United-States,<=50K\n0,Private,215395,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,343476,11th,7,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,178363,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,Private,95872,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,?,<=50K\n3,Private,90907,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Outlying-US(Guam-USVI-etc),>50K\n2,Private,165309,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,208358,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,171069,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Black,Male,4,0,2,United-States,>50K\n3,Private,53540,Some-college,10,Divorced,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,>50K\n3,Private,29433,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,175622,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,231865,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,266336,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,3,United-States,>50K\n2,Self-emp-not-inc,190290,Some-college,10,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,0,4,France,<=50K\n4,Self-emp-not-inc,45319,12th,8,Married-civ-spouse,Other-service,Husband,White,Male,1,0,0,Canada,<=50K\n0,Never-worked,131593,11th,7,Never-married,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Local-gov,177913,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,457357,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,253811,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n3,Private,501671,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,State-gov,227128,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,39137,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,121038,HS-grad,9,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,State-gov,119570,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,?,99297,HS-grad,9,Married-civ-spouse,?,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Cambodia,<=50K\n1,Self-emp-not-inc,169460,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,261639,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,2,0,2,United-States,<=50K\n0,Private,214542,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,141410,Some-college,10,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,370549,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,>50K\n2,Self-emp-not-inc,234767,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,104196,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,State-gov,52262,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,England,<=50K\n0,?,285775,Bachelors,13,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,235336,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,165539,Some-college,10,Never-married,Priv-house-serv,Not-in-family,Black,Female,0,0,4,Jamaica,<=50K\n2,Private,362815,Some-college,10,Separated,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,State-gov,292816,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,66692,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,120910,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,116133,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,247043,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,215616,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,131415,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,169604,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,230858,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,73968,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,339897,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,258406,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,Mexico,<=50K\n1,Private,180574,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,88808,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,179627,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,149823,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,60639,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,46492,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,48520,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,306538,12th,8,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,204678,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,218676,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,95455,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,335655,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,194436,9th,5,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,152724,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,242482,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,162041,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,291854,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,48343,Some-college,10,Never-married,?,Other-relative,Black,Female,0,0,2,United-States,<=50K\n4,?,153113,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,1,0,1,United-States,<=50K\n2,Private,80680,10th,6,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,211601,Assoc-voc,11,Never-married,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Self-emp-inc,264554,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,319998,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,194228,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,236247,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,123983,Masters,14,Divorced,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,3,South,<=50K\n3,Private,166215,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,178623,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,174102,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,292217,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,?,<=50K\n2,Private,198452,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,96497,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,?,194660,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,216924,HS-grad,9,Divorced,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,206721,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n2,State-gov,49105,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Federal-gov,164021,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,91608,Prof-school,15,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,323963,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,70037,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,289950,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,213149,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,320451,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,4,0,2,Philippines,>50K\n0,Private,351952,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,146015,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,281704,Some-college,10,Never-married,Farming-fishing,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,786418,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,214689,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,193188,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,142020,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n4,?,317311,HS-grad,9,Widowed,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,213416,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Federal-gov,326048,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,191265,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,348986,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,1,Taiwan,<=50K\n0,Private,126613,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,?,40032,Bachelors,13,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,150057,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,Poland,<=50K\n2,Private,113725,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,3,0,2,United-States,>50K\n0,Private,140500,10th,6,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,113364,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,176219,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n0,Private,146189,HS-grad,9,Never-married,Sales,Other-relative,Amer-Indian-Eskimo,Female,0,0,4,United-States,<=50K\n2,Private,83993,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,>50K\n1,Private,194336,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n4,State-gov,349434,Bachelors,13,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,142020,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,48894,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,226295,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,77313,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,305935,HS-grad,9,Divorced,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,287988,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,49275,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,102865,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,209146,Masters,14,Divorced,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,173001,Some-college,10,Married-civ-spouse,Tech-support,Own-child,White,Female,0,3,2,United-States,>50K\n2,Private,277256,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,4,3,United-States,>50K\n3,Private,20534,Masters,14,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,60227,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,176936,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,49255,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n4,?,232787,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,235095,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,190531,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,1,United-States,<=50K\n4,Without-pay,209291,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,109053,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,3,United-States,<=50K\n0,Private,183594,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,361608,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,257028,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,Haiti,<=50K\n1,Private,66561,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,176486,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,565313,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,198953,Some-college,10,Divorced,Protective-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,State-gov,99106,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,213140,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,66384,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,483201,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,?,466458,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,90446,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Self-emp-not-inc,165017,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,120596,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,345310,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,?,94746,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,Portugal,<=50K\n2,Local-gov,338611,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,275366,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,188872,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,359397,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,Other,Male,0,0,2,United-States,<=50K\n1,Private,158800,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,31510,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,358740,11th,7,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,257148,Bachelors,13,Widowed,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,174525,1st-4th,2,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,Dominican-Republic,<=50K\n3,Private,161599,HS-grad,9,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,193494,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,231832,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,146834,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,63927,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,278403,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,241141,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,?,<=50K\n4,Local-gov,127463,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,175017,11th,7,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,2,United-States,<=50K\n3,Private,56741,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,107683,Assoc-voc,11,Married-civ-spouse,Craft-repair,Wife,White,Female,2,0,2,United-States,>50K\n2,Private,270324,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,Jamaica,<=50K\n3,State-gov,304512,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,167049,Bachelors,13,Married-civ-spouse,Priv-house-serv,Wife,White,Female,0,0,0,United-States,>50K\n2,Self-emp-inc,88973,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,210736,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,73514,HS-grad,9,Never-married,Other-service,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,75785,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,137723,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,0,United-States,<=50K\n1,Private,220043,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,132247,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,65390,12th,8,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,177144,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,1,India,<=50K\n3,Local-gov,358668,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,188328,Masters,14,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,350510,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,72257,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,183668,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,1,United-States,<=50K\n3,Private,168262,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,153536,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,189703,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,147703,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,173670,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,231832,Some-college,10,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,33223,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,130021,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,50990,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,308241,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,254025,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,377622,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,217802,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,39477,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,138946,7th-8th,4,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,35603,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,142624,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n0,Private,92609,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,111232,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,203518,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,233571,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,45093,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,175431,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,1,0,2,United-States,<=50K\n4,Private,225063,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n0,Private,391495,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,162312,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,1,Philippines,<=50K\n1,Self-emp-not-inc,151733,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,172026,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,323164,10th,6,Never-married,Craft-repair,Own-child,Other,Female,0,0,1,El-Salvador,<=50K\n4,?,129824,7th-8th,4,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,203715,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,156040,5th-6th,3,Separated,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,186168,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,154227,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,141327,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,103925,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,118633,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,207540,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,4,0,3,United-States,>50K\n3,Self-emp-not-inc,106728,Assoc-acdm,12,Divorced,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,192237,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,132879,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,4,0,2,Italy,>50K\n0,Private,148345,11th,7,Never-married,Protective-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,326292,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,33975,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,112115,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,129357,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,175958,Some-college,10,Separated,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,125317,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,424934,10th,6,Widowed,Other-service,Not-in-family,White,Female,0,0,2,Portugal,<=50K\n1,Private,204648,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n3,Private,186256,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n1,Local-gov,126569,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,89813,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,149576,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,220426,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,72055,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,94939,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,104917,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,314645,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,108604,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,153542,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,226902,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,450200,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,279129,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,242619,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-inc,357223,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,206951,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,70737,9th,5,Widowed,Handlers-cleaners,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,200939,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,192128,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,188798,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n0,?,202920,Assoc-acdm,12,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,205407,HS-grad,9,Divorced,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,?,24008,Some-college,10,Never-married,?,Own-child,White,Male,0,2,2,United-States,<=50K\n3,Private,172962,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,83791,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,304838,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,165858,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,>50K\n1,Private,110592,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,164416,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,345339,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,129806,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,205100,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n1,Local-gov,250551,HS-grad,9,Married-civ-spouse,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,285832,Masters,14,Married-civ-spouse,Sales,Wife,White,Female,0,0,4,United-States,<=50K\n0,Private,338717,11th,7,Never-married,Handlers-cleaners,Not-in-family,Other,Male,0,0,1,United-States,<=50K\n2,State-gov,187802,Some-college,10,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,215895,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,2,0,3,United-States,>50K\n1,Private,100135,Bachelors,13,Never-married,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,137616,9th,5,Never-married,Sales,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,341672,HS-grad,9,Married-spouse-absent,Adm-clerical,Other-relative,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n2,Private,322044,Some-college,10,Divorced,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,149347,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,293809,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,93639,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,166297,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n4,Private,167840,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,3,2,United-States,<=50K\n1,Private,180656,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,?,<=50K\n2,Self-emp-not-inc,197176,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,207965,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Federal-gov,23940,Some-college,10,Divorced,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,67433,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,1,United-States,<=50K\n0,Private,190916,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,235892,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,240767,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,194515,Some-college,10,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,156464,10th,6,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,117728,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,195727,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,133454,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,191177,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,2,2,United-States,<=50K\n4,State-gov,71075,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,233740,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,185455,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,141445,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,131712,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,3,United-States,>50K\n0,Private,210338,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Private,465334,11th,7,Divorced,Farming-fishing,Unmarried,White,Male,0,0,0,United-States,<=50K\n3,Private,168069,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,80557,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,110622,Bachelors,13,Never-married,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,40255,Assoc-voc,11,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,402748,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,Canada,<=50K\n4,Private,97030,10th,6,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,39477,Some-college,10,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,152351,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,229939,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,131230,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,182211,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,57435,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,>50K\n1,?,225654,HS-grad,9,Never-married,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,252424,Assoc-voc,11,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,Cambodia,<=50K\n3,?,155509,11th,7,Divorced,?,Unmarried,Black,Female,0,0,0,Haiti,<=50K\n2,Private,29591,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,101774,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n2,Local-gov,74194,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,171156,Some-college,10,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,145637,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Female,4,0,3,United-States,>50K\n1,Private,172714,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,188528,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Canada,>50K\n3,Federal-gov,89705,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,165107,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,347873,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Vietnam,<=50K\n0,?,298342,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,53482,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,162958,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,366483,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Federal-gov,335481,Some-college,10,Separated,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,>50K\n2,Private,197609,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,1,2,United-States,<=50K\n1,State-gov,160731,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,Germany,<=50K\n1,Private,210848,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,1,Mexico,<=50K\n4,Private,196126,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,201519,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,121124,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,State-gov,203518,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,254230,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,136531,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,108505,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,31095,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,149347,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,188246,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,1,3,United-States,<=50K\n3,State-gov,185859,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,227879,Assoc-voc,11,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,75541,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,99385,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,403061,1st-4th,2,Never-married,Machine-op-inspct,Other-relative,White,Female,0,0,2,Mexico,<=50K\n0,State-gov,82067,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,140434,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,159268,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,162945,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,365430,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,163678,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,230919,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,112264,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,192900,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,355856,Bachelors,13,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n0,?,156916,Some-college,10,Never-married,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,172927,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,2,4,United-States,<=50K\n0,?,170125,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,305379,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,206974,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,482096,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Local-gov,267843,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,173927,Some-college,10,Never-married,Tech-support,Own-child,Other,Female,0,0,1,Jamaica,<=50K\n0,Private,225865,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,State-gov,261278,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,4,?,<=50K\n4,?,180082,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,115187,Assoc-voc,11,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,451603,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,145041,Bachelors,13,Divorced,Machine-op-inspct,Other-relative,White,Male,0,4,3,Dominican-Republic,<=50K\n1,Self-emp-not-inc,132705,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,177695,HS-grad,9,Married-civ-spouse,Other-service,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,197033,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,307267,Masters,14,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,341643,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,2,3,United-States,<=50K\n1,Private,256362,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,153484,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,308077,Some-college,10,Separated,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,156266,Assoc-acdm,12,Never-married,Exec-managerial,Own-child,Amer-Indian-Eskimo,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,106634,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,1,United-States,>50K\n4,Private,198435,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,37210,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,237914,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,186106,7th-8th,4,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,236731,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Cuba,<=50K\n0,Self-emp-inc,465725,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,343121,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,298435,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,3,2,Cuba,<=50K\n2,State-gov,255824,Masters,14,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,255252,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,184167,12th,8,Married-civ-spouse,Craft-repair,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Private,145419,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,87310,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Self-emp-not-inc,55048,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,104052,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,41163,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,502633,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,176279,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,279833,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,1,2,United-States,<=50K\n0,Private,254351,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,284916,9th,5,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,?,<=50K\n0,Self-emp-not-inc,188925,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,180954,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,108023,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,197058,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,1,0,2,United-States,<=50K\n4,Private,100303,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,473133,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,State-gov,27051,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,163708,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,United-States,>50K\n4,Private,52765,HS-grad,9,Divorced,Other-service,Other-relative,White,Female,0,0,4,United-States,<=50K\n2,Self-emp-inc,84924,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,181705,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,52012,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,167691,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Federal-gov,182470,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,200834,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,State-gov,76075,Assoc-voc,11,Divorced,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,200574,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,29144,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,34722,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,177907,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n3,Private,238481,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,182541,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,142426,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,4,0,0,United-States,>50K\n0,Private,216413,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,30070,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,82699,Prof-school,15,Divorced,Prof-specialty,Not-in-family,Black,Female,4,0,4,United-States,>50K\n1,Private,236861,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,114328,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,198229,Prof-school,15,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,138892,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,271927,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,?,220258,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,212588,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,477867,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,394927,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,155611,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Female,1,0,2,United-States,<=50K\n2,Private,109351,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,206520,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,156526,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,315437,HS-grad,9,Separated,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,181665,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,3,United-States,<=50K\n2,Private,60594,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,221233,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,176900,Some-college,10,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,64563,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,99408,Some-college,10,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,219869,Some-college,10,Widowed,Farming-fishing,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,135056,Masters,14,Never-married,Prof-specialty,Own-child,White,Female,3,0,3,United-States,>50K\n0,Private,79077,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,255830,Assoc-acdm,12,Divorced,Craft-repair,Unmarried,Black,Female,2,0,2,United-States,<=50K\n2,Self-emp-not-inc,22245,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,150154,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,State-gov,342049,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,99927,HS-grad,9,Widowed,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,191784,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n2,Private,175883,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,328239,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,107231,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,155818,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,282421,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,?,<=50K\n2,Private,241998,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,55363,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,137240,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,361951,Bachelors,13,Never-married,Sales,Not-in-family,Black,Male,0,0,3,?,<=50K\n0,State-gov,311311,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,186299,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,168055,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,305423,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,393715,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,36440,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,106761,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,235014,Assoc-voc,11,Widowed,?,Not-in-family,White,Female,0,4,2,United-States,<=50K\n1,Local-gov,249932,11th,7,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,382242,Bachelors,13,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,?,<=50K\n1,Private,213152,11th,7,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,State-gov,26898,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,435356,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,103963,HS-grad,9,Widowed,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,185860,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,188999,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,108654,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,1,United-States,<=50K\n2,Private,54953,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,130620,Some-college,10,Never-married,Adm-clerical,Other-relative,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n1,Private,273884,HS-grad,9,Married-spouse-absent,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,392518,Assoc-acdm,12,Married-spouse-absent,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,198766,HS-grad,9,Widowed,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,66935,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,135500,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,111697,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,141288,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,296450,7th-8th,4,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,94448,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,138200,Assoc-acdm,12,Never-married,Farming-fishing,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,217826,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,Haiti,<=50K\n4,?,182836,Some-college,10,Married-civ-spouse,?,Wife,White,Female,1,0,2,United-States,>50K\n4,Self-emp-not-inc,46366,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,168275,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,214514,7th-8th,4,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,107439,Some-college,10,Never-married,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,164909,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Federal-gov,329426,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Local-gov,181974,7th-8th,4,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,176468,HS-grad,9,Divorced,Priv-house-serv,Unmarried,Black,Female,0,0,1,United-States,<=50K\n3,State-gov,187686,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,229769,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Mexico,<=50K\n2,Private,45975,12th,8,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Italy,<=50K\n2,Private,187702,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,102585,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,327112,11th,7,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,167558,7th-8th,4,Married-civ-spouse,?,Wife,White,Female,0,0,2,Mexico,<=50K\n1,Private,296538,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,169560,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,185283,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,224793,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,2,2,United-States,<=50K\n0,Federal-gov,478457,11th,7,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,104413,Some-college,10,Separated,Tech-support,Other-relative,Black,Female,2,0,2,United-States,<=50K\n1,Self-emp-not-inc,175710,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,?,<=50K\n1,Private,85632,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,102359,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,237546,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,96245,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,91453,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,131039,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,1,Trinadad&Tobago,<=50K\n3,Private,106176,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Male,0,4,2,United-States,<=50K\n3,Private,329797,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,153932,11th,7,Married-civ-spouse,Craft-repair,Own-child,White,Male,1,0,1,United-States,<=50K\n1,State-gov,52738,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,25932,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,78374,Some-college,10,Never-married,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Private,653215,11th,7,Widowed,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,318061,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,State-gov,260782,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,340580,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,45564,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,209051,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,100821,HS-grad,9,Married-spouse-absent,Priv-house-serv,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,86268,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,95680,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,1,0,2,United-States,>50K\n1,Private,327164,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Other-relative,White,Male,0,0,2,United-States,>50K\n0,?,117210,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,136175,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,232591,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,29144,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,64875,Assoc-voc,11,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,184011,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,244246,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,3,0,3,United-States,>50K\n2,Private,357173,HS-grad,9,Separated,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,203181,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,299508,HS-grad,9,Divorced,Tech-support,Unmarried,Black,Female,0,0,3,United-States,<=50K\n1,Private,198493,Assoc-acdm,12,Never-married,Adm-clerical,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,Local-gov,358747,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,91039,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,34918,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,97159,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,212600,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,?,>50K\n4,Private,90113,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,96705,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,156873,11th,7,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,136358,Masters,14,Divorced,Sales,Unmarried,Other,Female,0,0,0,Peru,<=50K\n2,Private,227065,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,?,>50K\n2,Local-gov,193144,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,317660,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,85492,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,203628,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,183801,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,132686,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,50818,Masters,14,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,State-gov,160812,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,212286,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,77072,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,176155,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,219211,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,356934,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,143266,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,?,>50K\n2,?,194809,Bachelors,13,Never-married,?,Own-child,White,Female,0,0,3,United-States,<=50K\n4,Private,138157,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,437825,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,165503,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,?,<=50K\n4,Private,152053,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,211273,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,576645,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,<=50K\n2,?,148951,Bachelors,13,Divorced,?,Not-in-family,White,Female,0,0,0,Ecuador,<=50K\n2,Private,38145,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,66619,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,126613,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,France,<=50K\n3,State-gov,135854,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,132145,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,194920,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,260387,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Cuba,<=50K\n4,Private,176388,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,77009,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,385177,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,510643,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,100135,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,297697,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Private,179481,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,134045,Assoc-voc,11,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,275034,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,127651,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,3,0,2,United-States,>50K\n0,Private,237262,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,274445,HS-grad,9,Never-married,?,Own-child,White,Male,0,2,0,United-States,<=50K\n2,?,141583,Bachelors,13,Never-married,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,?,294642,HS-grad,9,Separated,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,181179,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,184493,HS-grad,9,Separated,Handlers-cleaners,Own-child,White,Female,0,1,1,United-States,<=50K\n3,Local-gov,125892,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,118235,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,0,2,United-States,<=50K\n0,Private,119329,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,189843,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,167357,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,1,2,United-States,<=50K\n3,Private,103803,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Private,145175,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,158846,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,203313,7th-8th,4,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,125954,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,102178,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,139364,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,265007,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Ecuador,<=50K\n1,Private,61996,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n4,Private,209790,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,117779,12th,8,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,173326,HS-grad,9,Never-married,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,318046,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n4,Local-gov,204021,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,236157,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,42900,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,144002,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,126954,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,228649,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,126094,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,113106,HS-grad,9,Never-married,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Private,118941,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,205675,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,89295,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,173858,HS-grad,9,Never-married,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,1,0,2,?,<=50K\n1,Private,168251,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,143059,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,41737,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,3,1,United-States,<=50K\n3,Private,266598,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,181796,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,214731,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,219869,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,4,United-States,>50K\n0,Private,211968,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,38707,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,58700,9th,5,Never-married,Farming-fishing,Other-relative,Other,Female,0,0,2,Mexico,<=50K\n0,Private,160261,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,0,France,<=50K\n1,Private,160594,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,152307,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,100079,HS-grad,9,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,3,China,<=50K\n0,Private,279472,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,149102,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,177625,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,178678,10th,6,Divorced,Adm-clerical,Unmarried,White,Female,0,1,3,United-States,<=50K\n4,Self-emp-not-inc,21383,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,86019,11th,7,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,181153,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,?,>50K\n2,Federal-gov,223749,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,33087,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,253190,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,165278,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,279452,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,Mexico,<=50K\n2,Private,290660,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,274883,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n4,Self-emp-not-inc,35468,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,195318,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,256362,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Italy,>50K\n3,Private,148169,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n4,Self-emp-not-inc,538099,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,186682,HS-grad,9,Never-married,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,156797,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n1,Private,162257,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,208881,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,159168,Assoc-voc,11,Widowed,Exec-managerial,Unmarried,White,Female,0,4,2,United-States,>50K\n4,Private,172740,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n3,Federal-gov,186256,HS-grad,9,Divorced,Farming-fishing,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,110861,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,128699,HS-grad,9,Married-spouse-absent,Adm-clerical,Unmarried,White,Female,0,0,2,Ecuador,<=50K\n1,Private,271933,Some-college,10,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,102320,Assoc-voc,11,Separated,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,117674,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,190273,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,217747,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,99830,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,343068,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,State-gov,204052,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,267912,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,Mexico,<=50K\n1,Private,207537,Some-college,10,Separated,Sales,Not-in-family,White,Male,1,0,3,United-States,<=50K\n2,Private,256864,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,306678,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,101204,Some-college,10,Married-civ-spouse,Tech-support,Husband,Black,Male,2,0,2,United-States,<=50K\n2,Private,77373,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,371103,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,316271,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,51916,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,159008,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,153475,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,118549,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,315081,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,122622,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,81786,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,194752,HS-grad,9,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,208662,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,173944,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,4,0,4,United-States,>50K\n1,State-gov,49325,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,425502,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,55360,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,432480,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,155781,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,4,1,United-States,>50K\n2,Local-gov,216473,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,185366,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,247515,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n4,Private,210673,10th,6,Widowed,Adm-clerical,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,Private,107435,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,217300,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,39803,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,482211,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,169549,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,?,>50K\n0,Private,353542,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,114200,HS-grad,9,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,245173,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,212895,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,?,95636,10th,6,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,271486,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,258836,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,288530,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,47589,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,295099,Some-college,10,Divorced,Tech-support,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,275338,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,168553,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,142766,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,72887,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n1,Private,102946,Some-college,10,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Female,0,2,2,United-States,<=50K\n4,Self-emp-not-inc,244749,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,Cuba,<=50K\n2,Private,166115,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,213383,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,107231,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,130969,9th,5,Never-married,?,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,221977,1st-4th,2,Married-spouse-absent,Priv-house-serv,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n2,Private,43467,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,357596,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,146497,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,317809,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,190290,Bachelors,13,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,194573,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,>50K\n4,?,144461,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n3,Local-gov,240638,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,52776,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,50524,12th,8,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,324023,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,110916,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,203924,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,214868,Assoc-voc,11,Never-married,Adm-clerical,Other-relative,Black,Female,0,3,2,United-States,<=50K\n1,Private,275466,10th,6,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,198708,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,179241,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,154981,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,178811,Assoc-voc,11,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,130018,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,87504,Bachelors,13,Divorced,Tech-support,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,377414,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,177927,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,137490,5th-6th,3,Separated,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,262617,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,30682,7th-8th,4,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,119684,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,>50K\n3,Private,187938,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,122353,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,75755,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,91316,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,134789,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,115892,11th,7,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,104457,Bachelors,13,Married-spouse-absent,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n3,Local-gov,230767,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,Cuba,<=50K\n4,Private,227332,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,160264,Some-college,10,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,174533,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,53114,Some-college,10,Widowed,Sales,Not-in-family,White,Female,1,0,0,United-States,<=50K\n0,Private,163870,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,228709,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,172571,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,335542,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,2,3,United-States,<=50K\n4,Local-gov,241404,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,197189,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,102058,1st-4th,2,Married-civ-spouse,?,Husband,White,Male,0,0,2,Portugal,<=50K\n2,Self-emp-inc,104813,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,261437,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,366842,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,?,>50K\n0,?,121468,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,214994,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,229709,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,249039,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,142287,Some-college,10,Divorced,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,259323,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,238281,11th,7,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,156774,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,86153,HS-grad,9,Never-married,Tech-support,Unmarried,White,Female,0,0,2,Germany,<=50K\n4,Private,93997,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Self-emp-inc,91039,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,224198,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,221336,HS-grad,9,Widowed,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,128012,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,?,<=50K\n3,Local-gov,231166,HS-grad,9,Separated,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,79702,Some-college,10,Never-married,Adm-clerical,Other-relative,Black,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,132320,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,?,33355,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n3,?,177557,HS-grad,9,Divorced,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,148549,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,301563,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,113106,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,304175,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,State-gov,230200,Bachelors,13,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,313444,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,247328,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,132565,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,539019,Some-college,10,Never-married,Farming-fishing,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,114292,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,227608,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,185554,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,181372,Bachelors,13,Never-married,Farming-fishing,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,344592,HS-grad,9,Never-married,Sales,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,102326,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,220647,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,4,3,United-States,<=50K\n0,?,30246,11th,7,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,496743,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,161508,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,30494,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,256764,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,1,2,United-States,>50K\n0,?,49819,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,?,338281,Assoc-voc,11,Married-civ-spouse,?,Wife,White,Female,0,0,0,Iran,<=50K\n0,Local-gov,256356,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,318644,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,227594,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,165695,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,127728,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,123681,Assoc-acdm,12,Separated,Sales,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Private,168038,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,1,3,United-States,>50K\n1,Private,154950,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,148298,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,63042,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,101444,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,455379,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,211097,5th-6th,3,Divorced,Other-service,Unmarried,Other,Female,0,0,0,Honduras,<=50K\n4,Local-gov,153264,HS-grad,9,Widowed,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,263220,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,180138,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,208946,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,?,<=50K\n1,Private,321709,Assoc-acdm,12,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,215981,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,26880,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,<=50K\n1,Self-emp-not-inc,90705,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,185068,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,0,Puerto-Rico,<=50K\n2,Private,268390,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Mexico,<=50K\n3,Private,102058,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,421132,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,191803,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,0,?,<=50K\n4,Private,181954,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,1,Iran,<=50K\n0,?,34505,11th,7,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,93973,11th,7,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Mexico,<=50K\n4,Private,355459,12th,8,Widowed,Priv-house-serv,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,173586,7th-8th,4,Never-married,Other-service,Own-child,Black,Male,0,0,3,United-States,<=50K\n1,Private,312055,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,353479,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,>50K\n0,Private,321426,Some-college,10,Never-married,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,228752,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,281647,Bachelors,13,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,161615,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,187376,Assoc-acdm,12,Separated,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,234994,7th-8th,4,Separated,Craft-repair,Unmarried,White,Male,0,0,2,Puerto-Rico,<=50K\n4,?,169329,9th,5,Married-civ-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,216116,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,Jamaica,<=50K\n2,Private,109204,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,88879,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,200863,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,223811,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,360761,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,166275,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,149102,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,117381,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,306993,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,232475,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,165867,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,268234,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n4,Self-emp-inc,110457,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,329174,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Private,109472,9th,5,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,418176,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,380390,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,36064,12th,8,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-inc,95835,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Local-gov,250770,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,67603,9th,5,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,352045,Masters,14,Separated,Craft-repair,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,Private,196742,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,303942,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,246687,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,187793,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,205816,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,182343,12th,8,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,42959,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,140644,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Male,0,4,3,United-States,<=50K\n0,Private,183264,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,294671,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,88926,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,64667,Some-college,10,Divorced,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n1,Private,416946,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,208570,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,116649,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,87052,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,102869,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,123552,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Ireland,<=50K\n1,Private,157262,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,146890,9th,5,Never-married,Farming-fishing,Own-child,Black,Male,0,0,0,United-States,<=50K\n4,Private,257200,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,452402,Doctorate,16,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,0,United-States,<=50K\n2,Private,531055,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,298539,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,95989,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,162572,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Local-gov,75313,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n4,Private,117162,10th,6,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,173461,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,349986,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,204244,9th,5,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,36222,HS-grad,9,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-inc,320811,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,82676,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,152189,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,52565,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n4,?,121319,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,144301,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,162869,Some-college,10,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,179241,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,State-gov,62327,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,121012,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,60001,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,105582,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,454076,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,155818,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,434081,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,265386,Assoc-acdm,12,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,44671,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,190296,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n1,Federal-gov,198827,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,22228,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,109857,Assoc-voc,11,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,190968,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,75235,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,127605,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,Italy,>50K\n1,Private,318982,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,229636,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,Mexico,<=50K\n3,Private,233802,HS-grad,9,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,28119,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,State-gov,198363,7th-8th,4,Widowed,Other-service,Not-in-family,Black,Female,1,0,2,United-States,<=50K\n4,Local-gov,153914,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,143582,Masters,14,Widowed,Sales,Own-child,Asian-Pac-Islander,Female,0,0,3,United-States,<=50K\n4,?,78786,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,3,1,United-States,<=50K\n2,State-gov,126333,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,182689,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,1,United-States,>50K\n0,Private,35245,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,160120,Some-college,10,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Male,1,0,2,?,<=50K\n2,Self-emp-not-inc,400287,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,0,United-States,>50K\n0,Private,50610,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,349826,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,35890,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,174209,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,63225,1st-4th,2,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,164519,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,81145,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,73514,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,Asian-Pac-Islander,Female,0,0,3,United-States,<=50K\n3,Local-gov,452402,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,3,0,1,United-States,>50K\n0,?,318056,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,285897,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,>50K\n0,?,194404,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,434710,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,177142,Bachelors,13,Never-married,Tech-support,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,182863,Bachelors,13,Separated,Tech-support,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,394645,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,110457,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,292465,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,238433,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,160631,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,0,Yugoslavia,<=50K\n1,Private,285657,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,236907,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,378418,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,213279,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,105503,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,79160,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,?,139391,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,Ireland,>50K\n1,Self-emp-not-inc,190027,Masters,14,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,4,United-States,>50K\n4,Private,160758,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,361280,Doctorate,16,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,Japan,<=50K\n2,Private,199288,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n3,Self-emp-not-inc,204698,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,213354,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,282579,HS-grad,9,Never-married,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,99783,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,446947,Bachelors,13,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,3,United-States,<=50K\n4,Private,186202,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,177951,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,258364,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,200727,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,33404,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,4,0,3,United-States,>50K\n4,Federal-gov,31115,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,301915,11th,7,Separated,Sales,Not-in-family,Other,Female,0,0,1,Mexico,<=50K\n2,Private,201908,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,168071,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,347623,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,35890,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,154227,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,161532,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,178282,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,263561,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,201783,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,161153,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,193125,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,126060,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,4,0,2,United-States,>50K\n3,Private,186826,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,1,2,United-States,>50K\n1,Private,156192,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,193094,HS-grad,9,Never-married,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,472411,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,147069,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,300195,Some-college,10,Divorced,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,91417,Assoc-voc,11,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,182342,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,Italy,<=50K\n1,Private,258406,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,87239,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,294406,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,41385,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,2,0,2,United-States,<=50K\n4,Private,197414,7th-8th,4,Married-spouse-absent,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,323212,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,31532,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,127610,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,0,Greece,<=50K\n1,Private,163189,Some-college,10,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,594187,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,269323,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,1,2,United-States,>50K\n1,Private,96480,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,2,United-States,>50K\n4,Private,200316,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,212780,HS-grad,9,Never-married,Sales,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n3,Self-emp-inc,120121,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,367240,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,117606,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,122749,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n4,Private,169560,10th,6,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,269890,HS-grad,9,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,303426,HS-grad,9,Divorced,Other-service,Unmarried,Asian-Pac-Islander,Male,2,0,2,Philippines,<=50K\n0,Private,112835,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,226503,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,207733,1st-4th,2,Widowed,Other-service,Unmarried,White,Female,0,0,2,Puerto-Rico,<=50K\n0,Private,275421,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,165883,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,236676,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,?,171578,Some-college,10,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,685955,Bachelors,13,Never-married,Sales,Unmarried,Black,Male,0,0,3,United-States,<=50K\n1,Private,72887,Some-college,10,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Private,135304,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,218781,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,126540,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,261943,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Guatemala,<=50K\n1,State-gov,111843,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,1,0,2,United-States,<=50K\n4,Self-emp-not-inc,401203,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,117400,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,?,113658,10th,6,Divorced,?,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,166822,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,>50K\n1,Private,151322,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,102684,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,152652,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,193416,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,103323,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,33975,Bachelors,13,Separated,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,163665,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,187485,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,110327,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,179498,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,197915,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,103995,Doctorate,16,Widowed,Prof-specialty,Not-in-family,White,Female,4,0,3,United-States,>50K\n3,Private,123807,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n0,Private,43535,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,200316,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,125832,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,4,Canada,<=50K\n3,State-gov,71691,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Private,168212,Some-college,10,Married-spouse-absent,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,98320,Some-college,10,Divorced,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,1,0,2,United-States,<=50K\n2,Private,173307,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,442116,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,130849,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,159015,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,147921,Prof-school,15,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,268022,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,253357,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,339954,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,347623,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,1,0,2,United-States,<=50K\n3,Federal-gov,169112,Prof-school,15,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,166213,HS-grad,9,Divorced,Tech-support,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,216765,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n3,Private,335997,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n0,?,354236,10th,6,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,178120,HS-grad,9,Widowed,Priv-house-serv,Other-relative,Black,Female,0,0,0,United-States,<=50K\n3,Private,312631,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,3,United-States,>50K\n4,Local-gov,31924,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,1,0,2,United-States,<=50K\n1,Federal-gov,96483,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Female,0,0,3,United-States,>50K\n1,Private,305304,11th,7,Separated,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,111722,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,4,United-States,<=50K\n0,Private,197554,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,257126,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,101597,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,220115,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,210844,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,Columbia,<=50K\n0,Self-emp-inc,66935,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,Private,192813,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,Portugal,<=50K\n1,Self-emp-not-inc,95639,11th,7,Married-civ-spouse,Prof-specialty,Husband,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,223881,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,223105,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,192644,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,4,United-States,>50K\n2,Self-emp-not-inc,58683,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,162282,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n2,State-gov,239539,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Private,117849,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,99237,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,?,149343,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,193882,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,107845,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,268295,5th-6th,3,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,71269,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,204598,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,1,0,4,United-States,<=50K\n0,?,98283,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,188694,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,201908,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,191137,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,129786,HS-grad,9,Separated,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,302903,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,0,0,2,United-States,>50K\n1,Private,143526,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,182117,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,172753,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,139770,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,215686,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,181388,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,2,United-States,>50K\n4,Private,81973,Bachelors,13,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,4,0,2,United-States,>50K\n2,Private,328581,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,110110,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,174201,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,137421,Masters,14,Married-spouse-absent,Exec-managerial,Not-in-family,Asian-Pac-Islander,Male,0,0,1,India,>50K\n1,Private,153927,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,187649,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,149992,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,234640,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Local-gov,311569,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,182383,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n4,State-gov,344381,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,3,0,4,United-States,>50K\n1,Self-emp-not-inc,280570,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,215039,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,339442,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,168065,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,47534,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Local-gov,116428,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,121789,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,143139,10th,6,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,187790,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,140559,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,184025,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,257824,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,89226,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,3,0,2,Greece,>50K\n0,Private,145917,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,207301,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,135388,12th,8,Widowed,Machine-op-inspct,Not-in-family,White,Male,0,1,2,United-States,>50K\n0,Private,266467,Assoc-voc,11,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,200047,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,121040,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,199694,Assoc-acdm,12,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,301007,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n4,Self-emp-not-inc,253759,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,120839,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,33342,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,205195,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,362873,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,El-Salvador,<=50K\n0,Private,104614,HS-grad,9,Never-married,Protective-serv,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,32280,HS-grad,9,Never-married,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,191777,12th,8,Never-married,Other-service,Own-child,Black,Female,0,0,0,?,<=50K\n2,Private,144169,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,264076,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,119164,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,126845,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,228395,Bachelors,13,Never-married,Sales,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,242654,Some-college,10,Divorced,Sales,Unmarried,Black,Female,0,1,2,Honduras,<=50K\n4,Self-emp-not-inc,30951,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,48855,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,50791,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n4,Local-gov,248739,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,165418,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,79464,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,321247,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,104269,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,129573,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,222142,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,126613,11th,7,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,145548,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,331861,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,156261,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,173944,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,69739,10th,6,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,Portugal,<=50K\n1,Private,266345,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,278006,HS-grad,9,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,82578,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,154164,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,?,<=50K\n0,Private,250038,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,175468,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,State-gov,435835,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,135601,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n0,State-gov,162945,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,244115,11th,7,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,351902,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,291414,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,278736,12th,8,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n2,Private,138975,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,165295,5th-6th,3,Never-married,Other-service,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n3,Self-emp-inc,93557,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,176315,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,1,0,0,United-States,<=50K\n1,Private,187167,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,241582,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,247328,11th,7,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,157778,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,4,0,3,United-States,>50K\n4,Self-emp-not-inc,67765,11th,7,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,4,United-States,>50K\n0,?,229431,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,192203,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,93326,Some-college,10,Separated,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,118889,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,State-gov,237028,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,156127,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,151325,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,311350,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,273465,Assoc-acdm,12,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,172646,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,0,United-States,<=50K\n3,Private,379797,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,131827,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n1,State-gov,158734,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,233533,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,246367,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Local-gov,142049,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,101119,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,104830,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,1,0,1,United-States,<=50K\n2,Private,173981,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Italy,>50K\n4,Private,195338,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,64253,11th,7,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n4,Private,182062,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,111696,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,168071,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,314369,HS-grad,9,Divorced,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Private,178877,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,111483,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,?,192321,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,171757,7th-8th,4,Widowed,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Federal-gov,157313,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,38772,11th,7,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,72338,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,132626,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,176240,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,202692,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,70021,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,181242,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,196707,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,81534,1st-4th,2,Widowed,Farming-fishing,Not-in-family,Asian-Pac-Islander,Male,1,0,0,Philippines,<=50K\n0,Private,137658,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,253190,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,233059,9th,5,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,177787,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,193026,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,271464,Masters,14,Separated,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,199689,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,190653,Assoc-voc,11,Married-civ-spouse,Armed-Forces,Husband,White,Male,0,0,2,?,>50K\n2,Private,359389,Bachelors,13,Divorced,Other-service,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,Self-emp-not-inc,181717,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,>50K\n3,Private,245127,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Outlying-US(Guam-USVI-etc),<=50K\n0,Private,274398,Assoc-voc,11,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,344624,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,169037,HS-grad,9,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,221406,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,211707,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n4,?,185939,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,227026,Bachelors,13,Never-married,Craft-repair,Unmarried,White,Female,0,0,2,Nicaragua,<=50K\n2,Private,187847,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,238144,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,243660,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,102476,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,238405,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,187479,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,168294,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,129893,HS-grad,9,Married-civ-spouse,?,Husband,Black,Male,0,1,1,United-States,<=50K\n3,Private,172642,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,208066,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,247558,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,99233,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n3,Private,430278,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,204374,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,Poland,<=50K\n1,Private,136832,9th,5,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,151135,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,95875,9th,5,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,360494,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,187581,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,98659,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,252242,Doctorate,16,Divorced,Sales,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,411238,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,199083,Masters,14,Divorced,Transport-moving,Not-in-family,White,Male,0,4,3,United-States,>50K\n2,Private,222573,HS-grad,9,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,245317,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,216414,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,236396,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,311826,Some-college,10,Never-married,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,172538,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,43712,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,272669,Assoc-acdm,12,Never-married,Adm-clerical,Unmarried,Asian-Pac-Islander,Male,0,0,1,Hong,<=50K\n3,Private,137299,Assoc-acdm,12,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,171305,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,3,United-States,>50K\n1,Local-gov,190027,HS-grad,9,Divorced,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,376416,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,236831,12th,8,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,170148,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,366425,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,95864,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,England,>50K\n4,Private,37435,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,151835,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,149419,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,224238,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,359972,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,England,>50K\n4,Private,23063,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,198282,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,211020,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,Germany,>50K\n2,Private,104196,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,133819,HS-grad,9,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,328734,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,4,2,United-States,<=50K\n1,Self-emp-not-inc,41210,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,225399,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,13862,HS-grad,9,Never-married,?,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Local-gov,43959,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,83827,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,157332,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,163706,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n2,Private,211517,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,2,2,United-States,<=50K\n4,?,92852,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,192626,HS-grad,9,Separated,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,115439,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,98769,Assoc-voc,11,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,>50K\n1,Private,66473,Some-college,10,Never-married,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,138077,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,>50K\n1,Local-gov,339388,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,195520,Assoc-voc,11,Never-married,Adm-clerical,Other-relative,White,Male,0,0,2,Ireland,<=50K\n4,Private,204680,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,184948,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,356231,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,3,4,United-States,<=50K\n3,Private,204334,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,England,>50K\n4,Self-emp-inc,96660,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n2,Private,184871,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,298950,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,238802,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,242670,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,183186,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,34125,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,158996,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,203883,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,248160,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,548361,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,203914,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n3,State-gov,91121,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,139397,10th,6,Separated,Exec-managerial,Unmarried,White,Female,0,0,0,Ecuador,<=50K\n4,Private,208640,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,183013,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,161141,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,343333,Bachelors,13,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,>50K\n1,State-gov,210866,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,359854,Bachelors,13,Never-married,Priv-house-serv,Other-relative,White,Female,0,0,1,Mexico,<=50K\n3,Self-emp-inc,235646,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,157588,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,4,United-States,<=50K\n3,Private,200734,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,2,?,<=50K\n0,Private,212213,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,248941,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,291831,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,State-gov,114191,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,151580,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,331498,Doctorate,16,Never-married,Other-service,Own-child,White,Male,0,0,2,?,<=50K\n0,Private,139989,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,187167,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,299528,Some-college,10,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Female,0,0,3,Taiwan,<=50K\n2,Private,226608,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,306361,HS-grad,9,Never-married,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,213416,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,Mexico,<=50K\n0,Private,85139,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,48779,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,146365,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,355856,5th-6th,3,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n3,Private,39264,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,117028,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,266631,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,Haiti,<=50K\n1,Private,152263,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,387074,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,245211,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,3,2,United-States,<=50K\n3,Private,136455,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,State-gov,153486,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,105479,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,409902,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,?,133515,Assoc-acdm,12,Never-married,?,Own-child,White,Female,1,0,2,United-States,<=50K\n2,Private,89202,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,?,185692,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,157024,10th,6,Never-married,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n3,Private,230936,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,57298,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,82488,Masters,14,Never-married,Prof-specialty,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n1,Private,606111,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,235182,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,150336,Some-college,10,Divorced,Tech-support,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,145175,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,186719,Some-college,10,Separated,Craft-repair,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,325538,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,192995,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,103596,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,207172,11th,7,Never-married,Protective-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,237729,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,1,0,4,United-States,<=50K\n2,Private,121718,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,196344,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,100235,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,161153,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,303619,11th,7,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,141646,7th-8th,4,Widowed,Farming-fishing,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,293726,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,190772,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,1,1,?,<=50K\n1,Local-gov,91124,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,318912,Bachelors,13,Never-married,Other-service,Own-child,Black,Male,0,0,3,United-States,<=50K\n3,Private,355978,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,81468,HS-grad,9,Widowed,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,183672,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,140826,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,4,0,3,?,>50K\n2,Private,146659,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,294720,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,284629,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,128091,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,153643,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,173968,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,?,168479,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,303177,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Self-emp-not-inc,189030,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,261584,Some-college,10,Separated,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,131230,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,138852,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,137532,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,67222,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n1,Private,278736,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,143574,HS-grad,9,Married-spouse-absent,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,283725,Masters,14,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,131404,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Private,185015,5th-6th,3,Married-spouse-absent,Priv-house-serv,Other-relative,White,Female,0,0,2,El-Salvador,<=50K\n2,Federal-gov,47902,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,272017,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,224559,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,1,United-States,<=50K\n4,Private,138247,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,365465,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,69887,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,203488,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,57691,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,193815,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Self-emp-not-inc,37284,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,317434,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,198613,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,126060,Prof-school,15,Never-married,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,187560,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,331863,Some-college,10,Separated,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,State-gov,52498,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,256466,Masters,14,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,3,0,2,China,>50K\n2,Local-gov,215618,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,150057,10th,6,Separated,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,213714,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,173095,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,?,143774,HS-grad,9,Divorced,?,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,277488,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,240468,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,197609,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,72294,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,219155,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,130277,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,2,2,United-States,<=50K\n4,Private,100718,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,151474,HS-grad,9,Divorced,Handlers-cleaners,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,209955,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,23892,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,206927,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,60135,10th,6,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n4,?,95630,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,229516,7th-8th,4,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,127740,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n2,State-gov,252662,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,4,3,United-States,>50K\n0,?,171080,12th,8,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,?,118358,HS-grad,9,Never-married,?,Other-relative,White,Female,0,0,3,?,<=50K\n1,Private,160594,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,1,3,United-States,>50K\n1,Local-gov,393376,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Federal-gov,321494,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,289517,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,331395,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,199609,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,178596,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,198996,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,222654,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,293510,12th,8,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,176185,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,Japan,>50K\n1,Private,370509,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,167381,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,300445,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,339602,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,329222,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,2,2,Laos,<=50K\n3,Self-emp-not-inc,183668,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,30014,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,120820,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,33021,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,295662,Bachelors,13,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,183168,Some-college,10,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,148738,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,214738,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,196758,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Federal-gov,105527,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,221495,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,211938,10th,6,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,209320,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,298785,9th,5,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,459192,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,342642,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,46015,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,219199,10th,6,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,285858,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,592029,HS-grad,9,Widowed,Sales,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,132247,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,330543,Preschool,1,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,176613,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n4,Private,174895,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,200408,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,219687,Some-college,10,Widowed,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,174466,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,339921,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n3,Local-gov,330080,11th,7,Married-spouse-absent,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,151504,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,275300,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,178322,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,State-gov,209482,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,157831,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,224314,Bachelors,13,Widowed,Exec-managerial,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,?,144461,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,1,0,0,United-States,<=50K\n2,Self-emp-not-inc,114580,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,4,United-States,<=50K\n3,Self-emp-inc,321764,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,197907,HS-grad,9,Never-married,Tech-support,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,State-gov,199884,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,332975,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,37599,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,52199,Bachelors,13,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,45795,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,154120,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,217530,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,2,El-Salvador,<=50K\n4,Self-emp-not-inc,426263,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,406664,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,269799,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,127738,9th,5,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,330724,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,0,?,<=50K\n3,Private,138999,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,152744,Some-college,10,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n1,Private,155459,Bachelors,13,Never-married,Protective-serv,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,222596,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,209173,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,513977,Some-college,10,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,186410,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,230684,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,136986,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,647591,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,264300,Assoc-voc,11,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,95967,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,?,<=50K\n1,Private,187802,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,2,2,United-States,>50K\n2,Self-emp-inc,270079,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,141379,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,176653,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,176900,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,200217,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,188331,11th,7,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,158363,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,247422,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,247120,HS-grad,9,Married-civ-spouse,Transport-moving,Other-relative,White,Female,0,0,3,?,<=50K\n0,Local-gov,37932,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,32280,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,197286,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,228595,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,81534,11th,7,Never-married,Other-service,Other-relative,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n0,Private,165457,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,209109,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,141817,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n4,?,249043,11th,7,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,185640,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,400195,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,267912,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,370032,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,191957,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,361405,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,103580,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,116632,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,185480,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,94113,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n1,Self-emp-not-inc,75654,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Greece,<=50K\n2,Private,104727,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,302406,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,116993,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,274528,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,218382,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,>50K\n1,Federal-gov,170425,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,148437,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,24264,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,78923,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,>50K\n3,Self-emp-inc,106169,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,211860,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,250821,Prof-school,15,Never-married,Farming-fishing,Other-relative,White,Male,0,0,3,United-States,<=50K\n1,Private,202558,Assoc-voc,11,Married-civ-spouse,Tech-support,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,96480,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,215912,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,182211,Some-college,10,Divorced,Sales,Own-child,White,Female,0,0,4,United-States,<=50K\n1,Federal-gov,206392,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,150057,Bachelors,13,Divorced,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,105044,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,168195,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,54377,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,118174,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,4,0,0,United-States,>50K\n3,Private,229375,Some-college,10,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,109414,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,3,2,Philippines,>50K\n2,Private,158275,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,1,2,United-States,<=50K\n2,Private,32185,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,228452,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,170174,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,162370,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,260253,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,252392,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,99897,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,52596,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,?,>50K\n2,Private,48093,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,275244,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Private,173316,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,162164,Some-college,10,Never-married,Priv-house-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,166425,11th,7,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,203463,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,4,0,2,United-States,>50K\n1,Private,77792,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,120384,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,257470,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n2,Private,171482,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,<=50K\n1,Private,162298,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,170273,Some-college,10,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,384774,7th-8th,4,Divorced,Tech-support,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,State-gov,120131,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,159442,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,Ireland,>50K\n1,Private,303942,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,33436,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,193407,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,51985,Some-college,10,Never-married,Other-service,Own-child,White,Male,1,0,0,United-States,<=50K\n1,Local-gov,127651,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,152246,Some-college,10,Never-married,Handlers-cleaners,Own-child,Asian-Pac-Islander,Male,0,0,2,Outlying-US(Guam-USVI-etc),<=50K\n4,Private,124137,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,1,0,2,United-States,<=50K\n0,?,209735,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,241552,11th,7,Never-married,Other-service,Own-child,White,Female,0,2,0,United-States,<=50K\n2,Private,174295,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,165815,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,566066,Bachelors,13,Never-married,Protective-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,269373,12th,8,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,143604,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,294605,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,122381,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,104443,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,2,2,United-States,<=50K\n1,Private,208116,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,328011,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,375079,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,210945,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,169100,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,30126,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,165737,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,0,Japan,<=50K\n0,Private,200295,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,553588,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n4,Local-gov,164970,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,199031,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,187750,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,89413,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,188171,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,392502,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,73883,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,76860,5th-6th,3,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,4,0,2,China,>50K\n0,Private,376683,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Private,189013,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,190385,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,154874,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,175032,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,338432,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,30725,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,319280,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,280957,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,256813,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,276213,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,161496,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,399531,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,168951,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,108140,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,358677,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,127424,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,3,?,>50K\n2,Self-emp-not-inc,163559,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Asian-Pac-Islander,Female,0,0,3,?,<=50K\n2,Self-emp-not-inc,390746,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,187981,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,187715,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,>50K\n0,Private,192449,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,381581,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,211049,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,214242,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Poland,<=50K\n1,Private,190759,11th,7,Separated,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,209301,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,118614,Masters,14,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,124971,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,198704,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,32587,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,>50K\n3,Local-gov,227261,Some-college,10,Divorced,Protective-serv,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,250988,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,147206,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,25265,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Local-gov,188446,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,206017,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,287821,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Federal-gov,223163,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,38973,10th,6,Widowed,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,370274,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,271559,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,1,Columbia,<=50K\n4,Private,171331,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,201127,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,64874,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,202683,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,103851,11th,7,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,196266,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,303601,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,43150,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,193889,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,?,<=50K\n1,Private,177036,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,135304,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,1,United-States,<=50K\n0,Private,25982,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,State-gov,103063,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,328239,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,142724,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,198452,Some-college,10,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,285897,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,34568,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,136986,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n1,Self-emp-not-inc,404062,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,Portugal,>50K\n2,Private,80479,Bachelors,13,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,433325,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Self-emp-not-inc,368797,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,157473,Masters,14,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,193130,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,91667,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,153078,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,4,0,2,Hong,>50K\n1,Private,179673,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,236648,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,77357,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,267796,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,335276,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,189102,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-not-inc,203051,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Private,167440,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,186087,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,334267,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,172246,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,117963,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,?,374116,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,304074,Some-college,10,Widowed,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,212195,HS-grad,9,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,United-States,>50K\n2,Self-emp-not-inc,52781,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Local-gov,176671,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,175778,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,340126,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,237581,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,Mexico,>50K\n3,Local-gov,246891,Masters,14,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,>50K\n0,Private,181659,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,209900,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,151826,10th,6,Divorced,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,210013,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,120131,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,366421,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,137578,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,89625,10th,6,Separated,Sales,Own-child,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,298945,Bachelors,13,Widowed,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Local-gov,108247,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,103642,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,?,181528,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,131699,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,154089,11th,7,Never-married,Sales,Unmarried,White,Male,0,0,0,United-States,<=50K\n2,Private,247081,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,276839,Some-college,10,Married-spouse-absent,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,166115,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,192936,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,247425,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,198196,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,260254,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,176360,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,?,214731,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,107564,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,29312,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,190610,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,296858,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,294431,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,259818,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,161111,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,?,85995,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,108501,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,135296,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,491607,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,170278,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,Philippines,<=50K\n2,Private,309932,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,117310,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,234289,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,199925,Assoc-voc,11,Never-married,Adm-clerical,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,31540,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,202606,HS-grad,9,Separated,Other-service,Not-in-family,Black,Female,0,0,1,Haiti,<=50K\n0,Private,239577,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,110013,Masters,14,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,230054,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,203558,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Self-emp-not-inc,222883,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Private,61518,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,246891,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,194995,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,207025,HS-grad,9,Divorced,Priv-house-serv,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Self-emp-inc,128715,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,180168,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,270495,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,191196,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n1,State-gov,624572,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,488706,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-not-inc,52131,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,134635,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,197033,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,112576,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,134737,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,154237,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,103200,Masters,14,Married-spouse-absent,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Taiwan,<=50K\n0,Private,179599,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,274900,7th-8th,4,Married-civ-spouse,?,Other-relative,White,Male,0,0,2,Dominican-Republic,<=50K\n0,Private,138580,12th,8,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,187034,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,263568,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,1,0,1,United-States,<=50K\n0,Private,142332,12th,8,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,174090,Assoc-voc,11,Never-married,Sales,Unmarried,White,Female,2,0,3,United-States,>50K\n2,Federal-gov,103323,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,108670,Assoc-voc,11,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,?,326640,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,99185,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,365328,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,53220,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,2,0,United-States,<=50K\n0,Private,369084,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,55377,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,Jamaica,>50K\n4,Private,271094,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,165014,Some-college,10,Married-civ-spouse,Sales,Own-child,Other,Female,0,0,0,Mexico,<=50K\n2,Local-gov,284086,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n3,Federal-gov,186256,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,84250,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,189404,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,152667,12th,8,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,239755,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,271756,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,332657,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,141029,HS-grad,9,Separated,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,41281,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Federal-gov,27444,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,267945,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,177675,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,135716,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,91355,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,53292,Assoc-acdm,12,Widowed,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,256466,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,3,Philippines,<=50K\n2,Private,126701,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,64671,1st-4th,2,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,Without-pay,123004,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Own-child,White,Female,0,2,2,United-States,>50K\n1,Private,167350,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,State-gov,160062,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,35683,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,146565,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,168166,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,117779,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,1,0,4,United-States,<=50K\n4,Local-gov,310085,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,262413,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,2,Italy,<=50K\n4,Private,152874,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,0,2,2,United-States,<=50K\n1,Private,35314,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,32719,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,224566,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,190290,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,2,2,United-States,<=50K\n1,Private,331806,HS-grad,9,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,328610,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,221533,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,350169,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,Japan,<=50K\n1,Private,125131,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,>50K\n4,?,158437,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,2,Hungary,<=50K\n2,Private,180714,HS-grad,9,Never-married,Other-service,Unmarried,Black,Male,0,0,3,United-States,<=50K\n2,Private,284086,9th,5,Divorced,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,119422,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,327060,HS-grad,9,Never-married,?,Unmarried,Black,Male,0,0,0,United-States,<=50K\n1,Private,171813,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,20308,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,45037,10th,6,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,139157,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,228931,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,220729,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,147230,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,79586,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n2,Self-emp-inc,279914,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,2,United-States,>50K\n4,Private,142769,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,226525,10th,6,Divorced,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,178759,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,106705,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,194161,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Italy,>50K\n2,Private,225385,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,331960,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,156877,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,271354,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,33117,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,212437,Assoc-acdm,12,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,121228,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,2,3,United-States,<=50K\n4,Self-emp-not-inc,142139,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,165001,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,294623,5th-6th,3,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,244413,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Male,0,0,1,Dominican-Republic,<=50K\n1,Private,146014,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,197014,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,169528,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,162397,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,154641,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,251585,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,46322,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,197042,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,111829,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,229005,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Private,298546,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Federal-gov,111483,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,>50K\n4,Private,104973,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,383300,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,135162,1st-4th,2,Married-spouse-absent,Adm-clerical,Not-in-family,White,Male,0,0,2,?,<=50K\n2,Private,87520,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,183258,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,206546,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,199212,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,2,United-States,>50K\n2,Local-gov,207685,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,213277,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,129980,Some-college,10,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,118089,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Self-emp-not-inc,236391,11th,7,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,209057,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,98044,Preschool,1,Never-married,Other-service,Not-in-family,White,Male,0,0,1,El-Salvador,<=50K\n0,Private,154192,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,48268,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,158762,10th,6,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,87569,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Female,0,2,2,United-States,<=50K\n1,?,274800,HS-grad,9,Separated,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,230684,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,1,2,United-States,<=50K\n3,Private,155496,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,22494,Some-college,10,Married-spouse-absent,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,200610,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,<=50K\n3,State-gov,54985,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,207807,10th,6,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,1,United-States,<=50K\n0,Private,294263,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,204226,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,188242,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,4,0,2,United-States,>50K\n3,Private,20956,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,55567,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,State-gov,251325,Some-college,10,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Female,0,0,0,?,<=50K\n1,Self-emp-not-inc,75417,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,185556,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,96732,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,Mexico,<=50K\n1,Private,323985,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,Federal-gov,198186,10th,6,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,147884,Some-college,10,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,285000,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,91882,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,196434,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,191968,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,222756,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Male,2,0,2,United-States,>50K\n1,Private,66384,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,165937,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,243076,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,193026,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,State-gov,126894,Doctorate,16,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n0,Private,214399,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,3,United-States,<=50K\n4,?,98979,Some-college,10,Married-civ-spouse,?,Husband,Black,Male,0,0,0,United-States,>50K\n1,Private,191177,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,420351,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,159849,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,128392,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,2,0,2,United-States,>50K\n1,Private,130492,11th,7,Divorced,Craft-repair,Unmarried,Other,Male,0,0,1,United-States,<=50K\n3,Private,59380,Some-college,10,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,130532,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,535740,HS-grad,9,Never-married,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,186452,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,276418,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,3,1,United-States,<=50K\n0,?,383715,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,418617,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,El-Salvador,<=50K\n0,Private,607118,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,230592,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,212932,10th,6,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,321865,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,240755,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,167571,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,165586,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,132267,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,308082,Preschool,1,Never-married,Other-service,Not-in-family,White,Female,0,0,0,El-Salvador,<=50K\n1,Self-emp-not-inc,402812,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n0,Local-gov,136357,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,117549,11th,7,Never-married,Sales,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Private,482677,10th,6,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,112658,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,120461,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,83444,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,93699,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,174794,Some-college,10,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,157332,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,420973,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,176471,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,113708,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,82497,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,169512,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,>50K\n2,Private,157765,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,277946,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,195808,11th,7,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,57852,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,99699,Bachelors,13,Separated,Prof-specialty,Not-in-family,Black,Female,0,2,2,United-States,<=50K\n1,Private,296466,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,248476,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,409902,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,4,United-States,<=50K\n0,?,187161,HS-grad,9,Married-civ-spouse,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,220237,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,303187,Some-college,10,Never-married,Handlers-cleaners,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,285890,Bachelors,13,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,63322,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,159937,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,1,United-States,>50K\n1,Private,208725,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,325159,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,89698,HS-grad,9,Widowed,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,399088,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,134922,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,245866,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,406328,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,203523,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,272432,HS-grad,9,Never-married,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,State-gov,182271,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,193746,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,203505,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,118484,Some-college,10,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,391591,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,30069,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Private,179731,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n1,Private,128016,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Private,139473,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,130302,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,250630,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,115222,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n0,Private,198700,Some-college,10,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,394191,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,163140,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,243786,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,248406,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,?,33860,Some-college,10,Never-married,?,Not-in-family,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n2,Private,138342,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,115254,Some-college,10,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,173504,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,89259,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,152940,Masters,14,Never-married,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,58124,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,201908,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,156780,HS-grad,9,Married-spouse-absent,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n1,Private,152924,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,115963,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,375313,Some-college,10,Never-married,Craft-repair,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Local-gov,126754,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,State-gov,223725,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,298871,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n0,Local-gov,306352,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,2,Mexico,>50K\n2,Private,166879,11th,7,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,125040,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,198493,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,86298,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,249039,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,217200,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,145139,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,146343,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,102976,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,24013,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,162745,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,128045,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,245937,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,426431,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,409902,10th,6,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,580591,1st-4th,2,Married-spouse-absent,Farming-fishing,Not-in-family,White,Male,0,0,1,Mexico,<=50K\n4,Self-emp-not-inc,130585,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n0,Private,201145,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,196107,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,178353,9th,5,Divorced,Machine-op-inspct,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,195508,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,224716,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,?,280901,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,169076,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,141584,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,253267,5th-6th,3,Separated,Other-service,Unmarried,White,Female,0,0,1,Cuba,<=50K\n1,Private,203776,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,449172,Bachelors,13,Divorced,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,174329,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,91674,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,202958,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,205680,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,193932,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,177635,12th,8,Married-spouse-absent,Transport-moving,Unmarried,White,Male,0,0,2,Mexico,<=50K\n4,?,180422,Assoc-acdm,12,Never-married,?,Not-in-family,White,Male,2,0,2,United-States,<=50K\n0,Private,231335,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,141058,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n0,Federal-gov,114365,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,212856,Assoc-acdm,12,Never-married,Protective-serv,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Private,64292,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,96609,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,?,438427,Assoc-acdm,12,Separated,?,Unmarried,Black,Female,0,0,3,United-States,<=50K\n4,?,144026,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,105997,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,154430,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,162954,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,94826,5th-6th,3,Never-married,Craft-repair,Own-child,White,Male,0,0,2,Guatemala,<=50K\n1,Private,54897,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,135020,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,367819,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n0,Private,225531,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,4,2,United-States,<=50K\n3,Private,165937,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,131230,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,95647,HS-grad,9,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n4,Private,115880,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,262278,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,Black,Male,4,0,2,United-States,>50K\n2,Private,126755,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,150211,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,188694,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n3,Self-emp-inc,59840,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,144752,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n4,Private,192042,HS-grad,9,Married-civ-spouse,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,230806,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,364946,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,133986,10th,6,Separated,Transport-moving,Unmarried,White,Female,0,0,4,United-States,<=50K\n0,?,201418,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,236543,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,238386,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,96062,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,202565,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,194063,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,243733,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,4,United-States,>50K\n0,Private,403519,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,2,1,United-States,<=50K\n3,State-gov,104353,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,Black,Female,0,0,2,United-States,>50K\n0,Private,239539,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Private,104089,Assoc-voc,11,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,93585,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,139277,HS-grad,9,Widowed,Craft-repair,Unmarried,White,Female,0,0,2,Italy,<=50K\n0,Private,124971,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,82566,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,145964,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Federal-gov,177855,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,263656,11th,7,Never-married,Sales,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,199191,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,England,>50K\n2,Private,212840,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,191330,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,193720,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,Greece,>50K\n2,Self-emp-inc,172927,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,51185,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,186145,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,181307,Doctorate,16,Separated,Prof-specialty,Not-in-family,White,Male,0,1,2,United-States,<=50K\n0,Private,180052,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,?,61791,9th,5,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,52505,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,48976,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,281012,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,Asian-Pac-Islander,Female,0,0,2,China,>50K\n1,Private,156464,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,94358,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,296858,Masters,14,Married-civ-spouse,Armed-Forces,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,84790,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,177054,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,131239,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Federal-gov,49398,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,27242,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Philippines,<=50K\n2,Local-gov,113324,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,295591,Preschool,1,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,Private,48549,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Local-gov,384627,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,1,0,0,United-States,<=50K\n0,Private,266062,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,State-gov,208117,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,315476,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,20308,Some-college,10,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,169204,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,319831,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,80356,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,313473,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,209529,11th,7,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n0,Private,214956,11th,7,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,557853,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,1,United-States,<=50K\n3,Private,78529,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,446390,Some-college,10,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,>50K\n2,Local-gov,256253,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,61898,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,181152,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,1,United-States,<=50K\n1,Private,90409,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,237713,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,176727,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,285367,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Male,2,0,1,United-States,<=50K\n2,Private,135293,Masters,14,Separated,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,?,105044,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,2,0,2,United-States,>50K\n3,State-gov,98010,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n4,Private,162301,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,103824,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,115677,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n3,Local-gov,319079,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,134912,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,35985,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,245317,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,35743,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,231004,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,237295,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,43311,5th-6th,3,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,El-Salvador,<=50K\n0,Private,154583,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,278515,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,266062,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,172425,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n3,Self-emp-not-inc,102388,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,195554,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,265368,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,2,United-States,>50K\n0,Private,100669,Assoc-voc,11,Never-married,Craft-repair,Own-child,Asian-Pac-Islander,Male,0,0,0,United-States,<=50K\n2,Private,233511,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-inc,223566,Prof-school,15,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,95552,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,159676,HS-grad,9,Divorced,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,?,80978,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,70584,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,153475,11th,7,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,104489,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,146325,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,246900,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,187589,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,65535,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,38540,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,191389,5th-6th,3,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,Italy,<=50K\n0,Private,231492,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,130007,10th,6,Divorced,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,487751,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,52240,Some-college,10,Never-married,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,172906,Assoc-acdm,12,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Private,158685,12th,8,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,307693,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n2,Private,202922,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,119563,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,161444,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,82242,Bachelors,13,Never-married,Exec-managerial,Other-relative,White,Male,0,0,2,Germany,<=50K\n4,Private,357233,HS-grad,9,Widowed,Handlers-cleaners,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Private,177596,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,Puerto-Rico,<=50K\n1,Private,209085,HS-grad,9,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,241306,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,19447,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,195104,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,109456,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,34887,HS-grad,9,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,202435,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,191821,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,228372,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,78374,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,Japan,<=50K\n4,Private,129786,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,410439,HS-grad,9,Divorced,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-inc,152307,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,<=50K\n2,Private,33895,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,178390,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,114011,11th,7,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,157839,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Federal-gov,97355,Some-college,10,Separated,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,369781,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,225515,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,138513,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,138248,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,149141,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,233955,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Federal-gov,150533,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,233369,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,188779,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,Private,287927,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,3,England,<=50K\n0,?,96483,Some-college,10,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n2,Private,165215,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,0,Poland,<=50K\n1,Private,107142,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,<=50K\n3,Private,82008,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,116044,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,160101,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,356934,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,204527,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,1,0,2,United-States,<=50K\n2,Private,276276,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,110408,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,187022,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,117528,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,180439,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,185870,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,290504,Some-college,10,Never-married,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,162264,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,253716,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,190525,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,283227,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,375221,11th,7,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Private,194971,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,3,China,<=50K\n1,Private,198091,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,224462,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,198751,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n1,Private,221166,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,277777,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,1,0,2,United-States,>50K\n1,Local-gov,247156,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,1,0,2,United-States,>50K\n0,Private,61777,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,<=50K\n4,Private,67928,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,204596,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,27881,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,332884,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,178551,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,215917,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Private,284701,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,286990,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,366204,7th-8th,4,Widowed,Priv-house-serv,Unmarried,Black,Female,1,0,0,United-States,<=50K\n0,Private,163519,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,123780,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,108140,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,469263,HS-grad,9,Divorced,Craft-repair,Unmarried,Black,Male,0,0,3,United-States,<=50K\n3,Private,216558,Some-college,10,Separated,Craft-repair,Other-relative,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n3,Private,149218,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,113453,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,23545,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,409443,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,Mexico,<=50K\n1,Local-gov,152744,Masters,14,Never-married,Prof-specialty,Other-relative,Asian-Pac-Islander,Female,1,0,2,United-States,<=50K\n1,Private,166543,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,224217,11th,7,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n2,State-gov,244522,HS-grad,9,Divorced,Protective-serv,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,148121,Some-college,10,Widowed,Exec-managerial,Unmarried,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n4,Private,295425,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,255424,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,97214,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,158703,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,478972,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,180383,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,159123,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,231230,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,134232,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,90729,11th,7,Never-married,Machine-op-inspct,Unmarried,Other,Male,0,0,2,United-States,<=50K\n2,Private,275338,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,410446,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,120475,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Federal-gov,127048,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,State-gov,113544,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,233727,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,210064,Some-college,10,Widowed,Prof-specialty,Unmarried,White,Male,0,0,0,United-States,<=50K\n3,Private,77462,Some-college,10,Separated,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,108001,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,379522,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n1,State-gov,51461,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,147270,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,118212,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,189792,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,225623,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n2,Private,248445,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,218490,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,?,379883,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,312232,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,234470,Assoc-acdm,12,Widowed,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,389942,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,79483,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,389279,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,107581,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,176458,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,70505,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,259646,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,235743,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,177449,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n1,Private,103432,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,Portugal,>50K\n3,Private,347088,5th-6th,3,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,275924,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,Mexico,>50K\n1,Private,162113,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n0,Private,147497,5th-6th,3,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,37232,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,4,0,4,United-States,>50K\n1,Private,441949,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,285855,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,103123,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,207076,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,36270,HS-grad,9,Married-spouse-absent,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,206927,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,236415,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,108035,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,225395,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n1,State-gov,140239,HS-grad,9,Separated,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,338033,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,314164,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,337065,7th-8th,4,Divorced,Farming-fishing,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,State-gov,340899,Doctorate,16,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,102096,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,31141,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,312818,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,0,United-States,>50K\n2,Local-gov,270147,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,97279,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,247328,5th-6th,3,Never-married,Transport-moving,Not-in-family,White,Male,0,0,1,El-Salvador,<=50K\n3,Self-emp-not-inc,31267,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,1,0,4,United-States,<=50K\n1,Private,220066,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,159269,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,3,Yugoslavia,<=50K\n3,Private,155107,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,354351,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,129053,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,255822,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,192323,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,176244,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,223019,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,243243,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,4,United-States,<=50K\n0,State-gov,194630,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,98980,HS-grad,9,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,0,0,4,United-States,>50K\n2,Private,223792,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,415706,10th,6,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,68781,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,113718,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,1,2,United-States,<=50K\n2,Local-gov,152587,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,120268,Some-college,10,Married-civ-spouse,Craft-repair,Own-child,White,Male,0,0,3,United-States,>50K\n2,Private,52870,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,193895,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,239662,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,1,2,United-States,<=50K\n3,Local-gov,201040,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Mexico,>50K\n2,State-gov,25806,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n3,Private,130667,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,141807,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,108037,Doctorate,16,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,129311,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,149218,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,337276,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,24366,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,?,149855,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,3,4,United-States,<=50K\n1,Self-emp-inc,49020,Assoc-acdm,12,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,165007,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,83413,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,103331,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,4,0,2,United-States,>50K\n4,Private,142689,11th,7,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,398575,Some-college,10,Never-married,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,166301,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,53703,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,274907,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,226525,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,36440,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,81965,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,111192,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,214238,11th,7,Never-married,Sales,Other-relative,White,Female,0,0,1,Mexico,<=50K\n2,Private,115932,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,173730,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,123157,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,220283,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,155066,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,244246,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,Poland,<=50K\n2,Private,112264,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,200450,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,128609,HS-grad,9,Divorced,?,Not-in-family,Black,Male,0,0,2,United-States,>50K\n4,Private,340591,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,43712,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,316820,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,44142,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,311311,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,?,143417,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,264166,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,Mexico,<=50K\n2,Private,112656,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,123844,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,146760,Some-college,10,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,225516,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,114366,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,199227,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,299347,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,74194,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,2,2,United-States,<=50K\n0,Private,244408,HS-grad,9,Married-civ-spouse,Exec-managerial,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n1,Private,198289,12th,8,Never-married,Farming-fishing,Other-relative,White,Male,0,0,2,Puerto-Rico,<=50K\n4,State-gov,89451,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,149433,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,236999,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,34943,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,40666,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,329170,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,2,0,4,United-States,<=50K\n1,Private,122999,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,118972,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,205068,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,195124,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Columbia,<=50K\n0,Private,143184,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n3,?,90290,HS-grad,9,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,175232,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,319366,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,Haiti,>50K\n1,Private,61559,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,247869,Some-college,10,Separated,Transport-moving,Unmarried,White,Male,0,0,3,United-States,>50K\n2,Private,204158,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n2,Private,239755,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,198210,HS-grad,9,Never-married,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,190961,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,171193,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Female,0,2,1,United-States,<=50K\n1,Private,110073,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,England,>50K\n0,Private,163885,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,99783,Assoc-voc,11,Married-civ-spouse,Other-service,Wife,White,Female,0,3,2,United-States,<=50K\n0,Private,430930,11th,7,Never-married,Priv-house-serv,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,200450,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,226296,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,2,3,United-States,<=50K\n2,Self-emp-inc,214690,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,181008,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,England,>50K\n1,Local-gov,345779,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,58350,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,164647,HS-grad,9,Divorced,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,142809,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,105803,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,195067,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,289964,Some-college,10,Separated,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,194813,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,303211,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,37932,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,268832,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,63925,5th-6th,3,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,189897,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,635913,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,?,296228,Some-college,10,Never-married,?,Unmarried,Other,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,138162,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,164757,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,236013,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n4,Private,149912,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,85384,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Self-emp-not-inc,30008,Masters,14,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,209744,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,161027,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,131662,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,Germany,<=50K\n3,Private,115971,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,88373,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,211399,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,Local-gov,273989,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,124614,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,263439,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,19896,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,229732,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n4,?,169611,Some-college,10,Married-civ-spouse,?,Wife,White,Female,2,0,0,United-States,>50K\n2,Private,220237,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,Greece,>50K\n3,Self-emp-not-inc,130779,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,152540,Bachelors,13,Divorced,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,168330,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,485944,Bachelors,13,Never-married,Sales,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,199539,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,210521,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,244175,11th,7,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,223763,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,183580,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,?,<=50K\n2,Private,63596,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,108540,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n2,Self-emp-not-inc,116632,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n0,Private,175414,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Federal-gov,290624,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,557805,Assoc-voc,11,Never-married,Sales,Other-relative,White,Female,0,0,2,El-Salvador,<=50K\n0,Private,19410,HS-grad,9,Never-married,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n3,Private,206357,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,216385,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,2,2,Haiti,<=50K\n3,Private,120131,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,>50K\n4,Private,131699,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,157778,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,302679,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,133292,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,243567,11th,7,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,72442,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,43909,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,108307,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,87467,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,State-gov,99185,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,37274,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,342434,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,234372,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,107627,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,?,377725,Bachelors,13,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,30271,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,368570,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,316820,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n4,State-gov,176538,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,265786,5th-6th,3,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,82393,Some-college,10,Married-civ-spouse,Other-service,Other-relative,Asian-Pac-Islander,Male,0,0,1,Philippines,<=50K\n3,Private,318259,Some-college,10,Separated,Tech-support,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,157980,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,173981,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n3,Federal-gov,165050,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,303652,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,393376,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,121912,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,351770,9th,5,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,198841,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Canada,>50K\n2,Local-gov,139160,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,?,214810,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,137385,Some-college,10,Never-married,Tech-support,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n2,Private,86643,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,200089,11th,7,Never-married,Farming-fishing,Other-relative,White,Male,0,0,2,El-Salvador,<=50K\n1,Private,219199,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,142711,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n1,Private,626493,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,177125,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,181776,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,2,4,United-States,<=50K\n2,Private,257250,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,444134,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,24688,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,33731,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,209443,Bachelors,13,Married-AF-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,140854,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,330142,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,29488,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,279129,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,177940,Assoc-acdm,12,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,3,United-States,<=50K\n0,Private,391403,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,334365,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,171356,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,71145,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,>50K\n0,Private,36943,Assoc-acdm,12,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,285787,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,433580,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,50197,9th,5,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n4,State-gov,139611,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,3,2,India,>50K\n1,Private,187802,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,241431,12th,8,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,121775,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,136758,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,493034,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,132139,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,100109,11th,7,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,?,<=50K\n0,Private,198861,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,2,2,United-States,<=50K\n0,Private,273226,11th,7,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,323054,HS-grad,9,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n0,Private,284895,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,191324,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,92565,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n3,Private,234690,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Cuba,>50K\n0,Private,258509,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,State-gov,178897,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,1,0,2,United-States,<=50K\n4,Private,220788,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,376548,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,228592,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,177216,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n1,?,211743,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,23813,10th,6,Divorced,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,Private,195688,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,124953,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,?,<=50K\n1,Private,129775,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,339644,HS-grad,9,Married-spouse-absent,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,149648,11th,7,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,187492,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,336523,12th,8,Never-married,Other-service,Other-relative,Black,Male,0,0,0,United-States,<=50K\n2,State-gov,222530,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Female,0,1,2,United-States,<=50K\n3,Self-emp-inc,140644,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,265201,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,Germany,<=50K\n3,Private,135246,11th,7,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,89202,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,296394,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,66544,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,4,United-States,<=50K\n2,Self-emp-not-inc,165815,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,187722,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,187046,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,397877,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,258827,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,119529,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,97212,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,47235,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n4,Private,359972,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,97212,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,72036,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,174938,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,201404,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,234791,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,State-gov,85632,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,147411,5th-6th,3,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,127388,Assoc-acdm,12,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,116657,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,194608,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,108706,11th,7,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,158343,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,914061,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,190174,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,456736,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,167882,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,3,0,4,United-States,>50K\n2,Self-emp-inc,557349,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,310483,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,78261,HS-grad,9,Separated,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,172571,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,230229,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,183017,HS-grad,9,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,230329,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,46537,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,409622,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Mexico,<=50K\n3,Private,190482,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,113588,HS-grad,9,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,246891,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,>50K\n3,Private,193081,Preschool,1,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,Haiti,<=50K\n3,?,284477,5th-6th,3,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,141420,HS-grad,9,Married-civ-spouse,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,197325,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,443336,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,66304,9th,5,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,180783,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,218763,Masters,14,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,388252,Bachelors,13,Never-married,Tech-support,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,55614,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,307643,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n0,?,33241,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,58065,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,<=50K\n4,State-gov,172348,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,138790,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n0,State-gov,117393,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,227220,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,241306,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,189565,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,182714,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,113839,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,92531,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,119629,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,322585,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,277347,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,221955,5th-6th,3,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,Mexico,<=50K\n2,Private,149347,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,207116,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,174077,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,22418,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,255252,11th,7,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,159138,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,414991,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,282678,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Federal-gov,164195,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,143436,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,202046,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Canada,<=50K\n0,?,202214,Some-college,10,Never-married,?,Own-child,White,Female,0,2,2,United-States,<=50K\n3,Private,236731,1st-4th,2,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Dominican-Republic,<=50K\n0,Local-gov,307267,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,144514,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,155913,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,206383,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,233994,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,123291,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,195602,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,151888,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,103995,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,263796,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,111499,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,202222,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,230246,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,37778,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,144752,10th,6,Never-married,Handlers-cleaners,Own-child,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n1,Private,220931,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,68840,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,205339,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,172837,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,3,United-States,>50K\n2,State-gov,159131,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,207284,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,350824,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,116080,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n2,Self-emp-not-inc,183081,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,177974,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,4,United-States,<=50K\n4,Private,192849,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,169882,11th,7,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,137320,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,0,United-States,<=50K\n1,Private,251521,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,329791,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,261319,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,343052,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,53271,HS-grad,9,Never-married,Transport-moving,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,Private,129024,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,179768,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,144949,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,150861,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,112763,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,116358,HS-grad,9,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Self-emp-not-inc,331374,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,152811,10th,6,Widowed,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,202699,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,1,United-States,>50K\n3,Private,92847,7th-8th,4,Widowed,Priv-house-serv,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,137142,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,953588,11th,7,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,225544,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,116289,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,279912,Some-college,10,Never-married,Tech-support,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,256630,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,3,Canada,>50K\n2,Private,259727,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,4,0,0,United-States,>50K\n3,Private,331650,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,182006,11th,7,Never-married,Adm-clerical,Not-in-family,White,Female,2,0,1,United-States,<=50K\n0,Private,277708,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,64874,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,376455,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,125000,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,390369,Assoc-acdm,12,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,250423,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,27802,Masters,14,Separated,Prof-specialty,Not-in-family,White,Male,0,2,3,United-States,<=50K\n3,Private,137192,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,0,Philippines,<=50K\n1,State-gov,200068,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Other,Female,0,0,2,United-States,<=50K\n1,Private,220656,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,199501,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,Black,Female,0,0,3,Jamaica,<=50K\n1,Private,181613,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,2,United-States,<=50K\n1,Private,329432,Masters,14,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,139180,11th,7,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,263612,HS-grad,9,Never-married,?,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,402363,Masters,14,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n0,Private,256545,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,246439,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,182615,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,131401,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,259138,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,1,0,2,United-States,<=50K\n2,Private,107503,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,State-gov,205482,HS-grad,9,Married-spouse-absent,Transport-moving,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,184833,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,2,0,3,United-States,<=50K\n2,Private,395997,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,158438,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,190044,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n2,Self-emp-not-inc,184378,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,118983,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,99127,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,106982,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,<=50K\n3,Private,213668,Some-college,10,Separated,Machine-op-inspct,Not-in-family,White,Male,3,0,4,United-States,>50K\n3,Local-gov,159689,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,354923,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,200207,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,98590,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,221947,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,160634,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,179237,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,97771,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,237399,11th,7,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,276559,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,178251,Masters,14,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,145370,Bachelors,13,Married-civ-spouse,Sales,Husband,Black,Male,4,0,3,United-States,>50K\n0,Private,249271,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,257562,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,115842,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,341368,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,?,172232,Some-college,10,Never-married,?,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,67151,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,228649,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,164198,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n4,Local-gov,190228,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,>50K\n0,Private,179707,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-inc,132559,Doctorate,16,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,473547,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,284526,5th-6th,3,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Cuba,<=50K\n0,Private,112854,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,271012,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,298995,Some-college,10,Never-married,Tech-support,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n0,Never-worked,273905,HS-grad,9,Married-spouse-absent,?,Other-relative,White,Male,0,0,1,United-States,<=50K\n2,Private,172712,Bachelors,13,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,>50K\n1,Private,205249,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,3,?,<=50K\n0,Private,375698,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,Japan,<=50K\n2,State-gov,355756,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,147951,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,156493,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,215857,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,Mexico,<=50K\n0,Private,88824,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,242150,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,295010,Some-college,10,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,>50K\n4,Self-emp-not-inc,268781,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n3,Private,360593,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,2,0,2,United-States,<=50K\n1,Private,306678,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,377018,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,131230,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,457710,11th,7,Never-married,?,Own-child,White,Male,0,0,0,Mexico,<=50K\n1,Self-emp-inc,229732,Assoc-acdm,12,Divorced,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,159879,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,204629,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,46580,HS-grad,9,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,471768,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Private,117802,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,175537,Some-college,10,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,247734,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,190885,7th-8th,4,Widowed,Other-service,Unmarried,White,Female,0,0,2,Mexico,<=50K\n3,Private,117849,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,103794,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,2,0,1,United-States,<=50K\n1,Self-emp-not-inc,222450,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,558344,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n0,Private,131825,11th,7,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,166181,HS-grad,9,Widowed,Priv-house-serv,Own-child,Black,Female,0,0,1,United-States,<=50K\n0,Private,179392,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,232149,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,96748,Bachelors,13,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,177895,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,207066,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,127749,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,216129,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,401273,Some-college,10,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,245356,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,30846,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,216414,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,361390,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,Italy,<=50K\n1,Private,255364,Some-college,10,Divorced,Other-service,Own-child,White,Male,1,0,1,United-States,<=50K\n0,Private,197377,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,197816,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,194140,Some-college,10,Separated,Machine-op-inspct,Unmarried,Black,Male,0,0,3,United-States,<=50K\n4,?,110122,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,102130,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,85815,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n1,Private,176064,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,38946,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,249463,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,175665,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,111385,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,165867,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,347890,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,49249,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,125417,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,4,United-States,>50K\n4,Self-emp-not-inc,30958,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,State-gov,191001,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,3,0,2,United-States,>50K\n0,Self-emp-not-inc,68073,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,233149,12th,8,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,204596,12th,8,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,109996,9th,5,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,2,Hong,<=50K\n1,Private,251170,HS-grad,9,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,140001,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,State-gov,347445,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,229126,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n3,Private,235431,Preschool,1,Never-married,Sales,Unmarried,Black,Female,0,0,2,Haiti,<=50K\n4,Private,209280,HS-grad,9,Separated,Sales,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,172013,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,133876,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,152727,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,139838,10th,6,Separated,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,153885,Some-college,10,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,21174,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,101266,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,?,<=50K\n1,Private,99548,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Local-gov,220669,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,2,0,2,United-States,<=50K\n2,Private,91716,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,112115,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,220978,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,121012,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,242804,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,192654,Bachelors,13,Widowed,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,111706,1st-4th,2,Never-married,Machine-op-inspct,Unmarried,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n2,Private,174196,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Private,312500,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,39623,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,355468,10th,6,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,62726,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,75867,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,76449,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,111567,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,112945,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,191389,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,208584,Assoc-acdm,12,Separated,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,99340,5th-6th,3,Separated,Machine-op-inspct,Unmarried,White,Female,0,0,2,Dominican-Republic,<=50K\n3,Self-emp-not-inc,308087,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,166843,HS-grad,9,Widowed,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,?,122433,10th,6,Divorced,?,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,Private,103573,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,264735,Masters,14,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n4,Self-emp-not-inc,281792,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,184081,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,Jamaica,<=50K\n0,Private,381741,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,98630,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,161631,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,152591,Some-college,10,Divorced,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,150080,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,278141,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,229328,12th,8,Widowed,Sales,Unmarried,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n1,Private,278916,Some-college,10,Separated,Handlers-cleaners,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Federal-gov,421871,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Male,2,0,3,United-States,<=50K\n1,Private,164193,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,189265,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,384959,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,67320,HS-grad,9,Widowed,Other-service,Other-relative,Black,Female,0,0,2,United-States,<=50K\n3,Private,174655,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n1,Local-gov,327203,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,51148,HS-grad,9,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,287380,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,131608,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,122857,HS-grad,9,Never-married,Priv-house-serv,Not-in-family,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n1,Private,259609,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,104509,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,203435,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,148429,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,106950,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,87402,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,265638,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,430710,HS-grad,9,Separated,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,193116,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,94880,Some-college,10,Married-spouse-absent,Craft-repair,Other-relative,White,Male,0,0,2,Mexico,<=50K\n4,Private,186427,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,348287,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,77498,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,199058,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,156464,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,Germany,<=50K\n2,Private,202508,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,?,>50K\n2,Private,131309,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,99679,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,136309,Assoc-acdm,12,Never-married,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,294451,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,104719,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,207889,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,215890,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,341239,HS-grad,9,Never-married,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,58326,11th,7,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,176544,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,2,3,United-States,<=50K\n2,Private,216149,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,274637,9th,5,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,163870,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,?,<=50K\n3,?,287575,HS-grad,9,Separated,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,268292,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,343061,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,Cuba,<=50K\n3,Local-gov,481258,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,181129,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,153302,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,235891,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n1,Self-emp-not-inc,41210,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,152109,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,114072,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,83066,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,1,United-States,<=50K\n0,Private,110230,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-inc,137421,HS-grad,9,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n3,Self-emp-inc,222829,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,89652,Masters,14,Widowed,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-inc,375807,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,184224,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,54639,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,77764,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,61523,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,54614,Assoc-voc,11,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,188246,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,?,<=50K\n0,Private,267594,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,140499,HS-grad,9,Never-married,Protective-serv,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,73471,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,33487,Some-college,10,Divorced,Adm-clerical,Other-relative,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,?,201179,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,165814,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n2,State-gov,46221,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,172246,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n1,Federal-gov,148207,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,389725,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,2,Germany,>50K\n1,Private,343519,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,67317,Assoc-acdm,12,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,257728,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,264554,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,224567,11th,7,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,24038,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,210945,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,123727,HS-grad,9,Separated,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,?,78374,Bachelors,13,Never-married,?,Not-in-family,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n0,Private,138266,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,147098,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,211695,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,196480,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,373699,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,189680,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,342458,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,161155,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,116601,Masters,14,Divorced,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,3,Nicaragua,<=50K\n4,Self-emp-inc,127605,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n0,Self-emp-not-inc,47541,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,134779,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,2,0,2,United-States,<=50K\n2,Self-emp-not-inc,177937,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,114758,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n4,Local-gov,287277,9th,5,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,173113,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,169785,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Self-emp-not-inc,280508,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,360077,11th,7,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,>50K\n3,Private,165229,12th,8,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,282753,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,308812,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,132519,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,185053,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,>50K\n2,Local-gov,261899,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,119939,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,276165,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,361280,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n1,Private,195770,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,289804,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,247115,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,3,United-States,<=50K\n3,Federal-gov,50459,HS-grad,9,Divorced,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,260617,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,155066,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,227210,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Local-gov,47270,Assoc-acdm,12,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,111128,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n2,Private,119929,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,157248,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,174386,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,21755,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n1,Private,261646,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,590204,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,2,United-States,>50K\n2,Private,679853,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,Dominican-Republic,<=50K\n2,Private,144928,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,>50K\n1,?,88513,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,110663,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,182187,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,Haiti,>50K\n2,Private,160703,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,279323,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,131425,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,180288,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n2,Self-emp-inc,123490,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,421474,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,100079,Doctorate,16,Married-spouse-absent,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,4,0,3,China,>50K\n1,Private,95639,11th,7,Never-married,Handlers-cleaners,Other-relative,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,169002,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,49893,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,234994,Some-college,10,Divorced,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,137232,Bachelors,13,Married-spouse-absent,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,27067,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,193385,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n1,Private,181372,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,189143,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,115422,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n4,Self-emp-not-inc,163510,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n1,Private,241998,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,106838,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n4,?,50746,10th,6,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,325658,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,244688,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,244721,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,170721,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,105803,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,152810,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,138944,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,392668,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,192257,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Japan,<=50K\n4,?,23275,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-inc,46577,Bachelors,13,Widowed,Farming-fishing,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,174325,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,307767,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,192698,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,443809,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,Private,218100,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,516701,Masters,14,Never-married,Exec-managerial,Not-in-family,Black,Male,0,1,3,?,>50K\n0,Private,123173,Some-college,10,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,241697,Some-college,10,Married-spouse-absent,Sales,Unmarried,Amer-Indian-Eskimo,Male,0,2,2,United-States,<=50K\n3,Self-emp-inc,233149,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,357939,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,73928,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n3,State-gov,88528,Masters,14,Never-married,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,245073,7th-8th,4,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Private,148824,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n1,Private,106276,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,185039,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,210310,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,150767,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,3,United-States,<=50K\n4,?,31327,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,30237,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,264765,Some-college,10,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,148069,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,41493,Bachelors,13,Divorced,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,168539,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,?,108233,Assoc-acdm,12,Separated,?,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n0,State-gov,66692,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,122747,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,176289,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,238017,HS-grad,9,Widowed,Tech-support,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n1,Private,41099,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,190151,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,109159,HS-grad,9,Widowed,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,176949,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n4,Private,293899,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,168262,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n4,Private,208862,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,69477,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,443546,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,Germany,<=50K\n0,?,202989,Some-college,10,Never-married,?,Own-child,White,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,49996,HS-grad,9,Widowed,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n4,State-gov,220618,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,127875,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,217517,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,162151,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n3,Federal-gov,314871,Some-college,10,Separated,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,193960,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,198103,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,State-gov,106466,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,122109,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,254152,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,249449,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,184169,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-inc,156192,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,175509,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,297266,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,188073,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,?,221312,Some-college,10,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n4,Private,121552,7th-8th,4,Widowed,Other-service,Unmarried,Black,Male,0,0,0,United-States,<=50K\n2,Private,177134,HS-grad,9,Married-civ-spouse,Sales,Wife,Black,Female,0,0,2,United-States,<=50K\n4,Private,127921,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,210794,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,149366,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Peru,<=50K\n0,Private,214303,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,?,<=50K\n0,?,205940,9th,5,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,?,43535,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,<=50K\n3,Private,158924,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,270437,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,266505,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,37070,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Japan,<=50K\n1,Federal-gov,56419,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,0,South,<=50K\n3,Private,389270,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,205266,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,104575,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,99220,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,178313,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,103614,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,114882,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,186977,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Columbia,<=50K\n0,Private,208893,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Self-emp-inc,84231,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,129240,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,113061,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,243409,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,28132,12th,8,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,77975,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,241580,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,165599,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,85374,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,45604,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,109501,5th-6th,3,Separated,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,289405,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,75759,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,144808,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,?,231511,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,155890,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n4,Self-emp-not-inc,108496,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,101562,Some-college,10,Divorced,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,116372,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,Private,58037,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,339025,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,0,1,2,Vietnam,<=50K\n1,Private,117659,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,372898,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,199426,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,36023,HS-grad,9,Married-spouse-absent,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,?,186535,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,1,0,0,United-States,<=50K\n2,Private,57600,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,369522,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,28572,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,215323,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,81846,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n4,Private,535762,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,0,United-States,<=50K\n4,Private,239405,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,1,Jamaica,<=50K\n2,Local-gov,43998,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,408417,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,43705,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,176841,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,El-Salvador,<=50K\n0,Private,120676,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,207685,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,3,0,1,United-States,>50K\n1,Private,157708,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,126349,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,277647,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,174426,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,37869,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,150025,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Peru,<=50K\n3,Self-emp-not-inc,155496,Some-college,10,Never-married,Other-service,Unmarried,White,Female,1,0,2,United-States,<=50K\n2,Private,174748,Bachelors,13,Divorced,Exec-managerial,Unmarried,Black,Female,2,0,2,United-States,>50K\n2,Self-emp-inc,140915,Bachelors,13,Married-civ-spouse,Tech-support,Husband,Other,Male,0,0,2,France,>50K\n0,Private,187161,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,181820,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,438321,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,342121,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,135162,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,289448,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,4,1,Philippines,<=50K\n2,Self-emp-not-inc,157237,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,184542,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,70234,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,170412,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,171184,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,Dominican-Republic,<=50K\n4,?,141076,HS-grad,9,Divorced,?,Not-in-family,Black,Female,2,0,2,United-States,<=50K\n4,Private,168145,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,172594,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,133582,5th-6th,3,Never-married,Farming-fishing,Not-in-family,White,Male,1,0,2,Mexico,<=50K\n3,Private,214840,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,?,301408,Some-college,10,Never-married,?,Own-child,White,Female,0,2,1,United-States,<=50K\n1,Private,97723,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n3,Self-emp-not-inc,318351,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,Canada,>50K\n0,Private,69911,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,200316,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,265426,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,66687,Some-college,10,Separated,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,107417,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,140782,HS-grad,9,Separated,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,212210,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,144012,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,40021,Doctorate,16,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,228493,1st-4th,2,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,4,Mexico,<=50K\n2,State-gov,114714,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,>50K\n0,Private,188758,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,176862,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,107793,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,333052,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,175958,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,125012,Bachelors,13,Married-spouse-absent,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Private,135296,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,31171,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,Private,103860,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,90582,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,216292,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,188588,5th-6th,3,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,3,1,United-States,<=50K\n1,?,173178,Some-college,10,Never-married,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,193720,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,<=50K\n1,Private,218542,9th,5,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,138845,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,86837,Some-college,10,Married-spouse-absent,Adm-clerical,Not-in-family,Asian-Pac-Islander,Male,0,0,3,Philippines,<=50K\n0,State-gov,125010,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,50149,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,241350,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,3,0,3,United-States,>50K\n4,Private,39895,7th-8th,4,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,258289,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,183151,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,?,228614,7th-8th,4,Married-civ-spouse,?,Husband,Black,Male,0,0,1,United-States,<=50K\n3,Private,192236,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,3,United-States,<=50K\n2,?,161664,Some-college,10,Married-civ-spouse,?,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,105381,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,235786,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,118947,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,168817,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,24361,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,321223,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,146810,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,94041,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,1,0,1,United-States,<=50K\n2,Self-emp-not-inc,814850,9th,5,Divorced,Other-service,Not-in-family,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n2,Private,331649,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,209894,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,229954,Assoc-acdm,12,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,287547,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,184018,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,332409,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,113364,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,134813,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,>50K\n0,Private,273049,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,334277,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,196395,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,United-States,>50K\n3,Private,138069,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,358259,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,362067,Assoc-voc,11,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,209947,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,122244,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,116546,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,213934,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n1,Local-gov,24988,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Female,0,1,4,United-States,>50K\n3,Private,157229,Assoc-acdm,12,Married-civ-spouse,Sales,Wife,Asian-Pac-Islander,Female,0,0,2,India,<=50K\n1,Private,162442,Some-college,10,Never-married,Craft-repair,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,231604,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,187031,Masters,14,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,172714,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,198286,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,220001,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,262656,HS-grad,9,Never-married,Other-service,Unmarried,Black,Male,0,0,1,United-States,<=50K\n1,Private,203776,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,193815,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,Local-gov,173242,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,108126,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,2,1,United-States,<=50K\n4,Private,199021,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,92968,Assoc-acdm,12,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,173682,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,277533,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,90896,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,212600,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,261671,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n4,Private,86321,HS-grad,9,Widowed,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,119992,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,427812,9th,5,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Puerto-Rico,<=50K\n1,Private,55849,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,271354,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,131239,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,State-gov,139157,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,>50K\n2,State-gov,305541,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,151159,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,84726,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Wife,White,Female,0,0,2,Germany,<=50K\n3,?,175530,5th-6th,3,Separated,?,Own-child,White,Female,0,0,3,Mexico,<=50K\n2,Local-gov,364782,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,137304,Bachelors,13,Married-civ-spouse,Tech-support,Wife,Black,Female,0,0,2,United-States,>50K\n0,Private,197613,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,171615,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,105824,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Local-gov,250745,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,352451,7th-8th,4,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,123947,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,178983,Masters,14,Separated,Sales,Unmarried,White,Female,2,0,3,United-States,<=50K\n3,Private,101299,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,124993,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,178478,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,67001,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,97295,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,169515,Bachelors,13,Divorced,Protective-serv,Not-in-family,Black,Female,0,0,2,United-States,>50K\n3,Private,121253,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,1,2,United-States,>50K\n3,Federal-gov,35546,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,111635,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,207419,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,143083,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,248094,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,170685,Some-college,10,Divorced,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,116143,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,223426,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,0,0,4,Canada,>50K\n0,Private,370548,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,245525,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,408229,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,181434,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,213225,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,2,England,>50K\n0,Private,199915,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,143104,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,874728,HS-grad,9,Never-married,Adm-clerical,Other-relative,Black,Male,0,0,2,United-States,<=50K\n2,Private,27661,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,216116,HS-grad,9,Divorced,Protective-serv,Unmarried,Black,Female,0,0,2,United-States,>50K\n2,Private,193882,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,98180,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n4,Self-emp-not-inc,92353,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,184762,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,3,United-States,<=50K\n0,?,148294,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,128777,Some-college,10,Never-married,Sales,Other-relative,Black,Female,0,0,2,United-States,<=50K\n4,Private,108098,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,233511,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,223792,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,43904,HS-grad,9,Divorced,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,239864,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,159075,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,103474,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,90896,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,145290,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,155818,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,35864,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Other,Male,0,0,4,Iran,>50K\n0,Private,394954,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,State-gov,34637,9th,5,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,<=50K\n1,Private,38223,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,352105,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,291096,1st-4th,2,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,144391,Bachelors,13,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,44013,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,134890,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,497525,10th,6,Separated,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,195520,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,35166,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,180514,Bachelors,13,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,262153,11th,7,Married-spouse-absent,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,91039,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,30140,9th,5,Never-married,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,125791,Bachelors,13,Never-married,Prof-specialty,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Private,337505,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Japan,<=50K\n4,Private,258775,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,153152,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,3,2,United-States,<=50K\n0,?,120998,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,434102,11th,7,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,342768,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,160786,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,279440,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Self-emp-not-inc,67240,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,196963,Assoc-acdm,12,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,115239,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,133937,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,31714,Some-college,10,Divorced,Adm-clerical,Other-relative,White,Female,2,0,2,United-States,<=50K\n1,Private,347623,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,174848,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,106873,11th,7,Widowed,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,49687,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,3,2,United-States,<=50K\n2,Private,256294,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n4,State-gov,33155,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,131939,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,95256,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,198901,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,177287,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,144925,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Private,188243,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,>50K\n1,Self-emp-not-inc,198068,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,116960,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,172496,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Poland,<=50K\n0,?,399449,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,251990,HS-grad,9,Separated,Adm-clerical,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n3,Federal-gov,28683,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,109133,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,24504,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,201495,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Private,187634,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,77391,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,36601,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Canada,>50K\n1,Self-emp-not-inc,197193,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,184762,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,Greece,<=50K\n2,Private,200671,HS-grad,9,Divorced,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,186539,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,199816,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,171351,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,119793,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Portugal,>50K\n2,Local-gov,38876,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,145242,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,0,United-States,>50K\n0,?,292774,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,217251,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,195253,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,139854,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,State-gov,145072,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,108085,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,72055,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,82628,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,49156,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,187666,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,225456,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Federal-gov,286253,HS-grad,9,Married-spouse-absent,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,168548,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,190384,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Federal-gov,362835,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Germany,>50K\n3,Private,243322,HS-grad,9,Married-spouse-absent,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,49105,Assoc-voc,11,Separated,Adm-clerical,Own-child,White,Female,1,0,2,United-States,<=50K\n0,Private,72520,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,200352,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,146660,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,247328,Assoc-voc,11,Separated,Sales,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Private,304605,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Canada,>50K\n1,Private,309778,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,248476,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,129624,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Cambodia,<=50K\n1,Private,97723,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,143404,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,127264,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,179191,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,230824,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,410615,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,224998,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,3,2,United-States,>50K\n4,Self-emp-not-inc,54553,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,225165,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,75257,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,33155,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,450141,Some-college,10,Divorced,Protective-serv,Not-in-family,White,Male,0,1,2,United-States,<=50K\n1,Private,441949,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,Mexico,>50K\n0,Private,131341,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Cuba,<=50K\n0,Private,227548,12th,8,Married-civ-spouse,Other-service,Husband,Black,Male,1,0,2,United-States,<=50K\n2,Self-emp-inc,38876,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,143756,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,308275,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,3,0,4,United-States,>50K\n1,Private,173586,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,196074,9th,5,Never-married,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,178877,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,285580,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,219220,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,Germany,<=50K\n1,Federal-gov,228696,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,Mexico,<=50K\n2,Private,185405,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,240124,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,96130,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,2,England,<=50K\n1,Private,329172,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,147280,11th,7,Never-married,Other-service,Own-child,Other,Male,0,0,2,United-States,<=50K\n1,Private,197252,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,118544,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,183169,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,205810,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,Black,Female,0,2,2,United-States,<=50K\n0,Private,132556,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,438429,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,104293,Assoc-acdm,12,Never-married,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,506830,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,56072,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,164300,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,Puerto-Rico,<=50K\n1,Private,274577,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,36568,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,223548,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,Mexico,<=50K\n1,Local-gov,478277,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,254672,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Female,1,0,2,United-States,<=50K\n0,Private,171538,HS-grad,9,Divorced,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,?,220302,10th,6,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,87135,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,138626,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,179069,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,88824,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,159623,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n4,?,350525,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n3,Self-emp-not-inc,276369,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,96862,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n0,Private,245486,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Local-gov,209899,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,306122,Bachelors,13,Never-married,Other-service,Not-in-family,Black,Female,0,0,2,United-States,>50K\n1,Private,240763,11th,7,Divorced,Transport-moving,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,323069,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,179579,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,162034,Some-college,10,Divorced,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,291181,HS-grad,9,Never-married,Sales,Other-relative,White,Female,0,0,1,Mexico,<=50K\n1,Private,356823,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,4,0,2,United-States,>50K\n2,Private,312271,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,182714,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,184569,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,129762,HS-grad,9,Divorced,Other-service,Other-relative,White,Female,0,0,2,Scotland,<=50K\n0,Private,216867,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,155489,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,256179,Some-college,10,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,3,United-States,>50K\n4,Private,51063,10th,6,Divorced,Other-service,Not-in-family,Black,Male,0,0,4,United-States,<=50K\n2,State-gov,164898,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,202206,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,Puerto-Rico,<=50K\n3,Private,115358,7th-8th,4,Married-civ-spouse,Priv-house-serv,Wife,Black,Female,0,0,0,United-States,<=50K\n2,Local-gov,343068,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Private,152908,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,217802,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Black,Male,3,0,2,United-States,>50K\n4,Self-emp-not-inc,380498,Bachelors,13,Widowed,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,257124,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,313635,Prof-school,15,Separated,Prof-specialty,Not-in-family,Black,Male,2,0,2,United-States,<=50K\n1,Private,168906,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,99146,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,190325,11th,7,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,272715,10th,6,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,118598,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,97213,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,39388,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,190916,11th,7,Divorced,Sales,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Private,61308,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,27856,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,State-gov,151580,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,248011,11th,7,Divorced,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,188615,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,62932,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,32510,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n4,?,155977,Some-college,10,Widowed,?,Unmarried,Black,Female,0,0,3,United-States,<=50K\n4,Federal-gov,250873,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,257942,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,334141,7th-8th,4,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,?,144210,11th,7,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,87535,Doctorate,16,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,222011,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,33895,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,168997,Some-college,10,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,163576,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n4,Private,98035,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n0,?,41356,Assoc-voc,11,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,245361,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,109133,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,?,<=50K\n4,?,111563,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,124256,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,1,United-States,<=50K\n0,?,227521,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,197060,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,332125,HS-grad,9,Never-married,Machine-op-inspct,Other-relative,White,Male,1,0,1,United-States,<=50K\n0,Private,348867,HS-grad,9,Never-married,Sales,Other-relative,Black,Female,0,0,0,United-States,<=50K\n1,Self-emp-inc,118584,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,110622,Bachelors,13,Divorced,Sales,Unmarried,Asian-Pac-Islander,Female,0,0,0,South,<=50K\n0,Private,43535,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,84784,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,2,2,United-States,<=50K\n0,Private,266600,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,1,0,2,United-States,<=50K\n1,Private,106935,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,265518,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,289653,Assoc-voc,11,Widowed,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,340917,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,3,?,>50K\n2,Private,56651,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,374833,1st-4th,2,Married-spouse-absent,Farming-fishing,Unmarried,White,Male,0,0,2,Mexico,<=50K\n2,Private,210198,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,240448,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,206869,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,149689,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,75594,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,110331,Prof-school,15,Married-civ-spouse,Other-service,Wife,White,Female,0,0,3,United-States,>50K\n3,Private,353787,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,142909,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,54102,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,339116,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,?,<=50K\n4,?,50783,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,415500,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,79586,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,China,>50K\n3,Private,142757,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,United-States,>50K\n2,Private,91716,HS-grad,9,Divorced,Sales,Unmarried,White,Male,0,0,4,United-States,<=50K\n0,Private,376393,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n4,Private,129762,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,Scotland,<=50K\n1,Private,293017,Some-college,10,Never-married,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,54583,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,54472,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,129767,Assoc-acdm,12,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,109217,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,189265,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,221680,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,200947,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,Italy,<=50K\n0,Private,402136,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,119411,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,?,<=50K\n3,Self-emp-not-inc,136258,Some-college,10,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,35459,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,157640,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,181384,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,81253,HS-grad,9,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,1,United-States,<=50K\n0,Private,152668,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,?,387063,10th,6,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,?,132256,Bachelors,13,Widowed,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,106964,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n0,?,214238,HS-grad,9,Married-spouse-absent,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,218068,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,162572,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,160009,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,164488,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,209794,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,119033,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,3,United-States,<=50K\n1,Private,311446,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,Germany,<=50K\n1,Private,128065,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,101016,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,33493,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,130078,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,3,0,1,?,>50K\n1,Private,284826,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,169112,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Hungary,>50K\n2,Federal-gov,362006,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,124906,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,2,1,United-States,<=50K\n3,Private,229247,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,2,United-States,<=50K\n4,Self-emp-inc,170993,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Private,157641,Bachelors,13,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,224632,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,159732,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,4,2,United-States,<=50K\n4,Self-emp-not-inc,221801,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,347074,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,3,0,United-States,<=50K\n1,Private,143059,HS-grad,9,Married-civ-spouse,Transport-moving,Wife,White,Female,0,3,1,United-States,>50K\n0,Private,37072,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,137815,9th,5,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,594194,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,284716,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,202662,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,191754,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,175985,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,108196,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,?,<=50K\n2,Private,51198,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,384276,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,368355,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,221661,10th,6,Never-married,Sales,Not-in-family,White,Female,0,0,1,Mexico,<=50K\n3,Private,108435,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n4,?,176827,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,209547,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,197565,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,3,1,United-States,>50K\n4,Private,180418,12th,8,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,45880,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,203953,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Puerto-Rico,>50K\n4,Self-emp-not-inc,178748,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Private,203171,Assoc-voc,11,Never-married,Tech-support,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,132057,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Black,Male,3,0,3,United-States,>50K\n1,Local-gov,40142,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,224541,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,El-Salvador,<=50K\n4,Self-emp-not-inc,221252,Masters,14,Divorced,Sales,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n1,Private,133766,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,244945,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,171393,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,326903,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,91257,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,El-Salvador,<=50K\n2,Private,118001,11th,7,Never-married,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,168906,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,267912,10th,6,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,Mexico,<=50K\n3,Private,327406,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,4,United-States,>50K\n0,Private,141876,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,185177,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,191807,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,114942,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,204470,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,195844,Doctorate,16,Never-married,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n2,Private,184659,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,224466,Some-college,10,Divorced,Craft-repair,Unmarried,Black,Male,0,0,1,United-States,<=50K\n3,Local-gov,149551,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,2,0,3,United-States,<=50K\n3,Private,113522,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,116993,Prof-school,15,Widowed,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,>50K\n2,Private,277434,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,167948,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,South,>50K\n4,Self-emp-inc,273239,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,322789,10th,6,Married-civ-spouse,Protective-serv,Husband,White,Male,0,1,2,United-States,<=50K\n0,Local-gov,240517,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,230112,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Local-gov,211385,11th,7,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n1,Private,109282,7th-8th,4,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,367904,10th,6,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,Mexico,<=50K\n2,Private,34278,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,221281,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,179671,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,106721,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,<=50K\n1,Private,152951,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,315203,7th-8th,4,Never-married,Other-service,Not-in-family,White,Female,0,0,1,Dominican-Republic,<=50K\n2,Private,117728,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,192017,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,186575,11th,7,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,120837,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,289430,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,Mexico,<=50K\n2,Private,304175,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Local-gov,251841,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,33611,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,38122,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,72143,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,385183,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,37345,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,290964,Assoc-voc,11,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,52839,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,134737,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,1,0,4,United-States,<=50K\n0,Private,55465,10th,6,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,377401,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,323497,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,334693,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-inc,163215,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n3,Private,178530,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,368256,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,191137,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,212513,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,147653,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,173307,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,212760,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,101238,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,306868,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,117509,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,151835,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,291998,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,4,United-States,>50K\n2,Private,136986,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,201984,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,187060,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,Canada,<=50K\n3,Private,174928,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n1,Private,445480,12th,8,Married-civ-spouse,Machine-op-inspct,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,265781,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,377107,Some-college,10,Separated,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n2,Private,347890,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,199336,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,111593,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,258657,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,39207,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Private,159770,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,309463,9th,5,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Federal-gov,215419,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,Canada,<=50K\n3,Self-emp-not-inc,177533,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,251239,9th,5,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n2,Federal-gov,134307,Bachelors,13,Divorced,Prof-specialty,Not-in-family,Black,Female,0,2,2,United-States,<=50K\n0,Private,24598,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,140676,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,143542,11th,7,Widowed,Machine-op-inspct,Other-relative,White,Male,0,0,0,United-States,<=50K\n4,?,38189,HS-grad,9,Never-married,?,Not-in-family,Black,Male,1,0,2,United-States,<=50K\n1,Private,158291,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,118503,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,71609,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,203653,Bachelors,13,Divorced,Sales,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,181751,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,162358,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,<=50K\n4,?,231315,Assoc-acdm,12,Widowed,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Federal-gov,181940,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,213226,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,452963,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,268039,Some-college,10,Divorced,Handlers-cleaners,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,141841,HS-grad,9,Separated,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,194733,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,214008,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,?,147755,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,1,?,<=50K\n2,State-gov,273869,HS-grad,9,Divorced,Protective-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,160261,Some-college,10,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Male,0,0,4,?,<=50K\n0,Private,48029,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,163460,Some-college,10,Never-married,Sales,Own-child,Black,Male,0,0,0,United-States,<=50K\n3,Private,112529,5th-6th,3,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,109075,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,2,0,3,United-States,<=50K\n1,Private,182699,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,101867,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n4,Local-gov,382245,HS-grad,9,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,200290,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n0,State-gov,35805,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,157541,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n4,Local-gov,192085,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,33795,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,345459,Some-college,10,Never-married,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,105520,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Local-gov,114752,Bachelors,13,Widowed,Adm-clerical,Unmarried,Asian-Pac-Islander,Female,0,0,1,Philippines,<=50K\n0,Private,98572,11th,7,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,83984,Masters,14,Divorced,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,317846,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,State-gov,319027,HS-grad,9,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Private,84319,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,298470,Bachelors,13,Widowed,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,278322,Doctorate,16,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,169182,9th,5,Widowed,Other-service,Not-in-family,White,Female,0,0,2,Cuba,<=50K\n4,Private,498267,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,94314,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,1,0,0,United-States,<=50K\n1,Private,50053,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,107302,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,110009,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,?,<=50K\n2,Private,154174,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,147110,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,141122,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,162164,11th,7,Widowed,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,?,168095,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,134664,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n4,Private,95644,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,198183,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,538583,11th,7,Separated,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,?,308499,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,108837,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,27227,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,3,1,United-States,>50K\n2,Federal-gov,117022,Assoc-voc,11,Divorced,Handlers-cleaners,Unmarried,Black,Male,0,2,2,United-States,<=50K\n4,Private,133884,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,602513,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,114495,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n2,Private,171087,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,53373,10th,6,Never-married,Other-service,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,323810,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,?,<=50K\n3,Private,200325,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,322092,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,209739,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,589809,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,105838,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,119522,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,258579,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,123200,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,4,2,Puerto-Rico,>50K\n1,Private,275771,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Local-gov,33386,Some-college,10,Widowed,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,101016,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,2,United-States,>50K\n4,Private,217434,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,187229,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,207772,HS-grad,9,Divorced,Tech-support,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,179717,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,260978,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,Philippines,<=50K\n2,Private,280570,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,179001,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,State-gov,79089,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,85420,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,United-States,<=50K\n0,Local-gov,244074,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,259087,11th,7,Widowed,Craft-repair,Unmarried,White,Female,0,0,2,?,<=50K\n0,Private,361138,HS-grad,9,Never-married,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,309311,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,46756,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,272339,HS-grad,9,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,82521,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,103759,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,150675,10th,6,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,180096,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,157991,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,164170,Some-college,10,Never-married,Sales,Other-relative,Asian-Pac-Islander,Female,0,0,1,Philippines,<=50K\n0,Private,186946,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,201159,Assoc-acdm,12,Widowed,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,61343,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,130534,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,222635,11th,7,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,169768,Bachelors,13,Separated,Tech-support,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,72922,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,66440,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,338836,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n3,Local-gov,122206,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,145704,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,State-gov,88215,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n0,Private,114873,HS-grad,9,Never-married,Protective-serv,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n0,Private,240063,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,167015,Bachelors,13,Widowed,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,354230,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,124827,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,225739,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,188102,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n3,Local-gov,349986,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,112763,HS-grad,9,Divorced,Handlers-cleaners,Own-child,White,Female,1,0,2,United-States,<=50K\n4,Private,242589,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,366929,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,154210,11th,7,Married-spouse-absent,Sales,Own-child,Asian-Pac-Islander,Male,0,0,1,India,<=50K\n1,Private,274222,1st-4th,2,Never-married,Transport-moving,Other-relative,Other,Male,0,0,2,El-Salvador,<=50K\n3,State-gov,166459,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,36222,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,240063,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Federal-gov,158847,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,203488,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,96062,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Portugal,<=50K\n3,Self-emp-not-inc,126077,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,162580,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,413699,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,303692,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,184682,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,217801,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,306496,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,110171,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Italy,<=50K\n2,State-gov,89625,HS-grad,9,Never-married,Protective-serv,Other-relative,Asian-Pac-Islander,Female,0,0,2,United-States,<=50K\n0,?,234108,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,270147,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Self-emp-not-inc,195891,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,Iran,<=50K\n3,Private,131160,Bachelors,13,Divorced,Other-service,Not-in-family,White,Female,4,0,2,United-States,>50K\n4,Private,93211,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,2,Canada,<=50K\n2,Private,181661,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,146365,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,219671,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Private,203523,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,1,0,0,United-States,<=50K\n0,?,268145,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,137421,HS-grad,9,Married-spouse-absent,Other-service,Other-relative,Other,Male,0,0,2,Mexico,<=50K\n1,Private,302679,12th,8,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,421474,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,98524,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,Canada,>50K\n1,Private,282313,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,157786,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,83508,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,0,United-States,<=50K\n4,State-gov,167687,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,1,United-States,<=50K\n2,Self-emp-not-inc,187272,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,South,<=50K\n2,Federal-gov,187089,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,167625,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n4,Private,190955,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,185846,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,55764,Some-college,10,Never-married,Handlers-cleaners,Unmarried,Black,Male,0,0,2,United-States,<=50K\n4,Private,164110,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,117444,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,164593,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,?,<=50K\n2,Private,22610,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,303942,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,378126,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,3,2,United-States,<=50K\n2,Self-emp-inc,231491,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,69481,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,199018,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,255252,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,193871,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,23892,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,201156,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,3,United-States,>50K\n1,Private,171468,Some-college,10,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,255454,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,2,0,1,Haiti,>50K\n1,Private,207258,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,134440,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,107737,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,193190,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,114774,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,4,3,United-States,<=50K\n0,Private,507492,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,Guatemala,<=50K\n0,Private,209955,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,Canada,<=50K\n2,Private,298635,Bachelors,13,Married-civ-spouse,Other-service,Husband,Other,Male,0,0,2,Iran,>50K\n3,Private,175600,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,294592,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,40955,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,268996,Assoc-voc,11,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,175323,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,125010,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,?,201871,12th,8,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,203171,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,53774,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,0,United-States,<=50K\n1,Private,247867,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,126135,Some-college,10,Never-married,Farming-fishing,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,82067,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,224559,10th,6,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,127675,Masters,14,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,101825,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n3,Self-emp-not-inc,259412,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,166977,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,2,United-States,>50K\n4,Private,546118,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,110318,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,175856,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,156763,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,213897,Masters,14,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,3,2,Hong,>50K\n0,Private,44493,Assoc-voc,11,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,201156,11th,7,Never-married,Craft-repair,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,375703,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,293594,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,4,2,Puerto-Rico,<=50K\n2,Local-gov,183850,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,84433,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,?,296485,Assoc-voc,11,Separated,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,214026,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,104235,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,1,United-States,<=50K\n2,Private,212894,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,446647,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,530099,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,42850,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,?,<=50K\n2,Private,120277,Some-college,10,Divorced,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Private,146554,HS-grad,9,Separated,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n0,Private,44793,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,182907,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,341797,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Canada,>50K\n1,Private,226441,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,48988,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,252646,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,?,214695,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,189194,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,68021,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,117369,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,340094,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,161113,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,279243,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,110669,10th,6,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,121470,12th,8,Never-married,Transport-moving,Own-child,White,Male,0,0,0,?,<=50K\n2,Private,453686,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,281219,Assoc-voc,11,Divorced,Sales,Unmarried,White,Female,0,1,2,United-States,<=50K\n1,Private,235738,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,272167,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,84737,Bachelors,13,Widowed,?,Other-relative,Asian-Pac-Islander,Male,0,0,1,China,<=50K\n4,Private,176965,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,101701,Bachelors,13,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,0,United-States,<=50K\n1,Private,22405,HS-grad,9,Separated,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,4,United-States,<=50K\n3,Private,98815,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,195897,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,96718,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,126361,11th,7,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,0,United-States,>50K\n1,State-gov,56365,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,0,China,<=50K\n1,Federal-gov,344073,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Male,0,1,3,United-States,<=50K\n1,Private,306388,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,143604,Some-college,10,Never-married,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,174592,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,48553,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,358355,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,2,Mexico,<=50K\n3,Private,443377,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,229656,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,1,2,United-States,>50K\n2,Private,115516,Masters,14,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n4,Private,189665,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n3,Self-emp-not-inc,105010,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Private,320510,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,175943,HS-grad,9,Married-civ-spouse,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,89172,Masters,14,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,281627,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,1,1,United-States,>50K\n0,State-gov,1117718,Some-college,10,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Private,108293,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,152035,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,38389,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,213902,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Local-gov,192812,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,301911,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,3,Japan,>50K\n3,Private,89041,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,96483,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Other-relative,Asian-Pac-Islander,Female,2,0,2,United-States,>50K\n0,Private,209970,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,110622,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,State-gov,250807,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,State-gov,391257,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,135521,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n0,?,108670,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,179533,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,250117,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,3,United-States,>50K\n1,State-gov,101562,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,223275,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,126060,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,State-gov,168035,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,175382,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,170540,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,243240,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,381769,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,104545,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,74040,Bachelors,13,Divorced,Sales,Not-in-family,Asian-Pac-Islander,Female,0,0,1,South,<=50K\n2,Federal-gov,275366,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,194360,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,State-gov,334693,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,146764,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,184975,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,336508,11th,7,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,427248,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,186489,11th,7,Married-civ-spouse,?,Husband,White,Male,0,4,2,United-States,<=50K\n1,Private,258364,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,214215,11th,7,Married-civ-spouse,Other-service,Other-relative,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,49448,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,261198,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,270535,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n1,Self-emp-not-inc,218993,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,155489,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,?,151866,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,98828,HS-grad,9,Widowed,Prof-specialty,Unmarried,Other,Female,0,0,1,Puerto-Rico,<=50K\n0,Private,233495,9th,5,Divorced,Priv-house-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,182203,Some-college,10,Divorced,Machine-op-inspct,Unmarried,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Private,33394,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n0,?,171583,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,80411,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,161295,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,>50K\n3,?,178341,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,311523,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Other,Male,0,0,2,Iran,<=50K\n0,Private,315130,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,67311,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,Canada,<=50K\n3,Private,44907,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,104849,Masters,14,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,China,>50K\n1,Private,225768,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,186666,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,69510,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,171242,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,197420,HS-grad,9,Never-married,Priv-house-serv,Unmarried,White,Female,0,0,2,Mexico,<=50K\n3,Private,224087,10th,6,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,140141,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,175943,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,318259,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,190027,HS-grad,9,Separated,Tech-support,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,233838,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n3,?,117847,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,49092,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,171524,10th,6,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Columbia,<=50K\n3,Private,237868,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n3,Self-emp-not-inc,34067,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,25005,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,177437,Bachelors,13,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,185267,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,104397,HS-grad,9,Married-civ-spouse,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,33331,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,29128,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,State-gov,328228,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,227411,Assoc-voc,11,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,169117,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,238267,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,118551,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,47541,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,92374,Some-college,10,Never-married,Exec-managerial,Not-in-family,Other,Male,4,0,3,India,>50K\n4,Local-gov,224598,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,1,?,<=50K\n1,Private,131568,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,183319,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,309932,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,197311,HS-grad,9,Divorced,Priv-house-serv,Unmarried,White,Female,0,0,4,United-States,<=50K\n1,Private,292120,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,117310,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,308647,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,135785,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,179008,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,205188,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,193945,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,State-gov,258589,Masters,14,Never-married,Craft-repair,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,State-gov,107142,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,23157,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,47203,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,4,United-States,<=50K\n1,Private,279923,Some-college,10,Never-married,Sales,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,192386,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,2,0,2,United-States,<=50K\n0,Private,188569,Masters,14,Never-married,Exec-managerial,Own-child,White,Female,2,0,3,United-States,>50K\n2,Private,68748,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n3,Private,239155,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,165346,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,4,United-States,<=50K\n0,Private,392082,Some-college,10,Never-married,Adm-clerical,Own-child,Other,Male,0,0,2,United-States,<=50K\n2,Private,379522,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,Germany,<=50K\n1,Private,109917,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,109097,11th,7,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,State-gov,202765,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,125892,Masters,14,Married-civ-spouse,Exec-managerial,Other-relative,White,Male,4,0,2,United-States,>50K\n1,Private,119411,HS-grad,9,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,88231,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,188561,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,191681,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,36999,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n4,Private,161662,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Local-gov,111994,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,247711,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,4,3,United-States,<=50K\n2,Private,271282,11th,7,Divorced,Protective-serv,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,314739,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,195148,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,358121,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,101266,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,278391,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,206751,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,161147,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,1,0,2,United-States,<=50K\n3,Private,301431,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,347000,12th,8,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n2,State-gov,25806,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,China,<=50K\n0,Private,181557,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,20057,Some-college,10,Never-married,Protective-serv,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,Private,190107,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,30269,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,Private,117767,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,406734,Masters,14,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,236696,Assoc-acdm,12,Never-married,Craft-repair,Own-child,White,Male,0,0,0,Iran,<=50K\n0,Private,354691,12th,8,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,199720,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Self-emp-not-inc,31606,Bachelors,13,Married-civ-spouse,Prof-specialty,Other-relative,White,Female,0,0,0,United-States,>50K\n2,Federal-gov,133973,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,323639,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,225724,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,144391,Masters,14,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,34173,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,219074,Some-college,10,Divorced,Adm-clerical,Not-in-family,Black,Female,0,0,3,United-States,>50K\n0,Private,379525,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,2,0,1,United-States,<=50K\n0,Local-gov,287160,11th,7,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,130386,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,409815,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,212143,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n1,?,33404,HS-grad,9,Divorced,?,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,235567,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,306868,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,353550,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,107302,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n4,Self-emp-not-inc,169435,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,105817,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,68879,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,206288,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,1,Vietnam,<=50K\n1,Private,187936,10th,6,Never-married,Craft-repair,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n2,Private,226081,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,414448,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,?,<=50K\n1,Local-gov,79190,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,<=50K\n2,Private,34996,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,318415,Some-college,10,Divorced,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,?,214800,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,148334,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,160120,Doctorate,16,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,>50K\n4,Self-emp-not-inc,285692,Masters,14,Married-spouse-absent,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,461725,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,104329,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,37284,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,154374,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,209650,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,227529,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,249382,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,305781,11th,7,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,147989,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,207604,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,367260,Doctorate,16,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,147523,9th,5,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,2,El-Salvador,<=50K\n3,Self-emp-not-inc,193116,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,Mexico,<=50K\n0,Private,50119,10th,6,Never-married,Other-service,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n3,Private,262579,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,244910,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,120902,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,217241,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,65098,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,364491,11th,7,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,209739,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,72338,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n3,Private,215895,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,32289,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,209464,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,98170,7th-8th,4,Widowed,Other-service,Not-in-family,White,Female,1,0,0,United-States,<=50K\n2,Private,271665,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,124111,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,121944,7th-8th,4,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,121424,Bachelors,13,Separated,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,State-gov,33795,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,150429,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,3,United-States,>50K\n4,Private,124771,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,204160,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,243616,5th-6th,3,Separated,Craft-repair,Unmarried,Black,Female,2,0,2,United-States,<=50K\n2,Private,168556,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,186224,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,187332,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,113433,Some-college,10,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,268598,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,137733,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,210318,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,106662,Bachelors,13,Separated,Sales,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,162667,HS-grad,9,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,2,Ecuador,<=50K\n0,Private,187577,Assoc-acdm,12,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,89495,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,1,0,0,United-States,<=50K\n2,Local-gov,247082,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,157059,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,282643,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,69249,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,131766,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Female,1,0,2,United-States,<=50K\n0,Private,163665,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,165097,Some-college,10,Never-married,Exec-managerial,Other-relative,White,Male,0,3,2,United-States,<=50K\n1,Private,194668,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,116372,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,113635,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,162994,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,266803,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,3,Canada,>50K\n0,Private,230482,9th,5,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,299831,Assoc-voc,11,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,172830,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,144084,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,295737,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,207685,Bachelors,13,Separated,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,161423,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-not-inc,122109,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Female,0,0,4,United-States,<=50K\n2,Private,215892,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,4,3,United-States,>50K\n2,Private,176517,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,220821,7th-8th,4,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n4,Local-gov,249043,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,Cuba,<=50K\n0,Private,58949,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,158438,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,154950,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,200445,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n4,?,224357,Some-college,10,Married-civ-spouse,?,Husband,White,Male,1,0,0,United-States,<=50K\n1,Federal-gov,103651,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n0,?,316524,Bachelors,13,Never-married,?,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,200046,Bachelors,13,Separated,Sales,Unmarried,White,Male,0,4,2,United-States,>50K\n1,Private,193285,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,146015,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,195096,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,221078,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,186375,Assoc-voc,11,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,44983,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,29770,Some-college,10,Widowed,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,State-gov,266565,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,State-gov,235431,HS-grad,9,Separated,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,153788,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,4,0,2,United-States,>50K\n3,Private,280030,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,158352,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Local-gov,109972,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,278940,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,174395,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,141592,10th,6,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,295525,Some-college,10,Divorced,Protective-serv,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,121321,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,198955,9th,5,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n1,?,105189,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,Germany,<=50K\n2,Private,186191,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,208967,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,159399,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,?,169600,Some-college,10,Married-spouse-absent,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,262656,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,284629,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,182189,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,4,United-States,>50K\n3,Federal-gov,38819,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,191873,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,125082,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,67791,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,334744,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,198841,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,218490,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,188386,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,95661,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,Germany,<=50K\n3,Self-emp-not-inc,79011,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,Asian-Pac-Islander,Male,0,0,4,United-States,<=50K\n4,Self-emp-not-inc,103368,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,119176,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,90928,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,218009,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,460259,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Local-gov,405284,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n3,Self-emp-not-inc,26502,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,157569,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,252168,Some-college,10,Never-married,Other-service,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n1,Federal-gov,269353,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,Other,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,52822,Bachelors,13,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,?,365996,HS-grad,9,Widowed,?,Unmarried,White,Female,2,0,2,United-States,>50K\n0,State-gov,147548,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,216411,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,Puerto-Rico,>50K\n2,State-gov,122493,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,41680,Some-college,10,Divorced,Tech-support,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,100818,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,2,0,2,United-States,<=50K\n2,Private,30056,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-inc,83141,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,178768,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,376957,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,0,United-States,<=50K\n1,Private,194740,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,160728,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,198170,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,200967,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,State-gov,116493,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n4,?,117349,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,188615,Some-college,10,Separated,Prof-specialty,Not-in-family,White,Male,0,4,3,Canada,>50K\n3,Private,849067,12th,8,Divorced,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,193459,Assoc-acdm,12,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,Outlying-US(Guam-USVI-etc),<=50K\n3,State-gov,177487,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,151971,Some-college,10,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-inc,169152,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Female,4,0,4,Greece,>50K\n4,Private,108929,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,290861,11th,7,Married-spouse-absent,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,168826,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,216414,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,324053,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,197399,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,138069,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,184553,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,328734,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,336167,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Private,195748,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,590941,Doctorate,16,Never-married,Prof-specialty,Unmarried,White,Female,0,1,2,United-States,<=50K\n2,Private,211580,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,?,73117,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,166863,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,141350,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,133937,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,282192,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Private,237582,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,262758,Assoc-acdm,12,Never-married,Other-service,Unmarried,Black,Male,0,1,3,United-States,<=50K\n3,Self-emp-inc,188694,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,104196,12th,8,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,172232,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,103432,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,183544,9th,5,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,276289,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,3,?,<=50K\n4,Private,111209,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,176862,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,201229,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,186689,HS-grad,9,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,3,United-States,<=50K\n1,Private,177675,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,337505,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,>50K\n3,Private,156148,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,209642,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n4,Private,159474,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,75073,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,121362,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,2,United-States,>50K\n0,Private,321369,10th,6,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,?,49665,HS-grad,9,Divorced,?,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,219155,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,329144,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n2,Local-gov,161475,HS-grad,9,Married-civ-spouse,Protective-serv,Wife,Black,Female,0,0,4,United-States,<=50K\n4,Self-emp-inc,99554,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,277488,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,286352,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,109457,10th,6,Married-civ-spouse,Craft-repair,Other-relative,White,Male,0,0,3,United-States,<=50K\n1,Private,236304,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,>50K\n1,Private,399601,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,396758,Some-college,10,Divorced,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,21798,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,?,278130,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,192173,9th,5,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,43546,Some-college,10,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,87546,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,135891,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n1,Private,312923,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,33432,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,270379,HS-grad,9,Never-married,Tech-support,Other-relative,Black,Female,0,0,1,United-States,<=50K\n0,Private,134829,11th,7,Never-married,Other-service,Own-child,White,Male,1,0,0,United-States,<=50K\n2,Federal-gov,155106,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n0,?,145989,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,270221,Some-college,10,Divorced,Adm-clerical,Own-child,White,Male,0,0,2,United-States,>50K\n0,Private,123226,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,154641,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,145570,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,229983,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,140892,Masters,14,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,278303,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,127139,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,360689,11th,7,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,19513,HS-grad,9,Never-married,Sales,Own-child,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Private,50490,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,34067,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,359808,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,>50K\n1,Private,105422,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,28171,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Male,2,0,2,United-States,>50K\n4,State-gov,49230,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,165107,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,112706,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,28297,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,104748,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,129137,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,1,0,1,United-States,<=50K\n1,Private,298871,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,2,2,Iran,>50K\n1,Local-gov,229716,Some-college,10,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,113752,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,198739,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,277256,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,114937,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,206206,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,197861,12th,8,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,38925,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,34309,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,219122,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,Italy,<=50K\n2,Self-emp-not-inc,51494,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,173422,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,116773,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,?,<=50K\n1,Private,252340,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,213799,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,110100,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,146325,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,383352,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,369258,Bachelors,13,Never-married,Handlers-cleaners,Other-relative,White,Female,0,0,2,Mexico,<=50K\n3,Private,239865,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,200783,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,243580,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,132563,Prof-school,15,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n2,Self-emp-not-inc,390369,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,403788,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,State-gov,68346,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,?,<=50K\n4,Private,136413,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,208791,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,572285,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,90992,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,156056,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,194102,Some-college,10,Never-married,Prof-specialty,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,Private,149102,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,40151,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,356934,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n1,Federal-gov,72514,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,4,United-States,<=50K\n3,Local-gov,174126,HS-grad,9,Widowed,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,324386,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,159544,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,114691,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,222829,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,298699,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,216845,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,Mexico,<=50K\n2,Private,321824,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,97541,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,329425,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,148287,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,251346,9th,5,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,Puerto-Rico,<=50K\n1,Private,143766,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,49647,Assoc-voc,11,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,233363,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,180427,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,30111,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,360527,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,135803,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n4,Federal-gov,608441,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,720428,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,269784,10th,6,Separated,Handlers-cleaners,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,423311,HS-grad,9,Married-AF-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,343591,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,37088,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,154430,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,113588,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,2,1,United-States,<=50K\n3,Private,190072,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,272132,Prof-school,15,Married-spouse-absent,Prof-specialty,Not-in-family,White,Female,0,0,4,?,<=50K\n2,Federal-gov,32000,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-inc,164190,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,4,2,United-States,<=50K\n0,Private,233781,11th,7,Never-married,Other-service,Own-child,Black,Male,0,0,0,United-States,<=50K\n0,Private,401762,11th,7,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,169186,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,164309,11th,7,Separated,Exec-managerial,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,170800,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,37232,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,373403,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,192014,Bachelors,13,Separated,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,172034,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,322770,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,Black,Male,0,2,2,Jamaica,>50K\n2,Private,269168,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,>50K\n1,Private,302570,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,103710,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,113364,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,121966,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,416745,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n1,Local-gov,154548,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,4,United-States,<=50K\n2,Private,188794,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,156266,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,?,270599,1st-4th,2,Widowed,?,Not-in-family,White,Female,0,0,0,Mexico,<=50K\n2,Private,19914,Some-college,10,Never-married,Adm-clerical,Own-child,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,246439,Assoc-acdm,12,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,101833,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,177695,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n0,Private,167868,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,82561,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,31848,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,?,117310,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,355918,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,49115,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,102875,11th,7,Married-civ-spouse,Handlers-cleaners,Own-child,Black,Male,0,0,0,United-States,<=50K\n4,?,194456,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,284648,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,4,United-States,>50K\n3,Self-emp-not-inc,73134,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,172600,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,4,0,3,United-States,>50K\n4,?,244856,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,184303,7th-8th,4,Never-married,Priv-house-serv,Other-relative,White,Female,0,0,2,Guatemala,<=50K\n0,State-gov,154610,Bachelors,13,Married-spouse-absent,Handlers-cleaners,Not-in-family,White,Female,0,2,0,United-States,<=50K\n1,Private,260560,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,360770,1st-4th,2,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,Dominican-Republic,<=50K\n0,Private,315877,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,128258,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,179336,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,168165,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,109015,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,2,2,United-States,<=50K\n0,Private,89154,1st-4th,2,Never-married,Other-service,Other-relative,White,Male,0,0,2,El-Salvador,<=50K\n1,?,260954,7th-8th,4,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,85399,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,240841,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,119742,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,30656,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,263561,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,108838,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,259351,HS-grad,9,Never-married,Other-service,Other-relative,Amer-Indian-Eskimo,Male,0,0,2,Mexico,<=50K\n2,Private,159449,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,195322,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,179481,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,195388,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,123429,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,116580,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,270043,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,232586,Bachelors,13,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,4,United-States,<=50K\n3,Private,164519,HS-grad,9,Widowed,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,141118,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,177955,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,2,Mexico,<=50K\n2,Local-gov,149337,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Italy,>50K\n2,Private,68896,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,?,<=50K\n1,Private,22422,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,215453,1st-4th,2,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Local-gov,170772,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,36011,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,<=50K\n1,Private,133839,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,50567,Some-college,10,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,203233,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,4,United-States,>50K\n3,Private,187510,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Federal-gov,290625,Some-college,10,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,127573,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,50316,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,169473,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,25894,Doctorate,16,Divorced,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,India,>50K\n2,Private,106900,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,157473,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,122612,Masters,14,Married-civ-spouse,Sales,Wife,Asian-Pac-Islander,Female,0,0,1,Japan,>50K\n0,Private,132187,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,52151,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,212705,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,436361,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,3,0,United-States,>50K\n2,Private,184456,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,3,Greece,<=50K\n4,Local-gov,142297,10th,6,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n4,Federal-gov,54701,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,245211,Masters,14,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Poland,>50K\n3,Private,98975,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,463601,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Black,Male,0,0,2,United-States,<=50K\n1,Private,297991,Bachelors,13,Married-civ-spouse,Sales,Not-in-family,Asian-Pac-Islander,Female,0,3,4,Cambodia,>50K\n2,Private,196554,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,113511,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,152710,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,147171,Some-college,10,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,2,Trinadad&Tobago,<=50K\n3,Private,52724,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,177482,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n4,Private,219537,7th-8th,4,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,350106,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,197947,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n0,Private,253583,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,58751,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,206889,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,151399,12th,8,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Local-gov,50563,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,United-States,>50K\n1,Private,63861,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,165517,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n4,?,43103,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,123983,Some-college,10,Never-married,?,Own-child,Other,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,32172,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,192644,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,230919,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,115772,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,Scotland,<=50K\n4,Self-emp-not-inc,51687,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,191803,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,170721,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,?,132053,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,170842,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,51973,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,?,72621,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,State-gov,205895,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,99185,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,Greece,<=50K\n1,Private,149726,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,372525,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,165107,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,273767,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,227266,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,89334,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,199202,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,326587,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,144351,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n4,Federal-gov,119254,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,193881,Masters,14,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,271000,HS-grad,9,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,?,74685,10th,6,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,123291,10th,6,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,557359,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,197403,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,184245,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,Columbia,<=50K\n0,Private,92898,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,105788,5th-6th,3,Separated,Other-service,Unmarried,Black,Female,0,0,2,Scotland,<=50K\n0,?,205940,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,212120,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,351772,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,309582,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,2,0,3,United-States,>50K\n1,Private,244650,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,2,1,United-States,<=50K\n4,Self-emp-not-inc,290670,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,167877,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,454024,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,Black,Female,0,0,1,United-States,<=50K\n1,Private,125531,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,220603,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n4,Private,180645,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,98725,10th,6,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,431515,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,122612,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n0,Federal-gov,190290,HS-grad,9,Never-married,Armed-Forces,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,174839,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,1,United-States,>50K\n3,Federal-gov,83610,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,273534,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,383885,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,<=50K\n0,Private,188618,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,95653,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,204908,11th,7,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,221172,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,97723,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,200401,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,Columbia,<=50K\n2,State-gov,205153,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,170174,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,26947,Bachelors,13,Widowed,Prof-specialty,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,?,154938,11th,7,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,125832,9th,5,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Self-emp-not-inc,150361,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,113183,Masters,14,Divorced,Prof-specialty,Not-in-family,Other,Female,0,0,2,United-States,<=50K\n0,Private,39551,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,62854,11th,7,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,?,31285,7th-8th,4,Separated,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,199217,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n3,Local-gov,40690,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,315671,7th-8th,4,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,180339,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,119545,Bachelors,13,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,195508,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,364657,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,168598,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,>50K\n1,Private,178683,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,123116,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,<=50K\n1,Private,251117,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n1,Local-gov,42893,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,2,0,2,United-States,<=50K\n0,Private,386492,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,249869,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,116219,Some-college,10,Divorced,Other-service,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,168981,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n4,?,137894,Bachelors,13,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,>50K\n0,State-gov,139091,Some-college,10,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n0,Private,219199,11th,7,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,191455,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,325374,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,2,United-States,>50K\n4,?,180425,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,149871,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,342730,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,252392,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,193864,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,139364,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,1,United-States,>50K\n0,Private,253612,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,287168,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,364952,10th,6,Married-spouse-absent,Other-service,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,82797,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,100135,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,287831,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,3,United-States,>50K\n2,Self-emp-not-inc,140108,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,1,United-States,<=50K\n0,Private,180917,HS-grad,9,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,29036,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,221581,HS-grad,9,Married-spouse-absent,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,382882,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,237091,HS-grad,9,Married-civ-spouse,Priv-house-serv,Other-relative,White,Female,0,0,0,Columbia,<=50K\n0,Private,134252,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,269354,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,226922,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Self-emp-not-inc,191841,Bachelors,13,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,37805,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,590522,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,<=50K\n3,Private,202752,12th,8,Separated,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,?,304872,Some-college,10,Widowed,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,228075,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,163831,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,32326,Bachelors,13,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,179809,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,76878,7th-8th,4,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Italy,<=50K\n2,Federal-gov,210492,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Male,0,0,2,United-States,<=50K\n4,Private,105582,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,160369,Some-college,10,Married-civ-spouse,Priv-house-serv,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,364986,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,169278,Some-college,10,Widowed,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,76081,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,State-gov,234135,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,187693,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,188362,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,235271,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,4,United-States,>50K\n1,Private,51854,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,103772,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,300670,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,>50K\n2,Private,175990,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,173590,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,156866,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Other,Male,0,0,2,United-States,>50K\n4,Private,71388,9th,5,Separated,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,228659,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,110844,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,149908,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Female,4,0,2,United-States,>50K\n1,Private,93021,5th-6th,3,Never-married,Machine-op-inspct,Unmarried,Other,Female,0,0,2,?,<=50K\n0,Local-gov,273187,HS-grad,9,Never-married,Protective-serv,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,62419,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,218957,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,1,?,<=50K\n3,Private,182715,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,157103,Assoc-voc,11,Never-married,Tech-support,Own-child,Black,Male,0,3,2,United-States,<=50K\n3,Private,133963,Some-college,10,Widowed,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,151724,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,196502,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,199816,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,413365,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,195568,Some-college,10,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,186245,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,279231,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n3,Federal-gov,171870,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,127651,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,289605,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,130856,Assoc-voc,11,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,49539,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,263081,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n4,Local-gov,205759,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,358655,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,186299,Preschool,1,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,289517,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,105686,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,81132,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Asian-Pac-Islander,Male,0,0,4,Philippines,>50K\n0,Private,68302,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,443546,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,195891,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,?,>50K\n1,Private,99462,HS-grad,9,Divorced,Tech-support,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Private,224541,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,?,262502,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,2,1,United-States,<=50K\n2,Private,118921,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,155489,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,248919,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,1,United-States,>50K\n3,Private,139268,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,4,0,2,United-States,>50K\n2,Self-emp-not-inc,245056,Preschool,1,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,Haiti,<=50K\n2,Private,433592,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,336624,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,3,0,2,United-States,>50K\n3,Private,177858,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n2,Private,207066,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,56750,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,110331,9th,5,Divorced,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,84801,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Local-gov,175897,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,369027,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,170411,HS-grad,9,Divorced,Protective-serv,Own-child,White,Male,2,0,2,United-States,<=50K\n3,Private,171751,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,61518,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,214235,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n2,?,56483,Some-college,10,Married-AF-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n2,Private,154993,Some-college,10,Separated,Craft-repair,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,160594,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,258298,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,192666,12th,8,Separated,Machine-op-inspct,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,156602,Bachelors,13,Never-married,Sales,Own-child,White,Male,1,0,2,United-States,<=50K\n1,Private,122116,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,73433,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,4,2,Canada,<=50K\n3,Private,99185,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,203828,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Italy,<=50K\n4,Local-gov,101480,Assoc-voc,11,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,134815,9th,5,Never-married,Craft-repair,Unmarried,White,Male,0,1,2,United-States,<=50K\n2,State-gov,235195,Some-college,10,Separated,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,93169,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,36091,Masters,14,Never-married,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Private,124318,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,188861,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,1,3,United-States,>50K\n1,Private,194402,Masters,14,Never-married,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n2,Self-emp-not-inc,54651,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Cuba,>50K\n1,Private,169496,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,34366,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,State-gov,213076,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,161132,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,479406,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,<=50K\n2,Private,115618,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-inc,158033,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,108682,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,430195,11th,7,Separated,Other-service,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,138107,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,215014,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,183279,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,119056,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,>50K\n3,Private,158583,Some-college,10,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,242464,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,35204,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,233149,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n3,Private,182855,7th-8th,4,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,189346,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,82726,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,179481,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,167428,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,182117,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,162970,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,421633,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,State-gov,231931,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,132749,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,254351,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,State-gov,152109,Assoc-voc,11,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,100479,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,222405,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,117028,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,91163,HS-grad,9,Separated,Other-service,Other-relative,Black,Female,0,0,2,United-States,<=50K\n2,Private,150104,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,114605,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,348152,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,174463,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,180243,Bachelors,13,Never-married,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,238816,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,275848,12th,8,Never-married,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n3,Private,114520,HS-grad,9,Divorced,Sales,Unmarried,White,Male,0,0,0,United-States,<=50K\n1,State-gov,275880,Bachelors,13,Separated,Exec-managerial,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,188148,Some-college,10,Never-married,Handlers-cleaners,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Private,112494,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,1,United-States,>50K\n0,?,159771,10th,6,Never-married,?,Own-child,Black,Male,0,0,0,England,<=50K\n1,Private,278736,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,?,354351,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,252257,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,128230,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,321456,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,200825,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,86143,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Male,0,0,0,Philippines,<=50K\n2,State-gov,353687,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n2,Local-gov,212847,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,154589,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,1,United-States,>50K\n2,Private,183765,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,186672,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,Jamaica,<=50K\n3,Private,249096,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,190784,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,144516,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,124993,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,111376,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,300838,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,2,Mexico,<=50K\n1,Private,359049,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,1,3,United-States,<=50K\n2,?,100669,HS-grad,9,Never-married,?,Own-child,Asian-Pac-Islander,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,366089,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,110998,Some-college,10,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n0,Private,60409,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,129263,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,219967,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,171540,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,61708,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,294760,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,209280,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,208881,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,191535,11th,7,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,143851,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n3,Private,161599,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,?,<=50K\n0,Self-emp-not-inc,428299,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,199366,10th,6,Married-spouse-absent,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,484911,HS-grad,9,Never-married,Craft-repair,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n2,Private,390243,HS-grad,9,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,204502,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,114419,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,79436,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,141272,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Local-gov,123749,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,101173,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,39518,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,250038,9th,5,Never-married,Farming-fishing,Other-relative,White,Male,0,0,2,Mexico,<=50K\n4,Self-emp-inc,165695,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n3,Local-gov,225594,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,<=50K\n0,State-gov,51979,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,177675,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,199739,Some-college,10,Divorced,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,27408,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,110169,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,179488,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,118551,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,>50K\n4,Self-emp-not-inc,157845,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,68899,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,58535,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,191503,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n1,Private,113364,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,204742,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,163870,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,2,?,<=50K\n3,Private,208122,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Other,Male,0,0,3,United-States,>50K\n3,Local-gov,174361,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,265077,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,1,0,0,United-States,<=50K\n3,Private,241648,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,94145,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,178449,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,236804,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,187830,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,1,United-States,>50K\n2,Private,66208,Prof-school,15,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,219155,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n2,Private,99146,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,142889,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,136164,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,154410,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,113293,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,195096,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,172641,7th-8th,4,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,265072,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,4,3,United-States,>50K\n0,Private,305874,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,312446,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,391312,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n1,Private,234976,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,<=50K\n0,Private,199495,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,98561,HS-grad,9,Widowed,Tech-support,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Private,176452,9th,5,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,3,?,>50K\n0,Private,188996,9th,5,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,State-gov,171926,Masters,14,Divorced,Exec-managerial,Unmarried,White,Male,2,0,3,United-States,>50K\n2,Local-gov,310260,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,72257,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,86643,Assoc-acdm,12,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,115815,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,227475,Bachelors,13,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,324550,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,138105,HS-grad,9,Separated,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,146189,11th,7,Never-married,Sales,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,478836,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,513440,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,151806,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,363253,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,?,>50K\n3,Self-emp-inc,263925,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,57617,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,208761,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,169092,HS-grad,9,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,2,?,<=50K\n1,Private,173854,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,4,0,3,United-States,>50K\n3,Local-gov,116906,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Female,0,4,1,United-States,<=50K\n2,Private,163769,10th,6,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,72630,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n4,?,141409,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,24968,9th,5,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,118514,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,116834,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,99185,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,121769,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,160271,7th-8th,4,Separated,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,123429,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,744929,HS-grad,9,Never-married,Exec-managerial,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,143604,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,284582,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,?,229363,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,0,United-States,<=50K\n3,Private,161482,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,>50K\n0,Self-emp-not-inc,107452,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,534775,Some-college,10,Never-married,Tech-support,Unmarried,Black,Female,0,0,3,United-States,<=50K\n3,Private,183200,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,<=50K\n2,Private,169980,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,3,United-States,>50K\n0,Private,299047,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,92475,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,114758,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n4,Self-emp-not-inc,55894,Prof-school,15,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,98466,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,170174,Assoc-voc,11,Never-married,Adm-clerical,Own-child,White,Male,4,0,2,United-States,>50K\n3,Private,335177,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,2,?,<=50K\n0,Private,511231,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Local-gov,257849,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,208751,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,383566,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,England,>50K\n3,?,214605,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,243664,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,176716,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,366618,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,83748,Some-college,10,Married-civ-spouse,Exec-managerial,Other-relative,Asian-Pac-Islander,Female,0,0,4,South,<=50K\n4,Private,278585,HS-grad,9,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,106942,7th-8th,4,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,372898,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,183610,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,106061,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n4,State-gov,138130,HS-grad,9,Never-married,Tech-support,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,43479,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,118520,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Federal-gov,207284,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,598995,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,1,0,2,United-States,>50K\n3,State-gov,141608,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,230912,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,309170,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,27459,HS-grad,9,Married-spouse-absent,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,266674,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n3,Private,93127,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,27444,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,131415,Bachelors,13,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,105428,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,2,2,United-States,<=50K\n2,Private,139364,Bachelors,13,Married-spouse-absent,Exec-managerial,Not-in-family,White,Male,4,0,2,Ireland,>50K\n2,Private,236936,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,>50K\n1,Private,109204,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,456592,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,173201,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,Cuba,<=50K\n0,?,183408,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,111721,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,268258,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,Black,Male,3,0,3,United-States,>50K\n4,Private,128258,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,525848,11th,7,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Private,124090,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,249043,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,2,0,2,United-States,<=50K\n2,Private,119098,Some-college,10,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,30772,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,49414,Some-college,10,Never-married,Adm-clerical,Own-child,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n1,Private,197886,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,>50K\n2,Without-pay,334291,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,171156,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,109498,9th,5,Widowed,?,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,83411,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,4,2,United-States,>50K\n3,Local-gov,209968,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,223921,12th,8,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,133403,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,75763,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,200401,HS-grad,9,Separated,Transport-moving,Own-child,White,Male,0,0,1,Columbia,<=50K\n2,Private,55272,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,194970,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,143385,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,180317,Assoc-voc,11,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,255176,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Local-gov,175120,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,66687,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,Portugal,>50K\n1,Federal-gov,115705,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,3,United-States,>50K\n0,?,197732,11th,7,Never-married,?,Own-child,White,Female,0,0,0,England,<=50K\n1,Private,111883,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,210474,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,123472,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,257621,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,341943,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,181242,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,187164,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,308365,HS-grad,9,Never-married,Craft-repair,Other-relative,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,263439,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,442359,HS-grad,9,Never-married,Sales,Own-child,White,Female,3,0,0,United-States,>50K\n3,Self-emp-not-inc,166368,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,116968,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,1,0,2,United-States,<=50K\n1,Private,57593,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,247507,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,213512,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,71691,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,?,147719,Some-college,10,Never-married,?,Not-in-family,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n2,Private,244835,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n0,?,285830,HS-grad,9,Never-married,?,Own-child,Asian-Pac-Islander,Female,0,0,0,Laos,<=50K\n2,Private,386461,5th-6th,3,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,Mexico,<=50K\n2,Private,154714,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,281401,5th-6th,3,Divorced,Sales,Other-relative,White,Female,0,0,1,Mexico,<=50K\n1,Private,189251,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Female,0,0,2,Iran,<=50K\n2,State-gov,102729,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,327766,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,>50K\n1,Private,168479,Masters,14,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,249352,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,300008,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,296466,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,199277,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,Portugal,>50K\n2,Private,174242,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,130968,9th,5,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,288440,Bachelors,13,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,208358,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,Self-emp-not-inc,458168,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,37894,HS-grad,9,Separated,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,107410,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n0,?,96844,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,402016,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,258869,Doctorate,16,Separated,Priv-house-serv,Unmarried,White,Female,0,0,1,Nicaragua,<=50K\n1,Private,114087,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,2,0,2,United-States,<=50K\n1,Private,116294,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,>50K\n0,Private,241523,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,163053,10th,6,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Never-worked,162908,11th,7,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,State-gov,310049,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n2,Private,293475,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,169015,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,325762,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,101561,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n2,State-gov,52131,HS-grad,9,Divorced,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,163867,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,204663,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,1,0,2,United-States,<=50K\n0,?,233136,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,48846,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,264351,Bachelors,13,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,Ecuador,<=50K\n2,Private,190205,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,254450,Assoc-voc,11,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,29974,Assoc-voc,11,Never-married,Farming-fishing,Own-child,White,Male,4,0,2,United-States,>50K\n3,Federal-gov,40641,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,256401,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,203833,10th,6,Never-married,Farming-fishing,Not-in-family,Black,Male,0,0,1,Haiti,<=50K\n1,Private,277455,HS-grad,9,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,176335,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,394690,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,State-gov,71996,Bachelors,13,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,110787,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,0,United-States,<=50K\n2,State-gov,157641,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,164724,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,173792,Some-college,10,Separated,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n0,Private,163595,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,132412,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,?,<=50K\n0,Private,193089,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,190525,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,175878,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,161902,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,211494,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,3,0,2,?,>50K\n2,Private,89003,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,344200,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,62345,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n1,Private,85799,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,?,26754,Bachelors,13,Married-civ-spouse,?,Wife,Asian-Pac-Islander,Female,0,0,0,China,<=50K\n1,Private,193347,Some-college,10,Divorced,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,176814,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,336162,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,98975,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,3,0,3,United-States,>50K\n4,Private,150943,Bachelors,13,Widowed,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,131573,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,138852,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,35406,Assoc-voc,11,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,167159,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,80680,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Self-emp-inc,87584,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,56121,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,284358,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,224969,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,209034,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,?,177733,7th-8th,4,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,57366,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,35633,7th-8th,4,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,191177,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,199826,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Self-emp-not-inc,94323,9th,5,Married-civ-spouse,Craft-repair,Wife,Amer-Indian-Eskimo,Female,0,3,0,United-States,<=50K\n4,?,380268,Prof-school,15,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,272752,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,228386,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,Black,Female,0,0,4,United-States,<=50K\n0,Private,187149,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Local-gov,335439,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,250630,Bachelors,13,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,272182,Some-college,10,Married-civ-spouse,Tech-support,Husband,Black,Male,2,0,2,United-States,<=50K\n1,Private,108574,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,State-gov,185400,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,406641,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Private,103634,HS-grad,9,Never-married,Protective-serv,Unmarried,White,Male,0,0,1,United-States,<=50K\n3,Private,261059,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,199374,Masters,14,Widowed,Sales,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,405172,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,?,>50K\n1,Private,147654,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,129301,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,173789,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,212588,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,457453,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Private,187493,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,Germany,>50K\n4,Self-emp-not-inc,20953,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,3,4,United-States,<=50K\n2,Private,131650,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,290754,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,?,<=50K\n3,State-gov,114396,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,202102,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,318360,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,160916,9th,5,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,?,913447,HS-grad,9,Divorced,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,293931,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,230824,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,?,358355,12th,8,Never-married,?,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,584259,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,208726,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,185556,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n4,Local-gov,100054,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,172307,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,30840,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n1,Private,147839,Some-college,10,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,54465,Assoc-voc,11,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Federal-gov,418640,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,Haiti,>50K\n1,Private,222130,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,187336,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,321024,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,351262,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,181597,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,694105,HS-grad,9,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n4,Private,241013,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,223881,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,?,>50K\n2,Federal-gov,158796,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,Philippines,<=50K\n2,Private,248476,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,191277,Assoc-voc,11,Divorced,Craft-repair,Own-child,White,Male,0,0,1,Thailand,<=50K\n1,Private,273876,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,402848,HS-grad,9,Married-spouse-absent,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n4,Private,82906,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,2,0,1,England,<=50K\n2,Private,153682,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n3,Private,122412,Doctorate,16,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,1,United-States,>50K\n1,Private,216819,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,189680,HS-grad,9,Separated,Craft-repair,Unmarried,White,Male,0,0,2,United-States,>50K\n2,Private,147099,Masters,14,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,288158,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,146161,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,?,104454,Bachelors,13,Widowed,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,91475,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,151868,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,4,United-States,<=50K\n0,Private,118306,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,51543,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,69727,1st-4th,2,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Private,186000,10th,6,Separated,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,48280,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,186935,11th,7,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Federal-gov,104164,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,339677,Masters,14,Divorced,Tech-support,Unmarried,White,Female,0,0,2,?,>50K\n2,Private,123586,Bachelors,13,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,3,4,?,>50K\n0,Private,150073,Some-college,10,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,1,United-States,<=50K\n0,Private,59792,Masters,14,Never-married,Tech-support,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,24185,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,4,United-States,<=50K\n4,?,97295,HS-grad,9,Widowed,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Without-pay,35034,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,118714,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,230883,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,4,3,United-States,>50K\n2,Private,149224,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,3,United-States,<=50K\n1,Private,122493,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,441227,11th,7,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n2,Local-gov,272792,Masters,14,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,3,United-States,>50K\n2,Private,303521,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,2,0,2,United-States,<=50K\n2,Private,226027,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,94565,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,336848,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,162688,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,State-gov,45737,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,147393,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,335997,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,96645,Doctorate,16,Never-married,Craft-repair,Other-relative,Black,Male,0,0,0,United-States,<=50K\n1,State-gov,43712,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,101825,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,2,0,2,United-States,<=50K\n2,Private,102318,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,132125,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,229741,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,1,2,United-States,<=50K\n4,State-gov,265201,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,?,332884,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,179481,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,304833,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,341632,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,Black,Male,0,0,3,United-States,<=50K\n0,Private,140001,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,159123,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,229636,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n1,Private,34996,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,67198,7th-8th,4,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,4,United-States,<=50K\n4,Self-emp-inc,323636,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,182689,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,51392,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,177905,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,143152,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n2,State-gov,211286,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,217962,Some-college,10,Never-married,Machine-op-inspct,Unmarried,Black,Male,0,0,2,?,<=50K\n3,Private,309212,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,Germany,<=50K\n0,?,97261,Some-college,10,Never-married,?,Own-child,White,Male,1,0,1,United-States,<=50K\n3,Self-emp-not-inc,188273,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,93977,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,163234,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Local-gov,166423,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,239071,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,190292,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,170783,Assoc-voc,11,Divorced,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,239539,HS-grad,9,Never-married,Craft-repair,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Private,164200,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,177647,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,86750,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,4,United-States,<=50K\n0,Private,176981,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,134153,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Black,Male,2,0,2,United-States,>50K\n1,Private,245372,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,168236,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,448841,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,144778,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,3,0,3,United-States,>50K\n1,Private,155914,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,117362,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,82783,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,207301,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,606347,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n4,Local-gov,149981,HS-grad,9,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,234972,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,437566,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,243266,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Dominican-Republic,>50K\n1,Private,203408,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,297510,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,211683,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,167375,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,297449,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,342634,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,Cambodia,<=50K\n3,Private,38819,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,164571,HS-grad,9,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,122353,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,192652,Some-college,10,Never-married,Exec-managerial,Unmarried,White,Male,0,0,0,United-States,<=50K\n0,Private,401241,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,2,0,1,United-States,<=50K\n1,Private,116539,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,162834,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,226525,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,219404,5th-6th,3,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n2,Private,33394,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,75804,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,359972,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,201247,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,220109,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,118479,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n2,Private,189123,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,495366,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,53776,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,375980,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,185152,11th,7,Widowed,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,230095,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,349198,7th-8th,4,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,59916,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,214150,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,255187,Some-college,10,Never-married,Tech-support,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,?,129624,Some-college,10,Never-married,?,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Private,274577,Assoc-acdm,12,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,257756,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n1,?,1024535,11th,7,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n4,Private,186808,11th,7,Married-civ-spouse,Protective-serv,Husband,Black,Male,2,0,3,United-States,>50K\n3,Private,147519,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,4,United-States,<=50K\n3,Private,173630,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,136986,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,3,United-States,>50K\n2,Private,163287,Bachelors,13,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,150804,HS-grad,9,Never-married,Transport-moving,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Vietnam,<=50K\n1,Private,189565,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,165484,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,177727,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Self-emp-inc,190762,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,246463,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,27661,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,3,United-States,>50K\n1,Private,288419,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n4,Local-gov,258641,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,200540,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,4,3,United-States,>50K\n0,Private,305423,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,148906,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,262645,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,243368,Preschool,1,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n2,Private,106698,HS-grad,9,Widowed,Transport-moving,Not-in-family,White,Female,4,0,3,United-States,>50K\n0,Private,186365,Some-college,10,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,304001,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,1,0,2,United-States,<=50K\n2,Self-emp-inc,125645,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,111468,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,1,0,2,United-States,<=50K\n1,Private,136331,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,213849,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,138994,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n1,Private,177562,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,2,0,1,United-States,<=50K\n0,Private,36251,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,100904,Some-college,10,Separated,Other-service,Unmarried,Other,Female,0,0,4,United-States,<=50K\n1,Private,427474,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,3,1,Mexico,<=50K\n2,Private,280549,Bachelors,13,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,75454,12th,8,Divorced,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,203488,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,49795,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,152710,10th,6,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,199172,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,382583,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,127295,HS-grad,9,Never-married,Exec-managerial,Not-in-family,Other,Male,0,0,0,Iran,<=50K\n3,Local-gov,274200,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,130540,Masters,14,Never-married,Prof-specialty,Own-child,White,Male,0,1,2,United-States,>50K\n0,Private,117789,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,83141,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,229826,Some-college,10,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-inc,176837,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,234096,9th,5,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,353317,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,302312,HS-grad,9,Divorced,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,181015,10th,6,Never-married,Other-service,Other-relative,White,Male,0,0,0,United-States,<=50K\n2,State-gov,230329,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n3,Private,164984,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,229051,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,204415,11th,7,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,319883,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,0,?,>50K\n0,Private,185836,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,228516,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,>50K\n3,Self-emp-not-inc,242519,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,164866,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,33001,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,811615,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n2,Local-gov,300099,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,State-gov,146305,Some-college,10,Divorced,Tech-support,Not-in-family,Other,Female,0,0,3,United-States,<=50K\n2,Private,167482,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,4,United-States,<=50K\n0,Private,106615,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,White,Female,1,0,1,United-States,<=50K\n1,Private,204663,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,171234,Assoc-voc,11,Never-married,Prof-specialty,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,167174,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,3,China,>50K\n0,?,112137,Some-college,10,Never-married,?,Own-child,Other,Female,0,0,0,?,<=50K\n0,Private,116167,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,106159,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,4,0,3,United-States,>50K\n3,State-gov,295826,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,2,3,United-States,<=50K\n1,Private,233461,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Mexico,<=50K\n4,Private,289605,9th,5,Never-married,Craft-repair,Not-in-family,White,Male,0,2,1,United-States,<=50K\n0,?,348533,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,197886,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,120126,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,61343,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,116197,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,236503,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Federal-gov,47341,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,248990,11th,7,Married-civ-spouse,Machine-op-inspct,Other-relative,White,Male,0,0,2,Mexico,<=50K\n0,Private,190776,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,179048,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,213842,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,188274,Assoc-acdm,12,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,86849,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-inc,67671,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Canada,>50K\n0,Private,243890,HS-grad,9,Never-married,Other-service,Other-relative,Black,Male,0,0,2,United-States,<=50K\n3,Private,279337,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,333636,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,3,?,>50K\n4,State-gov,312351,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,175935,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,245297,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,State-gov,70209,Doctorate,16,Divorced,Prof-specialty,Not-in-family,White,Female,4,0,3,United-States,>50K\n3,Local-gov,251588,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,176992,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,1,0,2,United-States,<=50K\n3,State-gov,87205,Assoc-acdm,12,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,65545,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,289579,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,360689,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,3,United-States,<=50K\n2,Private,57600,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,247082,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,3,United-States,>50K\n2,Local-gov,188798,11th,7,Separated,Prof-specialty,Unmarried,Other,Female,0,0,1,United-States,<=50K\n0,Private,232261,9th,5,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,35507,Some-college,10,Never-married,?,Own-child,White,Female,1,0,2,United-States,<=50K\n1,Private,301298,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,27016,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,166789,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,1,3,United-States,<=50K\n0,Private,94071,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,117767,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,219155,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Italy,<=50K\n0,Private,34616,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,112341,Assoc-voc,11,Married-spouse-absent,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,220656,11th,7,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,239990,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,139180,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Private,215656,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,<=50K\n2,State-gov,157999,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n0,Private,41763,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,299725,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,198813,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,431551,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,1,Mexico,<=50K\n0,Private,195767,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,140649,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,295706,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,324284,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,352426,Some-college,10,Never-married,Sales,Unmarried,White,Male,0,0,3,Mexico,<=50K\n2,Private,126569,Bachelors,13,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,?,<=50K\n4,Private,106390,5th-6th,3,Widowed,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n1,Private,134813,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,95644,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,254148,5th-6th,3,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Private,367819,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,162370,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,204387,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n4,Federal-gov,248288,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,175070,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,4,United-States,<=50K\n3,Local-gov,169182,7th-8th,4,Divorced,Other-service,Unmarried,White,Female,0,0,2,Columbia,<=50K\n2,Self-emp-not-inc,223242,Some-college,10,Married-spouse-absent,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,State-gov,461929,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,>50K\n0,Self-emp-not-inc,114483,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,260543,Masters,14,Widowed,Machine-op-inspct,Other-relative,Asian-Pac-Islander,Female,0,0,2,China,<=50K\n1,Private,237466,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,335605,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,169122,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,176907,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,264300,Assoc-acdm,12,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,159187,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,303090,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n1,Private,393702,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,?,177923,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,191265,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,215074,Some-college,10,Married-civ-spouse,Other-service,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,30597,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,28996,Assoc-voc,11,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,188069,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,53481,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,410034,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,64544,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,204379,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,255252,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,178037,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,Ireland,<=50K\n1,Private,184303,5th-6th,3,Married-spouse-absent,Priv-house-serv,Other-relative,White,Female,0,0,0,El-Salvador,<=50K\n0,Private,175800,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,263569,11th,7,Married-civ-spouse,Farming-fishing,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,234508,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,195372,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,265295,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,184846,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,2,0,2,United-States,>50K\n3,Private,185400,11th,7,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,360689,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,200598,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,227615,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,308144,Bachelors,13,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n2,Private,259674,Masters,14,Married-civ-spouse,Exec-managerial,Husband,Black,Male,2,0,3,United-States,>50K\n2,Private,182217,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,230997,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,393702,HS-grad,9,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,130957,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,4,United-States,>50K\n0,Private,210444,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n2,?,172775,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Self-emp-inc,167914,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,2,3,United-States,<=50K\n4,Private,533660,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,205997,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n1,Private,253616,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,148549,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,113649,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,233322,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,325775,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,94235,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,336274,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,147510,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Private,191389,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,174503,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,160968,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,122797,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,52263,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,249282,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,227332,Bachelors,13,Never-married,Sales,Other-relative,Asian-Pac-Islander,Male,0,0,3,?,<=50K\n2,Local-gov,289238,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,153790,Assoc-acdm,12,Widowed,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,101563,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,176240,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,131608,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Ireland,>50K\n1,Private,198286,Some-college,10,Married-spouse-absent,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,222204,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,Self-emp-not-inc,148169,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,347867,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,170413,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,2,2,United-States,<=50K\n1,Private,133861,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n3,Federal-gov,289572,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,445382,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,3,0,2,United-States,>50K\n2,Self-emp-not-inc,151809,HS-grad,9,Divorced,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n3,Private,368355,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,73333,Prof-school,15,Never-married,Farming-fishing,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,117585,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,107960,Some-college,10,Never-married,Transport-moving,Own-child,Asian-Pac-Islander,Male,0,0,0,China,<=50K\n1,Private,196117,Bachelors,13,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,126569,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,216741,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,254167,10th,6,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,102444,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,38848,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,55360,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Male,0,0,2,United-States,<=50K\n0,Private,140117,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,66883,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-not-inc,154728,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,184756,Bachelors,13,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,193047,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,231342,Assoc-acdm,12,Divorced,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,310483,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,174778,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,3,2,United-States,<=50K\n2,Federal-gov,130749,Bachelors,13,Separated,Exec-managerial,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Private,22463,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,159473,HS-grad,9,Never-married,Machine-op-inspct,Own-child,Black,Female,0,0,2,United-States,<=50K\n4,Private,81605,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,68684,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,229799,Some-college,10,Never-married,?,Other-relative,White,Male,0,0,2,?,<=50K\n0,Private,124661,11th,7,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,117381,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,53206,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,172962,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,243923,9th,5,Married-civ-spouse,?,Husband,White,Male,0,0,0,Mexico,<=50K\n4,Private,132753,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,67339,Bachelors,13,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,0,United-States,<=50K\n4,Private,101577,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Asian-Pac-Islander,Female,0,2,0,United-States,<=50K\n2,Private,101192,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,State-gov,42901,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,88787,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,306504,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,183746,HS-grad,9,Never-married,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,242965,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,427475,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,180512,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,97723,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,208915,HS-grad,9,Widowed,Craft-repair,Other-relative,Asian-Pac-Islander,Female,0,0,2,Cambodia,<=50K\n2,Local-gov,189189,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,327258,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,3,3,China,>50K\n1,Private,254494,Some-college,10,Never-married,Exec-managerial,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,105577,Some-college,10,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,215124,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,192936,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,274158,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Black,Male,1,0,2,United-States,>50K\n0,Private,318822,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,171889,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n0,Private,94492,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,73820,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,32989,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,197382,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,164526,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,153205,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,4,India,>50K\n2,Private,269168,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,?,<=50K\n2,Local-gov,126847,Masters,14,Married-spouse-absent,Prof-specialty,Unmarried,White,Female,2,0,3,United-States,>50K\n3,State-gov,211112,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,100049,HS-grad,9,Married-spouse-absent,Handlers-cleaners,Not-in-family,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n3,State-gov,104542,Masters,14,Widowed,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,186479,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,225272,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,346871,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,142712,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,46221,Prof-school,15,Never-married,Exec-managerial,Not-in-family,White,Male,2,0,3,United-States,>50K\n3,Private,349151,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,111883,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,46691,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,291789,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Private,201699,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,2,2,United-States,<=50K\n0,Federal-gov,219262,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,160079,Masters,14,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,193374,HS-grad,9,Never-married,Priv-house-serv,Not-in-family,White,Male,0,2,2,United-States,<=50K\n2,?,111268,Assoc-voc,11,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,410632,Some-college,10,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,33943,Some-college,10,Married-civ-spouse,Protective-serv,Husband,Other,Male,0,0,2,United-States,>50K\n2,Federal-gov,269792,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,153328,Some-college,10,Never-married,Handlers-cleaners,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,162859,HS-grad,9,Divorced,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,?,258664,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,22406,Bachelors,13,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,65624,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,326232,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,363257,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n1,Private,51700,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,19678,Bachelors,13,Married-AF-spouse,Sales,Wife,Asian-Pac-Islander,Female,0,0,3,Philippines,>50K\n0,Private,109199,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,State-gov,36741,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Private,347653,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,37514,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,355781,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n3,Private,86709,Some-college,10,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,22933,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,83922,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Federal-gov,182637,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n4,Private,80149,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,2,Germany,>50K\n4,Self-emp-not-inc,231770,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,>50K\n4,?,116746,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,189759,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,46221,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,3,United-States,>50K\n4,State-gov,198145,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,35864,Bachelors,13,Never-married,Sales,Not-in-family,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n2,Federal-gov,259307,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,129007,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,169104,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n2,Private,198097,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,224640,Assoc-acdm,12,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,35603,11th,7,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,85982,Masters,14,Never-married,Prof-specialty,Not-in-family,Amer-Indian-Eskimo,Female,0,0,3,United-States,<=50K\n2,Private,230467,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,2,Germany,<=50K\n1,Self-emp-not-inc,168782,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,142566,10th,6,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,195337,HS-grad,9,Never-married,Adm-clerical,Unmarried,Other,Female,1,0,2,United-States,<=50K\n0,?,99829,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,133456,HS-grad,9,Married-civ-spouse,Sales,Other-relative,White,Female,0,0,1,United-States,>50K\n1,Local-gov,133136,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,1,United-States,<=50K\n0,Private,211531,12th,8,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,243867,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Local-gov,284871,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Federal-gov,188563,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,2,2,United-States,<=50K\n4,Private,173866,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,114939,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,259425,Assoc-voc,11,Never-married,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,59159,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,189439,HS-grad,9,Married-spouse-absent,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,157786,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,289636,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,154174,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,70857,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,183000,Some-college,10,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,United-States,>50K\n2,Private,123104,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,254620,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Local-gov,156464,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,124987,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,413720,HS-grad,9,Married-civ-spouse,Transport-moving,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,169886,12th,8,Separated,Other-service,Unmarried,White,Female,0,0,3,Dominican-Republic,<=50K\n1,Private,228873,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,110622,Masters,14,Divorced,Exec-managerial,Other-relative,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n1,?,170513,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,4,United-States,<=50K\n0,?,221418,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,151287,Masters,14,Separated,Exec-managerial,Not-in-family,Black,Male,0,0,0,United-States,<=50K\n0,Private,235218,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,111567,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,251603,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,136109,11th,7,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,178610,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,192289,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,Puerto-Rico,<=50K\n1,Private,49325,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n3,Private,229375,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,125791,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,75619,HS-grad,9,Divorced,Transport-moving,Other-relative,White,Male,0,0,3,United-States,<=50K\n4,Private,247483,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,289448,Masters,14,Never-married,Prof-specialty,Unmarried,Asian-Pac-Islander,Female,0,0,2,China,>50K\n0,?,122048,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,177940,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,399088,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,1,2,United-States,<=50K\n2,Private,108293,Assoc-voc,11,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,178249,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,2,2,United-States,>50K\n0,Local-gov,52262,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,143046,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,3,United-States,>50K\n4,Private,124318,HS-grad,9,Divorced,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,57855,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,355954,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,143582,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,Asian-Pac-Islander,Female,0,0,2,South,<=50K\n1,Private,220915,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,172025,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,109813,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n2,Private,175600,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,389856,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,Mexico,<=50K\n2,Self-emp-not-inc,167081,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n2,Self-emp-inc,179228,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n0,?,214542,Bachelors,13,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,1210504,10th,6,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,426263,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,146225,10th,6,Never-married,Other-service,Own-child,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,240810,12th,8,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,100113,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,3,2,United-States,<=50K\n0,Private,214542,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,41506,11th,7,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,200121,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,199919,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,60668,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,208347,11th,7,Never-married,Machine-op-inspct,Not-in-family,Other,Female,0,0,2,Puerto-Rico,<=50K\n2,State-gov,184105,Assoc-voc,11,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,255979,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,251585,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,132222,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,164733,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,327902,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,<=50K\n1,Private,262092,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,299047,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,146735,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,154568,HS-grad,9,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,>50K\n2,Private,160916,7th-8th,4,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,161444,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,246282,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,4,?,<=50K\n0,?,147031,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,220531,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,282202,HS-grad,9,Never-married,Exec-managerial,Unmarried,White,Male,0,0,2,Mexico,<=50K\n1,Private,122485,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,2,0,2,United-States,<=50K\n1,Private,227012,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,116502,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,163606,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,211413,11th,7,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,?,171637,HS-grad,9,Married-civ-spouse,?,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,204387,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,0,United-States,>50K\n0,?,214542,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,70100,HS-grad,9,Divorced,Craft-repair,Own-child,White,Male,0,0,2,Portugal,<=50K\n2,Private,96115,Bachelors,13,Separated,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,181758,HS-grad,9,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,?,278557,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n1,Federal-gov,168931,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,Other,Female,0,0,2,United-States,>50K\n1,Private,397962,10th,6,Married-civ-spouse,Other-service,Wife,Black,Female,0,0,1,United-States,<=50K\n1,Private,113922,HS-grad,9,Separated,Transport-moving,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,318025,HS-grad,9,Never-married,Other-service,Other-relative,White,Male,0,0,0,United-States,<=50K\n0,Private,287357,11th,7,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,33219,Assoc-acdm,12,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-inc,51646,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,1,United-States,>50K\n3,Federal-gov,51664,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,195721,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,255575,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n1,Private,200835,Bachelors,13,Married-spouse-absent,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Japan,<=50K\n0,?,140414,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,165579,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,38948,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,184685,Some-college,10,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,2,United-States,<=50K\n0,Private,86939,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,215251,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,Germany,<=50K\n3,Private,151159,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,385266,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-inc,289349,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,3,0,4,Germany,>50K\n0,Private,232060,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,290429,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,179048,12th,8,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,1,?,>50K\n4,Self-emp-not-inc,98466,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,142801,Masters,14,Widowed,Prof-specialty,Not-in-family,Black,Female,0,0,2,United-States,>50K\n4,Self-emp-inc,119938,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,172186,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,2,0,2,United-States,<=50K\n2,Local-gov,198759,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,2,2,United-States,>50K\n2,State-gov,74305,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n4,Self-emp-inc,120796,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,190091,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,197286,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,State-gov,129499,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,30875,Bachelors,13,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,>50K\n2,Self-emp-not-inc,172822,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,107417,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,226902,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,86557,Some-college,10,Never-married,Sales,Other-relative,Other,Female,0,0,1,United-States,<=50K\n1,Private,98900,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,38707,Bachelors,13,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,286730,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,221797,12th,8,Never-married,Adm-clerical,Own-child,White,Female,1,0,0,United-States,<=50K\n3,?,175029,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,<=50K\n3,?,32184,HS-grad,9,Married-civ-spouse,?,Wife,White,Female,2,0,0,United-States,<=50K\n1,Federal-gov,144949,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,33084,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,156464,Some-college,10,Never-married,Tech-support,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,152307,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,341954,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,2,2,?,<=50K\n0,Private,235720,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,161187,12th,8,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,90017,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Portugal,<=50K\n4,Private,192671,11th,7,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n2,Self-emp-not-inc,102178,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,293136,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Local-gov,99568,10th,6,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,81413,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,31053,Some-college,10,Divorced,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,31008,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,221801,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,76491,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,286732,HS-grad,9,Widowed,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,196707,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Federal-gov,23698,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,224699,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,131291,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,0,United-States,<=50K\n0,?,372665,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,64506,Assoc-voc,11,Divorced,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Private,139388,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,425830,Assoc-acdm,12,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,99146,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,59200,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,Private,37482,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,188798,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,Private,100285,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,228446,Some-college,10,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,19491,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,1,0,2,United-States,<=50K\n1,Private,156967,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,187581,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,154490,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,101345,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,150533,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,93213,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,257046,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,421837,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,101593,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,122215,HS-grad,9,Widowed,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n3,Private,272780,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,3,Mexico,>50K\n0,Private,190591,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Private,78374,HS-grad,9,Married-civ-spouse,Sales,Other-relative,Asian-Pac-Islander,Female,0,0,2,?,<=50K\n1,?,121468,Bachelors,13,Married-civ-spouse,?,Wife,Asian-Pac-Islander,Female,0,0,2,Hong,<=50K\n1,Private,280927,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n0,Private,202480,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,110088,1st-4th,2,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,?,129632,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,225860,Assoc-acdm,12,Married-spouse-absent,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,310545,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,1,El-Salvador,<=50K\n1,Private,178664,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,198759,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,259301,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,234537,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n4,Private,427770,12th,8,Divorced,Priv-house-serv,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,?,331702,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,180052,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,205337,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,236091,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,33895,HS-grad,9,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,?,447210,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,226441,Bachelors,13,Never-married,Prof-specialty,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,State-gov,119721,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,455995,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,105813,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,350917,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,State-gov,191814,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,399296,5th-6th,3,Married-civ-spouse,Other-service,Other-relative,White,Female,0,0,0,Mexico,<=50K\n3,Private,201595,HS-grad,9,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n2,Private,143003,Masters,14,Married-civ-spouse,Tech-support,Husband,Asian-Pac-Islander,Male,0,2,2,China,>50K\n1,Private,74784,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,?,313045,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,303781,Some-college,10,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,236769,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,264961,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,144962,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,435836,10th,6,Married-civ-spouse,Other-service,Wife,White,Female,0,0,1,United-States,>50K\n3,Private,186539,HS-grad,9,Divorced,Craft-repair,Other-relative,White,Male,0,0,3,United-States,>50K\n0,Private,181796,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,398397,Masters,14,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,196280,Assoc-voc,11,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,31670,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,65535,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n3,Local-gov,97680,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,0,United-States,>50K\n4,Self-emp-not-inc,47178,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n0,Self-emp-not-inc,151818,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,304530,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,197651,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,108054,HS-grad,9,Widowed,Transport-moving,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,179666,Some-college,10,Divorced,Transport-moving,Unmarried,White,Male,0,0,1,England,<=50K\n2,Private,142909,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,231261,12th,8,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n2,State-gov,119008,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,Black,Female,0,3,2,United-States,<=50K\n1,Private,168138,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,217850,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,343242,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,167087,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,200179,HS-grad,9,Divorced,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,172252,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,132879,Doctorate,16,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,240063,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,3,United-States,<=50K\n0,Private,425816,Some-college,10,Never-married,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,167571,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,1,0,1,United-States,<=50K\n2,Private,85566,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,123799,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,194690,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,266347,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,68268,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,145574,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,128666,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,119411,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,276552,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,305423,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,104236,Assoc-acdm,12,Divorced,Adm-clerical,Unmarried,White,Female,1,0,2,United-States,<=50K\n3,?,136455,Some-college,10,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,139889,11th,7,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n2,Local-gov,102953,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,320183,11th,7,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n3,Private,83407,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,>50K\n0,?,118910,Some-college,10,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,99254,Masters,14,Divorced,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,State-gov,198766,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,191832,12th,8,Never-married,Other-service,Unmarried,White,Male,0,0,2,?,<=50K\n0,Private,146178,HS-grad,9,Separated,Adm-clerical,Other-relative,White,Male,0,0,3,United-States,<=50K\n1,Private,132879,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,94706,Bachelors,13,Married-spouse-absent,Sales,Unmarried,Asian-Pac-Islander,Male,0,0,2,Laos,<=50K\n3,Local-gov,273767,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,3,0,2,United-States,>50K\n2,Self-emp-not-inc,204235,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n4,?,181782,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n2,State-gov,144860,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,185832,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n4,Private,55314,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,United-States,>50K\n2,Private,200194,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,339772,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n3,Private,159755,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,200207,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,31493,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,168981,HS-grad,9,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,201729,Some-college,10,Never-married,Protective-serv,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,105749,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n4,Federal-gov,101847,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,<=50K\n3,Private,110646,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,3,United-States,<=50K\n1,Private,139753,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,1,0,3,United-States,<=50K\n0,Private,255004,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,341954,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,124330,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,64563,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,2,2,United-States,>50K\n1,Self-emp-not-inc,29254,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,?,182810,Some-college,10,Never-married,?,Not-in-family,White,Female,0,1,2,United-States,>50K\n1,?,139051,11th,7,Separated,?,Unmarried,Black,Female,0,0,3,United-States,<=50K\n1,Private,151053,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n4,Self-emp-inc,45702,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n1,?,138685,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,164193,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,109651,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,126364,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,328199,HS-grad,9,Never-married,Tech-support,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Self-emp-not-inc,180096,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,197613,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,170195,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Federal-gov,56482,10th,6,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,23686,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,2,2,United-States,<=50K\n4,State-gov,200916,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,160261,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,4,1,Hong,<=50K\n0,Private,129232,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,249727,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n1,State-gov,106448,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,313852,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,213722,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,Greece,<=50K\n1,Private,120645,Assoc-acdm,12,Married-civ-spouse,Tech-support,Wife,Black,Female,0,0,2,United-States,<=50K\n4,?,167336,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,199419,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,3,United-States,<=50K\n0,Private,132053,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,311570,Assoc-acdm,12,Never-married,Tech-support,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,187203,Bachelors,13,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,201240,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,150999,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,24961,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,152021,11th,7,Divorced,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,374116,11th,7,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,35370,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,>50K\n2,Private,65291,Bachelors,13,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,1,United-States,<=50K\n4,?,283889,HS-grad,9,Married-spouse-absent,?,Not-in-family,Black,Male,0,2,2,United-States,<=50K\n2,Self-emp-not-inc,48585,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,132661,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,267583,10th,6,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,313297,5th-6th,3,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,Mexico,<=50K\n4,Private,290578,7th-8th,4,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,246038,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,125461,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Male,0,1,2,United-States,<=50K\n4,Self-emp-not-inc,206149,7th-8th,4,Never-married,Other-service,Unmarried,Black,Female,0,0,3,United-States,<=50K\n4,Local-gov,205718,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,Canada,<=50K\n2,Private,241153,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,186706,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,3,0,2,United-States,>50K\n4,Private,216574,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Private,49570,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,82161,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,191268,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,59469,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,197947,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,180131,Bachelors,13,Separated,Sales,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Private,156732,11th,7,Never-married,Other-service,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,Private,415755,7th-8th,4,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,228254,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,414599,Assoc-acdm,12,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,Guatemala,<=50K\n1,Private,357173,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n3,?,111282,7th-8th,4,Married-civ-spouse,?,Husband,White,Male,2,0,4,United-States,>50K\n2,Private,174308,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Federal-gov,61885,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,123838,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,119170,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,2,2,United-States,<=50K\n4,Private,219426,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-inc,184920,HS-grad,9,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Local-gov,187385,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n1,Private,234258,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,387663,Some-college,10,Married-spouse-absent,Farming-fishing,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,151817,Masters,14,Separated,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,187203,Prof-school,15,Married-civ-spouse,Sales,Husband,White,Male,3,0,2,United-States,>50K\n1,Local-gov,67187,HS-grad,9,Divorced,Adm-clerical,Not-in-family,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n3,Private,194526,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,73266,Some-college,10,Never-married,Transport-moving,Own-child,Asian-Pac-Islander,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,340755,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,168552,HS-grad,9,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,188069,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,198587,HS-grad,9,Never-married,Sales,Unmarried,Black,Female,0,0,3,United-States,<=50K\n0,State-gov,33423,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Federal-gov,190910,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,195963,7th-8th,4,Never-married,Transport-moving,Not-in-family,Other,Male,0,0,3,Puerto-Rico,<=50K\n0,Private,345066,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n2,Private,195258,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,262233,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n4,Private,78707,9th,5,Married-civ-spouse,Other-service,Wife,White,Female,2,0,1,United-States,<=50K\n3,Self-emp-inc,251240,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,4,3,United-States,>50K\n2,Self-emp-not-inc,414056,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,174564,12th,8,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,236242,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,White,Female,0,1,2,United-States,<=50K\n1,Private,31286,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,234476,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,414916,HS-grad,9,Never-married,Tech-support,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,223881,Some-college,10,Divorced,Tech-support,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,284651,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,38948,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,157887,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,70796,HS-grad,9,Married-civ-spouse,Priv-house-serv,Wife,Black,Female,0,0,0,United-States,<=50K\n3,Local-gov,97176,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,146834,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,490645,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,<=50K\n4,State-gov,31577,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,145437,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,State-gov,21798,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,4,0,2,Germany,>50K\n4,Private,142076,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,136230,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,184169,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,>50K\n0,Private,175778,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,59174,HS-grad,9,Widowed,Prof-specialty,Unmarried,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n3,Private,123713,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,?,222362,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,>50K\n3,Private,108435,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,39182,Assoc-acdm,12,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,203051,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Federal-gov,363418,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,113054,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,163320,Assoc-acdm,12,Never-married,Protective-serv,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,118710,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,170066,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,135267,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,361278,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,165867,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,300497,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,State-gov,255928,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,27305,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,29933,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,265434,11th,7,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,145643,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,?,162041,HS-grad,9,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,119562,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,<=50K\n1,Private,115549,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,39590,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,97676,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,34973,11th,7,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,312446,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,88909,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,117381,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Self-emp-inc,513977,HS-grad,9,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Federal-gov,39089,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,2,0,3,United-States,<=50K\n1,Private,341672,Bachelors,13,Never-married,Sales,Not-in-family,Asian-Pac-Islander,Male,1,0,2,Taiwan,<=50K\n1,Local-gov,400535,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,338042,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,216734,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,224207,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Male,2,0,3,United-States,<=50K\n4,Private,107897,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,205903,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,191754,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,216225,Assoc-acdm,12,Married-civ-spouse,Sales,Wife,White,Female,0,0,3,United-States,>50K\n1,Private,125762,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,201062,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,Black,Female,0,0,2,Jamaica,<=50K\n3,Local-gov,25820,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,Amer-Indian-Eskimo,Male,0,0,3,United-States,<=50K\n1,Private,553405,Assoc-voc,11,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,78022,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,207631,5th-6th,3,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,3,Mexico,<=50K\n1,Private,203988,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,122194,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,318918,10th,6,Never-married,Farming-fishing,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,264453,Assoc-voc,11,Divorced,Exec-managerial,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,183523,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,98881,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,184101,11th,7,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Local-gov,135056,HS-grad,9,Separated,Adm-clerical,Other-relative,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,71457,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,2,0,0,United-States,<=50K\n3,Self-emp-not-inc,96459,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,2,4,United-States,>50K\n3,Private,134184,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Private,153132,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,173991,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,96219,HS-grad,9,Never-married,Tech-support,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,152744,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n4,Private,182142,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,48915,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,24126,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,263984,HS-grad,9,Married-civ-spouse,Other-service,Husband,Black,Male,0,0,2,Puerto-Rico,<=50K\n2,Local-gov,118261,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Private,106141,7th-8th,4,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,355320,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,0,Germany,>50K\n0,?,497414,7th-8th,4,Never-married,?,Not-in-family,White,Female,0,0,1,Mexico,<=50K\n2,Private,118915,Bachelors,13,Separated,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,75885,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n1,State-gov,93806,Some-college,10,Never-married,Adm-clerical,Other-relative,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,255058,Bachelors,13,Divorced,Prof-specialty,Own-child,White,Male,0,4,2,United-States,<=50K\n2,Private,120277,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,120046,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Private,209384,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,742903,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,147869,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,208117,10th,6,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Private,268965,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,41210,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,128378,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,131291,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,187046,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,221672,12th,8,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,70100,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,199586,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,243743,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,State-gov,375931,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,139012,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Vietnam,>50K\n2,Private,167617,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,88269,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,70377,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,431745,Some-college,10,Never-married,Other-service,Not-in-family,Black,Female,0,0,0,United-States,<=50K\n1,Private,72967,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,174373,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n2,Private,145178,HS-grad,9,Separated,Tech-support,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,?,138938,HS-grad,9,Married-civ-spouse,?,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,113948,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,135446,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,43711,HS-grad,9,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,137304,Bachelors,13,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,4,United-States,<=50K\n0,Private,180690,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,135384,HS-grad,9,Separated,Machine-op-inspct,Unmarried,Black,Female,0,0,2,United-States,<=50K\n3,Private,178137,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,113440,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n2,Private,110243,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,165981,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,246635,Some-college,10,Never-married,Sales,Own-child,White,Female,1,0,0,United-States,<=50K\n1,Private,553405,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,137506,9th,5,Widowed,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,313730,Assoc-acdm,12,Never-married,Farming-fishing,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Federal-gov,102684,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,Private,265579,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,1,0,2,United-States,<=50K\n4,Private,101338,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Greece,>50K\n4,Private,188903,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,231919,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,?,139770,Masters,14,Married-civ-spouse,?,Wife,White,Female,0,0,3,United-States,>50K\n2,Private,253006,Some-college,10,Separated,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,258425,Assoc-voc,11,Never-married,Sales,Not-in-family,Amer-Indian-Eskimo,Male,1,0,2,United-States,<=50K\n3,Private,168837,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n3,Private,177536,Some-college,10,Divorced,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,168524,Some-college,10,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,108551,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,180477,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Self-emp-not-inc,162970,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,104196,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,329288,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,3,United-States,>50K\n4,Self-emp-not-inc,39398,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,70240,Some-college,10,Never-married,Other-service,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n0,Private,153021,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Local-gov,152461,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,171892,Assoc-acdm,12,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,128141,11th,7,Separated,Tech-support,Unmarried,White,Female,0,0,2,Puerto-Rico,<=50K\n4,Private,182574,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,1,0,1,United-States,<=50K\n1,Local-gov,189145,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Self-emp-inc,218835,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,132994,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,3,0,2,United-States,>50K\n3,Private,83103,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,198103,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,177812,Bachelors,13,Never-married,?,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,144154,Bachelors,13,Never-married,Prof-specialty,Unmarried,White,Female,0,0,4,United-States,<=50K\n4,Private,169086,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,?,>50K\n2,Private,140854,7th-8th,4,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Portugal,<=50K\n0,?,218875,Some-college,10,Never-married,?,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,187589,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,112271,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,3,2,United-States,>50K\n1,Private,199118,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,Mexico,<=50K\n3,Private,178642,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,113708,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Federal-gov,185670,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,303187,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Asian-Pac-Islander,Male,0,0,2,?,>50K\n3,Private,209739,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n3,Self-emp-not-inc,155278,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,119422,Assoc-voc,11,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,326292,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,212512,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,33440,HS-grad,9,Separated,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,State-gov,185180,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n3,Self-emp-not-inc,504941,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,192014,9th,5,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,192755,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,Canada,>50K\n4,Private,220896,Prof-school,15,Divorced,Other-service,Not-in-family,White,Male,4,0,3,United-States,>50K\n0,Private,189832,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n2,Private,235638,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Private,134367,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,171231,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Peru,<=50K\n0,Private,253529,12th,8,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n3,State-gov,210557,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,362835,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,186479,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,115360,Masters,14,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,184806,Assoc-acdm,12,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,450141,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,1,United-States,>50K\n2,Private,200479,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,152493,HS-grad,9,Divorced,Transport-moving,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,135791,Bachelors,13,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,3,Cuba,>50K\n2,Self-emp-not-inc,50096,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,173832,Masters,14,Divorced,Sales,Not-in-family,White,Male,4,0,2,United-States,>50K\n3,Private,224198,HS-grad,9,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n4,Private,111553,9th,5,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,191380,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,34242,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,100054,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,3,4,United-States,>50K\n4,Private,110103,HS-grad,9,Widowed,Craft-repair,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,196626,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n4,?,194480,11th,7,Married-civ-spouse,?,Husband,White,Male,0,4,0,United-States,>50K\n1,Private,190040,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,152629,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Federal-gov,263162,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,205581,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,155434,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,415922,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Other,Male,0,0,1,United-States,<=50K\n2,Local-gov,221581,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,135089,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,117774,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Portugal,<=50K\n3,Private,206707,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,315640,Masters,14,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Iran,>50K\n3,Private,210736,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,3,United-States,>50K\n0,Local-gov,456665,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,133935,Some-college,10,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,106982,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,4,United-States,<=50K\n1,Private,126569,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,209954,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,46401,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,410216,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,410509,HS-grad,9,Divorced,Handlers-cleaners,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,382199,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,84130,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,243380,Masters,14,Divorced,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,329635,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,>50K\n0,?,265434,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,241752,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,172579,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,121179,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,76131,5th-6th,3,Married-civ-spouse,Other-service,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n4,Private,138777,Bachelors,13,Married-civ-spouse,Protective-serv,Wife,White,Female,0,0,2,Germany,>50K\n2,State-gov,195212,Masters,14,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,3,United-States,<=50K\n0,Private,315135,Some-college,10,Never-married,Transport-moving,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,248406,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,283314,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,4,0,2,?,>50K\n1,Private,231601,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,126807,Masters,14,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,1,4,United-States,<=50K\n1,Private,198513,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,162651,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,Columbia,<=50K\n4,Private,197613,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,>50K\n3,Private,184098,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,187505,Masters,14,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,?,<=50K\n3,Private,174655,7th-8th,4,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,161123,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,390369,1st-4th,2,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,Mexico,<=50K\n1,Self-emp-not-inc,354520,HS-grad,9,Married-spouse-absent,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,364631,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,Mexico,<=50K\n1,Private,323120,Assoc-acdm,12,Never-married,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,>50K\n4,State-gov,32367,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,30102,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,2,3,?,<=50K\n3,Private,229259,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,274818,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,248476,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,98080,Some-college,10,Never-married,Adm-clerical,Other-relative,Other,Male,0,0,2,India,<=50K\n2,Private,56651,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,216411,1st-4th,2,Never-married,Adm-clerical,Unmarried,White,Female,0,0,1,Dominican-Republic,<=50K\n1,Private,226443,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,4,United-States,>50K\n0,Private,119039,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,136277,Bachelors,13,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Local-gov,149508,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,449376,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,2,United-States,>50K\n0,Private,143450,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,0,United-States,<=50K\n2,Private,227065,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,175801,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,260346,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,54159,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,143246,Some-college,10,Never-married,Machine-op-inspct,Own-child,Black,Female,1,0,2,United-States,<=50K\n1,Private,115854,Some-college,10,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,138441,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,149704,10th,6,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,22245,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,104017,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,2,3,United-States,<=50K\n3,Private,165468,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,181557,Some-college,10,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,220694,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,Local-gov,190330,Assoc-voc,11,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,109209,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,187815,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Federal-gov,236648,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,53306,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,418645,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,217530,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,Mexico,<=50K\n0,Private,135066,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Local-gov,38455,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,53553,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,117857,11th,7,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,379376,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,191932,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Female,0,4,2,United-States,<=50K\n1,Private,234067,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,348886,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,142490,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,?,155190,Bachelors,13,Married-civ-spouse,?,Husband,Black,Male,1,0,0,United-States,<=50K\n0,Private,176178,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,149220,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,188047,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Private,258102,Assoc-voc,11,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,185216,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,219701,12th,8,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,Cuba,<=50K\n2,Private,235523,HS-grad,9,Separated,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n1,Private,100375,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,24273,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n0,Private,224115,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,187795,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,3,3,United-States,>50K\n1,Private,161007,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,262723,Some-college,10,Never-married,Machine-op-inspct,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,33474,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,167151,Bachelors,13,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,3,United-States,<=50K\n2,Self-emp-inc,222532,Prof-school,15,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,48195,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,89089,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,179151,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,192589,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,236149,HS-grad,9,Never-married,Exec-managerial,Own-child,White,Female,0,0,3,United-States,<=50K\n4,Private,110820,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,113464,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Other,Male,0,0,2,Dominican-Republic,<=50K\n2,Self-emp-not-inc,248929,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,257758,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,198660,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,195532,Bachelors,13,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,208046,HS-grad,9,Never-married,Sales,Own-child,Black,Female,0,0,0,United-States,<=50K\n1,Self-emp-inc,72744,HS-grad,9,Divorced,Other-service,Unmarried,White,Male,0,0,1,United-States,<=50K\n4,?,65072,10th,6,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,313479,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n4,Private,262681,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n3,State-gov,305319,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,158958,HS-grad,9,Never-married,Priv-house-serv,Other-relative,Black,Female,0,0,2,Honduras,<=50K\n0,Self-emp-not-inc,47039,Assoc-voc,11,Never-married,Farming-fishing,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,150057,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,55861,Assoc-acdm,12,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,225053,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n2,Self-emp-not-inc,136986,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,?,342480,11th,7,Never-married,?,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,?,335124,Some-college,10,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,29874,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,191146,Some-college,10,Divorced,Sales,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,154164,Assoc-acdm,12,Married-civ-spouse,Adm-clerical,Husband,White,Male,4,0,0,?,>50K\n2,Private,287008,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,397877,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,1,United-States,<=50K\n2,Local-gov,108765,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,Private,215251,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n4,Private,132586,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,328610,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,264048,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,>50K\n4,?,98867,5th-6th,3,Widowed,?,Not-in-family,Black,Male,0,0,1,United-States,<=50K\n2,Private,166289,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,186328,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Federal-gov,59948,HS-grad,9,Never-married,Prof-specialty,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Private,231793,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,487770,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,167536,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,250736,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,4,United-States,<=50K\n0,?,197057,10th,6,Never-married,?,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Self-emp-not-inc,448026,Assoc-voc,11,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,217775,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,3,United-States,>50K\n0,Federal-gov,154394,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,244933,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,155362,HS-grad,9,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,187563,Masters,14,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n4,?,108398,11th,7,Widowed,?,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,69235,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,<=50K\n4,Private,174032,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n1,State-gov,174957,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,31781,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,1,1,United-States,<=50K\n3,Private,278322,Bachelors,13,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,144514,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,>50K\n2,Private,255824,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,443858,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,114066,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,103860,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,132839,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,State-gov,290688,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,?,187439,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n0,Private,170302,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,White,Male,0,3,2,United-States,<=50K\n3,Private,74984,10th,6,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,94774,10th,6,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,72896,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,197749,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,1,2,United-States,<=50K\n1,Private,182509,Assoc-voc,11,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,233117,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,102729,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,172706,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Self-emp-inc,295254,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,101426,HS-grad,9,Never-married,Protective-serv,Unmarried,Black,Male,0,0,2,United-States,<=50K\n4,Private,185603,10th,6,Widowed,Tech-support,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,289620,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,?,>50K\n2,Private,179137,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,222199,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,320305,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n0,?,111340,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,1,2,United-States,<=50K\n1,Federal-gov,86150,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,2,0,2,United-States,>50K\n4,Private,124648,HS-grad,9,Widowed,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,175761,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n0,Private,148948,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,230355,Some-college,10,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,Cuba,<=50K\n3,State-gov,277203,Some-college,10,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,176690,9th,5,Widowed,Other-service,Not-in-family,White,Female,0,0,2,England,<=50K\n4,Self-emp-inc,119182,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Greece,<=50K\n1,Private,181353,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,311293,HS-grad,9,Never-married,Other-service,Own-child,Black,Male,0,0,1,United-States,<=50K\n0,?,132962,12th,8,Never-married,?,Own-child,Black,Male,0,0,1,United-States,<=50K\n2,Private,155478,Some-college,10,Divorced,Tech-support,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,46706,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,142326,Assoc-voc,11,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,220454,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,105779,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,362623,10th,6,Married-civ-spouse,Other-service,Husband,White,Male,0,1,1,Mexico,<=50K\n2,Private,115806,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,351324,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,131712,11th,7,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,181118,HS-grad,9,Never-married,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,?,214087,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,181291,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,Italy,<=50K\n2,Private,146908,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,262777,Masters,14,Separated,Exec-managerial,Unmarried,Asian-Pac-Islander,Male,0,0,2,China,<=50K\n3,Local-gov,394765,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,207335,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,>50K\n0,Private,133712,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,State-gov,105479,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,140092,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,53232,Prof-school,15,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,3,United-States,>50K\n4,Private,178154,10th,6,Widowed,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,202203,Bachelors,13,Never-married,Adm-clerical,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,49340,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,106207,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,103064,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,79190,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,473449,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,189498,11th,7,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,149902,Masters,14,Never-married,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n2,Private,150098,Some-college,10,Married-civ-spouse,Sales,Husband,Black,Male,0,0,3,United-States,>50K\n2,Private,100451,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,193094,HS-grad,9,Never-married,Craft-repair,Own-child,White,Female,0,0,3,United-States,<=50K\n2,Private,202950,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,161444,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,303867,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,143912,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,?,42623,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,?,145064,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,199287,9th,5,Never-married,Priv-house-serv,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,250733,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,372525,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,338162,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,2,United-States,<=50K\n2,Private,154210,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,United-States,>50K\n3,Private,158702,10th,6,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,199118,Some-college,10,Never-married,Tech-support,Own-child,White,Female,0,0,2,Nicaragua,<=50K\n4,Private,104455,Some-college,10,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,2,Philippines,<=50K\n2,Self-emp-not-inc,210013,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,440934,Some-college,10,Never-married,Sales,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,375833,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,289551,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,272729,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,State-gov,176185,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,155403,HS-grad,9,Divorced,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n3,Federal-gov,239321,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,365745,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,107399,HS-grad,9,Separated,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,93394,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n1,Self-emp-not-inc,143078,Assoc-voc,11,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,283314,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,3,2,United-States,>50K\n2,Private,194668,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,170301,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-inc,162442,Masters,14,Never-married,Sales,Own-child,White,Female,4,0,2,United-States,>50K\n0,Private,456430,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,337424,Prof-school,15,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,160291,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,Germany,<=50K\n2,Private,341204,Bachelors,13,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,148084,HS-grad,9,Never-married,Handlers-cleaners,Unmarried,Black,Female,0,0,2,Dominican-Republic,<=50K\n1,Private,102476,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,183161,12th,8,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,160029,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,152066,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,175942,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,?,>50K\n4,Private,387669,1st-4th,2,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,179824,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,107882,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,?,65249,12th,8,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,267879,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,1,0,3,United-States,>50K\n3,Private,150999,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,69758,Assoc-acdm,12,Divorced,Protective-serv,Not-in-family,Asian-Pac-Islander,Male,0,0,3,United-States,>50K\n0,Private,180795,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,257283,HS-grad,9,Never-married,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,196344,5th-6th,3,Married-civ-spouse,Other-service,Husband,White,Male,0,2,1,Mexico,<=50K\n1,Private,155151,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-not-inc,368140,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,95079,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n2,?,254773,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,181659,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,?,215620,HS-grad,9,Never-married,?,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,169104,HS-grad,9,Married-civ-spouse,Exec-managerial,Other-relative,Asian-Pac-Islander,Male,0,0,4,Thailand,<=50K\n1,Local-gov,100446,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n3,Private,189680,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,376474,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,112494,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,?,212491,HS-grad,9,Divorced,?,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n4,Local-gov,254218,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,421200,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,426589,HS-grad,9,Married-spouse-absent,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,335015,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,78605,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,123749,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,245500,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Female,0,0,1,?,<=50K\n1,Private,226443,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Self-emp-not-inc,150630,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,257124,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,127865,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,2,0,1,United-States,<=50K\n3,Private,27432,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,161765,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,42703,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,209641,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,93131,Some-college,10,Never-married,Other-service,Not-in-family,Asian-Pac-Islander,Male,1,0,0,China,<=50K\n3,Private,191821,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,410009,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,334291,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,4,2,United-States,<=50K\n4,Self-emp-inc,274363,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,102456,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,0,United-States,<=50K\n1,State-gov,102268,Some-college,10,Never-married,Prof-specialty,Own-child,White,Male,0,0,1,United-States,<=50K\n3,Private,220979,Some-college,10,Divorced,Tech-support,Not-in-family,Amer-Indian-Eskimo,Male,4,0,2,United-States,>50K\n1,?,111377,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,187847,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,40052,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,133375,HS-grad,9,Divorced,Sales,Unmarried,White,Female,0,0,3,United-States,<=50K\n3,Private,226032,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,211032,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,392812,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,174724,Assoc-voc,11,Divorced,Adm-clerical,Own-child,Black,Female,1,0,2,United-States,<=50K\n3,Local-gov,363405,Some-college,10,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,42750,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,174112,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n4,Self-emp-not-inc,326936,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,188069,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,366089,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,162160,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,114937,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,2,0,2,United-States,<=50K\n4,Private,189932,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n4,Private,168447,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,154210,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Other,Male,0,3,2,Japan,>50K\n1,Private,211331,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,Mexico,<=50K\n4,Private,157749,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,84587,5th-6th,3,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,0,Japan,<=50K\n1,Private,269246,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,305129,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,253556,HS-grad,9,Divorced,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,229116,Prof-school,15,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,4,United-States,>50K\n2,Private,111381,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,121201,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,271836,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,0,3,United-States,>50K\n3,Private,116641,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,0,France,<=50K\n2,Private,171524,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,2,?,<=50K\n3,Private,145409,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,174575,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,149135,Assoc-acdm,12,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,234096,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,210973,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,269323,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,29087,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,177831,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Hungary,<=50K\n2,Self-emp-not-inc,167106,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,4,South,<=50K\n4,Private,118993,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,102875,Assoc-acdm,12,Never-married,Sales,Own-child,White,Female,0,0,2,India,<=50K\n4,?,29106,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,101103,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,135020,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,182390,11th,7,Separated,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Private,174102,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,?,138153,Some-college,10,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,162282,Assoc-acdm,12,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Private,280292,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,307294,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,Private,94156,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,4,United-States,>50K\n4,?,199033,9th,5,Married-civ-spouse,?,Wife,Black,Female,0,0,1,United-States,<=50K\n4,Private,57408,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,210922,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,?,<=50K\n0,Private,138994,HS-grad,9,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n0,Private,416103,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Private,166740,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,United-States,>50K\n3,Self-emp-inc,139268,7th-8th,4,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,165474,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,100316,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,?,49028,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Self-emp-inc,85566,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,4,United-States,<=50K\n1,Private,58150,HS-grad,9,Separated,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,376548,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,398166,11th,7,Never-married,Other-service,Own-child,Black,Male,0,0,2,United-States,<=50K\n2,Private,86797,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,161819,HS-grad,9,Separated,Other-service,Unmarried,Black,Female,0,0,1,United-States,<=50K\n0,Private,187125,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,226535,HS-grad,9,Never-married,Transport-moving,Not-in-family,White,Male,2,0,2,United-States,<=50K\n0,Self-emp-inc,216889,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,3,2,United-States,>50K\n4,Self-emp-not-inc,135517,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,336951,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,State-gov,101603,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n1,Local-gov,205931,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n2,Federal-gov,105119,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,200316,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,132859,10th,6,Never-married,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,Private,137031,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,184975,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,165539,HS-grad,9,Never-married,Prof-specialty,Not-in-family,Black,Female,2,0,2,Jamaica,<=50K\n1,Private,48099,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,47012,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,3,0,2,United-States,>50K\n4,Local-gov,195409,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,20795,HS-grad,9,Divorced,Protective-serv,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,State-gov,484879,Bachelors,13,Separated,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,276087,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Federal-gov,45937,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,3,United-States,>50K\n3,Federal-gov,102359,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n1,Private,186824,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,203914,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,209808,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,228516,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,49424,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,359067,Some-college,10,Never-married,Sales,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,340171,HS-grad,9,Separated,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,142037,11th,7,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,157437,Bachelors,13,Never-married,Protective-serv,Not-in-family,White,Female,2,0,3,United-States,<=50K\n3,State-gov,142287,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n1,State-gov,189794,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,258289,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,352849,HS-grad,9,Never-married,Sales,Other-relative,Black,Female,0,2,1,United-States,<=50K\n2,Private,162322,Assoc-voc,11,Separated,Craft-repair,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,200295,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,?,296372,HS-grad,9,Divorced,?,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,190333,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,211518,Bachelors,13,Divorced,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Private,264098,10th,6,Widowed,Transport-moving,Not-in-family,White,Female,1,0,2,United-States,<=50K\n2,Private,393762,Some-college,10,Separated,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Local-gov,181970,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,110901,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,4,3,United-States,>50K\n0,Private,92863,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,111368,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,?,83601,12th,8,Widowed,?,Unmarried,White,Female,0,0,4,United-States,<=50K\n3,Private,164682,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,3,?,<=50K\n2,Private,166549,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,176566,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Wife,White,Female,1,0,2,United-States,>50K\n0,Private,201613,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,285294,Assoc-acdm,12,Never-married,Adm-clerical,Other-relative,Black,Female,0,0,3,United-States,<=50K\n4,Private,100301,10th,6,Widowed,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,120320,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,218650,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,339482,Masters,14,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,301352,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Local-gov,225978,Assoc-voc,11,Separated,Craft-repair,Unmarried,Black,Male,0,0,2,United-States,<=50K\n2,Private,337778,Some-college,10,Divorced,Sales,Not-in-family,White,Male,2,0,2,United-States,<=50K\n3,Private,241350,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,266645,12th,8,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,100863,12th,8,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,227386,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,315971,Masters,14,Divorced,Other-service,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,177287,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,Private,169022,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,205803,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Federal-gov,277700,Some-college,10,Never-married,Tech-support,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Self-emp-not-inc,166371,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,?,295763,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,152519,Doctorate,16,Widowed,Prof-specialty,Not-in-family,White,Male,4,0,0,United-States,>50K\n2,Private,438696,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,34266,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,136391,HS-grad,9,Never-married,Tech-support,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,435638,Some-college,10,Never-married,Craft-repair,Not-in-family,White,Male,0,2,2,United-States,<=50K\n0,Private,51973,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Female,0,0,3,Japan,<=50K\n2,Private,109800,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n2,Federal-gov,260761,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,109015,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,>50K\n1,Private,272944,Bachelors,13,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,290498,Preschool,1,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,176864,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,State-gov,169914,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,205759,Doctorate,16,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,271075,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n0,Private,239995,11th,7,Never-married,Sales,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,Private,65663,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,259865,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,Mexico,<=50K\n2,Self-emp-not-inc,34722,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,225821,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,1,2,United-States,>50K\n2,Private,191503,5th-6th,3,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n3,?,110243,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,3,0,United-States,>50K\n1,Self-emp-not-inc,227429,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,Yugoslavia,<=50K\n3,Private,174452,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,209085,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,192386,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,2,United-States,>50K\n0,?,234877,11th,7,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,320862,Some-college,10,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,535027,Some-college,10,Never-married,Transport-moving,Unmarried,Black,Male,0,0,0,United-States,<=50K\n1,Private,137421,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,1,Japan,<=50K\n3,Private,195727,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,163229,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,1,United-States,<=50K\n1,Private,50753,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,4,United-States,<=50K\n3,Private,173503,12th,8,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,29078,11th,7,Never-married,Adm-clerical,Own-child,Amer-Indian-Eskimo,Female,0,0,0,United-States,<=50K\n1,Private,360743,Bachelors,13,Never-married,Adm-clerical,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,142149,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,464552,5th-6th,3,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n4,Private,47444,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,>50K\n2,Private,24721,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n3,Private,187492,Bachelors,13,Divorced,Craft-repair,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n3,Private,229318,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,Trinadad&Tobago,<=50K\n3,Private,358382,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n4,State-gov,119832,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,414166,12th,8,Never-married,Other-service,Own-child,Black,Female,0,0,1,United-States,<=50K\n1,Private,147638,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,Asian-Pac-Islander,Female,0,0,2,Hong,<=50K\n1,Self-emp-not-inc,33016,Assoc-voc,11,Divorced,Other-service,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,?,386962,10th,6,Never-married,?,Own-child,White,Male,0,0,2,Mexico,<=50K\n1,Private,39248,Bachelors,13,Never-married,Tech-support,Not-in-family,Other,Male,0,0,2,United-States,<=50K\n4,Private,232683,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,0,France,>50K\n1,Self-emp-not-inc,55912,9th,5,Never-married,Craft-repair,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Private,113152,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,150095,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,114424,Some-college,10,Separated,Machine-op-inspct,Other-relative,Black,Female,0,0,2,United-States,<=50K\n1,?,408417,Some-college,10,Married-AF-spouse,?,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,163167,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,86009,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n1,Federal-gov,316582,HS-grad,9,Married-civ-spouse,Other-service,Wife,White,Female,2,0,2,United-States,>50K\n3,Self-emp-not-inc,165219,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,99829,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,275095,9th,5,Widowed,Exec-managerial,Unmarried,White,Female,0,0,3,United-States,<=50K\n2,Private,167650,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,141820,10th,6,Divorced,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,108253,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Other,Female,0,0,2,United-States,<=50K\n2,Private,156526,Some-college,10,Separated,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-inc,185366,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,314739,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,336101,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,49115,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Self-emp-inc,186488,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,Puerto-Rico,<=50K\n0,?,158826,12th,8,Never-married,?,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Private,218415,11th,7,Separated,Handlers-cleaners,Other-relative,White,Female,0,0,2,United-States,<=50K\n1,Private,76978,HS-grad,9,Never-married,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n3,Private,213408,9th,5,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,Cuba,<=50K\n2,Private,181265,Assoc-acdm,12,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Local-gov,242956,11th,7,Never-married,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,226696,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,186932,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n3,Federal-gov,107079,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,154950,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,48597,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,196344,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,>50K\n2,Self-emp-inc,152414,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,222966,9th,5,Widowed,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,272671,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,235520,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,232766,HS-grad,9,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,309990,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,3,0,3,United-States,>50K\n2,Private,95639,HS-grad,9,Separated,Handlers-cleaners,Unmarried,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,?,177161,HS-grad,9,Never-married,?,Own-child,Other,Female,0,0,2,United-States,<=50K\n3,Local-gov,133963,HS-grad,9,Separated,Adm-clerical,Not-in-family,White,Female,0,0,1,?,<=50K\n2,Self-emp-not-inc,240786,Bachelors,13,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Local-gov,141186,Masters,14,Divorced,Prof-specialty,Not-in-family,White,Female,3,0,1,United-States,>50K\n4,Private,109221,7th-8th,4,Widowed,Priv-house-serv,Not-in-family,White,Female,0,4,3,Puerto-Rico,<=50K\n1,Private,337953,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,1,0,2,United-States,<=50K\n3,State-gov,189762,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,?,40190,12th,8,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n4,Private,171824,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,83545,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,>50K\n1,Private,142712,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,91893,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,443701,Some-college,10,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n2,Private,438427,Some-college,10,Never-married,Sales,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,69372,Doctorate,16,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,2,India,>50K\n3,Private,243190,Assoc-voc,11,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,109762,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,91189,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Federal-gov,205805,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,2,United-States,>50K\n3,Local-gov,212050,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,152652,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,157193,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,1,Italy,<=50K\n1,Private,36592,11th,7,Never-married,Farming-fishing,Unmarried,White,Male,0,0,3,United-States,<=50K\n2,Private,192664,Some-college,10,Divorced,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,99280,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,168621,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,127048,Some-college,10,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,167610,HS-grad,9,Divorced,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,108320,HS-grad,9,Divorced,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,369643,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,232388,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,513719,HS-grad,9,Separated,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,Private,27073,Some-college,10,Never-married,Adm-clerical,Unmarried,Other,Female,0,0,2,United-States,<=50K\n3,Private,105428,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,167284,Assoc-voc,11,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,320615,7th-8th,4,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n1,Local-gov,47284,HS-grad,9,Never-married,Protective-serv,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,45156,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,166269,Some-college,10,Divorced,Sales,Unmarried,White,Male,0,0,3,United-States,<=50K\n1,Federal-gov,236418,Some-college,10,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,311478,HS-grad,9,Never-married,Handlers-cleaners,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,256908,Doctorate,16,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,256796,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,2,United-States,<=50K\n1,Private,168138,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,244413,12th,8,Married-civ-spouse,Craft-repair,Husband,Black,Male,0,0,1,Ecuador,<=50K\n4,?,52728,Bachelors,13,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,?,223019,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,215443,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,4,United-States,<=50K\n2,Private,99665,9th,5,Married-civ-spouse,Tech-support,Wife,White,Female,0,0,4,United-States,<=50K\n2,Private,243485,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,169872,HS-grad,9,Separated,Adm-clerical,Unmarried,White,Female,2,0,2,United-States,<=50K\n3,Private,116338,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,109989,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,144401,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Asian-Pac-Islander,Female,0,0,2,Philippines,>50K\n0,Local-gov,199555,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,1,2,United-States,<=50K\n1,Self-emp-not-inc,105229,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,185216,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,155278,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,371408,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n4,Private,177271,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,234891,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,Private,356286,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n4,?,225894,Preschool,1,Widowed,?,Not-in-family,White,Female,0,0,2,Guatemala,<=50K\n0,Private,181781,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n0,Private,197756,Some-college,10,Married-civ-spouse,Sales,Wife,White,Female,0,0,1,United-States,<=50K\n4,Local-gov,216269,Assoc-acdm,12,Widowed,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Private,33931,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,151584,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,>50K\n3,Private,286085,Some-college,10,Widowed,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,111385,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,280966,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,221178,HS-grad,9,Separated,Other-service,Other-relative,White,Male,0,0,1,United-States,<=50K\n4,Private,74422,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,Mexico,<=50K\n0,Private,103031,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n3,State-gov,209739,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,State-gov,112137,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Other,Female,0,0,2,Canada,<=50K\n1,Private,138537,11th,7,Never-married,Exec-managerial,Not-in-family,Black,Male,0,0,3,United-States,<=50K\n4,Private,135378,7th-8th,4,Widowed,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n3,Private,175006,1st-4th,2,Separated,Machine-op-inspct,Other-relative,Black,Male,0,0,3,United-States,<=50K\n1,Private,182194,Assoc-acdm,12,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,194686,Some-college,10,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,0,United-States,<=50K\n1,Private,70034,Some-college,10,Never-married,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,439263,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,Black,Male,0,0,2,United-States,>50K\n4,Private,74749,HS-grad,9,Married-civ-spouse,Sales,Wife,White,Female,0,0,0,United-States,<=50K\n1,Private,231638,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,197623,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,148409,Some-college,10,Never-married,?,Own-child,White,Male,0,2,2,United-States,<=50K\n0,?,40299,10th,6,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,96260,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-not-inc,62143,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,4,0,2,United-States,>50K\n0,Private,193027,HS-grad,9,Married-spouse-absent,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,?,334105,11th,7,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Private,31411,11th,7,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,?,<=50K\n4,Private,140516,Some-college,10,Widowed,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,155963,9th,5,Divorced,Transport-moving,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,119891,Masters,14,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,206609,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,425444,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,White,Female,4,0,3,United-States,>50K\n3,Private,114674,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n3,Federal-gov,57679,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Male,0,0,2,United-States,<=50K\n3,Private,213140,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n3,Local-gov,295494,Assoc-voc,11,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,182862,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,1,United-States,<=50K\n3,Private,178529,11th,7,Divorced,Protective-serv,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,214031,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,4,United-States,<=50K\n0,Private,350538,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n1,Private,238073,Some-college,10,Never-married,Machine-op-inspct,Other-relative,White,Male,0,0,2,Columbia,<=50K\n1,Private,194640,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,139770,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,1,United-States,>50K\n0,Private,164177,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,99470,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n4,Private,359367,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,45612,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,Black,Female,0,0,0,United-States,<=50K\n1,Private,127651,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,193132,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,314798,Some-college,10,Never-married,Protective-serv,Not-in-family,White,Male,0,0,3,United-States,>50K\n1,Private,108438,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,165815,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,?,136172,11th,7,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n0,Private,127159,Some-college,10,Never-married,Other-service,Other-relative,White,Female,0,0,1,?,<=50K\n0,?,220168,Some-college,10,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n3,Private,127677,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,119941,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,93699,HS-grad,9,Widowed,Machine-op-inspct,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,Federal-gov,196649,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Private,332763,HS-grad,9,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,158363,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,249039,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,1,0,2,United-States,>50K\n2,Private,454585,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,Mexico,<=50K\n1,Private,121321,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,229148,HS-grad,9,Divorced,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,221284,HS-grad,9,Never-married,Sales,Own-child,White,Male,0,0,4,United-States,<=50K\n2,Private,428251,Some-college,10,Never-married,Craft-repair,Unmarried,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,77660,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,3,United-States,>50K\n0,Private,139722,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,Puerto-Rico,<=50K\n1,State-gov,171151,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,94819,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,214546,Some-college,10,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-inc,190482,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,283029,9th,5,Never-married,Craft-repair,Own-child,White,Male,0,0,3,United-States,<=50K\n3,?,142719,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,244172,5th-6th,3,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,120238,Assoc-voc,11,Never-married,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,66095,Some-college,10,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,129232,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,44121,Some-college,10,Married-civ-spouse,Other-service,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,243678,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,118786,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,176900,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,155574,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,76625,Assoc-acdm,12,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,192254,Some-college,10,Never-married,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Private,238802,HS-grad,9,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,247731,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,397606,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n0,?,370057,Some-college,10,Never-married,?,Unmarried,White,Female,0,0,2,United-States,<=50K\n4,?,190873,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,145879,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n3,Private,618130,HS-grad,9,Divorced,Other-service,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,542762,Bachelors,13,Never-married,Sales,Other-relative,Black,Male,0,0,3,United-States,<=50K\n1,Private,144124,Some-college,10,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,190539,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,92462,Assoc-acdm,12,Never-married,Sales,Unmarried,Black,Male,0,0,1,United-States,<=50K\n3,Private,129974,Bachelors,13,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,State-gov,254890,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,261207,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,Cuba,>50K\n2,Self-emp-inc,206362,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,27804,Some-college,10,Divorced,Priv-house-serv,Unmarried,Amer-Indian-Eskimo,Female,0,0,1,United-States,<=50K\n4,Self-emp-not-inc,771836,Assoc-acdm,12,Divorced,Prof-specialty,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,101108,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n3,Private,255466,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,204494,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,4,Germany,<=50K\n3,Private,271918,9th,5,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,152619,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,107318,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,130773,Bachelors,13,Never-married,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,?,117210,Some-college,10,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Private,148549,Assoc-acdm,12,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,181530,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,365739,Assoc-acdm,12,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,182429,HS-grad,9,Widowed,Transport-moving,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,381510,Some-college,10,Never-married,Other-service,Own-child,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-not-inc,116789,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n2,Self-emp-inc,196554,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,335878,Assoc-acdm,12,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,184120,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,260084,Some-college,10,Divorced,Adm-clerical,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Private,160369,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,164901,11th,7,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,?,96844,HS-grad,9,Married-civ-spouse,?,Other-relative,White,Female,0,0,2,United-States,<=50K\n4,Private,124566,5th-6th,3,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,473133,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,335223,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,380086,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,3,0,3,United-States,>50K\n1,Private,198265,1st-4th,2,Never-married,Exec-managerial,Own-child,White,Male,0,0,1,United-States,<=50K\n1,?,32207,HS-grad,9,Divorced,?,Not-in-family,White,Male,0,0,4,United-States,<=50K\n4,Private,288684,5th-6th,3,Divorced,Machine-op-inspct,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,302604,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,>50K\n4,Private,170290,HS-grad,9,Divorced,Other-service,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Self-emp-inc,195398,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,2,3,Canada,>50K\n3,Local-gov,256923,Some-college,10,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,2,United-States,>50K\n1,Private,464552,9th,5,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,Mexico,<=50K\n1,Private,112754,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,0,0,3,United-States,<=50K\n4,Private,176118,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,>50K\n4,?,316627,10th,6,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,146628,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,1,0,1,United-States,<=50K\n1,Private,108822,HS-grad,9,Never-married,Transport-moving,Unmarried,White,Male,0,0,2,United-States,<=50K\n1,Private,208608,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,1,United-States,<=50K\n0,Private,317019,11th,7,Separated,Other-service,Unmarried,White,Female,0,0,0,United-States,<=50K\n0,Private,250978,Some-college,10,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,4,United-States,<=50K\n3,Private,224559,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n4,State-gov,138593,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,175614,10th,6,Never-married,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,396099,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,122493,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,2,United-States,>50K\n3,Local-gov,182677,Bachelors,13,Married-civ-spouse,Protective-serv,Husband,Asian-Pac-Islander,Male,0,0,3,Philippines,<=50K\n4,State-gov,247624,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,210458,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,Mexico,<=50K\n0,Private,91639,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,334096,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Female,0,0,1,United-States,<=50K\n0,?,183945,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n3,Private,78022,Assoc-acdm,12,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n3,Self-emp-inc,318351,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,2,2,United-States,<=50K\n0,Private,233280,Bachelors,13,Divorced,Adm-clerical,Not-in-family,White,Female,3,0,4,United-States,>50K\n0,Private,100188,Some-college,10,Never-married,Prof-specialty,Own-child,White,Female,0,0,0,United-States,<=50K\n1,Private,85572,Bachelors,13,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,61735,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,>50K\n0,Private,206827,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,3,United-States,<=50K\n0,Private,210053,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Local-gov,172016,Bachelors,13,Divorced,Prof-specialty,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,?,138153,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n1,?,183855,11th,7,Never-married,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,188362,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n2,Private,191429,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,357596,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,278404,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,180339,HS-grad,9,Never-married,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n2,Private,355431,Some-college,10,Divorced,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,223212,Some-college,10,Never-married,Handlers-cleaners,Other-relative,White,Male,0,0,2,Mexico,<=50K\n1,Private,116371,HS-grad,9,Divorced,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,?,199018,Some-college,10,Divorced,?,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Private,435266,Doctorate,16,Separated,Exec-managerial,Not-in-family,White,Female,4,0,3,United-States,>50K\n4,Private,345697,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,>50K\n3,Private,253973,10th,6,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,?,191149,Bachelors,13,Married-civ-spouse,?,Wife,White,Female,0,0,1,United-States,>50K\n2,Private,197344,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,437161,Some-college,10,Never-married,Other-service,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,?,94268,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n3,Self-emp-inc,207841,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,2,0,2,United-States,>50K\n1,Private,46492,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,State-gov,190325,Some-college,10,Divorced,Tech-support,Unmarried,Black,Female,0,0,3,United-States,<=50K\n3,Private,158944,HS-grad,9,Widowed,Craft-repair,Not-in-family,White,Female,0,0,3,United-States,>50K\n2,Private,228598,Some-college,10,Married-civ-spouse,Handlers-cleaners,Husband,Other,Male,0,0,2,Mexico,<=50K\n0,Private,349156,HS-grad,9,Never-married,Farming-fishing,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,246974,12th,8,Never-married,?,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Private,386120,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,4,0,2,United-States,>50K\n1,Private,220678,5th-6th,3,Never-married,Handlers-cleaners,Own-child,Black,Female,0,0,2,Dominican-Republic,<=50K\n2,Private,462964,HS-grad,9,Divorced,Exec-managerial,Not-in-family,White,Male,1,0,3,United-States,<=50K\n0,Private,158603,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,?,167261,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,412933,12th,8,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n4,Local-gov,167027,Some-college,10,Widowed,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,194829,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,145636,Assoc-voc,11,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,3,United-States,>50K\n4,?,123632,Bachelors,13,Never-married,?,Not-in-family,Black,Female,0,0,1,United-States,<=50K\n3,Private,27614,HS-grad,9,Separated,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,324854,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,22245,Some-college,10,Divorced,Sales,Not-in-family,White,Male,0,0,2,Outlying-US(Guam-USVI-etc),<=50K\n1,Private,101352,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,?,238721,Bachelors,13,Divorced,?,Own-child,Black,Female,0,0,2,United-States,<=50K\n0,Private,289982,11th,7,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,399449,11th,7,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n2,?,142804,HS-grad,9,Divorced,?,Unmarried,White,Female,0,0,0,United-States,<=50K\n1,Private,121427,Bachelors,13,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,230959,Bachelors,13,Never-married,Adm-clerical,Own-child,Asian-Pac-Islander,Female,0,0,2,Philippines,<=50K\n2,Private,191342,Masters,14,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,Taiwan,>50K\n4,Private,285610,11th,7,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,207369,Some-college,10,Never-married,Exec-managerial,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Federal-gov,80485,Masters,14,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,State-gov,33474,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,173658,Some-college,10,Separated,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n2,Local-gov,202207,HS-grad,9,Married-spouse-absent,Protective-serv,Not-in-family,White,Male,0,0,4,Germany,>50K\n2,Private,174242,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,2,0,3,United-States,>50K\n0,Private,349212,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,?,54978,7th-8th,4,Never-married,?,Own-child,White,Female,0,0,0,United-States,<=50K\n0,State-gov,81993,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,3,United-States,<=50K\n3,Private,311395,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,?,149017,12th,8,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Self-emp-not-inc,156532,7th-8th,4,Divorced,Farming-fishing,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,53470,Bachelors,13,Divorced,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n1,Private,212578,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,2,United-States,<=50K\n0,Private,227465,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,423605,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,El-Salvador,<=50K\n3,Private,149337,Assoc-acdm,12,Married-spouse-absent,Craft-repair,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,State-gov,38353,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,325385,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,196252,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,110538,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,3,United-States,<=50K\n4,Private,71385,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Female,0,0,0,United-States,<=50K\n1,Private,178449,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,>50K\n1,Federal-gov,366533,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,336326,11th,7,Never-married,Craft-repair,Unmarried,White,Male,1,0,2,United-States,<=50K\n0,Private,335439,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,2,3,United-States,<=50K\n2,Private,184471,Some-college,10,Divorced,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Federal-gov,133526,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,618808,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n0,Private,408385,10th,6,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,156192,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,126569,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,300773,Assoc-acdm,12,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,152109,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,260275,11th,7,Separated,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n0,Private,209650,12th,8,Never-married,Other-service,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,573446,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n2,Private,253189,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,200426,Some-college,10,Never-married,Other-service,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,109667,Masters,14,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n2,?,173651,Assoc-acdm,12,Married-civ-spouse,?,Husband,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,432204,Assoc-acdm,12,Married-civ-spouse,Other-service,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,252013,Some-college,10,Never-married,Adm-clerical,Unmarried,White,Female,0,0,2,Japan,<=50K\n4,?,461484,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,2,0,United-States,>50K\n0,Private,191889,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,112507,12th,8,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,224531,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,Poland,>50K\n0,?,83783,7th-8th,4,Never-married,?,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,346783,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,Cuba,>50K\n3,Federal-gov,72896,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,171393,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,2,2,United-States,<=50K\n1,Private,294931,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,White,Male,0,0,2,Germany,<=50K\n1,Private,198986,HS-grad,9,Never-married,Craft-repair,Other-relative,White,Male,0,0,2,United-States,<=50K\n0,?,264767,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,225623,Some-college,10,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,2,United-States,<=50K\n0,?,48610,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n4,Private,113974,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,334991,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,362059,12th,8,Never-married,Craft-repair,Own-child,White,Male,0,0,1,United-States,<=50K\n2,Private,389725,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n0,Private,330774,9th,5,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,149910,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,Black,Female,0,0,2,United-States,<=50K\n3,Self-emp-inc,99401,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,104266,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,136440,Some-college,10,Never-married,Sales,Own-child,Asian-Pac-Islander,Female,0,0,0,South,<=50K\n4,Private,65478,HS-grad,9,Widowed,Priv-house-serv,Not-in-family,White,Female,0,0,2,England,<=50K\n0,Private,56121,5th-6th,3,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,3,Mexico,<=50K\n2,Private,143939,Some-college,10,Separated,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,231853,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,3,2,United-States,>50K\n0,Private,267706,HS-grad,9,Never-married,Sales,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,328301,Some-college,10,Never-married,Tech-support,Unmarried,White,Female,2,0,3,United-States,>50K\n1,Private,36340,11th,7,Divorced,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,112780,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,113045,Masters,14,Widowed,Exec-managerial,Unmarried,White,Male,4,0,2,United-States,>50K\n3,Private,196504,7th-8th,4,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,State-gov,136216,HS-grad,9,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,158242,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,180062,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,206888,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,77370,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-not-inc,349151,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,1,0,2,United-States,<=50K\n3,Private,113843,Some-college,10,Divorced,Other-service,Unmarried,White,Female,0,0,2,United-States,<=50K\n3,State-gov,176917,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n4,?,226078,11th,7,Divorced,?,Unmarried,Black,Female,0,0,1,United-States,<=50K\n1,Local-gov,177828,Bachelors,13,Separated,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,137304,HS-grad,9,Widowed,Machine-op-inspct,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n1,Private,138441,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,30840,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,149546,Some-college,10,Divorced,Craft-repair,Unmarried,White,Male,0,0,1,United-States,<=50K\n1,Private,145182,HS-grad,9,Never-married,Protective-serv,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,Local-gov,270379,HS-grad,9,Never-married,Adm-clerical,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n0,Self-emp-inc,378036,12th,8,Never-married,Farming-fishing,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,118535,Some-college,10,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n0,Private,239057,HS-grad,9,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Private,107740,HS-grad,9,Widowed,Handlers-cleaners,Not-in-family,White,Male,0,0,1,United-States,<=50K\n4,Private,194804,Preschool,1,Separated,Transport-moving,Not-in-family,Black,Male,4,0,2,United-States,>50K\n3,Self-emp-not-inc,225065,Bachelors,13,Separated,Other-service,Unmarried,White,Female,0,0,2,Nicaragua,<=50K\n1,Private,165412,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Male,0,0,0,United-States,<=50K\n1,Private,211199,10th,6,Never-married,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,State-gov,172962,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,190748,HS-grad,9,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,455379,12th,8,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,112917,Assoc-voc,11,Married-civ-spouse,Tech-support,Husband,Other,Male,0,0,2,Mexico,<=50K\n1,Private,299383,HS-grad,9,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n2,Private,22245,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,49683,Some-college,10,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,1,United-States,<=50K\n4,Local-gov,32846,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,1,0,1,United-States,<=50K\n1,Private,46947,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Private,165609,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n2,Private,226894,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,2,United-States,<=50K\n1,Private,143231,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,173730,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,259425,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,168747,Bachelors,13,Never-married,Transport-moving,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,?,210652,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,40915,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,180303,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Asian-Pac-Islander,Male,0,0,4,South,<=50K\n3,Private,182541,10th,6,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,1,2,United-States,>50K\n1,Private,67306,HS-grad,9,Never-married,Adm-clerical,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n0,?,38032,HS-grad,9,Never-married,?,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,257395,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,29261,Assoc-voc,11,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,205977,Some-college,10,Married-civ-spouse,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,<=50K\n0,?,216639,Some-college,10,Never-married,?,Not-in-family,White,Female,0,3,2,United-States,<=50K\n0,Private,134768,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,32184,HS-grad,9,Divorced,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,138037,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,1,3,United-States,<=50K\n3,Private,174426,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,3,3,United-States,>50K\n2,Private,50162,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n3,Federal-gov,193998,Some-college,10,Never-married,Craft-repair,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,187901,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,Private,82050,11th,7,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,2,0,2,United-States,>50K\n0,?,20057,Some-college,10,Never-married,?,Not-in-family,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n1,Private,144200,HS-grad,9,Never-married,Handlers-cleaners,Other-relative,Other,Male,0,0,1,Columbia,<=50K\n4,Self-emp-not-inc,29441,7th-8th,4,Married-spouse-absent,Farming-fishing,Unmarried,White,Male,0,0,0,United-States,<=50K\n4,Private,195695,HS-grad,9,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Private,274502,Some-college,10,Widowed,Other-service,Unmarried,White,Female,0,0,1,United-States,<=50K\n4,?,239900,HS-grad,9,Divorced,?,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,191954,Assoc-voc,11,Never-married,Sales,Own-child,White,Male,0,0,2,United-States,<=50K\n1,Private,172304,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,174575,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n0,Private,325744,Some-college,10,Never-married,Sales,Other-relative,White,Male,0,0,2,United-States,<=50K\n2,Private,26252,Some-college,10,Separated,Other-service,Unmarried,Amer-Indian-Eskimo,Female,0,0,2,United-States,<=50K\n4,Private,176904,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,504871,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,141702,HS-grad,9,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Private,399088,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n1,Local-gov,409282,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,532969,HS-grad,9,Married-civ-spouse,Other-service,Other-relative,White,Male,0,0,2,Nicaragua,<=50K\n0,?,213366,Some-college,10,Never-married,?,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,188888,12th,8,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,24126,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,151369,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,1,United-States,>50K\n0,Private,115759,HS-grad,9,Never-married,Sales,Not-in-family,White,Female,0,0,1,United-States,<=50K\n4,Self-emp-inc,171355,Masters,14,Divorced,Prof-specialty,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Private,310175,12th,8,Never-married,Other-service,Own-child,White,Female,0,0,0,United-States,<=50K\n2,Private,216116,HS-grad,9,Divorced,Adm-clerical,Unmarried,Black,Female,0,0,2,Jamaica,<=50K\n0,Private,204141,Some-college,10,Never-married,Adm-clerical,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,212894,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,Greece,<=50K\n3,Private,120121,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n4,Self-emp-not-inc,190997,10th,6,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,224566,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,4,0,2,United-States,>50K\n1,Private,235847,Bachelors,13,Never-married,Other-service,Own-child,White,Female,0,0,1,United-States,<=50K\n4,?,124319,Masters,14,Married-civ-spouse,?,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,193807,HS-grad,9,Never-married,Craft-repair,Not-in-family,White,Male,0,2,2,United-States,<=50K\n4,Self-emp-not-inc,215926,7th-8th,4,Married-civ-spouse,Other-service,Husband,White,Male,0,0,1,United-States,<=50K\n0,?,455665,HS-grad,9,Never-married,?,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,36423,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,32878,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,2,United-States,>50K\n0,Private,96862,HS-grad,9,Never-married,Adm-clerical,Own-child,White,Female,1,0,2,United-States,<=50K\n0,Private,187879,9th,5,Never-married,Other-service,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Local-gov,36296,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,75615,HS-grad,9,Married-civ-spouse,Sales,Husband,Black,Male,0,0,2,United-States,<=50K\n0,Private,168807,10th,6,Never-married,Other-service,Own-child,White,Male,0,0,1,United-States,<=50K\n0,State-gov,161783,Bachelors,13,Never-married,Transport-moving,Not-in-family,Black,Male,0,0,2,?,<=50K\n2,State-gov,13492,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Amer-Indian-Eskimo,Male,0,0,4,United-States,<=50K\n4,Private,119769,HS-grad,9,Widowed,Priv-house-serv,Unmarried,Black,Female,0,0,0,United-States,<=50K\n2,Private,313914,Bachelors,13,Separated,Farming-fishing,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,172584,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,1,3,United-States,<=50K\n1,Private,112425,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,157783,HS-grad,9,Married-civ-spouse,Other-service,Husband,Asian-Pac-Islander,Male,0,0,1,Vietnam,<=50K\n1,Private,356882,HS-grad,9,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,156852,Assoc-voc,11,Widowed,Tech-support,Unmarried,White,Female,0,0,0,United-States,<=50K\n4,Self-emp-not-inc,175177,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,>50K\n1,Private,425127,9th,5,Married-civ-spouse,Other-service,Other-relative,White,Female,0,0,1,United-States,<=50K\n1,Local-gov,99761,Assoc-voc,11,Divorced,Adm-clerical,Own-child,White,Female,0,0,2,United-States,<=50K\n2,Private,272950,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,174295,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n0,Local-gov,157678,HS-grad,9,Married-spouse-absent,Machine-op-inspct,Unmarried,White,Female,1,0,2,United-States,<=50K\n3,Private,186224,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,142587,11th,7,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Private,131091,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Self-emp-not-inc,71269,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,311551,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Male,0,0,3,United-States,<=50K\n0,Self-emp-inc,171041,Bachelors,13,Never-married,Handlers-cleaners,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Self-emp-not-inc,140915,HS-grad,9,Married-civ-spouse,Sales,Husband,Asian-Pac-Islander,Male,0,0,3,South,<=50K\n3,Private,41223,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,292569,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,2,0,4,United-States,>50K\n2,Private,132921,Assoc-acdm,12,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-inc,177271,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,4,0,4,United-States,>50K\n4,Self-emp-inc,314482,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,>50K\n2,Private,310531,10th,6,Separated,Handlers-cleaners,Unmarried,Black,Male,0,0,2,United-States,<=50K\n1,Private,145490,11th,7,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,181200,HS-grad,9,Divorced,Transport-moving,Not-in-family,White,Male,0,1,2,United-States,>50K\n1,Private,152951,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,Canada,>50K\n2,Private,85668,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,316606,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,4,3,United-States,<=50K\n2,Private,220694,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,408585,7th-8th,4,Married-civ-spouse,Farming-fishing,Own-child,White,Female,0,0,2,Mexico,<=50K\n2,Federal-gov,36699,Some-college,10,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,104280,Some-college,10,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,97254,11th,7,Never-married,Sales,Not-in-family,Amer-Indian-Eskimo,Male,2,0,2,United-States,<=50K\n1,Private,186420,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n2,Private,112482,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,317733,Doctorate,16,Widowed,Tech-support,Unmarried,White,Male,0,4,2,United-States,>50K\n4,Private,235136,7th-8th,4,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,Dominican-Republic,<=50K\n1,Private,229729,Bachelors,13,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,46677,Some-college,10,Widowed,Other-service,Unmarried,Black,Female,0,0,0,United-States,<=50K\n3,Federal-gov,277946,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,263727,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,74501,Masters,14,Never-married,Sales,Own-child,White,Female,0,0,3,United-States,<=50K\n1,Private,143776,Masters,14,Never-married,Prof-specialty,Not-in-family,Black,Male,0,0,2,?,>50K\n4,Private,179130,HS-grad,9,Divorced,Sales,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,State-gov,386568,Some-college,10,Separated,Prof-specialty,Not-in-family,White,Female,0,0,0,United-States,<=50K\n0,Private,413110,HS-grad,9,Never-married,Other-service,Other-relative,Black,Female,0,0,0,United-States,<=50K\n2,Private,72618,Assoc-voc,11,Divorced,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n2,Private,102288,Some-college,10,Divorced,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n3,Self-emp-not-inc,321851,Assoc-voc,11,Separated,Exec-managerial,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,180871,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,124483,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,0,0,3,India,<=50K\n2,Private,72791,Some-college,10,Married-civ-spouse,Craft-repair,Husband,Black,Male,2,0,2,United-States,>50K\n3,State-gov,263215,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,4,United-States,<=50K\n1,Local-gov,207668,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,198953,Bachelors,13,Never-married,Exec-managerial,Not-in-family,Black,Female,0,0,2,United-States,<=50K\n3,Private,38819,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,107306,Some-college,10,Divorced,Craft-repair,Not-in-family,White,Male,0,0,3,Canada,<=50K\n0,Private,161962,Some-college,10,Never-married,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,Private,192305,Some-college,10,Divorced,Prof-specialty,Unmarried,White,Female,0,0,0,United-States,<=50K\n2,Private,449925,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,>50K\n2,Local-gov,131167,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Local-gov,268482,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n1,State-gov,103642,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,2,2,United-States,<=50K\n2,Private,201723,HS-grad,9,Married-civ-spouse,Other-service,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,186224,Assoc-voc,11,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Self-emp-not-inc,139703,Some-college,10,Never-married,Prof-specialty,Not-in-family,Black,Female,0,0,3,United-States,>50K\n1,Private,397995,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Private,259323,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n0,State-gov,427515,HS-grad,9,Never-married,Adm-clerical,Own-child,Black,Female,0,0,0,United-States,<=50K\n0,?,187937,Some-college,10,Never-married,?,Other-relative,White,Female,0,0,3,United-States,<=50K\n1,Private,177437,10th,6,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,162442,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,3,United-States,<=50K\n3,Private,83407,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n4,?,265201,Some-college,10,Married-civ-spouse,?,Husband,White,Male,0,0,0,United-States,<=50K\n0,Private,109005,Some-college,10,Never-married,Sales,Own-child,White,Female,0,0,0,United-States,<=50K\n3,Local-gov,56915,HS-grad,9,Divorced,Exec-managerial,Unmarried,Amer-Indian-Eskimo,Male,0,0,0,United-States,<=50K\n2,Private,210830,Assoc-voc,11,Divorced,Prof-specialty,Unmarried,White,Female,0,0,2,United-States,<=50K\n0,Private,194848,Bachelors,13,Never-married,Tech-support,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,109351,Assoc-voc,11,Separated,Adm-clerical,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,105813,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,2,0,4,United-States,<=50K\n1,Private,123430,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,4,United-States,<=50K\n2,Private,105896,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Self-emp-not-inc,135020,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Germany,<=50K\n1,Private,136077,HS-grad,9,Never-married,Exec-managerial,Not-in-family,White,Male,0,0,3,United-States,<=50K\n0,Private,151463,11th,7,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n2,Private,73333,Some-college,10,Never-married,Exec-managerial,Not-in-family,White,Female,1,0,2,United-States,<=50K\n3,Local-gov,43705,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,320465,HS-grad,9,Never-married,Other-service,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n0,Private,237811,Some-college,10,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,Trinadad&Tobago,<=50K\n2,State-gov,190910,HS-grad,9,Married-civ-spouse,Farming-fishing,Other-relative,White,Male,0,0,2,United-States,<=50K\n1,Self-emp-inc,348326,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,163287,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,97723,Assoc-acdm,12,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,>50K\n4,State-gov,160383,10th,6,Widowed,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Federal-gov,263690,Masters,14,Married-civ-spouse,Other-service,Husband,Black,Male,1,0,2,Trinadad&Tobago,<=50K\n2,Private,278926,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,189017,12th,8,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,87546,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n0,Local-gov,145112,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Private,107308,Assoc-voc,11,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,<=50K\n4,Local-gov,132668,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n3,Private,175600,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,176608,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Wife,White,Female,0,0,2,United-States,<=50K\n2,Private,217054,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Local-gov,244813,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,United-States,<=50K\n2,Private,77373,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n2,Private,135823,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,174864,Bachelors,13,Divorced,Exec-managerial,Unmarried,White,Male,0,0,2,United-States,>50K\n2,Private,121676,Some-college,10,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,108140,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,>50K\n3,Private,185041,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n3,Self-emp-inc,144579,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,4,United-States,<=50K\n1,Private,143280,HS-grad,9,Never-married,Adm-clerical,Other-relative,White,Female,0,0,2,United-States,<=50K\n2,Private,242804,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n3,Private,156926,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n3,Self-emp-inc,103948,Assoc-voc,11,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,249720,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,203635,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,4,United-States,>50K\n1,Private,136721,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,114865,HS-grad,9,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n0,Private,166517,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,96219,Bachelors,13,Never-married,Exec-managerial,Other-relative,White,Female,0,0,2,United-States,<=50K\n3,Private,236197,12th,8,Widowed,Handlers-cleaners,Not-in-family,Asian-Pac-Islander,Male,0,0,2,Guatemala,<=50K\n2,Private,357118,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,3,2,United-States,<=50K\n0,Private,193787,Bachelors,13,Married-civ-spouse,Sales,Wife,White,Female,0,0,2,United-States,<=50K\n1,Private,607658,Bachelors,13,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Local-gov,47298,Doctorate,16,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,>50K\n3,Local-gov,258121,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,3,2,United-States,>50K\n3,Private,411037,10th,6,Divorced,Sales,Unmarried,White,Female,0,0,1,United-States,<=50K\n0,Private,228724,Some-college,10,Never-married,Prof-specialty,Not-in-family,White,Male,0,0,0,United-States,<=50K\n1,Private,179008,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,422933,Masters,14,Never-married,Exec-managerial,Own-child,White,Male,0,0,2,United-States,>50K\n1,Private,32452,Masters,14,Married-civ-spouse,Adm-clerical,Wife,White,Female,0,0,0,United-States,>50K\n1,Private,54363,Assoc-acdm,12,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n1,Private,113397,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,Japan,<=50K\n2,Private,159080,HS-grad,9,Married-civ-spouse,Adm-clerical,Own-child,White,Female,0,0,0,United-States,<=50K\n4,State-gov,354948,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,1,0,2,United-States,>50K\n1,Private,162572,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,4,0,3,United-States,>50K\n1,Private,108140,HS-grad,9,Divorced,Craft-repair,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Private,126104,10th,6,Divorced,Other-service,Unmarried,White,Female,0,0,4,United-States,<=50K\n2,Private,145522,Bachelors,13,Never-married,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n0,Private,61777,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,1,United-States,<=50K\n2,Private,139057,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,2,India,<=50K\n1,Private,113129,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,202062,Bachelors,13,Never-married,Prof-specialty,Own-child,Black,Male,0,0,2,United-States,<=50K\n1,Private,31341,HS-grad,9,Divorced,Adm-clerical,Unmarried,White,Female,0,0,2,United-States,<=50K\n1,Private,196125,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,2,United-States,>50K\n1,Private,44559,HS-grad,9,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,1,United-States,<=50K\n1,Self-emp-not-inc,202153,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,1,0,2,United-States,<=50K\n1,Private,238980,Some-college,10,Never-married,Sales,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,156873,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,32805,HS-grad,9,Never-married,Other-service,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,273088,Some-college,10,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,0,United-States,<=50K\n2,Self-emp-not-inc,241055,Masters,14,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,157028,Bachelors,13,Never-married,Sales,Not-in-family,White,Male,0,0,3,United-States,<=50K\n1,Local-gov,304252,Assoc-acdm,12,Divorced,Exec-managerial,Not-in-family,Asian-Pac-Islander,Female,0,0,2,Vietnam,<=50K\n4,Private,106910,Assoc-voc,11,Divorced,Other-service,Other-relative,Asian-Pac-Islander,Female,0,0,2,Outlying-US(Guam-USVI-etc),<=50K\n4,Private,127728,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,>50K\n2,State-gov,178876,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Amer-Indian-Eskimo,Male,0,0,2,United-States,<=50K\n0,Private,78181,Some-college,10,Never-married,Other-service,Other-relative,White,Female,0,0,2,United-States,<=50K\n0,?,212661,Some-college,10,Never-married,?,Own-child,White,Female,0,0,1,United-States,<=50K\n1,Private,288229,HS-grad,9,Married-civ-spouse,Tech-support,Wife,Asian-Pac-Islander,Female,2,0,2,United-States,>50K\n0,Private,205883,HS-grad,9,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n2,Local-gov,268205,Bachelors,13,Divorced,Prof-specialty,Unmarried,White,Female,0,0,3,United-States,<=50K\n0,Private,113735,Some-college,10,Divorced,Adm-clerical,Other-relative,White,Female,0,0,0,United-States,<=50K\n2,Private,390243,HS-grad,9,Never-married,Craft-repair,Own-child,Black,Male,0,0,2,United-States,<=50K\n3,Private,137984,HS-grad,9,Divorced,Craft-repair,Unmarried,White,Female,0,0,2,United-States,<=50K\n2,Private,160785,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n0,Private,86150,HS-grad,9,Never-married,Exec-managerial,Own-child,Asian-Pac-Islander,Female,0,0,0,United-States,<=50K\n1,Private,244268,11th,7,Married-civ-spouse,Craft-repair,Husband,White,Male,0,2,3,United-States,<=50K\n1,Self-emp-inc,177828,HS-grad,9,Divorced,Sales,Unmarried,White,Male,0,0,3,United-States,>50K\n1,Private,337953,Bachelors,13,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,254711,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,0,United-States,<=50K\n4,Private,127084,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,3,United-States,<=50K\n0,Private,156618,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,2,0,United-States,<=50K\n1,Private,201697,HS-grad,9,Separated,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n3,Self-emp-not-inc,187830,Bachelors,13,Divorced,Craft-repair,Not-in-family,White,Male,4,0,0,United-States,>50K\n3,Private,240612,HS-grad,9,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,Peru,>50K\n4,Self-emp-not-inc,190160,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,0,United-States,<=50K\n1,Private,109001,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Local-gov,297322,Some-college,10,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,2,United-States,>50K\n1,Private,335015,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,3,United-States,<=50K\n3,Private,174964,7th-8th,4,Married-civ-spouse,Transport-moving,Husband,White,Male,0,0,2,United-States,<=50K\n1,Federal-gov,408813,HS-grad,9,Married-civ-spouse,Craft-repair,Wife,White,Female,0,0,2,United-States,>50K\n2,Private,115332,HS-grad,9,Married-civ-spouse,Transport-moving,Husband,Black,Male,0,0,3,United-States,<=50K\n1,Local-gov,170482,HS-grad,9,Married-civ-spouse,Protective-serv,Husband,Black,Male,0,3,2,United-States,<=50K\n1,Private,113688,Some-college,10,Divorced,Other-service,Not-in-family,White,Female,0,0,1,United-States,<=50K\n1,Private,133770,Bachelors,13,Never-married,Prof-specialty,Not-in-family,Asian-Pac-Islander,Male,1,0,3,Philippines,<=50K\n4,Private,161964,12th,8,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n1,Private,34572,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,4,United-States,>50K\n0,Private,198259,HS-grad,9,Never-married,Handlers-cleaners,Own-child,White,Female,0,0,1,United-States,<=50K\n4,?,144872,HS-grad,9,Married-civ-spouse,?,Husband,White,Male,0,0,1,Canada,<=50K\n4,Private,161944,Some-college,10,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,3,United-States,>50K\n3,Private,29887,Bachelors,13,Divorced,Tech-support,Not-in-family,White,Male,0,1,2,United-States,<=50K\n2,Federal-gov,238980,Masters,14,Never-married,Adm-clerical,Not-in-family,White,Male,0,0,2,United-States,<=50K\n2,Private,275677,Assoc-voc,11,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,24529,Assoc-voc,11,Married-civ-spouse,Sales,Husband,White,Male,2,0,3,United-States,>50K\n3,Self-emp-not-inc,311631,Bachelors,13,Married-civ-spouse,Sales,Husband,White,Male,0,0,2,United-States,>50K\n0,Private,105460,9th,5,Never-married,Craft-repair,Own-child,White,Male,0,0,0,United-States,<=50K\n0,Private,374763,11th,7,Separated,Craft-repair,Not-in-family,White,Male,0,0,2,Mexico,<=50K\n0,Private,242136,HS-grad,9,Divorced,Machine-op-inspct,Not-in-family,Black,Male,0,0,2,United-States,<=50K\n1,Private,112115,HS-grad,9,Never-married,Machine-op-inspct,Not-in-family,White,Male,0,0,2,United-States,<=50K\n3,Self-emp-inc,77132,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,2,Canada,>50K\n4,?,26711,Assoc-voc,11,Married-civ-spouse,?,Husband,White,Male,1,0,0,United-States,<=50K\n4,Private,117909,Assoc-voc,11,Married-civ-spouse,Prof-specialty,Husband,White,Male,3,0,2,United-States,>50K\n2,Private,229647,Bachelors,13,Never-married,Tech-support,Not-in-family,White,Female,0,2,2,United-States,<=50K\n2,Private,149347,Masters,14,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,>50K\n2,Local-gov,23157,Masters,14,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,3,3,United-States,>50K\n0,Private,93977,HS-grad,9,Never-married,Machine-op-inspct,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-inc,159691,Some-college,10,Divorced,Exec-managerial,Not-in-family,White,Female,0,0,2,United-States,<=50K\n1,Private,176967,Some-college,10,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,344436,HS-grad,9,Widowed,Sales,Other-relative,White,Female,0,0,0,United-States,<=50K\n1,Private,430340,Some-college,10,Never-married,Sales,Not-in-family,White,Female,0,0,2,United-States,<=50K\n2,Private,202168,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,4,0,3,United-States,>50K\n3,Private,82720,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,269623,Some-college,10,Never-married,Craft-repair,Own-child,White,Male,0,0,2,United-States,<=50K\n4,Self-emp-not-inc,136405,HS-grad,9,Widowed,Farming-fishing,Not-in-family,White,Male,0,0,1,United-States,<=50K\n3,Local-gov,139347,Masters,14,Married-civ-spouse,Prof-specialty,Wife,White,Female,0,0,2,?,>50K\n3,Private,224655,HS-grad,9,Separated,Priv-house-serv,Not-in-family,White,Female,0,0,1,United-States,<=50K\n2,Private,247547,Assoc-voc,11,Never-married,Adm-clerical,Unmarried,Black,Female,0,0,2,United-States,<=50K\n4,Private,292710,Assoc-acdm,12,Divorced,Prof-specialty,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,173449,HS-grad,9,Married-civ-spouse,Handlers-cleaners,Husband,White,Male,0,0,2,United-States,<=50K\n3,Private,285570,HS-grad,9,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,2,United-States,<=50K\n4,Private,89686,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,3,United-States,<=50K\n1,Private,440129,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,2,United-States,<=50K\n0,Private,350977,HS-grad,9,Never-married,Other-service,Own-child,White,Female,0,0,2,United-States,<=50K\n3,Local-gov,349230,Masters,14,Divorced,Other-service,Not-in-family,White,Male,0,0,2,United-States,<=50K\n1,Private,245211,Bachelors,13,Never-married,Prof-specialty,Own-child,White,Male,0,0,2,United-States,<=50K\n2,Private,215419,Bachelors,13,Divorced,Prof-specialty,Not-in-family,White,Female,0,0,2,United-States,<=50K\n4,?,321403,HS-grad,9,Widowed,?,Other-relative,Black,Male,0,0,2,United-States,<=50K\n2,Private,374983,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,White,Male,0,0,3,United-States,<=50K\n2,Private,83891,Bachelors,13,Divorced,Adm-clerical,Own-child,Asian-Pac-Islander,Male,2,0,2,United-States,<=50K\n1,Self-emp-inc,182148,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,3,United-States,>50K\n"
  },
  {
    "path": "6.scikit-learn/data/titanic_openml.csv",
    "content": "\"pclass\",\"survived\",\"name\",\"sex\",\"age\",\"sibsp\",\"parch\",\"ticket\",\"fare\",\"cabin\",\"embarked\",\"boat\",\"body\",\"home.dest\"\n1,1,\"Allen, Miss. Elisabeth Walton\",\"female\",29,0,0,\"24160\",211.3375,\"B5\",\"S\",\"2\",?,\"St Louis, MO\"\n1,1,\"Allison, Master. Hudson Trevor\",\"male\",0.9167,1,2,\"113781\",151.55,\"C22 C26\",\"S\",\"11\",?,\"Montreal, PQ / Chesterville, ON\"\n1,0,\"Allison, Miss. Helen Loraine\",\"female\",2,1,2,\"113781\",151.55,\"C22 C26\",\"S\",?,?,\"Montreal, PQ / Chesterville, ON\"\n1,0,\"Allison, Mr. Hudson Joshua Creighton\",\"male\",30,1,2,\"113781\",151.55,\"C22 C26\",\"S\",?,135,\"Montreal, PQ / Chesterville, ON\"\n1,0,\"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)\",\"female\",25,1,2,\"113781\",151.55,\"C22 C26\",\"S\",?,?,\"Montreal, PQ / Chesterville, ON\"\n1,1,\"Anderson, Mr. Harry\",\"male\",48,0,0,\"19952\",26.55,\"E12\",\"S\",\"3\",?,\"New York, NY\"\n1,1,\"Andrews, Miss. Kornelia Theodosia\",\"female\",63,1,0,\"13502\",77.9583,\"D7\",\"S\",\"10\",?,\"Hudson, NY\"\n1,0,\"Andrews, Mr. Thomas Jr\",\"male\",39,0,0,\"112050\",0,\"A36\",\"S\",?,?,\"Belfast, NI\"\n1,1,\"Appleton, Mrs. Edward Dale (Charlotte Lamson)\",\"female\",53,2,0,\"11769\",51.4792,\"C101\",\"S\",\"D\",?,\"Bayside, Queens, NY\"\n1,0,\"Artagaveytia, Mr. Ramon\",\"male\",71,0,0,\"PC 17609\",49.5042,?,\"C\",?,22,\"Montevideo, Uruguay\"\n1,0,\"Astor, Col. John Jacob\",\"male\",47,1,0,\"PC 17757\",227.525,\"C62 C64\",\"C\",?,124,\"New York, NY\"\n1,1,\"Astor, Mrs. John Jacob (Madeleine Talmadge Force)\",\"female\",18,1,0,\"PC 17757\",227.525,\"C62 C64\",\"C\",\"4\",?,\"New York, NY\"\n1,1,\"Aubart, Mme. Leontine Pauline\",\"female\",24,0,0,\"PC 17477\",69.3,\"B35\",\"C\",\"9\",?,\"Paris, France\"\n1,1,\"Barber, Miss. Ellen 'Nellie'\",\"female\",26,0,0,\"19877\",78.85,?,\"S\",\"6\",?,?\n1,1,\"Barkworth, Mr. Algernon Henry Wilson\",\"male\",80,0,0,\"27042\",30,\"A23\",\"S\",\"B\",?,\"Hessle, Yorks\"\n1,0,\"Baumann, Mr. John D\",\"male\",?,0,0,\"PC 17318\",25.925,?,\"S\",?,?,\"New York, NY\"\n1,0,\"Baxter, Mr. Quigg Edmond\",\"male\",24,0,1,\"PC 17558\",247.5208,\"B58 B60\",\"C\",?,?,\"Montreal, PQ\"\n1,1,\"Baxter, Mrs. James (Helene DeLaudeniere Chaput)\",\"female\",50,0,1,\"PC 17558\",247.5208,\"B58 B60\",\"C\",\"6\",?,\"Montreal, PQ\"\n1,1,\"Bazzani, Miss. Albina\",\"female\",32,0,0,\"11813\",76.2917,\"D15\",\"C\",\"8\",?,?\n1,0,\"Beattie, Mr. Thomson\",\"male\",36,0,0,\"13050\",75.2417,\"C6\",\"C\",\"A\",?,\"Winnipeg, MN\"\n1,1,\"Beckwith, Mr. Richard Leonard\",\"male\",37,1,1,\"11751\",52.5542,\"D35\",\"S\",\"5\",?,\"New York, NY\"\n1,1,\"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)\",\"female\",47,1,1,\"11751\",52.5542,\"D35\",\"S\",\"5\",?,\"New York, NY\"\n1,1,\"Behr, Mr. Karl Howell\",\"male\",26,0,0,\"111369\",30,\"C148\",\"C\",\"5\",?,\"New York, NY\"\n1,1,\"Bidois, Miss. Rosalie\",\"female\",42,0,0,\"PC 17757\",227.525,?,\"C\",\"4\",?,?\n1,1,\"Bird, Miss. 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  {
    "path": "6.scikit-learn/environment.yml",
    "content": "name: pyparis_sklearn\ndependencies:\n  - scikit-learn >= 0.20\n  - pandas\n  - matplotlib\n  - jupyter"
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  {
    "path": "6.scikit-learn/notebook.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"作者的github：https://github.com/glemaitre/pyparis-2018-sklearn\\n\",\n    \"    \\n\",\n    \"    翻译和整理：光城，黄海广\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# A more advanced introduction to scikit-learn\\n\",\n    \"# scikit-learn的高级介绍\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will draw couple of plots during the tutorial. We activate matplotlib to show the plots inline in the notebook.\\n\",\n    \"\\n\",\n    \"在本节教程中将会绘制几个图形，于是我们激活matplotlib,使得在notebook中显示内联图。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Why this tutorial?\\n\",\n    \"\\n\",\n    \"## 为什么要出这个教程？\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`scikit-learn` provides state-of-the-art machine learning algorithms. \\n\",\n    \"These algorithms, however, cannot be directly used on raw data. Raw data needs to be preprocessed beforehand. Thus, besides machine learning algorithms, `scikit-learn` provides a set of preprocessing methods. Furthermore, `scikit-learn` provides connectors for pipelining these estimators (i.e., transformer, regressor, classifier, clusterer, etc.).\\n\",\n    \"\\n\",\n    \"In this tutorial, we will present the set of `scikit-learn` functionalities allowing for pipelining estimators, evaluating those pipelines, tuning those pipelines using hyper-parameters optimization, and creating complex preprocessing steps.\\n\",\n    \"\\n\",\n    \"`scikit-learn` 提供最先进的机器学习算法。 但是，这些算法不能直接用于原始数据。 原始数据需要事先进行预处理。 因此，除了机器学习算法之外，scikit-learn还提供了一套预处理方法。此外，`scikit-learn` 提供用于流水线化这些估计器的连接器(即转换器，回归器，分类器，聚类器等)。\\n\",\n    \"\\n\",\n    \"在本教程中,将介绍`scikit-learn` 函数集，允许流水线估计器、评估这些流水线、使用超参数优化调整这些流水线以及创建复杂的预处理步骤。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1. Basic use-case: train and test a classifier\\n\",\n    \"## 1.基本用例：训练和测试分类器\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For this first example, we will train and test a classifier on a dataset. We will use this example to recall the API of `scikit-learn`.\\n\",\n    \"\\n\",\n    \"对于第一个示例，我们将在数据集上训练和测试一个分类器。 我们将使用此示例来回忆`scikit-learn`的API。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will use the `digits` dataset which is a dataset of hand-written digits.\\n\",\n    \"\\n\",\n    \"我们将使用`digits`数据集，这是一个手写数字的数据集。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.datasets import load_digits\\n\",\n    \"\\n\",\n    \"X, y = load_digits(return_X_y=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Each row in `X` contains the intensities of the 64 image pixels. For each sample in `X`, we get the ground-truth `y` indicating the digit written.\\n\",\n    \"\\n\",\n    \"`X`中的每行包含64个图像像素的强度。 对于`X`中的每个样本，我们得到表示所写数字对应的`y`。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The digit in the image is 0\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x25630f906a0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(X[0].reshape(8, 8), cmap='gray');# 下面完成灰度图的绘制\\n\",\n    \"# 灰度显示图像\\n\",\n    \"plt.axis('off')# 关闭坐标轴\\n\",\n    \"print('The digit in the image is {}'.format(y[0]))# 格式化打印\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In machine learning, we should evaluate our model by training and testing it on distinct sets of data. `train_test_split` is a utility function to split the data into two independent sets. The `stratify` parameter enforces the classes distribution of the train and test datasets to be the same than the one of the entire dataset.\\n\",\n    \"\\n\",\n    \"在机器学习中，我们应该通过在不同的数据集上进行训练和测试来评估我们的模型。`train_test_split` 是一个用于将数据拆分为两个独立数据集的效用函数。`stratify`参数可强制将训练和测试数据集的类分布与整个数据集的类分布相同。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)\\n\",\n    \"\\n\",\n    \"# 划分数据为训练集与测试集,添加stratify参数，以使得训练和测试数据集的类分布与整个数据集的类分布相同。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once we have independent training and testing sets, we can learn a machine learning model using the `fit` method. We will use the `score` method to test this method, relying on the default accuracy metric.\\n\",\n    \"\\n\",\n    \"一旦我们拥有独立的培训和测试集，我们就可以使用`fit`方法学习机器学习模型。 我们将使用`score`方法来测试此方法，依赖于默认的准确度指标。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy score of the LogisticRegression is 0.96\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import LogisticRegression# 求出Logistic回归的精确度得分\\n\",\n    \"\\n\",\n    \"clf = LogisticRegression(solver='lbfgs', multi_class='ovr', max_iter=5000, random_state=42)\\n\",\n    \"clf.fit(X_train, y_train)\\n\",\n    \"accuracy = clf.score(X_test, y_test)\\n\",\n    \"print('Accuracy score of the {} is {:.2f}'.format(clf.__class__.__name__, accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The API of `scikit-learn` is consistent across classifiers. Thus, we can easily replace the `LogisticRegression` classifier by a `RandomForestClassifier`. These changes are minimal and only related to the creation of the classifier instance.\\n\",\n    \"\\n\",\n    \"`scikit-learn`的API在分类器中是一致的。因此，我们可以通过`RandomForestClassifier`轻松替换`LogisticRegression`分类器。这些更改很小，仅与分类器实例的创建有关。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy score of the RandomForestClassifier is 0.96\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"# RandomForestClassifier轻松替换LogisticRegression分类器\\n\",\n    \"clf = RandomForestClassifier(n_estimators=100, n_jobs=-1, random_state=42)\\n\",\n    \"clf.fit(X_train, y_train)\\n\",\n    \"accuracy = clf.score(X_test, y_test)\\n\",\n    \"print('Accuracy score of the {} is {:.2f}'.format(clf.__class__.__name__, accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Exercise\\n\",\n    \"\\n\",\n    \"Do the following exercise:\\n\",\n    \"\\n\",\n    \"#### 练习\\n\",\n    \"完成接下来的练习：\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Load the breast cancer dataset. Import the functions `load_breast_cancer` from `sklearn.datasets`\\n\",\n    \"* 加载乳腺癌数据集。从`sklearn.datasets`导入函数`load_breast_cancer`。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load solutions/01_1_solutions.py\\n\",\n    \"from sklearn.datasets import load_breast_cancer\\n\",\n    \"\\n\",\n    \"X_breast, y_breast = load_breast_cancer(return_X_y=True)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Split the dataset to keep 30% of it for testing using `sklearn.model_selection.train_test_split`. Make sure to stratify the data (i.e., use the `stratify` parameter) and set the `random_state` to `0`.\\n\",\n    \"* 使用`sklearn.model_selection.train_test_split`拆分数据集并保留30％的数据集以进行测试。确保对数据进行分层（即使用 `stratify`参数）并将`random_state`设置为`0`。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load solutions/01_2_solutions.py\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"X_breast_train, X_breast_test, y_breast_train, y_breast_test = train_test_split(X_breast, y_breast, stratify=y_breast, random_state=0, test_size=0.3)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Train a supervised classifier using the training data.\\n\",\n    \"* 使用训练数据训练监督分类器。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GradientBoostingClassifier(criterion='friedman_mse', init=None,\\n\",\n       \"              learning_rate=0.1, loss='deviance', max_depth=3,\\n\",\n       \"              max_features=None, max_leaf_nodes=None,\\n\",\n       \"              min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"              min_samples_leaf=1, min_samples_split=2,\\n\",\n       \"              min_weight_fraction_leaf=0.0, n_estimators=100,\\n\",\n       \"              n_iter_no_change=None, presort='auto', random_state=0,\\n\",\n       \"              subsample=1.0, tol=0.0001, validation_fraction=0.1,\\n\",\n       \"              verbose=0, warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# %load solutions/01_3_solutions.py\\n\",\n    \"from sklearn.ensemble import GradientBoostingClassifier\\n\",\n    \"\\n\",\n    \"clf = GradientBoostingClassifier(n_estimators=100, random_state=0)\\n\",\n    \"clf.fit(X_breast_train, y_breast_train)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Use the fitted classifier to predict the classification labels for the testing set.\\n\",\n    \"* 使用拟合分类器预测测试集的分类标签。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load solutions/01_4_solutions.py\\n\",\n    \"y_pred = clf.predict(X_breast_test)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Compute the balanced accuracy on the testing set. You need to import `balanced_accuracy_score` from `sklearn.metrics`\\n\",\n    \"* 计算测试集的`balanced_accuracy_score`精度。您需要从`sklearn.metrics`导入`balanced_accuracy_score`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy score of the GradientBoostingClassifier is 0.94\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# %load solutions/01_5_solutions.py\\n\",\n    \"from sklearn.metrics import balanced_accuracy_score\\n\",\n    \"\\n\",\n    \"accuracy = balanced_accuracy_score(y_breast_test, y_pred)\\n\",\n    \"print('Accuracy score of the {} is {:.2f}'.format(clf.__class__.__name__, accuracy))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2. More advanced use-case: preprocess the data before training and testing a classifier\\n\",\n    \"## 2.更高级的用例：在训练和测试分类器之前预处理数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.1 Standardize your data\\n\",\n    \"### 2.1 标准化您的数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Preprocessing might be required before learning a model. For instance, a user could be interested in creating hand-crafted features or an algorithm might make some apriori assumptions about the data. \\n\",\n    \"\\n\",\n    \"In our case, the solver used by the `LogisticRegression` expects the data to be normalized. Thus, we need to standardize the data before training the model. To observe this necessary condition, we will check the number of iterations required to train the model.\\n\",\n    \"\\n\",\n    \"在学习模型之前可能需要预处理。例如，一个用户可能对创建手工制作的特征或者算法感兴趣，那么他可能会对数据进行一些先验假设。\\n\",\n    \"\\n\",\n    \"在我们的例子中，`LogisticRegression`使用的求解器期望数据被规范化。因此，我们需要在训练模型之前标准化数据。为了观察这个必要条件，我们将检查训练模型所需的迭代次数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LogisticRegression required 2152 iterations to be fitted\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"\\n\",\n    \"clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=5000, random_state=42)\\n\",\n    \"clf.fit(X_train, y_train)\\n\",\n    \"print('{} required {} iterations to be fitted'.format(clf.__class__.__name__, clf.n_iter_[0]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The `MinMaxScaler` transformer is used to normalise the data. This scaler should be applied in the following way: learn (i.e., `fit` method) the statistics on a training set and standardize (i.e., `transform` method) both the training and testing sets. Finally, we will train and test the model and the scaled datasets.\\n\",\n    \"\\n\",\n    \"`MinMaxScaler`变换器用于规范化数据。该标量应该以下列方式应用：学习（即，`fit`方法）训练集上的统计数据并标准化（即，`transform`方法）训练集和测试集。 最后，我们将训练和测试这个模型并得到归一化后的数据集。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy score of the LogisticRegression is 0.96\\n\",\n      \"LogisticRegression required 177 iterations to be fitted\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.preprocessing import MinMaxScaler\\n\",\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"\\n\",\n    \"scaler = MinMaxScaler()\\n\",\n    \"X_train_scaled = scaler.fit_transform(X_train)\\n\",\n    \"X_test_scaled = scaler.transform(X_test)\\n\",\n    \"\\n\",\n    \"clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000, random_state=42)\\n\",\n    \"clf.fit(X_train_scaled, y_train)\\n\",\n    \"accuracy = clf.score(X_test_scaled, y_test)\\n\",\n    \"print('Accuracy score of the {} is {:.2f}'.format(clf.__class__.__name__, accuracy))\\n\",\n    \"print('{} required {} iterations to be fitted'.format(clf.__class__.__name__, clf.n_iter_[0]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"By scaling the data, the convergence of the model happened much faster than with the unscaled data.\\n\",\n    \"\\n\",\n    \"通过归一化数据，模型的收敛速度要比未归一化的数据快得多。(迭代次数变少了)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.2 The wrong preprocessing patterns\\n\",\n    \"### 2.2 错误的预处理模式\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We highlighted how to preprocess and adequately train a machine learning model. It is also interesting to spot what would be the wrong way of preprocessing data. There are two potential mistakes which are easy to make but easy to spot.\\n\",\n    \"\\n\",\n    \"我们强调了如何预处理和充分训练机器学习模型。发现预处理数据的错误方法也很有趣。其中有两个潜在的错误，易于犯错但又很容易发现。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The first pattern is to standardize the data before spliting the full set into training and testing sets.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"第一种模式是在整个数据集分成训练和测试集之前标准化数据。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy score of the LogisticRegression is 0.96\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"scaler = MinMaxScaler()\\n\",\n    \"X_scaled = scaler.fit_transform(X)\\n\",\n    \"X_train_prescaled, X_test_prescaled, y_train_prescaled, y_test_prescaled = train_test_split(\\n\",\n    \"    X_scaled, y, stratify=y, random_state=42)\\n\",\n    \"\\n\",\n    \"clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000, random_state=42)\\n\",\n    \"clf.fit(X_train_prescaled, y_train_prescaled)\\n\",\n    \"accuracy = clf.score(X_test_prescaled, y_test_prescaled)\\n\",\n    \"print('Accuracy score of the {} is {:.2f}'.format(clf.__class__.__name__, accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The second pattern is to standardize the training and testing sets independently. It comes back to call the `fit` methods on both training and testing sets. Thus, the training and testing sets are standardized differently.\\n\",\n    \"\\n\",\n    \"第二种模式是独立地标准化训练和测试集。它回来在训练和测试集上调用`fit`方法。因此，训练和测试集的标准化不同。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy score of the LogisticRegression is 0.96\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"scaler = MinMaxScaler()\\n\",\n    \"X_train_prescaled = scaler.fit_transform(X_train)\\n\",\n    \"X_test_prescaled = scaler.fit_transform(X_test)\\n\",\n    \"\\n\",\n    \"clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000, random_state=42)\\n\",\n    \"clf.fit(X_train_prescaled, y_train)\\n\",\n    \"accuracy = clf.score(X_test_prescaled, y_test)\\n\",\n    \"print('Accuracy score of the {} is {:.2f}'.format(clf.__class__.__name__, accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.3 Keep it simple, stupid: use the pipeline connector from `scikit-learn`\\n\",\n    \"### 2.3 保持简单，笨的方法：使用`scikit-learn`的管道连接器\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The two previous patterns are an issue with data leaking. However, this is difficult to prevent such a mistake when one has to do the preprocessing by hand. Thus, `scikit-learn` introduced the `Pipeline` object. It sequentially connects several transformers and a classifier (or a regressor). We can create a pipeline as:\\n\",\n    \"\\n\",\n    \"前面提到的两个模式是数据泄漏的问题。然而，当必须手动进行预处理时，很难防止这种错误。因此,`scikit-learn`引入了`Pipeline`对象。它依次连接多个转换器和分类器（或回归器）。我们可以创建一个如下管道：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.pipeline import Pipeline\\n\",\n    \"\\n\",\n    \"pipe = Pipeline(steps=[('scaler', MinMaxScaler()),\\n\",\n    \"                       ('clf', LogisticRegression(solver='lbfgs', multi_class='auto', random_state=42))])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We see that this pipeline contains the parameters of both the scaler and the classifier. Sometimes, it can be tedious to give a name to each estimator in the pipeline. `make_pipeline` will give a name automatically to each estimator which is the lower case of the class name.\\n\",\n    \"\\n\",\n    \"我们看到这个管道包含了缩放器(归一化)和分类器的参数。 有时，为管道中的每个估计器命名可能会很繁琐。 而`make_pipeline`将自动为每个估计器命名，这是类名的小写。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.pipeline import make_pipeline\\n\",\n    \"pipe = make_pipeline(MinMaxScaler(),\\n\",\n    \"                     LogisticRegression(solver='lbfgs', multi_class='auto', random_state=42, max_iter=1000))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The pipeline will have an identical API. We use `fit` to train the classifier and `score` to check the accuracy. However, calling `fit` will call the method `fit_transform` of all transformers in the pipeline. Calling `score` (or `predict` and `predict_proba`) will call internally `transform` of all transformers in the pipeline. It corresponds to the normalization procedure in Sect. 2.1.\\n\",\n    \"\\n\",\n    \"管道将具有相同的API。 我们使用`fit`来训练分类器和`socre`来检查准确性。 然而，调用`fit`会调用管道中所有变换器的`fit_transform`方法。 调用`score`（或`predict`和`predict_proba`）将调用管道中所有转换器的内部变换。 它对应于本文2.1中的规范化过程。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy score of the Pipeline is 0.96\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"pipe.fit(X_train, y_train)\\n\",\n    \"accuracy = pipe.score(X_test, y_test)\\n\",\n    \"print('Accuracy score of the {} is {:.2f}'.format(pipe.__class__.__name__, accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can check all the parameters of the pipeline using `get_params()`.\\n\",\n    \"\\n\",\n    \"我们可以使用`get_params()`检查管道的所有参数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'logisticregression': LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"           intercept_scaling=1, max_iter=1000, multi_class='auto',\\n\",\n       \"           n_jobs=None, penalty='l2', random_state=42, solver='lbfgs',\\n\",\n       \"           tol=0.0001, verbose=0, warm_start=False),\\n\",\n       \" 'logisticregression__C': 1.0,\\n\",\n       \" 'logisticregression__class_weight': None,\\n\",\n       \" 'logisticregression__dual': False,\\n\",\n       \" 'logisticregression__fit_intercept': True,\\n\",\n       \" 'logisticregression__intercept_scaling': 1,\\n\",\n       \" 'logisticregression__max_iter': 1000,\\n\",\n       \" 'logisticregression__multi_class': 'auto',\\n\",\n       \" 'logisticregression__n_jobs': None,\\n\",\n       \" 'logisticregression__penalty': 'l2',\\n\",\n       \" 'logisticregression__random_state': 42,\\n\",\n       \" 'logisticregression__solver': 'lbfgs',\\n\",\n       \" 'logisticregression__tol': 0.0001,\\n\",\n       \" 'logisticregression__verbose': 0,\\n\",\n       \" 'logisticregression__warm_start': False,\\n\",\n       \" 'memory': None,\\n\",\n       \" 'minmaxscaler': MinMaxScaler(copy=True, feature_range=(0, 1)),\\n\",\n       \" 'minmaxscaler__copy': True,\\n\",\n       \" 'minmaxscaler__feature_range': (0, 1),\\n\",\n       \" 'steps': [('minmaxscaler', MinMaxScaler(copy=True, feature_range=(0, 1))),\\n\",\n       \"  ('logisticregression',\\n\",\n       \"   LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"             intercept_scaling=1, max_iter=1000, multi_class='auto',\\n\",\n       \"             n_jobs=None, penalty='l2', random_state=42, solver='lbfgs',\\n\",\n       \"             tol=0.0001, verbose=0, warm_start=False))]}\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe.get_params()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Exercise\\n\",\n    \"\\n\",\n    \"Reuse the breast dataset of the first exercise to train a `SGDClassifier` which you can import from `linear_model`. Make a pipeline with this classifier and a `StandardScaler` transformer imported from `sklearn.preprocessing`. Train and test this pipeline.\\n\",\n    \"\\n\",\n    \"### 练习\\n\",\n    \"重用第一个练习的乳腺癌数据集来训练,可以从`linear_model`导入`SGDClassifier`。 使用此分类器和从`sklearn.preprocessing`导入的`StandardScaler`变换器来创建管道。然后训练和测试这条管道。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy score of the Pipeline is 0.95\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\\n\",\n      \"  FutureWarning)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# %load solutions/02_solutions.py\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from sklearn.linear_model import SGDClassifier\\n\",\n    \"\\n\",\n    \"pipe = make_pipeline(StandardScaler(), SGDClassifier(max_iter=1000))\\n\",\n    \"pipe.fit(X_breast_train, y_breast_train)\\n\",\n    \"y_pred = pipe.predict(X_breast_test)\\n\",\n    \"accuracy = balanced_accuracy_score(y_breast_test, y_pred)\\n\",\n    \"print('Accuracy score of the {} is {:.2f}'.format(pipe.__class__.__name__, accuracy))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 3. When more is better than less: cross-validation instead of single split\\n\",\n    \"### 3.当更多优于更少时：交叉验证而不是单独拆分\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Splitting the data is necessary to evaluate the statistical model performance. However, it reduces the number of samples which can be used to learn the model. Therefore, one should use cross-validation whenever possible. Having multiple splits will give information about the model stability as well. \\n\",\n    \"\\n\",\n    \"`scikit-learn` provides three functions: `cross_val_score`, `cross_val_predict`, and [`cross_validate`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html). The latter provides more information regarding fitting time, training and testing scores. I can also return multiple scores at once.\\n\",\n    \"\\n\",\n    \"分割数据对于评估统计模型性能是必要的。 但是，它减少了可用于学习模型的样本数量。 因此，应尽可能使用交叉验证。有多个拆分也会提供有关模型稳定性的信息。\\n\",\n    \"\\n\",\n    \"`scikit-learn`提供了三个函数：`cross_val_score`，`cross_val_predict`和`cross_validate`。 后者提供了有关拟合时间，训练和测试分数的更多信息。 我也可以一次返回多个分数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import cross_validate\\n\",\n    \"\\n\",\n    \"pipe = make_pipeline(MinMaxScaler(),\\n\",\n    \"                     LogisticRegression(solver='lbfgs', multi_class='auto',\\n\",\n    \"                                        max_iter=1000, random_state=42))\\n\",\n    \"scores = cross_validate(pipe, X, y, cv=3, return_train_score=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Using the cross-validate function, we can quickly check the training and testing scores and make a quick plot using `pandas`.\\n\",\n    \"\\n\",\n    \"使用交叉验证函数，我们可以快速检查训练和测试分数，并使用`pandas`快速绘图。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>fit_time</th>\\n\",\n       \"      <th>score_time</th>\\n\",\n       \"      <th>test_score</th>\\n\",\n       \"      <th>train_score</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0.154587</td>\\n\",\n       \"      <td>0.000997</td>\\n\",\n       \"      <td>0.925249</td>\\n\",\n       \"      <td>0.988285</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0.132646</td>\\n\",\n       \"      <td>0.000997</td>\\n\",\n       \"      <td>0.943239</td>\\n\",\n       \"      <td>0.984975</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0.127659</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.924497</td>\\n\",\n       \"      <td>0.993339</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   fit_time  score_time  test_score  train_score\\n\",\n       \"0  0.154587    0.000997    0.925249     0.988285\\n\",\n       \"1  0.132646    0.000997    0.943239     0.984975\\n\",\n       \"2  0.127659    0.000000    0.924497     0.993339\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"df_scores = pd.DataFrame(scores)\\n\",\n    \"df_scores\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x25635300550>\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAX0AAAD9CAYAAABQvqc9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAFXVJREFUeJzt3X+wXOV93/H3x+KHiaVggsitLRGJJqRFNTKxb7Ad1/aNm7gQOmBQxkBqd5h0Ru04jCeZIY6YtjhWy4BrktYpZCbylBiauJRREoYY2UAVLXRqxwZqJBAaURkTI+S6jusovoADot/+sUfusrrS3ftDuhc979fMjs55nuec8+zR2c+e+5w9u6kqJElteM1Cd0CSdOwY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGnLDQHRi2fPnyWr169UJ347jx3HPP8brXvW6huyFNyeNz/jzyyCN/WVVnTNdu0YX+6tWrefjhhxe6G8eNXq/HxMTEQndDmpLH5/xJ8hejtHN4R5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktSQRXdzlmYnyYyX8feRpfZ4pn+cqKopH6t+43OHrZPUHkNfkhpi6EtSQwx9SWqIoS9JDRkp9JNckGR3kj1JNkxRvyrJ1iQ7kvSSrByo+0SSx7vH5fPZeUnSzEwb+kmWALcAFwJrgCuTrBlqdhNwe1WtBTYCN3TLXgS8BTgPeBvw60l+eP66L0maiVE+p38+sKeqngJIcgdwCfDEQJs1wK9109uAuwbKH6iqA8CBJNuBC4A756HvTXrzx+9j/wsvzWiZ1RvumVH7U085ke0fe9+MlpH06jBK6K8AnhmY30v/rH3QdmAd8CngUmBZktO78o8l+W3gh4Cf5ZVvFpqh/S+8xNM3XjRy+9n8MtFM3yQkvXqMEvpT3eo5fGfPNcDNSa4CHgSeBQ5U1X1Jfhr4IvBt4EvAgUM2kKwH1gOMjY3R6/VG7X+TZrJ/JicnZ7U//T/QsTDb41OzN0ro7wXOHJhfCewbbFBV+4DLAJIsBdZV1f6u7nrg+q7us8D/HN5AVW0CNgGMj4+Xv5l5BF+4Z0Zn7rP6DdIZbkOaLX8j99gb5dM7DwFnJzkryUnAFcDdgw2SLE9ycF3XArd25Uu6YR6SrAXWAvfNV+clSTMz7Zl+VR1IcjVwL7AEuLWqdibZCDxcVXcDE8ANSYr+8M6vdIufCPy37svA/hr4YHdRV5K0AEb6ls2q2gJsGSq7bmB6M7B5iuW+T/8TPJKkRcCvVn6VWXbOBs697ZD7447stpluA2D0TwhJevUw9F9lvrfrRj+yKWnW/O4dSWqIoS9JDTH0Jakhjum/Cs14zP0LM//uHUnHJ0P/VWYmF3Gh/wYx02UkHb8c3pGkhhj6ktQQh3eOE91XXUxd94mpy6uGvyxV0vHOM/3jRFVN+di2bdth6yS1x9CXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGjBT6SS5IsjvJniSH/EBrklVJtibZkaSXZOVA3b9NsjPJriS/kyN9X4Ak6aiaNvSTLAFuAS4E1gBXJlkz1Owm4PaqWgtsBG7olv0Z4J3AWuBNwE8D75m33kuSZmSUM/3zgT1V9VRVvQjcAVwy1GYNsLWb3jZQX8BrgZOAk4ETgW/NtdOSpNkZJfRXAM8MzO/tygZtB9Z105cCy5KcXlVfov8m8M3ucW9V7ZpblyVJszXKVytPNQY//BWN1wA3J7kKeBB4FjiQ5CeAc4CDY/z3J3l3VT34ig0k64H1AGNjY/R6vZGfgI5scnLS/alFy+Pz2Bsl9PcCZw7MrwT2DTaoqn3AZQBJlgLrqmp/F+Z/XlWTXd3ngbfTf2MYXH4TsAlgfHy8JiYmZvVkdKher4f7U4uVx+exN8rwzkPA2UnOSnIScAVw92CDJMuTHFzXtcCt3fQ3gPckOSHJifQv4jq8I0kLZNrQr6oDwNXAvfQD+86q2plkY5KLu2YTwO4kTwJjwPVd+Wbga8Bj9Mf9t1fVn87vU5AkjWqkn0usqi3AlqGy6wamN9MP+OHlXgb+2Rz7KEmaJ96RK0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0JekhowU+kkuSLI7yZ4kG6aoX5Vka5IdSXpJVnblP5vk0YHH95O8f76fhCRpNNOGfpIlwC3AhcAa4Moka4aa3QTcXlVrgY3ADQBVta2qzquq84D3As8D981j/yVJMzDKmf75wJ6qeqqqXgTuAC4ZarMG2NpNb5uiHuAXgc9X1fOz7awkaW5OGKHNCuCZgfm9wNuG2mwH1gGfAi4FliU5vaq+M9DmCuC3p9pAkvXAeoCxsTF6vd5Indf0Jicn3Z9atDw+j71RQj9TlNXQ/DXAzUmuAh4EngUO/GAFyRuAc4F7p9pAVW0CNgGMj4/XxMTECN3SKHq9Hu5PLVYen8feKKG/FzhzYH4lsG+wQVXtAy4DSLIUWFdV+weafAD4k6p6aW7dlSTNxShj+g8BZyc5K8lJ9Idp7h5skGR5koPruha4dWgdVwL/ea6dlSTNzbShX1UHgKvpD83sAu6sqp1JNia5uGs2AexO8iQwBlx/cPkkq+n/pfDAvPZckjRjowzvUFVbgC1DZdcNTG8GNh9m2afpXwyWJC0w78iVpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGjJS6Ce5IMnuJHuSbJiiflWSrUl2JOklWTlQ92NJ7kuyK8kT3Q+lS5IWwLShn2QJcAtwIbAGuDLJmqFmNwG3V9VaYCNww0Dd7cAnq+oc4Hzgf89HxyVJMzfKmf75wJ6qeqqqXgTuAC4ZarMG2NpNbztY3705nFBV9wNU1WRVPT8vPZckzdgoob8CeGZgfm9XNmg7sK6bvhRYluR04CeBv0ryx0m+muST3V8OkqQFcMIIbTJFWQ3NXwPcnOQq4EHgWeBAt/53AT8FfAP4L8BVwH98xQaS9cB6gLGxMXq93qj91zQmJyfdn1q0PD6PvVFCfy9w5sD8SmDfYIOq2gdcBpBkKbCuqvYn2Qt8taqe6uruAt7OUOhX1SZgE8D4+HhNTEzM6snoUL1eD/enFiuPz2NvlOGdh4Czk5yV5CTgCuDuwQZJlic5uK5rgVsHlj0tyRnd/HuBJ+bebUnSbEwb+lV1ALgauBfYBdxZVTuTbExycddsAtid5ElgDLi+W/Zl+kM/W5M8Rn+o6NPz/iwkSSMZZXiHqtoCbBkqu25gejOw+TDL3g+snUMfJUnzxDtyJakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUkJFCP8kFSXYn2ZNkwxT1q5JsTbIjSS/JyoG6l5M82j3uns/OS5Jm5oTpGiRZAtwC/DywF3goyd1V9cRAs5uA26vqtiTvBW4APtTVvVBV581zvyVJszDKmf75wJ6qeqqqXgTuAC4ZarMG2NpNb5uiXpK0CIwS+iuAZwbm93Zlg7YD67rpS4FlSU7v5l+b5OEkf57k/XPqrSRpTqYd3gEyRVkNzV8D3JzkKuBB4FngQFf3Y1W1L8nfBv4syWNV9bVXbCBZD6wHGBsbo9frjf4MdESTk5PuTy1aHp/H3iihvxc4c2B+JbBvsEFV7QMuA0iyFFhXVfsH6qiqp5L0gJ8Cvja0/CZgE8D4+HhNTEzM4qloKr1eD/enFiuPz2NvlOGdh4Czk5yV5CTgCuAVn8JJsjzJwXVdC9zalZ+W5OSDbYB3AoMXgCVJx9C0oV9VB4CrgXuBXcCdVbUzycYkF3fNJoDdSZ4ExoDru/JzgIeTbKd/gffGoU/9SJKOoVGGd6iqLcCWobLrBqY3A5unWO6LwLlz7KMkaZ54R64kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JashIX60sSbOVTPWLq9OrGv5VVs0Hz/QlHVVVddjHqt/43GHrdHQY+pLUEENfkhpi6EtSQwx9SWrISKGf5IIku5PsSbJhivpVSbYm2ZGkl2TlUP0PJ3k2yc3z1XFJ0sxNG/pJlgC3ABcCa4Ark6wZanYTcHtVrQU2AjcM1f9r4IG5d1eSNBejnOmfD+ypqqeq6kXgDuCSoTZrgK3d9LbB+iRvBcaA++beXUnSXIxyc9YK4JmB+b3A24babAfWAZ8CLgWWJTkd+C7wW8CHgH9wuA0kWQ+sBxgbG6PX643YfU1ncnLS/alFzePz2Bol9Ke6nW74zolrgJuTXAU8CDwLHAA+DGypqmeOdFdeVW0CNgGMj4/XxMTECN3SKHq9Hu5PLVpfuMfj8xgbJfT3AmcOzK8E9g02qKp9wGUASZYC66pqf5J3AO9K8mFgKXBSksmqOuRisCTp6Bsl9B8Czk5yFv0z+CuAXxpskGQ58H+q6v8C1wK3AlTVPx5ocxUwbuBL0sKZ9kJuVR0ArgbuBXYBd1bVziQbk1zcNZsAdid5kv5F2+uPUn8lSXMw0rdsVtUWYMtQ2XUD05uBzdOs4zPAZ2bcQ0nSvPGOXElqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSEjffeOJE3nzR+/j/0vvDTj5VZvuGfktqeeciLbP/a+GW9D/5+hL2le7H/hJZ6+8aIZLTPTH/mZyRuEpubwjiQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWrISKGf5IIku5PsSbJhivpVSbYm2ZGkl2TlQPkjSR5NsjPJP5/vJyBJGt20oZ9kCXALcCGwBrgyyZqhZjcBt1fVWmAjcENX/k3gZ6rqPOBtwIYkb5yvzkuSZmaUM/3zgT1V9VRVvQjcAVwy1GYNsLWb3nawvqperKq/6cpPHnF7kqSjZJQQXgE8MzC/tysbtB1Y101fCixLcjpAkjOT7OjW8Ymq2je3LkuSZmuUr2HIFGU1NH8NcHOSq4AHgWeBAwBV9QywthvWuSvJ5qr61is2kKwH1gOMjY3R6/Vm8hx0BJOTk+5PHTMzPdZmc3x6PM/NKKG/FzhzYH4l8Iqz9e7s/TKAJEuBdVW1f7hNkp3Au4DNQ3WbgE0A4+PjNZPv4tCRzfS7TaRZ+8I9Mz7WZnx8zmIbeqVRhnceAs5OclaSk4ArgLsHGyRZnuTguq4Fbu3KVyY5pZs+DXgnsHu+Oi9JmplpQ7+qDgBXA/cCu4A7q2pnko1JLu6aTQC7kzwJjAHXd+XnAF9Osh14ALipqh6b5+cgSRrRSF+tXFVbgC1DZdcNTG9maMimK78fWDvHPkqS5okfoZSkhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIaMdEeuJE1n2TkbOPe2Q35Yb3q3zWQbABfNfBv6AUNf0rz43q4bj/o2Tj3lxKO+jeOdoS9pXjx949Rn4MlUP8kxvarhn+3QfHBMX9JRVVWHfWzbtu2wdTo6DH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQ7LYboJI8m3gLxa6H8eR5cBfLnQnpMPw+Jw/q6rqjOkaLbrQ1/xK8nBVjS90P6SpeHweew7vSFJDDH1Jaoihf/zbtNAdkI7A4/MYc0xfkhrimb4kNcTQl3SIJK9P8uFZLvurSX5ovvuk+WHoL4DZvqCSbEny+qPRJ2nI64FZhT7wq8AxC/0kS47Vto4Hhv7CmPIFNd3BW1W/UFV/ddR6NSJfZE24EfjxJI8m+WSSX0/yUJIdST4OkOR1Se5Jsj3J40kuT/IR4I3AtiTbplpxkiVJPtMt81iSX+vKfyLJf+3W9z+S/Hj6PjnQ9vKu7USSbUk+CzzWlX0wyVe6Pv+ex+lhHOmnzHwcnQdwB/AC8CjwELAN+CzwRFd/F/AIsBNYP7Dc0/TvYFwN7AI+3bW5DzjlCNv7CPAEsAO4oytbCvw+/RfMDmBdV35lV/Y48ImBdUwCG4EvA38feCvwQNfPe4E3LPR+9TGvx+hq4PFu+n30P2UT+ieKnwPeDawDPj2wzKndv08Dy4+w7rcC9w/Mv77798vApd30a+n/tbAOuB9YAowB3wDeAEwAzwFnde3PAf4UOLGb/13gnyz0flyMjwXvQIuPoRfUKw7eruxHun9P6cL39G5+MPQPAOd15XcCHzzC9vYBJ3fTB19gnwD+/UCb0+ifoX0DOAM4Afgz4P1dfQEf6KZPBL4InNHNXw7cutD71cdRO0Zv6o69R7vHHuCfAj8JfL07lt41sOx0oX8a8DXgPwAXdG8ky4C9U7T9d8AvD8z/J+Di7nWzbaD86u44P9jH3cBvLvR+XIyPE9Bi8JWq+vrA/EeSXNpNnwmcDXxnaJmvV9Wj3fQj9F+kh7MD+MMkd9H/KwLg54ArDjaoqu8meTfQq6pvAyT5Q/pndHcBLwN/1DX/O8CbgPuTQP8s7JujPVW9CgW4oap+75CK5K3ALwA3JLmvqjZOt7LuWHsz8A+BXwE+QP86wOG2fTjPDbW7raqunW77rXNMf3H4wcGbZIJ+IL+jqt4MfJX+n7rD/mZg+mU44hv4RcAt9P+sfiTJCfRfJMM3aRzpBfb9qnp5oN3Oqjqve5xbVe87wrJ69fke/bNv6A/f/XKSpQBJViT50SRvBJ6vqj+g/9fAW6ZY9hBJlgOvqao/Av4V8Jaq+mtgb5L3d21O7j4B9CBweXcd4Az6JyFfmWK1W4FfTPKj3fI/kmTVXHbA8crQXxhHelGcCny3qp5P8neBt89lQ0leA5xZVduAj9K/iLyU/nWAqwfanUZ/TPU9SZZ3F8GupD9uP2w3cEaSd3TLnpjk782ln1pcquo7wH9P8jjw8/SvOX0pyWPAZvrH77nAV5I8CvwL4N90i28CPn+4C7nACqDXLfcZ4ODZ+Yfo/5W7g/7w4d8C/oT+X6rb6Q83frSq/tcU/X0C+JfAfd3y99Mf+9cQ78hdIN2nDtbSv6D7rar6R135yfSHU1bQhSv9sclekqeBcfqh/bmqelO3zDXA0qr6zSm2cyL9C8Wn0j9D/4OqurE7azt49v8y8PGq+uMkv0T/RRhgS1V9tFvPZFUtHVjvecDvdOs9gf71gU/P4y6SdBQY+pLUEC/kSjpqknwZOHmo+ENV9dhC9Eee6R9XktwCvHOo+FNV9fsL0R9Ji4+hL0kN8dM7ktQQQ1+SGmLoS1JDDH1JaoihL0kN+X9kxn8Z+kPMtgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x25635298eb8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"df_scores[['train_score', 'test_score']].boxplot()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Exercise\\n\",\n    \"\\n\",\n    \"Use the pipeline of the previous exercise and make a cross-validation instead of a single split evaluation.\\n\",\n    \"\\n\",\n    \"#### 练习\\n\",\n    \"使用上一个练习的管道并进行交叉验证，而不是单个拆分评估。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\\n\",\n      \"  FutureWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\\n\",\n      \"  FutureWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\\n\",\n      \"  FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x2563538fd68>\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x25635336358>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load solutions/03_solutions.py\\n\",\n    \"pipe = make_pipeline(StandardScaler(), SGDClassifier(max_iter=1000))\\n\",\n    \"scores = cross_validate(pipe, X_breast, y_breast, scoring='balanced_accuracy', cv=3, return_train_score=True)\\n\",\n    \"df_scores = pd.DataFrame(scores)\\n\",\n    \"df_scores[['train_score', 'test_score']].boxplot()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4. Hyper-parameters optimization: fine-tune the inside of a pipeline\\n\",\n    \"\\n\",\n    \"## 4.超参数优化：微调管道内部\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Sometimes you would like to find the parameters of a component of the pipeline which lead to the best accuracy. We already saw that we could check the parameters of a pipeline using `get_params()`.\\n\",\n    \"\\n\",\n    \"有时您希望找到管道组件的参数，从而获得最佳精度。 我们已经看到我们可以使用`get_params()`检查管道的参数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'memory': None,\\n\",\n       \" 'sgdclassifier': SGDClassifier(alpha=0.0001, average=False, class_weight=None,\\n\",\n       \"        early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\\n\",\n       \"        l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=1000,\\n\",\n       \"        n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\\n\",\n       \"        power_t=0.5, random_state=None, shuffle=True, tol=None,\\n\",\n       \"        validation_fraction=0.1, verbose=0, warm_start=False),\\n\",\n       \" 'sgdclassifier__alpha': 0.0001,\\n\",\n       \" 'sgdclassifier__average': False,\\n\",\n       \" 'sgdclassifier__class_weight': None,\\n\",\n       \" 'sgdclassifier__early_stopping': False,\\n\",\n       \" 'sgdclassifier__epsilon': 0.1,\\n\",\n       \" 'sgdclassifier__eta0': 0.0,\\n\",\n       \" 'sgdclassifier__fit_intercept': True,\\n\",\n       \" 'sgdclassifier__l1_ratio': 0.15,\\n\",\n       \" 'sgdclassifier__learning_rate': 'optimal',\\n\",\n       \" 'sgdclassifier__loss': 'hinge',\\n\",\n       \" 'sgdclassifier__max_iter': 1000,\\n\",\n       \" 'sgdclassifier__n_iter': None,\\n\",\n       \" 'sgdclassifier__n_iter_no_change': 5,\\n\",\n       \" 'sgdclassifier__n_jobs': None,\\n\",\n       \" 'sgdclassifier__penalty': 'l2',\\n\",\n       \" 'sgdclassifier__power_t': 0.5,\\n\",\n       \" 'sgdclassifier__random_state': None,\\n\",\n       \" 'sgdclassifier__shuffle': True,\\n\",\n       \" 'sgdclassifier__tol': None,\\n\",\n       \" 'sgdclassifier__validation_fraction': 0.1,\\n\",\n       \" 'sgdclassifier__verbose': 0,\\n\",\n       \" 'sgdclassifier__warm_start': False,\\n\",\n       \" 'standardscaler': StandardScaler(copy=True, with_mean=True, with_std=True),\\n\",\n       \" 'standardscaler__copy': True,\\n\",\n       \" 'standardscaler__with_mean': True,\\n\",\n       \" 'standardscaler__with_std': True,\\n\",\n       \" 'steps': [('standardscaler',\\n\",\n       \"   StandardScaler(copy=True, with_mean=True, with_std=True)),\\n\",\n       \"  ('sgdclassifier',\\n\",\n       \"   SGDClassifier(alpha=0.0001, average=False, class_weight=None,\\n\",\n       \"          early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\\n\",\n       \"          l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=1000,\\n\",\n       \"          n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\\n\",\n       \"          power_t=0.5, random_state=None, shuffle=True, tol=None,\\n\",\n       \"          validation_fraction=0.1, verbose=0, warm_start=False))]}\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe.get_params()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Hyper-parameters can be optimized by an exhaustive search. [`GridSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) provides such utility and does a cross-validated grid-search over a parameter grid.\\n\",\n    \"\\n\",\n    \"Let's give an example in which we would like to optimize the `C` and `penalty` parameters of the `LogisticRegression` classifier.\\n\",\n    \"\\n\",\n    \"可以通过穷举搜索来优化超参数。`GridSearchCV`提供此类实用程序，并通过参数网格进行交叉验证的网格搜索。\\n\",\n    \"\\n\",\n    \"如下例子，我们希望优化`LogisticRegression`分类器的`C`和`penalty`参数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=3, error_score='raise-deprecating',\\n\",\n       \"       estimator=Pipeline(memory=None,\\n\",\n       \"     steps=[('minmaxscaler', MinMaxScaler(copy=True, feature_range=(0, 1))), ('logisticregression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"          intercept_scaling=1, max_iter=5000, multi_class='auto',\\n\",\n       \"          n_jobs=None, penalty='l2', random_state=42, solver='saga',\\n\",\n       \"          tol=0.0001, verbose=0, warm_start=False))]),\\n\",\n       \"       fit_params=None, iid='warn', n_jobs=-1,\\n\",\n       \"       param_grid={'logisticregression__C': [0.1, 1.0, 10], 'logisticregression__penalty': ['l2', 'l1']},\\n\",\n       \"       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\\n\",\n       \"       scoring=None, verbose=0)\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import GridSearchCV\\n\",\n    \"\\n\",\n    \"pipe = make_pipeline(MinMaxScaler(),\\n\",\n    \"                     LogisticRegression(solver='saga', multi_class='auto',\\n\",\n    \"                                        random_state=42, max_iter=5000))\\n\",\n    \"param_grid = {'logisticregression__C': [0.1, 1.0, 10],\\n\",\n    \"              'logisticregression__penalty': ['l2', 'l1']}\\n\",\n    \"grid = GridSearchCV(pipe, param_grid=param_grid, cv=3, n_jobs=-1, return_train_score=True)\\n\",\n    \"grid.fit(X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"When fitting the grid-search object, it finds the best possible parameter combination on the training set (using cross-validation). We can introspect the results of the grid-search by accessing the attribute `cv_results_`. It allows us to check the effect of the parameters on the model performance.\\n\",\n    \"\\n\",\n    \"在拟合网格搜索对象时，它会在训练集上找到最佳的参数组合（使用交叉验证）。 我们可以通过访问属性`cv_results_`来得到网格搜索的结果。 通过这个属性允许我们可以检查参数对模型性能的影响\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>mean_fit_time</th>\\n\",\n       \"      <th>mean_score_time</th>\\n\",\n       \"      <th>mean_test_score</th>\\n\",\n       \"      <th>mean_train_score</th>\\n\",\n       \"      <th>param_logisticregression__C</th>\\n\",\n       \"      <th>param_logisticregression__penalty</th>\\n\",\n       \"      <th>params</th>\\n\",\n       \"      <th>rank_test_score</th>\\n\",\n       \"      <th>split0_test_score</th>\\n\",\n       \"      <th>split0_train_score</th>\\n\",\n       \"      <th>split1_test_score</th>\\n\",\n       \"      <th>split1_train_score</th>\\n\",\n       \"      <th>split2_test_score</th>\\n\",\n       \"      <th>split2_train_score</th>\\n\",\n       \"      <th>std_fit_time</th>\\n\",\n       \"      <th>std_score_time</th>\\n\",\n       \"      <th>std_test_score</th>\\n\",\n       \"      <th>std_train_score</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0.480382</td>\\n\",\n       \"      <td>0.040559</td>\\n\",\n       \"      <td>0.942836</td>\\n\",\n       \"      <td>0.955084</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>l2</td>\\n\",\n       \"      <td>{'logisticregression__C': 0.1, 'logisticregres...</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.951542</td>\\n\",\n       \"      <td>0.952968</td>\\n\",\n       \"      <td>0.935268</td>\\n\",\n       \"      <td>0.959956</td>\\n\",\n       \"      <td>0.941573</td>\\n\",\n       \"      <td>0.952328</td>\\n\",\n       \"      <td>0.056607</td>\\n\",\n       \"      <td>1.155489e-02</td>\\n\",\n       \"      <td>0.006717</td>\\n\",\n       \"      <td>0.003455</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1.089751</td>\\n\",\n       \"      <td>0.026928</td>\\n\",\n       \"      <td>0.892353</td>\\n\",\n       \"      <td>0.903496</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>l1</td>\\n\",\n       \"      <td>{'logisticregression__C': 0.1, 'logisticregres...</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.885463</td>\\n\",\n       \"      <td>0.905935</td>\\n\",\n       \"      <td>0.908482</td>\\n\",\n       \"      <td>0.902113</td>\\n\",\n       \"      <td>0.883146</td>\\n\",\n       \"      <td>0.902439</td>\\n\",\n       \"      <td>0.279431</td>\\n\",\n       \"      <td>8.141014e-04</td>\\n\",\n       \"      <td>0.011425</td>\\n\",\n       \"      <td>0.001730</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1.362025</td>\\n\",\n       \"      <td>0.019614</td>\\n\",\n       \"      <td>0.963623</td>\\n\",\n       \"      <td>0.986634</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>l2</td>\\n\",\n       \"      <td>{'logisticregression__C': 1.0, 'logisticregres...</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.977974</td>\\n\",\n       \"      <td>0.985442</td>\\n\",\n       \"      <td>0.955357</td>\\n\",\n       \"      <td>0.987764</td>\\n\",\n       \"      <td>0.957303</td>\\n\",\n       \"      <td>0.986696</td>\\n\",\n       \"      <td>0.044381</td>\\n\",\n       \"      <td>1.316415e-02</td>\\n\",\n       \"      <td>0.010263</td>\\n\",\n       \"      <td>0.000949</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4.539531</td>\\n\",\n       \"      <td>0.000665</td>\\n\",\n       \"      <td>0.953229</td>\\n\",\n       \"      <td>0.978837</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>l1</td>\\n\",\n       \"      <td>{'logisticregression__C': 1.0, 'logisticregres...</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.964758</td>\\n\",\n       \"      <td>0.977604</td>\\n\",\n       \"      <td>0.950893</td>\\n\",\n       \"      <td>0.977753</td>\\n\",\n       \"      <td>0.943820</td>\\n\",\n       \"      <td>0.981153</td>\\n\",\n       \"      <td>1.073243</td>\\n\",\n       \"      <td>4.699094e-04</td>\\n\",\n       \"      <td>0.008710</td>\\n\",\n       \"      <td>0.001639</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>3.466066</td>\\n\",\n       \"      <td>0.000998</td>\\n\",\n       \"      <td>0.968077</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>l2</td>\\n\",\n       \"      <td>{'logisticregression__C': 10, 'logisticregress...</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.977974</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.962054</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.964045</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.227521</td>\\n\",\n       \"      <td>5.947204e-07</td>\\n\",\n       \"      <td>0.007103</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>9.693750</td>\\n\",\n       \"      <td>0.000665</td>\\n\",\n       \"      <td>0.960653</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>l1</td>\\n\",\n       \"      <td>{'logisticregression__C': 10, 'logisticregress...</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.973568</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.957589</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.950562</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.904692</td>\\n\",\n       \"      <td>4.704713e-04</td>\\n\",\n       \"      <td>0.009643</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   mean_fit_time  mean_score_time  mean_test_score  mean_train_score  \\\\\\n\",\n       \"0       0.480382         0.040559         0.942836          0.955084   \\n\",\n       \"1       1.089751         0.026928         0.892353          0.903496   \\n\",\n       \"2       1.362025         0.019614         0.963623          0.986634   \\n\",\n       \"3       4.539531         0.000665         0.953229          0.978837   \\n\",\n       \"4       3.466066         0.000998         0.968077          1.000000   \\n\",\n       \"5       9.693750         0.000665         0.960653          1.000000   \\n\",\n       \"\\n\",\n       \"  param_logisticregression__C param_logisticregression__penalty  \\\\\\n\",\n       \"0                         0.1                                l2   \\n\",\n       \"1                         0.1                                l1   \\n\",\n       \"2                           1                                l2   \\n\",\n       \"3                           1                                l1   \\n\",\n       \"4                          10                                l2   \\n\",\n       \"5                          10                                l1   \\n\",\n       \"\\n\",\n       \"                                              params  rank_test_score  \\\\\\n\",\n       \"0  {'logisticregression__C': 0.1, 'logisticregres...                5   \\n\",\n       \"1  {'logisticregression__C': 0.1, 'logisticregres...                6   \\n\",\n       \"2  {'logisticregression__C': 1.0, 'logisticregres...                2   \\n\",\n       \"3  {'logisticregression__C': 1.0, 'logisticregres...                4   \\n\",\n       \"4  {'logisticregression__C': 10, 'logisticregress...                1   \\n\",\n       \"5  {'logisticregression__C': 10, 'logisticregress...                3   \\n\",\n       \"\\n\",\n       \"   split0_test_score  split0_train_score  split1_test_score  \\\\\\n\",\n       \"0           0.951542            0.952968           0.935268   \\n\",\n       \"1           0.885463            0.905935           0.908482   \\n\",\n       \"2           0.977974            0.985442           0.955357   \\n\",\n       \"3           0.964758            0.977604           0.950893   \\n\",\n       \"4           0.977974            1.000000           0.962054   \\n\",\n       \"5           0.973568            1.000000           0.957589   \\n\",\n       \"\\n\",\n       \"   split1_train_score  split2_test_score  split2_train_score  std_fit_time  \\\\\\n\",\n       \"0            0.959956           0.941573            0.952328      0.056607   \\n\",\n       \"1            0.902113           0.883146            0.902439      0.279431   \\n\",\n       \"2            0.987764           0.957303            0.986696      0.044381   \\n\",\n       \"3            0.977753           0.943820            0.981153      1.073243   \\n\",\n       \"4            1.000000           0.964045            1.000000      0.227521   \\n\",\n       \"5            1.000000           0.950562            1.000000      0.904692   \\n\",\n       \"\\n\",\n       \"   std_score_time  std_test_score  std_train_score  \\n\",\n       \"0    1.155489e-02        0.006717         0.003455  \\n\",\n       \"1    8.141014e-04        0.011425         0.001730  \\n\",\n       \"2    1.316415e-02        0.010263         0.000949  \\n\",\n       \"3    4.699094e-04        0.008710         0.001639  \\n\",\n       \"4    5.947204e-07        0.007103         0.000000  \\n\",\n       \"5    4.704713e-04        0.009643         0.000000  \"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df_grid = pd.DataFrame(grid.cv_results_)\\n\",\n    \"df_grid\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"By default, the grid-search object is also behaving as an estimator. Once it is fitted, calling `score` will fix the hyper-parameters to the best parameters found.\\n\",\n    \"\\n\",\n    \"默认情况下，网格搜索对象也表现为估计器。 一旦它被`fit`后，调用`score`将超参数固定为找到的最佳参数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'logisticregression__C': 10, 'logisticregression__penalty': 'l2'}\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"grid.best_params_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Besides this is possible to call the grid-search as any other classifier to make predictions.\\n\",\n    \"\\n\",\n    \"此外，可以将网格搜索称为任何其他分类器以进行预测。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy score of the GridSearchCV is 0.96\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"accuracy = grid.score(X_test, y_test)\\n\",\n    \"print('Accuracy score of the {} is {:.2f}'.format(grid.__class__.__name__, accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Up to know, we only make the fitting of the grid-search on a single split. However, as previously stated, we might be interested to make an outer cross-validation to estimate the performance of the model and different sample of data and check the potential variation in performance. Since grid-search is an estimator, we can use it directly within the `cross_validate` function.\\n\",\n    \"\\n\",\n    \"最重要的是，我们只对单个分割进行网格搜索。 但是，如前所述，我们可能有兴趣进行外部交叉验证，以估计模型的性能和不同的数据样本，并检查性能的潜在变化。 由于网格搜索是一个估计器，我们可以直接在`cross_validate`函数中使用它。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>fit_time</th>\\n\",\n       \"      <th>score_time</th>\\n\",\n       \"      <th>test_score</th>\\n\",\n       \"      <th>train_score</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>32.169982</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.928571</td>\\n\",\n       \"      <td>0.985774</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>33.664034</td>\\n\",\n       \"      <td>0.000999</td>\\n\",\n       \"      <td>0.946578</td>\\n\",\n       \"      <td>0.997496</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>31.734155</td>\\n\",\n       \"      <td>0.000998</td>\\n\",\n       \"      <td>0.924497</td>\\n\",\n       \"      <td>0.993339</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    fit_time  score_time  test_score  train_score\\n\",\n       \"0  32.169982    0.000000    0.928571     0.985774\\n\",\n       \"1  33.664034    0.000999    0.946578     0.997496\\n\",\n       \"2  31.734155    0.000998    0.924497     0.993339\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"scores = cross_validate(grid, X, y, cv=3, n_jobs=-1, return_train_score=True)\\n\",\n    \"df_scores = pd.DataFrame(scores)\\n\",\n    \"df_scores\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Exercise\\n\",\n    \"\\n\",\n    \"Reuse the previous pipeline for the breast dataset and make a grid-search to evaluate the difference between a `hinge` and `log` loss. Besides, fine-tune the `penalty`.\\n\",\n    \"\\n\",\n    \"#### 练习\\n\",\n    \"重复使用乳腺癌数据集的先前管道并进行网格搜索以评估`hinge`(铰链) 和`log`(对数)损失之间的差异。此外，微调`penalty`。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\\n\",\n      \"  FutureWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\\n\",\n      \"  FutureWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\\n\",\n      \"  FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'sgdclassifier__loss': 'log', 'sgdclassifier__penalty': 'l2'}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\\n\",\n      \"  FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x2563541a1d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load solutions/04_solutions.py\\n\",\n    \"pipe = make_pipeline(StandardScaler(), SGDClassifier(max_iter=1000))\\n\",\n    \"param_grid = {'sgdclassifier__loss': ['hinge', 'log'],\\n\",\n    \"              'sgdclassifier__penalty': ['l2', 'l1']}\\n\",\n    \"grid = GridSearchCV(pipe, param_grid=param_grid, cv=3, n_jobs=-1)\\n\",\n    \"scores = cross_validate(grid, X_breast, y_breast, scoring='balanced_accuracy', cv=3, return_train_score=True)\\n\",\n    \"df_scores = pd.DataFrame(scores)\\n\",\n    \"df_scores[['train_score', 'test_score']].boxplot()\\n\",\n    \"\\n\",\n    \"grid.fit(X_breast_train, y_breast_train)\\n\",\n    \"print(grid.best_params_)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5. Summary: my scikit-learn pipeline in less than 10 lines of code (skipping the import statements)\\n\",\n    \"## 5.总结：我的scikit-learn管道只有不到10行代码（跳过import语句）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x256353f2978>\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x256354007b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"from sklearn.preprocessing import MinMaxScaler\\n\",\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"from sklearn.pipeline import make_pipeline\\n\",\n    \"from sklearn.model_selection import GridSearchCV\\n\",\n    \"from sklearn.model_selection import cross_validate\\n\",\n    \"\\n\",\n    \"pipe = make_pipeline(MinMaxScaler(),\\n\",\n    \"                     LogisticRegression(solver='saga', multi_class='auto', random_state=42, max_iter=5000))\\n\",\n    \"param_grid = {'logisticregression__C': [0.1, 1.0, 10],\\n\",\n    \"              'logisticregression__penalty': ['l2', 'l1']}\\n\",\n    \"grid = GridSearchCV(pipe, param_grid=param_grid, cv=3, n_jobs=-1)\\n\",\n    \"scores = pd.DataFrame(cross_validate(grid, X, y, cv=3, n_jobs=-1, return_train_score=True))\\n\",\n    \"scores[['train_score', 'test_score']].boxplot()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 6. Heterogeneous data: when you work with data other than numerical\\n\",\n    \"# 6.异构数据：当您使用数字以外的数据时\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Up to now, we used `scikit-learn` to train model using numerical data.\\n\",\n    \"\\n\",\n    \"到目前为止，我们使用`scikit-learn`来训练使用数值数据的模型。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],\\n\",\n       \"       [ 0.,  0.,  0., ..., 10.,  0.,  0.],\\n\",\n       \"       [ 0.,  0.,  0., ..., 16.,  9.,  0.],\\n\",\n       \"       ...,\\n\",\n       \"       [ 0.,  0.,  1., ...,  6.,  0.,  0.],\\n\",\n       \"       [ 0.,  0.,  2., ..., 12.,  0.,  0.],\\n\",\n       \"       [ 0.,  0., 10., ..., 12.,  1.,  0.]])\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`X` is a NumPy array of `float` values only. However, datasets can contains mixed types.\\n\",\n    \"\\n\",\n    \"`X`是仅包含浮点值的`NumPy`数组。 但是，数据集可以包含混合类型。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>pclass</th>\\n\",\n       \"      <th>survived</th>\\n\",\n       \"      <th>name</th>\\n\",\n       \"      <th>sex</th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>sibsp</th>\\n\",\n       \"      <th>parch</th>\\n\",\n       \"      <th>ticket</th>\\n\",\n       \"      <th>fare</th>\\n\",\n       \"      <th>cabin</th>\\n\",\n       \"      <th>embarked</th>\\n\",\n       \"      <th>boat</th>\\n\",\n       \"      <th>body</th>\\n\",\n       \"      <th>home.dest</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Allen, Miss. Elisabeth Walton</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>29.0000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>24160</td>\\n\",\n       \"      <td>211.3375</td>\\n\",\n       \"      <td>B5</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>St Louis, MO</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Allison, Master. Hudson Trevor</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>0.9167</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>113781</td>\\n\",\n       \"      <td>151.5500</td>\\n\",\n       \"      <td>C22 C26</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Montreal, PQ / Chesterville, ON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Allison, Miss. Helen Loraine</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>2.0000</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>113781</td>\\n\",\n       \"      <td>151.5500</td>\\n\",\n       \"      <td>C22 C26</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Montreal, PQ / Chesterville, ON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Allison, Mr. Hudson Joshua Creighton</td>\\n\",\n       \"      <td>male</td>\\n\",\n       \"      <td>30.0000</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>113781</td>\\n\",\n       \"      <td>151.5500</td>\\n\",\n       \"      <td>C22 C26</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>135.0</td>\\n\",\n       \"      <td>Montreal, PQ / Chesterville, ON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\\n\",\n       \"      <td>female</td>\\n\",\n       \"      <td>25.0000</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>113781</td>\\n\",\n       \"      <td>151.5500</td>\\n\",\n       \"      <td>C22 C26</td>\\n\",\n       \"      <td>S</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Montreal, PQ / Chesterville, ON</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   pclass  survived                                             name     sex  \\\\\\n\",\n       \"0       1         1                    Allen, Miss. Elisabeth Walton  female   \\n\",\n       \"1       1         1                   Allison, Master. Hudson Trevor    male   \\n\",\n       \"2       1         0                     Allison, Miss. Helen Loraine  female   \\n\",\n       \"3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \\n\",\n       \"4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \\n\",\n       \"\\n\",\n       \"       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\\\\n\",\n       \"0  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \\n\",\n       \"1   0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \\n\",\n       \"2   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \\n\",\n       \"3  30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \\n\",\n       \"4  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \\n\",\n       \"\\n\",\n       \"                         home.dest  \\n\",\n       \"0                     St Louis, MO  \\n\",\n       \"1  Montreal, PQ / Chesterville, ON  \\n\",\n       \"2  Montreal, PQ / Chesterville, ON  \\n\",\n       \"3  Montreal, PQ / Chesterville, ON  \\n\",\n       \"4  Montreal, PQ / Chesterville, ON  \"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"data = pd.read_csv(os.path.join('data', 'titanic_openml.csv'), na_values='?')\\n\",\n    \"data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The `titanic` dataset contains both categorical, text, and numeric features. We will use this dataset to predict whether a passenger survived the Titanic or not. \\n\",\n    \"\\n\",\n    \"Let's split the data into training and testing sets and use the `survived` column as a target.\\n\",\n    \"\\n\",\n    \"泰坦尼克号数据集包含分类，文本和数字特征。 我们将使用此数据集来预测乘客是否在泰坦尼克号中幸存下来。\\n\",\n    \"\\n\",\n    \"让我们将数据拆分为训练和测试集，并将幸存列用作目标。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y = data['survived']\\n\",\n    \"X = data.drop(columns='survived')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"One could try a `LogisticRegression` classifier and see how good it is performing.\\n\",\n    \"\\n\",\n    \"首先，可以尝试使用`LogisticRegression`分类器，看看它的表现有多好。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\\n\",\n      \"  FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"ename\": \"ValueError\",\n     \"evalue\": \"could not convert string to float: 'S'\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mValueError\\u001b[0m                                Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-53-45c060082178>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m      1\\u001b[0m \\u001b[0mclf\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mLogisticRegression\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 2\\u001b[1;33m \\u001b[0mclf\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mfit\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mX_train\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0my_train\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;31m#这里肯定会报错。\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\logistic.py\\u001b[0m in \\u001b[0;36mfit\\u001b[1;34m(self, X, y, sample_weight)\\u001b[0m\\n\\u001b[0;32m   1286\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   1287\\u001b[0m         X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype, order=\\\"C\\\",\\n\\u001b[1;32m-> 1288\\u001b[1;33m                          accept_large_sparse=solver != 'liblinear')\\n\\u001b[0m\\u001b[0;32m   1289\\u001b[0m         \\u001b[0mcheck_classification_targets\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0my\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   1290\\u001b[0m         \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mclasses_\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0munique\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0my\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\validation.py\\u001b[0m in \\u001b[0;36mcheck_X_y\\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\\u001b[0m\\n\\u001b[0;32m    754\\u001b[0m                     \\u001b[0mensure_min_features\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mensure_min_features\\u001b[0m\\u001b[1;33m,\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    755\\u001b[0m                     \\u001b[0mwarn_on_dtype\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mwarn_on_dtype\\u001b[0m\\u001b[1;33m,\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 756\\u001b[1;33m                     estimator=estimator)\\n\\u001b[0m\\u001b[0;32m    757\\u001b[0m     \\u001b[1;32mif\\u001b[0m \\u001b[0mmulti_output\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    758\\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\validation.py\\u001b[0m in \\u001b[0;36mcheck_array\\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\\u001b[0m\\n\\u001b[0;32m    525\\u001b[0m             \\u001b[1;32mtry\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    526\\u001b[0m                 \\u001b[0mwarnings\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0msimplefilter\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m'error'\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mComplexWarning\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 527\\u001b[1;33m                 \\u001b[0marray\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0masarray\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0marray\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdtype\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mdtype\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0morder\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0morder\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    528\\u001b[0m             \\u001b[1;32mexcept\\u001b[0m \\u001b[0mComplexWarning\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    529\\u001b[0m                 raise ValueError(\\\"Complex data not supported\\\\n\\\"\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\numpy\\\\core\\\\numeric.py\\u001b[0m in \\u001b[0;36masarray\\u001b[1;34m(a, dtype, order)\\u001b[0m\\n\\u001b[0;32m    490\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    491\\u001b[0m     \\\"\\\"\\\"\\n\\u001b[1;32m--> 492\\u001b[1;33m     \\u001b[1;32mreturn\\u001b[0m \\u001b[0marray\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0ma\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdtype\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mcopy\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;32mFalse\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0morder\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0morder\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    493\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    494\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mValueError\\u001b[0m: could not convert string to float: 'S'\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clf = LogisticRegression()\\n\",\n    \"clf.fit(X_train, y_train)#这里肯定会报错。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Whoops, most of the classifiers are designed to work with numerical data. Therefore, we need to convert the categorical data into numeric features. The simplest way is to one-hot encode each categorical feature with the `OneHotEncoder`. Let's give an example for the `sex` and `embarked` columns. Note that we also encounter some data which are missing. We will use a `SimpleImputer` to replace the missing values with a constant values.\\n\",\n    \"\\n\",\n    \"哎呀，大多数分类器都设计用于处理数值数据。 因此，我们需要将分类数据转换为数字特征。 最简单的方法是使用`OneHotEncoder`对每个分类特征进行读热编码。 让我们以`sex`与`embarked`列为例。 请注意，我们还会遇到一些缺失的数据。 我们将使用`SimpleImputer`用常量值替换缺失值。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0., 1., 0., 0., 1., 0.],\\n\",\n       \"       [0., 1., 1., 0., 0., 0.],\\n\",\n       \"       [0., 1., 0., 0., 1., 0.],\\n\",\n       \"       ...,\\n\",\n       \"       [0., 1., 0., 0., 1., 0.],\\n\",\n       \"       [1., 0., 0., 0., 1., 0.],\\n\",\n       \"       [1., 0., 0., 0., 1., 0.]])\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.impute import SimpleImputer\\n\",\n    \"from sklearn.preprocessing import OneHotEncoder\\n\",\n    \"ohe = make_pipeline(SimpleImputer(strategy='constant'), OneHotEncoder())\\n\",\n    \"X_encoded = ohe.fit_transform(X_train[['sex', 'embarked']])\\n\",\n    \"X_encoded.toarray()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This way, it is possible to encode the categorical features. However, we also want to standardize the numerical features. Thus, we need to split the original data into 2 subgroups and apply a different preprocessing: (i) one-hot encoding for the categorical data and (ii) standard scaling for the numerical data. We also need to handle missing values in both cases. For the categorical column, we replace the missing values by the string `'missing_values'` which will be interpreted as a category on its own. For the numerical data, we will replace the missing data by the mean values of the feature of interest.\\n\",\n    \"\\n\",\n    \"这样，可以对分类特征进行编码。 但是，我们也希望标准化数字特征。 因此，我们需要将原始数据分成2个子组并应用不同的预处理：（i）分类数据的独热编；（ii）数值数据的标准缩放(归一化)。 我们还需要处理两种情况下的缺失值： 对于分类列，我们将字符串'`missing_values`'替换为缺失值，该字符串将自行解释为类别。 对于数值数据，我们将用感兴趣的特征的平均值替换缺失的数据。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"col_cat = ['sex', 'embarked']\\n\",\n    \"col_num = ['age', 'sibsp', 'parch', 'fare']\\n\",\n    \"\\n\",\n    \"X_train_cat = X_train[col_cat]\\n\",\n    \"X_train_num = X_train[col_num]\\n\",\n    \"X_test_cat = X_test[col_cat]\\n\",\n    \"X_test_num = X_test[col_num]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"\\n\",\n    \"scaler_cat = make_pipeline(SimpleImputer(strategy='constant'), OneHotEncoder())\\n\",\n    \"X_train_cat_enc = scaler_cat.fit_transform(X_train_cat)\\n\",\n    \"X_test_cat_enc = scaler_cat.transform(X_test_cat)\\n\",\n    \"\\n\",\n    \"scaler_num = make_pipeline(SimpleImputer(strategy='mean'), StandardScaler())\\n\",\n    \"X_train_num_scaled = scaler_num.fit_transform(X_train_num)\\n\",\n    \"X_test_num_scaled = scaler_num.transform(X_test_num)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We should apply these transformations on the training and testing sets as we did in Sect. 2.1\\n\",\n    \"\\n\",\n    \"我们应该像在本文2.1中那样在训练和测试集上应用这些变换。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from scipy import sparse\\n\",\n    \"\\n\",\n    \"X_train_scaled = sparse.hstack((X_train_cat_enc,\\n\",\n    \"                                sparse.csr_matrix(X_train_num_scaled)))\\n\",\n    \"X_test_scaled = sparse.hstack((X_test_cat_enc,\\n\",\n    \"                               sparse.csr_matrix(X_test_num_scaled)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once the transformation is done, we can combine the informations which are all numerical now. Finally, we use our `LogisticRegression` classifier as a model.\\n\",\n    \"\\n\",\n    \"转换完成后，我们现在可以组合所有数值的信息。最后，我们使用`LogisticRegression`分类器作为模型。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy score of the LogisticRegression is 0.79\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clf = LogisticRegression(solver='lbfgs')\\n\",\n    \"clf.fit(X_train_scaled, y_train)\\n\",\n    \"accuracy = clf.score(X_test_scaled, y_test)\\n\",\n    \"print('Accuracy score of the {} is {:.2f}'.format(clf.__class__.__name__, accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The above pattern of first transforming the data and then fitting/scoring the classifier is exactly the one of Sect. 2.1. Therefore, we would like to use a pipeline for such purpose. However, we would also like to have different processing on different columns of our matrix. The `ColumnTransformer` transformer or the `make_column_transformer` function should be used. It is used to automatically apply different pipeline on different columns.\\n\",\n    \"\\n\",\n    \"上面首先转换数据然后拟合/评分分类器的模式恰好是本节2.1的模式之一。因此，我们希望为此目的使用管道。但是，我们还希望对矩阵的不同列进行不同的处理。应使用`ColumnTransformer`转换器或`make_column_transformer`函数。它用于在不同的列上自动应用不同的管道。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy score of the Pipeline is 0.79\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\compose\\\\_column_transformer.py:739: DeprecationWarning: `make_column_transformer` now expects (transformer, columns) as input tuples instead of (columns, transformer). This has been introduced in v0.20.1. `make_column_transformer` will stop accepting the deprecated (columns, transformer) order in v0.22.\\n\",\n      \"  warnings.warn(message, DeprecationWarning)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.compose import make_column_transformer\\n\",\n    \"\\n\",\n    \"pipe_cat = make_pipeline(SimpleImputer(strategy='constant'), OneHotEncoder(handle_unknown='ignore'))\\n\",\n    \"pipe_num = make_pipeline(SimpleImputer(), StandardScaler())\\n\",\n    \"preprocessor = make_column_transformer((col_cat, pipe_cat), (col_num, pipe_num))\\n\",\n    \"\\n\",\n    \"pipe = make_pipeline(preprocessor, LogisticRegression(solver='lbfgs'))\\n\",\n    \"\\n\",\n    \"pipe.fit(X_train, y_train)\\n\",\n    \"accuracy = pipe.score(X_test, y_test)\\n\",\n    \"print('Accuracy score of the {} is {:.2f}'.format(pipe.__class__.__name__, accuracy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Besides, it can also be used in another pipeline. Thus, we will be able to use all `scikit-learn` utilities as `cross_validate` or `GridSearchCV`.\\n\",\n    \"\\n\",\n    \"此外，它还可以被使用在另一个管道。 因此，我们将能够使用所有`scikit-learn`实用程序作为`cross_validate`或`GridSearchCV`。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'columntransformer': ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,\\n\",\n       \"          transformer_weights=None,\\n\",\n       \"          transformers=[('pipeline-1', Pipeline(memory=None,\\n\",\n       \"      steps=[('simpleimputer', SimpleImputer(copy=True, fill_value=None, missing_values=nan,\\n\",\n       \"        strategy='constant', verbose=0)), ('onehotencoder', OneHotEncoder(categorical_features=None, categories=None,\\n\",\n       \"        dtype=<class 'numpy.float64'>, ha...r', StandardScaler(copy=True, with_mean=True, with_std=True))]), ['age', 'sibsp', 'parch', 'fare'])]),\\n\",\n       \" 'columntransformer__n_jobs': None,\\n\",\n       \" 'columntransformer__pipeline-1': Pipeline(memory=None,\\n\",\n       \"      steps=[('simpleimputer', SimpleImputer(copy=True, fill_value=None, missing_values=nan,\\n\",\n       \"        strategy='constant', verbose=0)), ('onehotencoder', OneHotEncoder(categorical_features=None, categories=None,\\n\",\n       \"        dtype=<class 'numpy.float64'>, handle_unknown='ignore',\\n\",\n       \"        n_values=None, sparse=True))]),\\n\",\n       \" 'columntransformer__pipeline-1__memory': None,\\n\",\n       \" 'columntransformer__pipeline-1__onehotencoder': OneHotEncoder(categorical_features=None, categories=None,\\n\",\n       \"        dtype=<class 'numpy.float64'>, handle_unknown='ignore',\\n\",\n       \"        n_values=None, sparse=True),\\n\",\n       \" 'columntransformer__pipeline-1__onehotencoder__categorical_features': None,\\n\",\n       \" 'columntransformer__pipeline-1__onehotencoder__categories': None,\\n\",\n       \" 'columntransformer__pipeline-1__onehotencoder__dtype': numpy.float64,\\n\",\n       \" 'columntransformer__pipeline-1__onehotencoder__handle_unknown': 'ignore',\\n\",\n       \" 'columntransformer__pipeline-1__onehotencoder__n_values': None,\\n\",\n       \" 'columntransformer__pipeline-1__onehotencoder__sparse': True,\\n\",\n       \" 'columntransformer__pipeline-1__simpleimputer': SimpleImputer(copy=True, fill_value=None, missing_values=nan,\\n\",\n       \"        strategy='constant', verbose=0),\\n\",\n       \" 'columntransformer__pipeline-1__simpleimputer__copy': True,\\n\",\n       \" 'columntransformer__pipeline-1__simpleimputer__fill_value': None,\\n\",\n       \" 'columntransformer__pipeline-1__simpleimputer__missing_values': nan,\\n\",\n       \" 'columntransformer__pipeline-1__simpleimputer__strategy': 'constant',\\n\",\n       \" 'columntransformer__pipeline-1__simpleimputer__verbose': 0,\\n\",\n       \" 'columntransformer__pipeline-1__steps': [('simpleimputer',\\n\",\n       \"   SimpleImputer(copy=True, fill_value=None, missing_values=nan,\\n\",\n       \"          strategy='constant', verbose=0)),\\n\",\n       \"  ('onehotencoder', OneHotEncoder(categorical_features=None, categories=None,\\n\",\n       \"          dtype=<class 'numpy.float64'>, handle_unknown='ignore',\\n\",\n       \"          n_values=None, sparse=True))],\\n\",\n       \" 'columntransformer__pipeline-2': Pipeline(memory=None,\\n\",\n       \"      steps=[('simpleimputer', SimpleImputer(copy=True, fill_value=None, missing_values=nan, strategy='mean',\\n\",\n       \"        verbose=0)), ('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True))]),\\n\",\n       \" 'columntransformer__pipeline-2__memory': None,\\n\",\n       \" 'columntransformer__pipeline-2__simpleimputer': SimpleImputer(copy=True, fill_value=None, missing_values=nan, strategy='mean',\\n\",\n       \"        verbose=0),\\n\",\n       \" 'columntransformer__pipeline-2__simpleimputer__copy': True,\\n\",\n       \" 'columntransformer__pipeline-2__simpleimputer__fill_value': None,\\n\",\n       \" 'columntransformer__pipeline-2__simpleimputer__missing_values': nan,\\n\",\n       \" 'columntransformer__pipeline-2__simpleimputer__strategy': 'mean',\\n\",\n       \" 'columntransformer__pipeline-2__simpleimputer__verbose': 0,\\n\",\n       \" 'columntransformer__pipeline-2__standardscaler': StandardScaler(copy=True, with_mean=True, with_std=True),\\n\",\n       \" 'columntransformer__pipeline-2__standardscaler__copy': True,\\n\",\n       \" 'columntransformer__pipeline-2__standardscaler__with_mean': True,\\n\",\n       \" 'columntransformer__pipeline-2__standardscaler__with_std': True,\\n\",\n       \" 'columntransformer__pipeline-2__steps': [('simpleimputer',\\n\",\n       \"   SimpleImputer(copy=True, fill_value=None, missing_values=nan, strategy='mean',\\n\",\n       \"          verbose=0)),\\n\",\n       \"  ('standardscaler',\\n\",\n       \"   StandardScaler(copy=True, with_mean=True, with_std=True))],\\n\",\n       \" 'columntransformer__remainder': 'drop',\\n\",\n       \" 'columntransformer__sparse_threshold': 0.3,\\n\",\n       \" 'columntransformer__transformer_weights': None,\\n\",\n       \" 'columntransformer__transformers': [('pipeline-1', Pipeline(memory=None,\\n\",\n       \"        steps=[('simpleimputer', SimpleImputer(copy=True, fill_value=None, missing_values=nan,\\n\",\n       \"          strategy='constant', verbose=0)), ('onehotencoder', OneHotEncoder(categorical_features=None, categories=None,\\n\",\n       \"          dtype=<class 'numpy.float64'>, handle_unknown='ignore',\\n\",\n       \"          n_values=None, sparse=True))]), ['sex', 'embarked']),\\n\",\n       \"  ('pipeline-2', Pipeline(memory=None,\\n\",\n       \"        steps=[('simpleimputer', SimpleImputer(copy=True, fill_value=None, missing_values=nan, strategy='mean',\\n\",\n       \"          verbose=0)), ('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True))]), ['age',\\n\",\n       \"    'sibsp',\\n\",\n       \"    'parch',\\n\",\n       \"    'fare'])],\\n\",\n       \" 'logisticregression': LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"           intercept_scaling=1, max_iter=100, multi_class='warn',\\n\",\n       \"           n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\\n\",\n       \"           tol=0.0001, verbose=0, warm_start=False),\\n\",\n       \" 'logisticregression__C': 1.0,\\n\",\n       \" 'logisticregression__class_weight': None,\\n\",\n       \" 'logisticregression__dual': False,\\n\",\n       \" 'logisticregression__fit_intercept': True,\\n\",\n       \" 'logisticregression__intercept_scaling': 1,\\n\",\n       \" 'logisticregression__max_iter': 100,\\n\",\n       \" 'logisticregression__multi_class': 'warn',\\n\",\n       \" 'logisticregression__n_jobs': None,\\n\",\n       \" 'logisticregression__penalty': 'l2',\\n\",\n       \" 'logisticregression__random_state': None,\\n\",\n       \" 'logisticregression__solver': 'lbfgs',\\n\",\n       \" 'logisticregression__tol': 0.0001,\\n\",\n       \" 'logisticregression__verbose': 0,\\n\",\n       \" 'logisticregression__warm_start': False,\\n\",\n       \" 'memory': None,\\n\",\n       \" 'steps': [('columntransformer',\\n\",\n       \"   ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,\\n\",\n       \"            transformer_weights=None,\\n\",\n       \"            transformers=[('pipeline-1', Pipeline(memory=None,\\n\",\n       \"        steps=[('simpleimputer', SimpleImputer(copy=True, fill_value=None, missing_values=nan,\\n\",\n       \"          strategy='constant', verbose=0)), ('onehotencoder', OneHotEncoder(categorical_features=None, categories=None,\\n\",\n       \"          dtype=<class 'numpy.float64'>, ha...r', StandardScaler(copy=True, with_mean=True, with_std=True))]), ['age', 'sibsp', 'parch', 'fare'])])),\\n\",\n       \"  ('logisticregression',\\n\",\n       \"   LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"             intercept_scaling=1, max_iter=100, multi_class='warn',\\n\",\n       \"             n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\\n\",\n       \"             tol=0.0001, verbose=0, warm_start=False))]}\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipe.get_params()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\compose\\\\_column_transformer.py:739: DeprecationWarning: `make_column_transformer` now expects (transformer, columns) as input tuples instead of (columns, transformer). This has been introduced in v0.20.1. `make_column_transformer` will stop accepting the deprecated (columns, transformer) order in v0.22.\\n\",\n      \"  warnings.warn(message, DeprecationWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x25636651c88>\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x256366397f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pipe_cat = make_pipeline(SimpleImputer(strategy='constant'), OneHotEncoder(handle_unknown='ignore'))\\n\",\n    \"pipe_num = make_pipeline(StandardScaler(), SimpleImputer())\\n\",\n    \"preprocessor = make_column_transformer((col_cat, pipe_cat), (col_num, pipe_num))\\n\",\n    \"\\n\",\n    \"pipe = make_pipeline(preprocessor, LogisticRegression(solver='lbfgs'))\\n\",\n    \"\\n\",\n    \"param_grid = {'columntransformer__pipeline-2__simpleimputer__strategy': ['mean', 'median'],\\n\",\n    \"              'logisticregression__C': [0.1, 1.0, 10]}\\n\",\n    \"grid = GridSearchCV(pipe, param_grid=param_grid, cv=5, n_jobs=-1)\\n\",\n    \"scores = pd.DataFrame(cross_validate(grid, X, y, scoring='balanced_accuracy', cv=5, n_jobs=-1, return_train_score=True))\\n\",\n    \"scores[['train_score', 'test_score']].boxplot()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Exercise\\n\",\n    \"\\n\",\n    \"Do the following exercise:\\n\",\n    \"\\n\",\n    \"Load the adult dataset located in `./data/adult_openml.csv`. Make your own `ColumnTransformer` preprocessor. Pipeline it with a classifier. Fine tune it and check the prediction accuracy within a cross-validation.\\n\",\n    \"\\n\",\n    \"#### 练习\\n\",\n    \"完成接下来的练习：\\n\",\n    \"\\n\",\n    \"加载位于`./data/adult_openml.csv`中的成人数据集。 制作自己的`ColumnTransformer`预处理器，并用分类器管道化它。对其进行微调并在交叉验证中检查预测准确性。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Read the adult dataset located in `./data/adult_openml.csv` using `pd.read_csv`.\\n\",\n    \"\\n\",\n    \"* 使用`pd.read_csv`读取位于`./data/adult_openml.csv`中的成人数据集。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load solutions/05_1_solutions.py\\n\",\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"data = pd.read_csv(os.path.join('data', 'adult_openml.csv'))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>workclass</th>\\n\",\n       \"      <th>fnlwgt</th>\\n\",\n       \"      <th>education</th>\\n\",\n       \"      <th>education-num</th>\\n\",\n       \"      <th>marital-status</th>\\n\",\n       \"      <th>occupation</th>\\n\",\n       \"      <th>relationship</th>\\n\",\n       \"      <th>race</th>\\n\",\n       \"      <th>sex</th>\\n\",\n       \"      <th>capitalgain</th>\\n\",\n       \"      <th>capitalloss</th>\\n\",\n       \"      <th>hoursperweek</th>\\n\",\n       \"      <th>native-country</th>\\n\",\n       \"      <th>class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>State-gov</td>\\n\",\n       \"      <td>77516</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Adm-clerical</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Self-emp-not-inc</td>\\n\",\n       \"      <td>83311</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>215646</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Handlers-cleaners</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>234721</td>\\n\",\n       \"      <td>11th</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Handlers-cleaners</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>338409</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Wife</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Cuba</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>284582</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Wife</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>160187</td>\\n\",\n       \"      <td>9th</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>Married-spouse-absent</td>\\n\",\n       \"      <td>Other-service</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Jamaica</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Self-emp-not-inc</td>\\n\",\n       \"      <td>209642</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>45781</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>159449</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>280464</td>\\n\",\n       \"      <td>Some-college</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>State-gov</td>\\n\",\n       \"      <td>141297</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Asian-Pac-Islander</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>India</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>122272</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Adm-clerical</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>205019</td>\\n\",\n       \"      <td>Assoc-acdm</td>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Sales</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>121772</td>\\n\",\n       \"      <td>Assoc-voc</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Craft-repair</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Asian-Pac-Islander</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>245487</td>\\n\",\n       \"      <td>7th-8th</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Transport-moving</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Amer-Indian-Eskimo</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Mexico</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Self-emp-not-inc</td>\\n\",\n       \"      <td>176756</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Farming-fishing</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>186824</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Machine-op-inspct</td>\\n\",\n       \"      <td>Unmarried</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>28887</td>\\n\",\n       \"      <td>11th</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Sales</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Self-emp-not-inc</td>\\n\",\n       \"      <td>292175</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Unmarried</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>193524</td>\\n\",\n       \"      <td>Doctorate</td>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>302146</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Separated</td>\\n\",\n       \"      <td>Other-service</td>\\n\",\n       \"      <td>Unmarried</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Federal-gov</td>\\n\",\n       \"      <td>76845</td>\\n\",\n       \"      <td>9th</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Farming-fishing</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>117037</td>\\n\",\n       \"      <td>11th</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Transport-moving</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>109015</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Tech-support</td>\\n\",\n       \"      <td>Unmarried</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Local-gov</td>\\n\",\n       \"      <td>216851</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Tech-support</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>168294</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Craft-repair</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>180211</td>\\n\",\n       \"      <td>Some-college</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Asian-Pac-Islander</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>South</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>367260</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>193366</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Craft-repair</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48812</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>26711</td>\\n\",\n       \"      <td>Assoc-voc</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48813</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>117909</td>\\n\",\n       \"      <td>Assoc-voc</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48814</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>229647</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Tech-support</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48815</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>149347</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48816</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Local-gov</td>\\n\",\n       \"      <td>23157</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48817</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>93977</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Machine-op-inspct</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48818</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Self-emp-inc</td>\\n\",\n       \"      <td>159691</td>\\n\",\n       \"      <td>Some-college</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48819</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>176967</td>\\n\",\n       \"      <td>Some-college</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Protective-serv</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48820</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>344436</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Widowed</td>\\n\",\n       \"      <td>Sales</td>\\n\",\n       \"      <td>Other-relative</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48821</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>430340</td>\\n\",\n       \"      <td>Some-college</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Sales</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48822</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>202168</td>\\n\",\n       \"      <td>Prof-school</td>\\n\",\n       \"      <td>15</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48823</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>82720</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Craft-repair</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48824</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>269623</td>\\n\",\n       \"      <td>Some-college</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Craft-repair</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48825</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Self-emp-not-inc</td>\\n\",\n       \"      <td>136405</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Widowed</td>\\n\",\n       \"      <td>Farming-fishing</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48826</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Local-gov</td>\\n\",\n       \"      <td>139347</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Wife</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48827</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>224655</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Separated</td>\\n\",\n       \"      <td>Priv-house-serv</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48828</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>247547</td>\\n\",\n       \"      <td>Assoc-voc</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Adm-clerical</td>\\n\",\n       \"      <td>Unmarried</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48829</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>292710</td>\\n\",\n       \"      <td>Assoc-acdm</td>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48830</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>173449</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Handlers-cleaners</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48831</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>285570</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Adm-clerical</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48832</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>89686</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Sales</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48833</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>440129</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Craft-repair</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48834</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>350977</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Other-service</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48835</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Local-gov</td>\\n\",\n       \"      <td>349230</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Other-service</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48836</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>245211</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48837</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>215419</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48838</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>321403</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Widowed</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>Other-relative</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48839</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>374983</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48840</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>83891</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Adm-clerical</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>Asian-Pac-Islander</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&lt;=50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48841</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Self-emp-inc</td>\\n\",\n       \"      <td>182148</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"      <td>&gt;50K</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>48842 rows × 15 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       age         workclass  fnlwgt     education  education-num  \\\\\\n\",\n       \"0        2         State-gov   77516     Bachelors             13   \\n\",\n       \"1        3  Self-emp-not-inc   83311     Bachelors             13   \\n\",\n       \"2        2           Private  215646       HS-grad              9   \\n\",\n       \"3        3           Private  234721          11th              7   \\n\",\n       \"4        1           Private  338409     Bachelors             13   \\n\",\n       \"5        2           Private  284582       Masters             14   \\n\",\n       \"6        3           Private  160187           9th              5   \\n\",\n       \"7        3  Self-emp-not-inc  209642       HS-grad              9   \\n\",\n       \"8        1           Private   45781       Masters             14   \\n\",\n       \"9        2           Private  159449     Bachelors             13   \\n\",\n       \"10       2           Private  280464  Some-college             10   \\n\",\n       \"11       1         State-gov  141297     Bachelors             13   \\n\",\n       \"12       0           Private  122272     Bachelors             13   \\n\",\n       \"13       1           Private  205019    Assoc-acdm             12   \\n\",\n       \"14       2           Private  121772     Assoc-voc             11   \\n\",\n       \"15       1           Private  245487       7th-8th              4   \\n\",\n       \"16       0  Self-emp-not-inc  176756       HS-grad              9   \\n\",\n       \"17       1           Private  186824       HS-grad              9   \\n\",\n       \"18       2           Private   28887          11th              7   \\n\",\n       \"19       2  Self-emp-not-inc  292175       Masters             14   \\n\",\n       \"20       2           Private  193524     Doctorate             16   \\n\",\n       \"21       3           Private  302146       HS-grad              9   \\n\",\n       \"22       1       Federal-gov   76845           9th              5   \\n\",\n       \"23       2           Private  117037          11th              7   \\n\",\n       \"24       4           Private  109015       HS-grad              9   \\n\",\n       \"25       4         Local-gov  216851     Bachelors             13   \\n\",\n       \"26       0           Private  168294       HS-grad              9   \\n\",\n       \"27       3                 ?  180211  Some-college             10   \\n\",\n       \"28       2           Private  367260       HS-grad              9   \\n\",\n       \"29       3           Private  193366       HS-grad              9   \\n\",\n       \"...    ...               ...     ...           ...            ...   \\n\",\n       \"48812    4                 ?   26711     Assoc-voc             11   \\n\",\n       \"48813    4           Private  117909     Assoc-voc             11   \\n\",\n       \"48814    2           Private  229647     Bachelors             13   \\n\",\n       \"48815    2           Private  149347       Masters             14   \\n\",\n       \"48816    2         Local-gov   23157       Masters             14   \\n\",\n       \"48817    0           Private   93977       HS-grad              9   \\n\",\n       \"48818    4      Self-emp-inc  159691  Some-college             10   \\n\",\n       \"48819    1           Private  176967  Some-college             10   \\n\",\n       \"48820    4           Private  344436       HS-grad              9   \\n\",\n       \"48821    1           Private  430340  Some-college             10   \\n\",\n       \"48822    2           Private  202168   Prof-school             15   \\n\",\n       \"48823    3           Private   82720       HS-grad              9   \\n\",\n       \"48824    0           Private  269623  Some-college             10   \\n\",\n       \"48825    4  Self-emp-not-inc  136405       HS-grad              9   \\n\",\n       \"48826    3         Local-gov  139347       Masters             14   \\n\",\n       \"48827    3           Private  224655       HS-grad              9   \\n\",\n       \"48828    2           Private  247547     Assoc-voc             11   \\n\",\n       \"48829    4           Private  292710    Assoc-acdm             12   \\n\",\n       \"48830    1           Private  173449       HS-grad              9   \\n\",\n       \"48831    3           Private  285570       HS-grad              9   \\n\",\n       \"48832    4           Private   89686       HS-grad              9   \\n\",\n       \"48833    1           Private  440129       HS-grad              9   \\n\",\n       \"48834    0           Private  350977       HS-grad              9   \\n\",\n       \"48835    3         Local-gov  349230       Masters             14   \\n\",\n       \"48836    1           Private  245211     Bachelors             13   \\n\",\n       \"48837    2           Private  215419     Bachelors             13   \\n\",\n       \"48838    4                 ?  321403       HS-grad              9   \\n\",\n       \"48839    2           Private  374983     Bachelors             13   \\n\",\n       \"48840    2           Private   83891     Bachelors             13   \\n\",\n       \"48841    1      Self-emp-inc  182148     Bachelors             13   \\n\",\n       \"\\n\",\n       \"              marital-status         occupation    relationship  \\\\\\n\",\n       \"0              Never-married       Adm-clerical   Not-in-family   \\n\",\n       \"1         Married-civ-spouse    Exec-managerial         Husband   \\n\",\n       \"2                   Divorced  Handlers-cleaners   Not-in-family   \\n\",\n       \"3         Married-civ-spouse  Handlers-cleaners         Husband   \\n\",\n       \"4         Married-civ-spouse     Prof-specialty            Wife   \\n\",\n       \"5         Married-civ-spouse    Exec-managerial            Wife   \\n\",\n       \"6      Married-spouse-absent      Other-service   Not-in-family   \\n\",\n       \"7         Married-civ-spouse    Exec-managerial         Husband   \\n\",\n       \"8              Never-married     Prof-specialty   Not-in-family   \\n\",\n       \"9         Married-civ-spouse    Exec-managerial         Husband   \\n\",\n       \"10        Married-civ-spouse    Exec-managerial         Husband   \\n\",\n       \"11        Married-civ-spouse     Prof-specialty         Husband   \\n\",\n       \"12             Never-married       Adm-clerical       Own-child   \\n\",\n       \"13             Never-married              Sales   Not-in-family   \\n\",\n       \"14        Married-civ-spouse       Craft-repair         Husband   \\n\",\n       \"15        Married-civ-spouse   Transport-moving         Husband   \\n\",\n       \"16             Never-married    Farming-fishing       Own-child   \\n\",\n       \"17             Never-married  Machine-op-inspct       Unmarried   \\n\",\n       \"18        Married-civ-spouse              Sales         Husband   \\n\",\n       \"19                  Divorced    Exec-managerial       Unmarried   \\n\",\n       \"20        Married-civ-spouse     Prof-specialty         Husband   \\n\",\n       \"21                 Separated      Other-service       Unmarried   \\n\",\n       \"22        Married-civ-spouse    Farming-fishing         Husband   \\n\",\n       \"23        Married-civ-spouse   Transport-moving         Husband   \\n\",\n       \"24                  Divorced       Tech-support       Unmarried   \\n\",\n       \"25        Married-civ-spouse       Tech-support         Husband   \\n\",\n       \"26             Never-married       Craft-repair       Own-child   \\n\",\n       \"27        Married-civ-spouse                  ?         Husband   \\n\",\n       \"28                  Divorced    Exec-managerial   Not-in-family   \\n\",\n       \"29        Married-civ-spouse       Craft-repair         Husband   \\n\",\n       \"...                      ...                ...             ...   \\n\",\n       \"48812     Married-civ-spouse                  ?         Husband   \\n\",\n       \"48813     Married-civ-spouse     Prof-specialty         Husband   \\n\",\n       \"48814          Never-married       Tech-support   Not-in-family   \\n\",\n       \"48815     Married-civ-spouse     Prof-specialty         Husband   \\n\",\n       \"48816     Married-civ-spouse    Exec-managerial         Husband   \\n\",\n       \"48817          Never-married  Machine-op-inspct       Own-child   \\n\",\n       \"48818               Divorced    Exec-managerial   Not-in-family   \\n\",\n       \"48819     Married-civ-spouse    Protective-serv         Husband   \\n\",\n       \"48820                Widowed              Sales  Other-relative   \\n\",\n       \"48821          Never-married              Sales   Not-in-family   \\n\",\n       \"48822     Married-civ-spouse     Prof-specialty         Husband   \\n\",\n       \"48823     Married-civ-spouse       Craft-repair         Husband   \\n\",\n       \"48824          Never-married       Craft-repair       Own-child   \\n\",\n       \"48825                Widowed    Farming-fishing   Not-in-family   \\n\",\n       \"48826     Married-civ-spouse     Prof-specialty            Wife   \\n\",\n       \"48827              Separated    Priv-house-serv   Not-in-family   \\n\",\n       \"48828          Never-married       Adm-clerical       Unmarried   \\n\",\n       \"48829               Divorced     Prof-specialty   Not-in-family   \\n\",\n       \"48830     Married-civ-spouse  Handlers-cleaners         Husband   \\n\",\n       \"48831     Married-civ-spouse       Adm-clerical         Husband   \\n\",\n       \"48832     Married-civ-spouse              Sales         Husband   \\n\",\n       \"48833     Married-civ-spouse       Craft-repair         Husband   \\n\",\n       \"48834          Never-married      Other-service       Own-child   \\n\",\n       \"48835               Divorced      Other-service   Not-in-family   \\n\",\n       \"48836          Never-married     Prof-specialty       Own-child   \\n\",\n       \"48837               Divorced     Prof-specialty   Not-in-family   \\n\",\n       \"48838                Widowed                  ?  Other-relative   \\n\",\n       \"48839     Married-civ-spouse     Prof-specialty         Husband   \\n\",\n       \"48840               Divorced       Adm-clerical       Own-child   \\n\",\n       \"48841     Married-civ-spouse    Exec-managerial         Husband   \\n\",\n       \"\\n\",\n       \"                     race     sex  capitalgain  capitalloss  hoursperweek  \\\\\\n\",\n       \"0                   White    Male            1            0             2   \\n\",\n       \"1                   White    Male            0            0             0   \\n\",\n       \"2                   White    Male            0            0             2   \\n\",\n       \"3                   Black    Male            0            0             2   \\n\",\n       \"4                   Black  Female            0            0             2   \\n\",\n       \"5                   White  Female            0            0             2   \\n\",\n       \"6                   Black  Female            0            0             0   \\n\",\n       \"7                   White    Male            0            0             2   \\n\",\n       \"8                   White  Female            4            0             3   \\n\",\n       \"9                   White    Male            2            0             2   \\n\",\n       \"10                  Black    Male            0            0             4   \\n\",\n       \"11     Asian-Pac-Islander    Male            0            0             2   \\n\",\n       \"12                  White  Female            0            0             1   \\n\",\n       \"13                  Black    Male            0            0             3   \\n\",\n       \"14     Asian-Pac-Islander    Male            0            0             2   \\n\",\n       \"15     Amer-Indian-Eskimo    Male            0            0             2   \\n\",\n       \"16                  White    Male            0            0             1   \\n\",\n       \"17                  White    Male            0            0             2   \\n\",\n       \"18                  White    Male            0            0             3   \\n\",\n       \"19                  White  Female            0            0             2   \\n\",\n       \"20                  White    Male            0            0             3   \\n\",\n       \"21                  Black  Female            0            0             0   \\n\",\n       \"22                  Black    Male            0            0             2   \\n\",\n       \"23                  White    Male            0            3             2   \\n\",\n       \"24                  White  Female            0            0             2   \\n\",\n       \"25                  White    Male            0            0             2   \\n\",\n       \"26                  White    Male            0            0             2   \\n\",\n       \"27     Asian-Pac-Islander    Male            0            0             3   \\n\",\n       \"28                  White    Male            0            0             4   \\n\",\n       \"29                  White    Male            0            0             2   \\n\",\n       \"...                   ...     ...          ...          ...           ...   \\n\",\n       \"48812               White    Male            1            0             0   \\n\",\n       \"48813               White    Male            3            0             2   \\n\",\n       \"48814               White  Female            0            2             2   \\n\",\n       \"48815               White    Male            0            0             3   \\n\",\n       \"48816               White    Male            0            3             3   \\n\",\n       \"48817               White    Male            0            0             2   \\n\",\n       \"48818               White  Female            0            0             2   \\n\",\n       \"48819               White    Male            0            0             2   \\n\",\n       \"48820               White  Female            0            0             0   \\n\",\n       \"48821               White  Female            0            0             2   \\n\",\n       \"48822               White    Male            4            0             3   \\n\",\n       \"48823               White    Male            0            0             2   \\n\",\n       \"48824               White    Male            0            0             2   \\n\",\n       \"48825               White    Male            0            0             1   \\n\",\n       \"48826               White  Female            0            0             2   \\n\",\n       \"48827               White  Female            0            0             1   \\n\",\n       \"48828               Black  Female            0            0             2   \\n\",\n       \"48829               White    Male            0            0             2   \\n\",\n       \"48830               White    Male            0            0             2   \\n\",\n       \"48831               White    Male            0            0             2   \\n\",\n       \"48832               White    Male            0            0             3   \\n\",\n       \"48833               White    Male            0            0             2   \\n\",\n       \"48834               White  Female            0            0             2   \\n\",\n       \"48835               White    Male            0            0             2   \\n\",\n       \"48836               White    Male            0            0             2   \\n\",\n       \"48837               White  Female            0            0             2   \\n\",\n       \"48838               Black    Male            0            0             2   \\n\",\n       \"48839               White    Male            0            0             3   \\n\",\n       \"48840  Asian-Pac-Islander    Male            2            0             2   \\n\",\n       \"48841               White    Male            0            0             3   \\n\",\n       \"\\n\",\n       \"      native-country  class  \\n\",\n       \"0      United-States  <=50K  \\n\",\n       \"1      United-States  <=50K  \\n\",\n       \"2      United-States  <=50K  \\n\",\n       \"3      United-States  <=50K  \\n\",\n       \"4               Cuba  <=50K  \\n\",\n       \"5      United-States  <=50K  \\n\",\n       \"6            Jamaica  <=50K  \\n\",\n       \"7      United-States   >50K  \\n\",\n       \"8      United-States   >50K  \\n\",\n       \"9      United-States   >50K  \\n\",\n       \"10     United-States   >50K  \\n\",\n       \"11             India   >50K  \\n\",\n       \"12     United-States  <=50K  \\n\",\n       \"13     United-States  <=50K  \\n\",\n       \"14                 ?   >50K  \\n\",\n       \"15            Mexico  <=50K  \\n\",\n       \"16     United-States  <=50K  \\n\",\n       \"17     United-States  <=50K  \\n\",\n       \"18     United-States  <=50K  \\n\",\n       \"19     United-States   >50K  \\n\",\n       \"20     United-States   >50K  \\n\",\n       \"21     United-States  <=50K  \\n\",\n       \"22     United-States  <=50K  \\n\",\n       \"23     United-States  <=50K  \\n\",\n       \"24     United-States  <=50K  \\n\",\n       \"25     United-States   >50K  \\n\",\n       \"26     United-States  <=50K  \\n\",\n       \"27             South   >50K  \\n\",\n       \"28     United-States  <=50K  \\n\",\n       \"29     United-States  <=50K  \\n\",\n       \"...              ...    ...  \\n\",\n       \"48812  United-States  <=50K  \\n\",\n       \"48813  United-States   >50K  \\n\",\n       \"48814  United-States  <=50K  \\n\",\n       \"48815  United-States   >50K  \\n\",\n       \"48816  United-States   >50K  \\n\",\n       \"48817  United-States  <=50K  \\n\",\n       \"48818  United-States  <=50K  \\n\",\n       \"48819  United-States  <=50K  \\n\",\n       \"48820  United-States  <=50K  \\n\",\n       \"48821  United-States  <=50K  \\n\",\n       \"48822  United-States   >50K  \\n\",\n       \"48823  United-States  <=50K  \\n\",\n       \"48824  United-States  <=50K  \\n\",\n       \"48825  United-States  <=50K  \\n\",\n       \"48826              ?   >50K  \\n\",\n       \"48827  United-States  <=50K  \\n\",\n       \"48828  United-States  <=50K  \\n\",\n       \"48829  United-States  <=50K  \\n\",\n       \"48830  United-States  <=50K  \\n\",\n       \"48831  United-States  <=50K  \\n\",\n       \"48832  United-States  <=50K  \\n\",\n       \"48833  United-States  <=50K  \\n\",\n       \"48834  United-States  <=50K  \\n\",\n       \"48835  United-States  <=50K  \\n\",\n       \"48836  United-States  <=50K  \\n\",\n       \"48837  United-States  <=50K  \\n\",\n       \"48838  United-States  <=50K  \\n\",\n       \"48839  United-States  <=50K  \\n\",\n       \"48840  United-States  <=50K  \\n\",\n       \"48841  United-States   >50K  \\n\",\n       \"\\n\",\n       \"[48842 rows x 15 columns]\"\n      ]\n     },\n     \"execution_count\": 57,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Split the datasets into a data and a target. The target corresponds to the `class` column. For the data, drop the columns `fnlwgt`, `capitalgain`, and `capitalloss`.\\n\",\n    \"\\n\",\n    \"* 将数据集拆分为数据和目标。 目标对应于类列。 对于数据，删除列`fnlwgt`，`capitalgain`和`capitalloss`。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load solutions/05_2_solutions.py\\n\",\n    \"y = data['class']\\n\",\n    \"X = data.drop(columns=['class', 'fnlwgt', 'capitalgain', 'capitalloss'])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>age</th>\\n\",\n       \"      <th>workclass</th>\\n\",\n       \"      <th>education</th>\\n\",\n       \"      <th>education-num</th>\\n\",\n       \"      <th>marital-status</th>\\n\",\n       \"      <th>occupation</th>\\n\",\n       \"      <th>relationship</th>\\n\",\n       \"      <th>race</th>\\n\",\n       \"      <th>sex</th>\\n\",\n       \"      <th>hoursperweek</th>\\n\",\n       \"      <th>native-country</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>State-gov</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Adm-clerical</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Self-emp-not-inc</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Handlers-cleaners</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>11th</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Handlers-cleaners</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Wife</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Cuba</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Wife</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>9th</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>Married-spouse-absent</td>\\n\",\n       \"      <td>Other-service</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Jamaica</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Self-emp-not-inc</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Some-college</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>State-gov</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Asian-Pac-Islander</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>India</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Adm-clerical</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Assoc-acdm</td>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Sales</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Assoc-voc</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Craft-repair</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Asian-Pac-Islander</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>7th-8th</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Transport-moving</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Amer-Indian-Eskimo</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Mexico</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Self-emp-not-inc</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Farming-fishing</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Machine-op-inspct</td>\\n\",\n       \"      <td>Unmarried</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>11th</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Sales</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Self-emp-not-inc</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Unmarried</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Doctorate</td>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Separated</td>\\n\",\n       \"      <td>Other-service</td>\\n\",\n       \"      <td>Unmarried</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Federal-gov</td>\\n\",\n       \"      <td>9th</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Farming-fishing</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>11th</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Transport-moving</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Tech-support</td>\\n\",\n       \"      <td>Unmarried</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Local-gov</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Tech-support</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Craft-repair</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>Some-college</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>Asian-Pac-Islander</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>South</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Craft-repair</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48812</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>Assoc-voc</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48813</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Assoc-voc</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48814</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Tech-support</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48815</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48816</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Local-gov</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48817</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Machine-op-inspct</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48818</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Self-emp-inc</td>\\n\",\n       \"      <td>Some-college</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48819</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Some-college</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Protective-serv</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48820</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Widowed</td>\\n\",\n       \"      <td>Sales</td>\\n\",\n       \"      <td>Other-relative</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48821</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Some-college</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Sales</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48822</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Prof-school</td>\\n\",\n       \"      <td>15</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48823</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Craft-repair</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48824</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Some-college</td>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Craft-repair</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48825</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Self-emp-not-inc</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Widowed</td>\\n\",\n       \"      <td>Farming-fishing</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48826</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Local-gov</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Wife</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48827</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Separated</td>\\n\",\n       \"      <td>Priv-house-serv</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48828</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Assoc-voc</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Adm-clerical</td>\\n\",\n       \"      <td>Unmarried</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48829</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Assoc-acdm</td>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48830</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Handlers-cleaners</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48831</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Adm-clerical</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48832</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Sales</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48833</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Craft-repair</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48834</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Other-service</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48835</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Local-gov</td>\\n\",\n       \"      <td>Masters</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Other-service</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48836</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Never-married</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48837</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Not-in-family</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Female</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48838</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>HS-grad</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>Widowed</td>\\n\",\n       \"      <td>?</td>\\n\",\n       \"      <td>Other-relative</td>\\n\",\n       \"      <td>Black</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48839</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Prof-specialty</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48840</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Private</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Divorced</td>\\n\",\n       \"      <td>Adm-clerical</td>\\n\",\n       \"      <td>Own-child</td>\\n\",\n       \"      <td>Asian-Pac-Islander</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48841</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Self-emp-inc</td>\\n\",\n       \"      <td>Bachelors</td>\\n\",\n       \"      <td>13</td>\\n\",\n       \"      <td>Married-civ-spouse</td>\\n\",\n       \"      <td>Exec-managerial</td>\\n\",\n       \"      <td>Husband</td>\\n\",\n       \"      <td>White</td>\\n\",\n       \"      <td>Male</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>United-States</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>48842 rows × 11 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       age         workclass     education  education-num  \\\\\\n\",\n       \"0        2         State-gov     Bachelors             13   \\n\",\n       \"1        3  Self-emp-not-inc     Bachelors             13   \\n\",\n       \"2        2           Private       HS-grad              9   \\n\",\n       \"3        3           Private          11th              7   \\n\",\n       \"4        1           Private     Bachelors             13   \\n\",\n       \"5        2           Private       Masters             14   \\n\",\n       \"6        3           Private           9th              5   \\n\",\n       \"7        3  Self-emp-not-inc       HS-grad              9   \\n\",\n       \"8        1           Private       Masters             14   \\n\",\n       \"9        2           Private     Bachelors             13   \\n\",\n       \"10       2           Private  Some-college             10   \\n\",\n       \"11       1         State-gov     Bachelors             13   \\n\",\n       \"12       0           Private     Bachelors             13   \\n\",\n       \"13       1           Private    Assoc-acdm             12   \\n\",\n       \"14       2           Private     Assoc-voc             11   \\n\",\n       \"15       1           Private       7th-8th              4   \\n\",\n       \"16       0  Self-emp-not-inc       HS-grad              9   \\n\",\n       \"17       1           Private       HS-grad              9   \\n\",\n       \"18       2           Private          11th              7   \\n\",\n       \"19       2  Self-emp-not-inc       Masters             14   \\n\",\n       \"20       2           Private     Doctorate             16   \\n\",\n       \"21       3           Private       HS-grad              9   \\n\",\n       \"22       1       Federal-gov           9th              5   \\n\",\n       \"23       2           Private          11th              7   \\n\",\n       \"24       4           Private       HS-grad              9   \\n\",\n       \"25       4         Local-gov     Bachelors             13   \\n\",\n       \"26       0           Private       HS-grad              9   \\n\",\n       \"27       3                 ?  Some-college             10   \\n\",\n       \"28       2           Private       HS-grad              9   \\n\",\n       \"29       3           Private       HS-grad              9   \\n\",\n       \"...    ...               ...           ...            ...   \\n\",\n       \"48812    4                 ?     Assoc-voc             11   \\n\",\n       \"48813    4           Private     Assoc-voc             11   \\n\",\n       \"48814    2           Private     Bachelors             13   \\n\",\n       \"48815    2           Private       Masters             14   \\n\",\n       \"48816    2         Local-gov       Masters             14   \\n\",\n       \"48817    0           Private       HS-grad              9   \\n\",\n       \"48818    4      Self-emp-inc  Some-college             10   \\n\",\n       \"48819    1           Private  Some-college             10   \\n\",\n       \"48820    4           Private       HS-grad              9   \\n\",\n       \"48821    1           Private  Some-college             10   \\n\",\n       \"48822    2           Private   Prof-school             15   \\n\",\n       \"48823    3           Private       HS-grad              9   \\n\",\n       \"48824    0           Private  Some-college             10   \\n\",\n       \"48825    4  Self-emp-not-inc       HS-grad              9   \\n\",\n       \"48826    3         Local-gov       Masters             14   \\n\",\n       \"48827    3           Private       HS-grad              9   \\n\",\n       \"48828    2           Private     Assoc-voc             11   \\n\",\n       \"48829    4           Private    Assoc-acdm             12   \\n\",\n       \"48830    1           Private       HS-grad              9   \\n\",\n       \"48831    3           Private       HS-grad              9   \\n\",\n       \"48832    4           Private       HS-grad              9   \\n\",\n       \"48833    1           Private       HS-grad              9   \\n\",\n       \"48834    0           Private       HS-grad              9   \\n\",\n       \"48835    3         Local-gov       Masters             14   \\n\",\n       \"48836    1           Private     Bachelors             13   \\n\",\n       \"48837    2           Private     Bachelors             13   \\n\",\n       \"48838    4                 ?       HS-grad              9   \\n\",\n       \"48839    2           Private     Bachelors             13   \\n\",\n       \"48840    2           Private     Bachelors             13   \\n\",\n       \"48841    1      Self-emp-inc     Bachelors             13   \\n\",\n       \"\\n\",\n       \"              marital-status         occupation    relationship  \\\\\\n\",\n       \"0              Never-married       Adm-clerical   Not-in-family   \\n\",\n       \"1         Married-civ-spouse    Exec-managerial         Husband   \\n\",\n       \"2                   Divorced  Handlers-cleaners   Not-in-family   \\n\",\n       \"3         Married-civ-spouse  Handlers-cleaners         Husband   \\n\",\n       \"4         Married-civ-spouse     Prof-specialty            Wife   \\n\",\n       \"5         Married-civ-spouse    Exec-managerial            Wife   \\n\",\n       \"6      Married-spouse-absent      Other-service   Not-in-family   \\n\",\n       \"7         Married-civ-spouse    Exec-managerial         Husband   \\n\",\n       \"8              Never-married     Prof-specialty   Not-in-family   \\n\",\n       \"9         Married-civ-spouse    Exec-managerial         Husband   \\n\",\n       \"10        Married-civ-spouse    Exec-managerial         Husband   \\n\",\n       \"11        Married-civ-spouse     Prof-specialty         Husband   \\n\",\n       \"12             Never-married       Adm-clerical       Own-child   \\n\",\n       \"13             Never-married              Sales   Not-in-family   \\n\",\n       \"14        Married-civ-spouse       Craft-repair         Husband   \\n\",\n       \"15        Married-civ-spouse   Transport-moving         Husband   \\n\",\n       \"16             Never-married    Farming-fishing       Own-child   \\n\",\n       \"17             Never-married  Machine-op-inspct       Unmarried   \\n\",\n       \"18        Married-civ-spouse              Sales         Husband   \\n\",\n       \"19                  Divorced    Exec-managerial       Unmarried   \\n\",\n       \"20        Married-civ-spouse     Prof-specialty         Husband   \\n\",\n       \"21                 Separated      Other-service       Unmarried   \\n\",\n       \"22        Married-civ-spouse    Farming-fishing         Husband   \\n\",\n       \"23        Married-civ-spouse   Transport-moving         Husband   \\n\",\n       \"24                  Divorced       Tech-support       Unmarried   \\n\",\n       \"25        Married-civ-spouse       Tech-support         Husband   \\n\",\n       \"26             Never-married       Craft-repair       Own-child   \\n\",\n       \"27        Married-civ-spouse                  ?         Husband   \\n\",\n       \"28                  Divorced    Exec-managerial   Not-in-family   \\n\",\n       \"29        Married-civ-spouse       Craft-repair         Husband   \\n\",\n       \"...                      ...                ...             ...   \\n\",\n       \"48812     Married-civ-spouse                  ?         Husband   \\n\",\n       \"48813     Married-civ-spouse     Prof-specialty         Husband   \\n\",\n       \"48814          Never-married       Tech-support   Not-in-family   \\n\",\n       \"48815     Married-civ-spouse     Prof-specialty         Husband   \\n\",\n       \"48816     Married-civ-spouse    Exec-managerial         Husband   \\n\",\n       \"48817          Never-married  Machine-op-inspct       Own-child   \\n\",\n       \"48818               Divorced    Exec-managerial   Not-in-family   \\n\",\n       \"48819     Married-civ-spouse    Protective-serv         Husband   \\n\",\n       \"48820                Widowed              Sales  Other-relative   \\n\",\n       \"48821          Never-married              Sales   Not-in-family   \\n\",\n       \"48822     Married-civ-spouse     Prof-specialty         Husband   \\n\",\n       \"48823     Married-civ-spouse       Craft-repair         Husband   \\n\",\n       \"48824          Never-married       Craft-repair       Own-child   \\n\",\n       \"48825                Widowed    Farming-fishing   Not-in-family   \\n\",\n       \"48826     Married-civ-spouse     Prof-specialty            Wife   \\n\",\n       \"48827              Separated    Priv-house-serv   Not-in-family   \\n\",\n       \"48828          Never-married       Adm-clerical       Unmarried   \\n\",\n       \"48829               Divorced     Prof-specialty   Not-in-family   \\n\",\n       \"48830     Married-civ-spouse  Handlers-cleaners         Husband   \\n\",\n       \"48831     Married-civ-spouse       Adm-clerical         Husband   \\n\",\n       \"48832     Married-civ-spouse              Sales         Husband   \\n\",\n       \"48833     Married-civ-spouse       Craft-repair         Husband   \\n\",\n       \"48834          Never-married      Other-service       Own-child   \\n\",\n       \"48835               Divorced      Other-service   Not-in-family   \\n\",\n       \"48836          Never-married     Prof-specialty       Own-child   \\n\",\n       \"48837               Divorced     Prof-specialty   Not-in-family   \\n\",\n       \"48838                Widowed                  ?  Other-relative   \\n\",\n       \"48839     Married-civ-spouse     Prof-specialty         Husband   \\n\",\n       \"48840               Divorced       Adm-clerical       Own-child   \\n\",\n       \"48841     Married-civ-spouse    Exec-managerial         Husband   \\n\",\n       \"\\n\",\n       \"                     race     sex  hoursperweek native-country  \\n\",\n       \"0                   White    Male             2  United-States  \\n\",\n       \"1                   White    Male             0  United-States  \\n\",\n       \"2                   White    Male             2  United-States  \\n\",\n       \"3                   Black    Male             2  United-States  \\n\",\n       \"4                   Black  Female             2           Cuba  \\n\",\n       \"5                   White  Female             2  United-States  \\n\",\n       \"6                   Black  Female             0        Jamaica  \\n\",\n       \"7                   White    Male             2  United-States  \\n\",\n       \"8                   White  Female             3  United-States  \\n\",\n       \"9                   White    Male             2  United-States  \\n\",\n       \"10                  Black    Male             4  United-States  \\n\",\n       \"11     Asian-Pac-Islander    Male             2          India  \\n\",\n       \"12                  White  Female             1  United-States  \\n\",\n       \"13                  Black    Male             3  United-States  \\n\",\n       \"14     Asian-Pac-Islander    Male             2              ?  \\n\",\n       \"15     Amer-Indian-Eskimo    Male             2         Mexico  \\n\",\n       \"16                  White    Male             1  United-States  \\n\",\n       \"17                  White    Male             2  United-States  \\n\",\n       \"18                  White    Male             3  United-States  \\n\",\n       \"19                  White  Female             2  United-States  \\n\",\n       \"20                  White    Male             3  United-States  \\n\",\n       \"21                  Black  Female             0  United-States  \\n\",\n       \"22                  Black    Male             2  United-States  \\n\",\n       \"23                  White    Male             2  United-States  \\n\",\n       \"24                  White  Female             2  United-States  \\n\",\n       \"25                  White    Male             2  United-States  \\n\",\n       \"26                  White    Male             2  United-States  \\n\",\n       \"27     Asian-Pac-Islander    Male             3          South  \\n\",\n       \"28                  White    Male             4  United-States  \\n\",\n       \"29                  White    Male             2  United-States  \\n\",\n       \"...                   ...     ...           ...            ...  \\n\",\n       \"48812               White    Male             0  United-States  \\n\",\n       \"48813               White    Male             2  United-States  \\n\",\n       \"48814               White  Female             2  United-States  \\n\",\n       \"48815               White    Male             3  United-States  \\n\",\n       \"48816               White    Male             3  United-States  \\n\",\n       \"48817               White    Male             2  United-States  \\n\",\n       \"48818               White  Female             2  United-States  \\n\",\n       \"48819               White    Male             2  United-States  \\n\",\n       \"48820               White  Female             0  United-States  \\n\",\n       \"48821               White  Female             2  United-States  \\n\",\n       \"48822               White    Male             3  United-States  \\n\",\n       \"48823               White    Male             2  United-States  \\n\",\n       \"48824               White    Male             2  United-States  \\n\",\n       \"48825               White    Male             1  United-States  \\n\",\n       \"48826               White  Female             2              ?  \\n\",\n       \"48827               White  Female             1  United-States  \\n\",\n       \"48828               Black  Female             2  United-States  \\n\",\n       \"48829               White    Male             2  United-States  \\n\",\n       \"48830               White    Male             2  United-States  \\n\",\n       \"48831               White    Male             2  United-States  \\n\",\n       \"48832               White    Male             3  United-States  \\n\",\n       \"48833               White    Male             2  United-States  \\n\",\n       \"48834               White  Female             2  United-States  \\n\",\n       \"48835               White    Male             2  United-States  \\n\",\n       \"48836               White    Male             2  United-States  \\n\",\n       \"48837               White  Female             2  United-States  \\n\",\n       \"48838               Black    Male             2  United-States  \\n\",\n       \"48839               White    Male             3  United-States  \\n\",\n       \"48840  Asian-Pac-Islander    Male             2  United-States  \\n\",\n       \"48841               White    Male             3  United-States  \\n\",\n       \"\\n\",\n       \"[48842 rows x 11 columns]\"\n      ]\n     },\n     \"execution_count\": 59,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0        <=50K\\n\",\n       \"1        <=50K\\n\",\n       \"2        <=50K\\n\",\n       \"3        <=50K\\n\",\n       \"4        <=50K\\n\",\n       \"5        <=50K\\n\",\n       \"6        <=50K\\n\",\n       \"7         >50K\\n\",\n       \"8         >50K\\n\",\n       \"9         >50K\\n\",\n       \"10        >50K\\n\",\n       \"11        >50K\\n\",\n       \"12       <=50K\\n\",\n       \"13       <=50K\\n\",\n       \"14        >50K\\n\",\n       \"15       <=50K\\n\",\n       \"16       <=50K\\n\",\n       \"17       <=50K\\n\",\n       \"18       <=50K\\n\",\n       \"19        >50K\\n\",\n       \"20        >50K\\n\",\n       \"21       <=50K\\n\",\n       \"22       <=50K\\n\",\n       \"23       <=50K\\n\",\n       \"24       <=50K\\n\",\n       \"25        >50K\\n\",\n       \"26       <=50K\\n\",\n       \"27        >50K\\n\",\n       \"28       <=50K\\n\",\n       \"29       <=50K\\n\",\n       \"         ...  \\n\",\n       \"48812    <=50K\\n\",\n       \"48813     >50K\\n\",\n       \"48814    <=50K\\n\",\n       \"48815     >50K\\n\",\n       \"48816     >50K\\n\",\n       \"48817    <=50K\\n\",\n       \"48818    <=50K\\n\",\n       \"48819    <=50K\\n\",\n       \"48820    <=50K\\n\",\n       \"48821    <=50K\\n\",\n       \"48822     >50K\\n\",\n       \"48823    <=50K\\n\",\n       \"48824    <=50K\\n\",\n       \"48825    <=50K\\n\",\n       \"48826     >50K\\n\",\n       \"48827    <=50K\\n\",\n       \"48828    <=50K\\n\",\n       \"48829    <=50K\\n\",\n       \"48830    <=50K\\n\",\n       \"48831    <=50K\\n\",\n       \"48832    <=50K\\n\",\n       \"48833    <=50K\\n\",\n       \"48834    <=50K\\n\",\n       \"48835    <=50K\\n\",\n       \"48836    <=50K\\n\",\n       \"48837    <=50K\\n\",\n       \"48838    <=50K\\n\",\n       \"48839    <=50K\\n\",\n       \"48840    <=50K\\n\",\n       \"48841     >50K\\n\",\n       \"Name: class, Length: 48842, dtype: object\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* The target is not encoded. Use the `sklearn.preprocessing.LabelEncoder` to encode the class.\\n\",\n    \"\\n\",\n    \"* 目标未编码。使用`sklearn.preprocessing.LabelEncoder`对类进行编码。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load solutions/05_3_solutions.py\\n\",\n    \"from sklearn.preprocessing import LabelEncoder\\n\",\n    \"\\n\",\n    \"encoder = LabelEncoder()\\n\",\n    \"y = encoder.fit_transform(y)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 0, 0, ..., 0, 0, 1])\"\n      ]\n     },\n     \"execution_count\": 62,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"y\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Create a list containing the name of the categorical columns. Similarly, do the same for the numerical data.\\n\",\n    \"\\n\",\n    \"* 创建一个包含分类列名称的列表。 同样，对数值数据也一样。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load solutions/05_4_solutions.py\\n\",\n    \"col_cat = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'native-country', 'sex']\\n\",\n    \"col_num = ['age', 'hoursperweek']\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Create a pipeline to one-hot encode the categorical data. Use the `KBinsDiscretizer` for the numerical data. Import it from `sklearn.preprocessing`.\\n\",\n    \"* 创建一个管道以对分类数据进行读热编码。 使用`KBinsDiscretizer`作为数值数据。 从`sklearn.preprocessing`导入它。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load solutions/05_5_solutions.py\\n\",\n    \"from sklearn.preprocessing import OneHotEncoder\\n\",\n    \"from sklearn.preprocessing import KBinsDiscretizer\\n\",\n    \"\\n\",\n    \"pipe_cat = OneHotEncoder(handle_unknown='ignore')\\n\",\n    \"pipe_num = KBinsDiscretizer()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Create a `preprocessor` by using the `make_column_transformer`. You should apply the good pipeline to the good column.\\n\",\n    \"* 使用`make_column_transformer`创建预处理器。 您应该将好的管道应用于好的列。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\compose\\\\_column_transformer.py:739: DeprecationWarning: `make_column_transformer` now expects (transformer, columns) as input tuples instead of (columns, transformer). This has been introduced in v0.20.1. `make_column_transformer` will stop accepting the deprecated (columns, transformer) order in v0.22.\\n\",\n      \"  warnings.warn(message, DeprecationWarning)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# %load solutions/05_6_solutions.py\\n\",\n    \"from sklearn.compose import make_column_transformer\\n\",\n    \"preprocessor = make_column_transformer((col_cat, pipe_cat, ), (col_num, pipe_num))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"* Pipeline the preprocessor with a `LogisticRegression` classifier. Subsequently define a grid-search to find the best parameter `C`. Train and test this workflow in a cross-validation scheme using `cross_validate`.\\n\",\n    \"\\n\",\n    \"* 使用`LogisticRegression`分类器对预处理器进行管道传输。 随后定义网格搜索以找到最佳参数`C`.使用`cross_validate`在交叉验证方案中训练和测试此工作流程。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x256366cbe10>\"\n      ]\n     },\n     \"execution_count\": 52,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x25636618ac8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load solutions/05_7_solutions.py\\n\",\n    \"from sklearn.pipeline import make_pipeline\\n\",\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"from sklearn.model_selection import GridSearchCV\\n\",\n    \"from sklearn.model_selection import cross_validate\\n\",\n    \"\\n\",\n    \"pipe = make_pipeline(preprocessor, LogisticRegression(solver='lbfgs', max_iter=1000))\\n\",\n    \"param_grid = {'logisticregression__C': [0.1, 1.0, 10]}\\n\",\n    \"grid = GridSearchCV(pipe, param_grid=param_grid, cv=5, n_jobs=-1)\\n\",\n    \"scores = pd.DataFrame(cross_validate(grid, X, y, scoring='balanced_accuracy', cv=3, n_jobs=-1, return_train_score=True))\\n\",\n    \"scores[['train_score', 'test_score']].boxplot(whis=10)\\n\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "6.scikit-learn/solutions/01_1_solutions.py",
    "content": "from sklearn.datasets import load_breast_cancer\n\nX_breast, y_breast = load_breast_cancer(return_X_y=True)\n"
  },
  {
    "path": "6.scikit-learn/solutions/01_2_solutions.py",
    "content": "from sklearn.model_selection import train_test_split\n\nX_breast_train, X_breast_test, y_breast_train, y_breast_test = train_test_split(X_breast, y_breast, stratify=y_breast, random_state=0, test_size=0.3)\n"
  },
  {
    "path": "6.scikit-learn/solutions/01_3_solutions.py",
    "content": "from sklearn.ensemble import GradientBoostingClassifier\n\nclf = GradientBoostingClassifier(n_estimators=100, random_state=0)\nclf.fit(X_breast_train, y_breast_train)\n"
  },
  {
    "path": "6.scikit-learn/solutions/01_4_solutions.py",
    "content": "y_pred = clf.predict(X_breast_test)\n"
  },
  {
    "path": "6.scikit-learn/solutions/01_5_solutions.py",
    "content": "from sklearn.metrics import balanced_accuracy_score\n\naccuracy = balanced_accuracy_score(y_breast_test, y_pred)\nprint('Accuracy score of the {} is {:.2f}'.format(clf.__class__.__name__, accuracy))\n"
  },
  {
    "path": "6.scikit-learn/solutions/01_solutions.py",
    "content": "from sklearn.datasets import load_breast_cancer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.metrics import balanced_accuracy_score\n\nX_breast, y_breast = load_breast_cancer(return_X_y=True)\nX_breast_train, X_breast_test, y_breast_train, y_breast_test = train_test_split(X_breast, y_breast, stratify=y_breast, random_state=0, test_size=0.3)\nclf = GradientBoostingClassifier(n_estimators=100, random_state=0)\nclf.fit(X_breast_train, y_breast_train)\ny_pred = clf.predict(X_breast_test)\naccuracy = balanced_accuracy_score(y_breast_test, y_pred)\nprint('Accuracy score of the {} is {:.2f}'.format(clf.__class__.__name__, accuracy))\n"
  },
  {
    "path": "6.scikit-learn/solutions/02_solutions.py",
    "content": "from sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import SGDClassifier\n\npipe = make_pipeline(StandardScaler(), SGDClassifier(max_iter=1000))\npipe.fit(X_breast_train, y_breast_train)\ny_pred = pipe.predict(X_breast_test)\naccuracy = balanced_accuracy_score(y_breast_test, y_pred)\nprint('Accuracy score of the {} is {:.2f}'.format(pipe.__class__.__name__, accuracy))\n"
  },
  {
    "path": "6.scikit-learn/solutions/03_solutions.py",
    "content": "pipe = make_pipeline(StandardScaler(), SGDClassifier(max_iter=1000))\nscores = cross_validate(pipe, X_breast, y_breast, scoring='balanced_accuracy', cv=3, return_train_score=True)\ndf_scores = pd.DataFrame(scores)\ndf_scores[['train_score', 'test_score']].boxplot()\n"
  },
  {
    "path": "6.scikit-learn/solutions/04_solutions.py",
    "content": "pipe = make_pipeline(StandardScaler(), SGDClassifier(max_iter=1000))\nparam_grid = {'sgdclassifier__loss': ['hinge', 'log'],\n              'sgdclassifier__penalty': ['l2', 'l1']}\ngrid = GridSearchCV(pipe, param_grid=param_grid, cv=3, n_jobs=-1)\nscores = cross_validate(grid, X_breast, y_breast, scoring='balanced_accuracy', cv=3, return_train_score=True)\ndf_scores = pd.DataFrame(scores)\ndf_scores[['train_score', 'test_score']].boxplot()\n\ngrid.fit(X_breast_train, y_breast_train)\nprint(grid.best_params_)\n"
  },
  {
    "path": "6.scikit-learn/solutions/05_1_solutions.py",
    "content": "import os\nimport pandas as pd\n\ndata = pd.read_csv(os.path.join('data', 'adult_openml.csv'))\n"
  },
  {
    "path": "6.scikit-learn/solutions/05_2_solutions.py",
    "content": "y = data['class']\nX = data.drop(columns=['class', 'fnlwgt', 'capitalgain', 'capitalloss'])\n"
  },
  {
    "path": "6.scikit-learn/solutions/05_3_solutions.py",
    "content": "from sklearn.preprocessing import LabelEncoder\n\nencoder = LabelEncoder()\ny = encoder.fit_transform(y)\n"
  },
  {
    "path": "6.scikit-learn/solutions/05_4_solutions.py",
    "content": "col_cat = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'native-country', 'sex']\ncol_num = ['age', 'hoursperweek']\n"
  },
  {
    "path": "6.scikit-learn/solutions/05_5_solutions.py",
    "content": "from sklearn.preprocessing import OneHotEncoder\nfrom sklearn.preprocessing import KBinsDiscretizer\n\npipe_cat = OneHotEncoder(handle_unknown='ignore')\npipe_num = KBinsDiscretizer()\n"
  },
  {
    "path": "6.scikit-learn/solutions/05_6_solutions.py",
    "content": "from sklearn.compose import make_column_transformer\npreprocessor = make_column_transformer((col_cat, pipe_cat, ), (col_num, pipe_num))\n"
  },
  {
    "path": "6.scikit-learn/solutions/05_7_solutions.py",
    "content": "from sklearn.pipeline import make_pipeline\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.model_selection import cross_validate\n\npipe = make_pipeline(preprocessor, LogisticRegression(solver='lbfgs', max_iter=1000))\nparam_grid = {'logisticregression__C': [0.1, 1.0, 10]}\ngrid = GridSearchCV(pipe, param_grid=param_grid, cv=5, n_jobs=-1)\nscores = pd.DataFrame(cross_validate(grid, X, y, scoring='balanced_accuracy', cv=3, n_jobs=-1, return_train_score=True))\nscores[['train_score', 'test_score']].boxplot(whis=10)\n"
  },
  {
    "path": "6.scikit-learn/solutions/05_solutions.py",
    "content": "from sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import OrdinalEncoder\nfrom sklearn.preprocessing import KBinsDiscretizer\n\n# Read the data\ndata = pd.read_csv(os.path.join('data', 'adult_openml.csv'))\n# Split the label from the data which we will use for classification\ny = data['class']\nX = data.drop(columns=['class', 'fnlwgt', 'capitalgain', 'capitalloss'])\n\n# Encode the class\nencoder = LabelEncoder()\ny = encoder.fit_transform(y)\n\n# Define the different column\ncol_cat = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'native-country']\ncol_ord = ['sex']\ncol_num = ['age', 'hoursperweek']\n\n# Define the different pipeline\npipe_cat = OneHotEncoder(handle_unknown='ignore')\npipe_ord = OrdinalEncoder()\npipe_num = KBinsDiscretizer()\npreprocessor = ColumnTransformer([('trans_cat', pipe_cat, col_cat),\n                                  ('trans_ord', pipe_ord, col_ord),\n                                  ('trans_num', pipe_num, col_num)])\n# Define the pipeline to use\npipe = make_pipeline(preprocessor, LogisticRegression(solver='lbfgs', max_iter=1000))\nparam_grid = {'logisticregression__C': [0.1, 1.0, 10]}\ngrid = GridSearchCV(pipe, param_grid=param_grid, cv=5, n_jobs=-1)\nscores = pd.DataFrame(cross_validate(grid, X, y, scoring='balanced_accuracy', cv=3, n_jobs=-1, return_train_score=True))\nscores[['train_score', 'test_score']].boxplot(whis=10)\n"
  },
  {
    "path": "7.machine-learning/README.md",
    "content": "## 一、斯坦福大学2014（吴恩达）机器学习教程中文笔记及资源\n\n**github地址**：https://github.com/fengdu78/Coursera-ML-AndrewNg-Notes\n\n\n\n文件夹说明：\n\n**docx**：笔记的**word**版本\n\n**markdown**：笔记的**markdown**版本\n\n**html**：笔记的**html**版本\n\n**images**：笔记的图片\n\n**ppt**：课程的原版课件\n\n**srt**：课程的中英文字幕（**mp4**文件需要在百度云下载，大家可以用记事本或者字幕编辑软件来编辑字幕，共同完善，百度云链接：https://pan.baidu.com/s/1h8QjqBlOm0Exh7orm9teMQ 密码：d3we，下载后解压）\n\n**code**：课程的**python**代码（有一部分是国外大牛写的）\n\n机器学习视频下载链接：https://pan.baidu.com/s/1raoOPOg 密码：48m8，包含视频和字幕，下载后解压，建议用**potplayer**播放，此视频与**mp4**一致。\n\n[笔记在线阅读](http://www.ai-start.com/ml2014)\n\n笔记pdf版本下载 ：见**github**根目录。\n\n\n\n## 二、李航《统计学习方法》的代码实现及ppt\n\n**github地址**：https://github.com/fengdu78/lihang-code\n\n**代码目录**\n\n第1章 统计学习方法概论\n\n第2章 感知机\n\n第3章 k近邻法\n\n第4章 朴素贝叶斯\n\n第5章 决策树\n\n第6章 逻辑斯谛回归\n\n第7章 支持向量机\n\n第8章 提升方法\n\n第9章 EM算法及其推广\n\n第10章 隐马尔可夫模型\n\n第11章 条件随机场\n\n第12章 监督学习方法总结\n\n\n\n## 三、周志华老师的《机器学习》的解答--南瓜书PumpkinBook\n\n作者：Datawhale\n\n**github地址**：\n\nhttps://github.com/datawhalechina/pumpkin-book\n\n**在线阅读地址**：\n\nhttps://datawhalechina.github.io/pumpkin-book/\n\n周志华老师的《机器学习》（西瓜书）是机器学习领域的经典入门教材之一，周老师为了使尽可能多的读者通过西瓜书对机器学习有所了解, 所以在书中对部分公式的推导细节没有详述，但是这对那些想深究公式推导细节的读者来说可能“不太友好”。\n\n近日有个github仓库“南瓜书(PumpkinBook)”对西瓜书里比较难理解的公式加以解析，以及对部分公式补充具体的推导细节。\n\n该仓库拥有者**Datawhale**是一个非盈利的开源学习组织，致力于构建一个纯粹的学习圈子，帮助学习者更好地成长。目前已经更新完前7章，并诚挚欢迎每一位西瓜书读者前来参与完善本书：**一个人可以走的很快，但是一群人却可以走的更远。**\n\n\n\n### 使用说明\n\n南瓜书仅仅是西瓜书的一些细微补充而已，里面的内容都是以西瓜书的内容为前置知识进行表述的，所以南瓜书的最佳使用方法是以西瓜书为主线，遇到自己推导不出来或者看不懂的公式时再来查阅南瓜书。若南瓜书里没有你想要查阅的公式，可以[点击这里](https://github.com/datawhalechina/pumpkin-book/issues)提交你希望补充推导或者解析的公式编号，我们看到后会尽快进行补充。\n\n### 在线阅读地址\n\n在线阅读地址：https://datawhalechina.github.io/pumpkin-book\n\n### 目录\n\n- 第1章 [绪论](https://datawhalechina.github.io/pumpkin-book/#/chapter1/chapter1)\n- 第2章 [模型评估与选择](https://datawhalechina.github.io/pumpkin-book/#/chapter2/chapter2)\n- 第3章 [线性模型](https://datawhalechina.github.io/pumpkin-book/#/chapter3/chapter3)\n- 第4章 [决策树](https://datawhalechina.github.io/pumpkin-book/#/chapter4/chapter4)\n- 第5章 [神经网络](https://datawhalechina.github.io/pumpkin-book/#/chapter5/chapter5)\n- 第6章 [支持向量机](https://datawhalechina.github.io/pumpkin-book/#/chapter6/chapter6)\n- 第7章 [贝叶斯分类器](https://datawhalechina.github.io/pumpkin-book/#/chapter7/chapter7)\n- 第8章 [集成学习](https://datawhalechina.github.io/pumpkin-book/#/chapter8/chapter8)\n- 第9章 [聚类](https://datawhalechina.github.io/pumpkin-book/#/chapter9/chapter9)\n- 第10章 [降维与度量学习](https://datawhalechina.github.io/pumpkin-book/#/chapter10/chapter10)\n- 第11章 [特征选择与稀疏学习](https://datawhalechina.github.io/pumpkin-book/#/chapter11/chapter11)\n- 第12章 [计算学习理论](https://datawhalechina.github.io/pumpkin-book/#/chapter12/chapter12)\n- 第13章 [半监督学习](https://datawhalechina.github.io/pumpkin-book/#/chapter13/chapter13)\n- 第14章 [概率图模型](https://datawhalechina.github.io/pumpkin-book/#/chapter14/chapter14)\n- 第15章 规则学习（正在完成中）\n- 第16章 [强化学习](https://datawhalechina.github.io/pumpkin-book/#/chapter16/chapter16)\n\n## \n\n## 四、台大林轩田《机器学习基石》系列课程教材的习题解答\n\n**github地址**：https://github.com/Doraemonzzz/Learning-from-data\n\n**课程简介**\n\n台湾大学林轩田老师的《机器学习基石》《机器学习技法》课程由浅入深、内容全面，基本涵盖了机器学习领域的很多方面。其作为机器学习的入门和进阶资料非常适合。\n\n林轩田机器学习基石这门课有一个配套教材：《Learning From Data》（LFD），林轩田也是编者之一。这本书的主页为：amlbook.com/\n\n豆瓣上关于这本书的评分高达9.4，还是很不错的，值得推荐！可以配套视频一起学习。这本书是台大林轩田老师的机器学习课程配套教材，内容通俗易懂，非常精彩，不是单纯罗列公式，是一本非常适合入门的机器学习书籍。教材的上半部分（第一章到第五章）是精髓，补充部分（第六章到第九章）有部分章节稍显仓促，而且有一些小错误，第九章部分实际应用可能较少，但是总的来说，本书绝对是一本不可多得的好书。\n\n但是尽管该书是一本入门书籍，要吃透这本书还是需要相当多的时间，尤其是课后习题部分，有的难度非常大。\n\n针对这个问题，有位清华大学的硕士生秦臻在学习的过程中把《Learning From Data》的习题都整理了一遍，方便自己以后查阅和他人参考。前后历时一年多，除了第六章和第九章少部分习题以外，其他所有习题均已完成。作者在又花了半年时间优化，经作者同意，在本站予以公布。\n\n\n\n补充说明：仓库中有四类文件，分别是Jupyter Notebook，py，pdf，md，代码部分保存在Jupyter Notebook和py文件中。\n\n**资源下载**\n\n1. 习题和笔记在秦臻同学的github（还会更新）：\n   https://github.com/Doraemonzzz/Learning-from-data\n2. 《机器学习基石》《机器学习技法》课程视频和ppt以及教材《Learning From Data》\n   链接:\n\n https://pan.baidu.com/s/1oAMX5vYbDtobCXZiM9gEEQ\n\n提取码: h5c3"
  },
  {
    "path": "8.deep-learning/Deep-Learning-Papers-Reading-Roadmap/README.md",
    "content": "# 深度学习论文阅读路线图\n## 0.导语\n>作者：Floodsung\n>\n>出处：https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap\n>\n>翻译：黄海广\n\n如果您是深度学习领域的新手，那么您可能会遇到的第一个问题是“我应该从哪篇论文开始阅读？”\n\n本文是深度学习论文的阅读路线图！\n\n该路线图是根据以下四个准则构建的：\n\n- 从轮廓到细节\n- 从旧到最新\n- 从通用到特定领域\n- 专注于最新技术\n\n您会发现许多非常新的论文，但确实值得阅读。\n\n此外，作者将继续在此路线图中添加论文。\n\n**译者注：**\n\n总共127篇论文，我已经将论文打包。\n\n我已经将论文全部下载了，放到百度云提供下载。\n\n百度云地址：\n\n链接：https://pan.baidu.com/s/17Xcg6-mzRjlNKEIj2lSPiw\n\n提取码：fnks\n\n我将本文的所有论文放在我的数据科学的github中：\n\n https://github.com/fengdu78/Data-Science-Notes/tree/master/8.deep-learning/Deep-Learning-Papers-Reading-Roadmap \n\n论文我做成了zotero格式，可以直接在zotero中导入，如果没有安装zotero，那么也可以下载分类好的pdf文件，按照本文论文目录进行分类了。\n\n使用方法：\n\n1.zotero 中阅读，先导入到zotero，阅读论文只需要在红框中输入论文名称即可搜到。\n\n![img](zotero.png)\n\n2.直接下载文件阅读\n\n![img](pdf.png)\n\n[toc]\n\n## 论文目录\n\n### 1 深度学习的历史和基础\n\n#### 1.0 图书\n\n[0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. \"Deep learning.\" An MIT Press book. (2015). [html] (Deep Learning Bible, you can read this book while reading following papers.) ⭐️⭐️⭐️⭐️⭐️\n\n#### 1.1 回顾\n\n[1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. \"Deep learning.\" Nature 521.7553 (2015): 436-444. [pdf] (Three Giants' Survey) ⭐️⭐️⭐️⭐️⭐️\n\n#### 1.2 深度信念网络（DBN）（深度学习前夜的里程碑）\n\n[2] Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. \"A fast learning algorithm for deep belief nets.\" Neural computation 18.7 (2006): 1527-1554. [pdf](Deep Learning Eve) ⭐️⭐️⭐️\n\n[3] Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. \"Reducing the dimensionality of data with neural networks.\" Science 313.5786 (2006): 504-507. [pdf] (Milestone, Show the promise of deep learning) ⭐️⭐️⭐️\n\n#### 1.3 ImageNet的发展（深度学习从这里爆发）\n\n[4] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. \"Imagenet classification with deep convolutional neural networks.\" Advances in neural information processing systems. 2012. [pdf] (AlexNet, Deep Learning Breakthrough) ⭐️⭐️⭐️⭐️⭐️\n\n[5] Simonyan, Karen, and Andrew Zisserman. \"Very deep convolutional networks for large-scale image recognition.\" arXiv preprint arXiv:1409.1556 (2014). [pdf] (VGGNet,Neural Networks become very deep!) ⭐️⭐️⭐️\n\n[6] Szegedy, Christian, et al. \"Going deeper with convolutions.\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. [pdf] (GoogLeNet) ⭐️⭐️⭐️\n\n[7] He, Kaiming, et al. \"Deep residual learning for image recognition.\" arXiv preprint arXiv:1512.03385 (2015). [pdf] (ResNet,Very very deep networks, CVPR best paper) ⭐️⭐️⭐️⭐️⭐️\n\n#### 1.4 语音识别的发展\n\n[8] Hinton, Geoffrey, et al. \"Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.\" IEEE Signal Processing Magazine 29.6 (2012): 82-97. [pdf] (Breakthrough in speech recognition)⭐️⭐️⭐️⭐️\n\n[9] Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. \"Speech recognition with deep recurrent neural networks.\" 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013. [pdf] (RNN)⭐️⭐️⭐️\n\n[10] Graves, Alex, and Navdeep Jaitly. \"Towards End-To-End Speech Recognition with Recurrent Neural Networks.\" ICML. Vol. 14. 2014. [pdf]⭐️⭐️⭐️\n\n[11] Sak, Haşim, et al. \"Fast and accurate recurrent neural network acoustic models for speech recognition.\" arXiv preprint arXiv:1507.06947 (2015). [pdf] (Google Speech Recognition System) ⭐️⭐️⭐️\n\n[12] Amodei, Dario, et al. \"Deep speech 2: End-to-end speech recognition in english and mandarin.\" arXiv preprint arXiv:1512.02595 (2015). [pdf] (Baidu Speech Recognition System) ⭐️⭐️⭐️⭐️\n\n[13] W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig \"Achieving Human Parity in Conversational Speech Recognition.\" arXiv preprint arXiv:1610.05256 (2016). [pdf] (State-of-the-art in speech recognition, Microsoft) ⭐️⭐️⭐️⭐️\n\n阅读以上论文后，您将对深度学习的历史，深度学习模型的基本架构（包括CNN，RNN，LSTM）以及如何将深度学习应用于图像和语音识别问题有基本的了解。 以下论文将带您深入了解深度学习方法，不同应用领域和领域的深度学习。 我建议您根据自己的兴趣和研究方向选择以下论文。\n\n### 2 深度学习方法\n\n#### 2.1 深度学习模型\n\n[14] Hinton, Geoffrey E., et al. \"Improving neural networks by preventing co-adaptation of feature detectors.\" arXiv preprint arXiv:1207.0580 (2012). [pdf] (Dropout) ⭐️⭐️⭐️\n\n[15] Srivastava, Nitish, et al. \"Dropout: a simple way to prevent neural networks from overfitting.\" Journal of Machine Learning Research 15.1 (2014): 1929-1958. [pdf] ⭐️⭐️⭐️\n\n[16] Ioffe, Sergey, and Christian Szegedy. \"Batch normalization: Accelerating deep network training by reducing internal covariate shift.\" arXiv preprint arXiv:1502.03167 (2015). [pdf] (An outstanding Work in 2015) ⭐️⭐️⭐️⭐️\n\n[17] Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer normalization.\" arXiv preprint arXiv:1607.06450 (2016). [pdf] (Update of Batch Normalization) ⭐️⭐️⭐️⭐️\n\n[18] Courbariaux, Matthieu, et al. \"Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1.\" [pdf] (New Model,Fast)  ⭐️⭐️⭐️\n\n[19] Jaderberg, Max, et al. \"Decoupled neural interfaces using synthetic gradients.\" arXiv preprint arXiv:1608.05343 (2016). [pdf] (Innovation of Training Method,Amazing Work) ⭐️⭐️⭐️⭐️⭐️\n\n[20] Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. \"Net2net: Accelerating learning via knowledge transfer.\" arXiv preprint arXiv:1511.05641 (2015). [pdf] (Modify previously trained network to reduce training epochs) ⭐️⭐️⭐️\n\n[21] Wei, Tao, et al. \"Network Morphism.\" arXiv preprint arXiv:1603.01670 (2016). [pdf] (Modify previously trained network to reduce training epochs) ⭐️⭐️⭐️\n\n#### 2.2 优化方法\n\n[22] Sutskever, Ilya, et al. \"On the importance of initialization and momentum in deep learning.\" ICML (3) 28 (2013): 1139-1147. [pdf] (Momentum optimizer) ⭐️⭐️\n\n[23] Kingma, Diederik, and Jimmy Ba. \"Adam: A method for stochastic optimization.\" arXiv preprint arXiv:1412.6980 (2014). [pdf] (Maybe used most often currently) ⭐️⭐️⭐️\n\n[24] Andrychowicz, Marcin, et al. \"Learning to learn by gradient descent by gradient descent.\" arXiv preprint arXiv:1606.04474 (2016). [pdf] (Neural Optimizer,Amazing Work) ⭐️⭐️⭐️⭐️⭐️\n\n[25] Han, Song, Huizi Mao, and William J. Dally. \"Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding.\" CoRR, abs/1510.00149 2 (2015). [pdf] (ICLR best paper, new direction to make NN running fast,DeePhi Tech Startup) ⭐️⭐️⭐️⭐️⭐️\n\n[26] Iandola, Forrest N., et al. \"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size.\" arXiv preprint arXiv:1602.07360 (2016). [pdf] (Also a new direction to optimize NN,DeePhi Tech Startup) ⭐️⭐️⭐️⭐️\n\n#### 2.3 无监督学习/ 深度生成模型\n\n[27] Le, Quoc V. \"Building high-level features using large scale unsupervised learning.\" 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013. [pdf] (Milestone, Andrew Ng, Google Brain Project, Cat) ⭐️⭐️⭐️⭐️\n\n[28] Kingma, Diederik P., and Max Welling. \"Auto-encoding variational bayes.\" arXiv preprint arXiv:1312.6114 (2013). [pdf] (VAE) ⭐️⭐️⭐️⭐️\n\n[29] Goodfellow, Ian, et al. \"Generative adversarial nets.\" Advances in Neural Information Processing Systems. 2014. [pdf] (GAN,super cool idea) ⭐️⭐️⭐️⭐️⭐️\n\n[30] Radford, Alec, Luke Metz, and Soumith Chintala. \"Unsupervised representation learning with deep convolutional generative adversarial networks.\" arXiv preprint arXiv:1511.06434 (2015). [pdf] (DCGAN) ⭐️⭐️⭐️⭐️\n\n[31] Gregor, Karol, et al. \"DRAW: A recurrent neural network for image generation.\" arXiv preprint arXiv:1502.04623 (2015). [pdf] (VAE with attention, outstanding work) ⭐️⭐️⭐️⭐️⭐️\n\n[32] Oord, Aaron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. \"Pixel recurrent neural networks.\" arXiv preprint arXiv:1601.06759 (2016). [pdf] (PixelRNN) ⭐️⭐️⭐️⭐️\n\n[33] Oord, Aaron van den, et al. \"Conditional image generation with PixelCNN decoders.\" arXiv preprint arXiv:1606.05328 (2016). [pdf] (PixelCNN) ⭐️⭐️⭐️⭐️\n\n#### 2.4 RNN / 序列到序列模型\n\n[34] Graves, Alex. \"Generating sequences with recurrent neural networks.\" arXiv preprint arXiv:1308.0850 (2013). [pdf] (LSTM, very nice generating result, show the power of RNN) ⭐️⭐️⭐️⭐️\n\n[35] Cho, Kyunghyun, et al. \"Learning phrase representations using RNN encoder-decoder for statistical machine translation.\" arXiv preprint arXiv:1406.1078 (2014). [pdf] (First Seq-to-Seq Paper) ⭐️⭐️⭐️⭐️\n\n[36] Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. \"Sequence to sequence learning with neural networks.\" Advances in neural information processing systems. 2014. [pdf] (Outstanding Work) ⭐️⭐️⭐️⭐️⭐️\n\n[37] Bahdanau, Dzmitry, KyungHyun Cho, and Yoshua Bengio. \"Neural Machine Translation by Jointly Learning to Align and Translate.\" arXiv preprint arXiv:1409.0473 (2014). [pdf] ⭐️⭐️⭐️⭐️\n\n[38] Vinyals, Oriol, and Quoc Le. \"A neural conversational model.\" arXiv preprint arXiv:1506.05869 (2015). [pdf] (Seq-to-Seq on Chatbot) ⭐️⭐️⭐️\n\n#### 2.5 神经图灵机\n\n[39] Graves, Alex, Greg Wayne, and Ivo Danihelka. \"Neural turing machines.\" arXiv preprint arXiv:1410.5401 (2014). [pdf] (Basic Prototype of Future Computer) ⭐️⭐️⭐️⭐️⭐️\n\n[40] Zaremba, Wojciech, and Ilya Sutskever. \"Reinforcement learning neural Turing machines.\" arXiv preprint arXiv:1505.00521 362 (2015). [pdf] ⭐️⭐️⭐️\n\n[41] Weston, Jason, Sumit Chopra, and Antoine Bordes. \"Memory networks.\" arXiv preprint arXiv:1410.3916 (2014). [pdf] ⭐️⭐️⭐️\n\n[42] Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. \"End-to-end memory networks.\" Advances in neural information processing systems. 2015. [pdf] ⭐️⭐️⭐️⭐️\n\n[43] Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. \"Pointer networks.\" Advances in Neural Information Processing Systems. 2015. [pdf] ⭐️⭐️⭐️⭐️\n\n[44] Graves, Alex, et al. \"Hybrid computing using a neural network with dynamic external memory.\" Nature (2016). [pdf] (Milestone,combine above papers' ideas) ⭐️⭐️⭐️⭐️⭐️\n\n#### 2.6 深度强化学习\n\n[45] Mnih, Volodymyr, et al. \"Playing atari with deep reinforcement learning.\" arXiv preprint arXiv:1312.5602 (2013). [pdf]) (First Paper named deep reinforcement learning) ⭐️⭐️⭐️⭐️\n\n[46] Mnih, Volodymyr, et al. \"Human-level control through deep reinforcement learning.\" Nature 518.7540 (2015): 529-533. [pdf] (Milestone) ⭐️⭐️⭐️⭐️⭐️\n\n[47] Wang, Ziyu, Nando de Freitas, and Marc Lanctot. \"Dueling network architectures for deep reinforcement learning.\" arXiv preprint arXiv:1511.06581 (2015). [pdf] (ICLR best paper,great idea)  ⭐️⭐️⭐️⭐️\n\n[48] Mnih, Volodymyr, et al. \"Asynchronous methods for deep reinforcement learning.\" arXiv preprint arXiv:1602.01783 (2016). [pdf] (State-of-the-art method) ⭐️⭐️⭐️⭐️⭐️\n\n[49] Lillicrap, Timothy P., et al. \"Continuous control with deep reinforcement learning.\" arXiv preprint arXiv:1509.02971 (2015). [pdf] (DDPG) ⭐️⭐️⭐️⭐️\n\n[50] Gu, Shixiang, et al. \"Continuous Deep Q-Learning with Model-based Acceleration.\" arXiv preprint arXiv:1603.00748 (2016). [pdf] (NAF) ⭐️⭐️⭐️⭐️\n\n[51] Schulman, John, et al. \"Trust region policy optimization.\" CoRR, abs/1502.05477 (2015). [pdf] (TRPO) ⭐️⭐️⭐️⭐️\n\n[52] Silver, David, et al. \"Mastering the game of Go with deep neural networks and tree search.\" Nature 529.7587 (2016): 484-489. [pdf] (AlphaGo) ⭐️⭐️⭐️⭐️⭐️\n\n#### 2.7 深度迁移学习/终身学习/特别是针对强化学习\n\n[53] Bengio, Yoshua. \"Deep Learning of Representations for Unsupervised and Transfer Learning.\" ICML Unsupervised and Transfer Learning 27 (2012): 17-36. [pdf] (A Tutorial) ⭐️⭐️⭐️\n\n[54] Silver, Daniel L., Qiang Yang, and Lianghao Li. \"Lifelong Machine Learning Systems: Beyond Learning Algorithms.\" AAAI Spring Symposium: Lifelong Machine Learning. 2013. [pdf] (A brief discussion about lifelong learning)  ⭐️⭐️⭐️\n\n[55] Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. \"Distilling the knowledge in a neural network.\" arXiv preprint arXiv:1503.02531 (2015). [pdf] (Godfather's Work) ⭐️⭐️⭐️⭐️\n\n[56] Rusu, Andrei A., et al. \"Policy distillation.\" arXiv preprint arXiv:1511.06295 (2015). [pdf] (RL domain) ⭐️⭐️⭐️\n\n[57] Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. \"Actor-mimic: Deep multitask and transfer reinforcement learning.\" arXiv preprint arXiv:1511.06342 (2015). [pdf] (RL domain) ⭐️⭐️⭐️\n\n[58] Rusu, Andrei A., et al. \"Progressive neural networks.\" arXiv preprint arXiv:1606.04671 (2016). [pdf] (Outstanding Work, A novel idea) ⭐️⭐️⭐️⭐️⭐️\n\n#### 2.8 One-Shot深度学习\n\n[59] Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. \"Human-level concept learning through probabilistic program induction.\" Science 350.6266 (2015): 1332-1338. [pdf] (No Deep Learning,but worth reading) ⭐️⭐️⭐️⭐️⭐️\n\n[60] Koch, Gregory, Richard Zemel, and Ruslan Salakhutdinov. \"Siamese Neural Networks for One-shot Image Recognition.\"(2015) [pdf] ⭐️⭐️⭐️\n\n[61] Santoro, Adam, et al. \"One-shot Learning with Memory-Augmented Neural Networks.\" arXiv preprint arXiv:1605.06065 (2016). [pdf] (A basic step to one shot learning) ⭐️⭐️⭐️⭐️\n\n[62] Vinyals, Oriol, et al. \"Matching Networks for One Shot Learning.\" arXiv preprint arXiv:1606.04080 (2016). [pdf] ⭐️⭐️⭐️\n\n[63] Hariharan, Bharath, and Ross Girshick. \"Low-shot visual object recognition.\" arXiv preprint arXiv:1606.02819 (2016). [pdf] (A step to large data) ⭐️⭐️⭐️⭐️\n\n### 3 应用\n\n#### 3.1 NLP(自然语言处理)\n\n[1] Antoine Bordes, et al. \"Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing.\" AISTATS(2012) [pdf] ⭐️⭐️⭐️⭐️\n\n[2] Mikolov, et al. \"Distributed representations of words and phrases and their compositionality.\" ANIPS(2013): 3111-3119 [pdf] (word2vec) ⭐️⭐️⭐️\n\n[3] Sutskever, et al. \"“Sequence to sequence learning with neural networks.\" ANIPS(2014) [pdf] ⭐️⭐️⭐️\n\n[4] Ankit Kumar, et al. \"“Ask Me Anything: Dynamic Memory Networks for Natural Language Processing.\" arXiv preprint arXiv:1506.07285(2015) [pdf] ⭐️⭐️⭐️⭐️\n\n[5] Yoon Kim, et al. \"Character-Aware Neural Language Models.\" NIPS(2015) arXiv preprint arXiv:1508.06615(2015) [pdf] ⭐️⭐️⭐️⭐️\n\n[6] Jason Weston, et al. \"Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks.\" arXiv preprint arXiv:1502.05698(2015) [pdf] (bAbI tasks) ⭐️⭐️⭐️\n\n[7] Karl Moritz Hermann, et al. \"Teaching Machines to Read and Comprehend.\" arXiv preprint arXiv:1506.03340(2015) [pdf] (CNN/DailyMail cloze style questions) ⭐️⭐️\n\n[8] Alexis Conneau, et al. \"Very Deep Convolutional Networks for Natural Language Processing.\" arXiv preprint arXiv:1606.01781(2016) [pdf] (state-of-the-art in text classification) ⭐️⭐️⭐️\n\n[9] Armand Joulin, et al. \"Bag of Tricks for Efficient Text Classification.\" arXiv preprint arXiv:1607.01759(2016) [pdf] (slightly worse than state-of-the-art, but a lot faster) ⭐️⭐️⭐️\n\n#### 3.2 目标检测\n\n[1] Szegedy, Christian, Alexander Toshev, and Dumitru Erhan. \"Deep neural networks for object detection.\" Advances in Neural Information Processing Systems. 2013. [pdf] ⭐️⭐️⭐️\n\n[2] Girshick, Ross, et al. \"Rich feature hierarchies for accurate object detection and semantic segmentation.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. [pdf] (RCNN) ⭐️⭐️⭐️⭐️⭐️\n\n[3] He, Kaiming, et al. \"Spatial pyramid pooling in deep convolutional networks for visual recognition.\" European Conference on Computer Vision. Springer International Publishing, 2014. [pdf] (SPPNet) ⭐️⭐️⭐️⭐️\n\n[4] Girshick, Ross. \"Fast r-cnn.\" Proceedings of the IEEE International Conference on Computer Vision. 2015. [pdf] ⭐️⭐️⭐️⭐️\n\n[5] Ren, Shaoqing, et al. \"Faster R-CNN: Towards real-time object detection with region proposal networks.\" Advances in neural information processing systems. 2015. [pdf] ⭐️⭐️⭐️⭐️\n\n[6] Redmon, Joseph, et al. \"You only look once: Unified, real-time object detection.\" arXiv preprint arXiv:1506.02640 (2015). [pdf] (YOLO,Oustanding Work, really practical) ⭐️⭐️⭐️⭐️⭐️\n\n[7] Liu, Wei, et al. \"SSD: Single Shot MultiBox Detector.\" arXiv preprint arXiv:1512.02325 (2015). [pdf] ⭐️⭐️⭐️\n\n[8] Dai, Jifeng, et al. \"R-FCN: Object Detection via\nRegion-based Fully Convolutional Networks.\" arXiv preprint arXiv:1605.06409 (2016). [pdf] ⭐️⭐️⭐️⭐️\n\n[9] He, Gkioxari, et al. \"Mask R-CNN\" arXiv preprint arXiv:1703.06870 (2017). [pdf] ⭐️⭐️⭐️⭐️\n\n#### 3.3 目标跟踪\n\n[1] Wang, Naiyan, and Dit-Yan Yeung. \"Learning a deep compact image representation for visual tracking.\" Advances in neural information processing systems. 2013. [pdf] (First Paper to do visual tracking using Deep Learning,DLT Tracker) ⭐️⭐️⭐️\n\n[2] Wang, Naiyan, et al. \"Transferring rich feature hierarchies for robust visual tracking.\" arXiv preprint arXiv:1501.04587 (2015). [pdf] (SO-DLT) ⭐️⭐️⭐️⭐️\n\n[3] Wang, Lijun, et al. \"Visual tracking with fully convolutional networks.\" Proceedings of the IEEE International Conference on Computer Vision. 2015. [pdf] (FCNT) ⭐️⭐️⭐️⭐️\n\n[4] Held, David, Sebastian Thrun, and Silvio Savarese. \"Learning to Track at 100 FPS with Deep Regression Networks.\" arXiv preprint arXiv:1604.01802 (2016). [pdf] (GOTURN,Really fast as a deep learning method,but still far behind un-deep-learning methods) ⭐️⭐️⭐️⭐️\n\n[5] Bertinetto, Luca, et al. \"Fully-Convolutional Siamese Networks for Object Tracking.\" arXiv preprint arXiv:1606.09549 (2016). [pdf] (SiameseFC,New state-of-the-art for real-time object tracking) ⭐️⭐️⭐️⭐️\n\n[6] Martin Danelljan, Andreas Robinson, Fahad Khan, Michael Felsberg. \"Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking.\" ECCV (2016) [pdf] (C-COT) ⭐️⭐️⭐️⭐️\n\n[7] Nam, Hyeonseob, Mooyeol Baek, and Bohyung Han. \"Modeling and Propagating CNNs in a Tree Structure for Visual Tracking.\" arXiv preprint arXiv:1608.07242 (2016). [pdf] (VOT2016 Winner,TCNN) ⭐️⭐️⭐️⭐️\n\n#### 3.4 图像标注\n\n[1] Farhadi,Ali,etal. \"Every picture tells a story: Generating sentences from images\". In Computer VisionECCV 2010. Springer Berlin Heidelberg:15-29, 2010. [pdf] ⭐️⭐️⭐️\n\n[2] Kulkarni, Girish, et al. \"Baby talk: Understanding and generating image descriptions\". In Proceedings of the 24th CVPR, 2011. [pdf]⭐️⭐️⭐️⭐️\n\n[3] Vinyals, Oriol, et al. \"Show and tell: A neural image caption generator\". In arXiv preprint arXiv:1411.4555, 2014. [pdf]⭐️⭐️⭐️\n\n[4] Donahue, Jeff, et al. \"Long-term recurrent convolutional networks for visual recognition and description\". In arXiv preprint arXiv:1411.4389 ,2014. [pdf]\n\n[5] Karpathy, Andrej, and Li Fei-Fei. \"Deep visual-semantic alignments for generating image descriptions\". In arXiv preprint arXiv:1412.2306, 2014. [pdf]⭐️⭐️⭐️⭐️⭐️\n\n[6] Karpathy, Andrej, Armand Joulin, and Fei Fei F. Li. \"Deep fragment embeddings for bidirectional image sentence mapping\". In Advances in neural information processing systems, 2014. [pdf]⭐️⭐️⭐️⭐️\n\n[7] Fang, Hao, et al. \"From captions to visual concepts and back\". In arXiv preprint arXiv:1411.4952, 2014. [pdf]⭐️⭐️⭐️⭐️⭐️\n\n[8] Chen, Xinlei, and C. Lawrence Zitnick. \"Learning a recurrent visual representation for image caption generation\". In arXiv preprint arXiv:1411.5654, 2014. [pdf]⭐️⭐️⭐️⭐️\n\n[9] Mao, Junhua, et al. \"Deep captioning with multimodal recurrent neural networks (m-rnn)\". In arXiv preprint arXiv:1412.6632, 2014. [pdf]⭐️⭐️⭐️\n\n[10] Xu, Kelvin, et al. \"Show, attend and tell: Neural image caption generation with visual attention\". In arXiv preprint arXiv:1502.03044, 2015. [pdf]⭐️⭐️⭐️⭐️⭐️\n\n#### 3.5 机器翻译\n\nSome milestone papers are listed in RNN / Seq-to-Seq topic.\n\n[1] Luong, Minh-Thang, et al. \"Addressing the rare word problem in neural machine translation.\" arXiv preprint arXiv:1410.8206 (2014). [pdf] ⭐️⭐️⭐️⭐️\n\n[2] Sennrich, et al. \"Neural Machine Translation of Rare Words with Subword Units\". In arXiv preprint arXiv:1508.07909, 2015. [pdf]⭐️⭐️⭐️\n\n[3] Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. \"Effective approaches to attention-based neural machine translation.\" arXiv preprint arXiv:1508.04025 (2015). [pdf] ⭐️⭐️⭐️⭐️\n\n[4] Chung, et al. \"A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation\". In arXiv preprint arXiv:1603.06147, 2016. [pdf]⭐️⭐️\n\n[5] Lee, et al. \"Fully Character-Level Neural Machine Translation without Explicit Segmentation\". In arXiv preprint arXiv:1610.03017, 2016. [pdf]⭐️⭐️⭐️⭐️⭐️\n\n[6] Wu, Schuster, Chen, Le, et al. \"Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation\". In arXiv preprint arXiv:1609.08144v2, 2016. [pdf] (Milestone) ⭐️⭐️⭐️⭐️\n\n#### 3.6 机器人\n\n[1] Koutník, Jan, et al. \"Evolving large-scale neural networks for vision-based reinforcement learning.\" Proceedings of the 15th annual conference on Genetic and evolutionary computation. ACM, 2013. [pdf] ⭐️⭐️⭐️\n\n[2] Levine, Sergey, et al. \"End-to-end training of deep visuomotor policies.\" Journal of Machine Learning Research 17.39 (2016): 1-40. [pdf] ⭐️⭐️⭐️⭐️⭐️\n\n[3] Pinto, Lerrel, and Abhinav Gupta. \"Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours.\" arXiv preprint arXiv:1509.06825 (2015). [pdf] ⭐️⭐️⭐️\n\n[4] Levine, Sergey, et al. \"Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection.\" arXiv preprint arXiv:1603.02199 (2016). [pdf] ⭐️⭐️⭐️⭐️\n\n[5] Zhu, Yuke, et al. \"Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning.\" arXiv preprint arXiv:1609.05143 (2016). [pdf] ⭐️⭐️⭐️⭐️\n\n[6] Yahya, Ali, et al. \"Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search.\" arXiv preprint arXiv:1610.00673 (2016). [pdf] ⭐️⭐️⭐️⭐️\n\n[7] Gu, Shixiang, et al. \"Deep Reinforcement Learning for Robotic Manipulation.\" arXiv preprint arXiv:1610.00633 (2016). [pdf] ⭐️⭐️⭐️⭐️\n\n[8] A Rusu, M Vecerik, Thomas Rothörl, N Heess, R Pascanu, R Hadsell.\"Sim-to-Real Robot Learning from Pixels with Progressive Nets.\" arXiv preprint arXiv:1610.04286 (2016). [pdf] ⭐️⭐️⭐️⭐️\n\n[9] Mirowski, Piotr, et al. \"Learning to navigate in complex environments.\" arXiv preprint arXiv:1611.03673 (2016). [pdf] ⭐️⭐️⭐️⭐️\n\n#### 3.7 Art\n\n[1] Mordvintsev, Alexander; Olah, Christopher; Tyka, Mike (2015). \"Inceptionism: Going Deeper into Neural Networks\". Google Research. [html] (Deep Dream)\n⭐️⭐️⭐️⭐️\n\n[2] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. \"A neural algorithm of artistic style.\" arXiv preprint arXiv:1508.06576 (2015). [pdf] (Outstanding Work, most successful method currently) ⭐️⭐️⭐️⭐️⭐️\n\n[3] Zhu, Jun-Yan, et al. \"Generative Visual Manipulation on the Natural Image Manifold.\" European Conference on Computer Vision. Springer International Publishing, 2016. [pdf] (iGAN) ⭐️⭐️⭐️⭐️\n\n[4] Champandard, Alex J. \"Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks.\" arXiv preprint arXiv:1603.01768 (2016). [pdf] (Neural Doodle) ⭐️⭐️⭐️⭐️\n\n[5] Zhang, Richard, Phillip Isola, and Alexei A. Efros. \"Colorful Image Colorization.\" arXiv preprint arXiv:1603.08511 (2016). [pdf] ⭐️⭐️⭐️⭐️\n\n[6] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. \"Perceptual losses for real-time style transfer and super-resolution.\" arXiv preprint arXiv:1603.08155 (2016). [pdf] ⭐️⭐️⭐️⭐️\n\n[7] Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. \"A learned representation for artistic style.\" arXiv preprint arXiv:1610.07629 (2016). [pdf] ⭐️⭐️⭐️⭐️\n\n[8] Gatys, Leon and Ecker, et al.\"Controlling Perceptual Factors in Neural Style Transfer.\" arXiv preprint arXiv:1611.07865 (2016). [pdf] (control style transfer over spatial location,colour information and across spatial scale)⭐️⭐️⭐️⭐️\n\n[9] Ulyanov, Dmitry and Lebedev, Vadim, et al. \"Texture Networks: Feed-forward Synthesis of Textures and Stylized Images.\" arXiv preprint arXiv:1603.03417(2016). [pdf] (texture generation and style transfer) ⭐️⭐️⭐️⭐️\n\n#### 3.8 目标分割\n\n[1] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.” in CVPR, 2015. [pdf] ⭐️⭐️⭐️⭐️⭐️\n\n[2] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. \"Semantic image segmentation with deep convolutional nets and fully connected crfs.\" In ICLR, 2015. [pdf] ⭐️⭐️⭐️⭐️⭐️\n\n[3] Pinheiro, P.O., Collobert, R., Dollar, P. \"Learning to segment object candidates.\" In: NIPS. 2015. [pdf] ⭐️⭐️⭐️⭐️\n\n[4] Dai, J., He, K., Sun, J. \"Instance-aware semantic segmentation via multi-task network cascades.\" in CVPR. 2016 [pdf] ⭐️⭐️⭐️\n\n[5] Dai, J., He, K., Sun, J. \"Instance-sensitive Fully Convolutional Networks.\" arXiv preprint arXiv:1603.08678 (2016). [pdf] ⭐️⭐️⭐️\n\n## 论文下载\n\n百度云地址发布：\n\n链接：https://pan.baidu.com/s/17Xcg6-mzRjlNKEIj2lSPiw\n\n提取码：fnks"
  },
  {
    "path": "8.deep-learning/PyTorch_beginner/1.Tensors.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 前言\\n\",\n    \"\\n\",\n    \"原文翻译自：[Deep Learning with PyTorch: A 60 Minute Blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html)\\n\",\n    \"\\n\",\n    \"翻译：[林不清](https://www.zhihu.com/people/lu-guo-92-42-88)\\n\",\n    \"\\n\",\n    \"整理：机器学习初学者公众号 ![gongzhong](images/gongzhong.jpg)\\n\",\n    \"\\n\",\n    \"## 目录\\n\",\n    \"\\n\",\n    \"[60分钟入门PyTorch（一）——Tensors](https://zhuanlan.zhihu.com/p/347676809)\\n\",\n    \"\\n\",\n    \"[60分钟入门PyTorch（二）——Autograd自动求导](https://zhuanlan.zhihu.com/p/347672836)\\n\",\n    \"\\n\",\n    \"[60分钟入门Pytorch（三）——神经网络](https://zhuanlan.zhihu.com/p/347678492)\\n\",\n    \"\\n\",\n    \"[60分钟入门PyTorch（四）——训练一个分类器](https://zhuanlan.zhihu.com/p/347681137)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tensors\\n\",\n    \"\\n\",\n    \"Tensors张量是一种特殊的数据结构，它和数组还有矩阵十分相似。在Pytorch中，我们使用tensors来给模型的输入输出以及参数进行编码。\\n\",\n    \"Tensors除了张量可以在gpu或其他专用硬件上运行来加速计算之外，其他用法类似于Numpy中的ndarrays。如果你熟悉ndarrays，您就会熟悉tensor的API。如果没有，请按照这个教程，快速了解一遍API。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 初始化Tensor\\n\",\n    \"创建Tensor有多种方法，如：\\n\",\n    \"#### 直接从数据创建\\n\",\n    \"可以直接利用数据创建tensor,数据类型会被自动推断出\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data = [[1, 2],[3, 4]]\\n\",\n    \"x_data = torch.tensor(data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 从Numpy创建\\n\",\n    \"Tensor 可以直接从numpy的array创建（反之亦然-参见`bridge-to-np-label`）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np_array = np.array(data)\\n\",\n    \"x_np = torch.from_numpy(np_array)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 从其他tensor创建\\n\",\n    \"新的tensor保留了参数tensor的一些属性（形状，数据类型），除非显式覆盖\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Ones Tensor: \\n\",\n      \" tensor([[1, 1],\\n\",\n      \"        [1, 1]]) \\n\",\n      \"\\n\",\n      \"Random Tensor: \\n\",\n      \" tensor([[0.6075, 0.4581],\\n\",\n      \"        [0.5631, 0.1357]]) \\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x_ones = torch.ones_like(x_data) # retains the properties of x_data\\n\",\n    \"print(f\\\"Ones Tensor: \\\\n {x_ones} \\\\n\\\")\\n\",\n    \"\\n\",\n    \"x_rand = torch.rand_like(x_data, dtype=torch.float) # overrides the datatype of x_data\\n\",\n    \"print(f\\\"Random Tensor: \\\\n {x_rand} \\\\n\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 从常数或者随机数创建\\n\",\n    \"``shape``是关于tensor维度的一个元组，在下面的函数中，它决定了输出tensor的维数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Random Tensor: \\n\",\n      \" tensor([[0.7488, 0.0891, 0.8417],\\n\",\n      \"        [0.0783, 0.5984, 0.5709]]) \\n\",\n      \"\\n\",\n      \"Ones Tensor: \\n\",\n      \" tensor([[1., 1., 1.],\\n\",\n      \"        [1., 1., 1.]]) \\n\",\n      \"\\n\",\n      \"Zeros Tensor: \\n\",\n      \" tensor([[0., 0., 0.],\\n\",\n      \"        [0., 0., 0.]])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"shape = (2,3,)\\n\",\n    \"rand_tensor = torch.rand(shape)\\n\",\n    \"ones_tensor = torch.ones(shape)\\n\",\n    \"zeros_tensor = torch.zeros(shape)\\n\",\n    \"\\n\",\n    \"print(f\\\"Random Tensor: \\\\n {rand_tensor} \\\\n\\\")\\n\",\n    \"print(f\\\"Ones Tensor: \\\\n {ones_tensor} \\\\n\\\")\\n\",\n    \"print(f\\\"Zeros Tensor: \\\\n {zeros_tensor}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Tensor的属性\\n\",\n    \"Tensor的属性包括形状，数据类型以及存储的设备\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Shape of tensor: torch.Size([3, 4])\\n\",\n      \"Datatype of tensor: torch.float32\\n\",\n      \"Device tensor is stored on: cpu\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tensor = torch.rand(3,4)\\n\",\n    \"\\n\",\n    \"print(f\\\"Shape of tensor: {tensor.shape}\\\")\\n\",\n    \"print(f\\\"Datatype of tensor: {tensor.dtype}\\\")\\n\",\n    \"print(f\\\"Device tensor is stored on: {tensor.device}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Tensor的操作\\n\",\n    \"Tensor有超过100个操作，包括 transposing, indexing, slicing, mathematical operations, linear algebra, random sampling,更多详细的介绍请点击[这里](https://pytorch.org/docs/stable/torch.html)\\n\",\n    \"\\n\",\n    \"它们都可以在GPU上运行（速度通常比CPU快），如果你使用的是Colab，通过编辑>笔记本设置来分配一个GPU。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# We move our tensor to the GPU if available\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"  tensor = tensor.to('cuda')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"尝试列表中的一些操作。如果你熟悉NumPy API，你会发现tensor的API很容易使用。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 标准的numpy类索引和切片:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tensor([[1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1.]])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tensor = torch.ones(4, 4)\\n\",\n    \"tensor[:,1] = 0\\n\",\n    \"print(tensor)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 合并tensors \\n\",\n    \"可以使用``torch.cat``来沿着特定维数连接一系列张量。 [``torch.stack``](https://pytorch.org/docs/stable/generated/torch.stack.html)另一个加入op的张量与``torch.cat``有细微的不同\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tensor([[1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.]])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"t1 = torch.cat([tensor, tensor, tensor], dim=1)\\n\",\n    \"print(t1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 增加tensors\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tensor.mul(tensor) \\n\",\n      \" tensor([[1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1.]]) \\n\",\n      \"\\n\",\n      \"tensor * tensor \\n\",\n      \" tensor([[1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1.]])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# This computes the element-wise product\\n\",\n    \"print(f\\\"tensor.mul(tensor) \\\\n {tensor.mul(tensor)} \\\\n\\\")\\n\",\n    \"# Alternative syntax:\\n\",\n    \"print(f\\\"tensor * tensor \\\\n {tensor * tensor}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"它计算两个tensor之间的矩阵乘法\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tensor.matmul(tensor.T) \\n\",\n      \" tensor([[3., 3., 3., 3.],\\n\",\n      \"        [3., 3., 3., 3.],\\n\",\n      \"        [3., 3., 3., 3.],\\n\",\n      \"        [3., 3., 3., 3.]]) \\n\",\n      \"\\n\",\n      \"tensor @ tensor.T \\n\",\n      \" tensor([[3., 3., 3., 3.],\\n\",\n      \"        [3., 3., 3., 3.],\\n\",\n      \"        [3., 3., 3., 3.],\\n\",\n      \"        [3., 3., 3., 3.]])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"tensor.matmul(tensor.T) \\\\n {tensor.matmul(tensor.T)} \\\\n\\\")\\n\",\n    \"# Alternative syntax:\\n\",\n    \"print(f\\\"tensor @ tensor.T \\\\n {tensor @ tensor.T}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 原地操作\\n\",\n    \"带有后缀``_``的操作表示的是原地操作，例如： ``x.copy_(y)``, ``x.t_()``将改变 ``x``.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tensor([[1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1.],\\n\",\n      \"        [1., 0., 1., 1.]]) \\n\",\n      \"\\n\",\n      \"tensor([[6., 5., 6., 6.],\\n\",\n      \"        [6., 5., 6., 6.],\\n\",\n      \"        [6., 5., 6., 6.],\\n\",\n      \"        [6., 5., 6., 6.]])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(tensor, \\\"\\\\n\\\")\\n\",\n    \"tensor.add_(5)\\n\",\n    \"print(tensor)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<div class=\\\"alert alert-info\\\"><h4>注意</h4>\\n\",\n    \"    <p>原地操作虽然会节省许多空间，但是由于会立刻清除历史记录所以在计算导数时可能会有问题，因此不建议使用</p></div>\\n\",\n    \"    \\n\",\n    \"### Tensor转换为Numpt 数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"t: tensor([1., 1., 1., 1., 1.])\\n\",\n      \"n: [1. 1. 1. 1. 1.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"t = torch.ones(5)\\n\",\n    \"print(f\\\"t: {t}\\\")\\n\",\n    \"n = t.numpy()\\n\",\n    \"print(f\\\"n: {n}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"tensor的变化反映在NumPy数组中。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"t: tensor([2., 2., 2., 2., 2.])\\n\",\n      \"n: [2. 2. 2. 2. 2.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"t.add_(1)\\n\",\n    \"print(f\\\"t: {t}\\\")\\n\",\n    \"print(f\\\"n: {n}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Numpy数组转换为Tensor\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n = np.ones(5)\\n\",\n    \"t = torch.from_numpy(n)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"NumPy数组的变化反映在tensor中\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"t: tensor([2., 2., 2., 2., 2.], dtype=torch.float64)\\n\",\n      \"n: [2. 2. 2. 2. 2.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.add(n, 1, out=n)\\n\",\n    \"print(f\\\"t: {t}\\\")\\n\",\n    \"print(f\\\"n: {n}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "8.deep-learning/PyTorch_beginner/2.Autograd自动求导.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 前言\\n\",\n    \"\\n\",\n    \"原文翻译自：[Deep Learning with PyTorch: A 60 Minute Blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html)\\n\",\n    \"\\n\",\n    \"翻译：[林不清](https://www.zhihu.com/people/lu-guo-92-42-88)\\n\",\n    \"\\n\",\n    \"整理：机器学习初学者公众号 ![gongzhong](images/gongzhong.jpg)\\n\",\n    \"\\n\",\n    \"## 目录\\n\",\n    \"\\n\",\n    \"[60分钟入门PyTorch（一）——Tensors](https://zhuanlan.zhihu.com/p/347676809)\\n\",\n    \"\\n\",\n    \"[60分钟入门PyTorch（二）——Autograd自动求导](https://zhuanlan.zhihu.com/p/347672836)\\n\",\n    \"\\n\",\n    \"[60分钟入门Pytorch（三）——神经网络](https://zhuanlan.zhihu.com/p/347678492)\\n\",\n    \"\\n\",\n    \"[60分钟入门PyTorch（四）——训练一个分类器](https://zhuanlan.zhihu.com/p/347681137)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Autograd：自动求导\\n\",\n    \"\\n\",\n    \"torch.autograd是pytorch自动求导的工具，也是所有神经网络的核心。我们首先先简单了解一下这个包如何训练神经网络。\\n\",\n    \"\\n\",\n    \"**背景介绍**\\n\",\n    \"\\n\",\n    \"    神经网络(NNs)是作用在输入数据上的一系列嵌套函数的集合，这些函数由权重和误差来定义，被存储在PyTorch中的tensors中。\\n\",\n    \"    神经网络训练的两个步骤：\\n\",\n    \"    前向传播：在前向传播中，神经网络通过将接收到的数据与每一层对应的权重和误差进行运算来对正确的输出做出最好的预测。\\n\",\n    \"    反向传播：在反向传播中，神经网络调整其参数使得其与输出误差成比例。反向传播基于梯度下降策略，是链式求导法则的一个应用，以目标的负梯度方向对参数进行调整。\\n\",\n    \"    更加详细的介绍可以参照下述地址：\\n\",\n    \"[3Blue1Brown](https://www.youtube.com/watch?v=tIeHLnjs5U8)\\n\",\n    \"\\n\",\n    \"**Pytorch应用**\\n\",\n    \"\\n\",\n    \"来看一个简单的示例，我们从torchvision加载一个预先训练好的resnet18模型，接着创建一个随机数据tensor来表示一有3个通道、高度和宽度为64的图像，其对应的标签初始化为一些随机值。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading: \\\"https://download.pytorch.org/models/resnet18-5c106cde.pth\\\" to C:\\\\Users\\\\hai_g/.cache\\\\torch\\\\checkpoints\\\\resnet18-5c106cde.pth\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"eecbf11d7b4a414a9e3032de9e86c5c2\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"HBox(children=(FloatProgress(value=0.0, max=46827520.0), HTML(value='')))\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch, torchvision\\n\",\n    \"model = torchvision.models.resnet18(pretrained=True)\\n\",\n    \"data = torch.rand(1, 3, 64, 64)\\n\",\n    \"labels = torch.rand(1, 1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"接下来，我们将输入数据向输出方向传播到模型的每一层中来预测输出，这就是前向传播。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"prediction = model(data) # 前向传播\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们利用模型的预测输出和对应的权重来计算误差，然后反向传播误差。完成计算后，您可以调用.backward()并自动计算所有梯度。此张量的梯度将累积到.grad属性中。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"loss = (prediction - labels).sum()\\n\",\n    \"loss.backward() # 反向传播\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"接着，我们加载一个优化器，在本例中，SGD的学习率为0.01，momentum 为0.9。我们在优化器中注册模型的所有参数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optim = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.9)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"最后，我们调用`.step()`来执行梯度下降，优化器通过存储在`.grad`中的梯度来调整每个参数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optim.step() #梯度下降\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在，你已经具备了训练神经网络所需所有条件。下面几节详细介绍了Autograd包的工作原理——可以跳过它们。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"********************************************\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Autograd中的求导**\\n\",\n    \"\\n\",\n    \"先来看一下`autograd`是如何收集梯度的。我们创建两个张量a和b并设置requires_grad = True以跟踪它的计算。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"a = torch.tensor([2., 3.], requires_grad=True)\\n\",\n    \"b = torch.tensor([6., 4.], requires_grad=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"接着在``a``和``b``的基础上创建张量``Q``\\n\",\n    \"\\\\begin{align}Q = 3a^3 - b^2\\\\end{align}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"Q = 3*a**3 - b**2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"假设``a``和``b``是一个神经网络的权重，``Q``是它的误差，在神经网络训练中，我们需要w.r.t参数的误差梯度，即\\n\",\n    \"\\n\",\n    \"\\\\begin{align}\\\\frac{\\\\partial Q}{\\\\partial a} = 9a^2\\\\end{align}\\n\",\n    \"\\n\",\n    \"\\\\begin{align}\\\\frac{\\\\partial Q}{\\\\partial b} = -2b\\\\end{align}\\n\",\n    \"当我们调用``Q``的``.backward()``时，autograd计算这些梯度并把它们存储在张量的 ``.grad``属性中。我们需要在``Q.backward()``中显式传递``gradient``，``gradient``是一个与`` Q ``相同形状的张量，它表示Q w.r.t本身的梯度，即\\n\",\n    \"\\\\begin{align}\\\\frac{dQ}{dQ} = 1\\\\end{align}\\n\",\n    \"同样，我们也可以将``Q``聚合为一个标量并隐式向后调用，如``Q.sum().backward()``。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"external_grad = torch.tensor([1., 1.])\\n\",\n    \"Q.backward(gradient=external_grad)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在梯度都被存放在``a.grad``和``b.grad``中\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tensor([True, True])\\n\",\n      \"tensor([True, True])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 检查一下存储的梯度是否正确\\n\",\n    \"print(9*a**2 == a.grad)\\n\",\n    \"print(-2*b == b.grad)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**可选阅读----用autograd进行向量计算**\\n\",\n    \"\\n\",\n    \"在数学上，如果你有一个向量值函数𝑦⃗ =𝑓(𝑥⃗ ) ，则𝑦⃗ 相对于𝑥⃗ 的梯度是雅可比矩阵：\\n\",\n    \"\\\\begin{align}J\\n\",\n    \"     =\\n\",\n    \"      \\\\left(\\\\begin{array}{cc}\\n\",\n    \"      \\\\frac{\\\\partial \\\\bf{y}}{\\\\partial x_{1}} &\\n\",\n    \"      ... &\\n\",\n    \"      \\\\frac{\\\\partial \\\\bf{y}}{\\\\partial x_{n}}\\n\",\n    \"      \\\\end{array}\\\\right)\\n\",\n    \"     =\\n\",\n    \"     \\\\left(\\\\begin{array}{ccc}\\n\",\n    \"      \\\\frac{\\\\partial y_{1}}{\\\\partial x_{1}} & \\\\cdots & \\\\frac{\\\\partial y_{1}}{\\\\partial x_{n}}\\\\\\\\\\n\",\n    \"      \\\\vdots & \\\\ddots & \\\\vdots\\\\\\\\\\n\",\n    \"      \\\\frac{\\\\partial y_{m}}{\\\\partial x_{1}} & \\\\cdots & \\\\frac{\\\\partial y_{m}}{\\\\partial x_{n}}\\n\",\n    \"      \\\\end{array}\\\\right)\\\\end{align}\\n\",\n    \"      \\n\",\n    \"一般来说，torch.autograd是一个计算雅可比向量积的引擎。 也就是说，给定任何向量𝑣=(𝑣1𝑣2...𝑣𝑚)𝑇，计算乘积𝐽⋅𝑣。如果𝑣恰好是标量函数的梯度𝑙=𝑔(𝑦⃗ )，即$v={{(\\\\frac{\\\\partial l}{\\\\partial {{y}_{1}}}\\\\cdots \\\\frac{\\\\partial l}{\\\\partial {{y}_{m}}})}^{T}}$ 然后根据链式法则，雅可比向量乘积将是𝑙相对于𝑥⃗ 的梯度\\n\",\n    \"\\n\",\n    \"\\\\begin{align}J^{T}\\\\cdot \\\\vec{v}=\\\\left(\\\\begin{array}{ccc}\\n\",\n    \"      \\\\frac{\\\\partial y_{1}}{\\\\partial x_{1}} & \\\\cdots & \\\\frac{\\\\partial y_{m}}{\\\\partial x_{1}}\\\\\\\\\\n\",\n    \"      \\\\vdots & \\\\ddots & \\\\vdots\\\\\\\\\\n\",\n    \"      \\\\frac{\\\\partial y_{1}}{\\\\partial x_{n}} & \\\\cdots & \\\\frac{\\\\partial y_{m}}{\\\\partial x_{n}}\\n\",\n    \"      \\\\end{array}\\\\right)\\\\left(\\\\begin{array}{c}\\n\",\n    \"      \\\\frac{\\\\partial l}{\\\\partial y_{1}}\\\\\\\\\\n\",\n    \"      \\\\vdots\\\\\\\\\\n\",\n    \"      \\\\frac{\\\\partial l}{\\\\partial y_{m}}\\n\",\n    \"      \\\\end{array}\\\\right)=\\\\left(\\\\begin{array}{c}\\n\",\n    \"      \\\\frac{\\\\partial l}{\\\\partial x_{1}}\\\\\\\\\\n\",\n    \"      \\\\vdots\\\\\\\\\\n\",\n    \"      \\\\frac{\\\\partial l}{\\\\partial x_{n}}\\n\",\n    \"      \\\\end{array}\\\\right)\\\\end{align}\\n\",\n    \"\\n\",\n    \"雅可比向量积的这种特性使得将外部梯度馈送到具有非标量输出的模型中非常方便。``external_grad`` 代表$\\\\vec{v}$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**图计算**\\n\",\n    \"\\n\",\n    \"从概念上讲，autograd在由[函数](https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)对象组成的有向无环图(DAG)中保存数据(tensor)和所有执行的操作(以及产生的新tensor)的记录，在这个DAG中，叶节点是输入数据，根节点是输出数据，通过从根节点到叶节点跟踪这个图，您可以使用链式法则自动计算梯度。\\n\",\n    \"\\n\",\n    \"在前向传播中，autograd同时完成两件事情：\\n\",\n    \"* 运行所请求的操作来计算结果tensor\\n\",\n    \"* 保持DAG中操作的梯度\\n\",\n    \"\\n\",\n    \"在反向传播中，当在DAG根节点上调用``.backward()``时，反向传播启动，``autograd``接下来完成：\\n\",\n    \"* 计算每一个``.grad_fn``的梯度\\n\",\n    \"* 将它们累加到各自张量的.grad属性中\\n\",\n    \"* 利用链式法则，一直传播到叶节点\\n\",\n    \"\\n\",\n    \"下面是DAG的可视化表示的示例。图中，箭头表示前向传播的方向，节点表示向前传递中每个操作的向后函数。蓝色标记的叶节点代表叶张量 ``a``和``b``\\n\",\n    \"\\n\",\n    \"![autograd](images/autograd.jpg)\\n\",\n    \"\\n\",\n    \"<div class=\\\"alert alert-info\\\"><h4>注意</h4><p>**DAG在PyTorch中是动态的**\\n\",\n    \" 值得注意的是图是重新开始创建的; 在调用每一个``.backward()``后，autograd开始填充一个新图，这就是能够在模型中使用控制流语句的原因。你可以根据需求在每次迭代时更改形状、大小和操作。\\n\",\n    \"  </p></div>\\n\",\n    \"\\n\",\n    \"\\n\",\n    \" \\n\",\n    \"``torch.autograd``追踪所有``requires_grad``为``True``的张量的相关操作。对于不需要梯度的张量，将此属性设置为False将其从梯度计算DAG中排除。\\n\",\n    \"操作的输出张量将需要梯度，即使只有一个输入张量``requires_grad=True``。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Does `a` require gradients? : False\\n\",\n      \"Does `b` require gradients?: True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = torch.rand(5, 5)\\n\",\n    \"y = torch.rand(5, 5)\\n\",\n    \"z = torch.rand((5, 5), requires_grad=True)\\n\",\n    \"\\n\",\n    \"a = x + y\\n\",\n    \"print(f\\\"Does `a` require gradients? : {a.requires_grad}\\\")\\n\",\n    \"b = x + z\\n\",\n    \"print(f\\\"Does `b` require gradients?: {b.requires_grad}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在神经网络中，不计算梯度的参数通常称为冻结参数。如果您事先知道您不需要这些参数的梯度，那么“冻结”部分模型是很有用的(这通过减少autograd计算带来一些性能好处)。\\n\",\n    \"另外一个常见的用法是微调一个[预训练好的网络](https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html)，在微调的过程中，我们冻结大部分模型——通常，只修改分类器来对新的<标签>做出预测,让我们通过一个小示例来演示这一点。与前面一样，我们加载一个预先训练好的resnet18模型，并冻结所有参数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch import nn, optim\\n\",\n    \"\\n\",\n    \"model = torchvision.models.resnet18(pretrained=True)\\n\",\n    \"\\n\",\n    \"# 冻结网络中所有的参数\\n\",\n    \"for param in model.parameters():\\n\",\n    \"    param.requires_grad = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"假设我们想在一个有10个标签的新数据集上微调模型。在resnet中，分类器是最后一个线性层模型``model.fc``。我们可以简单地用一个新的线性层(默认未冻结)代替它作为我们的分类器。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model.fc = nn.Linear(512, 10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在除了``model.fc``的参数外，模型的其他参数均被冻结，参与计算的参数是``model.fc``的权值和偏置。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 只优化分类器\\n\",\n    \"optimizer = optim.SGD(model.fc.parameters(), lr=1e-2, momentum=0.9)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"注意，尽管我们注册了优化器中所有参数，但唯一参与梯度计算(并因此在梯度下降中更新)的参数是分类器的权值和偏差。\\n\",\n    \"[torch.no_grad()](https://pytorch.org/docs/stable/generated/torch.no_grad.html)中也具有相同的功能。\\n\",\n    \"\\n\",\n    \"**拓展阅读**\\n\",\n    \"* [就地修改操作以及多线程Autograd](https://pytorch.org/docs/stable/notes/autograd.html)\\n\",\n    \"* [反向模式autodiff的示例](https://colab.research.google.com/drive/1VpeE6UvEPRz9HmsHh1KS0XxXjYu533EC)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "8.deep-learning/PyTorch_beginner/3.神经网络.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 前言\\n\",\n    \"\\n\",\n    \"原文翻译自：[Deep Learning with PyTorch: A 60 Minute Blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html)\\n\",\n    \"\\n\",\n    \"翻译：[林不清](https://www.zhihu.com/people/lu-guo-92-42-88)\\n\",\n    \"\\n\",\n    \"整理：机器学习初学者公众号 ![gongzhong](images/gongzhong.jpg)\\n\",\n    \"\\n\",\n    \"## 目录\\n\",\n    \"\\n\",\n    \"[60分钟入门PyTorch（一）——Tensors](https://zhuanlan.zhihu.com/p/347676809)\\n\",\n    \"\\n\",\n    \"[60分钟入门PyTorch（二）——Autograd自动求导](https://zhuanlan.zhihu.com/p/347672836)\\n\",\n    \"\\n\",\n    \"[60分钟入门Pytorch（三）——神经网络](https://zhuanlan.zhihu.com/p/347678492)\\n\",\n    \"\\n\",\n    \"[60分钟入门PyTorch（四）——训练一个分类器](https://zhuanlan.zhihu.com/p/347681137)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 神经网络\\n\",\n    \"\\n\",\n    \"可以使用`torch.nn`包来构建神经网络.\\n\",\n    \"你已知道`autograd`包,`nn`包依赖`autograd`包来定义模型并求导.一个`nn.Module`包含各个层和一个`forward(input)`方法,该方法返回`output`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"例如,我们来看一下下面这个分类数字图像的网络.\\n\",\n    \"\\n\",\n    \"![neural](images/neural.png)\\n\",\n    \"\\n\",\n    \"他是一个简单的前馈神经网络,它接受一个输入,然后一层接着一层的输入,直到最后得到结果。\\n\",\n    \"\\n\",\n    \"神经网络的典型训练过程如下:\\n\",\n    \"\\n\",\n    \"* 定义神经网络模型,它有一些可学习的参数(或者权重);\\n\",\n    \"* 在数据集上迭代;\\n\",\n    \"* 通过神经网络处理输入;\\n\",\n    \"* 计算损失(输出结果和正确值的差距大小)\\n\",\n    \"* 将梯度反向传播会网络的参数;\\n\",\n    \"* 更新网络的参数,主要使用如下简单的更新原则:`weight = weight - learning_rate * gradient`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 定义网络\\n\",\n    \"\\n\",\n    \"我们先定义一个网络：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Net(\\n\",\n      \"  (conv1): Conv2d(1, 6, kernel_size=(3, 3), stride=(1, 1))\\n\",\n      \"  (conv2): Conv2d(6, 16, kernel_size=(3, 3), stride=(1, 1))\\n\",\n      \"  (fc1): Linear(in_features=576, out_features=120, bias=True)\\n\",\n      \"  (fc2): Linear(in_features=120, out_features=84, bias=True)\\n\",\n      \"  (fc3): Linear(in_features=84, out_features=10, bias=True)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class Net(nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(Net, self).__init__()\\n\",\n    \"        # 1 input image channel, 6 output channels, 3x3 square convolution\\n\",\n    \"        # kernel\\n\",\n    \"        self.conv1 = nn.Conv2d(1, 6, 3)\\n\",\n    \"        self.conv2 = nn.Conv2d(6, 16, 3)\\n\",\n    \"        # an affine operation: y = Wx + b\\n\",\n    \"        self.fc1 = nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension \\n\",\n    \"        self.fc2 = nn.Linear(120, 84)\\n\",\n    \"        self.fc3 = nn.Linear(84, 10)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        # Max pooling over a (2, 2) window\\n\",\n    \"        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\\n\",\n    \"        # If the size is a square you can only specify a single number\\n\",\n    \"        x = F.max_pool2d(F.relu(self.conv2(x)), 2)\\n\",\n    \"        x = x.view(-1, self.num_flat_features(x))\\n\",\n    \"        x = F.relu(self.fc1(x))\\n\",\n    \"        x = F.relu(self.fc2(x))\\n\",\n    \"        x = self.fc3(x)\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"    def num_flat_features(self, x):\\n\",\n    \"        size = x.size()[1:]  # all dimensions except the batch dimension\\n\",\n    \"        num_features = 1\\n\",\n    \"        for s in size:\\n\",\n    \"            num_features *= s\\n\",\n    \"        return num_features\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"net = Net()\\n\",\n    \"print(net)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"你只需定义`forward`函数,`backward`函数(计算梯度)在使用`autograd`时自动为你创建.你可以在`forward`函数中使用`Tensor`的任何操作。\\n\",\n    \"\\n\",\n    \"`net.parameters()`返回模型需要学习的参数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"10\\n\",\n      \"torch.Size([6, 1, 3, 3])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"params = list(net.parameters())\\n\",\n    \"print(len(params))\\n\",\n    \"print(params[0].size())  # conv1's .weight\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"构造一个随机的32*32的输入，注意:这个网络(LeNet)期望的输入大小是32*32.如果使用MNIST数据集来训练这个网络,请把图片大小重新调整到32*32.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tensor([[-0.0765,  0.0522,  0.0820,  0.0109,  0.0004,  0.0184,  0.1024,  0.0509,\\n\",\n      \"          0.0917, -0.0164]], grad_fn=<AddmmBackward>)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"input = torch.randn(1, 1, 32, 32)\\n\",\n    \"out = net(input)\\n\",\n    \"print(out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"将所有参数的梯度缓存清零,然后进行随机梯度的的反向传播.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"net.zero_grad()\\n\",\n    \"out.backward(torch.randn(1, 10))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<div class=\\\"alert alert-info\\\"><h4>注意</h4>\\n\",\n    \"``torch.nn``只支持小批量输入,整个torch.nn包都只支持小批量样本,而不支持单个样本\\n\",\n    \"例如,``nn.Conv2d``将接受一个4维的张量,每一维分别是$nSamples\\\\times nChannels\\\\times Height\\\\times Width$(样本数*通道数*高*宽).\\n\",\n    \"如果你有单个样本,只需使用`input.unsqueeze(0)`来添加其它的维数.\\n\",\n    \"在继续之前,我们回顾一下到目前为止见过的所有类.\\n\",\n    \"\\n\",\n    \"### 回顾\\n\",\n    \"\\n\",\n    \"* `torch.Tensor`-支持自动编程操作（如`backward()`）的多维数组。 同时保持梯度的张量。\\n\",\n    \"* `nn.Module`-神经网络模块.封装参数,移动到GPU上运行,导出,加载等\\n\",\n    \"* `nn.Parameter`-一种张量,当把它赋值给一个`Module`时,被自动的注册为参数.\\n\",\n    \"* `autograd.Function`-实现一个自动求导操作的前向和反向定义, 每个张量操作都会创建至少一个`Function`节点，该节点连接到创建张量并对其历史进行编码的函数。\\n\",\n    \"\\n\",\n    \"#### 现在,我们包含了如下内容:\\n\",\n    \"\\n\",\n    \"* 定义一个神经网络\\n\",\n    \"* 处理输入和调用`backward`\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"#### 剩下的内容:\\n\",\n    \"\\n\",\n    \"* 计算损失值\\n\",\n    \"* 更新神经网络的权值\\n\",\n    \"\\n\",\n    \"### 损失函数\\n\",\n    \"一个损失函数接受一对(output, target)作为输入(output为网络的输出,target为实际值),计算一个值来估计网络的输出和目标值相差多少。\\n\",\n    \"\\n\",\n    \"在nn包中有几种不同的[损失函数](https://pytorch.org/docs/nn.html#loss-functions>).一个简单的损失函数是:`nn.MSELoss`,它计算输入和目标之间的均方误差。\\n\",\n    \"\\n\",\n    \"例如:\\n\",\n    \"</div>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tensor(1.5801, grad_fn=<MseLossBackward>)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"output = net(input)\\n\",\n    \"target = torch.randn(10)  # a dummy target, for example\\n\",\n    \"target = target.view(1, -1)  # make it the same shape as output\\n\",\n    \"criterion = nn.MSELoss()\\n\",\n    \"\\n\",\n    \"loss = criterion(output, target)\\n\",\n    \"print(loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在,你反向跟踪``loss``,使用它的``.grad_fn``属性,你会看到向下面这样的一个计算图:\\n\",\n    \" input -> conv2d -> relu -> maxpool2d -> conv2d -> relu -> maxpool2d\\n\",\n    \"          -> view -> linear -> relu -> linear -> relu -> linear\\n\",\n    \"          -> MSELoss\\n\",\n    \"          -> loss\\n\",\n    \"          \\n\",\n    \"所以, 当你调用``loss.backward()``,整个图被区分为损失以及图中所有具有``requires_grad = True``的张量，并且其``.grad`` 张量的梯度累积。\\n\",\n    \"\\n\",\n    \"为了说明,我们反向跟踪几步:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<MseLossBackward object at 0x0000023193A40E08>\\n\",\n      \"<AddmmBackward object at 0x0000023193A40E48>\\n\",\n      \"<AccumulateGrad object at 0x0000023193A40E08>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(loss.grad_fn)  # MSELoss\\n\",\n    \"print(loss.grad_fn.next_functions[0][0])  # Linear\\n\",\n    \"print(loss.grad_fn.next_functions[0][0].next_functions[0][0])  # ReLU\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 反向传播\\n\",\n    \"为了反向传播误差,我们所需做的是调用`loss.backward()`.你需要清除已存在的梯度,否则梯度将被累加到已存在的梯度。\\n\",\n    \"\\n\",\n    \"现在,我们将调用`loss.backward()`,并查看conv1层的偏置项在反向传播前后的梯度。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"conv1.bias.grad before backward\\n\",\n      \"tensor([0., 0., 0., 0., 0., 0.])\\n\",\n      \"conv1.bias.grad after backward\\n\",\n      \"tensor([ 0.0013,  0.0068,  0.0096,  0.0039, -0.0105, -0.0016])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"net.zero_grad()     # zeroes the gradient buffers of all parameters\\n\",\n    \"\\n\",\n    \"print('conv1.bias.grad before backward')\\n\",\n    \"print(net.conv1.bias.grad)\\n\",\n    \"\\n\",\n    \"loss.backward()\\n\",\n    \"\\n\",\n    \"print('conv1.bias.grad after backward')\\n\",\n    \"print(net.conv1.bias.grad)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在，我们知道了该如何使用损失函数\\n\",\n    \"#### 稍后阅读:\\n\",\n    \"\\n\",\n    \"神经网络包包含了各种用来构成深度神经网络构建块的模块和损失函数,一份完整的文档查看[这里](https://pytorch.org/docs/nn)\\n\",\n    \"\\n\",\n    \"#### 唯一剩下的内容:\\n\",\n    \"* 更新网络的权重\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 更新权重\\n\",\n    \"实践中最简单的更新规则是随机梯度下降(SGD)．\\n\",\n    \"\\n\",\n    \"`weight=weight−learning_rate∗gradient`\\n\",\n    \"\\n\",\n    \"我们可以使用简单的Python代码实现这个规则。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.01\\n\",\n    \"for f in net.parameters():\\n\",\n    \"    f.data.sub_(f.grad.data * learning_rate)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"然而,当你使用神经网络是,你想要使用各种不同的更新规则,比如``SGD``,``Nesterov-SGD``,``Adam``, ``RMSPROP``等.为了能做到这一点,我们构建了一个包``torch.optim``实现了所有的这些规则.使用他们非常简单：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch.optim as optim\\n\",\n    \"\\n\",\n    \"# create your optimizer\\n\",\n    \"optimizer = optim.SGD(net.parameters(), lr=0.01)\\n\",\n    \"\\n\",\n    \"# in your training loop:\\n\",\n    \"optimizer.zero_grad()   # zero the gradient buffers\\n\",\n    \"output = net(input)\\n\",\n    \"loss = criterion(output, target)\\n\",\n    \"loss.backward()\\n\",\n    \"optimizer.step()    # Does the update\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**注意**\\n\",\n    \"\\n\",\n    \"观察如何使用`optimizer.zero_grad()`手动将梯度缓冲区设置为零。 这是因为梯度是反向传播部分中的说明那样是累积的。\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "8.deep-learning/PyTorch_beginner/4.训练一个分类器.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 前言\\n\",\n    \"\\n\",\n    \"原文翻译自：[Deep Learning with PyTorch: A 60 Minute Blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html)\\n\",\n    \"\\n\",\n    \"翻译：[林不清](https://www.zhihu.com/people/lu-guo-92-42-88)\\n\",\n    \"\\n\",\n    \"整理：机器学习初学者公众号 ![gongzhong](images/gongzhong.jpg)\\n\",\n    \"\\n\",\n    \"## 目录\\n\",\n    \"\\n\",\n    \"[60分钟入门PyTorch（一）——Tensors](https://zhuanlan.zhihu.com/p/347676809)\\n\",\n    \"\\n\",\n    \"[60分钟入门PyTorch（二）——Autograd自动求导](https://zhuanlan.zhihu.com/p/347672836)\\n\",\n    \"\\n\",\n    \"[60分钟入门Pytorch（三）——神经网络](https://zhuanlan.zhihu.com/p/347678492)\\n\",\n    \"\\n\",\n    \"[60分钟入门PyTorch（四）——训练一个分类器](https://zhuanlan.zhihu.com/p/347681137)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 训练一个分类器\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"你已经学会如何去定义一个神经网络,计算损失值和更新网络的权重。\\n\",\n    \"\\n\",\n    \"你现在可能在思考：数据哪里来呢？\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 关于数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通常，当你处理图像，文本，音频和视频数据时，你可以使用标准的Python包来加载数据到一个numpy数组中.然后把这个数组转换成`torch.*Tensor`。\\n\",\n    \"* 对于图像,有诸如Pillow,OpenCV包等非常实用\\n\",\n    \"* 对于音频,有诸如scipy和librosa包\\n\",\n    \"* 对于文本,可以用原始Python和Cython来加载,或者使用NLTK和SpaCy\\n\",\n    \"对于视觉,我们创建了一个`torchvision`包,包含常见数据集的数据加载,比如Imagenet,CIFAR10,MNIST等,和图像转换器,也就是`torchvision.datasets`和`torch.utils.data.DataLoader`。\\n\",\n    \"\\n\",\n    \"这提供了巨大的便利,也避免了代码的重复。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在这个教程中,我们使用CIFAR10数据集,它有如下10个类别:’airplane’,’automobile’,’bird’,’cat’,’deer’,’dog’,’frog’,’horse’,’ship’,’truck’。这个数据集中的图像大小为3\\\\*32\\\\*32,即,3通道,32\\\\*32像素。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![cifar10](images/cifar10.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 训练一个图像分类器\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们将按照下列顺序进行:\\n\",\n    \"* 使用`torchvision`加载和归一化CIFAR10训练集和测试集.\\n\",\n    \"* 定义一个卷积神经网络\\n\",\n    \"* 定义损失函数\\n\",\n    \"* 在训练集上训练网络\\n\",\n    \"* 在测试集上测试网络\\n\",\n    \"\\n\",\n    \"### 1. 加载和归一化CIFAR10\\n\",\n    \"使用`torchvision`加载CIFAR10是非常容易的。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torchvision\\n\",\n    \"import torchvision.transforms as transforms\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"torchvision的输出是[0,1]的PILImage图像,我们把它转换为归一化范围为[-1, 1]的张量。\\n\",\n    \"<div class=\\\"alert alert-info\\\"><h4>注意</h4><p>如果在Windows上运行时出现BrokenPipeError，尝试将torch.utils.data.DataLoader()的num_worker设置为0。</p></div>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data\\\\cifar-10-python.tar.gz\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"dfeaa5d49cb44bc0a927ab4196629477\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Failed download. Trying https -> http instead. Downloading http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data\\\\cifar-10-python.tar.gz\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"2a44cd06f498404cab0bfb8a0f762c68\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"ename\": \"URLError\",\n     \"evalue\": \"<urlopen error [Errno 11001] getaddrinfo failed>\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mConnectionAbortedError\\u001b[0m                    Traceback (most recent call last)\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\torchvision\\\\datasets\\\\utils.py\\u001b[0m in \\u001b[0;36mdownload_url\\u001b[1;34m(url, root, filename, md5)\\u001b[0m\\n\\u001b[0;32m     70\\u001b[0m                 \\u001b[0murl\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mfpath\\u001b[0m\\u001b[1;33m,\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m---> 71\\u001b[1;33m                 \\u001b[0mreporthook\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mgen_bar_updater\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m     72\\u001b[0m             )\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\urllib\\\\request.py\\u001b[0m in \\u001b[0;36murlretrieve\\u001b[1;34m(url, filename, reporthook, data)\\u001b[0m\\n\\u001b[0;32m    275\\u001b[0m             \\u001b[1;32mwhile\\u001b[0m \\u001b[1;32mTrue\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 276\\u001b[1;33m                 \\u001b[0mblock\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mfp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mread\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mbs\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    277\\u001b[0m                 \\u001b[1;32mif\\u001b[0m \\u001b[1;32mnot\\u001b[0m \\u001b[0mblock\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\http\\\\client.py\\u001b[0m in \\u001b[0;36mread\\u001b[1;34m(self, amt)\\u001b[0m\\n\\u001b[0;32m    456\\u001b[0m             \\u001b[0mb\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mbytearray\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mamt\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 457\\u001b[1;33m             \\u001b[0mn\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mreadinto\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mb\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    458\\u001b[0m             \\u001b[1;32mreturn\\u001b[0m \\u001b[0mmemoryview\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mb\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[0mn\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mtobytes\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\http\\\\client.py\\u001b[0m in \\u001b[0;36mreadinto\\u001b[1;34m(self, b)\\u001b[0m\\n\\u001b[0;32m    500\\u001b[0m         \\u001b[1;31m# (for example, reading in 1k chunks)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 501\\u001b[1;33m         \\u001b[0mn\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mfp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mreadinto\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mb\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    502\\u001b[0m         \\u001b[1;32mif\\u001b[0m \\u001b[1;32mnot\\u001b[0m \\u001b[0mn\\u001b[0m \\u001b[1;32mand\\u001b[0m \\u001b[0mb\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\socket.py\\u001b[0m in \\u001b[0;36mreadinto\\u001b[1;34m(self, b)\\u001b[0m\\n\\u001b[0;32m    588\\u001b[0m             \\u001b[1;32mtry\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 589\\u001b[1;33m                 \\u001b[1;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_sock\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mrecv_into\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mb\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    590\\u001b[0m             \\u001b[1;32mexcept\\u001b[0m \\u001b[0mtimeout\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\ssl.py\\u001b[0m in \\u001b[0;36mrecv_into\\u001b[1;34m(self, buffer, nbytes, flags)\\u001b[0m\\n\\u001b[0;32m   1070\\u001b[0m                   self.__class__)\\n\\u001b[1;32m-> 1071\\u001b[1;33m             \\u001b[1;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mread\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mnbytes\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mbuffer\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m   1072\\u001b[0m         \\u001b[1;32melse\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\ssl.py\\u001b[0m in \\u001b[0;36mread\\u001b[1;34m(self, len, buffer)\\u001b[0m\\n\\u001b[0;32m    928\\u001b[0m             \\u001b[1;32mif\\u001b[0m \\u001b[0mbuffer\\u001b[0m \\u001b[1;32mis\\u001b[0m \\u001b[1;32mnot\\u001b[0m \\u001b[1;32mNone\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 929\\u001b[1;33m                 \\u001b[1;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_sslobj\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mread\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mlen\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mbuffer\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    930\\u001b[0m             \\u001b[1;32melse\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mConnectionAbortedError\\u001b[0m: [WinError 10053] 你的主机中的软件中止了一个已建立的连接。\",\n      \"\\nDuring handling of the above exception, another exception occurred:\\n\",\n      \"\\u001b[1;31mgaierror\\u001b[0m                                  Traceback (most recent call last)\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\urllib\\\\request.py\\u001b[0m in \\u001b[0;36mdo_open\\u001b[1;34m(self, http_class, req, **http_conn_args)\\u001b[0m\\n\\u001b[0;32m   1318\\u001b[0m                 h.request(req.get_method(), req.selector, req.data, headers,\\n\\u001b[1;32m-> 1319\\u001b[1;33m                           encode_chunked=req.has_header('Transfer-encoding'))\\n\\u001b[0m\\u001b[0;32m   1320\\u001b[0m             \\u001b[1;32mexcept\\u001b[0m \\u001b[0mOSError\\u001b[0m \\u001b[1;32mas\\u001b[0m \\u001b[0merr\\u001b[0m\\u001b[1;33m:\\u001b[0m \\u001b[1;31m# timeout error\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\http\\\\client.py\\u001b[0m in \\u001b[0;36mrequest\\u001b[1;34m(self, method, url, body, headers, encode_chunked)\\u001b[0m\\n\\u001b[0;32m   1251\\u001b[0m         \\u001b[1;34m\\\"\\\"\\\"Send a complete request to the server.\\\"\\\"\\\"\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 1252\\u001b[1;33m         \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_send_request\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mmethod\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0murl\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mbody\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mheaders\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mencode_chunked\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m   1253\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\http\\\\client.py\\u001b[0m in \\u001b[0;36m_send_request\\u001b[1;34m(self, method, url, body, headers, encode_chunked)\\u001b[0m\\n\\u001b[0;32m   1297\\u001b[0m             \\u001b[0mbody\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0m_encode\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mbody\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;34m'body'\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 1298\\u001b[1;33m         \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mendheaders\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mbody\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mencode_chunked\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mencode_chunked\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m   1299\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\http\\\\client.py\\u001b[0m in \\u001b[0;36mendheaders\\u001b[1;34m(self, message_body, encode_chunked)\\u001b[0m\\n\\u001b[0;32m   1246\\u001b[0m             \\u001b[1;32mraise\\u001b[0m \\u001b[0mCannotSendHeader\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 1247\\u001b[1;33m         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\"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\socket.py\\u001b[0m in \\u001b[0;36mcreate_connection\\u001b[1;34m(address, timeout, source_address)\\u001b[0m\\n\\u001b[0;32m    706\\u001b[0m     \\u001b[0merr\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[1;32mNone\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 707\\u001b[1;33m     \\u001b[1;32mfor\\u001b[0m \\u001b[0mres\\u001b[0m \\u001b[1;32min\\u001b[0m \\u001b[0mgetaddrinfo\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mhost\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mport\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;36m0\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mSOCK_STREAM\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    708\\u001b[0m         \\u001b[0maf\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0msocktype\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mproto\\u001b[0m\\u001b[1;33m,\\u001b[0m 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verified'\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    135\\u001b[0m             \\u001b[1;32mreturn\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 136\\u001b[1;33m         \\u001b[0mdownload_and_extract_archive\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0murl\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mroot\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mfilename\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mfilename\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mmd5\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mtgz_md5\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    137\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    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\\u001b[0mdownload_url\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0murl\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdownload_root\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mfilename\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mmd5\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    250\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    251\\u001b[0m     \\u001b[0marchive\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mos\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mpath\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mjoin\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mdownload_root\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mfilename\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\torchvision\\\\datasets\\\\utils.py\\u001b[0m in \\u001b[0;36mdownload_url\\u001b[1;34m(url, root, filename, md5)\\u001b[0m\\n\\u001b[0;32m     78\\u001b[0m                 urllib.request.urlretrieve(\\n\\u001b[0;32m     79\\u001b[0m                     \\u001b[0murl\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mfpath\\u001b[0m\\u001b[1;33m,\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m---> 80\\u001b[1;33m                     \\u001b[0mreporthook\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mgen_bar_updater\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m     81\\u001b[0m                 )\\n\\u001b[0;32m     82\\u001b[0m             \\u001b[1;32melse\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\urllib\\\\request.py\\u001b[0m in \\u001b[0;36murlretrieve\\u001b[1;34m(url, filename, reporthook, data)\\u001b[0m\\n\\u001b[0;32m    245\\u001b[0m     \\u001b[0murl_type\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mpath\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0msplittype\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0murl\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    246\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 247\\u001b[1;33m     \\u001b[1;32mwith\\u001b[0m \\u001b[0mcontextlib\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mclosing\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0murlopen\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0murl\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdata\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m)\\u001b[0m \\u001b[1;32mas\\u001b[0m \\u001b[0mfp\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    248\\u001b[0m         \\u001b[0mheaders\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mfp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0minfo\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    249\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\urllib\\\\request.py\\u001b[0m in \\u001b[0;36murlopen\\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\\u001b[0m\\n\\u001b[0;32m    220\\u001b[0m     \\u001b[1;32melse\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    221\\u001b[0m         \\u001b[0mopener\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0m_opener\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 222\\u001b[1;33m     \\u001b[1;32mreturn\\u001b[0m \\u001b[0mopener\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mopen\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0murl\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdata\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mtimeout\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    223\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    224\\u001b[0m \\u001b[1;32mdef\\u001b[0m \\u001b[0minstall_opener\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mopener\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\urllib\\\\request.py\\u001b[0m in \\u001b[0;36mopen\\u001b[1;34m(self, fullurl, data, timeout)\\u001b[0m\\n\\u001b[0;32m    523\\u001b[0m             \\u001b[0mreq\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mmeth\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mreq\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    524\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 525\\u001b[1;33m         \\u001b[0mresponse\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_open\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mreq\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdata\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    526\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    527\\u001b[0m         \\u001b[1;31m# post-process response\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\urllib\\\\request.py\\u001b[0m in \\u001b[0;36m_open\\u001b[1;34m(self, req, data)\\u001b[0m\\n\\u001b[0;32m    541\\u001b[0m         \\u001b[0mprotocol\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mreq\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mtype\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    542\\u001b[0m         result = self._call_chain(self.handle_open, protocol, protocol +\\n\\u001b[1;32m--> 543\\u001b[1;33m                                   '_open', req)\\n\\u001b[0m\\u001b[0;32m    544\\u001b[0m         \\u001b[1;32mif\\u001b[0m \\u001b[0mresult\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    545\\u001b[0m             \\u001b[1;32mreturn\\u001b[0m \\u001b[0mresult\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\urllib\\\\request.py\\u001b[0m in \\u001b[0;36m_call_chain\\u001b[1;34m(self, chain, kind, meth_name, *args)\\u001b[0m\\n\\u001b[0;32m    501\\u001b[0m         \\u001b[1;32mfor\\u001b[0m \\u001b[0mhandler\\u001b[0m \\u001b[1;32min\\u001b[0m \\u001b[0mhandlers\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    502\\u001b[0m             \\u001b[0mfunc\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mgetattr\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mhandler\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mmeth_name\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 503\\u001b[1;33m             \\u001b[0mresult\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mfunc\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    504\\u001b[0m             \\u001b[1;32mif\\u001b[0m \\u001b[0mresult\\u001b[0m \\u001b[1;32mis\\u001b[0m \\u001b[1;32mnot\\u001b[0m \\u001b[1;32mNone\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    505\\u001b[0m                 \\u001b[1;32mreturn\\u001b[0m \\u001b[0mresult\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\urllib\\\\request.py\\u001b[0m in \\u001b[0;36mhttp_open\\u001b[1;34m(self, req)\\u001b[0m\\n\\u001b[0;32m   1345\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   1346\\u001b[0m     \\u001b[1;32mdef\\u001b[0m \\u001b[0mhttp_open\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mreq\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 1347\\u001b[1;33m         \\u001b[1;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mdo_open\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mhttp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mclient\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mHTTPConnection\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mreq\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m   1348\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   1349\\u001b[0m     \\u001b[0mhttp_request\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mAbstractHTTPHandler\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mdo_request_\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\urllib\\\\request.py\\u001b[0m in \\u001b[0;36mdo_open\\u001b[1;34m(self, http_class, req, **http_conn_args)\\u001b[0m\\n\\u001b[0;32m   1319\\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\\n\\u001b[0;32m   1320\\u001b[0m             \\u001b[1;32mexcept\\u001b[0m \\u001b[0mOSError\\u001b[0m \\u001b[1;32mas\\u001b[0m \\u001b[0merr\\u001b[0m\\u001b[1;33m:\\u001b[0m \\u001b[1;31m# timeout error\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 1321\\u001b[1;33m                 \\u001b[1;32mraise\\u001b[0m \\u001b[0mURLError\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0merr\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m   1322\\u001b[0m             \\u001b[0mr\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mh\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mgetresponse\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   1323\\u001b[0m         \\u001b[1;32mexcept\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mURLError\\u001b[0m: <urlopen error [Errno 11001] getaddrinfo failed>\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"transform = transforms.Compose(\\n\",\n    \"    [transforms.ToTensor(),\\n\",\n    \"     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"\\n\",\n    \"trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\\n\",\n    \"                                        download=True, transform=transform)\\n\",\n    \"trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\\n\",\n    \"                                          shuffle=True, num_workers=2)\\n\",\n    \"\\n\",\n    \"testset = torchvision.datasets.CIFAR10(root='./data', train=False,\\n\",\n    \"                                       download=True, transform=transform)\\n\",\n    \"testloader = torch.utils.data.DataLoader(testset, batch_size=4,\\n\",\n    \"                                         shuffle=False, num_workers=2)\\n\",\n    \"\\n\",\n    \"classes = ('plane', 'car', 'bird', 'cat',\\n\",\n    \"           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\\n\",\n    \"#这个过程有点慢，会下载大约340mb图片数据。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们展示一些有趣的训练图像。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"# functions to show an image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def imshow(img):\\n\",\n    \"    img = img / 2 + 0.5     # unnormalize\\n\",\n    \"    npimg = img.numpy()\\n\",\n    \"    plt.imshow(np.transpose(npimg, (1, 2, 0)))\\n\",\n    \"    plt.show()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# get some random training images\\n\",\n    \"dataiter = iter(trainloader)\\n\",\n    \"images, labels = dataiter.next()\\n\",\n    \"\\n\",\n    \"# show images\\n\",\n    \"imshow(torchvision.utils.make_grid(images))\\n\",\n    \"# print labels\\n\",\n    \"print(' '.join('%5s' % classes[labels[j]] for j in range(4)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2. 定义一个卷积神经网络\\n\",\n    \"从之前的神经网络一节复制神经网络代码,并修改为接受3通道图像取代之前的接受单通道图像。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class Net(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(Net, self).__init__()\\n\",\n    \"        self.conv1 = nn.Conv2d(3, 6, 5)\\n\",\n    \"        self.pool = nn.MaxPool2d(2, 2)\\n\",\n    \"        self.conv2 = nn.Conv2d(6, 16, 5)\\n\",\n    \"        self.fc1 = nn.Linear(16 * 5 * 5, 120)\\n\",\n    \"        self.fc2 = nn.Linear(120, 84)\\n\",\n    \"        self.fc3 = nn.Linear(84, 10)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.pool(F.relu(self.conv1(x)))\\n\",\n    \"        x = self.pool(F.relu(self.conv2(x)))\\n\",\n    \"        x = x.view(-1, 16 * 5 * 5)\\n\",\n    \"        x = F.relu(self.fc1(x))\\n\",\n    \"        x = F.relu(self.fc2(x))\\n\",\n    \"        x = self.fc3(x)\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"net = Net()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3. 定义损失函数和优化器\\n\",\n    \"我们使用交叉熵作为损失函数,使用带动量的随机梯度下降。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch.optim as optim\\n\",\n    \"\\n\",\n    \"criterion = nn.CrossEntropyLoss()\\n\",\n    \"optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4. 训练网络\\n\",\n    \"这是开始有趣的时刻，我们只需在数据迭代器上循环,把数据输入给网络,并优化。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for epoch in range(2):  # loop over the dataset multiple times\\n\",\n    \"\\n\",\n    \"    running_loss = 0.0\\n\",\n    \"    for i, data in enumerate(trainloader, 0):\\n\",\n    \"        # get the inputs; data is a list of [inputs, labels]\\n\",\n    \"        inputs, labels = data\\n\",\n    \"\\n\",\n    \"        # zero the parameter gradients\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"\\n\",\n    \"        # forward + backward + optimize\\n\",\n    \"        outputs = net(inputs)\\n\",\n    \"        loss = criterion(outputs, labels)\\n\",\n    \"        loss.backward()\\n\",\n    \"        optimizer.step()\\n\",\n    \"\\n\",\n    \"        # print statistics\\n\",\n    \"        running_loss += loss.item()\\n\",\n    \"        if i % 2000 == 1999:    # print every 2000 mini-batches\\n\",\n    \"            print('[%d, %5d] loss: %.3f' %\\n\",\n    \"                  (epoch + 1, i + 1, running_loss / 2000))\\n\",\n    \"            running_loss = 0.0\\n\",\n    \"\\n\",\n    \"print('Finished Training')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"保存一下我们的训练模型\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"PATH = './cifar_net.pth'\\n\",\n    \"torch.save(net.state_dict(), PATH)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"点击[这里](https://pytorch.org/docs/stable/notes/serialization.html)查看关于保存模型的详细介绍\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 5. 在测试集上测试网络\\n\",\n    \"我们在整个训练集上训练了两次网络,但是我们还需要检查网络是否从数据集中学习到东西。\\n\",\n    \"\\n\",\n    \"我们通过预测神经网络输出的类别标签并根据实际情况进行检测，如果预测正确,我们把该样本添加到正确预测列表。\\n\",\n    \"\\n\",\n    \"第一步，显示测试集中的图片一遍熟悉图片内容。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dataiter = iter(testloader)\\n\",\n    \"images, labels = dataiter.next()\\n\",\n    \"\\n\",\n    \"# print images\\n\",\n    \"imshow(torchvision.utils.make_grid(images))\\n\",\n    \"print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"接下来，让我们重新加载我们保存的模型(注意:保存和重新加载模型在这里不是必要的，我们只是为了说明如何这样做)：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"net = Net()\\n\",\n    \"net.load_state_dict(torch.load(PATH))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在我们来看看神经网络认为以上图片是什么?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"outputs = net(images)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"输出是10个标签的概率。一个类别的概率越大,神经网络越认为他是这个类别。所以让我们得到最高概率的标签。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"_, predicted = torch.max(outputs, 1)\\n\",\n    \"\\n\",\n    \"print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\\n\",\n    \"                              for j in range(4)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这结果看起来非常的好。\\n\",\n    \"\\n\",\n    \"接下来让我们看看网络在整个测试集上的结果如何。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"correct = 0\\n\",\n    \"total = 0\\n\",\n    \"with torch.no_grad():\\n\",\n    \"    for data in testloader:\\n\",\n    \"        images, labels = data\\n\",\n    \"        outputs = net(images)\\n\",\n    \"        _, predicted = torch.max(outputs.data, 1)\\n\",\n    \"        total += labels.size(0)\\n\",\n    \"        correct += (predicted == labels).sum().item()\\n\",\n    \"\\n\",\n    \"print('Accuracy of the network on the 10000 test images: %d %%' % (\\n\",\n    \"    100 * correct / total))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"结果看起来好于偶然，偶然的正确率为10%,似乎网络学习到了一些东西。\\n\",\n    \"\\n\",\n    \"那在什么类上预测较好，什么类预测结果不好呢？\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class_correct = list(0. for i in range(10))\\n\",\n    \"class_total = list(0. for i in range(10))\\n\",\n    \"with torch.no_grad():\\n\",\n    \"    for data in testloader:\\n\",\n    \"        images, labels = data\\n\",\n    \"        outputs = net(images)\\n\",\n    \"        _, predicted = torch.max(outputs, 1)\\n\",\n    \"        c = (predicted == labels).squeeze()\\n\",\n    \"        for i in range(4):\\n\",\n    \"            label = labels[i]\\n\",\n    \"            class_correct[label] += c[i].item()\\n\",\n    \"            class_total[label] += 1\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for i in range(10):\\n\",\n    \"    print('Accuracy of %5s : %2d %%' % (\\n\",\n    \"        classes[i], 100 * class_correct[i] / class_total[i]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"接下来干什么?\\n\",\n    \"\\n\",\n    \"我们如何在GPU上运行神经网络呢?\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 在GPU上训练\\n\",\n    \"你是如何把一个Tensor转换GPU上,你就如何把一个神经网络移动到GPU上训练。这个操作会递归遍历有所模块,并将其参数和缓冲区转换为CUDA张量。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"# Assume that we are on a CUDA machine, then this should print a CUDA device:\\n\",\n    \"#假设我们有一台CUDA的机器，这个操作将显示CUDA设备。\\n\",\n    \"print(device)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"接下来假设我们有一台CUDA的机器，然后这些方法将递归遍历所有模块并将其参数和缓冲区转换为CUDA张量：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"net.to(device)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"请记住，你也必须在每一步中把你的输入和目标值转换到GPU上:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"inputs, labels = inputs.to(device), labels.to(device)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"为什么我们没注意到GPU的速度提升很多?那是因为网络非常的小。\\n\",\n    \"\\n\",\n    \"#### 实践:\\n\",\n    \"尝试增加你的网络的宽度(第一个`nn.Conv2d`的第2个参数, 第二个`nn.Conv2d`的第一个参数,他们需要是相同的数字),看看你得到了什么样的加速。\\n\",\n    \"\\n\",\n    \"#### 实现的目标:\\n\",\n    \"* 深入了解了PyTorch的张量库和神经网络\\n\",\n    \"* 训练了一个小网络来分类图片\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 在多GPU上训练\\n\",\n    \"如果你希望使用所有GPU来更大的加快速度,请查看选读:[数据并行](https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 接下来做什么?\\n\",\n    \"* 训练神经网络玩电子游戏\\n\",\n    \"* 在ImageNet上训练最好的ResNet\\n\",\n    \"* 使用对抗生成网络来训练一个人脸生成器\\n\",\n    \"* 使用LSTM网络训练一个字符级的语言模型\\n\",\n    \"* 更多示例\\n\",\n    \"* 更多教程\\n\",\n    \"* 在论坛上讨论PyTorch\\n\",\n    \"* 在Slack上与其他用户聊天\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "8.deep-learning/PyTorch_beginner/README.md",
    "content": "# 60分钟入门深度学习工具-PyTorch(目录)\n\n\n\n**作者**：Soumith Chintala\n\n\n原文翻译自：[Deep Learning with PyTorch: A 60 Minute Blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html)\n\n翻译：[林不清](https://www.zhihu.com/people/lu-guo-92-42-88)\n\n整理：机器学习初学者公众号 ![gongzhong](images/gongzhong.jpg)\n\ngithub：<https://github.com/fengdu78>\n\n代码全部测试通过。\n\n配置环境：PyTorch 1.5+，Python 3.7+\n\n\n\n\n## 本教程的目标：\n\n- 在高层次上理解PyTorch的张量(Tensor)库和神经网络\n- 训练一个小型神经网络对图像进行分类\n- 本教程假设您对numpy有基本的了解\n\n**注意**： 务必确认您已经安装了 torch 和 torchvision 两个包。\n\n\n\n## 目录\n\n\n\n* 1.[Tensors](1.Tensors.ipynb)\n* 2.[AUTOGRAD](2.Autograd自动求导.ipynb)\n* 3.[神经网络](3.神经网络.ipynb)\n* 4.[训练一个分类器](4.训练一个分类器.ipynb)\n\n  \n\n"
  },
  {
    "path": "8.deep-learning/README.md",
    "content": "## 一、Coursera深度学习教程中文笔记\n\n**github地址**：https://github.com/fengdu78/deeplearning_ai_books\n\n包含笔记、视频等资源。\n\n**课程概述**\n\nhttps://mooc.study.163.com/university/deeplearning_ai#/c\n\n这些课程专为已有一定基础（基本的编程知识，熟悉**Python**、对机器学习有基本了解），想要尝试进入人工智能领域的计算机专业人士准备。介绍显示：“深度学习是科技业最热门的技能之一，本课程将帮你掌握深度学习。”\n\n在这5堂课中，学生将可以学习到深度学习的基础，学会构建神经网络，并用在包括吴恩达本人在内的多位业界顶尖专家指导下创建自己的机器学习项目。**Deep Learning Specialization**对卷积神经网络 (**CNN**)、递归神经网络 (**RNN**)、长短期记忆 (**LSTM**) 等深度学习常用的网络结构、工具和知识都有涉及。\n\n课程中也会有很多实操项目，帮助学生更好地应用自己学到的深度学习技术，解决真实世界问题。这些项目将涵盖医疗、自动驾驶、和自然语言处理等时髦领域，以及音乐生成等等。**Coursera**上有一些特定方向和知识的资料，但一直没有比较全面、深入浅出的深度学习课程——《深度学习专业》的推出补上了这一空缺。\n\n课程的语言是**Python**，使用的框架是**Google**开源的**TensorFlow**。最吸引人之处在于，课程导师就是吴恩达本人，两名助教均来自斯坦福计算机系。完成课程所需时间根据不同的学习进度，大约需要3-4个月左右。学生结课后，**Coursera**将授予他们**Deep Learning Specialization**结业证书。\n\n“我们将帮助你掌握深度学习，理解如何应用深度学习，在人工智能业界开启你的职业生涯。”吴恩达在课程页面中提到。\n\n\n\n**有同学提供了一个离线视频的下载**：链接：https://pan.baidu.com/s/1ciq3qHo0lgoD3MLRwfeqnA 密码：0kim\n\n\n\n## 二、《python深度学习》的代码翻译版\n\n**github地址**：https://github.com/fengdu78/machine_learning_beginner/tree/master/deep-learning-with-python-notebooks\n\n《**python深度学习**》由Keras之父、现任Google人工智能研究员的弗朗索瓦•肖莱（François Chollet）执笔，详尽介绍了用Python和Keras进行深度学习的探索实践，包括计算机视觉、自然语言处理、产生式模型等应用。书中包含30多个代码示例，步骤讲解详细透彻。\n\n作者在github公布了代码，代码几乎囊括了本书所有知识点。在学习完本书后，读者将具备搭建自己的深度学习环境、建立图像识别模型、生成图像和文字等能力。但是有一个小小的遗憾：代码的解释和注释是全英文的，即使英文水平较好的朋友看起来也很吃力。\n\n**本站认为，这本书和代码是初学者入门深度学习及Keras最好的工具。**\n\n**本站对全部代码做了中文解释和注释，并下载了代码所需要的一些数据集（尤其是“猫狗大战”数据集），并对其中一些图像进行了本地化，代码全部测试通过。（请按照文件顺序运行，代码前后有部分关联）。**\n\n**以下代码包含了全书80%左右的知识点**\n\n**代码目录：**\n\n- 2.1: A first look at a neural network（ 初识神经网络）\n- 3.5: Classifying movie reviews（电影评论分类：二分类问题）\n- 3.6: Classifying newswires（新闻分类：多分类问题 ）\n- 3.7: Predicting house prices（预测房价：回归问题）\n\n- 4.4: Underfitting and overfitting（ 过拟合与欠拟合）\n\n- 5.1: Introduction to convnets（卷积神经网络简介）\n\n- 5.2: Using convnets with small datasets（在小型数据集上从头开始训练一个卷积\n\n- 5.3: Using a pre-trained convnet（使用预训练的卷积神经网络）\n\n- 5.4: Visualizing what convnets learn（卷积神经网络的可视化）\n\n- 6.1: One-hot encoding of words or characters（单词和字符的 one-hot 编码）\n\n- 6.1: Using word embeddings（使用词嵌入）\n- 6.2: Understanding RNNs（理解循环神经网络）\n\n- 6.3: Advanced usage of RNNs（循环神经网络的高级用法）\n\n- 6.4: Sequence processing with convnets（用卷积神经网络处理序列）\n\n- 8.1: Text generation with LSTM（使用 LSTM 生成文本）\n\n- 8.2: Deep dream（DeepDream）\n\n- 8.3: Neural style transfer（ 神经风格迁移）\n\n- 8.4: Generating images with VAEs（用变分自编码器生成图像）\n\n- 8.5: Introduction to GANs（生成式对抗网络简介）\n\n**作者的github：**\nhttps://github.com/fchollet/deep-learning-with-python-notebooks\n\n**《python深度学习》的购买地址：**\nhttps://item.jd.com/12409581.html\n\n**data目录的数据下载：**\n https://pan.baidu.com/s/1Ni8apGSR56Dyf7nOKBVPNQ 提取码：sdgi\n本书运行代码要下载大量的数据，如kaggle“猫狗大战”等数据，我们已经下载了所有本书需要的数据放在上面那个下载链接了。\n\n\n\n## 三、 强烈推荐的TensorFlow、Pytorch和Keras的样例资源\n\n**整合过的github地址：**https://github.com/fengdu78/machine_learning_beginner/tree/master/deep-learning-with-tensorflow-keras-pytorch\n\n**TensorFlow**、**Keras**和**Pytorch**是目前深度学习的主要框架，也是入门深度学习必须掌握的三大框架，但是官方文档相对内容较多，初学者往往无从下手。本人从github里搜到三个非常不错的学习资源，并对资源目录进行翻译，强烈建议初学者下载学习，这些资源包含了大量的代码示例（含数据集），个人认为，只要把以上资源运行一次，不懂的地方查官方文档，很快就能理解和运用这三大框架。\n\n### 1、TensorFlow\n\n**资源地址：**\n\nhttps://github.com/aymericdamien/TensorFlow-Examples\n\n**资源介绍：**\n\n本资源旨在通过示例轻松深入了解TensorFlow。\n为了便于阅读，它包括notebook和带注释的源代码。\n\n它适合想要找到关于TensorFlow的清晰简洁示例的初学者。\n除了传统的“原始”TensorFlow实现，您还可以找到最新的TensorFlow\nAPI实践（例如layers,estimator,dataset, ......）。\n\n最后更新（07/25/2018）：添加新示例（GBDT，Word2Vec）和 TF1.9兼容性！ （TF v1.9\n+推荐）。\n\n**配置环境：**\n\npython 3.6以上，TensorFlow 1.8+\n\n**资源目录：**\n\n0  - 先决条件\n\n- 机器学习简介\n- MNIST数据集简介\n\n1  - 简介\n\n- Hello\n  World(包含notebook和py源代码)。非常简单的例子，学习如何使用TensorFlow打印“hello world”。\n- 基本操作(包含notebook和py源代码)。一个涵盖TensorFlow基本操作的简单示例。\n- TensorFlow Eager API基础知识(包含notebook和py源代码)。开始使用TensorFlow的Eager API。\n\n2  - 基础模型\n\n- 线性回归(包含notebook和py源代码)。使用TensorFlow实现线性回归。\n- 线性回归（eager api）(包含notebook和py源代码)。使用TensorFlow的Eager\n  API实现线性回归。\n- Logistic回归(包含notebook和py源代码)。使用TensorFlow实现Logistic回归。\n- Logistic回归（eager api）(包含notebook和py源代码)。使用TensorFlow的Eager API实现Logistic回归。\n- 最近邻(包含notebook和py源代码)。使用TensorFlow实现最近邻算法。\n- K-Means(包含notebook和py源代码)。使用TensorFlow构建K-Means分类器。\n- 随机森林(包含notebook和py源代码)。使用TensorFlow构建随机森林分类器。\n- Gradient Boosted Decision Tree（GBDT）(包含notebook和py源代码)。使用TensorFlow构建梯度提升决策树（GBDT）。\n- Word2Vec（词嵌入）(包含notebook和py源代码)。使用TensorFlow从Wikipedia数据构建词嵌入模型（Word2Vec）。\n\n3  - 神经网络\n\n- **监督学习部分**\n- 简单神经网络(包含notebook和py源代码)。构建一个简单的神经网络（如多层感知器）来对MNIST数字数据集进行分类。\n  Raw TensorFlow实现。\n- 简单神经网络（tf.layers / estimator api）(包含notebook和py源代码)。使用TensorFlow'layers'和'estimator'API构建一个简单的神经网络（如：Multi-layer Perceptron）来对MNIST数字数据集进行分类。\n- 简单神经网络（Eager API）(包含notebook和py源代码)。使用TensorFlow Eager API构建一个简单的神经网络（如多层感知器）来对MNIST数字数据集进行分类。\n- 卷积神经网络(包含notebook和py源代码)。构建卷积神经网络以对MNIST数字数据集进行分类。\n  Raw TensorFlow实现。\n- 卷积神经网络（tf.layers / estimator api）(包含notebook和py源代码)。使用TensorFlow'layers'和'estimator'API构建卷积神经网络，对MNIST数字数据集进行分类。\n- 递归神经网络（LSTM）(包含notebook和py源代码)。构建递归神经网络（LSTM）以对MNIST数字数据集进行分类。\n- 双向LSTM(包含notebook和py源代码)。构建双向递归神经网络（LSTM）以对MNIST数字数据集进行分类。\n- 动态LSTM(包含notebook和py源代码)。构建一个递归神经网络（LSTM），执行动态计算以对不同长度的序列进行分类。\n- **无监督**\n- 自动编码器(包含notebook和py源代码)。构建自动编码器以将图像编码为较低维度并重新构建它。\n- 变分自动编码器（(包含notebook和py源代码)。构建变分自动编码器（VAE），对噪声进行编码和生成图像。\n- GAN（Generative Adversarial\n  Networks）(包含notebook和py源代码)。构建生成对抗网络（GAN）以从噪声生成图像。\n- DCGAN（Deep Convolutional Generative Adversarial\n  Networks）(包含notebook和py源代码)。构建深度卷积生成对抗网络（DCGAN）以从噪声生成图像。\n\n4  - 工具\n\n- 保存和还原模型(包含notebook和py源代码)。使用TensorFlow保存和还原模型。\n- Tensorboard  -\n  图形和损失可视化(包含notebook和py源代码)。使用Tensorboard可视化计算图并绘制损失。\n- Tensorboard  -\n  高级可视化(包含notebook和py源代码)。深入了解Tensorboard;可视化变量，梯度等......\n\n5  - 数据管理\n\n- 构建图像数据集(包含notebook和py源代码)。使用TensorFlow数据队列，从图像文件夹或数据集文件构建您自己的图像数据集。\n- TensorFlow数据集API(包含notebook和py源代码)。引入TensorFlow数据集API以优化输入数据管道。\n\n6  - 多GPU\n\n- 多GPU的基本操作(包含notebook和py源代码)。在TensorFlow中引入多GPU的简单示例。\n- 在多GPU上训练神经网络(包含notebook和py源代码)。一个清晰简单的TensorFlow实现，用于在多个GPU上训练卷积神经网络。\n\n数据集\n\n- 一些示例需要MNIST数据集进行训练和测试。官方网站：http://yann.lecun.com/exdb/mnist/\n\n###2、Keras\n\n**资源地址：**\n\nhttps://github.com/erhwenkuo/deep-learning-with-keras-notebooks\n\n**资源介绍：**\n\n这个github的repository主要是**ErhWen Kuo**在学习Keras的一些记录及练习。希望在学习过程中发现到一些好的信息与示例也可以对想要学习使用Keras来解决问题的同学带来帮助。这些notebooks主要是使用Python 3.6与Keras 2.1.1版本跑在一台配置Nivida 1080Ti的Windows 10的机台所产生的结果，但有些部份会参杂一些Tensorflow与其它的函式库的介绍。\n\n**配置环境：**\n\npython 3.6以上，Keras 2.1.1\n\n**资源目录：**\n\n0.图象数据集/工具介绍\n\n- 0.0: COCO API解说与简单示例\n- 0.1:土炮自制扑克牌图象数据集\n- 0.2:使用Pillow来进行图像处理\n\n1.Keras API示例\n\n- 1.0:使用图像增强来进行深度学习\n- 1.1:如何使用Keras函数式API进行深度学习\n- 1.2:从零开始构建VGG网络来学习Keras\n- 1.3:使用预训练的模型来分类照片中的物体\n- 1.4:使用图像增强来训练小数据集\n- 1.5:使用预先训练的卷积网络模型\n- 1.6:卷积网络模型学习到什么的可视化\n- 1.7:构建自动编码器（Autoencoder）\n- 1.8:序列到序列（Seq-to-Seq）学习介绍\n- 1.9: One-hot编码工具程序介绍\n- 1.10:循环神经网络（RNN）介绍\n- 1.11: LSTM的返回序列和返回状态之间的区别\n- 1.12:用LSTM来学习英文字母表顺序\n\n2.图像分类（Image Classification）\n\n- 2.0: Julia（Chars74K）字母图像分类\n- 2.1:交通标志图像分类\n- 2.2:辛普森卡通图像角色分类\n- 2.3:时尚服饰图像分类\n- 2.4:人脸关键点辨识\n- 2.5: Captcha验证码分类\n- 2.6: Mnist手写图像分类（MLP）\n- 2.7: Mnist手写图像分类（CNN）\n\n3.目标检测（Object Recognition）\n\n- 3.0: YOLO目标检测算法概念与介绍\n- 3.1: YOLOv2目标检测示例\n- 3.2:浣熊（Racoon）检测-YOLOv2模型训练与调整\n- 3.3:浣熊（Racoon）检测-YOLOv2模型的使用\n- 3.4:袋鼠（Kangaroo）检测-YOLOv2模型训练与调整\n- 3.5:双手（Hands）检测-YOLOv2模型训练与调整\n- 3.6:辛普森卡通图象角色（Simpson）检测-YOLOv2模型训练与调整\n- 3.7: MS COCO图象检测-YOLOv2模型训练与调整\n\n4.物体分割（Object Segmentation）\n\n5.关键点检测（Keypoint Detection）\n\n6.图象标题（Image Caption）\n\n7.人脸检测识别（Face Detection/Recognition）\n\n- 7.0:人脸检测- OpenCV（Haar特征分类器）\n- 7.1:人脸检测- MTCNN（Multi-task Cascaded Convolutional Networks）\n- 7.2:人脸识别-脸部检测、对齐&裁剪\n- 7.3:人脸识别-人脸部特征提取&人脸分类器\n- 7.4:人脸识别-转换、对齐、裁剪、特征提取与比对\n- 7.5:脸部关键点检测（dlib）\n- 7.6:头部姿态（Head pose）估计（dlib）\n\n8.自然语言处理（Natural Language Processing）\n\n- 8.0:词嵌入（word embeddings）介绍\n- 8.1:使用结巴（jieba）进行中文分词\n- 8.2: Word2vec词嵌入（word embeddings）的基本概念\n- 8.3:使用结巴（jieba）进行歌词分析\n- 8.4:使用gensim训练中文词向量（word2vec）\n\n### 3、Pytorch\n\n**资源地址：**\n\nhttps://github.com/yunjey/pytorch-tutorial\n\n**资源介绍：**\n\n这个资源为深度学习研究人员提供了学习PyTorch的教程代码大多数模型都使用少于30行代码实现。\n在开始本教程之前，建议先看完Pytorch官方教程。\n\n**配置环境：**\n\npython 2.7或者3.5以上，pytorch 0.4\n\n**资源目录：**\n\n1.基础知识\n\n- PyTorch基础知识\n- 线性回归\n- Logistic回归\n- 前馈神经网络\n\n2.中级\n\n- 卷积神经网络\n- 深度残差网络\n- 递归神经网络\n- 双向递归神经网络\n- 语言模型（RNN-LM）\n\n3.高级\n\n- 生成性对抗网络\n- 变分自动编码器\n- 神经风格转移\n- 图像字幕（CNN-RNN）\n\n4.工具\n\n- PyTorch中的TensorBoard\n\n 总结\n\n### 4. 60分钟入门深度学习工具-PyTorch\n\n**作者**：Soumith Chintala\n\n原文翻译自：<https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>\n\n\n\n**github地址：**https://github.com/fengdu78/Data-Science-Notes/tree/master/8.deep-learning/PyTorch_beginner\n\n#### 本教程的目标：\n\n- 在高层次上理解PyTorch的张量(Tensor)库和神经网络\n- 训练一个小型神经网络对图像进行分类\n- 本教程假设您对numpy有基本的了解\n\n**注意**： 务必确认您已经安装了 torch 和 torchvision 两个包。\n\n\n\n#### 目录\n\n\n\n- 1.Pytorch是什么？(1.PyTorch是什么？.ipynb)\n\n- 2.AUTOGRAD(2.AUTOGRAD.ipynb)\n\n- 3.神经网络(3.神经网络.ipynb)\n\n- 4.训练一个分类器(4.训练一个分类器.ipynb)\n\n- 5.数据并行(5.数据并行.ipynb)\n\n### 5.PaddlePaddle（飞桨）-入门及资源\n\ngithub地址：https://github.com/fengdu78/PaddlePaddle-Tutorial   （更新中）\n\n  PaddlePaddle（飞桨）是一个简单易用、高效灵活、可扩展的深度学习平台。\n\n  #### PaddlePaddle最新版本: [Fluid 1.5.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.5)\n\n  1. Paddle官网：https://www.paddlepaddle.org.cn\n  2. Paddle使用指南：https://www.paddlepaddle.org.cn/documentation/docs/zh/1.5/user_guides/index_cn.html?from=paddlenav\n  3. Paddle的github：https://github.com/PaddlePaddle\n  4. 开放模型库：https://github.com/PaddlePaddle/models \n  5. 教程：https://github.com/PaddlePaddle/book\n  6. PaddleHub：https://github.com/PaddlePaddle/PaddleHub\n\n  \n\n### 6.图解word2vec(翻译)\n**github地址：**https://github.com/fengdu78/machine_learning_beginner/tree/master/word2vec\n\n### 7.深度学习阅读路线图\n\n本文是深度学习论文的阅读路线图！\n\n该路线图是根据以下四个准则构建的：\n\n- 从轮廓到细节\n- 从旧到最新\n- 从通用到特定领域\n- 专注于最新技术\n\n您会发现许多非常新的论文，但确实值得阅读。\n\n此外，作者将继续在此路线图中添加论文。\n\ngithub地址：\n\nhttps://github.com/fengdu78/Data-Science-Notes/tree/master/8.deep-learning/Deep-Learning-Papers-Reading-Roadmap\n\n"
  },
  {
    "path": "8.deep-learning/word2vec/README.md",
    "content": "\n# 图解Word2vec\n\n本文作者：[jalammar](https://twitter.com/jalammar),[作者主页](https://jalammar.github.io/)\n\n翻译：[黄海广](https://github.com/fengdu78)\n\n我发现嵌入的概念是机器学习中最迷人的想法之一。如果您曾经使用Siri，Google智能助理，Alexa，谷歌翻译，甚至智能手机键盘进行下一词预测，那么您很有可能从这个已经成为自然语言处理模型核心的想法中受益。在过去的几十年中，使用嵌入技术进行神经模型已有相当大的发展（最近的发展包括BERT和GPT2 等尖端模型的语境化嵌入）。\n\n自2013年以来，word2vec一直是一种有效创建单词嵌入的方法。除了词嵌入字的方法之外，它的一些概念已经被证明可以在非语言任务中有效地创建推荐引擎和理解顺序数据。比如Airbnb，阿里巴巴，Spotify和Anghami这样的公司都从NLP世界中创造出这一优秀的工具并将其用于生产中，从而为新型推荐引擎提供支持。\n\n我们将讨论嵌入的概念，以及使用word2vec生成嵌入的机制。\n\n让我们从一个例子开始，了解使用向量来表示事物。\n\n您是否知道五个数字（向量）的列表可以代表您的个性？\n\n## 个性嵌入：你的个性怎么样？\n\n使用0到100的范围表示你的个性（其中0是最内向的，100是最外向的）。\n\n五大人格特质测试，这些测试会问你一个问题列表，然后在很多方面给你打分，内向/外向就是其中之一。\n![](images/1.png)\n\n<center>图:测试结果示例。它可以真正告诉你很多关于你自己的事情，并且在学术、个人和职业成功方面都具有预测能力。</center>\n假设我的测试得分为38/100。我们可以用这种方式绘制：\n![](images/2.png)\n\n\n让我们将范围切换到从-1到1：\n![](images/3.png)\n\n了解一个人，一个维度的信息不够，所以让我们添加另一个维度 - 测试中另一个特征的得分。\n![](images/4.png)\n\n\n你可能不知道每个维度代表什么，但仍然可以从一个人的个性的向量表示中获得了很多有用的信息。\n\n我们现在可以说这个向量部分代表了我的个性。当你想要将另外两个人与我进行比较时，这种表示的有用性就出现了。在下图中，两个人中哪一个更像我？\n![](images/5.png)\n\n处理向量时，计算相似度得分的常用方法是余弦相似度：\n![](images/6.png)\n\n一号人物与我的余弦相似度得分高，所以我们的性格比较相似。\n\n然而，两个方面还不足以捕获有关不同人群的足够信息。几十年的心理学研究已经研究了五个主要特征（以及大量的子特征）。所以我们在比较中使用所有五个维度：\n![](images/7.png)\n\n我们没法在二维上绘制出来五个维度，这是机器学习中的常见挑战，我们经常需要在更高维度的空间中思考。但好处是余弦相似度仍然有效。它适用于任意数量的维度：\n![](images/8.png)\n\n嵌入的两个中心思想：\n\n- 我们可以将人（事物）表示为数字的向量。\n- 我们可以很容易地计算出相似的向量彼此之间的关系。\n![](images/9.png)\n\n## 词嵌入\n\n我们导入在维基百科上训练的GloVe向量\n\n\n```python\nimport gensim\nimport gensim.downloader as api\nmodel = api.load('glove-wiki-gigaword-50')\n```\n\n\n\n单词“king”的词嵌入表示：\n\n\n```python\nmodel[\"king\"]\n```\n\n\n\n\n    array([ 0.50451 ,  0.68607 , -0.59517 , -0.022801,  0.60046 , -0.13498 ,\n           -0.08813 ,  0.47377 , -0.61798 , -0.31012 , -0.076666,  1.493   ,\n           -0.034189, -0.98173 ,  0.68229 ,  0.81722 , -0.51874 , -0.31503 ,\n           -0.55809 ,  0.66421 ,  0.1961  , -0.13495 , -0.11476 , -0.30344 ,\n            0.41177 , -2.223   , -1.0756  , -1.0783  , -0.34354 ,  0.33505 ,\n            1.9927  , -0.04234 , -0.64319 ,  0.71125 ,  0.49159 ,  0.16754 ,\n            0.34344 , -0.25663 , -0.8523  ,  0.1661  ,  0.40102 ,  1.1685  ,\n           -1.0137  , -0.21585 , -0.15155 ,  0.78321 , -0.91241 , -1.6106  ,\n           -0.64426 , -0.51042 ], dtype=float32)\n\n\n\n查看“king”最相似的单词\n\n\n```python\nmodel.most_similar(\"king\")\n```\n\n\n\n\n    [('prince', 0.8236179351806641),\n     ('queen', 0.7839042544364929),\n     ('ii', 0.7746230363845825),\n     ('emperor', 0.7736247181892395),\n     ('son', 0.766719400882721),\n     ('uncle', 0.7627150416374207),\n     ('kingdom', 0.7542160749435425),\n     ('throne', 0.7539913654327393),\n     ('brother', 0.7492411136627197),\n     ('ruler', 0.7434253096580505)]\n\n\n\n这是一个包含50个数字的列表，我们无法说清楚里面的值代表什么。我们把所有这些数字放在一行，以便我们可以比较其他单词向量。让我们根据它们的值对单元格进行颜色编码（如果它们接近2则为红色，如果它们接近0则为白色，如果它们接近-2则为蓝色）\n\n\n```python\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nplt.figure(figsize=(15, 1))\nsns.heatmap([model[\"king\"]],\n            xticklabels=False,\n            yticklabels=False,\n            cbar=False,\n            vmin=-2,\n            vmax=2,\n            linewidths=0.7)\nplt.show()\n```\n\n\n![png](images/output_14_0.png)\n\n\n我们将忽略数字并仅查看颜色以指示单元格的值，我们将“King”与其他词语进行对比：\n\n\n```python\nplt.figure(figsize=(15, 4))\nsns.heatmap([\n    model[\"king\"],\n    model[\"man\"],\n    model[\"woman\"],\n    model[\"king\"] - model[\"man\"] + model[\"woman\"],\n    model[\"queen\"],\n],\n            cbar=True,\n            xticklabels=False,\n            yticklabels=False,\n            linewidths=1)\nplt.show()\n```\n\n\n![png](images/output_16_0.png)\n\n\n看看“man”和“woman”是如何彼此更相似的，他们中的任何一个都是“king”？这告诉你一些事情。这些向量表示捕获了这些单词的信息/含义/关联。\n\n这是另一个示例列表（通过垂直扫描列来查找具有相似颜色的列）：\n![](images/10.png)\n\n有几点需要指出：\n\n- 所有这些不同的单词都有一个直的红色列。它们在这个维度上是相似的（我们不知道每个维度代码是什么）\n- 你可以看到“woman”和“girl”在很多地方是如何相似的。与“man”和“boy”一样\n- “boy”和“girl”也有彼此相似的地方，但与“woman”或“man”不同。这些是否可以编写一个模糊的青年概念？可能。\n- 除了最后一个字之外的所有字都代表着人。我添加了一个对象“water”来显示类别之间的差异。例如，您可以看到蓝色列一直向下并在嵌入“water”之前停止。\n- 有一个明显的地方，“king”和“queen”彼此相似，并与所有其他人不同。\n\n## 类比\n\n我们可以添加和减去单词嵌入并获得有趣的结果，最有名的例子是公式： “king” - “man” + “woman”:\n\n\n```python\nmodel.most_similar(positive=[\"king\",\"woman\"],negative=[\"man\"])\n```\n\n\n\n\n    [('queen', 0.8523603677749634),\n     ('throne', 0.7664334177970886),\n     ('prince', 0.759214460849762),\n     ('daughter', 0.7473883032798767),\n     ('elizabeth', 0.7460220456123352),\n     ('princess', 0.7424569725990295),\n     ('kingdom', 0.7337411642074585),\n     ('monarch', 0.7214490175247192),\n     ('eldest', 0.7184861898422241),\n     ('widow', 0.7099430561065674)]\n\n\n\n我们可以像以前一样想象这个类比：\n![](images/11.png)\n\n## 语言建模\n\n\n如果想要给出NLP应用程序的示例，最好的示例之一将是智能手机键盘的下一个字（词）预测功能。这是数十亿人每天使用数百次的功能。\n![](images/12.png)\n\n下一个字（词）预测是一项可以通过语言模型解决的任务。语言模型可以采用单词列表（比方说两个单词），并尝试预测它们之后的单词。\n\n在上面的屏幕截图中，我们可以将模型视为接受这两个绿色单词（thou shalt）并返回建议列表（“not”是具有最高概率的那个字）的模型：\n ![](images/13.png)\n\n\n我们可以把模型想象成这个黑盒子：\n\n ![](images/14.png)\n\n\n但实际上，该模型不会只输出一个单词。它实际上输出了它所知道的所有单词的概率分数（模型的“词汇表”，其范围可以从几千到一百多万个字（词））。然后应用程序必须找到分数最高的单词，并将其呈现给用户。\n\n ![](images/15.png)\n图：神经语言模型的输出是模型知道的所有单词的概率分数。我们在这里将概率称为百分比，比如概率40％将在输出向量中表示为0.4。\n\n经过训练，早期的神经语言模型（[Bengio 2003](http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf)）将分三步计算预测：\n\n   ![](images/16.png)\n\n\n在讨论嵌入时，第一步对我们来说最相关。训练过程的结果之一是这个矩阵包含我们词汇表中每个单词的嵌入。在预测时间内，我们只查找输入字的嵌入，并使用它们来计算预测：\n   ![](images/17.png)\n\n现在让我们转到训练过程，以了解嵌入矩阵是如何工作的。\n\n## 语言模型的训练\n\n与大多数其他机器学习模型相比，语言模型具有巨大优势。即：我们所有的书籍，文章，维基百科内容和其他形式的大量文本数据可以作为训练数据。与此相比，许多其他机器学习模型需要手动设计特征和专门收集的数据。\n\n\n单词通过我们查看它们往往会出现在旁边的其他单词来嵌入。其机制就是这样\n1.\t我们获得了大量文本数据（例如，所有维基百科文章）。然后\n2.\t我们有一个窗口（比如说三个单词），我们会对所有文本进行滑动。\n3.\t滑动窗口为我们的模型生成训练样本\n\n![](images/wikipedia-sliding-window.png)\n\n当这个窗口滑动文本时，我们（虚拟地）生成一个用于训练模型的数据集。为了准确看看它是如何完成的，让我们看看滑动窗口如何处理这个短语：\n\n当我们开始时，窗口在句子的前三个单词上：\n![](images/lm-sliding-window.png)\n\n\n我们将前两个单词作为特征，将第三个单词作为标签：\n\n![](images/lm-sliding-window-2.png)\n\n\n我们现在已经在数据集中生成了第一个样本，我们稍后可以使用它来训练语言模型。\n\n然后我们将窗口滑动到下一个位置并创建第二个样本：\n![](images/lm-sliding-window-3.png)\n\n现在生成第二个示例。\n\n很快我们就会有一个更大的数据集，在不同的单词对之后，这些数据集会出现：\n![](images/lm-sliding-window-4.png)\n\n\n在实践中，模型往往在我们滑动窗口时进行训练。但我发现逻辑上将“数据集生成”阶段与训练阶段分开是更清楚的。除了基于神经网络的语言建模方法之外，一种称为N-gram的技术通常用于训练语言模型。\n\n要了解这种从N-gram到神经模型的转换如何反映现实世界的产品，建议看这篇[2015年博客文章](https://blog.swiftkey.com/neural-networks-a-meaningful-leap-for-mobile-typing/)，介绍他们的神经语言模型并将其与之前的N-gram模型进行比较。\n\n## 两边看\n\n给了你句子前面的内容，进行填空：\n ![](images/jay_was_hit_by_a_.png) \n我在这里给你的背景是空格之前的五个字（以及之前提到的“bus”）。我相信大多数人都会猜到空格里的这个词会是“bus”。但是，如果我再给你一条信息:空格之后的一句话，那会改变你的答案吗？\n ![](images/jay_was_hit_by_a_bus.png)  \n这完全改变了应该留在空格中的内容。“red”这个词现在最可能填到空格中。我们从中学到的是特定词语之前和之后的词语都具有信息价值。事实证明，考虑两个方向（我们猜测的单词左侧和右侧的单词）会让词嵌入做得更好。\n\n让我们看看我们如何调整我们训练模型的方式来解决这个问题。\n\n## Skipgram\n\n我们不仅可以查看在目标词之前的两个单词，还可以查看其后的两个单词。\n ![](images/continuous-bag-of-words-example.png) \n如果我们这样做，我们实际构建和训练模型的数据集将如下所示：\n![](images/continuous-bag-of-words-dataset.png)\n\n这被称为连续词袋结构，并在word2vec论文 [one of the word2vec papers ](https://arxiv.org/pdf/1301.3781.pdf)中进行过描述。\n\n另一种结构与连续词袋结构略有不同，但也可以也显示出良好结果。这个结构试图使用当前词来猜测相邻词，而不是根据其上下文（它之前和之后的词）猜测一个词。我们可以想到它在训练文本上滑动的窗口如下所示：\n ![](images/skipgram-sliding-window.png) \n<center>图：绿色框中的字将是输入字，每个粉色框将是可能的输出。</center>\n粉色框具有不同的阴影，因为此滑动窗口实际上在我们的训练数据集中创建了四个单独的样本：\n![](images/skipgram-sliding-window-samples.png)\n\n\n此方法称为skipgram架构。我们可以执行以下操作将滑动窗口可视化：\n\n ![](images/skipgram-sliding-window-1.png)\n\n这会将这四个样本添加到我们的训练数据集中：\n![](images/skipgram-sliding-window-2.png)\n\n然后我们将窗口滑动到下一个位置：\n![](images/skipgram-sliding-window-3.png)\n\n\n这将产生我们的下四个样本：\n![](images/skipgram-sliding-window-4.png)\n\n接着滑动几个位置之后，我们有更多的样本：\n![](images/skipgram-sliding-window-5.png)\n\n\n## 重新审视训练过程\n\n现在我们已经从现有的运行文本中提取了我们的skipgram训练数据集，让我们看看我们如何使用它来训练预测相邻单词的基本神经语言模型。\n![](images/skipgram-language-model-training.png)\n\n我们从数据集中的第一个样本开始。我们把特征提供给未经训练的模型，要求它预测一个合适的相邻单词。\n![](images/skipgram-language-model-training-2.png)\n\n该模型进行三个步骤并输出预测向量（概率分配给其词汇表中的每个单词）。由于该模型未经过训练，因此在此阶段的预测肯定是错误的。但那没关系。我们知道应该它将猜到哪个词：我们目前用于训练模型的行中的标签/输出单元格：\n![](images/skipgram-language-model-training-3.png)\n\n<center>图：“目标向量”的词（字）概率为1，其他词（字）的概率都是0。</center>\n我们减去两个向量，得到一个误差向量：\n\n ![](images/skipgram-language-model-training-4.png)\n\n现在可以使用此误差向量来更新模型，以便下次当“not”作为输入时，模型更有可能猜测“thou”。\n ![](images/skipgram-language-model-training-5.png)\n\n\n这就是训练的第一步。我们继续使用数据集中的下一个样本进行相同的处理，然后是下一个样本，直到我们覆盖了数据集中的所有样本。这就结束了一个epcho的训练。我们继续训练多个epcho，然后我们就有了训练好的模型，我们可以从中提取嵌入矩阵并将其用于任何其他应用。\n\n虽然这加深了我们对该过程的理解，但仍然不是word2vec实际上的训练过程。\n\n## 负采样\n\n回想一下这个神经语言模型如何计算其预测的三个步骤： \n  ![](images/language-model-expensive.png)\n\n从计算的角度来看，第三步非常消耗资源：尤其是我们将在数据集中为每个训练样本做一次（很可能数千万次）。我们需要做一些事情来提高效率。\n\n一种方法是将目标分成两个步骤：\n1.\t生成高质量的单词嵌入（不要担心下一个单词预测）。\n2.\t使用这些高质量的嵌入来训练语言模型（进行下一个单词预测）。\n\n我们将专注于第1步，因为我们专注于嵌入。要使用高性能模型生成高质量嵌入，我们可以从预测相邻单词切换模型的任务：\n ![](images/predict-neighboring-word.png)  \n并将其切换到一个取输入和输出字的模型，并输出一个分数，表明它们是否是邻居（0表示“不是邻居”，1表示“邻居”）。\n  ![](images/are-the-words-neighbors.png) \n\n这个简单的改变，将我们需要的模型从神经网络改为逻辑回归模型：因此它变得更简单，计算速度更快。\n\n这个改变要求我们切换数据集的结构 - 标签现在是一个值为0或1的新列。它们将全部为1，因为我们添加的所有单词都是邻居。\n  ![](images/word2vec-training-dataset.png) \n\n\n现在可以以极快的速度计算 - 在几分钟内处理数百万个示例。但是我们需要关闭一个漏洞。如果我们所有的例子都是正面的（目标：1），我们打开自己的智能模型的可能性总是返回1 - 达到100％的准确性，但什么都不学习并生成垃圾嵌入。\n   ![](images/word2vec-smartass-model.png)\n\n为了解决这个问题，我们需要在数据集中引入负样本 - 不是邻居的单词样本。我们的模型需要为这些样本返回0。现在这是一个挑战，模型必须努力解决，而且速度还要快。\n\n![](images/word2vec-negative-sampling.png)\n\n<center>图：对于我们数据集中的每个样本，我们添加了负样本。它们具有相同的输入词和0标签。</center>\n但是我们填写什么作为输出词？我们从词汇表中随机抽取单词\n![](images/word2vec-negative-sampling-2.png)\n\n这个想法的灵感来自[Noise-contrastive estimation](http://proceedings.mlr.press/v9/gutmann10a/gutmann10a.pdf)。我们将实际信号（相邻单词的正例）与噪声（随机选择的不是邻居的单词）进行对比。这是计算量和统计效率的巨大折衷。\n\n## 带负采样的skipgram（SGNS）\n我们现在已经介绍了word2vec中的两个核心思想：\n\n负采样和skipgram。\n![](images/skipgram-with-negative-sampling.png)\n\n## Word2vec训练流程\n\n现在我们已经建立了skipgram和负采样的两个中心思想，我们可以继续仔细研究实际的word2vec训练过程。\n\n在训练过程开始之前，我们预先处理我们正在训练模型的文本。在这一步中，我们确定词汇量的大小（我们称之为`vocab_size`，比如说，将其视为10,000）以及哪些词属于它。\n在训练阶段的开始，我们创建两个矩阵 - `Embedding`矩阵和`Context`矩阵。这两个矩阵在我们的词汇表中嵌入了每个单词（这`vocab_size`是他们的维度之一）。\n第二个维度是我们希望每次嵌入的时间长度（`embedding_size`- 300是一个常见值，但我们在本文前面的例子是50。）。\n ![](images/word2vec-embedding-context-matrix.png) \n\n在训练过程开始时，我们用随机值初始化这些矩阵。然后我们开始训练过程。在每个训练步骤中，我们采取一个正样本及其相关的负样本。我们来看看我们的第一组：\n ![](images/word2vec-training-example.png) \n现在我们有四个单词：输入单词`not`和输出/上下文单词:( `thou`实际邻居）,`aaron`，和`taco`（负样本）。我们继续查找它们的嵌入 - 对于输入词，我们查看`Embedding`矩阵。对于上下文单词，我们查看`Context`矩阵（即使两个矩阵都在我们的词汇表中嵌入了每个单词）。\n ![](images/word2vec-lookup-embeddings.png)\n\n然后，我们计算输入嵌入与每个上下文嵌入的点积。。在每种情况下，会产生一个数字，该数字表示输入和上下文嵌入的相似性。\n ![](images/word2vec-training-dot-product.png)\n\n现在我们需要一种方法将这些分数转化为看起来像概率的东西 ：使用sigmoid函数把概率转换为0和1。\n ![](images/word2vec-training-dot-product-sigmoid.png)\n\n现在我们可以将sigmoid操作的输出视为这些样本的模型输出。您可以看到`taco`得分最高`aaron`，并且在sigmoid操作之前和之后仍然具有最低分。\n既然未经训练的模型已做出预测，并且看到我们有一个实际的目标标签要比较，那么让我们计算模型预测中的误差。为此，我们只从目标标签中减去sigmoid分数。\n ![](images/word2vec-training-error.png)\n<center>图：$error= target-sigmoid_scores$</center>\n这是“机器学习”的“学习”部分。现在，我们可以利用这个错误分数调整`not`，`thou`，`aaron`和`taco`的嵌入,使下一次我们做出这一计算，结果会更接近目标分数。\n  ![](images/word2vec-training-update.png)\n训练步骤到此结束。我们从这一步骤中得到稍微好一点的嵌入（`not`，`thou`，`aaron`和`taco`）。我们现在进行下一步（下一个正样本及其相关的负样本）,并再次执行相同的过程。\n   ![](images/word2vec-training-example-2.png)\n当我们循环遍历整个数据集多次时，嵌入继续得到改进。然后我们可以停止训练过程，丢弃`Context`矩阵，并使用`Embeddings`矩阵作为下一个任务的预训练嵌入。\n\n## 窗口大小和负样本数量\nword2vec训练过程中的两个关键超参数是窗口大小和负样本的数量。\n![](images/word2vec-window-size.png)\n\n不同的窗口大小可以更好地提供不同的任务。\n\n一种启发式方法是较小的窗口嵌入（2-15），其中两个嵌入之间的高相似性得分表明这些单词是可互换的（注意，如果我们只查看周围的单词，反义词通常可以互换 - 例如，好的和坏的经常出现在类似的情境中）。\n\n使用较大的窗口嵌入（15-50，甚至更多）会得到相似性更能指示单词相关性的嵌入。实际上，您通常需要对嵌入过程提供注释指导，为您的任务带来有用的相似感。\n\nGensim默认窗口大小为5（输入字本身加上输入字之前的两个字和输入字之后的两个字）。\n![](images/word2vec-negative-samples.png)\n负样本的数量是训练过程的另一个因素。原始论文里负样本数量为5-20。它还指出，当你拥有足够大的数据集时，2-5似乎已经足够了。Gensim默认为5个负样本。\n## 结论\n\n我希望你现在对词嵌入和word2vec算法有所了解。我也希望现在当你读到一篇提到“skip gram with negative sampling”（SGNS）的论文时，你会对这些概念有了更好的认识。\n\n本文作者：[jalammar](https://twitter.com/jalammar)。\n\n## 参考文献和进一步阅读材料\n-   [Distributed Representations of Words and Phrases and their Compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) [pdf]\n\n-   [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/pdf/1301.3781.pdf) [pdf]\n\n-   [A Neural Probabilistic Language Model](http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf) [pdf]\n\n-   [Speech and Language Processing](https://web.stanford.edu/~jurafsky/slp3/) by Dan Jurafsky and James H. Martin is a leading resource for NLP. Word2vec is tackled in Chapter 6.\n\n-   [Neural Network Methods in Natural Language Processing](https://www.amazon.com/Language-Processing-Synthesis-Lectures-Technologies/dp/1627052984) by [Yoav Goldberg](https://twitter.com/yoavgo) is a great read for neural NLP topics.\n\n-   [Chris McCormick](http://mccormickml.com/) has written some great blog posts about Word2vec. He also just released [The Inner Workings of word2vec](https://www.preview.nearist.ai/paid-ebook-and-tutorial), an E-book focused on the internals of word2vec.\n  \n-   Want to read the code? Here are two options:\n\n    -   Gensim’s [python implementation](https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/models/word2vec.py) of word2vec\n    \n-   Mikolov’s original [implementation in C](https://github.com/tmikolov/word2vec/blob/master/word2vec.c) – better yet, this [version with detailed comments](https://github.com/chrisjmccormick/word2vec_commented/blob/master/word2vec.c) from Chris McCormick.\n  \n-   [Evaluating distributional models of compositional semantics](http://sro.sussex.ac.uk/id/eprint/61062/1/Batchkarov,%20Miroslav%20Manov.pdf)\n\n-   [On word embeddings](http://ruder.io/word-embeddings-1/index.html), [part 2](http://ruder.io/word-embeddings-softmax/)\n\n-   [Dune](https://www.amazon.com/Dune-Frank-Herbert/dp/0441172717/)\n\n"
  },
  {
    "path": "8.deep-learning/word2vec/Visualizing_embeddings.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 图解Word2vec\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"本文作者：[jalammar](https://twitter.com/jalammar),[作者主页](https://jalammar.github.io/)\\n\",\n    \"\\n\",\n    \"翻译：[黄海广](https://github.com/fengdu78)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 正文开始\\n\",\n    \"我发现嵌入的概念是机器学习中最迷人的想法之一。如果您曾经使用Siri，Google智能助理，Alexa，谷歌翻译，甚至智能手机键盘进行下一词预测，那么您很有可能从这个已经成为自然语言处理模型核心的想法中受益。在过去的几十年中，使用嵌入技术进行神经模型已有相当大的发展（最近的发展包括BERT和GPT2 等尖端模型的语境化嵌入）。\\n\",\n    \"\\n\",\n    \"自2013年以来，Word2vec一直是一种有效创建单词嵌入的方法。除了词嵌入字的方法之外，它的一些概念已经被证明可以在非语言任务中有效地创建推荐引擎和理解顺序数据。比如Airbnb，阿里巴巴，Spotify和Anghami这样的公司都从NLP世界中创造出这一优秀的工具并将其用于生产中，从而为新型推荐引擎提供支持。\\n\",\n    \"\\n\",\n    \"我们将讨论嵌入的概念，以及使用word2vec生成嵌入的机制。\\n\",\n    \"\\n\",\n    \"让我们从一个例子开始，了解使用向量来表示事物。\\n\",\n    \"\\n\",\n    \"您是否知道五个数字（向量）的列表可以代表您的个性？\\n\",\n    \"\\n\",\n    \"## 个性嵌入：你的个性怎么样？\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"使用0到100的范围表示你的个性（其中0是最内向的，100是最外向的）。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"五大人格特质测试，这些测试会问你一个问题列表，然后在很多方面给你打分，内向/外向就是其中之一。\\n\",\n    \"![](images/1.png)\\n\",\n    \" \\n\",\n    \"<center>图:测试结果示例。它可以真正告诉你很多关于你自己的事情，并且在学术、个人和职业成功方面都具有预测能力。</center>\\n\",\n    \"\\n\",\n    \"假设我的测试得分为38/100。我们可以用这种方式绘制：\\n\",\n    \"![](images/2.png)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"让我们将范围切换到从-1到1：\\n\",\n    \"![](images/3.png)\\n\",\n    \"\\n\",\n    \"了解一个人，一个维度的信息不够，所以让我们添加另一个维度 - 测试中另一个特征的得分。\\n\",\n    \"![](images/4.png)\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"你可能不知道每个维度代表什么，但仍然可以从一个人的个性的向量表示中获得了很多有用的信息。\\n\",\n    \"\\n\",\n    \"我们现在可以说这个向量部分代表了我的个性。当你想要将另外两个人与我进行比较时，这种表示的有用性就出现了。在下图中，两个人中哪一个更像我？\\n\",\n    \"![](images/5.png)\\n\",\n    \"\\n\",\n    \"处理向量时，计算相似度得分的常用方法是余弦相似度：\\n\",\n    \"![](images/6.png)\\n\",\n    \" \\n\",\n    \"一号人物与我的余弦相似度得分高，所以我们的性格比较相似。\\n\",\n    \"\\n\",\n    \"然而，两个方面还不足以捕获有关不同人群的足够信息。几十年的心理学研究已经研究了五个主要特征（以及大量的子特征）。所以我们在比较中使用所有五个维度：\\n\",\n    \"![](images/7.png)\\n\",\n    \" \\n\",\n    \"我们没法在二维上绘制出来五个维度，这是机器学习中的常见挑战，我们经常需要在更高维度的空间中思考。但好处是余弦相似度仍然有效。它适用于任意数量的维度：\\n\",\n    \"![](images/8.png)\\n\",\n    \" \\n\",\n    \"嵌入的两个中心思想：\\n\",\n    \"\\n\",\n    \"- 我们可以将人（事物）表示为数字的向量。\\n\",\n    \"- 我们可以很容易地计算出相似的向量彼此之间的关系。\\n\",\n    \"![](images/9.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 词嵌入\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们导入在维基百科上训练的GloVe向量\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"colab\": {},\n    \"colab_type\": \"code\",\n    \"id\": \"oZ6HIOcIPZ6A\",\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\gensim\\\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\\n\",\n      \"  warnings.warn(\\\"detected Windows; aliasing chunkize to chunkize_serial\\\")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import gensim\\n\",\n    \"import gensim.downloader as api\\n\",\n    \"model = api.load('glove-wiki-gigaword-50')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"单词“king”的词嵌入表示：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"colab\": {},\n    \"colab_type\": \"code\",\n    \"id\": \"4Jvn_IHsPj5X\",\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0.50451 ,  0.68607 , -0.59517 , -0.022801,  0.60046 , -0.13498 ,\\n\",\n       \"       -0.08813 ,  0.47377 , -0.61798 , -0.31012 , -0.076666,  1.493   ,\\n\",\n       \"       -0.034189, -0.98173 ,  0.68229 ,  0.81722 , -0.51874 , -0.31503 ,\\n\",\n       \"       -0.55809 ,  0.66421 ,  0.1961  , -0.13495 , -0.11476 , -0.30344 ,\\n\",\n       \"        0.41177 , -2.223   , -1.0756  , -1.0783  , -0.34354 ,  0.33505 ,\\n\",\n       \"        1.9927  , -0.04234 , -0.64319 ,  0.71125 ,  0.49159 ,  0.16754 ,\\n\",\n       \"        0.34344 , -0.25663 , -0.8523  ,  0.1661  ,  0.40102 ,  1.1685  ,\\n\",\n       \"       -1.0137  , -0.21585 , -0.15155 ,  0.78321 , -0.91241 , -1.6106  ,\\n\",\n       \"       -0.64426 , -0.51042 ], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model[\\\"king\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"查看“king”最相似的单词\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 253\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"13FYk39cP1tI\",\n    \"outputId\": \"557ba322-01b4-4198-fbe9-3a166e1af1d1\",\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[('prince', 0.8236179351806641),\\n\",\n       \" ('queen', 0.7839042544364929),\\n\",\n       \" ('ii', 0.7746230363845825),\\n\",\n       \" ('emperor', 0.7736247181892395),\\n\",\n       \" ('son', 0.766719400882721),\\n\",\n       \" ('uncle', 0.7627150416374207),\\n\",\n       \" ('kingdom', 0.7542160749435425),\\n\",\n       \" ('throne', 0.7539913654327393),\\n\",\n       \" ('brother', 0.7492411136627197),\\n\",\n       \" ('ruler', 0.7434253096580505)]\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.most_similar(\\\"king\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这是一个包含50个数字的列表，我们无法说清楚里面的值代表什么。我们把所有这些数字放在一行，以便我们可以比较其他单词向量。让我们根据它们的值对单元格进行颜色编码（如果它们接近2则为红色，如果它们接近0则为白色，如果它们接近-2则为蓝色）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"colab\": {},\n    \"colab_type\": \"code\",\n    \"id\": \"4fMHY4BWP7qM\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x24e8b6ccba8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(15, 1))\\n\",\n    \"sns.heatmap([model[\\\"king\\\"]],\\n\",\n    \"            xticklabels=False,\\n\",\n    \"            yticklabels=False,\\n\",\n    \"            cbar=False,\\n\",\n    \"            vmin=-2,\\n\",\n    \"            vmax=2,\\n\",\n    \"            linewidths=0.7)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们将忽略数字并仅查看颜色以指示单元格的值，我们将“King”与其他词语进行对比：\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 248\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"XOu-mUHAQGy6\",\n    \"outputId\": \"859d3158-9ee3-46ff-ff2f-9ec40b43823d\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x24e8b8427b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(15, 4))\\n\",\n    \"sns.heatmap([\\n\",\n    \"    model[\\\"king\\\"],\\n\",\n    \"    model[\\\"man\\\"],\\n\",\n    \"    model[\\\"woman\\\"],\\n\",\n    \"    model[\\\"king\\\"] - model[\\\"man\\\"] + model[\\\"woman\\\"],\\n\",\n    \"    model[\\\"queen\\\"],\\n\",\n    \"],\\n\",\n    \"            cbar=True,\\n\",\n    \"            xticklabels=False,\\n\",\n    \"            yticklabels=False,\\n\",\n    \"            linewidths=1)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab\": {},\n    \"colab_type\": \"code\",\n    \"id\": \"n_Ox61QfQQZ9\"\n   },\n   \"source\": [\n    \"看看“man”和“woman”是如何彼此更相似的，他们中的任何一个都是“king”？这告诉你一些事情。这些向量表示捕获了这些单词的信息/含义/关联。\\n\",\n    \"\\n\",\n    \"这是另一个示例列表（通过垂直扫描列来查找具有相似颜色的列）：\\n\",\n    \"![](images/10.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"有几点需要指出：\\n\",\n    \"\\n\",\n    \"- 所有这些不同的单词都有一个直的红色列。它们在这个维度上是相似的（我们不知道每个维度代码是什么）\\n\",\n    \"- 你可以看到“woman”和“girl”在很多地方是如何相似的。与“man”和“boy”一样\\n\",\n    \"- “boy”和“girl”也有彼此相似的地方，但与“woman”或“man”不同。这些是否可以编写一个模糊的青年概念？可能。\\n\",\n    \"- 除了最后一个字之外的所有字都代表着人。我添加了一个对象“water”来显示类别之间的差异。例如，您可以看到蓝色列一直向下并在嵌入“water”之前停止。\\n\",\n    \"- 有一个明显的地方，“king”和“queen”彼此相似，并与所有其他人不同。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 类比\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们可以添加和减去单词嵌入并获得有趣的结果，最有名的例子是公式： “king” - “man” + “woman”:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[('queen', 0.8523603677749634),\\n\",\n       \" ('throne', 0.7664334177970886),\\n\",\n       \" ('prince', 0.759214460849762),\\n\",\n       \" ('daughter', 0.7473883032798767),\\n\",\n       \" ('elizabeth', 0.7460220456123352),\\n\",\n       \" ('princess', 0.7424569725990295),\\n\",\n       \" ('kingdom', 0.7337411642074585),\\n\",\n       \" ('monarch', 0.7214490175247192),\\n\",\n       \" ('eldest', 0.7184861898422241),\\n\",\n       \" ('widow', 0.7099430561065674)]\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.most_similar(positive=[\\\"king\\\",\\\"woman\\\"],negative=[\\\"man\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们可以像以前一样想象这个类比：\\n\",\n    \"![](images/11.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 语言建模\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"如果想要给出NLP应用程序的示例，最好的示例之一将是智能手机键盘的下一个字（词）预测功能。这是数十亿人每天使用数百次的功能。\\n\",\n    \"![](images/12.png)\\n\",\n    \"  \\n\",\n    \"下一个字（词）预测是一项可以通过语言模型解决的任务。语言模型可以采用单词列表（比方说两个单词），并尝试预测它们之后的单词。\\n\",\n    \"\\n\",\n    \"在上面的屏幕截图中，我们可以将模型视为接受这两个绿色单词（thou shalt）并返回建议列表（“not”是具有最高概率的那个字）的模型：\\n\",\n    \" ![](images/13.png)\\n\",\n    \"  \\n\",\n    \"\\n\",\n    \"我们可以把模型想象成这个黑盒子：\\n\",\n    \"\\n\",\n    \" ![](images/14.png)\\n\",\n    \"  \\n\",\n    \"\\n\",\n    \"但实际上，该模型不会只输出一个单词。它实际上输出了它所知道的所有单词的概率分数（模型的“词汇表”，其范围可以从几千到一百多万个字（词））。然后应用程序必须找到分数最高的单词，并将其呈现给用户。\\n\",\n    \"\\n\",\n    \" ![](images/15.png)\\n\",\n    \"图：神经语言模型的输出是模型知道的所有单词的概率分数。我们在这里将概率称为百分比，比如概率40％将在输出向量中表示为0.4。\\n\",\n    \"\\n\",\n    \"经过训练，早期的神经语言模型（[Bengio 2003](http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf)）将分三步计算预测：\\n\",\n    \"\\n\",\n    \"   ![](images/16.png)\\n\",\n    \" \\n\",\n    \"\\n\",\n    \"在讨论嵌入时，第一步对我们来说最相关。训练过程的结果之一是这个矩阵包含我们词汇表中每个单词的嵌入。在预测时间内，我们只查找输入字的嵌入，并使用它们来计算预测：\\n\",\n    \"   ![](images/17.png)\\n\",\n    \" \\n\",\n    \"现在让我们转到训练过程，以了解嵌入矩阵是如何工作的。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 语言模型的训练\\n\",\n    \"\\n\",\n    \"与大多数其他机器学习模型相比，语言模型具有巨大优势。即：我们所有的书籍，文章，维基百科内容和其他形式的大量文本数据可以作为训练数据。与此相比，许多其他机器学习模型需要手动设计特征和专门收集的数据。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"单词通过我们查看它们往往会出现在旁边的其他单词来嵌入。其机制就是这样\\n\",\n    \"1.\\t我们获得了大量文本数据（例如，所有维基百科文章）。然后\\n\",\n    \"2.\\t我们有一个窗口（比如说三个单词），我们会对所有文本进行滑动。\\n\",\n    \"3.\\t滑动窗口为我们的模型生成训练样本\\n\",\n    \"\\n\",\n    \"![](images/wikipedia-sliding-window.png)\\n\",\n    \" \\n\",\n    \"当这个窗口滑动文本时，我们（虚拟地）生成一个用于训练模型的数据集。为了准确看看它是如何完成的，让我们看看滑动窗口如何处理这个短语：\\n\",\n    \"\\n\",\n    \"当我们开始时，窗口在句子的前三个单词上：\\n\",\n    \"![](images/lm-sliding-window.png)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"我们将前两个单词作为特征，将第三个单词作为标签：\\n\",\n    \"\\n\",\n    \"![](images/lm-sliding-window-2.png)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"我们现在已经在数据集中生成了第一个样本，我们稍后可以使用它来训练语言模型。\\n\",\n    \"\\n\",\n    \"然后我们将窗口滑动到下一个位置并创建第二个样本：\\n\",\n    \"![](images/lm-sliding-window-3.png)\\n\",\n    \" \\n\",\n    \"现在生成第二个示例。\\n\",\n    \"\\n\",\n    \"很快我们就会有一个更大的数据集，在不同的单词对之后，这些数据集会出现：\\n\",\n    \"![](images/lm-sliding-window-4.png)\\n\",\n    \" \\n\",\n    \"\\n\",\n    \"在实践中，模型往往在我们滑动窗口时进行训练。但我发现逻辑上将“数据集生成”阶段与训练阶段分开是更清楚的。除了基于神经网络的语言建模方法之外，一种称为N-gram的技术通常用于训练语言模型。\\n\",\n    \"\\n\",\n    \"要了解这种从N-gram到神经模型的转换如何反映现实世界的产品，建议看这篇[2015年博客文章](https://blog.swiftkey.com/neural-networks-a-meaningful-leap-for-mobile-typing/)，介绍他们的神经语言模型并将其与之前的N-gram模型进行比较。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 两边看\\n\",\n    \"\\n\",\n    \"给了你句子前面的内容，进行填空：\\n\",\n    \" ![](images/jay_was_hit_by_a_.png) \\n\",\n    \"我在这里给你的背景是空格之前的五个字（以及之前提到的“bus”）。我相信大多数人都会猜到空格里的这个词会是“bus”。但是，如果我再给你一条信息:空格之后的一句话，那会改变你的答案吗？\\n\",\n    \" ![](images/jay_was_hit_by_a_bus.png)  \\n\",\n    \"这完全改变了应该留在空格中的内容。“red”这个词现在最可能填到空格中。我们从中学到的是特定词语之前和之后的词语都具有信息价值。事实证明，考虑两个方向（我们猜测的单词左侧和右侧的单词）会让词嵌入做得更好。\\n\",\n    \"\\n\",\n    \"让我们看看我们如何调整我们训练模型的方式来解决这个问题。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Skipgram\\n\",\n    \"\\n\",\n    \"我们不仅可以查看在目标词之前的两个单词，还可以查看其后的两个单词。\\n\",\n    \" ![](images/continuous-bag-of-words-example.png) \\n\",\n    \"如果我们这样做，我们实际构建和训练模型的数据集将如下所示：\\n\",\n    \"![](images/continuous-bag-of-words-dataset.png)\\n\",\n    \"  \\n\",\n    \"这被称为连续词袋结构，并在word2vec论文 [one of the word2vec papers ](https://arxiv.org/pdf/1301.3781.pdf)中进行过描述。\\n\",\n    \"\\n\",\n    \"另一种结构与连续词袋结构略有不同，但也可以也显示出良好结果。这个结构试图使用当前词来猜测相邻词，而不是根据其上下文（它之前和之后的词）猜测一个词。我们可以想到它在训练文本上滑动的窗口如下所示：\\n\",\n    \" ![](images/skipgram-sliding-window.png) \\n\",\n    \"<center>图：绿色框中的字将是输入字，每个粉色框将是可能的输出。</center>\\n\",\n    \"\\n\",\n    \"粉色框具有不同的阴影，因为此滑动窗口实际上在我们的训练数据集中创建了四个单独的样本：\\n\",\n    \"![](images/skipgram-sliding-window-samples.png)\\n\",\n    \"  \\n\",\n    \"\\n\",\n    \"此方法称为skipgram架构。我们可以执行以下操作将滑动窗口可视化：\\n\",\n    \"\\n\",\n    \" ![](images/skipgram-sliding-window-1.png)\\n\",\n    \"\\n\",\n    \"这会将这四个样本添加到我们的训练数据集中：\\n\",\n    \"![](images/skipgram-sliding-window-2.png)\\n\",\n    \" \\n\",\n    \"然后我们将窗口滑动到下一个位置：\\n\",\n    \"![](images/skipgram-sliding-window-3.png)\\n\",\n    \" \\n\",\n    \"\\n\",\n    \"这将产生我们的下四个样本：\\n\",\n    \"![](images/skipgram-sliding-window-4.png)\\n\",\n    \" \\n\",\n    \"接着滑动几个位置之后，我们有更多的样本：\\n\",\n    \"![](images/skipgram-sliding-window-5.png)\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 重新审视训练过程\\n\",\n    \"\\n\",\n    \"现在我们已经从现有的运行文本中提取了我们的skipgram训练数据集，让我们看看我们如何使用它来训练预测相邻单词的基本神经语言模型。\\n\",\n    \"![](images/skipgram-language-model-training.png)\\n\",\n    \" \\n\",\n    \"我们从数据集中的第一个样本开始。我们把特征提供给未经训练的模型，要求它预测一个合适的相邻单词。\\n\",\n    \"![](images/skipgram-language-model-training-2.png)\\n\",\n    \" \\n\",\n    \"该模型进行三个步骤并输出预测向量（概率分配给其词汇表中的每个单词）。由于该模型未经过训练，因此在此阶段的预测肯定是错误的。但那没关系。我们知道应该它将猜到哪个词：我们目前用于训练模型的行中的标签/输出单元格：\\n\",\n    \"![](images/skipgram-language-model-training-3.png)\\n\",\n    \" \\n\",\n    \"<center>图：“目标向量”的词（字）概率为1，其他词（字）的概率都是0。</center>\\n\",\n    \"\\n\",\n    \"我们减去两个向量，得到一个误差向量：\\n\",\n    \"\\n\",\n    \" ![](images/skipgram-language-model-training-4.png)\\n\",\n    \"\\n\",\n    \"现在可以使用此误差向量来更新模型，以便下次当“not”作为输入时，模型更有可能猜测“thou”。\\n\",\n    \" ![](images/skipgram-language-model-training-5.png)\\n\",\n    \" \\n\",\n    \"\\n\",\n    \"这就是训练的第一步。我们继续使用数据集中的下一个样本进行相同的处理，然后是下一个样本，直到我们覆盖了数据集中的所有样本。这就结束了一个epcho的训练。我们继续训练多个epcho，然后我们就有了训练好的模型，我们可以从中提取嵌入矩阵并将其用于任何其他应用。\\n\",\n    \"\\n\",\n    \"虽然这加深了我们对该过程的理解，但仍然不是word2vec实际上的训练过程。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 负采样\\n\",\n    \"\\n\",\n    \"回想一下这个神经语言模型如何计算其预测的三个步骤： \\n\",\n    \"  ![](images/language-model-expensive.png)\\n\",\n    \"\\n\",\n    \"从计算的角度来看，第三步非常消耗资源：尤其是我们将在数据集中为每个训练样本做一次（很可能数千万次）。我们需要做一些事情来提高效率。\\n\",\n    \"\\n\",\n    \"一种方法是将目标分成两个步骤：\\n\",\n    \"1.\\t生成高质量的单词嵌入（不要担心下一个单词预测）。\\n\",\n    \"2.\\t使用这些高质量的嵌入来训练语言模型（进行下一个单词预测）。\\n\",\n    \"\\n\",\n    \"我们将专注于第1步，因为我们专注于嵌入。要使用高性能模型生成高质量嵌入，我们可以从预测相邻单词切换模型的任务：\\n\",\n    \" ![](images/predict-neighboring-word.png)  \\n\",\n    \"并将其切换到一个取输入和输出字的模型，并输出一个分数，表明它们是否是邻居（0表示“不是邻居”，1表示“邻居”）。\\n\",\n    \"  ![](images/are-the-words-neighbors.png) \\n\",\n    \" \\n\",\n    \"这个简单的改变，将我们需要的模型从神经网络改为逻辑回归模型：因此它变得更简单，计算速度更快。\\n\",\n    \"\\n\",\n    \"这个改变要求我们切换数据集的结构 - 标签现在是一个值为0或1的新列。它们将全部为1，因为我们添加的所有单词都是邻居。\\n\",\n    \"  ![](images/word2vec-training-dataset.png) \\n\",\n    \" \\n\",\n    \"\\n\",\n    \"现在可以以极快的速度计算 - 在几分钟内处理数百万个示例。但是我们需要关闭一个漏洞。如果我们所有的例子都是正面的（目标：1），我们打开自己的智能模型的可能性总是返回1 - 达到100％的准确性，但什么都不学习并生成垃圾嵌入。\\n\",\n    \"   ![](images/word2vec-smartass-model.png)\\n\",\n    \"   \\n\",\n    \"为了解决这个问题，我们需要在数据集中引入负样本 - 不是邻居的单词样本。我们的模型需要为这些样本返回0。现在这是一个挑战，模型必须努力解决，而且速度还要快。\\n\",\n    \"\\n\",\n    \"![](images/word2vec-negative-sampling.png)\\n\",\n    \"\\n\",\n    \"<center>图：对于我们数据集中的每个样本，我们添加了负样本。它们具有相同的输入词和0标签。</center>\\n\",\n    \"\\n\",\n    \"但是我们填写什么作为输出词？我们从词汇表中随机抽取单词\\n\",\n    \"![](images/word2vec-negative-sampling-2.png)\\n\",\n    \" \\n\",\n    \"这个想法的灵感来自[Noise-contrastive estimation](http://proceedings.mlr.press/v9/gutmann10a/gutmann10a.pdf)。我们将实际信号（相邻单词的正例）与噪声（随机选择的不是邻居的单词）进行对比。这是计算量和统计效率的巨大折衷。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 带负采样的skipgram（SGNS）\\n\",\n    \"我们现在已经介绍了word2vec中的两个核心思想：\\n\",\n    \"\\n\",\n    \"负采样和skipgram。\\n\",\n    \"![](images/skipgram-with-negative-sampling.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Word2vec训练流程\\n\",\n    \"\\n\",\n    \"现在我们已经建立了skipgram和负采样的两个中心思想，我们可以继续仔细研究实际的word2vec训练过程。\\n\",\n    \"\\n\",\n    \"在训练过程开始之前，我们预先处理我们正在训练模型的文本。在这一步中，我们确定词汇量的大小（我们称之为`vocab_size`，比如说，将其视为10,000）以及哪些词属于它。\\n\",\n    \"在训练阶段的开始，我们创建两个矩阵 - `Embedding`矩阵和`Context`矩阵。这两个矩阵在我们的词汇表中嵌入了每个单词（这`vocab_size`是他们的维度之一）。\\n\",\n    \"第二个维度是我们希望每次嵌入的时间长度（`embedding_size`- 300是一个常见值，但我们在本文前面的例子是50。）。\\n\",\n    \" ![](images/word2vec-embedding-context-matrix.png) \\n\",\n    \" \\n\",\n    \"在训练过程开始时，我们用随机值初始化这些矩阵。然后我们开始训练过程。在每个训练步骤中，我们采取一个正样本及其相关的负样本。我们来看看我们的第一组：\\n\",\n    \" ![](images/word2vec-training-example.png) \\n\",\n    \"现在我们有四个单词：输入单词`not`和输出/上下文单词:( `thou`实际邻居）,`aaron`，和`taco`（负样本）。我们继续查找它们的嵌入 - 对于输入词，我们查看`Embedding`矩阵。对于上下文单词，我们查看`Context`矩阵（即使两个矩阵都在我们的词汇表中嵌入了每个单词）。\\n\",\n    \" ![](images/word2vec-lookup-embeddings.png)\\n\",\n    \" \\n\",\n    \"然后，我们计算输入嵌入与每个上下文嵌入的点积。。在每种情况下，会产生一个数字，该数字表示输入和上下文嵌入的相似性。\\n\",\n    \" ![](images/word2vec-training-dot-product.png)\\n\",\n    \"  \\n\",\n    \"现在我们需要一种方法将这些分数转化为看起来像概率的东西 ：使用sigmoid函数把概率转换为0和1。\\n\",\n    \" ![](images/word2vec-training-dot-product-sigmoid.png)\\n\",\n    \"  \\n\",\n    \"现在我们可以将sigmoid操作的输出视为这些样本的模型输出。您可以看到`taco`得分最高`aaron`，并且在sigmoid操作之前和之后仍然具有最低分。\\n\",\n    \"既然未经训练的模型已做出预测，并且看到我们有一个实际的目标标签要比较，那么让我们计算模型预测中的误差。为此，我们只从目标标签中减去sigmoid分数。\\n\",\n    \" ![](images/word2vec-training-error.png)\\n\",\n    \"<center>图：$error= target-sigmoid_scores$</center>\\n\",\n    \"\\n\",\n    \"这是“机器学习”的“学习”部分。现在，我们可以利用这个错误分数调整`not`，`thou`，`aaron`和`taco`的嵌入,使下一次我们做出这一计算，结果会更接近目标分数。\\n\",\n    \"  ![](images/word2vec-training-update.png)\\n\",\n    \"训练步骤到此结束。我们从这一步骤中得到稍微好一点的嵌入（`not`，`thou`，`aaron`和`taco`）。我们现在进行下一步（下一个正样本及其相关的负样本）,并再次执行相同的过程。\\n\",\n    \"   ![](images/word2vec-training-example-2.png)\\n\",\n    \"当我们循环遍历整个数据集多次时，嵌入继续得到改进。然后我们可以停止训练过程，丢弃`Context`矩阵，并使用`Embeddings`矩阵作为下一个任务的预训练嵌入。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 窗口大小和负样本数量\\n\",\n    \"word2vec训练过程中的两个关键超参数是窗口大小和负样本的数量。\\n\",\n    \"![](images/word2vec-window-size.png)\\n\",\n    \"\\n\",\n    \"不同的窗口大小可以更好地提供不同的任务。\\n\",\n    \"\\n\",\n    \"一种启发式方法是较小的窗口嵌入（2-15），其中两个嵌入之间的高相似性得分表明这些单词是可互换的（注意，如果我们只查看周围的单词，反义词通常可以互换 - 例如，好的和坏的经常出现在类似的情境中）。\\n\",\n    \"\\n\",\n    \"使用较大的窗口嵌入（15-50，甚至更多）会得到相似性更能指示单词相关性的嵌入。实际上，您通常需要对嵌入过程提供注释指导，为您的任务带来有用的相似感。\\n\",\n    \"\\n\",\n    \"Gensim默认窗口大小为5（输入字本身加上输入字之前的两个字和输入字之后的两个字）。\\n\",\n    \"![](images/word2vec-negative-samples.png)\\n\",\n    \"负样本的数量是训练过程的另一个因素。原始论文里负样本数量为5-20。它还指出，当你拥有足够大的数据集时，2-5似乎已经足够了。Gensim默认为5个负样本。\\n\",\n    \"## 结论\\n\",\n    \"\\n\",\n    \"我希望你现在对词嵌入和word2vec算法有所了解。我也希望现在当你读到一篇提到“skip gram with negative sampling”（SGNS）的论文时，你会对这些概念有了更好的认识。\\n\",\n    \"\\n\",\n    \"本文作者：[jalammar](https://twitter.com/jalammar)。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 参考文献和进一步阅读材料\\n\",\n    \"-   [Distributed Representations of Words and Phrases and their Compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) [pdf]\\n\",\n    \"\\n\",\n    \"-   [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/pdf/1301.3781.pdf) [pdf]\\n\",\n    \"\\n\",\n    \"-   [A Neural Probabilistic Language Model](http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf) [pdf]\\n\",\n    \"\\n\",\n    \"-   [Speech and Language Processing](https://web.stanford.edu/~jurafsky/slp3/) by Dan Jurafsky and James H. Martin is a leading resource for NLP. Word2vec is tackled in Chapter 6.\\n\",\n    \"\\n\",\n    \"-   [Neural Network Methods in Natural Language Processing](https://www.amazon.com/Language-Processing-Synthesis-Lectures-Technologies/dp/1627052984) by [Yoav Goldberg](https://twitter.com/yoavgo) is a great read for neural NLP topics.\\n\",\n    \"\\n\",\n    \"-   [Chris McCormick](http://mccormickml.com/) has written some great blog posts about Word2vec. He also just released [The Inner Workings of\\n\",\n    \"    word2vec](https://www.preview.nearist.ai/paid-ebook-and-tutorial), an E-book focused on the internals of word2vec.\\n\",\n    \"\\n\",\n    \"-   Want to read the code? Here are two options:\\n\",\n    \"\\n\",\n    \"    -   Gensim’s [python implementation](https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/models/word2vec.py) of\\n\",\n    \"        word2vec\\n\",\n    \"\\n\",\n    \"    -   Mikolov’s original [implementation in C](https://github.com/tmikolov/word2vec/blob/master/word2vec.c) – better\\n\",\n    \"        yet, this [version with detailed\\n\",\n    \"        comments](https://github.com/chrisjmccormick/word2vec_commented/blob/master/word2vec.c) from\\n\",\n    \"        Chris McCormick.\\n\",\n    \"\\n\",\n    \"-   [Evaluating distributional models of compositional semantics](http://sro.sussex.ac.uk/id/eprint/61062/1/Batchkarov,%20Miroslav%20Manov.pdf)\\n\",\n    \"\\n\",\n    \"-   [On word embeddings](http://ruder.io/word-embeddings-1/index.html), [part 2](http://ruder.io/word-embeddings-softmax/)\\n\",\n    \"\\n\",\n    \"-   [Dune](https://www.amazon.com/Dune-Frank-Herbert/dp/0441172717/)\\n\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"include_colab_link\": true,\n   \"name\": \"Loading and visualizing embeddings in Gensim.ipynb\",\n   \"provenance\": [],\n   \"version\": \"0.3.2\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "9.feature-engineering/1.引言.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[原版（英文）图书地址](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/)\\n\",\n    \"\\n\",\n    \"**翻译**：[apachecn](https://github.com/apachecn)，[翻译版本地址](https://github.com/apachecn/feature-engineering-for-ml-zh)\\n\",\n    \"\\n\",\n    \"**代码修改和整理**：[黄海广](https://github.com/fengdu78)，原文修改成jupyter notebook格式，并增加和修改了部分代码，测试全部通过，所有数据集已经放在[百度云](data/README.md)下载。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 一、引言\\n\",\n    \"\\n\",\n    \"> 译者：[@ZhipengYe](https://github.com/ZhipengYe)\\n\",\n    \"\\n\",\n    \"机器学习将数据拟合到数学模型中来获得结论或者做出预测。这些模型吸纳特征作为输入。特征就是原始数据某方面的数学表现。在机器学习流水线中特征位于数据和模型之间。特征工程是一项从数据中提取特征，然后转换成适合机器学习模型的格式的艺术。这是机器学习流水线关键的一步，因为正确的特征可以减轻建模的难度，并因此使流水线能输出更高质量的结果。从业者们认为构建机器学习流水线的绝大多数时间都花在特征工程和数据清洗上。然后，尽管它很重要，这个话题却很少单独讨论。也许是因为正确的特征只能在模型和数据的背景中定义。由于数据和模型如此多样化，所以很难概括项目中特征工程的实践。\\n\",\n    \"\\n\",\n    \"尽管如此，特征工程不仅仅是一种临时实践。工作中有更深层的原则，最好就地进行说明。本书的每一章都针对一个数据问题：如何表示文本数据或图像数据，如何降低自动生成的特征的维度，何时以及如何规范化等等。把它看作是一个相互联系的短篇小说集，而不是一本长篇小说。每章都提供了大量现有特征工程技术的插图。它们一起阐明了总体原则。\\n\",\n    \"\\n\",\n    \"掌握主题不仅仅是了解定义并能够推导出公式。仅仅知道这个机制是如何工作的以及它可以做什么是不够的。它还必须包括理解为什么要这样设计，它如何与其他技术相关联，以及每种方法的优缺点是什么。掌握就是要准确地知道如何完成某件事，对底层原则有一个感觉，并将其整合到我们已知的知识网络中。一个人通过读一本相关的书并不会成为某个东西的主人，尽管一本好书可以打开新的门。它必须涉及实践——将想法用于实践，这是一个反复的过程。随着每一次迭代，我们都会更好地了解这些想法，并在应用这些想法时变得越来越娴熟和富有创造性。本书的目标是促进其思想的应用。\\n\",\n    \"\\n\",\n    \"本书首先尝试传授感觉，其次是数学。我们不是只讨论如何完成某些事情，而是试图引导发现原因。我们的目标是提供观点背后的感觉，以便读者了解如何以及何时应用它们。对于以不同方式学习的人们来说，有大量的描述和图片。提出数学公式是为了使感觉更加精确，并且还可以将本书与其他现有的知识结合起来。\\n\",\n    \"\\n\",\n    \"本书中的代码示例在 Python 中给出，使用各种免费和开源软件包。NumPy 库提供数字向量和矩阵操作。Pandas 是一个强大的数据框架，是 Python 中数据科学的基石。Scikit-learn 是一个通用机器学习软件包，涵盖了广泛的模型和特征变换器。Matplotlib 和 Seaborn 的样式库提供了绘图和可视化。你可以在我们的 github 仓库中找到这些例子作为 Jupyter notebooks。\\n\",\n    \"\\n\",\n    \"前几章开始较缓慢，为刚刚开始使用数据科学和机器学习的人们提供了一个桥梁。\\n\",\n    \"\\n\",\n    \"第 1 章从数字数据的基本特征工程开始：过滤，合并，缩放，日志转换和能量转换以及交互功能。\\n\",\n    \"\\n\",\n    \"第 2 章和第 3 章深入探讨了自然文本的特征工程：bag-of-words，n-gram 和短语检测。\\n\",\n    \"\\n\",\n    \"第 4 章将 tf-idf 作为特征缩放的例子，并讨论它的工作原理。\\n\",\n    \"\\n\",\n    \"围绕第 5 章讨论分类变量的高效编码技术，包括特征哈希和 bin-counting，步伐开始加速。\\n\",\n    \"\\n\",\n    \"当我们在第 6 章中进行主成分分析时，我们深入机器学习的领域。\\n\",\n    \"\\n\",\n    \"第 7 章将 k-means 看作一种特征化技术，它说明了模型堆叠的有效理论。\\n\",\n    \"\\n\",\n    \"第 8 章都是关于图像的，在特征提取方面比文本数据更具挑战性。在得出深度学习是最新图像特征提取技术的解释之前，我们着眼于两种手动特征提取技术 SIFT 和 HOG。\\n\",\n    \"\\n\",\n    \"我们在第 9 章中完成了一个端到端示例中的几种不同技术，为学术论文数据集创建了一个推荐器。\\n\",\n    \"\\n\",\n    \"## 概括提示\\n\",\n    \"\\n\",\n    \"作者建议本书中的插图最好以彩色显示。真的，你应该在第 7 章中打印出彩色版本的“瑞士卷”，然后粘贴到你的书中。你的审美意识会感谢我们。\\n\",\n    \"\\n\",\n    \"特征工程是一个广泛的话题，每天都在发明更多的方法，特别是在自动特征学习方面。为了将本书的范围限制在一个可管理的大小，我们不得不做一些削减。本书没有讨论音频数据的傅里叶分析，但它是一个与线性代数中的特征分析密切相关的美丽主题（我们在第 4 章和第 6 章中会介绍）。我们也忽略了与傅立叶分析密切相关的随机特征的讨论。我们通过对图像数据的深度学习来介绍特征学习，但不会深入到积极开发中的众多深度学习模型中。超出范围的还有先进的研究思路，如随机投影，复杂文本特征模型（如 word2vec 和 Brown 聚类）以及潜在空间模型（如潜在狄利克雷分析和矩阵分解）。如果这些话对你来说毫无意义，那么你很幸运。如果特征学习的前沿是你的兴趣所在，那么这可能不是为你准备的书。\\n\",\n    \"\\n\",\n    \"本书假设掌握了基本机器学习概念的知识，例如“什么是模型”和“什么是矢量”。它并不要求掌握了数学或统计学。线性代数，概率分布和优化的经验是有帮助的，但不是必需的。\\n\",\n    \"\\n\",\n    \"## 机器学习流水线\\n\",\n    \"\\n\",\n    \"在深入研究特征工程之前，让我们花点时间看看整个机器学习流水线。这将帮助我们更好地了解应用的大方向。为此，让我们从数据和模型等基本概念入手。\\n\",\n    \"\\n\",\n    \"### 数据\\n\",\n    \"\\n\",\n    \"我们所说的数据是对现实世界现象的观察。例如，股票市场数据可能涉及对每日股票价格的观察，个别公司的收益公告，甚至专家的意见文章。个人生物识别数据可以包括我们的心率，血糖，血压等的测量数据。客户情报数据包括诸如：“Alice 在周日买两本书”，“Bob 浏览网站上的这些页面”和“查理点击了上周的特别优惠链接”。我们可以在不同领域得到无数的数据例子。\\n\",\n    \"\\n\",\n    \"### 任务\\n\",\n    \"\\n\",\n    \"我们为什么收集数据？因为数据可以帮助我们回答很多问题。这些问题可能是：“我应该投资哪些股票？”，“我怎么样才能活得更健康？”，或者“我如何理解顾客变化的口味，以便我的企业能够更好的服务他们？”。\\n\",\n    \"\\n\",\n    \"从数据中获取答案的路途充满了谜题。最开始充满希望的方法可能行不通。最初只是一个预感却可能是最佳解决方案。数据的工作流程通常是一个多阶段的迭代过程。例如，股票价格是在交易所观察到的，由汤普森路透社等中间人汇总，存储在数据库中，由公司购买，在 Hadoop 集群中转换为 Hive 存储，通过脚本从商店中抽出，进行二次抽样，由另一个脚本处理和清理，转储为文件，转换为可以在 R，Python 或 Scala 中最喜欢的建模库中尝试的格式，将预测转储回 csv 文件，由评估程序分析，迭代多次，最后由生产团队用 C++ 或 Java 重写，运行所有数据，并将最终预测输出到另一个数据库。\\n\",\n    \"\\n\",\n    \"然而，如果我们暂时忽略工具和系统的混乱，我们可能可以看到上述过程涉及到的两个数学实体，它们是机器学习的黄油和面包：模型和特征。\\n\",\n    \"\\n\",\n    \"### 模型\\n\",\n    \"\\n\",\n    \"尝试着从数据中了解世界就像用不完整的拼图以及一堆额外的碎片拼凑出现实。这就是数学模型，特别是统计模型的由来。统计语言中包含许多频繁数据特征的概念：误差，冗余或者缺失。误差数据是测量中的错误导致的。冗余数据意味着数据的多个方面传达完全相同的信息。例如，星期几可以作为“星期一”，“星期二”，...“星期天”的分类变量存在，并且再次被包括为 0 到 6 之间的整数里。如果某些数据点中不存在这种星期几的信息，那么这个数据就产生了缺失。数据的数学模型描述数据不同方面的关系。举个例子。一个预测股票价格的模型可能是一个将公司收益历史、过去股票价格和行业映射到预测股票价格的公式。推荐音乐的模型可能考量了用户的相似处，然后给欣赏很多同样歌曲的用户推荐一样的艺术家。\\n\",\n    \"\\n\",\n    \"数学公式将数量相互关联。但是原始数据通常不是数字。（“爱丽丝周三买了‘指环王’三部曲”的行为不是数字，她后来对这本书的评论也不是。）必须有一样东西把两者连接在一起。这就是特征的由来。\\n\",\n    \"\\n\",\n    \"### 特征\\n\",\n    \"\\n\",\n    \"特征是原始数据的数学表示。有很多方法可以将原始数据转换为数学测量值，这也是为什么特征最终看起来与许多事情相似。自然的，特征必须来自可用数据的类型。可能它们与模型相关联的事实也没那么明显；一些模型更适合某些类型的特征，反之亦然。正确的特征应该与手头的任务相关并且容易被模型摄取。特征工程是指给定数据、模型和任务是制定最佳特征的过程。\\n\",\n    \"\\n\",\n    \"特征的数量也是很重要的。如果没有足够的信息特征，模型就无法完成最终的任务。如果存在太多的特征，或者如果它们大多数是无关紧要的，那么训练这个模型会更加的棘手并且代价更多。在训练过程中可能会出现一些错误影响模型的表现。\\n\",\n    \"\\n\",\n    \"### 模型评估\\n\",\n    \"\\n\",\n    \"特征和模型位于原始数据和期望的观察结果之间。在机器学习工作流程中，我们不仅挑选模型，还挑选特征。这是一个双节杆，一个选择会影响另一个。良好的特征使后续的建模步骤变得简单，并且所得到的模型能更容易实现所需的任务。糟糕的特征可能需要更复杂的模型才能达到相同的性能水平。在本书的其余部分中，我们将介绍不同类型的特征，并讨论它们对不同类型数据和模型的优缺点。不要犹豫，让我们开始吧！\\n\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "9.feature-engineering/2.简单数字的奇特技巧.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[原版（英文）图书地址](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/)\\n\",\n    \"\\n\",\n    \"**翻译**：[apachecn](https://github.com/apachecn)，[翻译版本地址](https://github.com/apachecn/feature-engineering-for-ml-zh)\\n\",\n    \"\\n\",\n    \"**代码修改和整理**：[黄海广](https://github.com/fengdu78)，原文修改成jupyter notebook格式，并增加和修改了部分代码，测试全部通过，所有数据集已经放在[百度云](data/README.md)下载。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 二、简单数字的奇特技巧\\n\",\n    \"\\n\",\n    \"> 译者：[@coboe](https://github.com/coboe)\\n\",\n    \"> \\n\",\n    \"> 校对者：[@ZiseonJiao](https://github.com/emengjzs)\\n\",\n    \"\\n\",\n    \"在深入研究诸如文本和图像这样的复杂数据类型之前，让我们先从最简单的数字数据开始。它们可能来自各种来源：地理位置或人、购买的价格、传感器的测量、交通计数等。数字数据已经是数学模型容易消化的格式。这并不意味着不再需要特征工程。好的特征不仅代表数据的显著方面，而且符合模型的假设。因此，转换常常是必要的。数字特征工程技术是基础。当原始数据被转换为数字特征时，它们可以被应用。\\n\",\n    \"\\n\",\n    \"数值数据的第一个健全检查是大小是否重要。我们只需要知道它是正面的还是负面的？或者我们只需要知道一个非常粗粒度的大小？这一明智的检查对于自动累积数尤其重要，比如统计，每天访问网站的次数，餐馆所获得的评论数量等等。\\n\",\n    \"\\n\",\n    \"接下来，考虑特征的规模。最大值和最小值是什么？它们跨越几个数量级吗？输入特性平滑的模型对输入的尺度敏感。例如，3x+ 1是输入X的简单线性函数，其输出的规模直接取决于输入的比例。其他例子包括k-均值聚类，最近邻居方法，RBF内核，以及使用欧几里得距离的任何东西。对于这些模型和建模组件，通常规范化特征以使输出保持在预期的规模上通常是一个好主意。\\n\",\n    \"\\n\",\n    \"另一方面，逻辑函数对输入特征量表不敏感。无论输入是什么，它们的输出都是二进制的。例如，逻辑，并采取任何两个变量和输出1，当且仅当两个输入均为真。逻辑函数的另一个例子是阶跃函数“输入x大于5”。决策树模型由输入特征的阶跃函数组成。因此，基于空间划分树（决策树、梯度提升机、随机森林）的模型对尺度不敏感。唯一的例外是如果输入的规模随着时间的增长而增长，那么如果该特征是某种类型的累积计数。最终它将生长在树被训练的范围之外。如果可能是这样的话，那么就有必要周期性地重新调整输入。另一个解决方案是第5章讨论的bin计数方法。\\n\",\n    \"\\n\",\n    \"考虑数值特征的分布也是很重要的。分布总结了承担特定价值的可能性。输入特征的分布对某些模型比其他模型更重要。例如，线性回归模型的训练过程假定预测误差分布得像高斯。这通常是好的，除非预测目标在几个数量级上扩散。在这种情况下，高斯误差假设可能不再成立。解决这一问题的一种方法是转变产出目标，以驯服规模的增长。（严格地说，这将是目标工程，而不是特征工程。）对数变换，这是一种功率变换，将变量的分布接近高斯。另一个解决方案是第5章讨论的bin计数方法。\\n\",\n    \"\\n\",\n    \"除了裁剪模型或培训过程的假设, 多个功能可以组合成更复杂的功能。希望复杂的功能能够更简洁地捕捉原始数据中的重要信息。通过使输入功能更加 \\\"雄辩\\\", 模型本身可以更简单, 更容易进行培训和评估, 并做出更好的预测。作为一个极端的, 复杂的特点本身可能是统计模型的输出。这是一个称为模型堆叠的概念, 我们将在7章和8章中更详细地讨论。在本章中, 我们给出了复杂特征的最简单示例: 交互功能。\\n\",\n    \"\\n\",\n    \"交互特征易于制定，但特征的组合导致更多的输入到模型中。为了减少计算开销，通常需要使用自动特征选择来修剪输入特征。\\n\",\n    \"\\n\",\n    \"我们将从标量、向量和空间的基本概念开始，然后讨论尺度、分布、交互特征和特征选择。\\n\",\n    \"\\n\",\n    \"## 标量、向量和空间\\n\",\n    \"\\n\",\n    \"在我们开始之前, 我们需要定义一些基本概念, 这本书的其余部分。单个数字特征也称为标量。标量的有序列表称为向量。向量位于向量空间中。在绝大多数机器学习应用中, 对模型的输入通常表示为数字向量。本书的其余部分将讨论将原始数据转换为数字向量的最佳实践策略. \\n\",\n    \"\\n\",\n    \"向量可以被可视化为空间中的一个点。（有时人们从原点到那一点画一条线和一个箭头。在这本书中，我们将主要使用这一点。例如，假设我们有一个二维向量$v=[1，-1]$。也就是说，向量包含两个数，在第一方向$d1$中，向量具有1的值，并且在第二方向$d2$中，它具有$-1$的值。我们可以在二维图中绘制$v$。\\n\",\n    \"\\n\",\n    \"![A single vector](images/chapter2/A_single_vector.png)\\n\",\n    \"\\n\",\n    \"**Figure 2-1. A single vector.**\\n\",\n    \"\\n\",\n    \"在数据世界中, 抽象向量及其特征维度具有实际意义。例如, 它可以代表一个人对歌曲的偏爱。每首歌都是一个特征, 其中1的值相当于大拇指向上,$-1$ 个拇指向下。假设向量 $v$ 表示一个听众 Bob 的喜好。Bob喜欢 Bob Dylan 的  “Blowin’ in the Wind” 和 Lady Gaga 的 \\\"Poker Face\\\"。其他人可能有不同的喜好。总的来说, 数据集合可以在特征空间中可视化为点云. \\n\",\n    \"\\n\",\n    \"相反，一首歌可以由一组人的个人喜好来表示。假设只有两个听众，Alice 和 Bob。Alice 喜欢 Leonard Cohen 的 “Poker Face”, “Blowin’ in the Wind” 和 “Hallelujah”，但讨厌 Katy Perry 的 “Roar” 和 Radiohead 的 “Creep”。Bob 喜欢 “Roar\\\", “Hallelujah”和“Blowin’ in the Wind”，但讨厌 “Poker Face” 和 “Creep” 。在听众的空间里，每一首歌都是一个点。就像我们可以在特征空间中可视化数据一样，我们可以在数据空间中可视化特征。图2-2显示了这个例子。\\n\",\n    \"\\n\",\n    \"![Illustration of feature space vs. data space](images/chapter2/Illustration_of_feature_space_vs_data_space.png)\\n\",\n    \"\\n\",\n    \"**Figure 2-2. Illustration of feature space vs. data space.**\\n\",\n    \"\\n\",\n    \"## 处理计数\\n\",\n    \"\\n\",\n    \"在大数据时代，计数可以快速积累而不受约束。用户可以将歌曲或电影放在无限播放中，或者使用脚本反复检查流行节目的门票可用性，这会导致播放次数或网站访问计数迅速上升。当数据可以以高的体积和速度产生时，它们很可能包含一些极值。这是一个好主意，检查他们的规模，并确定是否保持它们作为原始数字，将它们转换成二进制变量，以指示存在，或将它们放入粗粒度。\\n\",\n    \"\\n\",\n    \"## 二值化\\n\",\n    \"\\n\",\n    \"Million Song 数据集中的用户品味画像包含了一百万个用户在 Echo Nest 的完整音乐聆听历史。下面是有关数据集的一些相关统计数据。\\n\",\n    \"\\n\",\n    \"###  Echo Nest 品味画像数据集的统计\\n\",\n    \"\\n\",\n    \"- 有超过4800万个用户ID、音乐ID和监听计数三元组。\\n\",\n    \"- 完整的数据集包含1019318个独特用户和384546首独特歌曲。\\n\",\n    \"- 引文：Echo Nest 品味画像的数据子集，官方的 Million Song 数据集的用户数据集，可从这里获得：http://labrosa.ee.columbia.edu/millionsong/tasteprofile\\n\",\n    \"\\n\",\n    \"假设任务是建立一个推荐器向用户推荐歌曲。推荐器的一个组件可以预测用户将对一首特别的歌曲会有多少喜欢。由于数据包含实际的听歌次数，这应该是预测的目标吗？如果一个大的听计数意味着用户真的喜欢这首歌，反之亦然，那是正确的。然而，数据表明，虽然99%的听计数是24或更低，也有一些听计数数以千计，最大为9667。（如图2-3所示，直方图最接近于0的bin中的峰值。但是超过10000个三元组的计数更大，几千个则有几个。这些值异常大；如果我们试图预测实际的听计数，那么模型将被这些大的值拉离。\\n\",\n    \"\\n\",\n    \"![Histogram of listen counts in the user taste profile of the Million Song Dataset. Note that the y-axis is on a log scale.](images/chapter2/listen_count.png)\\n\",\n    \"\\n\",\n    \"**Figure 2-3. Histogram of listen counts in the user taste profile of the Million Song Dataset. Note that the y-axis is on a log scale.**\\n\",\n    \"\\n\",\n    \"在 Million Song 数据集中，原始监听计数不是用户口味的可靠度量。（在统计术语中，健壮性意味着该方法在各种各样的条件下工作。）用户有不同的听力习惯。有些人可能把他们最喜欢的歌曲放在无限的循环中，而其他人可能只在特殊的场合品尝它们。很难说听一首歌20次的人一定喜欢听10次的人的两倍。\\n\",\n    \"\\n\",\n    \"用户偏好的更健壮表示是使计数二元化和修剪所有大于1的计数为1。换句话说，如果用户至少听过一首歌，那么我们将其视为用户喜欢歌曲。这样，模型不需要花费周期来预测原始计数之间的微小差异。二进制目标是用户偏好的简单而稳健的度量。\\n\",\n    \"\\n\",\n    \"### 例2-1:使 Million Song 数据集中听歌计数二进制化\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"listen_count = pd.read_csv(\\n\",\n    \"    'data/train_triplets.txt.zip', header=None, delimiter='\\\\t')\\n\",\n    \"# The table contains user-song-count triplets. Only non-zero counts are\\n\",\n    \"# included. Hence to binarize the count, we just need to set the entire\\n\",\n    \"# count column to 1.\\n\",\n    \"listen_count[2] = 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这是我们设计模型目标变量的一个例子。严格地说, 目标不是一个特征, 因为它不是输入。但有时我们确实需要修改目标以解决正确的问题。\\n\",\n    \"\\n\",\n    \"## 量化或装箱\\n\",\n    \"\\n\",\n    \"对于本练习, 我们从第 6 轮 Yelp 数据集挑战中采集数据, 并创建一个更小的分类数据集。Yelp 数据集包含用户对来自北美和欧洲十个城市的企业的评论。每个商户都标记为零个或多个类别。以下是有关数据集的相关统计信息。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 关于第 6 轮 Yelp 数据集的统计\\n\",\n    \"\\n\",\n    \"- 有782种商户类别。\\n\",\n    \"- 完整的数据集包含 1569264 个（约1.6M）评论和 61184 个（61K）商户。\\n\",\n    \"- “餐厅”（990627个评论）和“夜生活”（210028个评论）是最流行的类别，评论计数是明智的。\\n\",\n    \"- 没有一个商户同时属于餐厅和夜生活分类。因此，这两组评论之间没有重叠。\\n\",\n    \"\\n\",\n    \"每个商户都有一个评论计数。假设我们的任务是使用协同过滤来预测用户可能给企业的评级。评论计数可能是一个有用的输入特征，因为通常在流行和良好的评级之间有很强的相关性。现在的问题是，我们应该使用原始评论计数或进一步处理它吗？图2-4显示了所有商户评论计数的直方图。我们看到和音乐听歌计数一样的模式。大部分的统计数字都很小，但一些企业有成千上万的评论。\\n\",\n    \"\\n\",\n    \"### 例2-2:在YELP数据集中可视化商户评论计数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import json\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"### Load the data about businesses\\n\",\n    \"biz_file = open('data/yelp_academic_dataset_business.json')\\n\",\n    \"biz_df = pd.DataFrame([json.loads(x) for x in biz_file.readlines()])\\n\",\n    \"biz_file.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0,0.5,'Occurrence')\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x14e831c4470>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"### Plot the histogram of the review counts\\n\",\n    \"sns.set_style('whitegrid')\\n\",\n    \"fig, ax = plt.subplots()\\n\",\n    \"biz_df['review_count'].hist(ax=ax, bins=100)\\n\",\n    \"ax.set_yscale('log')\\n\",\n    \"ax.tick_params(labelsize=14)\\n\",\n    \"ax.set_xlabel('Review Count', fontsize=14)\\n\",\n    \"ax.set_ylabel('Occurrence', fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Figure 2-4. Histogram of business review counts in the Yelp reviews dataset. The y-axis is on a log-scale.**\\n\",\n    \"\\n\",\n    \"对于许多模型来说，跨越数个数量级的原始计数是有问题的。在线性模型中，相同的线性系数必须对计数的所有可能值工作。大量的计数也可能破坏无监督学习方法，如k-均值聚类，它使用相似性函数来测量数据点之间的相似性。k-均值使用数据点之间的欧几里得距离。数据向量的一个元素中的大计数将超过所有其他元素中的相似性，这可能会丢弃整个相似性度量。\\n\",\n    \"\\n\",\n    \"一种解决方案是通过量化计数来包含标量。换句话说，我们将计数分组到容器中，并且去掉实际的计数值。量化将连续数映射成离散数。我们可以把离散化的数字看作是代表强度度量的容器的有序的序列。\\n\",\n    \"\\n\",\n    \"为了量化数据，我们必须决定每一个箱子应该有多宽。解决方案分为固定宽度或自适应两种类型。我们将给出每个类型的例子。\\n\",\n    \"\\n\",\n    \"### 固定宽度装箱\\n\",\n    \"\\n\",\n    \"对于固定宽度装箱, 每个 bin 都包含一个特定的数值范围。范围可以是定制设计或自动分割, 它们可以线性缩放或指数缩放。例如, 我们可以将一个人的年龄分组为十年: 0-9 岁归纳到bin 1, 10-19 年归纳到 bin 2 等。要从计数映射到 bin, 只需除以 bin 的宽度并取整部分。\\n\",\n    \"\\n\",\n    \"也经常看到定制设计的年龄范围更适合于生活的阶段：\\n\",\n    \"- 0-12 岁\\n\",\n    \"- 12-17 岁\\n\",\n    \"- 18-24 岁\\n\",\n    \"- 25-34 岁\\n\",\n    \"- 35-44 岁\\n\",\n    \"- 45-54 岁\\n\",\n    \"- 55-64 岁\\n\",\n    \"- 65-74 岁\\n\",\n    \"- 75 岁以上\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"当数字跨越多个数量级时，最好用10个幂（或任何常数的幂）来分组：0－9、10-99、100-999、100-9999等。容器宽度呈指数增长，从O（10）、O（100）到O（1000）和以上。要从计数映射到bin，取计数的log值。指数宽度的划分与对数变换非常相关，我们在“对数变换”中讨论。\\n\",\n    \"\\n\",\n    \"### 例2-3:用固定宽度的箱进行量化计数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([84, 45, 51, 18, 50, 78, 40, 25, 75, 17, 39, 44, 69, 53, 35,  5,  8,\\n\",\n       \"       51, 63, 34])\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"### Generate 20 random integers uniformly between 0 and 99\\n\",\n    \"small_counts = np.random.randint(0, 100, 20)\\n\",\n    \"small_counts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([8, 4, 5, 1, 5, 7, 4, 2, 7, 1, 3, 4, 6, 5, 3, 0, 0, 5, 6, 3],\\n\",\n       \"      dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.floor_divide(small_counts, 10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"### An array of counts that span several magnitudes\\n\",\n    \"large_counts = [\\n\",\n    \"    296, 8286, 64011, 80, 3, 725, 867, 2215, 7689, 11495, 91897, 44, 28, 7971,\\n\",\n    \"    926, 122, 22222\\n\",\n    \"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([2., 3., 4., 1., 0., 2., 2., 3., 3., 4., 4., 1., 1., 3., 2., 2., 4.])\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"### Map to exponential-width bins via the log function\\n\",\n    \"np.floor(np.log10(large_counts))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 分位数装箱\\n\",\n    \"\\n\",\n    \"固定宽度装箱很容易计算。但是如果计数有很大的差距, 那么将会有许多空的垃圾箱没有数据。该问题可以通过基于数据分布的垃圾箱自适应定位来解决。这可以使用分发的分位数来完成。\\n\",\n    \"\\n\",\n    \"分位数是将数据划分为相等部分的值。例如, 中位数将数据分成一半;一半的数据是较小的, 一半大于中位数。分位数把数据分成几个部分, 十分位数把数据划分成十份。示例2-4 演示如何计算 Yelp 商户评论数的十等分, 图2-5 覆盖直方图上的十等分。这就更清楚地说明了对更小的计数的歪斜。\\n\",\n    \"\\n\",\n    \"### 例 2-4:计算 Yelp 商户评论数的十分位数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.1     3.0\\n\",\n       \"0.2     3.0\\n\",\n       \"0.3     4.0\\n\",\n       \"0.4     5.0\\n\",\n       \"0.5     6.0\\n\",\n       \"0.6     8.0\\n\",\n       \"0.7    12.0\\n\",\n       \"0.8    23.0\\n\",\n       \"0.9    50.0\\n\",\n       \"Name: review_count, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"deciles = biz_df['review_count'].quantile([.1, .2, .3, .4, .5, .6, .7, .8, .9])\\n\",\n    \"deciles\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0,0.5,'Occurrence')\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x14e833d9d68>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"### Visualize the deciles on the histogram\\n\",\n    \"sns.set_style('whitegrid')\\n\",\n    \"fig, ax = plt.subplots()\\n\",\n    \"biz_df['review_count'].hist(ax=ax, bins=100)\\n\",\n    \"for pos in deciles:\\n\",\n    \"    handle = plt.axvline(pos, color='r')\\n\",\n    \"ax.legend([handle], ['deciles'], fontsize=14)\\n\",\n    \"ax.set_yscale('log')\\n\",\n    \"ax.set_xscale('log')\\n\",\n    \"ax.tick_params(labelsize=14)\\n\",\n    \"ax.set_xlabel('Review Count', fontsize=14)\\n\",\n    \"ax.set_ylabel('Occurrence', fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Figure 2-5. Deciles of the review counts in the Yelp reviews dataset. Note that both x- and y-axes are in log scale.**\\n\",\n    \"\\n\",\n    \"为了计算分位数和映射数据到分位数箱，我们可以使用 `Pandas` 库。 `pandas.DataFrame.quantile` 和 `pandas.Series.quantile` 用于计算分位数。`pandas.qcut`将数据映射到所需数量的分位数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 例2-5:按分位数分箱计数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3, 0, 0, 1, 1, 2, 2, 3, 3, 0, 0, 2, 1, 0, 3], dtype=int64)\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"### Continue example Example 2-3 with large_counts\\n\",\n    \"import pandas as pd\\n\",\n    \"### Map the counts to quartiles\\n\",\n    \"pd.qcut(large_counts, 4, labels=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.25     122.0\\n\",\n       \"0.50     926.0\\n\",\n       \"0.75    8286.0\\n\",\n       \"dtype: float64\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"### Compute the quantiles themselves\\n\",\n    \"large_counts_series = pd.Series(large_counts)\\n\",\n    \"large_counts_series.quantile([0.25, 0.5, 0.75])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 对数转换\\n\",\n    \"\\n\",\n    \"在“量化或装箱”中，我们简要地介绍了把计数的对数映射到指数宽度箱的概念。让我们现在再看一看。\\n\",\n    \"\\n\",\n    \"对数函数是指数函数的逆。它定义为$\\\\log _{a}\\\\left({a}^{x}\\\\right)=X$\\n\",\n    \"其中 $a$ 为正常数, $x$ 可以是任何正数。由于$a^0=1$,我们有$\\\\log _{{a}}(1)=0$。这意味着对数函数将小范围的数字 (0、1) 映射到负数的整个范围$(-\\\\infty, 0)$。函数$\\\\log _{{a}}(10)=0$ 将 $[1、10]$ 映射到 $[0、1]$、将$[10、100]$ 映射到 $[1、2]$ 等等。换言之, 对数函数压缩大数的范围, 并扩展小数的范围。越大的 $x$ , $log(x)$的增量越慢。通过查看 $log$ 函数的图像, 可以更容易地消化这一点。(见图2-6)。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"![The log function](images/chapter2/2-6.png)\\n\",\n    \"\\n\",\n    \"**Figure 2-6. The log function compresses the high numeric range and expands the low range. Note how the horizontal x values from 100 to 1000 got compressed into just 2.0 to 3.0 in the vertical y range, while the tiny horizontal portion of x values less than 100 are mapped to the rest of the vertical range.**\\n\",\n    \"\\n\",\n    \"对数变换是处理具有重尾分布的正数的有力工具。（重尾分布在尾部范围内的概率比高斯分布的概率大）。它将分布在高端的长尾压缩成较短的尾部，并将低端扩展成较长的头部。图2-7比较d对数转换之前和之后的YELP商户评论计数的直方图。Y轴现在都在正常（线性）尺度上。在$(0.5,1)$范围内的底部图中增加的仓间隔是由于在1和10之间只有10个可能的整数计数。请注意，原始审查计数非常集中在低计数区域，离群值在4000以上。对数变换后，直方图不集中在低端，更分散在X轴上。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 例2-6:可视化对数变换前后评论数分布\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(23.625,0.5,'Occurrence')\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x14e8353e208>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, (ax1, ax2) = plt.subplots(2,1)\\n\",\n    \"fig.tight_layout(pad=0, w_pad=4.0, h_pad=4.0)\\n\",\n    \"biz_df['review_count'].hist(ax=ax1, bins=100)\\n\",\n    \"ax1.tick_params(labelsize=14)\\n\",\n    \"ax1.set_xlabel('review_count', fontsize=14)\\n\",\n    \"ax1.set_ylabel('Occurrence', fontsize=14)\\n\",\n    \"biz_df['log_review_count'] = np.log(biz_df['review_count'] + 1)\\n\",\n    \"biz_df['log_review_count'].hist(ax=ax2, bins=100)\\n\",\n    \"ax2.tick_params(labelsize=14)\\n\",\n    \"ax2.set_xlabel('log10(review_count))', fontsize=14)\\n\",\n    \"ax2.set_ylabel('Occurrence', fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Figure 2-7. Comparison of Yelp business review counts before (top) and after (bottom) log transformation.**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"另一个例子是来自 UC Irvine 机器学习库的在线新闻流行数据集。以下是有关数据集的相关统计信息。\\n\",\n    \"\\n\",\n    \"### 在线新闻流行数据集的统计\\n\",\n    \"\\n\",\n    \"- 该数据集包括 MasHabor 在两年的时间内出版的 39797 个新闻文章的60个特征。\\n\",\n    \"- 引证: K. Fernandes, P. Vinagre 和 P. Cortez . 一种用于预测在线新闻的流行程度的主动智能决策支持系统。2015 第十七届 EPIA 活动, 葡萄牙人工智能会议论文集, 9月, 葡萄牙科英布拉。\\n\",\n    \"\\n\",\n    \"目的是利用这些特征来预测文章在社交媒体上的用分享数量表示的流行度。在本例中, 我们将只关注一个特征——文章中的单词数。图2-8 显示了对数转换前后特征的直方图。请注意, 在对数转换后, 分布看起来更高斯, 除了长度为零的文章 (无内容) 的断裂。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\ipykernel_launcher.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\\n\",\n      \"  \\\"\\\"\\\"Entry point for launching an IPython kernel.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df = pd.read_csv('data/OnlineNewsPopularity.csv', delimiter=', ')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>url</th>\\n\",\n       \"      <th>timedelta</th>\\n\",\n       \"      <th>n_tokens_title</th>\\n\",\n       \"      <th>n_tokens_content</th>\\n\",\n       \"      <th>n_unique_tokens</th>\\n\",\n       \"      <th>n_non_stop_words</th>\\n\",\n       \"      <th>n_non_stop_unique_tokens</th>\\n\",\n       \"      <th>num_hrefs</th>\\n\",\n       \"      <th>num_self_hrefs</th>\\n\",\n       \"      <th>num_imgs</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>min_positive_polarity</th>\\n\",\n       \"      <th>max_positive_polarity</th>\\n\",\n       \"      <th>avg_negative_polarity</th>\\n\",\n       \"      <th>min_negative_polarity</th>\\n\",\n       \"      <th>max_negative_polarity</th>\\n\",\n       \"      <th>title_subjectivity</th>\\n\",\n       \"      <th>title_sentiment_polarity</th>\\n\",\n       \"      <th>abs_title_subjectivity</th>\\n\",\n       \"      <th>abs_title_sentiment_polarity</th>\\n\",\n       \"      <th>shares</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>http://mashable.com/2013/01/07/amazon-instant-...</td>\\n\",\n       \"      <td>731.0</td>\\n\",\n       \"      <td>12.0</td>\\n\",\n       \"      <td>219.0</td>\\n\",\n       \"      <td>0.663594</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.815385</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.100000</td>\\n\",\n       \"      <td>0.7</td>\\n\",\n       \"      <td>-0.350000</td>\\n\",\n       \"      <td>-0.600</td>\\n\",\n       \"      <td>-0.200000</td>\\n\",\n       \"      <td>0.500000</td>\\n\",\n       \"      <td>-0.187500</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.187500</td>\\n\",\n       \"      <td>593</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>http://mashable.com/2013/01/07/ap-samsung-spon...</td>\\n\",\n       \"      <td>731.0</td>\\n\",\n       \"      <td>9.0</td>\\n\",\n       \"      <td>255.0</td>\\n\",\n       \"      <td>0.604743</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.791946</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.033333</td>\\n\",\n       \"      <td>0.7</td>\\n\",\n       \"      <td>-0.118750</td>\\n\",\n       \"      <td>-0.125</td>\\n\",\n       \"      <td>-0.100000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.500000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>711</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>http://mashable.com/2013/01/07/apple-40-billio...</td>\\n\",\n       \"      <td>731.0</td>\\n\",\n       \"      <td>9.0</td>\\n\",\n       \"      <td>211.0</td>\\n\",\n       \"      <td>0.575130</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.663866</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.100000</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>-0.466667</td>\\n\",\n       \"      <td>-0.800</td>\\n\",\n       \"      <td>-0.133333</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.500000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1500</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>http://mashable.com/2013/01/07/astronaut-notre...</td>\\n\",\n       \"      <td>731.0</td>\\n\",\n       \"      <td>9.0</td>\\n\",\n       \"      <td>531.0</td>\\n\",\n       \"      <td>0.503788</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.665635</td>\\n\",\n       \"      <td>9.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.136364</td>\\n\",\n       \"      <td>0.8</td>\\n\",\n       \"      <td>-0.369697</td>\\n\",\n       \"      <td>-0.600</td>\\n\",\n       \"      <td>-0.166667</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.500000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1200</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>http://mashable.com/2013/01/07/att-u-verse-apps/</td>\\n\",\n       \"      <td>731.0</td>\\n\",\n       \"      <td>13.0</td>\\n\",\n       \"      <td>1072.0</td>\\n\",\n       \"      <td>0.415646</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.540890</td>\\n\",\n       \"      <td>19.0</td>\\n\",\n       \"      <td>19.0</td>\\n\",\n       \"      <td>20.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.033333</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>-0.220192</td>\\n\",\n       \"      <td>-0.500</td>\\n\",\n       \"      <td>-0.050000</td>\\n\",\n       \"      <td>0.454545</td>\\n\",\n       \"      <td>0.136364</td>\\n\",\n       \"      <td>0.045455</td>\\n\",\n       \"      <td>0.136364</td>\\n\",\n       \"      <td>505</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 61 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                 url  timedelta  \\\\\\n\",\n       \"0  http://mashable.com/2013/01/07/amazon-instant-...      731.0   \\n\",\n       \"1  http://mashable.com/2013/01/07/ap-samsung-spon...      731.0   \\n\",\n       \"2  http://mashable.com/2013/01/07/apple-40-billio...      731.0   \\n\",\n       \"3  http://mashable.com/2013/01/07/astronaut-notre...      731.0   \\n\",\n       \"4   http://mashable.com/2013/01/07/att-u-verse-apps/      731.0   \\n\",\n       \"\\n\",\n       \"   n_tokens_title  n_tokens_content  n_unique_tokens  n_non_stop_words  \\\\\\n\",\n       \"0            12.0             219.0         0.663594               1.0   \\n\",\n       \"1             9.0             255.0         0.604743               1.0   \\n\",\n       \"2             9.0             211.0         0.575130               1.0   \\n\",\n       \"3             9.0             531.0         0.503788               1.0   \\n\",\n       \"4            13.0            1072.0         0.415646               1.0   \\n\",\n       \"\\n\",\n       \"   n_non_stop_unique_tokens  num_hrefs  num_self_hrefs  num_imgs   ...    \\\\\\n\",\n       \"0                  0.815385        4.0             2.0       1.0   ...     \\n\",\n       \"1                  0.791946        3.0             1.0       1.0   ...     \\n\",\n       \"2                  0.663866        3.0             1.0       1.0   ...     \\n\",\n       \"3                  0.665635        9.0             0.0       1.0   ...     \\n\",\n       \"4                  0.540890       19.0            19.0      20.0   ...     \\n\",\n       \"\\n\",\n       \"   min_positive_polarity  max_positive_polarity  avg_negative_polarity  \\\\\\n\",\n       \"0               0.100000                    0.7              -0.350000   \\n\",\n       \"1               0.033333                    0.7              -0.118750   \\n\",\n       \"2               0.100000                    1.0              -0.466667   \\n\",\n       \"3               0.136364                    0.8              -0.369697   \\n\",\n       \"4               0.033333                    1.0              -0.220192   \\n\",\n       \"\\n\",\n       \"   min_negative_polarity  max_negative_polarity  title_subjectivity  \\\\\\n\",\n       \"0                 -0.600              -0.200000            0.500000   \\n\",\n       \"1                 -0.125              -0.100000            0.000000   \\n\",\n       \"2                 -0.800              -0.133333            0.000000   \\n\",\n       \"3                 -0.600              -0.166667            0.000000   \\n\",\n       \"4                 -0.500              -0.050000            0.454545   \\n\",\n       \"\\n\",\n       \"   title_sentiment_polarity  abs_title_subjectivity  \\\\\\n\",\n       \"0                 -0.187500                0.000000   \\n\",\n       \"1                  0.000000                0.500000   \\n\",\n       \"2                  0.000000                0.500000   \\n\",\n       \"3                  0.000000                0.500000   \\n\",\n       \"4                  0.136364                0.045455   \\n\",\n       \"\\n\",\n       \"   abs_title_sentiment_polarity  shares  \\n\",\n       \"0                      0.187500     593  \\n\",\n       \"1                      0.000000     711  \\n\",\n       \"2                      0.000000    1500  \\n\",\n       \"3                      0.000000    1200  \\n\",\n       \"4                      0.136364     505  \\n\",\n       \"\\n\",\n       \"[5 rows x 61 columns]\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df['log_n_tokens_content'] = np.log10(df['n_tokens_content'] + 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 例2-7:可视化在有对数变换和没有对数变换时新闻文章流行度的分布\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(23.625,0.5,'Number of Articles')\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x14e85713320>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, (ax1, ax2) = plt.subplots(2,1)\\n\",\n    \"fig.tight_layout(pad=0, w_pad=4.0, h_pad=4.0)\\n\",\n    \"df['n_tokens_content'].hist(ax=ax1, bins=100)\\n\",\n    \"ax1.tick_params(labelsize=14)\\n\",\n    \"ax1.set_xlabel('Number of Words in Article', fontsize=14)\\n\",\n    \"ax1.set_ylabel('Number of Articles', fontsize=14)\\n\",\n    \"\\n\",\n    \"df['log_n_tokens_content'].hist(ax=ax2, bins=100)\\n\",\n    \"ax2.tick_params(labelsize=14)\\n\",\n    \"ax2.set_xlabel('Log of Number of Words', fontsize=14)\\n\",\n    \"ax2.set_ylabel('Number of Articles', fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Figure 2-8. Comparison of word counts in Mashable news articles before (top) and after (bottom) log transformation.**\\n\",\n    \"\\n\",\n    \"## 对数转换实战\\n\",\n    \"\\n\",\n    \"让我们看看在监督学习中对数转换如何执行。我们将使用上面的两个数据集。对于 Yelp 评论数据集, 我们将使用评论的数量来预测商户的平均评级。对于 Mashable 的新闻文章, 我们将使用文章中的字数来预测其流行程度。由于输出是连续的数字, 我们将使用简单的线性回归作为模型。我们在没有对数变换和有对数变换的特色上，使用 Scikit Learn 执行10折交叉验证的线性回归。模型由 R 方评分来评估, 它测量训练后的回归模型预测新数据的良好程度。好的模型有较高的 R 方分数。一个完美的模型得到最高分1。分数可以是负的, 一个坏的模型可以得到一个任意低的负评分。通过交叉验证, 我们不仅得到了分数的估计, 还获得了方差, 这有助于我们判断两种模型之间的差异是否有意义。\\n\",\n    \"\\n\",\n    \"### 例2-8:使用对数转换 YELP 评论数预测平均商户评级\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"import json\\n\",\n    \"from sklearn import linear_model\\n\",\n    \"from sklearn.model_selection import cross_val_score\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"## Using the previously loaded Yelp reviews dataframe,\\n\",\n    \"## compute the log transform of the Yelp review count.\\n\",\n    \"## Note that we add 1 to the raw count to prevent the logarithm from\\n\",\n    \"## exploding into negative infinity in case the count is zero.\\n\",\n    \"biz_df['log_review_count'] = np.log10(biz_df['review_count'] + 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>business_id</th>\\n\",\n       \"      <th>categories</th>\\n\",\n       \"      <th>city</th>\\n\",\n       \"      <th>full_address</th>\\n\",\n       \"      <th>latitude</th>\\n\",\n       \"      <th>longitude</th>\\n\",\n       \"      <th>name</th>\\n\",\n       \"      <th>neighborhoods</th>\\n\",\n       \"      <th>open</th>\\n\",\n       \"      <th>review_count</th>\\n\",\n       \"      <th>stars</th>\\n\",\n       \"      <th>state</th>\\n\",\n       \"      <th>type</th>\\n\",\n       \"      <th>log_review_count</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>rncjoVoEFUJGCUoC1JgnUA</td>\\n\",\n       \"      <td>[Accountants, Professional Services, Tax Servi...</td>\\n\",\n       \"      <td>Peoria</td>\\n\",\n       \"      <td>8466 W Peoria Ave\\\\nSte 6\\\\nPeoria, AZ 85345</td>\\n\",\n       \"      <td>33.581867</td>\\n\",\n       \"      <td>-112.241596</td>\\n\",\n       \"      <td>Peoria Income Tax Service</td>\\n\",\n       \"      <td>[]</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>AZ</td>\\n\",\n       \"      <td>business</td>\\n\",\n       \"      <td>0.602060</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0FNFSzCFP_rGUoJx8W7tJg</td>\\n\",\n       \"      <td>[Sporting Goods, Bikes, Shopping]</td>\\n\",\n       \"      <td>Phoenix</td>\\n\",\n       \"      <td>2149 W Wood Dr\\\\nPhoenix, AZ 85029</td>\\n\",\n       \"      <td>33.604054</td>\\n\",\n       \"      <td>-112.105933</td>\\n\",\n       \"      <td>Bike Doctor</td>\\n\",\n       \"      <td>[]</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>AZ</td>\\n\",\n       \"      <td>business</td>\\n\",\n       \"      <td>0.778151</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3f_lyB6vFK48ukH6ScvLHg</td>\\n\",\n       \"      <td>[]</td>\\n\",\n       \"      <td>Phoenix</td>\\n\",\n       \"      <td>1134 N Central Ave\\\\nPhoenix, AZ 85004</td>\\n\",\n       \"      <td>33.460526</td>\\n\",\n       \"      <td>-112.073933</td>\\n\",\n       \"      <td>Valley Permaculture Alliance</td>\\n\",\n       \"      <td>[]</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>AZ</td>\\n\",\n       \"      <td>business</td>\\n\",\n       \"      <td>0.698970</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>usAsSV36QmUej8--yvN-dg</td>\\n\",\n       \"      <td>[Food, Grocery]</td>\\n\",\n       \"      <td>Phoenix</td>\\n\",\n       \"      <td>845 W Southern Ave\\\\nPhoenix, AZ 85041</td>\\n\",\n       \"      <td>33.392210</td>\\n\",\n       \"      <td>-112.085377</td>\\n\",\n       \"      <td>Food City</td>\\n\",\n       \"      <td>[]</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>AZ</td>\\n\",\n       \"      <td>business</td>\\n\",\n       \"      <td>0.778151</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>PzOqRohWw7F7YEPBz6AubA</td>\\n\",\n       \"      <td>[Food, Bagels, Delis, Restaurants]</td>\\n\",\n       \"      <td>Glendale Az</td>\\n\",\n       \"      <td>6520 W Happy Valley Rd\\\\nSte 101\\\\nGlendale Az, ...</td>\\n\",\n       \"      <td>33.712797</td>\\n\",\n       \"      <td>-112.200264</td>\\n\",\n       \"      <td>Hot Bagels &amp; Deli</td>\\n\",\n       \"      <td>[]</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>AZ</td>\\n\",\n       \"      <td>business</td>\\n\",\n       \"      <td>1.176091</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"              business_id                                         categories  \\\\\\n\",\n       \"0  rncjoVoEFUJGCUoC1JgnUA  [Accountants, Professional Services, Tax Servi...   \\n\",\n       \"1  0FNFSzCFP_rGUoJx8W7tJg                  [Sporting Goods, Bikes, Shopping]   \\n\",\n       \"2  3f_lyB6vFK48ukH6ScvLHg                                                 []   \\n\",\n       \"3  usAsSV36QmUej8--yvN-dg                                    [Food, Grocery]   \\n\",\n       \"4  PzOqRohWw7F7YEPBz6AubA                 [Food, Bagels, Delis, Restaurants]   \\n\",\n       \"\\n\",\n       \"          city                                       full_address   latitude  \\\\\\n\",\n       \"0       Peoria         8466 W Peoria Ave\\\\nSte 6\\\\nPeoria, AZ 85345  33.581867   \\n\",\n       \"1      Phoenix                  2149 W Wood Dr\\\\nPhoenix, AZ 85029  33.604054   \\n\",\n       \"2      Phoenix              1134 N Central Ave\\\\nPhoenix, AZ 85004  33.460526   \\n\",\n       \"3      Phoenix              845 W Southern Ave\\\\nPhoenix, AZ 85041  33.392210   \\n\",\n       \"4  Glendale Az  6520 W Happy Valley Rd\\\\nSte 101\\\\nGlendale Az, ...  33.712797   \\n\",\n       \"\\n\",\n       \"    longitude                          name neighborhoods  open  review_count  \\\\\\n\",\n       \"0 -112.241596     Peoria Income Tax Service            []  True             3   \\n\",\n       \"1 -112.105933                   Bike Doctor            []  True             5   \\n\",\n       \"2 -112.073933  Valley Permaculture Alliance            []  True             4   \\n\",\n       \"3 -112.085377                     Food City            []  True             5   \\n\",\n       \"4 -112.200264             Hot Bagels & Deli            []  True            14   \\n\",\n       \"\\n\",\n       \"   stars state      type  log_review_count  \\n\",\n       \"0    5.0    AZ  business          0.602060  \\n\",\n       \"1    5.0    AZ  business          0.778151  \\n\",\n       \"2    5.0    AZ  business          0.698970  \\n\",\n       \"3    3.5    AZ  business          0.778151  \\n\",\n       \"4    3.5    AZ  business          1.176091  \"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"biz_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"R-squared score without log transform: 0.00215 (+/- 0.00329)\\n\",\n      \"R-squared score with log transform: 0.00136 (+/- 0.00328)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"## Train linear regression models to predict the average stars rating of a business,\\n\",\n    \"## using the review_count feature with and without log transformation\\n\",\n    \"## Compare the 10-fold cross validation score of the two models\\n\",\n    \"m_orig = linear_model.LinearRegression()\\n\",\n    \"scores_orig = cross_val_score(\\n\",\n    \"    m_orig, biz_df[['review_count']], biz_df['stars'], cv=10)\\n\",\n    \"m_log = linear_model.LinearRegression()\\n\",\n    \"scores_log = cross_val_score(\\n\",\n    \"    m_log, biz_df[['log_review_count']], biz_df['stars'], cv=10)\\n\",\n    \"print(\\\"R-squared score without log transform: %0.5f (+/- %0.5f)\\\" %\\n\",\n    \"      (scores_orig.mean(), scores_orig.std() * 2))\\n\",\n    \"print(\\\"R-squared score with log transform: %0.5f (+/- %0.5f)\\\" %\\n\",\n    \"      (scores_log.mean(), scores_log.std() * 2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"从实验的结果来看, 两个简单的模型 (有对数变换和没有对数变换) 在预测目标时同样不好, 而有对数变换的特征表现略差。真令人失望!这并不奇怪, 他们都不是很好, 因为他们都只使用一个功能。但是, 人们本来希望日志转换的功能执行得更好。\\n\",\n    \"\\n\",\n    \"让我们看看对数转换在线新闻流行数据集上如何表现。\\n\",\n    \"\\n\",\n    \"### 例2-9:利用经过对数转换在线新闻数据中的词数量预测文章流行度\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"R-squared score without log transform: -0.00242 (+/- 0.00509)\\n\",\n      \"R-squared score with log transform: -0.00114 (+/- 0.00418)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"## Download the Online News Popularirty dataset from UCI, then use\\n\",\n    \"## Pandas to load the file into a dataframe\\n\",\n    \"\\n\",\n    \"## Take the log transform of the 'n_tokens_content' feature, which\\n\",\n    \"## represents the number of words (tokens) in a news article.\\n\",\n    \"df['log_n_tokens_content'] = np.log10(df['n_tokens_content'] + 1)\\n\",\n    \"\\n\",\n    \"## Train two linear regression models to predict the number of shares\\n\",\n    \"## of an article, one using the original feature and the other the\\n\",\n    \"## log transformed version.\\n\",\n    \"m_orig = linear_model.LinearRegression()\\n\",\n    \"scores_orig = cross_val_score(\\n\",\n    \"    m_orig, df[['n_tokens_content']], df['shares'], cv=10)\\n\",\n    \"m_log = linear_model.LinearRegression()\\n\",\n    \"scores_log = cross_val_score(\\n\",\n    \"    m_log, df[['log_n_tokens_content']], df['shares'], cv=10)\\n\",\n    \"print(\\\"R-squared score without log transform: %0.5f (+/- %0.5f)\\\" %\\n\",\n    \"      (scores_orig.mean(), scores_orig.std() * 2))\\n\",\n    \"print(\\\"R-squared score with log transform: %0.5f (+/- %0.5f)\\\" %\\n\",\n    \"      (scores_log.mean(), scores_log.std() * 2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"置信区间仍然重叠，但具有对数变换特征的模型比没有对数变换的表现更好。为什么对数转换在这个数据集上更成功？我们可以通过观察输入特征和目标值的散点图来得到线索。如图2-9的底部面板所示，对数变换重塑了$X$轴，将目标值（大于200000个份额）中的大离群值进一步拉向轴的右手侧。这给线性模型在输入特征空间的低端更多的“呼吸空间”。没有对数转换（上部面板），在输入值变化下非常小的情况下，模型有更大的压力下适应非常不同的目标值。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 例2-10:可视化新闻流程度预测问题中输入输出相关性\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0,0.5,'Number of Shares')\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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9LVnJYXFyMl156CUqlEn379oXJZMLx48dRXV2Njz76CHfeeadTgm5rPDU5jM/aDV2FvtHycLUKB5MfdVZo5ATt9QuI6vD6tl+8tu1be72+co9L8rFyfRkZGXjwwQcxb948SxvEmpoapKamIiMjA2vWrGlZtORU52wkhvaWExEREZnJGgT72LFjmDhxoiUxBOoGmp44cSJnEvFAYWpVk5YTERERmclKDkNCQnDmzJlGy0+fPm017Rx5hqThEVAprXuRq5S+SBoe0UoRERERUVsh67Hy2LFjMWfOHEydOhX33HMPgLr5jpcuXYpx48a5NEBqOnOnE/ZWJiIioqaSlRxOmDABer0eixcvxtWrVwEAwcHBmDBhAl544QVXxkfNlBAbzmSQiIiImkxWcqhQKDB16lRMnToVly5dQkBAAB8nExEREbVDspLD+jp37uyKOIiIiIjIA8jqkEJERERE3oHJIRERERFZyEoOly5dinPnzrk6FiIiIiJqZbKSww8//BAmk8nxikRERETUpsnqkDJ69GgsW7YMEydORFhYGAICAqzKfXz4dLq15Wh1bWJcw7YSJxERkbeSlRzu2rULZWVl+Oyzz2yWFxUVOTUoapocrQ5JmwpgNAkAgK5Cj6RNBQDgUYlXjlaHlM2F0BvraqF1FXqkbC4E4FlxEhEReTNZyWF2drar46Amql8Dp1AAtcK63GgSSN963KOSruwdJy2JoZneaEL2jpMeFScREZE3k5UcxsXFAQDKysrw888/Q6PR4MaNG+jSpYtLgyPbGtbACWF7vStVRjdG5di5Cn2TlhMREZH7yWosWFlZiWnTpuHhhx9GYmIiysvLkZqainHjxuHSpUuujpEasFUD1xaEqVVNWk5ERETuJys5XLBgAa5cuYKvvvrK0hklOTkZADB//nzXRUc2ya1pU6uULo6kaZKGR0Cl9LVaplL6Iml4RCtFRERERA3JSg53796NlJQUhIf/1i7s9ttvR1paGg4ePOiy4Mg2OTVtSh8F0h6PckM08iXEhiNzTDTC1SooAISrVcgcE832hkRERB5EVpvDmzdvQqlsXAtlMBggpBq8kcskDY+wanMIAEpfBYL8/XBVb/ToIWISYsM9Mi4iIiKqIys5HDJkCBYtWoSFCxdalp0+fRrz5s3DI4884qrYSII5uWrueIH2xhrkOIRERETeTVZymJqaipSUFNx3330QQmD06NHQ6/V44IEH8Prrr7s6RrKhuTVw9sYaBMBxCImIiLycrOSwQ4cOePfdd3H27FmUlJSgpqYGPXr0QK9evVwdn9eyrsE777QaPHtjDZr/b6uMySF5M9aoE5E3kZUcAkBtbS1OnTqFU6dOQalUIigoiMmhi+RodUjaWABjbb0ZTzZaz3jSnJtVjlYHXTPGGuQ4hOTNOLMPEXkbWclhaWkpEhMTceXKFdxxxx0QQuD06dO4/fbbsXTpUnTr1s3VcXqVtM+PWxJDM2OtQNrndTOe2LpZTV+fj/StxzH3sSibNyzzNlLMPaBtJY+dWnlIHNbaUGvizD5E5G1kDWUza9YsxMbGYv/+/di8eTO2bNmCffv2oVu3bkhNTXV1jF6nQm97ZhPzcqlBsK9UGZG0qQA5Wl2jMnsDZ5vHGkwaHgGlj6JReaWhxuZ7uoM5qdVV6CHwW61Na8VD3ocz+xCRt5GVHJ44cQJTpkxBUFCQZVnHjh0xY8YMfPfddy4Ljmyzd1Myz6nclG3MYw0mxIajQ2DjymSjSVjaJLqbozaSRK7GmX2IyNvISg41Gg0OHz7caHleXh4iIyOdHpS3u/UW249xzcsd3ZRszakstU24WmX1aKxCYj7m1qolYa0NtTbO7ENE3kayzeHbb79t+X+3bt2QkZGB3NxcREdHw8fHBz/88AO2b9+OP/3pT24J1JuMuqcrPso9Y3M5YHsQbEdsbWPrBhemVtlsd9hatSSeFk97x/adjbV0XFEiorZGMjk8evSo1et+/frhypUr+Prrry3LNBoNCgulOzlQ8+wpLne4PFDpI5kcKlB3k69/85J7gxvcJxgf555B/e4wrVlLIjeppZZjr1xpnNmHiLyJZHK4bt06d8ZB9dh7lJqj1SFpUwGMJulpCwVg86bu6AaXo9Xh0zwdGr7zTaMJ09fnI3vHyWbVmLSkNoq1Nu7DXrlERAQ0YZzDvXv34tSpUzAYDFbLFQoFJk+e7PTAvJm9R6npW4/bTQzN9EYTXt1gPTaiI1I9ms17a1iTJCfpc0ZtFGtt3IPtO4mICJCZHKakpGDr1q3o1asXAgMDrcqYHDpf0vAIq0GwAUDpo0DS8AhMX58v+31MQmDG+nxMX58PX4UCJiEQbqfmTU4SUL+nsJykr7Vro9iGTj627yQiIkBmcrhjxw4sWbIEQ4cOdXU8ZNZwuMHGww/KYk4vTeK32Vamr8/H61sKkfFENIDfHtn6/C+BdERXocerGwoaras3mpD2+XGr95R6N/MjclclbjlaHdK3Hrfquc02dPaxfScREQEyk8PQ0FCEhoa6Ohb6n+wdJxs9OjaPNahS+kBvrG3xPioNJry6sQA+gKWGUk5iCNTlqVLrVuiNDttEAoD6FmWjmsekjQVI33ocFVVGh8mivcSy4aPs+tiGThrbdxIRESAzOUxPT0d6ejqee+45dO3aFT4+1sMjDhgwwCXBeSup+Y91FXrceovSKckhAJhqBeQPhlNHAUjWBpo5SgyVPgoIgUbJm7FWWGr6bM0nbeaoHaO92WAAtqGzh+07iYhIVnJ4/PhxFBUVITk5uVGZQqFAUVGR0wMj22wNcO1q5vaKvjIfOzvSIdBPcrDt+oy1wmYvaUftGB0lf2xDR62B7V+JqK2QlRyuWLECM2bMwB//+MdGHVLIveTU3Dl7f4ueiWnUQaYlrlQZoVYpJeeQbqhhzaBUzao5KZTqWGE2uE9wEyNu25iUtD6OIUlEbYms5NDf3x9DhgyxmluZWoc7E0Pz/prSQ1quSkMNlD4K2QmnuWZw49HGM8eYmWsEHc0gIzXIOND0RMrTEy8mJZ6htXvtExE1hazk8G9/+xsWLFiAmTNnolu3bvDzs96sYRtEIkfkjNXYkK5CL1kjqAAsvWoTYsNxtPSyzSkIAek2h3ITKXNCqKvQW9Xk2ku83J1E1o+xISYl7scxJImoLZGVHL799tu4dOkS9u3bZ7OcbQ6ptQlYJ2T2agcBIPbvOxv1ipaq3Xl1QwGOll7GnuLyRglhwxTXVuJlL+mMcEErDXu9tc2ak5R4ei2pJ+MYkkTUlshKDrOzs10dB1GLadJ3Iu3xKIedUgRg1Ss6ZXMhjpZelqyVNAlhVQvpqM6z4ftIJZ1pnx9HfPdAfLn2Z0uHnz/e9zvMT4h2sAf7HPXWBpqelLTk8TSTSo4hSURti6zkMC4uztVxtDqDwYB58+bhyy+/hL+/P1544QVMnDixtcOiJqjQGzH9fzPCNIXeaJJ8BN1c8Vm7LUmQVNJZoTdi28nfOuXUT0JbkiA6qhU0z7Zji1Qi19w2c7aSSvOsPfZm63EndySvHEOSiNoSWcnhww8/DIVCeoqOvXv3OiueVrNw4UJotVqsWbMGFy5cwMyZMxEWFoZRo0a1dmjUBplnomlOZx5zgnjv7bc1muXFLMjfF1UGk80BwB3NdNMh0M9mUmIvkZOi+99MN1JJjq2ksn4bzenr8zFjfT6evb+7zYTY0WDnLU223Nlhp7ljSLLmlaj98tTPt0IIxwPXbdmyxep1TU0Nzp49iy1btmD69Ol48sknXRagO1RVVeH+++/HP//5TwwaNAgAsHz5cuzfvx+ffPKJ5HZFRUWIjIx0ejx3JG9z+nsStSU+CqBXcBB++rWyyT30b71FiWqjCVV2BotvzpBQ4WoVBvcJxp7i8kZf5PU7ANWfxzw4sBbHyqph7pTvA6BhVEofIPtpjc0bwpycQnyce8YqVpXSF5ljoi3rz8kpxCffnJVsmtDw5mPrGADHtZq2bmJytmvI2TfD1rq5Ovr+lxuXpyYHZvV/v8ykav2beyyeeA5cdX+vz1b78Iafb2eTe1yykkMpe/bswapVq+wmUG3Bd999h/Hjx6OgoAABAQEAgG+++QYTJkxAfn5+o97ZZkwOichZblH64M0x91jVjM5Yn28ziQ1Xq3Aw+VHMySmUbBIRrlbhjs4qHCq5bDcRVvoqAAGrYaVsJaANk1Q52zXk7JuhVOerhufSFex9/8s9zqacj9ZIoOz9fpn/wAqv94dCc66tqxKklp4vdySH8Vm7bTY7Mn++XUHucbVoDJqePXvi+PHjLXkLj1BeXo5OnTpZEkMA6NKlC4xGIy5fvtyKkRGRt6gy1iJpYwFytDoAdTVyUkmduV3px3bayuoq9DjoIDEE6oaVajjeqLk9KVB3k22YGMrZzhZ7bVebQ6rzVcNz6W5yj1PueuYESlehh8BvzR9cfXyffHNWsqzhEF7pW48369o6+3cCaL3z1VSePMSVrDaHhw8fbrSssrISa9asQe/evZ0elLvp9Xr4+/tbLTO/NhgMrRESEXkhY62QNQ2kube5KwfFN+/fXpJqb7umlDX3Zmhvu/rn0t3kHqfc9VprEHW506XqjSbJERIcXVtXJEhtZdB5Tx7iSlZy+OKLLzZaplQqER0djfnz5zs9KHcLCAholASaX6tU0hepurqaYzwSkVOdq9CjqKgIwUF++LWyxuY646M7uPy7JzjID0VFRU2+SZu3kyqzdUz2tnG0L6lzBPx2Ll3h5s2bLT5OuevZS6Bc+XvgowBaOmuqo2vr7N8JwDnny971dZbx0R3wzqGbqK43KUSAr8Itn29HZCWHxcXFro6jVYWGhuLatWswGAyWGsPy8nL4+/ujU6dOktsFBAS4qE3CKRe8JxG1BWFqFSIjIzH7Dx0btcVSAHj2/u6YMqquw0mQfykqDfbHtHREqu3g7D/0RWRkOMLU5yWHY2o4BWb97WyxdUyOtrFn9h86SrbLBH47l65gr+2W3OOUu57UNXDl8QHA+PtqZA/zpVYpUV1T2+Rr6+zfCcA558sdbQ4jI4HwMPe2JZWbdPqmpaWl2Sqora2FEELWj71hbtoCtVqNDz74AAMGDEC3bt0AAJ999hn0ej2eeeYZye0uXryI4OBgp8ezZNePTn9PInIPH8VvDfV7qX3xa6XJkrz4wP6jYKWPAmmPR6FP147o07Ujut2qQqHuKm7crEG4WoW/j+6LVwbfaVk/XK3Cf4vKILdboQJAfK/bUCtgec+0x6Pw+6j/Z7WfNx6723KD6hzkj30/lKOmXhKoAPDc/d3xwqAektvZYuuYHG1jT5+uHXHxRjWO/XK1UVn9c+kK9r7/5R6n3PVsXQOV0hdvPHa3y44PAB7tE4qLN6pxXHfN6ve24R1fpfTF/IS+GBoZ2uRr6+zfCcA558tV9/eG+nTtiAkP9MD0oXdhwgM9XHo9AfnHJdlbuU+fPrKSPoVCgRMnTjQ9Qg/zxhtv4OjRo8jKykJ5eTlmzpyJefPmYeTIkZLbuPIvC/ZYpvZGAUCl9LE7xIyUW29RYtQ9XS1DsHRSKVFlqIGhwRzd5mFk6g9VY95227HzljEj1SqlZTad+r0a1Q2GwVEoACHq1lcoYDXlIgCrcSjrv6eZox6tjraXo2H8QgBX9UbJYWva41iLzjqXTeGOmqX6POkaeFIsUtpCb+XW0OKhbI4cOSK5UVlZGZYsWQKdTodRo0Zh0aJFzY/UQ+j1eqSlpWHnzp0ICgpCYmIiEhMT7W6Tn59v1cOZiIiIyFNVV1dDo9E4XK9J4xzW1tZi7dq1WLp0KYKDg/HGG29g4MCBLQqUiIiIiDyHrA4pAKDVapGeno7Tp0/jL3/5CyZMmAClUunK2IiIiIjIzRwmhxUVFcjOzsbmzZvxyCOPYNmyZQgP96y2BURERETkHHaTw40bN2LRokUICgrC0qVLMWTIEHfFRUREREStQLLN4bhx41BQUICwsDC8+OKLCAwMlHyTp556ymUBEhEREZH7SCaHjz4qb9JnhUKBr776yqlBEREREVHr8JEq2L17t6wfJoauYTAYkJqaigEDBiA+Ph7vvfdea4dEdpw5cwZ/+ctfMGDAADz00EPIyspCdXWBTNCFAAAgAElEQVQ1AECn0yExMREajQYjRozAvn37rLbNzc3FY489hpiYGDz//PMoLS21Kl+3bh0eeughxMbGIiUlBVVVVW47LrL2+uuv4/nnn7e8Li4uxtixYxETE4MxY8bg2LFjVutv374dw4YNQ0xMDF5++WVcunTJUiaEwFtvvYWBAwdiwIABWLBgAUymls12Qk1nNBqRmZmJ++67D/fddx/mzp1rmT6Vn9227+rVq3jttdcQFxeHBx98EP/4xz8sn7OKigpMmzYN/fr1w6OPPootW7ZYbduSz3ebJ8gjzZs3T4waNUoUFhaK//73vyI2NlZ88cUXrR0W2VBdXS1GjBghpk6dKn766SfxzTffiCFDhojMzExRW1srHn/8cTFjxgzx448/ipUrV4p77rlHnDlzRgghxLlz54RGoxGrVq0SP/74o5g+fboYOXKkMJlMQgghduzYIfr16yd27doljh07JkaNGiVSU1Nb83C91qFDh8Rdd90lnnvuOSGEEJWVlSI+Pl5kZGSIn376ScyfP1/cf//94vr160IIIQoKCkR0dLT49NNPRVFRkXjuuedEYmKi5f0++OAD8cADD4hvvvlG5ObmigceeECsXLmyVY7Nm82fP18MHjxYHD16VOTl5YnBgweLxYsX87PbTsyYMUM899xz4uTJk+Lw4cMiPj5evPfee0IIISZPniyef/55UVxcLDZu3Cj69u0r8vLyhBAt/3y3dUwOPVBlZaWIjo4WBw8etCxbtmyZGDduXCtGRVK+/fZbERUVJW7cuGFZ9vnnn4tBgwaJQ4cOiejoaMsXihBC/PnPfxaLFy8WQgixZMkSq+taVVUlYmNjLdd+/PjxlnXN++rbt6/Vvsj1KisrxZAhQ8S4ceMsyeHGjRvFI488YkkGamtrxbBhw8SGDRuEEEIkJSWJV1991fIe586dE3fddZc4ffq0EEKIhx9+2LKuEELk5OSIhx56yF2HREKIq1eviqioKHHgwAHLsk8//VRMmDCBn912ol+/fuK///2v5XVmZqaYMGGCKC0ttfo8CiHE7NmzLZ/Zln6+2zrJx8rUeoqLi2EwGNC/f3/Lsv79+6OwsBA1NTWtGBnZ0rNnT6xatQpBQUGWZQqFAgaDAQUFBbj77rvRoUMHS1n//v2Rn58PACgoKMCAAQMsZSqVClFRUdBqtTCZTCgsLLQq12g0MJlMsidPJ+d46623EBcXh7i4OMuygoIC9OvXDz4+dV+jCoUC/fr1g1artZTXv3Zdu3ZFeHg4tFotysrKcP78edx7772W8v79++PChQs4f/68m46K8vLyEBgYiEGDBlmWjRkzBu+//z4/u+2EWq3G559/Dr1ej7KyMuzfvx9RUVEoKChAcHAwbr/9dsu6Da9vcz/f7QGTQw9UXl6OTp06WU3N16VLFxiNRly+fLkVIyNbbrvtNqubS21tLT766CP0798f5eXlCAkJsVq/c+fOuHDhAgBIlpeVleHatWuorq62Kvfz84NarbZsT66n1Wrx5ZdfYtasWVbL7V07APj1118ly8vLywHAqrxLly4AwGvrRmfOnEF4eDi++OILjBo1CoMHD8aCBQtgMBj42W0n5s6diyNHjqBfv3546KGH0KVLF0ydOrVF1xew//luD2TPkELuo9fr4e/vb7XM/NrcUJo8V2ZmJoqKirBp0yasWbOm0UxC/v7+MBqNAKSvtcFgwM2bNy2vbZWT6xkMBrz++uuYPXs2OnXqZFVm79oBwM2bN5t0bfkZd7/Kykr88ssv+Oijj5Ceno7Kykqkp6ejpqYGer2en9124MyZM7j77rsxZcoU3LhxA/PmzcOCBQugVqttXh+j0QghRIs+3+0Bk0MPFBAQ0OgXzPxapVK1RkgkgxACGRkZ+OSTT/D222+jd+/eCAgIwI0bN6zWMxgMlnFDpa61Wq221BzbKrc37ig5z7Jly3D77bdjxIgRjcqkrp2jaxsYGGiVCJoTEH7G3c/Pzw83btxAdnY2unfvDgCYOXMmZs6ciSeeeIKf3TbuzJkzePPNN7F79278v//3/wDUXbfExERMnz5d8vooFIoWfb7bA9mPlQ8ePGjppr1lyxZMmjQJ77zzjuWvKHKe0NBQXLt2zeoXr7y8HP7+/o1qL8gz1NbWYvbs2fjPf/6Dt956C0OHDgVQdy3NjxDNLl68iODgYIfl5pvMxYsXLWU1NTWoqKho9DiDXGPr1q04cOAAYmNjERsbi9WrV+Po0aOIjY2VdW3rX7v65aGhoZbXZub3Mm9PrhcSEgI/Pz9LYggAPXr0QHV1NYKDg/nZbeO+//57BAUFWRJDAOjbty9MJhMMBoPk5xOQ991tb/u2TlZyuGrVKkyZMgVnz55FXl4e5syZg5CQEGzfvh0LFixwdYxeJzIyEkql0qpha15eHqKiouDnx8peT5SVlYWtW7fi3Xffxe9//3vL8piYGBQXF1uNb5aXlweNRmMp/+677yxler0eJ06cgEajgY+PD6Kjo5GXl2cpz8/Ph6+vLyIjI91wVLRu3Tp88cUXyMnJQU5ODp5++mn07dsXOTk5iImJgVarhfjfPAJCCGi1WqtrW//anT9/HufOnYNGo0FoaCjCwsKsyvPy8hASEoKuXbu69yC9mEajQU1NDU6ePGlZVlJSgqCgIGg0Gn5227iQkBBcu3bNqpNXSUkJAOChhx5CWVkZfvnlF0tZXl4eYmJiAKBFn+92QU6X5sGDB4s9e/YIIYRITU0Vzz77rBCibpyfgQMHuqATNaWmpooRI0aIgoICsWvXLtGvXz+xbdu21g6LbNBqteKuu+4SK1euFL/++qvVT01NjRg5cqSYOnWq+OGHH8TKlStFTEyMOHv2rBBCiLNnz4ro6GixfPly8eOPP4oZM2aIUaNGWYZP+OKLL4RGoxE7duwQx44dE3/4wx/E3LlzW/FovdvixYstQ9lcv35d3H///SI9PV38+OOPIiMjQwwcONAy9Ml3330noqKixPr160VxcbF4/vnnxUsvvWR5r5UrV4r4+Hhx6NAh8c0334gHH3xQrFq1qlWOy5u9/PLL4oknnhCFhYXi22+/FYMHDxaZmZn87LYDRqNRjB49Wvz5z38WRUVFQqvViscee0wkJSUJIYRITEwU48ePF0VFRWLTpk2ib9++4rvvvhNCtPzz3dbJSg7vuecece7cOSGEEA8++KB4//33hRBClJaWitjYWNdF58WqqqrEzJkzhUajEfHx8WL16tWtHRJJyMrKEnfddZfNH6PRKE6fPi2effZZ0bdvXzFy5Eixf/9+q+337t0rhg8fLu655x7x/PPPi9LSUqvylStXioEDB4r+/fuL5ORkodfr3Xl4VE/95FCIuj+QExISRN++fcWTTz4pCgsLrdbfvHmzeOSRR4RGoxGvvPKKuHTpkqWspqZGZGZminvvvVfExcWJrKwsS2JB7nP9+nWRnJws+vXrJ+Li4sSbb74pDAaDEELws9sOXLhwQUybNk3ExcWJ+Ph4MW/ePMt1uHjxopg8ebKIjo4WgwcPFjk5OVbbtuTz3dZJzq1c35gxYzBkyBB07twZaWlp+PLLLxEWFob58+ejpKQEH3/8sTsqOYmIiIjIxWQ1YJs1axamTZuGq1evYvLkybjjjjuQnp6O3bt3Y+XKla6OkYiIiIjcRFbNIVDXG/P69euW3rIXL15Ep06dGo0D5U3y8/OtBqp2lerqarfsx1N5+/EDPAc8fu8+foDnwNuPH+A5cMbxV1dXy+o0I7vr6+XLl7Fx40aUlpYiKSkJR44cQa9evRAREdGiQNuygIAAt/Q8Kyoq8uoebt5+/ADPAY/fu48faBvnIEerQ/aOkzhXoUeYWoWk4RFIiA13ynu3heN3NW8/B844frnTN8oayqawsBDDhw/H4cOH8cUXX6CqqgpHjhzB008/jQMHDrQoUCIiorYuR6tDyuZC6Cr0EAB0FXqkbC5EjlbX2qERNZms5DArKwuTJk3C2rVrLY+R09LSMGnSJCxatMilARIREXm67B0noTearJbpjSZk7zgpsQWR55KVHJ44ccLm9FGjR4/GqVOnnB4UERFRW3KuQt+k5USeTFZy2LlzZ8uo4vWZR/QnIiLyZmFq23NiSy0n8mSyOqRMnDgRqampmDhxIoQQOHjwIM6fP4+1a9fitddec3WMREREHi1peARSNhdaPVpWKX2RNNx7O21S2yUrORw7diyCg4OxevVqBAYGYtGiRejRowcyMjIwcuRIV8dIRETk0cy9kl3VW5nInWQlh0uXLsWYMWM4EwoREZGEhNhwJoPULshqc/jhhx/CZDI5XpGIiIiI2jRZyeHo0aOxbNkylJSUQK/Xo7a21uqHiIiIiNoHWY+Vd+3ahbKyMnz22Wc2y+WOuE1EREREnk1Wcpidne3qOIiIiIjIA8hKDuPi4iTLLly44LRgiIiIiKh1yUoOS0pKsHDhQvz444+WNoZCCBgMBlRUVPCxMhEREVE7IatDSmpqKq5evYrJkyfjypUrmDhxIkaNGgW9Xo/MzExXx0hEREREbiKr5vD777/H+vXrERkZiZycHPTs2RPPPvssevTogQ0bNiAhIcHVcRIRERGRG8iqOfTz88P//d//AQB69uxpeYw8aNAg/PDDD66LjoiIiIjcSlZy2L9/f6xevRp6vR59+/bFrl27UFtbi4KCAgQEBLg6RiIionYtR6tDfNZu9Ejehvis3cjR6lo7JPJispLD5ORkHDp0CP/+978xevRoXLlyBffeey9ee+01jB8/3tUxEhERtVs5Wh1SNhdCV6GHAKCr0CNlcyETRGo1stoc9urVCzt27IBer4dKpcKnn36KI0eOQK1WQ6PRuDpGIiKidit7x0nojdZT1OqNJmTvOMm5mqlVyEoOza5fv46LFy8CqEsYAeDs2bP43e9+5/zIiIiIvMC5Cn2Tlrd1OVodsnecxLkKPcLUKiQNj2AS7GFkJYcHDhzArFmzcPnyZQB1YxwqFArLvxznkIjIM/FG7PnC1CrobCSCYWpVK0TjWuZH6OaaUvMjdAD8vfQgspLDefPmYeDAgXjppZcsvZaJiMiz8UbcNiQNj7C6TgCgUvoiaXhEK0blGnyE3jbISg7Pnz+P999/n4+PiYjaEN6I2wbztfCGGl5ve4TeVslKDu+//358++23TA6JiNoQb7oRt/XH5wmx4W0q3ubypkfobZlkcvj2229b/h8cHIy5c+di//796N69O3x8rEfA+etf/+q6CImIqFm85UbMx+dthzc9Qm/LJJPDo0ePWr3WaDS4ePGipbeymUKhcE1kRETUIt5yI+bj87bDmx6ht2WSyeG6devcGQcRETmZt9yIvenxeXvgLY/Q2zKHbQ6PHTuGPn36wN/fHwCwe/duHDp0CLfddhueeuophISEuDxIIiJqHm+4EXvL43Mid5GcPu/y5csYM2YMxo4di7NnzwIAVq9ejSlTpiA/Px/ffvstRo8ejZKSErcFS0RE1FDS8AiolL5Wy9rj43Mid5FMDpcsWQJ/f3/s3LkTvXr1wvXr1/HOO+9g0KBB2LRpE9asWYNnnnkGixcvdme8REREVhJiw5E5JhrhahUUAMLVKmSOiW73NaZEriL5WHnv3r146623LMPX7N+/H9XV1Rg7dqxlnWHDhuGTTz5xfZRERER2eMPjcyJ3kaw5vHLlCsLCwiyvDx8+DD8/PwwaNMiyTK1Ww2AwuDZCIiIiInIbyeSwa9euOH36NACgtrYWX3/9NWJjY9GhQwfLOkePHrVKIImIiIiobZNMDhMSEvDmm29i586dmD9/PsrKyjB+/HhL+bFjx7B48WKMHDnSLYESERERketJJoeTJ0/GAw88gNTUVGzfvh3Tpk3DiBEjAAAZGRl45plnEBUVhUmTJsne2datWxEREWH188orrwAAdDodEhMTodFoMGLECOzbt89q29zcXDz22GOIiYnB888/j9LSUqvydevW4aGHHkJsbCxSUlJQVVVlKTMYDEhNTcWAAQMQHx+P9957z2pbR/smIiIi8haSyaGvry9mzZqFb775Brm5uZYkDgCeeuopbN68GStWrLCMfyjHTz/9hGHDhuHAgQOWn6ysLAgh8Morr0CtVmPTpk144oknMG3aNMsQOufPn8fLL7+Mxx9/HJ9++im6dOmCV155BbW1tQCAnTt3YsmSJZg7dy7Wrl2LwsJCZGVlWfa7cOFCaLVarFmzBunp6VixYgW2bdsGAA73TURERORNJJNDeyIiInD33Xc3ebuSkhJEREQgODjY8tOxY0fk5ubi559/xt///nfceeedmDRpEmJjY7Fp0yYAwIYNG9CnTx9MnDgRd955J958802cP38eubm5AIB//etfeO655zBkyBBER0cjLS0NW7ZsQWVlJaqqqrBhwwbMnj0bffv2xdChQ/HSSy/ho48+AgCH+yYiIiLyJs1KDpvrp59+Qo8ePRotLygowN13323V2aV///7Iz8+3lA8YMMBSplKpEBUVBa1WC5PJhMLCQqtyjUYDk8mEoqIiFBcXw2AwoH///lbvXVhYiJqaGof7JiIiIvImbksODQYDzp49iz179uD3v/89hg4din/84x8wGAwoLy9vNA1f586dceHCBQCQLC8rK8O1a9dQXV1tVe7n5we1Wo0LFy6gvLwcnTp1QkBAgKW8S5cuMBqNuHz5ssN9ExEREXkTyUGwN27ciJEjRyIoKMgpOyotLUVNTQ1uueUWvPPOOzhz5gwyMjJQWVmJ6upqKJVKq/X9/f1hNBoBAHq9vlHbRn9/fxgMBty8edPy2lZ5TU2NzTKgLmHV6/V2921PdXU1ioqKZBx9y9y8edMt+/FU3n78AM8Bj9+7jx/gOfD24wd4Dtx5/JLJ4fz58zFo0CAEBQUhMjISBw8exG233dbsHfXu3Ru5ubm49dZbAQB9+vSBEAKvvvoqnn76ady4ccNqfYPBgMDAQABAQEBAo8G2DQYD1Gq1pUbQVnlgYCAUCoXNMqDu8XRAQIDdfdsTEBCAyMhIh+u1VFFRkVv246m8/fgBngMev3cfP8Bz4O3HD/AcOOP45SaXkslh9+7dMWXKFPTu3RtCCKSnp1s9mq1v4cKFsnZmTgzNevXqBaPRiJCQEBQXF1uVXbx4EcHBwQCA0NBQlJeXNyrv3bu3JUG8ePEi7rrrLgBATU0NKioqEBISAh8fH1y7dg0Gg8FSY1heXg5/f3906tQJoaGhdvdNRERE5E0k2xy+//77iIuLg59fXf7o4+MDX19fmz9y7Ny5E4MGDbKqxTtx4gQ6duwIjUaD4uJiq7EJ8/LyoNFoAAAxMTH47rvvLGV6vR4nTpyARqOBj48PoqOjkZeXZynPz8+Hr68vIiMjERkZCaVSCa1Wa/XeUVFR8PPzQ0xMjN19ExGRc+VodYjP2o0eydsQn7UbOVpda4dERPVI1hyGhoZi9uzZAIBffvkF6enp6NixY7N3NGDAAAgh8MYbb2Dy5MkoLS3FwoULMWHCBMTFxSEsLAzJycmYOnUq9uzZg4KCAmRkZAAAnnzySaxevRorVqzAsGHDsHz5coSFhWHgwIEAgPHjx2POnDmIiIhA165dkZ6ejieffNLSXjIhIQHp6enIyspCeXk5PvjgA8ybNw8AHO6biIicJ0erQ8rmQuiNJgCArkKPlM2FAICE2PDWDI2I/kcyOaxv3bp1qKqqwr///W+cOnUKJpMJPXv2xKhRo2S3Q7z11luxevVqZGZmYsyYMejQoQPGjRuHyZMnQ6FQYPny5Xj99dcxZswYdO/eHUuXLkW3bt0AAN26dcO7776LzMxM/POf/0RMTAyWL18OH5+6is9Ro0ZBp9MhLS0NBoMBw4YNQ3JysmXfKSkpSEtLw5///GcEBQVhypQplmn/fH197e6biIicJ3vHSUtiaKY3mpC94ySTQyIPoRBCCEcrFRcX46WXXoJSqUTfvn1hMplw/PhxVFdX46OPPsKdd97pjlg9jrsax7IRrncfP8BzwONvP8ffI3kbbN10FAB+zholuV17OgfN4e3HD/AcOKtDipz3kFVzmJGRgQcffBDz5s2ztEGsqalBamoqMjIysGbNmhYFS0RE3iFMrYKuQm9zORF5BlmDYB87dgwTJ060JIZA3UDTEydO5EwiREQkW9LwCKiU1h0ZVUpfJA2PaKWIiKghWclhSEgIzpw502j56dOnraadIyIisichNhyZY6IRrlZBASBcrULmmGi2NyTyILIeK48dOxZz5szB1KlTcc899wCom+946dKlGDdunEsDJCKi9iUhNpzJIJEHk5UcTpgwAXq9HosXL8bVq1cBAMHBwZgwYQJeeOEFV8ZHRERERG4kKzlUKBSYOnUqpk6dikuXLiEgIICPk4mIiIjaIVnJYX2dO3d2RRxERERE5AFkdUghIiIiIu/A5JCIiIiILGQlh0uXLsW5c+dcHQsRERERtTJZyeGHH34Ik8nkeEUiImrXcrQ6xGftRo/kbYjP2o0cra61QyIiJ5OVHI4ePRrLli1DSUkJ9Ho9amtrrX6IiKj9y9HqkLK5ELoKPQQAXYUeKZsLmSA6CRNv8hSyeivv2rULZWVl+Oyzz2yWFxUVOTUoIiLyPNk7TkJvtH6KpDea8OqGAgDgwNYtYE68zefXnHgDPK/kfrKSw+zsbFfHQURELpSj1SF7x0mcq9AjTK1C0vCIJicd5yr0NpebhGAi00JSiXf2jpM8p+R2spLDuLg4AEBZWRl+/vlnaDQa3LhxA126dHFpcERE1HLOqpUKU6ugk0gQmci0jFTiLbWcyJVktTmsrKzEtGnT8PDDDyMxMRHl5eVITU3FuHHjcOnSJVfHSERELWCvVqopkoZHQKX0lSxnItN8YWpVk5YTuZKs5HDBggW4cuUKvvrqKwQEBAAAkpOTAQDz5893XXRERNRizqqVSogNR+aYaPgqFDbLmcg0n63EW6X0RdLwiFaKiLyZrORw9+7dSElJQXj4b48Lbr/9dqSlpeHgwYMuC46IiFrOmbVSCbHhWPRMDBMZJzMn3uFqFRQAwtUqZI6J5mN6ahWy2hzevHkTSqWy0XKDwQAhhNODIiIi50kaHmHV5hBoWTJnTlha2sHFFZzR8aa1JMSGt5lYqX2TlRwOGTIEixYtwsKFCy3LTp8+jXnz5uGRRx5xVWxEROQErkjmPDGRaUnHm7acVBI5m6zkMDU1FSkpKbjvvvsghMDo0aOh1+vxwAMP4PXXX3d1jERE1EKemMw5W3OHg+EYg0TWZCWHHTp0wLvvvouzZ8+ipKQENTU16NGjB3r16uXq+Lya9V+y5/mXLBE1SXuuDbN1bM3teMMxBtuH9vz77m6ykkMAqK2txalTp3Dq1CkolUoEBQUxOXShHK0OSRsLYKyta9Opq9AjaSNnISAiedpzbZjUsXVSKVGhNzZa31HHG6nkUVehR45W55Hni4mQtfb8+94aZCWHpaWlSExMxJUrV3DHHXdACIHTp0/j9ttvx9KlS9GtWzdXx+l10j4/bkkMzYy1AmmfH+cvOhE51JLaME9PPKSOLVDpA5XSt8kdb+wN7u2JCQYTocZY++tcsoaymTVrFmJjY7F//35s3rwZW7Zswb59+9CtWzekpqa6OkavZOuvX3vLiYjqa+4jVnPioavQQ6Au8ZixPh9zcgpdEGXzSB1DRZWxWcPB2BvcuzmDhbuaswY1b084w4xzyao5PHHiBDIzMxEUFGRZ1rFjR8yYMQNjxoxxWXBERNQ8UrVhjh6x2ko8BICPc8/g3ttv84haGHvH1pyON+b1p6/Pt1nuaQkGE6HGmvv7TrbJqjnUaDQ4fPhwo+V5eXmIjIx0elAE3HpL43El7S0nIqqvuTNuSCUYAvCYmilXzCaSEBuO8DYyhV17mGovR6tDfNZu9Ejehvis3cjR6lr0fpxhxrkkaw7ffvtty/+7deuGjIwM5ObmIjo6Gj4+Pvjhhx+wfft2/OlPf3JLoN5m1D1d8VHuGZvLiYgcae7Yhvba33lKzZSrBuF29mDhrtJW4pTiijaTnjwwe1skmRwePXrU6nW/fv1w5coVfP3115ZlGo0GhYWe0w6lPdlTXN6k5UTkPaQ6jNhafjD5Uavt4rN22715Jg2PwIz1+bA191WYWuUxnVWcPW6j+bj0RhMUgOX4A5WyHrC1aJ9NPZdtPRFyVecRbxjL010kk8N169a5Mw5qgG1KiMgWqVqXo6WX8WmeTrI2xlFtTf1E5RZ/X1QarG/eKqUvBvcJbpe9ZBuem/qJ8ZUqI2asz8f09fkId2IS1tLas7acCPH+5vlkj3O4d+9enDp1CgaDwWq5QqHA5MmTnR6Yt2PjWiKyRarW5ZNvzsLUYK77+rUxjnq41k9UKg0mKH0VCPL3w1W9EWFqFQb3CXa4j4Y8pZbREVvnpj7zEdtL4Jp6rO4eesWTrgXvb55PVnKYkpKCrVu3olevXggMDLQqY3LoGknDI6wGwQYApY+izbQpISLXkKpdaZi0NVzfXm2NrUTFaBIICvBD/tzfW2q5HO2jPrk1Y46SFnO5rkIPX8XPMAnR5Bo8R/toSo2VrQSuObWAjmrPGsY8ProDmtP/M0erQ/rW47hS9dswaK1d49vW20x6A1nJ4Y4dO7BkyRIMHTrU1fFQPbUOXhOR95GaBcRXobCZvJlrY+zV1jhKVBzVrNmq8ZGqGZu+Ph/ZO05aEoGGSVX9R7h3dFbhUMllS82d+fgarmcvUZSTuNnrhGNL/fOVo9Xh1Q0FNmtUX91gPatV/YTPx871shVz9n49svdvs3u8DRPKwX2CrZoaNIyvtQaIbuttJr2BrOQwNDQUoaGhro6F6knfehymBjOkmGoF0rdyhhQib5Wj1aHSUNNoudJHgbFxv2uUCCh9FaisrkGP5G3wk+hX4SgpuiN5m91yWzU+OVqd3fc1J0Iewl8AACAASURBVGiBSh+bYyqa17H3HvXXS9pUgLTPj1segddPNBw9TjfXStbvhOKIORl2VKNqEsKSiALWibCtbczn0l4yLjWVqq2E8uPcM3aPqTXb+LXlNpPeQFZymJ6ejvT0dDz33HPo2rUrfHysv2UGDBjgkuC8Wf1HAHKWE1H7tfvUdbz02W7JZKlDoB/mJ0Tj3ttvs9TGqG9R4sbNGksto7EZjx4cJUu+CkWjGUhytDr8bYPtwaTr0xtNdmsjm8JoEpbjbFgzaG/e5IadUMwJolqlhEJR933bMGmsnww7qlEFrBNRe+sqADzZvy5hmiExGLfleGuFVQ2sVJtSR9ePbfxIiqzk8Pjx4ygqKkJycnKjMoVCgaKiIqcHRkREdcnWO4cuotokfauv+N8fjfVrY+Kzdrv0j0mV0tfm1HTZO06iVm4VnIvUf2Qq9chYgcbJmkDdlHsNh/8xJ9yd/pc0zvhfYib3UbScGjoB4KPcM9hTXC7ZdKCh+olwU2sBvaENuyd1wmlrZCWHK1aswIwZM/DHP/6xUYcUIiJynewdJ+0mhgDgo1CgR/I2qxtgU9rQNYe5/eCrGwrwx/t+h/kJ0QA8ZzgSXYUeOVodBvcJtjmhgNQZNZ+3honFs/d3x/ojZy2dBJtyfqXaF0rtX+mrkP3e5kRYKqGUfFxebxfuSKLcnai5YqBtbyIrOfT398eQIUOs5lYmIqKWc0ZP2vodNcxjHjalDV1LmITAR7lnsPHoL6iu8axuc3/bkA9fhfxEC6jLmebkFOLf35yx1IDqKvQ2E0y55CaGZkYHfww0JJWomtui1j+W+vuwNYxR/SQKANI+P25JOm+9RYm5j0U1Obmak1No1f7RHYmau4cKam9kJYd/+9vfsGDBAsycORPdunWDn5/1Zg3bIBIRkW0NH1NWGmosyYCuQo/p6/OR9vlxpD0eZfexKAD4KNDopq83mlqUyDSXpyWGQN25qW1iYmZ+vNsedAj0w7233yZ5PDqJYYz0RhPSPj+Oyuoaq+HUrlQZ8erGAhwtvYw9xeWyagFztDqbHWNcnahxoO2WkZUcvv3227h06RL27dtns5xtDomIrNmqEQSsa2mk2pVV6I2WmpWk4RGYLtFBobXb9pFnu1JltNQOSpH6w0Pqd9NUK6ySTUe1gNk7TkrWYEuN6eiMR84caLtlZCWH2dnZro6DiKjdyNHqkLSpwKpG0NyDV25CZ65ZGdwn2FVhkhdwddtTQLr9KWC/ps5HocAdydusmkCYk82NR88g99QVmISAr0KBP973OzwbIXtSNw603UKyznRcXJyr42h1BoMB8+bNw5dffgl/f3+88MILmDhxYmuHRURtUPrW443ajTWnlq+lbd2I3Mnc/vSj3DOWwbrtNYswt8W09cj5YMnlRu/79QlffG1jmhh7NY+e3lvZU3tUy0oOH374YSjsNOrdu3evs+JpNQsXLoRWq8WaNWtw4cIFzJw5E2FhYRg1alRrh0bklep/aQYH+WH2Hzo67UtT7hdy06Z2q+uReustSo5HSl7P3H7Wmc5cM+GO5G2Wz5qtWXTqd8raduy85bN4ubIa6VuPY8b6fKtmHq5IzJry/eKpPaoVQjhurbtlyxar1zU1NTh79iy2bNmC6dOn48knn3RZgO5QVVWF+++/H//85z8xaNAgAMDy5cuxf/9+fPLJJ5LbFRUVIbI5k13KYG9WgtNZ3pWwuvI8txWecA7kfOHZSpbCG3wR1y+T+lehAOx9MwX6KnCziT06iYiaIlytwi3+Pvjx10rLsvhet+HjiQMbzVldfwD1azeNNp8UmAdXr6iqm8mnsrrGZtvOhmNtmjnjPiD3PWTVHD7xxBM2l8fGxmLVqlVtPjksLi6GwWBA//79Lcv69++P5cuXo6amplHvbCJvI+cv3Ibr1B9eJWljAaD4bYgOc5nUv47+ZGViSESuZutx+MGSyxi2eC9OX6qyajpi/p+9wcvrl9lrC+oJPapbNAZNz549cfz4cWfF0mrKy8vRqVMnBAQEWJZ16dIFRqMRly9ftrMlkXdwND+t1DpmxlrR5LHbiIg80Y+/Vrr0+8wTelTLqhI7fPhwo2WVlZVYs2YNevfu7fSg3E2v18Pf399qmfm1wWCQ3K66urpVhvHxtqGDbt686XXH3FBrnwN7Y4aZ4/KEv3aJiNqyAF8Fxkd3sPl97877gKzk8MUXX2y0TKlUIjo6GvPnz3d6UO4WEBDQKAk0v1appDP4gIAAF7YDOyVZ0tptz9zNE9rbtbbWPgdh6vOSY4aZ45Jah4iIbLv1FiVu8feT1SnGWW0O5ZCVHBYXF7coGE8XGhqKa9euwWAwWGoMy8vL4e/vj06dOrVydEStT86YYbbWMVP6KKzaHBIRtVW9Q4IatTlsDpXSt1nTEbqDZJvD2tpa2T9tXWRkJJRKJbRarWVZXl4eoqKiWq0zilSPZG/rqUyeISE2HJljohGuVkGBut50mWOirb7U6q8DwDKnbbhaheynY5D9VEyjMkf/hqtV6B3SeE73QN+mzZdLRCRF6uvE1vdPfK/b8N+/PYLsp2Jw6y1Ky3JFvW3ie90GW2+pVilx6y1Kye9QTyKZ+dx99912xzY0UygUOHHihFODcjeVSoWEhASkp6cjKysL5eXl+OCDDzBv3rxWjcucCLb2I0UioC75c/RF5mid5n4RevtnwNuPH+A58PbjBzzrHMj5PmzLJMc5PHLkiORGZWVlWLJkCXQ6HUaNGoVFixa5LEB30ev1SEtLw86dOxEUFITExEQkJiba3SY/P9+qhzMRERGRp6quroZGo3G4nqxBsM1qa2uxdu1aLF26FMHBwXjjjTcwcODAFgVKRERERJ5DdoM6rVaL9PR0nD59Gn/5y18wYcIEKJVKxxsSERERUZvhMDmsqKhAdnY2Nm/ejEceeQTLli1DeHj7fc5ORERE5M3sJocbN27EokWLEBQUhKVLl2LIkCHuiouIiIiIWoFkm8Nx48ahoKAAYWFhePHFFxEYGCj5Jk899ZTLAiQiIiIi95FMDh999FF5b6BQ4KuvvnJqUERERETUOiQHwd69e7esHyaGLWcwGJCamooBAwYgPj4e7733nuS6xf+/vXsPa+LK+wD+pYB4gWpZL1vvBAkgCUmQq9eKuDyAupYtFVQKshZ1FcXaishaEWFBt+IFsKKIurBUqyAKrG1FH291QdCArIqQoIC4CqXacpMAOe8fLPMyhmDScqnxfJ4nz+OcOTPzO3OOh5M5M5PiYixatAgCgQDu7u64fft2H0baO9Qpv5+fH0xNTVmf7OzsPoy298hkMsybNw/Xr19Xmqeqqgp+fn4QCoVwcXHB5cuX+zDC3qfKOdiyZYtCGzh69GjfBdkLKioqsHLlStjY2GDmzJmIiopCc3Nzl3k1sQ9Qp/ya2gdIpVL4+vpCJBJh9uzZSEhIUJpXE9uAOuXX1DbQISQkBN7e3krX5+TkYP78+RAIBPD29kZ5eXnPB0Gofrd9+3bi5uZGioqKyPnz54lIJCKZmZkK+RoaGsi0adNIREQEkUgkJDw8nNjb25O6urp+iLrnqFp+QgiZMWMGycrKItXV1cynubm5jyPueS9evCCrV68mXC6XfP/9913mkcvlZMGCBWT9+vWktLSUxMfHE0tLS1JRUdHH0fYOVc4BIYR4enqShIQEVhtobGzsw0h7VnNzM3FxcSEBAQFEIpGQ3NxcMmfOHBIZGamQVxP7AHXKT4hm9gEymYzMnj2bbNq0iTx8+JBcvHiRiEQicubMGYW8mtgG1Ck/IZrZBjpcv36dcLlcsnTp0i7XP378mAiFQnLw4EFSWlpKAgMDiaurK2lra+vROOjgsJ81NDQQPp/P+mMYFxdHPD09FfKePHmSvPfee0wjkMvlZO7cueTrr7/us3h7mjrlr6urI1wul1RWVvZliL2utLSULFiwgMyfP7/bgdH169cJn89n/RHw8fEh0dHRfRVqr1H1HBBCiK2tLcnJyenD6HpXXl4esbCwIPX19Uza2bNnydSpUxXyamIfoE75NbUPqKysJOvWrSNNTU1M2urVq8lf//pXhbya2AbUKb+mtgFC2v8ezpkzh3h6eiodHO7Zs4f197GxsZGIRKJu+8xfQum0MtU3iouLIZPJMGXKFCZtypQpKCoqQmtrKytvYWEhrKys8NZb7dWmpaUFKysr1m9Cv27UKb9EIoGenh5Gjx7d12H2qvz8fEybNg0nTpzoNl9hYSEmT54MfX19Jm3KlCkoKCjo7RB7narnoKamBs+fP4eRkVEfRdb7OBwODh48iCFD/v83XLW0tCCTyRTyamIfoE75NbUPGDt2LPbs2YOBAweCEIKbN28iLy+vyx+Z0MQ2oE75NbUNAMDu3btha2sLW1tbpXkKCwthY2PDLA8aNAgWFhY9Xv8qvwSb6h01NTUYOnQo62f4hg8fjpaWFvz4448YOXIkK+/LfxR/97vfobi4uM/i7WnqlF8ikeDtt9/G+vXrcfPmTfz+979HQEAAZs2a1R+h9xhPT0+V8tXU1LDOB9Be/0+ePOmNsPqUqudAIpFAR0cHe/fuxZUrV/DOO+/A19cX7u7uvRxh7zE0NMTUqVOZZblcjuTkZNYXpg6a2AeoU35N7QM6mzlzJqqrqzF79mw4OzsrrNfENtDZq8qvqW1ALBbjm2++QWZmJhITE5XmU/Z34OnTpz0aD71y2M+ampowYMAAVlrH8svfnJXl7eob9utCnfJLpVI0NDTA0dERCQkJmDVrFlauXInCwsI+i7c/NTU1Kfwq0YABA9DS0tJPEfW9srIyAICZmRkOHTqEDz74AJ9//jnOnTvXz5H1nMjISNy7dw8bNmxQWKeJfcDLuiv/m9AH7N+/H/v378edO3cQGRmpsF7T28Cryq+JbUAmkyEkJASbN2/G0KFDu83bV/VPrxz2Mz09PYVK7VgeNGiQSnm7ewflb5065f/000+xatUqvP322wDaBwh37tzB8ePHIRAI+ibgfqSnp4f6+npW2ute/+pavHgx3NzcMGzYMADtbaC8vBxfffUVXFxc+jm6X4cQgoiICHz11VfYu3cvTExMFPJoYh/QQZXyvwl9AJ/PBwC8ePECQUFB2LhxI2swoMltAHh1+TWxDcTFxWHChAkq9WHK6r+jT+wp9MphPxs1ahR+/vlnVmXX1NRgwIABCt8gRo0ahZqaGlbaDz/8gBEjRvRJrL1BnfJra2szHUIHDoeD6urqPom1v2li/atLS0tLoRPkcDg9PqXS1+RyOTZv3ozjx49j9+7dcHJy6jKfprYBVcuvqX3A06dPFV4LZ2xsjJaWFoUvhJrYBtQpvya2gYyMDFy7dg0ikQgikQiHDx9Gfn4+RCKRQt6+qn86OOxn5ubm0NXVZd1MevPmTVhYWEBHh31hVyAQQCwWg/zvveWEEIjFYgiFwj6NuSepU/61a9ciNDSUlXbv3j2NejihOwKBAMXFxWhsbGTSbt68+VrXv7qioqKwYsUKVtq9e/fA4XD6KaKeERUVhYyMDMTExOAPf/iD0nya2AcAqpdfU/sAqVSKgIAA1NbWMml37tyBoaEhDA0NWXk1sQ2oU35NbANJSUnIzMxEeno60tPT4eHhAR6Ph/T0dIW8AoEAt27dYpabmppw9+7dnq//Hn32mfpFtmzZQlxcXEhhYSHJzs4mVlZWJCsrixBCSHV1NfN4f11dHbG3tyfbtm0jpaWlJCIigjg4OLzW77ciRPXynz59mvB4PHL27Fny4MEDsnfvXsLn80l5eXl/ht+jXn6NS21tLfOKj9bWVuLq6koCAgJISUkJiY+PJwKBQONe6dDdOcjNzSVmZmbk2LFjpLy8nCQnJxMLCwuSl5fXX+H+amKxmHC5XBIfH896b1t1dTUhRPP7AHXKr6l9gEwmI/PmzSPLly8nEomEXLx4kTg4OJCjR48SQjS/DahTfk1tA51FR0czr7JpbW1lvcexsrKS8Pl8sn//flJaWkrWr19P3Nzc6HsONVFjYyPZuHEjEQqFZNq0aeTw4cPMOi6XS1JTU5nlwsJCsnDhQsLj8cif/vQnUlRU1B8h9yh1yp+UlEScnJwIj8cj7u7u5MaNG/0Rcq95eWA0e/Zssm/fPmb54cOHZMmSJYTH4xFXV1dy9erV/gizV73qHGRlZRE3NzfC4/GIi4sL+fbbb/sjzB4TFRVFuFxul5+WlhaN7wPULb+m9gFVVVVkxYoVRCQSkenTp5MDBw4QuVxOCHkz/g6oU35NbQMdOg8OKysrCZfLZb3b9dKlS8TZ2ZlYWloSb2/vXhkYK/1tZYqiKIqiKOrNQ+85pCiKoiiKohh0cEhRFEVRFEUx6OCQoiiKoiiKYtDBIUVRFEVRFMWgg0OKoiiKoiiKQQeHFEVRFEVRFIMODilKA5iamuL69ev9HQaA9l8rmDNnDgQCAS5fvqywPicnByUlJQCAtLQ0zJw5s8eO7e3tDUdHR7x48YKV/ujRI5iamqK8vLzHjtVh06ZN+PTTT3t8v6qqr6/H4sWLwefzsXv3bta60NBQLFmyhJUmlUphamqKv/zlL6z077//Hubm5vjpp596JC4vLy/ExMSovZ23t7dCOdQlFothamqK7du3q5S/srISly5dAqBeW/kt/b+jqJ5EB4cURfWomJgYTJw4EVlZWbCzs1NY7+Pjgx9++KHXjl9VVYUvv/yy1/b/W3Pq1CmUl5cjPT0dy5YtY62zsbHBnTt30NbWxqTl5ORg5MiRyMvLg1wuZ9ILCgpgamqq8JvmfS0mJgb+/v6/ah9ZWVmYMGECMjMzWb/brszmzZuZn/B89913ce3aNYwdO/ZXxUBRrzM6OKQoqkfV1dWBx+Nh7NixGDhwYJ8ff8yYMTh8+DCkUmmfH7s/1NfXY9y4cTA2NsawYcNY66ytrdHU1ITS0lImLTc3F4sXL0ZLSwvu3LnDpBcUFMDW1rbP4lZm2LBhGDJkyC/evq2tDefOnYO/vz8aGhq6vHrdHW1tbYwYMQLa2tq/OAaKet3RwSFFvQHEYjG8vLwgFArh6OiIf/7zn6z1R48exYwZM2BlZYXw8HB4e3sjLS2ty301Nzfjiy++wKxZsyAUCrFy5UpUVVUBABwdHXHjxg0cOHAAjo6OCtt2pC1btow15RgXFwd7e3tYW1sjKioKnX+46cSJE5gzZw5EIhG8vLxw+/btbss6f/58TJ48GWFhYUrzODo64uTJk8xybm4uTE1N0draykwrXrhwAY6OjhCJRIiKisL9+/fh7u7OlLmxsZHZvqGhAWvWrAGfz8e8efNYMcpkMkRERMDe3h52dnZYt24dc+W041hxcXGwsbFBcHBwl/GmpaXB1dUVlpaWcHd3R25uLoD2Ke2YmBhmGvXRo0es7UaNGoXx48cz8RBCkJubi2nTpkEkEiEnJ4fJe/v2bWZwKJfLkZCQACcnJ1haWmLp0qUoLi5m8pqammLPnj2wt7eHr68vAOD8+fNwdnaGUChEeHg4qw7/+9//Yvny5bCysoKtrS2Cg4PR0NDQZVk7Tytv2rQJ4eHh+OSTTyAUCuHs7Ky0XXbIyclBbW0t3nvvPdjZ2Snk37RpE4KCgrBw4ULY2dkhICCAabPe3t4K08rPnj3Dhg0bMGXKFDg4OCAiIgKtra0Kx+2uninqdUMHhxSl4aRSKXx8fGBjY4PTp08jICAAf//733Hu3DkAwNmzZ7F3714EBwfjxIkTePToEfLy8pTub+vWrfjuu++wY8cOnDhxAq2trVi1ahXa2tpw6tQpiEQi+Pj44NSpUwrbdqTt2bMHfn5+AICnT5+itLQUKSkpCAsLw9GjR5n7vy5evMjEdvr0acycORM+Pj6orq7utsyhoaHIy8tDRkbGLzllAIBDhw5h//79CA0NxZEjR7B27Vp89tlnOHToEPLy8pCamsrkvXjxIiZNmoT09HRMnz4dvr6+zL170dHRKCgoQHx8PJKSkkAIwYoVK1iDp/z8fKSmpnY5nZqWloawsDD4+/vjzJkzmDZtGvz9/fH48WOEhITAz88PlpaWuHbtGt59912F7a2trVFYWAgAKC4uRmtrKywsLGBra8sMDsvKyvDzzz/DxsYGQPtgPTExkTnvY8eOxfLly1FfX8/s98KFC0hJSUFISAgkEgkCAwPh5eWF1NRUyGQyZpoWAMLCwqCjo4PU1FQkJiZCLBbjwIEDKtXD8ePHYW5ujrS0NEyfPh2hoaF4/vy50vyZmZng8/kYPnw4HB0dcfXqVfz444+sPGfPnsXq1atx6NAh/O1vf2PabFf3SK5ZswZVVVU4duwY4uLikJ2djUOHDinkU6WeKep1QQeHFKXhvv76a5iamuKTTz6BkZER3n//fSxduhQJCQkAgJSUFHh7e8PV1RUmJibYsWOH0ungn376CWfOnEFISAjs7e1hamqKL774AhUVFbh69SoMDQ2hq6uLQYMGwdDQUGH7jrShQ4cyU4c6OjoIDw8Hh8OBq6srzMzMmKtUCQkJ8Pf3h5OTEyZOnIhVq1aBx+Oxrvp1ZfLkyfDy8sKOHTtYAxp1rFq1CmZmZvjjH/+IYcOGwc3NDQ4ODrCxsYGtrS3KysqYvBYWFggMDISxsTE2btwIQ0NDZGRkoKmpCcnJydi2bRsEAgG4XC527twJiUSCmzdvMtt/9NFHGD9+PIyMjBTiSEpKwpIlS7Bw4UIYGRlhw4YNMDMzQ1JSEgwMDDB48GDo6OgonQq1sbFhrhzm5ubC2toa2trasLOzw61bt9DS0oLCwkLmfkNCCJKTk7FmzRrMmTMHxsbG2L59O3R0dHDmzBlmv4sWLQKHw4GJiQlSU1NhZWUFX19fGBsbY8uWLRgxYgSTt6qqCgYGBhgzZgx4PB5iY2OxcOFCleqBy+Xi448/BofDwfr169Hc3MyaJu9MJpPh/PnzcHJyAgDMnTsXra2tCl8SzM3NMXfuXFhaWsLAwIBpsy9Py5eWliI/Px87duwAj8eDlZUVQkNDWWUDoHI9U9Trgg4OKUrDSaVSCAQCVppIJGIGN/fv3wefz2fWDR06tMtBCgA8fPgQcrmctb9hw4bByMjoF9/j984770BfX59ZNjAwQHNzMxN7dHQ0RCIR87l16xYePnz4yv0GBgYCaL9K+Ut0fiBBT08Po0ePZpYHDhzIetCBx+Mx/37rrbdgbm4OqVSKyspKtLS0YMmSJUz8Dg4OaG5uxoMHD5htxowZozSOrupPKBSyBqfdsba2hlQqRWNjI3JycpiHhDrq/N69exCLxcyUcm1tLZ4/f846pq6uLng8HquOO8fc8QR05/ydl9euXYtvvvkG9vb2WLt2LYqLi5W2sZeNGzeO+XdHO+lqWhcALl26hLq6OmZwOHLkSAiFQqSnp7PyqfqwiUQigb6+PiZMmMCkzZo1Cx988AErn6r1TFGvC53+DoCiqN6lp6enkCaXy5knWLW1tRWmvpRNhXW1L6D9IYDOT8Sq4623FL+jdhy/ra0NQUFBmD59Omv94MGDX7lfAwMDbNy4EZs2beryqenOuopdR4fdPXYVp7J1crkcurq6zH47rvJ1ZmhoyEw9KzuvALq8iqvO+R4/fjyGDx+OoqIi5OfnIyAgAED7AK5jsH379m2sWbOm21hePmZ3MXfsv4OTkxMuX76M7OxsXLlyBcHBwbh27RqioqJeGX/n/XRQ1j4zMzMBtN932kEul4MQguLiYpiZmQEABgwY8MrjKjt2V15VzxT1uqFXDilKwxkbGzP3nHUQi8XMlZtJkyaxnlqtr69X+o638ePHQ0dHh7W/Z8+eoby8HBwOp8djNzIywpMnTzBhwgTmk5iYiBs3bqi0/YIFC2BtbY3IyEhWuq6uLmu6ubKy8lfF2fHeRqD9qtbdu3dhbGyMcePGQVtbG8+ePWPiNzQ0RGRkJPMQz6twOByF+issLFT5yhvQfvUwIyMDWlpaMDc3Z9Lt7OxQUFAAiUTC3G9oYGCAESNGsI7Z8WSzsmOamJigqKiIWW5ra8P9+/eZ5d27d+PJkyf48MMPERsbi/DwcPzrX/9SOX5V1NfX4/Lly/Dz80N6ejrzOXHiBHR1dRWuHqpi4sSJqK+vR0VFBZN28uRJfPTRR6x8PVHPFPVbQgeHFKUh/vOf/+DKlSusT8cLkktKShAdHY0HDx4gPT0dKSkpWLp0KYD2p0OTk5Px7bffQiqVIiQkBI2NjdDS0lI4xuDBg+Hp6YmIiAjk5OTg/v372LhxI0aNGoUZM2aoFOfgwYNRWlqKurq6V+ZdtmwZkpKScPr0aVRUVCA2NhapqalqDUS3bt2q8AALn8/H8ePHUVJSghs3buDIkSMq768rYrEYcXFxkEqlCA8PR0tLC+bPnw99fX14eHhg+/bt+Pe//w2pVIqgoCCUlJRg4sSJKu172bJlSElJQXp6Oh48eIBdu3bh3r17+PDDD1WOz9raGllZWbCxsWFd5bS1tcWlS5cwadIk1vsN/fz8EBsbiwsXLkAqleLzzz9Hc3Mz5s2b1+X+PTw8cPfuXcTGxqKsrAyRkZF48uQJs76srAxhYWG4e/cuysrK8N1338HCwkLl+FWRnZ2NlpYW+Pr6gsvlMh+BQABnZ2dkZGQonY4eMmQIKioqUFtby0qfNGkSpk6dipCQEBQXFyM/Px9ffvmlwpXsnqhnivotodPKFKUhdu3apZB26tQp8Pl8xMfHY+fOnUhMTMTo0aMRFBQEDw8PAICbmxvKy8sRGhqK5uZmeHh4YOzYsUqn1D777DMQQrBu3TrIZDJMnToVx44de+U0YwdfX1/s2rULjx8/Zt2X1hVXV1fU1tYiNjYW1dXV4HA4iIuLY139ehVjY2P4+fkhPj6eSQsMDERwcDDc3d1hZGSEwMBArFu3TuV9vuz9999nnsA1MTFBfHw8M/UdHByMnTt3Mg9TWFlZ4fDhwyq/X4ahfQAAAXlJREFUA9LZ2Rk1NTXYt28fampqYG5ujiNHjsDExETl+GxsbNDY2KjwHkM+nw8tLS2FdF9fX9TX12Pr1q2oq6uDUChEUlIShg8f3uX+J06ciAMHDiAyMhIHDx7E3LlzWV8WQkNDERYWBl9fX8hkMtjb23fZXn+NzMxMTJ8+HaNGjVJY5+XlhczMTFy9erXLbRctWoSgoCB8/PHH2LdvH2vdzp07ERYWBk9PTwwZMgTu7u7485//rLCPX1vPFPVbokXoc/YU9Ua7ceMGxo0bx7wGpbW1Ffb29oiLi3vlvXoURVGU5qFXDinqDZednQ2xWIxt27ZhyJAh+Mc//gF9fX0IhcL+Do2iKIrqB/TKIUW94err6xEWFobLly+jubkZIpEIISEhmDRpUn+HRlEURfUDOjikKIqiKIqiGPRpZYqiKIqiKIpBB4cURVEURVEUgw4OKYqiKIqiKAYdHFIURVEURVEMOjikKIqiKIqiGHRwSFEURVEURTH+D+SrieA15eetAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x14e84dc7518>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig2, (ax1, ax2) = plt.subplots(2, 1,figsize=(10, 4))\\n\",\n    \"fig.tight_layout(pad=0.4, w_pad=4.0, h_pad=6.0)\\n\",\n    \"ax1.scatter(df['n_tokens_content'], df['shares'])\\n\",\n    \"ax1.tick_params(labelsize=14)\\n\",\n    \"ax1.set_xlabel('Number of Words in Article', fontsize=14)\\n\",\n    \"ax1.set_ylabel('Number of Shares', fontsize=14)\\n\",\n    \"\\n\",\n    \"ax2.scatter(df['log_n_tokens_content'], df['shares'])\\n\",\n    \"ax2.tick_params(labelsize=14)\\n\",\n    \"ax2.set_xlabel('Log of the Number of Words in Article', fontsize=14)\\n\",\n    \"ax2.set_ylabel('Number of Shares', fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"**Figure 2-9. Scatter plot of number of words (input) vs. number of shares (target) in the Online News dataset. The top plot visualizes the original feature, and the bottom plot shows the scatter plot after log transformation.**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"将此与应用于YELP评论数据集的相同散点图进行比较。图2-10看起来与图2-9非常不同。在1到5，步长0.5的区间，平均星级是离散的。高评论计数（大致＞2500评论）与较高的平均星级评级相关。但这种关系远不是线性的。没有一种清晰的方法可以根据输入来预测平均星级。从本质上讲，该图表明，评论数及其对数都是平均星级的不良线性预测因子。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(23.625,0.5,'Average Star Rating')\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x14e84d594e0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"### 例2-11。可视化 Yelp 商户评论预测中输入与输出的相关性。\\n\",\n    \"fig, (ax1, ax2) = plt.subplots(2,1)\\n\",\n    \"fig.tight_layout(pad=0, w_pad=4.0, h_pad=4.0)\\n\",\n    \"ax1.scatter(biz_df['review_count'], biz_df['stars'])\\n\",\n    \"ax1.tick_params(labelsize=14)\\n\",\n    \"ax1.set_xlabel('Review Count', fontsize=14)\\n\",\n    \"ax1.set_ylabel('Average Star Rating', fontsize=14)\\n\",\n    \"\\n\",\n    \"ax2.scatter(biz_df['log_review_count'], biz_df['stars'])\\n\",\n    \"ax2.tick_params(labelsize=14)\\n\",\n    \"ax2.set_xlabel('Log of Review Count', fontsize=14)\\n\",\n    \"ax2.set_ylabel('Average Star Rating', fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Figure 2-10. Scatter plot of review counts (input) vs. average star ratings (target) in the Yelp Reviews dataset. The top panel plots the original review count, and the bottom panel plots the review count after log transformation.**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 数据可视化的重要性\\n\",\n    \"\\n\",\n    \"对数变换在两个不同数据集上的影响的比较，说明了可视化数据的重要性。在这里，我们故意保持输入和目标变量简单，以便我们可以很容易地可视化它们之间的关系。如图2-10所示的曲线，立即显示所选择的模型（线性）不可能代表所选择的输入和目标之间的关系。另一方面，人们可以令人信服地在给定平均星级模拟评论数的分布。在建立模型时，最好直观地检查输入和输出之间的关系，以及不同输入特征之间的关系。\\n\",\n    \"\\n\",\n    \"### 功率变换：对数变换的推广\\n\",\n    \"\\n\",\n    \"对数变换是一个称为功率变换的变换族的特殊例子。在统计方面，这些是方差稳定的变换。为了理解为什么方差稳定是好的，考虑泊松分布。这是一个方差等于它的平均值的重尾分布。因此，它的质量中心越大，其方差越大，尾部越重。功率变换改变变量的分布，使得方差不再依赖于平均值。例如，假设一个随机变量X具有泊松分布。假如我们使用开平方根变换$X$, $\\\\widetilde{X}=\\\\sqrt{X}$的方差大致是恒定的, 而不是等于平均值。\\n\",\n    \"\\n\",\n    \"![](../images/chapter2/2-11.png)\\n\",\n    \"\\n\",\n    \"**Figure 2-11. A rough illustration of the Poisson distribution. λ represents the mean of the distribution. As λ increases, not only does the mode of of the distribution shift to the right, but the mass spreads out and the variance becomes larger. The Poisson distribution is an example distribution where the variance increases along with the mean.**\\n\",\n    \"\\n\",\n    \"平方根变换和对数变换的简单推广称为Box-Cox变换：\\n\",\n    \"$$\\n\",\n    \"\\\\tilde{x}=\\\\left\\\\{\\\\begin{array}{ll}{\\\\frac{x^{2}-1}{\\\\lambda}} & {\\\\text { if } \\\\lambda \\\\neq 0} \\\\\\\\ {\\\\ln (x)} & {\\\\text { if } \\\\lambda=0}\\\\end{array}\\\\right.\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"图2-12， 展示出了在  $\\\\lambda =0$（log变换），$\\\\lambda =0.25$,$\\\\lambda =0.5$（平方根的缩放和移位版本），$\\\\lambda =0.75$, 和$\\\\lambda =1.5$时的Box-Cox变换。设置$\\\\lambda$ 小于1时压缩较高的值，并且设置$\\\\lambda$大于1时具有相反的效果。\\n\",\n    \"\\n\",\n    \"![](images/chapter2/2-12.png)\\n\",\n    \"\\n\",\n    \"**Figure 2-12. Box-Cox transforms for different values of $\\\\lambda$.**\\n\",\n    \"\\n\",\n    \"只有当数据为正值时, Box-Cox 公式才能工作。对于非正数据, 可以通过加上固定常量来移动数值。当应用 Box-Cox 变换或更一般的功率变换时, 我们必须确定参数 $\\\\lambda$ 的值。这可能是通过最大似然(找到的$\\\\lambda$,使产生的变换信号的高斯似然最大) 或贝叶斯方法。完全介绍 Box-Cox 和一般功率变换的使用超出了本书的范围。感兴趣的读者可以通过 Jack Johnston 和John DiNardo (McGraw Hill) 编写的Econometric Methods 找到更多关于幂转换的信息。幸运的是, Scipy 的数据包包含了一个 Box-Cox 转换的实现, 其中包括查找最佳变换参数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 例2-12:Yelp商户评论数的 Box-Cox 变换\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from scipy import stats\\n\",\n    \"\\n\",\n    \"# Continuing from the previous example, assume biz_df contains\\n\",\n    \"# the Yelp business reviews data\\n\",\n    \"# Box-Cox transform assumes that input data is positive.\\n\",\n    \"# Check the min to make sure.\\n\",\n    \"biz_df['review_count'].min()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Index(['business_id', 'categories', 'city', 'full_address', 'latitude',\\n\",\n       \"       'longitude', 'name', 'neighborhoods', 'open', 'review_count', 'stars',\\n\",\n       \"       'state', 'type', 'log_review_count'],\\n\",\n       \"      dtype='object')\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"biz_df.columns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Setting input parameter lmbda to 0 gives us the log transform (without constant offset)\\n\",\n    \"rc_log = stats.boxcox(biz_df['review_count'], lmbda=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"biz_df['rc_log']=rc_log\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"-0.5631160899391674\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# By default, the scipy implementation of Box-Cox transform finds the lmbda parameter\\n\",\n    \"# that will make the output the closest to a normal distribution\\n\",\n    \"rc_bc, bc_params = stats.boxcox(biz_df['review_count'])\\n\",\n    \"bc_params\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"biz_df['rc_bc']=rc_bc\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"图2-13 提供了原始和转换评论数分布的可视化比较。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(29.125,0.5,'Occurrence')\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x14e84c4b748>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"### 例2-13。可视化评论数的原始、对数转换和 Box-Cox 转换的直方图。\\n\",\n    \"\\n\",\n    \"fig, (ax1, ax2, ax3) = plt.subplots(3, 1)\\n\",\n    \"fig.tight_layout(pad=0, w_pad=4.0, h_pad=4.0)\\n\",\n    \"# original review count histogram\\n\",\n    \"biz_df['review_count'].hist(ax=ax1, bins=100)\\n\",\n    \"ax1.set_yscale('log')\\n\",\n    \"ax1.tick_params(labelsize=14)\\n\",\n    \"ax1.set_title('Review Counts Histogram', fontsize=14)\\n\",\n    \"ax1.set_xlabel('')\\n\",\n    \"ax1.set_ylabel('Occurrence', fontsize=14)\\n\",\n    \"\\n\",\n    \"# review count after log transform\\n\",\n    \"biz_df['rc_log'].hist(ax=ax2, bins=100)\\n\",\n    \"ax2.set_yscale('log')\\n\",\n    \"ax2.tick_params(labelsize=14)\\n\",\n    \"ax2.set_title('Log Transformed Counts Histogram', fontsize=14)\\n\",\n    \"ax2.set_xlabel('')\\n\",\n    \"ax2.set_ylabel('Occurrence', fontsize=14)\\n\",\n    \"\\n\",\n    \"# review count after optimal Box-Cox transform\\n\",\n    \"biz_df['rc_bc'].hist(ax=ax3, bins=100)\\n\",\n    \"ax3.set_yscale('log')\\n\",\n    \"ax3.tick_params(labelsize=14)\\n\",\n    \"ax3.set_title('Box-Cox Transformed Counts Histogram', fontsize=14)\\n\",\n    \"ax3.set_xlabel('')\\n\",\n    \"ax3.set_ylabel('Occurrence', fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Figure 2-13. Box-Cox transformation of Yelp business review counts.**\\n\",\n    \"\\n\",\n    \"概率图是一种直观地比较数据分布与理论分布的简单方法。这本质上是观察到散点图的与理论分位数。图2-14显示YELP评论数的原始数据和转换后数据相对正态分布的概率图。由于观测数据是严格正的，高斯可以是负的，所以分位数在负端上永远不会匹配。所以我们关注的是正数这的一边。在这方面，原始评论数明显比正常分布更重尾。（有序值上升到4000，而理论位数仅延伸到4）。简单的对数变换和最优的 Box-Cox 变换都使正尾部接近正态分布。最优的 Box-Cox 变换比对数变换更缩小尾部，由于尾部在红色对角线等值线下平展可以明显看出。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 例 2-14:原始和变换后的数据相对正态分布的概率图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5,1,'Probplot after Box-Cox transform')\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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GqVWnXy/Pzwd29Yd2eVpYKA33OnDm0a9eO06dP07JlS8IbYk8BIYQQNy3jafYzZ0pff+VKw7o9rSwVBvq1a9cIDg7GyckJf39/lFK1UZcQQghRZUVvSytLQ7xeXhqrBpZJTEwE4OLFizg4yFg0QgghGobSTrMX1VCvl5emwnQODw9nzpw5xMXFMXXqVGbNmlUbdQkhhBBVVt7tZw35enlpKuzl3qlTJ7Zs2VIbtQghhBDVytOz9NPtXl7a9XJ7UmGg33///eh0OtNzNzc3tm/fXqNFCSGEENVh6VLLW9XAvk6zF1VhoH/77bcAKKU4evSo6bkQQghRX0VEaNfPz57VBo9p1Ejrze7pqYW5vZxmL8qq+9D1ej0uLi4EBAQQFxdXG3UJIYQQVjHelqbTgZOT9j001DzhSloaZGfDpk0N+7a0ilR4hP7mm2+aTrlfunRJerkLIYSoN4qP/macbqT4HdbGCVfsNczBikDv2LGj6XHnzp0JDAys0YKEEEIIa1V0W1pRDXHCFVuUGeh79+4F4NZbb7VYfvjwYfr371+zVQkhhBAViIgof8CY4uxlAJmylBnoX331VZkvkkAXQghRl4yn2q1lrz3biyoz0F9//fVSl1+6dKnGihFCCCGs8fLLFZ9q1+m0a+leXvbbs72oCq+hr127lk8//ZT8/HxycnJo3759uUfvQgghRE2KiNB6rpfnZgnxoioM9D179rBnzx6WLVvGuHHjeO2116zacVBQEO7u7gC0bduWJ554gqVLl+Lo6Ej//v158cUXMRgMLFy4kOPHj6PX61myZAleXl5Va5EQQgi7FREBY8eWvd4eR4CzVoWB3qxZM/R6PZmZmXh5eZGdnV3hTnNzcwHYtGmTadnIkSNZt24d7dq1Y8KECRw7dozz58+Tl5fHli1biImJ4Y033mDDhg1VaI4QQgh7NXkyvPdeyVvSirL36+TlqTDQb7/9diIjI2nUqBFvvvkmGRkZFe40ISGB7OxswsLCKCgo4KWXXiIvLw/PP7oY9u/fnwMHDpCammq6Dc7Pz4+jR49WsTlCCCHsTUSEds28otPsHh431yn24ioM9EWLFpGcnMywYcP417/+xerVqyvcqaurK+PHjyckJITTp0/z/PPPc8stt5jWN2nShHPnzpGRkYGbm5tpuaOjIwUFBTg5lV1Wbm4u8fHxFdZglJOTY9P2DYG9tcne2gPSpobC3tpkb+0B+PzzRixZYiAnp/xBzVxdDcycmUx8/PVaqqzyaurnVGZyDh06lKCgIIKDg2nTpg0AoaGhVu20Q4cOeHl5odPp6NChA+7u7ly7ds20PjMzk1tuuYWcnBwyMzNNyw0GQ7lhDuDi4kKXLl2sqgMgPj7epu0bAntrk721B6RNDYW9tcne2hMRAfPmKZTSlbudoyP87W8OjBnTBmhTO8VVga0/J2vDv8w/eTZv3kyjRo2YMGECU6ZM4X//+5/Vbx4ZGckbb7wBQEpKCtnZ2TRu3JizZ8+ilGLv3r3cfffd+Pv7s2fPHgBiYmLw8fGx+j2EEELYn4gIaNlSu+Xs6aepMMx1Ovj445v7VLtRmYfDLVq0YNy4cYwbN47Y2Fi2bdvG22+/zZAhQ5g8eXK5Ow0ODmb27Nk89dRT6HQ6li1bhoODA6+++iqFhYX079+fnj170qNHD/bt28eTTz6JUoply5ZVewOFEELUfxERMHEiFDlpWyGdDl54QcLcqMJr6AC+vr4YDAZ0Oh3bt2+vMND1ej1vvvlmieVbt261eO7g4MCiRYtsKFcIIYS9Md6KZpxYxRoeHrBmjYR5UeUG+vnz5/niiy/4+uuv8fb2ZtSoUSxYsKC2ahNCCGHnIiK0qU7LuxWtOA8PuHy55mpqqMoM9KeffprLly8THBzMP/7xDzw8PGqzLiGEEHZu8mSwdegRvV47MhcllRnoL774In369KnNWoQQQtwkKhPmbm7awDJymr10ZfZylzAXQghREyIibAlzhYcHfPIJ3LghYV6e8u/UF0IIIapZeLh1202aBHFxCVy+LEFuDQl0IYQQtWbyZDhzpuLtJk2C9etrvh57UuY19P79+wOQn59PdnY2rVu35uLFi3h4eLBr165aK1AIIUTDZ+01c50ONm2SI/LKKDPQ9+7dC8Crr77K9OnTad26NSkpKbz++uu1VpwQQoiGr1s3iIureDtHRxn1rSoqPOWelJRE69atAWjVqhXJyck1XpQQQgj7YG2Yg4R5VVU4Upy3tzczZszA19eXmJgYAgICaqMuIYQQDVREBISFQV6e9a9xdJQwr6oKA33x4sXs2bOHU6dOMXz4cAYPHlwbdQkhhGhgKnNvudGECdVby82owlPuWVlZHDp0iN9++43CwkLOWNM9UQghxE0jIgKcnCof5tKjvXpUGOhz5syhXbt2nD59mpYtWxJu7Q2EQggh7F63bto0p7ZMrALaHwCffKKN4S5hXj0qDPRr164RHByMk5MT/v7+KFtG0BdCCGGXJk/WbjGztsNbUV27Qn6+XDOvblZNn5qYmAjAxYsXcXCQsWiEEOJm9MAD8P33VdtH165w7Fj11CMsVZjOc+fOZc6cOcTFxTF16lRmzZpVG3UJIYSoRxo3rnqYT5okYV6TKjxC//HHH9myZUtt1CKEEKKeqUrPddCulf/973J6vTZUeIT+v//9j0JbezsIIYRo0B54QLtGXtkwN3Z6k2vltafCI/SrV68SGBhI27Zt0el06HQ6Nm/eXBu1CSGEqGW2jOxWFrkNrW5UGOjvvfdebdQhhBCijjk6gsFQ+dffcQecP1999QjbVHjK3cnJidWrVzN//nz27dvH5cuXa6MuIYQQtaBNG+3Uuk5X+TA3nl6XMK9bFQb6vHnzePzxx8nLy+Puu+9m6dKltVGXEEKIGtKtmznEL1yo/H6MA8PIdfL6ocJAz83NpW/fvuh0Ojp27IiLi0tt1CWEEKKaGYO8qtfIGzXSglxCvH6pMND1ej0//vgjBoOBmJgY9Hp9bdQlhBCiGhhHdKuOIAcYPBiysqq+H1H9rJptbfny5Vy9epUPP/yQhQsX1kJZQgghqkKv106FVxfpuV7/VRjot99+O6tXr66NWoQQQlRSRAQ8/XSnat+vBHnDUWag9+/fH4D8/Hyys7Np3bo1KSkptGjRgl27dtVagUIIIUqn0xVfUn1zbeh0sGmTXCdvSMr86e/du5e9e/cSGBjIjh07TF++vr61WZ8QQog/GK+FG79qwqRJWoc3g0HCvKGp8JR7UlISrVu3BqBVq1YkJyfXeFFCCCGq/zp4WQYPhp07a/597J7BADk52kw2daDCQPf29mbGjBn4+voSExNDQEBAbdQlhBA3nebN4dq12nu/m3oqU6W07voZGdrXjRsVP65oO2P3/w8+gLCwWm9ShYE+Y8YMYmJiOHnyJMOHD2fw4MG1UZcQQti9mjptXp5GjRrgbWdKQW6u9cFrbQgrZd37OzmBuzu4uZm/u7lB+/bmx8Z17u7w0EM1+nGUWWZFG0yaNIl//vOfDBw4sBbKEUII+1MXwV1crfZWz8+3PXjLCGGf69chMxOsnfXTwaFk8Lq7awPNF19e2nalPW4gA6pVGOhNmzbl448/pkOHDjg4aH3ojD3ghRBCWKrb8FaAuQCrDkALCrTArEzwlnUknJdnfcmlhWvLltChA7i5kZ6XRwsvr9KDtrQQdnWt6x9Cnakw0Js3b05CQgIJCQmmZRLoQoibWePGkJ1d+++rw0BjsnDnBm5k4EaGxeOw4LMM6+9sDtcXrQhhWxrSqFHJEG3WDNq2Lf8It6x1jRtrR9TlSImPp0WXLlX85G4OFQb6lClTSE5OpnXr1rRt27Y2ahJCiDpX9YM8RSOySw3eyj/OLP8tI//4Aq2LfGkhettt1p1mLu25o2NVPxRRg8oM9MzMTKZPn861a9do06YNp0+fxsPDg7feegs3N7farFEIIaqVOaw7//FdoSfPIkTvrWIIu5GBI9bNR1qAIzdwJwM30/cM3DiLp+nxDdzJxI0FK8sO4ZPJydzl7w9NmmiBLm4qZQb6m2++ybBhwwgKCjIt++yzz1ixYgWLFi2qleKEEKJU+fmmU8ZdPS1D1Jqw/amU5c4UWPXWBnQlgvcG7lzkdk5xZ4nlFT3OwI1cXCh67bsoaztiAxTodNq9b+KmVGagJyQkMH/+fItlISEhREZGlvEKIYQoyVFXSBMyyz2StfXo15Vc0/4rmkAsgyYlgjQND07T3qbgNT7OphFlhW9VybjpoirKDHQnp9JXOVbzNRSDwcDChQs5fvw4er2eJUuW4OXlVa3vUZqICAgPh7NnwdMTli4tf5jDsrYvbTmYl7VooT2/csX8OC1NuxRVWAgeHtrAQpl/XBpzcNAGG9LpyvvLvHNZKxooe2sPNNw2KZqQWUagXseNAzaHcCHWd7rKxrXE0et1buECd9gcvNoV5yaoahzfvDrZcuQthDXKDPRmzZpx5MgRevToYVp25MgRmjZtWq0F7Ny5k7y8PLZs2UJMTAxvvPEGGzZsqNb3KC4iAiZMMA+ucOaM9hxKD/Wytt+3Dz7+2HL5uHFaGBvv2khLM++n6GPjLZVFl4EW5lDRP3Z7uyXD3toDtdMmhSs5Nl3TrWi7JmTigHVJk4u+1BC9TEubg1e7PtyEwor76TZIEt6iNpT5r+f//u//mDRpEvfeey/t2rUjKSmJAwcOVHvYRkVFERgYCICfnx9Hjx6t1v2XJjy85EhJWVna8tICvaztN24sOdZBbYy7LBom52Kdrqoawm5k4IR1g20U4sAN3EuEaBJtK3XaOQM38pFOV0VJaIu6Vmagt23blsjISHbv3s25c+fw9fVl2rRpNK7mQeczMjIses07OjpSUFBQ5in/3Nxc4uPjrd5/Tk5Oie3Pnu1MaUdQZ88q4uMTSlle+vaFharU5aLhc6Sg0td3y1qnx7q/9oydroqH6CVuIxHvUtdV9Li8TlcCsOqshCIu7niZa234b6nGlPb/XUMnbbJeuee3XFxcePDBB6v9TYtyc3MjM9N8b6XBYCgzzI01dbFhkIH4+PgS23t6aqfHi/P01JW677K2d3TUWT0aoag5OgzldrqqTAg3Isfq98+kcYkQvUIL0y1Hxa8JW9Ppqr5e97U/xj/KdVYcYeuA+j3ASWn/3zV00iasDv86v2Dl7+/PDz/8wPDhw4mJicHHx6fG33PpUstr4qANWGTs0Gbt9mPHWl5DB3B2tryGLopTNCar0sFbWgg3wfqZJnJwKTVEL3J7ubcVldfpyoAMtlHfWHv6Oz4+we7CQty86jzQhwwZwr59+3jyySdRSrFs2bIaf0/jdXJre7mXt32/fnXRy722TvUrXMitcvAWD2FrO13l41TqYBuXaVlh0JbV6aoA5xr+zIzs8XJM9bSpWTO4erXq1QghLNV5oDs4ONTJQDVjxpR/m5q125e3vKaUeVSRl2f9vL3WPq7qDEdupcxwVOzxmbQ0vLp1K7HO2cWFFkCLav30aoc9HvnZY5uEsCd1Hug3LeMMR9bO21vksefFi9rhe/FtqjrD0a23QseOtk0rWA0zHGXFx4MEhRBCVIkEujUMBvP0guVNGWjL4xzrO10Vn+FI5+ioTbBQ0QxHZU20YMUMR0IIIRqWmzvQf/wRIiMrDuHMCmY4KsrFpfQQtXaGo+KPmzQpMcPRGTvs9SmEEKJqbu5A/+47rZu6u7tlkHp62h68bm4yw5EQQog6c3MH+qJF2pcQQgjRwMmFVCGEEMIOSKALIYQQdkACXQghhLADOqUa1hxBMTExuLi41HUZQgghRK3Izc3Fz8+vwu0aXKALIYQQoiQ55S6EEELYAQl0IYQQwg5IoAshhBB2QAJdCCApKYkuXbowcuRI09cjjzxCZGSkzfsKDQ3l22+/tek169ats2rWwbCwMK5cuWLTvvfu3cugQYMIDg4mMTGRl156yabXl6ZTp04211GW3bt3s2bNmmrZV0U2bNjAwIEDmT17dq28nxC16eYeKU6IIlxdXdm+fbvpeUpKCg8//DDdu3enc+fOdViZ2b59+2x+zVdffUVISAiTJ0/mp59+4vfff6+ByirvyJEjpKen18p7RUZGsmrVKu6fMGuvAAAgAElEQVS+++5aeT8hatNNE+iJiYmMGjWK/fv3N+jb3rKyspg+fTrp6ek0atSIlStX0qJFQ5wx3OzGjRvMmDGDjIwM8vPzmTVrFr169arrsmjVqhVeXl6cPn2auLg4IiMjyc7Oxs3NjU2bNvHuu+/y1Vdf4ejoSIcOHZg3bx633norAN999x0bN24kLS2Npk2b8uWXX5KUlERoaCiBgYEcPnwYpRTz588vES4nT55k0aJFXLt2DZ1OR1hYGEFBQaajyrFjx7Jx40Zat25tes3ly5eZP38+aWlppKam0qZNG95++23+9a9/8f333+Pi4sKNGzfYuXMnKSkpjB8/ng8++IDo6GhWrVpFdnY2Dg4OvPjiiwwaNIjPP//cor0dOnTg+PHj6PV6lixZYlFvWZ/DmTNnmDNnDunp6dx6660opXjkkUd47LHHTK89fPgwmzdvprCwEHd3d7y8vCze9/3332fhwoWcOXOGa9eu0aRJE1atWkXHjh0JDQ3Fz8+P6OhokpOT6du3L4sXL8ZgMLB48WKio6Nxdnambdu2vP7664SHh5OSkkJ4eDgvv/wyrq6uzJ49m9tuuw2lFEFBQTz33HMkJSUxZswYvL29OX/+PG+88Qavvvoq/fr14+jRoxQWFjJ16lS2bNnCb7/9Rvfu3XnrrbdwqMMZDPPz85kzZw7nz58nLy+PSZMmMXjw4DqrpzoUFhYyd+5cfv/9dxwdHXn99dfx9PSs67KqLC0tjccee4wPP/wQb2/v6t25ugncuHFDPf/886pPnz4qJyenrsupko8++kitW7dOKaXUtm3b1OLFi+u4oqpbs2aN+uijj5RSSiUmJqqgoKBar+HcuXPKz8/PYll0dLTq3bu3unDhgtq2bZvq3bu3unHjhlJKqcjISPXEE0+ozMxMpZRSa9euVWFhYUoppZ5++mk1ceJE9dprr6khQ4aoXr16qd27d6tz584pHx8f9eWXXyqllNq9e7fq16+fysvLU2vXrlWvvfaays/PV4MHD1Y7duxQSil18eJFFRgYqKKjo5VSSvn4+Ki0tLQS9f/9739X77//vlJKKYPBoJ577jn1wQcfKKWUmjlzpvrb3/6mlFLq4MGD6qGHHlJKKXXt2jU1dOhQde7cOdN7DRgwQJ0/f96ivTt27FAzZ85USil16NAh9cILL5jqKO9zGDVqlIqIiFBKKXXq1CnVs2dPtW3bthK1G9uulCrxOX/zzTcWv+Pz5s1TixYtMn3OU6dOVYWFherGjRuqf//+6sCBA+qXX35Rw4YNUwaDQSml1IoVK1RUVJRSSqlBgwap2NhYtXHjRtWzZ081cOBApZRS169fVyNGjFD/+c9/TD+nX375xfS74ePjo3bu3KmUUmr+/Plq0KBB6saNGyonJ0f169fPtP+6EhkZqZYsWaKUUurKlSvqvvvuq9N6qsN3332nZs2apZTSfm9feOGFOq6o6vLy8tTkyZPV0KFD1alTp6p9/3Z/hK6UYt68efzlL39h8uTJdV1OlT377LMUFhYCcOHCBVq2bFnHFVXds88+i/6PWeoKCwvr7AxKTk4OI0eONNXRvHlzVq5caToS7tSpE25ubgDs2bOHxx57jMaNGwPwzDPP8N5775GXlwdAcHAweXl5DB06lMWLF7N//368vb1p2rQpI0aMAOC+++7D0dGR48ePm2o4ffo0ubm5DB06FNDOEgwdOpQff/yx3LMWY8eO5ddff+Wjjz7i9OnTnDx5kp49e5bb3piYGFJTU5kyZYppmU6nM9VjbG9UVBSBgYEA+Pn5cfToUdP2ZX0Oly9fJjY2lk8++QQAb29v+vTpU249RkU/52HDhtGuXTs2bdrEmTNn+Pnnny0+h0GDBuHg4ICbmxteXl6kp6fTt29fHB0dCQkJoX///jz44IP4+vpavEerVq3Izc3Fw8MDAHd3dx577DH27NlDz549cXJyshjIw9nZmfvvvx8AT09PevXqZarxtttuq7VLBmUZNmwYDz74oOm5Y7EplxuiBx54gIEDBwL283/d8uXLefLJJ9m4cWON7N+uAv2zzz7j448/tlh2xx13MHz48HpzDdQWpbVn2bJl+Pr68swzz3DixAk++uijOqqucsprU2pqKjNmzGDOnDl1Ulvxa+jFGUMLwGAwoNPpAK1NH374IQUFBQQFBXHhwgXOnTvHuHHj+Omnn1BK4eSk/VMr/h+twWCwWFZYWGjar5FSioKCgnJrX7lyJbGxsTz++OPce++9FBQUoCoYM6qwsBBvb28+++wz07KUlBRatGjBv//9b1N7MzIyTOFVvA1FPwfj84KCAtMfZUVrsDZkin7On376KVu3bmXMmDGMGDGCZs2akZSUZFrv6upqeqzT6VBKccstt7B9+3aio6M5ePAgr7zyCuPHj2fMmDGmbY3hXJSxdgC9Xm/6mYEW6EXb6ezsbFVbakuTJk0A7Wc1depUXnnllTquqHo4OTkxc+ZMvvvuO9auXVvX5VTJ559/TosWLQgMDKyxQLerXu4hISH85z//sfj6/fff2bZtG6GhoaSmphIWFlbXZVqttPYYjzT+8Y9/EBERUS09lmtTWW06fvw4zz77LNOmTeOee+6p6zIrFBgYyLZt28jKyiIkJIQ///nP9O7dm6+//poePXoQExODUorMzEySk5NNR7hXrlxhz549AOzatQtnZ2d8fHxM++3YsSNOTk7897//BbSA3bFjB3/6058ALRRLC/e9e/cyduxYgoKC8PDwYP/+/aYzOUU5OjqSn58PaEfbZ86c4ZdffgEgPj6eBx98kJSUFIvXuLm5kZmZaXpuMBhK/RwANm3aRO/evXF3d8ff35/PP/8cgHPnznHgwIESf6yU1yZjux599FFCQkLo0KEDu3btKrVdRf3www88++yz9OrVi5deeomgoCCLswrGNnXp0oW0tDRA68fxxRdfmD7nhig5OZlnnnmGkSNHms4C2YPly5ezY8cO5s2bZ/o9a4i2bdvG/v37CQ0NJT4+npkzZ5Kamlqt72FXR+il+e6770yP77//fj788MM6rKbq3n//fVq1akVQUBCNGze2i1Nrp06d4uWXX+btt99uMGdSgoODSU5OJiQkBIPBgJeXF6tWrTKtN57CvXr1Ku3bt6dPnz4kJSXh4uLC9u3bWbVqFa6urrz77rsWP0NnZ2fWr1/PkiVLWLduHYWFhUyZMsV0unrYsGGEhoaybt06iz8EpkyZwooVK1izZg3Ozs74+/tz9uzZEnXfeeeduLi4EBwczGeffcbatWtZsWIFubm5KKVYsWIFbdu25eeffza9xt/fnx9++IHhw4cTExODj4+PKfTL+xyWL19OeHg4n376Ka1ataJt27YWR9RGffr04dVXX2Xx4sV069bNYl1YWBjz58833T7o5+fHiRMnyv3ZDBgwgD179vDwww/TuHFjmjZtyuLFi0tsN2fOHF544QVGjBhBXl4eI0aM4LHHHuP8+fPl7r8+unz5sumz6tu3b12XUy2++OILUlJSmDhxIo0aNUKn0zXo/+8iIiJMj0NDQ1m4cKGpE211uanGcr///vv55ptvGnQv98uXLzNz5kzy8vIoLCxk+vTpBAQE1HVZVTJp0iSOHz9OmzZtAO3oacOGDXVcVfX46aef2Lx5M6tXryYpKYkRI0Zw6NChui7LJgaDgYULF3LixAmUUixbtsyq3rkbNmxg6NCheHt7c+PGDR555BH++te/cuedd9ZC1RVLSkriL3/5C1u3bq3rUqpsyZIlfPPNN3Ts2NG07K9//Wupf0A1FFlZWcyePZvLly9TUFDA888/zwMPPFDXZVULY6BXdy/3myrQhahLDTXQK+ubb75hw4YNODg4UFhYyFNPPcXo0aPruiwh7JYEuhBCCGEH7KpTnBBCCHGzkkAXQggh7IAEuhBCCGEHGtxtazExMTb1Us/NzW3QvdpLY29tsrf2gLSpobC3Ntlbe0DaZNy+6MiFZWlwge7i4kKXLl2s3j4+Pt6m7RsCe2uTvbUHpE0Nhb21yd7aAw2rTREREB4OZ8+CpycsXQpFBig0sbVN8fHxVm0np9yFEEKIUkREQPv24OCgfS8yNkyp206YAGfOgFLa9wkTyn9NdZNAF0IIcdMoLaTLWmZLQIeHQ/GRabOytOW1pcGdchdCCCFsYTwVfuYM6HRaQIP2fNw4bdkfEyWagrtRo7IDurTT6KWMtFzu8pogR+hCCCEaNOMRtk4HTk7a95YttS+dDkJDtaAGc5gb5eebw9woKwv+mLenhLIC2tPTtuU1QQJdCCFEgxMRYQ7sp582B7ZxMr60NHMoV+d4qGUF9NKlUGTmX0B7vnRp9b13RSTQhRBC1GtFj8AdHMwhXtZRdHXw8LAtoMeMgY0bwctLq8/LS3te2un5miLX0IUQQtSpiAiYOBEyM0tbazmlcnXPPuLsbHkNHbTgXrNGe2zNbWhGY8bUboAXJ4EuhBCi1pUf4kXpqv29jR3jvLzMR9xlBbfNAa2U9gZ1QAJdCCFEjZs8Gd57r/qPsK1VPMSLB3Wljqxzc+HIEYiKMn/FxcH69Vr3+VomgS6EEKJaRURAWFjJ3uO1wcEBDAbtGjjAlSvWnS6vUE4OxMaagzs6Go4e1brJAzRrBgEBMHUq/PnPVW5HZUigCyGEqLS6DO+iPDy0697Vcg07OxsOHzYHd1QUHDsGBQXa+hYttPCePh38/bXHHTrU2al2Iwl0IYQQFYqIgLFjzbeF1QfVEuJZWebwLnra3NjQli21wB4+XPseEGDuyl7P2BToJ0+eJCMjAwcHB9566y1eeOEF+vbtW1O1CSGEqEOTJ8OGDXVdhcLYMa7KAZ6ZCTExluEdH6+dowe49VYtsB95xBze7drVy/AujU2BvmDBAsLDw1m3bh3Tpk1j5cqVEuhCCGEn6jrA3dy0jnNFAzs+PqFys61lZMChQ5bXvBMSzOHdqpUW2I89Zg7vNm0aTHiXxqZAd3Jy4q677iI/Px8/Pz8K69O5FyGEEDbp1k07u1yXdDp44QWtY3ilXb9uDm/jNe/jx81d6lu31gI7JMR8zfuOOxp0eJfGpkDX6XRMnz6dAQMG8PXXX9OoUaOaqksIIUQ1e+AB+P77uq2hygGenm555B0VBSdOmNe3aaMF9pNPmo+8W7eultrrO5sCffXq1Rw5coT77ruPgwcPsnr16pqqSwghRDVo3FjrtF1XXF3hb3+r5HXva9dofPAg/Oc/5vA+dcq8vm1bLbCfftoc3q1aVVvtDY1Nga7X64mOjmbHjh0MHDiQ9PR0mjVrVu5rgoKCcHd3B6Bt27a8/vrrpnVbt25l8+bNODk5MWnSJAYNGlSJJgghhAAIC2vHwYN1W8OkSZU8+r5yxXy63HjqPDERL+N6T08tsJ99Vvvu7w+33VZ9hdsBmwJ9zpw5DBgwgF9++YWWLVsSHh7OJ598Uub2ubm5AGzatKnEutTUVDZt2sS2bdvIzc1l9OjR9OvXD71eb2MThBDi5uToaO7jpWlSa+89eDDs3FnJF6elWV7vjoqC3383r2/fXgvt8eM527IlnkFBWg90US6bAv3atWsEBwfz5Zdf4u/vj6pgDL+EhASys7MJCwujoKCAv/zlL/j5+QEQGxtLr1690Ov16PV6PD09SUhIwNfXt/KtEUIIO1ZxH66a7eRVqRC/fNnyendUlHmuU4COHbXwnjDBfORtHOYNyIyPlzC3ks0DyyQmJgJw8eJFHBzKn33V1dWV8ePHExISwunTp3n++ef59ttvcXJyIiMjw3QqHqBJkyZkZGRU+P65ubnEx8dbXW9OTo5N2zcE9tYme2sPSJsaivreph49OlFYWDykazK0LQ/SvL1z+fe/f7dYVt7H5ZiWhuuxY7jGxdHo2DFcjx3D+eJF0/q8du3I6daN7OBgcrp2JadrVwxNm1ru5NIl7esP9f1nVBk11SabAj08PJw5c+aQmJjI1KlTWbBgQbnbd+jQAS8vL3Q6HR06dKBZs2akpqbSunVr3NzcyCwyzU5mZqZFwJfFxcXFpnsS4+PjK3cPYz1mb22yt/aAtKmhqI9tqss7qQYP1hU7AncFyvh8Ll60vN4dFQVJSeb1d90FAweaO6v16oW+WTP0wC021FQff0ZVZWubrA1/mwK9U6dObNmyxertIyMjOXHiBAsXLiQlJYWMjAxu/ePUia+vL2+//Ta5ubnk5eWRmJiIj4+PLeUIIUSDV5cBfscdcP68FRteuGB5vTsqSlsGWgN8fGDAAPM93r16QfEjb1HjbAr0+++/H12R3z43Nze2b99e5vbBwcHMnj2bp556Cp1Ox7Jly9i0aROenp4MHjyY0NBQRo8ejVKKadOm4eLiUvmWCCFEA1CyI1vtKrcXulJaUBe/5m08ba7TQefOMGiQxZE3VpxdFTXPpkD/9ttvAVBKcfToUdPzsuj1et58802LZf7+/qbHo0aNYtSoUbaUIIQQDUpEhHabdO0wj3sO4OxczixoSmmnyIsGd3Q0pKRo6x0ctPAeMsQc3n5+2visol6y+T50o4CAAN56661qL0gIIRq6ujqN7u2dy6lTriVXKAVnz5a8VSw1VVvv4ABdu8KwYebw7tkTmtTebXCi6mwK9DfffNN0yv3SpUsV9nIXQoibRV2EeNeu2jTdRvHxv4PqrN0WVvzI+/JlbSNHR20Q94cfNl/z7tlTG1JONGg2BXrHjh1Njzt37kxgYGC1FySEEA1BXQS4g0Ox+ciVgt9+NwV3uz17tElJrlzR1js5QffultOB+vqCzMNhl6wK9L179wKYeqgbHT58mP79+1d/VUIIUQ/VRYibxu9SChITYUuRo+7oaLh6VVvv7IzTnXdaTgfao4c2mLq4KVgV6F999VWZ6yTQhRD2TK+H/Pzae79mzeBqmkGbhCQqCmZEmwM8Pd1cVI8e2nSgxvDu3p3ff/vN7u7ZFtazKtCLTqhS1KUio/kIIYQ9qa2jcR0GDAknLa95Nz+kzfEN4OKinSZ/6inzNe/u3bVQF6IIm66hr127lk8//ZT8/HxycnJo3759uUfvQgjRUHTrBnFxNfseDhTiwwniNxUJ70OHoPMfw167uGgd1MaMMR95d+um3X8mRAVsCvQ9e/awZ88eli1bxrhx43jttddqqi4hhKhxXbt2qrF9O1BIZxIIIIoAoni5f7QW3pmZEIp2bdvPD8aONYd3ly4S3qLSbAr0Zs2aodfryczMxMvLi+zs7JqqSwghaoTlqfTqOa/uSIFFeAcQhR8xNCFL26BxYzD4QViYeUaxLl20XuhCVBObfptuv/12IiMjadSoEW+++aZVs6MJIURde+AB+P770tbYHuiOFNCVOIvw7slhGvPHAU6TJtpwqP7PmY+8O3fW7v8WogbZFOiLFi0iOTmZYcOG8a9//YvVq1fXVF1CCFEl1THkqhP5dOOYRXj7EksjcgC4gRvugb0gYKI5vH18JLxFnbAq0IcOHUpQUBDBwcG0adMGgNDQ0BotTAghKqOyvdOdyaM7RwkgCn+iTeHtSi4A13EnGn/WM5npEVp4u991lzbaixD1gFWBvnnzZrZv386ECRNo06YNo0aN4r777qvp2oQQwmq2BLmeXFN4G796cAQXtJlMrtGUaPxZx0tEEUA0/njdfyfffe/AwJopX4gqsyrQW7Rowbhx4xg3bhyxsbFs27aNt99+myFDhjB58uSarlEIIUplTYi7kEMPjliEd3eOokcbLeYqzYgigDW8bNriNzqicDCP0iZEA2BzF0tfX18MBgM6nY7t27dLoAshalV5Ie5KNr7EmoLbn2i6cxRnCgC4QnOiCOAt/mIK79/pQNHOcRLioqGyOtDPnz/PF198wddff423tzejRo1iwYIFNVmbEEKYFA/yRmTRk8Om690BRNGNYzihzV5yGQ+iCGAlM0zhfQYvLHu2a/OHS4gLe2BVoD/99NNcvnyZ4OBg/vGPf+Dh4VHTdQkhbnJFA7wxmfTlsMVp8y7Em8L7ErcSRQD/ZgTR+BNFAGfxpLzb0pSC+PgEGftc2A2rAv3FF1+kT58+NV2LEELgpsvAjximFgnvziTgiAGAi7QiigC+IMi0RRJtseaecjkSF/bMqkCvbJjn5+czZ84czp8/T15eHpMmTWLw4MGm9R999BGRkZG0aNECgNdee81iznUhhJ27cQMOHWLafeZr3tdJwAEteZO5nSgCiCTYFN4XuANbB4SRIBc3gxodd/DLL7+kWbNmrFy5kqtXr/Loo49aBPqxY8dYvnw53bt3r8kyhBD1wfXr2ljmf0xKcvaLKNpmncABxWrgPHcQRQBbeMIU3hdpXem3GzwYdu6svvKFqO9qNNCHDRvGgw8+aHruWGz0pGPHjrFx40ZSU1MZOHAgEydOrMlyhBC1JT1dm7+76JSgJ0+aVp+jLdEE8FfGmK55p3B7tby1HI2Lm5VOqYp//fv37w9op9Czs7Np3bo1Fy9exMPDg127dlX4JhkZGUyaNIlRo0YxYsQI0/J33nmH0aNH4+bmxosvvshTTz3FoEGDyt1XTEwMLi4uFb6nUU5ODq6urlZv3xDYW5vsrT1wc7XJIT0d17g4XOPiaHTsGK7HjqE/d860/gyefwzO0uuPI++7SeW2aqhImb7HxR2v1B7s7edkb+0BaZORVZ03lQ2mT5+uLly4oJRS6uLFi+rll1+u8DUXLlxQjz76qPrss88slhsMBnX9+nXT808++US98847Fe4vLi7OlpJt3r4hsLc22Vt7lLLjNqWlKfXf/yr1xhtKhYQo1bGjUtpBsVKgfsdLRfKYms1SNZRvVUsuFV1d7V/V0iY7Ym/tUUraZMv2Np1yT0pKonVr7ZpWq1atSE5OLnf7y5cvExYWxvz58+nbt6/FuoyMDB5++GG+/vprGjduzE8//cTjjz9uSzlCiJp0+bLFaXPvgwfh/HnT6t/o8McR9/Om4VGvULO3tMrpdCHKZlOge3t7M2PGDHx9fYmJiSEgIKDc7d977z2uX7/O+vXrWb9+PQAhISFkZ2fzxBNPMG3aNJ555hn0ej19+/aV8eGFqCupqZbXu6Oi4OxZ83pvb3J69GDu+alE4080/lylRa2VJ0EuRMVsCvTFixezZ88eTp06xfDhwy16rJdm7ty5zJ07t8z1QUFBBAUF2VKCEKKqUlLMoW08Ai9yzfsEdxFNX6J40XTknZ7YDBK1UdVqi4S4ELaxKdCzsrI4dOgQqampeHp6cubMGby8vGqqNiFEVSUnWwZ3VJTFaXN8fKB/f6b/U7tN7BC9uE7TMnZW82EuIS5E5dkU6HPmzGHAgAH88ssvtGzZkvDwcD755JOaqk0IYYsLFyxOmV/4TxR3oPVzMaDjOJ2IYqDpHu8Y/Lhx4hY4UbdlS4gLUT1sCvRr164RHBzMl19+ib+/P0r+JQpR+5TSjrKLXu+OjoaLF7X1Dg4cM3QmigdM93jH4EcG7nVb9x+aNYOrV+u6CiHsj80DyyQmJgJw8eJFHBwcqr0gIW4mFc/nrWjHOYvpQAOIohWXACjEgTi6Es1Q85G3wY8smtR47daQv/mFqD02BfrcuXOZM2cOiYmJTJ06VaZPFaIKSoa5woszFsEdQBS3chmAAhyJoytfM9wU3ofpSTaNa7320kh4C1G3bAr0H3/8kS1bttRULULcPJSiPactpgP1J5qWpAGQjxPH6MaXPGLaIhZfcmhUl0VTVsc4CXMh6p5N58z/97//UVhYWFO11KqICGjfHhwctO8REZXbvrTlRZe1bKl9FX2s04GTk/a9ZUtwc9Me63Tg6Kh9d3AwLyv+1bVr5zLXNcQve2tPyTYpvHWJjNJtZbluJjt1D3DFwYPf6UgkIbzKKlpymX/xKJNYzz38hDs36EUMz/EBG5jMz9xbZ2FuHJctLi6hzDHbhBB1z6Yj9KtXrxIYGEjbtm3R6XTodDo2b95cU7XVmIgImDABsrK052fOaM8Bxoyxfvt9++Djjy2Xjxun/Seel6ctS0sz76foY+PfRUWXARi0KZ8r+E9SV1ETGxj7aY8OA94kWhx1+xNNc64BkIczsfhaTAd6hB7kYf38BLVJwlqIhsOmQH/vvfdqqo5aFR5uDmGjrCxteWmBXtb2Gzeag9koP796axX1lw4Dd3HS4nq3P9E05ToAueiJxddiOtCjdCcffZ3WLSEthH2yKdCdnJxMc5s/+OCDdOrUiTZt2tRUbTWm6IiWVVluJ1cfhBV0GPDhhMU1714c4hZuAJCDC4fpSQRjTFvE0bXOw7s4CXMh7JdNgT5v3jzGjRvH+vXrufvuu5k1axZbt26tqdpqjKendnq8tOW2bO/oKKFujxwopBPHLcLbjxjcyQAgG1di8OMfPGO6zzuOrhTgXG01SPAKIWxlU6Dn5ubSt29fNmzYQMeOHW2al7w+WbrU8po4QOPG2nJbth871vIaOoCzs+U1dFG/OVJAZxIsbhXzIwY3MgHIohEx+PF3njXFezxdKLR9CAchhKhRNv2vpNfr+fHHHzEYDMTExKDX16/TidYyXicPD9dOp3t6aqFd2vXzirbv16/k8qLbtvhjQqorV8yP09LMR/ceHpCTA5lafuDgoHWM0+nKO0qr3Ukyal7ttMeRAroQX+LIuzHZAGTSmEP04gPGm7ZIoDMGHCvxbpVvkxydCyEqw+bZ1pYvX87Vq1f58MMPWbhwYQ2VVfPGjCk7wG3ZvrzlNSU+PoEuXbrU3BvUshppT34+xMVZDo96+LD21xNo9wr26gUBEyEgAAICaOLjQ39HR/pXw9vb289ICFH/2RTot99+O6tXr66pWoSonLw8OHbMclzzw4chN1db7+4O/v4waZIpvPHx0U6HCCGEnbAq0Pv3145Z8vPzyc7OpnXr1qSkpNCiRQt27dpVowUKYSEvD44csZwONDbW3Gnhllu08H7xRXN433mnhCQM0j0AACAASURBVLcQwu5ZFeh79+4F4NVXX2X69OmmQH/99ddrtDhxk8vN1cK76GnzI0fMN/s3a6aF98svm8O7Y0cJbyHETcmmU+5JSUm0bt0agFatWpGcnFzu9gaDgYULF3L8+HH0ej1LlizBy8vLtH7r1q1s3rwZJycnJk2axKBBgyrRBGEXcnK0I+2i4X30KBQUaOubN9cC+y9/0b77+2vhrbOnzoFCCFF5NgW6t7c3M2bMwNfXl5iYGAICAsrdfufOneTl5bFlyxZiYmJ444032LBhAwCpqals2rSJbdu2kZuby+jRo+nXr1+D7TkvbJCdrV3j/uN6d4d9+yAx0RzeHh5aaL/6qvnIu317CW8hhCiHTYE+Y8YMYmJiOHnyJMOHD2fw4MHlbh8VFUVgYCAAfn5+HD161LQuNjaWXr16odfr0ev1eHp6kpCQgK+vbyWaIeqtrCyIibG85h0XZx6Rp2VLCjp3hscfN4e3p6eEtxBC2MimQJ80aRL//Oc/GThwoFXbZ2Rk4ObmZnru6OhIQUEBTk5OZGRk4O7ublrXpEkTMjIyKtxnbm4u8fHxVteck5Nj0/YNQX1tky4rC9eEBFyPHcM1Lg7XY8dw+e03dH/MOFPg4UFO167kPP882V27ktOtGwW3305Obi6urq7aTrKyICGhDltRPerrz6gqpE31n721B6RNtrAp0Js2bcrHH39Mhw4dcPij45GxB3xp3NzcyDSOmIJ2Td3JyanUdZmZmRYBXxYXFxeb7u+Nj4+3u/uB60WbMjLg0CHLW8USEszTxd1+u3a0PXq06Zq3U5s2uOl0uBXbVb1oTzWTNjUM9tYme2sPSJuM21vDpkBv3rw5CQkJJBQ5giov0P39/fnhhx8YPnw4MTEx+Pj4mNb5+vry9ttvk5ubS15eHomJiRbrRT1y/bo5vI2nzo8fNw9pdscdWmiHhJhPm99xR93WLIQQNxmbAn3KlCkkJyfTunVr2rZtW+H2Q4YMYd++fTz55JMopVi2bBkfffQRnp6eDB48mNDQUEaPHo1SimnTpjXYseHtSnq65ZF3VBScOGFe36aNFthPPWUO79tvr7t6hRBCAFYGemZmJtOnT+fatWu0adOG06dP4+HhwVtvvWVxjbw4BwcHFi1aZLHM29vb9HjUqFGMGjWqkqWLKrt2zbKzWlQUnDplXt+unRbYoaHmW8Vataq7eoUQQpTJqkB/8803GTZsGEFBQaZln332GStWrCgR2KKeunLFMryjo7VbxYy8vLTQHjfOHN633lp39QohhLCJVYGekJDA/PnzLZaFhIQQGRlZI0WJKkpLs7zeHRUFv/9uXt+hgxbY48ebw7tly7qrVwghRJVZFejGnunFOTpWZlpJUa0uX7Y8ZR4VBWfOmNd37Ah33w0TJ5rD2ziPqxBCCLthVaA3a9aMI0eO0KNHD9OyI0eO0LRp0xorTJTi0iWIisLj22+1ydajouDcOfP6O++EPn1gyhQtvHv10oZMFUIIYfesCvT/+7//Y9KkSdx77720a9eOpKQkDhw4YBrGVdSAixctr3dHRUFSEgC3gTb9Z//+5qPuXr20yUqEEELclKwK9LZt2xIZGcnu3bs5d+4cvr6+TJs2jcaNG9d0fTeHCxdK9ja/cEFbp9NBp04wYIDpNrHjjRvTqXfvuq1ZCCFEvWL1feguLi48+OCDNVmL/VNKC+ri17wvXtTW63TQuTPcf7/5Hm8/Pyg2gp7BzoZBFEIIUXU2DSwjbKCUdoq8aHBHR0NKirbewQG6dIGhQ83h3bMnlHNfvxBCCFEWCfTqoJS5k1rRa96pqdp6R0fo2hX+/GfzNe+ePaFJk7qtWwghhN2QQLeVUtptYcWPvC9f1tY7OkL37vDww+Yjb19fkP4GQgghapAEenmU0gZkKR7eV65o652ctPAeOdIyvI1TgQohhBC1RALdSCltKNTi4X3tmrbe2Rl69IDHHzeHd/fuEt5CCCHqhZs70OPi4KOPzOGdnq4t1+u1I+0nntCudxvDW2aDE0IIUU/d3IG+bp0W6L6+ltOBduumhboQQgjRQNzcgb5+vRbqZYxVL4QQQjQUDnVdQJ3S6STMhRBC2IWbO9CFEEIIOyGBLoQQQtgBnVJK1XURtoiJicFFepsLIYS4SeTm5uLn51fhdg0u0IUQQghRkpxyF0IIIeyABLoQQghhByTQhRBCCDsggS7sRlJSEl26dGHkyJGmr/9v787joqr3P46/hh0lRcXccSGXMhFxAW9umQlXo9DckkhcytCr5pa7mRtuqem9rpWV13JPbU+zfmZdARfEfcEVRcIFBdnh+/vjxMAkKsjBgeHzfDx4AOec+Z7Pd0Z5z/me75zz8ssvs3nz5gK3FRgYyA8//FCgxyxdupTp06c/dLsBAwZwM/sGP/m0d+9enn/+eXr06EFUVBTDhg0r0OPz0rFjR3x8fHjllVfw8/Oja9eubNiwodDt5paamsrixYvx9/c37mfVqlXoNXXn119/5cMPP9SlrYdZvnw5HTp0YMKECY9lf0IUlFxVRVgUBwcHtm/fbvw9NjaWl156iWeffZZGjRqZsbIcv//+e4Ef8+2339KzZ0+GDBlCaGgo58+f16WWBQsW0KRJEwBiYmLw8fGhXbt2VKtWrdBtK6UYMmQIdevWZcOGDdjb23Pr1i0GDx5MUlIS77zzTqH3ceTIEW5n34OhiG3evJkFCxbQokWLx7I/IQqq1AR6VFQUvXr14o8//ijRH3tLSkpi9OjR3L59G0dHR+bPn0/FihXNXVahJCQkMHbsWBITE0lPT2f8+PE0a9ZMl7arVKlC7dq1uXDhAsePH2fz5s0kJyfj5OTE2rVr+c9//sO3336LtbU1devWZcqUKVSuXBmAnTt3smrVKlJSUvDz8yM4OJjo6GgCAwNp27Ythw8fRinF1KlT7/kjf+bMGaZPn050dDQJCQlMnjwZf39/49Fdv379WLVqlUlwXr9+nalTp3Ljxg3i4uKoUaMGixcv5quvvuLnn3/G3t6ehIQEdu3aRWxsLAMHDuTjjz/m4MGDLFiwgOTkZKysrPjXv/7F888/z9atW+/p74Nk/5sqU6YMAPv372fevHkkJycTFxdHhQoVcHFxoWHDhhw9epR169Zx8+ZNunXrxoIFC/D29jZpLzw8nHPnzrFq1Sqsra0BqFChAvPmzePKlSsAXLt2jWnTpnHlyhWUUvj7+zNo0CD27dvHiBEj2LFjB5UrV6Zfv354e3szdOhQY/uHDx9m/fr1ZGZm8sQTT1C7dm2T/q5cuZJp06Zx8eJF4uPjKVu2LAsWLKBevXoEBgZSvXp1fv75Z8qVK0fr1q2ZMWMGWVlZzJgxg4MHD2Jra0vNmjUJCQlh0qRJxMbGMmnSJEaMGIGnp2eedUdHRxMQEICbmxtXrlxhzpw5jBkzhueee46jR4+SmZnJ8OHD2bBhA+fOnePZZ59l4cKFWFkVbrA0PT2diRMncuXKFdLS0ggODuaFF14oVJvmlpmZyeTJkzl//jzW1taEhITg6upq7rIK7caNG3Tv3p1PPvkENzc3fRtXpUBCQoJ68803lbe3t0pJSTF3OYWyZs0atXTpUqWUUlu2bFEzZswwc0WF9+GHH6o1a9YopZSKiopS/v7+j9TO5cuXlYeHh8mygwcPqpYtW6qrV6+qLVu2qJYtW6qEhASllFKbN29WvXv3Vnfv3lVKKbVkyRI1YMAApZRSr7/+uho8eLBKT09XCQkJytfXV/3666/q8uXLqkGDBmrHjh1KKaV+/fVX9dxzz6m0tDS1ZMkS9f7776v09HT1wgsvqDfffFP5+Piot99+W7Vt21YdPHhQKaVUgwYN1I0bN+6p/9NPP1UrV65USimVlZWlBg0apD7++GOllFLjxo1TH330kVJKqX379qmuXbsqpZSKj49XnTt3VpcvX1ZKKXXt2jXVrl07deXKlXv6+3fPP/+86ty5s3r55ZeVj4+PatSokVq4cKFSSqmbN2+q1q1bq4iICPXjjz+q4OBg1apVK/Xjjz+qwYMHq4CAALVy5UoVFBSkli9fnmf7H3/8sRo+fPgDX7OAgAD1ySefKKWUunPnjvLz81PffPONUkqphQsXqkGDBqmlS5eqAQMGqMzMzHsen/2cK6Xu6e/3339v8v9jypQpavr06UoppTp27Kg8PT1Vjx49VEJCgmrTpo363//+p8LDw5Wvr6/KyspSSik1b948deDAAePzFRkZ+cC6s/99hIeHK6WU8fddu3YppZSaOnWqev7551VCQoJKSUlRzz33nLH9wti8ebOaOXOmUkp77dq3b1/oNs1t586davz48Uop7d/822+/beaKCi8tLU0NGTJEde7cWZ09e1b39i3+CF0pxZQpUxg1ahRDhgwxdzmFFhQURGZmJgBXr17FxcXFzBUVXlBQEHZ/3d0uMzOzUCMoKSkpvPLKK8a2KlSowPz5841Hwg0bNsTJyQmAPXv20L17d+MR6RtvvMGKFStIS0sDoEePHtjY2ODk5ISPjw9//PEHbm5ulC9fHj8/PwDat2+PtbU1p06dMtZw4cIFUlNT8ff3p2LFimzYsIHOnTvz22+/PXDkoV+/fuzfv581a9Zw4cIFzpw5Q9OmTR/Y34iICOLi4kyOXA0Gg7Ge3P3NS+4h98uXLxMUFET9+vV54okncHV1pWnTpoSEhNC1a1eUUiQkJHDs2DE2bdqEn58fjRs3ZvDgwXm2bWVl9cBz5UlJSRw8eJBPPvkEgCeeeILu3buzZ88eunbtyrBhw+jbty9ffvklX3/9db6OYnP319fXl1q1arF27VouXrxIWFiY8fl3cHAgODiYn376CScnJ2rXrs3t27dp3bo11tbW9OzZkzZt2uDj44O7u3u+627atCk2NjYmFwGxtbWlY8eOALi6utKsWTNjjU8++aQupwx8fX3x8fEx/p49IlKSderUiQ4dOgCW87du7ty59OnTh1WrVhVJ+xYV6Js2beKzzz4zWVa9enW6dOlSbM6fFkRe/Zk9ezbu7u688cYbnD59mjVr1pipukfzoD7FxcUxduxYJk6c+Mjt//0c+t9lhzdAVlYWBoPB5PeMjAzj77n/KCqlsPnrRj5//2N59+5dRo4cSWJiIpmZmfz2229kZGTQpUsXQkNDjY/P3XZe5s+fT2RkJK+++ipeXl5kZGQ8dPJYZmYmbm5ubNq0ybgsNjaWihUr8vXXX5v092Fq1apFx44dCQ8Pp3379sbnJjExEScnJ2MfrK2tuXTpEvb29ly6dInbt2/j7OzMpEmTOHr0KAB9+vShadOmfPbZZ2RmZpo8Z5GRkaxdu5b33nvvnv7lfg0SEhKIi4vDYDBw8eLFfJ1ayt3fL774go0bNxIQEICfnx/Ozs5ER0cDULFiRRwdHY3bGgwGlFKUK1eO7du3c/DgQfbt28c777zDwIEDCQgIMKnxQXXb2dkZ/62AFui5/53Z2to+tB8FVbZsWUB7rYYPH67L/ITiwMbGhnHjxrFz506WLFli7nIKZevWrVSsWJG2bdsWWaBb1Cz3nj178s0335h8nT9/ni1bthAYGEhcXBwDBgwwd5n5lld/so8WPv/8c9atW6fLbOfH6X59OnXqFEFBQYwcOZJWrVo9llratm3Lli1bSEpKAmDt2rW0bNnSOFqwbds2lFLcvn2b77//nrZt2wJw8+ZN9uzZA8Du3bspV64cP/zwA3379uWll17iu+++w9HRkZ9++gnQRg1+/PFH/vGPfwDaG4K8wn3v3r3069cPf39/KlWqxB9//GEcjcnN2tqa9PR0ADw8PLh48SLh4eEAnDhxAh8fH2JjYwv8fCQlJREeHo67uzseHh6cO3eOyMhInJycOHv2LOHh4bRq1YqMjAzGjx/PnDlzeOmll5g0aRIAs2bNYvv27Wzfvp3XXnuNZs2aUa9ePUJCQkhNTQW0eQIzZ86kZs2aODk50bRpU9atWwdoAb5t2zbj8zRp0iRefvllQkJCGDNmDAkJCXk+F/d7o7R37166detGz549qVu3Lrt3787z+cztl19+ISgoiGbNmjFs2DD8/f2Nb1KyPaxuc4mJieGNN94wfprAUsydO5cff/yRKVOmGP+vlkRbtmzhjz/+IDAwkBMnTjBu3Dji4uJ03YdFHaHnZefOncafO3bsaBwmK6lWrlxJlSpV8Pf3p0yZMhYxtHb27FlGjBjB4sWLH+tISo8ePYiJiaFnz55kZWVRu3ZtFixYYFyfPZSakpLC66+/jre3N9HR0djb27N9+3YWLFiAg4MD//nPf0xeB1tbW5YtW8bMmTO5du0at27dYsyYMcZJY76+vgQGBrJ06VIaNGhgfNzQoUOZN28eH374Iba2tnh6enLp0qV76n7qqaewt7enR48ebNq0iSVLljBv3jxSU1NRSjFv3jxq1qxJWFjYQ5+DMWPG4ODggMFgIDk5mX/+85+8+uqrAHz44YfMmDGDuLg47t69S0hICLdv30YpRYcOHWjTpg2tWrWiR48erFu3zuQoNtuSJUtYtGgR3bt3x9ramqysLPz9/Rk4cCCgDflPnz6drVu3kpaWhp+fH927d2fdunXExMQYn4s2bdowZcoUFi9ebNK+t7c3Y8aMYcaMGTRu3Nhk3YABA5g6darxY4seHh6cPn36gc9Hu3bt2LNnDy+99BJlypShfPnyzJgx457t7ld39mS/x+369evG/rZu3dosNeht27ZtxMbGMnjwYBwdHTEYDCX67132G0DQPhY7bdo04wRcvZSqa7l37NiR77//vkTPcr9+/Trjxo0jLS2NzMxMRo8eTfPmzc1dVqEEBwdz6tQpatSoAWhHQMuXLzdzVXmLjo7Gz8+PQ4cO5Wv70NBQ1q9fz6JFi4q4sqKTlZXFtGnTOH36NEopZs+erf/sXDOIjo5m1KhRbNy40dylFNrMmTP5/vvvqVevnnHZ6tWrcXBwMGNVhZOUlMSECRO4fv06GRkZvPnmm3Tq1MncZekiO9D1/n9UqgJdiMIqaKALIcTjIoEuhBBCWACLmhQnhBBClFYS6EIIIYQFkEAXQgghLECJ+9haREREgWapp6amluhZ7XmxtD5ZWn9A+lRSWFqfLK0/IH3K3j731Qfvp8QFur29PU8//XS+tz9x4kSBti8JLK1PltYfkD6VFJbWJ0vrD0ifsrfPDxlyF0IIISxAiTtCF0IIIYqdO3cgPBwiI+GVVyDXRX4eFwl0IYQQoiAyMuDoUQgNzfk6cQKyL+tSrZoEuhBCCFHsREfDvn054X3gAGTfKKZSJfDygt69oVUr7SsfdwYsChLoQgghRLaEBNi/Pye8w8Lg6lVtnZ0deHjAwIHg7a0Feb16kOv2uOYkgS6EEKJ0ysyE48dNj76PH4esLG29mxt06KAFt5eXFubF+CN0EuhCCCFKh6tXTc97798PiYnaugoVtOHy7t218G7VClxczFtvAZk10NPT05k4cSJXrlwhLS2N4OBgXnjhBXOWJIQQwhLcvaud684d4NHR2jpbW2jaFPr1yzn6rl+/2AydPyqzBvqOHTtwdnZm/vz53Lp1i27dukmgCyGEKJjMTDh50jS8jx7VlgPUrQtt2uSEd7NmUILvFX8/Zg10X19ffHx8jL9bW1ubsRohhBAlwrVrpuEdHq5NZgMoX14bLp8wIWfo/MknzVvvY1Is7oeemJhIcHAwvXr1ws/P74HbFvRa7ikpKThY2DsxS+uTpfUHpE8lhaX1ydL6A5B66xbO587hGBlp/LKNiQFA2diQ0qABKe7uJP/1lVanDlgV74ugPsrrlJ9LxZp9UlxMTAxDhw6lb9++Dw1zkGu5g+X1ydL6A9KnksLS+lTi+5OVBadOmRx9q8hIDNlD57VrQ9u2xqFzg6cnjo6OOAIVzFp4wRTVtdzNGujXr19nwIABTJ06ldatW5uzFCGEEI/bn3/eO3R++7a2rlw5aNmSGwMH4tK1qxbiVaqYt95iTtdAP3PmDImJiVhZWbFw4ULefvvtBwb1ihUruHPnDsuWLWPZsmUArF692uKGjIQQotRLToZDh0wD/MIFbZ21NTRpAn365Exca9QIrKyIO3ECl5I86vAY6Rro7733HpMmTWLp0qWMHDmS+fPnPzDQJ0+ezOTJk/UsQQghhLllZcGZM6bhffiwdg10gFq1tNAeOlT77ukJZcuat2YLoGug29jYUL9+fdLT0/Hw8CAz+7yHEEIIy3X9uml4h4VBfLy2zskJWraEMWNyjr6rVTNvvRZK10A3GAyMHj2adu3a8d133+Ho6Khn80IIIcwtNfXeofNz57R1Vlbw7LPQs2dOeD/9tDakLoqcroG+aNEijhw5Qvv27dm3bx+LFi3Ss3khhBCPk1Jw9qxpeEdEQHq6tr5GDS20Bw/Wvjdvrh2RC7PQNdDt7Ow4ePAgP/74Ix06dOD27ds4OzvruQshhBBF5cYNbbg899D5zZvaurJloUULGDky5+i7Rg3z1itM6Prp+4kTJ1KrVi0uXLiAi4sLkyZN0rN5IYQQeklL0z4m9u9/Q2Cgdi1zFxfo0gWmT9eue96tG6xapU1oi4+HX3+FuXO1G5gUMszXrdMO5g2GB38980yjh25jMGij+gYD2Nho311ctC8rq/v/XKeOVse6ddrPuZdl15jX8gf1qSDb603XI/T4+Hh69OjBjh078PT0pBhchE4IIYRScP686dD5oUPa+XDQJql5ecHAgYze6MXqQ81JOFoOjgIfm7VyIH83TMm+42n2XOwbN3LW3e/nixehf3/tDUBaWs6yt96C33+Hzz6DpCTT5QABAffuf906bX1+ty8Kul9YJioqCoBr165hVcwvvyeEEJbEz68uUVFQnnhaEYYXocavylwHIAlHDtCcfQwzro2OqQnbDLDNzB0wg+zpALklJWkDE3//oFZSEkyalHdAT5qUE+b52b4o6BrokyZNYuLEiURFRTF8+HDee+89PZsXQohSoSB38bQhHXci8SKUKX9FdCNOAZCFgZM04hteMob3UZ4lA9siqtxy3O9T15cu6bO8KOga6A0bNmTDhg16NimEEBZBn1ttK2pz0eTI25ODOJICQCxPEooXawkkFC/Cackdyuux41LH2jrvUHd1zXt7V1dtmD2/2xcFXQO9Y8eOGHL9q3VycmL79u167kIIIYodfcL6XuW4TUvCTQK8Cn8CkIwDB/FkOcHGtRepTX7POQuNra3pOXSAMmWgXz/Tc+jZy2fNyrudWbNMz6E/bPuioGug//DDDwAopTh69KjxdyGEKOmKKrSzWZNBE46YhHcjTmKFNrn4JA35AV/j2kjcS8nQuSI/b1KsrLSJcdlH1pUqactv3oSKFfP+2dU1J3AnTdKGx7OXBQTAc8/lvTwv2cvzu31R0P1z6NmaN2/OwoUL9WxeCCGKXFEHt0ZRi8sm4d2cA5QhGYA4XAjFiy95zTh0Hl+ibhCafwYDvP02/HV/rnucOHHysdwSNq/gDQgoWCAXdHu96RroH3zwgXHI/c8//5RZ7kKIYu2ZZxo+lv04kXDP0Hk1rgGQgj2HaMYq3jKuPU9disvQeXDw/cNWFC+6Bnq9evWMPzdq1Ii2bdvq2bwQQjySTp3g55/zWqN/aFqTQWOOmYT3Mxw3Dp2fpj676GRce5impGP3kFbzy3R42tHx3o9SCculS6Dv3bsXgMqVK5ssP3z4MG3atNFjF0IIUSDr1sHrrz9sq8IHeg2iTcK7Bfspi5aiN6hIKF5soieheBFGK25RscD7yG8wP67haVE86RLo33777X3XSaALIR6Xoj7/XZZEWrDfJMBrcBWAVOyIwIOPGWhcG4UbD3rTIBfTFHrSJdBDQkLyXP7nn3/q0bwQQuSpKAPcikye4bhJeDfmGNZo1xg9ixu/0sG4NgIP0rA3Pl7CWjxuup5DX7JkCV988QXp6emkpKRQp06dBx69CyFEQRVViFfj6j1D50+QCMBNKhBGK76im3Ho/AYuKAVmnNQshAldA33Pnj3s2bOH2bNn079/f95//309mxdClEJFEeBluEtzDpgEeC2iAUjHhgg8+Ix+xrWns+rjazDgq38pQuhG10B3dnbGzs6Ou3fvUrt2bZKTk/VsXghRiugV5AayaMRJvAjFm314EcqzHMUG7bqe56jLXtoYw/t/yc1o6eBAS+Bf+pQgxGOha6BXrVqVzZs34+joyAcffEBiYqKezQshSoHCBvmTxJocebcknPLcASCe8oTRihAmMOVrL047O9OgTRvqAa8VvnQhzErXQJ8+fToxMTH4+vry1VdfsWjRIj2bF0JYqEcNcQeS8eSgSYDXQbtDRgbWHKYp6wgwrj2Z2YDOVlZ0/uvxmSdO6NMBIYoBXQK9c+fO+Pv706NHD2rUqAFAYGCgHk0LISzU/S/2kjcDWTTgtEl4uxOJLRkAXKA2oXixhOGE4sUhmpFMGZSCIUXUByGKE10Cff369Wzfvp233nqLGjVq0KtXL9q3b69H00IIC1KQI3EX4kzCuxVhOHMbgDs8QRitmMe7xlnnsVQF5ONiovTSJdArVqxI//796d+/P5GRkWzZsoXFixfz4osvMmSIvDcWojTLT4jbk0IzDpkEeD3OA5CJFUdowgZ65wyd04gsrI2PlxAXQudz6ADu7u5kZWVhMBjYvn27BLoQpUTjxnD8eH62VNTnjEl4N+UwdqQDcJmahOJlvM/3AZqTRFnTFiTAhbiHboF+5coVtm3bxnfffYebmxu9evXivffe06t5IUQx9qAwr8R1WhFmMnRekVsAJODEflqwkFHGeI+h+n33I0EuxP3pEuivv/46169fp0ePHnz++edUyr6zvBDC4jxoCN2OVDyIMDn6foooQBs6P0ZjtvCqce1xnjEZOr8fCXIhHk6XQP/Xv/6Ft7e3Hk0JIYox0zBXuBFlEt4eRGBPGgBXqE4oDCW+NAAAFfxJREFUXqzmTULxYj8tuItTvvclIS5EwegS6BLmQlg+q/h4fPjBZOjchRsA3KUM+2nBh4wwxvsVahZ4HxLiQjw63SfFCSFKptxH37ak0ZTDJkffDTnDD0AWBo7zDNt5xbj2GI3JfMQ/JxLiQuhDAl2I0k4p6lmdp0+u8G7GIRxIBSCGqoTixRr6G4fOEyj3yLtzdoZbt/QqXgiRTZdAb9OmDQDp6ekkJydTrVo1rl27RqVKldi9e7ceuxBC6CU+HsLCIDRU+woL4xxxACThyAGa82/+ZYz3y9QCHv0C63IELsTjoUug7927F4AxY8YwevRoqlWrRmxsLCEhIXo0L4R4BAYD2JCOO5EmQ+eNOGXc5jhPE0pX49qjPEsGtrrVIGEuxOOj65B7dHQ01apVA6BKlSrExMTo2bwQ4kGUgosXITSUhX1C2UsonhzEkRQAYnmSULxYSyCheBFOS+5QXtfdCyHMR9dAd3NzY+zYsbi7uxMREUHz5s3z9bjDhw+zYMEC1q5dq2c5Qli2O3cgPDxn6Dw0FGJjAQjGgYN4Gq+2FooXF6lNYYbOhRDFm66BPmPGDPbs2cPZs2fp0qULL7zwwkMfs3r1anbs2IGjo6OepQhhWTIy4OhR0/A+cSLnsLhhQ/DxAS8v8PKiXAt3XYfONYr7vSGQo3MhzE/XQE9KSuLQoUPExcXh6urKxYsXqV279gMf4+rqytKlS3n33Xf1LEWIEiHvq64pahJtct67OQcoSxIAcbj8tbSPceg8/lQFOAV8rn+N2WF94sRJnn76af13IITQhUEp/d5bDx8+nHbt2rF161bGjBnDwoUL+e9///vQx0VHRzNq1Cg2btz40G0jIiKwt7fPd00pKSk4ODjke/uSwNL6ZGn9gfz16ZlnGgIGnEikBftNArw62vyTVOw4iGeuNV6cpy6PZ+hcAYrjx7VJdKX1dSpJLK0/IH3Klp8307oeocfHx9OjRw927NiBp6cnOr5XMLK3ty/QUcKJEycs7qjC0vpkaf2BB/QpMxOOHYPQUFbzAV6E0phjWKH9XzlNfX7mBWN4H6Yp6dg95uqzGVDKAGj9KFWvUwllaf0B6VP29vmh+4VloqK0GzFcu3YNKysrvZsXomS5csX0vPf+/XD3LgDdqEgoXmymB6F4EUYrblGxyEuS891CWCZdA33y5MlMnDiRqKgohg8fLrdPFaVLYiIcOAChodTYtUu7n+iVK9o6W1vw8ID+/Y0T11waPIXMOhdC6EXXQP/tt9/YsGFDgR9Xs2bNfJ0/F6LYyMzUZpnnPvo+ehSysgBwqFUL2rUzhjceHmBh5wGFEMWLrmPi//d//0dmZqaeTRaZdeugTh2wstK+r1v3aNvntTz3MhcX7Sv3zwYD2Nho311cwMlJ+9lgAGtr7buVVc6yv38980yj+64riV8loT/VDDH4G7YRYpjAbkNH7thUgCZNYNAgbq3exI+RVZmeNYmufENl/sT+8kUMX36B4Z0RGFp7Y3B0uKdNc5DhdiEsl65H6Ldu3aJt27bUrFkTg8GAwWBg/fr1eu5CF+vWwVtvQZL2KSAuXtR+BwgIyP/2v/8On31murx/f+2PdZp2S2hu3MhpJ/fP2e97ci8D4wHeQ/7wWtowbfHqjyNJNOeAyaxzVy4DkI4Nh2lqvNpaKF6coT5K3/fGErxCiALTNdBXrFihZ3NFZtKknBDOlpSkLc8r0O+3/apVOcGcLT1d31pF0TKQRSNOmoR3E45gg/bCnqcOf/APFv219hDNSEEugiSEKH50DXQbGxvmz5/PrVu38PHxoWHDhtSoUUPPXeji0iV9lpeQswsilyeJNQnvloRTnjsA3KYcYbRiDuONs87/pIqZKxZCiPzRNdCnTJlC//79WbZsGS1atGD8+PHFcrKbq6s2PJ7X8oJsb20toV6cOZCMJwdNArwO2guZgTWRuPMFfY1rT9FQ96FzIYR4XHQN9NTUVFq3bs3y5cupV69ega7o9jjNmmV6ThygTBlteUG279fP9Bw6aJ9Oyn0OXTweBrKozxm8CMWbfXgRijuR2JIBwEVcCcWLpQwjFC8O4kkyZcxctRBC6EfXQLezs+O3334jKyuLiIgI7OzMdYWrB8s+Tz5pkjac7uqqhXZe588ftv1zz927PPe2Ff+6TsjNmzk/37iRc3RfqRKkpBivNYKVlTYxzmB40MSo+98ko2QqeH9ciMOLUFoRZvxegXgA7vAE4bRkPmONR9+xVC2Cuh/k0V8jmRAnhHgUut9tbe7cudy6dYtPPvmEadOm6dm8rgIC7h/gBdn+QcuLiqXdJOOh/UlJgYiInM9779sH589r66ystI+PefXKudNYo0a8YG3Nw+/1V3Qs7TUSQhR/ugZ61apVWbRokZ5NitJGKTh71vSCLREROR8fqFlTC+7gYO178+ZQtqx5axZCiGJAl0Bv06YNAOnp6SQnJ1OtWjViY2OpWLEiu3fv1mMXwlLduEHZPXtgwwYtvMPCtPMToAV1y5YwalTOFdeqVzdvvUIIUUzpEuh79+4FYMyYMYwePdoY6CEhIXo0LyxFaiocPmx69H32LK6gDZ03bgzdu+eE9zPPaJMNhBBCPJSuQ+7R0dFUq1YNgCpVqhATE6Nn86IkUQrOnTMN70OHcqb/V6+uhfagQVysWpXa3bvDE0+Yt2YhhCjBdA10Nzc3xo4di7u7OxERETRv3lzP5kVxduuWNlyeHd5hYXD9urauTBlo0QJGjMg5+q5Z0/jQpBMnJMyFEKKQdA30sWPHEhERwZkzZ+jSpQsvvGDOecaiyKSlQWSk6dH36dPaOoMBnn4a/Py04Pb21obSbXT9pyaEEOJvdP0rGxwczJdffkmHDh30bFaYk1Jw4YJpeB88qJ0PB6hSRQvufv207y1bQrlyZi1ZCCFKI10DvXz58nz22WfUrVsXKyvtEprZM+BFCREfD+HhpgEeF6etc3DQPiY2dGjO0Lmrq/nuBSqEEMJI10CvUKECJ0+e5OTJk8ZlEujFWHo6HDliGt65XjsaNYIuXXLCu0kT7dq2Qgghih1dA33o0KHExMRQrVo1auaa9CSKAaW0a9H+feg8OVlbX7myFtoBATlD587O5q1ZCCFEvukS6Hfv3mX06NHEx8dTo0YNLly4QKVKlVi4cCFOTk567EIU1J079w6dx8Zq6+ztwdMTBg/OOfquU0eGzoUQogTTJdA/+OADfH198ff3Ny7btGkT8+bNY/r06XrsQjxIRgYcPWoa3idO5Nzlo0ED6Nw5J7zd3aGY3jhHCCHEo9El0E+ePMnUqVNNlvXs2ZPNmzfr0bz4G5tr1+DYsZzwPnAg5x6ulSppod27t/a9VSuoUMG8BQshhChyugS6zX0+Y2wtl+0svMRE2L/f5Oi7/tWr2jo7O2jWDAYNyjn6rldPhs6FEKIU0iXQnZ2dOXLkCE2aNDEuO3LkCOXLl9ej+dIjMxOOHzcdOj92TLtBOsBTT8Hzz3Otdm2qvvIKNG2qnQ8XQghR6ukS6O+++y7BwcF4eXlRq1YtoqOj+d///sfy5cv1aN5yXb1qGt7792tH5KANk3t55dyspFUrbTgduHXiBFXlXttCCCFy0SXQa9asyebNm/n111+5fPky7u7ujBw5kjJlyujRvGW4e1c71507wKOjtXW2tuDhAUFBOUPnTz0lQ+dCCCHyTbfPodvb2+Pj46NXcyVbVpY2yzx3eB89qg2pg3aeu23bnPD28NCuwiaEEEI8Irljhh6uXTMN7/BwSEjQ1jk7a8PlL7+cM3ReubJ56xVCCGFxJNALKilJu8Ja7gC/dElbZ2OjTVQLDMw5+q5fH/66rr0QQghRVCTQHyQrC06dMg3vyMicofM6daB1a3jnHS28mzUDR0ezliyEEKJ0kkDP7c8/7x06v31bW1eunDZcPn58ztB5lSrmrVcIIYT4S+kO9KtXYcOGnAC/cEFbbm2t3VmsTx/w9tYCvGFDGToXQghRbJXuQJ8wAT7/XLund6tWOff5bt4c5CN3QgghSpDSHeirV8PcuVC1qrkrEUIIIQqldI8h29lJmAshhLAIpTvQhRBCCAshgS6EEEJYAINSSpm7iIKIiIjAXu4wJoQQopRITU3Fw8PjoduVuEAXQgghxL1kyF0IIYSwABLoQgghhAWQQBdCCCEsgAS6EEIIYQEk0IUQQggLUGoCPSoqiubNm5OammruUgolKSmJ4OBg+vbty8CBA7l586a5Syq0hIQE3n77bV5//XV69+7NoUOHzF2Sbnbu3Mno0aPNXUahZGVlMXXqVHr37k1gYCAXL140d0m6OHz4MIGBgeYuQxfp6emMHTuWvn370qNHD37++Wdzl1RomZmZTJgwgT59+hAQEMClS5fMXZIubty4Qfv27YmKitK97VIR6ImJicydOxc7Oztzl1JoGzdupHHjxnzxxRd07dqVZcuWmbukQluzZg3e3t7897//JSQkhOnTp5u7JF3MnDmTDz74gKysLHOXUii7du0iLS2NDRs2MHr0aObMmWPukgpt9erVTJ48ucS/wc+2Y8cOnJ2d+eKLL1i9ejUzZswwd0mF9ssvvwCwfv16hg8fTkhIiJkrKrz09HSmTp2Kg4NDkbRv8YGulGLKlCmMGjUKR0dHc5dTaEFBQQQHBwNw9epVXFxczFxR4QUFBdGnTx9Ae1duKRcO8vT0ZNq0aeYuo9AOHDhA27ZtAfDw8ODo0aNmrqjwXF1dWbp0qbnL0I2vry8jRoww/m5tbW3GavTRqVMn4xsTS/lbN3fuXPr06cOTTz5ZJO1b1N3WNm3axGeffWayrHr16nTp0oVGjRqZqapHl1d/Zs+ejbu7O2+88QanT59mzZo1Zqru0TyoT3FxcYwdO5aJEyeaqbpHc78+denShdDQUDNVpZ/ExEScnJyMv1tbW5ORkYGNTcn98+Hj40N0dLS5y9BN2bJlAe21Gj58OO+8846ZK9KHjY0N48aNY+fOnSxZssTc5RTK1q1bqVixIm3btmXVqlVFsg+Lv1Lciy++SNW/7qgWERGBu7s769atM3NV+oiKimLw4MHs2rXL3KUU2qlTpxg1ahTvvvsu7du3N3c5ugkNDWX9+vUsWrTI3KU8spCQEJo2bUqXLl0AaNeuHXv27DFzVYUXHR3NqFGj2Lhxo7lL0UVMTAxDhw41nke3JHFxcfTq1Ytvv/2WMmXKmLucRxIQEIDBYMBgMHDixAnq1KnD8uXLqVy5sm77KLlvsfNp586dxp87duzIJ598YsZqCm/lypVUqVIFf39/ypQpYxFDa2fPnmXEiBEsXry4RI6kWDpPT09++eUXunTpQkREBA0aNDB3SeJvrl+/zoABA5g6dSqtW7c2dzm62LZtG7GxsQwePBhHR0cMBkOJ/nuX+0AyMDCQadOm6RrmUAoC3dK8+uqrjBs3ji1btpCZmcns2bPNXVKhffDBB6SlpTFr1iwAnJycWL58uZmrEtlefPFFfv/9d/r06YNSyiL+zVmaFStWcOfOHZYtW2acKLt69eoim3z1OHTu3JkJEyYQEBBARkYGEydOtJj5NUXF4ofchRBCiNLA4me5CyGEEKWBBLoQQghhASTQhRBCCAsggS6EEEJYAAl0IYQQwgJIoAvxGM2ZM4fAwEB8fX3p0KEDgYGBDB8+nNDQUEaOHFnk+z916hTh4eEAjBw5krS0tAI9/rnnniuKsvIUHx/P119/DcCqVauIjIxk69atLFiw4LHVIERJIp9DF+IxGj9+PKBdBvLcuXOMGTMG4LFdIvann37CxcWFli1bFvur1506dYrdu3fj5+fHW2+9BWgXIRJC5E0CXYhi4uLFiwwaNIibN2/y/PPPM2zYME6dOsXMmTMBcHZ2Zvbs2TzxxBPMmTOHAwcOAPDSSy/Rr18/xo8fT3x8PPHx8axcuZKPPvqI8PBwlFIEBQXh6enJV199ha2tLY0bN+add97h+++/JyYmhsmTJ5Oeno6DgwOLFi3i+vXrzJkzh6ysLO7cucPkyZPx9PTMs+61a9eyefNmXFxcMBgMvPnmm1y5csX4hiU1NZV//vOf7N69m7CwMP79738DkJKSwty5c7G1tWX06NFUrVqVy5cv06RJE95//31WrFjByZMn2bBhA4cOHTJeejb3fr/55hsMBgNdunThjTfe4KeffmL16tXY2NhQo0YN5s2bh5WVDESK0kECXYhiIjU1lWXLlpGZmUmHDh0YNmwYU6ZMYfbs2Tz11FNs2rSJjz76CA8PD6Kjo9m4cSMZGRn07dsXb29vALy9vQkKCuL//u//iI6OZv369aSmptKrVy/Wrl1Lt27dcHFxwd3d3bjfuXPn8tZbb9GuXTu+++47jh8/zp07dxg3bhwNGzbk66+/ZuvWrXkGelxcnDFYQbuS4YOcOXOG+fPnU6VKFVasWMEPP/yAn58fFy5c4OOPP8bR0ZFOnToRFxfH22+/zfr16+nduzeHDh0yaefs2bN89913fPHFFxgMBoKCgmjTpg3ffPMNQUFBdO3alW3btpGYmEi5cuUK+9IIUSJIoAtRTNSvXx87OzsA453MoqKieP/99wHtXsp169YlKiqKFi1aYDAYsLW1pWnTpkRFRQFQt25dAE6fPs2xY8cIDAwEICMjg6tXr+a53/Pnz9OsWTMA41Hw/v37WbZsGQ4ODty9e9fkbmu5Xb161aTu7HZyy30xyipVqjBr1izKlClDbGys8U2Cq6urcR+VK1d+6H3KT58+zdWrVwkKCgLg9u3bXLp0iQkTJrBy5Uq+/PJL6tWrR6dOnR7YjhCWRAJdiGLCYDDcs6xu3brMnTuX6tWrc+DAAeLi4rC3t2fr1q0EBQWRnp7OoUOH6Natm0kb9erVw8vLixkzZpCVlcWyZcuoWbMmBoOBrKwsk324ublx5MgR/vGPf7Bjxw5u375tnHzm5ubGkiVLuHLlSp41165dm3PnzpGcnIydnR3Hjx+na9eu2NvbExcXB8CxY8eM20+ePJldu3bh5OTEuHHjjGGfV9+trKzuqTVbvXr1eOqpp/joo48wGAx8+umnNGjQgA0bNjBs2DAqVarE1KlT2blzp/G5EcLSSaALUYxNmzaNcePGkZmZCcCsWbOoW7cuYWFh9O7dm/T0dHx9fWncuLHJ4zp27EhYWBh9+/YlKSmJTp064eTkxLPPPsu8efNwc3Mzbvvuu+8ydepUli9fjoODA/PnzycjI4MhQ4ZQqVIlqlatyq1bt/Ksz9nZmaFDh/L666/j7OxMeno6AG3btuXLL7/ktddeo3Hjxsb7db/yyiv06tWLcuXK4eLiwp9//nnfvru6unL69Gk+/fTTe9Y1atSI1q1b89prr5GWloa7uztVqlTB3d2d/v374+zsTNmyZenQoUNBnm4hSjS5OYsQQjcjR46kT58+eHl5mbsUIUodmf4phBBCWAA5QhdCCCEsgByhCyGEEBZAAl0IIYSwABLoQgghhAWQQBdCCCEsgAS6EEIIYQEk0IUQQggL8P/IrybS9RxTSAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x14e8403ff60>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"fig2, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(8, 6))\\n\",\n    \"# fig.tight_layout(pad=4, w_pad=5.0, h_pad=0.0)\\n\",\n    \"prob1 = stats.probplot(biz_df['review_count'], dist=stats.norm, plot=ax1)\\n\",\n    \"ax1.set_xlabel('')\\n\",\n    \"ax1.set_title('Probplot against normal distribution')\\n\",\n    \"prob2 = stats.probplot(biz_df['rc_log'], dist=stats.norm, plot=ax2)\\n\",\n    \"ax2.set_xlabel('')\\n\",\n    \"ax2.set_title('Probplot after log transform')\\n\",\n    \"prob3 = stats.probplot(biz_df['rc_bc'], dist=stats.norm, plot=ax3)\\n\",\n    \"ax3.set_xlabel('Theoretical quantiles')\\n\",\n    \"ax3.set_title('Probplot after Box-Cox transform')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Figure 2-14. Comparing the distribution of raw and transformed review counts against the Normal distribution.**\\n\",\n    \"\\n\",\n    \"## 特征缩放或归一化\\n\",\n    \"\\n\",\n    \"某些特征的值有界的，如纬度或经度。其他数值特征 (如数量) 可能会在无界的情况下增加。那些关于输入是平滑函数的模型, 如线性回归、逻辑回归或任何涉及矩阵的东西, 都受输入的数值范围影响。另一方面, 基于树的模型不太在意这个。如果你的模型对输入特征的数值范围敏感, 则特征缩放可能会有所帮助。顾名思义, 特征缩放会更改特征值的数值范围。有时人们也称它为特征规范化。功能缩放通常分别针对单个特征进行。有几种常见的缩放操作, 每个类型都产生不同的特征值分布。\\n\",\n    \"\\n\",\n    \"### Min-max缩放\\n\",\n    \"\\n\",\n    \"设$X$是一个单独的特征值（即，在某些数据点中的一个特征值），以及 $min(x)$ 和 $max(x)$ ，分别是整个数据集上该特征的最小值和最大值。Min-max缩放压缩（或拉伸）所有特征值到$[0, 1 ]$的范围内。图2-15演示了这个概念。最小最大尺度的公式是\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\widetilde{x}=\\\\frac{x-\\\\min (x)}{\\\\max (x)-\\\\min (x)}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"![](../images/chapter2/2-15.png)\\n\",\n    \"\\n\",\n    \"**Figure 2-15. Min-max scaling**\\n\",\n    \"\\n\",\n    \"### 标准化（方差缩放）\\n\",\n    \"\\n\",\n    \"特征标准化的定义为：\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\widetilde{x}=\\\\frac{x-\\\\operatorname{mean}(x)}{\\\\operatorname{var}(x)}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"减去特征 (所有数据点) 的平均值并除以方差。因此, 它也可以称为方差缩放。缩放后的特征的平均值为0, 方差为1。如果原始特征具有高斯分布, 则缩放特征为标准高斯。图2-16 包含了标准化的说明。\\n\",\n    \"\\n\",\n    \"![](images/chapter2/2-16.png)\\n\",\n    \"\\n\",\n    \"**Figure 2-16. Illustration of feature standardization**\\n\",\n    \"\\n\",\n    \"## 不要中心化稀疏数据\\n\",\n    \"\\n\",\n    \"最小最大缩放和标准化都从原始特征值中减去一个数量。对于最小最大缩放, 移动量是当前特征的所有值中最小的。对于标准化, 移动的量是平均值。如果移动量不是零, 则这两种转换可以将稀疏特征（大部分值为零）的向量转换为一个稠密的向量。这反过来会给分类器带来巨大的计算负担, 取决于它是如何实现的。词袋是一种稀疏表示, 大多数分类库都对稀疏输入进行优化。如果现在的表示形式包含了文档中没有出现的每个单词, 那就太可怕了。请谨慎对稀疏特征执行最小最大缩放和标准化操作。\\n\",\n    \"\\n\",\n    \"### L2 normalization\\n\",\n    \"\\n\",\n    \"这项技术通过所谓的 L2 范数 (也称为欧几里德范数) 正常化 (划分) 原始特征值。\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\widetilde{x}=\\\\frac{x}{\\\\|x\\\\|_{2}}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"L2范数度量向量在坐标空间中的长度。这个定义可以从众所周知的勾股定理中得到，给出三角形两边的长度，可以得到斜边长度。\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\|x\\\\|_{2}=\\\\sqrt{x_{1}^{2}+x_{2}^{2}+\\\\ldots+x_{m}^{2}}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"L2 范数将求特征的各数据点的平方和, 然后取平方根。L2 规范化后, 该特征列具有范数1。它也可以称为 L2 缩放。(不严谨的说, 缩放意味着和常量相乘, 而规范化可能涉及许多操作。）图2-17说明了 L2 规范化。\\n\",\n    \"\\n\",\n    \"![](images/chapter2/2-17.png)\\n\",\n    \"\\n\",\n    \"**Figure 2-17. Illustration of L2 feature normalization**\\n\",\n    \"\\n\",\n    \"## 数据空间与特征空间\\n\",\n    \"\\n\",\n    \"请注意，图2-17中的说明是在数据空间中，而不是特征空间。还可以对数据点进行L2归一化，而不是特征，这将导致具有单位范数（范数为1）的数据向量。（参见[词袋](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch03.html#sec-bag-of-words)中关于数据向量和特征向量的互补性质的讨论）不管缩放方法如何，特征缩放总是将特征除以常数（也称为归一化常数）。因此，它不会改变单特征分布的形状。我们将用在线新闻文章标记计数来说明这一点。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 例子 2-15。特征缩放示例。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\ipykernel_launcher.py:7: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\\n\",\n      \"  import sys\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([219., 255., 211., ..., 442., 682., 157.])\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import sklearn.preprocessing as preproc\\n\",\n    \"\\n\",\n    \"# Load the online news popularity dataset\\n\",\n    \"df = pd.read_csv('data/OnlineNewsPopularity.csv', delimiter=', ')\\n\",\n    \"\\n\",\n    \"# Look at the original data - the number of words in an article\\n\",\n    \"df['n_tokens_content'].as_matrix()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.02584376, 0.03009205, 0.02489969, ..., 0.05215955, 0.08048147,\\n\",\n       \"       0.01852726])\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Min-max scaling\\n\",\n    \"df['minmax'] = preproc.minmax_scale(df[['n_tokens_content']])\\n\",\n    \"df['minmax'].as_matrix()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([-0.69521045, -0.61879381, -0.71219192, ..., -0.2218518 ,\\n\",\n       \"        0.28759248, -0.82681689])\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Standardization - note that by definition, some outputs will be negative\\n\",\n    \"df['standardized'] = preproc.StandardScaler().fit_transform(df[['n_tokens_content']])\\n\",\n    \"df['standardized'].as_matrix()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.00152439, 0.00177498, 0.00146871, ..., 0.00307663, 0.0047472 ,\\n\",\n       \"       0.00109283])\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# L2-normalization\\n\",\n    \"df['l2_normalized'] = preproc.normalize(df[['n_tokens_content']], axis=0)\\n\",\n    \"df['l2_normalized'].as_matrix()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们也可以可视化用不同的特征缩放方法后的数据的分布。如图2-18所示，与对数变换不同，特征缩放不会改变分布的形状；只有数据的规模发生变化。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 例2-16。绘制原始数据和缩放数据的直方图。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(29.125,0.5,'Number of articles')\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1f210dbce10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,1)\\n\",\n    \"fig.tight_layout(pad=0, w_pad=1.0, h_pad=2.0)\\n\",\n    \"# fig.tight_layout()\\n\",\n    \"\\n\",\n    \"df['n_tokens_content'].hist(ax=ax1, bins=100)\\n\",\n    \"ax1.tick_params(labelsize=14)\\n\",\n    \"ax1.set_xlabel('Article word count', fontsize=14)\\n\",\n    \"# ax1.set_ylabel('Number of articles', fontsize=14)\\n\",\n    \"\\n\",\n    \"df['minmax'].hist(ax=ax2, bins=100)\\n\",\n    \"ax2.tick_params(labelsize=14)\\n\",\n    \"ax2.set_xlabel('Min-max scaled word count', fontsize=14)\\n\",\n    \"ax2.set_ylabel('Number of articles', fontsize=14)\\n\",\n    \"\\n\",\n    \"df['standardized'].hist(ax=ax3, bins=100)\\n\",\n    \"ax3.tick_params(labelsize=14)\\n\",\n    \"ax3.set_xlabel('Standardized word count', fontsize=14)\\n\",\n    \"# ax3.set_ylabel('Number of articles', fontsize=14)\\n\",\n    \"\\n\",\n    \"df['l2_normalized'].hist(ax=ax4, bins=100)\\n\",\n    \"ax4.tick_params(labelsize=14)\\n\",\n    \"ax4.set_xlabel('L2-normalized word count', fontsize=14)\\n\",\n    \"ax4.set_ylabel('Number of articles', fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Figure 2-18. Original and scaled news article word counts. Note that only the scale of the x-axis changes; the shape of the distribution stays the same with feature scaling.**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在一组输入特征在比例上差异很大的情况下，特征缩放非常有用。例如，一个流行的电子商务网站的每日访问者数量可能是十万，而实际销售额可能是几千。如果这两种功能都投入到模型中，那么该模型需要在确定要做什么的同时平衡它们的规模。输入特征的极大变化会导致模型训练算法的数值稳定性问题。在这些情况下，标准化功能是个好主意。第4章将详细介绍处理自然文本时的特征缩放，包括使用示例。\\n\",\n    \"\\n\",\n    \"## 交互特征\\n\",\n    \"\\n\",\n    \"简单的成对交互特征是两个特征的积。类似逻辑与。它以成对条件表示结果：“购买来自邮政编码98121”和“用户的年龄在18到35之间”。这一点对基于决策树的模型没有影响，但发交互特征对广义线性模型通常很有帮助。\\n\",\n    \"\\n\",\n    \"一个简单的线性模型使用单个输入特征线性组合$x_1$，$x_2$，... $x_n$来预测结果$y$\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"y=w_{i} x_{l}+w_{2} x_{2}+\\\\ldots+w_{n} x_{n}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"一个简单的扩展线性模型的方法是包含输入特征对的组合，如下所示：\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"y=w_{1} x_{1}+w_{2} x_{2}+\\\\ldots+w_{n} x_{n}+w_{1,1} x_{1} x_{1}+w_{1,2} x_{1} x_{2}+w_{1,3} x_{1} x_{3}+\\\\ldots\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"这使我们能够捕获特征之间的相互影响，因此它们被称为交互特征。如果$x_1$和$x_2$是二元的，那么它们的积 $x_1x_2$ 是逻辑函数 $x_1\\\\ AND\\\\ x_2$ 假设问题是根据他或她的个人资料信息预测客户的偏好。在这种情况下，交互特征不是仅基于用户的年龄或位置进行预测，而交互特征允许模型基于具有特定年龄和特定位置的用户进行预测。\\n\",\n    \"\\n\",\n    \"在例2-17中，我们使用 UCI 在线新闻数据集中的成对交互特征来预测每篇新闻文章的分享数量。交互特征导致精度超过单身特征。两者都比例2-9表现得更好，它使用文章正文中单词数的单个预测器（有或没有经过对数变换）。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 例 2--17:用于预测的交互特征示例\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Index(['url', 'timedelta', 'n_tokens_title', 'n_tokens_content',\\n\",\n       \"       'n_unique_tokens', 'n_non_stop_words', 'n_non_stop_unique_tokens',\\n\",\n       \"       'num_hrefs', 'num_self_hrefs', 'num_imgs', 'num_videos',\\n\",\n       \"       'average_token_length', 'num_keywords', 'data_channel_is_lifestyle',\\n\",\n       \"       'data_channel_is_entertainment', 'data_channel_is_bus',\\n\",\n       \"       'data_channel_is_socmed', 'data_channel_is_tech',\\n\",\n       \"       'data_channel_is_world', 'kw_min_min', 'kw_max_min', 'kw_avg_min',\\n\",\n       \"       'kw_min_max', 'kw_max_max', 'kw_avg_max', 'kw_min_avg', 'kw_max_avg',\\n\",\n       \"       'kw_avg_avg', 'self_reference_min_shares', 'self_reference_max_shares',\\n\",\n       \"       'self_reference_avg_sharess', 'weekday_is_monday', 'weekday_is_tuesday',\\n\",\n       \"       'weekday_is_wednesday', 'weekday_is_thursday', 'weekday_is_friday',\\n\",\n       \"       'weekday_is_saturday', 'weekday_is_sunday', 'is_weekend', 'LDA_00',\\n\",\n       \"       'LDA_01', 'LDA_02', 'LDA_03', 'LDA_04', 'global_subjectivity',\\n\",\n       \"       'global_sentiment_polarity', 'global_rate_positive_words',\\n\",\n       \"       'global_rate_negative_words', 'rate_positive_words',\\n\",\n       \"       'rate_negative_words', 'avg_positive_polarity', 'min_positive_polarity',\\n\",\n       \"       'max_positive_polarity', 'avg_negative_polarity',\\n\",\n       \"       'min_negative_polarity', 'max_negative_polarity', 'title_subjectivity',\\n\",\n       \"       'title_sentiment_polarity', 'abs_title_subjectivity',\\n\",\n       \"       'abs_title_sentiment_polarity', 'shares', 'minmax', 'standardized',\\n\",\n       \"       'l2_normalized'],\\n\",\n       \"      dtype='object')\"\n      ]\n     },\n     \"execution_count\": 61,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn import linear_model\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"import sklearn.preprocessing as preproc\\n\",\n    \"\\n\",\n    \"### Assume df is a Pandas dataframe containing the UCI online news dataset\\n\",\n    \"df.columns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"### Select the content-based features as singleton features in the model,\\n\",\n    \"### skipping over the derived features\\n\",\n    \"features = ['n_tokens_title', 'n_tokens_content',\\n\",\n    \"            'n_unique_tokens', 'n_non_stop_words', 'n_non_stop_unique_tokens',\\n\",\n    \"            'num_hrefs', 'num_self_hrefs', 'num_imgs', 'num_videos',\\n\",\n    \"            'average_token_length', 'num_keywords', 'data_channel_is_lifestyle',\\n\",\n    \"            'data_channel_is_entertainment', 'data_channel_is_bus',\\n\",\n    \"            'data_channel_is_socmed', 'data_channel_is_tech',\\n\",\n    \"            'data_channel_is_world']\\n\",\n    \"\\n\",\n    \"X = df[features]\\n\",\n    \"y = df[['shares']]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(39644, 170)\"\n      ]\n     },\n     \"execution_count\": 63,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"### Create pairwise interaction features, skipping the constant bias term\\n\",\n    \"X2 = preproc.PolynomialFeatures(include_bias=False).fit_transform(X)\\n\",\n    \"X2.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"### Create train/test sets for both feature sets\\n\",\n    \"X1_train, X1_test, X2_train, X2_test, y_train, y_test = train_test_split(X, X2, y, test_size=0.3, random_state=123)\\n\",\n    \"\\n\",\n    \"def evaluate_feature(X_train, X_test, y_train, y_test):\\n\",\n    \"#   Fit a linear regression model on the training set and score on the test set\\n\",\n    \"    model = linear_model.LinearRegression().fit(X_train, y_train)\\n\",\n    \"    r_score = model.score(X_test, y_test)\\n\",\n    \"    return (model, r_score)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"R-squared score with singleton features: 0.00924\\n\",\n      \"R-squared score with pairwise features: 0.0113280910\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"### Train models and compare score on the two feature sets\\n\",\n    \"(m1, r1) = evaluate_feature(X1_train, X1_test, y_train, y_test)\\n\",\n    \"(m2, r2) = evaluate_feature(X2_train, X2_test, y_train, y_test)\\n\",\n    \"print(\\\"R-squared score with singleton features: %0.5f\\\" % r1)\\n\",\n    \"print(\\\"R-squared score with pairwise features: %0.10f\\\" % r2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"构造交互特征非常简单，但它们使用起来很昂贵。使用成对交互特征的线性模型的训练和得分时间将从$O(n)$到$O(n^2)$，其中$n$是单身特征的数量。\\n\",\n    \"\\n\",\n    \"围绕高阶交互特征的计算成本有几种方法。可以在所有交互特征之上执行特征选择，选择前几个。或者，可以更仔细地制作更少数量的复杂特征。两种策略都有其优点和缺点。特征选择采用计算手段来选择问题的最佳特征。（这种技术不限于交互特征。）一些特征选择技术仍然需要训练多个具有大量特征的模型。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"手工制作的复杂特征可以具有足够的表现力，所以只需要少量这些特征，这可以缩短模型的训练时间。但是特征本身的计算可能很昂贵，这增加了模型评分阶段的计算成本。手工制作（或机器学习）的复杂特征的好例子可以在第8章有关图像特征中找到。现在让我们看看一些特征选择技巧。\\n\",\n    \"\\n\",\n    \"## 特征选择\\n\",\n    \"\\n\",\n    \"特征选择技术会删除非有用的特征，以降低最终模型的复杂性。最终目标是快速计算的简约模型，预测准确性降低很小或不会降低。为了得到这样的模型，一些特征选择技术需要训练多个候选模型。换句话说，特征选择并不是减少训练时间，实际上有些技巧增加了整体训练时间，但是减少了模型评分时间。\\n\",\n    \"\\n\",\n    \"粗略地说，特征选择技术分为三类。\\n\",\n    \"\\n\",\n    \"- Filtering（过滤）: 预处理可以删除那些不太可能对模型有用的特征。例如，可以计算每个特征与响应变量之间的相关或相互信息，并筛除相关信息或相互信息低于阈值的特征。第3章讨论了文本特征的过滤技术的例子。过滤比下面的包装（wrapper）技术便宜得多，但是他们没有考虑到正在使用的模型。因此他们可能无法为模型选择正确的特征。最好先保守地进行预过滤，以免在进行模型训练步骤之前无意中消除有用的特征。\\n\",\n    \"\\n\",\n    \"- Wrapper methods（包装方法）：这些技术是昂贵的，但它们允许您尝试特征子集，这意味着你不会意外删除自身无法提供信息但在组合使用时非常有用的特征。包装方法将模型视为提供特征子集质量分数的黑盒子。shi一个独立的方法迭代地改进子集。\\n\",\n    \"\\n\",\n    \"- Embedded methods（嵌入式方法）：嵌入式方法执行特征选择作为模型训练过程的一部分。 例如，决策树固有地执行特征选择，因为它在每个训练步骤选择一个要在其上进行树分裂的特征。另一个例子是$L1$正则，它可以添加到任何线性模型的训练目标中。$L1$鼓励模型使用一些特征而不是许多特征。因此它也被称为模型的稀疏约束。嵌入式方法将特征选择作为模型训练过程的一部分。它们不如包装方法那么强大，但也远不如包装方法那么昂贵。与过滤相比，嵌入式方法会选择特定于模型的特征。从这个意义上讲，嵌入式方法在计算费用和结果质量之间取得平衡。\\n\",\n    \"\\n\",\n    \"特征选择的全面处理超出了本书的范围。有兴趣的读者可以参考 Isabelle Guyon 和 André Elisseeff 撰写的调查报告“变量和特征选择介绍”（“An Introduction to Variable and Feature Selection”）。\\n\",\n    \"\\n\",\n    \"## 总结\\n\",\n    \"\\n\",\n    \"本章讨论了许多常见的数字特征工程技术：量化，缩放（又称规范化），对数变换（一种功率变换），交互特征以及处理大量交互特征所需的特征选择技术的简要总结。在统计机器学习中，所有数据最终归结为数字特征。因此，所有道路最终都会指向某种数字特征工程技术。为了结束特征工程这个游戏，保证这些工具方便使用！\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## 参考书目\\n\",\n    \"\\n\",\n    \"Guyon, Isabell, and André Elisseeff. 2003. Journal of Machine Learning Research Special Issue on Variable and Feature Selection. 3(Mar):1157--1182.\\n\",\n    \"\\n\",\n    \"Johnston, Jack, and John DiNardo. 1997. Econometric Methods (Fourth Edition). New York: McGraw Hill.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "9.feature-engineering/3.文本数据.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[原版（英文）图书地址](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/)\\n\",\n    \"\\n\",\n    \"**翻译**：[apachecn](https://github.com/apachecn)，[翻译版本地址](https://github.com/apachecn/feature-engineering-for-ml-zh)\\n\",\n    \"\\n\",\n    \"**代码修改和整理**：[黄海广](https://github.com/fengdu78)，原文修改成jupyter notebook格式，并增加和修改了部分代码，测试全部通过，所有数据集已经放在[百度云](data/README.md)下载。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 三、文本数据: 展开、过滤和分块\\n\",\n    \"\\n\",\n    \"> 译者：[@kkejili](https://github.com/kkejili)\\n\",\n    \"> \\n\",\n    \"> 校对者：[@HeYun](https://github.com/KyrieHee)\\n\",\n    \"\\n\",\n    \"如果让你来设计一个算法来分析以下段落，你会怎么做？\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"Emma knocked on the door. No answer. She knocked again and waited. There was a large maple tree next to the house. Emma looked up the tree and saw a giant raven perched at the treetop. Under the afternoon sun, the raven gleamed magnificently. Its beak was hard and pointed, its claws sharp and strong. It looked regal and imposing. It reigned the tree it stood on. The raven was looking straight at Emma with its beady black eyes. Emma felt slightly intimidated. She took a step back from the door and tentatively said, “hello?” \\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"该段包含很多信息。我们知道它谈到了到一个名叫Emma的人和一只乌鸦。这里有一座房子和一棵树，艾玛正想进屋，却看到了乌鸦。这只华丽的乌鸦注意到艾玛，她有点害怕，但正在尝试交流。\\n\",\n    \"\\n\",\n    \"那么，这些信息的哪些部分是我们应该提取的显着特征？首先，提取主要角色艾玛和乌鸦的名字似乎是个好主意。接下来，注意房子，门和树的布置可能也很好。关于乌鸦的描述呢？Emma的行为呢，敲门，退后一步，打招呼呢？\\n\",\n    \"\\n\",\n    \"本章介绍文本特征工程的基础知识。我们从词袋（bags of words）开始，这是基于字数统计的最简单的文本功能。一个非常相关的变换是 tf-idf，它本质上是一种特征缩放技术。它将被我在（下一篇）章节进行全面讨论。本章首先讨论文本特征提取，然后讨论如何过滤和清洗这些特征。\\n\",\n    \"\\n\",\n    \"## Bag of X：把自然文本变成平面向量\\n\",\n    \"\\n\",\n    \"无论是构建机器学习模型还是特征工程，其结果应该是通俗易懂的。简单的事情很容易尝试，可解释的特征和模型相比于复杂的更易于调试。简单和可解释的功能并不总是会得到最精确的模型。但从简单开始就是一个好主意，仅在绝对必要时我们可以增加其复杂性。\\n\",\n    \"\\n\",\n    \"对于文本数据，我们可以从称为 BOW 的字数统计开始。字数统计表中并没有特别费力来寻找`\\\"Emma\\\"`或乌鸦这样有趣的实体。但是这两个词在该段落中被重复提到，并且它们在这里的计数比诸如`\\\"hello\\\"`之类的随机词更高。对于此类简单的文档分类任务，字数统计通常比较适用。它也可用于信息检索，其目标是检索与输入文本相关的文档集。这两个任务都很好解释词级特征，因为某些特定词的存在可能是本文档主题内容的重要指标。\\n\",\n    \"\\n\",\n    \"## 词袋\\n\",\n    \"\\n\",\n    \"在词袋特征中，文本文档被转换成向量。（向量只是 n 个数字的集合。）向量包含词汇表中每个单词可能出现的数目。 如果单词`\\\"aardvark\\\"`在文档中出现三次，则该特征向量在与该单词对应的位置上的计数为 3。 如果词汇表中的单词没有出现在文档中，则计数为零。 例如，“这是一只小狗，它是非常可爱”的句子具有如图所示的 BOW 表示\\n\",\n    \"\\n\",\n    \"![图3-1](images/chapter3/3-1.PNG)\\n\",\n    \"\\n\",\n    \"图 3-1 转换词成向量描述图\\n\",\n    \"\\n\",\n    \"BOW 将文本文档转换为平面向量。 它是“平面的”，因为它不包含任何原始的文本结构。 原文是一系列词语。但是词袋向量并没有序列；它只是记得每个单词在文本中出现多少次。 它不代表任何词层次结构的概念。 例如，“动物”的概念包括“狗”，“猫”，“乌鸦”等。但是在一个词袋表示中，这些词都是矢量的相同元素。\\n\",\n    \"\\n\",\n    \"![图3-2](images/chapter3/3-2.PNG)\\n\",\n    \"\\n\",\n    \"图 3-2 两个等效的词向量，向量中单词的排序不重要，只要它在数据集中的个数和文档中出现数量是一致的。\\n\",\n    \"\\n\",\n    \"重要的是特征空间中数据的几何形状。 在一个词袋矢量中，每个单词成为矢量的一个维度。如果词汇表中有 n 个单词，则文档将成为n维空间中的一个点。 很难想象二维或三维以外的任何物体的几何形状，所以我们必须使用我们的想象力。 图3-3显示了我们的例句在对应于“小狗”和“可爱”两个维度的特征空间中的样子。\\n\",\n    \"\\n\",\n    \"![图3-3](images/chapter3/3-3.PNG)\\n\",\n    \"\\n\",\n    \"图 3-3 特征空间中文本文档的图示\\n\",\n    \"\\n\",\n    \"![图3-4](images/chapter3/3-4.PNG)\\n\",\n    \"\\n\",\n    \"图 3-4 三维特征空间\\n\",\n    \"\\n\",\n    \"图 3-3 和图 3-4 描绘了特征空间中的数据向量。 坐标轴表示单个单词，它们是词袋表示下的特征，空间中的点表示数据点（文本文档）。 有时在数据空间中查看特征向量也是有益的。 特征向量包含每个数据点中特征的值。 轴表示单个数据点和点表示特征向量。 图 3-5 展示了一个例子。 通过对文本文档进行词袋特征化，一个特征是一个词，一个特征向量包含每个文档中这个词的计数。 这样，一个单词被表示为一个“一个词向量”。正如我们将在第 4 章中看到的那样，这些文档词向量来自词袋向量的转置矩阵。\\n\",\n    \"\\n\",\n    \"![图3-5](images/chapter3/3-5.PNG)\\n\",\n    \"\\n\",\n    \"## Bag-of-N-gram \\n\",\n    \"\\n\",\n    \"Bag-of-N-gram 或者 bag-of-ngram 是 BOW 的自然延伸。 n-gram 是 n 个有序的记号（token）。一个词基本上是一个 1-gram，也被称为一元模型。当它被标记后，计数机制可以将单个词进行计数，或将重叠序列计数为 n-gram。例如，`\\\"Emma knocked on the door\\\"`这句话会产生 n-gram，如`\\\"Emma knocked\\\"`，`\\\"knocked on\\\"`，`\\\"on the\\\"`，`\\\"the door\\\"`。\\n\",\n    \"N-gram 保留了文本的更多原始序列结构，故 bag-of-ngram可以提供更多信息。但是，这是有代价的。理论上，用 k 个独特的词，可能有 k 个独立的 2-gram（也称为 bigram）。在实践中，并不是那么多，因为不是每个单词后都可以跟一个单词。尽管如此，通常有更多不同的 n-gram（n > 1）比单词更多。这意味着词袋会更大并且有稀疏的特征空间。这也意味着 n-gram 计算，存储和建模的成本会变高。n 越大，信息越丰富，成本越高。\\n\",\n    \"\\n\",\n    \"为了说明随着 n 增加 n-gram 的数量如何增加，我们来计算纽约时报文章数据集上的 n-gram。我们使用 Pandas 和 scikit-learn 中的`CountVectorizer`转换器来计算前 10,000 条评论的 n-gram。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import json \\n\",\n    \"from sklearn.feature_extraction.text import CountVectorizer \\n\",\n    \"# Load the first 10,000 reviews \\n\",\n    \"f = open('data/yelp_academic_dataset_review.json') \\n\",\n    \"js = [] \\n\",\n    \"for i in range(10000): \\n\",\n    \"    js.append(json.loads(f.readline())) \\n\",\n    \"f.close() \\n\",\n    \"review_df = pd.DataFrame(js) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"备注：所有数据集已经放在[百度云](data/README.md)下载。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>business_id</th>\\n\",\n       \"      <th>date</th>\\n\",\n       \"      <th>review_id</th>\\n\",\n       \"      <th>stars</th>\\n\",\n       \"      <th>text</th>\\n\",\n       \"      <th>type</th>\\n\",\n       \"      <th>user_id</th>\\n\",\n       \"      <th>votes</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>9yKzy9PApeiPPOUJEtnvkg</td>\\n\",\n       \"      <td>2011-01-26</td>\\n\",\n       \"      <td>fWKvX83p0-ka4JS3dc6E5A</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>My wife took me here on my birthday for breakf...</td>\\n\",\n       \"      <td>review</td>\\n\",\n       \"      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\\n\",\n       \"      <td>{'funny': 0, 'useful': 5, 'cool': 2}</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>ZRJwVLyzEJq1VAihDhYiow</td>\\n\",\n       \"      <td>2011-07-27</td>\\n\",\n       \"      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>I have no idea why some people give bad review...</td>\\n\",\n       \"      <td>review</td>\\n\",\n       \"      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\\n\",\n       \"      <td>{'funny': 0, 'useful': 0, 'cool': 0}</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\\n\",\n       \"      <td>2012-06-14</td>\\n\",\n       \"      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>love the gyro plate. Rice is so good and I als...</td>\\n\",\n       \"      <td>review</td>\\n\",\n       \"      <td>0hT2KtfLiobPvh6cDC8JQg</td>\\n\",\n       \"      <td>{'funny': 0, 'useful': 1, 'cool': 0}</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\\n\",\n       \"      <td>2010-05-27</td>\\n\",\n       \"      <td>G-WvGaISbqqaMHlNnByodA</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\\n\",\n       \"      <td>review</td>\\n\",\n       \"      <td>uZetl9T0NcROGOyFfughhg</td>\\n\",\n       \"      <td>{'funny': 0, 'useful': 2, 'cool': 1}</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>6ozycU1RpktNG2-1BroVtw</td>\\n\",\n       \"      <td>2012-01-05</td>\\n\",\n       \"      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>General Manager Scott Petello is a good egg!!!...</td>\\n\",\n       \"      <td>review</td>\\n\",\n       \"      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\\n\",\n       \"      <td>{'funny': 0, 'useful': 0, 'cool': 0}</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"              business_id        date               review_id  stars  \\\\\\n\",\n       \"0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \\n\",\n       \"1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \\n\",\n       \"2  6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  IESLBzqUCLdSzSqm0eCSxQ      4   \\n\",\n       \"3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  G-WvGaISbqqaMHlNnByodA      5   \\n\",\n       \"4  6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \\n\",\n       \"\\n\",\n       \"                                                text    type  \\\\\\n\",\n       \"0  My wife took me here on my birthday for breakf...  review   \\n\",\n       \"1  I have no idea why some people give bad review...  review   \\n\",\n       \"2  love the gyro plate. Rice is so good and I als...  review   \\n\",\n       \"3  Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \\n\",\n       \"4  General Manager Scott Petello is a good egg!!!...  review   \\n\",\n       \"\\n\",\n       \"                  user_id                                 votes  \\n\",\n       \"0  rLtl8ZkDX5vH5nAx9C3q5Q  {'funny': 0, 'useful': 5, 'cool': 2}  \\n\",\n       \"1  0a2KyEL0d3Yb1V6aivbIuQ  {'funny': 0, 'useful': 0, 'cool': 0}  \\n\",\n       \"2  0hT2KtfLiobPvh6cDC8JQg  {'funny': 0, 'useful': 1, 'cool': 0}  \\n\",\n       \"3  uZetl9T0NcROGOyFfughhg  {'funny': 0, 'useful': 2, 'cool': 1}  \\n\",\n       \"4  vYmM4KTsC8ZfQBg-j5MWkw  {'funny': 0, 'useful': 0, 'cool': 0}  \"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"review_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"29222 368943 881620\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Create feature transformers for unigram, bigram, and trigram.\\n\",\n    \"# The default ignores single-character words, which is useful in practice because it trims\\n\",\n    \"# uninformative words. But we explicitly include them in this example for illustration purposes.\\n\",\n    \"bow_converter = CountVectorizer(token_pattern='(?u)\\\\\\\\b\\\\\\\\w+\\\\\\\\b')\\n\",\n    \"bigram_converter = CountVectorizer(\\n\",\n    \"    ngram_range=(2, 2), token_pattern='(?u)\\\\\\\\b\\\\\\\\w+\\\\\\\\b')\\n\",\n    \"trigram_converter = CountVectorizer(\\n\",\n    \"    ngram_range=(3, 3), token_pattern='(?u)\\\\\\\\b\\\\\\\\w+\\\\\\\\b')\\n\",\n    \"# Fit the transformers and look at vocabulary size\\n\",\n    \"bow_converter.fit(review_df['text'])\\n\",\n    \"words = bow_converter.get_feature_names()\\n\",\n    \"bigram_converter.fit(review_df['text'])\\n\",\n    \"bigram = bigram_converter.get_feature_names()\\n\",\n    \"trigram_converter.fit(review_df['text'])\\n\",\n    \"trigram = trigram_converter.get_feature_names()\\n\",\n    \"print(len(words), len(bigram), len(trigram))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['0', '00', '000', '007', '00a', '00am', '00pm', '01', '02', '03']\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Sneak a peek at the ngram themselves\\n\",\n    \"words[:10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['zuzu was',\\n\",\n       \" 'zuzus room',\\n\",\n       \" 'zweigel wine',\\n\",\n       \" 'zwiebel kräuter',\\n\",\n       \" 'zy world',\\n\",\n       \" 'zzed in',\\n\",\n       \" 'éclairs napoleons',\\n\",\n       \" 'école lenôtre',\\n\",\n       \" 'ém all',\\n\",\n       \" 'òc châm']\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bigram[-10:] \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['0 0 eye',\\n\",\n       \" '0 20 less',\\n\",\n       \" '0 39 oz',\\n\",\n       \" '0 39 pizza',\\n\",\n       \" '0 5 i',\\n\",\n       \" '0 50 to',\\n\",\n       \" '0 6 can',\\n\",\n       \" '0 75 oysters',\\n\",\n       \" '0 75 that',\\n\",\n       \" '0 75 to']\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trigram[:10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![图3-6](images/chapter3/3-6.PNG)\\n\",\n    \"\\n\",\n    \"图3-6 Number of unique n-gram in the first 10,000 reviews of the Yelp dataset\\n\",\n    \"\\n\",\n    \"### 过滤清洗特征\\n\",\n    \"\\n\",\n    \"我们如何清晰地将信号从噪声中分离出来？ 通过过滤，使用原始标记化和计数来生成简单词表或 n-gram 列表的技术变得更加可用。 短语检测，我们将在下面讨论，可以看作是一个特别的 bigram 过滤器。 以下是执行过滤的几种方法。\\n\",\n    \"\\n\",\n    \"### 停用词\\n\",\n    \"\\n\",\n    \"分类和检索通常不需要对文本有深入的理解。 例如，在`\\\"Emma knocked on the door\\\"`一句中，`\\\"on\\\"`和`\\\"the\\\"`这两个词没有包含很多信息。 代词、冠词和介词大部分时间并没有显示出其价值。流行的 Python NLP 软件包 NLTK 包含许多语言的语言学家定义的停用词列表。 （您将需要安装 NLTK 并运行`nltk.download()`来获取所有的好东西。）各种停用词列表也可以在网上找到。 例如，这里有一些来自英语停用词的示例词\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"Sample words from the nltk stopword list\\n\",\n    \"a, about, above, am, an, been, didn’t, couldn’t, i’d, i’ll, itself, let’s, myself, our, they, through, when’s, whom, ... \\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"请注意，该列表包含撇号，并且这些单词没有大写。 为了按原样使用它，标记化过程不得去掉撇号，并且这些词需要转换为小写。\\n\",\n    \"\\n\",\n    \"### 基于频率的过滤\\n\",\n    \"\\n\",\n    \"停用词表是一种去除空洞特征常用词的方法。还有其他更统计的方法来理解“常用词”的概念。在搭配提取中，我们看到依赖于手动定义的方法，以及使用统计的方法。同样的想法也适用于文字过滤。我们也可以使用频率统计。\\n\",\n    \"\\n\",\n    \"### 高频词\\n\",\n    \"\\n\",\n    \"频率统计对滤除语料库专用常用词以及通用停用词很有用。例如，纽约时报文章数据集中经常出现“纽约时报”和其中单个单词。“议院”这个词经常出现在加拿大议会辩论的Hansard语料库中的“众议院”一词中，这是一种用于统计机器翻译的流行数据集，因为它包含所有文档的英文和法文版本。这些词在普通语言中有意义，但不在语料库中。手动定义的停用词列表将捕获一般停用词，但不是语料库特定的停用词。\\n\",\n    \"\\n\",\n    \"表 3-1 列出了 Yelp 评论数据集中最常用的 40 个单词。在这里，频率被认为是它们出现在文件（评论）中的数量，而不是它们在文件中的数量。正如我们所看到的，该列表涵盖了许多停用词。它也包含一些惊喜。`\\\"s\\\"`和`\\\"t\\\"`在列表中，因为我们使用撇号作为标记化分隔符，并且诸如`\\\"Mary's\\\"`或`\\\"did not\\\"`之类的词被解析为`\\\"Mary s\\\"`和`\\\"didn t\\\"`。词`\\\"good\\\"`，`\\\"food\\\"`和`\\\"great\\\"`分别出现在三分之一的评论中。但我们可能希望保留它们，因为它们对于情感分析或业务分类非常有用。\\n\",\n    \"\\n\",\n    \"![图3-biao](images/chapter3/biao.PNG)\\n\",\n    \"\\n\",\n    \"最常用的单词最可以揭示问题，并突出显示通常有用的单词通常在该语料库中曾出现过多次。 例如，纽约时报语料库中最常见的词是“时代”。实际上，它有助于将基于频率的过滤与停用词列表结合起来。还有一个棘手的问题，即何处放置截止点。 不幸的是这里没有统一的答案。在大多数情况下截断还需手动确定，并且在数据集改变时可能需要重新检查。\\n\",\n    \"\\n\",\n    \"### 稀有词\\n\",\n    \"\\n\",\n    \"根据任务的不同，可能还需要筛选出稀有词。对于统计模型而言，仅出现在一个或两个文档中的单词更像噪声而非有用信息。例如，假设任务是根据他们的 Yelp 评论对企业进行分类，并且单个评论包含`\\\"gobbledygook\\\"`这个词。基于这一个词，我们将如何说明这家企业是餐厅，美容院还是一间酒吧？即使我们知道在这种情况下的这种生意发生在酒吧，它也会对于其他包含`\\\"gobbledygook\\\"`这个词的评论来说，这可能是一个错误。\\n\",\n    \"\\n\",\n    \"不仅稀有词不可靠，而且还会产生计算开销。这套 160 万个 Yelp 评论包含 357,481 个独特单词（用空格和标点符号表示），其中 189,915 只出现在一次评论中，41,162 次出现在两次评论中。超过 60% 的词汇很少发生。这是一种所谓的重尾分布，在现实世界的数据中非常普遍。许多统计机器学习模型的训练时间随着特征数量线性地变化，并且一些模型是二次的或更差的。稀有词汇会产生大量的计算和存储成本，而不会带来额外的收益。\\n\",\n    \"\\n\",\n    \"根据字数统计，可以很容易地识别和修剪稀有词。或者，他们的计数可以汇总到一个特殊的垃圾箱中，可以作为附加功能。图3-7展示了一个短文档中的表示形式，该短文档包含一些常用单词和两个稀有词`\\\"gobbledygook\\\"`和`\\\"zylophant\\\"`。通常单词保留自己的计数，可以通过停用词列表或其他频率进一步过滤方法。这些难得的单词会失去他们的身份并被分组到垃圾桶功能中.\\n\",\n    \"\\n\",\n    \"![图3-7](images/chapter3/3-7.PNG)\\n\",\n    \"\\n\",\n    \"由于在计算整个语料库之前不会知道哪些词很少，因此需要收集垃圾桶功能作为后处理步骤。\\n\",\n    \"\\n\",\n    \"由于本书是关于特征工程的，因此我们将重点放在特征上。但稀有概念也适用于数据点。如果文本文档很短，那么它可能不包含有用的信息，并且在训练模型时不应使用该信息。\\n\",\n    \"\\n\",\n    \"应用此规则时必须谨慎。维基百科转储包含许多不完整的存根，可能安全过滤。另一方面，推文本身就很短，并且需要其他特征和建模技巧。\\n\",\n    \"\\n\",\n    \"### 词干解析（Stemming）\\n\",\n    \"\\n\",\n    \"简单解析的一个问题是同一个单词的不同变体会被计算为单独的单词。例如，`\\\"flower\\\"`和`\\\"flowers\\\"`在技术上是不同的记号，`\\\"swimmer\\\"`，`\\\"swimming\\\"`和`\\\"swim\\\"`也是如此，尽管它们的含义非常接近。如果所有这些不同的变体都映射到同一个单词，那将会很好。\\n\",\n    \"\\n\",\n    \"词干解析是一项 NLP 任务，试图将单词切分为基本的语言词干形式。有不同的方法。有些基于语言规则，其他基于观察统计。被称为词形化的算法的一个子类将词性标注和语言规则结合起来。\\n\",\n    \"\\n\",\n    \"Porter stemmer 是英语中使用最广泛的免费词干工具。原来的程序是用 ANSI C 编写的，但是很多其他程序包已经封装它来提供对其他语言的访问。尽管其他语言的努力正在进行，但大多数词干工具专注于英语。\\n\",\n    \"\\n\",\n    \"以下是通过 NLTK Python 包运行 Porter stemmer 的示例。正如我们所看到的，它处理了大量的情况，包括将`\\\"sixties\\\"`和`\\\"sixty\\\"`转变为同一根`\\\"sixti\\\"`。但这并不完美。单词`\\\"goes\\\"`映射到`\\\"goe\\\"`，而`\\\"go\\\"`映射到它自己。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import nltk \\n\",\n    \"stemmer = nltk.stem.porter.PorterStemmer() \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'flower'\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stemmer.stem('flowers')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'zero'\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stemmer.stem('zeroes')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'stemmer'\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stemmer.stem('stemmer')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'sixti'\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stemmer.stem('sixties')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'sixti'\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stemmer.stem('sixty')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'goe'\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stemmer.stem('goes')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'go'\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"stemmer.stem('go') \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"词干解析的确有一个计算成本。 最终收益是否大于成本取决于应用程序。\\n\",\n    \"\\n\",\n    \"### 含义的原子：从单词到 N-gram 到短语\\n\",\n    \"\\n\",\n    \"词袋的概念很简单。但是，一台电脑怎么知道一个词是什么？文本文档以数字形式表示为一个字符串，基本上是一系列字符。也可能会遇到 JSON blob 或 HTML 页面形式的半结构化文本。但即使添加了标签和结构，基本单位仍然是一个字符串。如何将字符串转换为一系列的单词？这涉及解析和标记化的任务，我们将在下面讨论。\\n\",\n    \"\\n\",\n    \"### 解析和分词\\n\",\n    \"\\n\",\n    \"当字符串包含的不仅仅是纯文本时，解析是必要的。例如，如果原始数据是网页，电子邮件或某种类型的日志，则它包含额外的结构。人们需要决定如何处理日志中的标记，页眉，页脚或无趣的部分。如果文档是网页，则解析器需要处理 URL。如果是电子邮件，则可能需要特殊字段，例如 From，To 和 Subject 需要被特别处理，否则，这些标题将作为最终计数中的普通单词统计，这可能没有用处。\\n\",\n    \"\\n\",\n    \"解析后，文档的纯文本部分可以通过标记。这将字符串（一系列字符）转换为一系列记号。然后可以将每个记号计为一个单词。分词器需要知道哪些字符表示一个记号已经结束，另一个正在开始。空格字符通常是好的分隔符，正如标点符号一样。如果文本包含推文，则不应将井号（`#`）用作分隔符（也称为分隔符）。\\n\",\n    \"\\n\",\n    \"有时，分析需要使用句子而不是整个文档。例如，n-gram 是一个句子的概括，不应超出句子范围。更复杂的文本特征化方法，如 word2vec 也适用于句子或段落。在这些情况下，需要首先将文档解析为句子，然后将每个句子进一步标记为单词。\\n\",\n    \"\\n\",\n    \"### 字符串对象\\n\",\n    \"\\n\",\n    \"字符串对象有各种编码，如 ASCII 或 Unicode。纯英文文本可以用 ASCII 编码。 一般语言需要 Unicode。 如果文档包含非 ASCII 字符，则确保分词器可以处理该特定编码。否则，结果将不正确。\\n\",\n    \"\\n\",\n    \"### 短语检测的搭配提取\\n\",\n    \"\\n\",\n    \"连续的记号能立即被转化成词表和 n-gram。但从语义上讲，我们更习惯于理解短语，而不是 n-gram。在计算自然语言处理中，有用短语的概念被称为搭配。用 Manning 和 Schütze（1999：141）的话来说：“搭配是一个由两个或两个以上单词组成的表达，它们对应于某种常规的说话方式。”\\n\",\n    \"\\n\",\n    \"搭配比其部分的总和更有意义。例如，`\\\"strong tea\\\"`具有超越`\\\"great physical strength\\\"`和`\\\"tea\\\"`的不同含义，因此被认为是搭配。另一方面，“可爱的小狗”这个短语恰恰意味着它的部分总和：“可爱”和“小狗”。因此，它不被视为搭配。\\n\",\n    \"\\n\",\n    \"搭配不一定是连续的序列。`\\\"Emma knocked on the door\\\"`一词被认为包含搭配`\\\"knock door\\\"`，因此不是每一个搭配都是一个 n-gram。相反，并不是每个 n-gram 都被认为是一个有意义的搭配。\\n\",\n    \"\\n\",\n    \"由于搭配不仅仅是其部分的总和，它们的含义也不能通过单个单词计数来充分表达。作为一种表现形式，词袋不足。袋子的 ngram 也是有问题的，因为它们捕获了太多无意义的序列（考虑`\\\"this is in the bag-of-ngram example\\\"`），而没有足够的有意义的序列。\\n\",\n    \"\\n\",\n    \"搭配作为功能很有用。但是，如何从文本中发现并提取它们呢？一种方法是预先定义它们。如果我们努力尝试，我们可能会找到各种语言的全面成语列表，我们可以通过文本查看任何匹配。这将是非常昂贵的，但它会工作。如果语料库是非常特定领域的并且包含深奥的术语，那么这可能是首选的方法。但是这个列表需要大量的手动管理，并且需要不断更新语料库。例如，分析推文，博客和文章可能不太现实。\\n\",\n    \"\\n\",\n    \"自从统计 NLP 过去二十年出现以来，人们越来越多地选择用于查找短语的统计方法。统计搭配提取方法不是建立固定的短语和惯用语言列表，而是依赖不断发展的数据来揭示当今流行的语言。\\n\",\n    \"\\n\",\n    \"### 基于频率的方法\\n\",\n    \"\\n\",\n    \"一个简单的黑魔法是频繁发生的 n-gram。这种方法的问题是最常发生的，这种可能不是最有用的。 表 3-2 显示了整个 Yelp 评论数据集中最流行的 bigram（`n=2`）。 正如我们所知的，按文件计数排列的最常见的十大常见术语是非常通用的术语，并不包含太多含义。\\n\",\n    \"\\n\",\n    \"![图3-biaod](images/chapter3/biaod.PNG)\\n\",\n    \"\\n\",\n    \"### 用于搭配提取的假设检验\\n\",\n    \"\\n\",\n    \"原始流行度计数（Raw popularity count）是一个比较粗糙的方法。我们必须找到更聪慧的统计数据才能够轻松挑选出有意义的短语。关键的想法是看两个单词是否经常出现在一起。回答这个问题的统计机制被称为假设检验。\\n\",\n    \"\\n\",\n    \"假设检验是将噪音数据归结为“是”或“否”的答案。它涉及将数据建模为从随机分布中抽取的样本。随机性意味着人们永远无法 100% 的确定答案；总会有异常的机会。所以答案附在概率上。例如，假设检验的结果可能是“这两个数据集来自同一分布，其概率为 95%”。对于假设检验的温和介绍，请参阅可汗学院关于假设检验和 p 值的教程。\\n\",\n    \"\\n\",\n    \"在搭配提取的背景下，多年来已经提出了许多假设检验。最成功的方法之一是基于似然比检验（Dunning，1993）。对于给定的一对单词，该方法测试两个假设观察的数据集。假设 1（原假设）表示，词语 1 独立于词语 2 出现。另一种说法是说，看到词语1对我们是否看到词语2没有影响。假设 2（备选假设）说，看到词 1 改变了看到单词 2 的可能性。我们采用备选假设来暗示这两个单词形成一个共同的短语。因此，短语检测（也称为搭配提取）的似然比检验提出了以下问题：给定文本语料库中观察到的单词出现更可能是从两个单词彼此独立出现的模型中生成的，或者模型中两个词的概率纠缠？\\n\",\n    \"\\n\",\n    \"这是有用的。让我们算一点。（数学非常精确和简洁地表达事物，但它确实需要与自然语言完全不同的分析器。）\\n\",\n    \"\\n\",\n    \"![图3-gongshi](images/chapter3/gongshi.PNG)\\n\",\n    \"\\n\",\n    \"似然函数`L(Data; H)`表示在单词对的独立模型或非独立模型下观察数据集中词频的概率。为了计算这个概率，我们必须对如何生成数据做出另一个假设。最简单的数据生成模型是二项模型，其中对于数据集中的每个单词，我们抛出一个硬币，并且如果硬币朝上出现，我们插入我们的特殊单词，否则插入其他单词。在此策略下，特殊词的出现次数遵循二项分布。二项分布完全由词的总数，词的出现次数和词首概率决定。\\n\",\n    \"\\n\",\n    \"似然比检验分析常用短语的算法收益如下。 \\n\",\n    \"\\n\",\n    \"1.  计算所有单体词的出现概率：`p(w)`。 \\n\",\n    \"2.  计算所有唯一双元的条件成对词发生概率：`p(W2 × W1)`\\n\",\n    \"3.  计算所有唯一的双对数似然比对数。 \\n\",\n    \"4.  根据它们的似然比排序双字节。 \\n\",\n    \"5.  以最小似然比值作为特征。\\n\",\n    \"\\n\",\n    \"### 掌握似然比测试\\n\",\n    \"\\n\",\n    \"关键在于测试比较的不是概率参数本身，而是在这些参数（以及假设的数据生成模型）下观察数据的概率。可能性是统计学习的关键原则之一。但是在你看到它的前几次，这绝对是一个令人困惑的问题。一旦你确定了逻辑，它就变得直观了。\\n\",\n    \"\\n\",\n    \"还有另一种基于点互信息的统计方法。但它对真实世界文本语料库中常见的罕见词很敏感。因此它不常用，我们不会在这里展示它。\\n\",\n    \"\\n\",\n    \"请注意，搭配抽取的所有统计方法，无论是使用原始频率，假设测试还是点对点互信息，都是通过过滤候选词组列表来进行操作的。生成这种清单的最简单和最便宜的方法是计算 n-gram。它可能产生不连续的序列，但是它们计算成本颇高。在实践中，即使是连续 n-gram，人们也很少超过 bi-gram 或 tri-gram，因为即使在过滤之后，它们的数量也很多。为了生成更长的短语，还有其他方法，如分块或与词性标注相结合。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"### 分块（Chunking）和词性标注（part-of-Speech Tagging）\\n\",\n    \"\\n\",\n    \"分块比 n-gram 要复杂一点，因为它基于词性，基于规则的模型形成了记号序列。\\n\",\n    \"\\n\",\n    \"例如，我们可能最感兴趣的是在问题中找到所有名词短语，其中文本的实体，主题最为有趣。 为了找到这个，我们使用词性标记每个作品，然后检查该标记的邻域以查找词性分组或“块”。 定义单词到词类的模型通常是语言特定的。 几种开源 Python 库（如 NLTK，Spacy 和 TextBlob）具有多种语言模型。\\n\",\n    \"\\n\",\n    \"为了说明 Python 中的几个库如何使用词性标注非常简单地进行分块，我们再次使用 Yelp 评论数据集。 我们将使用 spacy 和 TextBlob 来评估词类以找到名词短语。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd \\n\",\n    \"import json \\n\",\n    \"# Load the first 10 reviews \\n\",\n    \"f = open('data/yelp_academic_dataset_review.json') \\n\",\n    \"js = [] \\n\",\n    \"for i in range(10): \\n\",\n    \"    js.append(json.loads(f.readline())) \\n\",\n    \"f.close() \\n\",\n    \"review_df = pd.DataFrame(js) \\n\",\n    \"## First we'll walk through spaCy's functions \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# !pip install -U spacy\\n\",\n    \"# !python -m spacy download en#使用spacy，需要安装上述两个步骤，去掉注释即可运行\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['General', 'PROPN', 'NNP']\\n\",\n      \"['Manager', 'PROPN', 'NNP']\\n\",\n      \"['Scott', 'PROPN', 'NNP']\\n\",\n      \"['Petello', 'PROPN', 'NNP']\\n\",\n      \"['is', 'VERB', 'VBZ']\\n\",\n      \"['a', 'DET', 'DT']\\n\",\n      \"['good', 'ADJ', 'JJ']\\n\",\n      \"['egg', 'NOUN', 'NN']\\n\",\n      \"['!', 'PUNCT', '.']\\n\",\n      \"['!', 'PUNCT', '.']\\n\",\n      \"['!', 'PUNCT', '.']\\n\",\n      \"['Not', 'ADV', 'RB']\\n\",\n      \"['to', 'PART', 'TO']\\n\",\n      \"['go', 'VERB', 'VB']\\n\",\n      \"['into', 'ADP', 'IN']\\n\",\n      \"['detail', 'NOUN', 'NN']\\n\",\n      \"[',', 'PUNCT', ',']\\n\",\n      \"['but', 'CCONJ', 'CC']\\n\",\n      \"['let', 'VERB', 'VB']\\n\",\n      \"['me', 'PRON', 'PRP']\\n\",\n      \"['assure', 'VERB', 'VB']\\n\",\n      \"['you', 'PRON', 'PRP']\\n\",\n      \"['if', 'ADP', 'IN']\\n\",\n      \"['you', 'PRON', 'PRP']\\n\",\n      \"['have', 'VERB', 'VBP']\\n\",\n      \"['any', 'DET', 'DT']\\n\",\n      \"['issues', 'NOUN', 'NNS']\\n\",\n      \"['(', 'PUNCT', '-LRB-']\\n\",\n      \"['albeit', 'ADP', 'IN']\\n\",\n      \"['rare', 'ADJ', 'JJ']\\n\",\n      \"[')', 'PUNCT', '-RRB-']\\n\",\n      \"['speak', 'VERB', 'VBP']\\n\",\n      \"['with', 'ADP', 'IN']\\n\",\n      \"['Scott', 'PROPN', 'NNP']\\n\",\n      \"['and', 'CCONJ', 'CC']\\n\",\n      \"['treat', 'VERB', 'VB']\\n\",\n      \"['the', 'DET', 'DT']\\n\",\n      \"['guy', 'NOUN', 'NN']\\n\",\n      \"['with', 'ADP', 'IN']\\n\",\n      \"['some', 'DET', 'DT']\\n\",\n      \"['respect', 'NOUN', 'NN']\\n\",\n      \"['as', 'ADP', 'IN']\\n\",\n      \"['you', 'PRON', 'PRP']\\n\",\n      \"['state', 'VERB', 'VBP']\\n\",\n      \"['your', 'DET', 'PRP$']\\n\",\n      \"['case', 'NOUN', 'NN']\\n\",\n      \"['and', 'CCONJ', 'CC']\\n\",\n      \"['I', 'PRON', 'PRP']\\n\",\n      \"[\\\"'d\\\", 'AUX', 'MD']\\n\",\n      \"['be', 'VERB', 'VB']\\n\",\n      \"['surprised', 'ADJ', 'JJ']\\n\",\n      \"['if', 'ADP', 'IN']\\n\",\n      \"['you', 'PRON', 'PRP']\\n\",\n      \"['do', 'VERB', 'VBP']\\n\",\n      \"[\\\"n't\\\", 'ADV', 'RB']\\n\",\n      \"['walk', 'VERB', 'VB']\\n\",\n      \"['out', 'ADV', 'RB']\\n\",\n      \"['totally', 'ADV', 'RB']\\n\",\n      \"['satisfied', 'ADJ', 'JJ']\\n\",\n      \"['as', 'ADP', 'IN']\\n\",\n      \"['I', 'PRON', 'PRP']\\n\",\n      \"['just', 'ADV', 'RB']\\n\",\n      \"['did', 'VERB', 'VBD']\\n\",\n      \"['.', 'PUNCT', '.']\\n\",\n      \"['Like', 'INTJ', 'UH']\\n\",\n      \"['I', 'PRON', 'PRP']\\n\",\n      \"['always', 'ADV', 'RB']\\n\",\n      \"['say', 'VERB', 'VBP']\\n\",\n      \"['.....', 'PUNCT', '.']\\n\",\n      \"['\\\"', 'PUNCT', \\\"''\\\"]\\n\",\n      \"['Mistakes', 'NOUN', 'NNS']\\n\",\n      \"['are', 'VERB', 'VBP']\\n\",\n      \"['inevitable', 'ADJ', 'JJ']\\n\",\n      \"[',', 'PUNCT', ',']\\n\",\n      \"['it', 'PRON', 'PRP']\\n\",\n      \"[\\\"'s\\\", 'VERB', 'VBZ']\\n\",\n      \"['how', 'ADV', 'WRB']\\n\",\n      \"['we', 'PRON', 'PRP']\\n\",\n      \"['recover', 'VERB', 'VBP']\\n\",\n      \"['from', 'ADP', 'IN']\\n\",\n      \"['them', 'PRON', 'PRP']\\n\",\n      \"['that', 'DET', 'WDT']\\n\",\n      \"['is', 'VERB', 'VBZ']\\n\",\n      \"['important', 'ADJ', 'JJ']\\n\",\n      \"['\\\"', 'PUNCT', \\\"''\\\"]\\n\",\n      \"['!', 'PUNCT', '.']\\n\",\n      \"['!', 'PUNCT', '.']\\n\",\n      \"['!', 'PUNCT', '.']\\n\",\n      \"['\\\\n\\\\n', 'SPACE', '_SP']\\n\",\n      \"['Thanks', 'NOUN', 'NNS']\\n\",\n      \"['to', 'ADP', 'IN']\\n\",\n      \"['Scott', 'PROPN', 'NNP']\\n\",\n      \"['and', 'CCONJ', 'CC']\\n\",\n      \"['his', 'DET', 'PRP$']\\n\",\n      \"['awesome', 'ADJ', 'JJ']\\n\",\n      \"['staff', 'NOUN', 'NN']\\n\",\n      \"['.', 'PUNCT', '.']\\n\",\n      \"['You', 'PRON', 'PRP']\\n\",\n      \"[\\\"'ve\\\", 'VERB', 'VB']\\n\",\n      \"['got', 'VERB', 'VBN']\\n\",\n      \"['a', 'DET', 'DT']\\n\",\n      \"['customer', 'NOUN', 'NN']\\n\",\n      \"['for', 'ADP', 'IN']\\n\",\n      \"['life', 'NOUN', 'NN']\\n\",\n      \"['!', 'PUNCT', '.']\\n\",\n      \"['!', 'PUNCT', '.']\\n\",\n      \"['..........', 'PUNCT', '.']\\n\",\n      \"[':', 'PUNCT', ':']\\n\",\n      \"['^', 'PUNCT', 'LS']\\n\",\n      \"[')', 'PUNCT', '-RRB-']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import spacy \\n\",\n    \"# preload the language model \\n\",\n    \"nlp = spacy.load('en') \\n\",\n    \"# We can create a Pandas Series of spaCy nlp variables \\n\",\n    \"doc_df = review_df['text'].apply(nlp) \\n\",\n    \"# spaCy gives you fine grained parts of speech using: (.pos_) \\n\",\n    \"# and coarse grained parts of speech using: (.tag_) \\n\",\n    \"for doc in doc_df[4]: \\n\",\n    \"    print([doc.text, doc.pos_, doc.tag_]) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[General Manager Scott Petello, a good egg, detail, me, you, you, any issues, Scott, the guy, some respect, you, your case, I, you, I, I, \\\"Mistakes, it, we, them, Thanks, Scott, his awesome staff, You, a customer, life]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print([chunk for chunk in doc_df[4].noun_chunks]) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[nltk_data] Downloading package averaged_perceptron_tagger to\\n\",\n      \"[nltk_data]     C:\\\\Users\\\\haigu\\\\AppData\\\\Roaming\\\\nltk_data...\\n\",\n      \"[nltk_data]   Package averaged_perceptron_tagger is already up-to-\\n\",\n      \"[nltk_data]       date!\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import nltk\\n\",\n    \"nltk.download('averaged_perceptron_tagger')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[('General', 'NNP'),\\n\",\n       \" ('Manager', 'NNP'),\\n\",\n       \" ('Scott', 'NNP'),\\n\",\n       \" ('Petello', 'NNP'),\\n\",\n       \" ('is', 'VBZ'),\\n\",\n       \" ('a', 'DT'),\\n\",\n       \" ('good', 'JJ'),\\n\",\n       \" ('egg', 'NN'),\\n\",\n       \" ('Not', 'RB'),\\n\",\n       \" ('to', 'TO'),\\n\",\n       \" ('go', 'VB'),\\n\",\n       \" ('into', 'IN'),\\n\",\n       \" ('detail', 'NN'),\\n\",\n       \" ('but', 'CC'),\\n\",\n       \" ('let', 'VB'),\\n\",\n       \" ('me', 'PRP'),\\n\",\n       \" ('assure', 'VB'),\\n\",\n       \" ('you', 'PRP'),\\n\",\n       \" ('if', 'IN'),\\n\",\n       \" ('you', 'PRP'),\\n\",\n       \" ('have', 'VBP'),\\n\",\n       \" ('any', 'DT'),\\n\",\n       \" ('issues', 'NNS'),\\n\",\n       \" ('albeit', 'IN'),\\n\",\n       \" ('rare', 'NN'),\\n\",\n       \" ('speak', 'NN'),\\n\",\n       \" ('with', 'IN'),\\n\",\n       \" ('Scott', 'NNP'),\\n\",\n       \" ('and', 'CC'),\\n\",\n       \" ('treat', 'VB'),\\n\",\n       \" ('the', 'DT'),\\n\",\n       \" ('guy', 'NN'),\\n\",\n       \" ('with', 'IN'),\\n\",\n       \" ('some', 'DT'),\\n\",\n       \" ('respect', 'NN'),\\n\",\n       \" ('as', 'IN'),\\n\",\n       \" ('you', 'PRP'),\\n\",\n       \" ('state', 'NN'),\\n\",\n       \" ('your', 'PRP$'),\\n\",\n       \" ('case', 'NN'),\\n\",\n       \" ('and', 'CC'),\\n\",\n       \" ('I', 'PRP'),\\n\",\n       \" (\\\"'d\\\", 'MD'),\\n\",\n       \" ('be', 'VB'),\\n\",\n       \" ('surprised', 'VBN'),\\n\",\n       \" ('if', 'IN'),\\n\",\n       \" ('you', 'PRP'),\\n\",\n       \" ('do', 'VBP'),\\n\",\n       \" (\\\"n't\\\", 'RB'),\\n\",\n       \" ('walk', 'VB'),\\n\",\n       \" ('out', 'RP'),\\n\",\n       \" ('totally', 'RB'),\\n\",\n       \" ('satisfied', 'JJ'),\\n\",\n       \" ('as', 'IN'),\\n\",\n       \" ('I', 'PRP'),\\n\",\n       \" ('just', 'RB'),\\n\",\n       \" ('did', 'VBD'),\\n\",\n       \" ('Like', 'IN'),\\n\",\n       \" ('I', 'PRP'),\\n\",\n       \" ('always', 'RB'),\\n\",\n       \" ('say', 'VBP'),\\n\",\n       \" ('..', 'VBP'),\\n\",\n       \" ('Mistakes', 'NNS'),\\n\",\n       \" ('are', 'VBP'),\\n\",\n       \" ('inevitable', 'JJ'),\\n\",\n       \" ('it', 'PRP'),\\n\",\n       \" (\\\"'s\\\", 'VBZ'),\\n\",\n       \" ('how', 'WRB'),\\n\",\n       \" ('we', 'PRP'),\\n\",\n       \" ('recover', 'VBP'),\\n\",\n       \" ('from', 'IN'),\\n\",\n       \" ('them', 'PRP'),\\n\",\n       \" ('that', 'WDT'),\\n\",\n       \" ('is', 'VBZ'),\\n\",\n       \" ('important', 'JJ'),\\n\",\n       \" ('Thanks', 'NNS'),\\n\",\n       \" ('to', 'TO'),\\n\",\n       \" ('Scott', 'NNP'),\\n\",\n       \" ('and', 'CC'),\\n\",\n       \" ('his', 'PRP$'),\\n\",\n       \" ('awesome', 'JJ'),\\n\",\n       \" ('staff', 'NN'),\\n\",\n       \" ('You', 'PRP'),\\n\",\n       \" (\\\"'ve\\\", 'VBP'),\\n\",\n       \" ('got', 'VBN'),\\n\",\n       \" ('a', 'DT'),\\n\",\n       \" ('customer', 'NN'),\\n\",\n       \" ('for', 'IN'),\\n\",\n       \" ('life', 'NN'),\\n\",\n       \" ('^', 'NN')]\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"## We can do the same feature transformations using Textblob \\n\",\n    \"from textblob import TextBlob \\n\",\n    \"# The default tagger in TextBlob uses the PatternTagger, which is fine for our example. \\n\",\n    \"# You can also specify the NLTK tagger, which works better for incomplete sentences. \\n\",\n    \"blob_df = review_df['text'].apply(TextBlob) \\n\",\n    \"blob_df[4].tags \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['general manager', 'scott petello', 'good egg', 'scott', \\\"n't walk\\\", '... ..', 'mistakes', 'thanks', 'scott', 'awesome staff', '... ... ...']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print([np for np in blob_df[4].noun_phrases]) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"你可以看到每个库找到的名词短语有些不同。spacy 包含英语中的常见单词，如`\\\"a\\\"`和`\\\"the\\\"`，而 TextBlob 则删除这些单词。这反映了规则引擎的差异，它驱使每个库都认为是“名词短语”。 你也可以写你的词性关系来定义你正在寻找的块。使用 Python 进行自然语言处理可以深入了解从头开始用 Python 进行分块。\\n\",\n    \"\\n\",\n    \"## 总结\\n\",\n    \"\\n\",\n    \"词袋模型易于理解和计算，对分类和搜索任务很有用。但有时单个单词太简单，不足以将文本中的某些信息封装起来。为了解决这个问题，人们寄希望于比较长的序列。Bag-of-ngram 是 BOW 的自然概括，这个概念仍然容于理解，而且它的计算开销这就像 BOW 一样容易。\\n\",\n    \"\\n\",\n    \"Bag of-ngram 生成更多不同的 ngram。它增加了特征存储成本，以及模型训练和预测阶段的计算成本。虽然数据点的数量保持不变，但特征空间的维度现在更大。因此数据密度更为稀疏。n 越高，存储和计算成本越高，数据越稀疏。由于这些原因，较长的 n-gram 并不总是会使模型精度的得到提高（或任何其他性能指标）。人们通常在`n = 2`或 3 时停止。较少的 n-gram 很少被使用。\\n\",\n    \"\\n\",\n    \"防止稀疏性和成本增加的一种方法是过滤 n-gram 并保留最有意义的短语。这是搭配抽取的目标。理论上，搭配（或短语）可以在文本中形成非连续的标记序列。然而，在实践中，寻找非连续词组的计算成本要高得多并且没有太多的收益。因此搭配抽取通常从一个候选人名单中开始，并利用统计方法对他们进行过滤。\\n\",\n    \"\\n\",\n    \"所有这些方法都将一系列文本标记转换为一组断开的计数。与一个序列相比，一个集合的结构要少得多；他们导致平面特征向量。\\n\",\n    \"\\n\",\n    \"在本章中，我们用简单的语言描述文本特征化技术。这些技术将一段充满丰富语义结构的自然语言文本转化为一个简单的平面向量。我们讨论一些常用的过滤技术来降低向量维度。我们还引入了 ngram 和搭配抽取作为方法，在平面向量中添加更多的结构。下一章将详细介绍另一种常见的文本特征化技巧，称为 tf-idf。随后的章节将讨论更多方法将结构添加回平面向量。\\n\",\n    \"\\n\",\n    \"## 参考文献\\n\",\n    \"\\n\",\n    \"Dunning, Ted. 1993. “Accurate methods for the statistics of surprise and\\n\",\n    \"\\n\",\n    \"coincidence.” ACM Journal of Computational Linguistics, special issue on using large corpora , 19:1 (61—74).\\n\",\n    \"\\n\",\n    \"“Hypothesis Testing and p-Values.” Khan Academy, accessed May 31,\\n\",\n    \"\\n\",\n    \"2016,<https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/hypothesis-testing-and-p-values>.\\n\",\n    \"\\n\",\n    \"Manning,Christopher D. and Hinrich Schütze. 1999. Foundations of StatisticalNatural Language Processing . Cambridge, Massachusettes: MIT Press.\\n\",\n    \"\\n\",\n    \"Sometimes people call it the document “vector.” The vector extends from the original and ends at the specified point. For our purposes, “vector” and “point” are the same thing.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "9.feature-engineering/4.特征缩放的效果：从词袋到_TF-IDF.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[原版（英文）图书地址](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/)\\n\",\n    \"\\n\",\n    \"**翻译**：[apachecn](https://github.com/apachecn)，[翻译版本地址](https://github.com/apachecn/feature-engineering-for-ml-zh)\\n\",\n    \"\\n\",\n    \"**代码修改和整理**：[黄海广](https://github.com/fengdu78)，原文修改成jupyter notebook格式，并增加和修改了部分代码，测试全部通过，所有数据集已经放在[百度云](data/README.md)下载。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 四、特征缩放的效果：从词袋到 TF-IDF\\n\",\n    \"\\n\",\n    \"> 译者：[@gin](https://github.com/tirtile)\\n\",\n    \"> \\n\",\n    \"> 校对者：[@HeYun](https://github.com/KyrieHee)\\n\",\n    \"\\n\",\n    \"字袋易于生成，但远非完美。假设我们平等的统计所有单词，有些不需要的词也会被强调。在第三章提过一个例子，Emma and the raven。我们希望在文档表示中能强调两个主要角色。示例中，“Eama”和“raven”都出现了3词，但是“the”的出现高达8次，“and”出现了次，另外“it”以及“was”也都出现了4词。仅仅通过简单的频率统计，两个主要角色并不突出。这是有问题的。\\n\",\n    \"\\n\",\n    \"其他的像是“magnificently,” “gleamed,” “intimidated,” “tentatively,” 和“reigned,”这些辅助奠定段落基调的词也是很好的选择。它们表示情绪，这对数据科学家来说可能是非常有价值的信息。 所以，理想情况下，我们会倾向突出对有意义单词的表示。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## Tf-Idf: 词袋的小转折\\n\",\n    \"\\n\",\n    \"Tf-Idf 是词袋的一个小小的转折。它表示词频-逆文档频。tf-idf不是查看每个文档中每个单词的原始计数，而是查看每个单词计数除以出现该单词的文档数量的标准化计数。\\n\",\n    \"$$\\n\",\n    \"{bow}(w, d)=\\\\# \\\\text {times} \\\\text { word } w \\\\text { appears in document } d\\n\",\n    \"$$\\n\",\n    \"$$\\n\",\n    \"t f-i d f(w, d)=\\\\frac{{bow}(w, d) \\\\times N}{(\\\\# \\\\text {documents in which word w appears)}}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$N$代表数据集中所有文档的数量。分数$\\\\frac{{bow}(w, d) \\\\times N}{(\\\\# \\\\text {documents in which word w appears)}}$就是所谓的逆文件频率。如果一个单词出现在许多文档中，则其逆文档频率接近1。如果单词出现在较少文档中，则逆文档频率要高得多。\\n\",\n    \"\\n\",\n    \"或者，我们可以对原始逆文档频率进行对数转换，可以将1变为0，并使得较大的数字（比1大得多）变小。（稍后更多内容）\\n\",\n    \"\\n\",\n    \"如果我们定义 tf-idf 为：\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"t f-i d f(w, d)=\\\\frac{{bow}(w, d) \\\\times N}{(\\\\# \\\\text {documents in which word w appears)}}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"那么每个文档中出现的单词都将被有效清零，并且只出现在少数文档中的单词的计数将比以前更大。\\n\",\n    \"\\n\",\n    \"让我们看一些图片来了解它的具体内容。图4-1展示了一个包含4个句子的简单样例：“it is a puppy,” “it is a cat,” “it is a kitten,” 以及 “that is a dog and this is a pen.” 我们将这些句子绘制在“puppy”，“cat”以及“is”三个词的特征空间上。\\n\",\n    \"\\n\",\n    \"![图 4-1: 关于猫和狗的四个句子](images/chapter4/4-1.png)\\n\",\n    \"\\n\",\n    \"<center><h5>图 4-1: 关于猫和狗的四个句子</h5></center>\\n\",\n    \"\\n\",\n    \"现在让我们看看对逆文档频进行对数变换之后，相同四个句子的tf-idf表示。 图4-2显示了相应特征空间中的文档。可以注意到，单词“is”被有效地消除，因为它出现在该数据集中的所有句子中。另外，单词“puppy”和“cat”都只出现在四个句子中的一个句子中，所以现在这两个词计数得比之前更高（$log(4)=1.38...>1$）。因此tf-idf使罕见词语更加突出，并有效地忽略了常见词汇。它与第3章中基于频率的滤波方法密切相关，但比放置严格截止阈值更具数学优雅性。\\n\",\n    \"![Figure 4-2: 图4-1中四个句子的Tf-idf表示](images/chapter4/4-2.png)\\n\",\n    \"\\n\",\n    \"<center><h5>Figure 4-2: 图4-1中四个句子的Tf-idf表示</h5></center>\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## Tf-Idf的含义\\n\",\n    \"\\n\",\n    \"Tf-idf使罕见的单词更加突出，并有效地忽略了常见单词。\\n\",\n    \"\\n\",\n    \"## 测试\\n\",\n    \"\\n\",\n    \"Tf-idf通过乘以一个常量来转换字数统计特性。因此，它是特征缩放的一个例子，这是第2章介绍的一个概念。特征缩放在实践中效果有多好？ 我们来比较简单文本分类任务中缩放和未缩放特征的表现。 coding时间到！\\n\",\n    \"\\n\",\n    \"本次实践， 我们依旧采用了Yelp评论数据集。Yelp数据集挑战赛第6轮包含在美国六个城市将近一百六十万商业评论。\\n\",\n    \"### 样例4-1：使用python加载和清洗Yelp评论数据集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import json\\n\",\n    \"import pandas as pd\\n\",\n    \"## Load Yelp Business data\\n\",\n    \"biz_f = open('data/yelp_academic_dataset_business.json')\\n\",\n    \"biz_df = pd.DataFrame([json.loads(x) for x in biz_f.readlines()])\\n\",\n    \"biz_f.close()\\n\",\n    \"## Load Yelp Reviews data\\n\",\n    \"review_file = open('data/yelp_academic_dataset_review.json')\\n\",\n    \"review_df = pd.DataFrame([json.loads(x) for x in review_file.readlines()])\\n\",\n    \"review_file.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(11537, 13)\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"biz_df.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(229907, 8)\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"review_df.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Pull out only Nightlife and Restaurants businesses\\n\",\n    \"two_biz = biz_df[biz_df.apply(\\n\",\n    \"    lambda x: 'Nightlife' in x['categories'] or 'Restaurants' in x['categories'\\n\",\n    \"                                                                   ],\\n\",\n    \"    axis=1)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(4816, 13)\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"two_biz.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(11537, 13)\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"biz_df.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Join with the reviews to get all reviews on the two types of business\\n\",\n    \"twobiz_reviews = two_biz.merge(review_df, on='business_id', how='inner')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(166038, 20)\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"twobiz_reviews.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Trim away the features we won't use\\n\",\n    \"twobiz_reviews = twobiz_reviews[['business_id',\\n\",\n    \"                        'name',\\n\",\n    \"                        'stars_y',\\n\",\n    \"                        'text',\\n\",\n    \"                        'categories']]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Create the target column--True for Nightlife businesses, and False otherwise\\n\",\n    \"twobiz_reviews['target'] = twobiz_reviews.apply(\\n\",\n    \"    lambda x: 'Nightlife' in x['categories'], axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 建立分类数据集\\n\",\n    \"\\n\",\n    \"让我们看看是否可以使用评论来区分餐厅或夜生活场所。为了节省训练时间，仅使用一部分评论。这两个类别之间的评论数目有很大差异。这是所谓的类不平衡数据集。对于构建模型来说，不平衡的数据集存在着一个问题:这个模型会把大部分精力花费在比重更大的类上。由于我们在这两个类别都有大量的数据，解决这个问题的一个比较好方法是将数目较大的类（餐厅）进行下采样，使之与数目较小的类（夜生活）数目大致相同。下面是一个示例工作流程。\\n\",\n    \"\\n\",\n    \"1. 随机抽取10%夜生活场所评论以及2.1%的餐厅评论（选取合适的百分比使得每个种类的数目大致一样）\\n\",\n    \"\\n\",\n    \"2. 将数据集分成比例为7：3的训练集和测试集。在这个例子里，训练集包括29，264条评论，测试集有12542条。\\n\",\n    \"\\n\",\n    \"3. 训练数据包括46，924个不同的单词，这是词袋表示中特征的数量。\\n\",\n    \"\\n\",\n    \"### 样例4-2：创建一个分类数据集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Create a class-balanced subsample to play with\\n\",\n    \"nightlife = twobiz_reviews[\\n\",\n    \"    twobiz_reviews.apply(lambda x: 'Nightlife' in x['categories'], axis=1)]\\n\",\n    \"restaurants = twobiz_reviews[\\n\",\n    \"    twobiz_reviews.apply(lambda x: 'Restaurants' in x['categories'], axis=1)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(30136, 6)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"nightlife.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(158430, 6)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"restaurants.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"nightlife_subset = nightlife.sample(frac=0.1, random_state=123)\\n\",\n    \"restaurant_subset = restaurants.sample(frac=0.021, random_state=123)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(3014, 6)\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"nightlife_subset.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(3327, 6)\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"restaurant_subset.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"combined = pd.concat([nightlife_subset, restaurant_subset])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"combined['target'] = combined.apply(\\n\",\n    \"    lambda x: 'Nightlife' in x['categories'], axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>business_id</th>\\n\",\n       \"      <th>name</th>\\n\",\n       \"      <th>stars_y</th>\\n\",\n       \"      <th>text</th>\\n\",\n       \"      <th>categories</th>\\n\",\n       \"      <th>target</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>103709</th>\\n\",\n       \"      <td>2ceeU8e3nZjaPfGmLwh4kg</td>\\n\",\n       \"      <td>Casey Moore's Oyster House</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Been here a couple times over the last few yea...</td>\\n\",\n       \"      <td>[Bars, Seafood, Irish, Pubs, Nightlife, Restau...</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>52043</th>\\n\",\n       \"      <td>JokKtdXU7zXHcr20Lrk29A</td>\\n\",\n       \"      <td>Four Peaks Brewing Co</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>Over the top service. One of my favorite meals...</td>\\n\",\n       \"      <td>[Bars, Food, Breweries, Pubs, Nightlife, Ameri...</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>134438</th>\\n\",\n       \"      <td>-yxfBYGB6SEqszmxJxd97A</td>\\n\",\n       \"      <td>Quiessence Restaurant</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>On a trip where I ate at some very nice and up...</td>\\n\",\n       \"      <td>[Wine Bars, Bars, American (New), Nightlife, R...</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>142453</th>\\n\",\n       \"      <td>fjAQGf-iJlVjD2vizzuORQ</td>\\n\",\n       \"      <td>Giligin's Bar</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Stumbled on Giligins a few nights ago.  I knew...</td>\\n\",\n       \"      <td>[Bars, Nightlife]</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>67583</th>\\n\",\n       \"      <td>DjdA1xbHki_lopCSxf-Egg</td>\\n\",\n       \"      <td>Greasewood Flat</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>Thinking about one of these burgers is making ...</td>\\n\",\n       \"      <td>[Burgers, Bars, Hot Dogs, Nightlife, Restaurants]</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                   business_id                        name  stars_y  \\\\\\n\",\n       \"103709  2ceeU8e3nZjaPfGmLwh4kg  Casey Moore's Oyster House        2   \\n\",\n       \"52043   JokKtdXU7zXHcr20Lrk29A       Four Peaks Brewing Co        5   \\n\",\n       \"134438  -yxfBYGB6SEqszmxJxd97A       Quiessence Restaurant        4   \\n\",\n       \"142453  fjAQGf-iJlVjD2vizzuORQ               Giligin's Bar        4   \\n\",\n       \"67583   DjdA1xbHki_lopCSxf-Egg             Greasewood Flat        5   \\n\",\n       \"\\n\",\n       \"                                                     text  \\\\\\n\",\n       \"103709  Been here a couple times over the last few yea...   \\n\",\n       \"52043   Over the top service. One of my favorite meals...   \\n\",\n       \"134438  On a trip where I ate at some very nice and up...   \\n\",\n       \"142453  Stumbled on Giligins a few nights ago.  I knew...   \\n\",\n       \"67583   Thinking about one of these burgers is making ...   \\n\",\n       \"\\n\",\n       \"                                               categories  target  \\n\",\n       \"103709  [Bars, Seafood, Irish, Pubs, Nightlife, Restau...    True  \\n\",\n       \"52043   [Bars, Food, Breweries, Pubs, Nightlife, Ameri...    True  \\n\",\n       \"134438  [Wine Bars, Bars, American (New), Nightlife, R...    True  \\n\",\n       \"142453                                  [Bars, Nightlife]    True  \\n\",\n       \"67583   [Burgers, Bars, Hot Dogs, Nightlife, Restaurants]    True  \"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"combined.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\model_selection\\\\_split.py:2179: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\\n\",\n      \"  FutureWarning)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Split into training and test data sets\\n\",\n    \"import sklearn.model_selection as modsel\\n\",\n    \"training_data, test_data = modsel.train_test_split(\\n\",\n    \"    combined, train_size=0.7, random_state=123)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(4438, 6)\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"training_data.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(1903, 6)\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_data.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 用tf-idf转换缩放词袋\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"这个实验的目标是比较词袋，tf-idf以及L2归一化对于线性分类的作用。注意，做tf-idf接着做L2归一化和单独做L2归一化是一样的。所以我们需要只需要3个特征集合：词袋，tf-idf，以及逐词进行L2归一化后的词袋。\\n\",\n    \"\\n\",\n    \"在这个例子中，我们将使用Scikit-learn的CountVectorizer将评论文本转化为词袋。所有的文本特征化方法都依赖于标记器（tokenizer），该标记器能够将文本字符串转换为标记（词）列表。在这个例子中，Scikit-learn的默认标记模式是查找2个或更多字母数字字符的序列。标点符号被视为标记分隔符。\\n\",\n    \"\\n\",\n    \"## 测试集上进行特征缩放\\n\",\n    \"\\n\",\n    \"特征缩放的一个细微之处是它需要了解我们在实践中很可能不知道的特征统计，例如均值，方差，文档频率，L2范数等。为了计算tf-idf表示，我们不得不根据训练数据计算逆文档频率，并使用这些统计量来调整训练和测试数据。在Scikit-learn中，将特征变换拟合到训练集上相当于收集相关统计数据。然后可以将拟合过的变换应用于测试数据。\\n\",\n    \"\\n\",\n    \"### 样例4-3：特征变换\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.feature_extraction import text\\n\",\n    \"# Represent the review text as a bag-of-words\\n\",\n    \"bow_transform = text.CountVectorizer()\\n\",\n    \"X_tr_bow = bow_transform.fit_transform(training_data['text'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_te_bow = bow_transform.transform(test_data['text'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"18565\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(bow_transform.vocabulary_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y_tr = training_data['target']\\n\",\n    \"y_te = test_data['target']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Create the tf-idf representation using the bag-of-words matrix\\n\",\n    \"tfidf_trfm = text.TfidfTransformer(norm=None)\\n\",\n    \"X_tr_tfidf = tfidf_trfm.fit_transform(X_tr_bow)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_te_tfidf = tfidf_trfm.transform(X_te_bow)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sklearn.preprocessing as preproc\\n\",\n    \"# Just for kicks, l2-normalize the bag-of-words representation\\n\",\n    \"X_tr_l2 = preproc.normalize(X_tr_bow, axis=0)\\n\",\n    \"X_te_l2 = preproc.normalize(X_te_bow, axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"当我们使用训练统计来衡量测试数据时，结果看起来有点模糊。测试集上的最小-最大比例缩放不再整齐地映射到零和一。L2范数，平均数和方差统计数据都将显得有些偏离。这比缺少数据的问题好一点。例如，测试集可能包含训练数据中不存在的单词，并且对于新的单词没有相应的文档频。通常的解决方案是简单地将测试集中新的单词丢弃。这似乎是不负责任的，但训练集上的模型在任何情况下都不会知道如何处理新词。一种稍微不太好的方法是明确地学习一个“垃圾”单词，并将所有罕见的频率单词映射到它，即使在训练集中也是如此，正如“罕见词汇”中所讨论的那样。\\n\",\n    \"\\n\",\n    \"## 使用逻辑回归进行分类\\n\",\n    \"\\n\",\n    \"逻辑回归是一个简单的线性分类器。通过对输入特征的加权组合，输入到一个sigmoid函数。sigmoid函数将任何实数平滑的映射到介于0和1之间。如图4-3绘制sigmoid函数曲线。由于逻辑回归比较简单，因此它通常是最先接触的分类器。\\n\",\n    \"\\n\",\n    \"![Figure 4-3: sigmoid函数](images/chapter4/4-3.png)\\n\",\n    \"\\n\",\n    \"<center><h5>Figure 4-3: sigmoid函数</h5></center>\\n\",\n    \"\\n\",\n    \"图4-3是sigmoid函数的插图。该函数将输入的实数x转换为一个0到1之间的数。它有一组参数w，表示围绕中点0.5增加的斜率。截距项b表示函数输出穿过中点的输入值。如果sigmoid输出大于0.5，则逻辑分类器将预测为正例，否则为反例。通过改变w和b，可以控制决策的改变，以及决策响应该点周围输入值变化的速度。\\n\",\n    \"\\n\",\n    \"### 样例4-4：使用默认参数训练逻辑回归分类器\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.linear_model import LogisticRegression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def simple_logistic_classify(X_tr, y_tr, X_test, y_test, description, _C=1.0):\\n\",\n    \"    ## Helper function to train a logistic classifier and score on test data\\n\",\n    \"    m = LogisticRegression(C=_C).fit(X_tr, y_tr)\\n\",\n    \"    s = m.score(X_test, y_test)\\n\",\n    \"    print('Test score with', description, 'features:', s)\\n\",\n    \"    return m\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\\n\",\n      \"  FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test score with bow features: 0.7677351550183921\\n\",\n      \"Test score with l2-normalized features: 0.7856016815554387\\n\",\n      \"Test score with tf-idf features: 0.7540725170782975\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"m1 = simple_logistic_classify(X_tr_bow, y_tr, X_te_bow, y_te, 'bow')\\n\",\n    \"m2 = simple_logistic_classify(X_tr_l2, y_tr, X_te_l2, y_te, 'l2-normalized')\\n\",\n    \"m3 = simple_logistic_classify(X_tr_tfidf, y_tr, X_te_tfidf, y_te, 'tf-idf')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"矛盾的是，结果表明最准确的分类器是使用BOW特征的分类器。出乎意料我们之外。事实证明，造成这种情况的原因是没有很好地“调整”分类器，这是比较分类器时一个常见的错误。\\n\",\n    \"\\n\",\n    \"## 使用正则化调整逻辑回归\\n\",\n    \"\\n\",\n    \"逻辑回归有些华而不实。 当特征的数量大于数据点的数量时，找到最佳模型的问题被认为是欠定的。 解决这个问题的一种方法是在训练过程中增加额外的约束条件。 这就是所谓的正则化，技术细节将在下一节讨论。\\n\",\n    \"\\n\",\n    \"逻辑回归的大多数实现允许正则化。为了使用这个功能，必须指定一个正则化参数。正则化参数是在模型训练过程中未自动学习的超参数。相反，他们必须手动进行调整，并将其提供给训练算法。这个过程称为超参数调整。（有关如何评估机器学习模型的详细信息，请参阅评估机器学习模型（Evaluating Machine Learning Models））.调整超参数的一种基本方法称为网格搜索：指定一个超参数值网格，并且调谐器以编程方式在网格中搜索最佳超参数设置 格。 找到最佳超参数设置后，使用该设置对整个训练集进行训练，并比较测试集上这些同类最佳模型的性能。\\n\",\n    \"\\n\",\n    \"## 重点：比较模型时调整超参数\\n\",\n    \"\\n\",\n    \"比较模型或特征时，调整超参数非常重要。 软件包的默认设置将始终返回一个模型。 但是除非软件在底层进行自动调整，否则很可能会返回一个基于次优超参数设置的次优模型。 分类器性能对超参数设置的敏感性取决于模型和训练数据的分布。 逻辑回归对超参数设置相对稳健（或不敏感）。 即便如此，仍然有必要找到并使用正确的超参数范围。 否则，一个模型相对于另一个模型的优点可能仅仅是由于参数的调整，并不能反映模型或特征的实际表现。\\n\",\n    \"\\n\",\n    \"即使是最好的自动调整软件包仍然需要指定搜索的上限和下限，并且找到这些限制可能需要几次手动尝试。\\n\",\n    \"\\n\",\n    \"在本例中，我们手动将逻辑正则化参数的搜索网格设置为{1e-5，0.001，0.1，1，10，100}。 上限和下限花费了几次尝试来缩小范围。 表4-1给出了每个特征集合的最优超参数设置。\\n\",\n    \"\\n\",\n    \"<center><h5>Table4-1.对夜场和餐厅的Yelp评论进行逻辑回归的最佳参数设置</h5></center>\\n\",\n    \"\\n\",\n    \" Method | L2 Regularization\\n\",\n    \"--------|-----------\\n\",\n    \"BOW | 0.1 \\n\",\n    \"L2-normalized| 10\\n\",\n    \"TF-IDF | 0.01\\n\",\n    \"\\n\",\n    \"我们也想测试tf-idf和BOW之间的精度差异是否是由于噪声造成的。 为此，我们使用k折交叉验证来模拟具有多个统计独立的数据集。它将数据集分为k个折叠。交叉验证过程通过分割后的数据进行迭代，使用除除去某一折之外的所有内容进行训练，并用那一折验证结果。Scikit-Learn中的GridSearchCV功能通过交叉验证进行网格搜索。 图4-4显示了在每个特征集上训练的模型的精度测量分布箱线图。 盒子中线表示中位精度，盒子本身表示四分之一和四分之三分位之间的区域，而线则延伸到剩余的分布。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"### 通过重采样估计方差\\n\",\n    \"\\n\",\n    \"现代统计方法假设底层数据是随机分布的。 数据导出模型的性能测量也受到随机噪声的影响。 在这种情况下，基于相似数据的数据集，不止一次进行测量总是比较好的。 这给了我们一个测量的置信区间。 K折交叉验证就是这样一种策略。 重采样是另一种从相同底层数据集生成多个小样本的技术。 有关重采样的更多详细信息，请参见评估机器学习模型。\\n\",\n    \"\\n\",\n    \"### 样例4-5：使用网格搜索调整逻辑回归超参数\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"param_grid_ = {'C': [1e-5, 1e-3, 1e-1, 1e0, 1e1, 1e2]}\\n\",\n    \"bow_search = modsel.GridSearchCV(\\n\",\n    \"    LogisticRegression(), cv=5, param_grid=param_grid_)\\n\",\n    \"l2_search = modsel.GridSearchCV(\\n\",\n    \"    LogisticRegression(), cv=5, param_grid=param_grid_)\\n\",\n    \"tfidf_search = modsel.GridSearchCV(\\n\",\n    \"    LogisticRegression(), cv=5, param_grid=param_grid_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\\n\",\n      \"  FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=5, error_score='raise-deprecating',\\n\",\n       \"       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"          intercept_scaling=1, max_iter=100, multi_class='warn',\\n\",\n       \"          n_jobs=None, penalty='l2', random_state=None, solver='warn',\\n\",\n       \"          tol=0.0001, verbose=0, warm_start=False),\\n\",\n       \"       fit_params=None, iid='warn', n_jobs=None,\\n\",\n       \"       param_grid={'C': [1e-05, 0.001, 0.1, 1.0, 10.0, 100.0]},\\n\",\n       \"       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\\n\",\n       \"       scoring=None, verbose=0)\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bow_search.fit(X_tr_bow, y_tr)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.765209553853087\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bow_search.best_score_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\\n\",\n      \"  FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=5, error_score='raise-deprecating',\\n\",\n       \"       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"          intercept_scaling=1, max_iter=100, multi_class='warn',\\n\",\n       \"          n_jobs=None, penalty='l2', random_state=None, solver='warn',\\n\",\n       \"          tol=0.0001, verbose=0, warm_start=False),\\n\",\n       \"       fit_params=None, iid='warn', n_jobs=None,\\n\",\n       \"       param_grid={'C': [1e-05, 0.001, 0.1, 1.0, 10.0, 100.0]},\\n\",\n       \"       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\\n\",\n       \"       scoring=None, verbose=0)\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"l2_search.fit(X_tr_l2, y_tr)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.7708427219468229\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"l2_search.best_score_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\\n\",\n      \"  FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"GridSearchCV(cv=5, error_score='raise-deprecating',\\n\",\n       \"       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"          intercept_scaling=1, max_iter=100, multi_class='warn',\\n\",\n       \"          n_jobs=None, penalty='l2', random_state=None, solver='warn',\\n\",\n       \"          tol=0.0001, verbose=0, warm_start=False),\\n\",\n       \"       fit_params=None, iid='warn', n_jobs=None,\\n\",\n       \"       param_grid={'C': [1e-05, 0.001, 0.1, 1.0, 10.0, 100.0]},\\n\",\n       \"       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\\n\",\n       \"       scoring=None, verbose=0)\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tfidf_search.fit(X_tr_tfidf, y_tr)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.7893195132942767\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tfidf_search.best_score_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'C': 0.1}\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bow_search.best_params_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'C': 1.0}\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"l2_search.best_params_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'C': 0.001}\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tfidf_search.best_params_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:125: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\\n\",\n      \"  warnings.warn(*warn_args, **warn_kwargs)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:125: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\\n\",\n      \"  warnings.warn(*warn_args, **warn_kwargs)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:125: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\\n\",\n      \"  warnings.warn(*warn_args, **warn_kwargs)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:125: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\\n\",\n      \"  warnings.warn(*warn_args, **warn_kwargs)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:125: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\\n\",\n      \"  warnings.warn(*warn_args, **warn_kwargs)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:125: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\\n\",\n      \"  warnings.warn(*warn_args, **warn_kwargs)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:125: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\\n\",\n      \"  warnings.warn(*warn_args, **warn_kwargs)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'mean_fit_time': array([0.02672362, 0.04170284, 0.14934993, 0.23928313, 0.37921972,\\n\",\n       \"        0.42493615]),\\n\",\n       \" 'mean_score_time': array([0.00079846, 0.00099654, 0.00039368, 0.00079846, 0.00059032,\\n\",\n       \"        0.00039887]),\\n\",\n       \" 'mean_test_score': array([0.54912123, 0.72239748, 0.76520955, 0.75259126, 0.74132492,\\n\",\n       \"        0.72577738]),\\n\",\n       \" 'mean_train_score': array([0.54912118, 0.77022266, 0.94338642, 0.99273309, 0.99909867,\\n\",\n       \"        0.99988734]),\\n\",\n       \" 'param_C': masked_array(data=[1e-05, 0.001, 0.1, 1.0, 10.0, 100.0],\\n\",\n       \"              mask=[False, False, False, False, False, False],\\n\",\n       \"        fill_value='?',\\n\",\n       \"             dtype=object),\\n\",\n       \" 'params': [{'C': 1e-05},\\n\",\n       \"  {'C': 0.001},\\n\",\n       \"  {'C': 0.1},\\n\",\n       \"  {'C': 1.0},\\n\",\n       \"  {'C': 10.0},\\n\",\n       \"  {'C': 100.0}],\\n\",\n       \" 'rank_test_score': array([6, 5, 1, 2, 3, 4]),\\n\",\n       \" 'split0_test_score': array([0.54842342, 0.76013514, 0.78490991, 0.76576577, 0.75112613,\\n\",\n       \"        0.73423423]),\\n\",\n       \" 'split0_train_score': array([0.54901408, 0.76676056, 0.94309859, 0.99239437, 0.99887324,\\n\",\n       \"        0.99971831]),\\n\",\n       \" 'split1_test_score': array([0.55067568, 0.72072072, 0.77702703, 0.76801802, 0.7545045 ,\\n\",\n       \"        0.75112613]),\\n\",\n       \" 'split1_train_score': array([0.54873239, 0.76591549, 0.93830986, 0.99183099, 0.99859155,\\n\",\n       \"        1.        ]),\\n\",\n       \" 'split2_test_score': array([0.5518018 , 0.71734234, 0.75900901, 0.72972973, 0.72635135,\\n\",\n       \"        0.70720721]),\\n\",\n       \" 'split2_train_score': array([0.54873239, 0.77070423, 0.94450704, 0.99183099, 0.99943662,\\n\",\n       \"        1.        ]),\\n\",\n       \" 'split3_test_score': array([0.54791432, 0.70800451, 0.77113867, 0.75422773, 0.7361894 ,\\n\",\n       \"        0.71476888]),\\n\",\n       \" 'split3_train_score': array([0.54970431, 0.77499296, 0.94564911, 0.99436778, 0.99915517,\\n\",\n       \"        0.99971839]),\\n\",\n       \" 'split4_test_score': array([0.54678692, 0.70574972, 0.73393461, 0.74520857, 0.73844419,\\n\",\n       \"        0.72153326]),\\n\",\n       \" 'split4_train_score': array([0.5494227 , 0.77274007, 0.9453675 , 0.99324134, 0.99943678,\\n\",\n       \"        1.        ]),\\n\",\n       \" 'std_fit_time': array([0.00416257, 0.00363773, 0.0199039 , 0.03759725, 0.02942488,\\n\",\n       \"        0.03680945]),\\n\",\n       \" 'std_score_time': array([3.99235538e-04, 7.44843452e-07, 4.82230192e-04, 3.99236620e-04,\\n\",\n       \"        4.82177678e-04, 4.88519238e-04]),\\n\",\n       \" 'std_test_score': array([0.00184359, 0.01968314, 0.01777104, 0.0140838 , 0.0102844 ,\\n\",\n       \"        0.01548232]),\\n\",\n       \" 'std_train_score': array([0.00038594, 0.00346014, 0.00268904, 0.00096673, 0.00032855,\\n\",\n       \"        0.00013798])}\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bow_search.cv_results_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>bow</th>\\n\",\n       \"      <th>l2</th>\\n\",\n       \"      <th>tfidf</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0.549121</td>\\n\",\n       \"      <td>0.548220</td>\\n\",\n       \"      <td>0.589004</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0.722397</td>\\n\",\n       \"      <td>0.548220</td>\\n\",\n       \"      <td>0.789320</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0.765210</td>\\n\",\n       \"      <td>0.593961</td>\\n\",\n       \"      <td>0.759351</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0.752591</td>\\n\",\n       \"      <td>0.770843</td>\\n\",\n       \"      <td>0.747634</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0.741325</td>\\n\",\n       \"      <td>0.769491</td>\\n\",\n       \"      <td>0.730509</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>0.725777</td>\\n\",\n       \"      <td>0.751465</td>\\n\",\n       \"      <td>0.702118</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"        bow        l2     tfidf\\n\",\n       \"0  0.549121  0.548220  0.589004\\n\",\n       \"1  0.722397  0.548220  0.789320\\n\",\n       \"2  0.765210  0.593961  0.759351\\n\",\n       \"3  0.752591  0.770843  0.747634\\n\",\n       \"4  0.741325  0.769491  0.730509\\n\",\n       \"5  0.725777  0.751465  0.702118\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"########\\n\",\n    \"# Plot the cross validation results in a box-and-whiskers plot to\\n\",\n    \"# visualize and compare classifier performance\\n\",\n    \"########\\n\",\n    \"search_results = pd.DataFrame.from_dict({\\n\",\n    \"    'bow':\\n\",\n    \"    bow_search.cv_results_['mean_test_score'],\\n\",\n    \"    'tfidf':\\n\",\n    \"    tfidf_search.cv_results_['mean_test_score'],\\n\",\n    \"    'l2':\\n\",\n    \"    l2_search.cv_results_['mean_test_score']\\n\",\n    \"})\\n\",\n    \"search_results\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Table4-2.每个超参数设置的平均交叉验证分类器准确度。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 画出交叉验证结果\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Our usual matplotlib incantations. Seaborn is used here to make\\n\",\n    \"# the plot pretty\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"sns.set_style(\\\"whitegrid\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x25b2a909eb8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = sns.boxplot(data=search_results, width=0.4)\\n\",\n    \"ax.set_ylabel('Accuracy', size=14)\\n\",\n    \"ax.tick_params(labelsize=14)\\n\",\n    \"# plt.savefig('tfidf_gridcv_results.png')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>Figure 4-4: 分类器精度在每个特征集和正则化设置下的分布。 准确度是以5折交叉验证的平均准确度来衡量的</center>\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"在图4-4中，L2归一化后的特征结果看起来非常糟糕。 但不要被蒙蔽了 。准确率低是由于正则化参数设置不恰当造成的 - 实际证明次优超参数会得到相当错误的结论。 如果我们使用每个特征集的最佳超参数设置来训练模型，则不同特征集的测试精度非常接近。\\n\",\n    \"\\n\",\n    \"### 示例4-6：最终的训练和测试步骤来比较不同的特征集\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\\n\",\n      \"  FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test score with bow features: 0.7682606410930111\\n\",\n      \"Test score with l2-normalized features: 0.7856016815554387\\n\",\n      \"Test score with tf-idf features: 0.792433000525486\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"m1 = simple_logistic_classify(\\n\",\n    \"    X_tr_bow, y_tr, X_te_bow, y_te, 'bow', _C=bow_search.best_params_['C'])\\n\",\n    \"m2 = simple_logistic_classify(\\n\",\n    \"    X_tr_l2,\\n\",\n    \"    y_tr,\\n\",\n    \"    X_te_l2,\\n\",\n    \"    y_te,\\n\",\n    \"    'l2-normalized',\\n\",\n    \"    _C=l2_search.best_params_['C'])\\n\",\n    \"m3 = simple_logistic_classify(\\n\",\n    \"    X_tr_tfidf,\\n\",\n    \"    y_tr,\\n\",\n    \"    X_te_tfidf,\\n\",\n    \"    y_te,\\n\",\n    \"    'tf-idf',\\n\",\n    \"    _C=tfidf_search.best_params_['C'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.54912123, 0.72239748, 0.76520955, 0.75259126, 0.74132492,\\n\",\n       \"       0.72577738])\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"bow_search.cv_results_['mean_test_score']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>Table4-3.BOW, Tf-Idf,以及L2正则化的最终分类精度</center>\\n\",\n    \"\\n\",\n    \"Feature Set |Test Accuracy\\n\",\n    \"--------|-----------\\n\",\n    \"Bag-of-Words | 0.7682606410930111\\n\",\n    \"L2 -normalized | 0.7856016815554387\\n\",\n    \"Tf-Idf | 0.792433000525486\\n\",\n    \"\\n\",\n    \"适当的调整提高了所有特征集的准确性，并且所有特征集在正则化后进行逻辑回归得到了相近的准确率。tf-idf模型准确率略高，但这点差异可能没有统计学意义。 这些结果是完全神秘的。 如果特征缩放效果不如vanilla词袋的效果好，那为什么要这么做呢？ 如果tf-idf没有做任何事情，为什么总是要这么折腾？ 我们将在本章的其余部分中探索答案。\\n\",\n    \"\\n\",\n    \"## 深入：发生了什么？\\n\",\n    \"为了明白结果背后隐含着什么，我们必须考虑模型是如何使用特征的。对于类似逻辑回归这种线性模型来说，是通过所谓的数据矩阵的中间对象来实现的。\\n\",\n    \"数据矩阵包含以固定长度平面向量表示的数据点。 根据词袋向量，数据矩阵也被称为文档词汇矩阵。 图3-1显示了一个向量形式的词袋向量，图4-1显示了特征空间中的四个词袋向量。 要形成文档词汇矩阵，只需将文档向量取出，平放，然后将它们堆叠在一起。 这些列表示词汇表中所有可能的单词。 由于大多数文档只包含所有可能单词的一小部分，因此该矩阵中的大多数都是零，是一个稀疏矩阵。\\n\",\n    \"\\n\",\n    \"![Figure 4-5: 包含5个文档7个单词的文档-词汇矩阵](images/chapter4/4-5.png)\\n\",\n    \"\\n\",\n    \"<center>Figure 4-5: 包含5个文档7个单词的文档-词汇矩阵</center>\\n\",\n    \"\\n\",\n    \"特征缩放方法本质上是对数据矩阵的列操作。特别的，tf-idf和L2归一化都将整列（例如n-gram特征）乘上一个常数。\\n\",\n    \"\\n\",\n    \"## Tf-idf=列缩放\\n\",\n    \"Tf-idf和L2归一化都是数据矩阵上的列操作。 正如附录A所讨论的那样，训练线性分类器归结为寻找最佳的线性组合特征，这是数据矩阵的列向量。 解空间的特征是列空间和数据矩阵的空间。训练过的线性分类器的质量直接取决于数据矩阵的零空间和列空间。 大的列空间意味着特征之间几乎没有线性相关性，这通常是好的。 零空间包含“新”数据点，不能将其表示为现有数据的线性组合; 大的零空间可能会有问题。（强烈建议希望对诸如线性决策表面，特征分解和矩阵的基本子空间等概念进行的回顾的读者阅读附录A。)\\n\",\n    \"\\n\",\n    \"列缩放操作如何影响数据矩阵的列空间和空间？ 答案是“不是很多”。但是在tf-idf和L2归一化之间有一个小小的差别。\\n\",\n    \"\\n\",\n    \"由于几个原因，数据矩阵的零空间可能很大。 首先，许多数据集包含彼此非常相似的数据点。 这使得有效的行空间与数据集中数据的数量相比较小。 其次，特征的数量可以远大于数据的数量。 词袋特别擅长创造巨大的特征空间。 在我们的Yelp例子中，训练集中有29K条评论，但有47K条特征。 而且，不同单词的数量通常随着数据集中文档的数量而增长。 因此，添加更多的文档不一定会降低特征与数据比率或减少零空间。\\n\",\n    \"\\n\",\n    \"在词袋模型中，与特征数量相比，列空间相对较小。 在相同的文档中可能会出现数目大致相同的词，相应的列向量几乎是线性相关的，这导致列空间不像它可能的那样满秩。 这就是所谓的秩亏。 （就像动物缺乏维生素和矿物质一样，矩阵秩亏，输出空间也不会像应该那样蓬松）。\\n\",\n    \"\\n\",\n    \"秩亏行空间和列空间导致模型空间预留过度的问题。 线性模型为数据集中的每个特征配置权重参数。 如果行和列空间满秩$^1$，那么该模型将允许我们在输出空间中生成任何目标向量。 当模型不满秩时，模型的自由度比需要的更大。 这使得找出解决方案变得更加棘手。\\n\",\n    \"\\n\",\n    \"可以通过特征缩放来解决数据矩阵的不满秩问题吗？ 让我们来看看。\\n\",\n    \"\\n\",\n    \"列空间被定义为所有列向量的线性组合：$a_{1} v_{1}+a_{2} v_{2}+\\\\ldots+a_{n} v_{n}$。比方说，特征缩放用一个常数倍来替换一个列向量，$v_{1}=c v_{1}$。但是我们仍然可以通过用$\\\\widetilde{a_{1}}=\\\\frac{a_{1}}{c}$来替换$a_1$，生成原始的线性组合。看起来，特征缩放不会改变列空间的秩。类似地，特征缩放不会影响空间的秩，因为可以通过反比例缩放权重向量中的对应条目来抵消缩放的特征列。\\n\",\n    \"\\n\",\n    \"但是，仍然存在一个陷阱。 如果标量为0，则无法恢复原始线性组合;$v_1$消失了。 如果该向量与所有其他列线性无关，那么我们已经有效地缩小了列空间并放大了零空间。\\n\",\n    \"\\n\",\n    \"如果该向量与目标输出不相关，那么这将有效地修剪掉噪声信号，这是一件好事。 这是tf-idf和L2归一化之间的关键区别。 L2归一化永远不会计算零的范数，除非该向量包含全零。 如果向量接近零，那么它的范数也接近于零。 按照小规范划分将突出向量并使其变大。\\n\",\n    \"\\n\",\n    \"另一方面，如图4-2所示，Tf-idf可以生成接近零的缩放因子。 当这个词出现在训练集中的大量文档中时，会发生这种情况。 这样的话有可能与目标向量没有很强的相关性。 修剪它可以使模型专注于列空间中的其他方向并找到更好的解决方案。 准确度的提高可能不会很大，因为很少有噪声方向可以通过这种方式修剪。\\n\",\n    \"\\n\",\n    \"在特征缩放的情况下，L2和tf-idf对于模型的收敛速度确实有促进。 这是该数据矩阵有一个更小的条件数的标志。 事实上，L2归一化使得条件数几乎一致。 但情况并非条件数越多，解决方案越好。 在这个实验中，L2归一化收敛比BOW或tf-idf快得多。 但它对过拟合也更敏感：它需要更多的正则化，并且对优化期间的迭代次数更敏感。\\n\",\n    \"\\n\",\n    \"## 总结\\n\",\n    \"\\n\",\n    \"在本章中，我们使用tf-idf作为入口点，详细分析特征变换如何影响（或不）模型。Tf-idf是特征缩放的一个例子，所以我们将它的性能与另一个特征缩放方法-L2标准化进行了对比。\\n\",\n    \"\\n\",\n    \"结果并不如预期。Tf-idf和L2归一化不会提高最终分类器的准确度，而不会超出纯词袋。 在获得了一些统计建模和线性代数处理知识之后，我们意识到了为什么：他们都没有改变数据矩阵的列空间。\\n\",\n    \"\\n\",\n    \"两者之间的一个小区别是，tf-idf可以“拉伸”字数以及“压缩”它。 换句话说，它使一些数字更大，其他数字更接近\\n\",\n    \"归零。 因此，tf-idf可以完全消除无意义的单词。\\n\",\n    \"\\n\",\n    \"我们还发现了另一个特征缩放效果：它改善了数据矩阵的条件数，使线性模型的训练速度更快。 L2标准化和tf-idf都有这种效果。\\n\",\n    \"\\n\",\n    \"总而言之，正确的特征缩放可以有助于分类。 正确的缩放突出了信息性词语，并降低了常见单词的权重。 它还可以改善数据矩阵的条件数。 正确的缩放并不一定是统一的列缩放。\\n\",\n    \"\\n\",\n    \"这个故事很好地说明了在一般情况下分析特征工程的影响的难度。 更改特征会影响训练过程和随后的模型。 线性模型是容易理解的模型。 然而，它仍然需要非常谨慎的实验方法和大量的深刻的数学知识来区分理论和实际的影响。 对于更复杂的模型或特征转换来说，这是不可能的。\\n\",\n    \"\\n\",\n    \"## 参考书目\\n\",\n    \"\\n\",\n    \"Strang, Gilbert. 2006. Linear Algebra and Its Applications. Brooks Cole Cengage, fourth edition.\\n\",\n    \"\\n\",\n    \"$^1$ 严格地说，矩阵矩阵的行空间和列空间不能都是满秩的。 两个子空间的最大秩是m（行数）和n（列数）中的较小者。 这就是我们所说的满秩。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "9.feature-engineering/5.类别特征.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[原版（英文）图书地址](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/)\\n\",\n    \"\\n\",\n    \"**翻译**：[apachecn](https://github.com/apachecn)，[翻译版本地址](https://github.com/apachecn/feature-engineering-for-ml-zh)\\n\",\n    \"\\n\",\n    \"**代码修改和整理**：[黄海广](https://github.com/fengdu78)，原文修改成jupyter notebook格式，并增加和修改了部分代码，测试全部通过，所有数据集已经放在[百度云](data/README.md)下载。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 五、类别特征：机器鸡时代的鸡蛋计数\\n\",\n    \"\\n\",\n    \"> 译者：[@ZhenLeiXu](https://github.com/HadXu)\\n\",\n    \"\\n\",\n    \"一个类别特征，见名思义，就是用来表达一种类别或标签。比如，一个类别特征能够表达世界上的主要城市，一年四季，或者说一个公司的产品(石油、路程、技术)。在真实世界的数据集中，类别值的数量总是无限的。同时这些值一般可以用数值来表示。但是，与其他数值变量不一样的是，类别特征的数值变量无法与其他数值变量进行比较大小。(作为行业类型，石油与旅行无法进行比较)它们被称之为非序的。\\n\",\n    \"\\n\",\n    \"一个简单的问题可以作为测试是否应该是一个分类变量的试金石测试：“两个价值有多么不同，或者只是它们不同？”500美元的股票价格比100美元的价格高5倍。 所以股票价格应该用一个连续的数字变量表示。 另一方面，公司的产业（石油，旅游，技术等）应该无法被比较的，也就是类别特征。\\n\",\n    \"\\n\",\n    \"大的分类变量在交易记录中特别常见。 对于实例中，许多Web服务使用id作为分类变量来跟踪用户具有数百至数百万的值，取决于唯一的数量服务的用户。 互联网交易的IP地址是另一个例子一个很大的分类变量。 它们是分类变量，因为即使用户ID和IP地址是数字，它们的大小通常与任务无关在眼前。 例如，在进行欺诈检测时，IP地址可能是相关的个人交易。 某些IP地址或子网可能会产生更多欺骗性交易比其他人。 但是`164.203.x.x`的子网本质上并不多欺诈性比`164.202.x.x`; 子网的数值无关紧要。\\n\",\n    \"\\n\",\n    \"文档语料库的词汇可以被解释为一个大的分类变量，类别是唯一的单词。 它可能在计算上很昂贵代表如此多的不同类别。 如果一个类别（例如，单词）出现多个数据点（文档）中的时间，然后我们可以将它表示为一个计数并表示所有的类别通过他们的统计数字。 这被称为bin-counting。 我们用分类变量的共同表示开始讨论，并且最终蜿蜒曲折地讨论了大范围的bin-counting问题变量，这在现代数据集中非常普遍。\\n\",\n    \"\\n\",\n    \"## 对类别特征进行编码\\n\",\n    \"\\n\",\n    \"分类变量的类别通常不是数字。例如，眼睛的颜色可以是“黑色”，“蓝色”，“棕色”等。因此，需要使用编码方法将这些非数字类别变为数字。 简单地将一个整数（比如1到k）分配给k个可能的类别中的每一个都是诱人的。 但是，由此产生的价值观可以互相授权，这在类别中不应该被允许。\\n\",\n    \"\\n\",\n    \"## One-hot 编码\\n\",\n    \"\\n\",\n    \"将类别特征进行表示一个最好的办法就是使用一组比特位来表达。每一位代表一个可能的类别。 如果该变量不能一次成为多个类别，那么该组中只有一位可以是1。 这被称为独热编码，它在Scikit Learn中实现[sklearn.preprocessing.OneHotEncoder](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)。 每个位都是一个特征。 因此是一个绝对的具有k个可能类别的变量被编码为长度为k的特征向量。\\n\",\n    \"\\n\",\n    \"表5-1 对3个城市的类别进行独热编码\\n\",\n    \"\\n\",\n    \"|City|e1|e2|e3|\\n\",\n    \"|:-:|:-:|:-:|:-:|\\n\",\n    \"|San Francisco|1|0|0|\\n\",\n    \"|New York|0|1|0|\\n\",\n    \"|Seattle|0|0|1|\\n\",\n    \"\\n\",\n    \"独热编码非常易于理解。 但它使用的是比严格必要的更多的一点。 如果我们看到k-1位是零，那么最后一位必须是1，因为变量必须具有k个值中的一个。 在数学上，可以写下这个约束条件为“所有位的和必须等于1”。\\n\",\n    \"\\n\",\n    \"等式 5-1. 独热编码```e1,e2,e3```限制条件。\\n\",\n    \"\\n\",\n    \"$$e1+e2+e3+...+ek=1$$\\n\",\n    \"\\n\",\n    \"因此，我们有一个线性的依赖性。 线性相关特征，就像我们一样在```tfidf```中发现，有点烦人，因为它意味着训练线性模型不会是唯一的。 特征的不同线性组合可以做出同样的预测，所以我们需要跳过额外条件的来理解特征对预测的影响。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## dummy编码\\n\",\n    \"\\n\",\n    \"独热编码的问题是它允许$k$个自由度，其中变量本身只需要$k-1$。 虚拟编码通过仅使用表示中的$k-1$个特征来消除额外的自由度。 \\n\",\n    \"\\n\",\n    \"公共汽车下面有一个特征，由全零向量表示。 这被称为参考类别。 虚拟编码和独热编码都是在Pandas中以[pandas.get_dummies](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html)的形式实现的。\\n\",\n    \"\\n\",\n    \"表5-2 对3个城市的类别进行dummy编码\\n\",\n    \"\\n\",\n    \"|City|e1|e2|\\n\",\n    \"|:-:|:-:|:-:|\\n\",\n    \"|San Francisco|1|0|\\n\",\n    \"|New York|0|1|\\n\",\n    \"|Seattle|0|0|\\n\",\n    \"\\n\",\n    \"使用虚拟编码进行建模的结果比单编码更易解释。这很容易在简单的线性回归问题中看到。 假设我们有一些数据关于三个城市的公寓租赁价格：旧金山，纽约和西雅图。（见表5-3）\\n\",\n    \"\\n\",\n    \"表5-3 三个不同城市的公寓价格数据集\\n\",\n    \"\\n\",\n    \"|id|city|Rent|\\n\",\n    \"|:-:|:-:|:-:|\\n\",\n    \"|0|SF|3999|\\n\",\n    \"|1|SF|4000|\\n\",\n    \"|2|SF|4001|\\n\",\n    \"|3|NYC|3499|\\n\",\n    \"|4|NYC|3500|\\n\",\n    \"|5|NYC|3501|\\n\",\n    \"|6|Seattle|2499|\\n\",\n    \"|7|Seattle|2500|\\n\",\n    \"|8|Seattle|2501|\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"![图5-1](images/chapter5/5-1.jpg)\\n\",\n    \"图5-1 公寓租金价格在one-hot编码中的向量空间表示。点的大小表达了数据集中租金不同价格的平均数。\\n\",\n    \"\\n\",\n    \"我们这时能够仅仅依靠城市这一个变量来建立线性回归来预测租金的价格。\\n\",\n    \"\\n\",\n    \"线性回归模型可以这样写\\n\",\n    \"\\n\",\n    \"$$y=w_1x_1+w_2x_2+w_3x_3+...+w_nx_n$$\\n\",\n    \"\\n\",\n    \"习惯上我们还添加一个常量来，这样的话当$x$全部为0，$y$不会为0.\\n\",\n    \"\\n\",\n    \"例5-1.在独热编码上的线性回归\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3333.3333333333335\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"from sklearn import linear_model\\n\",\n    \"df = pd.DataFrame({\\n\",\n    \"    'City':\\n\",\n    \"    ['SF', 'SF', 'SF', 'NYC', 'NYC', 'NYC', 'Seattle', 'Seattle', 'Seattle'],\\n\",\n    \"    'Rent': [3999, 4000, 4001, 3499, 3500, 3501, 2499, 2500, 2501]\\n\",\n    \"})\\n\",\n    \"df['Rent'].mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Rent</th>\\n\",\n       \"      <th>city_NYC</th>\\n\",\n       \"      <th>city_SF</th>\\n\",\n       \"      <th>city_Seattle</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>3999</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>4000</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>4001</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>3499</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>3500</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>3501</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>2499</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>2500</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>2501</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Rent  city_NYC  city_SF  city_Seattle\\n\",\n       \"0  3999         0        1             0\\n\",\n       \"1  4000         0        1             0\\n\",\n       \"2  4001         0        1             0\\n\",\n       \"3  3499         1        0             0\\n\",\n       \"4  3500         1        0             0\\n\",\n       \"5  3501         1        0             0\\n\",\n       \"6  2499         0        0             1\\n\",\n       \"7  2500         0        0             1\\n\",\n       \"8  2501         0        0             1\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"one_hot_df = pd.get_dummies(df, prefix=['city'])\\n\",\n    \"one_hot_df\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 166.66666667,  666.66666667, -833.33333333]])\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model = linear_model.LinearRegression()\\n\",\n    \"model.fit(one_hot_df[['city_NYC', 'city_SF', 'city_Seattle']],\\n\",\n    \"          one_hot_df[['Rent']])\\n\",\n    \"model.coef_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([3333.33333333])\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.intercept_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"使用dummy code进行回归\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Rent</th>\\n\",\n       \"      <th>city_SF</th>\\n\",\n       \"      <th>city_Seattle</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>3999</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>4000</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>4001</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>3499</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>3500</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>3501</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>2499</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>2500</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>2501</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Rent  city_SF  city_Seattle\\n\",\n       \"0  3999        1             0\\n\",\n       \"1  4000        1             0\\n\",\n       \"2  4001        1             0\\n\",\n       \"3  3499        0             0\\n\",\n       \"4  3500        0             0\\n\",\n       \"5  3501        0             0\\n\",\n       \"6  2499        0             1\\n\",\n       \"7  2500        0             1\\n\",\n       \"8  2501        0             1\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dummy_df = pd.get_dummies(df, prefix=['city'], drop_first=True)\\n\",\n    \"dummy_df\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\\n\",\n       \"         normalize=False)\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.fit(dummy_df[['city_SF', 'city_Seattle']], dummy_df['Rent'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  500., -1000.])\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.coef_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3500.0\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.intercept_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"通过独热编码，截距项表示目标变量的全局均值租金价格，并且每个线性系数表示该城市的平均租金与全局平均值的差异。\\n\",\n    \"\\n\",\n    \"通过虚拟编码，偏差系数代表响应的平均值参考类别的变量y，在这个例子中是纽约市。该第i个特征的系数等于平均响应之间的差异第i类别的值和参考类别的平均值。\\n\",\n    \"\\n\",\n    \"表5-4：线性回归学得的系数\\n\",\n    \"\\n\",\n    \"|id|x1|x2|x3|b|\\n\",\n    \"|:-:|:-:|:-:|:-:|:-:|\\n\",\n    \"|one-hot|166.67|666.67|-833.33|3333.33|\\n\",\n    \"|dummy coding|0|500|-1000|3500|\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## Effect编码\\n\",\n    \"\\n\",\n    \"分类变量编码的另一种变体称为Effect编码。 Effect编码与虚拟编码非常相似，区别在于参考类别现在由所有-1的向量表示。\\n\",\n    \"\\n\",\n    \"表5-5: Effect编码表示3个城市\\n\",\n    \"\\n\",\n    \"|City|e1|e2|\\n\",\n    \"|:-:|:-:|:-:|\\n\",\n    \"|San Francisco|1|0|\\n\",\n    \"|New York|0|1|\\n\",\n    \"|Seattle|-1|-1|\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Effect编码与虚拟编码非常相似，但是在线性回归中更容易被拟合。例子5-2表达了运行机理。截距项表示目标的全球平均值变量，单个系数表示各个类别的平均值与全球平均值有多少差异。 （这被称为类别或级别的主要效果，因此名称为“效果编码”。）独热编码实际上具有相同的截距和系数，但在这种情况下，每个城市都有线性系数。 在效果编码中，没有单一特征代表参考类别。 因此，参考类别的影响需要分别计算为所有其他类别的系数的负和。(查看[what is effect coding?](https://stats.idre.ucla.edu))\\n\",\n    \"\\n\",\n    \"例子5-2 Effect编码的线性回归\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Rent</th>\\n\",\n       \"      <th>city_SF</th>\\n\",\n       \"      <th>city_Seattle</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>3999</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>4000</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>4001</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>3499</td>\\n\",\n       \"      <td>-1.0</td>\\n\",\n       \"      <td>-1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>3500</td>\\n\",\n       \"      <td>-1.0</td>\\n\",\n       \"      <td>-1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>3501</td>\\n\",\n       \"      <td>-1.0</td>\\n\",\n       \"      <td>-1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>2499</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>2500</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>2501</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Rent  city_SF  city_Seattle\\n\",\n       \"0  3999      1.0           0.0\\n\",\n       \"1  4000      1.0           0.0\\n\",\n       \"2  4001      1.0           0.0\\n\",\n       \"3  3499     -1.0          -1.0\\n\",\n       \"4  3500     -1.0          -1.0\\n\",\n       \"5  3501     -1.0          -1.0\\n\",\n       \"6  2499      0.0           1.0\\n\",\n       \"7  2500      0.0           1.0\\n\",\n       \"8  2501      0.0           1.0\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"effect_df = dummy_df.copy()\\n\",\n    \"effect_df.loc[3:5, ['city_SF', 'city_Seattle']] = -1.0\\n\",\n    \"effect_df\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\\n\",\n       \"         normalize=False)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.fit(effect_df[['city_SF', 'city_Seattle']], effect_df['Rent'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 666.66666667, -833.33333333])\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.coef_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3333.3333333333335\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.intercept_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 类别变量的优点和缺点\\n\",\n    \"\\n\",\n    \"独热，虚拟和效果编码非常相似。 他们每个人都有优点和缺点。 独热编码是多余的，它允许多个有效模型一样的问题。 非唯一性有时候对解释有问题。该优点是每个特征都明显对应于一个类别。 此外，失踪数据可以编码为全零矢量，输出应该是整体目标变量的平均值。\\n\",\n    \"\\n\",\n    \"虚拟编码和效果编码不是多余的。 他们产生独特和可解释的模型。 虚拟编码的缺点是它不能轻易处理缺少数据，因为全零矢量已经映射到参考类别。它还编码每个类别相对于参考类别的影响，其中看起来很奇怪。 效果编码通过使用不同的代码来避免此问题参考类别。 但是，所有-1的矢量都是一个密集的矢量，对于存储和计算来说都很昂贵。 因此，Pandas和Scikit Learn等流行的ML软件包选择了虚拟编码或独热编码，而不是效应编码。当类别数量变得非常多时，所有三种编码技术都会失效大。 需要不同的策略来处理非常大的分类变量。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## 处理大量的类别特征\\n\",\n    \"\\n\",\n    \"互联网上的自动数据收集可以生成大量的分类变量。这在诸如定向广告和欺诈检测等应用中很常见。 在有针对性的广告中，任务是根据用户的搜索查询或当前页面将用户与一组广告进行匹配。 功能包括用户ID，广告的网站域，搜索查询，当前页面以及这些功能的所有可能的成对连词。 （查询是一个文本字符串，可以切分成常用的文本特征，但查询通常很短，通常由短语组成，因此在这种情况下最好的行为通常是保持完整，或 通过哈希函数来简化存储和比较，我们将在下面更详细地讨论哈希。）其中每一个都是一个非常大的分类变量。 我们面临的挑战是如何找到一个能够提高内存效率的优秀特征表示，并生成训练速度快的准确模型。\\n\",\n    \"\\n\",\n    \"对于这种类别特征处理的方案有：\\n\",\n    \"\\n\",\n    \"1. 对编码不做任何事情。 使用便宜的训练简单模型。 在许多机器上将独热编码引入线性模型（逻辑回归或线性支持向量机）。\\n\",\n    \"\\n\",\n    \"2. 压缩编码，有两种方式\\n\",\n    \"  - a. 对特征进行哈希--在线性回归中特别常见\\n\",\n    \"  - b. bin-counting--在线性回归中与树模型都常见\\n\",\n    \"\\n\",\n    \"使用one-hot编码是可行的。在微软搜索广告研究中，Graepel等人 [2010]报告在贝叶斯概率回归模型中使用这种二值特征，可以使用简单更新在线进行培训。 与此同时，其他组织则争论压缩方法。 来自雅虎的研究人员 通过特征散列方式[Weinberger et al.2009年]。 尽管McMahan等人[2013]在谷歌的广告引擎上尝试了功能哈希，并没有找到显着的改进。 然而，微软的其他人则被认为是计数[Bilenko，2015]。\\n\",\n    \"\\n\",\n    \"我们将会看到，所有这些想法都有利有弊。 我们将首先描述解决方案本身，然后讨论他们的权衡。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## 特征哈希\\n\",\n    \"\\n\",\n    \"散列函数是一个确定性函数，它映射一个潜在的无界整数到有限整数范围$[1，m]$。 由于输入域可能大于输出范围，多个数字可能会映射到相同的输出。 这被称为a碰撞。 统一的散列函数可确保大致相同数量的数字被映射到每个$m$箱。 在视觉上，我们可以将散列函数视为一台机器可以吸入编号的球并将它们传送到一个m箱。 球与相同的号码将始终被路由到同一个bin。\\n\",\n    \"\\n\",\n    \"散列函数可以为任何可以用数字表示的对象构造（对于可以存储在计算机上的任何数据都是如此）：数字，字符串，复杂的结构等。\\n\",\n    \"\\n\",\n    \"![](images/chapter5/5-2.jpg)\\n\",\n    \"\\n\",\n    \"图5-2 哈希编码\\n\",\n    \"\\n\",\n    \"当有很多特征时，存储特征向量可能占用很多空间。 特征散列将原始特征向量压缩为m维通过对特征ID应用散列函数来创建矢量。 例如，如果原件特征是文档中的单词，那么散列版本将具有固定的词汇大小为m，无论输入中有多少独特词汇。\\n\",\n    \"\\n\",\n    \"例5-3 对单词的特征哈希\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def hash_features(word_list, m):\\n\",\n    \"    output = [0] * m\\n\",\n    \"\\n\",\n    \"    for word in word_list:\\n\",\n    \"        index = hash_fcn(word) % m\\n\",\n    \"        output[index] += 1\\n\",\n    \"\\n\",\n    \"    return output\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"功能散列的另一个变体添加了一个符号组件，因此计数也是从哈希箱中增加或减少。 这确保了内部产品之间散列特征与原始特征的期望值相同。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def hash_features(word_list, m):\\n\",\n    \"    output = [0] * m\\n\",\n    \"    for word in word_list:\\n\",\n    \"        index = hash_fcn(word) % m\\n\",\n    \"        sign_bit = sign_hash(word) % 2\\n\",\n    \"        if (sign_bit == 0):\\n\",\n    \"            output[index] -= 1\\n\",\n    \"        else:\\n\",\n    \"            output[index] += 1\\n\",\n    \"    return output\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"哈希后内积的值在时间复杂度在```O(1/(m**0.5))```.所以哈希表m的大小可以根据可接受的错误来选择。在实践中，选择合适的m可能需要一些试验和错误。特征哈希可以用于涉及特征内积的模型矢量和系数，例如线性模型和核心方法。 它一直证明在垃圾邮件过滤任务中取得成功[Weinberger等，2009]。在有针对性的广告案例中，McMahan et al. [2013年]报告不能将预测误差降低到可接受的水平，除非m的数量级为数十亿。散列特征的一个缺点是散列特征是聚合的原始特征，不再可解释。\\n\",\n    \"\\n\",\n    \"在这个例子中，我们将使用Yelp评论数据集来演示存储和,解释性使用的为sklearn的库```FeatureHasher```。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import json\\n\",\n    \"js = []\\n\",\n    \"with open('data/yelp_academic_dataset_review.json') as f:\\n\",\n    \"    for i in range(10000):\\n\",\n    \"        js.append(json.loads(f.readline()))\\n\",\n    \"\\n\",\n    \"review_df = pd.DataFrame(js)\\n\",\n    \"\\n\",\n    \"m = len(review_df.business_id.unique())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"4174\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"m\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.feature_extraction import FeatureHasher\\n\",\n    \"h = FeatureHasher(n_features=m, input_type='string')\\n\",\n    \"f = h.transform(review_df['business_id'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['9yKzy9PApeiPPOUJEtnvkg',\\n\",\n       \" 'ZRJwVLyzEJq1VAihDhYiow',\\n\",\n       \" '6oRAC4uyJCsJl1X0WZpVSA',\\n\",\n       \" '_1QQZuf4zZOyFCvXc0o6Vg',\\n\",\n       \" '6ozycU1RpktNG2-1BroVtw']\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"review_df['business_id'].unique().tolist()[0:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0., 0., 0., ..., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., ..., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., ..., 0., 0., 0.],\\n\",\n       \"       ...,\\n\",\n       \"       [0., 0., 0., ..., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., ..., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., ..., 0., 0., 0.]])\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"f.toarray()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们看看特征的存储\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sys import getsizeof\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Our pandas Series, in bytes:  790104\\n\",\n      \"Our hashed numpy array, in bytes:  56\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Our pandas Series, in bytes: ', getsizeof(review_df['business_id']))\\n\",\n    \"print('Our hashed numpy array, in bytes: ', getsizeof(f))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们可以清楚地看到如何使用特征散列会以计算方式使我们受益，牺牲直接的用户解释能力。 这是一个容易的权衡来接受何时从数据探索和可视化发展到机器学习管道对于大型数据集。\\n\",\n    \"\\n\",\n    \"## bin-counting\\n\",\n    \"\\n\",\n    \"Bin-counting是机器学习中常见的重新发现之一。 从广告点击率预测到硬件分支预测，它已经被重新创建并用于各种应用[Yeh and Patt，1991; Lee等人，1998; Pavlov等，2009; 李等人，2010]。 然而，因为它是一种特征工程技术，而不是一种建模或优化方法，所以没有关于该主题的研究论文。 关于该技术最详细的描述可以在Misha Bilenko的博客文章“Big Learning Made with Easy”以及相关的幻灯片中找到。\\n\",\n    \"\\n\",\n    \"bin-counting的想法非常简单：而不是使用分类变量作为特征，而不是使用条件概率的目标在该价值下。 换句话说，而不是编码的身份分类值，计算该值和该值之间的关联统计量我们希望预测的目标。 对于那些熟悉Na?veBayes分类器的人来说，这个统计学应该敲响一下钟，因为它是该类的条件概率假设所有功能都是独立的。 最好用一个例。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"表5-6. bin-counting的例子\\n\",\n    \"\\n\",\n    \"|User|Number of clicks|Number of non-clicks|probability of click|QueryHash,AdDomain|Number of clicks|Number of non-clicks|probability of click|\\n\",\n    \"|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\\n\",\n    \"|Alice|5|120|0.0400|0x598fd4fe,foo.com|5000|30000|0.167|\\n\",\n    \"|bob|20|230|0.0800|0x50fa3cc0,bar.org|100,900,0.100|\\n\",\n    \"|...|\\n\",\n    \"|joe|2|3|0.400|0x437a45e1,qux.net|6,18,0.250|\\n\",\n    \"\\n\",\n    \"Bin-counting假定历史数据可用于计算统计。 表5-6包含分类变量每个可能值的汇总历史计数。 根据用户点击任何广告的次数以及未点击的次数，我们可以计算用户“Alice”点击任何广告的概率。 同样，我们可以计算任何查询 - 广告 - 域组合的点击概率。 在训练时，每当我们看到“爱丽丝”时，都使用她的点击概率作为模型的输入特征。 QueryHash-AdDomain对也是如此，例如“0x437a45e1，qux.net”。\\n\",\n    \"\\n\",\n    \"假设有10,000个用户。 独热编码会生成一个稀疏矢量长度为10,000，在列中对应于值的单个1当前数据点。 Bin-counting将所有10,000个二进制列编码为一个功能的真实值介于0和1之间。\\n\",\n    \"\\n\",\n    \"除了历史点击概率外，我们还可以包含其他功能：原始计数本身（点击次数和非点击次数），对数比率或任何其他概率的衍生物。 我们的例子是预测广告点击率，通过率。 但该技术很容易应用于一般的二元分类。 它也可以使用通常的技术容易地扩展到多级分类将二元分类器扩展到多个类，即通过一对多优势比或其他多类标签编码。\\n\",\n    \"\\n\",\n    \"## Bin-counting的优势比和对数比\\n\",\n    \"\\n\",\n    \"比值比通常定义在两个二元变量之间。 它通过提出这样一个问题来看待他们的联想强度：“当$X$为真时，$Y$有多大可能是真的”。例如，我们可能会问，“Alice点击广告的可能性大于一般人口？“在这里，$X$是二进制变量”是Alice是当前用户“，而$Y$是变量”点击广告与否“。 该计算使用所谓的双向列联表（基本上，四个数字对应于$X$和$Y$的四种可能组合）。\\n\",\n    \"\\n\",\n    \"表5-7. 偶然发生的用户点击事件\\n\",\n    \"\\n\",\n    \"||click|Non-Click|Total|\\n\",\n    \"|:-:|:-:|:-:|:-:| \\n\",\n    \"|Alice|5|120|125|\\n\",\n    \"|Not Alice|995|18880|19875|\\n\",\n    \"|Total|1000|19000|20000|\\n\",\n    \"\\n\",\n    \"给定输入变量$X$和目标变量$Y$，优势比定义为:$优势比=(P(Y=1|X=1)/(P(Y=0|X=1))/(P(Y=1|X=0)/(P(Y=0|X=0))))$\\n\",\n    \"\\n\",\n    \"在我们的例子中，这意味着“爱丽丝点击广告而不是点击的可能性”和“其他人点击而非点击的可能性有多大”之间的比率。在这种情况下，数字是\\n\",\n    \"![](images/chapter5/5-3.jpg)\\n\",\n    \"\\n\",\n    \"更简单地说，我们可以看看分子，它检查多少可能性单个用户（Alice）是否点击广告而不是点击。 这适合大型具有许多值的分类变量，而不仅仅是两个。\\n\",\n    \"$$\\n\",\n    \"\\\\frac{5 / 125}{120 / 125}=0.04166\\n\",\n    \"$$\\n\",\n    \"概率比率可能很容易变得非常小或非常大。 （例如，将会有几乎不会点击广告的用户，也可能是点击广告的用户更频繁得多）日志转换再次来到我们的救援。 另一个对数的有用特性是它将一个划分变为一个减法。\\n\",\n    \"$$\\n\",\n    \"\\\\log \\\\left(\\\\frac{5}{125}\\\\right)-\\\\log \\\\left(\\\\frac{120}{125}\\\\right)=-3.178\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"简而言之，bin-counting将分类变量转换为有关的统计信息值。 它变成了一个大的，稀疏的分类变量的二进制表示变成一个非常小，密集的实值数值表示。\\n\",\n    \"\\n\",\n    \"![](images/chapter5/5-6.jpg)\\n\",\n    \"\\n\",\n    \"图5-3 分类变量的独热编码与二进制计数统计的说明。\\n\",\n    \"\\n\",\n    \"在实施方面，垃圾箱计数需要在每个类别之间存储地图及其相关计数。 （其余的统计数据可以从中得到原始计数）。因此它需要$O(k)$空间，其中$k$是唯一值的数量的分类变量。\\n\",\n    \"\\n\",\n    \"我们采用Kaggle的比赛[Avazu](https://www.kaggle.com/c/avazu-ctr-prediction)举个例子。\\n\",\n    \"\\n\",\n    \"#### Avazu Click数据集\\n\",\n    \"\\n\",\n    \"* 有24个变量，包括'点击'，一个二进制点击/不点击计数器和'device_id'，用于跟踪显示广告的设备。\\n\",\n    \"* 完整的数据集包含4,0428,967个观测值，其中有2,686,408个独特的设备。\\n\",\n    \"\\n\",\n    \"mAvazu竞赛使用广告数据来预测点击率，但我们将使用它来演示如何bin计数可以大大减少大的特征空间流数据量。\\n\",\n    \"\\n\",\n    \"编者注：这个数据集特别大，大概6g，我们读取前10k行存储为`train_subset.csv`，并传到了[百度云](data/README.md)，可能跑出来的结果与原书不一致，但不影响学习。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 例子5-6 Bin-counting例子\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1075\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"#读取前面的10k行\\n\",\n    \"df = pd.read_csv('data/train_subset.csv')\\n\",\n    \"#有多少独立的特征\\n\",\n    \"len(df['device_id'].unique())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>id</th>\\n\",\n       \"      <th>click</th>\\n\",\n       \"      <th>hour</th>\\n\",\n       \"      <th>C1</th>\\n\",\n       \"      <th>banner_pos</th>\\n\",\n       \"      <th>site_id</th>\\n\",\n       \"      <th>site_domain</th>\\n\",\n       \"      <th>site_category</th>\\n\",\n       \"      <th>app_id</th>\\n\",\n       \"      <th>app_domain</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>device_type</th>\\n\",\n       \"      <th>device_conn_type</th>\\n\",\n       \"      <th>C14</th>\\n\",\n       \"      <th>C15</th>\\n\",\n       \"      <th>C16</th>\\n\",\n       \"      <th>C17</th>\\n\",\n       \"      <th>C18</th>\\n\",\n       \"      <th>C19</th>\\n\",\n       \"      <th>C20</th>\\n\",\n       \"      <th>C21</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1000009418151094273</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>14102100</td>\\n\",\n       \"      <td>1005</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1fbe01fe</td>\\n\",\n       \"      <td>f3845767</td>\\n\",\n       \"      <td>28905ebd</td>\\n\",\n       \"      <td>ecad2386</td>\\n\",\n       \"      <td>7801e8d9</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>15706</td>\\n\",\n       \"      <td>320</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>1722</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>35</td>\\n\",\n       \"      <td>-1</td>\\n\",\n       \"      <td>79</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>10000169349117863715</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>14102100</td>\\n\",\n       \"      <td>1005</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1fbe01fe</td>\\n\",\n       \"      <td>f3845767</td>\\n\",\n       \"      <td>28905ebd</td>\\n\",\n       \"      <td>ecad2386</td>\\n\",\n       \"      <td>7801e8d9</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>15704</td>\\n\",\n       \"      <td>320</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>1722</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>35</td>\\n\",\n       \"      <td>100084</td>\\n\",\n       \"      <td>79</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>10000371904215119486</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>14102100</td>\\n\",\n       \"      <td>1005</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1fbe01fe</td>\\n\",\n       \"      <td>f3845767</td>\\n\",\n       \"      <td>28905ebd</td>\\n\",\n       \"      <td>ecad2386</td>\\n\",\n       \"      <td>7801e8d9</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>15704</td>\\n\",\n       \"      <td>320</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>1722</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>35</td>\\n\",\n       \"      <td>100084</td>\\n\",\n       \"      <td>79</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>10000640724480838376</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>14102100</td>\\n\",\n       \"      <td>1005</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1fbe01fe</td>\\n\",\n       \"      <td>f3845767</td>\\n\",\n       \"      <td>28905ebd</td>\\n\",\n       \"      <td>ecad2386</td>\\n\",\n       \"      <td>7801e8d9</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>15706</td>\\n\",\n       \"      <td>320</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>1722</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>35</td>\\n\",\n       \"      <td>100084</td>\\n\",\n       \"      <td>79</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>10000679056417042096</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>14102100</td>\\n\",\n       \"      <td>1005</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>fe8cc448</td>\\n\",\n       \"      <td>9166c161</td>\\n\",\n       \"      <td>0569f928</td>\\n\",\n       \"      <td>ecad2386</td>\\n\",\n       \"      <td>7801e8d9</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>18993</td>\\n\",\n       \"      <td>320</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>2161</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>35</td>\\n\",\n       \"      <td>-1</td>\\n\",\n       \"      <td>157</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 24 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                     id  click      hour    C1  banner_pos   site_id  \\\\\\n\",\n       \"0   1000009418151094273      0  14102100  1005           0  1fbe01fe   \\n\",\n       \"1  10000169349117863715      0  14102100  1005           0  1fbe01fe   \\n\",\n       \"2  10000371904215119486      0  14102100  1005           0  1fbe01fe   \\n\",\n       \"3  10000640724480838376      0  14102100  1005           0  1fbe01fe   \\n\",\n       \"4  10000679056417042096      0  14102100  1005           1  fe8cc448   \\n\",\n       \"\\n\",\n       \"  site_domain site_category    app_id app_domain ...  device_type  \\\\\\n\",\n       \"0    f3845767      28905ebd  ecad2386   7801e8d9 ...            1   \\n\",\n       \"1    f3845767      28905ebd  ecad2386   7801e8d9 ...            1   \\n\",\n       \"2    f3845767      28905ebd  ecad2386   7801e8d9 ...            1   \\n\",\n       \"3    f3845767      28905ebd  ecad2386   7801e8d9 ...            1   \\n\",\n       \"4    9166c161      0569f928  ecad2386   7801e8d9 ...            1   \\n\",\n       \"\\n\",\n       \"  device_conn_type    C14  C15  C16   C17  C18  C19     C20  C21  \\n\",\n       \"0                2  15706  320   50  1722    0   35      -1   79  \\n\",\n       \"1                0  15704  320   50  1722    0   35  100084   79  \\n\",\n       \"2                0  15704  320   50  1722    0   35  100084   79  \\n\",\n       \"3                0  15706  320   50  1722    0   35  100084   79  \\n\",\n       \"4                0  18993  320   50  2161    0   35      -1  157  \\n\",\n       \"\\n\",\n       \"[5 rows x 24 columns]\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def click_counting(x, bin_column):\\n\",\n    \"    clicks = pd.Series(\\n\",\n    \"        x[x['click'] > 0][bin_column].value_counts(), name='clicks')\\n\",\n    \"    no_clicks = pd.Series(\\n\",\n    \"        x[x['click'] < 1][bin_column].value_counts(), name='no_clicks')\\n\",\n    \"\\n\",\n    \"    counts = pd.DataFrame([clicks, no_clicks]).T.fillna('0')\\n\",\n    \"    counts['total'] = counts['clicks'].astype(\\n\",\n    \"        'int64') + counts['no_clicks'].astype('int64')\\n\",\n    \"\\n\",\n    \"    return counts\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def bin_counting(counts):\\n\",\n    \"    counts['N+'] = counts['clicks'].astype('int64').divide(\\n\",\n    \"        counts['total'].astype('int64'))\\n\",\n    \"    counts['N-'] = counts['no_clicks'].astype('int64').divide(\\n\",\n    \"        counts['total'].astype('int64'))\\n\",\n    \"    counts['log_N+'] = counts['N+'].divide(counts['N-'])\\n\",\n    \"\\n\",\n    \"    #    If we wanted to only return bin-counting properties, we would filter here\\n\",\n    \"    bin_counts = counts.filter(items=['N+', 'N-', 'log_N+'])\\n\",\n    \"    return counts, bin_counts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"bin_column = 'device_id'\\n\",\n    \"device_clicks = click_counting(df.filter(items= [bin_column, 'click']), bin_column)\\n\",\n    \"device_all, device_bin_counts = bin_counting(device_clicks)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1075\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# check to make sure we have all the devices\\n\",\n    \"len(device_bin_counts)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>clicks</th>\\n\",\n       \"      <th>no_clicks</th>\\n\",\n       \"      <th>total</th>\\n\",\n       \"      <th>N+</th>\\n\",\n       \"      <th>N-</th>\\n\",\n       \"      <th>log_N+</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>a99f214a</th>\\n\",\n       \"      <td>1561</td>\\n\",\n       \"      <td>7163</td>\\n\",\n       \"      <td>8724</td>\\n\",\n       \"      <td>0.178932</td>\\n\",\n       \"      <td>0.821068</td>\\n\",\n       \"      <td>0.217925</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>c357dbff</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>15</td>\\n\",\n       \"      <td>17</td>\\n\",\n       \"      <td>0.117647</td>\\n\",\n       \"      <td>0.882353</td>\\n\",\n       \"      <td>0.133333</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>a167aa83</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3c0208dc</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"         clicks no_clicks  total        N+        N-    log_N+\\n\",\n       \"a99f214a   1561      7163   8724  0.178932  0.821068  0.217925\\n\",\n       \"c357dbff      2        15     17  0.117647  0.882353  0.133333\\n\",\n       \"a167aa83      0         9      9  0.000000  1.000000  0.000000\\n\",\n       \"3c0208dc      0         9      9  0.000000  1.000000  0.000000\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"device_all.sort_values(by = 'total', ascending=False).head(4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Our pandas Series, in bytes:  730104\\n\",\n      \"Our bin-counting feature, in bytes:  95699\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# We can see how this can change model evaluation time by comparing raw vs. bin-counting size\\n\",\n    \"from sys import getsizeof\\n\",\n    \"\\n\",\n    \"print('Our pandas Series, in bytes: ',\\n\",\n    \"      getsizeof(df.filter(items=['device_id', 'click'])))\\n\",\n    \"print('Our bin-counting feature, in bytes: ', getsizeof(device_bin_counts))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 关于稀有类别\\n\",\n    \"\\n\",\n    \"就像罕见的词，罕见的类别需要特殊的处理。 想想一个用户每年登录一次：几乎没有数据可以可靠估计她广告的点击率。 而且，稀有类别会在计数表中浪费空间。解决这个问题的一种方法是通过补偿，一种积累的简单技术一个特殊垃圾箱中所有稀有类别的数量。 如果计数大于一定的门槛，那么这个类别就有自己的统计数字。 否则，使用来自回退箱的统计数据。 这基本上会恢复单个的统计信息罕见类别与所有罕见类别的统计数据进行比较。 当使用back-off方法，它有助于为统计信息添加二进制指标来自后退箱。\\n\",\n    \"\\n\",\n    \"![ ](images/chapter5/5-7.jpg)\\n\",\n    \"\\n\",\n    \"图5-4\\n\",\n    \"\\n\",\n    \"如果罕见类别获得收益，它可以使用自己的统计数据进行建模，从而超过回退库的阈值。\\n\",\n    \"\\n\",\n    \"还有另一种方法来处理这个问题，称为count-min sketch [Cormode和Muthukrishnan，2005]。 在这种方法中，所有类别，罕见或频繁类似通过多个散列函数进行映射，输出范围为$m$，远小于类别的数量$k$。 当检索一个统计量时，计算所有的哈希值该类别，并返回最小的统计量。 拥有多个散列函数减轻单个散列函数内碰撞的可能性。 该计划有效因为可以做出散列函数次数$m$，散列表大小小于$k$，类别的数量，仍然保持较低的整体碰撞可能性。\\n\",\n    \"\\n\",\n    \"![](images/chapter5/5-9.jpg)\\n\",\n    \"\\n\",\n    \"由于二进制计数依赖于历史数据来生成必要的统计数据需要通过数据收集期等待，导致了数据收集时间的轻微延迟学习管道。 这也意味着当数据分布发生变化时，计数需要更新。 数据变化越快，计数需要的次数越多重新计算。 这对于目标应用程序尤其重要广告，用户偏好和热门查询变化非常快，而且缺乏适应当前的分布可能意味着广告的巨大损失平台。\\n\",\n    \"\\n\",\n    \"有人可能会问，为什么不使用相同的数据集来计算相关统计量并训练模型？这个想法看起来很无辜。这里最大的问题是统计涉及目标变量，这是模型试图预测的。使用输出来计算输入特征会导致一个称为泄漏的有害问题。简而言之，泄漏意味着信息被揭示给模型，从而使它有更好的预测的不切实际的优势。当测试数据泄露到训练集中，或者未来的数据泄漏到过去时，可能会发生这种情况。任何时候都会向模型提供在生产中实时进行预测时应该无法访问的信息，这会导致泄漏。 Kaggle的维基提供了更多泄漏示例以及为什么它对机器学习应用程序不利。\\n\",\n    \"\\n\",\n    \"如果二进制计数程序使用当前数据点的标签来计算输入统计量的一部分，则这构成直接泄漏。防止这种情况的一种方法是在计数收集（用于计算箱计数统计）和训练之间进行严格分离，即使用较早批次的数据点进行计数，将当前数据点用于训练（将分类变量映射到历史统计我们刚刚收集），并使用未来的数据点进行测试。这解决了泄漏问题，但引入了上述延迟（输入统计信息，因此模型将跟踪当前数据）。\\n\",\n    \"\\n\",\n    \"事实证明，还有另一种基于差别隐私的解决方案。 如果统计数据的分布保持大致相同或不存在任何一个数据点，则该统计近似是防漏的。 在实践中，增加一个分布拉普拉斯$(0,1)$的小随机噪声足以掩盖单个数据点的任何潜在泄漏。 这个想法可以结合一次性计算来制定当前数据的统计数据。 \\n\",\n    \"\\n\",\n    \"Owen Zhang在他的“赢得数据科学竞赛”的演讲中详细介绍了这个技巧。\\n\",\n    \"\\n\",\n    \"## Counts without bounds\\n\",\n    \"\\n\",\n    \"如果在越来越多的历史数据下统计数据不断更新，原始数量将无限增长。这可能是模型的问题。训练有素的模型“知道”输入数据直至观察到的比例。一个训练有素的决策树可能会说“当x大于3时，预测为1”。一个经过训练的线性模型可能会说“乘以0.7的多个x并查看结果是否大于全局平均值”。这些可能是x介于0和5之间。但是除此之外会发生什么？没有人知道。\\n\",\n    \"当输入计数增加时，模型将需要重新训练以适应当前的比例。如果计数积累得相当缓慢，那么有效量表不会变得太快，并且模型不需要过于频繁地重新训练。但是当计数增加很快时，频繁的再培训将是一个麻烦。\\n\",\n    \"\\n\",\n    \"出于这个原因，使用标准化计数通常会更好。\\n\",\n    \"\\n\",\n    \"以已知的时间间隔为界。例如，估计的点击率是介于$[0,1]$之间。另一种方法是采取对数变换，即施加一个严格的限制，但是当数量非常大时，增加速度会很慢。\\n\",\n    \"这两种方法都不能防止转移投入分布，例如，去年的芭比娃娃现在已经过时，人们将不再点击这些广告。该模型需要重新训练以适应输入数据分布中的这些更根本性的变化，否则整个流程将需要迁移到模型不断适应输入的在线学习环境。\\n\",\n    \"\\n\",\n    \"## 总结\\n\",\n    \"\\n\",\n    \"#### Plain one-hot encoding\\n\",\n    \"\\n\",\n    \"空间复杂度：$O(n)$\\n\",\n    \"\\n\",\n    \"时间复杂度：$O(nk)$\\n\",\n    \"\\n\",\n    \"优点:\\n\",\n    \"\\n\",\n    \"* 容易实现\\n\",\n    \"* 更高的精度\\n\",\n    \"* 在线学习特别容易扩展\\n\",\n    \"\\n\",\n    \"缺点\\n\",\n    \"\\n\",\n    \"* 计算不足\\n\",\n    \"* 如果类别增加则不能够使用\\n\",\n    \"* 对线性模型以外的任何其他方法都不可行\\n\",\n    \"* 对于大数据集需要分布式训练\\n\",\n    \"\\n\",\n    \"#### Feature hashing\\n\",\n    \"\\n\",\n    \"空间复杂度：$O(n)$\\n\",\n    \"\\n\",\n    \"时间复杂度：$O(nm)$\\n\",\n    \"\\n\",\n    \"优点:\\n\",\n    \"\\n\",\n    \"* 容易实现\\n\",\n    \"* 容易训练\\n\",\n    \"* 容易扩展到新类别\\n\",\n    \"* 容易处理稀有类别\\n\",\n    \"* 在线学习容易扩展\\n\",\n    \"\\n\",\n    \"缺点:\\n\",\n    \"\\n\",\n    \"* 只能够使用线性或核模型\\n\",\n    \"* 哈希编码很难解释\\n\",\n    \"* 精度有争议\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## Bin-counting\\n\",\n    \"\\n\",\n    \"空间复杂度：$O(n+k)$\\n\",\n    \"\\n\",\n    \"时间复杂度：$O(n)$\\n\",\n    \"\\n\",\n    \"优点:\\n\",\n    \"\\n\",\n    \"* 训练快\\n\",\n    \"* 能够使用树模型\\n\",\n    \"* 容易扩展到新列类别\\n\",\n    \"* 容易处理稀有类别\\n\",\n    \"* 可解释\\n\",\n    \"\\n\",\n    \"缺点：\\n\",\n    \"\\n\",\n    \"* 需要利用历史信息\\n\",\n    \"* 对于在线学习有困难\\n\",\n    \"* 会有数据泄露\\n\",\n    \"\\n\",\n    \"正如我们所看到的，没有任何方法是完美的。 选择使用哪一个取决于所需的型号。 \\n\",\n    \"\\n\",\n    \"线性模型比较简单，因此可以进行训练处理非压缩表示，例如独热编码。 \\n\",\n    \"\\n\",\n    \"基于树的模型，另一方面，需要反复搜索右侧分割的所有特征，并且是因此限于小型表示，如箱计数。 \\n\",\n    \"\\n\",\n    \"哈希函数处于在这两个极端之间，但是由此产生的精确度各有不同。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "9.feature-engineering/6.降维：用_PCA_压缩数据集.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[原版（英文）图书地址](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/)\\n\",\n    \"\\n\",\n    \"**翻译**：[apachecn](https://github.com/apachecn)，[翻译版本地址](https://github.com/apachecn/feature-engineering-for-ml-zh)\\n\",\n    \"\\n\",\n    \"**代码修改和整理**：[黄海广](https://github.com/fengdu78)，原文修改成jupyter notebook格式，并增加和修改了部分代码，测试全部通过，所有数据集已经放在[百度云](data/README.md)下载。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 六、降维：用 PCA 压缩数据集\\n\",\n    \"\\n\",\n    \"> 译者：[@cn-Wziv](https://github.com/cn-Wziv)\\n\",\n    \"> \\n\",\n    \"> 校对者：[@HeYun](https://github.com/KyrieHee)\\n\",\n    \"\\n\",\n    \"通过自动数据收集和特征生成技术，可以快速获得大量特征，但并非所有这些都有用。在[第 3 章](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch03.html#chap-basic-text)和\\n\",\n    \"在[第 4 章](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch04.html#chap-tfidf)中，我们讨论了基于频率的滤波和特征缩放修剪无信息的特征。现在我们来仔细讨论一下使用*主成分分析*（PCA）进行数据降维。\\n\",\n    \"\\n\",\n    \"本章标志着进入基于模型的特征工程技术。在这之前，大多数技术可以在不参考数据的情况下定义。对于实例中，基于频率的过滤可能会说“删除所有小于`n`的计数“，这个程序可以在没有进一步输入的情况下进行数据本身。 另一方面，基于模型的技术则需要来自数据的信息。例如，PCA 是围绕数据的主轴定义的。 在之前的技术中，数据，功能和模型之间从来没有明确的界限。从这一点前进，差异变得越来越模糊。这正是目前关于特征学习研究的兴奋之处。\\n\",\n    \"\\n\",\n    \"## 引言\\n\",\n    \"\\n\",\n    \"降维是关于摆脱“无信息的信息”的同时保留关键点。有很多方法可以定义“无信息”。PCA 侧重于线性依赖的概念。在“[矩阵的剖析](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/app01.html#sec-basic-linalg)”中，我们将数据矩阵的列空间描述为所有特征向量的跨度。如果列空间与特征的总数相比较小，则大多数特征是几个关键特征的线性组合。如果在下一步管道是一个线性模型，然后线性相关的特征会浪费空间和计算能力。为了避免这种情况，主成分分析尝试去通过将数据压缩成更低维的线性来减少这种“绒毛”子空间。\\n\",\n    \"\\n\",\n    \"在特征空间中绘制一组数据点。每个数据点都是一个点，整个数据点集合形成一个 blob。在[图 6-1(a) ](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch06.html#fig-data-blobs)中，数据点在两个特征维度上均匀分布，blob 填充空间。在这个示例中，列空间具有完整的等级。但是，如果其中一些特征是其他特征的线性组合，那么该 blob 看起来不会那么丰满; 它看起来更像[图 6-1(b)](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch06.html#fig-data-blobs)，这是一个平面斑点，其中特征 1 是特征 2 的重复（或标量倍数）。在这种情况下，我们说该 blob 的本征维数是 1，即使它位于二维空间之中。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"在实践中，事情很少完全相同。这更可能是我们看到非常接近平等但不完全相同的特征。在这种情况下，数据 blob 可能如[图 6-1(c) ](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch06.html#fig-data-blobs)所示。这是一个憔悴的一团。要是我们想要减少传递给模型的特征的数量，那么我们可以用一个新特征替换特征 1 和特征 2，可能称之为位于两个特征之间的对线的 1.5 特征。原始数据集可以是用一个数字充分表示——沿着特征方 1.5 的方向——而不是两个维度$f1$和$f2$。\\n\",\n    \"\\n\",\n    \"![图6-1](images/chapter_6/6-1.png)\\n\",\n    \"\\n\",\n    \"**图 6-1 特征空间中的数据 blobs(a)，满秩数据 blob(b)，低维数据 blob(c)，近似低维的数据 blob**\\n\",\n    \"\\n\",\n    \"这里的关键思想是**用一些充分总结原始特征空间中包含的信息的新特征取代冗余特征**。当只有两个特征的时候新特征很容易得到。这在当原始特征空间具有数百或数千个维度时将变得很难。我们需要一种数学描述我们正在寻找的新功能的方法。这样我们就可以使用优化技术来找到它们。\\n\",\n    \"\\n\",\n    \"数学上定义“充分总结信息”的一种方法要求就是这样说新数据 blob 应该保留尽可能多的原来的列。我们是将数据块压扁成平坦的数据饼，但我们希望数据饼尽可能在正确的方向上。这意味着我们需要一种衡量特征列的方法。特征列与距离有关。但是在一些数据点中距离的概念有些模糊。可以测量任意两对之间的最大距离点。但事实证明，这是一个非常困难的数学优化功能。另一种方法是测量任意一对点之间的平均距离，或者等价地，每个点与它们的平均值之间的平均距离，即方差。事实证明，这优化起来要容易得多。（生活很难，统计学家已经学会了采取简便方法）在数学上，这体现为最大化新特征空间中数据点的方差。\\n\",\n    \"\\n\",\n    \"## 导航线性代数公式的提示\\n\",\n    \"\\n\",\n    \"为了保持面向线性代数的世界，保持跟踪哪些数量标量，它们是向量，向量的方向是垂直还是水平。知道你的矩阵的维度，因为他们经常告诉你感兴趣的向量是否在行或列中。绘制矩阵和向量作为页面上的矩形，并确保形状匹配。就像通过记录测量单位（距离以英里，速度以英里/小时计）一样，在代数中可以得到很大的代数，在线性代数中，所有人都需要的是尺寸。\\n\",\n    \"\\n\",\n    \"## 求导\\n\",\n    \"\\n\",\n    \"## 提示和符号\\n\",\n    \"如前所述，让$X$表示$n×d$数据矩阵，其中$n$是数据点的数量$d$是特征的数量。令$X$是包含单个数据点的列向量（所以$x$是$X$中其中一行的转置）。设$W$表示我们试图找到的新的特征向量或主要分量之一。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 矩阵的奇异值分解（SVD）\\n\",\n    \"\\n\",\n    \"任何矩形矩阵都可以分解为三个特定形状和特征的矩阵：\\n\",\n    \"\\n\",\n    \"$X=UΣV^T$\\n\",\n    \"\\n\",\n    \"这里，$U$ 和$V$是正交矩阵（即$UU^T=I$ 并且$VV^T=I$，\\n\",\n    \"$Σ$ 是对角线包含$X$的奇异值的矩阵，它可以是正的，零的或负的。\\n\",\n    \"\\n\",\n    \"假设 $X$ 有$n$行$d$列且$n≥d$，那么$U$的大小为$n\\\\times d$，$Σ$和$V$的大小为$d\\\\times d$。（请参阅[“奇异值分解（SVD）”](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/app01.html#sec-svd)来获得矩阵的 SVD 和特征分解的完整评论。）\\n\",\n    \"\\n\",\n    \"公式 6-1 有用的平方和标识\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\|w\\\\|_{2}^{2}=w w^{T}=\\\\sum w^{2}\\n\",\n    \"$$\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 线性投影\\n\",\n    \"\\n\",\n    \"让我们逐步分解 PCA 的推导。PCA 使用线性投影将数据转换为新的特征空间。 [图 6-2(c)](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch06.html#fig-pca)说明了线性投影是什么样子。当我们将$x$投影到 $w$ 时，投影的长度与两者之间的内积成正比，用$w$ （它与它本身的内积）进行规范归一化。稍后，我们将$w$ 单位化。所以只有相关部分是分子。\\n\",\n    \"\\n\",\n    \"公式 6-2 投影坐标\\n\",\n    \"\\n\",\n    \"$$z=x^Tw$$\\n\",\n    \"\\n\",\n    \"请注意，$z$是一个标量，而 $x$和$v$是列向量。由于有一堆的数据点，我们可以制定所有投影坐标的向量$z$在新特征 $v$。这里，$X$ 是熟悉的数据矩阵，其中每行是数据点，得到的 $z$是一个列向量。\\n\",\n    \"\\n\",\n    \"公式6-4 投影坐标向量\\n\",\n    \"\\n\",\n    \"$$z=Xw$$\\n\",\n    \"\\n\",\n    \"![图6-2](images/chapter_6/6-2.png)\\n\",\n    \"\\n\",\n    \"图 6-2 PCA 的插图\\n\",\n    \"\\n\",\n    \"（a）特征空间中的原始数据，（b）以数据为中心 （c）将数据向量$x$投影到另一向量$v$上，（d）使投影坐标的方差最大化的方向是$X^TX$ 的主要特征向量。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 方差和经验方差\\n\",\n    \"\\n\",\n    \"下一步是计算投影的方差。方差定义为到均值的距离的平方的期望。\\n\",\n    \"\\n\",\n    \"公式 6-6 随机变量$Z$的方差\\n\",\n    \"\\n\",\n    \"$$Var(Z)=E[Z-E(Z)]^2$$\\n\",\n    \"有一个小问题：我们提出的问题没有提到平均值 $E(Z)$；它是一个自由变量。一个解决方案是从公式中删除它,从每个数据点中减去平均值。结果数据集的平均值为零，这意味着方差仅仅是 $Z^2$几何的期望值，减去平均值会产生数据居中效应。 （见[图 6-2](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch06.html#fig-pca)（$a-b$））。密切相关的量是两个随机变量$Z_1$和$Z_2$之间的协方差。把它看作是方差思想的扩展（单个随机变量）到两个随机变量。\\n\",\n    \"\\n\",\n    \"公式 6-8 两个随机变量$Z_1$和$Z_2$之间的协方差\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\operatorname{Cov}\\\\left(Z_{1}, Z_{2}\\\\right)=E\\\\left[\\\\left(Z_{1}-E\\\\left(Z_{1}\\\\right)\\\\right)\\\\left(Z_{2}-E\\\\left(Z_{2}\\\\right)\\\\right)\\\\right]\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"当随机变量的均值为零时，它们的协方差与它们的线性相一致相关性 $E[Z_1,Z_2]$。 稍后我们会听到更多关于这个概念的信息。\\n\",\n    \"\\n\",\n    \"数据上定义了统计量，如方差和期望值分配。 \\n\",\n    \"\\n\",\n    \"在实践中，我们没有真正的分布，但只有一堆观察数据点$z1, ..., z_n$。这被称为经验分布，它给我们一个方差的经验估计。\\n\",\n    \"\\n\",\n    \"公式 6-10 基于观察$z$得到$Z$的经验方差\\n\",\n    \"\\n\",\n    \"## 公式法：第一个要素\\n\",\n    \"\\n\",\n    \"结合[公式 6-2](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch06.html#projection-coordinate)中 $z_i$ 的定义，我们有以下公式用于最大化投影数据的方差。（我们从中删除分母`n-1`经验方差的定义，因为它是一个全局常数而不是影响价值最大化的地方。）\\n\",\n    \"\\n\",\n    \"公式 6-12 主成分的目标函数\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\max _{n} \\\\sum_{i=1}^{n}\\\\left(x^{T} w^{2}\\\\right)^{2},where\\\\ w^{T} w=1\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"这里的约束迫使 $w$与其自身的内积为 1，这是相当于说向量必须有单位长度。这是因为我们只关心 $w$的方向而不是$w$的大小。$w$的大小是 1 不必要的自由度，所以我们把它设置为任意的值。\\n\",\n    \"\\n\",\n    \"## 主要成分：矩阵-向量表达式\\n\",\n    \"\\n\",\n    \"接下来是棘手的一步。[公式 6-12](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch06.html#pca1)中的平方和是相当的繁琐。它在矩阵向量格式中会更清晰。我们能做到吗？答案是肯定的。关键在于平方和的同一性：一组平方项的和等于向量的平方范数，其元素是那些项，这相当于向量的内积。有了这个标识，我们可以用矩阵向量表示法重写[公式 6-12](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch06.html#pca1)。\\n\",\n    \"\\n\",\n    \"6-4 主成分的目标函数，矩阵-向量表达式\\n\",\n    \"\\n\",\n    \"$$max_w \\\\, w^Tw, where \\\\, w^Tw = 1$$\\n\",\n    \"\\n\",\n    \"PCA 的这种表述更明确地提出了目标：我们寻找一个输入最大化输出的标准的方向。这听起来很熟悉吗？ 答案在于$X$的奇异值分解（SVD）。最优 $w$是$X$ 的主要左奇异向量，它也是$X^TX$的主特征向量。投影数据被称为原始数据的主成分。\\n\",\n    \"\\n\",\n    \"## 主成分的一般解决方案\\n\",\n    \"\\n\",\n    \"这个过程可以重复。一旦找到第一个主成分，我们就可以重新运行[公式 6-14](https://translate.google.cn/#en/zh-CN/This%20process%20can%20be%20repeated.%20Once%20we%20find%20the%20first%20principal%20component%2C%20we%20can%0Arerun%20Equation%206-14%20with%20the%20added%20constraint%20that%20the%20new%20vector%20be%20orthogonal%20to%0Athe%20previously%20found%20vectors)，并添加新向量与之正交的约束条件先前发现的向量.\\n\",\n    \"\\n\",\n    \"公式 6-16 目标函数的`k + 1`个主成分\\n\",\n    \"\\n\",\n    \"$$max_w \\\\, w^Tw where \\\\, w^Tw = 1 \\\\, and \\\\, w^Tw_1 = \\\\ldots w^Tw_n = 0$$\\n\",\n    \"\\n\",\n    \"该解是$X$的第$k+1$个左奇异向量，按奇异值降序排序。因此，前$k$个主成分对应于前$k$个$X$的左奇异向量。\\n\",\n    \"\\n\",\n    \"## 转换功能\\n\",\n    \"\\n\",\n    \"一旦找到主成分，我们可以使用线性转换特征投影。令$X=UΣV^T$ 是$X$和$S$的 SVD，第$k$列中包含的矩阵前$k$个左奇异向量。$X$的维数为$nxd$，其中$d$是个数原始特征，并且 $V_k$具有尺寸$d\\\\times k$。 而不是单个投影如[公式 6-4 ](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch06.html#projection-vector)中的向量，我们可以同时投影到$a$中的多个向量投影矩阵。\\n\",\n    \"\\n\",\n    \"公式 6-18 PCA 投影矩阵\\n\",\n    \"\\n\",\n    \"$$W = V_k$$\\n\",\n    \"\\n\",\n    \"投影坐标矩阵易于计算，并且可以使用奇异向量彼此正交的事实来进一步简化。\\n\",\n    \"\\n\",\n    \"公式 6-19 简单的 PCA 转换\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"Z=X W=X V_{k}=U \\\\Sigma V^{T} V_{k}=U_{k} \\\\Sigma_{k}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"投影值仅仅是第一个按比例缩放的前k个右向奇异向量$k$个奇异值。因此，整个 PCA 解决方案，成分和投影可以通过$X$的 SVD 方便地获得。\\n\",\n    \"\\n\",\n    \"## 实现 PCA\\n\",\n    \"\\n\",\n    \"PCA 的许多推导涉及首先集中数据，然后采取特征协方差矩阵的分解。但实现 PCA 的最简单方法是通对中心数据矩阵进行奇异值分解。\\n\",\n    \"\\n\",\n    \"### PCA 实现步骤\\n\",\n    \"\\n\",\n    \"公式 6-20 数据矩阵中心化\\n\",\n    \"\\n\",\n    \"$$C = X-1μ^T$$，其中`1`是全部是 1 的列向量，并且$μ$是包含$X$的平均行数的列向量。\\n\",\n    \"\\n\",\n    \"公式 6-21 计算 SVD\\n\",\n    \"\\n\",\n    \"$$C = U\\\\Sigma V^T$$\\n\",\n    \"\\n\",\n    \"公式 6-22 主成分 \\n\",\n    \"\\n\",\n    \"前$k$个主分量是$V$的前$k$列，即右奇异向量对应于$k$个最大奇异值。\\n\",\n    \"\\n\",\n    \"公式 6-23 转换数据\\n\",\n    \"\\n\",\n    \"转换后的数据只是$U$的前$k$列。（如果需要 whitening，然后通过逆奇异值对向量进行缩放。这需要选择奇异值不为零。参见[“whitening 和 ZCA”](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch06.html#sec-whitening)）\\n\",\n    \"\\n\",\n    \"## 使用PCA \\n\",\n    \"\\n\",\n    \"让我们更好地了解 PCA 如何将其应用于某些图像数据。[MNIST](http://yann.lecun.com/exdb/mnist/) 数据集包含从 0 到 9 的手写数字的图像。原始图像是`28 x 28`像素。使用 scikit-learn 分发图像的较低分辨率子集，其中每个图像被下采样为`8×8`像素。原始数据在 scikit 学习有 64 个维度。我们应用 PCA 并使用第一个可视化数据集三个主要部分。\\n\",\n    \"\\n\",\n    \"### 示例 6-1:scikit-learn 数字数据集（MNIST 数据集的一个子集）的主成分分析\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn import datasets\\n\",\n    \"from sklearn.decomposition import PCA\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Load the data\\n\",\n    \"digits_data = datasets.load_digits()\\n\",\n    \"n = len(digits_data.images)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(1797, 64)\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Each image is represented as an 8-by-8 array.\\n\",\n    \"# Flatten this array as input to PCA.\\n\",\n    \"image_data = digits_data.images.reshape((n, -1))\\n\",\n    \"image_data.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0.14890594, 0.13618771, 0.11794594, 0.08409979, 0.05782415,\\n\",\n       \"       0.0491691 , 0.04315987, 0.03661373, 0.03353248, 0.03078806,\\n\",\n       \"       0.02372341, 0.02272697, 0.01821863])\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Fit a PCA transformer to the dataset.\\n\",\n    \"# The number of components is automatically chosen to account for \\n\",\n    \"# at least 80% of the total variance.\\n\",\n    \"pca_transformer = PCA(n_components=0.8)\\n\",\n    \"pca_images = pca_transformer.fit_transform(image_data)\\n\",\n    \"pca_transformer.explained_variance_ratio_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.4030395858767508\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pca_transformer.explained_variance_ratio_[:3].sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, ..., 8, 9, 8])\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Groundtruth label of the number appearing in each image\\n\",\n    \"labels = digits_data.target\\n\",\n    \"labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n    \"%matplotlib notebook\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5,0,'Principal component 3')\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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/nAx/4QNrjjiksLMAviRkLHQLMgPU1TcjZFHNkmrLweDy4XC58Ph+1tbWsXbs2QbaVj/QHnHK8Gv9Qq0Tc2NiI3W5PKCjRMf0wmUwUFRVNWCANh8M0NDQghKCvr4+mpiYikUi8ok6b+sjHmsKZkLLIFuMLQIQQ8YBj165dXHjhhVmNK4SIv2DD4TDhcBghBE899RS/+MUvALjhhhv4whe+kBEhj6EAOCal/IJ242uSkFXpmqIoWRVzpBMhj4yM4HK54j4PZWVlE8bPNyFrizqklPT39+NyuXA4HKxbty5tIs41txmNRme8UutMg9lsxmKxUFxcnJCj1io+urq68Hq9CdI8reIjE9Kb64Q8XYt6iqKwZcsWGhoauO222+J6fPV4VVVVdHR0TDFKUlgBqxDifcDzgBsYfU0Rcj6KOaaKkFUNLzBlVVs+CVlNWUgp6evrw+VyUVhYyPr16zOapo0vvsgEavfq1tbW+DgOhyNOIrl6UMzmlEU2SKaysFgslJWVJTVjUhe1BgcH8fl8E3r3FRYWJpgxaTHXCTlZusftdudcVWo0Gjl48CDDw8O89a1v5dixYxP2yfJ+NhEj5Y8Q80I2A8WvCULOZzGHyWSa0HFXXShzuVyYTKa0iynyScjq1PfIkSM4nU42bNiQERGryMaCMxqN0tHRQWtrKxUVFQmuc36/H4/Hw+joKJ2dnbS/FMTdLIj4wWgRLDu7mHPetBSb/bXX6TqT3n2qGZMqzVP/vVZ9oLq5QcyMSZv2OJMX9aZCJBKZYCcA+SFkFSUlJVx44YXs2bOH4eHheFTe3t7O4sWLMx5vzGBopxDCRoyYFcA+pwlZvWFHR0cpKirKiYhVaFMW2rSAzWaLS8cyGStXQlZNh/r6+giHw0m9LjKBuvqfzgOllne3tLSwYMGCeDGLoiiEw+EJioRwUOGVn70cO44BlJDEtXeYkVE3886KJuRPU5nZz6UIOdeodTIzJu2LsLu7G7fbzSuvvDJB8WG1WvOu9JhpQlYUJWnKwuv15rRe0tfXh9lspqSkBL/fzxNPPMGnPvUpLrroIn7729+ya9cuHnroIa655pqMxx5b1DsHeDNwQEr5sBBi8ZwkZG0xh9frpaWlhY0bN+ZlbFX21t3dTVNTE06nM6P8rBa5ELKUkp6eHpqamiguLqaiooKFCxfmRMaQngWnlDJOxPPmzWPr1q0Jsp9UD7jJbGDjZYtZtqGMkgo7Dfv7eOE3zQR7LGx774YJFXOqmb06LQ+Hw/h8Psxm85yQ1k2XDjmZNO/FF19k06ZN8WKXZGZM413zskWqiHW6kCyHrL64c3nhdXV1ccMNN8TXmt7+9rfz5je/mbVr17Jr1y7uuusuzj77bG6++ea0xxRCGKSUUeAS4HpgOeAAHgaum1OEnKyYQxWN52v8wcFBuru7MRgMbNq0KacbL5uWSVr3t5KSkrjpUH19fV7SH5Odk5SSrq4umpubKS8vz9hnQxgEGy+tjP/dOxRCSrAWxG7DVPlTdVre09NDa2trXN87Pj+dL+vNmcJMFoZAbCHRbDanNGNSpXktLS2Ew2FMJtMExUc60ryZ7t+XalEv0/Wh8diwYQMHDhyYsL22tjYX/3H1hHYATxCTvl00tk3OCUKerJgj10IOdXw1R6oWc6xZsybn887kZtGSYWlp6QT3t3w1KE22cKa+BJqamigrK2PLli1YrbnlfNtfHeKVf3QCsPmKqknPR52Wd3Z2smrVKqxW6wTrzba2tri+d3za43Q6uk2GmSwMmQypzJjC4XB8xtLT04PX603ok6glay0BzwaVxWy5tkmgPlwDQDGwARge21Y1O+/UNKEaw0ymIc4lLaAoStzwZ8GCBWzduhWAV155JbcTzwDRaDQeEU9GhqrKIldoiV2bFikpKWHz5s15sQDtODHMUw/EbD3PuWoJC5dnbmKUynpTSyLjHd20RD2TNpCpMJNeFtnAbDZTWlqa1Ixpsj6Jo6OjOJ3OGVN2JDOiHxkZyWg9Z6Ywlq4A+BOwC7gU2CeE+BUQPKMJWSXfyaYm2bwlI5EIra2tdHZ2snjxYs4999z4G1iNxqcb0WiUrq4uWlpaKC8vnzIqVQtDcoU6Tk9PDy6Xi+Li4rx6MXfWu3nyR/UAnP3GStbuXJiXcVWkIhG1h5/H42FgYCBuFKSqEVSynm7PaS1mOmWRD2jNmManltT8dH9/P729vbS1tcX9JrTRdCppXrZI9mIbHh6elT4WEDenbxJCfB3YB2wCXgWeOKMJGfI3VYeYKL+lpYXe3l6qqqrYsWPHhDdvtp2Z04VWuTB//vy087T5IGSVuA4fPkxpaWnOOfLxCAcU/vlAffzvR3Z3c2R3NyUL7Vxx29QpoGx1yFrZ2HijILWsWes/4ff7OX78eEJEPR1lzWciIaeC1oyps7OTlStXYrPZpuyTOP5lmK/rMVuNhSBeOm0DNgKjwO8AH+A44wk5XUx28weDQZqbm+nv72fp0qXs2LFjxqaSqswMiOep1fRIJgtmRqORcDic1Tmo8j21cemKFStYuDC/kat6HKl5d4YDsRdIYDS7884VWv+JioqK+PZ9+/axaNGieMcRtaxZmztV0x655ErnajWjNoUwmTQv2cswX30SZ6sXsqZ1063ANcBiYmXUJmDBa4KQ1YW98VGO3++nqamJ4eFhqqurWbFixYw/IAaDgZaWFjo7OxO0vNmMk20H68bGRhwOBxs2bKC1tTUv0WCyB8hiN/Guu89JsvfsghBiwiKXNnfq8Xhoa2tLqJbTRtPp+iPPVIQ80wtc6SzqpXoZKooSz0+Pl+aNV3xYLJaUBS+zuH2TILawdxtwmZSySfvhGU/I6dxoZrM53lMNiHfFGB0dpaamhjVr1mR8w+bD76G9vZ2RkRGKi4tz7hCSaepGJWKbzZago85nCijfOJ2l05PlTtUiDFWN4Pf7E2R5KpGMj/RmkpBPR6CRDYxGY1IzplR9Eo1GI8FgkM7OzgQzptkaIWsW9fYCi4QQg0BE/XPGE3I6UCPk0dFRXC4XgUCA2tpazjrrrKweiHS9h5NBURTa29tpb29n4cKFlJeXs2TJkpyj0nRVFqoXssViSWrBOR2kNzg4yKOPPkp3dzdms5mdO3eyadOmvB7jdEE7JddCK8sbb7upEnQoFEpZZZZPzDQhT8dLM5U0b3h4GJfLFV8E93q9fOlLX2JgYIBFixYxf/581q1bx8aNGzN6Xtva2njPe94Trzl43/vex0c+8hEGBwe5/vrraW5uprq6mt/85jfZlmf7iPXU+xsx2ZsPOPN76qVDqIqicOzYMQwGQ0rntUyglk9n8gNrJXSLFi2KKzcOHz6cV3VEKqhtmrTdQVKNk48IWY3+IpEIv/71r/H5fBgMBsLhME8++SSFhYVx/9q5iHRkeeFwmCNHjqAoSk79+6SUvPLKK7zwwgv4fD5sNhtXXnklNTU1wNzNVQPxmUhV1Skt++9//3s+//nP43Q66e/v59577+X73/9+Rs+ryWTinnvuYfPmzYyOjrJlyxYuvfRSHnzwQS6++GLuvPNO7r77bu6++26+/vWvZ3TOYwb1h4EXgHJgAVDEXPdDHhoaorGxEZ/Px5IlS+I3aK7IxBNZUZSUEjrIn8FQKiIdHh6moaEBo9GYltdGPghZncbb7Xa6u7vx+Xw4HA7e9773cfToUR5//HH27duXMSHPBbc3rSyvq6uLLVu2JEjGxvfv0yoRCgsLk3pP7Nu3j2effRaI3ZuBQICWlpbXBCEnKwoxGAyEQiEuuugi3vCGN2Q17qJFi1i0aBEATqeTNWvW0NHRwZ/+9Cd2794NxLyQL7zwwowJeWxR7ztCiJXEXN6GgB4ppXLGE/L4m1PbGcNisbBy5UoGBwfzKltKp/pP7SKdiohV5IuQx6cs3G533AB95cqVaXeQzoWQtZI9m81GIBCId0QxGAy43W4sFgtGo5HBwcGsjjGXoL5cJuvfp20L1dHRkeA9oS4g7tmzB4CdO3dSXV2N1WpNUOjMJCHP9ALiTDQ4bW5u5sCBA5x77rn09PTEiXrRokX09vZmPJ4Qwg7cCLydWHRsBZ4XQtxxxhOyCq0PsMPhSMiPjoyM5LWYYzKTerWopKurK95FerKpUj4jZEVRGBkZoaGhASll2jagWmQThWrLulWzITVl4fF4OHr0KB6Ph0ceeYS+vj4gNnOor69PWPSaq1FctpisLZQaTTc2NsbvxWeeeYZnnnkGu93OVVddxeLFi+P311xr36RiMkLOh/Wmx+Phuuuu45vf/GbaQU0qaCRvK4kR8lVSyt6xzz4BfPOMJ2QtGRQVFSX1AU7mYZwLkqUsIpEILS0tdHd3U1VVlXYX6XwRss/nY3h4mJMnT7J8+fKso4NM5HOq9afL5aKkpCReTagWmKgtcN74xjfyt7/9LU7GEDMSKi8vj0vIvF5vgqF9sin6TKQszoSUiHaBSzUBikQilJeXE4lEcLvd/P3vf2fTpk1Eo9F4/76+vr54pdx0RbEzsUiphaoPHw9VvZQLwuEw1113He985zu59tprAaioqKCrq4tFixbR1dXFggULshm6DBhSyXgM+4CdZzwhh0Ih3G73pOW9quwtX9CmLMLhMC0tLfT09GRExCpyJWSPx0NDQwOhUAir1ZpgDp8N1IW3yaCmhRoaGnA6nUmvvfaBX7RoER/4wAcYGRlhz549NDY2Ul1dPcHZTevjq52iq8oEdfputVpnrWFQJsgHKarkKoTgXe96F8FgkB/84AfxhSiAzs5OBgYG4t7IqixPu4iYq+WmitMRISdrxKB6oGcLKSU333wza9as4WMf+1h8+9VXX81DDz3EnXfembEXsjz1tm8DOoUQnwKeBAqJWXEeOuPvatUYfjLkw/FNC1X72NDQQE9PD0uWLMm6ui/bCjt1uhoMBuMR8d69ezMeZzymyiEPDQ1RX1+PzWabtCuJKxDik819HPIGed/Tf8RAYoR77rnnJj22qiXVFguEw+F4RVdfXx9tbW0JPslqNJ2PyO9MK2cuKSlBUZT4wqDf70dKmVApZ7FYcDqdCYvaagGGKstTLTfNZvMEJ7dMXn4z2ZkEkhsLAQnXIBs899xz/PSnP2X9+vVxieZXv/pV7rzzTt7+9rdz//33s3TpUh5++OGMx5ZSNggh/gf4OHAdMQ3yQ8APz3hCTufhMZlMeYuQQ6EQAwMDuN1uli9fnnOZtdFoJBAIpL2/1+ulsbGRQCBAXV1dvKWPlDIv0+1UaQG32019fT1GozGpflmLQDTKu0500R9RMAk4VFnHhg4XBimpqKjgoosuymiqp3ZtKCgoYOnSpRQWFk7wSNAWZGjz0tPlQzFbYLVaWblyJcePH+cHP/hBfPvKlSvjz0ayRb1UBRjaasTOzs4EJ7fx1YjJ7vvTkbJIZU6fC84777yU4zz55JM5jw90Av9NTH88LKUcgTlQqZcOzGZzzhFyKBSiubmZvr4+SktLcTqdLFmyJOdzSzdl4fP54hI+lYi1L6N8RXXjI+TR0VEaGhqIRqOsWLEirbzcYU+A/ohCucnIvzYs4/dL53FXy3o2FVh5eE1q7+NMkMojQRv5aX0oMrHfPJMiZIBLL70Uk8nEkSNHADjrrLMS5F6ZqCwsFgsWi2WCW14gEIjrp8f37tO+ADPV5+eKybyQZ9vvqC7qCSE2AbcA24npj6NCiB8C984JQp5qsSeXlEUoFKKpqYn+/n6WLVvGjh07GBwcZGBgINvTTcBUhOz3+2lsbMTj8VBXV8e8efOm9UZTCdnr9dLQ0EAwGGTFihUZrVj7oxIBFBoFZoOg3GTCKgSuQO6zlKmin2SRn7Zr83j7zfF51Jkkk3zBYrFw+eWXc/nllyf9PNc0glaWl8wgSG0S0N7ejsfjQQhBOBxOuLbTNUtJRsgej2dWeiEDBmLNTD9EbFHvHAAhRDHwW6BnThDyVMjGMlN1gBsYGGDZsmUJxkP5zEmnImS/34/L5WJkZIS6urqsy7wzRTgcpr+/n+HhYZYvXz4hEk8H6wqsmAS0BCPceLKTPSN+FCCi5DaVzPb7p+rarL54tF1HgsEgwWAwQZJnsURoa7ubwaG/I4SVysoPsmjhe2ZdBJYK0Wg0LwtYVMZIAAAgAElEQVR245FMltfZ2Uk4HKa4uHhCpxGr1TohP51rvjlZisTtducsUZsmqA+ACzgG8R57biHESWB0ThByPuVQwWCQpqYmBgcHqa6uTsjFqZhOQg4EArhcLtxuN7W1taxdu3ZGHvxgMIjL5aKvr4/H/Y+zP7CfvuY+CswFvLX2rXxo44cwGdK7XcrMJr62bD53NPfx3Ig/vt1hnF06Y4PBMKG8ORKJcPDgQcrLy/F6vbS3tzPq+TpwEAApg7S3/y9QwqKFV2f928ykvG4mC0MURcFisVBSUpIgvVTd8tQmAYODg3i9XoAJi7OZ+iKP33e2GgtpUAB8SgixAnhZCHHe2PaOOUHI+UAgEKCpqYmhoSFqampYtWpVypsiX9phdaxoNBonxKGhIWpra7NyoMsG4XA4npKpqamhdEEpH3s8JvMxCRPesJefnfgZRoORD2/8cNrjbiq0sWdjNZ2hMN/tHOJJt4/XF+Vudj8TRGYwGOKSvHBkmAMHDgGwcsUf6e9/hsGhb9DR8WPa2xbFbSG1edR0FrVmsqJtpgk5WWcbrVve+FmKujg7OjpKV1dXgi/yVLK8ZPfDLO4Wop6sD2gg1uj0zcTW8gqAS+YEIadzY6tG8ONvTG1EWlNTw+rVq6ccL58RsqIouN1uXnrppbSPPxnSlW1FIhGam5vp6elh2bJlbN++HYPBwMjoCNfMu4Yr9rkpf+QF/lkT5L43GXn80G/50NpbEWnkAhv9QS450jZh+wcX5VY5NRMENv76BQOtgMRiWUxJSTV2u43BoW8gRB9bt20jEonEoz5tDz9VlZBKkjdd/fSklIReHiD4r26kNwI2I8atJppGn+XRJx/DP+LGWlDARbd8iKXr8++4l6kOWSt11CpvJpPlaVMeqRRBs5GQxxb0DFLKr6TaZ04QcjpQi0PUt7dqTq8ScSYRaT4iZO1iodFoZMeOHTkTjhptT/ZAaM2O1DZVWmIwGoxcNe8qin59IxLon2chKhQKekcZuu8+yj58KkoONTfT95m7CB45gnDYKfvoR7G+7W3sOto64bh3VJaxxpFbp+rTgXAk1hDYaIzlSU0mJ2AgGo3pfU0mU9LpuapKUNsWqW53arSntjfKJ6SU/OaJh/jJ0K8YWjpCoeLgkx03UnVgHgcO/zr2PSwWgl4vHa8enhWEnAqTyfJUtUdHRweBQIB9+/bF0x4NDQ3xgqVscdNNN/HII4+wYMGCuHIlX7abUsqoEOIi4CLABkSBELEFv6/MrqTeNEKNav1+P0ePHo3nCbdv387ChQszIsNc+uqFQiFOnjzJiy++SGFhYbyyLx/R32Rlz9FolNbW1rgRzfbt21m2bNmEKE1VWVT96U+0/+EefrMjCkLwjt0S/959p8YLBum66WaCR46AyYT0+Rn46tfY968X6AsrlJsMvLq5li8vi5nlPDHszfn7wfSnLMZHyGZTjGgVxQNAJDIKRDEYYhGvO6Lw0cZuVuxvZN1LLv6vK2ampCoSampqWLduHdu2bePss8+msrISo9HIwMAAXq+Xffv2cfDgQRoaGuIRdrbmTj858RO+NfpDhkwjWLHgMfo48vpe9h7/HQDb3vb/uOgjO9jywW6Mtffw0ss7GR5+NoerNRHTXalnsVgoKytjyZIl1NXVUVJSwtatW6mrq8PpdHLo0CH+8pe/8P3vf59t27Zxww034PF4MjrGjTfeyGOPPZaw7e677+biiy+mvr6eiy++mLvvvjuXr/FdYumLk0ALMMhc8UOG9KeyJ06cIBQKzehimYpwOExzczO9vb1x+Vy+p6zJquxU4+7m5mYqKipSus6NH+Mlaycfe/pTANzwuMK6Von9llPVdaGjr6IMDGAoK2PZP/7O6J//TP9/fon+x/6BuGoX1miUgd4eCjDnTfI2U7+X9jhW21JAEAp1Egr14h6JvdBstqUAfKqplyfdMQldUEr+q2OQhRYTV5VPjNC0UV9ZWRmhUIj169cnLHaN74itTXtM1ltOkQoPHX8QgJt73soW71oKFTvWEzaOW4M0hQ/RXP9tFpf1jX1HK4oygntkDyUl5yUdMxvMZOm0qnnWatLvuOMOFEVh48aNXHXVVRw/fjzeDSdd7Ny5k+bm5oRt+bDdBBBCGIFOKeXnk30+Jwh5MqgFFUNDQ/G36kwTsep1Md0NVLUWnFJKuru7aWpqory8PO2mqQaDgWPeY3zr6W8BsGu3wpv2x6JS8/K6+H7RgB+EwFBQgDCbMZaVgcVC9Yt7MF51PZ1RwUd6vBwMS6ISwhKOHTuWQDCzsYJufARuNpVQUnI+w8NPc/CVy+LbFy68gaGIwj/HyPjZDcv4l9vH/9fSx4+6h5MS8vjjqMULqRa7tBrftra2hI4j2kVEo9FI/XA9gWjMQOv+ij9wP3+gLFzM11s/yvrS82nyvsLCLTHtfMcLCzDJ1Vx266cxGpOXvmeLmSbkZPeQmkO22Wx560yTD9tNDX4nhLgX+Cux6HgAGJRSDs4JQk5GsGqJsd/vp7a2Nt6IMp9kPNkCmtb9LRevi0xgMBiIRCL09vbS2NhIcXFx3IEtXfgiPr7X/b343x+52Mlfdkao6gjw1W/8L84rrgDAumYNmExE2troeN/7Cbz4IkSjlAz08d/LF/EfDd28HAYQIKDAKFi8eDE/Ov4jnup5iqHIEDZh47yi83jnsndSWlQ6pR/F6TKor635Es3NX0nQIZeXvZGD3iASqLKYqLCYeH1xjNyag1PPBqZSWaSy3lQ7jng8Hjo6OuKlzU2yCQtmQoSpCVSiFEArHfz3oof43+Y7cMwPYTDFXtaVO3qBXl49dozVq/4Pkyl/mt3TESGPx2xd1BtDIbAOOJ9TeeQyoANYNycIWQuPx4PL5cLv9yeUGPt8vrwbDCUTpat+yJ2dnSxZsiRj97dcEAqFOHz4MMXFxWzatAm7PQuZmYCoPJX28Cg+MIC7QCBDofh2Y2kp5Z//HAN3fZbg3r2o1GJwONjidLBnwzI6wxGN5M2BucDM7zpi+UyTMBGQAZ5wP0HxYDHXGq9N6keh/pkpf4RkL1mTqYjly78OJE5RhyKxfH3hmL7aaTRgJFapqB3HHVH4fEsffx3yYhWCjywuZVehKavgQNtxRHvO1m4rhk4jhqjCN5s/icfg490rPk2jvRVFRjBawghhQcoQ/n4rwgSU9OFquouz1v4s4/NIhZn2Xp5OL2Qt8mS7CVAL7JBSbkj24ZwgZNUIXXU/q6urm9A3z2w2590TWVu2qaoXOjo64uqFTG7MXFzGhoeHqa+vx+/3s2LFivjUKhsUWYv4tv0zzP/c5wAQVity7Lop117MNYeaOOgJYBdwS9TEtQ89yEKjkZEf3Y9v927sO3aw/YCLKGAkVicKMcmbzWjmljW3cFHlRdQU1fC3lr/x1Ze+yt6hvXx828fj56A1YO/p6aGxsRFFUYhEIkQiEcLhcFYFBPlGqSn2+3qU2AtsVImiAA5Doo+CNs9sjI7ia7+HYzwHmOns+lDOVX9CCOrm1REhQlREGTV6GTF6kUhM0oRiiBLxGokEwxhMcPKP1SzesBbbtj/h9R4jGlUwGPJDojPpljcT3UJU5GK7OQ5h4JgQYh0wAviBIOCXUobnBCEHg0GOHTtGbW1tQh5OC5PJFK8MygfUCFltXtre3k5lZWXGRKwdK9MocGRkhPr6eoQQrF69ms7OzpzzskIIlJJi7Oefh3/PXmQwiKGkBPtb38LV515GtyeAEYlfCq75/KdBSrqNRhhTd5T8+y3cZCnmx11DKMA6h5W7lpTHJW83rbkpfqwubxdRohRbEquqknUYllJy/PhxbDZbQgHB+MKMXP0oMiGUapsZAbSHIvSEIjw3RrrV1lO/wfg887GTH8Hhf27s0zDt7f+LxbKAeeVXZH3OAIXmQi4q38nj/U/xrhWfjm8/370ZK1aWrL4IYXwQYYC67ZtZftF5dPf9AbCw/6WXMIjp8UeeTqQypx8dHc2pUu8d73gHu3fvpr+/n6qqKr74xS/mbLup6RYCUAl8G9hDTPZmBvYDD88JQrZarWzdunXSffJpwQkxEm1ra6O/v5/Fixezffv2rKfVageSdP+9akofiUQSHNjy1TEas5lF996bsOnxti6624YoEZJnN9XwiDvAb99wJW/756MYFAXL2rXM++QnsK5axV1S8snFsSljKnJ7rus57j9+PwC3rrt1ylMSQsQ1v9rpqLYwo6urKy4bm6zzSL5QajJyUbGDJ90+zjvUEt9+y8JT0VlzIBzPM5cZPDj8LwBwh/g/vmt9CX/gPrq7H8qKkMcXgdxqvwZR4ucfJbFjXLHgcv79+OVIA1xww+00uroZHHyUwnW/pLvvlwCUl19OXe02FEWJLyJqCzEsFkvCdcykG/Z0I5U5vdolJVv88pe/TLo9F9tNDRkPEct9eYDFgAVYSEz2NndSFlMhHxacEPux29vb6e3tZcGCBTkRsYp02yb5fD4aGhriqQlttw2Y2Og0H3C73Zw8eZIOgxWBmWKrGafdTrk/wuff9m5+cf17OHzuqvj+YqAB818+hK3rZaTZQXDnZwhvuiFhzD3de/jEC58A4Pb1t7Nl/paszy9VYUay5qBahYLT6cThcEyIpjOdcn+9ZsGE/PCby04txGnzzGrVXy8L6KYMs3kj/gAEAhMLaVJBSklv38N0dvyAcGQAQ6iARbb3UxjciMNv5mP2m/mPY++O7Xws9h/3QoVSIaipvguDwUp//x8BmFd+NcuW3QnE7p3xvh6q/4SaPmptbY3PMjOR5E0XUnkhz9Y2XGNRcocQQgE2A71AN3BCytjCzZwgZJheC06IEXFHRwetra1UVFRQWVlJWVlZXhabpqr8CwQCNDY2Mjo6OqkFZyb98KaCx+Ohvr6eaDTKqlWrqLM7+PL+BloCYd51tJXn3d6JDm6RAMoPLsYi/ChSYAz7sD35Gbp7vZRf9kEA9vfu52PPx7wy3r/2/exasSvtc0pXZSGESNl5RI2m1T5+kEguRqMxowe62GTkm3UL+WaKz7V5ZrXqz48du1Cr/05V/QGceKGPQ090EBiNYLEbOf8dtVSuOfWy6er6Me0d34l9T8VC1OIleHYzC2vehrAZQULgX92EX4l19TZvKKOnvJulgNFYQG3NF6it+UJa300ryRvfakt94Q0PD9Pe3h5/4YVCITo7OxMkedOFyczpZ5sTn8YLeRtwG7CcWD65EviTEOLzUkrvnCHkqZBtXz1ta/sFCxawbds2zGZz3Pg8H0hFyKFQCJfLxeDgYFrFLKlSFk/d/z3q9z5POODHbLWx+vyLOO//3YDRNDHf7Pf749WM46Pw/16+iI/Wd/Iv96lcvNbBTXQdxIYfb8TMA207WePs4uJ5r7Kg7fcofBBv2BuPjAF+Xv9zfl7/c2qKavjBBae6XUwXkikUxut93W43Xq+XAwcOTIimM5mqq+mEmn9186I3gtsM/sucSMCOnyqTASl9xKr+HAghOPxUJwce7QDAaDYQ8it01o/ECVlKhc6u/wOgsvhDmB+twBB2YFBseJ44gSg0UfCu5TjevBTevPTUybzYk/O10yKVJM/n83HkyJH4M6Omj1TJabZubqmQjJB9Pl/GhSAzBNUL+SbgqJQyPm0UQvwSeBvw4Jwh5Kmip0z9J9QKt5aWlnhre+0CgtFonDZCVqv6+vr6qK6untR5TotvPNvDbtcI/vAJHBYjb920iDsvX8mrT8dyXyarjXAwwOEnHsVit7Pjbe+M/1uV/IeGhjCZTGzbtm3CMbc47by8dQUdwTDfbuvn8SEPO0s0N384ZrUZiho569KrWF3uQe7/EnZ/Fx5icjoleup7esKxktahwFDG1yxfGE8uXq+X5uZmVqxYEY+mW1paJhjaa6fqyRB8vpfg7i4EEDJCcRh+c0SwbpVgAb28p2CUSCjmImezLSUalRx6oguALW+qYvGqYix2IybLqQjT5ztBNBpr99XhvhdeB6ZACUubPofZV0Z0IIj/z60UvnfldF2uSWEwGLDZbFRVneoKo7ba8ng8CU1WjUZjwrXMxsQ+GSEPDw/PdutNAzHvCtULOUosUp47OeR0kO4bWUoZLzUuLy/nnHPOSfrQmUymvMnoVEJWNcxdXV0sXbo07sCWLh494QbAYTbiCyn8fF87hVYT7/36dygoLcVis3N09xP888ffp/3Vw0BiAYtqO/rCCy8kvV4XvNw4Qc72oap58c/lwg1EMVBqCVBz8AtUFMSm6ERjMxOnxckzb30mvr/f34jL9Tm8vqPse3ETZWVXUFP92ZTVYzNVGKI2Bk3WFTuZA5l24cvpdGKz2gg+1x37Rzsr+GHHII+ZFbyLHbyPzWxmPzVD70F1WFi48AaGOn0o4djs5qW/tvPSX9uxF5m57P2rsDpij2k4MowQVqQMYjcvRxn2EiroomfL/axZ9iCj3z6K0u1LyIPPZD41mQZZW9as1e5qpY29vb14PJ6EVltal7xUz0Cy483WohAppfrI/BG4XgjxcWLyt02Ak7GM/2uGkKeCttS4rKxsygq3fMrohBB0d3dTX19PZWVl1sUkP3/nKqyKn/VrVvLwSx3c9edj7HEN8rFLlvPCwz+n49hRuhtOAFC5Zh0tLS20t7cndX1Ltrh18+Iyftw5iAKsL7Dx+ZoK1hbYTu3gKGcvF/I6nqK6cDi+OSQn3mZSSo6f+ADhcC9CmJAywuDgoxgNNmpqkpb5zwgmI7Bkhvbqwtfo6Cgej4f+/n6iPX6WhceivWd6+BBwPVFuP97NA6Z/Y+2lBdiUp4lIA38dNvLcv+7mFmcAo3kJSlhSusiOokhGegM8+6sm3nT7WgDMplKEMCKlgbUrf8LwfS/SeN5H8fqOE/WGYnY1xsTfbCZ1wZkUhaSSNgaDwfjMpL+/H5/PFyf18TOTZN9tNpvTj0XEfxNC+IB3AZcA9cAHpJTdMIcIOZMIeLwvbU9PDy6Xi9LSUjZv3ozNZptkhBjykbJQFwrb2tooKSmZ0vhnKlSX2fnfJzv5ynMvcqAtFi3vqI1FeM0H9jPQfmo1f8AXpCISSXpMNRIdf03fVean/uTdHB08ittkp9lxO1uL3pawz5YP3E3QWkikvwme/AoF3c/T4i1l4bhzVZQRwuE+QLBl83O4R/ZSX387o56Xs/7+pwPaha9582KzhbB9BN/+JohIGlAwAdUY+ZbNzrWBKPfs2UXl8jAHPQfHRgnzm45f8jbuQAjBlR9eS8iv8NsvH2Sww4uMSoRBYLVWIWUYiKKYPIiVEpAIxYjnRycRCMxrShJ+t1z76WWCXKv0tK221GsJqWcmfr8/odWWw+GY1YQ8Zr15BfCElHK3un2spx4whwg5HWgLMKSU9Pb24nK5KC4uTpuIVaja4WygpkWamppYsGABtbW18TFzgcFg4MUOPy3uUy+KFRWx3Og1d36BjiYXh575J10vPkf/gT3U3XhzynHGm/kHlSDvf+r9DAQGMAkT/oifr7/0dRY4FnBB5QWx7xWNYr1vO4IoFmFEjM3SDvhWM15lazQWYTEvIBTu4dVj74lLv5yFqSVwM5GyyEdEaXCYwCCQQvJ+6aXOaeOHHiPzg0YE0OVz0+95BYAvLvoiJ4In+G3k9yiRKAZpxDsaRAlKpASjKeYFAjEv5tLSSxgcfDRmdDQmIHF2b42R8YYy7JdXJpzLVP7Y+cR0lU2nmpns3buX8vLyuGrmnnvuYf/+/RQVFVFcXMyGDRu45JJLMvJy0eKxxx7jIx/5CIqicMstt3DnnXdm/R00hSFfAxqEEA3ECkKiwCNCiLdKKfvnDCGn8xCpxSGDg4M0NjZSVFSUtedDNhGy+hJobGykrKwsvlDY1dUVb6ueC4xGI1+8sJzyyhoeP9bLt//p4r/+fpLqkZOMYsRZXMyG151H14vPoUxy7snUGscGjzEQGKDMWsbfrvkbf3H9ha/s/woPvvpgnJBHB/o5PLCILWUdGFDo9hfyz55aLJt30t3dnVBYIITAZq8mFO7B5zsRP47TefaE83nsu8fobVYzrgNAExfesJyl6/LrV5AvGEqtoEiEBCeCCnNMjiZMAhGBkOjHhGSBdQGX7riUtaNr+cXjv6Cp/BXq+jfzx68diY9VXmuhv78/rk5IqiXefCfGtyTPu890+6aZIv9oNIrZbE7I899///1873vfw+fzUVtby/PPP88FF1yQFSErisJtt93G448/TlVVFVu3buXqq69m7dq1WZ2vpjDEIqWsH/t/dXHPCXjhNRQhSylRFIWXX36Z4uJiNm7cmLTKJ11kEiFLKenv76exsRGn0zkhGs9HB5JoVLKnZYRyEWVlRSG9o0EEYBjtZ/cPfh47jtmCEo4ZBC3ftiPlWMkM+AORAAKBw+zAZDBRaivFarTSPNI8dvwofcPDvGQ4n2fru4kqCvaiYtZceik1r7sAr88XVyvEyHgIn28vYKCu7puEgi20td9Da9s9zJt3VcKx/Z7w2PmDMAgMBgMmy/SQTD4iZGEzYl5dTPjoMH/EGTNYBMJ1hax98TnObduL49gywmZoXXqQ8tUrMAgDT9f+msuqL6Nxfz8AyzaVsOICZ5JS8XdQufjfcTqdU2p95yohp/Kx8Hg8rFu3juuvv57rr78+6/H37dvH8uXL47PXXbt28ac//SlrQtbgBSHEHcATxKr1lgOjUko/zCFCTvUQSSkZGBigsbGRUCjE8uXLczLfUZFuocng4CANDQ3YbDY2bNiQ9CWQD0LuGPZz++9ikab1kacIRmIR7uvWLWNZ0Wbajh5CCYewFTpZe+ElnHtt6ptV7T+oxeqy1ZgMJto97dz2z9vY37sfRSqEo2G6urpwuVxUVFTw7q9+Iz4T0VpMah1GFEWhr+8FWn0CIZw0uWxEo0UgBJGIJx4Rji93rtxop3JNCXXrKjEYpm+hajJCdvvDfPmxBh57tQ+rycBtO5dx4/aqCf/GfsUSQoA4OoxEEl1Twj/6/8VFA/8CIGKIYg4b6Hj1MNa6RURlFKNV8Pqra3j99TUpj68tFdfab6YqFZ9pQp4pV77pNhbq6OhgyZIl8b9XVVWxd+/enMcllrL4NrAWcAArgHjucM4QcjIMDAzQ0NCA3W5n3bp1dHR05O0NPhWJut1u6uvrMZlMrF27doKIPpOxxuNL+77E462P44v4sJvsXFVzFR9cdzuvqylmb7ObYCRKsc3I28+p4vaL6rCYMitNTpayKLGW8Lltn+Ozez7L3p5TN6YVK0NDQ5xzzjlpTw2NRiPz52+mo7MARXGD+CCI2MvNZtuYUO5sNpsJ+GLXpnW/n9b9fo7OG+SyW1fjKM6/+c1UOeq7/nKC3fWxkDcYifKNp5qocFq4cl1Fwn7CaqTkLdV8OHSE3fWDiFfdvL/lccyA/fWX8WvxEwLmCG+6ZCcv9r4IwJKCJeMPNwGZlopbLJa4CqSgoGBayTlVx+npQCpCHhkZyQshJ7sP8qFWkVI2AFeOyd3CUsqj2s/nJCGrUanVauWss86Kk2G+/Cwg9Y8zOjpKfX09UkpWrlw5oUljMkxFyJ9s6OSRgVG8ShSHQRAdLqIwEsRhsuOP+PlN/W8IjgS5ZcUbuHm5jde//vU53TypKv7Wz1vP4295nMa+Rr538Hsc8h7i3IXnZjWNMxodrFp5H83NX8Lnj0X2pSVvoLr6s5jNp3LDoVCIjmdPEPSGMRZG8HTBSH+Qxx86wpZrF8yoX/KwL8wzDTEyfvL2c3mucZDP/bWeB/a0TyBkFV++ahVffqyBlw68ilnG7j3/c//gahbis0a4WXk3I4Wx7e9c+c6kY0yFyUrFOzs7GRgYoK2tDZ8vplFOFk3nA7MhZTEyMpIXlUVVVRVtbac6p7e3t7N48eKcx1UX96SUB7V/Vz+fM4QshGBoaIiGhgbMZnPSqDRXP4vJ4PV6aWhoiKdFMjHInoqQf90bk7DZggF8VhsUXcb1g0v43Juv5MFXfsZ9TfdxzHuMT73+Uxw4cCD3HGgKNcO1j1xLlCgGDESJEfb7Nrwv6+MUFq5j3bpfT7qPxWLhzR9eD0BzczNht5E9v+jG2ysxGAwJfslqia7T6cza4W2yHHLLkB8pobLYxgKnlR21sd+4dSiQcrxiu5n/fusa2laE+Md3Yzn8sqql+H1eGBzg/Ffm8cT5Q9yy9hYuqboko3OdCmazmYKCAhRFiedCU7WGMpvN8euWravbbOkWkg9z+q1bt1JfX09TUxOVlZX86le/4he/+EXW42mIWIrYDSaIrfUlPGhzhpDV3nWrV69O2QLcbDbn1RMZYt4PjY2NeL1eli9fntKPeTJMRcj/PLsW/wU7sQeD/PXCy/ift9/IPmnigY9fwQ/OHwXgvKXnYTKZ8uL2Nj5CDofDuFwuLnBewNOjTxMlyprSNXx888dZWTq9ZboyKuk47qZiuZO2iOThxlE2R2HUIPmnqYB3ropFLdoS3fEpj1w8KbQY9sUWFwusMSJwWk0YBPhDypSLgXZnUey6Ggy85TNf4uSrr/Lsvf/FglEHT139W8Q0pRLG55BT+VBoi1u0jVa15c1Op3PS8ubZECHnK4dsMpm49957ufzyy1EUhZtuuomzzjor6/G0xDv2/0lzY3OGkC0Wy5QNDfMZIQeDQQKBAAcPHqSuro758+dnHZkajUbsDzxA0+0fQXq9CLsd5zVXU37HHQizmVq7la/96Gfsixp42RdTSWw+foQljUNwfuwnrCuuS6qOyAYqIatdUDo7O6murua/rvyvtL9jvqrDvMMhnnogphJSBJwjY3fyS7VWnmntZ6HFxMUlBSlLdLUdnbUqD5Vo1KhQfbgnI9YSR4yMvMHYy3M0GCEqwWExTvl9i+ZXoEQiyGiUoNdD0OsBKTGYjDCNlXTpLupZLBbKy8snNFpNVpAxvrzZ4YiZI800ISdLtaQyrc8GV155JVdeeWXO4zz99BLPIdAAACAASURBVNNceOGFDwMNxLqEDI/9GSDmYdEopeyCOUTIMP0WnBB7wJuamhgYGMBsNrNly5acbwCj0Yj92eeQgLDbkX4/I7/6NYbCQso+/GGklDwRkjRoetrVdLSy/OJruXXdYu47ch/fOvgtLl92eU7noUIIQV9fH8eOHYub78/UgzYe1kITC2oK6Wv1YlQkXptgx4WLsa5z8EzbAPd1DXFxSWp3r2SeFIqiJPgoaFMeqkIkEAhMSHksK7UjBHS4A/SOBnnBFTNFWlo6dUGRxeGg9pxzadj7HD+/47b49ldtNTy4pz2pUiMfyEVlkU6peF9fH36/HyEEwWCQnp4eSkpKpr2reCQSmeDqNlt9kMdecseJ8W0dUAwUEFNZ1AL3AV8VQhjnFCFPhVy6hkQiEZqbm+np6WHZsmWsWLGCAwcO5CVFIISg7wufZ+vll2NwOBj5/e/p/+J/4t+7j+F3x/rlfcFiRfnh99ldVsEDV1/Pfde9i12GUdaVlyIQhKKhqQ80BVS9dFdXFyUlJRMc7vINpS+A/y8tKF2xohjzWSXYr1yC0DicmS1G3vjBNfzL7ePm+i4WGwUfOHsx/WOG8K5A5r+n0WikqKgoYcFVTXl0d3fj9Xo5ceJE0pTHzuVl7K4f5OJvn1KavHd71YRjSH8E32PtRF4dBpPAunMhr3/XTRjNZk48uxsJHCtcxTNl5/OPFEqNfCDfsrdkpeIQe8nt378fk8lEX19f3J7WZrNNMAvKx4tnMondbPNCXrduHVLKz061n5RSeU0RcjYqC+20fcmSJQkmPPlMgSgVFQz96H4C+/cTfCVWVutetozOhka+YS3noC+E6d9upDgYACkJm8yc+NKn+dCHYuR1yZLcFoTUziBWq5WFCxdSVlaWlIwbej3c+YdXOdw5AsCb11fwn1etocCa2a0kpcT7y0bkaBgMAqKS8NFhMBtwvGnphP3PclgxAh2K5MaTnewZ8U80yM8BasqjpKSEaDTK8uXLgYkpj+uqfPhGjezrVrAYBTdvq+DSVWUTxvP9pZVIfewaEZEEn+rC7jSz8e03cXvnGqIS/vjes7igNzSlUiMX5NrOKF0YjUYMBgNVVacifSklgUAgfv3UruJa6021uCXTc0yWQw4EAtl1Wp9mjJWvC2KR8cXAamKNTSNAAHhUStkCesoiJaLRaLx5aappe75VG75nniZc3xD/e9nZmyhct57nX26MbShwMlIYi+oueGkPxoik2FLMW+reklZfuqTH9Pk4efIkkUiEVatWUVRUhMvlSnodpZS89ycH6B0NYjYIwlHJI4d7sJuNfPmaidK3yfKxMqAgxyrwij6xnkjTKL7fNKG0Jl90LTMb+YTTwNdGozw3cqrMXGuQnw+MP+dkKY/zzz2V8vB4PBw6dChB5VFosmNpiJGx8/azCDeOEPhrG8E9fbSUmpAS5jsMVBRZ2VEYm3ZPptTIBTOZ1x1/7YQQ2O127HY78+fPj2/XWm92d3fj8XgSr18aRvapvJDTkZmeDoypK24E1gBXAYeBQmAVMevNFiGEYU4R8lRIp+ectkNIRUXFpA5s+aiw0x7X84lPMNrWxuLWNkIPPUTwBz+k4tprOddi4CVfkIjJTJFnlDc9+xTvfeRhyq57O09e++mpB0+CUChEQ0MDIyMjrFixImExJ1mlHoDbH6HPE0QIeOnTF/K8a5Bbf/EK+1uGJ+w7FYTNiCg0I0fDeB6sJzoY85Y2Lk1dQLPWYuDPi2xESkr5bucQT7p9vL4oFhH96e4v0NNwImH/y277ONVnn5PxuU2FyVIeHo8Hf/MwZglhm+Rg/WGKjQWUA9HBAIOeWGrJbhIIITJSamSDmazUSxeprDe1RvZqqbi2D6K2LVQyQp7FXsjq/74JuJFYZHy/lPJlIcS9jHlaAEmMas9g5HIza/2Qy8vL08qf5iNCjkQivHLgY1iOPsPAqhCGSgdBsT0W7YdC2AwGfrW+mhc+8GHmv/IypkgEd0EhjW+5jpWf/MSE8aYql9Ua0tfW1rJmzZoJ1y1VYUix3USF00r3SJDrf7SflsGYNGprdeYPgRACQ7kVZTRMtOdUxBs+OMAfDvXQsrmU2y6pwayJgG8YiBAlgrHLFzfI/+CimObUPxLTapttNoQwgBCYssx/Z3MfaVUeYbcVH03YiuysW1eLZ3AERDsyLOlpaUQC3pBCd3c3YXPhBKVGuuXZ6WA2EnIypFLJaPsgqqXiKnm3t7cnFLfMVutNze8WIZa2MAHnCyGOARuBn6o7zClCzgZSSvr6+mhsbKSkpGRKY3otcvFEjkajtLa20tHRgaH/CSq+ZwEsSHMYEX4WgILLLgPAYLXyxdvv5ITvVIeS76xYjEiyip3MOlM9ntqktbKycoIhfbIxxkMIQe28ArpHghzrHo1v37I0UYivpkHC4XB8lX78FFTpC6A0e2LyeKsBAqeO94aoiT/vH+C7ZsFH31Ab336dw8DvfFEUYJ3Dyl1LylnjSPyt1l/6JpZuOJt5y2qyIqJ8rNSLsQ4fMhjFYrFQ4ihiVAIWA288bzNf2/8CHm+Ql3/3a4ZOHuYDwsjxim0cOhS7Vnc/N8TzLTF3u8nKs9PBTBHydCkckvVBVK037XZ7XHP+ox/9iJdeeun/Z++8w+Oor/X/mdmu3iUXSbZlqxg3LMk21dRAYnq4QCAELrkp5IchkJBwQ0ggiYNNwoXQQqimhmrABIMhBAgGV7AxttV779qm7TO/P0YznpVW0qrY5jF5n8ePrC2zs6OZM+d7zvu+B5vNxvr161m8eDELFiyYENPj5Zdf5vbbb6esrIwdO3ZQUnJwlXXnnXfy+OOPYzAYuO+++zjrrLGZTbrjvx6F7vYwcDNwFtAFNAx+r6MrQ44G+gxS9bqIjY2dkA3nRDyRZVmmtbWV+vp6srKyWL58OX7X6zQuvANzeSVCIEAoViZ4aiqzdRnwC8fk0OUPsrnXyd1N3axp6OS89OHZwNCyjN7yMy0tLSoTfFEUI7JRqjtdfFrbi0GAh69YQk2Xm7Wbq1i7uZILlkzD7/dTU1OD3W4nLy9PE+IMXYLGx8eT6LZgAYVUrAvGCCDLsAQDt5R3hwXka+ONrE42RjSHCvqVm9Xnb77K52++SmJmFufcfBuxScMbbmNh0n7IyRble9j9SM4AgVqn9nh8jImT56YQ+/Fz9HnqATDKIRa0b8VoX0gwKZ2tg8H4zydZKOuTeXyfn0c+ruPkWbHjFrYcroB8ODNxQVAc/zIzMzWp+P33388TTzzB3r176evr4y9/+Qtr164lK2voaISxsWDBAjZs2MCPfvSjsMcPHDjACy+8wP79+2ltbeWMM86gsrIy6hq9LMsvDu6/G7gbxYpzq/41R1VAjtYTuaenh/r6esxmMwsWLJjwlFqj0Ri1j7GaiVdXVw8riRiT8rGvzsZsDuEeUAZfzpx5HoLJhCTL/LvfzYrEGApirXQEgsrgTClyRiKKonaT6Ovro7KyktjY2HEZ8I/UHO33BBEFSI4xc/ycFGYk2bjr3SrcviB1dXW0trZqc/mCwSB+v5+UlBRSU1O1i1VlLTgFB2ZkBATkwZ/KcVKS5t2ESLINz25GysQSM7KwxsWTljOLhr27sXe08/HTj3H29b+I6juPtf3xQIwxYpybQLDKgfO+g94xlhVKY+u206bz2rsNADw7+2q+N8OFtOUVyj54l7nfV4ygZiRaOevkZczvcfH4vs9pcQRobGzUlKb6ump8fPyIN9nDFSiPZPNQvw/FxcX85Cc/mdT2i4qKIj7+xhtvcNlll2GxWJg9ezZz585lx44dHHfcyFa2egiCsAhlurQNRRBSIQjCAVmW7eprjqqAPBYcDgdOp5P6+vpRJdbRItqSRW9vL1VVVaMGxpC0G/fAQTOT2Jh5ADT7AlxVpjxuFQW8g4F4VWrkbrIoirhcLsrKygDCzJWixUgli6KsOGLMBrrdfhb/4QOCg/syL0kJ4MuXL9canbIsYzAYkGVZU/2pUBs6fZY6TL5kLRiDNhyDvQQ5f7aZxsZGreQx2g333F/8Rvt/Z10Nb9z5G9qrKkZ8/aFGzLk5w3jIpmOUZbds70FAJi41jX/fehau3h6e3/IKjo72YfLslDgrogC+oKzV+0OhEAMDAzidzjDOr56lEB8fj8ViOSoD8khjqRwOx4Qy4mjR0tLCihUrtN9nzpxJS0vLeDaxDqU88W8UUcg1wHLgevUFR1VAHumCdblcVFVVEQqFSEpKIi8vb9LBGMYuWTgcDqqqqjAYDGMGxvi420lPN+MP7KCx8X7q6v9MevoqMsxGTk2KZYt9AK8kk2w0cFlmEjdlpw/bhtfrxeFwMDAwQFFR0YRNVkYKyO/c9Vv+u6o87LGqovP41fcuZsv9f2TTkOfOXn0zs5eWattSA7UsywQCw5kZAyYHfw+Y+T5WfmGKxVY8R1OD1dXV4fF4MBqNPFv3LB/3fUyPv4dYYyznzjqXVdJx5BQtxGSx0tvSCLKMcQIuZlPFdBBsRmIvnAUXDn/O61JKGGarUiIzx8QgiCIBn5dEm3JJjibPNhgMERV0Xq8Xp9MZViLyer00NDSQmJg4aS+P0XC4A/JkfSzOOOMM2tvbhz2+Zs0azj///IjvmYwl5+BUkFRZlvXTzB4TBOGA/nVHVUAeioGBAaqrq/F6vcydO5eUlBTKy8unjDs8EstC7/yWn58/audXliX6+z/BaEzEZJqBxeIFBKRB5Z1VFFk/f7hQQo9gUCkZdHV1YbPZxu02NxQj0d4GBpkMosmMIAoYDAZ+ds5i0uKtuO1KgDVZrQiiiIDAAwceYktN2PnGXcffxcoZKzEa07DH9iKLfkwepatuC8RzLCEkwCKBzWYjJiaGzMxMRFGkqakJl8/F63uV8UUGDLiDbl6ofoH91ZsofiAZ0WBAGrxJFp506oSPwaGENU4JpH6vwj32DwwgSxImi5VZKTETkmfrOb96lsKuXbtITU3F4/GMWPKYCpnz4TSnDwQCkw7I//znP8f9uZO05DQBDkEQ/gfYA3hQpoV8CSAIguGoU+qpdyuv10tNTQ1Op5O8vDzS0tK056ZSzDG0ZKH/3KHc3pHg87Ww/4DSPOjtsyDLSnMqLW3s7q1evJKTk8OKFSuoqKiYdB00kkmR3+/HP+ilMf+0syhccQKpObO0/VDzhMVnnUPuoqWkzZrDJZsvARfEGGMQBSVIW41KYDEYDNhPew9H727yProXQ8iGjMzSwRxhlxRg/bNf8sQVCwDlgg8EAthMNq4pvIZTp59KXlIemxo28cfP/khTdoCSGiUYG20xZCxaSnzBQlpbWzU1WKTM8LH2Pl7qctLoCyADT2fFkHWIpbeJmVkgCLh6unD399J8QOkbJGRmkTTY9ItGnh0t9DV8YFjJo7a2llAohNVqDbPgHE2YMRSHe7r1oXR6GwnnnXcel19+OTfddBOtra1UVVWxbNmyaN8uAk7gOmA3kA4sAN4TBOFxoBG446gKyMFgkLKyMvr7+5kzZw7z588fdkJNxs9iKNSSRSAQoK6uju7u7hE/17DppxjKNiL4XcimWEKLLiN0+h2YzRkkJ51MX/+nyLIPozGJzMyLyc25LuJndt5+B+5330V2u5EsFoTTTmPZb3+DaZAhMhViFX3JQi8dl0PKzWff5jfZt/lNEjOncd4vfkPq29cQ7AWwsOuNV9j1xiuk5xr57mkNZCZ7gQFM8SewqOBPmI1Kduh2VxOs8UCyiO88AedrEpmIyMh8RJA/yV4SPcpIeNXi1Ov1kp+fz1XTrwKUTKnZ2YyExPSs2Xz/r49oQSEYDGoGOE1NTcMyQzXwbOh2UudTzgeLIOCWpl6cMRTWuHgScmbjaKgNMxpafNY5wEFTez0P+ZvHZIy0uVERqQQznpKHyorRCzMiBd6vivXmVHghv/baa6xevZquri5WrVrFkiVL2Lx5M8cccwyXXHIJ8+fPx2g08uCDD47nO9uBHwz+Pw2wDP5LBZKBPgBhnNnUV9NOaRChUIjm5mYyMzNHvKhaWloIBALMmjVr0p/n9/v59NNPMZlM5ObmMn369BGzBMudygUlm2IQAoqgInjcDYROuRWAhoYGDAYDM2eOngnVLlYsRmWLBcGnZNNJ//N9UlavBqC6upr4+PiwyRHjhd1up7GxkdTUVOrq6pg+fTrZ2dlsXHcHXreLtNzZNH7xOV6Xk9zFS7kodiMv74nDK5nJsHmpdcQz5zv1mGODBGUwDv4p9voSuWjZa6Tb0rE3fUboycEPHPSykJHpyXuT/CvuAA7eDDo6OpgzZ06YxakkSWxp3cLNWxVq4L3H38uxqQcnVqtNRfX1KvtEleyqwboyJGCyWrkzYMEtw30pJgqNkJMzeplosti65WMG9u+mZudWDCYzJedfzKKzzpnym8HOnTspLS2d8PtVYYZ6vNQbW2xsbFig7unpwe/3H/LjBtDR0YHX6yU3Nzfs8dNPP52PPvroK+Vn8cUXX1BdXc3FF1+cAXwXqOeg/aYbaJNlWSP1H1UZssFgGLPLOh6q2kiQJInm5mYaGxuRZTkqe0r/D7cix2eCOQ5xz7MY374Jsf5jTXE2VmbrdDqprKzEtG4tc0pKiEtLC3OFQ4nHNEuwrsnOgZpeYkSB/52VwfeyxsfFVZeyRqOR0tJSTCYTLlcVs1eV43YrNK6F805n19Nu2irL4Vi4NPdLAsf/jFDeGeS7QtR1X40sw6POBaxITGSJ9AmZooO1n63l7hPvJjatgI7kt7D05yJKJoJmO3057xJa0o8sy3R0dFBXV8e0aQq/+ZVXXsHlcmE2m1m8eDGWfIsWjH+6+KccN+M4LauXJAlJkjSGBxxsKKrji7KyshBFkaWD8+j+VNEBskRXVxcxAQ99fX1awImPjx/X8j0aGC1WTv/R9Zz+o+vHfvERRCRhhuqTrGd5eDweTCYToVBoQiWP8WCkDNnv90dN7TxcUI8fkAScikJ3SwLMwAxgA/C/R2UNGaIzGJpoyUIvr05PT2f58uXs3LkzqmWLnJqH4cM1iI2fIrYoQy1Ds07WnjcYDFqNVg+Px0NVVRU+n09pEBYX03Pf/dh1rnCSz0fd8Sfg8/m5cc199CYkYRRgQJK5rbaDaWYTZ6aMzSpxuVxUVlYSCoVITEwkYe06mvfsQUamfU2Afkcc8dOMiOYg7XW7kOXpGC0WZL+HGmcKOVvuwfrp3bj8xxBYYcQcF+RnWUG83s+RBKj1Gdjj2gOA0RZH4n8fQ03t7QwMKPS0lOTTyUz7X3bt2kVcXBzFxQon96WXXgKULNfv97Nz507KK8shBa5dcC3fyf+O9rz+pwp9gJZlWbvxqT8tFguiKCBIAllZmeQIEhkZGVpW2N7ePmz5figZC191RPJJbmhQeNUxMTETKnmMB6qtpx5fVS/k+fPnM3/+fGRZrgLOUx8XBMGA0uiTQLHehKMsQ44GE7HgVH2Cq6urxy2v1kOsfhexq+zgdtMPEtCHZsjq2KTe3l7mzp0b1pgc6goXqKwEoGL+InrjE0l29LO56jP+9V9XcEttOw8194wakPVGQwUFBZjNZsrKygj29CgvSLXhFaHuHWUqsmAQkUMSIFN08unY+7p4vVk5lURBRpIF5hQ6MccFGRg4SIWr9RmwGg5eSHFxC1i86BVAaYhWV1fT0NBDUVGRRhGUZZkTTjiBgoIC0tLS2LVnF++/+z4z3TPZn7KfZyqe4ZmKZ5iTMIdHT3s08nEXxYhBWs2gZVnWanFu9wBSjBmj0ThM1BIIBLQgrZ8+oq9Jx8XFHTEzf4CKTzvY/lojBpPAZb9betg+V5Ik4uLiSE9PD3N205c89LX8oSWP8bA8RsqQ4avnhazywAVBOBulRPw2KAFYEIQilAkijerrj7qAPNVTQ/r7+6msrMRqtbJ48WJiYmImvG+ByzcguDsRKzZh/Hgdxg9ux3+MQlRVA/LQsUn5+fnDTrLpjz5KqLsH97/+Rd9DD2FITSX7H2/S7JcQ9jcS4/US3L6d1CuuwiIIVHt8kXaHUChEQ0MDbW1tzJkzh8LCQmRZJhgMYjKZ8Hm9GAH/yWdjsb5HbNYAA1025JCE0RZk1opplJx/MaHAeUzr+CMdtdVIwSBJqS4SZg4QkuHRbgvTTHB+ko/zk3zUJ10AgL+2lp7f/hb/foUW5ykpIePXt5KenT3MwvGEE07Qfnc4FFtLn0H5Tq6AIjPu9fWO62+hBlkteMqghuXp06eHUf+GilqSkpK094dCoTCHMpfLpZVG9Nn0oZyeocLV62P7a8q1LYVAkuTDljmO1NSLtuShZr3RsDwiBWSfz3dIhylMFLrjfz5QAbwtCEKsLMtu4McoY53uFQRBlGVZOuoC8liINiA7nU6qqpRZbkVFRSMKScZyVwNAlhBqP0TOPR45pgjJ2Y6MAMGDJQpRFHE4HGzbtm1E/2VZkvBs3Yq1pATzvGSCXZ0gCMihEH2PPU7qvv0Yv/sTWjKyuOmKH7GzvDmiibssy7S1tWkNu+OOO05TgKnNsAULFtAMhADLa69jnhlg3vl9YdtJSjoXh72fpK6dXPCzW8Acg2Hv3xnYegufywn4MVHnt9Ab9HNuIsQYDPzI5cf86InYempIXAA1LVkE+kVsu3ZheOophNtGHqxQU1PDrm27EBD4yQU/YV3uupGPeZRYW9/BMx39eAAjAr/2Grmvop0PlswGImTSQ5SHsixrQRcOMlRUWpkq0x8acNTtTVVGJ8sy/35W8c0WxIOPTXT7QzNtg3H0MsN4WBYjjYZSzewj2W+qxy02NjbixOmvqtObDgYGbTYHgzEoar0u/Yu+lgF5tBqyx+Ohuroaj8fDvHnzxqTRqAF+1LtzfyPmly4DQDZaEYKKICBYdJ5WDlH5w8uXLx9xW8HWVtp/otCkBIsFeZBlEfuNbzDw74+wVVXzC0lkzTWr2ZZ4kAOtN3Hv6emhqqqKxMRErWGnDzCCIBzkbOdkIyYlIZdMx1/0HoQg6+MlmK88ncamu3E4HsPsnsX0968GQBKMiHIQUQQDJmxCgLtmysiycgNMTFyO+dOXEftqQFSyuIxH7sffKtH10xvxfr57xENYW1vLq6++CsCpp55Kbk6Ossr48gVEVzuyOZ7g4isInPwrMIydjaqN2br2PjyC0pUPAkFJpi94MCMelkkPvlf9Gal5CGC1WrHZbJqoRU8rczgceL1edu7ciclkGlaXnkgQrdreRXeTm6QsG/ZOpWktT1A2HSnTHivUTpb2NpKZfaSSx8DAAJIkabP7YmJivrJeyLpj8jpwqSAIZhRD+hwgAagafF6GozAgj3Uyj3SCqk5l/f39w2q2oyEq3m98FqG8MxDrP0IIepFtKYQWX0Hv4h9TuWsXFouF+fPn09DQMGpgN6SnYzvpRDzbtiP7fIhJScRfeCEp/+8nSC4Xoe4eFr/3T167+Yd0zc3n5Vtu570+FycnxeJyuaioqMBgMLBo0SJsNhuSJGmrBX0gVpH1qFKTdTg+hwP/RHTLiO80kHLDyTQ23YMse8hbuJxQxXLEtt2IIT9Bawqdsy4CcT5ITyPTDEBs7ElMy/pfDuRvQwr4mP/pLRiMIex33om7XFEAOlISef+mH+Pu68Vss1F08hksv/g7NDU388orSq35pJNOUmhcfjfmrfcCIIsmBL8T486HQTASGKQSjgT1ppSens5DpQvGHUhGax7qs+mhQdpkMpGamkpaWhq9vb2UlpZqA0OdTifd3d0MDAxgMBiG1aVHC6wDdj/bXlWaaiddPod/3Ltf25/xBuRImXY0OFQ85Eglj127dpGdnY3H46Grq4tnnnmGN954A0EQuP3221myZAmnn376hOwRbr75Zt58803MZjN5eXk8+eSTWqCfiPWmClmWNwmCkAJ8C8V2UwJulWV5z+DzMhxlPGRQ6ktjBchPP/2U448/Xnu9Orx09uzZTJs2bVwZyt69e5k9e/a4/vj6sUn5+fkkJCTg8/nYt2+fxiyIFvoyhmixMPuTA0iCgBgKIQ1eIA/ESWR4XOTn52sz49RgESkQa9vdsgVraSmyWWbX9pORRJ9Swxi87hITj2d+0d9G3rdBSll/fz8tLS0abS0uLo7St87HaPBT/880vL3KTWhPdjqtKQlh8ueFZ5/LjpYu7cahNlPT0lK5alYnoYJVyGkFGL58CfM7NyIn5uL94acR98fj8VA52ADNz88/LHxVfXBW/9/X10dDQwOLFy9WGz6apSQo56SaFTqdzoiNML3D2ycv1lGzqxuDSSQm0YRrcPpKQrqFmSfJFC9fHPX+Vm7rZNurDbpMW+Cy3x+LyTx6sN2zZw/z588/LHXcnTt3UlJSEnbevvfee2zatIlVq1axZ88efvCDH4zJ6Y+Ed999l9NOOw2j0cgvf/lLANatW8eBAwf4zne+w44dOyZkvcmgb9ZgUBZQeMhxgEPWBeGvXYasIhQK0dTUREtLy7DhpePBeJqEo41NmqjZ/dAyxrdXXcyrp30TyWAg3yjwnYCd0oxcMjKKNMqXWlsc7ViF2tvp+umN6pckZUaI/sshqBAtSEk+nTlzfjvm/qkik5kzZzJjxgwEQcBVVoYQDIIB3JddiqsdEje8xoIuB8f8YR0z5ubT8Nl2PnziYep270JKy9a25xss07jdAwRPuEl7XHA0gSwh24aXmEKhEHV1dfT09DBv3ryw+XiHGvqSh9frpbKyEkmSWLhwIUajcVgmXbWtm10bmzEYBS65YwnZ2cp31zfCOjs7NbmzzWbDYVeu51BAwtmtjNgCsHf4mBGMng00UqYdDQ6nUg+GX+cOh4Pc3FwuvPBCLrwwgqNTlPjG4FAIgBUrVmgrsymw3jwfOHPwV2nwnwm4BUVSDRyFAXksKE5jAbZu3cq0adOiMmwfDdGULKIZmzRRyfPQMsb/e/9NrnJ341j1LWZMzyUnJz+sYgxXRQAAIABJREFUYafPxEaDmJyM5dgl+Pbth0AAqzONfOd3SCi9CiGKC09lpyQmJlJSUhLGMjD5DrI+8s46C18gjbbX38AYkgjGxFFeXk7dns+RZQnRYOC73/0uhn+8hf+ttwg2N4MsM32Qmwwg1vwT8yd/BlBqyIPQC0xmzJhBaWnpEeENq9Nh2tr/DjyPKFqw2T5FFE1hr3H2eNm1sXnwd5lgIAiCrH2XmJgYYmNjNVGLugJJTXbQ163Q8QK+ILWbBQQBll+Zik92Rd3c2/2OYiVpMIl8+LRKq5R5+/4yzvxBAbaEkWvzh9OgPtKq/lDUkJ944gkuvfRSYEqsN+8C/grUoMRdC0qGHKZSO+oC8ohTjnWTMwAWL148ZRacI2W24xmbNNFuuGixMO2BBwClNlpeXk4oFOK4447DaDSO2LAbc7s2G1mPPz7u/VGFLJIkccwxx0Q0/zcXFKCmce0/+AHeTiXAW5ctIzMvj/ovPmfbZ9sAOPbcb+PxeJBffx1DaysAsslER0M9SRnpxHfswPrqlQD4T70dKVehyDmdTioqKoiJiaG4uHjCS+ndu3fz3nvvYTQauf7668d981a9sFNSJOB5Zf/l0GCj8+A+CYLAv5/T124FTCYzJrNh1OahxWIhc1oGWdOV5mEoGKLh/d3IMoREHy67i507d4axFUYStYSCg9sekmn3t3vwe4OjBuRDMaB1PJ9jt9uZM2dOhHcMRzTWm2vWrMFoNHLFFVdonzsU4/y++2RZvnesFx11ATkS1FFNcXFxLF26lIqKiik7eSKVGiYyNmkyUGXVBoOB+fPnU1tbiyiKozbsphpqLb6np4e5c+eO6HRn/OhOTLufAKsEkkDuyZ0EPSKtA5eR+utbafxyD2/fuxaA4y+7ivkrlADrW3sncihE5403gtNFMBCgZ+cGMnYqdb6Wwv9hYPo52Hp7aW9vZ2BgQKvPTxR2u5333nsPIKzuHg18Pp+mely4cCE1Nf8z+Ezk1UXltk66G90kT7PRPzj0VRW06AOn/gZx7bXXDjOCArj0d0sQBAG73Y7RZCA/P39EUYtal46Pj2f5t3MoXqXUXUMBmTf+/CUgcOEvFxCf+tWQJI9UGplK682nnnqKf/zjH7z//vvadTMZ683BMtsMQRAeAT4AelHMhHplWa7Wv/aoDsh2u52qqiqMRmPYqKaptOAcalI/0bFJE4HP56Oqqgq3201BQQGJiYla176srIyEhAQSExMnPKIqGqgzAhsbG8nOzh6zLCD4HQh+RcyBqGSDprRYMm74M80HvuSt//sjAMu+fRmLz1qlvc+yQLHhFAwGZEFgV3uQ9Q1GGuRnkBF4p+oO5lT/HYd1Br2L1mAymWhubiY+Pp6EhIRxK+hkWWbjxo3KZ47jZqZaora1tZGXl0d6ejodHa/gcn9JjG0eA57aYe9x9/v59OU6AE6+Yh4b/29vxG0PvUEYDAYt8x/aPAwGg3R1dWEwGAgEAgiCMKKoxeVyDRO1xNhiEURllktM4ldHcDGSSs/hcExJyeKdd95h3bp1fPTRR2EisMlYbw7Gh89Q/I8vRylXJKI4wH1DEARBbewddQFZEATcbjdVVVUEg0HmzZs3jDA+1RacPp9P84GAiY1NGg/UbLSzs1Oz+1QbdqIosnz5clwuFw6Hg9raWo1KpQanhISEKfFh6O3t1eTkQ+vEIyFw5p0Ezrxz2ON+j4dNfzko9Njz9kb2vL2RlBnZXPir3w97/Uc1dmoNyrBTC37cAQmj6CbR6OfEE0/Ugo3D4aClpQWn06mJOBISErSscKSVy969e2lrayMtLY0eVUI+BtSbcVpaGqWlpRgMBnz+DmrrFPe6ufPWsnfvJcPet/sdJfMymEQ+WF+pPf7Wffv4xo+KiEkwj3mD0DcP1f1IT0/XmoL6ckcwGNS2oZYxpk2bFiZqcblcLL8mEafTyWef78JqtYaVPCwWi7aNw+kjEUkUAlMnDLnuuuvw+XyceabSf1uxYgUPP/zwpKw3Y2JikGX5/w19XBCU4tRRzbIIBAIcOHCAOXPmjLhsnoifxUiQJIn29na6u7vJz8+ftB/raLU4WZZpaWmhoaGBmTNnsmLFiogNO7PZTEpKShibQF2yOhwO6urqcLvdYUF6NBP3oRgYGNBUjAsWLJiUnPzgd5MG/TEU+AcUi1KP3R7x9T9eOZvv5uZx/cv7GQiYsf/XBgbyDnoGGwwGbXafCkmSNDpZe3u7VutWl+3qcfB6vWzevBmAc889l/Xr14+67+pKJRAIsHDhwrDj0dSk1PdF0Upl5Y3qt+XLfd9lftEjmM1pBAMHa7eObu9g7Vamr81DwBuChOhuEH6/n6qqKvx+v8Y1H4pI47SGPma1WomJiSEjIyNM1KIeu9bWVnw+nyZqUb/v4agjq7L+oZgqL+Tq6uoRn7v11lu59dbROe56lJWV8c4773D11VeTkpLyGEpDzwH0AC6U+Xpf6t9z1AVks9k8pv/rVJQs1LFJ7e3t2Gw2iouLJ30yGgwGbSk6FN3d3YPNoRSWLVs27oadyWQaFqSDwWDY4Fe3240oilpgSkhICAvS6nfu6+vTRmJNFSwxscTdvIo36t6g2dWMJEv8/ay/Mzth9vAXyzI2vx1nVw1GgwEhGIpo9iTLMp+/3Uz1zi4G7H5MVgP5KzIoWZXNjBkzALSM0OFwaNMz9u9X6F4Gg0FTBwI8++yzXHLJJdrqR1X7tba2auWJoZAk3+BPL15vIyAiy+DxVBEKOYE0Trw0j5JzFB/hUFDitXVfAAIX/2ox8alWnE7nqDcIVQof3Po3SmseQTZa8SwMn2+oIpKoJZLyUH+Th4Pnz9AJ4k6nE7vdrrnwqaIWNZuOjY2dUjrcaBnyVATkqYR6rQ5eowEgF8WMPh6YCXwK/Fi13oSjMCBDdAZDPl9kw52xMHRs0uLFi6mvr5+SzEA1UdefcCpbwGQysXjx4qgUdtFCdTQbGqTVTFofpEVRZGBggBkzZlBcXDypi2zHjh3s3buXvj7FG+Oaa64hNTWVN+vfpMGp8GDNohl3wK29p++BB3C+8CKyx4MsihjuuIPs9HTElTeP+DlBv8TefyrUJNEgEPCG2P9hG6IoaAFQFEUtgKhobW2lo6ODUCikmRnpHf9mzJiBLMvU1taSnp6ulSciIS/vd+TmKHxpSfKx54vzEQSBJYv/gdWqlBOMZpE4s3JDkUIS4qDUXa3dbtmyRXmd0cjrr7+ubfvZZ5/lnHPOoampiWTByYKaR5QnpKCieY4SE1UeiqJIUlISVqsVt9vNwoULNVGLy+WipaUFt9uNLMsjilrGi5FqyF6v9ytlTA+QmZlJZmYmO3fuBHheluWPI71ODcZwlAbksaAYrrvG9R69F3JmZqbGnBgYGJj0yCQV+gahakc5MDBAQUEBCQkJUxaIx9oHvVS1t7eXyspKbDYbqampuN1udu7cqRnEqDXp8fjcfvnll/T2Ku5sBoNBuzneVnobISnEzZ/cjDPgDHuPr68PebCMIYRCEAoRGqGcoX0Xk8iSs2cye1EKSVkxVO3s5JMXaqnf26MF5Ej45je/ySmnnAIoAeDxxx9HEAS++93vIkkS1dXV2pLdbrdTV1enHYuhDmUG0YrBogxNkKTAIPdYxmyOPNFFNIh8b114s0j9mweDQfr6+rSEo7u7mwMHDnDskiVkvHk5ALIwddnoSD4e+uZhKBSis7MzrHmoHgv1/UPd3fSiFn2QjsbSNlJAVpOvr5r1pppcVVRUgCKZjhiQ9fhaBuTxlixU74OEhARKSkrCOK1TPTTV7/fT0tJCZ2entgwej8JuqqA2RkVR1DJzryvACw98hnL+SyhNYiUoFl4MBuNBFy+V2RApSH/rW99CkiQ2bNgQtlI5JuUYAESdiYLqC+04+2zyr79+eOPm7i0jfgdBFDj2Gwfls65eP7IMltjRm48mk0mrU+pXLF6vl/b29rDyhM/n01YUbW1teDwezGZz2HFQDYNE0cTyZTtH/exIOPvss1m5ciVw8AYBcM4551BUVIRx73MY2nYjpRUhdFciSTLP37aLS9ecOKZL23ihD9Jut5uysjLi4uLIz8/X6s1DM2m9qEXdhizLeDwereTR3NyM3+8PO3ZxcXHYbLaw8z0YDI6YCX/VArK6P4MEgrMEQfCgTJy2o4xvqpZluV//nqMyIE+VJ7JaLjAajSxatChi82qqArJ6gn7xxRfk5uaO2LA71FADoN1uH+Z2JwgClhgjwUEBgRSUkSSZmHgTK45bqqjNBoNTY2MjLpcLURTJ69hEZvM7mFzNgMz0az5ATh3u8zwUXV1dOCud5ObmDvOFvvf9Gp7b2cKAP4RBFPjvZ/aQHmdh8+oVEbfVdKCPPZsVFVzJt7IjviYSDAYD11xzjeY9MrQ8YbUqg1j19WO1tqrWpVWWi742Px6Wi3qD8Pl81NbWajfl/Px8RFc7ls1K2abv5P8j+dVVgIAUkpFCMoZDcIVLkkR9fT1dXV0UFhZGZDdE0zw0m82aob0apPVmSx0dHXg8njBRi8fjGUbjHM2w/khCjUELFy4EJRCXDv4zAnOAO4DnVS9kOEoD8lgwmUxjWnCGjU0ahU6jnkiTQVdXF9XV1ciyTGFhIenp6RNW2E0Uqqqwubk5YgAEsMQa+c7vS7Tf37znS7qb3BSeqAyVNRgMJCUlhfFBQ6EQ1id+hmmwNhwSTBz4fDvyjKB2gQ49fuoF7PF4OKn0pIgXm9MXxO1Xjk9QkglKMv2eyH/T5vI+/vmYMiaq9Pwcps2Ljh6lshZ8Pt+IqsNIMJvNpKamhrF8VJaL0+nUWC56X+DRVhSyLNPc3ExLSwt5eXn8/Oc/154zblGogrLRhmnD/2iPX5TySwT3a2CZFtU+Rwu73U55eTkZGRmjcs7Hah4O9ZVWfxoMBpKTk0lJSQmb1KIyPPr7+7Hb7dTX1xMXF0dfXx+dnZ1Torq97bbbeOONNxBFkYyMDNavX8/06dORZZkbbriBTZs2ERMTw/r161m6dPRpLKqvuMpJB+5Tnd0E5cIycnB8k0YvOioD8ljBa6SsNhAIUFNTo7EIorXgnCgcDgeVlZWYzWaWLFlCU1PTYakTD4WqZExNTaW0tDSqbMPZ46WnxY0gwrxlI4+oNxgMhM65j5AUxLLhakSfg8KiQoI17zDb9zhJch/ic//HjpL7IL2AgYEBjSebk5Mz4r7c9q0CbvtWwZj72VrZz3uPKMF46beyWbBybHWVGgCbm5uZM2cOGRkZk/47jMRyUbnSTU1NWl9Dz5UGqKqqIikpKWLzUBgcciAEPSTQgiSICLJMmrEBp98JTE1ADgaD1NTU4HK5wkRW48FEmoeyLGvMn8REhRc9a9YsrZHY2NjIyy+/zIEDB1i6dCn5+fncfvvtFBYWjnv/br75Zn7/e4Xzft999/G73/2Ohx9+mLfffpuqqiqqqqrYvn071157Ldu3bx91W4Ig8NZbb/Hiiy+q4rD/FQThTeAlWZb9KKyLYTgqA/JYGGrkM3RsUkFBwSENhF6vl6qqKrxerybvlSSJhIQEampqwppE422YjQdut5vKykqtJDOeLnXF1k5kCbLmJhCTMLqSS5p2LKA0nATAIBqwVW9ElJXGniSaSYu38Nzu9cjt6XwDxXFrU+0mNrEJgB98/79JSEoZF7sj4A3xz8cPCi32/auVff9qJSnLxqrrF0R8j91up6KiguTkZJYtW3ZIHcyMRmPEFYXL5aK/v5+ysjI8Hg9WqxWfz0dzc7MWqNUblf/su7Ef+wv+ce8+DIKfKzKuR5Lh2e6HOTc5b0xj+WigUi5zcnIirpwmi7Gah+pPv9+v3bRU/viZZ55Jeno669ev57HHHqOysnLMyfMjQS+zd7vd2vd84403+N73vocgCKxYsYL+/n7a2tqYNm30m93f/vY3TjrpJFatWsWjjz76InA98AXwpZ7qpsdRGZDHOmH0CqPW1lbq6+tHHJsULaIhxas83q6uLi0D1zfs0tLSSE9Pj0g9G6q0i42NnfCFoa4EnE4n8+bNG7fkVJJkKrd1IgjQlmFg1YPbaezzIMsyb/x4GXnpkbOnXe3J7OvKpe/X60DOIH7OMVxheRur5GPr1q0QBxa8+DEjYUCxjZUxEaS5pY2BakV2rGaQY0miJVlGlg6WQ/xe5fz3uIavjlRrVK/XO67yxFRDZZ20traSnZ2tUezcbjcOh4OOjg6qq6sJhULExMSQkJBA9YduXFIaJpNESDICMi4pNUzpNxH4/X4qKiqQJImlS5dOaLDvRKEP0nrXvjlz5mCz2cLq0ps2baKlpQWDwUBRUdEYWx4dt956K08//TSJiYl88MEHAJpFrwrV6W2sgOzxeLj5ZqW+L8vyBkEQvg90D/4ekZp1VAbksaBacG7bto3k5GRKS0snZaytSk5HDAw617fs7OwxG3ZDqWcQrrTTy6HVwKTv5o8EbWxRXZ225FJPOhU///nPx8zGWyvs+D1BjBYDL3f2UdejmOFYjCIu38gNzgM9SfT5LYCMLAi0S6nKbEGUzPCnZ/0On8/Pg6+o06NlQCCAiZ7eXk455RStceh0OjXT+0iSaIPBgMVm5Ko/LR/1u6jqx6ampikrT0wUHo9HayLrHepUKtnQGXQqlczjVpR7gYDI3zpeGLRCJ0zpNx6oQpOGhgby8vLIyBi5JHWo4fP5KC8vx2AwDJPnd3Z28rOf/QxRFPnLX/4S1fbGcnpbs2YNa9as4c477+SBBx7gjjvumLDT244dOzj99NPJz8/n4Ycf/i1wPMoYp72AF9g+NDB/7QKy3W4P65hPhexXdXwbNpR0kCtaVVWlub6parzxNuwi1SADgQAOhwOHw0FnZycDAwOYTKawIK3ShlRRQ3p6OsXFxezdu5dgMEjQJyELEiAjSmbW/3w7AgJX/Wk5oiHyfh34uA1Zhnml6fx+WQohSea6F7/E6R2dbfKNWW3IPicb2krw+wNccP4FxGx5HcHr59JLL0WaHotBjmGpMJcsn5+3zfXIyAiCSGVlJaeccgqiKI4oiXY4HLS2tuJyubSx9OpxUIO0Huq5oM4XPFKdetUvub29nfz8/KgUkIIgaIKWb/4gE687ALKM2+Xh7b9UgAD55xo5ULMHa4s1jOGh96EYCo/HQ1lZGTabLWp/kkMBlfdfX1/PvHnzSEtLC3vu1Vdf5U9/+hN33HEHF154YdQ30bGc3lRcfvnlrFq1ijvuuGPCTm9PPfUUlZWVqtQ9F3gDOA+4EqW4X4DOnB6O0oAc6Y+jH5tUWFhIWVnZlJ1sQx3fQGnYVVRUYLFYtOXeVDfs1Blt+m6+3+/XgnR7eztut5tAIIDZbCYnJ4fU1FSsViurV6/G5w6yYe0e2mw7CRqc2DzTEQSBmHjTqPPUupvciEaBwhMyScxQ6s7iKF9Ftdy0CW5kmwFj0ElQNkXsjAuCQLGYxx7jy8jEk2jwYQ9ZR61vq00ffQ1wtCAdGxuL3W4nGAxSVFR0SI2gxkJ/fz8VFRWkpaWxbNmyiU2t0Sn9YhLNiIPc4+UnLkU0CGHDVVtaWvB6vVgsljD/DovFQlNTE+3t7RQUFBxRGbLX66WsrAyLxTLsptDR0cFNN91EbGwsH3zwQVigniyqqqqYN28eABs3btQag+eddx4PPPAAl112Gdu3bycxMXHMcgXABRdcoP1/3bp110SzD0dlQNZjpLFJKtNiKoKynrWhjunx+XwUFBQQHx9/WJkTZrOZtLQ0EhISqK2tJRAIkJ+fDyg3ifLycu2CTEhIYNnVyWzY4EIURKYlzcbuCmk0tpFwuY76Fg0Crm7MfhePBL/FC6HTOFV+F4vso7nfQ26E10tSkH2CGZCxhxT70pNPPjnqz5PcAfr/by/Iypx1ZQ1kAAz0XqYEntjYWCRJYt++fVE7wE0lAoGApsScKGshEiIp/dRpzvrSg8/n03xMmpqasNvtmM1mMjIy8Hq9uN3uCU/AnijUnk5TU9OwEWeSJPHKK69w99138/vf/57zzz9/yvftlltuoaKiAlEUyc3N5eGHHwYUIdOmTZuYO3cuMTExPPnkk1P6uXoclQFZEARtbJI6vHTo2KSpVtiphuTd3d1hJ9PhVtipXhutra3Mnj07jDGiihdkWdYuyE8//VSpwcYmYK8JIQiQkqd8n8k2cdSboSfrcgpW3sFLz+yntmuAUwafD771SwRrN4hGLC9cjByXifeHW+lkBx7MgJLpnXrqqeTmRgrdI0AUEGxG5EEBC0EZJJmgRSYkhTj++OO1oKvKetUVheoApwoRhrIaJgv9UnzWrFkUFhYekZq1xWIhJSWF/v5+ZFlm+fLlmM1mrT5fU1MzzLZ1PI6A44VaKomJiaGkpCTseLe3t3PjjTeSkJDAhx9+OKKL42ShN5LSQxAEHnzwwUPymUNxVAZkv9/P9u3bNYvKSCfQWOKQaKG6hamWn0dKYafWq2tqasjIyBiVsiUIAlarFbPZTGNjI4IgMCt5Ie0yJOdY8QZd7NvXit/vx2azhdWko2l+6m8K+kbZH84rIiTJfHDHU8p+hLwIyCAFQAogO3voXbubHUILCEr5odSYP6Z731CINiPJP1cmLQcCAXof3oexT8Zckk5BYXhg14sz9A5wkVgNsbGxYTXp8QbpgYEBysvLsVqtR7Q+Cwc9SmbMmBE2wTmSoEUt/TQ0NGjqS/2qYrzm/3qoTdXm5uZh9XNJknjppZe45557WLNmDeeee+5XTh491RDGqTI7fE7Uk4Cq8hrtJKmqqiIxMXHCHWRZljWFndFoZPr06cyYMSNs1M/hyorVEU5Wq5W8vLyop5TU1tby6quvYjKZSO8+noBH4tT/zid3gXJRqMdRrUk7nU78fr9Gt0pISGD957288FmbJmE2iJBogscvymbWrFlhf4NtrzzPvvffwe/1IiFiMhmJS07minX3A+De1IhzVxtPWz4cfIeAWTAiWAykpaVp882igcoUaC1rYPanZgQBkm5YhBg/sSCoN8hRj4UapPV0xEhBWi81LigomPJhnONBIBCgsrISv99PUVHRhCbahEIhLZN2OBxhghZ9Nj1WkPZ4PBw4cIC4uDjmzp0b9vq2tjZ++tOfkpKSwj333HNYJ4UfIkQVCI7KDFmV8Y6GyZQs1O681Wpl6dKlmlXj4VbYqSWBic6P++yzz5Blmdkz8ulrkjBaDGQXHQwWgiAo43xiYhSy/UA3tgcXIxxUenIbcJsIeTxDUDIQlMBjMJCXlzd8fz0eAl6vIg5BQgr48boONpljv5WD4bQMxAc+1m5qfoLgC2IvL6dhabH22pwd2xFGyFBVD5L4+HgKQjMIyF0Yc+ImHIwhPJNWO+x6L2V1hqKeH5yQkEAoFKKmpoasrKwjNvUawidwz549m8zM0fsEoyGSRF7fRG1ra6OysjKs9KP+M5lMyLKsraAKCwuHbefvf/87999/P3/84x9ZtWrVUZ8V63FUZsigBKvRvltzczOhUGhctUnV48Lv95Ofn6817FR1VzRUq6mANla+rW1S3Nn7778fv9/PvJiT6akKMf+kLJZfOGvkN3j6sD12EgS9yu8hP0ghAtYU/l38MOkZGVr2NNoS/6S7t2D3BHnm6mNZPHNkX4mQ3U7rRd9GHnSEk/1+CIUwpKUx4523h31nVfDicrkoKCggLjaO/ru/QPaGiLskD3PBoc9MVX5wb28vTU1N+P1+LBbLsPPicJYrvF4v5eXlmEwm8vPzD9tnR1pVBAIB/H4/cXFx5ObmkpiYqJXBWltbueGGG8jMzOTuu+/+yhnOTxJf3ww5GozHpF51QOvt7Y2osEtISGDZsmXaCWi322ltbcXpVLI//ZJ2JAOZaKCWSWpra8nMzJy0tHf16tUAPH/bLo3GNipsyXhW79P2xfDEGVh6DtA/779Ycdxxw4xk1Iuxo6MjrFkWCknIHPQsGAmGxESy3z/IG2377pX4Dxwg/pL/CgvGeiFDbm6u1sj0V9uRvSEEs4hp7uTnrUULu91OS0sL8+bN0xqpak26q6trWCatnh9THSj1pkRDWQuHA/pVxbRp07QkorCwEFmW6evro6GhgbVr19Ld3U1DQwPXXnstP/zhD4+2YBw1jtoMORAIjHrB9/T0aPaBI0E/HSQ3N1dr+oyHOaFmjGodVm2KqAE6MTExKnqRWie22Wzk5eUdVhlrpH1p2PMRpdt+BAh4rv0M4sYI5sA9/6zmuZ0teAISogAGAZIsAg9+K31MKXSguZnWCy4EQWDGprcwDgY6tTwRFxdHXl5eWFBzPFdFsMaBZVkGsWdHb7k5UbhcLsrLy4mPjycvL2/Upp9eaaeeG8FgcFhNeqJB2uVyUVZWRlJSEnPmzDmknhzR7ktycjJz5swJu3G3tLSwevVq0tPTOeOMM6ioqGD//v289tprR6y8c4jwnwx5NIxWQ5ZlWasJZmRkhCnsxtuwi1RvU2fZORwOampqcLvdmsIuMTExbPKEz+fTPBbUMsmRgt/v1/Z3qWcnyBJSzvFRBWMAlz/EgDrQU1b+eSQDM2fOxOFw0NzcrDWIhq4qXBteA0nCUlKCMT0dh8Oh8USHQpV/h9oGwCBgLRk+624qEQqFqK2tpb+/X5vuMhb0SjtVZCDLslaT7u7upq6uTjNk15c7RmO6SJJEXV0dPT09FBYWjruvMJWQJImGhgYt8Rkq3HnmmWf461//yl133cVZZ531taoVj4SvbYasTsRYsmRJ2ONqPTgmJoa5c+dqCrtDzZxQFXZ2ux2Hw4HHo5j1BINBZsyYwcyZMyfUEZ8K6O0oZ82aRVZGOjEPLgKfHf8FjxOad/aUfp7qeKaxO+x2Mn95C4LHg+GWWwgsPZampib27NkTZtWolkSuvfbaw3ZxqyUI9W801Z+rD9JqNh0IBIaVO8xms6b6y8zMJCcn54hmmE6nk7KyMtLS0pg1a1bYvjROVMDjAAAgAElEQVQ3N7N69WpmzZrFXXfdNarf+FGEr3eGPNaFMZSHrI62DwQCmpz2SCjsUlNTw7Lz+Ph4nE4n+/fvH0Y5OxR1x6Ho7e2lqqoqzCtZrP0X+BxgiiU05/Qp/0yDwRDmV+H55FM6PR6wWmnMSIe2NsxmM8cdd5x2HDZv3kxXVxfHHnvsYQnGXq+XiooKBEHg2GOPPWQlJEEQiI2NJTY2dlgm7XQ66e3t1QzvBUEgKyuLuLg4gsHgpAyzJgp9hl5UVBS2opMkiaeeeopHHnmEP//5z5xxxhn/yYqH4KgNyGNBLVnoG3Zq4+NIzLCDg4b1qlpJvaBUf9ehS1p1WKTKZkhMTJwyZocqAZckiYULF4aZMJk+ewzkEMGFl4Lh0Hfs7c8/jyxJeFasYOGSJdrSV7UpbW1tpbu7WxPkVFRURO2AN17oKVtqg/dwQx+kRVGkp6dHs1FVg3RDQ8OEhT0ThSrNT09Pp6SkJCwrbmxsZPXq1eTl5bFly5YjWnr7KuOoLVnoecGRIEkSH330EWazecINu6mCOmFaHRk1npNVrypTl7V6O8rExMRxMTtCoRANDQ10dnaOGHBs9y8AvxPv1e8jp86Nel/HC5U7O3DJpRh8Pqa98HfMs2cPe91HH33E9u3bycnJ4dvf/nZYo0xfnx/qgDdeqAEnJSWF2bNnH9FGmc/n0zL0goKCiIFWL+xRj4k+SOvd3yYDSZKora2lr69vmFmTJEk8+eSTPPbYY9x9992cfvrpX9esOKov/bULyOpFXltbi8/n46STTppww24q9lENfnl5eVM2Mkpfg7Xb7cOYHZEM7vWUumnTppGdnX1Ea5DNPc1c/K+LkSOccp98+xOM4kEvigceeACfz8cFF1yguXXpoR866nA4GBgYwGw2hx0PtYkaCcFgkOrqalwuF4WFhUfUHU414GlsbGTu3Llhw1Wjfb867Vk9HmqQ1jdSow3S6oy9rKwscnJywo5hQ0MD1113HYWFhaxbt+6IHrevAP5TQx4KtekRGxtLcXExu3bt0oyI1PccjkCsN5iZPn36hG0XR4K+BqtOOtAzO1SDe6PRqC1ju7q6sNlsh7QeGg3UqSrN3c0kmBIIyEqd3x/yE5JDpFpTMQgHM9P6+np8Ph8mk4k5c+ZE3GakoaN6m9K2tjY8Ho/mgKdf3qs3KT2/+UjB7XZTXl5OXFzchP2b9erLzEyFHSPLMl6vF4fDQX9/P42Njfj9fqxWaxi7Q99UVpkldrt9mFudJEk8/vjjPPnkk9xzzz2ccsopX9eseNw4ajNkSZK0pp3qhRwKhcjPz9cadl988QXBYFBb2icmJh7yYKTKruPj45kzZ84RabyoUI+L0+nEarUSDAbDgtLhOB4qVKphbW2tNrpIfxFf/c+rKesr49oF13J10dXa4y+//DJ1dXUUFxdz+umTazDqfYP7+vpwOBwYjUaysrJITk6ekuX9RKDSxzo7OyksLDwsrAR9kFaPic/n00yp+vr6mDZtGrNnzw5LJurq6li9ejXHHHMMa9euPWKjsA4V3n33XT788EO++c1vsmjRovH8Lb7eJQvVZ0CdIq06SekHJwqCgN/v16hmdrtdYzKofOCRDGPGC7VOrMquvyrL3tzcXKZNm6YFP/UiVP/5fD5sNlvY8ZhqZofb7aaiogKr1crcuXOH3aRaXC1c/M7FCAhsXLWRNNvBurYq/7766qunRImmBr+Ojg7y8/OJiYmJeDzUrFEv/T0UUGmYkehjhxvBYJCKigocDgdJSUl4PB58Ph+dnZ18+OGHAGzZsoUHH3yQlStXHlVZcTAY5LrrrmPPnj2cdtpptLW1MX36dNasWRPtJr7eJQufz8eOHTu0KdJAREtMi8VCRkaG5vqmZzJ0dnZSXV2NJElafW0iTTLV6etIdeX16O/vp7KyUhsrP/RmY7VasVqtYcdDbQypzA5VUaYP0hNpcKnL3r6+PgoKCkbMNl6vfR1JlihOLw4LxnBQ/j0VUEtaqn2p+jceejwiLe/1dMSxxBvRIBQKUV1djdPpPKJDV1Wodp0zZ85k/vz5YYOCP//8c2pqavB6vcycOZMbbriB66+/nu9///tHdJ+nEh9//DGCILBt2zYANm/ezMaNG3E4HMTHx0/ZzeeozZDVC0cQhEk37NTBmpGaZGpQGkqv0teJVdHAkcxufD6fZoxUUFAwqQtclf2qKwun0xl201KD0kjfV20g1tTUMHPmzFEFFSE5xNkbz8YVcLH2uLWsnLFywvs9EgKBAFVVVXi9XgoLC8c9Z3GoTakq3hhqrhTtykKdfzhz5sxhpZvDDbWhOTAwQFFRUdgorVAoxCOPPMJzzz3Hvffeq011UYcIT+XKoampie9973u0t7cjiiI//OEPueGGG+jt7eXSSy/VDP9feumlQ+aDceDAAebPnw8oA0x/8YtfaCuDKPD1LlmowTMpKUkLwlN5YqtNMjUoDQwMaPVXo9FIR0eH5iNwJI3I9QM0p5LJEelz9J17p9OpmcuoN67Y2FgGBga0WYPz5s0b86Ld2r6Vm7bchM1g493z39XYFVMBvSnRZC0pI21bXWkN9arQNw71KxS/36/1OgoKCo6YMlNFT08PVVVVZGdnM3369LBjU11dzerVqykuLuYPf/jDlAwLHg1tbW20tbWxdOlSnE4nxcXFvP7666xfv56UlBRuueUW1q5dS19fH+vWrZv056kr6ZGwbds27rnnHl588cVoN/n1LlmUl5fzs5/9DLvdTmFhIcXFxZSWlrJ48eJRB2ZGC6PROGwKtFoO8Pv9mM1ment78Xq9U16PjhZqFqo6wx3KDD3SNGhVuKF6dvT39xMKhTRFojrTcLQT/8WqF5FkiXNmnTOlwVhlLMTGxh6S6R0jKexUzri+HKauVux2O3l5eVEN0DyUUFcMPp+PJUuWDGNX/PWvf+WFF17gvvvu48QTTzws+zRt2jTtuMTHx1NUVERLSwtvvPGGlqVeddVVnHLKKZMKyGp/RdUlDIUkSYiiSENDg1am2717N6FQiJKS8c2ajISjNkNWEQgE2L9/P9u2bWPnzp3s2bMHURQ59thjWbp0KaWlpeTn50+K5B8MBqmvr6enp4e5c+dqzSU1S1KzaIfDMal6dLRwu91UVlZiNBqZN2/eEc209Pzm6dOnk5mZGcaRjkQ30+/vWRvPwh1w89w3niM3fhxz9UZAKBSirq6O3t7eUevWhwtut5sDBw4AEBMTg9vt1s6R8UzfmCp0d3dTVVWleJZkZYXdLCsrK7n++utZtmwZv//976cksZkI6uvrOfnkk9m3bx85OTn09/drzyUnJ9PX1zfhbTc0NPDQQw9x+eWX88QTT3DRRRexcuXwMtl9992n1Y+ff/55/vSnP401iPfrXbIYCbIs43K5+Oyzz7QgXVlZSVpaGiUlJRQXF7Ns2bKolq/6Ja9a7xsruI5Uj1az6MTExAkryVQOr8oqOZKjggCtPGEymZg3b96IlDGfzxd209IzGaZS8qsuwb8KwhdZljV/4IKCgrC6p376hlr+AYaZ3E/l/gcCASoqKgiFQhQWFob9rYLBIA899BAvv/wy999/P8cff/yUfe544XK5WLlyJbfeeisXXXQRSUlJUxKQ1cwX4Morr+Stt97irLPOYv369RHP2+uuu47HHnuMK6+8kj//+c/R3Ni/3iWLkSAIAvHx8ZxyyimccsopwMHAumPHDrZt28ajjz6qSYeLi4spKSnh2GOPJS4uTguUvb29VFdXk5SUNK4lr35pr4o2AoEATqcTu91OZ2dnWD1aDdSj8V/1N4bs7Gzmzp17RBtBKrOku7ub/Pz8MZsskZguXq8Xu91Ob28v9fX1w5pk4yn/qBPBJUkatgQ/EnA6nZSXl5OcnExpaemw7FevqlShV1+qNqWqhae+Rj+RIK2aWUWqo5eXl3P99ddzwgkn8MknnxzRYxcIBPj2t7/NFVdcwUUXXQRAZmYmbW1tTJs2jba2tnHPyNSzrtSgrPoyX3zxxVgsloj15MzMTJ5++mkuueQSQPn7TMUq5muXIUcL1aRm+/btbN++nd27dxMIBJg9ezbNzc2sXLmSG2+88ZAteVVqlZo5+v3+YSZCRqMxTGgy1KD9SECtW0+fPn1KmSX6+qu+/BMXF6fdtIZmjXrb0InIjKcaarmkr6+PwsLCSRvsjDT8QM92GS1I+/1+KioqkGWZwsLCsFVIMBjkgQceYMOGDTz44IMsX758Uvs6WciyzFVXXUVKSgr33nuv9vjNN99Mamqq1tTr7e3lrrvuimqb+qz4/fff59577+XKK6/kvPPO48svv+SWW27hrrvuorj44CzHQCAw7BrTb2cU/KdkMdX49a9/zYYNGzj11FPp7e3lwIEDxMfHa1l0aWnpIaO3Da1Hq/VXQRCYOXMm6enph6QeHS08Hg8VFRVa3fpwKNr0S3u1/KOugFTZc3Jy8rCJxkcCKo93+vTpZGdnH7IVjL6RqporGQyGYUFaVUXm5eUNyyrLyspYvXo1K1eu5Le//e0RX1GAIjg56aSTWLhwoXaO//GPf2T58uVccsklNDY2kpOTw8svvzzuCdUPPvggW7duZdGiRezZs4djjjmGX/3qV/zmN7+hr6+PW265heeff57rrrtuMmyS/wTkqcb27dspKSnRLm5Zlunp6WHHjh1s376dHTt20NTURE5ODqWlpRQXF1NcXKxR76YC6lip1tZWZs2ahc1m08odLpcLg8EQJn2eaD06WqgGSV1dXVGVJw41VBc0u91ObGwsfr8/7JgcCkvO0aBnLBQWFh6RRphaElPFLH19fYiiSEZGBklJScTFxRETE4Msy/zlL39h48aNPPTQQ5SWlh72fT3UGFpaePfddzn77LPZvHkzZ555Jps3b+btt9/mxBNP5Nxzz+Wmm25i3759JCYm8sorr0yml/GfgHwkoFoRqqWOXbt24Xa7mT9/PiUlJZSUlLBo0aIJZZBqUyo9PZ1Zs2ZFnj0XCGjZkZ7FoG8aThVhXxUwfBWaZHCwFjrUC0N/TIa6velr9FPtm6xmoZEYC4cbeqHS3LlzSUpK0o7H3r17ufXWW/H7/WRnZ3Pdddfx/9s787iqqrWPfxegiJEDqKSQCgIKqKlpYunNFFGuZaaZY85mpqaVmlfvW3hzyKslWu9VK+16y/RV0wbHhMI0B8AJUcAJKhUHHJhBODzvH/ucfc/BCeUwqOf7+ZwP++y9z95rb9b+7bWe51nP+stf/qL7OB40CgsLiYyMpFWrVtSoUYMBAwaQn5/P2rVrMRgMLF++nNjYWIYPH07Lli311ncJsQlyReH69evExsbqIn3kyBEqV65My5YtdZH29va+paCZkgDZ2dnh4+Nz162s4tqji0tOTo5Fecq7S2uavcPe3r7Y5pK8vDyLF5cpcY65SN/riys3N5eEhAQcHBzw9fUt1wRSoF1rfHw8lSpVwtfX18IGmp+fT1hYGJs2bWLWrFkUFhYSExODh4cHQ4YMKcdSlw6//PIL48aN45lnniErK4shQ4bg5+fHyy+/zLvvvkuvXr04efIkn332GU8//TQ9e/bUf1tCx51NkCsqIkJ6ejrR0dG6qePUqVPUrVtXt0e3bt0aR0dHwsPD8fDwwMfH565tY7c7/63io00RIDdzBplnHbNmee4Vk/kmJSWlxNPcm+eoMN2Xu43sMDkRz549W+LyWAPz6BtfX98byhMXF8ebb75JcHAw06dPL3W7//Dhw9m4cSN16tQhLi4OoFSHPhd1tl29epXp06fzzjvvUK1aNZo2bUpoaChjxoxh7dq1fPjhh0RGRvLoo4+Smppq7bwzNkG+nzA9zHv37mXfvn1s2rSJixcvEhgYSPv27WndujUtWrQoNfunKT7aJEbm9ujq1avrYly3bt1yn0AT/psFzdXV9Zbmm5JyqxdX0Xhge3t7srKyiI+Pp1q1ajRq1KjcnYi5ubnEx8dTpUoVfHx8LF4k+fn5fPzxx2zdupXFixfTqlWrMinTr7/+irOzM4MHD9YFecqUKaUy9NlcjE0jZ3Nzc3nrrbfIyckhPj6eN954gyFDhpCXl4e9vT2DBw8mODiYoUOH6se50xDqu8AmyPcrixYtIiEhgffee49Lly7pA1gOHjyIiPDEE0/orejGjRuX2nDs/Px8i+no7e3tLVJxlnbqyVuVyZTspkmTJmWeBe1mgzby8vIQEdzd3XFzc7vneGBrICKcPXuWM2fO6ClnzTly5AhvvvkmISEhTJs2rcz/f8nJyTz//PO6IDdu3JjIyEg9jrhjx44kJiZa5Vz5+fmMGjUKJycnnnrqKYYNG8aoUaNISUlh6dKluLu7c+XKFZYuXcr48eNxcHAoTfObTZDvV271VjbF4u7fv5+oqCiioqJITEykZs2aFqF3RRPB3AvmeYHNu993skffayrOO2GaeispKalCOMngv+k6a9euTc2aNfVIBlPvwmQCKqvIDlPLr2rVqnh7e9+QuGj+/PmEh4ezZMkSWrRoUapluRVFBdlaI+2KcuzYMZYuXYqDgwMhISH07t2bXbt2kZKSwvr166lZsybPPvssoaGhtGjRgn/961/6S7SYccV3i02QHwZM3nyTwzA6OpqUlBQ8PT31hEotW7akWrVqxRYEUzSHm5sbDRo0uG3lNB+wkZaWpk+yWjTLW0kqeHZ2NgkJCXr3u7wHv5hSUmZlZeHn53fT2FTz0Zf3Mo/f3WBuuy46DBvg8OHDTJgwgeeff56pU6eWq5OxNAS5qICmpqYyatQo0tLSWLt2La6uroSFhfHFF18QFxfHkSNH+Oqrrzh//jx9+vThhRdesM7F3R6bID+sFBYWcuLECfbu3UtUVBQHDhwgNzeXpk2b6iIdEBBww4OZm5vL8ePHEZESpX80DfO9lT26uDM/FxYW6sn9GzduXO65OUAbiXjy5MkbZlopDuaz06Snp5Obm2sxb929mICys7OJj4/XR2qa907y8vKYN28ev/zyC0uXLqV58+Z3dezSwNomC/Pe5K5du3B1daVJkybs27eP2bNnM2HCBDp16oRSimeffRZ/f38WL158w29LqVVsjk2QbfyXvLw8Dh06pNuj4+LiqFq1Kq1ataJFixYcOHAAT09P+vXrVyrRAXcbH20a2Waazbi8nYimASegiYg1IhLMIzvMZ4A2zT5iCkm8WY/APDlRkyZNbnhZHTp0iAkTJtCzZ0+mTJlS7r0KE0UFuSRDn03k5+czfvx4oqOjeeKJJ/jzzz/ZsmUL//znP8nIyGDo0KE0btyYP/74g+DgYPbt26cPsy8DITZhE2Qbt0ZEuHr1Kp9//jkLFy6kfv36ZGVl4e7urjsMn3zySVxcXErF9ikiFlne0tLSyM/Px8nJidzcXOzs7PD39y/3qePN5x8si3wYN0tsbzAYLCI77OzsOH78ONWrV8fLy+uGVvHcuXPZuXMnS5YsoVmzZqVa3ruhf//+REZGkpqaipubGzNmzKBnz553PfS5aDzwgQMH+OCDD9iwYQOgZWuzt7dn6dKljB49mnbt2tGnTx99Ts1y8j3Ysr3ZuDVKKb3i7969m4YNG+qOvH379rFjxw7mz59PRkaGRYL/5s2bW2X4r1JKn7/Pzc1Njw74/fff9XIdO3bM6vbou8FkDnjkkUduOv9gaXCzxPaFhYVkZWXpTsTMzEycnJzIz88nJSWF69ev4+7uztGjR5k4cSK9e/cmMjKywrSKTaxateqm6yMiIor1e5OY2tvbk5eXh1KKypUrc/LkSQs7/ooVKwgICODcuXMEBwdz4MABvc4opayWma00sLWQbdyW/Px84uLidHt0bGws9vb2Fgn+fXx8SlTBMzMzSUhI0O2g5sJ3J3t09erVreYcM2Ga9urChQsVxnadmZlJfHw8Li4ueHp6IiJ6VMdnn33Ghg0buHbtGn/961/p1q0bL7zwQoUod2mwfft2xo8fT2BgIF5eXkyZMoUGDRoQHh6u9wiGDx/OrFmzqFu3bnm2is2xmSzulsmTJ/Pjjz9SuXJlGjVqxJdffqlX6jlz5rBs2TLs7e1ZtGgRXbt2LefSlg8mITBP8G/Kr2EeelecBP/ms3c0adLEIv/v7TDZo00inZOTY7Vhz+np6SQkJODq6oqnp2e5265Njs3U1FT8/PxuSNkZExPDW2+9Rd++fRk5ciSxsbHExMTQt2/fUs9FsXXrViZMmIDBYGDkyJFMnTrV6ucwiWlhYSEFBQXMnDmT7OxsXnrpJVxdXXnllVeYOnUqTk5OvP/++0ybNo3IyEgSExN180cZ24pvhU2Q75affvqJTp064eDgwLvvvgvA3LlzOXbsGP379ycqKopz584RFBTE8ePHK2y3p6wx2VlNCf6jo6O5dOkSPj4+esa7Vq1a8cgjj+gibUq8Y410lLeyRz/yyCN6K/pO0yAZDAZOnTpFeno6TZo0KXfbNWiJ7OPj46lVqxYNGza0EJTc3Fxmz57Nvn37+Oyzz/Dz8yvTshkMBnx9fdm+fTseHh60adOGVatW6bMyW4OiIlpYWEijRo0ICAhg48aNgJZA3/Q8btu2jdjYWLKyspg7d25FaBWbYxPkkrBhwwbWrVvHypUrmTNnDgB/+9vfAOjatSuhoaG0a9euPItYoTEYDCQkJOi5Og4cOIDBYMDb25vk5GQ6duzIpEmTSm2k3d3ER5virt3d3fHw8Cj3B7mwsFDvOfj5+d3wcoiKiuKdd96hf//+TJw4sUwnzjWxZ88eQkND2bZtG8ANz0hJMDcxZGRk8MEHH9C2bVt69+7N3r17GTRoELt379bzOA8aNIhJkybdMNilgtmKbU69krB8+XL69u0LwNmzZwkMDNS3eXh4cPbs2fIq2n2Bvb09AQEBBAQEMHz4cEBLBB4WFkanTp34/fffCQoKolq1ahamjuLMS1gcTNMbOTs7U69ePcByho3k5GQyMzP1fMkNGjSwdjKZeyI9PZ34+Hjc3Nx48sknLe5FTk4OM2fO5MCBA6xcuZImTZqUWznPnj1rYRLx8PBg3759JT6ueav4/PnzvPnmmzg7O/Of//yH5ORk3nnnHZ5//nleffVVZsyYQWpqKgcPHrxhMIyIVCQxLjYPnSAHBQVx/vz5G9bPmjWLF198UV92cHBg4MCBgPbPLUp5t6LuR5o3b87hw4d1j7iIkJqaqif4//rrrzlz5gwNGjSwCL2rXr26Ve63vb09NWrU0GOeMzMz8fb2xtHRkfT0dBITE61qj74bDAYDp0+f5tq1azRt2vSGnsPevXuZNGkSgwYNYv78+eUuNqX1TNjZ2ZGWlkZoaCgNGzakVatWTJ06lZ9++olNmzaxfv16FixYQGBgIDNnzqRZs2YsXLiQBg0sZyS/X5/Ph06Qw8PDb7t9xYoVbNy4kYiICP2f6uHhwZ9//qnvc+bMGb3VdS+sXbuW0NBQ4uPjiYqKonXr1vq2B9l52KFDB4vvSilq165N9+7d6d69O6C1kE6dOsW+ffvYvn07c+bMITs72yLBf7Nmze55YEZOTg4JCQk4OjpaTE5rah2b26OvXr1KcnIyBQUFN+SPtqYgXrt2jYSEBOrVq0fr1q0txCQ7O5sPPviAQ4cOsXr1anx9fa123pJg7WfCxIEDB5gzZw4eHh7s37+fHTt2MHXqVIKDgzlx4gQ7d+6kWbNmLFq0iMGDB7Ns2TLc3NwqgtPOKthsyGZs3bqVt99+mx07dlgMADh69CgDBgzQnXqdO3fmxIkT9/xQxsfHY2dnx+jRo5k/f74uyDbn4c25fv06hw8f1vN1xMXF4ejoaJHgv1GjRnfMuWGa+upmWdBuh8kebT7sGbBIHmQ+I3lxMTkSMzIybpoTY/fu3UyePJkhQ4Ywfvz4ClUPCgoK8PX1JSIiAnd3d9q0acM333xDQEBAsY9RVET379/P7NmzsbOzY+3atQA0adKE4cOHM2XKFJKTk5k3bx7+/v6MHTuWAQMGkJ2dzXfffWf16ysFbE69u8Xb25u8vDx96HBgYCBLliwBNDPG8uXLcXBwICwsjJCQkBKfr2PHjhaCbHMeFg8RIS0tzSLBvyliwxQb3bp1a2rVqoVSirNnz3Lu3Dlq1qyJp6enVYTN3B6dlpZGVlYWDg4OFqaO28VHX716lcTExJs6ErOysvjHP/5BXFwcn332GT4+PiUub2mwefNmJk6ciMFgYPjw4UyfPr3YvzUX4/379+upVD///HO2bdvG5MmTadu2LTExMXTt2pWYmBg8PT1JTEzE19cXpRQFBQXs3LmT5557rrQu0ZrYBLmiU1SQx40bR2BgIIMGDQJgxIgRhISE8PLLL5dnMe8LTLkdTAIdHR3N5cuXcXR0JC8vT7/PpTnpq3l8dFpamp48yDxfh52dHSdOnCA7Oxs/Pz+LUY8iwm+//ca7777L8OHDeeONNypUq9japKenM3bsWBITE6lTpw5t2rRh4sSJzJw5Ezc3N4YPH46LiwsjRowgIyODNWvW6L8tKCgol+iSEmCLsihPiuM8LIrNeXjvKKVo0KABDRo04JVXXiE5OZnevXvz3HPP4e3tzbfffsvf//53lFI3JPi3luhVqlQJV1dXvYdlnjzoypUruhA7Ozvz2GOPkZeXR+XKlfUZR0JDQ0lISGDdunU0atTIKmWqSBQNQ/vqq69QSuk9nKCgILp06UKvXr345ptv2LRpE6+++irLli274Vj3mRgXmwfzqioAd3Ie3ozScpQ8jLi7u7N+/XoL77vJFhwTE0NUVBRz584lMTERFxcXi9C7u02reSuUUjg5OVGpUiWuXLlClSpVaNGiBQaDgbS0NFJSUpg7dy5btmwhOzubjh07smDBAho2bFjic1dE7O3tSU1NZcuWLbz66qukp6fz5JNPAuDl5cX06dOZMGEC0dHRREREcOHCBbKzs6lSpQp2dnb3Y6v4rrn/3QmEPDEAABVoSURBVJJWYNmyZfz0008WibLLgx49erB69Wry8vJISkrixIkTPPXUUyU+7tatW2ncuDHe3t58+OGHVihpxadSpUo3DYVydnamY8eOTJkyhbVr13L48GFWr17NX/7yF44cOcKYMWMIDAykf//+zJ8/nx07dpCenn7T3ktxSE1NJTo6murVq9OiRQucnJxwdnbG3d2dxx9/nLy8POrXr8+CBQto06YNc+fOZfv27da4Bbdl7dq1BAQEYGdnR0xMjMW2OXPm4O3tTePGjfWBH9ZgxYoVBAcHEx8fD2jJ6SMiIigsLARg4MCBeHl5AfD6668zadIkqlatqtuaH3QxBrRWw118HkiUUhIcHCwtW7aUgQMHSl5enoiIFBYW6vuYL5eU9evXi7u7u1SuXFnq1KkjwcHB+raZM2eKl5eX+Pr6yubNm0t8roKCAvHy8pJTp05JXl6eNG/eXI4ePVri4z7IGAwGiY+Ply+//FLGjBkjbdu2lZYtW8qrr74qYWFh8ttvv8m1a9ckKyvrlp9r165JVFSU/Pbbb3L58mWLbZmZmbJ582Zp3ry5LF68WAwGQ5lf47FjxyQhIUGeffZZiY6O1tcfPXpUmjdvLrm5uXL69Gnx8vKSgoKCEp8vPT1d+vTpI4cPH7ZYHxISIhMnTpQffvhBBg4cKAMHDrS4H+Vxb0qJYmnsQy/IR44cEU9PTxERycrKkueff14iIyMt9jFVinXr1kl+fn6Zl7Ek7N6920LwZ8+eLbNnzy7HEt2f5OTkyJ49e2TBggUyYMAAeeKJJ+Tpp5+WcePGyb///W+Ji4uTjIwMycrKkr1790p4eLicPHlSMjMzLcT4/PnzMmrUKOnSpYskJSWV92XdIMhF60dwcLDs3r272Mf7448/JDc394b1p06dkscff1yuXbsmIiKZmZkiIvL777/L0qVLpW/fvhIaGnqvl3E/UCyNfQj6ALcnIiJCj528fPky/v7+HDlyhJYtW5KamoqXlxd2dnZkZ2eTlJSkd5sMBgNKKf1TUSmtIa4PG1WqVCEwMFAfQi8iXLlyhejoaPbu3cuaNWs4deoUIkKdOnWYNGmSxeAVESEyMpJp06YxduxYlixZUiEHMpQ0TcC4ceNwdXVl+fLl+joRoWHDhvTs2ZPVq1czevRoHnnkEU6ePMn169d57bXXGDx4sD5lWAXLQVGmPPSCHBMTQ35+Pnl5efzwww8kJSXx0ksv8eGHH7Jhwwa8vb157rnnCA4OtoiOuF2FkYqRfxWwRW6UFkopXF1d6datG926dePo0aMMHDiQkSNH4uLiwi+//MK8efPIyMjA19eXixcv4uTkxI8//kj9+vXLpIxlGeljEtGlS5fSsWNHwsPDCQoKskif2blzZxYvXkxeXh61atVi7ty5jB07Fn9/f/3lJfdpDgpr8dAL8p49e/D39yckJIRq1aoxadIkAgMDmThxIn369OGdd96hevXq9OnTB3d3d8LCwpgxYwZpaWn4+/vj4+ND+/btLSqRqQKbKuPp06f1BDZljS1yo2zw9vYmPDxcH4I9YMAAQItNjo2N5ccff+S9994r01ZxWUT6mITY3t4eg8HAY489xttvv83UqVPZuXMnTk5Ousi++OKLVK9enU2bNrFjxw6WLVumx+CbnpmHvrFQXNuGPIA25Ly8PHF1ddW/m5wXV69elYCAAElJSdG3BQYGyrZt20RExNPTU0aNGiVhYWHi4+OjrxcROXTokMTGxlqcZ9u2bbJs2TIREcnPzxeDwWBVJ+HtyM/PF09PTzl9+rTu1IuLi7Pa8YcNGya1a9eWgIAAfd3ly5clKChIvL29JSgoSK5cuWK189mwLkVtyHFxcRZOPU9Pz1s69czr8LfffiubNm3S9+3Ro4dMmDDBYr+b1fnCwsIyexbKGZtT706Eh4eLs7OziIhcv35dX79//35p06aN/r2goEA8PT3lwoULcv78efHy8pLz58+LiMjkyZNl3rx5IiLy8ccfS//+/aVDhw7i5eWlC/XRo0eL5S0urYq5adMm8fHxES8vL5k5c6ZVj71jxw7Zv3+/hSBPnjxZ5syZIyIic+bMkSlTplj1nDZKjrUifc6dOycvv/yy9OzZU0aOHCn9+vUTg8GgPyc///yziNy8bj9AERTFwSbId+LMmTOyceNGERGL6ImwsDAJCQnRv0dHR0vr1q1FRCQiIkLatWsnIlooz+zZs2X+/PmSlpYm9erVk8TERBERycjIkEuXLklqaqrUqlVLLly4ID///LN8+OGHsmTJEtm8ebNkZ2eLyI2VtbCwUK+sZ8+eLaWrtx5JSUkWguzr6yvnzp0TEe2B9fX1La+i2bAyRUX0888/l//93/8VEZGxY8eKq6urhIWFiYjI8uXLxcvLS3Jycsq8nBWQYmlsxXPzliHu7u50795dCzcxCzr39PTU80mAlkTFNDuBKREKaFEZV69epUGDBsTGxuLh4YGvry+5ubk4OztTq1YtkpOT8fb2pk6dOsTHx7NgwQIuX75MWFgY06dP1+3M69atIzo6mvz8fAs7WnBwMD/88EMZ3RHrcOHCBX3G5Lp163Lx4sVyLpGNkmIavGFnZ8elS5c4evQoACNHjuSVV16hW7du1KlTh5UrV/Lpp5+ya9cuhg0bxosvvsjVq1fLs+j3FQ+1U88khkUdCT169LD43qlTJ9q3bw/AoUOH9KlikpOTSUtLw8/Pjz179ujOD1P4DmgC7unpCWghRT169GDatGmMHDmSrl27cvnyZZydnTl27BirVq3i1KlTzJ07V8+DfP36dVq1anXT8hcWFmpvVTs7mzPERqlw/fp1KleurDsjv/rqK2bNmkWtWrXo2LEjr732GiJCQUEB7733HqDVy2+++YbWrVvz8ccfl2fx7zse6hbyrUTMYDBYfG/fvj2dOnUCtAo5btw4ABwdHalXrx5OTk507dqVatWqsXr1alJTUykoKADg8OHDBAYGkp2dzbVr1/TjpKSk0KFDB5KSkqhSpQrdu3dn5cqVfPTRR3zxxRcAxMbG4ujoiIeHx03LaWdnh729vcV1FBYW6kJdXri5uZGSkgJo12nqXdi4v9i5cyeTJ08GtMT+CxYs4KuvvmL//v2sXLmSnJwcvvzyS86fP4+Liwv/8z//w4gRI+jevTtTp07VGyblWRfvNx5qQb4VReMgTd010ETQlDKxXbt2vP/++3h5efH4448TEhLCmjVr6NChA7/++isGg4Fff/2VJ598kgsXLrB9+3Z9GvdDhw5RtWpV8vPzWbBgAdOmTSM4OJhXXnlFr8C7d+/WZ4gw2ZhAS1u4cuVKevXqxbx58/Tuo6l8RVvM5r8tC3r06MGKFSsALX/BrWJe75Y///yT5557Dj8/PwICAli4cCEAV65coUuXLvj4+NClS5cHvos8efJkmjRpQvPmzXnppZcscrBYMw9F27Zt+eijj0hOTsbJyYkzZ85w/Phx8vPzadCgAS+88AJ//PEHzs7OTJo0iaSkJHx8fFi0aBH169fX65yt93YXFNfYLA+gU6+k3C4qoqCgQHJzc2XOnDmSm5sr3333nbzwwgsybtw4+frrryUwMFBWrVolu3btki5dusiZM2dEROT111+XGTNmiIjI0KFD9WgFc2fKhAkTpF+/frJhwwYZN26cLFmyRERE9u3bJ5MnT5YlS5box7sVBoPBKl7ufv36yWOPPSYODg7i7u4uX3zxhaSmpkqnTp3E29tbOnXqJJcvXy7xeUQ0B+H+/ftFRHOo+vj4yNGjRx+6qI5t27bpTugpU6bo12uNPBRF94+Pj5datWrJyZMn5ezZszJ8+HBZtGiRvv3pp5/WUw2YRyo9ZBEUxcE2dLq0KfrmN5k6TIHy9vb2TJ06ldzcXM6cOUPjxo1p164dGzduZNSoUfTr148rV66Ql5fHjh07aNq0KWvWrGHRokUAxMXFMXToUItzRUdHk5aWxvjx43n66afp2bMnoGXv2rhxI/Xr12fnzp0cO3aMhQsXEhcXx/Hjx3F3d6devXr6MOqbDVC4lyGrq1atuun6iIiIuzpOcahbt67uLHz00Ufx8/Pj7NmzfP/990RGRgIwZMgQOnbsyNy5c61+/opCcHCwvhwYGMi6desA+P777+nXrx+Ojo54enri7e1NVFTUXc04Y/r/f/LJJ3h7exMSEsKbb77J5MmTWb9+Pd27d+fTTz/F3t6eevXqkZ2drU93ZnKMi9GvYePusQmyFSkqZmJ0GmZnZ3P+/Hlq165Nr1696NWrl75PjRo1GDp0KD/++CNbtmzBx8cHHx8fcnJySEpKonnz5sB/BbmwsJCcnByaNm2KiHD9+nUcHR1ZtmwZgwYN0qNDWrduTUxMDGlpacyePZuAgAD27NnDoEGDCA4OJiYmhmbNmtG+fXvdxGFeflPZKyrJyckcPHiQtm3bPtRRHcuXL6dv375AyfNQgGb+GTlyJJmZmSxatAgRYeLEibz00kssXryYMWPGEBUVxZo1a2jcuDHffvutnjLTNtqu5NgEuRQxVUwXFxdCQ0PJyMgAtOG0ptmO7ezsGDZsGMOGDQMgMzMTR0dHUlJSePHFF6lZs6bFMWvVqkV0dDTVqlUDNMdiYWEhFy5cICgoiLy8PBwdHbl69Spubm7ExMTQsGFDFi9eTNWqVfH19SU7OxsXFxdGjx7NF198Qfv27Vm0aBG+vr40atTolhOGFhYWVohkSpmZmfTu3ZuwsDD9PjxoFCcPxaxZs3BwcGDgwIHA3eehMHWTzf/XJ06coE6dOqxfv15f9+ijjzJjxgzGjh1L165dGTFiBEopHn/8cby8vB6YGZ8rAjZBLiPs7e2pUaMGgC7GJkyZ4+zs7HB2dgagfv36N526pmHDhrz22ms888wztGjRgl69etGqVSsCAgKIi4sjKCiIY8eOYW9vT+3atfnjjz/o3LkzVatWxWAwkJSUxFtvvYWbmxtJSUkcP36c9u3b88knn9CsWTMqVarEwYMHWbt2LU888QSgtZpcXFz0h8689VzWoXf5+fn07t2bgQMH6j0NU1RH3bp1H5iojjvloVixYgUbN24kIiJCv+93k4fCJKJKKS5evIiLiwsODg5ER0dz7tw5ALKzs/WZsJ955hk6d+7M559/zqxZs/D39yc8PJxu3brpLWQbVqC4xmbbp2w/gN3ttgFtgQlAD+O6gcCvwAfA58BcoAqwEHjFuE87IMm4XBuYAwwGHgFOmx1rKPCpcTkI2AocBFYA1Yzrne5UfoyT6FrxnijgP0BYkfXzgKnG5anAP610vipAFHAYOArMMK73BPYBJ4D/AyqXcd3oBhwDahdZH2Asq6OxjKcB+5vdR7Pl0cb9/gN0AB4FEoGOZvsMAdyBSmbragOPleV1Pwyfci+A7WOFf6ImVJWBTsBM4CXj+o7GB62z8fvfgW+Ny82AT4D2wDPAb2bHewbYC1QD4oCngRrG/Qcaz7cKCAU+A6YAjxannCW8zvZoM5/HAoeMn78CrkCEUSAjABcr3ldn43IlowgHAmuAfsb1S4AxZfz/Pgn8aXYPlphtmw6cMopqSJHfTQaaGpdrAl8CHwL1gb8Bi4DmQA/jMd4AfgQizcX/ZiJv+1jnYzNZ3KcorZ+qRKRQtKfkOvCz8WMiGrgCnDN+7wD8Ylz2QWtJnQL6ANeVUnYiUogmfAfRhPiCiOw2njMG6AWsA1yAOsD3wCggFVhu3K874AbsEZF4U2GM5TSV33QulFIOIlJwp2sWkV3cejr1znf6/d1iLG+m8Wsl40fQXnwDjOtXoL2YFlv7/Lcpl/dtts0CZpmvM7vX+9Ba1qCZK7sCq0XkD6XUN8AgtLrwPpAO+AObRcTi2kTEcuSUDaths8Tfp4hGofk6pZSdUsrebJ8sEYkVkVTj967AAuPmdCBZRFLQWn25QDel1HPAs2hd8dbAfrNT+KO1znzRRH65iGwCtqO1qlBKDUIT8vpAmFJqsHF9Q6VUR6VUTWNZzMs+UinlZl72ioJSyl4pdQi4iHadp4BrZi+QM2jd+QqJUsre7F5HA/9QSn0qIpeA8UB3pVQtEfkd2IX2oh0nIpEi8i+TGFfE/82DiE2QHyCMreXbtl5EJM/4N1xEZhtXN0ezO78IfIRmmtiB1h1+VClVXSnliWbK+A5ogSbIppb3X4CdSilvYAzgh2bK+BbNbg2aHXINMF8plaCUGgiglOoG9AUyipZdaTyrlBqulPK7p5tSQkTEICItAA/gKbRru2G3si1V8THdU6XUa2gt5/9De/G2FZFvgW1AmHHfHWgv4MO3Oo6N0sUmyA8pphaPUWjdRWS9iIwWkVYissXYXd+MVkd2AF+j2Sr3oDkU/wRMY3ZbotlunwZigANoD/l4oIpSqhbQFFgpIiPQuvdtlVKPAi+jtcT3KaVGFSmmM5p9eCKwXSlVbuETInINzZYaCNRQSpnMfR7898VUIVBKjVNKdVdKeRi/r0K7/wtF5Aja/f/IaPb6B+CvlHrd+PN/G4XZRjlgsyE/pJi1eM6i2RJvsOUau7qjjds8ROSMUsoRyAbOiUi2cVdvtCiE6kBfEZlgOoZSqgqaqNcDTC3yS4CPiGQopVLQIkO2oJlNUEop4wshV0TWK6Wc0ITQdL4yQSlVG8gXkWvGMgShRa/8gvYiWY3W8v++LMt1K5RSzwLvAWlorfZLSqmFaHb3tiLyu/HefqSU6gq8LyKhSqkZQFW4wZRko4yxtZAfckTkuohEGZctHGtGk4GdcdsZ48OcJyJT0LzvKKUeA74UkXy06IerSql/KKW6KaX80RxhbmgP/BHjoWsCGcbl5kCCiBwRkRPGc5lMAKbyBAK/AznWvv47UBf4RSkVi2Z/3S4iG4F3gbeVUifRWvA3BoyXMUopF7QXxUYR6QXMAArR7u+7QHWlVFezezsOmKSUaiIi34vIzcfA2yhTbC1kG7fE+PBKke8WyyJyHhhhFOurSqlZaHHME9Fa32PRTBJ2IlKglKqOJmJnjK3nGsDpO0RaeAJby9qOKSKxaOaYoutPo9mTKwwickUpNROt5b5ARGKVUtWAQmPLeBbwvlIq0vhSPa6U6iAiCeVbchvm2FrINkqEMbJDmQl0vIi8KyLdRGSEiOQCCWiDUEBrdXoAl9E8+hcxRi2oIkP9RESMZgNnILmMLum+RUTeQ3PCTlFK/RVojBYFArAS7R5+Ybb/QdBDKG1UAGwtZBsl4iahdwrtRa+H5RkdSUeMwp1gtFnmo8UunwaOKqXeFpFl5scxirxJtFPK5orue/qhmY72AN1F5CJo/yel1DS03ooF5j0fG+XL/wO3LvgJu83KVgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x14aa1b2e0b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"ax = fig.add_subplot(111, projection='3d')\\n\",\n    \"for i in range(100):\\n\",\n    \"    ax.scatter(\\n\",\n    \"        pca_images[i, 0],\\n\",\n    \"        pca_images[i, 1],\\n\",\n    \"        pca_images[i, 2],\\n\",\n    \"        marker=r'${}$'.format(labels[i]),\\n\",\n    \"        s=64)\\n\",\n    \"\\n\",\n    \"ax.set_xlabel('Principal component 1')\\n\",\n    \"ax.set_ylabel('Principal component 2')\\n\",\n    \"ax.set_zlabel('Principal component 3')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"图 6-3 PCA 预测 MNIST 数据的子集。标记对应于图像标签。\\n\",\n    \"\\n\",\n    \"前 6 个投影图像的图片显示在[图 6-3 ](http://https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch06.html#fig-mnist-pca)的 3D 图中。标记对应于标签。前三个主成分大约占数据集总差的 40%。这绝不是完美的，但它允许方便的低维可视化。我们看到 PCA 组类似数字彼此接近。数字 0 和 6 位于同一区域，如同 1 和 17，3 和 9。该空间大致分为 0，4 和 6 一侧，其余数字是其他类的。\\n\",\n    \"\\n\",\n    \"由于数字之间有相当多的重叠，因此很难清楚的将它们在投影空间中使用线性分类器分开。因此，如果任务是分类手写数字并且选择的模型是一个线性分类器，前三个主成分不足以作为功能。尽管如此有趣的是只有 3 个可以捕获多少个 64 维数据集尺寸。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 白化和 PCA\\n\",\n    \"\\n\",\n    \"由于目标函数中的正交性约束，PCA 变换产生了很好的附带作用：转换后的特征不再相关。再换句话说，特征向量对之间的内积是零。这很容易使用奇异向量的正交性来证明这一点：结果是包含奇异值的平方的对角矩阵表示每个特征向量与其自身的相关性，也称为其 L2 范数。\\n\",\n    \"\\n\",\n    \"有时候，将特征的比例标准化为1.在信号中是有用的处理方式，这就是所谓的*白化*。 它产生了一组与自身具有单位相关性，并且彼此之间的相关性为零的结果。在数学上，*白化*可以通过将 PCA 变换乘以反奇异值。\\n\",\n    \"\\n\",\n    \"公式 6-24 PCA 白化\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"W_{\\\\text {white}}=V_{k} \\\\Sigma_{k}^{-1}\\n\",\n    \"$$\\n\",\n    \"$$\\n\",\n    \"Z_{\\\\text {white}}=X V_{k} \\\\Sigma_{k}^{-1}=U \\\\Sigma V^T V_{k} \\\\Sigma_{k}^{-1}=U_{k}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"白化与维度降低无关；可以独立执行不受其他的干扰。例如，零相分量分析（ZCA）（Bell 和 Sejnowski，1996）是一种与 PCA 密切相关的白化转化，但事实并非减少特征的数量。ZCA 白化使用全套主要特征$V$ 没有减少，并且包括一个额外的乘法回到$V^T$)。\\n\",\n    \"\\n\",\n    \"公式 6-25 ZCA 白化\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"W_{\\\\text {ZCA}}=V\\\\Sigma^{-1}V^T\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"Z_{\\\\text {zca}}=X V\\\\Sigma^{-1}V^T=U \\\\Sigma V^T V \\\\Sigma^{-1}=U\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"简单的 PCA 投影（[公式 6-19](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch06.html#pca-projection)）在新特征中产生坐标空间，主成分作为基础。这些坐标表示只有投影向量的长度，而不是方向。乘以主成分给我们的长度和方向。另一个有效解释是，多余的乘法将坐标旋转回原点原始特征空间。（$V$是正交矩阵，并且正交矩阵旋转他们的输入不拉伸或压缩）。所以 ZCA 白化产生的数据尽可能接近原始数据（欧几里德距离）。\\n\",\n    \"\\n\",\n    \"## 主成分分析的局限性\\n\",\n    \"\\n\",\n    \"当使用 PCA 进行降维时，必须解决使用多少个主成分（$k$）的问题。像所有的超参数一样，这个数字可以根据最终模型的质量进行调整。但也有启发式算法不涉及高度的计算方法。\\n\",\n    \"\\n\",\n    \"一种可能性是选择`k`来解释总方差的所需比例。（该选项在 scikit-learn 软件包的 PCA 中可用）投影到第$k$个分量上：$\\\\left\\\\|X_{V_{k}}\\\\right\\\\|^{2}=\\\\left\\\\|u_{k} \\\\sigma_{k}\\\\right\\\\|^{2}=\\\\sigma_{k}^{2}$，这是正方形 $X$的第$k$个最大奇异值。$a$的奇异值的有序列表矩阵被称为其频谱。因此，为了确定要使用多少个成分，人们可以对数据矩阵进行简单的频谱分析并选择阈值保留足够的差异。\\n\",\n    \"\\n\",\n    \"## 基于占用方差的k选择\\n\",\n    \"\\n\",\n    \"要保留足够的成分覆盖数据总方差的80%，请这样选择$k$：$$\\n\",\n    \"\\\\frac{\\\\sum_{i=1}^{k} \\\\sigma_{i}^{2}}{\\\\sum_{i=1}^{d} \\\\sigma_{i}^{2}} \\\\geq 0.8\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"另一种选择$k$的方法涉及数据集的固有维度。这个是一个更朦胧的概念，但也可以从频谱中确定。基本上，如果谱包含一些很大的奇异值和一些小奇异值，那么可能只是收获最大的奇异值并丢弃其余的值。有时候其余的频谱不是很小，但头部和尾部值之间有很大差距。这也是一个合理的截止点。 该方法需要光谱进行视觉检查，因此不能作为自动化管线的一部分执行。\\n\",\n    \"\\n\",\n    \"对 PCA 的一个关键批评是转变相当复杂，并且结果因此很难解释。主成分和投影向量是真实的价值，可能是积极的或消极的。主成分是（居中）行的基本线性组合，以及投影值为列的线性组合。例如，在股票申报中，每个因素都是股票收益时间片的线性组合。那是什么意思？学习因素很难附加人为的理解原因。因此，分析师很难相信结果。如果你不能解释你为什么正在把数十亿其他人的钱放在特定的股票上，你可能会这样做将不会选择使用该模型。\\n\",\n    \"\\n\",\n    \"PCA 在计算上是繁杂的的。它依赖于 SVD，这是一个昂贵的过程。计算一个矩阵的全 SVD 需要 $O(nd^2+d^3)$操作 [Golub 和 Van Loan，2012]，假设$n≥d$，即数据点比特征更多。即使我们只需要$k$个主成分，计算截断 SVD（$k$个最大奇异值和向量）仍然需要 $O((n+d)^2*k)= O(n^2k)$操作。这是令人望而生畏的当有大量数据点或特征时。\\n\",\n    \"\\n\",\n    \"以流媒体方式，批量更新或者从 PCA 执行 PCA是 很困难的完整数据的样本。SVD 的流式计算，更新 SVD 和从一个子样本计算 SVD 都是很难研究的问题。算法存在，但代价是精度降低。一个含义是人们应该期待将测试数据投影到主成分上时代表性较低在训练集上找到。随着数据分布的变化，人们不得不这样做重新计算当前数据集中的主成分。\\n\",\n    \"\\n\",\n    \"最后，最好不要将 PCA 应用于原始计数（字数，音乐播放次数，电影观看次数等）。这是因为这种计数通常包含在内大的异常值。（这个概率非常高，有粉丝观看了 314,582 次“指环王”，这让其余的人望而生畏计数）。正如我们所知，PCA 在特征中查找线性相关性。相关性和方差统计对大的异常值非常敏感; 单一大量的数据可能会改变很多。 所以，首先修剪是个好主意大数值的数据（“[基于频率的滤波](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch03.html#sec-freq-filter)”）或应用缩放变换如 tf-idf（[第 4 章](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch04.html#chap-tfidf)）或日志转换（“[日志转换](https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/ch02.html#sec-log-transform)”）。\\n\",\n    \"\\n\",\n    \"## 用例\\n\",\n    \"\\n\",\n    \"PCA 通过查找线性相关模式来减少特征空间维度功能之间。由于涉及 SVD，PCA 计算数千个功能的代价很高。但是对于少量的实值特征而言，它非常重要值得尝试。\\n\",\n    \"\\n\",\n    \"PCA 转换会丢弃数据中的信息。因此，下游模型可能会训练成本更低，但可能不太准确。在 MNIST 数据集上，有一些观察到使用来自 PCA 的降维数据导致不太准确分类模型。在这些情况下，使用 PCA 有好处和坏处。\\n\",\n    \"\\n\",\n    \"PCA 最酷的应用之一是时间序列的异常检测。Lakhina，Crovella 和 Diot [2004] 使用 PCA 来检测和诊断异常互联网流量。他们专注于数量异常情况，即当出现波动或波动时减少从一个网络区域到另一个网络区域的通信量。这些突然更改可能表示配置错误的网络或协调的拒绝服务攻击。无论哪种方式，知道何时何地发生这种变化对互联网都是有价值的运营商。\\n\",\n    \"\\n\",\n    \"由于互联网上的交通总量非常之多，孤立的激增规模很小地区很难被发现。一个相对较小的主干链路处理很多交通。 他们的重要见解是，数量异常会影响到多个链接同时（因为网络数据包需要跳过多个节点才能到达他们的网络目的地）。将每个链接视为一项功能，并将每个链接的流量数量对待时间步骤作为测量。数据点是流量测量的时间片跨越网络上的所有链接。这个矩阵的主成分表明了网络上的整体流量趋势。其余的成分代表了剩余信号，其中包含异常。\\n\",\n    \"\\n\",\n    \"PCA 也经常用于金融建模。在这些用例中，它作为一种类型工作因子分析，一组旨在描述观察结果的统计方法使用少量未观察因素的数据变异性。在因素分析中应用程序，目标是找到解释性成分，而不是转换数据。\\n\",\n    \"\\n\",\n    \"像股票收益这样的财务数量往往是相互关联的。股票可以同时上下移动（正相关），也可以相反移动方向（负相关）。为了平衡波动和降低风险，投资组合需要多种不相关的股票其他。（如果篮子要下沉，不要把所有的鸡蛋放在一个篮子里）寻找强大的相关模式有助于决定投资策略。\\n\",\n    \"\\n\",\n    \"股票关联模式可能在行业范围内。 例如，科技股可能会上涨并一起下跌，而当油价高企时，航空股往往下跌。 但行业可能不是解释结果的最好方式。 分析师也在寻找观察到的统计数据中意外的相关性 特别是*文体因素模型* [Connor，1995] 在个体股票时间序列矩阵上运行 PCA 返回寻找共同变化的股票。 在这个用例中，最终目标是主成分本身，而不是转换后的数据。\\n\",\n    \"\\n\",\n    \"从图像中学习时，ZCA 可作为预处理步骤。在自然的图像中，相邻像素通常具有相似的颜色。ZCA 白化可以消除这种相关性，这允许后续的建模工作集中在更有趣的图像上结构。Alex Krizhevsky 的“[学习多层特征](http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf)”的论文图像“包含很好的示例，说明 ZCA 影响自然图片。\\n\",\n    \"\\n\",\n    \"许多深度学习模型使用 PCA 或 ZCA 作为预处理步骤，但事实并非如此总是显示是必要的。在“Factored 3-Way Restricted Boltzmann Machines forModeling Natural Images”中，Ranzato et al，评论，“白化不是必要的，但加快了算法的收敛速度。在“[An Analysis of Single-Layer Networks in Unsupervised Feature Learning](http://ai.stanford.edu/~ang/papers/aistats11-AnalysisSingleLayerUnsupervisedFeatureLearning.pdf)”中，Coates 等人 发现 ZCA 白化是有帮助的对于某些号，但不是全部。（请注意，本文中的模型是无监督功能学习模型。 所以 ZCA 被用作其他功能的特征方法工程方法。方法的堆叠和链接在机器中很常见学习管道。）\\n\",\n    \"\\n\",\n    \"## 总结\\n\",\n    \"\\n\",\n    \"这结束了对 PCA 的讨论。关于 PCA 需要记住的两件事是其机制（线性投影）和目标（最大化方差预计数据）。该解决方案涉及协方差的特征分解矩阵，它与数据矩阵的 SVD 密切相关。人们还可以记住 PCA 的精神图像将数据挤压成像蓬松一样的煎饼可能。PCA 是模型驱动特征工程的一个示例。（应该立即怀疑当一个目标函数进入时，一个模型潜伏在背景中场景）。这里的建模假设是方差充分代表了包含在数据中的信息。等价地，该模型寻找线性特征之间的相关性。这在几个应用程序中用于减少相关性或在输入中找到共同因素。PCA 是一种众所周知的降维方法。但它有其局限性作为高计算成本和无法解释的结果。它作为一个预先定义好处理步骤，特别是在特征之间存在线性相关时。当被看作是一种消除线性相关的方法时，PCA 与其相关白化的概念。其表兄 ZCA 以可解释的方式使数据变白，但是不会降低维度。\\n\",\n    \"\\n\",\n    \"## 参考书目\\n\",\n    \"\\n\",\n    \"Bell, Anthony J. and Terrence J. Sejnowski. 1996. “Edges are the ‘Independent\\n\",\n    \"Components’ of Natural Scenes.” Proceedings of the Conference on Neural Information Processing Systems (NIPS) .\\n\",\n    \"\\n\",\n    \"Coates, Adam, Andrew Y. Ng, and Honglak Lee. 2011. “An Analysis of Single-Layer\\n\",\n    \"Networks in Unsupervised Feature Learning.\\\" International conference on artificial intelligence and statistics .\\n\",\n    \"\\n\",\n    \"Connor, Gregory. 1995. “The Three Types of Factor Models: A Comparison of Their Explanatory Power.\\\" Financial Analysts Journal 51, no. 3: 42-46. http://www.jstor.org/stable/4479845.\\n\",\n    \"\\n\",\n    \"Golub, Gene H., and Charles F. Van Loan. 2012. Matrix Computations . Baltimore and London: Johns Hopkins University Press; fourth edition.\\n\",\n    \"\\n\",\n    \"Krizhevsky, Alex. 2009. “Learning Multiple Layers of Features from Tiny Images.”\\n\",\n    \"MSc thesis, University of Toronto.\\n\",\n    \"\\n\",\n    \"Lakhina, Anukool, Mark Crovella, and Christophe Diot. 2004. “Diagnosing network-wide traffic anomalies.” Proceedings of the 2004 conference on Applications, technologies,architectures, and protocols for computer communications (SIGCOMM ’04). DOI=http://dx.doi.org/10.1145/1015467.1015492\\n\",\n    \"\\n\",\n    \"Ranzato, Marc’Aurelio, Alex Krizhevsky, and Geoffrey E. Hinton. 2010. “Factored 3-Way Restricted Boltzmann Machines for Modeling Natural Images.\\\" Proceedings of the 13-th International Conference on Artificial Intelligence and Statistics (AISTATS 2010) \"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "9.feature-engineering/7.非线性特征提取和模型堆叠.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[原版（英文）图书地址](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/)\\n\",\n    \"\\n\",\n    \"**翻译**：[apachecn](https://github.com/apachecn)，[翻译版本地址](https://github.com/apachecn/feature-engineering-for-ml-zh)\\n\",\n    \"\\n\",\n    \"**代码修改和整理**：[黄海广](https://github.com/fengdu78)，原文修改成jupyter notebook格式，并增加和修改了部分代码，测试全部通过，所有数据集已经放在[百度云](data/README.md)下载。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 七、非线性特征提取和模型堆叠\\n\",\n    \"\\n\",\n    \"> 译者：[@friedhelm739](https://github.com/friedhelm739)\\n\",\n    \"\\n\",\n    \"当在数据一个线性子空间像扁平饼时 PCA 是非常有用的。但是如果数据形成更复杂的形状呢？一个平面（线性子空间）可以推广到一个 *流形* （非线性子空间），它可以被认为是一个被各种拉伸和滚动的表面。\\n\",\n    \"\\n\",\n    \"如果线性子空间是平的纸张，那么卷起的纸张就是非线性流形的例子。你也可以叫它瑞士卷。（见图 7-1），一旦滚动，二维平面就会变为三维的。然而，它本质上仍是一个二维物体。换句话说，它具有低的内在维度，这是我们在“直觉”中已经接触到的一个概念。如果我们能以某种方式展开瑞士卷，我们就可以恢复到二维平面。这是非线性降维的目标，它假定流形比它所占据的全维更简单，并试图展开它。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tt0 = 3 * np.pi / 2 * (1 + 2 * np.arange(0, 1.25, 0.01))\\n\",\n    \"hh = np.arange(0, 1.125, 0.125) * 30\\n\",\n    \"xx = np.transpose(np.tile(np.multiply(tt0, np.cos(tt0)), (len(hh), 1)))\\n\",\n    \"yy = np.tile(hh, (len(tt0), 1))\\n\",\n    \"zz = np.transpose(np.tile(np.multiply(tt0, np.sin(tt0)), (len(hh), 1)))\\n\",\n    \"cc = np.transpose(\\n\",\n    \"    np.tile((tt0 - tt0.min()) / (tt0.max() - tt0.min()), (len(hh), 1)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n    \"from matplotlib import cm\\n\",\n    \"import seaborn as sns\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sns.set_style('whitegrid')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x2cff6263320>\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x2cff624dbe0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(12, 10))\\n\",\n    \"ax = fig.add_subplot(111, projection='3d')\\n\",\n    \"ax.plot_surface(xx, yy, zz, rstride=1, cstride=1, \\n\",\n    \"                linewidth=0, antialiased=False, \\n\",\n    \"                facecolors=cm.seismic_r(cc))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"关键是，即使当大流形看起来复杂，每个点周围的局部邻域通常可以很好地近似于一片平坦的表面。换句话说，他们学习使用局部结构对全局结构进行编码。非线性降维也被称为非线性嵌入，或流形学习。非线性嵌入可有效地将高维数据压缩成低维数据。它们通常用于 2-D 或 3-D 的可视化。\\n\",\n    \"\\n\",\n    \"然而，特征工程的目的并不是要使特征维数尽可能低，而是要达到任务的正确特征。在这一章中，正确的特征是代表数据空间特征的特征。\\n\",\n    \"\\n\",\n    \"聚类算法通常不是局部结构化学习的技术。但事实上也可以用他们这么做。彼此接近的点（由数据科学家使用某些度量可以定义的“接近度”）属于同一个簇。给定聚类，数据点可以由其聚类成员向量来表示。如果簇的数量小于原始的特征数，则新的表示将比原始的具有更小的维度；原始数据被压缩成较低的维度。\\n\",\n    \"\\n\",\n    \"与非线性嵌入技术相比，聚类可以产生更多的特征。但是如果最终目标是特征工程而不是可视化，那这不是问题。\\n\",\n    \"\\n\",\n    \"我们将提出一个使用 k 均值聚类算法来进行结构化学习的思想。它简单易懂，易于实践。与非线性流体降维相反，k 均值执行非线性流形特征提取更容易解释。如果正确使用它，它可以是特征工程的一个强大的工具。\\n\",\n    \"\\n\",\n    \"## k 均值聚类\\n\",\n    \"\\n\",\n    \"k 均值是一种聚类算法。聚类算法根据数据在空间中的排列方式来分组数据。它们是无监督的，因为它们不需要任何类型的标签，使用算法仅基于数据本身的几何形状来推断聚类标签。\\n\",\n    \"\\n\",\n    \"聚类算法依赖于 *度量* ，它是度量数据点之间的紧密度的测量。最流行的度量是欧几里德距离或欧几里得度量。它来自欧几里得几何学并测量两点之间的直线距离。我们对它很熟悉，因为这是我们在日常现实中看到的距离。\\n\",\n    \"\\n\",\n    \"两个向量$X$和$Y$之间的欧几里得距离是$X-Y$的 L2 范数。（见 L2 范数的“L2 标准化”），在数学语言中，它通常被写成$‖ x - y ‖$。\\n\",\n    \"\\n\",\n    \"k 均值建立一个硬聚类，意味着每个数据点被分配给一个且只分配一个集群。该算法学习定位聚类中心，使得每个数据点和它的聚类中心之间的欧几里德距离的总和最小化。对于那些喜欢阅读公式而非语言的人来说，目标函数是：\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\min _{C_{1}, \\\\ldots, C_{k}, \\\\mu_{1}, \\\\ldots, \\\\mu_{k}} \\\\sum_{i=1}^{k} \\\\sum_{x \\\\in C_{i}}| | x-\\\\mu_{i}| |_{2}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"每个簇 $C_i$ 包含数据点的子集。簇$i$的中心等于簇中所有数据点的平均值：$\\\\mu_{i}=\\\\sum_{x \\\\in C_{i}} x / n_{i}$，其中 $n_i$ 表示簇$i$中的数据点的数目。\\n\",\n    \"\\n\",\n    \"图 7-2 显示了 k 均值在两个不同的随机生成数据集上的工作。（a）中的数据是由具有相同方差但不同均值的随机高斯分布生成的。（c）中的数据是随机产生的。这些问题很容易解决，k 均值做得很好。（结果可能对簇的数目敏感，数目必须给算法）。这个例子的代码如例 7-1 所示。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"### 例 7-1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.cluster import KMeans\\n\",\n    \"from sklearn.datasets import make_blobs\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n_data = 1000\\n\",\n    \"seed = 1\\n\",\n    \"n_centers = 4\\n\",\n    \"\\n\",\n    \"# Generate random Gaussian blobs and run K-means\\n\",\n    \"blobs, blob_labels = make_blobs(\\n\",\n    \"    n_samples=n_data, n_features=2, centers=n_centers, random_state=seed)\\n\",\n    \"clusters_blob = KMeans(\\n\",\n    \"    n_clusters=n_centers, random_state=seed).fit_predict(blobs)\\n\",\n    \"\\n\",\n    \"# Generate data uniformly at random and run K-means\\n\",\n    \"uniform = np.random.rand(n_data, 2)\\n\",\n    \"clusters_uniform = KMeans(\\n\",\n    \"    n_clusters=n_centers, random_state=seed).fit_predict(uniform)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(-0.05629127394003485,\\n\",\n       \" 1.0556515256943935,\\n\",\n       \" -0.06307640900583997,\\n\",\n       \" 1.0629275741592705)\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x2cff90f1f28>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"figure = plt.figure(figsize=(20, 10))\\n\",\n    \"plt.subplot(221)\\n\",\n    \"plt.scatter(\\n\",\n    \"    blobs[:, 0], blobs[:, 1], c=blob_labels, edgecolors='k', cmap='PuRd')\\n\",\n    \"plt.title(\\\"(a) Four randomly generated blobs\\\", fontsize=14)\\n\",\n    \"plt.axis('off')\\n\",\n    \"\\n\",\n    \"plt.subplot(222)\\n\",\n    \"plt.scatter(\\n\",\n    \"    blobs[:, 0], blobs[:, 1], c=clusters_blob, edgecolors='k', cmap='PuRd')\\n\",\n    \"plt.title(\\\"(b) Clusters found via K-means\\\", fontsize=14)\\n\",\n    \"plt.axis('off')\\n\",\n    \"\\n\",\n    \"plt.subplot(223)\\n\",\n    \"plt.scatter(uniform[:, 0], uniform[:, 1], edgecolors='k')\\n\",\n    \"plt.title(\\\"(c) 1000 randomly generated pois\\\", fontsize=14)\\n\",\n    \"plt.axis('off')\\n\",\n    \"\\n\",\n    \"plt.subplot(224)\\n\",\n    \"plt.scatter(\\n\",\n    \"    uniform[:, 0],\\n\",\n    \"    uniform[:, 1],\\n\",\n    \"    c=clusters_uniform,\\n\",\n    \"    edgecolors='k',\\n\",\n    \"    cmap='PuRd')\\n\",\n    \"plt.title(\\\"(d) Clusters found via K-means\\\", fontsize=14)\\n\",\n    \"plt.axis('off')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 曲面拼接聚类\\n\",\n    \"\\n\",\n    \"应用聚类一般假定存在自然簇，即在其他空的空间中存在密集的数据区域。在这些情况下，有一个正确的聚类数的概念，人们已经发明了聚类指数用于测量数据分组的质量，以便选择k。\\n\",\n    \"\\n\",\n    \"然而，当数据像如图 7-2（c）那样均匀分布时，不再有正确的簇数。在这种情况下，聚类算法的作用是矢量量化，即将数据划分成有限数量的块。当使用量化矢量而不是原始矢量时，可以基于可接受的近似误差来选择簇的数目。\\n\",\n    \"\\n\",\n    \"从视觉上看，k 均值的这种用法可以被认为是如图 7-3 那样用补丁覆盖数据表面。如果在瑞士卷数据集上运行 k 均值，这确实是我们所得到的。例 7-2 使用`sklearn`生成瑞士卷上的嘈杂数据集，将其用 k 均值聚类，并使用 Matplotlib 可视化聚类结果。数据点根据它们的簇 ID 着色。\\n\",\n    \"\\n\",\n    \"![图7-3](images/chapter7/7-3.png)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"### 例 7-2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n    \"from sklearn import manifold, datasets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X, color = datasets.samples_generator.make_swiss_roll(n_samples=1500)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 用100 K-均值聚类估计数据集 \\n\",\n    \"clusters_swiss_roll = KMeans(n_clusters=100, random_state=seed).fit_predict(X)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x2cff995fc88>\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x2cff964a940>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 展示用数据集，其中颜色是K-均值聚类的id\\n\",\n    \"fig2 = plt.figure(figsize=(10, 8))\\n\",\n    \"ax = fig2.add_subplot(111, projection='3d')\\n\",\n    \"ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=clusters_swiss_roll, cmap='Spectral')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"图7-5\\n\",\n    \"\\n\",\n    \"在这个例子中，我们在瑞士卷表面上随机生成 1500 个点，并要求 k 均值用 100 个簇来近似它。我们提出 100 这个数字，因为它看起来相当大，使每一簇覆盖了相当小的空间。结果看起来不错；簇群确实是很小的的，并且流体的不同部分被映射到不同的簇。不错！但我们完成了吗？\\n\",\n    \"\\n\",\n    \"问题是，如果我们选择一个太小的$K$，那么从多元学习的角度来看，结果不会那么好。图 7-5 显示了 k 均值用 10 个簇在瑞士卷的输出。我们可以清楚地看流体的完全的部分都被映射到相同的簇（例如黄色、紫色、绿色和品红簇）的数据。\\n\",\n    \"\\n\",\n    \"如果数据在空间中均匀分布，则选择正确的$k$就被归结为球填充问题。在$D$维中，可以拟合半径约为$R$的$1/r$的$D$次幂的球。每个 k 均值聚类是一个球面，半径是用质心表示球面中的点的最大误差。因此，如果我们愿意容忍每个数据点$R$的最大逼近误差，那么簇的数目是$O((1/R)^D)$，其中$D$是数据的原始特征空间的维数。\\n\",\n    \"\\n\",\n    \"对于 k 均值来说，均匀分布是最坏的情况。如果数据密度不均匀，那么我们将能够用更少的簇来表示更多的数据。一般来说，很难知道数据在高维空间中是如何分布的。我们可以保守的选择更大的 K。但是它不能太大，因为`K`将成为下一步建模步骤的特征数量。\\n\",\n    \"\\n\",\n    \"## 用于分类的 k 均值特征化\\n\",\n    \"\\n\",\n    \"当使用 k 均值作为特征化过程时，数据点可以由它的簇成员（分类变量群组成员的稀疏独热编码）来表示，我们现在来说明。\\n\",\n    \"\\n\",\n    \"如果目标变量也是可用的，那么我们可以选择将该信息作为对聚类过程的提示。一种合并目标信息的方法是简单地将目标变量作为 k 均值算法的附加输入特征。由于目标是最小化在所有输入维度上的总欧氏距离，所以聚类过程将试图平衡目标值和原始特征空间中的相似性。可以在聚类算法中对目标值进行缩放以获得更多或更少的关注。目标的较大差异将产生更多关注分类边界的聚类。\\n\",\n    \"\\n\",\n    \"## k 均值特征化\\n\",\n    \"\\n\",\n    \"聚类算法分析数据的空间分布。因此，k 均值特征化创建了一个压缩的空间索引，该数据可以在下一阶段被馈送到模型中。这是模型堆叠（stacking）的一个例子。\\n\",\n    \"\\n\",\n    \"例 7-3 显示了一个简单的 k 均值特征。它被定义为可以训练数据和变换任何新数据的类对象。为了说明在聚类时使用和不使用目标信息之间的差异，我们将特征化器应用到使用`sklearn`的 *make——moons* 函数（例 7-4）生成的合成数据集。然后我们绘制簇边界的 Voronoi 图。图 7-6 展示出了结果的比较。底部面板显示没有目标信息训练的集群。注意，许多簇跨越两个类之间的空空间。顶部面板表明，当聚类算法被给定目标信息时，聚类边界可以沿着类边界更好地对齐。\\n\",\n    \"\\n\",\n    \"### 例 7-3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from scipy.spatial import Voronoi, voronoi_plot_2d\\n\",\n    \"from sklearn.cluster import KMeans\\n\",\n    \"from sklearn.datasets import make_moons\\n\",\n    \"from sklearn.preprocessing import OneHotEncoder\\n\",\n    \"import sklearn\\n\",\n    \"import scipy\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class KMeansFeaturizer:\\n\",\n    \"    \\\"\\\"\\\"Transforms numeric data into k-means cluster memberships.\\n\",\n    \"    \\n\",\n    \"    This transformer runs k-means on the input data and converts each data point\\n\",\n    \"    into the id of the closest cluster. If a target variable is present, it is \\n\",\n    \"    scaled and included as input to k-means in order to derive clusters that\\n\",\n    \"    obey the classification boundary as well as group similar points together.\\n\",\n    \"\\n\",\n    \"    Parameters\\n\",\n    \"    ----------\\n\",\n    \"    k: integer, optional, default 100\\n\",\n    \"        The number of clusters to group data into.\\n\",\n    \"\\n\",\n    \"    target_scale: float, [0, infty], optional, default 5.0\\n\",\n    \"        The scaling factor for the target variable. Set this to zero to ignore\\n\",\n    \"        the target. For classification problems, larger `target_scale` values \\n\",\n    \"        will produce clusters that better respect the class boundary.\\n\",\n    \"\\n\",\n    \"    random_state : integer or numpy.RandomState, optional\\n\",\n    \"        This is passed to k-means as the generator used to initialize the \\n\",\n    \"        kmeans centers. If an integer is given, it fixes the seed. Defaults to \\n\",\n    \"        the global numpy random number generator.\\n\",\n    \"\\n\",\n    \"    Attributes\\n\",\n    \"    ----------\\n\",\n    \"    cluster_centers_ : array, [k, n_features]\\n\",\n    \"        Coordinates of cluster centers. n_features does count the target column.\\n\",\n    \"    将数字型数据输入k-均值聚类.          \\n\",\n    \"    在输入数据上运行k-均值并且把每个数据点设定为它的簇id. \\n\",\n    \"    如果存在目标变量，则将其缩放并包含为k-均值的输入，以导出服从分类边界以及组相似点的簇。\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, k=100, target_scale=5.0, random_state=None):\\n\",\n    \"        self.k = k\\n\",\n    \"        self.target_scale = target_scale\\n\",\n    \"        self.random_state = random_state\\n\",\n    \"        self.cluster_encoder = OneHotEncoder().fit(\\n\",\n    \"            np.array(range(k)).reshape(-1, 1))\\n\",\n    \"\\n\",\n    \"    def fit(self, X, y=None):\\n\",\n    \"        # 在输入数据上运行k-均值，并找到中心.\\n\",\n    \"        \\\"\\\"\\\"Runs k-means on the input data and find centroids.\\n\",\n    \"\\n\",\n    \"        If no target is given (`y` is None) then run vanilla k-means on input\\n\",\n    \"        `X`. \\n\",\n    \"\\n\",\n    \"        If target `y` is given, then include the target (weighted by \\n\",\n    \"        `target_scale`) as an extra dimension for k-means clustering. In this \\n\",\n    \"        case, run k-means twice, first with the target, then an extra iteration\\n\",\n    \"        without.\\n\",\n    \"\\n\",\n    \"        After fitting, the attribute `cluster_centers_` are set to the k-means\\n\",\n    \"        centroids in the input space represented by `X`.\\n\",\n    \"\\n\",\n    \"        Parameters\\n\",\n    \"        ----------\\n\",\n    \"        X : array-like or sparse matrix, shape=(n_data_points, n_features)\\n\",\n    \"\\n\",\n    \"        y : vector of length n_data_points, optional, default None\\n\",\n    \"            If provided, will be weighted with `target_scale` and included in \\n\",\n    \"            k-means clustering as hint.\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        if y is None:\\n\",\n    \"            # 没有目标变量运行k-均值\\n\",\n    \"            # No target variable, just do plain k-means\\n\",\n    \"            km_model = KMeans(\\n\",\n    \"                n_clusters=self.k, n_init=20, random_state=self.random_state)\\n\",\n    \"            km_model.fit(X)\\n\",\n    \"\\n\",\n    \"            self.km_model_ = km_model\\n\",\n    \"            self.cluster_centers_ = km_model.cluster_centers_\\n\",\n    \"            return self\\n\",\n    \"        # 有目标信息，使用合适的缩减并把输入数据输入k-均值\\n\",\n    \"        # There is target information. Apply appropriate scaling and include\\n\",\n    \"        # into input data to k-means\\n\",\n    \"        data_with_target = np.hstack((X, y[:, np.newaxis] * self.target_scale))\\n\",\n    \"\\n\",\n    \"        # 在数据和目标上建立预训练k-均值模型\\n\",\n    \"        km_model_pretrain = KMeans(\\n\",\n    \"            n_clusters=self.k, n_init=20, random_state=self.random_state)\\n\",\n    \"        km_model_pretrain.fit(data_with_target)\\n\",\n    \"        #运行k-均值第二次获得簇在原始空间没有目标信息。使用预先训练中发现的质心进行初始化。\\n\",\n    \"        #通过一个迭代的集群分配和质心重新计算。\\n\",\n    \"        km_model = KMeans(\\n\",\n    \"            n_clusters=self.k,\\n\",\n    \"            init=km_model_pretrain.cluster_centers_[:, :2],\\n\",\n    \"            n_init=1,\\n\",\n    \"            max_iter=1)\\n\",\n    \"        km_model.fit(X)\\n\",\n    \"\\n\",\n    \"        self.km_model = km_model\\n\",\n    \"        self.cluster_centers_ = km_model.cluster_centers_\\n\",\n    \"        return self\\n\",\n    \"\\n\",\n    \"    def transform(self, X, y=None):\\n\",\n    \"        # 为每个输入数据点输出最接近的簇id。\\n\",\n    \"        clusters = self.km_model.predict(X)\\n\",\n    \"        return self.cluster_encoder.transform(clusters.reshape(-1, 1))\\n\",\n    \"\\n\",\n    \"    def fit_transform(self, X, y=None):\\n\",\n    \"        self.fit(X, y)\\n\",\n    \"        return self.transform(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"seed = 1\\n\",\n    \"training_data, training_labels = make_moons(\\n\",\n    \"    n_samples=2000, noise=0.2, random_state=seed)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\preprocessing\\\\_encoders.py:368: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\\n\",\n      \"If you want the future behaviour and silence this warning, you can specify \\\"categories='auto'\\\".\\n\",\n      \"In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\\n\",\n      \"  warnings.warn(msg, FutureWarning)\\n\",\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\preprocessing\\\\_encoders.py:368: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\\n\",\n      \"If you want the future behaviour and silence this warning, you can specify \\\"categories='auto'\\\".\\n\",\n      \"In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\\n\",\n      \"  warnings.warn(msg, FutureWarning)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"kmf_hint = KMeansFeaturizer(\\n\",\n    \"    k=100, target_scale=10, random_state=seed).fit(training_data,\\n\",\n    \"                                                   training_labels)\\n\",\n    \"kmf_no_hint = KMeansFeaturizer(\\n\",\n    \"    k=100, target_scale=0, random_state=seed).fit(training_data,\\n\",\n    \"                                                  training_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def kmeans_voronoi_plot(X, y, cluster_centers, ax):\\n\",\n    \"    \\\"\\\"\\\"Plots the Voronoi diagram of the kmeans clusters overlayed with the data\\\"\\\"\\\"\\n\",\n    \"    ax.scatter(X[:, 0], X[:, 1], c=y, cmap='Set1', alpha=0.2)\\n\",\n    \"    vor = Voronoi(cluster_centers)\\n\",\n    \"    voronoi_plot_2d(vor, ax=ax, show_vertices=False, alpha=0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\spatial\\\\_plotutils.py:20: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0.\\n\",\n      \"  was_held = ax.ishold()\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5,1,'K-Means without Target Hint')\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x2cff9ab5eb8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(10, 8))\\n\",\n    \"ax = plt.subplot(211, aspect='equal')\\n\",\n    \"fig.tight_layout(pad=0, w_pad=4.0, h_pad=4.0)\\n\",\n    \"kmeans_voronoi_plot(training_data, training_labels, kmf_hint.cluster_centers_,\\n\",\n    \"                    ax)\\n\",\n    \"ax.set_title('K-Means with Target Hint')\\n\",\n    \"ax2 = plt.subplot(212, aspect='equal')\\n\",\n    \"kmeans_voronoi_plot(training_data, training_labels,\\n\",\n    \"                    kmf_no_hint.cluster_centers_, ax2)\\n\",\n    \"ax2.set_title('K-Means without Target Hint')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"让我们测试 k 均值特征分类的有效性。例 7-5 对 k 均值簇特征增强的输入数据应用 Logistic 回归。比较了与使用径向基核的支持向量机（RBF SVM）、K 近邻（KNN）、随机森林（RF）和梯度提升树（GBT）的结果。随机森林和梯度提升树是最流行的非线性分类器，具有最先进的性能。RBF 支持向量机是欧氏空间的一种合理的非线性分类器。KNN 根据其 K 近邻的平均值对数据进行分类。（请参阅“分类器概述”来概述每个分类器。）\\n\",\n    \"\\n\",\n    \"分类器的默认输入数据是数据的 2D 坐标。Logistic 回归也给出了簇成员特征（在图 7-7 中标注为“k 均值的 LR”）。作为基线，我们也尝试在二维坐标（标记为“LR”）上进行逻辑回归。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 例 7-4\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"from sklearn.svm import SVC\\n\",\n    \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\\n\",\n      \"  FutureWarning)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#生成与训练数据相同分布的测试数据\\n\",\n    \"test_data, test_labels = make_moons(n_samples=2000, noise=0.3)\\n\",\n    \"\\n\",\n    \"# 使用k-均值特技器生成簇特征\\n\",\n    \"training_cluster_features = kmf_hint.transform(training_data)\\n\",\n    \"test_cluster_features = kmf_hint.transform(test_data)\\n\",\n    \"\\n\",\n    \"# 将新的输入特征和聚类特征整合\\n\",\n    \"training_with_cluster = scipy.sparse.hstack((training_data,\\n\",\n    \"                                             training_cluster_features))\\n\",\n    \"test_with_cluster = scipy.sparse.hstack((test_data, test_cluster_features))\\n\",\n    \"\\n\",\n    \"# 建立分类器\\n\",\n    \"lr_cluster = LogisticRegression(random_state=seed).fit(training_with_cluster,\\n\",\n    \"                                                       training_labels)\\n\",\n    \"\\n\",\n    \"classifier_names = ['LR', 'kNN', 'RBF SVM', 'Random Forest', 'Boosted Trees']\\n\",\n    \"classifiers = [\\n\",\n    \"    LogisticRegression(random_state=seed),\\n\",\n    \"    KNeighborsClassifier(5),\\n\",\n    \"    SVC(gamma=2, C=1, random_state=seed),\\n\",\n    \"    RandomForestClassifier(\\n\",\n    \"        max_depth=5, n_estimators=10, max_features=1, random_state=seed),\\n\",\n    \"    GradientBoostingClassifier(\\n\",\n    \"        n_estimators=10, learning_rate=1.0, max_depth=5, random_state=seed)\\n\",\n    \"]\\n\",\n    \"for model in classifiers:\\n\",\n    \"    model.fit(training_data, training_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# 辅助函数使用ROC评估分类器性能\\n\",\n    \"def test_roc(model, data, labels):\\n\",\n    \"    if hasattr(model, \\\"decision_function\\\"):\\n\",\n    \"        predictions = model.decision_function(data)\\n\",\n    \"    else:\\n\",\n    \"        predictions = model.predict_proba(data)[:, 1]\\n\",\n    \"    fpr, tpr, _ = sklearn.metrics.roc_curve(labels, predictions)\\n\",\n    \"    return fpr, tpr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0,0.5,'True Positive Rate')\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x2cffa4efda0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 显示结果\\n\",\n    \"plt.figure()\\n\",\n    \"fpr_cluster, tpr_cluster = test_roc(lr_cluster, test_with_cluster, test_labels)\\n\",\n    \"plt.plot(fpr_cluster, tpr_cluster, 'r-', label='LR with k-means')\\n\",\n    \"\\n\",\n    \"for i, model in enumerate(classifiers):\\n\",\n    \"    fpr, tpr = test_roc(model, test_data, test_labels)\\n\",\n    \"    plt.plot(fpr, tpr, label=classifier_names[i])\\n\",\n    \"\\n\",\n    \"plt.plot([0, 1], [0, 1], 'k--')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.xlabel('False Positive Rate', fontsize=14)\\n\",\n    \"plt.ylabel('True Positive Rate', fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 可选择的密集化\\n\",\n    \"\\n\",\n    \"与独热簇相反，数据点也可以由其逆距离的密集向量表示到每个聚类中心。这比简单的二值化簇保留了更多的信息，但是现在表达是密集的。这里有一个折衷方案。一个热集群成员导致一个非常轻量级的稀疏表示，但是一个可能需要较大的$K$来表示复杂形状的数据。反向距离表示是密集的，这对于建模步骤可能花费更昂贵，但是这可以需要较小的$K$。\\n\",\n    \"\\n\",\n    \"稀疏和密集之间的折衷是只保留最接近的簇的$p$的逆距离。但是现在$P$是一个额外的超参数需要去调整。（现在你能理解为什么特征工程需要这么多的步骤吗？），天下没有免费的午餐。\\n\",\n    \"\\n\",\n    \"## 总结\\n\",\n    \"\\n\",\n    \"使用 k 均值将空间数据转换为模型堆叠的一个例子，其中一个模型的输入是另一个模型的输出。堆叠的另一个例子是使用决策树类型模型（随机森林或梯度提升树）的输出作为线性分类器的输入。堆叠已成为近年来越来越流行的技术。非线性分类器训练和维护是昂贵的。堆叠的关键一点是将非线性引入特征，并且使用非常简单的、通常是线性的模型作为最后一层。该特征可以离线训练，这意味着可以使用昂贵的模型，这需要更多的计算能力或内存，但产生有用的特征。顶层的简单模型可以很快地适应在线数据的变化分布。这是精度和速度之间的一个很好的折衷，这经常被应用于需要快速适应改变数据分布的应用，比如目标广告。\\n\",\n    \"\\n\",\n    \"## 模型堆叠的关键点\\n\",\n    \"\\n\",\n    \"复杂的基础层（通常是昂贵的模型）产生良好的（通常是非线性的）特征，随后结合简单并且快速的顶层模型。这常常在模型精度和速度之间达到正确的平衡。\\n\",\n    \"\\n\",\n    \"与使用非线性分类器相比，采用 logistic 回归的 k 均值更容易进行训练和存储。表 7-1 是多个机器学习模型的计算和记忆的训练和预测复杂性的图表。$n$表示数据点的数量，$D$（原始）特征的数量。\\n\",\n    \"\\n\",\n    \"对于 k 均值，训练时间是$O(nkd)$，因为每次迭代涉及计算每个数据点和每个质心（$k$）之间的$d$维距离。我们乐观地假设迭代次数不是$n$的函数，尽管并不普遍适用。预测需要计算新的数据点与质心（$k$）之间的距离，即$O(kd)$。存储空间需求是$O(kd)$，对于$K$质心的坐标。\\n\",\n    \"\\n\",\n    \"logistic 训练和预测在数据点的数量和特征维度上都是线性的。RBF SVM 训练是昂贵的，因为它涉及计算每一对输入数据的核矩阵。RBF SVM 预测比训练成本低，在支持向量$S$和特征维数$D$的数目上是线性的。改进的树模型训练和预测在数据大小和模型的大小上线性的（$t$个树，每个最多 2 的$m$次幂子叶，其中$m$是树的最大深度）。KNN 的实现根本不需要训练时间，因为训练数据本身本质上是模型。花费全都在预测时间，输入必须对每个原始训练点进行评估，并部分排序以检索 K 近邻。\\n\",\n    \"\\n\",\n    \"总体而言，k 均值 +LR 是在训练和预测时间上唯一的线性组合（相对于训练数据$O(nd)$的大小和模型大小$O(kd)$）。复杂度最类似于提升树，其成本在数据点的数量、特征维度和模型的大小（$O(2^m*t)$）中是线性的。很难说 k 均值 +LR 或提升树是否会产生更小的模型，这取决于数据的空间特征。\\n\",\n    \"\\n\",\n    \"![图T7-1](images/chapter7/T7-1.png)\\n\",\n    \"\\n\",\n    \"## 数据泄露的潜力\\n\",\n    \"\\n\",\n    \"那些记得我们对数据泄露的谨慎（参见“防止数据泄露（桶计数：未来的日子）”）可能会问 k 均值特化步骤中的目标变量是否也会导致这样的问题。答案是“是的”，但并不像桶计数（Bin-counting）计算的那么多。如果我们使用相同的数据集来学习聚类和建立分类模型，那么关于目标的信息将泄漏到输入变量中。因此，对训练数据的精度评估可能过于乐观，但是当在保持验证集或测试集上进行评估时，偏差会消失。此外，泄漏不会像桶计数那么糟糕（参见“桶计数”），因为聚类算法的有损压缩将抽象掉一些信息。要格外小心防止泄漏，人们可以始终保留一个单独的数据集来导出簇，就像在桶计数下一样。\\n\",\n    \"\\n\",\n    \"k 均值特化对有实数、有界的数字特征是有用的，这些特征构成空间中密集区域的团块。团块可以是任何形状，因为我们可以增加簇的数量来近似它们。（与经典的类别聚类不同，我们不关心真正的簇数；我们只需要覆盖它们。）\\n\",\n    \"\\n\",\n    \"k 均值不能处理欧几里得距离没有意义的特征空间，也就是说，奇怪的分布式数字变量或类别变量。如果特征集包含这些变量，那么有几种处理它们的方法：\\n\",\n    \"\\n\",\n    \"1.  仅在实值的有界数字特征上应用 k 均值特征。\\n\",\n    \"\\n\",\n    \"2.  定义自定义度量（参见第？章以处理多个数据类型并使用 k 中心点算法。（k 中心点类似于 k 均值，但允许任意距离度量。）\\n\",\n    \"\\n\",\n    \"3.  类别变量可以转换为装箱统计（见“桶计数”），然后使用 k 均值进行特征化。\\n\",\n    \"\\n\",\n    \"结合处理分类变量和时间序列的技术，k 均值特化可以自适应的处理经常出现在客户营销和销售分析中的丰富数据。所得到的聚类可以被认为是用户段，这对于下一个建模步骤是非常有用的特征。\\n\",\n    \"\\n\",\n    \"我们将在下一章中讨论的深度学习，是通过将神经网络层叠在一起，将模型堆叠提升到一个全新的水平。ImageNet 挑战的两个赢家使用了 13 层和 22 层神经网络。就像 k 均值一样，较低层次的深度学习模型是无监督的。它们利用大量可用的未标记的训练图像，并寻找产生良好图像特征的像素组合。\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "9.feature-engineering/8.自动化特征提取器：图像特征提取和深度学习.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[原版（英文）图书地址](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/)\\n\",\n    \"\\n\",\n    \"**翻译**：[apachecn](https://github.com/apachecn)，[翻译版本地址](https://github.com/apachecn/feature-engineering-for-ml-zh)\\n\",\n    \"\\n\",\n    \"**代码修改和整理**：[黄海广](https://github.com/fengdu78)，原文修改成jupyter notebook格式，并增加和修改了部分代码，测试全部通过，所有数据集已经放在[百度云](data/README.md)下载。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 八、自动化特征提取器：图像特征提取和深度学习\\n\",\n    \"\\n\",\n    \"> 译者：[@friedhelm739](https://github.com/friedhelm739)\\n\",\n    \"\\n\",\n    \"视觉和声音是人类固有的感觉输入。我们的大脑是可以迅速进化我们的能力来处理视觉和听觉信号的，一些系统甚至在出生前就对刺激做出反应。另一方面，语言技能是学习得来的。他们需要几个月或几年的时间来掌握。许多人天生就具有视力和听力的天赋，但是我们所有人都必须有意训练我们的大脑去理解和使用语言。\\n\",\n    \"\\n\",\n    \"有趣的是，机器学习的情况是相反的。我们已经在文本分析应用方面取得了比图像或音频更多的进展。以搜索问题为例。人们在信息检索和文本检索方面已经取得了相当多年的成功，而图像和音频搜索仍在不断完善。在过去五年中，深度学习模式的突破最终预示着期待已久的图像和语音分析的革命。\\n\",\n    \"\\n\",\n    \"进展的困难与从相应类型的数据中提取有意义特征的困难直接相关。机器学习模型需要语义上有意义的特征进行语义意义的预测。在文本分析中，特别是对于英语这样的语言，其中一个基本的语义单位（一个词）很容易提取，可以很快地取得进展。另一方面，图像和音频被记录为数字像素或波形。图像中的单个“原子”是像素。在音频数据中，它是波形强度的单一测量。它们包含的语义信息远少于数据文本。因此，在图像和音频上的特征提取和工程任务比文本更具挑战性。\\n\",\n    \"\\n\",\n    \"在过去的二十年中，计算机视觉研究已经集中在人工标定上，用于提取良好的图像特征。在一段时间内，图像特征提取器，如 SIFT 和 HOG 是标准步骤。深度学习研究的最新发展已经扩展了传统机器学习模型的范围，将自动特征提取作为基础层。他们本质上取代手动定义的特征图像提取器与手动定义的模型，自动学习和提取特征。人工标定仍然存在，只是进一步深入到建模中去。\\n\",\n    \"\\n\",\n    \"在本章中，我们将从流行的图像特征提取SIFT和HOG入手，深入研究本书所涵盖的最复杂的建模机制：深度学习的特征工程。\\n\",\n    \"\\n\",\n    \"## 最简单的图像特征（为什么他们不好使）\\n\",\n    \"\\n\",\n    \"从图像中提取的哪些特征是正确的呢？答案当然取决于我们试图用这些特征来做什么。假设我们的任务是图像检索：我们得到一张图片并要求从图像数据库中得到相似的图片。我们需要决定如何表示每个图像，以及如何测量它们之间的差异。我们可以看看图像中不同颜色的百分比吗？图8-1展示了两幅具有大致相同颜色轮廓但有着非常不同含义的图片；一个看起来像蓝色天空中的白云，另一个是希腊国旗。因此，颜色信息可能不足以表征图像。\\n\",\n    \"\\n\",\n    \"![图8-1](images/chapter8/8-1.png)\\n\",\n    \"\\n\",\n    \"另一个比较简单的想法是测量图像之间的像素值差异。首先，调整图像的宽度和高度。每个图像由像素值矩阵表示。矩阵可以通过一行或一列被堆叠成一个长向量。每个像素的颜色（例如，颜色的 RGB 编码）现在是图像的特征。最后，测量长像素向量之间的欧几里得距离。这绝对可以区分希腊国旗和白云。但作为相似性度量，它过于严格。云可以呈现一千种不同的形状，仍然是一朵云。它可以移动到图像的一边，或者一半可能位于阴影中。所有这些转换都会增加欧几里得距离，但是他们不应该改变图片仍然是云的事实。\\n\",\n    \"\\n\",\n    \"问题是单个像素不携带足够的图像语义信息。因此，使用它们用于分析结果是非常糟糕的。在 1999 年，计算机视觉研究者想出了一种更好的方法来使用图像的统计数据来表示图像。它叫做 Scale Invariant Feature Transform（SIFT）。\\n\",\n    \"\\n\",\n    \"SIFT最初是为对象识别的任务而开发的，它不仅涉及将图像正确地标记为包含对象，而且确定其在图像中的位置。该过程包括在可能的尺度金字塔上分析图像，检测可以指示对象存在的兴趣点，提取关于兴趣点的特征（通常称为计算机视觉中的图像描述符），并确定对象的姿态。\\n\",\n    \"\\n\",\n    \"多年来，SIFT 的使用扩展到不仅提取兴趣点，而且遍及整个图像的特征。SIFT 特征提取过程非常类似于另一种称为 Histogram of Oriented Gradients （HOG）的技术。它们都计算梯度方向的直方图。现在我们详细地描述一下。\\n\",\n    \"\\n\",\n    \"## 人工特征提取：SIFT 与 HOG\\n\",\n    \"\\n\",\n    \"### 图像梯度\\n\",\n    \"\\n\",\n    \"要比原始像素值做得更好，我们必须以某种方式将像素组织成更多信息单元。相邻像素之间的差异通常是非常有用的特征。通常情况下像素值在对象的边界处是不同，当存在阴影、图案内或纹理表面时。相邻像素之间的差值称为图像梯度。\\n\",\n    \"\\n\",\n    \"计算图像梯度的最简单的方法是分别计算图像沿水平（$X$）和垂直（$Y$）轴的差异，然后将它们合成为二维矢量。这涉及两个 1D 差分操作，可以用矢量掩模或滤波器方便地表示。掩码$(1, 0, -1)$可以得到在左像素和右像素之间的差异或者上像素和下像素之间的差异，取决于我们应用掩码的方向。当然也有二维梯度滤波器。但在本例中，1D 滤波器就足够了。\\n\",\n    \"\\n\",\n    \"为了对图像应用滤波器，我们执行卷积。它涉及翻转滤波器和内积与一小部分的图像，然后移动到下一个块。卷积在信号处理中很常见。我们将使用`*`来表示操作：\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"[a\\\\quad b\\\\quad c]*[1\\\\quad 2\\\\quad 3]=c*1+b*2+a*3\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"在像素点$(i,j)$的$x$梯度和$y$梯度为：\\n\",\n    \"$$\\n\",\n    \"g_{x}(i, j)=[1\\\\quad 0\\\\quad -1]*[I(i-1,j)\\\\quad I(i,j)\\\\quad I(i+1,j)]=-1*I(i-1,j)+1 * I(i + 1,j)\\n\",\n    \"$$\\n\",\n    \"$$\\n\",\n    \"g_{y}(i, j)=[1\\\\quad 0\\\\quad -1]*[I(i,j-1)\\\\quad I(i,j)\\\\quad I(i,j+1)]=-1*I(i,j-1)+1 * I(i ,j+ 1)\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"他们一起组成了梯度：\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\nabla I(i, j)=\\\\left[\\\\begin{array}{l}{g_{x}(i, j)} \\\\\\\\ {g_{y}(i, j)}\\\\end{array}\\\\right]\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"向量可以通过它的方向和大小来完全描述。梯度的大小等于梯度的欧几里得范数，这表明像素值在像素周围变化得多大。梯度的位置或方向取决于水平方向和垂直方向上的变化的相对大小；图 8-2 说明了这些数学概念。\\n\",\n    \"\\n\",\n    \"![图8-2](images/chapter8/8-2.png)\\n\",\n    \"<center>图 8-2</center>\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"图8—3展出了由垂直和水平梯度组成的图像梯度的示例。每个示例是一个 9 像素的图像。每个像素用灰度值标记。（较小的数字对应于较深的颜色）中心像素的梯度显示在每个图像下面。左侧的图像包含水平条纹，其中颜色仅垂直变化。因此，水平梯度为零，梯度垂直为非零。中心图像包含垂直条纹，因此水平梯度为零。右边的图像包含对角线条纹，斜率也是对角线。\\n\",\n    \"\\n\",\n    \"![图8-3](images/chapter8/8-3.png)\\n\",\n    \"<center>图 8-3</center>\\n\",\n    \"\\n\",\n    \"它们能在真实的图像上发挥作用吗？在例 8-1 中，我们使用图 8-4 所示的猫的水平和垂直梯度上来实验。由于梯度是在原始图像的每个像素位置计算的，所以我们得到两个新的矩阵，每个矩阵可以被可视化为图像。\\n\",\n    \"\\n\",\n    \"![图8-4](images/chapter8/8-4.png)\\n\",\n    \"<center>图 8-4</center>\\n\",\n    \"\\n\",\n    \"### 例 8-1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from skimage.feature import hog\\n\",\n    \"from skimage import data, color, exposure\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"image = color.rgb2gray(data.chelsea())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\skimage\\\\feature\\\\_hog.py:239: skimage_deprecation: Argument `visualise` is deprecated and will be changed to `visualize` in v0.16\\n\",\n      \"  'be changed to `visualize` in v0.16', skimage_deprecation)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fd, hog_image = hog(\\n\",\n    \"    image,\\n\",\n    \"    orientations=8,\\n\",\n    \"    pixels_per_cell=(16, 16),\\n\",\n    \"    cells_per_block=(1, 1),\\n\",\n    \"    visualise=True)\\n\",\n    \"\\n\",\n    \"hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x25815ef8550>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True)\\n\",\n    \"\\n\",\n    \"ax1.axis('off')\\n\",\n    \"ax1.imshow(image, cmap=plt.cm.gray)\\n\",\n    \"ax1.set_title('Input image')\\n\",\n    \"ax1.set_adjustable('box-forced')\\n\",\n    \"\\n\",\n    \"ax2.axis('off')\\n\",\n    \"ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)\\n\",\n    \"ax2.set_title('Histogram of Oriented Gradients')\\n\",\n    \"ax2.set_adjustable('box-forced')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"注意，水平梯度提取出强烈的垂直模式，如猫眼睛的内边缘，而垂直梯度则提取强的水平模式，如晶须和眼睛的上下眼睑。这乍看起来似乎有些矛盾，如果我们仔细考虑一下，这还是有道理的。水平（$X$）梯度识别水平方向上的变化。强的垂直图案在大致相同的$X$位置上跨越多个$Y$像素。因此，垂直图案导致像素值的水平差异。这也是我们的眼睛也能察觉到的。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"gx = np.empty(image.shape, dtype=np.double)\\n\",\n    \"gx[:, 0] = 0\\n\",\n    \"gx[:, -1] = 0\\n\",\n    \"gx[:, 1:-1] = image[:, :-2] - image[:, 2:]\\n\",\n    \"gy = np.empty(image.shape, dtype=np.double)\\n\",\n    \"gy[0, :] = 0\\n\",\n    \"gy[-1, :] = 0\\n\",\n    \"gy[1:-1, :] = image[:-2, :] - image[2:, :]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x25816762be0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, (ax1, ax2, ax3) = plt.subplots(\\n\",\n    \"    1, 3, figsize=(12, 8), sharex=True, sharey=True)\\n\",\n    \"\\n\",\n    \"ax1.axis('off')\\n\",\n    \"ax1.imshow(image, cmap=plt.cm.gray)\\n\",\n    \"ax1.set_title('Original image')\\n\",\n    \"ax1.set_adjustable('box-forced')\\n\",\n    \"\\n\",\n    \"ax2.axis('off')\\n\",\n    \"ax2.imshow(gx, cmap=plt.cm.gray)\\n\",\n    \"ax2.set_title('Horizontal gradients')\\n\",\n    \"ax2.set_adjustable('box-forced')\\n\",\n    \"\\n\",\n    \"ax3.axis('off')\\n\",\n    \"ax3.imshow(gy, cmap=plt.cm.gray)\\n\",\n    \"ax3.set_title('Vertical gradients')\\n\",\n    \"ax3.set_adjustable('box-forced')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 梯度方向直方图\\n\",\n    \"\\n\",\n    \"单个图像梯度可以识别图像邻域中的微小差异。但是我们的眼睛看到的图案比那更大。例如，我们看到一整只猫的胡须，而不仅仅是一个小部分。人类视觉系统识别区域中的连续模式。因此，我们咋就图像梯度邻域有仍然有很多的工作要做。\\n\",\n    \"\\n\",\n    \"我们如何精确地归纳向量？统计学家会回答：“看分布！SIFT 和 HOG 都走这条路。它们计算（正则化）梯度矢量直方图作为图像特征。直方图将数据分成容器并计算每容器中有多少，这是一个（不规范的）经验分布。规范化确保数和为 1，用数学语言描述为它具有单位 L 范数。\\n\",\n    \"\\n\",\n    \"图像梯度是矢量，矢量可以由两个分量来表示：方向和幅度。因此，我们仍然需要决定如何设计直方图来表示这两个分量。SIFT 和 HOG 提供了一个解决方案，其中图像梯度被它们的方向角所包括，由每个梯度的大小加权。以下是流程：\\n\",\n    \"\\n\",\n    \"1.  将 0° - 360° 分成相等大小的容器。\\n\",\n    \"\\n\",\n    \"2.  对于邻域中的每个像素，将权重$W$添加到对应于其方向角的容器中。\\n\",\n    \"\\n\",\n    \"3.  $W$是梯度的大小和其他相关信息的函数。例如，其他相关信息可以是像素到图像贴片中心的逆距离。其思想是，如果梯度较大，权重应该很大，而图像邻域中心附近的像素比远离像素的像素更大。\\n\",\n    \"\\n\",\n    \"4.  正则化直方图。\\n\",\n    \"\\n\",\n    \"图 8-5 提供了由`4x4`像素的图像邻域构成的8个容器的梯度方向直方图的图示。\\n\",\n    \"\\n\",\n    \"![图8-5](images/chapter8/8-5.png)\\n\",\n    \"\\n\",\n    \"当然，在基本的梯度方向直方图算法中还有许多选项。像通常一样，正确的设置可能高度依赖于想要分析的特定图像。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 有多少容器？\\n\",\n    \"\\n\",\n    \"他们的跨度是从 0° - 360°（有符号梯度）还是 0° - 180°（无符号梯度）？\\n\",\n    \"\\n\",\n    \"具有更多的容器导致梯度方向的细粒度量化，因此会保留更多关于原始梯度的信息。但是，有太多的容器是不必要的，并可能导致过度拟合训练数据。例如，在图像中识别猫可能不依赖于精确地取向在 3° 的猫的晶须。\\n\",\n    \"\\n\",\n    \"还有一个问题，容器是否应该跨越 0 - 360°，这将沿$Y$轴保持梯度，或跨越 0°- 180°，这将不会保留垂直梯度的符号。Dalal 与 Triggs 是 HOG 论文的最初作者，实验确定从 0 - 180° 跨越的 9 个容器是最好的，而 SIFT 论文推荐了 8 个跨越 0° - 360° 的容器。\\n\",\n    \"\\n\",\n    \"### 使用什么样的权重函数？\\n\",\n    \"\\n\",\n    \"HOG 论文比较各种梯度幅度加权方案：其大小本身、平方、平方根、二值化等等。没有改变的平面大小在实验中表现最好。\\n\",\n    \"\\n\",\n    \"SIFT 还使用梯度的原始大小。最重要的是，它希望避免图像描述符在图像窗口位置的微小变化中的突然变化。因此，它使用从窗口中心测量的高斯距离函数来衡量来自邻域边缘的梯度。换言之，梯度幅值乘以高斯函数 $\\\\frac{1}{2 \\\\pi \\\\sigma^{2}} e^{-\\\\left\\\\|p-p_{0}\\\\right\\\\|^{2} / 2 \\\\sigma^{2}}$，其中$P$是产生梯度的像素的位置，$P0$图像邻域的中心位置，并且$\\\\sigma$为高斯的宽度，$\\\\sigma$被设置为邻域半径的一半。\\n\",\n    \"\\n\",\n    \"SIFT 还希望避免从单个图像梯度方向的微小变化来改变方向直方图中的大的变化。因此，它使用一个插值技巧，将权重从一个梯度扩展到相邻的方向箱。特别地，根箱（梯度分配的箱）得到加权幅度的 1 倍的投票。每个相邻的容器得到 1-D 的投票，其中$D$是来自根容器的直方图箱单元的差异。\\n\",\n    \"\\n\",\n    \"总的来说，SIFT 的单一图像梯度的投票是\\n\",\n    \"\\n\",\n    \"$w_{(\\\\nabla p, b)}=w_{b} \\\\sigma\\\\left\\\\|\\\\nabla_{p}\\\\right\\\\|$\\n\",\n    \"\\n\",\n    \"其中 $\\\\nabla_{p}$ 是在箱$b$的像素点$p$的梯度，$Wb$是权值$b$的插值，$\\\\sigma$是$p$距离中心的高斯距离。\\n\",\n    \"\\n\",\n    \"### 邻域怎么定义？他们应该怎样覆盖图片？\\n\",\n    \"\\n\",\n    \"HOG 和 SIFT 都基于图像邻域的两层表示：首先，将相邻像素组织成单元，然后将相邻单元组织成块。计算每个单元的方向直方图，并将单元直方图矢量连接起来，形成整个块的最终特征描述符。\\n\",\n    \"\\n\",\n    \"SIFT 使用`16x16`像素的单元，将其组织成 8 个方向的容器，然后通过`4x4`单元的块分组，使得图像邻域的`4x4x8=128`个特征。\\n\",\n    \"\\n\",\n    \"HOG 论文实验用矩形和圆形形状的单元和块。矩形单元称为 R-HOG。最好的 R-HOG 设置为`8x8`像素的 9 个定向仓，每个分组为`2x2`个单元的块。圆形窗口称为 C-HOG，具有由中心单元的半径确定的变量、单元是否径向分裂、外单元的宽度等。\\n\",\n    \"\\n\",\n    \"无论邻域如何组织，它们通常重叠形成整个图像的特征向量。换言之，单元和块在水平方向和垂直方向上横移图像，一次只有几个像素，以覆盖整个图像。\\n\",\n    \"\\n\",\n    \"邻域结构的主要组成部分是多层次的组织和重叠的窗口，其在图像上移动。在深度学习网络的设计中使用了相同的成分。\\n\",\n    \"\\n\",\n    \"### 什么样的归一化？\\n\",\n    \"\\n\",\n    \"归一化处理出特征描述符，使得它们具有可比的大小。它是缩放的同义词，我们在第 4 章中讨论过。我们发现，文本特征的特征缩放（以 tf-idf 的形式）对分类精度没有很大影响。图像特征与文字区别很大，其对在自然图像中出现的照明和对比度的变化可能是非常敏感的。例如，在强烈的聚光灯下观察苹果的图像，而不是透过窗户发出柔和的散射光。即使物体是相同的，图像梯度也会有非常不同的幅度。为此，计算机视觉中的图像特征通常从全局颜色归一化开始，以消除照度和对比度方差。对于 SIFT 和 HOG 来说，结果表明，只要我们对特征进行归一化，这种预处理是不必要的。\\n\",\n    \"\\n\",\n    \"SIFT 遵循归一化-阈值-归一化方案。首先，块特征向量归一化为单位长度（L2 标准化）。然后，将特征剪辑除以最大值以摆脱极端的照明效果，如从相机的色彩饱和度。最后，将剪切特征再次归一化到单位长度。\\n\",\n    \"\\n\",\n    \"HOG 论文实验涉及不同的归一化方案例如 L1 和 L2，包括 SIFT 论文中标归一化-阈值-归一化方案。他们发现L1归一化比其他的方法稍显不靠谱。\\n\",\n    \"\\n\",\n    \"## SIFT 结构\\n\",\n    \"\\n\",\n    \"SIFT 需要相当多的步骤。HOG 稍微简单，但是遵循许多相同的基本步骤，如梯度直方图和归一化。图 8-6 展示了 SIFT 体系结构。从原始图像中的感兴趣区域开始，首先将区域划分为网格。然后将每个网格单元进一步划分为子网格。每个子网格元素包含多个像素，并且每个像素产生梯度。每个子网格元素产生加权梯度估计，其中权重被选择以使得子网格元素之外的梯度可以贡献。然后将这些梯度估计聚合成子网格的方向直方图，其中梯度可以具有如上所述的加权投票。然后将每个子网格的方向直方图连接起来，形成整个网格的长梯度方向直方图。（如果网格被划分为`2x2`子网格，那么将有 4 个梯度方向直方图拼接成一个。）这是网格的特征向量。从这开始，它经过一个归一化-阈值-归一化过程。首先，将向量归一化为单位范数。然后，将单个值剪辑除以最大阈值。最后，再次对阈值向量进行归一化处理。这是图像块的最终 SIFT 特征描述。\\n\",\n    \"\\n\",\n    \"![图8-6](images/chapter8/8-6.png)\\n\",\n    \"\\n\",\n    \"## 基于深度神经网络的图像特征提取\\n\",\n    \"\\n\",\n    \"SIFT 和 HOG 在定义良好的图像特征方面走了很久。然而，计算机视觉的最新成果来自一个非常不同的方向：深度神经网络模型。这一突破发生在 2012 的 ImageNet 挑战中，多伦多大学的一组研究人员几乎将前一年的获奖者的错误率减半。他们强调他们的方法是“深度学习”。与以前的神经网络模型不同，最新一代包含许多层叠在彼此之上的神经网络层和变换。ImageNet 2012 的获奖模型随后被称为 AlexNet ，其神经网络有 13 层。之后 ImageNet 2014 的获胜者有 27 层。\\n\",\n    \"\\n\",\n    \"从表面上看，叠层神经网络的机制与 SIFT 和 HOG 的图像梯度直方图有很大的不同。但是 AlxNETA 的可视化显示，前几层本质上是计算边缘梯度和其他简单的操作，很像 SIFT 和 HOG。随后的层将局部模式组合成更全局的模式。最终的结果是一个比以前更强大的特征提取器。\\n\",\n    \"\\n\",\n    \"堆叠层的神经网络（或任何其他分类模型）的基础思想不是新的。但是，训练这种复杂的模型需要大量的数据和强大的计算能力，这是直到最近才有的。ImageNet 数据集包含来自 1000 个类的 120 万个图像的标记集。现代 GPU 加速了矩阵向量计算，这是许多机器学习模型的核心，包括神经网络。深度学习方法的成功取决于大量可用的数据和大量的 GPU 小时。\\n\",\n    \"\\n\",\n    \"深度学习架构可以由若干类型的层组成。例如，AlxNETs 包含卷积、全连接层、归一化层和最大池化层。现在我们将依次查看每一层的内容。\\n\",\n    \"\\n\",\n    \"## 全连接层\\n\",\n    \"\\n\",\n    \"所有神经网络的核心是输入的线性函数。我们在第4章中遇到的逻辑回归是神经网络的一个示例。全连接的神经网络只是所有输入特征的一组线性函数。回想一个线性函数可以被写为输入特征向量与权重向量之间的内积，加上一个可能的常数项。线性函数的集合可以表示为矩阵向量乘积，其中权重向量成为权重矩阵。\\n\",\n    \"\\n\",\n    \"### 全连接层的数学定义\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"z=Wx+b\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"$W$的每一行是将整个输入向量$X$映射成$Z$中的单个输出的权重向量。$b$是表示每个神经元恒定偏移（或偏置）的标量。\\n\",\n    \"\\n\",\n    \"全连接层之所以如此命名，是因为在每一个输入都要在每个输出中使用。在数学上，这意味着对矩阵$W$中的值没有限制。（如我们将很快看到的，卷积层仅利用每个输出的一小部分输入。）在图中，一个完全连接的神经网络可以由一个完整的二部图表示，其中前一层的每个结点输出都连接到下一层的每个输入。\\n\",\n    \"\\n\",\n    \"![图8-7](images/chapter8/8-7.png)\\n\",\n    \"\\n\",\n    \"全连接层包含尽可能多的参数。因此，它们是昂贵的。这种密集连接允许网络检测可能涉及所有输入的全局模式。由于这个原因，AlexNet 的最后两层完全连接。在输入为条件下输出仍然是相互独立的。\\n\",\n    \"\\n\",\n    \"## 卷积层\\n\",\n    \"\\n\",\n    \"与全连接层相反，卷积层仅使用每个输出的输入子集。通过在输入上移动窗，每次使用几个特征产生输出。为了简单起见，可以对输入的不同集合使用相同的权重，而不是重新学习新权重。数学上，卷积算子以两个函数作为输入，并产生一个函数作为输出。它翻转一个输入函数，将其移动到另一个函数上，并在每个点上在乘法曲线下输出总面积。计算曲线下总面积的方法是取其积分。操作符在输入中是对称的，这意味着不管我们翻转第一个输入还是第二个输入，输出都是一样的。\\n\",\n    \"\\n\",\n    \"卷积定义为\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\left(f^{*} g\\\\right)(t)=\\\\int_{-\\\\infty}^{\\\\infty} f(\\\\tau) g(t-\\\\tau) d \\\\tau=\\\\int_{-\\\\infty}^{\\\\infty} g(\\\\tau) f(t-\\\\tau) d \\\\tau\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"我们已经看到了一个简单的卷积的示例，当我们看着图像梯度（“图像梯度”）。但是卷积的数学定义似乎仍有点复杂。用信号处理的示例来解释卷积后的结果是最容易的。\\n\",\n    \"\\n\",\n    \"想象一下，我们有一个小黑匣子。为了看到黑匣子的作用，我们通过一个单一的刺激单位。无论输出看起来如何，我们记录在一张纸上。我们等到对最初的刺激没有反应为止。随时间变化的函数称为响应函数；我们称之为响应函数$g(t)$。\\n\",\n    \"\\n\",\n    \"想想一下现在我们有一个疯狂的函数$f(t)$，随后将它输入黑盒中。在时间$t=0$时，$f(0)$与黑盒进行通讯，随后用$f(0)$乘以$g(0)$。在时间$t=1$，$f(1)$进入黑盒，随后与$g(0$相乘。在相同的时间，黑盒持续回复信号$f(0)$，它现在是与$g(1)$相乘了。所以在$t=1$时的输出为$(f(0)*g(1))+(f(1)*g(0))$。在$t=2$时，输出会变得更复杂，当$f(2)$进入图片后，这时$f(0)$与$f(1)$持续产生回复。所以在$t=2$时的总输出为$(f(0)*g(2))+(f(1)*g(1))+(f(2)*g(0))$。通过这种方式，响应函数在时间上有效地被翻转，其中$\\\\tau=0$总是与当前进入黑匣子的信息进行交互，并且响应函数的尾部与先前发生的函数进行交互。图 8—8 展示了这一过程。到目前为止，为了便于描述，我们已经把时间离散了。在现实中，时间是连续的，所以总和是一个积分。\\n\",\n    \"\\n\",\n    \"这个黑箱被称为线性系统，因为它不比标量乘法和求和更疯狂。卷积算子清楚地捕捉线性系统的影响。\\n\",\n    \"\\n\",\n    \"### 卷积的思想\\n\",\n    \"\\n\",\n    \"卷积算子捕获线性系统的效果，该线性系统将输入信号与其响应函数相乘，求出所有过去输入响应的和。\\n\",\n    \"\\n\",\n    \"在上面的示例中，$g(t)$用来表示响应函数，$f(t)$表示输入。但是由于卷积是对称的，响应和输出实际上并不重要。输出只是两者的结合。$g(t)$也称为滤波器。\\n\",\n    \"\\n\",\n    \"![图8-8](images/chapter8/8-8.png)\\n\",\n    \"\\n\",\n    \"图像是二维信号，所以我们需要一个二维滤波器。二维卷积滤波器通过取两个变量的积分来推广一维情形。由于数字图像具有离散像素，卷积积分变成离散和。此外，由于像素的数量是有限的，滤波函数只需要有限数量的元素。在图像处理中，二维卷积滤波器也被称为核或掩模。\\n\",\n    \"\\n\",\n    \"2 维卷积的离散定义\\n\",\n    \"$$\\n\",\n    \"\\\\left(f^{*} g\\\\right)[i, j]=\\\\sum_{u=0}^{m} \\\\sum_{v=0}^{n} f[u, v] g[i-u, j-v]\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"当将卷积滤波器应用于图像时，我们不需要定义一个覆盖整个图像的巨型滤波器。相反，只覆盖几个像素的滤波器就够了，并且在图像上应用相同的滤波器，并在在水平和垂直像素方向上移动。\\n\",\n    \"\\n\",\n    \"因为在图像中使用相同的滤波器，所以我们只需要定义一组小的参数。权衡是滤波器只能在一个小像素邻域内吸收信息。换言之，卷积神经网络识别局部信息而不是全局信息。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 卷积滤波器示例\\n\",\n    \"\\n\",\n    \"在这个示例中，我们将高斯滤波器应用于图像。高斯函数在零附近形成光滑对称的图形。滤波器在附近函数值产生加权平均值。当应用于图像时，它具有模糊附近像素值的效果。二维高斯滤波器是由 $G(x, y)=\\\\frac{1}{2 \\\\pi \\\\sigma} e^{-\\\\frac{x^{2}+y^{2}}{2 \\\\sigma^{2}}}$ 定义的，其中$\\\\sigma$为高斯函数的标准差，它控制着图形的宽度。\\n\",\n    \"\\n\",\n    \"在下面的代码示例中，我们将首先创建二维高斯滤波器，然后将它与猫图像进行卷积以产生模糊的猫。注意，这不是计算高斯滤波器的最精确的方法，但它是最容易理解的。采取在每个离散点的加权平均值，而不是简单的点估计是更好的实现方法。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ind = [-1., -0.5, 0., 0.5, 1.]\\n\",\n    \"X,Y = np.meshgrid(ind, ind)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-1. , -0.5,  0. ,  0.5,  1. ],\\n\",\n       \"       [-1. , -0.5,  0. ,  0.5,  1. ],\\n\",\n       \"       [-1. , -0.5,  0. ,  0.5,  1. ],\\n\",\n       \"       [-1. , -0.5,  0. ,  0.5,  1. ],\\n\",\n       \"       [-1. , -0.5,  0. ,  0.5,  1. ]])\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0.36787944, 0.53526143, 0.60653066, 0.53526143, 0.36787944],\\n\",\n       \"       [0.53526143, 0.77880078, 0.8824969 , 0.77880078, 0.53526143],\\n\",\n       \"       [0.60653066, 0.8824969 , 1.        , 0.8824969 , 0.60653066],\\n\",\n       \"       [0.53526143, 0.77880078, 0.8824969 , 0.77880078, 0.53526143],\\n\",\n       \"       [0.36787944, 0.53526143, 0.60653066, 0.53526143, 0.36787944]])\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# G is the unnormalized Gaussian kernel where the value at (0,0) is 1.0\\n\",\n    \"G = np.exp(-(np.multiply(X,X) + np.multiply(Y,Y))/2)\\n\",\n    \"G\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from skimage import data, color\\n\",\n    \"cat = color.rgb2gray(data.chelsea())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy import signal\\n\",\n    \"blurred_cat = signal.convolve2d(cat, G, mode='valid')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x25816dabd30>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True)\\n\",\n    \"\\n\",\n    \"ax1.axis('off')\\n\",\n    \"ax1.imshow(cat, cmap=plt.cm.gray)\\n\",\n    \"ax1.set_title('Input image')\\n\",\n    \"ax1.set_adjustable('box-forced')\\n\",\n    \"\\n\",\n    \"ax2.axis('off')\\n\",\n    \"ax2.imshow(blurred_cat, cmap=plt.cm.gray)\\n\",\n    \"ax2.set_title('After convolving with a Gaussian filter')\\n\",\n    \"ax2.set_adjustable('box-forced')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"AlxNet 中的卷积层是三维的。换言之，它们对前一层的三维像素进行操作。第一卷积神经网络采用原始 RGB 图像，并在所有三个颜色通道中学习用于局部图像邻域的卷积滤波器。随后的层跨越空间和内核尺寸将其作为输入像素。\\n\",\n    \"\\n\",\n    \"## 线性整流函数（Relu）变换\\n\",\n    \"\\n\",\n    \"神经网络的输出通常通过另一个非线性变换，也称为激活函数。常见的选择是 `tanh` 函数（在 -1 和 1 之间有界的光滑非线性函数），`sigmoid` 函数（0 到 1 之间的平滑非线性函数），或者称为整流线性单元的函数（`Relu`）。`Relu` 是一个线性函数的简单变化，其中负部分被归零。换言之，它修剪了负值，但留下无穷的正边界。`Relu` 的范围从 0 到无穷大。\\n\",\n    \"\\n\",\n    \"整流线性单元（`Relu`）是线性函数，负部分归零。\\n\",\n    \"\\n\",\n    \"$$RELU(x)=max(0,x)$$\\n\",\n    \"\\n\",\n    \"`tanh` 函数，在 -1 和 1 之间有界的光滑非线性函数。\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\tanh (x)=\\\\frac{\\\\sinh (x)}{\\\\cosh (x)}=\\\\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"`sigmoid` 函数，0 到 1 之间的平滑非线性函数。\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\text { sigmoid }(x)=\\\\frac{1}{1+e^{-x}}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"![图8-10](images/chapter8/8-10.png)\\n\",\n    \"\\n\",\n    \"`Relu`变换对原始图像或高斯滤波器等非负函数没有影响。然而，经过训练的神经网络，无论是完全连接的还是卷积的，都有可能输出负值。AlxNet 使用 `Relu` 代替其他变换，在训练过程中可以更快的收敛，它适用于每一个卷积和全连接层。\\n\",\n    \"\\n\",\n    \"## 响应归一化层\\n\",\n    \"\\n\",\n    \"在第 4 章和本章之前的讨论之后，归一化对大家来说应该是一个熟悉的概念。归一化将个体输出通过集体总响应的函数来划分。因此，理解归一化的另一种方式是，它在邻居之间产生竞争，因为现在每个输出的强度都相对于其邻居进行测量。AlexNet 在不同内核的每个位置上归一化输出。\\n\",\n    \"\\n\",\n    \"局部响应归一化引起相邻核之间的竞争\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"y_{k}=x_{k} /\\\\left(c+\\\\alpha \\\\sum_{\\\\ell \\\\in \\\\text { neighborhood\\\\ of } k} x_{\\\\ell}^{2}\\\\right)^{\\\\beta}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"其中，$x_k$是第$k$个核的输出，$y_k$是相对于邻域中的其他核的归一化响应。对每个输出位置分别执行归一化。换言之，对于每个输出位置$(i,j)$，在附近的卷积核输出上进行归一化。注意，这与在图像邻域或输出位置上的归一化不相同。内核邻域的尺寸$c$、$\\\\alpha$和$\\\\beta$的大小超参数都是通过图像验证集调整的。\\n\",\n    \"\\n\",\n    \"![图8-11](images/chapter8/8-11.png)\\n\",\n    \"\\n\",\n    \"## 池化层\\n\",\n    \"\\n\",\n    \"池化层将多个输入组合成单个输出。当卷积滤波器在图像上移动时，它为其尺寸下的每个邻域生成输出。池化层迫使局部图像邻域产生一个值而不是许多值。这减少了在深度学习网络的中间层中的输出数量，这有效地减少了过拟合训练数据的概率。\\n\",\n    \"\\n\",\n    \"有多种方法汇集输入：平均，求和（或计算一个广义范数），或取最大值。池化层通过图像或中间输出层移动。Alexnet 使用最大池化层，以 2 像素（或输出）的步幅移动图像，并在 3 个邻居之间汇集。\\n\",\n    \"\\n\",\n    \"![图8-12](images/chapter8/8-12.png)\\n\",\n    \"\\n\",\n    \"## AlexNet 的结构\\n\",\n    \"\\n\",\n    \"AlexNet 包含 5 个卷积层、2 个响应归一化层、3 个最大池化层和 2 个全连接层。与最终分类输出层一起，模型中共有 13 层神经网络层，形成8层层组。详情请参阅图 8-12。\\n\",\n    \"\\n\",\n    \"输入图像首先被缩放到`256x256`像素。输入实际上是`224x224`大小的随机图像，具有 3 个颜色通道。前两个卷积层各有一个响应归一化层和一个最大池化层，最后一个卷积层接着是最大池化层。原始文件用两个 GPU 来分割训练数据和计算。层之间的通信主要限于在相同的 GPU 内。在层组 2 和 3 之间，并且在层组 5 之后是例外的。在这些边界点，下一层从两个 GPU 上的前一层的内核的像素作为输入。每个中间层都使用 `Relu` 变换。\\n\",\n    \"\\n\",\n    \"![图8-13](images/chapter8/8-13.png)\\n\",\n    \"\\n\",\n    \"![图8-14](images/chapter8/8-14.png)\\n\",\n    \"\\n\",\n    \"注意 AlexNet 是 SIFT/HOG 特征提取器的梯度直方图标准化规范化体系结构（参见图8 -6），但具有更多的层。（这就是“深度学习”中的“深度”）不同于 SIFT/HOG，卷积核和全连接权值是从数据中学习的，而不是预定义的。此外，SIFT 中的归一化步骤在整个图像区域上遍及特征向量执行，而 AlexNet 中的响应归一化层在卷积核上归一化。\\n\",\n    \"\\n\",\n    \"深入的来看，模型从局部图像邻域中提取特征开始。每个后续层建立在先前层的输出上，有效地覆盖原始图像的相继较大区域。因此，即使前五个卷积层都具有相当小的内核宽度，后面的层依然能够制定更多的全局特征。端部的全连接层是最具全局性的。\\n\",\n    \"\\n\",\n    \"尽管特征的要点在概念上是清晰的，但是很难想象每个层挑选出的实际特征。图 8- 14 和图 8-15 显示了由特征型学习的前两层卷积核的可视化。第一层包括在不同的方向上的灰度边缘和纹理的检测器，以及颜色斑点和纹理。第二层似乎是各种光滑图案的检测器。\\n\",\n    \"\\n\",\n    \"![图8-15](images/chapter8/8-15.png)\\n\",\n    \"\\n\",\n    \"![图8-16](images/chapter8/8-16.png)\\n\",\n    \"\\n\",\n    \"尽管该领域有巨大的进步，图像特征仍然是一门艺术而不是科学。十年前，人工制作的特征提取步骤结合了图像梯度、边缘检测、定位、空间提示、平滑和归一化等。如今，深度学习架构师构建了封装相同想法的模型，但是这些参数是从训练图像中自动学习的。\\n\",\n    \"\\n\",\n    \"## 总结\\n\",\n    \"\\n\",\n    \"接近尾声，我们在直觉上更好地理解为什么最直接和简单的图像特征在执行任务时将永远不是最有用的，如图像分类。与其将每个像素表示为原子单位相反，更重要的是考虑像素与它们附近的其他像素之间的关系。我们可以将这些技术如 SIFT 和 HOG 一样，通过分析邻域的梯度更好地提取整个图像的特征，发展技术以适应其他任务。\\n\",\n    \"\\n\",\n    \"近年来的又一次飞跃将更深层次的神经网络应用于计算机视觉，以进一步推动图像的特征提取。这里要记住的重要一点是，深度学习堆叠了许多层的神经网络和相互转换。这些层中的一些，当单独检查时，开始梳理出类似的特征，这些特征可以被识别为人类视觉的构建块：定义线条、梯度、颜色图。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "9.feature-engineering/9.回到特征：将它们放到一起.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[原版（英文）图书地址](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**代码修改和整理**：[黄海广](https://github.com/fengdu78)，原文修改成jupyter notebook格式，并增加和修改了部分代码，测试全部通过，所有数据集已经放在[百度云](data/README.md)下载。\\n\",\n    \"\\n\",\n    \"**备注**：请等待，数据集已经下载，代码还未测试，先放上翻译。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 九、回到特征：将它们放到一起\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"当第一次看到图1-1 中从数据到结果的路径时，很可能会无所适从。纵贯本书，我们的重点在于介绍特征工程的基本原则，我们使用的是玩具模型和简单明了的数据集，这些例子是有意设计成有说明性和启发性的。\\n\",\n    \"\\n\",\n    \"机器学习的常见例子是展示最理想的情况和最佳结果，这掩盖了本书中描述的路径中的艰辛。既然基础已经打好，我们就离开模拟数据的简单世界，投入到使用真实的、结构化数据集的特征工程中。在前进的每个阶段中，我们都会研究如何从原始数据生成特征，如何进行特征转换，以及特征工程中需要何种权衡取舍。\\n\",\n    \"\\n\",\n    \"先说一下，这个综合示例的目标不是为数据集建立最好的模型，而是演示一下本书中几种技术的实际应用，以及如何更加深入地研究一下各种技术是否可以为建模过程提供价值。\\n\",\n    \"\\n\",\n    \"## 基于物品的协同过滤\\n\",\n    \"\\n\",\n    \"我们的任务是使用Microsoft Academic Graph数据集的子样本为学术论文构建推荐器。 对于正在搜索引文但没使用Google学术搜索的所有人来说，这应该非常方便。 以下是有关数据集的一些相关统计信息：\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Microsoft Academic Graph 数据集\\n\",\n    \"这个数据集包含 166 192 182 篇论文，可经由 Open Academic Graph 获取，\\n\",\n    \"- 只能用于研究目的。\\n\",\n    \"- 完整数据集的大小是 104GB。\\n\",\n    \"- 每条观测有 18 个变量用以标识论文，包括论文题目、论文摘要、作者姓名、关键字和研究领域。\\n\",\n    \"\\n\",\n    \"**备注**：这个数据集已经下载并处理好了，如果只是为了跑通本文代码，就不需要再下载了，本文数据我已经放到了[百度云](data/)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"这个数据集被设计成易于使用数据库存储和读取。对于机器学习模型来说，它可能不够整洁，需要做一些基本的数据整理。有些教师喜欢省略这个步骤，让学生直接建模并得到结果，但我们可不这么做，我们一切都从头开始。\\n\",\n    \"\\n\",\n    \"第一步是将一些变量整理为正确的形式，建立一个基于项目的协同过滤器，看看能否快速有效地找到那些非常相似的论文。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 基于物品的协同过滤的起源\\n\",\n    \"这种方法最初是由 Amazon 公司开发的，作为基于用户的商品推荐算法的一种改进。Sarawar 等人详细介绍了将推荐算法从基于用户切换到基于物品的过程中的困难和收获(Sarawar et al. (2001))。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"基于物品的协同过滤方法根据物品之间的相似程度来提供推荐。这项工作分为两个阶段： 首先找出物品之间的相似度评分，然后对所有评分进行排序，找到前 $N$ 个相似项目作为推荐。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 建立基于物品的推荐器\\n\",\n    \"基于物品的推荐器完成以下三项任务。\\n\",\n    \"\\n\",\n    \"1.\\t生成关于“事物”或物品的信息。\\n\",\n    \"2.\\t对所有物品进行评分，找出与某个项目“相似”的其他物品。\\n\",\n    \"3.\\t返回评分排序 + 物品。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 第一步：数据导入、清理和特征解析\\n\",\n    \"与所有优秀的科学实验一样，我们从一个假设开始。在这个例子中，我们假定那些大约在同一时间而且在同一研究领域发表的论文对用户是最有用的。我们使用一种简单的方法从完整数据集的一个子样本中解析出这些字段。在生成了简单的稀疏数组后，我们在整个物品数组上运行基于物品的协同过滤器，看看能否得到满意的结果。\\n\",\n    \"\\n\",\n    \"基于物品的协同过滤器使用相似度评分来比较物品。在这个例子中，余弦相似度可以在两个非零向量之间提供合理的比较。下面的例子使用的就是余弦距离，它是余弦相似度在正空间中的补集，即：\\n\",\n    \"\\n\",\n    \"$$D_C(A,B)=1-S_C(A,B)$$\\n\",\n    \"其中 $D_C$ 是余弦距离，而 $S_C$ 表示余弦相似度。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 学术论文推荐器：简单方法\\n\",\n    \"第一步就是导入和检查数据集。在例 9-1  中，我们先导入数据，然后选择出一些可用的字段，以此来开始实验。在保留的字段中仍然含有丰富的信息，如图 9-1 所示。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 例 9-1：导入并过滤数据\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(20000, 19)\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model_df = pd.read_json('data/mag_subset20K.txt', lines=True)\\n\",\n    \"\\n\",\n    \"model_df.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Index(['abstract', 'authors', 'doc_type', 'doi', 'fos', 'id', 'issue',\\n\",\n       \"       'keywords', 'lang', 'n_citation', 'page_end', 'page_start', 'publisher',\\n\",\n       \"       'references', 'title', 'url', 'venue', 'volume', 'year'],\\n\",\n       \"      dtype='object')\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model_df.columns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(20000, 9)\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# filter out non-English articles\\n\",\n    \"# keep abstract, authors, fos, keywords, year, title\\n\",\n    \"# model_df = model_df[model_df.lang == 'en'].drop_duplicates(\\n\",\n    \"#     subset='title', keep='first').drop([\\n\",\n    \"#         'doc_type', 'doi', 'id', 'issue', 'lang', 'n_citation', 'page_end',\\n\",\n    \"#         'page_start', 'publisher', 'references', 'url', 'venue', 'volume'\\n\",\n    \"#     ],\\n\",\n    \"#                                        axis=1)\\n\",\n    \"model_df = model_df.drop_duplicates(\\n\",\n    \"    subset='title', keep='first').drop([\\n\",\n    \"        'doc_type', 'doi', 'id', 'issue', 'n_citation', 'page_end',\\n\",\n    \"        'page_start', 'publisher', 'venue', 'volume'\\n\",\n    \"    ],\\n\",\n    \"                                       axis=1)\\n\",\n    \"model_df.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>abstract</th>\\n\",\n       \"      <th>authors</th>\\n\",\n       \"      <th>fos</th>\\n\",\n       \"      <th>keywords</th>\\n\",\n       \"      <th>lang</th>\\n\",\n       \"      <th>references</th>\\n\",\n       \"      <th>title</th>\\n\",\n       \"      <th>url</th>\\n\",\n       \"      <th>year</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>A system and method for maskless direct write ...</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>[Electronic engineering, Computer hardware, En...</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>en</td>\\n\",\n       \"      <td>[354c172f-d877-4e60-a7eb-c1b1cf03ce4d, 76cf106...</td>\\n\",\n       \"      <td>System and Method for Maskless Direct Write Li...</td>\\n\",\n       \"      <td>[http://www.freepatentsonline.com/y2016/021111...</td>\\n\",\n       \"      <td>2015</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>[{'name': 'Ahmed M. Alluwaimi'}]</td>\\n\",\n       \"      <td>[Biology, Virology, Immunology, Microbiology]</td>\\n\",\n       \"      <td>[paratuberculosis, of, subspecies, proceedings...</td>\\n\",\n       \"      <td>en</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>The dilemma of the Mycobacterium avium subspec...</td>\\n\",\n       \"      <td>[http://www.omicsonline.org/proceedings/the-di...</td>\\n\",\n       \"      <td>2016</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                            abstract  \\\\\\n\",\n       \"0  A system and method for maskless direct write ...   \\n\",\n       \"1                                                NaN   \\n\",\n       \"\\n\",\n       \"                            authors  \\\\\\n\",\n       \"0                               NaN   \\n\",\n       \"1  [{'name': 'Ahmed M. Alluwaimi'}]   \\n\",\n       \"\\n\",\n       \"                                                 fos  \\\\\\n\",\n       \"0  [Electronic engineering, Computer hardware, En...   \\n\",\n       \"1      [Biology, Virology, Immunology, Microbiology]   \\n\",\n       \"\\n\",\n       \"                                            keywords lang  \\\\\\n\",\n       \"0                                                NaN   en   \\n\",\n       \"1  [paratuberculosis, of, subspecies, proceedings...   en   \\n\",\n       \"\\n\",\n       \"                                          references  \\\\\\n\",\n       \"0  [354c172f-d877-4e60-a7eb-c1b1cf03ce4d, 76cf106...   \\n\",\n       \"1                                                NaN   \\n\",\n       \"\\n\",\n       \"                                               title  \\\\\\n\",\n       \"0  System and Method for Maskless Direct Write Li...   \\n\",\n       \"1  The dilemma of the Mycobacterium avium subspec...   \\n\",\n       \"\\n\",\n       \"                                                 url  year  \\n\",\n       \"0  [http://www.freepatentsonline.com/y2016/021111...  2015  \\n\",\n       \"1  [http://www.omicsonline.org/proceedings/the-di...  2016  \"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model_df.head(2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>图 9-1：Microsoft Academic Graph数据集的前两行</center>\\n\",\n    \"\\n\",\n    \"从表 9-1 中可以非常清楚地看出，需要何种程度的数据整理才能将原始数据转换为更适合建模的形式。列表和字典便于数据存储，但如果不经过一些解包操作的话，就不够整洁，不能很好地适应机器学习（Wickham, 2014）。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>表9-1：model_df的数据概述</center>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"|Field name|Description|Field type|# NaN|\\n\",\n    \"|:-:|:-:|:-:|:-:|\\n\",\n    \"|abstract|paper abstract|string\\t|4393|\\n\",\n    \"|authors|author names and affiliations|list of dict, keys = name, org|1|\\n\",\n    \"|fos|fields of study|list of strings|1733|\\n\",\n    \"|keywords|keywords|list of strings|4294|\\n\",\n    \"|title|paper title|string|0|\\n\",\n    \"|year|published year|int|0|\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在例 9-2 中，我们先重点关注两个字段，将它们从列表和整数转换为特征数组，如图 9-2所示。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"####  例 9-2：协同过滤阶段 1：建立物品特征矩阵\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"9325\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"unique_fos = sorted(list({ feature\\n\",\n    \"                          for paper_row in model_df.fos.fillna('0')\\n\",\n    \"                          for feature in paper_row }))\\n\",\n    \"\\n\",\n    \"unique_year = sorted(model_df['year'].astype('str').unique())\\n\",\n    \"\\n\",\n    \"len(unique_fos + unique_year)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"13251\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model_df.shape[0] - pd.isnull(model_df['fos']).sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"9150\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(unique_fos)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['Ancient history',\\n\",\n       \" 'Dentistry',\\n\",\n       \" 'Functional residual capacity',\\n\",\n       \" 'Hierarchical database model',\\n\",\n       \" 'Irrigation',\\n\",\n       \" 'Mycology',\\n\",\n       \" 'Noise temperature',\\n\",\n       \" 'Powder diffraction',\\n\",\n       \" 'Random test generator',\\n\",\n       \" 'Reaction intermediate',\\n\",\n       \" 'Rotating wave approximation',\\n\",\n       \" 'Schwarz lemma',\\n\",\n       \" 'Service delivery framework',\\n\",\n       \" 'Social environment',\\n\",\n       \" 'Transforming growth factor']\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import random\\n\",\n    \"[unique_fos[i] for i in sorted(random.sample(range(len(unique_fos)), 15)) ]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def feature_array(x, unique_array):\\n\",\n    \"    row_dict = {}\\n\",\n    \"    for i in x.index:\\n\",\n    \"        var_dict = {}\\n\",\n    \"        \\n\",\n    \"        for j in range(len(unique_array)):\\n\",\n    \"            if type(x[i]) is list:\\n\",\n    \"                if unique_array[j] in x[i]:\\n\",\n    \"                    var_dict.update({unique_array[j]: 1})\\n\",\n    \"                else:\\n\",\n    \"                    var_dict.update({unique_array[j]: 0})\\n\",\n    \"            else:    \\n\",\n    \"                if unique_array[j] == str(x[i]):\\n\",\n    \"                    var_dict.update({unique_array[j]: 1})\\n\",\n    \"                else:\\n\",\n    \"                    var_dict.update({unique_array[j]: 0})\\n\",\n    \"        \\n\",\n    \"        row_dict.update({i : var_dict})\\n\",\n    \"    \\n\",\n    \"    feature_df = pd.DataFrame.from_dict(row_dict, dtype='str').T\\n\",\n    \"    \\n\",\n    \"    return feature_df\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Wall time: 1min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%time year_features = feature_array(model_df['year'], unique_year)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Wall time: 58min 30s\\n\",\n      \"Size of fos feature array:  5856418904\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%time fos_features = feature_array(model_df['fos'], unique_fos)\\n\",\n    \"\\n\",\n    \"from sys import getsizeof\\n\",\n    \"print('Size of fos feature array: ', getsizeof(fos_features))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"9325\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"year_features.shape[1] + fos_features.shape[1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Wall time: 24.9 s\\n\",\n      \"Size of first feature array:  5969031694\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# now looking at 10399 x  7760 array for our feature space\\n\",\n    \"\\n\",\n    \"%time first_features = fos_features.join(year_features).T\\n\",\n    \"\\n\",\n    \"first_size = getsizeof(first_features)\\n\",\n    \"\\n\",\n    \"print('Size of first feature array: ', first_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will start with a simple example of building a recommender with just a few fields, building sparse arrays of available features to calculate for the cosine similary between papers. We will see if reasonably similar papers can be found in a timely manner.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(9325, 20000)\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"first_features.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>19990</th>\\n\",\n       \"      <th>19991</th>\\n\",\n       \"      <th>19992</th>\\n\",\n       \"      <th>19993</th>\\n\",\n       \"      <th>19994</th>\\n\",\n       \"      <th>19995</th>\\n\",\n       \"      <th>19996</th>\\n\",\n       \"      <th>19997</th>\\n\",\n       \"      <th>19998</th>\\n\",\n       \"      <th>19999</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0-10 V lighting control</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1-planar graph</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1/N expansion</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10G-PON</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 20000 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                        0     1     2     3     4     5     6     7     8      \\\\\\n\",\n       \"0                           0     0     0     0     0     0     0     0     0   \\n\",\n       \"0-10 V lighting control     0     0     0     0     0     0     0     0     0   \\n\",\n       \"1-planar graph              0     0     0     0     0     0     0     0     0   \\n\",\n       \"1/N expansion               0     0     0     0     0     0     0     0     0   \\n\",\n       \"10G-PON                     0     0     0     0     0     0     0     0     0   \\n\",\n       \"\\n\",\n       \"                        9      ...  19990 19991 19992 19993 19994 19995 19996  \\\\\\n\",\n       \"0                           0  ...      0     0     0     0     0     0     0   \\n\",\n       \"0-10 V lighting control     0  ...      0     0     0     0     0     0     0   \\n\",\n       \"1-planar graph              0  ...      0     0     0     0     0     0     0   \\n\",\n       \"1/N expansion               0  ...      0     0     0     0     0     0     0   \\n\",\n       \"10G-PON                     0  ...      0     0     0     0     0     0     0   \\n\",\n       \"\\n\",\n       \"                        19997 19998 19999  \\n\",\n       \"0                           0     0     0  \\n\",\n       \"0-10 V lighting control     0     0     0  \\n\",\n       \"1-planar graph              0     0     0  \\n\",\n       \"1/N expansion               0     0     0  \\n\",\n       \"10G-PON                     0     0     0  \\n\",\n       \"\\n\",\n       \"[5 rows x 20000 columns]\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"first_features.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>图 9-2：first_features 的头部——原始数据集中观测（论文）的索引是列，特征是行</center>\\n\",\n    \"\\n\",\n    \"我们成功地将一个较小的数据集（大约 1 万行原始数据）转换成了 2.5GB 的特征。但对于需要快速迭代的数据探索过程来说，这种方法太笨重了。我们需要更快速的方法，使得出的特征占用更少的计算资源和实验时间。\\n\",\n    \"\\n\",\n    \"稍安勿躁，不妨先看一下，现在这种特征在下一阶段能为我们做出多么好的推荐（见例 9-3）。我们定义“好”的推荐就是与输入相似的论文。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"####  例 9-3   协同过滤阶段 2：查找相似物品\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# from scipy.spatial.distance import cosine\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# def item_collab_filter(features_df):\\n\",\n    \"#     item_similarities = pd.DataFrame(\\n\",\n    \"#         index=features_df.columns, columns=features_df.columns)\\n\",\n    \"#     item_similarities.fillna(0)#后面添加的，把nan填充为0\\n\",\n    \"#     for i in features_df.columns:\\n\",\n    \"#         for j in features_df.columns:\\n\",\n    \"#             item_similarities.loc[i][j] = 1 - cosine(features_df[i],\\n\",\n    \"#                                                      features_df[j])\\n\",\n    \"\\n\",\n    \"#     return item_similarities\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from scipy.spatial.distance import cosine\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def item_collab_filter(features_df):\\n\",\n    \"    item_similarities = pd.DataFrame(\\n\",\n    \"        index=features_df.columns, columns=features_df.columns)\\n\",\n    \"    for i in features_df.columns:\\n\",\n    \"        for j in features_df.columns:\\n\",\n    \"            item_similarities.loc[i][j] = 1 - cosine(\\n\",\n    \"                features_df[i].tolist(),\\n\",\n    \"                features_df[j].tolist())  #这里有改动，增加了.tolist()\\n\",\n    \"\\n\",\n    \"    return item_similarities\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>19990</th>\\n\",\n       \"      <th>19991</th>\\n\",\n       \"      <th>19992</th>\\n\",\n       \"      <th>19993</th>\\n\",\n       \"      <th>19994</th>\\n\",\n       \"      <th>19995</th>\\n\",\n       \"      <th>19996</th>\\n\",\n       \"      <th>19997</th>\\n\",\n       \"      <th>19998</th>\\n\",\n       \"      <th>19999</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0-10 V lighting control</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1-planar graph</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1/N expansion</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10G-PON</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14-3-3 protein</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2-choice hashing</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20th-century philosophy</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2D Filters</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2D computer graphics</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2DEG</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3-D Secure</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3D computer graphics</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3D lookup table</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3D pose estimation</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3D radar</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3D reconstruction</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3D single-object recognition</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3G</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3G MIMO</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>40-bit encryption</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5-HT1 receptor</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5-HT2 receptor</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5-HT5A receptor</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5052 aluminium alloy</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>56-bit encryption</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6111 aluminium alloy</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>78xx</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>ABO blood group system</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>AC motor</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1988</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1989</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1990</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1991</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1992</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1993</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1994</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1995</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n   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      \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1998</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n    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      \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2012</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2013</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2014</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2015</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2016</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2017</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>9325 rows × 20000 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                             0     1     2     3     4     5     6     7      \\\\\\n\",\n       \"0                                0     0     0     0     0     0     0     0   \\n\",\n       \"0-10 V lighting control          0     0     0     0     0     0     0     0   \\n\",\n       \"1-planar graph                   0     0     0     0     0     0     0     0   \\n\",\n       \"1/N expansion                    0     0     0     0     0     0     0     0   \\n\",\n       \"10G-PON                          0     0     0     0     0     0     0     0   \\n\",\n       \"14-3-3 protein                   0     0     0     0     0     0     0     0   \\n\",\n       \"2-choice hashing                 0     0     0     0     0     0     0     0   \\n\",\n       \"20th-century philosophy          0     0     0     0     0     0     0     0   \\n\",\n       \"2D Filters                       0     0     0     0     0     0     0     0   \\n\",\n       \"2D computer graphics             0     0     0     0     0     0     0     0   \\n\",\n       \"2DEG                             0     0     0     0     0     0     0     0   \\n\",\n       \"3-D Secure                       0     0     0     0     0     0     0     0   \\n\",\n       \"3D computer graphics             0     0     0     0     0     0     0     0   \\n\",\n       \"3D lookup table                  0     0     0     0     0     0     0     0   \\n\",\n       \"3D pose estimation               0     0     0     0     0     0     0     0   \\n\",\n       \"3D radar                         0     0     0     0     0     0     0     0   \\n\",\n       \"3D reconstruction                0     0     0     0     0     0     0     0   \\n\",\n       \"3D single-object recognition     0     0     0     0     0     0     0     0   \\n\",\n       \"3G                               0     0     0     0     0     0     0     0   \\n\",\n       \"3G MIMO                          0     0    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     \"                             8     9      ...  19990 19991 19992 19993 19994  \\\\\\n\",\n       \"0                                0     0  ...      0     0     0     0     0   \\n\",\n       \"0-10 V lighting control          0     0  ...      0     0     0     0     0   \\n\",\n       \"1-planar graph                   0     0  ...      0     0     0     0     0   \\n\",\n       \"1/N expansion                    0     0  ...      0     0     0     0     0   \\n\",\n       \"10G-PON                          0     0  ...      0     0     0     0     0   \\n\",\n       \"14-3-3 protein                   0     0  ...      0     0     0     0     0   \\n\",\n       \"2-choice hashing                 0     0  ...      0     0     0     0     0   \\n\",\n       \"20th-century philosophy          0     0  ...      0     0     0     0     0   \\n\",\n       \"2D Filters                       0     0  ...      0     0     0     0     0   \\n\",\n       \"2D computer graphics           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   0   \\n\",\n       \"40-bit encryption                0     0  ...      0     0     0     0     0   \\n\",\n       \"5-HT1 receptor                   0     0  ...      0     0     0     0     0   \\n\",\n       \"5-HT2 receptor                   0     0  ...      0     0     0     0     0   \\n\",\n       \"5-HT5A receptor                  0     0  ...      0     0     0     0     0   \\n\",\n       \"5052 aluminium alloy             0     0  ...      0     0     0     0     0   \\n\",\n       \"56-bit encryption                0     0  ...      0     0     0     0     0   \\n\",\n       \"6111 aluminium alloy             0     0  ...      0     0     0     0     0   \\n\",\n       \"78xx                             0     0  ...      0     0     0     0     0   \\n\",\n       \"ABO blood group system           0     0  ...      0     0     0     0     0   \\n\",\n       \"AC motor                         0     0  ...      0     0     0     0     0   \\n\",\n       \"...                            ...   ...  ...    ...   ...   ...   ...   ...   \\n\",\n       \"1988                             0     0  ...      0     0     0     0     0   \\n\",\n       \"1989                             0     0  ...      0     0     0     0     0   \\n\",\n       \"1990                             0     0  ...      0     0     0     0     0   \\n\",\n       \"1991                             0     0  ...      0     0     0     0     0   \\n\",\n       \"1992                             0     0  ...      0     0     0     0     0   \\n\",\n       \"1993                             0     0  ...      0     0     0     0     0   \\n\",\n       \"1994                             0     0  ...      0     0     0     1     0   \\n\",\n       \"1995                             0     0  ...      0     0     0     0     0   \\n\",\n       \"1996                             0     0  ...      0     1     0     0     0   \\n\",\n       \"1997                             0     0  ...      0     0     0     0     0   \\n\",\n       \"1998                             0     0  ...      0     0     0     0     0   \\n\",\n       \"1999                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2000                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2001                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2002                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2003                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2004                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2005                             0     0  ...      1     0     0     0     0   \\n\",\n       \"2006                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2007                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2008                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2009                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2010                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2011                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2012                             0     1  ...      0     0     0     0     0   \\n\",\n       \"2013                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2014                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2015                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2016                             0     0  ...      0     0     0     0     0   \\n\",\n       \"2017                             0     0  ...      0     0     0     0     0   \\n\",\n       \"\\n\",\n       \"                             19995 19996 19997 19998 19999  \\n\",\n       \"0                                0     0     0     0     0  \\n\",\n       \"0-10 V lighting control          0     0     0     0     0  \\n\",\n       \"1-planar graph                   0     0     0     0     0  \\n\",\n       \"1/N expansion                    0     0     0     0     0  \\n\",\n       \"10G-PON                          0     0     0     0     0  \\n\",\n       \"14-3-3 protein                   0     0     0     0     0  \\n\",\n       \"2-choice hashing                 0     0     0     0     0  \\n\",\n       \"20th-century philosophy          0     0     0     0     0  \\n\",\n       \"2D Filters                       0     0     0     0     0  \\n\",\n       \"2D computer graphics             0     0     0     0     0  \\n\",\n       \"2DEG                             0     0     0     0     0  \\n\",\n       \"3-D Secure                       0     0     0     0     0  \\n\",\n       \"3D computer graphics             0     0     0     0     0  \\n\",\n       \"3D lookup table                  0     0     0     0     0  \\n\",\n       \"3D pose estimation               0     0     0     0     0  \\n\",\n       \"3D radar                         0     0     0     0     0  \\n\",\n       \"3D reconstruction                0     0     0     0     0  \\n\",\n       \"3D single-object recognition     0     0     0     0     0  \\n\",\n       \"3G                               0     0     0     0     0  \\n\",\n       \"3G MIMO                          0     0     0     0     0  \\n\",\n       \"40-bit encryption                0     0     0     0     0  \\n\",\n       \"5-HT1 receptor                   0     0     0     0     0  \\n\",\n       \"5-HT2 receptor                   0     0     0     0     0  \\n\",\n       \"5-HT5A receptor                  0     0     0     0     0  \\n\",\n       \"5052 aluminium alloy             0     0     0     0     0  \\n\",\n       \"56-bit encryption                0     0     0     0     0  \\n\",\n       \"6111 aluminium alloy             0     0     0     0     0  \\n\",\n       \"78xx                             0     0     0     0     0  \\n\",\n       \"ABO blood group system           0     0     0     0     0  \\n\",\n       \"AC motor                         0     0     0     0     0  \\n\",\n       \"...                            ...   ...   ...   ...   ...  \\n\",\n       \"1988                             0     0     0     0     0  \\n\",\n       \"1989                             0     0     0     0     0  \\n\",\n       \"1990                             0     0     0     0     0  \\n\",\n       \"1991                             0     0     0     0     0  \\n\",\n       \"1992                             0     0     0     0     0  \\n\",\n       \"1993                             0     0     0     0     0  \\n\",\n       \"1994                             0     0     0     0     0  \\n\",\n       \"1995                             0     0     0     0     0  \\n\",\n       \"1996                             0     0     0     0     0  \\n\",\n       \"1997                             0     0     0     0     0  \\n\",\n       \"1998                             0     0     0     0     0  \\n\",\n       \"1999                             0     0     0     0     0  \\n\",\n       \"2000                             0     0     0     0     0  \\n\",\n       \"2001                             0     0     0     0     0  \\n\",\n       \"2002                             0     0     0     0     0  \\n\",\n       \"2003                             0     0     0     0     0  \\n\",\n       \"2004                             0     0     0     0     0  \\n\",\n       \"2005                             0     0     0     0     0  \\n\",\n       \"2006                             0     0     0     0     0  \\n\",\n       \"2007                             0     0     0     0     0  \\n\",\n       \"2008                             0     0     0     0     0  \\n\",\n       \"2009                             0     0     0     0     0  \\n\",\n       \"2010                             0     0     0     0     0  \\n\",\n       \"2011                             0     0     0     0     0  \\n\",\n       \"2012                             0     0     0     0     0  \\n\",\n       \"2013                             0     1     0     0     0  \\n\",\n       \"2014                             0     0     0     0     0  \\n\",\n       \"2015                             0     0     0     0     1  \\n\",\n       \"2016                             0     0     0     0     0  \\n\",\n       \"2017                             1     0     0     0     0  \\n\",\n       \"\\n\",\n       \"[9325 rows x 20000 columns]\"\n      ]\n     },\n     \"execution_count\": 54,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"first_features\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Wall time: 1h 3min 18s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%time first_items = item_collab_filter(first_features.loc[:, 0:1000])\\n\",\n    \"#这一步时间非常长，大概要1小时\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Why does it take so long for us to calculate the item similarities using only two features? We are taking the dot product of a 10,399 × 1,000 我们只是使用两个特征来计算项目相似度，为什么计算时间如此之长？因为我们使用了嵌套 for 循环来计算一个 10 399×1000 的矩阵的点积。如果向模型中添加了更多观测，那每次循环的时间还会增加。注意，我们只筛选出了英文论文，这只是整个可用数据集的一个子集。当得到一个差不多“好”的结果时，还需要回到更大的数据集合上进行测试，看看这是不是最好的结果。\\n\",\n    \"\\n\",\n    \"怎么才能做得更快一些呢？因为每次只需要一个结果，所以可以修改一下函数，指定我们需要的前几个结果的数量，每次只计算一个项目。我们以后会这么做，因为需要持续改进实验。眼下还是使用全特征空间，理解一下在实际数据集上使用暴力算法时迭代造成的影响。\\n\",\n    \"\\n\",\n    \"要得到好的推荐，需要一种更好的特征转换方法。我们有足够的观测来改进吗？让我们绘制一幅热图（见例 9-4），看看是否有彼此相似的论文。结果显示在图 9-3 中。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 例 9-4:论文推荐热图\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"import numpy as np\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21863ac3860>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.set()\\n\",\n    \"ax = sns.heatmap(\\n\",\n    \"    first_items.fillna(0),\\n\",\n    \"    vmin=0,\\n\",\n    \"    vmax=1,\\n\",\n    \"    cmap=\\\"YlGnBu\\\",\\n\",\n    \"    xticklabels=250,\\n\",\n    \"    yticklabels=250)\\n\",\n    \"ax.tick_params(labelsize=12)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>图 9-3：相似论文热图，基于两个初始特征：发表年份和研究领域</center>\\n\",\n    \"\\n\",\n    \"颜色较暗的像素表示彼此相似的项目。黑色对角线说明了余弦相似度能正确地表示出每篇论文都与它本身是最相似的。但是，因为有个特征中有很多 NaN 值，所以对角线是断断续续的。可以看出，尽管多数项目是彼此不相似的（这说明我们的数据集来源非常广泛），但还是有一些相似度评分很高的候选值。定性地看，这些可能是好的推荐，也可能不是， 但至少说明了我们的方法不是一无是处。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"例 9-5 给出了将物品相似度转换为推荐的方法。值得庆幸的是：我们还有大量可用的特征，改进空间非常大。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 例 9-5：基于物品的协同过滤推荐\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def paper_recommender(paper_index, items_df):\\n\",\n    \"    print('Based on the paper: \\\\nindex = ', paper_index)\\n\",\n    \"    print(model_df.iloc[paper_index])\\n\",\n    \"    top_results = items_df.loc[paper_index].sort_values(\\n\",\n    \"        ascending=False).head(4)\\n\",\n    \"    print('\\\\nTop three results: ')\\n\",\n    \"    order = 1\\n\",\n    \"    for i in top_results.index.tolist()[-3:]:\\n\",\n    \"        print(order, '. Paper index = ', i)\\n\",\n    \"        print('Similarity score: ', top_results[i])\\n\",\n    \"        print(model_df.iloc[i], '\\\\n')\\n\",\n    \"        if order < 5: order += 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Based on the paper: \\n\",\n      \"index =  2\\n\",\n      \"abstract                                                    NaN\\n\",\n      \"authors       [{'name': 'Jovana P. Lekovich', 'org': 'Weill ...\\n\",\n      \"fos                                                         NaN\\n\",\n      \"keywords                                                    NaN\\n\",\n      \"lang                                                         en\\n\",\n      \"references                                                  NaN\\n\",\n      \"title         Should endometriosis be an indication for intr...\\n\",\n      \"url           [http://www.fertstert.org/article/S0015-0282(1...\\n\",\n      \"year                                                       2015\\n\",\n      \"Name: 2, dtype: object\\n\",\n      \"\\n\",\n      \"Top three results: \\n\",\n      \"1 . Paper index =  71\\n\",\n      \"Similarity score:  1.0\\n\",\n      \"abstract                                              NaN\\n\",\n      \"authors                    [{'name': 'Harold S. Wilson'}]\\n\",\n      \"fos                                                   NaN\\n\",\n      \"keywords                                              NaN\\n\",\n      \"lang                                                  NaN\\n\",\n      \"references                                            NaN\\n\",\n      \"title         Chapter III. “My Blood Is Like Champagne”62\\n\",\n      \"url                                                   NaN\\n\",\n      \"year                                                 2015\\n\",\n      \"Name: 71, dtype: object \\n\",\n      \"\\n\",\n      \"2 . Paper index =  798\\n\",\n      \"Similarity score:  1.0\\n\",\n      \"abstract                                                    NaN\\n\",\n      \"authors                              [{'name': 'غفاری، سعیده'}]\\n\",\n      \"fos                                                         NaN\\n\",\n      \"keywords      [جستجو در پایان نامه های دانشجویی دانشگاه فردو...\\n\",\n      \"lang                                                         fa\\n\",\n      \"references                                                  NaN\\n\",\n      \"title         تهیه نانوکامپوزیت پلیمری توسط نانوذرات اکسید ف...\\n\",\n      \"url           [http://thesis.um.ac.ir/rs/59364.pdf, http://t...\\n\",\n      \"year                                                       2015\\n\",\n      \"Name: 798, dtype: object \\n\",\n      \"\\n\",\n      \"3 . Paper index =  2\\n\",\n      \"Similarity score:  1.0\\n\",\n      \"abstract                                                    NaN\\n\",\n      \"authors       [{'name': 'Jovana P. Lekovich', 'org': 'Weill ...\\n\",\n      \"fos                                                         NaN\\n\",\n      \"keywords                                                    NaN\\n\",\n      \"lang                                                         en\\n\",\n      \"references                                                  NaN\\n\",\n      \"title         Should endometriosis be an indication for intr...\\n\",\n      \"url           [http://www.fertstert.org/article/S0015-0282(1...\\n\",\n      \"year                                                       2015\\n\",\n      \"Name: 2, dtype: object \\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"paper_recommender(2, first_items)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"好消息是返回的最相似论文就是我们要找的论文，坏消息是即使我们使用了选定的特征，另外两篇论文似乎与我们的搜索初衷相去甚远。\\n\",\n    \"\\n\",\n    \"“是的，是的，”你可能会说，“但现在是大数据时代，这会解决我们的问题！我们难道不能通过更多数据找出更好的结果吗？”可能会，但即使大数据也不能弥补糟糕的数据和特征选择所造成的恶果。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![](images/chapter9/9-4.png)\\n\",\n    \"<center>图 9-4：机器学习(https://xkcd.com/1838/)</center>\\n\",\n    \"现在的暴力方法太慢了，远算不上是智能的、迭代的特征工程。下面试验一下新的特征工程技术，看看是否能提高计算速度，找到更合适的特征和搜索结果的更好方式。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 第二步：更多特征工程和更智能的模型\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"最初的方法是创建一个巨大的、稀疏的数组，然后通过一个筛选器暴力求解。有多种方式可以改进这种方法。下一步的重点是使用更好的技术来处理两个初始特征，并修改基于物品的协同过滤方法来加快迭代。\\n\",\n    \"\\n\",\n    \"首先，在假设中的两个变量上，试验一下本书介绍过的精彩的特征工程技巧。在更加深入地研究了特征之后，我们可以选择那些适合每种变量的技术，将变量转换为适合推荐系统的“更好”的特征。\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"### 学术论文推荐器：第2轮\\n\",\n    \"先看出版年份。2.2.2 节（“Quantization or Binning”）中介绍了为什么使用原始计数作为特征不适合那些使用相似度度量的方法。例 9-6 和图 9-5 会研究如何对 year 进行转换，以使它更加适合我们选择的模型。\\n\",\n    \"\\n\",\n    \"#### 例 9-6：等宽分箱 + 虚拟编码（第 1 部分）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Year spread:  1800  -  2017\\n\",\n      \"Quantile spread:\\n\",\n      \" 0.25    1992.0\\n\",\n      \"0.50    2005.0\\n\",\n      \"0.75    2012.0\\n\",\n      \"Name: year, dtype: float64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Year spread: \\\", model_df['year'].min(), \\\" - \\\", model_df['year'].max())\\n\",\n    \"print(\\\"Quantile spread:\\\\n\\\", model_df['year'].quantile([0.25, 0.5, 0.75]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0,0.5,'Occurrence')\"\n      ]\n     },\n     \"execution_count\": 61,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21863b797f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot years to see the distribution\\n\",\n    \"fig, ax = plt.subplots()\\n\",\n    \"model_df['year'].hist(ax=ax, bins= model_df['year'].max() - model_df['year'].min())\\n\",\n    \"ax.tick_params(labelsize=12)\\n\",\n    \"ax.set_xlabel('Year Count', fontsize=12)\\n\",\n    \"ax.set_ylabel('Occurrence', fontsize=12)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>图 9-5：数据集中 10 000 多篇学术论文的原始出版年份分布</center>\\n\",\n    \"从图 9-5 中的偏态分布来看，出版年份非常适合分箱操作。\\n\",\n    \"\\n\",\n    \"我们将根据变量的取值范围来分箱，而不是唯一的特征值数量。为了进一步压缩特征空间，我们对分箱结果进行虚拟编码（见例 9-7）。Pandas 的内置函数可以完成这两项任务。这些方法的结果很容易解释，所以我们可以对转换后的特征做一个快速检查（见图 9-6），再进行后面的工作。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 例 9-7   定宽分箱 + 虚拟编码（第 2 部分）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"217\"\n      ]\n     },\n     \"execution_count\": 62,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# we'll base our bins on the range of the variable, rather than the unique number of features\\n\",\n    \"model_df['year'].max() - model_df['year'].min()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# binning here (by 10 years)\\n\",\n    \"bins = int(round((model_df['year'].max() - model_df['year'].min()) / 10))\\n\",\n    \"\\n\",\n    \"temp_df = pd.DataFrame(index=model_df.index)\\n\",\n    \"temp_df['yearBinned'] = pd.cut(model_df['year'].tolist(), bins, precision=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"We have reduced from 175 to 21 features representing the year.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# now we only have as many bins as we created(grouping together by 10 years)\\n\",\n    \"print('We have reduced from', len(model_df['year'].unique()), 'to',\\n\",\n    \"      len(temp_df['yearBinned'].values.unique()),\\n\",\n    \"      'features representing the year.')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>(1800.0, 1810.0]</th>\\n\",\n       \"      <th>(1810.0, 1820.0]</th>\\n\",\n       \"      <th>(1820.0, 1830.0]</th>\\n\",\n       \"      <th>(1830.0, 1839.0]</th>\\n\",\n       \"      <th>(1839.0, 1849.0]</th>\\n\",\n       \"      <th>(1849.0, 1859.0]</th>\\n\",\n       \"      <th>(1859.0, 1869.0]</th>\\n\",\n       \"      <th>(1869.0, 1879.0]</th>\\n\",\n       \"      <th>(1879.0, 1889.0]</th>\\n\",\n       \"      <th>(1889.0, 1899.0]</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>(1918.0, 1928.0]</th>\\n\",\n       \"      <th>(1928.0, 1938.0]</th>\\n\",\n       \"      <th>(1938.0, 1948.0]</th>\\n\",\n       \"      <th>(1948.0, 1958.0]</th>\\n\",\n       \"      <th>(1958.0, 1968.0]</th>\\n\",\n       \"      <th>(1968.0, 1978.0]</th>\\n\",\n       \"      <th>(1978.0, 1987.0]</th>\\n\",\n       \"      <th>(1987.0, 1997.0]</th>\\n\",\n       \"      <th>(1997.0, 2007.0]</th>\\n\",\n       \"      <th>(2007.0, 2017.0]</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 22 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   (1800.0, 1810.0]  (1810.0, 1820.0]  (1820.0, 1830.0]  (1830.0, 1839.0]  \\\\\\n\",\n       \"0                 0                 0                 0                 0   \\n\",\n       \"1                 0                 0                 0                 0   \\n\",\n       \"2                 0                 0                 0                 0   \\n\",\n       \"3                 0                 0                 0                 0   \\n\",\n       \"4                 0                 0                 0                 0   \\n\",\n       \"\\n\",\n       \"   (1839.0, 1849.0]  (1849.0, 1859.0]  (1859.0, 1869.0]  (1869.0, 1879.0]  \\\\\\n\",\n       \"0                 0                 0                 0                 0   \\n\",\n       \"1                 0                 0                 0                 0   \\n\",\n       \"2                 0                 0                 0                 0   \\n\",\n       \"3                 0                 0                 0                 0   \\n\",\n       \"4                 0                 0                 0                 0   \\n\",\n       \"\\n\",\n       \"   (1879.0, 1889.0]  (1889.0, 1899.0]        ...         (1918.0, 1928.0]  \\\\\\n\",\n       \"0                 0                 0        ...                        0   \\n\",\n       \"1                 0                 0        ...                        0   \\n\",\n       \"2                 0                 0        ...                        0   \\n\",\n       \"3                 0                 0        ...                        0   \\n\",\n       \"4                 0                 0        ...                        0   \\n\",\n       \"\\n\",\n       \"   (1928.0, 1938.0]  (1938.0, 1948.0]  (1948.0, 1958.0]  (1958.0, 1968.0]  \\\\\\n\",\n       \"0                 0                 0                 0                 0   \\n\",\n       \"1                 0                 0                 0                 0   \\n\",\n       \"2                 0                 0                 0                 0   \\n\",\n       \"3                 0                 0                 1                 0   \\n\",\n       \"4                 0                 0                 0                 0   \\n\",\n       \"\\n\",\n       \"   (1968.0, 1978.0]  (1978.0, 1987.0]  (1987.0, 1997.0]  (1997.0, 2007.0]  \\\\\\n\",\n       \"0                 0                 0                 0                 0   \\n\",\n       \"1                 0                 0                 0                 0   \\n\",\n       \"2                 0                 0                 0                 0   \\n\",\n       \"3                 0                 0                 0                 0   \\n\",\n       \"4                 0                 0                 0                 0   \\n\",\n       \"\\n\",\n       \"   (2007.0, 2017.0]  \\n\",\n       \"0                 1  \\n\",\n       \"1                 1  \\n\",\n       \"2                 1  \\n\",\n       \"3                 0  \\n\",\n       \"4                 1  \\n\",\n       \"\\n\",\n       \"[5 rows x 22 columns]\"\n      ]\n     },\n     \"execution_count\": 65,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_yrs = pd.get_dummies(temp_df['yearBinned'])\\n\",\n    \"X_yrs.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"IntervalIndex([(1800.0, 1810.0], (1810.0, 1820.0], (1820.0, 1830.0], (1830.0, 1839.0], (1839.0, 1849.0] ... (1968.0, 1978.0], (1978.0, 1987.0], (1987.0, 1997.0], (1997.0, 2007.0], (2007.0, 2017.0]]\\n\",\n       \"              closed='right',\\n\",\n       \"              dtype='interval[float64]')\"\n      ]\n     },\n     \"execution_count\": 66,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_yrs.columns.categories\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0,0.5,'Counts')\"\n      ]\n     },\n     \"execution_count\": 67,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x21863c63898>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# let's look at the new distribution\\n\",\n    \"fig, ax = plt.subplots()\\n\",\n    \"X_yrs.sum().plot.bar(ax=ax)\\n\",\n    \"ax.tick_params(labelsize=8)\\n\",\n    \"ax.set_xlabel('Binned Years', fontsize=12)\\n\",\n    \"ax.set_ylabel('Counts', fontsize=12)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>图 9-6：分箱之后新特征 X_yrs 的分布</center>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"按照 10 年的宽度进行分箱，我们保留了原始变量中的基本分布。如果需要对其他分布进行分箱，可以修改一下分箱设置，改变变量在模型中的呈现方式。因为我们使用的是余弦相似度，所以这样做没有问题。下面接着处理模型中最初包含的另外一个特征。研究领域特征空间对初始模型的大小和处理时间有非常显著的影响。\\n\",\n    \"\\n\",\n    \"检查一下已经完成的工作。通过解析字符串列表，我们在第一关中创建了一个“短语袋”。既然已经有了一个非常好用的稀疏数组，就应该使用这个更高效的数据类型来表示这个短语袋。例 9-8 演示了将 Pandas 数据框转换为 NumPy 稀疏数组之后对计算时间的影响。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 例 9-8:将短语袋从 pd.Series 转换为 NumPy 稀疏数组\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"unique_fos = sorted(list({ feature\\n\",\n    \"                          for paper_row in model_df.fos.fillna('0')\\n\",\n    \"                          for feature in paper_row }))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def feature_array(x, unique_array):\\n\",\n    \"    row_dict = {}\\n\",\n    \"    for i in x.index:\\n\",\n    \"        var_dict = {}\\n\",\n    \"        \\n\",\n    \"        for j in range(len(unique_array)):\\n\",\n    \"            if type(x[i]) is list:\\n\",\n    \"                if unique_array[j] in x[i]:\\n\",\n    \"                    var_dict.update({unique_array[j]: 1})\\n\",\n    \"                else:\\n\",\n    \"                    var_dict.update({unique_array[j]: 0})\\n\",\n    \"            else:    \\n\",\n    \"                if unique_array[j] == str(x[i]):\\n\",\n    \"                    var_dict.update({unique_array[j]: 1})\\n\",\n    \"                else:\\n\",\n    \"                    var_dict.update({unique_array[j]: 0})\\n\",\n    \"        \\n\",\n    \"        row_dict.update({i : var_dict})\\n\",\n    \"    \\n\",\n    \"    feature_df = pd.DataFrame.from_dict(row_dict, dtype='str').T\\n\",\n    \"    \\n\",\n    \"    return feature_df\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Wall time: 57min 51s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%time fos_features = feature_array(model_df['fos'], unique_fos)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <th>0-10 V lighting control</th>\\n\",\n       \"      <th>1-planar graph</th>\\n\",\n       \"      <th>1/N expansion</th>\\n\",\n       \"      <th>10G-PON</th>\\n\",\n       \"      <th>14-3-3 protein</th>\\n\",\n       \"      <th>2-choice hashing</th>\\n\",\n       \"      <th>20th-century philosophy</th>\\n\",\n       \"      <th>2D Filters</th>\\n\",\n       \"      <th>2D computer graphics</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>open</th>\\n\",\n       \"      <th>pH</th>\\n\",\n       \"      <th>photoperiodism</th>\\n\",\n       \"      <th>r-process</th>\\n\",\n       \"      <th>route</th>\\n\",\n       \"      <th>strictfp</th>\\n\",\n       \"      <th>string</th>\\n\",\n       \"      <th>van der Waals force</th>\\n\",\n       \"      <th>Ćuk converter</th>\\n\",\n       \"      <th>μ operator</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>2 rows × 9150 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   0 0-10 V lighting control 1-planar graph 1/N expansion 10G-PON  \\\\\\n\",\n       \"0  0                       0              0             0       0   \\n\",\n       \"1  0                       0              0             0       0   \\n\",\n       \"\\n\",\n       \"  14-3-3 protein 2-choice hashing 20th-century philosophy 2D Filters  \\\\\\n\",\n       \"0              0                0                       0          0   \\n\",\n       \"1              0                0                       0          0   \\n\",\n       \"\\n\",\n       \"  2D computer graphics    ...     open pH photoperiodism r-process route  \\\\\\n\",\n       \"0                    0    ...        0  0              0         0     0   \\n\",\n       \"1                    0    ...        0  0              0         0     0   \\n\",\n       \"\\n\",\n       \"  strictfp string van der Waals force Ćuk converter μ operator  \\n\",\n       \"0        0      0                   0             0          0  \\n\",\n       \"1        0      0                   0             0          0  \\n\",\n       \"\\n\",\n       \"[2 rows x 9150 columns]\"\n      ]\n     },\n     \"execution_count\": 71,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fos_features.head(2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_fos = fos_features.values\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Our pandas Series, in bytes:  5856418904\\n\",\n      \"Our hashed numpy array, in bytes:  112\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# We can see how this will make a difference in the future by looking at the size of each\\n\",\n    \"print('Our pandas Series, in bytes: ', getsizeof(fos_features))\\n\",\n    \"print('Our hashed numpy array, in bytes: ', getsizeof(X_fos))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"真是太棒了！把两个特征组合在一起，一起输入过滤器（见例 9-9），重新运行推荐器（见例 9-10）看看能否得到更好的结果。在过滤器中，我们使用了 scikit-learn 的余弦相似度函数。我们还是每次只对一个物品进行推荐，目的是节省计算时间。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"####  例 9-9：协同过滤阶段 1+2：建立项目特征矩阵，搜索相似项目\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"9172\"\n      ]\n     },\n     \"execution_count\": 74,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_yrs.shape[1] + X_fos.shape[1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Wall time: 2.06 s\\n\",\n      \"Size of second feature array, in bytes:  1467520112\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# now looking at 10399 x  7623 array for our feature space\\n\",\n    \"\\n\",\n    \"%time second_features = np.append(X_fos, X_yrs, axis = 1)\\n\",\n    \"\\n\",\n    \"second_size = getsizeof(second_features)\\n\",\n    \"\\n\",\n    \"print('Size of second feature array, in bytes: ', second_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The power of feature engineering saves us, in bytes:  -665280615\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"The power of feature engineering saves us, in bytes: \\\", 802239497 - second_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.metrics.pairwise import cosine_similarity\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def piped_collab_filter(features_matrix, index, top_n):\\n\",\n    \"\\n\",\n    \"    item_similarities = 1 - cosine_similarity(features_matrix[index:index + 1],\\n\",\n    \"                                              features_matrix).flatten()\\n\",\n    \"    related_indices = [\\n\",\n    \"        i for i in item_similarities.argsort()[::-1] if i != index\\n\",\n    \"    ]\\n\",\n    \"\\n\",\n    \"    return [(index, item_similarities[index])\\n\",\n    \"            for index in related_indices][0:top_n]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 例 9-10：基于物品的协同过滤推荐：第 2 轮\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def paper_recommender(items_df, paper_index, top_n):\\n\",\n    \"    if paper_index in model_df.index:\\n\",\n    \"\\n\",\n    \"        print('Based on the paper:')\\n\",\n    \"        print('Paper index = ', model_df.loc[paper_index].name)\\n\",\n    \"        print('Title :', model_df.loc[paper_index]['title'])\\n\",\n    \"        print('FOS :', model_df.loc[paper_index]['fos'])\\n\",\n    \"        print('Year :', model_df.loc[paper_index]['year'])\\n\",\n    \"        print('Abstract :', model_df.loc[paper_index]['abstract'])\\n\",\n    \"        print('Authors :', model_df.loc[paper_index]['authors'], '\\\\n')\\n\",\n    \"\\n\",\n    \"        # define the location index for the DataFrame index requested\\n\",\n    \"        array_ix = model_df.index.get_loc(paper_index)\\n\",\n    \"\\n\",\n    \"        top_results = piped_collab_filter(items_df, array_ix, top_n)\\n\",\n    \"\\n\",\n    \"        print('\\\\nTop', top_n, 'results: ')\\n\",\n    \"\\n\",\n    \"        order = 1\\n\",\n    \"        for i in range(len(top_results)):\\n\",\n    \"            print(order, '. Paper index = ',\\n\",\n    \"                  model_df.iloc[top_results[i][0]].name)\\n\",\n    \"            print('Similarity score: ', top_results[i][1])\\n\",\n    \"            print('Title :', model_df.iloc[top_results[i][0]]['title'])\\n\",\n    \"            print('FOS :', model_df.iloc[top_results[i][0]]['fos'])\\n\",\n    \"            print('Year :', model_df.iloc[top_results[i][0]]['year'])\\n\",\n    \"            print('Abstract :', model_df.iloc[top_results[i][0]]['abstract'])\\n\",\n    \"            print('Authors :', model_df.iloc[top_results[i][0]]['authors'],\\n\",\n    \"                  '\\\\n')\\n\",\n    \"            if order < top_n: order += 1\\n\",\n    \"\\n\",\n    \"    else:\\n\",\n    \"        print('Whoops! Choose another paper. Try something from here: \\\\n',\\n\",\n    \"              model_df.index[100:200])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Based on the paper:\\n\",\n      \"Paper index =  2\\n\",\n      \"Title : Should endometriosis be an indication for intracytoplasmic sperm injection (ICSI) in fresh IVF cycles\\n\",\n      \"FOS : nan\\n\",\n      \"Year : 2015\\n\",\n      \"Abstract : nan\\n\",\n      \"Authors : [{'name': 'Jovana P. Lekovich', 'org': 'Weill Cornell Medical College, New York, NY'}, {'name': 'G.D. Palermo', 'org': 'Weill Medical College of Cornell University, New York, NY'}, {'name': 'Nigel Pereira', 'org': 'The Ronald O. Perelman and Claudia Cohen Center, New York, NY'}, {'name': 'Zev Rosenwaks', 'org': 'Weill Cornell Medical College, New York, NY'}] \\n\",\n      \"\\n\",\n      \"\\n\",\n      \"Top 3 results: \\n\",\n      \"1 . Paper index =  13686\\n\",\n      \"Similarity score:  1.0\\n\",\n      \"Title : Electromagnetic System Design and Visual Simulation\\n\",\n      \"FOS : ['Simulation', 'Systems engineering', 'Engineering', 'Engineering drawing']\\n\",\n      \"Year : 2004\\n\",\n      \"Abstract : An electromagnet design system is developed using Visual C++ language and OpenGL technology to visualize parametric 3D model. The system consists of primary design, optimization design, dynamic and static characteristics, and visual simulation. All empirical parameters and curves used in design process are stored in database. Through human-computer interactions, an electromagnetic system can be designed conveniently with the results and characteristics curves displayed in graphic model. Using this system can greatly shorten the process of product design, and the results satisfy technical requirements.\\n\",\n      \"Authors : [{'name': 'Niu Chunping', 'org': \\\"School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China)\\\"}] \\n\",\n      \"\\n\",\n      \"2 . Paper index =  4857\\n\",\n      \"Similarity score:  1.0\\n\",\n      \"Title : An Investigation of the Action and Haemolytic Effect of Glyceryl Guaiacolate in the Horse\\n\",\n      \"FOS : ['Respiration', 'Carbon dioxide', 'Oxygen', 'Surgery']\\n\",\n      \"Year : 1978\\n\",\n      \"Abstract : SUMMARY#R##N##R##N#Glyceryl guaiacolate (GGE) was found to be a useful and safe casting agent when given by rapid intravenous infusion. It was administered to premedicated horses under controlled conditions at various concentrations from 10 to 20 per cent GGE solution. The onset and degree of relaxation was dependent only on the speed of infusion. For casting adult horses 350 to 450 ml of 15 per cent solution must be given within 30 to 60 seconds. A slight transient hypoxaemia occurred which seemed to be related to the animal being in lateral recumbency rather than the depressive action of GGE on respiratory function. At the higher concentrations (20 per cent solution) 2 cases of aseptic thrombosis of the jugular vein occurred.#R##N##R##N##R##N##R##N#There were no significant changes in the blood picture associated with GGE administration apart from some transient haemolysis in horses dosed with 20 per cent solution. However, if the solution was adequately stabilised this did not occur. The haemolytic threshold without stabilizing substances was found to lie between 16 and 20 per cent. For daily use a prepacked 15 per cent stable solution was recommended.#R##N##R##N##R##N##R##N#RESUME#R##N##R##N#L'ester glycerique du gaiacol s'est reveleetre un facteur relaxeur utile lorsqu'on l'injecte par voie intraveineuse rapide. On l'a administrea des chevaux premediques a des concentrations variables en solution de 10 %a 20 %. L'apparition et le degre de relaxation dependent seulement de la vitesse de perfusion. Pour coucher des chevaux adultes 350 a 450 ml de la solution doivent etre injectes en 30 a 60 secondes. Une legere et passagere hypoxie fut constatee qui semble n'etre provoquee que par le decubitus lateral des animaux plus que due a une action depressive du medicament sur la fonction respiratoire. A des concentrations elevees (20%) deux cas de trombose aseptique de la veine jugulaire furent constates.#R##N##R##N##R##N##R##N#On ne constata point de modifications significatives dans la formule sanguine a la suite de l'injection de glyceryl gaiacolate a l'exception d'une hemolyse passagere lors de concentrations de l'ordre de 20 %.#R##N##R##N##R##N##R##N#Ceci n'apparut d'ailleurs que lors de l'emploi de solutions non stabilisees. Le seuil d'apparition de l'hemolyse se situerait pour des concentrations de 16 a 20 %.#R##N##R##N##R##N##R##N#L'emploi de solutions a 15 % est recommande.#R##N##R##N##R##N##R##N#ZUSAMMENFASSUNG#R##N##R##N#Guiacolglyzerin-Aether (GGE) wurde als nutzliches und sicheres Mittel zum Niederlegen von Pferden befunden, wenn es rasch intravenos infundiert wird. Praemedizierte Pferde erhielten GGE in Konzentrationen von 10 bis 20 % unter kontrollierten Bedingungen. Eintritt und Grad der Relaxierung hingen nur von der Infusionsgeschwindigkeit ab. Um erwachsene Pferde niederzulegen, mussen 350 bis 450 ml der 15 prozentigen Losung innert 30 bis 60 Sekunden gegeben werden. Eine leichte, vorubergehende Hypoxaemie trat auf, die anscheinend eher der Seitenlage des Pferdes als der atmungsbeeintrachtigenden Wirkung des GGE zuzuschreiben war. Bei hoheren Konzentrationen (20 %) waren zwei Falle von aseptischer Thrombose der Jugularvenen zu verzeichnen.#R##N##R##N##R##N##R##N#Das Blutbild wies keine signifikanten Veranderungen auf, ausser einer vorubergehenden Haemolyse mit der 20 prozentigen Losung. Wenn die Losung adaequat stabilisiert war, trat die Haemolyse nicht auf. Die haemolytische Schwelle ohne Stabilisatoren liegt zwischen 16 und 20 Prozent. Fur den taglichen Gebrauch wird eine vorverpackte, 15 prozentige stabile Losung empfohlen.\\n\",\n      \"Authors : [{'name': 'Schatzmann U', 'org': 'Klinik fur Nutztiere und Pferde, University of Berne, Langgasstrasse 124, Berne, Switzerland'}, {'name': 'Tschudi P', 'org': 'Klinik fur Nutztiere und Pferde, University of Berne, Langgasstrasse 124, Berne, Switzerland'}, {'name': 'James P. Held', 'org': 'Klinik fur Nutztiere und Pferde, University of Berne, Langgasstrasse 124, Berne, Switzerland'}, {'name': 'B. Muhlebach', 'org': 'Klinik fur Nutztiere und Pferde, University of Berne, Langgasstrasse 124, Berne, Switzerland'}] \\n\",\n      \"\\n\",\n      \"3 . Paper index =  11762\\n\",\n      \"Similarity score:  1.0\\n\",\n      \"Title : Spongiosis and sclerosis of the frontal sinus\\n\",\n      \"FOS : nan\\n\",\n      \"Year : 1954\\n\",\n      \"Abstract : nan\\n\",\n      \"Authors : [{'name': 'Schlosshauer B'}] \\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"paper_recommender(second_features, 2, 3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"说实话，我并不认为这次特征选择的效果有多么好。这些字段中有很多缺失值。下面继续看一下能否找出一些信息更丰富的特征。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 找到你的位置\\n\",\n    \"在 Pandas 数据框和 NumPy 矩阵之间的转换中，索引会令人迷惑——索引的大小相同， 但其分配的位置却不一样。为了解决这个问题，Pandas 提供了 `.iloc`、`.loc` 和 `.get_loc` 三种方法，如例 9-11 所示。\\n\",\n    \"\\n\",\n    \"- `.loc` 返回基于初始 Pandas 数据框的索引，可以让我们引用具体的论文。\\n\",\n    \"- `.iloc` 使用整数位置，和 NumPy 数组的索引是一样的。\\n\",\n    \"- `.get_loc` 可以帮助我们在已知数据框索引时找出整数位置。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 例 9-11:在转换时维护索引分配\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"abstract      A microprocessor includes hardware registers t...\\n\",\n       \"authors                        [{'name': 'Mark John Ebersole'}]\\n\",\n       \"fos           [Embedded system, Parallel computing, Computer...\\n\",\n       \"keywords                                                    NaN\\n\",\n       \"lang                                                         en\\n\",\n       \"references    [1bdfcbc2-29de-4c81-836a-eb672338a081, 1da9e4c...\\n\",\n       \"title         Microprocessor that enables ARM ISA program to...\\n\",\n       \"url           [http://www.freepatentsonline.com/y2013/030501...\\n\",\n       \"year                                                       2013\\n\",\n       \"Name: 21, dtype: object\"\n      ]\n     },\n     \"execution_count\": 80,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model_df.loc[21]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"abstract      A microprocessor includes hardware registers t...\\n\",\n       \"authors                        [{'name': 'Mark John Ebersole'}]\\n\",\n       \"fos           [Embedded system, Parallel computing, Computer...\\n\",\n       \"keywords                                                    NaN\\n\",\n       \"lang                                                         en\\n\",\n       \"references    [1bdfcbc2-29de-4c81-836a-eb672338a081, 1da9e4c...\\n\",\n       \"title         Microprocessor that enables ARM ISA program to...\\n\",\n       \"url           [http://www.freepatentsonline.com/y2013/030501...\\n\",\n       \"year                                                       2013\\n\",\n       \"Name: 21, dtype: object\"\n      ]\n     },\n     \"execution_count\": 81,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model_df.iloc[21]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 82,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"30\"\n      ]\n     },\n     \"execution_count\": 82,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model_df.index.get_loc(30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 第三步：更多特征=更多信息\\n\",\n    \"到此为止，我们的实验并没有支持初始假设，即仅靠出版年份和研究领域就足以推荐出相似的论文。既然如此，有以下几种选择。\\n\",\n    \"\\n\",\n    \"- 使用原始数据集中更多的数据，看看能否得到更好的结果。\\n\",\n    \"- 花费更多时间去探索数据，看看能否找到一个足够密集的集合来提供好的推荐。\\n\",\n    \"- 添加更多特征，继续迭代当前模型。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"第一种选择假设问题在于我们对数据的抽样，这是有可能的，但这样做和图 9-4 中翻动数据垃圾堆找寻更好答案的比喻是一样的。\\n\",\n    \"\\n\",\n    \"第二种选择可以更好地理解原始数据。这应该在数据探索过程中，根据特征和模型选择决策的变化不断地重新进行。在本例中，初始的子样本选择就反映了这个步骤。因为在数据集中还有更多变量可用，所以我们就不再重新进行这个步骤了。\\n\",\n    \"\\n\",\n    \"最后看第三种选择，添加更多特征，在当前模型的基础上继续前进。加入更多关于每个项目的信息可以改善相似度评分，进而得到更好的推荐。\\n\",\n    \"\\n\",\n    \"根据我们最初的探索，下一步的工作将集中在信息量最丰富的两个字段上：论文摘要和作者姓名。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 学术论文推荐器：第3轮\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"回顾一下第 4 章，我们可以知道论文摘要非常适合使用 tf-idf 来过滤掉噪声并找出显著相关的单词。我们在例 9-12 中实现了 tf-idf。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 例 9-12：停用词 +tf-idf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 83,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# need to fill in NaN for sklearn\\n\",\n    \"filled_df = model_df.fillna('None')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 84,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0    A system and method for maskless direct write ...\\n\",\n       \"1                                                 None\\n\",\n       \"2                                                 None\\n\",\n       \"3                                                 None\\n\",\n       \"4    早期発見と治療成績の向上で担癌患者の生存期間が延びており，それに伴い重複癌を経験する機会も増...\\n\",\n       \"Name: abstract, dtype: object\"\n      ]\n     },\n     \"execution_count\": 84,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# abstract: stopwords, frequency based filtering (tf-idf?)\\n\",\n    \"filled_df['abstract'].head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 85,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<20000x99896 sparse matrix of type '<class 'numpy.float64'>'\\n\",\n       \"\\twith 592505 stored elements in Compressed Sparse Row format>\"\n      ]\n     },\n     \"execution_count\": 85,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.feature_extraction.text import TfidfVectorizer\\n\",\n    \"\\n\",\n    \"vectorizer = TfidfVectorizer(\\n\",\n    \"    sublinear_tf=True, max_df=0.5, stop_words='english')\\n\",\n    \"X_abstract = vectorizer.fit_transform(filled_df['abstract'])\\n\",\n    \"\\n\",\n    \"X_abstract\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"n_samples: 20000, n_features: 99896\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"n_samples: %d, n_features: %d\\\" % X_abstract.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 88,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"MemoryError\",\n     \"evalue\": \"\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mMemoryError\\u001b[0m                               Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-88-9635c2b3f99c>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m \\u001b[0mthird_features\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mappend\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0msecond_features\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mX_abstract\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mtoarray\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0maxis\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[1;36m1\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\sparse\\\\compressed.py\\u001b[0m in \\u001b[0;36mtoarray\\u001b[1;34m(self, order, out)\\u001b[0m\\n\\u001b[0;32m    941\\u001b[0m         \\u001b[1;32mif\\u001b[0m \\u001b[0mout\\u001b[0m \\u001b[1;32mis\\u001b[0m \\u001b[1;32mNone\\u001b[0m \\u001b[1;32mand\\u001b[0m \\u001b[0morder\\u001b[0m \\u001b[1;32mis\\u001b[0m \\u001b[1;32mNone\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    942\\u001b[0m             \\u001b[0morder\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_swap\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m'cf'\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m0\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 943\\u001b[1;33m         \\u001b[0mout\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_process_toarray_args\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0morder\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mout\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m    944\\u001b[0m         \\u001b[1;32mif\\u001b[0m \\u001b[1;32mnot\\u001b[0m \\u001b[1;33m(\\u001b[0m\\u001b[0mout\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mflags\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mc_contiguous\\u001b[0m \\u001b[1;32mor\\u001b[0m \\u001b[0mout\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mflags\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mf_contiguous\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m    945\\u001b[0m             \\u001b[1;32mraise\\u001b[0m \\u001b[0mValueError\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m'Output array must be C or F contiguous'\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32mC:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\scipy\\\\sparse\\\\base.py\\u001b[0m in \\u001b[0;36m_process_toarray_args\\u001b[1;34m(self, order, out)\\u001b[0m\\n\\u001b[0;32m   1128\\u001b[0m             \\u001b[1;32mreturn\\u001b[0m \\u001b[0mout\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   1129\\u001b[0m         \\u001b[1;32melse\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 1130\\u001b[1;33m             \\u001b[1;32mreturn\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mzeros\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mshape\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdtype\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mdtype\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0morder\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0morder\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m   1131\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m   1132\\u001b[0m     \\u001b[1;32mdef\\u001b[0m \\u001b[0m__numpy_ufunc__\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mfunc\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mmethod\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mpos\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0minputs\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;33m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mMemoryError\\u001b[0m: \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"third_features = np.append(second_features, X_abstract.toarray(), axis = 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"paper_recommender(third_features, 2, 3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"论文作者比较混乱，而且参差不齐，我们把它整理成字典，再对它进行 one-hot 编码，这样可以降低计算负载，如例 9-13 所示。\\n\",\n    \"\\n\",\n    \"#### 例 9-13：使用 scikit-learn 的 DictVectorizer 进行 one-hot 编码\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 89,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>authors</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>None</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>[{'name': 'Ahmed M. Alluwaimi'}]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>[{'name': 'Jovana P. Lekovich', 'org': 'Weill ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>[{'name': 'P. M. Voltes'}]</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>[{'name': '高田和外'}, {'name': 'ほか'}]</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                             authors\\n\",\n       \"0                                               None\\n\",\n       \"1                   [{'name': 'Ahmed M. Alluwaimi'}]\\n\",\n       \"2  [{'name': 'Jovana P. Lekovich', 'org': 'Weill ...\\n\",\n       \"3                         [{'name': 'P. M. Voltes'}]\\n\",\n       \"4                 [{'name': '高田和外'}, {'name': 'ほか'}]\"\n      ]\n     },\n     \"execution_count\": 89,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"authors_df = pd.DataFrame(filled_df.authors)\\n\",\n    \"authors_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 90,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import json\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 91,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"authors_list = []\\n\",\n    \"\\n\",\n    \"for row in authors_df.itertuples():\\n\",\n    \"    # create a dictionary from each Series index\\n\",\n    \"    if type(row.authors) is str:\\n\",\n    \"        y = {'None': row.Index}\\n\",\n    \"    if type(row.authors) is list:\\n\",\n    \"        # add these keys + values to our running dictionary    \\n\",\n    \"        y = dict.fromkeys(row.authors[0].values(), row.Index)\\n\",\n    \"    authors_list.append(y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'None': 0},\\n\",\n       \" {'Ahmed M. Alluwaimi': 1},\\n\",\n       \" {'Jovana P. Lekovich': 2, 'Weill Cornell Medical College, New York, NY': 2},\\n\",\n       \" {'P. M. Voltes': 3},\\n\",\n       \" {'高田和外': 4}]\"\n      ]\n     },\n     \"execution_count\": 92,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"authors_list[0:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 93,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0., 0., 0., ..., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., ..., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., ..., 0., 0., 0.],\\n\",\n       \"       ...,\\n\",\n       \"       [0., 0., 0., ..., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., ..., 0., 0., 0.],\\n\",\n       \"       [0., 0., 0., ..., 0., 0., 0.]])\"\n      ]\n     },\n     \"execution_count\": 93,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.feature_extraction import DictVectorizer\\n\",\n    \"v = DictVectorizer(sparse=False)\\n\",\n    \"D = authors_list\\n\",\n    \"X_authors = v.fit_transform(D)\\n\",\n    \"\\n\",\n    \"X_authors\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 94,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"n_samples: 20000, n_features: 25154\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"n_samples: %d, n_features: %d\\\" % X_authors.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"NameError\",\n     \"evalue\": \"name 'third_features' is not defined\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mNameError\\u001b[0m                                 Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-95-8954394db0bd>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m      1\\u001b[0m \\u001b[1;31m# now looking at 5167 x  38070 array for our feature space\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      2\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 3\\u001b[1;33m \\u001b[0mfourth_features\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mappend\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mthird_features\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mX_authors\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0maxis\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[1;36m1\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n      \"\\u001b[1;31mNameError\\u001b[0m: name 'third_features' is not defined\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# now looking at 5167 x  38070 array for our feature space\\n\",\n    \"\\n\",\n    \"fourth_features = np.append(third_features, X_authors, axis = 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在可以使用推荐器看看这些新特征的效果了。例 9-14 给出了结果。\\n\",\n    \"#### 例 9-14：基于项目的协同过滤推荐：第 3 轮\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"paper_recommender(fourth_features, 2, 3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"尽管某些字段中有缺失数据，但最后一轮特征工程得到的前 3 个结果都向我们推荐了医学领域的其他论文。\\n\",\n    \"\\n\",\n    \"这个数据集中的论文范围非常广泛。例如，论文的随机抽样中可以包括以下研究领域： “Coupling  constant”“Evapotranspiratioin”“Hash function”“IVMS”“Meditation”“Pareto analysis”“Second-generation wavelet transform”“Slip”和“Spiral  galaxy”。考虑到这 1 万多篇论文中共有 7604 个唯一的研究领域，这些最终结果应该是向着正确方向前进。我们的工作正逐步接近有用的模型，我们对此非常有信心。\\n\",\n    \"\\n\",\n    \"对更多文本型变量继续迭代，比如找出论文题目中的名词短语，或对关键字进行词干提取，都可以使我们更加接近“最佳”推荐。\\n\",\n    \"\\n\",\n    \"需要注意的是，这里所说的“最佳”只是所有推荐器和搜索引擎追求的一种理想状态。我们要搜索出一个对用户最有帮助的结果，这不一定能从数据中直接表现出来。特征工程可以抽象出显著的特征并将其转化为一种表示形式，以使算法能揭示出其中包含的显式和隐式信息。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 总结\\n\",\n    \"正如你看到的，建立一个机器学习模型非常容易，但要建立一个好模型并得到有用的结果则需要花很多时间做很多工作。在本章中，我们为了获得更好的结果，检验了可能的变量集合，使用多种特征工程方法进行了试验。在这里，“更好”的含义不仅包括从训练和测试中得到好的结果，还包括使模型更简洁，以及减少在各种试验上的迭代时间。\\n\",\n    \"\\n\",\n    \"本书开头说过，要精通一门学科，需要深入理解其中的原理，以便获得直觉，进而有效地将知识应用到工作中。希望从本书中你能获得必要的方法和工具，提高工作的效率和效果，同时扩展你的数学与计算机能力，更好地理解为什么特征工程是开发有用的机器学习模型的一项基本技能。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 参考文献\\n\",\n    \"Sarwar, Badrul, George Karypis, Joseph Konstan, and John Riedl. “Item-Based Collaborative Filtering Recommendation Algorithms.” Proceedings of the 10th International Conference on the World Wide Web (2001) 285–295.\\n\",\n    \"\\n\",\n    \"Sinha, Arnab, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. “An Overview of Microsoft Academic Service (MAS) and Applications.” Proceedings of the 24th International Conference on the World Wide Web (2015): 243–246.\\n\",\n    \"\\n\",\n    \"Tang, Jie, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. “ArnetMiner: Extraction and Mining of Academic Social Networks.” Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2008): 990–998.\\n\",\n    \"\\n\",\n    \"Wickham, Hadley. “Tidy Data.” The Journal of Statistical Software 59 (2014).\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "9.feature-engineering/FeatureSelectorUsage/FeatureSelectorUsage.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 特征工程的神器-基于Python的特征自动化选择代码\\n\",\n    \">本文介绍一个特征选择神器：特征选择器是用于减少机器学习数据集的维数的工具，可以傻瓜式地进行特征选择。\\n\",\n    \">\\n\",\n    \">来源：[Will Koehrsen](https://github.com/WillKoehrsen)\\n\",\n    \">\\n\",\n    \">代码整理及注释翻译：[黄海广](https://github.com/fengdu78)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 实现的功能\\n\",\n    \"该选择器基于python编写，有五种方法来标识要删除的特征：\\n\",\n    \"\\n\",\n    \"- 缺失值\\n\",\n    \"- 唯一值\\n\",\n    \"- 共线特征\\n\",\n    \"- 零重要性特征\\n\",\n    \"- 低重要性特征\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 使用方法\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"在这个Jupyter文件中， 我们将使用 `FeatureSelector` 类来选择数据集中要删除的特征，这个类提供五种方法来查找要删除的功能：  \\n\",\n    \"\\n\",\n    \"1. 查找缺失分数大于指定阈值的列\\n\",\n    \"2. 查找只有唯一值的特征\\n\",\n    \"3. 查找由相关系数大于指定值的共线特征\\n\",\n    \"4. 使用梯度增强算法查找具有零重要性的特征\\n\",\n    \"5. 使用梯度增强算法查找中查找对指定的累积特征重要性无贡献的特征 \\n\",\n    \"\\n\",\n    \" `FeatureSelector` 仍在进一步开发中! 欢迎大家在github提交PR.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from feature_selector import FeatureSelector\\n\",\n    \"\\n\",\n    \"import pandas as pd\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 示例数据集\\n\",\n    \"\\n\",\n    \"该数据集被用作Kaggle上房屋信用违约风险竞赛的(https://www.kaggle.com/c/home-credit-default-risk)  一部分(数据放在`data`目录里)。它旨在用于有监督的机器学习分类任务，其目的是预测客户是否会拖欠贷款。您可以在此处下载整个数据集，我们将处理10,000行的一小部分样本。\\n\",\n    \"\\n\",\n    \"特征选择器旨在用于机器学习任务，但可以应用于任何数据集。基于特征重要性的方法需要使用机器学习的监督学习问题。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>SK_ID_CURR</th>\\n\",\n       \"      <th>TARGET</th>\\n\",\n       \"      <th>NAME_CONTRACT_TYPE</th>\\n\",\n       \"      <th>CODE_GENDER</th>\\n\",\n       \"      <th>FLAG_OWN_CAR</th>\\n\",\n       \"      <th>FLAG_OWN_REALTY</th>\\n\",\n       \"      <th>CNT_CHILDREN</th>\\n\",\n       \"      <th>AMT_INCOME_TOTAL</th>\\n\",\n       \"      <th>AMT_CREDIT</th>\\n\",\n       \"      <th>AMT_ANNUITY</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>FLAG_DOCUMENT_18</th>\\n\",\n       \"      <th>FLAG_DOCUMENT_19</th>\\n\",\n       \"      <th>FLAG_DOCUMENT_20</th>\\n\",\n       \"      <th>FLAG_DOCUMENT_21</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>247408</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>108000.0</td>\\n\",\n       \"      <td>172512.0</td>\\n\",\n       \"      <td>13477.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>153916</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Revolving loans</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>135000.0</td>\\n\",\n       \"      <td>180000.0</td>\\n\",\n       \"      <td>9000.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>229065</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>112500.0</td>\\n\",\n       \"      <td>463500.0</td>\\n\",\n       \"      <td>20547.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>282013</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>135000.0</td>\\n\",\n       \"      <td>549882.0</td>\\n\",\n       \"      <td>17739.0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>142266</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Cash loans</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>90000.0</td>\\n\",\n       \"      <td>518562.0</td>\\n\",\n       \"      <td>20695.5</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 122 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\\\\n\",\n       \"0      247408       0         Cash loans           F            Y   \\n\",\n       \"1      153916       0    Revolving loans           F            Y   \\n\",\n       \"2      229065       0         Cash loans           F            N   \\n\",\n       \"3      282013       0         Cash loans           F            N   \\n\",\n       \"4      142266       0         Cash loans           F            N   \\n\",\n       \"\\n\",\n       \"  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\\\\n\",\n       \"0               N             2          108000.0    172512.0      13477.5   \\n\",\n       \"1               Y             2          135000.0    180000.0       9000.0   \\n\",\n       \"2               Y             0          112500.0    463500.0      20547.0   \\n\",\n       \"3               Y             0          135000.0    549882.0      17739.0   \\n\",\n       \"4               Y             0           90000.0    518562.0      20695.5   \\n\",\n       \"\\n\",\n       \"              ...              FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\\\\n\",\n       \"0             ...                             0                0   \\n\",\n       \"1             ...                             0                0   \\n\",\n       \"2             ...                             0                0   \\n\",\n       \"3             ...                             0                0   \\n\",\n       \"4             ...                             0                0   \\n\",\n       \"\\n\",\n       \"  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\\\\n\",\n       \"0                0                0                        0.0   \\n\",\n       \"1                0                0                        0.0   \\n\",\n       \"2                0                0                        0.0   \\n\",\n       \"3                0                0                        0.0   \\n\",\n       \"4                0                0                        0.0   \\n\",\n       \"\\n\",\n       \"  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\\\\n\",\n       \"0                       0.0                         0.0   \\n\",\n       \"1                       0.0                         0.0   \\n\",\n       \"2                       0.0                         1.0   \\n\",\n       \"3                       0.0                         0.0   \\n\",\n       \"4                       0.0                         0.0   \\n\",\n       \"\\n\",\n       \"   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\\\\n\",\n       \"0                        0.0                        0.0   \\n\",\n       \"1                        0.0                        0.0   \\n\",\n       \"2                        0.0                        0.0   \\n\",\n       \"3                        0.0                        0.0   \\n\",\n       \"4                        0.0                        1.0   \\n\",\n       \"\\n\",\n       \"   AMT_REQ_CREDIT_BUREAU_YEAR  \\n\",\n       \"0                         1.0  \\n\",\n       \"1                         0.0  \\n\",\n       \"2                         7.0  \\n\",\n       \"3                         1.0  \\n\",\n       \"4                         1.0  \\n\",\n       \"\\n\",\n       \"[5 rows x 122 columns]\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train = pd.read_csv('data/credit_example.csv')\\n\",\n    \"train_labels = train['TARGET']\\n\",\n    \"train.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"数据集中有几个分类列。`FeatureSelector`处理这些特征重要性的时候使用独热编码。 \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train = train.drop(columns = ['TARGET'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 实施\\n\",\n    \"\\n\",\n    \"在`FeatureSelector`具有用于标识列，以除去五种特征 :\\n\",\n    \"\\n\",\n    \"* `identify_missing`（查找缺失值）\\n\",\n    \"* `identify_single_unique`（查找唯一值）\\n\",\n    \"* `identify_collinear`（查找共线特征）\\n\",\n    \"* `identify_zero_importance` （查找零重要特征）\\n\",\n    \"* `identify_low_importance`（查找低重要特征）\\n\",\n    \"\\n\",\n    \"这些方法找到要根据指定条件删除的特征。 标识的特征存储在 `FeatureSelector`的 `ops` 属性（Python词典）中。 我们可以手动删除已识别的特征，也可以使用 `FeatureSelector`中的删除特征函数真正删除特征。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 创建实例\\n\",\n    \"\\n\",\n    \"`FeatureSelector` 仅需要一个在行中具有观察值而在列中具有特征的数据集（标准结构化数据）。 我们正在处理机器学习的分类问题，因此我们也需要训练的标签。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fs = FeatureSelector(data = train, labels = train_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 1. 缺失值\\n\",\n    \"\\n\",\n    \"第一种特征选择方法很简单：找到丢失分数大于指定阈值的任何列。 在此示例中，我们将使用阈值0.6，这对应于查找缺失值超过60％的特征。 （此方法不会首先对特征进行一次独热编码）。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"17 features with greater than 0.60 missing values.\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fs.identify_missing(missing_threshold=0.6)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \" 可以通过 `FeatureSelector` 对象的`ops`词典访问已确定要删除的特征。  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['OWN_CAR_AGE',\\n\",\n       \" 'YEARS_BUILD_AVG',\\n\",\n       \" 'COMMONAREA_AVG',\\n\",\n       \" 'FLOORSMIN_AVG',\\n\",\n       \" 'LIVINGAPARTMENTS_AVG',\\n\",\n       \" 'NONLIVINGAPARTMENTS_AVG',\\n\",\n       \" 'YEARS_BUILD_MODE',\\n\",\n       \" 'COMMONAREA_MODE',\\n\",\n       \" 'FLOORSMIN_MODE',\\n\",\n       \" 'LIVINGAPARTMENTS_MODE']\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"missing_features = fs.ops['missing']\\n\",\n    \"missing_features[:10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们还可以绘制数据集中所有列的缺失列分数的直方图。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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47eZ6hp556KtXB/oGBgXrsscf0xx9/6MyZM04ZcFyqVClVrVpVu3fvtjvduHz5chmGoXbt2mVofbVr19amTZvUsWNHderUSU8//bSqVasmHx+fFJ9v7dq19fvvv+v5559X586d9fTTT8vf319ubm4Z+qWe3vc9eQ6yJk2apNhnXFxc1KJFC02bNi3d282TJ486duyoL7/8UkuXLtXQoUNty5LHTCUPwr/fkCFDUrTdunVLx44d05YtWyTZ7+vOcOjQIcXExKhq1aqpXsFbo0YNeXt7KyIiQtHR0SpcuLBq166tyMhIdevWzfb51KhRQ/ny5VPv3r0zXENoaKgCAgLSXD558mT973//+9v11K5dW/Pnz9fYsWO1b98+NW3aVHXr1pWXl5ddsH+Q5O9aw4YN5eXllWJ5/fr1VahQIZ0/f17nz59XsWLFtH37drm6uqpx48Yp+jdv3vyBf6Ck9jPP0f3Aw8NDvr6+dm3JQbpUqVIpXk/yae67d++mWR/MQ/BCpjkynURa8x5FRUXp+++/144dOxQZGamoqCgZhmH7JWncNyVEWn/1Ztbly5clSSVKlEizT8mSJbV//35b32Rpva7kI3h/d1l7eradPDbk8uXLTrvSq02bNjp8+LBWr16t7t27S/ozeJUqVUp+fn4ZWte4ceP02muv6eDBg/riiy/0xRdfyNvbW40aNVKnTp3sjv4MHz5c58+f16ZNm/T111/r66+/Vv78+dWwYUM9++yzduP5HiS97/vFixclpb3POHLFZHBwsKZOnaply5bp9ddfl8VikWEY+vnnn+Xq6ppmEDh79qxCQ0O1d+9enT59WteuXZOkVP+IcIbk13748OEUv7T/6o8//lDhwoU1YsQIXbhwQVu2bNFXX32lr776Sp6enmrQoIE6dOiQqUlNMyMoKEi//fabZs+erUWLFmnRokVydXVVzZo11aZNG4WEhKQYf/lX6f2uXbt2TVeuXFG+fPl09+5dFSlSJNV1e3l56ZFHHtHNmzdTXdcjjzySarsj+0GBAgVSLE9+bMbULsgcgheyRGp/Ge7YsUOvvPKKYmNjVaxYMT355JMqX768qlSpohIlSqS4HP9BM7NnRnK4e9APvuQ+f/0B7Kxfmo5sOzNat26tDz/8UCtWrFD37t11+vRpHT58WC+//HKG1/X4449r4cKF2r17t9asWaOtW7fq2LFjWrJkiZYsWaJ+/frpjTfekPTnL6vp06frt99+U1hYmLZs2aJDhw5p1apVWrVqldq0aZOuWfHT+74nT7thpDGnW1rtD1KyZEnVq1dPW7du1e7duxUQEKDt27frwoULatasmR577LEUz/npp580atQo3bt3T2XKlFHdunVVoUIFVa1aVQkJCemeEf9B/hryk0+9lShRQjVr1nzgc5MHYBcsWFAzZ87UkSNHbJ9P8sDvVatWKSgoKEvm8bNYLHrzzTfVq1cvrVq1Sps2bdLu3bsVHh6u8PBwhYaGau7cuQ+c2Daj3/PknzcP2kcetCy1n3mO7geZmdMMWY/ghWzBMAyNHj1asbGxeuedd/T888/bLT9y5EiK5ySfLknrUv5t27YpKipKdevWTfWXX1qS1/ugMTZnz56VpHRfPZjdt12iRAn5+flp9+7dunLliu2S/ORxI46oVauWatWqJUm6evWqFi1apMmTJ2vGjBnq2bOn3YStlStXVuXKlfWvf/1Lt27d0ooVKzR+/HgtX75cvXv3Vo0aNTL3Av9P8lil5KM/f/XXqRfSKyQkRFu3btXSpUsVEBBgm/YjtYlVb9++rbFjxypPnjyaOnVqitNWK1asSNc2kwNDamOZDMNIceQled8qUaJEhsNSlSpVVKVKFQ0ZMkQ3b97U8uXLNWHCBC1dulS9e/dOdY43MxQrVkx9+vSxXRm7bds2vffee4qIiNCCBQvUt2/fNJ+bnu9a8pQPPj4+KlSokPLmzavo6GjdvXvXNkY1WWxsrG7evJnuPwKctR8g52FwPbKFqKgonT17VgULFkwRuiTZ5qe5/6/45F/qmzZtSnWdn3zyid54440Mn8KpVq2aPDw8dOTIEVvIud+ZM2d0+PBh5c+fX9WqVUvXOtOrVq1aslgs2rJli27fvp1i+fbt2xUdHa2SJUs6fSLRNm3aKCkpSWFhYVqxYoUqVKjwt6ek/urkyZMKCgpKMbWCj4+P+vfvL19fXyUlJenSpUu6efOmgoODU4wRLFCggLp06WIbEJxWSHJE8qz+GzduTHF0wjAMbdiwwaH1tmzZUo888ojWrFmju3fvas2aNSpSpIiefvrpFH2PHj2q2NhY2zQIf5U8tufvTksnX0hw9erVFMsOHz6cYjyPn5+f3NzcdOjQoVQv8rh48aJat26tvn376s6dO7p27ZqCg4PVvn17u34FCxZU165dVb9+fUmOh9XMGDJkiAIDA+3+6HJzc1OjRo1sp8rv329S++4nT4OxefPmVKeB2LJli65fv64yZcqoaNGicnNzk7+/v+7du5fqz5wNGzZkaIZ8Z+0HyHkIXsgWChQooLx58+rmzZspbmQbFhamqVOnSrIfaFq3bl1VrFhRR44cSTEgNzQ0VAcOHFClSpVsg1qTT83dunXrgbV4eHioS5cuSkpK0rBhw+yuioyOjtbQoUOVlJSkkJAQp8+JU6pUKTVr1ky3b9/W8OHDFRsba1t29uxZjRkzRpJSDFJ3hmeeeUYWi0Vz5szRsWPHHDraVbp0aUVFRWnz5s0prpI8dOiQfv/9d3l6eqpcuXIqWLCgkpKSdPz48RRXv547d0579uxRnjx5nBpua9WqpapVq+r48eOaMmWKLXwZhqEpU6bo6NGjkjJ+ytjNzU1BQUGKiorS1KlTbaHy/ittkyUPgj558qQiIyNt7UlJSQoNDdXChQsl/f1A6HLlyilv3ryKjIy0XXwi/TkhZ2r3I/Xy8lLnzp0VGxurESNG2O3Xt2/f1ltvvaWTJ0/K09NTHh4eKlSokBISEnT06FF99913dus6e/as9u7dKxcXF6fPNZYePj4+un79uiZNmmT3M+HOnTtavXq1JNmNTUw+OnX/d7906dJq0qSJbty4oTfffFN37tyxLTt9+rTefvttSbIFOUm2CwomTpxoOxom/fnH2AcffCAp/fuOs/YD5DycakS24O7urq5du+q7775T7969Vbt2bdvEm6dOnbINcr1165bi4uLk7u4ui8Wijz/+WH369NHkyZO1ZMkSVaxYUadPn9bRo0fl5eVlNz6oTJkykqSpU6dq7969at++fZqDt4cOHaojR45o586dat68uW1AePIkiXXr1rWNU3K29957T5GRkVq3bp1tpv87d+4oPDxc8fHxCgoKyvBl/OlRrFgx1ahRQ3v37pXk2GlGFxcXvffeexo0aJD+9a9/qWrVqipZsqSuXbum3bt36969e3r77bdtV12NHTtWPXr00MSJEzV//nyVL19et2/f1u7du3X37l298sorKlWqlFNf58SJE9WjRw9NmTJFK1asUMWKFXXixAmdOHFCpUqV0tmzZ9N9g/H7derUSXPmzNGMGTNksVjSvEVQuXLl1KhRI23cuFHt27dXnTp15ObmpsOHD+vixYuqWLGiIiIi/nbqES8vL9t3ZsCAAapTp47c3d0VHh4uHx8f1axZ0zZje7Lhw4frt99+05YtW9SiRQtVr15d7u7u2rNnj27cuKFy5crp3XfftfUfO3asevXqpXHjxmnevHkqX768bt26pV27dik+Pl6vvfbaAwenPywDBw7U+vXr9csvvyg8PNwWzvfv36/o6GjVq1dPbdq0sfUvXbq0tm7dqnfffVc///yzXnrpJfn5+WncuHHq2bOnVq9eraZNm6pWrVp237WOHTva3RmgefPm6tixoxYvXqy2bdsqMDBQhmFo+/bttlPn6d13nLUfIOfhiBeyjZEjR+rf//63KlSooAMHDthmjX/llVf0008/KTAwUElJSXZ/3SffCqhr166Ki4vTunXrdPnyZbVr106LFi2ym5epW7du6tChg6Q/b1x86NChNGtxd3fXzJkzNXLkSJUpU0bbt2/Xzp07VbFiRb333nuaNWuW3N3dH8r74OPjox9++EGDBg2Sj4+PrVZ/f39NnjxZH3300UO78i15Tq+qVauqdOnSDq2jRYsWmjFjhp5++mlduHBBa9eu1YkTJ/T000/r22+/VZcuXWx9n3zySc2dO1etWrXSzZs3tW7dOh0+fFi1atXS559/rtdff90pr+t+vr6+Wrhwodq2bavo6GitW7dO+fLl0xdffKFmzZpJkkP37axcubJtUHRgYOADA+Nnn32mQYMGqXjx4goPD9e+fftUpEgRDR8+XD/++KPKly+vixcv2o7ApWXkyJEaOXKkypYtq127dunQoUNq166d5s+fn+rVbR4eHvrmm29s+/X+/fsVHh6uxx9/XIMHD9b8+fPt5veqWbOm5s6dq5YtW+r69etau3atjhw5ooCAAH355ZdOuQjAEYULF9b333+vbt26yd3dXZs3b9aOHTtUrFgxjRw5Ul9//bVdABo0aJCaNGmimJgYbdq0yTZf2KOPPqoFCxbotddeU6FChWzftVq1aumzzz6z3SHifhMmTNDbb7+tUqVKadu2bTp48KA6dOigr7/+WlLG9h1n7QfIWSyGI5fxAEAOdOvWLV24cEElSpRIde6mAQMGaN26dfr6669THZ+Ff7YTJ04of/78KlasWIpAduDAAXXu3Fk1a9bU999/n0UVIifgiBeAf4zo6Gg9++yzCgkJSTHW79dff9XGjRvl7e2dJfcfRPb35ZdfqkmTJlqwYIFde2xsrD7++GNJfx7xBR6EI14A/lEGDhyoNWvWyMvLSzVr1pSHh4fOnTunw4cPK1++fPrss8/UpEmTrC4T2dCePXvUu3dvxcfHq0qVKipVqpTu3LmjvXv36tatW6pfv36K05zAXxG8APyjJCQkaPHixVq8eLFOnz6tW7du6dFHH1VgYKBefPHFVG/tAiQ7fvy4vvnmG4WHh+vy5ctyc3NT2bJl1b59e3Xt2jXVK1mB+xG8AAAATMIYLwAAAJMQvAAAAEySK0cABgQEKD4+PkP35wMAAHDElStX5Obmpl27dv1t31wZvO7evZvqjWMBAACcLTExMcX9X9OSK4NX8l3n165dm8WVAACA3C75rhfpwRgvAAAAkxC8AAAATELwAgAAMAnBCwAAwCQELwAAAJMQvAAAAExC8AIAADAJwQsAAMAkBC8AAACTELwAAABMQvACAAAwCcELAADAJAQvAAAAkxC8AAAATOKa1QXkRIZhKDY2NqvLcJr8+fPLYrFkdRkAAOR6BC8HxMbGysvLK6vLcJrbt2/L09Mzq8sAACDX41QjAACASTjilUmXJOXEY0UxkopmdREAAPzDELwyyVM5M3gBAADzcaoRAADAJAQvAAAAkxC8AAAATELwAgAAMAnBCwAAwCQELwAAAJMQvAAAAExC8AIAADAJwQsAAMAkBC8AAACTELwAAABMQvACAAAwCcELAADAJAQvAAAAkxC8AAAATJKtgtf169c1YsQIBQYGqnbt2nr11Vd1+fJlSdL+/fvVuXNn+fv7q2nTplqwYEEWVwsAAJAx2Sp4DRo0SLGxsVq9erXWr18vFxcXvf3227px44b69++vDh06aOfOnRo/frwmTpyoAwcOZHXJAAAA6eaa1QUkO3TokPbv36+tW7fKy8tLkvT+++/rypUrCgsLk7e3t7p37y5JqlevnoKCghQaGio/P7+sLBsAACDdss0RrwMHDqhChQqaP3++WrRooQYNGmjSpEl67LHHFBERIavVate/QoUKOnr0aBZVCwAAkHHZJnjduHFDx44dU2RkpBYvXqyffvpJly5d0ptvvqmYmBh5eHjY9Xd3d1dsbGwWVQsAAJBx2SZ4ubm5SZJGjx4tLy8vPfrooxoyZIg2btwowzAUFxdn1z8uLk6enp5ZUSoAAIBDsk3wqlChgpKSkpSQkGBrS0pKkiRVrlxZERERdv1PnDihihUrmlojAABAZmSb4FW/fn2VKlVKo0aNUkxMjKKjozV58mQ1b95c7dq1U1RUlGbPnq2EhARt375dS5cuVUiVx6ELAAAgAElEQVRISFaXDQAAkG7ZJnjlzZtX3333nVxcXNSqVSu1atVKjz/+uCZMmKBChQpp5syZWrlypQIDAzVmzBiNGTNGdevWzeqyAQAA0i3bTCchSUWLFtXkyZNTXVa9enXNmzfP5IoAAACcJ9sc8QIAAMjtCF4AAAAmIXgBAACYhOAFAABgEoIXAACASQheAAAAJiF4AQAAmITgBQAAYBKCFwAAgEkIXgAAACYheAEAAJiE4AUAAGASghcAAIBJCF4AAAAmIXgBAACYhOAFAABgEoIXAACASQheAAAAJiF4AQAAmITgBQAAYBKCFwAAgEkIXgAAACYheAEAAJiE4AUAAGASghcAAIBJCF4AAAAmIXgBAACYhOAFAABgEoIXAACASQheAAAAJiF4AQAAmITgBQAAYBKCFwAAgEkIXgAAACYheAEAAJiE4AUAAGASghcAAIBJCF4AAAAmIXgBAACYhOAFAABgEoIXAACASQheAAAAJiF4AQAAmITgBQAAYBKCFwAAgEkIXgAAACYheAEAAJgkWwWv5cuXq0qVKvL397f9N3z4cEnSxo0bFRQUpBo1aqh169Zav359FlcLAACQMa5ZXcD9Dh48qPbt22vixIl27ZGRkRo0aJA++eQTNW7cWGFhYRoyZIjCwsJUtGjRLKoWAAAgY7LVEa+DBw+qWrVqKdoXL16sgIAANW/eXK6urmrTpo1q166tH374IQuqBAAAcEy2OeKVlJSkw4cPy8PDQ9OnT9e9e/fUqFEjvfHGGzpx4oSsVqtd/woVKujo0aNZVC0AAEDGZZsjXtHR0apSpYpatWql5cuXa968eYqMjNTw4cMVExMjDw8Pu/7u7u6KjY3NomoBAAAyLtsc8Xr00UcVGhpqe+zh4aHhw4erS5cuCgwMVFxcnF3/uLg4eXp6ml0mAACAw7LNEa+jR4/qo48+kmEYtrb4+HjlyZNHfn5+ioiIsOt/4sQJVaxY0ewyAQAAHJZtgpe3t7dCQ0M1ffp0JSYm6sKFC/rwww/VsWNHdejQQeHh4Vq+fLkSExO1fPlyhYeHq3379lldNgAAQLplm+D1+OOPa9q0aVq7dq3q1KmjkJAQVa9eXf/+979Vvnx5ffnll5o2bZpq166tqVOn6osvvlDZsmWzumwAAIB0yzZjvCSpTp06mjdvXqrLGjZsqIYNG5pcEQAAgPNkmyNeAAAAuR3BCwAAwCQELwAAAJMQvAAAAExC8AIAADAJwQsAAMAkBC8AAACTELwAAABMQvACAAAwiVOD14kTJ3Tq1ClnrhIAACDXcDh4zZs3T++++67t8eDBgxUUFKQ2bdro1VdfVXx8vDPqAwAAyDUcCl6LFy/Wu+++qz179kiSNmzYoLCwMD355JNq37691q9fr1mzZjm1UAAAgJzOoZtkz5s3T1WrVlVoaKgkadWqVXJ1ddXUqVNVuHBhWSwWLVu2TC+//LJTiwUAAMjJHDriFRERoeDgYLm7u0uStm7dqqpVq6pw4cKSJH9/f505c8Z5VQIAAOQCDgUvi8WivHnzSpJOnjypS5cuqW7durbld+7ckYeHh3MqBAAAyCUcCl5lypRReHi4JGnp0qWyWCxq2LChJCkxMVHLly9X6dKlnVclAABALuBQ8AoODtayZcvUrl07ffXVVypbtqwCAgIUERGhkJAQHThwQN26dXN2rQAAADmaQ4Pru3fvroSEBC1atEj169fXqFGjJP15tOv8+fN6/fXX1aFDB6cWCgAAkNM5FLwkqU+fPurTp49dm9Vq1datW+Xm5pbZugAAAHIdh4OXJN29e1c7d+7U+fPn1bhxY3l4eOju3bt67LHHnFUfAABAruHwzPVhYWFq0qSJ+vXrp3fffVcnT57Uvn371LRpUyZPBQAASIVDwWvPnj16/fXX5e3tbTdJapEiRVS8eHF98MEHWrNmjdOKBAAAyA0cCl7//e9/VaJECS1atEi9evWSYRiSpEqVKmnRokUqW7asZs+e7cw6AQAAcjyHgte+ffsUHBwsDw8PWSwWu2VeXl7q3LmzTpw44ZQCAQAAcguHgld8fLweeeSRNJe7uroqLi7O4aIAAAByI4eCV9myZW0z16dm3bp1zFwPAADwFw4Fr5CQEK1cuVKzZs1STEyMpD/v33j9+nW9++672r59u9q3b+/UQgEAAHI6h+bx6tGjh/bu3atJkybpgw8+kMVi0WuvvaaYmBgZhqEGDRqod+/ezq4VAAAgR3MoeFksFn3yySdq0aKFfvnlF0VGRurevXvy9/dXq1atFBwcrDx5HJ4iDAAAIFdyKHh9//33qlevnlq3bq3WrVs7uyYAAIBcyaHDUh999JGWLl3q7FoAAAByNYeCV548eVSoUCFn1wIAAJCrORS8+vbtq6+++kqbNm1SUlKSs2sCAADIlRwa47Vv3z7dvn1b/fv3l5ubmwoVKiQXFxe7PhaLhfs1AgAA3Meh4HX8+HF5e3vL29vb1pZ8v8a0HgMAAPzTORS81q1b5+w6AAAAcj0m2wIAADCJQ0e8pkyZ8rd9LBaLBg4c6MjqAQAAciWnBy+LxSLDMAheAAAAf+FQ8Pr2229TtN27d09XrlzRkiVLdOnSJX311VeZLg4AACA3cSh41alTJ81lQUFB6tGjh2bOnKkxY8Y4XBgAAEBu4/TB9RaLRW3bttXKlSudvWoAAIAc7aFc1Xjnzh3dunXrYawaAAAgx3LoVGNa4uPjdfDgQX3zzTcqX768M1cNAACQ4zkUvCpVqiSLxZLmcsMw9NZbbzlcFAAAQG7kUPCqXbt26itzdVWRIkUUEhLywAH4AAAA/0QOBa/vvvvO2XXYuXfvnvr06aMSJUroP//5jyRp48aN+uijj3T27FkVK1ZMI0aMUJMmTR5qHQAAAM7k0OD6kSNHav/+/Wku37Ztm/r27etwUVOmTNGuXbtsjyMjIzVo0CD961//0q5duzRo0CANGTJEly5dcngbAAAAZnMoeC1evFhnz55Nc3l4eLjCw8MdKmjbtm0KCwtTy5Yt7bYXEBCg5s2by9XVVW3atFHt2rX1ww8/OLQNAACArJCuU41nzpxR27ZtlZiYaGsbPny4hg8fnuZzKlasmOFirl69qtGjR2vq1KmaPXu2rf3EiROyWq12fStUqKCjR49meBsAAABZJV3B64knntDgwYP166+/SpJ27dqlsmXLysfHJ0VfFxcX+fj4qF+/fhkqJCkpScOHD9cLL7ygSpUq2S2LiYmRh4eHXZu7u7tiY2MztA0AAICslO7B9f369bOFqUqVKmnAgAEKCgpyWiHTpk2Tm5ubevbsmWKZh4eH4uLi7Nri4uLk6enptO0DAAA8bA5d1fgwTvEtWbJEly9fVkBAgCTZgtaaNWvUvXt3HT582K7/iRMnVK1aNafXAQAA8LBkaub63377TTExMTIMw9aWmJioW7duadOmTXr//ffTva6/3tsxeQLW//znP/r99981a9YsLV++XC1btlRYWJjCw8M1evTozJQPAABgKoeC15kzZ/TKK6/o1KlTD+yXkeD1IOXLl9eXX36pjz76SKNHj1aJEiX0xRdfqGzZsk5ZPwAAgBkcCl6fffaZTp06pVatWsnd3V1LlixR//79dfXqVYWFhSkhIUHff/99pgpLnjg1WcOGDdWwYcNMrRMAACArOTSP144dO9SmTRt9+umnGjVqlAzDUOPGjTVu3DgtWLBALi4uCgsLc3atAAAAOZpDwev69eu2QfAFCxZUsWLFdOTIEUlS6dKlFRwcrNWrVzuvSgAAgFzAoeD11zm1SpYsqRMnTtgeW61W/fHHH5mrDAAAIJdxKHhZrVZt3brV9viJJ57QoUOHbI+vXLmS+coAAAByGYeCV4cOHbR69WoNHDhQMTExaty4sQ4ePKjJkydr+fLl+u6771Lc4gcAAOCfzqGrGjt37qyIiAjNnTtXrq6uat68uRo1aqRp06bJYrEob968GjRokLNrBQAAyNEcnkB11KhRevXVV5UvXz5J0pdffqlffvlF169fV4MGDVS+fHmnFQkAAJAbZGrmem9v7/+/IldXtW/fPtMFAQAA5FYOjfGSpPj4eM2YMUNdu3ZVw4YNtWvXLh0+fFjvv/++rl696swaAQAAcgWHjnjdvXtXvXv31r59+5QvXz7Fx8crISFBUVFRCg0N1ebNmzV37lz5+Pg4u14AAIAcy6EjXv/73/+0f/9+jR8/XmvXrrXdJLtVq1Z65513dO7cOU2bNs2phQIAAOR0DgWv5cuXq3379goJCZGLi4ut3WKxqFu3bgoODtaGDRucVSMAAECu4FDwunDhgmrUqJHmcj8/P2auBwAA+AuHgleBAgUeOID+zJkzKlCggMNFAQAA5EYOBa+6detqwYIFunXrVoplZ8+e1bx581S7du1MFwcAAJCbOHRV46BBg9SpUye1b99ejRo1ksVi0dq1a7V27Vr9+OOPunfvnl555RVn1woAAJCjOXTEq2zZspo1a5by58+v77//XoZhaM6cOZozZ44KFSqk//3vf6pUqZKzawUAAMjRHJ653s/PT8uWLdOxY8d06tQpJSUlqWTJkqpWrZry5HF4XlYAAIBcK1O3DJIkX19f+fr6OqMWAACAXC1dh6YqV66spUuXPuxaAAAAcrV0Ba/kmenvd+3aNVWuXFnbtm1zelEAAAC5UaYGY6UWyAAAAJA6RsEDAACYhOAFAABgEoIXAACASQheAAAAJkn3PF6rV6/W6dOnbY/j4uJksVi0ZMkS7d69O0V/i8WigQMHOqdKAACAXCDdwSssLExhYWEp2n/66adU+xO8AAAA7KUreE2cOPFh1wEAAJDrpSt4dezY8WHXAQAAkOsxuB4AAMAkBC8AAACTELwAAABMQvACAAAwSbqC1x9//PGw6wAAAMj10hW8goODNX/+fNvjKVOm6Pjx4w+tKAAAgNwoXcHr9u3bunXrlu0xwQsAACDj0jWPV5kyZTRlyhTt379fnp6ekqQffvhBW7ZsSfM5FotFEyZMcE6VAAAAuUC6gtfo0aM1ZMgQ2y2DLBaLdu7cqZ07d6b5HIIXAACAvXQFr8DAQG3dulVXrlxRfHy8mjdvrlGjRqlZs2YPuz4AAIBcI903ybZYLCpSpIikP28h9OSTT6pEiRIPrTAAAIDcJt3B637JN82+ffu2Nm/erHPnzsnNzU3FixdXgwYN5O7u7tQiAQAAcgOHgpckrVq1Sv/+97918+ZNGYYh6c+jYl5eXho7dqzatGnjtCIBAAByA4eC1/79+zVs2DAVKFBAgwcPVsWKFZWUlKTjx49rzpw5GjFihEqWLCk/Pz9n1wsAAJBjORS8/vvf/8rb21tLliyRj4+Prb1ly5bq1q2b2rdvr+nTp+vzzz93WqEAAAA5nUP3aty7d6+ee+45u9CVzMfHR88995x2796d6eIAAAByE4eCV2xsbKqhK1nhwoXtZrpPr23btqlz586qWbOmnnrqKb3//vuKi4uT9Ofpzc6dO8vf319NmzbVggULHCkdAAAgyzgUvEqWLKnt27enuXz79u0qXrx4htYZHR2tl19+Wd26ddOuXbu0ePFihYeH66uvvtKNGzfUv39/dejQQTt37tT48eM1ceJEHThwwJHyAQAAsoRDwatdu3ZavXq1pkyZovj4eFt7fHy8pkyZojVr1mT4qsbChQtr69atCg4OlsVi0fXr13X37l0VLlxYYWFh8vb2Vvfu3eXq6qp69eopKChIoaGhjpQPAACQJRwaXN+vXz9t3LhRU6ZM0cyZM/XEE0/IYrHo9OnTio2NVZUqVdS/f/8Mr9fLy0uS1KhRI126dEkBAQEKDg7Wp59+KqvVate3QoUKWrhwoSPlAwAAZAmHjni5ubnp22+/1aBBg1SiRAlFRkbq5MmTKlGihF577TWFhoZmahLVsLAw/frrr8qTJ48GDx6smJgYeXh42PVxd3dXbGysw9sAAAAwm8MTqLq7u2vgwIEaOHCgM+uxrdvd3V3Dhw9X586d1bNnzxSD9ePi4uTp6en0bQMAADwsDh3xehj27NmjZ555JsWYsbx586pChQqKiIiw63/ixAlVrFjR7DIBAAAclm2Cl6+vr+Li4vTxxx8rPj5e58+f16RJk9SpUye1atVKUVFRmj17thISErR9+3YtXbpUISEhWV02AABAujl8qtHZPD09NX36dE2YMEFPPfWUChQooKCgIA0cOFBubm6aOXOmxo8fr88//1yFCxfWmDFjVLdu3awuGwAAIN2yTfCS/rxScebMmakuq169uubNm2dyRQAAAM6TbU41AgAA5HYOBa+RI0dq//79aS7ftm2b+vbt63BRAAAAuZFDwWvx4sU6e/ZsmsvDw8MVHh7ucFEAAAC5UbrGeJ05c0Zt27ZVYmKirW348OEaPnx4ms9hqgcAAAB76QpeTzzxhAYPHqxff/1VkrRr1y6VLVtWPj4+Kfq6uLjIx8dH/fr1c26lAAAAOVy6r2rs16+fLUxVqlRJAwYMUFBQ0EMrDAAAILdxaDqJo0ePOrsOAACAXC9T83idOXNGV65cUVJSUqrLa9eunZnVAwAA5CoOBa+oqCi9/vrr2rVr1wP7/fbbbw4VBQAAkBs5FLw+/vhj7dy5U/Xq1VP16tXl5ubm7LoAwFSGYSg2Njary3Ca/Pnzy2KxZHUZAP7CoeC1YcMGtW3bVh9//LGz6wGALBEbGysvL6+sLsNpbt++LU9Pz6wuA8BfODSBamxsLDeoBgAAyCCHjnhVqFBBp06dcnYtAJAtXJKUE48VxUgqmtVFAHggh4LXyy+/rJEjR6p169aqXr26s2sCgCzlqZwZvABkfw4Fr/DwcHl7e6tLly4qXbq0HnvssRSDOC0Wi7755hunFAkAAJAbOBS85syZY/t3ZGSkIiMjU/ThahoAAAB7zFwPAABgEoeuagQAAEDGOXTE66effkpXvw4dOjiyegAAgFzJoeD11ltvpWsMF8ELAADg/3MoeE2cODFFW2JioqKiorR8+XIZhqEJEyZkujgAAIDcxKHg1bFjxzSX9e3bV506ddKGDRvk5+fncGEAAAC5jdMH17u5uSk4ODjd48AAAAD+KR7KVY0uLi6Kiop6GKsGAADIsZwevC5evKi5c+eqVKlSzl41AABAjubQGK9mzZql2n737l1FR0crKSlJb7/9dqYKAwAAyG0cCl6GYaTanj9/fpUpU0YhISEPHIAPAADwT+RQ8Fq3bp2z6wAAAMj1Mj3GyzAMRUdH6/bt286oBwAAINdy6IiXJF27dk0ffvihwsLCFBMTI0ny8vJSq1atNHToUBUuXNhpRQIAAOQGDgWvmzdvqmvXrjp9+rRKly6twMBA3bt3TydPntTChQu1Y8cOLV68WF5eXs6uFwAAIMdyKHj997//1ZkzZ/T++++rc+fOdssWLlyot99+W9OmTdOwYcOcUiQAAEBu4NAYr9WrV6t9+/YpQpckderUSR06dFBYWFimiwMAAMhNHApef/zxh2rUqJHm8ieffFIXL150uCgAAIDcyKHg9cgjj+jChQtpLj937hzjuwAAAP7CoeAVGBiouXPn6tSpUymW/f777/r+++9Vp06dTBcHAACQmzg0uH7gwIFat26dOnTooPbt26t8+fKyWCyKiIjQzz//LIvFoldffdXZtQIAAORoDgWv8uXLa/r06Ro1apTmz59vt6xUqVIaN26crFarUwoEAADILRyeQDUgIECrVq3SkSNHdObMGRmGoSeeeEJVqlRRnjyZnhAfAAAg13E4eEmSxWJR1apVVbVqVUVFRcnb25vQBQAAkIYMpaSFCxeqQ4cOSkpKSrFs0qRJatSokebNm+e04gAAAHKTdAevDz/8UGPGjNGJEycUGRmZYrmrq6tu3LihsWPHaty4cc6sEQAAIFdIV/Bav369ZsyYoTp16mjlypUqV65cij4TJ07U6tWrVb16dYWGhmrLli1OLxYAACAnS1fwmjt3rkqUKKEZM2aoZMmSafYrVqyYZsyYoUKFCmnOnDlOKxIAACA3SFfwOnjwoDp27Ki8efP+bd8CBQqoQ4cO2r9/f6aLAwAAyE3SFbxiYmJUtGjRdK+0bNmyunXrlsNFAQAA5EbpCl6PPfaYrly5ku6VRkdHy8fHJ8PFHD16VC+88ILq1Kmjp556SiNGjFB0dLQkaf/+/ercubP8/f3VtGlTLViwIMPrBwAAyErpCl7VqlXT2rVr073SsLAwlSlTJkOFxMXF6aWXXpK/v782b96sZcuW6fr16xo1apRu3Lih/v37q0OHDtq5c6fGjx+viRMn6sCBAxnaBgAAQFZKV/Dq2LGjDh8+rNmzZ/9t39mzZ+vIkSPq2LFjhgq5cOGCKlWqpIEDB8rNzU2FChXSc889p507dyosLEze3t7q3r27XF1dVa9ePQUFBSk0NDRD2wAAAMhK6Zq5vkmTJmrZsqUmTZqk/fv3q0ePHnryySfl6vrn0xMSErR3716FhoYqLCxMtWvXVtu2bTNUSLly5TR9+nS7tlWrVqlq1aqKiIhIce/HChUqaOHChRnaBgAAQFZK9y2DJk6cqDx58mjFihVauXKlXFxc5O3trXv37unmzZtKSkqSYRhq0aKFJkyYYAtljjAMQ59++qnWr1+vOXPm6Ntvv5WHh4ddH3d3d8XGxjq8DQAAALOlOx15enrq008/1ebNm/XTTz/p4MGDunz5slxcXFSmTBnVrl1b7dq1U0BAQKYKun37tkaOHKnDhw9rzpw58vX1lYeHR4qrJOPi4uTp6ZmpbQEAAJgpw4elGjRooAYNGjyMWnTmzBn169dPxYsX18KFC1W4cGFJktVqTTET/okTJ1SxYsWHUgcAAMDDkKGbZD9MN27cUO/evVWzZk3NmDHDFrokqUWLFoqKitLs2bOVkJCg7du3a+nSpQoJCcnCigEAADLG8YFYTvbjjz/qwoULtjFk99u7d69mzpyp8ePH6/PPP1fhwoU1ZswY1a1bN4uqBQAAyLhsE7xeeOEFvfDCC2kur169uubNm2diRQAAAM6VbU41AgAA5HYELwAAAJMQvAAAAExC8AIAADAJwQsAAMAkBC8AAACTELwAAABMQvACAAAwCcELAADAJNlm5noAQOYY9/07JiYmy+pwpvz588tisWR1GYDTELwAIJeIve/fRYsWzbI6nOn27dvy9PTM6jIAp+FUIwAAgEk44gUAudAlSTn1OFGMpNxxvA5IieAFALmQp3Ju8AJyM041AgAAmITgBQAAYBKCFwAAgEkIXgAAACYheAEAAJiE4AUAAGASghcAAIBJCF4AAAAmIXgBAACYhOAFAABgEoIXAACASQheAAAAJiF4AQAAmITgBQAAYBKCFwAAgEkIXgAAACYheAEAAJiE4AUAAGASghcAAIBJCF4AAAAmIXgBAACYhOAFAABgEoIXAACASQheAAAAJiF4AQAAmITgBQAAYBKCFwAAgEkIXgAAACYheAEAAJiE4AUAAGASghcAAIBJCF4AAAAmyZbBKzo6Wi1atNCOHTtsbfv371fnzp3l7++vpk2basGCBVlYIQAAQMZlu+C1e/duPffcczpz5oyt7caNG+rfv786dOignTt3avz48Zo4caIOHDiQhZUCAABkTLYKXosXL9Ybb7yh119/3a49LCxM3t7e6t69u1xdXVWvXj0FBQUpNDQ0iyoFAADIuGwVvBo0aKDVq1erTZs2du0RERGyWq12bRUqVNDRo0fNLA8AACBTXLO6gPs99thjqbbHxMTIw8PDrs3d3V2xsbFmlAUAAOAU2eqIV1o8PDwUFxdn1xYXFydPT88sqggAACDjckTwslqtioiIsGs7ceKEKlasmEUVAQAAZFyOCF4tWrRQVFSUZs+erYSEBG3fvl1Lly5VSEhIVpcGAACQbjkieBUqVEgzZ87UypUrFRgYqDFjxmjMmDGqW7duVpcGAACQbtlqcP39jh07Zve4evXqmjdvXhZVAwAAkHk54ogXAABAbkDwAgAAMAnBCwAAwCQELwAAAJMQvAAAAExC8AIAADAJwQsAAMAkBC8AAACTELwAAABMkm1nrsfDZdz375iYmCyrw1ny588vi8WS1WUAAPBABK9/qNj7/l20aNEsq8NZbt++LU9Pz6wuAwCAB+JUIwAAgEk44gVdkpQTjxXFSMr5x+oAAP8kBC/IUzkzeAEAkNNwqhEAAMAkBC8AAACTELwAAABMQvACAAAwCcELAADAJAQvAAAAkzCdBIBMMwxDsbGxf98xG8sNt84CkP0RvABkWmxsrLy8vLK6DADI9jjVCAAAYBKOeAFwqpx6C6rLkspldREAcj2CFwCnyqm3oMqJNQPIeTjVCAAAYBKCFwAAgEkIXgAAACYheAEAAJiE4AUAAGASghcAAIBJCF4AAAAmIXgBAACYhOAFAABgEoIXAACASQheAAAAJiF4AQAAmITgBQAAYBKCFwAAgEkIXgAAACYheAEAAJiE4AUAAGASghcAAIBJXLO6AOCfzjAMxcbGZnUZmRITE5PVJQBAjkDwArJYbGysvLy8sroMAIAJONUIAABgkhx1xOvq1at6++23FR4eLhcXFz377LN688035eqao14GkKZLkjyzuggHXJZULquLAIAcIEclliFDhqho0aLatGmToqKiNGDAAM2ePVsvvfRSVpcGOIWncmbwyok1A0BWyDGnGk+fPq3w8HANHz5cHh4eKlWqlF599VWFhoZmdWkAAADpkmOOeEVERMjb21tFixa1tZUvX14XLlzQzZs3VbBgwSypK6deyxWTxr9zErvXkIOvqru/9pz6KnLd/pRlVWRObngNUu75biN78fTMHsfmc0zwiomJkYeHh11b8uPY2Fi74HX58mXdu3dPzZo1eyi1GIahMmXKSJICH8oWzFHm//6fK15DYE5+FWJ/yibK/N//eSrCZZwAABW2SURBVA1Zr8z//T+nf7eRfZQr9/BGol68eFEuLi7p6ptjglf+/Pl1584du7bkx39Nsfny5VN8fPxDq+X/tXfvQU2d6R/Av1xEsaKI0IpL1Q5CEAhJgHJRu2rEIhe5atGK21i6jrWigrtb6m1bbVm0o1SdFbUKrboDlBBxWyBcRNbWKohFBW1XwSpgu1ZwsXSpXJL394dD1jQh5PiTE7o+nxln5M2bnO+T6zPnvDkxMzMb1AeQEEIIIb8elpaWsLKyMm7uIGd5bFxcXNDe3o7W1lbY29sDABobGzF+/HjY2Nhoza2pqTFFREIIIYQQg341i+snT54MHx8fpKam4qeffkJzczP27t2LBQsWmDoaIYQQQohRzBhjzNQhjNXa2ootW7agqqoK5ubmiIqKwh/+8Aejj6sSQgghhJjSr6rxIoQQQgj5NfvVHGo0hba2NqxcuRK+vr7w9/fHe++9h97eXr1z//GPf2D+/PkQi8UICQnByZMneU6rH5ca+pSUlAzaN0IfBZcasrOzERwcDIlEguDg4CFznjdja1Cr1dizZw9mzpwJiUSC+fPno6ioyASJ9XuU59PVq1chEolQVVXFU0rDuNTw2muvQSgUQiKRaP6dOnWK58S6uNRQXV2NhQsXQiKRYObMmdi/fz/PaftnbB2vvfaa1mMgkUggEAiwefNmE6TWxuWx+PjjjyGVSuHt7Y358+ejpKSE57T6calBoVBg3rx5kEgkiIuLw7lz53hOO7C7d+9i7ty5Bt9zTPqZzUi/4uPj2bp161hnZydrampiYWFh7MMPP9SZ9+233zKhUMjKyspYT08PKywsZF5eXuxf//qXCVJrM7YGxhjr7u5mBw4cYO7u7mz27Nk8J+2fsTWUlZUxX19fVltby9RqNfvqq6+Yr68vUyqVJkitzdgaDh8+zKRSKbt58yZjjLGKigrm5uam+dvUuDyfGGOss7OThYeHM1dXV3b27Fkek/aPSw3+/v6sqqqK54QDM7aGhoYGJhKJmEKhYGq1mn399dfMz8+PFRcXmyC1Lq7Ppz55eXls5syZ7Pbt2zykNMzYGiorK1lgYCBrbGxkjDGmVCqZm5sba25u5juyDmNrKC8vZ56enqyiooL19vaykpISJhKJNDUNBTU1NSwoKMjge46pP7Op8erHjRs3mKurq9YDUVhYyGbNmqUzd+fOnWzZsmVaYwkJCWzXrl2DntMQLjUw9uDFl5CQwNLT04dM48WlhqNHj7L9+/drjb3xxhts69atg57TEC41qFQq9p///IcxxlhXVxeTy+VMIpEMiQ8Yrs8nxhh788032QcffDBkGi8uNTQ1NTE3NzfW0dHBZ8QBcalhy5YtLDk5WWvs+vXr7Icffhj0nAN5lOcTY4w1NjYyLy8vdu7cucGOOCAuNWRmZrKAgADW0NDA1Go1KysrY0KhkH3//fd8RtbBpYa1a9eylJQUrbGEhAS2ffv2Qc9pDIVCwWbNmsUKCwsNvueY+jObDjX2Y6Az5T+soaEBrq6uWmNTpkzBN998w0vW/nCpAQDef/99HDx4EBMnTuQzpkFcaliyZAmWL1+u+butrQ3nzp2Dp6cnb3n14VKDubk5Ro4ciS+++AIikQgbNmzAmjVr8PTTT/MdWwfX51NBQQFu3ryJVatW8RnTIC411NXV4amnnkJSUhICAgIQHh4OuVzOd2QdXGq4dOkSnJyckJycDH9/f4SEhKC6uhoODg58x9bB9fnU55133kFUVBR8fX35iGkQlxrCwsJgb2+P0NBQeHh4YM2aNUhLS8P48eP5jq2FSw0qlQojR47UGjM3N8f169d5yTqQGTNmoKysDKGhoQbnmfozmxqvfgx0pvyB5o4YMUJnHt+41ADA5G8A+nCtoc+dO3fw+9//Hp6enggPDx/UjAN5lBr8/PxQV1eHrKwsfPDBB0NinReXOhobG5Geno4dO3YMqW8dc6mhu7sbYrEYSUlJ+Pzzz5GSkoL33nsPxcXFvOXVh0sN9+7dw+HDhxEREYHTp09jy5Yt2LZtG5RKJW95+/Mor4uamhpcvHhxyDTzXGro6emBm5sb8vLycOHCBWzZsgUbNmzAP//5T97y6sOlhuDgYBQUFKC6uhq9vb0oLy/HmTNn0NXVxVteQxwcHGBpOfDpSU39mU2NVz+4nCnf2toa9+/f1xq7f/++yX8XiksNQ9Wj1HDhwgUsWLAAzz33HDIyMox6IQ6mR6nBysoKlpaWCAwMRGRkJD799NNBzzkQY+vo6upCUlIS1q9fjwkTJvCacSBcHouoqCgcPHgQ7u7uGDZsGGbMmIGoqCiTN15carCyssKcOXMwa9YsWFpa4vnnn0dkZKTJawAe7XWRm5uLkJCQIbHHDuBWw9atW+Hi4gIvLy9YWVkhNjYWYrEYx44d4y2vPlxqCAsLw9q1a7Fp0ybN3qXw8HCT/VbyozL1ZzY1Xv14+Ez5ffo7U76rqyuuXbumNdbQ0AAXFxdesvaHSw1DFdca5HI5ZDIZXnnlFezYscPon3AYTFxqSEtLQ1pamtZYd3c3bG1teclqiLF11NXV4caNG9iwYQN8fX01h4RWrFiBt99+m+/YWrg8FnK5XKdB6e7uxvDhw3nJ2h8uNTg7O+v8fJpKpQIbAmcR4vra7u3txYkTJxAREcFnTIO41PDdd9/pPBaWlpYYNmwYL1n7w6WGO3fu4IUXXkBJSQnOnj2Lbdu2obGx0eTLObgy+Wc2LyvJfqUWL17MkpKSWEdHh+abHrt379aZ19DQwIRCISssLNR8Q0IoFLLr16+bILU2Y2t4WH5+/pBZXM+Y8TUolUrm4eHBTp06ZYKUhhlbQ1lZGROJRKy6upqpVCp24sQJJhKJ2Pnz502QWtejPJ8YY0NmcT1jxteQlZXFAgMD2eXLl5lKpWInT54cMou6ja3hyy+/ZO7u7qygoICp1WpWXV3NxGIxKy8vN0FqXVyeT/X19czd3Z3dv3+f55SGGVtDeno68/f3Z/X19UylUrHi4mImFArZlStXTJBam7E1FBYWshkzZrCWlhZ2//59lpWVxXx9fVlra6sJUhtm6D3H1J/Z1HgZcOfOHZaYmMj8/PxYQEAAS0tLY729vYwxxsRiMTt+/Lhm7qlTp1hERAQTi8UsLCyMVVZWmiq2Fi419BlqjZexNYSHhzM3NzcmFou1/m3atMmU8Rlj3B6HvLw89uKLLzJvb28WExMzpBrJR3k+MTa0Gi9ja1Cr1eyvf/0rmz17NvPy8mJhYWFD5jQMXB6HyspKFhMTwyQSCZszZw7Lzs42VWwdXOooLi5mgYGBporaL2Nr6OnpYbt372azZ89m3t7eLDo6esi8trk8Dnv27GHTp09nEomExcfHs8uXL5sqtkG/fM8ZSp/ZdOZ6QgghhBCe0BovQgghhBCeUONFCCGEEMITarwIIYQQQnhCjRchhBBCCE+o8SKEEEII4Qk1XoQQQgghPKHGixBCCCGEJ9R4EfKE2rNnDwQCAQQCAf72t78ZnCuVSiEQCLB06VKd61dVVQ1KvpSUFAgEArS0tAzK7Q+kr2ZD//bs2WOSbABw48YNrb+lUimkUqlpwhBCjGbaXw8mhAwJSqUSS5Ys0XvZhQsXcOvWLZ3xuXPnYuLEiXB2dh6UTHFxcQgMDISdnd2g3L6xtm/f3u9lAoGAxyT/9e6776KyshLl5eWasfXr15skCyGEG2q8CHnCTZo0CTU1NWhtbYW9vb3O5UVFRRg3bhza2tq0xt3c3ODm5jZouSQSCSQSyaDdvrEiIyNNHUFHRUWFzlhQUJAJkhBCuKJDjYQ84UJCQqBWq1FaWqpzGWMMSqUS8+bNM0EyQgj530ONFyFPuICAANjZ2UGpVOpcVlNTg9u3byMsLEznMn1rvK5cuYIVK1bghRdegKenJ+bMmYN3330X7e3tWtctKirCokWL4OfnB7FYjMjISGRmZkKtVmvm/HKNV1VVFQQCAQoLC7Fv3z7MnTsXnp6ekEql2LVrF3p7e7W28dNPPyE1NRWzZs2CUChETEwMTp48CZlM9tjXQikUCk22BQsWwNPTE/PmzUNXVxcAoKysDK+++ir8/f3h4eEBf39/rFixAvX19Tq39eWXXyIhIQF+fn7w8fFBXFwcSkpKAAAtLS0QCAS4desWbt26pbXOTN8ar3v37iEtLQ1z5syBp6cnAgMDkZycjMbGRq15KSkpkEgkaGlpwdq1a+Hv7w8vLy8sWrQIX3zxxWO9rwh50tGhRkKecObm5pg7dy7kcrnO4cbCwkI4OjrC29t7wNtpbm7GK6+8AgcHB8hkMowePRoXL17E0aNHcenSJeTm5sLMzAylpaVITk7G9OnTsWbNGpibm0OpVGLbtm1oa2vDH//4R4Pb2blzJxhjiIuLw+jRo6FQKLB3716YmZlh9erVAIDu7m787ne/w5UrVxAdHQ1PT0/U1tZi5cqVsLGxwahRo4y+f+7evat33NraGtbW1lpjGzduRFBQEBYsWIDOzk4MHz4cH3/8MVJTU+Hn54dVq1Zh2LBhqK+vR0FBAWpra1FRUYGnnnoKAJCXl4dNmzbhN7/5DWQyGcaMGQOFQoHVq1dj69atCA8Px/bt2/GXv/wFAPDWW2/1u86stbUVixcvRnNzM6KiouDl5YWWlhZkZ2ejoqICBw8ehK+vr2Z+T08PXn75ZUydOhWrV69Ge3s7srKysHz5chQVFWHy5MlG32eEkP5R40UIQWhoKHJzc1FaWoqXX34ZAKBSqVBaWoqoqCiYmZkNeBulpaX48ccfcejQIXh5eQEAFi5ciFGjRqG6uho//PADnnnmGeTn58Pa2hoffvghzM0f7HR/6aWXIJPJdPbE6NPV1YWioiKMHj0awIM1WL/97W+Rl5enabyOHDmCy5cvIyUlBcuWLQMALFmyBFOmTEF6ejqnxiswMFDv+KpVq5CYmKg1NmXKFGzfvl1zf6lUKmRkZMDd3R0fffQRLCwsNHNHjx6NQ4cO4fTp03jxxRc1e+gmTZqE/Px8TcbY2FjMnz8fu3fvRmxsLCIjI7Fr1y5N7f3ZuXMnmpqakJqaitjYWM14dHQ0oqOjsX79ehQXF2sy9fT0QCqV4u2339bMdXJywp/+9CccO3YMSUlJRt9nhJD+UeNFCIGfnx/s7e2hVCo1jdfZs2fR1tam9zCjPo6OjgCA999/HytXroSPjw+srKyQkpKiNW/8+PHo7OzEO++8g7i4OEydOhUWFhY4cuSIUduZPXu2pukCgJEjR8LZ2Rl1dXWasaKiItjY2CA+Pl7ruq+++ir2799v1Hb6ZGVl6R1/9tln9WZ7uEm1sLDAqVOn8PPPP2s1XZ2dnRg2bJjm/8CDQ4ydnZ1YvHixVmM4YsQIHDhwABYWFppGdSB9a/YmTZqEmJgYrctcXFwQGRkJuVyOy5cva5pkAIiIiNCaKxQKAQB37twxaruEkIFR40UIgbm5OYKDg5GTk4O2tjaMGzdOc3jJw8PDqNsIDg5GbGwsFAoFZDIZRowYAR8fH8ycORNRUVEYM2YMACAxMRFff/01cnJykJOTAzs7OwQEBCAoKAjBwcGwtDT8tqTvm5dWVlZQqVSav7/99ltMmjRJ09w8PG/ixIno6OgwqiYAmDZtmtFzHRwc9GY7f/48iouL0dTUhObmZnz33XdgjAGAZl1b31o2fafneO6554zOAAD//ve/0dHRgeeff17v3koXFxfNNh9uvH6Z38rKSisjIeT/jxbXE0IAPPh2Y9/hxZ6eHpSXlxu9twt4sHcnNTUV5eXl2LhxI6ZNm4a6ujqkpqYiJCQETU1NAB40Tp988gnkcjkSExPh7OyMsrIyJCcnY8mSJejp6TG4HWP2+vT09Giahl8aPny40TVx9fBerT6bN2+GTCbD+fPnMWHCBMTHxyMzMxObN2/Wmtf35QBjDusOpK+p6++2+hqpX95Hj2PbhBDDqPEihAAAfHx88PTTT6O4uBinT59Ge3s7p8br1q1bOHPmDJycnLB06VJkZGTgzJkzSE5ORltbG7Kzs8EYw9WrV3Hp0iUIhUKsWrUKR48exdmzZxEUFIQLFy48lm/RTZ48GTdu3NDZU6NWq3Hz5s3/9+0bq6amBrm5uQgPD8dnn32G1NRULFu2DIGBgbh3757WXCcnJwAP9tb90t///ne89dZbuH37tlHbtbOzw6hRo9DQ0KBpwh527do1AP89PEwI4Q81XoQQAP893FhTU4Ps7Gy4ublxOiv9vn37IJPJcPHiRc2YpaUlRCIRgAd7g8zMzJCYmIjXX39d63DfqFGjNN/O07fXiKvQ0FC0t7fj2LFjWuP5+fk6p7YYTH3bcnV11dqbdPfuXcjlcgDQHCKdNm0arK2t8cknn+Dnn3/WzO3u7saBAwdQUVGBcePGAXjwWBk6/Nf3TdWbN29CoVBoXdbY2IhPP/0Uzz77LNzd3R9PoYQQo9EaL0KIRmhoKI4cOYLKykqsW7eO03VlMhmKi4uxfPlyLFq0CE5OTrh9+zays7NhY2ODl156CcCDNV7r1q1DXFwcYmJiMGbMGHzzzTfIzc3F1KlTOa2pMpSlsLAQGzduRG1tLTw8PFBfX4/jx4/rrPsaTN7e3rC1tcW+ffvQ2dkJJycntLS0ID8/X9N4/vjjjwAAW1tbpKSk4M9//jNiYmIQHR0Na2trHD9+HNeuXUN6erpm/Zu9vT0uXbqErKwsSCQSiMVinW2vW7cO1dXV2LBhA86dOweRSISWlhbk5ORoDgvToUVC+EeNFyFEQyKRwNHREd9//z1CQ0M5XdfZ2RlHjx5FRkYGCgoK0NbWBltbWwQEBOCNN97AxIkTAQDh4eGwtrbGRx99hEOHDqGjowOOjo5YunQpXn/99QEX1xvD2toahw8fRnp6Ok6cOIGCggIIBALs27cPb775Zr/rvx43Ozs7ZGZmYufOncjJyUF3dzeeeeYZBAcHY9myZZg3bx4+//xzJCQkAAAWLVoER0dHHDx4EBkZGbCwsMDUqVORmZmJ6dOna253zZo12Lx5M3bs2IGIiAi9jZeDgwPkcjn27t2LiooKfPbZZ7C1tYVUKsWKFSsG7Tc2CSGGmTF9CwAIIeRX7O7du7CxsdHZu6VWqyEWiyESiYw+fQUhhDxOtMaLEPI/Z9euXRCJRGhubtYaVyqV6Orq0ruHiBBC+EB7vAgh/3Nqa2sRHx+PCRMmYOHChRg7diyuXr2KvLw8jB07FgqFAmPHjjV1TELIE4gaL0LI/6SvvvoKBw4cQH19Pe7duwcHBwdIpVKsXLkSdnZ2po5HCHlCUeNFCCGEEMITWuNFCCGEEMITarwIIYQQQnhCjRchhBBCCE+o8SKEEEII4Qk1XoQQQgghPKHGixBCCCGEJ9R4EUIIIYTwhBovQgghhBCeUONFCCGEEMKT/wPDee7wmGaMQgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x148c63a3630>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fs.plot_missing()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"有关缺失分数的详细信息，我们可以访问`missing_stats`属性，这是所有特征缺失分数的DataFrame。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>missing_fraction</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>COMMONAREA_AVG</th>\\n\",\n       \"      <td>0.6953</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>COMMONAREA_MODE</th>\\n\",\n       \"      <td>0.6953</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>COMMONAREA_MEDI</th>\\n\",\n       \"      <td>0.6953</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>NONLIVINGAPARTMENTS_AVG</th>\\n\",\n       \"      <td>0.6945</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>NONLIVINGAPARTMENTS_MODE</th>\\n\",\n       \"      <td>0.6945</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>NONLIVINGAPARTMENTS_MEDI</th>\\n\",\n       \"      <td>0.6945</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>LIVINGAPARTMENTS_MEDI</th>\\n\",\n       \"      <td>0.6846</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>LIVINGAPARTMENTS_AVG</th>\\n\",\n       \"      <td>0.6846</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>LIVINGAPARTMENTS_MODE</th>\\n\",\n       \"      <td>0.6846</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>FONDKAPREMONT_MODE</th>\\n\",\n       \"      <td>0.6820</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                          missing_fraction\\n\",\n       \"COMMONAREA_AVG                      0.6953\\n\",\n       \"COMMONAREA_MODE                     0.6953\\n\",\n       \"COMMONAREA_MEDI                     0.6953\\n\",\n       \"NONLIVINGAPARTMENTS_AVG             0.6945\\n\",\n       \"NONLIVINGAPARTMENTS_MODE            0.6945\\n\",\n       \"NONLIVINGAPARTMENTS_MEDI            0.6945\\n\",\n       \"LIVINGAPARTMENTS_MEDI               0.6846\\n\",\n       \"LIVINGAPARTMENTS_AVG                0.6846\\n\",\n       \"LIVINGAPARTMENTS_MODE               0.6846\\n\",\n       \"FONDKAPREMONT_MODE                  0.6820\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fs.missing_stats.head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2. 唯一值\\n\",\n    \"\\n\",\n    \"下一个方法很简单：找到只有一个唯一值的所有特征。 （这不会对特征进行独热编码）。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"4 features with a single unique value.\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fs.identify_single_unique()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['FLAG_MOBIL', 'FLAG_DOCUMENT_10', 'FLAG_DOCUMENT_12', 'FLAG_DOCUMENT_17']\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"single_unique = fs.ops['single_unique']\\n\",\n    \"single_unique\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们可以绘制数据集中每个特征中唯一值数量的直方图。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x148c75f01d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fs.plot_unique()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"最后，我们可以访问一个DataFrame，其中包含每个特征的唯一值数量。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>nunique</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>CODE_GENDER</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>REG_CITY_NOT_LIVE_CITY</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>FLAG_DOCUMENT_10</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>ENTRANCES_MEDI</th>\\n\",\n       \"      <td>41</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>LANDAREA_AVG</th>\\n\",\n       \"      <td>1477</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                        nunique\\n\",\n       \"CODE_GENDER                   3\\n\",\n       \"REG_CITY_NOT_LIVE_CITY        2\\n\",\n       \"FLAG_DOCUMENT_10              1\\n\",\n       \"ENTRANCES_MEDI               41\\n\",\n       \"LANDAREA_AVG               1477\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fs.unique_stats.sample(5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 3. 共线(高相关性) 特征\\n\",\n    \"\\n\",\n    \"该方法基于皮尔森相关系数找到共线特征对。 对于高于指定阈值（就绝对值而言）的每一对，它标识要删除的变量之一。 我们需要传递一个 `correlation_threshold`。\\n\",\n    \"\\n\",\n    \"此方法基于在：https://chrisalbon.com/machine_learning/feature_selection/drop_highly_correlated_features/ 中找到的代码。\\n\",\n    \"\\n\",\n    \"对于每一对，将要删除的特征是在DataFrame中列排序方面排在最后的特征。 （除非`one_hot = True`，否则此方法不会预先对数据进行一次独热编码。因此，仅在数字列之间计算相关性）\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"24 features with a correlation magnitude greater than 0.97.\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fs.identify_collinear(correlation_threshold=0.975)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['AMT_GOODS_PRICE',\\n\",\n       \" 'FLAG_EMP_PHONE',\\n\",\n       \" 'YEARS_BUILD_MODE',\\n\",\n       \" 'COMMONAREA_MODE',\\n\",\n       \" 'ELEVATORS_MODE']\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"correlated_features = fs.ops['collinear']\\n\",\n    \"correlated_features[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们可以查看阈值以上相关性的热图。 将要删除的特征位于x轴上。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x148c78018d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fs.plot_collinear()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"要绘制数据中的所有相关性，我们可以将 `plot_all = True` 传递给 `plot_collinear`函数。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x148c77f8f98>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fs.plot_collinear(plot_all=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"21 features with a correlation magnitude greater than 0.98.\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x148c77f8208>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fs.identify_collinear(correlation_threshold=0.98)\\n\",\n    \"fs.plot_collinear()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"要查看阈值以上的相关细节，我们访问`record_collinear` 属性，它是一个DataFrame。  `drop_feature` 将被删除，并且对于每个将被删除的特征，它与 `corr_feature`可能存在多个相关性，而这些相关性都高于`correlation_threshold`。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>corr_feature</th>\\n\",\n       \"      <th>corr_value</th>\\n\",\n       \"      <th>drop_feature</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>AMT_CREDIT</td>\\n\",\n       \"      <td>0.987232</td>\\n\",\n       \"      <td>AMT_GOODS_PRICE</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>DAYS_EMPLOYED</td>\\n\",\n       \"      <td>-0.999533</td>\\n\",\n       \"      <td>FLAG_EMP_PHONE</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>YEARS_BUILD_AVG</td>\\n\",\n       \"      <td>0.992120</td>\\n\",\n       \"      <td>YEARS_BUILD_MODE</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>COMMONAREA_AVG</td>\\n\",\n       \"      <td>0.988074</td>\\n\",\n       \"      <td>COMMONAREA_MODE</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>FLOORSMAX_AVG</td>\\n\",\n       \"      <td>0.984663</td>\\n\",\n       \"      <td>FLOORSMAX_MODE</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      corr_feature  corr_value      drop_feature\\n\",\n       \"0       AMT_CREDIT    0.987232   AMT_GOODS_PRICE\\n\",\n       \"1    DAYS_EMPLOYED   -0.999533    FLAG_EMP_PHONE\\n\",\n       \"2  YEARS_BUILD_AVG    0.992120  YEARS_BUILD_MODE\\n\",\n       \"3   COMMONAREA_AVG    0.988074   COMMONAREA_MODE\\n\",\n       \"4    FLOORSMAX_AVG    0.984663    FLOORSMAX_MODE\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fs.record_collinear.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 4. 零重要特征\\n\",\n    \"此方法依赖于机器学习模型来识别要删除的特征。 因此，它是有标签的监督学习问题。 该方法通过使用LightGBM库中实现的梯度增强机找到特征重要性。\\n\",\n    \"\\n\",\n    \"为了减少所计算的特征重要性的差异，默认情况下对模型进行了10次训练。 默认情况下，还使用验证集（训练数据的15％）通过提前停止训练模型，以识别要训练的最优估计量。 可以将以下参数传递给`identify_zero_importance` 方法：\\n\",\n    \"\\n\",\n    \"* `task`: 可以是 `classification` 或 `regression`。 指标和标签必须与任务匹配。\\n\",\n    \"* `eval_metric`: 用于提前停止的度量（例如，用于分类的auc或用于回归的`l2` ）。 要查看可用指标的列表，请参阅LightGBM文档：(http://testlightgbm.readthedocs.io/en/latest/Parameters.html#metric-parameters)。\\n\",\n    \"* `n_iterations`: 训练次数。 特征重要性是在训练运行中平均得出的 (默认为10)。\\n\",\n    \"* `early_stopping`: 训练模型时是否使用提前停止（默认= True）。 当验证集的性能对于指定数量的估计量（此实现中默认为100）不再降低时，提早停止将停止训练估计量（决策树）。 早停是一种正则化形式，用于防止训练数据过拟合。\\n\",\n    \"\\n\",\n    \"首先对数据进行一次独热编码，以供模型使用。 这意味着某些零重要性特征可以通过一键编码来创建。 要查看单编码的列，我们可以访问 `FeatureSelector`的`one_hot_features` 。\\n\",\n    \"\\n\",\n    \"**注意**：与其他方法相比，模型的特征重要性是不确定的（具有少许随机性）。 每次运行此方法时，其结果都可能更改。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training Gradient Boosting Model\\n\",\n      \"\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[72]\\tvalid_0's binary_logloss: 0.254264\\tvalid_0's auc: 0.746911\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[108]\\tvalid_0's binary_logloss: 0.251856\\tvalid_0's auc: 0.75226\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[18]\\tvalid_0's binary_logloss: 0.258582\\tvalid_0's auc: 0.747935\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[33]\\tvalid_0's binary_logloss: 0.254186\\tvalid_0's auc: 0.754061\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[9]\\tvalid_0's binary_logloss: 0.266813\\tvalid_0's auc: 0.722839\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[48]\\tvalid_0's binary_logloss: 0.254795\\tvalid_0's auc: 0.750713\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[5]\\tvalid_0's binary_logloss: 0.269148\\tvalid_0's auc: 0.743539\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[21]\\tvalid_0's binary_logloss: 0.257916\\tvalid_0's auc: 0.738772\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[58]\\tvalid_0's binary_logloss: 0.245383\\tvalid_0's auc: 0.782554\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[42]\\tvalid_0's binary_logloss: 0.259282\\tvalid_0's auc: 0.715016\\n\",\n      \"\\n\",\n      \"84 features with zero importance after one-hot encoding.\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fs.identify_zero_importance(task = 'classification', eval_metric = 'auc', \\n\",\n    \"                            n_iterations = 10, early_stopping = True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"运行梯度提升模型需要对特征进行独热编码。 这些特征保存在 `FeatureSelector`的 `one_hot_features` 属性中。 原始特征保存在`base_features`中。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"There are 121 original features\\n\",\n      \"There are 134 one-hot features\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"one_hot_features = fs.one_hot_features\\n\",\n    \"base_features = fs.base_features\\n\",\n    \"print('There are %d original features' % len(base_features))\\n\",\n    \"print('There are %d one-hot features' % len(one_hot_features))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`FeatureSelector` 的 `data` 属性保存原始DataFrame。 独热编码后， `data_all`属性将保留原始数据以及独热编码特征。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>NAME_CONTRACT_TYPE_Cash loans</th>\\n\",\n       \"      <th>NAME_CONTRACT_TYPE_Revolving loans</th>\\n\",\n       \"      <th>CODE_GENDER_F</th>\\n\",\n       \"      <th>CODE_GENDER_M</th>\\n\",\n       \"      <th>CODE_GENDER_XNA</th>\\n\",\n       \"      <th>FLAG_OWN_CAR_N</th>\\n\",\n       \"      <th>FLAG_OWN_CAR_Y</th>\\n\",\n       \"      <th>FLAG_OWN_REALTY_N</th>\\n\",\n       \"      <th>FLAG_OWN_REALTY_Y</th>\\n\",\n       \"      <th>NAME_TYPE_SUITE_Children</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>FLAG_DOCUMENT_18</th>\\n\",\n       \"      <th>FLAG_DOCUMENT_19</th>\\n\",\n       \"      <th>FLAG_DOCUMENT_20</th>\\n\",\n       \"      <th>FLAG_DOCUMENT_21</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\\n\",\n       \"      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>7.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>10 rows × 255 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   NAME_CONTRACT_TYPE_Cash loans  NAME_CONTRACT_TYPE_Revolving loans  \\\\\\n\",\n       \"0                              1                                   0   \\n\",\n       \"1                              0                                   1   \\n\",\n       \"2                              1                                   0   \\n\",\n       \"3                              1                                   0   \\n\",\n       \"4                              1                                   0   \\n\",\n       \"5                              1                                   0   \\n\",\n       \"6                              1                                   0   \\n\",\n       \"7                              1                                   0   \\n\",\n       \"8                              1                                   0   \\n\",\n       \"9                              1                                   0   \\n\",\n       \"\\n\",\n       \"   CODE_GENDER_F  CODE_GENDER_M  CODE_GENDER_XNA  FLAG_OWN_CAR_N  \\\\\\n\",\n       \"0              1              0                0               0   \\n\",\n       \"1              1              0                0               0   \\n\",\n       \"2              1              0                0               1   \\n\",\n       \"3              1              0                0               1   \\n\",\n       \"4              1              0                0               1   \\n\",\n       \"5              1              0                0               0   \\n\",\n       \"6              1              0                0               1   \\n\",\n       \"7              1              0                0               1   \\n\",\n       \"8              1              0                0               1   \\n\",\n       \"9              0              1                0               0   \\n\",\n       \"\\n\",\n       \"   FLAG_OWN_CAR_Y  FLAG_OWN_REALTY_N  FLAG_OWN_REALTY_Y  \\\\\\n\",\n       \"0               1                  1                  0   \\n\",\n       \"1               1                  0                  1   \\n\",\n       \"2               0                  0                  1   \\n\",\n       \"3               0                  0                  1   \\n\",\n       \"4               0                  0                  1   \\n\",\n       \"5               1                  1                  0   \\n\",\n       \"6               0                  0                  1   \\n\",\n       \"7               0                  0                  1   \\n\",\n       \"8               0                  0                  1   \\n\",\n       \"9               1                  0                  1   \\n\",\n       \"\\n\",\n       \"   NAME_TYPE_SUITE_Children             ...              FLAG_DOCUMENT_18  \\\\\\n\",\n       \"0                         0             ...                             0   \\n\",\n       \"1                         0             ...                             0   \\n\",\n       \"2                         0             ...                             0   \\n\",\n       \"3                         0             ...                             0   \\n\",\n       \"4                         0             ...                             0   \\n\",\n       \"5                         0             ...                             0   \\n\",\n       \"6                         0             ...                             0   \\n\",\n       \"7                         0             ...                             0   \\n\",\n       \"8                         0             ...                             0   \\n\",\n       \"9                         0             ...                             0   \\n\",\n       \"\\n\",\n       \"   FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  \\\\\\n\",\n       \"0                 0                 0                 0   \\n\",\n       \"1                 0                 0                 0   \\n\",\n       \"2                 0                 0                 0   \\n\",\n       \"3                 0                 0                 0   \\n\",\n       \"4                 0                 0                 0   \\n\",\n       \"5                 0                 0                 0   \\n\",\n       \"6                 0                 0                 0   \\n\",\n       \"7                 0                 0                 0   \\n\",\n       \"8                 0                 0                 0   \\n\",\n       \"9                 0                 0                 0   \\n\",\n       \"\\n\",\n       \"   AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\\\\n\",\n       \"0                         0.0                        0.0   \\n\",\n       \"1                         0.0                        0.0   \\n\",\n       \"2                         0.0                        0.0   \\n\",\n       \"3                         0.0                        0.0   \\n\",\n       \"4                         0.0                        0.0   \\n\",\n       \"5                         NaN                        NaN   \\n\",\n       \"6                         0.0                        0.0   \\n\",\n       \"7                         0.0                        0.0   \\n\",\n       \"8                         0.0                        0.0   \\n\",\n       \"9                         NaN                        NaN   \\n\",\n       \"\\n\",\n       \"   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\\\\n\",\n       \"0                         0.0                        0.0   \\n\",\n       \"1                         0.0                        0.0   \\n\",\n       \"2                         1.0                        0.0   \\n\",\n       \"3                         0.0                        0.0   \\n\",\n       \"4                         0.0                        0.0   \\n\",\n       \"5                         NaN                        NaN   \\n\",\n       \"6                         0.0                        1.0   \\n\",\n       \"7                         0.0                        0.0   \\n\",\n       \"8                         0.0                        0.0   \\n\",\n       \"9                         NaN                        NaN   \\n\",\n       \"\\n\",\n       \"   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \\n\",\n       \"0                        0.0                         1.0  \\n\",\n       \"1                        0.0                         0.0  \\n\",\n       \"2                        0.0                         7.0  \\n\",\n       \"3                        0.0                         1.0  \\n\",\n       \"4                        1.0                         1.0  \\n\",\n       \"5                        NaN                         NaN  \\n\",\n       \"6                        0.0                         0.0  \\n\",\n       \"7                        1.0                         0.0  \\n\",\n       \"8                        0.0                         0.0  \\n\",\n       \"9                        NaN                         NaN  \\n\",\n       \"\\n\",\n       \"[10 rows x 255 columns]\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fs.data_all.head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们可以使用多种方法来检查特征重要性的结果。 首先，我们可以访问具有零重要性的特征列表。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['ORGANIZATION_TYPE_Mobile',\\n\",\n       \" 'ORGANIZATION_TYPE_Military',\\n\",\n       \" 'ORGANIZATION_TYPE_Legal Services',\\n\",\n       \" 'FLAG_DOCUMENT_7',\\n\",\n       \" 'ORGANIZATION_TYPE_Trade: type 2']\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"zero_importance_features = fs.ops['zero_importance']\\n\",\n    \"zero_importance_features[10:15]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 画出特征重要性\\n\",\n    \"\\n\",\n    \"使用 `plot_feature_importances` 的特征重要性画图将向我们显示 `plot_n` 最重要的特征（按归一化比例将特征加总为1）。 它还向我们显示了累积特征重要性与特征数量之间的关系。\\n\",\n    \"\\n\",\n    \"当我们绘制特征重要性时，我们可以传递一个阈值，该阈值标识达到指定的累积特征重要性所需的特征数量。 例如，`threshold = 0.99`将告诉我们占总重要性的99％所需的特征数量。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x148c9c3bfd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x148c761ef60>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"115 features required for 0.99 of cumulative importance\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fs.plot_feature_importances(threshold = 0.99, plot_n = 12)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"在 `FeatureSelector`中的 `feature_importances` 属性中可以访问所有的特征重要性。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>feature</th>\\n\",\n       \"      <th>importance</th>\\n\",\n       \"      <th>normalized_importance</th>\\n\",\n       \"      <th>cumulative_importance</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>EXT_SOURCE_2</td>\\n\",\n       \"      <td>116.8</td>\\n\",\n       \"      <td>0.094042</td>\\n\",\n       \"      <td>0.094042</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>EXT_SOURCE_3</td>\\n\",\n       \"      <td>108.9</td>\\n\",\n       \"      <td>0.087681</td>\\n\",\n       \"      <td>0.181723</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>EXT_SOURCE_1</td>\\n\",\n       \"      <td>72.7</td>\\n\",\n       \"      <td>0.058535</td>\\n\",\n       \"      <td>0.240258</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>DAYS_BIRTH</td>\\n\",\n       \"      <td>58.8</td>\\n\",\n       \"      <td>0.047343</td>\\n\",\n       \"      <td>0.287601</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>SK_ID_CURR</td>\\n\",\n       \"      <td>50.9</td>\\n\",\n       \"      <td>0.040982</td>\\n\",\n       \"      <td>0.328583</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>DAYS_REGISTRATION</td>\\n\",\n       \"      <td>50.0</td>\\n\",\n       \"      <td>0.040258</td>\\n\",\n       \"      <td>0.368841</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>DAYS_ID_PUBLISH</td>\\n\",\n       \"      <td>49.8</td>\\n\",\n       \"      <td>0.040097</td>\\n\",\n       \"      <td>0.408937</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>DAYS_LAST_PHONE_CHANGE</td>\\n\",\n       \"      <td>47.2</td>\\n\",\n       \"      <td>0.038003</td>\\n\",\n       \"      <td>0.446940</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>DAYS_EMPLOYED</td>\\n\",\n       \"      <td>47.0</td>\\n\",\n       \"      <td>0.037842</td>\\n\",\n       \"      <td>0.484783</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>AMT_INCOME_TOTAL</td>\\n\",\n       \"      <td>37.1</td>\\n\",\n       \"      <td>0.029871</td>\\n\",\n       \"      <td>0.514654</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                  feature  importance  normalized_importance  \\\\\\n\",\n       \"0            EXT_SOURCE_2       116.8               0.094042   \\n\",\n       \"1            EXT_SOURCE_3       108.9               0.087681   \\n\",\n       \"2            EXT_SOURCE_1        72.7               0.058535   \\n\",\n       \"3              DAYS_BIRTH        58.8               0.047343   \\n\",\n       \"4              SK_ID_CURR        50.9               0.040982   \\n\",\n       \"5       DAYS_REGISTRATION        50.0               0.040258   \\n\",\n       \"6         DAYS_ID_PUBLISH        49.8               0.040097   \\n\",\n       \"7  DAYS_LAST_PHONE_CHANGE        47.2               0.038003   \\n\",\n       \"8           DAYS_EMPLOYED        47.0               0.037842   \\n\",\n       \"9        AMT_INCOME_TOTAL        37.1               0.029871   \\n\",\n       \"\\n\",\n       \"   cumulative_importance  \\n\",\n       \"0               0.094042  \\n\",\n       \"1               0.181723  \\n\",\n       \"2               0.240258  \\n\",\n       \"3               0.287601  \\n\",\n       \"4               0.328583  \\n\",\n       \"5               0.368841  \\n\",\n       \"6               0.408937  \\n\",\n       \"7               0.446940  \\n\",\n       \"8               0.484783  \\n\",\n       \"9               0.514654  \"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fs.feature_importances.head(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"我们可以使用这些结果来仅选择“$n$”个最重要的特征。 例如，如果我们希望排名前100位的重要性最高，则可以执行以下操作。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"100\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"one_hundred_features = list(fs.feature_importances.loc[:99, 'feature'])\\n\",\n    \"len(one_hundred_features)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 5. 低重要性特征\\n\",\n    \"此方法使用梯度提升算法（必须首先运行`identify_zero_importance`）通过查找达到指定的累积总特征重要性所需的最低特征重要性的特征，来构建特征重要性。 例如，如果我们输入0.99，则将找到最不重要的特征重要性，这些特征总的不到总特征重要性的99％。\\n\",\n    \"\\n\",\n    \"使用此方法时，我们必须已经运行了`identify_zero_importance` ，并且需要传递一个`cumulative_importance` ，该值占总特征重要性的一部分。\\n\",\n    \"\\n\",\n    \"**注意：**此方法建立在梯度提升模型的重要性基础之上，并且还是不确定的。 我建议使用不同的参数多次运行这两种方法，并测试每个结果的特征集，而不是只选择一个数字。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"114 features required for cumulative importance of 0.99 after one hot encoding.\\n\",\n      \"125 features do not contribute to cumulative importance of 0.99.\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fs.identify_low_importance(cumulative_importance = 0.99)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"要删除的低重要性特征是指那些对指定的累积重要性无贡献的特征。这些特征也可以在 `ops` 词典中找到。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['NAME_HOUSING_TYPE_With parents',\\n\",\n       \" 'ORGANIZATION_TYPE_Government',\\n\",\n       \" 'OCCUPATION_TYPE_Security staff',\\n\",\n       \" 'ELEVATORS_MODE',\\n\",\n       \" 'NAME_FAMILY_STATUS_Separated']\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"low_importance_features = fs.ops['low_importance']\\n\",\n    \"low_importance_features[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 删除特征\\n\",\n    \"一旦确定了要删除的特征，便可以通过多种方式删除这些特征。 我们可以访问`removal_ops`词典中的任何功能列表，并手动删除列。 我们还可以使用 `remove` 方法，传入标识我们要删除的特征的方法。\\n\",\n    \"\\n\",\n    \"此方法返回结果数据，然后我们可以将其用于机器学习。 仍然可以在特征选择器的 `data` 属性中访问原始数据。\\n\",\n    \"\\n\",\n    \"请注意用于删除特征的方法！ 在使用删除特征之前，最好先检查将要`remove`的特征。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Removed 17 features.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_no_missing = fs.remove(methods = ['missing'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Removed 101 features.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_no_missing_zero = fs.remove(methods = ['missing', 'zero_importance'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"要从所有方法中删除特征，请传入 `method='all'`。 在执行此操作之前，我们可以使用`check_removal`检查将删除了多少个特征。 这将返回已被识别为要删除的所有特征的列表。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Total of 150 features identified for removal\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['ORGANIZATION_TYPE_Industry: type 2',\\n\",\n       \" 'WALLSMATERIAL_MODE_Stone, brick',\\n\",\n       \" 'CODE_GENDER_XNA',\\n\",\n       \" 'AMT_REQ_CREDIT_BUREAU_DAY',\\n\",\n       \" 'ORGANIZATION_TYPE_Security Ministries',\\n\",\n       \" 'FONDKAPREMONT_MODE',\\n\",\n       \" 'FLAG_DOCUMENT_7',\\n\",\n       \" 'NAME_TYPE_SUITE_Other_B',\\n\",\n       \" 'ORGANIZATION_TYPE_Industry: type 13',\\n\",\n       \" 'NAME_CONTRACT_TYPE_Revolving loans',\\n\",\n       \" 'OCCUPATION_TYPE_Sales staff',\\n\",\n       \" 'OCCUPATION_TYPE_IT staff',\\n\",\n       \" 'ORGANIZATION_TYPE_Transport: type 3',\\n\",\n       \" 'NAME_TYPE_SUITE_Other_A',\\n\",\n       \" 'REG_REGION_NOT_LIVE_REGION']\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"all_to_remove = fs.check_removal()\\n\",\n    \"all_to_remove[10:25]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"现在我们可以删除所有已识别的特征。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\\n\",\n      \"\\n\",\n      \"Removed 150 features.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_removed = fs.remove(methods = 'all')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 处理独热特征\\n\",\n    \"\\n\",\n    \"如果我们查看返回的DataFrame，可能会注意到原始数据中没有的几个新列。 这些是在对数据进行独热编码以进行机器学习时创建的。 要删除所有独热特征，我们可以将 `keep_one_hot = False` 传递给 `remove` 方法。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\\n\",\n      \"\\n\",\n      \"Removed 189 features including one-hot features.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_removed_all = fs.remove(methods = 'all', keep_one_hot=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Original Number of Features 121\\n\",\n      \"Final Number of Features:  66\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Original Number of Features', train.shape[1])\\n\",\n    \"print('Final Number of Features: ', train_removed_all.shape[1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 使用所有方法的替代选项\\n\",\n    \"\\n\",\n    \"如果我们不想一次运行一个识别方法，则可以使用`identify_all` 在一次调用中运行所有方法。 对于此功能，我们需要传入参数字典以用于每种单独的识别方法。\\n\",\n    \"\\n\",\n    \"以下代码在一个调用中完成了上述步骤。\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"17 features with greater than 0.60 missing values.\\n\",\n      \"\\n\",\n      \"4 features with a single unique value.\\n\",\n      \"\\n\",\n      \"21 features with a correlation magnitude greater than 0.98.\\n\",\n      \"\\n\",\n      \"Training Gradient Boosting Model\\n\",\n      \"\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[100]\\tvalid_0's binary_logloss: 0.251527\\tvalid_0's auc: 0.747956\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[26]\\tvalid_0's binary_logloss: 0.255572\\tvalid_0's auc: 0.740053\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[92]\\tvalid_0's binary_logloss: 0.242676\\tvalid_0's auc: 0.778073\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[36]\\tvalid_0's binary_logloss: 0.255766\\tvalid_0's auc: 0.735214\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[39]\\tvalid_0's binary_logloss: 0.258468\\tvalid_0's auc: 0.724103\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[41]\\tvalid_0's binary_logloss: 0.257077\\tvalid_0's auc: 0.730639\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[55]\\tvalid_0's binary_logloss: 0.243672\\tvalid_0's auc: 0.779974\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[69]\\tvalid_0's binary_logloss: 0.253203\\tvalid_0's auc: 0.747206\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[79]\\tvalid_0's binary_logloss: 0.24819\\tvalid_0's auc: 0.761093\\n\",\n      \"Training until validation scores don't improve for 100 rounds.\\n\",\n      \"Early stopping, best iteration is:\\n\",\n      \"[55]\\tvalid_0's binary_logloss: 0.244782\\tvalid_0's auc: 0.77629\\n\",\n      \"\\n\",\n      \"77 features with zero importance after one-hot encoding.\\n\",\n      \"\\n\",\n      \"115 features required for cumulative importance of 0.99 after one hot encoding.\\n\",\n      \"124 features do not contribute to cumulative importance of 0.99.\\n\",\n      \"\\n\",\n      \"151 total features out of 255 identified for removal after one-hot encoding.\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fs = FeatureSelector(data = train, labels = train_labels)\\n\",\n    \"\\n\",\n    \"fs.identify_all(selection_params = {'missing_threshold': 0.6, 'correlation_threshold': 0.98, \\n\",\n    \"                                    'task': 'classification', 'eval_metric': 'auc', \\n\",\n    \"                                     'cumulative_importance': 0.99})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\\n\",\n      \"\\n\",\n      \"Removed 151 features.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_removed_all_once = fs.remove(methods = 'all', keep_one_hot = True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>feature</th>\\n\",\n       \"      <th>importance</th>\\n\",\n       \"      <th>normalized_importance</th>\\n\",\n       \"      <th>cumulative_importance</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>EXT_SOURCE_2</td>\\n\",\n       \"      <td>166.1</td>\\n\",\n       \"      <td>0.093525</td>\\n\",\n       \"      <td>0.093525</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>EXT_SOURCE_3</td>\\n\",\n       \"      <td>147.4</td>\\n\",\n       \"      <td>0.082995</td>\\n\",\n       \"      <td>0.176520</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>EXT_SOURCE_1</td>\\n\",\n       \"      <td>97.4</td>\\n\",\n       \"      <td>0.054842</td>\\n\",\n       \"      <td>0.231363</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>DAYS_BIRTH</td>\\n\",\n       \"      <td>83.3</td>\\n\",\n       \"      <td>0.046903</td>\\n\",\n       \"      <td>0.278266</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>SK_ID_CURR</td>\\n\",\n       \"      <td>71.0</td>\\n\",\n       \"      <td>0.039977</td>\\n\",\n       \"      <td>0.318243</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"        feature  importance  normalized_importance  cumulative_importance\\n\",\n       \"0  EXT_SOURCE_2       166.1               0.093525               0.093525\\n\",\n       \"1  EXT_SOURCE_3       147.4               0.082995               0.176520\\n\",\n       \"2  EXT_SOURCE_1        97.4               0.054842               0.231363\\n\",\n       \"3    DAYS_BIRTH        83.3               0.046903               0.278266\\n\",\n       \"4    SK_ID_CURR        71.0               0.039977               0.318243\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fs.feature_importances.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"由于特征重要性已更改，因此删除的特征数略有差异。由缺失的（`missing`）、单一的（`single_unique`）和共线（ `collinear`）确定要删除的特征数量将保持不变，因为它们是确定性的，但是由于多次训练模型，零重要性（ `zero_importance` ）和低重要性（`low_importance` ）的特征数量可能会有所不同。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 结论\\n\",\n    \"\\n\",\n    \"本笔记本演示了如何使用`FeatureSelector`类从数据集中删除特征。此实现中有几个重要注意事项：\\n\",\n    \"- 在机器学习模型的多次运行中，特征重要性将发生变化。\\n\",\n    \"- 决定是否保留从一个独热编码创建的额外特征。\\n\",\n    \"- 为不同的参数尝试几个不同的值，以确定哪些参数最适合机器学习任务。\\n\",\n    \"- 对于相同的参数，缺失的（`missing`）、单一的（`single_unique`）和共线（ `collinear`）的输出将保持不变。\\n\",\n    \"- 特征选择是机器学习工作流中的一个关键步骤，它可能需要多次迭代来优化。\\n\",\n    \"我很感激你对这个项目的任何评论、反馈或帮助。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 参考\\n\",\n    \"Jupyter notebook：\\n\",\n    \"\\n\",\n    \"https://github.com/WillKoehrsen/feature-selector\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "9.feature-engineering/FeatureSelectorUsage/README.md",
    "content": "# 特征工程的神器-基于Python的特征自动化选择代码\n>本文介绍一个特征选择神器：特征选择器是用于减少机器学习数据集的维数的工具，可以傻瓜式地进行特征选择。\n>\n>来源：[Will Koehrsen](https://github.com/WillKoehrsen)\n>\n>代码整理及注释翻译：[黄海广](https://github.com/fengdu78)\n\n## 实现的功能\n该选择器基于python编写，有五种方法来标识要删除的特征：\n\n- 缺失值\n- 唯一值\n- 共线特征\n- 零重要性特征\n- 低重要性特征"
  },
  {
    "path": "9.feature-engineering/FeatureSelectorUsage/data/AirQualityUCI.csv",
    "content": "Date,Time,CO(GT),PT08.S1(CO),NMHC(GT),C6H6(GT),PT08.S2(NMHC),NOx(GT),PT08.S3(NOx),NO2(GT),PT08.S4(NO2),PT08.S5(O3),T,RH,AH\n10/03/2004,18.00.00,2,6,1360,150,11,9,1046,166,1056,113,1692,1268,13\n10/03/2004,19.00.00,2,1292,112,9,4,955,103,1174,92,1559,972,13,3\n10/03/2004,20.00.00,2,2,1402,88,9,0,939,131,1140,114,1555,1074,11\n10/03/2004,21.00.00,2,2,1376,80,9,2,948,172,1092,122,1584,1203,11\n10/03/2004,22.00.00,1,6,1272,51,6,5,836,131,1205,116,1490,1110,11\n10/03/2004,23.00.00,1,2,1197,38,4,7,750,89,1337,96,1393,949,11\n11/03/2004,00.00.00,1,2,1185,31,3,6,690,62,1462,77,1333,733,11\n11/03/2004,01.00.00,1,1136,31,3,3,672,62,1453,76,1333,730,10,7\n11/03/2004,02.00.00,0,9,1094,24,2,3,609,45,1579,60,1276,620,10\n11/03/2004,03.00.00,0,6,1010,19,1,7,561,-200,1705,-200,1235,501,10\n11/03/2004,04.00.00,-200,1011,14,1,3,527,21,1818,34,1197,445,10,1\n11/03/2004,05.00.00,0,7,1066,8,1,1,512,16,1918,28,1182,422,11\n11/03/2004,06.00.00,0,7,1052,16,1,6,553,34,1738,48,1221,472,10\n11/03/2004,07.00.00,1,1,1144,29,3,2,667,98,1490,82,1339,730,10\n11/03/2004,08.00.00,2,1333,64,8,0,900,174,1136,112,1517,1102,10,8\n11/03/2004,09.00.00,2,2,1351,87,9,5,960,129,1079,101,1583,1028,10\n11/03/2004,10.00.00,1,7,1233,77,6,3,827,112,1218,98,1446,860,10\n11/03/2004,11.00.00,1,5,1179,43,5,0,762,95,1328,92,1362,671,10\n11/03/2004,12.00.00,1,6,1236,61,5,2,774,104,1301,95,1401,664,9\n11/03/2004,13.00.00,1,9,1286,63,7,3,869,146,1162,112,1537,799,8\n11/03/2004,14.00.00,2,9,1371,164,11,5,1034,207,983,128,1730,1037,8\n11/03/2004,15.00.00,2,2,1310,79,8,8,933,184,1082,126,1647,946,8\n11/03/2004,16.00.00,2,2,1292,95,8,3,912,193,1103,131,1591,957,9\n11/03/2004,17.00.00,2,9,1383,150,11,2,1020,243,1008,135,1719,1104,9\n11/03/2004,18.00.00,4,8,1581,307,20,8,1319,281,799,151,2083,1409,10\n11/03/2004,19.00.00,6,9,1776,461,27,4,1488,383,702,172,2333,1704,9\n11/03/2004,20.00.00,6,1,1640,401,24,0,1404,351,743,165,2191,1654,9\n11/03/2004,21.00.00,3,9,1313,197,12,8,1076,240,957,136,1707,1285,9\n11/03/2004,22.00.00,1,5,965,61,4,7,749,94,1325,85,1333,821,8\n11/03/2004,23.00.00,1,913,26,2,6,629,47,1565,53,1252,552,8,2\n12/03/2004,00.00.00,1,7,1080,55,5,9,805,122,1254,97,1375,816,8\n12/03/2004,01.00.00,1,9,1044,53,6,4,829,133,1247,110,1378,832,7\n12/03/2004,02.00.00,1,4,988,40,4,1,718,82,1396,91,1304,692,7\n12/03/2004,03.00.00,0,8,889,21,1,9,574,-200,1680,-200,1187,512,7\n12/03/2004,04.00.00,-200,831,10,1,1,506,21,1893,32,1134,384,6,1\n12/03/2004,05.00.00,0,6,847,7,1,0,501,30,1895,44,1155,394,6\n12/03/2004,06.00.00,0,8,927,17,1,8,571,56,1685,71,1223,487,6\n12/03/2004,07.00.00,1,4,1091,33,4,4,730,109,1387,104,1361,748,6\n12/03/2004,08.00.00,4,4,1587,202,17,9,1236,307,897,141,1900,1400,7\n12/03/2004,09.00.00,-200,1545,-200,22,1,1353,-200,767,-200,2058,1588,9,2\n12/03/2004,10.00.00,3,1,1350,208,14,0,1118,187,912,122,1712,1237,13\n12/03/2004,11.00.00,2,7,1263,166,11,6,1037,216,969,143,1598,1167,14\n12/03/2004,12.00.00,2,1,1206,114,10,2,986,143,1035,113,1537,959,15\n12/03/2004,13.00.00,2,5,1252,140,11,0,1016,160,1008,116,1593,983,16\n12/03/2004,14.00.00,2,7,1287,169,12,8,1078,163,949,123,1660,1061,16\n12/03/2004,15.00.00,2,9,1353,185,14,2,1122,190,922,126,1740,1139,15\n12/03/2004,16.00.00,2,8,1309,165,12,7,1073,178,954,120,1657,1112,15\n12/03/2004,17.00.00,2,4,1274,133,11,7,1041,150,1006,119,1610,994,16\n12/03/2004,18.00.00,3,9,1510,233,19,3,1277,206,812,149,1910,1410,15\n12/03/2004,19.00.00,3,7,1525,242,18,2,1246,202,821,145,1847,1448,14\n12/03/2004,20.00.00,6,6,1843,488,32,6,1610,340,624,170,2390,1887,12\n12/03/2004,21.00.00,4,4,1598,333,20,1,1299,274,752,149,1941,1627,12\n12/03/2004,22.00.00,3,5,1484,215,14,3,1127,253,839,139,1723,1491,11\n12/03/2004,23.00.00,5,4,1677,367,21,8,1346,300,741,134,2062,1657,9\n13/03/2004,00.00.00,2,7,1280,122,9,6,964,193,963,113,1544,1285,9\n13/03/2004,01.00.00,1,9,1196,67,7,4,873,139,1071,97,1463,1144,9\n13/03/2004,02.00.00,1,6,1184,43,5,4,782,83,1176,82,1365,1043,8\n13/03/2004,03.00.00,1,7,1172,46,5,4,783,-200,1179,-200,1380,996,7\n13/03/2004,04.00.00,-200,1147,56,6,2,821,109,1132,83,1412,992,7,0\n13/03/2004,05.00.00,1,978,30,2,6,625,62,1420,65,1274,819,8,3\n13/03/2004,06.00.00,1,2,1100,27,2,9,646,53,1406,60,1268,835,7\n13/03/2004,07.00.00,1,5,1112,47,5,1,770,139,1228,77,1409,940,6\n13/03/2004,08.00.00,2,7,1336,132,11,8,1043,256,935,96,1678,1192,6\n13/03/2004,09.00.00,3,7,1408,239,15,1,1153,295,830,119,1777,1411,9\n13/03/2004,10.00.00,3,2,1447,160,12,9,1081,250,869,126,1667,1465,12\n13/03/2004,11.00.00,4,1,1542,283,16,1,1184,296,808,158,1780,1583,15\n13/03/2004,12.00.00,3,6,1451,210,14,0,1117,239,875,161,1679,1387,18\n13/03/2004,13.00.00,2,8,1328,154,12,3,1059,153,987,124,1600,1101,19\n13/03/2004,14.00.00,2,1207,112,8,6,924,118,1088,102,1488,850,18,0\n13/03/2004,15.00.00,2,1240,108,9,2,947,119,1049,116,1532,947,18,4\n13/03/2004,16.00.00,2,5,1306,111,10,2,987,138,1004,124,1554,1078,17\n13/03/2004,17.00.00,2,3,1326,97,10,6,1000,148,976,125,1602,1084,16\n13/03/2004,18.00.00,3,2,1473,191,15,5,1163,227,831,148,1779,1395,16\n13/03/2004,19.00.00,4,2,1609,258,19,6,1286,277,758,165,1922,1612,15\n13/03/2004,20.00.00,4,2,1611,284,19,2,1274,279,754,161,1915,1697,15\n13/03/2004,21.00.00,4,2,1621,269,18,3,1247,283,762,159,1860,1886,15\n13/03/2004,22.00.00,3,1,1444,180,13,1,1089,214,844,143,1748,1624,14\n13/03/2004,23.00.00,2,6,1418,116,10,9,1010,172,892,130,1603,1536,14\n14/03/2004,00.00.00,2,9,1534,93,11,0,1013,190,889,129,1611,1535,13\n14/03/2004,01.00.00,2,8,1484,131,11,9,1045,174,880,119,1624,1530,14\n14/03/2004,02.00.00,2,5,1367,92,8,6,925,128,953,104,1543,1337,12\n14/03/2004,03.00.00,2,4,1344,132,9,7,968,-200,921,-200,1620,1278,11\n14/03/2004,04.00.00,-200,1130,56,5,2,773,70,1130,82,1452,1051,12,1\n14/03/2004,05.00.00,1,2,1062,32,3,7,691,53,1272,70,1377,929,11\n14/03/2004,06.00.00,1,1076,29,2,5,618,44,1395,63,1333,872,11,6\n14/03/2004,07.00.00,0,9,1028,27,2,4,615,74,1384,67,1340,853,10\n14/03/2004,08.00.00,1,4,1155,36,4,2,722,101,1225,84,1414,959,11\n14/03/2004,09.00.00,1,6,1235,57,6,4,828,118,1055,83,1527,1093,12\n14/03/2004,10.00.00,2,2,1332,129,8,6,923,144,952,98,1614,1225,14\n14/03/2004,11.00.00,2,8,1445,148,10,9,1009,176,878,114,1696,1355,16\n14/03/2004,12.00.00,2,8,1416,145,10,7,1002,161,907,119,1677,1262,19\n14/03/2004,13.00.00,2,1281,93,7,5,880,113,1084,104,1525,980,21,2\n14/03/2004,14.00.00,1,8,1207,84,7,5,879,103,1104,102,1490,872,21\n14/03/2004,15.00.00,1,9,1258,99,8,2,906,112,1081,107,1511,900,21\n14/03/2004,16.00.00,3,1458,150,11,9,1045,170,974,129,1646,1099,22,2\n14/03/2004,17.00.00,2,9,1438,156,12,0,1051,180,943,128,1668,1206,21\n14/03/2004,18.00.00,2,5,1478,122,12,2,1055,160,929,121,1671,1262,19\n14/03/2004,19.00.00,4,6,1808,262,20,6,1312,261,753,157,1993,1698,18\n14/03/2004,20.00.00,5,9,1898,341,23,1,1381,325,681,173,2103,1905,17\n14/03/2004,21.00.00,3,4,1560,214,14,7,1140,217,784,146,1818,1648,16\n14/03/2004,22.00.00,2,1,1324,100,9,0,940,146,924,121,1587,1423,16\n14/03/2004,23.00.00,2,2,1349,79,8,8,933,152,933,119,1617,1349,14\n15/03/2004,00.00.00,1,8,1239,66,7,4,872,104,985,99,1547,1250,14\n15/03/2004,01.00.00,1,8,1239,73,6,9,853,106,1010,93,1543,1174,14\n15/03/2004,02.00.00,1,8,1224,66,7,0,855,108,998,88,1566,1149,13\n15/03/2004,03.00.00,1,1,1078,44,4,4,734,-200,1128,-200,1487,1021,12\n15/03/2004,04.00.00,-200,1078,44,4,0,711,66,1150,71,1468,1013,12,3\n15/03/2004,05.00.00,1,1075,39,3,9,703,88,1156,74,1464,1010,11,9\n15/03/2004,06.00.00,1,4,1157,51,6,4,830,138,1030,80,1584,1083,11\n15/03/2004,07.00.00,2,2,1314,107,9,7,966,228,897,89,1710,1235,11\n15/03/2004,08.00.00,5,5,1797,336,25,9,1451,360,652,114,2323,1680,12\n15/03/2004,09.00.00,8,1,1961,618,36,7,1701,478,537,149,2665,2184,14\n15/03/2004,10.00.00,5,8,1771,438,26,6,1470,394,622,157,2262,1973,17\n15/03/2004,11.00.00,4,2,1564,334,20,1,1300,319,710,155,2029,1798,19\n15/03/2004,12.00.00,3,1,1430,221,14,1,1120,201,831,134,1783,1522,22\n15/03/2004,13.00.00,2,9,1417,207,14,9,1146,171,830,119,1831,1404,23\n15/03/2004,14.00.00,2,9,1400,191,15,4,1162,159,838,111,1829,1263,23\n15/03/2004,15.00.00,2,5,1317,185,12,1,1053,153,926,104,1707,1137,24\n15/03/2004,16.00.00,2,3,1318,141,11,5,1033,143,950,99,1675,1068,24\n15/03/2004,17.00.00,2,8,1445,214,14,8,1141,156,857,110,1824,1252,23\n15/03/2004,18.00.00,6,1,1917,471,32,1,1601,314,631,162,2447,1843,22\n15/03/2004,19.00.00,8,2040,685,39,2,1754,404,542,187,2679,2122,20,4\n15/03/2004,20.00.00,6,5,1895,538,31,0,1573,320,565,165,2443,1992,18\n15/03/2004,21.00.00,4,2,1595,319,19,9,1294,256,678,145,2058,1707,16\n15/03/2004,22.00.00,3,2,1439,224,15,3,1159,193,764,125,1856,1494,15\n15/03/2004,23.00.00,1,4,1142,67,6,9,852,89,1008,101,1547,1164,15\n16/03/2004,00.00.00,2,1,1304,155,11,1,1019,127,852,103,1731,1272,14\n16/03/2004,01.00.00,1,2,1074,49,5,4,784,79,1098,88,1483,1040,14\n16/03/2004,02.00.00,0,8,968,29,2,8,640,43,1320,61,1386,867,14\n16/03/2004,03.00.00,0,7,929,25,2,3,608,-200,1376,-200,1364,826,13\n16/03/2004,04.00.00,-200,941,25,2,6,626,59,1316,59,1373,840,12,3\n16/03/2004,05.00.00,0,6,937,17,2,0,585,38,1412,52,1348,793,12\n16/03/2004,06.00.00,0,9,1017,27,3,5,681,82,1246,64,1418,870,11\n16/03/2004,07.00.00,1,3,1171,50,5,1,770,99,1162,70,1467,930,11\n16/03/2004,08.00.00,3,4,1541,218,16,2,1185,263,770,97,1889,1407,11\n16/03/2004,09.00.00,3,7,1539,285,19,7,1287,229,698,95,2055,1507,13\n16/03/2004,10.00.00,5,3,1735,437,25,1,1431,396,628,150,2211,1843,17\n16/03/2004,11.00.00,4,1,1571,327,20,0,1297,314,730,162,1973,1729,21\n16/03/2004,12.00.00,3,3,1452,283,18,3,1250,217,776,154,1868,1583,24\n16/03/2004,13.00.00,4,1579,366,22,3,1359,252,724,161,1998,1671,25,3\n16/03/2004,14.00.00,3,8,1466,318,20,4,1309,263,773,161,1897,1491,25\n16/03/2004,15.00.00,2,8,1280,228,14,6,1136,180,893,128,1675,1240,27\n16/03/2004,16.00.00,2,9,1407,201,16,6,1197,184,905,129,1759,1313,28\n16/03/2004,17.00.00,2,9,1389,199,15,8,1173,190,898,133,1739,1363,28\n16/03/2004,18.00.00,3,4,1447,237,17,8,1235,184,859,139,1778,1296,23\n16/03/2004,19.00.00,3,9,1551,261,19,1,1271,181,800,137,1875,1432,21\n16/03/2004,20.00.00,3,2,1474,230,15,8,1173,166,854,143,1776,1432,20\n16/03/2004,2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0,3,6,1358,-200,14,8,1144,184,727,115,1851,1243,17\n01/05/2004,23.00.00,3,7,1385,-200,13,3,1094,190,765,129,1769,1175,17\n02/05/2004,00.00.00,2,3,1167,-200,8,4,914,105,876,99,1599,1126,17\n02/05/2004,01.00.00,1,8,1072,-200,7,9,894,76,893,74,1646,1118,17\n02/05/2004,02.00.00,0,9,938,-200,5,5,789,38,989,55,1536,864,16\n02/05/2004,03.00.00,1,6,1000,-200,6,6,838,-200,910,-200,1580,942,14\n02/05/2004,04.00.00,-200,854,-200,3,3,670,26,1141,35,1460,684,14,8\n02/05/2004,05.00.00,0,6,815,-200,2,6,628,33,1212,37,1426,689,14\n02/05/2004,06.00.00,0,7,866,-200,3,6,689,57,1138,45,1456,767,13\n02/05/2004,07.00.00,0,8,911,-200,4,8,751,48,1036,46,1518,817,14\n02/05/2004,08.00.00,1,2,988,-200,5,9,806,87,951,68,1568,940,16\n02/05/2004,09.00.00,1,4,1046,-200,6,9,851,94,920,77,1595,908,17\n02/05/2004,10.00.00,1,6,1085,-200,8,5,918,89,890,79,1639,871,21\n02/05/2004,11.00.00,1,3,1008,-200,6,1,816,78,1015,72,1531,689,23\n02/05/2004,12.00.00,1,3,1004,-200,6,2,821,72,1031,68,1510,629,24\n02/05/2004,13.00.00,1,1,948,-200,4,8,754,64,1086,64,1436,602,24\n02/05/2004,14.00.00,1,2,1026,-200,6,8,847,70,1010,73,1519,617,24\n02/05/2004,15.00.00,1,5,1047,-200,7,0,859,92,948,84,1538,677,23\n02/05/2004,16.00.00,1,1,1018,-200,5,5,790,69,1047,67,1443,580,25\n02/05/2004,17.00.00,1,9,1160,-200,9,5,957,131,884,106,1637,861,24\n02/05/2004,18.00.00,2,3,1269,-200,11,9,1047,144,809,114,1721,1080,22\n02/05/2004,19.00.00,3,3,1394,-200,13,8,1111,209,736,123,1853,1288,21\n02/05/2004,20.00.00,2,6,1292,-200,10,7,1003,170,789,108,1753,1163,19\n02/05/2004,21.00.00,2,2,1212,-200,9,7,966,152,840,100,1691,1043,19\n02/05/2004,22.00.00,2,3,1137,-200,10,2,985,171,858,118,1607,1045,19\n02/05/2004,23.00.00,2,1080,-200,8,3,910,153,923,121,1548,929,19,1\n03/05/2004,00.00.00,1,6,1031,-200,7,1,861,97,903,94,1534,825,18\n03/05/2004,01.00.00,0,8,917,-200,3,0,654,37,1169,52,1414,545,17\n03/05/2004,02.00.00,0,6,888,-200,2,2,598,26,1228,39,1397,500,16\n03/05/2004,03.00.00,0,4,872,-200,1,7,564,-200,1284,-200,1385,494,15\n03/05/2004,04.00.00,-200,876,-200,1,5,547,25,1256,33,1351,505,16,0\n03/05/2004,05.00.00,0,7,964,-200,3,7,697,61,1067,57,1516,717,15\n03/05/2004,06.00.00,1,3,1147,-200,8,2,906,152,815,80,1687,1126,16\n03/05/2004,07.00.00,3,4,1493,-200,21,1,1328,283,577,101,2193,1597,16\n03/05/2004,08.00.00,4,7,1597,-200,26,5,1466,320,518,109,2400,1687,16\n03/05/2004,09.00.00,3,4,1483,-200,17,4,1222,318,597,109,2054,1529,17\n03/05/2004,10.00.00,2,5,1222,-200,12,9,1080,201,742,111,1755,1366,21\n03/05/2004,11.00.00,2,1060,-200,10,9,1010,135,858,102,1623,1074,23,9\n03/05/2004,12.00.00,1,9,1048,-200,11,0,1013,127,886,96,1584,940,26\n03/05/2004,13.00.00,1,7,1051,-200,10,8,1008,102,911,90,1561,921,28\n03/05/2004,14.00.00,1,4,998,-200,10,0,977,74,947,69,1496,695,30\n03/05/2004,15.00.00,1,5,1058,-200,11,2,1023,81,896,74,1580,795,30\n03/05/2004,16.00.00,2,4,1203,-200,16,8,1204,129,790,102,1744,1079,30\n03/05/2004,17.00.00,3,9,1491,-200,23,6,1392,190,630,128,2149,1473,26\n03/05/2004,18.00.00,4,2,1467,-200,23,3,1385,192,623,130,2084,1493,24\n03/05/2004,19.00.00,3,7,1407,-200,19,4,1279,163,660,120,2009,1308,23\n03/05/2004,20.00.00,3,2,1324,-200,13,1,1086,149,692,113,1903,1189,19\n03/05/2004,21.00.00,2,1214,-200,9,8,970,119,777,100,1771,1076,18,7\n03/05/2004,22.00.00,1,1,1016,-200,5,3,780,61,967,70,1566,806,19\n03/05/2004,23.00.00,1,5,1135,-200,6,7,842,84,872,83,1653,973,18\n04/05/2004,00.00.00,1,3,1065,-200,5,5,789,79,936,81,1620,929,18\n04/05/2004,01.00.00,1,999,-200,3,7,692,58,1038,69,1588,778,16,4\n04/05/2004,02.00.00,0,6,911,-200,2,2,599,32,1189,48,1517,633,16\n04/05/2004,03.00.00,0,4,873,-200,1,5,545,-200,1308,-200,1471,497,15\n04/05/2004,04.00.00,-200,881,-200,1,5,546,21,1335,28,1465,458,15,6\n04/05/2004,05.00.00,0,6,967,-200,2,9,646,49,1117,47,1552,579,15\n04/05/2004,06.00.00,1,9,1280,-200,9,9,976,180,780,88,1912,1003,16\n04/05/2004,07.00.00,4,5,1564,-200,23,0,1379,302,552,109,2306,1393,16\n04/05/2004,08.00.00,4,3,1505,-200,22,3,1358,258,556,113,2248,1406,16\n04/05/2004,09.00.00,3,2,1309,-200,16,2,1186,192,649,104,1973,1308,17\n04/05/2004,10.00.00,1,3,1035,-200,7,9,895,84,898,65,1657,863,20\n04/05/2004,11.00.00,2,2,1148,-200,12,4,1062,128,753,87,1825,1004,21\n04/05/2004,12.00.00,2,6,1152,-200,13,6,1104,145,725,99,1860,1023,21\n04/05/2004,13.00.00,2,3,1131,-200,12,3,1059,132,772,100,1784,978,21\n04/05/2004,14.00.00,2,4,1142,-200,12,1,1053,136,753,105,1814,979,20\n04/05/2004,15.00.00,2,6,1197,-200,12,9,1082,140,753,97,1851,971,19\n04/05/2004,16.00.00,1,2,960,-200,6,3,828,55,971,54,1565,617,19\n04/05/2004,17.00.00,1,6,1077,-200,10,6,1001,67,836,59,1764,717,19\n04/05/2004,18.00.00,3,9,1377,-200,20,6,1313,191,612,109,2120,1220,18\n04/05/2004,19.00.00,4,7,1405,-200,22,1,1353,227,602,125,2185,1296,17\n04/05/2004,20.00.00,5,4,1484,-200,20,8,1319,264,591,132,2166,1413,16\n04/05/2004,21.00.00,3,1187,-200,11,8,1042,196,766,110,1840,1093,16,6\n04/05/2004,22.00.00,0,9,870,-200,4,3,729,43,1096,53,1475,586,18\n04/05/2004,23.00.00,1,1,932,-200,5,0,765,58,1054,61,1543,575,17\n05/05/2004,00.00.00,0,7,83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93,-200,16,5,1196,195,675,149,1888,1152,30\n01/07/2004,21.00.00,1,9,1078,-200,10,2,985,106,778,111,1697,925,28\n01/07/2004,22.00.00,2,1,1140,-200,11,6,1035,107,726,112,1779,1059,27\n01/07/2004,23.00.00,2,5,1239,-200,14,3,1127,134,648,120,1930,1201,26\n02/07/2004,00.00.00,2,3,1171,-200,13,3,1093,117,669,105,1862,1205,26\n02/07/2004,01.00.00,1,7,1065,-200,10,0,977,83,736,90,1733,1103,25\n02/07/2004,02.00.00,1,5,1024,-200,7,6,882,77,791,87,1662,1072,25\n02/07/2004,03.00.00,0,8,873,-200,3,4,679,-200,1010,-200,1501,713,26\n02/07/2004,04.00.00,-200,874,-200,3,3,668,37,1043,54,1513,737,25,3\n02/07/2004,05.00.00,0,5,887,-200,2,9,647,50,1046,61,1500,740,25\n02/07/2004,06.00.00,0,8,993,-200,5,6,795,60,888,63,1629,770,24\n02/07/2004,07.00.00,2,6,1299,-200,17,5,1225,179,620,102,2077,1327,25\n02/07/2004,08.00.00,4,9,1529,-200,29,0,1526,300,476,149,2559,1763,27\n02/07/2004,09.00.00,2,9,1320,-200,17,0,1210,263,595,147,2052,1477,29\n02/07/2004,10.00.00,2,8,1226,-200,15,6,1168,265,650,158,1952,1311,33\n02/07/2004,11.00.00,1,8,1105,-200,10,1,980,164,777,112,1745,980,34\n02/07/2004,12.00.00,1,8,1160,-200,11,7,1040,122,720,108,1810,961,35\n02/07/2004,13.00.00,1,9,1125,-200,12,0,1051,125,730,120,1771,923,36\n02/07/2004,14.00.00,1,7,1099,-200,11,0,1015,125,763,117,1732,839,38\n02/07/2004,15.00.00,1,7,1124,-200,11,2,1021,115,745,119,1735,827,38\n02/07/2004,16.00.00,1,9,1105,-200,10,5,995,162,778,149,1681,821,37\n02/07/2004,17.00.00,2,1,1035,-200,11,8,1043,149,804,174,1647,749,34\n02/07/2004,18.00.00,2,4,1065,-200,13,8,1110,161,757,163,1691,871,33\n02/07/2004,19.00.00,3,1051,-200,17,3,1220,201,739,161,1729,1077,31,4\n02/07/2004,20.00.00,2,8,1073,-200,14,2,1124,209,752,154,1688,1175,29\n02/07/2004,21.00.00,1,9,1008,-200,10,7,1004,133,817,124,1588,1125,27\n02/07/2004,22.00.00,2,1,1090,-200,12,7,1073,133,745,122,1693,1124,26\n02/07/2004,23.00.00,1,8,1048,-200,10,1,983,134,780,119,1643,1064,25\n03/07/2004,00.00.00,2,5,1131,-200,13,1,1087,164,705,131,1738,1240,24\n03/07/2004,01.00.00,1,1,953,-200,7,4,874,72,854,84,1528,899,24\n03/07/2004,02.00.00,0,7,897,-200,4,6,745,50,968,77,1461,807,23\n03/07/2004,03.00.00,0,7,900,-200,4,7,750,-200,932,-200,1494,817,21\n03/07/2004,04.00.00,0,4,828,-200,2,8,639,27,1077,41,1371,573,22\n03/07/2004,05.00.00,0,7,892,-200,4,5,739,74,964,68,1479,768,20\n03/07/2004,06.00.00,0,7,914,-200,5,9,809,69,912,69,1530,807,20\n03/07/2004,07.00.00,1,8,1096,-200,11,1,1017,162,722,101,1743,1204,21\n03/07/2004,08.00.00,2,3,1173,-200,14,4,1130,208,648,120,1885,1401,23\n03/07/2004,09.00.00,1,9,1125,-200,11,1,1016,168,712,124,1725,1256,25\n03/07/2004,10.00.00,1,7,1079,-200,9,9,976,139,781,113,1669,999,29\n03/07/2004,11.00.00,1,3,974,-200,8,8,930,104,872,97,1536,712,33\n03/07/2004,12.00.00,1,5,977,-200,9,9,973,99,865,94,1550,712,36\n03/07/2004,13.00.00,1,3,912,-200,7,4,873,89,946,86,1436,614,36\n03/07/2004,14.00.00,0,8,896,-200,6,1,817,57,992,70,1426,554,37\n03/07/2004,15.00.00,0,8,896,-200,6,2,822,64,1002,70,1391,722,37\n03/07/2004,16.00.00,1,1,956,-200,7,1,862,65,930,82,1479,636,38\n03/07/2004,17.00.00,1,2,970,-200,8,0,900,79,909,93,1524,664,36\n03/07/2004,18.00.00,1,2,929,-200,7,3,869,80,942,89,1454,624,35\n03/07/2004,19.00.00,1,4,947,-200,8,7,926,81,905,90,1484,659,31\n03/07/2004,20.00.00,1,1,1003,-200,7,5,880,59,864,67,1537,695,29\n03/07/2004,21.00.00,1,5,1045,-200,9,0,940,90,822,94,1658,920,27\n03/07/2004,22.00.00,2,1125,-200,11,6,1035,122,728,109,1726,1150,26,7\n03/07/2004,23.00.00,1,9,1138,-200,10,2,985,120,742,103,1710,1141,25\n04/07/2004,00.00.00,2,2,1217,-200,12,3,1060,126,660,101,1840,1227,24\n04/07/2004,01.00.00,1,1,983,-200,6,4,831,68,847,71,1585,887,24\n04/07/2004,02.00.00,1,1,1007,-200,7,2,864,65,816,71,1639,987,22\n04/07/2004,03.00.00,0,8,957,-200,6,1,816,-200,846,-200,1613,903,22\n04/07/2004,04.00.00,0,6,914,-200,4,3,725,38,949,49,1550,780,21\n04/07/2004,05.00.00,0,6,886,-200,3,8,702,31,971,43,1528,767,20\n04/07/2004,06.00.00,0,6,899,-200,4,0,708,40,972,44,1559,780,21\n04/07/2004,07.00.00,0,6,942,-200,4,2,723,36,968,42,1573,698,23\n04/07/2004,08.00.00,1,1,1022,-200,6,1,817,64,835,69,1669,821,23\n04/07/2004,09.00.00,1,1009,-200,6,0,810,56,859,65,1634,772,25,4\n04/07/2004,10.00.00,1,1025,-200,5,9,809,56,877,68,1606,690,29,6\n04/07/2004,11.00.00,1,977,-200,5,4,783,43,939,58,1543,586,33,9\n04/07/2004,12.00.00,0,8,936,-200,4,9,757,40,1007,52,1459,523,35\n04/07/2004,13.00.00,0,7,884,-200,3,9,703,34,1076,46,1394,488,37\n04/07/2004,14.00.00,0,4,809,-200,3,2,664,28,1185,40,1270,427,38\n04/07/2004,15.00.00,0,5,852,-200,4,1,718,32,1109,47,1307,468,39\n04/07/2004,16.00.00,0,6,872,-200,4,0,708,39,1106,56,1331,489,38\n04/07/2004,17.00.00,0,7,839,-200,4,8,752,52,1102,64,1298,515,37\n04/07/2004,18.00.00,0,9,854,-200,5,5,788,60,1064,71,1310,562,35\n04/07/2004,19.00.00,1,6,971,-200,9,1,942,104,900,103,1541,776,32\n04/07/2004,20.00.00,1,6,985,-200,9,1,944,102,866,97,1541,833,30\n04/07/2004,21.00.00,1,7,1027,-200,10,1,981,120,817,108,1631,1023,28\n04/07/2004,22.00.00,1,3,1012,-200,7,4,876,89,853,87,1580,967,26\n04/07/2004,23.00.00,1,9,1086,-200,9,9,973,121,766,104,1656,1089,25\n05/07/2004,00.00.00,1,5,1025,-200,9,0,939,107,790,93,1635,1103,24\n05/07/2004,01.00.00,0,6,848,-200,3,7,692,41,1034,55,1412,734,25\n05/07/2004,02.00.00,0,4,839,-200,3,0,652,34,1075,49,1405,671,22\n05/07/2004,03.00.00,0,3,814,-200,2,5,618,-200,1097,-200,1400,696,21\n05/07/2004,04.00.00,-200,816,-200,2,6,624,41,1073,56,1422,729,19,7\n05/07/2004,05.00.00,0,4,799,-200,2,4,613,38,1120,43,1387,641,21\n05/07/2004,06.00.00,0,7,879,-200,5,6,792,59,906,57,1521,825,21\n05/07/2004,07.00.00,2,1104,-200,14,3,1125,165,697,80,1815,1179,21,6\n05/07/2004,08.00.00,3,9,1344,-200,27,7,1496,287,519,134,2311,1651,23\n05/07/2004,09.00.00,3,6,1226,-200,19,1,1272,317,632,157,1901,1474,26\n05/07/2004,10.00.00,3,2,1157,-200,15,9,1176,330,691,175,1779,1370,30\n05/07/2004,11.00.00,2,2,1059,-200,12,3,1060,204,798,147,1638,1141,34\n05/07/2004,12.00.00,2,2,1076,-200,12,2,1057,189,778,140,1633,1076,36\n05/07/2004,13.00.00,2,1,1066,-200,11,7,1041,165,783,137,1641,1036,37\n05/07/2004,14.00.00,1,8,1049,-200,10,4,994,139,813,123,1580,881,39\n05/07/2004,15.00.00,2,1097,-200,12,7,1074,156,776,138,1671,960,40,4\n05/07/2004,16.00.00,1,8,1087,-200,11,1,1018,130,791,132,1624,883,40\n05/07/2004,17.00.00,3,1269,-200,19,0,1269,173,633,174,1942,1221,39,3\n05/07/2004,18.00.00,4,3,1328,-200,24,1,1405,302,584,203,2072,1518,37\n05/07/2004,19.00.00,3,5,1200,-200,19,1,1270,239,654,176,1906,1264,33\n05/07/2004,20.00.00,2,5,1098,-200,13,6,1105,139,732,125,1768,1061,30\n05/07/2004,21.00.00,1,8,1058,-200,10,7,1004,92,776,98,1684,947,28\n05/07/2004,22.00.00,1,9,1081,-200,11,3,1025,104,738,103,1757,1059,27\n05/07/2004,23.00.00,1,9,1062,-200,9,6,962,108,776,104,1674,1030,26\n06/07/2004,00.00.00,1,9,1078,-200,9,8,971,107,752,105,1694,1065,25\n06/07/2004,01.00.00,0,9,917,-200,4,9,761,55,931,75,1497,893,25\n06/07/2004,02.00.00,0,7,881,-200,4,1,718,43,965,60,1510,837,23\n06/07/2004,03.00.00,0,6,865,-200,3,3,671,-200,1012,-200,1477,807,23\n06/07/2004,04.00.00,0,4,829,-200,2,2,602,23,1123,36,1410,596,23\n06/07/2004,05.00.00,0,7,913,-200,4,6,743,65,931,66,1540,884,21\n06/07/2004,06.00.00,1,6,1077,-200,10,2,985,165,742,97,1780,1170,21\n06/07/2004,07.00.00,3,8,1337,-200,23,3,1386,274,565,115,2196,1596,22\n06/07/2004,08.00.00,5,1,1467,-200,31,0,1575,369,472,145,2460,1873,24\n06/07/2004,09.00.00,3,4,1257,-200,17,6,1227,308,611,162,1954,1608,26\n06/07/2004,10.00.00,2,6,1227,-200,14,3,1125,227,675,147,1835,1470,30\n06/07/2004,11.00.00,2,2,1188,-200,13,7,1108,178,693,132,1818,1275,34\n06/07/2004,12.00.00,3,4,1301,-200,19,3,1276,267,631,179,1953,1407,38\n06/07/2004,13.00.00,3,4,1201,-200,19,3,1278,278,657,174,1892,1302,39\n06/07/2004,14.00.00,1,8,1058,-200,11,3,1026,165,794,132,1622,931,39\n06/07/2004,15.00.00,2,1037,-200,12,7,1075,172,789,151,1604,917,40,2\n06/07/2004,16.00.00,2,4,1055,-200,15,3,1157,216,762,168,1674,985,39\n06/07/2004,17.00.00,3,8,1179,-200,22,5,1364,306,661,192,1845,1265,39\n06/07/2004,18.00.00,4,4,1298,-200,25,0,1430,317,632,194,1946,2475,37\n06/07/2004,19.00.00,3,9,1213,-200,21,1,1327,251,654,155,1864,2021,34\n06/07/2004,20.00.00,4,1233,-200,23,8,1398,230,613,159,1975,1834,32,6\n06/07/2004,21.00.00,2,5,1086,-200,14,9,1145,154,737,125,1717,1426,30\n06/07/2004,22.00.00,2,1,1056,-200,12,8,1078,137,765,108,1668,1272,28\n06/07/2004,23.00.00,2,4,1138,-200,14,3,1128,133,681,110,1737,1490,27\n07/07/2004,00.00.00,2,1,1112,-200,14,3,1128,117,675,99,1771,1440,26\n07/07/2004,01.00.00,1,6,992,-200,9,2,948,106,773,95,1609,1271,26\n07/07/2004,02.00.00,0,8,895,-200,5,1,767,55,931,69,1463,985,25\n07/07/2004,03.00.00,0,5,843,-200,3,0,655,-200,1046,-200,1377,826,24\n07/07/2004,04.00.00,0,4,828,-200,2,6,626,27,1073,45,1362,754,25\n07/07/2004,05.00.00,0,5,894,-200,4,0,709,46,997,53,1432,853,24\n07/07/2004,06.00.00,1,3,1072,-200,9,9,976,144,740,88,1673,1340,23\n07/07/2004,07.00.00,4,1,1425,-200,27,3,1486,392,512,136,2305,1869,24\n07/07/2004,08.00.00,4,1,1428,-200,27,9,1500,358,496,141,2275,2021,26\n07/07/2004,09.00.00,4,1359,-200,24,5,1415,381,572,189,2081,1902,29,6\n07/07/2004,10.00.00,2,2,1182,-200,14,2,1122,261,730,166,1707,1527,34\n07/07/2004,11.00.00,2,1117,-200,11,9,1045,207,761,154,1623,1260,38,3\n07/07/2004,12.00.00,2,1119,-200,13,1,1088,155,741,149,1640,1124,40,4\n07/07/2004,13.00.00,2,2,1068,-200,15,1,1152,193,739,142,1683,1309,42\n07/07/2004,14.00.00,2,2,1090,-200,14,1,1120,195,743,130,1629,1283,42\n07/07/2004,15.00.00,1,8,1056,-200,11,3,1024,159,792,121,1555,1068,41\n07/07/2004,16.00.00,1,8,1098,-200,10,8,1007,120,799,116,1546,937,41\n07/07/2004,17.00.00,3,3,1318,-200,21,5,1339,228,635,177,1925,1416,39\n07/07/2004,18.00.00,4,3,1348,-200,21,8,1347,348,599,233,1903,1611,36\n07/07/2004,19.00.00,5,3,1502,-200,29,8,1546,337,538,223,2199,1919,35\n07/07/2004,20.00.00,4,3,1373,-200,22,5,1364,282,597,184,2005,1745,33\n07/07/2004,21.00.00,3,6,1397,-200,21,8,1346,228,580,165,2017,1837,31\n07/07/2004,22.00.00,2,4,1286,-200,17,1,1214,168,620,132,1880,1759,31\n07/07/2004,23.00.00,2,1,1222,-200,15,4,1161,173,661,122,1827,1593,30\n08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0.00,4,9,1426,-200,25,6,1445,667,477,151,1970,1782,24\n02/11/2004,13.00.00,5,1,1535,-200,31,2,1578,583,440,150,2125,1921,25\n02/11/2004,14.00.00,7,5,1678,-200,37,9,1727,726,390,190,2318,2039,25\n02/11/2004,15.00.00,5,8,1605,-200,32,3,1603,674,415,168,2199,1912,25\n02/11/2004,16.00.00,5,9,1559,-200,31,5,1586,544,421,127,2197,1827,25\n02/11/2004,17.00.00,6,8,1713,-200,38,0,1728,715,390,140,2386,1919,24\n02/11/2004,18.00.00,7,9,1751,-200,44,1,1853,789,358,158,2496,2013,23\n02/11/2004,19.00.00,8,1723,-200,44,9,1869,737,359,121,2563,1919,21,8\n02/11/2004,20.00.00,9,2,1778,-200,48,2,1935,859,349,119,2643,1927,20\n02/11/2004,21.00.00,5,9,1461,-200,29,1,1529,668,450,96,2111,1573,20\n02/11/2004,22.00.00,3,7,1258,-200,19,1,1272,454,541,77,1804,1325,19\n02/11/2004,23.00.00,2,6,1138,-200,13,9,1113,337,610,66,1641,1203,19\n03/11/2004,00.00.00,1,1,936,-200,6,8,849,127,798,56,1381,947,20\n03/11/2004,01.00.00,2,6,1167,-200,13,2,1090,281,626,62,1620,1106,18\n03/11/2004,02.00.00,2,2,1071,-200,10,7,1003,252,665,58,1542,1045,17\n03/11/2004,03.00.00,0,9,866,-200,4,1,717,-200,909,-200,1328,827,17\n03/11/2004,04.00.00,-200,836,-200,2,9,647,65,985,37,1271,777,16,4\n03/11/2004,05.00.00,0,5,823,-200,2,7,630,69,1033,35,1253,748,16\n03/11/2004,06.00.00,0,7,890,-200,4,3,727,151,917,50,1290,833,16\n03/11/2004,07.00.00,1,3,1019,-200,8,0,901,231,750,64,1438,960,17\n03/11/2004,08.00.00,2,2,1177,-200,15,5,1166,297,621,75,1667,1157,18\n03/11/2004,09.00.00,3,7,1348,-200,23,8,1398,515,505,101,1847,1444,20\n03/11/2004,10.00.00,3,7,1341,-200,21,0,1323,499,516,94,1786,1522,23\n03/11/2004,11.00.00,3,4,1307,-200,19,3,1278,445,527,95,1767,1452,26\n03/11/2004,12.00.00,3,4,1305,-200,19,7,1287,464,525,124,1771,1437,26\n03/11/2004,13.00.00,2,6,1177,-200,13,2,1090,298,612,86,1632,1209,26\n03/11/2004,14.00.00,2,5,1192,-200,13,0,1085,266,617,79,1638,1170,26\n03/11/2004,15.00.00,2,5,1214,-200,12,9,1080,323,611,87,1642,1190,26\n03/11/2004,16.00.00,2,7,1240,-200,15,1,1152,352,575,94,1699,1278,25\n03/11/2004,17.00.00,3,2,1271,-200,16,3,1188,372,563,107,1703,1305,25\n03/11/2004,18.00.00,3,7,1333,-200,20,5,1312,383,520,107,1853,1364,23\n03/11/2004,19.00.00,4,6,1390,-200,24,4,1414,455,487,117,1959,1428,23\n03/11/2004,20.00.00,3,3,1266,-200,18,7,1261,355,541,97,1807,1285,22\n03/11/2004,21.00.00,2,4,1140,-200,12,7,1072,267,626,90,1597,1143,21\n03/11/2004,22.00.00,2,8,1240,-200,15,6,1168,308,582,86,1714,1235,20\n03/11/2004,23.00.00,2,7,1187,-200,14,2,1122,313,589,77,1641,1208,19\n04/11/2004,00.00.00,1,8,1043,-200,9,9,975,216,675,66,1489,1041,18\n04/11/2004,01.00.00,1,1,948,-200,6,3,826,137,802,57,1382,897,19\n04/11/2004,02.00.00,1,2,955,-200,5,2,775,140,831,55,1362,862,17\n04/11/2004,03.00.00,0,8,915,-200,4,2,721,-200,891,-200,1315,816,16\n04/11/2004,04.00.00,0,6,832,-200,2,9,644,94,990,40,1239,747,17\n04/11/2004,05.00.00,0,5,846,-200,2,6,628,79,1025,40,1218,727,16\n04/11/2004,06.00.00,0,7,897,-200,4,7,750,156,874,46,1270,799,15\n04/11/2004,07.00.00,0,8,908,-200,5,1,767,153,868,53,1272,807,17\n04/11/2004,08.00.00,3,5,1348,-200,22,2,1357,464,526,84,1833,1324,16\n04/11/2004,09.00.00,3,6,1384,-200,23,3,1386,435,504,88,1833,1404,17\n04/11/2004,10.00.00,-200,1473,-200,26,3,1463,683,462,143,1948,1605,20,6\n04/11/2004,11.00.00,-200,1360,-200,19,7,1287,523,517,130,1755,1478,23,6\n04/11/2004,12.00.00,-200,1305,-200,17,4,1223,460,545,120,1720,1414,23,7\n04/11/2004,13.00.00,-200,1337,-200,20,2,1302,417,507,139,1786,1465,24,7\n04/11/2004,14.00.00,-200,1414,-200,24,4,1414,486,478,139,1896,1588,25,9\n04/11/2004,15.00.00,-200,1463,-200,24,5,1417,512,467,156,1900,1651,26,8\n04/11/2004,16.00.00,-200,1530,-200,24,8,1425,595,457,143,1975,1652,26,6\n04/11/2004,17.00.00,-200,1915,-200,45,9,1889,1052,340,186,2603,2110,24,8\n04/11/2004,18.00.00,8,7,1882,-200,47,5,1920,1014,328,171,2609,2204,23\n04/11/2004,19.00.00,5,8,1589,-200,33,0,1619,633,406,124,2216,1780,22\n04/11/2004,20.00.00,4,3,1463,-200,27,2,1484,485,443,97,2011,1590,21\n04/11/2004,21.00.00,3,6,1380,-200,21,8,1345,480,493,92,1856,1419,20\n04/11/2004,22.00.00,3,9,1383,-200,21,3,1332,572,495,96,1855,1415,19\n04/11/2004,23.00.00,2,4,1181,-200,13,3,1094,345,600,74,1599,1180,18\n05/11/2004,00.00.00,1,6,1061,-200,8,8,931,195,699,65,1481,1015,18\n05/11/2004,01.00.00,1,8,1091,-200,9,2,945,213,698,62,1467,1014,18\n05/11/2004,02.00.00,1,5,1010,-200,7,6,884,216,719,59,1430,938,16\n05/11/2004,03.00.00,0,7,868,-200,3,6,688,-200,922,-200,1249,777,17\n05/11/2004,04.00.00,0,5,852,-200,2,8,639,73,1009,41,1204,723,16\n05/11/2004,05.00.00,0,4,826,-200,2,2,601,67,1056,38,1180,702,16\n05/11/2004,06.00.00,0,5,862,-200,3,4,677,96,973,43,1192,725,16\n05/11/2004,07.00.00,0,8,951,-200,5,7,800,162,803,52,1257,836,16\n05/11/2004,08.00.00,2,1210,-200,16,3,1188,305,583,75,1575,1129,16,2\n05/11/2004,09.00.00,6,2,1601,-200,37,9,1727,725,389,118,2252,1718,16\n05/11/2004,10.00.00,4,5,1477,-200,25,5,1443,658,469,126,1878,1646,19\n05/11/2004,11.00.00,4,6,1458,-200,24,2,1408,710,471,151,1889,1664,20\n05/11/2004,12.00.00,3,3,1354,-200,20,9,1321,445,504,107,1799,1496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30/11/2004,02.00.00,1,2,1014,-200,4,5,737,124,1509,75,1288,765,13\n30/11/2004,03.00.00,0,8,897,-200,2,0,581,-200,1184,-200,1181,636,13\n30/11/2004,04.00.00,0,6,878,-200,1,2,518,50,1294,42,1149,539,13\n30/11/2004,05.00.00,0,6,840,-200,1,4,534,47,1268,38,1109,483,13\n30/11/2004,06.00.00,0,7,879,-200,1,9,575,110,1262,64,1111,537,11\n30/11/2004,07.00.00,1,1,999,-200,3,6,690,193,1027,86,1192,707,11\n30/11/2004,08.00.00,2,5,1251,-200,11,0,1015,475,751,127,1453,1070,12\n30/11/2004,09.00.00,3,6,1351,-200,17,5,1226,420,648,122,1649,1248,13\n30/11/2004,10.00.00,3,6,1312,-200,15,9,1177,439,711,141,1588,1261,13\n30/11/2004,11.00.00,3,3,1201,-200,13,4,1098,461,763,139,1500,1209,14\n30/11/2004,12.00.00,2,1120,-200,8,8,932,238,897,105,1301,1039,14,6\n30/11/2004,13.00.00,1,9,1071,-200,9,4,955,232,940,103,1311,977,14\n30/11/2004,14.00.00,2,2,1085,-200,9,7,967,241,943,108,1321,976,14\n30/11/2004,15.00.00,2,2,1113,-200,10,1,980,278,984,109,1311,995,15\n30/11/2004,16.00.00,3,8,1310,-200,20,2,1301,496,746,149,1694,1350,14\n30/11/2004,17.00.00,3,5,1183,-200,14,2,1122,464,897,143,1476,1215,12\n30/11/2004,18.00.00,5,2,1362,-200,22,8,1373,600,755,159,1698,1461,11\n30/11/2004,19.00.00,6,3,1408,-200,28,0,1502,692,640,178,1843,1598,11\n30/11/2004,20.00.00,6,8,1471,-200,30,3,1559,731,605,168,1939,1669,11\n30/11/2004,21.00.00,4,1,1157,-200,12,4,1063,491,905,160,1394,1223,10\n30/11/2004,22.00.00,2,3,1061,-200,8,0,899,284,1067,117,1277,939,9\n30/11/2004,23.00.00,1,9,1006,-200,6,5,836,245,1226,113,1210,902,9\n01/12/2004,00.00.00,2,2,1039,-200,7,5,880,273,1198,113,1251,933,9\n01/12/2004,01.00.00,1,3,886,-200,3,8,699,157,1900,94,1106,766,10\n01/12/2004,02.00.00,1,2,900,-200,3,8,701,117,1955,76,1127,749,9\n01/12/2004,03.00.00,0,9,856,-200,3,0,654,-200,2042,-200,1100,709,8\n01/12/2004,04.00.00,-200,857,-200,2,8,638,123,2061,68,1083,746,8,5\n01/12/2004,05.00.00,0,6,844,-200,2,5,618,100,2095,63,1064,757,8\n01/12/2004,06.00.00,0,9,927,-200,3,3,671,135,2009,74,1098,792,8\n01/12/2004,07.00.00,1,4,1007,-200,7,0,857,301,1699,96,1248,921,8\n01/12/2004,08.00.00,3,4,1318,-200,18,1,1243,641,1261,121,1638,1318,9\n01/12/2004,09.00.00,5,1396,-200,24,6,1419,849,1104,145,1799,1547,9,5\n01/12/2004,10.00.00,4,7,1337,-200,19,7,1288,861,1192,158,1648,1516,10\n01/12/2004,11.00.00,3,2,1202,-200,13,0,1083,629,1376,143,1410,1340,11\n01/12/2004,12.00.00,3,5,1309,-200,17,1,1212,737,1267,182,1533,1556,13\n01/12/2004,13.00.00,4,1351,-200,19,3,1278,712,1249,163,1623,1582,13,6\n01/12/2004,14.00.00,5,7,1501,-200,28,4,1514,933,1090,178,1898,1791,13\n01/12/2004,15.00.00,5,1426,-200,22,9,1375,836,1143,162,1751,1631,13,0\n01/12/2004,16.00.00,5,6,1544,-200,25,7,1446,987,1070,171,1834,1679,12\n01/12/2004,17.00.00,6,2,1566,-200,26,8,1474,985,1049,174,1887,1681,12\n01/12/2004,18.00.00,7,5,1547,-200,29,2,1532,1040,1009,185,1972,1718,12\n01/12/2004,19.00.00,3,1124,-200,11,9,1047,401,1401,126,1419,1193,12,5\n01/12/2004,20.00.00,2,6,1090,-200,10,1,981,279,1513,113,1390,1044,11\n01/12/2004,21.00.00,2,1,1034,-200,7,2,865,280,1732,111,1280,925,11\n01/12/2004,22.00.00,1,8,1040,-200,7,1,859,280,1705,107,1293,889,11\n01/12/2004,23.00.00,2,1060,-200,7,5,877,327,1648,107,1313,929,11,3\n02/12/2004,00.00.00,2,2,1076,-200,8,1,903,316,1627,102,1330,958,11\n02/12/2004,01.00.00,1,9,975,-200,5,5,787,223,1812,96,1238,844,11\n02/12/2004,02.00.00,1,6,987,-200,5,7,796,180,1789,86,1254,830,11\n02/12/2004,03.00.00,1,1,928,-200,3,8,699,-200,1930,-200,1198,785,11\n02/12/2004,04.00.00,0,9,889,-200,2,9,646,131,2047,70,1163,743,12\n02/12/2004,05.00.00,0,7,880,-200,2,4,610,101,2095,59,1150,711,12\n02/12/2004,06.00.00,0,9,919,-200,4,0,711,189,1667,74,1195,819,12\n02/12/2004,07.00.00,1,4,1073,-200,7,2,867,336,768,89,1329,981,12\n02/12/2004,08.00.00,4,2,1435,-200,21,6,1341,804,501,131,1787,1480,12\n02/12/2004,09.00.00,5,1477,-200,27,9,1501,860,444,139,1972,1691,13,3\n02/12/2004,10.00.00,4,4,1337,-200,19,2,1275,745,525,145,1655,1548,14\n02/12/2004,11.00.00,3,4,1308,-200,16,4,1191,685,537,137,1559,1523,15\n02/12/2004,12.00.00,2,8,1245,-200,14,8,1143,639,569,128,1531,1406,15\n02/12/2004,13.00.00,3,4,1295,-200,16,8,1205,668,538,128,1579,1476,16\n02/12/2004,14.00.00,2,9,1338,-200,16,9,1208,469,539,123,1600,1481,18\n02/12/2004,15.00.00,3,9,1405,-200,19,0,1269,605,500,136,1735,1534,19\n02/12/2004,16.00.00,4,1456,-200,19,8,1291,641,490,137,1753,1592,18,1\n02/12/2004,17.00.00,4,9,1516,-200,24,8,1423,774,455,151,1881,1688,16\n02/12/2004,18.00.00,7,7,1707,-200,37,6,1721,1001,375,171,2260,1940,15\n02/12/2004,19.00.00,9,4,1816,-200,43,9,1851,1184,341,171,2405,2069,15\n02/12/2004,20.00.00,7,8,1713,-200,36,6,1699,1020,371,161,2196,1943,15\n02/12/2004,21.00.00,6,6,1646,-200,30,8,1570,964,401,154,2049,1846,15\n02/12/2004,22.00.00,4,7,1468,-200,22,0,1351,741,472,127,1783,1611,15\n02/12/2004,23.00.00,4,8,1443,-200,20,9,1323,757,486,115,1760,1487,14\n03/12/2004,00.00.00,3,2,1289,-200,13,1,1087,564,587,102,1515,1208,14\n03/12/2004,01.00.00,2,6,1194,-200,9,3,950,434,667,100,1433,1043,14\n03/12/2004,02.00.00,2,1,1165,-200,9,4,957,308,661,86,1452,979,14\n03/12/2004,03.00.00,1,3,1003,-200,4,7,747,-200,843,-200,1289,806,14\n03/12/2004,04.00.00,1,1,986,-200,3,8,700,133,883,80,1249,768,14\n03/12/2004,05.00.00,0,7,930,-200,2,5,623,103,975,74,1215,720,13\n03/12/2004,06.00.00,0,9,988,-200,4,0,709,178,897,82,1267,785,13\n03/12/2004,07.00.00,1,5,1094,-200,7,0,857,289,739,96,1378,903,13\n03/12/2004,08.00.00,3,8,1528,-200,21,1,1328,736,487,124,1839,1389,13\n03/12/2004,09.00.00,6,1628,-200,29,0,1528,928,416,142,2081,1649,13,9\n03/12/2004,10.00.00,5,1,1555,-200,23,0,1377,897,455,154,1834,1621,14\n03/12/2004,11.00.00,3,6,1290,-200,13,0,1084,596,586,144,1544,1362,15\n03/12/2004,12.00.00,3,1280,-200,14,9,1144,481,555,144,1599,1624,16,4\n03/12/2004,13.00.00,3,1,1302,-200,15,7,1169,495,544,139,1636,1605,16\n03/12/2004,14.00.00,3,3,1332,-200,18,1,1242,460,524,138,1716,1608,17\n03/12/2004,15.00.00,3,4,1332,-200,15,5,1163,445,548,134,1664,1522,18\n03/12/2004,16.00.00,4,6,1493,-200,23,8,1399,618,466,150,1893,1708,19\n03/12/2004,17.00.00,6,9,1648,-200,32,6,1612,858,401,173,2177,1885,17\n03/12/2004,18.00.00,5,1,1482,-200,24,0,1404,634,463,134,1883,1657,16\n03/12/2004,19.00.00,4,9,1501,-200,23,6,1392,575,476,128,1832,1645,15\n03/12/2004,20.00.00,6,1,1601,-200,30,7,1568,738,422,132,2111,1729,15\n03/12/2004,21.00.00,6,2,1421,-200,20,9,1320,739,496,134,1804,1556,14\n03/12/2004,22.00.00,3,1232,-200,12,7,1074,472,606,107,1504,1317,14,0\n03/12/2004,23.00.00,3,4,1270,-200,14,0,1117,542,586,101,1528,1290,13\n04/12/2004,00.00.00,3,5,1257,-200,13,7,1108,577,592,94,1520,1287,12\n04/12/2004,01.00.00,3,2,1246,-200,12,8,1077,568,621,99,1462,1235,12\n04/12/2004,02.00.00,2,9,1166,-200,11,0,1013,451,643,90,1408,1144,12\n04/12/2004,03.00.00,2,1,1062,-200,8,2,906,-200,724,-200,1305,1004,11\n04/12/2004,04.00.00,-200,1005,-200,6,4,829,242,779,71,1246,944,11,6\n04/12/2004,05.00.00,1,7,1032,-200,6,8,848,277,751,71,1266,972,11\n04/12/2004,06.00.00,1,7,1034,-200,7,4,876,291,740,66,1305,968,11\n04/12/2004,07.00.00,1,3,1024,-200,6,2,822,277,800,70,1266,954,12\n04/12/2004,08.00.00,2,8,1243,-200,13,7,1108,510,595,90,1539,1201,12\n04/12/2004,09.00.00,2,5,1148,-200,9,6,963,451,680,102,1395,1017,12\n04/12/2004,10.00.00,2,7,1182,-200,10,4,994,584,654,102,1431,1039,12\n04/12/2004,11.00.00,1,5,1015,-200,6,5,835,260,796,94,1263,911,12\n04/12/2004,12.00.00,2,6,1175,-200,10,6,1002,492,668,122,1422,1059,13\n04/12/2004,13.00.00,3,1,1224,-200,11,9,1046,580,630,124,1476,1213,11\n04/12/2004,14.00.00,2,5,1123,-200,8,4,916,373,709,103,1346,1003,11\n04/12/2004,15.00.00,2,6,1187,-200,9,6,961,497,685,115,1408,1017,11\n04/12/2004,16.00.00,2,5,1116,-200,7,1,860,413,758,107,1316,937,12\n04/12/2004,17.00.00,2,3,1131,-200,8,3,912,444,728,120,1356,987,12\n04/12/2004,18.00.00,3,1,1215,-200,10,9,1012,533,653,120,1441,1131,13\n04/12/2004,19.00.00,1,9,1077,-200,6,9,852,325,773,108,1302,969,13\n04/12/2004,20.00.00,2,5,1210,-200,10,6,1000,439,679,115,1456,1062,13\n04/12/2004,21.00.00,3,1,1181,-200,9,4,956,506,689,124,1400,1058,13\n04/12/2004,22.00.00,2,1088,-200,6,9,851,349,766,109,1307,959,14,0\n04/12/2004,23.00.00,1,9,1054,-200,5,6,794,235,830,93,1282,887,13\n05/12/2004,00.00.00,2,4,1096,-200,6,3,826,378,795,103,1285,911,13\n05/12/2004,01.00.00,2,1042,-200,6,5,834,304,808,106,1267,914,12,4\n05/12/2004,02.00.00,1,2,887,-200,3,4,679,180,969,96,1132,786,12\n05/12/2004,03.00.00,1,884,-200,3,0,651,-200,1034,-200,1105,741,12,2\n05/12/2004,04.00.00,1,862,-200,2,8,639,148,1033,81,1100,727,12,3\n05/12/2004,05.00.00,0,8,821,-200,2,0,584,106,1132,70,1070,686,12\n05/12/2004,06.00.00,0,6,797,-200,1,5,543,87,1235,60,1047,648,12\n05/12/2004,07.00.00,0,7,802,-200,1,4,538,96,1237,63,1042,645,12\n05/12/2004,08.00.00,0,8,848,-200,1,9,578,120,1160,68,1068,679,13\n05/12/2004,09.00.00,0,9,863,-200,2,4,610,130,1088,68,1086,696,13\n05/12/2004,10.00.00,1,889,-200,3,1,661,140,1001,66,1115,735,12,9\n05/12/2004,11.00.00,1,3,969,-200,4,4,733,176,901,72,1191,803,13\n05/12/2004,12.00.00,1,7,1013,-200,5,6,795,209,826,81,1257,848,13\n05/12/2004,13.00.00,2,4,1100,-200,6,7,842,309,774,89,1296,882,12\n05/12/2004,14.00.00,1,7,1019,-200,5,5,788,226,828,85,1267,812,11\n05/12/2004,15.00.00,1,8,1061,-200,7,7,887,246,745,86,1367,889,12\n05/12/2004,16.00.00,1,5,940,-200,3,7,692,208,934,83,1177,749,11\n05/12/2004,17.00.00,1,1,904,-200,2,8,640,154,1022,77,1152,682,11\n05/12/2004,18.00.00,2,4,1120,-200,8,9,935,275,744,93,1385,922,12\n05/12/2004,19.00.00,2,987,-200,4,6,746,281,880,103,1212,833,12,1\n05/12/2004,20.00.00,1,5,971,-200,4,4,734,237,905,90,1187,808,12\n05/12/2004,21.00.00,1,7,965,-200,4,4,733,225,909,93,1173,790,12\n05/12/2004,22.00.00,1,873,-200,2,8,641,143,1025,72,1120,685,12,5\n05/12/2004,23.00.00,1,892,-200,3,3,672,146,1012,73,1111,706,13,1\n06/12/2004,00.00.00,1,857,-200,2,3,605,115,1094,64,1071,636,13,2\n06/12/2004,01.00.00,0,9,901,-200,3,2,662,125,1034,66,1127,678,13\n06/12/2004,02.00.00,1,1,901,-200,3,1,661,150,1022,71,1124,714,13\n06/12/2004,03.00.00,1,1,904,-200,3,0,649,-200,983,-200,1157,726,12\n06/12/2004,04.00.00,0,6,857,-200,1,7,562,78,1140,54,1130,646,11\n06/12/2004,05.00.00,0,7,881,-200,2,3,602,108,1034,58,1157,685,11\n06/12/2004,06.00.00,0,9,913,-200,3,8,701,181,926,72,1219,750,12\n06/12/2004,07.00.00,1,3,1012,-200,6,1,817,302,819,89,1314,878,12\n06/12/2004,08.00.00,3,1,1314,-200,15,8,1173,587,589,112,1627,1238,12\n06/12/2004,09.00.00,4,9,1479,-200,26,4,1465,712,471,131,2018,1572,13\n06/12/2004,10.00.00,4,7,1299,-200,18,5,1254,698,566,161,1662,1488,15\n06/12/2004,11.00.00,3,2,1221,-200,13,2,1090,576,641,135,1489,1424,18\n06/12/2004,12.00.00,2,5,1171,-200,12,0,1049,438,655,119,1418,1338,18\n06/12/2004,13.00.00,3,8,1316,-200,19,9,1293,562,538,144,1682,1559,19\n06/12/2004,14.00.00,3,3,1208,-200,16,4,1191,467,573,130,1589,1415,19\n06/12/2004,15.00.00,2,5,1145,-200,13,8,1112,323,621,109,1481,1331,20\n06/12/2004,16.00.00,2,7,1189,-200,15,6,1167,391,599,114,1541,1315,18\n06/12/2004,17.00.00,2,9,1173,-200,15,7,1170,379,597,115,1551,1289,17\n06/12/2004,18.00.00,3,1,1237,-200,18,0,1239,387,570,123,1620,1389,16\n06/12/2004,19.00.00,4,1253,-200,17,9,1238,433,571,138,1613,1351,15,9\n06/12/2004,20.00.00,3,1,1155,-200,14,3,1126,349,617,123,1511,1267,15\n06/12/2004,21.00.00,2,7,1175,-200,12,8,1077,337,634,112,1480,1279,15\n06/12/2004,22.00.00,1,7,1001,-200,7,3,871,245,806,87,1264,1071,16\n06/12/2004,23.00.00,1,5,1014,-200,5,8,804,236,871,79,1233,994,14\n07/12/2004,00.00.00,1,4,966,-200,5,1,770,191,900,74,1206,956,14\n07/12/2004,01.00.00,1,4,987,-200,5,5,786,273,901,75,1220,936,13\n07/12/2004,02.00.00,1,1,888,-200,3,5,680,211,985,70,1138,826,13\n07/12/2004,03.00.00,1,1,911,-200,4,2,719,-200,947,-200,1170,839,11\n07/12/2004,04.00.00,-200,828,-200,2,6,627,146,1074,64,1103,748,11,9\n07/12/2004,05.00.00,0,6,857,-200,2,3,604,126,1126,63,1091,740,12\n07/12/2004,06.00.00,0,8,886,-200,2,8,641,146,1082,67,1098,783,14\n07/12/2004,07.00.00,1,964,-200,4,3,730,243,985,71,1146,891,12,9\n07/12/2004,08.00.00,2,8,1230,-200,12,6,1070,450,761,98,1431,1197,11\n07/12/2004,09.00.00,3,4,1236,-200,16,8,1203,499,681,114,1566,1311,12\n07/12/2004,10.00.00,3,3,1322,-200,17,3,1219,574,682,130,1591,1472,14\n07/12/2004,11.00.00,4,1,1407,-200,19,8,1292,679,643,155,1638,1694,16\n07/12/2004,12.00.00,3,5,1357,-200,21,6,1339,572,657,141,1583,1639,17\n07/12/2004,13.00.00,3,1289,-200,18,0,1239,396,733,118,1635,1469,18,8\n07/12/2004,14.00.00,3,6,1257,-200,16,0,1179,455,818,134,1545,1415,19\n07/12/2004,15.00.00,3,1199,-200,13,7,1108,423,818,123,1450,1250,19,2\n07/12/2004,16.00.00,2,6,1181,-200,14,1,1121,356,629,117,1466,1236,17\n07/12/2004,17.00.00,2,6,1175,-200,13,9,1112,350,628,110,1484,1212,16\n07/12/2004,18.00.00,3,1,1202,-200,14,3,1126,359,606,115,1480,1212,15\n07/12/2004,19.00.00,3,2,1170,-200,13,3,1095,358,618,116,1470,1195,15\n07/12/2004,20.00.00,2,4,1115,-200,11,6,1036,289,649,99,1381,1200,14\n07/12/2004,21.00.00,2,6,1130,-200,11,3,1026,359,657,115,1374,1209,14\n07/12/2004,22.00.00,2,4,1195,-200,11,0,1013,355,646,104,1345,1276,14\n07/12/2004,23.00.00,1,8,1111,-200,7,7,887,244,720,92,1262,1132,14\n08/12/2004,00.00.00,1,7,1092,-200,7,0,855,194,750,84,1231,1061,15\n08/12/2004,01.00.00,1,9,1111,-200,7,0,856,176,746,81,1254,1036,14\n08/12/2004,02.00.00,1,4,992,-200,4,4,731,170,1035,77,1149,911,15\n08/12/2004,03.00.00,1,5,1051,-200,6,1,815,-200,1305,-200,1203,951,12\n08/12/2004,04.00.00,1,7,1080,-200,7,9,896,212,1075,75,1262,1033,11\n08/12/2004,05.00.00,1,893,-200,3,1,655,146,990,69,1097,816,12,0\n08/12/2004,06.00.00,0,9,923,-200,3,2,664,177,988,67,1121,822,11\n08/12/2004,07.00.00,1,950,-200,4,0,713,172,917,68,1139,841,11,8\n08/12/2004,08.00.00,1,947,-200,3,2,666,159,985,68,1115,795,12,8\n08/12/2004,09.00.00,1,1,973,-200,4,0,712,184,923,69,1157,831,13\n08/12/2004,10.00.00,1,7,1111,-200,7,6,884,310,751,81,1280,1025,13\n08/12/2004,11.00.00,2,3,1206,-200,9,8,969,363,701,95,1365,1178,14\n08/12/2004,12.00.00,2,1,1202,-200,9,5,958,302,732,92,1330,1181,16\n08/12/2004,13.00.00,2,7,1261,-200,11,2,1022,424,635,113,1407,1250,17\n08/12/2004,14.00.00,2,3,1102,-200,6,9,855,258,763,98,1279,1039,17\n08/12/2004,15.00.00,1,2,997,-200,4,6,742,179,911,82,1180,899,16\n08/12/2004,16.00.00,1,4,1049,-200,5,5,790,221,870,96,1183,1019,15\n08/12/2004,17.00.00,1,9,1082,-200,6,5,834,303,800,107,1207,1057,15\n08/12/2004,18.00.00,2,6,1177,-200,10,3,987,429,692,119,1324,1229,16\n08/12/2004,19.00.00,2,4,1166,-200,8,9,935,357,756,114,1301,1135,16\n08/12/2004,20.00.00,3,5,1352,-200,14,0,1118,438,625,127,1524,1188,14\n08/12/2004,21.00.00,3,5,1210,-200,9,2,947,457,697,121,1404,1060,13\n08/12/2004,22.00.00,2,2,1161,-200,8,2,909,355,721,109,1370,962,13\n08/12/2004,23.00.00,2,3,1142,-200,7,2,866,348,815,107,1329,939,13\n09/12/2004,00.00.00,1,9,1054,-200,5,5,789,241,886,100,1254,864,14\n09/12/2004,01.00.00,1,926,-200,2,9,649,134,1035,83,1145,741,14,9\n09/12/2004,02.00.00,0,8,873,-200,2,0,583,94,1155,66,1108,642,14\n09/12/2004,03.00.00,0,5,832,-200,1,2,518,-200,1264,-200,1061,539,15\n09/12/2004,04.00.00,0,4,803,-200,0,7,466,33,1451,30,1034,449,14\n09/12/2004,05.00.00,0,4,818,-200,0,7,463,33,1509,29,1046,437,14\n09/12/2004,06.00.00,0,4,8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00,680,-200,1075,1222,3\n02/02/2005,04.00.00,2,5,1058,-200,4,8,753,210,764,121,986,1149,3\n02/02/2005,05.00.00,2,0,1040,-200,3,6,688,185,846,115,920,1090,3\n02/02/2005,06.00.00,2,2,1069,-200,4,5,737,257,792,125,946,1114,2\n02/02/2005,07.00.00,2,4,1042,-200,5,2,771,297,795,136,941,1089,3\n02/02/2005,08.00.00,4,0,1322,-200,15,1,1151,573,566,174,1286,1446,2\n02/02/2005,09.00.00,6,7,1549,-200,27,3,1485,883,422,224,1638,1930,2\n02/02/2005,10.00.00,5,6,1416,-200,19,6,1285,824,501,230,1390,1809,5\n02/02/2005,11.00.00,4,3,1337,-200,14,8,1143,693,560,205,1236,1769,9\n02/02/2005,12.00.00,4,0,1281,-200,14,5,1132,714,577,204,1206,1696,12\n02/02/2005,13.00.00,2,6,1037,-200,10,1,981,388,721,139,1000,1086,13\n02/02/2005,14.00.00,2,2,1028,-200,10,1,983,337,728,133,979,1048,13\n02/02/2005,15.00.00,1,9,984,-200,8,3,913,326,762,127,930,990,14\n02/02/2005,16.00.00,1,8,1010,-200,9,0,940,329,759,124,960,1027,14\n02/02/2005,17.00.00,3,1,1113,-200,13,4,1096,465,645,158,1092,1359,13\n02/02/2005,18.00.00,4,8,1253,-200,19,5,1284,612,536,171,1285,1618,10\n02/02/2005,19.00.00,5,8,1400,-200,27,8,1499,759,461,193,1527,1869,8\n02/02/2005,20.00.00,5,9,1470,-200,31,4,1584,771,436,195,1641,2033,8\n02/02/2005,21.00.00,4,1,1281,-200,15,6,1168,655,571,212,1195,1756,6\n02/02/2005,22.00.00,1,8,1084,-200,7,0,855,332,748,193,939,1446,5\n02/02/2005,23.00.00,1,7,1097,-200,7,8,892,287,719,167,979,1423,4\n03/02/2005,00.00.00,1,7,1032,-200,5,6,791,242,793,156,915,1278,4\n03/02/2005,01.00.00,1,5,1046,-200,6,0,812,242,782,153,940,1265,3\n03/02/2005,02.00.00,1,1,975,-200,3,9,707,192,860,128,902,1107,3\n03/02/2005,03.00.00,0,9,932,-200,2,8,642,-200,950,-200,871,1014,3\n03/02/2005,04.00.00,-200,0,926,-200,2,6,623,134,954,96,873,1011,2\n03/02/2005,05.00.00,0,3,932,-200,2,9,645,142,916,105,882,1006,1\n03/02/2005,06.00.00,0,4,937,-200,2,8,640,194,923,130,870,1010,2\n03/02/2005,07.00.00,0,6,996,-200,4,2,724,216,861,134,938,1067,1\n03/02/2005,08.00.00,3,0,1382,-200,16,7,1202,728,534,275,1352,1630,1\n03/02/2005,09.00.00,5,6,1544,-200,29,1,1530,975,416,309,1706,2030,2\n03/02/2005,10.00.00,4,9,1429,-200,20,7,1317,947,495,322,1425,1916,5\n03/02/2005,11.00.00,3,5,1312,-200,15,5,1164,790,570,333,1230,1761,8\n03/02/2005,12.00.00,2,2,1205,-200,10,3,989,551,643,235,1084,1597,11\n03/02/2005,13.00.00,2,7,1322,-200,13,8,1110,565,568,241,1208,1776,12\n03/02/2005,14.00.00,2,7,1301,-200,12,7,1074,532,574,214,1255,1636,13\n03/02/2005,15.00.00,2,1,1280,-200,11,4,1030,459,597,174,1246,1480,14\n03/02/2005,16.00.00,2,0,1256,-200,10,2,986,407,612,157,1240,1413,15\n03/02/2005,17.00.00,2,4,1358,-200,14,3,1128,434,545,170,1344,1611,14\n03/02/2005,18.00.00,6,7,1713,-200,32,4,1605,980,383,212,1870,2186,12\n03/02/2005,19.00.00,6,4,1655,-200,29,0,1527,966,393,249,1758,2203,10\n03/02/2005,20.00.00,3,9,1444,-200,17,6,1227,646,485,231,1414,1891,9\n03/02/2005,21.00.00,2,7,1314,-200,12,6,1069,471,571,216,1271,1662,8\n03/02/2005,22.00.00,3,0,1368,-200,15,5,1164,528,524,217,1350,1674,6\n03/02/2005,23.00.00,2,1,1195,-200,9,1,943,378,645,203,1149,1462,6\n04/02/2005,00.00.00,1,2,1083,-200,6,4,828,235,734,161,1067,1273,6\n04/02/2005,01.00.00,1,2,1055,-200,5,1,771,217,781,147,1030,1174,5\n04/02/2005,02.00.00,0,7,973,-200,3,3,672,141,887,111,954,1031,4\n04/02/2005,03.00.00,0,9,1039,-200,5,3,777,-200,778,-200,1031,1082,2\n04/02/2005,04.00.00,0,8,949,-200,3,1,657,158,895,115,948,991,5\n04/02/2005,05.00.00,0,4,920,-200,2,2,601,87,974,80,920,925,3\n04/02/2005,06.00.00,0,7,989,-200,3,9,703,194,843,122,988,1017,1\n04/02/2005,07.00.00,1,4,1100,-200,6,9,854,401,714,186,1075,1187,1\n04/02/2005,08.00.00,4,0,1411,-200,19,7,1288,887,492,306,1472,1638,2\n04/02/2005,09.00.00,3,9,1328,-200,20,1,1300,654,499,301,1405,1672,4\n04/02/2005,10.00.00,3,1,1308,-200,16,3,1189,566,534,274,1325,1656,7\n04/02/2005,11.00.00,3,2,1280,-200,14,3,1127,649,558,247,1277,1641,12\n04/02/2005,12.00.00,1,5,1056,-200,7,0,857,309,735,169,1028,1117,15\n04/02/2005,13.00.00,1,2,1019,-200,6,3,825,229,781,138,994,901,14\n04/02/2005,14.00.00,1,9,1084,-200,8,6,923,312,712,149,1085,1046,12\n04/02/2005,15.00.00,1,5,998,-200,6,6,841,276,792,165,979,884,12\n04/02/2005,16.00.00,1,4,987,-200,6,1,816,236,816,156,959,821,11\n04/02/2005,17.00.00,1,9,1079,-200,8,8,932,312,719,184,1018,1056,11\n04/02/2005,18.00.00,2,2,1096,-200,9,4,954,345,687,197,1085,1139,9\n04/02/2005,19.00.00,3,0,1146,-200,11,8,1043,386,639,204,1169,1248,8\n04/02/2005,20.00.00,2,6,1125,-200,11,3,1025,356,653,207,1147,1200,7\n04/02/2005,21.00.00,2,8,1130,-200,9,7,968,467,680,208,1102,1220,7\n04/02/2005,22.00.00,1,6,1017,-200,5,8,805,287,807,174,958,1032,6\n04/02/2005,23.00.00,1,3,1017,-200,5,3,776,239,843,157,946,971,6\n05/02/2005,00.00.00,1,6,1041,-200,5,3,778,266,828,166,947,1011,5\n05/02/2005,01.00.00,1,5,1034,-200,5,1,769,261,816,167,952,1049,4\n05/02/2005,02.00.00,1,3,1009,-200,4,8,751,223,828,154,939,1053,4\n05/02/2005,03.00.00,1,6,1105,-200,6,4,828,-200,735,-200,997,1157,2\n05/02/2005,04.00.00,2,0,1084,-200,7,0,859,272,703,164,1017,1163,1\n05/02/2005,05.00.00,1,3,989,-200,4,4,731,226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141,1184,102,637,465,0\n02/03/2005,01.00.00,0,8,765,-200,1,5,544,122,1253,93,601,405,0\n02/03/2005,02.00.00,0,6,761,-200,1,3,525,79,1307,67,605,332,0\n02/03/2005,03.00.00,0,5,788,-200,0,9,486,-200,1294,-200,650,420,-1\n02/03/2005,04.00.00,-200,0,790,-200,0,7,471,81,1302,75,669,532,-1\n02/03/2005,05.00.00,0,4,804,-200,0,9,489,77,1262,73,698,630,-1\n02/03/2005,06.00.00,0,5,831,-200,1,4,537,124,1158,96,722,768,-1\n02/03/2005,07.00.00,0,7,881,-200,3,5,680,159,1023,110,813,866,-1\n02/03/2005,08.00.00,2,4,1099,-200,11,0,1013,396,690,150,1056,1254,-1\n02/03/2005,09.00.00,2,8,1100,-200,12,4,1065,479,669,169,1072,1347,0\n02/03/2005,10.00.00,2,0,1042,-200,8,3,910,438,774,181,922,1216,4\n02/03/2005,11.00.00,2,2,1026,-200,8,9,935,466,747,187,942,1151,7\n02/03/2005,12.00.00,1,4,905,-200,5,7,796,288,900,146,767,699,9\n02/03/2005,13.00.00,1,2,891,-200,5,5,790,205,927,131,764,584,9\n02/03/2005,14.00.00,1,9,968,-200,9,0,938,311,790,161,897,836,9\n02/03/2005,15.00.00,1,3,909,-200,6,3,826,246,898,143,802,626,10\n02/03/2005,16.00.00,2,0,938,-200,9,1,945,356,804,176,855,878,11\n02/03/2005,17.00.00,2,0,971,-200,8,4,917,397,802,178,885,952,11\n02/03/2005,18.00.00,3,4,1148,-200,17,5,1226,579,624,207,1126,1460,9\n02/03/2005,19.00.00,5,1,1299,-200,23,3,1384,819,523,240,1325,1855,7\n02/03/2005,20.00.00,5,3,1315,-200,19,0,1269,754,551,236,1282,1860,6\n02/03/2005,21.00.00,2,8,1180,-200,11,1,1020,473,656,199,1054,1629,5\n02/03/2005,22.00.00,1,6,1070,-200,6,2,821,300,754,170,936,1454,4\n02/03/2005,23.00.00,1,4,1031,-200,5,0,763,235,818,153,905,1348,5\n03/03/2005,00.00.00,1,4,1047,-200,5,3,778,207,799,140,907,1326,4\n03/03/2005,01.00.00,1,4,1030,-200,4,2,724,204,832,138,893,1285,4\n03/03/2005,02.00.00,1,1,986,-200,3,5,682,155,875,117,884,1205,3\n03/03/2005,03.00.00,0,9,992,-200,3,0,655,-200,876,-200,887,1220,3\n03/03/2005,04.00.00,1,0,1076,-200,4,1,715,179,783,119,947,1448,3\n03/03/2005,05.00.00,1,0,1104,-200,4,0,713,143,754,108,1001,1433,2\n03/03/2005,06.00.00,1,2,1160,-200,5,1,770,212,702,121,1063,1447,2\n03/03/2005,07.00.00,1,4,1217,-200,6,7,846,263,650,136,1120,1481,2\n03/03/2005,08.00.00,3,0,1457,-200,15,6,1167,648,496,175,1422,1785,3\n03/03/2005,09.00.00,4,2,1337,-200,17,1,1212,717,529,211,1406,1756,5\n03/03/2005,10.00.00,2,1,1111,-200,8,8,931,389,687,186,1069,1368,7\n03/03/2005,11.00.00,2,0,1165,-200,6,9,852,438,692,177,1155,1197,3\n03/03/2005,12.00.00,1,6,1129,-200,5,4,783,282,757,147,1127,1084,3\n03/03/2005,13.00.00,1,4,1092,-200,4,9,759,268,773,150,1088,1039,5\n03/03/2005,14.00.00,1,6,1096,-200,5,3,777,248,767,148,1107,961,6\n03/03/2005,15.00.00,1,5,1108,-200,5,4,783,272,758,151,1115,964,6\n03/03/2005,16.00.00,1,7,1124,-200,5,5,787,291,740,151,1147,941,6\n03/03/2005,17.00.00,1,9,1216,-200,7,5,879,363,670,165,1252,1111,6\n03/03/2005,18.00.00,3,8,1437,-200,16,7,1202,660,507,197,1510,1459,5\n03/03/2005,19.00.00,5,4,1473,-200,18,2,1245,907,476,228,1539,1519,4\n03/03/2005,20.00.00,4,2,1396,-200,15,5,1164,639,508,196,1488,1412,4\n03/03/2005,21.00.00,3,8,1285,-200,10,9,1013,574,568,192,1331,1336,4\n03/03/2005,22.00.00,2,3,1206,-200,8,7,928,373,621,161,1235,1285,4\n03/03/2005,23.00.00,2,1,1179,-200,7,3,868,330,664,156,1205,1240,4\n04/03/2005,00.00.00,2,3,1213,-200,7,5,877,336,654,158,1206,1282,4\n04/03/2005,01.00.00,2,3,1142,-200,6,8,849,347,680,154,1173,1189,4\n04/03/2005,02.00.00,1,7,1089,-200,4,7,750,241,765,138,1108,1117,3\n04/03/2005,03.00.00,1,2,982,-200,2,5,617,-200,891,-200,977,1024,4\n04/03/2005,04.00.00,0,6,888,-200,1,3,526,96,1134,92,912,744,4\n04/03/2005,05.00.00,0,9,979,-200,2,2,602,147,925,103,993,928,4\n04/03/2005,06.00.00,1,1,1023,-200,3,6,690,210,833,117,1049,1006,3\n04/03/2005,07.00.00,1,9,1153,-200,7,5,878,409,682,139,1180,1173,3\n04/03/2005,08.00.00,3,7,1393,-200,16,8,1204,719,506,167,1497,1568,3\n04/03/2005,09.00.00,6,1,1567,-200,28,0,1502,959,399,205,1848,1913,5\n04/03/2005,10.00.00,4,6,1334,-200,15,8,1174,744,524,216,1432,1643,6\n04/03/2005,11.00.00,3,3,1280,-200,15,5,1165,583,533,192,1435,1605,7\n04/03/2005,12.00.00,3,3,1330,-200,14,9,1145,597,537,188,1399,1668,8\n04/03/2005,13.00.00,3,4,1349,-200,16,4,1193,550,507,189,1493,1710,10\n04/03/2005,14.00.00,3,6,1349,-200,15,6,1167,499,515,192,1443,1632,12\n04/03/2005,15.00.00,3,9,1344,-200,16,1,1182,470,525,194,1411,1545,12\n04/03/2005,16.00.00,4,8,1337,-200,16,6,1198,568,521,217,1401,1557,11\n04/03/2005,17.00.00,3,9,1316,-200,15,9,1177,525,544,178,1392,1529,12\n04/03/2005,18.00.00,3,6,1253,-200,13,3,1094,481,575,183,1327,1430,10\n04/03/2005,19.00.00,3,6,1258,-200,14,9,1144,414,558,189,1336,1509,8\n04/03/2005,20.00.00,4,6,1326,-200,18,5,1253,565,514,225,1479,1615,8\n04/03/2005,21.00.00,2,3,1109,-200,7,9,896,303,710,175,1132,1242,7\n04/03/2005,22.00.00,1,6,1029,-200,5,0,762,221,820,153,1049,1065,6\n04/03/2005,23.00.00,1,8,1112,-200,6,5,833,260,759,156,1104,1180,6\n05/03/2005,00.00.00,2,2,1100,-200,6,0,810,327,768,172,1070,1158,6\n05/03/2005,01.00.00,2,1,1136,-200,6,8,849,344,715,164,1132,1216,5\n05/03/2005,02.00.00,1,7,1000,-200,4,0,712,270,848,157,989,971,5\n05/03/2005,03.00.00,1,5,1041,-200,4,4,732,-200,789,-200,1003,1076,4\n05/03/2005,04.00.00,-200,0,1009,-200,4,0,710,191,819,136,974,1011,4\n05/03/2005,05.00.00,1,1,975,-200,3,5,681,136,889,110,948,813,4\n05/03/2005,06.00.00,1,1,993,-200,4,3,727,194,810,128,989,1001,3\n05/03/2005,07.00.00,0,8,872,-200,2,0,587,168,1082,132,866,655,4\n05/03/2005,08.00.00,1,3,1017,-200,6,1,818,254,842,156,1034,933,5\n05/03/2005,09.00.00,2,3,1070,-200,8,7,928,410,701,185,1112,1088,7\n05/03/2005,10.00.00,1,1,915,-200,3,1,658,166,957,121,866,562,10\n05/03/2005,11.00.00,1,1,978,-200,5,2,772,162,870,117,955,586,13\n05/03/2005,12.00.00,1,5,968,-200,5,3,777,188,830,133,929,617,14\n05/03/2005,13.00.00,1,6,1008,-200,6,7,843,199,787,136,998,705,13\n05/03/2005,14.00.00,2,2,1025,-200,8,3,913,272,724,162,1035,814,13\n05/03/2005,15.00.00,1,5,983,-200,5,9,806,180,820,132,966,615,13\n05/03/2005,16.00.00,1,8,1018,-200,7,3,868,255,751,162,1015,804,13\n05/03/2005,17.00.00,2,0,1101,-200,8,4,916,251,721,159,1125,861,11\n05/03/2005,18.00.00,1,9,1116,-200,7,7,888,258,695,156,1176,980,8\n05/03/2005,19.00.00,2,5,1161,-200,9,1,945,344,654,177,1205,1077,8\n05/03/2005,20.00.00,2,6,1212,-200,10,9,1010,380,622,185,1284,1214,7\n05/03/2005,21.00.00,3,2,1225,-200,10,2,985,464,635,199,1255,1169,7\n05/03/2005,22.00.00,2,2,1154,-200,7,8,891,304,697,166,1197,1079,6\n05/03/2005,23.00.00,1,9,1140,-200,7,0,856,258,707,152,1171,1147,6\n06/03/2005,00.00.00,2,4,1201,-200,9,2,949,358,665,170,1220,1260,6\n06/03/2005,01.00.00,1,8,1011,-200,4,6,742,278,842,160,998,929,7\n06/03/2005,02.00.00,0,9,867,-200,2,1,590,116,1088,100,826,465,7\n06/03/2005,03.00.00,0,8,856,-200,1,5,545,-200,1166,-200,803,398,7\n06/03/2005,04.00.00,0,7,859,-200,1,3,527,60,1203,57,810,342,6\n06/03/2005,05.00.00,0,6,859,-200,0,8,479,51,1285,48,822,313,5\n06/03/2005,06.00.00,0,5,842,-200,0,6,454,33,1433,32,788,292,5\n06/03/2005,07.00.00,0,6,856,-200,1,1,505,80,1246,72,833,367,5\n06/03/2005,08.00.00,0,6,867,-200,1,0,500,103,1219,86,818,387,5\n06/03/2005,09.00.00,0,7,877,-200,1,5,541,93,1148,79,829,399,7\n06/03/2005,10.00.00,0,7,869,-200,1,4,535,108,1162,87,783,376,9\n06/03/2005,11.00.00,0,9,900,-200,2,0,582,107,1081,85,792,377,11\n06/03/2005,12.00.00,1,0,899,-200,2,3,603,122,1055,97,782,379,12\n06/03/2005,13.00.00,1,0,895,-200,2,6,624,122,1002,96,787,387,14\n06/03/2005,14.00.00,1,4,992,-200,6,7,844,156,830,113,990,552,13\n06/03/2005,15.00.00,3,0,1104,-200,13,3,1095,246,609,163,1210,930,13\n06/03/2005,16.00.00,1,5,933,-200,4,0,711,205,912,146,850,554,12\n06/03/2005,17.00.00,1,7,973,-200,5,3,778,257,839,167,916,723,11\n06/03/2005,18.00.00,2,3,1069,-200,8,6,923,228,735,162,1036,875,10\n06/03/2005,19.00.00,2,5,1055,-200,6,2,821,321,797,194,942,949,8\n06/03/2005,20.00.00,2,1,1032,-200,5,3,778,270,834,185,926,869,8\n06/03/2005,21.00.00,1,5,982,-200,4,1,714,203,879,158,905,747,7\n06/03/2005,22.00.00,1,4,968,-200,3,5,680,201,918,156,891,740,6\n06/03/2005,23.00.00,1,4,979,-200,4,1,718,207,883,157,903,807,6\n07/03/2005,00.00.00,1,4,935,-200,3,2,666,180,939,141,836,653,5\n07/03/2005,01.00.00,1,2,942,-200,3,2,666,140,960,116,848,553,5\n07/03/2005,02.00.00,0,9,883,-200,1,7,561,93,1080,84,785,412,5\n07/03/2005,03.00.00,0,6,841,-200,0,7,470,-200,1311,-200,708,289,5\n07/03/2005,04.00.00,0,4,843,-200,0,5,443,35,1490,34,730,262,5\n07/03/2005,05.00.00,0,4,872,-200,0,6,460,47,1359,45,755,288,4\n07/03/2005,06.00.00,0,5,862,-200,0,8,474,68,1312,59,753,310,5\n07/03/2005,07.00.00,0,7,917,-200,1,7,562,131,1099,99,827,418,5\n07/03/2005,08.00.00,1,5,1022,-200,5,2,771,289,812,165,941,769,5\n07/03/2005,09.00.00,1,9,1022,-200,6,6,837,322,755,168,978,881,6\n07/03/2005,10.00.00,1,6,961,-200,5,5,789,330,839,169,910,816,8\n07/03/2005,11.00.00,1,4,912,-200,4,4,731,339,912,162,811,684,9\n07/03/2005,12.00.00,1,3,896,-200,4,4,731,316,911,155,799,627,10\n07/03/2005,13.00.00,1,3,890,-200,4,3,726,218,921,136,779,523,10\n07/03/2005,14.00.00,1,2,891,-200,4,5,740,177,919,124,788,502,10\n07/03/2005,15.00.00,1,3,894,-200,4,5,737,278,913,158,805,604,9\n07/03/2005,16.00.00,1,4,907,-200,4,8,753,313,886,167,819,682,9\n07/03/2005,17.00.00,1,7,973,-200,6,3,827,351,825,176,888,846,9\n07/03/2005,18.00.00,2,5,1035,-200,9,6,961,374,726,204,985,1027,7\n07/03/2005,19.00.00,3,8,1146,-200,15,0,1147,431,621,209,1148,1315,6\n07/03/2005,20.00.00,4,2,1176,-200,15,4,1162,468,600,224,1137,1363,6\n07/03/2005,21.00.00,2,8,1057,-200,8,9,935,368,720,211,982,1236,5\n07/03/2005,22.00.00,1,5,974,-200,5,0,763,239,861,174,865,1076,4\n07/03/2005,23.00.00,1,8,1044,-200,6,5,834,292,781,181,924,1200,3\n08/03/2005,00.00.00,1,5,988,-200,4,4,734,205,860,157,868,1081,3\n08/03/2005,01.00.00,1,5,1016,-200,5,3,781,255,799,154,918,1174,2\n08/03/2005,02.00.00,1,2,929,-200,3,1,659,220,920,147,851,1033,2\n08/03/2005,03.00.00,0,8,876,-200,1,9,576,-200,1040,-200,815,900,1\n08/03/2005,04.00.00,-200,0,872,-200,1,7,558,114,1054,101,807,853,0\n08/03/2005,05.00.00,0,7,888,-200,1,9,574,153,1035,114,820,886,1\n08/03/2005,06.00.00,0,7,927,-200,2,8,638,224,949,134,846,949,0\n08/03/2005,07.00.00,1,3,991,-200,5,5,786,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  },
  {
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0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntrain,31,1,3,2,7,2,2,0,5,6,1,3,2,2,6,0,3,6,1,0,2,0,4,4,0,0,5,4,1,7,2,6,0,3,9,0,7,2,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,3,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0\ntrain,35,1,2,3,8,0,7,0,2,4,0,5,5,1,3,1,2,7,0,0,0,2,3,5,0,0,3,6,0,3,6,5,0,4,8,1,5,2,3,0,0,2,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntrain,33,1,3,2,8,0,4,0,5,9,0,0,0,3,6,0,4,5,0,0,0,2,5,3,0,2,2,5,2,0,9,9,0,0,9,0,1,5,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntrain,39,1,3,2,9,1,5,1,3,7,1,2,2,3,5,1,5,3,3,0,3,1,2,2,3,2,3,3,1,3,6,5,3,1,4,5,3,3,3,1,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntrain,40,1,3,4,10,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,0,0,3,6,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntrain,3,1,2,4,1,1,6,1,2,5,1,3,2,4,3,1,4,4,2,1,0,4,1,3,2,2,3,4,1,3,6,6,1,3,7,2,3,4,3,1,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntrain,1,1,3,3,1,1,4,2,3,7,1,1,1,4,4,4,4,2,6,0,0,2,1,1,4,2,3,2,0,0,9,6,3,0,3,6,1,3,4,2,0,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntrain,13,1,2,3,3,0,2,0,7,5,2,3,3,3,5,2,4,4,2,0,0,2,2,4,2,2,1,4,2,9,0,6,0,3,7,2,3,4,3,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntrain,3,1,2,3,1,0,6,0,3,6,0,3,0,7,2,0,7,2,0,0,0,5,2,3,0,2,3,5,0,0,9,5,2,2,5,4,0,4,4,2,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntrain,36,1,1,2,8,0,6,1,2,1,2,6,5,3,2,2,5,2,2,0,0,4,1,3,2,3,3,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,2,0,0,6,0,4,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntrain,35,1,4,4,8,1,4,1,4,6,0,3,2,2,5,0,0,9,2,1,1,3,3,2,0,4,5,0,0,3,6,6,1,2,6,3,0,9,0,0,0,4,5,0,0,0,0,0,0,0,1,0,0,3,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntrain,33,1,3,4,8,0,6,0,3,5,1,4,3,3,4,0,1,8,1,0,0,2,3,5,1,1,1,4,4,7,2,4,0,5,8,1,5,3,1,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntrain,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntrain,33,1,3,3,8,0,6,1,2,7,1,2,1,4,4,1,2,6,1,0,1,3,2,4,1,1,2,6,1,5,4,5,2,3,6,3,2,5,2,1,0,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,2,8,0,6,0,3,5,0,4,1,1,8,2,2,6,0,0,1,2,6,1,0,2,1,5,3,1,8,8,1,1,8,1,3,3,3,0,0,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,2,2,0,5,0,4,5,2,2,1,4,5,5,4,0,5,0,0,4,0,0,4,3,0,2,1,3,6,9,0,0,7,2,1,1,5,4,0,6,8,2,0,0,6,0,4,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1\ntest,39,1,3,3,9,1,4,2,3,5,2,3,2,3,6,2,4,4,2,1,1,3,2,2,1,1,5,2,1,1,8,6,2,2,6,3,2,4,3,1,0,3,5,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,2,3,2,4,5,4,1,2,4,4,2,4,4,2,1,1,5,1,2,3,1,3,2,2,3,6,7,2,1,7,2,2,5,3,1,0,4,4,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,1,4,2,3,5,0,4,4,3,2,1,2,6,1,0,1,3,3,3,1,1,2,5,1,5,4,5,1,4,9,0,2,5,2,1,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,35,1,2,4,8,2,5,1,2,8,0,1,2,5,3,1,5,4,2,0,0,3,3,3,1,1,5,4,0,8,1,8,1,1,4,5,2,5,2,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,3,4,2,2,9,0,0,0,5,4,4,4,2,4,3,0,2,0,2,6,1,0,2,0,0,9,5,4,0,3,6,1,3,4,2,2,6,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,4,1,2,4,1,0,7,2,0,9,0,0,1,7,2,3,4,2,2,0,0,4,3,1,2,3,1,4,1,7,2,7,0,2,7,2,3,3,3,1,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,2,3,0,7,0,2,9,0,0,0,2,7,2,3,5,0,0,0,5,2,3,0,4,0,5,0,0,9,6,1,2,6,3,0,7,2,0,0,4,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,34,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,2,1,0,6,8,0,1,1,1,7,1,5,4,2,1,0,4,3,0,2,2,2,4,0,5,4,6,2,1,2,7,2,3,3,2,1,5,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1\ntest,38,1,3,2,9,0,6,3,0,9,0,0,0,4,5,2,4,4,2,2,0,4,2,1,2,3,2,4,0,9,0,7,0,2,6,3,2,5,3,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,6,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,2,4,10,0,6,0,3,6,0,3,3,4,3,0,2,7,1,0,8,1,0,1,2,1,6,2,0,0,9,3,3,3,2,7,3,6,1,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,1,4,6,0,5,2,3,3,1,6,6,3,1,2,4,3,2,1,0,5,2,1,2,2,3,3,1,9,0,5,0,4,5,4,5,3,3,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,41,1,3,3,10,1,7,1,2,8,1,1,1,3,6,1,4,5,2,1,2,2,3,1,2,1,3,4,0,1,8,5,4,1,5,4,2,3,4,1,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,2,5,3,9,0,6,0,3,9,0,0,0,3,6,0,4,5,0,0,0,3,6,0,0,2,3,5,0,3,6,9,0,0,9,0,6,0,3,0,0,3,5,2,0,0,6,0,4,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,1,5,1,4,2,3,4,1,4,5,4,1,1,4,4,2,1,0,3,4,0,1,1,0,7,0,8,1,6,0,3,7,2,4,0,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,4,5,0,5,2,2,9,0,0,0,6,3,7,0,2,3,5,0,0,0,2,6,0,2,2,0,0,9,9,0,0,2,7,0,2,5,2,0,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,2,8,0,4,2,4,3,2,4,4,4,2,0,5,4,0,0,0,7,0,2,0,5,0,4,0,7,2,7,0,2,9,0,4,5,0,0,0,3,3,0,0,3,6,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,0,3,0,6,7,0,2,2,2,6,2,5,3,1,0,0,6,0,2,1,2,4,3,0,6,3,5,1,3,8,1,2,6,2,0,0,3,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,5,1,2,4,1,0,5,3,2,5,1,4,3,4,3,1,3,6,2,1,1,3,4,2,1,2,1,6,1,6,3,6,1,3,7,2,4,3,2,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,4,2,9,2,4,2,2,8,0,1,1,3,6,1,3,6,1,0,0,2,4,4,1,1,2,5,2,9,0,8,1,1,8,1,1,6,2,2,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,4,2,3,7,1,2,2,3,4,1,3,5,1,1,1,2,3,3,2,2,2,4,1,4,5,6,2,2,7,2,1,4,4,1,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,5,5,1,5,1,3,4,1,5,5,3,2,1,2,7,1,0,1,3,3,4,1,1,2,4,3,8,1,3,1,5,8,1,5,3,1,0,0,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,4,2,3,0,7,2,0,9,0,0,0,0,8,2,4,4,2,0,0,2,5,0,2,0,3,5,0,4,5,9,0,0,7,2,0,4,5,0,0,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,1,4,1,4,7,1,2,2,4,4,2,4,4,3,1,2,2,2,1,2,2,3,3,1,4,5,6,2,1,5,4,3,3,3,2,1,4,6,0,0,0,6,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,2,5,2,3,1,5,2,2,5,6,3,1,1,4,5,1,0,0,1,3,5,1,1,2,6,1,8,1,5,0,4,8,1,6,3,1,1,0,2,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,4,3,2,1,5,1,3,6,1,2,0,3,6,7,1,1,5,0,0,1,1,3,4,2,3,1,0,0,9,7,2,1,5,4,0,1,3,4,1,7,8,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,2,2,6,6,3,0,1,5,4,2,6,2,1,0,0,4,2,4,1,2,4,3,0,8,1,5,0,4,6,3,4,3,0,1,2,5,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,0,3,2,4,8,0,1,1,1,8,2,2,5,2,0,0,3,4,1,2,2,0,6,1,1,8,8,1,0,6,3,3,5,2,0,0,3,3,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,2,3,10,0,2,0,7,7,2,0,0,9,0,0,2,7,0,0,0,2,7,0,0,0,3,6,0,0,9,7,0,2,9,0,0,0,9,0,0,6,4,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,2,3,1,5,6,1,3,2,2,6,1,7,2,1,1,0,5,1,3,1,4,3,2,1,7,2,6,1,2,6,3,2,3,5,0,0,4,6,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,3,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,3,3,7,0,6,2,2,7,0,2,2,3,5,1,4,5,1,1,2,3,2,1,2,2,0,5,0,4,5,6,1,2,8,1,3,5,1,0,1,4,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,29,1,2,5,7,2,2,2,4,6,0,3,3,4,2,0,1,8,1,0,0,1,4,5,0,1,1,4,5,9,0,4,3,3,9,0,1,8,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,0,3,4,2,9,0,0,0,3,6,5,3,2,7,0,0,2,0,1,7,1,1,2,0,0,9,5,4,0,2,7,0,2,4,1,3,8,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,4,2,3,2,2,0,5,9,0,0,0,0,9,4,5,1,2,2,0,4,0,2,3,4,2,2,0,0,9,5,4,0,0,9,0,0,6,3,0,7,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,2,3,2,3,1,2,2,5,7,2,1,1,4,5,4,5,1,3,0,0,5,2,1,5,3,1,2,0,7,2,7,1,2,5,4,1,4,5,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,21,1,1,2,5,2,2,2,4,6,0,3,6,3,1,0,2,7,2,0,0,3,1,5,0,2,1,3,5,9,0,4,3,3,9,0,4,5,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,2,2,5,3,2,4,4,0,5,0,4,4,2,3,2,3,4,4,0,0,2,0,4,4,2,0,0,4,0,9,4,2,4,5,4,3,5,1,1,0,3,6,1,0,0,6,0,0,0,0,0,0,6,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,0,2,0,7,6,0,3,5,3,2,3,4,2,7,0,0,0,2,0,4,0,4,1,0,0,9,9,0,0,2,7,2,2,5,0,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,4,2,8,0,7,0,2,7,2,0,0,4,5,0,3,6,2,2,0,2,3,2,0,2,4,4,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,38,1,3,2,9,0,6,3,0,9,0,0,0,4,5,2,4,4,2,2,0,4,2,1,2,3,2,4,0,9,0,7,0,2,6,3,2,5,3,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,1,4,2,3,5,0,4,4,3,2,1,2,6,1,0,1,3,3,3,1,1,2,5,1,5,4,5,1,4,9,0,2,5,2,1,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,2,9,0,3,1,5,8,0,1,1,2,7,2,3,5,2,0,0,2,3,3,2,0,2,5,1,9,0,7,0,2,9,0,3,6,0,0,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,1,5,0,5,0,5,0,6,3,3,5,2,0,5,4,2,0,0,4,2,2,2,2,2,4,2,9,0,5,3,3,9,0,5,2,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,3,4,7,2,4,0,4,7,0,2,0,3,6,1,1,7,2,0,0,3,3,3,1,0,2,5,2,1,8,5,2,2,7,2,0,7,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,2,2,4,6,0,4,2,3,5,0,4,4,2,4,0,0,9,0,0,2,2,3,3,0,0,2,3,5,9,0,7,0,2,9,0,2,7,0,0,0,3,1,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,1,6,1,2,6,1,2,2,3,4,1,3,5,2,1,1,4,2,2,1,2,3,4,1,2,7,5,2,3,5,4,2,4,3,1,1,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,33,1,2,3,8,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0\ntest,32,1,3,3,7,1,5,2,2,5,1,3,3,3,4,2,4,4,2,1,1,4,2,2,2,2,3,2,2,4,5,4,2,3,6,3,2,4,4,1,0,4,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,0,2,3,5,6,0,3,2,0,7,0,6,3,0,0,0,7,0,2,0,3,4,3,0,4,5,9,0,0,7,2,0,7,0,2,0,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,3,8,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,2,6,0,2,9,0,0,0,3,6,5,3,2,4,0,0,5,0,0,3,2,3,2,0,0,9,7,2,0,2,7,0,5,0,4,0,7,7,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,2,2,1,4,2,3,7,1,2,1,3,6,2,4,3,3,1,0,4,2,1,2,2,3,3,0,2,7,7,1,1,5,4,1,3,5,1,0,5,8,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,2,3,0,4,2,4,4,2,5,5,2,3,2,5,3,2,0,0,5,0,3,2,2,4,2,0,5,4,5,2,4,5,4,0,3,2,5,0,7,4,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,1,5,1,3,7,1,2,1,4,5,2,4,3,3,0,1,3,3,1,3,2,2,3,1,3,6,6,2,1,5,4,2,2,4,2,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,27,2,2,4,6,0,5,2,3,7,0,2,2,5,3,0,1,8,0,0,0,2,4,3,0,0,1,7,2,9,0,8,0,1,7,2,5,4,0,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,3,2,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,1,3,7,1,2,2,3,5,2,4,4,3,1,2,2,2,1,3,2,3,3,1,3,6,6,2,1,5,4,3,2,3,2,1,4,3,0,0,0,7,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,5,0,4,5,0,4,3,4,3,2,5,3,4,2,0,2,0,3,5,0,2,2,0,4,5,7,0,2,7,2,0,4,5,0,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,3,2,4,7,1,2,2,3,5,1,7,1,4,0,0,5,1,1,2,3,4,1,0,6,3,7,1,2,4,5,3,4,3,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,27,1,1,5,6,0,5,0,4,3,1,5,7,2,0,1,4,5,4,0,0,4,1,2,1,1,5,1,3,9,0,7,0,2,9,0,2,7,1,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,3,2,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,7,0,0,0,6,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0\ntest,23,1,2,2,5,2,2,4,2,2,5,2,2,5,2,0,4,5,0,0,0,4,0,5,0,2,3,5,0,9,0,5,2,2,9,0,0,6,3,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,2,4,10,0,8,0,1,9,0,0,0,8,1,1,4,5,4,1,4,1,2,1,2,1,6,2,0,1,8,7,2,0,5,4,4,5,0,1,1,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,3,3,0,7,2,0,9,0,0,0,3,6,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,7,2,0,2,7,0,0,7,2,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,0,6,0,3,9,0,0,0,3,6,0,0,9,0,0,3,0,6,0,0,0,3,6,0,9,0,7,2,0,9,0,3,5,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,5,8,2,5,0,2,7,0,2,2,5,2,2,0,7,2,2,0,2,4,0,2,0,3,5,0,7,2,7,0,2,7,2,2,3,3,2,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,4,3,2,2,5,1,2,6,1,3,0,2,7,4,4,2,3,1,0,5,2,1,2,2,3,2,1,1,8,8,0,1,6,3,1,3,4,3,1,5,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,2,3,5,6,3,0,0,3,6,0,1,8,0,0,0,1,6,3,0,1,1,8,0,3,6,6,3,0,9,0,3,5,2,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,0,6,0,3,6,0,3,0,0,9,0,3,6,2,0,0,2,5,2,0,2,4,4,0,7,2,3,6,0,6,3,7,0,2,0,0,3,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,2,2,5,0,4,3,0,6,0,0,9,0,0,4,0,3,3,0,0,4,4,2,4,5,9,0,0,9,0,2,6,2,0,0,3,4,0,0,4,6,6,0,0,0,3,4,0,4,0,0,0,6,0,0,0,0,0,0,0,1,1,1,0,0,0,1,1,0,2,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,4,1,0,5,6,3,0,0,4,5,5,3,2,5,0,0,3,1,2,4,3,1,2,0,0,9,6,3,0,2,7,0,3,4,3,1,7,7,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,0,7,2,0,9,0,0,0,3,6,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,7,2,0,2,7,0,0,7,2,0,5,8,0,0,0,7,0,4,0,2,0,0,3,0,0,0,0,2,0,0,0,0,0,0,0,0,4,0,1,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,2,4,2,3,5,2,3,2,4,3,1,3,5,1,1,0,3,3,2,2,2,2,5,1,6,3,7,1,2,7,2,3,4,3,1,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,2,5,1,0,9,0,0,9,0,0,0,7,2,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,9,0,0,0,9,2,3,3,2,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,2,7,1,0,7,0,2,0,2,7,4,2,4,4,0,0,0,2,4,4,0,2,2,3,1,8,7,2,0,5,4,2,3,2,3,0,5,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,0,4,0,5,9,0,0,0,7,2,0,0,9,0,0,0,0,5,4,0,0,0,7,2,9,0,5,0,4,9,0,4,4,2,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,2,2,4,8,0,3,2,5,5,2,3,3,5,3,0,3,6,1,0,5,1,3,2,1,2,4,3,2,5,4,4,5,1,7,2,2,5,1,2,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,1,5,0,5,1,4,2,1,7,6,2,1,1,7,1,1,0,0,4,1,4,1,1,5,4,0,8,1,3,1,6,8,1,6,3,1,0,0,2,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,2,3,1,5,1,4,8,1,1,1,2,7,3,5,2,2,0,0,5,2,1,2,4,2,2,0,1,8,9,0,0,5,4,2,4,4,0,0,4,8,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,4,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,1,1\ntest,3,1,2,4,1,2,7,0,0,6,1,2,2,6,2,0,4,5,3,0,0,5,0,2,1,1,3,4,2,0,9,6,0,3,6,3,4,4,0,2,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,4,2,8,1,4,1,4,7,1,2,0,0,9,0,3,6,3,0,0,3,5,0,0,0,5,4,0,0,9,6,2,1,5,4,0,6,0,3,0,5,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,1,5,0,4,7,0,2,2,2,6,3,5,1,6,0,0,2,0,1,4,1,4,0,0,0,9,7,2,0,2,7,2,2,6,0,0,5,6,2,0,0,6,0,0,0,0,0,0,3,0,0,3,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1\ntest,8,1,2,3,2,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,4,3,7,0,3,0,6,9,0,0,0,5,4,0,1,8,1,0,0,4,1,5,0,1,0,4,5,4,5,9,0,0,4,5,1,4,4,1,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,2,7,2,1,1,1,4,2,2,1,2,5,1,2,7,4,4,2,7,2,3,3,3,0,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,0,4,7,0,2,0,1,8,0,3,6,0,0,0,3,3,4,0,1,2,5,2,9,0,7,2,0,9,0,2,6,1,0,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,3,5,0,2,6,0,3,2,0,7,0,4,5,3,0,0,0,3,3,3,0,2,5,0,2,7,2,5,3,6,3,2,3,4,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,4,3,1,2,6,2,2,2,3,4,2,4,4,1,0,2,3,5,0,1,1,3,5,0,3,6,5,3,2,7,2,2,2,3,3,0,5,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,2,8,0,5,1,3,7,0,2,0,0,9,1,3,5,3,0,0,2,3,3,3,0,2,4,1,2,7,6,3,0,6,3,4,4,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,3,8,1,5,1,3,7,0,2,2,5,3,2,3,4,2,0,0,3,4,2,1,1,3,5,0,7,2,6,1,2,6,3,2,5,3,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,4,2,3,0,7,2,0,9,0,0,0,0,8,2,4,4,2,0,0,2,5,0,2,0,3,5,0,4,5,9,0,0,7,2,0,4,5,0,0,5,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,9,0,0,9,0,0,0,2,7,0,3,6,3,0,0,2,5,0,2,2,2,5,0,0,9,6,3,0,3,6,0,5,4,0,0,4,3,0,0,0,6,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0\ntest,40,1,5,3,10,1,1,5,3,9,0,0,0,1,8,0,6,3,1,0,6,3,0,0,3,1,6,0,0,0,9,6,3,0,9,0,1,6,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,2,3,2,6,0,3,6,1,3,1,0,8,2,6,2,2,1,0,5,0,3,2,3,3,2,1,4,5,5,2,2,5,4,2,3,4,1,1,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,7,1,3,3,2,1,5,0,4,7,0,2,1,4,4,4,5,1,4,0,0,4,0,1,2,1,5,3,0,7,2,6,1,2,5,4,1,1,8,0,0,5,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,3,5,0,7,0,2,6,0,3,3,2,4,2,3,4,0,2,0,3,4,0,3,2,0,5,0,0,9,7,2,0,7,2,4,2,4,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,3,7,1,2,2,3,4,1,3,5,2,0,2,2,2,3,2,2,2,3,2,1,8,6,2,2,5,4,3,3,3,1,1,4,8,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,2,5,1,4,2,4,7,1,1,1,3,6,1,2,7,0,0,0,2,5,3,0,1,1,6,3,8,1,5,1,4,7,2,2,5,2,1,0,3,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,0,7,2,0,9,0,0,2,5,2,2,5,2,1,0,0,3,3,3,1,2,3,4,1,6,3,7,0,2,7,2,1,3,3,2,0,6,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,10,1,5,3,3,0,5,0,4,9,0,0,0,0,8,5,4,0,7,0,0,2,0,0,7,0,2,0,0,0,9,9,0,0,4,5,0,0,9,0,0,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,1,3,2,5,9,0,0,0,3,6,1,7,2,1,1,0,8,1,1,1,2,2,5,0,5,4,6,1,3,5,4,2,6,2,0,0,3,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,0,4,7,0,2,1,5,4,2,4,3,3,0,3,2,2,1,4,2,2,2,0,2,7,8,1,0,5,4,2,4,4,1,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,2,5,2,4,2,4,7,1,2,8,0,1,5,4,0,1,0,0,3,3,3,0,5,1,3,0,8,1,7,1,2,6,3,3,5,1,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,2,7,0,0,7,1,2,1,3,5,0,4,5,2,1,0,3,2,3,1,3,2,4,1,0,9,6,2,2,5,4,0,5,3,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,1,4,1,5,5,1,4,3,4,2,0,4,5,1,1,2,4,3,1,1,3,3,3,1,2,7,5,1,4,7,2,5,3,2,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,20,1,2,2,5,1,5,1,4,4,3,2,2,6,1,1,5,4,1,0,0,5,1,4,1,2,4,4,0,9,0,7,2,1,8,1,4,3,2,2,0,4,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,2,2,5,7,0,2,0,2,7,0,4,5,0,0,0,0,9,0,0,0,0,9,0,9,0,5,0,4,9,0,4,5,0,0,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,0,5,1,3,6,0,3,3,3,3,0,1,8,0,0,0,4,0,5,0,1,1,5,3,9,0,7,0,2,9,0,6,2,1,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,4,1,4,6,1,3,1,5,4,1,5,3,1,0,1,4,2,3,1,3,4,2,1,2,7,4,0,5,7,2,2,3,3,1,1,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,3,4,6,1,2,1,2,7,0,3,6,0,0,0,2,2,6,0,1,1,7,1,9,0,7,0,2,7,2,6,3,1,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,2,2,3,0,9,0,0,9,0,0,0,5,4,0,5,4,7,0,0,2,0,0,0,4,0,5,0,0,9,9,0,0,7,2,0,5,2,4,0,6,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,4,7,1,3,3,3,5,1,4,4,2,4,1,3,6,2,1,1,3,2,3,1,2,1,4,2,2,7,5,1,4,6,3,3,4,2,1,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,2,6,0,2,9,0,0,0,3,6,5,3,2,4,0,0,5,0,0,3,2,3,2,0,0,9,7,2,0,2,7,0,5,0,4,0,7,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,3,5,2,5,1,3,3,3,4,4,3,3,2,5,2,2,0,0,3,1,4,1,3,4,2,1,8,1,6,0,3,8,1,4,2,3,0,1,5,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,9,0,0,5,0,4,0,5,4,0,0,9,0,0,2,0,3,5,0,0,0,9,0,9,0,3,2,4,7,2,9,0,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,4,9,1,5,2,1,6,1,3,3,4,3,2,5,3,2,0,0,5,1,2,1,2,2,4,1,7,2,5,1,3,6,3,5,2,2,1,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0\ntest,37,1,3,3,8,0,5,0,4,8,0,1,0,3,6,0,3,6,0,2,0,5,3,1,0,2,4,4,1,1,8,8,1,0,8,1,0,6,3,0,0,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,2,4,2,3,0,2,0,7,9,0,0,0,0,9,0,3,6,0,0,0,7,0,2,0,2,0,6,2,0,9,9,0,0,9,0,2,0,5,2,0,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,2,5,0,3,1,6,5,2,3,3,2,5,1,4,5,1,0,0,3,4,2,1,2,2,5,1,8,1,6,1,2,8,1,6,3,1,0,0,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,1,2,5,0,5,2,2,1,4,5,5,4,1,1,6,2,1,0,0,6,1,3,1,4,3,3,0,9,0,3,2,4,6,3,3,4,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,4,8,1,4,1,4,7,1,2,4,4,2,0,4,5,0,0,0,9,0,0,0,4,0,5,0,7,2,6,2,1,5,4,5,4,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,0,5,1,3,6,1,3,2,4,4,2,2,6,2,0,0,2,3,3,2,1,2,5,1,5,4,6,1,2,6,3,3,4,2,2,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,4,0,5,9,0,0,0,4,5,1,2,7,1,0,0,3,5,2,1,2,1,7,1,4,5,6,2,2,7,2,2,5,2,1,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,10,1,3,2,3,0,5,0,4,7,2,0,0,4,5,4,4,2,1,0,0,6,1,2,1,3,3,3,1,7,2,9,0,0,5,4,2,3,4,2,0,5,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,4,10,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,4,1,4,7,2,1,0,3,6,2,5,2,5,1,0,3,1,1,3,2,3,1,1,1,8,6,0,3,4,5,1,1,6,2,1,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,1,2,4,6,0,5,2,2,6,0,3,3,6,1,1,4,5,1,0,0,5,2,2,1,3,0,4,3,7,2,7,0,2,9,0,5,2,2,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,0,3,2,4,6,0,3,3,4,3,2,4,4,5,0,3,2,0,0,3,2,3,2,0,7,2,5,2,3,4,5,3,2,5,0,0,4,6,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,4,7,0,3,0,6,7,0,2,2,4,4,0,3,6,4,0,0,0,3,3,3,0,3,3,2,9,0,4,2,3,9,0,3,4,2,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,4,9,0,4,0,5,7,0,2,2,6,2,3,5,1,6,1,0,2,0,1,6,0,2,2,0,6,3,7,2,0,3,6,2,4,4,1,0,5,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,33,1,3,2,8,0,4,2,4,8,1,0,0,2,7,0,3,6,0,0,0,4,3,3,0,1,3,6,0,3,6,6,3,0,9,0,2,5,2,2,0,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,5,1,3,7,0,2,1,2,6,1,2,7,1,0,1,2,5,2,1,1,2,6,1,1,8,8,0,1,8,1,0,4,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,0,3,0,6,3,4,2,2,5,2,4,3,2,8,0,0,1,1,0,5,2,3,1,0,0,9,9,0,0,2,7,0,6,1,3,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,11,3,2,3,3,1,3,4,2,9,0,0,3,6,1,3,6,0,2,0,0,0,4,4,3,0,3,4,0,3,6,7,1,1,5,4,3,2,3,3,0,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,2,5,1,3,2,4,6,1,2,2,3,5,0,4,5,2,0,0,0,5,3,0,1,4,3,1,4,5,5,1,3,7,2,2,5,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,5,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,5,1,3,4,1,0,9,0,0,9,0,0,0,6,3,0,6,3,4,0,0,4,0,3,3,0,4,3,0,4,6,9,0,0,6,4,3,4,4,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,4,9,0,7,0,2,9,0,0,0,6,3,1,4,5,3,1,3,1,2,1,2,1,5,2,0,2,7,6,3,0,5,4,5,2,0,1,1,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,2,3,1,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,2,2,4,3,1,3,8,0,1,2,2,6,4,5,0,5,0,0,3,0,2,5,2,1,2,0,5,4,4,2,4,1,8,2,1,4,3,0,6,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,0,4,0,5,9,0,0,0,1,8,1,2,7,1,0,0,2,4,2,1,1,2,6,1,6,3,8,1,1,7,2,2,4,4,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,0,5,5,2,3,2,2,6,0,4,5,0,0,0,4,4,1,0,0,2,7,1,1,8,6,2,2,8,1,4,1,0,4,0,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,2,2,1,1,3,6,8,0,1,1,2,6,2,3,4,2,0,0,3,4,1,2,3,1,4,1,5,4,7,2,0,7,2,1,2,5,2,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,2,2,6,6,0,3,3,3,4,0,4,5,1,0,0,3,5,2,1,2,0,7,1,8,1,5,0,4,9,0,0,0,9,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,4,7,0,3,0,6,2,0,7,6,0,3,0,2,7,4,0,0,0,0,5,0,2,0,7,0,6,3,3,0,6,7,2,7,0,2,0,0,2,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,26,1,1,6,6,0,5,2,2,2,1,7,7,2,0,0,0,9,0,0,1,2,5,2,0,0,1,6,3,9,0,1,0,8,8,1,8,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,2,7,0,6,0,4,6,0,3,0,1,8,0,4,5,0,0,0,4,2,4,0,2,4,3,2,9,0,6,3,0,9,0,2,7,2,0,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,2,2,3,4,1,3,5,1,1,1,2,3,3,2,2,2,4,1,4,5,6,2,2,7,2,1,4,4,1,0,4,3,2,0,0,6,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,2,3,7,0,2,1,6,2,2,3,5,3,0,1,2,4,1,2,2,1,3,2,2,7,6,2,2,6,3,2,4,4,0,0,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,6,3,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,2,6,0,2,9,0,0,0,3,6,5,3,2,4,0,0,5,0,0,3,2,3,2,0,0,9,7,2,0,2,7,0,5,0,4,0,7,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,0,8,1,1,2,2,6,4,4,2,1,4,4,2,0,0,3,1,4,2,2,3,4,0,9,0,7,1,2,6,3,5,3,1,0,0,2,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,0,9,0,0,5,0,4,4,2,3,0,3,6,0,0,2,0,3,5,2,0,0,5,3,0,9,3,2,4,7,2,5,0,4,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,5,1,0,6,0,3,6,0,3,3,5,2,1,5,3,1,0,1,4,4,0,1,0,5,2,3,8,1,4,3,3,4,5,3,2,3,3,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,0,6,1,2,1,2,6,3,4,3,2,5,3,1,0,0,5,2,2,1,4,2,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,4,8,0,5,1,4,6,1,3,2,4,3,1,3,6,2,0,1,4,3,2,1,2,2,4,2,4,5,4,0,5,7,2,5,2,2,1,1,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,1,6,2,2,7,1,2,5,3,2,0,5,4,0,0,0,0,9,0,0,0,0,6,3,5,4,7,1,2,7,2,3,4,0,3,0,4,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,2,5,0,4,1,4,2,3,4,3,4,2,1,5,4,1,0,0,2,2,4,1,1,3,4,2,8,1,6,1,3,8,1,5,4,1,0,0,2,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,3,3,2,3,5,1,3,3,4,3,3,4,3,4,1,0,3,1,2,3,2,2,3,1,1,8,6,2,2,4,5,3,2,3,1,2,5,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,6,2,2,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,6,2,1,0,3,6,0,6,3,3,0,5,2,0,0,2,4,4,0,0,0,9,6,1,2,6,3,0,7,0,2,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,5,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,2,2,6,1,2,1,4,5,4,1,5,2,0,0,3,3,2,3,1,5,2,0,3,6,7,0,2,8,1,2,5,2,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,1,3,7,1,2,0,3,6,0,3,6,0,0,0,0,6,3,0,0,0,9,0,5,4,9,0,0,5,4,0,9,0,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0\ntest,31,1,2,3,7,3,5,0,2,9,0,0,0,6,3,3,5,2,2,0,0,7,0,1,1,2,4,3,0,5,4,7,2,0,7,2,2,4,2,1,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,20,1,4,3,5,1,4,1,4,7,1,2,0,2,7,2,0,7,5,0,0,2,0,3,2,2,5,2,0,6,3,6,2,1,5,4,7,2,0,0,0,2,2,2,0,0,6,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,1,6,0,3,9,0,0,0,4,5,8,1,0,9,0,0,0,0,0,9,0,0,0,0,0,9,7,2,0,2,7,0,1,3,5,0,7,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,2,3,3,5,0,5,0,4,7,0,2,0,2,7,2,1,7,0,2,0,1,1,5,2,0,0,7,0,4,5,6,1,2,7,2,0,6,3,0,0,4,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,0,3,3,4,6,3,1,1,5,3,2,4,3,1,0,1,6,1,2,1,3,3,2,1,2,7,4,0,5,7,2,3,3,2,2,1,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,1,5,7,0,7,1,1,5,0,4,6,2,2,1,3,6,1,0,0,1,1,8,1,1,1,4,5,8,1,3,0,6,9,0,3,6,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,0,4,2,3,5,1,3,2,4,3,0,3,6,1,0,1,2,2,5,1,1,2,5,3,8,1,6,1,3,6,3,4,4,1,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,9,0,0,0,9,0,0,0,5,4,0,0,9,0,0,5,0,0,4,0,0,5,4,0,0,9,9,0,0,9,0,4,1,4,2,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,2,2,5,1,3,3,3,4,2,4,4,2,1,1,4,2,2,2,2,3,2,2,4,5,4,2,3,6,3,2,4,4,1,0,4,1,1,0,0,5,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,2,1,0,5,1,3,8,0,1,1,3,6,1,4,5,2,1,1,4,2,2,1,3,0,6,0,7,2,7,1,2,8,1,1,7,2,0,1,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,2,4,2,0,6,0,3,6,0,3,4,0,5,3,4,3,2,2,0,6,0,0,3,1,5,2,0,9,0,9,0,0,2,7,6,0,2,0,2,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,24,1,2,3,5,2,3,0,5,4,2,3,2,6,2,5,2,3,0,0,0,5,2,3,4,3,0,3,0,5,4,2,0,7,7,2,4,3,2,2,0,4,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,4,0,5,7,0,2,2,1,6,0,2,7,0,0,0,3,6,0,0,1,1,6,2,4,5,5,2,3,8,1,3,4,2,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,0,3,4,2,9,0,0,0,3,6,5,3,2,7,0,0,2,0,1,7,1,1,2,0,0,9,5,4,0,2,7,0,2,4,1,3,8,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0\ntest,11,2,3,2,3,1,2,1,5,7,1,1,1,3,5,3,5,2,3,1,0,4,2,2,2,3,3,2,1,3,6,7,1,2,5,4,2,3,4,2,1,5,6,2,0,0,5,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,4,9,0,4,0,5,7,0,2,2,6,2,3,5,1,6,1,0,2,0,1,6,0,2,2,0,6,3,7,2,0,3,6,2,4,4,1,0,5,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,2,5,0,3,9,0,0,0,3,6,2,4,3,2,0,0,4,2,2,2,2,3,3,0,0,9,5,0,4,7,2,0,6,3,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,5,8,0,6,1,3,6,0,3,2,4,3,1,3,5,5,1,1,2,2,1,2,1,4,3,0,1,8,5,1,3,6,3,5,3,1,1,1,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,5,1,2,7,0,0,6,0,3,2,5,3,0,5,5,3,2,0,2,2,2,0,4,5,0,0,2,7,5,2,3,0,9,1,3,3,3,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,0,5,0,4,7,0,2,2,4,3,0,7,2,0,0,0,7,0,2,0,3,5,2,0,9,0,9,0,0,9,0,4,5,0,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,2,3,0,2,4,4,7,0,2,1,4,4,3,5,2,2,0,0,4,1,3,1,3,3,3,1,5,4,7,0,2,6,3,3,3,3,1,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,4,3,3,1,3,1,5,7,1,2,1,2,7,1,2,7,0,0,0,3,3,4,0,1,1,6,3,8,1,6,1,3,7,2,2,6,2,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,18,5,2,2,4,3,2,1,4,6,1,2,5,2,2,4,3,2,2,0,1,4,2,3,1,2,3,4,1,6,3,6,2,2,6,3,5,2,2,1,0,3,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,1,5,7,2,2,1,5,4,0,5,7,1,1,0,1,8,0,0,0,0,5,4,0,0,0,5,4,8,1,4,0,5,7,2,5,4,1,0,0,2,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,5,2,3,5,1,4,1,4,4,5,3,1,2,3,0,4,1,1,5,3,1,1,1,2,7,5,1,3,5,4,3,1,4,2,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0\ntest,1,1,2,4,1,2,7,0,0,9,0,0,0,7,2,4,5,0,6,0,0,3,0,0,6,3,0,0,0,0,9,7,2,0,3,6,0,0,9,0,0,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,3,4,3,9,0,0,0,1,8,0,4,5,1,0,0,3,5,2,1,2,1,4,3,9,0,5,3,2,7,2,0,3,3,4,0,7,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,4,3,0,2,4,4,7,0,2,4,0,5,4,0,5,2,0,0,4,1,3,0,0,5,3,3,9,0,7,0,2,6,3,6,0,3,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,5,7,0,5,0,4,5,0,4,4,4,2,0,2,7,0,0,0,3,6,0,0,0,3,5,2,9,0,5,0,4,9,0,6,2,2,0,0,2,2,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,0,2,0,7,6,0,3,5,3,2,4,4,2,7,0,0,0,2,0,5,0,4,1,0,0,9,9,0,0,2,7,1,3,6,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,2,2,2,5,0,3,1,6,4,1,5,3,0,6,0,0,9,0,0,0,0,4,5,0,0,0,4,5,9,0,5,1,3,8,1,5,4,0,0,0,2,2,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,0,4,7,2,0,0,3,6,0,4,5,2,0,2,0,4,3,2,0,2,5,0,4,5,7,2,0,5,4,2,2,3,3,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,4,9,1,5,2,2,6,1,2,2,5,3,1,1,7,1,1,1,3,3,2,1,2,2,5,1,3,6,6,2,2,7,2,4,3,2,1,1,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,16,1,2,1,4,1,2,1,6,4,2,4,6,3,1,6,1,3,4,0,0,0,0,5,1,0,6,0,3,8,1,5,1,4,5,4,5,3,1,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,4,0,5,9,0,0,0,4,5,0,3,6,0,0,0,0,5,5,0,0,2,5,2,9,0,3,2,4,9,0,6,3,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,4,2,9,0,5,1,3,7,0,2,0,0,9,1,3,5,3,0,0,2,3,3,3,0,2,4,1,2,7,6,3,0,6,3,4,4,2,0,0,3,5,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,3,3,0,6,1,2,7,1,1,5,1,4,5,4,0,4,0,0,5,1,0,1,8,0,0,0,9,0,8,1,1,4,5,0,5,4,0,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,0,4,4,0,5,3,2,5,1,3,6,0,0,2,2,4,3,0,1,3,5,1,2,7,5,1,3,7,2,3,3,3,0,0,3,3,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,1,4,1,4,6,0,3,2,2,5,1,4,5,2,1,1,3,3,2,2,2,2,4,1,3,6,6,1,2,6,3,3,2,3,1,1,4,6,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,5,1,3,7,1,2,3,2,5,1,3,6,2,1,1,2,3,3,1,1,1,5,2,5,4,5,2,2,7,2,4,4,1,1,1,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,2,8,0,2,0,7,3,4,2,0,4,5,0,2,7,0,0,0,4,5,0,0,0,2,7,0,0,9,6,3,0,7,2,3,3,4,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,4,0,5,7,0,2,2,6,2,3,5,1,6,1,0,2,0,1,6,0,2,2,0,6,3,7,2,0,3,6,2,4,4,1,0,5,6,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,4,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0\ntest,33,1,3,3,8,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,3,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,5,7,1,1,0,4,5,0,6,3,1,1,0,4,2,3,0,0,4,3,3,0,9,6,2,2,8,1,0,6,0,3,0,5,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,2,5,2,4,1,3,3,2,5,6,2,2,2,5,4,1,0,0,2,3,4,1,2,3,5,1,8,1,5,0,4,8,1,7,2,1,0,1,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,0,5,2,2,6,1,2,2,3,4,3,1,6,2,0,0,3,3,2,2,1,2,5,0,2,7,7,0,2,8,1,2,5,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,1,4,1,4,6,0,3,0,1,8,0,3,6,3,0,1,0,0,6,1,2,1,4,3,0,9,6,1,2,6,3,0,4,5,0,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,2,3,5,2,3,2,5,4,2,5,3,2,1,1,4,3,2,2,2,3,2,1,1,8,6,2,2,6,3,1,4,4,1,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,5,0,4,7,0,2,2,2,6,0,2,7,0,2,2,0,6,0,1,0,2,6,0,2,7,4,4,2,8,1,5,2,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,3,2,3,3,1,0,6,1,2,6,0,3,1,4,4,3,2,5,1,0,1,4,2,4,1,3,2,4,1,3,6,7,0,2,7,2,0,4,5,0,0,4,6,2,3,0,6,6,0,0,0,0,0,0,0,0,0,0,6,0,0,1,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,1\ntest,33,1,2,4,8,0,7,0,2,7,0,2,0,5,4,2,2,6,4,0,2,0,2,2,3,0,2,3,2,0,9,5,4,0,6,3,0,7,2,0,0,4,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,2,3,4,2,3,4,3,1,1,2,2,3,3,1,3,3,1,2,7,7,2,1,5,4,2,4,3,1,1,4,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,2,3,0,5,0,4,6,0,3,1,2,7,3,2,4,1,0,1,4,1,4,1,3,2,1,3,0,9,6,3,0,4,5,0,4,2,4,1,6,6,0,0,4,6,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,41,1,2,5,10,0,6,0,3,3,2,5,0,8,1,4,1,4,4,1,2,2,2,0,4,1,5,0,0,0,9,7,1,2,6,3,0,4,5,0,0,4,4,0,0,3,6,0,0,0,0,5,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,3,8,2,5,0,3,6,2,2,0,3,6,2,6,2,2,0,0,6,0,2,2,2,4,3,0,2,7,7,2,0,6,3,6,0,3,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,3,7,0,5,1,4,6,1,2,3,4,3,2,3,5,3,1,1,2,2,1,3,1,3,2,2,4,5,5,2,2,6,3,4,3,2,1,1,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,2,3,7,1,5,1,2,6,2,2,1,7,2,1,4,5,1,0,0,4,3,3,1,3,1,3,3,1,8,8,0,1,6,3,1,5,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,4,9,0,7,0,2,3,1,6,4,2,4,0,1,8,1,0,0,1,3,5,1,1,1,4,4,8,1,3,0,6,8,1,6,2,2,1,0,2,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,2,3,1,4,0,5,5,0,4,4,2,4,2,4,4,3,1,0,3,4,0,3,2,1,4,0,8,1,4,1,5,8,1,6,3,1,1,1,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,3,1,5,6,0,3,2,3,5,0,4,5,2,0,0,4,4,1,1,0,2,4,3,5,4,5,1,3,7,2,4,3,2,0,0,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,2,7,1,3,2,5,5,2,2,3,2,5,1,2,7,1,0,0,3,4,3,1,1,1,6,2,8,1,7,1,2,8,1,3,5,2,1,1,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,2,2,4,7,0,3,3,4,5,1,4,2,5,2,1,4,5,2,1,0,1,5,2,1,1,4,4,1,3,6,7,1,2,7,2,5,3,1,1,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,2,0,0,5,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,3,8,0,3,0,6,3,2,4,3,2,5,3,4,4,0,2,0,4,2,3,3,2,0,5,0,7,2,5,2,2,5,4,2,4,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,1,4,8,1,5,2,2,4,0,5,6,3,1,3,2,5,4,0,1,2,2,2,3,1,1,3,2,5,4,4,2,4,4,5,3,3,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1\ntest,5,1,3,3,1,0,5,0,4,8,0,1,3,3,3,1,3,6,1,1,6,2,1,0,3,1,3,2,2,4,5,7,2,0,5,4,3,3,4,1,1,4,3,2,0,0,5,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,1,5,0,5,1,4,2,1,7,6,2,1,1,7,1,1,0,0,4,1,4,1,1,5,4,0,8,1,3,1,6,8,1,6,3,1,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,25,1,2,5,6,1,6,1,2,5,1,4,4,3,2,2,5,3,1,0,0,6,1,2,1,2,3,4,2,8,1,6,1,3,4,5,5,3,1,2,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,0,5,2,3,5,2,3,3,1,5,1,6,2,1,0,0,5,3,1,1,4,1,4,1,8,1,8,0,1,9,0,4,4,2,0,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,0,3,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,3,0,6,6,0,3,1,3,6,1,5,4,1,0,1,4,3,3,1,1,4,5,0,9,0,5,0,4,5,4,5,4,1,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,4,8,0,4,0,5,5,0,4,3,2,5,0,2,7,0,0,2,0,3,4,0,0,3,3,4,0,9,6,0,3,9,0,6,3,0,0,0,2,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,4,1,0,2,0,7,6,0,3,5,3,2,3,4,2,7,0,0,0,2,0,4,0,4,1,0,0,9,9,0,0,2,7,2,2,5,0,0,4,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,3,5,2,6,1,1,7,1,2,3,3,4,1,5,4,2,1,0,4,1,3,1,3,2,4,1,4,5,7,1,1,5,4,2,6,2,1,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,4,3,2,4,1,3,4,1,3,4,1,4,3,4,2,3,5,2,5,0,0,3,1,2,4,1,4,1,0,8,1,4,1,5,6,3,5,3,1,1,1,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,3,3,0,3,0,6,9,0,0,0,3,6,5,2,2,2,0,0,5,0,2,2,5,0,2,0,0,9,6,0,3,9,0,0,4,5,0,0,4,8,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,0,7,0,2,9,0,0,0,3,6,2,4,4,0,0,0,5,4,0,0,2,3,5,0,7,2,9,0,0,9,0,2,4,3,0,0,3,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,3,3,5,0,6,1,2,7,1,2,1,4,4,1,2,6,1,0,1,3,2,4,1,1,2,6,1,5,4,5,2,3,6,3,2,5,2,1,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,4,2,3,5,2,2,2,4,3,3,4,3,5,2,2,5,3,2,0,1,3,1,4,1,2,3,3,1,6,3,4,0,5,7,2,3,2,3,2,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,1,3,7,1,2,3,4,3,0,3,6,0,0,0,2,2,6,0,2,3,3,3,5,4,9,0,0,5,4,2,2,3,3,0,5,5,0,0,0,6,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,1,4,2,3,5,2,3,2,4,4,3,2,4,3,1,1,2,3,2,1,3,2,4,1,1,8,6,2,2,6,3,3,5,2,1,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,2,9,0,3,1,5,8,0,1,1,2,7,2,3,5,2,0,0,2,3,3,2,0,2,5,1,9,0,7,0,2,9,0,3,6,0,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,0,6,0,3,7,0,2,0,6,3,2,6,2,2,0,0,4,0,4,2,2,4,2,0,6,3,7,2,0,4,5,0,1,6,2,0,5,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,1,4,0,5,7,0,2,2,5,3,1,6,2,2,0,0,6,0,2,1,1,6,2,0,8,1,5,1,4,6,3,1,1,8,0,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,0,7,0,2,6,0,3,3,2,4,2,3,4,0,2,0,3,4,0,3,2,0,5,0,0,9,7,2,0,7,2,4,2,4,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,8,1,4,2,2,0,7,2,0,9,0,0,0,2,7,4,2,4,4,1,0,4,2,1,4,2,1,4,0,0,9,8,1,0,5,4,0,6,3,0,0,4,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,4,1,2,3,6,1,2,1,4,4,5,4,0,4,1,0,4,0,1,4,3,2,1,0,2,7,5,2,2,3,6,2,3,3,2,2,6,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,1,2,0,6,3,0,6,5,2,2,3,3,3,3,0,0,1,2,4,3,1,4,3,1,1,8,6,1,3,6,3,1,4,5,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,5,2,3,5,1,4,3,4,3,2,4,4,3,1,1,3,2,2,2,2,2,4,1,4,5,6,1,3,6,3,4,2,3,1,0,3,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,0,3,0,6,3,2,4,3,2,5,3,4,4,0,2,0,4,2,3,3,2,0,5,0,7,2,5,2,2,5,4,2,4,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,7,1,4,2,2,1,5,2,3,8,1,0,0,1,8,5,4,1,5,1,0,4,1,0,5,4,1,1,0,0,9,7,1,2,4,5,1,3,6,1,0,5,6,2,0,0,6,0,0,0,0,0,0,0,5,0,2,0,4,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0\ntest,8,1,2,4,2,0,4,0,5,7,0,2,2,6,2,3,5,1,6,1,0,2,0,1,6,0,2,2,0,6,3,7,2,0,3,6,2,4,4,1,0,5,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,3,4,2,2,9,0,0,0,5,4,4,4,2,4,3,0,2,0,2,6,1,0,2,0,0,9,5,4,0,3,6,1,3,4,2,2,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,8,1,3,3,2,0,4,1,4,6,1,3,3,1,6,4,2,4,3,0,0,5,2,1,3,3,1,4,0,1,8,7,0,2,8,1,1,6,3,0,0,4,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,2,3,1,5,1,3,8,1,1,1,3,6,4,4,2,4,1,0,3,1,1,3,2,3,2,0,1,8,7,1,1,4,5,1,3,4,2,1,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,5,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0\ntest,23,2,2,2,5,0,5,2,2,4,2,4,4,4,2,2,4,3,3,1,0,2,1,4,5,2,1,2,2,7,2,6,1,3,8,1,4,3,3,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,0,7,0,2,7,0,2,0,4,5,0,0,9,0,0,0,2,7,0,0,0,0,9,0,9,0,7,0,2,9,0,3,4,2,2,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,0,6,1,3,7,0,2,0,3,6,0,4,5,0,0,3,2,3,2,2,0,5,0,3,0,9,7,1,2,7,2,3,0,6,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,2,2,0,3,2,5,7,0,2,2,4,4,3,5,2,3,0,0,4,1,2,2,3,3,3,0,5,4,7,0,2,4,5,2,4,3,1,1,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,2,3,2,6,0,3,6,1,3,1,0,8,2,6,2,2,1,0,5,0,3,2,3,3,2,1,4,5,5,2,2,5,4,2,3,4,1,1,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,34,1,5,2,8,0,8,1,0,9,0,0,0,1,8,1,5,4,2,2,0,4,1,2,2,2,2,4,0,0,9,5,4,0,7,2,0,7,2,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,3,0,6,7,0,2,0,3,6,2,1,7,0,0,2,0,6,2,1,0,2,4,3,5,4,7,0,2,7,2,3,3,2,2,0,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,2,7,1,3,2,5,5,2,2,3,2,5,1,2,7,1,0,0,3,4,3,1,1,1,6,2,8,1,7,1,2,8,1,3,5,2,1,1,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,3,3,7,2,3,2,4,5,2,3,1,3,5,1,1,8,1,0,0,7,1,1,0,1,1,5,3,1,8,5,2,3,6,3,1,7,1,1,1,3,3,2,0,0,5,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,0,5,1,3,7,1,2,3,2,5,1,3,6,2,1,1,2,3,3,1,1,1,5,2,5,4,5,2,2,7,2,4,4,1,1,1,3,5,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,4,7,0,5,3,2,4,1,4,5,4,1,2,4,3,1,1,0,5,2,1,1,2,3,4,1,9,0,5,0,4,5,4,5,3,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,1,3,2,5,9,0,0,0,3,6,1,7,2,1,1,0,8,1,1,1,2,2,5,0,5,4,6,1,3,5,4,2,6,2,0,0,3,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,18,10,2,2,4,1,1,1,7,2,2,6,6,2,2,5,2,2,3,1,0,3,1,2,5,1,2,1,2,6,3,4,0,5,4,5,4,1,4,2,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,5,2,2,5,1,0,4,2,3,5,0,4,4,4,2,4,0,5,4,0,0,0,4,2,4,0,0,5,0,4,5,5,0,4,5,4,1,3,5,1,0,5,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,4,7,2,2,2,4,6,0,3,3,4,3,0,3,6,1,0,0,4,2,4,0,1,2,4,3,9,0,4,3,3,9,0,1,8,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,3,9,0,7,0,2,9,0,0,0,4,5,0,4,5,0,0,3,0,6,0,0,0,3,6,0,0,9,7,2,0,5,4,0,0,9,0,0,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,4,3,2,4,1,2,1,5,2,7,0,2,1,6,3,2,5,2,5,2,1,1,0,2,4,3,1,2,0,2,7,7,0,2,5,4,1,1,8,0,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,3,5,0,9,0,0,9,0,0,0,4,5,1,0,8,0,0,1,2,3,3,1,0,1,6,2,6,3,5,0,4,7,2,3,5,1,1,0,3,2,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0\ntest,33,1,2,3,8,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,0,4,0,5,7,0,2,2,5,2,4,5,0,9,0,0,0,0,0,9,0,0,0,0,6,3,9,0,0,2,7,0,2,5,2,0,6,8,2,0,0,6,0,0,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,23,1,1,1,5,1,4,2,3,4,1,4,5,4,1,1,4,4,2,1,0,3,4,0,1,1,0,7,0,8,1,6,0,3,7,2,4,0,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,0,7,0,2,9,0,0,0,5,4,0,2,7,1,0,2,1,5,2,1,1,2,4,3,5,4,8,1,0,8,1,4,3,2,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,1,3,2,1,4,1,4,7,1,2,6,3,0,0,0,9,2,0,0,3,0,4,0,3,0,3,3,6,3,6,2,1,5,4,6,3,0,0,0,2,8,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,1,2,5,0,5,2,2,1,4,5,5,4,1,1,6,2,1,0,0,6,1,3,1,4,3,3,0,9,0,3,2,4,6,3,3,4,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,3,7,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0\ntest,11,1,2,3,3,0,6,1,2,7,1,1,1,8,1,5,4,0,4,0,0,1,4,0,5,4,0,0,0,9,0,8,1,1,4,5,0,5,4,0,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,2,3,0,2,4,4,7,0,2,0,1,8,4,3,3,2,0,0,2,1,5,1,4,0,5,0,3,6,7,0,2,6,3,3,1,6,1,0,5,8,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,0,3,3,4,5,0,4,4,1,5,0,0,9,0,0,0,4,0,5,0,0,0,5,4,9,0,6,0,3,9,0,1,6,3,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,3,3,4,6,1,2,1,2,7,0,3,6,0,0,0,3,3,4,0,1,1,6,3,9,0,7,0,2,7,2,7,2,1,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,0,7,0,2,6,0,3,3,3,4,0,5,4,2,0,0,4,4,0,2,2,0,5,0,4,5,7,2,0,7,2,0,3,4,3,0,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,3,4,2,2,9,0,0,3,4,3,3,4,3,2,0,0,3,1,5,2,4,1,3,0,0,9,7,2,0,9,0,0,5,3,2,0,5,4,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,1,4,1,4,7,1,2,0,2,7,2,0,7,5,0,0,2,0,3,2,2,5,2,0,6,3,6,2,1,5,4,7,2,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,12,2,3,2,3,0,6,0,3,7,2,0,3,3,4,3,3,4,1,0,0,5,4,1,0,5,1,4,0,0,9,9,0,0,5,4,3,4,3,0,0,3,7,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,0,6,0,3,7,0,2,2,2,6,0,3,6,0,0,0,2,8,0,0,1,2,6,0,2,7,9,0,0,9,0,5,0,4,0,0,4,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,3,5,2,0,7,0,2,0,3,6,7,1,2,7,0,0,0,1,2,7,0,1,1,2,2,7,8,1,0,4,5,1,1,5,4,0,6,8,2,0,0,6,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,6,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,2,2,2,3,5,2,2,0,6,3,1,4,5,3,0,0,4,0,3,2,1,2,2,4,8,1,5,2,2,5,4,2,7,0,0,1,3,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,4,3,8,0,3,2,4,8,0,1,1,1,8,2,2,5,2,0,0,3,4,1,2,2,0,6,1,1,8,8,1,0,6,3,3,5,2,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,3,3,2,3,1,5,5,2,2,2,4,4,1,5,4,2,0,0,4,3,1,1,3,1,5,1,8,1,6,2,2,8,1,2,5,2,1,1,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,4,3,2,1,5,1,3,6,1,2,0,3,6,7,1,1,5,0,0,1,1,3,4,2,3,1,0,0,9,7,2,1,5,4,0,1,3,4,1,7,8,0,0,0,5,0,0,0,0,0,0,0,5,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,23,1,1,2,5,2,3,1,5,2,2,5,6,3,1,1,4,5,1,0,0,1,3,5,1,1,2,6,1,8,1,5,0,4,8,1,6,3,1,1,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,4,2,8,0,5,0,4,9,0,0,0,0,9,0,4,5,0,2,0,0,7,0,2,0,0,7,0,7,2,4,5,0,7,2,4,2,2,2,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,0,5,5,2,3,2,2,6,0,4,5,0,0,0,4,4,1,0,0,2,7,1,1,8,6,2,2,8,1,4,1,0,4,0,5,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,0,6,0,3,9,0,0,0,5,4,7,1,1,1,0,0,8,0,0,4,3,2,1,0,0,9,7,2,0,7,2,0,0,1,2,6,0,8,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,3,9,0,6,1,3,7,1,2,1,1,8,0,2,7,1,0,0,1,5,3,1,1,1,7,1,6,3,6,1,2,9,0,1,7,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,5,8,1,4,2,3,3,0,6,5,3,2,2,2,6,2,2,0,3,2,3,1,1,2,4,2,8,1,2,2,5,5,4,7,1,0,0,1,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,3,4,0,3,4,3,3,0,4,5,0,5,4,2,0,0,2,5,2,2,0,2,6,0,9,0,5,0,4,9,0,5,2,3,0,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,7,1,2,1,2,2,5,0,2,7,2,0,0,6,3,5,3,2,2,2,0,3,4,0,3,3,0,4,0,0,9,7,0,2,4,5,0,0,7,2,0,6,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,1,4,2,3,7,1,2,2,4,4,2,4,4,2,1,1,3,2,3,2,2,3,4,1,8,1,6,0,3,6,3,3,4,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,2,5,1,3,2,4,6,1,2,2,3,5,0,4,5,2,0,0,0,5,3,0,1,4,3,1,4,5,5,1,3,7,2,2,5,3,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,2,9,0,5,1,4,4,4,2,2,3,4,2,3,4,2,0,0,3,3,2,2,2,2,5,1,7,2,7,0,2,9,0,2,2,5,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,1,4,0,5,7,0,2,2,5,3,1,6,2,2,0,0,6,0,2,1,1,6,2,0,8,1,5,1,4,6,3,1,1,8,0,0,5,4,0,0,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,0,5,9,0,0,0,2,7,1,1,8,1,0,0,2,6,2,1,1,1,5,4,3,6,6,1,2,7,2,3,5,2,1,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,0,5,1,4,5,2,3,2,2,6,0,2,7,2,0,0,1,3,5,1,1,1,6,2,8,1,6,1,3,9,0,2,6,1,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,6,0,3,5,1,4,2,3,5,0,1,8,1,0,0,3,2,5,1,1,2,5,2,7,2,4,0,5,8,1,3,3,1,3,0,4,5,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,6,2,2,8,0,1,1,2,6,4,3,2,5,1,0,2,2,1,4,2,1,3,0,2,7,6,3,1,4,5,1,1,5,3,0,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,5,2,0,0,6,0,5,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,2,2,0,5,6,1,3,3,1,6,0,1,8,3,0,1,0,2,4,0,0,2,3,4,6,3,6,0,3,9,0,3,6,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,2,4,2,3,5,2,3,2,4,3,1,3,5,1,1,0,3,3,2,2,2,2,5,1,6,3,7,1,2,7,2,3,4,3,1,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,0,4,2,4,5,1,3,2,5,3,2,3,4,3,1,0,2,3,3,3,1,2,3,1,3,6,7,1,2,5,4,3,3,3,1,0,4,6,0,0,0,7,6,5,0,1,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,2,1,6,0,3,2,3,4,1,2,7,1,0,2,2,1,5,1,2,2,5,1,1,8,7,0,2,7,2,0,5,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,4,6,2,1,1,5,3,0,3,6,1,0,0,4,3,2,1,1,2,6,0,6,3,6,0,3,5,4,2,2,5,0,0,4,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,3,3,7,1,5,2,3,5,1,3,0,4,5,0,1,8,0,0,1,3,0,6,0,1,1,5,4,8,1,6,1,3,7,2,3,6,0,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,2,3,1,4,2,3,6,1,2,1,3,6,2,5,3,2,1,0,5,1,3,2,3,3,3,1,7,2,5,2,3,7,2,2,3,4,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,3,9,0,0,0,2,7,0,3,6,0,0,0,0,7,2,0,0,0,7,2,1,8,7,1,1,9,0,1,4,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,2,2,2,4,0,4,9,0,0,0,0,9,4,5,0,7,0,0,2,0,0,7,0,2,0,0,0,9,7,2,0,0,9,0,2,7,0,0,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,2,4,2,3,0,2,0,7,9,0,0,0,0,9,0,3,6,0,0,0,7,0,2,0,2,0,6,2,0,9,9,0,0,9,0,2,0,5,2,0,5,7,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,3,7,1,2,2,3,5,2,4,4,3,1,2,2,2,1,3,2,3,3,1,3,6,6,2,1,5,4,3,2,3,2,1,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,2,5,1,4,1,4,2,2,5,5,3,1,1,4,4,2,0,0,2,2,4,2,1,2,5,1,7,2,6,0,3,7,2,4,3,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,4,1,2,4,1,0,5,2,3,5,1,4,3,4,3,2,4,4,3,1,1,3,2,2,2,2,2,4,1,4,5,6,1,3,6,3,4,2,3,1,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,5,2,3,5,1,4,1,4,4,5,3,1,2,3,0,4,1,1,5,3,1,1,1,2,7,5,1,3,5,4,3,1,4,2,1,5,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,5,0,4,7,2,0,0,2,7,2,5,3,2,0,0,4,2,2,2,2,4,2,1,8,1,9,0,0,5,4,2,4,3,2,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,3,3,1,0,2,2,5,9,0,0,0,4,5,2,6,2,2,5,0,2,0,0,3,2,4,0,0,9,0,5,4,0,2,7,0,0,7,2,0,6,6,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,3,7,1,2,0,5,4,3,5,1,2,1,0,4,1,2,2,3,4,1,1,2,7,7,0,2,5,4,1,2,5,2,1,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,2,4,2,2,7,0,2,2,4,3,1,5,4,1,0,0,3,1,4,1,1,4,4,0,2,7,5,3,2,5,4,2,5,3,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,23,1,1,2,5,2,4,1,3,3,2,5,6,2,2,2,5,4,1,0,0,2,3,4,1,2,3,5,1,8,1,5,0,4,8,1,7,2,1,0,1,3,3,0,0,0,5,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,3,5,1,6,1,2,6,1,3,2,4,4,1,5,4,1,0,0,3,2,4,1,1,3,5,1,8,1,8,1,1,7,2,2,7,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,2,8,0,3,2,4,5,2,3,3,4,3,0,4,5,0,0,0,5,4,0,0,3,0,6,0,7,2,9,0,0,9,0,3,6,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,4,7,0,4,0,5,9,0,0,0,7,2,0,0,9,0,0,0,0,5,4,0,0,0,7,2,9,0,5,0,4,9,0,4,4,2,0,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,1,5,1,3,6,1,2,0,5,4,4,0,5,2,1,1,2,3,2,3,0,6,0,0,0,9,6,1,3,7,2,3,4,2,1,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1\ntest,33,1,2,2,8,0,3,2,4,5,2,3,3,4,3,0,4,5,0,0,0,5,4,0,0,3,0,6,0,7,2,9,0,0,9,0,3,6,0,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,1,6,1,2,6,1,2,2,3,4,1,3,5,2,1,1,4,2,2,1,2,3,4,1,2,7,5,2,3,5,4,2,4,3,1,1,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,5,2,2,0,5,3,2,9,0,0,0,0,9,3,3,3,4,2,0,3,1,1,0,3,0,3,4,6,3,9,0,0,3,6,0,3,6,0,0,4,8,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,1\ntest,23,1,2,2,5,0,4,1,5,4,3,3,3,2,4,1,4,4,2,0,0,2,2,4,1,3,2,3,3,8,1,6,1,2,8,1,4,4,1,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,4,0,5,9,0,0,0,2,7,1,2,6,1,0,0,3,3,3,1,1,3,6,1,5,4,6,1,2,7,2,1,4,4,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,2,3,4,3,1,3,5,2,2,2,3,4,1,6,2,1,0,2,4,2,2,1,3,4,2,0,5,4,8,1,1,5,4,2,1,6,1,0,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,4,3,8,1,4,2,3,7,1,2,0,0,9,0,0,9,0,0,0,0,5,4,0,0,0,5,4,9,0,6,2,2,7,2,0,4,5,0,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,20,1,2,2,5,1,5,1,4,4,3,2,2,6,1,1,5,4,1,0,0,5,1,4,1,2,4,4,0,9,0,7,2,1,8,1,4,3,2,2,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,1,2,4,6,2,7,1,1,5,0,4,3,2,4,3,2,4,4,3,0,3,1,1,3,2,3,1,3,6,3,3,4,3,3,6,5,2,0,0,3,5,1,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,4,3,8,0,5,3,1,7,2,0,0,2,7,1,5,4,2,0,0,5,2,1,2,1,4,3,0,3,6,4,3,2,6,3,1,0,8,0,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,4,2,2,2,2,5,7,1,2,2,4,4,5,3,2,8,1,0,0,0,0,7,0,2,0,0,0,9,3,5,2,2,7,0,2,5,2,1,6,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,2,3,10,0,6,0,3,3,1,5,0,0,9,0,6,3,3,0,0,4,0,3,0,6,0,3,0,0,9,6,1,2,6,3,0,9,0,0,0,3,4,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,0,5,9,0,0,0,3,6,0,4,5,0,0,0,2,5,3,0,2,2,5,2,0,9,9,0,0,9,0,1,5,4,0,0,4,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,4,0,5,7,0,2,2,1,6,0,2,7,0,0,0,3,6,0,0,1,1,6,2,4,5,5,2,3,8,1,3,4,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,2,3,10,2,2,1,5,8,0,1,0,6,3,6,3,0,0,0,3,3,0,3,3,2,4,0,0,0,9,7,2,0,1,8,3,0,0,6,0,6,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,2,4,10,1,4,0,5,7,0,2,5,3,2,0,2,7,2,1,1,3,2,2,0,0,3,6,0,3,6,6,2,1,4,5,6,2,0,2,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,2,7,1,0,7,0,2,0,2,7,4,2,4,4,0,0,0,2,4,4,0,2,2,3,1,8,7,2,0,5,4,2,3,2,3,0,5,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,5,6,1,5,2,2,5,1,3,4,5,1,1,5,4,3,1,0,4,1,2,3,2,2,2,3,6,3,7,1,1,5,4,4,4,2,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,33,1,3,4,8,0,4,0,5,5,0,4,3,2,5,0,2,7,0,0,2,0,3,4,0,0,3,3,4,0,9,6,0,3,9,0,6,3,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,2,7,0,0,6,1,2,2,6,2,0,4,5,3,0,0,5,0,2,1,1,3,4,2,0,9,6,0,3,6,3,4,4,0,2,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,1,3,8,1,1,1,2,7,0,2,7,1,0,4,1,1,5,1,2,3,4,1,1,8,7,1,1,8,1,2,3,2,4,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,0,7,1,2,8,0,1,1,4,5,0,5,4,2,0,0,5,2,2,2,2,2,4,1,0,9,6,2,2,5,4,0,3,6,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,2,5,0,3,9,0,0,1,3,5,7,2,0,9,0,0,0,0,0,6,2,1,0,0,0,9,7,2,0,2,7,1,2,3,4,1,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,3,10,0,5,1,4,8,1,0,0,4,5,1,3,6,2,0,5,1,2,0,2,1,6,2,0,2,7,7,2,0,3,6,5,2,2,1,0,3,3,0,0,4,6,0,0,0,0,3,0,3,0,0,0,0,5,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,0,4,1,5,5,1,4,3,2,4,0,1,8,1,0,0,1,0,8,0,0,1,5,3,8,1,6,1,3,7,2,7,2,0,0,0,2,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,1,4,2,3,5,2,3,2,3,6,2,4,4,2,1,1,3,2,2,1,1,5,2,1,1,8,6,2,2,6,3,2,4,3,1,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,0,4,1,4,4,2,4,4,4,2,2,4,3,3,1,0,2,2,3,3,2,3,2,2,2,7,8,1,1,5,4,2,2,5,1,0,5,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,5,1,2,5,1,2,4,1,5,3,5,1,2,5,3,6,1,0,2,1,1,4,2,4,1,1,3,6,7,2,1,5,4,2,3,4,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,4,2,0,7,0,2,9,0,0,2,5,3,0,4,5,0,0,0,4,1,5,0,0,5,4,1,9,0,7,0,2,5,4,4,4,2,0,0,3,7,1,0,0,5,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,6,2,2,7,1,2,0,3,6,0,0,9,2,0,0,3,3,2,0,0,0,9,0,3,6,7,1,2,7,2,0,4,0,5,0,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,0,5,0,4,7,0,2,2,4,3,0,7,2,0,0,0,7,0,2,0,3,5,2,0,9,0,9,0,0,9,0,4,5,0,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,2,3,3,3,1,3,5,1,4,3,3,4,3,5,2,3,0,1,4,2,2,3,3,2,3,1,6,3,5,0,4,4,5,3,2,3,1,0,4,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,0,4,1,4,6,1,3,3,1,6,4,2,4,3,0,0,5,2,1,3,3,1,4,0,1,8,7,0,2,8,1,1,6,3,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,4,1,2,4,1,2,4,2,3,6,0,3,3,2,4,2,4,3,2,2,1,4,2,1,2,2,4,2,0,3,6,6,1,3,5,4,1,5,3,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,0,6,0,3,7,0,2,0,5,4,2,4,4,2,2,0,3,3,2,3,2,0,4,0,2,7,7,2,0,2,7,0,5,4,0,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,5,8,1,5,2,2,5,1,3,3,5,1,1,5,3,3,1,0,4,1,2,2,3,3,2,1,5,4,7,1,1,5,4,3,3,3,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,2,2,5,0,4,3,0,6,0,0,9,0,0,4,0,3,3,0,0,4,4,2,4,5,9,0,0,9,0,2,6,2,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,0,4,2,4,7,1,2,2,5,3,0,2,7,2,0,1,2,4,2,1,1,1,7,1,6,3,6,1,2,7,2,6,3,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,2,8,0,6,0,3,5,0,4,1,1,8,2,2,6,0,0,1,2,6,1,0,2,1,5,3,1,8,8,1,1,8,1,3,3,3,0,0,3,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,0,9,0,0,5,0,4,4,3,3,0,3,6,0,0,2,0,3,5,0,3,0,6,0,0,9,3,2,4,7,2,6,3,0,0,0,2,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,4,1,4,7,1,2,2,4,3,1,2,6,4,0,0,2,2,1,2,2,3,3,0,2,7,6,2,1,5,4,2,5,2,2,0,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,4,3,1,3,5,2,3,3,4,3,1,3,5,2,0,1,3,3,2,1,1,2,4,2,7,2,7,2,0,4,5,3,3,3,1,1,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,1,2,4,6,1,4,2,3,5,0,4,6,0,3,5,0,4,0,0,0,9,0,0,0,5,0,0,4,4,5,5,1,4,9,0,0,0,9,0,0,5,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,2,7,0,0,6,1,2,2,6,2,0,4,5,3,0,0,5,0,2,1,1,3,4,2,0,9,6,0,3,6,3,4,4,0,2,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,1,2,1,0,5,0,4,0,3,6,6,3,0,3,3,3,0,0,0,6,0,3,0,4,2,2,2,7,2,5,0,4,7,2,5,4,0,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,0,4,1,5,5,1,4,3,2,4,0,1,8,1,0,0,1,0,8,0,0,1,5,3,8,1,6,1,3,7,2,7,2,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,4,8,0,0,0,9,9,0,0,0,3,6,0,2,7,3,0,0,2,5,0,0,2,3,5,0,4,5,7,2,0,5,4,0,0,9,0,0,5,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,17,1,2,4,4,0,5,3,2,5,1,4,4,4,2,1,4,5,2,1,1,3,1,4,2,1,2,4,1,4,5,6,1,2,7,2,4,2,3,1,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,0,2,7,0,2,0,7,2,0,2,7,0,1,0,4,4,1,0,1,1,5,3,3,6,8,1,0,7,2,0,5,4,0,0,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,21,2,2,4,5,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,1,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,1,4,1,4,6,0,3,2,2,5,1,4,5,2,1,1,3,3,2,2,2,2,4,1,3,6,6,1,2,6,3,3,2,3,1,1,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,5,3,1,6,3,2,4,0,1,8,1,0,0,1,0,7,0,0,2,2,5,8,1,4,2,4,7,2,7,2,0,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,20,1,2,2,5,1,5,1,4,4,3,2,2,6,1,1,5,4,1,0,0,5,1,4,1,2,4,4,0,9,0,7,2,1,8,1,4,3,2,2,0,4,2,0,3,0,0,0,0,6,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,4,2,3,0,7,0,2,7,2,0,1,1,8,2,3,5,1,0,0,7,2,1,0,3,2,5,0,0,9,9,0,0,6,3,2,4,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,5,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,4,1,2,2,2,5,7,1,2,2,4,4,5,3,2,8,1,0,0,0,0,7,0,2,0,0,0,9,3,5,2,2,7,0,2,5,2,1,6,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,4,1,0,5,2,2,9,0,0,5,2,3,5,1,3,7,0,0,2,0,0,1,1,5,3,0,0,9,6,3,0,5,4,0,1,4,5,0,6,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,31,1,2,3,7,0,4,0,5,3,0,6,6,2,2,1,2,7,0,0,0,0,5,4,0,0,0,6,3,9,0,5,0,4,9,0,6,3,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,2,7,0,0,9,0,0,1,6,3,0,6,3,3,0,0,5,0,2,2,1,5,2,1,1,8,7,2,0,7,2,1,6,1,0,2,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,1,2,3,0,6,1,2,1,2,6,5,3,2,2,5,2,2,0,0,4,1,3,2,3,3,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,4,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,8,0,1,7,0,2,0,7,2,0,4,5,0,1,0,2,6,2,0,1,2,7,1,1,8,7,2,0,7,2,0,6,3,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,30,1,5,3,7,0,0,0,9,9,0,0,0,0,9,9,0,0,9,0,0,0,0,0,9,0,0,0,0,0,9,9,0,0,0,9,0,0,3,3,3,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,2,4,10,0,5,2,2,7,1,1,0,6,3,2,0,7,2,2,3,2,1,2,1,1,4,3,1,1,8,8,0,1,5,4,2,4,3,2,0,4,4,0,4,0,0,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,2,2,9,0,0,0,6,3,7,0,2,3,5,0,0,0,2,6,0,2,2,0,0,9,9,0,0,2,7,0,2,5,2,0,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,2,5,3,4,1,2,1,3,5,4,3,2,1,4,5,1,0,0,3,1,5,1,1,3,4,2,8,1,7,0,2,6,3,5,3,1,1,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,2,2,0,3,4,2,9,0,0,0,3,6,5,3,2,7,0,0,2,0,1,7,1,1,2,0,0,9,5,4,0,2,7,0,2,4,1,3,8,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,7,1,3,2,2,2,2,0,6,5,3,2,0,4,5,5,4,0,5,0,0,4,0,0,5,2,2,0,0,4,5,6,3,0,6,3,0,3,2,5,0,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,5,3,3,2,2,0,5,9,0,0,1,1,8,5,4,1,5,0,0,3,1,1,5,1,4,1,0,0,9,9,0,0,4,5,3,2,4,0,0,4,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,3,7,1,4,1,4,4,2,3,5,4,1,0,2,7,3,0,0,3,2,3,0,0,3,2,4,3,6,6,1,3,6,3,7,2,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,1,6,2,2,7,1,2,5,3,2,0,5,4,0,0,0,0,9,0,0,0,0,6,3,5,4,7,1,2,7,2,3,4,0,3,0,4,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,4,8,0,6,0,3,7,0,2,0,3,6,0,3,6,2,1,2,1,3,3,1,0,4,5,0,1,8,7,2,0,7,2,4,3,3,0,0,3,3,0,0,4,7,0,0,0,0,3,0,3,0,0,0,0,4,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,0,4,7,2,0,0,3,6,0,4,5,2,0,2,0,4,3,2,0,2,5,0,4,5,7,2,0,5,4,2,2,3,3,0,5,3,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,3,1,0,5,0,4,6,0,3,3,3,4,9,0,0,6,0,0,3,0,0,6,3,0,0,0,0,9,6,0,3,3,6,0,3,2,4,0,7,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,1,1,1,7,8,1,1,1,4,5,1,3,5,3,1,1,1,4,2,2,1,2,4,2,2,7,8,1,0,5,4,4,3,2,1,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,3,6,0,5,1,3,7,1,2,3,5,1,0,5,4,0,0,0,2,2,6,0,4,2,2,2,1,8,9,0,0,5,4,3,3,2,2,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,0,0,4,6,0,0,0,0,3,0,3,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,1,4,1,1,5,2,2,4,0,5,6,3,1,3,2,5,4,0,1,2,2,2,3,1,1,3,2,5,4,4,2,4,4,5,3,3,3,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,3,1,2,4,1,2,7,0,0,6,1,2,2,6,2,0,4,5,3,0,0,5,0,2,1,1,3,4,2,0,9,6,0,3,6,3,4,4,0,2,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,0,5,3,2,7,1,1,1,4,5,2,5,3,2,0,1,3,2,2,2,3,1,4,0,5,4,7,1,1,5,4,2,2,5,1,0,4,7,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,5,9,0,5,4,0,0,0,9,7,0,2,0,0,9,0,0,0,0,6,3,0,0,0,6,3,7,2,7,1,2,7,2,7,2,0,0,0,1,5,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,37,2,2,3,8,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,5,1,1,5,2,2,5,1,3,3,5,1,1,5,3,3,1,0,4,1,2,2,3,3,2,1,5,4,7,1,1,5,4,3,3,3,1,0,3,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,1,4,2,8,0,6,0,3,6,0,3,3,0,6,0,2,7,0,0,0,0,9,0,0,0,0,9,0,2,7,9,0,0,9,0,3,0,6,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,2,2,0,5,6,1,3,3,1,6,0,1,8,3,0,1,0,2,4,0,0,2,3,4,6,3,6,0,3,9,0,3,6,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,7,0,0,2,9,0,0,0,6,3,1,4,4,2,0,0,3,0,4,2,0,3,4,0,0,9,7,2,0,4,5,0,5,4,2,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,0,7,0,2,4,1,4,4,2,4,0,2,7,1,0,1,3,3,3,1,1,2,5,2,5,4,5,2,3,8,1,6,1,3,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,2,5,3,4,1,2,1,3,5,4,3,2,1,4,5,1,0,0,3,1,5,1,1,3,4,2,8,1,7,0,2,6,3,5,3,1,1,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,8,1,4,3,2,2,5,0,3,7,0,2,0,2,7,2,5,3,2,0,0,3,4,2,2,2,1,4,2,6,3,7,0,2,3,6,2,0,6,2,0,6,7,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,6,1,3,7,0,2,1,3,6,1,2,7,1,0,1,3,3,4,1,2,1,6,1,1,8,8,0,1,8,1,0,2,7,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,3,3,7,1,5,2,3,5,1,3,0,5,4,0,1,8,0,0,2,3,0,5,0,1,1,4,5,8,1,6,1,3,7,2,3,6,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,2,5,2,9,1,5,0,3,7,1,2,0,0,9,0,4,5,0,0,0,6,3,0,0,4,0,5,0,2,7,6,2,2,6,3,0,4,5,0,0,4,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,2,1,7,5,2,3,3,3,5,0,1,8,0,0,0,2,7,1,0,1,1,8,0,5,4,5,4,0,9,0,3,2,5,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,2,2,3,3,0,4,2,5,5,1,3,4,3,3,1,6,2,2,1,0,5,2,2,2,2,4,2,1,5,4,5,1,3,7,2,2,3,3,0,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,28,2,1,5,6,2,5,2,0,0,0,9,7,2,0,2,1,7,2,0,2,2,1,4,1,0,2,2,5,8,1,0,0,9,9,0,2,7,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,2,4,4,0,5,0,4,3,4,2,2,3,5,3,0,0,1,2,5,2,0,3,5,0,3,7,5,2,2,7,2,1,5,4,0,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,27,1,1,6,6,1,5,2,3,5,1,3,5,4,1,4,5,1,5,0,0,4,0,0,5,4,1,0,1,8,1,6,1,3,7,2,5,4,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,0,5,1,3,7,1,2,3,2,5,1,3,6,2,1,1,2,3,3,1,1,1,5,2,5,4,5,2,2,7,2,4,4,1,1,1,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,5,7,2,5,2,1,3,0,6,6,2,2,0,2,7,2,0,1,2,1,5,0,2,3,5,1,2,7,6,0,3,9,0,3,6,1,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,2,4,10,1,4,0,5,7,0,2,5,3,2,0,2,7,2,1,1,3,2,2,0,0,3,6,0,3,6,6,2,1,4,5,6,2,0,2,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,1,3,2,5,9,0,0,0,3,6,1,7,2,1,1,0,8,1,1,1,2,2,5,0,5,4,6,1,3,5,4,2,6,2,0,0,3,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,39,1,3,3,9,2,7,0,0,8,1,1,1,4,5,0,6,3,2,2,0,4,2,1,2,5,1,3,1,0,9,7,2,1,2,7,0,6,3,1,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,2,3,8,0,5,2,2,6,1,2,2,3,4,3,1,6,2,0,0,3,3,2,2,1,2,5,0,2,7,7,0,2,8,1,2,5,2,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,3,0,6,7,0,2,2,2,6,2,4,3,1,0,0,6,0,2,1,3,4,3,0,6,3,5,1,3,8,1,2,5,3,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,4,2,8,0,7,0,2,7,2,0,0,4,5,0,3,6,2,2,0,2,3,2,0,2,4,4,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,5,6,2,2,2,4,4,1,3,6,1,1,1,3,3,2,1,2,3,4,1,5,4,6,2,2,8,1,4,4,2,1,1,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,4,2,3,2,4,1,3,7,1,1,1,2,6,3,4,2,3,0,0,4,2,1,3,3,3,2,0,2,7,9,0,0,5,4,2,3,5,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,0,4,2,4,8,1,1,1,2,7,0,2,7,1,0,0,2,4,4,1,1,2,6,2,6,3,7,2,1,9,0,1,3,5,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,2,7,0,0,8,1,1,1,4,5,0,6,3,2,2,0,4,2,1,2,5,1,3,1,0,9,7,2,1,2,7,0,6,3,1,0,4,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,1,3,2,4,7,1,2,1,2,6,2,5,3,2,0,0,5,1,3,2,2,3,3,0,6,3,7,1,2,5,4,1,4,4,1,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,4,3,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,5,1,4,7,1,2,1,3,5,0,3,6,1,0,0,3,4,2,1,3,1,5,1,8,1,6,1,2,9,0,1,5,3,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,0,5,9,0,0,0,9,0,0,0,9,5,0,0,0,5,0,0,0,5,4,0,5,5,9,0,0,9,0,5,5,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,0,2,4,4,7,0,2,1,0,8,1,0,8,2,0,0,4,1,3,0,0,1,4,4,9,0,7,0,2,6,3,4,0,5,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,3,4,3,1,7,1,1,1,4,5,1,6,2,1,0,0,6,1,2,1,4,2,3,1,4,5,4,2,3,7,2,2,2,5,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,4,2,0,6,0,3,6,0,3,4,0,5,3,4,3,2,2,0,6,0,0,3,1,5,2,0,9,0,9,0,0,2,7,6,0,2,0,2,4,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,2,3,2,2,7,0,0,2,7,2,0,0,5,4,5,4,1,2,0,0,7,1,1,3,4,2,1,0,1,8,5,2,3,4,5,1,6,2,1,0,4,7,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,6,0,3,2,5,3,2,2,5,2,0,0,3,3,2,2,0,0,7,0,5,4,6,0,3,7,2,3,2,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,0,4,7,1,1,1,4,5,2,4,4,3,0,1,2,2,2,4,2,1,4,0,5,4,7,2,0,8,1,3,2,4,1,0,4,4,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,2,1,7,5,2,3,3,3,5,0,1,8,0,0,0,2,7,1,0,1,1,8,0,5,4,5,4,0,9,0,3,2,5,0,0,3,3,0,0,0,5,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,1,4,1,4,7,1,2,2,4,4,2,4,4,3,1,2,2,2,1,2,2,3,3,1,4,5,6,2,1,5,4,3,3,3,2,1,4,5,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,0,6,0,3,7,1,2,2,1,6,1,3,5,2,1,0,3,3,2,1,2,2,4,1,6,3,5,2,3,7,2,3,5,2,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,4,3,0,5,3,2,4,1,4,4,2,4,1,4,4,1,1,0,5,2,1,1,2,3,4,1,9,0,5,0,4,5,4,3,4,4,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0\ntest,33,2,4,2,8,0,4,0,5,9,0,0,0,3,6,0,3,6,0,0,0,2,6,2,0,2,2,6,0,0,9,4,4,1,9,0,2,5,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,6,0,2,7,0,2,1,4,4,2,3,5,3,0,0,3,2,2,2,1,1,5,2,4,5,6,0,3,7,2,1,4,4,0,0,4,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,4,8,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,0,6,1,3,5,0,4,3,3,4,1,3,5,1,0,1,6,1,3,1,2,2,3,3,2,7,7,0,2,7,2,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,1,1,5,6,1,4,1,4,4,0,5,5,4,1,2,3,4,5,0,0,3,1,1,2,2,1,3,2,8,1,4,2,4,4,5,1,3,5,1,1,5,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,39,2,3,3,9,2,6,0,3,6,1,3,1,0,8,1,3,6,2,1,0,4,0,3,1,2,2,3,3,2,7,5,2,2,5,4,1,4,2,3,1,5,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,0,9,0,0,5,0,4,4,2,3,0,3,6,0,0,2,0,3,5,2,0,0,5,3,0,9,3,2,4,7,2,5,0,4,0,0,2,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,2,3,2,5,1,3,2,4,6,1,2,2,2,6,0,3,6,2,0,0,0,6,2,0,1,4,4,1,4,5,5,1,3,7,2,3,5,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,0,3,3,4,6,2,2,2,5,3,0,2,7,1,0,1,2,2,4,1,1,2,6,1,7,2,6,1,2,7,2,5,4,1,0,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,5,0,4,7,0,2,2,2,5,1,3,6,0,1,3,2,3,2,2,2,1,6,0,4,5,4,5,0,2,7,2,5,0,2,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,2,1,6,0,3,2,3,4,1,2,7,1,0,2,2,1,5,1,2,2,5,1,1,8,7,0,2,7,2,0,5,4,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,12,2,4,2,3,2,4,2,2,9,0,0,0,0,8,2,4,4,2,0,0,7,1,0,0,3,4,3,0,2,7,9,0,0,9,0,0,0,4,5,0,8,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,4,0,4,0,6,6,0,0,0,6,6,0,0,0,0,0,5,0,0,0,0,0,0,1,0,1,1,0,0,0,2,2,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,2,2,1,5,1,2,6,2,2,1,3,6,5,3,2,1,0,0,5,3,2,3,4,1,2,1,2,7,8,0,1,6,3,2,5,3,0,0,4,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,4,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,3,3,0,6,1,2,7,1,1,5,1,4,5,4,0,4,0,0,5,1,0,1,8,0,0,0,9,0,8,1,1,4,5,0,5,4,0,0,5,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,4,1,5,7,2,0,0,3,6,1,6,2,2,0,5,1,2,0,5,1,2,3,0,1,8,8,1,0,3,6,3,2,2,4,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,0,3,2,4,8,0,1,1,1,8,2,2,5,2,0,0,3,4,1,2,2,0,6,1,1,8,8,1,0,6,3,3,5,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,0,6,0,3,7,0,2,2,2,6,3,4,2,4,0,3,2,0,0,5,2,3,0,0,0,9,6,3,0,4,5,0,3,3,3,0,5,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,3,4,7,2,4,0,4,7,0,2,0,3,6,1,1,7,2,0,0,3,3,3,1,0,2,5,2,1,8,5,2,2,7,2,0,7,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,35,1,3,3,8,0,7,0,2,7,0,2,2,0,7,1,4,5,0,0,0,4,3,3,0,2,2,3,3,7,2,4,1,4,8,1,2,4,4,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,2,3,2,4,5,4,1,2,4,4,2,4,4,2,1,1,5,1,2,3,1,3,2,2,3,6,7,2,1,7,2,2,5,3,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,1,5,3,2,6,0,3,4,2,3,0,4,5,3,0,0,2,2,4,3,0,1,4,1,5,4,6,1,2,9,0,3,5,1,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,23,1,2,2,5,2,4,1,3,3,3,4,4,3,2,2,4,4,1,0,0,2,3,4,1,2,3,5,1,8,1,6,0,3,8,1,6,3,1,0,1,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,2,2,0,0,0,0,5,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,0,4,1,5,5,1,4,3,2,4,0,1,8,1,0,0,1,0,8,0,0,1,5,3,8,1,6,1,3,7,2,7,2,0,0,0,2,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,1,4,1,5,8,1,1,1,2,7,2,2,6,3,1,1,2,4,1,3,1,1,5,1,4,5,5,4,1,7,2,4,2,4,0,1,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,0,5,5,2,3,2,2,6,0,4,5,0,0,0,4,4,1,0,0,2,7,1,1,8,6,2,2,8,1,4,1,0,4,0,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,1,3,5,7,0,2,1,1,8,0,1,8,1,1,0,1,6,3,1,1,0,5,3,9,0,5,2,3,7,2,1,6,3,0,0,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,5,2,0,3,6,0,6,0,0,6,4,0,0,0,0,0,6,0,0,0,0,0,1,0,1,1,0,2,0,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,0,3,3,4,6,2,2,2,5,3,0,2,7,1,0,1,2,2,4,1,1,2,6,1,7,2,6,1,2,7,2,5,4,1,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,4,3,2,0,5,0,5,9,0,0,0,0,9,0,5,5,0,0,0,9,0,0,0,4,0,5,0,0,9,6,3,0,2,7,0,9,0,0,0,3,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,1,3,3,1,0,8,0,1,6,0,3,2,2,5,3,5,1,4,0,0,5,1,1,3,3,2,2,1,5,4,2,4,3,4,5,1,4,4,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,2,5,2,4,1,3,3,2,5,6,2,2,2,5,4,1,0,0,2,3,4,1,2,3,5,1,8,1,5,0,4,8,1,7,2,1,0,1,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,26,1,2,5,6,1,4,2,3,3,0,6,5,3,2,2,2,6,2,2,0,3,2,3,1,1,2,4,2,8,1,2,2,5,5,4,7,1,0,0,1,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,0,5,1,4,7,1,2,1,3,5,0,3,6,1,0,0,3,4,2,1,3,1,5,1,8,1,6,1,2,9,0,1,5,3,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,8,1,4,3,2,1,5,1,3,7,1,2,0,2,7,2,5,2,2,1,0,5,2,1,2,3,3,2,1,1,8,7,0,2,5,4,1,2,6,1,1,5,7,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,5,1,1,5,2,3,5,1,4,1,8,1,6,2,1,6,2,0,1,1,1,5,3,1,1,1,1,8,5,1,3,5,4,1,1,3,3,3,8,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,26,3,2,4,6,0,3,0,6,5,0,4,4,4,2,0,0,9,0,0,0,0,7,2,0,0,0,5,4,7,2,5,0,4,9,0,6,2,2,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,4,1,4,6,1,3,1,5,4,3,4,3,1,0,1,4,2,4,1,2,4,2,1,2,7,4,0,5,7,2,3,3,2,2,1,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,2,2,5,9,0,0,0,3,6,0,5,4,3,0,0,4,2,1,1,1,5,3,0,1,8,7,0,2,7,2,2,5,3,0,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,0,5,2,2,4,2,4,4,4,3,1,3,5,2,1,0,3,2,3,2,2,1,3,2,6,3,6,1,3,8,1,4,3,3,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,0,5,2,2,7,0,2,2,5,3,0,0,9,0,0,0,2,7,0,0,0,0,9,0,5,4,9,0,0,9,0,5,4,0,0,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,1,5,3,2,6,0,3,3,4,3,0,2,7,1,0,0,3,3,4,1,0,2,6,1,4,5,6,1,2,9,0,1,7,2,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,1,2,1,0,5,0,4,0,3,6,6,3,0,3,3,3,0,0,0,6,0,3,0,4,2,2,2,7,2,5,0,4,7,2,5,4,0,0,0,2,6,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,5,7,1,1,0,4,5,0,6,3,1,1,0,4,2,3,0,0,4,3,3,0,9,6,2,2,8,1,0,6,0,3,0,5,4,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,2,5,0,4,1,4,2,3,4,3,4,2,1,5,4,1,0,0,2,2,4,1,1,3,4,2,8,1,6,1,3,8,1,5,4,1,0,0,2,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,4,7,2,6,2,2,2,0,7,4,3,2,0,4,6,1,0,1,1,1,7,0,1,1,7,1,1,8,5,0,4,9,0,4,3,3,0,0,3,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,2,0,0,0,0,0,0,1,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,12,2,4,2,3,0,2,0,7,9,0,0,0,0,9,0,3,6,0,0,0,7,0,2,0,2,0,6,2,0,9,9,0,0,9,0,2,0,5,2,0,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,7,2,2,2,2,0,2,2,5,6,2,1,3,2,4,4,5,1,4,0,0,4,1,1,3,4,1,2,1,6,3,7,0,2,3,6,3,2,3,2,1,4,6,2,0,0,6,0,0,0,0,0,0,0,5,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,1\ntest,37,1,3,3,8,0,7,0,2,6,0,3,3,2,4,3,3,4,3,0,2,0,3,2,3,0,2,5,0,2,7,3,0,6,7,2,3,4,2,2,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,5,1,1,5,2,2,5,1,3,3,5,1,1,5,3,3,1,0,4,1,2,2,3,3,2,1,5,4,7,1,1,5,4,3,3,3,1,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,5,9,0,5,4,0,0,0,9,7,0,2,0,0,9,0,0,0,0,6,3,0,0,0,6,3,7,2,7,1,2,7,2,7,2,0,0,0,1,5,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1\ntest,8,1,2,4,2,2,2,2,5,7,1,2,2,4,4,5,3,2,8,1,0,0,0,0,7,0,2,0,0,0,9,3,5,2,2,7,0,2,5,2,1,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,1\ntest,28,2,1,5,6,2,5,2,0,0,0,9,7,2,0,2,1,7,2,0,2,2,1,4,1,0,2,2,5,8,1,0,0,9,9,0,2,7,0,0,0,3,1,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,0,4,7,0,2,2,5,2,3,4,2,5,0,0,4,0,0,5,0,3,2,0,6,3,6,3,0,2,7,3,4,3,0,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,11,1,2,4,3,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,6,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,1,5,3,2,6,0,3,4,2,3,0,4,5,3,0,0,2,2,4,3,0,1,4,1,5,4,6,1,2,9,0,3,5,1,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,4,2,3,7,0,2,0,2,7,0,2,7,0,0,2,4,0,3,0,0,5,4,0,0,9,7,0,2,9,0,3,6,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,4,3,7,2,7,0,0,6,1,3,2,1,7,0,4,5,2,1,0,3,2,4,1,2,3,4,1,0,9,5,2,3,7,2,0,4,4,1,0,5,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,4,1,0,4,0,5,7,0,2,2,5,2,4,5,0,9,0,0,0,0,0,9,0,0,0,0,6,3,9,0,0,2,7,0,2,5,2,0,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,2,2,3,0,4,0,5,5,2,2,0,6,3,0,3,6,4,0,2,2,3,0,0,0,0,5,4,3,6,6,2,2,6,3,3,4,2,1,0,3,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,0,7,1,2,8,0,1,1,4,5,0,5,4,2,0,0,5,2,2,2,2,2,4,1,0,9,6,2,2,5,4,0,3,6,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,1,1,2,4,1,2,5,2,1,9,0,0,0,6,3,4,5,0,6,1,0,3,0,0,2,5,2,0,0,0,9,5,4,0,0,9,0,0,7,2,1,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,24,1,2,3,5,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,2,4,10,0,6,0,3,6,0,3,3,4,3,0,2,7,1,0,8,1,0,1,2,1,6,2,0,0,9,3,3,3,2,7,3,6,1,0,0,3,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,2,2,6,6,0,3,1,5,4,0,4,5,3,0,0,1,5,1,3,1,0,2,4,2,7,5,0,4,9,0,0,0,9,0,0,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,3,3,1,4,1,4,9,0,0,0,5,4,3,5,2,5,0,0,1,1,3,2,2,5,2,0,0,9,7,2,0,2,7,0,2,5,3,1,6,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,3,4,2,2,9,0,0,1,2,7,1,3,6,1,0,0,3,3,3,1,2,3,5,0,0,9,7,2,0,9,0,0,2,7,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,4,0,5,6,2,2,2,4,4,0,4,5,0,0,4,2,3,1,1,1,4,3,1,0,9,6,2,1,4,5,3,2,5,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,2,2,7,0,2,2,5,3,0,0,9,0,0,0,2,7,0,0,0,0,9,0,5,4,9,0,0,9,0,5,4,0,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0\ntest,24,1,3,3,5,1,1,1,7,5,2,2,2,3,5,1,1,8,0,0,0,3,2,4,0,1,1,7,2,8,1,6,1,3,8,1,5,3,1,1,0,2,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,0,7,0,2,6,0,3,3,3,4,0,5,4,2,0,0,4,4,0,2,2,0,5,0,4,5,7,2,0,7,2,0,3,4,3,0,6,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,3,4,2,2,9,0,0,0,5,4,4,4,2,4,3,0,2,0,2,6,1,0,2,0,0,9,5,4,0,3,6,1,3,4,2,2,6,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,16,2,2,1,4,0,2,0,7,5,0,4,2,5,2,9,0,0,2,0,0,3,2,5,0,0,9,0,0,9,0,4,0,5,8,1,9,0,0,0,0,1,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,1,1,2,6,5,1,4,3,2,4,1,2,7,0,0,0,2,4,4,0,1,1,5,3,8,1,4,1,5,8,1,5,4,1,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,4,3,0,7,0,2,6,0,3,4,2,4,3,3,3,4,0,2,0,4,1,3,0,2,5,0,1,8,3,0,6,7,2,4,3,2,2,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,5,2,8,0,9,0,0,9,0,0,0,0,9,3,0,6,4,0,3,0,0,3,2,0,4,4,0,0,9,3,6,0,0,9,0,3,5,2,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,28,1,2,5,6,1,7,1,1,6,1,3,3,5,1,2,4,4,2,2,3,2,1,1,2,1,4,3,1,3,6,5,0,4,5,4,1,4,3,2,0,4,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,2,5,0,3,9,0,0,0,6,3,0,5,4,2,0,0,7,0,0,2,3,2,4,0,3,6,7,0,2,6,3,2,4,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,4,3,0,5,3,2,4,1,4,4,2,4,1,4,4,1,1,0,5,2,1,1,2,3,4,1,9,0,5,0,4,5,4,3,4,4,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,9,0,0,5,0,4,4,3,3,0,3,6,0,0,2,0,3,5,0,3,0,6,0,0,9,3,2,4,7,2,6,3,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,6,2,2,7,0,2,2,4,4,0,3,6,1,0,0,3,3,3,1,1,4,4,2,0,9,7,1,2,7,2,0,7,2,0,0,3,5,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,0,6,0,3,9,0,0,0,3,6,0,0,9,0,0,3,0,6,0,0,0,3,6,0,9,0,7,2,0,9,0,3,5,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,2,2,2,4,1,4,6,1,3,1,4,5,2,5,2,2,2,0,3,1,3,3,2,2,3,1,6,3,7,1,2,6,3,3,4,3,0,0,3,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,0,6,1,2,7,1,2,1,4,4,1,2,6,1,0,1,3,2,4,1,1,2,6,1,5,4,5,2,3,6,3,2,5,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,0,1,3,6,1,3,5,5,0,4,0,0,9,0,0,0,0,3,6,0,0,0,3,6,9,0,5,1,3,8,1,5,4,0,0,0,2,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,1,5,3,2,6,0,3,3,4,3,0,2,7,1,0,0,3,3,4,1,0,2,6,1,4,5,6,1,2,9,0,1,7,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,4,3,3,1,4,1,4,7,1,2,0,3,6,4,3,3,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,3,2,2,2,8,4,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,4,7,0,6,1,3,5,0,4,4,4,2,2,3,5,2,0,0,4,3,2,2,3,0,5,2,7,2,6,0,3,9,0,5,2,2,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,2,1,1,5,0,2,4,4,2,2,5,5,4,0,0,7,2,0,0,0,7,0,2,0,5,0,4,0,9,0,7,0,2,9,0,4,4,2,0,0,3,3,2,0,0,5,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,5,1,2,4,1,0,5,2,3,5,1,4,3,4,3,2,4,4,3,1,1,3,2,2,2,2,2,4,1,4,5,6,1,3,6,3,4,2,3,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,15,3,1,4,4,1,5,2,2,4,0,5,6,3,1,3,2,5,4,0,1,2,2,2,3,1,1,3,2,5,4,4,2,4,4,5,3,3,3,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,3,7,1,2,2,3,5,2,4,4,3,1,2,2,2,1,3,2,3,3,1,3,6,6,2,1,5,4,3,2,3,2,1,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,0,9,0,0,7,0,2,2,3,5,4,3,3,4,0,0,3,2,2,2,2,3,3,0,0,9,9,0,0,3,6,2,0,4,4,0,7,7,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,4,8,1,5,2,3,5,1,3,0,5,4,0,3,6,0,0,4,4,0,1,0,3,5,1,1,2,7,6,1,3,7,2,1,8,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,2,3,2,0,2,0,7,3,4,2,2,3,5,2,2,5,0,0,0,4,5,0,1,0,4,4,1,2,7,6,3,0,7,2,3,3,3,1,0,4,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,3,1,1,4,1,1,5,2,2,4,0,5,6,3,1,3,2,5,4,0,1,2,2,2,3,1,1,3,2,5,4,4,2,4,4,5,3,3,3,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,7,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,7,1,2,1,4,4,1,2,6,1,0,1,3,2,4,1,1,2,6,1,5,4,5,2,3,6,3,2,5,2,1,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,2,2,0,5,6,1,3,3,1,6,0,1,8,3,0,1,0,2,4,0,0,2,3,4,6,3,6,0,3,9,0,3,6,0,0,0,2,1,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,2,4,3,2,0,5,0,5,9,0,0,0,0,9,0,5,5,0,0,0,9,0,0,0,4,0,5,0,0,9,6,3,0,2,7,0,9,0,0,0,3,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,3,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,1,4,0,5,7,0,2,2,5,3,1,6,2,2,0,0,6,0,2,1,1,6,2,0,8,1,5,1,4,6,3,1,1,8,0,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,2,3,1,5,1,3,8,1,1,1,3,6,4,4,2,4,1,0,3,1,1,3,2,3,2,0,1,8,7,1,1,4,5,1,3,4,2,1,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,1,2,8,1,1,1,5,3,0,3,6,2,0,2,3,2,3,1,2,3,4,1,3,6,6,2,2,6,3,2,7,1,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,7,2,0,0,0,0,0,0,0,0,0,0,6,0,0,6,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,0,0,1,1,0,0,0,0,0,0\ntest,21,1,3,3,5,1,4,1,5,6,2,2,2,3,5,1,3,6,1,0,0,3,3,2,1,2,1,5,2,8,1,6,1,2,8,1,3,5,2,1,1,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,1,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,2,9,2,0,2,5,9,0,0,0,3,6,2,4,4,2,0,0,3,5,0,2,0,3,5,0,0,9,9,0,0,7,2,0,5,4,0,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,2,4,3,9,0,7,0,2,6,0,3,3,2,4,0,3,6,0,0,0,0,2,7,0,0,2,4,3,4,5,6,0,3,9,0,3,4,3,0,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,1,2,8,1,4,1,4,2,2,5,5,3,1,1,4,4,2,0,0,2,2,4,2,1,2,5,1,7,2,6,0,3,7,2,4,3,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0\ntest,38,1,2,3,9,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,1,3,0,5,7,1,2,1,4,5,3,4,2,4,0,0,4,2,0,4,3,1,1,2,6,3,6,2,2,3,6,4,2,3,2,0,4,6,2,0,0,6,0,5,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,2,2,4,2,2,5,2,2,5,2,0,4,5,0,0,0,4,0,5,0,2,3,5,0,9,0,5,2,2,9,0,0,6,3,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,1,4,1,4,7,1,2,2,4,3,1,2,6,4,0,0,2,2,1,2,2,3,3,0,2,7,6,2,1,5,4,2,5,2,2,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,3,3,7,0,4,1,4,6,1,3,1,4,5,1,3,5,3,0,1,4,1,2,1,2,4,3,1,2,7,4,0,5,7,2,2,2,2,4,1,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,0,3,0,6,5,2,3,2,3,4,4,3,3,6,0,0,3,0,0,3,2,4,0,2,5,4,5,2,3,5,4,5,2,3,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,5,1,4,2,3,4,1,3,5,3,0,1,3,1,2,2,1,4,3,2,4,5,4,2,3,6,3,4,3,2,0,1,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,3,1,5,6,0,3,2,2,6,0,4,5,2,0,0,3,4,2,1,0,2,5,3,5,4,5,1,3,7,2,4,4,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,4,3,8,1,4,2,4,5,2,3,0,0,9,6,3,0,2,0,0,7,0,0,0,9,0,0,0,1,8,4,2,4,6,3,0,5,0,4,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0\ntest,10,2,4,3,3,1,5,1,4,7,1,2,0,0,9,2,0,7,0,0,0,3,0,6,0,2,0,7,0,0,9,6,2,1,4,5,0,3,5,2,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,1,2,0,0,6,0,0,0,0,3,0,3,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,2,4,2,0,6,0,3,6,0,3,4,0,5,3,4,3,2,2,0,6,0,0,3,1,5,2,0,9,0,9,0,0,2,7,6,0,2,0,2,4,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,4,7,0,6,1,3,5,0,4,4,4,2,2,3,5,2,0,0,4,3,2,2,3,0,5,2,7,2,6,0,3,9,0,5,2,2,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,3,5,0,2,6,0,3,2,0,7,0,4,5,3,0,0,0,3,3,3,0,2,5,0,2,7,2,5,3,6,3,2,3,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,0,9,0,0,5,0,4,0,0,9,0,0,9,0,0,2,0,3,5,0,3,0,6,0,9,0,3,2,4,7,2,9,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,3,6,0,4,3,2,5,2,3,3,5,2,1,3,6,1,1,0,3,2,4,1,2,2,3,3,6,3,6,1,3,8,1,4,2,3,0,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,6,2,2,6,0,3,3,2,5,0,3,6,0,0,9,0,0,0,3,0,2,0,4,0,9,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,5,3,1,6,3,2,4,0,1,8,1,0,0,1,0,7,0,0,2,2,5,8,1,4,2,4,7,2,7,2,0,0,0,2,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,4,6,2,1,1,5,3,0,3,6,1,0,0,4,3,2,1,1,2,6,0,6,3,6,0,3,5,4,2,2,5,0,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,0,5,1,3,7,1,2,3,2,5,1,3,6,2,1,1,2,3,3,1,1,1,5,2,5,4,5,2,2,7,2,4,4,1,1,1,3,1,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,3,2,1,5,1,3,7,1,2,0,2,7,3,4,2,2,1,0,4,3,1,2,3,2,3,1,1,8,7,0,2,5,4,1,3,4,2,1,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,3,3,8,0,3,0,6,9,0,0,2,2,5,0,4,5,0,0,0,3,6,0,0,0,4,5,0,9,0,5,0,4,6,3,5,4,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,4,7,0,9,0,0,5,0,4,4,2,3,0,3,6,0,0,2,0,3,5,2,0,0,5,3,0,9,3,2,4,7,2,5,0,4,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,3,8,0,4,0,5,5,2,3,2,3,4,3,3,3,7,0,0,2,0,1,4,3,2,2,1,3,6,5,2,3,6,3,3,2,4,2,0,5,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,2,8,0,5,1,3,7,0,2,0,0,9,1,3,5,3,0,0,2,3,3,3,0,2,4,1,2,7,6,3,0,6,3,4,4,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,31,1,2,2,7,1,3,2,5,5,2,2,3,2,5,1,2,7,1,0,0,3,4,3,1,1,1,6,2,8,1,7,1,2,8,1,3,5,2,1,1,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,2,2,7,0,2,2,5,3,0,0,9,0,0,0,2,7,0,0,0,0,9,0,5,4,9,0,0,9,0,5,4,0,0,0,3,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,3,2,2,3,2,4,5,0,4,3,3,3,4,3,3,2,2,2,3,2,0,4,3,2,2,0,4,5,5,2,3,6,3,5,2,3,0,0,3,7,0,0,4,6,0,0,0,2,3,0,0,0,0,0,0,7,0,0,0,0,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,0,2,3,5,6,3,0,0,4,5,0,0,9,0,0,0,0,0,9,0,0,0,9,0,5,4,4,3,3,9,0,0,5,4,0,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,6,1,3,8,1,1,1,3,6,0,3,6,0,0,2,3,4,1,0,2,4,4,1,2,7,7,1,2,6,3,2,2,6,0,0,4,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,2,1,0,5,1,3,8,0,1,1,3,6,1,4,5,2,1,1,4,2,2,1,3,0,6,0,7,2,7,1,2,8,1,1,7,2,0,1,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,1,3,5,7,0,2,2,2,6,0,3,6,2,1,0,1,5,1,2,2,0,5,1,9,0,5,2,3,7,2,3,4,2,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,1,4,1,4,7,1,2,0,2,7,2,0,7,5,0,0,2,0,3,2,2,5,2,0,6,3,6,2,1,5,4,7,2,0,0,0,2,3,0,0,3,0,6,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,2,2,5,3,2,4,4,0,5,0,4,4,2,3,2,3,4,4,0,0,2,0,4,4,2,0,0,4,0,9,4,2,4,5,4,3,5,1,1,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,3,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,2,5,1,3,2,4,6,1,2,2,3,5,0,4,5,2,0,0,0,5,3,0,1,4,3,1,4,5,5,1,3,7,2,2,5,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,2,3,2,5,1,3,2,4,6,1,2,2,2,6,0,3,6,2,0,0,0,6,2,0,1,4,4,1,4,5,5,1,3,7,2,3,5,2,0,0,3,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,4,6,2,1,1,5,3,0,3,6,1,0,0,4,3,2,1,1,2,6,0,6,3,6,0,3,5,4,2,2,5,0,0,4,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,2,1,4,6,3,3,3,1,3,1,6,5,4,0,5,2,2,3,1,0,5,1,1,3,4,1,2,2,9,0,3,1,6,3,6,2,2,4,3,1,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,4,7,0,4,0,5,9,0,0,0,7,2,0,0,9,0,0,0,0,5,4,0,0,0,7,2,9,0,5,0,4,9,0,4,4,2,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,4,0,2,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0\ntest,9,1,4,3,3,0,6,1,3,9,0,0,0,1,8,0,4,5,0,0,0,3,4,3,0,0,5,4,0,4,5,7,2,0,9,0,0,9,0,0,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,0,2,2,5,5,2,2,2,3,4,0,3,6,1,0,1,1,1,7,1,1,3,5,1,8,1,4,2,3,7,2,4,5,1,0,0,2,1,2,0,0,6,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,1,3,7,1,2,3,4,3,0,3,6,0,0,0,2,2,6,0,2,3,3,3,5,4,9,0,0,5,4,2,2,3,3,0,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,0,7,2,0,7,0,2,0,5,4,0,3,6,0,0,0,2,0,7,0,0,4,5,1,1,8,5,2,2,7,2,0,8,1,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,4,3,8,0,4,1,5,6,2,2,0,4,5,0,4,5,4,0,0,3,3,0,0,4,0,5,0,5,4,6,2,2,8,1,0,0,4,5,0,7,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,2,4,2,8,2,5,0,2,9,0,0,0,0,8,0,7,2,0,0,0,5,4,0,0,5,0,4,0,0,9,7,2,0,5,4,2,4,4,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,4,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1\ntest,38,1,2,3,9,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,4,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,0,3,1,6,8,1,1,1,2,6,1,3,6,0,0,0,3,5,2,0,1,2,6,1,6,3,6,2,2,9,0,2,7,1,0,0,3,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,2,2,4,8,0,3,2,5,5,2,3,3,5,3,0,3,6,1,0,5,1,3,2,1,2,4,3,2,5,4,4,5,1,7,2,2,5,1,2,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,2,4,8,1,0,0,2,7,0,3,6,0,0,0,4,3,3,0,1,3,6,0,3,6,6,3,0,9,0,2,5,2,2,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,2,8,0,5,0,4,3,2,4,4,3,3,1,4,5,2,0,0,3,4,1,1,3,0,3,3,2,7,5,2,2,8,1,2,5,2,0,0,3,5,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,40,1,3,3,10,0,6,2,2,6,0,3,3,2,5,0,3,6,0,0,9,0,0,0,3,0,2,0,4,0,9,5,2,3,7,2,5,4,0,0,0,2,3,2,0,0,7,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,3,5,1,5,2,2,5,2,2,2,5,2,2,2,6,2,0,0,2,3,2,2,1,1,6,0,9,0,7,2,1,7,2,3,1,6,1,0,4,2,1,0,0,6,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,2,5,2,1,7,2,0,0,2,7,4,4,2,3,0,0,5,2,1,2,3,3,2,1,0,9,9,0,0,4,5,1,2,7,0,0,5,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,8,0,1,0,3,6,0,3,6,1,2,1,3,3,2,2,0,1,7,0,4,5,7,2,0,7,2,4,3,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,9,0,0,5,0,4,0,5,4,0,0,9,0,0,2,0,3,5,0,0,0,9,0,9,0,3,2,4,7,2,9,0,0,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,3,5,1,5,1,3,6,1,2,0,0,9,0,0,9,2,1,1,2,3,2,0,0,5,4,0,9,0,6,1,3,7,2,4,5,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,38,1,3,3,9,0,3,3,4,6,1,2,1,2,7,0,3,6,0,0,0,2,2,6,0,1,1,7,1,9,0,7,0,2,7,2,6,3,1,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,0,7,2,0,7,0,2,0,3,6,0,2,7,0,0,0,8,0,1,0,0,2,5,2,5,4,5,2,2,7,2,0,5,3,1,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,3,3,7,2,3,2,4,5,2,3,1,3,5,1,1,8,1,0,0,7,1,1,0,1,1,5,3,1,8,5,2,3,6,3,1,7,1,1,1,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,4,3,1,1,5,1,3,6,1,2,0,2,7,8,1,1,6,0,0,2,2,1,5,3,2,1,0,0,9,7,2,1,5,4,0,1,4,5,1,7,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,1,5,2,3,6,1,3,3,4,2,1,4,4,3,1,0,4,1,2,2,2,2,3,2,4,5,6,1,2,6,3,3,3,3,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,4,0,0,4,0,0,0,0,0,4,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,2,1,4,6,0,9,0,0,2,0,7,4,3,3,0,4,5,2,0,0,4,2,2,0,2,3,3,2,9,0,2,0,7,9,0,4,4,0,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,4,3,7,1,4,1,4,7,1,2,0,2,7,2,0,7,5,0,0,2,0,3,2,2,5,2,0,6,3,6,2,1,5,4,7,2,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,0,7,1,1,7,1,2,2,3,5,1,3,5,2,1,0,4,3,1,2,2,1,5,1,3,6,6,2,2,5,4,3,3,3,0,0,4,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,2,5,0,4,1,4,2,3,4,3,4,2,1,5,4,1,0,0,2,2,4,1,1,3,4,2,8,1,6,1,3,8,1,5,4,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,0,6,0,3,7,0,2,2,2,6,0,3,6,0,0,0,2,8,0,0,1,2,6,0,2,7,9,0,0,9,0,5,0,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,18,2,2,2,4,1,2,1,6,2,2,6,6,2,1,5,4,1,2,1,0,4,1,3,2,2,5,2,1,6,3,3,1,6,4,5,6,2,2,1,1,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,2,8,0,4,2,4,3,2,4,4,4,2,0,5,4,0,0,0,7,0,2,0,5,0,4,0,7,2,7,0,2,9,0,4,5,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,2,3,2,5,1,3,2,4,6,1,2,2,2,6,0,3,6,2,0,0,0,6,2,0,1,4,4,1,4,5,5,1,3,7,2,3,5,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,2,3,0,2,4,4,7,0,2,0,1,8,4,3,3,2,0,0,2,1,5,1,4,0,5,0,3,6,7,0,2,6,3,3,1,6,1,0,5,8,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,0,7,0,2,6,0,3,3,3,4,0,5,4,2,0,0,4,4,0,2,2,0,5,0,4,5,7,2,0,7,2,0,3,4,3,0,6,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,5,1,0,5,0,4,4,0,5,4,3,3,2,2,6,5,0,0,4,0,0,2,0,2,0,6,4,5,5,2,4,5,4,3,3,0,3,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,34,1,3,2,8,3,5,0,2,6,0,3,2,0,7,0,4,5,3,0,0,0,3,3,3,0,2,5,0,2,7,2,5,3,6,3,2,3,4,0,0,4,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,4,4,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0\ntest,8,1,2,4,2,1,4,2,4,5,2,3,0,6,3,6,3,0,0,0,0,6,3,0,0,6,0,3,0,0,9,6,1,2,7,2,0,0,9,0,0,4,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,38,1,3,2,9,3,6,1,1,5,0,4,2,1,7,1,5,4,1,0,0,3,1,5,1,1,5,4,1,9,0,8,1,1,8,1,3,5,2,1,0,3,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,2,3,1,4,7,1,2,1,4,5,1,6,3,4,1,0,4,1,1,3,2,3,1,2,6,3,7,1,2,5,4,5,3,1,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,4,1,4,7,1,2,2,4,4,2,4,4,3,1,2,2,2,1,2,2,3,3,1,4,5,6,2,1,5,4,3,3,3,2,1,4,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0\ntest,41,1,3,3,10,0,7,0,2,6,0,3,3,1,6,0,6,3,3,0,4,2,0,1,3,2,5,1,0,0,9,3,3,3,2,7,1,5,4,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,0,5,2,2,7,0,2,2,5,3,0,0,9,0,0,0,2,7,0,0,0,0,9,0,5,4,9,0,0,9,0,5,4,0,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,1,3,7,1,2,3,4,3,0,3,6,0,0,0,2,2,6,0,2,3,3,3,5,4,9,0,0,5,4,2,2,3,3,0,5,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,38,1,3,4,9,2,2,2,4,9,0,0,0,4,5,2,2,6,2,0,0,2,2,4,2,2,0,6,0,9,0,7,0,2,4,5,0,9,0,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,33,1,2,4,8,0,5,4,1,6,1,2,2,5,2,1,1,8,1,0,0,2,5,2,1,1,1,8,0,4,5,7,0,2,8,1,4,4,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,6,0,2,7,0,2,1,4,4,2,3,5,3,0,0,3,2,2,2,1,1,5,2,4,5,6,0,3,7,2,1,4,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,6,1,3,3,2,0,2,0,7,3,4,2,2,3,5,2,2,5,0,0,0,4,5,0,1,0,4,4,1,2,7,6,3,0,7,2,3,3,3,1,0,4,8,2,0,0,6,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,26,3,2,4,6,0,3,0,6,5,0,4,4,4,2,0,0,9,0,0,0,0,7,2,0,0,0,5,4,7,2,5,0,4,9,0,6,2,2,1,0,3,1,0,0,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,2,3,0,4,0,5,9,0,0,5,2,3,2,5,2,6,0,0,3,0,0,1,1,2,3,2,7,2,9,0,0,7,2,6,0,3,0,0,3,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,2,3,3,9,2,6,0,3,6,1,3,1,0,8,1,3,6,2,1,0,4,0,3,1,2,2,3,3,2,7,5,2,2,5,4,1,4,2,3,1,5,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,4,2,4,2,2,6,4,2,4,0,2,7,1,0,0,2,0,7,0,0,2,1,7,7,2,2,2,6,7,2,7,2,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,5,9,0,5,4,0,0,0,9,7,0,2,0,0,9,0,0,0,0,6,3,0,0,0,6,3,7,2,7,1,2,7,2,7,2,0,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,36,1,3,3,8,0,6,0,3,6,2,2,1,4,5,2,2,5,3,1,1,3,1,3,2,2,2,3,1,4,5,7,1,1,6,3,2,4,2,2,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,2,1,0,5,1,3,8,0,1,1,3,6,1,4,5,2,1,1,4,2,2,1,3,0,6,0,7,2,7,1,2,8,1,1,7,2,0,1,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,4,8,1,4,2,3,5,2,3,2,5,3,2,3,5,2,1,2,2,3,2,1,1,4,3,1,1,8,6,2,2,6,3,2,6,2,1,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,6,1,3,3,2,0,7,0,2,9,0,0,0,4,5,1,4,5,1,0,2,0,7,0,1,2,0,7,1,4,5,7,2,0,8,1,3,3,4,0,0,3,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,2,3,0,5,4,2,4,3,2,5,0,4,5,0,0,0,2,2,5,0,2,3,3,2,9,0,3,0,6,9,0,7,2,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,1,3,2,4,7,1,2,1,4,4,2,6,2,2,0,0,5,2,1,2,4,2,3,0,6,3,7,1,2,5,4,2,4,4,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,4,10,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,2,3,0,5,0,4,6,0,3,1,2,7,3,2,4,1,0,1,4,1,4,1,3,2,1,3,0,9,6,3,0,4,5,0,4,2,4,1,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,2,2,0,5,6,1,3,3,1,6,0,1,8,3,0,1,0,2,4,0,0,2,3,4,6,3,6,0,3,9,0,3,6,0,0,0,2,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,0,3,8,1,1,1,3,5,1,4,5,1,1,3,2,3,1,2,1,3,4,0,2,7,5,4,0,6,3,3,4,4,0,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,1,3,0,5,2,3,5,0,9,0,5,5,0,2,0,0,2,2,4,4,0,5,0,0,0,9,5,0,4,5,4,0,0,5,5,0,7,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,3,3,1,0,7,2,0,7,0,2,0,4,5,3,3,5,0,3,0,5,0,2,3,2,0,3,2,7,2,9,0,0,6,3,2,5,3,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,2,2,4,1,0,5,1,3,6,1,3,3,4,3,3,3,4,3,1,1,3,2,2,3,2,2,3,1,3,6,6,1,3,7,2,3,2,4,1,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,2,0,9,0,0,3,4,3,3,4,3,1,0,0,1,4,4,1,1,5,2,2,2,7,7,0,2,7,2,1,3,4,3,0,7,3,0,2,0,7,0,6,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,2,1,0,4,2,4,9,0,0,0,4,5,3,6,1,7,0,0,2,0,0,4,5,0,1,0,0,9,5,4,0,0,9,0,3,6,0,0,5,8,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,2,4,10,0,6,0,3,6,0,3,3,4,3,0,2,7,1,0,8,1,0,1,2,1,6,2,0,0,9,3,3,3,2,7,3,6,1,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,1,3,7,1,2,2,3,5,2,4,4,3,1,2,2,2,1,3,2,3,3,1,3,6,6,2,1,5,4,3,2,3,2,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,1,4,1,5,6,2,1,1,4,5,2,5,3,1,0,0,6,1,2,1,3,4,3,0,1,8,7,2,0,8,1,1,3,6,1,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,3,8,0,5,0,4,6,0,3,1,6,3,1,6,3,1,0,3,3,1,3,1,1,6,3,1,0,9,6,3,0,4,5,0,3,1,3,2,8,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,1,4,2,3,5,0,4,3,4,2,3,4,3,3,0,0,3,1,2,4,3,0,1,3,3,6,6,0,3,4,5,3,3,3,1,0,4,6,0,0,0,6,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,26,1,2,4,6,2,4,2,2,3,0,6,5,2,2,2,5,2,2,0,0,5,0,2,1,3,3,2,1,6,3,8,0,1,4,5,3,4,2,1,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,0,6,0,3,6,2,2,1,4,5,2,2,5,3,1,1,3,1,3,2,2,2,3,1,4,5,7,1,1,6,3,2,4,2,2,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,1,4,2,3,6,1,2,1,3,6,2,5,3,2,1,0,5,1,3,2,3,3,3,1,7,2,5,2,3,7,2,2,3,4,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,2,10,1,5,1,3,6,1,3,0,4,5,0,3,6,1,0,3,3,0,4,0,2,4,3,0,3,6,6,1,2,6,3,0,0,9,0,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,1,5,0,3,5,1,4,3,5,1,4,3,3,3,0,0,5,1,1,2,4,2,3,0,4,5,6,1,3,4,5,2,2,3,3,1,6,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,31,1,2,2,7,1,3,2,5,5,2,2,3,2,5,1,2,7,1,0,0,3,4,3,1,1,1,6,2,8,1,7,1,2,8,1,3,5,2,1,1,3,1,0,0,0,6,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,2,3,2,2,2,5,0,3,9,0,0,3,3,4,7,2,0,9,0,0,0,0,0,0,6,3,0,0,0,9,7,2,0,2,7,3,6,0,0,0,3,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,1,5,1,4,7,1,2,1,4,5,4,4,2,4,0,1,3,1,2,4,2,2,3,0,4,5,6,2,1,4,5,1,2,6,1,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,4,9,1,5,2,1,6,1,3,3,4,3,2,5,3,2,0,0,5,1,2,1,2,2,4,1,7,2,5,1,3,6,3,5,2,2,1,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,3,4,2,2,9,0,0,3,4,3,3,4,3,2,0,0,3,1,5,2,4,1,3,0,0,9,7,2,0,9,0,0,5,3,2,0,5,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,3,5,0,2,6,0,3,2,0,7,0,4,5,3,0,0,0,3,3,3,0,2,5,0,2,7,2,5,3,6,3,2,3,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,2,3,10,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,2,4,8,1,0,0,2,7,0,3,6,0,0,0,4,3,3,0,1,3,6,0,3,6,6,3,0,9,0,2,5,2,2,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,1,5,1,3,6,1,2,0,5,4,4,0,5,2,1,1,2,3,2,3,0,6,0,0,0,9,6,1,3,7,2,3,4,2,1,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,9,0,0,0,6,2,2,1,5,4,4,3,2,5,1,0,2,1,2,3,2,2,2,1,6,3,6,0,3,7,2,4,0,4,2,1,5,4,2,0,0,6,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,0,7,0,2,6,0,3,3,2,4,2,3,4,0,2,0,3,4,0,3,2,0,5,0,0,9,7,2,0,7,2,4,2,4,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,2,3,3,2,1,4,0,4,7,0,2,2,2,6,3,4,3,4,0,2,3,0,1,4,2,3,1,0,0,9,7,2,0,4,5,2,3,3,2,1,6,8,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,0,3,3,3,5,2,2,2,2,5,2,6,2,3,0,0,6,0,2,1,2,5,1,0,0,9,9,0,0,4,5,2,3,5,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,2,8,0,3,2,4,5,2,3,3,4,3,0,4,5,0,0,0,5,4,0,0,3,0,6,0,7,2,9,0,0,9,0,3,6,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,9,0,0,9,0,0,0,5,4,0,5,4,0,0,0,1,8,1,0,0,1,8,0,1,8,5,0,4,9,0,1,4,0,5,0,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,3,5,1,4,1,4,2,2,5,5,3,1,1,6,3,1,0,0,4,1,4,1,2,5,2,1,8,1,4,0,5,8,1,7,2,1,0,1,2,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,2,4,1,3,3,3,4,4,3,2,2,4,4,1,0,0,2,3,4,1,2,3,5,1,8,1,6,0,3,8,1,6,3,1,0,1,2,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,6,0,3,2,0,7,3,3,3,0,2,7,0,0,0,2,3,4,1,1,2,6,1,5,4,1,2,7,7,2,6,2,2,0,1,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,1,3,2,4,6,1,2,2,3,5,1,3,6,1,0,0,3,3,3,1,1,3,5,2,7,2,5,1,3,7,2,3,5,2,1,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,3,1,0,2,2,5,9,0,0,0,4,5,2,6,2,2,5,0,2,0,0,3,2,4,0,0,9,0,5,4,0,2,7,0,0,7,2,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,0,5,0,4,7,0,2,2,4,3,0,7,2,0,0,0,7,0,2,0,3,5,2,0,9,0,9,0,0,9,0,4,5,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,0,4,0,5,5,2,3,2,4,4,3,3,4,6,0,0,2,0,2,3,2,2,2,2,4,5,5,2,3,6,3,3,3,3,1,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,2,3,0,5,0,4,9,0,0,0,3,6,0,4,5,0,0,0,4,5,0,0,0,4,5,0,3,6,7,1,1,5,4,2,7,0,0,0,3,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,6,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1\ntest,33,1,2,3,8,0,3,3,4,6,1,2,1,2,7,0,3,6,0,0,0,3,3,4,0,1,1,6,3,9,0,7,0,2,7,2,7,2,1,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,2,7,2,1,1,1,4,2,2,1,2,5,1,2,7,4,4,2,7,2,3,3,3,0,1,4,3,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,3,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,9,0,0,9,0,0,0,5,4,0,5,4,0,0,0,1,8,1,0,0,1,8,0,1,8,5,0,4,9,0,1,4,0,5,0,6,3,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,5,6,2,2,2,4,4,1,3,6,1,1,1,3,3,2,1,2,3,4,1,5,4,6,2,2,8,1,4,4,2,1,1,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,0,9,0,0,7,0,2,2,3,5,5,2,3,3,0,0,3,2,1,2,3,3,2,0,0,9,9,0,0,3,6,2,0,4,4,0,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,4,2,3,7,0,2,0,2,7,0,2,7,0,0,2,4,0,3,0,0,5,4,0,0,9,7,0,2,9,0,3,6,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,2,5,1,6,0,2,5,3,2,3,1,5,3,5,1,2,0,0,5,0,2,1,1,5,3,1,5,4,5,0,4,6,3,4,3,1,1,1,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,0,4,8,0,1,0,4,5,0,1,8,0,1,0,1,8,1,0,1,1,8,0,3,6,8,1,0,9,0,1,3,5,0,0,4,3,2,0,0,6,0,0,0,0,3,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,1\ntest,23,1,2,3,5,0,5,1,4,4,3,3,1,5,3,2,4,3,2,1,1,5,2,1,2,2,4,3,0,8,1,5,1,4,7,2,4,4,1,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,2,5,0,3,9,0,0,0,6,3,0,5,4,2,0,0,7,0,0,2,3,2,4,0,3,6,7,0,2,6,3,2,4,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,3,5,0,2,6,0,3,2,0,7,0,4,5,3,0,0,0,3,3,3,0,2,5,0,2,7,2,5,3,6,3,2,3,4,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,0,5,9,0,0,0,9,0,0,0,9,5,0,0,0,5,0,0,0,5,4,0,5,5,9,0,0,9,0,5,5,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,3,5,0,5,2,2,2,0,7,7,2,0,4,4,2,0,2,0,7,0,1,2,3,3,2,0,9,0,7,0,2,5,4,3,4,0,3,0,5,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,18,1,1,2,4,1,3,0,6,2,1,7,6,2,1,6,1,2,3,0,0,2,0,4,3,1,5,1,1,8,1,2,0,7,6,3,6,2,1,1,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,4,8,0,8,0,1,8,0,1,1,7,1,0,5,4,1,0,0,5,1,3,1,4,1,4,1,2,7,7,1,2,4,5,4,1,5,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,1,4,2,3,6,2,1,1,4,5,3,5,2,5,0,1,3,1,2,4,2,2,2,0,2,7,6,1,2,4,5,1,1,8,1,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,32,1,1,4,7,0,3,1,5,5,2,3,9,0,0,0,3,6,5,0,0,0,0,5,0,2,3,0,5,5,4,6,1,2,8,1,6,3,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,1,7,0,2,7,0,2,1,3,6,3,5,1,6,0,0,2,0,1,6,1,3,1,0,4,5,7,2,1,5,4,1,1,8,0,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0\ntest,9,1,1,2,3,0,6,1,2,1,2,6,5,3,2,2,5,2,2,0,0,4,1,3,2,3,3,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,2,5,1,2,8,0,1,2,5,3,1,5,4,2,0,0,3,3,3,1,1,5,4,0,8,1,8,1,1,4,5,2,5,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,0,3,8,1,1,1,3,5,1,4,5,1,1,3,2,3,1,2,1,3,4,0,2,7,5,4,0,6,3,3,4,4,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,2,4,2,3,0,2,0,7,9,0,0,0,0,9,0,3,6,0,0,0,7,0,2,0,2,0,6,2,0,9,9,0,0,9,0,2,0,5,2,0,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,1,5,2,3,6,1,3,3,4,2,1,4,4,3,1,0,4,1,2,2,2,2,3,2,4,5,6,1,2,6,3,3,3,3,1,0,4,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,0,8,1,1,2,2,6,4,4,2,1,4,4,2,0,0,3,1,4,2,2,3,4,0,9,0,7,1,2,6,3,5,3,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,1,4,2,3,7,1,2,1,3,6,2,4,3,3,1,0,4,2,1,2,2,3,3,0,2,7,7,1,1,5,4,1,3,5,1,0,5,7,0,0,4,6,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,27,1,1,5,6,0,5,0,4,3,1,5,7,2,0,1,4,5,4,0,0,4,1,2,1,1,5,1,3,9,0,7,0,2,9,0,2,7,1,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,2,3,2,0,2,5,9,0,0,0,3,6,2,4,4,2,0,0,3,5,0,2,0,3,5,0,0,9,9,0,0,7,2,0,5,4,0,0,4,8,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,0,4,0,5,8,1,1,1,4,5,5,3,1,6,0,0,2,1,1,5,1,4,1,1,9,0,7,0,2,4,5,1,2,6,0,0,5,6,2,0,0,6,0,0,0,0,0,0,0,5,0,0,0,3,0,0,1,4,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,1,1,0,0\ntest,38,1,2,3,9,0,2,3,5,6,0,3,0,5,4,4,5,0,3,0,0,0,6,0,2,0,3,4,0,9,0,9,0,0,4,5,5,5,0,0,0,2,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,4,3,7,1,4,1,4,7,1,2,0,3,6,0,0,9,5,0,0,2,0,3,0,0,6,3,0,6,3,6,2,1,5,4,7,2,0,0,0,2,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,2,5,3,9,0,6,0,3,9,0,0,0,3,6,0,4,5,0,0,0,3,6,0,0,2,3,5,0,3,6,9,0,0,9,0,6,0,3,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0\ntest,39,1,3,3,9,2,7,0,0,8,1,1,1,4,5,0,6,3,2,2,0,4,2,1,2,5,1,3,1,0,9,7,2,1,2,7,0,6,3,1,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,3,8,2,4,2,3,5,2,3,2,4,3,1,3,5,1,1,0,3,3,2,2,2,2,5,1,6,3,7,1,2,7,2,3,4,3,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,39,1,3,2,9,0,3,1,6,8,1,1,1,2,6,1,3,6,0,0,0,3,5,2,0,1,2,6,1,6,3,6,2,2,9,0,2,7,1,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,4,3,0,5,3,2,4,1,4,4,2,4,1,4,4,1,1,0,5,2,1,1,2,3,4,1,9,0,5,0,4,5,4,3,4,4,0,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,5,1,4,4,1,5,0,2,7,0,0,0,1,2,6,0,0,2,5,2,6,3,6,1,2,8,1,2,6,0,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,7,1,2,2,3,4,2,3,4,3,1,1,2,2,3,3,1,3,3,1,2,7,7,2,1,5,4,2,4,3,1,1,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,12,2,3,2,3,0,6,0,3,7,2,0,3,3,4,3,3,4,1,0,0,5,4,1,0,5,1,4,0,0,9,9,0,0,5,4,3,4,3,0,0,3,7,2,0,0,6,0,0,0,2,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,32,1,3,3,7,0,2,3,5,6,3,0,0,4,5,0,0,9,0,0,0,0,0,9,0,0,0,9,0,5,4,4,3,3,9,0,0,5,4,0,0,4,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,2,5,1,4,2,3,4,1,4,3,4,2,2,4,4,1,2,0,4,4,0,2,2,0,5,0,6,3,6,0,3,7,2,4,0,5,0,0,4,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,4,10,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,0,5,3,5,0,0,0,0,3,0,0,0,0,0,0,6,0,0,0,0,0,0,1,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,2,2,1,1,3,6,8,0,1,1,2,6,2,3,4,2,0,0,3,4,1,2,3,1,4,1,5,4,7,2,0,7,2,1,2,5,2,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,1,4,0,5,8,1,1,0,4,5,4,2,4,4,0,0,3,0,3,4,2,1,4,0,4,5,7,2,0,5,4,1,3,5,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,3,3,0,5,2,3,7,1,1,1,3,6,2,5,3,4,0,0,3,2,0,3,2,3,3,0,1,8,8,0,1,5,4,0,5,4,0,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,2,1,6,0,3,2,3,4,1,2,7,1,0,2,2,1,5,1,2,2,5,1,1,8,7,0,2,7,2,0,5,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,0,2,2,6,6,0,3,1,5,4,0,4,5,3,0,0,1,5,1,3,1,0,2,4,2,7,5,0,4,9,0,0,0,9,0,0,6,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,0,0,4,6,0,0,0,3,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,2,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,1,4,1,5,6,2,1,1,4,5,2,5,3,1,0,0,6,1,2,1,3,4,3,0,1,8,7,2,0,8,1,1,3,6,1,0,5,6,0,0,0,5,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,26,1,2,4,6,0,7,0,2,4,0,5,4,5,1,1,3,5,2,0,0,4,1,3,1,3,2,2,3,8,1,2,1,7,8,1,4,4,2,0,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,3,0,0,0,6,0,0,0,0,0,0,0,5,0,2,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,7,1,0,7,0,2,4,1,4,1,5,4,1,0,0,4,5,1,1,3,1,5,1,5,4,4,0,5,8,1,1,7,1,1,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,0,4,1,5,6,1,3,3,3,3,0,3,6,0,0,0,4,2,4,0,2,2,5,2,9,0,6,0,3,7,2,5,4,1,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,0,4,7,0,2,2,5,2,3,4,2,5,0,0,4,0,0,5,0,3,2,0,6,3,6,3,0,2,7,3,4,3,0,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,5,3,1,0,5,0,4,9,0,0,0,5,4,0,4,5,4,0,0,1,4,1,0,4,0,5,1,1,8,9,0,0,4,5,1,4,4,1,0,4,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,13,1,3,2,3,0,5,0,4,5,0,4,2,4,3,3,6,0,1,0,0,5,1,3,1,6,2,2,0,3,6,9,0,0,6,3,2,2,3,4,0,7,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,3,2,5,5,4,1,8,0,0,0,1,0,8,0,0,1,0,0,9,9,0,0,4,5,0,6,3,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,2,8,0,8,0,1,5,0,4,2,1,7,0,5,4,1,0,0,1,4,4,1,1,2,6,1,2,7,4,0,5,5,4,4,3,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,0,5,9,0,0,0,3,6,1,2,7,1,0,0,3,4,2,1,1,1,6,1,5,4,6,1,2,7,2,2,4,3,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,0,6,0,3,7,0,2,0,5,4,2,4,4,2,2,0,3,3,2,3,2,0,4,0,2,7,7,2,0,2,7,0,5,4,0,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1\ntest,22,1,2,3,5,1,3,0,5,4,1,5,5,4,1,1,5,3,2,0,0,6,0,2,1,3,1,3,4,5,4,6,0,3,6,3,1,1,6,1,1,5,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,2,5,2,3,1,5,2,2,5,6,3,1,1,4,5,1,0,0,1,3,5,1,1,2,6,1,8,1,5,0,4,8,1,6,3,1,1,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,3,2,5,1,3,3,4,8,1,1,1,4,5,0,4,5,1,0,0,3,5,2,1,2,3,5,0,3,6,6,1,2,8,1,2,6,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,1,6,0,2,3,0,6,4,0,5,0,5,4,0,0,0,0,5,4,0,3,3,3,2,6,3,2,1,6,7,2,3,4,0,0,3,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,1,6,1,2,5,1,3,0,5,4,0,5,4,0,0,0,0,5,4,0,0,0,9,0,0,9,6,1,3,7,2,3,4,3,1,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,2,4,8,0,4,2,4,2,2,6,4,2,4,0,2,7,1,0,0,2,0,7,0,0,2,1,7,7,2,2,2,6,7,2,7,2,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,2,5,0,3,9,0,0,0,3,6,2,5,3,2,0,0,4,2,2,2,2,3,4,0,3,6,7,2,0,6,3,2,0,7,0,0,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,3,1,1,5,3,2,5,5,4,2,0,8,1,1,0,0,5,2,2,1,2,6,2,0,2,7,5,2,2,8,1,4,5,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,8,0,1,7,0,2,0,7,2,0,4,5,0,1,0,2,6,2,0,1,2,7,1,1,8,7,2,0,7,2,0,6,3,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,3,5,0,2,6,0,3,2,0,7,0,4,5,3,0,0,0,3,3,3,0,2,5,0,2,7,2,5,3,6,3,2,3,4,0,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,5,0,4,5,1,3,3,2,5,1,4,4,2,1,0,3,3,2,1,2,2,4,1,7,2,5,1,4,7,2,3,5,2,0,0,3,4,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,2,0,7,5,2,2,0,7,2,0,0,9,0,0,0,0,6,3,0,0,0,9,0,3,6,9,0,0,9,0,0,9,0,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,5,7,0,5,0,4,5,0,4,4,4,2,0,2,7,0,0,0,3,6,0,0,0,3,5,2,9,0,5,0,4,9,0,6,2,2,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,0,5,1,4,7,1,2,1,3,5,0,3,6,1,0,0,3,4,2,1,3,1,5,1,8,1,6,1,2,9,0,1,5,3,0,0,3,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,5,2,8,0,8,1,0,9,0,0,0,1,8,1,5,4,2,2,0,4,1,2,2,2,2,4,0,0,9,5,4,0,7,2,0,7,2,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0\ntest,9,1,4,2,3,2,4,1,3,7,1,1,1,2,6,3,4,2,3,0,0,4,2,1,3,3,3,2,0,2,7,9,0,0,5,4,2,3,5,0,0,4,4,2,0,0,5,0,4,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,2,3,0,4,2,4,4,2,5,5,2,3,2,5,3,2,0,0,5,0,3,2,2,4,2,0,5,4,5,2,4,5,4,0,3,2,5,0,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,0,5,9,0,0,0,3,6,1,2,7,1,0,0,3,4,2,1,1,1,6,1,5,4,6,1,2,7,2,2,4,3,1,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,5,0,4,7,0,2,2,2,5,1,3,6,0,1,3,2,3,2,2,2,1,6,0,4,5,4,5,0,2,7,2,5,0,2,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,2,3,2,3,5,1,4,4,2,3,3,5,1,5,1,1,3,0,1,5,2,1,2,0,4,5,5,2,3,5,4,5,1,2,1,1,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,1,3,3,6,2,5,2,1,7,0,2,2,2,6,1,5,3,2,1,1,3,2,3,2,1,5,3,0,3,6,7,1,2,4,5,1,3,6,1,0,4,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,1,4,2,3,6,1,2,1,3,6,2,5,3,2,1,0,5,1,3,2,3,3,3,1,7,2,5,2,3,7,2,2,3,4,0,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,4,3,1,3,5,2,3,2,3,4,1,2,6,3,0,0,3,0,4,0,0,3,4,3,8,1,7,2,0,4,5,4,4,1,1,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,4,1,1,4,2,3,7,1,1,0,6,3,4,5,0,8,0,0,0,0,1,6,3,0,0,0,0,9,6,3,0,3,6,0,0,1,8,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,0,5,9,0,0,0,3,6,0,1,8,2,0,0,2,6,0,1,0,2,2,5,8,1,9,0,0,7,2,5,3,1,0,0,3,3,2,0,0,6,6,0,0,2,4,3,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,1,0,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,5,0,4,7,0,2,2,2,6,0,2,7,0,2,2,0,6,0,1,0,2,6,0,2,7,4,4,2,8,1,5,2,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,6,0,0,4,6,0,0,0,0,5,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,2,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0\ntest,38,2,3,4,9,0,9,0,0,9,0,0,0,4,5,1,0,8,0,0,1,2,3,4,1,0,1,6,2,6,3,5,0,4,7,2,2,6,1,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,2,3,3,0,2,0,7,5,2,3,3,3,5,2,4,4,2,0,0,2,2,4,2,2,1,4,2,9,0,6,0,3,7,2,3,4,3,0,0,4,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,4,2,8,0,5,1,4,8,1,0,0,1,8,0,4,5,0,0,0,5,2,2,0,2,3,5,0,2,7,5,4,0,9,0,1,7,2,1,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,2,2,2,5,1,3,2,4,2,4,3,3,3,3,2,5,3,2,0,0,5,3,0,2,1,4,4,0,5,4,7,0,2,9,0,5,3,2,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,2,4,2,3,0,9,0,0,9,0,0,0,0,9,0,3,6,0,0,0,5,3,2,0,3,0,6,0,2,7,5,4,0,5,4,0,4,5,0,0,4,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,1,5,1,3,6,1,2,0,3,6,4,3,3,2,0,0,1,1,7,1,3,4,2,0,0,9,7,2,1,5,4,0,3,1,2,4,8,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,7,1,2,2,3,4,2,3,4,3,1,1,2,2,3,3,1,3,3,1,2,7,7,2,1,5,4,2,4,3,1,1,4,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,3,1,5,6,0,3,2,2,6,0,4,5,2,0,0,3,4,2,1,0,2,5,3,5,4,5,1,3,7,2,4,4,2,0,0,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,2,9,0,4,1,5,6,0,3,3,3,4,0,1,8,0,0,0,4,0,5,0,1,2,6,2,9,0,5,0,4,9,0,4,4,2,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,0,2,0,7,9,0,0,0,5,4,3,4,2,2,0,0,7,0,0,3,4,2,0,0,0,9,9,0,0,5,4,2,0,3,3,2,7,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,4,9,1,6,0,2,7,0,2,0,5,4,0,0,9,5,0,0,0,0,4,0,0,0,5,4,9,0,6,0,3,7,2,0,4,5,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,26,1,2,4,6,0,7,0,2,4,0,5,4,5,1,1,3,5,2,0,0,4,1,3,1,3,2,2,3,8,1,2,1,7,8,1,4,4,2,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,26,3,2,4,6,0,3,0,6,5,0,4,4,4,2,0,0,9,0,0,0,0,7,2,0,0,0,5,4,7,2,5,0,4,9,0,6,2,2,1,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,7,2,0,7,0,2,0,3,6,0,2,7,0,0,0,8,0,1,0,0,2,5,2,5,4,5,2,2,7,2,0,5,3,1,0,4,4,2,3,0,0,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,2,1,1,3,1,6,7,0,2,2,1,7,2,4,4,3,1,0,3,1,2,4,1,2,4,1,2,7,8,0,1,5,4,1,4,2,3,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,7,1,3,3,2,1,5,0,4,7,0,2,1,4,4,4,5,1,4,0,0,4,0,1,2,1,5,3,0,7,2,6,1,2,5,4,1,1,8,0,0,5,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,4,0,5,7,0,2,2,6,2,3,5,1,6,1,0,2,0,1,6,0,2,2,0,6,3,7,2,0,3,6,2,4,4,1,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,5,7,1,1,0,4,5,0,6,3,1,1,0,4,2,3,0,0,4,3,3,0,9,6,2,2,8,1,0,6,0,3,0,5,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,4,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,2,8,1,4,2,4,7,1,2,6,0,3,0,6,3,3,1,0,4,1,2,0,0,6,3,0,9,0,6,1,3,5,4,0,9,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,0,6,0,3,7,1,2,2,1,6,1,3,5,2,1,0,3,3,2,1,2,2,4,1,6,3,5,2,3,7,2,3,5,2,0,0,3,4,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,2,7,0,0,9,0,0,1,6,3,0,6,3,3,0,0,5,0,2,2,1,5,2,1,1,8,7,2,0,7,2,1,6,1,0,2,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,4,8,1,4,2,3,5,2,3,2,5,3,2,3,5,2,1,2,2,3,2,1,1,4,3,1,1,8,6,2,2,6,3,2,6,2,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,1,3,7,1,2,2,3,5,2,4,4,3,1,2,2,2,1,3,2,3,3,1,3,6,6,2,1,5,4,3,2,3,2,1,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,3,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,5,2,8,0,8,1,0,9,0,0,0,1,8,1,5,4,2,2,0,4,1,2,2,2,2,4,0,0,9,5,4,0,7,2,0,7,2,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,3,5,0,2,6,0,3,2,4,4,2,4,4,4,0,1,3,2,2,3,2,2,3,2,4,5,7,2,1,4,5,2,3,4,1,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,2,5,0,1,0,8,3,2,4,3,2,5,0,1,8,0,0,0,1,4,5,0,1,0,5,4,8,1,4,0,5,8,1,8,1,0,0,0,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,2,7,0,0,6,1,2,2,6,2,0,4,5,3,0,0,5,0,2,1,1,3,4,2,0,9,6,0,3,6,3,4,4,0,2,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,3,8,1,5,2,3,5,1,3,0,2,7,0,2,7,0,0,0,5,0,4,0,0,4,3,3,9,0,6,1,3,7,2,3,3,4,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,1,4,0,5,8,1,1,0,4,5,4,2,4,4,0,0,3,0,3,4,2,1,4,0,4,5,7,2,0,5,4,1,3,5,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,1,5,3,2,6,0,3,3,4,3,0,2,7,1,0,0,3,3,4,1,0,2,6,1,4,5,6,1,2,9,0,1,7,2,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,7,0,2,0,4,5,0,3,6,1,0,0,4,3,2,1,1,3,6,1,4,5,6,3,0,8,1,3,3,4,1,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,2,3,1,2,1,5,7,2,0,0,2,7,2,5,2,3,1,0,4,2,0,2,4,2,2,0,0,9,7,1,2,6,3,1,5,3,1,0,4,8,2,0,0,6,0,4,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,11,1,3,2,3,1,3,0,5,6,2,1,0,5,4,3,5,1,3,0,0,4,2,1,3,3,2,2,0,0,9,6,3,0,5,4,1,2,4,4,0,7,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,0,6,0,3,9,0,0,0,3,6,0,0,9,0,0,3,0,6,0,0,0,3,6,0,9,0,7,2,0,9,0,3,5,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,16,1,1,2,4,0,0,3,6,1,2,7,7,2,1,7,2,0,0,5,0,4,0,0,3,3,4,0,0,6,3,2,0,7,6,3,6,1,2,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,2,7,1,2,2,5,5,2,3,1,3,6,1,3,6,0,0,0,3,3,3,0,1,2,6,1,8,1,5,1,4,8,1,3,5,2,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,1,2,8,1,1,1,5,3,0,3,6,2,0,2,3,2,3,1,2,3,4,1,3,6,6,2,2,6,3,2,7,1,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,2,1,6,0,3,2,3,4,1,2,7,1,0,2,2,1,5,1,2,2,5,1,1,8,7,0,2,7,2,0,5,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,3,6,1,3,1,3,6,4,3,3,3,0,0,4,2,1,3,2,3,2,0,1,8,8,1,0,5,4,1,3,5,1,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,1,4,2,3,5,0,4,3,4,2,3,4,3,3,0,0,3,1,2,4,3,0,1,3,3,6,6,0,3,4,5,3,3,3,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,7,1,3,2,2,0,4,2,4,9,0,0,0,4,5,4,2,3,3,0,0,3,2,3,2,2,0,5,0,2,7,4,0,5,3,6,0,4,0,5,0,6,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,0,4,2,4,7,1,2,0,0,9,0,9,0,1,0,0,3,3,3,0,0,0,9,0,5,4,7,1,1,8,1,1,6,2,1,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,2,4,2,2,7,0,2,2,4,3,1,5,4,1,0,0,3,1,4,1,1,4,4,0,2,7,5,3,2,5,4,2,5,3,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,2,3,5,6,3,0,0,3,6,0,1,8,0,0,0,1,6,3,0,1,1,8,0,3,6,6,3,0,9,0,3,5,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,2,2,3,0,4,0,5,4,0,5,3,3,3,0,3,6,2,0,0,2,0,6,1,0,2,6,2,7,2,7,0,2,9,0,5,4,0,0,0,2,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,2,4,1,4,4,1,4,0,2,7,1,7,1,2,0,0,7,2,0,1,7,2,0,0,9,0,7,0,2,3,6,2,3,1,2,2,6,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,5,3,1,6,3,2,4,0,1,8,1,0,0,1,0,7,0,0,2,2,5,8,1,4,2,4,7,2,7,2,0,0,0,2,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,1,3,2,1,4,1,4,7,1,2,6,3,0,0,0,9,2,0,0,3,0,4,0,3,0,3,3,6,3,6,2,1,5,4,6,3,0,0,0,2,8,0,0,4,6,0,4,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,2,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,0,4,9,0,0,1,4,5,2,5,3,3,2,2,2,1,2,3,2,3,2,1,2,7,4,3,3,6,3,0,4,0,0,5,9,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,1,4,1,4,7,1,2,0,6,3,0,6,3,4,0,0,2,2,1,0,5,0,4,0,3,6,6,2,1,5,4,0,9,0,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,4,1,5,3,1,1,6,2,1,0,4,5,1,5,4,3,0,3,2,2,1,2,1,4,3,0,4,5,7,1,1,6,3,5,3,2,0,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,1,3,7,1,2,0,3,6,0,3,6,0,0,0,0,6,3,0,0,0,9,0,5,4,9,0,0,5,4,0,9,0,0,0,3,5,1,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,4,6,1,3,1,3,5,3,3,3,3,0,0,5,1,2,3,2,3,3,0,1,8,8,1,0,6,3,1,3,5,1,0,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,2,4,2,3,5,2,3,2,4,3,1,3,5,1,1,0,3,3,2,2,2,2,5,1,6,3,7,1,2,7,2,3,4,3,1,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,1,1,3,3,1,0,7,0,2,7,2,0,0,4,5,5,4,0,5,0,2,2,0,1,7,2,0,0,0,0,9,5,4,0,0,9,0,2,6,2,0,6,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,5,1,2,3,1,1,4,2,4,5,2,3,3,5,2,2,4,3,3,1,1,4,2,1,2,3,2,3,1,6,3,6,1,2,7,2,2,3,3,1,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,1,6,1,2,7,1,2,1,3,5,2,4,4,2,1,1,4,2,2,2,2,3,4,0,4,5,7,1,1,5,4,2,3,3,2,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,4,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1\ntest,33,1,4,2,8,0,5,0,4,8,0,1,0,1,8,0,1,8,0,0,0,1,5,4,0,1,1,7,1,9,0,7,2,0,9,0,3,6,1,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,2,4,3,4,2,0,5,4,0,3,6,2,0,2,2,3,2,0,1,5,3,1,2,7,5,2,3,6,3,0,6,2,2,1,5,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,4,3,8,1,4,2,3,7,1,2,0,0,9,0,0,9,0,0,0,0,5,4,0,0,0,5,4,9,0,6,2,2,7,2,0,4,5,0,0,4,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,7,1,2,8,1,1,1,3,6,1,4,5,2,1,2,2,3,1,2,1,3,4,0,1,8,5,4,1,5,4,2,3,4,1,0,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,6,3,0,0,9,0,0,0,3,6,6,2,2,5,2,0,3,0,0,5,3,0,2,0,0,9,9,0,0,0,9,0,0,6,3,0,6,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,2,9,0,4,1,5,6,0,3,3,3,4,0,1,8,0,0,0,4,0,5,0,1,2,6,2,9,0,5,0,4,9,0,4,4,2,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,2,2,1,1,3,6,8,0,1,1,2,6,2,3,4,2,0,0,3,4,1,2,3,1,4,1,5,4,7,2,0,7,2,1,2,5,2,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,3,5,2,5,1,3,3,3,4,4,3,3,2,5,2,2,0,0,3,1,4,1,3,4,2,1,8,1,6,0,3,8,1,4,2,3,0,1,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,3,2,0,0,5,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,2,7,0,0,7,1,2,1,3,5,0,4,5,2,1,0,3,2,3,1,3,2,4,1,0,9,6,2,2,5,4,0,5,3,1,0,4,6,2,0,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,4,3,8,0,4,0,5,9,0,0,0,4,5,4,1,5,1,0,0,4,3,3,1,3,0,5,0,9,0,9,0,0,6,3,0,4,4,1,0,5,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,4,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,2,2,6,6,0,3,3,3,4,0,4,5,1,0,0,3,5,2,1,2,0,7,1,8,1,5,0,4,9,0,0,0,9,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,3,4,2,2,9,0,0,1,2,7,1,3,6,1,0,0,3,3,3,1,2,3,5,0,0,9,7,2,0,9,0,0,2,7,1,0,4,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,3,1,2,4,1,1,4,1,4,7,1,2,0,9,0,0,0,9,4,0,0,2,2,1,0,0,5,4,0,5,5,6,2,1,5,4,5,5,0,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,0,6,0,3,9,0,0,0,3,6,0,0,9,0,0,3,0,6,0,0,0,3,6,0,9,0,7,2,0,9,0,3,5,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,1,5,1,4,6,1,3,2,1,6,3,3,4,2,0,0,5,1,2,2,2,3,4,0,1,8,7,2,0,7,2,1,4,4,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,2,2,6,1,2,1,4,5,4,1,5,2,0,0,3,3,2,3,1,5,2,0,3,6,7,0,2,8,1,2,5,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,4,2,1,5,2,2,7,1,2,1,5,4,1,1,7,2,1,1,1,5,2,2,1,1,5,2,2,7,4,3,2,7,2,3,3,3,0,1,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,3,1,1,6,0,3,9,0,0,0,4,5,8,1,0,9,0,0,0,0,0,9,0,0,0,0,0,9,7,2,0,2,7,0,1,3,5,0,7,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,2,3,8,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,6,0,0,3,6,0,4,0,0,4,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,1,4,2,3,7,1,2,1,3,6,1,7,1,3,0,0,6,1,1,2,2,5,2,0,7,2,7,2,1,6,3,1,5,4,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,2,3,10,0,6,0,3,7,0,2,0,6,3,0,3,6,0,0,9,0,0,0,3,0,6,0,0,0,9,6,2,1,7,2,2,4,2,2,1,5,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,4,0,0,5,5,4,0,0,6,3,6,2,2,6,0,0,2,2,1,6,2,0,2,0,0,9,7,2,0,2,7,0,3,3,4,1,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,1,3,8,1,1,1,2,7,0,2,7,1,0,4,1,1,5,1,2,3,4,1,1,8,7,1,1,8,1,2,3,2,4,0,5,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,2,3,3,1,0,6,1,2,6,0,3,1,4,4,3,2,5,1,0,1,4,2,4,1,3,2,4,1,3,6,7,0,2,7,2,0,4,5,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,3,5,2,0,7,0,2,0,3,6,7,1,2,7,0,0,0,1,2,7,0,1,1,2,2,7,8,1,0,4,5,1,1,5,4,0,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,4,2,3,0,7,2,0,9,0,0,0,0,8,2,4,4,2,0,0,2,5,0,2,0,3,5,0,4,5,9,0,0,7,2,0,4,5,0,0,5,7,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,2,5,1,2,8,0,1,2,5,3,1,5,4,2,0,0,3,3,3,1,1,5,4,0,8,1,8,1,1,4,5,2,5,2,0,0,3,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,3,0,6,7,0,2,1,3,6,2,5,2,2,0,1,3,4,0,2,2,1,5,0,1,8,6,3,0,5,4,3,1,6,0,0,4,4,0,0,0,5,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,3,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,3,6,0,5,2,2,4,2,4,4,4,3,1,3,5,2,1,0,3,2,3,2,2,1,3,2,6,3,6,1,3,8,1,4,3,3,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,4,4,0,2,9,0,0,0,3,6,2,5,2,2,0,1,4,1,3,2,2,3,3,0,1,8,9,0,0,4,5,2,3,5,0,0,4,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,6,0,3,5,1,4,2,3,5,0,1,8,1,0,0,3,2,5,1,1,2,5,2,7,2,4,0,5,8,1,3,3,1,3,0,4,5,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,2,3,0,2,4,4,7,0,2,0,1,8,4,3,3,2,0,0,2,1,5,1,4,0,5,0,3,6,7,0,2,6,3,3,1,6,1,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,3,5,2,4,1,3,3,2,5,5,3,2,2,5,3,1,0,0,3,2,4,1,2,4,3,1,8,1,5,0,4,8,1,6,2,1,0,1,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,2,2,4,7,0,6,1,3,6,1,3,4,3,3,0,1,8,0,0,1,2,4,3,0,1,1,5,3,7,2,5,1,3,8,1,5,2,3,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,2,9,4,2,1,4,7,1,2,1,3,6,3,4,2,3,0,0,4,1,2,3,3,2,3,1,4,5,6,2,2,5,4,2,3,3,3,0,6,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,7,2,0,0,6,0,0,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,0,6,1,2,1,2,6,3,4,3,2,5,3,1,0,0,5,2,2,1,4,2,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,2,7,1,0,7,0,2,0,2,7,4,2,4,4,0,0,0,2,4,4,0,2,2,3,1,8,7,2,0,5,4,2,3,2,3,0,5,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,4,6,2,1,1,5,3,0,3,6,1,0,0,4,3,2,1,1,2,6,0,6,3,6,0,3,5,4,2,2,5,0,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,0,3,0,6,7,0,2,2,2,6,2,4,3,1,0,0,6,0,2,1,3,4,3,0,6,3,5,1,3,8,1,2,5,3,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,7,1,2,8,1,1,1,3,6,1,4,5,2,1,2,2,3,1,2,1,3,4,0,1,8,5,4,1,5,4,2,3,4,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,5,8,1,5,2,2,5,1,3,3,5,1,1,5,3,3,1,0,4,1,2,2,3,3,2,1,5,4,7,1,1,5,4,3,3,3,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,0,4,2,4,0,5,4,2,5,3,0,4,5,0,0,0,2,4,4,0,0,4,4,2,9,0,7,0,2,9,0,4,2,3,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,3,0,6,6,0,3,1,3,6,1,5,4,1,0,1,4,3,3,1,1,4,5,0,9,0,5,0,4,5,4,5,4,1,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,2,7,0,0,9,0,0,0,3,6,0,3,6,0,0,0,2,5,2,1,2,1,5,1,4,5,7,2,0,9,0,3,5,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,5,7,1,1,0,5,4,0,8,1,1,1,0,4,2,3,0,0,5,4,1,0,9,6,2,2,8,1,0,8,0,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,1,5,1,4,7,1,2,1,4,5,4,4,2,4,0,1,3,1,2,4,2,2,3,0,4,5,6,2,1,4,5,1,2,6,1,0,5,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,2,9,0,4,2,4,7,1,2,3,4,3,1,1,8,1,0,0,3,6,0,1,1,1,8,0,3,6,7,1,1,8,1,0,7,2,0,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,0,4,1,5,4,3,3,3,2,4,1,4,4,2,0,0,2,2,4,1,3,2,3,3,8,1,6,1,2,8,1,4,4,1,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,6,2,3,3,2,1,4,0,4,7,0,2,2,2,6,3,4,3,4,0,2,3,0,1,4,2,3,1,0,0,9,7,2,0,4,5,2,3,3,2,1,6,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,2,3,8,0,5,2,2,6,1,2,2,3,4,3,1,6,2,0,0,3,3,2,2,1,2,5,0,2,7,7,0,2,8,1,2,5,2,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,4,1,0,5,6,3,0,0,4,5,3,3,4,3,0,0,0,0,6,3,0,3,3,0,0,9,6,3,0,2,7,0,0,5,0,4,9,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,4,3,8,0,4,1,5,6,2,2,0,4,5,0,4,5,4,0,0,3,3,0,0,4,0,5,0,5,4,6,2,2,8,1,0,0,4,5,0,7,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,26,1,2,5,6,1,1,0,8,5,1,4,5,4,1,1,3,5,1,1,1,6,1,2,1,1,3,5,1,9,0,3,1,6,6,3,7,1,1,1,1,2,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,3,2,4,1,2,1,5,2,7,0,2,1,6,3,2,5,2,5,2,1,1,0,2,4,3,1,2,0,2,7,7,0,2,5,4,1,1,8,0,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,12,1,3,2,3,0,4,0,5,9,0,0,0,2,7,3,3,4,3,0,0,3,3,2,3,3,0,4,0,9,0,9,0,0,6,3,0,6,2,2,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,2,8,0,4,2,4,3,2,4,4,4,2,0,5,4,0,0,0,7,0,2,0,5,0,4,0,7,2,7,0,2,9,0,4,5,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,11,2,2,5,3,2,4,4,0,5,0,4,4,2,3,2,3,4,4,0,0,2,0,4,4,2,0,0,4,0,9,4,2,4,5,4,3,5,1,1,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,4,2,8,0,5,1,4,8,1,0,0,1,8,0,4,5,0,0,0,5,2,2,0,2,3,5,0,2,7,5,4,0,9,0,1,7,2,1,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,3,3,0,3,7,2,1,1,6,3,0,2,7,1,0,0,2,3,4,0,2,1,7,1,6,3,7,1,2,7,2,5,3,1,1,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,2,3,3,1,0,6,1,2,6,0,3,1,4,4,3,2,5,1,0,1,4,2,4,1,3,2,4,1,3,6,7,0,2,7,2,0,4,5,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,2,4,10,0,6,0,3,6,0,3,3,3,4,0,2,7,0,0,6,3,0,1,0,2,4,2,3,0,9,4,3,3,4,5,6,0,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,4,1,5,7,1,2,2,2,6,1,3,6,0,0,0,3,2,4,0,1,2,5,2,7,2,6,2,2,9,0,4,4,2,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,4,1,1,5,1,3,6,1,2,0,4,5,8,1,1,5,0,0,1,1,4,4,1,4,1,0,0,9,7,2,1,5,4,0,1,5,4,1,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,3,5,0,2,9,0,0,0,5,4,2,4,4,2,0,0,6,0,2,1,2,3,4,0,6,3,7,2,0,7,2,3,4,2,1,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,0,9,0,0,7,0,2,2,3,5,4,3,3,4,0,0,3,2,2,2,2,3,3,0,0,9,9,0,0,3,6,2,0,4,4,0,7,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,1,3,8,1,6,1,2,1,0,8,8,1,1,0,4,5,1,0,2,2,4,1,0,4,1,5,1,2,7,3,0,6,9,0,8,1,1,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,1,2,1,6,4,1,5,4,3,2,5,2,2,5,0,0,2,1,2,4,1,3,2,1,4,5,5,1,3,5,4,3,2,3,1,1,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,2,8,0,5,1,3,7,0,2,0,0,9,1,3,5,3,0,0,2,3,3,3,0,2,4,1,2,7,6,3,0,6,3,4,4,2,0,0,3,3,2,0,0,5,0,6,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,7,1,2,1,4,4,1,2,6,1,0,1,3,2,4,1,1,2,6,1,5,4,5,2,3,6,3,2,5,2,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,4,9,0,7,0,2,3,1,6,4,2,4,0,1,8,1,0,0,1,3,5,1,1,1,4,4,8,1,3,0,6,8,1,6,2,2,1,0,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,2,4,2,3,2,6,1,1,7,1,1,0,2,7,3,5,2,4,1,0,3,1,2,3,2,2,3,1,2,7,7,2,1,4,5,1,2,5,1,1,6,8,2,0,0,6,0,6,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,0,9,0,0,5,0,4,4,2,3,0,3,6,0,0,2,0,3,5,2,0,0,5,3,0,9,3,2,4,7,2,5,0,4,0,0,2,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,2,9,0,3,3,4,0,3,6,5,4,1,2,4,3,1,0,0,4,3,1,1,1,3,5,1,9,0,4,0,5,7,2,5,1,3,1,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,1,5,7,0,7,1,1,5,0,4,6,2,2,1,3,6,1,0,0,1,1,8,1,1,1,4,5,8,1,3,0,6,9,0,3,6,0,0,0,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,4,3,8,0,4,1,5,6,2,2,0,4,5,0,4,5,4,0,0,3,3,0,0,4,0,5,0,5,4,6,2,2,8,1,0,0,4,5,0,7,5,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,2,8,0,6,0,3,6,0,3,3,0,6,0,2,7,0,0,0,0,9,0,0,0,0,9,0,2,7,9,0,0,9,0,3,0,6,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,4,2,3,7,0,2,0,2,7,0,2,7,0,0,2,4,0,3,0,0,5,4,0,0,9,7,0,2,9,0,3,6,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,0,6,1,3,6,1,3,3,2,5,0,2,7,0,0,1,2,4,3,0,1,2,5,2,6,3,5,1,3,8,1,5,2,3,0,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,6,1,3,7,0,2,1,3,6,1,2,7,1,0,1,3,3,4,1,2,1,6,1,1,8,8,0,1,8,1,0,2,7,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,2,4,2,2,7,0,2,2,4,3,1,5,4,1,0,0,3,1,4,1,1,4,4,0,2,7,5,3,2,5,4,2,5,3,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,1,5,1,3,7,0,2,2,4,4,3,4,3,3,2,1,3,2,0,4,2,2,2,1,5,4,6,2,2,3,6,2,3,4,2,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,2,5,0,6,1,2,1,2,6,3,5,1,2,6,2,1,0,0,4,1,3,1,3,4,2,0,9,0,5,1,3,5,4,3,2,5,0,0,4,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,2,3,3,1,7,1,2,7,1,2,2,3,5,1,7,2,1,0,0,7,2,1,1,4,3,2,1,2,7,8,0,1,6,3,1,6,2,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,3,2,1,3,5,2,2,2,5,2,2,4,4,2,2,0,2,3,1,3,2,0,4,0,6,3,7,0,2,6,3,5,0,0,4,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,4,2,0,0,6,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,4,5,1,4,1,5,6,1,2,3,4,3,1,4,5,1,0,0,3,1,6,1,1,2,5,2,4,6,5,1,4,8,1,4,3,2,1,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,0,7,0,2,7,0,2,0,4,5,0,0,9,0,0,0,2,7,0,0,0,0,9,0,9,0,7,0,2,9,0,3,4,2,2,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,2,2,3,0,4,2,4,7,1,2,3,4,3,2,6,1,5,0,0,3,1,0,4,2,2,3,0,7,2,7,1,1,8,1,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,6,1,3,8,1,1,1,3,6,0,3,6,0,0,2,3,4,1,0,2,4,4,1,2,7,7,1,2,6,3,2,2,6,0,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,2,3,3,9,2,6,0,3,6,1,3,1,0,8,1,3,6,2,1,0,4,0,3,1,2,2,3,3,2,7,5,2,2,5,4,1,4,2,3,1,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,4,2,3,0,2,3,5,6,0,3,2,0,7,0,6,3,0,0,0,7,0,2,0,3,4,3,0,4,5,9,0,0,7,2,0,7,0,2,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,6,0,2,7,0,2,1,4,4,2,3,5,3,0,0,3,2,2,2,1,1,5,2,4,5,6,0,3,7,2,1,4,4,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,39,1,2,4,9,3,5,1,1,4,2,4,4,4,1,2,3,5,2,2,1,2,3,1,2,1,2,3,2,7,2,5,0,4,7,2,6,2,2,1,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,3,1,2,4,1,0,9,0,0,9,0,0,2,4,3,0,3,6,3,0,2,2,2,2,2,1,3,3,2,1,8,4,5,0,4,5,5,3,1,0,0,2,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,2,0,5,2,2,2,6,2,0,3,6,5,0,0,1,0,4,2,0,2,4,2,4,5,4,4,2,3,6,3,6,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,7,0,2,7,0,2,2,0,7,1,4,5,0,0,0,4,3,3,0,2,2,3,3,7,2,4,1,4,8,1,2,4,4,0,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,21,2,2,4,5,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,6,3,0,0,9,0,0,0,3,6,6,2,2,5,2,0,3,0,0,5,3,0,2,0,0,9,9,0,0,0,9,0,0,6,3,0,6,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,33,1,2,4,8,0,1,3,6,6,0,3,3,3,3,0,1,8,1,1,0,1,8,1,1,1,0,8,1,9,0,5,2,3,7,2,7,2,1,0,0,2,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,9,1,2,3,3,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,3,2,2,3,2,4,5,0,4,3,3,3,4,3,3,2,2,2,3,2,0,4,3,2,2,0,4,5,5,2,3,6,3,5,2,3,0,0,3,7,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,2,3,8,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,27,2,2,4,6,0,4,2,3,5,0,4,4,2,4,0,0,9,0,0,2,2,3,3,0,0,2,3,5,9,0,7,0,2,9,0,2,7,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,0,5,2,3,5,2,3,3,1,5,1,6,2,1,0,0,5,3,1,1,4,1,4,1,8,1,8,0,1,9,0,4,4,2,0,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,0,3,3,4,6,3,1,1,5,3,2,4,3,1,0,1,6,1,2,1,3,3,2,1,2,7,4,0,5,7,2,3,3,2,2,1,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,1,5,3,2,6,0,3,3,4,3,0,2,7,1,0,0,3,3,4,1,0,2,6,1,4,5,6,1,2,9,0,1,7,2,0,0,3,6,2,0,0,6,0,5,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,9,1,3,3,3,0,3,0,6,7,0,2,2,2,6,2,4,3,1,0,0,6,0,2,1,3,4,3,0,6,3,5,1,3,8,1,2,5,3,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,2,3,4,2,3,4,3,1,1,2,2,3,3,1,3,3,1,2,7,7,2,1,5,4,2,4,3,1,1,4,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,2,8,0,8,1,0,9,0,0,0,2,7,2,3,4,3,1,0,4,1,1,3,2,2,4,0,0,9,7,2,0,6,3,0,6,3,0,0,4,3,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,2,7,0,0,9,0,0,1,4,4,0,5,4,3,0,0,5,0,2,1,1,5,3,1,1,8,7,2,0,7,2,1,5,2,0,1,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,4,3,7,1,4,1,4,7,1,2,0,3,6,0,0,9,5,0,0,2,0,3,0,0,6,3,0,6,3,6,2,1,5,4,7,2,0,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,0,5,9,0,0,0,2,7,1,1,8,1,0,0,2,6,2,1,1,1,5,4,3,6,6,1,2,7,2,3,5,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,2,3,10,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,4,3,0,5,3,2,4,1,4,5,4,1,3,2,4,1,1,0,5,2,1,3,1,2,2,3,9,0,5,0,4,5,4,3,4,3,0,0,4,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,1,1,8,5,3,0,2,7,0,2,5,4,0,0,0,9,0,0,0,0,3,6,0,0,0,4,5,9,0,6,3,0,9,0,5,4,0,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,1,1,5,1,3,2,4,8,1,0,9,0,0,0,9,0,0,0,0,9,0,0,0,6,3,0,0,9,0,9,0,0,0,9,9,0,0,0,0,1,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,6,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,0,6,0,3,3,0,6,3,4,3,0,3,6,2,0,0,2,4,4,1,0,2,4,3,9,0,5,0,4,5,4,5,3,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,4,8,0,5,0,4,9,0,0,0,4,5,0,3,6,0,0,0,3,6,0,0,2,0,7,0,4,5,9,0,0,9,0,5,4,0,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0\ntest,33,1,3,3,8,0,7,0,2,5,1,4,4,1,5,0,2,7,0,0,0,1,2,6,0,0,2,5,2,6,3,6,1,2,8,1,2,6,0,1,0,3,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,22,1,2,2,5,0,6,1,2,1,2,6,3,5,1,2,6,2,1,0,0,4,1,3,1,3,4,2,0,9,0,5,1,3,5,4,3,2,5,0,0,4,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,39,2,5,2,9,1,5,0,3,7,1,2,0,0,9,0,4,5,0,0,0,6,3,0,0,4,0,5,0,2,7,6,2,2,6,3,0,4,5,0,0,4,5,1,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,2,9,0,5,1,3,7,0,2,0,0,9,1,3,5,3,0,0,2,3,3,3,0,2,4,1,2,7,6,3,0,6,3,4,4,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,4,6,1,3,1,3,5,3,3,3,3,0,0,5,1,2,3,2,3,3,0,1,8,8,1,0,6,3,1,3,5,1,0,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,4,2,8,0,5,0,4,9,0,0,0,0,9,0,4,5,0,2,0,0,7,0,2,0,0,7,0,7,2,4,5,0,7,2,4,2,2,2,0,4,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,2,3,1,4,3,1,5,5,3,2,3,5,2,3,1,0,4,1,2,3,2,3,2,1,7,2,5,1,3,4,5,3,2,3,1,1,4,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,3,2,5,2,2,2,5,5,1,4,2,2,6,0,5,4,1,0,0,4,3,2,1,1,2,6,0,2,7,5,1,3,8,1,2,6,1,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,7,0,1,6,0,3,2,4,4,4,4,2,3,0,0,5,1,1,3,2,3,2,0,0,9,6,3,2,5,4,1,4,4,1,0,5,4,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,3,2,5,1,3,3,4,8,1,1,1,4,5,0,4,5,1,0,0,3,5,2,1,2,3,5,0,3,6,6,1,2,8,1,2,6,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,0,4,0,5,9,0,0,0,7,2,0,0,9,0,0,0,0,5,4,0,0,0,7,2,9,0,5,0,4,9,0,4,4,2,0,0,3,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,1,2,4,1,0,5,1,4,7,1,2,1,5,3,6,2,2,6,1,0,1,1,1,6,1,1,2,1,2,7,7,1,1,2,7,2,4,4,1,0,4,6,2,0,0,6,0,5,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,1,5,2,3,6,1,3,3,4,2,1,4,4,3,1,0,4,1,2,2,2,2,3,2,4,5,6,1,2,6,3,3,3,3,1,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,2,3,2,6,0,3,6,1,3,1,0,8,2,6,2,2,1,0,5,0,3,2,3,3,2,1,4,5,5,2,2,5,4,2,3,4,1,1,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,2,5,0,3,1,6,5,2,3,3,2,5,1,4,5,1,0,0,3,4,2,1,2,2,5,1,8,1,6,1,2,8,1,6,3,1,0,0,2,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,3,2,1,5,1,3,7,1,2,0,2,7,3,4,2,2,1,0,4,3,1,2,3,2,3,1,1,8,7,0,2,5,4,1,3,4,2,1,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,1,4,1,4,7,1,2,2,4,4,2,4,4,3,1,2,2,2,1,2,2,3,3,1,4,5,6,2,1,5,4,3,3,3,2,1,4,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,5,8,1,4,1,4,7,1,2,4,4,2,0,6,3,0,0,0,9,0,0,0,3,4,3,0,9,0,6,2,1,5,4,6,0,3,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,3,7,1,2,0,5,4,3,5,1,2,1,0,4,1,2,2,3,4,1,1,2,7,7,0,2,5,4,1,2,5,2,1,6,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,3,3,2,1,2,5,6,2,1,1,4,4,0,4,5,1,3,1,4,1,2,2,2,3,3,1,1,8,6,1,3,7,2,1,3,6,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,1,4,2,4,6,1,3,0,5,4,0,2,7,0,0,0,0,0,9,0,0,2,2,5,5,4,5,1,3,7,2,0,7,0,2,0,4,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,4,7,0,4,3,2,7,0,2,2,5,2,0,4,5,0,0,0,4,2,4,0,1,3,4,2,9,0,3,0,6,7,2,2,7,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,0,3,8,1,1,1,3,5,1,4,5,1,1,3,2,3,1,2,1,3,4,0,2,7,5,4,0,6,3,3,4,4,0,0,4,4,0,0,4,6,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,0,5,0,4,5,1,4,2,4,4,1,2,7,0,0,1,2,1,6,1,1,0,2,6,8,1,4,2,4,8,1,2,7,1,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,0,5,1,3,7,1,2,3,2,5,1,3,6,2,1,1,2,3,3,1,1,1,5,2,5,4,5,2,2,7,2,4,4,1,1,1,3,5,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,0,4,0,5,9,0,0,0,2,7,3,3,4,3,0,0,3,3,2,3,3,0,4,0,9,0,9,0,0,6,3,0,6,2,2,0,4,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,3,4,2,2,9,0,0,3,4,3,3,4,3,2,0,0,3,1,5,2,4,1,3,0,0,9,7,2,0,9,0,0,5,3,2,0,5,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,0,7,1,2,7,1,2,3,3,3,2,3,5,3,0,3,3,2,0,2,2,4,3,0,1,8,5,2,3,3,6,3,4,4,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,3,3,0,6,0,3,9,0,0,0,5,4,0,9,0,0,0,0,5,0,4,0,0,6,3,0,5,4,7,2,0,7,2,0,9,0,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,4,1,1,4,1,4,6,2,2,0,4,5,0,6,3,3,0,0,4,0,3,0,3,3,3,0,0,9,7,2,1,4,5,1,2,4,2,1,6,8,0,0,0,6,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,4,2,9,0,5,1,4,7,1,2,1,1,8,0,1,8,1,0,0,2,4,4,1,1,1,5,4,8,1,6,1,2,9,0,2,6,2,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,35,1,3,3,8,0,7,0,2,7,0,2,2,0,7,1,4,5,0,0,0,4,3,3,0,2,2,3,3,7,2,4,1,4,8,1,2,4,4,0,0,3,5,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,4,3,9,0,7,2,0,9,0,0,0,0,9,0,2,7,0,0,0,2,2,6,0,2,0,7,0,7,2,7,2,0,9,0,0,7,2,0,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,6,3,0,9,0,0,0,4,5,2,4,4,2,2,0,4,2,1,2,3,2,4,0,9,0,7,0,2,6,3,2,5,3,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,9,1,4,2,3,0,2,3,5,6,0,3,2,0,7,0,6,3,0,0,0,7,0,2,0,3,4,3,0,4,5,9,0,0,7,2,0,7,0,2,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0\ntest,31,1,2,4,7,0,9,0,0,9,0,0,1,8,1,0,4,5,0,0,0,1,7,1,0,0,0,9,0,8,1,4,5,0,4,5,1,8,0,0,0,3,1,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,4,2,3,0,6,1,3,7,1,2,1,2,7,1,3,6,0,0,0,3,2,4,0,1,3,5,1,8,1,7,1,1,9,0,2,3,4,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,5,6,2,2,2,4,4,1,3,6,1,1,1,3,3,2,1,2,3,4,1,5,4,6,2,2,8,1,4,4,2,1,1,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,9,1,3,3,3,1,5,2,1,6,1,3,2,3,5,1,3,5,1,0,0,3,3,3,1,2,2,5,1,8,1,5,1,3,6,3,5,2,2,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,0,5,9,0,0,0,3,6,1,2,7,1,0,0,3,4,2,1,1,1,6,1,5,4,6,1,2,7,2,2,4,3,1,0,4,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,2,1,2,5,0,3,9,0,0,1,3,5,4,5,0,9,0,0,0,0,0,9,0,0,0,0,0,9,7,2,0,2,7,0,0,5,0,4,0,8,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,3,4,3,9,0,0,0,1,8,0,4,5,1,0,0,3,5,2,1,2,1,4,3,9,0,5,3,2,7,2,0,3,3,4,0,7,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,36,1,2,3,8,1,5,1,3,7,0,2,2,5,3,2,3,4,2,0,0,3,4,2,1,1,3,5,0,7,2,6,1,2,6,3,2,5,3,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,3,3,4,0,0,5,5,4,0,0,3,6,4,3,2,4,0,0,3,1,2,1,6,0,2,0,0,9,7,2,0,2,7,0,1,4,3,2,9,7,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,0,4,7,0,2,2,5,2,3,4,2,5,0,0,4,0,0,5,0,3,2,0,6,3,6,3,0,2,7,3,4,3,0,0,4,5,2,0,0,0,0,0,0,0,0,0,3,3,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,0,0,4,5,0,0,0,0,4,0,0,0,0,0,6,6,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,1,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,0,5,0,4,8,0,1,0,3,6,0,3,6,0,2,0,5,3,1,0,2,4,4,1,1,8,8,1,0,8,1,0,6,3,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,2,8,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,5,3,8,0,6,2,2,6,0,3,3,0,6,0,3,6,0,0,0,2,7,0,0,0,0,9,0,6,3,4,0,5,9,0,5,2,2,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,9,0,0,5,0,4,2,4,3,0,2,7,0,0,0,2,1,7,0,0,2,5,3,5,4,7,0,2,5,4,5,2,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,6,1,2,6,1,2,2,3,4,1,3,5,2,1,1,4,2,2,1,2,3,4,1,2,7,5,2,3,5,4,2,4,3,1,1,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,40,1,3,3,10,0,3,0,6,9,0,0,0,4,5,2,0,7,2,0,5,0,2,0,2,1,5,3,0,2,7,7,2,0,7,2,4,2,4,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,2,9,0,3,2,5,5,3,2,1,6,2,1,4,4,1,1,1,6,1,2,1,2,2,5,0,8,1,5,1,4,8,1,6,3,1,1,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,1,1,2,4,1,1,4,2,3,7,1,1,0,6,3,4,5,0,8,0,0,0,0,1,6,3,0,0,0,0,9,6,3,0,3,6,0,0,1,8,0,0,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,4,0,5,5,2,2,3,4,3,2,1,7,4,0,2,2,3,0,0,0,3,5,1,1,8,6,2,2,6,3,0,6,3,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,39,2,5,3,9,0,6,0,3,9,0,0,0,3,6,0,4,5,0,0,0,3,6,0,0,2,3,5,0,3,6,9,0,0,9,0,6,0,3,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,2,2,5,7,0,2,0,6,3,0,2,7,0,0,0,3,4,3,0,2,0,7,0,9,0,7,2,0,9,0,6,3,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,3,8,0,5,0,4,6,0,3,2,3,4,2,4,4,2,0,2,3,2,3,2,1,5,2,1,0,9,6,3,0,4,5,0,4,4,2,1,6,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,0,6,0,3,6,0,3,3,6,1,1,4,4,0,0,2,3,2,4,1,1,3,4,2,1,8,5,2,2,5,4,2,4,4,1,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,7,0,0,4,6,0,0,0,0,5,2,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,0,6,1,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,3,4,3,1,7,1,1,1,4,5,1,6,2,1,0,0,6,1,2,1,4,2,3,1,4,5,4,2,3,7,2,2,2,5,1,0,4,4,0,0,0,6,0,6,0,0,0,0,0,0,0,0,0,4,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,1,1,3,3,1,0,7,2,0,9,0,0,0,3,6,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,7,2,0,2,7,0,0,7,2,0,5,8,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,2,4,3,3,0,4,2,4,8,1,1,1,3,6,1,7,2,4,0,0,3,3,0,1,3,3,3,0,1,8,8,0,1,4,5,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,2,2,9,0,0,0,6,3,7,0,2,3,5,0,0,0,2,6,0,2,2,0,0,9,9,0,0,2,7,0,2,5,2,0,6,3,0,0,0,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,3,8,0,5,1,3,6,1,3,2,4,4,2,2,6,2,0,0,2,3,3,2,1,2,5,1,5,4,6,1,2,6,3,3,4,2,2,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,12,1,3,2,3,4,1,0,5,6,3,0,0,4,5,5,3,2,5,0,0,3,1,2,4,3,1,2,0,0,9,6,3,0,2,7,0,3,4,3,1,7,7,2,0,0,5,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,4,3,0,5,3,2,4,1,4,5,4,1,3,2,4,1,1,0,5,2,1,3,1,2,2,3,9,0,5,0,4,5,4,3,4,3,0,0,4,6,0,0,0,6,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0\ntest,12,1,3,2,3,0,3,3,3,5,2,2,2,2,5,2,6,2,3,0,0,6,0,2,1,2,5,1,0,0,9,9,0,0,4,5,2,3,5,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,4,2,3,0,2,3,5,6,0,3,2,0,7,0,6,3,0,0,0,7,0,2,0,3,4,3,0,4,5,9,0,0,7,2,0,7,0,2,0,4,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,4,9,1,5,2,2,6,1,2,2,5,3,1,1,7,1,1,1,3,3,2,1,2,2,5,1,3,6,6,2,2,7,2,4,3,2,1,1,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,1,4,1,4,9,0,0,0,5,4,3,5,2,5,0,0,1,1,3,2,2,5,2,0,0,9,7,2,0,2,7,0,2,5,3,1,6,8,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,4,2,8,0,7,0,2,7,2,0,0,4,5,0,3,6,2,2,0,2,3,2,0,2,4,4,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,2,4,2,3,1,4,0,5,5,0,4,1,1,7,5,2,3,4,0,0,5,0,1,3,2,2,3,1,8,1,7,0,2,6,3,2,4,1,3,0,5,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,0,4,5,0,4,3,4,3,0,0,9,0,0,0,0,5,4,0,0,0,7,2,6,3,6,1,2,6,3,9,0,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,2,2,0,5,6,1,3,3,1,6,0,1,8,3,0,1,0,2,4,0,0,2,3,4,6,3,6,0,3,9,0,3,6,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,2,4,8,1,0,0,2,7,0,3,6,0,0,0,4,3,3,0,1,3,6,0,3,6,6,3,0,9,0,2,5,2,2,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,39,1,2,3,9,0,7,0,2,6,0,3,3,2,4,2,3,4,0,2,0,3,4,0,3,2,0,5,0,0,9,7,2,0,7,2,4,2,4,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,12,1,3,2,3,6,3,0,0,9,0,0,0,3,6,6,2,2,5,2,0,3,0,0,5,3,0,2,0,0,9,9,0,0,0,9,0,0,6,3,0,6,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,4,6,1,5,1,2,6,1,3,3,5,2,2,3,4,3,1,1,3,2,1,2,1,2,3,2,5,4,6,0,3,6,3,3,3,3,1,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,2,5,1,4,2,3,4,1,4,4,4,2,2,4,4,2,1,0,3,4,0,3,2,0,6,0,7,2,6,0,3,7,2,4,0,5,0,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,3,7,0,2,2,5,5,2,3,2,4,4,0,3,6,0,0,0,2,5,3,0,1,1,8,1,9,0,7,0,2,7,2,5,4,1,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,33,1,3,3,8,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,33,2,4,2,8,0,5,1,4,8,1,0,0,1,8,0,4,5,0,0,0,5,2,2,0,2,3,5,0,2,7,5,4,0,9,0,1,7,2,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,2,8,0,4,2,4,7,1,2,3,4,3,2,4,5,3,0,0,3,4,0,2,1,1,5,0,5,4,7,1,1,8,1,0,6,3,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,7,1,2,1,4,4,1,2,6,1,0,1,3,2,4,1,1,2,6,1,5,4,5,2,3,6,3,2,5,2,1,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,4,8,0,6,0,3,7,0,2,0,3,6,0,3,6,2,1,2,1,3,3,1,0,4,5,0,1,8,7,2,0,7,2,4,3,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,2,4,2,3,0,9,0,0,9,0,0,0,0,9,0,3,6,0,0,0,5,3,2,0,3,0,6,0,2,7,5,4,0,5,4,0,4,5,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,2,3,3,5,0,9,0,0,5,0,4,3,0,6,0,5,4,3,0,0,2,0,4,3,0,3,3,0,3,6,5,2,3,7,2,4,2,3,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,3,3,1,4,1,4,7,1,2,0,3,6,4,3,3,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,3,2,2,2,8,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,0,7,0,2,6,0,3,3,2,4,2,3,4,0,2,0,3,4,0,3,2,0,5,0,0,9,7,2,0,7,2,4,2,4,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,4,2,5,0,5,1,4,8,1,1,1,2,6,1,3,6,0,0,0,5,1,3,0,1,3,5,1,7,2,8,1,1,9,0,2,6,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,2,4,8,1,0,0,2,7,0,3,6,0,0,0,4,3,3,0,1,3,6,0,3,6,6,3,0,9,0,2,5,2,2,0,4,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,1,4,8,1,4,1,4,7,1,2,9,0,0,0,0,9,0,0,0,9,0,0,0,9,0,0,0,9,0,6,2,1,5,4,5,5,0,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,2,2,2,5,5,1,4,3,4,3,0,6,3,1,0,0,4,3,2,1,2,4,3,0,3,6,5,1,3,8,1,3,5,2,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,3,8,1,5,2,3,5,1,3,0,2,7,0,2,7,0,0,0,5,0,4,0,0,4,3,3,9,0,6,1,3,7,2,3,3,4,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,11,1,2,4,3,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,6,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,1,5,0,4,8,0,1,1,3,5,3,5,3,3,0,0,3,1,2,3,2,3,3,0,5,4,9,0,0,0,9,2,3,4,1,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,1,4,8,1,4,1,4,6,1,3,9,0,0,0,0,9,0,0,0,3,4,3,0,0,5,0,4,0,9,5,2,2,4,5,9,0,0,0,0,1,3,2,0,0,6,0,0,0,0,0,0,2,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,2,2,2,3,5,0,2,5,2,3,3,4,3,4,3,2,4,0,0,3,0,2,4,3,0,2,0,9,0,3,0,6,9,0,2,3,4,2,1,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,2,5,2,1,7,2,0,0,2,7,4,4,2,3,0,0,5,2,1,2,3,3,2,1,0,9,9,0,0,4,5,1,2,7,0,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,5,9,0,5,4,0,0,0,9,7,0,2,0,0,9,0,0,0,0,6,3,0,0,0,6,3,7,2,7,1,2,7,2,7,2,0,0,0,1,5,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,1,3,7,1,2,0,3,6,0,3,6,0,0,0,0,6,3,0,0,0,9,0,5,4,9,0,0,5,4,0,9,0,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,2,7,0,0,7,1,2,1,4,5,0,3,6,1,1,0,2,3,4,1,1,3,5,1,0,9,6,2,2,5,4,0,7,2,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,6,3,0,9,0,0,0,4,5,2,4,4,2,2,0,4,2,1,2,3,2,4,0,9,0,7,0,2,6,3,2,5,3,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,5,0,2,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,3,5,0,2,9,0,0,0,5,4,2,4,4,2,0,0,6,0,2,1,2,3,4,0,6,3,7,2,0,7,2,3,4,2,1,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,3,7,0,5,1,3,7,1,2,3,5,2,0,4,5,0,0,0,2,2,6,0,3,3,2,3,3,6,9,0,0,5,4,2,3,3,3,0,5,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,4,0,5,9,0,0,0,3,6,1,2,7,1,0,0,3,4,2,1,1,1,6,1,5,4,6,1,2,7,2,2,4,3,1,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,4,7,1,3,1,5,5,1,4,3,4,3,0,2,8,0,0,0,1,4,4,0,1,1,5,3,8,1,5,0,4,7,2,6,3,1,1,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,5,1,3,7,1,2,3,2,5,1,3,6,2,1,1,2,3,3,1,1,1,5,2,5,4,5,2,2,7,2,4,4,1,1,1,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,19,1,1,3,4,2,0,0,7,0,1,8,8,1,0,6,3,0,2,3,3,3,0,0,5,2,3,0,0,4,5,5,1,4,6,3,6,0,2,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,2,3,0,5,9,0,0,0,4,5,7,1,1,3,1,0,3,2,1,5,2,2,1,0,9,0,5,0,4,6,3,0,2,7,0,0,5,4,2,0,0,0,0,0,0,0,0,0,0,5,0,2,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,2,2,0,8,0,1,7,0,2,0,1,8,0,7,2,5,0,0,3,2,1,3,3,1,2,3,2,7,6,3,0,2,7,2,3,3,2,0,5,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,0,7,1,1,7,1,2,2,3,5,1,3,5,2,1,0,4,3,1,2,2,1,5,1,3,6,6,2,2,5,4,3,3,3,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,4,6,1,5,1,2,6,1,3,3,5,2,2,3,4,3,1,1,3,2,1,2,1,2,3,2,5,4,6,0,3,6,3,3,3,3,1,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,3,5,1,1,1,7,5,2,2,2,3,5,1,1,8,0,0,0,3,2,4,0,1,1,7,2,8,1,6,1,3,8,1,5,3,1,1,0,2,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,0,1,3,5,7,0,2,2,2,6,0,3,6,2,1,0,1,5,1,2,2,0,5,1,9,0,5,2,3,7,2,3,4,2,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,4,5,1,4,1,5,6,1,2,3,4,3,1,4,5,1,0,0,3,1,6,1,1,2,5,2,4,6,5,1,4,8,1,4,3,2,1,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,7,0,2,9,0,0,1,3,6,0,1,8,0,0,0,1,2,6,0,0,1,6,3,9,0,7,0,2,5,4,3,6,1,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,2,2,0,9,0,0,9,0,0,0,4,5,0,5,4,5,0,0,0,0,4,2,0,6,2,0,0,9,9,0,0,4,5,0,0,7,0,2,8,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,35,1,2,4,8,0,9,0,0,5,0,4,4,2,3,0,3,6,0,0,2,0,3,5,2,0,0,5,3,0,9,3,2,4,7,2,5,0,4,0,0,2,5,0,0,0,6,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,2,5,1,0,2,6,5,1,3,1,4,5,1,5,4,0,0,1,1,3,6,0,1,1,8,1,9,0,4,1,4,9,0,1,5,1,3,0,5,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,2,3,3,5,0,5,0,4,7,0,2,0,2,7,2,1,7,0,2,0,1,1,5,2,0,0,7,0,4,5,6,1,2,7,2,0,6,3,0,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,4,3,3,1,3,1,6,7,1,1,1,2,7,1,2,6,0,0,0,2,5,2,0,1,2,6,2,8,1,4,2,4,8,1,3,5,1,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,4,0,5,9,0,0,0,4,5,0,3,6,0,0,0,0,5,5,0,0,2,5,2,9,0,3,2,4,9,0,6,3,0,0,0,2,4,2,0,0,0,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,0,3,3,4,5,0,4,4,1,5,0,0,9,0,0,0,4,0,5,0,0,0,5,4,9,0,6,0,3,9,0,1,6,3,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,1,3,2,4,7,1,2,1,2,6,2,5,3,2,0,0,5,1,3,2,2,3,3,0,6,3,7,1,2,5,4,1,4,4,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,1,4,1,4,7,1,2,2,4,3,1,2,6,4,0,0,2,2,1,2,2,3,3,0,2,7,6,2,1,5,4,2,5,2,2,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,3,4,2,2,9,0,0,0,5,4,4,4,2,4,3,0,2,0,2,6,1,0,2,0,0,9,5,4,0,3,6,1,3,4,2,2,6,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,2,3,2,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,4,2,3,0,7,2,0,9,0,0,0,0,8,2,4,4,2,0,0,2,5,0,2,0,3,5,0,4,5,9,0,0,7,2,0,4,5,0,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,0,8,0,1,7,0,2,0,1,8,0,7,2,5,0,0,3,2,1,3,3,1,2,3,2,7,6,3,0,2,7,2,3,3,2,0,5,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,4,7,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,2,3,3,9,2,6,0,3,6,1,3,1,0,8,1,3,6,2,1,0,4,0,3,1,2,2,3,3,2,7,5,2,2,5,4,1,4,2,3,1,5,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,3,10,0,5,1,4,8,1,0,0,4,5,1,3,6,2,0,5,1,2,0,2,1,6,2,0,2,7,7,2,0,3,6,5,2,2,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,2,2,4,2,2,5,2,2,5,2,0,4,5,0,0,0,4,0,5,0,2,3,5,0,9,0,5,2,2,9,0,0,6,3,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,5,1,3,7,0,2,1,2,6,1,2,7,1,0,1,2,5,2,1,1,2,6,1,1,8,8,0,1,8,1,0,4,5,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,3,8,0,3,0,6,3,2,4,3,2,5,3,4,4,0,2,0,4,2,3,3,2,0,5,0,7,2,5,2,2,5,4,2,4,4,0,0,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,5,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,1,6,1,3,5,0,4,0,5,4,0,4,5,5,0,0,4,0,0,0,3,6,0,0,4,5,5,2,4,4,5,2,3,3,2,1,5,8,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,3,6,0,4,3,2,5,2,3,3,5,2,1,3,6,1,1,0,3,2,4,1,2,2,3,3,6,3,6,1,3,8,1,4,2,3,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,4,2,5,0,5,1,4,8,1,1,1,2,6,1,3,6,0,0,0,5,1,3,0,1,3,5,1,7,2,8,1,1,9,0,2,6,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,3,5,0,5,1,4,2,1,7,3,4,2,2,5,2,1,1,1,7,2,1,1,4,1,4,0,8,1,6,1,3,7,2,3,6,1,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,1,4,2,3,7,1,2,2,4,4,2,4,4,2,1,1,3,2,3,2,2,3,4,1,8,1,6,0,3,6,3,3,4,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,0,9,0,0,6,0,3,3,3,3,1,6,2,0,3,0,4,0,3,0,4,2,3,0,2,7,4,2,3,5,4,3,5,0,2,0,4,3,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,2,6,0,2,9,0,0,0,3,6,5,3,2,4,0,0,5,0,0,3,2,3,2,0,0,9,7,2,0,2,7,0,5,0,4,0,7,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,2,9,0,3,1,5,8,0,1,4,1,5,1,1,7,2,0,0,2,3,3,1,0,1,2,6,9,0,7,0,2,9,0,7,2,0,0,0,2,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,3,1,0,4,0,5,7,0,2,3,3,4,3,3,4,5,0,0,3,0,2,1,3,3,2,1,1,8,6,1,2,7,2,2,3,4,1,1,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,1,5,0,4,1,5,7,1,2,4,2,3,1,3,6,0,0,0,2,2,6,0,1,1,5,2,6,3,6,2,2,9,0,4,3,3,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,2,3,2,0,2,5,9,0,0,0,3,6,2,4,4,2,0,0,3,5,0,2,0,3,5,0,0,9,9,0,0,7,2,0,5,4,0,0,4,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,1,5,1,1,4,1,4,7,1,2,8,1,1,0,3,6,5,0,0,2,0,2,5,3,1,1,0,1,8,6,2,1,5,4,4,1,1,4,0,6,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,1,3,7,1,2,3,4,3,0,3,6,0,0,0,2,2,6,0,2,3,3,3,5,4,9,0,0,5,4,2,2,3,3,0,5,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,5,7,0,5,0,4,5,0,4,4,4,2,0,2,7,0,0,0,3,6,0,0,0,3,5,2,9,0,5,0,4,9,0,6,2,2,0,0,2,2,1,0,0,6,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,4,2,0,0,0,0,5,0,0,0,0,0,7,0,0,0,4,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,30,2,1,5,7,0,6,0,3,6,0,3,5,4,0,2,3,5,0,0,4,0,0,5,2,0,4,3,2,2,7,5,2,2,5,4,2,4,4,0,0,4,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,0,6,0,3,7,2,0,1,2,6,2,4,4,1,0,0,5,2,1,0,3,2,4,0,0,9,9,0,0,5,4,2,4,4,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,3,3,0,5,2,3,7,1,1,1,3,6,2,5,3,4,0,0,3,2,0,3,2,3,3,0,1,8,8,0,1,5,4,0,5,4,0,0,4,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,2,5,2,2,7,2,0,0,3,6,2,3,5,3,0,2,3,1,2,4,0,2,4,0,1,8,7,2,0,6,3,5,2,2,2,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,2,2,1,6,6,0,3,4,4,2,1,1,8,0,0,0,1,2,6,0,1,1,4,5,9,0,5,0,4,9,0,6,3,1,1,0,2,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,0,5,9,0,0,0,3,6,1,2,7,1,0,0,3,4,2,1,1,1,6,1,5,4,6,1,2,7,2,2,4,3,1,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,0,3,1,5,3,4,2,1,7,1,0,4,5,0,0,0,4,2,3,0,3,2,4,0,7,2,4,1,4,9,0,3,5,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,2,9,3,6,1,1,5,0,4,2,1,7,1,5,4,1,0,0,3,1,5,1,1,5,4,1,9,0,8,1,1,8,1,3,5,2,1,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,6,0,3,6,3,0,0,4,5,5,4,0,7,0,0,2,0,0,5,0,4,0,0,0,9,6,3,0,4,5,0,0,5,4,0,7,3,2,0,0,5,0,0,0,0,0,0,0,5,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,1,5,0,3,2,4,4,2,4,4,5,1,1,6,3,0,0,0,6,1,3,0,4,3,3,0,9,0,5,0,4,8,1,7,1,1,0,0,2,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,4,7,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,7,1,3,3,2,1,5,0,4,7,0,2,1,4,4,4,5,1,4,0,0,4,0,1,2,1,5,3,0,7,2,6,1,2,5,4,1,1,8,0,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,13,1,3,3,3,1,4,1,5,7,1,1,0,4,5,9,0,0,5,0,0,0,0,4,3,6,0,0,0,9,0,6,2,2,8,1,0,4,5,0,0,5,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,3,2,0,0,5,0,0,0,0,3,2,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,3,4,7,2,4,0,4,7,0,2,0,3,6,1,1,7,2,0,0,3,3,3,1,0,2,5,2,1,8,5,2,2,7,2,0,7,2,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,2,5,2,1,7,2,0,0,2,7,4,4,2,3,0,0,5,2,1,2,3,3,2,1,0,9,9,0,0,4,5,1,2,7,0,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,29,1,4,3,7,2,4,0,4,7,0,2,0,2,7,1,1,8,1,0,0,1,2,6,1,0,1,7,1,1,8,5,2,2,7,2,0,8,1,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,1,1,3,3,1,1,4,2,3,7,1,1,1,4,4,4,4,2,6,0,0,2,1,1,4,2,3,2,0,0,9,6,3,0,3,6,1,3,4,2,0,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,4,2,3,2,5,2,0,9,0,0,0,0,9,0,5,4,0,0,0,5,2,2,0,2,5,3,0,4,5,7,1,2,6,3,0,9,0,0,0,3,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,1,5,3,2,6,0,3,3,4,3,0,2,7,1,0,0,3,3,4,1,0,2,6,1,4,5,6,1,2,9,0,1,7,2,0,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,0,7,2,0,9,0,0,0,3,6,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,7,2,0,2,7,0,0,7,2,0,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,6,2,2,8,0,1,1,2,6,4,3,2,5,1,0,2,2,1,4,2,1,3,0,2,7,6,3,1,4,5,1,1,5,3,0,6,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,4,1,4,7,2,1,0,3,6,2,5,2,5,1,0,3,1,1,3,2,3,1,1,1,8,6,0,3,4,5,1,1,6,2,1,6,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,0,4,0,5,5,2,3,2,4,4,3,3,4,6,0,0,2,0,2,3,2,2,2,2,4,5,5,2,3,6,3,3,3,3,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,0,4,0,5,4,1,5,5,1,4,0,4,5,0,0,0,3,6,1,0,3,0,6,1,5,4,6,0,3,7,2,6,3,0,0,0,2,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,2,9,2,4,2,2,8,0,1,1,3,6,1,3,6,1,0,0,2,4,4,1,1,2,5,2,9,0,8,1,1,8,1,1,6,2,2,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,4,2,2,3,8,0,1,0,6,3,1,0,8,0,0,0,1,4,4,1,0,0,0,8,9,0,6,2,1,2,7,6,0,3,1,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,1,1,5,6,2,5,2,0,0,0,9,7,2,0,2,1,7,2,0,1,3,3,2,2,0,1,4,4,8,1,0,0,9,9,0,4,5,0,0,0,2,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,5,2,2,1,5,1,3,6,1,3,0,1,8,0,1,8,8,0,1,1,0,1,0,1,8,1,0,1,8,6,1,2,6,3,0,0,9,0,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,4,2,0,7,0,2,9,0,0,2,5,3,0,4,5,0,0,0,4,1,5,0,0,5,4,1,9,0,7,0,2,5,4,4,4,2,0,0,3,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,1,5,8,0,0,3,6,3,0,6,6,3,0,0,9,0,4,0,0,5,0,0,3,3,0,4,0,9,0,9,0,0,6,3,3,6,0,0,0,2,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,6,0,3,5,2,2,2,2,5,0,2,7,0,0,0,2,7,0,0,0,0,9,0,2,7,9,0,0,7,2,0,9,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,4,7,0,4,2,3,5,0,4,4,4,2,0,2,7,0,0,0,4,0,5,0,1,1,3,5,9,0,7,0,2,9,0,8,1,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,11,1,3,3,3,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,4,7,0,7,1,2,6,0,3,3,4,3,0,1,8,0,0,0,3,0,6,0,1,1,8,1,9,0,8,0,1,9,0,7,1,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,2,4,2,8,0,5,1,4,8,1,0,0,1,8,0,4,5,0,0,0,5,2,2,0,2,3,5,0,2,7,5,4,0,9,0,1,7,2,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,33,1,2,4,8,0,7,0,2,7,0,2,0,7,2,0,2,7,0,1,0,4,4,1,0,1,1,5,3,3,6,8,1,0,7,2,0,5,4,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,3,8,0,4,1,4,6,1,3,1,6,2,2,2,5,1,0,2,3,3,2,2,1,3,3,1,3,6,4,0,5,7,2,2,3,1,3,2,6,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,2,2,2,5,0,5,2,2,4,2,4,4,4,2,2,4,3,3,1,0,2,1,4,5,2,1,2,2,7,2,6,1,3,8,1,4,3,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,3,5,0,5,1,4,2,1,7,3,4,2,2,5,2,1,1,1,7,2,1,1,4,1,4,0,8,1,6,1,3,7,2,3,6,1,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,2,7,1,0,7,0,2,0,2,7,4,2,4,4,0,0,0,2,4,4,0,2,2,3,1,8,7,2,0,5,4,2,3,2,3,0,5,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,4,4,8,1,4,1,4,6,0,3,2,2,5,0,0,9,2,1,1,3,3,2,0,4,5,0,0,3,6,6,1,2,6,3,0,9,0,0,0,4,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,1,3,3,1,0,8,0,1,6,0,3,2,2,5,3,5,1,4,0,0,5,1,1,3,3,2,2,1,5,4,2,4,3,4,5,1,4,4,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,3,3,4,6,1,2,1,2,7,0,3,6,0,0,0,3,3,4,0,1,1,6,3,9,0,7,0,2,7,2,7,2,1,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,8,1,2,3,2,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,4,0,5,7,0,2,2,6,2,3,5,1,6,1,0,2,0,1,6,0,2,2,0,6,3,7,2,0,3,6,2,4,4,1,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,6,2,2,7,1,2,0,3,6,0,0,9,2,0,0,3,3,2,0,0,0,9,0,3,6,7,1,2,7,2,0,4,0,5,0,6,3,2,0,0,5,0,6,0,0,0,0,0,1,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,3,8,2,3,1,5,6,2,2,0,9,0,0,9,0,1,0,0,7,0,2,0,0,5,4,0,0,9,7,1,2,5,4,0,0,9,0,0,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,4,8,1,5,2,3,5,1,3,0,5,4,0,3,6,0,0,4,4,0,1,0,3,5,1,1,2,7,6,1,3,7,2,1,8,0,0,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,4,9,0,4,4,2,3,2,4,4,4,2,0,2,7,0,0,0,1,3,5,0,1,3,6,1,9,0,6,0,3,7,2,6,3,1,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,2,8,0,3,1,5,6,1,3,0,0,9,0,5,4,0,0,0,0,4,5,0,0,5,4,0,9,0,5,1,3,7,2,9,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,0,4,4,0,5,3,2,5,1,3,6,0,0,2,2,4,3,0,1,3,5,1,2,7,5,1,3,7,2,3,3,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,2,4,0,4,7,2,0,0,6,3,3,2,4,4,0,0,0,2,4,5,0,0,4,0,6,3,5,2,2,5,4,4,4,0,0,2,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,39,1,3,3,9,1,4,2,3,7,1,2,2,4,4,2,4,4,2,1,1,3,2,3,2,2,3,4,1,8,1,6,0,3,6,3,3,4,3,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,22,2,3,2,5,1,1,0,8,3,4,2,2,3,5,2,2,6,1,1,0,2,4,3,1,2,1,4,4,8,1,7,1,2,8,1,2,3,3,2,1,6,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,4,7,1,5,1,3,8,1,1,2,5,3,2,3,5,1,1,1,5,3,1,1,1,3,5,1,4,5,7,0,2,5,4,2,3,4,1,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,7,1,3,2,2,0,0,0,9,6,3,0,0,4,5,0,3,6,9,0,0,0,0,0,3,0,6,0,0,0,9,6,3,0,0,9,2,4,4,0,1,4,7,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,0,2,4,4,7,0,2,1,0,8,1,0,8,2,0,0,4,1,3,0,0,1,4,4,9,0,7,0,2,6,3,4,0,5,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,1,2,4,1,1,5,2,3,5,1,4,4,4,1,4,2,4,4,2,0,2,2,1,2,1,3,1,4,1,8,5,1,3,5,4,1,3,3,3,1,6,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,4,3,7,2,4,0,4,7,0,2,0,2,7,1,1,8,1,0,0,1,2,6,1,0,1,7,1,1,8,5,2,2,7,2,0,8,1,0,0,3,3,2,0,0,0,0,5,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,3,7,1,4,1,4,5,1,4,3,3,3,0,4,5,1,1,1,4,2,2,1,2,3,4,1,2,7,5,1,4,7,2,3,3,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,1,2,8,0,1,1,4,4,0,4,5,1,0,0,4,2,2,1,1,3,4,2,0,9,7,1,2,6,3,0,5,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,0,4,7,2,0,0,3,6,0,4,5,2,0,2,0,4,3,2,0,2,5,0,4,5,7,2,0,5,4,2,2,3,3,0,5,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,4,1,2,2,2,5,7,1,2,2,4,4,5,3,2,8,1,0,0,0,0,7,0,2,0,0,0,9,3,5,2,2,7,0,2,5,2,1,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,37,1,2,3,8,2,4,2,3,5,2,3,2,4,3,1,3,5,1,1,0,3,3,2,2,2,2,5,1,6,3,7,1,2,7,2,3,4,3,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,2,3,2,4,2,4,7,1,2,2,3,5,3,5,2,3,1,1,4,2,2,3,2,3,2,1,6,3,7,1,2,6,3,1,3,5,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,8,2,0,0,0,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,1,4,2,3,7,2,1,1,6,3,1,1,8,1,1,2,1,4,3,1,1,3,6,1,1,8,4,4,2,7,2,4,3,3,0,1,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,2,0,7,6,0,3,5,3,2,3,4,2,7,0,0,0,2,0,4,0,4,1,0,0,9,9,0,0,2,7,2,2,5,0,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,8,0,1,9,0,0,0,7,2,0,1,8,1,0,4,1,4,0,1,1,5,4,0,1,8,7,2,0,9,0,7,1,2,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,3,3,7,0,4,1,4,6,1,3,1,4,5,1,3,5,3,0,1,4,1,2,1,2,4,3,1,2,7,4,0,5,7,2,2,2,2,4,1,6,2,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,34,2,2,4,8,0,3,2,5,5,2,3,3,5,3,0,3,6,1,0,5,1,3,2,1,2,4,3,2,5,4,4,5,1,7,2,2,5,1,2,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,0,4,2,3,5,1,3,2,4,3,0,3,6,1,0,1,2,2,5,1,1,2,5,3,8,1,6,1,3,6,3,4,4,1,0,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,2,10,0,6,0,3,6,3,0,0,5,4,1,7,2,3,0,3,3,0,1,4,2,2,2,0,0,9,4,5,0,3,6,0,4,4,2,0,5,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,9,0,0,9,0,0,0,2,7,0,3,6,3,0,0,2,5,0,2,2,2,5,0,0,9,6,3,0,3,6,0,5,4,0,0,4,3,0,0,3,6,0,0,0,0,6,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,2,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,0,9,0,0,9,0,0,0,3,6,0,3,6,4,0,0,0,3,3,0,3,0,6,0,0,9,6,2,2,6,3,3,4,2,0,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,4,2,7,0,9,0,0,5,0,4,0,0,9,0,0,9,0,0,2,0,3,5,0,4,0,5,0,9,0,3,2,4,7,2,9,0,0,0,0,1,1,0,0,0,6,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,2,2,2,5,0,5,2,2,4,2,4,4,4,2,2,4,3,3,1,0,2,1,4,5,2,1,2,2,7,2,6,1,3,8,1,4,3,3,0,0,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,1,2,8,1,0,0,4,5,0,4,5,0,0,0,5,1,4,0,1,5,4,0,4,5,8,1,0,9,0,2,5,2,2,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,3,2,1,5,1,3,7,1,2,0,2,7,3,4,2,2,1,0,4,3,1,2,3,2,3,1,1,8,7,0,2,5,4,1,3,4,2,1,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,36,1,4,3,8,1,4,2,3,7,1,2,0,0,9,0,0,9,0,0,0,0,5,4,0,0,0,5,4,9,0,6,2,2,7,2,0,4,5,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,30,1,2,4,7,0,3,3,4,5,0,4,4,3,3,0,0,9,0,0,0,3,0,6,0,0,0,5,4,9,0,6,0,3,9,0,2,5,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,39,1,3,3,9,1,4,1,4,7,1,2,2,4,4,2,4,4,3,1,2,2,2,1,2,2,3,3,1,4,5,6,2,1,5,4,3,3,3,2,1,4,5,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,1,6,1,2,6,1,2,2,3,4,1,3,5,2,1,1,4,2,2,1,2,3,4,1,2,7,5,2,3,5,4,2,4,3,1,1,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,2,8,0,7,2,0,9,0,0,0,0,9,2,3,5,2,0,0,3,2,3,2,0,3,5,0,9,0,6,0,3,7,2,5,2,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,2,3,1,4,0,5,5,0,4,4,2,4,2,4,4,3,1,0,3,4,0,3,2,1,4,0,8,1,4,1,5,8,1,6,3,1,1,1,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,2,1,6,0,3,2,3,4,1,2,7,1,0,2,2,1,5,1,2,2,5,1,1,8,7,0,2,7,2,0,5,4,0,0,4,3,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,4,2,8,2,4,0,3,7,2,0,0,3,6,1,4,4,3,0,0,3,3,1,1,2,3,2,3,7,2,6,2,2,7,2,2,3,4,2,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,3,5,3,4,1,3,2,2,5,5,3,1,3,4,3,2,0,0,4,1,3,2,1,4,3,1,8,1,6,0,3,7,2,5,2,2,0,1,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,2,5,3,0,9,0,0,2,5,4,4,3,2,6,0,0,3,1,0,4,2,3,1,0,0,9,5,4,0,0,9,0,0,8,1,0,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,0,4,0,5,3,0,6,6,2,2,1,2,7,0,0,0,0,5,4,0,0,0,6,3,9,0,5,0,4,9,0,6,3,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,2,5,0,4,1,4,2,3,4,3,4,2,1,5,4,1,0,0,2,2,4,1,1,3,4,2,8,1,6,1,3,8,1,5,4,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,5,0,4,7,0,2,2,2,6,0,2,7,0,2,2,0,6,0,1,0,2,6,0,2,7,4,4,2,8,1,5,2,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,6,0,3,6,0,3,3,1,6,0,1,8,0,2,1,0,6,0,1,0,2,7,0,3,6,5,2,3,8,1,8,1,1,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,3,3,5,3,1,1,8,1,1,1,1,8,1,5,3,1,0,0,5,3,1,1,3,3,3,0,1,8,9,0,0,5,4,3,2,4,0,0,4,8,2,0,0,6,0,6,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,1,6,1,2,4,1,5,5,3,2,3,4,3,4,0,0,3,1,2,3,1,3,3,1,4,5,5,0,4,5,4,5,2,3,1,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,5,1,2,3,1,1,4,2,4,5,2,3,3,5,2,2,4,3,3,1,1,4,2,1,2,3,2,3,1,6,3,6,1,2,7,2,2,3,3,1,0,4,3,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0\ntest,22,2,3,2,5,1,3,2,4,6,1,2,2,2,6,0,3,6,2,0,0,0,6,2,0,1,4,4,1,4,5,5,1,3,7,2,3,5,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,4,2,9,1,4,1,4,8,1,1,0,0,9,0,5,4,3,0,0,0,0,6,0,0,5,4,0,3,6,7,2,1,3,6,0,0,0,9,0,8,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,0,4,1,5,6,1,2,2,2,6,1,3,6,0,0,0,3,3,4,0,2,1,5,2,8,1,6,2,2,9,0,6,2,1,0,0,2,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,2,5,0,3,1,6,6,2,1,1,4,4,2,2,5,2,0,0,3,2,3,2,1,2,5,1,8,1,7,1,2,7,2,2,6,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,4,1,2,4,1,1,3,0,5,5,1,4,0,7,2,6,3,0,5,0,0,5,0,0,5,2,3,0,0,2,7,4,1,5,4,5,0,0,2,5,2,9,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,2,9,0,3,1,5,8,0,1,1,2,7,2,3,5,2,0,0,2,3,3,2,0,2,5,1,9,0,7,0,2,9,0,3,6,0,0,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,1,5,2,3,5,1,4,1,4,4,3,5,1,2,1,0,6,1,1,3,2,4,1,1,1,8,5,1,3,5,4,1,4,2,4,1,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,2,2,2,5,0,5,2,2,4,2,4,4,4,2,2,4,3,3,1,0,2,1,4,5,2,1,2,2,7,2,6,1,3,8,1,4,3,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,1,4,0,5,8,1,1,0,4,5,4,2,4,4,0,0,3,0,3,4,2,1,4,0,4,5,7,2,0,5,4,1,3,5,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,8,1,3,3,2,1,7,0,1,6,0,3,2,4,4,4,3,2,3,0,0,5,1,1,3,2,3,2,0,0,9,6,3,2,5,4,1,4,4,1,0,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,4,3,9,1,4,1,4,7,1,2,0,2,7,2,0,7,5,0,0,2,0,3,2,2,5,2,0,6,3,6,2,1,5,4,7,2,0,0,0,2,3,0,0,4,6,0,0,0,0,4,1,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,2,7,1,3,2,5,5,2,2,3,2,5,1,2,7,1,0,0,3,4,3,1,1,1,6,2,8,1,7,1,2,8,1,3,5,2,1,1,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,8,1,3,3,2,0,4,1,4,6,1,3,3,1,6,4,2,4,3,0,0,5,2,1,3,3,1,4,0,1,8,7,0,2,8,1,1,6,3,0,0,4,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,2,3,2,2,7,0,0,2,7,2,0,0,5,4,5,4,1,2,0,0,7,1,1,3,4,2,1,0,1,8,5,2,3,4,5,1,6,2,1,0,4,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,28,5,2,4,6,2,5,0,3,4,0,5,4,3,2,2,3,4,2,2,0,5,2,0,2,2,4,3,0,3,6,5,0,4,4,5,2,3,5,0,1,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,2,3,8,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,6,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,0,2,2,5,5,2,3,4,4,2,3,5,2,2,1,2,6,1,1,3,4,1,2,0,8,1,4,2,4,8,1,2,5,1,3,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,6,0,3,6,0,3,3,2,4,2,2,6,2,0,0,3,2,3,2,0,0,5,3,0,9,6,0,3,4,5,3,1,3,3,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,29,1,2,3,7,1,4,1,4,5,1,4,3,3,3,0,4,5,1,1,1,4,2,2,1,2,3,4,1,2,7,5,1,4,7,2,3,3,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,0,5,0,4,8,0,1,0,3,6,0,3,6,0,2,0,5,3,1,0,2,4,4,1,1,8,8,1,0,8,1,0,6,3,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,0,2,3,5,6,3,0,0,4,5,0,0,9,0,0,0,0,0,9,0,0,0,9,0,5,4,4,3,3,9,0,0,5,4,0,0,4,1,2,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,2,2,1,3,1,4,5,2,3,0,6,4,6,4,0,6,0,0,4,0,0,5,0,4,0,0,9,0,3,0,6,5,4,0,0,6,4,0,7,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,3,3,0,4,2,4,7,1,2,0,0,9,0,9,0,1,0,0,3,3,3,0,0,0,9,0,5,4,7,1,1,8,1,1,6,2,1,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,4,2,4,2,2,6,4,2,4,0,2,7,1,0,0,2,0,7,0,0,2,1,7,7,2,2,2,6,7,2,7,2,0,0,0,2,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,3,4,2,2,9,0,0,3,4,3,3,4,3,2,0,0,3,1,5,2,4,1,3,0,0,9,7,2,0,9,0,0,5,3,2,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,2,1,0,4,2,4,9,0,0,0,4,5,3,6,1,7,0,0,2,0,0,4,5,0,1,0,0,9,5,4,0,0,9,0,3,6,0,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,4,2,8,0,4,0,5,9,0,0,0,3,6,0,3,6,0,0,0,2,6,2,0,2,2,6,0,0,9,4,4,1,9,0,2,5,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,2,3,3,2,1,4,0,4,7,0,2,2,2,6,3,4,3,4,0,2,3,0,1,4,2,3,1,0,0,9,7,2,0,4,5,2,3,3,2,1,6,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,8,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,1,1,2,4,1,0,2,0,7,6,0,3,5,3,2,3,4,2,7,0,0,0,2,0,4,0,4,1,0,0,9,9,0,0,2,7,2,2,5,0,0,4,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,3,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,35,1,3,3,8,0,6,2,2,7,0,2,2,4,4,0,3,6,1,0,0,3,3,3,1,1,4,4,2,0,9,7,1,2,7,2,0,7,2,0,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,3,9,0,6,1,3,7,1,2,1,1,8,0,2,7,1,0,0,1,5,3,1,1,1,7,1,6,3,6,1,2,9,0,1,7,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,2,2,5,7,0,2,0,2,7,0,4,5,0,0,0,0,9,0,0,0,0,9,0,9,0,5,0,4,9,0,4,5,0,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,0,4,4,0,5,3,2,5,1,3,6,0,0,2,2,4,3,0,1,3,5,1,2,7,5,1,3,7,2,3,3,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,24,1,4,2,5,0,5,1,4,8,1,1,1,2,6,1,3,6,0,0,0,5,1,3,0,1,3,5,1,7,2,8,1,1,9,0,2,6,2,0,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,0,3,4,2,9,0,0,0,3,6,5,3,2,7,0,0,2,0,1,7,1,1,2,0,0,9,5,4,0,2,7,0,2,4,1,3,8,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,4,7,0,4,3,2,7,0,2,2,5,2,0,4,5,0,0,0,4,2,4,0,1,3,4,2,9,0,3,0,6,7,2,2,7,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,1,3,6,6,0,3,3,3,3,0,1,8,1,1,0,1,8,1,1,1,0,8,1,9,0,5,2,3,7,2,7,2,1,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0\ntest,33,1,3,2,8,0,4,2,4,8,1,0,0,2,7,0,3,6,0,0,0,4,3,3,0,1,3,6,0,3,6,6,3,0,9,0,2,5,2,2,0,4,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,4,5,4,2,0,4,5,2,2,6,1,3,3,3,4,3,1,0,1,0,6,2,2,2,4,0,9,0,6,0,3,7,2,7,2,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,3,8,0,6,1,2,7,1,1,2,4,4,2,3,4,2,0,1,5,2,0,2,2,3,3,0,3,6,4,2,4,6,3,2,3,4,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,6,0,0,3,6,0,3,2,2,6,3,3,4,2,0,0,4,2,2,2,3,0,4,0,3,6,5,4,0,6,3,0,4,4,2,0,5,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,3,9,0,7,2,0,9,0,0,0,0,9,0,2,7,0,0,0,2,2,6,0,2,0,7,0,7,2,7,2,0,9,0,0,7,2,0,0,4,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,2,9,1,1,0,7,8,0,1,1,3,6,1,5,4,1,0,0,3,5,1,1,2,2,4,1,1,8,7,2,1,5,4,3,0,6,1,0,4,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,0,5,5,2,3,2,2,6,0,4,5,0,0,0,4,4,1,0,0,2,7,1,1,8,6,2,2,8,1,4,1,0,4,0,5,5,2,0,0,7,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,3,7,1,6,1,3,4,1,5,3,3,4,0,5,4,1,1,1,5,1,3,1,1,5,3,1,4,5,4,2,4,7,2,6,3,1,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,7,1,4,2,2,1,5,2,3,8,1,0,0,1,8,5,4,1,5,1,0,4,1,0,5,4,1,1,0,0,9,7,1,2,4,5,1,3,6,1,0,5,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,4,8,0,5,0,4,9,0,0,0,4,5,0,3,6,0,0,0,3,6,0,0,2,0,7,0,4,5,9,0,0,9,0,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,5,2,1,6,1,3,2,3,5,1,3,5,1,0,0,3,3,3,1,2,2,5,1,8,1,5,1,3,6,3,5,2,2,1,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,2,2,6,6,0,3,3,3,4,0,4,5,1,0,0,3,5,2,1,2,0,7,1,8,1,5,0,4,9,0,0,0,9,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,4,8,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,3,3,7,0,4,1,4,6,1,3,1,4,5,1,3,5,3,0,1,4,1,2,1,2,4,3,1,2,7,4,0,5,7,2,2,2,2,4,1,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,3,8,0,3,0,6,3,2,4,3,2,5,3,4,4,0,2,0,4,2,3,3,2,0,5,0,7,2,5,2,2,5,4,2,4,4,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,4,1,0,6,2,2,9,0,0,0,4,5,3,6,0,4,0,0,4,2,0,4,2,4,0,0,0,9,6,3,0,3,6,0,6,3,0,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,3,0,6,9,0,0,2,2,5,0,4,5,0,0,0,3,6,0,0,0,4,5,0,9,0,5,0,4,6,3,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,7,1,2,2,3,4,2,3,4,3,1,1,2,2,3,3,1,3,3,1,2,7,7,2,1,5,4,2,4,3,1,1,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,2,5,3,0,9,0,0,2,5,4,4,3,2,6,0,0,3,1,0,4,2,3,1,0,0,9,5,4,0,0,9,0,0,8,1,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,3,6,0,5,1,4,3,2,5,5,3,3,2,3,4,1,1,0,4,2,3,1,4,1,3,2,7,2,6,1,3,8,1,5,3,2,0,0,3,1,0,4,0,6,0,0,0,0,0,0,0,0,0,0,6,6,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,5,2,3,5,1,4,1,4,4,5,3,1,2,3,0,4,1,1,5,3,1,1,1,2,7,5,1,3,5,4,3,1,4,2,1,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1\ntest,37,1,3,3,8,0,4,1,4,6,1,3,1,5,4,1,5,3,1,0,1,4,2,3,1,3,4,2,1,2,7,4,0,5,7,2,2,3,3,1,1,5,4,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,1,4,7,0,5,1,3,6,1,3,5,4,1,0,0,9,0,0,0,0,4,5,0,0,0,9,0,9,0,6,1,2,6,3,3,6,0,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,5,2,3,5,1,4,3,4,3,2,4,4,3,1,1,3,2,2,2,2,2,4,1,4,5,6,1,3,6,3,4,2,3,1,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,0,9,0,0,5,0,4,4,3,3,0,3,6,0,0,2,0,3,5,0,3,0,6,0,0,9,3,2,4,7,2,6,3,0,0,0,2,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,4,3,8,0,4,0,5,9,0,0,0,4,5,4,1,5,1,0,0,4,3,3,1,3,0,5,0,9,0,9,0,0,6,3,0,4,4,1,0,5,3,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,3,1,5,6,0,3,2,2,6,0,4,5,2,0,0,3,4,2,1,0,2,5,3,5,4,5,1,3,7,2,4,4,2,0,0,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,1,4,1,5,6,2,1,1,4,5,2,5,3,1,0,0,6,1,2,1,3,4,3,0,1,8,7,2,0,8,1,1,3,6,1,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,1,2,3,0,5,1,4,4,3,2,6,3,1,4,5,1,2,0,0,4,3,2,1,1,1,7,2,8,1,8,0,1,9,0,3,5,2,0,0,3,6,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,5,0,4,5,0,4,4,2,4,2,3,5,4,2,0,0,3,0,2,1,4,3,0,0,9,5,3,2,6,3,4,2,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,2,5,0,3,9,0,0,0,3,6,2,4,3,2,0,0,4,2,2,2,2,3,3,0,0,9,5,0,4,7,2,0,6,3,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,28,7,2,6,6,0,6,2,2,9,0,0,4,2,4,2,1,6,5,2,0,0,3,0,2,1,4,0,4,4,5,6,3,0,0,9,7,0,1,2,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,1,4,2,3,7,2,1,1,6,3,1,1,8,1,1,2,1,4,3,1,1,3,6,1,1,8,4,4,2,7,2,4,3,3,0,1,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,4,7,0,5,0,4,3,0,6,4,2,3,0,0,9,0,0,0,3,4,3,0,0,0,6,3,7,2,2,2,6,9,0,5,4,0,0,0,2,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,3,4,2,1,3,4,3,3,5,2,0,4,5,0,0,0,4,4,2,0,2,3,4,1,9,0,7,1,1,8,1,3,6,1,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,3,1,2,5,0,3,9,0,0,1,3,5,7,2,0,9,0,0,0,0,0,6,2,1,0,0,0,9,7,2,0,2,7,1,2,3,4,1,7,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,24,1,2,1,5,3,4,2,1,3,4,3,4,5,2,0,4,5,0,0,0,2,3,4,0,2,3,5,1,9,0,7,1,1,8,1,1,6,3,0,0,4,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,2,1,0,6,8,0,1,1,1,7,1,5,4,2,1,0,4,3,0,2,2,2,4,0,5,4,6,2,1,2,7,2,3,3,2,1,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,29,1,2,3,7,1,5,1,2,6,2,2,1,7,2,1,4,5,1,0,0,4,3,3,1,3,1,3,3,1,8,8,0,1,6,3,1,5,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,4,10,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,2,5,1,3,2,5,6,1,2,1,2,7,1,2,7,0,0,0,2,4,3,0,1,1,5,3,8,1,3,2,5,8,1,3,5,1,1,0,3,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,5,8,1,4,2,3,3,0,6,5,3,2,2,2,6,2,2,0,3,2,3,1,1,2,4,2,8,1,2,2,5,5,4,7,1,0,0,1,3,3,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,4,3,1,1,5,1,3,6,1,2,0,2,7,8,1,1,6,0,0,2,2,1,5,3,2,1,0,0,9,7,2,1,5,4,0,1,4,5,1,7,8,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0\ntest,10,1,3,3,3,0,5,0,4,8,1,0,1,3,5,1,5,3,3,0,0,2,1,3,0,3,4,3,0,0,9,9,0,0,5,4,2,4,4,0,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,4,7,0,8,0,1,6,1,2,1,3,5,0,3,6,1,0,0,3,2,4,1,2,2,4,3,6,3,5,0,4,7,2,6,3,1,1,0,2,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,12,1,3,2,3,4,1,0,5,6,3,0,0,4,5,5,3,2,5,0,0,3,1,2,4,3,1,2,0,0,9,6,3,0,2,7,0,3,4,3,1,7,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,0,1,3,6,1,3,5,5,0,4,0,0,9,0,0,0,0,3,6,0,0,0,3,6,9,0,5,1,3,8,1,5,4,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,0,7,0,2,7,2,0,0,4,5,5,4,0,5,0,2,2,0,1,7,2,0,0,0,0,9,5,4,0,0,9,0,2,6,2,0,6,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,26,1,1,5,6,0,5,0,4,5,0,4,8,1,0,1,1,8,1,0,1,2,1,7,1,1,1,1,7,9,0,5,0,4,9,0,4,5,1,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,5,1,2,4,1,0,5,2,3,5,1,4,3,4,3,2,4,4,3,1,1,3,2,2,2,2,2,4,1,4,5,6,1,3,6,3,4,2,3,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,2,3,0,5,7,2,0,0,5,4,1,5,4,1,0,0,4,1,4,1,3,2,5,0,8,1,9,0,0,6,3,4,1,4,1,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,1,4,1,5,8,1,1,1,2,7,2,2,6,3,1,1,2,4,1,3,1,1,5,1,4,5,5,4,1,7,2,4,2,4,0,1,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,12,1,3,2,3,0,6,0,3,7,2,0,1,2,6,2,4,4,1,0,0,5,2,1,0,3,2,4,0,0,9,9,0,0,5,4,2,4,4,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,0,2,0,6,4,1,4,5,1,1,0,3,3,3,1,2,0,6,0,2,7,7,2,0,2,7,0,6,3,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,3,2,1,5,0,3,7,1,2,3,3,4,0,5,4,3,0,0,0,5,2,0,4,3,0,2,0,9,6,2,2,6,3,0,5,3,2,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,34,1,3,2,8,1,5,1,4,6,1,3,2,1,6,3,3,4,2,0,0,5,1,2,2,2,3,4,0,1,8,7,2,0,7,2,1,4,4,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,2,4,2,4,7,1,2,2,3,5,3,5,2,3,1,1,4,2,2,3,2,3,2,1,6,3,7,1,2,6,3,1,3,5,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,1,3,8,0,9,0,0,5,0,4,5,4,1,0,1,8,0,0,2,0,3,5,0,4,0,5,0,0,9,3,2,4,7,2,8,1,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,2,5,1,4,1,4,2,2,5,5,3,1,1,4,4,2,0,0,2,2,4,2,1,2,5,1,7,2,6,0,3,7,2,4,3,2,1,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,0,6,2,2,7,0,2,2,3,5,1,4,5,1,1,2,3,2,1,2,2,0,5,0,4,5,6,1,2,8,1,3,5,1,0,1,4,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,1,4,2,3,6,2,1,2,7,1,2,3,5,3,1,0,2,2,2,2,1,1,4,2,7,2,7,2,1,5,4,3,2,4,1,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,2,2,2,9,0,6,1,3,5,1,3,2,2,5,0,2,7,0,0,1,2,3,5,0,1,2,6,2,6,3,5,2,3,8,1,8,1,1,0,0,2,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,9,1,2,4,3,0,4,4,2,7,0,2,2,5,3,0,4,5,0,0,0,4,2,3,0,2,3,3,2,9,0,3,0,6,7,2,2,7,0,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,3,5,1,2,2,6,6,1,2,1,2,6,1,2,7,0,0,0,3,4,3,0,1,1,6,2,8,1,5,1,4,8,1,3,5,1,1,0,3,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,9,0,0,9,0,0,0,2,7,0,3,6,3,0,0,2,5,0,2,2,2,5,0,0,9,6,3,0,3,6,0,5,4,0,0,4,3,0,0,3,6,0,0,0,2,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,2,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,2,3,0,4,2,4,9,0,0,0,5,4,3,5,2,7,0,0,2,0,0,4,4,0,2,0,0,9,5,4,0,0,9,0,3,6,0,0,5,8,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,1,3,7,1,2,0,3,6,0,3,6,0,0,0,0,6,3,0,0,0,9,0,5,4,9,0,0,5,4,0,9,0,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,8,1,2,4,2,1,4,2,4,5,2,3,0,6,3,6,3,0,0,0,0,6,3,0,0,6,0,3,0,0,9,6,1,2,7,2,0,0,9,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,34,1,3,3,8,1,5,1,4,6,1,3,1,2,7,4,2,4,2,0,0,6,1,1,4,1,2,3,0,1,8,8,1,0,6,3,1,2,5,3,0,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,3,5,1,3,2,5,5,2,2,4,3,3,1,4,5,1,0,0,3,1,4,1,1,3,3,3,7,2,6,1,3,8,1,2,5,3,1,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,1,4,2,3,5,0,4,3,4,2,3,4,3,3,0,0,3,1,2,4,3,0,1,3,3,6,6,0,3,4,5,3,3,3,1,0,4,6,2,2,0,6,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,2,8,0,4,2,4,3,2,4,4,4,2,0,5,4,0,0,0,7,0,2,0,5,0,4,0,7,2,7,0,2,9,0,4,5,0,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,8,2,2,3,2,1,4,0,5,5,1,3,3,3,4,2,4,3,2,1,0,4,1,2,2,2,2,3,1,4,5,5,2,3,5,4,3,2,4,2,1,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,6,0,4,0,0,6,0,0,0,7,6,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,1,0,0,0,5,4,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,4,2,3,0,7,0,2,7,2,0,1,1,8,2,3,5,1,0,0,7,2,1,0,3,2,5,0,0,9,9,0,0,6,3,2,4,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,2,3,0,4,1,5,6,1,2,2,2,6,1,3,6,0,0,0,3,3,4,0,2,1,5,2,8,1,6,2,2,9,0,6,2,1,0,0,2,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,2,4,0,4,8,1,0,0,4,5,1,7,1,4,0,0,4,1,2,3,2,2,4,0,8,1,9,0,0,6,3,3,3,3,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,4,0,5,9,0,0,0,3,6,1,2,7,1,0,0,3,4,2,1,1,1,6,1,5,4,6,1,2,7,2,2,4,3,1,0,4,4,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,1,3,0,6,6,0,3,1,3,6,1,5,4,1,0,1,4,3,3,1,1,4,5,0,9,0,5,0,4,5,4,5,4,1,0,0,3,4,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,0,2,2,5,7,0,2,0,2,7,0,4,5,0,0,0,0,9,0,0,0,0,9,0,9,0,5,0,4,9,0,4,5,0,0,0,3,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,1,4,2,3,7,1,2,1,3,6,2,4,3,3,1,0,4,2,1,2,2,3,3,0,2,7,7,1,1,5,4,1,3,5,1,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,3,3,7,1,5,2,3,5,1,3,0,4,5,0,1,8,0,0,1,3,0,6,0,1,1,5,4,8,1,6,1,3,7,2,3,6,0,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,1,2,2,5,5,2,2,4,4,1,1,4,4,2,0,0,3,2,4,1,2,1,5,1,7,2,6,1,3,8,1,2,5,2,1,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,0,7,0,2,7,2,0,0,7,2,5,4,0,5,2,0,2,0,0,6,0,3,0,0,0,9,6,3,0,2,7,0,0,5,2,2,8,8,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,0,4,2,4,5,1,3,2,5,3,2,2,6,3,1,0,1,4,2,3,1,2,4,1,2,7,7,1,2,5,4,2,4,3,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,3,3,0,5,2,3,7,1,1,1,3,6,2,5,3,4,0,0,3,2,0,3,2,3,3,0,1,8,8,0,1,5,4,0,5,4,0,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,5,0,4,5,0,4,3,4,3,2,5,3,4,2,0,2,0,3,5,0,2,2,0,4,5,7,0,2,7,2,0,4,5,0,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,7,0,2,7,0,2,2,0,7,1,4,5,0,0,0,4,3,3,0,2,2,3,3,7,2,4,1,4,8,1,2,4,4,0,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,1,4,2,3,5,0,4,3,4,2,3,4,3,3,0,0,3,1,2,4,3,0,1,3,3,6,6,0,3,4,5,3,3,3,1,0,4,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,9,0,0,5,0,4,4,3,3,0,3,6,0,0,2,0,3,5,0,3,0,6,0,0,9,3,2,4,7,2,6,3,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,19,1,2,3,4,2,2,2,4,4,1,5,5,3,2,5,2,3,4,1,0,2,1,2,2,1,4,3,0,6,3,4,1,5,5,4,5,3,1,1,1,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,8,0,1,9,0,0,0,4,5,0,3,6,1,1,1,2,5,0,1,0,4,5,0,0,9,6,3,0,6,3,5,4,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,5,2,3,5,1,4,1,4,4,5,3,1,2,3,0,4,1,1,5,3,1,1,1,2,7,5,1,3,5,4,3,1,4,2,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,0,7,0,2,7,2,0,0,4,5,5,4,0,5,0,2,2,0,1,7,2,0,0,0,0,9,5,4,0,0,9,0,2,6,2,0,6,8,2,0,0,6,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1\ntest,33,1,3,4,8,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,1,2,4,1,2,4,2,3,6,0,3,3,2,4,2,4,3,2,2,1,4,2,1,2,2,4,2,0,3,6,6,1,3,5,4,1,5,3,1,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0\ntest,39,1,2,3,9,4,3,1,3,5,2,3,6,2,2,0,2,7,0,0,0,0,9,0,0,0,0,5,4,9,0,7,2,0,4,5,5,2,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,2,0,5,2,2,2,6,2,0,3,6,5,0,0,1,0,4,2,0,2,4,2,4,5,4,4,2,3,6,3,6,0,0,0,2,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0\ntest,6,1,3,3,2,1,5,2,3,7,1,2,0,2,7,4,4,2,5,1,0,3,1,1,5,1,2,2,1,1,8,7,0,2,4,5,2,2,4,2,1,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,4,1,4,9,0,0,0,4,5,3,6,1,2,0,0,4,2,2,2,3,3,2,0,0,9,7,2,0,2,7,0,1,6,3,1,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,9,1,2,3,3,3,1,1,4,7,1,2,2,3,4,2,4,4,2,1,0,2,3,3,2,2,3,4,1,7,2,6,2,2,6,3,4,3,3,1,1,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,3,5,0,2,9,0,0,0,6,3,3,5,2,2,0,0,7,0,1,1,2,4,3,0,5,4,7,2,0,7,2,2,4,2,1,0,4,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,4,0,0,5,5,4,0,0,6,3,6,2,2,6,0,0,2,2,1,6,2,0,2,0,0,9,7,2,0,2,7,0,3,3,4,1,6,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,16,1,2,2,4,0,2,0,7,3,0,6,6,2,2,2,4,3,0,0,0,2,4,4,1,1,2,4,2,7,2,3,0,6,4,5,7,1,1,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,2,4,10,0,6,0,3,6,0,3,3,4,3,0,2,7,1,0,8,1,0,1,2,1,6,2,0,0,9,3,3,3,2,7,3,6,1,0,0,3,3,0,0,0,6,0,0,0,0,5,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,2,5,1,4,2,4,2,2,6,6,3,1,2,4,4,2,0,0,3,2,3,2,2,2,5,1,8,1,6,0,3,8,1,2,6,2,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,2,2,3,0,9,0,0,4,0,5,5,0,4,5,0,4,0,0,0,9,0,0,0,5,0,4,0,5,4,9,0,0,4,5,0,9,0,0,0,4,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,0,4,7,1,1,1,4,5,2,4,4,3,0,1,2,2,2,4,2,1,4,0,5,4,7,2,0,8,1,3,2,4,1,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,3,3,7,1,6,1,2,6,1,2,2,3,4,1,3,5,2,1,1,4,2,2,1,2,3,4,1,2,7,5,2,3,5,4,2,4,3,1,1,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,2,3,0,4,8,0,1,1,5,4,1,4,5,1,0,0,5,3,1,1,1,1,7,1,1,8,8,1,1,4,5,2,4,2,2,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,4,0,0,4,6,0,0,0,0,4,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,2,7,0,0,9,0,0,1,6,3,0,6,3,3,0,0,5,0,2,2,1,5,2,1,1,8,7,2,0,7,2,1,6,1,0,2,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,2,4,2,2,0,6,0,3,9,0,0,0,1,8,4,5,1,5,1,0,2,2,1,5,2,2,2,1,0,9,8,1,0,3,6,0,3,5,2,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,2,5,0,3,1,5,3,4,2,1,7,1,0,4,5,0,0,0,4,2,3,0,3,2,4,0,7,2,4,1,4,9,0,3,5,2,0,0,3,2,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,4,2,3,2,4,1,3,7,1,1,1,2,6,3,4,2,3,0,0,4,2,1,3,3,3,2,0,2,7,9,0,0,5,4,2,3,5,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,2,8,0,4,2,3,5,1,3,2,7,1,0,4,5,0,0,0,3,2,4,0,1,1,7,1,9,0,6,0,3,7,2,2,4,3,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,7,1,3,3,2,0,5,2,3,7,0,2,1,6,2,2,3,5,3,0,1,2,4,1,2,2,1,3,2,2,7,6,2,2,6,3,2,4,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,8,0,1,8,1,0,0,4,5,2,4,4,3,0,5,1,0,2,3,3,3,2,0,0,9,4,5,0,1,8,0,5,3,2,0,5,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,0,0,0,6,5,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,3,2,2,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,2,4,10,0,6,0,3,6,0,3,3,4,3,0,2,7,1,0,8,1,0,1,2,1,6,2,0,0,9,3,3,3,2,7,3,6,1,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,2,7,0,0,7,1,2,1,4,5,0,3,6,1,1,0,2,3,4,1,1,3,5,1,0,9,6,2,2,5,4,0,7,2,1,0,4,3,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,0,4,5,0,4,3,4,3,0,0,9,0,0,0,0,5,4,0,0,0,7,2,6,3,6,1,2,6,3,9,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,4,0,5,7,0,2,2,6,2,3,5,1,6,1,0,2,0,1,6,0,2,2,0,6,3,7,2,0,3,6,2,4,4,1,0,5,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,4,6,2,1,1,5,3,0,3,6,1,0,0,4,3,2,1,1,2,6,0,6,3,6,0,3,5,4,2,2,5,0,0,4,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,1,2,8,0,6,1,2,1,2,6,5,3,2,2,5,2,2,0,0,4,1,3,2,3,3,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,41,1,2,4,10,1,4,0,5,7,0,2,5,3,2,0,2,7,2,1,1,3,2,2,0,0,3,6,0,3,6,6,2,1,4,5,6,2,0,2,0,3,4,0,0,0,7,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,0,5,5,2,3,2,2,6,0,4,5,0,0,0,4,4,1,0,0,2,7,1,1,8,6,2,2,8,1,4,1,0,4,0,5,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,1,4,2,3,5,2,3,2,3,6,2,4,4,2,1,1,3,2,2,1,1,5,2,1,1,8,6,2,2,6,3,2,4,3,1,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,1,6,0,2,7,0,2,1,4,4,2,3,5,3,0,0,3,2,2,2,1,1,5,2,4,5,6,0,3,7,2,1,4,4,0,0,4,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,5,1,2,5,1,2,4,1,5,3,5,1,2,5,3,6,1,0,2,1,1,4,2,4,1,1,3,6,7,2,1,5,4,2,3,4,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,3,7,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,1,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,3,2,1,5,1,3,7,1,2,0,2,7,3,4,2,2,1,0,4,3,1,2,3,2,3,1,1,8,7,0,2,5,4,1,3,4,2,1,6,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,2,2,0,3,3,3,5,2,2,2,2,5,2,6,2,3,0,0,6,0,2,1,2,5,1,0,0,9,9,0,0,4,5,2,3,5,0,0,4,8,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,4,3,3,1,5,1,3,7,1,2,0,3,6,3,4,2,3,1,0,4,2,1,3,2,3,2,1,1,8,7,0,2,5,4,1,2,5,2,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,2,8,1,3,3,3,5,0,4,3,1,5,1,4,5,1,0,0,6,1,3,0,3,2,5,0,6,3,6,2,3,8,1,7,1,1,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,4,6,2,1,1,5,3,0,3,6,1,0,0,4,3,2,1,1,2,6,0,6,3,6,0,3,5,4,2,2,5,0,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,1,3,8,1,1,1,2,7,0,2,7,1,0,4,1,1,5,1,2,3,4,1,1,8,7,1,1,8,1,2,3,2,4,0,5,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,3,6,2,4,1,3,3,2,5,5,3,2,2,5,3,1,0,0,3,2,4,1,2,4,3,1,8,1,5,0,4,8,1,6,2,1,0,1,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,1,4,2,3,7,1,1,0,6,3,4,5,0,8,0,0,0,0,1,6,3,0,0,0,0,9,6,3,0,3,6,0,0,1,8,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,9,0,0,0,5,4,0,2,7,1,0,2,1,5,2,1,1,2,4,3,5,4,8,1,0,8,1,4,3,2,0,0,3,3,2,0,0,6,0,4,0,0,0,0,0,3,0,0,0,5,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,6,2,2,8,1,0,0,6,3,1,5,4,1,0,6,2,1,0,2,1,7,1,0,2,7,7,2,0,2,7,3,3,1,3,0,5,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,2,7,2,1,1,1,4,2,2,1,2,5,1,2,7,4,4,2,7,2,3,3,3,0,1,4,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,3,4,2,1,3,4,3,3,5,2,0,4,5,0,0,0,4,4,2,0,2,3,4,1,9,0,7,1,1,8,1,3,6,1,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,0,6,1,3,5,0,4,3,3,4,1,3,5,1,0,1,6,1,3,1,2,2,3,3,2,7,7,0,2,7,2,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,1,6,1,2,4,1,5,5,3,2,3,4,3,4,0,0,3,1,2,3,1,3,3,1,4,5,5,0,4,5,4,5,2,3,1,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,4,2,4,2,2,6,4,2,4,0,2,7,1,0,0,2,0,7,0,0,2,1,7,7,2,2,2,6,7,2,7,2,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,8,0,1,0,3,6,0,3,6,1,2,1,3,3,2,2,0,1,7,0,4,5,7,2,0,7,2,4,3,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,0,9,0,0,7,0,2,2,3,5,4,3,3,4,0,0,3,2,2,2,2,3,3,0,0,9,9,0,0,3,6,2,0,4,4,0,7,7,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,2,9,0,4,2,4,7,1,2,3,4,3,1,1,8,1,0,0,3,6,0,1,1,1,8,0,3,6,7,1,1,8,1,0,7,2,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,2,5,2,1,7,2,0,0,2,7,4,4,2,3,0,0,5,2,1,2,3,3,2,1,0,9,9,0,0,4,5,1,2,7,0,0,5,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,1,6,0,3,9,0,0,0,4,5,8,1,0,9,0,0,0,0,0,9,0,0,0,0,0,9,7,2,0,2,7,0,1,3,5,0,7,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,10,1,3,2,3,0,4,2,4,9,0,0,0,5,4,3,5,2,7,0,0,2,0,0,4,4,0,2,0,0,9,5,4,0,0,9,0,3,6,0,0,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,1,3,7,1,2,2,3,5,2,4,4,3,1,2,2,2,1,3,2,3,3,1,3,6,6,2,1,5,4,3,2,3,2,1,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,7,1,3,2,2,0,9,0,0,9,0,0,0,4,5,0,5,4,5,0,0,0,0,4,2,0,6,2,0,0,9,9,0,0,4,5,0,0,7,0,2,8,6,1,0,0,7,0,5,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,2,8,0,4,2,4,3,2,4,4,4,2,0,5,4,0,0,0,7,0,2,0,5,0,4,0,7,2,7,0,2,9,0,4,5,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,3,1,2,5,1,2,1,6,1,3,6,6,3,1,1,7,2,3,1,1,4,1,2,2,2,5,1,1,5,4,5,0,4,4,5,2,1,6,1,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,4,4,8,1,4,1,4,6,0,3,2,2,5,0,0,9,2,1,1,3,3,2,0,4,5,0,0,3,6,6,1,2,6,3,0,9,0,0,0,4,5,0,0,4,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,3,3,2,3,1,3,7,1,1,1,1,8,4,4,1,1,0,0,7,1,1,1,6,1,2,0,1,8,9,0,0,6,3,3,2,5,0,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,2,2,4,7,0,6,1,3,6,1,3,4,3,3,0,1,8,0,0,1,2,4,3,0,1,1,5,3,7,2,5,1,3,8,1,5,2,3,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,9,1,2,4,3,0,5,3,2,4,1,4,4,2,4,1,4,4,1,1,0,5,2,1,1,2,3,4,1,9,0,5,0,4,5,4,3,4,4,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,1,2,4,6,1,5,2,3,5,1,4,3,6,0,6,0,3,4,2,0,2,1,1,6,0,0,3,0,6,3,5,1,3,5,4,3,2,3,2,1,5,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,1,4,1,5,5,1,4,3,4,2,0,4,5,1,1,2,4,3,1,1,3,3,3,1,2,7,5,1,4,7,2,5,3,2,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,34,1,4,3,8,1,4,2,3,7,1,2,0,0,9,0,0,9,0,0,0,0,5,4,0,0,0,5,4,9,0,6,2,2,7,2,0,4,5,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,0,3,3,4,5,0,4,4,3,3,0,0,9,0,0,0,3,0,6,0,0,0,5,4,9,0,6,0,3,9,0,2,5,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,33,2,3,3,8,0,4,0,5,7,0,2,2,1,6,0,2,7,0,0,0,3,6,0,0,1,1,6,2,4,5,5,2,3,8,1,3,4,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0\ntest,38,1,2,2,9,0,3,1,5,8,0,1,4,1,5,1,1,7,2,0,0,2,3,3,1,0,1,2,6,9,0,7,0,2,9,0,7,2,0,0,0,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,7,1,2,8,1,1,1,3,6,1,4,5,2,1,2,2,3,1,2,1,3,4,0,1,8,5,4,1,5,4,2,3,4,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,4,7,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,1,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,1,4,4,2,5,0,4,4,5,1,0,1,8,1,0,0,2,3,4,1,0,1,6,2,3,6,5,1,4,9,0,1,7,2,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0\ntest,40,1,3,3,10,0,5,1,4,8,1,0,0,4,5,1,3,6,2,0,5,1,2,0,2,1,6,2,0,2,7,7,2,0,3,6,5,2,2,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,5,1,2,3,4,2,7,0,2,2,6,2,2,5,3,3,1,2,2,0,3,2,2,4,2,0,6,3,7,0,2,6,3,1,4,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,3,5,3,2,1,3,5,2,2,2,5,2,2,4,4,2,2,0,2,3,1,3,2,0,4,0,6,3,7,0,2,6,3,5,0,0,4,0,5,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,6,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,1,5,3,2,6,0,3,3,4,3,0,2,7,1,0,0,3,3,4,1,0,2,6,1,4,5,6,1,2,9,0,1,7,2,0,0,3,6,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,9,1,3,3,3,1,7,0,1,6,0,3,2,4,4,4,4,2,3,0,0,5,1,1,3,2,3,2,0,0,9,6,3,2,5,4,1,4,4,1,0,5,4,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,1,6,1,3,6,2,1,1,3,5,1,2,6,1,0,0,3,3,3,1,3,1,5,2,7,2,7,2,1,8,1,3,4,1,1,2,5,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,3,3,1,0,7,2,0,7,0,2,0,4,5,3,3,5,0,3,0,5,0,2,3,2,0,3,2,7,2,9,0,0,6,3,2,5,3,0,0,3,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,2,1,6,0,3,2,3,4,1,2,7,1,0,2,2,1,5,1,2,2,5,1,1,8,7,0,2,7,2,0,5,4,0,0,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,4,7,1,4,2,4,6,1,3,0,5,4,0,2,7,0,0,0,0,0,9,0,0,2,2,5,5,4,5,1,3,7,2,0,7,0,2,0,4,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,0,5,3,2,3,4,3,1,8,1,0,5,4,0,0,0,5,3,1,0,5,1,4,0,8,1,4,1,5,9,0,7,2,1,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,2,4,10,1,4,0,5,7,0,2,5,3,2,0,2,7,2,1,1,3,2,2,0,0,3,6,0,3,6,6,2,1,4,5,6,2,0,2,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,2,1,1,5,0,2,4,4,2,2,5,5,4,0,0,7,2,0,0,0,7,0,2,0,5,0,4,0,9,0,7,0,2,9,0,4,4,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,0,4,0,5,5,2,3,2,4,4,3,3,4,6,0,0,2,0,2,3,2,2,2,2,4,5,5,2,3,6,3,3,3,3,1,0,4,6,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,0,7,0,2,9,0,0,0,5,4,0,2,7,1,0,2,1,5,2,1,1,2,4,3,5,4,8,1,0,8,1,4,3,2,0,0,3,6,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,0,3,1,5,8,0,1,1,1,7,2,3,5,2,0,0,2,3,3,2,0,2,4,3,9,0,7,0,2,9,0,4,5,0,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,3,0,6,9,0,0,0,4,5,0,0,9,0,0,6,0,3,0,0,0,6,3,0,0,9,7,2,0,7,2,6,3,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,2,5,1,0,9,0,0,9,0,0,0,7,2,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,9,0,0,0,9,2,3,3,2,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,2,2,3,8,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,34,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,0,9,0,0,9,0,0,0,3,6,0,3,6,4,0,0,0,3,3,0,3,0,6,0,0,9,6,2,2,6,3,3,4,2,0,0,3,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,2,2,3,8,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,3,5,0,5,1,4,2,1,7,3,4,2,2,5,2,1,1,1,7,2,1,1,4,1,4,0,8,1,6,1,3,7,2,3,6,1,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,2,5,1,4,2,3,4,1,4,4,4,2,2,4,4,2,1,0,3,4,0,3,2,0,6,0,7,2,6,0,3,7,2,4,0,5,0,0,4,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,0,7,2,0,9,0,0,4,3,2,1,6,3,1,0,0,3,3,4,1,1,3,5,1,8,1,7,0,2,7,2,1,4,3,3,0,6,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,33,1,3,3,8,0,7,1,2,8,0,1,1,4,4,0,4,5,1,0,0,4,2,2,1,1,3,4,2,0,9,7,1,2,6,3,0,5,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,6,2,2,7,0,2,2,4,4,0,3,6,1,0,0,3,3,3,1,1,4,4,2,0,9,7,1,2,7,2,0,7,2,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,0,4,0,5,9,0,0,0,2,7,3,3,4,3,0,0,3,3,2,3,3,0,4,0,9,0,9,0,0,6,3,0,6,2,2,0,4,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,27,2,2,4,6,0,4,2,3,5,0,4,4,2,4,0,0,9,0,0,2,2,3,3,0,0,2,3,5,9,0,7,0,2,9,0,2,7,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,2,3,2,9,0,6,1,2,7,1,1,0,4,5,1,6,3,0,0,0,1,4,5,1,0,1,8,0,5,4,8,1,1,4,5,0,4,5,1,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,2,5,1,2,8,0,1,2,5,3,1,5,4,2,0,0,3,3,3,1,1,5,4,0,8,1,8,1,1,4,5,2,5,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,2,2,6,6,2,2,0,6,3,9,0,0,0,0,0,6,0,3,9,0,0,0,0,0,9,5,3,2,4,5,3,0,2,2,2,7,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,0,4,1,5,4,3,3,3,2,4,1,4,4,2,0,0,2,2,4,1,3,2,3,3,8,1,6,1,2,8,1,4,4,1,0,0,2,3,2,2,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,4,3,1,1,4,1,4,7,1,2,0,0,9,3,6,0,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,0,5,0,4,9,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,2,8,0,5,0,4,3,2,4,4,3,3,1,4,5,2,0,0,3,4,1,1,3,0,3,3,2,7,5,2,2,8,1,2,5,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,1,3,2,5,9,0,0,0,3,6,1,7,2,1,1,0,8,1,1,1,2,2,5,0,5,4,6,1,3,5,4,2,6,2,0,0,3,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,0,5,9,0,0,0,3,6,0,4,5,0,0,0,2,5,3,0,2,2,5,2,0,9,9,0,0,9,0,1,5,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,1,4,1,4,7,1,2,0,2,7,2,0,7,5,0,0,2,0,3,2,2,5,2,0,6,3,6,2,1,5,4,7,2,0,0,0,2,3,0,0,0,6,0,0,0,0,4,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,11,1,2,3,3,4,3,1,3,5,2,3,3,4,3,1,3,5,2,0,1,3,3,2,1,1,2,4,2,7,2,7,2,0,4,5,3,3,3,1,1,4,6,0,0,0,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,5,6,2,2,2,4,4,1,3,6,1,1,1,3,3,2,1,2,3,4,1,5,4,6,2,2,8,1,4,4,2,1,1,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,8,1,4,3,2,2,5,1,2,8,1,1,0,3,6,3,4,2,4,1,0,3,2,1,3,3,2,2,1,1,8,8,0,1,4,5,1,4,4,1,1,4,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,7,0,2,7,0,2,2,0,7,1,4,5,0,0,0,4,3,3,0,2,2,3,3,7,2,4,1,4,8,1,2,4,4,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,25,2,2,3,6,1,5,2,2,4,2,4,4,4,2,2,5,3,4,0,0,5,0,0,2,2,3,3,0,6,3,7,2,0,4,5,4,2,3,0,0,3,1,0,0,0,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,3,1,2,5,1,0,5,0,4,4,0,5,4,3,3,2,2,6,5,0,0,4,0,0,2,0,2,0,6,4,5,5,2,4,5,4,3,3,0,3,0,5,6,0,0,0,5,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,1,4,4,2,5,0,4,4,5,1,0,1,8,1,0,0,2,3,4,1,0,1,6,2,3,6,5,1,4,9,0,1,7,2,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,2,1,7,5,2,3,3,3,5,0,1,8,0,0,0,2,7,1,0,1,1,8,0,5,4,5,4,0,9,0,3,2,5,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,4,1,0,6,2,2,9,0,0,0,4,5,3,6,0,4,0,0,4,2,0,4,2,4,0,0,0,9,6,3,0,3,6,0,6,3,0,0,4,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,3,5,0,5,2,3,1,1,8,8,1,1,5,4,2,2,0,0,7,1,1,3,4,1,1,1,9,0,4,2,4,5,4,1,5,3,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,5,1,4,4,1,5,0,2,7,0,0,0,1,2,6,0,0,2,5,2,6,3,6,1,2,8,1,2,6,0,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,2,4,1,3,3,2,5,5,3,2,2,5,3,1,0,0,3,2,4,1,2,4,3,1,8,1,5,0,4,8,1,6,2,1,0,1,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,2,4,2,4,7,1,2,6,0,3,1,8,0,3,0,0,1,1,5,0,3,6,1,0,6,3,7,1,2,6,3,4,4,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,0,6,1,3,7,0,2,2,4,3,3,2,5,3,1,2,2,3,1,2,2,2,3,2,2,7,6,1,3,6,3,4,2,3,1,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,0,4,0,5,7,0,2,2,6,2,3,5,1,6,1,0,2,0,1,6,0,2,2,0,6,3,7,2,0,3,6,2,4,4,1,0,5,6,2,0,0,6,0,0,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,2,1,2,5,0,3,9,0,0,1,3,5,4,5,0,9,0,0,0,0,0,9,0,0,0,0,0,9,7,2,0,2,7,0,0,5,0,4,0,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,1,2,9,0,7,0,2,6,0,3,5,4,0,0,0,9,0,0,0,1,3,6,0,0,0,9,0,9,0,3,2,5,6,3,5,4,0,0,0,2,5,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,0,5,1,4,5,2,3,2,2,6,0,2,7,2,0,0,1,3,5,1,1,1,6,2,8,1,6,1,3,9,0,2,6,1,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,6,1,3,9,0,0,0,4,5,6,3,1,6,0,0,2,1,1,5,2,2,1,0,0,9,7,2,0,2,7,0,1,5,4,1,7,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,0,6,1,3,8,1,1,1,3,6,0,3,6,0,0,2,3,4,1,0,2,4,4,1,2,7,7,1,2,6,3,2,2,6,0,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,1,4,2,3,5,0,4,4,3,2,1,2,6,1,0,1,3,3,3,1,1,2,5,1,5,4,5,1,4,9,0,2,5,2,1,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,40,1,2,4,10,0,6,0,3,6,0,3,3,4,3,0,2,7,1,0,8,1,0,1,2,1,6,2,0,0,9,3,3,3,2,7,3,6,1,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,6,1,3,9,0,0,0,4,5,6,3,1,6,0,0,2,1,1,5,2,2,1,0,0,9,7,2,0,2,7,0,1,5,4,1,7,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,5,8,1,4,2,3,3,0,6,5,3,2,2,2,6,2,2,0,3,2,3,1,1,2,4,2,8,1,2,2,5,5,4,7,1,0,0,1,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,2,5,0,1,0,8,3,2,4,3,2,5,0,1,8,0,0,0,1,4,5,0,1,0,5,4,8,1,4,0,5,8,1,8,1,0,0,0,2,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,0,6,0,3,4,0,5,1,2,6,1,4,5,0,0,2,2,4,2,0,2,3,5,1,4,5,5,2,3,7,2,5,3,2,0,0,3,4,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,4,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,2,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,26,1,1,6,6,0,5,2,2,2,2,6,6,3,0,2,4,3,2,0,0,2,0,5,2,2,3,3,0,9,0,7,0,2,4,5,4,4,0,2,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,2,2,5,0,4,3,0,6,0,0,9,0,0,4,0,3,3,0,0,4,4,2,4,5,9,0,0,9,0,2,6,2,0,0,3,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,2,9,0,3,2,5,5,3,2,1,6,2,1,4,4,1,1,1,6,1,2,1,2,2,5,0,8,1,5,1,4,8,1,6,3,1,1,0,2,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,2,3,1,5,5,2,2,2,4,4,1,5,4,2,0,0,4,3,1,1,3,1,5,1,8,1,6,2,2,8,1,2,5,2,1,1,4,4,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,22,1,2,4,5,0,5,3,2,4,1,4,4,5,1,2,4,3,1,1,0,5,2,1,1,2,3,3,1,9,0,5,0,4,5,4,6,3,1,0,0,2,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,4,1,0,9,0,0,9,0,0,0,6,3,0,6,3,4,0,0,4,0,3,3,0,4,3,0,4,6,9,0,0,6,4,3,4,4,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,10,1,3,2,3,1,5,1,3,8,1,1,1,3,6,4,4,2,4,1,0,3,1,1,3,2,3,2,0,1,8,7,1,1,4,5,1,3,4,2,1,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,1,9,0,2,4,4,7,0,2,0,7,2,4,4,1,1,0,0,1,4,5,4,1,0,4,0,7,2,7,0,2,6,3,1,1,8,1,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,21,1,2,3,5,2,4,1,3,6,0,3,5,3,1,1,6,2,0,0,0,6,3,0,1,3,0,4,2,8,1,5,1,3,5,4,5,3,0,1,0,2,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,0,6,0,3,6,2,2,1,4,5,2,2,5,3,1,1,3,1,3,2,2,2,3,1,4,5,7,1,1,6,3,2,4,2,2,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,4,2,7,2,6,0,1,6,2,2,0,0,9,0,0,9,0,0,0,0,3,6,0,0,0,6,3,6,3,7,0,2,6,3,0,9,0,0,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,4,0,5,3,0,6,5,2,2,1,2,7,0,0,2,2,3,4,0,1,4,5,1,2,7,5,1,4,7,2,1,4,5,0,0,4,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,1,5,4,0,9,0,0,1,6,3,4,3,2,6,0,0,2,2,0,5,2,3,1,0,0,9,5,4,0,0,9,0,0,8,1,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,5,9,0,5,4,0,0,0,9,7,0,2,0,0,9,0,0,0,0,6,3,0,0,0,6,3,7,2,7,1,2,7,2,7,2,0,0,0,1,5,0,0,0,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1\ntest,13,1,4,2,3,0,7,0,2,7,2,0,1,1,8,2,3,5,1,0,0,7,2,1,0,3,2,5,0,0,9,9,0,0,6,3,2,4,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,4,1,0,5,6,3,0,0,4,5,5,3,2,5,0,0,3,1,2,4,3,1,2,0,0,9,6,3,0,2,7,0,3,4,3,1,7,7,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,0,2,4,4,9,0,0,0,3,6,0,4,5,1,0,0,3,3,2,2,2,2,5,1,9,0,5,3,2,6,3,0,4,5,1,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,34,1,2,3,8,0,5,2,2,6,1,2,2,3,4,3,1,6,2,0,0,3,3,2,2,1,2,5,0,2,7,7,0,2,8,1,2,5,2,0,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,3,5,2,1,2,5,7,0,2,0,6,3,0,2,7,1,0,0,2,7,1,1,2,0,7,1,8,1,8,1,0,8,1,2,7,1,1,1,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,2,3,7,1,5,1,2,6,2,2,1,7,2,1,4,5,1,0,0,4,3,3,1,3,1,3,3,1,8,8,0,1,6,3,1,5,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,3,3,1,4,0,5,7,0,2,2,5,3,1,6,2,2,0,0,6,0,2,1,1,6,2,0,8,1,5,1,4,6,3,1,1,8,0,0,5,4,0,0,0,6,0,4,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0\ntest,11,1,2,4,3,0,7,0,2,6,0,3,4,2,4,3,3,3,4,0,2,0,4,1,3,0,2,5,0,1,8,3,0,6,7,2,4,3,2,2,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,2,4,2,0,6,0,3,6,0,3,4,0,5,3,4,3,2,2,0,6,0,0,3,1,5,2,0,9,0,9,0,0,2,7,6,0,2,0,2,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,3,8,0,4,4,2,7,2,1,1,7,2,0,3,6,1,0,2,4,1,4,1,1,4,4,2,7,2,8,1,1,7,2,5,3,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,21,2,2,3,5,0,3,2,5,4,0,5,6,2,2,0,2,7,0,0,0,4,0,5,0,1,0,4,4,5,4,3,0,6,7,2,6,3,0,0,0,1,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,4,3,7,2,7,0,0,6,1,3,2,1,7,0,4,5,2,1,0,3,2,4,1,2,3,4,1,0,9,5,2,3,7,2,0,4,4,1,0,5,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,8,1,2,4,2,0,7,0,2,9,0,0,2,5,3,0,4,5,0,0,0,4,1,5,0,0,5,4,1,9,0,7,0,2,5,4,4,4,2,0,0,3,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,2,5,2,9,1,5,0,3,7,1,2,0,0,9,0,4,5,0,0,0,6,3,0,0,4,0,5,0,2,7,6,2,2,6,3,0,4,5,0,0,4,5,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,26,1,2,5,6,0,8,1,1,5,0,4,3,5,1,1,3,5,1,0,0,2,1,6,1,1,3,3,2,6,3,4,0,5,9,0,6,3,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,1,4,1,4,7,1,2,0,3,6,0,6,3,0,0,0,9,0,0,0,0,0,9,0,9,0,6,2,1,5,4,5,4,0,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,0,7,2,0,9,0,0,0,3,6,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,7,2,0,2,7,0,0,7,2,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,2,2,0,4,2,4,9,0,0,0,5,4,3,5,2,7,0,0,2,0,0,4,4,0,2,0,0,9,5,4,0,0,9,0,3,6,0,0,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,23,2,1,1,5,0,2,4,4,2,2,5,5,4,0,0,7,2,0,0,0,7,0,2,0,5,0,4,0,9,0,7,0,2,9,0,4,4,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,2,2,6,6,0,3,1,5,4,0,4,5,3,0,0,1,5,1,3,1,0,2,4,2,7,5,0,4,9,0,0,0,9,0,0,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,32,1,2,4,7,0,4,2,3,5,0,4,4,4,2,0,2,7,0,0,0,4,0,5,0,1,1,3,5,9,0,7,0,2,9,0,8,1,1,0,0,2,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,4,3,1,1,5,1,3,6,1,2,0,2,7,8,1,1,6,0,0,2,2,1,5,3,2,1,0,0,9,7,2,1,5,4,0,1,4,5,1,7,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,9,0,0,0,5,4,0,2,7,1,0,2,1,5,2,1,1,2,4,3,5,4,8,1,0,8,1,4,3,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,5,1,2,4,1,0,5,2,3,5,1,4,3,4,3,2,4,4,3,1,1,3,2,2,2,2,2,4,1,4,5,6,1,3,6,3,4,2,3,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0\ntest,24,1,3,3,5,1,4,0,4,6,1,3,2,3,5,1,3,5,3,0,0,3,3,2,1,2,3,4,0,9,0,5,3,2,8,1,2,3,4,2,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,4,1,2,3,1,2,1,2,5,5,3,2,2,5,3,5,3,1,2,0,0,5,2,1,2,4,3,1,1,8,1,7,2,1,5,4,2,2,3,3,1,6,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,4,3,8,4,0,0,5,4,0,5,0,4,5,0,0,9,2,0,0,2,0,5,0,0,0,3,6,9,0,7,2,0,4,5,6,3,0,0,0,2,4,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,15,3,1,4,4,1,5,2,2,4,0,5,6,3,1,3,2,5,4,0,1,2,2,2,3,1,1,3,2,5,4,4,2,4,4,5,3,3,3,0,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,2,3,3,1,7,1,2,7,1,2,2,3,5,1,7,2,1,0,0,7,2,1,1,4,3,2,1,2,7,8,0,1,6,3,1,6,2,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,24,1,2,1,5,3,4,2,1,3,4,3,4,5,2,0,4,5,0,0,0,2,3,4,0,2,3,5,1,9,0,7,1,1,8,1,1,6,3,0,0,4,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,4,3,2,4,1,2,1,5,2,7,0,2,1,6,3,2,5,2,5,2,1,1,0,2,4,3,1,2,0,2,7,7,0,2,5,4,1,1,8,0,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,12,1,3,2,3,0,6,0,3,7,2,0,1,2,6,2,4,4,1,0,0,5,2,1,0,3,2,4,0,0,9,9,0,0,5,4,2,4,4,0,0,4,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,40,1,2,4,10,0,6,0,3,6,0,3,3,4,3,0,2,7,1,0,8,1,0,1,2,1,6,2,0,0,9,3,3,3,2,7,3,6,1,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,2,3,0,5,7,2,0,0,5,4,1,8,1,3,0,0,4,1,3,1,2,4,4,0,5,4,9,0,0,6,3,2,1,4,3,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,3,2,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,0,6,0,3,9,0,0,0,3,6,0,0,9,0,0,3,0,6,0,0,0,3,6,0,9,0,7,2,0,9,0,3,5,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,35,1,2,4,8,1,4,1,4,6,1,3,2,4,4,2,4,4,3,1,1,3,2,2,3,2,2,3,2,3,6,6,2,2,5,4,4,2,3,1,1,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,6,1,0,3,6,1,3,1,3,6,4,3,3,5,2,0,2,1,1,5,1,3,2,0,2,7,6,1,2,5,4,0,3,4,3,1,6,6,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,22,1,2,2,5,2,4,4,0,5,0,4,3,4,2,2,3,5,3,0,0,1,2,5,2,0,3,5,0,3,7,5,2,2,7,2,1,5,4,0,0,4,2,0,0,0,5,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0\ntest,38,1,2,3,9,0,5,0,4,3,2,5,4,3,3,2,4,4,2,1,0,5,1,2,2,2,3,3,1,8,1,4,1,5,8,1,5,4,1,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,23,1,1,3,5,1,6,1,3,2,2,6,5,3,2,2,5,3,1,0,0,2,2,5,1,2,4,4,1,8,1,5,0,4,8,1,6,3,1,0,1,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,7,0,2,9,0,0,1,3,6,0,1,8,0,0,0,1,2,6,0,0,1,6,3,9,0,7,0,2,5,4,3,6,1,0,0,2,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,0,4,1,5,4,3,3,2,3,4,1,4,5,1,0,0,2,3,4,1,1,3,4,1,8,1,6,1,2,8,1,3,4,2,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,0,4,7,0,2,0,1,8,0,3,6,0,0,0,3,3,4,0,1,2,5,2,9,0,7,2,0,9,0,2,6,1,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,6,0,3,4,1,4,4,3,2,4,2,4,2,0,0,2,2,4,2,2,3,3,1,6,3,0,0,9,2,7,7,1,1,1,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,3,4,7,0,5,1,3,6,1,3,1,5,4,0,0,9,0,0,0,0,3,6,0,0,0,9,0,9,0,6,1,2,6,3,5,4,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,5,6,2,2,2,4,4,1,3,6,1,1,1,3,3,2,1,2,3,4,1,5,4,6,2,2,8,1,4,4,2,1,1,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,2,4,2,3,5,2,3,2,4,3,1,3,5,1,1,0,3,3,2,2,2,2,5,1,6,3,7,1,2,7,2,3,4,3,1,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,39,1,3,3,9,0,5,1,3,7,1,2,3,2,5,1,3,6,2,1,1,2,3,3,1,1,1,5,2,5,4,5,2,2,7,2,4,4,1,1,1,3,5,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,7,1,3,3,2,5,0,0,4,6,3,0,0,3,6,6,2,2,6,2,0,0,0,2,7,0,0,2,0,0,9,7,2,0,2,7,0,0,0,7,2,0,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,6,2,8,1,8,0,0,7,1,1,0,1,8,0,5,5,0,0,0,5,0,5,0,3,0,0,6,6,3,3,5,2,9,0,0,5,5,0,0,4,3,2,0,0,6,0,0,0,0,4,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,2,4,10,0,7,0,2,5,0,4,3,5,2,1,4,4,2,0,4,3,2,0,2,1,4,2,2,3,6,4,2,3,4,5,4,3,0,2,0,4,3,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,25,1,2,5,6,0,5,0,4,2,2,5,3,6,0,1,4,5,4,0,0,2,2,2,1,2,4,2,2,9,0,5,0,4,9,0,6,2,1,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,3,9,2,5,0,3,9,0,0,0,3,6,2,4,3,2,0,0,4,2,2,2,2,3,3,0,0,9,5,0,4,7,2,0,6,3,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,0,5,2,2,7,0,2,2,5,3,0,0,9,0,0,0,2,7,0,0,0,0,9,0,5,4,9,0,0,9,0,5,4,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,1,5,1,4,6,1,3,1,2,7,4,2,4,2,0,0,6,1,1,4,1,2,3,0,1,8,8,1,0,6,3,1,2,5,3,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,0,6,0,3,3,0,6,1,4,4,0,2,7,2,0,0,2,4,4,1,0,1,4,4,9,0,5,0,4,5,4,3,6,1,0,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,2,2,4,8,0,3,2,5,5,2,3,3,5,3,0,3,6,1,0,5,1,3,2,1,2,4,3,2,5,4,4,5,1,7,2,2,5,1,2,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,21,1,2,3,5,1,4,0,5,5,2,3,3,3,4,0,2,7,1,0,0,1,3,5,0,2,2,5,2,5,4,6,0,3,7,2,4,3,2,0,0,3,1,0,2,0,8,6,0,0,0,0,0,0,0,0,0,6,4,0,0,0,0,0,0,1,0,5,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0\ntest,30,1,2,5,7,0,7,0,2,5,0,4,4,4,2,0,2,7,0,0,0,5,0,5,0,0,3,6,0,5,4,5,2,3,5,4,7,2,0,0,0,2,2,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,3,3,1,4,1,4,7,1,2,0,3,6,4,3,3,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,3,2,2,2,8,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,0,5,0,4,5,2,3,4,4,2,2,3,4,3,0,0,2,5,1,2,3,1,4,1,8,1,7,2,0,7,2,3,4,3,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,0,7,2,0,7,0,2,0,5,4,0,2,7,0,0,0,6,0,3,0,0,2,6,2,3,6,5,2,2,7,2,0,5,3,1,0,4,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,5,2,1,6,1,3,2,3,5,1,3,5,1,0,0,3,3,3,1,2,2,5,1,8,1,5,1,3,6,3,5,2,2,1,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,3,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0\ntest,30,1,2,4,7,0,5,3,2,4,1,4,5,4,1,2,4,3,1,1,0,5,2,1,1,2,3,4,1,9,0,5,0,4,5,4,5,3,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,4,5,1,4,1,5,6,1,2,3,4,3,1,4,5,1,0,0,3,1,6,1,1,2,5,2,4,6,5,1,4,8,1,4,3,2,1,0,3,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,3,3,7,0,6,2,2,7,0,2,2,3,5,1,4,5,1,1,2,3,2,1,2,2,0,5,0,4,5,6,1,2,8,1,3,5,1,0,1,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,1,2,8,1,1,1,5,3,0,3,6,2,0,2,3,2,3,1,2,3,4,1,3,6,6,2,2,6,3,2,7,1,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,2,3,0,5,0,4,6,0,3,1,2,7,3,2,4,1,0,1,4,1,4,1,3,2,1,3,0,9,6,3,0,4,5,0,4,2,4,1,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,0,4,2,4,9,0,0,0,3,6,7,2,0,3,0,0,3,0,3,5,3,2,0,0,2,7,7,1,1,4,5,2,0,4,3,2,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,4,9,0,2,4,4,7,0,2,4,0,5,4,0,5,2,0,0,4,1,3,0,0,5,3,3,9,0,7,0,2,6,3,6,0,3,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,3,8,1,5,0,3,4,2,4,3,2,4,2,5,3,1,0,0,4,2,3,2,2,3,2,1,3,6,5,1,3,7,2,4,3,3,1,1,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,2,5,0,2,0,7,4,4,2,0,7,2,0,5,4,0,0,0,4,4,2,0,3,3,4,0,9,0,4,0,5,7,2,5,4,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,0,5,9,0,0,0,3,6,1,2,7,1,0,0,3,4,2,1,1,1,6,1,5,4,6,1,2,7,2,2,4,3,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,1,5,3,2,6,0,3,3,4,3,0,2,7,1,0,0,3,3,4,1,0,2,6,1,4,5,6,1,2,9,0,1,7,2,0,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,4,8,1,5,2,3,5,1,3,0,5,4,0,3,6,0,0,4,4,0,1,0,3,5,1,1,2,7,6,1,3,7,2,1,8,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,3,10,0,6,2,2,6,0,3,3,2,5,0,3,6,0,0,9,0,0,0,3,0,2,0,4,0,9,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,4,2,8,2,0,2,6,7,2,0,1,0,8,2,2,5,0,0,0,3,3,3,1,1,3,3,2,9,0,5,0,4,3,6,3,4,3,0,0,3,7,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,6,1,3,9,0,0,0,4,5,6,3,1,6,0,0,2,1,1,5,2,2,1,0,0,9,7,2,0,2,7,0,1,5,4,1,7,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,0,3,3,4,6,1,2,1,4,5,0,3,6,0,0,0,3,3,4,0,1,1,6,2,9,0,7,0,2,7,2,6,3,1,0,0,2,2,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,1,1,5,1,1,1,4,4,1,1,8,7,1,1,0,4,5,0,0,0,3,2,5,1,1,1,6,2,8,1,1,0,8,9,0,8,1,1,0,0,2,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,1,4,1,4,7,1,2,1,5,4,0,1,8,5,0,0,2,0,2,1,1,5,4,0,2,7,6,2,1,5,4,1,4,5,1,0,4,6,1,0,0,7,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,4,0,0,2,5,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,4,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,23,1,1,3,5,0,5,0,5,0,6,3,7,2,1,0,5,4,2,0,0,4,2,2,1,1,4,1,4,9,0,5,3,3,9,0,5,2,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,2,2,2,3,3,2,9,0,0,0,0,9,2,3,4,2,0,0,6,2,0,0,2,4,4,0,2,7,9,0,0,9,0,0,0,4,5,0,8,7,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,39,2,5,2,9,1,5,0,3,7,1,2,0,0,9,0,4,5,0,0,0,6,3,0,0,4,0,5,0,2,7,6,2,2,6,3,0,4,5,0,0,4,5,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,4,1,1,5,1,3,6,1,2,0,4,5,8,1,1,5,0,0,1,1,4,4,1,4,1,0,0,9,7,2,1,5,4,0,1,5,4,1,7,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,39,1,3,3,9,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,5,0,0,0,5,0,0,0,0,4,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,2,3,9,0,0,0,7,2,0,1,4,5,3,3,4,4,0,0,3,2,2,2,2,2,5,1,4,5,6,1,3,6,3,3,4,2,2,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,6,2,1,1,4,4,1,3,6,2,0,1,3,3,2,2,1,2,5,1,5,4,6,1,2,6,3,2,4,3,1,1,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,0,2,3,5,6,3,0,0,4,5,0,0,9,0,0,0,0,0,9,0,0,0,9,0,5,4,4,3,3,9,0,0,5,4,0,0,4,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,4,8,0,4,0,5,5,0,4,3,2,5,0,2,7,0,0,2,0,3,4,0,0,3,3,4,0,9,6,0,3,9,0,6,3,0,0,0,2,3,0,0,2,0,0,0,0,0,3,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,4,8,0,6,2,3,6,0,3,0,5,4,1,5,4,1,0,0,4,1,4,1,2,3,4,0,5,4,7,0,2,6,3,2,3,4,2,0,5,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,2,3,3,2,1,4,0,4,7,0,2,2,2,6,3,4,3,4,0,2,3,0,1,4,2,3,1,0,0,9,7,2,0,4,5,2,3,3,2,1,6,8,2,0,0,6,0,0,0,0,0,0,0,6,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,4,9,1,6,0,2,7,0,2,0,5,4,0,0,9,5,0,0,0,0,4,0,0,0,5,4,9,0,6,0,3,7,2,0,4,5,0,0,4,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,0,5,9,0,0,0,3,6,0,4,5,0,0,0,2,5,3,0,2,2,5,2,0,9,9,0,0,9,0,1,5,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,3,1,2,4,1,2,4,0,4,7,1,2,2,6,2,5,3,2,4,1,1,3,1,2,4,2,2,2,1,1,8,6,3,0,2,7,1,2,3,3,1,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,2,5,3,9,0,6,0,3,9,0,0,0,3,6,0,4,5,0,0,0,3,6,0,0,2,3,5,0,3,6,9,0,0,9,0,6,0,3,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,2,5,0,7,2,0,9,0,0,4,3,2,1,6,3,1,0,0,3,3,4,1,1,3,5,1,8,1,7,0,2,7,2,1,4,3,3,0,6,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,3,9,2,5,0,3,9,0,0,0,3,6,2,4,3,2,0,0,4,2,2,2,2,3,3,0,0,9,5,0,4,7,2,0,6,3,0,0,4,4,3,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,5,0,4,7,2,0,0,3,6,3,5,2,1,0,0,5,2,2,1,3,3,2,1,8,1,9,0,0,5,4,2,3,3,2,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,28,4,2,5,6,1,5,0,3,5,0,4,4,4,2,3,3,3,6,0,0,2,2,0,6,0,0,3,0,3,6,5,0,4,2,7,3,2,4,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,4,0,5,7,2,0,0,3,6,0,5,4,3,0,0,0,6,0,3,0,0,5,2,0,9,9,0,0,6,3,4,0,5,0,0,3,4,0,0,3,6,0,0,0,0,6,0,0,0,4,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0\ntest,8,1,2,3,2,1,5,0,3,7,1,2,3,3,4,0,5,4,3,0,0,0,5,2,0,4,3,0,2,0,9,6,2,2,6,3,0,5,3,2,0,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,1,4,1,5,5,1,4,3,4,2,0,4,5,1,1,2,4,3,1,1,3,3,3,1,2,7,5,1,4,7,2,5,3,2,0,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,2,2,5,3,2,4,4,0,5,0,4,4,2,3,2,3,4,4,0,0,2,0,4,4,2,0,0,4,0,9,4,2,4,5,4,3,5,1,1,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,11,1,4,3,3,0,4,0,5,9,0,0,0,2,7,5,4,0,5,0,2,3,0,0,5,3,2,0,0,0,9,7,2,0,4,5,0,2,5,2,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,4,8,0,5,1,4,6,1,3,2,4,3,1,3,6,2,0,1,4,3,2,1,2,2,4,2,4,5,4,0,5,7,2,5,2,2,1,1,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,4,6,1,3,1,3,5,3,3,3,3,0,0,5,1,2,3,2,3,3,0,1,8,8,1,0,6,3,1,3,5,1,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,3,1,1,5,0,4,7,0,2,2,2,6,3,5,1,6,0,0,3,0,1,4,1,5,0,0,0,9,7,2,0,2,7,2,1,6,0,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,4,8,0,5,0,4,9,0,0,0,4,5,0,3,6,0,0,0,3,6,0,0,2,0,7,0,4,5,9,0,0,9,0,5,4,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,4,9,0,7,0,2,3,1,6,4,2,4,0,1,8,1,0,0,1,3,5,1,1,1,4,4,8,1,3,0,6,8,1,6,2,2,1,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,4,8,0,6,0,3,5,1,4,3,3,4,0,1,8,1,0,0,2,3,5,1,1,1,4,4,7,2,4,0,5,8,1,5,3,1,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,4,1,2,4,1,1,5,2,3,5,1,4,4,4,1,4,2,4,4,2,0,2,2,1,2,1,3,1,4,1,8,5,1,3,5,4,1,3,3,3,1,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,2,10,1,5,1,3,6,1,3,0,3,6,0,2,7,4,0,2,2,0,3,0,2,6,2,0,2,7,6,1,2,6,3,0,0,9,0,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,0,5,0,5,9,0,0,0,9,0,0,9,0,0,0,0,9,0,0,0,9,0,0,0,9,0,9,0,0,5,5,0,9,0,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,5,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,33,1,5,3,8,0,6,2,2,6,0,3,3,0,6,0,3,6,0,0,0,2,7,0,0,0,0,9,0,6,3,4,0,5,9,0,5,2,2,0,0,2,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,4,5,0,0,6,0,3,1,6,3,4,4,2,7,1,0,1,0,2,6,1,1,2,0,1,8,3,2,5,7,2,2,4,1,3,0,5,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,3,3,2,3,2,4,5,4,1,2,4,4,2,4,4,2,1,1,5,1,2,3,1,3,2,2,3,6,7,2,1,7,2,2,5,3,1,0,4,4,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,1,3,1,5,4,2,4,0,4,5,0,6,3,0,0,0,6,0,3,0,6,0,3,0,3,6,6,0,3,8,1,3,3,4,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,1,4,1,4,2,2,5,5,3,1,1,4,4,2,0,0,2,2,4,2,1,2,5,1,7,2,6,0,3,7,2,4,3,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,3,3,1,0,6,0,3,8,0,1,0,4,5,4,3,3,2,3,0,2,3,1,5,2,0,3,0,2,7,7,2,0,2,7,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,2,3,3,1,7,1,2,7,1,2,2,3,5,1,7,2,1,0,0,7,2,1,1,4,3,2,1,2,7,8,0,1,6,3,1,6,2,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,2,1,6,0,3,2,3,4,1,2,7,1,0,2,2,1,5,1,2,2,5,1,1,8,7,0,2,7,2,0,5,4,0,0,4,3,0,0,4,0,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,2,3,3,7,0,7,0,2,9,0,0,0,4,5,2,3,5,2,0,0,3,4,2,2,2,1,5,0,1,8,5,1,4,6,3,2,6,2,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,5,1,1,5,2,3,5,1,4,2,6,2,2,3,4,4,1,0,2,4,1,3,1,2,4,1,2,7,5,1,3,5,4,1,2,4,3,1,6,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,1,5,0,4,0,5,9,0,0,4,4,2,1,8,0,1,0,0,8,1,1,1,8,1,0,0,7,2,7,1,1,7,2,0,2,4,4,0,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,5,2,3,5,1,4,1,4,4,5,3,1,2,3,0,4,1,1,5,3,1,1,1,2,7,5,1,3,5,4,3,1,4,2,1,5,6,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,1,5,3,4,2,1,3,4,3,4,5,2,0,4,5,0,0,0,2,3,4,0,2,3,5,1,9,0,7,1,1,8,1,1,6,3,0,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,3,9,1,4,1,4,7,1,2,0,2,7,2,0,7,5,0,0,2,0,3,2,2,5,2,0,6,3,6,2,1,5,4,7,2,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,2,2,4,2,2,5,2,2,5,2,0,4,5,0,0,0,4,0,5,0,2,3,5,0,9,0,5,2,2,9,0,0,6,3,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,0,7,0,2,4,0,5,5,1,3,1,2,7,0,0,0,2,3,5,0,0,3,6,0,3,6,5,0,4,8,1,5,2,3,0,0,2,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,0,4,5,1,3,2,1,7,1,4,5,1,1,0,2,3,3,1,3,1,4,1,7,2,5,1,4,7,2,3,5,2,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,7,0,1,6,0,3,2,4,4,4,4,2,3,0,0,5,1,1,3,2,3,2,0,0,9,6,3,2,5,4,1,4,4,1,0,5,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,4,8,0,6,0,3,7,0,2,0,3,6,0,3,6,2,1,2,1,3,3,1,0,4,5,0,1,8,7,2,0,7,2,4,3,3,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,4,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1\ntest,3,1,2,3,1,0,6,1,3,5,0,4,3,3,4,1,3,5,1,0,1,6,1,3,1,2,2,3,3,2,7,7,0,2,7,2,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,2,4,8,1,0,0,2,7,0,3,6,0,0,0,4,3,3,0,1,3,6,0,3,6,6,3,0,9,0,2,5,2,2,0,4,3,2,2,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,23,1,2,3,5,1,1,4,4,3,1,5,3,3,4,2,1,6,1,1,0,5,0,4,1,2,1,6,1,9,0,2,0,7,7,2,6,1,3,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,1,2,2,5,5,2,2,4,4,1,1,4,4,2,0,0,3,2,4,1,2,1,5,1,7,2,6,1,3,8,1,2,5,2,1,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0\ntest,38,1,3,3,9,1,7,1,0,7,0,2,4,1,4,1,5,4,1,0,0,4,5,1,1,3,1,5,1,5,4,4,0,5,8,1,1,7,1,1,0,3,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,26,3,2,4,6,0,3,0,6,5,0,4,4,4,2,0,0,9,0,0,0,0,7,2,0,0,0,5,4,7,2,5,0,4,9,0,6,2,2,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,0,4,0,5,5,2,3,2,4,4,3,3,4,6,0,0,2,0,2,3,2,2,2,2,4,5,5,2,3,6,3,3,3,3,1,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,4,8,1,4,2,3,5,2,3,2,5,3,2,3,5,2,1,2,2,3,2,1,1,4,3,1,1,8,6,2,2,6,3,2,6,2,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,4,2,9,0,4,0,5,8,0,1,1,1,8,1,4,5,1,0,0,2,0,7,1,2,2,5,1,8,1,5,4,1,8,1,5,4,0,0,0,2,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,5,1,2,4,1,0,5,2,3,5,1,4,3,4,3,2,4,4,3,1,1,3,2,2,2,2,2,4,1,4,5,6,1,3,6,3,4,2,3,1,0,3,3,0,3,4,6,0,0,0,0,3,0,0,0,0,0,0,6,0,0,0,0,0,0,1,1,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,13,1,3,2,3,1,5,1,3,8,1,1,1,3,6,4,4,2,4,1,0,3,1,1,3,2,3,2,0,1,8,7,1,1,4,5,1,3,4,2,1,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,4,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,3,1,3,3,1,2,4,3,2,9,0,0,0,4,5,3,4,3,5,0,0,4,0,0,5,2,0,3,0,3,6,5,4,0,3,6,0,5,2,3,0,5,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,4,1,1,5,1,3,7,0,2,2,4,4,3,4,3,3,2,1,3,2,0,4,2,2,2,1,5,4,6,2,2,3,6,2,3,4,2,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,3,8,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,3,2,5,2,2,2,6,5,2,2,2,2,6,4,4,1,5,0,0,4,0,0,2,2,2,3,2,0,9,4,2,4,4,5,3,0,2,3,2,7,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,35,1,2,3,8,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,24,1,2,2,5,0,4,2,4,0,5,4,2,5,3,0,4,5,0,0,0,2,4,4,0,0,4,4,2,9,0,7,0,2,9,0,4,2,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,6,0,3,5,1,4,2,3,5,0,1,8,1,0,0,3,2,5,1,1,2,5,2,7,2,4,0,5,8,1,3,3,1,3,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,28,3,2,4,6,1,6,1,3,7,1,2,2,6,2,3,2,4,4,1,1,2,2,2,3,1,2,4,1,3,6,7,0,2,5,4,3,3,3,1,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,1,3,7,1,2,0,3,6,0,3,6,0,0,0,0,6,3,0,0,0,9,0,5,4,9,0,0,5,4,0,9,0,0,0,3,5,0,0,0,6,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,2,2,2,4,0,4,9,0,0,0,0,9,4,5,0,7,0,0,2,0,0,7,0,2,0,0,0,9,7,2,0,0,9,0,2,7,0,0,5,8,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,12,2,4,2,3,0,2,0,7,9,0,0,0,0,9,0,3,6,0,0,0,7,0,2,0,2,0,6,2,0,9,9,0,0,9,0,2,0,5,2,0,5,7,0,0,0,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1\ntest,39,1,2,2,9,1,4,1,4,2,2,5,5,3,1,1,4,4,2,0,0,2,2,4,2,1,2,5,1,7,2,6,0,3,7,2,4,3,2,1,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,3,3,4,6,1,2,1,2,7,0,3,6,0,0,0,3,3,4,0,1,1,6,3,9,0,7,0,2,7,2,7,2,1,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,39,1,4,3,9,0,6,1,3,7,1,2,1,1,8,0,2,7,1,0,0,1,5,3,1,1,1,7,1,6,3,6,1,2,9,0,1,7,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,3,3,2,3,1,3,7,1,1,1,1,8,4,4,1,1,0,0,7,1,1,1,6,1,2,0,1,8,9,0,0,6,3,3,2,5,0,0,4,8,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,22,1,2,2,5,0,4,1,5,4,3,3,5,2,3,1,4,5,1,0,0,2,3,4,1,1,4,4,1,8,1,6,1,2,8,1,6,3,1,0,0,2,2,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,4,6,1,2,2,3,4,3,5,2,3,0,0,3,0,4,3,2,4,0,2,4,5,6,1,2,6,3,4,3,0,3,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,3,3,5,1,2,0,6,6,0,3,3,4,3,1,2,7,0,0,0,3,2,4,1,2,1,4,2,9,0,7,0,2,6,3,5,2,2,1,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,0,3,0,6,7,0,2,2,2,6,2,5,3,1,0,0,6,0,2,1,2,4,3,0,6,3,5,1,3,8,1,2,6,2,0,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,1,1,3,3,1,1,6,0,3,9,0,0,0,4,5,8,1,0,9,0,0,0,0,0,9,0,0,0,0,0,9,7,2,0,2,7,0,1,3,5,0,7,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,1,4,2,3,5,2,3,2,4,4,2,6,3,2,1,1,3,2,2,1,1,4,3,1,1,8,6,2,2,6,3,1,4,3,2,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,2,2,3,4,1,3,5,1,1,1,2,3,3,2,2,2,4,1,4,5,6,2,2,7,2,1,4,4,1,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,1,2,4,6,2,7,1,1,5,0,4,3,2,4,3,2,4,4,3,0,3,1,1,3,2,3,1,3,6,3,3,4,3,3,6,5,2,0,0,3,5,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,2,2,6,1,2,1,4,5,4,1,5,2,0,0,3,3,2,3,1,5,2,0,3,6,7,0,2,8,1,2,5,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,4,2,8,0,5,1,3,7,0,2,0,0,9,1,3,5,3,0,0,2,3,3,3,0,2,4,1,2,7,6,3,0,6,3,4,4,2,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,2,3,5,2,3,2,5,4,2,5,3,2,1,1,4,3,2,2,2,3,2,1,1,8,6,2,2,6,3,1,4,4,1,0,4,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,1,4,2,3,7,1,2,1,3,6,1,7,1,3,0,0,6,1,1,2,2,5,2,0,7,2,7,2,1,6,3,1,5,4,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,0,2,2,5,5,2,3,4,4,2,3,5,2,2,1,2,6,1,1,3,4,1,2,0,8,1,4,2,4,8,1,2,5,1,3,0,4,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,2,3,3,1,0,6,1,2,6,0,3,1,4,4,3,2,5,1,0,1,4,2,4,1,3,2,4,1,3,6,7,0,2,7,2,0,4,5,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,1,4,0,5,7,0,2,2,5,3,1,6,2,2,0,0,6,0,2,1,1,6,2,0,8,1,5,1,4,6,3,1,1,8,0,0,5,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,10,1,3,3,3,0,9,0,0,7,0,2,2,3,5,5,2,3,3,0,0,3,2,1,2,3,3,2,0,0,9,9,0,0,3,6,2,0,4,4,0,7,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1\ntest,33,1,2,4,8,0,7,0,2,4,2,3,3,2,4,0,0,9,3,0,0,0,0,6,2,0,0,3,4,5,5,5,2,3,9,0,3,2,4,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,1,7,0,1,6,0,3,2,4,4,4,4,2,3,0,0,5,1,1,3,2,3,2,0,0,9,6,3,2,5,4,1,4,4,1,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,2,7,2,1,1,1,4,2,2,1,2,5,1,2,7,4,4,2,7,2,3,3,3,0,1,4,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,6,1,2,0,0,9,0,0,9,2,1,1,2,3,2,0,0,5,4,0,9,0,6,1,3,7,2,4,5,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,3,0,6,3,4,2,1,5,4,1,4,5,2,0,0,3,4,0,2,3,1,4,0,0,9,9,0,0,2,7,0,2,7,1,0,6,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,11,1,2,3,3,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,4,6,2,1,1,5,3,0,3,6,1,0,0,4,3,2,1,1,2,6,0,6,3,6,0,3,5,4,2,2,5,0,0,4,5,2,0,0,6,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,0,6,1,3,5,0,4,3,3,4,1,3,5,1,0,1,6,1,3,1,2,2,3,3,2,7,7,0,2,7,2,0,5,4,0,0,4,6,0,0,4,6,0,0,0,0,4,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,1,4,2,3,5,2,3,2,4,4,2,6,3,2,1,1,3,2,2,1,1,4,3,1,1,8,6,2,2,6,3,1,4,3,2,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,5,3,10,0,0,5,5,9,0,0,0,0,9,0,9,0,5,0,5,0,0,0,9,0,0,0,0,0,9,7,2,0,7,2,4,2,3,0,0,3,4,2,0,0,6,0,0,0,0,6,4,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,0,9,0,0,5,0,4,0,0,9,0,0,9,0,0,2,0,3,5,0,3,0,6,0,9,0,3,2,4,7,2,9,0,0,0,0,1,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,4,6,1,3,1,3,5,3,3,3,3,0,0,5,1,2,3,2,3,3,0,1,8,8,1,0,6,3,1,3,5,1,0,5,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,3,1,2,4,2,2,9,0,0,0,4,5,5,4,1,5,4,0,1,0,1,8,1,0,1,0,0,9,3,6,0,1,8,1,3,4,2,2,6,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,8,1,2,4,2,1,3,1,4,7,1,1,3,5,1,4,5,0,2,0,0,6,0,2,2,5,2,2,0,0,9,7,1,2,3,6,3,5,2,0,0,3,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,3,2,8,0,5,1,3,7,0,2,1,2,6,1,2,7,1,0,1,2,5,2,1,1,2,6,1,1,8,8,0,1,8,1,0,4,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,2,9,0,5,0,4,5,1,3,3,2,5,1,4,4,2,1,0,3,3,2,1,2,2,4,1,7,2,5,1,4,7,2,3,5,2,0,0,3,4,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,3,7,1,2,0,2,7,5,3,2,3,1,0,4,2,1,4,3,2,2,1,1,8,7,0,2,5,4,1,2,4,3,1,6,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,0,3,4,2,6,0,3,3,3,4,0,5,4,2,0,0,2,1,6,0,1,4,2,3,2,7,6,0,3,7,2,1,3,5,0,0,5,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,19,1,2,4,4,2,2,1,5,4,0,5,5,3,2,0,1,8,0,0,0,0,5,4,0,0,0,7,2,7,2,4,0,5,7,2,6,2,1,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,6,2,2,7,1,2,0,3,6,0,0,9,2,0,0,3,3,2,0,0,0,9,0,3,6,7,1,2,7,2,0,4,0,5,0,6,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,2,4,1,3,8,0,1,1,5,4,1,4,4,1,0,0,6,1,3,0,3,2,4,0,5,4,5,2,3,6,3,2,4,3,1,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,37,1,2,4,8,0,5,1,4,6,1,3,2,4,3,1,3,6,2,0,1,4,3,2,1,2,2,4,2,4,5,4,0,5,7,2,5,2,2,1,1,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,5,5,1,5,1,3,4,1,5,5,3,2,1,2,7,1,0,1,3,3,4,1,1,2,4,3,8,1,3,1,5,8,1,5,3,1,0,0,2,2,2,0,0,5,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,0,3,3,4,5,0,4,4,3,3,0,0,9,0,0,0,3,0,6,0,0,0,5,4,9,0,6,0,3,9,0,2,5,3,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,0,4,7,2,0,0,3,6,0,4,5,2,0,2,0,4,3,2,0,2,5,0,4,5,7,2,0,5,4,2,2,3,3,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,1,6,1,2,5,1,3,2,4,3,1,4,4,2,1,0,4,1,3,2,2,3,4,1,3,6,6,1,3,7,2,3,4,3,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,3,1,2,5,0,3,9,0,0,1,3,5,7,2,0,9,0,0,0,0,0,6,2,1,0,0,0,9,7,2,0,2,7,1,2,3,4,1,7,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,1,4,2,4,9,0,0,0,4,5,5,3,2,5,0,0,2,2,1,5,2,2,2,0,0,9,7,2,0,2,7,0,2,3,3,2,7,8,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,6,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,4,4,8,1,4,1,4,6,0,3,2,2,5,0,0,9,2,1,1,3,3,2,0,4,5,0,0,3,6,6,1,2,6,3,0,9,0,0,0,4,5,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,4,8,1,4,2,3,5,2,3,2,5,3,2,3,5,2,1,2,2,3,2,1,1,4,3,1,1,8,6,2,2,6,3,2,6,2,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,0,4,1,5,6,1,2,2,2,6,1,3,6,0,0,0,3,3,4,0,2,1,5,2,8,1,6,2,2,9,0,6,2,1,0,0,2,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,2,2,2,5,0,3,1,6,4,1,5,3,0,6,0,0,9,0,0,0,0,4,5,0,0,0,4,5,9,0,5,1,3,8,1,5,4,0,0,0,2,2,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,0,4,1,5,4,3,3,2,3,4,1,4,5,1,0,0,2,3,4,1,1,3,4,1,8,1,6,1,2,8,1,3,4,2,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,2,2,2,5,0,5,2,2,4,2,4,4,4,2,2,4,3,3,1,0,2,1,4,5,2,1,2,2,7,2,6,1,3,8,1,4,3,3,0,0,3,3,2,0,0,0,0,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,2,2,4,2,2,5,2,2,5,2,0,4,5,0,0,0,4,0,5,0,2,3,5,0,9,0,5,2,2,9,0,0,6,3,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,2,2,5,3,2,4,4,0,5,0,4,4,2,3,2,3,4,4,0,0,2,0,4,4,2,0,0,4,0,9,4,2,4,5,4,3,5,1,1,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,1,1,3,3,1,0,7,0,2,7,2,0,0,4,5,5,4,0,5,0,2,2,0,1,7,2,0,0,0,0,9,5,4,0,0,9,0,2,6,2,0,6,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,39,2,5,3,9,0,6,0,3,9,0,0,0,3,6,0,4,5,0,0,0,3,6,0,0,2,3,5,0,3,6,9,0,0,9,0,6,0,3,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,27,1,2,4,6,2,4,2,3,6,0,3,3,2,4,2,4,3,2,2,1,4,2,1,2,2,4,2,0,3,6,6,1,3,5,4,1,5,3,1,0,4,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,4,1,1,6,1,3,5,0,4,0,5,4,0,4,5,5,0,0,4,0,0,0,3,6,0,0,4,5,5,2,4,4,5,2,3,3,2,1,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,1,5,1,3,6,1,2,0,5,4,4,0,5,2,1,1,2,3,2,3,0,6,0,0,0,9,6,1,3,7,2,3,4,2,1,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,24,1,3,3,5,1,1,1,7,5,2,2,2,3,5,1,1,8,0,0,0,3,2,4,0,1,1,7,2,8,1,6,1,3,8,1,5,3,1,1,0,2,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,5,1,2,3,1,0,5,1,4,5,1,4,3,2,5,2,3,4,3,1,0,1,4,2,3,1,2,4,1,3,6,6,1,3,6,3,3,5,1,2,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,4,8,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,0,4,2,3,5,1,3,2,4,3,0,3,6,1,0,1,2,2,5,1,1,2,5,3,8,1,6,1,3,6,3,4,4,1,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,5,7,1,1,0,5,4,0,8,1,1,1,0,4,2,3,0,0,5,4,1,0,9,6,2,2,8,1,0,8,0,1,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,3,7,0,2,2,5,5,2,3,2,4,4,0,3,6,0,0,0,2,5,3,0,1,1,8,1,9,0,7,0,2,7,2,5,4,1,0,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,4,1,2,3,1,0,5,2,2,4,2,4,4,4,3,1,3,5,2,1,0,3,2,3,2,2,1,3,2,6,3,6,1,3,8,1,4,3,3,0,0,3,6,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,4,2,8,0,2,4,4,8,0,1,1,3,6,0,6,3,3,2,0,2,3,1,3,3,0,3,1,9,0,5,2,3,7,2,1,5,3,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,4,1,0,9,0,0,9,0,0,0,6,3,0,6,3,4,0,0,4,0,3,3,0,4,3,0,4,6,9,0,0,6,4,3,4,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,5,6,0,5,0,4,2,2,5,3,6,0,1,4,5,4,0,0,2,2,2,1,2,4,2,2,9,0,5,0,4,9,0,6,2,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,1,2,8,1,0,0,4,5,0,4,5,0,0,0,5,1,4,0,1,5,4,0,4,5,8,1,0,9,0,2,5,2,2,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,16,1,2,2,4,3,2,0,5,1,2,6,6,3,1,5,4,1,2,2,0,4,0,2,1,1,6,2,1,8,1,2,0,7,3,6,7,2,0,1,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,2,3,0,3,4,2,9,0,0,0,1,8,1,7,1,2,0,0,4,0,4,2,3,5,1,0,0,9,5,4,0,2,7,0,1,4,4,1,6,8,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,2,0,9,0,0,3,4,3,3,4,3,1,0,0,1,4,4,1,1,5,2,2,2,7,7,0,2,7,2,1,3,4,3,0,7,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,4,2,6,0,6,1,2,7,1,2,0,0,9,0,5,4,0,0,0,0,4,5,0,0,5,4,0,9,0,5,2,3,6,3,9,0,0,0,0,2,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,5,3,3,0,5,0,4,9,0,0,0,0,8,5,4,0,7,0,0,2,0,0,7,0,2,0,0,0,9,9,0,0,4,5,0,0,9,0,0,5,8,2,0,0,6,0,5,0,0,0,0,0,6,0,0,0,4,0,0,0,0,2,1,0,0,1,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0\ntest,1,1,2,4,1,0,2,0,7,6,0,3,5,3,2,3,4,2,7,0,0,0,2,0,4,0,4,1,0,0,9,9,0,0,2,7,2,2,5,0,0,4,8,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,2,2,0,2,0,7,5,2,2,0,2,7,2,4,4,4,0,0,2,3,2,3,1,2,4,0,0,9,6,3,0,3,6,0,5,4,0,0,5,8,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,0,2,1,7,3,5,2,2,6,2,0,4,5,0,0,0,4,2,4,0,2,3,5,0,6,3,5,1,4,9,0,2,6,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,0,3,3,4,6,2,2,2,5,3,0,2,7,1,0,1,2,2,4,1,1,2,6,1,7,2,6,1,2,7,2,5,4,1,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,1,4,0,5,7,0,2,2,4,4,1,4,4,3,0,0,3,4,1,2,1,2,3,2,2,7,7,2,1,4,5,4,0,5,2,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,1,6,1,2,5,1,3,0,5,4,0,5,4,0,0,0,0,5,4,0,0,0,9,0,0,9,6,1,3,7,2,3,4,3,1,0,4,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,1,3,2,4,7,1,2,1,2,6,2,5,3,2,0,0,5,1,3,2,2,3,3,0,6,3,7,1,2,5,4,1,4,4,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,3,3,1,4,1,4,9,0,0,0,5,4,3,5,2,5,0,0,1,1,3,2,2,5,2,0,0,9,7,2,0,2,7,0,2,5,3,1,6,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,3,2,1,4,1,4,7,1,2,0,0,9,3,6,0,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,0,5,0,4,9,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,23,1,2,2,5,0,1,3,6,1,3,5,5,0,4,0,0,9,0,0,0,0,3,6,0,0,0,3,6,9,0,5,1,3,8,1,5,4,0,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,4,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,6,3,0,0,9,0,0,0,3,6,6,2,2,5,2,0,3,0,0,5,3,0,2,0,0,9,9,0,0,0,9,0,0,6,3,0,6,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,5,3,10,0,0,5,5,9,0,0,0,0,9,0,9,0,5,0,5,0,0,0,9,0,0,0,0,0,9,7,2,0,7,2,4,2,3,0,0,3,4,2,0,3,6,0,0,0,0,6,0,0,0,0,0,0,6,0,0,0,0,0,1,0,1,1,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,24,1,2,3,5,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,4,3,2,1,4,1,4,7,1,2,0,3,6,4,3,3,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,3,2,2,2,8,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,4,2,8,0,4,0,5,9,0,0,0,3,6,0,3,6,0,0,0,2,6,2,0,2,2,6,0,0,9,4,4,1,9,0,2,5,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,0,2,6,0,3,3,1,6,0,6,3,3,0,4,2,0,1,3,2,5,1,0,0,9,3,3,3,2,7,1,5,4,0,0,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,32,1,3,3,7,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,31,1,2,3,7,0,6,0,3,3,0,6,3,4,3,0,3,6,2,0,0,2,4,4,1,0,2,4,3,9,0,5,0,4,5,4,5,3,1,0,0,2,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,2,10,0,6,0,3,6,3,0,0,5,4,1,7,2,3,0,3,3,0,1,4,2,2,2,0,0,9,4,5,0,3,6,0,4,4,2,0,5,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,4,9,0,7,0,2,3,1,6,4,2,4,0,1,8,1,0,0,1,3,5,1,1,1,4,4,8,1,3,0,6,8,1,6,2,2,1,0,2,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,4,5,3,3,0,4,3,1,5,5,4,1,4,2,3,4,0,0,1,3,3,3,0,3,5,0,6,3,3,2,4,4,5,5,1,2,2,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,2,3,2,4,5,2,3,3,2,4,2,2,5,3,0,0,4,2,2,0,3,3,3,1,2,7,5,2,3,6,3,2,4,2,2,2,6,4,2,0,0,5,0,4,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,3,2,1,5,1,3,7,1,2,0,3,6,3,5,1,3,1,0,4,1,2,3,3,2,2,1,1,8,7,0,2,5,4,1,3,5,1,1,5,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,7,0,2,7,0,2,2,0,7,1,4,5,0,0,0,4,3,3,0,2,2,3,3,7,2,4,1,4,8,1,2,4,4,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,5,1,2,3,1,0,5,1,4,5,1,4,3,2,5,2,3,4,3,1,0,1,4,2,3,1,2,4,1,3,6,6,1,3,6,3,3,5,1,2,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,9,0,0,5,0,4,0,0,9,0,5,4,0,0,2,0,3,5,0,0,0,9,0,9,0,3,2,4,7,2,6,2,2,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,3,8,0,5,2,2,6,1,2,2,3,4,3,1,6,2,0,0,3,3,2,2,1,2,5,0,2,7,7,0,2,8,1,2,5,2,0,0,3,5,0,0,4,0,0,0,0,0,3,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,3,5,1,6,1,3,2,2,6,5,3,2,2,5,3,1,0,0,2,2,5,1,2,4,4,1,8,1,5,0,4,8,1,6,3,1,0,1,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,3,2,4,1,2,1,5,2,7,0,2,1,6,3,2,5,2,5,2,1,1,0,2,4,3,1,2,0,2,7,7,0,2,5,4,1,1,8,0,0,5,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,3,3,8,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,10,2,4,3,3,1,5,1,4,7,1,2,0,0,9,2,0,7,0,0,0,3,0,6,0,2,0,7,0,0,9,6,2,1,4,5,0,3,5,2,0,5,8,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,3,0,6,6,0,3,1,3,6,1,5,4,1,0,1,4,3,3,1,1,4,5,0,9,0,5,0,4,5,4,5,4,1,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,7,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,4,9,0,5,0,4,2,0,7,4,3,2,2,4,3,2,0,0,4,2,2,1,1,4,3,1,3,6,7,0,2,7,2,5,0,3,2,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,26,1,1,6,6,3,2,2,4,4,2,4,7,1,1,1,1,8,0,0,0,0,4,5,0,1,1,1,7,8,1,5,0,4,7,2,8,1,0,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,0,7,2,0,9,0,0,0,3,6,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,7,2,0,2,7,0,0,7,2,0,5,8,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,2,5,0,6,1,2,1,2,6,5,3,1,2,6,2,1,0,0,4,3,2,1,3,2,4,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,1,1,4,6,0,5,2,3,3,1,6,6,3,1,2,4,3,2,1,0,5,2,1,2,2,3,3,1,9,0,5,0,4,5,4,5,3,3,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,0,4,3,3,7,1,2,1,4,4,0,4,5,0,0,0,5,2,3,0,2,2,5,2,9,0,8,0,1,7,2,4,4,2,0,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,0,4,2,4,6,0,3,4,4,2,0,2,7,0,0,0,2,4,4,0,2,0,7,0,9,0,3,3,4,7,2,5,2,2,0,0,3,4,2,0,0,6,0,5,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,6,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,11,2,2,5,3,2,4,4,0,5,0,4,4,2,3,2,3,4,4,0,0,2,0,4,4,2,0,0,4,0,9,4,2,4,5,4,3,5,1,1,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,1,6,1,3,6,2,1,1,3,5,1,2,6,1,0,0,3,3,3,1,3,1,5,2,7,2,7,2,1,8,1,3,4,1,1,2,5,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,5,0,4,7,0,2,2,4,3,0,7,2,0,0,0,7,0,2,0,3,5,2,0,9,0,9,0,0,9,0,4,5,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,2,4,1,3,3,2,5,5,3,2,2,6,2,1,0,0,3,2,4,1,3,3,4,1,8,1,5,0,4,8,1,6,2,1,0,1,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,0,6,1,3,6,1,3,3,2,5,0,2,7,0,0,1,2,4,3,0,1,2,5,2,6,3,5,1,3,8,1,5,2,3,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,2,5,0,1,0,8,3,2,4,3,2,5,0,1,8,0,0,0,1,4,5,0,1,0,5,4,8,1,4,0,5,8,1,8,1,0,0,0,2,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,3,4,7,2,4,0,4,7,0,2,0,3,6,1,1,7,2,0,0,3,3,3,1,0,2,5,2,1,8,5,2,2,7,2,0,7,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,4,2,9,0,6,0,3,7,1,2,2,1,6,1,3,5,2,1,0,3,3,2,1,2,2,4,1,6,3,5,2,3,7,2,3,5,2,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,0,3,3,4,3,1,6,4,2,3,0,6,3,1,0,0,2,3,5,1,2,5,3,1,9,0,6,0,3,7,2,6,2,1,1,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,2,4,7,1,3,1,5,5,1,4,3,4,3,0,2,8,0,0,0,1,4,4,0,1,1,5,3,8,1,5,0,4,7,2,6,3,1,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,9,0,0,5,0,4,3,2,5,0,0,9,0,0,2,0,3,5,0,4,0,5,0,5,4,3,2,4,7,2,9,0,0,0,0,1,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,20,1,2,2,5,1,5,1,4,4,3,2,2,6,1,1,5,4,1,0,0,5,1,4,1,2,4,4,0,9,0,7,2,1,8,1,4,3,2,2,0,4,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,3,7,0,2,2,5,5,2,3,2,4,4,0,3,6,0,0,0,2,5,3,0,1,1,8,1,9,0,7,0,2,7,2,5,4,1,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,0,9,0,0,9,0,0,0,3,6,0,3,6,4,0,0,0,3,3,0,3,0,6,0,0,9,6,2,2,6,3,3,4,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,2,3,2,3,1,2,2,5,7,2,1,1,4,5,4,5,1,3,0,0,5,2,1,5,3,1,2,0,7,2,7,1,2,5,4,1,4,5,1,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,5,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0\ntest,30,1,2,4,7,0,3,3,4,5,0,4,4,2,3,0,0,9,0,0,0,2,0,7,0,0,0,4,5,9,0,6,0,3,9,0,5,1,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,0,7,2,0,9,0,0,0,3,6,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,7,2,0,2,7,0,0,7,2,0,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,5,1,4,8,1,0,0,4,5,1,3,6,2,0,5,1,2,0,2,1,6,2,0,2,7,7,2,0,3,6,5,2,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,2,4,3,2,1,4,1,4,7,1,2,0,3,6,4,3,3,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,3,2,2,2,8,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,2,2,5,7,0,2,0,2,7,0,4,5,0,0,0,0,9,0,0,0,0,9,0,9,0,5,0,4,9,0,4,5,0,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,5,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,0,6,1,3,5,0,4,3,3,4,1,3,5,1,0,1,6,1,3,1,2,2,3,3,2,7,7,0,2,7,2,0,5,4,0,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,2,9,4,1,0,5,6,3,0,0,4,5,3,3,4,3,0,0,0,0,6,3,0,3,3,0,0,9,6,3,0,2,7,0,0,5,0,4,9,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,26,1,1,6,6,0,5,1,4,1,1,8,7,2,0,0,0,9,0,0,1,1,5,3,0,0,1,3,6,9,0,1,0,8,6,3,8,1,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,0,4,7,1,1,1,4,5,2,4,4,3,0,1,2,2,2,4,2,1,4,0,5,4,7,2,0,8,1,3,2,4,1,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,5,9,0,5,4,0,0,0,9,7,0,2,0,0,9,0,0,0,0,6,3,0,0,0,6,3,7,2,7,1,2,7,2,7,2,0,0,0,1,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,2,10,1,3,0,5,6,1,2,2,3,5,2,4,4,2,0,5,0,2,2,3,0,3,3,0,2,7,5,3,1,3,6,4,0,4,2,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,18,1,2,3,4,0,3,0,6,0,3,6,5,3,1,4,4,2,4,2,0,4,0,1,3,2,3,2,0,4,5,4,0,5,6,3,6,2,2,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,2,3,0,6,0,0,0,0,0,0,0,5,0,0,0,4,0,0,0,0,4,1,1,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0\ntest,39,1,2,3,9,0,7,0,2,6,0,3,3,2,4,2,3,4,0,2,0,3,4,0,3,2,0,5,0,0,9,7,2,0,7,2,4,2,4,0,0,3,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,4,1,0,3,0,6,3,4,2,2,5,2,4,3,2,8,0,0,1,1,0,5,2,3,1,0,0,9,9,0,0,2,7,0,6,1,3,0,5,8,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,4,2,1,4,2,4,5,2,3,0,6,3,6,3,0,0,0,0,6,3,0,0,6,0,3,0,0,9,6,1,2,7,2,0,0,9,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,1,4,1,4,7,1,2,0,0,9,0,0,9,5,0,0,2,0,3,4,0,5,0,0,9,0,6,2,1,5,4,5,5,0,0,0,3,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,3,8,0,5,2,2,6,1,2,2,3,4,3,1,6,2,0,0,3,3,2,2,1,2,5,0,2,7,7,0,2,8,1,2,5,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,4,6,2,7,1,1,5,0,4,3,2,4,3,2,4,4,3,0,3,1,1,3,2,3,1,3,6,3,3,4,3,3,6,5,2,0,0,3,5,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,0,4,2,4,7,1,2,2,5,3,0,2,7,2,0,1,2,4,2,1,1,1,7,1,6,3,6,1,2,7,2,6,3,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,4,0,5,7,0,2,2,6,2,3,5,1,6,1,0,2,0,1,6,0,2,2,0,6,3,7,2,0,3,6,2,4,4,1,0,5,6,0,0,0,6,0,0,0,0,0,0,0,5,0,0,0,4,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,1,0\ntest,37,1,3,3,8,0,5,0,4,8,0,1,0,3,6,0,3,6,0,2,0,5,3,1,0,2,4,4,1,1,8,8,1,0,8,1,0,6,3,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,2,0,9,0,0,3,4,3,3,4,3,1,0,0,1,4,4,1,1,5,2,2,2,7,7,0,2,7,2,1,3,4,3,0,7,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,4,2,8,0,5,1,3,7,0,2,0,0,9,1,3,5,3,0,0,2,3,3,3,0,2,4,1,2,7,6,3,0,6,3,4,4,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,3,1,0,6,0,3,4,0,5,2,3,4,4,4,2,0,4,0,5,0,0,3,2,4,2,0,3,6,5,2,2,5,4,2,2,3,3,0,5,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,0,4,0,5,9,0,0,0,2,7,3,3,4,3,0,0,3,3,2,3,3,0,4,0,9,0,9,0,0,6,3,0,6,2,2,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,6,1,2,6,1,2,2,3,4,1,3,5,2,1,1,4,2,2,1,2,3,4,1,2,7,5,2,3,5,4,2,4,3,1,1,4,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,1,2,3,6,2,4,1,3,3,2,5,5,3,2,2,5,3,1,0,0,3,2,4,1,2,4,3,1,8,1,5,0,4,8,1,6,2,1,0,1,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,5,1,4,7,1,2,1,3,5,0,3,6,1,0,0,3,4,2,1,3,1,5,1,8,1,6,1,2,9,0,1,5,3,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,3,5,0,2,4,4,2,2,6,5,2,3,4,5,1,1,0,0,7,1,0,1,6,2,1,0,9,0,6,1,3,5,4,2,1,6,1,1,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,3,3,1,5,1,4,7,1,2,0,0,9,5,4,0,2,0,0,5,0,2,3,6,0,0,0,0,9,6,2,1,4,5,0,0,7,2,0,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,4,2,4,2,2,6,4,2,4,0,2,7,1,0,0,2,0,7,0,0,2,1,7,7,2,2,2,6,7,2,7,2,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,2,8,0,6,0,3,5,0,4,1,1,8,2,2,6,0,0,1,2,6,1,0,2,1,5,3,1,8,8,1,1,8,1,3,3,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,0,3,2,5,6,1,3,3,3,4,2,2,6,2,0,1,2,3,4,2,1,2,4,2,1,8,4,0,5,7,2,2,4,3,1,1,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,3,1,5,8,0,1,1,2,7,2,3,5,2,0,0,2,3,3,2,0,2,5,1,9,0,7,0,2,9,0,3,6,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,4,3,0,2,4,4,7,0,2,4,0,5,4,0,5,2,0,0,4,1,3,0,0,5,3,3,9,0,7,0,2,6,3,6,0,3,0,0,3,8,1,0,0,6,0,0,0,2,0,0,0,0,0,0,0,3,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1\ntest,9,1,2,3,3,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,4,1,0,5,6,3,0,0,4,5,5,3,2,5,0,0,3,1,2,4,3,1,2,0,0,9,6,3,0,2,7,0,3,4,3,1,7,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,1,4,1,4,3,1,3,5,2,3,9,0,0,0,0,9,2,0,1,3,3,2,0,0,4,5,0,0,9,7,2,0,4,5,3,0,3,3,0,5,6,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,2,4,1,3,3,2,5,5,3,2,2,6,2,1,0,0,3,2,4,1,3,3,4,1,8,1,5,0,4,8,1,6,2,1,0,1,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,26,3,2,4,6,0,3,0,6,5,0,4,4,4,2,0,0,9,0,0,0,0,7,2,0,0,0,5,4,7,2,5,0,4,9,0,6,2,2,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,4,6,2,1,1,5,3,0,3,6,1,0,0,4,3,2,1,1,2,6,0,6,3,6,0,3,5,4,2,2,5,0,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,4,0,0,4,6,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,3,3,7,0,6,2,2,7,0,2,2,3,5,1,4,5,1,1,2,3,2,1,2,2,0,5,0,4,5,6,1,2,8,1,3,5,1,0,1,4,3,0,5,0,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,5,7,0,5,0,4,5,0,4,4,4,2,0,2,7,0,0,0,3,6,0,0,0,3,5,2,9,0,5,0,4,9,0,6,2,2,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,1,2,1,0,5,0,4,0,3,6,6,3,0,3,3,3,0,0,0,6,0,3,0,4,2,2,2,7,2,5,0,4,7,2,5,4,0,0,0,2,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,2,7,1,0,7,0,2,0,2,7,4,2,4,4,0,0,0,2,4,4,0,2,2,3,1,8,7,2,0,5,4,2,3,2,3,0,5,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,5,1,2,4,1,0,5,2,3,5,1,4,3,4,3,2,4,4,3,1,1,3,2,2,2,2,2,4,1,4,5,6,1,3,6,3,4,2,3,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,0,4,5,1,3,2,1,7,1,4,5,1,1,0,2,3,3,1,3,1,4,1,7,2,5,1,4,7,2,3,5,2,0,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,2,3,1,5,7,0,2,0,4,5,5,3,2,6,0,0,3,0,0,5,2,2,2,0,0,9,7,2,0,3,6,3,0,1,4,3,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,9,0,0,9,0,0,0,2,7,0,3,6,3,0,0,2,5,0,2,2,2,5,0,0,9,6,3,0,3,6,0,5,4,0,0,4,3,2,0,0,7,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,8,1,2,4,2,2,2,2,5,7,1,2,2,4,4,5,3,2,8,1,0,0,0,0,7,0,2,0,0,0,9,3,5,2,2,7,0,2,5,2,1,6,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0\ntest,10,1,5,3,3,0,5,0,4,9,0,0,0,0,8,5,4,0,7,0,0,2,0,0,7,0,2,0,0,0,9,9,0,0,4,5,0,0,9,0,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,1,1,3,1,1,0,7,2,2,5,5,4,1,4,2,3,1,1,0,1,6,1,4,1,2,3,0,8,1,4,2,4,7,2,1,4,4,0,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,0,3,1,5,6,1,3,2,3,4,2,3,5,3,0,0,2,3,2,2,2,2,4,2,5,4,5,1,3,7,2,3,2,4,1,0,4,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,4,3,2,4,1,4,4,1,4,0,7,2,4,3,4,3,0,0,6,1,0,1,4,5,0,0,9,0,7,0,2,3,6,1,4,1,3,3,8,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,4,3,2,2,5,1,2,6,1,3,0,2,7,4,4,2,3,1,0,5,2,1,2,2,3,2,1,1,8,8,0,1,6,3,1,3,4,3,1,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,0,3,1,6,6,2,1,1,4,4,2,2,5,2,0,0,3,2,3,2,1,2,5,1,8,1,7,1,2,7,2,2,6,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,2,6,1,2,6,2,2,0,3,6,3,5,2,2,0,0,2,2,4,2,2,4,3,0,0,9,5,2,3,6,3,2,3,2,4,0,6,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,3,5,1,3,2,5,5,2,2,4,3,3,1,4,5,1,0,0,3,1,4,1,1,3,3,3,7,2,6,1,3,8,1,2,5,3,1,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,4,9,2,2,2,4,9,0,0,0,4,5,2,2,6,2,0,0,2,2,4,2,2,0,6,0,9,0,7,0,2,4,5,0,9,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,1,1,2,6,5,1,4,3,2,4,1,2,7,0,0,0,2,4,4,0,1,1,5,3,8,1,4,1,5,8,1,5,4,1,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,2,0,5,2,2,2,6,2,0,3,6,5,0,0,1,0,4,2,0,2,4,2,4,5,4,4,2,3,6,3,6,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,2,3,3,1,0,6,1,2,6,0,3,1,4,4,3,2,5,1,0,1,4,2,4,1,3,2,4,1,3,6,7,0,2,7,2,0,4,5,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,2,2,4,6,0,5,2,3,7,0,2,2,5,3,0,1,8,0,0,0,2,4,3,0,0,1,7,2,9,0,8,0,1,7,2,5,4,0,0,0,2,1,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,4,10,0,7,0,2,5,0,4,3,4,2,2,4,4,2,0,4,4,0,0,3,2,4,0,2,2,7,4,2,3,4,5,6,0,0,3,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,2,5,0,1,0,8,3,2,4,3,2,5,0,1,8,0,0,0,1,4,5,0,1,0,5,4,8,1,4,0,5,8,1,8,1,0,0,0,2,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,9,0,0,0,9,0,0,2,5,3,0,2,7,9,0,0,0,0,0,0,0,3,6,0,4,5,9,0,0,6,3,3,4,2,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,1,4,1,4,8,0,1,2,5,3,3,3,4,5,1,0,2,1,1,3,2,2,3,1,3,6,6,3,1,3,6,2,1,4,2,2,7,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,5,9,0,5,4,0,0,0,9,7,0,2,0,0,9,0,0,0,0,6,3,0,0,0,6,3,7,2,7,1,2,7,2,7,2,0,0,0,1,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,2,8,0,2,0,7,9,0,0,0,0,8,3,0,6,3,0,0,0,3,3,3,0,0,6,0,0,9,5,2,2,5,4,6,0,0,3,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,2,1,0,5,1,3,8,0,1,1,3,6,1,4,5,2,1,1,4,2,2,1,3,0,6,0,7,2,7,1,2,8,1,1,7,2,0,1,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,4,3,7,0,4,0,5,8,1,0,0,3,6,0,0,9,0,0,0,2,5,2,0,0,0,8,1,7,2,7,0,2,7,2,2,4,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,0,5,0,4,9,0,0,0,2,7,3,4,2,6,0,1,0,2,0,6,1,0,2,1,4,5,7,2,0,7,2,1,1,8,0,0,4,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,1,1,5,6,2,5,2,0,0,0,9,7,2,0,2,1,7,2,0,1,3,3,2,2,0,1,4,4,8,1,0,0,9,9,0,4,5,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,24,1,2,2,5,0,3,1,6,5,2,3,3,2,5,1,4,5,1,0,0,3,4,2,1,2,2,5,1,8,1,6,1,2,8,1,6,3,1,0,0,2,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,1,4,2,4,9,0,0,0,4,5,5,3,2,5,0,0,2,2,1,5,2,2,2,0,0,9,7,2,0,2,7,0,2,3,3,2,7,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,5,6,0,5,0,4,2,2,5,3,6,0,1,4,5,4,0,0,2,2,2,1,2,4,2,2,9,0,5,0,4,9,0,6,2,1,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,2,4,2,3,0,9,0,0,9,0,0,0,0,9,0,3,6,0,0,0,5,3,2,0,3,0,6,0,2,7,5,4,0,5,4,0,4,5,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,2,7,0,0,9,0,0,2,3,4,0,5,4,5,0,0,4,0,1,1,2,4,2,2,1,8,7,2,0,7,2,2,4,3,0,1,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,3,7,0,5,1,3,7,1,2,3,5,2,0,4,5,0,0,0,2,2,6,0,3,3,2,3,3,6,9,0,0,5,4,2,3,3,3,0,5,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,0,2,7,0,2,0,7,2,0,2,7,0,1,0,4,4,1,0,1,1,5,3,3,6,8,1,0,7,2,0,5,4,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,3,6,2,1,1,1,4,2,1,2,4,3,1,2,7,4,4,2,7,2,3,3,3,0,1,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,5,0,4,8,0,1,0,3,6,0,3,6,0,2,0,5,3,1,0,2,4,4,1,1,8,8,1,0,8,1,0,6,3,0,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,0,7,2,0,9,0,0,2,5,2,2,5,2,1,0,0,3,3,3,1,2,3,4,1,6,3,7,0,2,7,2,1,3,3,2,0,6,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,0,5,0,5,9,0,0,0,9,0,0,9,0,0,0,0,9,0,0,0,9,0,0,0,9,0,9,0,0,5,5,0,9,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,4,1,4,7,1,2,2,4,4,2,4,4,3,1,2,2,2,1,2,2,3,3,1,4,5,6,2,1,5,4,3,3,3,2,1,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,2,9,0,4,2,4,7,1,2,3,4,3,1,1,8,1,0,0,3,6,0,1,1,1,8,0,3,6,7,1,1,8,1,0,7,2,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,2,3,0,4,0,5,9,0,0,0,1,8,1,2,7,1,0,0,2,4,2,1,1,2,6,1,6,3,8,1,1,7,2,2,4,4,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,1,4,2,3,5,2,3,2,4,4,3,2,4,3,1,1,2,3,2,1,3,2,4,1,1,8,6,2,2,6,3,3,5,2,1,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,3,7,0,3,0,6,5,0,4,1,4,4,0,0,9,0,0,0,0,2,7,0,0,0,4,5,8,1,6,0,3,9,0,2,5,3,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,2,8,0,6,2,1,6,1,2,0,1,8,1,1,8,1,0,0,2,2,5,1,0,1,6,3,8,1,6,1,2,8,1,4,4,2,0,1,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,0,6,0,3,7,2,0,1,2,6,2,4,4,1,0,0,5,2,1,0,3,2,4,0,0,9,9,0,0,5,4,2,4,4,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,2,8,0,3,2,4,5,2,3,3,4,3,0,4,5,0,0,0,5,4,0,0,3,0,6,0,7,2,9,0,0,9,0,3,6,0,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,4,2,9,0,2,3,5,6,0,3,2,0,7,0,6,3,0,0,0,7,0,2,0,3,4,3,0,4,5,9,0,0,7,2,0,7,0,2,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,41,1,3,3,10,1,7,1,2,8,1,1,1,3,6,1,4,5,2,1,2,2,3,1,2,1,3,4,0,1,8,5,4,1,5,4,2,3,4,1,0,4,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,9,1,2,3,3,2,4,1,3,3,2,5,5,3,2,2,5,3,1,0,0,3,2,4,1,2,4,3,1,8,1,5,0,4,8,1,6,2,1,0,1,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,4,8,0,0,0,9,9,0,0,0,3,6,0,2,7,3,0,0,2,5,0,0,2,3,5,0,4,5,7,2,0,5,4,0,0,9,0,0,5,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,4,1,4,6,1,3,1,5,4,1,5,3,1,0,1,4,2,3,1,3,4,2,1,2,7,4,0,5,7,2,2,3,3,1,1,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,3,8,0,5,0,4,6,0,3,1,6,3,1,6,3,1,0,3,3,1,3,1,1,6,3,1,0,9,6,3,0,4,5,0,3,1,3,2,8,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,17,1,2,4,4,0,5,3,2,5,1,4,4,4,2,1,4,5,2,1,1,3,1,4,2,1,2,4,1,4,5,6,1,2,7,2,4,2,3,1,0,3,5,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,4,3,3,0,3,0,6,7,0,2,1,1,7,1,4,4,1,0,0,5,0,4,1,3,2,4,0,8,1,5,1,3,8,1,1,5,4,0,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,33,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,3,10,0,5,1,4,8,0,1,1,4,4,3,3,4,2,1,2,3,2,1,2,2,3,3,1,4,5,6,2,1,6,3,1,3,5,1,1,6,3,2,0,0,6,0,0,0,0,0,0,0,0,0,3,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,11,1,2,4,3,0,5,3,2,4,1,4,5,4,1,3,2,4,1,1,0,5,2,1,3,1,2,2,3,9,0,5,0,4,5,4,3,4,3,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,6,2,2,7,1,2,0,3,6,0,0,9,0,0,0,3,6,0,0,0,0,9,0,0,9,7,1,2,7,2,0,9,0,0,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,3,4,3,1,7,1,1,1,4,5,1,6,2,1,0,0,6,1,2,1,4,2,3,1,4,5,4,2,3,7,2,2,2,5,1,0,4,4,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0\ntest,24,2,2,2,5,3,2,1,4,6,1,2,4,3,2,4,3,2,2,0,1,4,2,3,1,2,4,3,1,6,3,6,2,2,6,3,5,2,2,1,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,3,3,5,3,1,1,8,1,1,1,1,8,1,5,3,1,0,0,5,3,1,1,3,3,3,0,1,8,9,0,0,5,4,3,2,4,0,0,4,8,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,5,6,1,5,2,2,5,1,3,4,5,1,1,5,4,3,1,0,4,1,2,3,2,2,2,3,6,3,7,1,1,5,4,4,4,2,1,0,3,1,2,0,0,6,0,0,0,0,0,0,2,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,2,2,2,2,2,4,9,0,0,0,2,7,2,7,0,2,0,0,7,0,0,3,6,0,0,0,0,9,7,2,0,6,3,0,0,5,4,0,7,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,4,3,7,2,4,0,4,7,0,2,0,2,7,1,1,8,1,0,0,1,2,6,1,0,1,7,1,1,8,5,2,2,7,2,0,8,1,0,0,3,3,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,1,4,2,3,7,1,2,1,3,6,2,4,3,3,1,0,4,2,1,2,2,3,3,0,2,7,7,1,1,5,4,1,3,5,1,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,5,7,1,1,0,5,4,0,8,1,1,1,0,4,2,3,0,0,5,4,1,0,9,6,2,2,8,1,0,8,0,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,0,5,5,2,3,2,2,6,0,4,5,0,0,0,4,4,1,0,0,2,7,1,1,8,6,2,2,8,1,4,1,0,4,0,5,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,4,2,8,0,7,0,2,3,0,6,0,0,9,0,3,6,2,0,0,2,2,5,0,3,3,4,0,0,9,6,0,3,7,2,6,2,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,23,1,1,1,5,1,4,2,3,4,1,4,5,4,1,1,4,4,2,1,0,3,4,0,1,1,0,7,0,8,1,6,0,3,7,2,4,0,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,5,7,0,5,0,4,5,0,4,4,4,2,0,2,7,0,0,0,3,6,0,0,0,3,5,2,9,0,5,0,4,9,0,6,2,2,0,0,2,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,0,4,1,4,6,1,3,3,1,6,4,2,4,3,0,0,5,2,1,3,3,1,4,0,1,8,7,0,2,8,1,1,6,3,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,6,0,3,2,5,3,2,2,5,2,0,0,3,3,2,2,0,0,7,0,5,4,6,0,3,7,2,3,2,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,0,7,0,2,7,2,0,0,4,5,5,4,0,5,0,2,2,0,1,7,2,0,0,0,0,9,5,4,0,0,9,0,2,6,2,0,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,4,9,0,5,0,4,6,0,3,3,3,4,2,4,4,3,0,2,3,2,1,3,1,5,2,1,0,9,6,3,0,4,5,0,4,4,1,1,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,5,1,4,8,1,0,0,4,5,1,3,6,2,0,5,1,2,0,2,1,6,2,0,2,7,7,2,0,3,6,5,2,2,1,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,2,5,2,4,1,3,3,2,5,6,2,2,2,5,4,1,0,0,2,3,4,1,2,3,5,1,8,1,5,0,4,8,1,7,2,1,0,1,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,5,1,0,4,0,5,7,0,2,2,7,0,4,4,2,6,0,0,0,0,3,4,3,0,2,0,9,0,9,0,0,0,9,0,0,9,0,0,5,6,2,0,0,7,0,0,0,0,0,0,0,0,3,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,4,1,0,6,2,2,6,0,3,3,3,4,1,5,4,5,1,2,1,2,1,3,2,4,2,0,1,8,6,2,2,5,4,6,1,1,1,1,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,22,1,2,3,5,1,3,2,5,5,2,2,4,3,3,1,4,5,1,0,0,3,1,4,1,1,3,3,3,7,2,6,1,3,8,1,2,5,3,1,0,4,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,6,1,3,9,0,0,0,4,5,6,3,1,6,0,0,2,1,1,5,2,2,1,0,0,9,7,2,0,2,7,0,1,5,4,1,7,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,2,8,0,6,2,1,6,1,2,0,1,8,1,1,8,1,0,0,2,2,5,1,0,1,6,3,8,1,6,1,2,8,1,4,4,2,0,1,3,3,2,0,2,6,0,0,0,0,3,0,0,0,0,0,6,6,0,0,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,2,2,3,4,1,3,5,1,1,1,2,3,3,2,2,2,4,1,4,5,6,2,2,7,2,1,4,4,1,0,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,0,7,2,0,9,0,0,0,3,6,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,7,2,0,2,7,0,0,7,2,0,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,4,2,9,0,2,4,4,7,0,2,1,0,8,1,0,8,2,0,0,4,1,3,0,0,1,4,4,9,0,7,0,2,6,3,4,0,5,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,2,3,3,2,1,4,0,4,7,0,2,2,2,6,3,4,3,4,0,2,3,0,1,4,2,3,1,0,0,9,7,2,0,4,5,2,3,3,2,1,6,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,2,5,3,0,9,0,0,2,5,4,4,3,2,6,0,0,3,1,0,4,2,3,1,0,0,9,5,4,0,0,9,0,0,8,1,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,1,3,2,4,7,1,2,2,3,5,1,7,1,4,0,0,5,1,1,2,3,4,1,0,6,3,7,1,2,4,5,3,4,3,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,33,1,2,4,8,0,7,0,2,7,0,2,0,7,2,0,2,7,0,1,0,4,4,1,0,1,1,5,3,3,6,8,1,0,7,2,0,5,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,4,7,2,1,0,5,4,2,3,4,4,0,0,1,4,2,3,0,2,4,1,3,6,5,4,1,6,4,1,5,2,0,2,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,2,3,3,9,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,5,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,38,1,4,2,9,1,4,1,4,6,0,3,0,1,8,0,3,6,3,0,1,0,0,6,1,2,1,4,3,0,9,6,1,2,6,3,0,4,5,0,0,5,4,2,0,0,6,0,6,0,1,0,0,3,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,5,1,1,5,2,3,5,1,4,2,6,2,2,3,4,4,1,0,2,4,1,3,1,2,4,1,2,7,5,1,3,5,4,1,2,4,3,1,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,39,1,2,3,9,0,4,0,5,9,0,0,2,4,3,0,0,9,0,0,0,4,5,0,0,0,0,9,0,6,3,9,0,0,9,0,3,4,3,1,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,3,7,0,5,1,3,6,1,3,2,4,4,2,2,6,2,0,0,2,3,3,2,1,2,5,1,5,4,6,1,2,6,3,3,4,2,2,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,1,3,7,1,2,2,3,5,2,4,4,3,1,2,2,2,1,3,2,3,3,1,3,6,6,2,1,5,4,3,2,3,2,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,1,4,1,4,7,1,2,2,4,3,1,2,6,4,0,0,2,2,1,2,2,3,3,0,2,7,6,2,1,5,4,2,5,2,2,0,4,6,0,0,0,6,6,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,4,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,4,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0\ntest,8,1,2,4,2,0,7,0,2,9,0,0,2,5,3,0,4,5,0,0,0,4,1,5,0,0,5,4,1,9,0,7,0,2,5,4,4,4,2,0,0,3,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,1,6,0,2,7,0,2,0,5,4,0,0,9,5,0,0,0,0,4,0,0,0,5,4,9,0,6,0,3,7,2,0,4,5,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,2,3,4,9,0,9,0,0,9,0,0,0,4,5,1,0,8,0,0,1,2,3,4,1,0,1,6,2,6,3,5,0,4,7,2,2,6,1,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,27,2,2,5,6,1,5,3,1,7,0,2,2,6,1,2,4,4,2,1,2,2,0,3,1,1,5,2,0,8,1,8,0,1,7,2,2,7,1,0,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,4,7,2,1,0,5,4,2,3,4,4,0,0,1,4,2,3,0,2,4,1,3,6,5,4,1,6,4,1,5,2,0,2,6,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,0,4,0,5,3,0,6,5,2,2,1,2,7,0,0,2,2,3,4,0,1,4,5,1,2,7,5,1,4,7,2,1,4,5,0,0,4,5,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1\ntest,38,1,4,2,9,1,4,1,4,6,0,3,0,1,8,0,3,6,3,0,1,0,0,6,1,2,1,4,3,0,9,6,1,2,6,3,0,4,5,0,0,5,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,1,7,1,2,8,1,1,1,3,6,1,4,5,2,1,2,2,3,1,2,1,3,4,0,1,8,5,4,1,5,4,2,3,4,1,0,4,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,7,0,1,6,0,3,2,4,4,4,3,2,3,0,0,5,1,1,3,2,3,2,0,0,9,6,3,2,5,4,1,4,4,1,0,5,7,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,33,1,2,4,8,0,5,1,4,6,0,3,3,4,2,1,1,8,0,2,0,1,7,0,0,1,2,6,0,0,9,7,2,0,7,2,7,2,1,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,0,4,1,5,7,1,1,1,4,5,2,5,2,2,1,0,4,1,3,2,2,4,2,0,5,4,7,1,2,5,4,1,2,5,2,1,6,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,2,1,6,0,3,2,3,4,1,2,7,1,0,2,2,1,5,1,2,2,5,1,1,8,7,0,2,7,2,0,5,4,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,2,3,1,2,2,5,6,2,2,1,4,5,2,4,4,2,1,0,4,2,3,1,2,2,3,2,8,1,6,1,3,7,2,3,4,2,1,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,0,7,2,0,9,0,0,0,3,6,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,7,2,0,2,7,0,0,7,2,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,2,2,4,6,0,4,2,3,5,0,4,4,2,4,0,0,9,0,0,2,2,3,3,0,0,2,3,5,9,0,7,0,2,9,0,2,7,0,0,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,4,7,1,3,1,5,5,1,4,3,4,3,0,2,8,0,0,0,1,4,4,0,1,1,5,3,8,1,5,0,4,7,2,6,3,1,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,3,1,1,7,0,2,8,1,0,0,5,4,4,1,5,4,0,2,2,2,0,3,1,3,3,0,0,9,6,3,0,3,6,3,3,3,2,0,5,8,0,0,0,6,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,2,7,2,1,1,1,4,2,2,1,2,5,1,2,7,4,4,2,7,2,3,3,3,0,1,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,0,7,0,2,9,0,0,0,2,7,2,3,4,2,0,0,4,1,3,2,3,2,2,2,1,8,7,2,0,7,2,0,4,2,3,1,6,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,4,0,5,7,0,2,2,1,6,0,2,7,0,0,0,3,6,0,0,1,1,6,2,4,5,5,2,3,8,1,3,4,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,1,3,2,4,7,1,2,1,2,6,2,5,3,2,0,0,5,1,3,2,2,3,3,0,6,3,7,1,2,5,4,1,4,4,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,2,2,4,8,0,3,2,5,5,2,3,3,5,3,0,3,6,1,0,5,1,3,2,1,2,4,3,2,5,4,4,5,1,7,2,2,5,1,2,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,4,2,8,0,5,1,4,8,1,0,0,1,8,0,4,5,0,0,0,5,2,2,0,2,3,5,0,2,7,5,4,0,9,0,1,7,2,1,0,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,2,5,0,4,0,5,3,0,6,5,0,4,3,3,4,0,0,0,3,2,4,0,2,3,3,2,9,0,3,2,5,9,0,9,0,0,0,0,1,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,1,3,8,1,4,1,4,7,1,2,6,3,0,0,0,9,0,0,0,9,0,0,0,3,0,3,3,6,3,6,2,1,5,4,6,3,0,0,0,2,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,4,7,0,8,0,1,6,1,2,1,3,5,0,3,6,1,0,0,3,2,4,1,2,2,4,3,6,3,5,0,4,7,2,6,3,1,1,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,0,5,9,0,0,0,3,6,0,4,5,0,0,0,2,5,3,0,2,2,5,2,0,9,9,0,0,9,0,1,5,4,0,0,4,3,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,32,1,2,4,7,0,6,1,3,5,0,4,4,4,2,2,3,5,2,0,0,4,3,2,2,3,0,5,2,7,2,6,0,3,9,0,5,2,2,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,0,4,0,5,9,0,0,0,7,2,0,0,9,0,0,0,0,5,4,0,0,0,7,2,9,0,5,0,4,9,0,4,4,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,24,1,3,2,5,1,3,2,5,6,1,2,1,2,7,1,2,7,0,0,0,2,4,3,0,1,1,5,3,8,1,3,2,5,8,1,3,5,1,1,0,3,2,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,2,7,0,0,6,1,2,2,6,2,0,4,5,3,0,0,5,0,2,1,1,3,4,2,0,9,6,0,3,6,3,4,4,0,2,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,0,3,0,6,9,0,0,0,3,6,5,2,2,2,0,0,5,0,2,2,5,0,2,0,0,9,6,0,3,9,0,0,4,5,0,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,5,1,0,3,9,0,0,0,5,4,5,4,0,8,0,0,0,0,1,9,0,0,0,0,1,8,5,4,0,1,8,1,3,4,2,0,6,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,3,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,3,3,2,1,2,5,6,2,1,1,4,4,0,4,5,1,3,1,4,1,2,2,2,3,3,1,1,8,6,1,3,7,2,1,3,6,0,0,4,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,0,4,4,0,5,3,2,5,1,3,6,0,0,2,2,4,3,0,1,3,5,1,2,7,5,1,3,7,2,3,3,3,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,20,1,2,4,5,1,3,1,5,5,1,4,3,4,3,0,2,8,0,0,0,1,4,4,0,1,1,5,3,8,1,5,0,4,7,2,6,3,1,1,0,3,2,0,0,0,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,4,3,3,1,5,1,3,7,1,2,0,3,6,3,4,2,3,1,0,4,2,1,3,2,3,2,1,1,8,7,0,2,5,4,1,2,5,2,1,5,6,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,1\ntest,41,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,4,2,0,0,6,0,5,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,3,1,2,6,1,2,2,0,5,7,0,2,2,7,1,4,5,1,5,2,0,3,1,1,6,3,0,1,1,3,6,9,0,0,4,5,5,3,3,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,2,3,2,0,2,5,9,0,0,0,3,6,2,4,4,2,0,0,3,5,0,2,0,3,5,0,0,9,9,0,0,7,2,0,5,4,0,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,3,2,4,6,0,3,3,4,3,2,4,4,5,0,3,2,0,0,3,2,3,2,0,7,2,5,2,3,4,5,3,2,5,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,4,2,3,0,7,0,2,7,2,0,1,1,8,2,3,5,1,0,0,7,2,1,0,3,2,5,0,0,9,9,0,0,6,3,2,4,4,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,0,2,6,0,3,3,1,6,0,6,3,3,0,4,2,0,1,3,2,5,1,0,0,9,3,3,3,2,7,1,5,4,0,0,4,4,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,1,3,2,4,6,1,2,2,3,5,0,4,5,2,0,0,0,5,3,0,1,4,3,1,4,5,5,1,3,7,2,2,5,3,0,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,32,1,4,3,7,2,7,0,0,6,1,3,2,1,7,0,4,5,2,1,0,3,2,4,1,2,3,4,1,0,9,5,2,3,7,2,0,4,4,1,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,3,3,2,4,0,4,6,0,3,2,5,3,2,5,3,2,1,0,3,2,2,1,2,3,3,1,6,3,7,2,0,6,3,2,3,3,1,2,6,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,2,5,2,3,1,5,2,2,5,6,3,1,1,4,5,1,0,0,1,3,5,1,1,2,6,1,8,1,5,0,4,8,1,6,3,1,1,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,2,3,0,5,4,2,4,3,2,5,0,4,5,0,0,0,2,2,5,0,2,3,3,2,9,0,3,0,6,9,0,7,2,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,4,7,2,1,0,5,4,2,3,4,4,0,0,1,4,2,3,0,2,4,1,3,6,5,4,1,6,4,1,5,2,0,2,6,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,2,0,0,5,0,0,0,0,0,0,0,6,0,2,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,1,5,4,0,9,0,0,1,6,3,4,3,2,6,0,0,2,2,0,5,2,3,1,0,0,9,5,4,0,0,9,0,0,8,1,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,0,3,1,5,6,1,3,2,3,4,2,3,5,3,0,0,2,3,2,2,2,2,4,2,5,4,5,1,3,7,2,3,2,4,1,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,6,1,2,0,0,9,0,0,9,2,1,1,2,3,2,0,0,5,4,0,9,0,6,1,3,7,2,4,5,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,2,7,0,0,9,0,0,1,6,3,0,6,3,3,0,0,5,0,2,2,1,5,2,1,1,8,7,2,0,7,2,1,6,1,0,2,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,3,0,4,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0\ntest,2,1,2,4,1,0,5,0,5,9,0,0,0,9,0,0,9,0,0,5,0,0,0,5,5,0,0,4,0,6,3,7,2,0,3,6,2,4,4,1,0,5,6,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1\ntest,36,1,2,4,8,0,6,2,2,5,0,4,4,4,2,2,0,7,0,0,0,9,0,0,0,2,0,7,0,9,0,4,2,4,9,0,9,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,0,7,0,2,7,0,2,0,4,5,0,0,9,0,0,0,2,7,0,0,0,0,9,0,9,0,7,0,2,9,0,3,4,2,2,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,1,6,1,2,5,1,3,0,5,4,0,5,4,0,0,0,0,5,4,0,0,0,9,0,0,9,6,1,3,7,2,3,4,3,1,0,4,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,2,2,3,2,6,2,0,2,7,0,2,3,3,4,2,3,5,3,0,0,4,0,3,2,2,0,4,2,3,6,9,0,0,5,4,0,0,9,0,0,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,1,5,0,4,1,5,7,1,2,4,2,3,1,3,6,0,0,0,2,2,6,0,1,1,5,2,6,3,6,2,2,9,0,4,3,3,0,0,3,2,2,0,0,6,0,6,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,2,8,2,7,0,0,5,2,2,0,4,5,3,0,6,4,0,0,0,0,5,2,0,0,4,3,0,9,2,2,5,9,0,4,0,0,0,5,9,3,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,5,0,4,7,0,2,2,4,3,0,7,2,0,0,0,7,0,2,0,3,5,2,0,9,0,9,0,0,9,0,4,5,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,4,3,2,4,1,2,1,5,2,7,0,2,1,6,3,2,5,2,5,2,1,1,0,2,4,3,1,2,0,2,7,7,0,2,5,4,1,1,8,0,0,5,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,4,3,8,0,4,1,5,6,2,2,0,4,5,0,4,5,4,0,0,3,3,0,0,4,0,5,0,5,4,6,2,2,8,1,0,0,4,5,0,7,6,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,2,3,2,3,6,1,2,3,5,2,1,3,6,1,0,0,2,2,6,1,1,2,6,1,3,6,6,1,3,8,1,2,6,1,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,4,7,0,3,3,4,5,0,4,4,3,3,0,0,9,0,0,0,3,0,6,0,0,0,5,4,9,0,6,0,3,9,0,2,5,3,0,0,3,1,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,0,0,0,9,9,0,0,0,5,4,4,5,0,9,0,0,0,0,0,9,0,0,0,0,0,9,0,9,0,0,9,0,0,5,4,0,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,0,6,1,3,3,0,6,6,1,3,2,2,5,2,0,0,4,3,1,2,2,0,5,1,8,1,5,0,4,9,0,5,2,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,2,2,2,5,5,1,4,3,4,3,0,6,3,1,0,0,4,3,2,1,2,4,3,0,3,6,5,1,3,8,1,3,5,2,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,1,4,0,5,8,1,1,0,4,5,4,2,4,4,0,0,3,0,3,4,2,1,4,0,4,5,7,2,0,5,4,1,3,5,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,2,8,0,4,2,4,3,2,4,4,4,2,0,5,4,0,0,0,7,0,2,0,5,0,4,0,7,2,7,0,2,9,0,4,5,0,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,3,2,0,9,0,0,9,0,0,2,2,6,3,4,3,2,0,0,4,3,2,2,1,4,3,0,2,7,6,3,0,3,6,2,5,0,2,1,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,0,6,1,3,7,0,2,0,3,6,0,4,5,0,0,3,2,3,2,2,0,5,0,3,0,9,7,1,2,7,2,3,0,6,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,5,6,1,6,1,2,5,1,4,4,3,2,2,5,3,1,0,0,6,1,2,1,2,3,4,2,8,1,6,1,3,4,5,5,3,1,2,0,4,1,0,0,0,6,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1\ntest,41,1,3,3,10,0,7,1,2,8,1,1,1,5,3,0,3,6,2,0,2,3,2,3,1,2,3,4,1,3,6,6,2,2,6,3,2,7,1,1,0,3,4,2,0,0,6,0,0,0,2,0,0,2,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,4,1,0,5,6,3,0,0,4,5,5,3,2,5,0,0,3,1,2,4,3,1,2,0,0,9,6,3,0,2,7,0,3,4,3,1,7,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,3,7,1,2,0,5,4,3,5,1,2,1,0,4,1,2,2,3,4,1,1,2,7,7,0,2,5,4,1,2,5,2,1,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,2,5,0,3,9,0,0,0,6,3,0,5,4,2,0,0,7,0,0,2,3,2,4,0,3,6,7,0,2,6,3,2,4,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,4,0,0,0,6,0,0,0,0,5,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,2,8,0,6,0,3,5,0,4,1,1,8,2,2,6,0,0,1,2,6,1,0,2,1,5,3,1,8,8,1,1,8,1,3,3,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,28,1,2,4,6,1,7,1,2,7,1,2,2,6,1,3,5,2,3,3,1,2,2,1,3,1,2,3,2,2,7,7,0,2,5,4,2,4,3,2,0,4,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,5,1,2,4,1,0,5,2,3,5,1,4,3,4,3,2,4,4,3,1,1,3,2,2,2,2,2,4,1,4,5,6,1,3,6,3,4,2,3,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,4,2,4,2,2,6,4,2,4,0,2,7,1,0,0,2,0,7,0,0,2,1,7,7,2,2,2,6,7,2,7,2,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,2,3,5,6,3,0,0,3,6,0,1,8,0,0,0,1,6,3,0,1,1,8,0,3,6,6,3,0,9,0,3,5,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,3,2,2,5,1,2,8,1,1,0,3,6,3,4,2,4,1,0,3,2,1,3,3,2,2,1,1,8,8,0,1,4,5,1,4,4,1,1,4,7,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,5,0,0,4,9,0,0,0,5,4,3,6,0,2,0,2,4,2,2,3,4,0,2,0,0,9,9,0,0,4,5,0,3,6,0,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,0,4,5,1,3,2,1,7,1,4,5,1,1,0,2,3,3,1,3,1,4,1,7,2,5,1,4,7,2,3,5,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,0,8,0,1,7,0,2,0,1,8,0,7,2,5,0,0,3,2,1,3,3,1,2,3,2,7,6,3,0,2,7,2,3,3,2,0,5,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,0,6,2,2,9,0,0,1,8,1,7,2,1,5,4,0,0,1,0,7,1,1,0,1,1,8,6,3,0,0,9,1,0,5,4,0,8,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,6,0,3,5,1,4,2,3,5,0,1,8,1,0,0,3,2,5,1,1,2,5,2,7,2,4,0,5,8,1,3,3,1,3,0,4,5,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,1,3,1,6,4,2,4,4,3,3,4,4,2,2,1,0,5,1,2,2,3,2,4,0,4,5,5,1,3,7,2,3,2,3,2,0,4,4,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,39,2,2,2,9,0,6,1,3,5,1,3,2,2,5,0,2,7,0,0,1,2,3,5,0,1,2,6,2,6,3,5,2,3,8,1,8,1,1,0,0,2,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,2,1,7,5,2,3,3,3,5,0,1,8,0,0,0,2,7,1,0,1,1,8,0,5,4,5,4,0,9,0,3,2,5,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,3,2,1,4,1,4,7,1,2,0,3,6,4,3,3,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,3,2,2,2,8,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,2,4,2,3,2,4,2,2,9,0,0,0,0,8,2,4,4,2,0,0,7,1,0,0,3,4,3,0,2,7,9,0,0,9,0,0,0,4,5,0,8,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,33,1,2,4,8,0,5,1,4,6,0,3,3,4,2,1,1,8,0,2,0,1,7,0,0,1,2,6,0,0,9,7,2,0,7,2,7,2,1,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,0,3,0,6,9,0,0,0,5,4,4,2,4,3,0,0,5,2,2,2,2,2,4,1,6,3,8,0,1,9,0,3,3,2,2,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,7,0,2,9,0,0,1,3,6,0,1,8,0,0,0,1,2,6,0,0,1,6,3,9,0,7,0,2,5,4,3,6,1,0,0,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,5,1,2,4,1,1,5,2,3,5,1,4,2,5,2,4,3,3,4,2,0,2,2,1,4,2,2,2,2,1,8,5,1,3,5,4,1,2,4,3,1,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0\ntest,7,1,2,4,2,1,2,2,5,5,1,4,2,6,2,2,2,6,2,1,0,4,1,2,1,1,2,5,1,7,2,5,1,4,5,4,4,3,3,1,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,5,2,2,1,5,1,3,6,1,3,0,1,8,0,1,8,8,0,1,1,0,1,0,1,8,1,0,1,8,6,1,2,6,3,0,0,9,0,0,5,8,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,3,1,1,4,1,1,5,0,3,5,0,4,7,2,1,2,4,4,6,0,0,2,2,0,2,0,0,7,0,1,8,5,0,4,2,7,4,2,4,0,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,0,5,5,2,3,2,2,6,0,4,5,0,0,0,4,4,1,0,0,2,7,1,1,8,6,2,2,8,1,4,1,0,4,0,5,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,0,4,3,3,7,1,2,1,4,4,0,4,5,0,0,0,5,2,3,0,2,2,5,2,9,0,8,0,1,7,2,4,4,2,0,0,3,1,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,2,2,6,6,0,3,3,3,4,0,4,5,1,0,0,3,5,2,1,2,0,7,1,8,1,5,0,4,9,0,0,0,9,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,0,3,2,5,6,1,3,3,3,4,2,2,6,2,0,1,2,3,4,2,1,2,4,2,1,8,4,0,5,7,2,2,4,3,1,1,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,0,5,0,4,9,0,0,0,4,5,0,4,5,0,0,0,4,0,5,0,4,0,5,0,0,9,9,0,0,9,0,5,2,2,0,0,3,7,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,4,1,0,5,2,2,9,0,0,1,6,3,4,5,1,7,0,0,2,0,0,4,4,1,2,0,0,9,6,3,0,5,4,0,2,7,1,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,2,4,2,4,7,1,2,2,3,5,3,5,2,3,1,1,4,2,2,3,2,3,2,1,6,3,7,1,2,6,3,1,3,5,1,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,2,3,2,3,1,2,2,5,7,2,1,1,4,5,4,5,1,3,0,0,5,2,1,5,3,1,2,0,7,2,7,1,2,5,4,1,4,5,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,33,1,3,3,8,1,5,1,3,7,1,2,2,3,5,2,4,4,3,1,2,2,2,1,3,2,3,3,1,3,6,6,2,1,5,4,3,2,3,2,1,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,1,4,2,3,7,1,2,1,3,6,1,7,1,3,0,0,6,1,1,2,2,5,2,0,7,2,7,2,1,6,3,1,5,4,1,0,4,6,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0\ntest,11,1,2,3,3,0,5,0,4,6,0,3,4,3,2,1,5,4,1,0,1,1,3,4,1,1,6,3,1,0,9,6,3,0,4,5,0,2,7,1,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,1,1,6,6,1,5,2,3,5,1,3,5,4,1,4,5,1,5,0,0,4,0,0,5,4,1,0,1,8,1,6,1,3,7,2,5,4,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,3,0,6,9,0,0,0,4,5,2,0,7,2,0,5,0,2,0,2,1,5,3,0,2,7,7,2,0,7,2,4,2,4,0,0,3,3,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,2,3,5,2,3,2,5,4,2,5,3,2,1,1,4,3,2,2,2,3,2,1,1,8,6,2,2,6,3,1,4,4,1,0,4,4,0,0,0,6,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,4,7,0,3,0,6,2,0,7,6,0,3,0,2,7,4,0,0,0,0,5,0,2,0,7,0,6,3,3,0,6,7,2,7,0,2,0,0,2,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,2,2,5,7,0,2,0,2,7,0,4,5,0,0,0,0,9,0,0,0,0,9,0,9,0,5,0,4,9,0,4,5,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,4,1,6,1,2,2,5,2,1,1,8,1,0,0,2,5,2,1,1,1,8,0,4,5,7,0,2,8,1,4,4,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,0,3,0,6,7,0,2,2,2,6,2,4,3,1,0,0,6,0,2,1,3,4,3,0,6,3,5,1,3,8,1,2,5,3,0,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,2,3,1,4,7,1,1,0,4,5,2,6,2,2,0,0,5,0,2,2,4,2,2,0,5,4,8,0,1,7,2,2,3,5,0,0,4,7,2,0,0,5,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,2,1,7,5,2,3,3,3,5,0,1,8,0,0,0,2,7,1,0,1,1,8,0,5,4,5,4,0,9,0,3,2,5,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,0,5,9,0,0,0,9,0,0,9,0,0,0,0,9,0,0,0,9,0,0,0,9,0,9,0,0,5,5,0,9,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,29,1,2,3,7,1,5,1,2,6,2,2,1,7,2,1,4,5,1,0,0,4,3,3,1,3,1,3,3,1,8,8,0,1,6,3,1,5,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,0,5,9,0,0,0,3,6,1,2,7,1,0,0,3,4,2,1,1,1,6,1,5,4,6,1,2,7,2,2,4,3,1,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,2,3,2,4,5,1,4,6,2,2,4,3,3,4,0,0,5,0,0,2,1,5,1,2,4,5,5,2,3,4,5,2,3,2,3,0,5,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,40,1,3,3,10,0,3,0,6,9,0,0,0,4,5,2,0,7,2,0,5,0,2,0,2,1,5,3,0,2,7,6,2,2,5,4,4,2,4,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,2,7,0,0,6,1,2,2,6,2,0,4,5,3,0,0,5,0,2,1,1,3,4,2,0,9,6,0,3,6,3,4,4,0,2,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,3,3,7,2,3,2,4,5,2,3,1,3,5,1,1,8,1,0,0,7,1,1,0,1,1,5,3,1,8,5,2,3,6,3,1,7,1,1,1,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,7,0,0,0,8,0,0,0,0,0,0,0,6,0,0,7,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0\ntest,8,1,2,2,2,3,6,0,1,8,1,0,5,2,2,0,2,7,0,0,0,3,0,6,2,0,0,4,3,9,0,7,2,0,2,7,5,0,4,0,0,4,8,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,3,0,6,9,0,0,2,2,5,0,4,5,0,0,0,3,6,0,0,0,4,5,0,9,0,5,0,4,6,3,5,4,0,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,9,0,0,7,0,2,2,3,5,2,2,5,2,0,1,3,4,2,1,1,2,5,2,3,6,6,2,2,7,2,3,2,4,1,1,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,9,0,0,5,0,4,3,2,5,0,0,9,0,0,2,0,3,5,0,4,0,5,0,5,4,3,2,4,7,2,9,0,0,0,0,1,4,0,0,4,6,6,0,0,0,5,2,0,0,0,0,6,6,0,0,0,0,5,0,0,1,1,1,0,0,0,3,1,0,0,0,0,1,1,0,0,0,0,1,0\ntest,6,1,3,3,2,2,5,0,3,9,0,0,1,3,5,7,2,0,9,0,0,0,0,0,6,2,1,0,0,0,9,7,2,0,2,7,1,2,3,4,1,7,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,2,7,0,0,7,1,2,1,4,5,0,3,6,1,1,0,2,3,4,1,1,3,5,1,0,9,6,2,2,5,4,0,7,2,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,39,1,3,2,9,4,1,0,5,6,3,0,0,4,5,3,3,4,3,0,0,0,0,6,3,0,3,3,0,0,9,6,3,0,2,7,0,0,5,0,4,9,5,0,0,0,6,0,4,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,1,3,8,1,1,1,2,7,0,2,7,1,0,4,1,1,5,1,2,3,4,1,1,8,7,1,1,8,1,2,3,2,4,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,3,5,1,5,1,3,6,1,2,0,0,9,0,0,9,2,1,1,2,3,2,0,0,5,4,0,9,0,6,1,3,7,2,4,5,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,3,2,1,2,2,0,6,4,2,3,1,4,5,2,5,2,1,1,0,3,0,5,2,5,1,3,0,2,7,6,2,2,2,7,0,3,2,4,1,8,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,3,3,1,0,6,0,3,8,0,1,0,4,5,4,3,3,2,3,0,2,3,1,5,2,0,3,0,2,7,7,2,0,2,7,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,8,2,3,3,2,0,5,2,3,7,1,2,2,3,5,3,4,3,4,0,0,4,1,0,4,2,2,2,0,1,8,8,0,1,6,3,0,5,4,0,0,4,7,0,0,0,6,0,0,0,0,0,0,0,6,0,0,0,3,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0\ntest,13,1,3,2,3,1,5,1,4,7,1,2,0,5,4,0,5,4,4,0,1,3,1,2,0,0,0,9,0,7,2,6,2,1,4,5,0,2,7,0,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,4,3,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,4,2,9,0,5,1,3,7,0,2,0,0,9,1,3,5,3,0,0,2,3,3,3,0,2,4,1,2,7,6,3,0,6,3,4,4,2,0,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,4,2,0,3,5,0,4,2,4,4,2,5,2,2,0,0,5,0,3,2,4,3,2,0,4,5,5,1,3,5,4,0,4,4,1,1,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,2,3,2,5,1,3,2,4,6,1,2,2,2,6,0,3,6,2,0,0,0,6,2,0,1,4,4,1,4,5,5,1,3,7,2,3,5,2,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,3,9,0,7,2,0,9,0,0,0,0,9,0,2,7,0,0,0,2,2,6,0,2,0,7,0,7,2,7,2,0,9,0,0,7,2,0,0,4,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,3,8,0,4,1,4,6,1,3,1,5,3,1,5,3,1,0,1,2,1,5,1,1,5,3,1,3,6,4,0,5,7,2,3,3,1,3,1,5,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,29,1,3,3,7,2,3,2,4,5,2,3,1,3,5,1,1,8,1,0,0,7,1,1,0,1,1,5,3,1,8,5,2,3,6,3,1,7,1,1,1,3,3,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,2,5,2,2,7,2,0,0,3,6,2,3,5,3,0,2,3,1,2,4,0,2,4,0,1,8,7,2,0,6,3,5,2,2,2,0,3,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,1,2,1,0,5,0,4,0,3,6,6,3,0,3,3,3,0,0,0,6,0,3,0,4,2,2,2,7,2,5,0,4,7,2,5,4,0,0,0,2,6,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,0,4,0,5,9,0,0,2,4,3,0,0,9,0,0,0,4,5,0,0,0,0,9,0,6,3,9,0,0,9,0,3,4,3,1,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,9,0,0,5,0,4,4,3,3,0,3,6,0,0,2,0,3,5,0,3,0,6,0,0,9,3,2,4,7,2,6,3,0,0,0,2,3,0,0,3,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,3,5,2,4,1,3,3,2,5,5,3,2,2,5,3,1,0,0,3,2,4,1,2,4,3,1,8,1,5,0,4,8,1,6,2,1,0,1,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,4,8,0,5,0,4,9,0,0,0,4,5,0,3,6,0,0,0,3,6,0,0,2,0,7,0,4,5,9,0,0,9,0,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,0,3,1,5,6,1,3,2,3,4,2,3,5,3,0,0,2,3,2,2,2,2,4,2,5,4,5,1,3,7,2,3,2,4,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,7,1,3,3,2,1,5,0,4,7,0,2,1,4,4,4,5,1,4,0,0,4,0,1,2,1,5,3,0,7,2,6,1,2,5,4,1,1,8,0,0,5,6,2,0,0,5,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,3,5,2,0,7,0,2,0,3,6,7,1,2,7,0,0,0,1,2,7,0,1,1,2,2,7,8,1,0,4,5,1,1,5,4,0,6,8,2,0,0,5,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,1,3,1,5,4,2,4,0,4,5,0,6,3,0,0,0,6,0,3,0,6,0,3,0,3,6,6,0,3,8,1,3,3,4,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,0,9,0,0,5,0,4,4,3,3,0,3,6,0,0,2,0,3,5,0,3,0,6,0,0,9,3,2,4,7,2,6,3,0,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,0,6,1,3,5,0,4,3,3,4,1,3,5,1,0,1,6,1,3,1,2,2,3,3,2,7,7,0,2,7,2,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,4,3,9,5,4,0,1,9,0,0,0,3,6,1,4,5,3,1,0,4,1,2,2,2,1,4,2,3,6,8,0,1,6,3,1,6,3,1,1,4,5,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0\ntest,7,1,2,3,2,1,2,1,6,5,0,4,3,3,4,6,3,0,5,1,0,3,0,2,5,2,2,1,0,7,2,5,2,2,2,7,0,3,3,4,1,7,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,5,1,3,7,0,2,1,2,6,1,2,7,1,0,1,2,5,2,1,1,2,6,1,1,8,8,0,1,8,1,0,4,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,4,3,7,0,3,0,6,9,0,0,0,5,4,0,1,8,1,0,0,4,1,5,0,1,0,4,5,4,5,9,0,0,4,5,1,4,4,1,0,4,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,8,0,1,0,3,6,0,3,6,1,2,1,3,3,2,2,0,1,7,0,4,5,7,2,0,7,2,4,3,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,6,1,3,7,0,2,1,4,5,1,2,6,1,0,2,3,2,3,1,2,3,4,2,2,7,7,1,2,7,2,1,4,4,1,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,2,4,2,3,0,2,0,7,9,0,0,0,0,9,0,3,6,0,0,0,7,0,2,0,2,0,6,2,0,9,9,0,0,9,0,2,0,5,2,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,2,3,0,4,2,4,4,2,5,5,2,3,2,5,3,2,0,0,5,0,3,2,2,4,2,0,5,4,5,2,4,5,4,0,3,2,5,0,7,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,0,5,3,2,7,1,1,0,4,5,2,2,5,3,0,0,4,2,2,2,2,1,4,1,4,5,6,1,2,8,1,4,2,4,1,0,3,7,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,0,2,4,2,3,3,2,4,0,0,9,3,0,0,0,0,6,2,0,0,3,4,5,5,5,2,3,9,0,3,2,4,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,8,1,3,3,2,2,4,2,2,6,1,2,2,2,5,3,3,4,2,2,0,5,1,2,2,3,1,2,3,2,7,6,1,3,4,5,2,1,4,3,0,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,2,3,0,5,0,4,9,0,0,0,3,6,0,4,5,0,0,0,4,5,0,0,0,4,5,0,3,6,7,1,1,5,4,2,7,0,0,0,3,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,4,3,7,0,3,0,6,9,0,0,0,5,4,0,1,8,1,0,0,4,1,5,0,1,0,4,5,4,5,9,0,0,4,5,1,4,4,1,0,4,1,2,0,0,6,0,0,0,0,0,0,4,0,0,0,0,5,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,4,9,0,7,0,2,9,0,0,0,6,3,1,4,5,3,1,3,1,2,1,2,1,5,2,0,2,7,6,3,0,5,4,5,2,0,1,1,4,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,1,4,1,5,6,2,1,1,4,5,2,5,3,1,0,0,6,1,2,1,3,4,3,0,1,8,7,2,0,8,1,1,3,6,1,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,34,1,3,3,8,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,5,1,2,4,1,0,5,2,3,5,1,4,3,4,3,2,4,4,3,1,1,3,2,2,2,2,2,4,1,4,5,6,1,3,6,3,4,2,3,1,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,0,6,0,3,7,1,2,2,1,6,1,3,5,2,1,0,3,3,2,1,2,2,4,1,6,3,5,2,3,7,2,3,5,2,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,2,0,5,2,2,2,6,2,0,3,6,5,0,0,1,0,4,2,0,2,4,2,4,5,4,4,2,3,6,3,6,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,0,5,2,2,7,0,2,2,5,3,0,0,9,0,0,0,2,7,0,0,0,0,9,0,5,4,9,0,0,9,0,5,4,0,0,0,3,3,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,23,1,1,3,5,0,5,2,2,2,0,7,7,2,0,4,4,2,0,2,0,7,0,1,2,3,3,2,0,9,0,7,0,2,5,4,3,4,0,3,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,3,3,7,1,5,2,3,5,1,3,0,4,5,0,1,8,0,0,1,3,0,6,0,1,1,5,4,8,1,6,1,3,7,2,3,6,0,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,6,3,0,0,9,0,0,0,3,6,6,2,2,5,2,0,3,0,0,5,3,0,2,0,0,9,9,0,0,0,9,0,0,6,3,0,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,20,1,1,6,5,0,6,0,3,0,0,9,9,0,0,0,0,9,0,0,0,0,3,6,0,0,0,3,6,9,0,0,0,9,9,0,9,0,0,0,0,1,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,2,3,1,4,0,5,5,0,4,4,2,4,2,4,4,3,1,0,3,4,0,3,2,1,4,0,8,1,4,1,5,8,1,6,3,1,1,1,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,4,3,3,1,5,0,4,9,0,0,0,3,6,5,3,2,4,0,0,5,0,1,4,2,2,2,0,0,9,5,4,0,4,5,0,2,5,2,2,7,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,4,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,2,5,0,4,1,4,2,3,4,3,4,2,1,5,4,1,0,0,2,2,4,1,1,3,4,2,8,1,6,1,3,8,1,5,4,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0\ntest,16,1,1,2,4,2,4,0,4,2,1,7,6,2,1,7,1,2,3,0,0,3,0,4,4,2,2,2,2,5,4,4,0,5,5,4,6,1,2,2,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,2,0,9,0,0,3,4,3,3,4,3,1,0,0,1,4,4,1,1,5,2,2,2,7,7,0,2,7,2,1,3,4,3,0,7,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,1,5,2,3,5,1,4,1,4,4,3,5,1,2,1,0,6,1,1,3,2,4,1,1,1,8,5,1,3,5,4,1,4,2,4,1,6,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,1,5,1,1,5,2,2,5,1,4,4,4,1,1,7,2,2,1,0,6,1,1,2,4,2,2,1,4,5,7,1,2,5,4,4,3,3,1,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,4,2,10,1,5,1,3,6,1,3,0,3,6,0,2,7,4,0,2,2,0,3,0,2,6,2,0,2,7,6,1,2,6,3,0,0,9,0,0,5,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,0,3,0,6,9,0,0,0,4,5,2,0,7,2,0,5,0,2,0,2,1,5,3,0,2,7,7,2,0,7,2,4,2,4,0,0,3,5,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,2,3,3,9,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,0,2,0,7,6,0,3,5,3,2,3,4,2,7,0,0,0,2,0,4,0,4,1,0,0,9,9,0,0,2,7,2,2,5,0,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,4,2,3,0,7,2,0,9,0,0,0,0,8,2,4,4,2,0,0,2,5,0,2,0,3,5,0,4,5,9,0,0,7,2,0,4,5,0,0,5,7,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,5,6,2,2,2,4,4,1,3,6,1,1,1,3,3,2,1,2,3,4,1,5,4,6,2,2,8,1,4,4,2,1,1,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,1,5,1,3,6,1,2,0,3,6,4,3,3,2,0,0,1,1,7,1,3,4,2,0,0,9,7,2,1,5,4,0,3,1,2,4,8,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,1,4,1,4,7,1,2,2,4,3,1,2,6,4,0,0,2,2,1,2,2,3,3,0,2,7,6,2,1,5,4,2,5,2,2,0,4,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,2,3,8,0,5,2,2,6,1,2,2,3,4,3,1,6,2,0,0,3,3,2,2,1,2,5,0,2,7,7,0,2,8,1,2,5,2,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,2,2,4,8,0,3,2,5,5,2,3,3,5,3,0,3,6,1,0,5,1,3,2,1,2,4,3,2,5,4,4,5,1,7,2,2,5,1,2,0,4,6,2,0,0,5,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,4,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,4,2,8,0,5,0,4,9,0,0,0,0,9,0,4,5,0,2,0,0,7,0,2,0,0,7,0,7,2,4,5,0,7,2,4,2,2,2,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,2,7,1,2,2,5,5,2,3,1,3,6,1,3,6,0,0,0,3,3,3,0,1,2,6,1,8,1,5,1,4,8,1,3,5,2,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,2,5,1,0,9,0,0,9,0,0,0,7,2,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,9,0,0,0,9,2,3,3,2,1,5,6,2,0,0,7,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,27,2,2,4,6,0,4,2,3,5,0,4,4,2,4,0,0,9,0,0,2,2,3,3,0,0,2,3,5,9,0,7,0,2,9,0,2,7,0,0,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,1,2,8,1,1,1,5,3,0,3,6,2,0,2,3,2,3,1,2,3,4,1,3,6,6,2,2,6,3,2,7,1,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,3,3,0,4,2,4,7,1,2,0,0,9,0,9,0,1,0,0,3,3,3,0,0,0,9,0,5,4,7,1,1,8,1,1,6,2,1,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,0,6,0,3,4,0,5,1,2,6,1,4,5,0,0,2,2,4,2,0,2,3,5,1,4,5,5,2,3,7,2,5,3,2,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,2,3,4,3,1,3,5,2,2,2,3,4,1,6,2,1,0,2,4,2,2,1,3,4,2,0,5,4,8,1,1,5,4,2,1,6,1,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,4,6,1,3,1,3,5,3,3,3,3,0,0,5,1,2,3,2,3,3,0,1,8,8,1,0,6,3,1,3,5,1,0,5,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,5,1,3,7,0,2,1,2,6,1,2,7,1,0,1,2,5,2,1,1,2,6,1,1,8,8,0,1,8,1,0,4,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,4,1,4,9,0,0,0,4,5,3,6,1,2,0,0,4,2,2,2,3,3,2,0,0,9,7,2,0,2,7,0,1,6,3,1,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,5,1,3,7,2,1,1,4,5,3,4,3,2,0,1,6,2,0,2,3,2,3,0,4,5,3,3,4,7,2,2,3,4,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,1,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0\ntest,11,1,4,2,3,0,6,1,3,9,0,0,0,2,7,3,4,3,5,1,0,2,2,1,4,1,2,2,1,0,9,8,1,0,4,5,1,2,6,2,1,6,6,2,3,0,6,6,0,0,0,0,0,0,3,0,0,6,4,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,0,5,9,0,0,0,3,6,0,4,5,0,0,0,2,5,3,0,2,2,5,2,0,9,9,0,0,9,0,1,5,4,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,2,4,10,0,7,0,2,9,0,0,0,6,3,1,4,5,3,1,3,1,2,1,2,1,5,2,0,2,7,6,3,0,5,4,5,2,0,1,1,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,2,3,3,1,2,4,0,4,8,0,1,0,0,9,6,3,0,4,0,0,3,0,3,6,3,0,0,0,0,9,3,4,3,5,4,0,0,0,5,4,0,8,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,2,3,0,3,4,2,9,0,0,0,1,8,1,7,1,2,0,0,4,0,4,2,3,5,1,0,0,9,5,4,0,2,7,0,1,4,4,1,6,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,5,1,3,7,0,2,1,2,6,1,2,7,1,0,1,2,5,2,1,1,2,6,1,1,8,8,0,1,8,1,0,4,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,24,1,2,2,5,0,2,2,7,4,2,3,3,5,2,1,3,6,0,0,0,2,4,4,0,1,3,5,0,9,0,4,0,5,7,2,3,4,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,2,3,2,5,1,3,2,4,6,1,2,2,2,6,0,3,6,2,0,0,0,6,2,0,1,4,4,1,4,5,5,1,3,7,2,3,5,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,3,5,0,2,9,0,0,0,1,8,5,4,0,4,1,0,2,1,2,4,2,3,1,0,0,9,3,6,0,2,7,0,1,4,2,2,7,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,1,4,2,4,5,2,3,3,5,2,2,4,3,3,1,1,4,2,1,2,3,2,3,1,6,3,6,1,2,7,2,2,3,3,1,0,4,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,4,3,0,2,4,4,7,0,2,4,0,5,4,0,5,2,0,0,4,1,3,0,0,5,3,3,9,0,7,0,2,6,3,6,0,3,0,0,3,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,1,3,2,4,7,1,2,1,2,6,3,5,1,3,0,0,5,1,1,2,2,4,3,0,6,3,7,1,2,5,4,3,3,4,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,2,3,0,5,5,2,3,0,2,7,1,7,2,2,0,0,4,0,4,2,1,6,2,0,4,5,7,2,0,6,3,4,5,1,0,0,3,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,3,1,0,6,0,3,8,0,1,0,4,5,4,3,3,2,3,0,2,3,1,5,2,0,3,0,2,7,7,2,0,2,7,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,3,9,1,4,1,4,4,1,4,0,0,9,5,0,5,5,0,0,0,0,5,4,0,5,0,0,0,9,5,1,4,5,4,0,5,5,0,0,4,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,1,3,3,8,0,1,3,5,7,0,2,1,1,8,0,1,8,1,1,0,1,6,3,1,1,0,5,3,9,0,5,2,3,7,2,1,6,3,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,1,5,3,2,6,0,3,4,2,3,0,4,5,3,0,0,2,2,4,3,0,1,4,1,5,4,6,1,2,9,0,3,5,1,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,4,7,2,2,2,4,6,0,3,3,4,3,0,3,6,1,0,0,4,2,4,0,1,2,4,3,9,0,4,3,3,9,0,1,8,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,0,5,3,2,3,4,3,1,8,1,0,5,4,0,0,0,5,3,1,0,5,1,4,0,8,1,4,1,5,9,0,7,2,1,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,1,2,4,1,0,5,1,4,7,1,2,1,5,3,6,2,2,6,1,0,1,1,1,6,1,1,2,1,2,7,7,1,1,2,7,2,4,4,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,5,7,1,1,0,5,4,0,8,1,1,1,0,4,2,3,0,0,5,4,1,0,9,6,2,2,8,1,0,8,0,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,21,2,2,4,5,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,1,4,1,4,7,1,2,0,2,7,2,0,7,5,0,0,2,0,3,2,2,5,2,0,6,3,6,2,1,5,4,7,2,0,0,0,2,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,8,1,4,2,2,2,2,2,4,9,0,0,0,2,7,2,7,0,2,0,0,7,0,0,3,6,0,0,0,0,9,7,2,0,6,3,0,0,5,4,0,7,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,12,1,3,3,3,2,1,2,5,6,2,1,1,4,4,0,4,5,1,3,1,4,1,2,2,2,3,3,1,1,8,6,1,3,7,2,1,3,6,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,1,5,0,4,7,0,2,2,2,6,3,5,1,6,0,0,3,0,1,4,1,5,0,0,0,9,7,2,0,2,7,2,1,6,0,0,5,8,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,2,2,1,5,4,3,2,3,5,2,1,5,4,1,0,0,6,1,3,1,4,1,4,1,5,4,7,1,2,7,2,2,3,4,1,0,4,3,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,0,6,0,3,7,1,2,2,1,6,1,3,5,2,1,0,3,3,2,1,2,2,4,1,6,3,5,2,3,7,2,3,5,2,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,18,1,1,2,4,2,2,1,6,1,1,7,6,2,1,6,2,2,4,0,0,5,0,1,3,2,4,2,0,9,0,2,1,6,7,2,4,4,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,2,2,3,8,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,3,2,1,4,7,0,2,1,5,4,4,6,0,2,1,0,4,2,2,2,2,4,2,0,7,2,6,3,1,2,7,3,1,4,2,2,7,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,3,3,2,4,0,4,4,2,3,2,3,4,3,4,2,4,1,0,3,2,1,4,2,2,2,1,5,4,6,2,2,4,5,3,3,3,2,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,2,3,7,0,2,1,6,2,2,3,5,3,0,1,2,4,1,2,2,1,3,2,2,7,6,2,2,6,3,2,4,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,4,2,9,0,2,3,5,6,0,3,2,0,7,0,6,3,0,0,0,7,0,2,0,3,4,3,0,4,5,9,0,0,7,2,0,7,0,2,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,3,7,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,4,7,1,4,2,4,6,1,3,0,5,4,0,2,7,0,0,0,0,0,9,0,0,2,2,5,5,4,5,1,3,7,2,0,7,0,2,0,4,2,0,0,1,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,5,7,0,5,0,4,5,0,4,4,4,2,0,2,7,0,0,0,3,6,0,0,0,3,5,2,9,0,5,0,4,9,0,6,2,2,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,12,1,3,3,3,2,1,2,5,6,2,1,1,4,4,0,4,5,1,3,1,4,1,2,2,2,3,3,1,1,8,6,1,3,7,2,1,3,6,0,0,4,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,0,4,0,5,4,0,5,0,4,5,3,0,6,4,0,0,3,1,2,0,0,3,6,0,4,5,9,0,0,0,9,0,4,5,0,0,4,8,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,4,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,2,5,0,6,1,2,1,2,6,6,2,1,4,4,2,1,0,0,6,1,3,1,3,4,2,0,9,0,5,1,3,5,4,4,2,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,4,2,3,7,0,2,0,2,7,0,2,7,0,0,2,4,0,3,0,0,5,4,0,0,9,7,0,2,9,0,3,6,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,1,2,3,6,1,2,1,6,3,2,4,4,4,2,4,4,2,2,0,0,5,1,2,2,3,3,2,1,8,1,7,1,2,6,3,2,3,3,2,1,5,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,33,2,3,3,8,0,6,1,3,7,0,2,1,3,6,1,2,7,1,0,1,3,3,4,1,2,1,6,1,1,8,8,0,1,8,1,0,2,7,0,0,4,3,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,2,2,3,8,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,1,2,8,1,1,1,5,3,0,3,6,2,0,2,3,2,3,1,2,3,4,1,3,6,6,2,2,6,3,2,7,1,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,2,4,2,3,0,2,0,7,9,0,0,0,0,9,0,3,6,0,0,0,7,0,2,0,2,0,6,2,0,9,9,0,0,9,0,2,0,5,2,0,5,7,0,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,22,1,2,2,5,0,6,1,2,1,2,6,3,5,1,2,6,2,1,0,0,4,1,3,1,3,4,2,0,9,0,5,1,3,5,4,3,2,5,0,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,3,1,5,6,0,3,2,2,6,0,4,5,2,0,0,3,4,2,1,0,2,5,3,5,4,5,1,3,7,2,4,4,2,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,4,1,2,4,1,0,7,2,0,9,0,0,1,7,2,3,4,2,2,0,0,4,3,1,2,3,1,4,1,7,2,7,0,2,7,2,3,3,3,1,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,1,4,1,4,7,1,2,0,3,6,0,6,3,0,0,0,9,0,0,0,0,0,9,0,9,0,6,2,1,5,4,5,4,0,0,0,2,6,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,4,2,3,0,7,0,2,7,2,0,1,1,8,2,3,5,1,0,0,7,2,1,0,3,2,5,0,0,9,9,0,0,6,3,2,4,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0\ntest,33,1,3,3,8,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,0,6,1,2,7,1,2,1,4,4,1,2,6,1,0,1,3,2,4,1,1,2,6,1,5,4,5,2,3,6,3,2,5,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,0,6,1,3,8,1,1,1,3,6,0,3,6,0,0,2,3,4,1,0,2,4,4,1,2,7,7,1,2,6,3,2,2,6,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,3,8,0,4,1,4,6,1,3,1,6,2,2,2,5,1,0,2,3,3,2,2,1,3,3,1,3,6,4,0,5,7,2,2,3,1,3,2,6,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,0,3,3,4,6,1,2,1,4,5,0,3,6,0,0,0,3,3,4,0,1,1,6,2,9,0,7,0,2,7,2,6,3,1,0,0,2,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,6,1,3,8,1,1,1,3,6,0,3,6,0,0,2,3,4,1,0,2,4,4,1,2,7,7,1,2,6,3,2,2,6,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,1,4,0,4,6,1,3,2,4,3,3,4,3,3,0,0,4,2,1,3,2,2,3,2,2,7,6,2,2,4,5,2,2,4,2,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,2,7,2,2,0,5,6,1,3,2,2,6,0,3,6,1,0,2,0,4,4,0,0,5,4,1,7,2,6,0,3,9,0,7,2,0,0,0,2,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,3,8,0,5,0,4,6,0,3,2,3,4,2,4,4,2,0,2,3,2,3,2,1,5,2,1,0,9,6,3,0,4,5,0,4,4,2,1,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,1,4,2,3,4,1,4,3,4,2,2,4,4,1,2,0,4,4,0,2,2,0,5,0,6,3,6,0,3,7,2,4,0,5,0,0,4,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,3,4,7,0,5,1,3,6,1,3,1,5,4,0,0,9,0,0,0,0,3,6,0,0,0,9,0,9,0,6,1,2,6,3,5,4,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,33,1,2,4,8,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,3,3,0,5,0,4,8,1,0,1,3,5,1,5,3,3,0,0,2,1,3,0,3,4,3,0,0,9,9,0,0,5,4,2,4,4,0,0,4,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,1,1,0,8,5,2,2,2,5,2,2,4,4,3,0,4,0,1,3,3,0,3,4,0,2,7,6,2,1,6,3,5,0,1,4,0,5,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,1,4,2,4,5,2,3,3,5,2,2,4,3,3,1,1,4,2,1,2,3,2,3,1,6,3,6,1,2,7,2,2,3,3,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,4,2,8,0,4,0,5,9,0,0,0,3,6,0,3,6,0,0,0,2,6,2,0,2,2,6,0,0,9,4,4,1,9,0,2,5,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0\ntest,38,1,2,3,9,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,4,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,1,4,2,4,5,2,3,3,5,2,2,4,3,3,1,1,4,2,1,2,3,2,3,1,6,3,6,1,2,7,2,2,3,3,1,0,4,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,1,4,0,5,5,1,3,0,0,9,0,4,5,0,0,0,6,0,3,0,2,3,5,0,0,9,5,2,3,5,4,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,5,8,1,4,1,4,7,1,2,4,4,2,0,6,3,0,0,0,9,0,0,0,3,4,3,0,9,0,6,2,1,5,4,6,0,3,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,0,2,4,2,3,3,2,4,0,0,9,3,0,0,0,0,6,2,0,0,3,4,5,5,5,2,3,9,0,3,2,4,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0\ntest,36,1,2,5,8,1,4,2,3,3,0,6,5,3,2,2,2,6,2,2,0,3,2,3,1,1,2,4,2,8,1,2,2,5,5,4,7,1,0,0,1,3,3,0,0,0,6,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,25,2,2,4,6,0,5,0,4,2,0,7,4,3,2,2,4,3,2,0,0,4,2,2,1,1,4,3,1,3,6,7,0,2,7,2,5,0,3,2,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,33,1,4,3,8,0,6,0,3,7,0,2,2,2,6,0,3,6,0,0,0,2,8,0,0,1,2,6,0,2,7,9,0,0,9,0,5,0,4,0,0,4,3,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,5,1,2,4,1,0,5,2,3,5,1,4,3,4,3,2,4,4,3,1,1,3,2,2,2,2,2,4,1,4,5,6,1,3,6,3,4,2,3,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,2,3,1,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,4,8,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,29,1,3,4,7,2,4,0,4,7,0,2,0,3,6,1,1,7,2,0,0,3,3,3,1,0,2,5,2,1,8,5,2,2,7,2,0,7,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,0,3,1,5,6,1,3,2,3,4,2,3,5,3,0,0,2,3,2,2,2,2,4,2,5,4,5,1,3,7,2,3,2,4,1,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,0,5,9,0,0,0,9,0,0,0,9,5,0,0,0,5,0,0,0,5,4,0,5,5,9,0,0,9,0,5,5,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,0,2,6,0,3,3,1,6,0,6,3,3,0,4,2,0,1,3,2,5,1,0,0,9,3,3,3,2,7,1,5,4,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,4,3,2,1,5,1,3,7,1,2,0,2,7,2,5,2,2,1,0,5,2,1,2,3,3,2,1,1,8,7,0,2,5,4,1,2,6,1,1,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,2,3,2,3,6,1,2,3,5,2,1,3,6,1,0,0,2,2,6,1,1,2,6,1,3,6,6,1,3,8,1,2,6,1,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,2,9,2,2,0,5,0,3,6,6,2,2,0,3,6,1,0,0,3,4,2,0,1,3,4,3,8,1,5,0,4,9,0,7,2,0,1,0,2,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,4,1,4,9,0,0,0,4,5,3,6,1,2,0,0,4,2,2,2,3,3,2,0,0,9,7,2,0,2,7,0,1,6,3,1,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,2,10,0,6,0,3,6,3,0,0,5,4,1,7,2,3,0,3,3,0,1,4,2,2,2,0,0,9,4,5,0,3,6,0,4,4,2,0,5,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,2,3,10,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,13,1,3,2,3,2,2,0,6,6,0,3,0,0,9,0,5,4,1,1,0,3,3,4,0,0,0,9,0,9,0,7,1,1,4,5,5,4,0,0,0,3,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,3,10,0,6,2,2,6,0,3,3,2,5,0,3,6,0,0,9,0,0,0,3,0,2,0,4,0,9,5,2,3,7,2,5,4,0,0,0,2,3,0,0,3,6,5,0,0,0,6,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,3,1,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,0,7,0,2,6,0,3,3,3,4,0,5,4,2,0,0,4,4,0,2,2,0,5,0,4,5,7,2,0,7,2,0,3,4,3,0,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,3,5,2,4,2,3,5,2,3,2,4,3,1,3,5,1,1,0,3,3,2,2,2,2,5,1,6,3,7,1,2,7,2,3,4,3,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,1,2,1,0,5,0,4,0,3,6,6,3,0,3,3,3,0,0,0,6,0,3,0,4,2,2,2,7,2,5,0,4,7,2,5,4,0,0,0,2,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,6,2,3,6,0,3,0,3,6,1,6,3,2,0,0,5,2,2,2,2,4,2,0,7,2,7,0,2,6,3,3,3,3,1,0,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,26,1,1,6,6,0,5,2,2,2,1,7,7,2,0,0,0,9,0,0,1,2,5,2,0,0,1,6,3,9,0,1,0,8,8,1,8,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,2,9,0,5,1,4,4,4,2,2,3,4,2,3,4,2,0,0,3,3,2,2,2,2,5,1,7,2,7,0,2,9,0,2,2,5,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,3,8,4,3,1,3,5,2,3,2,4,3,1,3,5,2,0,0,2,4,2,1,2,4,2,2,6,3,7,2,0,4,5,1,4,3,2,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,0,5,3,2,3,4,3,1,8,1,0,5,4,0,0,0,5,3,1,0,5,1,4,0,8,1,4,1,5,9,0,7,2,1,0,0,2,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,3,5,3,0,0,6,2,4,3,4,5,1,2,5,3,1,0,0,5,3,2,1,2,3,4,1,4,5,5,2,2,7,2,1,4,4,2,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,26,1,3,3,6,0,4,1,4,6,1,3,2,5,3,2,3,5,2,0,1,3,2,3,1,2,3,3,2,3,6,4,0,5,7,2,3,3,2,2,1,4,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,2,3,3,3,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,38,1,4,2,9,0,6,0,3,4,0,5,1,2,6,1,4,5,0,0,2,2,4,2,0,2,3,5,1,4,5,5,2,3,7,2,5,3,2,0,0,3,4,0,0,4,0,0,0,0,0,5,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,24,1,3,3,5,1,4,1,3,6,1,3,2,4,3,0,6,3,5,0,0,4,0,0,1,1,5,2,2,0,9,6,2,2,6,3,2,4,3,0,1,5,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,2,2,1,4,2,3,7,1,2,1,3,6,2,4,3,3,1,0,4,2,1,2,2,3,3,0,2,7,7,1,1,5,4,1,3,5,1,0,5,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,2,2,4,2,2,5,2,2,5,2,0,4,5,0,0,0,4,0,5,0,2,3,5,0,9,0,5,2,2,9,0,0,6,3,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,1,0,0,0,6,0,0,0,0,5,3,0,0,0,0,0,6,0,0,0,0,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,1,2,3,8,0,9,0,0,5,0,4,4,3,3,0,3,6,0,0,2,0,3,5,0,3,0,6,0,0,9,3,2,4,7,2,6,3,0,0,0,2,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,5,2,2,5,1,0,4,2,3,5,0,4,4,4,2,4,0,5,4,0,0,0,4,2,4,0,0,5,0,4,5,5,0,4,5,4,1,3,5,1,0,5,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,5,1,1,5,1,3,3,0,6,6,2,2,3,4,3,5,1,0,2,0,3,3,1,3,3,1,6,3,3,1,6,4,5,7,1,1,1,1,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,2,1,3,5,0,5,1,4,5,1,4,6,2,2,1,4,4,0,0,0,4,3,3,0,2,3,3,2,7,2,4,0,5,7,2,7,1,2,0,0,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,2,4,3,2,0,5,0,5,9,0,0,0,0,9,0,5,5,0,0,0,9,0,0,0,4,0,5,0,0,9,6,3,0,2,7,0,9,0,0,0,3,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,38,1,3,3,9,1,5,1,3,7,1,2,1,4,5,2,4,3,3,0,1,3,3,1,3,2,2,3,1,3,6,6,2,1,5,4,2,2,4,2,0,5,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,26,1,1,5,6,0,5,0,4,5,0,4,8,1,0,2,2,6,1,0,1,7,1,1,1,2,2,2,4,9,0,5,0,4,9,0,2,5,2,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,4,8,0,6,0,3,7,0,2,0,3,6,0,3,6,2,1,2,1,3,3,1,0,4,5,0,1,8,7,2,0,7,2,4,3,3,0,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,0,6,1,2,1,2,6,3,4,3,2,5,3,1,0,0,5,2,2,1,4,2,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,2,2,0,7,2,0,9,0,0,0,2,7,4,2,4,4,1,0,4,2,1,4,2,1,4,0,0,9,8,1,0,5,4,0,6,3,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1\ntest,34,1,5,2,8,0,8,1,0,9,0,0,0,1,8,1,5,4,2,2,0,4,1,2,2,2,2,4,0,0,9,5,4,0,7,2,0,7,2,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,5,7,0,9,0,0,9,0,0,0,5,4,0,4,5,0,0,0,2,5,2,0,0,3,6,0,4,5,5,0,4,9,0,7,2,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,2,5,1,2,9,0,0,0,4,5,3,4,3,3,1,0,5,1,2,2,4,2,3,0,7,2,7,2,1,7,2,1,8,1,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,2,2,5,3,2,4,4,0,5,0,4,4,2,3,2,3,4,4,0,0,2,0,4,4,2,0,0,4,0,9,4,2,4,5,4,3,5,1,1,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,0,4,7,0,2,0,1,8,0,3,6,0,0,0,3,3,4,0,1,2,5,2,9,0,7,2,0,9,0,2,6,1,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,6,0,3,5,2,2,2,2,5,0,2,7,0,0,0,2,7,0,0,0,0,9,0,2,7,9,0,0,7,2,0,9,0,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,2,4,3,3,0,4,2,4,8,1,1,1,3,6,1,7,2,4,0,0,3,3,0,1,3,3,3,0,1,8,8,0,1,4,5,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,9,1,2,2,3,1,2,4,4,8,1,1,1,6,3,2,7,1,2,0,0,7,1,1,2,5,2,1,0,6,3,8,1,1,7,2,1,5,3,1,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,26,1,2,5,6,0,8,1,1,5,0,4,3,5,1,1,3,5,1,0,0,2,1,6,1,1,3,3,2,6,3,4,0,5,9,0,6,3,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,3,5,0,5,1,4,4,3,3,1,5,3,2,4,3,2,1,1,5,2,1,2,2,4,3,0,8,1,5,1,4,7,2,4,4,1,1,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,8,0,1,9,0,0,0,4,5,0,3,6,1,1,1,2,5,0,1,0,4,5,0,0,9,6,3,0,6,3,5,4,0,0,0,3,3,0,0,4,0,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,0,4,9,0,0,0,3,6,3,5,2,4,0,3,2,2,0,5,1,2,2,0,3,6,9,0,0,5,4,3,3,2,3,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,1,4,2,3,4,3,3,3,2,5,0,3,6,1,0,0,2,3,3,1,1,2,6,1,9,0,5,0,4,8,1,6,3,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,2,5,0,6,1,2,1,2,6,5,3,1,2,6,2,1,0,0,4,3,2,1,3,2,4,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,6,2,1,0,3,6,0,6,3,3,0,5,2,0,0,2,4,4,0,0,0,9,6,1,2,6,3,0,7,0,2,0,5,3,0,0,3,6,0,0,0,0,5,0,0,5,0,0,0,6,0,0,0,0,0,0,0,1,2,0,0,0,0,2,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,2,3,1,5,5,2,2,2,4,4,1,5,4,2,0,0,4,3,1,1,3,1,5,1,8,1,6,2,2,8,1,2,5,2,1,1,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,1,4,2,3,7,1,2,1,3,6,1,7,1,3,0,0,6,1,1,2,2,5,2,0,7,2,7,2,1,6,3,1,5,4,1,0,4,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,1,5,1,4,6,1,3,1,2,7,4,2,4,2,0,0,6,1,1,4,1,2,3,0,1,8,8,1,0,6,3,1,2,5,3,0,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,4,1,0,9,0,0,9,0,0,0,6,3,0,6,3,4,0,0,4,0,3,3,0,4,3,0,4,6,9,0,0,6,4,3,4,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,7,0,2,7,0,2,2,0,7,1,4,5,0,0,0,4,3,3,0,2,2,3,3,7,2,4,1,4,8,1,2,4,4,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,2,7,0,0,9,0,0,1,4,4,0,5,4,3,0,0,5,0,2,1,1,5,3,1,1,8,7,2,0,7,2,1,5,2,0,1,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,39,1,3,3,9,0,5,1,3,7,1,2,3,2,5,1,3,6,2,1,1,2,3,3,1,1,1,5,2,5,4,5,2,2,7,2,4,4,1,1,1,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,4,3,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,0,4,7,2,0,0,3,6,0,4,5,2,0,2,0,4,3,2,0,2,5,0,4,5,7,2,0,5,4,2,2,3,3,0,5,4,2,0,0,5,0,4,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,3,3,5,3,1,1,8,1,1,1,1,8,1,5,3,1,0,0,5,3,1,1,3,3,3,0,1,8,9,0,0,5,4,3,2,4,0,0,4,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,10,1,4,3,3,5,3,1,1,8,1,1,1,1,8,1,5,3,1,0,0,5,3,1,1,3,3,3,0,1,8,9,0,0,5,4,3,2,4,0,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,24,1,2,4,5,0,5,2,2,4,1,5,5,4,1,1,3,5,1,1,0,3,2,5,1,2,2,3,2,8,2,5,1,3,8,1,5,4,1,0,0,3,2,0,0,0,6,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,26,1,1,4,6,1,5,2,2,4,0,5,6,3,1,3,2,5,4,0,1,2,2,2,3,1,1,3,2,5,4,4,2,4,4,5,3,3,3,0,0,3,1,2,2,0,5,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,2,4,4,7,0,2,0,7,2,4,1,4,1,0,0,4,1,5,1,4,0,5,0,5,4,7,0,2,6,3,1,4,2,4,0,6,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,3,10,1,3,1,5,8,0,1,1,4,4,2,4,4,3,0,2,3,2,2,2,2,3,4,0,1,8,5,4,0,5,4,1,3,4,2,1,6,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,1,2,8,1,1,1,5,3,0,3,6,2,0,2,3,2,3,1,2,3,4,1,3,6,6,2,2,6,3,2,7,1,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,5,0,4,7,0,2,2,4,3,0,7,2,0,0,0,7,0,2,0,3,5,2,0,9,0,9,0,0,9,0,4,5,0,0,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,4,6,2,7,1,1,5,0,4,3,2,4,3,2,4,4,3,0,3,1,1,3,2,3,1,3,6,3,3,4,3,3,6,5,2,0,0,3,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,1,3,5,7,0,2,2,2,6,0,3,6,2,1,0,1,5,1,2,2,0,5,1,9,0,5,2,3,7,2,3,4,2,0,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,2,0,7,6,1,3,3,4,3,1,6,3,2,0,0,2,1,5,2,1,5,1,2,2,7,6,0,3,3,6,7,2,1,0,0,2,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,39,1,4,2,9,1,5,3,1,8,0,1,0,0,9,1,4,4,1,0,0,1,3,6,1,1,4,3,3,8,1,9,0,0,7,2,0,7,1,0,1,4,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,0,4,8,0,1,1,3,6,0,4,5,0,2,2,0,6,0,2,0,2,5,0,1,8,3,5,1,7,2,3,2,5,0,0,4,4,0,0,3,6,0,0,0,0,5,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,4,6,1,3,1,3,5,3,3,3,3,0,0,5,1,2,3,2,3,3,0,1,8,8,1,0,6,3,1,3,5,1,0,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,3,2,0,5,9,0,0,1,4,4,2,6,2,3,2,0,5,0,0,2,2,2,4,1,4,5,6,3,0,4,5,0,3,5,2,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,2,3,3,2,0,5,2,3,7,1,2,2,3,5,3,4,3,4,0,0,4,1,0,4,2,2,2,0,1,8,8,0,1,6,3,0,5,4,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,2,8,0,3,1,5,6,1,3,0,0,9,0,5,4,0,0,0,0,4,5,0,0,5,4,0,9,0,5,1,3,7,2,9,0,0,0,0,2,3,2,0,0,0,0,6,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,0,3,0,6,9,0,0,0,3,6,5,2,2,2,0,0,5,0,2,2,5,0,2,0,0,9,6,0,3,9,0,0,4,5,0,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,5,0,4,5,0,4,3,4,3,2,5,3,4,2,0,2,0,3,5,0,2,2,0,4,5,7,0,2,7,2,0,4,5,0,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,2,3,8,0,7,0,2,7,0,2,0,6,4,1,4,5,1,1,0,3,3,3,1,2,0,6,0,2,7,7,2,0,2,7,0,6,3,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,5,7,2,5,2,1,3,0,6,6,2,2,0,2,7,2,0,1,2,1,5,0,2,3,5,1,2,7,6,0,3,9,0,3,6,1,0,0,3,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,2,2,6,6,0,3,3,3,4,0,4,5,1,0,0,3,5,2,1,2,0,7,1,8,1,5,0,4,9,0,0,0,9,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,2,1,2,3,1,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,6,2,1,1,4,4,1,3,6,2,0,1,3,3,2,2,1,2,5,1,5,4,6,1,2,6,3,2,4,3,1,1,4,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,2,5,1,4,2,3,4,1,4,3,4,2,2,4,4,1,2,0,4,4,0,2,2,0,5,0,6,3,6,0,3,7,2,4,0,5,0,0,4,2,2,0,0,0,0,4,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,8,1,3,2,2,2,5,0,3,9,0,0,0,3,6,2,5,3,2,0,0,4,2,2,2,2,3,4,0,3,6,7,2,0,6,3,2,0,7,0,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,0,2,7,0,2,0,7,2,0,2,7,0,1,0,4,4,1,0,1,1,5,3,3,6,8,1,0,7,2,0,5,4,0,0,4,3,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,9,0,0,9,0,0,0,2,7,0,3,6,3,0,0,2,5,0,2,2,2,5,0,0,9,6,3,0,3,6,0,5,4,0,0,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,4,2,8,0,5,1,4,8,1,0,0,1,8,0,4,5,0,0,0,5,2,2,0,2,3,5,0,2,7,5,4,0,9,0,1,7,2,1,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,9,0,0,9,0,0,0,2,7,0,3,6,3,0,0,2,5,0,2,2,2,5,0,0,9,6,3,0,3,6,0,5,4,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,4,2,2,5,1,0,7,0,2,7,0,2,2,7,0,2,6,2,6,0,0,3,0,0,3,1,6,0,0,0,9,9,0,0,6,3,2,2,5,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,6,1,3,7,0,2,1,3,6,1,2,7,1,0,1,3,3,4,1,2,1,6,1,1,8,8,0,1,8,1,0,2,7,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,5,7,1,1,0,5,4,0,8,1,1,1,0,4,2,3,0,0,5,4,1,0,9,6,2,2,8,1,0,8,0,1,0,3,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,3,6,0,5,1,3,7,1,2,3,5,1,0,5,4,0,0,0,2,2,6,0,4,2,2,2,1,8,9,0,0,5,4,3,3,2,2,0,4,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,2,2,2,5,0,3,1,6,4,1,5,3,0,6,0,0,9,0,0,0,0,4,5,0,0,0,4,5,9,0,5,1,3,8,1,5,4,0,0,0,2,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,3,2,1,3,5,2,2,2,5,2,2,4,4,2,2,0,2,3,1,3,2,0,4,0,6,3,7,0,2,6,3,5,0,0,4,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,4,8,1,5,2,3,5,1,3,0,5,4,0,3,6,0,0,4,4,0,1,0,3,5,1,1,2,7,6,1,3,7,2,1,8,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,2,5,1,0,9,0,0,9,0,0,0,7,2,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,9,0,0,0,9,2,3,3,2,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,2,7,2,2,0,5,6,1,3,2,2,6,0,3,6,1,0,2,0,4,4,0,0,5,4,1,7,2,6,0,3,9,0,7,2,0,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,2,6,1,2,6,2,2,0,3,6,3,5,2,2,0,0,2,2,4,2,2,4,3,0,0,9,5,2,3,6,3,2,3,2,4,0,6,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,2,5,2,1,7,2,0,0,2,7,4,4,2,3,0,0,5,2,1,2,3,3,2,1,0,9,9,0,0,4,5,1,2,7,0,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,2,2,2,3,7,1,0,2,9,0,0,2,5,3,2,7,0,0,0,0,0,6,3,3,0,2,5,0,3,6,5,4,0,3,6,0,3,2,4,0,6,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,0,5,0,4,7,0,2,2,5,2,3,4,2,5,0,0,4,0,0,5,0,3,2,0,6,3,6,3,0,2,7,3,4,3,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,4,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0\ntest,30,1,1,6,7,0,6,0,3,2,0,7,7,2,1,1,2,7,2,2,2,1,2,2,1,1,4,2,3,7,2,3,0,6,6,3,7,0,2,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,1,4,2,8,0,3,1,5,6,1,3,0,0,9,0,5,4,0,0,0,0,4,5,0,0,5,4,0,9,0,5,1,3,7,2,9,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,3,9,0,6,1,3,7,1,2,1,1,8,0,2,7,1,0,0,1,5,3,1,1,1,7,1,6,3,6,1,2,9,0,1,7,2,0,0,3,5,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,4,2,3,7,0,2,0,2,7,0,2,7,0,0,2,4,0,3,0,0,5,4,0,0,9,7,0,2,9,0,3,6,0,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,3,3,7,1,5,2,3,5,1,3,0,5,4,0,1,8,0,0,2,3,0,5,0,1,1,4,5,8,1,6,1,3,7,2,3,6,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,5,1,3,7,0,2,1,2,6,1,2,7,1,0,1,2,5,2,1,1,2,6,1,1,8,8,0,1,8,1,0,4,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,2,0,9,0,0,3,4,3,3,4,3,1,0,0,1,4,4,1,1,5,2,2,2,7,7,0,2,7,2,1,3,4,3,0,7,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,2,3,0,5,7,2,0,0,4,5,1,6,2,3,0,0,4,1,3,2,2,3,4,0,8,1,9,0,0,6,3,2,2,4,1,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,2,8,0,5,0,4,3,2,4,4,3,3,1,4,5,2,0,0,3,4,1,1,3,0,3,3,2,7,5,2,2,8,1,2,5,2,0,0,3,5,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,4,0,5,9,0,0,0,3,6,1,2,7,1,0,0,3,4,2,1,1,1,6,1,5,4,6,1,2,7,2,2,4,3,1,0,4,4,2,0,0,5,0,5,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,1,0,0,1,0,3,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,21,2,2,4,5,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,2,0,0,7,0,6,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,0,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,2,8,0,5,0,4,5,1,4,4,2,4,1,3,5,0,0,1,5,2,2,1,3,0,4,2,6,3,4,2,4,8,1,5,3,2,0,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,2,3,2,3,0,6,0,3,7,2,0,3,3,4,3,3,4,1,0,0,5,4,1,0,5,1,4,0,0,9,9,0,0,5,4,3,4,3,0,0,3,7,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,2,4,10,0,6,0,3,6,0,3,3,4,3,0,2,7,1,0,8,1,0,1,2,1,6,2,0,0,9,3,3,3,2,7,3,6,1,0,0,3,3,0,0,3,6,0,0,0,0,5,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,3,5,1,3,0,6,6,3,1,2,0,7,0,3,7,0,0,0,3,4,3,0,1,2,6,0,7,2,7,2,1,5,4,6,3,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,5,7,0,4,0,5,9,0,0,0,8,1,0,0,9,0,0,0,0,5,4,0,0,0,8,1,9,0,5,0,4,9,0,4,4,1,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,3,5,2,0,7,0,2,0,3,6,7,1,2,7,0,0,0,1,2,7,0,1,1,2,2,7,8,1,0,4,5,1,1,5,4,0,6,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,4,2,9,0,5,1,4,7,1,2,1,1,8,0,1,8,1,0,0,2,4,4,1,1,1,5,4,8,1,6,1,2,9,0,2,6,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,6,2,2,7,1,2,0,3,6,0,0,9,2,0,0,3,3,2,0,0,0,9,0,3,6,7,1,2,7,2,0,4,0,5,0,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,5,2,2,1,5,1,3,6,1,3,0,1,8,0,1,8,8,0,1,1,0,1,0,1,8,1,0,1,8,6,1,2,6,3,0,0,9,0,0,5,8,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,4,3,10,1,4,1,4,6,0,3,0,3,6,0,6,3,2,0,5,0,0,3,4,2,3,1,1,0,9,6,1,2,6,3,0,1,8,0,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,1,5,2,2,6,1,3,2,4,4,1,5,3,3,0,1,3,1,3,2,2,2,3,2,6,3,5,2,3,5,4,4,3,2,1,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1\ntest,31,1,2,4,7,0,4,0,5,5,2,2,2,3,4,0,1,8,2,0,0,1,4,4,1,1,2,3,4,8,1,4,0,5,8,1,5,4,1,1,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,2,3,0,4,0,5,5,0,4,2,5,3,4,3,2,2,0,0,4,2,3,2,4,2,3,0,0,9,6,1,2,6,3,0,4,4,2,0,5,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,3,1,2,3,1,0,4,0,5,5,2,3,2,4,4,3,3,4,6,0,0,2,0,2,3,2,2,2,2,4,5,5,2,3,6,3,3,3,3,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,7,1,2,1,4,4,1,2,6,1,0,1,3,2,4,1,1,2,6,1,5,4,5,2,3,6,3,2,5,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,2,7,0,0,9,0,0,1,6,3,0,6,3,3,0,0,5,0,2,2,1,5,2,1,1,8,7,2,0,7,2,1,6,1,0,2,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,3,3,2,1,2,5,6,2,1,1,4,4,0,4,5,1,3,1,4,1,2,2,2,3,3,1,1,8,6,1,3,7,2,1,3,6,0,0,4,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,0,5,0,4,5,2,3,2,2,5,0,8,1,0,0,0,7,1,2,0,3,6,1,0,0,9,7,2,0,7,2,0,5,4,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1\ntest,22,2,3,3,5,0,5,0,4,7,0,2,0,2,7,2,1,7,0,2,0,1,1,5,2,0,0,7,0,4,5,6,1,2,7,2,0,6,3,0,0,4,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,37,1,3,3,8,0,5,0,4,7,2,0,0,3,6,3,5,2,1,0,0,5,2,2,1,3,3,2,1,8,1,9,0,0,5,4,2,3,3,2,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,2,8,0,4,2,4,3,2,4,4,4,2,0,5,4,0,0,0,7,0,2,0,5,0,4,0,7,2,7,0,2,9,0,4,5,0,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,9,0,0,9,0,0,0,5,4,0,5,4,0,0,0,1,8,1,0,0,1,8,0,1,8,5,0,4,9,0,1,4,0,5,0,6,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,0,3,3,4,5,0,4,4,1,5,0,0,9,0,0,0,4,0,5,0,0,0,5,4,9,0,6,0,3,9,0,1,6,3,0,0,4,4,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,2,5,0,6,1,2,1,2,6,6,2,1,4,4,2,1,0,0,6,1,3,1,3,4,2,0,9,0,5,1,3,5,4,4,2,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,6,2,0,0,6,0,0,0,0,0,0,0,0,0,3,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,0,6,0,3,4,0,5,1,2,6,1,4,5,0,0,2,2,4,2,0,2,3,5,1,4,5,5,2,3,7,2,5,3,2,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,0,4,2,3,5,1,3,2,4,3,0,3,6,1,0,1,2,2,5,1,1,2,5,3,8,1,6,1,3,6,3,4,4,1,0,0,3,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,3,8,0,5,0,4,6,0,3,2,3,4,2,4,4,2,0,2,3,2,3,2,1,5,2,1,0,9,6,3,0,4,5,0,4,4,2,1,6,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,3,3,8,0,7,0,2,8,0,1,0,3,6,0,3,6,1,2,1,3,3,2,2,0,1,7,0,4,5,7,2,0,7,2,4,3,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,9,1,3,3,3,1,5,2,1,6,1,3,2,3,5,1,3,5,1,0,0,3,3,3,1,2,2,5,1,8,1,5,1,3,6,3,5,2,2,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0\ntest,8,1,4,2,2,1,3,2,4,8,1,0,0,2,7,3,5,2,4,1,0,4,2,0,3,4,2,1,0,0,9,7,1,2,5,4,1,4,4,1,0,4,7,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,3,1,1,4,1,4,3,1,3,5,2,3,9,0,0,0,0,9,2,0,1,3,3,2,0,0,4,5,0,0,9,7,2,0,4,5,3,0,3,3,0,5,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,4,0,5,7,0,2,2,1,6,0,2,7,0,0,0,3,6,0,0,1,1,6,2,4,5,5,2,3,8,1,3,4,2,0,0,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,9,0,0,9,0,0,0,2,7,0,3,6,3,0,0,2,5,0,2,2,2,5,0,0,9,6,3,0,3,6,0,5,4,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,0,6,1,3,5,0,4,3,3,4,1,3,5,1,0,1,6,1,3,1,2,2,3,3,2,7,7,0,2,7,2,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,2,5,1,2,8,0,1,2,5,3,1,5,4,2,0,0,3,3,3,1,1,5,4,0,8,1,8,1,1,4,5,2,5,2,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,4,3,3,0,6,1,3,9,0,0,0,1,8,0,4,5,0,0,0,3,4,3,0,0,5,4,0,4,5,7,2,0,9,0,0,9,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,3,1,0,6,0,3,4,0,5,2,3,4,4,4,2,0,4,0,5,0,0,3,2,4,2,0,3,6,5,2,2,5,4,2,2,3,3,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,2,8,0,3,1,5,8,0,1,1,1,7,2,3,5,2,0,0,2,3,3,2,0,2,4,3,9,0,7,0,2,9,0,4,5,0,0,0,3,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,7,0,2,7,0,2,2,0,7,1,4,5,0,0,0,4,3,3,0,2,2,3,3,7,2,4,1,4,8,1,2,4,4,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,5,3,10,0,0,5,5,9,0,0,0,0,9,0,9,0,5,0,5,0,0,0,9,0,0,0,0,0,9,7,2,0,7,2,4,2,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,2,5,0,2,8,0,1,1,3,6,2,2,6,0,2,4,2,2,1,1,3,5,2,0,1,8,4,5,0,2,7,5,1,0,4,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,2,3,3,5,0,9,0,0,5,0,4,3,0,6,0,5,4,3,0,0,2,0,4,3,0,3,3,0,3,6,5,2,3,7,2,4,2,3,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,2,3,1,4,7,1,2,1,4,5,1,6,3,4,1,0,4,1,1,3,2,3,1,2,6,3,7,1,2,5,4,5,3,1,0,0,3,4,2,0,0,0,0,4,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,0,4,2,4,7,1,2,2,5,3,0,2,7,2,0,1,2,4,2,1,1,1,7,1,6,3,6,1,2,7,2,6,3,1,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,0,4,0,5,9,0,0,0,2,7,3,3,4,3,0,0,3,3,2,3,3,0,4,0,9,0,9,0,0,6,3,0,6,2,2,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,7,1,1,1,6,1,3,3,4,4,2,4,4,2,0,0,3,2,3,2,2,2,4,1,6,3,5,1,3,6,3,3,3,3,1,1,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0\ntest,16,3,2,3,4,1,4,0,4,6,1,2,4,3,2,1,6,2,2,0,0,4,0,3,1,2,2,5,1,4,5,6,0,3,6,3,4,3,2,1,0,3,1,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,2,3,3,0,2,0,7,5,2,3,3,3,5,2,4,4,2,0,0,2,2,4,2,2,1,4,2,9,0,6,0,3,7,2,3,4,3,0,0,4,6,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,1,3,8,1,6,1,2,1,0,8,8,1,1,0,4,5,1,0,2,2,4,1,0,4,1,5,1,2,7,3,0,6,9,0,8,1,1,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,6,2,1,0,3,6,0,6,3,3,0,5,2,0,0,2,4,4,0,0,0,9,6,1,2,6,3,0,7,0,2,0,5,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,2,3,2,3,4,1,1,4,7,1,1,1,1,7,2,5,2,5,0,0,3,1,1,4,2,3,2,1,2,7,8,1,0,5,4,1,2,6,2,0,5,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,5,3,4,3,1,3,5,2,3,0,7,2,0,6,3,0,0,5,0,5,0,0,0,7,2,0,5,4,7,2,0,4,5,3,3,3,0,0,3,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,2,7,1,0,7,0,2,0,2,7,4,2,4,4,0,0,0,2,4,4,0,2,2,3,1,8,7,2,0,5,4,2,3,2,3,0,5,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,3,2,5,2,2,2,5,5,1,4,2,2,6,0,5,4,1,0,0,4,3,2,1,1,2,6,0,2,7,5,1,3,8,1,2,6,1,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,2,9,0,5,0,4,5,2,3,4,4,2,0,5,4,0,0,0,5,4,1,0,4,2,4,0,0,9,7,2,0,7,2,0,8,1,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,3,3,0,4,2,4,7,1,2,0,0,9,0,9,0,1,0,0,3,3,3,0,0,0,9,0,5,4,7,1,1,8,1,1,6,2,1,0,4,8,2,0,0,6,0,0,0,0,0,0,0,4,0,2,0,4,2,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,2,0,1,0,1,1,0,0,1,0,0\ntest,9,1,4,3,3,0,3,0,6,7,0,2,1,1,7,1,4,4,1,0,0,5,0,4,1,3,2,4,0,8,1,5,1,3,8,1,1,5,4,0,0,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,2,10,1,5,1,3,6,1,3,0,3,6,0,2,7,4,0,2,2,0,3,0,2,6,2,0,2,7,6,1,2,6,3,0,0,9,0,0,5,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,3,5,0,9,0,0,9,0,0,0,4,5,1,0,8,0,0,1,2,3,3,1,0,1,6,2,6,3,5,0,4,7,2,3,5,1,1,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,2,2,5,6,1,5,3,1,7,0,2,2,6,1,2,4,4,2,1,2,2,0,3,1,1,5,2,0,8,1,8,0,1,7,2,2,7,1,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,2,4,2,2,2,2,5,7,1,2,2,4,4,5,3,2,8,1,0,0,0,0,7,0,2,0,0,0,9,3,5,2,2,7,0,2,5,2,1,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,33,2,3,3,8,0,9,0,0,9,0,0,0,2,7,0,3,6,3,0,0,2,5,0,2,2,2,5,0,0,9,6,3,0,3,6,0,5,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,3,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,2,5,0,3,9,0,0,0,5,4,5,5,0,5,0,0,3,2,0,5,3,0,2,0,2,7,7,2,0,2,7,3,0,3,3,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,2,2,4,2,2,5,2,2,5,2,0,4,5,0,0,0,4,0,5,0,2,3,5,0,9,0,5,2,2,9,0,0,6,3,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,7,0,2,6,0,3,3,2,4,3,3,4,3,0,2,0,3,2,3,0,2,5,0,2,7,3,0,6,7,2,3,4,2,2,0,4,4,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,37,1,1,4,8,1,4,1,4,7,1,2,9,0,0,0,0,9,0,0,0,9,0,0,0,9,0,0,0,9,0,6,2,1,5,4,5,5,0,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,2,3,0,4,1,5,6,1,2,2,2,6,1,3,6,0,0,0,3,3,4,0,2,1,5,2,8,1,6,2,2,9,0,6,2,1,0,0,2,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,4,3,8,0,4,1,5,6,2,2,0,4,5,0,4,5,4,0,0,3,3,0,0,4,0,5,0,5,4,6,2,2,8,1,0,0,4,5,0,7,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,2,5,0,6,1,2,1,2,6,5,3,1,2,6,2,1,0,0,4,3,2,1,3,2,4,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,1,2,2,5,4,1,5,4,4,2,0,6,3,2,0,0,5,1,2,1,3,5,2,0,2,7,4,1,5,7,2,5,3,2,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,4,1,2,5,0,2,7,0,2,2,3,5,5,3,2,7,0,0,2,0,0,6,2,2,0,0,0,9,3,5,2,4,5,2,0,3,1,3,8,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,2,5,1,3,2,5,6,1,2,1,2,7,1,2,7,0,0,0,2,4,3,0,1,1,5,3,8,1,3,2,5,8,1,3,5,1,1,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,2,3,3,2,1,4,0,4,7,0,2,2,2,6,3,4,3,4,0,2,3,0,1,4,2,3,1,0,0,9,7,2,0,4,5,2,3,3,2,1,6,8,2,0,0,5,0,4,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,26,3,2,4,6,0,3,0,6,5,0,4,4,4,2,0,0,9,0,0,0,0,7,2,0,0,0,5,4,7,2,5,0,4,9,0,6,2,2,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,1,5,0,4,7,0,2,2,2,6,3,5,1,6,0,0,2,0,1,4,1,4,0,0,0,9,7,2,0,2,7,2,2,6,0,0,5,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,1,2,5,1,2,1,6,3,0,6,7,1,2,5,1,4,3,0,0,1,5,1,2,1,3,4,1,8,1,5,0,4,6,3,8,0,1,0,0,2,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,3,6,2,1,1,1,4,2,1,2,4,3,1,2,7,4,4,2,7,2,3,3,3,0,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,2,2,4,7,0,6,1,3,6,1,3,4,3,3,0,1,8,0,0,1,2,4,3,0,1,1,5,3,7,2,5,1,3,8,1,5,2,3,0,0,3,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,0,4,2,4,0,5,4,2,5,3,0,4,5,0,0,0,2,4,4,0,0,4,4,2,9,0,7,0,2,9,0,4,2,3,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,1,5,1,3,5,1,3,2,3,4,2,3,5,2,1,1,3,1,3,2,2,2,3,2,4,5,6,2,2,6,3,3,4,2,1,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,1,5,0,4,8,0,1,1,3,5,3,5,3,3,0,0,3,1,2,3,2,3,3,0,5,4,9,0,0,0,9,2,3,4,1,1,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,4,0,5,7,0,2,2,1,6,0,2,7,0,0,0,3,6,0,0,1,1,6,2,4,5,5,2,3,8,1,3,4,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,2,5,0,3,9,0,0,0,6,3,0,5,4,2,0,0,7,0,0,2,3,2,4,0,3,6,7,0,2,6,3,2,4,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,0,2,4,4,9,0,0,0,3,6,0,4,5,1,0,0,3,3,2,2,2,2,5,1,9,0,5,3,2,6,3,0,4,5,1,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,0,5,2,3,5,2,3,3,1,5,1,6,2,1,0,0,5,3,1,1,4,1,4,1,8,1,8,0,1,9,0,4,4,2,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,0,6,1,3,6,1,3,3,2,5,0,2,7,0,0,1,2,4,3,0,1,2,5,2,6,3,5,1,3,8,1,5,2,3,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,6,2,2,6,0,3,3,2,5,0,3,6,0,0,9,0,0,0,3,0,2,0,4,0,9,5,2,3,7,2,5,4,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,3,4,2,2,8,1,0,0,5,4,4,3,2,5,0,0,2,1,2,4,2,2,1,2,0,9,9,0,0,4,5,2,2,5,0,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,0,6,0,3,9,0,0,0,3,6,0,0,9,0,0,3,0,6,0,0,0,3,6,0,9,0,7,2,0,9,0,3,5,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,0,4,0,5,8,0,1,1,1,8,1,4,5,1,0,0,2,0,7,1,2,2,5,1,8,1,5,4,1,8,1,5,4,0,0,0,2,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,0,5,1,3,6,1,3,2,4,4,2,2,6,2,0,0,2,3,3,2,1,2,5,1,5,4,6,1,2,6,3,3,4,2,2,0,4,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0\ntest,11,1,3,2,3,2,4,2,3,6,2,2,1,4,5,1,6,3,2,0,1,4,2,2,1,5,1,3,0,6,3,7,1,2,5,4,1,2,5,2,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,2,2,5,7,0,2,0,2,7,0,4,5,0,0,0,0,9,0,0,0,0,9,0,9,0,5,0,4,9,0,4,5,0,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,4,0,5,7,0,2,2,1,6,0,2,7,0,0,0,3,6,0,0,1,1,6,2,4,5,5,2,3,8,1,3,4,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,21,1,3,2,5,0,0,2,7,2,4,3,2,1,7,4,4,2,0,0,0,0,0,9,0,3,5,1,1,5,4,5,0,4,5,4,4,3,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,3,8,0,4,1,4,6,1,3,1,6,2,2,2,5,1,0,2,3,3,2,2,1,3,3,1,3,6,4,0,5,7,2,2,3,1,3,2,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,3,8,1,5,2,3,5,1,3,0,2,7,0,2,7,0,0,0,5,0,4,0,0,4,3,3,9,0,6,1,3,7,2,3,3,4,0,0,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,8,1,3,2,2,2,4,1,4,6,1,3,1,4,5,2,5,2,2,2,0,3,1,3,3,2,2,3,1,6,3,7,1,2,6,3,3,4,3,0,0,3,7,2,0,0,6,0,6,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,0,2,7,0,2,0,7,2,0,2,7,0,1,0,4,4,1,0,1,1,5,3,3,6,8,1,0,7,2,0,5,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,1,4,2,3,4,3,3,3,2,5,0,3,6,1,0,0,2,3,3,1,1,2,6,1,9,0,5,0,4,8,1,6,3,0,0,0,2,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,0,6,0,3,7,0,2,0,3,6,0,3,6,1,1,1,2,3,3,1,0,2,6,0,2,7,7,2,0,7,2,4,3,3,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,4,2,3,0,7,1,1,7,0,2,0,0,9,0,5,5,2,0,2,2,3,2,0,0,4,5,0,0,9,5,2,2,4,5,0,5,0,5,0,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,0,6,1,3,7,0,2,0,3,6,0,4,5,0,0,3,2,3,2,2,0,5,0,3,0,9,7,1,2,7,2,3,0,6,0,0,4,4,0,0,4,6,0,0,0,0,3,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,2,1,0,6,8,0,1,1,1,7,1,5,4,2,1,0,4,3,0,2,2,2,4,0,5,4,6,2,1,2,7,2,3,3,2,1,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,2,3,2,2,2,5,0,3,9,0,0,3,3,4,7,2,0,9,0,0,0,0,0,0,6,3,0,0,0,9,7,2,0,2,7,3,6,0,0,0,3,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,24,1,2,3,5,1,5,1,3,4,2,4,4,4,2,2,4,4,2,0,0,3,3,3,1,1,2,5,1,7,2,6,0,3,7,2,3,3,2,2,0,4,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,5,3,1,1,5,2,3,5,1,4,0,4,5,0,9,0,4,2,0,2,1,1,0,4,0,5,0,0,9,5,1,3,5,4,3,2,3,2,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,1,4,1,4,5,1,4,5,1,3,0,4,5,2,1,1,4,1,3,2,2,1,4,2,1,8,5,1,4,7,2,2,6,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,20,1,2,2,5,1,5,1,4,4,3,2,2,6,1,1,5,4,1,0,0,5,1,4,1,2,4,4,0,9,0,7,2,1,8,1,4,3,2,2,0,4,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,0,6,0,3,7,2,0,1,2,6,2,4,4,1,0,0,5,2,1,0,3,2,4,0,0,9,9,0,0,5,4,2,4,4,0,0,4,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,4,3,9,0,7,0,2,4,1,4,4,2,4,0,2,7,1,0,1,3,3,3,1,1,2,5,2,5,4,5,2,3,8,1,6,1,3,1,0,3,5,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,2,3,2,3,2,3,7,0,2,2,2,5,2,5,2,1,0,0,3,2,4,1,2,3,4,0,4,5,4,1,4,6,3,3,3,3,1,0,4,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,1,3,7,1,2,2,3,5,2,4,4,3,1,2,2,2,1,3,2,3,3,1,3,6,6,2,1,5,4,3,2,3,2,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,2,3,3,1,0,6,1,2,6,0,3,1,4,4,3,2,5,1,0,1,4,2,4,1,3,2,4,1,3,6,7,0,2,7,2,0,4,5,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,1,4,1,5,8,1,1,1,2,7,2,2,6,3,1,1,2,4,1,3,1,1,5,1,4,5,5,4,1,7,2,4,2,4,0,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,3,7,1,5,1,2,6,2,2,1,7,2,1,4,5,1,0,0,4,3,3,1,3,1,3,3,1,8,8,0,1,6,3,1,5,4,0,0,4,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,3,3,7,1,6,1,2,6,1,2,2,3,4,1,3,5,2,1,1,4,2,2,1,2,3,4,1,2,7,5,2,3,5,4,2,4,3,1,1,4,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,3,8,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,35,1,2,3,8,0,4,4,2,7,2,1,1,7,2,0,3,6,1,0,2,4,1,4,1,1,4,4,2,7,2,8,1,1,7,2,5,3,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,3,5,1,4,1,4,2,2,5,5,3,1,1,6,3,1,0,0,4,1,4,1,2,5,2,1,8,1,4,0,5,8,1,7,2,1,0,1,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,0,7,0,2,7,0,2,0,4,5,0,0,9,0,0,0,2,7,0,0,0,0,9,0,9,0,7,0,2,9,0,3,4,2,2,0,4,1,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,4,2,3,2,4,1,3,7,1,1,1,2,6,3,4,2,3,0,0,4,2,1,3,3,3,2,0,2,7,9,0,0,5,4,2,3,5,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,38,1,3,3,9,2,3,2,4,5,2,3,3,2,4,2,2,5,3,0,0,4,2,2,0,3,3,3,1,2,7,5,2,3,6,3,2,4,2,2,2,6,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,4,2,2,5,1,0,7,0,2,7,0,2,2,7,0,2,6,2,6,0,0,3,0,0,3,1,6,0,0,0,9,9,0,0,6,3,2,2,5,0,0,4,6,2,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0\ntest,32,2,2,4,7,0,3,3,4,5,1,4,2,5,2,1,4,5,2,1,0,1,5,2,1,1,4,4,1,3,6,7,1,2,7,2,5,3,1,1,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,1,4,1,4,7,1,2,0,5,4,0,8,1,4,0,0,2,2,1,0,4,0,5,0,5,4,6,2,1,5,4,0,9,0,0,0,3,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,7,0,2,7,0,2,2,0,7,1,4,5,0,0,0,4,3,3,0,2,2,3,3,7,2,4,1,4,8,1,2,4,4,0,0,3,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,0,6,1,3,7,0,2,0,3,6,0,4,5,0,0,3,2,3,2,2,0,5,0,3,0,9,7,1,2,7,2,3,0,6,0,0,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,33,1,3,3,8,0,9,0,0,5,0,4,0,0,9,0,5,4,0,0,2,0,3,5,0,0,0,9,0,9,0,3,2,4,7,2,6,2,2,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,2,2,4,7,0,3,3,4,5,1,4,2,5,2,1,4,5,2,1,0,1,5,2,1,1,4,4,1,3,6,7,1,2,7,2,5,3,1,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,20,1,2,3,5,2,2,2,4,3,2,5,3,3,3,4,3,2,5,1,1,2,1,1,4,2,1,3,0,8,1,3,0,6,5,4,3,2,4,2,0,4,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,0,7,1,2,6,0,3,3,4,3,0,1,8,0,0,0,3,0,6,0,1,1,8,1,9,0,8,0,1,9,0,7,1,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,3,1,0,7,0,2,7,2,0,0,4,5,5,4,0,5,0,2,2,0,1,7,2,0,0,0,0,9,5,4,0,0,9,0,2,6,2,0,6,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,1,5,1,3,7,2,1,1,3,5,4,4,2,3,1,1,4,2,1,3,3,2,2,1,1,8,6,3,1,5,4,2,4,4,1,0,4,6,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,25,1,2,5,6,1,5,2,2,5,1,3,4,5,1,1,5,4,3,1,0,4,1,2,3,2,2,2,3,6,3,7,1,1,5,4,4,4,2,1,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,0,3,0,6,3,2,4,3,2,5,3,4,4,0,2,0,4,2,3,3,2,0,5,0,7,2,5,2,2,5,4,2,4,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,1,5,6,4,2,4,0,4,0,5,7,2,1,2,2,6,3,0,0,3,2,3,1,1,4,2,3,8,1,2,0,7,5,4,4,1,4,2,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,2,10,0,6,0,3,6,3,0,0,5,4,1,7,2,3,0,3,3,0,1,4,2,2,2,0,0,9,4,5,0,3,6,0,4,4,2,0,5,3,2,0,0,6,0,0,0,0,3,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,8,0,1,0,3,6,0,3,6,1,2,1,3,3,2,2,0,1,7,0,4,5,7,2,0,7,2,4,3,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,2,3,3,1,0,6,1,2,6,0,3,1,4,4,3,2,5,1,0,1,4,2,4,1,3,2,4,1,3,6,7,0,2,7,2,0,4,5,0,0,4,6,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,4,1,2,3,1,0,5,2,2,4,2,4,4,4,3,1,3,5,2,1,0,3,2,3,2,2,1,3,2,6,3,6,1,3,8,1,4,3,3,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,0,5,0,4,5,2,3,2,2,5,0,8,1,0,0,0,7,1,2,0,3,6,1,0,0,9,7,2,0,7,2,0,5,4,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,1,2,1,0,5,0,4,0,3,6,6,3,0,3,3,3,0,0,0,6,0,3,0,4,2,2,2,7,2,5,0,4,7,2,5,4,0,0,0,2,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,3,8,0,6,1,2,7,1,1,2,4,4,2,3,4,2,0,1,5,2,0,2,2,3,3,0,3,6,4,2,4,6,3,2,3,4,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,1,2,0,0,0,0,0,0,0,0,0,0,4,0,2,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,5,1,4,7,1,2,1,3,5,0,3,6,1,0,0,3,4,2,1,3,1,5,1,8,1,6,1,2,9,0,1,5,3,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,5,1,2,3,1,0,5,1,4,5,1,4,3,2,5,2,3,4,3,1,0,1,4,2,3,1,2,4,1,3,6,6,1,3,6,3,3,5,1,2,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,1,5,1,4,7,1,2,1,4,5,4,4,2,4,0,1,3,1,2,4,2,2,3,0,4,5,6,2,1,4,5,1,2,6,1,0,5,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,3,3,2,1,5,1,3,3,3,4,1,5,4,1,1,1,4,3,1,2,1,3,4,1,6,3,5,2,3,7,2,4,4,3,0,0,3,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,0,4,1,4,6,1,3,1,5,4,1,5,3,1,0,1,4,2,3,1,3,4,2,1,2,7,4,0,5,7,2,2,3,3,1,1,5,4,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1\ntest,37,1,2,4,8,1,4,2,3,5,2,3,2,5,3,2,3,5,2,1,2,2,3,2,1,1,4,3,1,1,8,6,2,2,6,3,2,6,2,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,4,2,3,0,2,3,5,6,0,3,2,0,7,0,6,3,0,0,0,7,0,2,0,3,4,3,0,4,5,9,0,0,7,2,0,7,0,2,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,1,5,0,4,7,0,2,2,2,6,3,5,1,6,0,0,3,0,1,4,1,5,0,0,0,9,7,2,0,2,7,2,1,6,0,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,4,1,2,4,1,1,5,2,3,5,1,4,4,4,1,4,2,4,4,2,0,2,2,1,2,1,3,1,4,1,8,5,1,3,5,4,1,3,3,3,1,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,0,5,0,4,4,1,4,3,4,2,3,2,5,2,0,0,3,4,2,2,2,1,4,2,8,1,7,2,0,6,3,3,4,3,0,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,0,9,0,0,7,0,2,2,3,5,4,3,3,4,0,0,3,2,2,2,2,3,3,0,0,9,9,0,0,3,6,2,0,4,4,0,7,7,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,3,2,7,0,2,3,2,6,0,5,4,1,0,0,4,5,0,1,2,2,5,0,2,7,7,0,2,8,1,2,2,4,2,0,5,3,2,3,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,4,8,0,6,0,3,5,1,4,3,3,4,0,1,8,1,0,0,2,3,5,1,1,1,4,4,7,2,4,0,5,8,1,5,3,1,1,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,1,3,8,1,1,1,2,7,0,2,7,1,0,4,1,1,5,1,2,3,4,1,1,8,7,1,1,8,1,2,3,2,4,0,5,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,6,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,35,1,2,3,8,0,4,4,2,7,2,1,1,7,2,0,3,6,1,0,2,4,1,4,1,1,4,4,2,7,2,8,1,1,7,2,5,3,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,3,8,1,5,2,3,5,1,3,0,2,7,0,2,7,0,0,0,5,0,4,0,0,4,3,3,9,0,6,1,3,7,2,3,3,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,3,0,0,0,7,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,2,5,10,0,6,0,3,3,2,5,0,8,1,4,1,4,4,1,2,2,2,0,4,1,5,0,0,0,9,7,1,2,6,3,0,4,5,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,1,3,8,1,1,1,2,7,0,2,7,1,0,4,1,1,5,1,2,3,4,1,1,8,7,1,1,8,1,2,3,2,4,0,5,4,0,0,4,6,0,0,0,0,3,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,26,3,2,4,6,0,3,0,6,5,0,4,4,4,2,0,0,9,0,0,0,0,7,2,0,0,0,5,4,7,2,5,0,4,9,0,6,2,2,1,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,30,1,3,3,7,1,5,2,3,5,1,3,0,5,4,0,1,8,0,0,2,3,0,5,0,1,1,4,5,8,1,6,1,3,7,2,3,6,0,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,20,1,2,3,5,1,4,2,3,6,2,2,2,4,3,1,2,6,1,0,0,3,2,4,1,1,1,6,2,9,0,7,2,2,8,1,3,3,3,1,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,1,7,1,2,8,1,1,1,3,6,1,4,5,2,1,2,2,3,1,2,1,3,4,0,1,8,5,4,1,5,4,2,3,4,1,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,2,6,1,2,6,2,2,0,3,6,0,6,3,0,0,3,2,0,4,0,2,2,5,0,4,5,5,2,3,6,3,5,4,0,0,0,3,5,2,0,0,0,0,3,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,8,0,1,0,3,6,0,3,6,1,2,1,3,3,2,2,0,1,7,0,4,5,7,2,0,7,2,4,3,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,7,1,2,2,3,4,2,3,4,3,1,1,2,2,3,3,1,3,3,1,2,7,7,2,1,5,4,2,4,3,1,1,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,2,4,10,0,5,2,2,7,1,1,0,6,3,2,0,7,2,2,3,2,1,2,1,1,4,3,1,1,8,8,0,1,5,4,2,4,3,2,0,4,4,0,0,3,5,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,2,2,9,0,0,0,6,3,7,0,2,3,5,0,0,0,2,6,0,2,2,0,0,9,9,0,0,2,7,0,2,5,2,0,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,3,9,0,6,1,3,7,1,2,1,1,8,0,2,7,1,0,0,1,5,3,1,1,1,7,1,6,3,6,1,2,9,0,1,7,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,3,8,1,5,2,3,5,1,3,0,2,7,0,2,7,0,0,0,5,0,4,0,0,4,3,3,9,0,6,1,3,7,2,3,3,4,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,5,7,2,5,2,1,3,0,6,6,2,2,0,2,7,2,0,1,2,1,5,0,2,3,5,1,2,7,6,0,3,9,0,3,6,1,0,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,5,7,1,1,0,5,4,0,8,1,1,1,0,4,2,3,0,0,5,4,1,0,9,6,2,2,8,1,0,8,0,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0\ntest,40,1,5,3,10,0,0,5,5,9,0,0,0,0,9,0,9,0,5,0,5,0,0,0,9,0,0,0,0,0,9,7,2,0,7,2,4,2,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,27,1,1,5,6,2,5,2,0,0,0,9,7,2,0,2,1,7,2,0,1,3,3,2,2,0,1,4,4,8,1,0,0,9,9,0,4,5,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,2,3,3,0,2,0,7,5,2,3,3,3,5,2,4,4,2,0,0,2,2,4,2,2,1,4,2,9,0,6,0,3,7,2,3,4,3,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,5,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,8,2,4,3,2,0,5,0,5,9,0,0,0,0,9,0,5,5,0,0,0,9,0,0,0,4,0,5,0,0,9,6,3,0,2,7,0,9,0,0,0,3,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,0,3,3,4,5,0,4,4,3,3,0,0,9,0,0,0,3,0,6,0,0,0,5,4,9,0,6,0,3,9,0,2,5,3,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,0,7,1,2,8,0,1,1,4,5,0,5,4,2,0,0,5,2,2,2,2,2,4,1,0,9,6,2,2,5,4,0,3,6,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,2,3,1,4,0,5,5,0,4,4,2,4,2,4,4,3,1,0,3,4,0,3,2,1,4,0,8,1,4,1,5,8,1,6,3,1,1,1,2,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,10,1,3,3,3,0,5,0,4,8,1,0,1,3,5,1,5,3,3,0,0,2,1,3,0,3,4,3,0,0,9,9,0,0,5,4,2,4,4,0,0,4,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,5,0,4,7,0,2,2,4,3,0,7,2,0,0,0,7,0,2,0,3,5,2,0,9,0,9,0,0,9,0,4,5,0,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,4,0,0,5,5,4,0,0,6,3,6,2,2,6,0,0,2,2,1,6,2,0,2,0,0,9,7,2,0,2,7,0,3,3,4,1,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,3,1,4,1,0,5,4,2,4,0,5,5,5,5,0,4,1,0,2,1,3,3,2,3,0,3,0,9,4,4,1,2,7,0,5,5,0,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,28,1,1,6,6,4,3,1,3,5,2,3,9,0,0,0,3,6,0,0,0,0,0,9,0,0,3,2,4,9,0,7,2,0,4,5,9,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,9,0,0,9,0,0,0,2,7,0,3,6,3,0,0,2,5,0,2,2,2,5,0,0,9,6,3,0,3,6,0,5,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,4,6,2,7,1,1,5,0,4,3,2,4,3,2,4,4,3,0,3,1,1,3,2,3,1,3,6,3,3,4,3,3,6,5,2,0,0,3,5,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,2,7,1,0,7,0,2,0,2,7,4,2,4,4,0,0,0,2,4,4,0,2,2,3,1,8,7,2,0,5,4,2,3,2,3,0,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,4,3,8,0,7,0,2,9,0,0,0,4,5,2,5,3,0,2,0,6,0,2,2,3,3,3,0,4,5,9,0,0,3,6,0,3,3,3,0,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,2,3,1,2,4,4,8,1,1,1,6,3,2,7,1,2,0,0,7,1,1,2,5,2,1,0,6,3,8,1,1,7,2,1,5,3,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,2,3,2,5,1,3,2,4,6,1,2,2,2,6,0,3,6,2,0,0,0,6,2,0,1,4,4,1,4,5,5,1,3,7,2,3,5,2,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,5,1,3,3,1,1,4,2,3,7,0,2,2,4,4,1,4,5,1,0,0,6,1,3,0,3,2,5,0,6,3,5,2,3,7,2,3,3,3,1,0,4,3,0,0,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,1,2,1,0,5,0,4,0,3,6,6,3,0,3,3,3,0,0,0,6,0,3,0,4,2,2,2,7,2,5,0,4,7,2,5,4,0,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,6,2,0,0,5,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,2,2,2,9,0,6,1,3,5,1,3,2,2,5,0,2,7,0,0,1,2,3,5,0,1,2,6,2,6,3,5,2,3,8,1,8,1,1,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,4,4,0,2,7,1,1,1,2,6,3,4,2,4,1,1,3,2,1,5,2,1,2,0,1,8,7,2,0,3,6,1,2,4,3,1,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,3,6,2,1,1,1,4,2,1,2,4,3,1,2,7,4,4,2,7,2,3,3,3,0,1,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,0,3,0,6,3,2,4,3,2,5,3,4,4,0,2,0,4,2,3,3,2,0,5,0,7,2,5,2,2,5,4,2,4,4,0,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,2,3,4,2,3,4,3,1,1,2,2,3,3,1,3,3,1,2,7,7,2,1,5,4,2,4,3,1,1,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,1,7,0,2,7,0,2,1,3,6,3,5,1,6,0,0,2,0,1,6,1,3,1,0,4,5,7,2,1,5,4,1,1,8,0,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,1,5,1,4,6,1,3,2,1,6,3,3,4,2,0,0,5,1,2,2,2,3,4,0,1,8,7,2,0,7,2,1,4,4,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,27,2,2,4,6,0,4,2,3,5,0,4,4,2,4,0,0,9,0,0,2,2,3,3,0,0,2,3,5,9,0,7,0,2,9,0,2,7,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,0,4,8,0,1,1,3,6,0,4,5,0,2,2,0,6,0,2,0,2,5,0,1,8,3,5,1,7,2,3,2,5,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,0,5,2,3,5,2,3,3,1,5,1,6,2,1,0,0,5,3,1,1,4,1,4,1,8,1,8,0,1,9,0,4,4,2,0,0,3,6,0,0,0,6,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,2,9,0,4,1,5,6,0,3,3,3,4,0,1,8,0,0,0,4,0,5,0,1,2,6,2,9,0,5,0,4,9,0,4,4,2,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,8,1,3,3,2,0,4,1,4,6,1,3,3,1,6,4,2,4,3,0,0,5,2,1,3,3,1,4,0,1,8,7,0,2,8,1,1,6,3,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,4,1,0,5,6,3,0,0,4,5,5,3,2,5,0,0,3,1,2,4,3,1,2,0,0,9,6,3,0,2,7,0,3,4,3,1,7,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,20,1,1,2,5,4,0,0,5,4,1,5,5,0,4,9,0,0,0,0,0,9,0,0,0,9,0,0,0,9,0,3,0,6,7,2,5,4,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,2,8,0,6,2,1,6,1,2,0,1,8,1,1,8,1,0,0,2,2,5,1,0,1,6,3,8,1,6,1,2,8,1,4,4,2,0,1,3,3,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,5,2,2,1,5,1,3,6,1,3,0,1,8,0,1,8,8,0,1,1,0,1,0,1,8,1,0,1,8,6,1,2,6,3,0,0,9,0,0,5,8,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,0,4,1,4,6,1,3,1,5,4,3,4,3,1,0,1,4,2,4,1,2,4,2,1,2,7,4,0,5,7,2,3,3,2,2,1,4,4,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,4,3,3,1,3,1,6,7,1,1,1,2,7,1,2,6,0,0,0,2,5,2,0,1,2,6,2,8,1,4,2,4,8,1,3,5,1,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,2,9,2,0,2,5,9,0,0,0,3,6,2,4,4,2,0,0,3,5,0,2,0,3,5,0,0,9,9,0,0,7,2,0,5,4,0,0,4,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,3,8,0,3,0,6,3,2,4,3,2,5,3,4,4,0,2,0,4,2,3,3,2,0,5,0,7,2,5,2,2,5,4,2,4,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,6,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,3,1,0,2,2,5,9,0,0,0,4,5,2,6,2,2,5,0,2,0,0,3,2,4,0,0,9,0,5,4,0,2,7,0,0,7,2,0,6,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,4,0,5,9,0,0,0,3,6,1,2,7,1,0,0,3,4,2,1,1,1,6,1,5,4,6,1,2,7,2,2,4,3,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,2,6,0,2,9,0,0,0,3,6,5,3,2,4,0,0,5,0,0,3,2,3,2,0,0,9,7,2,0,2,7,0,5,0,4,0,7,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,21,1,3,3,5,1,4,1,5,6,2,2,2,3,5,1,3,6,1,0,0,3,3,2,1,2,1,5,2,8,1,6,1,2,8,1,3,5,2,1,1,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,0,4,1,5,6,1,2,2,2,6,1,3,6,0,0,0,3,3,4,0,2,1,5,2,8,1,6,2,2,9,0,6,2,1,0,0,2,4,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,3,3,0,5,2,3,7,1,1,1,3,6,2,5,3,4,0,0,3,2,0,3,2,3,3,0,1,8,8,0,1,5,4,0,5,4,0,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,2,4,2,3,3,0,2,5,9,0,0,0,2,7,2,4,4,0,0,0,2,0,7,0,3,3,4,0,0,9,7,0,2,5,4,0,5,0,2,2,6,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,8,0,1,7,0,2,0,7,2,0,4,5,0,1,0,2,6,2,0,1,2,7,1,1,8,7,2,0,7,2,0,6,3,0,0,4,3,0,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,10,1,4,3,3,1,4,1,4,9,0,0,0,3,6,4,5,1,5,0,0,2,1,1,2,2,5,2,0,0,9,7,2,0,2,7,0,2,4,3,1,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,33,1,3,3,8,0,5,1,4,7,2,1,0,5,4,2,3,4,4,0,0,1,4,2,3,0,2,4,1,3,6,5,4,1,6,4,1,5,2,0,2,6,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,9,0,0,5,0,4,4,3,3,0,3,6,0,0,2,0,3,5,0,3,0,6,0,0,9,3,2,4,7,2,6,3,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,2,7,2,1,1,1,4,2,2,1,2,5,1,2,7,4,4,2,7,2,3,3,3,0,1,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,1,4,2,3,5,2,3,2,4,4,2,6,3,2,1,1,3,2,2,1,1,4,3,1,1,8,6,2,2,6,3,1,4,3,2,0,5,6,2,0,0,6,0,4,0,0,0,0,0,1,2,0,0,3,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,0,1,1,0,0,1,0,0,2,0,0,0\ntest,3,1,2,4,1,2,7,0,0,6,1,2,2,6,2,0,4,5,3,0,0,5,0,2,1,1,3,4,2,0,9,6,0,3,6,3,4,4,0,2,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,4,9,6,0,0,3,7,2,0,0,5,4,4,3,4,0,0,0,6,3,0,3,0,3,4,0,0,9,7,2,0,9,0,0,4,0,5,0,7,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,0,3,0,6,7,0,2,2,2,6,2,4,3,1,0,0,6,0,2,1,3,4,3,0,6,3,5,1,3,8,1,2,5,3,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0\ntest,2,1,2,3,1,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,1,4,2,3,7,1,2,2,4,4,2,4,4,2,1,1,3,2,3,2,2,3,4,1,8,1,6,0,3,6,3,3,4,3,0,0,3,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,5,0,4,7,0,2,2,2,5,1,3,6,0,1,3,2,3,2,2,2,1,6,0,4,5,4,5,0,2,7,2,5,0,2,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,3,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,17,1,2,4,4,0,5,3,2,5,1,4,4,4,2,1,4,5,2,1,1,3,1,4,2,1,2,4,1,4,5,6,1,2,7,2,4,2,3,1,0,3,5,2,0,0,6,0,5,0,0,0,0,0,6,0,0,0,4,0,4,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,2,0,0,0,0\ntest,38,1,3,2,9,0,6,3,0,9,0,0,0,4,5,2,4,4,2,2,0,4,2,1,2,3,2,4,0,9,0,7,0,2,6,3,2,5,3,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,39,1,2,3,9,0,7,0,2,6,0,3,3,2,4,2,3,4,0,2,0,3,4,0,3,2,0,5,0,0,9,7,2,0,7,2,4,2,4,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,6,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,2,0,0,6,0,0,0,0,0,0,0,5,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,0,6,1,2,7,1,2,1,4,4,1,2,6,1,0,1,3,2,4,1,1,2,6,1,5,4,5,2,3,6,3,2,5,2,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,4,3,8,0,4,1,5,6,2,2,0,4,5,0,4,5,4,0,0,3,3,0,0,4,0,5,0,5,4,6,2,2,8,1,0,0,4,5,0,7,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,9,0,0,9,0,0,0,5,4,0,5,4,0,0,0,1,8,1,0,0,1,8,0,1,8,5,0,4,9,0,1,4,0,5,0,6,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,4,3,0,5,3,2,4,1,4,5,4,1,3,2,4,1,1,0,5,2,1,3,1,2,2,3,9,0,5,0,4,5,4,3,4,3,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,2,3,3,2,0,5,5,3,3,0,2,7,4,5,0,3,0,0,6,0,0,4,1,4,0,0,4,5,6,0,3,4,5,3,2,2,2,2,7,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,41,1,3,3,10,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,4,2,7,0,9,0,0,5,0,4,0,0,9,0,0,9,0,0,2,0,3,5,0,4,0,5,0,9,0,3,2,4,7,2,9,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,2,7,0,0,9,0,0,2,3,4,0,5,4,5,0,0,4,0,1,1,2,4,2,2,1,8,7,2,0,7,2,2,4,3,0,1,4,6,0,0,0,6,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,5,5,1,5,1,3,4,1,5,5,3,2,1,2,7,1,0,1,3,3,4,1,1,2,4,3,8,1,3,1,5,8,1,5,3,1,0,0,2,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,5,1,3,3,1,2,4,0,4,7,0,2,0,4,5,1,1,8,3,0,0,1,3,3,1,0,4,5,1,1,8,5,2,2,7,2,0,8,1,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,0,6,2,2,5,0,4,4,4,2,2,0,7,0,0,0,9,0,0,0,2,0,7,0,9,0,4,2,4,9,0,9,0,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,1,4,2,3,5,2,3,2,4,4,3,2,4,3,1,1,2,3,2,1,3,2,4,1,1,8,6,2,2,6,3,3,5,2,1,0,3,5,0,0,0,5,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,4,3,1,2,5,2,1,8,0,1,1,1,8,6,3,1,7,1,0,1,1,1,7,1,1,1,0,1,8,5,4,1,2,7,1,1,7,1,0,6,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,2,4,1,3,5,2,2,2,3,4,2,3,5,2,0,0,4,2,3,2,2,1,4,2,4,5,6,1,2,7,2,4,3,2,1,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,7,1,2,8,1,1,1,3,6,1,4,5,2,1,2,2,3,1,2,1,3,4,0,1,8,5,4,1,5,4,2,3,4,1,0,4,4,0,0,4,5,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,0,0,0,9,6,0,3,3,2,5,2,4,4,3,0,0,2,3,2,0,2,3,4,0,9,0,7,2,0,9,0,3,4,3,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,2,2,0,5,6,1,3,3,1,6,0,1,8,3,0,1,0,2,4,0,0,2,3,4,6,3,6,0,3,9,0,3,6,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,2,2,0,5,6,1,3,3,1,6,0,1,8,3,0,1,0,2,4,0,0,2,3,4,6,3,6,0,3,9,0,3,6,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,41,1,3,2,10,1,5,1,3,6,1,3,0,4,5,0,3,6,1,0,3,3,0,4,0,2,4,3,0,3,6,6,1,2,6,3,0,0,9,0,0,5,4,0,0,2,5,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,0,3,3,4,6,2,2,2,5,3,0,2,7,1,0,1,2,2,4,1,1,2,6,1,7,2,6,1,2,7,2,5,4,1,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,5,7,1,1,0,4,5,0,6,3,1,1,0,4,2,3,0,0,4,3,3,0,9,6,2,2,8,1,0,6,0,3,0,5,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,2,10,1,5,1,3,6,1,3,0,4,5,0,3,6,1,0,3,3,0,4,0,2,4,3,0,3,6,6,1,2,6,3,0,0,9,0,0,5,4,2,0,0,6,0,0,0,0,0,0,0,3,0,2,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,6,1,3,7,0,2,1,4,5,1,2,6,1,0,2,3,2,3,1,2,3,4,2,2,7,7,1,2,7,2,1,4,4,1,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,0,4,0,5,4,1,5,5,1,4,0,4,5,0,0,0,3,6,1,0,3,0,6,1,5,4,6,0,3,7,2,6,3,0,0,0,2,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,2,3,0,7,2,0,7,0,2,0,7,2,0,1,8,0,0,0,6,0,3,0,0,1,8,1,1,8,5,2,2,7,2,0,3,5,2,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,1,7,2,5,1,2,4,4,2,1,6,3,1,6,3,1,0,0,6,2,2,1,5,1,3,1,5,4,7,0,2,7,2,4,4,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,2,2,5,7,2,0,0,4,5,0,7,2,0,0,0,7,2,0,0,2,6,2,0,4,5,6,0,3,6,3,3,4,2,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,2,2,6,1,2,1,4,5,4,1,5,2,0,0,3,3,2,3,1,5,2,0,3,6,7,0,2,8,1,2,5,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,6,2,1,0,3,6,0,6,3,3,0,5,2,0,0,2,4,4,0,0,0,9,6,1,2,6,3,0,7,0,2,0,5,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,4,7,2,3,0,5,4,2,4,4,4,2,0,0,9,0,0,0,5,3,2,0,0,0,5,4,7,2,4,0,5,9,0,4,3,3,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,3,4,6,1,2,1,2,7,0,3,6,0,0,0,2,2,6,0,1,1,7,1,9,0,7,0,2,7,2,6,3,1,0,0,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,2,3,4,3,1,3,5,2,2,2,3,4,1,6,2,1,0,2,4,2,2,1,3,4,2,0,5,4,8,1,1,5,4,2,1,6,1,0,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,0,6,0,3,8,0,1,0,3,6,2,5,3,3,0,2,3,2,1,3,1,3,3,1,3,6,7,2,0,6,3,2,4,3,0,0,4,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,4,3,0,4,4,2,7,0,2,2,5,3,0,4,5,0,0,0,4,2,3,0,2,3,3,2,9,0,3,0,6,7,2,2,7,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,3,8,0,5,0,4,6,0,3,1,6,3,1,6,3,1,0,3,3,1,3,1,1,6,3,1,0,9,6,3,0,4,5,0,3,1,3,2,8,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,2,3,2,2,0,6,6,3,0,1,4,4,0,3,6,0,0,1,1,5,4,0,2,1,6,0,8,1,9,0,0,9,0,2,2,4,2,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,5,0,4,7,0,2,1,0,8,0,0,9,3,0,2,3,1,3,0,2,7,1,0,9,0,5,3,2,5,4,1,6,3,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,5,6,0,5,0,4,2,2,5,3,6,0,1,4,5,4,0,0,2,2,2,1,2,4,2,2,9,0,5,0,4,9,0,6,2,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,2,5,1,3,2,5,6,1,2,1,2,7,1,2,7,0,0,0,2,4,3,0,1,1,5,3,8,1,3,2,5,8,1,3,5,1,1,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,4,2,3,2,4,1,3,7,1,1,1,2,6,3,4,2,3,0,0,4,2,1,3,3,3,2,0,2,7,9,0,0,5,4,2,3,5,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,1,3,6,6,0,3,3,3,3,0,1,8,1,1,0,1,8,1,1,1,0,8,1,9,0,5,2,3,7,2,7,2,1,0,0,2,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,34,1,2,3,8,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,2,4,1,0,5,0,5,9,0,0,0,9,0,0,9,0,0,5,0,0,0,5,5,0,0,4,0,6,3,7,2,0,3,6,2,4,4,1,0,5,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,0,4,0,5,3,0,6,6,2,2,1,2,7,0,0,0,0,5,4,0,0,0,6,3,9,0,5,0,4,9,0,6,3,0,0,0,2,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,0,6,1,2,1,2,6,3,5,1,2,6,2,1,0,0,4,1,3,1,3,4,2,0,9,0,5,1,3,5,4,3,2,5,0,0,4,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,3,2,0,7,0,2,9,0,0,0,4,5,2,5,3,0,2,0,6,0,2,2,3,3,3,0,4,5,9,0,0,3,6,0,3,3,3,0,6,7,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,2,5,2,1,7,2,0,0,2,7,4,4,2,3,0,0,5,2,1,2,3,3,2,1,0,9,9,0,0,4,5,1,2,7,0,0,5,7,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,7,1,2,2,3,4,2,3,4,3,1,1,2,2,3,3,1,3,3,1,2,7,7,2,1,5,4,2,4,3,1,1,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,4,3,2,2,6,0,2,9,0,0,0,4,5,4,5,0,2,4,0,3,0,0,6,3,0,0,0,0,9,7,2,0,0,9,0,0,4,4,2,9,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,2,10,1,5,1,3,7,1,2,2,3,5,1,5,3,3,0,3,1,2,2,3,2,3,3,1,3,6,5,3,1,4,5,3,3,3,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,4,0,5,9,0,0,0,4,5,0,3,6,0,0,0,0,5,5,0,0,2,5,2,9,0,3,2,4,9,0,6,3,0,0,0,2,4,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,7,1,3,3,2,1,5,1,4,7,1,2,1,4,5,4,4,2,4,0,1,3,1,2,4,2,2,3,0,4,5,6,2,1,4,5,1,2,6,1,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,24,1,3,3,5,1,1,1,7,5,2,2,2,3,5,1,1,8,0,0,0,3,2,4,0,1,1,7,2,8,1,6,1,3,8,1,5,3,1,1,0,2,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,4,7,0,6,1,3,5,0,4,4,4,2,2,3,5,2,0,0,4,3,2,2,3,0,5,2,7,2,6,0,3,9,0,5,2,2,0,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,2,5,0,3,9,0,0,0,6,3,0,5,4,2,0,0,7,0,0,2,3,2,4,0,3,6,7,0,2,6,3,2,4,4,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,8,1,3,3,2,1,6,2,1,7,0,2,1,2,6,4,3,2,5,1,0,2,1,1,5,2,1,2,0,2,7,6,3,1,4,5,1,2,6,2,0,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,8,1,2,3,2,2,3,2,4,5,0,4,3,3,3,4,3,3,2,2,2,3,2,0,4,3,2,2,0,4,5,5,2,3,6,3,5,2,3,0,0,3,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,4,2,9,0,2,3,5,6,0,3,2,0,7,0,6,3,0,0,0,7,0,2,0,3,4,3,0,4,5,9,0,0,7,2,0,7,0,2,0,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,1,4,0,5,7,0,2,5,3,2,0,2,7,2,1,1,3,2,2,0,0,3,6,0,3,6,6,2,1,4,5,6,2,0,2,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,3,1,1,6,0,3,9,0,0,0,4,5,8,1,0,9,0,0,0,0,0,9,0,0,0,0,0,9,7,2,0,2,7,0,1,3,5,0,7,8,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,2,9,0,5,1,4,4,4,2,2,3,4,2,3,4,2,0,0,3,3,2,2,2,2,5,1,7,2,7,0,2,9,0,2,2,5,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,29,1,2,4,7,2,2,2,4,6,0,3,3,4,3,0,3,6,1,0,0,4,2,4,0,1,2,4,3,9,0,4,3,3,9,0,1,8,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,4,2,3,0,6,1,3,7,1,2,1,2,7,1,3,6,0,0,0,3,2,4,0,1,3,5,1,8,1,7,1,1,9,0,2,3,4,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,4,0,5,9,0,0,0,4,5,1,2,7,1,0,0,3,5,2,1,2,1,7,1,4,5,6,2,2,7,2,2,5,2,1,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,2,3,7,1,4,1,4,5,1,4,3,3,3,0,4,5,1,1,1,4,2,2,1,2,3,4,1,2,7,5,1,4,7,2,3,3,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,2,3,1,5,1,3,8,1,1,1,3,6,4,4,2,4,1,0,3,1,1,3,2,3,2,0,1,8,7,1,1,4,5,1,3,4,2,1,5,8,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,2,1,6,0,3,2,3,4,1,2,7,1,0,2,2,1,5,1,2,2,5,1,1,8,7,0,2,7,2,0,5,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,3,10,0,6,2,2,6,0,3,3,2,5,0,3,6,0,0,9,0,0,0,3,0,2,0,4,0,9,5,2,3,7,2,5,4,0,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,2,2,3,0,9,0,0,4,0,5,5,0,4,5,0,4,0,0,0,9,0,0,0,5,0,4,0,5,4,9,0,0,4,5,0,9,0,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,1,4,2,3,5,0,4,4,3,2,1,2,6,1,0,1,3,3,3,1,1,2,5,1,5,4,5,1,4,9,0,2,5,2,1,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,4,2,3,7,1,2,2,3,4,1,3,5,1,1,1,2,3,3,2,2,2,4,1,4,5,6,2,2,7,2,1,4,4,1,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,3,3,7,0,4,2,4,5,1,3,2,5,3,2,4,4,2,1,0,1,2,5,2,1,4,3,1,5,4,7,1,2,5,4,2,2,3,3,0,5,3,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,2,1,4,6,0,9,0,0,2,0,7,4,3,3,0,4,5,2,0,0,4,2,2,0,2,3,3,2,9,0,2,0,7,9,0,4,4,0,1,0,3,1,2,0,0,0,0,6,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,2,10,4,4,1,1,6,2,2,2,3,4,0,4,5,1,2,3,3,1,2,2,2,2,3,1,3,6,7,1,1,5,4,4,2,4,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,1,3,2,4,6,0,3,4,4,2,1,3,6,1,0,0,3,1,5,1,2,2,3,3,7,2,6,0,3,6,3,4,3,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,2,3,3,5,0,1,2,6,7,0,2,0,2,7,1,5,3,2,0,0,5,0,3,1,4,0,4,0,4,5,9,0,0,7,2,0,6,3,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,0,4,1,5,6,1,2,2,2,6,1,3,6,0,0,0,3,3,4,0,2,1,5,2,8,1,6,2,2,9,0,6,2,1,0,0,2,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,2,7,0,5,0,4,5,1,3,3,2,5,1,4,4,2,1,0,3,3,2,1,2,2,4,1,7,2,5,1,4,7,2,3,5,2,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,2,3,4,0,0,5,2,7,0,2,5,3,2,6,2,1,0,0,3,2,3,1,3,3,3,0,4,5,5,0,4,9,0,1,2,4,2,1,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,1,4,1,4,5,1,4,5,1,3,0,4,5,2,1,1,4,1,3,2,2,1,4,2,1,8,5,1,4,7,2,2,6,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,0,5,9,0,0,0,3,6,0,4,5,0,0,0,2,5,3,0,2,2,5,2,0,9,9,0,0,9,0,1,5,4,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,0,0,0,0,0,0\ntest,41,1,2,3,10,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,0,5,1,4,5,1,3,3,3,4,1,3,5,2,0,0,3,3,3,1,1,3,4,1,8,1,6,1,3,8,1,3,3,3,1,0,3,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,0,5,2,2,7,0,2,2,5,3,0,0,9,0,0,0,2,7,0,0,0,0,9,0,5,4,9,0,0,9,0,5,4,0,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,0,4,7,2,0,0,3,6,0,4,5,2,0,2,0,4,3,2,0,2,5,0,4,5,7,2,0,5,4,2,2,3,3,0,5,4,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,0,4,7,0,2,0,1,8,0,3,6,0,0,0,3,3,4,0,1,2,5,2,9,0,7,2,0,9,0,2,6,1,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,4,0,5,7,2,0,0,4,5,0,0,9,0,0,0,2,4,4,0,0,0,7,2,5,4,8,0,1,8,1,2,4,4,0,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,0,5,2,2,7,0,2,2,5,3,0,0,9,0,0,0,2,7,0,0,0,0,9,0,5,4,9,0,0,9,0,5,4,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,2,3,3,9,2,6,0,3,6,1,3,1,0,8,1,3,6,2,1,0,4,0,3,1,2,2,3,3,2,7,5,2,2,5,4,1,4,2,3,1,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,0,4,0,5,4,1,5,5,1,4,0,4,5,0,0,0,3,6,1,0,3,0,6,1,5,4,6,0,3,7,2,6,3,0,0,0,2,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,2,4,8,1,0,0,2,7,0,3,6,0,0,0,4,3,3,0,1,3,6,0,3,6,6,3,0,9,0,2,5,2,2,0,4,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,2,9,0,3,3,4,0,3,6,5,4,1,2,4,3,1,0,0,4,3,1,1,1,3,5,1,9,0,4,0,5,7,2,5,1,3,1,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,3,5,0,5,2,2,2,0,7,7,2,0,4,4,2,0,2,0,7,0,1,2,3,3,2,0,9,0,7,0,2,5,4,3,4,0,3,0,5,3,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,0,7,2,0,5,2,2,4,1,4,0,4,5,3,0,0,6,0,1,1,0,1,6,3,6,3,4,4,2,3,6,5,4,0,0,0,2,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,1,6,7,0,3,2,4,6,3,0,7,1,2,0,1,8,0,0,0,1,1,8,0,1,1,3,5,7,2,6,3,0,9,0,8,0,1,1,0,2,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,2,5,1,4,2,3,4,1,4,4,4,2,2,4,4,2,1,0,3,4,0,3,2,0,6,0,7,2,6,0,3,7,2,4,0,5,0,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,1,3,5,2,2,0,6,2,0,7,6,2,2,2,4,4,0,0,0,4,2,4,1,1,3,4,2,7,2,3,0,6,7,2,7,2,1,0,0,2,2,0,0,0,6,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,2,7,0,5,0,4,5,1,3,3,2,5,1,4,4,2,1,0,3,3,2,1,2,2,4,1,7,2,5,1,4,7,2,3,5,2,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,2,2,5,1,3,3,3,4,2,4,4,2,1,1,4,2,2,2,2,3,2,2,4,5,4,2,3,6,3,2,4,4,1,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,3,5,0,5,0,5,0,6,3,7,2,1,0,5,4,2,0,0,4,2,2,1,1,4,1,4,9,0,5,3,3,9,0,5,2,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,3,8,1,5,2,3,5,1,3,0,2,7,0,2,7,0,0,0,5,0,4,0,0,4,3,3,9,0,6,1,3,7,2,3,3,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,2,4,2,3,2,4,2,2,9,0,0,0,0,8,2,4,4,2,0,0,7,1,0,0,3,4,3,0,2,7,9,0,0,9,0,0,0,4,5,0,8,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,4,1,2,4,1,0,5,2,3,5,1,4,3,4,3,2,4,4,3,1,1,3,2,2,2,2,2,4,1,4,5,6,1,3,6,3,4,2,3,1,0,3,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,2,2,4,6,0,5,2,3,7,0,2,2,5,3,0,1,8,0,0,0,2,4,3,0,0,1,7,2,9,0,8,0,1,7,2,5,4,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,2,3,0,7,2,0,7,0,2,0,7,2,0,1,8,0,0,0,6,0,3,0,0,1,8,1,1,8,5,2,2,7,2,0,3,5,2,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,4,7,0,6,1,3,5,0,4,4,4,2,2,3,5,2,0,0,4,3,2,2,3,0,5,2,7,2,6,0,3,9,0,5,2,2,0,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,4,3,9,0,6,1,3,7,1,2,1,1,8,0,2,7,1,0,0,1,5,3,1,1,1,7,1,6,3,6,1,2,9,0,1,7,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,2,4,10,0,6,0,3,6,0,3,3,4,3,0,2,7,1,0,8,1,0,1,2,1,6,2,0,0,9,3,3,3,2,7,3,6,1,0,0,3,3,0,5,0,7,0,0,7,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,4,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,26,1,1,5,6,0,4,2,4,3,0,6,6,2,1,1,1,7,0,0,2,2,4,2,0,1,4,5,0,8,1,3,0,6,9,0,2,5,2,1,0,4,1,0,0,0,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,30,1,2,4,7,1,4,2,3,5,0,4,4,3,2,1,2,6,1,0,1,3,3,3,1,1,2,5,1,5,4,5,1,4,9,0,2,5,2,1,0,4,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,4,0,5,3,0,6,5,2,2,1,2,7,0,0,2,2,3,4,0,1,4,5,1,2,7,5,1,4,7,2,1,4,5,0,0,4,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,2,4,3,3,1,5,1,4,7,1,2,0,0,9,2,0,7,0,0,0,3,0,6,0,2,0,7,0,0,9,6,2,1,4,5,0,3,5,2,0,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,1,4,1,4,7,1,2,0,6,3,0,6,3,4,0,0,2,2,1,0,5,0,4,0,3,6,6,2,1,5,4,0,9,0,0,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,2,4,2,4,7,1,2,2,0,7,1,8,0,1,0,0,4,4,1,0,1,5,3,0,2,7,7,1,2,6,3,4,2,4,0,0,3,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,3,3,2,1,2,5,6,2,1,1,4,4,0,4,5,1,3,1,4,1,2,2,2,3,3,1,1,8,6,1,3,7,2,1,3,6,0,0,4,7,2,0,0,5,0,5,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,4,5,0,0,6,0,3,1,6,3,4,4,2,7,1,0,1,0,2,6,1,1,2,0,1,8,3,2,5,7,2,2,4,1,3,0,5,7,2,0,0,6,0,0,0,2,0,0,0,0,0,0,0,6,2,0,0,2,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,2,1,0,0,1,0,1\ntest,26,3,2,4,6,0,3,0,6,5,0,4,4,4,2,0,0,9,0,0,0,0,7,2,0,0,0,5,4,7,2,5,0,4,9,0,6,2,2,1,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,2,4,1,3,8,0,1,1,5,4,1,4,4,1,0,0,6,1,3,0,3,2,4,0,5,4,5,2,3,6,3,2,4,3,1,0,4,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,0,7,2,0,5,2,2,4,1,4,0,4,5,3,0,0,6,0,1,1,0,1,6,3,6,3,4,4,2,3,6,5,4,0,0,0,2,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,2,10,1,3,0,5,6,1,2,2,3,5,2,4,4,2,0,5,0,2,2,3,0,3,3,0,2,7,5,3,1,3,6,4,0,4,2,0,5,3,0,3,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,2,2,5,3,2,4,4,0,5,0,4,4,2,3,2,3,4,4,0,0,2,0,4,4,2,0,0,4,0,9,4,2,4,5,4,3,5,1,1,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,1,5,0,5,1,4,2,1,7,6,2,1,1,7,1,1,0,0,4,1,4,1,1,5,4,0,8,1,3,1,6,8,1,6,3,1,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,2,4,2,2,0,6,0,3,9,0,0,0,1,8,4,5,1,5,1,0,2,2,1,5,2,2,2,1,0,9,8,1,0,3,6,0,3,5,2,0,5,8,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,2,5,2,2,4,2,4,2,4,3,2,5,3,2,0,1,2,2,4,2,1,3,3,1,4,5,6,0,3,5,4,3,2,3,1,1,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,1,3,8,1,1,1,2,7,0,2,7,1,0,4,1,1,5,1,2,3,4,1,1,8,7,1,1,8,1,2,3,2,4,0,5,4,0,0,4,6,0,0,0,0,4,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,1,5,1,3,8,1,1,1,3,6,4,4,2,4,1,0,3,1,1,3,2,3,2,0,1,8,7,1,1,4,5,1,3,4,2,1,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,3,7,0,5,1,3,6,1,3,2,4,4,2,2,6,2,0,0,2,3,3,2,1,2,5,1,5,4,6,1,2,6,3,3,4,2,2,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,5,3,3,0,5,0,4,9,0,0,0,0,8,5,4,0,7,0,0,2,0,0,7,0,2,0,0,0,9,9,0,0,4,5,0,0,9,0,0,5,8,2,0,0,6,0,0,0,0,0,0,0,5,0,2,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,1,3,5,7,0,2,1,1,8,0,1,8,1,1,0,1,6,3,1,1,0,5,3,9,0,5,2,3,7,2,1,6,3,0,0,4,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,2,9,1,1,0,7,8,0,1,1,3,6,1,5,4,1,0,0,3,5,1,1,2,2,4,1,1,8,7,2,1,5,4,3,0,6,1,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,0,6,0,3,7,0,2,0,3,6,0,3,6,1,1,1,2,3,3,1,0,2,6,0,2,7,7,2,0,7,2,4,3,3,0,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,3,6,2,1,1,1,4,2,1,2,4,3,1,2,7,4,4,2,7,2,3,3,3,0,1,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,36,1,3,3,8,0,6,0,3,6,2,2,1,4,5,2,2,5,3,1,1,3,1,3,2,2,2,3,1,4,5,7,1,1,6,3,2,4,2,2,0,4,3,0,0,3,6,0,0,0,0,5,0,0,0,5,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0\ntest,28,7,2,6,6,0,6,2,2,9,0,0,4,2,4,2,1,6,5,2,0,0,3,0,2,1,4,0,4,4,5,6,3,0,0,9,7,0,1,2,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,2,3,2,3,0,6,0,3,7,2,0,3,3,4,3,3,4,1,0,0,5,4,1,0,5,1,4,0,0,9,9,0,0,5,4,3,4,3,0,0,3,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,41,1,3,3,10,0,7,0,3,8,1,1,1,3,5,1,4,5,1,1,3,2,3,1,2,1,3,4,0,2,7,5,4,0,6,3,3,4,4,0,0,4,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,3,3,5,3,1,1,8,1,1,1,1,8,1,5,3,1,0,0,5,3,1,1,3,3,3,0,1,8,9,0,0,5,4,3,2,4,0,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,5,1,2,3,1,0,5,1,4,5,1,4,3,2,5,2,3,4,3,1,0,1,4,2,3,1,2,4,1,3,6,6,1,3,6,3,3,5,1,2,0,4,3,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,3,6,0,0,9,0,0,1,7,1,2,4,4,2,1,4,4,0,1,2,3,5,1,0,2,7,6,3,0,0,9,1,1,7,1,0,5,6,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,0,4,2,4,8,1,1,1,2,7,0,2,7,1,0,0,2,4,4,1,1,2,6,2,6,3,7,2,1,9,0,1,3,5,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,0,4,0,5,5,2,3,2,3,4,3,3,3,7,0,0,2,0,1,4,3,2,2,1,3,6,5,2,3,6,3,3,2,4,2,0,5,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,3,2,1,5,1,3,7,1,2,0,3,6,3,5,1,3,1,0,4,1,2,3,3,2,2,1,1,8,7,0,2,5,4,1,3,5,1,1,5,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,4,8,0,2,0,7,9,0,0,0,6,3,2,4,4,2,0,0,2,2,5,2,2,2,5,0,7,2,5,1,3,6,3,5,0,4,1,1,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,7,0,2,7,0,2,2,0,7,1,4,5,0,0,0,4,3,3,0,2,2,3,3,7,2,4,1,4,8,1,2,4,4,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,40,1,3,3,10,0,3,0,6,9,0,0,0,4,5,2,0,7,2,0,5,0,2,0,2,1,5,3,0,2,7,7,2,0,7,2,4,2,4,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,3,6,0,5,1,3,7,1,2,3,5,1,0,5,4,0,0,0,2,2,6,0,4,2,2,2,1,8,9,0,0,5,4,3,3,2,2,0,4,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,2,4,3,3,1,5,1,4,7,1,2,0,0,9,2,0,7,0,0,0,3,0,6,0,2,0,7,0,0,9,6,2,1,4,5,0,3,5,2,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,0,7,0,2,9,0,0,0,3,6,2,4,4,0,0,0,5,4,0,0,2,3,5,0,7,2,9,0,0,9,0,2,4,3,0,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,1,2,5,6,1,5,2,3,5,1,3,5,1,3,1,1,8,6,0,0,3,0,0,1,1,4,0,5,6,3,6,1,3,7,2,6,3,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,2,2,2,9,0,6,1,3,5,1,3,2,2,5,0,2,7,0,0,1,2,3,5,0,1,2,6,2,6,3,5,2,3,8,1,8,1,1,0,0,2,5,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,2,3,0,4,2,4,4,2,5,5,2,3,2,5,3,2,0,0,5,0,3,2,2,4,2,0,5,4,5,2,4,5,4,0,3,2,5,0,7,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,5,8,1,4,2,3,3,0,6,5,3,2,2,2,6,2,2,0,3,2,3,1,1,2,4,2,8,1,2,2,5,5,4,7,1,0,0,1,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,0,7,0,2,6,0,3,3,2,4,2,3,4,0,2,0,3,4,0,3,2,0,5,0,0,9,7,2,0,7,2,4,2,4,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,1,5,1,3,4,2,4,4,4,2,2,4,4,2,0,0,3,3,3,1,1,2,5,1,7,2,6,0,3,7,2,3,3,2,2,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,5,7,2,5,2,1,3,0,6,6,2,2,0,2,7,2,0,1,2,1,5,0,2,3,5,1,2,7,6,0,3,9,0,3,6,1,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,5,7,0,5,0,4,5,0,4,4,4,2,0,2,7,0,0,0,3,6,0,0,0,3,5,2,9,0,5,0,4,9,0,6,2,2,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,2,5,0,6,1,2,1,2,6,5,3,2,2,5,2,2,0,0,4,1,3,2,3,3,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,4,3,8,2,7,0,0,9,0,0,0,4,5,0,3,6,2,0,0,6,0,2,0,1,4,4,0,2,7,7,2,0,7,2,0,7,2,0,0,3,3,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,2,4,3,3,1,5,1,4,7,1,2,0,0,9,2,0,7,0,0,0,3,0,6,0,2,0,7,0,0,9,6,2,1,4,5,0,3,5,2,0,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1\ntest,33,1,3,3,8,0,6,2,1,6,0,3,2,3,4,1,2,7,1,0,2,2,1,5,1,2,2,5,1,1,8,7,0,2,7,2,0,5,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,3,5,0,2,9,0,0,0,5,4,2,4,4,2,0,0,6,0,2,1,2,3,4,0,6,3,7,2,0,7,2,3,4,2,1,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,11,1,3,3,3,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,3,4,0,3,6,0,3,3,2,5,0,5,4,0,0,0,4,5,0,0,0,5,4,0,0,9,9,0,0,5,4,2,7,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,16,1,2,3,4,3,0,0,6,1,0,8,5,2,2,6,3,0,4,0,0,3,0,3,3,2,4,0,0,9,0,3,0,6,2,7,3,3,4,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,4,7,0,5,0,4,3,0,6,4,2,3,0,0,9,0,0,0,3,4,3,0,0,0,6,3,7,2,2,2,6,9,0,5,4,0,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,2,2,3,4,1,3,5,1,1,1,2,3,3,2,2,2,4,1,4,5,6,2,2,7,2,1,4,4,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,29,1,2,2,7,1,5,1,2,6,2,2,1,5,4,1,6,3,1,0,0,5,3,2,1,4,2,3,1,2,7,8,0,1,6,3,2,5,3,0,0,4,3,0,0,0,6,0,0,0,0,0,0,4,0,0,0,0,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,3,3,1,4,9,0,0,0,5,4,6,3,0,7,1,0,2,0,0,7,2,0,0,0,0,9,2,7,0,0,9,0,0,3,3,4,0,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,9,0,0,9,0,0,0,5,4,0,5,4,0,0,0,1,8,1,0,0,1,8,0,1,8,5,0,4,9,0,1,4,0,5,0,6,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,7,1,3,3,2,1,5,0,4,7,0,2,1,4,5,3,5,1,4,0,0,4,0,1,3,1,4,2,0,6,3,6,1,2,5,4,1,1,7,0,0,5,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,4,3,0,5,3,2,4,1,4,4,2,4,1,4,4,1,1,0,5,2,1,1,2,3,4,1,9,0,5,0,4,5,4,3,4,4,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,1,4,0,4,8,0,1,3,4,2,1,5,3,1,0,0,3,1,5,2,0,1,5,2,7,2,8,1,0,7,2,6,1,2,1,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,2,5,0,3,1,6,6,2,1,1,4,4,2,2,5,2,0,0,3,2,3,2,1,2,5,1,8,1,7,1,2,7,2,2,6,2,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,1,2,1,0,5,0,4,0,3,6,6,3,0,3,3,3,0,0,0,6,0,3,0,4,2,2,2,7,2,5,0,4,7,2,5,4,0,0,0,2,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,2,5,0,4,0,5,3,0,6,5,0,4,3,3,4,0,0,0,3,2,4,0,2,3,3,2,9,0,3,2,5,9,0,9,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,4,8,0,6,0,3,7,0,2,0,3,6,0,3,6,2,1,2,1,3,3,1,0,4,5,0,1,8,7,2,0,7,2,4,3,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,4,7,2,1,0,5,4,2,3,4,4,0,0,1,4,2,3,0,2,4,1,3,6,5,4,1,6,4,1,5,2,0,2,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,3,5,0,5,1,4,2,1,7,3,4,2,2,5,2,1,1,1,7,2,1,1,4,1,4,0,8,1,6,1,3,7,2,3,6,1,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,8,1,4,3,2,0,7,0,2,9,0,0,0,4,5,2,5,3,0,2,0,6,0,2,2,3,3,3,0,4,5,9,0,0,3,6,0,3,3,3,0,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,11,1,3,3,3,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,0,3,4,2,6,0,3,3,3,4,0,5,4,2,0,0,2,1,6,0,1,4,2,3,2,7,6,0,3,7,2,1,3,5,0,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,5,0,0,3,5,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0\ntest,24,1,2,2,5,0,3,1,5,3,4,2,1,7,1,0,4,5,0,0,0,4,2,3,0,3,2,4,0,7,2,4,1,4,9,0,3,5,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,4,1,0,5,2,2,9,0,0,5,2,3,5,1,3,7,0,0,2,0,0,1,1,5,3,0,0,9,6,3,0,5,4,0,1,4,5,0,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,37,2,2,3,8,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,2,2,3,4,1,3,5,1,1,1,2,3,3,2,2,2,4,1,4,5,6,2,2,7,2,1,4,4,1,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,1,1,2,4,1,0,6,2,2,9,0,0,1,8,1,7,2,1,5,4,0,0,1,0,7,1,1,0,1,1,8,6,3,0,0,9,1,0,5,4,0,8,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,2,3,3,8,0,2,3,5,6,3,0,0,3,6,0,1,8,0,0,0,1,6,3,0,1,1,8,0,3,6,6,3,0,9,0,3,5,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,4,3,1,3,5,2,3,2,4,3,1,3,5,2,0,0,2,4,2,1,2,4,2,2,6,3,7,2,0,4,5,1,4,3,2,0,4,6,2,0,0,5,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1\ntest,19,1,1,4,4,4,4,0,2,5,0,4,9,0,0,0,0,9,3,0,3,4,0,0,0,0,0,4,5,9,0,7,2,0,4,5,5,2,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,2,3,1,5,1,4,8,1,1,1,2,7,3,5,2,2,0,0,5,2,1,2,4,2,2,0,1,8,9,0,0,5,4,2,4,4,0,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1\ntest,20,1,3,3,5,1,4,1,5,6,1,2,1,2,6,1,2,7,1,0,0,4,4,1,1,1,2,6,1,8,1,7,1,2,8,1,3,5,2,1,1,3,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,2,2,3,0,9,0,0,4,0,5,5,0,4,5,0,4,0,0,0,9,0,0,0,5,0,4,0,5,4,9,0,0,4,5,0,9,0,0,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,5,1,1,5,2,3,5,1,4,1,8,1,6,2,1,6,2,0,1,1,1,5,3,1,1,1,1,8,5,1,3,5,4,1,1,3,3,3,8,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,36,1,3,3,8,1,5,1,3,5,2,3,0,0,9,3,5,1,0,0,0,8,0,1,3,3,1,2,0,0,9,6,1,3,6,3,0,2,3,3,2,8,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,4,3,2,0,7,0,2,9,0,0,0,4,5,2,5,3,0,2,0,6,0,2,2,3,3,3,0,4,5,9,0,0,3,6,0,3,3,3,0,6,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,1,7,1,2,8,1,1,1,3,6,1,4,5,2,1,2,2,3,1,2,1,3,4,0,1,8,5,4,1,5,4,2,3,4,1,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,4,9,0,5,0,4,6,0,3,3,3,4,2,4,4,3,0,2,3,2,1,3,1,5,2,1,0,9,6,3,0,4,5,0,4,4,1,1,5,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,1,4,1,1,5,0,3,5,0,4,7,2,1,2,4,4,6,0,0,2,2,0,2,0,0,7,0,1,8,5,0,4,2,7,4,2,4,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,7,0,2,7,0,2,2,0,7,1,4,5,0,0,0,4,3,3,0,2,2,3,3,7,2,4,1,4,8,1,2,4,4,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,3,7,1,2,0,2,7,5,3,2,3,1,0,4,2,1,4,3,2,2,1,1,8,7,0,2,5,4,1,2,4,3,1,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,7,1,3,3,2,1,5,0,4,7,0,2,1,4,4,4,5,1,4,0,0,4,0,1,2,1,5,3,0,7,2,6,1,2,5,4,1,1,8,0,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,4,2,5,0,5,1,4,8,1,1,1,2,6,1,3,6,0,0,0,5,1,3,0,1,3,5,1,7,2,8,1,1,9,0,2,6,2,0,0,3,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,2,3,4,1,0,5,6,3,0,0,4,5,5,3,2,5,0,0,3,1,2,4,3,1,2,0,0,9,6,3,0,2,7,0,3,4,3,1,7,7,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,0,6,0,3,8,0,1,0,3,6,2,5,3,3,0,2,3,2,1,3,1,3,3,1,3,6,7,2,0,6,3,2,4,3,0,0,4,6,2,0,0,5,0,4,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,4,1,0,5,6,3,0,0,4,5,5,3,2,5,0,0,3,1,2,4,3,1,2,0,0,9,6,3,0,2,7,0,3,4,3,1,7,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,0,3,0,6,7,0,2,2,2,6,2,5,3,1,0,0,6,0,2,1,2,4,3,0,6,3,5,1,3,8,1,2,6,2,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,0,4,2,4,0,5,4,2,5,3,0,4,5,0,0,0,2,4,4,0,0,4,4,2,9,0,7,0,2,9,0,4,2,3,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,2,9,0,4,2,4,7,1,2,3,4,3,1,1,8,1,0,0,3,6,0,1,1,1,8,0,3,6,7,1,1,8,1,0,7,2,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,32,1,2,5,7,0,9,0,0,9,0,0,0,5,4,0,4,5,0,0,0,2,5,2,0,0,3,6,0,4,5,5,0,4,9,0,7,2,0,1,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,2,2,3,2,2,0,5,3,4,3,3,4,3,6,2,2,6,0,0,2,2,0,6,2,0,2,0,7,2,7,2,0,3,6,2,2,6,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,7,0,1,6,0,3,2,4,4,4,3,2,3,0,0,5,1,1,3,2,3,2,0,0,9,6,3,2,5,4,1,4,4,1,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,4,1,1,4,1,1,4,1,4,3,1,6,6,3,0,3,6,0,3,0,0,6,0,0,3,6,0,0,0,6,3,4,1,4,7,2,0,0,0,3,6,0,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,5,7,1,5,4,1,7,1,1,2,6,2,3,5,2,3,0,0,5,1,1,3,3,2,2,1,8,1,6,1,3,6,3,5,1,1,2,0,4,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,4,6,2,1,1,5,3,0,3,6,1,0,0,4,3,2,1,1,2,6,0,6,3,6,0,3,5,4,2,2,5,0,0,4,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,3,9,0,1,3,6,9,0,0,0,2,7,0,4,5,0,0,0,2,2,5,0,0,3,6,0,3,6,8,1,0,9,0,0,9,0,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,37,1,3,4,8,0,6,1,2,8,0,1,1,6,3,0,4,5,1,0,0,4,2,4,0,1,4,4,1,2,7,7,0,2,8,1,2,4,4,0,0,4,4,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,2,3,3,9,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,1,4,1,4,7,1,2,1,5,4,0,1,8,5,0,0,2,0,2,1,1,5,4,0,2,7,6,2,1,5,4,1,4,5,1,0,4,6,0,0,4,6,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,4,1,2,2,2,5,7,1,2,2,4,4,5,3,2,8,1,0,0,0,0,7,0,2,0,0,0,9,3,5,2,2,7,0,2,5,2,1,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,26,1,1,6,6,0,5,2,2,2,1,7,7,2,0,0,0,9,0,0,1,2,5,2,0,0,1,6,3,9,0,1,0,8,8,1,8,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,5,1,1,5,2,2,5,1,3,3,5,1,1,5,3,3,1,0,4,1,2,2,3,3,2,1,5,4,7,1,1,5,4,3,3,3,1,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,3,7,0,5,0,4,7,2,0,0,6,3,0,0,9,0,0,0,2,2,5,0,0,0,6,3,4,5,8,0,1,8,1,2,4,4,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,0,3,3,4,5,0,4,4,3,3,0,0,9,0,0,0,3,0,6,0,0,0,5,4,9,0,6,0,3,9,0,2,5,3,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,6,2,2,6,0,3,3,2,5,0,3,6,0,0,9,0,0,0,3,0,2,0,4,0,9,5,2,3,7,2,5,4,0,0,0,2,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,2,3,0,5,7,2,0,0,4,5,1,6,3,2,0,0,3,2,3,1,2,4,4,0,8,1,9,0,0,6,3,1,2,6,1,0,5,8,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,1,2,8,1,0,0,4,5,0,4,5,0,0,0,5,1,4,0,1,5,4,0,4,5,8,1,0,9,0,2,5,2,2,0,4,6,2,0,0,5,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,4,2,3,7,0,2,0,2,7,0,2,7,0,0,2,4,0,3,0,0,5,4,0,0,9,7,0,2,9,0,3,6,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,4,3,8,2,7,0,0,9,0,0,0,4,5,0,3,6,2,0,0,6,0,2,0,1,4,4,0,2,7,7,2,0,7,2,0,7,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,1,4,1,4,7,1,2,2,4,4,2,4,4,3,1,2,2,2,1,2,2,3,3,1,4,5,6,2,1,5,4,3,3,3,2,1,4,5,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,4,3,0,2,4,4,7,0,2,4,0,5,4,0,5,2,0,0,4,1,3,0,0,5,3,3,9,0,7,0,2,6,3,6,0,3,0,0,3,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,38,1,2,3,9,0,4,2,3,5,1,3,2,4,3,0,3,6,1,0,1,2,2,5,1,1,2,5,3,8,1,6,1,3,6,3,4,4,1,0,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,2,5,1,6,2,2,7,1,2,5,3,2,0,5,4,0,0,0,0,9,0,0,0,0,6,3,5,4,7,1,2,7,2,3,4,0,3,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,23,1,2,2,5,0,4,1,5,4,3,3,3,2,4,1,4,4,2,0,0,2,2,4,1,3,2,3,3,8,1,6,1,2,8,1,4,4,1,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,4,0,5,9,0,0,0,4,5,1,2,7,1,0,0,3,5,2,1,2,1,7,1,4,5,6,2,2,7,2,2,5,2,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,3,4,2,2,9,0,0,0,5,4,4,4,2,4,3,0,2,0,2,6,1,0,2,0,0,9,5,4,0,3,6,1,3,4,2,2,6,8,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,3,8,2,4,2,3,5,2,3,2,4,3,1,3,5,1,1,0,3,3,2,2,2,2,5,1,6,3,7,1,2,7,2,3,4,3,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,5,7,2,2,2,4,6,0,3,3,4,2,0,1,8,1,0,0,1,4,5,0,1,1,4,5,9,0,4,3,3,9,0,1,8,0,0,0,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,1,5,1,3,6,1,2,0,5,4,4,0,5,2,1,1,2,3,2,3,0,6,0,0,0,9,6,1,3,7,2,3,4,2,1,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,2,3,2,4,8,1,1,1,5,4,2,1,7,2,0,0,3,3,2,2,1,1,7,0,9,0,6,2,2,7,2,2,3,2,4,0,6,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,9,1,3,3,3,3,4,3,1,7,1,1,1,4,5,1,6,2,1,0,0,6,1,2,1,4,2,3,1,4,5,4,2,3,7,2,2,2,5,1,0,4,4,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,29,1,4,3,7,2,4,0,4,7,0,2,0,2,7,1,1,8,1,0,0,1,2,6,1,0,1,7,1,1,8,5,2,2,7,2,0,8,1,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,1,2,5,0,5,2,2,1,4,5,5,4,1,1,6,2,1,0,0,6,1,3,1,4,3,3,0,9,0,3,2,4,6,3,3,4,3,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,1,4,8,1,5,2,2,4,0,5,6,3,1,3,2,5,4,0,1,2,2,2,3,1,1,3,2,5,4,4,2,4,4,5,3,3,3,0,0,3,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,4,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,1,0\ntest,9,1,4,3,3,1,3,1,5,7,1,2,1,2,7,1,2,7,0,0,0,3,3,4,0,1,1,6,3,8,1,6,1,3,7,2,2,6,2,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,6,2,2,7,0,2,2,4,4,0,3,6,1,0,0,3,3,3,1,1,4,4,2,0,9,7,1,2,7,2,0,7,2,0,0,3,5,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,3,2,4,7,1,2,2,3,5,1,7,1,4,0,0,5,1,1,2,3,4,1,0,6,3,7,1,2,4,5,3,4,3,1,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,0,4,1,5,6,1,3,3,3,3,0,3,6,0,0,0,4,2,4,0,2,2,5,2,9,0,6,0,3,7,2,5,4,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,3,1,2,5,3,0,9,0,0,2,5,4,4,3,2,6,0,0,3,1,0,4,2,3,1,0,0,9,5,4,0,0,9,0,0,8,1,0,5,8,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,2,3,1,5,2,3,8,1,1,1,4,4,1,6,3,2,0,0,3,5,1,2,1,2,5,1,1,8,8,0,1,7,2,2,5,3,0,0,4,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,1,3,2,4,7,1,2,1,2,6,3,5,1,3,0,0,5,1,1,2,2,4,3,0,6,3,7,1,2,5,4,3,3,4,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,4,1,5,7,1,2,2,2,6,1,3,6,0,0,0,3,2,4,0,1,2,5,2,7,2,6,2,2,9,0,4,4,2,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,1,6,0,3,9,0,0,0,4,5,8,1,0,9,0,0,0,0,0,9,0,0,0,0,0,9,7,2,0,2,7,0,1,3,5,0,7,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,3,3,7,0,4,1,4,6,1,3,1,4,5,1,3,5,3,0,1,4,1,2,1,2,4,3,1,2,7,4,0,5,7,2,2,2,2,4,1,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,4,3,1,3,5,2,3,3,4,3,1,3,5,2,0,1,3,3,2,1,1,2,4,2,7,2,7,2,0,4,5,3,3,3,1,1,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,4,2,0,6,0,3,3,2,5,2,4,3,2,4,3,4,1,2,2,2,0,3,2,3,2,1,1,8,7,1,2,6,3,1,4,4,1,0,4,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,2,2,5,7,0,2,0,6,3,0,2,7,0,0,0,3,4,3,0,2,0,7,0,9,0,7,2,0,9,0,6,3,0,0,0,3,3,0,0,0,6,0,4,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,2,3,8,0,5,2,2,6,1,2,2,3,4,3,1,6,2,0,0,3,3,2,2,1,2,5,0,2,7,7,0,2,8,1,2,5,2,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,6,1,2,0,0,9,0,0,9,2,1,1,2,3,2,0,0,5,4,0,9,0,6,1,3,7,2,4,5,0,0,0,3,1,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,1,3,7,1,2,2,3,5,2,4,4,3,1,2,2,2,1,3,2,3,3,1,3,6,6,2,1,5,4,3,2,3,2,1,4,3,0,0,0,6,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0\ntest,33,1,4,3,8,0,6,0,3,9,0,0,0,3,6,0,0,9,0,0,3,0,6,0,0,0,3,6,0,9,0,7,2,0,9,0,3,5,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,2,3,10,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,0,6,1,3,6,1,3,3,2,5,0,2,7,0,0,1,2,4,3,0,1,2,5,2,6,3,5,1,3,8,1,5,2,3,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,7,2,3,2,2,4,2,1,4,5,2,3,2,2,5,4,4,2,3,1,1,4,1,2,3,2,3,2,1,4,5,6,1,2,5,4,2,3,3,2,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,4,5,3,3,0,4,0,1,8,8,1,1,6,2,1,5,0,0,3,0,1,4,1,5,1,0,5,4,5,0,4,5,4,5,1,4,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,2,2,1,3,1,5,7,1,2,0,9,0,0,5,5,2,2,1,4,1,2,0,0,3,4,2,0,9,5,2,2,4,5,2,3,2,3,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,4,1,4,7,1,2,2,4,4,2,4,4,3,1,2,2,2,1,2,2,3,3,1,4,5,6,2,1,5,4,3,3,3,2,1,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,0,4,0,5,3,0,6,6,2,2,1,1,7,0,0,0,0,5,4,0,0,0,6,3,9,0,5,0,4,9,0,6,3,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,1,4,2,8,0,8,1,0,9,0,0,0,2,7,2,3,4,3,1,0,4,1,1,3,2,2,4,0,0,9,7,2,0,6,3,0,6,3,0,0,4,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,2,3,4,2,3,4,3,1,1,2,2,3,3,1,3,3,1,2,7,7,2,1,5,4,2,4,3,1,1,4,5,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,1,6,0,2,7,0,2,0,5,4,0,0,9,5,0,0,0,0,4,0,0,0,5,4,9,0,6,0,3,7,2,0,4,5,0,0,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,1,6,1,2,5,1,3,0,5,4,0,5,4,0,0,0,0,5,4,0,0,0,9,0,0,9,6,1,3,7,2,3,4,3,1,0,4,1,0,0,0,6,0,0,0,0,0,0,0,0,0,2,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,3,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,2,2,2,5,0,3,1,6,4,1,5,3,0,6,0,0,9,0,0,0,0,4,5,0,0,0,4,5,9,0,5,1,3,8,1,5,4,0,0,0,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,3,4,3,1,7,1,1,1,4,5,1,6,2,1,0,0,6,1,2,1,4,2,3,1,4,5,4,2,3,7,2,2,2,5,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,0,9,0,0,6,0,3,4,3,2,0,5,4,3,0,0,2,2,4,1,1,3,4,0,6,3,6,0,3,4,5,6,0,3,0,0,3,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,3,5,0,5,1,4,2,1,7,3,4,2,2,5,2,1,1,1,7,2,1,1,4,1,4,0,8,1,6,1,3,7,2,3,6,1,1,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,4,3,1,1,5,1,3,6,1,2,0,2,7,8,1,1,6,0,0,2,2,1,5,3,2,1,0,0,9,7,2,1,5,4,0,1,4,5,1,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,7,1,2,4,2,3,2,0,5,6,0,3,4,4,1,2,7,0,2,2,0,5,0,0,4,5,0,0,0,0,9,7,0,2,6,3,4,2,1,4,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,2,2,6,1,2,1,4,5,4,1,5,2,0,0,3,3,2,3,1,5,2,0,3,6,7,0,2,8,1,2,5,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,0,3,2,4,8,0,1,1,1,8,2,2,5,2,0,0,3,4,1,2,2,0,6,1,1,8,8,1,0,6,3,3,5,2,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,2,2,2,3,2,2,0,5,3,4,3,3,4,3,6,2,2,6,0,0,2,2,0,6,2,0,2,0,7,2,7,2,0,3,6,2,2,6,0,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,3,10,1,5,1,3,6,1,2,2,4,4,2,4,4,2,0,4,3,1,1,3,2,4,2,0,1,8,6,3,1,3,6,2,2,5,2,1,5,3,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0\ntest,4,2,2,4,1,0,5,1,3,6,1,3,3,4,3,3,3,4,3,1,1,3,2,2,3,2,2,3,1,3,6,6,1,3,7,2,3,2,4,1,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,2,1,6,0,3,2,3,4,1,2,7,1,0,2,2,1,5,1,2,2,5,1,1,8,7,0,2,7,2,0,5,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,4,2,9,2,4,2,2,8,0,1,1,3,6,1,3,6,1,0,0,2,4,4,1,1,2,5,2,9,0,8,1,1,8,1,1,6,2,2,0,4,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,2,5,1,0,9,0,0,9,0,0,0,7,2,6,3,0,6,2,0,2,0,0,7,2,0,0,0,0,9,9,0,0,0,9,2,3,3,2,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,2,2,6,6,0,3,3,3,4,0,4,5,1,0,0,3,5,2,1,2,0,7,1,8,1,5,0,4,9,0,0,0,9,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,1,2,1,0,5,0,4,0,3,6,6,3,0,3,3,3,0,0,0,6,0,3,0,4,2,2,2,7,2,5,0,4,7,2,5,4,0,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,2,5,0,2,2,7,4,2,3,3,5,2,1,3,6,0,0,0,2,4,4,0,1,3,5,0,9,0,4,0,5,7,2,3,4,2,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,3,7,1,2,0,5,4,3,5,1,2,1,0,4,1,2,2,3,4,1,1,2,7,7,0,2,5,4,1,2,5,2,1,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,6,0,3,5,2,2,2,2,5,0,2,7,0,0,0,2,7,0,0,0,0,9,0,2,7,9,0,0,7,2,0,9,0,0,0,3,4,2,0,0,6,0,5,0,2,0,0,0,0,0,0,0,4,0,0,0,0,3,1,0,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,29,1,2,3,7,1,4,1,4,5,1,4,3,3,3,0,4,5,1,1,1,4,2,2,1,2,3,4,1,2,7,5,1,4,7,2,3,3,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,1,6,1,2,5,1,3,2,4,3,1,4,4,2,1,0,4,1,3,2,2,3,4,1,3,6,6,1,3,7,2,3,4,3,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,1,2,8,0,1,1,4,4,0,4,5,1,0,0,4,2,2,1,1,3,4,2,0,9,7,1,2,6,3,0,5,4,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,2,3,1,2,1,5,7,2,0,0,2,7,2,5,2,3,1,0,4,2,0,2,4,2,2,0,0,9,7,1,2,6,3,1,5,3,1,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,6,2,2,6,0,3,3,2,5,0,3,6,0,0,9,0,0,0,3,0,2,0,4,0,9,5,2,3,7,2,5,4,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,0,5,0,4,7,0,2,2,5,2,3,4,2,5,0,0,4,0,0,5,0,3,2,0,6,3,6,3,0,2,7,3,4,3,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0\ntest,31,1,2,4,7,0,6,0,3,4,0,5,3,4,3,0,4,5,2,0,0,2,4,3,1,0,2,4,3,9,0,5,0,4,5,4,6,2,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,2,3,2,4,5,0,4,3,3,3,4,3,3,2,2,2,3,2,0,4,3,2,2,0,4,5,5,2,3,6,3,5,2,3,0,0,3,4,0,0,4,6,0,0,0,2,4,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,12,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,4,1,0,5,2,2,7,0,2,2,4,4,0,4,5,1,0,0,3,2,5,0,1,4,3,2,2,7,6,0,3,8,1,2,4,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,6,2,3,3,2,1,4,0,4,7,0,2,2,2,6,3,4,3,4,0,2,3,0,1,4,2,3,1,0,0,9,7,2,0,4,5,2,3,3,2,1,6,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,28,3,2,4,6,1,6,1,3,7,1,2,2,6,2,3,2,4,4,1,1,2,2,2,3,1,2,4,1,3,6,7,0,2,5,4,3,3,3,1,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,1,5,2,3,5,1,3,0,3,6,0,2,7,0,0,2,3,0,4,0,1,3,3,3,7,2,6,1,3,7,2,3,5,2,0,0,3,6,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,2,9,0,6,3,0,9,0,0,0,4,5,2,4,4,2,2,0,4,2,1,2,3,2,4,0,9,0,7,0,2,6,3,2,5,3,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,6,2,2,7,1,2,0,3,6,0,0,9,2,0,0,3,3,2,0,0,0,9,0,3,6,7,1,2,7,2,0,4,0,5,0,6,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,0,5,2,2,6,1,2,2,3,4,3,1,6,2,0,0,3,3,2,2,1,2,5,0,2,7,7,0,2,8,1,2,5,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,6,3,0,9,0,0,0,4,5,2,4,4,2,2,0,4,2,1,2,3,2,4,0,9,0,7,0,2,6,3,2,5,3,0,0,3,4,0,0,0,6,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,2,4,10,0,7,0,2,5,0,4,3,5,2,1,4,4,2,0,4,3,2,0,2,1,4,2,2,3,6,4,2,3,4,5,4,3,0,2,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,1,7,0,2,7,0,2,1,3,6,3,5,1,6,0,0,2,0,1,6,1,3,1,0,4,5,7,2,1,5,4,1,1,8,0,0,5,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,4,3,7,1,4,1,4,7,1,2,0,3,6,0,0,9,5,0,0,2,0,3,0,0,6,3,0,6,3,6,2,1,5,4,7,2,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,3,1,1,2,1,0,5,0,4,0,3,6,6,3,0,3,3,3,0,0,0,6,0,3,0,4,2,2,2,7,2,5,0,4,7,2,5,4,0,0,0,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,2,2,2,5,0,3,1,6,4,1,5,3,0,6,0,0,9,0,0,0,0,4,5,0,0,0,4,5,9,0,5,1,3,8,1,5,4,0,0,0,2,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,2,1,0,6,8,0,1,1,1,7,1,5,4,2,1,0,4,3,0,2,2,2,4,0,5,4,6,2,1,2,7,2,3,3,2,1,5,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,1,2,2,6,6,1,2,1,2,6,1,2,7,0,0,0,3,4,3,0,1,1,6,2,8,1,5,1,4,8,1,3,5,1,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,4,7,0,4,0,5,3,0,6,6,2,2,1,1,7,0,0,0,0,5,4,0,0,0,6,3,9,0,5,0,4,9,0,6,3,0,0,0,2,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,5,7,1,1,0,4,5,0,6,3,1,1,0,4,2,3,0,0,4,3,3,0,9,6,2,2,8,1,0,6,0,3,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,2,9,2,0,2,5,9,0,0,0,3,6,2,4,4,2,0,0,3,5,0,2,0,3,5,0,0,9,9,0,0,7,2,0,5,4,0,0,4,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,10,1,3,3,3,0,9,0,0,7,0,2,2,3,5,5,2,3,3,0,0,3,2,1,2,3,3,2,0,0,9,9,0,0,3,6,2,0,4,4,0,7,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,2,4,2,2,4,1,4,3,1,5,4,3,2,3,4,3,3,1,0,3,1,3,3,2,2,3,1,6,3,5,1,4,5,4,2,3,4,1,0,4,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,0,4,0,5,8,0,1,1,1,8,1,4,5,1,0,0,2,0,7,1,2,2,5,1,8,1,5,4,1,8,1,5,4,0,0,0,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,3,5,1,4,1,4,6,0,3,2,2,5,1,4,5,2,1,1,3,3,2,2,2,2,4,1,3,6,6,1,2,6,3,3,2,3,1,1,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,7,1,2,1,4,4,1,2,6,1,0,1,3,2,4,1,1,2,6,1,5,4,5,2,3,6,3,2,5,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,0,2,3,4,6,0,3,2,0,7,0,3,6,0,0,0,3,3,4,0,2,0,7,0,9,0,7,0,2,9,0,2,5,3,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,4,3,8,2,7,0,0,9,0,0,0,4,5,0,3,6,2,0,0,6,0,2,0,1,4,4,0,2,7,7,2,0,7,2,0,7,2,0,0,3,3,0,6,0,0,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,2,9,0,5,1,4,7,1,2,1,1,8,0,1,8,1,0,0,2,4,4,1,1,1,5,4,8,1,6,1,2,9,0,2,6,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,3,0,3,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1\ntest,8,1,3,3,2,2,5,2,1,7,2,0,0,2,7,4,4,2,3,0,0,5,2,1,2,3,3,2,1,0,9,9,0,0,4,5,1,2,7,0,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,2,2,2,4,0,4,9,0,0,0,0,9,4,5,0,7,0,0,2,0,0,7,0,2,0,0,0,9,7,2,0,0,9,0,2,7,0,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,6,2,3,6,0,3,0,3,6,1,6,3,2,0,0,5,2,2,2,2,4,2,0,7,2,7,0,2,6,3,3,3,3,1,0,4,4,2,0,0,5,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,1,4,2,3,5,2,3,2,4,4,3,2,4,3,1,1,2,3,2,1,3,2,4,1,1,8,6,2,2,6,3,3,5,2,1,0,3,5,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,1,4,0,5,7,0,2,2,5,3,1,6,2,2,0,0,6,0,2,1,1,6,2,0,8,1,5,1,4,6,3,1,1,8,0,0,5,4,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,4,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,0\ntest,27,1,2,5,6,1,5,2,3,5,1,3,5,1,3,1,1,8,6,0,0,3,0,0,1,1,4,0,5,6,3,6,1,3,7,2,6,3,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,0,5,9,0,0,0,9,0,0,0,9,5,0,0,0,5,0,0,0,5,4,0,5,5,9,0,0,9,0,5,5,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,3,3,8,0,6,1,3,7,0,2,1,3,6,1,2,7,1,0,1,3,3,4,1,2,1,6,1,1,8,8,0,1,8,1,0,2,7,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0\ntest,22,1,2,3,5,1,5,2,2,5,2,2,2,5,2,2,2,6,2,0,0,2,3,2,2,1,1,6,0,9,0,7,2,1,7,2,3,1,6,1,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,38,1,2,3,9,1,5,1,3,5,1,3,2,3,4,2,3,5,2,1,1,3,1,3,2,2,2,3,2,4,5,6,2,2,6,3,3,4,2,1,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,1,4,1,1,5,2,2,4,0,5,6,3,1,3,2,5,4,0,1,2,2,2,3,1,1,3,2,5,4,4,2,4,4,5,3,3,3,0,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,3,1,1,6,1,2,6,1,2,2,3,4,1,3,5,2,1,1,4,2,2,1,2,3,4,1,2,7,5,2,3,5,4,2,4,3,1,1,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,3,5,2,4,1,3,3,2,5,5,3,2,2,5,3,1,0,0,3,2,4,1,2,4,3,1,8,1,5,0,4,8,1,6,2,1,0,1,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,0,4,2,4,7,1,2,2,5,3,0,2,7,2,0,1,2,4,2,1,1,1,7,1,6,3,6,1,2,7,2,6,3,1,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,0,4,5,0,4,3,4,3,0,0,9,0,0,0,0,5,4,0,0,0,7,2,6,3,6,1,2,6,3,9,0,0,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,20,1,2,3,5,1,4,2,3,6,2,2,2,4,3,1,2,6,1,0,0,3,2,4,1,1,1,6,2,9,0,7,2,2,8,1,3,3,3,1,0,4,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,2,2,4,6,0,5,2,3,7,0,2,2,5,3,0,1,8,0,0,0,2,4,3,0,0,1,7,2,9,0,8,0,1,7,2,5,4,0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,2,0,5,2,2,2,6,2,0,3,6,5,0,0,1,0,4,2,0,2,4,2,4,5,4,4,2,3,6,3,6,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,2,5,1,3,2,4,6,1,2,2,3,5,0,4,5,2,0,0,0,5,3,0,1,4,3,1,4,5,5,1,3,7,2,2,5,3,0,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,6,0,3,5,2,2,2,2,5,0,2,7,0,0,0,2,7,0,0,0,0,9,0,2,7,9,0,0,7,2,0,9,0,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0\ntest,35,1,3,3,8,0,4,0,5,5,2,3,2,2,6,0,4,5,0,0,0,4,4,1,0,0,2,7,1,1,8,6,2,2,8,1,4,1,0,4,0,5,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,2,3,2,4,8,1,1,1,5,4,2,1,7,2,0,0,3,3,2,2,1,1,7,0,9,0,6,2,2,7,2,2,3,2,4,0,6,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,27,1,2,5,6,1,4,2,3,3,0,6,5,3,2,2,2,6,2,2,0,3,2,3,1,1,2,4,2,8,1,2,2,5,5,4,7,1,0,0,1,3,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,1,4,2,3,5,2,3,2,4,4,2,6,3,2,1,1,3,2,2,1,1,4,3,1,1,8,6,2,2,6,3,1,4,3,2,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,37,1,3,3,8,0,4,1,4,6,1,3,1,5,4,1,5,3,1,0,1,4,2,3,1,3,4,2,1,2,7,4,0,5,7,2,2,3,3,1,1,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,3,3,7,1,5,2,3,5,1,3,0,5,4,0,1,8,0,0,2,3,0,5,0,1,1,4,5,8,1,6,1,3,7,2,3,6,0,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,3,4,6,1,2,1,2,7,0,3,6,0,0,0,2,2,6,0,1,1,7,1,9,0,7,0,2,7,2,6,3,1,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,2,5,0,3,1,6,6,2,1,1,4,4,2,2,5,2,0,0,3,2,3,2,1,2,5,1,8,1,7,1,2,7,2,2,6,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,34,1,2,4,8,0,7,0,2,7,0,2,0,5,4,2,2,6,4,0,2,0,2,2,3,0,2,3,2,0,9,5,4,0,6,3,0,7,2,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,5,2,8,0,8,1,0,9,0,0,0,1,8,1,5,4,2,2,0,4,1,2,2,2,2,4,0,0,9,5,4,0,7,2,0,7,2,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,9,1,2,3,3,1,4,0,5,7,0,2,2,5,3,1,6,2,2,0,0,6,0,2,1,1,6,2,0,8,1,5,1,4,6,3,1,1,8,0,0,5,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,5,0,0,0,6,0,5,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,0,4,1,5,3,1,6,3,2,4,0,1,8,1,0,0,1,0,7,0,0,2,2,5,8,1,4,2,4,7,2,7,2,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,2,2,4,8,0,3,2,5,5,2,3,3,5,3,0,3,6,1,0,5,1,3,2,1,2,4,3,2,5,4,4,5,1,7,2,2,5,1,2,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,3,7,0,3,0,6,5,0,4,1,4,4,0,0,9,0,0,0,0,2,7,0,0,0,4,5,8,1,6,0,3,9,0,2,5,3,1,0,4,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,6,2,3,3,2,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,21,1,1,2,5,2,2,2,4,6,0,3,6,3,1,0,2,7,2,0,0,3,1,5,0,2,1,3,5,9,0,4,3,3,9,0,4,5,0,0,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,3,0,6,9,0,0,0,4,5,2,0,7,2,0,5,0,2,0,2,1,5,3,0,2,7,7,2,0,7,2,4,2,4,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,0,4,0,5,3,0,6,6,2,2,1,2,7,0,0,0,0,5,4,0,0,0,6,3,9,0,5,0,4,9,0,6,3,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,7,0,2,4,0,5,3,4,3,2,2,6,4,0,2,1,0,3,2,1,3,4,2,4,5,6,0,3,6,3,4,3,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,0,4,2,4,5,1,3,2,5,3,2,2,6,3,1,0,1,4,2,3,1,2,4,1,2,7,7,1,2,5,4,2,4,3,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,3,3,5,2,1,0,6,7,1,2,2,4,4,2,5,2,3,0,0,4,2,0,3,3,1,3,0,9,0,7,1,2,6,3,2,4,2,1,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,4,9,0,3,5,1,5,0,4,4,4,2,2,4,4,1,1,0,3,2,4,2,1,3,3,2,7,2,6,1,2,6,3,0,2,4,3,0,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,1,5,1,3,6,1,2,2,4,4,2,4,4,2,0,4,3,1,1,3,2,4,2,0,1,8,6,3,1,3,6,2,2,5,2,1,5,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,4,3,3,1,3,1,5,7,1,2,1,2,7,1,2,7,0,0,0,3,3,4,0,1,1,6,3,8,1,6,1,3,7,2,2,6,2,1,0,3,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,0,5,9,0,0,0,2,7,1,1,8,1,0,0,2,6,2,1,1,1,5,4,3,6,6,1,2,7,2,3,5,2,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,2,5,2,3,1,5,2,2,5,6,3,1,1,4,5,1,0,0,1,3,5,1,1,2,6,1,8,1,5,0,4,8,1,6,3,1,1,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,3,8,0,4,1,4,6,1,3,1,6,2,2,2,5,1,0,2,3,3,2,2,1,3,3,1,3,6,4,0,5,7,2,2,3,1,3,2,6,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,1,3,2,8,0,5,1,3,7,0,2,1,2,6,1,2,7,1,0,1,2,5,2,1,1,2,6,1,1,8,8,0,1,8,1,0,4,5,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,2,2,2,1,1,6,9,0,0,0,2,7,7,2,1,7,0,0,1,1,1,6,1,2,1,0,0,9,8,1,0,1,8,0,1,7,2,1,6,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,1,3,1,5,4,2,4,0,4,5,0,6,3,0,0,0,6,0,3,0,6,0,3,0,3,6,6,0,3,8,1,3,3,4,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,2,4,2,4,7,1,2,2,3,5,3,5,2,3,1,1,4,2,2,3,2,3,2,1,6,3,7,1,2,6,3,1,3,5,1,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,10,1,4,2,3,2,6,0,3,6,1,3,1,0,8,2,6,2,2,1,0,5,0,3,2,3,3,2,1,4,5,5,2,2,5,4,2,3,4,1,1,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,22,1,2,2,5,0,3,1,6,6,2,1,1,4,4,2,2,5,2,0,0,3,2,3,2,1,2,5,1,8,1,7,1,2,7,2,2,6,2,0,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,2,7,0,0,7,1,2,1,3,5,0,4,5,2,1,0,3,2,3,1,3,2,4,1,0,9,6,2,2,5,4,0,5,3,1,0,4,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,2,8,0,4,2,4,3,2,4,4,4,2,0,5,4,0,0,0,7,0,2,0,5,0,4,0,7,2,7,0,2,9,0,4,5,0,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,4,8,0,6,0,3,7,0,2,0,3,6,0,3,6,2,1,2,1,3,3,1,0,4,5,0,1,8,7,2,0,7,2,4,3,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,5,8,2,6,2,2,2,0,7,3,4,3,0,2,7,1,0,1,1,1,7,0,1,1,4,4,3,6,5,0,4,9,0,2,5,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,2,5,1,3,2,4,6,1,2,2,3,5,0,4,5,2,0,0,0,5,3,0,1,4,3,1,4,5,5,1,3,7,2,2,5,3,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,0,5,2,3,9,0,0,3,4,3,2,4,4,4,2,0,4,0,0,4,2,2,2,0,3,6,9,0,0,2,7,0,3,6,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,22,2,3,3,5,0,9,0,0,5,0,4,3,0,6,0,5,4,3,0,0,2,0,4,3,0,3,3,0,3,6,5,2,3,7,2,4,2,3,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,2,3,0,3,0,6,6,0,3,1,4,4,5,3,1,1,0,0,8,1,0,1,6,2,1,0,8,1,7,0,2,3,6,1,2,4,4,1,7,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,1,4,1,4,7,1,2,1,5,4,0,1,8,5,0,0,2,0,2,1,1,5,4,0,2,7,6,2,1,5,4,1,4,5,1,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,2,9,0,6,3,0,9,0,0,0,4,5,2,4,4,2,2,0,4,2,1,2,3,2,4,0,9,0,7,0,2,6,3,2,5,3,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,4,3,7,0,4,0,5,8,1,0,0,3,6,0,0,9,0,0,0,2,5,2,0,0,0,8,1,7,2,7,0,2,7,2,2,4,4,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,23,1,2,3,5,0,4,2,3,2,3,5,5,3,1,5,3,1,5,0,0,3,2,0,4,2,0,3,0,1,8,5,0,4,5,4,0,0,9,0,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,2,2,3,0,9,0,0,4,0,5,5,0,4,5,0,4,0,0,0,9,0,0,0,5,0,4,0,5,4,9,0,0,4,5,0,9,0,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,3,9,2,4,2,3,5,2,3,2,4,3,1,3,5,1,1,0,3,3,2,2,2,2,5,1,6,3,7,1,2,7,2,3,4,3,1,0,3,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,2,2,4,7,0,3,3,4,5,1,4,2,5,2,1,4,5,2,1,0,1,5,2,1,1,4,4,1,3,6,7,1,2,7,2,5,3,1,1,0,3,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,7,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,38,1,3,3,9,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,31,1,3,3,7,0,4,1,5,5,1,4,3,2,4,0,1,8,1,0,0,1,0,8,0,0,1,5,3,8,1,6,1,3,7,2,7,2,0,0,0,2,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,2,2,2,5,0,3,9,0,0,0,3,6,2,5,3,2,0,0,4,2,2,2,2,3,4,0,3,6,7,2,0,6,3,2,0,7,0,0,5,7,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,0,2,6,0,3,3,1,6,0,6,3,3,0,4,2,0,1,3,2,5,1,0,0,9,3,3,3,2,7,1,5,4,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,3,5,1,5,1,3,6,1,2,0,0,9,0,0,9,2,1,1,2,3,2,0,0,5,4,0,9,0,6,1,3,7,2,4,5,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,4,2,3,2,4,1,3,7,1,1,1,2,6,3,4,2,3,0,0,4,2,1,3,3,3,2,0,2,7,9,0,0,5,4,2,3,5,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,3,4,0,3,6,0,3,3,2,5,0,5,4,0,0,0,4,5,0,0,0,5,4,0,0,9,9,0,0,5,4,2,7,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,0,4,7,1,1,1,4,5,2,4,4,3,0,1,2,2,2,4,2,1,4,0,5,4,7,2,0,8,1,3,2,4,1,0,4,4,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,2,3,0,5,0,4,9,0,0,0,0,9,2,3,4,4,0,0,4,0,2,4,2,0,4,0,0,9,9,0,0,5,4,2,2,6,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,3,5,1,3,2,5,5,2,2,4,3,3,1,4,5,1,0,0,3,1,4,1,1,3,3,3,7,2,6,1,3,8,1,2,5,3,1,0,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,0,7,1,2,6,0,3,3,4,3,0,1,8,0,0,0,3,0,6,0,1,1,8,1,9,0,8,0,1,9,0,7,1,1,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,3,3,1,0,5,1,3,7,1,2,3,2,5,1,3,6,2,1,1,2,3,3,1,1,1,5,2,5,4,5,2,2,7,2,4,4,1,1,1,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,1,5,3,2,6,0,3,4,2,3,0,4,5,3,0,0,2,2,4,3,0,1,4,1,5,4,6,1,2,9,0,3,5,1,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,4,8,1,4,2,3,5,0,4,5,0,4,8,0,1,0,0,0,9,0,0,0,8,0,0,1,1,8,5,1,4,9,0,0,0,9,0,0,5,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,4,0,5,9,0,0,0,4,5,0,3,6,0,0,0,0,5,5,0,0,2,5,2,9,0,3,2,4,9,0,6,3,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,0,5,0,5,9,0,0,0,9,0,0,9,0,0,0,0,9,0,0,0,9,0,0,0,9,0,9,0,0,5,5,0,9,0,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,1,4,1,1,5,2,2,4,0,5,6,3,1,3,2,5,4,0,1,2,2,2,3,1,1,3,2,5,4,4,2,4,4,5,3,3,3,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,2,3,0,5,4,2,4,3,2,5,0,4,5,0,0,0,2,2,5,0,2,3,3,2,9,0,3,0,6,9,0,7,2,0,0,0,2,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,1,3,2,5,0,2,7,2,0,0,6,3,5,3,2,2,2,0,3,4,0,3,3,0,4,0,0,9,7,0,2,4,5,0,0,7,2,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,3,8,0,7,0,2,6,0,3,3,2,4,3,3,4,3,0,2,0,3,2,3,0,2,5,0,2,7,3,0,6,7,2,3,4,2,2,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,9,1,3,2,3,2,4,2,4,7,1,2,2,3,5,3,5,2,3,1,1,4,2,2,3,2,3,2,1,6,3,7,1,2,6,3,1,3,5,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,3,7,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,1,3,8,1,1,1,2,7,0,2,7,1,0,4,1,1,5,1,2,3,4,1,1,8,7,1,1,8,1,2,3,2,4,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,20,1,3,3,5,1,1,2,6,5,1,3,3,3,4,0,5,4,2,0,1,3,3,2,2,2,2,5,0,3,6,5,2,2,6,3,2,4,3,0,0,4,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,33,1,2,4,8,0,5,4,1,6,1,2,2,5,2,1,1,8,1,0,0,2,5,2,1,1,1,8,0,4,5,7,0,2,8,1,4,4,2,0,0,3,3,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,1,4,1,4,5,1,4,5,1,3,0,4,5,2,1,1,4,1,3,2,2,1,4,2,1,8,5,1,4,7,2,2,6,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,26,1,2,5,6,0,8,1,1,5,0,4,3,5,1,1,3,5,1,0,0,2,1,6,1,1,3,3,2,6,3,4,0,5,9,0,6,3,0,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,4,7,1,3,1,5,5,1,4,3,4,3,0,2,8,0,0,0,1,4,4,0,1,1,5,3,8,1,5,0,4,7,2,6,3,1,1,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,4,4,8,1,4,1,4,6,0,3,2,2,5,0,0,9,2,1,1,3,3,2,0,4,5,0,0,3,6,6,1,2,6,3,0,9,0,0,0,4,5,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,2,3,2,3,0,6,0,3,7,2,0,3,3,4,3,3,4,1,0,0,5,4,1,0,5,1,4,0,0,9,9,0,0,5,4,3,4,3,0,0,3,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,3,5,1,4,1,5,7,1,1,0,4,5,9,0,0,5,0,0,0,0,4,3,6,0,0,0,9,0,6,2,2,8,1,0,4,5,0,0,5,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,0,4,7,0,2,2,5,2,3,4,2,5,0,0,4,0,0,5,0,3,2,0,6,3,6,3,0,2,7,3,4,3,0,0,4,5,2,0,0,6,0,0,0,0,0,0,0,5,0,0,0,4,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0\ntest,36,1,1,3,8,1,6,1,2,1,0,8,8,1,1,0,4,5,1,0,2,2,4,1,0,4,1,5,1,2,7,3,0,6,9,0,8,1,1,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,1,2,3,6,6,0,0,3,4,0,5,4,0,5,0,4,5,0,0,0,0,6,3,0,0,5,4,0,6,3,4,0,5,6,3,3,4,2,2,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,4,8,1,4,2,3,5,2,3,2,5,3,2,3,5,2,1,2,2,3,2,1,1,4,3,1,1,8,6,2,2,6,3,2,6,2,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,4,7,1,4,2,4,6,1,3,0,5,4,0,2,7,0,0,0,0,0,9,0,0,2,2,5,5,4,5,1,3,7,2,0,7,0,2,0,4,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,2,4,3,4,2,0,5,4,0,3,6,2,0,2,2,3,2,0,1,5,3,1,2,7,5,2,3,6,3,0,6,2,2,1,5,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,4,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,0,4,0,5,9,0,0,2,4,3,0,0,9,0,0,0,4,5,0,0,0,0,9,0,6,3,9,0,0,9,0,3,4,3,1,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,0,5,0,4,9,0,0,2,2,5,3,3,4,3,0,0,2,4,1,3,2,0,4,2,0,9,9,0,0,6,3,2,3,5,0,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,4,2,2,0,7,2,0,9,0,0,0,2,7,4,2,4,4,1,0,4,2,1,4,2,1,4,0,0,9,8,1,0,5,4,0,6,3,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,0,6,1,3,3,0,6,6,1,3,2,2,5,2,0,0,4,3,1,2,2,0,5,1,8,1,5,0,4,9,0,5,2,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,7,2,2,4,2,0,5,0,5,5,5,0,0,9,0,3,6,0,5,0,0,5,0,0,3,2,5,0,0,0,9,6,3,0,3,6,0,3,5,2,0,6,6,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,2,2,2,5,1,3,2,4,2,4,3,3,3,3,2,5,3,2,0,0,5,3,0,2,1,4,4,0,5,4,7,0,2,9,0,5,3,2,0,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,2,3,10,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,0,4,0,5,6,0,3,3,4,3,4,3,4,4,3,0,0,4,0,5,0,0,4,0,0,9,6,2,2,3,6,2,3,4,0,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,6,0,3,5,1,4,2,3,5,0,1,8,1,0,0,3,2,5,1,1,2,5,2,7,2,4,0,5,8,1,3,3,1,3,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,2,5,0,3,9,0,0,1,3,5,7,2,0,9,0,0,0,0,0,6,2,1,0,0,0,9,7,2,0,2,7,1,2,3,4,1,7,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,6,0,3,2,5,3,2,2,5,2,0,0,3,3,2,2,0,0,7,0,5,4,6,0,3,7,2,3,2,5,0,0,4,3,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0\ntest,33,1,2,3,8,3,4,0,3,6,0,3,3,2,5,0,5,4,0,0,0,4,5,0,0,0,5,4,0,0,9,9,0,0,5,4,2,7,0,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,20,1,2,3,5,1,4,2,3,6,2,2,2,4,3,1,2,6,1,0,0,3,2,4,1,1,1,6,2,9,0,7,2,2,8,1,3,3,3,1,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,2,3,1,5,1,4,7,1,2,0,5,4,0,5,4,4,0,1,3,1,2,0,0,0,9,0,7,2,6,2,1,4,5,0,2,7,0,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,3,8,0,5,2,2,6,1,2,2,3,4,3,1,6,2,0,0,3,3,2,2,1,2,5,0,2,7,7,0,2,8,1,2,5,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,4,3,3,0,3,0,6,7,0,2,1,1,7,1,4,4,1,0,0,5,0,4,1,3,2,4,0,8,1,5,1,3,8,1,1,5,4,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,2,3,0,5,5,2,3,0,2,7,1,7,2,2,0,0,4,0,4,2,1,6,2,0,4,5,7,2,0,6,3,4,5,1,0,0,3,7,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,3,2,1,5,1,3,7,1,2,0,2,7,2,5,2,2,1,0,5,2,1,2,3,3,2,1,1,8,7,0,2,5,4,1,2,6,1,1,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,28,7,2,6,6,0,6,2,2,9,0,0,4,2,4,2,1,6,5,2,0,0,3,0,2,1,4,0,4,4,5,6,3,0,0,9,7,0,1,2,0,3,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,2,5,1,4,2,4,7,1,1,1,3,6,1,2,7,0,0,0,2,5,3,0,1,1,6,3,8,1,5,1,4,7,2,2,5,2,1,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,2,3,3,2,4,1,4,4,1,4,4,2,4,1,5,3,3,0,0,5,1,1,1,3,2,4,0,8,1,7,0,2,3,6,4,2,2,2,0,4,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,4,2,10,1,5,1,3,6,1,3,0,3,6,0,2,7,4,0,2,2,0,3,0,2,6,2,0,2,7,6,1,2,6,3,0,0,9,0,0,5,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1\ntest,4,3,2,4,1,2,1,5,2,7,0,2,1,6,3,2,5,2,5,2,1,1,0,2,4,3,1,2,0,2,7,7,0,2,5,4,1,1,8,0,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,3,3,3,4,0,0,5,5,4,0,0,3,6,4,3,2,4,0,0,3,1,2,1,6,0,2,0,0,9,7,2,0,2,7,0,1,4,3,2,9,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,2,8,0,6,0,3,5,0,4,1,1,8,2,2,6,0,0,1,2,6,1,0,2,1,5,3,1,8,8,1,1,8,1,3,3,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,3,5,2,4,2,3,5,2,3,2,4,3,1,3,5,1,1,0,3,3,2,2,2,2,5,1,6,3,7,1,2,7,2,3,4,3,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,3,8,0,4,1,4,6,1,3,1,6,2,2,2,5,1,0,2,3,3,2,2,1,3,3,1,3,6,4,0,5,7,2,2,3,1,3,2,6,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,5,2,4,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,1,1,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,41,1,2,3,10,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,3,1,2,3,1,0,6,0,3,5,1,3,3,4,2,3,2,5,3,0,0,4,3,0,2,2,0,3,3,3,6,6,1,2,5,4,2,0,7,0,0,5,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,2,2,2,5,0,5,2,2,4,2,4,4,4,2,2,4,3,3,1,0,2,1,4,5,2,1,2,2,7,2,6,1,3,8,1,4,3,3,0,0,3,3,2,2,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,5,0,4,6,0,3,3,3,4,2,4,4,3,0,2,3,2,1,3,1,5,2,1,0,9,6,3,0,4,5,0,4,4,1,1,5,5,2,0,0,5,0,6,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,1,2,3,8,0,2,1,7,5,2,3,3,3,5,0,1,8,0,0,0,2,7,1,0,1,1,8,0,5,4,5,4,0,9,0,3,2,5,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,9,1,1,2,3,0,6,1,2,1,2,6,5,3,2,2,5,2,2,0,0,4,1,3,2,3,3,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,4,6,2,1,1,5,3,0,3,6,1,0,0,4,3,2,1,1,2,6,0,6,3,6,0,3,5,4,2,2,5,0,0,4,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,1,3,7,1,2,3,4,3,0,3,6,0,0,0,2,2,6,0,2,3,3,3,5,4,9,0,0,5,4,2,2,3,3,0,5,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,7,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,2,3,2,3,1,5,6,2,2,2,3,5,3,5,2,2,1,0,4,2,2,2,2,3,3,1,4,5,7,2,2,6,3,2,3,4,2,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,2,3,2,2,2,5,0,3,9,0,0,3,3,4,7,2,0,9,0,0,0,0,0,0,6,3,0,0,0,9,7,2,0,2,7,3,6,0,0,0,3,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,1,4,1,5,7,1,1,0,4,5,9,0,0,5,0,0,0,0,4,3,6,0,0,0,9,0,6,2,2,8,1,0,4,5,0,0,5,6,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,2,4,2,3,0,2,0,7,9,0,0,0,0,9,0,3,6,0,0,0,7,0,2,0,2,0,6,2,0,9,9,0,0,9,0,2,0,5,2,0,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,1,2,3,0,5,1,4,4,3,2,6,3,1,4,5,1,2,0,0,4,3,2,1,1,1,7,2,8,1,8,0,1,9,0,3,5,2,0,0,3,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,3,8,0,4,1,4,6,1,3,1,5,3,1,5,3,1,0,1,2,1,5,1,1,5,3,1,3,6,4,0,5,7,2,3,3,1,3,1,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,2,5,1,2,4,0,5,4,4,1,5,3,2,3,2,0,3,3,0,5,2,1,2,0,1,8,8,1,1,1,8,3,0,2,1,4,9,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,1,4,1,5,5,1,4,3,4,2,0,4,5,1,1,2,4,3,1,1,3,3,3,1,2,7,5,1,4,7,2,5,3,2,0,0,3,6,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,1,4,2,3,6,1,2,1,3,6,2,5,3,2,1,0,5,1,3,2,3,3,3,1,7,2,5,2,3,7,2,2,3,4,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,1,3,5,0,3,0,6,5,0,4,5,3,1,1,2,7,0,0,0,1,2,7,0,1,1,4,5,8,1,6,0,3,9,0,6,1,2,1,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,3,0,6,9,0,0,2,2,5,0,4,5,0,0,0,3,6,0,0,0,4,5,0,9,0,5,0,4,6,3,5,4,0,0,0,2,3,2,2,0,7,0,6,0,0,0,0,0,0,0,0,0,3,0,0,0,0,2,1,1,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,3,1,2,3,1,3,2,1,3,5,2,2,2,5,2,2,4,4,2,2,0,2,3,1,3,2,0,4,0,6,3,7,0,2,6,3,5,0,0,4,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,38,1,2,2,9,0,4,2,4,7,1,2,3,4,3,1,1,8,1,0,0,3,6,0,1,1,1,8,0,3,6,7,1,1,8,1,0,7,2,0,0,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,2,2,1,1,3,6,8,0,1,1,2,6,2,3,4,2,0,0,3,4,1,2,3,1,4,1,5,4,7,2,0,7,2,1,2,5,2,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,4,3,3,0,6,1,3,9,0,0,0,1,8,0,4,5,0,0,0,3,4,3,0,0,5,4,0,4,5,7,2,0,9,0,0,9,0,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,3,8,0,5,2,3,5,2,3,2,6,1,1,3,5,2,1,0,3,1,4,1,1,2,3,3,4,5,7,1,2,6,3,3,4,3,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,4,3,10,1,4,1,4,6,0,3,0,3,6,0,6,3,2,0,5,0,0,3,4,2,3,1,1,0,9,6,1,2,6,3,0,1,8,0,0,5,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,2,4,2,4,7,1,2,6,0,3,1,8,0,3,0,0,1,1,5,0,3,6,1,0,6,3,7,1,2,6,3,4,4,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,2,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,5,2,3,5,1,4,1,4,4,5,3,1,2,3,0,4,1,1,5,3,1,1,1,2,7,5,1,3,5,4,3,1,4,2,1,5,6,2,0,0,5,0,5,0,0,0,0,0,5,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,3,5,1,5,2,2,5,2,2,2,5,2,2,2,6,2,0,0,2,3,2,2,1,1,6,0,9,0,7,2,1,7,2,3,1,6,1,0,4,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,0,6,1,3,5,0,4,3,3,4,1,3,5,1,0,1,6,1,3,1,2,2,3,3,2,7,7,0,2,7,2,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,2,8,0,4,2,4,3,2,4,4,4,2,0,5,4,0,0,0,7,0,2,0,5,0,4,0,7,2,7,0,2,9,0,4,5,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,4,2,3,7,1,2,2,3,4,1,3,5,1,1,1,2,3,3,2,2,2,4,1,4,5,6,2,2,7,2,1,4,4,1,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,0,4,2,3,5,1,4,3,4,3,2,6,2,1,0,0,2,3,4,1,2,3,5,0,4,5,5,2,3,4,5,3,3,3,1,0,4,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,4,1,2,4,1,1,5,0,4,4,2,4,4,4,2,2,4,4,3,0,0,4,1,2,3,2,3,3,0,6,3,7,1,2,6,3,3,3,4,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,41,1,3,3,10,1,1,1,7,8,1,1,1,4,5,1,3,5,3,1,1,1,4,2,2,1,2,4,2,2,7,8,1,0,5,4,4,3,2,1,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,4,2,4,2,2,6,4,2,4,0,2,7,1,0,0,2,0,7,0,0,2,1,7,7,2,2,2,6,7,2,7,2,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1\ntest,13,1,3,2,3,0,4,0,5,8,1,1,1,4,5,5,3,1,6,0,0,2,1,1,5,1,4,1,1,9,0,7,0,2,4,5,1,2,6,0,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,3,5,1,6,1,3,2,2,6,5,3,2,2,5,3,1,0,0,2,2,5,1,2,4,4,1,8,1,5,0,4,8,1,6,3,1,0,1,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,8,0,1,0,3,6,0,3,6,1,2,1,3,3,2,2,0,1,7,0,4,5,7,2,0,7,2,4,3,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,3,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,8,1,5,3,2,0,5,0,4,9,0,0,0,0,8,5,4,0,7,0,0,2,0,0,7,0,2,0,0,0,9,9,0,0,4,5,0,0,9,0,0,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,2,5,3,9,0,6,0,3,9,0,0,0,3,6,0,4,5,0,0,0,3,6,0,0,2,3,5,0,3,6,9,0,0,9,0,6,0,3,0,0,3,5,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,6,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,2,4,8,1,0,0,2,7,0,3,6,0,0,0,4,3,3,0,1,3,6,0,3,6,6,3,0,9,0,2,5,2,2,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,4,1,4,7,2,1,0,3,6,2,5,2,5,1,0,3,1,1,3,2,3,1,1,1,8,6,0,3,4,5,1,1,6,2,1,6,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,38,1,4,2,9,0,6,0,3,4,0,5,1,2,6,1,4,5,0,0,2,2,4,2,0,2,3,5,1,4,5,5,2,3,7,2,5,3,2,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,3,1,3,4,1,0,9,0,0,9,0,0,0,6,3,0,6,3,4,0,0,4,0,3,3,0,4,3,0,4,6,9,0,0,6,4,3,4,4,0,0,4,6,2,0,0,5,0,4,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,0,4,2,4,7,1,2,2,5,3,0,2,7,2,0,1,2,4,2,1,1,1,7,1,6,3,6,1,2,7,2,6,3,1,0,0,2,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,3,3,0,3,0,6,9,0,0,0,3,6,5,2,2,2,0,0,5,0,2,2,5,0,2,0,0,9,6,0,3,9,0,0,4,5,0,0,4,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,4,2,3,0,7,0,2,7,2,0,1,1,8,2,3,5,1,0,0,7,2,1,0,3,2,5,0,0,9,9,0,0,6,3,2,4,4,0,0,4,6,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,3,1,3,3,1,4,8,1,0,0,7,2,4,3,3,6,0,0,1,1,3,6,1,1,1,3,0,9,9,0,0,4,5,3,3,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,2,8,0,8,1,0,9,0,0,0,2,7,2,3,4,3,1,0,4,1,1,3,2,2,4,0,0,9,7,2,0,6,3,0,6,3,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,3,2,4,1,2,1,5,2,7,0,2,1,6,3,2,5,2,5,2,1,1,0,2,4,3,1,2,0,2,7,7,0,2,5,4,1,1,8,0,0,5,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,4,1,5,7,1,1,0,5,4,0,8,1,1,1,0,4,2,3,0,0,5,4,1,0,9,6,2,2,8,1,0,8,0,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,2,3,8,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,4,1,4,7,1,2,2,4,3,1,2,6,4,0,0,2,2,1,2,2,3,3,0,2,7,6,2,1,5,4,2,5,2,2,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,0,6,1,3,7,0,2,0,3,6,0,4,5,0,0,3,2,3,2,2,0,5,0,3,0,9,7,1,2,7,2,3,0,6,0,0,4,4,1,0,0,5,0,0,0,0,0,0,0,3,0,0,0,3,0,0,0,0,2,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,1,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,3,1,2,5,3,0,9,0,0,2,5,4,4,3,2,6,0,0,3,1,0,4,2,3,1,0,0,9,5,4,0,0,9,0,0,8,1,0,5,8,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,0,6,1,2,1,2,6,3,4,3,2,5,3,1,0,0,5,2,2,1,4,2,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,5,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,0,6,0,3,9,0,0,0,3,6,0,0,9,0,0,3,0,6,0,0,0,3,6,0,9,0,7,2,0,9,0,3,5,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,13,2,2,2,3,4,3,1,3,5,2,3,0,9,0,0,5,5,0,0,0,9,0,0,0,0,0,9,0,5,5,7,2,0,4,5,0,0,5,5,0,7,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,2,2,3,0,9,0,0,9,0,0,0,5,4,0,5,4,7,0,0,2,0,0,0,4,0,5,0,0,9,9,0,0,7,2,0,5,2,4,0,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,3,5,0,5,0,5,0,6,3,7,2,1,0,5,4,2,0,0,4,2,2,1,1,4,1,4,9,0,5,3,3,9,0,5,2,3,0,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,2,5,0,3,1,6,6,2,1,1,4,4,2,2,5,2,0,0,3,2,3,2,1,2,5,1,8,1,7,1,2,7,2,2,6,2,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,0,9,0,0,7,0,2,2,3,5,4,3,3,4,0,0,3,2,2,2,2,3,3,0,0,9,9,0,0,3,6,2,0,4,4,0,7,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,4,1,1,5,1,3,7,0,2,2,4,4,3,4,3,3,2,1,3,2,0,4,2,2,2,1,5,4,6,2,2,3,6,2,3,4,2,0,4,8,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1\ntest,41,1,3,3,10,0,7,1,2,8,1,1,1,5,3,0,3,6,2,0,2,3,2,3,1,2,3,4,1,3,6,6,2,2,6,3,2,7,1,1,0,3,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,0,6,0,3,7,0,2,0,6,3,2,6,2,2,0,0,4,0,4,2,2,4,2,0,6,3,7,2,0,4,5,0,1,6,2,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,4,3,8,2,5,0,3,9,0,0,0,3,6,2,4,3,2,0,0,4,2,2,2,2,3,3,0,0,9,5,0,4,7,2,0,6,3,0,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,2,2,5,1,0,7,0,2,7,0,2,2,7,0,2,6,2,6,0,0,3,0,0,3,1,6,0,0,0,9,9,0,0,6,3,2,2,5,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,5,1,1,5,1,3,3,0,6,6,2,2,3,4,3,5,1,0,2,0,3,3,1,3,3,1,6,3,3,1,6,4,5,7,1,1,1,1,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,1,3,8,1,1,1,2,7,0,2,7,1,0,4,1,1,5,1,2,3,4,1,1,8,7,1,1,8,1,2,3,2,4,0,5,4,0,0,4,6,0,0,0,0,5,0,0,0,0,0,0,6,0,0,0,0,4,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,10,1,3,2,3,1,5,1,3,8,1,1,1,3,6,4,4,2,4,1,0,3,1,1,3,2,3,2,0,1,8,7,1,1,4,5,1,3,4,2,1,5,8,0,0,0,6,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,2,7,2,2,0,5,6,1,3,2,2,6,0,3,6,1,0,2,0,4,4,0,0,5,4,1,7,2,6,0,3,9,0,7,2,0,0,0,2,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,1,4,1,4,7,1,2,2,4,3,1,2,6,4,0,0,2,2,1,2,2,3,3,0,2,7,6,2,1,5,4,2,5,2,2,0,4,5,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,0,2,3,5,6,3,0,0,4,5,0,0,9,0,0,0,0,0,9,0,0,0,9,0,5,4,4,3,3,9,0,0,5,4,0,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,1,2,3,6,6,2,1,1,3,0,6,5,3,2,2,4,4,2,0,0,2,2,4,2,2,2,5,0,9,0,7,0,2,9,0,3,6,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,4,9,2,2,2,4,9,0,0,0,4,5,2,2,6,2,0,0,2,2,4,2,2,0,6,0,9,0,7,0,2,4,5,0,9,0,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,2,5,1,4,2,3,4,1,4,3,4,2,2,4,4,1,2,0,4,4,0,2,2,0,5,0,6,3,6,0,3,7,2,4,0,5,0,0,4,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,4,3,8,1,5,1,3,6,1,2,0,3,6,4,3,3,2,0,0,1,1,7,1,3,4,2,0,0,9,7,2,1,5,4,0,3,1,2,4,8,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0\ntest,39,1,3,3,9,0,9,0,0,9,0,0,0,3,6,0,3,6,4,0,0,0,3,3,0,3,0,6,0,0,9,6,2,2,6,3,3,4,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,3,4,0,3,6,0,3,3,2,5,0,5,4,0,0,0,4,5,0,0,0,5,4,0,0,9,9,0,0,5,4,2,7,0,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,13,1,3,3,3,1,4,1,5,6,2,1,1,4,5,2,5,3,1,0,0,6,1,2,1,3,4,3,0,1,8,7,2,0,8,1,1,3,6,1,0,5,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1\ntest,31,1,2,4,7,0,3,4,2,2,2,6,6,3,0,2,5,2,0,2,0,5,0,3,3,2,3,2,2,9,0,4,0,5,7,2,2,3,4,0,0,4,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,3,0,6,9,0,0,2,2,5,0,4,5,0,0,0,3,6,0,0,0,4,5,0,9,0,5,0,4,6,3,5,4,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,2,4,3,2,1,4,1,4,7,1,2,0,3,6,4,3,3,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,3,2,2,2,8,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,5,8,2,6,2,2,2,0,7,3,4,3,0,2,7,1,0,1,1,1,7,0,1,1,4,4,3,6,5,0,4,9,0,2,5,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,6,2,2,7,0,2,2,4,4,0,3,6,1,0,0,3,3,3,1,1,4,4,2,0,9,7,1,2,7,2,0,7,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,1,4,1,4,7,1,2,2,4,4,2,4,4,3,1,2,2,2,1,2,2,3,3,1,4,5,6,2,1,5,4,3,3,3,2,1,4,4,0,0,3,6,0,0,0,0,4,0,3,0,0,0,0,5,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,2,3,3,1,7,1,2,7,1,2,2,3,5,1,7,2,1,0,0,7,2,1,1,4,3,2,1,2,7,8,0,1,6,3,1,6,2,0,0,4,6,2,0,0,5,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,1\ntest,39,1,3,3,9,0,5,1,3,7,1,2,3,2,5,1,3,6,2,1,1,2,3,3,1,1,1,5,2,5,4,5,2,2,7,2,4,4,1,1,1,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,1,5,1,3,6,1,2,0,5,4,4,0,5,2,1,1,2,3,2,3,0,6,0,0,0,9,6,1,3,7,2,3,4,2,1,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,33,1,2,4,8,2,5,0,3,9,0,0,0,6,3,0,5,4,2,0,0,7,0,0,2,3,2,4,0,3,6,7,0,2,6,3,2,4,4,0,0,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,3,3,1,0,5,1,3,7,1,2,3,2,5,1,3,6,2,1,1,2,3,3,1,1,1,5,2,5,4,5,2,2,7,2,4,4,1,1,1,3,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,2,2,1,1,3,6,8,0,1,1,2,6,2,3,4,2,0,0,3,4,1,2,3,1,4,1,5,4,7,2,0,7,2,1,2,5,2,0,5,8,2,0,0,6,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,34,1,4,3,8,1,4,2,3,7,1,2,0,0,9,0,0,9,0,0,0,0,5,4,0,0,0,5,4,9,0,6,2,2,7,2,0,4,5,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,3,4,2,2,8,1,0,0,5,4,4,3,2,5,0,0,2,1,2,4,2,2,1,2,0,9,9,0,0,4,5,2,2,5,0,0,5,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,2,5,2,2,7,2,0,0,3,6,2,3,5,3,0,2,3,1,2,4,0,2,4,0,1,8,7,2,0,6,3,5,2,2,2,0,3,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,1,2,3,1,0,5,2,2,4,2,4,4,4,3,1,3,5,2,1,0,3,2,3,2,2,1,3,2,6,3,6,1,3,8,1,4,3,3,0,0,3,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,26,3,2,4,6,0,3,0,6,5,0,4,4,4,2,0,0,9,0,0,0,0,7,2,0,0,0,5,4,7,2,5,0,4,9,0,6,2,2,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,38,1,2,3,9,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,0,7,0,2,6,0,3,2,5,2,3,3,4,4,0,0,3,1,1,2,2,2,5,0,1,8,7,0,2,6,3,2,4,3,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,4,2,3,2,4,1,3,7,1,1,1,2,6,3,4,2,3,0,0,4,2,1,3,3,3,2,0,2,7,9,0,0,5,4,2,3,5,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,3,0,6,7,0,2,2,2,6,2,4,3,1,0,0,6,0,2,1,3,4,3,0,6,3,5,1,3,8,1,2,5,3,0,0,3,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,2,4,8,1,0,0,2,7,0,3,6,0,0,0,4,3,3,0,1,3,6,0,3,6,6,3,0,9,0,2,5,2,2,0,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,33,2,4,2,8,0,5,1,4,8,1,0,0,1,8,0,4,5,0,0,0,5,2,2,0,2,3,5,0,2,7,5,4,0,9,0,1,7,2,1,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,3,5,0,7,0,2,6,0,3,3,2,4,2,3,4,0,2,0,3,4,0,3,2,0,5,0,0,9,7,2,0,7,2,4,2,4,0,0,3,2,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,2,2,7,0,2,2,5,3,0,0,9,0,0,0,2,7,0,0,0,0,9,0,5,4,9,0,0,9,0,5,4,0,0,0,3,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,2,3,1,3,1,5,7,2,1,3,3,4,0,9,0,4,0,0,5,0,0,5,4,0,0,0,3,6,7,1,2,5,4,0,5,0,0,4,8,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,2,2,9,0,0,0,6,3,7,0,2,3,5,0,0,0,2,6,0,2,2,0,0,9,9,0,0,2,7,0,2,5,2,0,6,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1\ntest,1,1,3,4,1,1,5,1,3,6,1,2,0,4,5,8,1,1,5,0,0,1,1,4,4,1,4,1,0,0,9,7,2,1,5,4,0,1,5,4,1,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,30,1,3,3,7,1,5,2,3,5,1,3,0,4,5,0,1,8,0,0,1,3,0,6,0,1,1,5,4,8,1,6,1,3,7,2,3,6,0,0,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,2,2,7,1,5,1,2,6,2,2,1,5,4,1,6,3,1,0,0,5,3,2,1,4,2,3,1,2,7,8,0,1,6,3,2,5,3,0,0,4,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,9,0,0,5,0,4,0,0,9,0,5,4,0,0,2,0,3,5,0,0,0,9,0,9,0,3,2,4,7,2,6,2,2,0,0,2,3,0,0,0,6,0,0,0,2,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0\ntest,9,1,2,3,3,2,3,1,5,5,2,2,2,4,4,1,5,4,2,0,0,4,3,1,1,3,1,5,1,8,1,6,2,2,8,1,2,5,2,1,1,4,4,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,0,4,0,5,7,1,1,1,4,4,1,4,4,1,0,2,4,3,1,3,2,2,4,0,2,7,5,4,0,7,2,4,4,2,0,0,3,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,2,3,3,9,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,17,1,2,4,4,0,5,3,2,5,1,4,4,4,2,1,4,5,2,1,1,3,1,4,2,1,2,4,1,4,5,6,1,2,7,2,4,2,3,1,0,3,5,0,4,0,6,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,2,3,5,6,3,0,0,3,6,0,1,8,0,0,0,1,6,3,0,1,1,8,0,3,6,6,3,0,9,0,3,5,2,1,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,2,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,2,4,4,8,1,1,1,4,5,0,5,4,0,0,0,4,1,5,0,3,2,4,0,5,4,8,1,0,9,0,8,1,1,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,2,3,2,3,6,1,2,3,5,2,1,3,6,1,0,0,2,2,6,1,1,2,6,1,3,6,6,1,3,8,1,2,6,1,1,0,3,3,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0\ntest,36,1,3,3,8,2,4,3,2,9,0,0,0,4,5,3,4,3,5,0,0,4,0,0,5,2,0,3,0,3,6,5,4,0,3,6,0,5,2,3,0,5,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,5,2,1,6,1,3,2,3,5,1,3,5,1,0,0,3,3,3,1,2,2,5,1,8,1,5,1,3,6,3,5,2,2,1,0,3,4,2,0,0,5,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,3,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0\ntest,11,1,3,2,3,0,5,0,4,6,0,3,1,2,7,3,2,4,1,0,1,4,1,4,1,3,2,1,3,0,9,6,3,0,4,5,0,4,2,4,1,6,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,36,1,1,3,8,0,4,4,2,6,0,3,7,1,1,5,4,0,2,0,0,1,0,7,2,7,0,0,0,0,9,5,4,0,3,6,0,1,8,0,0,5,3,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,4,3,10,0,5,1,3,8,1,1,1,2,7,0,2,7,1,0,4,1,1,5,1,2,3,4,1,1,8,7,1,1,8,1,2,3,2,4,0,5,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,13,6,3,2,3,1,2,1,6,4,2,3,2,2,5,5,4,2,2,1,0,3,3,2,2,3,4,1,1,7,2,6,1,2,5,4,2,2,5,1,1,5,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,4,2,4,5,1,1,9,0,0,0,5,4,1,4,4,1,1,0,3,0,4,2,3,0,4,0,0,9,8,2,0,7,2,1,3,4,2,2,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,2,4,10,0,6,0,3,6,0,3,3,4,3,0,2,7,1,0,8,1,0,1,2,1,6,2,0,0,9,3,3,3,2,7,3,6,1,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,3,3,0,5,2,3,7,1,1,1,3,6,2,5,3,4,0,0,3,2,0,3,2,3,3,0,1,8,8,0,1,5,4,0,5,4,0,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,1,4,7,0,5,1,3,6,1,3,5,4,1,0,0,9,0,0,0,0,4,5,0,0,0,9,0,9,0,6,1,2,6,3,3,6,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,4,2,3,2,3,1,5,7,0,2,1,2,7,2,5,3,2,0,0,4,1,3,1,3,1,4,2,9,0,4,3,2,6,3,4,3,2,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,0,4,7,0,2,0,1,8,0,3,6,0,0,0,3,3,4,0,1,2,5,2,9,0,7,2,0,9,0,2,6,1,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,2,3,1,4,7,1,2,1,4,5,1,6,3,4,1,0,4,1,1,3,2,3,1,2,6,3,7,1,2,5,4,5,3,1,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,8,0,1,9,0,0,0,4,5,0,3,6,1,1,1,2,5,0,1,0,4,5,0,0,9,6,3,0,6,3,5,4,0,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,4,2,8,0,5,0,4,9,0,0,0,0,9,0,4,5,0,2,0,0,7,0,2,0,0,7,0,7,2,4,5,0,7,2,4,2,2,2,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,2,3,3,1,7,1,2,7,1,2,2,3,5,1,7,2,1,0,0,7,2,1,1,4,3,2,1,2,7,8,0,1,6,3,1,6,2,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,3,4,6,1,2,1,2,7,0,3,6,0,0,0,2,2,6,0,1,1,7,1,9,0,7,0,2,7,2,6,3,1,0,0,2,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,0,3,7,1,2,1,2,6,1,4,5,2,1,0,3,2,3,1,3,2,4,1,3,6,6,2,2,6,3,2,4,3,1,1,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,5,1,3,7,1,2,0,5,4,3,5,1,2,1,0,4,1,2,2,3,4,1,1,2,7,7,0,2,5,4,1,2,5,2,1,6,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,6,2,3,6,0,3,0,3,6,1,6,3,2,0,0,5,2,2,2,2,4,2,0,7,2,7,0,2,6,3,3,3,3,1,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,4,3,9,2,5,0,3,9,0,0,0,3,6,2,4,3,2,0,0,4,2,2,2,2,3,3,0,0,9,5,0,4,7,2,0,6,3,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,2,3,1,4,0,5,5,0,4,4,2,4,2,4,4,3,1,0,3,4,0,3,2,1,4,0,8,1,4,1,5,8,1,6,3,1,1,1,2,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,4,1,4,7,2,1,0,3,6,2,5,2,5,1,0,3,1,1,3,2,3,1,1,1,8,6,0,3,4,5,1,1,6,2,1,6,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,4,8,0,5,1,4,6,1,3,2,4,3,1,3,6,2,0,1,4,3,2,1,2,2,4,2,4,5,4,0,5,7,2,5,2,2,1,1,3,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,3,5,3,4,1,3,2,2,5,5,3,1,3,4,3,2,0,0,4,1,3,2,1,4,3,1,8,1,6,0,3,7,2,5,2,2,0,1,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,3,0,6,7,0,2,1,3,6,2,5,2,2,0,1,3,4,0,2,2,1,5,0,1,8,6,3,0,5,4,3,1,6,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,3,5,0,9,0,0,6,0,3,3,2,5,3,3,4,0,0,2,3,2,3,2,2,0,4,2,9,0,5,0,4,9,0,4,5,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,2,10,1,5,1,3,6,1,3,0,4,5,0,3,6,1,0,3,3,0,4,0,2,4,3,0,3,6,6,1,2,6,3,0,0,9,0,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,3,0,6,9,0,0,0,4,5,0,0,9,0,0,6,0,3,0,0,0,6,3,0,0,9,7,2,0,7,2,6,3,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,33,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,3,2,0,0,6,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,4,2,1,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,4,1,2,3,1,0,5,2,2,4,2,4,4,4,3,1,3,5,2,1,0,3,2,3,2,2,1,3,2,6,3,6,1,3,8,1,4,3,3,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0\ntest,38,1,4,3,9,0,6,0,3,7,0,2,2,2,6,0,3,6,0,0,0,2,8,0,0,1,2,6,0,2,7,9,0,0,9,0,5,0,4,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,1,4,2,3,7,1,2,2,3,4,1,3,5,1,1,1,2,3,3,2,2,2,4,1,4,5,6,2,2,7,2,1,4,4,1,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,0,9,0,0,5,0,4,0,0,9,0,0,9,0,0,2,0,3,5,0,3,0,6,0,9,0,3,2,4,7,2,9,0,0,0,0,1,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,0,2,3,4,6,0,3,2,0,7,0,3,6,0,0,0,3,3,4,0,2,0,7,0,9,0,7,0,2,9,0,2,5,3,0,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,2,4,7,1,3,1,5,5,1,4,3,4,3,0,2,8,0,0,0,1,4,4,0,1,1,5,3,8,1,5,0,4,7,2,6,3,1,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,1,4,1,4,7,1,2,2,4,3,1,2,6,4,0,0,2,2,1,2,2,3,3,0,2,7,6,2,1,5,4,2,5,2,2,0,4,5,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,1,2,3,6,2,4,1,3,3,2,5,5,3,2,2,5,3,1,0,0,3,2,4,1,2,4,3,1,8,1,5,0,4,8,1,6,2,1,0,1,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,28,2,1,5,6,2,5,2,0,0,0,9,7,2,0,2,1,7,2,0,2,2,1,4,1,0,2,2,5,8,1,0,0,9,9,0,2,7,0,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,2,7,0,5,0,4,5,1,3,3,2,5,1,4,4,2,1,0,3,3,2,1,2,2,4,1,7,2,5,1,4,7,2,3,5,2,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,18,1,1,2,4,2,4,2,2,2,0,7,8,1,0,5,4,1,2,1,0,3,1,3,2,2,4,2,1,8,1,2,0,7,5,4,5,3,1,0,0,2,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,2,2,5,7,0,2,0,6,3,0,2,7,0,0,0,3,4,3,0,2,0,7,0,9,0,7,2,0,9,0,6,3,0,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,2,3,2,1,4,1,3,6,1,2,6,0,3,0,3,6,3,0,0,3,3,2,0,0,6,3,0,9,0,8,1,1,4,5,6,0,0,3,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,3,4,6,1,2,1,2,7,0,3,6,0,0,0,2,2,6,0,1,1,7,1,9,0,7,0,2,7,2,6,3,1,0,0,2,4,2,0,0,6,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,2,5,2,1,7,2,0,0,2,7,4,4,2,3,0,0,5,2,1,2,3,3,2,1,0,9,9,0,0,4,5,1,2,7,0,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,0,9,0,0,5,0,4,2,3,4,0,3,6,0,0,2,0,3,5,1,0,0,7,2,4,5,3,2,4,7,2,6,2,2,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,2,7,0,0,8,1,1,1,4,5,0,6,3,2,2,0,4,2,1,2,5,1,3,1,0,9,7,2,1,2,7,0,6,3,1,0,4,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,26,1,1,5,6,0,3,1,5,5,2,3,9,0,0,0,2,7,5,0,0,0,0,5,0,2,3,0,5,5,4,6,1,2,8,1,7,2,0,0,0,1,1,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,6,2,2,8,0,1,1,2,6,4,3,2,5,1,0,2,2,1,4,2,1,3,0,2,7,6,3,1,4,5,1,1,5,3,0,6,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,2,1,4,6,0,9,0,0,2,0,7,4,3,3,0,4,5,2,0,0,4,2,2,0,2,3,3,2,9,0,2,0,7,9,0,4,4,0,1,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,4,3,0,5,1,3,6,2,2,4,4,2,3,5,1,5,2,0,3,2,0,4,0,3,1,2,0,9,8,0,1,4,5,3,3,3,2,0,4,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,7,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,39,1,4,3,9,0,6,1,3,7,1,2,1,1,8,0,2,7,1,0,0,1,5,3,1,1,1,7,1,6,3,6,1,2,9,0,1,7,2,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,2,9,0,5,1,4,4,4,2,2,3,4,2,3,4,2,0,0,3,3,2,2,2,2,5,1,7,2,7,0,2,9,0,2,2,5,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,18,1,1,2,4,1,1,1,8,1,1,8,6,2,2,7,2,0,6,1,0,3,0,0,3,2,4,1,0,6,3,3,0,6,3,6,4,5,1,1,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,2,3,5,0,7,0,2,6,0,3,3,2,4,2,3,4,0,2,0,3,4,0,3,2,0,5,0,0,9,7,2,0,7,2,4,2,4,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,3,5,0,2,6,0,3,2,0,7,0,4,5,3,0,0,0,3,3,3,0,2,5,0,2,7,2,5,3,6,3,2,3,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,4,0,0,0,0,0,0,7,0,0,0,0,0,2,0,6,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0\ntest,30,1,3,3,7,0,4,1,4,6,1,3,1,4,5,1,3,5,3,0,1,4,1,2,1,2,4,3,1,2,7,4,0,5,7,2,2,2,2,4,1,6,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,2,4,1,3,7,0,2,2,2,5,6,2,1,6,1,0,2,1,1,5,3,1,1,0,1,8,5,4,1,2,7,2,1,3,3,3,9,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,32,1,2,4,7,0,4,2,3,5,0,4,4,4,2,0,2,7,0,0,0,4,0,5,0,1,1,3,5,9,0,7,0,2,9,0,8,1,1,0,0,2,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,6,2,3,6,0,3,0,3,6,1,6,3,2,0,0,5,2,2,2,2,4,2,0,7,2,7,0,2,6,3,3,3,3,1,0,4,4,0,0,0,6,0,6,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1\ntest,41,1,3,3,10,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,4,2,0,0,5,0,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,4,1,2,4,1,1,5,2,3,5,1,4,4,4,1,4,2,4,4,2,0,2,2,1,2,1,3,1,4,1,8,5,1,3,5,4,1,3,3,3,1,6,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,2,3,3,1,7,1,2,7,1,2,2,3,5,1,7,2,1,0,0,7,2,1,1,4,3,2,1,2,7,8,0,1,6,3,1,6,2,0,0,4,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,1,3,1,5,7,1,2,2,4,4,3,3,3,3,2,0,4,1,1,3,3,2,3,1,7,2,6,1,3,5,4,2,2,4,2,0,5,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,4,3,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,2,7,0,2,0,7,9,0,0,0,3,6,1,2,7,0,0,0,1,1,8,0,1,3,6,0,9,0,9,0,0,9,0,1,8,1,0,0,3,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,3,7,1,4,1,5,6,2,2,2,3,5,1,3,6,1,0,0,3,3,2,1,2,1,5,2,8,1,6,1,2,8,1,3,5,2,1,1,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,4,3,1,2,5,2,1,8,0,1,1,1,8,6,3,1,7,1,0,1,1,1,7,1,1,1,0,1,8,5,4,1,2,7,1,1,7,1,0,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,2,3,3,1,0,6,1,2,6,0,3,1,4,4,3,2,5,1,0,1,4,2,4,1,3,2,4,1,3,6,7,0,2,7,2,0,4,5,0,0,4,6,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,3,5,0,5,1,4,2,1,7,3,4,2,2,5,2,1,1,1,7,2,1,1,4,1,4,0,8,1,6,1,3,7,2,3,6,1,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,0,5,0,4,7,2,0,0,2,7,2,5,3,2,0,0,4,2,2,2,2,4,2,1,8,1,9,0,0,5,4,2,4,3,2,0,5,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,0,3,0,6,6,0,3,2,2,6,3,4,3,4,0,2,3,1,0,3,2,3,3,0,2,7,6,2,2,5,4,3,4,2,1,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,4,3,3,1,3,1,5,7,1,2,1,2,7,1,2,7,0,0,0,3,3,4,0,1,1,6,3,8,1,6,1,3,7,2,2,6,2,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,4,3,7,2,7,0,0,6,1,3,2,1,7,0,4,5,2,1,0,3,2,4,1,2,3,4,1,0,9,5,2,3,7,2,0,4,4,1,0,5,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,0,4,0,5,9,0,0,0,7,2,0,0,9,0,0,0,0,5,4,0,0,0,7,2,9,0,5,0,4,9,0,4,4,2,0,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,3,7,0,5,0,4,5,1,4,2,4,4,1,2,7,0,0,1,2,1,6,1,1,0,2,6,8,1,4,2,4,8,1,2,7,1,0,0,3,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,5,1,4,7,1,2,1,4,5,0,2,7,1,0,0,6,2,2,1,2,1,6,1,8,1,6,1,2,9,0,3,3,3,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,4,3,3,5,3,1,1,8,1,1,1,1,8,1,5,3,1,0,0,5,3,1,1,3,3,3,0,1,8,9,0,0,5,4,3,2,4,0,0,4,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,3,3,5,1,1,1,7,5,2,2,2,3,5,1,1,8,0,0,0,3,2,4,0,1,1,7,2,8,1,6,1,3,8,1,5,3,1,1,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,1,4,1,5,7,1,2,1,4,4,2,3,4,3,0,2,2,3,1,3,1,3,3,2,3,6,7,2,1,5,4,3,2,3,2,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,2,0,5,2,2,2,6,2,0,3,6,5,0,0,1,0,4,2,0,2,4,2,4,5,4,4,2,3,6,3,6,0,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,1,5,1,4,6,1,2,2,3,5,1,3,6,1,1,1,3,3,2,1,2,2,5,2,5,4,5,2,2,7,2,4,4,2,1,1,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,2,3,8,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,6,2,0,0,6,6,0,0,0,0,0,0,0,0,3,0,4,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,0,2,0,7,6,0,3,5,3,2,3,4,2,7,0,0,0,2,0,4,0,4,1,0,0,9,9,0,0,2,7,2,2,5,0,0,4,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,10,1,3,3,3,0,5,0,4,8,1,0,1,3,5,1,5,3,3,0,0,2,1,3,0,3,4,3,0,0,9,9,0,0,5,4,2,4,4,0,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,6,7,0,4,1,4,6,0,3,5,2,2,3,2,4,1,1,1,2,3,3,0,0,3,4,3,5,4,6,0,3,6,3,0,6,3,0,0,4,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,4,2,8,1,4,1,4,7,1,2,0,0,9,0,3,6,3,0,0,3,5,0,0,0,5,4,0,0,9,6,2,1,5,4,0,6,0,3,0,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,1,6,0,3,9,0,0,0,4,5,8,1,0,9,0,0,0,0,0,9,0,0,0,0,0,9,7,2,0,2,7,0,1,3,5,0,7,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,26,1,2,5,6,0,8,1,1,5,0,4,3,5,1,1,3,5,1,0,0,2,1,6,1,1,3,3,2,6,3,4,0,5,9,0,6,3,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,4,3,8,2,7,0,0,9,0,0,0,4,5,0,3,6,2,0,0,6,0,2,0,1,4,4,0,2,7,7,2,0,7,2,0,7,2,0,0,3,3,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,1,3,7,1,2,2,3,4,2,3,4,3,1,1,2,2,3,3,1,3,3,1,2,7,7,2,1,5,4,2,4,3,1,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,1,6,0,3,9,0,0,0,4,5,8,1,0,9,0,0,0,0,0,9,0,0,0,0,0,9,7,2,0,2,7,0,1,3,5,0,7,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,41,1,4,3,10,0,5,1,3,8,1,1,1,2,7,0,2,7,1,0,4,1,1,5,1,2,3,4,1,1,8,7,1,1,8,1,2,3,2,4,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,2,1,2,4,1,0,5,0,5,9,0,0,0,9,0,0,9,0,0,5,0,0,0,5,5,0,0,4,0,6,3,7,2,0,3,6,2,4,4,1,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,1\ntest,9,1,2,2,3,1,4,0,5,5,0,4,4,2,4,2,4,4,3,1,0,3,4,0,3,2,1,4,0,8,1,4,1,5,8,1,6,3,1,1,1,2,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,2,3,8,1,4,1,4,5,1,4,3,3,3,0,4,5,1,1,1,4,2,2,1,2,3,4,1,2,7,5,1,4,7,2,3,3,3,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,8,1,3,3,2,0,5,3,2,7,1,1,1,4,5,2,5,3,2,0,1,3,2,2,2,3,1,4,0,5,4,7,1,1,5,4,2,2,5,1,0,4,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,4,3,9,0,6,1,3,7,1,2,1,1,8,0,2,7,1,0,0,1,5,3,1,1,1,7,1,6,3,6,1,2,9,0,1,7,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,1,4,4,2,5,0,4,4,5,1,0,1,8,1,0,0,2,3,4,1,0,1,6,2,3,6,5,1,4,9,0,1,7,2,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,21,1,3,3,5,1,4,1,5,6,2,2,2,3,5,1,3,6,1,0,0,3,3,2,1,2,1,5,2,8,1,6,1,2,8,1,3,5,2,1,1,3,1,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,2,9,0,3,1,5,8,0,1,1,2,7,2,3,5,2,0,0,2,3,3,2,0,2,5,1,9,0,7,0,2,9,0,3,6,0,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,5,8,0,6,1,3,6,0,3,2,4,3,1,3,5,5,1,1,2,2,1,2,1,4,3,0,1,8,5,1,3,6,3,5,3,1,1,1,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,3,7,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,4,8,0,6,0,3,7,0,2,0,3,6,0,3,6,2,1,2,1,3,3,1,0,4,5,0,1,8,7,2,0,7,2,4,3,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,4,9,0,4,0,5,7,0,2,2,6,2,3,5,1,6,1,0,2,0,1,6,0,2,2,0,6,3,7,2,0,3,6,2,4,4,1,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,38,1,4,3,9,1,4,1,4,7,1,2,0,2,7,2,0,7,5,0,0,2,0,3,2,2,5,2,0,6,3,6,2,1,5,4,7,2,0,0,0,2,3,0,0,0,6,0,0,0,0,3,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,2,3,2,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,9,0,0,9,0,0,0,2,7,0,3,6,3,0,0,2,5,0,2,2,2,5,0,0,9,6,3,0,3,6,0,5,4,0,0,4,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,2,3,1,4,0,5,5,0,4,4,2,4,2,4,4,3,1,0,3,4,0,3,2,1,4,0,8,1,4,1,5,8,1,6,3,1,1,1,2,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,0,5,1,3,7,2,1,1,4,5,3,4,3,2,0,1,6,2,0,2,3,2,3,0,4,5,3,3,4,7,2,2,3,4,0,0,4,4,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,0,7,0,2,7,0,2,0,4,5,0,0,9,0,0,0,2,7,0,0,0,0,9,0,9,0,7,0,2,9,0,3,4,2,2,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,0,5,5,2,3,2,2,6,0,4,5,0,0,0,4,4,1,0,0,2,7,1,1,8,6,2,2,8,1,4,1,0,4,0,5,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,2,3,0,5,0,4,6,0,3,1,2,7,3,2,4,1,0,1,4,1,4,1,3,2,1,3,0,9,6,3,0,4,5,0,4,2,4,1,6,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,0,1,3,6,1,3,5,5,0,4,0,0,9,0,0,0,0,3,6,0,0,0,3,6,9,0,5,1,3,8,1,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,38,1,2,3,9,1,5,1,3,5,1,4,3,3,4,2,2,6,1,1,1,2,4,2,1,1,2,5,1,4,5,6,1,2,7,2,3,5,2,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,6,2,2,7,1,2,0,3,6,0,0,9,2,0,0,3,3,2,0,0,0,9,0,3,6,7,1,2,7,2,0,4,0,5,0,6,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,3,4,7,2,4,0,4,7,0,2,0,3,6,1,1,7,2,0,0,3,3,3,1,0,2,5,2,1,8,5,2,2,7,2,0,7,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,4,3,7,2,7,0,0,6,1,3,2,1,7,0,4,5,2,1,0,3,2,4,1,2,3,4,1,0,9,5,2,3,7,2,0,4,4,1,0,5,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,4,8,0,4,0,5,5,0,4,3,2,5,0,2,7,0,0,2,0,3,4,0,0,3,3,4,0,9,6,0,3,9,0,6,3,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,0,7,0,2,6,0,3,3,3,4,0,5,4,2,0,0,4,4,0,2,2,0,5,0,4,5,7,2,0,7,2,0,3,4,3,0,6,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,0,5,2,2,5,4,0,2,7,0,0,4,5,0,0,0,7,2,0,0,4,0,5,0,0,9,5,0,4,9,0,0,2,7,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,2,9,1,4,1,4,8,1,1,0,0,9,0,5,4,3,0,0,0,0,6,0,0,5,4,0,3,6,7,2,1,3,6,0,0,0,9,0,8,5,2,0,0,6,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,5,1,3,7,0,2,1,2,6,1,2,7,1,0,1,2,5,2,1,1,2,6,1,1,8,8,0,1,8,1,0,4,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,4,8,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,3,2,2,3,2,4,5,0,4,3,3,3,4,3,3,2,2,2,3,2,0,4,3,2,2,0,4,5,5,2,3,6,3,5,2,3,0,0,3,7,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,0,5,2,2,6,1,2,2,3,4,3,1,6,2,0,0,3,3,2,2,1,2,5,0,2,7,7,0,2,8,1,2,5,2,0,0,3,5,0,0,0,6,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,4,1,2,3,1,0,5,2,2,4,2,4,4,4,3,1,3,5,2,1,0,3,2,3,2,2,1,3,2,6,3,6,1,3,8,1,4,3,3,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,38,1,3,3,9,1,6,0,2,5,1,3,2,3,4,2,3,5,2,0,2,1,2,4,2,1,2,3,3,2,7,5,2,3,8,1,7,1,2,1,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,2,5,8,1,5,2,2,5,1,3,3,5,1,1,5,3,3,1,0,4,1,2,2,3,3,2,1,5,4,7,1,1,5,4,3,3,3,1,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,2,2,2,5,1,3,2,4,2,4,3,3,3,3,2,5,3,2,0,0,5,3,0,2,1,4,4,0,5,4,7,0,2,9,0,5,3,2,0,0,3,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,2,5,0,4,1,4,2,3,4,3,4,2,1,5,4,1,0,0,2,2,4,1,1,3,4,2,8,1,6,1,3,8,1,5,4,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,2,2,1,2,1,5,7,1,1,1,3,5,3,5,2,3,1,0,4,2,2,2,3,3,2,1,3,6,7,1,2,5,4,2,3,4,2,1,5,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,1,3,6,1,2,2,4,4,2,2,6,2,1,1,2,3,2,2,1,2,4,2,6,3,6,1,3,7,2,3,4,2,1,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,31,1,2,3,7,3,5,0,2,9,0,0,0,6,3,3,5,2,2,0,0,7,0,1,1,2,4,3,0,5,4,7,2,0,7,2,2,4,2,1,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,4,3,2,0,7,0,2,9,0,0,0,4,5,2,5,3,0,2,0,6,0,2,2,3,3,3,0,4,5,9,0,0,3,6,0,3,3,3,0,6,7,2,0,0,7,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,33,1,2,4,8,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,0,7,1,2,6,0,3,3,4,3,0,1,8,0,0,0,3,0,6,0,1,1,8,1,9,0,8,0,1,9,0,7,1,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,2,2,0,8,0,1,7,0,2,0,1,8,0,7,2,5,0,0,3,2,1,3,3,1,2,3,2,7,6,3,0,2,7,2,3,3,2,0,5,7,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0\ntest,7,1,3,3,2,1,5,1,4,7,1,2,1,4,5,4,4,2,4,0,1,3,1,2,4,2,2,3,0,4,5,6,2,1,4,5,1,2,6,1,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,0,5,5,2,3,2,2,6,0,4,5,0,0,0,4,4,1,0,0,2,7,1,1,8,6,2,2,8,1,4,1,0,4,0,5,5,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,1,6,0,3,6,0,3,2,2,6,2,4,3,4,0,1,3,2,2,3,1,2,3,1,0,9,6,1,2,4,5,3,3,4,0,0,4,5,2,0,0,6,0,0,0,0,0,0,3,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1\ntest,28,2,1,5,6,2,5,2,0,0,0,9,7,2,0,2,1,7,2,0,2,2,1,4,1,0,2,2,5,8,1,0,0,9,9,0,2,7,0,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,2,10,0,6,0,3,6,3,0,0,5,4,1,7,2,3,0,3,3,0,1,4,2,2,2,0,0,9,4,5,0,3,6,0,4,4,2,0,5,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,3,8,0,7,0,2,4,0,5,5,1,3,1,2,7,0,0,0,2,3,5,0,0,3,6,0,3,6,5,0,4,8,1,5,2,3,0,0,2,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,5,6,2,2,2,4,4,1,3,6,1,1,1,3,3,2,1,2,3,4,1,5,4,6,2,2,8,1,4,4,2,1,1,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,0,6,0,3,3,0,6,3,4,3,0,3,6,2,0,0,2,4,4,1,0,2,4,3,9,0,5,0,4,5,4,5,3,1,0,0,2,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0\ntest,38,1,2,3,9,0,5,2,2,4,2,4,4,4,3,1,3,5,2,1,0,3,2,3,2,2,1,3,2,6,3,6,1,3,8,1,4,3,3,0,0,3,4,2,0,0,0,0,0,6,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,12,1,3,2,3,0,6,0,3,7,2,0,1,2,6,2,4,4,1,0,0,5,2,1,0,3,2,4,0,0,9,9,0,0,5,4,2,4,4,0,0,4,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,4,10,3,0,2,4,9,0,0,0,7,2,0,7,2,4,0,2,2,2,0,5,2,0,3,0,0,9,3,6,0,2,7,2,0,2,4,2,9,3,2,2,0,6,0,0,0,0,0,0,3,0,0,0,0,5,0,0,0,2,0,1,1,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0\ntest,16,8,2,3,4,3,3,1,4,3,2,5,4,4,2,3,5,2,3,1,0,5,1,2,3,3,3,2,1,4,5,5,1,4,3,6,2,4,3,2,1,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,2,3,2,0,4,0,5,6,0,3,3,4,3,0,7,2,5,0,0,3,0,2,3,5,0,2,0,0,9,6,3,0,6,3,0,2,3,4,0,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,2,2,3,8,1,4,1,4,7,1,2,4,4,2,0,3,6,0,0,0,9,0,0,0,3,1,5,1,7,2,6,2,1,5,4,5,3,1,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,13,1,3,2,3,0,4,0,5,8,1,1,1,4,5,5,3,1,6,0,0,2,1,1,5,1,4,1,1,9,0,7,0,2,4,5,1,2,6,0,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,4,9,1,5,0,4,4,2,4,3,3,4,1,2,7,1,0,2,2,4,3,1,1,3,5,1,2,8,5,1,4,8,1,5,3,2,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,2,4,7,2,2,2,4,6,0,3,3,4,3,0,3,6,1,0,0,4,2,4,0,1,2,4,3,9,0,4,3,3,9,0,1,8,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,1,4,2,3,7,1,1,0,5,4,3,6,0,7,0,0,0,0,2,5,4,0,0,0,0,9,6,3,0,3,6,0,0,3,6,0,9,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,0,6,0,3,9,0,0,0,3,6,0,0,9,0,0,3,0,6,0,0,0,3,6,0,9,0,7,2,0,9,0,3,5,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,11,2,2,5,3,2,4,4,0,5,0,4,4,2,3,2,3,4,4,0,0,2,0,4,4,2,0,0,4,0,9,4,2,4,5,4,3,5,1,1,0,3,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,0,2,4,4,8,1,1,1,4,5,0,5,4,0,0,0,4,1,5,0,3,2,4,0,5,4,8,1,0,9,0,8,1,1,0,0,2,3,1,0,0,0,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,4,1,1,5,1,2,6,1,3,3,5,2,2,3,4,3,1,1,3,2,1,2,1,2,3,2,5,4,6,0,3,6,3,3,3,3,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,4,7,1,3,1,5,5,1,4,3,4,3,0,2,8,0,0,0,1,4,4,0,1,1,5,3,8,1,5,0,4,7,2,6,3,1,1,0,3,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,1,3,5,0,5,0,5,0,6,3,7,2,1,0,5,4,2,0,0,4,2,2,1,1,4,1,4,9,0,5,3,3,9,0,5,2,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,4,2,3,0,7,0,2,7,2,0,1,1,8,2,3,5,1,0,0,7,2,1,0,3,2,5,0,0,9,9,0,0,6,3,2,4,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,2,5,1,1,5,2,3,5,1,4,2,6,2,2,3,4,4,1,0,2,4,1,3,1,2,4,1,2,7,5,1,3,5,4,1,2,4,3,1,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,3,9,0,5,2,2,4,2,4,4,4,3,1,3,5,2,1,0,3,2,3,2,2,1,3,2,6,3,6,1,3,8,1,4,3,3,0,0,3,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,4,3,0,4,4,2,7,0,2,2,5,3,0,4,5,0,0,0,4,2,3,0,2,3,3,2,9,0,3,0,6,7,2,2,7,0,0,0,3,4,2,0,0,5,0,0,0,0,0,0,0,3,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,31,2,2,4,7,0,2,1,7,6,2,2,5,0,4,0,1,8,3,0,0,0,4,2,0,1,3,4,2,9,0,8,1,0,7,2,5,1,4,0,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,3,10,1,3,1,5,8,0,1,1,4,4,2,4,4,3,0,2,3,2,2,2,2,3,4,0,1,8,5,4,0,5,4,1,3,4,2,1,6,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,4,3,0,6,1,3,8,1,1,1,4,5,2,5,2,2,2,0,4,1,1,2,1,4,3,1,7,2,6,1,3,2,7,5,2,2,1,1,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,5,1,3,7,1,2,2,3,5,2,4,4,3,1,2,2,2,1,3,2,3,3,1,3,6,6,2,1,5,4,3,2,3,2,1,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,2,7,2,1,1,1,4,2,2,1,2,5,1,2,7,4,4,2,7,2,3,3,3,0,1,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,4,7,2,6,2,2,2,0,7,4,3,2,0,4,6,1,0,1,1,1,7,0,1,1,7,1,1,8,5,0,4,9,0,4,3,3,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,4,9,0,9,0,0,5,0,4,4,2,3,0,3,6,0,0,2,0,3,5,2,0,0,5,3,0,9,3,2,4,7,2,5,0,4,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,0,5,0,4,3,2,5,4,3,3,2,4,4,2,1,0,5,1,2,2,2,3,3,1,8,1,4,1,5,8,1,5,4,1,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,1,2,4,6,1,5,1,2,6,1,3,3,5,2,2,3,4,3,1,1,3,2,1,2,1,2,3,2,5,4,6,0,3,6,3,3,3,3,1,0,4,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,4,10,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,0,4,0,5,9,0,0,0,7,2,0,0,9,0,0,0,0,5,4,0,0,0,7,2,9,0,5,0,4,9,0,4,4,2,0,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,2,5,3,0,9,0,0,2,5,4,4,3,2,6,0,0,3,1,0,4,2,3,1,0,0,9,5,4,0,0,9,0,0,8,1,0,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,37,1,3,4,8,0,6,1,2,8,0,1,1,6,3,0,4,5,1,0,0,4,2,4,0,1,4,4,1,2,7,7,0,2,8,1,2,4,4,0,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,5,2,2,5,1,0,4,2,3,5,0,4,4,4,2,4,0,5,4,0,0,0,4,2,4,0,0,5,0,4,5,5,0,4,5,4,1,3,5,1,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,22,1,2,2,5,0,8,1,1,2,2,6,4,4,2,1,4,4,2,0,0,3,1,4,2,2,3,4,0,9,0,7,1,2,6,3,5,3,1,0,0,2,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,2,3,3,0,6,1,2,7,1,1,5,1,4,5,4,0,4,0,0,5,1,0,1,8,0,0,0,9,0,8,1,1,4,5,0,5,4,0,0,5,4,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,3,3,3,2,4,0,4,8,1,0,0,4,5,1,7,1,4,0,0,4,1,2,3,2,2,4,0,8,1,9,0,0,6,3,3,3,3,1,0,3,4,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,1,4,1,4,6,1,3,2,4,4,1,3,6,1,1,1,2,4,2,1,2,2,4,1,4,5,5,2,3,7,2,3,4,2,1,1,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,4,3,9,0,1,3,6,9,0,0,0,2,7,0,4,5,0,0,0,2,2,5,0,0,3,6,0,3,6,8,1,0,9,0,0,9,0,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,2,7,2,1,1,1,4,2,2,1,2,5,1,2,7,4,4,2,7,2,3,3,3,0,1,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,4,2,9,1,4,1,4,6,0,3,0,1,8,0,3,6,3,0,1,0,0,6,1,2,1,4,3,0,9,6,1,2,6,3,0,4,5,0,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,12,1,3,3,3,4,0,0,5,5,4,0,0,3,6,4,3,2,4,0,0,3,1,2,1,6,0,2,0,0,9,7,2,0,2,7,0,1,4,3,2,9,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,2,3,3,3,0,3,9,0,0,0,3,6,4,4,2,2,0,0,5,1,2,3,5,0,2,0,0,9,7,1,1,1,8,1,2,6,2,0,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0\ntest,38,1,2,3,9,0,6,0,3,7,0,2,0,6,3,2,6,2,2,0,0,4,0,4,2,2,4,2,0,6,3,7,2,0,4,5,0,1,6,2,0,5,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,0,6,0,3,9,0,0,0,3,6,0,0,9,0,0,3,0,6,0,0,0,3,6,0,9,0,7,2,0,9,0,3,5,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,3,2,7,1,2,2,5,5,2,3,1,3,6,1,3,6,0,0,0,3,3,3,0,1,2,6,1,8,1,5,1,4,8,1,3,5,2,1,0,3,1,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,0,4,2,4,8,1,0,0,2,7,0,3,6,0,0,0,4,3,3,0,1,3,6,0,3,6,6,3,0,9,0,2,5,2,2,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,4,8,0,5,1,3,7,1,2,3,4,3,0,3,6,0,0,0,2,2,6,0,2,3,3,3,5,4,9,0,0,5,4,2,2,3,3,0,5,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,9,1,2,3,3,1,4,0,5,7,0,2,2,5,3,1,6,2,2,0,0,6,0,2,1,1,6,2,0,8,1,5,1,4,6,3,1,1,8,0,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,4,3,2,1,4,1,4,7,1,2,0,3,6,4,3,3,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,3,2,2,2,8,8,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,3,2,7,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,1,4,2,4,9,0,0,0,4,5,5,3,2,5,0,0,2,2,1,5,2,2,2,0,0,9,7,2,0,2,7,0,2,3,3,2,7,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,29,1,3,3,7,2,3,2,4,5,2,3,1,3,5,1,1,8,1,0,0,7,1,1,0,1,1,5,3,1,8,5,2,3,6,3,1,7,1,1,1,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,0,2,0,7,6,0,3,5,3,2,3,4,2,7,0,0,0,2,0,4,0,4,1,0,0,9,9,0,0,2,7,2,2,5,0,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,6,3,2,1,5,1,2,6,1,2,0,0,9,5,5,0,2,0,1,5,1,2,0,9,0,0,0,0,9,7,2,1,5,4,2,3,4,1,1,5,8,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,1,4,2,3,5,0,4,3,4,2,3,4,3,3,0,0,3,1,2,4,3,0,1,3,3,6,6,0,3,4,5,3,3,3,1,0,4,6,0,0,4,6,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,1,2,8,0,6,1,2,1,2,6,5,3,2,2,5,2,2,0,0,4,1,3,2,3,3,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,2,3,1,5,6,1,3,2,2,6,1,7,2,1,1,0,5,1,3,1,4,3,2,1,7,2,6,1,2,6,3,2,3,5,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,2,3,0,4,2,4,8,1,1,1,2,7,0,2,7,1,0,0,2,4,4,1,1,2,6,2,6,3,7,2,1,9,0,1,3,5,0,0,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,3,3,3,1,4,1,4,6,0,3,2,2,5,1,4,5,2,1,1,3,3,2,2,2,2,4,1,3,6,6,1,2,6,3,3,2,3,1,1,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,20,1,2,3,5,2,4,2,2,3,0,6,4,3,3,5,3,2,6,2,0,0,0,1,7,0,0,2,0,3,6,6,0,3,4,5,2,2,4,2,2,6,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,2,5,0,2,2,7,4,2,3,3,5,2,1,3,6,0,0,0,2,4,4,0,1,3,5,0,9,0,4,0,5,7,2,3,4,2,0,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,2,2,4,8,0,3,2,5,5,2,3,3,5,3,0,3,6,1,0,5,1,3,2,1,2,4,3,2,5,4,4,5,1,7,2,2,5,1,2,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,3,8,0,2,1,7,5,2,3,3,3,5,0,1,8,0,0,0,2,7,1,0,1,1,8,0,5,4,5,4,0,9,0,3,2,5,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,2,3,8,4,3,1,3,5,2,3,3,4,3,1,3,5,2,0,1,3,3,2,1,1,2,4,2,7,2,7,2,0,4,5,3,3,3,1,1,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,1,1,5,6,1,2,2,5,2,0,7,6,3,1,1,3,6,1,1,0,4,2,4,1,1,2,4,3,8,1,2,1,7,8,1,8,1,0,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,2,7,0,0,6,1,2,2,6,2,0,4,5,3,0,0,5,0,2,1,1,3,4,2,0,9,6,0,3,6,3,4,4,0,2,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,3,2,5,5,4,1,8,0,0,0,1,0,8,0,0,1,0,0,9,9,0,0,4,5,0,6,3,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,4,1,4,4,2,3,3,5,2,2,5,2,2,1,1,6,1,1,2,3,2,3,0,8,1,6,1,3,7,2,3,5,1,1,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,1,4,1,5,6,2,1,1,4,5,2,5,3,1,0,0,6,1,2,1,3,4,3,0,1,8,7,2,0,8,1,1,3,6,1,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,2,5,2,1,7,2,0,0,2,7,4,4,2,3,0,0,5,2,1,2,3,3,2,1,0,9,9,0,0,4,5,1,2,7,0,0,5,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,4,2,8,0,7,0,2,7,2,0,0,4,5,0,3,6,2,2,0,2,3,2,0,2,4,4,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,28,1,1,6,6,2,5,2,0,0,0,9,8,1,0,1,2,7,1,0,1,4,5,1,1,0,1,6,2,8,1,0,0,9,9,0,7,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,2,4,7,1,4,2,3,5,0,4,4,3,2,1,2,6,1,0,1,3,3,3,1,1,2,5,1,5,4,5,1,4,9,0,2,5,2,1,0,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,2,3,10,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,3,1,0,6,1,3,5,0,4,3,3,4,1,3,5,1,0,1,6,1,3,1,2,2,3,3,2,7,7,0,2,7,2,0,5,4,0,0,4,6,3,0,0,5,0,4,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,2,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,3,9,1,4,1,4,7,1,2,3,5,2,0,5,4,2,0,0,3,0,4,0,4,3,2,0,2,7,6,2,1,5,4,3,2,0,4,0,6,5,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,3,3,8,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,4,2,8,0,4,0,5,9,0,0,0,3,6,0,3,6,0,0,0,2,6,2,0,2,2,6,0,0,9,4,4,1,9,0,2,5,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,1,2,3,0,6,1,2,1,2,6,5,3,2,2,5,2,2,0,0,4,1,3,2,3,3,3,0,9,0,5,1,3,5,4,4,3,3,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,39,1,4,2,9,0,5,1,3,7,0,2,0,0,9,1,3,5,3,0,0,2,3,3,3,0,2,4,1,2,7,6,3,0,6,3,4,4,2,0,0,3,5,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,34,1,3,3,8,1,4,1,4,7,1,2,0,3,6,0,6,3,0,0,0,9,0,0,0,0,0,9,0,9,0,6,2,1,5,4,5,4,0,0,0,2,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,4,2,6,0,6,1,2,7,1,2,0,0,9,0,5,4,0,0,0,0,4,5,0,0,5,4,0,9,0,5,2,3,6,3,9,0,0,0,0,2,1,0,0,0,6,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,1\ntest,33,1,3,4,8,0,6,0,3,5,1,4,3,3,4,0,1,8,1,0,0,2,3,5,1,1,1,4,4,7,2,4,0,5,8,1,5,3,1,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,30,1,2,5,7,0,5,0,4,5,0,4,4,4,2,0,2,7,0,0,0,3,6,0,0,0,3,5,2,9,0,5,0,4,9,0,6,2,2,0,0,2,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,1,5,0,3,2,4,4,2,4,4,5,1,1,6,3,0,0,0,6,1,3,0,4,3,3,0,9,0,5,0,4,8,1,7,1,1,0,0,2,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,1,4,2,3,5,2,3,2,3,4,2,3,5,1,1,2,2,3,1,2,2,3,4,1,3,6,5,1,3,7,2,3,4,2,1,1,4,3,2,2,0,5,6,0,0,0,0,0,0,6,0,0,6,4,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,2,0,0,1,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,2,1,6,0,3,2,3,4,1,2,7,1,0,2,2,1,5,1,2,2,5,1,1,8,7,0,2,7,2,0,5,4,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,3,5,2,1,1,6,4,2,4,3,4,2,2,4,4,2,0,0,4,1,3,3,1,3,4,1,6,3,4,0,5,4,5,3,3,3,1,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,4,8,0,4,0,5,5,0,4,3,2,5,0,2,7,0,0,2,0,3,4,0,0,3,3,4,0,9,6,0,3,9,0,6,3,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,0,4,4,0,5,3,2,5,1,3,6,0,0,2,2,4,3,0,1,3,5,1,2,7,5,1,3,7,2,3,3,3,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,3,3,10,0,4,1,4,8,1,0,0,5,4,2,4,3,3,0,4,2,1,0,3,1,4,2,0,2,7,7,2,0,2,7,3,2,2,4,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,2,7,2,1,1,1,4,2,2,1,2,5,1,2,7,4,4,2,7,2,3,3,3,0,1,4,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,4,8,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,6,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,4,1,0,6,0,3,9,0,0,2,3,4,6,3,0,6,0,0,3,0,0,5,2,3,0,0,0,9,9,0,0,3,6,2,0,3,3,2,7,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,2,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,9,1,4,2,3,2,4,1,3,7,1,1,1,2,6,3,4,2,3,0,0,4,2,1,3,3,3,2,0,2,7,9,0,0,5,4,2,3,5,0,0,4,4,2,0,0,0,0,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,0,4,0,5,9,0,0,0,5,4,3,4,3,2,0,0,5,2,1,2,1,3,4,1,2,7,5,4,0,5,4,0,2,3,2,3,8,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,4,2,3,0,7,2,0,9,0,0,0,0,8,2,4,4,2,0,0,2,5,0,2,0,3,5,0,4,5,9,0,0,7,2,0,4,5,0,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,11,1,2,3,3,0,5,0,4,6,0,3,4,3,2,1,5,4,1,0,1,1,3,4,1,1,6,3,1,0,9,6,3,0,4,5,0,2,7,1,1,5,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,0,5,0,4,3,2,5,4,3,3,2,4,4,2,1,0,5,1,2,2,2,3,3,1,8,1,4,1,5,8,1,5,4,1,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,1,3,2,1,4,1,4,7,1,2,6,3,0,0,0,9,2,0,0,3,0,4,0,3,0,3,3,6,3,6,2,1,5,4,6,3,0,0,0,2,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,3,4,1,2,2,2,5,7,1,2,2,4,4,5,3,2,8,1,0,0,0,0,7,0,2,0,0,0,9,3,5,2,2,7,0,2,5,2,1,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,4,3,0,2,4,4,7,0,2,4,0,5,4,0,5,2,0,0,4,1,3,0,0,5,3,3,9,0,7,0,2,6,3,6,0,3,0,0,3,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0\ntest,41,1,3,2,10,0,7,0,2,9,0,0,0,3,6,2,5,2,0,0,2,5,2,0,4,4,0,2,0,4,5,4,5,0,5,4,2,5,2,0,0,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,2,4,3,2,1,4,1,4,7,1,2,0,3,6,4,3,3,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,3,2,2,2,8,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,0,5,9,0,0,0,3,6,1,2,7,1,0,0,3,4,2,1,1,1,6,1,5,4,6,1,2,7,2,2,4,3,1,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,28,3,2,4,6,1,6,1,3,7,1,2,2,6,2,3,2,4,4,1,1,2,2,2,3,1,2,4,1,3,6,7,0,2,5,4,3,3,3,1,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,1,3,8,0,4,4,2,6,0,3,7,1,1,5,4,0,2,0,0,1,0,7,2,7,0,0,0,0,9,5,4,0,3,6,0,1,8,0,0,5,3,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,2,3,2,0,4,0,5,6,0,3,3,4,3,0,7,2,5,0,0,3,0,2,3,5,0,2,0,0,9,6,3,0,6,3,0,2,3,4,0,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,2,7,0,0,7,1,2,1,3,5,0,4,5,2,1,0,3,2,3,1,3,2,4,1,0,9,6,2,2,5,4,0,5,3,1,0,4,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,3,4,2,2,9,0,0,0,5,4,4,4,2,4,3,0,2,0,2,6,1,0,2,0,0,9,5,4,0,3,6,1,3,4,2,2,6,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,21,2,2,4,5,1,5,1,3,6,0,3,4,3,3,1,3,5,2,1,0,3,3,2,1,1,2,5,2,5,4,6,0,3,6,3,3,3,2,1,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,5,2,2,5,1,0,4,2,3,5,0,4,4,4,2,4,0,5,4,0,0,0,4,2,4,0,0,5,0,4,5,5,0,4,5,4,1,3,5,1,0,5,3,2,0,0,6,0,6,0,0,0,0,0,4,0,0,0,3,0,0,0,0,2,1,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0\ntest,30,1,3,3,7,1,5,2,3,5,1,3,0,5,4,0,1,8,0,0,2,3,0,5,0,1,1,4,5,8,1,6,1,3,7,2,3,6,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,4,3,3,1,4,2,3,8,1,1,1,1,7,3,6,1,5,0,0,3,1,1,3,4,3,1,0,1,8,8,1,1,2,7,1,1,6,2,1,6,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,2,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,6,0,6,6,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,1,3,1,5,6,1,2,4,2,3,4,0,5,0,0,0,3,6,0,3,2,0,3,3,2,7,7,1,2,7,2,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,4,7,0,4,0,5,9,0,0,0,7,2,0,0,9,0,0,0,0,5,4,0,0,0,7,2,9,0,5,0,4,9,0,4,4,2,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,9,0,0,9,0,0,0,2,7,0,3,6,3,0,0,2,5,0,2,2,2,5,0,0,9,6,3,0,3,6,0,5,4,0,0,4,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,34,1,4,2,8,0,5,0,4,9,0,0,0,0,9,0,4,5,0,2,0,0,7,0,2,0,0,7,0,7,2,4,5,0,7,2,4,2,2,2,0,4,6,2,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,2,4,3,4,2,0,5,4,0,3,6,2,0,2,2,3,2,0,1,5,3,1,2,7,5,2,3,6,3,0,6,2,2,1,5,5,2,0,0,6,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,0,4,1,4,6,1,3,3,1,6,4,2,4,3,0,0,5,2,1,3,3,1,4,0,1,8,7,0,2,8,1,1,6,3,0,0,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,5,3,1,6,3,2,4,0,1,8,1,0,0,1,0,7,0,0,2,2,5,8,1,4,2,4,7,2,7,2,0,0,0,2,5,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,38,1,2,3,9,0,3,3,4,5,0,4,4,1,5,0,0,9,0,0,0,4,0,5,0,0,0,5,4,9,0,6,0,3,9,0,1,6,3,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,26,1,2,4,6,0,7,0,2,4,0,5,4,5,1,1,3,5,2,0,0,4,1,3,1,3,2,2,3,8,1,2,1,7,8,1,4,4,2,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,0,4,5,1,3,2,1,7,1,4,5,1,1,0,2,3,3,1,3,1,4,1,7,2,5,1,4,7,2,3,5,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,11,1,2,3,3,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,37,1,3,3,8,2,4,0,4,7,2,0,0,5,4,2,4,3,3,0,0,5,1,2,3,3,1,3,0,2,7,7,2,0,6,3,2,4,3,1,0,4,4,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,3,4,10,0,7,0,2,6,0,3,2,2,5,0,5,5,3,0,5,1,0,1,3,1,5,1,0,0,9,3,3,3,2,7,2,5,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,6,1,3,3,2,0,3,0,6,8,0,1,1,6,3,2,5,2,4,0,1,5,1,0,3,3,2,2,0,5,4,9,0,0,5,4,1,2,7,0,0,5,8,2,0,0,6,0,5,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,2,4,2,3,6,3,1,1,8,1,0,0,2,7,5,1,4,2,0,0,2,6,1,5,1,1,4,0,6,3,9,0,0,2,7,0,3,3,4,0,6,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,1,4,2,3,7,1,1,1,4,4,1,2,7,2,1,1,1,4,2,2,1,2,5,1,2,7,4,4,2,7,2,3,3,3,0,1,4,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,0,7,0,2,7,2,0,0,5,4,0,3,6,2,0,0,2,5,2,2,1,2,5,2,0,9,4,4,2,6,3,2,5,3,0,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,0,4,0,5,3,0,6,6,2,2,1,2,7,0,0,0,0,5,4,0,0,0,6,3,9,0,5,0,4,9,0,6,3,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,9,1,2,3,3,2,3,1,5,5,2,2,2,4,4,1,5,4,2,0,0,4,3,1,1,3,1,5,1,8,1,6,2,2,8,1,2,5,2,1,1,4,4,0,0,0,6,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,0,5,1,4,7,1,2,2,3,5,2,4,3,4,1,1,3,2,1,4,2,1,3,1,2,7,7,2,1,6,3,2,2,5,1,0,4,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,2,8,2,7,0,0,7,1,2,1,4,5,0,3,6,1,1,0,2,3,4,1,1,3,5,1,0,9,6,2,2,5,4,0,7,2,1,0,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,2,4,6,1,5,1,2,6,1,3,3,5,2,2,3,4,3,1,1,3,2,1,2,1,2,3,2,5,4,6,0,3,6,3,3,3,3,1,0,4,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,3,3,8,2,1,0,6,8,0,1,1,1,7,1,5,4,2,1,0,4,3,0,2,2,2,4,0,5,4,6,2,1,2,7,2,3,3,2,1,5,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,4,2,2,4,1,0,5,1,3,6,1,3,3,4,3,3,3,4,3,1,1,3,2,2,3,2,2,3,1,3,6,6,1,3,7,2,3,2,4,1,0,3,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,2,2,2,3,2,2,0,5,3,4,3,3,4,3,6,2,2,6,0,0,2,2,0,6,2,0,2,0,7,2,7,2,0,3,6,2,2,6,0,0,4,6,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,3,9,1,5,1,3,5,1,3,3,2,4,2,3,5,2,1,0,3,2,3,2,2,2,3,2,5,4,4,2,4,7,2,3,5,1,1,0,3,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,1,2,1,0,5,0,4,0,3,6,6,3,0,3,3,3,0,0,0,6,0,3,0,4,2,2,2,7,2,5,0,4,7,2,5,4,0,0,0,2,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,3,5,1,5,1,3,4,1,5,4,2,3,1,3,5,1,0,1,2,3,4,1,2,2,5,1,5,4,5,1,3,8,1,5,3,2,0,0,3,2,2,0,0,5,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,6,2,1,1,4,4,1,3,6,2,0,1,3,3,2,2,1,2,5,1,5,4,6,1,2,6,3,2,4,3,1,1,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,4,0,5,6,0,3,3,2,5,3,2,4,6,2,0,1,1,0,4,1,3,3,0,3,6,6,2,1,3,6,3,1,4,1,2,6,6,0,0,0,6,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,24,1,2,1,5,3,4,2,1,3,4,3,4,5,2,0,4,5,0,0,0,2,3,4,0,2,3,5,1,9,0,7,1,1,8,1,1,6,3,0,0,4,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,7,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,0,3,2,5,3,4,3,3,3,4,2,6,2,3,0,0,2,4,0,2,1,3,4,0,9,0,6,1,3,7,2,4,2,4,0,0,3,6,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,9,0,0,0,9,0,0,2,5,3,0,2,7,9,0,0,0,0,0,0,0,3,6,0,4,5,9,0,0,6,3,3,4,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,2,3,1,5,1,3,8,1,1,1,3,6,4,4,2,4,1,0,3,1,1,3,2,3,2,0,1,8,7,1,1,4,5,1,3,4,2,1,5,8,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,4,7,0,5,2,2,7,1,1,0,6,3,2,0,7,2,2,3,2,1,2,1,1,4,3,1,1,8,8,0,1,5,4,2,4,3,2,0,4,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,2,2,5,7,0,2,0,2,7,0,4,5,0,0,0,0,9,0,0,0,0,9,0,9,0,5,0,4,9,0,4,5,0,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,1,9,0,2,4,4,7,0,2,0,7,2,4,4,1,1,0,0,1,4,5,4,1,0,4,0,7,2,7,0,2,6,3,1,1,8,1,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,0,5,0,4,9,0,0,0,2,7,3,4,2,6,0,1,0,2,0,6,1,0,2,1,4,5,7,2,0,7,2,1,1,8,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,2,2,2,5,0,3,9,0,0,0,3,6,2,5,3,2,0,0,4,2,2,2,2,3,4,0,3,6,7,2,0,6,3,2,0,7,0,0,5,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,4,5,0,6,0,3,5,0,4,4,4,2,2,3,4,6,0,0,0,2,2,4,0,3,2,2,5,4,5,0,4,7,2,4,3,2,1,1,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,2,4,1,0,5,2,2,9,0,0,5,2,3,5,1,3,7,0,0,2,0,0,1,1,5,3,0,0,9,6,3,0,5,4,0,1,4,5,0,6,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,0,3,8,1,1,1,3,5,1,4,5,1,1,3,2,3,1,2,1,3,4,0,2,7,5,4,0,6,3,3,4,4,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,3,1,3,4,1,0,6,2,3,6,0,3,0,6,3,2,3,5,1,0,0,4,1,4,1,2,3,4,0,2,7,7,0,2,6,3,1,3,4,3,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,2,5,2,4,1,3,3,2,5,5,3,2,2,6,2,1,0,0,3,2,4,1,3,3,4,1,8,1,5,0,4,8,1,6,2,1,0,1,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,4,2,4,5,1,1,9,0,0,0,5,4,1,4,4,1,1,0,3,0,4,2,3,0,4,0,0,9,8,2,0,7,2,1,3,4,2,2,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,2,0,0,0,0,5,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,29,1,2,4,7,0,6,0,3,6,3,0,4,3,3,0,3,6,0,0,0,5,2,2,0,2,6,1,0,3,6,9,0,0,6,3,3,4,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,3,3,1,4,1,4,9,0,0,0,5,4,3,5,2,5,0,0,1,1,3,2,2,5,2,0,0,9,7,2,0,2,7,0,2,5,3,1,6,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,9,0,0,5,0,4,4,3,3,0,3,6,0,0,2,0,3,5,0,3,0,6,0,0,9,3,2,4,7,2,6,3,0,0,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,2,3,8,0,4,0,5,5,2,3,2,3,4,3,3,3,7,0,0,2,0,1,4,3,2,2,1,3,6,5,2,3,6,3,3,2,4,2,0,5,5,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,2,3,2,4,2,3,6,2,2,1,4,5,1,6,3,2,0,1,4,2,2,1,5,1,3,0,6,3,7,1,2,5,4,1,2,5,2,0,5,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,9,0,0,0,6,0,3,0,5,4,0,4,5,2,0,0,2,3,3,1,2,3,5,0,1,8,5,0,4,7,2,0,4,4,1,0,5,3,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,2,3,7,1,4,1,4,5,1,4,3,3,3,0,4,5,1,1,1,4,2,2,1,2,3,4,1,2,7,5,1,4,7,2,3,3,3,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,4,2,0,6,0,3,6,0,3,4,0,5,3,4,3,2,2,0,6,0,0,3,1,5,2,0,9,0,9,0,0,2,7,6,0,2,0,2,4,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,2,4,10,0,5,2,2,7,1,1,0,6,3,2,0,7,2,2,3,2,1,2,1,1,4,3,1,1,8,8,0,1,5,4,2,4,3,2,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,29,1,2,3,7,1,4,1,4,5,1,4,3,3,3,0,4,5,1,1,1,4,2,2,1,2,3,4,1,2,7,5,1,4,7,2,3,3,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,4,3,9,0,6,1,3,7,1,2,1,1,8,0,2,7,1,0,0,1,5,3,1,1,1,7,1,6,3,6,1,2,9,0,1,7,2,0,0,3,5,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,2,7,0,0,7,1,2,1,3,5,0,4,5,2,1,0,3,2,3,1,3,2,4,1,0,9,6,2,2,5,4,0,5,3,1,0,4,6,0,0,0,0,6,0,0,2,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,7,1,3,2,2,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,31,1,2,3,7,1,4,2,3,4,3,3,3,2,5,0,3,6,1,0,0,2,3,3,1,1,2,6,1,9,0,5,0,4,8,1,6,3,0,0,0,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,2,4,3,2,0,5,0,5,9,0,0,0,0,9,0,5,5,0,0,0,9,0,0,0,4,0,5,0,0,9,6,3,0,2,7,0,9,0,0,0,3,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,0,7,0,2,7,0,2,0,4,5,0,0,9,0,0,0,2,7,0,0,0,0,9,0,9,0,7,0,2,9,0,3,4,2,2,0,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,4,0,0,3,6,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,0,5,2,2,5,4,0,2,7,0,0,4,5,0,0,0,7,2,0,0,4,0,5,0,0,9,5,0,4,9,0,0,2,7,0,0,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,3,3,2,1,4,1,4,7,2,1,0,3,6,2,5,2,5,1,0,3,1,1,3,2,3,1,1,1,8,6,0,3,4,5,1,1,6,2,1,6,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,2,4,3,4,2,0,5,4,0,3,6,2,0,2,2,3,2,0,1,5,3,1,2,7,5,2,3,6,3,0,6,2,2,1,5,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,3,3,7,1,5,2,3,5,1,3,0,5,4,0,1,8,0,0,2,3,0,5,0,1,1,4,5,8,1,6,1,3,7,2,3,6,0,0,0,3,2,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,4,3,7,2,7,0,0,6,1,3,2,1,7,0,4,5,2,1,0,3,2,4,1,2,3,4,1,0,9,5,2,3,7,2,0,4,4,1,0,5,1,2,0,0,6,0,6,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,0,2,6,0,3,3,1,6,0,6,3,3,0,4,2,0,1,3,2,5,1,0,0,9,3,3,3,2,7,1,5,4,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,4,0,0,0,6,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0\ntest,33,2,2,3,8,0,7,1,2,5,1,4,4,1,5,0,2,7,0,0,1,2,6,1,0,2,1,7,1,8,1,5,1,4,8,1,5,3,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,25,6,2,3,6,2,2,1,5,3,1,5,4,3,2,2,4,4,2,1,1,4,1,3,2,2,2,4,2,7,2,4,1,5,6,3,4,3,2,2,1,4,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,0,5,5,2,3,2,2,6,0,4,5,0,0,0,4,4,1,0,0,2,7,1,1,8,6,2,2,8,1,4,1,0,4,0,5,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,2,2,5,0,2,1,7,3,5,2,2,6,2,0,4,5,0,0,0,4,2,4,0,2,3,5,0,6,3,5,1,4,9,0,2,6,2,0,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,3,3,2,1,7,0,1,6,0,3,2,4,4,4,3,2,3,0,0,5,1,1,3,2,3,2,0,0,9,6,3,2,5,4,1,4,4,1,0,5,7,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,3,3,9,1,4,2,3,5,2,3,2,4,4,3,2,4,3,1,1,2,3,2,1,3,2,4,1,1,8,6,2,2,6,3,3,5,2,1,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,2,3,2,3,0,2,0,7,5,4,0,0,6,3,5,2,2,2,0,0,5,2,0,2,3,2,2,0,0,9,5,4,0,9,0,0,2,5,2,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,23,1,1,1,5,1,4,2,3,4,1,4,5,4,1,1,4,4,2,1,0,3,4,0,1,1,0,7,0,8,1,6,0,3,7,2,4,0,5,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,1,6,1,2,7,1,2,1,7,1,1,4,5,0,0,4,1,0,4,4,0,1,5,0,5,4,4,2,3,7,2,0,2,3,4,0,7,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,2,4,8,0,9,0,0,5,0,4,4,2,3,0,3,6,0,0,2,0,3,5,2,0,0,5,3,0,9,3,2,4,7,2,5,0,4,0,0,2,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0\ntest,2,1,3,3,1,0,2,2,5,9,0,0,0,4,5,2,6,2,2,5,0,2,0,0,3,2,4,0,0,9,0,5,4,0,2,7,0,0,7,2,0,6,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,4,2,4,3,4,2,0,5,4,0,3,6,2,0,2,2,3,2,0,1,5,3,1,2,7,5,2,3,6,3,0,6,2,2,1,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,0,6,0,3,3,0,6,3,4,3,0,3,6,2,0,0,2,4,4,1,0,2,4,3,9,0,5,0,4,5,4,5,3,1,0,0,2,1,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,3,5,1,2,2,6,6,1,2,1,2,6,1,2,7,0,0,0,3,4,3,0,1,1,6,2,8,1,5,1,4,8,1,3,5,1,1,0,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,0,5,1,3,6,0,3,3,3,3,0,1,8,0,0,0,4,0,5,0,1,1,5,3,9,0,7,0,2,9,0,6,2,1,0,0,2,1,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,35,1,3,3,8,3,3,1,3,9,0,0,0,4,5,3,5,2,4,0,0,1,4,1,1,2,4,2,0,0,9,9,0,0,6,3,0,5,4,0,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,2,8,0,6,0,3,5,0,4,1,1,8,2,2,6,0,0,1,2,6,1,0,2,1,5,3,1,8,8,1,1,8,1,3,3,3,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,7,0,2,5,1,4,2,3,4,1,3,5,3,0,1,3,1,2,2,1,4,3,2,4,5,4,2,3,6,3,4,3,2,0,1,4,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,2,3,2,2,2,5,0,3,9,0,0,3,3,4,7,2,0,9,0,0,0,0,0,0,6,3,0,0,0,9,7,2,0,2,7,3,6,0,0,0,3,8,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,2,3,10,0,9,0,0,5,0,4,4,3,3,0,3,6,0,0,2,0,3,5,0,3,0,6,0,0,9,3,2,4,7,2,6,3,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0\ntest,33,2,4,2,8,0,4,0,5,9,0,0,0,3,6,0,3,6,0,0,0,2,6,2,0,2,2,6,0,0,9,4,4,1,9,0,2,5,2,0,0,3,3,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,4,8,0,4,0,5,9,0,0,0,7,2,0,0,9,0,0,0,0,5,4,0,0,0,7,2,9,0,5,0,4,9,0,4,4,2,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,2,2,5,3,2,4,4,0,5,0,4,4,2,3,2,3,4,4,0,0,2,0,4,4,2,0,0,4,0,9,4,2,4,5,4,3,5,1,1,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,1,4,1,4,6,2,3,3,4,3,1,4,5,1,1,0,3,2,4,1,2,2,5,1,5,4,6,1,3,7,2,3,4,3,1,0,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,6,0,3,8,0,1,0,3,6,2,5,3,3,0,2,3,2,1,3,1,3,3,1,3,6,7,2,0,6,3,2,4,3,0,0,4,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,39,1,2,2,9,1,3,1,5,4,2,3,1,4,5,0,7,2,1,0,0,3,3,4,1,1,4,3,2,5,4,3,2,4,6,3,3,4,2,1,0,3,5,2,0,0,6,0,0,0,0,0,0,0,2,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,22,1,1,4,5,0,7,1,1,7,1,2,5,4,1,4,4,2,1,1,0,6,3,1,1,1,3,5,1,9,0,5,0,4,5,4,5,3,2,0,0,3,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,1,1,2,4,1,0,2,0,7,6,0,3,5,3,2,3,4,2,7,0,0,0,2,0,4,0,4,1,0,0,9,9,0,0,2,7,2,2,5,0,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,1,2,1,0,5,0,4,0,3,6,6,3,0,3,3,3,0,0,0,6,0,3,0,4,2,2,2,7,2,5,0,4,7,2,5,4,0,0,0,2,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,1,4,2,2,2,2,2,4,9,0,0,0,2,7,2,7,0,2,0,0,7,0,0,3,6,0,0,0,0,9,7,2,0,6,3,0,0,5,4,0,7,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,34,1,4,2,8,0,5,0,4,9,0,0,0,0,9,0,4,5,0,2,0,0,7,0,2,0,0,7,0,7,2,4,5,0,7,2,4,2,2,2,0,4,6,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,36,1,2,5,8,1,4,2,3,3,0,6,5,3,2,2,2,6,2,2,0,3,2,3,1,1,2,4,2,8,1,2,2,5,5,4,7,1,0,0,1,3,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,36,1,4,3,8,0,7,0,2,4,1,4,4,2,4,0,2,7,1,0,1,3,3,3,1,1,2,5,2,5,4,5,2,3,8,1,6,1,3,1,0,3,3,2,4,0,6,6,0,0,0,0,0,0,0,2,0,0,6,0,0,1,0,4,1,1,0,1,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0\ntest,10,1,4,2,3,2,6,0,3,6,1,3,1,0,8,2,6,2,2,1,0,5,0,3,2,3,3,2,1,4,5,5,2,2,5,4,2,3,4,1,1,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,4,3,8,0,6,0,3,9,0,0,0,3,6,0,0,9,0,0,3,0,6,0,0,0,3,6,0,9,0,7,2,0,9,0,3,5,2,0,0,3,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,2,3,3,0,3,0,6,3,2,4,3,2,5,3,4,4,0,2,0,4,2,3,3,2,0,5,0,7,2,5,2,2,5,4,2,4,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,2,3,2,6,0,3,6,1,3,1,0,8,2,6,2,2,1,0,5,0,3,2,3,3,2,1,4,5,5,2,2,5,4,2,3,4,1,1,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,3,1,3,3,1,1,5,2,3,5,1,4,1,4,4,3,5,1,2,1,0,6,1,1,3,2,4,1,1,1,8,5,1,3,5,4,1,4,2,4,1,6,6,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,30,1,3,3,7,1,5,2,3,5,1,3,0,5,4,0,1,8,0,0,2,3,0,5,0,1,1,4,5,8,1,6,1,3,7,2,3,6,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,3,1,1,4,1,1,5,2,2,4,0,5,6,3,1,3,2,5,4,0,1,2,2,2,3,1,1,3,2,5,4,4,2,4,4,5,3,3,3,0,0,3,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,2,4,7,0,4,2,3,5,0,4,4,4,2,0,2,7,0,0,0,4,0,5,0,1,1,3,5,9,0,7,0,2,9,0,8,1,1,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,36,1,3,3,8,1,4,1,4,6,1,2,2,3,5,2,4,3,3,1,1,3,2,2,3,2,2,3,0,4,5,6,2,1,4,5,2,2,4,1,1,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,20,1,3,3,5,1,4,1,5,6,1,2,1,2,6,1,2,7,1,0,0,4,4,1,1,1,2,6,1,8,1,7,1,2,8,1,3,5,2,1,1,3,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,2,4,1,0,4,0,5,7,0,2,2,6,2,3,5,1,6,1,0,2,0,1,6,0,2,2,0,6,3,7,2,0,3,6,2,4,4,1,0,5,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,5,0,4,7,2,0,0,3,6,0,4,5,2,0,2,0,4,3,2,0,2,5,0,4,5,7,2,0,5,4,2,2,3,3,0,5,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,25,1,1,4,6,0,5,2,3,3,1,6,6,3,1,2,4,3,2,1,0,5,2,1,2,2,3,3,1,9,0,5,0,4,5,4,5,3,3,0,0,3,1,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,3,3,0,5,2,3,7,1,1,1,3,6,2,5,3,4,0,0,3,2,0,3,2,3,3,0,1,8,8,0,1,5,4,0,5,4,0,0,4,8,1,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,2,1,3,4,1,1,5,2,3,5,1,4,1,4,4,5,3,1,2,3,0,4,1,1,5,3,1,1,1,2,7,5,1,3,5,4,3,1,4,2,1,5,6,0,0,0,6,7,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,3,0,0,0,2,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0\ntest,13,1,4,2,3,0,7,0,2,7,2,0,1,1,8,2,3,5,1,0,0,7,2,1,0,3,2,5,0,0,9,9,0,0,6,3,2,4,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,2,2,0,6,4,2,4,3,0,6,7,0,2,3,0,0,0,4,2,5,2,2,2,0,0,9,6,1,3,6,3,0,0,0,4,5,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,12,1,4,2,3,2,5,2,1,7,2,0,0,1,8,4,4,1,0,0,0,4,2,3,0,7,1,1,0,2,7,6,3,1,4,5,0,3,4,3,0,6,7,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,36,1,2,2,8,0,5,0,4,5,1,4,4,2,4,1,3,5,0,0,1,5,2,2,1,3,0,4,2,6,3,4,2,4,8,1,5,3,2,0,0,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,2,3,3,8,0,2,3,5,6,3,0,0,3,6,0,1,8,0,0,0,1,6,3,0,1,1,8,0,3,6,6,3,0,9,0,3,5,2,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,1,1,3,3,1,2,5,3,0,9,0,0,2,5,4,4,3,2,6,0,0,3,1,0,4,2,3,1,0,0,9,5,4,0,0,9,0,0,8,1,0,5,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,8,0,1,9,0,0,0,4,5,0,4,5,3,1,2,2,2,1,2,1,4,3,0,3,6,6,2,1,5,4,0,0,7,2,0,6,3,0,0,0,6,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,32,1,2,3,7,0,4,2,3,5,1,3,3,4,2,0,3,6,0,0,0,3,2,4,0,1,2,6,1,9,0,6,0,3,7,2,4,4,1,0,0,3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,0,4,2,3,5,1,3,2,4,3,0,3,6,1,0,1,2,2,5,1,1,2,5,3,8,1,6,1,3,6,3,4,4,1,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,4,2,10,1,5,1,3,6,1,3,0,3,6,0,2,7,4,0,2,2,0,3,0,2,6,2,0,2,7,6,1,2,6,3,0,0,9,0,0,5,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,2,2,9,0,4,2,4,7,1,2,3,4,3,1,1,8,1,0,0,3,6,0,1,1,1,8,0,3,6,7,1,1,8,1,0,7,2,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,2,0,7,9,0,0,0,6,3,0,0,9,0,0,0,2,4,4,0,0,0,7,2,9,0,7,2,0,9,0,5,4,0,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,22,1,1,2,5,0,5,2,2,1,4,5,5,4,1,1,6,2,1,0,0,6,1,3,1,4,3,3,0,9,0,3,2,4,6,3,3,4,3,0,0,3,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,3,3,3,1,4,1,4,9,0,0,0,5,4,3,5,2,5,0,0,1,1,3,2,2,5,2,0,0,9,7,2,0,2,7,0,2,5,3,1,6,8,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,0,4,5,0,4,3,4,3,0,0,9,0,0,0,0,5,4,0,0,0,7,2,6,3,6,1,2,6,3,9,0,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,6,2,8,1,8,0,0,7,1,1,0,1,8,0,5,5,0,0,0,5,0,5,0,3,0,0,6,6,3,3,5,2,9,0,0,5,5,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,2,3,7,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,1,0,0,0,6,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,9,1,3,3,3,2,3,0,5,9,0,0,0,4,5,7,1,1,3,1,0,3,2,1,5,2,2,1,0,9,0,5,0,4,6,3,0,2,7,0,0,5,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,3,1,3,3,1,2,7,0,0,7,1,2,1,3,5,0,4,5,2,1,0,3,2,3,1,3,2,4,1,0,9,6,2,2,5,4,0,5,3,1,0,4,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1\ntest,8,1,3,3,2,0,4,1,4,6,1,3,3,1,6,4,2,4,3,0,0,5,2,1,3,3,1,4,0,1,8,7,0,2,8,1,1,6,3,0,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,2,3,4,9,0,9,0,0,9,0,0,0,4,5,1,0,8,0,0,1,2,3,4,1,0,1,6,2,6,3,5,0,4,7,2,2,6,1,1,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,33,1,3,3,8,0,4,1,4,7,0,2,2,3,5,0,2,7,0,0,3,0,3,3,2,0,2,3,4,3,6,5,2,3,7,2,5,4,0,0,0,2,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,3,3,1,4,1,4,7,0,2,2,3,4,3,2,5,3,0,0,3,3,2,3,3,0,4,2,5,4,4,3,3,3,6,4,3,2,0,2,4,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,4,6,2,1,1,5,3,0,3,6,1,0,0,4,3,2,1,1,2,6,0,6,3,6,0,3,5,4,2,2,5,0,0,4,5,0,0,4,6,0,0,0,0,3,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,4,9,0,3,5,1,5,0,4,4,4,2,2,4,4,1,1,0,3,2,4,2,1,3,3,2,7,2,6,1,2,6,3,0,2,4,3,0,6,4,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,8,2,4,3,2,1,4,1,4,7,1,2,0,3,6,4,3,3,0,0,0,9,0,0,3,0,6,0,0,0,9,6,2,1,5,4,0,3,2,2,2,8,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0\ntest,29,1,2,3,7,1,4,1,4,5,1,4,3,3,3,0,4,5,1,1,1,4,2,2,1,2,3,4,1,2,7,5,1,4,7,2,3,3,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\ntest,9,1,4,2,3,2,4,1,3,7,1,1,1,2,6,3,4,2,3,0,0,4,2,1,3,3,3,2,0,2,7,9,0,0,5,4,2,3,5,0,0,4,4,2,0,0,6,0,4,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,2,5,0,2,2,7,4,2,3,3,5,2,1,3,6,0,0,0,2,4,4,0,1,3,5,0,9,0,4,0,5,7,2,3,4,2,0,0,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,0,6,2,2,6,0,3,0,5,4,0,4,5,0,0,0,7,0,2,2,0,2,3,3,0,9,5,2,2,7,2,0,6,0,3,0,5,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,2,2,4,8,0,3,2,5,5,2,3,3,5,3,0,3,6,1,0,5,1,3,2,1,2,4,3,2,5,4,4,5,1,7,2,2,5,1,2,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,32,1,3,3,7,0,5,0,4,7,0,2,2,4,3,0,7,2,0,0,0,7,0,2,0,3,5,2,0,9,0,9,0,0,9,0,4,5,0,0,0,3,1,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,2,8,2,7,0,0,7,1,2,1,4,5,0,3,6,1,1,0,2,3,4,1,1,3,5,1,0,9,6,2,2,5,4,0,7,2,1,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,0,6,0,3,6,0,3,2,4,4,4,3,2,7,0,0,0,2,0,7,0,0,2,0,0,9,9,0,0,4,5,0,5,4,0,0,4,6,2,0,0,5,0,0,0,0,0,0,3,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,3,8,0,7,0,2,7,0,2,0,6,4,1,4,5,1,1,0,3,3,3,1,2,0,6,0,2,7,7,2,0,2,7,0,6,3,0,0,4,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,4,2,9,0,6,0,3,7,1,2,2,1,6,1,3,5,2,1,0,3,3,2,1,2,2,4,1,6,3,5,2,3,7,2,3,5,2,0,0,3,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,31,1,3,2,7,1,2,2,5,5,2,3,1,3,6,1,3,6,0,0,0,3,3,3,0,1,2,6,1,8,1,5,1,4,8,1,3,5,2,1,0,3,1,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,39,1,2,5,9,0,5,4,0,0,0,9,7,0,2,0,0,9,0,0,0,0,6,3,0,0,0,6,3,7,2,7,1,2,7,2,7,2,0,0,0,1,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,38,1,4,2,9,0,6,0,3,4,0,5,1,2,6,1,4,5,0,0,2,2,4,2,0,2,3,5,1,4,5,5,2,3,7,2,5,3,2,0,0,3,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,41,1,3,3,10,0,7,0,3,8,1,1,1,3,5,1,4,5,1,1,3,2,3,1,2,1,3,4,0,2,7,5,4,0,6,3,3,4,4,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,34,1,3,2,8,0,7,0,2,7,2,0,0,4,5,0,2,7,0,2,0,2,4,2,0,0,4,5,0,2,7,5,4,0,9,0,0,5,4,0,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,40,1,2,4,10,0,5,0,4,7,0,2,2,7,0,2,5,2,3,0,3,5,0,0,4,3,0,2,0,2,7,9,0,0,2,7,0,3,6,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,3,2,9,0,5,0,4,7,0,2,0,1,8,0,3,6,0,0,0,3,3,4,0,1,2,5,2,9,0,7,2,0,9,0,2,6,1,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,35,1,3,3,8,0,5,1,3,6,2,1,1,4,4,1,3,6,2,0,1,3,3,2,2,1,2,5,1,5,4,6,1,2,6,3,2,4,3,1,1,4,5,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,3,3,1,5,1,3,7,2,1,1,3,5,4,4,2,3,1,1,4,2,1,3,3,2,2,1,1,8,6,3,1,5,4,2,4,4,1,0,4,6,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,6,1,3,3,2,2,2,0,6,7,1,2,1,2,6,7,2,0,7,2,0,0,0,0,9,0,0,0,0,1,8,8,1,0,0,9,0,2,5,3,0,6,8,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,4,9,0,7,0,2,3,1,6,4,2,4,0,1,8,1,0,0,1,3,5,1,1,1,4,4,8,1,3,0,6,8,1,6,2,2,1,0,2,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,2,9,0,3,1,5,8,0,1,1,1,7,2,3,5,2,0,0,2,3,3,2,0,2,4,3,9,0,7,0,2,9,0,4,5,0,0,0,3,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0\ntest,35,1,3,3,8,0,5,0,4,4,0,5,3,2,5,1,3,6,0,0,2,2,4,3,0,1,3,5,1,2,7,5,1,3,7,2,3,3,3,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,5,1,3,3,1,1,4,2,3,7,0,2,2,4,4,1,4,5,1,0,0,6,1,3,0,3,2,5,0,6,3,5,2,3,7,2,3,3,3,1,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,27,1,1,6,6,1,3,1,5,1,1,7,5,4,0,1,2,6,4,0,0,0,2,4,2,0,2,3,4,9,0,3,0,6,7,2,6,3,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0\ntest,33,1,4,3,8,2,5,0,3,9,0,0,0,3,6,2,4,3,2,0,0,4,2,2,2,2,3,3,0,0,9,5,0,4,7,2,0,6,3,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,5,5,1,5,1,3,4,1,5,5,3,2,1,2,7,1,0,1,3,3,4,1,1,2,4,3,8,1,3,1,5,8,1,5,3,1,0,0,2,2,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,4,2,8,0,5,0,4,9,0,0,0,0,9,0,4,5,0,2,0,0,7,0,2,0,0,7,0,7,2,4,5,0,7,2,4,2,2,2,0,4,6,2,0,0,5,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,3,3,5,1,2,1,6,7,1,2,2,3,4,4,4,2,3,1,0,4,1,2,3,2,3,2,1,8,1,6,1,3,4,5,4,3,2,1,1,3,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,10,1,3,2,3,1,5,1,3,8,1,1,1,3,6,4,4,2,4,1,0,3,1,1,3,2,3,2,0,1,8,7,1,1,4,5,1,3,4,2,1,5,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,34,1,3,3,8,0,2,2,5,7,0,2,0,2,7,0,4,5,0,0,0,0,9,0,0,0,0,9,0,9,0,5,0,4,9,0,4,5,0,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,2,4,3,3,1,5,1,4,7,1,2,0,0,9,2,0,7,0,0,0,3,0,6,0,2,0,7,0,0,9,6,2,1,4,5,0,3,5,2,0,5,8,0,0,0,6,0,0,0,0,0,0,0,4,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0\ntest,39,2,5,2,9,1,5,0,3,7,1,2,0,0,9,0,4,5,0,0,0,6,3,0,0,4,0,5,0,2,7,6,2,2,6,3,0,4,5,0,0,4,5,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,3,0,6,7,0,2,0,4,5,0,0,9,0,0,0,0,5,4,0,0,0,6,3,5,4,5,3,3,9,0,4,5,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,23,1,2,3,5,3,2,1,3,5,2,2,2,5,2,2,4,4,2,2,0,2,3,1,3,2,0,4,0,6,3,7,0,2,6,3,5,0,0,4,0,5,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,40,1,2,4,10,0,6,0,3,6,0,3,3,3,4,0,2,7,0,0,6,3,0,1,0,2,4,2,3,0,9,4,3,3,4,5,6,0,3,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,3,3,9,0,6,2,3,6,0,3,0,3,6,1,6,3,2,0,0,5,2,2,2,2,4,2,0,7,2,7,0,2,6,3,3,3,3,1,0,4,4,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,39,1,2,4,9,0,4,1,5,3,1,6,3,2,4,0,1,8,1,0,0,1,0,7,0,0,2,2,5,8,1,4,2,4,7,2,7,2,0,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,4,1,5,6,2,2,2,4,4,1,3,6,1,1,1,3,3,2,1,2,3,4,1,5,4,6,2,2,8,1,4,4,2,1,1,3,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,41,1,3,3,10,0,5,1,3,7,1,2,1,4,4,0,2,7,1,0,3,2,2,3,1,2,3,4,1,4,5,6,3,1,7,2,2,5,1,2,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,2,3,3,3,2,4,1,3,6,1,2,2,3,4,3,4,3,3,1,0,4,1,2,3,2,3,2,1,3,6,6,2,2,6,3,2,3,4,2,1,5,8,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,11,1,3,2,3,1,4,0,5,6,2,1,2,3,5,3,6,0,2,0,0,5,1,1,2,3,4,1,0,2,7,7,2,0,5,4,1,0,5,3,0,6,6,2,0,0,6,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,1,3,6,6,0,3,3,3,3,0,1,8,1,1,0,1,8,1,1,1,0,8,1,9,0,5,2,3,7,2,7,2,1,0,0,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,13,1,3,2,3,2,3,1,5,6,1,3,2,2,6,1,7,2,1,1,0,5,1,3,1,4,3,2,1,7,2,6,1,2,6,3,2,3,5,0,0,4,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0\ntest,8,1,3,3,2,1,6,1,3,9,0,0,0,4,5,6,3,1,6,0,0,2,1,1,5,2,2,1,0,0,9,7,2,0,2,7,0,1,5,4,1,7,7,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,38,1,2,3,9,1,3,1,5,4,2,4,4,2,3,1,3,6,1,0,1,3,3,3,1,1,3,3,3,7,2,6,0,3,8,1,5,4,1,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,3,3,8,0,6,1,2,6,0,3,2,3,5,1,2,6,1,0,1,4,2,4,1,2,2,5,2,2,7,7,0,2,7,2,0,4,5,0,0,4,3,2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,1\ntest,12,1,3,2,3,0,7,2,0,7,2,0,0,6,3,2,7,0,2,0,0,7,0,0,2,3,4,0,0,0,9,7,2,0,7,2,0,6,3,0,0,4,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,3,3,8,0,3,0,6,9,0,0,2,2,5,0,4,5,0,0,0,3,6,0,0,0,4,5,0,9,0,5,0,4,6,3,5,4,0,0,0,2,3,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0\ntest,22,1,2,2,5,0,6,1,2,1,2,6,3,5,1,2,6,2,1,0,0,4,1,3,1,3,4,2,0,9,0,5,1,3,5,4,3,2,5,0,0,4,2,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,22,2,3,3,5,0,9,0,0,5,0,4,3,0,6,0,5,4,3,0,0,2,0,4,3,0,3,3,0,3,6,5,2,3,7,2,4,2,3,0,0,3,2,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,33,1,2,4,8,0,5,2,2,6,1,2,1,4,5,4,1,5,2,0,0,3,3,2,3,1,5,2,0,3,6,7,0,2,8,1,2,5,2,0,0,3,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,10,1,4,3,3,0,5,2,3,7,1,1,1,3,6,2,5,3,4,0,0,3,2,0,3,2,3,3,0,1,8,8,0,1,5,4,0,5,4,0,0,4,8,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,33,1,2,4,8,0,7,2,0,5,2,2,2,6,2,0,3,6,5,0,0,1,0,4,2,0,2,4,2,4,5,4,4,2,3,6,3,6,0,0,0,2,3,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\ntest,24,1,2,3,5,1,5,1,3,4,2,4,4,4,2,2,4,4,2,0,0,3,3,3,1,1,2,5,1,7,2,6,0,3,7,2,3,3,2,2,0,4,2,0,0,0,6,0,0,0,0,0,0,0,6,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1\ntest,36,1,2,3,8,1,5,1,3,7,0,2,2,5,3,2,3,4,2,0,0,3,4,2,1,1,3,5,0,7,2,6,1,2,6,3,2,5,3,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0\ntest,33,1,3,3,8,1,4,2,3,7,1,2,2,3,4,1,3,5,1,1,1,2,3,3,2,2,2,4,1,4,5,6,2,2,7,2,1,4,4,1,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\ntest,8,1,2,3,2,4,3,0,3,5,2,2,0,6,3,8,0,1,8,0,0,0,0,1,4,0,5,1,0,2,7,9,0,0,2,7,0,0,7,2,0,7,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0\n"
  },
  {
    "path": "9.feature-engineering/FeatureSelectorUsage/data/credit_example.csv",
    "content": "SK_ID_CURR,TARGET,NAME_CONTRACT_TYPE,CODE_GENDER,FLAG_OWN_CAR,FLAG_OWN_REALTY,CNT_CHILDREN,AMT_INCOME_TOTAL,AMT_CREDIT,AMT_ANNUITY,AMT_GOODS_PRICE,NAME_TYPE_SUITE,NAME_INCOME_TYPE,NAME_EDUCATION_TYPE,NAME_FAMILY_STATUS,NAME_HOUSING_TYPE,REGION_POPULATION_RELATIVE,DAYS_BIRTH,DAYS_EMPLOYED,DAYS_REGISTRATION,DAYS_ID_PUBLISH,OWN_CAR_AGE,FLAG_MOBIL,FLAG_EMP_PHONE,FLAG_WORK_PHONE,FLAG_CONT_MOBILE,FLAG_PHONE,FLAG_EMAIL,OCCUPATION_TYPE,CNT_FAM_MEMBERS,REGION_RATING_CLIENT,REGION_RATING_CLIENT_W_CITY,WEEKDAY_APPR_PROCESS_START,HOUR_APPR_PROCESS_START,REG_REGION_NOT_LIVE_REGION,REG_REGION_NOT_WORK_REGION,LIVE_REGION_NOT_WORK_REGION,REG_CITY_NOT_LIVE_CITY,REG_CITY_NOT_WORK_CITY,LIVE_CITY_NOT_WORK_CITY,ORGANIZATION_TYPE,EXT_SOURCE_1,EXT_SOURCE_2,EXT_SOURCE_3,APARTMENTS_AVG,BASEMENTAREA_AVG,YEARS_BEGINEXPLUATATION_AVG,YEARS_BUILD_AVG,COMMONAREA_AVG,ELEVATORS_AVG,ENTRANCES_AVG,FLOORSMAX_AVG,FLOORSMIN_AVG,LANDAREA_AVG,LIVINGAPARTMENTS_AVG,LIVINGAREA_AVG,NONLIVINGAPARTMENTS_AVG,NONLIVINGAREA_AVG,APARTMENTS_MODE,BASEMENTAREA_MODE,YEARS_BEGINEXPLUATATION_MODE,YEARS_BUILD_MODE,COMMONAREA_MODE,ELEVATORS_MODE,ENTRANCES_MODE,FLOORSMAX_MODE,FLOORSMIN_MODE,LANDAREA_MODE,LIVINGAPARTMENTS_MODE,LIVINGAREA_MODE,NONLIVINGAPARTMENTS_MODE,NONLIVINGAREA_MODE,APARTMENTS_MEDI,BASEMENTAREA_MEDI,YEARS_BEGINEXPLUATATION_MEDI,YEARS_BUILD_MEDI,COMMONAREA_MEDI,ELEVATORS_MEDI,ENTRANCES_MEDI,FLOORSMAX_MEDI,FLOORSMIN_MEDI,LANDAREA_MEDI,LIVINGAPARTMENTS_MEDI,LIVINGAREA_MEDI,NONLIVINGAPARTMENTS_MEDI,NONLIVINGAREA_MEDI,FONDKAPREMONT_MODE,HOUSETYPE_MODE,TOTALAREA_MODE,WALLSMATERIAL_MODE,EMERGENCYSTATE_MODE,OBS_30_CNT_SOCIAL_CIRCLE,DEF_30_CNT_SOCIAL_CIRCLE,OBS_60_CNT_SOCIAL_CIRCLE,DEF_60_CNT_SOCIAL_CIRCLE,DAYS_LAST_PHONE_CHANGE,FLAG_DOCUMENT_2,FLAG_DOCUMENT_3,FLAG_DOCUMENT_4,FLAG_DOCUMENT_5,FLAG_DOCUMENT_6,FLAG_DOCUMENT_7,FLAG_DOCUMENT_8,FLAG_DOCUMENT_9,FLAG_DOCUMENT_10,FLAG_DOCUMENT_11,FLAG_DOCUMENT_12,FLAG_DOCUMENT_13,FLAG_DOCUMENT_14,FLAG_DOCUMENT_15,FLAG_DOCUMENT_16,FLAG_DOCUMENT_17,FLAG_DOCUMENT_18,FLAG_DOCUMENT_19,FLAG_DOCUMENT_20,FLAG_DOCUMENT_21,AMT_REQ_CREDIT_BUREAU_HOUR,AMT_REQ_CREDIT_BUREAU_DAY,AMT_REQ_CREDIT_BUREAU_WEEK,AMT_REQ_CREDIT_BUREAU_MON,AMT_REQ_CREDIT_BUREAU_QRT,AMT_REQ_CREDIT_BUREAU_YEAR\n247408,0,Cash loans,F,Y,N,2,108000.0,172512.0,13477.5,144000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-12665,-2719,-6816.0,-4365,7.0,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.3955847670427957,0.7391046089557364,0.7910749129911773,0.1371,0.0554,0.9781,0.7008,0.0562,0.16,0.1379,0.3333,0.375,0.0579,0.1076,0.1299,0.0193,0.0714,0.1397,0.0575,0.9782,0.7125,0.0567,0.1611,0.1379,0.3333,0.375,0.0592,0.1175,0.1354,0.0195,0.0756,0.1384,0.0554,0.9781,0.7048,0.0565,0.16,0.1379,0.3333,0.375,0.0589,0.1095,0.1323,0.0194,0.0729,reg oper account,block of flats,0.1484,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-2622.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n153916,0,Revolving loans,F,Y,Y,2,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.007273999999999998,-12748,-471,-747.0,-4880,17.0,1,1,0,1,0,0,Cooking staff,4.0,2,2,TUESDAY,10,0,0,0,0,1,1,Restaurant,,0.5652092295106195,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-520.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n229065,0,Cash loans,F,N,Y,0,112500.0,463500.0,20547.0,463500.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.026392,-23441,365243,-14237.0,-4634,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.6427214865316639,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1285.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,7.0\n282013,0,Cash loans,F,N,Y,0,135000.0,549882.0,17739.0,459000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.019101,-22793,365243,-690.0,-4478,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.33175016802953683,0.5316861425197883,0.057,0.0273,0.9935,0.9116,0.0123,0.08,0.0459,0.2358,0.1667,0.0147,0.0431,0.0617,0.0167,0.0208,0.0546,0.0,0.9891,0.8563,0.0,0.0,0.0345,0.125,0.0417,0.0065,0.0441,0.027000000000000003,0.0233,0.0152,0.0562,0.0364,0.9955,0.9396,0.0104,0.04,0.0345,0.2083,0.0417,0.015,0.0445,0.0572,0.0233,0.0154,reg oper account,block of flats,0.0383,\"Stone, brick\",No,5.0,1.0,5.0,0.0,-177.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n142266,0,Cash loans,F,N,Y,0,90000.0,518562.0,20695.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-13829,-631,-4822.0,-1803,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Trade: type 7,0.39098251099910303,0.6275151016376028,0.324891229465852,0.0165,,0.9851,,,0.0,0.069,0.0417,,,,0.0112,,0.0,0.0168,,0.9851,,,0.0,0.069,0.0417,,,,0.0117,,0.0,0.0167,,0.9851,,,0.0,0.069,0.0417,,,,0.0114,,0.0,,block of flats,0.0088,Block,No,0.0,0.0,0.0,0.0,-718.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n330221,0,Cash loans,F,Y,N,0,157500.0,550980.0,40221.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006233,-19423,-680,-5690.0,-2955,14.0,1,1,0,1,0,0,Cleaning staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Bank,,0.5480601320633717,,0.0825,0.0564,0.9866,0.8164,0.02,0.08,0.069,0.375,0.4167,,0.0672,0.0838,0.0,0.0,0.084,0.0585,0.9866,0.8236,0.0202,0.0806,0.069,0.375,0.4167,,0.0735,0.0873,0.0,0.0,0.0833,0.0564,0.9866,0.8189,0.0201,0.08,0.069,0.375,0.4167,,0.0684,0.0853,0.0,0.0,reg oper account,block of flats,0.0768,Panel,No,0.0,0.0,0.0,0.0,-2020.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n171519,0,Cash loans,F,N,Y,0,126000.0,770292.0,35824.5,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.010032,-13191,-2910,-3309.0,-4403,,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.7200246348493619,0.4694472480075251,0.475849908720221,0.0825,0.0808,0.9757,0.6668,,0.0,0.1379,0.1667,,,,0.0594,,,0.084,0.0833,0.9757,0.6798,,0.0,0.1379,0.1667,,,,0.0492,,,0.0833,0.0808,0.9757,0.6713,,0.0,0.1379,0.1667,,,,0.0605,,,reg oper account,block of flats,0.0551,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n385229,0,Cash loans,F,N,Y,0,270000.0,532494.0,30699.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.011657,-15181,-350,-9208.0,-3202,,1,1,0,1,1,0,Managers,2.0,1,1,THURSDAY,13,1,1,0,0,0,0,Trade: type 7,0.3093584678484739,0.6854072000170753,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2062.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n273675,0,Cash loans,F,N,Y,0,112500.0,458725.5,17707.5,396000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.02461,-23737,365243,-1447.0,-3904,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.4751425510929402,0.6279908192952864,0.0062,0.0,0.9518,,,0.0,0.0345,0.0,,0.0213,,0.0059,,0.0,0.0063,0.0,0.9518,,,0.0,0.0345,0.0,,0.0218,,0.0061,,0.0,0.0062,0.0,0.9518,,,0.0,0.0345,0.0,,0.0217,,0.006,,0.0,,terraced house,0.0103,Wooden,Yes,0.0,0.0,0.0,0.0,-1118.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n171005,0,Cash loans,M,Y,Y,0,135000.0,175896.0,9108.0,126000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.003813,-15372,-766,-2670.0,-4426,13.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,8,0,0,0,0,1,1,School,,0.03694388970562258,,0.0825,0.0769,0.9771,0.6872,0.008,0.0,0.1379,0.1667,0.0417,,,0.0694,,,0.084,0.0798,0.9772,0.6994,0.0081,0.0,0.1379,0.1667,0.0417,,,0.0723,,,0.0833,0.0769,0.9771,0.6914,0.0081,0.0,0.1379,0.1667,0.0417,,,0.0707,,,reg oper spec account,block of flats,0.0581,Block,No,0.0,0.0,0.0,0.0,-21.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n368904,0,Cash loans,F,N,Y,2,72000.0,781920.0,28215.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.018634,-13629,-4943,-699.0,-4561,,1,1,1,1,0,0,Cooking staff,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Kindergarten,0.3612357570224348,0.18654123344793727,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1741.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n338922,0,Cash loans,F,N,N,1,180000.0,675000.0,32472.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009334,-15938,-8342,-8049.0,-2756,,1,1,1,1,0,0,Medicine staff,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Medicine,,0.7172644075912377,0.28371188263500075,0.0124,,0.9732,,,0.0,0.069,0.0417,,0.0264,,0.0101,,0.0162,0.0126,,0.9732,,,0.0,0.069,0.0417,,0.027000000000000003,,0.0105,,0.0171,0.0125,,0.9732,,,0.0,0.069,0.0417,,0.0269,,0.0103,,0.0165,,block of flats,0.0115,Wooden,No,3.0,1.0,3.0,1.0,-2903.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n266870,0,Revolving loans,M,Y,Y,1,94500.0,202500.0,10125.0,202500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010147,-14391,-515,-5363.0,-4390,9.0,1,1,0,1,0,0,Security staff,3.0,2,2,FRIDAY,10,0,0,0,0,1,1,Security,0.2491036714108397,0.6031369582338103,0.7958031328748403,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-405.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n307881,0,Cash loans,F,N,Y,1,225000.0,508495.5,21672.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.00963,-16779,-6992,-83.0,-330,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,10,0,0,0,1,0,1,School,,0.478481082750384,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-511.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n267426,0,Cash loans,F,Y,N,2,203400.0,294322.5,18940.5,243000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-16221,-385,-9230.0,-4107,16.0,1,1,0,1,0,0,Sales staff,4.0,2,2,SATURDAY,15,0,0,0,0,0,0,Self-employed,0.4684435648226956,0.4479062247729914,0.8083935912119442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2305.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n301311,1,Cash loans,F,N,Y,0,202500.0,1762110.0,48586.5,1575000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-21162,365243,-4553.0,-4553,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.5514755527193638,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1906.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n385327,0,Cash loans,M,N,Y,1,202500.0,640080.0,24259.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008625,-15687,-7215,-5596.0,-3977,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,Industry: type 9,,0.4932834728853558,0.6161216908872079,0.066,0.0442,0.9737,,,,0.1379,0.125,,,,0.0526,,,0.0672,0.0459,0.9737,,,,0.1379,0.125,,,,0.0549,,,0.0666,0.0442,0.9737,,,,0.1379,0.125,,,,0.0536,,,,,0.0476,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-367.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n167193,0,Cash loans,M,Y,Y,1,148500.0,380533.5,30640.5,328500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-13838,-2406,-3189.0,-4024,6.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.4647717459537136,0.6916303246268416,0.7597121819739279,0.2598,0.204,0.9861,0.8096,0.0652,0.28,0.2414,0.3333,0.375,0.1222,0.2118,0.2707,0.0,0.0,0.2647,0.2117,0.9861,0.8171,0.0658,0.282,0.2414,0.3333,0.375,0.125,0.2314,0.282,0.0,0.0,0.2623,0.204,0.9861,0.8121,0.0656,0.28,0.2414,0.3333,0.375,0.1243,0.2155,0.2755,0.0,0.0,reg oper spec account,block of flats,0.2486,Panel,No,0.0,0.0,0.0,0.0,-416.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n383858,0,Cash loans,M,N,Y,0,157500.0,225000.0,15034.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.02461,-10514,-1077,-1934.0,-2530,,1,1,1,1,0,0,Sales staff,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Other,,0.06460937802329063,,0.0124,0.0,0.9687,,,0.0,0.069,0.0417,,0.0223,,0.01,,0.0,0.0126,0.0,0.9687,,,0.0,0.069,0.0417,,0.0228,,0.0104,,0.0,0.0125,0.0,0.9687,,,0.0,0.069,0.0417,,0.0227,,0.0102,,0.0,,block of flats,0.0087,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-130.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n248242,0,Revolving loans,F,N,Y,0,202500.0,675000.0,33750.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.072508,-9795,-1528,-526.0,-526,,1,1,0,1,1,0,,2.0,1,1,THURSDAY,12,0,1,1,0,0,0,Business Entity Type 3,0.4920479296174688,0.5853544993116938,0.5280927512030451,0.2959,0.1899,0.9776,0.6940000000000001,0.0108,0.32,0.2759,0.3333,0.375,0.0,0.2412,0.282,0.0,0.0016,0.3015,0.19699999999999998,0.9777,0.706,0.0108,0.3222,0.2759,0.3333,0.375,0.0,0.2635,0.2938,0.0,0.0016,0.2987,0.1899,0.9776,0.6981,0.0108,0.32,0.2759,0.3333,0.375,0.0,0.2454,0.2871,0.0,0.0016,reg oper account,block of flats,0.2481,Panel,No,0.0,0.0,0.0,0.0,-397.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n187726,0,Cash loans,M,N,N,0,180000.0,296280.0,14251.5,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14940,-7574,-3853.0,-4776,,1,1,1,1,0,0,Core staff,2.0,2,2,SUNDAY,17,0,0,0,0,1,1,Police,0.14209489072944945,0.5682215450964254,0.4614823912998385,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1587.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n178210,0,Cash loans,F,N,Y,1,112500.0,473760.0,46989.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.030755,-14729,-3863,-2927.0,-1373,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Self-employed,,0.41853954984410335,,0.0608,0.0674,0.9816,0.7484,0.0284,0.0,0.1379,0.1667,0.0417,0.0473,0.0471,0.0539,0.0116,0.0515,0.062,0.0699,0.9816,0.7583,0.0287,0.0,0.1379,0.1667,0.0417,0.0484,0.0514,0.0561,0.0117,0.0545,0.0614,0.0674,0.9816,0.7518,0.0286,0.0,0.1379,0.1667,0.0417,0.0482,0.0479,0.0548,0.0116,0.0526,reg oper spec account,block of flats,0.0691,\"Stone, brick\",No,8.0,0.0,8.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n314459,0,Cash loans,M,Y,Y,0,270000.0,438084.0,34740.0,387000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.008625,-11758,-157,-1476.0,-1495,26.0,1,1,0,1,0,1,Laborers,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.11820985979508652,0.3944457156505145,0.3723336657058204,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n426212,0,Cash loans,F,Y,N,0,247500.0,450000.0,24543.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-19144,-1604,-2944.0,-2660,14.0,1,1,1,1,0,0,Core staff,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Government,,0.35896592485354034,0.25259869783397665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1355.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,3.0\n345809,0,Cash loans,M,Y,N,1,157500.0,857169.0,25191.0,715500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-15128,-2061,-7666.0,-4205,3.0,1,1,0,1,0,0,Managers,3.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.3766426231232876,0.5903558665758692,0.4812493411434029,0.0505,0.034,0.9796,0.7552,0.0175,0.04,0.069,0.2292,0.375,0.0371,0.0605,0.0579,0.0,0.011000000000000001,0.0273,0.0,0.9821,0.7648,0.0177,0.0,0.069,0.125,0.375,0.0258,0.0661,0.0212,0.0,0.0,0.051,0.034,0.9821,0.7585,0.0176,0.04,0.069,0.2292,0.375,0.0378,0.0616,0.078,0.0,0.0112,reg oper account,block of flats,0.0197,\"Stone, brick\",No,5.0,1.0,5.0,1.0,-1536.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n162088,0,Cash loans,F,N,Y,0,225000.0,254700.0,25191.0,225000.0,Family,Commercial associate,Higher education,Separated,House / apartment,0.04622,-19165,-4235,-4920.0,-2728,,1,1,0,1,0,0,,1.0,1,1,WEDNESDAY,13,0,1,1,0,0,0,Business Entity Type 3,,0.6768825078501225,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n105803,0,Cash loans,F,N,Y,2,135000.0,225000.0,14377.5,225000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.025164,-15207,-1971,-6980.0,-4448,,1,1,0,1,0,0,,4.0,2,2,THURSDAY,11,0,0,0,0,1,1,Other,0.5026060320565993,0.3559874756771383,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-508.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n104364,1,Cash loans,F,N,Y,0,108000.0,526500.0,26874.0,526500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-9711,-514,-2712.0,-76,,1,1,1,1,1,0,Sales staff,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.00025557641256708795,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,,,,,,\n436552,0,Cash loans,F,N,N,0,90000.0,288873.0,14805.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.032561,-19296,-2576,-8022.0,-2843,,1,1,0,1,0,0,Sales staff,1.0,1,1,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.7887849379844937,0.7544061731797895,0.3433,0.2238,0.9806,0.7348,,0.36,0.3103,0.3333,,0.218,,0.35700000000000004,,0.1828,0.3498,0.2322,0.9806,0.7452,,0.3625,0.3103,0.3333,,0.223,,0.3719,,0.1935,0.3466,0.2238,0.9806,0.7383,,0.36,0.3103,0.3333,,0.2218,,0.3634,,0.1866,,block of flats,0.3206,Others,No,0.0,0.0,0.0,0.0,-285.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n324308,0,Revolving loans,F,N,N,2,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.002042,-12818,-1351,-522.0,-1926,,1,1,1,1,0,0,Laborers,3.0,3,3,FRIDAY,12,0,0,0,0,0,0,Self-employed,,0.2983132541800768,,,,0.9856,,,,,,,,,0.0714,,,,,0.9856,,,,,,,,,0.0744,,,,,0.9856,,,,,,,,,0.0726,,,,,0.099,,No,0.0,0.0,0.0,0.0,-506.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n206155,0,Cash loans,F,N,Y,0,112500.0,50940.0,5166.0,45000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.00702,-18170,-2292,-9644.0,-1723,,1,1,1,1,1,0,Cooking staff,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Medicine,0.7162870626412197,0.2966588939789039,0.2807895743848605,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1170.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n448066,0,Cash loans,F,Y,N,1,135000.0,334152.0,16074.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.016612000000000002,-16729,-1199,-3823.0,-277,8.0,1,1,0,1,1,0,,3.0,2,2,THURSDAY,18,0,0,0,0,1,1,Business Entity Type 2,0.6849025094765665,0.5291674200592413,0.3031463744186309,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-113.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n377385,0,Cash loans,M,N,Y,0,90000.0,86346.0,8667.0,81000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.030755,-15889,-2539,-4030.0,-1654,,1,1,0,1,0,0,,1.0,2,2,SUNDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.4688551873072928,0.6552292238951613,0.8050196619153701,0.168,0.0645,0.9856,0.8028,0.127,0.04,0.0345,0.3333,0.375,0.0452,0.13699999999999998,0.0883,0.0,0.0,0.1712,0.067,0.9856,0.8105,0.1282,0.0403,0.0345,0.3333,0.375,0.0463,0.1497,0.092,0.0,0.0,0.1697,0.0645,0.9856,0.8054,0.1279,0.04,0.0345,0.3333,0.375,0.046,0.1394,0.0899,0.0,0.0,reg oper spec account,block of flats,0.0845,Panel,No,9.0,0.0,9.0,0.0,-533.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n117980,0,Cash loans,F,N,Y,0,81000.0,526500.0,27013.5,526500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-15953,-3090,-5170.0,-5170,,1,1,0,1,0,0,Cooking staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Kindergarten,,0.5497548968878996,0.6528965519806539,0.0928,0.0801,0.9786,,,,0.2069,0.1667,,0.0744,,0.0871,,,0.0945,0.0831,0.9786,,,,0.2069,0.1667,,0.0761,,0.0907,,,0.0937,0.0801,0.9786,,,,0.2069,0.1667,,0.0757,,0.0887,,,,block of flats,0.0685,Panel,No,7.0,0.0,7.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n126356,0,Cash loans,M,Y,Y,0,202500.0,360000.0,17446.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-20733,-4794,-5201.0,-3661,26.0,1,1,0,1,1,0,Drivers,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Medicine,,0.47780993935107896,0.36896873825284665,0.0928,0.0651,0.9776,,,0.0,0.2069,0.1667,,,,0.0856,,0.0183,0.0945,0.0676,0.9777,,,0.0,0.2069,0.1667,,,,0.0892,,0.0194,0.0937,0.0651,0.9776,,,0.0,0.2069,0.1667,,,,0.0872,,0.0187,,block of flats,0.0779,Panel,No,0.0,0.0,0.0,0.0,-897.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n452576,0,Cash loans,F,N,N,0,261000.0,481495.5,32305.5,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.003818,-20537,-7342,-10276.0,-4033,,1,1,1,1,1,0,Core staff,2.0,2,2,TUESDAY,6,0,0,0,0,0,0,Government,,0.5284808805272666,,0.0247,0.0,0.9801,0.728,0.0036,0.0,0.069,0.0833,0.0417,0.0091,0.0202,0.0142,0.0,0.0,0.0252,0.0,0.9801,0.7387,0.0036,0.0,0.069,0.0833,0.0417,0.0093,0.022000000000000002,0.0148,0.0,0.0,0.025,0.0,0.9801,0.7316,0.0036,0.0,0.069,0.0833,0.0417,0.0092,0.0205,0.0144,0.0,0.0,reg oper account,block of flats,0.0172,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-448.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n114351,0,Cash loans,F,N,N,0,270000.0,1288350.0,41562.0,1125000.0,Family,Working,Higher education,Civil marriage,Rented apartment,0.019101,-17979,-4647,-3452.0,-1528,,1,1,1,1,0,0,Managers,2.0,2,2,MONDAY,13,1,1,0,1,1,0,Business Entity Type 3,0.7114319060181145,0.7611887883234424,0.7407990879702335,0.0722,0.0499,0.9866,0.8164,0.0348,0.08,0.0345,0.4583,0.0417,0.0068,0.057999999999999996,0.0707,0.0039,0.012,0.0735,0.0518,0.9866,0.8236,0.0352,0.0806,0.0345,0.4583,0.0417,0.006999999999999999,0.0634,0.0736,0.0039,0.0127,0.0729,0.0499,0.9866,0.8189,0.0351,0.08,0.0345,0.4583,0.0417,0.0069,0.059000000000000004,0.07200000000000001,0.0039,0.0122,reg oper account,block of flats,0.0772,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-746.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n283508,1,Cash loans,M,Y,Y,1,157500.0,135000.0,9018.0,135000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.006305,-12627,-534,-2535.0,-2837,14.0,1,1,0,1,0,0,,3.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,Transport: type 2,,0.6290982382949823,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-2354.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n204053,1,Cash loans,F,N,Y,0,81000.0,521280.0,35262.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-10140,-1812,-4863.0,-1229,,1,1,1,1,0,0,Sales staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.5904299016325364,0.04727481802734645,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-867.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n109351,1,Cash loans,F,N,Y,0,112500.0,309073.5,26428.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.022625,-9298,-379,-1874.0,-1612,,1,1,1,1,1,0,Laborers,1.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Business Entity Type 1,0.28893944018308915,0.1596557610518511,0.3740208032583212,0.067,0.0389,0.9896,,0.0259,0.04,0.0345,0.3333,,0.0524,,0.0582,,0.0354,0.0683,0.0404,0.9896,,0.0261,0.0403,0.0345,0.3333,,0.0536,,0.0607,,0.0375,0.0677,0.0389,0.9896,,0.026000000000000002,0.04,0.0345,0.3333,,0.0533,,0.0593,,0.0361,,block of flats,0.0676,\"Stone, brick\",No,2.0,1.0,2.0,0.0,-572.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n169511,0,Cash loans,M,Y,N,0,180000.0,508495.5,24592.5,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008625,-10126,-1004,-4557.0,-2787,4.0,1,1,1,1,0,0,Sales staff,2.0,2,2,THURSDAY,18,0,0,0,1,1,0,Transport: type 4,0.21346994157078025,0.6222509153475654,0.14287252304131962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-544.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n200280,0,Cash loans,F,N,Y,1,126000.0,315000.0,17086.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.035792000000000004,-8255,-898,-8215.0,-875,,1,1,0,1,0,0,Core staff,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Bank,0.2674956484494,0.5187008727721591,0.5352762504724826,0.1021,0.1064,0.9771,0.6872,0.0114,0.0,0.2069,0.1667,0.2083,0.1437,0.0824,0.0916,0.0039,0.0104,0.10400000000000001,0.1104,0.9772,0.6994,0.0115,0.0,0.2069,0.1667,0.2083,0.147,0.09,0.0954,0.0039,0.011000000000000001,0.1031,0.1064,0.9771,0.6914,0.0114,0.0,0.2069,0.1667,0.2083,0.1462,0.0838,0.0932,0.0039,0.0106,reg oper account,block of flats,0.0743,Panel,No,0.0,0.0,0.0,0.0,-706.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n194056,0,Cash loans,M,N,N,0,225000.0,1125000.0,46426.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-11624,-3454,-1109.0,-4104,,1,1,0,1,1,0,Laborers,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Transport: type 3,,0.7369865131151553,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1735.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,7.0\n279545,0,Cash loans,F,Y,Y,2,225000.0,1054935.0,44824.5,904500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-11704,-275,-724.0,-1050,64.0,1,1,0,1,0,1,Accountants,4.0,1,1,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.8067203186562106,0.7267789704587652,0.07396514611722674,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-704.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n248407,0,Cash loans,F,N,Y,1,135000.0,547339.5,29691.0,409500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-11273,-1481,-2363.0,-3799,,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Insurance,0.6524260622882326,0.24380215232555885,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-891.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n347786,0,Cash loans,M,N,Y,0,90000.0,267102.0,17919.0,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.072508,-22258,365243,-12496.0,-4341,,1,0,0,1,1,0,,2.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.4466195790923816,0.6528965519806539,0.2894,0.2868,0.9762,0.6736,0.0,0.04,0.431,0.2396,0.2812,0.0,0.23600000000000002,0.2428,0.0,0.0457,0.1775,0.1915,0.9762,0.6864,0.0,0.0,0.069,0.1667,0.2083,0.0,0.1552,0.1401,0.0,0.0,0.3102,0.3009,0.9762,0.6779999999999999,0.0,0.0,0.5172,0.1667,0.2083,0.0,0.2548,0.266,0.0,0.0,reg oper account,block of flats,0.1944,Panel,No,1.0,0.0,1.0,0.0,-1508.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n134324,0,Cash loans,F,N,Y,0,202500.0,1314000.0,50179.5,1314000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-16971,-4206,-4679.0,-518,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.3931250071750642,0.8510087026616691,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,1.0,1.0,-542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n282172,0,Cash loans,M,N,Y,0,157500.0,142632.0,9661.5,126000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-23262,365243,-7584.0,-5193,,1,0,0,1,0,0,,2.0,3,3,SUNDAY,13,0,0,0,0,0,0,XNA,,0.515107782732612,0.3572932680336494,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1426.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n377468,0,Cash loans,F,N,Y,0,112500.0,497520.0,33376.5,450000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.030755,-15413,-1563,-3468.0,-3626,,1,1,0,1,0,0,Sales staff,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Trade: type 7,0.5303644889049318,0.6648412408942129,0.4561097392782771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n286491,0,Cash loans,F,N,N,0,157500.0,577912.5,30789.0,535500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.00963,-23040,365243,-1331.0,-4711,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.4739211442886992,0.5298898341969072,0.0309,0.0397,0.9732,0.6328,,0.0,0.069,0.125,0.1667,0.0153,0.0252,0.0233,0.0,0.0048,0.0315,0.0412,0.9732,0.6472,,0.0,0.069,0.125,0.1667,0.0157,0.0275,0.0243,0.0,0.0051,0.0312,0.0397,0.9732,0.6377,,0.0,0.069,0.125,0.1667,0.0156,0.0257,0.0237,0.0,0.0049,reg oper account,block of flats,0.0208,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n379369,0,Cash loans,F,N,N,1,135000.0,625356.0,17325.0,522000.0,Unaccompanied,Working,Higher education,Married,Co-op apartment,0.028663,-10061,-2685,-644.0,-2696,,1,1,0,1,0,0,Managers,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Postal,,0.5715822790778998,0.6178261467332483,0.0691,0.10800000000000001,0.9851,0.7959999999999999,0.0157,0.0,0.2069,0.1667,0.2083,0.0369,0.0538,0.0743,0.0116,0.018000000000000002,0.0704,0.1121,0.9851,0.804,0.0158,0.0,0.2069,0.1667,0.2083,0.0377,0.0588,0.0774,0.0117,0.0191,0.0697,0.10800000000000001,0.9851,0.7987,0.0158,0.0,0.2069,0.1667,0.2083,0.0375,0.0547,0.0757,0.0116,0.0184,reg oper account,block of flats,0.0624,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1453.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n147090,1,Cash loans,M,Y,N,0,337500.0,1125000.0,36292.5,1125000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.032561,-15444,-1001,-8048.0,-4094,14.0,1,1,1,1,0,0,,2.0,1,1,MONDAY,10,0,0,0,0,0,0,Insurance,0.33554670936119985,0.6601964410969293,0.11247434965561487,0.1216,0.1175,0.9826,0.762,0.0421,0.08,0.0345,0.5417,0.5833,0.0597,0.0983,0.0828,0.0039,0.065,0.1239,0.122,0.9826,0.7713,0.0425,0.0806,0.0345,0.5417,0.5833,0.0611,0.1074,0.0863,0.0039,0.0688,0.1228,0.1175,0.9826,0.7652,0.0424,0.08,0.0345,0.5417,0.5833,0.0608,0.1,0.0843,0.0039,0.0664,reg oper account,block of flats,0.1234,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-716.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n111092,1,Cash loans,M,N,N,0,112500.0,390447.0,19120.5,274500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.020246,-10134,-678,-103.0,-2814,,1,1,0,1,0,0,,1.0,3,3,MONDAY,9,0,0,0,0,0,0,Self-employed,0.1831542782769696,0.11012101691132636,0.20208660168203949,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-97.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n282901,1,Cash loans,F,N,N,1,112500.0,288873.0,15084.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14324,-1281,-6391.0,-5183,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,10,0,1,1,0,0,0,Business Entity Type 3,0.5446622190103599,0.2867111015167043,0.22383131747015547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-72.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,3.0,0.0\n278059,0,Cash loans,F,N,N,1,130500.0,450000.0,21109.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.007273999999999998,-12829,-1742,-6839.0,-3973,,1,1,0,1,1,0,Sales staff,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,Self-employed,,0.41349257497252856,,0.1485,0.0929,0.9861,0.8096,0.033,0.16,0.1379,0.3333,0.0417,0.0284,0.121,0.0886,0.0,0.0,0.1513,0.0964,0.9861,0.8171,0.0333,0.1611,0.1379,0.3333,0.0417,0.028999999999999998,0.1322,0.0923,0.0,0.0,0.1499,0.0929,0.9861,0.8121,0.0332,0.16,0.1379,0.3333,0.0417,0.0289,0.1231,0.0902,0.0,0.0,reg oper account,block of flats,0.1201,Panel,No,0.0,0.0,0.0,0.0,-653.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n112583,0,Revolving loans,F,N,Y,0,81000.0,405000.0,20250.0,405000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.018029,-16664,-732,-184.0,-224,,1,1,0,1,1,0,Sales staff,2.0,3,2,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,0.6157433779098226,0.6283019073031504,0.6545292802242897,0.2402,0.1584,0.9851,0.7959999999999999,0.1055,0.28,0.2414,0.3333,0.375,0.1624,0.1891,0.2631,0.0309,0.0321,0.2447,0.1643,0.9851,0.804,0.1065,0.282,0.2414,0.3333,0.375,0.1661,0.2066,0.2741,0.0311,0.034,0.2425,0.1584,0.9851,0.7987,0.1062,0.28,0.2414,0.3333,0.375,0.1652,0.1924,0.2678,0.0311,0.0328,reg oper account,block of flats,0.2716,Panel,No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n306690,0,Cash loans,M,Y,N,0,94500.0,402939.0,15061.5,306000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-16027,-576,-1575.0,-4512,10.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Trade: type 7,,0.5371410352990779,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n228485,0,Cash loans,M,N,Y,0,225000.0,450000.0,30073.5,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.028663,-9992,-645,-2279.0,-2649,,1,1,0,1,1,0,Managers,1.0,2,2,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.4641451131745141,0.5514971884313318,0.3233112448967859,0.2082,0.1636,0.9811,0.7416,0.0325,0.16,0.1379,0.3333,0.375,0.1309,0.1614,0.174,0.0386,0.1357,0.2122,0.1698,0.9811,0.7517,0.0328,0.1611,0.1379,0.3333,0.375,0.1339,0.1763,0.1813,0.0389,0.1437,0.2103,0.1636,0.9811,0.7451,0.0327,0.16,0.1379,0.3333,0.375,0.1332,0.1642,0.1771,0.0388,0.1386,reg oper account,block of flats,0.1841,\"Stone, brick\",No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n244320,0,Cash loans,F,Y,N,0,112500.0,469152.0,17869.5,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-9266,-229,-8296.0,-1589,10.0,1,1,1,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.1951895933733617,0.5308887265507224,0.3201633668633456,0.0753,0.0,0.9762,0.6736,0.0271,0.0,0.1034,0.1667,0.2083,0.040999999999999995,0.0605,0.0613,0.0039,0.0108,0.0767,0.0,0.9762,0.6864,0.0274,0.0,0.1034,0.1667,0.2083,0.042,0.0661,0.0639,0.0039,0.0114,0.076,0.0,0.9762,0.6779999999999999,0.0273,0.0,0.1034,0.1667,0.2083,0.0417,0.0616,0.0624,0.0039,0.011000000000000001,reg oper account,block of flats,0.0654,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-26.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,0.0\n129091,0,Cash loans,F,N,Y,0,202500.0,1546020.0,42642.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-20009,-256,-407.0,-3491,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.7248599048132662,0.4002108904667913,0.4489622731076524,0.1227,0.065,0.9866,0.8164,0.022000000000000002,0.12,0.1034,0.375,0.4167,0.0,0.1,0.0547,0.0,0.0553,0.125,0.0674,0.9866,0.8236,0.0222,0.1208,0.1034,0.375,0.4167,0.0,0.1093,0.057,0.0,0.0585,0.1239,0.065,0.9866,0.8189,0.0221,0.12,0.1034,0.375,0.4167,0.0,0.1018,0.0557,0.0,0.0564,not specified,block of flats,0.0847,Panel,No,0.0,0.0,0.0,0.0,-800.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n205284,0,Cash loans,F,N,Y,0,112500.0,760225.5,33615.0,679500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.003069,-20720,365243,-1246.0,-638,,1,0,0,1,0,0,,2.0,3,3,SATURDAY,11,0,0,0,1,0,0,XNA,,0.5681410664970301,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1405.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n174531,0,Cash loans,M,Y,N,0,94716.0,398160.0,11079.0,315000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Rented apartment,0.018209,-10049,-213,-7217.0,-449,18.0,1,1,1,1,0,0,Sales staff,1.0,3,3,MONDAY,8,0,0,0,1,1,0,Business Entity Type 3,0.3364913858061181,0.26668225035987114,0.6925590674998008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-707.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n165446,0,Cash loans,F,N,Y,0,58500.0,260640.0,25906.5,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.031329,-14899,-170,-5124.0,-1557,,1,1,0,1,0,0,Cooking staff,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Kindergarten,0.4205860707832871,0.16106610529422638,0.3506958432829587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n227904,0,Cash loans,F,N,Y,0,157500.0,614223.0,29547.0,549000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-18753,-2974,-1647.0,-2251,,1,1,1,1,1,0,Core staff,2.0,3,3,SATURDAY,7,0,0,0,1,1,0,Government,0.6894874471729528,0.5358522850540168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1918.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n125041,1,Cash loans,M,Y,N,1,135000.0,314055.0,16573.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.018801,-12873,-1921,-6069.0,-4635,3.0,1,1,0,1,0,0,,3.0,2,2,THURSDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.12061273948898292,0.07105512466873665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.0,1.0,16.0,1.0,-397.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n176845,0,Cash loans,M,Y,N,1,270000.0,1314000.0,48825.0,1314000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018209,-10884,-1903,-2461.0,-2461,8.0,1,1,0,1,0,0,Core staff,3.0,3,3,SATURDAY,14,0,0,0,1,1,0,Medicine,,0.11754141178847295,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n410171,1,Cash loans,M,N,Y,1,126000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-14212,-5206,-3366.0,-4298,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.19421511211361847,0.5620604831738043,0.0722,0.066,0.9776,0.6940000000000001,0.0089,0.0,0.1379,0.1667,0.2083,0.0614,0.0588,0.0652,0.0,0.0,0.0735,0.0685,0.9777,0.706,0.009000000000000001,0.0,0.1379,0.1667,0.2083,0.0628,0.0643,0.0679,0.0,0.0,0.0729,0.066,0.9776,0.6981,0.009000000000000001,0.0,0.1379,0.1667,0.2083,0.0624,0.0599,0.0664,0.0,0.0,reg oper account,block of flats,0.0562,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2817.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n147342,0,Cash loans,M,Y,Y,0,157500.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-18151,-1364,-1417.0,-1437,20.0,1,1,0,1,0,0,Drivers,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,Self-employed,,0.02302260968069571,,0.0928,0.1252,0.9771,0.6872,0.0119,0.0,0.2069,0.1667,0.0417,0.0,,0.0881,,0.0,0.0945,0.1299,0.9772,0.6994,0.012,0.0,0.2069,0.1667,0.0417,0.0,,0.0918,,0.0,0.0937,0.1252,0.9771,0.6914,0.012,0.0,0.2069,0.1667,0.0417,0.0,,0.0897,,0.0,,block of flats,0.0758,Panel,No,1.0,1.0,1.0,0.0,-2096.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n333077,0,Revolving loans,F,N,Y,1,112500.0,202500.0,10125.0,202500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018634,-12733,-1293,-6529.0,-436,,1,1,0,1,1,0,Core staff,3.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Self-employed,0.5382977686334671,0.4296856212660991,,0.0577,0.0636,0.9752,0.66,0.0029,0.0,0.1034,0.0833,0.125,0.0146,0.0471,0.0168,0.0,0.0,0.0588,0.066,0.9752,0.6733,0.0029,0.0,0.1034,0.0833,0.125,0.0149,0.0514,0.0175,0.0,0.0,0.0583,0.0636,0.9752,0.6645,0.0029,0.0,0.1034,0.0833,0.125,0.0148,0.0479,0.0171,0.0,0.0,reg oper account,block of flats,0.0132,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-453.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n201489,0,Cash loans,F,N,Y,1,135000.0,948582.0,27864.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-15407,-3521,-3633.0,-4505,,1,1,0,1,0,0,Sales staff,3.0,3,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.6030820051583641,0.5591649540370658,0.6940926425266661,0.0825,0.0659,0.9757,0.6668,0.0286,0.0,0.1379,0.1667,0.2083,0.0644,0.0664,0.0698,0.0039,0.0031,0.084,0.0683,0.9757,0.6798,0.0289,0.0,0.1379,0.1667,0.2083,0.0658,0.0725,0.0727,0.0039,0.0033,0.0833,0.0659,0.9757,0.6713,0.0288,0.0,0.1379,0.1667,0.2083,0.0655,0.0676,0.071,0.0039,0.0031,reg oper account,block of flats,0.0712,Panel,No,6.0,0.0,6.0,0.0,-2376.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n402157,0,Cash loans,F,N,Y,0,81000.0,634041.0,22905.0,481500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009657,-11524,-956,-5255.0,-3908,,1,1,0,1,0,1,,1.0,2,2,SATURDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.31537457797270463,0.5549467685334323,0.5546,0.5219,0.9831,0.7688,0.1932,0.6,0.5172,0.3333,0.375,0.35,0.4522,0.6065,0.0,0.0161,0.5651,0.5416,0.9831,0.7779,0.195,0.6042,0.5172,0.3333,0.375,0.358,0.494,0.6319,0.0,0.0171,0.56,0.5219,0.9831,0.7719,0.1944,0.6,0.5172,0.3333,0.375,0.3561,0.46,0.6174,0.0,0.0164,reg oper account,block of flats,0.5862,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n250457,0,Cash loans,F,Y,N,0,292500.0,1800000.0,66708.0,1800000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.04622,-11607,-3641,-4556.0,-1514,2.0,1,1,0,1,1,0,Core staff,1.0,1,1,THURSDAY,11,0,0,0,0,0,0,Police,0.5433772686880742,0.7651883068758394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-650.0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n288183,0,Cash loans,M,Y,N,2,337500.0,1007761.5,42826.5,927000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.031329,-11127,-3518,-2340.0,-3660,11.0,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,11,0,0,0,0,0,0,Transport: type 2,0.2733500324460745,0.5892554363696447,0.44539624156083396,0.11699999999999999,0.1694,0.9871,,0.018000000000000002,0.0,0.1552,0.1667,0.2083,,0.0946,0.1075,0.0425,0.0207,0.021,0.0263,0.9846,,0.0043,0.0,0.0345,0.1667,0.2083,,0.0184,0.022000000000000002,0.0078,0.0,0.1181,0.1694,0.9871,,0.0182,0.0,0.1552,0.1667,0.2083,,0.0962,0.1095,0.0427,0.0212,reg oper account,block of flats,0.0189,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1882.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n170826,0,Cash loans,F,N,N,0,112500.0,545913.0,14400.0,455688.0,,Working,Incomplete higher,Married,House / apartment,0.018029,-9457,-494,-8630.0,-137,,1,1,0,1,0,0,,2.0,3,2,FRIDAY,13,0,0,0,0,0,0,Transport: type 4,0.21417725278060706,0.6327797135067842,,0.0778,0.0588,0.9781,0.7008,0.0138,0.04,0.1034,0.25,0.2917,0.0537,0.0609,0.0736,0.0116,0.0118,0.0756,0.0526,0.9742,0.6602,0.0081,0.0,0.069,0.1667,0.2083,0.055,0.0661,0.0678,0.0,0.0,0.0786,0.0588,0.9781,0.7048,0.0139,0.04,0.1034,0.25,0.2917,0.0547,0.062,0.0749,0.0116,0.0121,reg oper account,block of flats,0.0633,Panel,No,5.0,0.0,5.0,0.0,-751.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n259664,0,Revolving loans,F,N,N,1,202500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,With parents,0.007273999999999998,-11012,-884,-122.0,-2451,,1,1,0,1,0,0,Accountants,2.0,2,2,SATURDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.4356867781947089,0.4411029045455352,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n373883,1,Cash loans,F,N,Y,1,99000.0,348264.0,23404.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.0105,-9131,-712,-1117.0,-1666,,1,1,0,1,0,0,Sales staff,3.0,3,3,TUESDAY,12,0,0,0,0,0,0,Restaurant,,0.0311448239523215,0.17146836689679945,0.0082,,0.9801,,,0.0,0.0345,0.0417,,0.0039,,0.0066,,0.0,0.0084,,0.9801,,,0.0,0.0345,0.0417,,0.004,,0.0069,,0.0,0.0083,,0.9801,,,0.0,0.0345,0.0417,,0.0039,,0.0068,,0.0,,block of flats,0.009000000000000001,Wooden,No,0.0,0.0,0.0,0.0,-368.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n387984,0,Cash loans,M,N,N,0,247500.0,626476.5,58702.5,580500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00823,-19433,-156,-3532.0,-2936,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.6103529167531909,0.7981372313187245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n449702,0,Cash loans,F,N,Y,0,99000.0,225000.0,15219.0,225000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.022625,-11125,-1012,-237.0,-3731,,1,1,1,1,1,0,Core staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,School,0.5583222029446437,0.454767056911715,0.2225807646753351,0.0165,0.0466,0.9831,0.7688,0.0019,0.0,0.069,0.0417,0.0833,0.0149,0.0134,0.0141,0.0,0.0,0.0168,0.0483,0.9831,0.7779,0.0019,0.0,0.069,0.0417,0.0833,0.0153,0.0147,0.0147,0.0,0.0,0.0167,0.0466,0.9831,0.7719,0.0019,0.0,0.069,0.0417,0.0833,0.0152,0.0137,0.0144,0.0,0.0,not specified,block of flats,0.0111,\"Stone, brick\",No,3.0,2.0,3.0,2.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n308161,0,Cash loans,M,Y,N,1,202500.0,284400.0,10849.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.04622,-9956,-444,-4661.0,-2618,6.0,1,1,0,1,0,0,Managers,3.0,1,1,WEDNESDAY,16,0,0,0,0,1,1,Transport: type 4,0.5418898659550209,0.65104802740528,0.5954562029091491,0.0619,,0.9826,,,0.0,0.1379,0.1667,,0.0095,,0.0343,,0.0,0.063,,0.9826,,,0.0,0.1379,0.1667,,0.0097,,0.0358,,0.0,0.0625,,0.9826,,,0.0,0.1379,0.1667,,0.0096,,0.0349,,0.0,,block of flats,0.0418,Panel,No,0.0,0.0,0.0,0.0,-1803.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n452809,0,Cash loans,F,N,Y,0,112500.0,673875.0,19440.0,562500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-22080,365243,-3031.0,-4350,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,0.7347560836256369,0.474368230987012,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1343.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n443211,0,Cash loans,F,N,Y,0,36000.0,107820.0,7798.5,90000.0,Unaccompanied,Pensioner,Lower secondary,Widow,House / apartment,0.007120000000000001,-22853,365243,-3646.0,-4482,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,XNA,,0.7142792864482229,0.7435593141311444,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1167.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n247960,0,Cash loans,F,N,Y,0,270000.0,779688.0,39937.5,630000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006233,-13765,-5693,-418.0,-2796,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Transport: type 2,,0.5003189634354869,0.09322509537747448,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-436.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n400898,1,Cash loans,F,N,Y,0,112500.0,592560.0,35937.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-15994,-635,-2523.0,-2613,,1,1,0,1,0,0,Cooking staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Restaurant,0.7670578193534353,0.5092836052180564,0.3108182544189319,0.0082,,0.9742,0.6464,0.0,0.0,0.2759,0.0417,0.0833,0.0042,0.0067,0.0037,0.0,0.0,0.0084,,0.9742,0.6602,0.0,0.0,0.2759,0.0417,0.0833,0.0043,0.0073,0.0038,0.0,0.0,0.0083,,0.9742,0.6511,0.0,0.0,0.2759,0.0417,0.0833,0.0043,0.0068,0.0037,0.0,0.0,reg oper account,block of flats,0.0029,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1352.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n224997,0,Cash loans,F,N,N,0,315000.0,755190.0,52690.5,675000.0,Family,Commercial associate,Higher education,Civil marriage,With parents,0.011657,-14679,-1282,-9883.0,-2929,,1,1,0,1,0,0,Managers,2.0,1,1,MONDAY,15,1,1,0,0,0,0,Business Entity Type 3,,0.725256539445682,0.7934490301648944,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1644.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n302914,0,Cash loans,M,Y,Y,0,157500.0,1298655.0,35712.0,1134000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007114,-21300,-1888,-11157.0,-4155,5.0,1,1,1,1,1,0,Managers,2.0,2,2,MONDAY,13,0,0,0,0,1,1,Self-employed,0.5672613000614756,0.6955758002308602,0.11987796089553485,0.0619,0.0627,0.9846,0.7892,0.0088,0.0,0.1379,0.1667,0.0417,0.0573,0.0504,0.0543,0.0,0.0,0.063,0.065,0.9846,0.7975,0.0089,0.0,0.1379,0.1667,0.0417,0.0587,0.0551,0.0566,0.0,0.0,0.0625,0.0627,0.9846,0.792,0.0089,0.0,0.1379,0.1667,0.0417,0.0583,0.0513,0.0553,0.0,0.0,reg oper account,block of flats,0.0475,Panel,No,0.0,0.0,0.0,0.0,-713.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n342638,0,Cash loans,M,N,Y,0,234000.0,760225.5,33484.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007114,-14184,-95,-8858.0,-3004,,1,1,1,1,1,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.3576029779801682,0.5609160735480856,0.4866531565147181,0.066,0.0,0.9762,0.6736,0.0062,0.0,0.1379,0.125,0.1667,0.0501,0.0504,0.0472,0.0154,0.0142,0.0672,0.0,0.9762,0.6864,0.0063,0.0,0.1379,0.125,0.1667,0.0512,0.0551,0.0492,0.0156,0.0151,0.0666,0.0,0.9762,0.6779999999999999,0.0062,0.0,0.1379,0.125,0.1667,0.0509,0.0513,0.0481,0.0155,0.0145,reg oper account,block of flats,0.0402,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-37.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n451312,0,Cash loans,F,N,Y,0,90000.0,254700.0,14350.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-23821,365243,-10606.0,-4506,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.6058607906653767,0.5884877883422673,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,1.0,-701.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n157034,0,Cash loans,F,N,N,2,135000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-13779,-4359,-6076.0,-4511,,1,1,0,1,0,0,Core staff,4.0,2,2,FRIDAY,11,0,0,0,0,0,0,University,0.4863160438692074,0.5508967253656891,0.4794489811780563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n350962,0,Cash loans,M,N,Y,0,360000.0,1971072.0,68643.0,1800000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.009549,-18781,-10711,-10450.0,-2290,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 2,,0.30258759856549483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,1.0,0.0,1.0,0.0,-470.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n450146,0,Cash loans,M,Y,N,1,238500.0,490495.5,47911.5,454500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007114,-12042,-715,-168.0,-4138,64.0,1,1,0,1,0,0,Managers,3.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 1,0.444180701590967,0.6551106549131976,0.6785676886853644,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1544.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n131972,0,Revolving loans,F,N,Y,0,67500.0,135000.0,6750.0,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-11858,-796,-3264.0,-4073,,1,1,1,1,1,0,Sales staff,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Self-employed,0.42112469781122347,0.2064127542531252,0.722392890081304,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1119.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n212253,0,Cash loans,F,N,Y,0,135000.0,397881.0,22347.0,328500.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.018029,-19591,-414,-3892.0,-3132,,1,1,0,1,0,0,High skill tech staff,1.0,3,3,FRIDAY,8,0,0,0,0,0,0,Government,0.6066059901364927,0.5173850673687946,0.6413682574954046,0.0619,,0.9856,,,,0.1379,0.1667,,0.0466,,0.0536,,0.0988,0.063,,0.9856,,,,0.1379,0.1667,,0.0476,,0.0558,,0.1046,0.0625,,0.9856,,,,0.1379,0.1667,,0.0474,,0.0545,,0.1009,,block of flats,0.0636,,No,0.0,0.0,0.0,0.0,-364.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n156939,0,Cash loans,F,Y,Y,0,90000.0,1040985.0,30568.5,909000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15418,-2486,-7925.0,-4903,3.0,1,1,0,1,1,0,Core staff,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Hotel,0.5546941329429712,0.7279844570594195,0.2866524828090694,0.0247,0.0409,0.9806,0.7348,0.013999999999999999,0.0,0.069,0.0833,0.125,0.0174,0.0202,0.0216,0.0,0.0,0.0252,0.0425,0.9806,0.7452,0.0141,0.0,0.069,0.0833,0.125,0.0177,0.022000000000000002,0.0225,0.0,0.0,0.025,0.0409,0.9806,0.7383,0.0141,0.0,0.069,0.0833,0.125,0.0177,0.0205,0.0219,0.0,0.0,,block of flats,0.0246,\"Stone, brick\",No,8.0,2.0,8.0,1.0,-2833.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n271422,0,Cash loans,F,Y,Y,1,157500.0,564124.5,41179.5,481500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-15568,-2741,-5405.0,-5392,24.0,1,1,0,1,0,0,,3.0,1,1,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.7475073258362533,0.5576551142713989,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n130304,0,Cash loans,F,Y,N,2,135000.0,509400.0,37066.5,450000.0,Children,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.020246,-15120,-1737,-2376.0,-3720,1.0,1,1,0,1,1,1,High skill tech staff,3.0,3,3,FRIDAY,13,0,0,0,0,1,1,Self-employed,0.7354239639681606,0.6505908348698884,0.5316861425197883,0.1196,0.1016,0.9826,0.762,0.0287,0.16,0.1379,0.3333,0.375,0.0364,0.0967,0.1455,0.0039,0.0058,0.1218,0.1055,0.9826,0.7713,0.028999999999999998,0.1611,0.1379,0.3333,0.375,0.0372,0.1056,0.1516,0.0039,0.0062,0.1207,0.1016,0.9826,0.7652,0.0289,0.16,0.1379,0.3333,0.375,0.037000000000000005,0.0983,0.1481,0.0039,0.0059,reg oper account,block of flats,0.1314,Panel,No,0.0,0.0,0.0,0.0,-281.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n194780,0,Cash loans,F,N,Y,0,180000.0,229230.0,22459.5,202500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.032561,-24840,-8038,-11087.0,-3778,,1,1,0,1,1,0,Managers,1.0,1,1,MONDAY,18,0,0,0,0,0,0,Medicine,,0.6899067006246438,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-1474.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n286173,0,Cash loans,M,N,N,0,117000.0,508495.5,21541.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008575,-10648,-3969,-2127.0,-2231,,1,1,1,1,0,0,Laborers,2.0,2,2,SATURDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.2151936377725556,0.4583479541749202,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1731.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n268787,0,Cash loans,M,Y,Y,0,112500.0,900000.0,35005.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.015221,-11131,-497,-5255.0,-3715,8.0,1,1,0,1,1,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.5948011485149465,0.8327850252992314,0.099,0.1545,0.9886,,,0.12,0.1034,0.375,,0.0221,,0.109,,0.0598,0.1008,0.1604,0.9886,,,0.1208,0.1034,0.375,,0.0227,,0.1136,,0.0633,0.0999,0.1545,0.9886,,,0.12,0.1034,0.375,,0.0225,,0.111,,0.061,,block of flats,0.0857,Panel,No,3.0,0.0,3.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n204828,0,Revolving loans,F,N,Y,0,157500.0,405000.0,20250.0,405000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.00702,-18512,365243,-12630.0,-813,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.15532399362816107,,0.0619,,0.9771,0.6804,,0.0,0.1034,0.1667,0.0417,,0.0496,0.0395,0.0039,,0.063,,0.9767,0.6929,,0.0,0.1034,0.1667,0.0417,,0.0542,0.0412,0.0039,,0.0625,,0.9771,0.6847,,0.0,0.1034,0.1667,0.0417,,0.0504,0.0402,0.0039,,reg oper account,block of flats,0.0416,,No,2.0,0.0,2.0,0.0,-2374.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n162911,0,Cash loans,M,N,Y,0,90000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.007120000000000001,-19170,-2062,-13253.0,-2684,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Industry: type 7,,0.751109633462441,0.7688075728291359,0.0165,0.0,0.9677,0.5579999999999999,,0.0,0.069,0.0417,,0.0318,0.0134,0.0078,,0.0,0.0168,0.0,0.9677,0.5753,,0.0,0.069,0.0417,,0.0325,0.0147,0.0081,,0.0,0.0167,0.0,0.9677,0.5639,,0.0,0.069,0.0417,,0.0323,0.0137,0.0079,,0.0,,block of flats,0.0107,Block,No,3.0,0.0,3.0,0.0,-570.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n211389,0,Cash loans,F,N,Y,2,112500.0,450000.0,30573.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-13533,-826,-2650.0,-2905,,1,1,1,1,1,0,,4.0,2,2,SATURDAY,8,0,0,0,0,0,0,Other,,0.5252483815989715,0.7366226976503176,0.2052,0.1656,0.9876,0.83,0.2093,0.24,0.2069,0.3333,0.375,0.0823,0.1614,0.205,0.027000000000000003,0.1325,0.209,0.1719,0.9876,0.8367,0.2112,0.2417,0.2069,0.3333,0.375,0.0842,0.1763,0.2135,0.0272,0.1402,0.2071,0.1656,0.9876,0.8323,0.2106,0.24,0.2069,0.3333,0.375,0.0838,0.1642,0.2086,0.0272,0.1352,reg oper account,block of flats,0.1792,Block,No,3.0,0.0,2.0,0.0,-1940.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n344672,0,Cash loans,F,Y,N,1,135000.0,360351.0,40882.5,324000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-14671,-136,-645.0,-619,1.0,1,1,0,1,1,0,Laborers,3.0,2,2,TUESDAY,15,0,0,0,0,1,1,Business Entity Type 3,0.13078555462027328,0.5852005711152046,0.5064842396679806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-415.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n379849,0,Revolving loans,M,N,Y,0,162000.0,495000.0,24750.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-14526,-443,-1072.0,-2917,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.5649458895795955,0.4596904504249018,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-308.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,6.0\n193379,0,Cash loans,F,Y,Y,0,202500.0,900000.0,52753.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-13292,-529,-619.0,-622,2.0,1,1,0,1,0,0,,2.0,2,1,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4629251549083357,0.6846853304637683,0.19294222771695085,0.1546,0.2292,0.998,0.9728,0.0449,0.0,0.2069,0.1667,0.0417,0.0,0.1261,0.1788,0.0,0.0,0.1576,0.2378,0.998,0.9739,0.0453,0.0,0.2069,0.1667,0.0417,0.0,0.1377,0.1863,0.0,0.0,0.1561,0.2292,0.998,0.9732,0.0452,0.0,0.2069,0.1667,0.0417,0.0,0.1283,0.182,0.0,0.0,not specified,block of flats,0.1652,Block,No,0.0,0.0,0.0,0.0,-1312.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n244243,0,Cash loans,F,N,N,0,112500.0,526491.0,18909.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-14853,-7000,-8022.0,-4405,,1,1,0,1,1,0,Cooking staff,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Other,,0.6680252844520036,0.7352209993926424,0.2134,0.0927,0.9811,,,0.0,0.069,0.3333,,0.1359,,0.0636,,0.4920000000000001,0.2174,0.0962,0.9811,,,0.0,0.069,0.3333,,0.139,,0.0662,,0.5209,0.2155,0.0927,0.9811,,,0.0,0.069,0.3333,,0.1382,,0.0647,,0.5023,,block of flats,0.1233,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-19.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n183271,0,Cash loans,F,N,Y,0,135000.0,1585899.0,43740.0,1417500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.02461,-16082,-101,-3548.0,-4384,,1,1,0,1,1,0,Core staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Kindergarten,,0.7257878133096538,0.7407990879702335,0.0196,,0.9687,,0.0025,0.0,0.069,0.0417,,0.0351,0.016,0.0174,0.0,0.0,0.02,,0.9687,,0.0025,0.0,0.069,0.0417,,0.036000000000000004,0.0174,0.0181,0.0,0.0,0.0198,,0.9687,,0.0025,0.0,0.069,0.0417,,0.0358,0.0162,0.0177,0.0,0.0,,block of flats,0.015,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1391.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n219780,1,Cash loans,M,N,Y,0,112500.0,148500.0,12033.0,148500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-11093,-106,-11093.0,-3669,,1,1,0,1,0,0,Low-skill Laborers,1.0,2,2,WEDNESDAY,15,0,0,0,1,1,0,Business Entity Type 3,,0.05162399408266919,0.3001077565791181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-523.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n100678,0,Cash loans,F,Y,Y,2,184500.0,1078200.0,31522.5,900000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.020246,-10598,-854,-558.0,-630,6.0,1,1,0,1,0,0,High skill tech staff,4.0,3,3,TUESDAY,9,0,0,0,0,1,1,Industry: type 2,0.2913389437488164,0.4726019397886651,0.4830501881366946,0.066,0.0799,0.9771,0.6872,0.0,0.0,0.1379,0.1667,0.0,0.0375,0.0538,0.0508,0.0,0.0459,0.0672,0.0829,0.9772,0.6994,0.0,0.0,0.1379,0.1667,0.0,0.0383,0.0588,0.0529,0.0,0.0486,0.0666,0.0799,0.9771,0.6914,0.0,0.0,0.1379,0.1667,0.0,0.0381,0.0547,0.0517,0.0,0.0469,not specified,block of flats,0.0499,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1823.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n159304,0,Revolving loans,F,N,Y,0,90000.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-16935,-4809,-1757.0,-4554,,1,1,0,1,1,0,High skill tech staff,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,Government,0.606631302869721,0.34750614308047256,0.4740512892789932,0.0948,0.1227,0.9826,0.762,0.0141,0.0,0.2069,0.1667,0.2083,0.0896,0.0765,0.1006,0.0039,0.0036,0.0966,0.1274,0.9826,0.7713,0.0143,0.0,0.2069,0.1667,0.2083,0.0917,0.0836,0.1048,0.0039,0.0038,0.0958,0.1227,0.9826,0.7652,0.0142,0.0,0.2069,0.1667,0.2083,0.0912,0.0778,0.1024,0.0039,0.0037,reg oper account,block of flats,0.0877,Panel,No,0.0,0.0,0.0,0.0,-1749.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n254669,0,Cash loans,M,Y,Y,0,337500.0,1546020.0,42642.0,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-17993,-626,-1281.0,-1554,6.0,1,1,0,1,0,0,Security staff,2.0,2,2,MONDAY,17,0,1,1,0,0,0,Security,,0.10576080310376897,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1468.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n351328,0,Cash loans,F,Y,Y,0,202500.0,663093.0,21519.0,553500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.018029,-23685,365243,-4387.0,-4389,38.0,1,0,0,1,0,0,,1.0,3,3,TUESDAY,7,0,0,0,0,0,0,XNA,,0.5054630948193832,0.5902333386185574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2470.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,4.0\n371556,0,Cash loans,M,N,Y,1,342000.0,840951.0,33480.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-15436,-2962,-5043.0,-4648,,1,1,0,1,0,0,Drivers,3.0,2,2,THURSDAY,10,0,0,0,0,1,1,Self-employed,,0.4666378611944033,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-651.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n220992,0,Cash loans,F,N,Y,0,99000.0,906615.0,30091.5,688500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.008473999999999999,-20471,365243,-7544.0,-88,,1,0,0,1,0,0,,1.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.15342566171523456,,,,0.9722,,,,0.2069,0.0833,,,,0.0575,,,,,0.9722,,,,0.2069,0.0,,,,0.0024,,,,,0.9722,,,,0.2069,0.0833,,,,0.0586,,,,,0.0018,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-477.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n162247,0,Revolving loans,F,N,Y,0,108000.0,247500.0,12375.0,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.009334,-20191,365243,-1328.0,-3639,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.5589115922826918,0.09445169198377182,,,0.9752,,,,0.1034,0.125,,,,0.0405,,,,,0.9752,,,,0.1034,0.125,,,,0.0422,,,,,0.9752,,,,0.1034,0.125,,,,0.0412,,,,,0.0341,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-456.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n249246,0,Cash loans,F,N,Y,0,94500.0,272520.0,17545.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.031329,-24302,365243,-4154.0,-4155,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,0.4804884613552088,0.18639859007666684,0.6313545365850379,0.0278,0.0412,0.9831,0.7688,0.0042,0.0,0.1034,0.0833,0.125,,0.0227,0.0251,0.0,0.0,0.0284,0.0428,0.9831,0.7779,0.0042,0.0,0.1034,0.0833,0.125,,0.0248,0.0261,0.0,0.0,0.0281,0.0412,0.9831,0.7719,0.0042,0.0,0.1034,0.0833,0.125,,0.0231,0.0255,0.0,0.0,reg oper account,block of flats,0.022000000000000002,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1647.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n217995,0,Cash loans,F,Y,N,0,360000.0,1125000.0,32895.0,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-17168,-1646,-1878.0,-708,64.0,1,1,0,1,1,0,,2.0,2,2,WEDNESDAY,19,0,0,0,0,1,1,Other,0.7866436512884524,0.5060042462595927,0.5567274263630174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1548.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n371541,0,Cash loans,F,N,Y,0,130500.0,528633.0,25560.0,472500.0,Family,Working,Incomplete higher,Separated,With parents,0.018634,-14963,-356,-1344.0,-1246,,1,1,0,1,0,0,Sales staff,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Other,,0.6139352959877447,0.5424451438613613,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-925.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n275853,0,Cash loans,F,N,N,0,76500.0,390960.0,27207.0,337500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00963,-17871,-11060,-8744.0,-1394,,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Medicine,,0.7601337934384922,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-791.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n108594,1,Cash loans,M,Y,N,1,202500.0,473760.0,56353.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-10964,-1045,-3861.0,-2221,21.0,1,1,0,1,0,0,,3.0,2,2,MONDAY,12,0,1,1,0,1,1,Industry: type 9,,0.5996824898664309,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n176547,0,Cash loans,F,N,Y,1,81000.0,254700.0,25321.5,225000.0,Family,Working,Higher education,Married,House / apartment,0.01885,-15356,-1437,-1806.0,-4823,,1,1,1,1,1,0,Core staff,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,School,,0.5313788394273955,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-506.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n112827,0,Cash loans,M,Y,Y,0,202500.0,640080.0,24259.5,450000.0,Children,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-19608,-873,-1586.0,-2325,9.0,1,1,0,1,0,0,Drivers,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.08239536774955909,0.3539876078507373,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-4.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n267905,0,Cash loans,F,N,Y,0,112500.0,971316.0,51880.5,918000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-22885,365243,-10667.0,-4618,,1,0,0,1,0,0,,2.0,3,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.6433087832286639,,0.0825,0.0657,0.9762,0.6736,0.0286,0.0,0.1379,0.1667,0.2083,0.0495,0.0672,0.0707,0.0,0.0,0.084,0.0682,0.9762,0.6864,0.0289,0.0,0.1379,0.1667,0.2083,0.0507,0.0735,0.0737,0.0,0.0,0.0833,0.0657,0.9762,0.6779999999999999,0.0288,0.0,0.1379,0.1667,0.2083,0.0504,0.0684,0.07200000000000001,0.0,0.0,reg oper account,block of flats,0.0713,Panel,No,3.0,0.0,3.0,0.0,-683.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n223988,0,Cash loans,M,Y,Y,0,270000.0,104256.0,10287.0,90000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.01885,-14706,-1566,-853.0,-5166,2.0,1,1,0,1,0,1,Core staff,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.34644336143272964,0.4207230624463584,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n186205,0,Cash loans,M,N,Y,0,427500.0,497520.0,36333.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-19939,365243,-2926.0,-2581,,1,0,0,1,0,1,,2.0,1,1,TUESDAY,14,1,0,0,0,0,0,XNA,0.5924342271512814,0.7085508336247286,0.1245194708919845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1899.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n180712,0,Cash loans,F,N,N,0,112500.0,675000.0,19867.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.014519999999999996,-21641,365243,-10547.0,-4968,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,XNA,,0.2318881971357951,0.4206109640437848,,,0.9856,0.8028,,,0.1379,0.3333,,,,,,,,,0.9856,0.8105,,,0.1379,0.3333,,,,,,,,,0.9856,0.8054,,,0.1379,0.3333,,,,,,,,block of flats,0.0603,,No,0.0,0.0,0.0,0.0,-1528.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n400447,0,Cash loans,F,N,Y,0,225000.0,675000.0,35419.5,675000.0,Other_A,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-21066,365243,-8811.0,-3687,,1,0,0,1,0,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.5953438800954148,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1118.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n148461,0,Cash loans,F,N,Y,0,315000.0,1983631.5,54679.5,1773000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-23317,365243,-10425.0,-4041,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,XNA,,0.057904478828910234,,0.1088,0.0925,0.9856,0.8028,0.0268,0.12,0.0862,0.3958,0.4375,0.0506,0.0887,0.1141,0.0,0.0,0.0714,0.0723,0.9856,0.8105,0.0261,0.0806,0.0345,0.3333,0.375,0.0498,0.0624,0.0933,0.0,0.0,0.1098,0.0925,0.9856,0.8054,0.027000000000000003,0.12,0.0862,0.3958,0.4375,0.0515,0.0902,0.1161,0.0,0.0,reg oper spec account,block of flats,0.0845,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-3053.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n131903,0,Cash loans,F,N,Y,0,135000.0,497520.0,30568.5,450000.0,Other_A,Pensioner,Secondary / secondary special,Married,House / apartment,0.072508,-20610,365243,-576.0,-4152,,1,0,0,1,1,0,,2.0,1,1,TUESDAY,12,1,0,0,1,0,0,XNA,,0.7210669843194215,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1551.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n341742,0,Cash loans,F,Y,Y,2,180000.0,1350000.0,43551.0,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-12851,-1987,-2229.0,-5052,23.0,1,1,0,1,0,0,Core staff,4.0,2,2,THURSDAY,13,0,0,0,0,0,0,Self-employed,,0.6014712676636255,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1771.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n354620,0,Cash loans,M,Y,N,0,495000.0,497520.0,33376.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00733,-10577,-960,-338.0,-2707,0.0,1,1,0,1,0,0,Private service staff,2.0,2,2,WEDNESDAY,12,1,1,0,0,0,0,Services,,0.5024564998910583,,0.0732,,0.9851,0.7959999999999999,,0.04,0.0345,0.3333,0.0417,,0.0597,0.0646,0.0,,0.0746,,0.9851,0.804,,0.0403,0.0345,0.3333,0.0417,,0.0652,0.0673,0.0,,0.0739,,0.9851,0.7987,,0.04,0.0345,0.3333,0.0417,,0.0607,0.0658,0.0,,reg oper account,block of flats,0.0532,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-287.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n119498,0,Cash loans,F,N,N,0,112500.0,241618.5,23665.5,229500.0,\"Spouse, partner\",State servant,Higher education,Married,House / apartment,0.006629,-15193,-3706,-186.0,-2440,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,School,,0.6331404156503074,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1870.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n149043,0,Cash loans,F,N,Y,2,202500.0,675000.0,28975.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-13277,-636,-1420.0,-4010,,1,1,1,1,1,0,Sales staff,4.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.2497689466285773,0.5581405375104213,0.5280927512030451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-669.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n246883,0,Cash loans,F,N,Y,0,157500.0,225000.0,13896.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-13921,-2542,-4781.0,-4754,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,1,1,0,Self-employed,,0.5814800824651016,0.3233112448967859,,,0.9742,,,,,,,,,0.0123,,,,,0.9742,,,,,,,,,0.0128,,,,,0.9742,,,,,,,,,0.0125,,,,,0.0097,,No,1.0,0.0,1.0,0.0,-327.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n138736,0,Cash loans,M,Y,N,0,135000.0,135000.0,15228.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Rented apartment,0.01885,-10258,-619,-7245.0,-1874,7.0,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,20,0,0,0,0,0,0,Self-employed,0.15271925306016348,0.4545447576236754,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,1.0,-620.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n328326,0,Cash loans,F,Y,Y,1,144000.0,601470.0,30838.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00702,-17439,-1143,-9193.0,-870,1.0,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Self-employed,0.7556319237114356,0.7294499405859021,0.6023863442690867,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1570.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n316532,0,Cash loans,M,Y,Y,0,135000.0,273636.0,27193.5,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008575,-20438,-1754,-5817.0,-3997,5.0,1,1,0,1,0,0,Security staff,2.0,2,2,TUESDAY,16,0,1,1,0,0,0,University,,0.7118273877797452,0.6594055320683344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1313.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n191055,0,Cash loans,M,N,Y,1,202500.0,573628.5,25398.0,463500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018634,-18921,-3143,-1147.0,-2483,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,1,1,0,Business Entity Type 3,,0.2740367850847375,0.2866524828090694,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-113.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n372655,0,Cash loans,M,Y,N,0,157500.0,1125000.0,33025.5,1125000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-15928,-1178,-7028.0,-674,18.0,1,1,1,1,1,0,Drivers,2.0,2,2,SATURDAY,13,0,0,0,0,1,1,Business Entity Type 1,,0.653305045644048,0.7106743858828587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1129.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n124216,0,Cash loans,M,N,Y,1,225000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018634,-16644,-718,-6974.0,-154,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Self-employed,0.5257449977901321,0.7094486613920431,,0.1082,0.1168,0.9831,0.7688,0.0493,0.0,0.2414,0.1667,0.2083,0.0208,0.0883,0.0997,0.0,0.0,0.1103,0.1212,0.9831,0.7779,0.0497,0.0,0.2414,0.1667,0.2083,0.0212,0.0964,0.1038,0.0,0.0,0.1093,0.1168,0.9831,0.7719,0.0496,0.0,0.2414,0.1667,0.2083,0.0211,0.0898,0.1015,0.0,0.0,org spec account,block of flats,0.1053,Panel,No,11.0,0.0,11.0,0.0,-1794.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n262808,1,Cash loans,M,Y,N,0,112500.0,942300.0,30528.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.031329,-9064,-748,-4993.0,-1555,19.0,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,9,1,1,0,1,1,0,Business Entity Type 3,,0.1235161326057024,,0.0619,0.0545,0.9876,0.83,0.0086,0.0,0.1379,0.1667,0.2083,0.0521,0.0504,0.0526,0.0,0.0,0.063,0.0566,0.9876,0.8367,0.0087,0.0,0.1379,0.1667,0.2083,0.0533,0.0551,0.0548,0.0,0.0,0.0625,0.0545,0.9876,0.8323,0.0087,0.0,0.1379,0.1667,0.2083,0.053,0.0513,0.0536,0.0,0.0,reg oper account,block of flats,0.0414,Panel,No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n283842,0,Cash loans,F,Y,N,0,450000.0,1350000.0,39604.5,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-16660,-4794,-769.0,-207,1.0,1,1,1,1,1,0,Core staff,2.0,2,2,TUESDAY,17,0,0,0,0,1,1,Self-employed,0.8300308865736192,0.6772458873094609,0.4507472818545589,0.2691,0.1273,0.9985,0.9728,0.1243,0.32,0.1379,0.5833,0.5417,0.1826,0.2101,0.3482,0.0425,0.1006,0.2742,0.1321,0.9985,0.9739,0.1254,0.3222,0.1379,0.5833,0.5417,0.1868,0.2296,0.3628,0.0428,0.1065,0.2717,0.1273,0.9985,0.9732,0.1251,0.32,0.1379,0.5833,0.5417,0.1858,0.2138,0.3544,0.0427,0.1027,reg oper account,block of flats,0.3637,Monolithic,No,0.0,0.0,0.0,0.0,-1372.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n168705,0,Cash loans,F,N,N,0,90000.0,123637.5,8122.5,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019101,-23984,365243,-7714.0,-4781,,1,0,0,1,0,0,,1.0,2,2,SUNDAY,11,0,0,0,0,0,0,XNA,,0.5528760593272872,0.7922644738669378,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,3.0\n281636,0,Cash loans,M,Y,Y,2,135000.0,1006920.0,37444.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007305,-16932,-231,-3575.0,-483,15.0,1,1,0,1,0,0,Managers,4.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Transport: type 4,0.6033845465661654,0.6527253229428417,0.7583930617144343,0.1649,0.1167,0.9876,0.8436,0.0382,0.16,0.1379,0.375,0.4167,0.0203,0.1345,0.1878,0.0,0.0,0.1681,0.1211,0.9876,0.8497,0.0385,0.1611,0.1379,0.375,0.4167,0.0207,0.1469,0.1956,0.0,0.0,0.1665,0.1167,0.9876,0.8457,0.0384,0.16,0.1379,0.375,0.4167,0.0206,0.1368,0.1911,0.0,0.0,reg oper account,block of flats,0.1477,Panel,No,0.0,0.0,0.0,0.0,-1490.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n220125,0,Cash loans,F,N,Y,0,112500.0,521280.0,28408.5,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.00702,-8655,-598,-8590.0,-1329,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Insurance,0.3432878963411213,0.7051206099584222,,0.1175,0.1025,0.9881,0.8640000000000001,0.0107,0.0,0.1724,0.1667,0.2083,0.0794,0.0958,0.0565,0.0,0.0,0.1197,0.1063,0.9861,0.8693,0.0108,0.0,0.1724,0.1667,0.2083,0.0812,0.1047,0.0156,0.0,0.0,0.1187,0.1025,0.9881,0.8658,0.0108,0.0,0.1724,0.1667,0.2083,0.0807,0.0975,0.0575,0.0,0.0,reg oper account,block of flats,0.0165,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n253999,0,Cash loans,F,N,Y,1,90000.0,616500.0,19885.5,616500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.015221,-14491,-2049,-91.0,-4573,,1,1,0,1,0,0,Cooking staff,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,Other,0.30685225858789666,0.10769124249263512,0.5867400085415683,0.0928,0.0801,0.9767,0.6804,0.0312,0.0,0.2069,0.1667,0.2083,0.0998,0.0756,0.0867,0.0,0.0,0.0945,0.0831,0.9767,0.6929,0.0315,0.0,0.2069,0.1667,0.2083,0.102,0.0826,0.0904,0.0,0.0,0.0937,0.0801,0.9767,0.6847,0.0314,0.0,0.2069,0.1667,0.2083,0.1015,0.077,0.0883,0.0,0.0,reg oper account,block of flats,0.0853,Panel,No,3.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n162769,0,Cash loans,M,Y,N,1,315000.0,1687266.0,68850.0,1575000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007273999999999998,-16069,-417,-1.0,-4051,4.0,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5737656471824375,0.33125086459090186,0.0557,,0.9861,0.8096,,0.0,0.1379,0.1667,0.0417,0.0,0.0454,0.0311,0.0,0.0,0.0567,,0.9861,0.8171,,0.0,0.1379,0.1667,0.0417,0.0,0.0496,0.0324,0.0,0.0,0.0562,,0.9861,0.8121,,0.0,0.1379,0.1667,0.0417,0.0,0.0462,0.0317,0.0,0.0,reg oper spec account,block of flats,0.0411,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n402946,0,Cash loans,F,N,Y,1,139500.0,1800000.0,62568.0,1800000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.011703,-12478,-3639,-919.0,-2698,,1,1,1,1,0,1,Managers,3.0,2,2,FRIDAY,20,0,0,0,0,0,0,Other,0.8730804725319089,0.6432134395022386,0.6706517530862718,0.0835,,0.9935,,,0.08,0.069,0.3333,,0.0061,,0.07,,0.0,0.0851,,0.9935,,,0.0806,0.069,0.3333,,0.0062,,0.0729,,0.0,0.0843,,0.9935,,,0.08,0.069,0.3333,,0.0062,,0.0712,,0.0,,block of flats,0.0971,Panel,No,0.0,0.0,0.0,0.0,-1051.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n208254,0,Cash loans,F,N,Y,1,360000.0,963472.5,40950.0,778500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00963,-14006,-2391,-721.0,-1269,,1,1,0,1,0,0,Sales staff,3.0,2,2,THURSDAY,15,0,0,0,0,0,0,Trade: type 3,0.3850653982720397,0.6068942389686699,,0.0577,0.0661,0.9821,0.7552,0.0094,0.0,0.1379,0.1667,0.2083,0.0418,0.0471,0.0559,0.0,0.0,0.0588,0.0686,0.9821,0.7648,0.0095,0.0,0.1379,0.1667,0.2083,0.0427,0.0514,0.0583,0.0,0.0,0.0583,0.0661,0.9821,0.7585,0.0095,0.0,0.1379,0.1667,0.2083,0.0425,0.0479,0.0569,0.0,0.0,reg oper account,block of flats,0.0491,Panel,No,0.0,0.0,0.0,0.0,-553.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n110981,1,Revolving loans,F,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006852,-8399,-409,-5013.0,-296,,1,1,0,1,0,0,Sales staff,2.0,3,3,FRIDAY,9,0,0,0,1,1,0,Business Entity Type 1,0.14446788390242624,0.048584806429715036,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-210.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n134511,0,Cash loans,M,N,Y,0,40500.0,170640.0,9922.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-23317,365243,-8172.0,-3971,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.04165343448461658,0.7106743858828587,0.0,,0.0,,,,0.0345,0.0417,,,,0.0197,,0.0462,0.0,,0.0005,,,,0.0345,0.0417,,,,0.0205,,0.0489,0.0,,0.0,,,,0.0345,0.0417,,,,0.02,,0.0472,,block of flats,0.0255,,No,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n180342,0,Cash loans,F,N,Y,0,54000.0,157500.0,9765.0,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019689,-22739,365243,-9519.0,-5855,,1,0,0,1,1,0,,1.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.5040964966711771,0.7958031328748403,0.0165,,0.9801,,,,0.069,0.0417,,,,0.0153,,,0.0168,,0.9801,,,,0.069,0.0417,,,,0.0159,,,0.0167,,0.9801,,,,0.069,0.0417,,,,0.0156,,,,block of flats,0.0121,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2135.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n164187,0,Cash loans,F,N,N,0,114750.0,572076.0,39946.5,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006207,-12703,-5160,-2652.0,-3428,,1,1,1,1,1,0,Core staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Transport: type 2,0.5104385558549649,0.5289494828328575,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-662.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n240853,0,Cash loans,F,Y,Y,0,112500.0,1125000.0,33025.5,1125000.0,Family,Working,Higher education,Married,House / apartment,0.00496,-11041,-1722,-4088.0,-3029,3.0,1,1,1,1,1,0,Laborers,2.0,2,2,MONDAY,15,0,0,0,1,0,1,Self-employed,,0.4554666035880196,0.5262949398096192,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,10.0,0.0,-490.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n337051,0,Cash loans,F,N,Y,0,315000.0,855882.0,36391.5,765000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.007273999999999998,-20862,365243,-7189.0,-4047,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.485147232411689,0.6577838002083306,0.0412,0.0,0.9776,0.6940000000000001,0.0161,0.0,0.069,0.1667,0.0417,0.0349,0.0336,0.039,0.0,0.0,0.042,0.0,0.9777,0.706,0.0163,0.0,0.069,0.1667,0.0417,0.0357,0.0367,0.0407,0.0,0.0,0.0416,0.0,0.9776,0.6981,0.0162,0.0,0.069,0.1667,0.0417,0.0355,0.0342,0.0397,0.0,0.0,reg oper spec account,block of flats,0.0395,Panel,No,2.0,0.0,2.0,0.0,-523.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n324924,0,Cash loans,M,Y,N,0,427500.0,1057500.0,31050.0,1057500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-17167,-1157,-2656.0,-723,5.0,1,1,1,1,1,0,Drivers,2.0,1,1,THURSDAY,15,0,0,0,0,0,0,Self-employed,,0.8025190043350982,0.2353105173615833,0.2598,0.2473,0.9866,0.8164,0.0567,0.0,0.4828,0.2083,0.25,0.0,0.2034,0.2456,0.0386,0.2078,0.2647,0.2567,0.9866,0.8236,0.0573,0.0,0.4828,0.2083,0.25,0.0,0.2222,0.2559,0.0389,0.22,0.2623,0.2473,0.9866,0.8189,0.0571,0.0,0.4828,0.2083,0.25,0.0,0.2069,0.25,0.0388,0.2122,reg oper account,block of flats,0.2694,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1897.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n423219,0,Cash loans,F,N,Y,0,112500.0,544500.0,15601.5,544500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.014519999999999996,-21462,365243,-9579.0,-4951,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.3454858866640793,0.7121551551910698,0.0371,0.0,0.9737,,,0.0,0.1034,0.0833,,0.0,,0.0191,,0.0357,0.0378,0.0,0.9737,,,0.0,0.1034,0.0833,,0.0,,0.0199,,0.0378,0.0375,0.0,0.9737,,,0.0,0.1034,0.0833,,0.0,,0.0194,,0.0364,,block of flats,0.0228,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2433.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n331188,0,Cash loans,M,Y,Y,2,112500.0,497520.0,39438.0,450000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.002042,-13776,-817,-1893.0,-4570,16.0,1,1,0,1,0,0,Drivers,4.0,3,3,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.2516381190651225,0.18303516721781032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n268354,0,Cash loans,F,N,N,0,157500.0,157500.0,6066.0,157500.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.018801,-23310,365243,-17456.0,-4025,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.7532537114285338,0.6706517530862718,0.0619,0.0512,0.9742,0.6464,0.0076,0.0,0.1034,0.1667,0.0417,0.0168,0.0504,0.051,0.0,0.0,0.063,0.0531,0.9742,0.6602,0.0076,0.0,0.1034,0.1667,0.0417,0.0172,0.0551,0.0531,0.0,0.0,0.0625,0.0512,0.9742,0.6511,0.0076,0.0,0.1034,0.1667,0.0417,0.0171,0.0513,0.0519,0.0,0.0,reg oper account,block of flats,0.0442,Panel,No,0.0,0.0,0.0,0.0,-1513.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n170671,0,Cash loans,F,N,N,0,90000.0,1125000.0,37174.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-22865,365243,-4672.0,-4616,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.6998327687570911,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-2981.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n375186,0,Cash loans,F,Y,Y,1,270000.0,2345760.0,71253.0,2025000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-10576,-938,-5.0,-1251,6.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,Construction,0.3928531840872357,0.5793818661059253,0.6722428897082422,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-416.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n199089,0,Cash loans,F,N,Y,0,157500.0,254700.0,25321.5,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.020246,-24985,-3624,-2490.0,-4759,,1,1,0,1,0,0,Cooking staff,1.0,3,3,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.07761628037302502,0.10015182033723906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-683.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n184654,0,Cash loans,F,N,Y,1,157500.0,970380.0,31302.0,810000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-14909,-735,-224.0,-4527,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Transport: type 4,0.7837333751449461,0.5643438323275315,0.646329897706246,0.0,,0.0,,,,,,,,,,,,0.0,,0.0005,,,,,,,,,,,,0.0,,0.0,,,,,,,,,,,,,block of flats,0.0,,No,5.0,1.0,5.0,0.0,-1537.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n111552,0,Cash loans,F,N,Y,0,90000.0,314100.0,14683.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.006207,-17789,-1813,-3841.0,-1340,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,16,0,0,0,0,0,0,Self-employed,0.4969134493904381,0.6848599821485614,0.6956219298394389,0.0753,0.0527,0.9851,0.7959999999999999,,0.08,0.069,0.3333,0.375,0.0401,0.0614,0.0748,0.0,0.0,0.0767,0.0547,0.9851,0.804,,0.0806,0.069,0.3333,0.375,0.040999999999999995,0.067,0.0779,0.0,0.0,0.076,0.0527,0.9851,0.7987,,0.08,0.069,0.3333,0.375,0.0408,0.0624,0.0762,0.0,0.0,reg oper account,block of flats,0.0651,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n306941,0,Cash loans,F,N,Y,1,157500.0,227520.0,14940.0,180000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.005002,-11619,-457,-3249.0,-639,,1,1,0,1,0,1,Sales staff,3.0,3,3,FRIDAY,11,0,0,0,0,0,0,Trade: type 7,,0.10242071882213208,0.5744466170995097,0.2598,0.1245,0.9836,0.7756,0.0,0.28,0.2414,0.3333,0.375,0.147,0.2101,0.2696,0.0077,0.002,0.2647,0.1292,0.9836,0.7844,0.0,0.282,0.2414,0.3333,0.375,0.1503,0.2296,0.2809,0.0078,0.0021,0.2623,0.1245,0.9836,0.7786,0.0,0.28,0.2414,0.3333,0.375,0.1495,0.2138,0.2745,0.0078,0.002,reg oper spec account,block of flats,0.2125,Panel,No,0.0,0.0,0.0,0.0,-498.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n195874,0,Cash loans,F,N,Y,0,112500.0,270000.0,26302.5,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.030755,-19720,-2757,-13544.0,-3248,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Cleaning,0.6109365498768096,0.6255092814305399,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2471.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n379256,0,Cash loans,M,Y,Y,0,67500.0,518562.0,20695.5,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010147,-22747,365243,-8660.0,-4064,11.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.7286118944589669,0.4650692149562261,0.1216,0.1297,0.9831,0.7688,0.0612,0.0,0.2759,0.1667,0.2083,0.1641,0.0992,0.122,0.0,0.0,0.1239,0.1346,0.9831,0.7779,0.0618,0.0,0.2759,0.1667,0.2083,0.1679,0.1084,0.1271,0.0,0.0,0.1228,0.1297,0.9831,0.7719,0.0616,0.0,0.2759,0.1667,0.2083,0.16699999999999998,0.1009,0.1242,0.0,0.0,reg oper spec account,block of flats,0.1295,Panel,No,2.0,0.0,2.0,0.0,-2181.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n284218,0,Cash loans,F,N,N,0,270000.0,729792.0,35239.5,630000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-10444,-2022,-958.0,-3103,,1,1,1,1,0,0,Managers,2.0,3,3,MONDAY,13,0,0,0,0,0,0,School,0.2328419825784297,0.5658110109425575,0.324891229465852,0.0124,,0.9687,,,0.0,0.069,0.0417,,0.0039,,0.0116,,0.0,0.0126,,0.9687,,,0.0,0.069,0.0417,,0.0,,0.0121,,0.0,0.0125,,0.9687,,,0.0,0.069,0.0417,,0.004,,0.0118,,0.0,,block of flats,0.0091,Wooden,Yes,0.0,0.0,0.0,0.0,-1881.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n227314,0,Cash loans,F,N,N,1,180000.0,305221.5,20524.5,252000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-9802,-2773,-492.0,-2411,,1,1,0,1,0,0,Sales staff,2.0,3,3,FRIDAY,5,0,0,0,0,0,0,Self-employed,,0.023138480094622504,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n332678,1,Cash loans,F,Y,Y,1,157500.0,168961.5,12424.5,139500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.020246,-11551,-321,-4401.0,-3298,2.0,1,1,0,1,0,0,Sales staff,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Emergency,0.23415985264528225,0.09268489735214706,0.3910549766342248,0.0619,0.0842,0.9717,0.6124,0.0629,0.0,0.1379,0.1667,0.2083,0.0708,0.0504,0.0672,0.0,0.0,0.063,0.0874,0.9717,0.6276,0.0635,0.0,0.1379,0.1667,0.2083,0.0724,0.0551,0.07,0.0,0.0,0.0625,0.0842,0.9717,0.6176,0.0633,0.0,0.1379,0.1667,0.2083,0.07200000000000001,0.0513,0.0684,0.0,0.0,reg oper account,block of flats,0.0872,Block,No,1.0,0.0,1.0,0.0,-141.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n239406,0,Cash loans,F,N,Y,0,67500.0,270000.0,21199.5,270000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-17184,-564,-419.0,-727,,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6680786270065107,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1292.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n272675,0,Cash loans,F,N,Y,0,157500.0,1037974.5,55431.0,981000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010147,-10331,-107,-2563.0,-2954,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,10,0,0,0,1,0,1,Medicine,0.6680219109180476,0.3042838643253667,0.5656079814115492,0.0825,0.08,0.9757,0.6668,0.0085,0.0,0.1379,0.1667,0.2083,0.1035,0.0672,0.0707,0.0,0.0,0.084,0.083,0.9757,0.6798,0.0086,0.0,0.1379,0.1667,0.2083,0.1059,0.0735,0.0736,0.0,0.0,0.0833,0.08,0.9757,0.6713,0.0086,0.0,0.1379,0.1667,0.2083,0.1053,0.0684,0.0719,0.0,0.0,org spec account,block of flats,0.0602,Panel,No,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n148560,0,Cash loans,F,N,Y,0,112500.0,284400.0,22599.0,225000.0,Unaccompanied,Working,Incomplete higher,Single / not married,Rented apartment,0.016612000000000002,-8005,-592,-2595.0,-663,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,15,0,0,0,1,1,0,Self-employed,0.19635168718961507,0.6888765462329369,0.1117563710719636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-461.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n280188,1,Cash loans,F,N,Y,0,112500.0,310671.0,19134.0,256500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-21504,365243,-2193.0,-3410,,1,0,0,1,1,0,,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,XNA,0.7336468920452199,0.1987235452178288,0.6296742509538716,,,0.9821,,,,,,,,,0.1478,,,,,0.9821,,,,,,,,,0.154,,,,,0.9821,,,,,,,,,0.1504,,,,,0.1169,,No,2.0,1.0,2.0,1.0,-1671.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n139700,0,Cash loans,F,Y,Y,1,202500.0,1006920.0,42790.5,900000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.010966,-10881,-3714,-142.0,-274,4.0,1,1,0,1,1,0,High skill tech staff,3.0,2,2,SATURDAY,10,0,0,0,1,0,1,Business Entity Type 3,,0.3236327526996169,,0.0876,0.048,0.9821,0.7552,0.0312,0.08,0.0345,0.4583,0.0417,0.0464,0.0714,0.0765,0.0,0.0,0.0893,0.0498,0.9821,0.7648,0.0315,0.0806,0.0345,0.4583,0.0417,0.0475,0.0781,0.0797,0.0,0.0,0.0885,0.048,0.9821,0.7585,0.0314,0.08,0.0345,0.4583,0.0417,0.0472,0.0727,0.0779,0.0,0.0,org spec account,block of flats,0.0772,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2257.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n297224,0,Cash loans,F,N,Y,1,112500.0,269550.0,14620.5,225000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-11623,-1466,-573.0,-2812,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Trade: type 7,,0.6355497118665108,0.392773860603134,,,0.9896,,,0.2,0.2414,0.375,,,,,,,,,0.9896,,,0.2014,0.2414,0.375,,,,,,,,,0.9896,,,0.2,0.2414,0.375,,,,,,,,block of flats,0.1903,Panel,No,3.0,0.0,3.0,0.0,-432.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n133868,0,Cash loans,M,Y,N,0,180000.0,270000.0,18396.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-9831,-343,-340.0,-325,2.0,1,1,0,1,0,0,,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Self-employed,,0.29532831088945344,0.12295541790885495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-130.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n360388,0,Cash loans,F,Y,N,0,540000.0,1126548.0,32283.0,983713.5,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-13926,-5201,-445.0,-4668,1.0,1,1,0,1,1,0,,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Other,0.5519522865611407,0.5940789037885398,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2735.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n289986,0,Cash loans,M,Y,N,0,202500.0,225000.0,11619.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-19703,-9218,-47.0,-2884,4.0,1,1,1,1,0,0,Laborers,2.0,3,3,MONDAY,17,0,0,0,0,0,0,Industry: type 9,,0.3354984179964272,0.178760465484575,0.0608,0.0656,0.9891,0.8504,0.0104,0.0,0.1379,0.1667,0.2083,0.0172,0.0496,0.0629,0.0,0.0,0.062,0.0681,0.9891,0.8563,0.0105,0.0,0.1379,0.1667,0.2083,0.0176,0.0542,0.0655,0.0,0.0,0.0614,0.0656,0.9891,0.8524,0.0105,0.0,0.1379,0.1667,0.2083,0.0175,0.0504,0.064,0.0,0.0,reg oper spec account,block of flats,0.0494,Panel,No,0.0,0.0,0.0,0.0,-2361.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n342115,0,Cash loans,F,N,Y,0,202500.0,573408.0,29407.5,495000.0,Unaccompanied,Pensioner,Lower secondary,Single / not married,House / apartment,0.018029,-23330,365243,-6910.0,-2394,,1,0,0,1,0,1,,1.0,3,3,FRIDAY,7,0,0,0,0,0,0,XNA,0.8552287293727748,0.6332419949884396,0.722392890081304,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1104.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n344005,1,Cash loans,F,N,Y,0,144000.0,153576.0,5787.0,121500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-14505,-2345,-4108.0,-4110,,1,1,1,1,1,0,Sales staff,2.0,3,3,THURSDAY,4,0,0,0,0,0,0,Self-employed,,0.5390705085192465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1753.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n367974,0,Cash loans,F,N,Y,0,216000.0,450000.0,21109.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.018209,-16495,-2624,-8311.0,-29,,1,1,0,1,0,1,,1.0,3,3,THURSDAY,14,0,0,0,0,0,0,Self-employed,0.6090207550775935,0.5363254271344091,0.17352743046491467,0.1237,0.1309,0.9776,0.6940000000000001,0.0159,0.0,0.2759,0.1667,0.0417,0.1156,0.1,0.1143,0.0039,0.0047,0.1261,0.1358,0.9777,0.706,0.0161,0.0,0.2759,0.1667,0.0417,0.1183,0.1093,0.1191,0.0039,0.005,0.1249,0.1309,0.9776,0.6981,0.016,0.0,0.2759,0.1667,0.0417,0.1177,0.1018,0.1164,0.0039,0.0048,org spec account,block of flats,0.0996,Panel,No,1.0,0.0,1.0,0.0,-1771.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n428652,0,Cash loans,F,N,Y,2,225000.0,526491.0,28048.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-11797,-2757,-8849.0,-2232,,1,1,0,1,0,0,,4.0,3,3,WEDNESDAY,11,0,0,0,0,0,0,Industry: type 9,,0.2489463318262683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-310.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n410271,0,Cash loans,F,N,N,0,225000.0,1546020.0,40914.0,1350000.0,Unaccompanied,Working,Higher education,Single / not married,Rented apartment,0.008019,-10754,-1541,-1507.0,-3416,,1,1,0,1,0,0,,1.0,2,2,MONDAY,14,0,0,0,0,0,0,Other,0.7217977889631387,0.5931715677473456,0.19747451156854226,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-281.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n255703,0,Cash loans,M,Y,N,0,148500.0,728460.0,57685.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009334,-12652,-1874,-631.0,-4142,24.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Self-employed,,0.6753501485093889,0.7610263695502636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1752.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n136066,0,Cash loans,F,N,N,0,292500.0,675000.0,53460.0,675000.0,Unaccompanied,Commercial associate,Lower secondary,Married,With parents,0.019101,-14265,-566,-1889.0,-4846,,1,1,1,1,1,0,Cleaning staff,2.0,2,2,TUESDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.5013154839258652,0.6696623813379322,0.17044612304522816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n177370,0,Cash loans,M,Y,Y,0,202500.0,450000.0,25965.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-21431,-476,-6554.0,-4413,12.0,1,1,0,1,0,0,Security staff,2.0,2,2,SATURDAY,17,0,0,0,0,0,0,Security,,0.5780810184320359,,0.0186,,0.9637,,,0.0,0.1034,0.0833,,,,0.0268,,0.0,0.0189,,0.9638,,,0.0,0.1034,0.0833,,,,0.0279,,0.0,0.0187,,0.9637,,,0.0,0.1034,0.0833,,,,0.0273,,0.0,,block of flats,0.0211,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-283.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n417850,0,Cash loans,F,N,Y,0,216000.0,168102.0,9778.5,148500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.010147,-24140,365243,-8021.0,-4367,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.5775529052746423,0.722392890081304,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-805.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n262313,0,Cash loans,F,N,Y,0,67500.0,299772.0,10894.5,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-17447,-560,-7967.0,-984,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,17,0,0,0,0,1,1,Industry: type 11,,0.3939966051419472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n368847,0,Revolving loans,F,N,Y,1,270000.0,495000.0,24750.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-12104,-1934,-5495.0,-2524,,1,1,0,1,0,0,Managers,3.0,3,3,TUESDAY,11,0,0,0,1,1,0,Business Entity Type 3,0.2433925886770144,0.2809416416480156,0.5954562029091491,0.0825,0.1146,0.9866,0.8164,,0.0,0.1379,0.1667,,0.0641,0.0672,0.0793,0.0,0.0,0.084,0.11900000000000001,0.9866,0.8236,,0.0,0.1379,0.1667,,0.0656,0.0735,0.0826,0.0,0.0,0.0833,0.1146,0.9866,0.8189,,0.0,0.1379,0.1667,,0.0653,0.0684,0.0807,0.0,0.0,reg oper account,block of flats,0.0705,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-183.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n355001,0,Cash loans,M,N,N,0,135000.0,327024.0,25965.0,270000.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,With parents,0.019689,-15104,-1699,-7622.0,-4789,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,12,0,1,1,0,1,1,Business Entity Type 3,,0.6208929800680432,0.6706517530862718,0.0811,0.0941,0.9796,0.7212,0.0099,0.0,0.1607,0.1667,0.1525,0.1025,0.0653,0.0718,0.0039,0.0042,0.0735,0.0881,0.9796,0.7321,0.0061,0.0,0.1379,0.1667,0.2083,0.0867,0.0624,0.0668,0.0,0.0,0.0729,0.0851,0.9796,0.7249,0.0064,0.0,0.1379,0.1667,0.2083,0.0939,0.059000000000000004,0.0665,0.0039,0.0045,reg oper account,block of flats,0.0556,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-160.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n191533,0,Cash loans,M,Y,Y,0,112500.0,348264.0,36697.5,315000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00733,-11317,-1747,-8834.0,-1995,16.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,16,0,0,0,1,1,0,Business Entity Type 3,0.6237714296511188,0.6490034275284726,0.7517237147741489,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-1677.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n426307,0,Cash loans,M,Y,Y,0,90000.0,450000.0,22018.5,450000.0,Unaccompanied,Commercial associate,Lower secondary,Married,House / apartment,0.00963,-13888,-5402,-443.0,-4628,15.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Government,,0.4764569857321481,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1536.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n379808,0,Cash loans,F,N,N,0,225000.0,2195086.5,62910.0,1962000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.025164,-18502,-3064,-7587.0,-2031,,1,1,0,1,1,0,Laborers,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5979383660483687,0.3376727217405312,0.2639,0.0,0.9831,0.7688,0.2278,0.24,0.1034,0.5417,0.5833,0.1914,0.2118,0.3422,0.0154,0.4204,0.2689,0.0,0.9831,0.7779,0.2298,0.2417,0.1034,0.5417,0.5833,0.1958,0.2314,0.3566,0.0156,0.4451,0.2665,0.0,0.9831,0.7719,0.2292,0.24,0.1034,0.5417,0.5833,0.1947,0.2155,0.3484,0.0155,0.4292,not specified,block of flats,0.4851,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-2664.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n295835,0,Revolving loans,M,N,Y,0,45000.0,180000.0,9000.0,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-19839,365243,-9327.0,-3385,,1,0,0,1,1,0,,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,XNA,,0.6017752492946981,0.4048783643353997,0.0732,0.0858,0.9781,0.7008,0.0072,0.0,0.1379,0.1667,0.2083,0.0439,0.057999999999999996,0.0633,0.0077,0.0144,0.0746,0.0891,0.9782,0.7125,0.0072,0.0,0.1379,0.1667,0.2083,0.0449,0.0634,0.0659,0.0078,0.0152,0.0739,0.0858,0.9781,0.7048,0.0072,0.0,0.1379,0.1667,0.2083,0.0447,0.059000000000000004,0.0644,0.0078,0.0147,reg oper account,block of flats,0.0568,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1405.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n221164,0,Cash loans,F,N,Y,0,108000.0,578619.0,18607.5,499500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-22768,365243,-12311.0,-4346,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.0022047983435766927,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-497.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n300391,0,Cash loans,F,N,N,0,135000.0,1971072.0,68512.5,1800000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.003818,-19068,-1581,-2659.0,-2580,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Housing,0.8604966890573817,0.7447235946234118,0.5620604831738043,0.0124,0.0252,0.9881,0.8368,0.0195,0.0,0.069,0.0417,0.0417,0.0302,0.0101,0.0126,0.0,0.0,0.0126,0.0261,0.9881,0.8432,0.0197,0.0,0.069,0.0417,0.0417,0.0309,0.011000000000000001,0.0132,0.0,0.0,0.0125,0.0252,0.9881,0.8390000000000001,0.0196,0.0,0.069,0.0417,0.0417,0.0307,0.0103,0.0129,0.0,0.0,reg oper account,block of flats,0.0099,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-553.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n143982,0,Cash loans,F,N,Y,0,67500.0,229500.0,11290.5,229500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-17896,-1120,-7741.0,-1430,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,13,0,0,0,1,1,0,Self-employed,0.16661934063679704,0.2967045232419317,0.5262949398096192,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-706.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n207063,0,Cash loans,F,N,Y,1,225000.0,379008.0,39946.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.010032,-12161,-1086,-1004.0,-2945,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.3409303304224112,0.006606087347627011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n186559,0,Cash loans,M,N,N,0,180000.0,808650.0,23773.5,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.016612000000000002,-16502,-8354,-2131.0,-35,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Police,,0.5973546846595864,0.6109913280868294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1510.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n324927,0,Cash loans,F,N,Y,1,157500.0,755190.0,38556.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.015221,-12150,-419,-889.0,-4156,,1,1,1,1,1,0,Accountants,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.5116556035812906,0.7805873896680612,0.5549467685334323,0.1856,0.1357,0.9836,0.7756,0.1061,0.2,0.1724,0.3333,0.375,0.1437,0.1513,0.11599999999999999,0.0116,0.0122,0.1891,0.1408,0.9836,0.7844,0.1071,0.2014,0.1724,0.3333,0.375,0.147,0.1653,0.1208,0.0117,0.0129,0.1874,0.1357,0.9836,0.7786,0.1068,0.2,0.1724,0.3333,0.375,0.1462,0.1539,0.11800000000000001,0.0116,0.0125,reg oper spec account,block of flats,0.1519,Panel,No,0.0,0.0,0.0,0.0,-1556.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n118360,0,Cash loans,F,N,Y,0,72000.0,135000.0,13279.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.004849,-21692,365243,-7239.0,-4583,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,,0.4798290582169067,,,,0.9677,,,,,,,,,,,,,,0.9677,,,,,,,,,,,,,,0.9677,,,,,,,,,,,,,,0.0076,,No,1.0,0.0,1.0,0.0,-458.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n257689,0,Cash loans,F,N,N,1,135000.0,398016.0,26041.5,360000.0,Other_A,Working,Secondary / secondary special,Single / not married,Rented apartment,0.005144,-9953,-1068,-1615.0,-2176,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,1,1,0,Business Entity Type 3,0.4492690292845299,0.5697874726550407,,,,0.9767,,,,,,,,,0.0087,,,,,0.9767,,,,,,,,,0.009000000000000001,,,,,0.9767,,,,,,,,,0.0088,,,,,0.0068,,No,0.0,0.0,0.0,0.0,-1357.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n329020,0,Cash loans,M,Y,N,1,202500.0,450000.0,24412.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.02461,-17817,-1111,-302.0,-1375,7.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5089939565547456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-941.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n233476,0,Cash loans,F,N,Y,0,90000.0,932643.0,27400.5,778500.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-22651,365243,-13117.0,-2610,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.3551256437854799,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-1448.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n349477,0,Revolving loans,M,N,Y,0,112500.0,157500.0,7875.0,157500.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.010643,-8357,-435,-6087.0,-755,,1,1,1,1,1,0,High skill tech staff,1.0,2,2,SUNDAY,14,0,0,0,0,0,0,Transport: type 4,0.2825369900889658,0.5685648799361184,0.3441550073724169,0.2392,0.3892,0.9906,0.8708,0.0,0.32,0.3103,0.375,0.375,0.2989,0.19,0.4314,0.0232,0.0759,0.2437,0.4039,0.9906,0.8759,0.0,0.3222,0.3103,0.375,0.375,0.3057,0.2075,0.4494,0.0233,0.0803,0.2415,0.3892,0.9906,0.8725,0.0,0.32,0.3103,0.375,0.375,0.3041,0.1932,0.4391,0.0233,0.0774,org spec account,block of flats,0.3558,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-145.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n378455,0,Cash loans,F,N,Y,1,225000.0,835380.0,35523.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.032561,-16815,-720,-1254.0,-340,,1,1,0,1,1,0,Laborers,2.0,1,1,MONDAY,19,0,0,0,0,0,0,Industry: type 4,,0.6831489884416679,0.6144143775673561,0.0062,0.0988,0.9702,,,0.0,0.1034,0.1667,,0.0178,,0.0641,,0.1006,0.0063,0.1025,0.9702,,,0.0,0.1034,0.1667,,0.0182,,0.0668,,0.1065,0.0062,0.0988,0.9702,,,0.0,0.1034,0.1667,,0.0182,,0.0653,,0.1027,,block of flats,0.0723,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1378.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n299111,0,Cash loans,F,N,Y,1,202500.0,835380.0,33259.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-14062,-244,-4177.0,-1133,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,7,0,0,0,0,0,0,Industry: type 4,0.441596168465457,0.5688330644526715,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-375.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n394028,0,Cash loans,M,N,Y,0,135000.0,855000.0,46512.0,855000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.026392,-10099,-2528,-3997.0,-1480,,1,1,0,1,0,1,Managers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.486583875976269,0.5620604831738043,0.2474,0.1339,0.9935,,,0.28,0.2414,0.375,,0.1913,,0.3063,,0.0,0.2521,0.1389,0.9935,,,0.282,0.2414,0.375,,0.1957,,0.3192,,0.0,0.2498,0.1339,0.9935,,,0.28,0.2414,0.375,,0.1947,,0.3118,,0.0,,block of flats,0.2409,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n382394,0,Cash loans,F,N,Y,0,112500.0,619965.0,18256.5,517500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018634,-18413,-5494,-6178.0,-1964,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.8163261506109738,0.629990723757851,0.4722533429586386,0.0722,0.0777,0.9796,0.7212,0.033,0.0,0.1379,0.1667,0.2083,0.0396,0.0588,0.0669,0.0,0.0,0.0735,0.0806,0.9796,0.7321,0.0333,0.0,0.1379,0.1667,0.2083,0.0405,0.0643,0.0697,0.0,0.0,0.0729,0.0777,0.9796,0.7249,0.0332,0.0,0.1379,0.1667,0.2083,0.0403,0.0599,0.0681,0.0,0.0,reg oper account,block of flats,0.0707,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-2539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n243935,0,Cash loans,F,N,Y,0,225000.0,801000.0,41026.5,801000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-12590,-3286,-1331.0,-369,,1,1,0,1,0,0,Private service staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Services,,0.6590573930868678,0.4206109640437848,0.1093,,0.9866,,,0.04,0.1034,0.3333,,,,0.1136,,0.0074,0.1113,,0.9866,,,0.0403,0.1034,0.3333,,,,0.1184,,0.0078,0.1103,,0.9866,,,0.04,0.1034,0.3333,,,,0.1157,,0.0075,,block of flats,0.091,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-644.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n199397,0,Cash loans,F,N,N,2,157500.0,521280.0,20394.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010147,-10501,-1279,-409.0,-2920,,1,1,0,1,1,0,,4.0,2,2,THURSDAY,14,0,0,0,0,1,1,Business Entity Type 2,0.563397063058401,0.4637979591637098,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1405.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,0.0\n426503,1,Cash loans,F,N,Y,0,270000.0,926136.0,39370.5,760500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.011703,-18450,-4402,-8824.0,-1375,,1,1,0,1,1,1,Laborers,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.6830701982729422,0.7517223300911986,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-655.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,1.0\n344346,0,Cash loans,F,N,Y,1,90000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.019101,-15893,-4941,-8155.0,-4892,,1,1,1,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Other,0.2369277386446981,0.7418989516405684,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1325.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n411252,0,Cash loans,F,N,Y,0,289800.0,724581.0,34987.5,625500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-22058,-5399,-951.0,-4388,,1,1,0,1,1,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Construction,,0.16642870718632394,0.07596568132476847,0.1361,0.1021,0.9985,0.9796,0.0166,0.08,0.069,0.3333,0.2083,0.0195,0.1059,0.0646,0.0309,0.0346,0.1387,0.1059,0.9985,0.9804,0.0167,0.0806,0.069,0.3333,0.2083,0.0199,0.1157,0.0674,0.0311,0.0366,0.1374,0.1021,0.9985,0.9799,0.0167,0.08,0.069,0.3333,0.2083,0.0198,0.1077,0.0658,0.0311,0.0353,reg oper account,block of flats,0.0928,Monolithic,No,0.0,0.0,0.0,0.0,-282.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n117088,0,Revolving loans,F,N,Y,1,54000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010006,-15704,-1199,-194.0,-3,,1,1,1,1,0,0,,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,School,,0.2811227965781889,,0.0021,,0.9776,,,,,0.0,,0.0219,,0.0016,,,0.0021,,0.9777,,,,,0.0,,0.0224,,0.0017,,,0.0021,,0.9776,,,,,0.0,,0.0223,,0.0016,,,,block of flats,0.0018,Wooden,No,0.0,0.0,0.0,0.0,-1052.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n388956,0,Cash loans,M,N,Y,0,180000.0,916299.0,36468.0,819000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.019101,-11901,-1222,-5804.0,-4238,,1,1,1,1,0,0,Drivers,2.0,2,2,FRIDAY,14,0,0,0,1,1,0,Legal Services,0.2468864349258274,0.5884348224449447,0.29708661164720285,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-584.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n161090,0,Cash loans,F,N,Y,3,135000.0,553500.0,33993.0,553500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.015221,-11532,-1920,-4589.0,-2972,,1,1,0,1,0,1,Core staff,5.0,2,2,FRIDAY,10,0,0,0,0,0,0,Security Ministries,,0.5230721867803303,0.3910549766342248,0.066,0.0175,0.9776,,,0.0,0.1379,0.1667,,0.0287,,0.051,,0.0527,0.0672,0.0182,0.9777,,,0.0,0.1379,0.1667,,0.0293,,0.0532,,0.0558,0.0666,0.0175,0.9776,,,0.0,0.1379,0.1667,,0.0292,,0.0519,,0.0538,,block of flats,0.057,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1318.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n293992,0,Cash loans,M,N,Y,0,157500.0,589050.0,25087.5,526500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.022625,-22805,-8915,-14228.0,-4745,,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,Medicine,,0.3916755944558979,,0.0804,0.0,0.9732,0.6328,0.0919,0.0,0.1379,0.1667,0.2083,0.0,0.0656,0.0617,0.0,0.0077,0.0819,0.0,0.9732,0.6472,0.0927,0.0,0.1379,0.1667,0.2083,0.0,0.0716,0.0643,0.0,0.0081,0.0812,0.0,0.9732,0.6377,0.0924,0.0,0.1379,0.1667,0.2083,0.0,0.0667,0.0628,0.0,0.0078,reg oper account,block of flats,0.0502,Panel,No,5.0,1.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n104302,0,Cash loans,F,N,Y,0,202500.0,886090.5,35271.0,792000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-20717,-1472,-7994.0,-4038,,1,1,0,1,1,0,Laborers,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4899714752199155,0.6817058776720116,0.0562,0.0752,0.9806,0.7348,0.0235,0.0,0.1379,0.1458,0.0417,0.013000000000000001,0.0378,0.0526,0.0,0.0027,0.0473,0.0729,0.9727,0.6406,0.0232,0.0,0.1379,0.125,0.0417,0.0125,0.0413,0.0548,0.0,0.0,0.0567,0.0752,0.9806,0.7383,0.0236,0.0,0.1379,0.1458,0.0417,0.0133,0.0385,0.0536,0.0,0.0027,reg oper account,block of flats,0.054000000000000006,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2390.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n334496,0,Cash loans,F,Y,Y,0,99000.0,254700.0,14350.5,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.015221,-15767,-3153,-3526.0,-4221,6.0,1,1,1,1,1,0,Core staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Other,,0.6634510989608053,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n251030,1,Cash loans,F,N,Y,0,360000.0,1288350.0,35428.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.0105,-11046,-2386,-198.0,-3558,,1,1,1,1,0,1,Accountants,2.0,3,3,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 1,0.7624589180116867,0.507348861969352,0.0785361445733672,0.1361,0.0227,0.9985,,0.0338,0.08,0.069,0.5,0.5417,0.0544,0.1084,0.1187,0.0116,0.0759,0.1061,0.0189,0.998,,0.0264,0.0403,0.0345,0.375,0.4167,0.0169,0.0882,0.1057,0.0039,0.0236,0.1374,0.0227,0.9985,,0.0341,0.08,0.069,0.5,0.5417,0.0554,0.1103,0.1208,0.0116,0.0775,not specified,block of flats,0.1578,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-380.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n405290,0,Cash loans,F,N,Y,0,157500.0,540000.0,17550.0,540000.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.01885,-22193,365243,-12473.0,-4905,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.7828151277795647,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-2219.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n287699,0,Cash loans,F,N,Y,0,117000.0,283500.0,12618.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.008865999999999999,-16180,-440,-4600.0,-4384,,1,1,0,1,0,0,Medicine staff,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Medicine,,0.5986741449399646,,0.067,0.0627,0.9767,0.6804,0.0084,0.0,0.1379,0.1667,0.2083,0.0988,0.0538,0.0509,0.0039,0.0872,0.0683,0.0651,0.9767,0.6929,0.0085,0.0,0.1379,0.1667,0.2083,0.1011,0.0588,0.053,0.0039,0.0923,0.0677,0.0627,0.9767,0.6847,0.0085,0.0,0.1379,0.1667,0.2083,0.1005,0.0547,0.0518,0.0039,0.08900000000000001,reg oper account,block of flats,0.0636,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-2882.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n130705,0,Cash loans,F,Y,Y,0,180000.0,971280.0,54234.0,900000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.011657,-22299,365243,-3917.0,-4255,3.0,1,0,0,1,1,0,,2.0,1,1,TUESDAY,14,0,0,0,0,0,0,XNA,0.8529828425511807,0.7457449320836144,0.7838324335199619,0.1113,0.0743,0.9801,,0.0161,0.12,0.1034,0.3333,0.375,0.0747,,0.0755,,,0.1134,0.0771,0.9801,,0.0163,0.1208,0.1034,0.3333,0.375,0.0765,,0.0786,,,0.1124,0.0743,0.9801,,0.0162,0.12,0.1034,0.3333,0.375,0.076,,0.0768,,,reg oper account,block of flats,0.0893,Panel,No,0.0,0.0,0.0,0.0,-1642.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n313473,0,Cash loans,F,Y,Y,0,67500.0,54000.0,5944.5,54000.0,Children,Pensioner,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-24537,365243,-13631.0,-4390,41.0,1,0,0,1,1,0,,1.0,2,2,SATURDAY,9,0,0,0,0,0,0,XNA,,0.5426328364427121,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-151.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n404001,1,Cash loans,F,Y,Y,0,157500.0,423000.0,20704.5,423000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-13040,-1097,-1907.0,-2953,2.0,1,1,1,1,1,0,Waiters/barmen staff,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Self-employed,0.7619943241134565,0.5725833407834949,0.11612491427195998,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1548.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n109059,0,Cash loans,F,N,N,1,360000.0,1288350.0,41692.5,1125000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-11053,-630,-2972.0,-2843,,1,1,0,1,1,0,Laborers,3.0,2,2,SATURDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.5393367395649199,0.4223696523543468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2464.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n226385,1,Cash loans,M,N,N,0,180000.0,607041.0,29331.0,490500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.006852,-12455,-1051,-6317.0,-4232,,1,1,0,1,0,0,Laborers,1.0,3,3,MONDAY,12,0,0,0,0,0,0,School,,0.030246360132370694,0.6690566947824041,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n344632,0,Revolving loans,M,Y,Y,0,315000.0,900000.0,45000.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-19886,-3318,-798.0,-3431,7.0,1,1,0,1,0,0,Laborers,2.0,3,1,THURSDAY,8,0,0,0,0,0,0,Transport: type 3,,0.6154372835511703,,0.0833,0.0,0.9762,0.7008,0.0185,0.04,0.1379,0.2,0.0208,0.0591,0.0958,0.0688,0.0116,0.016,0.0399,0.0,0.9747,0.6668,0.0169,0.0,0.069,0.1667,0.0,0.0278,0.0588,0.0323,0.0,0.0006,0.0666,0.0,0.9747,0.7048,0.0186,0.04,0.1034,0.1667,0.0208,0.0602,0.0975,0.0641,0.0116,0.0177,reg oper account,block of flats,0.025,\"Stone, brick\",No,7.0,1.0,7.0,0.0,-1150.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n296246,0,Cash loans,F,Y,Y,0,157500.0,270000.0,12586.5,270000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.01885,-9867,-389,-6987.0,-2524,6.0,1,1,0,1,0,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3034392619888164,0.7104438300271293,0.31703177858344445,0.066,0.0783,0.9742,,0.0091,,0.1379,0.1667,,,0.0538,0.0508,0.0193,0.1074,0.0672,0.0812,0.9742,,0.0092,,0.1379,0.1667,,,0.0588,0.0529,0.0195,0.1137,0.0666,0.0783,0.9742,,0.0091,,0.1379,0.1667,,,0.0547,0.0517,0.0194,0.1097,,block of flats,0.0683,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1074.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n129996,1,Cash loans,M,N,Y,0,157500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.019101,-17177,-489,-3280.0,-705,,1,1,0,1,1,0,Laborers,1.0,2,2,SUNDAY,13,0,0,0,0,1,1,Self-employed,,0.43656869419109534,0.1500851762342935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1474.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n254445,0,Cash loans,F,N,Y,0,135000.0,679500.0,26442.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.010032,-10990,-1616,-4428.0,-1071,,1,1,1,1,0,0,,2.0,2,2,MONDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.4606507050378558,0.4956658291397297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1029.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n263601,0,Cash loans,F,N,Y,0,144000.0,239850.0,23364.0,225000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.004849,-21423,-1413,-553.0,-4846,,1,1,1,1,0,0,,1.0,2,2,THURSDAY,10,0,1,1,0,1,1,Other,,0.6513708764512699,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1220.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n296910,0,Cash loans,M,N,N,0,135000.0,425133.0,13018.5,351000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-22896,365243,-8792.0,-3837,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.5437072121400618,0.4884551844437485,0.0485,0.0466,0.9747,0.6532,0.0041,0.0,0.1034,0.125,0.1667,0.0465,0.0387,0.0385,0.0039,0.0054,0.0494,0.0484,0.9747,0.6668,0.0041,0.0,0.1034,0.125,0.1667,0.0476,0.0422,0.0401,0.0039,0.0057,0.0489,0.0466,0.9747,0.6578,0.0041,0.0,0.1034,0.125,0.1667,0.0473,0.0393,0.0392,0.0039,0.0055,reg oper account,block of flats,0.0337,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-151.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n128624,0,Cash loans,F,N,Y,1,112500.0,180000.0,17532.0,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.025164,-21650,365243,-4609.0,-5087,,1,0,0,1,1,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,0.8553359517332313,0.5394291002018563,0.8327850252992314,0.0371,0.0,0.9757,,,0.0,0.1034,0.125,,0.0,,0.043,,0.0503,0.0378,0.0,0.9757,,,0.0,0.1034,0.125,,0.0,,0.0448,,0.0533,0.0375,0.0,0.9757,,,0.0,0.1034,0.125,,0.0,,0.0438,,0.0514,,block of flats,0.0338,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-217.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n268499,0,Cash loans,F,N,Y,1,193500.0,805072.5,29047.5,679500.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.006207,-9441,-291,-1330.0,-1249,,1,1,0,1,0,0,Core staff,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Government,0.6500751006316198,0.6686990229985663,,0.0866,0.1309,0.9786,0.7076,,0.0,0.2069,0.1667,0.2083,0.1006,0.0706,0.1026,0.0,0.0104,0.0882,0.1358,0.9786,0.7190000000000001,,0.0,0.2069,0.1667,0.2083,0.1029,0.0771,0.1069,0.0,0.011000000000000001,0.0874,0.1309,0.9786,0.7115,,0.0,0.2069,0.1667,0.2083,0.1024,0.0718,0.1045,0.0,0.0106,reg oper account,block of flats,0.0905,Panel,No,3.0,0.0,3.0,0.0,-1294.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,8.0\n243104,0,Cash loans,F,Y,Y,0,202500.0,740088.0,32728.5,661500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-20281,365243,-4505.0,-2006,7.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.4674162273099747,0.5172965813614878,0.066,0.0653,0.9767,0.6804,0.0282,0.0,0.1379,0.125,0.1667,0.0409,0.0538,0.0325,0.0,0.0,0.0672,0.0678,0.9767,0.6929,0.0284,0.0,0.1379,0.125,0.1667,0.0418,0.0588,0.0338,0.0,0.0,0.0666,0.0653,0.9767,0.6847,0.0284,0.0,0.1379,0.125,0.1667,0.0416,0.0547,0.033,0.0,0.0,reg oper account,block of flats,0.0409,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-762.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n425579,0,Cash loans,M,Y,Y,2,157500.0,348264.0,37638.0,315000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008865999999999999,-13762,-1628,-7889.0,-4375,1.0,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Construction,,0.7229674185475644,0.5549467685334323,0.067,0.053,0.9767,0.6804,0.0084,0.0,0.1379,0.1667,0.2083,0.064,0.0538,0.051,0.0039,0.0939,0.0683,0.055,0.9767,0.6929,0.0084,0.0,0.1379,0.1667,0.2083,0.0655,0.0588,0.0532,0.0039,0.0995,0.0677,0.053,0.9767,0.6847,0.0084,0.0,0.1379,0.1667,0.2083,0.0651,0.0547,0.052000000000000005,0.0039,0.0959,reg oper account,block of flats,0.0651,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-839.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n152224,0,Cash loans,M,N,N,0,225000.0,270000.0,19647.0,270000.0,Unaccompanied,Working,Higher education,Married,With parents,0.019101,-12115,-1393,-2313.0,-4437,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,10,0,1,1,0,1,1,Construction,0.3532348796878191,0.581102192854995,0.1419915427739129,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2119.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n316972,0,Revolving loans,F,N,Y,0,180000.0,540000.0,27000.0,540000.0,Family,State servant,Higher education,Single / not married,House / apartment,0.00702,-8861,-188,-8830.0,-1525,,1,1,0,1,0,0,,1.0,2,2,MONDAY,14,0,0,0,0,0,0,Government,0.4242891056942433,0.6267634291786175,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-456.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n244023,0,Cash loans,F,N,Y,0,157500.0,239850.0,25960.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009657,-19624,-1077,-281.0,-318,,1,1,1,1,0,0,Managers,2.0,2,2,SATURDAY,11,0,0,0,0,1,1,Other,,0.5700715505334721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-229.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n230070,0,Cash loans,F,N,Y,1,162000.0,675000.0,28728.0,675000.0,Family,Working,Secondary / secondary special,Married,Rented apartment,0.030755,-13291,-4247,-7431.0,-4658,,1,1,1,1,0,0,Core staff,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Kindergarten,0.3681909212954673,0.4251420789665428,0.6545292802242897,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n139122,1,Cash loans,F,N,Y,0,202500.0,730017.0,32287.5,652500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018634,-17846,-6700,-10509.0,-1401,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Government,0.6327802523088413,0.16394994020421727,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1041.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n239589,1,Cash loans,M,N,Y,0,180000.0,251280.0,17127.0,180000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006852,-12894,-661,-2301.0,-4117,,1,1,0,1,0,0,Low-skill Laborers,2.0,3,3,TUESDAY,8,0,1,1,0,1,1,Construction,0.17581816363738234,0.31221975968159443,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-160.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n179105,0,Cash loans,M,N,N,1,247500.0,679500.0,36202.5,679500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.001333,-18948,-2484,-3121.0,-2348,,1,1,1,1,1,0,,3.0,3,3,TUESDAY,8,0,0,0,0,0,0,Self-employed,,0.7365199843263099,0.3774042489507649,0.1021,0.0905,0.9816,0.7484,,0.0,0.2069,0.1667,,0.1187,,0.0892,,0.0307,0.10400000000000001,0.0939,0.9816,0.7583,,0.0,0.2069,0.1667,,0.1214,,0.0925,,0.0325,0.1031,0.0905,0.9816,0.7518,,0.0,0.2069,0.1667,,0.1207,,0.0908,,0.0313,reg oper account,block of flats,0.0765,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-571.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n377243,0,Cash loans,F,N,Y,2,121500.0,971280.0,56920.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006296,-12629,-3877,-2991.0,-4463,,1,1,0,1,0,0,,4.0,3,3,FRIDAY,14,0,0,0,0,0,0,Self-employed,,0.5342420359524598,0.6769925032909132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-260.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n160353,0,Cash loans,F,N,Y,1,76500.0,52128.0,5607.0,45000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-17847,365243,-4428.0,-1370,,1,0,0,1,1,0,,3.0,3,3,TUESDAY,9,0,0,0,0,0,0,XNA,,0.6045284393481609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-565.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n291056,0,Cash loans,F,Y,Y,0,135000.0,1125171.0,44622.0,1035000.0,Family,State servant,Higher education,Married,House / apartment,0.035792000000000004,-19931,-3075,-4242.0,-3493,2.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Other,,0.6384823925415725,0.7610263695502636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-533.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n367528,0,Cash loans,M,Y,N,1,108000.0,134775.0,11659.5,112500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-11961,-1189,-6096.0,-4116,1.0,1,1,0,1,0,0,Drivers,3.0,3,3,MONDAY,7,0,0,0,1,1,0,Business Entity Type 3,0.13336224587513282,0.4024438302308987,0.4938628816996244,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-307.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n147482,0,Cash loans,F,N,Y,2,225000.0,900000.0,26446.5,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008575,-12200,-2923,-6174.0,-4256,,1,1,0,1,1,0,Laborers,4.0,2,2,TUESDAY,16,0,0,0,1,0,1,Self-employed,,0.7050114743217336,0.5849900404894085,0.0165,0.0557,0.9871,,,0.0,0.069,0.0417,,0.0861,,0.0171,,0.0,0.0168,0.0578,0.9871,,,0.0,0.069,0.0417,,0.0881,,0.0178,,0.0,0.0167,0.0557,0.9871,,,0.0,0.069,0.0417,,0.0876,,0.0174,,0.0,,block of flats,0.0135,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-461.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n230857,0,Revolving loans,F,N,Y,1,112500.0,225000.0,11250.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.010276,-18905,365243,-7061.0,-2464,,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.5232794824808701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-965.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n126933,0,Cash loans,M,N,Y,0,202500.0,1197000.0,44487.0,1197000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.026392,-11945,-376,-574.0,-580,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Other,0.2845697774507717,0.12669696238205486,0.2851799046358216,0.2454,,0.9901,,,0.24,0.2069,0.375,,,,0.2482,,0.0461,0.25,,0.9901,,,0.2417,0.2069,0.375,,,,0.2586,,0.0488,0.2477,,0.9901,,,0.24,0.2069,0.375,,,,0.2527,,0.047,,block of flats,0.2053,Panel,No,7.0,0.0,7.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n311603,0,Cash loans,M,N,Y,0,67500.0,284400.0,16011.0,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.006008,-24117,365243,-4471.0,-2199,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.5959074418102865,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-484.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n316172,0,Cash loans,F,N,Y,0,31500.0,101880.0,10053.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-22515,365243,-6763.0,-4281,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,0.8633110473788744,0.2930897022262147,0.7557400501752248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n146182,0,Cash loans,M,N,Y,0,157500.0,251091.0,29799.0,238500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.010966,-13552,-2150,-5665.0,-4643,,1,1,0,1,1,0,Drivers,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Transport: type 3,,0.7312715715578644,0.1997705696341145,0.0557,0.0829,0.9806,0.7348,0.0076,0.0,0.1379,0.1667,0.2083,0.0,0.0445,0.0536,0.0039,0.095,0.0567,0.0861,0.9806,0.7452,0.0077,0.0,0.1379,0.1667,0.2083,0.0,0.0487,0.0558,0.0039,0.1005,0.0562,0.0829,0.9806,0.7383,0.0077,0.0,0.1379,0.1667,0.2083,0.0,0.0453,0.0546,0.0039,0.0969,reg oper account,block of flats,0.067,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-29.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n117665,0,Cash loans,M,Y,Y,0,225000.0,845811.0,33673.5,756000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-15738,-652,-6168.0,-4663,10.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.7290397606536647,0.7958031328748403,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1811.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n106927,0,Cash loans,F,N,Y,0,139500.0,225000.0,22252.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010147,-24657,365243,-770.0,-4589,,1,0,0,1,1,0,,1.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.22507719762425368,0.5709165417729987,0.0124,,0.9856,,,0.0,0.069,0.0417,,0.0069,,0.0114,,0.025,0.0126,,0.9856,,,0.0,0.069,0.0417,,0.0071,,0.0119,,0.0265,0.0125,,0.9856,,,0.0,0.069,0.0417,,0.0071,,0.0116,,0.0255,,block of flats,0.009000000000000001,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-326.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n130708,0,Cash loans,F,N,Y,0,72000.0,247500.0,12037.5,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-22844,365243,-8127.0,-4568,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.34536227671948433,0.5744466170995097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n163884,0,Cash loans,F,N,Y,2,67500.0,152820.0,16587.0,135000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-12678,-2633,-5963.0,-4083,,1,1,1,1,1,0,Laborers,4.0,2,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,0.4984868478676884,0.6043663609194857,0.7503751495159068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2198.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n425003,0,Cash loans,M,Y,Y,2,360000.0,1762110.0,67248.0,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-16041,-420,-8057.0,-2471,2.0,1,1,0,1,0,0,Laborers,4.0,1,1,TUESDAY,19,1,0,1,0,0,0,Business Entity Type 3,,0.5354334732477775,0.31547215492577346,0.5753,0.3152,0.9806,0.7348,0.0,0.96,0.4138,0.4583,0.5,0.0,0.469,0.5822,0.0,0.0564,0.5861,0.3271,0.9806,0.7452,0.0,0.9667,0.4138,0.4583,0.5,0.0,0.5124,0.6066,0.0,0.0598,0.5808,0.3152,0.9806,0.7383,0.0,0.96,0.4138,0.4583,0.5,0.0,0.4771,0.5927,0.0,0.0576,reg oper account,block of flats,0.4702,Panel,No,0.0,0.0,0.0,0.0,-226.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n104852,1,Cash loans,M,Y,Y,1,180000.0,429763.5,33952.5,351000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-16195,-1803,-8706.0,-4382,1.0,1,1,0,1,0,1,Laborers,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.6767894659368786,0.6206973630882127,0.5460231970049609,0.0825,0.0644,0.9752,,,0.0,0.1379,0.1667,,0.0702,,0.0683,,0.0097,0.084,0.0669,0.9752,,,0.0,0.1379,0.1667,,0.0718,,0.0712,,0.0103,0.0833,0.0644,0.9752,,,0.0,0.1379,0.1667,,0.0714,,0.0695,,0.01,,block of flats,0.0603,Panel,No,9.0,0.0,9.0,0.0,-1510.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n192487,0,Cash loans,F,Y,Y,1,112500.0,273636.0,15835.5,247500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.005084,-15375,-2827,-1624.0,-5049,4.0,1,1,1,1,0,0,Managers,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Government,0.6375222862306303,0.6671420825129923,0.6971469077844458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n302737,0,Cash loans,M,N,N,0,157500.0,953460.0,63976.5,900000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.014464,-22407,-651,-10117.0,-4535,,1,1,0,1,0,0,Security staff,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.7876167547149272,0.7194907850918436,0.0598,0.0241,0.9811,,,0.04,0.0345,0.3333,,,,0.0404,,0.0025,0.0609,0.025,0.9811,,,0.0403,0.0345,0.3333,,,,0.042,,0.0027,0.0604,0.0241,0.9811,,,0.04,0.0345,0.3333,,,,0.0411,,0.0026,,block of flats,0.0337,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-702.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n410168,0,Cash loans,M,Y,Y,0,162000.0,1724220.0,50544.0,1350000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18069,-4274,-6308.0,-1620,12.0,1,1,1,1,1,0,Laborers,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Other,0.7602553989745393,0.4174862092657629,0.5602843280409464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2002.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n165435,0,Cash loans,F,N,N,0,135000.0,573628.5,25398.0,463500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-13995,-3189,-946.0,-4318,,1,1,1,1,0,0,Sales staff,2.0,3,3,FRIDAY,8,0,0,0,0,0,0,Self-employed,0.5229318824509364,0.4679769671353141,0.6058362647264226,0.0392,0.0385,0.9806,0.7348,0.0039,0.0,0.069,0.1667,0.0417,,,0.0315,,0.0051,0.0399,0.04,0.9806,0.7452,0.0039,0.0,0.069,0.1667,0.0417,,,0.0328,,0.0054,0.0396,0.0385,0.9806,0.7383,0.0039,0.0,0.069,0.1667,0.0417,,,0.032,,0.0052,,block of flats,0.027999999999999997,Panel,No,0.0,0.0,0.0,0.0,-1136.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n454217,0,Cash loans,F,N,Y,0,202500.0,1024740.0,52452.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.02461,-18099,-6414,-2911.0,-1352,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Hotel,0.8296976581752423,0.6307448190062717,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1757.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n158255,0,Cash loans,F,Y,Y,0,193500.0,450000.0,41643.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-14995,-2779,-7475.0,-306,1.0,1,1,1,1,1,0,Medicine staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Medicine,,0.7223406763452098,0.5082869913916046,0.2423,0.1636,0.9896,,,0.24,0.2069,0.375,,,,0.2608,,0.0058,0.2468,0.1698,0.9896,,,0.2417,0.2069,0.375,,,,0.2717,,0.0062,0.2446,0.1636,0.9896,,,0.24,0.2069,0.375,,,,0.2655,,0.0059,,block of flats,0.2328,Block,No,5.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n343949,0,Cash loans,F,N,Y,2,104008.5,1170000.0,29704.5,1170000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-11655,-3294,-780.0,-3651,,1,1,1,1,1,0,Medicine staff,4.0,2,2,MONDAY,12,0,0,0,0,0,0,Kindergarten,0.6933386367591463,0.5619983177991307,,0.1897,,0.9876,,,0.2,0.1724,0.3333,,,,0.1943,,0.0047,0.1933,,0.9876,,,0.2014,0.1724,0.3333,,,,0.2025,,0.005,0.1915,,0.9876,,,0.2,0.1724,0.3333,,,,0.1978,,0.0048,,block of flats,0.1747,Block,No,3.0,0.0,3.0,0.0,-87.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,,,,,,\n388897,0,Cash loans,F,N,N,2,279000.0,906615.0,35415.0,688500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.008068,-13524,-5376,-309.0,-5116,,1,1,0,1,0,0,,4.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Military,0.33920048457717195,0.16957385277871256,0.4722533429586386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,-1460.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n151428,0,Cash loans,F,N,N,2,247500.0,679500.0,27076.5,679500.0,Unaccompanied,Working,Incomplete higher,Married,With parents,0.019101,-11513,-2511,-5024.0,-2687,,1,1,0,1,0,0,Accountants,4.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.5747703977065701,0.7710228349226064,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2433.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n100688,0,Cash loans,F,N,Y,2,135000.0,167121.0,13527.0,139500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.020713,-13103,-1270,-542.0,-1040,,1,1,0,1,0,0,Sales staff,4.0,3,3,MONDAY,6,0,0,0,0,0,0,Transport: type 4,,0.1072047010306742,0.3490552510751822,0.0124,0.0,0.9856,0.8028,0.0021,,0.069,0.0417,0.0833,0.0153,0.0101,0.0099,,0.0,0.0126,0.0,0.9856,0.8105,0.0021,,0.069,0.0417,0.0833,0.0156,0.011000000000000001,0.0104,,0.0,0.0125,0.0,0.9856,0.8054,0.0021,,0.069,0.0417,0.0833,0.0156,0.0103,0.0101,,0.0,reg oper spec account,block of flats,0.009000000000000001,Wooden,No,1.0,0.0,1.0,0.0,-563.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n435575,0,Revolving loans,F,Y,Y,2,315000.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006629,-14339,-2471,-1514.0,-2211,14.0,1,1,0,0,0,0,Accountants,4.0,2,2,THURSDAY,3,0,0,0,0,0,0,Government,0.7675880918822947,0.694560780116349,0.6658549219640212,0.0186,0.0358,0.9975,0.966,,0.0,0.0345,0.1667,0.0417,0.0016,0.0151,0.0481,0.0077,0.0483,0.0189,0.0372,0.9975,0.9673,,0.0,0.0345,0.1667,0.0417,0.0016,0.0165,0.0501,0.0078,0.0512,0.0187,0.0358,0.9975,0.9665,,0.0,0.0345,0.1667,0.0417,0.0016,0.0154,0.049,0.0078,0.0494,,block of flats,0.0379,Others,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n200410,0,Cash loans,F,Y,N,2,126000.0,450000.0,22018.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-13904,-1239,-115.0,-5812,11.0,1,1,1,1,1,0,,4.0,3,3,THURSDAY,13,0,0,0,1,0,1,Business Entity Type 3,0.6416017404495683,0.4504274606774829,,0.0959,0.0832,0.9786,0.7076,0.0448,0.0,0.1034,0.125,0.0417,0.0411,0.0748,0.0569,0.0154,0.0173,0.0977,0.0864,0.9786,0.7190000000000001,0.0452,0.0,0.1034,0.125,0.0417,0.042,0.0817,0.0592,0.0156,0.0183,0.0968,0.0832,0.9786,0.7115,0.0451,0.0,0.1034,0.125,0.0417,0.0418,0.0761,0.0579,0.0155,0.0176,reg oper account,block of flats,0.073,Panel,No,0.0,0.0,0.0,0.0,-2656.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n126552,0,Revolving loans,F,Y,N,0,90000.0,202500.0,10125.0,202500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0105,-17105,-3271,-7218.0,-361,15.0,1,1,0,0,0,0,Core staff,2.0,3,3,FRIDAY,14,0,0,0,0,1,1,Medicine,0.5535824928647581,0.3087507929554781,0.0576392903477425,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-604.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n102834,0,Cash loans,F,N,Y,1,247500.0,675000.0,38880.0,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-20170,365243,-6306.0,-3632,,1,0,0,1,0,0,,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,0.7675171220772522,0.7259749131527943,0.5190973382084597,0.099,0.1337,0.9881,,,0.0,0.2759,0.1667,,0.064,,0.0988,,,0.1008,0.1388,0.9881,,,0.0,0.2759,0.1667,,0.0655,,0.1029,,,0.0999,0.1337,0.9881,,,0.0,0.2759,0.1667,,0.0651,,0.1006,,,,block of flats,0.1002,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2701.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n303881,0,Revolving loans,F,N,N,0,270000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.011657,-22208,-589,-359.0,-4693,,1,1,0,1,0,0,Cleaning staff,2.0,1,1,THURSDAY,13,0,0,0,0,0,0,Industry: type 7,,0.5859010749186679,0.5513812618027899,0.0835,0.1314,0.9995,0.9932,0.0605,0.0,0.1379,0.0833,0.125,0.0678,0.0681,0.0649,0.0,0.0,0.0851,0.1364,0.9995,0.9935,0.0611,0.0,0.1379,0.0833,0.125,0.0693,0.0744,0.0676,0.0,0.0,0.0843,0.1314,0.9995,0.9933,0.0609,0.0,0.1379,0.0833,0.125,0.0689,0.0693,0.066,0.0,0.0,reg oper account,block of flats,0.051,Block,No,0.0,0.0,0.0,0.0,-329.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n431408,0,Cash loans,F,Y,Y,0,157500.0,870984.0,44599.5,778500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007114,-14411,-2147,-1098.0,-62,7.0,1,1,1,1,0,0,Managers,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Other,0.4593821532036948,0.4687719404948954,,0.0825,0.1167,0.9836,0.7756,0.0173,0.0,0.2069,0.1667,0.2083,0.114,0.0664,0.0841,0.0039,0.002,0.084,0.1211,0.9836,0.7844,0.0174,0.0,0.2069,0.1667,0.2083,0.1166,0.0725,0.0877,0.0039,0.0021,0.0833,0.1167,0.9836,0.7786,0.0174,0.0,0.2069,0.1667,0.2083,0.11599999999999999,0.0676,0.0856,0.0039,0.0021,reg oper account,block of flats,0.0662,Panel,No,0.0,0.0,0.0,0.0,-1640.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n206762,0,Cash loans,M,Y,Y,3,202500.0,1014493.5,51930.0,891000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-12231,-2348,-1545.0,-4641,65.0,1,1,0,1,0,0,,5.0,3,2,TUESDAY,12,0,0,0,0,1,1,Other,,0.4913486513207447,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1320.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n239220,0,Cash loans,M,Y,Y,0,270000.0,473760.0,51021.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-21508,-841,-7146.0,-2057,16.0,1,1,0,1,1,0,Core staff,2.0,1,1,THURSDAY,15,0,1,1,0,0,0,Other,0.8219064345658882,0.7145425207348761,0.34741822720026416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1906.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n221502,0,Cash loans,F,N,Y,3,54000.0,450000.0,17095.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-11954,-1042,-473.0,-2838,,1,1,1,1,1,0,,5.0,3,3,MONDAY,11,0,0,0,0,1,1,Other,,0.3771822452629681,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-574.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n265293,0,Cash loans,M,Y,N,0,337500.0,990000.0,96444.0,990000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.072508,-13699,-2879,-4176.0,-4723,6.0,1,1,0,1,1,0,Laborers,2.0,1,1,SUNDAY,16,0,0,0,0,0,0,Construction,0.3249581666640729,0.7470901244754585,0.2079641743053816,0.0784,0.047,0.9727,0.626,0.0467,0.0,0.1379,0.1667,0.2083,0.1642,0.0614,0.0663,0.0116,0.0207,0.084,0.0061,0.9727,0.6406,0.0108,0.0,0.1379,0.1667,0.2083,0.168,0.0579,0.0605,0.0,0.0,0.0833,0.0674,0.9727,0.631,0.0115,0.0,0.1379,0.1667,0.2083,0.1671,0.065,0.0691,0.0155,0.0266,reg oper account,block of flats,0.0583,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-835.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n416401,0,Revolving loans,F,N,N,0,112500.0,247500.0,12375.0,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.006233,-11205,-3184,-5356.0,-3822,,1,1,0,1,1,0,Sales staff,1.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Self-employed,0.35362271536450685,0.567336083306231,0.4066174366275036,0.0742,,0.9846,,,0.08,0.069,0.3333,,,,0.0757,,0.0169,0.0756,,0.9846,,,0.0806,0.069,0.3333,,,,0.0789,,0.0178,0.0749,,0.9846,,,0.08,0.069,0.3333,,,,0.0771,,0.0172,,block of flats,0.064,Panel,No,1.0,0.0,1.0,0.0,-1660.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n430644,0,Cash loans,F,N,Y,0,202500.0,522396.0,41274.0,472500.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.072508,-17714,-2640,-22.0,-1187,,1,1,0,1,0,1,High skill tech staff,2.0,1,1,SATURDAY,12,0,0,0,0,0,0,Transport: type 4,,0.7019415930067457,,0.1773,0.3294,0.9955,0.932,0.1722,0.16,0.069,0.7083,0.75,0.0,0.1404,0.3459,0.0193,0.04,0.1807,0.3418,0.9955,0.9347,0.1738,0.1611,0.069,0.7083,0.75,0.0,0.1534,0.3604,0.0195,0.0423,0.179,0.3294,0.9955,0.9329,0.1733,0.16,0.069,0.7083,0.75,0.0,0.1428,0.3521,0.0194,0.0408,reg oper account,block of flats,0.3749,Monolithic,No,0.0,0.0,0.0,0.0,-1336.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n154999,0,Cash loans,F,Y,Y,0,139500.0,675000.0,24376.5,675000.0,Children,Pensioner,Secondary / secondary special,Widow,House / apartment,0.015221,-21266,365243,-7570.0,-4545,14.0,1,0,0,1,1,0,,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,0.8733564389088037,0.6300926656844718,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-333.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,4.0\n301614,0,Cash loans,F,N,N,3,65250.0,284400.0,13387.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.031329,-11403,-1042,-1.0,-4041,,1,1,0,1,0,0,Security staff,5.0,2,2,FRIDAY,13,0,0,0,0,1,1,Agriculture,,0.1915258625230554,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,-535.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n390949,0,Cash loans,F,N,Y,1,202500.0,459000.0,29461.5,459000.0,Family,Commercial associate,Higher education,Single / not married,House / apartment,0.031329,-12696,-1126,-12002.0,-3982,,1,1,1,1,1,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Industry: type 4,0.26043740570646634,0.4035802476726009,0.4083588531230431,0.0825,0.0497,0.9791,0.7144,0.0092,0.0,0.1379,0.1667,0.2083,0.0772,0.0672,0.078,0.0,0.0,0.084,0.0516,0.9791,0.7256,0.0093,0.0,0.1379,0.1667,0.2083,0.079,0.0735,0.0813,0.0,0.0,0.0833,0.0497,0.9791,0.7182,0.0093,0.0,0.1379,0.1667,0.2083,0.0786,0.0684,0.0794,0.0,0.0,reg oper account,block of flats,0.0664,Block,No,9.0,0.0,9.0,0.0,-1912.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n139262,0,Cash loans,F,Y,Y,0,157500.0,835380.0,40320.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-15764,-428,-7001.0,-4068,3.0,1,1,0,1,0,0,Medicine staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Medicine,,0.09097793864426228,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n258226,0,Cash loans,F,N,Y,0,103500.0,159993.0,10354.5,121500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.00702,-18284,-316,-5875.0,-1826,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,17,0,0,0,0,0,0,Self-employed,0.6627672454926323,0.5106169986724627,0.4543210601605785,0.0928,,0.9781,,,,0.2069,0.1667,,,,0.0505,,,0.0945,,0.9782,,,,0.2069,0.1667,,,,0.0526,,,0.0937,,0.9781,,,,0.2069,0.1667,,,,0.0514,,,,block of flats,0.0591,Mixed,No,1.0,0.0,1.0,0.0,-1676.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,5.0\n108928,0,Cash loans,F,N,N,1,180000.0,911079.0,26239.5,760500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.032561,-12293,-380,-42.0,-4103,,1,1,0,1,0,0,High skill tech staff,2.0,1,1,WEDNESDAY,19,0,0,0,0,0,0,Transport: type 4,0.6431044940901639,0.627172578831715,0.4525335592581747,0.25,,0.9786,,,,0.2759,0.25,,,,0.1815,,,0.1239,,0.9786,,,,0.2069,0.1667,,,,0.0774,,,0.2524,,0.9786,,,,0.2759,0.25,,,,0.0758,,,,,0.3152,,No,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n422204,1,Cash loans,M,N,Y,0,67500.0,168102.0,20079.0,148500.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.007120000000000001,-17592,-1584,-9940.0,-1131,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Other,0.26397200379479485,0.08875309453615003,0.7922644738669378,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n439452,0,Cash loans,M,Y,N,2,90000.0,396706.5,31473.0,324000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003122,-13730,-133,-6730.0,-873,12.0,1,1,0,1,0,0,Laborers,4.0,3,3,SATURDAY,11,0,0,0,0,0,0,Industry: type 9,0.15659050526062204,0.6000813937611358,0.19747451156854226,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-2406.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n299356,0,Cash loans,F,N,Y,1,67500.0,563877.0,24021.0,504000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.018209,-13784,-6522,-4583.0,-3940,,1,1,0,1,0,0,Medicine staff,3.0,3,3,WEDNESDAY,13,0,0,0,0,1,1,Kindergarten,0.701595475946323,0.5022268953164489,0.2032521136204725,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1758.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n399116,0,Cash loans,F,Y,Y,1,157500.0,548775.0,52236.0,508500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-16680,-198,-9220.0,-220,18.0,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,4,0,0,0,0,0,0,Business Entity Type 3,,0.28306693069554445,0.8235493123400324,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n401831,0,Cash loans,F,N,Y,1,112500.0,500490.0,53901.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010032,-9964,-706,-747.0,-1195,,1,1,0,1,0,0,Accountants,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Industry: type 4,0.11596650275195786,0.3027168252129539,0.3360615207658242,0.10099999999999999,0.1148,0.9786,,,0.0,0.2069,0.1667,,0.0286,,0.0836,,0.0162,0.1029,0.1188,0.9786,,,0.0,0.2069,0.1667,,0.0076,,0.0771,,0.0,0.102,0.1145,0.9786,,,0.0,0.2069,0.1667,,0.0348,,0.0888,,0.003,,block of flats,0.0902,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-400.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n250417,0,Cash loans,M,Y,Y,1,225000.0,1174977.0,34483.5,1026000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18150,-568,-5477.0,-1694,8.0,1,1,0,1,0,0,Managers,3.0,2,2,THURSDAY,15,0,0,0,0,1,1,Trade: type 7,,0.6804210491947065,0.7700870700124128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1918.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n173550,0,Cash loans,M,N,Y,1,135000.0,450000.0,22018.5,450000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.025164,-10321,-1117,-10305.0,-2331,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Other,0.16241441706804885,0.2962028246127759,0.6041125998015721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-915.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n298784,0,Cash loans,F,N,Y,0,112500.0,450000.0,21780.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.009334,-21641,-907,-1089.0,-4875,,1,1,1,1,0,0,Security staff,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,School,,0.5410585425983052,0.6161216908872079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-216.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,1.0,0.0,1.0\n425664,0,Cash loans,F,N,Y,1,67500.0,450000.0,17095.5,450000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.0228,-16105,-860,-8843.0,-4092,,1,1,0,1,0,0,,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.6505153355363964,0.6714690632315883,,0.0557,0.0393,0.9866,0.8164,0.0133,0.08,0.069,0.3333,0.0417,0.0358,0.0454,0.068,0.0,0.0,0.0567,0.0408,0.9866,0.8236,0.0134,0.0806,0.069,0.3333,0.0417,0.0366,0.0496,0.0708,0.0,0.0,0.0562,0.0393,0.9866,0.8189,0.0134,0.08,0.069,0.3333,0.0417,0.0364,0.0462,0.0692,0.0,0.0,not specified,block of flats,0.0535,Panel,No,0.0,0.0,0.0,0.0,-1868.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n443838,0,Cash loans,M,Y,Y,1,135000.0,172021.5,18526.5,148500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15652,-3667,-1057.0,-4777,14.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Transport: type 4,,0.7622802227549145,0.8128226070575616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1775.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n217729,0,Cash loans,F,Y,Y,2,270000.0,585000.0,21010.5,585000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.0105,-11780,-1265,-2304.0,-355,0.0,1,1,0,1,0,0,,4.0,3,3,WEDNESDAY,14,0,0,0,1,1,0,Business Entity Type 3,,0.5696588184875808,,0.0196,0.0937,0.9836,0.7756,0.0045,0.0,0.069,0.1667,0.2083,0.0533,0.0151,0.0678,0.0039,0.0062,0.02,0.0972,0.9836,0.7844,0.0045,0.0,0.069,0.1667,0.2083,0.0545,0.0165,0.0695,0.0039,0.0016,0.0198,0.0937,0.9836,0.7786,0.0045,0.0,0.069,0.1667,0.2083,0.0542,0.0154,0.069,0.0039,0.0063,reg oper account,block of flats,0.0545,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-608.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n375495,1,Cash loans,M,N,N,0,157500.0,481500.0,23292.0,481500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-19072,-7301,-3141.0,-2620,,1,1,0,1,0,0,Drivers,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Government,,0.20351928728859525,0.6722428897082422,0.0825,0.0613,0.9742,0.6464,0.0096,0.0,0.1379,0.1667,0.0,0.0588,0.0664,0.0622,0.0039,0.0052,0.084,0.0636,0.9742,0.6602,0.0097,0.0,0.1379,0.1667,0.0,0.0601,0.0725,0.0649,0.0039,0.0055,0.0833,0.0613,0.9742,0.6511,0.0097,0.0,0.1379,0.1667,0.0,0.0598,0.0676,0.0634,0.0039,0.0053,reg oper account,block of flats,0.0545,\"Stone, brick\",No,12.0,2.0,12.0,2.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n402478,0,Cash loans,M,N,Y,0,126000.0,343800.0,13090.5,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-20125,-3516,-9327.0,-3679,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Military,,0.35347975868200704,0.4902575124990026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-21.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n283720,0,Cash loans,F,N,Y,0,135000.0,1113840.0,47322.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.02461,-17352,-4268,-11494.0,-897,,1,1,0,1,1,0,Medicine staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Medicine,,0.6427767207563795,0.7016957740576931,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-259.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n239265,0,Cash loans,F,Y,Y,2,180000.0,180000.0,17532.0,180000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.005313,-17370,-6113,-371.0,-931,13.0,1,1,1,1,0,0,,4.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Security Ministries,,0.6804543337472728,0.5779691187553125,,,0.9841,,,,,0.1667,,,,,,,,,0.9841,,,,,0.1667,,,,,,,,,0.9841,,,,,0.1667,,,,,,,,block of flats,0.0702,Panel,No,0.0,0.0,0.0,0.0,-1500.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n368791,0,Cash loans,F,N,N,0,202500.0,254700.0,17149.5,225000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.030755,-11881,-2765,-5812.0,-4102,,1,1,1,1,1,0,,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Trade: type 6,0.30572347300289016,0.5372171486560433,0.21640296051521946,0.133,0.1102,0.9796,0.7212,0.051,0.16,0.1379,0.3333,0.375,0.0546,0.1076,0.1321,0.0039,0.1261,0.1355,0.1143,0.9796,0.7321,0.0515,0.1611,0.1379,0.3333,0.375,0.0558,0.1175,0.1376,0.0039,0.1335,0.1343,0.1102,0.9796,0.7249,0.0514,0.16,0.1379,0.3333,0.375,0.0555,0.1095,0.1344,0.0039,0.1287,reg oper account,block of flats,0.1592,Panel,No,0.0,0.0,0.0,0.0,-2722.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n178313,0,Cash loans,M,Y,Y,1,202500.0,675000.0,26154.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-16627,-2194,-656.0,-176,3.0,1,1,0,1,0,0,Drivers,3.0,2,2,THURSDAY,16,0,0,0,0,1,1,Industry: type 11,,0.7167097457697379,0.3092753558842053,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1927.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n388062,0,Cash loans,M,Y,Y,2,90000.0,157050.0,8649.0,112500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-12785,-2731,-4423.0,-4429,17.0,1,1,0,1,0,0,Drivers,4.0,2,2,MONDAY,10,0,0,0,0,1,1,Emergency,,0.009631127761367677,0.6041125998015721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1634.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n391234,0,Cash loans,M,N,Y,2,67500.0,332842.5,16317.0,234000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-13035,-1152,-2506.0,-3980,,1,1,0,1,0,0,Laborers,4.0,3,3,MONDAY,5,0,0,0,0,0,0,School,,0.00012734004802677693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-77.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,1.0,0.0,0.0,2.0,4.0\n442221,0,Cash loans,F,Y,Y,1,81000.0,814041.0,26388.0,679500.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.020713,-17413,-2062,-394.0,-825,13.0,1,1,0,1,0,0,Core staff,3.0,3,3,MONDAY,12,0,0,0,0,0,0,School,0.7208969575192705,0.6028856853727219,0.3606125659189888,0.0701,0.0769,0.9821,0.7552,0.0078,0.0,0.1379,0.1667,0.2083,0.0769,0.0555,0.0624,0.0077,0.0158,0.0714,0.0798,0.9821,0.7648,0.0079,0.0,0.1379,0.1667,0.2083,0.0786,0.0606,0.065,0.0078,0.0167,0.0708,0.0769,0.9821,0.7585,0.0079,0.0,0.1379,0.1667,0.2083,0.0782,0.0564,0.0635,0.0078,0.0161,reg oper account,block of flats,0.0568,Panel,No,0.0,0.0,0.0,0.0,-821.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n277480,0,Cash loans,F,N,Y,0,202500.0,315000.0,14814.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010966,-19291,-128,-5277.0,-2843,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Industry: type 3,,0.5505991435380012,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,1.0,0.0,-135.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n451772,0,Cash loans,M,N,Y,0,180000.0,487971.0,51372.0,463500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008865999999999999,-15864,-98,-8528.0,-4683,,1,1,0,1,0,1,Drivers,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Industry: type 3,0.5039539987484819,0.4010405399859509,0.6023863442690867,0.0825,,0.9762,,,0.0,0.1379,0.1667,,,,0.0571,,,0.084,,0.9762,,,0.0,0.1379,0.1667,,,,0.0595,,,0.0833,,0.9762,,,0.0,0.1379,0.1667,,,,0.0582,,,,terraced house,0.0449,\"Stone, brick\",No,1.0,1.0,1.0,0.0,-665.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n324926,1,Cash loans,F,N,N,0,81000.0,170640.0,11533.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-10548,-1898,-4714.0,-2461,,1,1,1,1,1,0,Core staff,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Kindergarten,,0.05861353026068185,0.2925880073368475,0.3258,0.0,0.9871,0.8232,0.496,0.0,0.1724,0.3333,0.375,0.0,0.2656,0.3318,0.0,0.046,0.3319,0.0,0.9871,0.8301,0.5005,0.0,0.1724,0.3333,0.375,0.0,0.2902,0.3457,0.0,0.0487,0.3289,0.0,0.9871,0.8256,0.4991,0.0,0.1724,0.3333,0.375,0.0,0.2702,0.3377,0.0,0.0469,reg oper account,block of flats,0.2712,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-249.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n395853,0,Cash loans,F,N,N,0,121500.0,592560.0,32274.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-10295,-221,-4669.0,-1773,,1,1,0,1,1,0,Core staff,2.0,2,2,WEDNESDAY,4,0,0,0,1,1,0,Self-employed,0.3207299967007284,0.6285980607224058,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n313613,0,Cash loans,F,N,Y,0,54000.0,254700.0,14350.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,Municipal apartment,0.00702,-23917,365243,-5300.0,-4060,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.4199291784507629,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1845.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n430121,0,Cash loans,F,N,N,0,130500.0,143910.0,15268.5,135000.0,Unaccompanied,State servant,Incomplete higher,Married,House / apartment,0.025164,-10008,-2946,-613.0,-1126,,1,1,1,1,0,0,Core staff,2.0,2,2,SUNDAY,10,0,0,0,1,0,1,Emergency,0.4002459130154818,0.6061740572919522,0.5046813193144684,0.1282,0.1085,0.993,0.9116,0.0394,0.18,0.1434,0.3608,0.375,0.084,0.1042,0.1724,0.0015,0.0209,0.0483,0.0406,0.9926,0.902,0.0253,0.0806,0.0345,0.3333,0.2917,0.0344,0.0413,0.0765,0.0,0.0,0.1624,0.1171,0.993,0.9128,0.0298,0.18,0.1552,0.3333,0.375,0.10300000000000001,0.1334,0.1918,0.0,0.006,reg oper account,block of flats,0.2303,Panel,No,4.0,0.0,4.0,0.0,-1036.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n223706,0,Cash loans,M,Y,N,2,376875.0,1777212.0,47011.5,1588500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.02461,-13284,-5016,-2845.0,-4567,9.0,1,1,1,1,1,0,Managers,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,University,0.6079509868077536,0.6832673110717948,0.7713615919194317,0.3670000000000001,,0.9806,,0.1333,0.36,0.3103,0.3333,,0.1989,0.2975,0.3721,0.0077,0.0929,0.3739,,0.9806,,0.1346,0.3625,0.3103,0.3333,,0.2034,0.3251,0.3876,0.0078,0.0984,0.3706,,0.9806,,0.1342,0.36,0.3103,0.3333,,0.2024,0.3027,0.3787,0.0078,0.0949,,block of flats,0.3857,Panel,No,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n210559,0,Cash loans,F,Y,N,1,225000.0,1687266.0,64264.5,1575000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.026392,-15758,-3338,-1555.0,-4478,1.0,1,1,0,1,1,0,Managers,3.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.6855259820728979,0.7189774618877164,0.324891229465852,0.066,,0.9975,,,0.08,0.0345,0.6667,,,,0.1137,,0.1761,0.0672,,0.9975,,,0.0806,0.0345,0.6667,,,,0.1185,,0.1864,0.0666,,0.9975,,,0.08,0.0345,0.6667,,,,0.1158,,0.1798,,block of flats,0.1277,Mixed,No,2.0,1.0,2.0,0.0,-1431.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n239776,1,Cash loans,F,N,Y,0,126000.0,284400.0,18643.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.04622,-12390,-1018,-5481.0,-1339,,1,1,0,1,1,0,Laborers,2.0,1,1,FRIDAY,11,0,0,0,1,1,0,Business Entity Type 1,,0.2652733881023268,,0.0371,0.0622,0.9752,0.66,0.0049,0.0,0.1034,0.125,0.1667,0.0243,0.0303,0.0296,0.0116,0.0377,0.0378,0.0645,0.9752,0.6733,0.005,0.0,0.1034,0.125,0.1667,0.0248,0.0331,0.0308,0.0117,0.04,0.0375,0.0622,0.9752,0.6645,0.0049,0.0,0.1034,0.125,0.1667,0.0247,0.0308,0.0301,0.0116,0.0385,reg oper account,block of flats,0.0315,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-38.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n398890,0,Cash loans,M,Y,Y,1,112500.0,239850.0,27189.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-10814,-304,-4859.0,-1965,14.0,1,1,0,1,0,0,Drivers,3.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.3276328102963969,,0.0082,0.0004,0.9667,0.5444,0.0023,0.0,0.069,0.0417,0.0417,0.0,,0.0079,,0.0,0.0084,0.0004,0.9667,0.5622,0.0023,0.0,0.069,0.0417,0.0417,0.0,,0.0083,,0.0,0.0083,0.0004,0.9667,0.5505,0.0023,0.0,0.069,0.0417,0.0417,0.0,,0.0081,,0.0,reg oper account,block of flats,0.0071,Block,No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n431185,0,Cash loans,M,Y,N,2,202500.0,1293502.5,35698.5,1129500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.018029,-14545,365243,-2505.0,-4682,11.0,1,0,0,1,1,0,,4.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.7562812473477822,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-785.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n427732,0,Cash loans,F,N,Y,1,198000.0,454500.0,14661.0,454500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Municipal apartment,0.002506,-15201,-610,-7517.0,-362,,1,1,0,1,0,0,Cooking staff,3.0,2,2,TUESDAY,6,0,0,0,0,0,0,Other,,0.6188422104907426,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-806.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n260219,0,Cash loans,F,N,Y,2,270000.0,808650.0,26217.0,675000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.011703,-15332,-259,-2996.0,-5620,,1,1,0,1,0,1,Core staff,3.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.5194068425438881,0.6174485633078872,0.23791607950711405,0.0722,0.0,0.9806,,,,0.1379,0.1667,,0.0505,,0.0668,,0.0,0.0735,0.0,0.9806,,,,0.1379,0.1667,,0.0517,,0.0696,,0.0,0.0729,0.0,0.9806,,,,0.1379,0.1667,,0.0514,,0.068,,0.0,,block of flats,0.0569,Panel,No,0.0,0.0,0.0,0.0,-788.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n383677,0,Cash loans,F,N,Y,0,112500.0,229500.0,16830.0,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-22978,365243,-7941.0,-4763,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.6852751600110667,0.3539876078507373,0.0464,,0.9886,,,0.0,0.0345,0.3333,,,,0.0689,,0.0096,0.0473,,0.9886,,,0.0,0.0345,0.3333,,,,0.0718,,0.0102,0.0468,,0.9886,,,0.0,0.0345,0.3333,,,,0.0701,,0.0098,,block of flats,0.0563,\"Stone, brick\",No,8.0,0.0,7.0,0.0,-731.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n306626,0,Cash loans,F,N,Y,0,94500.0,406597.5,19687.5,351000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-22086,365243,-10483.0,-4946,,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.005623550429879748,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2365.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n243932,0,Cash loans,F,Y,Y,1,180000.0,103500.0,10363.5,103500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.0228,-12914,-3527,-3473.0,-4381,0.0,1,1,1,1,1,0,Sales staff,3.0,2,2,FRIDAY,16,0,0,0,0,0,0,Self-employed,0.6097151229330914,0.585418188865403,0.5442347412142162,0.0948,0.0367,0.9861,0.8096,0.0247,0.0,0.1379,0.1667,,0.076,,0.09,,0.0352,0.0966,0.0381,0.9861,0.8171,0.0249,0.0,0.1379,0.1667,,0.0777,,0.0938,,0.0372,0.0958,0.0367,0.9861,0.8121,0.0249,0.0,0.1379,0.1667,,0.0773,,0.0917,,0.0359,reg oper account,block of flats,0.092,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-1562.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n235028,0,Cash loans,F,N,Y,0,121500.0,1255680.0,41629.5,1125000.0,Unaccompanied,Working,Higher education,Widow,House / apartment,0.018029,-20862,-4978,-2831.0,-2835,,1,1,0,1,0,0,Core staff,1.0,3,3,FRIDAY,8,0,0,0,0,0,0,School,,0.3553360046576225,0.5460231970049609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1619.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n445414,0,Cash loans,F,N,Y,0,225000.0,654498.0,31617.0,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-17655,-569,-6851.0,-1195,,1,1,0,1,1,0,,2.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.7631498441419348,0.7507742655383427,0.7380196196295241,0.2216,0.1436,0.9771,,,0.24,0.2069,0.3333,,0.0,,0.1357,,0.0068,0.2258,0.1491,0.9772,,,0.2417,0.2069,0.3333,,0.0,,0.1414,,0.0072,0.2238,0.1436,0.9771,,,0.24,0.2069,0.3333,,0.0,,0.1382,,0.006999999999999999,,block of flats,0.1611,Panel,No,0.0,0.0,0.0,0.0,-3063.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n454257,0,Cash loans,F,N,Y,0,189000.0,862560.0,25348.5,720000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.072508,-23086,365243,-438.0,-4295,,1,0,0,1,0,0,,2.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.7046930279202601,0.3539876078507373,0.2891,0.1213,0.9965,0.9524,0.161,0.512,0.1241,0.9,0.8917,0.0025,0.2328,0.2973,0.0131,0.0342,0.1481,0.055999999999999994,0.9965,0.9543,0.0896,0.1611,0.0345,0.9583,1.0,0.0,0.1267,0.1758,0.0117,0.0329,0.1468,0.0545,0.9965,0.953,0.0919,0.16,0.0345,0.9583,1.0,0.0,0.11800000000000001,0.1723,0.0116,0.0324,reg oper spec account,block of flats,0.6408,Panel,No,1.0,0.0,1.0,0.0,-393.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n197919,0,Cash loans,F,N,Y,2,108000.0,528633.0,23413.5,472500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-17098,-1842,-2498.0,-620,,1,1,0,1,0,0,Core staff,4.0,2,2,TUESDAY,8,0,0,0,0,0,0,Hotel,,0.6328406877046258,0.7194907850918436,0.1485,0.1028,0.9791,0.7144,0.0256,0.16,0.1379,0.3333,0.375,0.2253,0.121,0.14400000000000002,0.0,0.0,0.1513,0.1067,0.9791,0.7256,0.0258,0.1611,0.1379,0.3333,0.375,0.2305,0.1322,0.1501,0.0,0.0,0.1499,0.1028,0.9791,0.7182,0.0257,0.16,0.1379,0.3333,0.375,0.2293,0.1231,0.1466,0.0,0.0,reg oper account,block of flats,0.1272,Panel,No,0.0,0.0,0.0,0.0,-671.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n293620,0,Cash loans,F,N,Y,1,90000.0,523597.5,22180.5,468000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-17459,-1101,-1001.0,-1003,,1,1,0,1,0,0,Core staff,3.0,3,3,WEDNESDAY,5,0,0,0,0,0,0,Postal,,0.5308887265507224,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-953.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n153862,0,Revolving loans,F,N,N,0,225000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.019101,-8929,-1196,-6.0,-488,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Mobile,,0.6569512257089027,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n337174,0,Cash loans,F,N,N,0,67500.0,225000.0,20821.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.031329,-14911,-228,-1749.0,-4151,,1,1,1,1,0,0,Sales staff,1.0,2,2,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.16646106222078114,0.6384369635082922,0.4525335592581747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1607.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n452400,0,Cash loans,F,N,Y,0,315000.0,280332.0,22603.5,234000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.014464,-19045,-5416,-10752.0,-2597,,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,4,0,0,0,0,0,0,Postal,0.7134818164369943,0.3874738254979765,0.20092608771597087,0.0639,0.0469,0.9747,0.6532,0.0058,0.0,0.1034,0.2083,0.2083,0.0316,0.0504,0.0499,0.0077,0.0189,0.0651,0.0487,0.9747,0.6668,0.0059,0.0,0.1034,0.2083,0.2083,0.0323,0.0551,0.052000000000000005,0.0078,0.02,0.0645,0.0469,0.9747,0.6578,0.0058,0.0,0.1034,0.2083,0.2083,0.0321,0.0513,0.0508,0.0078,0.0193,reg oper account,block of flats,0.0484,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-638.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n405161,0,Cash loans,F,N,N,0,202500.0,253737.0,26127.0,229500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.016612000000000002,-16931,-1849,-8754.0,-481,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,Construction,,0.7090879219235974,0.5919766183185521,0.0887,0.0089,0.9687,0.5716,0.0087,0.0,0.0345,0.125,0.1667,0.0175,0.0681,0.0391,0.0193,0.0329,0.0903,0.0093,0.9687,0.5884,0.0087,0.0,0.0345,0.125,0.1667,0.0179,0.0744,0.0408,0.0195,0.0349,0.0895,0.0089,0.9687,0.5773,0.0087,0.0,0.0345,0.125,0.1667,0.0178,0.0693,0.0398,0.0194,0.0336,reg oper account,block of flats,0.0427,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1873.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n235205,0,Revolving loans,F,N,Y,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.02461,-22377,-167,-13692.0,-3927,,1,1,0,1,0,0,Managers,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.7658163733118173,0.4938628816996244,0.0577,0.0805,0.9776,,,,0.1379,0.1667,,,,0.0505,0.0734,0.0859,0.0588,0.0835,0.9777,,,,0.1379,0.1667,,,,0.0527,0.0739,0.091,0.0583,0.0805,0.9776,,,,0.1379,0.1667,,,,0.0514,0.0738,0.0877,,block of flats,0.0584,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-261.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n374769,0,Revolving loans,F,N,Y,0,225000.0,495000.0,24750.0,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.04622,-15726,-1539,-4929.0,-4544,,1,1,0,1,0,0,,1.0,1,1,WEDNESDAY,14,0,1,1,0,0,0,Other,0.8169918776301954,0.7562610936276519,0.8027454758994178,0.10099999999999999,0.0469,0.9841,0.7824,0.0606,0.08,0.0345,0.5417,0.5833,0.0,0.0824,0.0937,0.0,0.0,0.1029,0.0487,0.9841,0.7909,0.0611,0.0806,0.0345,0.5417,0.5833,0.0,0.09,0.0977,0.0,0.0,0.102,0.0469,0.9841,0.7853,0.061,0.08,0.0345,0.5417,0.5833,0.0,0.0838,0.0954,0.0,0.0,reg oper account,block of flats,0.1068,Panel,No,0.0,0.0,0.0,0.0,-138.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n143594,0,Cash loans,M,N,Y,0,225000.0,542295.0,31261.5,490500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.04622,-16100,-4124,-1851.0,-4461,,1,1,0,1,0,0,Security staff,1.0,1,1,FRIDAY,10,0,1,1,0,0,0,Security,,0.721897486683045,,0.5711,0.3185,0.9965,0.9524,,0.4,0.1724,0.6667,0.6667,0.0,,0.7201,,0.2474,0.5819,0.3305,0.9965,0.9543,,0.4028,0.1724,0.6667,0.6667,0.0,,0.7503,,0.2619,0.5767,0.3185,0.9965,0.953,,0.4,0.1724,0.6667,0.6667,0.0,,0.7331,,0.2526,not specified,block of flats,0.8122,Monolithic,No,0.0,0.0,0.0,0.0,-11.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,5.0\n197630,0,Cash loans,F,N,Y,0,81000.0,135000.0,13351.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16055,-3686,-4899.0,-4770,,1,1,1,1,0,0,Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Self-employed,0.6255144405374307,0.7204111787185121,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1650.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n310271,1,Cash loans,M,N,Y,0,180000.0,309420.0,15178.5,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-16781,-1661,-6227.0,-335,,1,1,1,1,1,0,Laborers,2.0,2,2,MONDAY,9,0,1,1,0,1,1,Construction,,0.3273005099889225,0.19633396621345675,0.0577,,0.9811,,,0.0,0.1379,0.1667,,,,0.0513,,0.0,0.0588,,0.9811,,,0.0,0.1379,0.1667,,,,0.0534,,0.0,0.0583,,0.9811,,,0.0,0.1379,0.1667,,,,0.0522,,0.0,,block of flats,0.0524,Panel,No,0.0,0.0,0.0,0.0,-831.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n203526,0,Cash loans,M,N,Y,0,315000.0,1741972.5,46080.0,1557000.0,Unaccompanied,Working,Higher education,Widow,Office apartment,0.030755,-19488,-11450,-13459.0,-2966,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,13,0,0,0,0,0,0,University,,0.6334095750116939,0.22133520635446602,0.1495,1.0,0.9578,0.42200000000000004,0.026000000000000002,0.0,0.3103,0.1667,0.2083,0.0571,0.1017,0.1666,0.0927,0.1035,0.1523,1.0,0.9578,0.4446,0.0263,0.0,0.3103,0.1667,0.2083,0.0584,0.1111,0.1736,0.0934,0.1096,0.1509,1.0,0.9578,0.4297,0.0262,0.0,0.3103,0.1667,0.2083,0.057999999999999996,0.1035,0.1696,0.0932,0.1057,reg oper account,block of flats,0.1678,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1803.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n397999,0,Cash loans,F,N,Y,0,225000.0,269550.0,14751.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.0105,-19937,-2261,-11560.0,-3489,,1,1,0,1,1,0,Laborers,2.0,3,3,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 2,,0.2541086859947692,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n180638,0,Cash loans,M,Y,Y,0,166500.0,153576.0,12262.5,121500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-18046,-1460,-1545.0,-1571,5.0,1,1,0,1,0,0,Sales staff,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,Trade: type 3,0.10127755600966132,0.6295216417133035,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-344.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n166950,0,Cash loans,F,N,Y,2,67500.0,104256.0,10683.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-21763,365243,-9303.0,-4974,,1,0,0,1,0,0,,4.0,3,3,FRIDAY,6,0,0,0,0,0,0,XNA,0.14568593492217113,0.6015918214784672,0.6832688314232291,0.0918,0.0951,0.9871,,,,0.2759,0.1667,,0.0494,,,,,0.0935,0.0987,0.9871,,,,0.2759,0.1667,,0.0505,,,,,0.0926,0.0951,0.9871,,,,0.2759,0.1667,,0.0502,,,,,,,0.0781,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-2306.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n156327,0,Cash loans,F,N,Y,1,382500.0,1364593.5,52105.5,1170000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-20055,-1013,-1735.0,-3561,,1,1,0,1,1,0,Accountants,3.0,1,1,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.27584587790716103,0.2445163919946749,0.067,,0.9712,0.6056,,0.0,0.1034,0.1667,0.2083,0.0557,,0.0891,,0.0,0.0683,,0.9712,0.621,,0.0,0.1034,0.1667,0.2083,0.0569,,0.0928,,0.0,0.0677,,0.9712,0.6109,,0.0,0.1034,0.1667,0.2083,0.0566,,0.0907,,0.0,,block of flats,0.0701,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3840.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n345444,0,Cash loans,F,Y,N,0,157500.0,314100.0,21375.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019101,-11571,-270,-1310.0,-1134,3.0,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.26202391712241435,0.20669946565753744,0.2955826421513093,0.1237,0.0855,0.9801,,,0.0,0.069,0.1667,,0.0152,,0.0709,,0.0,0.1261,0.0887,0.9801,,,0.0,0.069,0.1667,,0.0155,,0.0739,,0.0,0.1249,0.0855,0.9801,,,0.0,0.069,0.1667,,0.0154,,0.0722,,0.0,,block of flats,0.0558,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-418.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n343840,0,Cash loans,M,Y,Y,1,225000.0,238500.0,25173.0,238500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-15263,-416,-413.0,-4670,22.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,16,0,1,1,0,1,1,Business Entity Type 3,,0.5099885726985488,0.7352209993926424,0.1284,0.0015,0.9752,,,0.0,0.0862,0.1667,,0.0146,,0.0608,,0.0014,0.0935,0.0011,0.9752,,,0.0,0.069,0.1667,,0.0148,,0.0621,,0.0,0.1296,0.0015,0.9752,,,0.0,0.0862,0.1667,,0.0149,,0.0619,,0.0014,,block of flats,0.0559,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n342458,0,Cash loans,F,N,Y,0,67500.0,495000.0,16488.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-12374,-1810,-2117.0,-3901,,1,1,0,1,0,0,Cooking staff,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Kindergarten,0.4300230783377255,0.4791905057066889,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1506.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n218628,0,Cash loans,F,N,N,0,283500.0,450000.0,35554.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.072508,-17630,-5080,-11648.0,-1193,,1,1,0,1,1,0,Cooking staff,2.0,1,1,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 1,0.4314471045545761,0.5444449322286112,0.5691487713619409,0.1619,0.0756,0.9781,,,0.24,0.1034,0.625,,,,0.1133,,,0.1649,0.0784,0.9782,,,0.2417,0.1034,0.625,,,,0.1175,,,0.1634,0.0756,0.9781,,,0.24,0.1034,0.625,,,,0.1153,,,,block of flats,0.1418,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n430382,1,Cash loans,F,Y,N,1,112500.0,1078200.0,31653.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.031329,-18020,-4598,-4557.0,-1552,9.0,1,1,0,1,0,0,Medicine staff,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Medicine,,0.2622583692422573,0.6109913280868294,0.066,0.0838,0.9767,0.6804,0.0079,0.0,0.1379,0.1667,0.2083,0.0628,0.0538,0.0628,0.0,0.0146,0.0672,0.087,0.9767,0.6929,0.0079,0.0,0.1379,0.1667,0.2083,0.0643,0.0588,0.0654,0.0,0.0155,0.0666,0.0838,0.9767,0.6847,0.0079,0.0,0.1379,0.1667,0.2083,0.0639,0.0547,0.0639,0.0,0.0149,reg oper account,block of flats,0.0568,\"Stone, brick\",No,4.0,1.0,4.0,1.0,-1805.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n320349,0,Cash loans,M,Y,N,0,270000.0,1800000.0,49500.0,1800000.0,\"Spouse, partner\",Commercial associate,Higher education,Civil marriage,House / apartment,0.005002,-11013,-191,-5303.0,-3441,11.0,1,1,0,1,1,0,Managers,2.0,3,3,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6993644018257359,0.537331315438331,,0.0711,0.0,0.9826,0.762,0.0132,0.0,0.1379,0.1667,0.2083,0.0436,0.0572,0.0644,0.0039,0.0133,0.0725,0.0,0.9826,0.7713,0.0133,0.0,0.1379,0.1667,0.2083,0.0446,0.0624,0.0671,0.0039,0.0141,0.0718,0.0,0.9826,0.7652,0.0132,0.0,0.1379,0.1667,0.2083,0.0443,0.0581,0.0656,0.0039,0.0136,org spec account,block of flats,0.0608,Panel,No,2.0,0.0,2.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n290920,0,Cash loans,M,N,Y,0,202500.0,314055.0,17167.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-15331,-933,-461.0,-967,,1,1,0,1,0,0,Laborers,1.0,3,3,TUESDAY,7,0,0,0,0,1,1,Business Entity Type 3,0.34031884284161384,0.6102384975406306,0.5531646987710016,0.066,0.0366,0.9876,0.83,0.0118,0.04,0.0345,0.3333,0.0417,,,0.0477,,0.0,0.0672,0.0379,0.9876,0.8367,0.012,0.0403,0.0345,0.3333,0.0417,,,0.0497,,0.0,0.0666,0.0366,0.9876,0.8323,0.0119,0.04,0.0345,0.3333,0.0417,,,0.0485,,0.0,,block of flats,0.044000000000000004,Panel,No,2.0,0.0,2.0,0.0,-615.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n408953,0,Cash loans,F,Y,Y,0,157500.0,781920.0,25969.5,675000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.006852,-14913,-5018,-1055.0,-4081,10.0,1,1,0,1,0,0,,2.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,Government,,0.19798985814359527,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-37.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,6.0\n255271,0,Revolving loans,M,Y,Y,1,270000.0,720000.0,36000.0,720000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.030755,-17127,-3613,-33.0,-683,11.0,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,Military,0.5879959077482477,0.7105652732082796,,0.2474,0.1157,1.0,0.9932,0.1441,0.32,0.1379,0.625,0.6667,0.2897,0.2017,0.3129,0.0,0.0,0.2521,0.1201,1.0,0.9935,0.1455,0.3222,0.1379,0.625,0.6667,0.2963,0.2204,0.326,0.0,0.0,0.2498,0.1157,1.0,0.9933,0.1451,0.32,0.1379,0.625,0.6667,0.2948,0.2052,0.3185,0.0,0.0,not specified,block of flats,0.3249,Panel,No,0.0,0.0,0.0,0.0,-1648.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n388058,0,Cash loans,M,N,N,1,135000.0,576072.0,21847.5,405000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-12372,-1545,-904.0,-4323,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Government,0.2574520581446945,0.3594642384730246,0.6479768603302221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1914.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n345105,0,Cash loans,F,N,Y,0,135000.0,225000.0,17667.0,225000.0,Family,Working,Incomplete higher,Single / not married,House / apartment,0.020713,-8819,-1182,-1425.0,-1482,,1,1,1,1,0,0,Cooking staff,1.0,3,3,TUESDAY,10,0,0,0,1,1,0,Restaurant,0.2489086165815756,0.43328501155725824,,0.0021,,0.9871,,,,0.069,0.0,,,,,,,0.0021,,0.9871,,,,0.069,0.0,,,,,,,0.0021,,0.9871,,,,0.069,0.0,,,,,,,,block of flats,0.0023,Panel,No,2.0,1.0,2.0,1.0,-1374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n422504,0,Cash loans,F,N,Y,0,135000.0,760122.0,24651.0,544500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.04622,-18660,-588,-5002.0,-2173,,1,1,0,1,1,0,Cleaning staff,1.0,1,1,FRIDAY,13,0,1,1,0,0,0,Other,,0.7267659434422771,,0.0825,,0.9767,,,,,,,,,0.0448,,0.0051,0.084,,0.9767,,,,,,,,,0.0467,,0.0054,0.0833,,0.9767,,,,,,,,,0.0456,,0.0052,,block of flats,0.0533,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1034.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n333710,0,Cash loans,F,N,Y,0,112500.0,592560.0,28638.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.02461,-20964,365243,-4000.0,-4315,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.6253197324761649,0.4794489811780563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1152.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,9.0\n376434,0,Cash loans,F,N,Y,0,157500.0,1515415.5,41800.5,1354500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-13166,-883,-6659.0,-4345,,1,1,0,1,1,0,Medicine staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.6520090088385847,0.7626485483914579,0.5849900404894085,0.066,0.0257,0.9886,0.8436,0.0071,0.0,0.1379,0.1667,0.0417,0.0448,0.0521,0.0679,0.0077,0.0068,0.0672,0.0267,0.9886,0.8497,0.0072,0.0,0.1379,0.1667,0.0417,0.0458,0.0569,0.0708,0.0078,0.0072,0.0666,0.0257,0.9886,0.8457,0.0072,0.0,0.1379,0.1667,0.0417,0.0456,0.053,0.0691,0.0078,0.006999999999999999,reg oper account,block of flats,0.0549,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-460.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n196877,0,Cash loans,F,N,Y,0,90000.0,167895.0,16483.5,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006629,-21780,365243,-8678.0,-4059,,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,0.6827467073493475,0.6554267959315662,0.7490217048463391,0.1144,0.0946,0.9871,0.8232,0.033,0.04,0.1034,0.25,0.0417,0.091,0.0904,0.1105,0.0135,0.0173,0.084,0.0849,0.9866,0.8236,0.0277,0.0,0.069,0.1667,0.0417,0.0832,0.0725,0.0987,0.0039,0.0063,0.1155,0.0946,0.9871,0.8256,0.0332,0.04,0.1034,0.25,0.0417,0.0926,0.0919,0.1125,0.0136,0.0177,reg oper account,block of flats,0.0908,Panel,No,5.0,1.0,5.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n451621,0,Cash loans,M,Y,N,0,270000.0,2156400.0,59301.0,1800000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-13574,-1610,-7321.0,-4913,4.0,1,1,0,1,1,1,Laborers,2.0,1,1,WEDNESDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.7734181091741013,0.5710359978312368,0.7557400501752248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,1.0,9.0,0.0,-442.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n324958,0,Cash loans,F,N,N,0,36900.0,528633.0,21096.0,472500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.014464,-22258,365243,-110.0,-5047,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,0.9118457394403278,0.7218008926606463,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,0.0,-1585.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n230981,0,Cash loans,F,N,Y,0,112500.0,267102.0,21105.0,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-11190,-1335,-3899.0,-2684,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,17,0,0,0,0,1,1,Industry: type 4,,0.3420622784635928,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-471.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n422362,0,Cash loans,M,N,Y,0,58500.0,47970.0,5044.5,45000.0,Unaccompanied,Pensioner,Lower secondary,Widow,House / apartment,0.0105,-24237,365243,-9278.0,-4665,,1,0,0,1,0,0,,1.0,3,3,FRIDAY,9,0,0,0,0,0,0,XNA,,0.15955310194012925,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n386941,1,Cash loans,F,N,Y,0,180000.0,360000.0,28570.5,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-9633,-1777,-4123.0,-1832,,1,1,0,1,0,1,Core staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Restaurant,,0.5423397556233779,0.5100895276257282,0.0399,,0.9752,,,0.0,0.0917,0.125,,,,0.0292,,0.016,0.0084,,0.9717,,,0.0,0.1034,0.1667,,,,0.0087,,0.0,0.05,,0.9767,,,0.0,0.1034,0.1667,,,,0.0287,,0.0028,,block of flats,0.032,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-253.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n109680,0,Revolving loans,F,N,Y,0,135000.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-13946,-3341,-7897.0,-2559,,1,1,0,1,0,0,Sales staff,1.0,3,2,WEDNESDAY,10,0,0,0,0,0,0,Other,0.3191471378754316,0.611241851035802,0.2176285202779586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-632.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n449833,0,Cash loans,F,Y,Y,0,202500.0,891126.0,35469.0,796500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-10826,-1229,-3514.0,-3517,0.0,1,1,0,1,0,0,Managers,1.0,3,3,FRIDAY,5,0,0,0,0,1,1,Business Entity Type 3,0.05796031204854985,0.5222156447517875,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2639.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n243771,1,Cash loans,M,Y,N,2,202500.0,1706728.5,50031.0,1525500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.014464,-13829,-356,-2626.0,-1206,22.0,1,1,1,1,1,0,Laborers,4.0,2,2,SATURDAY,3,0,0,0,0,0,0,Construction,,0.08566017511555732,0.11392239629221085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-73.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n308133,0,Cash loans,F,N,Y,0,157500.0,1350000.0,37255.5,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-17315,-662,-11246.0,-790,,1,1,0,1,1,0,Sales staff,2.0,1,1,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 1,,0.4506223419389232,,0.1567,0.0434,0.9771,0.6872,0.0125,0.08,0.0345,0.5417,0.5833,0.0,0.0824,0.0764,0.0039,0.0026,0.10400000000000001,0.0444,0.9772,0.6994,0.0126,0.0806,0.0345,0.5417,0.5833,0.0,0.09,0.0532,0.0039,0.0,0.1634,0.0429,0.9771,0.6914,0.0125,0.08,0.0345,0.5417,0.5833,0.0,0.0838,0.086,0.0039,0.0,reg oper account,block of flats,0.0645,Panel,No,1.0,0.0,1.0,0.0,-3632.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n203449,0,Cash loans,F,N,Y,0,41400.0,225000.0,8082.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.031329,-23503,365243,-6768.0,-4320,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.3313236938622792,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2283.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n169384,0,Cash loans,M,Y,N,0,135000.0,468000.0,15228.0,468000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020246,-18527,-3155,-8371.0,-2069,17.0,1,1,0,1,0,0,Drivers,2.0,3,3,MONDAY,9,0,0,0,0,0,0,Transport: type 3,,0.5929921351361775,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1118.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n211260,0,Cash loans,M,N,Y,0,112500.0,343800.0,12478.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009334,-23674,365243,-5085.0,-3790,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,XNA,,0.13073580186697265,0.3706496323299817,0.0124,,0.9821,,,0.0,0.1034,0.0417,,0.008,,0.015,,,0.0126,,0.9821,,,0.0,0.1034,0.0417,,0.0081,,0.0157,,,0.0125,,0.9821,,,0.0,0.1034,0.0417,,0.0081,,0.0153,,,,block of flats,0.0133,Wooden,No,0.0,0.0,0.0,0.0,-610.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n136381,0,Cash loans,F,N,Y,0,135000.0,450000.0,29299.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.007114,-11831,-635,-9676.0,-1661,,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.4522343544477716,0.2620553359511834,0.4632753280912678,0.1082,0.0813,0.9871,0.8232,0.0,0.12,0.1034,0.3333,0.0417,0.0926,0.0841,0.1126,0.0193,0.0038,0.1103,0.0844,0.9871,0.8301,0.0,0.1208,0.1034,0.3333,0.0417,0.0947,0.0918,0.1173,0.0195,0.004,0.1093,0.0813,0.9871,0.8256,0.0,0.12,0.1034,0.3333,0.0417,0.0942,0.0855,0.1146,0.0194,0.0039,reg oper account,block of flats,0.0894,Panel,No,0.0,0.0,0.0,0.0,-1053.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n401400,0,Cash loans,F,N,Y,2,157500.0,157500.0,10192.5,157500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006008,-13663,-926,-506.0,-5020,,1,1,0,1,0,0,Core staff,4.0,2,2,FRIDAY,12,0,0,0,0,0,0,Kindergarten,,0.5617614586765913,0.19182160241360605,0.2969,,0.995,0.932,,0.36,0.3103,0.3333,0.375,,0.2421,0.3326,0.0,0.0,0.3025,,0.995,0.9347,,0.3625,0.3103,0.3333,0.375,,0.2645,0.3466,0.0,0.0,0.2998,,0.995,0.9329,,0.36,0.3103,0.3333,0.375,,0.2463,0.3386,0.0,0.0,reg oper spec account,block of flats,0.2728,Panel,No,0.0,0.0,0.0,0.0,-538.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n315456,0,Cash loans,M,Y,Y,0,162000.0,450000.0,24412.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.010147,-10133,-1292,-4765.0,-2819,6.0,1,1,1,1,1,0,Drivers,1.0,2,2,MONDAY,9,0,0,0,1,1,0,Other,,0.19432464532193847,0.4794489811780563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-314.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n387533,0,Cash loans,F,N,N,0,67500.0,106974.0,12825.0,94500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00702,-12759,-1279,-1999.0,-1195,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3083866772277481,0.3111923024346083,0.4974688893052743,0.1237,0.1227,0.9896,0.8572,0.0,0.0,0.3793,0.1667,0.2083,0.0903,0.1009,0.1373,0.0,0.0589,0.1261,0.1274,0.9896,0.8628,0.0,0.0,0.3793,0.1667,0.2083,0.0923,0.1102,0.1431,0.0,0.0623,0.1249,0.1227,0.9896,0.8591,0.0,0.0,0.3793,0.1667,0.2083,0.0919,0.1026,0.1398,0.0,0.0601,reg oper account,block of flats,0.1208,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1690.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n422062,0,Cash loans,F,N,Y,0,135000.0,562491.0,27189.0,454500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-13274,-517,-2185.0,-2354,,1,1,0,1,0,0,Managers,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.5147693455393834,0.6448928305838731,0.4507472818545589,0.16699999999999998,0.1009,0.9886,0.8368,0.0347,0.16,0.1379,0.375,0.4167,0.0473,0.1345,0.1698,0.0077,0.0011,0.1702,0.1047,0.9886,0.8432,0.035,0.1611,0.1379,0.375,0.4167,0.0484,0.1469,0.1769,0.0078,0.0012,0.1686,0.1009,0.9886,0.8390000000000001,0.0349,0.16,0.1379,0.375,0.4167,0.0481,0.1368,0.1729,0.0078,0.0012,reg oper account,block of flats,0.1511,Panel,No,2.0,0.0,2.0,0.0,-1769.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n148787,0,Cash loans,F,Y,Y,0,67500.0,101880.0,9652.5,90000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-21593,-713,-12253.0,-4278,19.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Trade: type 3,,0.6021524966136308,0.7047064232963289,0.0722,0.0734,0.9831,0.7688,0.0073,0.0,0.1379,0.1667,0.2083,0.0526,0.0555,0.0599,0.0154,0.0473,0.0735,0.0762,0.9831,0.7779,0.0074,0.0,0.1379,0.1667,0.2083,0.0538,0.0606,0.0624,0.0156,0.05,0.0729,0.0734,0.9831,0.7719,0.0074,0.0,0.1379,0.1667,0.2083,0.0535,0.0564,0.061,0.0155,0.0483,reg oper account,block of flats,0.0607,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1140.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n257805,0,Cash loans,F,N,Y,0,90000.0,547344.0,17919.0,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019101,-21981,365243,-4987.0,-4989,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.6780006487555701,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1334.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n245580,0,Cash loans,M,Y,N,0,180000.0,379008.0,35203.5,360000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006629,-10691,-2782,-55.0,-1936,13.0,1,1,1,1,1,0,,2.0,2,2,MONDAY,5,0,0,0,0,0,0,Business Entity Type 3,,0.5347534777891141,0.8327850252992314,0.0203,,0.9796,,,0.0,0.0572,0.0417,,,,0.0126,,,0.0126,,0.9767,,,0.0,0.069,0.0417,,,,0.0089,,,0.0125,,0.9801,,,0.0,0.069,0.0417,,,,0.0098,,,,block of flats,0.0067,Wooden,No,0.0,0.0,0.0,0.0,-7.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n191529,0,Cash loans,F,Y,Y,0,315000.0,1223010.0,45454.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-14337,-2298,-1993.0,-4087,6.0,1,1,0,1,0,0,Managers,2.0,2,2,SUNDAY,13,0,0,0,1,0,1,Business Entity Type 3,0.4772611925844846,0.4082418115300484,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-62.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n178667,0,Cash loans,F,N,Y,0,72000.0,225000.0,12564.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-23895,365243,-11013.0,-3481,,1,0,0,1,1,0,,2.0,1,1,SATURDAY,19,0,0,0,0,0,0,XNA,0.8762270471128867,0.6914530693409198,0.7838324335199619,0.1485,0.065,0.9786,0.7076,0.0209,0.16,0.1379,0.3333,0.375,0.1377,0.121,0.1219,0.0,0.0,0.1513,0.0674,0.9786,0.7190000000000001,0.0211,0.1611,0.1379,0.3333,0.375,0.1409,0.1322,0.127,0.0,0.0,0.1499,0.065,0.9786,0.7115,0.021,0.16,0.1379,0.3333,0.375,0.1401,0.1231,0.1241,0.0,0.0,reg oper account,block of flats,0.0959,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1379.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n194317,1,Cash loans,M,Y,N,0,135000.0,550980.0,36949.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00496,-12036,-351,-386.0,-2028,15.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Industry: type 8,0.14682167178786865,0.02553601462754013,0.2721336844147212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-301.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n388928,0,Cash loans,F,N,Y,0,202500.0,493497.0,58698.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-11466,-965,-3773.0,-3769,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Self-employed,0.3634353692520646,0.5398093801106358,0.4596904504249018,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1035.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n326234,0,Revolving loans,F,Y,Y,0,121500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.028663,-19649,-4711,-7129.0,-2899,7.0,1,1,1,1,1,0,Laborers,1.0,2,2,FRIDAY,9,0,0,0,0,1,1,Industry: type 9,,0.6640660387458531,0.6479768603302221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1012.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n407913,0,Cash loans,M,N,N,2,180000.0,152820.0,15111.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15551,-1141,-8104.0,-4388,,1,1,0,1,0,0,Managers,4.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.7329313582723593,0.31703177858344445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1516.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n415291,0,Revolving loans,F,N,Y,2,94500.0,225000.0,11250.0,225000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.0105,-8838,-1967,-3593.0,-1495,,1,1,0,1,0,0,,4.0,3,3,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.01481817210448294,0.4014074137749511,0.034,0.0589,0.9896,0.8572,0.0042,0.0,0.1034,0.0833,0.125,0.0451,0.0277,0.0342,0.0,0.0,0.0347,0.0611,0.9896,0.8628,0.0043,0.0,0.1034,0.0833,0.125,0.0461,0.0303,0.0357,0.0,0.0,0.0344,0.0589,0.9896,0.8591,0.0042,0.0,0.1034,0.0833,0.125,0.0459,0.0282,0.0349,0.0,0.0,reg oper account,block of flats,0.0269,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n425848,0,Cash loans,F,Y,Y,0,67500.0,338314.5,19021.5,306000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008068,-22817,365243,-5224.0,-1231,16.0,1,0,0,1,0,0,,2.0,3,3,SUNDAY,7,0,0,0,0,0,0,XNA,,0.31378559982326043,0.7165702448010511,0.0021,,,,,,,0.0,,,,0.0015,,0.0,0.0021,,,,,,,0.0,,,,0.0016,,0.0,0.0021,,,,,,,0.0,,,,0.0015,,0.0,,block of flats,0.0012,,No,2.0,0.0,2.0,0.0,-649.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n392788,0,Cash loans,F,Y,Y,2,81000.0,454500.0,21865.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005084,-12739,-3900,-6781.0,-4666,14.0,1,1,1,1,0,0,Laborers,4.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Industry: type 7,0.4964427662091713,0.6472579586086145,0.6879328378491735,0.1515,0.1247,0.9806,0.7348,0.0821,0.0,0.069,0.1667,0.2083,0.0562,0.1236,0.0585,0.0116,0.0081,0.1544,0.1294,0.9806,0.7452,0.0829,0.0,0.069,0.1667,0.2083,0.0574,0.135,0.061,0.0117,0.0086,0.153,0.1247,0.9806,0.7383,0.0827,0.0,0.069,0.1667,0.2083,0.0571,0.1257,0.0596,0.0116,0.0083,reg oper spec account,block of flats,0.0927,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1656.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n113451,0,Cash loans,F,N,Y,0,157500.0,592560.0,31023.0,450000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.02461,-15855,-1361,-3147.0,-4559,,1,1,0,1,1,0,Sales staff,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3614315882515917,0.7286724265219101,0.5849900404894085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-752.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n339139,0,Cash loans,F,N,N,1,270000.0,808650.0,23773.5,675000.0,Family,State servant,Higher education,Separated,House / apartment,0.018801,-14398,-1781,-636.0,-3867,,1,1,0,1,0,0,Core staff,2.0,2,2,SUNDAY,12,0,0,0,1,1,0,Government,0.586526106240516,0.5242394831689787,0.28812959991785075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2325.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,0.0\n208834,0,Cash loans,F,N,Y,0,135000.0,1350000.0,35743.5,1350000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,With parents,0.018209,-8481,-331,-1287.0,-55,,1,1,0,1,1,0,Core staff,2.0,3,3,THURSDAY,8,0,0,0,1,1,0,Self-employed,0.5164472145262122,0.4947647888795022,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-138.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n311227,0,Cash loans,F,N,N,0,90000.0,298512.0,29524.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.007120000000000001,-18965,-4906,-9520.0,-2508,,1,1,0,1,0,0,Core staff,1.0,2,2,THURSDAY,7,0,0,0,0,0,0,Transport: type 2,0.4806539519421438,0.7068952967027645,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1526.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n217187,0,Cash loans,F,N,N,0,166500.0,976135.5,54634.5,904500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-19138,-3008,-10032.0,-2692,,1,1,1,1,0,0,Sales staff,2.0,3,3,WEDNESDAY,8,0,0,0,0,0,0,Self-employed,0.5155685848787238,0.33345396160023955,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1956.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n406714,0,Cash loans,M,N,Y,0,112500.0,148365.0,11781.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009334,-9718,-1786,-1882.0,-1872,,1,1,1,1,1,0,Laborers,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,Other,,0.6362028604453799,0.2340151665320674,0.1203,0.0,0.9806,0.7348,0.0008,0.12,0.1148,0.2358,0.2775,0.0033,0.0981,0.1111,0.0,0.0,0.0084,0.0,0.9846,0.7975,0.0008,0.0,0.0345,0.3333,0.375,0.0034,0.0073,0.0068,0.0,0.0,0.1218,0.0,0.9846,0.792,0.0008,0.12,0.1034,0.3333,0.375,0.0034,0.1,0.1126,0.0,0.0,org spec account,block of flats,0.1699,Panel,No,1.0,0.0,1.0,0.0,-1557.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n275480,0,Cash loans,M,Y,Y,0,225000.0,563341.5,44640.0,522000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010643,-12040,-2315,-962.0,-4259,2.0,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,,0.6957471002419962,0.8128226070575616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1822.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n320163,0,Cash loans,F,N,Y,0,292500.0,1575000.0,54877.5,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-22581,-1731,-11794.0,-4852,,1,1,0,1,1,0,Secretaries,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,School,0.9238054059685528,0.6782631646168626,0.7407990879702335,0.033,0.0423,0.9742,0.6464,0.0359,0.0,0.069,0.125,0.1667,0.0198,0.0269,0.017,0.0,0.0,0.0336,0.0439,0.9742,0.6602,0.0362,0.0,0.069,0.125,0.1667,0.0203,0.0294,0.0177,0.0,0.0,0.0333,0.0423,0.9742,0.6511,0.0361,0.0,0.069,0.125,0.1667,0.0201,0.0274,0.0173,0.0,0.0,reg oper account,block of flats,0.0196,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-864.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n100377,0,Cash loans,M,N,N,2,157500.0,239850.0,25447.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009549,-14252,-1281,-6024.0,-3427,,1,1,1,1,0,0,High skill tech staff,4.0,2,2,TUESDAY,10,0,0,0,1,0,1,Business Entity Type 3,0.6420731750249923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-344.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n292468,0,Cash loans,M,N,N,0,157500.0,706221.0,22909.5,589500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006207,-13705,-4543,-2650.0,-4352,,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Emergency,,0.6758485794289937,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2662.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n175581,0,Cash loans,F,Y,N,0,90000.0,157500.0,8541.0,157500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.01885,-12266,-165,-1199.0,-3725,8.0,1,1,0,1,0,0,Accountants,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Self-employed,,0.34251026865560763,0.3108182544189319,0.1856,0.1184,0.9861,0.8096,0.0432,0.2,0.1724,0.3333,0.375,,0.1513,0.2,0.0,0.0,0.1891,0.1229,0.9861,0.8171,0.0435,0.2014,0.1724,0.3333,0.375,,0.1653,0.2081,0.0,0.0,0.1874,0.1184,0.9861,0.8121,0.0434,0.2,0.1724,0.3333,0.375,,0.1539,0.2036,0.0,0.0,reg oper account,block of flats,0.1807,Panel,No,3.0,1.0,3.0,0.0,-349.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n221346,0,Cash loans,M,N,Y,0,63000.0,254700.0,14751.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-23953,365243,-12948.0,-4677,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.6151111756629916,0.33285056416487313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1664.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,9.0\n233032,0,Cash loans,F,N,Y,1,121500.0,675000.0,32602.5,675000.0,Family,Working,Higher education,Married,House / apartment,0.006670999999999999,-15983,-6462,-6084.0,-2397,,1,1,1,1,1,0,Cooking staff,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Military,0.7115389627631664,0.6979967109712157,0.3996756156233169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1089.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n455163,0,Cash loans,F,N,Y,1,180000.0,862560.0,25348.5,720000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010966,-18215,-5469,-5639.0,-1424,,1,1,0,1,0,0,Core staff,3.0,2,2,FRIDAY,14,0,0,0,0,0,0,School,0.6325830430232281,0.6456836677211093,0.4830501881366946,0.1474,0.0983,0.9811,0.7416,0.0182,0.16,0.1379,0.3333,0.0417,0.0923,0.1084,0.1337,0.0541,0.0627,0.1502,0.102,0.9811,0.7517,0.0184,0.1611,0.1379,0.3333,0.0417,0.0944,0.1185,0.1352,0.0545,0.0664,0.1489,0.0983,0.9811,0.7451,0.0183,0.16,0.1379,0.3333,0.0417,0.0939,0.1103,0.1361,0.0543,0.064,org spec account,block of flats,0.1256,Panel,No,0.0,0.0,0.0,0.0,-420.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n267189,0,Revolving loans,F,Y,Y,0,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-8674,-684,-3424.0,-780,24.0,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Mobile,,0.22505431392164046,0.3092753558842053,0.0165,,0.9732,,,0.0,0.069,0.0417,,0.0299,,0.0112,,0.0,0.0168,,0.9732,,,0.0,0.069,0.0417,,0.0306,,0.0117,,0.0,0.0167,,0.9732,,,0.0,0.069,0.0417,,0.0304,,0.0114,,0.0,,block of flats,0.0088,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-489.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n148842,0,Cash loans,F,N,Y,0,157500.0,704844.0,29862.0,630000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.0105,-15618,-816,-896.0,-49,,1,1,0,1,0,0,Sales staff,2.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.3136524225220223,0.6401161833252154,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1134.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n213670,1,Cash loans,M,Y,Y,0,81000.0,198666.0,13275.0,175500.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.016612000000000002,-8691,-1044,-8684.0,-1367,13.0,1,1,1,1,1,0,Drivers,1.0,2,2,THURSDAY,11,0,0,0,1,1,0,Security,,0.029397977631754495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-645.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n206043,0,Cash loans,F,Y,Y,0,202500.0,1350000.0,37125.0,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010556,-16271,-7489,-7608.0,-3436,27.0,1,1,0,1,1,0,High skill tech staff,2.0,3,3,MONDAY,15,0,0,0,0,0,0,Telecom,,0.4404722482609634,0.18088797776707397,0.0495,0.0461,0.9816,0.7484,0.0047,0.04,0.0345,0.3333,0.2083,0.0221,0.0403,0.0418,0.0,0.0,0.0504,0.0478,0.9816,0.7583,0.0047,0.0403,0.0345,0.3333,0.2083,0.0226,0.0441,0.0435,0.0,0.0,0.05,0.0461,0.9816,0.7518,0.0047,0.04,0.0345,0.3333,0.2083,0.0225,0.040999999999999995,0.0425,0.0,0.0,reg oper account,block of flats,0.0427,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1982.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n377078,0,Cash loans,F,N,N,0,193500.0,1125000.0,104107.5,1125000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.04622,-17508,-511,-1523.0,-1054,,1,1,0,1,0,0,Core staff,2.0,1,1,FRIDAY,13,0,0,0,0,1,1,Government,0.9112220269184816,0.7209790115458641,0.7958031328748403,0.2206,,0.9786,,,,0.1379,0.3333,,,,,,,0.2248,,0.9786,,,,0.1379,0.3333,,,,,,,0.2228,,0.9786,,,,0.1379,0.3333,,,,,,,,block of flats,0.1896,Panel,No,0.0,0.0,0.0,0.0,-1948.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n193268,0,Cash loans,F,Y,Y,0,157500.0,341280.0,21937.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-18284,-1874,-9909.0,-1836,17.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Other,0.3086638896245969,0.26365696495669394,0.3139166772114369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,9.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n339244,0,Cash loans,M,Y,Y,0,180000.0,1078200.0,31522.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-11608,-563,-5551.0,-3669,10.0,1,1,1,1,1,0,Laborers,1.0,2,2,THURSDAY,6,0,0,0,0,1,1,Construction,0.2814166392681885,0.6047009492104852,0.5762088360175724,0.0619,,0.9876,,,,0.1379,0.1667,,0.0141,,0.0642,,0.0314,0.063,,0.9876,,,,0.1379,0.1667,,0.0144,,0.0669,,0.0333,0.0625,,0.9876,,,,0.1379,0.1667,,0.0143,,0.0654,,0.0321,,block of flats,0.0574,Panel,No,0.0,0.0,0.0,0.0,-1664.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n254199,0,Cash loans,F,N,N,0,144000.0,433188.0,12042.0,283500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-19922,-9018,-293.0,-3123,,1,1,0,1,1,0,High skill tech staff,2.0,2,2,TUESDAY,19,0,0,0,0,0,0,Business Entity Type 2,,0.630240460872275,0.5190973382084597,0.2388,0.1008,0.9985,0.9864,0.0736,0.192,0.0828,0.6917,0.6667,0.0649,0.1807,0.2739,0.0097,0.1059,0.1071,0.0482,0.9985,0.9869,0.0624,0.0806,0.0345,0.625,0.6667,0.0159,0.1644,0.1358,0.0078,0.0307,0.1884,0.0678,0.9990000000000001,0.9866,0.0741,0.16,0.069,0.7083,0.6667,0.0257,0.1838,0.1908,0.0097,0.0677,reg oper account,block of flats,0.1849,Mixed,No,2.0,0.0,2.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n384588,0,Cash loans,M,Y,N,1,180000.0,1319269.5,36409.5,1152000.0,Unaccompanied,State servant,Academic degree,Married,House / apartment,0.035792000000000004,-13574,-2440,-7368.0,-3937,19.0,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Police,,0.5331209662931404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2058.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n244698,0,Cash loans,F,N,N,0,81000.0,291384.0,18837.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.020246,-20216,-2946,-2714.0,-3578,,1,1,0,1,0,0,Laborers,1.0,3,3,FRIDAY,9,0,0,0,0,0,0,Self-employed,,0.13740123005799315,0.6817058776720116,0.0722,0.0703,0.9801,0.728,0.0305,0.0,0.1379,0.1667,0.2083,0.0295,0.0588,0.0452,0.0039,0.0768,0.0735,0.0729,0.9801,0.7387,0.0308,0.0,0.1379,0.1667,0.2083,0.0302,0.0643,0.0471,0.0039,0.0813,0.0729,0.0703,0.9801,0.7316,0.0307,0.0,0.1379,0.1667,0.2083,0.03,0.0599,0.046,0.0039,0.0784,reg oper spec account,block of flats,0.0522,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2260.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n382443,0,Cash loans,M,Y,N,0,412308.0,1800000.0,45504.0,1800000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Office apartment,0.035792000000000004,-15111,-1486,-1169.0,-4109,2.0,1,1,1,1,0,0,Managers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Security Ministries,,0.4383660750821625,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-356.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n215776,1,Cash loans,F,N,Y,3,315000.0,1096020.0,56092.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-14217,-2190,-2956.0,-3220,,1,1,0,1,0,0,Sales staff,5.0,2,2,THURSDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.28584202578183243,0.5444883214865099,0.1510075350878296,0.0021,,0.9722,,,0.0,0.069,,,,,0.0769,,,0.0021,,0.9722,,,0.0,0.069,,,,,0.0801,,,0.0021,,0.9722,,,0.0,0.069,,,,,0.0783,,,,block of flats,0.0009,Wooden,No,0.0,0.0,0.0,0.0,-707.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n455720,0,Cash loans,M,N,Y,2,90000.0,414000.0,20133.0,414000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-12197,-1423,-5359.0,-4486,,1,1,1,1,1,0,Laborers,4.0,2,2,MONDAY,8,0,0,0,0,0,0,Government,,0.06817197143512119,0.4722533429586386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,0.0,-494.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n362625,0,Cash loans,F,Y,Y,0,225000.0,417024.0,25330.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002042,-16815,-523,-1326.0,-353,13.0,1,1,0,1,0,0,Sales staff,2.0,3,3,FRIDAY,12,0,0,0,0,0,0,Self-employed,0.535163499386852,0.3059715359768996,0.7530673920730478,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-879.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n438638,0,Revolving loans,F,N,Y,0,67500.0,135000.0,6750.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-22197,365243,-8697.0,-122,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.31490261818251625,0.7435593141311444,0.0557,0.0261,0.9786,,,0.04,0.0345,0.3333,,0.0382,,0.0466,,0.0,0.0567,0.0271,0.9786,,,0.0403,0.0345,0.3333,,0.039,,0.0486,,0.0,0.0562,0.0261,0.9786,,,0.04,0.0345,0.3333,,0.0388,,0.0475,,0.0,,block of flats,0.0367,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2295.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n379982,0,Cash loans,F,N,N,0,112500.0,595903.5,30555.0,481500.0,Other_A,Pensioner,Secondary / secondary special,Widow,House / apartment,0.004849,-20094,365243,-10741.0,-3624,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,XNA,,0.4786775297704238,0.7662336700704004,0.0872,0.0698,0.9856,0.7959999999999999,0.012,0.07200000000000001,0.0621,0.3417,0.2638,0.1338,0.0602,0.0772,0.0019,0.05,0.0756,0.0549,0.9831,0.7779,0.0109,0.0403,0.0345,0.3333,0.375,0.0835,0.0661,0.0382,0.0,0.0,0.0749,0.0595,0.9851,0.7987,0.0121,0.04,0.0345,0.3333,0.375,0.1015,0.0616,0.0661,0.0019,0.0,reg oper account,block of flats,0.1685,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1506.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n221433,0,Cash loans,F,Y,Y,0,157500.0,439740.0,23985.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17696,-4718,-8344.0,-1230,30.0,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Trade: type 7,0.6438122512776171,0.44072017579181694,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-471.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n347395,0,Cash loans,F,N,Y,0,148500.0,351792.0,15034.5,252000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.005002,-17937,-7905,-9786.0,-1437,,1,1,0,1,0,1,Core staff,2.0,3,3,FRIDAY,16,0,0,0,0,0,0,School,,0.7869905751224762,0.6479768603302221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1737.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n381841,0,Cash loans,M,Y,N,0,225000.0,508495.5,35518.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.020713,-15440,-241,-2367.0,-4590,17.0,1,1,0,1,0,0,Drivers,2.0,3,3,TUESDAY,12,0,0,0,1,1,0,Industry: type 9,0.3386360375563354,0.5298266121328677,0.520897599048938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n232230,0,Cash loans,F,Y,N,0,135000.0,639000.0,22950.0,639000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-11215,-1877,-2211.0,-2961,4.0,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.6565667219878174,0.31547215492577346,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-356.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n349852,0,Cash loans,F,Y,Y,0,180000.0,629320.5,32130.0,508500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-17628,-537,-6713.0,-1170,3.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Medicine,0.8028961701168509,0.5513403251085309,0.3740208032583212,0.1237,0.0,0.9906,,,0.12,0.1034,0.375,,,,,,,0.1261,0.0,0.9906,,,0.1208,0.1034,0.375,,,,,,,0.1249,0.0,0.9906,,,0.12,0.1034,0.375,,,,,,,,block of flats,0.0567,Panel,No,1.0,0.0,0.0,0.0,-948.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n333232,0,Cash loans,F,N,Y,0,135000.0,254700.0,14751.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-24337,-2686,-14035.0,-4971,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Housing,,0.7304887481322392,0.4722533429586386,0.1155,0.0793,0.9801,0.728,0.0255,0.08,0.1607,0.2221,0.2638,0.0819,0.0941,0.1077,0.0013,0.0017,0.063,0.0537,0.9801,0.7387,0.01,0.0,0.1379,0.1667,0.2083,0.0519,0.0551,0.053,0.0,0.0,0.0625,0.0535,0.9801,0.7316,0.0103,0.0,0.1379,0.1667,0.2083,0.057,0.0513,0.0524,0.0,0.0,reg oper account,block of flats,0.1749,Panel,No,1.0,0.0,1.0,0.0,-210.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n248918,1,Cash loans,F,N,N,0,54000.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-15437,-958,-8792.0,-4409,,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.3823122575990892,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n430900,0,Cash loans,F,N,Y,0,112500.0,1006920.0,42790.5,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006207,-21372,365243,-4302.0,-3500,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,0.6322727594688438,0.6845767368864439,0.7151031019926098,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-2880.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n151754,0,Cash loans,F,N,Y,0,67500.0,332946.0,17127.0,238500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.022625,-19463,-2416,-11106.0,-2641,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.7747384867174754,0.7338145369642702,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-2431.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,3.0\n145579,0,Cash loans,F,N,Y,2,112500.0,1082214.0,28678.5,945000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-15107,-2716,-4417.0,-5379,,1,1,0,1,1,0,Private service staff,4.0,2,2,FRIDAY,12,0,0,0,0,0,0,Self-employed,,0.3420081448615342,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,2.0,0.0,2.0,0.0,-1498.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n173782,0,Cash loans,F,Y,Y,3,67500.0,299772.0,14580.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-11112,-947,-3719.0,-2681,40.0,1,1,1,1,0,0,,5.0,2,2,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.4183796892795736,0.4539159117316768,0.14287252304131962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,4.0,5.0,3.0,-448.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n251791,0,Revolving loans,F,Y,Y,0,450000.0,900000.0,45000.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.026392,-21864,-2155,-8400.0,-4445,64.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,FRIDAY,15,0,0,0,0,1,1,Construction,0.9154459994874318,0.39146199301715656,0.4311917977993083,0.034,0.0786,0.9921,,,0.0,0.1034,0.0833,,0.0337,,0.0367,,0.0,0.0347,0.0816,0.9921,,,0.0,0.1034,0.0833,,0.0344,,0.0383,,0.0,0.0344,0.0786,0.9921,,,0.0,0.1034,0.0833,,0.0342,,0.0374,,0.0,,block of flats,0.0289,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1098.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n430043,0,Cash loans,F,N,Y,0,112500.0,225000.0,13045.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-19768,365243,-5240.0,-3301,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.6841657875525505,0.4614823912998385,0.6588,0.4569,0.9791,,,0.72,0.6207,0.3333,,0.4552,,0.6258,,0.0234,0.6712,0.4742,0.9791,,,0.725,0.6207,0.3333,,0.4656,,0.652,,0.0248,0.6651,0.4569,0.9791,,,0.72,0.6207,0.3333,,0.4631,,0.6371,,0.0239,,block of flats,0.4973,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n436141,0,Cash loans,F,N,Y,0,202500.0,675000.0,64219.5,675000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.010006,-18825,-2302,-8034.0,-2363,,1,1,0,1,0,0,Core staff,1.0,2,1,SATURDAY,9,0,0,0,0,0,0,Government,,0.630479939151501,0.2636468134452008,0.2969,0.376,0.9856,0.8028,0.0731,0.0,0.6897,0.1667,0.2083,0.2313,0.2396,0.3432,0.0116,0.0535,0.3025,0.3902,0.9856,0.8105,0.0738,0.0,0.6897,0.1667,0.2083,0.2366,0.2617,0.3576,0.0117,0.0566,0.2998,0.376,0.9856,0.8054,0.0736,0.0,0.6897,0.1667,0.2083,0.2353,0.2437,0.3494,0.0116,0.0546,reg oper account,block of flats,0.3282,Panel,No,0.0,0.0,0.0,0.0,-1869.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n113620,0,Cash loans,F,N,Y,0,135000.0,508495.5,22527.0,454500.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.035792000000000004,-21943,365243,-7766.0,-4607,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.7047430827221114,0.4794489811780563,0.057,0.0571,0.9826,0.762,0.0145,0.0,0.1241,0.1667,0.2083,0.0317,0.0479,0.0531,0.0,0.0147,0.0735,0.0458,0.9791,0.7256,0.0079,0.0,0.1379,0.1667,0.2083,0.0155,0.0643,0.0462,0.0,0.0,0.0578,0.0552,0.9791,0.7182,0.0091,0.0,0.1379,0.1667,0.2083,0.0344,0.0547,0.0512,0.0,0.0,reg oper account,block of flats,0.0348,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-470.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n164535,1,Cash loans,M,N,Y,0,135000.0,550980.0,43659.0,450000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-8249,-484,-5085.0,-361,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Housing,,0.14810645559046165,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-256.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n169180,0,Cash loans,M,N,Y,0,180000.0,1350000.0,47056.5,1350000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-23170,365243,-12535.0,-4120,,1,0,0,1,0,0,,2.0,3,3,MONDAY,15,0,0,0,0,0,0,XNA,,0.026177114672350985,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n248995,0,Revolving loans,F,N,Y,0,112500.0,247500.0,12375.0,247500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.0228,-21304,365243,-13986.0,-4209,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.2530656310243799,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-408.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n204034,0,Revolving loans,M,N,Y,0,157500.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-12867,-982,-6780.0,-4043,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.14504789320265893,0.429707056517204,0.4507472818545589,0.0907,0.0804,0.9811,,,0.0,0.2069,0.1667,,0.0567,,0.0851,,0.0519,0.0924,0.0835,0.9811,,,0.0,0.2069,0.1667,,0.057999999999999996,,0.0887,,0.0549,0.0916,0.0804,0.9811,,,0.0,0.2069,0.1667,,0.0577,,0.0867,,0.053,,block of flats,0.0989,Panel,No,1.0,1.0,1.0,1.0,-148.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n161003,0,Cash loans,M,N,N,0,126000.0,755190.0,35122.5,675000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.016612000000000002,-23208,365243,-6826.0,-4208,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,13,0,0,0,1,0,0,XNA,,0.3569054502913684,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1015.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n175512,0,Revolving loans,F,N,N,2,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13438,-4017,-4134.0,-4149,,1,1,0,1,0,0,Accountants,4.0,2,2,SUNDAY,11,0,0,0,0,0,0,Government,,0.5603774270034454,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-2456.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n380740,0,Cash loans,M,N,Y,1,247500.0,178038.0,13905.0,162000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-10587,-3625,-4593.0,-3046,,1,1,0,1,0,0,Core staff,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Police,0.6660075602302236,0.6100408357747829,,0.0639,0.0507,0.9841,0.7824,0.0041,0.04,0.0345,0.3333,0.375,0.0529,0.0521,0.0639,0.0039,0.001,0.0651,0.0526,0.9841,0.7909,0.0041,0.0403,0.0345,0.3333,0.375,0.0541,0.0569,0.0666,0.0039,0.001,0.0645,0.0507,0.9841,0.7853,0.0041,0.04,0.0345,0.3333,0.375,0.0538,0.053,0.065,0.0039,0.001,reg oper spec account,block of flats,0.0572,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-440.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n365947,0,Cash loans,F,N,N,1,247500.0,601470.0,30708.0,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.018029,-11209,-1771,-3233.0,-3700,,1,1,0,1,0,0,Sales staff,2.0,3,3,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.05269315741291127,0.3441550073724169,0.0732,,0.9856,,,0.08,0.069,0.3333,,,,0.0807,,0.0,0.0746,,0.9856,,,0.0806,0.069,0.3333,,,,0.084,,0.0,0.0739,,0.9856,,,0.08,0.069,0.3333,,,,0.0821,,0.0,,block of flats,0.0634,Panel,No,5.0,0.0,5.0,0.0,-421.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n279836,0,Cash loans,F,N,Y,0,90000.0,679500.0,26946.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-21625,-598,-7921.0,-4481,,1,1,0,1,0,0,Security staff,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Other,0.7893092710789389,0.6908275612835464,0.40314167665875134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-448.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n375268,0,Cash loans,F,N,Y,0,81000.0,134775.0,10206.0,112500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018634,-21651,-1778,-14129.0,-5181,,1,1,1,1,0,0,Laborers,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Trade: type 7,0.35542503137320913,0.4082576599656222,0.6785676886853644,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-873.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n288311,0,Cash loans,F,N,Y,1,252000.0,315000.0,18211.5,315000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.0228,-12638,-4134,-2385.0,-2372,,1,1,0,1,1,0,Managers,3.0,2,2,MONDAY,9,1,1,0,1,1,0,Other,0.6872404059903682,0.6722307957388698,0.6642482627052363,0.0969,0.1087,0.9965,0.9524,0.0225,0.0,0.1724,0.1667,0.2083,0.0685,0.0773,0.0784,0.0077,0.0113,0.0987,0.1128,0.9965,0.9543,0.0227,0.0,0.1724,0.1667,0.2083,0.0701,0.0845,0.0817,0.0078,0.012,0.0978,0.1087,0.9965,0.953,0.0226,0.0,0.1724,0.1667,0.2083,0.0697,0.0787,0.0798,0.0078,0.0115,reg oper account,block of flats,0.0792,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1390.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n355899,0,Revolving loans,F,Y,Y,0,180000.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.018801,-8581,-540,-7008.0,-1243,3.0,1,1,1,1,1,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Trade: type 7,0.25846388565041306,0.7048886686561411,,0.1237,0.0659,0.9886,0.8436,0.0471,0.12,0.1034,0.375,0.4167,0.075,0.1009,0.1283,0.0,0.0,0.1261,0.0684,0.9886,0.8497,0.0476,0.1208,0.1034,0.375,0.4167,0.0767,0.1102,0.1337,0.0,0.0,0.1249,0.0659,0.9886,0.8457,0.0474,0.12,0.1034,0.375,0.4167,0.0763,0.1026,0.1306,0.0,0.0,reg oper spec account,block of flats,0.1267,Panel,No,0.0,0.0,0.0,0.0,-433.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n332625,0,Cash loans,M,N,Y,0,135000.0,760225.5,32337.0,679500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-20442,-4497,-2611.0,-3948,,1,1,0,1,0,0,Security staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Culture,0.14976117546609385,0.6522493144668556,0.6817058776720116,0.0711,0.0563,0.9757,,,0.0,0.1379,0.1667,,0.0555,,0.0523,,0.0,0.0725,0.0584,0.9757,,,0.0,0.1379,0.1667,,0.0567,,0.0544,,0.0,0.0718,0.0563,0.9757,,,0.0,0.1379,0.1667,,0.0564,,0.0532,,0.0,,block of flats,0.0411,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-932.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n244977,0,Cash loans,F,N,Y,0,112500.0,536917.5,30109.5,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006629,-22734,365243,-1448.0,-4072,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,5,0,0,0,0,0,0,XNA,,0.3930832710091585,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1152.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n196267,0,Cash loans,F,Y,N,0,166500.0,1078200.0,31522.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-10057,-1530,-4730.0,-2739,7.0,1,1,1,1,1,0,Accountants,2.0,1,1,SATURDAY,18,0,0,0,0,0,0,Bank,0.3784956429460004,0.6649386877826894,0.38079968264891495,0.1237,0.0846,0.9911,0.8776,0.0281,0.12,0.1034,0.375,0.4167,0.0931,0.1009,0.1245,0.0,0.0,0.1261,0.0878,0.9911,0.8824,0.0283,0.1208,0.1034,0.375,0.4167,0.0952,0.1102,0.1298,0.0,0.0,0.1249,0.0846,0.9911,0.8792,0.0282,0.12,0.1034,0.375,0.4167,0.0947,0.1026,0.1268,0.0,0.0,reg oper account,block of flats,0.1133,Panel,No,0.0,0.0,0.0,0.0,-670.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n146455,0,Cash loans,F,Y,Y,0,112500.0,486265.5,32517.0,459000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.035792000000000004,-22949,365243,-2377.0,-5352,6.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,,0.4229043442739037,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-393.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n315570,0,Cash loans,F,N,Y,0,157500.0,948096.0,34182.0,720000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-13919,-1345,-7357.0,-4132,,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,School,,0.5702269698550578,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-967.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n169219,0,Cash loans,F,Y,N,0,112500.0,343800.0,13090.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-15816,-605,-744.0,-3858,7.0,1,1,1,1,1,0,Sales staff,2.0,2,2,TUESDAY,18,0,0,0,0,1,1,Business Entity Type 3,,0.693418363083505,0.42765737003502935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-936.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n160380,0,Cash loans,M,N,Y,0,112500.0,286704.0,23116.5,247500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.010276,-22941,365243,-14412.0,-4217,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.662512982842065,0.7476633896301825,0.0722,0.0811,0.9742,0.6464,0.0813,0.0,0.1379,0.1667,0.2083,0.0255,0.0555,0.0518,0.0154,0.0168,0.0735,0.0841,0.9742,0.6602,0.08199999999999999,0.0,0.1379,0.1667,0.2083,0.026000000000000002,0.0606,0.054000000000000006,0.0156,0.0178,0.0729,0.0811,0.9742,0.6511,0.0818,0.0,0.1379,0.1667,0.2083,0.0259,0.0564,0.0528,0.0155,0.0172,reg oper account,block of flats,0.0444,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n372820,0,Cash loans,M,Y,N,2,900000.0,1800000.0,49630.5,1800000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-14509,-5368,-1907.0,-4452,6.0,1,1,0,1,1,1,Managers,4.0,2,1,MONDAY,9,0,0,0,0,0,0,Transport: type 4,0.2559677946669344,0.7580784230271915,,0.23199999999999998,0.228,0.9965,0.9456,0.6568,0.4,0.1724,0.625,0.0417,0.0483,0.1673,0.34600000000000003,0.1004,0.1258,0.2363,0.2366,0.9965,0.9477,0.6628,0.4028,0.1724,0.625,0.0417,0.0494,0.1827,0.3605,0.1012,0.1332,0.2342,0.228,0.9965,0.9463,0.6609999999999999,0.4,0.1724,0.625,0.0417,0.0491,0.1702,0.3522,0.1009,0.1285,reg oper account,block of flats,0.3591,Monolithic,No,1.0,0.0,1.0,0.0,-1973.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n432103,0,Cash loans,F,N,Y,0,315000.0,1218118.5,48438.0,1120500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018029,-15536,-1559,-1309.0,-2173,,1,1,0,1,0,0,Laborers,1.0,3,3,FRIDAY,8,0,0,0,0,0,0,Government,,0.6057876825872915,0.30162489168411943,0.1046,,0.9841,,,0.0,0.1379,0.1667,,,,0.0523,,0.0,0.062,,0.9826,,,0.0,0.1379,0.1667,,,,0.0545,,0.0,0.1057,,0.9841,,,0.0,0.1379,0.1667,,,,0.0533,,0.0,,block of flats,0.0412,Panel,No,3.0,0.0,3.0,0.0,-831.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n427607,0,Revolving loans,F,N,Y,1,112500.0,180000.0,9000.0,180000.0,Family,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.006852,-12389,-2083,-3763.0,-4562,,1,1,0,1,1,0,Sales staff,2.0,3,3,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.5552421083958153,0.3982807668937485,0.7076993447402619,0.0711,0.0881,0.9826,0.762,0.0951,0.0,0.1379,0.1667,0.0208,0.0511,0.057999999999999996,0.0723,0.0,0.0485,0.063,0.0901,0.9816,0.7583,0.0331,0.0,0.1379,0.1667,0.0,0.0157,0.0551,0.0682,0.0,0.0025,0.0718,0.0881,0.9826,0.7652,0.0957,0.0,0.1379,0.1667,0.0208,0.052000000000000005,0.059000000000000004,0.0736,0.0,0.0495,reg oper spec account,block of flats,0.0576,\"Stone, brick\",No,5.0,0.0,4.0,0.0,-1231.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n172112,1,Cash loans,F,N,Y,0,112500.0,540000.0,39424.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-13317,-3801,-503.0,-4808,,1,1,1,1,0,0,Laborers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 2,,0.6183159501678289,0.3001077565791181,0.2031,,0.9866,,,0.0,0.4138,0.1667,,0.1478,,0.1803,,,0.2069,,0.9866,,,0.0,0.4138,0.1667,,0.1512,,0.1878,,,0.2051,,0.9866,,,0.0,0.4138,0.1667,,0.1504,,0.1835,,,,block of flats,0.1713,Panel,No,0.0,0.0,0.0,0.0,-351.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,0.0\n243785,0,Cash loans,F,Y,Y,1,76500.0,119893.5,14355.0,103500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-11740,-2626,-86.0,-2497,7.0,1,1,0,1,0,1,Laborers,3.0,2,2,SATURDAY,13,0,1,1,0,0,0,Self-employed,0.4193931485821288,0.4711251036499032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-418.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n240627,0,Cash loans,M,Y,Y,0,270000.0,1303812.0,34524.0,1138500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006852,-23471,-798,-377.0,-996,3.0,1,1,0,1,0,0,High skill tech staff,2.0,3,3,SUNDAY,6,0,0,0,0,0,0,Industry: type 7,0.8003151820947918,0.3007311244168093,,0.3876,0.3273,0.9990000000000001,,,0.36,0.3103,0.375,,0.1887,,0.3581,,0.0132,0.395,0.3396,0.9990000000000001,,,0.3625,0.3103,0.375,,0.1931,,0.3731,,0.0139,0.3914,0.3273,0.9990000000000001,,,0.36,0.3103,0.375,,0.192,,0.3645,,0.0134,,block of flats,0.3862,Monolithic,No,0.0,0.0,0.0,0.0,-616.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n324331,0,Cash loans,F,N,Y,0,85500.0,187929.0,14179.5,171000.0,\"Spouse, partner\",Pensioner,Lower secondary,Married,House / apartment,0.031329,-22555,365243,-9284.0,-4112,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.3558571387188817,0.4902575124990026,0.1021,,0.9876,,,0.0,0.2069,0.1667,,,,0.0902,,0.004,0.10400000000000001,,0.9876,,,0.0,0.2069,0.1667,,,,0.094,,0.0043,0.1031,,0.9876,,,0.0,0.2069,0.1667,,,,0.0918,,0.0041,,block of flats,0.071,Panel,No,3.0,0.0,3.0,0.0,-1392.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n272022,0,Cash loans,M,Y,N,0,157500.0,450000.0,21649.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-8585,-594,-3017.0,-1270,6.0,1,1,1,1,1,0,Security staff,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Security,,0.047476995148932485,0.28812959991785075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n411991,0,Cash loans,F,N,Y,2,126000.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00963,-13302,-2177,-509.0,-2084,,1,1,0,1,0,0,Core staff,4.0,2,2,MONDAY,13,0,0,0,0,0,0,School,,0.6294910409041904,0.5262949398096192,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1083.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n139562,0,Cash loans,M,N,N,0,103500.0,1125000.0,33025.5,1125000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-15815,365243,-9928.0,-3995,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,XNA,,0.5583400682298149,,0.0825,0.0,0.9856,0.8028,0.0,0.0,0.2069,0.1667,0.0417,0.0757,0.0,0.0838,0.0,0.0,0.084,0.0,0.9856,0.8105,0.0,0.0,0.2069,0.1667,0.0417,0.0775,0.0,0.0873,0.0,0.0,0.0833,0.0,0.9856,0.8054,0.0,0.0,0.2069,0.1667,0.0417,0.077,0.0,0.0853,0.0,0.0,reg oper spec account,block of flats,0.0659,\"Stone, brick\",No,9.0,0.0,9.0,0.0,-1777.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n311491,0,Cash loans,F,N,Y,0,67500.0,422451.0,15304.5,297000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-16910,-6875,-7828.0,-455,,1,1,0,1,1,0,,2.0,3,3,THURSDAY,10,0,0,0,0,0,0,Kindergarten,,0.15388920239248916,,0.0861,0.0777,0.9811,,0.0248,0.0,0.1207,0.1667,,0.0112,,0.0573,,0.0208,0.063,0.0692,0.9801,,0.0251,0.0,0.1034,0.1667,,0.0,,0.0387,,0.0158,0.0869,0.0777,0.9811,,0.025,0.0,0.1207,0.1667,,0.0114,,0.0583,,0.0212,,block of flats,0.035,Panel,No,4.0,0.0,4.0,0.0,-1456.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n250678,0,Cash loans,F,N,Y,0,171000.0,270000.0,16645.5,270000.0,Group of people,Working,Secondary / secondary special,Married,House / apartment,0.020246,-12942,-3950,-3955.0,-5622,,1,1,1,1,0,0,Waiters/barmen staff,2.0,3,3,THURSDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.4712178662768457,0.02015033881208547,0.44539624156083396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-911.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n376875,0,Revolving loans,F,N,N,0,200250.0,270000.0,13500.0,270000.0,Family,Commercial associate,Higher education,Single / not married,With parents,0.00496,-9229,-587,-3450.0,-1910,,1,1,1,1,1,0,Core staff,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Bank,0.5303402698270764,0.4440268550089631,0.4365064990977374,0.2005,,0.9861,0.8504,,0.36,0.1897,0.2917,0.0417,0.2567,0.2967,0.0386,0.0,0.0591,0.0378,,0.9836,0.8563,,0.3625,0.069,0.2083,0.0417,0.2626,0.3242,0.0377,0.0,0.0625,0.2025,,0.9861,0.8524,,0.36,0.1897,0.2917,0.0417,0.2612,0.3018,0.0393,0.0,0.0603,,block of flats,0.0322,Panel,No,2.0,0.0,2.0,0.0,-472.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n232743,0,Revolving loans,F,N,N,0,315000.0,1350000.0,67500.0,1350000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.04622,-14262,-1500,-623.0,-4346,,1,1,0,1,0,0,,1.0,1,1,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.6241572010257506,0.6344042327396211,,0.0495,,0.9737,,,0.0,0.069,0.125,,0.0394,,,,,0.0504,,0.9737,,,0.0,0.069,0.125,,0.0403,,,,,0.05,,0.9737,,,0.0,0.069,0.125,,0.0401,,,,,,block of flats,0.0339,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-238.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n373584,0,Cash loans,F,N,N,0,121500.0,1002870.0,42619.5,922500.0,Other_B,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.001276,-17439,-4104,-3776.0,-609,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,5,0,0,0,0,0,0,Agriculture,,0.42722769667095056,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1296.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n268121,0,Cash loans,F,N,N,0,99000.0,778500.0,22761.0,778500.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.019689,-14689,-2340,-1752.0,-4307,,1,1,0,1,1,0,Sales staff,1.0,2,2,FRIDAY,11,0,0,0,1,1,0,Business Entity Type 3,0.4294880118665125,0.3137338073276237,0.6430255641096323,0.0082,0.0,0.9747,,,0.0,0.0345,0.0417,,0.0055,,0.006,,0.0,0.0084,0.0,0.9747,,,0.0,0.0345,0.0417,,0.0056,,0.0063,,0.0,0.0083,0.0,0.9747,,,0.0,0.0345,0.0417,,0.0056,,0.0061,,0.0,,block of flats,0.0052,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1087.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n258339,0,Cash loans,F,N,Y,1,148500.0,278613.0,29385.0,252000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-11124,-608,-1507.0,-2748,,1,1,0,1,0,0,Sales staff,3.0,2,2,SUNDAY,14,0,0,0,0,0,0,Transport: type 4,,0.19709551790587,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n325611,0,Cash loans,M,Y,N,0,360000.0,1800000.0,56650.5,1800000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.009334,-16634,-191,-7258.0,-87,2.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,13,0,1,1,0,1,1,Government,0.20106933997894216,0.7091420496802089,,0.0082,0.0372,0.9712,0.6056,0.0,0.0,0.069,0.0417,0.0417,0.0064,0.0067,0.0088,0.0,0.0,0.0084,0.0386,0.9712,0.621,0.0,0.0,0.069,0.0417,0.0417,0.0066,0.0073,0.0091,0.0,0.0,0.0083,0.0372,0.9712,0.6109,0.0,0.0,0.069,0.0417,0.0417,0.0066,0.0068,0.0089,0.0,0.0,reg oper account,block of flats,0.0077,Wooden,No,0.0,0.0,0.0,0.0,-1869.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n224546,0,Cash loans,F,Y,Y,0,225000.0,1035000.0,68431.5,1035000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.011703,-23571,365243,-8375.0,-4593,8.0,1,0,0,1,1,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,0.8366267911469446,0.7176855102757849,0.7032033049040319,0.2103,0.0,0.9846,0.7892,0.0531,0.24,0.2069,0.3333,0.375,0.1652,0.0,0.2256,0.0,0.0759,0.2143,0.0,0.9846,0.7975,0.0536,0.2417,0.2069,0.3333,0.375,0.16899999999999998,0.0,0.2351,0.0,0.0803,0.2123,0.0,0.9846,0.792,0.0535,0.24,0.2069,0.3333,0.375,0.1681,0.0,0.2297,0.0,0.0774,,block of flats,0.223,Panel,No,0.0,0.0,0.0,0.0,-1929.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n423269,1,Cash loans,M,N,Y,0,157500.0,553806.0,26770.5,495000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00702,-12907,-784,-3895.0,-4477,,1,1,0,1,0,0,Security staff,2.0,2,2,SATURDAY,12,0,0,0,0,1,1,Medicine,,0.031285679447553005,0.14024318370802974,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-234.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n346882,0,Revolving loans,M,N,Y,0,112500.0,135000.0,6750.0,135000.0,Family,Working,Higher education,Separated,House / apartment,0.007120000000000001,-19105,-367,-4031.0,-2621,,1,1,1,1,1,0,Laborers,1.0,2,2,SUNDAY,11,0,0,0,0,0,0,School,,0.2712743514774837,0.6801388218428291,0.0639,0.0843,0.9841,0.7824,,0.0,0.1379,0.1667,0.2083,0.0478,0.0504,0.0364,0.0077,0.0458,0.0651,0.0875,0.9841,0.7909,,0.0,0.1379,0.1667,0.2083,0.0489,0.0551,0.0379,0.0078,0.0485,0.0645,0.0843,0.9841,0.7853,,0.0,0.1379,0.1667,0.2083,0.0487,0.0513,0.0371,0.0078,0.0468,reg oper account,block of flats,0.044000000000000004,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1915.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n229668,0,Cash loans,F,N,N,1,112500.0,247675.5,17365.5,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008625,-22657,365243,-9595.0,-5964,,1,0,0,1,0,0,,3.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.7677208652733204,0.3859146722745145,0.2969,0.2099,0.9871,0.8232,0.2082,0.32,0.2759,0.3333,0.375,0.0561,0.2421,0.3276,0.0,0.0,0.3025,0.2178,0.9871,0.8301,0.2101,0.3222,0.2759,0.3333,0.375,0.0574,0.2645,0.3414,0.0,0.0,0.2998,0.2099,0.9871,0.8256,0.2095,0.32,0.2759,0.3333,0.375,0.0571,0.2463,0.3335,0.0,0.0,reg oper account,block of flats,0.3538,Panel,No,1.0,0.0,0.0,0.0,-2317.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n139454,0,Cash loans,F,N,N,2,180000.0,540000.0,25020.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020246,-10139,-1037,-4263.0,-2249,,1,1,0,1,0,0,,3.0,3,3,THURSDAY,15,0,0,0,0,1,1,Restaurant,0.37603680928232175,0.3519121463185953,0.22383131747015547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,1.0,-462.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,2.0\n172668,0,Cash loans,M,Y,N,0,180000.0,1125000.0,35325.0,1125000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-9418,-1455,-4416.0,-2014,3.0,1,1,0,1,1,0,,1.0,1,1,SUNDAY,14,0,0,0,0,0,0,Construction,,0.7136898284207572,0.7136313997323308,0.033,0.0,0.9617,0.4764,0.063,0.0,0.1379,0.125,0.1667,0.0554,0.0235,0.0366,0.0154,0.0,0.0336,0.0,0.9618,0.4969,0.0635,0.0,0.1379,0.125,0.1667,0.0567,0.0257,0.0381,0.0156,0.0,0.0333,0.0,0.9617,0.4834,0.0634,0.0,0.1379,0.125,0.1667,0.0564,0.0239,0.0373,0.0155,0.0,reg oper account,block of flats,0.0344,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1599.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n262804,0,Cash loans,F,N,Y,0,315000.0,408780.0,22954.5,337500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-21597,365243,-9452.0,-4958,,1,0,0,1,0,0,,2.0,1,1,THURSDAY,11,0,0,0,0,0,0,XNA,,0.7006635620191495,0.7421816117614419,0.0608,,0.9866,0.8164,0.0085,0.0,0.1379,0.1667,0.2083,0.0,0.0496,0.0554,0.0,0.0,0.062,,0.9866,0.8236,0.0086,0.0,0.1379,0.1667,0.2083,0.0,0.0542,0.0577,0.0,0.0,0.0614,,0.9866,0.8189,0.0085,0.0,0.1379,0.1667,0.2083,0.0,0.0504,0.0564,0.0,0.0,reg oper account,block of flats,0.0482,,No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n414327,0,Cash loans,F,N,Y,0,54000.0,247500.0,11029.5,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.031329,-22381,365243,-2835.0,-4472,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.6597744230193255,,0.166,0.0674,0.9767,0.6804,0.0135,0.0,0.1034,0.1667,0.2083,0.0559,0.1353,0.0602,0.0039,0.028999999999999998,0.1691,0.07,0.9767,0.6929,0.0136,0.0,0.1034,0.1667,0.2083,0.0572,0.1478,0.0627,0.0039,0.0307,0.1676,0.0674,0.9767,0.6847,0.0135,0.0,0.1034,0.1667,0.2083,0.0569,0.1377,0.0613,0.0039,0.0296,reg oper account,block of flats,0.0537,\"Stone, brick\",No,1.0,1.0,1.0,0.0,-410.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n343217,0,Cash loans,F,N,Y,0,108000.0,292500.0,15997.5,292500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.014519999999999996,-20195,-1231,-856.0,-1846,,1,1,0,1,0,0,Accountants,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Industry: type 3,,0.6509834409492751,0.324891229465852,0.1706,0.0,0.9801,0.7484,,0.28,0.2414,0.2221,0.2917,0.1461,0.0967,0.2287,,0.0093,0.1208,0.0,0.9772,0.7125,,0.2417,0.2069,0.1667,0.2083,0.1494,0.1056,0.2383,,0.0099,0.1723,0.0,0.9781,0.7518,,0.28,0.2414,0.1667,0.2917,0.1486,0.0983,0.2328,,0.0095,,block of flats,0.1819,Panel,No,6.0,0.0,6.0,0.0,-1872.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n351803,0,Cash loans,F,N,N,0,135000.0,72000.0,7753.5,72000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.002134,-8686,-1450,-3463.0,-1354,,1,1,1,1,0,0,,1.0,3,3,MONDAY,11,0,0,0,0,1,1,Transport: type 4,0.16599673877624793,0.20442112828829687,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1222.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n400502,0,Cash loans,F,N,Y,0,56250.0,127350.0,12402.0,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009175,-23182,365243,-4353.0,-2194,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.2231455600821236,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-256.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n372654,0,Cash loans,F,N,N,1,36000.0,76410.0,8748.0,67500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-11472,-603,-548.0,-3240,,1,1,0,1,0,0,Core staff,3.0,2,2,THURSDAY,13,0,0,0,0,1,1,Kindergarten,0.161860830769272,0.6603337875012921,0.13259749523614614,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-376.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n322724,0,Cash loans,M,N,Y,0,76500.0,66829.5,5863.5,54000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.00963,-11620,-1888,-5608.0,-3265,,1,1,0,1,0,1,Laborers,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,Religion,,0.704000857210746,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1191.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n284418,0,Cash loans,F,N,N,0,112500.0,178290.0,17496.0,157500.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.019101,-24473,365243,-2571.0,-4769,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.33175016802953683,0.838724852442103,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-130.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n147817,0,Cash loans,M,Y,N,0,450000.0,284400.0,18643.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.007305,-12126,-1918,-267.0,-4125,23.0,1,1,0,1,1,0,High skill tech staff,2.0,3,3,FRIDAY,9,0,1,1,0,1,1,Industry: type 9,0.6174673688766886,0.5959232389680283,0.3441550073724169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n133307,0,Revolving loans,M,N,Y,0,67500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-18115,-216,-1876.0,-1668,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.3624134767579742,0.65793619131192,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-457.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n148079,0,Cash loans,F,N,N,0,135000.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-22218,-976,-2448.0,-3955,,1,1,0,1,1,0,Core staff,2.0,3,3,TUESDAY,13,0,0,0,0,0,0,Postal,,0.6983054663831425,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1813.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n242438,0,Cash loans,F,Y,Y,0,180000.0,117162.0,12618.0,103500.0,Unaccompanied,Working,Higher education,Widow,House / apartment,0.02461,-14016,-734,-8163.0,-4354,64.0,1,1,0,1,0,1,Accountants,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Transport: type 4,0.3416994115718464,0.6516985502858018,0.324891229465852,0.1227,0.1146,0.9791,,,0.0,0.2759,0.1667,,0.1289,,0.1138,,0.0,0.125,0.1189,0.9791,,,0.0,0.2759,0.1667,,0.1319,,0.1185,,0.0,0.1239,0.1146,0.9791,,,0.0,0.2759,0.1667,,0.1312,,0.1158,,0.0,,block of flats,0.0984,Panel,No,1.0,0.0,1.0,0.0,-229.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,9.0,0.0,2.0\n406751,0,Cash loans,F,N,N,0,225000.0,592560.0,31153.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.032561,-19297,-739,-678.0,-1949,,1,1,0,1,1,0,Private service staff,1.0,1,1,SUNDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6711795609727974,0.5046813193144684,0.1302,0.1262,0.9811,0.8844,0.0667,0.16,0.2069,0.2358,0.2083,0.0313,0.1664,0.142,0.0039,0.0105,0.0945,0.10300000000000001,0.9777,0.8889,0.0673,0.1611,0.2069,0.1667,0.2083,0.032,0.1818,0.0589,0.0039,0.0111,0.0937,0.0992,0.9776,0.8859,0.0671,0.16,0.2069,0.1667,0.2083,0.0318,0.1693,0.1439,0.0039,0.0107,reg oper account,block of flats,0.2147,Panel,No,10.0,0.0,10.0,0.0,-461.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n144664,0,Cash loans,F,N,Y,0,67500.0,284400.0,16011.0,225000.0,Family,Pensioner,Lower secondary,Married,House / apartment,0.00702,-24094,365243,-1714.0,-4661,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,0.7073646134344509,0.4231872262978964,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-122.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n102096,0,Cash loans,F,N,Y,0,135000.0,454500.0,20151.0,454500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.028663,-17406,-7808,-11244.0,-925,,1,1,0,1,0,0,Managers,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,School,0.6514512368455501,0.6091717365610725,0.19519840600440985,0.1031,0.0,0.9771,0.6872,0.01,0.0,0.2069,0.1667,0.2083,0.0469,0.0841,0.0891,0.0,0.0,0.105,0.0,0.9772,0.6994,0.0101,0.0,0.2069,0.1667,0.2083,0.048,0.0918,0.0929,0.0,0.0,0.1041,0.0,0.9771,0.6914,0.0101,0.0,0.2069,0.1667,0.2083,0.0478,0.0855,0.0907,0.0,0.0,reg oper account,block of flats,0.0701,\"Stone, brick\",No,9.0,0.0,9.0,0.0,-1974.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n418675,0,Cash loans,F,N,Y,1,67500.0,592560.0,31153.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-11852,-1617,-3199.0,-2620,,1,1,0,1,0,0,Cooking staff,3.0,3,3,MONDAY,13,0,0,0,0,0,0,School,,0.4177042637762371,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,3.0,7.0,1.0,-429.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n394213,0,Cash loans,F,N,Y,0,112500.0,225000.0,6187.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.030755,-12037,-1125,-277.0,-4728,,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,13,0,0,0,0,1,1,Trade: type 3,0.3353851218550091,0.4964976607416372,0.13177013253138142,0.1763,0.0644,0.9866,0.8164,0.0529,0.04,0.0345,0.3333,0.0417,0.0337,0.1437,0.0504,0.0,0.133,0.1796,0.0668,0.9866,0.8236,0.0534,0.0403,0.0345,0.3333,0.0417,0.0345,0.157,0.0525,0.0,0.1408,0.17800000000000002,0.0644,0.9866,0.8189,0.0532,0.04,0.0345,0.3333,0.0417,0.0343,0.1462,0.0513,0.0,0.1358,reg oper account,block of flats,0.0685,Panel,No,1.0,0.0,1.0,0.0,-1540.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n175800,0,Cash loans,M,Y,Y,0,225000.0,1140156.0,37809.0,1021500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.025164,-20099,-2655,-8896.0,-3563,5.0,1,1,0,1,0,0,Managers,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.4689894690775006,0.6633485550793495,0.25396280933631177,0.0165,0.0427,0.9722,0.6192,0.0019,0.0,0.069,0.0417,0.0833,0.0351,0.0134,0.0126,0.0,0.0,0.0168,0.0443,0.9722,0.6341,0.0019,0.0,0.069,0.0417,0.0833,0.0359,0.0147,0.0132,0.0,0.0,0.0167,0.0427,0.9722,0.6243,0.0019,0.0,0.069,0.0417,0.0833,0.0357,0.0137,0.0129,0.0,0.0,reg oper account,block of flats,0.011000000000000001,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1126.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n259478,0,Cash loans,F,N,Y,0,180000.0,313438.5,32247.0,283500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.032561,-16967,-2871,-699.0,-506,,1,1,0,1,0,0,Laborers,1.0,1,1,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.8570598765595376,0.6776186068417904,0.7252764347002191,0.0722,0.11,0.9478,,,0.28,0.2414,0.1667,,0.0462,,0.1132,,0.0765,0.0735,0.1142,0.9479,,,0.282,0.2414,0.1667,,0.0473,,0.11800000000000001,,0.081,0.0729,0.11,0.9478,,,0.28,0.2414,0.1667,,0.047,,0.1153,,0.0781,,block of flats,0.1179,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-738.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n447826,0,Cash loans,F,N,Y,0,112500.0,143910.0,14364.0,135000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.016612000000000002,-24645,365243,-5602.0,-3988,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.6368555073156181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-367.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n149838,0,Cash loans,F,N,Y,0,127350.0,270000.0,13117.5,270000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-21705,365243,-8326.0,-4338,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.6193372595472497,0.7209441499436497,0.5402,0.5124,0.9876,,,0.0,1.0,0.1667,,0.1219,,0.4881,,0.0043,0.5504,0.5317,0.9876,,,0.0,1.0,0.1667,,0.1247,,0.5086,,0.0045,0.5454,0.5124,0.9876,,,0.0,1.0,0.1667,,0.124,,0.4969,,0.0043,,block of flats,0.3849,Panel,No,1.0,1.0,1.0,1.0,-1223.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n247590,0,Cash loans,F,N,N,0,166500.0,1303200.0,46809.0,1125000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-14896,-93,-8957.0,-4236,,1,1,1,1,1,0,Medicine staff,2.0,1,1,FRIDAY,17,0,0,0,0,1,1,Other,,0.7061244620511731,0.4471785780453068,0.0588,,0.9821,,,0.04,0.0345,0.4583,,,,0.066,,0.0343,0.0599,,0.9821,,,0.0403,0.0345,0.4583,,,,0.0688,,0.0363,0.0593,,0.9821,,,0.04,0.0345,0.4583,,,,0.0672,,0.035,,block of flats,0.0519,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1995.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n255164,0,Revolving loans,F,N,Y,0,360000.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.032561,-20925,-4851,-9653.0,-4470,,1,1,0,1,0,0,,1.0,1,1,FRIDAY,18,0,0,0,0,0,0,Transport: type 4,,0.7915621381114892,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-290.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n213028,0,Cash loans,M,N,Y,0,225000.0,528633.0,25560.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-16617,-500,-4607.0,-159,,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,3,0,0,0,0,0,0,Business Entity Type 3,0.4352173997195424,0.4722749227543728,0.6075573001388961,0.0577,0.0486,0.9747,0.6532,,0.0,0.1034,0.1667,0.2083,0.0126,0.0471,0.0466,0.0,0.0083,0.0588,0.0504,0.9747,0.6668,,0.0,0.1034,0.1667,0.2083,0.0129,0.0514,0.0485,0.0,0.0088,0.0583,0.0486,0.9747,0.6578,,0.0,0.1034,0.1667,0.2083,0.0128,0.0479,0.0474,0.0,0.0084,reg oper account,block of flats,0.0496,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n304750,0,Revolving loans,M,N,N,1,144000.0,180000.0,9000.0,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15773,-5940,-3414.0,-3953,,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,Military,0.3435250775624593,0.4437083733604363,0.4135967602644276,0.0464,0.05,0.9851,0.7959999999999999,0.0086,0.0,0.1034,0.1667,0.2083,0.0488,0.0378,0.0424,0.0,0.0,0.0473,0.0519,0.9851,0.804,0.0086,0.0,0.1034,0.1667,0.2083,0.0499,0.0413,0.0442,0.0,0.0,0.0468,0.05,0.9851,0.7987,0.0086,0.0,0.1034,0.1667,0.2083,0.0496,0.0385,0.0431,0.0,0.0,reg oper account,block of flats,0.038,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1860.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n393659,0,Cash loans,M,Y,Y,0,292500.0,307944.0,30456.0,292500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.072508,-13976,-1434,-8112.0,-424,64.0,1,1,0,1,1,0,Cooking staff,2.0,1,1,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.3469559669726364,0.3706496323299817,0.0825,0.084,0.9752,0.66,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0672,0.0446,0.0,0.0,0.084,0.0872,0.9752,0.6733,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.0465,0.0,0.0,0.0833,0.084,0.9752,0.6645,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0684,0.0454,0.0,0.0,reg oper account,block of flats,0.0539,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-645.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n210662,0,Cash loans,F,N,Y,1,112500.0,112068.0,7618.5,99000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-11968,-1428,-5793.0,-3481,,1,1,1,1,1,1,Accountants,3.0,2,2,SATURDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.4852673988410692,0.12530787842823918,0.0742,0.0432,0.9861,0.8096,0.0277,0.08,0.069,0.3333,0.375,0.0425,0.0605,0.0749,0.0,0.0,0.0756,0.0448,0.9861,0.8171,0.0279,0.0806,0.069,0.3333,0.375,0.0434,0.0661,0.078,0.0,0.0,0.0749,0.0432,0.9861,0.8121,0.0278,0.08,0.069,0.3333,0.375,0.0432,0.0616,0.0762,0.0,0.0,not specified,block of flats,0.07400000000000001,Panel,No,0.0,0.0,0.0,0.0,-636.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n423807,0,Cash loans,M,Y,Y,0,157500.0,414792.0,21307.5,315000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-19563,-1564,-5631.0,-3115,8.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.5035009989876007,0.6891248913833483,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,5.0,6.0,5.0,-585.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n403200,0,Cash loans,M,N,Y,2,360000.0,765000.0,22495.5,765000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-16136,-6947,-4683.0,-4683,,1,1,1,1,0,0,Laborers,4.0,3,3,FRIDAY,7,0,0,0,0,0,0,Transport: type 2,0.6139096253829622,0.7691295091047624,0.2622489709189549,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1302.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n434842,0,Cash loans,M,N,N,0,180000.0,254700.0,27558.0,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,With parents,0.006629,-9980,-1534,-9775.0,-1480,,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Government,,0.30472845599048864,0.7180328113294772,,,0.9851,,,,0.069,0.0363,,,,,,,,,0.9841,,,,0.069,0.0417,,,,,,,,,0.9846,,,,0.069,0.0417,,,,,,,,block of flats,0.0166,Wooden,No,3.0,0.0,3.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n117447,0,Cash loans,F,N,Y,0,126000.0,202500.0,7843.5,202500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-9398,-160,-4046.0,-380,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Trade: type 2,,0.3205292329573365,,0.0619,,0.9891,,,0.0,0.1034,0.1667,,0.0537,,0.0376,,,0.063,,0.9891,,,0.0,0.1034,0.1667,,0.0549,,0.0392,,,0.0625,,0.9891,,,0.0,0.1034,0.1667,,0.0546,,0.0383,,,,block of flats,0.0583,Panel,No,0.0,0.0,0.0,0.0,-98.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n232613,0,Cash loans,F,N,N,0,157500.0,450000.0,30325.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.02461,-19688,-7655,-6105.0,-3179,,1,1,1,1,1,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 2,,0.6387851894495424,0.17249546677733105,0.1969,0.1017,0.9906,0.8708,,0.16,0.0345,0.3333,0.0417,,0.1572,0.1567,0.0154,0.0123,0.2006,0.1056,0.9906,0.8759,,0.1611,0.0345,0.3333,0.0417,,0.1717,0.1633,0.0156,0.0131,0.1988,0.1017,0.9906,0.8725,,0.16,0.0345,0.3333,0.0417,,0.1599,0.1595,0.0155,0.0126,reg oper account,block of flats,0.1768,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,9.0,0.0,0.0\n292087,0,Cash loans,F,N,Y,0,103500.0,191880.0,19107.0,180000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-14192,-3750,-2612.0,-4614,,1,1,1,1,0,0,Core staff,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Government,,0.7484635374771156,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,0.0,-1792.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n426462,0,Cash loans,F,N,Y,1,171000.0,610335.0,20299.5,463500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018029,-15013,-4208,-7437.0,-1135,,1,1,0,1,0,0,Core staff,3.0,3,3,FRIDAY,9,0,0,0,0,0,0,Kindergarten,,0.6488390239530133,,0.0814,0.0766,0.9786,0.7076,0.0103,0.0,0.1379,0.1667,0.2083,0.0364,0.0656,0.0746,0.0039,0.0078,0.083,0.0795,0.9786,0.7190000000000001,0.0104,0.0,0.1379,0.1667,0.2083,0.0372,0.0716,0.0777,0.0039,0.0083,0.0822,0.0766,0.9786,0.7115,0.0104,0.0,0.1379,0.1667,0.2083,0.037000000000000005,0.0667,0.0759,0.0039,0.008,reg oper account,block of flats,0.066,Panel,No,0.0,0.0,0.0,0.0,-1429.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n269051,0,Cash loans,M,Y,N,0,135000.0,521280.0,22216.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-9996,-1428,-22.0,-2530,10.0,1,1,1,1,1,0,Drivers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6242534459154839,0.18629294965553744,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1650.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n440101,0,Cash loans,M,Y,Y,0,225000.0,808650.0,26217.0,675000.0,Unaccompanied,Commercial associate,Higher education,Widow,House / apartment,0.072508,-21676,-2725,-861.0,-5212,14.0,1,1,0,1,0,0,Managers,1.0,1,1,TUESDAY,15,0,0,0,0,0,0,Security,0.7687042660715551,0.7990269300168009,0.5902333386185574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2332.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n186929,0,Cash loans,F,N,N,0,180000.0,263686.5,29880.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-19419,-491,-8473.0,-2809,,1,1,0,1,0,0,,2.0,1,1,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6192289887244781,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n166791,1,Cash loans,F,N,Y,0,180000.0,521280.0,28408.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Office apartment,0.014464,-15855,-3745,-4131.0,-4222,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,3,0,0,0,0,0,0,Business Entity Type 1,,0.5074253818012543,0.13937578009978951,0.0754,0.0777,0.9781,0.728,0.0,0.0532,0.1034,0.2292,0.3192,0.0636,0.07,0.0566,0.0058,0.0601,0.0735,0.0906,0.9777,0.706,0.0,0.0,0.069,0.3333,0.375,0.0331,0.0643,0.0128,0.0,0.0,0.0739,0.0849,0.9796,0.7518,0.0,0.04,0.1034,0.25,0.375,0.0732,0.0616,0.0469,0.0058,0.0048,reg oper spec account,block of flats,0.0527,Panel,No,0.0,0.0,0.0,0.0,-2930.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,2.0,8.0\n353453,0,Cash loans,F,N,Y,0,315000.0,291384.0,20407.5,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.011703,-19671,-7027,-406.0,-3082,,1,1,0,1,0,0,Accountants,1.0,2,2,THURSDAY,18,0,0,0,0,0,0,Other,,0.7319928999596642,0.7688075728291359,0.1856,0.0,0.9831,0.7688,0.0456,0.2,0.1724,0.3333,0.375,0.3675,0.1505,0.2007,0.0039,0.0014,0.1891,0.0,0.9831,0.7779,0.046,0.2014,0.1724,0.3333,0.375,0.3759,0.1644,0.2091,0.0039,0.0015,0.1874,0.0,0.9831,0.7719,0.0459,0.2,0.1724,0.3333,0.375,0.3739,0.1531,0.2043,0.0039,0.0015,org spec account,block of flats,0.1831,Panel,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n215163,0,Cash loans,F,N,N,0,135000.0,1006920.0,42790.5,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.019689,-20473,365243,-5565.0,-4021,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.7520609211473582,,0.033,0.0632,0.9702,0.5920000000000001,0.0446,0.0,0.069,0.125,0.1667,0.0452,0.0244,0.0379,0.0116,0.0135,0.0336,0.0656,0.9702,0.608,0.045,0.0,0.069,0.125,0.1667,0.0463,0.0266,0.0395,0.0117,0.0143,0.0333,0.0632,0.9702,0.5975,0.0449,0.0,0.069,0.125,0.1667,0.046,0.0248,0.0386,0.0116,0.0138,reg oper spec account,block of flats,0.0542,Mixed,No,1.0,0.0,1.0,0.0,-559.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n441912,0,Cash loans,F,N,Y,0,225000.0,101880.0,10206.0,90000.0,Family,Commercial associate,Higher education,Separated,House / apartment,0.011703,-18870,-907,-285.0,-2410,,1,1,0,1,0,0,,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Government,,0.7057204357921129,,0.3289,,0.998,,,0.16,0.1379,0.3333,,0.1873,,0.3054,,0.0839,0.3351,,0.998,,,0.1611,0.1379,0.3333,,0.1916,,0.3182,,0.0888,0.3321,,0.998,,,0.16,0.1379,0.3333,,0.1906,,0.3109,,0.0857,,block of flats,0.3258,Mixed,No,0.0,0.0,0.0,0.0,-489.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n343662,0,Cash loans,M,Y,N,0,225000.0,1800000.0,62568.0,1800000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.019101,-14339,-5577,-1924.0,-5150,2.0,1,1,1,1,0,0,Core staff,2.0,2,2,THURSDAY,19,0,0,0,0,1,1,Security Ministries,,0.6480015133571609,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1831.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n242613,0,Cash loans,F,N,N,0,112500.0,247500.0,11907.0,247500.0,Unaccompanied,Pensioner,Incomplete higher,Widow,House / apartment,0.030755,-21808,365243,-4847.0,-4862,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.672278976201302,0.5709165417729987,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-833.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n373999,0,Revolving loans,M,Y,N,2,135000.0,180000.0,9000.0,180000.0,Children,Commercial associate,Higher education,Married,House / apartment,0.009334,-12601,-2893,-2537.0,-4490,3.0,1,1,0,1,0,0,Accountants,4.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.6321062708013497,0.6543988360045198,0.7898803468924867,0.233,0.1445,0.9831,0.7688,0.0167,0.24,0.2069,0.3333,0.0417,0.1318,0.1891,0.2159,0.0039,0.0402,0.2374,0.15,0.9831,0.7779,0.0168,0.2417,0.2069,0.3333,0.0417,0.1348,0.2066,0.225,0.0039,0.0426,0.2352,0.1445,0.9831,0.7719,0.0168,0.24,0.2069,0.3333,0.0417,0.1341,0.1924,0.2198,0.0039,0.0411,reg oper spec account,block of flats,0.1786,Panel,No,0.0,0.0,0.0,0.0,-438.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n363323,0,Cash loans,F,N,Y,2,112500.0,225000.0,11074.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-13779,-2278,-2114.0,-2117,,1,1,1,1,1,0,Medicine staff,4.0,2,2,FRIDAY,11,0,0,0,0,0,0,Medicine,,0.5595530228847475,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-150.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n154740,0,Cash loans,M,N,Y,0,112500.0,272520.0,16672.5,225000.0,Unaccompanied,Commercial associate,Lower secondary,Single / not married,House / apartment,0.020713,-13589,-1305,-4873.0,-4597,,1,1,0,1,0,0,Drivers,1.0,3,3,TUESDAY,11,1,1,0,1,1,0,Self-employed,0.2440279997048793,0.31311265962214,0.3441550073724169,0.1175,0.0211,0.9806,0.7348,,0.0,0.2759,0.1667,0.0417,0.0243,,0.1089,,0.0206,0.1197,0.0219,0.9806,0.7452,,0.0,0.2759,0.1667,0.0417,0.0248,,0.1135,,0.0218,0.1187,0.0211,0.9806,0.7383,,0.0,0.2759,0.1667,0.0417,0.0247,,0.1109,,0.021,reg oper account,block of flats,0.0917,Panel,No,15.0,0.0,15.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n379695,0,Cash loans,M,Y,Y,0,180000.0,229500.0,24228.0,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-22949,365243,-5201.0,-4712,1.0,1,0,0,1,1,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,0.43312617807483184,0.721053789487657,0.34578480246959553,0.0433,,0.9911,,,0.0,0.1034,0.1667,,,,,,,0.0441,,0.9911,,,0.0,0.1034,0.1667,,,,,,,0.0437,,0.9911,,,0.0,0.1034,0.1667,,,,,,,,block of flats,0.0409,Panel,No,1.0,0.0,1.0,0.0,-955.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n337094,0,Cash loans,M,Y,Y,0,450000.0,143910.0,13455.0,135000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-11978,-234,-5970.0,-4118,9.0,1,1,1,1,1,0,Core staff,2.0,1,1,FRIDAY,11,1,1,0,1,1,0,Business Entity Type 3,0.3158506477049517,0.7803176106335563,0.6594055320683344,0.0186,,0.9826,,,0.0,0.1034,0.0417,,,,,,,0.0189,,0.9826,,,0.0,0.1034,0.0417,,,,,,,0.0187,,0.9826,,,0.0,0.1034,0.0417,,,,,,,,block of flats,0.013999999999999999,,No,0.0,0.0,0.0,0.0,-2450.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n142826,0,Cash loans,F,N,Y,0,67500.0,328405.5,14593.5,283500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.01885,-22453,365243,-2613.0,-4291,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.3846853263962318,0.8193176922872417,,,0.9791,,,,,,,,,0.1553,,,,,0.9791,,,,,,,,,0.1618,,,,,0.9791,,,,,,,,,0.158,,,,,0.1362,,No,1.0,0.0,1.0,0.0,-815.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n313958,0,Cash loans,M,Y,Y,2,112500.0,270000.0,15066.0,270000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.015221,-16060,-219,-2383.0,-4164,6.0,1,1,0,1,1,0,High skill tech staff,4.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.5758500066079526,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n222739,0,Cash loans,M,Y,Y,0,67500.0,50940.0,5089.5,45000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-24547,365243,-1984.0,-4511,2.0,1,0,0,1,1,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.6225130235786245,0.1852020815902493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1232.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n275918,0,Cash loans,F,N,N,1,81000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.00733,-16065,-1624,-6094.0,-5877,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,12,0,0,0,0,1,1,Business Entity Type 1,,0.6798977649312744,0.4135967602644276,0.0619,,0.9762,0.6736,,0.0,0.1034,0.1667,0.2083,,,0.0503,,,0.063,,0.9762,0.6864,,0.0,0.1034,0.1667,0.2083,,,0.0524,,,0.0625,,0.9762,0.6779999999999999,,0.0,0.1034,0.1667,0.2083,,,0.0512,,,reg oper account,block of flats,0.0527,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1546.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n318602,1,Cash loans,F,N,N,0,180000.0,254700.0,17019.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.00823,-18688,-1255,-10601.0,-2242,,1,1,1,1,0,0,,2.0,2,2,FRIDAY,15,1,1,0,1,1,0,Other,,0.14852355690934302,0.13937578009978951,0.1021,0.111,0.9811,0.7416,0.0457,0.0,0.2069,0.1667,0.2083,,0.0832,0.0878,0.0,0.0,0.10400000000000001,0.1152,0.9811,0.7517,0.0461,0.0,0.2069,0.1667,0.2083,,0.0909,0.0914,0.0,0.0,0.1031,0.111,0.9811,0.7451,0.046,0.0,0.2069,0.1667,0.2083,,0.0847,0.0893,0.0,0.0,reg oper account,block of flats,0.094,\"Stone, brick\",No,1.0,1.0,1.0,0.0,-1109.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n445152,0,Cash loans,F,Y,N,0,202500.0,540000.0,23008.5,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-11139,-842,-3317.0,-2079,7.0,1,1,0,1,0,0,Accountants,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Bank,,0.6695607873909883,0.2678689358444539,0.1979,0.1007,0.9841,0.7824,0.1297,0.24,0.2069,0.3333,0.375,0.3153,0.1614,0.1996,,0.0901,0.2017,0.1045,0.9841,0.7909,0.1309,0.2417,0.2069,0.3333,0.375,0.3225,0.1763,0.2079,,0.0954,0.1999,0.1007,0.9841,0.7853,0.1305,0.24,0.2069,0.3333,0.375,0.3208,0.1642,0.2031,,0.092,reg oper account,block of flats,0.2475,Block,No,0.0,0.0,0.0,0.0,-1798.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n255552,0,Cash loans,F,N,Y,0,81000.0,174132.0,18414.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-14633,-7437,-7759.0,-4456,,1,1,1,1,0,0,Low-skill Laborers,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.28948729929841244,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1760.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n343760,0,Cash loans,M,Y,N,2,180000.0,254700.0,27558.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.022625,-14316,-2240,-426.0,-4220,7.0,1,1,0,1,0,0,High skill tech staff,4.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.6334865536149565,0.7498689059346317,0.5567274263630174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2190.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n425999,1,Cash loans,M,Y,Y,0,112500.0,163008.0,17685.0,144000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.031329,-12167,-1519,-6242.0,-4542,23.0,1,1,1,1,1,0,Security staff,2.0,2,2,SUNDAY,8,0,0,0,0,0,0,Security,0.187254596392131,0.31444046811214393,0.20092608771597087,0.1485,0.0693,0.9851,0.7959999999999999,0.0752,0.16,0.1379,0.3333,0.375,0.0527,0.1196,0.14800000000000002,0.0068,0.0125,0.1502,0.046,0.9846,0.7975,0.0284,0.1611,0.1379,0.3333,0.375,0.0509,0.1304,0.1502,0.0039,0.0034,0.1494,0.0768,0.9851,0.7987,0.0906,0.16,0.1379,0.3333,0.375,0.0534,0.1214,0.1516,0.0058,0.0113,reg oper account,block of flats,0.1679,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n318454,0,Cash loans,F,N,Y,1,225000.0,957033.0,53568.0,904500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-16744,-4742,-8101.0,-283,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 2,,0.2800810254952981,0.7047064232963289,0.2928,0.1884,0.9866,0.8164,0.1069,0.28,0.2414,0.375,0.4167,0.0,0.2269,0.3026,0.0541,0.0494,0.2983,0.1955,0.9866,0.8236,0.1079,0.282,0.2414,0.375,0.4167,0.0,0.2479,0.3153,0.0545,0.0523,0.2956,0.1884,0.9866,0.8189,0.1076,0.28,0.2414,0.375,0.4167,0.0,0.2309,0.3081,0.0543,0.0504,reg oper account,block of flats,0.3072,Panel,No,2.0,0.0,2.0,0.0,-1959.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n293984,0,Revolving loans,M,Y,Y,2,229500.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.019689,-14702,-7163,-7210.0,-4939,11.0,1,1,0,1,0,1,Laborers,4.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 2,0.30493039841856723,0.5449601359191796,0.6109913280868294,0.0062,,0.9692,,,0.0,0.069,0.0417,,,,0.0068,,,0.0063,,0.9692,,,0.0,0.069,0.0417,,,,0.0071,,,0.0062,,0.9692,,,0.0,0.069,0.0417,,,,0.0069,,,,block of flats,0.006,Block,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n139065,0,Cash loans,F,N,N,2,135000.0,315000.0,19035.0,315000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.008019,-12948,-2536,-592.0,-1238,,1,1,0,1,0,0,Sales staff,4.0,2,2,FRIDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.20258873465025767,0.1574357021916809,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-273.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n280665,0,Cash loans,M,N,N,0,112500.0,364896.0,16200.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.010643,-13678,-2876,-7563.0,-4547,,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,16,0,1,1,1,0,0,Industry: type 9,0.33671640790177226,0.25524601316879314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-1836.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n261083,0,Cash loans,F,Y,Y,1,157500.0,855000.0,43785.0,855000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.015221,-12478,-2284,-5049.0,-3794,8.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,12,0,0,0,1,1,0,Construction,0.6071817129400032,0.5166369697386844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n307583,0,Cash loans,F,N,Y,0,81000.0,207711.0,16204.5,189000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-17905,-1654,-749.0,-783,,1,1,0,1,0,0,Core staff,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,Trade: type 7,0.4525891116811328,0.004864572097470814,0.7517237147741489,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n143573,0,Cash loans,M,Y,Y,1,135000.0,835605.0,24561.0,697500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-15145,-3521,-9272.0,-4888,24.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,SATURDAY,14,0,0,0,0,0,0,Trade: type 3,0.4292021328739655,0.7304586108166892,0.4578995512067301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1604.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n361185,0,Cash loans,M,N,N,0,90000.0,799299.0,32683.5,702000.0,Unaccompanied,Working,Higher education,Single / not married,Municipal apartment,0.009334,-9633,-515,-4365.0,-2282,,1,1,0,1,0,1,Laborers,1.0,2,2,TUESDAY,15,1,1,0,1,1,0,Trade: type 2,0.0827473404428338,0.4663875120287053,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-924.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n288351,0,Cash loans,F,N,N,0,157500.0,755190.0,32125.5,675000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.031329,-21265,365243,-6563.0,-3959,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,,0.7907057581329119,0.1096264336424546,0.10099999999999999,0.0471,0.9722,0.6192,0.0319,0.0,0.2069,0.125,0.1667,0.0462,0.0799,0.1194,0.0116,0.0353,0.1029,0.0488,0.9722,0.6341,0.0322,0.0,0.2069,0.125,0.1667,0.0473,0.0872,0.1244,0.0117,0.0374,0.102,0.0471,0.9722,0.6243,0.0321,0.0,0.2069,0.125,0.1667,0.047,0.0812,0.1216,0.0116,0.0361,reg oper account,block of flats,0.11900000000000001,Mixed,No,0.0,0.0,0.0,0.0,-2813.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n173643,0,Cash loans,F,N,Y,0,225000.0,790830.0,57676.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-9757,-940,-411.0,-954,,1,1,0,1,1,1,Sales staff,2.0,1,1,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.5177186176586238,0.5636071294127138,0.3656165070113335,0.1351,,0.9707,,,,0.1724,0.25,,,,0.1904,,0.1342,0.1376,,0.9707,,,,0.1724,0.25,,,,0.1984,,0.1421,0.1364,,0.9707,,,,0.1724,0.25,,,,0.1939,,0.1371,,,0.179,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-455.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n363637,1,Cash loans,M,Y,Y,0,112500.0,269374.5,21280.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020246,-18973,-4489,-8831.0,-591,1.0,1,1,0,1,1,1,Laborers,1.0,3,3,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.5323323511627468,0.2613201904890845,0.2793353208976285,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-111.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n428959,0,Cash loans,F,Y,Y,0,112500.0,253737.0,25227.0,229500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.00823,-12046,-140,-5618.0,-4186,2.0,1,1,0,1,1,0,Sales staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Trade: type 7,0.3805168139157226,0.7526069085366579,0.6785676886853644,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-813.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n325080,0,Cash loans,F,N,Y,1,135000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-11267,-1300,-2807.0,-1758,,1,1,1,1,1,0,,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,Security Ministries,,0.13156807239162407,0.5585066276769286,,,0.9682,,,,,,,,,0.0033,,,,,0.9682,,,,,,,,,0.0034,,,,,0.9682,,,,,,,,,0.0034,,,,block of flats,0.0052,,Yes,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n423309,0,Cash loans,F,N,N,0,112500.0,788103.0,26176.5,598500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.020713,-18954,-1901,-6018.0,-2450,,1,1,0,1,1,0,,1.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.7287790161125689,0.6518077430315677,0.5549467685334323,0.0196,,0.9811,0.7416,0.0046,0.0,0.1034,0.0417,0.0833,0.0121,0.016,0.0141,0.0,0.0,0.02,,0.9811,0.7517,0.0046,0.0,0.1034,0.0417,0.0833,0.0124,0.0174,0.0146,0.0,0.0,0.0198,,0.9811,0.7451,0.0046,0.0,0.1034,0.0417,0.0833,0.0124,0.0162,0.0143,0.0,0.0,reg oper account,block of flats,0.0136,Wooden,No,1.0,0.0,0.0,0.0,-2098.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n388301,1,Cash loans,F,N,Y,0,40500.0,101880.0,5818.5,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018209,-24125,365243,-3306.0,-4176,,1,0,0,1,0,0,,1.0,3,3,FRIDAY,6,0,0,0,0,0,0,XNA,,0.22013138323820805,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-814.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n372272,0,Cash loans,M,N,Y,0,157500.0,945000.0,25987.5,945000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.0228,-7910,-262,-2799.0,-305,,1,1,0,1,1,0,Sales staff,2.0,2,2,TUESDAY,16,0,0,0,0,1,1,Trade: type 2,0.32245022942140705,0.14293248984377782,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-201.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n225932,1,Cash loans,F,N,Y,0,130500.0,269550.0,24853.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.032561,-12303,-757,-337.0,-624,,1,1,0,1,0,0,Sales staff,2.0,1,1,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.3911755197971107,0.4632753280912678,1.0,,0.9985,,,1.0,0.7586,1.0,,0.298,,1.0,,0.2648,1.0,,0.9985,,,1.0,0.7586,1.0,,0.3048,,1.0,,0.2803,1.0,,0.9985,,,1.0,0.7586,1.0,,0.3031,,1.0,,0.2703,,block of flats,1.0,Panel,No,0.0,0.0,0.0,0.0,-44.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n317969,0,Cash loans,M,Y,N,0,117000.0,271066.5,10593.0,234000.0,\"Spouse, partner\",Pensioner,Higher education,Married,House / apartment,0.019101,-24069,365243,-2928.0,-3875,2.0,1,0,0,1,0,0,,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,XNA,,0.688801552736799,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1072.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n207028,0,Cash loans,M,Y,Y,2,180000.0,254700.0,26874.0,225000.0,Unaccompanied,Working,Higher education,Married,Municipal apartment,0.011703,-18331,-3915,-437.0,-1876,17.0,1,1,0,1,0,0,Drivers,4.0,2,2,MONDAY,15,0,0,0,0,1,1,Self-employed,0.4219761108159129,0.7400564572894972,0.7380196196295241,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1873.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n147576,0,Cash loans,F,Y,Y,0,67500.0,247500.0,12037.5,247500.0,Other_B,Pensioner,Secondary / secondary special,Widow,House / apartment,0.031329,-22679,365243,-8745.0,-4826,18.0,1,0,0,1,0,0,,1.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.05107222431811402,0.520897599048938,0.0608,0.0334,0.9851,,,0.0,0.0345,0.1667,,0.0808,,0.0314,,0.0016,0.062,0.0346,0.9851,,,0.0,0.0345,0.1667,,0.0826,,0.0327,,0.0017,0.0614,0.0334,0.9851,,,0.0,0.0345,0.1667,,0.0822,,0.032,,0.0016,,block of flats,0.025,Block,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n154194,0,Cash loans,M,N,Y,0,112500.0,95940.0,9472.5,90000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.031329,-23478,365243,-6434.0,-5288,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,0.8203080673931875,0.6483655438480871,0.3656165070113335,0.0619,,0.9771,,,0.0,0.1379,0.1667,,,,0.0515,,0.0357,0.063,,0.9772,,,0.0,0.1379,0.1667,,,,0.0537,,0.0378,0.0625,,0.9771,,,0.0,0.1379,0.1667,,,,0.0525,,0.0365,,block of flats,0.0483,,No,0.0,0.0,0.0,0.0,-437.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n127745,0,Cash loans,M,N,N,0,153607.5,568800.0,15003.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Rented apartment,0.010006,-8175,-463,-144.0,-683,,1,1,1,1,0,0,Core staff,1.0,2,2,MONDAY,12,1,1,0,1,1,0,Trade: type 2,0.1329806146559997,0.54484083411348,0.511891801533151,0.2928,0.2194,0.9881,0.8368,0.0,0.32,0.2759,0.3333,0.375,0.0,0.2387,0.3063,0.0,0.0146,0.2983,0.2277,0.9881,0.8432,0.0,0.3222,0.2759,0.3333,0.375,0.0,0.2608,0.3191,0.0,0.0155,0.2956,0.2194,0.9881,0.8390000000000001,0.0,0.32,0.2759,0.3333,0.375,0.0,0.2428,0.3118,0.0,0.015,reg oper account,block of flats,0.2944,Panel,No,0.0,0.0,0.0,0.0,-4.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n261030,0,Cash loans,F,N,Y,0,171000.0,405000.0,39451.5,405000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009334,-10479,-59,-4432.0,-3090,,1,1,0,1,1,0,Waiters/barmen staff,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.6320578633107976,0.5762088360175724,0.0722,0.0844,0.9791,0.728,0.0078,0.0,0.1379,0.1667,0.0,0.0235,0.0588,0.0656,0.0,0.0,0.0735,0.0876,0.9782,0.7387,0.0079,0.0,0.1379,0.1667,0.0,0.0241,0.0643,0.0684,0.0,0.0,0.0729,0.0844,0.9791,0.7316,0.0078,0.0,0.1379,0.1667,0.0,0.0239,0.0599,0.0668,0.0,0.0,reg oper spec account,block of flats,0.0516,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1999.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n392611,0,Cash loans,F,N,Y,0,202500.0,900000.0,35824.5,900000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.010966,-17790,-124,-8418.0,-1316,,1,1,0,1,1,1,,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.5721926222465098,0.3962195720630885,0.0722,0.0761,0.9831,0.7688,0.0086,0.0,0.1379,0.1667,0.2083,0.0634,0.0588,0.0666,0.0,0.0,0.0735,0.079,0.9831,0.7779,0.0087,0.0,0.1379,0.1667,0.2083,0.0649,0.0643,0.0693,0.0,0.0,0.0729,0.0761,0.9831,0.7719,0.0087,0.0,0.1379,0.1667,0.2083,0.0645,0.0599,0.0678,0.0,0.0,reg oper spec account,block of flats,0.0524,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n392913,0,Cash loans,M,N,N,0,157500.0,906228.0,46399.5,810000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.006629,-15409,-650,-4506.0,-2640,,1,1,1,1,0,0,Drivers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5007945827265349,,0.0072,,0.9737,,,,0.0345,0.0,,,,0.0031,,0.0028,0.0074,,0.9702,,,,0.0345,0.0,,,,0.0032,,0.003,0.0073,,0.9737,,,,0.0345,0.0,,,,0.0031,,0.0029,,terraced house,0.003,Wooden,Yes,0.0,0.0,0.0,0.0,-422.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n338802,0,Cash loans,F,N,Y,0,69300.0,130365.0,6786.0,99000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-23650,365243,-4200.0,-4201,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,0.663055635782359,0.5521353539049749,0.4561097392782771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-176.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n126183,0,Cash loans,M,Y,Y,2,135000.0,835605.0,29736.0,697500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-10944,-747,-203.0,-2917,20.0,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,18,0,0,0,1,1,0,Business Entity Type 3,,0.3653457049587889,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-231.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n392845,0,Revolving loans,M,Y,Y,1,225000.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-16831,-1082,-3590.0,-368,2.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,16,0,0,0,0,1,1,Industry: type 11,0.6553907738293415,0.6162701986004009,0.5656079814115492,0.0021,0.0,0.9662,0.5376,,0.0,0.069,0.0,0.0417,0.0,0.0017,0.0017,0.0,0.0,0.0021,0.0,0.9662,0.5557,,0.0,0.069,0.0,0.0417,0.0,0.0018,0.0018,0.0,0.0,0.0021,0.0,0.9662,0.5438,,0.0,0.069,0.0,0.0417,0.0,0.0017,0.0018,0.0,0.0,reg oper account,block of flats,0.0014,Wooden,No,1.0,0.0,1.0,0.0,-1578.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n389787,0,Cash loans,F,Y,Y,1,112500.0,99000.0,10525.5,99000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-14634,-3905,-4722.0,-4740,22.0,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,10,0,0,0,0,1,1,Business Entity Type 2,,0.5439838798221812,0.4066174366275036,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n311364,0,Cash loans,F,Y,N,0,256500.0,728460.0,44563.5,675000.0,Family,Commercial associate,Incomplete higher,Single / not married,Co-op apartment,0.001276,-9706,-1026,-2390.0,-2390,17.0,1,1,0,1,1,0,Sales staff,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.4512991302494701,0.6817058776720116,0.0948,0.1753,0.9871,0.8232,0.0218,0.0,0.3103,0.1667,0.0417,0.0004,0.0773,0.1019,0.0,0.0,0.0966,0.1819,0.9871,0.8301,0.022000000000000002,0.0,0.3103,0.1667,0.0417,0.0004,0.0845,0.1062,0.0,0.0,0.0958,0.1753,0.9871,0.8256,0.022000000000000002,0.0,0.3103,0.1667,0.0417,0.0004,0.0787,0.1037,0.0,0.0,reg oper account,block of flats,0.1101,Panel,No,1.0,0.0,1.0,0.0,-1616.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n338542,0,Cash loans,F,N,N,0,135000.0,305221.5,20524.5,252000.0,Unaccompanied,Commercial associate,Higher education,Separated,Municipal apartment,0.022625,-21970,-112,-2796.0,-4795,,1,1,0,1,0,0,Medicine staff,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Hotel,,0.728789142995527,0.11761373170805695,0.1649,0.0,0.9796,0.7212,0.2295,0.16,0.1379,0.3333,0.375,0.0,0.1345,0.1066,0.0,0.0,0.1681,0.0,0.9796,0.7321,0.2316,0.1611,0.1379,0.3333,0.375,0.0,0.1469,0.1111,0.0,0.0,0.1665,0.0,0.9796,0.7249,0.2309,0.16,0.1379,0.3333,0.375,0.0,0.1368,0.1085,0.0,0.0,reg oper account,block of flats,0.1255,Block,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,0.0\n226840,0,Cash loans,F,N,Y,0,90000.0,256500.0,15822.0,256500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.015221,-23379,365243,-10472.0,-4599,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.6033044432607436,0.6296742509538716,0.0598,0.0654,0.9786,0.7076,0.0,0.0,0.1379,0.1667,0.0833,0.0,0.0471,0.0533,0.0077,0.0572,0.0609,0.0679,0.9786,0.7190000000000001,0.0,0.0,0.1379,0.1667,0.0833,0.0,0.0514,0.0555,0.0078,0.0605,0.0604,0.0654,0.9786,0.7115,0.0,0.0,0.1379,0.1667,0.0833,0.0,0.0479,0.0543,0.0078,0.0584,reg oper account,block of flats,0.0582,\"Stone, brick\",No,3.0,1.0,3.0,0.0,-183.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n169765,0,Cash loans,F,N,N,0,76500.0,82111.5,9283.5,67500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00823,-13296,-3245,-7166.0,-5208,,1,1,0,1,1,0,Drivers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Postal,,0.3995024656233843,0.10822632266971416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,block of flats,0.0607,Panel,No,3.0,0.0,3.0,0.0,-419.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n274764,0,Cash loans,F,N,N,0,90000.0,225000.0,12694.5,225000.0,Family,Pensioner,Secondary / secondary special,Separated,House / apartment,0.025164,-24354,365243,-8395.0,-4668,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.4500810426945106,0.6801388218428291,0.0835,0.0662,0.9752,0.66,0.0303,0.0,0.1379,0.1667,0.2083,0.0169,0.0672,0.0695,0.0039,0.0029,0.0851,0.0687,0.9752,0.6733,0.0306,0.0,0.1379,0.1667,0.2083,0.0173,0.0735,0.0725,0.0039,0.0031,0.0843,0.0662,0.9752,0.6645,0.0305,0.0,0.1379,0.1667,0.2083,0.0172,0.0684,0.0708,0.0039,0.003,org spec account,block of flats,0.0553,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n266487,0,Cash loans,F,Y,Y,0,90000.0,225000.0,16371.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.018209,-9598,-1134,-3521.0,-2274,14.0,1,1,1,1,1,1,Cleaning staff,2.0,3,3,THURSDAY,10,0,0,0,0,0,0,Transport: type 4,0.3852642627409284,0.3352498313875732,0.07396514611722674,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-906.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n373829,0,Cash loans,F,N,N,0,90000.0,675000.0,34596.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-18371,-2767,-5170.0,-1913,,1,1,1,1,0,0,Sales staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,,0.4197587337625287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-822.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n289898,0,Cash loans,F,N,Y,1,180000.0,207306.0,8910.0,148500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.010006,-17316,-5989,-34.0,-836,,1,1,0,1,0,0,Core staff,3.0,2,1,SUNDAY,13,0,0,0,0,1,1,School,,0.4495939743692048,0.7267112092725122,0.0866,0.0763,0.9995,0.9932,0.0087,0.12,0.1034,0.25,0.0417,0.0459,0.0656,0.0484,0.0232,0.2217,0.0882,0.0792,0.9995,0.9935,0.0088,0.1208,0.1034,0.25,0.0417,0.047,0.0716,0.0504,0.0233,0.2347,0.0874,0.0763,0.9995,0.9933,0.0087,0.12,0.1034,0.25,0.0417,0.0467,0.0667,0.0492,0.0233,0.2264,reg oper account,block of flats,0.091,Block,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n290321,0,Cash loans,F,N,Y,0,157500.0,422505.0,36261.0,391500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-11834,-2129,-5860.0,-2716,,1,1,0,1,1,0,Sales staff,2.0,2,2,THURSDAY,10,0,0,0,1,0,1,Trade: type 7,0.4773163255463404,0.4958799375105476,0.5567274263630174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-910.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n201722,0,Cash loans,F,N,N,0,171000.0,544491.0,15916.5,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.002042,-21020,-13940,-12587.0,-903,,1,1,0,1,1,0,Managers,2.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,School,0.7168579591558362,0.3064407450789713,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1326.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,2.0\n239904,0,Cash loans,F,N,Y,0,112500.0,142200.0,9216.0,112500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.030755,-9961,-2934,-4621.0,-2651,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,17,0,0,0,1,1,0,Kindergarten,0.484253808568291,0.662014087234755,0.4668640059537032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-892.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n192977,0,Cash loans,M,N,N,2,202500.0,539100.0,25933.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-14500,-922,-6471.0,-5168,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,13,0,1,1,0,1,1,Military,0.5755998029015758,0.17536286777570634,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-677.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n444226,0,Cash loans,F,N,Y,0,90000.0,770292.0,32764.5,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-19664,-11198,-7063.0,-3179,,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Medicine,,0.3112673050275249,0.7209441499436497,0.2969,0.0182,0.9811,0.7416,0.0558,0.36,0.3103,0.3333,0.375,0.2862,0.2421,0.3151,0.0,0.2156,0.3025,0.0189,0.9811,0.7517,0.0563,0.3625,0.3103,0.3333,0.375,0.2927,0.2645,0.3283,0.0,0.2282,0.2998,0.0182,0.9811,0.7451,0.0561,0.36,0.3103,0.3333,0.375,0.2912,0.2463,0.3208,0.0,0.2201,reg oper account,block of flats,0.3116,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-343.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n204092,0,Cash loans,M,Y,N,3,180000.0,808650.0,23305.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010276,-13646,-1516,-1124.0,-4541,5.0,1,1,0,1,0,0,Security staff,5.0,2,2,FRIDAY,16,0,0,0,0,0,0,Industry: type 9,,0.22277428398490995,0.24318648044201235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-2.0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n155201,0,Cash loans,M,N,Y,2,180000.0,1467612.0,58203.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011703,-12640,-3968,-6691.0,-4264,,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,14,1,1,0,0,0,0,Industry: type 3,0.3661103554230653,0.3850062954713647,,0.099,0.1342,0.9886,0.8436,0.0177,0.0,0.2759,0.1667,0.2083,0.0895,0.0807,0.1001,0.0,0.0,0.1008,0.1392,0.9886,0.8497,0.0179,0.0,0.2759,0.1667,0.2083,0.0916,0.0882,0.1043,0.0,0.0,0.0999,0.1342,0.9886,0.8457,0.0179,0.0,0.2759,0.1667,0.2083,0.0911,0.0821,0.1019,0.0,0.0,reg oper account,block of flats,0.0884,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n101059,0,Cash loans,M,Y,Y,0,225000.0,454500.0,46570.5,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019689,-12229,-4078,-90.0,-4138,1.0,1,1,0,1,1,0,High skill tech staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Industry: type 9,0.4194598758037255,0.6782965676556185,0.6023863442690867,0.0371,0.0384,0.9891,0.8504,0.0115,0.04,0.0345,0.3333,0.375,0.068,0.0303,0.049,0.0,0.0,0.0378,0.0398,0.9891,0.8563,0.0116,0.0403,0.0345,0.3333,0.375,0.0696,0.0331,0.051,0.0,0.0,0.0375,0.0384,0.9891,0.8524,0.0115,0.04,0.0345,0.3333,0.375,0.0692,0.0308,0.0499,0.0,0.0,reg oper account,block of flats,0.0448,Panel,No,0.0,0.0,0.0,0.0,-1737.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,2.0\n127002,0,Cash loans,F,Y,N,2,157500.0,758214.0,32256.0,603000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.020713,-11360,-1874,-226.0,-1786,13.0,1,1,0,1,0,0,Cleaning staff,4.0,3,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.11022565109349693,0.37263154928810016,,0.0598,0.0709,0.9846,0.7892,0.0115,0.0,0.1379,0.1667,0.2083,0.0689,0.0488,0.0575,0.0,0.0,0.0609,0.0736,0.9846,0.7975,0.0116,0.0,0.1379,0.1667,0.2083,0.0705,0.0533,0.0599,0.0,0.0,0.0604,0.0709,0.9846,0.792,0.0116,0.0,0.1379,0.1667,0.2083,0.0701,0.0496,0.0585,0.0,0.0,reg oper account,block of flats,0.0515,Panel,No,0.0,0.0,0.0,0.0,-1721.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n164705,1,Cash loans,F,Y,Y,0,360000.0,917860.5,49032.0,850500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.022625,-15897,-1520,-6176.0,-2445,9.0,1,1,0,1,0,0,Managers,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Trade: type 7,,0.01857782014417876,0.06733950871403091,0.0715,0.0631,0.9881,0.8368,0.0172,0.08,0.069,0.3608,0.4025,0.0615,0.0583,0.073,0.0,0.0524,0.042,0.0282,0.9821,0.7648,0.0071,0.0403,0.0345,0.375,0.4167,0.033,0.0367,0.0442,0.0,0.0,0.0416,0.027999999999999997,0.9906,0.8725,0.0078,0.04,0.0345,0.375,0.4167,0.0337,0.0342,0.0432,0.0,0.0,reg oper account,block of flats,0.1819,Panel,No,0.0,0.0,0.0,0.0,-454.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n434193,0,Revolving loans,F,N,Y,0,180000.0,495000.0,24750.0,495000.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.010006,-20417,365243,-66.0,-3895,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,0.781139214579361,0.17078128702927053,0.6075573001388961,0.0918,0.0836,0.9801,0.728,0.102,0.0,0.2069,0.1667,0.2083,0.1059,0.0748,0.0785,0.0,0.0,0.0935,0.0868,0.9801,0.7387,0.1029,0.0,0.2069,0.1667,0.2083,0.1083,0.0817,0.0818,0.0,0.0,0.0926,0.0836,0.9801,0.7316,0.1026,0.0,0.2069,0.1667,0.2083,0.1077,0.0761,0.0799,0.0,0.0,reg oper account,block of flats,0.1175,Panel,No,2.0,1.0,2.0,1.0,-276.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n149924,0,Cash loans,M,Y,N,0,270000.0,1451047.5,59098.5,1354500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.01885,-10629,-583,-4673.0,-3299,5.0,1,1,1,1,0,0,,2.0,2,2,THURSDAY,10,0,1,1,0,1,1,Transport: type 4,0.1294558494861845,0.7198823277847108,0.5971924268337128,0.23199999999999998,,0.9866,,,0.24,0.2069,0.3333,,,,0.1529,,0.0064,0.2363,,0.9866,,,0.2417,0.2069,0.3333,,,,0.1593,,0.0068,0.2342,,0.9866,,,0.24,0.2069,0.3333,,,,0.1557,,0.0066,,block of flats,0.1988,Mixed,No,0.0,0.0,0.0,0.0,-569.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n301779,0,Cash loans,F,N,Y,0,112500.0,545040.0,26640.0,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.030755,-10194,-253,-4668.0,-2864,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Transport: type 4,0.3627648680210225,0.1626561934571403,0.28812959991785075,0.0103,0.0,0.9727,,,0.0,0.0345,0.0,,0.0,,0.0034,,0.0,0.0105,0.0,0.9727,,,0.0,0.0345,0.0,,0.0,,0.0035,,0.0,0.0104,0.0,0.9727,,,0.0,0.0345,0.0,,0.0,,0.0035,,0.0,,block of flats,0.0037,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n119182,0,Revolving loans,F,N,Y,0,270000.0,585000.0,29250.0,585000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.072508,-18063,-4041,-126.0,-1554,,1,1,0,1,0,1,,1.0,1,1,MONDAY,18,0,0,0,0,0,0,Other,0.8784161275238874,0.7689314160842795,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-346.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n187054,0,Cash loans,F,N,Y,0,90000.0,536917.5,30109.5,463500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.019101,-12438,-1107,-6576.0,-3991,,1,1,0,1,0,0,IT staff,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,School,0.5420412733822496,0.20977120641809047,0.7922644738669378,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2554.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n318603,0,Cash loans,F,N,Y,0,90000.0,675000.0,26541.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018209,-20786,365243,-331.0,-4157,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,12,0,0,0,0,0,0,XNA,0.8200981938698456,0.4972739296449821,0.8038850611746273,0.3701,0.1455,0.998,,,0.0,0.1724,0.1667,,0.127,,0.0704,,0.0,0.3771,0.1509,0.998,,,0.0,0.1724,0.1667,,0.1299,,0.0734,,0.0,0.3737,0.1455,0.998,,,0.0,0.1724,0.1667,,0.1293,,0.0717,,0.0,,block of flats,0.0904,\"Stone, brick\",No,3.0,1.0,3.0,0.0,-1341.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n322313,0,Cash loans,M,Y,Y,0,202500.0,1255680.0,41499.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006233,-21137,-854,-10762.0,-4382,13.0,1,1,0,1,1,0,Drivers,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Transport: type 3,,0.5713841383255198,0.5082869913916046,0.0598,0.0488,0.9851,0.7959999999999999,0.0222,0.0664,0.0572,0.3333,0.2638,0.0474,0.0476,0.0478,0.0064,0.0153,0.0273,0.0,0.9851,0.804,0.0,0.0806,0.069,0.3333,0.375,0.0077,0.0661,0.0188,0.0039,0.0035,0.076,0.0552,0.9851,0.7987,0.0185,0.08,0.069,0.3333,0.375,0.0448,0.0616,0.0508,0.0039,0.0058,reg oper account,block of flats,0.0263,Panel,No,0.0,0.0,0.0,0.0,-200.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,2.0\n136672,0,Cash loans,M,Y,Y,0,202500.0,1078200.0,31653.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.010032,-22321,365243,-10424.0,-4929,17.0,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,6,0,0,0,0,0,0,XNA,0.64234737482698,0.6227442360788253,0.7435593141311444,0.0227,0.0603,0.9737,0.6396,0.0231,0.0,0.1034,0.125,0.1667,0.1542,0.0185,0.0432,0.0,0.0,0.0231,0.0626,0.9737,0.6537,0.0234,0.0,0.1034,0.125,0.1667,0.1577,0.0202,0.045,0.0,0.0,0.0229,0.0603,0.9737,0.6444,0.0233,0.0,0.1034,0.125,0.1667,0.1569,0.0188,0.044000000000000004,0.0,0.0,,block of flats,0.0467,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n454803,0,Cash loans,F,Y,N,0,67500.0,270000.0,15628.5,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-22333,-6119,-5674.0,-4270,4.0,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,0.8460028291449742,0.4373326493715029,0.4596904504249018,0.2646,0.1803,0.9786,0.7076,0.0979,0.16,0.2069,0.2775,0.3192,0.1275,0.2152,0.2472,0.0025,0.0005,0.1261,0.122,0.9782,0.7125,0.0862,0.2417,0.2069,0.3333,0.375,0.0858,0.2975,0.107,0.0,0.0008,0.3373,0.2116,0.9786,0.7115,0.1033,0.24,0.2069,0.3333,0.375,0.1516,0.27699999999999997,0.3248,0.0,0.0007,reg oper account,block of flats,0.3072,Panel,No,0.0,0.0,0.0,0.0,-1558.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n395162,0,Cash loans,F,N,Y,0,157500.0,808650.0,23773.5,675000.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.04622,-14621,-367,-3249.0,-4407,,1,1,0,1,0,0,Sales staff,2.0,1,1,WEDNESDAY,17,0,0,0,0,0,0,Trade: type 3,0.46651370343625603,0.7052342679296125,0.3077366963789207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-566.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n227794,0,Cash loans,M,Y,Y,0,247500.0,675000.0,21906.0,675000.0,Family,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.011703,-18873,365243,-7398.0,-2390,8.0,1,0,0,1,0,0,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,XNA,0.406788144028456,0.7318470159294798,0.4776491548517548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-911.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n270362,0,Cash loans,F,N,Y,1,94500.0,463284.0,25996.5,382500.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-14681,-2204,-7415.0,-3967,,1,1,1,1,1,0,High skill tech staff,3.0,2,2,THURSDAY,12,0,0,0,0,0,0,Housing,,0.37679190994147616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1180.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n351634,0,Revolving loans,M,Y,Y,0,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018209,-17523,-8226,-1649.0,-1084,8.0,1,1,1,1,1,0,Managers,2.0,3,3,TUESDAY,11,0,0,0,0,0,0,Self-employed,0.6622988471386106,0.4018497319143061,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-229.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n117884,0,Revolving loans,M,N,Y,0,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-16257,-1708,-5681.0,-4136,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.2910579719771149,0.4380269228752889,0.6832688314232291,0.1187,0.0816,0.9861,0.83,0.052000000000000005,0.168,0.141,0.2421,0.0417,0.0757,0.2421,0.0917,,0.0124,0.0578,0.0572,0.9856,0.8367,0.0525,0.1208,0.1379,0.1667,0.0417,0.0421,0.2645,0.0573,,0.0037,0.1124,0.0673,0.9856,0.8323,0.0523,0.12,0.1379,0.1667,0.0417,0.0419,0.2463,0.055999999999999994,,0.008,reg oper account,block of flats,0.046,Panel,No,0.0,0.0,0.0,0.0,-161.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n327713,0,Cash loans,F,N,Y,0,121500.0,239850.0,23494.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23515,365243,-9942.0,-4158,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,XNA,,0.3542247319929012,0.3656165070113335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1853.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n199825,0,Cash loans,F,N,Y,0,270000.0,675000.0,47110.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-20463,365243,-722.0,-3377,,1,0,0,1,1,0,,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,XNA,0.7871654067742511,0.4901463467229177,0.3296550543128238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-284.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n275412,0,Cash loans,F,N,Y,0,81000.0,343800.0,16852.5,225000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-10266,-444,-4001.0,-2657,,1,1,1,1,0,0,Sales staff,1.0,3,3,SUNDAY,8,0,0,0,1,1,0,Business Entity Type 3,0.20812906723948948,0.15912544355063846,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1350.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n358296,0,Cash loans,F,N,Y,0,112500.0,1255680.0,41629.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.032561,-22958,365243,-6091.0,-4388,,1,0,0,1,1,0,,1.0,1,1,TUESDAY,11,0,0,0,0,0,0,XNA,,0.6153699999307374,0.5495965024956946,,,0.9483,,,,,,,,,,,,,,0.9484,,,,,,,,,,,,,,0.9483,,,,,,,,,,,,,block of flats,0.0337,,No,0.0,0.0,0.0,0.0,-467.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n252456,1,Cash loans,M,Y,Y,1,225000.0,1042560.0,61087.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16149,-1325,-2825.0,-4114,13.0,1,1,0,1,1,0,Drivers,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Self-employed,,0.6909489832936385,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2664.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n279829,0,Cash loans,F,N,N,1,103500.0,648000.0,18702.0,648000.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,With parents,0.003540999999999999,-8731,-369,-1362.0,-1252,,1,1,0,1,0,0,Core staff,3.0,1,1,SATURDAY,9,0,0,0,0,0,0,Government,0.08254293964231334,0.5768164518605912,0.2822484337007223,0.0186,,0.9831,,,,,0.0417,,0.0153,,,,,0.0189,,0.9831,,,,,0.0417,,0.0156,,,,,0.0187,,0.9831,,,,,0.0417,,0.0155,,,,,,,0.0129,Wooden,No,4.0,0.0,4.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n341593,0,Cash loans,F,N,Y,0,56349.0,270000.0,14656.5,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-17353,-2644,-6580.0,-900,,1,1,1,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Other,,0.571453757958032,,0.2608,0.1791,0.9776,,,0.2,0.1724,0.3333,,0.1054,,0.2206,,0.1463,0.2658,0.1858,0.9777,,,0.2014,0.1724,0.3333,,0.1078,,0.2299,,0.1549,0.2634,0.1791,0.9776,,,0.2,0.1724,0.3333,,0.1072,,0.2246,,0.1494,,block of flats,0.2555,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1166.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n162008,0,Cash loans,M,N,N,0,112500.0,260640.0,19615.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.008068,-8652,-552,-4885.0,-1341,,1,1,1,1,0,0,Security staff,1.0,3,3,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.15999406194655807,0.7205168755611916,0.3077366963789207,0.0649,0.0851,0.9811,0.7416,0.0076,0.0,0.1379,0.1667,0.0417,0.0473,0.0521,0.0603,0.0039,0.0036,0.0662,0.0883,0.9811,0.7517,0.0077,0.0,0.1379,0.1667,0.0417,0.0484,0.0569,0.0628,0.0039,0.0039,0.0656,0.0851,0.9811,0.7451,0.0077,0.0,0.1379,0.1667,0.0417,0.0482,0.053,0.0613,0.0039,0.0037,reg oper account,block of flats,0.0524,Mixed,No,4.0,0.0,4.0,0.0,-532.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n255178,0,Cash loans,F,N,Y,0,76500.0,495000.0,26370.0,495000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-15430,-294,-3004.0,-4250,,1,1,1,1,1,0,Core staff,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Kindergarten,,0.5709127927980233,0.3876253444214701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1752.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n328293,0,Revolving loans,F,Y,N,0,67500.0,157500.0,7875.0,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-16682,365243,-3457.0,-189,64.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,0.7334637559109815,0.6257602464687839,0.5154953751603267,0.0495,0.0269,0.9826,0.762,0.0095,0.08,0.0345,0.4583,0.0417,0.0,0.0403,0.0497,0.0,0.0,0.0504,0.0279,0.9826,0.7713,0.0096,0.0806,0.0345,0.4583,0.0417,0.0,0.0441,0.0518,0.0,0.0,0.05,0.0269,0.9826,0.7652,0.0096,0.08,0.0345,0.4583,0.0417,0.0,0.040999999999999995,0.0506,0.0,0.0,reg oper account,block of flats,0.0443,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-2075.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n238382,0,Cash loans,F,Y,N,0,157500.0,990000.0,35559.0,990000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.015221,-17081,-2927,-5026.0,-641,18.0,1,1,0,1,1,0,,2.0,2,2,TUESDAY,13,0,0,0,0,1,1,Other,0.5103965864018607,0.38068175952443467,0.3979463219016906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2558.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n363980,0,Cash loans,F,N,Y,0,270000.0,773460.0,55138.5,697500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.032561,-21863,365243,-13614.0,-5341,,1,0,0,1,1,1,,2.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,XNA,0.6368458802809316,0.7541183995760066,0.2418614865234661,0.0278,0.0475,0.9379,0.15,0.0072,0.0,0.1034,0.125,0.1667,0.0193,0.0202,0.0418,0.0116,0.0523,0.0284,0.0493,0.9379,0.1833,0.0072,0.0,0.1034,0.125,0.1667,0.0198,0.022000000000000002,0.0436,0.0117,0.0553,0.0281,0.0475,0.9379,0.1614,0.0072,0.0,0.1034,0.125,0.1667,0.0197,0.0205,0.0426,0.0116,0.0534,reg oper account,block of flats,0.0482,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1728.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,9.0\n285063,0,Cash loans,M,Y,Y,0,135000.0,1046142.0,33876.0,913500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.00496,-19534,365243,-7693.0,-3072,18.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.5050093922806072,0.7435593141311444,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-454.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n246420,0,Cash loans,M,N,N,0,157500.0,1133748.0,33277.5,990000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-9421,-344,-72.0,-2095,,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.14458591788273326,0.10992402467829904,0.3108182544189319,0.0062,0.0223,0.9687,,,0.0,0.069,0.0417,,0.0103,,0.0058,,0.0046,0.0063,0.0231,0.9687,,,0.0,0.069,0.0417,,0.0105,,0.0061,,0.0049,0.0062,0.0223,0.9687,,,0.0,0.069,0.0417,,0.0105,,0.0059,,0.0047,,block of flats,0.0056,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n187911,0,Cash loans,M,Y,Y,2,121500.0,900000.0,26316.0,900000.0,Family,Working,Higher education,Married,House / apartment,0.008575,-12552,-592,-6585.0,-4048,28.0,1,1,0,1,0,0,Drivers,4.0,2,2,SUNDAY,13,0,0,0,0,0,0,Government,0.2789350924824925,0.5922741570016928,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-503.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n117118,0,Cash loans,M,N,N,0,157500.0,835380.0,32818.5,675000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.006670999999999999,-8677,-1537,-839.0,-1371,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,19,0,0,0,0,0,0,Self-employed,,0.10754003944313376,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-14.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,,,,,,\n352689,0,Cash loans,M,Y,Y,0,135000.0,454500.0,18639.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-20174,-4754,-12147.0,-3516,12.0,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Business Entity Type 2,,0.559305098374861,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2724.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,0.0\n293215,0,Revolving loans,F,N,Y,0,112500.0,247500.0,12375.0,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008019,-20860,365243,-2389.0,-4166,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.6448026848809506,0.4382813743111921,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-172.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n266497,0,Cash loans,M,N,Y,0,144000.0,545040.0,26640.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.025164,-12272,-2340,-7034.0,-238,,1,1,0,1,1,0,Sales staff,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.4367846161296789,0.6647096653065743,0.3360615207658242,0.4959,0.2446,0.9876,0.83,0.1776,0.48,0.4138,0.375,0.4167,0.2956,0.3976,0.5097,0.0309,0.0277,0.5053,0.2539,0.9876,0.8367,0.1793,0.4834,0.4138,0.375,0.4167,0.3023,0.4343,0.531,0.0311,0.0293,0.5007,0.2446,0.9876,0.8323,0.1788,0.48,0.4138,0.375,0.4167,0.3007,0.4045,0.5189,0.0311,0.0283,not specified,block of flats,0.504,Panel,No,2.0,0.0,2.0,0.0,-552.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,4.0\n161780,0,Revolving loans,F,N,N,0,180000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.035792000000000004,-9215,-411,-3938.0,-1868,,1,1,0,1,0,1,HR staff,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,0.5182240258863687,0.5042058270461132,0.1419915427739129,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-215.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n303239,0,Cash loans,F,N,N,0,72000.0,117162.0,11542.5,103500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010276,-23671,365243,-11021.0,-4009,,1,0,0,1,0,0,,1.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.4608517337795258,0.8327850252992314,0.0907,0.0864,0.9846,,,0.0,0.2069,0.1667,,,,0.0895,,,0.0924,0.0896,0.9846,,,0.0,0.2069,0.1667,,,,0.0932,,,0.0916,0.0864,0.9846,,,0.0,0.2069,0.1667,,,,0.0911,,,,block of flats,0.0773,Panel,No,4.0,0.0,4.0,0.0,-596.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n194142,1,Cash loans,F,N,Y,0,135000.0,121500.0,6039.0,121500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.014464,-16757,-1595,-8638.0,-291,,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,5,0,0,0,0,0,0,Self-employed,,0.6876472820108157,0.3556387169923543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-787.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n285871,0,Cash loans,F,N,Y,0,202500.0,276277.5,13419.0,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010147,-22499,365243,-5190.0,-5147,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.7686904370380906,0.5549467685334323,0.0825,0.0449,0.9901,,,0.08,0.0345,0.5417,,0.0262,,0.0761,,0.0144,0.084,0.0466,0.9901,,,0.0806,0.0345,0.5417,,0.0268,,0.0793,,0.0153,0.0833,0.0449,0.9901,,,0.08,0.0345,0.5417,,0.0266,,0.0775,,0.0147,,block of flats,0.0631,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-528.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n384271,0,Cash loans,F,N,Y,0,157500.0,571446.0,16839.0,477000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006852,-23355,-10353,-11248.0,-3977,,1,1,0,1,0,0,IT staff,2.0,3,3,THURSDAY,8,0,0,0,0,0,0,University,,0.028968025658566376,0.6496203111237195,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2524.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n194825,0,Cash loans,M,Y,N,0,180000.0,149256.0,17010.0,135000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.004849,-9681,-576,-4012.0,-2098,8.0,1,1,1,1,0,0,Managers,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.32836257852264605,0.4674815520129408,0.7309873696832169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-564.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n259654,1,Cash loans,M,N,Y,0,90000.0,829224.0,26878.5,594000.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-16974,-170,-850.0,-530,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,,0.5188209751454279,0.3123653692278984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-768.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n222247,0,Cash loans,M,N,Y,0,202500.0,335592.0,21577.5,265500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-20379,-4215,-12530.0,-3224,,1,1,0,1,1,0,Security staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,,0.4520031812284137,0.3296550543128238,0.0052,,0.9727,,,,,0.0,,,,0.0023,,,0.0053,,0.9727,,,,,0.0,,,,0.0024,,,0.0052,,0.9727,,,,,0.0,,,,0.0024,,,,block of flats,,Wooden,No,0.0,0.0,0.0,0.0,-914.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n258877,0,Cash loans,F,N,N,0,180000.0,524866.5,25659.0,369000.0,Unaccompanied,Working,Lower secondary,Single / not married,House / apartment,0.018634,-10513,-574,-6381.0,-3090,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,14,0,0,0,1,1,0,Postal,0.6076159441127615,0.3337894094113413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n351729,1,Cash loans,F,N,Y,0,202500.0,450000.0,35554.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010643,-13691,-1415,-5741.0,-3209,,1,1,1,1,0,0,Security staff,1.0,2,2,SATURDAY,9,0,0,0,0,1,1,Government,0.4672595661525424,0.4349771956131108,0.12769879437277135,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-982.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n165693,1,Cash loans,F,N,Y,0,135000.0,431280.0,22149.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-17077,-404,-2338.0,-630,,1,1,1,1,1,0,Cooking staff,2.0,3,3,THURSDAY,6,0,0,0,0,0,0,Business Entity Type 3,,0.22195751804223568,0.7610263695502636,0.1237,0.1081,0.9786,0.7076,0.017,0.0,0.2759,0.1667,0.2083,0.049,0.0992,0.1119,0.0077,0.0063,0.1261,0.1122,0.9786,0.7190000000000001,0.0171,0.0,0.2759,0.1667,0.2083,0.0501,0.1084,0.1166,0.0078,0.0067,0.1249,0.1081,0.9786,0.7115,0.0171,0.0,0.2759,0.1667,0.2083,0.0499,0.1009,0.1139,0.0078,0.0065,reg oper account,block of flats,0.0987,Panel,No,7.0,0.0,7.0,0.0,-620.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n450576,0,Cash loans,M,Y,Y,0,225000.0,540000.0,42795.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010643,-20177,-777,-3597.0,-3617,16.0,1,1,1,1,0,0,Laborers,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,Construction,,0.6098171222848959,,0.0186,0.0,0.9811,,,0.0,0.1034,0.0417,,0.0545,,0.0191,,0.0,0.0189,0.0,0.9811,,,0.0,0.1034,0.0417,,0.0558,,0.02,,0.0,0.0187,0.0,0.9811,,,0.0,0.1034,0.0417,,0.0555,,0.0195,,0.0,,block of flats,0.0162,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1127.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n154714,0,Cash loans,F,Y,Y,0,45900.0,540000.0,21546.0,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-21741,365243,-9442.0,-3870,32.0,1,0,0,1,0,0,,2.0,3,3,MONDAY,6,0,0,0,0,0,0,XNA,,0.5889960699369411,0.6161216908872079,0.1072,0.0773,0.9851,0.7959999999999999,0.1911,0.12,0.1034,0.3333,0.0417,0.0144,0.0857,0.1112,0.0077,0.0186,0.1092,0.0802,0.9851,0.804,0.1928,0.1208,0.1034,0.3333,0.0417,0.0147,0.0937,0.1159,0.0078,0.0197,0.1083,0.0773,0.9851,0.7987,0.1923,0.12,0.1034,0.3333,0.0417,0.0146,0.0872,0.1132,0.0078,0.019,reg oper account,block of flats,0.1045,Panel,No,0.0,0.0,0.0,0.0,-909.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n332937,0,Cash loans,F,N,N,0,55350.0,545040.0,20547.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.0105,-19395,365243,-9014.0,-2921,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,XNA,,0.5808360162714618,0.5744466170995097,,0.2434,0.9861,,,0.32,0.2759,0.3333,,,,0.3011,,0.1964,,0.2526,0.9861,,,0.3222,0.2759,0.3333,,,,0.3138,,0.2079,,0.2434,0.9861,,,0.32,0.2759,0.3333,,,,0.3066,,0.2005,,,0.2796,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-894.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n340877,0,Cash loans,F,N,N,0,157500.0,824823.0,22810.5,688500.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.018209,-10987,-953,-5113.0,-3491,,1,1,0,1,0,0,Accountants,1.0,3,3,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.5582306489229593,0.6126231664059472,0.656158373001177,0.068,0.0759,0.9846,0.7892,0.0919,0.0,0.1379,0.1667,0.2083,0.0476,0.0555,0.0583,0.0,0.0202,0.0693,0.0788,0.9846,0.7975,0.0927,0.0,0.1379,0.1667,0.2083,0.0487,0.0606,0.0607,0.0,0.0214,0.0687,0.0759,0.9846,0.792,0.0924,0.0,0.1379,0.1667,0.2083,0.0484,0.0564,0.0593,0.0,0.0206,reg oper account,block of flats,0.0502,Panel,No,1.0,0.0,1.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n185864,0,Cash loans,F,N,N,0,270000.0,1258650.0,53325.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-17596,-1030,-178.0,-1093,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,15,0,1,1,0,1,1,Business Entity Type 2,,0.5011827307766367,0.1766525794312139,0.2577,0.2318,0.9891,,,0.28,0.2414,0.3333,,0.0495,,0.2744,,0.0033,0.2626,0.2406,0.9891,,,0.282,0.2414,0.3333,,0.0507,,0.2859,,0.0035,0.2602,0.2318,0.9891,,,0.28,0.2414,0.3333,,0.0504,,0.2793,,0.0034,,block of flats,0.2795,Block,Yes,2.0,0.0,2.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n402518,1,Cash loans,M,N,N,1,94500.0,244584.0,19453.5,193500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.006852,-10081,-603,-7989.0,-2735,,1,1,0,1,0,0,Low-skill Laborers,2.0,3,3,SATURDAY,12,0,0,0,1,1,0,Self-employed,0.2416971721872857,0.0038007561095146952,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n322943,0,Cash loans,M,N,Y,0,112500.0,328500.0,10525.5,328500.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-9186,-757,-4054.0,-1849,,1,1,0,1,1,0,Core staff,2.0,3,3,THURSDAY,9,0,0,0,0,1,1,Police,0.16428205614963315,0.4154242774663085,0.2366108235287817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-700.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n319038,1,Revolving loans,F,N,Y,1,49500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-14388,-3269,-1502.0,-4712,,1,1,1,1,1,0,Sales staff,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.5651922374181511,0.5762451984170266,0.23272477626794336,0.0227,,0.9856,,,,0.1034,0.0417,,,,0.0113,,,0.0231,,0.9856,,,,0.1034,0.0417,,,,0.0118,,,0.0229,,0.9856,,,,0.1034,0.0417,,,,0.0115,,,,block of flats,0.0138,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-380.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n417034,0,Cash loans,F,N,Y,1,157500.0,365175.0,28980.0,301500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-9967,-528,-113.0,-2241,,1,1,0,1,0,1,,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Restaurant,0.4132607228423372,0.4035539299613776,0.15286622915504153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n218922,0,Cash loans,F,Y,N,0,247500.0,2013840.0,53253.0,1800000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.007114,-16528,-6249,-5074.0,-76,15.0,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Government,,0.3827151319898409,0.326475210066026,0.0412,,0.9781,,,,0.069,0.2083,,,,0.0356,,0.0256,0.042,,0.9782,,,,0.069,0.2083,,,,0.037000000000000005,,0.0271,0.0416,,0.9781,,,,0.069,0.2083,,,,0.0362,,0.0261,,block of flats,0.0321,\"Stone, brick\",No,5.0,3.0,5.0,1.0,-1963.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n235859,0,Revolving loans,M,N,N,0,292500.0,337500.0,16875.0,337500.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.072508,-23023,365243,-2312.0,-4383,,1,0,0,1,0,0,,2.0,1,1,THURSDAY,10,0,0,0,0,0,0,XNA,0.8132863490436355,0.12685915992318075,0.6642482627052363,0.3613,0.1848,0.9836,0.7756,0.1459,0.44,0.1897,0.625,0.6667,0.0,0.2942,0.3888,0.0039,0.0071,0.3351,0.1483,0.9836,0.7844,0.1357,0.4028,0.1724,0.625,0.6667,0.0,0.292,0.3742,0.0039,0.0074,0.3648,0.1848,0.9836,0.7786,0.1468,0.44,0.1897,0.625,0.6667,0.0,0.2993,0.3958,0.0039,0.0072,reg oper account,block of flats,0.284,Panel,No,0.0,0.0,0.0,0.0,-2430.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n259851,0,Cash loans,F,N,N,2,193500.0,900000.0,24750.0,900000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.0228,-12085,-657,-5857.0,-4404,,1,1,1,1,1,0,Sales staff,4.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.4303437633914861,0.6684082851964619,0.1595195404777181,0.0041,,0.9722,,,,0.1379,0.0417,,0.0131,,0.0032,,,0.0042,,0.9722,,,,0.1379,0.0417,,0.0134,,0.0033,,,0.0042,,0.9722,,,,0.1379,0.0417,,0.0133,,0.0032,,,,block of flats,0.0032,Wooden,No,4.0,1.0,4.0,1.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n174130,0,Cash loans,M,N,Y,0,90000.0,450000.0,19822.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00963,-13436,-1621,-7588.0,-3893,,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Other,,0.4912557422193819,0.17982176508970435,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-780.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n206782,1,Cash loans,F,N,Y,1,99000.0,265851.0,22873.5,229500.0,Unaccompanied,Working,Incomplete higher,Civil marriage,With parents,0.003813,-11891,-448,-3690.0,-383,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,13,0,0,0,1,1,0,Business Entity Type 3,0.2512942582519669,0.2354160850071869,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1615.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n363807,0,Cash loans,M,Y,N,0,180000.0,990000.0,50422.5,990000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.015221,-21585,-1194,-4693.0,-4746,21.0,1,1,0,1,0,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,Construction,0.8276155072930528,0.5649566388666986,0.6109913280868294,0.0309,0.031,0.9722,,,0.0,0.069,0.125,,0.015,,0.0235,,0.0053,0.0315,0.0321,0.9722,,,0.0,0.069,0.125,,0.0153,,0.0245,,0.0057,0.0312,0.031,0.9722,,,0.0,0.069,0.125,,0.0152,,0.0239,,0.0055,,block of flats,0.0264,Block,No,0.0,0.0,0.0,0.0,-2016.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n221413,0,Cash loans,F,Y,Y,0,135000.0,117162.0,11542.5,103500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010966,-15197,-2313,-14673.0,-4089,4.0,1,1,0,1,1,0,Laborers,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,University,,0.7036179387119115,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2770.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n326318,1,Cash loans,F,N,Y,0,63000.0,422892.0,23742.0,382500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.020246,-22111,365243,-7244.0,-4194,,1,0,0,1,1,0,,2.0,3,3,THURSDAY,8,0,0,0,0,0,0,XNA,0.6385756577412139,0.481947111378841,0.8658959152750668,0.0722,0.0735,0.9841,0.7824,0.0181,0.0,0.1724,0.1667,0.2083,0.1207,0.057999999999999996,0.071,0.0039,0.0078,0.0735,0.0763,0.9841,0.7909,0.0183,0.0,0.1724,0.1667,0.2083,0.1234,0.0634,0.07400000000000001,0.0039,0.0083,0.0729,0.0735,0.9841,0.7853,0.0182,0.0,0.1724,0.1667,0.2083,0.1228,0.059000000000000004,0.0723,0.0039,0.008,reg oper account,block of flats,0.0658,Panel,No,1.0,0.0,1.0,0.0,-767.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n314834,0,Cash loans,M,Y,Y,1,157500.0,517266.0,26541.0,387000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-18018,-843,-11986.0,-1553,12.0,1,1,0,1,0,1,Drivers,3.0,2,2,FRIDAY,8,0,0,0,0,0,0,Transport: type 4,,0.3420278293317465,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-55.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n120627,0,Cash loans,M,N,Y,0,135000.0,840951.0,33480.0,679500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-20881,365243,-9352.0,-3840,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.7724519850473689,0.4668640059537032,0.4423,0.2858,0.9876,0.83,0.0718,0.48,0.4138,0.3333,0.375,0.2417,0.3606,0.4697,0.0,0.0,0.4506,0.2966,0.9876,0.8367,0.0725,0.4834,0.4138,0.3333,0.375,0.2472,0.3939,0.4894,0.0,0.0,0.4466,0.2858,0.9876,0.8323,0.0723,0.48,0.4138,0.3333,0.375,0.2459,0.3668,0.4782,0.0,0.0,reg oper account,block of flats,0.4307,Panel,No,0.0,0.0,0.0,0.0,-301.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n402303,0,Cash loans,F,N,Y,1,112500.0,162000.0,10674.0,162000.0,Unaccompanied,Working,Higher education,Widow,House / apartment,0.022625,-16283,-2711,-10359.0,-4631,,1,1,0,1,0,0,Private service staff,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,,0.6018119314305592,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1035.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n102240,0,Cash loans,M,N,N,0,180000.0,679500.0,36333.0,679500.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.010556,-10712,-921,-588.0,-3395,,1,1,1,1,1,0,Managers,2.0,3,3,THURSDAY,11,0,0,0,1,1,0,Business Entity Type 3,,0.5088354669818597,0.42589289800515295,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-500.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n424280,0,Cash loans,F,N,Y,0,270000.0,1002456.0,39883.5,810000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-11749,-4827,-5843.0,-3186,,1,1,0,1,1,0,,2.0,1,1,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.4805061134811367,0.6813048419527907,0.34578480246959553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2486,,No,0.0,0.0,0.0,0.0,-1497.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n102089,0,Cash loans,F,N,Y,0,144000.0,783927.0,34659.0,688500.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.02461,-18158,-5890,-12018.0,-1657,,1,1,1,1,1,0,Accountants,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Industry: type 7,,0.6460487859569967,0.6706517530862718,0.0392,,0.9831,0.7008,,0.0,0.1379,0.2083,,0.0507,0.0572,0.0513,0.0,0.0003,0.0084,,0.9782,0.7125,,0.0,0.1379,0.2083,,0.0519,0.0624,0.0744,0.0,0.0,0.0396,,0.9781,0.7048,,0.0,0.1379,0.2083,,0.0516,0.0581,0.0727,0.0,0.0,org spec account,block of flats,0.0562,Panel,No,0.0,0.0,0.0,0.0,-1054.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n194831,1,Cash loans,F,N,N,2,135000.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.020246,-13796,-2676,-5952.0,-5952,,1,1,0,1,1,0,,3.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,Restaurant,,0.008267393298023085,0.23272477626794336,0.0928,0.1017,0.9831,0.7688,0.0824,0.0,0.2069,0.1667,0.2083,0.0683,0.0756,0.0874,0.0,0.0,0.0945,0.1056,0.9831,0.7779,0.0831,0.0,0.2069,0.1667,0.2083,0.0698,0.0826,0.0911,0.0,0.0,0.0937,0.1017,0.9831,0.7719,0.0829,0.0,0.2069,0.1667,0.2083,0.0694,0.077,0.08900000000000001,0.0,0.0,reg oper account,block of flats,0.1138,Panel,No,0.0,0.0,0.0,0.0,-169.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n403557,0,Cash loans,F,N,Y,0,103500.0,1223010.0,51948.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-20576,-11224,-4068.0,-4129,,1,1,1,1,0,0,Laborers,2.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Industry: type 11,,0.37782973107777656,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n415215,0,Cash loans,M,Y,Y,1,202500.0,1546020.0,45333.0,1350000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.022625,-11670,-3084,-6964.0,-3890,4.0,1,1,0,1,1,0,Sales staff,3.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.7645039604907232,0.6195277080511546,0.166,0.1257,0.9906,0.8708,0.0936,0.16,0.1379,0.375,0.4167,0.0436,0.1345,0.19,0.0039,0.0011,0.1691,0.1305,0.9906,0.8759,0.0944,0.1611,0.1379,0.375,0.4167,0.0446,0.1469,0.1979,0.0039,0.0011,0.1676,0.1257,0.9906,0.8725,0.0941,0.16,0.1379,0.375,0.4167,0.0443,0.1368,0.1934,0.0039,0.0011,reg oper account,block of flats,0.2008,Panel,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n367405,0,Cash loans,M,Y,N,0,225000.0,1575000.0,43443.0,1575000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.031329,-18194,-7159,-3993.0,-1745,3.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.6103269134530295,,0.1485,0.1028,0.9796,0.7212,0.0256,0.16,0.1379,0.3333,0.375,0.2253,0.121,0.14400000000000002,0.0,0.0,0.1513,0.1067,0.9796,0.7321,0.0258,0.1611,0.1379,0.3333,0.375,0.2305,0.1322,0.1501,0.0,0.0,0.1499,0.1028,0.9796,0.7249,0.0257,0.16,0.1379,0.3333,0.375,0.2293,0.1231,0.1466,0.0,0.0,reg oper account,block of flats,0.1272,Panel,No,0.0,0.0,0.0,0.0,-2167.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n387793,0,Revolving loans,M,Y,Y,0,112500.0,382500.0,19125.0,382500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15067,-1082,-7519.0,-4963,4.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Government,,0.6254631784690547,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1928.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n335960,1,Cash loans,M,Y,Y,0,270000.0,225000.0,25447.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005002,-14608,-3659,-4257.0,-2826,30.0,1,1,1,1,0,1,Drivers,2.0,3,3,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.2446449307737009,0.5774622013019081,0.4101025731788671,0.0407,0.0769,0.9722,0.762,0.0081,0.0,0.1379,0.1042,0.2083,0.0334,0.0588,0.0358,0.0,0.0,0.0095,0.0798,0.9623,0.7713,0.0081,0.0,0.1379,0.0417,0.2083,0.0341,0.0643,0.0063,0.0,0.0,0.0411,0.0769,0.9722,0.7652,0.0081,0.0,0.1379,0.1042,0.2083,0.0339,0.0599,0.0364,0.0,0.0,,block of flats,0.055999999999999994,\"Stone, brick\",No,14.0,1.0,14.0,1.0,-541.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,1.0\n314901,0,Cash loans,M,Y,Y,0,315000.0,298512.0,35554.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-15237,-5983,-3328.0,-3328,15.0,1,1,0,1,1,0,Laborers,2.0,1,1,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4975874416657952,0.7929621560230599,,0.0735,0.0585,0.9752,,,0.04,0.1241,0.2333,,,,0.0389,,0.0058,0.0945,0.017,0.9747,,,0.0403,0.0345,0.1667,,,,0.0259,,0.0043,0.0781,0.0614,0.9747,,,0.04,0.1379,0.1667,,,,0.0368,,0.0043,,block of flats,0.033,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1747.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n391196,0,Cash loans,F,N,Y,0,112500.0,1009368.0,48690.0,886500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-19275,365243,-10334.0,-2654,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,XNA,,0.2345748106559173,0.31703177858344445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-338.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n164871,0,Cash loans,F,N,Y,0,135000.0,1546020.0,42642.0,1350000.0,Family,Working,Secondary / secondary special,Widow,House / apartment,0.020713,-23424,-3003,-12452.0,-5115,,1,1,0,1,0,0,,1.0,3,3,TUESDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.8920206822346525,0.39432036451590596,0.6178261467332483,0.0588,0.0809,0.9821,0.7552,0.0073,0.0,0.1379,0.1667,0.2083,0.0,0.0471,0.0513,0.0039,0.0485,0.0599,0.084,0.9821,0.7648,0.0074,0.0,0.1379,0.1667,0.2083,0.0,0.0514,0.0535,0.0039,0.0514,0.0593,0.0809,0.9821,0.7585,0.0074,0.0,0.1379,0.1667,0.2083,0.0,0.0479,0.0522,0.0039,0.0496,reg oper account,block of flats,0.0681,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-140.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,5.0\n347218,1,Cash loans,M,Y,Y,1,180000.0,447768.0,29920.5,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.020713,-8461,-575,-618.0,-827,0.0,1,1,0,1,0,0,Laborers,2.0,3,1,THURSDAY,9,0,0,0,1,1,0,Construction,,0.05516286267362582,,0.1066,0.0,0.9836,0.7756,0.022000000000000002,0.096,0.1241,0.3,0.0,0.0462,0.0854,0.1049,0.0069,0.0072,0.0756,0.0,0.9836,0.7844,0.012,0.1208,0.1034,0.3333,0.0,0.0227,0.0643,0.0732,0.0078,0.0063,0.1114,0.0,0.9836,0.7786,0.0181,0.12,0.1034,0.3333,0.0,0.0523,0.0898,0.1131,0.0078,0.0073,reg oper account,block of flats,0.0667,Panel,No,8.0,0.0,8.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n246013,0,Cash loans,F,N,Y,0,117000.0,106659.0,5580.0,81000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.031329,-23493,365243,-10863.0,-4342,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.724026068940153,,0.4392,0.2657,0.9816,0.7484,0.0738,0.32,0.2759,0.3333,0.375,0.2327,0.3547,0.3638,0.0154,0.1429,0.4475,0.2757,0.9816,0.7583,0.0745,0.3222,0.2759,0.3333,0.375,0.23800000000000002,0.3875,0.379,0.0156,0.1513,0.4434,0.2657,0.9816,0.7518,0.0743,0.32,0.2759,0.3333,0.375,0.2368,0.3608,0.3703,0.0155,0.1459,reg oper spec account,block of flats,0.3575,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-817.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n279013,0,Cash loans,F,N,Y,0,180000.0,993082.5,39514.5,913500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.04622,-19819,-1463,-13019.0,-2437,,1,1,0,1,1,0,Laborers,1.0,1,1,MONDAY,16,0,0,0,0,1,1,Self-employed,,0.7410228364119755,0.7338145369642702,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1042.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n152059,0,Revolving loans,F,N,Y,0,148500.0,157500.0,7875.0,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,With parents,0.00496,-19970,365243,-2817.0,-2820,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,0.7530207771255139,0.3972802319433649,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1313.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n367375,0,Cash loans,F,N,N,1,184500.0,378117.0,38290.5,342000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.04622,-9922,-399,-275.0,-1443,,1,1,0,1,1,0,Secretaries,2.0,1,1,TUESDAY,10,0,1,1,0,0,0,Other,0.3379691957185137,0.4939175288047203,0.39449540531239935,0.1392,0.0653,0.9965,0.9524,0.0699,0.24,0.1034,0.6667,0.7083,0.0113,0.111,0.2141,0.0116,0.0349,0.1418,0.0677,0.9965,0.9543,0.0706,0.2417,0.1034,0.6667,0.7083,0.0116,0.1212,0.2231,0.0117,0.037000000000000005,0.1405,0.0653,0.9965,0.953,0.0704,0.24,0.1034,0.6667,0.7083,0.0115,0.1129,0.218,0.0116,0.0357,not specified,block of flats,0.2142,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n394790,0,Revolving loans,F,Y,Y,0,720000.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.04622,-10132,-761,-4355.0,-2771,10.0,1,1,1,1,0,0,Managers,2.0,1,1,WEDNESDAY,10,0,1,1,0,1,1,Trade: type 2,0.3950037010046276,0.7441536333795272,0.3280631605201915,0.0825,0.0835,0.9781,0.7008,0.0495,0.0,0.1379,0.1667,0.2083,0.0516,0.0672,0.0661,0.0,0.0,0.084,0.0866,0.9782,0.7125,0.05,0.0,0.1379,0.1667,0.2083,0.0528,0.0735,0.0689,0.0,0.0,0.0833,0.0835,0.9781,0.7048,0.0498,0.0,0.1379,0.1667,0.2083,0.0525,0.0684,0.0673,0.0,0.0,reg oper account,block of flats,0.0791,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-733.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n114448,0,Revolving loans,F,N,Y,0,157500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-14131,-192,-8029.0,-4642,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,0.5481522137199464,0.2593075242205716,0.5902333386185574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-566.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n176033,0,Cash loans,F,Y,N,1,315000.0,724630.5,48622.5,684000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.003813,-15046,-2552,-1729.0,-3628,3.0,1,1,1,1,1,0,Accountants,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,,0.27310679517219155,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1354.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n347084,0,Cash loans,M,N,Y,0,202500.0,1113840.0,53716.5,900000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.006305,-16768,-2742,-3576.0,-273,,1,1,0,1,0,0,,1.0,3,3,THURSDAY,7,0,0,0,0,0,0,Housing,,0.30801854585369115,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n377244,0,Revolving loans,F,N,Y,2,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-15260,-3542,-5014.0,-5197,,1,1,1,1,0,0,Laborers,4.0,2,2,FRIDAY,11,0,0,0,0,0,0,Government,,0.6719609180998897,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-350.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n447365,0,Cash loans,F,N,N,0,31500.0,675000.0,22306.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-22207,365243,-2199.0,-5030,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.6538348214580048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-498.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n303894,0,Cash loans,F,N,Y,0,112500.0,675000.0,21906.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22806,365243,-4219.0,-4704,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.26525634018619443,0.41184855592423975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2065.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n404002,0,Cash loans,F,N,Y,0,112500.0,242950.5,16366.5,184500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.005002,-16926,-1307,-4886.0,-475,,1,1,1,1,1,1,Sales staff,2.0,3,3,TUESDAY,11,0,0,0,0,0,0,Construction,0.5926817885382255,0.6165442391112352,0.7001838506835805,0.0835,0.0473,0.9856,,,,0.1724,0.1667,,0.0189,,0.0751,,0.0431,0.0851,0.0491,0.9856,,,,0.1724,0.1667,,0.0194,,0.0782,,0.0457,0.0843,0.0473,0.9856,,,,0.1724,0.1667,,0.0193,,0.0764,,0.044000000000000004,,block of flats,0.0591,Block,No,10.0,0.0,9.0,0.0,-2377.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n329928,0,Cash loans,F,Y,Y,0,180000.0,597339.0,31954.5,553500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-17348,-5179,-6505.0,-840,15.0,1,1,0,1,0,0,Managers,2.0,1,1,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.6376019478196245,0.7255658011819083,0.722392890081304,0.0186,,0.9727,0.626,,0.0,0.0345,0.1667,0.2083,0.0246,,0.0314,,0.3284,0.0189,,0.9727,0.6406,,0.0,0.0345,0.1667,0.2083,0.0252,,0.0328,,0.3477,0.0187,,0.9727,0.631,,0.0,0.0345,0.1667,0.2083,0.0251,,0.032,,0.3353,reg oper account,block of flats,0.0961,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1095.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n436676,0,Revolving loans,F,N,Y,0,180000.0,157500.0,7875.0,157500.0,Children,State servant,Secondary / secondary special,Married,House / apartment,0.018029,-21938,-10802,-4765.0,-4794,,1,1,0,0,0,0,Managers,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Other,0.7396163099026506,0.462634375865165,0.4686596550493113,0.1577,0.198,0.9836,0.7756,0.0288,0.0,0.2759,0.1667,0.0417,,,0.1572,,0.006999999999999999,0.1607,0.2054,0.9836,0.7844,0.028999999999999998,0.0,0.2759,0.1667,0.0417,,,0.1638,,0.0074,0.1593,0.198,0.9836,0.7786,0.028999999999999998,0.0,0.2759,0.1667,0.0417,,,0.16,,0.0072,,block of flats,0.1409,Panel,No,6.0,0.0,6.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n238074,0,Cash loans,F,Y,Y,0,315000.0,943425.0,27715.5,787500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010147,-13000,-702,-1299.0,-5358,12.0,1,1,0,1,0,0,Accountants,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Industry: type 11,0.5130558096880602,0.5773181313160931,0.15855489979486306,0.0742,0.049,0.9786,0.7076,0.0364,0.08,0.069,0.3333,0.375,0.0103,0.0605,0.0696,0.0,0.0,0.0756,0.0508,0.9786,0.7190000000000001,0.0367,0.0806,0.069,0.3333,0.375,0.0106,0.0661,0.0725,0.0,0.0,0.0749,0.049,0.9786,0.7115,0.0366,0.08,0.069,0.3333,0.375,0.0105,0.0616,0.0709,0.0,0.0,reg oper spec account,block of flats,0.0747,Panel,No,1.0,1.0,1.0,1.0,-1708.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n161792,0,Cash loans,F,N,Y,0,72000.0,675000.0,21906.0,675000.0,Family,Pensioner,Secondary / secondary special,Separated,House / apartment,0.003069,-21621,365243,-3574.0,-4579,,1,0,0,1,1,0,,1.0,3,3,TUESDAY,11,0,0,0,0,0,0,XNA,0.8124675126756341,0.6473727821815991,0.6446794549585961,0.1505,0.0969,0.9851,,,0.16,0.1379,0.3333,,0.0221,,0.0908,,0.0004,0.1534,0.1006,0.9851,,,0.1611,0.1379,0.3333,,0.0226,,0.0946,,0.0005,0.152,0.0969,0.9851,,,0.16,0.1379,0.3333,,0.0225,,0.0925,,0.0004,,block of flats,0.12,Panel,No,0.0,0.0,0.0,0.0,-273.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n145744,0,Cash loans,M,Y,Y,2,225000.0,1006920.0,51543.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.004849,-16856,-1761,-4634.0,-398,8.0,1,1,0,1,0,0,Sales staff,4.0,2,2,WEDNESDAY,14,0,1,1,0,1,1,Construction,,0.6665299459499597,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-453.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n314018,0,Cash loans,M,N,Y,0,67500.0,148365.0,11781.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002042,-17932,-912,-9972.0,-1452,,1,1,1,1,0,0,,2.0,3,3,SATURDAY,12,0,0,0,0,0,0,Industry: type 1,,0.5062119572866605,0.6144143775673561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n331161,0,Revolving loans,F,N,Y,0,270000.0,630000.0,31500.0,630000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.010006,-15201,-3786,-9133.0,-4818,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,Industry: type 9,0.5887537650551348,0.7012502885352407,0.6313545365850379,0.0969,0.091,0.9801,0.728,,0.0,0.2069,0.1667,0.2083,0.0654,,,,0.0,0.0987,0.0945,0.9801,0.7387,,0.0,0.2069,0.1667,0.2083,0.0669,,,,0.0,0.0978,0.091,0.9801,0.7316,,0.0,0.2069,0.1667,0.2083,0.0666,,,,0.0,reg oper account,block of flats,0.0723,Panel,No,0.0,0.0,0.0,0.0,-306.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n287537,0,Cash loans,F,N,N,1,180000.0,808650.0,23773.5,675000.0,Family,State servant,Higher education,Married,House / apartment,0.006852,-12280,-2123,-2533.0,-2057,,1,1,0,1,0,0,High skill tech staff,3.0,3,3,MONDAY,5,0,0,0,0,0,0,Government,,0.5813736443412151,0.4418358231994413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1658.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n146027,0,Cash loans,F,N,N,2,81000.0,269550.0,21294.0,225000.0,Family,State servant,Higher education,Married,House / apartment,0.00496,-11267,-532,-5203.0,-830,,1,1,0,1,0,0,Core staff,4.0,2,2,MONDAY,13,0,0,0,0,0,0,Kindergarten,,0.4299857387404375,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-166.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n409402,0,Cash loans,M,Y,N,1,360000.0,508495.5,22396.5,454500.0,Unaccompanied,Working,Incomplete higher,Married,Rented apartment,0.006008,-15150,-1645,-1639.0,-2541,3.0,1,1,1,1,0,0,Core staff,3.0,2,2,MONDAY,22,1,1,1,1,1,1,Police,0.2637848458860356,0.697018573888921,0.4596904504249018,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1183.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n200150,0,Cash loans,F,N,N,1,225000.0,983299.5,37584.0,904500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-13788,-347,-7587.0,-5363,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.5519544725798413,0.5634350122463097,,0.5237,0.3756,0.9846,0.7892,0.1157,0.56,0.4828,0.3333,0.375,,0.4203,0.5537,0.0309,0.0312,0.5336,0.3898,0.9846,0.7975,0.1167,0.5639,0.4828,0.3333,0.375,,0.4591,0.5769,0.0311,0.033,0.5288,0.3756,0.9846,0.792,0.1164,0.56,0.4828,0.3333,0.375,,0.4275,0.5636,0.0311,0.0318,reg oper account,block of flats,0.5055,Panel,No,5.0,0.0,5.0,0.0,-1.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n245117,0,Cash loans,F,N,Y,1,135000.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002042,-13646,-953,-1160.0,-4900,,1,1,0,1,0,0,Cleaning staff,3.0,3,3,MONDAY,11,0,0,0,0,1,1,Self-employed,,0.2489463318262683,0.25396280933631177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,3.0,0.0,-648.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n102103,0,Revolving loans,M,Y,N,0,405000.0,405000.0,20250.0,405000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.025164,-12427,-1678,-1747.0,-3908,4.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.5398691341796813,0.4365064990977374,0.2443,0.1469,0.9965,0.9524,0.1461,0.28,0.2414,0.375,,0.0364,,0.2697,,0.1036,0.2489,0.1524,0.9965,0.9543,0.1475,0.282,0.2414,0.375,,0.0372,,0.281,,0.1097,0.2467,0.1469,0.9965,0.953,0.1471,0.28,0.2414,0.375,,0.037000000000000005,,0.2745,,0.1058,reg oper account,block of flats,0.292,Panel,No,2.0,0.0,2.0,0.0,-1503.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n295411,0,Cash loans,F,Y,Y,2,135000.0,497520.0,25758.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005144,-14104,-1107,-4216.0,-3591,7.0,1,1,0,1,0,0,Core staff,4.0,2,2,FRIDAY,11,0,0,0,0,0,0,Self-employed,,0.29153808623634725,0.19294222771695085,0.1237,0.0885,0.9806,0.7348,0.0,0.0,0.069,0.1667,0.2083,0.016,0.1009,0.0642,0.0,0.0,0.1261,0.0918,0.9806,0.7452,0.0,0.0,0.069,0.1667,0.2083,0.0164,0.1102,0.0669,0.0,0.0,0.1249,0.0885,0.9806,0.7383,0.0,0.0,0.069,0.1667,0.2083,0.0163,0.1026,0.0654,0.0,0.0,reg oper account,block of flats,0.0505,\"Stone, brick\",No,4.0,1.0,4.0,1.0,-185.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n250210,0,Cash loans,F,N,N,1,135000.0,343800.0,12478.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-16433,-370,-7119.0,-4359,,1,1,0,1,0,0,Core staff,3.0,2,2,MONDAY,17,0,0,0,0,0,0,Realtor,0.7221721641884424,0.5668045993076746,0.656158373001177,0.0845,0.0,0.9901,0.8640000000000001,0.0119,0.0,0.2069,0.1667,0.2083,0.0379,0.0681,0.0859,0.0039,0.0643,0.0861,0.0,0.9901,0.8693,0.012,0.0,0.2069,0.1667,0.2083,0.0387,0.0744,0.0895,0.0039,0.0681,0.0854,0.0,0.9901,0.8658,0.012,0.0,0.2069,0.1667,0.2083,0.0385,0.0693,0.0875,0.0039,0.0656,reg oper account,block of flats,0.0881,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2308.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,1.0,0.0\n132295,0,Revolving loans,F,N,Y,1,157500.0,157500.0,7875.0,157500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.00702,-12620,-653,-17.0,-17,,1,1,0,1,1,0,Managers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.3934774440811936,0.4911519033506087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1547.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n333822,0,Cash loans,F,Y,Y,1,202500.0,993082.5,39514.5,913500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-16789,-2745,-8957.0,-350,3.0,1,1,0,1,0,0,,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.757361196145808,0.7600380893688025,0.5154953751603267,0.0701,0.0636,0.9876,0.83,0.0207,0.08,0.069,0.3333,0.375,0.1051,0.0572,0.083,0.0,0.0263,0.0714,0.066,0.9876,0.8367,0.0209,0.0806,0.069,0.3333,0.375,0.1075,0.0624,0.0865,0.0,0.0278,0.0708,0.0636,0.9876,0.8323,0.0208,0.08,0.069,0.3333,0.375,0.107,0.0581,0.0845,0.0,0.0268,reg oper account,block of flats,0.1042,Panel,No,0.0,0.0,0.0,0.0,-291.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n314161,0,Cash loans,F,N,Y,1,180000.0,1436850.0,42142.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019101,-18032,-1742,-1059.0,-1587,,1,1,0,1,1,0,Laborers,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6776902578020443,0.6642482627052363,0.0979,,0.998,0.9728,,0.16,0.1379,0.25,0.2917,0.0797,,0.1092,,0.0716,0.0998,,0.998,0.9739,,0.1611,0.1379,0.25,0.2917,0.0815,,0.1137,,0.0758,0.0989,,0.998,0.9732,,0.16,0.1379,0.25,0.2917,0.0811,,0.1111,,0.0731,reg oper account,block of flats,0.121,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-504.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n146443,0,Cash loans,F,N,N,0,67500.0,247500.0,25488.0,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-20059,365243,-1686.0,-3615,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.5676956866622298,0.7490217048463391,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1206.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n251794,1,Cash loans,M,Y,Y,0,157500.0,254799.0,20259.0,193500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-8877,-416,-930.0,-1502,12.0,1,1,0,1,0,0,Sales staff,2.0,3,2,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.467198486380413,0.6195277080511546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n378819,0,Cash loans,F,Y,N,0,360000.0,473760.0,50400.0,450000.0,Family,Working,Higher education,Civil marriage,House / apartment,0.010006,-13554,-4232,-4044.0,-4714,0.0,1,1,1,1,1,0,Accountants,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Self-employed,0.6988511950857172,0.680654002440241,0.4920600938649263,0.0402,0.0657,0.9876,0.83,0.0119,0.0,0.069,0.1667,0.0417,,0.0328,0.0473,0.0,0.0025,0.040999999999999995,0.0681,0.9876,0.8367,0.012,0.0,0.069,0.1667,0.0417,,0.0358,0.0493,0.0,0.0026,0.0406,0.0657,0.9876,0.8323,0.012,0.0,0.069,0.1667,0.0417,,0.0333,0.0482,0.0,0.0025,reg oper account,block of flats,0.0393,Panel,No,1.0,0.0,1.0,0.0,-102.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n313661,1,Cash loans,F,N,N,0,112500.0,1195587.0,33007.5,1044000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020246,-14518,-2811,-1229.0,-5016,,1,1,0,1,0,0,Cleaning staff,1.0,3,3,FRIDAY,12,0,0,0,0,0,0,Government,,0.4047177006985797,0.4135967602644276,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n299831,0,Cash loans,M,Y,N,1,112500.0,1350000.0,39474.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-9302,-2535,-4041.0,-1960,10.0,1,1,1,1,1,0,Laborers,3.0,3,3,TUESDAY,21,0,0,0,0,1,1,Self-employed,0.19774888434486249,0.4877912659800369,0.7091891096653581,0.3711,,0.9886,0.8368,0.2852,0.36,0.3793,0.3333,0.2083,,0.3026,0.4021,,,0.3782,,0.9886,0.8432,0.2878,0.3625,0.3793,0.3333,0.2083,,0.3306,0.4189,,,0.3747,,0.9886,0.8390000000000001,0.287,0.36,0.3793,0.3333,0.2083,,0.3078,0.4093,,,reg oper account,block of flats,0.3603,Panel,No,3.0,0.0,3.0,0.0,-2588.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n255207,0,Cash loans,M,Y,Y,0,180000.0,215640.0,17419.5,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.011703,-16975,-2032,-2507.0,-504,13.0,1,1,0,1,0,0,Drivers,1.0,2,2,FRIDAY,16,0,0,0,0,1,1,Self-employed,0.4433777485212616,0.7216954938506975,,0.1247,0.0,0.9771,0.6872,0.0131,0.0,0.2069,0.1667,0.0417,0.0677,0.1017,0.1159,0.0,0.0,0.1271,0.0,0.9772,0.6994,0.0132,0.0,0.2069,0.1667,0.0417,0.0693,0.1111,0.1207,0.0,0.0,0.126,0.0,0.9771,0.6914,0.0132,0.0,0.2069,0.1667,0.0417,0.0689,0.1035,0.1179,0.0,0.0,reg oper account,block of flats,0.0983,Panel,No,0.0,0.0,0.0,0.0,-2204.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n379577,0,Cash loans,F,N,Y,0,112500.0,263686.5,28525.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-11595,-1094,-560.0,-2919,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,1,1,Housing,0.29796897843324505,0.5686024281927312,0.5884877883422673,0.0124,0.0293,0.9841,,,,0.069,0.0417,,0.0202,,0.0111,,,0.0126,0.0304,0.9841,,,,0.069,0.0417,,0.0206,,0.0116,,,0.0125,0.0293,0.9841,,,,0.069,0.0417,,0.0205,,0.0113,,,,block of flats,0.0087,Panel,No,1.0,0.0,1.0,0.0,-43.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n184738,0,Cash loans,F,N,Y,0,315000.0,900000.0,38263.5,900000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.010556,-22522,365243,-4187.0,-4688,,1,0,0,1,1,0,,2.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,XNA,0.7976147518428746,0.6135516813460672,0.3842068130556564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-227.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n200859,1,Cash loans,F,N,Y,1,157500.0,781695.0,30420.0,652500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-8300,-254,-1525.0,-966,,1,1,0,1,0,0,Core staff,3.0,2,2,THURSDAY,15,0,0,0,0,1,1,Kindergarten,0.12038769523557945,0.4335696204978291,0.04727481802734645,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n435347,1,Cash loans,M,Y,Y,0,180000.0,860112.0,55098.0,742500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-19213,-1134,-1968.0,-2680,12.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.1176344408802947,0.17456426555726348,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,1.0\n263042,1,Cash loans,M,N,Y,0,67500.0,319981.5,14224.5,243000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003813,-16113,-5219,-7600.0,-4083,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Housing,0.2398502307522265,0.0358064881964286,,0.0041,,0.9727,,,0.0,0.1379,0.0,,,,0.0028,,,0.0042,,0.9727,,,0.0,0.1379,0.0,,,,0.0029,,,0.0042,,0.9727,,,0.0,0.1379,0.0,,,,0.0029,,,,block of flats,0.0022,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-606.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n383863,0,Cash loans,F,N,Y,1,166500.0,795465.0,31675.5,711000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-16532,-9296,-6816.0,-94,,1,1,0,1,0,0,Core staff,3.0,2,2,SATURDAY,11,0,0,0,0,1,1,Medicine,,0.5246430667463854,0.7151031019926098,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-234.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n225594,1,Cash loans,M,Y,Y,0,171000.0,325908.0,17194.5,247500.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.016612000000000002,-17680,-1206,-4131.0,-277,10.0,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Industry: type 11,,0.6839294793930287,0.18848953379516767,0.3247,0.1802,0.9901,0.8640000000000001,0.12,0.2,0.1724,0.375,0.4167,0.1024,0.2648,0.3591,0.0,0.0,0.3309,0.187,0.9901,0.8693,0.1211,0.2014,0.1724,0.375,0.4167,0.1047,0.2893,0.3741,0.0,0.0,0.3279,0.1802,0.9901,0.8658,0.1207,0.2,0.1724,0.375,0.4167,0.1042,0.2694,0.3655,0.0,0.0,reg oper spec account,block of flats,0.3505,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-257.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,6.0,0.0,2.0\n383369,0,Revolving loans,F,N,Y,2,67500.0,270000.0,13500.0,270000.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,House / apartment,0.003069,-15731,-3499,-2284.0,-549,,1,1,0,1,0,0,,4.0,3,3,MONDAY,14,0,0,0,0,0,0,Other,,0.4781700552711602,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-856.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n217766,1,Cash loans,F,N,Y,2,72000.0,509602.5,30924.0,387000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-10959,-2582,-3348.0,-3404,,1,1,0,1,0,0,Medicine staff,4.0,3,3,FRIDAY,13,0,0,0,0,0,0,Medicine,,0.1385743139680205,0.5406544504453575,0.0299,0.0467,0.9752,0.66,0.0032,0.0,0.069,0.125,0.1667,0.0334,0.0235,0.0224,0.0039,0.0112,0.0305,0.0484,0.9752,0.6733,0.0032,0.0,0.069,0.125,0.1667,0.0342,0.0257,0.0233,0.0039,0.0118,0.0302,0.0467,0.9752,0.6645,0.0032,0.0,0.069,0.125,0.1667,0.034,0.0239,0.0228,0.0039,0.0114,reg oper account,block of flats,0.0218,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-1811.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n219777,0,Cash loans,M,N,Y,2,130500.0,326664.0,16677.0,234000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-12432,-3092,-1710.0,-5043,,1,1,0,1,0,1,Security staff,4.0,2,2,MONDAY,9,0,0,0,0,0,0,Industry: type 3,0.5797425103379787,0.2758720876222189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n438724,1,Cash loans,M,Y,Y,0,117000.0,417708.0,33133.5,369000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-19986,-901,-10150.0,-2953,12.0,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Self-employed,0.7509270014687265,0.014524506506080554,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-267.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n266775,0,Cash loans,F,N,N,2,85500.0,450000.0,12001.5,450000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-12678,-2971,-995.0,-4034,,1,1,0,1,0,0,Accountants,4.0,2,2,SUNDAY,12,0,0,0,0,0,0,Other,0.34075556554733377,0.3285149651920687,0.4135967602644276,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,7.0,0.0,7.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n441381,0,Cash loans,F,Y,Y,0,270000.0,770292.0,30676.5,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-16697,-1484,-3711.0,-245,9.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Trade: type 7,0.7257211414614352,0.17254745667104174,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,2.0,7.0,1.0,-1147.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,6.0\n100443,0,Cash loans,F,N,Y,0,144000.0,456273.0,23296.5,346500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010147,-23054,365243,-3975.0,-4748,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.4279341883428694,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n436810,0,Cash loans,F,Y,Y,0,360000.0,1006920.0,42790.5,900000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.0228,-22427,365243,-7015.0,-4750,5.0,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.4482685583598472,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n186122,0,Cash loans,F,N,Y,1,157500.0,647046.0,21001.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.020713,-19434,-2468,-5483.0,-2968,,1,1,0,1,0,0,,2.0,3,3,WEDNESDAY,5,0,0,0,0,0,0,Business Entity Type 1,,0.1308650822983572,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-411.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n366196,0,Cash loans,M,Y,Y,0,126000.0,254700.0,14751.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-23904,365243,-2124.0,-4298,16.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.6322256701557334,0.7898803468924867,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1663.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n204750,0,Cash loans,F,N,Y,0,135000.0,247275.0,17716.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-24118,-2070,-2927.0,-5895,,1,1,0,1,0,0,Managers,2.0,1,1,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.7195823744830012,0.7623356180684377,0.0247,0.0294,0.9727,,,,0.069,0.0833,,0.0526,,0.0198,,0.0073,0.0252,0.0306,0.9727,,,,0.069,0.0833,,0.0538,,0.0206,,0.0077,0.025,0.0294,0.9727,,,,0.069,0.0833,,0.0535,,0.0201,,0.0074,,block of flats,0.0156,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n249238,0,Revolving loans,M,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.015221,-11949,-1057,-5401.0,-1224,,1,1,0,1,0,0,Core staff,2.0,2,2,SUNDAY,14,0,0,0,1,0,1,Agriculture,,0.42935876640250864,0.2340151665320674,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-642.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n453679,0,Revolving loans,F,N,N,0,166500.0,135000.0,6750.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.010006,-23344,365243,-7344.0,-4780,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,12,0,0,0,1,0,0,XNA,,0.65694629747558,0.5762088360175724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,2.0,7.0,2.0,-675.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n394165,0,Cash loans,F,Y,N,2,67500.0,405000.0,19696.5,405000.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.018029,-9970,-1165,-287.0,-2146,64.0,1,1,0,1,0,0,Sales staff,4.0,3,3,TUESDAY,8,0,0,0,0,0,0,Self-employed,0.16773189623768334,0.39999569494796783,0.6246146584503397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-889.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n160403,0,Cash loans,F,N,Y,1,202500.0,601470.0,29065.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007305,-12576,-1364,-2207.0,-4794,,1,1,0,1,0,0,Sales staff,3.0,3,3,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.2921864727957453,0.6012563368737536,0.43473324875017305,0.2649,,0.9866,,0.0452,0.08,0.0345,0.3333,,,,0.1482,,0.0614,0.27,,0.9866,,0.0456,0.0806,0.0345,0.3333,,,,0.1544,,0.065,0.2675,,0.9866,,0.0454,0.08,0.0345,0.3333,,,,0.1508,,0.0627,,specific housing,0.1412,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1917.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n294053,0,Cash loans,M,N,Y,2,180000.0,450000.0,35685.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.02461,-9931,-864,-2121.0,-2562,,1,1,1,1,1,0,Sales staff,4.0,2,2,THURSDAY,14,0,0,0,0,0,0,Trade: type 2,0.2262034498754311,0.2185998061053863,0.2176285202779586,0.1711,0.0295,0.9786,0.7076,0.0392,0.0,0.069,0.1667,0.2083,0.0211,0.1395,0.0523,0.0,0.0,0.1744,0.0306,0.9786,0.7190000000000001,0.0395,0.0,0.069,0.1667,0.2083,0.0216,0.1524,0.0545,0.0,0.0,0.1728,0.0295,0.9786,0.7115,0.0394,0.0,0.069,0.1667,0.2083,0.0214,0.1419,0.0532,0.0,0.0,reg oper account,block of flats,0.0692,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-437.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n169922,0,Cash loans,F,N,N,1,67500.0,298512.0,29655.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.035792000000000004,-13891,-1304,-7933.0,-4339,,1,1,0,1,0,0,Sales staff,3.0,2,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,0.6359847746879911,0.5202238244803045,0.7886807751817684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-883.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n303500,1,Cash loans,F,N,Y,0,157500.0,474048.0,25843.5,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-17818,-5226,-6360.0,-541,,1,1,0,1,1,0,,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.0804507838529112,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-341.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n132590,0,Cash loans,F,N,Y,1,112500.0,211500.0,16735.5,211500.0,Family,Working,Higher education,Separated,House / apartment,0.006852,-15189,-1118,-5058.0,-4137,,1,1,0,1,0,0,Laborers,2.0,3,3,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.5578055249195629,0.4112405210459889,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2148.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n293314,0,Cash loans,F,N,N,0,162000.0,781920.0,25101.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-24001,365243,-8766.0,-3982,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,16,0,0,0,0,0,0,XNA,,0.5266332345079712,0.34741822720026416,0.0454,,0.9771,0.6328,0.018000000000000002,0.0,0.1034,0.1042,,0.0267,0.0134,0.0345,0.0,0.0155,0.0294,,0.9732,0.6472,0.0181,0.0,0.069,0.0417,,0.0129,0.0147,0.0119,0.0,0.004,0.0458,,0.9771,0.6377,0.0181,0.0,0.1034,0.1042,,0.0272,0.0137,0.0351,0.0,0.0159,reg oper account,block of flats,0.0511,Block,No,4.0,0.0,4.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n355208,0,Cash loans,M,N,Y,0,202500.0,229230.0,24453.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-10763,-3099,-1719.0,-3199,,1,1,1,1,1,0,Core staff,1.0,2,2,MONDAY,11,0,1,1,0,1,1,Military,0.16117726425912096,0.48911354827528297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n179122,0,Cash loans,F,N,N,1,157500.0,840951.0,43065.0,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-11632,-1548,-448.0,-742,,1,1,0,1,0,0,,3.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.26107126281905696,0.6449929804168458,0.3280631605201915,0.0082,0.0,0.9727,0.626,0.0006,0.0,0.0345,0.0417,0.0417,0.0,0.0067,0.0055,0.0,0.0,0.0084,0.0,0.9727,0.6406,0.0006,0.0,0.0345,0.0417,0.0417,0.0,0.0073,0.0057,0.0,0.0,0.0083,0.0,0.9727,0.631,0.0006,0.0,0.0345,0.0417,0.0417,0.0,0.0068,0.0056,0.0,0.0,reg oper account,block of flats,0.0043,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-650.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n326443,0,Cash loans,F,N,Y,0,99000.0,508495.5,22396.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-21043,365243,-4819.0,-4359,,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,0.7993882876895441,0.7591875712002032,0.5460231970049609,0.0722,0.0,0.9781,0.7008,0.091,0.0,0.1379,0.1667,0.2083,0.0,0.0588,0.064,0.0,0.0,0.0735,0.0,0.9782,0.7125,0.0918,0.0,0.1379,0.1667,0.2083,0.0,0.0643,0.0642,0.0,0.0,0.0729,0.0,0.9781,0.7048,0.0916,0.0,0.1379,0.1667,0.2083,0.0,0.0599,0.0644,0.0,0.0,reg oper account,block of flats,0.0498,\"Stone, brick\",No,2.0,2.0,2.0,2.0,-1656.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n263446,0,Cash loans,M,Y,Y,0,270000.0,675000.0,53460.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-9503,-165,-4327.0,-2189,9.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.19117395956819955,0.4418358231994413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-13.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n453429,1,Cash loans,F,Y,Y,0,153000.0,360000.0,37017.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.011657,-18806,-2217,-3780.0,-2200,33.0,1,1,1,1,1,0,Security staff,1.0,1,1,FRIDAY,13,0,1,1,0,1,1,Business Entity Type 3,0.5044445569987387,0.6812858493454154,0.7352209993926424,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1208.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n239198,0,Revolving loans,F,Y,Y,1,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-10236,-606,-2142.0,-518,1.0,1,1,0,1,0,0,Sales staff,3.0,3,3,FRIDAY,10,0,0,0,0,0,0,Self-employed,0.2648952994480302,0.5498414995602987,0.2608559142068693,0.0825,0.0723,0.9767,0.6804,0.0564,0.0,0.1379,0.1667,0.0417,0.0166,0.0664,0.0701,0.0039,0.003,0.084,0.075,0.9767,0.6929,0.0569,0.0,0.1379,0.1667,0.0417,0.017,0.0725,0.0731,0.0039,0.0032,0.0833,0.0723,0.9767,0.6847,0.0568,0.0,0.1379,0.1667,0.0417,0.0169,0.0676,0.0714,0.0039,0.0031,reg oper account,block of flats,0.0867,Panel,No,0.0,0.0,0.0,0.0,-510.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n424595,0,Cash loans,F,N,Y,1,90000.0,462694.5,30253.5,418500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-8414,-1205,-3486.0,-1104,,1,1,0,1,0,0,Core staff,3.0,3,3,MONDAY,13,0,0,0,0,0,0,Kindergarten,,0.4625908852351879,0.2940831009471255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-232.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n278082,0,Cash loans,M,Y,Y,1,229500.0,1724688.0,54153.0,1575000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.072508,-14279,-6918,-8356.0,-4774,6.0,1,1,0,1,0,0,,3.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,Military,,0.7677091664226601,0.8625103025687644,0.201,0.1,0.9826,0.762,0.0,0.24,0.1034,0.625,0.6667,0.139,0.1605,0.2412,0.0154,0.0026,0.2048,0.1038,0.9826,0.7713,0.0,0.2417,0.1034,0.625,0.6667,0.1422,0.1754,0.2513,0.0156,0.0027,0.203,0.1,0.9826,0.7652,0.0,0.24,0.1034,0.625,0.6667,0.1414,0.1633,0.2455,0.0155,0.0026,reg oper account,block of flats,0.1903,Panel,No,0.0,0.0,0.0,0.0,-605.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n402839,0,Cash loans,M,N,N,2,108000.0,545040.0,26640.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.019101,-16794,-2906,-5295.0,-346,,1,1,0,1,0,0,Low-skill Laborers,4.0,2,2,MONDAY,10,0,0,0,0,1,1,Industry: type 4,,0.6461887861631253,0.8590531292026357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2748.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n433586,1,Cash loans,M,N,N,0,315000.0,1260000.0,40774.5,1260000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-13377,-3602,-3665.0,-3447,,1,1,0,1,1,0,Drivers,2.0,3,3,THURSDAY,12,0,0,0,0,0,0,Self-employed,,0.11052022885116286,0.39449540531239935,0.0619,0.0597,0.9901,0.8640000000000001,0.0526,0.0,0.1379,0.1667,0.2083,0.0948,0.0504,0.0593,0.0,0.0,0.063,0.062,0.9901,0.8693,0.053,0.0,0.1379,0.1667,0.2083,0.09699999999999999,0.0551,0.0618,0.0,0.0,0.0625,0.0597,0.9901,0.8658,0.0529,0.0,0.1379,0.1667,0.2083,0.0965,0.0513,0.0603,0.0,0.0,reg oper account,block of flats,0.0754,Panel,No,0.0,0.0,0.0,0.0,-2354.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,3.0\n137050,0,Cash loans,F,N,Y,0,135000.0,450000.0,43969.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.026392,-16751,-2452,-1606.0,-288,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,Housing,0.4944363666957402,0.5827407941113236,0.4956658291397297,0.1814,0.175,0.9821,,,0.0,0.4828,0.1667,,0.2189,,0.17800000000000002,,0.0045,0.1849,0.1816,0.9821,,,0.0,0.4828,0.1667,,0.2239,,0.1854,,0.0048,0.1832,0.175,0.9821,,,0.0,0.4828,0.1667,,0.2227,,0.1812,,0.0046,,block of flats,0.1701,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1245.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n371128,0,Cash loans,M,Y,N,0,238500.0,521280.0,35262.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-14661,-1066,-3318.0,-4443,10.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.7338463703218298,0.6496203111237195,0.1082,0.1,0.9876,0.83,0.0251,0.12,0.1034,0.3333,0.375,0.0221,0.0883,0.0954,0.0039,0.0029,0.1103,0.1038,0.9876,0.8367,0.0253,0.1208,0.1034,0.3333,0.375,0.0226,0.0964,0.0994,0.0039,0.0031,0.1093,0.1,0.9876,0.8323,0.0252,0.12,0.1034,0.3333,0.375,0.0225,0.0898,0.0971,0.0039,0.003,reg oper account,block of flats,0.0893,Panel,No,3.0,1.0,3.0,1.0,-537.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,2.0\n134874,0,Cash loans,F,N,N,0,112500.0,306000.0,14395.5,306000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.018209,-11910,-574,-331.0,-4110,,1,1,0,1,0,0,Laborers,2.0,3,3,FRIDAY,10,0,0,0,1,1,0,Business Entity Type 3,0.16414132617717156,0.28305361746066343,0.3296550543128238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-623.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n429842,0,Cash loans,F,N,Y,0,135000.0,675000.0,32602.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-22074,365243,-12636.0,-4577,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.5628862869905201,0.722392890081304,0.1412,0.1111,0.9821,,,0.16,0.1379,0.3333,,,,0.1426,,0.0317,0.1439,0.1153,0.9821,,,0.1611,0.1379,0.3333,,,,0.1485,,0.0336,0.1426,0.1111,0.9821,,,0.16,0.1379,0.3333,,,,0.1451,,0.0324,,block of flats,0.149,Mixed,No,6.0,0.0,6.0,0.0,-1351.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n384225,0,Cash loans,M,Y,N,2,112500.0,292500.0,23580.0,292500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-12294,-2593,-1407.0,-2059,3.0,1,1,0,1,1,0,Core staff,4.0,2,2,TUESDAY,8,0,0,0,0,0,0,University,0.3793215070937765,0.7232607666076022,,0.1485,0.0894,0.9851,0.7959999999999999,0.0255,0.16,0.1379,0.3333,0.375,0.0328,0.121,0.1481,0.0,0.0039,0.1513,0.0928,0.9851,0.804,0.0257,0.1611,0.1379,0.3333,0.375,0.0335,0.1322,0.1543,0.0,0.0041,0.1499,0.0894,0.9851,0.7987,0.0256,0.16,0.1379,0.3333,0.375,0.0334,0.1231,0.1508,0.0,0.0039,reg oper account,block of flats,0.1313,Panel,No,2.0,1.0,2.0,0.0,-2238.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n100039,0,Cash loans,M,Y,N,1,360000.0,733315.5,39069.0,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.015221,-11694,-2060,-3557.0,-3557,3.0,1,1,0,1,0,0,Drivers,3.0,2,2,THURSDAY,10,0,0,0,1,1,0,Self-employed,,0.32174489566177183,0.41184855592423975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-697.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n447359,0,Cash loans,F,N,N,0,135000.0,360000.0,15381.0,360000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.014519999999999996,-16738,-881,-3318.0,-263,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,12,0,0,0,1,1,0,Kindergarten,0.6924545824414103,0.5186899542672203,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1752.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n112160,0,Cash loans,M,Y,Y,2,450000.0,1530517.5,42219.0,1368000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-17001,-5492,-1879.0,-466,3.0,1,1,0,1,0,0,Managers,4.0,1,1,SUNDAY,10,0,0,0,0,1,1,Industry: type 11,,0.7534487490744222,0.4596904504249018,0.1732,,0.995,0.932,0.0,0.24,0.1034,0.5417,0.0417,,0.1412,,0.0,,0.1765,,0.995,0.9347,0.0,0.2417,0.1034,0.5417,0.0417,,0.1543,,0.0,,0.1749,,0.995,0.9329,0.0,0.24,0.1034,0.5417,0.0417,,0.1437,,0.0,,reg oper account,block of flats,0.248,Panel,No,2.0,0.0,2.0,0.0,-1485.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n324583,0,Cash loans,F,N,Y,0,90000.0,286704.0,20520.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-16193,-339,-4782.0,-4547,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,12,0,0,0,1,1,0,Self-employed,,0.3020894456435957,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-500.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n115305,0,Cash loans,F,N,Y,0,135000.0,499221.0,25618.5,373500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-20595,-1165,-12422.0,-2180,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.4680096340981782,0.19294222771695085,0.0619,0.0495,0.9781,0.7008,0.0094,0.0,0.1379,0.1667,0.2083,0.0615,0.0504,0.0517,0.0,0.0,0.063,0.0514,0.9782,0.7125,0.0095,0.0,0.1379,0.1667,0.2083,0.0629,0.0551,0.0539,0.0,0.0,0.0625,0.0495,0.9781,0.7048,0.0095,0.0,0.1379,0.1667,0.2083,0.0626,0.0513,0.0527,0.0,0.0,reg oper spec account,block of flats,0.0407,Panel,No,0.0,0.0,0.0,0.0,-988.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n451174,0,Cash loans,F,N,Y,0,112500.0,1125000.0,33025.5,1125000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.014519999999999996,-22326,365243,-2834.0,-4073,,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.11793893237647875,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-795.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n247617,1,Cash loans,F,N,Y,0,121500.0,808650.0,26217.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.002042,-10699,-1430,-10.0,-451,,1,1,1,1,0,0,Waiters/barmen staff,2.0,3,3,MONDAY,12,0,1,1,0,1,1,Business Entity Type 3,,0.20514402347530927,0.3201633668633456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-459.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n222434,0,Cash loans,F,N,Y,0,146250.0,143910.0,14148.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.01885,-14801,-1422,-2548.0,-4275,,1,1,1,1,0,0,Accountants,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Other,0.4035840151588321,0.6814140377537151,,0.0237,0.0415,0.9866,0.8164,0.0177,,0.069,0.0833,,0.0142,0.0193,0.0264,,0.0,0.0242,0.0431,0.9866,0.8236,0.0179,,0.069,0.0833,,0.0145,0.0211,0.0275,,0.0,0.0239,0.0415,0.9866,0.8189,0.0178,,0.069,0.0833,,0.0144,0.0197,0.0269,,0.0,reg oper account,block of flats,0.0208,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-2522.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n182659,0,Cash loans,M,N,Y,0,225000.0,1569285.0,66618.0,1345500.0,Unaccompanied,Working,Higher education,Married,Co-op apartment,0.035792000000000004,-10888,-200,-123.0,-3396,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,17,0,0,0,0,1,1,Business Entity Type 1,0.4804355052698732,0.1670180769468052,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-21.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,0.0\n172227,0,Revolving loans,F,N,Y,0,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-12363,-1220,-3599.0,-249,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,11,0,0,0,0,1,1,Business Entity Type 1,,0.5172594796878771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-191.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n377052,0,Cash loans,F,Y,N,0,90000.0,536917.5,28737.0,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008625,-20269,365243,-2326.0,-3619,22.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.5982432424547262,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-16.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n100123,0,Cash loans,F,N,N,0,103500.0,675000.0,19737.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.020246,-23719,-8466,-3917.0,-4036,,1,1,1,1,0,0,Security staff,1.0,3,3,SATURDAY,16,0,0,0,0,0,0,Medicine,,0.3993660764222056,0.6738300778602003,0.0567,0.0629,0.9732,0.6328,0.0484,0.0,0.1034,0.1667,0.2083,0.0314,0.042,0.0308,0.0193,0.0148,0.0578,0.0652,0.9732,0.6472,0.0488,0.0,0.1034,0.1667,0.2083,0.0322,0.0459,0.0321,0.0195,0.0156,0.0573,0.0629,0.9732,0.6377,0.0487,0.0,0.1034,0.1667,0.2083,0.032,0.0428,0.0313,0.0194,0.0151,reg oper account,block of flats,0.0396,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n157223,0,Cash loans,F,N,Y,0,112500.0,254700.0,25191.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.001333,-24718,365243,-12503.0,-4978,,1,0,0,1,0,0,,1.0,3,3,TUESDAY,4,0,0,0,0,0,0,XNA,,0.6616078569466702,,0.1237,0.0882,0.9806,0.7348,0.044000000000000004,0.0,0.2759,0.1667,0.2083,0.1042,0.1,0.1033,0.0039,0.001,0.1261,0.0916,0.9806,0.7452,0.0444,0.0,0.2759,0.1667,0.2083,0.1066,0.1093,0.1072,0.0039,0.0011,0.1249,0.0882,0.9806,0.7383,0.0443,0.0,0.2759,0.1667,0.2083,0.106,0.1018,0.1052,0.0039,0.001,reg oper account,block of flats,0.1059,Panel,No,0.0,0.0,0.0,0.0,-623.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n326310,0,Cash loans,F,N,N,0,247500.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.010006,-17621,-1750,-402.0,-1172,,1,1,0,1,0,0,Managers,1.0,2,1,FRIDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.6084144002043608,0.7435411411580131,0.5190973382084597,0.3495,0.1335,0.9990000000000001,0.9864,0.1143,0.4,0.1724,0.6667,0.7083,0.1758,0.2849,0.3774,0.0193,0.1001,0.3561,0.1386,0.9990000000000001,0.9869,0.1153,0.4028,0.1724,0.6667,0.7083,0.1798,0.3113,0.3932,0.0195,0.106,0.3529,0.1335,0.9990000000000001,0.9866,0.115,0.4,0.1724,0.6667,0.7083,0.1789,0.2899,0.3842,0.0194,0.1022,reg oper account,block of flats,0.3811,Panel,No,1.0,0.0,1.0,0.0,-787.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n104601,0,Cash loans,M,N,N,0,225000.0,1575000.0,43443.0,1575000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.003540999999999999,-16957,-1718,-170.0,-488,,1,1,0,1,1,0,Drivers,1.0,1,1,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.4995055971216025,0.6084530354134621,,0.0804,0.0,0.9935,0.9116,0.0,0.0,0.069,0.1667,0.2083,0.0,0.0656,0.0643,0.0,0.12300000000000001,0.0819,0.0,0.9935,0.9151,0.0,0.0,0.069,0.1667,0.2083,0.0,0.0716,0.067,0.0,0.1302,0.0812,0.0,0.9935,0.9128,0.0,0.0,0.069,0.1667,0.2083,0.0,0.0667,0.0654,0.0,0.1256,reg oper account,block of flats,0.0645,Panel,No,0.0,0.0,0.0,0.0,-1100.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n386750,0,Revolving loans,M,N,Y,1,81000.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-17572,-2875,-939.0,-980,,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5763039348327678,,0.0825,0.0833,0.9767,0.6804,,0.0,0.1379,0.1667,0.0417,0.0992,0.0672,0.0756,0.0,0.0,0.084,0.0865,0.9767,0.6929,,0.0,0.1379,0.1667,0.0417,0.1015,0.0735,0.0788,0.0,0.0,0.0833,0.0833,0.9767,0.6847,,0.0,0.1379,0.1667,0.0417,0.1009,0.0684,0.077,0.0,0.0,reg oper account,block of flats,0.0595,Panel,No,1.0,0.0,1.0,0.0,-171.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n406169,0,Cash loans,F,Y,N,1,90000.0,381528.0,17784.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-10068,-1491,-2658.0,-2663,17.0,1,1,0,1,1,0,Laborers,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Religion,0.32243671101708216,0.3248977247797861,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1937.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n265549,0,Cash loans,F,N,Y,0,157500.0,521280.0,31630.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,With parents,0.018029,-8149,365243,-2987.0,-837,,1,0,0,1,0,0,,1.0,3,3,MONDAY,9,0,0,0,0,0,0,XNA,,0.3451101772469102,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-244.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n139806,1,Cash loans,M,N,N,0,135000.0,364896.0,24813.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-11782,-469,-513.0,-3008,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,15,0,0,0,0,1,1,Self-employed,0.06642433753307317,0.3180293219274338,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-626.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n210956,0,Cash loans,M,N,Y,0,430650.0,4031032.5,102033.0,3712500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.035792000000000004,-16475,-299,-41.0,-24,,1,1,1,1,0,0,Managers,1.0,2,2,MONDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.4679197438371288,0.4477494109451289,0.5100895276257282,0.0113,0.0456,0.9722,,,0.0,0.069,0.0417,,0.0112,,0.0123,,0.0112,0.0116,0.0474,0.9722,,,0.0,0.069,0.0417,,0.0115,,0.0128,,0.0119,0.0115,0.0456,0.9722,,,0.0,0.069,0.0417,,0.0114,,0.0125,,0.0115,,block of flats,0.0121,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n219038,0,Cash loans,F,N,N,1,171000.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009175,-10882,-669,-389.0,-1910,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,17,0,0,0,1,1,0,Business Entity Type 3,,0.10735758490008088,0.4206109640437848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-41.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n170772,0,Revolving loans,M,N,Y,0,112500.0,270000.0,13500.0,270000.0,Family,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.04622,-15036,-472,-7732.0,-4169,,1,1,0,1,0,0,Laborers,2.0,1,1,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.7549972235119549,,0.0928,,0.9821,,,0.0,0.2069,0.1667,,0.1103,,,,,0.0945,,0.9821,,,0.0,0.2069,0.1667,,0.1128,,,,,0.0937,,0.9821,,,0.0,0.2069,0.1667,,0.1122,,,,,,block of flats,0.0791,Panel,No,0.0,0.0,0.0,0.0,-280.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n306130,0,Cash loans,M,Y,Y,1,270000.0,1201207.5,35253.0,940500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-14985,-4615,-2982.0,-95,1.0,1,1,0,1,1,0,Medicine staff,3.0,3,2,WEDNESDAY,7,0,0,0,0,0,0,Medicine,,0.3816050366373444,0.5902333386185574,0.0629,0.027000000000000003,0.9786,0.7076,0.0085,0.0,0.1034,0.1667,0.2083,0.0347,0.0513,0.0529,0.0,0.0,0.0641,0.027999999999999997,0.9786,0.7190000000000001,0.0086,0.0,0.1034,0.1667,0.2083,0.0355,0.055999999999999994,0.0552,0.0,0.0,0.0635,0.027000000000000003,0.9786,0.7115,0.0085,0.0,0.1034,0.1667,0.2083,0.0353,0.0522,0.0539,0.0,0.0,reg oper account,block of flats,0.0463,Panel,No,3.0,0.0,3.0,0.0,-565.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n146519,0,Cash loans,F,N,N,0,135000.0,1040463.0,33561.0,868500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-12660,-775,-6511.0,-3187,,1,1,0,1,1,0,Accountants,2.0,1,1,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.2597908215400957,0.2580842039460289,0.2052,0.1703,0.9786,0.7076,0.0797,0.4,0.3448,0.1667,0.2083,,0.1673,0.1731,,,0.209,0.1767,0.9786,0.7190000000000001,0.0805,0.4028,0.3448,0.1667,0.2083,,0.1827,0.1804,,,0.2071,0.1703,0.9786,0.7115,0.0802,0.4,0.3448,0.1667,0.2083,,0.1702,0.1763,,,,block of flats,0.2091,Panel,No,0.0,0.0,0.0,0.0,-509.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n153907,0,Cash loans,F,N,Y,1,135000.0,755190.0,30078.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.010032,-14207,-934,-6857.0,-5457,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SATURDAY,4,0,0,0,0,0,0,Medicine,,0.6406399244535806,0.8463780633178121,0.1649,0.0835,0.9876,0.83,0.0575,0.16,0.1379,0.375,0.4167,0.1802,0.1345,0.1724,0.0,0.0,0.1681,0.0867,0.9876,0.8367,0.0581,0.1611,0.1379,0.375,0.4167,0.1843,0.1469,0.1796,0.0,0.0,0.1665,0.0835,0.9876,0.8323,0.0579,0.16,0.1379,0.375,0.4167,0.1834,0.1368,0.1754,0.0,0.0,reg oper account,block of flats,0.16699999999999998,Panel,No,0.0,0.0,0.0,0.0,-1668.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n180506,0,Cash loans,M,N,N,0,180000.0,247275.0,17716.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010147,-17260,-1580,-269.0,-741,,1,1,1,1,0,1,IT staff,2.0,2,2,THURSDAY,17,0,0,0,0,1,1,Business Entity Type 3,0.407157218755938,0.6815232136297555,0.31703177858344445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1701.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n332314,0,Cash loans,M,N,N,1,153000.0,521280.0,19449.0,450000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.04622,-17362,-5205,-10857.0,-895,,1,1,0,1,0,0,,3.0,1,1,WEDNESDAY,16,0,0,0,0,1,1,School,0.4585656134861141,0.6880604603396241,0.4066174366275036,0.3588,,0.9851,,,0.4,0.3448,0.3333,,,,0.3958,,0.1285,0.3655,,0.9851,,,0.4028,0.3448,0.3333,,,,0.4124,,0.136,0.3622,,0.9851,,,0.4,0.3448,0.3333,,,,0.4029,,0.1312,,block of flats,0.3113,,No,2.0,0.0,2.0,0.0,-1436.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n110634,0,Cash loans,F,N,Y,1,180000.0,469152.0,28480.5,405000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010032,-11081,-615,-5662.0,-3736,,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,5,0,0,0,0,0,0,Kindergarten,,0.6284806321198583,0.25533177083329,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2417.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n420062,0,Cash loans,F,N,Y,1,112500.0,314055.0,13963.5,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-15251,-8422,-6086.0,-4278,,1,1,0,1,0,0,Medicine staff,3.0,2,2,FRIDAY,7,0,0,0,0,0,0,Medicine,,0.5222811176078832,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1840.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n137646,0,Cash loans,F,N,Y,0,67500.0,312768.0,13905.0,270000.0,Family,Working,Higher education,Married,House / apartment,0.028663,-16003,-2302,-248.0,-4157,,1,1,1,1,1,0,Core staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,School,0.44648861741521695,0.3413244478332803,0.6127042441012546,0.0371,0.0824,0.9871,0.8232,,0.0,0.1034,0.125,,0.0529,,0.0395,,0.0083,0.0378,0.0855,0.9871,0.8301,,0.0,0.1034,0.125,,0.0541,,0.0412,,0.0088,0.0375,0.0824,0.9871,0.8256,,0.0,0.1034,0.125,,0.0538,,0.0402,,0.0085,,block of flats,0.0329,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-574.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n294778,0,Cash loans,F,N,Y,1,112500.0,517500.0,26550.0,517500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-13954,-2098,-172.0,-1171,,1,1,0,1,0,0,,3.0,2,2,SATURDAY,9,0,0,0,0,0,0,School,0.28017418781606285,0.5494355235135943,0.7570690154522959,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-934.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n384329,0,Cash loans,M,Y,Y,2,405000.0,1574532.0,62572.5,1350000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.018801,-13439,-3785,-443.0,-4167,14.0,1,1,0,1,0,0,Managers,4.0,2,2,FRIDAY,10,0,0,0,0,0,0,Security Ministries,0.4606776483886698,0.565332825368456,0.7503751495159068,0.1649,0.075,0.998,0.9728,0.1183,0.16,0.069,0.7083,0.7083,0.079,0.1345,0.18899999999999997,0.0,0.0,0.1681,0.0779,0.998,0.9739,0.1194,0.1611,0.069,0.7083,0.7083,0.0808,0.1469,0.1969,0.0,0.0,0.1665,0.075,0.998,0.9732,0.1191,0.16,0.069,0.7083,0.7083,0.0803,0.1368,0.1924,0.0,0.0,reg oper account,block of flats,0.2308,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-841.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n333892,0,Cash loans,F,N,Y,0,180000.0,459000.0,18090.0,459000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-20684,365243,-9176.0,-4176,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.6763418249701024,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n398529,0,Revolving loans,F,N,Y,0,225000.0,270000.0,13500.0,270000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-18524,-468,-407.0,-1970,,1,1,0,1,0,0,Accountants,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5907154269962531,0.363945238612397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-331.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n228246,0,Cash loans,F,Y,Y,0,112500.0,229500.0,12577.5,229500.0,Unaccompanied,Working,Incomplete higher,Separated,House / apartment,0.028663,-8374,-1560,-3200.0,-1037,1.0,1,1,0,1,0,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.4948139855963687,0.5832379256761245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-885.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n274979,0,Cash loans,F,Y,Y,1,225000.0,497520.0,53712.0,450000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.025164,-14241,-1360,-4458.0,-5750,21.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Transport: type 4,,0.5612499624255123,0.6528965519806539,0.1031,0.0962,0.9811,0.7348,0.0,0.0,0.1379,0.1667,0.2083,,0.0841,0.0884,0.0,0.0,0.105,0.0998,0.9811,0.7452,0.0,0.0,0.1379,0.1667,0.2083,,0.0918,0.0921,0.0,0.0,0.1041,0.0962,0.9811,0.7383,0.0,0.0,0.1379,0.1667,0.2083,,0.0855,0.09,0.0,0.0,reg oper spec account,block of flats,0.0695,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1727.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n171088,0,Cash loans,M,Y,Y,0,315000.0,578979.0,25632.0,517500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.032561,-24284,365243,-4221.0,-4666,8.0,1,0,0,1,1,0,,2.0,1,1,MONDAY,17,0,0,0,0,0,0,XNA,,0.6852893085879229,0.5709165417729987,0.0355,0.0515,0.993,,,0.064,0.0621,0.175,,0.0299,,0.0544,,0.0216,0.0105,0.0244,0.993,,,0.0403,0.0345,0.1667,,0.021,,0.0968,,0.0,0.0187,0.0515,0.993,,,0.04,0.0345,0.1667,,0.0265,,0.0314,,0.0319,,block of flats,0.0755,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n413304,0,Cash loans,F,N,N,1,135000.0,675000.0,32602.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-13856,-2941,-6571.0,-4194,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.4791426334627482,0.3758885898585745,0.5620604831738043,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-735.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n432437,0,Cash loans,F,N,Y,0,135000.0,953460.0,64107.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23067,365243,-370.0,-3001,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,0.5749432253305784,0.6591212678051052,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1213.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n393569,0,Revolving loans,M,N,Y,0,112500.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010556,-9727,-1038,-4539.0,-2184,,1,1,1,1,1,0,Low-skill Laborers,2.0,3,3,FRIDAY,18,0,0,0,0,0,0,Agriculture,,0.4724820307980102,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-609.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n213535,0,Cash loans,M,N,Y,0,270000.0,474048.0,24331.5,360000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-14179,-1653,-3607.0,-4581,,1,1,0,1,0,0,Laborers,2.0,1,1,MONDAY,17,0,1,1,1,0,0,Government,,0.6260265027456444,0.36896873825284665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n264308,0,Cash loans,F,Y,N,0,122850.0,450000.0,30073.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.072508,-20919,365243,-13243.0,-4248,12.0,1,0,0,1,0,0,,1.0,1,1,MONDAY,18,0,0,0,0,0,0,XNA,,0.7317955154374451,0.6296742509538716,0.6546,0.355,0.9816,,,0.78,0.4655,0.4792,,0.5492,,0.7296,,0.0366,0.5599,0.3684,0.9816,,,0.6042,0.4138,0.3333,,0.4611,,0.5473,,0.0027,0.6609999999999999,0.355,0.9816,,,0.78,0.4655,0.4792,,0.5587,,0.7428,,0.0374,,block of flats,0.4137,Panel,No,0.0,0.0,0.0,0.0,-1172.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n317663,0,Cash loans,M,N,Y,0,67500.0,215640.0,9625.5,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.022625,-22898,365243,-7663.0,-4411,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.3459854833274849,0.35895122857839673,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n149751,0,Cash loans,M,Y,Y,1,202500.0,755190.0,56461.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-15126,-2936,-13426.0,-4094,5.0,1,1,0,1,0,0,Laborers,3.0,3,3,THURSDAY,12,0,0,0,0,0,0,Construction,,0.5018715530866826,0.5495965024956946,,,0.9657,,,,,,,,,0.0469,,,,,0.9657,,,,,,,,,0.0489,,,,,0.9657,,,,,,,,,0.0478,,,,,0.0487,,No,7.0,0.0,7.0,0.0,-2539.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n193425,0,Cash loans,F,N,Y,0,90000.0,351000.0,27859.5,351000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-18803,-5671,-6873.0,-2333,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Kindergarten,,0.5665468546777386,0.7032033049040319,0.0495,0.0562,0.9846,,,0.0,0.0345,0.1667,,0.2472,,0.0323,,0.0777,0.0504,0.0583,0.9846,,,0.0,0.0345,0.1667,,0.2528,,0.0337,,0.0823,0.05,0.0562,0.9846,,,0.0,0.0345,0.1667,,0.2515,,0.0329,,0.0794,,block of flats,0.0254,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n179089,0,Cash loans,F,N,N,1,117000.0,66222.0,7083.0,58500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-11680,-1339,-2051.0,-4172,,1,1,0,1,0,1,Core staff,3.0,3,3,FRIDAY,13,0,0,0,0,0,0,Kindergarten,,0.4866221167718973,0.4135967602644276,0.2021,0.1727,0.9965,0.9524,0.0567,0.16,0.1379,0.375,0.4167,0.1179,0.1589,0.2284,0.027000000000000003,0.0428,0.2059,0.1792,0.9965,0.9543,0.0572,0.1611,0.1379,0.375,0.4167,0.1206,0.1736,0.23800000000000002,0.0272,0.0453,0.204,0.1727,0.9965,0.953,0.0571,0.16,0.1379,0.375,0.4167,0.1199,0.1616,0.2325,0.0272,0.0437,reg oper account,block of flats,0.18899999999999997,Monolithic,No,6.0,0.0,6.0,0.0,-1903.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n389427,0,Cash loans,M,N,Y,0,157500.0,318411.0,25285.5,288000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008575,-14416,-261,-3917.0,-4969,,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Transport: type 4,0.3248166328510215,0.3629912142669917,,0.1014,0.0971,0.9781,0.7008,,0.0,0.2297,0.1667,0.2083,0.08900000000000001,0.1,0.0646,0.0,0.0,0.063,0.0642,0.9782,0.7125,,0.0,0.2759,0.1667,0.2083,0.0652,0.1093,0.038,0.0,0.0,0.1207,0.0971,0.9781,0.7048,,0.0,0.2759,0.1667,0.2083,0.079,0.1018,0.0801,0.0,0.0,reg oper account,block of flats,0.0423,Panel,No,0.0,0.0,0.0,0.0,-208.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n310860,0,Cash loans,F,N,N,0,112500.0,787131.0,26145.0,679500.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,With parents,0.005084,-10800,-2843,-100.0,-3467,,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,16,0,0,0,0,1,1,Government,,0.001594635179194583,,,,1.0,,,0.04,0.0345,0.3333,,,,,,,,,1.0,,,0.0403,0.0345,0.3333,,,,,,,,,1.0,,,0.04,0.0345,0.3333,,,,,,,,block of flats,0.037000000000000005,,No,3.0,0.0,3.0,0.0,-213.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n141315,0,Cash loans,M,N,Y,0,67500.0,808650.0,26217.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-21975,365243,-13200.0,-4644,,1,0,0,1,1,0,,2.0,2,2,MONDAY,17,0,0,0,0,0,0,XNA,,0.5201419546626572,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14.0,0.0,14.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n226603,0,Cash loans,F,Y,Y,0,135000.0,706500.0,31248.0,706500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-16985,-2029,-4447.0,-534,5.0,1,1,1,1,1,0,,2.0,2,2,FRIDAY,16,0,0,0,0,1,1,Government,0.6537978767418944,0.6399448877919203,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2618.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n389186,0,Cash loans,M,N,Y,2,126000.0,598486.5,25290.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-12432,-1548,-452.0,-4441,,1,1,1,1,0,0,Laborers,4.0,2,2,MONDAY,11,0,0,0,0,1,1,Business Entity Type 1,0.2959666222039162,0.1942664499695638,0.5424451438613613,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-625.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n405502,0,Cash loans,F,N,Y,0,67500.0,450000.0,32742.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-19433,-2380,-8743.0,-2969,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Government,,0.2598076423985638,0.4938628816996244,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n275359,1,Cash loans,M,Y,Y,0,157500.0,835380.0,40320.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-20390,-6314,-356.0,-3919,14.0,1,1,1,1,1,0,Drivers,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Transport: type 4,,0.4917585514620969,0.08064954432143595,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1390.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n171186,0,Cash loans,F,N,N,0,202500.0,450000.0,25128.0,450000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.010147,-17274,-1348,-2540.0,-821,,1,1,0,1,1,0,Laborers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Government,,0.7231600866535602,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1852.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n435022,0,Cash loans,F,N,Y,1,140850.0,555273.0,21645.0,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-10855,-321,-983.0,-991,,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.27824401784054925,0.4637925209318071,0.2721336844147212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-356.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n433784,0,Cash loans,F,Y,Y,0,270000.0,1872517.5,65088.0,1710000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.008068,-9493,-747,-2385.0,-2142,18.0,1,1,0,1,0,0,Accountants,1.0,3,3,WEDNESDAY,5,0,0,0,0,1,1,Agriculture,0.6466470765221892,0.2214443571374176,0.3893387918468769,,,0.9856,,,0.0,0.1379,0.1667,,,,0.0657,,0.0055,,,0.9856,,,0.0,0.1379,0.1667,,,,0.0685,,0.0059,,,0.9856,,,0.0,0.1379,0.1667,,,,0.0669,,0.0056,,block of flats,0.0718,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n111999,0,Cash loans,F,N,N,1,202500.0,107820.0,7420.5,90000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-18321,-839,-6698.0,-1835,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,School,,0.6813997959529365,0.17560597946937906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1527.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,2.0\n217289,0,Cash loans,F,N,N,2,229500.0,807984.0,25366.5,697500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006852,-12772,-3229,-642.0,-1489,,1,1,0,1,0,0,Accountants,4.0,3,3,FRIDAY,5,0,0,0,0,0,0,Government,0.7129882454778265,0.4204139847984143,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,1.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2.0,2.0,0.0,0.0,0.0,0.0\n417036,0,Cash loans,M,N,N,0,112500.0,601470.0,29065.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.00496,-10306,-1597,-4090.0,-2869,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Self-employed,0.24594916069881456,0.4161519964693964,0.4794489811780563,0.0938,,0.9767,,,0.0,0.2069,0.1667,,0.0957,,,,,0.0756,,0.9767,,,0.0,0.2069,0.1667,,0.0761,,,,,0.0947,,0.9767,,,0.0,0.2069,0.1667,,0.0974,,,,,,block of flats,0.0478,,No,0.0,0.0,0.0,0.0,-513.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n368952,0,Revolving loans,F,N,Y,0,90000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018801,-9382,-1253,-4224.0,-472,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Government,0.4604646232306806,0.4174808912657607,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-287.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n117434,0,Cash loans,F,N,Y,0,171000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.009549,-15842,-2377,-7372.0,-3507,,1,1,1,1,1,0,Cleaning staff,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,Police,,0.6129811829083621,,0.0423,0.0272,0.9836,,,0.04,0.0345,0.3333,,0.0428,,0.068,,0.1009,0.0431,0.027999999999999997,0.9831,,,0.0403,0.0345,0.3333,,0.0332,,0.0709,,0.1069,0.0427,0.0272,0.9836,,,0.04,0.0345,0.3333,,0.0435,,0.0693,,0.1031,,specific housing,0.0531,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-448.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n195775,0,Cash loans,F,Y,Y,0,135000.0,1350000.0,37255.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-15606,-215,-6107.0,-3287,12.0,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,0.5791501369332983,0.7863116142054276,0.8193176922872417,0.5619,0.3333,0.9816,0.7484,0.1249,0.6,0.5172,0.3333,0.0,0.3589,0.4514,0.5998,0.0309,0.0124,0.5725,0.3459,0.9816,0.7583,0.126,0.6042,0.5172,0.3333,0.0,0.3671,0.4931,0.6249,0.0311,0.0132,0.5673,0.3333,0.9816,0.7518,0.1257,0.6,0.5172,0.3333,0.0,0.3651,0.4592,0.6106,0.0311,0.0127,reg oper account,block of flats,0.5985,Panel,No,1.0,1.0,1.0,1.0,-593.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,3.0\n139466,0,Cash loans,M,Y,Y,2,337500.0,729396.0,56574.0,688500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-11846,-3472,-894.0,-3478,13.0,1,1,0,1,0,0,High skill tech staff,4.0,1,1,THURSDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.7887667213568029,0.3556387169923543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2622.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n383322,0,Cash loans,F,N,Y,0,144000.0,640080.0,24259.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.020713,-18468,-301,-2700.0,-1898,,1,1,0,1,0,0,Core staff,2.0,3,3,SATURDAY,6,0,0,0,0,1,1,Government,,0.5696695400277333,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-872.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n291722,0,Cash loans,F,N,Y,1,107406.0,900000.0,23742.0,900000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.008473999999999999,-14020,-2521,-5047.0,-4674,,1,1,1,1,1,0,High skill tech staff,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Government,0.4766702023127334,0.12691972684528113,0.7992967832109371,0.1227,0.1045,0.9791,0.7144,0.0489,0.0,0.2759,0.1667,0.2083,0.0566,0.1,0.1129,,,0.125,0.1085,0.9791,0.7256,0.0493,0.0,0.2759,0.1667,0.2083,0.0579,0.1093,0.1177,,,0.1239,0.1045,0.9791,0.7182,0.0492,0.0,0.2759,0.1667,0.2083,0.0576,0.1018,0.115,,,reg oper account,block of flats,0.0976,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n408888,0,Cash loans,F,N,Y,1,112500.0,595903.5,30555.0,481500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010643,-18807,-3542,-1141.0,-1152,,1,1,0,1,1,0,Cooking staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Kindergarten,,0.4817887799015799,,,0.1141,0.9826,,,0.0,0.2069,0.1667,,,,0.09,,,,0.1184,0.9826,,,0.0,0.2069,0.1667,,,,0.0938,,,,0.1141,0.9826,,,0.0,0.2069,0.1667,,,,0.0917,,,,block of flats,0.077,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-445.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n358450,0,Cash loans,F,N,Y,0,225000.0,450000.0,27324.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.014519999999999996,-18191,-1449,-1455.0,-1706,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Services,,0.6242637044272646,0.8027454758994178,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,4.0,1.0,4.0,1.0,-1257.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,7.0\n411931,0,Cash loans,M,Y,Y,1,202500.0,1029658.5,34159.5,922500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16729,-4335,-4913.0,-277,18.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,14,0,0,0,0,1,1,Construction,0.4697908497051869,0.688318545015081,0.3296550543128238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-716.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n179975,0,Revolving loans,F,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.002042,-7790,-829,-2647.0,-471,,1,1,0,1,1,0,Sales staff,2.0,3,3,SATURDAY,14,0,0,0,0,0,0,Self-employed,,0.0015124670509348596,0.4578995512067301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n252122,0,Cash loans,F,Y,Y,0,216000.0,941472.0,37467.0,841500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006629,-22090,365243,-11855.0,-4287,13.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.6330337446128824,0.31547215492577346,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n383515,0,Cash loans,M,Y,Y,0,247500.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.026392,-18035,-4663,-5621.0,-1582,2.0,1,1,0,1,1,0,Core staff,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Advertising,0.6908823761795541,0.6766911696455241,0.5832379256761245,0.1144,0.0364,0.9881,0.8368,0.006,0.08,0.0345,0.625,0.6667,0.0858,0.0933,0.1146,0.0,0.0,0.1166,0.0378,0.9881,0.8432,0.0061,0.0806,0.0345,0.625,0.6667,0.0877,0.1019,0.1194,0.0,0.0,0.1155,0.0364,0.9881,0.8390000000000001,0.006,0.08,0.0345,0.625,0.6667,0.0873,0.0949,0.1167,0.0,0.0,reg oper account,block of flats,0.0901,Panel,No,5.0,1.0,5.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n417133,0,Cash loans,F,N,Y,0,135000.0,781920.0,34573.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-22403,365243,-12239.0,-4862,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.5564140897983525,0.4938628816996244,0.3670000000000001,0.2271,0.9831,0.7688,0.1491,0.44,0.3793,0.3333,0.375,0.2452,0.2992,0.4,0.0,0.0,0.3739,0.2357,0.9831,0.7779,0.1505,0.4431,0.3793,0.3333,0.375,0.2508,0.3269,0.4168,0.0,0.0,0.3706,0.2271,0.9831,0.7719,0.1501,0.44,0.3793,0.3333,0.375,0.2494,0.3044,0.4072,0.0,0.0,org spec account,block of flats,0.3962,Panel,No,4.0,0.0,4.0,0.0,-1324.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n142145,0,Cash loans,F,N,Y,1,157500.0,675000.0,26284.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-13362,-5409,-1043.0,-4196,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 1,0.7306497091318861,0.5811873593995798,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2424.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n248245,0,Cash loans,F,N,Y,0,157500.0,1040985.0,28755.0,909000.0,,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.010032,-16424,-1712,-47.0,-4991,,1,1,1,1,1,0,,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.6784778666173765,,0.1021,0.0,0.9762,,0.0119,0.0,0.2069,0.1667,0.2083,0.1102,0.0832,0.0761,0.0039,0.0497,0.10400000000000001,0.0,0.9762,,0.012,0.0,0.2069,0.1667,0.2083,0.1127,0.0909,0.0793,0.0039,0.0526,0.1031,0.0,0.9762,,0.0119,0.0,0.2069,0.1667,0.2083,0.1121,0.0847,0.0774,0.0039,0.0507,reg oper account,block of flats,0.0771,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-326.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n152741,0,Cash loans,F,Y,Y,1,157500.0,980937.0,47322.0,805500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-10884,-822,-2345.0,-2347,8.0,1,1,0,1,1,0,Sales staff,3.0,2,2,FRIDAY,17,1,1,0,0,0,0,Trade: type 7,0.2456804047432656,0.7259966637898903,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-554.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n440140,0,Cash loans,M,Y,N,0,202500.0,545040.0,26509.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-9307,-384,-4172.0,-1958,64.0,1,1,0,1,1,0,Drivers,1.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.14229004283020782,0.3342126051920896,0.266456808245056,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-247.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n456053,0,Cash loans,F,N,Y,3,135000.0,364896.0,28957.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-12564,-636,-4785.0,-4857,,1,1,0,1,0,0,Sales staff,5.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Trade: type 7,0.4790874829059169,0.2672771033543751,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-490.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n291058,0,Cash loans,F,N,N,2,139500.0,517500.0,16821.0,517500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.018801,-16634,365243,-641.0,-178,,1,0,0,1,1,0,,4.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,XNA,0.6675100675294671,0.5278759825270628,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1162.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n219387,0,Cash loans,F,N,N,0,40500.0,970380.0,28503.0,810000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-16878,-2563,-6475.0,-426,,1,1,0,1,0,0,,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,School,0.4393174215765072,0.39712315764189504,0.4668640059537032,0.1433,0.0906,0.9911,0.8776,0.1008,0.16,0.1379,0.3333,0.375,0.0198,0.11599999999999999,0.1498,0.0039,0.0151,0.146,0.094,0.9911,0.8824,0.1017,0.1611,0.1379,0.3333,0.375,0.0203,0.1267,0.1561,0.0039,0.016,0.1447,0.0906,0.9911,0.8792,0.1015,0.16,0.1379,0.3333,0.375,0.0202,0.11800000000000001,0.1525,0.0039,0.0154,reg oper account,block of flats,0.1762,Panel,No,8.0,1.0,8.0,0.0,-1641.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n187147,1,Cash loans,F,N,Y,1,180000.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-15252,-1640,-4906.0,-4394,,1,1,0,1,0,0,Sales staff,3.0,3,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.09428825315971602,0.5102235531689405,0.22009464485041005,0.1237,0.0897,0.9806,0.7348,0.0041,0.0,0.069,0.1667,0.2083,0.0392,0.1009,0.0727,0.0,0.0,0.1261,0.0931,0.9806,0.7452,0.0041,0.0,0.069,0.1667,0.2083,0.0401,0.1102,0.0757,0.0,0.0,0.1249,0.0897,0.9806,0.7383,0.0041,0.0,0.069,0.1667,0.2083,0.0399,0.1026,0.07400000000000001,0.0,0.0,reg oper account,block of flats,0.0594,Panel,No,0.0,0.0,0.0,0.0,-347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n141082,0,Cash loans,F,N,Y,0,112500.0,573408.0,19080.0,495000.0,Family,Working,Secondary / secondary special,Widow,House / apartment,0.008019,-23292,-1797,-12577.0,-4457,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.5393910696789003,,0.19899999999999998,0.0916,0.9806,0.7348,0.0288,0.0,0.4138,0.1667,0.0417,0.1245,0.158,0.1216,0.0193,0.0592,0.2027,0.0951,0.9806,0.7452,0.0291,0.0,0.4138,0.1667,0.0417,0.1274,0.1726,0.1267,0.0195,0.0627,0.2009,0.0916,0.9806,0.7383,0.028999999999999998,0.0,0.4138,0.1667,0.0417,0.1267,0.1608,0.1238,0.0194,0.0604,reg oper account,block of flats,0.1678,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n212278,0,Cash loans,F,Y,Y,0,90000.0,137520.0,5067.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-19684,-9517,-10631.0,-3178,7.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,School,,0.6007004814502882,0.7476633896301825,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n155794,0,Cash loans,M,Y,Y,1,135000.0,298512.0,31473.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010147,-11753,-1663,-2075.0,-2049,12.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,13,0,0,0,0,1,1,Construction,0.2274638206828888,0.3111219965028397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-294.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n160868,0,Cash loans,F,Y,Y,1,202500.0,1078200.0,34911.0,900000.0,Family,Working,Incomplete higher,Married,House / apartment,0.019101,-13454,-5319,-1525.0,-4213,4.0,1,1,0,1,0,0,Managers,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,,0.6443367684862095,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2534.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n319826,0,Cash loans,F,N,Y,0,99000.0,607041.0,28260.0,490500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-11479,-1744,-992.0,-1486,,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6568008804393438,0.5105077095534235,0.5154953751603267,0.768,0.5727,0.9846,0.7959999999999999,0.1808,0.84,0.7241,0.3333,0.375,0.4411,0.6262,0.8045,0.0,0.019,0.7826,0.5943,0.9846,0.804,0.1824,0.8459,0.7241,0.3333,0.375,0.4512,0.6841,0.8367,0.0,0.0201,0.7755,0.5727,0.9846,0.7987,0.1819,0.84,0.7241,0.3333,0.375,0.4488,0.637,0.8189,0.0,0.0194,reg oper account,block of flats,0.7089,Panel,No,0.0,0.0,0.0,0.0,-1347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n154817,0,Cash loans,M,Y,Y,0,270000.0,675000.0,49248.0,675000.0,Unaccompanied,Working,Higher education,Separated,Rented apartment,0.014519999999999996,-16832,-3022,-2055.0,-387,8.0,1,1,0,1,0,0,Managers,1.0,2,2,SATURDAY,7,0,0,0,1,1,0,Business Entity Type 3,0.5002569554240837,0.5938626782509017,0.19747451156854226,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,10.0,0.0,-737.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n437659,0,Cash loans,M,N,N,0,135000.0,848745.0,43465.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.04622,-14818,-1094,-4564.0,-4561,,1,1,0,1,0,0,Drivers,1.0,1,1,TUESDAY,12,0,0,0,0,1,1,Self-employed,,0.6135672360361775,,0.0691,0.0701,0.9786,0.7076,,0.0,0.1379,0.1667,0.2083,0.0291,0.0563,0.0565,,0.0085,0.0704,0.0727,0.9786,0.7190000000000001,,0.0,0.1379,0.1667,0.2083,0.0297,0.0615,0.0589,,0.009000000000000001,0.0697,0.0701,0.9786,0.7115,,0.0,0.1379,0.1667,0.2083,0.0296,0.0573,0.0575,,0.0087,reg oper account,block of flats,0.0445,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n430783,0,Cash loans,F,N,Y,0,157500.0,229365.0,17280.0,198000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-17783,-1721,-6328.0,-1307,,1,1,1,1,1,0,Core staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.7168220742025871,0.3321963227477029,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-348.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n379395,0,Cash loans,F,Y,N,0,247500.0,387000.0,21123.0,387000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,With parents,0.014519999999999996,-9374,-404,-2941.0,-2025,15.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.7094297939646409,0.6444169456524449,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-394.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n258509,0,Cash loans,F,Y,N,0,270000.0,539100.0,29376.0,450000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.04622,-11163,-781,-3255.0,-3255,3.0,1,1,1,1,0,1,Managers,1.0,1,1,WEDNESDAY,20,0,1,1,0,1,1,Trade: type 3,0.3802938850463132,0.6907341418058938,0.4543210601605785,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1761.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n299407,0,Revolving loans,M,Y,N,0,135000.0,247500.0,12375.0,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.030755,-11807,-176,-3116.0,-3936,12.0,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,17,0,0,0,1,1,1,Self-employed,,0.13326883248187338,0.5691487713619409,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n205927,0,Cash loans,F,N,Y,0,180000.0,283585.5,29187.0,256500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-17453,-258,-9462.0,-982,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,1,1,0,Transport: type 4,,0.7562530318233642,0.7032033049040319,,,0.9786,,,,0.0345,0.0,,0.0104,,0.0111,,,,,0.9786,,,,0.0345,0.0,,0.0106,,0.0116,,,,,0.9786,,,,0.0345,0.0,,0.0106,,0.0113,,,,block of flats,0.0094,,Yes,0.0,0.0,0.0,0.0,-1666.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n117778,0,Cash loans,F,N,Y,0,139500.0,667237.5,34195.5,576000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-22540,365243,-9287.0,-4565,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,XNA,0.6508147095168924,0.5728937101725355,0.3859146722745145,0.1113,0.0897,0.9871,0.8232,0.0495,0.12,0.1034,0.3333,0.0417,0.063,0.0908,0.1156,0.0,0.0,0.1134,0.0931,0.9871,0.8301,0.05,0.1208,0.1034,0.3333,0.0417,0.0644,0.0992,0.1204,0.0,0.0,0.1124,0.0897,0.9871,0.8256,0.0498,0.12,0.1034,0.3333,0.0417,0.0641,0.0923,0.1177,0.0,0.0,org spec account,block of flats,0.11800000000000001,Panel,No,0.0,0.0,0.0,0.0,-65.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,1.0,0.0,1.0,1.0,0.0\n165295,0,Revolving loans,F,N,Y,1,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.015221,-20064,-3115,-425.0,-356,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Trade: type 7,0.8421733775508772,0.5501391731782986,0.25946765482111994,0.0732,0.0399,0.9816,0.7484,0.009000000000000001,0.0,0.1379,0.1667,0.2083,0.0352,0.0588,0.0673,0.0039,0.0199,0.0746,0.0414,0.9816,0.7583,0.0091,0.0,0.1379,0.1667,0.2083,0.036000000000000004,0.0643,0.0701,0.0039,0.021,0.0739,0.0399,0.9816,0.7518,0.009000000000000001,0.0,0.1379,0.1667,0.2083,0.0358,0.0599,0.0685,0.0039,0.0203,reg oper account,block of flats,0.0622,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-124.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n300189,0,Cash loans,F,N,Y,0,202500.0,507469.5,32562.0,459000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-15903,-2051,-42.0,-4256,,1,1,1,1,1,1,Accountants,2.0,2,2,TUESDAY,11,0,0,0,1,1,0,Business Entity Type 3,0.7568849312920793,0.12339781221142028,0.3441550073724169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-546.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n108906,0,Cash loans,M,Y,N,1,382500.0,2925000.0,73944.0,2925000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.007120000000000001,-15752,-933,-1388.0,-3977,4.0,1,1,1,1,1,0,Managers,3.0,2,2,FRIDAY,14,1,1,0,1,1,0,Trade: type 7,0.5039849350891444,0.5916773017050581,0.3556387169923543,0.1546,0.1216,0.9911,,,0.16,0.1034,0.4583,,0.1414,,0.10099999999999999,,0.1893,0.1576,0.1262,0.9911,,,0.1611,0.1034,0.4583,,0.1446,,0.1053,,0.2004,0.1561,0.1216,0.9911,,,0.16,0.1034,0.4583,,0.1438,,0.1029,,0.1932,,block of flats,0.1335,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n132594,0,Cash loans,F,N,Y,0,72000.0,314100.0,13437.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018029,-21609,365243,-9647.0,-5068,,1,0,0,1,0,0,,1.0,3,3,SATURDAY,7,0,0,0,0,0,0,XNA,,0.16607103936144052,0.8027454758994178,0.0928,,0.9871,,,0.0,0.2069,0.1667,,0.0,,0.0857,,0.1777,0.0945,,0.9871,,,0.0,0.2069,0.1667,,0.0,,0.0893,,0.1882,0.0937,,0.9871,,,0.0,0.2069,0.1667,,0.0,,0.0872,,0.1815,,block of flats,0.106,Panel,No,5.0,0.0,5.0,0.0,-1244.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n173425,0,Cash loans,F,Y,Y,0,67500.0,98910.0,7654.5,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010966,-20110,365243,-898.0,-2462,17.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.6631238813971944,0.7194907850918436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1673.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n357666,0,Cash loans,M,N,Y,0,90000.0,170640.0,11034.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00702,-20712,-6113,-2194.0,-4159,,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Military,,0.3369381076670985,0.8590531292026357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,1.0,-465.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n301132,0,Cash loans,M,Y,Y,1,202500.0,450000.0,41274.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-15212,-1370,-2533.0,-3467,5.0,1,1,0,1,0,0,Laborers,3.0,2,1,THURSDAY,12,0,0,0,0,0,0,Other,,0.6474027333583675,0.3910549766342248,0.0443,0.0,0.9851,0.7959999999999999,0.0067,0.0,0.069,0.0417,0.0833,0.0336,0.0361,0.0208,0.0,0.0,0.0452,0.0,0.9851,0.804,0.0068,0.0,0.069,0.0417,0.0833,0.0343,0.0395,0.0217,0.0,0.0,0.0448,0.0,0.9851,0.7987,0.0067,0.0,0.069,0.0417,0.0833,0.0342,0.0368,0.0212,0.0,0.0,reg oper account,block of flats,0.02,Wooden,No,5.0,0.0,5.0,0.0,-1546.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n347203,0,Cash loans,F,N,Y,0,175500.0,573408.0,31234.5,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-17848,-1105,-8935.0,-1395,,1,1,0,1,0,0,Cleaning staff,2.0,3,3,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.2457384507787496,0.6925590674998008,0.0639,0.0413,0.9737,0.6396,0.0389,0.0,0.1379,0.125,0.0417,0.0,0.0513,0.0488,0.0039,0.0051,0.0651,0.0428,0.9737,0.6537,0.0393,0.0,0.1379,0.125,0.0417,0.0,0.055999999999999994,0.0509,0.0039,0.0054,0.0645,0.0413,0.9737,0.6444,0.0392,0.0,0.1379,0.125,0.0417,0.0,0.0522,0.0497,0.0039,0.0052,reg oper account,block of flats,0.0395,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n254020,0,Cash loans,F,N,N,0,387000.0,1381113.0,38110.5,1206000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.032561,-11650,-1793,-899.0,-709,,1,1,0,1,0,0,Managers,2.0,1,1,WEDNESDAY,15,0,0,0,0,0,0,Trade: type 3,0.3409660831131002,0.6959923814108895,0.24318648044201235,0.1423,0.0622,0.9737,,,,0.1034,0.0833,,,,0.0178,,,0.145,0.0645,0.9737,,,,0.1034,0.0833,,,,0.0185,,,0.1436,0.0622,0.9737,,,,0.1034,0.0833,,,,0.0181,,,,block of flats,0.0232,,No,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n365067,0,Cash loans,M,N,N,0,83916.0,450000.0,29533.5,450000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.030755,-8175,-465,-8175.0,-845,,1,1,1,1,1,0,Sales staff,1.0,2,2,TUESDAY,11,0,0,0,1,1,1,Trade: type 3,0.09492654072962776,0.5274018159673388,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-240.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,,,,,,\n315596,0,Cash loans,M,Y,Y,0,225000.0,900000.0,35694.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-23057,365243,-12704.0,-4441,10.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.3825653265034911,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n358645,0,Cash loans,F,Y,N,0,112500.0,1354500.0,37246.5,1354500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-21776,365243,-9175.0,-4146,1.0,1,0,0,1,1,0,,2.0,3,3,TUESDAY,17,0,0,0,0,0,0,XNA,0.7081708928268776,0.5283174027544615,0.5549467685334323,0.0928,0.1002,0.9771,0.6872,0.0158,0.0,0.2069,0.1667,0.2083,0.0918,0.0748,0.0806,0.0039,0.0043,0.0945,0.10400000000000001,0.9772,0.6994,0.016,0.0,0.2069,0.1667,0.2083,0.0939,0.0817,0.084,0.0039,0.0046,0.0937,0.1002,0.9771,0.6914,0.0159,0.0,0.2069,0.1667,0.2083,0.0934,0.0761,0.0821,0.0039,0.0044,reg oper spec account,block of flats,0.073,Panel,No,0.0,0.0,0.0,0.0,-1851.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n103636,0,Cash loans,F,Y,Y,0,675000.0,2085120.0,72607.5,1800000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.008019,-19306,-8112,-1077.0,-2831,3.0,1,1,0,1,0,1,Managers,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.7919256347467319,0.7012044743012037,0.7449321846094795,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1388.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n224981,0,Cash loans,M,Y,Y,0,202500.0,1800000.0,62698.5,1800000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-18265,-5892,-7669.0,-1818,20.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.4939149063265226,0.3208484074148765,0.8050196619153701,0.0082,0.0,0.9687,,,0.0,0.069,0.0417,,0.0,,0.0085,,0.0,0.0084,0.0,0.9687,,,0.0,0.069,0.0417,,0.0,,0.0089,,0.0,0.0083,0.0,0.9687,,,0.0,0.069,0.0417,,0.0,,0.0087,,0.0,,block of flats,0.0121,Block,No,0.0,0.0,0.0,0.0,-3638.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n205296,0,Cash loans,M,N,Y,2,67500.0,76410.0,8235.0,67500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006852,-11736,-1732,-4026.0,-3949,,1,1,1,1,1,0,,4.0,3,3,FRIDAY,9,0,0,0,1,1,0,Self-employed,,0.24457704533099706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n203025,0,Cash loans,F,N,Y,1,202500.0,225000.0,10557.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-16889,-2539,-6943.0,-428,,1,1,0,1,1,0,Sales staff,3.0,2,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,,0.5736800784792994,,0.0124,0.0142,0.9697,0.5852,0.0026,0.0,0.069,0.0417,0.0833,0.0156,0.0101,0.0143,0.0,0.0,0.0126,0.0148,0.9697,0.6014,0.0026,0.0,0.069,0.0417,0.0833,0.016,0.011000000000000001,0.0149,0.0,0.0,0.0125,0.0142,0.9697,0.5907,0.0026,0.0,0.069,0.0417,0.0833,0.0159,0.0103,0.0145,0.0,0.0,not specified,block of flats,0.0158,Block,No,0.0,0.0,0.0,0.0,-1685.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n190477,0,Cash loans,F,N,Y,2,90000.0,792162.0,35023.5,630000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-16007,-1267,-1103.0,-5737,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Industry: type 11,,0.6942777202309163,0.6848276586890367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1222.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n323921,0,Cash loans,M,Y,N,0,90000.0,225000.0,12915.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-15814,-341,-5348.0,-4608,6.0,1,1,1,1,1,0,Security staff,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,Security,,0.4675577655753127,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-275.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n203528,0,Cash loans,M,Y,N,0,135000.0,687600.0,19039.5,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.008865999999999999,-19459,-10967,-9078.0,-2993,2.0,1,1,0,1,0,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,Medicine,,0.712419135144131,0.7121551551910698,0.0959,,0.9876,,,,0.1724,0.1667,,0.0,,0.0985,,0.005,0.0977,,0.9876,,,,0.1724,0.1667,,0.0,,0.1026,,0.0053,0.0968,,0.9876,,,,0.1724,0.1667,,0.0,,0.1002,,0.0051,,block of flats,0.1304,Panel,No,4.0,1.0,4.0,1.0,-3.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n148943,0,Cash loans,M,N,Y,0,135000.0,497520.0,33246.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006233,-13821,-108,-985.0,-4688,,1,1,0,1,0,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.5889420937819377,0.6113665547062671,0.7764098512142026,0.10099999999999999,0.0991,0.9796,0.7212,0.0435,0.0,0.2069,0.1667,0.0417,0.0805,0.0815,0.087,0.0039,0.0158,0.1029,0.1029,0.9796,0.7321,0.0439,0.0,0.2069,0.1667,0.0417,0.0823,0.0891,0.0906,0.0039,0.0167,0.102,0.0991,0.9796,0.7249,0.0437,0.0,0.2069,0.1667,0.0417,0.0819,0.0829,0.0886,0.0039,0.0162,reg oper account,block of flats,0.0785,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2344.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n277791,1,Cash loans,F,N,N,0,117000.0,787131.0,28404.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008473999999999999,-20300,-5906,-6342.0,-3468,,1,1,1,1,0,0,Security staff,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Other,,0.2333441960924568,0.3201633668633456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-228.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n119417,0,Revolving loans,M,N,Y,0,72000.0,180000.0,9000.0,180000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.015221,-20033,365243,-7787.0,-3594,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,7,0,0,0,0,0,0,XNA,0.6450882734949691,0.5927968420430895,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1911.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n230937,0,Cash loans,F,N,Y,0,202500.0,1042560.0,40702.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.01885,-21286,-5998,-9673.0,-4607,,1,1,1,1,1,0,Managers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Housing,,0.7138685289548921,0.7623356180684377,0.1072,0.1408,0.9722,,,0.0,0.2069,0.1667,,0.0984,,0.0857,,0.0635,0.1092,0.1461,0.9722,,,0.0,0.2069,0.1667,,0.1007,,0.0893,,0.0672,0.1083,0.1408,0.9722,,,0.0,0.2069,0.1667,,0.1001,,0.0873,,0.0649,,block of flats,0.1117,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1558.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n257356,0,Cash loans,F,N,N,0,90000.0,781920.0,25101.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.030755,-20423,365243,-1375.0,-1495,,1,0,0,1,0,0,,1.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,0.5728010438680978,0.5943899935834603,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1395.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n122344,0,Cash loans,F,N,Y,0,112500.0,810000.0,29223.0,810000.0,Unaccompanied,State servant,Incomplete higher,Civil marriage,House / apartment,0.015221,-18848,-8648,-10068.0,-2379,,1,1,0,1,1,0,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Other,0.7847459722929848,0.4260455240440433,0.8106180215523969,0.0928,0.0811,0.9791,0.7144,0.0521,0.0,0.2069,0.1667,0.2083,0.0529,0.0723,0.0837,0.0154,0.016,0.0945,0.0842,0.9791,0.7256,0.0526,0.0,0.2069,0.1667,0.2083,0.0541,0.079,0.0872,0.0156,0.0169,0.0937,0.0811,0.9791,0.7182,0.0524,0.0,0.2069,0.1667,0.2083,0.0538,0.0735,0.0852,0.0155,0.0163,reg oper account,block of flats,0.0978,Panel,No,0.0,0.0,0.0,0.0,-3079.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n193734,0,Revolving loans,F,N,N,2,67500.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009175,-11867,-1289,-1467.0,-3458,,1,1,1,1,1,0,Sales staff,4.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,,0.3636538636411412,0.8106180215523969,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-425.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n455137,0,Cash loans,F,N,Y,1,247500.0,1943356.5,53572.5,1737000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.04622,-13556,-4296,-2739.0,-4600,,1,1,0,1,1,0,Managers,3.0,1,1,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.7113202408808591,,,,0.9767,,,,0.1379,0.1667,,,,,,,,,0.9767,,,,0.1379,0.1667,,,,,,,,,0.9767,,,,0.1379,0.1667,,,,,,,,block of flats,0.0603,Panel,No,0.0,0.0,0.0,0.0,-2689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n278986,0,Revolving loans,F,N,Y,0,144000.0,450000.0,22500.0,450000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.010032,-19957,365243,-1243.0,-3439,,1,0,0,1,0,0,,1.0,2,2,MONDAY,4,0,0,0,0,0,0,XNA,0.774548469908966,0.5546970814436831,0.7738956942145427,0.0567,0.0616,0.9727,0.626,0.0201,0.0,0.1034,0.1458,0.0417,0.0727,0.0269,0.0444,0.0,0.0029,0.0336,0.0442,0.9727,0.6406,0.0135,0.0,0.069,0.125,0.0417,0.0365,0.0294,0.027000000000000003,0.0,0.0,0.0573,0.0616,0.9727,0.631,0.0202,0.0,0.1034,0.1458,0.0417,0.07400000000000001,0.0274,0.0452,0.0,0.003,reg oper account,block of flats,0.0277,\"Stone, brick\",No,3.0,1.0,3.0,0.0,-171.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n234869,0,Cash loans,F,Y,Y,0,225000.0,344043.0,18792.0,297000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-17224,-1622,-5363.0,-749,1.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,,0.6008945360112614,0.17044612304522816,0.1928,,0.9906,0.8708,,0.16,0.1379,0.375,0.4167,0.1605,0.1563,0.2132,0.0039,0.0546,0.1964,,0.9906,0.8759,,0.1611,0.1379,0.375,0.4167,0.1642,0.1708,0.2221,0.0039,0.0578,0.1947,,0.9906,0.8725,,0.16,0.1379,0.375,0.4167,0.1633,0.159,0.217,0.0039,0.0557,org spec account,block of flats,0.1795,Mixed,No,0.0,0.0,0.0,0.0,-1693.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n263091,0,Cash loans,F,N,Y,0,76500.0,187704.0,6124.5,148500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-20287,365243,-3318.0,-3841,,1,0,0,1,1,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,0.7554931569775499,0.5933298699977649,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-2144.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n101559,1,Cash loans,M,N,Y,0,292500.0,835380.0,45445.5,675000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-9900,-91,-4759.0,-1546,,1,1,0,1,1,0,High skill tech staff,2.0,1,1,MONDAY,19,0,0,0,0,0,0,Other,0.12195306286994093,0.2953010065129613,,0.1021,0.0859,0.9762,0.6736,0.0407,0.0,0.2069,0.1667,0.2083,0.0,0.0824,0.091,0.0039,0.0038,0.10400000000000001,0.0892,0.9762,0.6864,0.0411,0.0,0.2069,0.1667,0.2083,0.0,0.09,0.0949,0.0039,0.004,0.1031,0.0859,0.9762,0.6779999999999999,0.040999999999999995,0.0,0.2069,0.1667,0.2083,0.0,0.0838,0.0927,0.0039,0.0039,reg oper account,block of flats,0.0947,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-140.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n157868,0,Cash loans,F,Y,Y,0,135000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-9959,-1531,-1874.0,-2632,64.0,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6883982930000149,0.4436153084085652,0.0103,0.019,0.9727,0.626,0.0,0.0,0.069,0.0417,0.0833,0.0077,0.0084,0.0073,0.0,0.0,0.0105,0.0197,0.9727,0.6406,0.0,0.0,0.069,0.0417,0.0833,0.0079,0.0092,0.0076,0.0,0.0,0.0104,0.019,0.9727,0.631,0.0,0.0,0.069,0.0417,0.0833,0.0078,0.0086,0.0074,0.0,0.0,reg oper account,block of flats,0.0065,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1120.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n235413,0,Cash loans,F,N,N,0,60750.0,808650.0,23773.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-18550,-4541,-9640.0,-1867,,1,1,0,1,0,0,Core staff,2.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,Kindergarten,0.6088818284114659,0.33195865932031354,0.7544061731797895,0.0546,0.0636,0.9757,,,0.0,0.069,0.1667,,0.0601,,0.0431,,,0.0557,0.066,0.9757,,,0.0,0.069,0.1667,,0.0615,,0.0449,,,0.0552,0.0636,0.9757,,,0.0,0.069,0.1667,,0.0612,,0.0439,,,,block of flats,0.0339,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1432.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n338267,0,Cash loans,F,N,N,1,202500.0,755190.0,32125.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-17502,-239,-4508.0,-1040,,1,1,0,1,0,0,Sales staff,3.0,3,3,FRIDAY,10,0,0,0,0,0,0,Self-employed,,0.5918727620631286,0.4992720153045617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-484.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n440510,0,Cash loans,F,N,N,0,81000.0,454500.0,44275.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-21593,-965,-4929.0,-3620,,1,1,1,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,1,1,Trade: type 3,,0.5335073927333462,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-168.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n276502,0,Cash loans,M,N,Y,0,270000.0,900000.0,29034.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007114,-18771,-10765,-1049.0,-2309,,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Industry: type 9,0.7953422084606332,0.6396576359058749,0.6754132910917112,0.0206,0.0225,,0.8232,,,0.0345,0.1667,0.2083,0.0229,,0.0221,,0.0,0.021,0.0233,,0.8301,,,0.0345,0.1667,0.2083,0.0234,,0.0231,,0.0,0.0208,0.0225,,0.8256,,,0.0345,0.1667,0.2083,0.0233,,0.0225,,0.0,,block of flats,0.0174,Panel,No,7.0,0.0,7.0,0.0,-1612.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n267408,0,Cash loans,F,N,N,0,90000.0,675000.0,18940.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-19465,-2234,-8800.0,-2993,,1,1,1,1,0,0,Security staff,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,Government,0.7689681975080324,0.7227702860958822,0.470456116119975,,,0.9786,0.7076,,0.0,0.0345,0.0,,,0.0076,0.0037,,,,,0.9747,0.6668,,0.0,0.0345,0.0,,,0.0073,0.003,,,,,0.9786,0.7115,,0.0,0.0345,0.0,,,0.0077,0.0038,,,not specified,block of flats,0.0038,Mixed,No,0.0,0.0,0.0,0.0,-1815.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n211455,0,Cash loans,M,Y,Y,0,243000.0,315000.0,24507.0,315000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-15882,-513,-735.0,-2785,4.0,1,1,1,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.7041694327025476,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-2080.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n352823,0,Cash loans,F,N,Y,0,270000.0,640080.0,29970.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-10234,-2207,-2978.0,-1151,,1,1,0,1,0,0,Core staff,2.0,1,1,SUNDAY,12,0,0,0,0,1,1,Government,0.4413934636670193,0.6971524813738856,0.11911906455945008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-585.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n344096,0,Cash loans,F,N,Y,0,85500.0,545040.0,20677.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014464,-16547,-2530,-7927.0,-100,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,6,0,0,0,0,0,0,Business Entity Type 3,,0.6441663651232546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-286.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n248645,0,Cash loans,F,N,Y,0,86400.0,180000.0,15529.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-13753,-453,-5612.0,-1636,,1,1,1,1,1,0,Sales staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,,0.6833004371603564,0.7366226976503176,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-371.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n410319,0,Cash loans,M,Y,N,0,135000.0,332473.5,12663.0,274500.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.018029,-8548,-1107,-8517.0,-900,12.0,1,1,0,1,0,1,Medicine staff,1.0,3,2,FRIDAY,10,0,0,0,1,1,0,Other,0.12591653210395215,0.33494775107473723,0.4048783643353997,0.0619,0.0603,0.9786,0.7076,0.0077,0.0,0.1379,0.1667,0.2083,0.0417,0.0504,0.0536,0.0,0.0,0.063,0.0626,0.9786,0.7190000000000001,0.0078,0.0,0.1379,0.1667,0.2083,0.0427,0.0551,0.0559,0.0,0.0,0.0625,0.0603,0.9786,0.7115,0.0077,0.0,0.1379,0.1667,0.2083,0.0425,0.0513,0.0546,0.0,0.0,reg oper account,block of flats,0.0485,Panel,No,1.0,0.0,1.0,0.0,-290.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n413471,0,Cash loans,M,N,Y,1,171000.0,562491.0,27189.0,454500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.006207,-11886,-1484,-249.0,-1782,,1,1,0,1,0,0,Drivers,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.2935851222380201,0.5497278329341682,0.5100895276257282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n447710,0,Cash loans,F,N,N,0,112500.0,845811.0,38349.0,756000.0,Other_A,Commercial associate,Higher education,Married,House / apartment,0.00496,-13559,-528,-958.0,-958,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,15,0,0,0,0,1,1,Self-employed,0.5246406601513608,0.6685633613606587,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-141.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n252683,0,Cash loans,M,Y,Y,0,76500.0,79632.0,3132.0,63000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.003813,-16509,-2895,-7933.0,-14,14.0,1,1,1,1,1,0,Drivers,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Security Ministries,,0.5094311597000737,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n319182,0,Cash loans,M,N,N,0,135000.0,544491.0,15102.0,454500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.006207,-9338,-1523,-11.0,-1999,,1,1,0,1,0,0,Managers,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Transport: type 4,0.4978504116565581,0.2966087064762397,,0.0412,0.0502,0.9985,0.9796,0.0058,0.0,0.069,0.1667,0.2083,0.0224,0.0336,0.0407,0.0,0.0,0.042,0.0521,0.9985,0.9804,0.0058,0.0,0.069,0.1667,0.2083,0.023,0.0367,0.0424,0.0,0.0,0.0416,0.0502,0.9985,0.9799,0.0058,0.0,0.069,0.1667,0.2083,0.0228,0.0342,0.0414,0.0,0.0,reg oper account,block of flats,0.0351,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n256072,0,Cash loans,M,N,Y,2,225000.0,1078200.0,38331.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-13402,-774,-2891.0,-4268,,1,1,0,1,0,1,High skill tech staff,4.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.2578003217505692,0.5667347970336964,0.4561097392782771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,5.0\n263723,0,Cash loans,F,Y,N,1,225000.0,1078200.0,34780.5,900000.0,Unaccompanied,Working,Higher education,Married,With parents,0.030755,-10107,-763,-4706.0,-2179,7.0,1,1,0,1,0,0,Sales staff,3.0,2,2,SUNDAY,13,0,0,0,1,1,0,Self-employed,,0.6496855730885756,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1117.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n129786,0,Cash loans,F,N,N,0,76500.0,545040.0,16654.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-18721,365243,-11662.0,-2263,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.7071625448418268,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-89.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n206811,0,Cash loans,F,Y,N,0,157500.0,247500.0,12703.5,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.025164,-10846,-2232,-1180.0,-3472,4.0,1,1,0,1,0,0,Accountants,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.4527862198715539,0.6327187350597645,0.5814837058057234,0.2778,0.0553,0.998,0.9728,0.0,0.24,0.2069,0.375,0.0417,0.1142,0.2265,0.2395,0.0,0.0055,0.1576,0.0566,0.998,0.9739,0.0,0.1208,0.1034,0.375,0.0417,0.1023,0.1377,0.1248,0.0,0.0,0.2805,0.0553,0.998,0.9732,0.0,0.24,0.2069,0.375,0.0417,0.1162,0.2304,0.2438,0.0,0.0056,not specified,block of flats,0.3619,Panel,No,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n137541,0,Revolving loans,M,N,Y,1,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-11021,-1155,-880.0,-3704,,1,1,0,1,0,0,,3.0,2,2,THURSDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.3518124059951913,0.1997705696341145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-510.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n448628,0,Cash loans,M,N,Y,1,148500.0,840996.0,24718.5,702000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006852,-10421,-2424,-4284.0,-3006,,1,1,0,1,0,0,Laborers,3.0,3,3,THURSDAY,10,0,0,0,0,1,1,Electricity,,0.003455110319119362,0.392773860603134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1916.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n382525,0,Cash loans,F,N,Y,0,117000.0,900000.0,46084.5,900000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.00496,-18009,365243,-719.0,-1566,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,XNA,0.7176718069981556,0.7747842790571937,0.5919766183185521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1290.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n206384,0,Cash loans,F,N,N,0,72000.0,450000.0,24543.0,450000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.035792000000000004,-18418,-10361,-6944.0,-1964,,1,1,1,1,0,0,Core staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,School,,0.7159410092822314,0.7992967832109371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1957.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,1.0,0.0\n317467,0,Cash loans,M,Y,Y,0,247500.0,500211.0,38839.5,463500.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.04622,-8804,-1201,-517.0,-1481,65.0,1,1,0,1,0,1,,2.0,1,1,MONDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.3091009322948161,0.5549467685334323,0.0082,,0.9752,,,,0.0345,0.0417,,,,0.0042,,,0.0084,,0.9752,,,,0.0345,0.0417,,,,0.0044,,,0.0083,,0.9752,,,,0.0345,0.0417,,,,0.0043,,,,block of flats,0.0049,,No,0.0,0.0,0.0,0.0,-792.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n136272,0,Cash loans,F,N,Y,0,112500.0,244584.0,15759.0,193500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.031329,-24178,365243,-13082.0,-4768,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.6773223609875936,0.8128226070575616,,0.0729,0.9767,0.6804,,0.0,0.2069,0.1667,,0.092,0.0756,0.0728,,0.0023,,0.0757,0.9767,0.6929,,0.0,0.2069,0.1667,,0.0941,0.0826,0.0758,,0.0024,,0.0729,0.9767,0.6847,,0.0,0.2069,0.1667,,0.0936,0.077,0.0741,,0.0023,reg oper account,block of flats,0.0577,Panel,No,1.0,0.0,1.0,0.0,-999.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n203446,0,Cash loans,F,Y,N,0,148500.0,1125000.0,36423.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-11086,-2670,-1197.0,-3334,12.0,1,1,0,1,1,0,Laborers,2.0,2,2,MONDAY,19,0,0,0,0,0,0,Postal,0.3428735152992493,0.6511026731514067,0.4471785780453068,0.2227,0.1625,0.9811,0.7416,0.14,0.24,0.2069,0.3333,0.375,0.1278,0.1807,0.2343,0.0039,0.0044,0.2269,0.1686,0.9811,0.7517,0.1413,0.2417,0.2069,0.3333,0.375,0.1307,0.1974,0.2441,0.0039,0.0046,0.2248,0.1625,0.9811,0.7451,0.1409,0.24,0.2069,0.3333,0.375,0.13,0.1838,0.2385,0.0039,0.0045,reg oper account,block of flats,0.2617,Panel,No,0.0,0.0,0.0,0.0,-794.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n143824,0,Cash loans,M,Y,Y,0,135000.0,225000.0,17775.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-10443,-179,-10443.0,-2905,16.0,1,1,1,1,1,0,Drivers,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,Self-employed,,0.44286111430258296,0.13680052191177486,0.0093,,0.9657,,,,0.0345,0.0417,,,,0.0095,,,0.0095,,0.9657,,,,0.0345,0.0417,,,,0.0099,,,0.0094,,0.9657,,,,0.0345,0.0417,,,,0.0096,,,,block of flats,0.0074,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n396629,0,Cash loans,F,N,N,0,112500.0,781920.0,34443.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.016612000000000002,-21209,365243,-7508.0,-3951,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,19,0,0,0,0,0,0,XNA,,0.6764088378348617,0.6347055309763198,0.0866,0.0515,0.9826,0.762,0.0,0.08,0.0345,0.4583,0.5,0.0534,0.0706,0.0341,0.0,0.0,0.0882,0.0534,0.9826,0.7713,0.0,0.0806,0.0345,0.4583,0.5,0.0547,0.0771,0.0356,0.0,0.0,0.0874,0.0515,0.9826,0.7652,0.0,0.08,0.0345,0.4583,0.5,0.0544,0.0718,0.0347,0.0,0.0,reg oper account,block of flats,0.0448,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-157.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,0.0\n444593,0,Revolving loans,M,Y,N,0,135000.0,382500.0,19125.0,382500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-14821,-7343,-4226.0,-944,19.0,1,1,0,1,0,1,Managers,2.0,3,3,MONDAY,9,0,0,0,0,0,0,Business Entity Type 1,,0.3862237784430115,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-287.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n251625,0,Cash loans,F,N,N,0,135000.0,550489.5,19773.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.019101,-18622,365243,-1300.0,-2167,,1,0,0,1,0,0,,1.0,2,2,SUNDAY,9,0,0,0,0,0,0,XNA,0.6222288558369266,0.6428670958493178,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-320.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n283978,0,Cash loans,F,N,Y,1,141016.5,135000.0,13216.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-14725,-366,-1132.0,-5169,,1,1,1,1,0,0,Managers,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4182033185270658,0.6366228382058557,0.6161216908872079,0.1557,0.0973,0.9791,0.7144,0.0297,0.16,0.1379,0.3333,,0.1219,,0.1396,,0.0,0.1586,0.1009,0.9791,0.7256,0.03,0.1611,0.1379,0.3333,,0.1247,,0.1455,,0.0,0.1572,0.0973,0.9791,0.7182,0.0299,0.16,0.1379,0.3333,,0.124,,0.1421,,0.0,org spec account,block of flats,0.1247,Panel,No,2.0,0.0,2.0,0.0,-827.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n387615,1,Cash loans,M,Y,Y,1,270000.0,1506816.0,49927.5,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-19966,-343,-4005.0,-3473,3.0,1,1,0,1,1,0,High skill tech staff,3.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Construction,0.7466454751788933,0.5382879867854745,0.2608559142068693,0.1268,0.1799,0.9945,0.9252,,0.48,0.4138,,0.375,,0.1009,0.077,0.0116,,0.1292,0.1867,0.9945,0.9281,,0.4834,0.4138,,0.375,,0.1102,0.0803,0.0117,,0.128,0.1799,0.9945,0.9262,,0.48,0.4138,,0.375,,0.1026,0.0784,0.0116,,reg oper spec account,block of flats,0.1011,Panel,No,0.0,0.0,0.0,0.0,-581.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n380626,0,Cash loans,F,N,Y,2,225000.0,1096020.0,55962.0,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.011703,-16159,-212,-2086.0,-2380,,1,1,0,1,0,1,Laborers,4.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Industry: type 11,,0.6959785005448529,0.4956658291397297,0.0041,,0.9702,,,,,0.0,,,,0.0009,,0.0024,0.0042,,0.9702,,,,,0.0,,,,0.001,,0.0026,0.0042,,0.9702,,,,,0.0,,,,0.001,,0.0025,,terraced house,0.0014,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1653.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n170853,0,Cash loans,F,N,Y,0,90000.0,251280.0,10773.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009334,-15343,-7270,-9425.0,-4136,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,13,0,0,0,0,1,1,Other,,0.7213351962093278,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-576.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n353249,0,Cash loans,M,Y,N,0,81000.0,550489.5,19773.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.025164,-23727,365243,-1057.0,-4825,12.0,1,0,0,1,0,0,,1.0,2,2,THURSDAY,19,0,0,0,0,0,0,XNA,,0.11385017989875568,0.7435593141311444,0.032,0.0107,0.9672,0.5512,0.0337,0.0,0.1034,0.0833,0.125,0.0376,0.0261,0.0243,0.0,0.0,0.0326,0.0111,0.9672,0.5688,0.034,0.0,0.1034,0.0833,0.125,0.0385,0.0285,0.0253,0.0,0.0,0.0323,0.0107,0.9672,0.5572,0.0339,0.0,0.1034,0.0833,0.125,0.0383,0.0265,0.0248,0.0,0.0,reg oper account,block of flats,0.0375,Others,No,0.0,0.0,0.0,0.0,-402.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,2.0\n270445,0,Cash loans,F,N,Y,0,202500.0,518562.0,20695.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.006008,-22819,-3397,-7091.0,-4295,,1,1,0,1,1,0,Laborers,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Government,,0.7004296043841214,0.7557400501752248,0.0711,0.079,0.9771,0.6872,0.008,0.0,0.1379,0.1667,0.2083,0.0545,0.0538,0.0561,0.0193,0.2516,0.0725,0.08199999999999999,0.9772,0.6994,0.0081,0.0,0.1379,0.1667,0.2083,0.0557,0.0588,0.0584,0.0195,0.2664,0.0718,0.079,0.9771,0.6914,0.0081,0.0,0.1379,0.1667,0.2083,0.0554,0.0547,0.0571,0.0194,0.2569,reg oper account,block of flats,0.1032,Panel,No,0.0,0.0,0.0,0.0,-1498.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n181803,0,Cash loans,F,N,Y,1,202500.0,314100.0,13963.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.005084,-15346,-1886,-298.0,-4084,,1,1,1,1,1,0,Core staff,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,Kindergarten,,0.17092378602024555,0.6658549219640212,0.0619,0.0655,0.9851,0.7959999999999999,0.0498,0.0,0.2069,0.1667,0.0,0.023,0.0504,0.0599,0.0,0.0,0.063,0.068,0.9851,0.804,0.0503,0.0,0.2069,0.1667,0.0,0.0235,0.0551,0.0624,0.0,0.0,0.0625,0.0655,0.9851,0.7987,0.0501,0.0,0.2069,0.1667,0.0,0.0234,0.0513,0.061,0.0,0.0,,block of flats,0.0471,Panel,No,0.0,0.0,0.0,0.0,-2867.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n239669,0,Cash loans,F,N,N,0,135000.0,417024.0,35806.5,360000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.01885,-19406,-1594,-11252.0,-2621,,1,1,0,1,1,0,Core staff,1.0,2,2,MONDAY,13,0,0,0,0,0,0,School,,0.5504097544115928,,0.1134,0.0661,0.9801,0.728,0.013000000000000001,0.12,0.1034,0.3333,0.375,0.4972,0.0908,0.1139,0.0077,0.0041,0.1155,0.0686,0.9801,0.7387,0.0131,0.1208,0.1034,0.3333,0.375,0.5085,0.0992,0.1187,0.0078,0.0044,0.1145,0.0661,0.9801,0.7316,0.0131,0.12,0.1034,0.3333,0.375,0.5058,0.0923,0.1159,0.0078,0.0042,,block of flats,0.0896,Panel,No,0.0,0.0,0.0,0.0,-1635.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n390398,0,Cash loans,F,N,Y,0,157500.0,263686.5,26208.0,238500.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.008473999999999999,-13358,-484,-848.0,-151,,1,1,0,1,0,0,Medicine staff,1.0,2,2,FRIDAY,17,0,0,0,0,0,0,Construction,,0.2858978721410488,0.5531646987710016,0.1005,0.0372,0.9757,0.6668,0.0125,0.0,0.3103,0.1458,0.1875,0.0318,0.0786,0.0922,0.0154,0.0723,0.062,0.0245,0.9737,0.6537,0.0076,0.0,0.1379,0.125,0.1667,0.0,0.0514,0.0556,0.0117,0.0687,0.1015,0.0372,0.9757,0.6713,0.0126,0.0,0.3103,0.1458,0.1875,0.0323,0.08,0.0939,0.0155,0.0738,reg oper account,block of flats,0.0706,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n237276,0,Cash loans,F,N,Y,0,121500.0,305221.5,14976.0,252000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-9913,-1250,-2580.0,-2592,,1,1,1,1,1,0,Laborers,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Housing,0.4290115731053716,0.5023088970630839,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.0,0.0,12.0,0.0,-1633.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n350212,0,Cash loans,M,Y,Y,0,112500.0,528633.0,22527.0,472500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-18717,365243,-2195.0,-2278,11.0,1,0,0,1,0,0,,2.0,3,3,MONDAY,7,0,0,0,0,0,0,XNA,0.5393652909857504,0.3191021544566901,,0.0866,0.06,0.9737,,,0.0,0.0345,0.1667,,0.0126,,0.0472,,0.044000000000000004,0.0882,0.0623,0.9737,,,0.0,0.0345,0.1667,,0.0129,,0.0492,,0.0466,0.0874,0.06,0.9737,,,0.0,0.0345,0.1667,,0.0129,,0.0481,,0.0449,,block of flats,0.0467,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-1624.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,2.0\n361827,0,Cash loans,F,N,Y,0,108000.0,521280.0,22216.5,450000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-20735,365243,-8734.0,-4213,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,0.7262926094518359,0.5609753159304787,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2332.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n368542,0,Revolving loans,M,N,N,0,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Rented apartment,0.018209,-18253,-980,-3662.0,-1793,,1,1,0,1,0,0,Security staff,2.0,3,3,TUESDAY,11,0,0,0,1,1,0,Business Entity Type 3,,0.4787757557574022,0.15663982703141147,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-675.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n259539,0,Revolving loans,F,N,N,0,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.0105,-8988,-1069,-1463.0,-1497,,1,1,0,1,0,0,Laborers,1.0,3,3,TUESDAY,11,0,0,0,1,1,0,Business Entity Type 3,,0.3631125768379373,0.4135967602644276,0.0247,0.0,0.9801,0.728,0.0034,0.0,0.0345,0.0417,0.0833,0.01,0.0202,0.0123,0.0,0.0,0.0252,0.0,0.9801,0.7387,0.0034,0.0,0.0345,0.0417,0.0833,0.0103,0.022000000000000002,0.0128,0.0,0.0,0.025,0.0,0.9801,0.7316,0.0034,0.0,0.0345,0.0417,0.0833,0.0102,0.0205,0.0125,0.0,0.0,reg oper account,block of flats,0.0115,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-495.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n124469,0,Cash loans,F,Y,Y,1,112500.0,691020.0,26905.5,495000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.018801,-9654,-2969,-9654.0,-1252,22.0,1,1,1,1,1,0,Laborers,3.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 2,0.33299343810467985,0.09298876893570553,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-551.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n146999,0,Cash loans,F,N,Y,2,180000.0,568800.0,20560.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-12806,-183,-1133.0,-1580,,1,1,1,1,1,0,High skill tech staff,4.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.2538858817273877,0.7317654705902802,0.375711009574066,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2172.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n331656,0,Cash loans,F,N,Y,2,157500.0,298512.0,28039.5,270000.0,Family,Working,Higher education,Civil marriage,Rented apartment,0.04622,-14404,-2375,-5357.0,-2174,,1,1,0,1,0,0,Core staff,4.0,1,1,MONDAY,12,0,0,0,0,0,0,Trade: type 2,0.8361356668359258,0.7473544683802145,0.7252764347002191,0.1485,,0.9856,,,0.16,0.1379,0.3333,,,,,,,0.1513,,0.9856,,,0.1611,0.1379,0.3333,,,,,,,0.1499,,0.9856,,,0.16,0.1379,0.3333,,,,,,,,block of flats,0.1244,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2253.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n378960,0,Revolving loans,F,N,Y,0,157500.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18453,-365,-7502.0,-1837,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,0.6783980561841689,0.35728197361233577,0.4048783643353997,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-166.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n452320,0,Cash loans,M,N,Y,0,247500.0,849415.5,43501.5,697500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006008,-16782,-4228,-7104.0,-334,,1,1,0,1,1,1,Laborers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Trade: type 7,,0.521850074005281,0.4974688893052743,0.0784,0.0609,0.9821,0.7552,0.0117,0.08,0.069,0.3333,0.375,0.1302,0.0639,0.0891,0.0,0.0,0.0798,0.0632,0.9821,0.7648,0.0118,0.0806,0.069,0.3333,0.375,0.1332,0.0698,0.0929,0.0,0.0,0.0791,0.0609,0.9821,0.7585,0.0117,0.08,0.069,0.3333,0.375,0.1325,0.065,0.0907,0.0,0.0,reg oper account,block of flats,0.0765,Panel,No,0.0,0.0,0.0,0.0,-1004.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n125566,0,Cash loans,F,N,Y,3,90000.0,890379.0,32112.0,751500.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.009549,-14204,-665,-1058.0,-6071,,1,1,0,1,0,0,Sales staff,5.0,2,2,SATURDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.5695577479329216,0.5547889046131542,0.7544061731797895,0.0082,,0.9717,,,0.0,0.0345,0.0417,,0.0211,,0.0082,,,0.0084,,0.9717,,,0.0,0.0345,0.0417,,0.0216,,0.0085,,,0.0083,,0.9717,,,0.0,0.0345,0.0417,,0.0214,,0.0083,,,,block of flats,0.0064,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-965.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n179483,0,Cash loans,F,Y,Y,1,184500.0,604152.0,29196.0,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008625,-15921,-1448,-5873.0,-1088,3.0,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.4734904539583336,0.19294222771695085,0.0742,0.0528,0.9891,,,0.08,0.069,0.3333,,0.0371,,0.0894,,0.0,0.0756,0.0548,0.9891,,,0.0806,0.069,0.3333,,0.038,,0.0932,,0.0,0.0749,0.0528,0.9891,,,0.08,0.069,0.3333,,0.0378,,0.0911,,0.0,,block of flats,0.0704,Panel,No,1.0,1.0,1.0,1.0,-334.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n269952,0,Cash loans,F,Y,N,0,141727.5,135000.0,12492.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.02461,-19236,-4383,-10520.0,-2775,4.0,1,1,1,1,1,0,Managers,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,School,,0.6011881799339323,0.6178261467332483,0.0113,0.0,0.9727,0.626,0.0009,0.0,0.0345,0.0417,0.0833,,0.0092,0.0075,0.0,0.0,0.0116,0.0,0.9727,0.6406,0.0009,0.0,0.0345,0.0417,0.0833,,0.0101,0.0079,0.0,0.0,0.0115,0.0,0.9727,0.631,0.0009,0.0,0.0345,0.0417,0.0833,,0.0094,0.0077,0.0,0.0,reg oper account,block of flats,0.0064,\"Stone, brick\",No,3.0,0.0,2.0,0.0,-2041.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n122119,0,Cash loans,F,Y,Y,0,135000.0,808650.0,23773.5,675000.0,Family,Working,Incomplete higher,Married,House / apartment,0.030755,-14982,-8067,-7470.0,-4063,13.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,School,0.7444840420735221,0.5212061763159243,0.5638350489514956,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1871.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n208206,0,Cash loans,M,Y,Y,0,180000.0,538704.0,26046.0,481500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-19342,-3978,-11480.0,-2882,22.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 1,,0.7236195285100189,0.6479768603302221,0.0371,0.0075,0.9722,,,0.0,0.1034,0.0833,,0.0485,,0.0299,,0.0,0.0378,0.0078,0.9722,,,0.0,0.1034,0.0833,,0.0496,,0.0311,,0.0,0.0375,0.0075,0.9722,,,0.0,0.1034,0.0833,,0.0494,,0.0304,,0.0,,block of flats,0.0235,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1355.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n441170,0,Cash loans,F,N,N,2,211500.0,1506816.0,49099.5,1350000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-13107,-591,-2610.0,-5214,,1,1,0,1,0,0,Core staff,4.0,2,2,FRIDAY,11,0,0,0,0,0,0,Self-employed,0.6990836874442521,0.2781757870795312,0.4418358231994413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n133088,0,Cash loans,F,N,Y,0,54000.0,152820.0,16177.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.025164,-20076,-660,-5052.0,-3576,,1,1,1,1,0,0,,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Other,,0.4372357934506031,0.7252764347002191,0.0186,0.0,0.9806,0.7348,0.0064,0.0,0.0345,0.0417,0.0833,0.0038,0.0151,0.0052,0.0,0.0162,0.0189,0.0,0.9806,0.7452,0.0065,0.0,0.0345,0.0417,0.0833,0.0039,0.0165,0.0054,0.0,0.0171,0.0187,0.0,0.9806,0.7383,0.0065,0.0,0.0345,0.0417,0.0833,0.0039,0.0154,0.0053,0.0,0.0165,reg oper account,block of flats,0.0076,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n365459,0,Cash loans,F,N,N,0,58500.0,387000.0,23512.5,387000.0,Family,Commercial associate,Secondary / secondary special,Married,With parents,0.031329,-12236,-1955,-5643.0,-532,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,,0.18933121367510056,,0.0082,0.0246,0.9722,0.6192,0.0007,0.0,0.0345,0.0417,0.0833,0.0222,0.0067,0.0054,0.0,0.0,0.0084,0.0255,0.9722,0.6341,0.0007,0.0,0.0345,0.0417,0.0833,0.0227,0.0073,0.0056,0.0,0.0,0.0083,0.0246,0.9722,0.6243,0.0007,0.0,0.0345,0.0417,0.0833,0.0226,0.0068,0.0055,0.0,0.0,reg oper account,block of flats,0.0046,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n267485,0,Cash loans,F,N,Y,0,135000.0,292500.0,30843.0,292500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-19939,-3856,-6398.0,-3484,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Other,,0.7173752609744921,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1291.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n122658,0,Cash loans,F,N,Y,0,135000.0,755190.0,30078.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.02461,-23230,365243,-509.0,-4691,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.6359143216564411,0.7517237147741489,0.0835,0.0752,0.9762,0.6736,0.0334,0.0,0.1379,0.1667,0.2083,0.0632,0.0672,0.0585,0.0039,0.0036,0.0851,0.078,0.9762,0.6864,0.0337,0.0,0.1379,0.1667,0.2083,0.0647,0.0735,0.0609,0.0039,0.0038,0.0843,0.0752,0.9762,0.6779999999999999,0.0336,0.0,0.1379,0.1667,0.2083,0.0643,0.0684,0.0595,0.0039,0.0036,org spec account,block of flats,0.046,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1487.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n106777,0,Cash loans,F,N,N,0,90000.0,508495.5,22527.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003122,-19717,-9276,-12850.0,-3230,,1,1,1,1,0,0,Core staff,2.0,3,3,FRIDAY,18,0,0,0,0,1,1,Medicine,0.689570710634277,0.6741263528980198,0.6109913280868294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2341.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n289037,0,Revolving loans,M,Y,Y,2,112500.0,270000.0,13500.0,270000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-13471,-2148,-2952.0,-4025,13.0,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.36024099576087804,0.4111981647644288,0.4920600938649263,0.0825,0.0819,0.9881,0.8368,0.0126,0.0,0.2069,0.1667,0.2083,0.085,0.0672,0.0784,0.0,0.0,0.084,0.085,0.9881,0.8432,0.0127,0.0,0.2069,0.1667,0.2083,0.087,0.0735,0.0817,0.0,0.0,0.0833,0.0819,0.9881,0.8390000000000001,0.0127,0.0,0.2069,0.1667,0.2083,0.0865,0.0684,0.0798,0.0,0.0,reg oper account,block of flats,0.0616,Panel,No,5.0,0.0,5.0,0.0,-304.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n313951,0,Revolving loans,M,N,Y,0,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-20448,-2347,-415.0,-3541,,1,1,0,1,0,0,,2.0,3,3,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.17338579670572365,0.6610235391308081,0.132,0.1107,0.9821,,,0.16,0.1379,0.3333,,0.1374,,0.1335,,0.1788,0.1345,0.1149,0.9821,,,0.1611,0.1379,0.3333,,0.1405,,0.1391,,0.1893,0.1332,0.1107,0.9821,,,0.16,0.1379,0.3333,,0.1398,,0.1359,,0.1826,,block of flats,0.1439,Panel,No,0.0,0.0,0.0,0.0,-1626.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n108172,0,Cash loans,F,N,Y,0,202500.0,508495.5,24592.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00823,-10192,-574,-1523.0,-2813,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.3146065723800206,0.29910574396291073,0.1674084472266241,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-471.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n262207,0,Cash loans,F,N,Y,0,121500.0,780363.0,31077.0,697500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-20334,-1610,-8893.0,-3288,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,14,0,0,0,0,1,1,Self-employed,0.7612046882482247,0.712468414704572,0.41534714488434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-981.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n368416,0,Cash loans,F,Y,Y,1,225000.0,314055.0,13963.5,238500.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.003813,-18843,-3463,-7657.0,-2362,14.0,1,1,0,1,0,0,Cleaning staff,3.0,2,2,MONDAY,8,0,0,0,0,0,0,School,,0.2453495532540649,0.2622489709189549,0.1309,0.0576,0.9752,0.66,0.0056,0.0,0.1034,0.1667,0.2083,0.0322,0.1051,0.048,0.0077,0.0538,0.1334,0.0597,0.9752,0.6733,0.0056,0.0,0.1034,0.1667,0.2083,0.0329,0.1148,0.0501,0.0078,0.057,0.1322,0.0576,0.9752,0.6645,0.0056,0.0,0.1034,0.1667,0.2083,0.0327,0.1069,0.0489,0.0078,0.055,reg oper account,block of flats,0.0525,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n429956,0,Cash loans,F,Y,Y,0,135000.0,124380.0,12744.0,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-22800,365243,-7621.0,-3932,8.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.7222529505474755,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1040.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n312574,0,Cash loans,F,N,N,2,90000.0,292500.0,14989.5,292500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.015221,-16714,-9686,-3010.0,-247,,1,1,1,1,1,0,Medicine staff,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Medicine,,0.6687910631515377,0.4812493411434029,0.0082,0.0,0.9682,0.5648,0.0013,0.0,0.069,0.0417,0.0833,0.0085,0.0067,0.0074,0.0,0.0,0.0084,0.0,0.9682,0.5818,0.0013,0.0,0.069,0.0417,0.0833,0.0086,0.0073,0.0077,0.0,0.0,0.0083,0.0,0.9682,0.5706,0.0013,0.0,0.069,0.0417,0.0833,0.0086,0.0068,0.0075,0.0,0.0,reg oper account,block of flats,0.0065,Mixed,Yes,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n306009,0,Cash loans,F,N,N,0,180000.0,1546020.0,40914.0,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.02461,-21120,-1262,-8832.0,-4280,,1,1,0,1,1,0,Accountants,2.0,2,2,THURSDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.7172955339665475,0.5546376644614049,0.4902575124990026,0.1299,0.1057,0.9846,0.7892,0.0315,0.0,0.0345,0.3333,0.375,0.0964,0.1051,0.0989,0.0039,0.0025,0.1324,0.1097,0.9846,0.7975,0.0317,0.0,0.0345,0.3333,0.375,0.0986,0.1148,0.1031,0.0039,0.0026,0.1312,0.1057,0.9846,0.792,0.0317,0.0,0.0345,0.3333,0.375,0.0981,0.1069,0.1007,0.0039,0.0025,,block of flats,0.1313,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n372690,0,Cash loans,F,N,Y,1,135000.0,270126.0,13923.0,193500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.0105,-9572,-1115,-1953.0,-2206,,1,1,0,1,0,0,Sales staff,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,Other,,0.4375909551688262,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n263569,0,Cash loans,F,N,Y,0,90000.0,508495.5,22527.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22224,365243,-5588.0,-4265,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,,0.30750142443597583,0.6380435278721609,0.1938,0.1025,0.9916,0.8844,0.0857,0.24,0.2069,0.375,0.4167,0.1603,0.158,0.2126,0.0,0.0,0.1975,0.1063,0.9916,0.8889,0.0865,0.2417,0.2069,0.375,0.4167,0.16399999999999998,0.1726,0.2215,0.0,0.0,0.1957,0.1025,0.9916,0.8859,0.0862,0.24,0.2069,0.375,0.4167,0.1631,0.1608,0.2165,0.0,0.0,reg oper account,block of flats,0.1672,Panel,No,6.0,0.0,6.0,0.0,-1580.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n140976,0,Cash loans,F,N,Y,0,180000.0,1125000.0,32895.0,1125000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-15681,-5446,-2671.0,-4299,,1,1,0,1,1,0,Accountants,1.0,2,2,SATURDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.5036537047551857,0.6144143775673561,0.1227,0.0583,0.9901,0.8640000000000001,0.0326,0.16,0.069,0.4583,0.5,0.0877,0.1,0.1334,0.0097,0.006999999999999999,0.125,0.0603,0.9901,0.8693,0.0305,0.1611,0.069,0.4583,0.5,0.0744,0.1093,0.1387,0.0,0.0052,0.1239,0.0583,0.9901,0.8658,0.0328,0.16,0.069,0.4583,0.5,0.0892,0.1018,0.1358,0.0097,0.0072,org spec account,block of flats,0.1199,Panel,No,0.0,0.0,0.0,0.0,-2586.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n261038,0,Cash loans,M,N,Y,0,202500.0,417024.0,33565.5,360000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-8656,-664,-8595.0,-1055,,1,1,0,1,1,0,,2.0,1,1,MONDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.6071863542801491,0.4400578303966329,0.10099999999999999,0.0304,0.9811,0.7416,0.0236,0.08,0.0345,0.5417,0.5833,0.1084,0.0824,0.0834,0.0,0.0,0.1029,0.0316,0.9811,0.7517,0.0238,0.0806,0.0345,0.5417,0.5833,0.1109,0.09,0.0869,0.0,0.0,0.102,0.0304,0.9811,0.7451,0.0238,0.08,0.0345,0.5417,0.5833,0.1103,0.0838,0.0849,0.0,0.0,reg oper account,block of flats,0.0785,Mixed,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n219032,1,Cash loans,M,Y,N,0,180000.0,540000.0,39294.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.007120000000000001,-9342,-797,-3615.0,-1751,8.0,1,1,0,1,0,0,Security staff,2.0,2,2,WEDNESDAY,10,1,1,0,1,1,0,Business Entity Type 3,0.11321773277001974,0.5311392361921923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-450.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n259043,0,Cash loans,F,N,N,0,67500.0,119925.0,11097.0,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18832,365243,-9674.0,-2375,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.16470066686484844,0.5100895276257282,0.1031,0.1545,0.9871,,,0.0,0.2759,0.1667,,0.0372,,0.1113,,0.0,0.105,0.1604,0.9871,,,0.0,0.2759,0.1667,,0.038,,0.11599999999999999,,0.0,0.1041,0.1545,0.9871,,,0.0,0.2759,0.1667,,0.0378,,0.1133,,0.0,,block of flats,0.0965,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1591.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n344362,0,Cash loans,M,Y,Y,0,292500.0,1129500.0,46134.0,1129500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.031329,-21969,365243,-1762.0,-4613,0.0,1,0,0,1,1,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.27782465305109544,0.6178261467332483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n449593,0,Cash loans,F,N,Y,0,180000.0,800500.5,34047.0,715500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.020246,-20188,365243,-44.0,-3537,,1,0,0,1,0,0,,1.0,3,3,SATURDAY,7,0,0,0,0,0,0,XNA,0.755365771206524,0.4147924702761077,0.5136937663039473,0.3155,0.2353,0.9821,,,0.36,0.3103,0.3333,,0.3075,,0.3464,,0.0,0.3214,0.2442,0.9821,,,0.3625,0.3103,0.3333,,0.3145,,0.3609,,0.0,0.3185,0.2353,0.9821,,,0.36,0.3103,0.3333,,0.3129,,0.3526,,0.0,,block of flats,0.2725,Panel,No,1.0,0.0,1.0,0.0,-769.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n114749,0,Cash loans,F,N,N,2,135000.0,1288350.0,37669.5,1125000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.003069,-10176,-2077,-3051.0,-1609,,1,1,1,1,1,0,Secretaries,4.0,3,3,MONDAY,14,0,0,0,0,0,0,Security Ministries,,0.6710154519449951,0.7016957740576931,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-32.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n204019,0,Cash loans,F,N,N,0,81000.0,135000.0,13833.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.007273999999999998,-19522,-2138,-6444.0,-3050,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,14,1,1,0,1,1,0,Self-employed,0.8438089854015678,0.20213681577007928,0.5884877883422673,0.1753,0.1576,0.9866,0.8164,0.0,0.0,0.069,0.1667,0.0417,0.0276,0.142,0.1236,0.0039,0.0026,0.1786,0.1635,0.9866,0.8236,0.0,0.0,0.069,0.1667,0.0417,0.0282,0.1552,0.1288,0.0039,0.0027,0.177,0.1576,0.9866,0.8189,0.0,0.0,0.069,0.1667,0.0417,0.027999999999999997,0.1445,0.1258,0.0039,0.0026,reg oper account,block of flats,0.1147,Panel,No,1.0,0.0,1.0,0.0,-1193.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n377668,0,Cash loans,M,N,N,1,74250.0,286704.0,14769.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.009334,-17563,-10770,-9479.0,-1064,,1,1,1,1,1,1,Laborers,3.0,2,2,FRIDAY,12,0,0,0,0,1,1,Transport: type 2,,0.4168003461271057,0.36227724703843145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2820.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n339922,0,Cash loans,F,Y,Y,0,202500.0,699322.5,55381.5,648000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-15005,-1443,-761.0,-4342,15.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Trade: type 7,0.5475938608685724,0.674328083195248,0.6446794549585961,0.0082,,0.9717,,,0.0,0.0345,0.0417,,0.0169,,0.0077,,0.0,0.0084,,0.9717,,,0.0,0.0345,0.0417,,0.0173,,0.0081,,0.0,0.0083,,0.9717,,,0.0,0.0345,0.0417,,0.0172,,0.0079,,0.0,,block of flats,0.0061,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1287.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n144608,0,Cash loans,F,N,N,0,180000.0,450000.0,43839.0,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.019689,-10723,-1310,-955.0,-3349,,1,1,1,1,1,0,Accountants,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.43862297339284895,0.09586826766353758,,0.0464,0.07400000000000001,0.9796,0.7212,0.0104,0.0,0.1379,0.1667,0.2083,0.0689,0.0378,0.0371,0.0039,0.1069,0.0473,0.0768,0.9796,0.7321,0.0105,0.0,0.1379,0.1667,0.2083,0.0705,0.0413,0.0387,0.0039,0.1131,0.0468,0.07400000000000001,0.9796,0.7249,0.0105,0.0,0.1379,0.1667,0.2083,0.0701,0.0385,0.0378,0.0039,0.1091,org spec account,block of flats,0.0581,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n295386,0,Cash loans,F,N,Y,0,202500.0,481500.0,34371.0,481500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.026392,-21476,365243,-5138.0,-4206,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.4725746874672691,0.6363761710860439,0.1433,,0.9925,,,0.16,0.1379,0.3333,,,,0.1493,,0.0274,0.146,,0.9926,,,0.1611,0.1379,0.3333,,,,0.1555,,0.028999999999999998,0.1447,,0.9925,,,0.16,0.1379,0.3333,,,,0.152,,0.027999999999999997,,block of flats,0.1234,Panel,No,1.0,0.0,1.0,0.0,-2135.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n178883,0,Cash loans,F,N,Y,0,202500.0,557770.5,24696.0,481500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.00733,-20635,365243,-5450.0,-3792,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.5846749669475833,0.4525335592581747,0.2144,,0.9841,,,0.0,,0.1667,,,,0.081,,,0.2185,,0.9841,,,0.0,,0.1667,,,,0.0844,,,0.2165,,0.9841,,,0.0,,0.1667,,,,0.0825,,,,block of flats,0.0958,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1272.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n387814,0,Cash loans,M,Y,N,0,202500.0,327024.0,21852.0,270000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.006629,-8615,-520,-4989.0,-1286,8.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.1490366383913173,0.5947747964118507,,0.0134,,0.9727,,,0.0,0.069,0.0417,,,,0.0114,,,0.0126,,0.9727,,,0.0,0.069,0.0417,,,,0.0118,,,0.0135,,0.9727,,,0.0,0.069,0.0417,,,,0.0116,,,,block of flats,0.0089,Wooden,Yes,0.0,0.0,0.0,0.0,-351.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n179401,0,Cash loans,F,N,Y,1,90000.0,533668.5,25803.0,477000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-10927,-703,-1870.0,-3480,,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,16,0,0,0,1,1,0,Industry: type 1,0.6321699924089726,0.5986268582020097,0.6769925032909132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-808.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n177490,1,Cash loans,M,Y,Y,0,225000.0,445500.0,27049.5,445500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-15507,-2248,-4296.0,-4383,15.0,1,1,1,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.5871862094792498,0.4414371756566872,0.4956658291397297,0.1031,0.1138,0.9757,0.6668,0.0115,0.0,0.2069,0.1667,0.2083,0.1059,0.0841,0.0905,0.0,0.0104,0.105,0.1181,0.9757,0.6798,0.0116,0.0,0.2069,0.1667,0.2083,0.1083,0.0918,0.0943,0.0,0.011000000000000001,0.1041,0.1138,0.9757,0.6713,0.0115,0.0,0.2069,0.1667,0.2083,0.1077,0.0855,0.0921,0.0,0.0106,reg oper account,block of flats,0.0797,Block,No,0.0,0.0,0.0,0.0,-955.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n254934,0,Cash loans,F,N,N,0,166500.0,1172470.5,33732.0,918000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.032561,-13973,-1642,-781.0,-4237,,1,1,0,1,0,0,Core staff,1.0,1,1,SATURDAY,16,0,0,0,0,0,0,Trade: type 7,,0.6180630399747914,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n114173,0,Cash loans,M,Y,Y,0,225000.0,1211049.0,39195.0,1057500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18425,-393,-529.0,-1951,2.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.6040578257332085,0.8038850611746273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-521.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n243246,0,Cash loans,M,N,Y,0,90000.0,513000.0,21735.0,513000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-17908,-3093,-3964.0,-1468,,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,,0.6388911423298937,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,1.0,-2678.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n152318,0,Cash loans,F,Y,N,0,225000.0,269550.0,21163.5,225000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.032561,-8842,-2234,-993.0,-993,0.0,1,1,0,1,0,0,,1.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Other,0.5996767442616309,0.6913130911102198,0.5478104658520093,0.0454,0.0,0.9518,0.3404,0.0113,0.0,0.2069,0.125,0.1667,0.0302,0.0286,0.0456,0.0386,0.042,0.0462,0.0,0.9518,0.3662,0.0114,0.0,0.2069,0.125,0.1667,0.0309,0.0312,0.0475,0.0389,0.0444,0.0458,0.0,0.9518,0.3492,0.0114,0.0,0.2069,0.125,0.1667,0.0307,0.0291,0.0464,0.0388,0.0429,not specified,block of flats,0.0511,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-1743.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n364541,0,Cash loans,F,N,Y,0,135000.0,225000.0,22050.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.026392,-25175,-16607,-17394.0,-4079,,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 2,,0.6694107864577534,0.82872883245649,0.0825,,0.9757,,,0.0,0.1379,0.1667,,,,0.0719,,0.0,0.084,,0.9757,,,0.0,0.1379,0.1667,,,,0.0749,,0.0,0.0833,,0.9757,,,0.0,0.1379,0.1667,,,,0.0731,,0.0,,block of flats,0.0565,Panel,No,0.0,0.0,0.0,0.0,-1558.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n165167,0,Cash loans,F,Y,Y,1,135000.0,835380.0,40320.0,675000.0,Family,Working,Higher education,Married,House / apartment,0.020713,-13199,-2089,-2671.0,-2678,11.0,1,1,0,1,0,0,Sales staff,3.0,3,1,MONDAY,12,0,0,0,0,0,0,Trade: type 7,,0.5689242380008578,,0.0711,0.0657,0.9856,,,0.08,0.069,0.3333,,0.0274,,0.0892,,0.0068,0.0725,0.0682,0.9856,,,0.0806,0.069,0.3333,,0.027999999999999997,,0.09300000000000001,,0.0072,0.0718,0.0657,0.9856,,,0.08,0.069,0.3333,,0.0279,,0.0908,,0.0069,,block of flats,0.0716,Panel,No,1.0,1.0,1.0,1.0,-51.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n392661,0,Cash loans,M,N,N,0,180000.0,1293502.5,35698.5,1129500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006296,-22276,-3004,-157.0,-5229,,1,1,0,1,0,0,Drivers,2.0,3,3,SATURDAY,10,0,0,0,1,1,0,Transport: type 4,,0.6878398227167543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n326334,0,Cash loans,M,Y,N,0,126000.0,510853.5,27841.5,441000.0,Other_A,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-8093,-219,-6304.0,-725,8.0,1,1,0,1,0,0,,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 2,0.19817975519865724,0.5282465604978989,0.28961123838200553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-419.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n392856,0,Cash loans,F,Y,N,0,135000.0,360000.0,37800.0,360000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018029,-11783,-2596,-5706.0,-2472,16.0,1,1,0,1,1,1,Core staff,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,Self-employed,0.740247384804173,0.5270747756895832,0.6296742509538716,0.1103,0.1259,0.9851,,,0.0,0.2759,0.1667,,,,0.0932,,0.0464,0.1124,0.1307,0.9851,,,0.0,0.2759,0.1667,,,,0.0971,,0.0491,0.1114,0.1259,0.9851,,,0.0,0.2759,0.1667,,,,0.0948,,0.0474,,block of flats,0.0834,Panel,No,0.0,0.0,0.0,0.0,-316.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n363978,0,Cash loans,F,N,Y,1,135000.0,521280.0,26743.5,450000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.019689,-14370,-1893,-220.0,-3377,,1,1,0,1,0,0,Cleaning staff,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Services,,0.5425297187680556,0.4525335592581747,0.1608,0.0523,0.9786,0.7076,0.0194,0.0,0.069,0.1667,0.2083,0.0955,0.1278,0.0526,0.0154,0.0102,0.1639,0.0543,0.9786,0.7190000000000001,0.0195,0.0,0.069,0.1667,0.2083,0.0977,0.1396,0.0548,0.0156,0.0108,0.1624,0.0523,0.9786,0.7115,0.0195,0.0,0.069,0.1667,0.2083,0.0972,0.13,0.0535,0.0155,0.0104,reg oper account,block of flats,0.0579,\"Stone, brick\",No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n392889,0,Cash loans,F,Y,Y,0,148500.0,305221.5,20524.5,252000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.019689,-8565,-1729,-818.0,-941,1.0,1,1,0,1,0,1,Managers,1.0,2,2,MONDAY,17,0,0,0,1,1,0,Self-employed,,0.4496264425948071,0.5172965813614878,0.0722,0.0804,0.9796,0.7212,0.0087,0.0,0.1379,0.1667,0.2083,0.0,0.0588,0.0681,0.0,0.0,0.0735,0.0834,0.9796,0.7321,0.0088,0.0,0.1379,0.1667,0.2083,0.0,0.0643,0.0709,0.0,0.0,0.0729,0.0804,0.9796,0.7249,0.0088,0.0,0.1379,0.1667,0.2083,0.0,0.0599,0.0693,0.0,0.0,reg oper account,block of flats,0.0583,Block,No,0.0,0.0,0.0,0.0,-588.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n218324,0,Cash loans,F,N,N,0,157500.0,143910.0,15628.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-19108,-2298,-3145.0,-2635,,1,1,1,1,1,0,Private service staff,2.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,Services,,0.6960155153943958,0.4507472818545589,0.2216,0.1691,0.9821,0.7552,0.0,0.24,0.2069,0.3333,0.375,0.1391,0.1807,0.2226,0.0,0.0,0.2258,0.1755,0.9821,0.7648,0.0,0.2417,0.2069,0.3333,0.375,0.1423,0.1974,0.2319,0.0,0.0,0.2238,0.1691,0.9821,0.7585,0.0,0.24,0.2069,0.3333,0.375,0.1415,0.1838,0.2266,0.0,0.0,not specified,block of flats,0.2168,Panel,No,0.0,0.0,0.0,0.0,-2776.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n218549,0,Revolving loans,F,N,N,0,90000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.00733,-18810,-1174,-8298.0,-2000,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,MONDAY,18,0,0,0,0,0,0,Transport: type 4,,0.5506370196048056,0.7366226976503176,0.1093,0.0867,0.9891,0.7552,0.0,0.16,0.1034,0.3542,,0.0176,0.121,0.1178,0.0,0.085,0.0714,0.09,0.9821,0.7648,0.0,0.1611,0.069,0.3333,,0.018000000000000002,0.1322,0.0915,0.0,0.0332,0.1103,0.0867,0.9891,0.7585,0.0,0.16,0.1034,0.3542,,0.0179,0.1231,0.1199,0.0,0.0868,reg oper account,block of flats,0.0852,Panel,No,3.0,0.0,3.0,0.0,-115.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n301367,1,Cash loans,F,N,Y,0,67500.0,269550.0,11547.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-22107,365243,-12193.0,-4150,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,6,0,0,0,0,0,0,XNA,0.7312025501857949,0.34112290759888264,0.6363761710860439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1511.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n104177,0,Cash loans,M,N,N,1,166500.0,135000.0,14305.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-11459,-1046,-5803.0,-3434,,1,1,1,1,0,0,Core staff,3.0,3,3,FRIDAY,15,0,0,0,0,0,0,Police,,0.4534985692208394,0.13094715293601816,0.1392,0.0591,0.9901,0.8640000000000001,0.0284,0.2,0.1724,0.375,0.4167,0.1299,0.1135,0.1765,0.0,0.0,0.1418,0.0613,0.9901,0.8693,0.0286,0.2014,0.1724,0.375,0.4167,0.1329,0.124,0.1839,0.0,0.0,0.1405,0.0591,0.9901,0.8658,0.0286,0.2,0.1724,0.375,0.4167,0.1322,0.1154,0.1797,0.0,0.0,reg oper account,block of flats,0.1543,Panel,No,0.0,0.0,0.0,0.0,-676.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n304962,0,Cash loans,F,N,Y,0,157500.0,1078200.0,31653.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-20288,365243,-4417.0,-3488,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.5862193710880279,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,5.0,0.0,5.0,0.0,-465.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n329150,0,Revolving loans,M,Y,Y,1,225000.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-15841,-1347,-3235.0,-4589,8.0,1,1,0,1,0,1,Core staff,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Medicine,,0.5840216752315184,0.470456116119975,0.0925,,0.9831,,,0.08,0.1207,0.25,,,,0.0508,,0.0113,0.0672,,0.9891,,,0.0,0.1379,0.1667,,,,0.053,,0.0,0.0791,,0.9831,,,0.08,0.1379,0.25,,,,0.0518,,0.0,,,0.0648,,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n412410,0,Cash loans,M,N,N,1,135000.0,263686.5,13522.5,238500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.022625,-8447,-828,-969.0,-1110,,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,18,0,0,0,0,0,0,Construction,,0.2858978721410488,0.2032521136204725,0.1974,0.1173,0.9945,0.9252,0.0855,0.28,0.2414,0.375,0.4167,0.126,0.161,0.2149,0.0,0.0087,0.0284,0.0,0.9906,0.8759,0.0193,0.0403,0.0345,0.3333,0.375,0.0231,0.0248,0.0279,0.0,0.0,0.1993,0.1173,0.9945,0.9262,0.086,0.28,0.2414,0.375,0.4167,0.1282,0.1637,0.2187,0.0,0.0088,not specified,block of flats,0.0353,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-477.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n309586,0,Revolving loans,M,N,Y,0,279000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.008019,-17019,-1656,-417.0,-417,,1,1,0,1,0,0,Drivers,1.0,2,2,MONDAY,14,0,0,0,1,1,0,Transport: type 4,,0.4590375111218346,0.13937578009978951,0.0052,,0.9727,0.626,,0.0,0.0345,0.0,0.0417,,0.0042,,0.0,,0.0053,,0.9727,0.6406,,0.0,0.0345,0.0,0.0417,,0.0046,,0.0,,0.0052,,0.9727,0.631,,0.0,0.0345,0.0,0.0417,,0.0043,,0.0,,reg oper account,block of flats,0.0024,Wooden,No,0.0,0.0,0.0,0.0,-372.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n383900,0,Cash loans,F,N,Y,0,90000.0,540000.0,19525.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-15772,-4301,-8779.0,-4591,,1,1,1,1,0,0,Cooking staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,School,0.5026612774712587,0.5866224428353461,0.2678689358444539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1050.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n133797,0,Cash loans,F,N,N,4,99000.0,474048.0,25843.5,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Rented apartment,0.009175,-12209,-1905,-678.0,-2684,,1,1,1,1,0,0,,6.0,2,2,THURSDAY,13,0,1,1,0,1,1,Government,0.3634067394292468,0.4308863991212592,0.3606125659189888,0.0814,0.1002,0.9871,,,0.0,0.2069,0.1667,,0.0851,,0.0824,,0.0231,0.083,0.10400000000000001,0.9871,,,0.0,0.2069,0.1667,,0.087,,0.0859,,0.0244,0.0822,0.1002,0.9871,,,0.0,0.2069,0.1667,,0.0866,,0.0839,,0.0236,,block of flats,0.0698,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-366.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,1.0,1.0\n107313,0,Cash loans,F,N,N,0,135000.0,728460.0,43375.5,675000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.025164,-11827,-3257,-4147.0,-4147,,1,1,1,1,1,0,Core staff,1.0,2,2,SUNDAY,13,0,0,0,0,1,1,School,0.5434649971651051,0.3508455967874809,0.5902333386185574,0.0289,0.0086,0.9732,0.6328,0.0284,0.0,0.069,0.125,0.1667,0.0389,0.0214,0.0314,0.0097,0.0308,0.0242,0.0083,0.9712,0.621,0.0249,0.0,0.069,0.125,0.1667,0.0113,0.0211,0.0298,0.0,0.0276,0.0291,0.0086,0.9732,0.6377,0.0286,0.0,0.069,0.125,0.1667,0.0396,0.0218,0.0319,0.0097,0.0314,reg oper account,block of flats,0.046,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-878.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n170796,0,Cash loans,M,N,Y,1,202500.0,157500.0,16960.5,157500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.072508,-11954,-849,-6077.0,-4561,,1,1,0,1,1,0,Core staff,3.0,1,1,FRIDAY,22,0,0,0,0,0,0,Police,,0.6539338027155115,0.3672910183026313,0.2072,0.1639,0.9791,0.7144,0.0193,0.24,0.2586,0.3125,0.125,0.1386,0.1689,0.1971,0.0039,0.0044,0.1502,0.1082,0.9712,0.621,0.0035,0.0,0.2069,0.1667,0.0417,0.1318,0.1313,0.1494,0.0,0.0,0.2092,0.1639,0.9791,0.7182,0.0194,0.24,0.2586,0.3125,0.125,0.141,0.1719,0.2006,0.0039,0.0044,reg oper account,block of flats,0.1991,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-494.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n399612,0,Cash loans,F,Y,Y,0,202500.0,1024740.0,41868.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.018801,-20773,-3210,-10517.0,-3422,9.0,1,1,1,1,0,0,Laborers,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Trade: type 2,0.4169370774900802,0.5401950452458685,0.6178261467332483,0.1113,0.0827,0.9851,0.7959999999999999,0.0712,0.12,0.1034,0.3333,0.375,0.0621,0.0908,0.1149,0.0,0.0,0.1134,0.0858,0.9851,0.804,0.0719,0.1208,0.1034,0.3333,0.375,0.0635,0.0992,0.1198,0.0,0.0,0.1124,0.0827,0.9851,0.7987,0.0717,0.12,0.1034,0.3333,0.375,0.0631,0.0923,0.11699999999999999,0.0,0.0,reg oper account,block of flats,0.13,Panel,No,1.0,0.0,1.0,0.0,-669.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n370038,0,Cash loans,F,N,Y,0,180000.0,450000.0,22018.5,450000.0,Family,Working,Secondary / secondary special,Married,With parents,0.015221,-9876,-120,-4599.0,-2090,,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Trade: type 3,,0.6359902624038338,0.5531646987710016,,,0.9796,,,,,,,,,0.1114,,,,,0.9796,,,,,,,,,0.1161,,,,,0.9796,,,,,,,,,0.1134,,,,,0.0924,,No,2.0,1.0,2.0,1.0,-192.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,1.0,0.0\n233719,0,Cash loans,F,N,N,0,126000.0,325908.0,19692.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-15350,-1394,-1398.0,-5054,,1,1,1,1,1,0,,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Industry: type 4,0.3689973597728454,0.16040532147836672,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2405.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n179522,0,Cash loans,F,N,Y,0,270000.0,397881.0,22972.5,328500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-16163,-1484,-6044.0,-2754,,1,1,0,1,0,0,Sales staff,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.688154322829371,0.7252764347002191,0.1,0.0816,0.9781,0.7008,0.0827,0.0,0.2069,0.1667,0.2083,0.0724,0.0807,0.0833,0.0039,0.0206,0.1019,0.0847,0.9782,0.7125,0.0835,0.0,0.2069,0.1667,0.2083,0.07400000000000001,0.0882,0.0868,0.0039,0.0218,0.10099999999999999,0.0816,0.9781,0.7048,0.0833,0.0,0.2069,0.1667,0.2083,0.0736,0.0821,0.0848,0.0039,0.021,reg oper account,block of flats,0.1152,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-556.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n154261,0,Revolving loans,M,Y,Y,1,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-17546,-3457,-8521.0,-1105,14.0,1,1,0,1,1,0,Drivers,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,,0.6007791560329692,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3283.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n102699,0,Revolving loans,F,N,N,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.035792000000000004,-8367,-346,-3730.0,-1008,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,17,1,1,0,1,1,0,Business Entity Type 3,,0.568972504458814,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-220.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n130480,0,Cash loans,F,N,N,0,135000.0,862560.0,25348.5,720000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-22006,365243,-2794.0,-5142,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,8,0,0,0,0,0,0,XNA,,0.4989194398889386,0.41885428862332175,0.1485,0.1237,0.9925,0.898,0.0383,0.16,0.1379,0.3333,0.375,0.1628,0.121,0.17,0.0,0.0,0.1513,0.1283,0.9926,0.902,0.0387,0.1611,0.1379,0.3333,0.375,0.1665,0.1322,0.1772,0.0,0.0,0.1499,0.1237,0.9925,0.8994,0.0386,0.16,0.1379,0.3333,0.375,0.1657,0.1231,0.1731,0.0,0.0,not specified,block of flats,0.1337,Panel,No,0.0,0.0,0.0,0.0,-2427.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n118825,0,Cash loans,F,Y,N,1,180000.0,241618.5,25371.0,229500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.0105,-12430,-2271,-6556.0,-4187,2.0,1,1,0,1,0,0,Private service staff,2.0,3,3,TUESDAY,15,0,0,0,0,0,0,Self-employed,,0.2926142044729505,0.5691487713619409,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1314.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n399554,0,Cash loans,F,Y,Y,0,360000.0,536917.5,32976.0,463500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.04622,-14977,-2839,-9081.0,-4587,1.0,1,1,0,1,0,0,Laborers,2.0,1,1,THURSDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.639289605853327,0.4866531565147181,0.0165,0.0,0.9781,0.7008,0.0008,0.0,0.069,0.0417,0.0833,0.0087,0.0134,0.0147,0.0,0.0,0.0168,0.0,0.9782,0.7125,0.0008,0.0,0.069,0.0417,0.0833,0.0089,0.0147,0.0153,0.0,0.0,0.0167,0.0,0.9781,0.7048,0.0008,0.0,0.069,0.0417,0.0833,0.0088,0.0137,0.0149,0.0,0.0,reg oper account,block of flats,0.012,\"Stone, brick\",No,4.0,3.0,4.0,3.0,-271.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n406457,1,Cash loans,M,Y,Y,1,180000.0,1223010.0,51948.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-12604,-5165,-6373.0,-4176,21.0,1,1,0,1,0,0,Laborers,3.0,2,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.36712724700492894,0.4650692149562261,0.0742,0.040999999999999995,0.9876,0.83,0.0434,0.08,0.069,0.3333,0.375,0.0968,0.0605,0.0749,0.0,0.0,0.0756,0.0426,0.9876,0.8367,0.0438,0.0806,0.069,0.3333,0.375,0.099,0.0661,0.078,0.0,0.0,0.0749,0.040999999999999995,0.9876,0.8323,0.0437,0.08,0.069,0.3333,0.375,0.0985,0.0616,0.0762,0.0,0.0,reg oper spec account,block of flats,0.0827,Panel,No,2.0,1.0,2.0,1.0,-2853.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n206864,0,Revolving loans,F,N,Y,0,72000.0,135000.0,6750.0,135000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.028663,-8557,-1388,-5593.0,-366,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.2219967581639757,0.7183673309614714,0.5064842396679806,0.0876,0.0053,0.9811,,,0.0,0.1724,0.1667,,0.0175,,0.0618,,0.0189,0.0735,0.0021,0.9806,,,0.0,0.1379,0.1667,,0.0155,,0.0473,,0.0,0.0885,0.0053,0.9811,,,0.0,0.1724,0.1667,,0.0178,,0.0629,,0.0193,,block of flats,0.052000000000000005,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-537.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n223724,0,Cash loans,F,N,N,0,67500.0,225000.0,8482.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-19749,-1148,-10526.0,-3289,,1,1,1,1,0,0,Cooking staff,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Medicine,0.6092754080544891,0.6470682128345087,0.7992967832109371,0.0,,0.0,,,,,,,,,,,,0.0,,0.0,,,,,,,,,,,,0.0,,0.0,,,,,,,,,,,,,block of flats,0.0,,No,0.0,0.0,0.0,0.0,-2568.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n348067,0,Revolving loans,F,N,Y,0,157500.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.009549,-10912,-440,-634.0,-2178,,1,1,0,1,0,0,Sales staff,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,Self-employed,,0.05876695759292529,0.6848276586890367,0.0567,0.0547,0.9801,,,,0.1379,0.1667,,0.0482,,0.0551,,,0.0578,0.0568,0.9801,,,,0.1379,0.1667,,0.0493,,0.0574,,,0.0573,0.0547,0.9801,,,,0.1379,0.1667,,0.049,,0.0561,,,,block of flats,0.0473,Panel,No,0.0,0.0,0.0,0.0,-117.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n431458,0,Cash loans,M,N,Y,0,315000.0,547344.0,28471.5,472500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-12632,-265,-1308.0,-4549,,1,1,0,1,0,0,Managers,2.0,1,1,MONDAY,9,0,0,0,0,0,0,Other,,0.7035860158981476,0.7106743858828587,0.1918,,0.9518,0.3404,,0.0,0.5172,0.2083,0.25,0.0645,,0.2933,,0.1751,0.1954,,0.9518,0.3662,,0.0,0.5172,0.2083,0.25,0.066,,0.3055,,0.1854,0.1936,,0.9518,0.3492,,0.0,0.5172,0.2083,0.25,0.0656,,0.2985,,0.1788,reg oper account,block of flats,0.2687,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-238.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n148305,0,Cash loans,M,N,N,1,112500.0,855882.0,34987.5,765000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-9651,-1196,-9632.0,-2288,,1,1,0,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,11,0,0,0,1,1,0,Trade: type 2,0.3108676041917593,0.10919230664415004,0.475849908720221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-450.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n198888,1,Cash loans,M,N,Y,0,49500.0,284400.0,10849.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.019101,-18739,-992,-5615.0,-2231,,1,1,0,1,0,0,Security staff,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.5952806612023652,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-594.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n201822,0,Revolving loans,M,Y,Y,0,171000.0,225000.0,11250.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008068,-21761,-13814,-4453.0,-4844,1.0,1,1,0,1,0,0,Drivers,2.0,3,3,TUESDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.6973701551241691,0.4741337779138602,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1350.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n103397,0,Cash loans,F,Y,Y,0,64557.0,225000.0,21168.0,225000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.030755,-8002,-123,-2870.0,-683,6.0,1,1,1,1,1,0,Sales staff,2.0,2,2,TUESDAY,10,0,0,0,1,1,1,Trade: type 3,0.2521567319344737,0.5112344628243211,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-103.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n356187,0,Cash loans,F,N,Y,0,135000.0,215865.0,21159.0,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.006852,-21697,365243,-11216.0,-4780,,1,0,0,1,0,0,,1.0,3,3,SUNDAY,7,0,0,0,0,0,0,XNA,,0.08295425691428872,0.7209441499436497,0.134,0.1498,0.9846,0.7892,0.0637,0.0,0.2759,0.1667,0.2083,0.3674,0.1076,0.1417,0.0077,0.0145,0.1366,0.1554,0.9846,0.7975,0.0643,0.0,0.2759,0.1667,0.2083,0.3758,0.1175,0.1477,0.0078,0.0153,0.1353,0.1498,0.9846,0.792,0.0641,0.0,0.2759,0.1667,0.2083,0.3738,0.1095,0.1443,0.0078,0.0148,reg oper account,block of flats,0.1494,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-264.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n386260,0,Cash loans,F,N,Y,2,315000.0,1024740.0,55719.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-10937,-168,-5242.0,-3595,,1,1,0,1,1,0,Managers,4.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.4546419440428928,0.6898645947923544,0.5406544504453575,0.0124,0.0,0.9697,0.5852,0.0024,0.0,0.069,0.0417,0.0833,0.0073,0.0084,0.0102,0.0077,0.0077,0.0126,0.0,0.9697,0.6014,0.0025,0.0,0.069,0.0417,0.0833,0.0074,0.0092,0.0106,0.0078,0.0081,0.0125,0.0,0.9697,0.5907,0.0025,0.0,0.069,0.0417,0.0833,0.0074,0.0086,0.0104,0.0078,0.0078,reg oper account,block of flats,0.011000000000000001,Block,No,2.0,0.0,2.0,0.0,-718.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n198051,0,Cash loans,M,Y,Y,2,225000.0,1125000.0,39987.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-14869,-3892,-2536.0,-4278,16.0,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,13,0,0,0,0,0,0,Industry: type 9,0.5275563546208303,0.7724250782834761,0.4101025731788671,0.1165,,0.996,0.9456,,0.08,0.0345,0.75,0.7917,,0.0941,0.1253,0.0039,,0.1187,,0.996,0.9477,,0.0806,0.0345,0.75,0.7917,,0.1028,0.1306,0.0039,,0.1176,,0.996,0.9463,,0.08,0.0345,0.75,0.7917,,0.0958,0.1276,0.0039,,,block of flats,0.1256,Mixed,No,5.0,0.0,5.0,0.0,-1568.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n108543,0,Cash loans,F,N,Y,2,180000.0,1259626.5,50085.0,1080000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00496,-13411,-1183,-7519.0,-3674,,1,1,0,1,0,0,Core staff,4.0,2,2,THURSDAY,18,0,0,0,0,0,0,Postal,0.6200426037867556,0.7563134909507577,0.20559813854932085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,-1352.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n323576,0,Revolving loans,F,N,Y,0,180000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.018209,-17112,-6259,-3542.0,-670,,1,1,0,1,0,0,Core staff,1.0,3,3,TUESDAY,7,0,0,0,0,0,0,School,,0.557584988453646,0.4992720153045617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1664.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n117558,0,Cash loans,F,Y,Y,0,225000.0,244512.0,13783.5,216000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.02461,-24224,365243,-12600.0,-2693,4.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,XNA,,0.7711965177397184,0.7922644738669378,0.1186,0.1025,0.9831,0.7688,0.0819,0.12,0.1034,0.3333,0.375,0.0818,0.0941,0.1323,0.0116,0.0159,0.1208,0.1063,0.9831,0.7779,0.0827,0.1208,0.1034,0.3333,0.375,0.0837,0.1028,0.1379,0.0117,0.0168,0.1197,0.1025,0.9831,0.7719,0.0824,0.12,0.1034,0.3333,0.375,0.0832,0.0958,0.1347,0.0116,0.0162,reg oper account,block of flats,0.1489,\"Stone, brick\",No,3.0,1.0,2.0,1.0,-1468.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n233576,1,Cash loans,M,Y,Y,0,225000.0,508495.5,24462.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-10572,-3680,-4993.0,-3019,8.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,Self-employed,0.09505344490726128,0.531084778905105,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-503.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n353334,0,Cash loans,F,N,Y,0,90000.0,225000.0,9661.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.009175,-21478,365243,-3964.0,-4001,,1,0,0,1,1,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.5620682935005448,0.6594055320683344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-660.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n326131,0,Cash loans,F,N,Y,0,135000.0,270000.0,19332.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.028663,-22419,365243,-2502.0,-5693,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.590255383876552,0.2750003523983893,0.0495,0.0878,0.9791,0.7144,0.0108,0.0,0.1379,0.1667,0.0417,0.0916,0.0403,0.0539,0.0,0.1122,0.0504,0.0911,0.9791,0.7256,0.0109,0.0,0.1379,0.1667,0.0417,0.0937,0.0441,0.0561,0.0,0.1188,0.05,0.0878,0.9791,0.7182,0.0108,0.0,0.1379,0.1667,0.0417,0.0932,0.040999999999999995,0.0548,0.0,0.1146,reg oper account,block of flats,0.0727,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n338548,1,Cash loans,F,Y,Y,2,450000.0,797814.0,31045.5,571500.0,Family,State servant,Incomplete higher,Married,House / apartment,0.072508,-12333,-5538,-6329.0,-4416,6.0,1,1,0,1,1,0,High skill tech staff,4.0,1,1,THURSDAY,12,0,0,0,0,0,0,Other,,0.6387549147169048,0.5919766183185521,0.1219,0.0794,0.9796,,,0.12,0.0948,0.3646,,0.0981,,0.0926,,0.3148,0.0714,0.0385,0.9742,,,0.0403,0.0345,0.3333,,0.0779,,0.0493,,0.022000000000000002,0.0791,0.0578,0.9742,,,0.04,0.0862,0.3333,,0.0774,,0.062,,0.0433,,block of flats,0.4089,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1044.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n342408,0,Cash loans,M,N,N,2,315000.0,1288350.0,37800.0,1125000.0,Family,Working,Higher education,Married,House / apartment,0.00702,-11151,-659,-163.0,-3656,,1,1,0,1,0,0,Security staff,4.0,2,2,FRIDAY,16,0,0,0,0,1,1,Industry: type 9,,0.5421606362377518,0.6246146584503397,0.1423,,0.9896,0.8504,,0.18,0.2586,0.25,0.125,,0.11599999999999999,0.121,0.0,0.0,0.0945,,0.9896,0.8563,,0.0,0.2069,0.1667,0.0417,,0.0826,0.0972,0.0,0.0,0.1436,,0.9896,0.8524,,0.18,0.2586,0.25,0.125,,0.11800000000000001,0.1232,0.0,0.0,reg oper spec account,block of flats,0.1806,Panel,No,3.0,0.0,3.0,0.0,-168.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,2.0,2.0\n411895,0,Cash loans,F,N,Y,1,135000.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.01885,-16205,-1612,-991.0,-4052,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.5814375081253027,0.7091891096653581,0.0722,0.0487,0.9826,0.762,0.0085,0.0,0.1379,0.1667,0.2083,0.0176,0.0588,0.0505,0.0,0.0,0.0735,0.0505,0.9826,0.7713,0.0086,0.0,0.1379,0.1667,0.2083,0.018000000000000002,0.0643,0.0526,0.0,0.0,0.0729,0.0487,0.9826,0.7652,0.0086,0.0,0.1379,0.1667,0.2083,0.0179,0.0599,0.0514,0.0,0.0,reg oper spec account,block of flats,0.0444,Panel,No,3.0,0.0,3.0,0.0,-1581.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n442343,0,Cash loans,F,N,Y,2,180000.0,1305000.0,38286.0,1305000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-15239,-148,-3247.0,-4400,,1,1,0,1,0,1,High skill tech staff,4.0,2,2,MONDAY,8,0,0,0,0,0,0,Other,,0.6719127136974666,,0.2234,0.155,0.9806,0.7348,0.0,0.16,0.1379,0.3333,0.375,0.1209,0.1816,0.2175,0.0025,0.0007,0.2279,0.1608,0.9806,0.7452,0.0,0.1611,0.1379,0.3333,0.375,0.1104,0.1983,0.226,0.0039,0.0,0.2259,0.155,0.9806,0.7383,0.0,0.16,0.1379,0.3333,0.375,0.1285,0.1847,0.2214,0.0039,0.001,reg oper account,block of flats,0.1894,Panel,No,0.0,0.0,0.0,0.0,-143.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n106596,0,Cash loans,F,N,N,0,54000.0,679500.0,19998.0,679500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-13203,-1995,-3698.0,-4443,,1,1,1,1,1,0,Managers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Medicine,0.5282458924400879,0.4938628680119328,0.6496203111237195,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1740.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n253769,0,Cash loans,F,Y,Y,0,165798.0,1822500.0,50116.5,1822500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-10759,-293,-4972.0,-2374,6.0,1,1,1,1,1,1,Accountants,2.0,1,1,TUESDAY,13,0,0,0,0,0,0,Trade: type 2,0.7039680816934248,0.7292902327474245,0.28371188263500075,0.11699999999999999,0.0676,0.9776,0.6940000000000001,0.0425,0.12,0.0862,0.3958,0.4375,0.1257,0.0954,0.07,0.0,0.001,0.0882,0.04,0.9777,0.706,0.0245,0.0806,0.0345,0.3333,0.375,0.1016,0.0771,0.0476,0.0,0.0,0.1181,0.0676,0.9776,0.6981,0.0428,0.12,0.0862,0.3958,0.4375,0.1279,0.0971,0.0712,0.0,0.001,reg oper account,block of flats,0.0573,Panel,No,0.0,0.0,0.0,0.0,-273.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n428029,0,Cash loans,M,Y,N,0,202500.0,159993.0,12771.0,121500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Office apartment,0.016612000000000002,-18759,-1846,-4227.0,-2262,24.0,1,1,0,1,0,1,Drivers,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,Transport: type 3,,0.08872480161200096,0.3996756156233169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n196571,0,Cash loans,F,Y,N,1,112500.0,306000.0,16150.5,306000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.028663,-13763,-694,-862.0,-4186,64.0,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.361985589772365,0.30162489168411943,0.0557,0.0245,0.9771,0.6872,0.0087,0.04,0.0345,0.3333,0.375,0.0187,0.0454,0.0439,0.0,0.0,0.0567,0.0255,0.9772,0.6994,0.0088,0.0403,0.0345,0.3333,0.375,0.0191,0.0496,0.0458,0.0,0.0,0.0562,0.0245,0.9771,0.6914,0.0088,0.04,0.0345,0.3333,0.375,0.019,0.0462,0.0447,0.0,0.0,reg oper account,block of flats,0.0424,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-301.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n219899,0,Cash loans,M,N,N,0,180000.0,265851.0,13702.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.006008,-12694,-871,-6743.0,-4098,,1,1,0,1,1,0,Laborers,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.265843085349869,0.2307838686704285,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-480.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n347861,0,Cash loans,F,N,Y,2,67500.0,166500.0,14373.0,166500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-12305,-186,-545.0,-2821,,1,1,1,1,0,0,Sales staff,4.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.8976364081348472,0.7631785625639897,0.5954562029091491,0.0515,0.0844,0.9906,0.8708,0.0073,0.0,0.1034,0.1667,0.2083,0.0146,0.042,0.0668,0.0,0.0,0.0525,0.0876,0.9906,0.8759,0.0073,0.0,0.1034,0.1667,0.2083,0.015,0.0459,0.0696,0.0,0.0,0.052000000000000005,0.0844,0.9906,0.8725,0.0073,0.0,0.1034,0.1667,0.2083,0.0149,0.0428,0.068,0.0,0.0,reg oper account,block of flats,0.0526,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1606.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n386033,0,Cash loans,M,Y,Y,0,270000.0,684657.0,22086.0,571500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-12073,-4025,-721.0,-3873,2.0,1,1,1,1,0,0,Drivers,2.0,1,1,WEDNESDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.0668299777511004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-325.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n111964,0,Cash loans,F,N,Y,0,99000.0,814041.0,23931.0,679500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-11835,-4150,-2109.0,-1927,,1,1,1,1,0,0,Accountants,2.0,2,2,THURSDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.7031251939256017,0.10015182033723906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1159.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n111858,0,Cash loans,F,N,N,0,90000.0,225000.0,11781.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Office apartment,0.025164,-11107,-1862,-3288.0,-2807,,1,1,1,1,1,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Medicine,0.31932003089861605,0.34977556168398866,0.5884877883422673,0.032,,0.9786,,,0.0,0.0345,0.0417,,,,0.015,,0.0,0.0326,,0.9786,,,0.0,0.0345,0.0417,,,,0.0156,,0.0,0.0323,,0.9786,,,0.0,0.0345,0.0417,,,,0.0152,,0.0,,block of flats,0.0118,Mixed,No,7.0,0.0,7.0,0.0,-535.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n102520,0,Cash loans,F,N,Y,0,117000.0,225000.0,26833.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-24781,365243,-7126.0,-4509,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.5768538164417215,0.7764098512142026,0.0825,,0.9791,,,0.0,0.2069,0.1667,,0.1072,,0.0715,,0.0663,0.084,,0.9791,,,0.0,0.2069,0.1667,,0.1096,,0.0745,,0.0702,0.0833,,0.9791,,,0.0,0.2069,0.1667,,0.1091,,0.0728,,0.0677,,block of flats,0.0707,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-110.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n391992,0,Cash loans,F,N,Y,0,247500.0,1921797.0,58396.5,1755000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22538,365243,-11247.0,-4594,,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,0.7183411784677906,0.6963624061144457,0.4365064990977374,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2190.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n361068,0,Cash loans,M,N,Y,1,225000.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-11279,-1977,-1952.0,-3948,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Transport: type 4,,0.5930026906829697,,0.0619,0.0622,0.9752,,,0.0,0.1034,0.1667,,0.0445,,0.0505,,0.0063,0.063,0.0645,0.9752,,,0.0,0.1034,0.1667,,0.0455,,0.0526,,0.0067,0.0625,0.0622,0.9752,,,0.0,0.1034,0.1667,,0.0452,,0.0514,,0.0065,,block of flats,0.0397,\"Stone, brick\",No,14.0,0.0,14.0,0.0,-204.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n208135,0,Cash loans,F,N,Y,0,112500.0,315000.0,25015.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-13412,-3257,-39.0,-972,,1,1,0,1,0,0,Managers,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Postal,0.11601997993780012,0.4501892931304269,0.3344541255096772,0.0928,0.0999,0.9801,,,0.0,0.2069,0.1667,,0.0831,,0.0876,,0.0,0.0945,0.1036,0.9801,,,0.0,0.2069,0.1667,,0.085,,0.0912,,0.0,0.0937,0.0999,0.9801,,,0.0,0.2069,0.1667,,0.0846,,0.0892,,0.0,,block of flats,0.0755,Panel,No,4.0,0.0,4.0,0.0,-494.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n153649,0,Cash loans,F,Y,N,0,90000.0,225000.0,22050.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.028663,-24579,365243,-56.0,-4472,19.0,1,0,0,1,0,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,,0.6571187655083792,0.7862666146611379,0.1206,0.0726,0.9717,0.6124,0.1064,0.0,0.1379,0.1667,0.2083,0.079,0.0975,0.0343,0.0039,0.0109,0.1229,0.0753,0.9717,0.6276,0.1074,0.0,0.1379,0.1667,0.2083,0.0808,0.1065,0.0358,0.0039,0.0115,0.1218,0.0726,0.9717,0.6176,0.1071,0.0,0.1379,0.1667,0.2083,0.0804,0.0992,0.035,0.0039,0.0111,reg oper account,block of flats,0.0558,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1004.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n213723,0,Cash loans,M,N,N,0,180000.0,397957.5,42858.0,378000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019689,-9251,-1165,-3798.0,-878,,1,1,0,1,0,1,Laborers,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.332895256597271,0.622922000268356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-574.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n408455,0,Cash loans,F,Y,Y,0,135000.0,247275.0,19548.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-9566,-577,-6109.0,-2203,13.0,1,1,1,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Industry: type 6,,0.5953596843244868,0.5028782772082183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-639.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n445996,0,Cash loans,F,N,Y,0,90000.0,270000.0,17383.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.014464,-21982,-937,-683.0,-3952,,1,1,1,1,0,0,Cleaning staff,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,School,,0.6531713037811341,0.35895122857839673,0.0278,0.0,0.9985,0.9796,0.0,0.0,0.1034,0.0833,0.0417,0.040999999999999995,0.0227,0.0222,0.0,0.0,0.0284,0.0,0.9985,0.9804,0.0,0.0,0.1034,0.0833,0.0417,0.042,0.0248,0.0232,0.0,0.0,0.0281,0.0,0.9985,0.9799,0.0,0.0,0.1034,0.0833,0.0417,0.0417,0.0231,0.0226,0.0,0.0,reg oper account,block of flats,0.0206,Block,No,0.0,0.0,0.0,0.0,-402.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n298726,0,Cash loans,F,N,Y,0,157500.0,675000.0,21906.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.018209,-16290,-2611,-6102.0,-4765,,1,1,0,1,1,0,,1.0,3,3,SUNDAY,9,0,0,0,0,0,0,Police,0.477453057674102,0.12438520856349793,0.5656079814115492,,,0.9876,,,,,,,,,0.0241,,,,,0.9876,,,,,,,,,0.0251,,,,,0.9876,,,,,,,,,0.0245,,,,,0.0279,,No,0.0,0.0,0.0,0.0,-2220.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n325372,0,Cash loans,M,N,Y,0,112500.0,557770.5,35775.0,481500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Rented apartment,0.007305,-14240,-624,-2803.0,-5184,,1,1,0,1,0,0,Cleaning staff,2.0,3,3,SUNDAY,7,0,0,0,1,1,0,Business Entity Type 3,0.5670334476951423,0.34853323467523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-459.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n428026,0,Cash loans,F,N,N,0,292500.0,1291500.0,51349.5,1188000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,Municipal apartment,0.072508,-23195,365243,-10411.0,-5050,,1,0,0,1,1,0,,1.0,1,1,MONDAY,11,0,0,0,0,0,0,XNA,,0.6847419799749898,0.5352762504724826,0.4897,0.2033,0.9856,0.8028,0.1933,0.56,0.2414,0.6667,0.0417,0.0,0.3892,0.5053,0.0463,0.0489,0.4989,0.2109,0.9856,0.8105,0.1951,0.5639,0.2414,0.6667,0.0417,0.0,0.4252,0.5265,0.0467,0.0518,0.4944,0.2033,0.9856,0.8054,0.1945,0.56,0.2414,0.6667,0.0417,0.0,0.3959,0.5144,0.0466,0.0499,reg oper account,block of flats,0.4081,Panel,No,6.0,0.0,6.0,0.0,-427.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n220598,0,Cash loans,M,Y,Y,0,360000.0,1746000.0,60817.5,1746000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-21020,-7312,-3345.0,-4524,11.0,1,1,0,1,0,1,High skill tech staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.7560185565209653,0.6937947880247003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1313.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n150682,0,Cash loans,M,Y,Y,1,337500.0,846000.0,30100.5,846000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.001276,-12433,-1015,-896.0,-4607,14.0,1,1,0,1,0,0,Secretaries,3.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.6185223599392178,0.05097933339282625,0.0814,0.0654,0.9791,0.7144,0.0097,0.0,0.1379,0.1667,0.0417,0.0623,0.0664,0.0649,0.0,0.0,0.083,0.0679,0.9791,0.7256,0.0098,0.0,0.1379,0.1667,0.0417,0.0637,0.0725,0.0677,0.0,0.0,0.0822,0.0654,0.9791,0.7182,0.0097,0.0,0.1379,0.1667,0.0417,0.0634,0.0676,0.0661,0.0,0.0,reg oper account,block of flats,0.0564,Panel,No,0.0,0.0,0.0,0.0,-677.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n146604,0,Cash loans,F,Y,Y,0,270000.0,1314117.0,36265.5,1147500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.011657,-23130,365243,-9641.0,-3959,32.0,1,0,0,1,0,0,,1.0,1,1,MONDAY,16,0,0,0,0,0,0,XNA,0.7523609513959563,0.6525270231165675,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2850.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n377981,0,Cash loans,M,Y,N,0,202500.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-11745,-1034,-6010.0,-1236,13.0,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,15,0,0,0,1,1,0,Self-employed,,0.23869869114295064,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1040.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n397907,0,Cash loans,F,N,Y,0,180000.0,808650.0,23773.5,675000.0,Family,State servant,Higher education,Married,House / apartment,0.030755,-17267,-3137,-4933.0,-808,,1,1,0,1,1,0,Managers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Postal,,0.6634852768343846,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-898.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n292048,1,Cash loans,M,N,Y,0,180000.0,519633.0,41184.0,481500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-15696,-541,-899.0,-1106,,1,1,0,1,1,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Industry: type 4,0.3402732011133605,0.5213153165359634,0.10015182033723906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-809.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n225358,0,Cash loans,F,Y,Y,0,54000.0,161730.0,6979.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-21001,365243,-1974.0,-1989,16.0,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.5555827483021147,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-156.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n294107,0,Cash loans,F,N,Y,0,112500.0,247500.0,24610.5,247500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.015221,-23291,-1736,-3678.0,-1084,,1,1,0,1,1,0,,1.0,2,2,MONDAY,8,0,0,0,0,0,0,Government,,0.5433599831806235,0.5726825047161584,0.0928,0.0693,0.9841,0.7824,0.0135,0.0,0.2069,0.1667,0.2083,0.0,0.0756,0.0791,0.0,0.0,0.0945,0.0719,0.9841,0.7909,0.0136,0.0,0.2069,0.1667,0.2083,0.0,0.0826,0.0824,0.0,0.0,0.0937,0.0693,0.9841,0.7853,0.0136,0.0,0.2069,0.1667,0.2083,0.0,0.077,0.0806,0.0,0.0,reg oper account,block of flats,0.0696,Panel,No,6.0,0.0,6.0,0.0,-203.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n396425,0,Cash loans,M,Y,N,1,202500.0,1528200.0,53248.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-11045,-1675,-1031.0,-3703,15.0,1,1,1,1,0,0,Managers,3.0,2,2,WEDNESDAY,19,0,0,0,0,1,1,Business Entity Type 3,0.4543964855922872,0.6084738739667993,,0.0619,0.0788,0.9866,0.8164,0.0,0.0,0.2069,0.1667,0.2083,0.1437,0.0504,0.0686,0.0,0.0923,0.063,0.0818,0.9866,0.8236,0.0,0.0,0.2069,0.1667,0.2083,0.1469,0.0551,0.0715,0.0,0.0977,0.0625,0.0788,0.9866,0.8189,0.0,0.0,0.2069,0.1667,0.2083,0.1462,0.0513,0.0699,0.0,0.0942,org spec account,block of flats,0.0741,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-811.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n294037,0,Cash loans,F,N,N,1,121500.0,808650.0,31464.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-10313,-546,-3757.0,-2781,,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,,0.4503408516622296,0.4170996682522097,0.0742,0.0173,0.9727,,,0.0,0.1379,0.1667,,0.0,,0.0463,,0.0303,0.0756,0.0179,0.9727,,,0.0,0.1379,0.1667,,0.0,,0.0483,,0.0321,0.0749,0.0173,0.9727,,,0.0,0.1379,0.1667,,0.0,,0.0471,,0.0309,,block of flats,0.0493,\"Stone, brick\",No,6.0,3.0,6.0,3.0,-557.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n131276,0,Cash loans,F,N,N,0,157500.0,598500.0,19435.5,598500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018209,-13486,-2293,-6005.0,-4052,,1,1,1,1,1,0,Cooking staff,2.0,3,3,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.4530271050456852,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2193.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n425146,0,Cash loans,F,N,Y,2,171000.0,524866.5,25659.0,369000.0,Family,Working,Lower secondary,Civil marriage,House / apartment,0.0105,-11003,-2558,-2164.0,-2479,,1,1,0,1,0,0,Sales staff,4.0,3,3,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.5977122880781701,0.7557400501752248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n413972,0,Cash loans,M,N,N,0,270000.0,450000.0,22977.0,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Municipal apartment,0.072508,-11634,-4220,-5843.0,-4239,,1,1,0,1,1,0,Core staff,2.0,1,1,SUNDAY,17,0,0,0,0,0,0,Police,0.6632037343098706,0.7252521821008756,0.3425288720742255,0.0825,0.0822,0.9747,,,0.0,0.1379,0.1667,,,,0.0687,,0.0,0.084,0.0854,0.9752,,,0.0,0.1379,0.1667,,,,0.0695,,0.0,0.0833,0.0823,0.9752,,,0.0,0.1379,0.1667,,,,0.0709,,0.0,,block of flats,0.0548,Panel,No,0.0,0.0,0.0,0.0,-1729.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n150446,0,Cash loans,F,Y,Y,1,112500.0,646920.0,20997.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.020713,-11514,-672,-703.0,-3303,17.0,1,1,0,1,0,0,Sales staff,2.0,3,3,MONDAY,7,0,0,0,0,0,0,Self-employed,0.4305626428441166,0.4441402239978596,0.2707073872651806,0.1227,0.0473,0.9851,0.7959999999999999,0.0164,0.0,0.2759,0.1667,0.2083,0.0227,0.1,0.1135,0.0,0.0,0.125,0.0491,0.9851,0.804,0.0166,0.0,0.2759,0.1667,0.2083,0.0232,0.1093,0.1183,0.0,0.0,0.1239,0.0473,0.9851,0.7987,0.0165,0.0,0.2759,0.1667,0.2083,0.0231,0.1018,0.1156,0.0,0.0,reg oper account,block of flats,0.0983,Panel,No,1.0,0.0,1.0,0.0,-847.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n326465,0,Cash loans,M,Y,N,0,292500.0,834048.0,53433.0,720000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-14118,-382,-2980.0,-4725,6.0,1,1,0,1,1,0,Managers,2.0,2,2,TUESDAY,18,1,1,0,1,1,0,Business Entity Type 3,,0.5967286297145571,,0.0763,0.0414,0.9906,0.8708,0.0132,0.08,0.069,0.3471,0.3958,0.0477,0.0639,0.0789,0.0,0.0013,0.0735,0.0425,0.9906,0.8759,0.0129,0.0806,0.069,0.3333,0.375,0.0488,0.0661,0.0786,0.0,0.0,0.0749,0.0409,0.9906,0.8725,0.0133,0.08,0.069,0.3333,0.3958,0.0486,0.065,0.078,0.0,0.0,reg oper account,block of flats,0.0672,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n356091,0,Cash loans,M,Y,Y,0,252000.0,792162.0,40576.5,630000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011657,-18930,-1650,-1959.0,-2448,7.0,1,1,0,1,0,1,Managers,2.0,1,1,WEDNESDAY,10,1,1,0,0,0,0,Business Entity Type 3,0.705617793736241,0.6919753439622042,0.392773860603134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1310.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,2.0\n160301,0,Cash loans,F,N,Y,0,81000.0,123993.0,9864.0,103500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006305,-11758,-1720,-933.0,-3048,,1,1,0,1,0,0,Core staff,2.0,3,3,SUNDAY,7,0,0,0,0,0,0,Kindergarten,,0.4397017071716393,0.2458512138252296,0.0165,0.0,0.9722,0.6192,0.0014,0.0,0.069,0.0417,0.0833,0.0141,0.0134,0.0117,0.0,0.0,0.0168,0.0,0.9722,0.6341,0.0014,0.0,0.069,0.0417,0.0833,0.0144,0.0147,0.0122,0.0,0.0,0.0167,0.0,0.9722,0.6243,0.0014,0.0,0.069,0.0417,0.0833,0.0143,0.0137,0.0119,0.0,0.0,reg oper spec account,block of flats,0.01,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-724.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n397236,0,Cash loans,F,N,Y,1,90000.0,343800.0,12478.5,225000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.008068,-10582,-3385,-3729.0,-1161,,1,1,0,1,0,0,Core staff,2.0,3,3,TUESDAY,4,0,0,0,0,0,0,Bank,0.462395544585937,0.025018266599423567,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n282863,0,Cash loans,M,Y,N,0,157500.0,485640.0,37579.5,450000.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-9950,-1086,-9275.0,-2564,7.0,1,1,1,1,0,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.0963545962962474,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-232.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n331915,1,Cash loans,F,N,N,0,166500.0,283419.0,22522.5,234000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.010032,-15169,-1338,-885.0,-1486,,1,1,1,1,0,0,Cleaning staff,1.0,2,2,SATURDAY,6,0,0,0,1,1,0,Business Entity Type 3,,0.3232499352655716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-505.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n359517,0,Cash loans,F,N,N,0,135000.0,1485000.0,56691.0,1485000.0,Family,Working,Secondary / secondary special,Married,With parents,0.006207,-9927,-1290,-9927.0,-2604,,1,1,1,1,0,0,Sales staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,,0.6055683299514125,0.633031641417419,,0.0657,0.9781,,,,0.1379,0.1667,,,,0.0646,,0.0,,0.0682,0.9782,,,,0.1379,0.1667,,,,0.0673,,0.0,,0.0657,0.9781,,,,0.1379,0.1667,,,,0.0657,,0.0,,,0.055,,No,0.0,0.0,0.0,0.0,-1140.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n318610,0,Cash loans,F,N,Y,0,112500.0,593010.0,23107.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-17022,-10361,-3421.0,-578,,1,1,0,1,1,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.3834541534150607,0.4650692149562261,0.033,0.0,0.9757,0.6668,0.0169,0.0,0.1034,0.0833,0.0417,0.0293,0.0269,0.026000000000000002,0.0077,0.0103,0.0336,0.0,0.9757,0.6798,0.0171,0.0,0.1034,0.0833,0.0417,0.03,0.0294,0.027000000000000003,0.0078,0.0109,0.0333,0.0,0.9757,0.6713,0.017,0.0,0.1034,0.0833,0.0417,0.0299,0.0274,0.0264,0.0078,0.0105,reg oper account,block of flats,0.0227,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n448220,0,Cash loans,F,Y,N,1,211500.0,2025000.0,58036.5,2025000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-11602,-2832,-6226.0,-3867,7.0,1,1,0,1,0,0,Drivers,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Other,0.5664669491744229,0.6604907218020648,0.2032521136204725,0.0676,0.0607,0.9742,0.6464,0.0066,0.0,0.1131,0.1667,0.2083,0.0128,0.0549,0.0555,0.0011,0.0013,0.084,0.0778,0.9742,0.6602,0.0042,0.0,0.1379,0.1667,0.2083,0.008,0.0735,0.0321,0.0,0.0,0.0833,0.075,0.9742,0.6511,0.0079,0.0,0.1379,0.1667,0.2083,0.0152,0.0676,0.0651,0.0,0.0,reg oper account,block of flats,0.0254,Panel,No,1.0,0.0,1.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n388684,0,Cash loans,M,Y,Y,1,202500.0,1006920.0,51543.0,900000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00496,-11559,-2310,-3798.0,-3875,15.0,1,1,0,1,0,0,Core staff,3.0,2,2,SATURDAY,14,0,0,0,0,1,1,Self-employed,,0.3817546985892514,0.3506958432829587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n322517,0,Cash loans,F,N,N,0,126000.0,432661.5,22252.5,373500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010643,-22040,365243,-1708.0,-3398,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,9,1,0,0,0,0,0,XNA,0.4451629695067764,0.3648996257123287,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n132077,0,Cash loans,M,Y,N,0,135000.0,450000.0,17095.5,450000.0,Unaccompanied,Working,Lower secondary,Civil marriage,Municipal apartment,0.018029,-19434,-775,-9053.0,-1421,30.0,1,1,0,1,0,0,Laborers,2.0,3,3,THURSDAY,7,0,0,0,0,1,1,Other,,0.28059720188384746,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-296.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n185546,0,Cash loans,F,N,N,3,126000.0,508495.5,25960.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005002,-12655,-1533,-3613.0,-2217,,1,1,1,1,0,0,Laborers,5.0,3,3,MONDAY,10,0,0,0,1,0,1,Business Entity Type 2,,0.5065727134057606,0.3441550073724169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-813.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n151970,0,Cash loans,F,Y,Y,0,67500.0,135000.0,9148.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-13918,-5303,-6127.0,-4298,8.0,1,1,1,1,0,0,Core staff,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Kindergarten,,0.3658173809220551,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-237.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n240044,0,Cash loans,M,Y,Y,0,225000.0,900000.0,23742.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-14791,-2495,-347.0,-3983,1.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.6460787881098881,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-577.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n438682,1,Cash loans,F,N,Y,0,202500.0,1042560.0,34456.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014464,-14477,-3623,-198.0,-404,,1,1,1,1,0,0,Laborers,2.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.19220353573491827,0.6263080369270495,,0.1278,0.0357,0.9801,0.728,0.0425,0.0,0.0345,0.1667,0.0417,0.0,0.1034,0.0438,0.0039,0.1056,0.1303,0.037000000000000005,0.9801,0.7387,0.0429,0.0,0.0345,0.1667,0.0417,0.0,0.1129,0.0456,0.0039,0.1118,0.1291,0.0357,0.9801,0.7316,0.0427,0.0,0.0345,0.1667,0.0417,0.0,0.1052,0.0446,0.0039,0.1078,reg oper account,specific housing,0.0574,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1756.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,4.0\n434601,0,Cash loans,F,N,N,1,90000.0,314100.0,11767.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.011657,-11770,-343,-9065.0,-1174,,1,1,0,1,1,0,Laborers,3.0,1,1,FRIDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.2050977803362224,0.6284602082045604,,0.0928,0.1035,0.9881,,,0.0,0.2069,0.1667,,0.0,,0.105,,0.0239,0.0945,0.1075,0.9881,,,0.0,0.2069,0.1667,,0.0,,0.1094,,0.0253,0.0937,0.1035,0.9881,,,0.0,0.2069,0.1667,,0.0,,0.1069,,0.0244,,block of flats,0.0826,Panel,No,0.0,0.0,0.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n318346,0,Cash loans,F,Y,N,2,144000.0,720000.0,21051.0,720000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-15155,-1943,-7681.0,-4458,0.0,1,1,0,1,0,0,Cooking staff,4.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Other,,0.6883560748697737,0.8038850611746273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1795.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n216485,0,Revolving loans,M,N,Y,0,247500.0,720000.0,36000.0,720000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-16686,-666,-484.0,-243,,1,1,0,1,0,0,Drivers,2.0,1,1,SUNDAY,11,1,1,0,0,0,0,Self-employed,0.7292347995748657,0.6841988631039662,0.5567274263630174,0.2443,0.1316,0.9806,0.7348,0.0545,0.26,0.2241,0.3333,0.375,0.0435,0.1992,0.2508,0.0,0.0003,0.1124,0.0597,0.9806,0.7452,0.0214,0.1208,0.1034,0.3333,0.375,0.0,0.0983,0.1254,0.0,0.0,0.2467,0.1316,0.9806,0.7383,0.0548,0.26,0.2241,0.3333,0.375,0.0443,0.2027,0.2553,0.0,0.0003,reg oper account,block of flats,0.3479,Panel,No,3.0,0.0,3.0,0.0,-430.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n350761,0,Cash loans,F,N,Y,0,90000.0,174132.0,17973.0,157500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.019101,-19542,-361,-7248.0,-3105,,1,1,1,1,0,0,Cleaning staff,2.0,2,2,MONDAY,12,0,0,0,0,1,1,Industry: type 3,,0.5614168860891452,0.7946285827840042,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3044.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n166817,0,Cash loans,F,N,Y,0,144000.0,395766.0,21600.0,283500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.010276,-12761,-2582,-6888.0,-4740,,1,1,0,1,0,1,Core staff,1.0,2,2,FRIDAY,17,0,0,0,0,1,1,School,0.4853974704844408,0.5796376414479775,0.3092753558842053,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1666.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n356827,0,Cash loans,F,N,Y,0,180000.0,1046898.0,44487.0,963000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.005313,-20347,365243,-5309.0,-3744,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.41729477336532667,0.7610263695502636,0.0103,0.0388,0.9921,0.8912,0.0025,0.0,0.0345,0.0417,0.0833,0.0163,0.0084,0.0075,0.0,0.0,0.0105,0.0402,0.9921,0.8955,0.0025,0.0,0.0345,0.0417,0.0833,0.0167,0.0092,0.0079,0.0,0.0,0.0104,0.0388,0.9921,0.8927,0.0025,0.0,0.0345,0.0417,0.0833,0.0166,0.0086,0.0077,0.0,0.0,reg oper account,block of flats,0.0125,Others,No,7.0,0.0,7.0,0.0,-1691.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n240625,0,Revolving loans,M,N,Y,0,270000.0,855000.0,42750.0,855000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-21967,-1192,-12880.0,-5474,,1,1,0,1,0,0,Managers,2.0,1,1,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6134739044199051,0.8128226070575616,0.1584,0.1331,0.9811,0.7416,0.0,0.24,0.1034,0.4583,0.0,0.0,0.1292,0.1798,0.0,0.1628,0.1481,0.0836,0.9811,0.7517,0.0,0.2417,0.1034,0.4583,0.0,0.0,0.1295,0.177,0.0,0.0049,0.1624,0.1307,0.9811,0.7451,0.0,0.24,0.1034,0.4583,0.0,0.0,0.1334,0.1831,0.0,0.2159,reg oper spec account,block of flats,0.1875,Panel,No,0.0,0.0,0.0,0.0,-2174.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n408826,0,Cash loans,M,Y,Y,1,292500.0,263686.5,27819.0,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.005084,-13482,-717,-4717.0,-2294,9.0,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,13,0,1,1,0,1,1,Business Entity Type 3,0.7063627221796892,0.4608843342366918,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1260.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n271788,0,Cash loans,F,Y,N,0,112500.0,601677.0,19683.0,423000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-11016,-823,-1114.0,-2390,4.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.2933853375266816,0.009041148941506906,,0.0041,,0.9722,,,,0.069,0.0,,,,0.0013,,,0.0042,,0.9722,,,,0.069,0.0,,,,0.0014,,,0.0042,,0.9722,,,,0.069,0.0,,,,0.0013,,,,block of flats,0.0015,Wooden,No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n393598,0,Cash loans,F,N,N,0,74250.0,1006920.0,39933.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-12557,-1798,-6065.0,-4926,,1,1,1,1,0,0,Laborers,2.0,3,3,SUNDAY,19,0,0,0,1,1,1,Business Entity Type 3,0.7834305740437816,0.3849182778508729,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-384.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n282458,0,Cash loans,M,N,Y,0,135000.0,71109.0,4671.0,54000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.002042,-22179,365243,-1318.0,-5728,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,XNA,,0.5292110061727456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-1287.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n394232,0,Cash loans,M,Y,Y,0,121500.0,804420.0,38830.5,706500.0,Unaccompanied,Working,Lower secondary,Civil marriage,With parents,0.003813,-8092,-188,-7994.0,-318,14.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Transport: type 4,,0.024409522776788983,0.3108182544189319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n331256,0,Cash loans,F,N,Y,0,220500.0,225171.0,9670.5,171000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018801,-22082,365243,-7906.0,-4593,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.530567397934671,0.7958031328748403,0.0381,0.03,0.9727,0.626,0.003,0.0,0.069,0.1667,0.2083,0.0192,0.0294,0.0246,0.0077,0.0333,0.0389,0.0312,0.9727,0.6406,0.003,0.0,0.069,0.1667,0.2083,0.0196,0.0321,0.0256,0.0078,0.0352,0.0385,0.03,0.9727,0.631,0.003,0.0,0.069,0.1667,0.2083,0.0195,0.0299,0.0251,0.0078,0.034,reg oper account,block of flats,0.032,\"Stone, brick\",No,8.0,0.0,8.0,0.0,-1289.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n131458,0,Cash loans,F,Y,Y,1,157500.0,1237684.5,49086.0,1138500.0,Other_A,State servant,Secondary / secondary special,Married,House / apartment,0.020713,-13785,-2223,-7131.0,-3448,10.0,1,1,0,1,0,0,Medicine staff,3.0,3,3,SATURDAY,9,0,0,0,0,1,1,Medicine,0.5693388562577247,0.3672898483486329,0.816092360478441,0.0619,0.064,0.9851,0.7959999999999999,0.0108,0.0,0.1379,0.1667,0.2083,0.0568,0.005,0.0591,0.0,0.0,0.063,0.0664,0.9851,0.804,0.0109,0.0,0.1379,0.1667,0.2083,0.0581,0.0055,0.0616,0.0,0.0,0.0625,0.064,0.9851,0.7987,0.0109,0.0,0.1379,0.1667,0.2083,0.0578,0.0051,0.0602,0.0,0.0,reg oper account,block of flats,0.0524,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n274261,0,Cash loans,F,N,Y,0,157500.0,450000.0,30073.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-10432,-3840,-1648.0,-2207,,1,1,0,1,1,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.31836973533268714,0.7368805305081394,0.4048783643353997,0.1497,0.0871,0.9871,0.8232,0.0,0.152,0.1586,0.2667,0.3083,0.0301,0.1215,0.1457,0.0,0.0568,0.0704,0.0,0.9851,0.804,0.0,0.2417,0.1379,0.3333,0.375,0.0,0.0588,0.0607,0.0,0.0026,0.128,0.0893,0.9871,0.8256,0.0,0.16,0.1379,0.3333,0.375,0.0,0.1052,0.136,0.0,0.0407,reg oper account,block of flats,0.2124,Panel,No,0.0,0.0,0.0,0.0,-1242.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n455077,0,Cash loans,F,Y,N,0,171000.0,1762110.0,51651.0,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-17575,-4176,-4274.0,-1107,4.0,1,1,0,1,1,0,Cleaning staff,2.0,1,1,TUESDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.5284481854602953,0.2276129150623945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1563.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n305395,0,Cash loans,F,N,Y,1,180000.0,1260000.0,34650.0,1260000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.028663,-15229,-3954,-5895.0,-4793,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,13,0,0,0,0,1,1,Business Entity Type 2,0.8270017419553949,0.7805237133262153,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n229572,0,Cash loans,M,N,N,2,189000.0,728460.0,44563.5,675000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.032561,-13586,-3102,-6278.0,-2500,,1,1,1,1,0,0,Drivers,4.0,1,1,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 1,,0.6403579515521828,0.3185955240537633,0.2515,0.1358,0.9806,0.0,0.1305,0.28,0.2241,0.3333,0.375,,,0.1576,,0.0013,0.2563,0.1409,0.9806,0.0,0.1317,0.282,0.2069,0.3333,0.375,,,0.1484,,0.0014,0.254,0.1358,0.9806,0.0,0.1313,0.28,0.2241,0.3333,0.375,,,0.1604,,0.0013,reg oper account,block of flats,0.1834,Others,No,4.0,0.0,4.0,0.0,-1651.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n264818,0,Cash loans,F,Y,N,1,112500.0,282690.0,11641.5,202500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.009334,-12772,-204,-1990.0,-4313,5.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,WEDNESDAY,13,0,0,0,1,1,1,Trade: type 6,0.5270782425615665,0.6198217548030023,0.4722533429586386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-168.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n159492,0,Cash loans,M,Y,Y,1,139500.0,1546020.0,45333.0,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010276,-12465,-3913,-979.0,-4152,9.0,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.3708949540594249,0.5819376798417124,0.3360615207658242,0.0165,0.0,0.9722,0.6192,0.0081,0.12,0.1034,0.0417,0.0833,0.0104,0.0134,0.0206,0.0,0.0,0.0168,0.0,0.9722,0.6341,0.0082,0.1208,0.1034,0.0417,0.0833,0.0107,0.0147,0.0215,0.0,0.0,0.0167,0.0,0.9722,0.6243,0.0082,0.12,0.1034,0.0417,0.0833,0.0106,0.0137,0.021,0.0,0.0,reg oper account,block of flats,0.0206,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-838.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n292852,0,Cash loans,F,N,Y,0,90000.0,314100.0,13963.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-15675,-955,-7782.0,-4556,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Housing,,0.4524636263197047,0.6956219298394389,0.1165,0.0638,0.9647,,,0.0,0.0345,0.125,,0.0307,,0.0657,,0.0,0.1187,0.0662,0.9647,,,0.0,0.0345,0.125,,0.0314,,0.0685,,0.0,0.1176,0.0638,0.9647,,,0.0,0.0345,0.125,,0.0313,,0.0669,,0.0,,block of flats,0.0545,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1488.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n426541,0,Cash loans,M,N,N,2,112500.0,52128.0,6313.5,45000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-13458,-2326,-1155.0,-4241,,1,1,0,1,0,0,Drivers,4.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.3635989867989231,0.5254065147692089,0.7180328113294772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2833.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n191643,0,Cash loans,F,Y,Y,0,90000.0,479700.0,50503.5,450000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-12557,-790,-3395.0,-1246,3.0,1,1,1,1,0,0,Sales staff,2.0,2,2,THURSDAY,7,0,0,0,0,0,0,Trade: type 7,,0.556225176118225,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-543.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n126654,0,Revolving loans,F,Y,N,0,225000.0,585000.0,29250.0,585000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20933,365243,-10159.0,-4480,21.0,1,0,0,1,1,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,0.8474425831874325,0.3609559514828684,0.7366226976503176,0.16699999999999998,0.0532,0.9861,,,0.08,0.069,0.3333,0.0417,0.0817,0.1362,0.1055,0.0,0.0,0.1702,0.0552,0.9861,,,0.0806,0.069,0.3333,0.0417,0.0835,0.1488,0.11,0.0,0.0,0.1686,0.0532,0.9861,,,0.08,0.069,0.3333,0.0417,0.0831,0.1385,0.1074,0.0,0.0,reg oper account,block of flats,0.083,Panel,No,0.0,0.0,0.0,0.0,-1939.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n118682,0,Cash loans,M,Y,Y,0,90000.0,544491.0,17563.5,454500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.031329,-13486,-3145,-3219.0,-4274,14.0,1,1,1,1,1,0,Laborers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Kindergarten,0.30355137488016626,0.2622583692422573,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1697.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n135740,1,Cash loans,F,N,N,1,292500.0,337761.0,18450.0,256500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Municipal apartment,0.011657,-17273,-580,-4040.0,-813,,1,1,0,1,0,0,Laborers,3.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6553724689738407,0.6058362647264226,0.0082,0.0,0.9712,0.6056,0.0,0.0,0.0345,0.0417,0.0417,0.0035,0.0067,0.005,0.0,0.0,0.0084,0.0,0.9712,0.621,0.0,0.0,0.0345,0.0417,0.0417,0.0036,0.0073,0.0052,0.0,0.0,0.0083,0.0,0.9712,0.6109,0.0,0.0,0.0345,0.0417,0.0417,0.0036,0.0068,0.0051,0.0,0.0,not specified,block of flats,0.0068,Wooden,Yes,11.0,1.0,10.0,1.0,-1218.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n151520,0,Cash loans,M,Y,Y,0,112500.0,454500.0,19386.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-19165,365243,-9153.0,-2567,14.0,1,0,0,1,1,0,,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,XNA,0.8045652732827687,0.6266611133851432,0.7688075728291359,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2531.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n367508,0,Revolving loans,M,Y,Y,2,180000.0,180000.0,9000.0,180000.0,Unaccompanied,State servant,Secondary / secondary special,Married,With parents,0.020713,-9318,-538,-7993.0,-1976,12.0,1,1,1,1,1,0,High skill tech staff,4.0,3,3,THURSDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.18420001264620567,0.2630757549915064,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2376.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n428976,0,Cash loans,M,Y,Y,1,211500.0,354276.0,13486.5,292500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-17494,-2876,-304.0,-1014,17.0,1,1,0,1,0,0,,3.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.7882999840171591,0.4382813743111921,0.1825,0.0895,0.9876,0.83,0.0536,0.2,0.1724,0.3333,0.375,0.0133,0.1488,0.1743,0.0,0.0324,0.1859,0.0928,0.9876,0.8367,0.054000000000000006,0.2014,0.1724,0.3333,0.375,0.0136,0.1625,0.1816,0.0,0.0344,0.1842,0.0895,0.9876,0.8323,0.0539,0.2,0.1724,0.3333,0.375,0.0135,0.1513,0.1775,0.0,0.0331,reg oper account,block of flats,0.1464,Panel,No,0.0,0.0,0.0,0.0,-2072.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n293212,0,Cash loans,F,N,N,0,99000.0,284985.0,12204.0,284985.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.019689,-22324,365243,-7614.0,-4137,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.6933718729689963,0.5919766183185521,0.0082,0.0,0.9762,,,0.0,0.0345,0.0417,,0.0,,0.0044,,0.0,0.0084,0.0,0.9762,,,0.0,0.0345,0.0417,,0.0,,0.0046,,0.0,0.0083,0.0,0.9762,,,0.0,0.0345,0.0417,,0.0,,0.0045,,0.0,,block of flats,0.005,Wooden,No,2.0,2.0,2.0,2.0,-753.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n129993,0,Cash loans,F,Y,N,1,234000.0,512064.0,18522.0,360000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018634,-14059,-3771,-5877.0,-4134,8.0,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,17,0,0,0,0,0,0,Self-employed,0.36454859007409135,0.7331624376782261,0.5406544504453575,0.2392,0.1113,0.9881,0.8368,0.1175,0.12,0.1034,0.3333,0.375,0.0974,0.195,0.1528,0.0,0.0,0.2437,0.1155,0.9881,0.8432,0.1186,0.1208,0.1034,0.3333,0.375,0.0996,0.213,0.1592,0.0,0.0,0.2415,0.1113,0.9881,0.8390000000000001,0.1183,0.12,0.1034,0.3333,0.375,0.0991,0.1984,0.1556,0.0,0.0,reg oper account,block of flats,0.1845,Panel,No,1.0,0.0,1.0,0.0,-1854.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n167219,0,Cash loans,F,N,Y,2,207000.0,862560.0,30690.0,720000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009657,-11564,-282,-5893.0,-4010,,1,1,0,1,0,0,High skill tech staff,4.0,2,2,MONDAY,11,0,0,0,1,1,0,Transport: type 4,0.4359062957001126,0.7083295100374625,0.7490217048463391,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1029.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n361587,1,Cash loans,F,N,Y,0,225000.0,358443.0,17563.5,252000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-17840,-7435,-9538.0,-1364,,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 1,,0.3444087123456521,0.13342925446731707,0.1129,0.1217,0.9786,0.7076,0.0126,0.0,0.2414,0.1667,0.2083,,0.092,0.0905,0.0,0.0199,0.105,0.1176,0.9782,0.7125,0.0096,0.0,0.2069,0.1667,0.2083,,0.0918,0.0939,0.0,0.0,0.114,0.1217,0.9786,0.7115,0.0126,0.0,0.2414,0.1667,0.2083,,0.0936,0.0921,0.0,0.0203,reg oper account,block of flats,0.0761,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2964.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n206900,1,Cash loans,M,Y,Y,0,90000.0,253737.0,13905.0,229500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009175,-12626,-369,-4209.0,-4220,13.0,1,1,0,1,0,1,,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.35957509447605257,0.586590625593766,0.05842784088634019,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-312.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n125579,0,Cash loans,M,N,Y,0,112500.0,431280.0,22149.0,360000.0,Unaccompanied,Pensioner,Higher education,Civil marriage,House / apartment,0.018029,-22291,365243,-1062.0,-1882,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,7,0,0,0,0,0,0,XNA,,0.3801457259040904,,0.1237,0.0974,0.9776,,,0.0,0.2069,0.1667,,0.0474,,0.1068,,0.0,0.1261,0.1011,0.9777,,,0.0,0.2069,0.1667,,0.0485,,0.1113,,0.0,0.1249,0.0974,0.9776,,,0.0,0.2069,0.1667,,0.0483,,0.1087,,0.0,,block of flats,0.0914,Panel,No,1.0,0.0,1.0,0.0,-132.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n392625,0,Cash loans,F,N,Y,1,99000.0,704844.0,34038.0,630000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006207,-14085,365243,-1626.0,-2912,,1,0,0,1,0,0,,3.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,0.6650493014015321,0.39709698065200977,0.5884877883422673,,,0.9786,,,,,,,,,0.0702,,,,,0.9786,,,,,,,,,0.0731,,,,,0.9786,,,,,,,,,0.0715,,,,,0.0552,,No,0.0,0.0,0.0,0.0,-233.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n224216,0,Cash loans,M,N,N,0,135000.0,573628.5,29416.5,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003122,-17696,-6158,-2507.0,-551,,1,1,0,1,0,0,Managers,2.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,Agriculture,,0.5390976759763035,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-3718.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n383922,0,Cash loans,F,N,Y,0,157500.0,502438.5,20169.0,502438.5,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.004849,-21062,365243,-12600.0,-4397,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,0.7172023146783897,0.7698509585067258,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-1682.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n301796,0,Cash loans,F,N,Y,0,135000.0,675000.0,22306.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-11175,-898,-11124.0,-3410,,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,9,0,0,0,0,1,1,Self-employed,,0.06652650446995872,0.8482442999507556,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n204411,0,Cash loans,F,Y,Y,0,112500.0,1546020.0,42642.0,1350000.0,Family,Working,Secondary / secondary special,Married,With parents,0.008625,-15653,-5141,-4281.0,-4383,16.0,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6255861146381101,0.8038850611746273,0.2536,0.1879,0.9841,,,0.28,0.2414,0.3333,,0.1503,,0.2663,,0.0,0.2584,0.195,0.9841,,,0.282,0.2414,0.3333,,0.1537,,0.2774,,0.0,0.2561,0.1879,0.9841,,,0.28,0.2414,0.3333,,0.1529,,0.2711,,0.0,,block of flats,0.3102,Panel,No,0.0,0.0,0.0,0.0,-970.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n375104,0,Cash loans,F,N,Y,0,76500.0,145957.5,15084.0,126000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-18587,-616,-9120.0,-2141,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 1,,0.24021999936835864,0.633031641417419,0.2052,0.2185,0.9771,,,0.0,0.4138,0.1667,,0.1088,,0.1807,,0.0,0.209,0.2268,0.9772,,,0.0,0.4138,0.1667,,0.1113,,0.1882,,0.0,0.2071,0.2185,0.9771,,,0.0,0.4138,0.1667,,0.1107,,0.1839,,0.0,,block of flats,0.1421,Panel,No,0.0,0.0,0.0,0.0,-78.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n278958,0,Cash loans,F,N,Y,0,81000.0,528633.0,21096.0,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-22986,365243,-9983.0,-4583,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,0.7676338199743274,0.6459037593451422,0.6380435278721609,0.1639,0.1591,0.9856,0.7824,0.0,0.0,0.4828,0.1667,0.2083,0.1356,0.1336,0.1863,0.0,0.0685,0.16699999999999998,0.1651,0.9856,0.7909,0.0,0.0,0.4828,0.1667,0.2083,0.1387,0.146,0.1941,0.0,0.0725,0.1655,0.1591,0.9856,0.7853,0.0,0.0,0.4828,0.1667,0.2083,0.1379,0.136,0.1897,0.0,0.0699,not specified,block of flats,0.1625,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1333.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n320423,0,Cash loans,F,Y,Y,0,108000.0,675000.0,22437.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.001417,-17926,-4825,-3341.0,-680,13.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,School,,0.4782246202781624,0.12140828616312165,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2205.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n241830,0,Cash loans,F,Y,N,1,180000.0,555273.0,17023.5,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15904,-146,-2467.0,-4330,10.0,1,1,0,1,0,0,Accountants,3.0,2,2,MONDAY,15,0,0,0,0,0,0,Transport: type 4,0.4865854509877083,0.6541069878450084,0.7544061731797895,0.1485,0.0871,0.9945,0.9184,,0.24,0.1034,0.5,0.5,0.08,0.121,0.1888,,0.0689,0.1513,0.0904,0.9945,0.9216,,0.2417,0.1034,0.5,0.5,0.0818,0.1322,0.1967,,0.0729,0.1499,0.0871,0.9945,0.9195,,0.24,0.1034,0.5,0.5,0.0814,0.1231,0.1922,,0.0703,reg oper account,block of flats,0.1994,Panel,No,1.0,0.0,1.0,0.0,-2678.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n212536,0,Cash loans,F,Y,Y,2,337500.0,835380.0,40320.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018029,-11833,-1437,-5743.0,-4328,7.0,1,1,0,1,0,0,,4.0,3,3,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4094059142498176,0.5667347970336964,0.6092756673894402,0.3371,0.3133,0.9871,0.8232,0.0967,0.24,0.5862,0.3333,0.2083,0.4008,0.2681,0.3735,0.0309,0.1429,0.3435,0.3251,0.9871,0.8301,0.0976,0.2417,0.5862,0.3333,0.2083,0.41,0.2929,0.3892,0.0311,0.1513,0.3404,0.3133,0.9871,0.8256,0.0973,0.24,0.5862,0.3333,0.2083,0.4078,0.2728,0.3802,0.0311,0.1459,reg oper account,block of flats,0.5003,Panel,No,2.0,0.0,2.0,0.0,-2211.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n454797,0,Cash loans,M,Y,Y,1,225000.0,760225.5,53041.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007114,-13243,-3077,-7362.0,-4955,17.0,1,1,0,1,0,1,,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,0.7362178178618641,0.20763331082102893,0.21885908222837447,0.0928,0.0935,0.9781,,,0.0,0.2069,0.1667,,0.0687,,0.0778,,0.0,0.0945,0.09699999999999999,0.9782,,,0.0,0.2069,0.1667,,0.0703,,0.0811,,0.0,0.0937,0.0935,0.9781,,,0.0,0.2069,0.1667,,0.0699,,0.0792,,0.0,,block of flats,0.0612,Panel,No,1.0,0.0,1.0,0.0,-1252.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n295536,0,Cash loans,M,Y,Y,1,270000.0,450000.0,20979.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-11004,-1185,-365.0,-3458,4.0,1,1,0,1,0,0,Managers,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6972771237642821,0.2392262794694045,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1167.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n147148,0,Cash loans,F,N,Y,0,157500.0,253377.0,13063.5,211500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-10348,-2453,-8343.0,-2973,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,11,0,0,0,1,1,0,Business Entity Type 2,,0.6220607136468278,0.4686596550493113,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2623.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n388541,0,Cash loans,F,N,N,0,225000.0,1329579.0,36693.0,1161000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-19131,-4438,-3022.0,-2667,,1,1,0,1,0,0,Accountants,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Industry: type 11,,0.526562365976249,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1427.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,3.0\n421108,0,Cash loans,F,N,Y,3,112500.0,227520.0,10737.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.015221,-11927,-587,-3193.0,-4480,,1,1,1,1,0,0,Sales staff,4.0,2,2,THURSDAY,9,0,0,0,0,0,0,Self-employed,,0.6361218769824711,0.17982176508970435,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,2.0,-378.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n153487,0,Cash loans,M,Y,Y,0,162000.0,485640.0,34668.0,450000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.003069,-19399,-10264,-10963.0,-2749,6.0,1,1,0,1,0,0,Core staff,2.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,School,,0.6687571551230549,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-347.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n418381,0,Cash loans,F,Y,N,0,202500.0,450000.0,17847.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-20427,365243,-10364.0,-3964,8.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,0.6162786510194042,0.6748033346784756,0.5495965024956946,0.1227,0.0986,0.9806,0.7348,0.0156,0.0,0.2759,0.1667,0.2083,0.1069,0.1,0.1036,0.0,0.0,0.125,0.1023,0.9806,0.7452,0.0158,0.0,0.2759,0.1667,0.2083,0.1093,0.1093,0.10800000000000001,0.0,0.0,0.1239,0.0986,0.9806,0.7383,0.0157,0.0,0.2759,0.1667,0.2083,0.1088,0.1018,0.1055,0.0,0.0,org spec account,block of flats,0.09,Panel,No,8.0,0.0,8.0,0.0,-1470.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n408801,0,Cash loans,F,Y,Y,0,90000.0,601470.0,30838.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-18692,-3326,-9116.0,-2229,15.0,1,1,1,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Medicine,,0.627111217974678,0.6296742509538716,0.0784,0.0904,0.9871,0.8232,0.0132,0.0,0.1724,0.1667,0.2083,0.0787,0.063,0.0722,0.0039,0.0042,0.0798,0.0938,0.9871,0.8301,0.0134,0.0,0.1724,0.1667,0.2083,0.0805,0.0689,0.0752,0.0039,0.0045,0.0791,0.0904,0.9871,0.8256,0.0133,0.0,0.1724,0.1667,0.2083,0.0801,0.0641,0.0734,0.0039,0.0043,reg oper account,block of flats,0.0577,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1911.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n158169,0,Cash loans,F,N,N,0,112500.0,545040.0,35617.5,450000.0,Other_B,Commercial associate,Higher education,Single / not married,House / apartment,0.035792000000000004,-9119,-373,-3797.0,-1786,,1,1,0,1,0,0,Accountants,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,Bank,0.30479552205331195,0.06406308975774537,0.4525335592581747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n258830,0,Cash loans,M,N,Y,1,225000.0,805536.0,34128.0,720000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-13458,-876,-4691.0,-2207,,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.4335803613171438,0.4311917977993083,0.1856,0.1407,0.9861,0.8096,0.1338,0.2,0.1724,0.3333,0.375,0.0998,0.1513,0.1991,0.0,0.0,0.1891,0.146,0.9861,0.8171,0.135,0.2014,0.1724,0.3333,0.375,0.1021,0.1653,0.2074,0.0,0.0,0.1874,0.1407,0.9861,0.8121,0.1347,0.2,0.1724,0.3333,0.375,0.1015,0.1539,0.2026,0.0,0.0,reg oper account,block of flats,0.2297,Panel,No,3.0,1.0,3.0,0.0,-502.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n239484,0,Cash loans,F,Y,N,0,103500.0,312768.0,25074.0,270000.0,Unaccompanied,Working,Higher education,Married,With parents,0.015221,-9926,-1670,-1437.0,-761,7.0,1,1,0,1,1,0,Accountants,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Advertising,,0.5338121514467667,,0.0619,0.0616,0.9796,0.7212,0.0271,0.0,0.1379,0.1667,0.2083,0.0464,0.0488,0.0513,0.0077,0.0087,0.063,0.0639,0.9796,0.7321,0.0274,0.0,0.1379,0.1667,0.2083,0.0475,0.0533,0.0535,0.0078,0.0092,0.0625,0.0616,0.9796,0.7249,0.0273,0.0,0.1379,0.1667,0.2083,0.0472,0.0496,0.0522,0.0078,0.0088,reg oper account,block of flats,0.0571,Panel,No,1.0,1.0,1.0,1.0,-844.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n301977,0,Cash loans,F,Y,Y,1,450000.0,1223010.0,48631.5,1125000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-15827,-2307,-8140.0,-4176,6.0,1,1,0,1,1,0,,2.0,1,1,MONDAY,11,0,0,0,0,0,0,Services,,0.7352917562341751,0.7490217048463391,0.0995,0.0615,0.9781,0.7008,0.1197,0.04,0.1034,0.3958,0.4375,0.1154,0.0769,0.0832,0.0193,0.0182,0.0788,0.0505,0.9742,0.6602,0.0922,0.0,0.0345,0.1667,0.2083,0.1181,0.0689,0.0662,0.0,0.0,0.1004,0.0615,0.9781,0.7048,0.1205,0.04,0.1034,0.3958,0.4375,0.1175,0.0782,0.0847,0.0194,0.0186,not specified,block of flats,0.0499,Panel,No,0.0,0.0,0.0,0.0,-2225.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n200288,0,Cash loans,M,Y,Y,1,137250.0,1024740.0,52452.0,900000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.0228,-18024,-3649,-3136.0,-1554,8.0,1,1,0,1,0,1,Managers,3.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,0.6463763788820489,0.6288685992191171,0.190705947811054,0.0629,0.0655,0.9781,0.7008,0.008,0.0,0.1379,0.1667,0.2083,0.0317,0.0504,0.0518,0.0039,0.0043,0.0641,0.0679,0.9782,0.7125,0.0081,0.0,0.1379,0.1667,0.2083,0.0324,0.0551,0.054000000000000006,0.0039,0.0045,0.0635,0.0655,0.9781,0.7048,0.0081,0.0,0.1379,0.1667,0.2083,0.0323,0.0513,0.0528,0.0039,0.0044,reg oper account,block of flats,0.0461,Panel,No,1.0,1.0,1.0,1.0,-3492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n453554,0,Cash loans,F,N,Y,0,112500.0,238500.0,13068.0,238500.0,Other_B,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.031329,-15903,-762,-2751.0,-4385,,1,1,0,1,0,0,Sales staff,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,Self-employed,0.37498999895272295,0.580686936821281,0.3001077565791181,0.0619,0.0524,0.9876,0.8232,,0.0,0.1552,0.1667,,0.0361,0.0504,0.0613,0.0,0.0356,0.063,0.027999999999999997,0.9871,0.8301,,0.0,0.1034,0.1667,,0.0146,0.0551,0.0638,0.0,0.0,0.0625,0.0524,0.9876,0.8256,,0.0,0.1552,0.1667,,0.0367,0.0513,0.0624,0.0,0.0363,org spec account,block of flats,0.0483,Panel,No,3.0,1.0,3.0,1.0,-469.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n416282,0,Cash loans,M,N,N,0,144000.0,203760.0,13266.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-15946,-1544,-7631.0,-4634,,1,1,1,1,0,0,Laborers,2.0,2,2,FRIDAY,17,0,0,0,1,1,1,Business Entity Type 2,,0.6736842420588163,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-408.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n236759,0,Cash loans,M,Y,Y,1,315000.0,719946.0,36886.5,643500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003069,-17284,-1695,-3229.0,-833,3.0,1,1,0,1,0,0,Security staff,3.0,3,3,FRIDAY,15,0,0,0,0,1,1,Government,,0.7264705604603539,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1220.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n317026,0,Cash loans,F,Y,Y,0,270000.0,639396.0,43033.5,571500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.04622,-18268,-4788,-10735.0,-1795,6.0,1,1,0,1,0,0,Laborers,2.0,1,1,SATURDAY,14,0,0,0,0,0,0,Government,,0.6877506045950147,0.7032033049040319,0.0825,0.0633,0.9757,0.6668,0.0071,0.0,0.1379,0.1667,0.2083,0.0331,0.0672,0.0691,0.0,0.0,0.084,0.0657,0.9757,0.6798,0.0072,0.0,0.1379,0.1667,0.2083,0.0338,0.0735,0.07200000000000001,0.0,0.0,0.0833,0.0633,0.9757,0.6713,0.0072,0.0,0.1379,0.1667,0.2083,0.0336,0.0684,0.0703,0.0,0.0,reg oper account,block of flats,0.0582,Panel,No,2.0,0.0,2.0,0.0,-1237.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n344668,0,Cash loans,F,N,Y,0,135000.0,1534500.0,42327.0,1534500.0,\"Spouse, partner\",Commercial associate,Incomplete higher,Married,House / apartment,0.018209,-10906,-1619,-5735.0,-1395,,1,1,0,1,0,1,,2.0,3,3,SATURDAY,11,0,0,0,1,1,0,Self-employed,0.4002544007134191,0.5499226868226267,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1588.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n428340,0,Cash loans,M,Y,Y,1,405000.0,781920.0,43789.5,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019101,-16474,-1119,-6963.0,-11,6.0,1,1,1,1,0,0,Managers,3.0,2,2,WEDNESDAY,10,0,0,0,1,1,0,Self-employed,0.4819405074046538,0.6744097175755929,0.11538666200733562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-1266.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n117066,0,Cash loans,M,Y,Y,0,112500.0,403249.5,15331.5,283500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-19468,-533,-13257.0,-2798,10.0,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,13,0,0,0,1,1,1,Security,,0.7620106605607707,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n354485,0,Cash loans,F,N,Y,2,270000.0,1589904.0,55395.0,1372500.0,Family,Working,Higher education,Married,Office apartment,0.010006,-16538,-321,-1166.0,-60,,1,1,0,1,0,0,Secretaries,4.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.7100143626028355,0.3632339568478801,0.7922644738669378,0.1969,0.1337,0.996,0.9456,,0.24,0.2069,0.3333,0.375,0.1069,,,,0.0,0.2006,0.1387,0.996,0.9477,,0.2417,0.2069,0.3333,0.375,0.1094,,,,0.0,0.1988,0.1337,0.996,0.9463,,0.24,0.2069,0.3333,0.375,0.1088,,,,0.0,reg oper account,block of flats,0.1606,Panel,No,3.0,0.0,3.0,0.0,-347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n350347,0,Revolving loans,M,N,Y,0,112500.0,180000.0,9000.0,,,Working,Secondary / secondary special,Married,House / apartment,0.030755,-13939,-697,-3444.0,-4853,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.33526888152328754,0.5752623801620012,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-221.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n296724,0,Cash loans,F,N,N,0,81000.0,283585.5,17478.0,256500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.018801,-20800,365243,-250.0,-2668,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,XNA,0.8240928623851208,0.7393196034933733,0.5726825047161584,0.0515,,0.9990000000000001,,,0.0,0.069,0.1667,,,,,,,0.0525,,0.9990000000000001,,,0.0,0.069,0.1667,,,,,,,0.052000000000000005,,0.9990000000000001,,,0.0,0.069,0.1667,,,,,,,,block of flats,0.0366,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-858.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n247132,0,Cash loans,F,N,Y,0,81000.0,225000.0,12694.5,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.002042,-20124,-4407,-4816.0,-3107,,1,1,1,1,0,0,Core staff,2.0,3,3,MONDAY,10,0,0,0,0,0,0,School,0.856512133332105,0.5698678767252234,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1015.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n399407,0,Cash loans,F,N,Y,0,180000.0,631332.0,61632.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008019,-17431,-3392,-6259.0,-972,,1,1,0,1,0,0,Sales staff,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6671450640010586,0.5315803127723091,0.7981372313187245,0.1412,0.0597,0.9826,0.762,0.0547,0.08,0.069,0.3333,0.375,0.0752,0.1143,0.1194,0.0039,0.0134,0.1439,0.0619,0.9826,0.7713,0.0552,0.0806,0.069,0.3333,0.375,0.0769,0.1249,0.1244,0.0039,0.0141,0.1426,0.0597,0.9826,0.7652,0.0551,0.08,0.069,0.3333,0.375,0.0765,0.1163,0.1215,0.0039,0.0136,reg oper account,block of flats,0.1296,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2450.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n179611,0,Cash loans,M,N,Y,0,135000.0,587619.0,19084.5,490500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-18532,-187,-4341.0,-2089,,1,1,0,1,0,0,Security staff,2.0,1,1,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6824243380555692,0.33285056416487313,0.0,0.0399,,0.6396,0.0026,0.0,0.0,0.0833,0.125,0.0,0.0,0.0185,0.0,0.0,0.0,0.0414,,0.6537,0.0026,0.0,0.0,0.0833,0.125,0.0,0.0,0.0193,0.0,0.0,0.0,0.0399,,0.6444,0.0026,0.0,0.0,0.0833,0.125,0.0,0.0,0.0189,0.0,0.0,not specified,,0.016,,No,1.0,0.0,1.0,0.0,-2258.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n438212,0,Cash loans,F,N,Y,0,103500.0,547344.0,25492.5,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006008,-21614,365243,-7788.0,-5075,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.6706339786848912,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-588.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n249858,0,Cash loans,F,Y,Y,0,382500.0,1575000.0,41548.5,1575000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.010556,-18361,-10623,-10594.0,-1886,4.0,1,1,0,1,0,0,Accountants,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.6016494732968661,0.8016009030071296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1755.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n388319,0,Cash loans,F,N,Y,2,112500.0,807984.0,26833.5,697500.0,Unaccompanied,State servant,Higher education,Married,With parents,0.010556,-12763,-3379,-4240.0,-1604,,1,1,0,1,0,0,Core staff,4.0,3,3,TUESDAY,11,0,0,0,0,0,0,School,0.6637997531865315,0.6668117930000182,0.6528965519806539,0.0577,0.0591,0.9801,0.728,0.0486,0.0,0.1379,0.125,0.1667,0.0163,0.0471,0.05,0.0,0.0,0.0588,0.0613,0.9801,0.7387,0.049,0.0,0.1379,0.125,0.1667,0.0167,0.0514,0.0521,0.0,0.0,0.0583,0.0591,0.9801,0.7316,0.0489,0.0,0.1379,0.125,0.1667,0.0166,0.0479,0.0509,0.0,0.0,reg oper account,block of flats,0.0659,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2946.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n401667,0,Cash loans,M,N,N,0,144000.0,254700.0,13068.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-19970,-3746,-258.0,-294,,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Trade: type 7,,0.5865800196859002,0.6817058776720116,,,0.9856,,,,,,,,,0.0276,,,,,0.9856,,,,,,,,,0.0287,,,,,0.9856,,,,,,,,,0.0281,,,,,0.0217,,No,0.0,0.0,0.0,0.0,-2214.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n422619,0,Cash loans,M,Y,N,0,360000.0,808650.0,26217.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-21512,365243,-3961.0,-4003,17.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.6593030334464031,0.8050196619153701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-3025.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n248080,1,Cash loans,M,Y,N,0,135000.0,284400.0,13387.5,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.022625,-8870,-1070,-8839.0,-1554,13.0,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 2,,0.4284267656500495,0.3046721837533529,0.1031,0.1352,0.9876,0.83,0.0156,0.0,0.2414,0.1667,0.2083,0.0708,0.0841,0.1125,0.0,0.0,0.105,0.1403,0.9876,0.8367,0.0157,0.0,0.2414,0.1667,0.2083,0.0724,0.0918,0.1173,0.0,0.0,0.1041,0.1352,0.9876,0.8323,0.0157,0.0,0.2414,0.1667,0.2083,0.07200000000000001,0.0855,0.1146,0.0,0.0,reg oper account,block of flats,0.1203,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1543.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n153442,0,Cash loans,F,Y,N,0,67500.0,157500.0,9634.5,157500.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.028663,-22359,365243,-2441.0,-4691,65.0,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,0.6970753193816883,0.3494722450176825,0.40314167665875134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n249685,0,Cash loans,M,N,N,0,225000.0,1006920.0,51543.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006233,-10924,-699,-704.0,-3581,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Business Entity Type 1,,0.5097754470565383,0.3876253444214701,0.0928,0.0799,0.9757,0.6668,0.1372,0.0,0.2069,0.1667,0.2083,0.0,0.0756,0.0869,0.0,0.0,0.0945,0.0829,0.9757,0.6798,0.1384,0.0,0.2069,0.1667,0.2083,0.0,0.0826,0.0905,0.0,0.0,0.0937,0.0799,0.9757,0.6713,0.1381,0.0,0.2069,0.1667,0.2083,0.0,0.077,0.0885,0.0,0.0,reg oper account,block of flats,0.0683,Panel,No,3.0,0.0,3.0,0.0,-1683.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n109702,0,Cash loans,M,Y,Y,0,157500.0,254700.0,25321.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-10478,-933,-331.0,-1023,4.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,15,0,1,1,0,1,1,Other,0.20307182502883567,0.6284653142264173,0.4101025731788671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-329.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2.0,2.0,0.0,0.0,1.0,3.0\n430660,0,Cash loans,F,N,Y,1,67500.0,101880.0,10827.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006008,-17546,-125,-9931.0,-1083,,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,Self-employed,0.5930402422753748,0.7111450848362874,0.6769925032909132,0.2598,0.0,0.9861,0.8096,0.1317,0.28,0.2414,0.3333,0.375,0.0326,0.2118,0.2714,0.0,0.0,0.2647,0.0,0.9861,0.8171,0.1329,0.282,0.2414,0.3333,0.375,0.0333,0.2314,0.2827,0.0,0.0,0.2623,0.0,0.9861,0.8121,0.1326,0.28,0.2414,0.3333,0.375,0.0332,0.2155,0.2763,0.0,0.0,reg oper account,block of flats,0.2855,Panel,No,1.0,0.0,1.0,0.0,-2440.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n122269,0,Cash loans,F,N,Y,0,135000.0,463284.0,14175.0,382500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.010966,-23342,365243,-1807.0,-4600,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,,0.7615185650528349,0.5370699579791587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1495.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,9.0\n237604,0,Cash loans,F,N,N,0,90000.0,545040.0,20677.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.01885,-14427,-2477,-3809.0,-4097,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,FRIDAY,14,0,0,0,1,1,0,Housing,,0.416630259262105,0.7209441499436497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1635.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n112292,0,Cash loans,M,Y,Y,1,135000.0,161730.0,8770.5,135000.0,Family,Working,Secondary / secondary special,Married,Rented apartment,0.018209,-11667,-728,-2835.0,-4091,23.0,1,1,1,1,1,0,Drivers,3.0,3,3,MONDAY,13,0,0,0,1,1,0,Construction,0.315020333870324,0.5270693248206509,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-498.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n199667,0,Cash loans,F,N,N,1,90000.0,961146.0,31135.5,688500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-14244,-1522,-3499.0,-3086,,1,1,0,1,0,0,,3.0,3,3,SUNDAY,9,0,0,0,0,0,0,Government,,0.2117098294505589,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1517.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n307391,1,Cash loans,M,Y,Y,0,180000.0,790830.0,57676.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019101,-9906,-105,-2774.0,-2576,5.0,1,1,0,1,0,0,Drivers,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5755188252377726,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-2365.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n294250,0,Cash loans,F,N,Y,2,81000.0,148365.0,10678.5,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-16727,-5814,-6396.0,-267,,1,1,1,1,0,0,Core staff,4.0,3,3,TUESDAY,7,0,0,0,0,1,1,Government,0.5600936387758103,0.18134207114045475,0.475849908720221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-510.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n172920,0,Cash loans,F,N,Y,0,193950.0,450000.0,19318.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.01885,-7936,-545,-7700.0,-160,,1,1,1,1,0,0,Sales staff,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Trade: type 2,0.16947777277333487,0.681978688913797,0.5744466170995097,0.0351,0.2452,0.9707,0.5988,0.0077,0.0,0.1034,0.125,0.1667,0.0627,0.0261,0.0249,,0.0527,0.0357,0.2544,0.9707,0.6145,0.0078,0.0,0.1034,0.125,0.1667,0.0641,0.0285,0.026000000000000002,,0.0558,0.0354,0.2452,0.9707,0.6042,0.0077,0.0,0.1034,0.125,0.1667,0.0638,0.0265,0.0254,,0.0539,reg oper account,block of flats,0.0403,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-88.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n352052,0,Cash loans,F,N,N,0,157500.0,545040.0,25407.0,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.018801,-8924,-222,-3616.0,-1579,,1,1,1,1,1,0,Realty agents,1.0,2,2,SUNDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.3817865905189824,0.3627889821039193,,0.1082,0.0763,0.9816,0.7484,0.0094,0.08,0.069,0.3333,0.375,0.0757,0.0866,0.0915,0.0077,0.044000000000000004,0.1103,0.0792,0.9816,0.7583,0.0095,0.0806,0.069,0.3333,0.375,0.0775,0.0946,0.0953,0.0078,0.0466,0.1093,0.0763,0.9816,0.7518,0.0094,0.08,0.069,0.3333,0.375,0.0771,0.0881,0.0932,0.0078,0.0449,reg oper spec account,block of flats,0.0816,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-115.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n254168,0,Cash loans,M,N,Y,0,189000.0,222768.0,19246.5,180000.0,Other_A,Commercial associate,Secondary / secondary special,Civil marriage,With parents,0.04622,-8347,-455,-8315.0,-1022,,1,1,0,1,0,1,Laborers,2.0,1,1,WEDNESDAY,16,0,1,1,0,0,0,Business Entity Type 3,0.08614283015946071,0.6562412122236836,0.2940831009471255,0.2227,0.1185,0.9767,0.6804,,0.24,0.2069,0.3333,,0.0443,,0.2094,,0.0,0.2269,0.1229,0.9767,0.6929,,0.2417,0.2069,0.3333,,0.0454,,0.2181,,0.0,0.2248,0.1185,0.9767,0.6847,,0.24,0.2069,0.3333,,0.0451,,0.2131,,0.0,reg oper account,block of flats,0.1647,Panel,No,1.0,0.0,1.0,0.0,-694.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n212028,0,Cash loans,F,Y,Y,0,33750.0,71955.0,7879.5,67500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-19436,365243,-4178.0,-1858,25.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,0.7037119617615839,0.4024280539999651,0.5064842396679806,,0.0439,0.9767,0.6804,0.0,0.0,0.1724,0.1667,,0.0321,0.0588,0.0454,,0.0875,,0.0456,0.9767,0.6929,0.0,0.0,0.1724,0.1667,,0.0328,0.0643,0.0473,,0.0926,,0.0439,0.9767,0.6847,0.0,0.0,0.1724,0.1667,,0.0326,0.0599,0.0463,,0.0893,not specified,block of flats,0.0548,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-922.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n231205,0,Cash loans,F,Y,N,0,193500.0,508495.5,24592.5,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009657,-11092,-2953,-2061.0,-2971,7.0,1,1,1,1,1,1,,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Other,0.3552225457512321,0.5961496430331898,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1448.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n381530,0,Cash loans,F,Y,N,1,292500.0,599472.0,33601.5,517500.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.014464,-10935,-1557,-1641.0,-997,5.0,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.5169129798004655,0.6329118188960771,0.2940831009471255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-440.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n401862,0,Cash loans,F,Y,N,0,202500.0,521136.0,54855.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.005144,-18655,-4341,-10092.0,-2140,15.0,1,1,1,1,1,0,High skill tech staff,1.0,2,2,TUESDAY,12,1,1,0,1,1,0,Self-employed,0.5209799141632802,0.7110777003253653,0.7490217048463391,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-433.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n162530,0,Cash loans,F,N,N,0,180000.0,315000.0,27076.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-19024,-471,-8917.0,-2495,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.6685245957479602,0.6722428897082422,0.2216,0.0855,0.9816,0.7484,0.0521,0.24,0.2069,0.3333,0.0417,0.0923,0.1807,0.2344,0.0,0.0,0.2258,0.0888,0.9816,0.7583,0.0526,0.2417,0.2069,0.3333,0.0417,0.0944,0.1974,0.2442,0.0,0.0,0.2238,0.0855,0.9816,0.7518,0.0524,0.24,0.2069,0.3333,0.0417,0.0939,0.1838,0.2386,0.0,0.0,reg oper account,block of flats,0.2128,Panel,No,0.0,0.0,0.0,0.0,-264.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n254237,0,Cash loans,M,Y,Y,0,157500.0,313438.5,20943.0,283500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.005084,-8919,-1188,-3736.0,-1607,12.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,16,0,0,0,1,1,0,Construction,0.3220930571660914,0.455086982629882,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1099.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n372735,0,Cash loans,M,Y,N,0,144000.0,397881.0,25555.5,328500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003813,-11971,-1176,-3786.0,-4367,11.0,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Mobile,0.4710923224565761,0.19337464027501505,0.7091891096653581,0.0928,0.0842,0.9811,0.7416,0.0128,0.0,0.2069,0.1667,0.2083,0.0688,0.0756,0.0872,0.0,0.0,0.0945,0.0874,0.9811,0.7517,0.0129,0.0,0.2069,0.1667,0.2083,0.0704,0.0826,0.0909,0.0,0.0,0.0937,0.0842,0.9811,0.7451,0.0128,0.0,0.2069,0.1667,0.2083,0.07,0.077,0.0888,0.0,0.0,reg oper account,block of flats,0.0756,Panel,No,0.0,0.0,0.0,0.0,-606.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n102124,0,Cash loans,F,N,Y,0,157500.0,675000.0,29731.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.015221,-16598,-168,-4658.0,-157,,1,1,1,1,1,0,Core staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.8506060064347235,0.6471481114481508,0.7764098512142026,0.0866,0.1006,0.9821,0.7552,0.0144,0.0,0.2069,0.1667,0.2083,0.0642,0.0706,0.0819,0.0,0.0,0.0882,0.1044,0.9821,0.7648,0.0145,0.0,0.2069,0.1667,0.2083,0.0656,0.0771,0.0853,0.0,0.0,0.0874,0.1006,0.9821,0.7585,0.0145,0.0,0.2069,0.1667,0.2083,0.0653,0.0718,0.0833,0.0,0.0,org spec account,block of flats,0.0723,Panel,No,0.0,0.0,0.0,0.0,-688.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n376588,1,Revolving loans,M,N,Y,1,135000.0,405000.0,20250.0,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.020246,-13621,-1800,-7342.0,-5257,,1,1,0,1,1,0,Drivers,3.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,Self-employed,,0.5447811812795327,,0.0742,0.0565,0.9796,0.7212,0.0,0.08,0.069,0.3333,0.375,0.0493,0.0605,0.0761,0.0,0.0,0.0756,0.0586,0.9796,0.7321,0.0,0.0806,0.069,0.3333,0.375,0.0504,0.0661,0.0793,0.0,0.0,0.0749,0.0565,0.9796,0.7249,0.0,0.08,0.069,0.3333,0.375,0.0502,0.0616,0.0775,0.0,0.0,not specified,block of flats,0.0599,Panel,No,0.0,0.0,0.0,0.0,-1287.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n318312,0,Cash loans,M,Y,Y,2,99000.0,331834.5,20295.0,252000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-17978,-265,-3937.0,-1403,13.0,1,1,0,1,0,0,Drivers,4.0,2,2,MONDAY,17,0,0,0,0,0,0,Industry: type 9,,0.534557615022518,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-442.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n311921,0,Cash loans,M,N,N,0,83250.0,612000.0,23845.5,612000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.025164,-23759,365243,-250.0,-4036,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.014253465324347318,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.0,0.0,12.0,0.0,-123.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n282522,0,Cash loans,M,Y,Y,0,180000.0,432000.0,19156.5,432000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.018209,-22918,365243,-9523.0,-4954,15.0,1,0,0,1,1,0,,1.0,3,3,MONDAY,6,0,0,0,0,0,0,XNA,0.6227730894679497,0.5283882438704686,0.3556387169923543,0.1031,0.0945,0.9796,0.7212,0.0,0.0,0.2069,0.1667,0.0417,0.0189,0.0841,0.0927,0.3861,0.0225,0.105,0.0981,0.9796,0.7321,0.0,0.0,0.2069,0.1667,0.0417,0.0194,0.0918,0.0966,0.3891,0.0239,0.1041,0.0945,0.9796,0.7249,0.0,0.0,0.2069,0.1667,0.0417,0.0193,0.0855,0.0944,0.3882,0.023,reg oper spec account,block of flats,0.0778,Panel,No,0.0,0.0,0.0,0.0,-1755.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n214293,0,Cash loans,M,N,N,1,81000.0,286704.0,10755.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014464,-17719,-1033,-4210.0,-1269,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,7,0,0,0,0,0,0,Industry: type 9,,0.14991997217460387,0.4382813743111921,0.0619,0.0641,0.9846,0.7892,0.0299,0.0,0.1379,0.1667,0.2083,0.0276,0.0496,0.0592,0.0039,0.0033,0.063,0.0666,0.9846,0.7975,0.0301,0.0,0.1379,0.1667,0.2083,0.0283,0.0542,0.0617,0.0039,0.0035,0.0625,0.0641,0.9846,0.792,0.03,0.0,0.1379,0.1667,0.2083,0.0281,0.0504,0.0603,0.0039,0.0034,reg oper spec account,block of flats,0.0658,Panel,No,0.0,0.0,0.0,0.0,-724.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n391890,0,Cash loans,F,N,N,0,180000.0,835380.0,23440.5,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.025164,-17832,-1769,-3056.0,-1372,,1,1,1,1,1,0,High skill tech staff,2.0,2,2,SUNDAY,17,0,0,0,0,0,0,Construction,,0.6939712934351642,0.4776491548517548,0.067,0.107,0.9821,0.7552,0.0266,0.0,0.1379,0.1667,0.2083,0.0971,0.0546,0.0629,0.0,0.0,0.0683,0.111,0.9821,0.7648,0.0268,0.0,0.1379,0.1667,0.2083,0.0993,0.0597,0.0656,0.0,0.0,0.0677,0.107,0.9821,0.7585,0.0267,0.0,0.1379,0.1667,0.2083,0.0988,0.0556,0.0641,0.0,0.0,reg oper account,block of flats,0.0727,Panel,No,0.0,0.0,0.0,0.0,-1824.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n178293,0,Cash loans,M,Y,Y,0,292500.0,1601001.0,42363.0,1431000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009657,-22268,365243,-5995.0,-4560,13.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,0.6734450495407501,0.711867757205439,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-703.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n449361,0,Cash loans,F,N,N,1,112500.0,268659.0,18081.0,243000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.00963,-13743,-5842,-3850.0,-157,,1,1,0,1,1,0,Laborers,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Industry: type 11,,0.6084321964647541,0.6956219298394389,0.0722,0.0549,0.9776,0.6940000000000001,0.0078,0.0,0.1379,0.1667,0.0417,0.0474,0.0572,0.0558,0.0077,0.0045,0.0735,0.0569,0.9777,0.706,0.0079,0.0,0.1379,0.1667,0.0417,0.0485,0.0624,0.0581,0.0078,0.0047,0.0729,0.0549,0.9776,0.6981,0.0079,0.0,0.1379,0.1667,0.0417,0.0482,0.0581,0.0568,0.0078,0.0046,reg oper account,block of flats,0.0491,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-29.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n130267,0,Cash loans,F,N,Y,0,157500.0,172021.5,17757.0,148500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21662,365243,-2541.0,-5188,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.7320915576842589,0.3344541255096772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-1852.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n208835,1,Cash loans,F,Y,N,0,135000.0,152820.0,14418.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-14001,-166,-6707.0,-728,24.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.337808269766308,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n147939,0,Cash loans,F,N,N,0,157500.0,1125000.0,29677.5,1125000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-15746,-2923,-8945.0,-2681,,1,1,0,1,0,1,Managers,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Bank,0.7883285755570802,0.42130941474272665,0.7209441499436497,0.1278,0.0911,0.4903,,,0.04,0.1724,0.3333,,,,0.2103,,0.2969,0.0,0.0945,0.0005,,,0.0403,0.1724,0.3333,,,,0.2191,,0.3144,0.1291,0.0911,0.4903,,,0.04,0.1724,0.3333,,,,0.214,,0.3032,,block of flats,0.0,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1593.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n430102,0,Cash loans,F,N,Y,0,135000.0,684738.0,25285.5,490500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-17065,-587,-272.0,-611,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Kindergarten,,0.3536047046465761,0.7801436381572275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-682.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,3.0\n100981,0,Cash loans,F,N,N,0,67500.0,312768.0,13774.5,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-21516,365243,-10609.0,-4795,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.2776667317418485,0.6956219298394389,0.0742,0.0738,0.9851,0.7959999999999999,0.0138,0.08,0.069,0.3333,0.375,0.0246,0.0605,0.0721,0.0,0.0,0.0756,0.0765,0.9851,0.804,0.0139,0.0806,0.069,0.3333,0.375,0.0252,0.0661,0.0751,0.0,0.0,0.0749,0.0738,0.9851,0.7987,0.0138,0.08,0.069,0.3333,0.375,0.025,0.0616,0.0734,0.0,0.0,reg oper account,block of flats,0.0567,Panel,No,3.0,0.0,3.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n400836,0,Cash loans,F,N,Y,1,90000.0,526500.0,26883.0,526500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-15725,-1121,-1591.0,-4070,,1,1,0,1,0,0,High skill tech staff,3.0,3,3,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.7926616966586688,0.4545068058950448,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-703.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n281920,0,Cash loans,M,Y,Y,2,360000.0,1649844.0,85558.5,1575000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-15239,-859,-7474.0,-3981,6.0,1,1,0,1,0,0,,4.0,1,1,MONDAY,18,0,0,0,0,0,0,Other,0.6098434271981302,0.7657575416562947,0.7517237147741489,0.4423,0.0927,0.9801,0.728,0.2003,0.48,0.4138,0.3333,0.375,0.0,0.3606,0.2608,0.0,0.0,0.4506,0.0962,0.9801,0.7387,0.2021,0.4834,0.4138,0.3333,0.375,0.0,0.3939,0.2717,0.0,0.0,0.4466,0.0927,0.9801,0.7316,0.2016,0.48,0.4138,0.3333,0.375,0.0,0.3668,0.2654,0.0,0.0,reg oper account,block of flats,0.3146,Panel,No,0.0,0.0,0.0,0.0,-799.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n175612,0,Cash loans,M,N,Y,1,103500.0,440784.0,34956.0,360000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.005313,-8616,-760,-1051.0,-1253,,1,1,0,1,0,1,Private service staff,3.0,2,2,TUESDAY,11,0,0,0,0,1,1,Services,0.14605818930360515,0.6758102527268129,0.4471785780453068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-664.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n336291,0,Cash loans,F,Y,Y,0,144000.0,180000.0,19030.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.014464,-23295,-4037,-9399.0,-4673,18.0,1,1,1,1,1,0,Accountants,1.0,2,2,TUESDAY,5,0,0,0,0,0,0,Emergency,0.7126697767196106,0.20637335445528265,0.6347055309763198,0.0928,0.1106,0.9861,0.8096,0.0127,0.0,0.2069,0.1667,0.2083,0.0717,0.0756,0.094,0.0,0.0,0.0945,0.1148,0.9861,0.8171,0.0128,0.0,0.2069,0.1667,0.2083,0.0734,0.0826,0.0979,0.0,0.0,0.0937,0.1106,0.9861,0.8121,0.0128,0.0,0.2069,0.1667,0.2083,0.073,0.077,0.0957,0.0,0.0,reg oper account,block of flats,0.0809,Panel,No,0.0,0.0,0.0,0.0,-2120.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n108995,0,Cash loans,F,N,Y,0,67500.0,781920.0,34573.5,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-22609,365243,-3271.0,-2683,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.6290216984140076,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1704.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n220683,0,Cash loans,F,Y,N,0,135000.0,675000.0,19867.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-19608,-2355,-10702.0,-3139,11.0,1,1,1,1,1,0,Medicine staff,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Medicine,0.6880074816454261,0.6704117513574395,0.4992720153045617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1898.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n343108,0,Cash loans,M,Y,N,1,180000.0,433057.5,23620.5,324000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-11333,-3851,-5871.0,-3789,7.0,1,1,0,1,0,1,Laborers,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Trade: type 3,0.4234278123517869,0.5554639597146767,0.3344541255096772,0.0835,0.0798,0.9886,0.8436,0.0404,0.08,0.069,0.3333,0.375,0.0298,0.0681,0.0879,0.0,0.0,0.0851,0.0828,0.9886,0.8497,0.0408,0.0806,0.069,0.3333,0.375,0.0304,0.0744,0.0915,0.0,0.0,0.0843,0.0798,0.9886,0.8457,0.0407,0.08,0.069,0.3333,0.375,0.0303,0.0693,0.0894,0.0,0.0,reg oper account,block of flats,0.0912,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1727.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n371050,0,Cash loans,M,Y,Y,0,189000.0,225000.0,14778.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.015221,-18335,-1379,-1271.0,-1869,1.0,1,1,0,1,1,0,Drivers,1.0,2,2,MONDAY,9,0,0,0,1,1,0,Self-employed,,0.3472582655045638,0.4382813743111921,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n173816,0,Revolving loans,F,N,Y,0,103500.0,135000.0,6750.0,135000.0,Family,State servant,Secondary / secondary special,Single / not married,House / apartment,0.015221,-7731,-1080,-2268.0,-358,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,13,0,0,0,1,1,0,Other,,0.5948275000717498,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n243314,0,Cash loans,F,N,Y,1,180000.0,265306.5,30064.5,252000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.007305,-12288,-1635,-1344.0,-3055,,1,1,0,1,0,1,Core staff,3.0,3,3,THURSDAY,10,0,0,0,0,0,0,Self-employed,0.6979257953274189,0.4837545231350281,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,2.0,-689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n106838,1,Cash loans,F,Y,Y,0,166500.0,450000.0,23562.0,450000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.020246,-10010,-210,-803.0,-2468,10.0,1,1,0,1,0,0,Sales staff,2.0,3,3,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.2737663522859173,0.35731210309945394,0.3825018041447388,0.3232,0.3082,0.997,0.9592,0.1084,0.42,0.2069,0.4167,0.3333,0.1873,0.2526,0.3467,0.0502,0.1351,0.2122,0.2373,0.997,0.9608,0.0627,0.282,0.1724,0.375,0.25,0.1457,0.18,0.2639,0.0233,0.0647,0.3263,0.3082,0.997,0.9597,0.109,0.42,0.2069,0.4167,0.3333,0.1906,0.257,0.3529,0.0505,0.138,reg oper account,block of flats,0.2465,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1582.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n121338,0,Cash loans,F,N,Y,0,112500.0,590337.0,25141.5,477000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.025164,-20632,-4880,-7087.0,-3277,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.19429383437005504,0.5513812618027899,0.2577,0.1588,0.9841,0.7824,0.158,0.28,0.2414,0.3333,0.375,0.0,0.2017,0.2284,0.0386,0.0337,0.2626,0.1648,0.9841,0.7909,0.1595,0.282,0.2414,0.3333,0.375,0.0,0.2204,0.2379,0.0389,0.0357,0.2602,0.1588,0.9841,0.7853,0.159,0.28,0.2414,0.3333,0.375,0.0,0.2052,0.2325,0.0388,0.0344,reg oper account,block of flats,0.2574,Panel,No,3.0,0.0,3.0,0.0,-1214.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n287414,0,Cash loans,F,N,Y,0,135000.0,415107.0,22648.5,346500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-17108,-901,-5015.0,-654,,1,1,0,1,0,1,Cleaning staff,2.0,2,2,SATURDAY,14,0,0,0,0,1,1,Restaurant,0.3026700518624394,0.6830590473938996,0.4650692149562261,0.0124,,0.9702,,,0.0,0.069,0.0417,,,,0.0141,,0.0064,0.0126,,0.9702,,,0.0,0.069,0.0417,,,,0.0146,,0.0068,0.0125,,0.9702,,,0.0,0.069,0.0417,,,,0.0143,,0.0066,,block of flats,0.0125,Others,No,3.0,0.0,3.0,0.0,-503.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n109919,0,Cash loans,F,N,Y,1,112500.0,310671.0,15241.5,256500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006305,-13237,-1567,-2822.0,-3753,,1,1,0,1,0,0,Sales staff,3.0,3,3,WEDNESDAY,4,0,0,0,0,0,0,Self-employed,,0.6171075990340423,0.6397075677637197,0.0371,0.0513,0.9821,0.7552,0.0053,0.0,0.1034,0.125,0.0417,0.0238,0.0303,0.0341,0.0,0.0,0.0378,0.0533,0.9821,0.7648,0.0053,0.0,0.1034,0.125,0.0417,0.0243,0.0331,0.0355,0.0,0.0,0.0375,0.0513,0.9821,0.7585,0.0053,0.0,0.1034,0.125,0.0417,0.0242,0.0308,0.0347,0.0,0.0,reg oper account,block of flats,0.0297,Panel,No,0.0,0.0,0.0,0.0,-2035.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,1.0,5.0\n147768,0,Cash loans,F,N,N,0,112500.0,143910.0,14148.0,135000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.002042,-22445,-13576,-9242.0,-4408,,1,1,0,1,0,0,HR staff,1.0,3,3,MONDAY,12,0,0,0,0,0,0,University,,0.1271937977014603,0.6313545365850379,0.0557,0.0445,0.9876,,,0.08,0.069,0.3333,,0.0628,,0.0647,,0.0313,0.0567,0.0462,0.9876,,,0.0806,0.069,0.3333,,0.0642,,0.0674,,0.0331,0.0562,0.0445,0.9876,,,0.08,0.069,0.3333,,0.0639,,0.0659,,0.0319,,block of flats,0.0563,Panel,No,0.0,0.0,0.0,0.0,-1516.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n119491,0,Cash loans,M,N,Y,0,225000.0,2517300.0,73732.5,2250000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-23202,-15474,-6205.0,-4550,,1,1,1,1,1,0,Laborers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Transport: type 2,,0.5375976893542143,0.7981372313187245,0.0784,0.037000000000000005,0.9752,0.66,0.0082,0.0,0.1379,0.1667,0.2083,0.0513,0.0025,0.0621,0.2934,0.0195,0.0798,0.0383,0.9752,0.6733,0.0082,0.0,0.1379,0.1667,0.2083,0.0525,0.0028,0.0647,0.2957,0.0207,0.0791,0.037000000000000005,0.9752,0.6645,0.0082,0.0,0.1379,0.1667,0.2083,0.0522,0.0026,0.0632,0.295,0.0199,reg oper account,block of flats,0.0575,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-402.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n403310,0,Cash loans,F,N,Y,0,90000.0,238500.0,13819.5,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.026392,-18430,-4518,-12475.0,-1973,,1,1,0,1,1,0,Sales staff,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,Self-employed,,0.5339971712247031,,0.2227,,0.9811,,,0.24,0.2069,0.3333,,,,0.1402,,0.3535,0.2269,,0.9811,,,0.2417,0.2069,0.3333,,,,0.1461,,0.3742,0.2248,,0.9811,,,0.24,0.2069,0.3333,,,,0.1427,,0.3609,,block of flats,0.1871,Panel,No,3.0,0.0,3.0,0.0,-2260.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n175873,0,Cash loans,M,N,N,0,270000.0,1546020.0,44437.5,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009334,-12398,-2406,-1121.0,-4953,,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Police,,0.5679479031877822,0.24318648044201235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n314998,0,Revolving loans,F,N,Y,0,90000.0,135000.0,6750.0,135000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-23017,365243,-9224.0,-4540,,1,0,0,1,1,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.6982778239540675,0.3876253444214701,0.0227,,0.9786,,,0.0,0.1034,0.0417,,0.0506,,0.018000000000000002,,0.0239,0.0231,,0.9786,,,0.0,0.1034,0.0417,,0.0517,,0.0188,,0.0253,0.0229,,0.9786,,,0.0,0.1034,0.0417,,0.0515,,0.0183,,0.0244,,block of flats,0.0153,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1827.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,7.0,0.0,0.0\n364769,0,Cash loans,M,N,Y,0,180000.0,593010.0,19260.0,495000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-22505,365243,-3020.0,-3998,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.4490745413499887,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n264703,0,Cash loans,F,N,Y,0,112500.0,781920.0,25839.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.019101,-17853,-716,-5012.0,-1407,,1,1,1,1,0,0,Waiters/barmen staff,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,0.3215856510672951,0.7107541248073208,0.6925590674998008,,,0.9722,,,,,,,,,0.0076,,,,,0.9722,,,,,,,,,0.008,,,,,0.9722,,,,,,,,,0.0078,,,,block of flats,0.006,,Yes,1.0,1.0,1.0,0.0,-1949.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n120091,0,Cash loans,M,Y,Y,0,144000.0,239850.0,25317.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-15863,-1353,-5817.0,-4673,8.0,1,1,0,1,1,0,Drivers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Medicine,,0.6396576359058749,0.6594055320683344,0.0742,0.0514,0.9841,0.7824,0.0207,0.08,0.069,0.3333,0.375,0.0316,0.0605,0.0761,0.0,0.0,0.0756,0.0533,0.9841,0.7909,0.0209,0.0806,0.069,0.3333,0.375,0.0323,0.0661,0.0793,0.0,0.0,0.0749,0.0514,0.9841,0.7853,0.0209,0.08,0.069,0.3333,0.375,0.0321,0.0616,0.0774,0.0,0.0,reg oper account,block of flats,0.0712,Panel,No,2.0,2.0,2.0,1.0,-747.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n356420,1,Cash loans,F,N,Y,1,54000.0,592560.0,15889.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.01885,-17092,-2884,-4335.0,-649,,1,1,0,1,0,0,Cleaning staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Industry: type 9,,0.703795757955896,0.4400578303966329,0.034,0.0322,0.9841,0.7824,0.0144,0.0,0.0345,0.0417,0.0833,0.0113,0.0277,0.0143,0.0,0.0,0.0347,0.0334,0.9841,0.7909,0.0145,0.0,0.0345,0.0417,0.0833,0.0115,0.0303,0.0149,0.0,0.0,0.0344,0.0322,0.9841,0.7853,0.0145,0.0,0.0345,0.0417,0.0833,0.0115,0.0282,0.0146,0.0,0.0,reg oper account,block of flats,0.0191,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-417.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,6.0,0.0,2.0\n184772,0,Cash loans,M,Y,Y,0,49500.0,544491.0,17694.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010556,-22115,365243,-11443.0,-5649,28.0,1,0,0,1,0,0,,2.0,3,3,TUESDAY,15,0,0,0,0,0,0,XNA,,0.653087083529007,0.34578480246959553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-235.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n163679,0,Cash loans,F,N,Y,0,67500.0,639000.0,27202.5,639000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-22955,365243,-2920.0,-4018,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.2631435910213423,0.7981372313187245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,0.0,-2087.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n253328,0,Cash loans,F,Y,Y,0,225000.0,453514.5,19975.5,391500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-18898,365243,-9907.0,-2297,15.0,1,0,0,1,0,0,,2.0,3,3,FRIDAY,12,0,0,0,0,0,0,XNA,,0.28288501595658594,0.4223696523543468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,-977.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n157747,0,Cash loans,F,N,Y,1,126000.0,254700.0,24939.0,225000.0,Unaccompanied,Working,Higher education,Separated,With parents,0.019101,-13263,-309,-1332.0,-964,,1,1,0,1,1,0,Core staff,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,School,0.5926049641718132,0.6969354420651954,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-319.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n411644,1,Cash loans,M,Y,N,0,112500.0,521280.0,25209.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.018634,-10550,-194,-10550.0,-2972,29.0,1,1,0,1,0,0,,1.0,2,2,FRIDAY,16,0,0,0,0,1,1,Transport: type 4,0.12528744879564224,0.4915836593477498,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1535.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n296370,0,Cash loans,F,N,N,0,315000.0,223884.0,26698.5,202500.0,Unaccompanied,Working,Higher education,Single / not married,Co-op apartment,0.072508,-15556,-2803,-9634.0,-3921,,1,1,0,1,1,0,,1.0,1,1,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 1,0.7970948547491407,0.7657261605789958,0.5709165417729987,0.5186,0.32799999999999996,0.9801,0.7212,0.0009,0.56,0.4828,0.3333,0.375,0.0,0.4219,0.4998,0.0039,0.0023,0.5284,0.3404,0.9801,0.7321,0.0009,0.5639,0.4828,0.3333,0.375,0.0,0.461,0.5207,0.0039,0.0024,0.5236,0.32799999999999996,0.9801,0.7249,0.0009,0.56,0.4828,0.3333,0.375,0.0,0.4293,0.5087,0.0039,0.0023,reg oper account,block of flats,0.3936,Panel,No,0.0,0.0,0.0,0.0,-1106.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n310844,0,Cash loans,F,N,N,0,135000.0,319500.0,14071.5,319500.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.026392,-12738,-826,-6653.0,-1385,,1,1,0,1,0,0,,1.0,2,2,FRIDAY,19,0,0,0,0,1,1,Other,0.7063242184179388,0.6956267329263471,0.2580842039460289,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1730.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,1.0,0.0,0.0,0.0,0.0\n352345,0,Cash loans,F,Y,N,2,135000.0,180000.0,21492.0,180000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.010643,-11065,-3890,-9267.0,-2037,18.0,1,1,0,1,0,0,Core staff,4.0,2,2,SUNDAY,17,0,0,0,0,1,1,Government,0.5210637410826722,0.6351596097091011,0.5779691187553125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1612.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n157449,0,Cash loans,M,Y,N,2,427500.0,1339884.0,35343.0,1170000.0,Children,Working,Higher education,Married,House / apartment,0.010006,-9351,-649,-82.0,-1968,2.0,1,1,1,1,0,0,Cooking staff,4.0,2,2,MONDAY,15,0,0,0,0,0,0,Self-employed,,0.7723751026252127,0.4578995512067301,0.2033,0.9397,0.9955,0.9388,0.1605,0.22399999999999998,0.1931,0.3333,0.375,0.1242,0.1652,0.2173,0.0039,0.0122,0.2248,0.1473,0.996,0.9477,0.0,0.2417,0.2069,0.3333,0.375,0.0584,0.0643,0.0742,0.0,0.0,0.2228,0.1473,0.996,0.9463,0.2136,0.24,0.2069,0.3333,0.375,0.1357,0.183,0.253,0.0039,0.0031,reg oper account,block of flats,0.2047,Panel,No,1.0,0.0,1.0,0.0,-1297.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n433339,0,Cash loans,M,N,Y,0,225000.0,1223010.0,51948.0,1125000.0,\"Spouse, partner\",Pensioner,Higher education,Civil marriage,House / apartment,0.005084,-19993,365243,-273.0,-3374,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,0.6144458534412479,0.5847280670472291,0.6041125998015721,0.0619,0.0759,,,,,0.1379,0.1667,,0.0061,,0.0347,,,0.063,0.0787,,,,,0.1379,0.1667,,0.0062,,0.0362,,,0.0625,0.0759,,,,,0.1379,0.1667,,0.0062,,0.0353,,,,block of flats,0.0441,\"Stone, brick\",No,11.0,0.0,11.0,0.0,-2789.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n344810,0,Cash loans,F,Y,N,0,270000.0,900000.0,45954.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-11456,-1199,-1661.0,-3096,4.0,1,1,1,1,1,0,Managers,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.7503677659674407,0.4575282950420064,0.324891229465852,0.0722,0.0584,0.998,0.966,0.0216,0.04,0.0345,0.375,0.4167,0.013000000000000001,0.0588,0.07200000000000001,0.0039,0.0445,0.0735,0.0606,0.998,0.9673,0.0218,0.0403,0.0345,0.375,0.4167,0.0133,0.0643,0.075,0.0039,0.0471,0.0729,0.0584,0.998,0.9665,0.0218,0.04,0.0345,0.375,0.4167,0.0132,0.0599,0.0733,0.0039,0.0454,reg oper account,block of flats,0.0776,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-573.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n249299,1,Cash loans,F,N,Y,0,135000.0,781695.0,25344.0,652500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-13569,-2220,-307.0,-1532,,1,1,0,1,0,1,Cooking staff,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Other,,0.0962879762199284,0.470456116119975,0.0124,0.0345,0.9637,,,0.0,0.069,0.0833,,0.0296,,0.0232,,0.0012,0.0126,0.0358,0.9638,,,0.0,0.069,0.0833,,0.0303,,0.0241,,0.0012,0.0125,0.0345,0.9637,,,0.0,0.069,0.0833,,0.0302,,0.0236,,0.0012,,block of flats,0.0185,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-860.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,5.0\n435486,0,Cash loans,M,N,N,2,247500.0,918000.0,32971.5,918000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-15621,-3146,-15621.0,-2931,,1,1,0,1,0,0,Managers,4.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6823713131892755,0.6821209536272256,0.4884551844437485,0.0933,0.0164,0.9891,0.8504,0.0137,0.0,0.2069,0.1667,0.2083,0.0521,0.0756,0.0906,0.0039,0.0345,0.0945,0.017,0.9891,0.8563,0.0139,0.0,0.2069,0.1667,0.2083,0.0533,0.0826,0.0944,0.0039,0.0365,0.0942,0.0164,0.9891,0.8524,0.0138,0.0,0.2069,0.1667,0.2083,0.053,0.077,0.0922,0.0039,0.0352,reg oper account,block of flats,0.0712,Panel,No,0.0,0.0,0.0,0.0,-1797.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n215896,0,Cash loans,M,N,Y,0,135000.0,610335.0,22050.0,463500.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.019101,-11911,-453,-6032.0,-4041,,1,1,0,1,0,0,Low-skill Laborers,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Postal,0.1872196214173288,0.6184810814063068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2225.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n340530,0,Cash loans,M,N,N,0,112500.0,83403.0,10026.0,72000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.002042,-9616,-145,-597.0,-1282,,1,1,0,1,0,0,Laborers,1.0,3,3,SATURDAY,13,0,0,0,0,0,0,Trade: type 6,,0.20747865258366724,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-568.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,5.0\n244039,0,Cash loans,F,N,Y,1,112500.0,254799.0,20259.0,193500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.035792000000000004,-8350,-463,-3215.0,-1037,,1,1,0,1,1,0,Waiters/barmen staff,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Self-employed,,0.6827133571877337,0.16048893062734468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-324.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n282648,0,Cash loans,F,Y,Y,0,247500.0,1527579.0,53226.0,1395000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-18855,-4667,-11430.0,-1842,4.0,1,1,0,1,0,0,Managers,1.0,2,2,SATURDAY,8,0,0,0,0,0,0,Military,,0.6191155494312557,0.4848514754962666,0.0619,0.0587,0.9762,,,0.0,0.1379,0.1667,,0.0,,0.0492,,0.0517,0.063,0.0609,0.9762,,,0.0,0.1379,0.1667,,0.0,,0.0513,,0.0547,0.0625,0.0587,0.9762,,,0.0,0.1379,0.1667,,0.0,,0.0501,,0.0528,,block of flats,0.043,Panel,No,1.0,1.0,1.0,1.0,-3151.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n412515,0,Cash loans,F,N,N,0,135000.0,781920.0,42547.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-15586,-137,-310.0,-4342,,1,1,1,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Industry: type 11,0.6178639925166423,0.6250430244802883,0.3706496323299817,0.0082,,0.9687,,,0.0,0.0345,0.0417,,0.0,,0.0075,,0.0157,0.0084,,0.9687,,,0.0,0.0345,0.0417,,0.0,,0.0079,,0.0166,0.0083,,0.9687,,,0.0,0.0345,0.0417,,0.0,,0.0077,,0.016,,block of flats,0.0059,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1730.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n418297,1,Cash loans,M,N,Y,0,112500.0,343800.0,13090.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-22838,365243,-11274.0,-969,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.2055186782891003,0.3996756156233169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-311.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n443070,0,Cash loans,F,N,N,0,211500.0,135000.0,14175.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019101,-13922,-1300,-6355.0,-4692,,1,1,0,1,1,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,1,1,0,Self-employed,0.5892459328931143,0.7243754849006387,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2230.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n423551,0,Cash loans,F,N,Y,0,90000.0,58500.0,6430.5,58500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.014519999999999996,-10713,-849,-1050.0,-3360,,1,1,0,1,0,1,High skill tech staff,1.0,2,2,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.22188990690710989,0.6089790876567933,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-354.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n272851,0,Cash loans,F,N,Y,0,360000.0,547272.0,35104.5,495000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.072508,-20248,365243,-10332.0,-3462,,1,0,0,1,1,0,,2.0,1,1,TUESDAY,14,0,0,0,0,0,0,XNA,,0.7191850761995178,0.2608559142068693,0.1646,0.0888,0.9861,0.9932,0.0,0.16,0.1148,0.5692,0.7917,0.0,0.0815,0.1129,0.0039,0.0671,0.1029,0.0411,0.9767,0.9935,0.0,0.0806,0.0345,0.3333,0.7917,0.0,0.0891,0.0641,0.0039,0.0253,0.1093,0.0448,0.9826,0.9933,0.0,0.08,0.0345,0.625,0.7917,0.0,0.0829,0.09300000000000001,0.0039,0.0622,reg oper account,block of flats,0.2108,Block,No,0.0,0.0,0.0,0.0,-1309.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n373100,0,Cash loans,F,N,Y,0,216000.0,781920.0,43789.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006008,-17065,-1671,-8310.0,-437,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5946061300251548,0.2263473957097693,0.0247,0.0941,0.9752,,,0.0,0.1034,0.125,,,,0.0467,,0.0,0.0252,0.0977,0.9752,,,0.0,0.1034,0.125,,,,0.0487,,0.0,0.025,0.0941,0.9752,,,0.0,0.1034,0.125,,,,0.0476,,0.0,,block of flats,0.0419,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1106.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n338611,1,Cash loans,F,N,N,0,72000.0,207306.0,8779.5,148500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.02461,-24412,365243,-10826.0,-4027,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.7035540910946677,0.4436153084085652,0.1856,0.2129,0.9861,,,0.0,0.4138,0.1667,,0.0647,,0.1735,,,0.1891,0.2209,0.9861,,,0.0,0.4138,0.1667,,0.0661,,0.1808,,,0.1874,0.2129,0.9861,,,0.0,0.4138,0.1667,,0.0658,,0.1767,,,,block of flats,0.1365,Panel,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n385363,0,Cash loans,F,N,N,2,202500.0,1288350.0,37669.5,1125000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.007273999999999998,-12241,-3035,-3648.0,-1199,,1,1,1,1,0,0,Laborers,3.0,2,2,TUESDAY,20,0,0,0,0,1,1,Business Entity Type 2,,0.5878786428370355,0.33285056416487313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1320.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n282447,0,Cash loans,F,Y,Y,0,180000.0,112068.0,12019.5,99000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-16804,-1122,-10950.0,-351,9.0,1,1,0,1,0,0,Accountants,2.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.7430738361791787,0.4974688893052743,0.2588,0.1958,0.9811,0.7416,,0.28,0.2414,0.3333,0.375,0.0707,0.21100000000000002,0.2711,0.0,0.0,0.2637,0.2032,0.9811,0.7517,,0.282,0.2414,0.3333,0.375,0.0723,0.2305,0.2824,0.0,0.0,0.2613,0.1958,0.9811,0.7451,,0.28,0.2414,0.3333,0.375,0.0719,0.2146,0.2759,0.0,0.0,reg oper account,block of flats,0.2134,Panel,No,2.0,0.0,2.0,0.0,-1004.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n137046,0,Cash loans,M,Y,N,0,450000.0,450000.0,21109.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.014519999999999996,-8424,-467,-8287.0,-1082,3.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,13,0,0,0,1,1,0,Business Entity Type 3,0.3301449048818094,0.5884718930708671,0.3723336657058204,0.0165,0.0,0.9856,0.8028,0.0074,0.0,0.069,0.0417,0.0417,0.0112,0.0134,0.0089,0.0,0.0,0.0168,0.0,0.9856,0.8105,0.0075,0.0,0.069,0.0417,0.0417,0.0115,0.0147,0.0093,0.0,0.0,0.0167,0.0,0.9856,0.8054,0.0075,0.0,0.069,0.0417,0.0417,0.0114,0.0137,0.0091,0.0,0.0,reg oper account,block of flats,0.0111,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-489.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n354502,0,Revolving loans,F,Y,Y,3,157500.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018209,-17806,-183,-6000.0,-1351,22.0,1,1,0,1,0,0,Sales staff,5.0,3,3,FRIDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.8325068064321551,0.6979874916366315,0.3296550543128238,0.1619,0.0924,0.9906,0.8708,0.0987,0.16,0.069,0.5417,0.0417,0.1226,0.1303,0.16699999999999998,0.0077,0.0093,0.1649,0.0958,0.9906,0.8759,0.0996,0.1611,0.069,0.5417,0.0417,0.1254,0.1423,0.174,0.0078,0.0099,0.1634,0.0924,0.9906,0.8725,0.0993,0.16,0.069,0.5417,0.0417,0.1247,0.1325,0.17,0.0078,0.0095,reg oper spec account,block of flats,0.1873,Monolithic,No,5.0,1.0,5.0,1.0,-1022.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n392150,0,Cash loans,F,N,N,0,112500.0,521280.0,27423.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-18098,-1101,-803.0,-1636,,1,1,0,1,1,0,Cooking staff,2.0,3,3,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.5253218077472587,0.4677319736828822,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n262244,0,Cash loans,F,Y,Y,1,90000.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-10463,-1172,-3127.0,-3127,10.0,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.6417316507636472,0.6986675550534175,0.0082,0.0,0.9707,0.5988,0.0009,0.0,0.0517,0.0208,0.0625,0.0135,0.0067,0.0072,0.0,0.0,0.0042,0.0,0.9692,0.5949,0.0,0.0,0.0345,0.0,0.0417,0.0076,0.0037,0.0037,0.0,0.0,0.0083,0.0,0.9707,0.6042,0.0009,0.0,0.0517,0.0208,0.0625,0.0137,0.0068,0.0073,0.0,0.0,reg oper account,block of flats,0.0028,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-324.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n246953,0,Cash loans,F,N,Y,0,99000.0,112500.0,12852.0,112500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.02461,-17842,-3740,-1120.0,-1381,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,,0.7300364651870721,0.4014074137749511,0.0928,0.0865,0.9841,0.7824,,0.0,0.2069,0.1667,,0.09300000000000001,,0.0865,,0.0,0.0945,0.0897,0.9841,0.7909,,0.0,0.2069,0.1667,,0.0951,,0.0901,,0.0,0.0937,0.0865,0.9841,0.7853,,0.0,0.2069,0.1667,,0.0946,,0.08800000000000001,,0.0,,block of flats,0.0744,Panel,No,0.0,0.0,0.0,0.0,-1284.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n144630,0,Cash loans,M,Y,Y,0,135000.0,254700.0,24939.0,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.025164,-24521,365243,-5669.0,-1909,8.0,1,0,0,1,0,1,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.5302569377395527,0.25396280933631177,0.1165,0.0795,0.9901,0.8640000000000001,,0.12,0.1034,0.375,0.375,0.0372,0.0941,0.1217,0.0039,0.0743,0.1187,0.0825,0.9901,0.8693,,0.1208,0.1034,0.375,0.375,0.0381,0.1028,0.1268,0.0039,0.0786,0.1176,0.0795,0.9901,0.8658,,0.12,0.1034,0.375,0.375,0.0379,0.0958,0.1238,0.0039,0.0758,,block of flats,0.1392,Panel,No,5.0,2.0,5.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n126036,0,Cash loans,F,Y,N,2,135000.0,450000.0,21109.5,450000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,With parents,0.030755,-12930,-1356,-3210.0,-2812,8.0,1,1,1,1,1,0,Sales staff,4.0,2,2,MONDAY,14,0,0,0,0,0,0,Trade: type 3,,0.4287052417282084,0.31703177858344445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2233.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n121366,0,Cash loans,F,N,Y,0,76500.0,598486.5,21496.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.003813,-23751,365243,-3077.0,-4139,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,7,0,0,0,0,0,0,XNA,,0.35319995237262514,,0.0103,,0.9752,,,0.0,,0.0417,,,,,,,0.0105,,0.9752,,,0.0,,0.0417,,,,,,,0.0104,,0.9752,,,0.0,,0.0417,,,,,,,,block of flats,0.0074,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2214.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n304327,0,Cash loans,F,N,Y,0,283500.0,2156400.0,57015.0,1800000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-18787,-1079,-12889.0,-2305,,1,1,0,1,0,0,,2.0,1,1,SUNDAY,11,0,1,1,0,0,0,Other,,0.7059020606932481,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-742.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,,,,,,\n346162,0,Cash loans,F,N,Y,0,135000.0,107820.0,5634.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-23439,365243,-441.0,-4014,,1,0,0,1,0,0,,2.0,2,2,MONDAY,8,0,0,0,0,0,0,XNA,0.5462204631113621,0.15767370804607614,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-438.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n323465,0,Revolving loans,F,Y,Y,0,117000.0,360000.0,18000.0,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-22060,-1150,-7390.0,-5346,2.0,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Self-employed,0.9016188950770352,0.51751065285348,0.7032033049040319,0.1392,0.1247,0.9901,,,0.12,0.1034,0.3333,,0.1174,,0.1493,,0.0024,0.1418,0.1294,0.9901,,,0.1208,0.1034,0.3333,,0.1201,,0.1556,,0.0026,0.1405,0.1247,0.9901,,,0.12,0.1034,0.3333,,0.1194,,0.152,,0.0025,,block of flats,0.1432,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2612.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n405900,0,Cash loans,M,Y,Y,2,180000.0,497520.0,32521.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-10692,-678,-4747.0,-3291,13.0,1,1,0,1,1,0,,4.0,3,3,FRIDAY,14,0,0,0,0,1,1,Other,0.1549746110896795,0.4647062636840848,0.324891229465852,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-880.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,1.0,7.0\n254300,1,Cash loans,M,N,Y,0,135000.0,139500.0,16555.5,139500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.022625,-20859,365243,-1390.0,-1334,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.45231735843494403,0.5867400085415683,0.1021,0.0309,0.9771,0.6872,0.0526,0.0,0.2069,0.1667,0.2083,0.0342,0.0832,0.0895,0.0,0.0,0.10400000000000001,0.0321,0.9772,0.6994,0.0531,0.0,0.2069,0.1667,0.2083,0.0349,0.0909,0.0933,0.0,0.0,0.1031,0.0309,0.9771,0.6914,0.0529,0.0,0.2069,0.1667,0.2083,0.0347,0.0847,0.0911,0.0,0.0,reg oper account,block of flats,0.0992,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-479.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n353254,0,Cash loans,F,N,N,1,135000.0,454500.0,36040.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-14482,-696,-625.0,-632,,1,1,1,1,1,0,,3.0,1,1,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.655540491135259,0.5886731160816913,0.3910549766342248,0.0979,0.0414,0.9831,0.7688,0.0,0.16,0.069,0.4583,0.0,0.0,0.0799,0.0996,0.0,0.0,0.0998,0.0429,0.9831,0.7779,0.0,0.1611,0.069,0.4583,0.0,0.0,0.0872,0.1037,0.0,0.0,0.0989,0.0414,0.9831,0.7719,0.0,0.16,0.069,0.4583,0.0,0.0,0.0812,0.1014,0.0,0.0,reg oper spec account,block of flats,0.0787,Panel,No,0.0,0.0,0.0,0.0,-569.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n218487,0,Cash loans,F,N,Y,0,117000.0,271066.5,19278.0,234000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.02461,-12733,-2653,-4726.0,-3092,,1,1,1,1,0,0,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.3386855321159559,0.6749328863455641,0.4507472818545589,0.0278,0.0562,0.9866,0.8164,,0.0,0.1034,0.0833,0.125,0.0233,0.0227,0.0155,,0.043,0.0284,0.0583,0.9866,0.8236,,0.0,0.1034,0.0833,0.125,0.0238,0.0248,0.0162,,0.0455,0.0281,0.0562,0.9866,0.8189,,0.0,0.1034,0.0833,0.125,0.0237,0.0231,0.0158,,0.0439,not specified,block of flats,0.0215,Panel,No,2.0,0.0,2.0,0.0,-1240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n344174,0,Cash loans,F,N,Y,0,378000.0,1327500.0,42952.5,1327500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-12220,-1956,-1030.0,-1725,,1,1,1,1,1,0,Managers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Self-employed,0.3962718617252648,0.3816256783297323,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-796.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n187596,0,Cash loans,F,N,Y,0,76500.0,942300.0,27135.0,675000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.01885,-17124,-2944,-4655.0,-679,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.6967737599579921,0.6577838002083306,0.0495,0.0579,0.9707,0.5988,0.0085,0.0,0.069,0.125,0.1667,0.0695,0.0403,0.0538,0.0,0.0,0.0504,0.0601,0.9707,0.6145,0.0085,0.0,0.069,0.125,0.1667,0.0711,0.0441,0.0561,0.0,0.0,0.05,0.0579,0.9707,0.6042,0.0085,0.0,0.069,0.125,0.1667,0.0707,0.040999999999999995,0.0548,0.0,0.0,,block of flats,0.0423,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n111733,0,Cash loans,M,N,Y,0,270000.0,868797.0,34587.0,702000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-17701,-1132,-4002.0,-997,,1,1,0,1,0,0,Sales staff,2.0,1,1,SUNDAY,13,0,0,0,0,0,0,Self-employed,0.4983608860796825,0.6910096844137753,0.42765737003502935,0.1031,0.0801,0.9767,0.6804,0.0112,0.0,0.1724,0.1667,0.2083,0.0987,0.0841,0.08900000000000001,0.0,0.0,0.105,0.0831,0.9767,0.6929,0.0113,0.0,0.1724,0.1667,0.2083,0.10099999999999999,0.0918,0.0927,0.0,0.0,0.1041,0.0801,0.9767,0.6847,0.0113,0.0,0.1724,0.1667,0.2083,0.1005,0.0855,0.0906,0.0,0.0,reg oper account,block of flats,0.09,Panel,No,2.0,0.0,2.0,0.0,-572.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n297928,0,Cash loans,M,Y,N,3,247500.0,315000.0,29884.5,315000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-13085,-336,-2509.0,-4724,1.0,1,1,0,1,0,0,Core staff,5.0,2,2,THURSDAY,14,0,0,0,0,0,0,Bank,0.6605826297075839,0.7741617021692597,0.5478104658520093,0.1856,0.1315,0.9965,0.9524,0.0727,0.2,0.1724,0.375,0.0417,0.1426,0.1513,0.2212,0.0386,0.0893,0.1891,0.1365,0.9965,0.9543,0.0734,0.2014,0.1724,0.375,0.0417,0.1458,0.1653,0.2305,0.0389,0.0945,0.1874,0.1315,0.9965,0.953,0.0732,0.2,0.1724,0.375,0.0417,0.1451,0.1539,0.2252,0.0388,0.0912,reg oper account,block of flats,0.2332,Panel,No,3.0,0.0,3.0,0.0,-375.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n245217,0,Cash loans,M,N,Y,0,225000.0,1616107.5,44572.5,1444500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.035792000000000004,-23231,365243,-2993.0,-4070,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.7047021291599941,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-370.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n193505,0,Cash loans,F,N,Y,1,157500.0,761067.0,27468.0,657000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.011703,-14730,-4190,-8859.0,-4293,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Other,0.7056398271161826,0.6661069521382146,0.7675231046555077,0.0062,0.0,0.9682,0.5648,0.0,0.0,0.0345,0.0833,0.125,0.0463,0.0042,0.0195,0.0039,0.004,0.0063,0.0,0.9682,0.5818,0.0,0.0,0.0345,0.0833,0.125,0.0473,0.0046,0.0203,0.0039,0.0042,0.0062,0.0,0.9682,0.5706,0.0,0.0,0.0345,0.0833,0.125,0.0471,0.0043,0.0198,0.0039,0.0041,reg oper account,block of flats,0.0178,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2199.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n321090,0,Cash loans,F,N,Y,0,159750.0,1223010.0,52632.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-19639,-135,-11291.0,-3132,,1,1,1,1,0,0,Sales staff,2.0,2,2,FRIDAY,15,1,1,1,1,1,1,Business Entity Type 1,,0.2108462011090325,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-10.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n326743,0,Cash loans,M,Y,N,1,180000.0,244584.0,14170.5,193500.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.0105,-10213,-2116,-5056.0,-2879,12.0,1,1,0,1,0,0,Security staff,3.0,3,3,WEDNESDAY,9,0,0,0,1,1,0,Security,,0.42288299673640795,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-309.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n334404,0,Cash loans,F,N,Y,0,112500.0,900000.0,35824.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.030755,-18625,-1203,-4894.0,-2179,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,11,0,0,0,0,1,1,Self-employed,,0.6233297242547708,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3216.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n202699,0,Revolving loans,M,N,Y,0,135000.0,450000.0,22500.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-13911,-2664,-7994.0,-2936,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,0.4493062107482647,0.7040373106828022,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1671.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n182948,0,Cash loans,F,N,Y,0,157500.0,1502730.0,54108.0,1327500.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.019689,-21301,-415,-398.0,-4164,,1,1,0,1,0,1,Security staff,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Self-employed,0.6578541625970947,0.16119616026770178,0.6430255641096323,,,0.9811,,,,,,,,,0.0163,,,,,0.9811,,,,,,,,,0.017,,,,,0.9811,,,,,,,,,0.0166,,,,,0.0145,,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n249487,0,Revolving loans,F,Y,Y,1,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010032,-9688,-930,-4478.0,-1100,19.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Industry: type 9,0.2145836851615257,0.3859749848603956,0.5954562029091491,0.1124,0.067,0.9896,0.8572,0.0509,0.12,0.1034,0.3333,0.375,0.0246,0.0908,0.1179,0.0039,0.0,0.1145,0.0695,0.9896,0.8628,0.0514,0.1208,0.1034,0.3333,0.375,0.0252,0.0992,0.1228,0.0039,0.0,0.1135,0.067,0.9896,0.8591,0.0512,0.12,0.1034,0.3333,0.375,0.025,0.0923,0.12,0.0039,0.0,reg oper account,block of flats,0.1205,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-320.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n310717,0,Cash loans,F,N,N,2,112500.0,276277.5,18819.0,238500.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.008473999999999999,-12601,-3362,-2267.0,-644,,1,1,0,1,0,0,Secretaries,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Agriculture,0.3950079258707803,0.6574882001591088,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1203.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n278398,0,Cash loans,M,Y,N,0,427500.0,1575000.0,43443.0,1575000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-14549,-1196,-6413.0,-4696,6.0,1,1,0,1,1,0,Managers,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.16899308520315112,0.4960056678294886,0.23791607950711405,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2225.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n420972,0,Cash loans,M,N,N,1,166500.0,234000.0,17028.0,234000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.014464,-18856,-3137,-163.0,-2406,,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,5,0,0,0,0,0,0,Business Entity Type 1,,0.5030086397982588,0.5154953751603267,0.0433,0.0491,0.9995,0.9932,0.0011,0.0,0.069,0.0833,0.0,0.0377,0.0353,0.0278,0.0,0.0028,0.0441,0.0509,0.9995,0.9935,0.0011,0.0,0.069,0.0833,0.0,0.0386,0.0386,0.028999999999999998,0.0,0.0029,0.0437,0.0491,0.9995,0.9933,0.0011,0.0,0.069,0.0833,0.0,0.0384,0.0359,0.0283,0.0,0.0028,reg oper account,block of flats,0.0225,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1739.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,1.0\n280055,0,Cash loans,M,Y,Y,1,180000.0,405000.0,29479.5,405000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-12285,-2889,-8852.0,-4760,13.0,1,1,0,1,0,0,Drivers,3.0,2,2,TUESDAY,17,0,0,0,0,0,0,Construction,0.18393847815540024,0.6331048600866256,0.2793353208976285,0.0619,,0.9851,,,0.0,0.1379,0.1667,,0.0138,,0.0374,,0.0517,0.063,,0.9851,,,0.0,0.1379,0.1667,,0.0141,,0.039,,0.0547,0.0625,,0.9851,,,0.0,0.1379,0.1667,,0.013999999999999999,,0.0381,,0.0528,,block of flats,0.061,Panel,No,3.0,0.0,3.0,0.0,-3106.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,1.0\n313715,0,Cash loans,F,N,N,0,247500.0,675000.0,20538.0,675000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.032561,-18548,-11706,-12510.0,-2055,,1,1,0,1,1,0,Managers,2.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Other,,0.8034185169185489,0.7530673920730478,0.2588,0.1477,0.9801,0.728,0.0408,0.28,0.2414,0.3333,0.375,0.0301,0.21100000000000002,0.259,0.0,0.1027,0.2637,0.1533,0.9801,0.7387,0.0357,0.282,0.2414,0.3333,0.375,0.0304,0.2305,0.2684,0.0,0.0941,0.2613,0.1477,0.9801,0.7316,0.0411,0.28,0.2414,0.3333,0.375,0.0306,0.2146,0.2636,0.0,0.1048,reg oper spec account,block of flats,0.2282,Panel,No,0.0,0.0,0.0,0.0,-2042.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n103604,0,Cash loans,F,Y,Y,0,180000.0,341280.0,21937.5,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.030755,-22836,365243,-9534.0,-4889,4.0,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,0.3232676779919174,0.5460173566002556,0.6041125998015721,0.0155,0.0,0.9687,0.5716,0.0,0.0,0.069,0.0833,,0.0101,,0.0151,,0.0163,0.0158,0.0,0.9687,0.5884,0.0,0.0,0.069,0.0833,,0.0104,,0.0158,,0.0172,0.0156,0.0,0.9687,0.5773,0.0,0.0,0.069,0.0833,,0.0103,,0.0154,,0.0166,reg oper spec account,block of flats,0.0119,\"Stone, brick\",No,1.0,1.0,1.0,0.0,-2179.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n147985,0,Cash loans,F,N,Y,0,135000.0,1107981.0,32526.0,967500.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.032561,-22988,365243,-9813.0,-4429,,1,0,0,1,0,0,,1.0,1,1,TUESDAY,16,0,0,0,0,0,0,XNA,0.8560203951994849,0.7717093051848359,0.6263042766749393,0.0247,0.0,0.9001,0.0,0.009000000000000001,,0.1034,0.125,0.1667,0.0383,0.0185,0.0307,0.0077,0.0309,0.0252,0.0,0.9002,0.0,0.0091,,0.1034,0.125,0.1667,0.0391,0.0202,0.032,0.0078,0.0327,0.025,0.0,0.9001,0.0,0.009000000000000001,,0.1034,0.125,0.1667,0.0389,0.0188,0.0312,0.0078,0.0315,reg oper account,block of flats,0.0358,,No,0.0,0.0,0.0,0.0,-521.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n313379,0,Cash loans,M,Y,N,1,270000.0,900000.0,23742.0,900000.0,\"Spouse, partner\",State servant,Higher education,Married,House / apartment,0.019101,-10478,-3091,-3661.0,-3143,2.0,1,1,0,1,0,1,High skill tech staff,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Government,0.3282475700221141,0.6482807850102987,0.6769925032909132,0.0907,0.10300000000000001,0.9786,0.7076,0.0452,0.0,0.2069,0.1667,0.2083,0.0528,0.07400000000000001,0.0804,0.0,0.0,0.0924,0.1069,0.9786,0.7190000000000001,0.0456,0.0,0.2069,0.1667,0.2083,0.0541,0.0808,0.0837,0.0,0.0,0.0916,0.10300000000000001,0.9786,0.7115,0.0455,0.0,0.2069,0.1667,0.2083,0.0538,0.0752,0.0818,0.0,0.0,reg oper account,block of flats,0.0879,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-28.0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n260318,0,Cash loans,F,N,Y,0,135000.0,237024.0,14629.5,180000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.022625,-12791,-694,-6823.0,-4370,,1,1,0,1,0,1,HR staff,1.0,2,2,THURSDAY,17,0,0,0,0,0,0,Bank,0.2460098220458752,0.6695656255568301,0.25396280933631177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2013.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n114742,0,Cash loans,M,Y,N,0,256500.0,450000.0,47254.5,450000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.010556,-21913,365243,-7219.0,-4280,9.0,1,0,0,1,1,0,,2.0,3,3,THURSDAY,18,0,0,0,0,0,0,XNA,,0.5865110793273655,0.6023863442690867,0.2505,0.1826,0.9896,0.8572,0.1068,0.28,0.2414,0.3333,0.375,0.2259,0.2042,0.2674,0.0,0.0,0.2553,0.1895,0.9896,0.8628,0.1078,0.282,0.2414,0.3333,0.375,0.231,0.2231,0.2786,0.0,0.0,0.2529,0.1826,0.9896,0.8591,0.1075,0.28,0.2414,0.3333,0.375,0.2298,0.2078,0.2722,0.0,0.0,reg oper account,block of flats,0.2687,Panel,No,1.0,1.0,1.0,1.0,-766.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n148851,1,Revolving loans,M,N,Y,1,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.01885,-16891,-460,-8978.0,-344,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Trade: type 3,0.5240365884132566,0.3945919761939621,0.25533177083329,0.1485,0.1148,0.9856,0.8028,0.0225,0.16,0.1379,0.3333,,0.0458,0.121,0.0937,0.0039,0.0098,0.1513,0.1192,0.9856,0.8105,0.0228,0.1611,0.1379,0.3333,,0.0468,0.1322,0.0977,0.0039,0.0104,0.1499,0.1148,0.9856,0.8054,0.0227,0.16,0.1379,0.3333,,0.0466,0.1231,0.0954,0.0039,0.01,reg oper account,block of flats,0.1232,\"Stone, brick\",No,,,,,-308.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n393149,0,Cash loans,F,N,Y,2,112500.0,343800.0,16155.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.006629,-13774,-1210,-4693.0,-4345,,1,1,1,1,0,0,High skill tech staff,4.0,2,2,SATURDAY,8,0,0,0,0,0,0,Transport: type 3,,0.6300722782268641,,,,0.9851,,,,0.0345,0.125,,,,0.04,,0.0183,,,0.9851,,,,0.0345,0.125,,,,0.0417,,0.0194,,,0.9851,,,,0.0345,0.125,,,,0.0407,,0.0187,,block of flats,0.0315,Mixed,No,0.0,0.0,0.0,0.0,-820.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n143935,0,Cash loans,F,N,Y,0,171000.0,408780.0,27445.5,337500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009657,-12995,-2594,-2595.0,-5592,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,1,1,0,Business Entity Type 3,0.3366670546082189,0.6666903218146782,0.13595104417329515,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n372246,1,Cash loans,M,Y,Y,1,202500.0,640080.0,31261.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006852,-11494,-722,-921.0,-3480,21.0,1,1,0,1,0,0,Managers,3.0,3,3,TUESDAY,6,0,0,0,0,1,1,Military,0.5032623430961459,0.11294219878479599,0.2340151665320674,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-383.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n281570,0,Cash loans,M,Y,Y,1,135000.0,1309500.0,42241.5,1309500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-15900,-1336,-1637.0,-4008,0.0,1,1,1,1,1,0,Drivers,3.0,1,1,MONDAY,14,0,0,0,0,0,0,Self-employed,0.5216813762407138,0.7157942288532351,,0.1144,0.2933,0.9871,0.8232,0.0923,0.04,0.2414,0.2917,0.2083,0.1564,0.0883,0.1236,0.0232,0.1536,0.1166,0.3044,0.9871,0.8301,0.0931,0.0403,0.2414,0.2917,0.2083,0.16,0.0964,0.1288,0.0233,0.1626,0.1155,0.2933,0.9871,0.8256,0.0928,0.04,0.2414,0.2917,0.2083,0.1591,0.0898,0.1258,0.0233,0.1568,reg oper account,block of flats,0.1811,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2292.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n103018,0,Revolving loans,M,Y,Y,0,270000.0,270000.0,13500.0,270000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Municipal apartment,0.028663,-14648,-284,-749.0,-2318,8.0,1,1,1,1,0,0,Drivers,2.0,2,2,THURSDAY,9,0,1,1,0,1,1,Industry: type 9,,0.5085949966625212,0.4938628816996244,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n112834,0,Cash loans,M,Y,Y,0,202500.0,1528200.0,53248.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010147,-12447,-1908,-6538.0,-4705,8.0,1,1,0,1,1,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,0.3881111388536091,0.7503361975499394,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-696.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n297210,0,Cash loans,F,N,Y,0,135000.0,521280.0,28408.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-16573,-408,-83.0,-128,,1,1,0,1,0,0,Laborers,2.0,3,3,THURSDAY,8,0,0,0,0,0,0,Electricity,,0.4734086817498388,0.3296550543128238,0.0619,0.0629,0.9811,0.7416,0.0101,0.0,0.1379,0.1667,0.2083,0.091,0.0504,0.0538,0.0,0.0,0.063,0.0653,0.9811,0.7517,0.0102,0.0,0.1379,0.1667,0.2083,0.09300000000000001,0.0551,0.0561,0.0,0.0,0.0625,0.0629,0.9811,0.7451,0.0101,0.0,0.1379,0.1667,0.2083,0.0925,0.0513,0.0548,0.0,0.0,reg oper spec account,block of flats,0.0478,Panel,No,3.0,0.0,3.0,0.0,-746.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n250433,0,Cash loans,F,Y,N,1,292873.5,900000.0,24750.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018801,-13757,-251,-6720.0,-842,4.0,1,1,1,1,0,0,Managers,3.0,2,2,THURSDAY,7,0,0,0,0,1,1,Trade: type 7,0.5970765721536168,0.580830692259332,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-44.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n147513,0,Cash loans,F,N,N,0,171000.0,286704.0,20812.5,247500.0,Unaccompanied,Working,Secondary / secondary special,Separated,Office apartment,0.04622,-19385,-5165,-7588.0,-2903,,1,1,0,1,1,0,,1.0,1,1,TUESDAY,14,0,0,0,0,1,1,Other,0.5530515987268494,0.7705398964720053,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n415184,0,Cash loans,M,Y,Y,0,180000.0,521280.0,26743.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-11206,-2722,-4643.0,-3873,8.0,1,1,0,1,1,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6019534089554641,0.746300213050371,0.0742,0.0514,0.9841,0.7824,0.0186,0.08,0.069,0.3333,0.375,0.0603,0.0605,0.076,0.0,0.0,0.0756,0.0533,0.9841,0.7909,0.0187,0.0806,0.069,0.3333,0.375,0.0617,0.0661,0.0792,0.0,0.0,0.0749,0.0514,0.9841,0.7853,0.0187,0.08,0.069,0.3333,0.375,0.0614,0.0616,0.0774,0.0,0.0,reg oper account,block of flats,0.0699,Panel,No,0.0,0.0,0.0,0.0,-1755.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n262141,0,Revolving loans,F,Y,Y,0,337500.0,675000.0,33750.0,675000.0,Unaccompanied,State servant,Higher education,Widow,House / apartment,0.006296,-16094,-4027,-5639.0,-4965,7.0,1,1,0,1,0,0,,1.0,3,3,MONDAY,12,0,0,0,0,0,0,Military,,0.5326582908567448,0.10684194768082178,0.0928,0.1,0.9801,,,0.0,0.2069,0.1667,,0.049,,0.0876,,0.0,0.0945,0.1038,0.9801,,,0.0,0.2069,0.1667,,0.0501,,0.0913,,0.0,0.0937,0.1,0.9801,,,0.0,0.2069,0.1667,,0.0498,,0.0892,,0.0,,block of flats,0.0689,Panel,No,0.0,0.0,0.0,0.0,-1243.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n184542,0,Cash loans,F,N,Y,0,135000.0,948816.0,27873.0,792000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018029,-17289,-1286,-5150.0,-838,,1,1,0,1,1,0,Laborers,2.0,3,2,WEDNESDAY,12,0,0,0,0,0,0,Postal,0.6939003075037842,0.13714225625705664,0.4812493411434029,0.0619,0.076,0.9876,0.83,0.0447,0.0,0.2069,0.1667,0.2083,0.0797,0.0504,0.08199999999999999,0.0,0.0,0.063,0.0789,0.9876,0.8367,0.0451,0.0,0.2069,0.1667,0.2083,0.0815,0.0551,0.0854,0.0,0.0,0.0625,0.076,0.9876,0.8323,0.0449,0.0,0.2069,0.1667,0.2083,0.0811,0.0513,0.0835,0.0,0.0,reg oper account,block of flats,0.0889,Panel,No,4.0,0.0,4.0,0.0,-1470.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n241181,0,Cash loans,F,N,Y,1,112500.0,339241.5,16627.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010276,-14701,-930,-5149.0,-1264,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Trade: type 7,,0.5778889978777294,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-389.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n343437,0,Cash loans,M,Y,Y,0,225000.0,1436850.0,42142.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-22678,365243,-9075.0,-3913,30.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,0.5746710135439508,0.7118856980918457,0.7238369900414456,0.1412,0.0692,0.9891,0.8504,0.0389,0.136,0.1172,0.3333,0.375,0.0281,0.1152,0.1138,0.0,0.0002,0.1513,0.0776,0.9901,0.8693,0.0257,0.1611,0.1379,0.3333,0.375,0.0321,0.1322,0.0947,0.0,0.0,0.1499,0.0748,0.9901,0.8658,0.0257,0.16,0.1379,0.3333,0.375,0.0319,0.1231,0.0925,0.0,0.0,reg oper account,block of flats,0.1199,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-200.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n413356,0,Cash loans,F,N,Y,0,135000.0,454500.0,25375.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.010147,-22697,365243,-7269.0,-4536,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,XNA,,0.7698703304766804,0.4241303111942548,0.0351,,0.9896,,,,0.069,0.1667,,,,0.0336,,,0.0357,,0.9896,,,,0.069,0.1667,,,,0.0351,,,0.0354,,0.9896,,,,0.069,0.1667,,,,0.0342,,,,block of flats,0.0265,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1351.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,6.0\n439124,0,Cash loans,F,N,Y,0,45000.0,74182.5,5274.0,67500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-20112,365243,-8178.0,-2676,,1,0,0,1,1,0,,2.0,3,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.6180527157495703,,0.0845,0.073,0.9886,0.8436,0.022000000000000002,0.08,0.0345,0.5417,0.5833,0.1134,0.0672,0.0789,0.0077,0.11900000000000001,0.0861,0.0758,0.9886,0.8497,0.0222,0.0806,0.0345,0.5417,0.5833,0.11599999999999999,0.0735,0.0822,0.0078,0.126,0.0854,0.073,0.9886,0.8457,0.0221,0.08,0.0345,0.5417,0.5833,0.1154,0.0684,0.0803,0.0078,0.1215,reg oper account,block of flats,0.0999,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-377.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n300964,0,Cash loans,F,Y,Y,1,180000.0,284256.0,32148.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003818,-9551,-393,-4145.0,-1961,10.0,1,1,1,1,1,1,Sales staff,3.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.4066665643950345,0.5536651658937924,0.4170996682522097,,,0.9622,,,,,,,,,0.0081,,,,,0.9623,,,,,,,,,0.0085,,,,,0.9622,,,,,,,,,0.0083,,,,block of flats,0.0064,,Yes,0.0,0.0,0.0,0.0,-1302.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n357463,0,Cash loans,F,N,Y,1,99000.0,942300.0,30528.0,675000.0,Family,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.01885,-17536,-5080,-4125.0,-1080,,1,1,0,1,0,0,Managers,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Medicine,0.6220522252178687,0.1806030679411708,0.4048783643353997,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1435.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n268061,0,Cash loans,F,N,Y,0,270000.0,1241968.5,40194.0,1084500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-22047,365243,-1130.0,-2186,,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.5459523087487694,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-710.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n369423,0,Cash loans,M,Y,Y,0,108000.0,677664.0,29979.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-16457,-5197,-1851.0,-20,4.0,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Government,0.4865523267382319,0.5712502464772474,0.41885428862332175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1889.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n352031,0,Revolving loans,F,N,Y,3,67500.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Civil marriage,With parents,0.035792000000000004,-12312,-1435,-6468.0,-4824,,1,1,1,1,0,0,Laborers,5.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Agriculture,,0.572374611976979,0.43473324875017305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1123.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n439741,0,Cash loans,F,N,Y,0,112500.0,225000.0,24363.0,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.028663,-9532,-264,-9521.0,-2208,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,University,,0.5461637085190019,,0.1876,0.1431,0.9871,0.8232,0.044000000000000004,0.2,0.1724,0.3333,0.375,0.0943,0.1521,0.1993,0.0039,0.0006,0.1912,0.1485,0.9871,0.8301,0.0444,0.2014,0.1724,0.3333,0.375,0.0964,0.1662,0.2077,0.0039,0.0007,0.1894,0.1431,0.9871,0.8256,0.0443,0.2,0.1724,0.3333,0.375,0.0959,0.1548,0.2029,0.0039,0.0007,reg oper account,block of flats,0.18100000000000002,Panel,No,0.0,0.0,0.0,0.0,-518.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n334078,0,Cash loans,F,Y,Y,0,135000.0,328365.0,32607.0,297000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.02461,-12128,-2477,-3832.0,-4733,11.0,1,1,0,1,0,0,Accountants,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.5621436491865577,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,3.0,1.0,-2583.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n233923,0,Cash loans,F,N,Y,0,81000.0,135000.0,10665.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-8085,-1346,-3647.0,-764,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Industry: type 3,,0.26634027113237657,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,0.0,-107.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n432047,0,Revolving loans,M,Y,Y,0,207000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-20183,-1597,-9429.0,-3738,1.0,1,1,0,1,1,0,Laborers,2.0,2,2,MONDAY,16,0,0,0,1,1,0,Business Entity Type 3,,0.44747366012889983,0.13595104417329515,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-213.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n284535,0,Cash loans,F,N,Y,2,135000.0,254700.0,17019.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.005313,-11265,-1311,-1126.0,-3907,,1,1,1,1,0,0,Laborers,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Industry: type 11,,0.4059669175250837,0.4830501881366946,0.0041,,0.9732,,,,,0.0,,,,,,,0.0042,,0.9732,,,,,0.0,,,,,,,0.0042,,0.9732,,,,,0.0,,,,,,,,block of flats,0.0022,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-413.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n396650,0,Cash loans,F,N,N,0,225000.0,728460.0,44563.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-22495,-4356,-2879.0,-4717,,1,1,0,1,0,0,Managers,2.0,3,2,SUNDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5461799693513062,0.3344541255096772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-899.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n257448,0,Cash loans,F,N,Y,0,85500.0,297130.5,14292.0,256500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006852,-23465,365243,-3372.0,-5213,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,5,0,0,0,0,0,0,XNA,,0.16933379793539902,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-714.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n143003,0,Cash loans,M,Y,N,1,414000.0,1762110.0,48456.0,1575000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-12151,-763,-1288.0,-3171,8.0,1,1,1,1,1,0,Managers,3.0,1,1,MONDAY,17,0,1,1,0,1,1,Business Entity Type 3,0.4220537310956185,0.6055735531279095,0.5656079814115492,0.0505,,0.9712,,,,0.1724,0.1667,,,,,,,0.0515,,0.9712,,,,0.1724,0.1667,,,,,,,0.051,,0.9712,,,,0.1724,0.1667,,,,,,,,block of flats,0.0516,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2540.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,7.0,0.0,1.0\n418677,0,Cash loans,F,N,Y,0,112500.0,1061599.5,31171.5,927000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.009657,-17350,-1447,-3862.0,-794,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,School,0.7519786672417879,0.6673120204997441,0.190705947811054,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2666.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n310588,0,Cash loans,F,Y,Y,0,270000.0,997974.0,39708.0,918000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-18776,-4170,-3094.0,-2278,7.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 2,,0.7655260376251741,0.5762088360175724,0.0505,0.0369,0.9906,0.8708,0.0195,0.08,0.069,0.4583,0.5,0.0666,0.0403,0.0586,0.0039,0.0501,0.0515,0.0383,0.9906,0.8759,0.0197,0.0806,0.069,0.4583,0.5,0.0681,0.0441,0.061,0.0039,0.0531,0.051,0.0369,0.9906,0.8725,0.0196,0.08,0.069,0.4583,0.5,0.0677,0.040999999999999995,0.0596,0.0039,0.0512,reg oper account,block of flats,0.057,Panel,No,1.0,0.0,1.0,0.0,-233.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n380580,0,Cash loans,M,Y,Y,0,135000.0,1152000.0,33813.0,1152000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-23304,-12796,-13394.0,-4811,17.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Industry: type 7,,0.34335778602781525,,0.1979,0.1146,0.9811,,,0.16,0.1379,0.3333,,,,0.1068,,0.1686,0.2017,0.11900000000000001,0.9811,,,0.1611,0.1379,0.3333,,,,0.1113,,0.1785,0.1999,0.1146,0.9811,,,0.16,0.1379,0.3333,,,,0.1087,,0.1722,,,0.2086,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-73.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n161870,0,Cash loans,M,Y,N,0,225000.0,167895.0,16605.0,157500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.032561,-11389,-639,-5799.0,-3999,11.0,1,1,0,1,1,0,Laborers,1.0,1,1,WEDNESDAY,19,0,1,1,0,1,1,Industry: type 11,,0.26217798977616924,0.6863823354047934,0.2515,0.0225,0.9861,0.8096,0.067,0.28,0.2414,0.3333,0.375,0.0335,0.2051,0.282,0.0,0.0,0.2563,0.0233,0.9861,0.8171,0.0676,0.282,0.2414,0.3333,0.375,0.0343,0.2241,0.2938,0.0,0.0,0.254,0.0225,0.9861,0.8121,0.0675,0.28,0.2414,0.3333,0.375,0.0341,0.2086,0.287,0.0,0.0,reg oper account,block of flats,0.2218,Panel,No,9.0,0.0,9.0,0.0,-1588.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,2.0,0.0,2.0\n164129,0,Cash loans,F,N,Y,0,67500.0,472500.0,18738.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-19724,-12908,-2879.0,-3257,,1,1,1,1,1,0,Laborers,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Postal,,0.1953537989258013,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2254.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n306548,0,Cash loans,M,N,N,1,112500.0,450000.0,24543.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-9546,-1498,-8233.0,-2086,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Security Ministries,0.12044667920898065,0.6383662912029129,,0.0773,0.0809,0.9791,0.7144,0.0341,0.0,0.1724,0.1667,0.2083,0.0501,0.063,0.0687,0.0,0.0,0.0788,0.084,0.9791,0.7256,0.0344,0.0,0.1724,0.1667,0.2083,0.0512,0.0689,0.0715,0.0,0.0,0.0781,0.0809,0.9791,0.7182,0.0343,0.0,0.1724,0.1667,0.2083,0.0509,0.0641,0.0699,0.0,0.0,reg oper account,block of flats,0.0727,Panel,No,0.0,0.0,0.0,0.0,-733.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n304928,0,Cash loans,F,N,Y,1,135000.0,323194.5,14364.0,279000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-12983,-342,-6984.0,-4346,,1,1,0,1,1,0,Laborers,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Transport: type 4,0.5038081555958023,0.7158920875537709,0.4632753280912678,0.0639,0.0533,0.9757,0.6668,0.0067,0.0,0.1379,0.125,0.1667,0.0684,0.0521,0.0495,0.0,0.0,0.0651,0.0554,0.9757,0.6798,0.0067,0.0,0.1379,0.125,0.1667,0.07,0.0569,0.0516,0.0,0.0,0.0645,0.0533,0.9757,0.6713,0.0067,0.0,0.1379,0.125,0.1667,0.0696,0.053,0.0504,0.0,0.0,reg oper account,block of flats,0.0511,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-708.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n357731,0,Cash loans,F,Y,Y,2,382500.0,1006920.0,42790.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-16361,-3170,-5084.0,-4507,5.0,1,1,0,1,0,0,Managers,4.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.6750118777998045,0.2454386389048397,0.4794489811780563,0.0021,,0.9712,0.6056,0.0,0.0,0.0,0.0,0.0417,0.0226,0.0017,0.0022,0.0,0.0,0.0021,,0.9712,0.621,0.0,0.0,0.0,0.0,0.0417,0.0231,0.0018,0.0023,0.0,0.0,0.0021,,0.9712,0.6109,0.0,0.0,0.0,0.0,0.0417,0.023,0.0017,0.0023,0.0,0.0,not specified,block of flats,0.0018,Wooden,No,1.0,0.0,1.0,0.0,-530.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n248004,0,Cash loans,M,N,Y,0,112500.0,450000.0,20983.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-16394,-2008,-4330.0,-4171,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6130745584602297,0.5937175866150576,0.2227,0.1275,0.9826,0.762,0.04,0.24,0.2069,0.3333,0.375,0.0919,0.1807,0.2229,0.0039,0.0051,0.2269,0.1324,0.9826,0.7713,0.0403,0.2417,0.2069,0.3333,0.375,0.094,0.1974,0.2323,0.0039,0.0054,0.2248,0.1275,0.9826,0.7652,0.0402,0.24,0.2069,0.3333,0.375,0.0935,0.1838,0.2269,0.0039,0.0052,reg oper account,block of flats,0.1983,Panel,No,0.0,0.0,0.0,0.0,-1525.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n208581,0,Cash loans,F,N,N,1,306000.0,1483231.5,51687.0,1354500.0,Family,Commercial associate,Higher education,Married,Rented apartment,0.01885,-15025,-3135,-3177.0,-5187,,1,1,0,1,0,0,Core staff,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,School,0.7690372875818187,0.6878069545022616,0.5762088360175724,0.0722,0.0417,0.9791,0.7144,0.0078,0.0,0.1379,0.1667,0.2083,0.0443,0.0588,0.0675,0.0,0.0,0.0735,0.0432,0.9791,0.7256,0.0078,0.0,0.1379,0.1667,0.2083,0.0453,0.0643,0.0704,0.0,0.0,0.0729,0.0417,0.9791,0.7182,0.0078,0.0,0.1379,0.1667,0.2083,0.0451,0.0599,0.0687,0.0,0.0,reg oper account,block of flats,0.0574,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-2860.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n398411,0,Revolving loans,F,N,Y,1,112500.0,315000.0,15750.0,315000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.030755,-18191,-773,-12293.0,-1720,,1,1,0,1,0,1,Accountants,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,Hotel,0.6504168577403703,0.6693527123284523,0.5602843280409464,0.11800000000000001,0.1216,0.9816,0.6872,0.0121,0.0132,0.2155,0.2188,0.125,0.0491,0.1101,0.0939,0.0077,0.0421,0.063,0.0508,0.9782,0.6864,0.0,0.0,0.2759,0.1667,0.0417,0.0104,0.1175,0.0621,0.0,0.0,0.1353,0.1565,0.9781,0.6914,0.0122,0.0,0.2759,0.1667,0.125,0.0399,0.11199999999999999,0.0987,0.0078,0.0145,reg oper spec account,block of flats,0.0613,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-961.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n410829,0,Cash loans,M,Y,Y,1,337500.0,1546020.0,45202.5,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008019,-18823,-3690,-3849.0,-2341,12.0,1,1,1,1,1,0,,3.0,2,2,MONDAY,17,0,0,0,0,1,1,Self-employed,0.6878120129779819,0.5378966549125547,,0.0144,,0.9737,,,0.0,0.069,0.0417,,0.0,,0.017,,0.035,0.0147,,0.9737,,,0.0,0.069,0.0417,,0.0,,0.0177,,0.037000000000000005,0.0146,,0.9737,,,0.0,0.069,0.0417,,0.0,,0.0173,,0.0357,,,0.0134,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1833.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n145345,0,Cash loans,M,Y,N,1,225000.0,1082214.0,31770.0,945000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-12984,-4516,-2778.0,-4671,16.0,1,1,0,1,1,0,Laborers,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,Transport: type 2,0.5166900341626581,0.7574080523592506,0.5028782772082183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1529.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n430199,1,Cash loans,F,N,Y,0,135000.0,284400.0,16456.5,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020713,-20045,-2131,-7538.0,-3570,,1,1,0,1,0,0,Laborers,2.0,3,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6592812719737255,0.2205897236346008,0.6246146584503397,0.0619,0.0628,0.9767,0.6804,0.0481,0.0,0.1379,0.1667,0.2083,0.0152,0.0504,0.0543,0.0,0.0,0.063,0.0652,0.9767,0.6929,0.0485,0.0,0.1379,0.1667,0.2083,0.0155,0.0551,0.0565,0.0,0.0,0.0625,0.0628,0.9767,0.6847,0.0484,0.0,0.1379,0.1667,0.2083,0.0155,0.0513,0.0552,0.0,0.0,reg oper account,block of flats,0.0427,Panel,No,2.0,2.0,2.0,0.0,-1517.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n404884,0,Cash loans,M,Y,Y,0,112500.0,269550.0,11547.0,225000.0,Family,Pensioner,Higher education,Married,House / apartment,0.006008,-23695,365243,-4094.0,-4460,10.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.4573383404812966,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-332.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n226185,0,Cash loans,F,N,N,0,36000.0,225000.0,10489.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005313,-13498,-705,-428.0,-1094,,1,1,1,1,0,0,Laborers,2.0,2,2,SATURDAY,16,0,0,0,1,1,0,Business Entity Type 3,0.6589396635555862,0.6818980571618615,0.4066174366275036,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,0.0,-282.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n145126,0,Revolving loans,M,Y,Y,1,81000.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-12992,-1426,-4964.0,-4549,12.0,1,1,1,1,1,0,IT staff,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Advertising,,0.54005382132142,,0.0155,0.0375,0.9841,,,,0.069,0.0417,,0.0069,,0.0046,,,0.0158,0.0389,0.9841,,,,0.069,0.0417,,0.006999999999999999,,0.0048,,,0.0156,0.0375,0.9841,,,,0.069,0.0417,,0.006999999999999999,,0.0047,,,,block of flats,0.0121,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n376439,0,Cash loans,F,N,N,1,270000.0,1800000.0,62698.5,1800000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-15654,-1872,-1401.0,-2991,,1,1,0,1,1,1,,3.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.715638502679085,0.4471785780453068,0.0825,,0.9796,,,0.0,0.1379,0.1667,,,,0.0732,,0.0,0.084,,0.9796,,,0.0,0.1379,0.1667,,,,0.0763,,0.0,0.0833,,0.9796,,,0.0,0.1379,0.1667,,,,0.0745,,0.0,,block of flats,0.0576,Panel,No,1.0,1.0,1.0,1.0,-1952.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n337717,0,Cash loans,F,Y,Y,0,247500.0,900000.0,57649.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-15074,-1615,-836.0,-1682,2.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.5192987207496993,0.7046702741080054,0.2314393514998941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n188981,0,Revolving loans,M,Y,Y,0,180000.0,495000.0,24750.0,495000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-21373,-867,-11165.0,-4926,6.0,1,1,0,1,1,0,Drivers,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.7484454609451799,0.6912057506676593,0.6848276586890367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n325546,0,Cash loans,M,N,N,0,157500.0,225000.0,17775.0,225000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.008625,-8574,-739,-3449.0,-1258,,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Self-employed,0.09370259567626082,0.3986843648998536,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-328.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n433052,0,Cash loans,F,N,N,0,137025.0,450000.0,17271.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Rented apartment,0.020713,-15783,-989,-2404.0,-2429,,1,1,1,1,0,0,Sales staff,1.0,3,2,MONDAY,17,1,1,0,1,1,0,Business Entity Type 3,,0.4716918016014176,0.4418358231994413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n358232,0,Cash loans,F,N,Y,0,112500.0,1071909.0,31473.0,936000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020246,-14066,-3009,-4343.0,-2868,,1,1,0,1,0,0,Core staff,2.0,3,3,MONDAY,13,0,0,0,0,0,0,Restaurant,0.25818444386662986,0.5281484693419329,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-412.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n260377,0,Cash loans,M,Y,Y,0,225000.0,1724688.0,59949.0,1575000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.003122,-19987,-134,-7931.0,-2827,14.0,1,1,1,1,0,0,Security staff,2.0,3,3,TUESDAY,16,0,1,1,0,1,1,Security,,0.6066177022387004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-545.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n383523,0,Cash loans,M,Y,Y,0,261000.0,1174090.5,49873.5,1080000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.0105,-22620,365243,-2813.0,-4973,24.0,1,0,0,1,0,0,,2.0,3,3,MONDAY,13,0,0,0,0,0,0,XNA,,0.7649524811718719,0.2580842039460289,0.0866,0.0974,0.9826,0.762,0.0117,0.0,0.1931,0.1667,0.0417,0.0748,0.0756,0.0846,0.0,0.0,0.063,0.1132,0.9821,0.7648,0.008,0.0,0.1379,0.1667,0.0417,0.0645,0.0826,0.0633,0.0,0.0,0.0937,0.1091,0.9821,0.7585,0.0118,0.0,0.2069,0.1667,0.0417,0.0761,0.077,0.0925,0.0,0.0,reg oper account,block of flats,0.0479,Panel,No,0.0,0.0,0.0,0.0,-2733.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n175742,0,Cash loans,F,N,Y,0,90000.0,1040985.0,30568.5,909000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010032,-20915,365243,-7897.0,-4221,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,3,0,0,0,0,0,0,XNA,,0.4969459268800682,0.16048893062734468,0.0021,0.0,0.9876,0.83,0.0,0.0,0.0345,0.0,0.0417,0.0204,0.0042,0.0026,0.0,0.0,0.0021,0.0,0.9876,0.8367,0.0,0.0,0.0345,0.0,0.0417,0.0209,0.0046,0.0027,0.0,0.0,0.0021,0.0,0.9876,0.8323,0.0,0.0,0.0345,0.0,0.0417,0.0208,0.0043,0.0026,0.0,0.0,org spec account,block of flats,0.002,Wooden,No,0.0,0.0,0.0,0.0,-2258.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n250295,1,Cash loans,F,N,Y,0,157500.0,311877.0,17046.0,252000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018634,-21273,365243,-9590.0,-4156,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,0.5632013616762742,0.306519760347349,0.3539876078507373,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-988.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n216932,0,Cash loans,F,Y,Y,0,126000.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Higher education,Married,Office apartment,0.00733,-10213,-939,-4777.0,-1559,3.0,1,1,0,1,1,0,Accountants,2.0,2,2,MONDAY,18,0,0,0,0,0,0,Housing,0.5470704331229103,0.6598873131913205,0.3046721837533529,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-337.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n256585,0,Cash loans,F,N,Y,0,144000.0,518562.0,22099.5,463500.0,Family,Working,Higher education,Single / not married,House / apartment,0.030755,-19249,-174,-10626.0,-2800,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,11,0,1,1,0,1,1,Military,,0.6996168226645239,0.6161216908872079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n261361,0,Cash loans,M,Y,Y,0,288000.0,679500.0,24201.0,679500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.015221,-22949,-4352,-2206.0,-4529,1.0,1,1,1,1,1,0,Managers,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Telecom,,0.6691445709862142,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1170.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n258806,0,Cash loans,F,N,Y,0,67500.0,66222.0,6912.0,58500.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.031329,-18609,-3327,-8493.0,-2120,,1,1,0,1,0,0,Medicine staff,1.0,2,2,THURSDAY,14,0,0,0,0,1,1,Hotel,0.6541119336851886,0.6493072319543185,0.6512602186973006,0.0124,0.0,0.9881,0.8368,0.0211,0.0,0.1034,0.0417,0.0417,0.0352,0.0101,0.0147,0.0,0.0,0.0126,0.0,0.9881,0.8432,0.0213,0.0,0.1034,0.0417,0.0417,0.036000000000000004,0.011000000000000001,0.0153,0.0,0.0,0.0125,0.0,0.9881,0.8390000000000001,0.0213,0.0,0.1034,0.0417,0.0417,0.0358,0.0103,0.0149,0.0,0.0,reg oper account,block of flats,0.0116,Wooden,No,0.0,0.0,0.0,0.0,-1583.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n213320,0,Cash loans,F,N,Y,0,157500.0,1125000.0,33025.5,1125000.0,Family,State servant,Higher education,Married,House / apartment,0.00496,-22304,-14221,-8080.0,-4621,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,University,,0.6607652720037369,0.5902333386185574,0.0969,0.0557,0.9871,0.8232,,0.08,0.069,0.4583,,,0.0841,0.1018,,,0.0987,0.0578,0.9871,0.8301,,0.0806,0.069,0.4583,,,0.0918,0.1061,,,0.0978,0.0557,0.9871,0.8256,,0.08,0.069,0.4583,,,0.0855,0.1037,,,,block of flats,0.0801,Panel,No,0.0,0.0,0.0,0.0,-1751.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n437600,0,Cash loans,F,Y,Y,2,126000.0,143910.0,14364.0,135000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.006852,-10399,-1501,-1386.0,-3079,15.0,1,1,1,1,0,0,Sales staff,4.0,3,3,THURSDAY,5,0,0,0,0,0,0,Trade: type 7,,0.21263827509403585,0.7610263695502636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1911.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n428383,0,Revolving loans,M,N,N,0,270000.0,202500.0,10125.0,202500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-12742,-1410,-2401.0,-4036,,1,1,0,1,1,0,Managers,2.0,1,1,FRIDAY,15,0,1,1,1,0,0,Business Entity Type 3,0.7064488865761154,0.6973094337193461,0.6817058776720116,0.1031,0.1026,0.9781,,,0.0,0.1724,0.1667,,0.0854,,0.0926,,0.0,0.105,0.1065,0.9782,,,0.0,0.1724,0.1667,,0.0873,,0.0965,,0.0,0.1041,0.1026,0.9781,,,0.0,0.1724,0.1667,,0.0869,,0.0943,,0.0,,block of flats,0.0795,Panel,No,0.0,0.0,0.0,0.0,-1757.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n334888,0,Cash loans,F,N,Y,0,126000.0,1324422.0,36549.0,1156500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-14052,-2778,-5701.0,-4504,,1,1,1,1,1,0,Sales staff,2.0,3,3,MONDAY,10,0,0,0,0,0,0,Self-employed,0.2266896486061973,0.5873487392389087,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1986.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n448026,0,Cash loans,M,N,Y,1,81000.0,161730.0,13095.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Widow,House / apartment,0.01885,-10506,-363,-4059.0,-1043,,1,1,1,1,0,0,Laborers,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,School,,0.4176670326770749,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n275104,0,Cash loans,M,Y,N,1,157500.0,585000.0,22666.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.02461,-14478,-328,-8532.0,-4101,13.0,1,1,1,1,1,0,High skill tech staff,3.0,2,2,TUESDAY,19,0,0,0,0,0,0,Other,,0.610534922277132,0.6479768603302221,0.0546,0.0212,0.9826,,,0.08,0.0345,0.5417,,0.0375,,0.0531,,0.0134,0.0557,0.022000000000000002,0.9826,,,0.0806,0.0345,0.5417,,0.0384,,0.0553,,0.0142,0.0552,0.0212,0.9826,,,0.08,0.0345,0.5417,,0.0382,,0.054000000000000006,,0.0137,,block of flats,0.0517,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1115.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n323789,0,Cash loans,F,Y,N,0,180000.0,1125000.0,46426.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006008,-12192,-2880,-717.0,-4069,15.0,1,1,1,1,1,0,Core staff,2.0,2,2,THURSDAY,17,0,0,0,0,1,1,Bank,0.6418537482221721,0.4547128359072316,0.22009464485041005,0.2237,,0.9965,0.9524,,0.24,0.2069,0.3333,0.375,,0.1824,0.2578,0.0039,0.0024,0.2279,,0.9965,0.9543,,0.2417,0.2069,0.3333,0.375,,0.1993,0.2686,0.0039,0.0025,0.2259,,0.9965,0.953,,0.24,0.2069,0.3333,0.375,,0.1856,0.2625,0.0039,0.0024,,block of flats,0.2832,Panel,No,0.0,0.0,0.0,0.0,-695.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n119228,0,Cash loans,F,Y,N,0,157500.0,454500.0,21865.5,454500.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.028663,-8575,-787,-2651.0,-1236,8.0,1,1,1,1,0,0,Sales staff,1.0,2,2,TUESDAY,11,0,0,0,1,1,0,Business Entity Type 3,0.4423003369908706,0.6920965161108026,0.4596904504249018,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1533.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n224477,0,Cash loans,F,N,Y,0,67500.0,95940.0,10309.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005313,-18518,-2095,-8050.0,-2066,,1,1,1,1,1,0,Core staff,2.0,2,2,WEDNESDAY,17,0,0,0,0,1,1,Postal,,0.5413897563083867,0.6296742509538716,0.0371,0.0418,0.9891,0.8504,0.0039,0.0,0.1034,0.125,0.1667,0.0992,0.0303,0.0372,0.0,0.0,0.0378,0.0434,0.9891,0.8563,0.0039,0.0,0.1034,0.125,0.1667,0.1014,0.0331,0.0387,0.0,0.0,0.0375,0.0418,0.9891,0.8524,0.0039,0.0,0.1034,0.125,0.1667,0.1009,0.0308,0.0379,0.0,0.0,reg oper account,block of flats,0.0314,Block,No,0.0,0.0,0.0,0.0,-261.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n131307,0,Revolving loans,F,N,Y,0,90000.0,135000.0,6750.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.02461,-22227,365243,-2859.0,-4357,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.4397178693983623,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n273674,0,Cash loans,F,N,Y,0,90000.0,675000.0,21775.5,675000.0,Family,Pensioner,Secondary / secondary special,Separated,House / apartment,0.019101,-21506,365243,-7641.0,-4674,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,,0.25634082347862586,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-996.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n264871,0,Cash loans,M,N,Y,0,99000.0,225459.0,23229.0,211500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-21301,365243,-10017.0,-4800,,1,0,0,1,1,0,,2.0,3,3,TUESDAY,8,0,0,0,0,0,0,XNA,0.7867697286939459,0.3810478727635754,0.6674577419214722,0.23199999999999998,0.2049,0.9871,0.8232,,0.28,0.2414,0.3333,,0.1551,,0.2779,,0.0056,0.2363,0.2126,0.9871,0.8301,,0.282,0.2414,0.3333,,0.1587,,0.2895,,0.0059,0.2342,0.2049,0.9871,0.8256,,0.28,0.2414,0.3333,,0.1578,,0.2828,,0.0057,,block of flats,0.2508,Panel,No,2.0,2.0,2.0,1.0,-1863.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n171474,0,Cash loans,M,N,N,0,166500.0,873000.0,34623.0,873000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.019101,-10642,-1247,-4353.0,-3333,,1,1,0,1,1,0,Core staff,2.0,2,2,WEDNESDAY,19,0,0,0,0,1,1,Trade: type 7,,0.7088803771703833,0.375711009574066,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,1.0,-921.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n274849,0,Cash loans,F,Y,Y,0,270000.0,1061599.5,31041.0,927000.0,Family,Commercial associate,Higher education,Single / not married,House / apartment,0.003818,-21029,-3107,-6948.0,-2862,7.0,1,1,0,1,0,0,Managers,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,Housing,0.8556637733358887,0.7095658448332834,,0.0928,0.0986,0.9866,0.8164,0.0217,0.06,0.1552,0.25,0.0417,,0.0756,0.1042,0.0,0.0087,0.0924,0.1024,0.9846,0.7975,0.0219,0.0,0.1034,0.1667,0.0417,,0.0808,0.1031,0.0,0.0,0.0937,0.0986,0.9866,0.8189,0.0218,0.06,0.1552,0.25,0.0417,,0.077,0.106,0.0,0.0088,reg oper account,block of flats,0.0779,Panel,No,0.0,0.0,0.0,0.0,-1169.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n222560,0,Cash loans,F,N,Y,0,135000.0,309420.0,14553.0,202500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00733,-15920,-733,-8074.0,-3732,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,13,0,0,0,1,1,0,Trade: type 3,0.4560318263767327,0.4989631748575836,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-229.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n283836,0,Cash loans,F,N,Y,0,495000.0,828837.0,48591.0,715500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Municipal apartment,0.04622,-14443,-1686,-6420.0,-4908,,1,1,0,1,1,0,Core staff,1.0,1,1,FRIDAY,11,1,1,0,0,1,0,Industry: type 9,0.8149064191631071,0.6544136726370908,0.6785676886853644,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-952.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n352231,0,Cash loans,F,N,Y,0,225000.0,675000.0,26770.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-16055,-4032,-3433.0,-3400,,1,1,0,1,1,0,Managers,2.0,3,3,MONDAY,14,0,0,0,0,0,0,Kindergarten,0.4259460628704532,0.5140699572399979,0.10822632266971416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2452.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n326725,0,Cash loans,F,N,Y,0,360000.0,2064186.0,56893.5,1845000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.010147,-20824,365243,-6556.0,-4339,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.5867284950826309,0.838724852442103,0.0619,0.0,0.9881,0.8368,0.0099,0.0,0.1379,0.1667,0.2083,,0.0504,0.0604,0.0,0.0,0.063,0.0,0.9881,0.8432,0.01,0.0,0.1379,0.1667,0.2083,,0.0551,0.063,0.0,0.0,0.0625,0.0,0.9881,0.8390000000000001,0.01,0.0,0.1379,0.1667,0.2083,,0.0513,0.0615,0.0,0.0,reg oper spec account,block of flats,0.0529,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n340147,0,Cash loans,M,N,N,0,171000.0,486000.0,,486000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018634,-10151,-472,-10127.0,-2787,,1,1,0,1,1,0,Security staff,2.0,2,2,WEDNESDAY,13,1,1,1,1,1,1,Security,,0.7063059413671156,0.3910549766342248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-295.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,2.0\n276669,0,Cash loans,F,N,N,0,99000.0,823621.5,27220.5,711000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-20423,-3870,-7829.0,-3550,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Trade: type 7,,0.6015236838785534,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-2585.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n222016,0,Cash loans,F,N,N,0,135000.0,540000.0,15606.0,540000.0,Family,Working,Incomplete higher,Married,House / apartment,0.014519999999999996,-11127,-1617,-1333.0,-2916,,1,1,1,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.17335841744825406,0.5231594701534924,0.4722533429586386,0.1711,0.1362,0.9851,0.7959999999999999,0.0606,0.16,0.1897,0.2917,0.3333,0.1193,0.1391,0.1747,0.0019,0.012,0.1891,0.127,0.9851,0.804,0.0148,0.2014,0.1724,0.3333,0.375,0.0736,0.0845,0.0895,0.0,0.0,0.1874,0.1324,0.9851,0.7987,0.0706,0.2,0.1897,0.3333,0.375,0.121,0.1531,0.1945,0.0019,0.0036,reg oper account,block of flats,0.1575,Panel,No,2.0,0.0,2.0,0.0,-1379.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n403606,0,Revolving loans,M,Y,Y,0,202500.0,1012500.0,50625.0,1012500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010966,-16486,-3214,-8981.0,-14,6.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,,0.6697978153088421,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-536.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n390629,0,Cash loans,F,Y,N,0,90000.0,114430.5,11277.0,103500.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.019101,-22364,365243,-1638.0,-4346,22.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.5732575184310608,0.7776594425716818,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2099.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n203731,0,Cash loans,F,N,Y,1,157500.0,270000.0,16443.0,270000.0,Family,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.008625,-13025,-3124,-5053.0,-4190,,1,1,1,1,0,0,Managers,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,0.12009221516099013,0.6452733323581661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-251.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n369382,0,Cash loans,F,N,Y,0,135000.0,472500.0,48546.0,454500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006233,-15549,-2220,-8529.0,-1185,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.4439134918479772,0.3706496323299817,0.0021,,0.9697,,,,,0.0,,,,0.0016,,,0.0021,,0.9697,,,,,0.0,,,,0.0017,,,0.0021,,0.9697,,,,,0.0,,,,0.0016,,,,block of flats,0.0013,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n148595,0,Revolving loans,M,Y,Y,0,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010032,-11912,-3782,-6008.0,-2891,65.0,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Security Ministries,0.36084423296191465,0.5590248002704187,0.363945238612397,0.2918,0.3744,0.9846,,,0.36,0.3103,0.3333,,0.2424,,0.316,,0.7067,0.2973,0.3885,0.9846,,,0.3625,0.3103,0.3333,,0.2479,,0.3292,,0.7481,0.2946,0.3744,0.9846,,,0.36,0.3103,0.3333,,0.2466,,0.3216,,0.7215,,block of flats,0.4022,\"Stone, brick\",No,1.0,0.0,0.0,0.0,-1427.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n163186,0,Cash loans,F,N,Y,1,94500.0,256500.0,14449.5,256500.0,\"Spouse, partner\",Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-13760,-5695,-4690.0,-4690,,1,1,0,1,0,0,Medicine staff,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Medicine,,0.7448108871459286,0.6479768603302221,0.1237,0.0975,0.9771,,,0.0,0.2759,0.1667,,0.08,,0.1035,,0.0,0.1261,0.1012,0.9772,,,0.0,0.2759,0.1667,,0.0818,,0.1078,,0.0,0.1249,0.0975,0.9771,,,0.0,0.2759,0.1667,,0.0814,,0.1053,,0.0,,block of flats,0.0814,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n141428,0,Cash loans,M,Y,N,1,225000.0,702000.0,25002.0,702000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-11426,-1695,-10.0,-980,10.0,1,1,0,1,0,0,Drivers,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Industry: type 3,,0.6356206204166656,0.21885908222837447,0.3144,0.1178,0.9995,0.9932,0.0,0.36,0.1379,0.875,0.6667,0.0,0.2555,0.3288,0.0039,0.0237,0.3204,0.1223,0.9995,0.9935,0.0,0.3625,0.1379,0.875,0.6667,0.0,0.2792,0.3425,0.0039,0.0251,0.3175,0.1178,0.9995,0.9933,0.0,0.36,0.1379,0.875,0.6667,0.0,0.2599,0.3347,0.0039,0.0242,reg oper account,block of flats,0.3883,Monolithic,No,1.0,1.0,1.0,0.0,-1376.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,2.0\n451012,0,Cash loans,M,N,N,0,135000.0,612612.0,26086.5,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-19597,-765,-6256.0,-2973,,1,1,0,1,1,0,Drivers,2.0,3,3,SATURDAY,7,0,0,0,0,0,0,Transport: type 4,0.6033380068750892,0.442197570037176,0.6512602186973006,0.1649,0.0825,0.9786,,,0.0,0.1034,0.1667,,0.1084,,0.0615,,0.0308,0.1681,0.0856,0.9786,,,0.0,0.1034,0.1667,,0.1109,,0.0641,,0.0326,0.1665,0.0825,0.9786,,,0.0,0.1034,0.1667,,0.1103,,0.0626,,0.0314,,block of flats,0.0551,Block,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n444890,0,Cash loans,F,N,Y,1,90000.0,545040.0,26509.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-10878,-135,-1763.0,-1778,,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,9,0,0,0,1,1,0,Kindergarten,0.2714893557000291,0.6385429607339289,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-915.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,0.0\n382148,0,Cash loans,F,N,Y,1,292500.0,91692.0,10476.0,81000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-15261,-5463,-7109.0,-4118,,1,1,0,1,0,0,Medicine staff,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Medicine,,0.6795978616483677,0.190705947811054,0.0871,0.0967,0.9791,0.7144,0.0,0.0,0.2069,0.1667,0.2083,0.0,0.071,0.0837,0.0,0.0,0.0882,0.1002,0.9791,0.7256,0.0,0.0,0.2069,0.1667,0.2083,0.0,0.0771,0.0869,0.0,0.0,0.08800000000000001,0.0967,0.9791,0.7182,0.0,0.0,0.2069,0.1667,0.2083,0.0,0.0723,0.0852,0.0,0.0,reg oper account,block of flats,0.0656,Panel,No,0.0,0.0,0.0,0.0,-1639.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n192446,0,Cash loans,F,N,Y,1,112500.0,298512.0,29655.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-14667,-2247,-4137.0,-4675,,1,1,0,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.44853863837398,0.7214582568521488,,0.1031,0.0795,0.9811,0.7484,0.0173,0.06,0.1207,0.2917,0.125,0.069,0.0836,0.0661,0.0019,0.0794,0.084,0.0765,0.9786,0.7190000000000001,0.0088,0.0,0.1034,0.1667,0.0417,0.0543,0.0735,0.0596,0.0,0.0,0.1041,0.0795,0.9801,0.7518,0.0174,0.06,0.1207,0.3333,0.125,0.0702,0.0851,0.0673,0.0019,0.081,reg oper account,block of flats,0.2038,Panel,No,1.0,0.0,1.0,0.0,-445.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n123934,0,Cash loans,F,N,N,2,189000.0,1350000.0,39474.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.003122,-9906,-1522,-1238.0,-2567,,1,1,1,1,0,0,Core staff,4.0,3,3,MONDAY,10,0,1,1,0,1,1,Trade: type 3,,0.13875714066742445,0.3656165070113335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-939.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n243754,0,Cash loans,F,Y,N,3,54000.0,328500.0,18985.5,328500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.019101,-13713,-2816,-1010.0,-4911,16.0,1,1,0,1,0,0,Core staff,5.0,2,2,TUESDAY,10,0,0,0,0,0,0,Government,,0.5150258537609612,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1708.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n263545,0,Cash loans,F,N,Y,0,180000.0,1271929.5,50571.0,1170000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18059,-1065,-6455.0,-1588,,1,1,0,1,0,0,Private service staff,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Self-employed,,0.6822489628079803,0.6545292802242897,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-2403.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n407470,0,Cash loans,F,N,N,5,133650.0,435276.0,34519.5,355500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-13163,-5279,-2134.0,-4134,,1,1,0,0,0,0,Laborers,7.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 1,,0.5069389285240896,0.5028782772082183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,1.0,-114.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n406515,0,Cash loans,F,N,Y,0,166500.0,413235.0,25411.5,337500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018634,-10074,-2204,-994.0,-2758,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,17,0,0,0,1,1,0,Business Entity Type 1,0.6016126111727691,0.2384285768881152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-760.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n211022,0,Cash loans,F,N,Y,0,135000.0,296280.0,14382.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.04622,-24300,365243,-6421.0,-4712,,1,0,0,1,0,0,,1.0,1,1,THURSDAY,9,0,0,0,0,0,0,XNA,,0.6810388906520594,,0.1649,0.1903,0.9826,0.762,0.2668,0.0,0.3621,0.1667,0.2083,0.1998,0.1341,0.1488,0.0019,0.1328,0.105,0.11800000000000001,0.9811,0.7517,0.1463,0.0,0.2069,0.1667,0.2083,0.1197,0.0918,0.095,0.0,0.0366,0.1665,0.1903,0.9826,0.7652,0.2685,0.0,0.3621,0.1667,0.2083,0.2033,0.1364,0.1514,0.0019,0.1356,reg oper spec account,block of flats,0.2125,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-340.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n155203,0,Cash loans,M,Y,N,1,112500.0,508495.5,20025.0,454500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-22902,365243,-486.0,-4127,20.0,1,0,0,1,0,0,,3.0,2,2,SATURDAY,16,0,0,0,0,0,0,XNA,,0.6875815215612652,0.7352209993926424,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n400671,0,Cash loans,F,Y,Y,0,90000.0,315000.0,22531.5,315000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-12742,-1098,-2140.0,-4297,4.0,1,1,1,1,1,0,Sales staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.3941166985832775,0.6594055320683344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-755.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n142405,0,Cash loans,F,N,Y,1,135000.0,1350000.0,39604.5,1350000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.006233,-16866,-8047,-1715.0,-421,,1,1,1,1,1,0,,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Medicine,0.8301056979700179,0.6548586317393682,0.13765446191826075,0.0742,0.0545,0.9831,,,0.08,0.069,0.3333,,0.0466,,0.0769,,,0.0756,0.0565,0.9831,,,0.0806,0.069,0.3333,,0.047,,0.0793,,,0.0749,0.0545,0.9831,,,0.08,0.069,0.3333,,0.0474,,0.0783,,,,block of flats,0.0599,Panel,No,1.0,1.0,1.0,1.0,-787.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n315819,0,Cash loans,F,N,Y,0,171000.0,491031.0,38128.5,463500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-22696,365243,-11646.0,-4274,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.5928285131871741,0.7922644738669378,0.0732,0.0428,0.9836,,,0.08,0.069,0.3333,,0.08199999999999999,,0.046,,,0.0746,0.0444,0.9836,,,0.0806,0.069,0.3333,,0.0839,,0.0479,,,0.0739,0.0428,0.9836,,,0.08,0.069,0.3333,,0.0835,,0.0468,,,,block of flats,0.0593,Panel,No,9.0,0.0,9.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n135339,0,Cash loans,F,N,Y,0,90000.0,203760.0,21748.5,180000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.035792000000000004,-19087,-2529,-3268.0,-2616,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5819323597507968,0.5370699579791587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-681.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n306644,0,Cash loans,M,Y,N,0,135000.0,1293502.5,35568.0,1129500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-16024,-589,-255.0,-1786,2.0,1,1,1,1,1,0,Laborers,2.0,3,3,FRIDAY,9,0,0,0,0,1,1,Construction,,0.4117965689535414,0.6161216908872079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1860.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n440010,0,Cash loans,F,N,N,0,112500.0,238896.0,9004.5,189000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-17437,-7823,-4471.0,-995,,1,1,1,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,School,,0.4288605653194732,0.6413682574954046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-436.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n336135,0,Cash loans,M,Y,N,0,1575000.0,269550.0,21739.5,225000.0,Other_A,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-8875,-224,-111.0,-1562,6.0,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Construction,0.2191655248361328,0.3622785620347796,0.18629294965553744,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-686.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n413388,0,Cash loans,F,N,Y,0,85500.0,76410.0,8154.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.015221,-16595,-4782,-957.0,-143,,1,1,1,1,0,0,Laborers,1.0,2,2,SUNDAY,14,0,0,0,0,1,1,Business Entity Type 1,,0.3475259768594262,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-960.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n430500,0,Cash loans,F,Y,Y,0,90000.0,590337.0,30271.5,477000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010032,-20364,365243,-36.0,-3906,11.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,7,0,0,0,0,0,0,XNA,,0.26851657869912976,0.6754132910917112,0.0928,0.0824,0.9776,0.6940000000000001,0.0391,0.0,0.2069,0.1667,0.2083,0.13,0.0756,0.0883,0.0,0.0,0.0945,0.0855,0.9777,0.706,0.0394,0.0,0.2069,0.1667,0.2083,0.133,0.0826,0.092,0.0,0.0,0.0937,0.0824,0.9776,0.6981,0.0393,0.0,0.2069,0.1667,0.2083,0.1323,0.077,0.0899,0.0,0.0,,block of flats,0.0908,Panel,No,0.0,0.0,0.0,0.0,-14.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n190224,0,Cash loans,F,N,Y,0,90000.0,279000.0,27724.5,279000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.035792000000000004,-11638,-564,-2042.0,-4061,,1,1,0,1,1,0,Cooking staff,1.0,2,2,WEDNESDAY,18,0,0,0,0,1,1,Business Entity Type 1,0.4215600456841806,0.6554465501818523,0.4848514754962666,0.1144,0.0,0.9796,0.6940000000000001,,0.0,0.1448,0.1667,0.2083,0.1024,0.1034,0.0619,0.0116,0.02,0.0945,0.0,0.9777,0.706,,0.0,0.1034,0.1667,0.2083,0.0192,0.1129,0.0461,0.0117,0.0181,0.128,0.0,0.9786,0.6981,,0.0,0.1034,0.1667,0.2083,0.1042,0.1052,0.063,0.0116,0.0174,reg oper account,block of flats,0.0183,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-661.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n429613,0,Cash loans,F,N,Y,0,90000.0,1293502.5,35698.5,1129500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.005313,-21357,365243,-8253.0,-4536,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.2887232685865858,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1831.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,8.0\n400403,0,Revolving loans,F,Y,Y,1,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-9231,-669,-3993.0,-1861,13.0,1,1,0,1,0,0,Core staff,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Postal,,0.4141397202940969,0.4329616670974407,0.1237,0.1763,0.9901,,,0.0,0.2069,0.1667,,,,0.1079,,0.0813,0.1261,0.183,0.9901,,,0.0,0.2069,0.1667,,,,0.1125,,0.0861,0.1249,0.1763,0.9901,,,0.0,0.2069,0.1667,,,,0.1099,,0.083,,block of flats,0.1125,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-239.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n370018,0,Cash loans,F,N,Y,1,225000.0,497520.0,39438.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-16593,-1326,-432.0,-129,,1,1,0,1,0,0,,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.7251735961441165,0.6479316796176987,0.4101025731788671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2292.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n161322,1,Cash loans,M,N,Y,0,270000.0,755190.0,36328.5,675000.0,Unaccompanied,Commercial associate,Lower secondary,Civil marriage,Rented apartment,0.035792000000000004,-17006,-1997,-2441.0,-349,,1,1,1,1,0,0,Laborers,2.0,2,2,MONDAY,11,0,0,0,0,1,1,Industry: type 4,0.4823333635853377,0.6773128022966716,0.4206109640437848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1335.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n446562,0,Cash loans,F,N,Y,0,270000.0,651816.0,21672.0,495000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009175,-18286,-379,-6135.0,-1827,,1,1,0,1,1,0,Core staff,2.0,2,2,FRIDAY,10,0,1,1,0,0,0,School,0.8518909545968789,0.6761598964391748,0.7091891096653581,0.1186,0.1545,0.9846,,,0.0,0.2759,0.1667,,0.1231,,0.107,,0.0552,0.1208,0.1604,0.9846,,,0.0,0.2759,0.1667,,0.1259,,0.1115,,0.0584,0.1197,0.1545,0.9846,,,0.0,0.2759,0.1667,,0.1253,,0.1089,,0.0563,,block of flats,0.0962,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2014.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n200786,0,Cash loans,M,N,Y,0,270000.0,284400.0,22599.0,225000.0,Family,Working,Higher education,Single / not married,House / apartment,0.010006,-8556,-664,-5197.0,-1221,,1,1,0,1,0,0,Core staff,1.0,2,1,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.5474046663419821,0.14644206667896328,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1332.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n294956,0,Cash loans,F,N,N,0,180000.0,143910.0,14364.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019101,-16163,-2998,-8021.0,-3027,,1,1,0,1,0,0,,2.0,2,2,MONDAY,14,0,0,0,1,1,1,Business Entity Type 3,,0.5002260262382606,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1373.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,9.0\n214410,0,Cash loans,F,N,Y,1,135000.0,550980.0,35343.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-20054,-1263,-3214.0,-3553,,1,1,0,1,1,1,High skill tech staff,3.0,3,3,FRIDAY,7,0,0,0,0,0,0,Construction,0.6803946863542948,0.2254855675489318,,0.0814,0.1059,0.9801,,,0.0,0.069,0.1667,,0.0313,,0.0793,,0.0,0.083,0.1099,0.9801,,,0.0,0.069,0.1667,,0.032,,0.0826,,0.0,0.0822,0.1059,0.9801,,,0.0,0.069,0.1667,,0.0319,,0.0807,,0.0,,block of flats,0.1122,Panel,No,2.0,0.0,2.0,0.0,-2191.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n210685,0,Cash loans,M,Y,Y,0,135000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-12505,-3055,-179.0,-4148,24.0,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,7,0,0,0,0,1,1,Mobile,,0.4250565715103312,0.13510601574017175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-312.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n344812,0,Cash loans,F,Y,N,0,112500.0,398160.0,10633.5,315000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.019101,-9700,-829,-445.0,-63,6.0,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Realtor,0.3275968936736887,0.6074314633144897,0.6178261467332483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-669.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n398917,0,Cash loans,F,N,Y,0,135000.0,398016.0,26041.5,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-19423,-5097,-4335.0,-2734,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,,0.07498416171897539,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-231.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n408382,0,Cash loans,M,Y,Y,1,162000.0,161730.0,9900.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-10536,-1830,-4989.0,-3223,2.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,THURSDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.1511497578178458,0.5968496565662158,0.7898803468924867,0.0515,0.0495,0.9752,0.66,0.0356,0.0,0.1034,0.1667,0.2083,0.0,0.0403,0.0435,0.0077,0.0368,0.0525,0.0514,0.9752,0.6733,0.0359,0.0,0.1034,0.1667,0.2083,0.0,0.0441,0.0453,0.0078,0.039,0.052000000000000005,0.0495,0.9752,0.6645,0.0358,0.0,0.1034,0.1667,0.2083,0.0,0.040999999999999995,0.0442,0.0078,0.0376,reg oper account,block of flats,0.0617,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1659.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n454442,1,Cash loans,M,N,Y,0,112500.0,582471.0,28458.0,409500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.003122,-16149,-1992,-3633.0,-5097,,1,1,0,1,0,0,Security staff,1.0,3,3,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.04612211752957219,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-590.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n145522,0,Cash loans,F,Y,Y,0,112500.0,760225.5,32337.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-15082,-3164,-5382.0,-5399,8.0,1,1,1,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,1,1,Industry: type 11,,0.6681222677066793,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1556.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n336821,0,Cash loans,F,Y,Y,1,292500.0,332946.0,18189.0,238500.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.01885,-10730,-534,-10721.0,-2751,18.0,1,1,0,1,0,0,,3.0,2,2,THURSDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.6035622263486479,0.5964654872536381,0.0720131886405828,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-413.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n333106,0,Revolving loans,M,N,Y,0,42750.0,135000.0,6750.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.02461,-21646,365243,-5797.0,-4474,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,18,0,0,0,0,0,0,XNA,,0.6177635941899362,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-713.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n141837,0,Cash loans,F,N,Y,0,292500.0,381096.0,27859.5,301500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-21580,365243,-6414.0,-5110,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.4353534438038657,0.3185955240537633,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n205221,0,Cash loans,F,N,Y,0,135000.0,665892.0,24592.5,477000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.010966,-20625,365243,-3063.0,-1013,,1,0,0,1,1,0,,1.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,0.6418700040987233,0.6842508352020509,0.248535557339522,0.2134,0.1417,0.9846,0.7892,0.0446,0.24,0.2069,0.3333,0.375,0.1488,0.1689,0.1968,0.0232,0.0754,0.2174,0.147,0.9846,0.7975,0.045,0.2417,0.2069,0.3333,0.375,0.1522,0.1846,0.2051,0.0233,0.0798,0.2155,0.1417,0.9846,0.792,0.0448,0.24,0.2069,0.3333,0.375,0.1514,0.1719,0.2003,0.0233,0.077,reg oper account,block of flats,0.1955,Panel,No,0.0,0.0,0.0,0.0,-356.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n184272,0,Revolving loans,F,Y,Y,1,112500.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-13000,-1494,-4304.0,-4304,8.0,1,1,0,1,0,0,Accountants,3.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.42653839300965496,0.542437452496109,0.816092360478441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-934.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n428998,0,Cash loans,F,N,Y,0,135000.0,300735.0,16443.0,243000.0,Unaccompanied,Commercial associate,Higher education,Widow,House / apartment,0.016612000000000002,-13471,-1797,-12910.0,-1998,,1,1,0,1,0,0,Sales staff,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Other,0.44313561130562135,0.516140016746929,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n249314,0,Cash loans,M,Y,Y,0,180000.0,224149.5,23098.5,193500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.016612000000000002,-14020,-1002,-7980.0,-4880,2.0,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Self-employed,,0.5728295005165132,,0.1897,0.18,0.9846,0.7892,0.033,0.2,0.1724,0.3333,0.375,0.1148,0.1505,0.1924,0.0193,0.0797,0.1933,0.1868,0.9846,0.7975,0.0333,0.2014,0.1724,0.3333,0.375,0.1174,0.1644,0.2003,0.0195,0.0832,0.1915,0.18,0.9846,0.792,0.0332,0.2,0.1724,0.3333,0.375,0.1168,0.1531,0.1958,0.0194,0.0813,reg oper account,block of flats,0.1859,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-929.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n168594,0,Cash loans,M,Y,Y,0,270000.0,1170000.0,46530.0,1170000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-23716,365243,-4293.0,-4318,2.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.5158068764797473,,0.0041,,0.9881,,,0.0,0.0345,0.0417,,,,0.0034,,0.0,0.0042,,0.9881,,,0.0,0.0345,0.0417,,,,0.0035,,0.0,0.0042,,0.9881,,,0.0,0.0345,0.0417,,,,0.0035,,0.0,,block of flats,0.0027,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-862.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n446493,0,Cash loans,M,Y,N,0,270000.0,799956.0,25245.0,607500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.072508,-19270,-5179,-7014.0,-2505,13.0,1,1,0,1,0,0,Laborers,1.0,1,1,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 2,,0.4882392938334225,0.19519840600440985,0.0722,0.1235,0.9622,0.4832,0.1409,0.24,0.2069,0.2083,0.25,0.0846,0.0471,0.0852,0.0541,0.1633,0.0735,0.1281,0.9623,0.5034,0.1421,0.2417,0.2069,0.2083,0.25,0.0865,0.0514,0.0888,0.0545,0.1729,0.0729,0.1235,0.9622,0.4901,0.1418,0.24,0.2069,0.2083,0.25,0.086,0.0479,0.0867,0.0543,0.1668,reg oper account,block of flats,0.1025,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-273.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n219686,0,Cash loans,F,N,Y,0,270000.0,1260702.0,41796.0,1129500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.00496,-16133,-3358,-4057.0,-4375,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.61963623157403,0.633031641417419,0.1191,,0.9821,,,0.18,0.1379,0.25,,,,0.1946,,,0.063,,0.9831,,,0.1611,0.1379,0.1667,,,,0.2028,,,0.1166,,0.9826,,,0.18,0.1379,0.25,,,,0.1981,,,,,0.0513,Panel,No,3.0,3.0,3.0,3.0,-1412.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n291388,0,Revolving loans,M,Y,Y,0,225000.0,450000.0,22500.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-20897,-2555,-1070.0,-4136,13.0,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Self-employed,,0.6825191139367341,0.7530673920730478,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-483.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n256495,1,Cash loans,M,N,N,0,135000.0,416052.0,15682.5,292500.0,Unaccompanied,Commercial associate,Higher education,Married,Rented apartment,0.02461,-10484,-492,-7.0,-3147,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,12,0,0,0,1,1,0,Business Entity Type 3,0.03273142943262305,0.17309138614934905,0.4938628816996244,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n291073,0,Cash loans,F,N,Y,0,157500.0,104256.0,9688.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018801,-12589,-1356,-6631.0,-4649,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.4147747581618271,0.00487251796982735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1220.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n433744,0,Cash loans,F,N,N,0,270000.0,922500.0,26973.0,922500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014464,-15881,-682,-16.0,-5403,,1,1,1,1,1,0,Core staff,2.0,2,2,SATURDAY,16,0,0,0,0,1,1,Military,0.4130314008639907,0.34422111119471743,0.2512394458905693,0.0247,0.0373,0.9732,0.6328,0.0021,0.0,0.1034,0.0417,0.0417,0.0,0.0202,0.0163,0.0,0.0,0.0252,0.0387,0.9732,0.6472,0.0021,0.0,0.1034,0.0417,0.0417,0.0,0.022000000000000002,0.017,0.0,0.0,0.025,0.0373,0.9732,0.6377,0.0021,0.0,0.1034,0.0417,0.0417,0.0,0.0205,0.0166,0.0,0.0,reg oper spec account,block of flats,0.0139,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n217409,0,Cash loans,M,Y,Y,0,180000.0,1200744.0,35239.5,1048500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008625,-12333,-2297,-5996.0,-4021,13.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.4164920782944129,0.5726825047161584,0.0412,0.0297,0.9796,0.7212,0.0047,0.0,0.069,0.1667,0.2083,0.0384,0.0336,0.0357,0.0,0.0,0.042,0.0308,0.9796,0.7321,0.0047,0.0,0.069,0.1667,0.2083,0.0393,0.0367,0.0372,0.0,0.0,0.0416,0.0297,0.9796,0.7249,0.0047,0.0,0.069,0.1667,0.2083,0.0391,0.0342,0.0364,0.0,0.0,reg oper account,block of flats,0.0306,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n313315,0,Cash loans,F,N,N,0,90000.0,270000.0,18783.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.026392,-9182,-415,-4009.0,-1876,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.13793137636671884,0.4763151634850632,0.6940926425266661,0.2515,0.2688,0.9831,,,0.0,0.5517,0.1667,,0.2799,,0.2301,,0.001,0.2563,0.27899999999999997,0.9831,,,0.0,0.5517,0.1667,,0.2863,,0.2397,,0.001,0.254,0.2688,0.9831,,,0.0,0.5517,0.1667,,0.2848,,0.2342,,0.001,,block of flats,0.2082,Panel,No,1.0,0.0,1.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n343007,0,Revolving loans,M,Y,Y,0,76500.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-20322,-900,-3957.0,-3493,19.0,1,1,0,1,0,0,Laborers,2.0,3,3,THURSDAY,12,0,0,0,0,1,1,Construction,,0.1524596212327866,0.6722428897082422,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-432.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n451593,0,Cash loans,M,N,Y,1,144000.0,135000.0,10795.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-10169,-1281,-4872.0,-1849,,1,1,0,1,1,0,,3.0,3,3,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 1,0.3633229005166146,0.5028719721356106,0.08226850764912726,0.1237,0.1202,0.9757,0.6668,0.0104,0.0,0.2069,0.1667,0.2083,0.0823,0.1009,0.0959,0.0,0.0,0.1261,0.1247,0.9757,0.6798,0.0105,0.0,0.2069,0.1667,0.2083,0.0842,0.1102,0.0999,0.0,0.0,0.1249,0.1202,0.9757,0.6713,0.0105,0.0,0.2069,0.1667,0.2083,0.0837,0.1026,0.0977,0.0,0.0,reg oper account,block of flats,0.0812,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-842.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n366633,0,Cash loans,M,Y,N,0,180000.0,173196.0,20682.0,153000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,With parents,0.032561,-9538,-195,-5427.0,-1449,15.0,1,1,0,1,0,0,Laborers,1.0,1,1,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.564204041831652,0.4294236843421945,0.0918,0.11699999999999999,0.9891,0.8504,0.0504,0.2,0.0862,0.4583,0.0417,0.1351,0.124,0.1505,0.0019,0.0011,0.0,0.1214,0.9891,0.8563,0.0422,0.1611,0.069,0.4583,0.0417,0.1382,0.1084,0.0888,0.0,0.0,0.0926,0.11699999999999999,0.9891,0.8524,0.0507,0.2,0.0862,0.4583,0.0417,0.1374,0.1261,0.1532,0.0019,0.0011,reg oper account,block of flats,0.1701,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n176091,0,Cash loans,F,N,N,0,180000.0,808650.0,24732.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-20783,365243,-7588.0,-4114,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,0.7265490887988826,0.16214456766623808,0.6832688314232291,0.5196,0.4409,0.9821,0.7552,0.0,0.56,0.4828,0.3333,0.375,0.0,0.4236,0.5518,0.0,0.0,0.5294,0.4575,0.9821,0.7648,0.0,0.5639,0.4828,0.3333,0.375,0.0,0.4628,0.5749,0.0,0.0,0.5246,0.4409,0.9821,0.7585,0.0,0.56,0.4828,0.3333,0.375,0.0,0.431,0.5617,0.0,0.0,reg oper account,block of flats,0.434,Panel,No,1.0,1.0,1.0,1.0,-2069.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n170165,0,Cash loans,F,N,N,0,180000.0,419679.0,15201.0,346500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.01885,-16538,-1405,-16538.0,-94,,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Postal,0.4451586029177975,0.2374491940995908,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,2.0,8.0,1.0,-28.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n371337,0,Cash loans,M,Y,N,0,112500.0,279000.0,16987.5,279000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.028663,-16344,-2433,-8039.0,-4743,16.0,1,1,0,1,0,0,,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Industry: type 5,,0.4659031889889849,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n228587,1,Cash loans,M,Y,Y,0,270000.0,270000.0,12640.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-19147,-4518,-9047.0,-2695,1.0,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.5811181619504285,0.2580842039460289,0.4948,0.0683,0.9886,0.9592,0.0132,0.54,0.4655,0.3542,0.0417,0.4362,0.0925,0.5631,0.0,0.0,0.1155,0.0709,0.9801,0.9608,0.0133,0.1208,0.1034,0.3333,0.0417,0.1383,0.10099999999999999,0.1393,0.0,0.0,0.4996,0.0683,0.9886,0.9597,0.0133,0.54,0.4655,0.3542,0.0417,0.4438,0.0941,0.5732,0.0,0.0,reg oper account,block of flats,0.8943,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n216638,0,Cash loans,F,N,Y,0,225000.0,261000.0,13473.0,261000.0,Unaccompanied,State servant,Secondary / secondary special,Widow,House / apartment,0.018209,-18438,-11564,-3713.0,-1991,,1,1,0,1,0,0,Medicine staff,1.0,3,3,MONDAY,11,0,0,0,0,0,0,Medicine,,0.4877912659800369,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,3.0,5.0,2.0,-1822.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n323990,0,Cash loans,F,N,Y,0,67500.0,63000.0,6228.0,63000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.008068,-20464,365243,-9367.0,-1000,,1,0,0,1,1,0,,1.0,3,3,MONDAY,14,0,0,0,0,0,0,XNA,,0.11500911949550376,0.6940926425266661,0.1031,0.0,0.9866,0.8164,0.019,0.0,0.069,0.1667,0.2083,0.0552,0.0841,0.0605,0.0,0.0,0.105,0.0,0.9866,0.8236,0.0192,0.0,0.069,0.1667,0.2083,0.0565,0.0918,0.0631,0.0,0.0,0.1041,0.0,0.9866,0.8189,0.0191,0.0,0.069,0.1667,0.2083,0.0562,0.0855,0.0616,0.0,0.0,,block of flats,0.057999999999999996,Panel,No,2.0,1.0,2.0,1.0,-566.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n135472,0,Revolving loans,M,Y,N,0,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-9873,-761,-4643.0,-2562,9.0,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,18,0,0,0,1,1,0,Self-employed,,0.6826565120887229,0.5190973382084597,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-629.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,8.0\n111962,1,Cash loans,M,Y,Y,2,270000.0,724581.0,23269.5,625500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-13477,365243,-7027.0,-327,7.0,1,0,0,1,1,0,,4.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,0.14884615722917854,0.3240062272136592,0.3296550543128238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-531.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n122018,0,Cash loans,M,N,N,2,261000.0,254700.0,24939.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-15446,-4129,-4830.0,-2716,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,9,0,0,0,0,1,1,Transport: type 2,0.2830730628258989,0.21844296456832893,0.646329897706246,0.0041,,0.9732,,,,,0.0,,,,,,,0.0042,,0.9732,,,,,0.0,,,,,,,0.0042,,0.9732,,,,,0.0,,,,,,,,,0.002,,No,0.0,0.0,0.0,0.0,-228.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n398869,1,Cash loans,M,N,Y,0,184500.0,1039500.0,41355.0,1039500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-21616,365243,-6695.0,-4697,,1,0,0,1,1,0,,2.0,3,3,THURSDAY,6,0,0,0,0,0,0,XNA,,0.3698140119079228,0.7688075728291359,0.3732,,0.9871,,,0.4,0.3448,0.3333,,0.2337,,0.4104,,0.0032,0.3803,,0.9871,,,0.4028,0.3448,0.3333,,0.239,,0.4276,,0.0033,0.3768,,0.9871,,,0.4,0.3448,0.3333,,0.2377,,0.4178,,0.0032,,block of flats,0.3786,Panel,No,0.0,0.0,0.0,0.0,-430.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n259248,0,Cash loans,F,N,Y,0,157500.0,1104997.5,36648.0,990000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.003069,-20081,365243,-6038.0,-3288,,1,0,0,1,1,0,,2.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.4586357027905215,0.4436153084085652,0.0412,0.0328,0.9906,,,0.0,0.069,0.1667,,0.0159,,0.0286,,0.0,0.042,0.034,0.9906,,,0.0,0.069,0.1667,,0.0162,,0.0297,,0.0,0.0416,0.0328,0.9906,,,0.0,0.069,0.1667,,0.0162,,0.0291,,0.0,,block of flats,0.0372,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1078.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n160803,0,Cash loans,F,N,N,0,112500.0,454500.0,14899.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009334,-22013,-2053,-5493.0,-4686,,1,1,1,1,1,0,Sales staff,1.0,2,2,SATURDAY,19,0,0,0,0,0,0,Trade: type 7,0.7733127558884649,0.2265988359266469,0.4489622731076524,0.0742,0.0488,0.9866,0.8164,0.0153,0.0,0.0345,0.1667,0.2083,0.0718,0.057999999999999996,0.048,0.0116,0.01,0.0756,0.0507,0.9866,0.8236,0.0154,0.0,0.0345,0.1667,0.2083,0.0735,0.0634,0.05,0.0117,0.0106,0.0749,0.0488,0.9866,0.8189,0.0154,0.0,0.0345,0.1667,0.2083,0.0731,0.059000000000000004,0.0488,0.0116,0.0102,reg oper account,block of flats,0.0482,\"Stone, brick\",No,1.0,0.0,0.0,0.0,-1699.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n442646,0,Cash loans,F,N,N,0,166500.0,543037.5,30451.5,463500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-15542,-931,-2373.0,-4380,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Self-employed,0.5565885501993898,0.6876519788967059,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-567.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n324215,0,Cash loans,F,N,Y,0,171000.0,1125000.0,37179.0,1125000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010032,-21106,-1084,-9283.0,-4076,,1,1,1,1,1,0,Security staff,2.0,2,2,WEDNESDAY,10,0,1,1,0,1,1,Business Entity Type 1,,0.5727385326363528,0.5513812618027899,0.067,0.0694,0.9816,0.7484,0.0189,0.0,0.1379,0.1667,0.0417,0.066,0.0546,0.0591,0.0,0.0,0.063,0.0565,0.9796,0.7321,0.0084,0.0,0.1379,0.1667,0.0417,0.0646,0.0551,0.0551,0.0,0.0,0.0677,0.0694,0.9816,0.7518,0.0191,0.0,0.1379,0.1667,0.0417,0.0672,0.0556,0.0602,0.0,0.0,reg oper account,block of flats,0.0462,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1223.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n259911,0,Cash loans,M,Y,Y,1,450000.0,777024.0,75694.5,720000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-16816,-2186,-4908.0,-342,3.0,1,1,0,1,0,0,Managers,3.0,1,1,WEDNESDAY,19,0,0,0,0,0,0,Business Entity Type 3,,0.7353683617804809,0.13177013253138142,0.068,,0.9732,0.6328,0.0122,0.0,0.1724,0.1667,0.2083,0.0142,0.0555,0.0344,0.0,0.0968,0.0693,,0.9732,0.6472,0.0123,0.0,0.1724,0.1667,0.2083,0.0146,0.0606,0.0359,0.0,0.1025,0.0687,,0.9732,0.6377,0.0123,0.0,0.1724,0.1667,0.2083,0.0145,0.0564,0.035,0.0,0.0988,not specified,block of flats,0.0645,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-208.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n260837,0,Revolving loans,F,N,Y,0,112500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.011657,-8498,-162,-710.0,-1171,,1,1,1,1,1,1,Sales staff,2.0,1,1,SUNDAY,20,0,0,0,1,1,0,Trade: type 3,,0.6638660002094555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-644.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n197754,0,Cash loans,F,N,Y,0,157500.0,1256400.0,36733.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14732,-2085,-6408.0,-3220,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.5762798568179329,0.6302353648927319,0.6940926425266661,0.2557,0.0479,0.9791,0.7144,0.0034,0.0,0.1034,0.1667,0.0417,0.0985,0.2068,0.1116,0.0077,0.0076,0.2605,0.0497,0.9791,0.7256,0.0035,0.0,0.1034,0.1667,0.0417,0.1008,0.2259,0.1163,0.0078,0.0081,0.2581,0.0479,0.9791,0.7182,0.0035,0.0,0.1034,0.1667,0.0417,0.1002,0.2104,0.1136,0.0078,0.0078,reg oper account,specific housing,0.0974,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2754.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n313653,0,Cash loans,F,N,Y,0,157500.0,417915.0,28057.5,378000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.020246,-16106,-621,-1335.0,-4541,,1,1,0,1,1,0,Sales staff,1.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.5582982248933555,0.03602607365329466,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-259.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n360906,0,Cash loans,F,N,Y,1,135000.0,1270746.0,40027.5,1138500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-13413,-2957,-1502.0,-1568,,1,1,0,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,,0.6603779288342263,0.5919766183185521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1590.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n144737,1,Cash loans,M,N,Y,0,157500.0,1075932.0,45715.5,922500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-18007,-3399,-6845.0,-1268,,1,1,0,1,0,0,Low-skill Laborers,2.0,3,3,THURSDAY,7,0,0,0,0,0,0,Construction,,0.5857790423480432,0.7407990879702335,0.0457,0.0286,0.9776,0.7348,0.0062,0.0,0.0459,0.125,0.0417,0.0191,0.0525,0.0211,0.0,0.0052,0.0084,0.0,0.9806,0.7452,0.0036,0.0,0.0345,0.1667,0.0417,0.004,0.0533,0.006999999999999999,0.0,0.0,0.0604,0.0419,0.9806,0.7383,0.0063,0.0,0.0345,0.1667,0.0417,0.027000000000000003,0.0534,0.0273,0.0,0.0,reg oper account,block of flats,0.0253,\"Stone, brick\",No,4.0,0.0,3.0,0.0,-264.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n229087,0,Cash loans,M,N,Y,0,103500.0,1323000.0,38812.5,1323000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-11500,-2129,-348.0,-4142,,1,1,1,1,1,0,Core staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 2,,0.688909352771063,0.8337869986815019,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2172.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n342602,0,Cash loans,M,Y,Y,0,202500.0,269550.0,13095.0,225000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-23243,365243,-10759.0,-4814,6.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.6585019312391047,0.7252764347002191,,,0.9737,,,,,,,,,0.0099,,,,,0.9737,,,,,,,,,0.0103,,,,,0.9737,,,,,,,,,0.01,,,,,0.0082,,No,1.0,0.0,1.0,0.0,-1025.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n425235,0,Cash loans,F,N,Y,0,225000.0,1546020.0,45202.5,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003122,-21853,365243,-12571.0,-5352,,1,0,0,1,0,0,,2.0,3,3,MONDAY,16,0,0,0,0,0,0,XNA,,0.40513397002952894,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-718.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n303597,0,Cash loans,F,N,N,0,112500.0,276277.5,16915.5,238500.0,Unaccompanied,Commercial associate,Incomplete higher,Married,Rented apartment,0.031329,-10019,-687,-9990.0,-177,,1,1,0,1,0,0,Managers,2.0,2,2,SATURDAY,11,0,0,0,1,1,0,Business Entity Type 3,0.06603947461189774,0.2299812758211297,,0.1649,0.09300000000000001,0.9856,0.8028,0.0374,0.16,0.1379,0.3333,0.375,0.0476,0.1345,0.1611,0.0,0.0,0.1681,0.0965,0.9856,0.8105,0.0377,0.1611,0.1379,0.3333,0.375,0.0487,0.1469,0.1678,0.0,0.0,0.1665,0.09300000000000001,0.9856,0.8054,0.0376,0.16,0.1379,0.3333,0.375,0.0485,0.1368,0.16399999999999998,0.0,0.0,reg oper account,block of flats,0.1471,Block,No,0.0,0.0,0.0,0.0,-735.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n237258,0,Cash loans,F,N,Y,2,306000.0,1395000.0,51696.0,1395000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-14058,-503,-8167.0,-4737,,1,1,0,1,0,0,Accountants,4.0,2,2,SUNDAY,13,0,0,0,0,0,0,Industry: type 9,0.688491120963106,0.6568723699168563,0.2940831009471255,0.0825,0.0592,0.9732,,,0.08,0.1379,0.1667,,,,0.0423,,0.0288,0.084,0.0614,0.9732,,,0.0806,0.1379,0.1667,,,,0.0441,,0.0305,0.0833,0.0592,0.9732,,,0.08,0.1379,0.1667,,,,0.043,,0.0295,,,0.0721,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2359.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n144636,0,Cash loans,F,N,N,0,112500.0,203760.0,16096.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.009175,-10231,-593,-5048.0,-2381,,1,1,1,1,0,0,Laborers,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Transport: type 3,,0.6727846517483742,0.1940678276718812,0.2258,0.1556,0.9861,,,0.24,0.2069,0.3333,,,,0.2277,,0.0023,0.23,0.1615,0.9861,,,0.2417,0.2069,0.3333,,,,0.2372,,0.0024,0.228,0.1556,0.9861,,,0.24,0.2069,0.3333,,,,0.2318,,0.0023,,block of flats,0.1796,Panel,No,0.0,0.0,0.0,0.0,-597.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n431503,0,Cash loans,F,Y,Y,1,292500.0,1033123.5,71901.0,990000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.0105,-16566,-288,-9092.0,-108,12.0,1,1,0,1,0,0,,2.0,3,3,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.11276923880916233,0.6413682574954046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0115,,No,0.0,0.0,0.0,0.0,-104.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n376754,0,Cash loans,M,N,Y,2,90000.0,90000.0,5904.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005313,-10627,-3496,-4933.0,-2431,,1,1,1,1,0,0,Sales staff,4.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Trade: type 2,0.33935504460516325,0.6793263926676324,0.6144143775673561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-42.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n411380,0,Cash loans,M,Y,Y,2,157500.0,1467612.0,58333.5,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,Rented apartment,0.018209,-16534,-4212,-4167.0,-78,11.0,1,1,0,1,1,0,High skill tech staff,4.0,3,3,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.2602212366827857,0.5643868427311791,0.7338145369642702,0.1371,0.1005,0.9856,0.8028,0.0,0.0,0.1724,0.1667,0.2083,0.07200000000000001,0.1118,0.0643,0.0,0.0,0.1397,0.1043,0.9856,0.8105,0.0,0.0,0.1724,0.1667,0.2083,0.0736,0.1221,0.067,0.0,0.0,0.1384,0.1005,0.9856,0.8054,0.0,0.0,0.1724,0.1667,0.2083,0.0732,0.1137,0.0654,0.0,0.0,reg oper account,block of flats,0.0506,Panel,No,0.0,0.0,0.0,0.0,-2877.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n410511,1,Cash loans,F,N,Y,2,67500.0,269550.0,23188.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002134,-12417,-1975,-3703.0,-3858,,1,1,0,1,0,0,Sales staff,4.0,3,3,WEDNESDAY,5,0,0,0,1,1,0,Self-employed,,0.0969981621076347,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n299081,0,Cash loans,F,N,N,0,135000.0,521280.0,25209.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003069,-20642,365243,-1109.0,-3417,,1,0,0,1,1,0,,2.0,3,3,SATURDAY,11,0,0,0,0,0,0,XNA,,0.6878820790723822,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1515.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n324292,0,Cash loans,M,Y,Y,0,171000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-12782,-1277,-302.0,-4909,11.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.5110432164426221,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1337.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n415200,0,Cash loans,M,Y,N,1,135000.0,450000.0,23107.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,With parents,0.030755,-12466,-3271,-3431.0,-4233,11.0,1,1,0,1,0,1,Drivers,3.0,2,2,WEDNESDAY,20,0,0,0,0,0,0,Government,0.2976565783111673,0.5758393243995081,0.5989262182569273,0.2031,0.1199,0.9965,0.9524,0.3713,0.12,0.0345,0.625,0.6667,0.0849,0.1538,,0.0541,,0.2069,0.1244,0.9965,0.9543,0.3747,0.1208,0.0345,0.625,0.6667,0.0869,0.168,,0.0545,,0.2051,0.1199,0.9965,0.953,0.3737,0.12,0.0345,0.625,0.6667,0.0864,0.1565,,0.0543,,org spec account,block of flats,0.2977,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1774.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n170752,0,Cash loans,F,Y,N,0,180000.0,86598.0,8694.0,76500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009549,-15328,-1989,-2813.0,-3134,9.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Self-employed,0.5925622820114825,0.5710627803697048,0.7295666907060153,0.1485,0.0807,0.9821,0.7552,0.0631,0.16,0.1379,0.3333,0.375,0.076,0.121,0.1522,0.0,0.0,0.1513,0.0837,0.9821,0.7648,0.0637,0.1611,0.1379,0.3333,0.375,0.0777,0.1322,0.1586,0.0,0.0,0.1499,0.0807,0.9821,0.7585,0.0635,0.16,0.1379,0.3333,0.375,0.0773,0.1231,0.155,0.0,0.0,reg oper spec account,block of flats,0.1403,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n330239,0,Cash loans,F,N,N,0,90000.0,450000.0,32742.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-10232,-1025,-7002.0,-2867,,1,1,1,1,1,0,Core staff,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Government,0.35164314979671696,0.18445645802414548,0.1997705696341145,0.0619,0.0502,0.9752,0.66,0.0051,0.0,0.1034,0.1667,0.2083,0.0377,0.0504,0.0508,0.0,0.0,0.063,0.0521,0.9752,0.6733,0.0052,0.0,0.1034,0.1667,0.2083,0.0386,0.0551,0.0529,0.0,0.0,0.0625,0.0502,0.9752,0.6645,0.0052,0.0,0.1034,0.1667,0.2083,0.0384,0.0513,0.0517,0.0,0.0,reg oper spec account,block of flats,0.04,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2415.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n380332,0,Cash loans,F,Y,Y,0,157500.0,521280.0,36409.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-16131,-751,-16131.0,-2911,6.0,1,1,0,1,1,0,Laborers,2.0,1,1,MONDAY,13,0,0,0,0,0,0,Business Entity Type 1,,0.7446404406446122,0.41534714488434,0.0423,0.0368,0.9488,0.2996,0.0299,0.12,0.2759,0.1667,0.2083,0.0373,,0.1042,,0.0784,0.0431,0.0382,0.9489,0.327,0.0301,0.1208,0.2759,0.1667,0.2083,0.0382,,0.1086,,0.083,0.0427,0.0368,0.9488,0.309,0.0301,0.12,0.2759,0.1667,0.2083,0.038,,0.1061,,0.0801,reg oper account,block of flats,0.1154,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n250723,0,Cash loans,M,Y,Y,0,202500.0,888840.0,37494.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-17270,-225,-994.0,-821,22.0,1,1,0,1,1,0,Laborers,2.0,1,1,FRIDAY,14,1,1,0,1,1,0,Business Entity Type 3,,0.6820640518196789,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2915.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n349530,0,Cash loans,F,Y,N,2,342000.0,976135.5,53622.0,904500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.001333,-13711,-3615,-3610.0,-5683,5.0,1,1,0,1,0,0,Laborers,4.0,3,3,MONDAY,12,0,0,0,0,0,0,Construction,0.3272795883066737,0.6365267170771043,0.813917469762627,0.1165,0.0719,0.9791,0.7144,0.0287,0.0,0.069,0.1667,0.2083,0.0485,0.0941,0.0477,0.0039,0.0275,0.1187,0.0747,0.9791,0.7256,0.028999999999999998,0.0,0.069,0.1667,0.2083,0.0496,0.1028,0.0497,0.0039,0.0291,0.1176,0.0719,0.9791,0.7182,0.0289,0.0,0.069,0.1667,0.2083,0.0494,0.0958,0.0485,0.0039,0.0281,reg oper account,block of flats,0.0592,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2498.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n197403,0,Cash loans,M,Y,Y,0,180000.0,450000.0,22018.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.072508,-17007,-591,-1372.0,-360,13.0,1,1,0,1,0,0,,1.0,1,1,THURSDAY,16,0,0,0,0,0,0,Construction,0.6115813902125944,0.7170470585694896,,0.0309,0.03,0.9737,0.6396,0.0272,0.0,0.069,0.1667,0.2083,0.0323,0.0252,0.0253,0.0,0.0,0.0315,0.0311,0.9737,0.6537,0.0275,0.0,0.069,0.1667,0.2083,0.0331,0.0275,0.0264,0.0,0.0,0.0312,0.03,0.9737,0.6444,0.0274,0.0,0.069,0.1667,0.2083,0.0329,0.0257,0.0257,0.0,0.0,reg oper account,block of flats,0.0199,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n245061,0,Cash loans,F,N,N,0,75600.0,286704.0,17455.5,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.04622,-21523,365243,-13721.0,-4129,,1,0,0,1,0,0,,1.0,1,1,FRIDAY,16,0,0,0,0,0,0,XNA,,0.6132975886232876,0.7826078370261895,0.0371,0.0037,0.9732,0.6328,0.0043,0.0,0.1034,0.0833,0.125,0.0527,0.0303,0.0303,0.0,0.0,0.0378,0.0038,0.9732,0.6472,0.0043,0.0,0.1034,0.0833,0.125,0.0539,0.0331,0.0316,0.0,0.0,0.0375,0.0037,0.9732,0.6377,0.0043,0.0,0.1034,0.0833,0.125,0.0536,0.0308,0.0309,0.0,0.0,reg oper account,block of flats,0.0262,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1610.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n380546,0,Cash loans,F,N,Y,0,135000.0,191880.0,20286.0,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.006207,-23138,365243,-6097.0,-4164,,1,0,0,1,0,0,,1.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.43149782241560297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-168.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n394466,0,Cash loans,M,Y,Y,0,202500.0,929623.5,63427.5,877500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-13972,-260,-4615.0,-3599,13.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,13,0,0,0,0,1,1,Trade: type 3,,0.1505648832229978,0.2678689358444539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-212.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n132406,0,Cash loans,F,N,Y,1,112500.0,450000.0,27324.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-12232,-1733,-1630.0,-3339,,1,1,0,1,0,0,Sales staff,3.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,Self-employed,,0.2962119420200029,0.2721336844147212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1212.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n286457,0,Cash loans,M,N,Y,0,225000.0,348264.0,37084.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.014464,-10939,-3137,-4289.0,-3182,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,5,0,0,0,0,0,0,Business Entity Type 3,0.463235048779968,0.2858978721410488,,0.051,0.0196,0.9687,0.5716,0.0583,0.0,0.1207,0.0833,0.125,0.0319,0.0416,0.0368,0.0,0.0,0.0305,0.0,0.9687,0.5884,0.0464,0.0,0.1034,0.0833,0.125,0.0286,0.0266,0.0296,0.0,0.0,0.0515,0.0196,0.9687,0.5773,0.0587,0.0,0.1207,0.0833,0.125,0.0324,0.0423,0.0375,0.0,0.0,not specified,block of flats,0.0475,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-324.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n455317,0,Cash loans,F,Y,Y,1,157500.0,916470.0,29565.0,765000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.020713,-9916,-1089,-186.0,-1978,12.0,1,1,0,1,0,0,Managers,3.0,3,2,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.4306970179370129,0.1743532335979072,0.4650692149562261,0.6505,0.2577,0.9995,0.9864,0.1155,0.24,0.2069,0.375,0.0417,0.1133,0.501,0.3944,0.1351,0.29,0.6628,0.2675,0.9995,0.9869,0.1165,0.2417,0.2069,0.375,0.0417,0.1159,0.5473,0.4109,0.1362,0.307,0.6568,0.2577,0.9995,0.9866,0.1162,0.24,0.2069,0.375,0.0417,0.1153,0.5096,0.4015,0.1359,0.2961,reg oper account,block of flats,0.4458,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n261257,0,Cash loans,F,N,N,1,135000.0,538704.0,26046.0,481500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.008865999999999999,-10682,-463,-5240.0,-3333,,1,1,0,1,0,1,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.05354716976771432,0.2172394886463352,0.8193176922872417,0.0124,,0.9806,,,0.0,0.1034,0.0417,,,,0.0069,,,0.0126,,0.9806,,,0.0,0.1034,0.0417,,,,0.0072,,,0.0125,,0.9806,,,0.0,0.1034,0.0417,,,,0.0071,,,,block of flats,0.0084,Wooden,Yes,1.0,0.0,1.0,0.0,-1356.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n250008,1,Cash loans,F,N,Y,1,90000.0,450000.0,27324.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-10844,-1778,-983.0,-998,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,17,0,0,0,1,1,0,Industry: type 11,,0.4384252980494013,0.6690566947824041,0.0041,,0.9717,,,,,0.0,,,,0.0023,,,0.0042,,0.9717,,,,,0.0,,,,0.0024,,,0.0042,,0.9717,,,,,0.0,,,,0.0023,,,,block of flats,0.0018,Wooden,Yes,1.0,1.0,1.0,1.0,-585.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n368367,0,Cash loans,F,N,Y,2,135000.0,765261.0,34578.0,684000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.01885,-14252,-1122,-2504.0,-2417,,1,1,0,1,1,0,Realty agents,4.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.6531853805800283,0.591196451629396,,0.0722,0.0805,0.9826,0.762,0.0079,0.0,0.1379,0.1667,,,0.0588,0.0651,,0.0286,0.0735,0.0835,0.9826,0.7713,0.0079,0.0,0.1379,0.1667,,,0.0643,0.0678,,0.0302,0.0729,0.0805,0.9826,0.7652,0.0079,0.0,0.1379,0.1667,,,0.0599,0.0662,,0.0292,,block of flats,0.0617,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-418.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n148199,0,Cash loans,F,N,N,0,247500.0,1288350.0,37795.5,1125000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.035792000000000004,-13457,-6300,-7483.0,-4368,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Transport: type 2,0.5704871917889518,0.7464077694565145,0.7180328113294772,0.1093,0.0609,0.9861,0.8096,0.0635,0.12,0.1034,0.3333,0.0417,0.0861,0.0883,0.0711,0.0039,0.0137,0.1113,0.0632,0.9861,0.8171,0.0641,0.1208,0.1034,0.3333,0.0417,0.08800000000000001,0.0964,0.07400000000000001,0.0039,0.0145,0.1103,0.0609,0.9861,0.8121,0.0639,0.12,0.1034,0.3333,0.0417,0.0876,0.0898,0.0723,0.0039,0.0139,reg oper account,block of flats,0.0936,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1606.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,0.0\n205063,0,Cash loans,M,N,N,0,90000.0,509400.0,34042.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-14848,-685,-3965.0,-1471,,1,1,1,1,0,0,Laborers,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Other,0.3337438539277991,0.2921482035930674,,0.0495,0.0,0.9732,0.6328,0.0041,0.0,0.1034,0.125,0.1667,0.0107,0.0403,0.0403,0.0,0.0,0.0504,0.0,0.9732,0.6472,0.0041,0.0,0.1034,0.125,0.1667,0.011000000000000001,0.0441,0.042,0.0,0.0,0.05,0.0,0.9732,0.6377,0.0041,0.0,0.1034,0.125,0.1667,0.0109,0.040999999999999995,0.040999999999999995,0.0,0.0,reg oper account,block of flats,0.0339,Block,No,3.0,0.0,3.0,0.0,-1445.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n148003,0,Cash loans,F,N,Y,0,180000.0,942300.0,27679.5,675000.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.006207,-18176,-11404,-7575.0,-1710,,1,1,1,1,1,0,Core staff,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Culture,,0.6563053394035595,0.7001838506835805,0.0929,0.0159,0.9891,0.8504,0.0002,0.1,0.0862,0.3333,0.375,0.0,0.0756,0.0599,0.0006,0.1224,0.0756,0.0,0.9891,0.8563,0.0,0.0806,0.069,0.3333,0.375,0.0,0.0661,0.0506,0.0,0.1005,0.0937,0.0179,0.9891,0.8524,0.0,0.1,0.0862,0.3333,0.375,0.0,0.077,0.0609,0.0,0.1246,reg oper account,block of flats,0.0588,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n162706,0,Cash loans,F,N,Y,1,112500.0,139306.5,14755.5,126000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-15837,-209,-4652.0,-4659,,1,1,0,1,0,0,Medicine staff,3.0,2,2,TUESDAY,8,0,0,0,0,1,1,Medicine,,0.493463848752391,0.6925590674998008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n138460,0,Cash loans,F,N,Y,1,225000.0,770292.0,30676.5,688500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.010006,-17493,-8692,-2884.0,-1044,,1,1,0,1,0,0,High skill tech staff,2.0,2,1,WEDNESDAY,12,0,0,0,0,0,0,Trade: type 6,,0.011523040813754413,0.5656079814115492,0.1113,0.0,0.9955,0.9388,0.1168,0.0,0.1724,0.1667,0.2083,0.1278,0.0841,0.126,0.0309,0.1077,0.1134,0.0,0.9955,0.9412,0.1178,0.0,0.1724,0.1667,0.2083,0.1307,0.0918,0.1313,0.0311,0.114,0.1124,0.0,0.9955,0.9396,0.1175,0.0,0.1724,0.1667,0.2083,0.13,0.0855,0.1283,0.0311,0.1099,reg oper account,block of flats,0.1292,Panel,No,2.0,0.0,2.0,0.0,-774.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n110466,0,Cash loans,F,N,Y,0,360000.0,1616107.5,44442.0,1444500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006008,-21277,365243,-10338.0,-4061,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.7071897146198783,0.5849900404894085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,6.0,1.0,-1597.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n415002,0,Cash loans,M,N,N,0,121500.0,675000.0,21906.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,Co-op apartment,0.019689,-17175,-433,-6467.0,-709,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Construction,,0.6358130567937719,0.34741822720026416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-805.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n305649,0,Cash loans,F,N,Y,0,67500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-19148,-576,-8609.0,-91,,1,1,1,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Other,,0.6431431789688805,0.2678689358444539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2321.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n236104,0,Cash loans,M,Y,N,0,72000.0,528633.0,25560.0,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010966,-16965,365243,-4718.0,-517,1.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.7685504331204781,0.18411615593071512,0.0722,0.0782,0.9762,0.6736,0.0093,0.0,0.1379,0.1667,,0.0469,,0.0626,,0.0036,0.0735,0.0811,0.9762,0.6864,0.0094,0.0,0.1379,0.1667,,0.0479,,0.0652,,0.0038,0.0729,0.0782,0.9762,0.6779999999999999,0.0094,0.0,0.1379,0.1667,,0.0477,,0.0637,,0.0037,org spec account,block of flats,0.05,\"Stone, brick\",No,9.0,0.0,9.0,0.0,-2063.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n210613,0,Cash loans,F,N,Y,0,130500.0,254700.0,24939.0,225000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.007114,-24634,365243,-16689.0,-4322,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.6651140567518646,0.4418358231994413,0.0928,0.096,0.9771,0.6872,0.0133,0.0,0.2069,0.1667,0.2083,0.0765,,0.0889,,0.0,0.0945,0.0996,0.9772,0.6994,0.0134,0.0,0.2069,0.1667,0.2083,0.0782,,0.0926,,0.0,0.0937,0.096,0.9771,0.6914,0.0134,0.0,0.2069,0.1667,0.2083,0.0778,,0.0905,,0.0,reg oper account,block of flats,0.0699,Panel,No,0.0,0.0,0.0,0.0,-2127.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,4.0\n292147,0,Revolving loans,M,Y,N,1,315000.0,630000.0,31500.0,630000.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.035792000000000004,-12056,-3850,-1773.0,-4404,7.0,1,1,0,1,0,0,HR staff,3.0,2,2,FRIDAY,8,0,0,0,0,0,0,Military,0.1687424763049805,0.5468790914841644,0.4382813743111921,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1471.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n143234,1,Cash loans,M,Y,Y,1,202500.0,1042560.0,40572.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.031329,-14479,-6980,-8559.0,-4283,3.0,1,1,0,1,0,0,Core staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Transport: type 2,0.3091998468191965,0.5631875670581673,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n257830,0,Cash loans,F,N,Y,0,216000.0,445095.0,34573.5,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.072508,-19031,-162,-12788.0,-2575,,1,1,0,1,1,0,Cleaning staff,1.0,1,1,THURSDAY,17,0,0,0,0,0,0,University,,0.4934091892169036,,0.1186,0.1424,0.9732,0.6328,0.0315,0.16,0.1379,0.2917,0.3333,0.0601,0.0941,0.1548,0.0116,0.1574,0.1208,0.1477,0.9732,0.6472,0.0318,0.1611,0.1379,0.2917,0.3333,0.0615,0.1028,0.1613,0.0117,0.1667,0.1197,0.1424,0.9732,0.6377,0.0317,0.16,0.1379,0.2917,0.3333,0.0612,0.0958,0.1576,0.0116,0.1607,reg oper account,block of flats,0.1561,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-268.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n351363,0,Cash loans,F,N,Y,1,135000.0,625500.0,18418.5,625500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-14099,-550,-3879.0,-4773,,1,1,0,1,1,0,Laborers,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6060679594124174,0.6966027859121129,0.4902575124990026,0.0082,0.0226,0.9712,0.6056,0.0019,0.0,0.069,0.0417,0.0417,0.0214,0.0067,0.0084,0.0,0.0,0.0084,0.0235,0.9712,0.621,0.0019,0.0,0.069,0.0417,0.0417,0.0219,0.0073,0.0087,0.0,0.0,0.0083,0.0226,0.9712,0.6109,0.0019,0.0,0.069,0.0417,0.0417,0.0218,0.0068,0.0085,0.0,0.0,reg oper account,block of flats,0.0076,Mixed,No,0.0,0.0,0.0,0.0,-799.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n119080,0,Revolving loans,M,N,Y,2,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-12977,-2038,-3539.0,-3523,,1,1,0,1,0,0,Laborers,4.0,2,2,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.2184267011711621,0.5958231869457908,0.7981372313187245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-1097.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n260612,1,Cash loans,F,N,N,0,112500.0,150948.0,11286.0,126000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020246,-19077,-2688,-10707.0,-2621,,1,1,1,1,0,0,Sales staff,2.0,3,3,MONDAY,7,0,0,0,0,0,0,Self-employed,,0.19998704674887066,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1574.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n245223,0,Cash loans,M,N,Y,1,112500.0,219042.0,24844.5,193500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-19869,-2278,-6730.0,-3405,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,18,0,0,0,0,0,0,Housing,,0.7837987380068884,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1719.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n126458,0,Cash loans,M,Y,Y,0,90000.0,592560.0,31153.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-15834,-7984,-5422.0,-4408,22.0,1,1,0,1,0,0,Core staff,2.0,1,1,MONDAY,16,0,0,0,0,0,0,Postal,,0.6638025610788935,,0.0577,0.0561,0.9856,,,0.0,0.1379,0.1667,,0.0475,,0.0608,,0.0295,0.0588,0.0582,0.9856,,,0.0,0.1379,0.1667,,0.0486,,0.0633,,0.0312,0.0583,0.0561,0.9856,,,0.0,0.1379,0.1667,,0.0484,,0.0619,,0.0301,,block of flats,0.0542,Panel,No,1.0,0.0,1.0,0.0,-243.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n447663,0,Cash loans,F,N,Y,0,72000.0,225000.0,17775.0,225000.0,Unaccompanied,State servant,Incomplete higher,Married,Rented apartment,0.020246,-8270,-685,-8036.0,-165,,1,1,0,1,1,0,Core staff,2.0,3,3,TUESDAY,15,0,0,0,0,1,1,Kindergarten,0.1058520258933134,0.16311836514176922,,0.066,0.0823,0.9821,0.7552,0.011000000000000001,0.0,0.1379,0.1667,0.2083,0.0787,0.0538,0.0362,0.0,0.0877,0.0672,0.0854,0.9821,0.7648,0.0111,0.0,0.1379,0.1667,0.2083,0.0805,0.0588,0.0377,0.0,0.0928,0.0666,0.0823,0.9821,0.7585,0.0111,0.0,0.1379,0.1667,0.2083,0.0801,0.0547,0.0368,0.0,0.0895,reg oper account,block of flats,0.0475,\"Stone, brick\",No,3.0,2.0,3.0,2.0,-1121.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n176660,0,Revolving loans,F,N,Y,2,45000.0,135000.0,6750.0,135000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.019689,-10812,-850,-3153.0,-3421,,1,1,0,1,0,0,Sales staff,4.0,2,2,MONDAY,14,0,0,0,0,0,0,Self-employed,0.4445233548010576,0.5347861205432357,0.807273923277881,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,6.0,1.0,-2447.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n171083,0,Cash loans,F,Y,N,0,103500.0,1272888.0,37215.0,1111500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.018801,-12214,-3217,-1437.0,-3762,14.0,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,5,0,0,0,0,0,0,Government,,0.11570537236810448,0.5762088360175724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2267.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n323202,0,Cash loans,F,N,Y,0,67500.0,254700.0,14620.5,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.031329,-24060,365243,-6864.0,-4162,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.6991432692532219,0.22133520635446602,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-395.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n251544,0,Cash loans,F,N,Y,1,180000.0,1255680.0,41629.5,1125000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-14686,-2210,-8758.0,-4057,,1,1,0,1,0,0,,3.0,1,1,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.671589650745095,0.5691487713619409,,,0.9851,,,,0.069,,,,,0.0568,,,,,0.9851,,,,0.069,,,,,0.0592,,,,,0.9851,,,,0.069,,,,,0.0578,,,,,0.0695,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n389779,0,Cash loans,F,N,Y,0,112500.0,385164.0,21024.0,292500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.010276,-19460,-1813,-2941.0,-2998,,1,1,0,1,1,0,Managers,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Postal,,0.6431281223390132,0.816092360478441,0.099,0.1627,0.9846,,,0.0,0.2759,0.1667,,0.0252,,0.1065,,0.0087,0.1008,0.1689,0.9846,,,0.0,0.2759,0.1667,,0.0258,,0.111,,0.0092,0.0999,0.1627,0.9846,,,0.0,0.2759,0.1667,,0.0257,,0.1084,,0.0089,,block of flats,0.0857,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-256.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n399819,0,Cash loans,F,N,Y,1,135000.0,450000.0,16965.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010643,-11765,-1441,-5956.0,-4114,,1,1,0,1,1,0,Cleaning staff,3.0,2,2,TUESDAY,11,0,1,1,0,0,0,Business Entity Type 3,,0.6503869967182332,0.6594055320683344,0.0897,0.1098,0.9826,0.762,0.0131,0.0,0.2069,0.1667,0.2083,0.08900000000000001,0.0731,0.0904,0.0,0.0,0.0914,0.1139,0.9826,0.7713,0.0133,0.0,0.2069,0.1667,0.2083,0.0911,0.0799,0.0941,0.0,0.0,0.0906,0.1098,0.9826,0.7652,0.0132,0.0,0.2069,0.1667,0.2083,0.0906,0.0744,0.092,0.0,0.0,reg oper account,block of flats,0.0783,Panel,No,0.0,0.0,0.0,0.0,-130.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n341310,0,Revolving loans,F,N,Y,2,135000.0,225000.0,11250.0,225000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.072508,-16645,-8986,-5615.0,-176,,1,1,0,1,0,1,Core staff,4.0,1,1,FRIDAY,16,0,0,0,0,0,0,School,0.8423589269535344,0.744881538147575,0.7557400501752248,0.2433,0.0887,0.9786,0.7076,0.1299,0.4,0.1724,0.4583,0.5,0.0,0.2,0.2306,0.0309,0.1504,0.2479,0.092,0.9786,0.7190000000000001,0.1311,0.4028,0.1724,0.4583,0.5,0.0,0.2185,0.2402,0.0311,0.1592,0.2457,0.0887,0.9786,0.7115,0.1307,0.4,0.1724,0.4583,0.5,0.0,0.2035,0.2347,0.0311,0.1536,reg oper account,block of flats,0.2141,Panel,No,0.0,0.0,0.0,0.0,-3159.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n238814,0,Cash loans,F,Y,N,2,112500.0,239850.0,23719.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.028663,-13479,-1064,-2130.0,-1592,3.0,1,1,0,1,0,0,Core staff,4.0,2,2,FRIDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.6227398626847176,0.6461037890254693,0.6144143775673561,0.1402,0.0599,0.9747,0.6532,0.0816,0.0,0.2759,0.1667,0.2083,0.0899,0.1143,0.1089,0.0,0.1365,0.1429,0.0621,0.9747,0.6668,0.0824,0.0,0.2759,0.1667,0.2083,0.0919,0.1249,0.1134,0.0,0.1445,0.1416,0.0599,0.9747,0.6578,0.0821,0.0,0.2759,0.1667,0.2083,0.0914,0.1163,0.1108,0.0,0.1394,reg oper account,block of flats,0.1599,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-974.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n169060,0,Cash loans,F,N,N,0,108000.0,568858.5,30987.0,432000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.006852,-12974,-242,-6899.0,-2229,,1,1,0,1,0,0,Core staff,1.0,3,3,SATURDAY,8,0,0,0,0,0,0,Self-employed,,0.6797978136747904,0.8454379217710598,0.0835,0.0682,0.9776,0.6940000000000001,0.0307,0.0,0.1379,0.1667,0.2083,0.0182,0.0672,0.0771,0.0039,0.0093,0.0851,0.0708,0.9777,0.706,0.031,0.0,0.1379,0.1667,0.2083,0.0186,0.0735,0.0804,0.0039,0.0098,0.0843,0.0682,0.9776,0.6981,0.0309,0.0,0.1379,0.1667,0.2083,0.0185,0.0684,0.0785,0.0039,0.0095,reg oper account,block of flats,0.0795,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n200628,0,Cash loans,M,N,Y,1,225000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-16565,-2284,-5157.0,-106,,1,1,0,1,1,0,,3.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Transport: type 4,,0.7109608784019691,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,0.0\n132196,0,Cash loans,F,Y,N,1,112500.0,422892.0,22027.5,382500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-8994,-854,-372.0,-1482,64.0,1,1,0,1,0,0,Core staff,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Trade: type 2,,0.3550605434276024,0.3910549766342248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-450.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n223228,0,Revolving loans,M,Y,N,1,180000.0,270000.0,13500.0,270000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-14750,-159,-4209.0,-594,14.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,10,1,1,0,1,1,0,Construction,,0.18765873947249204,,0.0412,0.0818,0.9901,0.8640000000000001,0.01,0.0,0.1379,0.1667,0.2083,0.0109,0.0336,0.0451,0.0,0.0,0.042,0.0849,0.9901,0.8693,0.0101,0.0,0.1379,0.1667,0.2083,0.0112,0.0367,0.047,0.0,0.0,0.0416,0.0818,0.9901,0.8658,0.0101,0.0,0.1379,0.1667,0.2083,0.0111,0.0342,0.0459,0.0,0.0,reg oper account,block of flats,0.040999999999999995,\"Stone, brick\",No,9.0,0.0,9.0,0.0,-31.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n332711,0,Cash loans,M,N,N,0,315000.0,900000.0,50256.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006233,-18246,-4508,-5421.0,-1487,,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,19,0,0,0,0,0,0,Self-employed,,0.7563819990064197,,0.1649,0.0091,0.9881,0.8368,0.0402,0.16,0.1379,0.375,0.4167,0.0305,0.1345,0.1696,0.0,0.0072,0.1681,0.0094,0.9881,0.8432,0.0406,0.1611,0.1379,0.375,0.4167,0.0312,0.1469,0.1767,0.0,0.0076,0.1665,0.0091,0.9881,0.8390000000000001,0.0404,0.16,0.1379,0.375,0.4167,0.031,0.1368,0.1726,0.0,0.0073,reg oper account,block of flats,0.1349,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1362.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n192189,0,Cash loans,M,N,Y,0,112500.0,135000.0,12622.5,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.01885,-8659,-1098,-6449.0,-1296,,1,1,0,1,1,0,Sales staff,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Trade: type 2,,,,0.0495,,0.9727,,,,0.1034,0.125,,,,0.0262,,0.0008,0.0504,,0.9727,,,,0.1034,0.125,,,,0.0273,,0.0008,0.05,,0.9727,,,,0.1034,0.125,,,,0.0266,,0.0008,,block of flats,0.0319,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-309.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n223347,0,Cash loans,F,N,Y,0,90000.0,414792.0,22630.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.002134,-11734,-1975,-1764.0,-530,,1,1,0,1,0,0,Accountants,1.0,3,3,THURSDAY,10,0,0,0,0,0,0,Restaurant,,0.07355887352487972,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n324813,0,Cash loans,F,N,N,0,153000.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.014464,-17202,-384,-2634.0,-759,,1,1,1,1,0,0,Sales staff,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Self-employed,,0.4704004927959871,0.28371188263500075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1038.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n447271,0,Cash loans,F,N,Y,0,157500.0,622413.0,31909.5,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-7868,-630,-975.0,-539,,1,1,0,1,0,0,Sales staff,2.0,3,3,THURSDAY,9,0,0,0,1,1,0,Self-employed,0.2308442749857845,0.2679928964948429,0.4525335592581747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-319.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n345116,0,Cash loans,M,Y,Y,0,315000.0,611905.5,64386.0,567000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006629,-18322,-3229,-2291.0,-1874,18.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Self-employed,,0.6769303331533043,,0.0031,,0.9762,,,,0.1034,0.0417,,,,0.0029,,,0.0032,,0.9762,,,,0.1034,0.0417,,,,0.003,,,0.0031,,0.9762,,,,0.1034,0.0417,,,,0.0029,,,,block of flats,0.0023,Wooden,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n132224,0,Cash loans,M,N,Y,1,90000.0,164952.0,13162.5,130500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008473999999999999,-14852,-747,-2265.0,-4046,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 2,,0.39306240351163785,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-480.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n108179,0,Cash loans,M,N,Y,0,315000.0,1288350.0,41692.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.0105,-16056,-2737,-2771.0,-2771,,1,1,0,1,0,0,Managers,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,Trade: type 7,,0.3895206593050639,0.4578995512067301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n220668,0,Cash loans,M,Y,N,0,180000.0,900000.0,32328.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009175,-13410,-487,-1528.0,-758,8.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 1,0.5761352345290935,0.5488453877648309,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-951.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n409693,0,Cash loans,F,N,N,1,148500.0,500211.0,28062.0,463500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-14232,-2483,-1153.0,-4236,,1,1,0,1,0,0,Cleaning staff,3.0,2,2,WEDNESDAY,17,0,1,1,0,0,0,Cleaning,,0.5888319554112929,0.107532175802471,0.1711,0.1889,0.9781,0.7008,0.0214,0.0,0.1034,0.1667,0.2083,0.0377,0.1387,0.0518,0.0039,0.0058,0.1744,0.1961,0.9782,0.7125,0.0216,0.0,0.1034,0.1667,0.2083,0.0386,0.1515,0.0539,0.0039,0.0061,0.1728,0.1889,0.9781,0.7048,0.0216,0.0,0.1034,0.1667,0.2083,0.0384,0.1411,0.0527,0.0039,0.0059,reg oper account,specific housing,0.0537,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1491.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n281823,0,Cash loans,F,N,Y,0,202500.0,612612.0,24426.0,495000.0,Family,Pensioner,Secondary / secondary special,Separated,House / apartment,0.018634,-23545,365243,-2442.0,-4107,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,XNA,,0.4598467187178888,0.5226973172821112,0.0371,0.0611,0.9796,,,0.0,0.1379,0.0833,,0.0119,,0.0318,,0.0,0.0378,0.0634,0.9796,,,0.0,0.1379,0.0833,,0.0121,,0.0332,,0.0,0.0375,0.0611,0.9796,,,0.0,0.1379,0.0833,,0.0121,,0.0324,,0.0,,block of flats,0.0277,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n428554,0,Cash loans,F,Y,Y,1,202500.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.005002,-11974,-1500,-5136.0,-2464,5.0,1,1,0,1,0,0,Managers,3.0,3,3,WEDNESDAY,17,0,0,0,0,0,0,Trade: type 7,0.5678427369178991,0.6026029385315065,0.6144143775673561,0.2062,0.1825,0.9876,0.8368,0.0037,0.2,0.1724,0.375,,0.0306,0.2345,0.1471,0.0039,0.0012,0.1261,0.1894,0.9876,0.8432,0.0037,0.1208,0.1034,0.375,,0.0313,0.2562,0.135,0.0039,0.0012,0.2082,0.1825,0.9876,0.8390000000000001,0.0037,0.2,0.1724,0.375,,0.0311,0.2386,0.1498,0.0039,0.0012,org spec account,block of flats,0.2213,Panel,No,1.0,0.0,1.0,0.0,-625.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n193253,0,Cash loans,F,N,Y,0,79650.0,343800.0,12478.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019101,-21684,365243,-2583.0,-4373,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.6504367184119941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n230048,0,Cash loans,F,Y,N,0,112500.0,180000.0,11502.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010147,-15001,-1508,-2618.0,-5683,12.0,1,1,1,1,0,0,Laborers,2.0,2,2,SUNDAY,17,0,0,0,0,1,1,Business Entity Type 1,0.3060500371415751,0.4857316941075731,0.190705947811054,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-343.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n397683,0,Cash loans,M,N,Y,0,31500.0,227520.0,15331.5,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.0105,-22564,365243,-5525.0,-3972,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,16,0,0,0,0,0,0,XNA,,0.20150626358587315,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,0.0,-2246.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n150603,0,Cash loans,F,N,Y,0,49500.0,194076.0,10291.5,162000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-23334,365243,-9612.0,-4039,,1,0,0,1,1,0,,2.0,3,2,THURSDAY,7,0,0,0,0,0,0,XNA,,0.4659412794952601,,0.1639,0.1654,0.9776,0.6940000000000001,0.0191,0.0,0.2759,0.1667,0.2083,0.0479,0.132,0.1397,0.0077,0.0118,0.16699999999999998,0.1717,0.9777,0.706,0.0193,0.0,0.2759,0.1667,0.2083,0.049,0.1442,0.1456,0.0078,0.0125,0.1655,0.1654,0.9776,0.6981,0.0193,0.0,0.2759,0.1667,0.2083,0.0487,0.1342,0.1422,0.0078,0.012,reg oper account,block of flats,0.1229,Panel,No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n138398,0,Revolving loans,M,Y,Y,0,351000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.025164,-8045,-1319,-698.0,-707,9.0,1,1,0,1,0,1,Managers,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Self-employed,,0.0258529781133212,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n374004,0,Cash loans,F,Y,Y,0,130500.0,166810.5,13036.5,144000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-14541,-290,-7912.0,-4131,2.0,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.4092306647481109,0.474379136056963,0.5602843280409464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1978.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n223963,0,Cash loans,F,N,Y,0,112500.0,545040.0,20677.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-14742,-2374,-7416.0,-72,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Trade: type 6,0.5860286800886958,0.6227750601946034,0.501075160239048,0.0626,,0.9906,0.8572,,0.10400000000000001,0.1103,0.3333,0.375,0.0008,0.045,0.0784,0.0,0.009000000000000001,0.0378,,0.9896,0.8628,,0.1611,0.1034,0.3333,0.375,0.0008,0.0331,0.0535,0.0,0.0,0.0651,,0.9906,0.8591,,0.12,0.1034,0.3333,0.375,0.0008,0.0457,0.0757,0.0,0.0,reg oper account,block of flats,0.0405,Panel,No,1.0,0.0,1.0,0.0,-1497.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n430959,0,Cash loans,M,N,N,0,157500.0,248760.0,26788.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010147,-10241,-1024,-10213.0,-843,,1,1,1,1,1,0,Laborers,1.0,2,2,SATURDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.1035412178746307,0.54321348330385,0.2807895743848605,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-520.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,1.0,2.0\n124706,0,Revolving loans,M,Y,Y,1,315000.0,225000.0,11250.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-17981,-601,-826.0,-1472,10.0,1,1,0,1,0,0,High skill tech staff,3.0,1,1,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.6434371571504709,0.4740356381472464,0.2692857999073816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-512.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n251439,0,Cash loans,M,Y,Y,0,180000.0,269550.0,19300.5,225000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.009175,-8558,-1391,-5054.0,-1126,6.0,1,1,0,1,1,0,Core staff,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Construction,0.18032546105932645,0.714462227408255,,0.0753,0.0,0.9816,,,0.0,0.069,0.1667,,0.0515,,0.0633,,0.0,0.0767,0.0,0.9816,,,0.0,0.069,0.1667,,0.0527,,0.066,,0.0,0.076,0.0,0.9816,,,0.0,0.069,0.1667,,0.0524,,0.0645,,0.0,,block of flats,0.0498,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-663.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n321715,0,Cash loans,F,N,N,0,72000.0,158301.0,15552.0,148500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018029,-24007,365243,-9682.0,-5732,,1,0,0,1,0,0,,1.0,3,2,WEDNESDAY,8,0,0,0,0,0,0,XNA,,0.18345851962993434,0.7324033228040929,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1754.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n268694,1,Cash loans,F,N,Y,0,67500.0,270000.0,29209.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-9553,-301,-4377.0,-1148,,1,1,0,1,1,0,Waiters/barmen staff,2.0,2,2,WEDNESDAY,18,0,0,0,0,1,1,Business Entity Type 1,,0.6301894997707842,,0.0124,,0.9747,,,0.0,0.1034,0.0417,,0.006,,0.0065,,0.0112,0.0126,,0.9747,,,0.0,0.1034,0.0417,,0.0061,,0.0067,,0.0119,0.0125,,0.9747,,,0.0,0.1034,0.0417,,0.0061,,0.0066,,0.0115,,block of flats,0.0075,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-34.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n357354,0,Cash loans,F,N,Y,0,135000.0,225000.0,26833.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.014519999999999996,-24587,365243,-13203.0,-4165,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.6040107537547653,0.7583930617144343,0.1856,0.138,0.9816,0.7484,0.0132,0.2,0.1724,0.3333,0.375,0.1149,0.1513,0.1924,0.0,0.0,0.1513,0.1142,0.9816,0.7583,0.0,0.1611,0.1379,0.3333,0.375,0.0986,0.1322,0.1606,0.0,0.0,0.1874,0.138,0.9816,0.7518,0.0133,0.2,0.1724,0.3333,0.375,0.1169,0.1539,0.1958,0.0,0.0,org spec account,block of flats,0.1813,Panel,No,0.0,0.0,0.0,0.0,-35.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n247634,0,Revolving loans,F,N,Y,0,225000.0,360000.0,18000.0,360000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.022625,-21295,-5603,-7533.0,-4574,,1,1,0,1,0,0,Core staff,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Medicine,0.862843062608096,0.7445489507138215,0.722392890081304,0.0371,0.0,0.9722,0.6192,0.0441,0.0,0.1034,0.0833,0.125,0.0,0.0303,0.0205,0.0,0.0,0.0378,0.0,0.9722,0.6341,0.0445,0.0,0.1034,0.0833,0.125,0.0,0.0331,0.0214,0.0,0.0,0.0375,0.0,0.9722,0.6243,0.0443,0.0,0.1034,0.0833,0.125,0.0,0.0308,0.0209,0.0,0.0,reg oper account,block of flats,0.0241,Block,No,,,,,-851.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n381108,0,Cash loans,F,N,Y,0,112500.0,768550.5,33984.0,621000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-22917,365243,-12957.0,-4196,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,7,0,0,0,0,0,0,XNA,,0.0263987712395555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-1074.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n136159,0,Cash loans,F,N,N,1,189000.0,1200744.0,35239.5,1048500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.04622,-11961,-1774,-6043.0,-3758,,1,1,0,1,0,0,High skill tech staff,3.0,1,1,THURSDAY,16,0,0,0,0,1,1,Business Entity Type 3,0.46520275808183,0.7022846138233848,,0.0082,,0.9717,,,0.0,0.0345,0.0417,,,,0.0084,,0.0028,0.0084,,0.9717,,,0.0,0.0345,0.0417,,,,0.0088,,0.003,0.0083,,0.9717,,,0.0,0.0345,0.0417,,,,0.0086,,0.0029,,block of flats,0.0043,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n169733,0,Cash loans,M,Y,Y,0,315000.0,991944.0,29002.5,828000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-21182,-219,-8662.0,-4118,20.0,1,1,1,1,0,0,Laborers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.8513425027991474,0.7948762614474829,0.6397075677637197,0.0722,0.1599,0.9657,0.5308,0.1066,0.0,0.3103,0.125,0.0,0.0,0.0588,0.0857,0.0,0.0,0.0735,0.166,0.9657,0.5492,0.1076,0.0,0.3103,0.125,0.0,0.0,0.0643,0.0893,0.0,0.0,0.0729,0.1599,0.9657,0.5371,0.1073,0.0,0.3103,0.125,0.0,0.0,0.0599,0.0873,0.0,0.0,reg oper account,block of flats,0.1257,\"Stone, brick\",No,4.0,1.0,4.0,0.0,-2378.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,4.0,0.0,1.0\n213574,0,Cash loans,F,N,Y,0,135000.0,247500.0,17356.5,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.028663,-19576,365243,-4457.0,-3117,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,7,0,0,0,0,0,0,XNA,,0.29239246038363786,0.6279908192952864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-359.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n126529,0,Cash loans,F,N,Y,0,77400.0,148500.0,14818.5,148500.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.015221,-23452,365243,-10159.0,-3952,,1,0,0,1,1,0,,1.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.5644943644999207,0.7324033228040929,0.4237,0.1273,0.9831,0.7688,0.1805,0.16,0.1379,0.3333,0.0417,0.1875,0.3455,0.2106,0.0154,0.1431,0.4317,0.1321,0.9831,0.7779,0.1821,0.1611,0.1379,0.3333,0.0417,0.1917,0.3774,0.2194,0.0156,0.1514,0.4278,0.1273,0.9831,0.7719,0.1816,0.16,0.1379,0.3333,0.0417,0.1907,0.3514,0.2144,0.0155,0.1461,reg oper account,block of flats,0.1982,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-177.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n106606,0,Cash loans,F,Y,N,0,90000.0,675000.0,17937.0,675000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.026392,-12084,-2400,-112.0,-4407,3.0,1,1,0,1,0,0,Core staff,2.0,2,2,SUNDAY,11,0,0,0,0,1,1,School,,0.4618516321522241,0.3556387169923543,0.2464,,0.9940000000000001,,,0.24,0.2069,0.375,,,,0.254,,0.0,0.2511,,0.9940000000000001,,,0.2417,0.2069,0.375,,,,0.2646,,0.0,0.2488,,0.9940000000000001,,,0.24,0.2069,0.375,,,,0.2585,,0.0,,block of flats,0.1997,Panel,No,4.0,3.0,4.0,0.0,-14.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n156580,0,Cash loans,F,N,Y,0,112500.0,440784.0,27094.5,360000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003069,-16704,-756,-1378.0,-254,,1,1,0,1,0,1,Laborers,2.0,3,3,FRIDAY,13,0,0,0,0,0,0,Services,0.5093539312921526,0.6382501737036957,,0.1629,,0.9925,,,0.16,0.1379,0.375,,,,0.0987,,,0.166,,0.9926,,,0.1611,0.1379,0.375,,,,0.1028,,,0.1645,,0.9925,,,0.16,0.1379,0.375,,,,0.1005,,,,block of flats,0.1407,Panel,No,0.0,0.0,0.0,0.0,-1315.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n375840,0,Cash loans,M,Y,Y,0,94500.0,363190.5,20979.0,328500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.006852,-23440,365243,-10555.0,-4560,18.0,1,0,0,1,0,0,,2.0,3,3,THURSDAY,10,0,0,0,0,0,0,XNA,,0.4343323731809489,0.7490217048463391,0.0619,0.0704,0.9846,0.7892,0.0628,0.0,0.1379,0.1667,0.2083,0.0454,0.0504,0.0332,0.0,0.0,0.063,0.0731,0.9846,0.7975,0.0634,0.0,0.1379,0.1667,0.2083,0.0464,0.0551,0.0346,0.0,0.0,0.0625,0.0704,0.9846,0.792,0.0632,0.0,0.1379,0.1667,0.2083,0.0462,0.0513,0.0338,0.0,0.0,not specified,block of flats,0.0605,Others,No,2.0,1.0,2.0,0.0,-1309.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n243028,0,Cash loans,F,N,Y,2,67500.0,269550.0,14242.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-11140,-3431,-5639.0,-966,,1,1,1,1,1,0,Laborers,4.0,3,3,MONDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.30015176658443504,0.2842132851733042,0.1940678276718812,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-319.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n230246,0,Cash loans,F,N,N,0,202500.0,243000.0,26167.5,243000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.04622,-11679,-1703,-30.0,-1223,,1,1,0,1,0,1,,2.0,1,1,SATURDAY,14,0,1,1,0,1,1,Other,0.5903002811736171,0.7854837287591241,0.3859146722745145,0.066,,,,,0.16,0.1379,0.6667,,,,0.1362,,,0.0672,,,,,0.1611,0.1379,0.6667,,,,0.1419,,,0.0666,,,,,0.16,0.1379,0.6667,,,,0.1386,,,,,0.1136,,No,0.0,0.0,0.0,0.0,-459.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n264406,0,Cash loans,F,N,Y,2,67500.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-13021,-1219,-583.0,-2876,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,9,0,0,0,0,0,0,Self-employed,0.4551175824227211,0.7004846626540118,0.5726825047161584,0.0165,0.0001,0.9786,,,0.0,0.069,0.0417,,0.0896,,0.0146,,0.0,0.0168,0.0001,0.9786,,,0.0,0.069,0.0417,,0.0916,,0.0152,,0.0,0.0167,0.0001,0.9786,,,0.0,0.069,0.0417,,0.0911,,0.0149,,0.0,,block of flats,0.0115,\"Stone, brick\",No,2.0,0.0,1.0,0.0,-598.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n371792,0,Revolving loans,F,N,Y,0,202500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.016612000000000002,-17686,-8479,-8421.0,-1245,,1,1,0,1,0,0,Managers,1.0,2,2,THURSDAY,18,0,0,0,0,0,0,Kindergarten,0.6378613062404764,0.5924061679297055,,0.2969,0.1817,0.9856,0.8028,0.1991,0.36,0.3103,0.3333,0.375,0.0,0.2404,0.3237,0.0077,0.0119,0.3025,0.1886,0.9856,0.8105,0.2009,0.3625,0.3103,0.3333,0.375,0.0,0.2626,0.3373,0.0078,0.0126,0.2998,0.1817,0.9856,0.8054,0.2003,0.36,0.3103,0.3333,0.375,0.0,0.2446,0.3295,0.0078,0.0122,reg oper account,block of flats,0.366,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n245424,0,Cash loans,M,Y,Y,2,76500.0,135000.0,13351.5,135000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.00496,-15876,-2247,-3035.0,-4654,17.0,1,1,1,1,1,0,Drivers,4.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Cleaning,0.6279979242189708,0.7352747308522088,0.5779691187553125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-554.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n102739,0,Cash loans,F,Y,N,0,180000.0,203760.0,11826.0,180000.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.008473999999999999,-13575,-3735,-933.0,-1492,21.0,1,1,0,1,0,1,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Government,0.5377155347692979,0.4305914894194511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n222018,0,Cash loans,M,Y,N,0,202500.0,634482.0,18180.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-12737,-4462,-2936.0,-4584,15.0,1,1,1,1,1,0,Laborers,2.0,3,3,TUESDAY,8,0,0,0,0,0,0,Transport: type 2,,0.5092836052180564,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n392040,0,Cash loans,M,N,N,0,225000.0,225000.0,24232.5,225000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.028663,-9908,-276,-316.0,-2568,,1,1,1,1,0,0,Drivers,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.5024447150865572,0.4919279801219599,0.41184855592423975,0.0464,,0.9881,0.8368,,0.0,0.1034,0.2083,0.25,,,0.0424,,,0.0473,,0.9881,0.8432,,0.0,0.1034,0.2083,0.25,,,0.0442,,,0.0468,,0.9881,0.8390000000000001,,0.0,0.1034,0.2083,0.25,,,0.0432,,,reg oper account,block of flats,0.0362,Panel,No,0.0,0.0,0.0,0.0,-1419.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n386676,0,Cash loans,M,Y,Y,0,202500.0,177768.0,9207.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-13966,-5102,-156.0,-4302,5.0,1,1,1,1,0,0,Core staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Military,,0.5438482617903118,0.6109913280868294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2131.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n194346,1,Cash loans,M,Y,Y,1,225000.0,640080.0,31261.5,450000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.006629,-14079,-702,-5315.0,-4689,1.0,1,1,0,1,0,0,Security staff,3.0,2,2,THURSDAY,9,0,0,0,0,1,1,Security,,0.3944666088570036,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-344.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n410465,0,Cash loans,M,N,Y,0,1665000.0,454455.0,14400.0,319500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.032561,-14576,-285,-8614.0,-4083,,1,1,0,1,0,1,Sales staff,2.0,1,1,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.14337552039267762,0.7035905764219379,0.34578480246959553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-189.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n186631,1,Cash loans,M,N,Y,2,85500.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.026392,-16723,-337,-3986.0,-201,,1,1,0,1,1,0,Laborers,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.3897858912829331,0.21518240418475384,0.633,0.3731,0.9891,0.8504,0.179,0.84,0.5862,0.4583,0.25,0.5518,0.5127,0.789,0.0154,0.0218,0.645,0.3872,0.9891,0.8563,0.1807,0.8459,0.5862,0.4583,0.25,0.5644,0.5601,0.8220000000000001,0.0156,0.023,0.6391,0.3731,0.9891,0.8524,0.1802,0.84,0.5862,0.4583,0.25,0.5614,0.5216,0.8031,0.0155,0.0222,reg oper account,block of flats,0.6253,Panel,No,0.0,0.0,0.0,0.0,-2917.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n294748,0,Cash loans,F,N,Y,0,202500.0,837000.0,24601.5,837000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.0228,-23086,365243,-10061.0,-2026,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.670363430983041,0.5919766183185521,0.0914,0.1159,0.9940000000000001,0.9592,,0.08,0.069,0.3054,0.3471,0.0228,,0.1162,,0.0102,0.0494,0.0443,0.997,0.9608,,0.0403,0.0345,0.375,0.4167,0.0,,0.0475,,0.0084,0.0749,0.1447,0.997,0.9597,,0.08,0.069,0.375,0.4167,0.0319,,0.1226,,0.0083,,block of flats,0.1576,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1506.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n230847,0,Cash loans,F,N,Y,0,157500.0,755190.0,34992.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.02461,-14579,-1403,-8651.0,-4804,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.4097757234110175,0.6087082804275225,0.5100895276257282,0.204,0.1283,0.9841,0.7552,0.0442,0.1884,0.1624,0.3383,0.375,0.1364,0.1452,0.2149,0.0027,0.0071,0.1513,0.0503,0.9811,0.7517,0.0189,0.1611,0.1379,0.3333,0.375,0.0299,0.0643,0.0771,0.0,0.0,0.1499,0.1036,0.9821,0.7585,0.0342,0.16,0.1379,0.3333,0.375,0.11,0.1223,0.1524,0.0,0.0018,,block of flats,0.1576,Panel,No,0.0,0.0,0.0,0.0,-1547.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n447286,0,Cash loans,F,N,Y,0,117000.0,119893.5,11988.0,103500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-21651,365243,-5805.0,-4893,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,0.777712346621335,0.6469583388148467,0.7062051096536562,0.1536,0.14400000000000002,0.9881,0.8368,0.0652,0.0,0.3448,0.1667,0.2083,0.0912,0.1252,0.1414,0.0,0.0,0.1565,0.1494,0.9881,0.8432,0.0658,0.0,0.3448,0.1667,0.2083,0.0933,0.1368,0.1473,0.0,0.0,0.1551,0.14400000000000002,0.9881,0.8390000000000001,0.0656,0.0,0.3448,0.1667,0.2083,0.0928,0.1274,0.1439,0.0,0.0,,block of flats,0.1469,Panel,No,2.0,0.0,2.0,0.0,-1758.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n265899,0,Revolving loans,F,N,Y,0,54000.0,157500.0,7875.0,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-22221,365243,-1727.0,-4955,,1,0,0,1,0,0,,2.0,3,3,MONDAY,8,0,0,0,0,0,0,XNA,,0.4808334100809631,0.520897599048938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-877.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n378729,0,Cash loans,F,N,N,1,67500.0,127350.0,8419.5,112500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,Municipal apartment,0.008865999999999999,-15307,-3229,-5775.0,-4018,,1,1,1,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.4492043892371392,0.5656079814115492,0.0082,,0.9906,,,0.0,0.0345,0.0417,,,,0.0069,,,0.0084,,0.9906,,,0.0,0.0345,0.0417,,,,0.0071,,,0.0083,,0.9906,,,0.0,0.0345,0.0417,,,,0.006999999999999999,,,,block of flats,0.0054,Wooden,No,0.0,0.0,0.0,0.0,-42.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n214699,0,Cash loans,M,N,N,0,157500.0,436032.0,16434.0,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010556,-14705,-1176,-3468.0,-4057,,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,11,0,0,0,0,1,1,Self-employed,,0.14628378918724633,0.39449540531239935,0.0557,0.0603,0.9945,0.9252,0.0063,0.0,0.1034,0.1667,0.2083,0.0643,0.0454,0.048,0.0,0.0158,0.0567,0.0625,0.9945,0.9281,0.0064,0.0,0.1034,0.1667,0.2083,0.0658,0.0496,0.05,0.0,0.0168,0.0562,0.0603,0.9945,0.9262,0.0063,0.0,0.1034,0.1667,0.2083,0.0654,0.0462,0.0488,0.0,0.0162,reg oper account,block of flats,0.0506,\"Stone, brick\",No,9.0,0.0,9.0,0.0,-1794.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,1.0\n178628,0,Cash loans,F,N,Y,0,49500.0,76410.0,7956.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-19414,-3547,-1550.0,-2912,,1,1,0,1,0,0,,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5925751224537327,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1633.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n260003,0,Revolving loans,F,N,N,0,90000.0,270000.0,13500.0,270000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.028663,-9995,-2941,-4345.0,-2663,,1,1,0,1,1,0,Core staff,1.0,2,2,SATURDAY,12,0,0,0,0,1,1,Culture,0.3643888845020135,0.4623353893014076,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,1.0,-919.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n435266,0,Cash loans,F,N,Y,1,135000.0,829584.0,29925.0,630000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-13564,-1260,-254.0,-1524,,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,15,0,0,0,0,0,0,Industry: type 7,,0.13369628608896836,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-460.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n228549,0,Cash loans,F,Y,N,0,157500.0,203760.0,11695.5,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008575,-20776,365243,-9975.0,-3466,19.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,17,0,0,0,0,0,0,XNA,0.7362143845610212,0.4712722200624683,0.4812493411434029,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,1.0,0.0,-1846.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n366041,0,Revolving loans,M,N,Y,1,247500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.018801,-8165,-281,-5015.0,-832,,1,1,0,1,0,0,Accountants,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4282365588396063,0.4869772172945406,0.097581612687446,0.1082,0.1318,0.9791,0.7144,0.023,0.08,0.069,0.3333,0.375,0.0197,0.0874,0.0916,0.0039,0.1468,0.1103,0.1368,0.9791,0.7256,0.0232,0.0806,0.069,0.3333,0.375,0.0201,0.0955,0.0955,0.0039,0.1554,0.1093,0.1318,0.9791,0.7182,0.0232,0.08,0.069,0.3333,0.375,0.02,0.0889,0.0933,0.0039,0.1499,reg oper spec account,block of flats,0.10400000000000001,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-273.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,0.0\n295191,0,Cash loans,M,Y,Y,0,135000.0,818671.5,39514.5,661500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.025164,-11760,-2139,-5293.0,-3462,9.0,1,1,0,1,0,0,Drivers,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,Other,,0.35618302113249833,0.7338145369642702,0.0649,0.0367,0.9771,0.6872,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0504,0.0587,0.0116,0.0366,0.0662,0.0381,0.9772,0.6994,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0551,0.0612,0.0117,0.0388,0.0656,0.0367,0.9771,0.6914,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0513,0.0598,0.0116,0.0374,reg oper account,block of flats,0.0542,Panel,No,4.0,0.0,4.0,0.0,-2614.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n304789,0,Cash loans,F,N,Y,1,99000.0,203760.0,13617.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010032,-10548,-894,-4918.0,-3227,,1,1,1,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,3,0,0,0,0,1,1,Other,,0.5387879532357699,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-534.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n436238,0,Cash loans,F,N,N,1,54000.0,521280.0,26743.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010556,-13154,-137,-7144.0,-2201,,1,1,0,1,0,0,Laborers,3.0,3,3,SUNDAY,15,0,0,0,0,0,0,Kindergarten,0.37670073516422414,0.0665971549121703,0.5388627065779676,0.1227,0.0772,0.9851,0.7959999999999999,0.2059,0.0,0.2759,0.1667,0.2083,0.1613,0.1,0.1262,0.0,0.0611,0.125,0.0801,0.9851,0.804,0.2078,0.0,0.2759,0.1667,0.2083,0.1649,0.1093,0.1314,0.0,0.0646,0.1239,0.0772,0.9851,0.7987,0.2072,0.0,0.2759,0.1667,0.2083,0.1641,0.1018,0.1284,0.0,0.0623,org spec account,block of flats,0.1126,Panel,No,2.0,0.0,2.0,0.0,-2272.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n252043,0,Cash loans,F,N,N,1,90000.0,168102.0,13410.0,148500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-13888,-2684,-736.0,-3205,,1,1,0,1,0,0,Cleaning staff,3.0,3,3,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 1,,0.4456847758504598,0.41534714488434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,4.0,5.0,4.0,-1357.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n132725,0,Cash loans,F,N,Y,0,225000.0,495972.0,27036.0,414000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-22646,365243,-11219.0,-4310,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,,0.3913890654571238,0.4418358231994413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-348.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n131659,0,Cash loans,F,N,Y,0,157500.0,769527.0,34024.5,612000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.022625,-18021,-2670,-6015.0,-1557,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Government,,0.7894981234092475,0.4794489811780563,0.0814,0.0362,0.4883,,,0.0,0.0517,0.1042,,0.0467,,0.0256,,0.1662,0.0042,0.0001,0.0,,,0.0,0.0345,0.0417,,0.0,,0.004,,0.0,0.0822,0.0362,0.4883,,,0.0,0.0517,0.1042,,0.0475,,0.0261,,0.1696,,block of flats,0.0019,Wooden,No,0.0,0.0,0.0,0.0,-1951.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n274343,0,Cash loans,M,N,N,0,315000.0,653328.0,19102.5,468000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,Municipal apartment,0.032561,-19476,-129,-8935.0,-2444,,1,1,0,1,0,0,Drivers,1.0,1,1,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.41874711392822295,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-74.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,7.0,1.0,2.0\n155757,0,Cash loans,F,N,Y,0,135000.0,450000.0,33777.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-10046,-813,-4786.0,-2720,,1,1,1,1,0,0,Private service staff,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Self-employed,,0.7056704774815327,0.4920600938649263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1794.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n126501,0,Cash loans,M,N,Y,1,112500.0,808650.0,23773.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-18405,-365,-1016.0,-1955,,1,1,0,1,0,0,Drivers,3.0,2,2,FRIDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.638406676110761,0.8277026681625442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n179099,0,Cash loans,F,N,Y,0,103500.0,135000.0,9148.5,135000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-8537,-1512,-8518.0,-1178,,1,1,1,1,1,0,Sales staff,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.4129782544088402,0.3774042489507649,0.1856,,0.9846,,,0.2,0.1724,0.3333,,,,0.2063,,0.0,0.1891,,0.9846,,,0.2014,0.1724,0.3333,,,,0.215,,0.0,0.1874,,0.9846,,,0.2,0.1724,0.3333,,,,0.21,,0.0,,block of flats,0.1623,Panel,No,0.0,0.0,0.0,0.0,-1288.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n358267,0,Cash loans,M,N,Y,0,72000.0,239850.0,23850.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.02461,-24179,365243,-13252.0,-4307,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,0.6749478832066412,0.6930370281121087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n183240,0,Cash loans,M,Y,Y,0,202500.0,1350000.0,53541.0,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-23510,-1144,-97.0,-4634,11.0,1,1,0,1,1,0,Security staff,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Security,,0.7637077598937734,0.4400578303966329,0.132,0.0764,1.0,0.9932,0.0314,0.08,0.069,0.375,0.4167,0.0487,0.1076,0.1106,0.0,0.0,0.1345,0.0792,1.0,0.9935,0.0317,0.0806,0.069,0.375,0.4167,0.0498,0.1175,0.1152,0.0,0.0,0.1332,0.0764,1.0,0.9933,0.0316,0.08,0.069,0.375,0.4167,0.0495,0.1095,0.1125,0.0,0.0,reg oper account,block of flats,0.1112,Mixed,No,9.0,0.0,9.0,0.0,-839.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n447681,0,Revolving loans,M,Y,Y,0,135000.0,382500.0,19125.0,382500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-12115,-1601,-301.0,-4109,64.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.1997152646616184,0.7302002037572615,0.5172965813614878,0.2227,,0.9891,,,0.24,0.2069,0.3333,,,,0.2291,,,0.2269,,0.9891,,,0.2417,0.2069,0.3333,,,,0.2387,,,0.2248,,0.9891,,,0.24,0.2069,0.3333,,,,0.2333,,,,block of flats,0.2751,,No,0.0,0.0,0.0,0.0,-1611.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n174870,0,Cash loans,F,Y,Y,0,157500.0,239850.0,23719.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-16199,-132,-398.0,-4468,4.0,1,1,1,1,0,0,Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Industry: type 3,,0.7826700974294104,0.2910973802776635,0.0,,0.0,,,0.0,,,,,,,,,0.0,,0.0005,,,0.0,,,,,,,,,0.0,,0.0,,,0.0,,,,,,,,,,block of flats,0.0,,No,0.0,0.0,0.0,0.0,-1407.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n331233,0,Revolving loans,F,Y,Y,0,157500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.008865999999999999,-12692,-176,-6440.0,-1908,7.0,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Restaurant,,0.6682628680931756,0.4561097392782771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-926.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n201224,0,Cash loans,M,N,N,1,180000.0,508495.5,21672.0,454500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.04622,-14739,-2398,-5167.0,-4341,,1,1,0,1,1,0,Laborers,3.0,1,1,SATURDAY,12,0,0,0,0,0,0,Industry: type 7,,0.7275468779984353,0.4329616670974407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1573.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n380290,0,Cash loans,M,Y,Y,0,337500.0,1193580.0,42417.0,855000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-18089,-1828,-1929.0,-1171,2.0,1,1,0,1,0,0,Managers,2.0,1,1,FRIDAY,13,0,0,0,0,0,0,Construction,,0.6921990257195981,0.3774042489507649,0.0495,0.0952,0.9806,0.9524,0.0,0.0,0.1148,0.1667,0.2083,0.0798,0.0525,0.0585,0.0,0.0199,0.02,0.0988,0.9965,0.9543,0.0,0.0,0.069,0.1667,0.2083,0.0,0.0551,0.0327,0.0,0.0021,0.0625,0.0952,0.9965,0.953,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0534,0.0655,0.0,0.003,reg oper account,block of flats,0.0366,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1871.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n136438,0,Revolving loans,M,N,Y,0,180000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Co-op apartment,0.019689,-12147,-2194,-5625.0,-1727,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,MONDAY,12,0,0,0,1,1,1,Transport: type 1,,0.4100603345237154,0.1245194708919845,0.066,0.0851,0.9821,0.7552,0.0089,0.0,0.1379,0.1667,0.2083,0.0607,0.0538,0.0595,0.0,0.0,0.0672,0.0883,0.9821,0.7648,0.009000000000000001,0.0,0.1379,0.1667,0.2083,0.0621,0.0588,0.062,0.0,0.0,0.0666,0.0851,0.9821,0.7585,0.0089,0.0,0.1379,0.1667,0.2083,0.0618,0.0547,0.0605,0.0,0.0,reg oper account,block of flats,0.0516,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-144.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n230986,0,Cash loans,F,Y,Y,0,180000.0,1293502.5,35698.5,1129500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-17570,-1370,-5711.0,-1109,10.0,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Self-employed,0.7519456932011329,0.621016507389684,0.5154953751603267,0.1211,0.0815,0.9806,,,0.0,0.1034,0.1667,,0.0286,,0.0573,,0.0024,0.1155,0.0835,0.9806,,,0.0,0.1034,0.1667,,0.0293,,0.0593,,0.0026,0.1223,0.0815,0.9806,,,0.0,0.1034,0.1667,,0.0291,,0.0584,,0.0025,,block of flats,0.0454,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1732.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n306575,1,Cash loans,M,Y,N,2,157500.0,675000.0,34465.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-15743,-1687,-8344.0,-4781,21.0,1,1,1,1,1,0,Security staff,4.0,2,2,TUESDAY,12,0,1,1,1,1,1,Business Entity Type 3,,0.3312219625026864,0.5567274263630174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-635.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n124203,0,Cash loans,F,N,Y,0,67500.0,242595.0,17383.5,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-18723,-3500,-6843.0,-2151,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Government,,0.20357246321933267,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-1049.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n270495,0,Cash loans,M,N,Y,2,135000.0,323194.5,25663.5,279000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-13534,-2557,-1847.0,-1921,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,11,0,0,0,0,1,1,Self-employed,0.4549553739137236,0.5510211578847956,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1769.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n294458,0,Cash loans,M,Y,N,1,360000.0,239850.0,28462.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-17752,-882,-2072.0,-1301,3.0,1,1,1,1,1,0,Managers,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Other,,0.5203275246894774,0.266456808245056,0.0608,0.0726,0.9965,0.9524,0.0117,0.0,0.1379,0.1667,0.0417,0.0144,0.0496,0.06,0.0,0.0019,0.062,0.0754,0.9965,0.9543,0.0118,0.0,0.1379,0.1667,0.0417,0.0147,0.0542,0.0625,0.0,0.002,0.0614,0.0726,0.9965,0.953,0.0118,0.0,0.1379,0.1667,0.0417,0.0146,0.0504,0.0611,0.0,0.0019,reg oper account,block of flats,0.0476,Panel,No,1.0,0.0,1.0,0.0,-404.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n194851,0,Cash loans,M,Y,Y,1,405000.0,675000.0,18693.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006629,-13721,-326,-1656.0,-3935,3.0,1,1,0,1,0,0,Sales staff,3.0,2,2,SATURDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.5485529759262233,0.5832379256761245,0.2526,0.2478,0.9846,0.7892,0.0881,0.15,0.2069,0.1875,0.125,0.0505,0.1986,0.2028,0.0338,0.1176,0.1334,0.1598,0.9841,0.7909,0.0497,0.0,0.1034,0.125,0.0417,0.0,0.1139,0.0545,0.0078,0.034,0.1629,0.154,0.9841,0.7853,0.0557,0.0,0.1034,0.125,0.0417,0.0576,0.1287,0.0548,0.0252,0.0595,reg oper account,block of flats,0.5123,Block,No,0.0,0.0,0.0,0.0,-299.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n263921,0,Cash loans,F,Y,Y,0,126000.0,270000.0,14778.0,270000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-14484,-912,-901.0,-4903,3.0,1,1,0,1,0,0,Medicine staff,2.0,2,2,MONDAY,18,0,0,0,0,0,0,Medicine,0.4990150053407284,0.7072485774047276,,0.1371,,0.9841,,,0.12,0.1034,0.3333,,0.1224,,0.1085,,0.0746,0.1397,,0.9841,,,0.1208,0.1034,0.3333,,0.1252,,0.113,,0.079,0.1384,,0.9841,,,0.12,0.1034,0.3333,,0.1246,,0.1104,,0.0762,,block of flats,0.1016,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-2558.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n404159,0,Cash loans,M,Y,N,0,337500.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-17898,-6042,-9519.0,-1461,6.0,1,1,0,1,1,0,Laborers,2.0,2,2,SATURDAY,16,0,1,1,0,1,1,Industry: type 9,0.6091807093469337,0.5276089293316822,0.6109913280868294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2236.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n415261,0,Cash loans,F,N,Y,3,180000.0,1206000.0,35262.0,1206000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-11526,-3016,-3876.0,-3884,,1,1,1,1,0,0,Laborers,5.0,2,2,MONDAY,9,0,1,1,0,0,0,Self-employed,,0.6608878041712681,0.3876253444214701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.0,0.0,13.0,0.0,-2521.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n115325,0,Cash loans,F,N,Y,0,270000.0,835380.0,40320.0,675000.0,Family,Commercial associate,Incomplete higher,Separated,House / apartment,0.04622,-17787,-1947,-2248.0,-1338,,1,1,0,1,0,0,Laborers,1.0,1,1,FRIDAY,11,0,0,0,0,0,0,Self-employed,0.6431917300857839,0.7142926747564741,,0.5175,0.1814,0.995,0.932,0.0,0.64,0.2759,0.6667,0.7083,0.0052,0.4219,0.4972,0.0,0.1972,0.5273,0.1882,0.995,0.9347,0.0,0.6445,0.2759,0.6667,0.7083,0.0053,0.461,0.518,0.0,0.2088,0.5225,0.1814,0.995,0.9329,0.0,0.64,0.2759,0.6667,0.7083,0.0053,0.4293,0.5061,0.0,0.2013,not specified,block of flats,0.4039,Mixed,No,0.0,0.0,0.0,0.0,-974.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n425125,0,Cash loans,F,Y,Y,0,90000.0,373311.0,16573.5,283500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-11841,-1871,-99.0,-1416,11.0,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Kindergarten,0.470270872304164,0.2717501302307735,0.5902333386185574,0.0103,0.0118,0.9632,0.4968,0.0109,0.0,0.069,0.125,0.1667,0.0816,0.0034,0.053,0.0232,0.0564,0.0105,0.0123,0.9633,0.5165,0.011000000000000001,0.0,0.069,0.125,0.1667,0.0834,0.0037,0.0552,0.0233,0.0597,0.0104,0.0118,0.9632,0.5035,0.011000000000000001,0.0,0.069,0.125,0.1667,0.083,0.0034,0.0539,0.0233,0.0576,not specified,block of flats,0.0599,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1142.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n422779,0,Cash loans,M,Y,Y,1,202500.0,675000.0,26284.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-13699,-855,-1201.0,-1864,1.0,1,1,0,1,1,0,Drivers,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Postal,,0.7031890949470191,0.18629294965553744,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2552.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n297240,0,Cash loans,M,N,Y,0,112500.0,241618.5,26149.5,229500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.022625,-17103,-1297,-6166.0,-633,,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.5890172445631778,,0.0021,,0.9737,,,0.0,,0.0,,,,0.001,,,0.0021,,0.9737,,,0.0,,0.0,,,,0.001,,,0.0021,,0.9737,,,0.0,,0.0,,,,0.001,,,,block of flats,0.0013,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-865.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n201944,0,Cash loans,F,N,Y,0,135000.0,201469.5,16047.0,153000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-16990,-2079,-8467.0,-536,,1,1,0,1,0,0,Laborers,2.0,3,3,SATURDAY,6,0,0,0,0,0,0,Business Entity Type 2,,0.04277805349362064,0.15474363127259447,0.2144,,0.9881,,,,,,,,,0.239,,0.0189,0.2185,,0.9881,,,,,,,,,0.249,,0.02,0.2165,,0.9881,,,,,,,,,0.2433,,0.0193,,block of flats,0.2734,,No,5.0,0.0,5.0,0.0,-1849.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n120373,0,Cash loans,M,N,N,0,112500.0,416052.0,12744.0,292500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-12070,-1554,-2591.0,-4501,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Industry: type 9,0.1685057869542542,0.2574554279933875,0.3962195720630885,0.0722,0.0706,0.9781,0.7008,0.0079,0.0,0.1379,0.1667,0.2083,0.0478,0.057999999999999996,0.0664,0.0039,0.0043,0.0735,0.0732,0.9782,0.7125,0.0079,0.0,0.1379,0.1667,0.2083,0.0489,0.0634,0.0692,0.0039,0.0046,0.0729,0.0706,0.9781,0.7048,0.0079,0.0,0.1379,0.1667,0.2083,0.0486,0.059000000000000004,0.0676,0.0039,0.0044,reg oper spec account,block of flats,0.0616,Block,No,1.0,1.0,1.0,1.0,-8.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n309402,0,Cash loans,M,Y,N,1,450000.0,500490.0,54031.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.072508,-9847,-502,-4369.0,-2527,1.0,1,1,0,1,1,1,Core staff,3.0,1,1,WEDNESDAY,13,0,1,1,0,0,0,Restaurant,,0.7125042512212635,0.1684161714286957,0.1031,0.1036,0.9757,0.6668,0.0,0.0,0.1724,0.1667,0.2083,0.0,0.0841,0.08900000000000001,0.0,0.0,0.105,0.1075,0.9757,0.6798,0.0,0.0,0.1724,0.1667,0.2083,0.0,0.0918,0.0927,0.0,0.0,0.1041,0.1036,0.9757,0.6713,0.0,0.0,0.1724,0.1667,0.2083,0.0,0.0855,0.0906,0.0,0.0,reg oper account,block of flats,0.07,Panel,No,0.0,0.0,0.0,0.0,-2403.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n332064,1,Cash loans,M,N,Y,0,85500.0,604152.0,28125.0,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003069,-19542,-4857,-3643.0,-3045,,1,1,1,1,1,0,Laborers,2.0,3,3,SATURDAY,15,0,0,0,0,0,0,Construction,,0.5691816439567673,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2065.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n127256,0,Cash loans,F,N,N,0,76500.0,225000.0,11619.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010147,-23056,365243,-5375.0,-5375,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.4508551366308872,0.4884551844437485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-823.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,1.0\n183675,0,Cash loans,F,N,Y,0,270000.0,1176556.5,38884.5,963000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.018801,-18011,-4126,-5109.0,-1467,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Medicine,0.7930928366688587,0.5934617737342018,0.470456116119975,0.0258,0.08,0.9687,0.5716,0.0323,0.0,0.1034,0.0833,0.125,0.0145,0.0202,0.0364,0.0039,0.0091,0.0263,0.083,0.9687,0.5884,0.0326,0.0,0.1034,0.0833,0.125,0.0148,0.022000000000000002,0.0379,0.0039,0.0096,0.026000000000000002,0.08,0.9687,0.5773,0.0325,0.0,0.1034,0.0833,0.125,0.0148,0.0205,0.037000000000000005,0.0039,0.0092,reg oper account,block of flats,0.0302,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2312.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n426766,0,Cash loans,F,N,Y,0,157500.0,1096924.5,46606.5,940500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-20070,-11131,-8210.0,-3579,,1,1,0,1,0,0,Laborers,2.0,1,1,MONDAY,13,0,0,0,0,0,0,Business Entity Type 1,,0.5562791531168824,0.3910549766342248,0.1031,0.1371,0.9886,,,0.0,0.1724,0.1667,,0.0,,0.1206,,0.0,0.105,0.1423,0.9886,,,0.0,0.1724,0.1667,,0.0,,0.1256,,0.0,0.1041,0.1371,0.9886,,,0.0,0.1724,0.1667,,0.0,,0.1227,,0.0,,block of flats,0.0948,Panel,No,4.0,0.0,4.0,0.0,-1830.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n288284,0,Cash loans,F,Y,Y,0,157500.0,1255680.0,41629.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-19377,-574,-8813.0,-2813,10.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6830644573720419,0.3542247319929012,0.4920600938649263,0.1515,0.105,0.9806,0.7348,0.0181,0.16,0.1379,0.3333,0.375,0.0611,0.1236,0.1455,0.0,0.0,0.1544,0.1089,0.9806,0.7452,0.0183,0.1611,0.1379,0.3333,0.375,0.0625,0.135,0.1516,0.0,0.0,0.153,0.105,0.9806,0.7383,0.0182,0.16,0.1379,0.3333,0.375,0.0622,0.1257,0.1481,0.0,0.0,reg oper account,block of flats,0.1144,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-981.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n141405,0,Cash loans,F,Y,N,0,247500.0,472500.0,46863.0,454500.0,Family,State servant,Higher education,Married,House / apartment,0.032561,-20747,-6720,-2200.0,-4307,6.0,1,1,1,1,1,0,Core staff,2.0,1,1,SATURDAY,17,0,0,0,0,1,1,School,,0.7330340759320005,0.7151031019926098,0.1433,0.0794,0.9965,0.9524,0.0634,0.08,0.0345,0.5417,0.5833,0.0138,0.1168,0.1371,0.0,0.0,0.146,0.0824,0.9965,0.9543,0.064,0.0806,0.0345,0.5417,0.5833,0.0142,0.1276,0.1428,0.0,0.0,0.1447,0.0794,0.9965,0.953,0.0638,0.08,0.0345,0.5417,0.5833,0.0141,0.1189,0.1395,0.0,0.0,reg oper account,block of flats,0.1078,Panel,No,0.0,0.0,0.0,0.0,-1648.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n394937,0,Cash loans,F,N,Y,0,193500.0,900000.0,38133.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008473999999999999,-17627,-10778,-5423.0,-1170,,1,1,1,1,1,0,Laborers,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Industry: type 5,,0.4436975781815092,0.5779691187553125,0.0742,,0.9881,,,0.08,0.069,0.3333,,,,0.0491,,,0.0756,,0.9881,,,0.0806,0.069,0.3333,,,,0.0512,,,0.0749,,0.9881,,,0.08,0.069,0.3333,,,,0.05,,,,,0.0631,Panel,No,0.0,0.0,0.0,0.0,-443.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n439974,0,Cash loans,F,N,Y,0,157500.0,1099350.0,35595.0,787500.0,Family,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.04622,-21914,365243,-7397.0,-4019,,1,0,0,1,0,0,,1.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,XNA,0.7094990315346225,0.7089074531733663,0.5638350489514956,0.0371,0.0,0.9727,0.626,0.0031,0.0,0.1034,0.0833,0.0417,0.08900000000000001,0.0303,0.0302,0.0,0.0117,0.0378,0.0,0.9727,0.6406,0.0031,0.0,0.1034,0.0833,0.0417,0.0911,0.0331,0.0315,0.0,0.0123,0.0375,0.0,0.9727,0.631,0.0031,0.0,0.1034,0.0833,0.0417,0.0906,0.0308,0.0308,0.0,0.0119,,block of flats,0.0263,\"Stone, brick\",No,3.0,2.0,3.0,2.0,-364.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n446761,0,Cash loans,M,Y,N,0,180000.0,568800.0,15642.0,450000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-18910,-1681,-10968.0,-2297,12.0,1,1,0,1,0,0,,2.0,1,1,FRIDAY,13,0,0,0,0,0,0,Construction,,0.7051842615636206,0.3606125659189888,0.21,0.09699999999999999,0.9851,0.7959999999999999,0.0789,0.24,0.1034,0.6667,0.7083,,0.1701,0.2587,0.0051,0.0073,0.1439,0.0678,0.9851,0.804,0.0528,0.1611,0.069,0.6667,0.7083,,0.1249,0.1842,0.0039,0.0014,0.2103,0.0969,0.9851,0.7987,0.0794,0.24,0.1034,0.6667,0.7083,,0.171,0.2575,0.0039,0.0038,reg oper account,block of flats,0.2081,Panel,No,0.0,0.0,0.0,0.0,-4.0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n154179,0,Revolving loans,F,N,Y,0,90000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.028663,-9717,-1570,-4384.0,-2390,,1,1,1,1,0,0,,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Hotel,0.374358332007036,0.4963336623035909,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-850.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n182229,0,Cash loans,F,N,Y,0,135000.0,700830.0,20619.0,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.02461,-17485,-5329,-11625.0,-1011,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,0.5626032635392532,0.6778001063858046,0.1206410299309233,0.0082,0.0,0.9722,0.6192,0.0008,0.0,0.0345,0.0417,0.0833,0.0127,,0.0056,,0.0,0.0084,0.0,0.9722,0.6341,0.0008,0.0,0.0345,0.0417,0.0833,0.013000000000000001,,0.0059,,0.0,0.0083,0.0,0.9722,0.6243,0.0008,0.0,0.0345,0.0417,0.0833,0.0129,,0.0057,,0.0,,block of flats,0.008,Block,No,1.0,0.0,1.0,0.0,-409.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,3.0\n284015,0,Revolving loans,M,Y,N,0,225000.0,382500.0,19125.0,382500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.019689,-16608,-1959,-7514.0,-1682,6.0,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,9,0,0,0,0,0,0,Construction,,0.5624880957582091,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1278.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n334075,0,Cash loans,M,Y,Y,0,135000.0,76410.0,8154.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.031329,-12951,-177,-503.0,-4713,8.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,8,0,0,0,0,1,1,Construction,0.6531593482453443,0.5473017269764806,0.6801388218428291,0.0711,0.0848,0.9786,,,0.0,0.1379,0.1667,,0.0,,0.067,,0.0302,0.0725,0.08800000000000001,0.9786,,,0.0,0.1379,0.1667,,0.0,,0.0699,,0.032,0.0718,0.0848,0.9786,,,0.0,0.1379,0.1667,,0.0,,0.0682,,0.0308,,block of flats,0.0593,Block,No,2.0,0.0,2.0,0.0,-2691.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,3.0\n237264,0,Cash loans,F,N,N,1,112500.0,143910.0,15399.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.00496,-10294,-969,-4114.0,-2100,,1,1,0,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,19,0,0,0,0,1,1,Business Entity Type 3,0.5850590534128043,0.41575887636381104,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-692.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n401853,0,Cash loans,F,Y,Y,2,63900.0,136287.0,10894.5,103500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-15913,-1638,-4477.0,-4456,25.0,1,1,0,1,0,0,Core staff,4.0,2,2,MONDAY,11,0,0,0,0,0,0,Postal,,0.5738405161746867,0.3185955240537633,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2405.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n141616,0,Cash loans,F,N,Y,0,270000.0,495000.0,15966.0,495000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-13903,-1388,-1674.0,-4069,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,7,0,1,1,0,0,0,Trade: type 6,0.6280185745457102,0.5553235650301274,0.5971924268337128,0.084,0.043,0.9935,,,0.1,0.0345,0.7917,0.75,0.0421,,0.1128,,0.0845,0.0767,0.0434,0.9891,,,0.0806,0.0345,0.7083,0.75,0.0,,0.0988,,0.0762,0.0848,0.043,0.9935,,,0.1,0.0345,0.7917,0.75,0.0428,,0.1148,,0.0863,,block of flats,0.1624,Monolithic,No,4.0,0.0,4.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n438632,0,Cash loans,M,N,Y,0,166500.0,1456587.0,45733.5,1305000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.009657,-19499,-197,-8941.0,-3036,,1,1,0,1,1,0,Managers,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 1,,0.5467111021641697,0.5638350489514956,0.3505,,0.993,0.9048,,0.32,0.2759,0.375,0.0417,0.1922,,,,,0.3571,,0.993,0.9085,,0.3222,0.2759,0.375,0.0417,0.1966,,,,,0.3539,,0.993,0.9061,,0.32,0.2759,0.375,0.0417,0.1956,,,,,,block of flats,0.3527,Panel,No,0.0,0.0,0.0,0.0,-876.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n153686,0,Cash loans,M,Y,N,0,112500.0,254700.0,16407.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-21028,-1350,-3372.0,-3491,14.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,13,0,0,0,0,1,1,Other,,0.30945129095132823,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n148061,0,Cash loans,M,Y,Y,0,450000.0,1286608.5,51156.0,1183500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-13639,-932,-3856.0,-611,10.0,1,1,0,1,0,1,Managers,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Industry: type 3,,0.5897475667402261,0.7281412993111438,0.091,0.1036,0.9801,0.728,0.0103,0.0,0.18100000000000002,0.1667,0.2083,,0.0733,0.0785,0.0039,0.045,0.105,0.0545,0.9777,0.706,0.0072,0.0,0.2069,0.1667,0.2083,,0.0441,0.046,0.0,0.0,0.1041,0.1118,0.9791,0.7182,0.0109,0.0,0.2069,0.1667,0.2083,,0.0838,0.0861,0.0,0.0,reg oper account,block of flats,0.0386,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1699.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n196300,0,Cash loans,F,Y,Y,0,112500.0,808650.0,26217.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006629,-17702,-5854,-198.0,-1206,13.0,1,1,0,1,0,0,Cooking staff,2.0,2,2,SATURDAY,5,0,0,0,0,0,0,Restaurant,,0.6062210397647882,,0.0912,0.0,0.9935,0.9116,0.0227,0.0844,0.1034,0.2683,0.06,0.0183,0.0743,0.0966,0.0004,0.0127,0.0945,0.0,0.9975,0.9608,0.0122,0.1208,0.1034,0.25,0.0417,0.0089,0.0826,0.0503,0.0,0.0,0.0937,0.0,0.9955,0.9396,0.0199,0.08,0.1034,0.25,0.0417,0.017,0.077,0.0907,0.0,0.0,reg oper account,block of flats,0.0476,Panel,No,3.0,0.0,3.0,0.0,-2202.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n329634,0,Cash loans,F,N,N,0,225000.0,964368.0,51511.5,832500.0,Unaccompanied,Working,Higher education,Widow,House / apartment,0.04622,-16251,-857,-1980.0,-1979,,1,1,0,1,0,0,Laborers,1.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Transport: type 2,0.7369587385083105,0.6518474455309757,0.7338145369642702,0.1113,0.0095,0.9846,0.7892,0.0603,0.12,0.1034,0.3333,0.375,0.0,0.0908,0.1167,0.0,0.0,0.1134,0.0099,0.9846,0.7975,0.0609,0.1208,0.1034,0.3333,0.375,0.0,0.0992,0.1216,0.0,0.0,0.1124,0.0095,0.9846,0.792,0.0607,0.12,0.1034,0.3333,0.375,0.0,0.0923,0.1188,0.0,0.0,reg oper account,block of flats,0.1248,Panel,No,0.0,0.0,0.0,0.0,-1483.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n337575,0,Cash loans,M,N,N,0,135000.0,450000.0,21109.5,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.015221,-11233,-2035,-140.0,-3663,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 2,,0.6185429985638057,0.5867400085415683,0.0928,0.0996,0.9791,0.7144,0.0117,0.0,0.2069,0.1667,0.2083,0.0825,0.0756,0.0882,0.0,0.0,0.0945,0.1034,0.9791,0.7256,0.0118,0.0,0.2069,0.1667,0.2083,0.0844,0.0826,0.0919,0.0,0.0,0.0937,0.0996,0.9791,0.7182,0.0118,0.0,0.2069,0.1667,0.2083,0.084,0.077,0.0898,0.0,0.0,reg oper account,block of flats,0.0929,Panel,No,4.0,0.0,4.0,0.0,-1460.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n336987,0,Cash loans,M,Y,Y,1,157500.0,269550.0,16416.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-10436,-948,-4753.0,-2748,14.0,1,1,0,1,0,0,Drivers,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.30424683149662923,0.3092753558842053,0.1299,0.1156,0.9767,0.6804,0.0364,0.0,0.2759,0.1667,0.2083,0.1361,0.1042,0.1195,0.0077,0.0077,0.1324,0.12,0.9767,0.6929,0.0367,0.0,0.2759,0.1667,0.2083,0.1392,0.1139,0.1245,0.0078,0.0082,0.1312,0.1156,0.9767,0.6847,0.0366,0.0,0.2759,0.1667,0.2083,0.1385,0.106,0.1217,0.0078,0.0079,reg oper account,block of flats,0.0957,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1099.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n358295,0,Cash loans,F,N,Y,0,292500.0,1211049.0,35541.0,1057500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-18714,-9775,-3508.0,-2250,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,1,1,Industry: type 9,0.7016731967415237,0.6555502511813629,0.6446794549585961,0.0165,0.0367,0.9916,,,0.0,0.069,0.0692,,0.0104,,0.0117,,0.0065,0.0189,0.0294,0.9881,,,0.0,0.069,0.0833,,0.0089,,0.0071,,0.0,0.0187,0.0406,0.993,,,0.0,0.069,0.0833,,0.0106,,0.0144,,0.0063,,block of flats,0.02,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-18.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n382291,0,Revolving loans,F,Y,Y,1,112500.0,337500.0,16875.0,337500.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.006629,-14865,-947,-2736.0,-4974,11.0,1,1,1,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.8300633080612883,0.6942220190408451,0.7076993447402619,0.0701,0.0,0.9940000000000001,,,0.08,0.069,0.3333,,0.0335,,0.0793,,0.0039,0.0714,0.0,0.9940000000000001,,,0.0806,0.069,0.3333,,0.0343,,0.0826,,0.0041,0.0708,0.0,0.9940000000000001,,,0.08,0.069,0.3333,,0.0341,,0.0807,,0.004,,block of flats,0.073,Panel,No,0.0,0.0,0.0,0.0,-512.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n204273,1,Cash loans,F,N,N,0,144000.0,568800.0,16429.5,450000.0,Family,State servant,Higher education,Civil marriage,House / apartment,0.008625,-9627,-2533,-4389.0,-2304,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,0.3033028894898244,0.2974991214050953,,0.1155,0.1045,0.9881,,,0.12,0.1034,0.3333,,,,0.1165,,0.0022,0.1176,0.1085,0.9881,,,0.1208,0.1034,0.3333,,,,0.1214,,0.0023,0.1166,0.1045,0.9881,,,0.12,0.1034,0.3333,,,,0.1186,,0.0022,,block of flats,0.0916,Panel,No,3.0,1.0,3.0,1.0,-470.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,,,,,,\n279685,0,Revolving loans,F,N,Y,2,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-11602,-359,-581.0,-369,,1,1,0,1,0,0,Core staff,4.0,2,2,THURSDAY,9,0,0,0,0,0,0,Kindergarten,0.20193122788275086,0.5924431286003118,,0.1423,0.0919,0.9796,,,0.16,0.1379,0.3333,,0.0866,,0.0917,,,0.145,0.0953,0.9796,,,0.1611,0.1379,0.3333,,0.0886,,0.0956,,,0.1436,0.0919,0.9796,,,0.16,0.1379,0.3333,,0.0881,,0.0934,,,,block of flats,0.1098,Panel,No,0.0,0.0,0.0,0.0,-249.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n213742,0,Cash loans,F,Y,Y,1,135000.0,1125000.0,44617.5,1125000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.016612000000000002,-15401,-8502,-4068.0,-4220,13.0,1,1,0,1,0,0,Medicine staff,3.0,2,2,THURSDAY,13,0,0,0,0,0,0,Other,0.8141318572431647,0.6435345520187479,0.7700870700124128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2548.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n112631,0,Cash loans,M,Y,Y,0,270000.0,495351.0,27787.5,459000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010147,-20703,-2239,-8426.0,-4215,38.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.5502528209189801,0.7826078370261895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n335670,0,Cash loans,M,Y,Y,0,180000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-15810,-115,-1195.0,-3461,4.0,1,1,0,1,1,0,Laborers,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.37821496771860397,0.5908793120810863,0.29708661164720285,0.068,0.0822,0.9737,0.6396,0.0085,0.0,0.1379,0.1667,0.2083,0.0423,0.0538,0.0504,0.0077,0.0785,0.0693,0.0853,0.9737,0.6537,0.0085,0.0,0.1379,0.1667,0.2083,0.0433,0.0588,0.0525,0.0078,0.0831,0.0687,0.0822,0.9737,0.6444,0.0085,0.0,0.1379,0.1667,0.2083,0.043,0.0547,0.0513,0.0078,0.0801,,block of flats,0.0835,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-419.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n273324,0,Cash loans,F,N,Y,0,135000.0,1071909.0,31473.0,936000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-20423,-13516,-12802.0,-3935,,1,1,0,1,0,0,Medicine staff,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,Medicine,,0.7266704001454548,0.7958031328748403,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-643.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n190136,1,Revolving loans,F,N,N,0,103500.0,247500.0,12375.0,247500.0,Unaccompanied,Pensioner,Lower secondary,Widow,Municipal apartment,0.02461,-23159,365243,-2943.0,-2954,,1,0,0,1,1,0,,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.3845197041839431,0.5100895276257282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n316108,1,Cash loans,F,N,Y,0,112500.0,412614.0,26500.5,364500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Rented apartment,0.019101,-18676,-3231,-241.0,-380,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.4138903749813488,,0.2629,0.1096,0.9990000000000001,0.9864,0.0793,0.32,0.1379,0.4583,0.0,0.0,0.2118,0.1989,0.0116,0.016,0.2679,0.1138,0.9990000000000001,0.9869,0.08,0.3222,0.1379,0.4583,0.0,0.0,0.2314,0.2072,0.0117,0.017,0.2654,0.1096,0.9990000000000001,0.9866,0.0798,0.32,0.1379,0.4583,0.0,0.0,0.2155,0.2025,0.0116,0.0164,reg oper account,block of flats,0.2155,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-250.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n307031,0,Revolving loans,F,Y,Y,0,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-16534,-7138,-5247.0,-88,64.0,1,1,1,1,1,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6588939708363128,0.7424638349263717,0.363945238612397,0.0371,0.0,0.9732,0.6328,0.023,0.0,0.1034,0.0833,0.125,0.0492,0.0303,0.0305,,0.0,0.0378,0.0,0.9732,0.6472,0.0232,0.0,0.1034,0.0833,0.125,0.0504,0.0331,0.0318,,0.0,0.0375,0.0,0.9732,0.6377,0.0231,0.0,0.1034,0.0833,0.125,0.0501,0.0308,0.0311,,0.0,reg oper account,block of flats,0.0366,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-3283.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n378890,0,Revolving loans,F,Y,Y,1,270000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.016612000000000002,-18492,-2712,-2181.0,-2045,7.0,1,1,1,1,1,0,Core staff,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Medicine,,0.6126698711195964,0.5849900404894085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-460.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n311530,0,Cash loans,F,N,Y,0,67500.0,101880.0,5976.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-21929,365243,-2880.0,-3983,,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.3138138522312069,0.7180328113294772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-887.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n379670,0,Cash loans,M,Y,Y,0,292500.0,593010.0,17469.0,495000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-18737,365243,-8707.0,-2261,27.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.5557825147751475,0.08616166238092926,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n357312,1,Cash loans,M,Y,Y,0,180000.0,900000.0,35694.0,900000.0,Family,Working,Higher education,Married,House / apartment,0.018801,-11993,-4826,-6134.0,-4542,14.0,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Other,0.6142636507771714,0.15556226903842502,0.2079641743053816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3259.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n217015,0,Cash loans,F,Y,N,0,135000.0,679500.0,26946.0,679500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.020713,-10303,-2620,-1383.0,-2969,16.0,1,1,1,1,0,0,High skill tech staff,1.0,3,2,TUESDAY,15,0,0,0,0,0,0,Government,0.30809829661401905,0.5921738197874822,0.4992720153045617,0.0691,0.0807,0.9762,0.6736,0.0592,0.0,0.1379,0.1667,0.2083,0.061,0.0538,0.0506,0.0116,0.0599,0.0704,0.0837,0.9762,0.6864,0.0597,0.0,0.1379,0.1667,0.2083,0.0624,0.0588,0.0528,0.0117,0.0634,0.0697,0.0807,0.9762,0.6779999999999999,0.0596,0.0,0.1379,0.1667,0.2083,0.0621,0.0547,0.0516,0.0116,0.0612,reg oper account,block of flats,0.0529,\"Stone, brick\",No,11.0,4.0,11.0,3.0,-693.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n262697,0,Cash loans,M,Y,N,0,144000.0,348264.0,22671.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.010966,-10343,-1888,-2759.0,-2775,16.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,16,0,0,0,1,1,0,Business Entity Type 3,,0.10200132839262299,0.8128226070575616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.0,0.0,11.0,0.0,-2036.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n289061,0,Cash loans,M,Y,Y,0,180000.0,1303812.0,42187.5,1138500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006305,-15991,-2689,-3964.0,-4390,2.0,1,1,0,1,1,0,Drivers,2.0,3,3,SATURDAY,7,0,0,0,0,1,1,Business Entity Type 1,,0.2642007375130107,0.816092360478441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-461.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n180003,0,Cash loans,F,N,Y,1,202500.0,640080.0,31131.0,450000.0,Family,Working,Higher education,Married,House / apartment,0.00496,-11092,-1668,-3591.0,-3599,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,16,0,0,0,0,1,0,Business Entity Type 3,,0.6795978616483677,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,8.0,0.0,-279.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n434278,0,Revolving loans,M,Y,Y,1,270000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.007120000000000001,-15045,-1615,-2539.0,-3960,12.0,1,1,1,1,0,0,High skill tech staff,3.0,2,2,SATURDAY,11,0,1,1,0,1,1,Self-employed,,,0.060443691326567524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-175.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n294449,0,Cash loans,M,Y,Y,0,225000.0,327024.0,17388.0,270000.0,Family,Working,Higher education,Civil marriage,House / apartment,0.008575,-15216,-499,-9258.0,-3982,6.0,1,1,0,1,1,0,IT staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6941152425565781,0.7992967832109371,0.1031,0.0839,0.9776,,,0.0,0.2069,0.1667,,0.0293,,0.0623,,,0.105,0.0871,0.9777,,,0.0,0.2069,0.1667,,0.03,,0.0649,,,0.1041,0.0839,0.9776,,,0.0,0.2069,0.1667,,0.0299,,0.0634,,,,block of flats,0.0722,Panel,No,0.0,0.0,0.0,0.0,-587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n447063,0,Cash loans,F,N,Y,2,135000.0,664299.0,52483.5,567000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.022625,-10020,-1079,-4752.0,-560,,1,1,0,1,0,0,Core staff,4.0,2,2,MONDAY,11,0,0,0,0,0,0,Government,0.34883694427384304,0.6251045222144528,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-448.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n242363,0,Cash loans,F,N,N,3,81000.0,355536.0,19287.0,270000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.018801,-11368,-3035,-3591.0,-3842,,1,1,1,1,0,0,,5.0,2,2,TUESDAY,12,0,0,0,1,1,1,Business Entity Type 3,0.4358758660292574,0.505971449590325,0.6023863442690867,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,-1995.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n334119,0,Cash loans,F,N,N,1,112500.0,215640.0,11137.5,180000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-16908,-10086,-4951.0,-456,,1,1,0,1,0,0,Core staff,3.0,2,2,SUNDAY,10,0,0,0,0,0,0,School,,0.7097100322760337,0.34741822720026416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2068.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n437055,0,Cash loans,F,N,Y,0,225000.0,1097491.5,45423.0,931500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.04622,-20067,-2271,-7239.0,-3624,,1,1,0,1,0,0,Managers,2.0,1,1,WEDNESDAY,12,0,1,1,0,0,0,Government,0.8564990964229872,0.7456578499621883,0.7121551551910698,0.0619,0.0735,0.9811,0.7416,0.0096,0.0,0.1379,0.1667,0.2083,0.0794,0.0504,0.0606,0.0,0.0,0.063,0.0763,0.9811,0.7517,0.0096,0.0,0.1379,0.1667,0.2083,0.0812,0.0551,0.0631,0.0,0.0,0.0625,0.0735,0.9811,0.7451,0.0096,0.0,0.1379,0.1667,0.2083,0.0808,0.0513,0.0616,0.0,0.0,reg oper account,block of flats,0.0476,Panel,No,0.0,0.0,0.0,0.0,-1167.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n440865,0,Cash loans,M,N,Y,0,135000.0,292500.0,28930.5,292500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005144,-20057,-666,-6916.0,-3511,,1,1,1,1,1,0,Drivers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3797274147911896,0.594469072139464,0.5424451438613613,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1589.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n256736,1,Cash loans,F,N,Y,0,315000.0,113760.0,9117.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15430,-1518,-6188.0,-2251,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,19,1,1,0,0,0,0,Business Entity Type 3,,0.4290266163800704,0.1684161714286957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-116.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n453734,0,Cash loans,F,N,Y,0,135000.0,774117.0,30829.5,625500.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.025164,-18380,-442,-1232.0,-1883,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,10,0,0,0,0,1,1,Kindergarten,0.7928112916907711,0.5341440920349505,0.15380258630671767,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2421.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n199992,0,Cash loans,F,N,Y,1,90000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.031329,-19848,-1830,-7282.0,-3384,,1,1,0,1,0,0,,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Self-employed,,0.7190746546942157,0.7764098512142026,0.0732,0.0769,0.9767,0.6804,0.057,0.0,0.1724,0.1667,0.2083,0.0626,0.0597,0.0649,0.0039,0.0478,0.0746,0.0798,0.9767,0.6929,0.0575,0.0,0.1724,0.1667,0.2083,0.064,0.0652,0.0676,0.0039,0.0506,0.0739,0.0769,0.9767,0.6847,0.0573,0.0,0.1724,0.1667,0.2083,0.0637,0.0607,0.0661,0.0039,0.0488,reg oper account,block of flats,0.0614,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n294355,0,Cash loans,F,N,Y,0,109458.0,497520.0,25888.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018801,-9682,-240,-3072.0,-584,,1,1,1,1,0,0,Core staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Trade: type 3,0.265287937379614,0.7569336166223561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-153.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n120992,0,Cash loans,M,N,Y,0,202500.0,363190.5,41107.5,328500.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.010032,-15591,-1217,-5516.0,-4125,,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,6,0,0,0,1,1,0,Business Entity Type 3,,0.14909137170416656,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n243218,0,Cash loans,M,N,Y,0,229500.0,375322.5,16029.0,324000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.010032,-21612,-7620,-9027.0,-4891,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,4,0,0,0,0,0,0,Government,,0.27935847355313265,0.8050196619153701,,,0.9881,,,,,,,,,0.0795,,,,,0.9881,,,,,,,,,0.0828,,,,,0.9881,,,,,,,,,0.0809,,,,,0.0625,,No,1.0,0.0,1.0,0.0,-994.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n135800,0,Cash loans,M,N,Y,0,90000.0,314055.0,16164.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018634,-15790,-2576,-192.0,-5646,,1,1,0,1,0,0,Security staff,1.0,2,2,FRIDAY,10,0,1,1,0,0,0,Security,,0.520616784407961,0.7738956942145427,0.1309,0.0917,0.9762,,,,0.1724,0.1667,,0.0219,,0.0482,,0.1111,0.1334,0.0952,0.9762,,,,0.1724,0.1667,,0.0224,,0.0502,,0.1176,0.1322,0.0917,0.9762,,,,0.1724,0.1667,,0.0223,,0.0491,,0.1134,,specific housing,0.0621,\"Stone, brick\",No,11.0,0.0,11.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n235178,0,Cash loans,F,N,N,0,180000.0,1475154.0,61006.5,1377000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-20824,-3201,-12597.0,-4210,,1,1,0,1,0,0,High skill tech staff,2.0,3,3,MONDAY,5,0,0,0,0,0,0,Self-employed,0.7241629991505429,0.3288382431705088,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,3.0,5.0,2.0,-3195.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n264321,0,Cash loans,F,Y,N,2,202500.0,314100.0,21375.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,With parents,0.011703,-9587,-548,-9587.0,-726,8.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Trade: type 3,0.23768608310513856,0.4388668335242824,0.3441550073724169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-409.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n232634,0,Cash loans,M,N,Y,1,157500.0,862560.0,25348.5,720000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.04622,-18746,-3060,-11257.0,-385,,1,1,0,1,0,0,Security staff,2.0,1,1,FRIDAY,12,0,1,1,0,0,0,Business Entity Type 3,0.21878153540538828,0.6999751527518998,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-1562.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n238596,0,Cash loans,M,N,Y,0,112500.0,521280.0,27423.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-14179,-2818,-6955.0,-4172,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6601571943169848,0.6785676886853644,0.0082,0.0,0.9767,0.6804,0.001,0.0,0.069,0.0417,0.0833,0.0259,0.0067,0.0031,0.0,0.0,0.0084,0.0,0.9767,0.6929,0.001,0.0,0.069,0.0417,0.0833,0.0264,0.0073,0.0032,0.0,0.0,0.0083,0.0,0.9767,0.6847,0.001,0.0,0.069,0.0417,0.0833,0.0263,0.0068,0.0031,0.0,0.0,reg oper account,block of flats,0.0033,Mixed,No,1.0,1.0,1.0,1.0,-207.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n114730,0,Cash loans,M,N,Y,1,180000.0,900000.0,67423.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-16146,-382,-2483.0,-3186,,1,1,0,1,1,0,Laborers,3.0,2,2,WEDNESDAY,7,0,0,0,0,1,1,Construction,,0.4986023619024518,,0.0309,0.0567,0.9856,,,0.0,0.0345,0.125,,0.0124,,0.0203,,0.0511,0.0315,0.0589,0.9856,,,0.0,0.0345,0.125,,0.0127,,0.0211,,0.0541,0.0312,0.0567,0.9856,,,0.0,0.0345,0.125,,0.0126,,0.0206,,0.0522,,block of flats,0.027000000000000003,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-734.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n168259,0,Cash loans,F,N,Y,1,157500.0,592560.0,28638.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.014464,-16634,-119,-9747.0,-159,,1,1,0,1,1,0,Waiters/barmen staff,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Medicine,,0.545383073371278,0.5832379256761245,0.0619,0.0721,0.9801,0.728,0.0858,0.0,0.1379,0.1667,0.2083,0.1415,0.0504,0.0554,0.0,0.0,0.063,0.0748,0.9801,0.7387,0.0866,0.0,0.1379,0.1667,0.2083,0.1447,0.0551,0.0577,0.0,0.0,0.0625,0.0721,0.9801,0.7316,0.0864,0.0,0.1379,0.1667,0.2083,0.1439,0.0513,0.0564,0.0,0.0,not specified,block of flats,0.0905,Panel,No,2.0,0.0,2.0,0.0,-818.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n269070,0,Cash loans,F,N,Y,2,135000.0,904500.0,26446.5,904500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.019689,-14568,-1793,-2642.0,-4346,,1,1,0,1,0,0,Managers,4.0,2,2,MONDAY,11,0,0,0,0,1,1,Government,,0.43623523045600265,0.6910212267577837,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2393.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n309631,0,Cash loans,F,Y,N,0,202500.0,873261.0,65290.5,787500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006008,-14546,-924,-7648.0,-5212,5.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,12,1,1,0,1,1,0,Business Entity Type 3,0.3650258368279557,0.5486612805275137,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-433.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,8.0\n294076,0,Cash loans,M,Y,Y,1,135000.0,285264.0,30852.0,252000.0,Family,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.019689,-13782,-455,-4395.0,-4395,12.0,1,1,0,1,0,1,Laborers,2.0,2,2,WEDNESDAY,8,0,0,0,0,1,1,Business Entity Type 3,,0.0557596837333328,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,5.0\n426119,0,Cash loans,F,N,Y,0,135000.0,490005.0,20889.0,423000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.035792000000000004,-17549,-1456,-8590.0,-1033,,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Medicine,0.8573747149154288,0.6959692464358666,0.5280927512030451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-3172.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n285073,0,Revolving loans,F,N,Y,0,112500.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011703,-14151,-770,-4924.0,-2265,,1,1,0,1,0,0,Sales staff,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Self-employed,0.4973222631951744,0.7138327941948426,0.7801436381572275,0.0247,0.0582,0.9712,0.6056,0.0149,0.0,0.1034,0.0833,0.125,0.0,0.0202,0.016,0.0,0.0,0.0252,0.0604,0.9712,0.621,0.0151,0.0,0.1034,0.0833,0.125,0.0,0.022000000000000002,0.0167,0.0,0.0,0.025,0.0582,0.9712,0.6109,0.015,0.0,0.1034,0.0833,0.125,0.0,0.0205,0.0163,0.0,0.0,,block of flats,0.0207,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-428.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n175235,0,Cash loans,M,N,Y,1,135000.0,253737.0,16650.0,229500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006207,-10416,-887,-2384.0,-2713,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5385597173016565,0.4135967602644276,0.0598,0.0118,0.9727,0.626,0.0,0.0,0.0517,0.1042,0.2083,0.2456,0.0488,0.0191,0.0,0.0,0.0609,0.0122,0.9727,0.6406,0.0,0.0,0.0345,0.0417,0.2083,0.0,0.0533,0.0112,0.0,0.0,0.0604,0.0118,0.9727,0.631,0.0,0.0,0.0517,0.1042,0.2083,0.2499,0.0496,0.0195,0.0,0.0,reg oper spec account,block of flats,0.0432,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1164.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n157610,0,Cash loans,M,N,Y,0,135000.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-8429,-224,-8429.0,-1107,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 2,0.266799948244953,0.7304241655666178,0.5082869913916046,0.1031,0.1087,0.9771,0.6872,0.0113,0.0,0.2069,0.1667,0.2083,0.07200000000000001,0.0841,0.0895,0.0,0.0,0.105,0.1128,0.9772,0.6994,0.0114,0.0,0.2069,0.1667,0.2083,0.0736,0.0918,0.0933,0.0,0.0,0.1041,0.1087,0.9771,0.6914,0.0114,0.0,0.2069,0.1667,0.2083,0.0732,0.0855,0.0911,0.0,0.0,reg oper account,block of flats,0.0934,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1087.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n142950,0,Cash loans,F,N,Y,2,99000.0,508495.5,20295.0,454500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.018634,-13363,-1891,-5143.0,-4188,,1,1,1,1,0,0,Core staff,4.0,2,2,SUNDAY,13,0,0,0,0,1,1,Security Ministries,0.517981257965457,0.09280633885268584,0.6279908192952864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-369.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n450564,0,Cash loans,M,N,N,0,112500.0,178776.0,8464.5,117000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Municipal apartment,0.008575,-19026,-679,-6219.0,-2520,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,19,0,0,0,0,0,0,Business Entity Type 1,,0.6424201422117459,0.5460231970049609,0.0796,0.0915,0.9841,0.7892,,0.032,0.1072,0.2033,0.2292,0.0778,,0.0491,,0.0342,0.063,0.0164,0.9796,0.7583,,0.0,0.0345,0.1667,0.2083,0.0601,,0.0275,,0.0363,0.0729,0.0992,0.9851,0.7987,,0.0,0.1034,0.1667,0.2083,0.0831,,0.0539,,0.035,reg oper account,block of flats,0.0348,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n373258,0,Cash loans,F,N,Y,0,103500.0,774000.0,22761.0,774000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-22944,365243,-282.0,-4924,,1,0,0,1,0,0,,2.0,3,3,SATURDAY,7,0,0,0,0,0,0,XNA,,0.3327447294295324,0.4902575124990026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-361.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n143062,0,Cash loans,M,Y,Y,0,135000.0,315000.0,15448.5,315000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.018209,-9970,-1039,-1711.0,-2637,6.0,1,1,0,1,0,0,,2.0,3,3,SUNDAY,9,0,0,0,0,0,0,Security Ministries,0.2407975390438268,0.4444803658169472,0.3031463744186309,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-802.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n273296,0,Cash loans,M,N,N,1,202500.0,327024.0,18904.5,270000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.002134,-8557,-1038,-3105.0,-1234,,1,1,0,1,0,0,Core staff,2.0,3,3,MONDAY,10,0,0,0,0,1,1,Military,0.10967116634111967,0.18316715333640235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-282.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n136290,0,Cash loans,M,N,Y,0,135000.0,450000.0,17095.5,450000.0,Family,Working,Higher education,Married,Municipal apartment,0.025164,-20198,-8010,-13.0,-3647,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Industry: type 11,,0.6728664853252685,0.5442347412142162,0.033,0.0412,0.9732,0.6328,0.0034,0.0,0.069,0.125,0.1667,0.0073,0.0261,0.0256,0.0039,0.0095,0.0336,0.0427,0.9732,0.6472,0.0035,0.0,0.069,0.125,0.1667,0.0074,0.0285,0.0266,0.0039,0.0101,0.0333,0.0412,0.9732,0.6377,0.0035,0.0,0.069,0.125,0.1667,0.0074,0.0265,0.026000000000000002,0.0039,0.0097,reg oper account,block of flats,0.0241,\"Stone, brick\",No,9.0,1.0,8.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n123425,0,Cash loans,F,N,Y,1,225000.0,135000.0,14454.0,135000.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,House / apartment,0.006233,-12765,-5202,-599.0,-2801,,1,1,0,1,0,0,,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,Medicine,,0.6861658478866812,,0.0124,0.0,0.9712,0.6056,0.0018,0.0,0.069,0.0417,0.0833,0.0139,0.0101,0.0153,0.0,0.0,0.0126,0.0,0.9712,0.621,0.0018,0.0,0.069,0.0417,0.0833,0.0142,0.011000000000000001,0.0159,0.0,0.0,0.0125,0.0,0.9712,0.6109,0.0018,0.0,0.069,0.0417,0.0833,0.0141,0.0103,0.0155,0.0,0.0,not specified,block of flats,0.013000000000000001,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-520.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n168907,0,Cash loans,F,N,Y,0,90000.0,239850.0,23494.5,225000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.022625,-24536,365243,-6225.0,-4586,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,,0.6901826497817267,0.8182475079127519,0.1701,0.1816,0.9821,0.7552,0.024,0.0,0.3448,0.1667,0.2083,0.1614,0.1387,0.12300000000000001,,0.0299,0.1733,0.1885,0.9821,0.7648,0.0243,0.0,0.3448,0.1667,0.2083,0.1651,0.1515,0.1282,,0.0316,0.1718,0.1816,0.9821,0.7585,0.0242,0.0,0.3448,0.1667,0.2083,0.1642,0.1411,0.1253,,0.0305,reg oper account,block of flats,0.1538,Block,No,0.0,0.0,0.0,0.0,-1785.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n201285,0,Cash loans,M,N,Y,0,157500.0,337500.0,26662.5,337500.0,Family,Working,Higher education,Civil marriage,House / apartment,0.026392,-18764,-422,-4225.0,-2287,,1,1,0,1,1,0,High skill tech staff,2.0,2,2,TUESDAY,18,0,0,0,0,1,1,Business Entity Type 3,0.4866009090111407,0.6955711697481629,0.41184855592423975,0.1485,0.0777,0.9806,0.7348,0.0722,0.16,0.1379,0.3333,0.375,0.1332,0.121,0.1422,0.0,0.0,0.1513,0.0807,0.9806,0.7452,0.0728,0.1611,0.1379,0.3333,0.375,0.1363,0.1322,0.1482,0.0,0.0,0.1499,0.0777,0.9806,0.7383,0.0726,0.16,0.1379,0.3333,0.375,0.1355,0.1231,0.1448,0.0,0.0,reg oper account,block of flats,0.1258,Panel,No,0.0,0.0,0.0,0.0,-96.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n451849,0,Cash loans,F,Y,N,1,144000.0,450000.0,17140.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.025164,-9869,-1582,-2458.0,-1907,8.0,1,1,1,1,1,0,Core staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Trade: type 2,0.6333531393589567,0.4862615669837812,0.5832379256761245,0.1639,0.1047,0.9876,0.83,0.0366,0.0,0.1034,0.3333,0.375,0.0171,0.1328,0.1609,0.0039,0.0135,0.16699999999999998,0.1086,0.9876,0.8367,0.0369,0.0,0.1034,0.3333,0.375,0.0175,0.1451,0.1676,0.0039,0.0143,0.1655,0.1047,0.9876,0.8323,0.0368,0.0,0.1034,0.3333,0.375,0.0174,0.1351,0.1638,0.0039,0.0138,reg oper account,block of flats,0.1495,\"Stone, brick\",No,4.0,1.0,4.0,0.0,-1.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n270980,0,Cash loans,M,N,Y,0,193500.0,1971072.0,68512.5,1800000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-15244,-6846,-9337.0,-4207,,1,1,0,1,1,0,Laborers,2.0,1,1,THURSDAY,10,0,0,0,0,1,1,Housing,0.6207275062170478,0.7443118084733518,,0.1959,0.0934,0.9846,0.7892,0.0715,0.32,0.1379,0.4583,0.5,,0.1597,0.2157,0.0,,0.1996,0.0969,0.9846,0.7975,0.0721,0.3222,0.1379,0.4583,0.5,,0.1745,0.2247,0.0,,0.1978,0.0934,0.9846,0.792,0.0719,0.32,0.1379,0.4583,0.5,,0.1625,0.2195,0.0,,not specified,block of flats,0.2248,Panel,No,0.0,0.0,0.0,0.0,-1101.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n161614,0,Cash loans,M,N,Y,0,157500.0,1183963.5,34749.0,927000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-17395,-5367,-5741.0,-924,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Industry: type 1,,0.5542649223776017,0.6594055320683344,0.1485,0.0703,0.9776,0.6940000000000001,0.019,0.16,0.1379,0.3333,0.375,0.0911,0.1194,0.14,0.0077,0.0073,0.1513,0.0729,0.9777,0.706,0.0192,0.1611,0.1379,0.3333,0.375,0.0932,0.1304,0.1459,0.0078,0.0078,0.1499,0.0703,0.9776,0.6981,0.0191,0.16,0.1379,0.3333,0.375,0.0927,0.1214,0.1425,0.0078,0.0075,reg oper account,block of flats,0.1117,Panel,No,0.0,0.0,0.0,0.0,-2557.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n114455,0,Cash loans,F,N,Y,0,117000.0,1078200.0,31653.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.009657,-23128,365243,-10880.0,-3908,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.6565420673031869,0.6642482627052363,0.1031,0.1093,0.9762,,,0.0,0.1724,0.1667,,0.0784,,0.0966,,0.0,0.105,0.1134,0.9762,,,0.0,0.1724,0.1667,,0.0802,,0.1007,,0.0,0.1041,0.1093,0.9762,,,0.0,0.1724,0.1667,,0.0798,,0.0984,,0.0,,block of flats,0.0826,Panel,No,0.0,0.0,0.0,0.0,-651.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n377556,0,Cash loans,F,N,Y,0,157500.0,251280.0,16996.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-15796,-668,-7647.0,-1285,,1,1,0,1,0,0,Security staff,2.0,2,2,MONDAY,15,0,0,0,1,1,0,Business Entity Type 3,,0.5455728317522018,0.6722428897082422,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-317.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n170764,0,Revolving loans,F,N,N,1,112500.0,202500.0,10125.0,,,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.01885,-11412,-1147,-9431.0,-3379,,1,1,1,1,1,1,Sales staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Self-employed,0.5812321177727086,0.7127013044303409,0.5262949398096192,0.1856,0.1131,0.9871,0.8232,0.0786,0.2,0.1724,0.3333,0.375,0.0577,0.1513,0.2005,0.0,0.0,0.1891,0.1174,0.9871,0.8301,0.0793,0.2014,0.1724,0.3333,0.375,0.059000000000000004,0.1653,0.2089,0.0,0.0,0.1874,0.1131,0.9871,0.8256,0.0791,0.2,0.1724,0.3333,0.375,0.0587,0.1539,0.2041,0.0,0.0,reg oper account,block of flats,0.2007,Panel,No,0.0,0.0,0.0,0.0,-1789.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n378840,0,Revolving loans,F,Y,Y,0,135000.0,495000.0,24750.0,495000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.030755,-15552,-3152,-1456.0,-4796,14.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Kindergarten,0.5833563276888979,0.7059610747040349,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1912.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n435940,0,Cash loans,F,N,Y,0,202500.0,315000.0,19399.5,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,Municipal apartment,0.020713,-23367,365243,-1485.0,-4107,,1,0,0,1,1,0,,1.0,3,1,WEDNESDAY,11,0,0,0,0,0,0,XNA,0.8775715006325779,0.5505829107583049,0.6863823354047934,0.332,0.0,0.9811,0.7416,0.0541,0.08,0.0345,0.3333,0.0,0.0536,0.2622,0.1237,0.0386,0.1893,0.3382,0.0,0.9811,0.7517,0.0546,0.0806,0.0345,0.3333,0.0,0.0548,0.2865,0.1289,0.0389,0.2004,0.3352,0.0,0.9811,0.7451,0.0544,0.08,0.0345,0.3333,0.0,0.0545,0.2668,0.1259,0.0388,0.1933,reg oper account,block of flats,0.168,Panel,No,0.0,0.0,0.0,0.0,-1499.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n135817,0,Cash loans,F,Y,N,1,292500.0,325908.0,17811.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.016612000000000002,-14048,-3229,-5502.0,-4757,24.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 1,0.3507991944036743,0.5173523055761707,0.32173528219668485,0.1268,0.0929,0.9757,0.6668,0.040999999999999995,0.0,0.2069,0.1667,0.2083,0.0786,0.1009,0.0941,0.0116,0.0085,0.1292,0.0964,0.9757,0.6798,0.0413,0.0,0.2069,0.1667,0.2083,0.0804,0.1102,0.0981,0.0117,0.009000000000000001,0.128,0.0929,0.9757,0.6713,0.0412,0.0,0.2069,0.1667,0.2083,0.0799,0.1026,0.0958,0.0116,0.0087,reg oper account,block of flats,0.0983,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2666.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n266866,0,Cash loans,M,N,N,0,225000.0,727785.0,26734.5,607500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-13677,-2217,-2470.0,-2839,,1,1,1,1,1,1,,2.0,1,1,TUESDAY,14,0,1,1,0,1,1,Trade: type 6,0.7352622567726067,0.6557230532566897,0.3539876078507373,0.0598,0.1115,0.9454,0.252,0.0351,0.0,0.1379,0.125,0.1667,0.0784,0.0445,0.0716,0.0193,0.0882,0.0609,0.1158,0.9454,0.2813,0.0354,0.0,0.1379,0.125,0.1667,0.0802,0.0487,0.0746,0.0195,0.0934,0.0604,0.1115,0.9454,0.262,0.0353,0.0,0.1379,0.125,0.1667,0.0797,0.0453,0.0729,0.0194,0.0901,reg oper account,block of flats,0.0755,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1768.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n454615,0,Cash loans,F,N,Y,0,144000.0,286704.0,23116.5,247500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.010032,-18276,-2152,-329.0,-1827,,1,1,0,1,1,0,Sales staff,2.0,2,2,THURSDAY,5,0,0,0,0,0,0,Self-employed,,0.2442620443014153,0.5849900404894085,0.1041,0.0843,0.9781,0.7008,0.0393,0.0,0.2069,0.1667,0.0,0.0063,0.0841,0.1098,0.0039,0.0,0.1061,0.0875,0.9782,0.7125,0.0397,0.0,0.2069,0.1667,0.0,0.0065,0.0918,0.1144,0.0039,0.0,0.1051,0.0843,0.9781,0.7048,0.0396,0.0,0.2069,0.1667,0.0,0.0064,0.0855,0.1117,0.0039,0.0,reg oper spec account,block of flats,0.0933,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n167743,0,Cash loans,F,N,Y,1,135000.0,373311.0,19188.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.04622,-18265,-1608,-9143.0,-1785,,1,1,0,1,0,0,Sales staff,2.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.6906916758191914,0.6933021304251026,0.6109913280868294,0.0505,0.0385,0.9786,0.7076,0.008,0.04,0.0345,0.3333,0.375,0.2773,0.0403,0.0415,0.0039,0.0186,0.0515,0.0399,0.9786,0.7190000000000001,0.0081,0.0403,0.0345,0.3333,0.375,0.2836,0.0441,0.0433,0.0039,0.0197,0.051,0.0385,0.9786,0.7115,0.008,0.04,0.0345,0.3333,0.375,0.2821,0.040999999999999995,0.0423,0.0039,0.019,reg oper account,block of flats,0.0367,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-514.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n263824,0,Cash loans,M,Y,Y,0,180000.0,858114.0,55615.5,810000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14070,-209,-727.0,-4370,6.0,1,1,0,1,1,0,Drivers,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Trade: type 3,0.7019210852801613,0.7195691367350867,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-330.0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n424045,0,Cash loans,F,N,Y,1,135000.0,544491.0,17563.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-17093,-3025,-1023.0,-634,,1,1,1,1,0,0,,3.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Realtor,0.7795771661973966,0.7712736803555418,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1716.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n293005,0,Cash loans,F,N,Y,0,29250.0,755190.0,33394.5,675000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-21551,365243,-8882.0,-4485,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.4191463564263912,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-457.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n277974,0,Cash loans,F,N,N,0,144000.0,512145.0,26280.0,427500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-13215,-649,-7044.0,-4118,,1,1,0,1,0,0,Realty agents,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Services,0.3386974113354026,0.4552713634603151,0.6577838002083306,0.0247,,0.9841,,,,0.1034,0.0833,,,0.0202,0.0248,,,0.0252,,0.9841,,,,0.1034,0.0833,,,0.022000000000000002,0.0259,,,0.025,,0.9841,,,,0.1034,0.0833,,,0.0205,0.0253,,,,block of flats,0.0215,\"Stone, brick\",No,2.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n449343,1,Cash loans,M,Y,N,2,270000.0,521280.0,28408.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010276,-12566,-3384,-3030.0,-3040,2.0,1,1,0,1,1,0,Core staff,4.0,2,2,THURSDAY,13,0,1,1,0,1,1,Medicine,,0.659160572452004,0.5814837058057234,0.0876,0.0619,0.9906,0.8776,0.0,0.08,0.0862,0.2917,0.25,0.0421,0.0084,0.1063,0.0,0.0174,0.0105,0.0106,0.9906,0.8824,0.0,0.0,0.0345,0.2083,0.25,0.0052,0.0092,0.0169,0.0,0.0184,0.0885,0.0619,0.9906,0.8792,0.0,0.08,0.0862,0.2917,0.25,0.0428,0.0086,0.1082,0.0,0.0178,reg oper account,block of flats,0.0166,Panel,No,0.0,0.0,0.0,0.0,-2580.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n286883,0,Cash loans,F,N,Y,0,112500.0,808650.0,23773.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-17037,-5474,-8196.0,-573,,1,1,1,1,1,0,Sales staff,2.0,1,1,WEDNESDAY,9,0,0,0,0,0,0,Trade: type 7,0.8113538502583766,0.7798862194235322,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1898.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n203693,0,Cash loans,F,N,Y,0,157500.0,76410.0,8748.0,67500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,With parents,0.022625,-13743,-578,-5096.0,-5079,,1,1,0,1,0,0,Accountants,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Bank,,0.6767055220632967,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-744.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n105989,0,Cash loans,M,Y,N,0,202500.0,755190.0,36459.0,675000.0,Family,State servant,Higher education,Married,House / apartment,0.019101,-14696,-4096,-2190.0,-4557,5.0,1,1,0,1,1,0,Core staff,2.0,2,2,THURSDAY,17,0,0,0,0,1,1,Government,0.6086818272268054,0.6640318899889551,0.6754132910917112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-471.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n122993,0,Cash loans,F,Y,Y,0,270000.0,1223010.0,51948.0,1125000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.025164,-15619,-1055,-9760.0,-5264,0.0,1,1,0,1,1,0,High skill tech staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.7563526514072288,0.6894339992025385,0.6722428897082422,0.1433,0.0057,0.9786,,,0.0,0.3448,0.1667,,0.0967,,0.1447,,0.001,0.146,0.0059,0.9786,,,0.0,0.3448,0.1667,,0.0989,,0.1508,,0.001,0.1447,0.0057,0.9786,,,0.0,0.3448,0.1667,,0.0984,,0.1473,,0.001,,block of flats,0.1143,Panel,No,1.0,0.0,1.0,0.0,-602.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n344656,0,Cash loans,F,N,N,2,135000.0,765000.0,20308.5,765000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-11633,-3733,-1375.0,-126,,1,1,0,1,1,0,Managers,3.0,2,2,MONDAY,18,0,0,0,1,1,1,Government,0.3520725332835806,0.7182921142141405,0.4101025731788671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1940.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n389913,0,Cash loans,F,N,Y,0,180000.0,755190.0,36459.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.032561,-22828,365243,-13725.0,-4995,,1,0,0,1,1,0,,2.0,1,1,SATURDAY,17,0,0,0,0,0,0,XNA,,0.7601218318586176,0.6940926425266661,0.2055,0.134,0.9811,0.7416,,0.24,0.1493,0.375,0.4167,0.0452,0.18100000000000002,0.2215,0.0077,0.0082,0.0714,0.0602,0.9801,0.7387,,0.0806,0.069,0.3333,0.375,0.0263,0.0652,0.0859,0.0,0.0,0.2113,0.1386,0.9816,0.7518,,0.24,0.1724,0.3333,0.375,0.046,0.1838,0.2361,0.0039,0.0054,reg oper account,block of flats,0.1826,Panel,No,0.0,0.0,0.0,0.0,-769.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n384075,0,Cash loans,F,Y,Y,1,247500.0,1006920.0,51543.0,900000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.022625,-18086,-10072,-5147.0,-1632,10.0,1,1,0,1,1,0,High skill tech staff,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Industry: type 2,0.4777596145327669,0.4470519861291081,0.4329616670974407,0.0691,0.0654,0.9767,0.6804,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0882,0.0538,0.052000000000000005,0.0116,0.0549,0.0704,0.0679,0.9767,0.6929,0.0071,0.0,0.1379,0.1667,0.2083,0.0902,0.0588,0.0542,0.0117,0.0582,0.0697,0.0654,0.9767,0.6847,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0898,0.0547,0.0529,0.0116,0.0561,reg oper account,block of flats,0.0567,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-550.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n199545,0,Cash loans,M,Y,N,0,202500.0,408330.0,18117.0,292500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.030755,-14377,-3361,-5083.0,-5096,32.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,16,0,0,0,0,1,1,Other,0.7854141571893336,0.5959495671224359,0.4525335592581747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2984.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n391593,0,Cash loans,M,Y,Y,0,135000.0,1256400.0,44644.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-13649,-544,-3105.0,-4200,9.0,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Self-employed,,0.22669849207941034,0.4543210601605785,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-581.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n320406,0,Cash loans,F,N,Y,0,130500.0,170640.0,9792.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.020713,-11894,-764,-1722.0,-2893,,1,1,1,1,1,0,Sales staff,1.0,3,3,THURSDAY,7,0,0,0,0,0,0,Self-employed,0.5530428587130894,0.4899714752199155,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n121117,0,Cash loans,F,N,N,3,103500.0,1201207.5,35253.0,940500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018029,-12563,-1170,-6499.0,-2096,,1,1,0,1,0,0,,5.0,3,3,FRIDAY,11,0,0,0,0,0,0,Other,,0.5894671273001695,0.8182475079127519,0.0082,,0.9622,,,,0.069,0.0417,,,,0.009000000000000001,,0.0,0.0084,,0.9623,,,,0.069,0.0417,,,,0.0094,,0.0,0.0083,,0.9622,,,,0.069,0.0417,,,,0.0092,,0.0,,,0.0071,Wooden,No,0.0,0.0,0.0,0.0,-1456.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n382888,0,Cash loans,F,N,Y,0,112500.0,388066.5,23868.0,351000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.020713,-19557,-4621,-6750.0,-3108,,1,1,0,1,0,0,Accountants,2.0,3,2,THURSDAY,7,0,0,0,0,0,0,Medicine,,0.31272243237061376,0.5262949398096192,0.0608,0.0958,0.9831,0.7688,0.0105,0.0,0.069,0.1667,0.2083,0.0602,0.0496,0.0701,0.0,0.0,0.062,0.0994,0.9831,0.7779,0.0106,0.0,0.069,0.1667,0.2083,0.0616,0.0542,0.073,0.0,0.0,0.0614,0.0958,0.9831,0.7719,0.0105,0.0,0.069,0.1667,0.2083,0.0613,0.0504,0.0713,0.0,0.0,reg oper account,block of flats,0.0616,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1333.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n240352,0,Cash loans,F,N,Y,0,225000.0,566055.0,16353.0,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019689,-23182,365243,-11122.0,-4259,,1,0,0,1,1,0,,1.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,0.7466036695220075,0.4205631869220435,0.4507472818545589,0.1072,0.1052,0.9846,0.7892,0.046,0.0,0.2069,0.1667,0.2083,0.0662,0.0874,0.0983,0.0,0.0,0.1092,0.1092,0.9846,0.7975,0.0465,0.0,0.2069,0.1667,0.2083,0.0677,0.0955,0.1024,0.0,0.0,0.1083,0.1052,0.9846,0.792,0.0463,0.0,0.2069,0.1667,0.2083,0.0673,0.0889,0.1,0.0,0.0,reg oper account,block of flats,0.1058,Block,No,3.0,1.0,3.0,1.0,-2881.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n295722,0,Cash loans,F,N,Y,0,135000.0,521280.0,25078.5,450000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.009175,-18082,-4100,-7601.0,-1615,,1,1,1,1,1,0,Accountants,1.0,2,2,MONDAY,10,0,0,0,0,0,0,Bank,,0.7648856340682669,0.4365064990977374,0.0825,0.0691,0.9776,0.6940000000000001,0.0099,0.0,0.1379,0.1667,0.2083,0.0285,0.0672,0.0754,0.0,0.0,0.084,0.0717,0.9777,0.706,0.01,0.0,0.1379,0.1667,0.2083,0.0292,0.0735,0.0785,0.0,0.0,0.0833,0.0691,0.9776,0.6981,0.01,0.0,0.1379,0.1667,0.2083,0.028999999999999998,0.0684,0.0767,0.0,0.0,reg oper account,block of flats,0.0647,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1649.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n407662,0,Cash loans,F,N,Y,0,202500.0,722430.0,23436.0,517500.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.019689,-18761,-420,-5847.0,-2284,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,0.5015298403435328,0.5857047564963921,0.7610263695502636,0.1392,0.094,0.9851,,,0.0,0.1034,0.1667,,0.0375,,0.0437,,0.0515,0.1418,0.0975,0.9851,,,0.0,0.1034,0.1667,,0.0384,,0.0455,,0.0546,0.1405,0.094,0.9851,,,0.0,0.1034,0.1667,,0.0382,,0.0444,,0.0526,,block of flats,0.0571,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n233654,0,Cash loans,M,N,Y,0,157500.0,724581.0,26023.5,625500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.019101,-9136,-2294,-1872.0,-1799,,1,1,0,1,0,1,Sales staff,1.0,2,2,SATURDAY,10,0,1,1,0,1,1,Business Entity Type 3,,0.677432275275692,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1721.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n306595,0,Cash loans,F,Y,Y,0,225000.0,1125000.0,44748.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-12080,-2780,-3815.0,-1140,6.0,1,1,1,1,1,0,Sales staff,2.0,2,2,TUESDAY,16,0,0,0,0,1,1,Self-employed,0.4039542907404006,0.3978092042053136,0.3825018041447388,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n145185,0,Cash loans,M,Y,N,2,135000.0,1006920.0,51543.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-10918,-1159,-3787.0,-3592,7.0,1,1,0,1,0,0,,4.0,3,3,TUESDAY,6,0,0,0,0,1,1,Housing,0.14706765880427025,0.3665786908675875,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1382.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n295698,0,Cash loans,M,N,N,1,315000.0,755190.0,40968.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.020713,-10090,-383,-3236.0,-316,,1,1,0,1,0,0,Drivers,3.0,3,3,THURSDAY,15,0,0,0,0,1,1,Self-employed,0.20327789928098933,0.31554457686043924,0.4722533429586386,0.2052,0.2434,0.9866,0.8164,0.0276,,0.5172,0.1667,0.2083,0.2374,0.1673,0.2113,,0.0,0.209,0.2526,0.9866,0.8236,0.0278,,0.5172,0.1667,0.2083,0.2428,0.1827,0.2201,,0.0,0.2071,0.2434,0.9866,0.8189,0.0277,,0.5172,0.1667,0.2083,0.2416,0.1702,0.2151,,0.0,reg oper spec account,block of flats,0.1812,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n452535,0,Cash loans,F,Y,N,0,67500.0,405000.0,20677.5,405000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14381,-848,-831.0,-4856,12.0,1,1,1,1,1,0,Sales staff,2.0,2,2,FRIDAY,17,0,0,0,0,1,1,Business Entity Type 3,0.6083849160735507,0.6265843691001678,0.7801436381572275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2633.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n361868,0,Cash loans,F,N,Y,2,112500.0,526491.0,19039.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-13730,-2016,-1407.0,-529,,1,1,0,1,0,0,Medicine staff,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.4665181268805555,0.3490552510751822,0.1437,0.1267,0.9851,,,0.136,0.1172,1.0,,0.0135,,0.0625,,0.0037,0.0567,0.0586,0.9856,,,0.0806,0.069,0.3333,,0.011000000000000001,,0.08,,0.0037,0.0749,0.059000000000000004,0.9856,,,0.08,0.069,0.3333,,0.0136,,0.0647,,0.0036,,block of flats,0.1007,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n351029,0,Cash loans,M,N,Y,0,202500.0,338314.5,22608.0,306000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-9569,-978,-4322.0,-2247,,1,1,0,1,0,0,,2.0,3,3,FRIDAY,10,0,0,0,0,1,1,Other,0.36556075026204,0.25970252363014523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-61.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n287408,1,Cash loans,F,N,N,1,90000.0,573628.5,21388.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.020246,-10115,-885,-7328.0,-1825,,1,1,0,1,0,0,,3.0,3,3,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.10588131174405356,0.2338962407216287,0.2580842039460289,0.368,0.2657,0.9896,0.8572,0.0917,0.38,0.3276,0.375,0.4167,0.2222,0.298,0.4133,0.0097,0.0149,0.3729,0.2633,0.9896,0.8628,0.0892,0.3625,0.3103,0.375,0.4167,0.2271,0.3232,0.4248,0.0078,0.0117,0.3716,0.2657,0.9896,0.8591,0.0922,0.38,0.3276,0.375,0.4167,0.2261,0.3031,0.4208,0.0097,0.0152,reg oper account,block of flats,0.3714,Panel,No,0.0,0.0,0.0,0.0,-476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n410197,0,Cash loans,F,Y,Y,1,45000.0,545040.0,16654.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-16349,-496,-5689.0,-473,8.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Religion,,0.6528046280435762,,0.0299,0.036000000000000004,0.9717,0.6124,0.0066,0.0,0.1379,0.0833,0.125,0.0466,0.0227,0.0319,0.0077,0.0114,0.0305,0.0374,0.9717,0.6276,0.0066,0.0,0.1379,0.0833,0.125,0.0477,0.0248,0.0332,0.0078,0.012,0.0302,0.036000000000000004,0.9717,0.6176,0.0066,0.0,0.1379,0.0833,0.125,0.0475,0.0231,0.0325,0.0078,0.0116,reg oper account,block of flats,0.0275,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-463.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n329558,1,Cash loans,M,Y,Y,0,225000.0,369720.0,29340.0,292500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.02461,-18574,-1886,-4325.0,-2055,10.0,1,1,0,1,1,0,,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.7670720340564574,0.7035449693568121,,0.066,0.0806,0.9762,,,,0.1379,0.1667,,,,0.0502,0.0077,0.0999,0.0672,0.0836,0.9762,,,,0.1379,0.1667,,,,0.0523,0.0078,0.1058,0.0666,0.0806,0.9762,,,,0.1379,0.1667,,,,0.0511,0.0078,0.102,,block of flats,0.0612,\"Stone, brick\",No,2.0,2.0,2.0,1.0,-1217.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n288272,0,Cash loans,F,N,Y,0,157500.0,582228.0,16011.0,486000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.018801,-9393,-1118,-6477.0,-1970,,1,1,1,1,1,1,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.21487731954648998,0.4769806698194782,0.3360615207658242,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1332.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,2.0\n430821,0,Cash loans,F,N,Y,0,198000.0,630000.0,40261.5,630000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.011703,-19506,365243,-463.0,-3003,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,17,0,0,0,0,0,0,XNA,,0.7479526956115845,0.4206109640437848,0.0959,0.0864,0.9776,0.6668,0.0147,0.0,0.1931,0.1667,0.2083,0.0681,0.0975,0.08900000000000001,0.0154,0.0198,0.105,0.0762,0.9786,0.6798,0.0148,0.0,0.2069,0.1667,0.2083,0.0615,0.1065,0.0744,0.0156,0.0,0.1041,0.0852,0.9786,0.6713,0.0147,0.0,0.2069,0.1667,0.2083,0.0687,0.0992,0.0943,0.0155,0.0145,reg oper account,block of flats,0.0685,Panel,No,0.0,0.0,0.0,0.0,-171.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n196170,0,Revolving loans,F,N,N,0,189000.0,540000.0,27000.0,540000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006629,-14771,-2134,-1130.0,-3583,,1,1,0,1,1,0,Managers,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,Military,0.8208494638911107,0.5930132461438938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2540.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n147081,0,Cash loans,F,N,Y,0,112500.0,584014.5,28224.0,522000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.005002,-8929,-934,-8104.0,-560,,1,1,0,1,0,0,Sales staff,2.0,3,3,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,,0.6558267147349346,0.5280927512030451,0.0938,0.0826,0.9886,0.8436,0.0375,0.0,0.1724,0.1667,0.2083,0.0738,0.0731,0.0799,0.0154,0.0491,0.0956,0.0858,0.9886,0.8497,0.0379,0.0,0.1724,0.1667,0.2083,0.0755,0.0799,0.0833,0.0156,0.052000000000000005,0.0947,0.0826,0.9886,0.8457,0.0378,0.0,0.1724,0.1667,0.2083,0.0751,0.0744,0.0813,0.0155,0.0501,,block of flats,0.094,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-873.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n124622,0,Cash loans,F,N,Y,0,81000.0,101880.0,10566.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-16678,-8323,-8443.0,-221,,1,1,1,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,1,1,Medicine,0.4375915420699776,0.5937941113307992,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,-1967.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n379578,0,Cash loans,F,N,N,0,243000.0,414612.0,17694.0,297000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.002506,-23189,-6512,-14615.0,-4230,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,3,0,0,0,0,0,0,School,,0.7044426799844888,0.6674577419214722,0.0907,0.1006,0.9796,0.7212,0.0,0.0,0.2069,0.1667,0.0417,0.0868,0.07400000000000001,0.0857,0.0,0.0083,0.0924,0.1044,0.9796,0.7321,0.0,0.0,0.2069,0.1667,0.0417,0.0888,0.0808,0.0893,0.0,0.0088,0.0916,0.1006,0.9796,0.7249,0.0,0.0,0.2069,0.1667,0.0417,0.0883,0.0752,0.0872,0.0,0.0085,reg oper account,block of flats,0.0692,Panel,No,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n134633,1,Cash loans,F,N,Y,0,31500.0,50940.0,5089.5,45000.0,Unaccompanied,Pensioner,Lower secondary,Single / not married,House / apartment,0.007120000000000001,-23251,365243,-2448.0,-4283,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.1915258625230554,0.2778856891082046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1420.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n271138,0,Cash loans,F,N,N,0,68400.0,634482.0,20596.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.002042,-22685,365243,-20569.0,-4861,,1,0,0,1,1,0,,2.0,3,3,TUESDAY,9,0,0,0,0,0,0,XNA,,0.5688116111747552,0.5136937663039473,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1135.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n300038,0,Cash loans,F,Y,Y,0,67500.0,229500.0,11718.0,229500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.008575,-20347,365243,-3709.0,-1507,9.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,0.41592744898980494,0.37670461995057664,0.5971924268337128,0.1237,,0.9846,0.7892,,0.0,0.069,0.1667,,,0.1009,,0.0,,0.1261,,0.9846,0.7975,,0.0,0.069,0.1667,,,0.1102,,0.0,,0.1249,,0.9846,0.792,,0.0,0.069,0.1667,,,0.1026,,0.0,,reg oper account,block of flats,0.0505,Panel,No,1.0,0.0,1.0,0.0,-423.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n310140,0,Cash loans,F,N,Y,1,67500.0,640080.0,29839.5,450000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.0105,-10630,-1218,-2705.0,-2873,,1,1,0,1,0,0,High skill tech staff,3.0,3,3,FRIDAY,16,0,0,0,0,1,1,Business Entity Type 3,0.4256456553438369,0.028999412783176044,,,,0.9876,,,,,,,,,0.0795,,,,,0.9876,,,,,,,,,0.0829,,,,,0.9876,,,,,,,,,0.081,,,,,0.0698,,No,0.0,0.0,0.0,0.0,-2111.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n265767,0,Cash loans,M,Y,N,0,112500.0,495000.0,15795.0,495000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Co-op apartment,0.026392,-10289,-177,-10244.0,-2628,16.0,1,1,0,1,0,0,Sales staff,1.0,2,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.6640108005113872,0.5624558064462015,0.4866531565147181,0.4588,0.3086,0.9841,0.7824,0.2191,0.52,0.4483,0.3333,,0.3033,0.369,0.547,0.0232,0.0151,0.4674,0.3203,0.9841,0.7909,0.2211,0.5236,0.4483,0.3333,,0.3103,0.4031,0.5699,0.0233,0.0159,0.4632,0.3086,0.9841,0.7853,0.2205,0.52,0.4483,0.3333,,0.3086,0.3754,0.5568,0.0233,0.0154,reg oper spec account,block of flats,0.5056,Panel,No,0.0,0.0,0.0,0.0,-439.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n357673,0,Cash loans,M,Y,Y,0,292500.0,795294.0,75640.5,765000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.022625,-20841,-380,-4305.0,-4289,2.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,1,1,Other,0.21750757820432795,0.7311512308408776,0.4884551844437485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n100601,0,Cash loans,F,N,Y,0,157500.0,284400.0,14724.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.04622,-13509,-908,-6251.0,-3906,,1,1,1,1,0,0,Laborers,2.0,1,1,TUESDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.6864718528356194,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1924.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n242211,0,Cash loans,F,Y,N,0,184500.0,675000.0,32602.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-20896,-4612,-4172.0,-3598,4.0,1,1,1,1,1,0,Sales staff,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Self-employed,,0.5250902433647509,0.5154953751603267,0.1227,0.1595,0.9861,0.8096,,0.0,0.2759,0.1667,0.2083,0.1102,0.1,0.0746,,,0.125,0.1655,0.9861,0.8171,,0.0,0.2759,0.1667,0.2083,0.1127,0.1093,0.0777,,,0.1239,0.1595,0.9861,0.8121,,0.0,0.2759,0.1667,0.2083,0.1121,0.1018,0.0759,,,reg oper account,block of flats,0.0964,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-817.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n169670,0,Revolving loans,M,N,Y,0,112500.0,157500.0,7875.0,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00733,-21483,365243,-11125.0,-4944,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,XNA,0.7703660165751197,0.6309587029449821,0.5762088360175724,0.1031,0.11199999999999999,0.9846,,,0.0,0.2069,0.1667,,0.0,,0.0869,,0.0313,0.105,0.1162,0.9846,,,0.0,0.2069,0.1667,,0.0,,0.0906,,0.0332,0.1041,0.11199999999999999,0.9846,,,0.0,0.2069,0.1667,,0.0,,0.0885,,0.032,reg oper account,block of flats,0.0765,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3144.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n262055,0,Cash loans,F,N,Y,2,216000.0,906615.0,32692.5,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-12856,-1681,-6157.0,-818,,1,1,0,1,0,0,Cleaning staff,4.0,2,2,TUESDAY,13,0,0,0,0,1,1,Other,0.4525080826232871,0.1573197078245665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-624.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n149291,0,Cash loans,F,N,Y,0,315000.0,1006920.0,42790.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-18974,-9008,-8798.0,-2526,,1,1,0,1,1,0,Managers,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Self-employed,0.8143203857515182,0.5896576208135093,0.4956658291397297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-357.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n168883,0,Cash loans,F,N,Y,1,225000.0,781920.0,26725.5,675000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.032561,-15949,-2165,-1688.0,-4977,,1,1,0,1,0,0,Medicine staff,2.0,1,1,WEDNESDAY,16,0,0,0,0,0,0,Kindergarten,,0.7076332784807198,0.7992967832109371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1714.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n119975,0,Cash loans,F,N,Y,0,225000.0,675000.0,19867.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-20514,365243,-10865.0,-4027,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.2319427311585574,0.4223696523543468,0.0784,0.0798,0.9747,,,,0.1379,0.1667,,0.0075,,0.0437,,0.0142,0.0798,0.0828,0.9747,,,,0.1379,0.1667,,0.0076,,0.0456,,0.015,0.0791,0.0798,0.9747,,,,0.1379,0.1667,,0.0076,,0.0445,,0.0145,,terraced house,0.0374,Panel,No,0.0,0.0,0.0,0.0,-1973.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n282418,0,Cash loans,F,N,Y,0,225000.0,1293165.0,37809.0,1012500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.016612000000000002,-19765,-7204,-4925.0,-3293,,1,1,0,1,1,0,Medicine staff,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Medicine,,0.7627514508139602,0.6347055309763198,0.0165,0.0371,0.9786,0.7076,0.0135,0.0,0.069,0.0417,0.0833,0.0453,0.0134,0.0148,0.0,0.0,0.0168,0.0385,0.9786,0.7190000000000001,0.0136,0.0,0.069,0.0417,0.0833,0.0463,0.0147,0.0154,0.0,0.0,0.0167,0.0371,0.9786,0.7115,0.0136,0.0,0.069,0.0417,0.0833,0.046,0.0137,0.0151,0.0,0.0,not specified,block of flats,0.019,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-354.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n364962,0,Cash loans,F,N,Y,0,90000.0,518562.0,20695.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010556,-21640,-2526,-258.0,-4876,,1,1,0,1,0,0,,1.0,3,3,MONDAY,15,0,0,0,0,0,0,Cleaning,,0.4758460864656018,0.4578995512067301,0.0546,0.0368,0.9831,,,0.0,0.069,0.1667,,0.0893,,0.0437,,0.0927,0.0557,0.0381,0.9831,,,0.0,0.069,0.1667,,0.0913,,0.0455,,0.0981,0.0552,0.0368,0.9831,,,0.0,0.069,0.1667,,0.0908,,0.0445,,0.0946,reg oper account,block of flats,0.0545,\"Stone, brick\",No,6.0,2.0,6.0,1.0,-636.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n443155,1,Cash loans,F,N,Y,0,112500.0,450346.5,23710.5,342000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.018801,-16230,-804,-2719.0,-3887,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.587203350852972,0.10331066747140047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n200915,0,Cash loans,M,Y,Y,0,315000.0,1971072.0,68512.5,1800000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-19526,-294,-9331.0,-563,8.0,1,1,1,1,1,1,Drivers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Transport: type 3,0.7366227459822184,0.5926595915098644,0.266456808245056,0.1021,0.1132,0.9757,0.6668,0.011000000000000001,0.0,0.2069,0.1667,0.2083,0.0975,0.0824,0.08800000000000001,0.0039,0.0057,0.10400000000000001,0.1175,0.9757,0.6798,0.0111,0.0,0.2069,0.1667,0.2083,0.0997,0.09,0.0917,0.0039,0.006,0.1031,0.1132,0.9757,0.6713,0.0111,0.0,0.2069,0.1667,0.2083,0.0992,0.0838,0.0896,0.0039,0.0058,reg oper account,block of flats,0.0704,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1341.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n140489,0,Cash loans,M,Y,Y,1,202500.0,127350.0,13176.0,112500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-14519,-3842,-1670.0,-4560,19.0,1,1,0,1,0,0,,3.0,2,2,TUESDAY,12,0,1,1,0,0,0,Business Entity Type 2,0.6135638630227205,0.5825599985622982,0.6023863442690867,0.2887,0.1226,0.9955,0.932,0.0629,0.24,0.2069,0.375,0.4167,0.0194,0.2353,0.2512,0.0,0.0,0.2941,0.1273,0.9955,0.9347,0.0635,0.2417,0.2069,0.375,0.4167,0.0198,0.2571,0.2617,0.0,0.0,0.2915,0.1226,0.9955,0.9329,0.0633,0.24,0.2069,0.375,0.4167,0.0197,0.2394,0.2557,0.0,0.0,reg oper spec account,block of flats,0.23199999999999998,Panel,No,5.0,0.0,5.0,0.0,-2204.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n268580,0,Cash loans,F,N,Y,0,180000.0,840951.0,35761.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-15590,-2632,-8929.0,-4297,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,10,0,0,0,0,1,1,Self-employed,,0.3848923917483889,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,-839.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n197580,0,Cash loans,F,N,Y,1,238500.0,396000.0,42777.0,396000.0,Family,Commercial associate,Secondary / secondary special,Married,Rented apartment,0.018209,-12314,-189,-846.0,-3445,,1,1,0,1,0,0,Cooking staff,3.0,3,3,THURSDAY,8,0,0,0,1,1,0,Other,0.3317224174209058,0.3323418731628999,0.7700870700124128,,0.0,0.9737,,,0.0,0.069,0.0417,,,,0.0053,,0.0076,,0.0,0.9737,,,0.0,0.069,0.0417,,,,0.0055,,0.0081,,0.0,0.9737,,,0.0,0.069,0.0417,,,,0.0053,,0.0078,,,0.0046,,No,5.0,0.0,5.0,0.0,-1580.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n368726,0,Cash loans,F,N,Y,0,256500.0,904500.0,38452.5,904500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-18636,-2279,-9947.0,-2177,,1,1,1,1,1,1,Core staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Self-employed,0.6045389755935027,0.1966287653792793,0.5919766183185521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2893.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n134663,0,Cash loans,M,N,Y,2,360000.0,728460.0,57685.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-16014,-1360,-8484.0,-4523,,1,1,0,1,0,1,Managers,4.0,1,1,FRIDAY,10,0,0,0,1,1,0,Business Entity Type 3,0.6715156688126322,0.7380617388288417,0.3139166772114369,0.2041,0.1238,0.9841,0.7824,,0.2,0.1724,0.3333,,0.0675,,0.212,,0.084,0.20800000000000002,0.1285,0.9841,0.7909,,0.2014,0.1724,0.3333,,0.069,,0.2209,,0.08900000000000001,0.2061,0.1238,0.9841,0.7853,,0.2,0.1724,0.3333,,0.0686,,0.2158,,0.0858,reg oper account,block of flats,0.185,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1022.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n359687,0,Cash loans,F,N,Y,0,225000.0,835380.0,23571.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.018801,-18663,-3527,-7112.0,-2213,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Industry: type 11,,0.5319614503423878,0.4578995512067301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n265029,0,Revolving loans,M,Y,N,1,180000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-18724,-6088,-2548.0,-2268,15.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,13,0,0,0,0,0,0,Industry: type 11,0.4996138810543581,0.4691313642854167,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-2198.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n187768,0,Cash loans,F,N,N,0,90000.0,326439.0,11853.0,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010966,-20434,365243,-1305.0,-3997,,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.13102185597578753,0.4794489811780563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n315860,0,Cash loans,F,N,N,0,427500.0,1125000.0,47794.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.072508,-23371,365243,-3555.0,-4122,,1,0,0,1,0,0,,1.0,1,1,MONDAY,15,0,0,0,0,0,0,XNA,,0.6321341431166888,0.20915469884100693,0.1608,0.1243,0.9712,0.6056,0.1552,0.2,0.1724,0.2917,0.3333,0.0,0.1177,0.1955,0.0618,0.1894,0.1639,0.129,0.9712,0.621,0.1566,0.2014,0.1724,0.2917,0.3333,0.0,0.1286,0.2037,0.0623,0.2005,0.1624,0.1243,0.9712,0.6109,0.1562,0.2,0.1724,0.2917,0.3333,0.0,0.1197,0.19899999999999998,0.0621,0.1934,reg oper account,block of flats,0.1949,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-425.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n315013,0,Cash loans,M,Y,Y,0,180000.0,1320849.0,52515.0,1215000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-15163,-2126,-1395.0,-4055,10.0,1,1,0,1,0,0,Laborers,2.0,1,1,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.4307512043311892,0.7056931864173831,0.4938628816996244,0.0216,0.0662,0.9856,,,0.08,0.069,0.375,,0.0,,0.1061,,0.0689,0.0221,0.0687,0.9856,,,0.0806,0.069,0.375,,0.0,,0.1106,,0.0729,0.0219,0.0662,0.9856,,,0.08,0.069,0.375,,0.0,,0.1081,,0.0703,,block of flats,0.0985,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1128.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n356195,0,Cash loans,M,N,Y,0,121500.0,343800.0,16024.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.005002,-17631,-2157,-260.0,-783,,1,1,0,1,0,0,Drivers,1.0,3,3,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4951147250360933,0.5335454889449429,0.2910973802776635,0.1031,0.0878,0.9831,0.7688,0.0108,0.0,0.2069,0.1667,0.2083,0.0187,0.0841,0.0917,0.0,0.0,0.105,0.0912,0.9831,0.7779,0.0109,0.0,0.2069,0.1667,0.2083,0.0192,0.0918,0.0955,0.0,0.0,0.1041,0.0878,0.9831,0.7719,0.0109,0.0,0.2069,0.1667,0.2083,0.0191,0.0855,0.0933,0.0,0.0,org spec account,block of flats,0.078,Panel,No,10.0,0.0,10.0,0.0,-251.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n363677,0,Revolving loans,F,Y,N,0,261000.0,765000.0,38250.0,765000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-18429,-1657,-1991.0,-1991,8.0,1,1,1,1,1,0,,2.0,1,1,SATURDAY,14,0,0,0,0,1,1,Self-employed,0.6756127199180423,0.7172732768311804,0.6380435278721609,0.0562,0.0,0.9757,0.6668,0.0243,0.0,0.1207,0.1042,0.1458,0.0954,0.0458,0.0329,0.0,0.0,0.0126,0.0,0.9727,0.6406,0.0069,0.0,0.069,0.0417,0.0833,0.0352,0.011000000000000001,0.0087,0.0,0.0,0.0567,0.0,0.9757,0.6713,0.0244,0.0,0.1207,0.1042,0.1458,0.09699999999999999,0.0466,0.0335,0.0,0.0,reg oper spec account,block of flats,0.068,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1204.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n260865,0,Cash loans,F,Y,Y,0,288000.0,1350000.0,52254.0,1350000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.010006,-22855,-1257,-207.0,-4711,12.0,1,1,0,1,0,0,Cleaning staff,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.476544514988861,0.27472903399293724,,0.2041,0.1255,0.9995,0.9932,0.052000000000000005,0.24,0.1724,0.4583,0.375,0.2074,0.1656,0.2395,0.0039,0.0284,0.20800000000000002,0.1302,0.9995,0.9935,0.0525,0.2417,0.1724,0.4583,0.375,0.2121,0.1809,0.2495,0.0039,0.0301,0.2061,0.1255,0.9995,0.9933,0.0523,0.24,0.1724,0.4583,0.375,0.21100000000000002,0.1685,0.2438,0.0039,0.028999999999999998,reg oper account,block of flats,0.1945,Panel,No,0.0,0.0,0.0,0.0,-2024.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n396291,0,Revolving loans,F,Y,Y,1,225000.0,202500.0,10125.0,202500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.010006,-9639,-806,-315.0,-2303,13.0,1,1,1,1,1,0,Managers,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Self-employed,0.4295551569431036,0.6312285306773464,0.25946765482111994,0.0722,0.0671,0.9796,0.7212,0.0074,0.0,0.1379,0.1667,0.0417,0.0148,0.0588,0.0673,0.0,0.0,0.0735,0.0697,0.9796,0.7321,0.0074,0.0,0.1379,0.1667,0.0417,0.0151,0.0643,0.0701,0.0,0.0,0.0729,0.0671,0.9796,0.7249,0.0074,0.0,0.1379,0.1667,0.0417,0.015,0.0599,0.0685,0.0,0.0,reg oper spec account,block of flats,0.08,Block,No,3.0,0.0,3.0,0.0,-1452.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n380156,1,Cash loans,F,N,Y,0,157500.0,297000.0,14445.0,297000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-15267,-4823,-3721.0,-3859,,1,1,1,1,0,1,Sales staff,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,0.5174471388182277,0.6504466623582217,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n346282,0,Cash loans,F,N,Y,1,135000.0,472500.0,24124.5,472500.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.02461,-15420,-239,-422.0,-4446,,1,1,1,1,1,0,Laborers,3.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.6150592423696931,0.5407598761730376,0.21155076420525776,0.1129,0.0532,0.9985,,,0.12,0.1207,0.25,,0.0,,0.1035,,0.1365,0.0546,0.0552,0.998,,,0.0403,0.069,0.1667,,0.0,,0.0624,,0.023,0.114,0.0532,0.9985,,,0.12,0.1207,0.25,,0.0,,0.1054,,0.1394,,block of flats,0.1704,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1524.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n270560,0,Cash loans,F,N,Y,0,135000.0,797557.5,26487.0,688500.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.010643,-23764,365243,-12665.0,-4506,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.628613376377087,0.6058362647264226,0.0186,0.0,0.9851,0.7959999999999999,0.0022,0.0,0.1034,0.0417,0.0417,0.0398,0.0151,0.0179,0.0,0.0,0.0189,0.0,0.9851,0.804,0.0022,0.0,0.1034,0.0417,0.0417,0.0407,0.0165,0.0187,0.0,0.0,0.0187,0.0,0.9851,0.7987,0.0022,0.0,0.1034,0.0417,0.0417,0.0405,0.0154,0.0182,0.0,0.0,reg oper account,block of flats,0.0153,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1059.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n134362,0,Cash loans,F,N,Y,0,112500.0,225000.0,12694.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.026392,-24082,365243,-9402.0,-4816,,1,0,0,1,1,0,,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.6620532293488427,0.722392890081304,0.0272,,0.7829,,,0.0,0.0603,0.0312,,,,0.0058,,0.0,0.0084,,0.9747,,,0.0,0.069,0.0417,,,,0.0058,,0.0,0.0083,,0.9747,,,0.0,0.069,0.0417,,,,0.0059,,0.0,,block of flats,0.0047,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-135.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n156466,0,Cash loans,M,N,Y,1,157500.0,423000.0,30910.5,423000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018209,-10302,-1148,-575.0,-2917,,1,1,0,1,0,0,Managers,3.0,3,3,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.2493848621351432,0.04824127674903801,0.4101025731788671,0.1021,0.1064,0.9806,0.7348,0.0114,0.0,0.2069,0.1667,0.0,0.0423,0.0807,0.0885,0.0116,0.0195,0.10400000000000001,0.1104,0.9806,0.7452,0.0115,0.0,0.2069,0.1667,0.0,0.0433,0.0882,0.0922,0.0117,0.0206,0.1031,0.1064,0.9806,0.7383,0.0115,0.0,0.2069,0.1667,0.0,0.043,0.0821,0.0901,0.0116,0.0199,reg oper account,block of flats,0.0738,Panel,No,1.0,0.0,1.0,0.0,-468.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n313777,0,Cash loans,M,N,N,0,112500.0,67500.0,8140.5,67500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.016612000000000002,-10445,-111,-4439.0,-3115,,1,1,1,1,0,0,Drivers,1.0,2,2,WEDNESDAY,12,0,0,0,1,1,0,Industry: type 1,0.0632671402724946,0.2983590299594093,0.5709165417729987,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-266.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n244407,0,Cash loans,F,N,Y,0,265500.0,341280.0,22936.5,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-22482,365243,-5259.0,-5291,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,XNA,,0.647547481044461,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3098.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n375190,0,Cash loans,F,N,Y,0,45000.0,585000.0,13045.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.005144,-17542,-7534,-8770.0,-1081,,1,1,0,1,0,0,Cooking staff,1.0,2,2,MONDAY,10,0,0,0,0,0,0,Medicine,,0.338890168905726,,0.0165,0.0456,0.9727,0.626,0.0015,0.0,0.069,0.0417,0.0833,0.0124,0.0134,0.0112,0.0,0.0,0.0168,0.0473,0.9727,0.6406,0.0015,0.0,0.069,0.0417,0.0833,0.0127,0.0147,0.0117,0.0,0.0,0.0167,0.0456,0.9727,0.631,0.0015,0.0,0.069,0.0417,0.0833,0.0126,0.0137,0.0114,0.0,0.0,reg oper account,block of flats,0.0096,\"Stone, brick\",No,25.0,4.0,25.0,3.0,-2.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n390789,0,Cash loans,F,N,Y,0,202500.0,364500.0,28336.5,364500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.015221,-16563,-833,-9133.0,-106,,1,1,0,1,0,0,Accountants,2.0,2,2,THURSDAY,11,0,0,0,0,1,1,Cleaning,0.6302295658282556,0.27852279947141795,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-563.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n200443,0,Cash loans,F,N,Y,0,427500.0,945000.0,40036.5,945000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-14645,-358,-8413.0,-184,,1,1,0,1,0,0,Secretaries,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.7477841747451686,0.6563447994511425,0.6127042441012546,0.1237,0.0862,0.9836,,,0.0,0.069,0.1667,,0.0,,0.0646,,0.0012,0.1261,0.0895,0.9836,,,0.0,0.069,0.1667,,0.0,,0.0673,,0.0013,0.1249,0.0862,0.9836,,,0.0,0.069,0.1667,,0.0,,0.0657,,0.0012,reg oper account,block of flats,0.0727,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-508.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n139357,0,Cash loans,F,N,N,0,135000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-9242,-233,-1923.0,-1925,,1,1,0,1,1,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 1,,0.4914907487471729,,0.2227,0.1435,0.9841,0.7144,0.0404,0.16,0.1379,0.3333,0.375,0.0534,0.1816,0.1778,0.0,0.0,0.2269,0.1476,0.9841,0.6602,0.0408,0.1611,0.1379,0.3333,0.375,0.0343,0.1983,0.1455,0.0,0.0,0.2248,0.1435,0.9841,0.7182,0.0407,0.16,0.1379,0.3333,0.375,0.0543,0.1847,0.18100000000000002,0.0,0.0,reg oper account,block of flats,0.1699,Panel,No,2.0,0.0,2.0,0.0,-436.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n216034,0,Cash loans,M,Y,Y,0,157500.0,1024740.0,55719.0,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010556,-20144,-2302,-11081.0,-3676,9.0,1,1,0,1,0,0,Laborers,2.0,3,3,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.6960247687101572,0.2778856891082046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1140.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n176002,0,Cash loans,M,Y,Y,0,225000.0,432000.0,21141.0,432000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-10936,-975,-5083.0,-3535,0.0,1,1,0,1,1,0,Sales staff,1.0,2,2,SATURDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.1363343208660577,0.6212263380626669,0.4557,0.3182,0.9856,0.8028,0.2153,0.52,0.4483,0.3333,0.375,0.5098,0.369,0.5391,0.0116,0.006,0.4643,0.3302,0.9856,0.8105,0.2173,0.5236,0.4483,0.3333,0.375,0.5214,0.4031,0.5617,0.0117,0.0064,0.4601,0.3182,0.9856,0.8054,0.2167,0.52,0.4483,0.3333,0.375,0.5186,0.3754,0.5488,0.0116,0.0062,reg oper account,block of flats,0.5426,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n144332,0,Cash loans,M,Y,Y,2,225000.0,1288350.0,41562.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-11146,-275,-2368.0,-3397,2.0,1,1,1,1,0,0,,4.0,2,2,FRIDAY,13,0,1,1,0,1,1,Industry: type 9,0.2198803667748999,0.4728308657188498,0.4471785780453068,0.0,,0.0,,,,,,,,,,,,0.0,,0.0005,,,,,,,,,,,,0.0,,0.0,,,,,,,,,,,,,block of flats,0.0,,No,3.0,1.0,3.0,1.0,-3003.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n113095,0,Cash loans,F,N,Y,0,139500.0,835380.0,36927.0,675000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.006207,-23327,365243,-11944.0,-4214,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,0.33213797642247744,0.6202339032792111,,0.1155,0.0177,0.9831,0.7688,0.0,0.12,0.1034,0.375,0.375,0.0675,0.0941,0.1161,0.0,0.0446,0.1176,0.0183,0.9831,0.7779,0.0,0.1208,0.1034,0.375,0.375,0.0691,0.1028,0.121,0.0,0.0472,0.1166,0.0177,0.9831,0.7719,0.0,0.12,0.1034,0.375,0.375,0.0687,0.0958,0.1182,0.0,0.0455,reg oper account,block of flats,0.1011,Panel,No,7.0,2.0,7.0,2.0,-2621.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n345951,0,Cash loans,M,N,Y,0,202500.0,697500.0,24840.0,697500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-17789,-1814,-3762.0,-1326,,1,1,0,1,0,0,Laborers,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,Self-employed,,0.7194897022393026,0.3996756156233169,0.0825,0.0863,0.9866,,,0.0,0.2069,0.1667,,0.0755,,0.0847,,0.0,0.084,0.0895,0.9866,,,0.0,0.2069,0.1667,,0.0772,,0.0882,,0.0,0.0833,0.0863,0.9866,,,0.0,0.2069,0.1667,,0.0768,,0.0862,,0.0,,block of flats,0.0741,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-118.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n236425,0,Cash loans,F,N,Y,0,171000.0,593010.0,19260.0,495000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.008019,-22820,365243,-3819.0,-3826,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.44456675933919343,0.2225807646753351,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-590.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n395613,1,Cash loans,F,N,Y,0,45000.0,343800.0,13090.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-18649,-3255,-7398.0,-2162,,1,1,0,1,0,0,Cooking staff,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.5881693527311451,0.2753001929234249,0.5298898341969072,0.1031,0.0554,0.9916,0.8844,0.0158,0.0,0.1034,0.1667,0.2083,0.0,0.0841,0.0852,0.0,0.0,0.0819,0.0,0.9901,0.8693,0.0151,0.0,0.069,0.1667,0.2083,0.0,0.0716,0.0511,0.0,0.0,0.1041,0.0554,0.9916,0.8859,0.0159,0.0,0.1034,0.1667,0.2083,0.0,0.0855,0.0867,0.0,0.0,not specified,block of flats,0.0529,Block,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n441144,0,Cash loans,F,N,N,0,202500.0,284400.0,14445.0,225000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.072508,-11823,-184,-5913.0,-3087,,1,1,0,1,1,0,,2.0,1,1,FRIDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.5979460354632936,0.7630639272516351,0.4650692149562261,0.0505,0.0603,0.9717,0.6124,0.0115,0.0,0.1034,0.125,0.1667,0.0,0.0403,0.0387,0.0039,0.0363,0.0515,0.0619,0.9717,0.6276,0.0088,0.0,0.1034,0.125,0.1667,0.0,0.0441,0.04,0.0039,0.0384,0.051,0.0603,0.9717,0.6176,0.0116,0.0,0.1034,0.125,0.1667,0.0,0.040999999999999995,0.0394,0.0039,0.037000000000000005,reg oper account,block of flats,0.0432,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n165012,0,Cash loans,F,N,N,0,121500.0,147726.0,14742.0,130500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,Municipal apartment,0.018801,-24095,365243,-2093.0,-4609,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.2039769382753839,0.25670557243930026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n448970,0,Cash loans,F,N,Y,0,130500.0,254700.0,30357.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006629,-12433,-478,-2928.0,-2801,,1,1,1,1,1,1,Accountants,2.0,2,2,FRIDAY,5,0,0,0,0,0,0,Other,,0.09676282670869116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-945.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n425269,0,Cash loans,F,Y,Y,0,315000.0,1483650.0,61357.5,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.009334,-22592,365243,-50.0,-2052,29.0,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,0.6263961074750742,0.5638652764226357,0.6430255641096323,0.3454,0.4748,0.9856,0.8028,0.0,0.4,0.3448,0.3333,0.2083,0.0666,0.4194,0.316,0.0,0.0,0.1796,0.4927,0.9826,0.7713,0.0,0.2014,0.1724,0.3333,0.0417,0.0173,0.4582,0.1078,0.0,0.0,0.3487,0.4748,0.9856,0.8054,0.0,0.4,0.3448,0.3333,0.2083,0.0677,0.4267,0.3217,0.0,0.0,reg oper account,block of flats,0.5251,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-824.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n397217,1,Cash loans,F,N,N,0,157500.0,149256.0,17010.0,135000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-11644,-344,-5337.0,-3297,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,15,0,0,0,0,1,1,School,,0.5922213489000812,0.4650692149562261,0.1031,0.0799,0.9767,,,0.0,0.1379,0.1667,,0.0291,,0.061,,0.0,0.105,0.083,0.9767,,,0.0,0.1379,0.1667,,0.0297,,0.0636,,0.0,0.1041,0.0799,0.9767,,,0.0,0.1379,0.1667,,0.0296,,0.0621,,0.0,,block of flats,0.0773,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2160.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n353308,0,Cash loans,F,Y,N,0,405000.0,2250000.0,59355.0,2250000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.008575,-23754,365243,-5277.0,-1651,8.0,1,0,0,1,1,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.6417215954560347,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,10.0,1.0,-732.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n236725,0,Cash loans,F,N,Y,2,207000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-14374,-1671,-6796.0,-20,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,14,0,0,0,0,0,0,Housing,,0.5748295372808809,0.4329616670974407,,,0.9717,,,,,,,,,0.0028,,,,,0.9717,,,,,,,,,0.0029,,,,,0.9717,,,,,,,,,0.0028,,,,block of flats,0.0022,,Yes,4.0,1.0,4.0,0.0,-332.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n432472,0,Cash loans,M,N,Y,0,58500.0,314100.0,17167.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.022625,-18723,365243,-12798.0,-2268,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,0.5862945676902446,0.047507660955509394,0.8327850252992314,0.1546,0.141,0.9776,0.6940000000000001,0.0711,0.0,0.3448,0.1667,0.2083,0.1471,0.1261,0.1565,0.0,0.0,0.1576,0.1463,0.9777,0.706,0.0718,0.0,0.3448,0.1667,0.2083,0.1505,0.1377,0.163,0.0,0.0,0.1561,0.141,0.9776,0.6981,0.0716,0.0,0.3448,0.1667,0.2083,0.1497,0.1283,0.1593,0.0,0.0,reg oper account,block of flats,0.1619,Panel,No,0.0,0.0,0.0,0.0,-197.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,1.0,0.0\n439044,0,Cash loans,F,N,Y,0,225000.0,218016.0,17352.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.010032,-15687,-130,-9796.0,-4732,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.7289170243827688,0.15947687550975229,0.4400578303966329,0.0928,0.0909,0.9866,,,0.0,0.2069,0.1667,,,,0.0827,,,0.0945,0.0944,0.9866,,,0.0,0.2069,0.1667,,,,0.0861,,,0.0937,0.0909,0.9866,,,0.0,0.2069,0.1667,,,,0.0842,,,,block of flats,0.1033,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n411780,0,Cash loans,F,N,Y,1,159300.0,675000.0,32602.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-20133,-7144,-5690.0,-3360,,1,1,1,1,0,0,Core staff,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Kindergarten,0.4182828978653327,0.7039416147692028,0.7309873696832169,0.0082,0.0,0.9617,,,0.0,0.069,0.0417,,0.0394,,0.0077,,0.0,0.0084,0.0,0.9618,,,0.0,0.069,0.0417,,0.0403,,0.008,,0.0,0.0083,0.0,0.9617,,,0.0,0.069,0.0417,,0.0401,,0.0079,,0.0,,block of flats,0.0088,Wooden,Yes,0.0,0.0,0.0,0.0,-2043.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n108482,0,Cash loans,F,N,Y,0,216000.0,1467612.0,62311.5,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.04622,-23347,-398,-8640.0,-4369,,1,1,0,1,0,0,,1.0,1,1,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6565420673031869,,0.0818,0.0491,0.9771,0.6872,0.0257,0.0,0.1607,0.1667,0.2083,0.0202,0.0661,0.0685,0.0025,0.0036,0.063,0.0,0.9757,0.6798,0.0071,0.0,0.1379,0.1667,0.2083,0.0147,0.0551,0.057999999999999996,0.0,0.0,0.0812,0.0569,0.9767,0.6847,0.0307,0.0,0.1379,0.1667,0.2083,0.0199,0.065,0.0615,0.0,0.0,reg oper account,block of flats,0.0537,\"Stone, brick\",No,2.0,1.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n433554,0,Cash loans,F,Y,Y,2,202500.0,400500.0,47659.5,400500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-15774,-1416,-2834.0,-4381,4.0,1,1,0,1,0,0,,4.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.5776862839675398,0.5989262182569273,0.0825,0.1149,0.9811,,,0.0,0.2069,0.1667,,0.0815,,0.07200000000000001,,0.0669,0.084,0.1193,0.9811,,,0.0,0.2069,0.1667,,0.0834,,0.075,,0.0708,0.0833,0.1149,0.9811,,,0.0,0.2069,0.1667,,0.0829,,0.0733,,0.0683,,block of flats,0.0712,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1851.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n343193,0,Revolving loans,F,N,Y,0,450000.0,900000.0,45000.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-17545,-4640,-275.0,-1091,,1,1,0,1,0,0,,2.0,1,1,THURSDAY,17,0,1,1,0,1,1,Business Entity Type 3,,0.7562248140853277,0.722392890081304,0.2969,0.0676,0.9975,0.9592,0.1291,0.16,0.069,0.7083,0.75,0.0539,0.2286,0.3335,0.0618,0.1181,0.3025,0.0702,0.9975,0.9608,0.1303,0.1611,0.069,0.7083,0.75,0.0551,0.2498,0.3475,0.0623,0.125,0.2998,0.0676,0.9975,0.9597,0.13,0.16,0.069,0.7083,0.75,0.0548,0.2326,0.3395,0.0621,0.1206,reg oper account,block of flats,0.4055,Monolithic,No,0.0,0.0,0.0,0.0,-211.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n255224,0,Cash loans,F,N,N,0,90000.0,101880.0,10053.0,90000.0,Unaccompanied,Pensioner,Higher education,Single / not married,Municipal apartment,0.020713,-21651,365243,-8437.0,-5010,,1,0,0,1,0,0,,1.0,3,3,SUNDAY,9,0,0,0,0,0,0,XNA,,0.3429782503909263,0.22009464485041005,0.2196,0.0957,0.9851,0.7959999999999999,0.044000000000000004,0.12,0.1034,0.3333,0.375,0.115,0.179,0.152,0.0,0.0009,0.2237,0.0993,0.9851,0.804,0.0444,0.1208,0.1034,0.3333,0.375,0.1176,0.1956,0.1584,0.0,0.0009,0.2217,0.0957,0.9851,0.7987,0.0443,0.12,0.1034,0.3333,0.375,0.11699999999999999,0.1821,0.1547,0.0,0.0009,reg oper account,block of flats,0.1438,Panel,No,6.0,0.0,6.0,0.0,-242.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n214052,0,Cash loans,F,Y,Y,0,112500.0,700830.0,20214.0,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20218,-2521,-6145.0,-2933,21.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.03726970067983722,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-738.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n262261,0,Cash loans,F,Y,Y,0,216000.0,152820.0,17370.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-14486,-2946,-309.0,-4571,6.0,1,1,0,1,0,1,Core staff,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Self-employed,0.7202971920096928,0.6116211127574459,0.4170996682522097,0.0876,0.0842,0.9965,0.9524,0.011000000000000001,0.0,0.1724,0.1667,0.2083,0.0664,0.0714,0.0464,0.0,0.11900000000000001,0.0893,0.0874,0.9965,0.9543,0.0111,0.0,0.1724,0.1667,0.2083,0.0679,0.0781,0.0484,0.0,0.126,0.0885,0.0842,0.9965,0.953,0.0111,0.0,0.1724,0.1667,0.2083,0.0676,0.0727,0.0473,0.0,0.1215,reg oper account,block of flats,0.0624,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n217908,0,Cash loans,F,N,Y,0,90000.0,95940.0,10201.5,90000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.016612000000000002,-10544,-905,-5478.0,-2246,,1,1,1,1,1,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Other,0.4746044657566589,0.39479050284071576,0.392773860603134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-432.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n428579,0,Cash loans,M,Y,Y,0,360000.0,801306.0,44874.0,742500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-16851,-1937,-9510.0,-142,5.0,1,1,0,1,1,1,Managers,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.5605982893624557,0.4382813743111921,0.0588,0.0599,0.9796,0.7212,0.0077,0.0,0.1379,0.1667,0.2083,0.0511,0.0471,0.0532,0.0039,0.1052,0.0599,0.0622,0.9796,0.7321,0.0077,0.0,0.1379,0.1667,0.2083,0.0523,0.0514,0.0554,0.0039,0.1114,0.0593,0.0599,0.9796,0.7249,0.0077,0.0,0.1379,0.1667,0.2083,0.052000000000000005,0.0479,0.0541,0.0039,0.1074,reg oper account,block of flats,0.0647,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-3318.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n283778,0,Cash loans,M,Y,Y,0,225000.0,450000.0,44509.5,450000.0,Unaccompanied,Working,Incomplete higher,Separated,House / apartment,0.026392,-12655,-389,-6796.0,-4355,7.0,1,1,1,1,1,0,Sales staff,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6137175860871732,0.4974688893052743,0.2278,0.2398,0.9821,,,0.0,0.5172,0.1667,,0.2484,,0.2029,,0.0081,0.2321,0.2488,0.9821,,,0.0,0.5172,0.1667,,0.254,,0.2114,,0.0086,0.23,0.2398,0.9821,,,0.0,0.5172,0.1667,,0.2527,,0.2065,,0.0083,,block of flats,0.1849,Panel,No,0.0,0.0,0.0,0.0,-1027.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n151377,0,Revolving loans,M,Y,Y,0,562500.0,495000.0,24750.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-19600,-5121,-10478.0,-3148,2.0,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Medicine,0.5435746539688875,0.6872009021720008,,0.1031,0.0556,0.9786,0.7076,0.013999999999999999,0.0,0.2069,0.1667,0.2083,0.0759,0.0841,0.08900000000000001,0.0,0.0,0.105,0.0577,0.9786,0.7190000000000001,0.0141,0.0,0.2069,0.1667,0.2083,0.0776,0.0918,0.0927,0.0,0.0,0.1041,0.0556,0.9786,0.7115,0.0141,0.0,0.2069,0.1667,0.2083,0.0772,0.0855,0.0906,0.0,0.0,reg oper account,block of flats,0.0776,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2207.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n269724,0,Cash loans,F,N,Y,0,135000.0,450000.0,11871.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-20837,365243,-6582.0,-4254,,1,0,0,1,1,0,,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,XNA,0.7615612098113392,0.7755541255131594,,0.0021,,0.9896,,,0.0,,0.0,,,,,,,0.0021,,0.9896,,,0.0,,0.0,,,,,,,0.0021,,0.9896,,,0.0,,0.0,,,,,,,,terraced house,0.0023,\"Stone, brick\",No,5.0,0.0,4.0,0.0,-1154.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n412259,0,Cash loans,F,N,N,0,121500.0,253737.0,27454.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.019101,-8964,-751,-3109.0,-268,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.2465825019131669,0.5758660297877313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-257.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n421748,0,Cash loans,F,Y,Y,1,238500.0,497520.0,39307.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.009175,-17172,-2026,-2415.0,-720,12.0,1,1,1,1,0,0,Cooking staff,3.0,2,2,TUESDAY,10,1,1,0,1,1,0,Business Entity Type 3,0.5101381397197217,0.6948576030774594,0.16048893062734468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-981.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n454507,0,Cash loans,F,N,Y,0,171000.0,855882.0,34987.5,765000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-9333,-227,-3901.0,-288,,1,1,0,1,0,0,Sales staff,2.0,1,1,FRIDAY,14,0,1,1,0,0,0,Trade: type 2,0.474888812793942,0.6642367567971226,0.2314393514998941,0.267,0.2175,0.9945,0.9252,0.2225,0.48,0.2069,0.4583,0.5,0.0344,0.2127,0.2412,0.0232,0.0821,0.2721,0.2257,0.9945,0.9281,0.2245,0.4834,0.2069,0.4583,0.5,0.0352,0.2323,0.2513,0.0233,0.0869,0.2696,0.2175,0.9945,0.9262,0.2239,0.48,0.2069,0.4583,0.5,0.035,0.2163,0.2455,0.0233,0.0838,reg oper account,block of flats,0.2075,Panel,No,0.0,0.0,0.0,0.0,-183.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,0.0\n447627,0,Cash loans,F,Y,Y,0,180000.0,270000.0,18040.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-17513,-3052,-5915.0,-1025,10.0,1,1,1,1,1,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.6400513986877296,0.7371221307843667,0.5513812618027899,0.1129,,0.9752,,,,0.1207,0.1667,,0.0471,,0.0406,,0.009000000000000001,0.0882,,0.9752,,,,0.1034,0.1667,,0.0374,,0.0416,,0.0096,0.114,,0.9752,,,,0.1207,0.1667,,0.0479,,0.0413,,0.0092,,block of flats,0.0453,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-936.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n235547,1,Cash loans,M,N,Y,1,135000.0,276903.0,25524.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.008473999999999999,-9891,-972,-923.0,-2455,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 2,0.21740829971568765,0.4130100625546403,0.3046721837533529,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-281.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n424573,0,Cash loans,F,N,N,0,292500.0,539100.0,27522.0,450000.0,Family,Working,Secondary / secondary special,Married,Rented apartment,0.035792000000000004,-10856,-233,-2260.0,-2436,,1,1,1,1,0,0,Sales staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.2606042928008824,0.6260060242007348,0.3425288720742255,0.0495,0.0607,0.9762,,,0.0,0.1034,0.125,,0.0439,,0.0337,,0.0,0.0504,0.0629,0.9762,,,0.0,0.1034,0.125,,0.0449,,0.0351,,0.0,0.05,0.0607,0.9762,,,0.0,0.1034,0.125,,0.0446,,0.0343,,0.0,,block of flats,0.0287,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-790.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n347601,0,Cash loans,M,Y,N,0,94500.0,533304.0,27360.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.003813,-15349,-452,-3502.0,-3307,5.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,7,0,0,0,0,1,1,Business Entity Type 3,,0.036051142961278766,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n135498,0,Cash loans,F,N,N,1,94500.0,175896.0,9337.5,126000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.016612000000000002,-12155,-1414,-5681.0,-1955,,1,1,0,1,0,0,Accountants,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Security Ministries,0.4598893080944838,0.6829927662873461,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1077.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n311314,0,Cash loans,F,N,Y,1,103500.0,787131.0,42066.0,679500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010556,-14858,-5073,-1796.0,-3376,,1,1,0,1,0,0,Laborers,3.0,3,3,SATURDAY,11,0,0,0,0,0,0,Self-employed,,0.1213942300722686,0.6642482627052363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-534.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,0.0\n408224,0,Cash loans,M,Y,Y,0,67500.0,225000.0,10944.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.014519999999999996,-13875,-1640,-5053.0,-3758,9.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,1,1,Housing,,0.269729533123615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-441.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n343285,0,Cash loans,M,N,Y,1,90000.0,270000.0,26833.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00496,-13198,-681,-2085.0,-2374,,1,1,1,1,0,0,Drivers,3.0,2,2,THURSDAY,13,0,0,0,0,1,1,Self-employed,0.2949270145292052,0.3660406292215708,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-538.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n363024,0,Cash loans,F,N,Y,1,180000.0,254700.0,30357.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-13869,-264,-7157.0,-4357,,1,1,0,1,0,0,Sales staff,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4969131267434961,,0.0866,0.0,0.9791,0.7144,0.0365,0.0,0.1379,0.1667,0.0417,0.0008,0.0706,0.0546,0.0,0.0,0.0882,0.0,0.9791,0.7256,0.0368,0.0,0.1379,0.1667,0.0417,0.0008,0.0771,0.0569,0.0,0.0,0.0874,0.0,0.9791,0.7182,0.0367,0.0,0.1379,0.1667,0.0417,0.0008,0.0718,0.0556,0.0,0.0,reg oper account,block of flats,0.0629,Block,No,4.0,0.0,4.0,0.0,-142.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n105854,0,Cash loans,F,N,Y,0,90000.0,490500.0,25042.5,490500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-18225,-3008,-6165.0,-1772,,1,1,1,1,1,0,High skill tech staff,2.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Medicine,0.5469631065866855,0.29350216593425416,0.4561097392782771,0.0722,0.0751,0.9841,0.7824,0.0075,0.0,0.1379,0.1667,0.2083,0.0353,0.0588,0.0662,0.0,0.0037,0.0735,0.0779,0.9841,0.7909,0.0076,0.0,0.1379,0.1667,0.2083,0.0361,0.0643,0.069,0.0,0.0039,0.0729,0.0751,0.9841,0.7853,0.0076,0.0,0.1379,0.1667,0.2083,0.0359,0.0599,0.0674,0.0,0.0038,,block of flats,0.057,Block,No,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n135317,0,Cash loans,F,Y,Y,1,157500.0,675000.0,24246.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.010006,-14078,-6818,-1740.0,-4000,17.0,1,1,0,1,0,0,Medicine staff,3.0,2,2,MONDAY,14,0,0,0,0,0,0,Other,,0.6371336145594456,0.3296550543128238,0.2454,,0.996,,,0.36,0.3103,0.25,,,,0.1621,,0.8651,0.25,,0.996,,,0.3625,0.3103,0.25,,,,0.1689,,0.9159,0.2477,,0.996,,,0.36,0.3103,0.25,,,,0.165,,0.8833,,block of flats,0.3156,Panel,No,5.0,1.0,5.0,0.0,-2396.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n381401,0,Cash loans,M,N,Y,1,90000.0,156384.0,12667.5,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16524,-1025,-1280.0,-58,,1,1,0,1,0,0,Security staff,3.0,2,2,MONDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.7374652118764101,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-858.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n338316,0,Cash loans,F,N,Y,0,180000.0,862560.0,25348.5,720000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-12506,-2234,-2881.0,-2070,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Postal,,0.30649187130741395,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1263.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n190723,0,Cash loans,F,N,N,0,117000.0,808650.0,23305.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018029,-22727,365243,-6705.0,-4441,,1,0,0,1,0,0,,1.0,3,3,SATURDAY,8,0,0,0,1,0,0,XNA,,0.3055120082529754,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-2894.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n114348,0,Cash loans,F,Y,Y,2,117000.0,521280.0,31630.5,450000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-11808,-1344,-4551.0,-953,7.0,1,1,0,1,0,0,Laborers,4.0,1,1,WEDNESDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.1010910469504485,0.6833666837606205,0.0720131886405828,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n295586,0,Cash loans,M,N,Y,1,157500.0,339948.0,38448.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.001333,-8151,-1240,-2607.0,-800,,1,1,0,1,0,0,Core staff,3.0,3,3,FRIDAY,6,0,0,0,0,1,1,Self-employed,,0.5466243938339647,0.4135967602644276,0.0021,0.0,0.9856,0.8028,0.0,0.0,0.069,0.0,0.0417,0.0,0.0017,0.0012,0.0,0.0,0.0021,0.0,0.9856,0.8105,0.0,0.0,0.069,0.0,0.0417,0.0,0.0018,0.0012,0.0,0.0,0.0021,0.0,0.9856,0.8054,0.0,0.0,0.069,0.0,0.0417,0.0,0.0017,0.0012,0.0,0.0,not specified,block of flats,0.0009,Wooden,No,1.0,0.0,1.0,0.0,-347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n101347,0,Cash loans,F,N,Y,0,94500.0,562500.0,23962.5,562500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.02461,-19340,365243,-1392.0,-2886,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,10,0,0,0,1,0,0,XNA,,0.6684470576654707,0.7380196196295241,,0.0663,0.9801,0.728,0.0035,0.0,0.069,0.1667,0.2083,0.0641,,0.0647,0.0077,0.012,,0.0688,0.9801,0.7387,0.0035,0.0,0.069,0.1667,0.2083,0.0656,,0.0675,0.0078,0.0127,,0.0663,0.9801,0.7316,0.0035,0.0,0.069,0.1667,0.2083,0.0652,,0.0659,0.0078,0.0122,,block of flats,0.0879,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n382343,0,Cash loans,M,N,Y,0,202500.0,1125000.0,60070.5,1125000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-16314,-616,-9729.0,-3907,,1,1,1,1,1,0,Drivers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Transport: type 4,0.5810255548424877,0.6095361143236555,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2295.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n225320,0,Cash loans,F,N,Y,0,67500.0,101880.0,5688.0,90000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-20653,-8813,-9001.0,-4043,,1,1,1,1,1,0,High skill tech staff,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.6852515782853295,0.3979463219016906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1142.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n128352,0,Cash loans,F,N,N,1,112500.0,202500.0,13698.0,202500.0,Children,Working,Higher education,Married,House / apartment,0.035792000000000004,-10026,-1094,-3828.0,-2147,,1,1,1,1,0,0,Drivers,3.0,2,2,FRIDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.6758896878499798,0.6640172542824371,0.28371188263500075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-734.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n205785,0,Cash loans,F,N,Y,0,270000.0,454500.0,45868.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.002506,-16070,-3406,-2459.0,-4697,,1,1,0,1,0,0,Accountants,2.0,2,2,THURSDAY,4,0,0,0,0,0,0,Business Entity Type 3,0.6724995832919839,0.7411571038037089,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,-729.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n189547,0,Cash loans,F,N,Y,1,90000.0,210249.0,11403.0,175500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.0105,-14710,-6375,-4745.0,-4745,,1,1,0,1,0,0,Sales staff,3.0,3,3,TUESDAY,15,0,0,0,0,0,0,Trade: type 7,,0.4902611074600112,,0.0305,0.0634,0.995,0.7756,0.0144,0.0,0.0759,0.1167,0.2083,0.0307,0.0883,0.0397,0.0039,0.001,0.0074,0.0279,0.998,0.7844,0.0146,0.0,0.0345,0.0833,0.2083,0.0158,0.0964,0.0258,0.0039,0.0,0.0146,0.0269,0.998,0.7786,0.0145,0.0,0.0345,0.125,0.2083,0.0193,0.0898,0.0281,0.0039,0.0,reg oper spec account,block of flats,0.0217,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1384.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n297394,0,Cash loans,M,Y,Y,1,90000.0,107820.0,8473.5,90000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.030755,-13684,-4559,-1694.0,-4638,4.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.5663149850941674,0.6711023383960877,0.7121551551910698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,0.0,-2391.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n223773,0,Cash loans,F,N,Y,0,135000.0,1130760.0,33192.0,810000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-16668,-2277,-7355.0,-14,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.3046288461921154,0.26651977539251576,0.5549467685334323,0.1598,,0.9796,,,,0.0345,0.1667,,,,0.0511,,,0.1628,,0.9796,,,,0.0345,0.1667,,,,0.0533,,,0.1613,,0.9796,,,,0.0345,0.1667,,,,0.052000000000000005,,,,block of flats,0.0621,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1940.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n351884,0,Cash loans,F,N,Y,0,90000.0,215640.0,9625.5,180000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.006852,-23487,365243,-8886.0,-4005,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,11,0,0,0,0,0,0,XNA,,0.0656639854621731,,0.1196,0.1623,0.9881,,,0.0,0.2759,0.1667,,0.0927,,0.1169,,0.0172,0.1218,0.1685,0.9881,,,0.0,0.2759,0.1667,,0.0948,,0.1218,,0.0182,0.1207,0.1623,0.9881,,,0.0,0.2759,0.1667,,0.0943,,0.11900000000000001,,0.0175,,block of flats,0.1615,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-341.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n100152,0,Cash loans,F,N,Y,0,157500.0,254412.0,9720.0,166500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.010032,-16083,-2764,-7358.0,-4654,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,,0.1654420733377721,0.7490217048463391,0.0742,0.0457,0.9821,0.7552,0.0,0.08,0.069,0.3333,0.375,0.0,0.0605,0.0783,0.0,0.0,0.0756,0.0474,0.9821,0.7648,0.0,0.0806,0.069,0.3333,0.375,0.0,0.0661,0.0815,0.0,0.0,0.0749,0.0457,0.9821,0.7585,0.0,0.08,0.069,0.3333,0.375,0.0,0.0616,0.0797,0.0,0.0,reg oper account,block of flats,0.0752,Panel,No,0.0,0.0,0.0,0.0,-1761.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n355495,0,Revolving loans,F,N,N,0,211500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.04622,-9343,-218,-4207.0,-1960,,1,1,0,1,1,1,,2.0,1,1,THURSDAY,15,0,1,1,0,0,0,Other,,0.7033670630131106,0.4101025731788671,0.0928,,0.9771,,,0.0,0.2069,0.1667,,,,0.061,,0.0974,0.0945,,0.9772,,,0.0,0.2069,0.1667,,,,0.0635,,0.1031,0.0937,,0.9771,,,0.0,0.2069,0.1667,,,,0.0621,,0.0995,,block of flats,0.0691,Panel,No,0.0,0.0,0.0,0.0,-2239.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n198367,0,Cash loans,F,N,Y,0,225000.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.011703,-12828,-187,-2116.0,-3802,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 1,,0.6804638432817122,0.2721336844147212,0.1227,0.0102,0.9762,0.6736,0.0154,0.0,0.2069,0.1667,0.0,0.1437,0.0975,0.1092,0.0116,0.0226,0.125,0.0106,0.9762,0.6864,0.0156,0.0,0.2069,0.1667,0.0,0.147,0.1065,0.1138,0.0117,0.024,0.1239,0.0102,0.9762,0.6779999999999999,0.0155,0.0,0.2069,0.1667,0.0,0.1462,0.0992,0.1112,0.0116,0.0231,reg oper account,block of flats,0.0996,Panel,No,1.0,0.0,1.0,0.0,-1075.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,2.0\n380738,0,Cash loans,M,Y,Y,3,135000.0,808650.0,29839.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-13438,-3324,-2078.0,-4347,20.0,1,1,1,1,0,0,,5.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.3923788961221287,0.7789161917432513,0.41534714488434,0.10099999999999999,0.2963,0.9906,,,0.0,0.2414,0.1667,,0.0721,,0.094,,0.0008,0.1029,0.3075,0.9906,,,0.0,0.2414,0.1667,,0.0737,,0.0979,,0.0009,0.102,0.2963,0.9906,,,0.0,0.2414,0.1667,,0.0733,,0.0957,,0.0009,,block of flats,0.0871,Panel,No,0.0,0.0,0.0,0.0,-1378.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n381440,0,Cash loans,M,N,Y,1,112500.0,370107.0,18895.5,319500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-15507,-2601,-722.0,-4669,,1,1,1,1,0,0,Sales staff,3.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,,0.6324493627999486,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-850.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n142341,0,Cash loans,M,Y,Y,2,121500.0,229500.0,15655.5,229500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-11108,-962,-2410.0,-3009,12.0,1,1,0,1,0,0,Laborers,4.0,2,2,FRIDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.09068539072110564,0.16640554618393638,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n198920,0,Cash loans,M,N,Y,2,135000.0,640080.0,24259.5,450000.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.003818,-14330,-798,-6161.0,-4405,,1,1,1,1,1,0,,3.0,2,2,SATURDAY,3,0,0,0,1,1,0,Self-employed,0.3832919786225313,0.5744660743763846,0.7517237147741489,0.0567,0.0519,0.9831,0.7688,0.0445,0.0,0.0948,0.1667,0.0417,0.0333,0.0454,0.0505,0.0039,0.003,0.063,0.04,0.9816,0.7583,0.0375,0.0,0.1034,0.1667,0.0417,0.0179,0.0533,0.0417,0.0,0.0,0.0625,0.0558,0.9831,0.7719,0.047,0.0,0.1034,0.1667,0.0417,0.039,0.0496,0.0542,0.0039,0.0031,reg oper account,block of flats,0.0518,Block,No,1.0,0.0,1.0,0.0,-462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n355631,0,Cash loans,M,N,Y,1,144000.0,1546020.0,45333.0,1350000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00733,-8523,-310,-818.0,-1187,,1,1,1,1,1,0,Core staff,3.0,2,2,MONDAY,17,1,1,0,1,1,0,Other,,0.5893454090585035,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n143052,0,Cash loans,F,N,Y,0,157500.0,274941.0,20686.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.009175,-20097,-2949,-528.0,-3376,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,14,0,1,1,0,0,0,Industry: type 3,,0.7382223552965772,0.4382813743111921,0.4113,0.2918,0.9826,0.762,0.0739,0.44,0.3793,0.3333,0.0417,0.1911,0.3345,0.4205,0.0039,0.0024,0.4191,0.3028,0.9826,0.7713,0.0745,0.4431,0.3793,0.3333,0.0417,0.1954,0.3655,0.4382,0.0039,0.0025,0.4153,0.2918,0.9826,0.7652,0.0743,0.44,0.3793,0.3333,0.0417,0.1944,0.3403,0.4281,0.0039,0.0024,reg oper account,block of flats,0.3313,Panel,No,0.0,0.0,0.0,0.0,-2368.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n132283,0,Cash loans,M,Y,Y,0,135000.0,772686.0,30069.0,553500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18886,-1413,-5539.0,-2445,10.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Self-employed,,0.645818731265154,0.807273923277881,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-516.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n340392,0,Cash loans,F,N,Y,1,270000.0,900000.0,60277.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.026392,-13476,-636,-2557.0,-2460,,1,1,1,1,1,1,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4550890856426861,0.6933811713064704,0.7209441499436497,0.0,,0.0,,,0.0,,,,,,,,,0.0,,0.0,,,0.0,,,,,,,,,0.0,,0.0,,,0.0,,,,,,,,,,block of flats,0.021,,No,3.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.0,2.0,0.0,1.0,0.0,0.0\n430798,0,Cash loans,M,Y,N,0,130500.0,325908.0,14485.5,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00823,-20710,365243,-4056.0,-4261,21.0,1,0,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,0.8392829745684001,0.6315593394274358,0.6144143775673561,0.0773,,0.9806,,,,0.1724,0.1667,,,,0.0726,,0.0119,0.0788,,0.9806,,,,0.1724,0.1667,,,,0.0756,,0.0126,0.0781,,0.9806,,,,0.1724,0.1667,,,,0.0739,,0.0121,,block of flats,0.0552,Panel,No,0.0,0.0,0.0,0.0,-2549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n261538,0,Cash loans,F,N,Y,0,94500.0,675000.0,21906.0,675000.0,Family,Working,Incomplete higher,Married,House / apartment,0.018209,-8361,-983,-7733.0,-378,,1,1,0,1,1,0,Core staff,2.0,3,3,SATURDAY,12,0,0,0,0,0,0,Bank,0.16252868615469834,0.33932144020851673,,0.2082,0.1337,0.9891,,,0.2,0.1724,0.375,,0.1348,,0.0309,,0.051,0.2122,0.1388,0.9891,,,0.2014,0.1724,0.375,,0.1379,,0.0322,,0.054000000000000006,0.2103,0.1337,0.9891,,,0.2,0.1724,0.375,,0.1372,,0.0314,,0.052000000000000005,,block of flats,0.1667,Panel,No,2.0,0.0,2.0,0.0,-866.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n353981,0,Cash loans,F,N,Y,1,67500.0,130320.0,13351.5,112500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006305,-13654,-683,-2350.0,-4743,,1,1,0,1,0,0,Waiters/barmen staff,3.0,3,3,WEDNESDAY,8,0,0,0,0,1,1,Transport: type 4,,0.4429096743590181,0.2608559142068693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1297.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n335693,1,Cash loans,F,N,Y,1,189000.0,135531.0,16213.5,117000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.007305,-13017,-632,-5378.0,-2111,,1,1,0,1,0,0,Core staff,3.0,3,3,SATURDAY,12,0,0,0,0,1,1,School,0.4213940818274817,0.28471179780730976,0.2866524828090694,0.1515,0.0444,0.9821,0.7552,0.03,0.04,0.0345,0.3333,0.375,0.0171,0.1219,0.0753,0.0077,0.0172,0.1544,0.046,0.9821,0.7648,0.0302,0.0403,0.0345,0.3333,0.375,0.0175,0.1331,0.0785,0.0078,0.0183,0.153,0.0444,0.9821,0.7585,0.0302,0.04,0.0345,0.3333,0.375,0.0174,0.124,0.0767,0.0078,0.0176,reg oper spec account,block of flats,0.0794,Panel,No,3.0,0.0,3.0,0.0,-408.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n385848,1,Cash loans,M,N,Y,2,180000.0,900000.0,35158.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-10480,-1296,-4613.0,-2857,,1,1,0,1,0,0,Drivers,4.0,2,2,SATURDAY,9,0,0,0,0,0,0,Self-employed,,0.3356008037103711,0.2692857999073816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-792.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n245951,0,Cash loans,M,N,N,1,103500.0,481495.5,33511.5,454500.0,,Working,Secondary / secondary special,Married,House / apartment,0.019689,-12542,-375,-309.0,-4453,,1,1,0,1,0,0,Security staff,3.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3920607099793215,0.3144216126439368,0.5388627065779676,0.1082,0.1308,0.9871,0.8232,0.0657,0.0,0.2414,0.1667,0.2083,0.0888,0.0874,0.1016,0.0,0.0,0.1103,0.1357,0.9871,0.8301,0.0663,0.0,0.2414,0.1667,0.2083,0.0908,0.0955,0.1058,0.0,0.0,0.1093,0.1308,0.9871,0.8256,0.0661,0.0,0.2414,0.1667,0.2083,0.0903,0.0889,0.1034,0.0,0.0,reg oper account,block of flats,0.1359,Panel,No,6.0,0.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n342766,0,Cash loans,F,Y,Y,1,90000.0,225171.0,12703.5,171000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.031329,-16031,-3881,-424.0,-3993,15.0,1,1,0,1,0,0,Medicine staff,3.0,2,2,SATURDAY,9,0,0,0,1,1,0,Medicine,,0.5735196252732798,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-703.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n260796,0,Revolving loans,M,N,N,0,121500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.018634,-8709,-271,-1879.0,-1368,,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,Industry: type 3,0.1263735257886365,0.3595548735624992,,,,0.9786,,,,,,,,,0.1872,,,,,0.9786,,,,,,,,,0.195,,,,,0.9786,,,,,,,,,0.1905,,,,,0.1469,,No,0.0,0.0,0.0,0.0,-76.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n377060,0,Cash loans,M,N,Y,0,32850.0,95940.0,9616.5,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.030755,-23642,365243,-10274.0,-3934,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.4894523400694562,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-546.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n404238,0,Cash loans,F,Y,Y,0,540000.0,418743.0,33214.5,342000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.008019,-11941,-2919,-5688.0,-4312,11.0,1,1,0,1,1,1,Sales staff,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,Trade: type 7,0.6760252216772162,0.384913100578256,0.17352743046491467,0.1443,0.0834,0.9786,0.7076,0.08,0.0,0.1034,0.1667,0.0417,0.0472,0.1177,0.0403,0.0,0.0,0.1471,0.0866,0.9786,0.7190000000000001,0.0807,0.0,0.1034,0.1667,0.0417,0.0483,0.1286,0.042,0.0,0.0,0.1457,0.0834,0.9786,0.7115,0.0805,0.0,0.1034,0.1667,0.0417,0.0481,0.1197,0.040999999999999995,0.0,0.0,reg oper account,block of flats,0.0437,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2078.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n189462,0,Cash loans,F,N,Y,0,252000.0,1135134.0,36382.5,1017000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-21639,365243,-2813.0,-4480,,1,0,0,1,1,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.16069709350347094,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-832.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n152213,0,Revolving loans,F,N,Y,1,94500.0,292500.0,14625.0,292500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-15585,-3336,-2114.0,-3047,,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 2,0.6004998187730336,0.7235889137183305,0.4471785780453068,0.0082,,0.9627,,,,0.2759,0.0,,,,0.0048,,0.0,0.0084,,0.9628,,,,0.2759,0.0,,,,0.005,,0.0,0.0083,,0.9627,,,,0.2759,0.0,,,,0.0049,,0.0,,block of flats,0.0038,Wooden,Yes,1.0,1.0,1.0,1.0,-237.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n441289,0,Cash loans,F,N,Y,1,135000.0,894906.0,26293.5,747000.0,Unaccompanied,Working,Incomplete higher,Married,Municipal apartment,0.020713,-14259,-558,-4432.0,-1318,,1,1,1,1,1,0,High skill tech staff,3.0,3,3,THURSDAY,13,0,0,0,1,1,0,Other,0.5060664267495772,0.3718548329614859,0.21155076420525776,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-543.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n365492,0,Cash loans,F,N,N,0,103500.0,647046.0,21001.5,463500.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018029,-18933,365243,-10455.0,-2468,,1,0,0,1,0,0,,1.0,3,3,TUESDAY,7,0,0,0,0,0,0,XNA,0.6002771353380915,0.4681403046891375,,0.099,,0.9781,,,0.0,0.2069,0.125,,,,0.0819,,0.0,0.1008,,0.9782,,,0.0,0.2069,0.125,,,,0.0854,,0.0,0.0999,,0.9781,,,0.0,0.2069,0.125,,,,0.0834,,0.0,,block of flats,0.0644,Panel,No,0.0,0.0,0.0,0.0,-2768.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n294396,0,Cash loans,M,Y,N,3,157500.0,522396.0,38142.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-13293,-1872,-1521.0,-4542,14.0,1,1,0,1,0,0,Drivers,5.0,2,2,THURSDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.6221326862793151,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2669.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n451808,0,Cash loans,F,N,N,2,540000.0,755190.0,30078.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018801,-13531,-6900,-5008.0,-5025,,1,1,0,1,0,0,Medicine staff,4.0,2,2,MONDAY,12,0,0,0,0,1,1,Medicine,0.4094251503537664,0.4791686760417807,0.7421816117614419,0.0412,0.0559,0.9851,,,0.0,0.1379,0.1667,,0.0,,0.0456,,0.0,0.042,0.057999999999999996,0.9851,,,0.0,0.1379,0.1667,,0.0,,0.0475,,0.0,0.0416,0.0559,0.9851,,,0.0,0.1379,0.1667,,0.0,,0.0464,,0.0,,block of flats,0.0501,\"Stone, brick\",No,10.0,0.0,10.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n214522,0,Cash loans,F,N,Y,0,202500.0,862560.0,25348.5,720000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-21548,365243,-9301.0,-4654,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.7552398415252212,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n184825,0,Cash loans,F,Y,N,2,135000.0,1125000.0,47664.0,1125000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.035792000000000004,-13495,-2357,-1950.0,-780,7.0,1,1,1,1,0,0,Core staff,4.0,2,2,SATURDAY,15,0,0,0,0,1,1,Medicine,0.6363182755672138,0.6878163456135402,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-624.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n415647,0,Cash loans,F,Y,Y,0,126000.0,700830.0,22608.0,585000.0,Unaccompanied,State servant,Higher education,Separated,House / apartment,0.007120000000000001,-13819,-164,-6629.0,-801,64.0,1,1,0,1,0,0,Accountants,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Kindergarten,0.7091636853284826,0.6971570982763083,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1272.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n291395,1,Cash loans,M,N,N,3,117000.0,256500.0,14044.5,256500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.007114,-11760,-2762,-2862.0,-4038,,1,1,0,1,0,0,Laborers,5.0,2,2,WEDNESDAY,13,0,0,0,1,1,0,Self-employed,0.2866474965189824,0.5937941113307992,0.7394117535524816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2045.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n126577,0,Revolving loans,F,N,Y,2,450000.0,900000.0,45000.0,900000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.072508,-12374,-5131,-75.0,-4247,,1,1,0,1,1,0,Core staff,3.0,1,1,WEDNESDAY,9,0,0,0,0,0,0,Bank,,0.7599662941713806,0.4365064990977374,0.1573,0.1802,0.9762,0.6736,0.0982,0.22399999999999998,0.1793,0.3417,0.3333,0.0247,0.0658,0.1618,0.0013,0.1008,0.0011,0.0612,0.9752,0.6733,0.0006,0.0,0.0345,0.1667,0.2083,0.0,0.0009,0.075,0.0,0.0,0.1197,0.1252,0.9771,0.6779999999999999,0.1478,0.08,0.2069,0.375,0.2083,0.0,0.0975,0.1041,0.0,0.0,org spec account,block of flats,0.4433,Block,No,0.0,0.0,0.0,0.0,-820.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n377400,0,Cash loans,M,Y,Y,0,90000.0,301896.0,22702.5,252000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-13055,-3803,-3771.0,-3778,27.0,1,1,0,1,0,1,Laborers,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.3539824435435924,0.6402219656822471,0.4884551844437485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2622.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n333031,0,Cash loans,F,N,Y,0,81000.0,284400.0,18306.0,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.031329,-23983,365243,-778.0,-4531,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,0.7905282550894341,0.5585881071756078,0.6754132910917112,0.0165,0.0,0.9831,0.7688,0.0019,0.0,0.069,0.0417,0.0833,0.0,0.0134,0.0135,0.0,0.0,0.0168,0.0,0.9831,0.7779,0.0019,0.0,0.069,0.0417,0.0833,0.0,0.0147,0.0141,0.0,0.0,0.0167,0.0,0.9831,0.7719,0.0019,0.0,0.069,0.0417,0.0833,0.0,0.0137,0.0138,0.0,0.0,reg oper account,block of flats,0.0117,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-474.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n306939,0,Cash loans,M,N,N,0,121500.0,545040.0,20677.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Municipal apartment,0.014464,-18591,-4469,-560.0,-2133,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,5,0,0,0,0,1,1,Transport: type 2,0.6307176435031897,0.642836971947398,0.6212263380626669,0.0371,0.0412,0.9757,,,0.0,0.1034,0.1042,,0.0804,,0.0304,,0.1223,0.0147,0.0206,0.9737,,,0.0,0.069,0.0417,,0.0822,,0.0073,,0.1295,0.0375,0.0412,0.9757,,,0.0,0.1034,0.1042,,0.0818,,0.0309,,0.1249,,block of flats,0.0689,\"Stone, brick\",No,5.0,2.0,5.0,2.0,-2615.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n191764,0,Cash loans,F,Y,Y,0,180000.0,325908.0,14485.5,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.007273999999999998,-19834,-723,-4245.0,-3229,24.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,SATURDAY,14,0,0,0,0,1,1,Trade: type 3,,0.036511425599269454,0.8245949709919925,0.0124,0.0353,0.9811,0.7416,0.0,0.0,0.069,0.0417,0.0833,0.0,0.0101,0.0105,0.0,0.005,0.0126,0.0366,0.9811,0.7517,0.0,0.0,0.069,0.0417,0.0833,0.0,0.011000000000000001,0.0109,0.0,0.0053,0.0125,0.0353,0.9811,0.7451,0.0,0.0,0.069,0.0417,0.0833,0.0,0.0103,0.0107,0.0,0.0051,reg oper account,block of flats,0.0093,Panel,No,0.0,0.0,0.0,0.0,-463.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n359902,1,Cash loans,M,Y,Y,0,225000.0,1078200.0,31653.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-13896,-132,-7699.0,-1622,1.0,1,1,0,1,0,0,Drivers,2.0,1,1,MONDAY,18,0,1,1,0,0,0,Business Entity Type 1,0.35794013459159163,0.04264215391520893,0.06294857668996477,0.1732,0.1157,0.9886,0.8436,0.0434,0.2,0.1724,0.3333,0.375,0.1347,0.1378,0.1789,0.0154,0.1586,0.1765,0.1201,0.9886,0.8497,0.0437,0.2014,0.1724,0.3333,0.375,0.1378,0.1506,0.1864,0.0156,0.1679,0.1749,0.1157,0.9886,0.8457,0.0436,0.2,0.1724,0.3333,0.375,0.13699999999999998,0.1402,0.1821,0.0155,0.1619,reg oper account,block of flats,0.1989,\"Stone, brick\",No,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n309513,0,Cash loans,M,N,Y,1,90000.0,807534.0,35698.5,652500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-14222,-2332,-5739.0,-4404,,1,1,0,1,0,0,Laborers,3.0,3,3,THURSDAY,9,0,0,0,0,0,0,Industry: type 11,0.33618752632978816,0.3509203065424874,0.7544061731797895,0.033,0.0404,0.9742,,,0.0,0.069,0.125,,0.0286,,0.0226,,0.0,0.0336,0.0419,0.9742,,,0.0,0.069,0.125,,0.0293,,0.0235,,0.0,0.0333,0.0404,0.9742,,,0.0,0.069,0.125,,0.0291,,0.023,,0.0,,block of flats,0.0193,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-892.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n196900,0,Cash loans,F,N,N,0,94500.0,225000.0,11781.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005313,-18226,-1221,-11150.0,-1761,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,Government,,0.4057349028800848,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-242.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n103241,0,Cash loans,F,N,N,1,103500.0,381528.0,18684.0,315000.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.006670999999999999,-11329,-95,-5641.0,-3944,,1,1,1,1,0,0,Laborers,2.0,2,2,THURSDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.3244425948804461,0.5864845629017803,0.4561097392782771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1663.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n192198,0,Cash loans,F,N,N,0,90000.0,544491.0,16047.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019101,-17001,-1614,-7105.0,-545,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,10,0,0,0,0,0,0,Trade: type 7,0.683338044964772,0.53804340986339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-380.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n333006,1,Cash loans,F,N,N,0,76500.0,545040.0,19705.5,450000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.032561,-20164,365243,-9834.0,-3697,,1,0,0,1,1,0,,1.0,1,1,WEDNESDAY,15,0,0,0,0,0,0,XNA,,0.6670789516124379,0.7544061731797895,0.2778,0.1972,0.9796,0.7212,,0.3,0.2586,0.3333,0.375,0.0638,,0.2879,,0.0072,0.2637,0.2016,0.9796,0.7321,,0.282,0.2414,0.3333,0.375,0.0373,,0.2795,,0.0059,0.2805,0.1972,0.9796,0.7249,,0.3,0.2586,0.3333,0.375,0.0649,,0.293,,0.0073,reg oper account,block of flats,0.2122,Panel,No,0.0,0.0,0.0,0.0,-2175.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n404305,0,Cash loans,M,Y,N,1,117000.0,288873.0,14805.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-12804,-315,-1883.0,-4340,14.0,1,1,0,1,0,0,Laborers,3.0,3,3,MONDAY,9,0,0,0,0,1,1,Electricity,,0.7476186293278091,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n449236,0,Cash loans,M,N,Y,0,81000.0,327024.0,18904.5,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.022625,-16463,365243,-1292.0,-3,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.6029642139780762,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-785.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n410330,0,Cash loans,M,N,Y,0,81000.0,269550.0,14242.5,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.008068,-14254,-2217,-5139.0,-4436,,1,1,1,1,0,0,Laborers,2.0,3,3,WEDNESDAY,6,0,0,0,1,1,0,Business Entity Type 3,,0.1582175713845228,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n422954,0,Cash loans,M,N,N,0,81000.0,296280.0,15255.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-18022,-1041,-3544.0,-1576,,1,1,1,1,1,0,Laborers,2.0,2,2,MONDAY,10,0,1,1,0,1,1,Agriculture,,0.6226517577753016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1612.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n278651,0,Cash loans,M,N,Y,1,112500.0,227520.0,15331.5,180000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.008625,-11649,-99,-5955.0,-4212,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,10,0,0,0,1,1,0,Business Entity Type 1,0.2556261999712559,0.2723260880515593,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-1350.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n287174,0,Cash loans,F,Y,Y,1,225000.0,257391.0,22149.0,238500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.025164,-13780,-3406,-2728.0,-4364,2.0,1,1,0,1,0,1,Managers,3.0,2,2,MONDAY,8,0,0,0,0,0,0,Self-employed,0.3198890072644241,0.6586838561138062,0.5884877883422673,0.0598,0.0786,0.9608,0.4628,0.0174,0.0,0.1379,0.1667,0.2083,0.0018,0.0445,0.0575,0.0193,0.0299,0.0609,0.0815,0.9608,0.4838,0.0175,0.0,0.1379,0.1667,0.2083,0.0018,0.0487,0.0599,0.0195,0.0316,0.0604,0.0786,0.9608,0.47,0.0175,0.0,0.1379,0.1667,0.2083,0.0018,0.0453,0.0586,0.0194,0.0305,reg oper account,block of flats,0.0612,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1371.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n356445,0,Cash loans,M,N,Y,0,202500.0,746626.5,26946.0,567000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-15768,-1986,-7235.0,-4460,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.3709155796940308,0.695126459090938,,0.1485,0.1086,0.9831,0.7688,0.0,0.16,0.1379,0.3333,0.0417,0.1096,0.121,0.153,0.0,0.0,0.1513,0.1127,0.9831,0.7779,0.0,0.1611,0.1379,0.3333,0.0417,0.1121,0.1322,0.1594,0.0,0.0,0.1499,0.1086,0.9831,0.7719,0.0,0.16,0.1379,0.3333,0.0417,0.1115,0.1231,0.1558,0.0,0.0,reg oper account,block of flats,0.1204,Panel,No,0.0,0.0,0.0,0.0,-1255.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n148280,0,Cash loans,F,N,N,1,157500.0,943425.0,27585.0,787500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010643,-9645,-2555,-5209.0,-800,,1,1,0,1,0,0,Private service staff,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Other,0.2287603276860029,0.6683210387885224,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1854.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n374557,0,Cash loans,M,N,N,0,225000.0,714154.5,21780.0,616500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-10101,-576,-563.0,-2758,,1,1,0,1,0,0,Sales staff,2.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.4552863770636297,0.4470952312495501,0.2471909382666521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n113638,0,Revolving loans,F,N,N,0,67500.0,135000.0,6750.0,135000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-7938,-795,-816.0,-615,,1,1,1,1,0,0,Laborers,1.0,2,2,SUNDAY,12,0,0,0,0,1,1,Housing,,0.04099160736044115,0.43473324875017305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,1.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n171738,0,Cash loans,M,Y,N,0,81000.0,143910.0,14364.0,135000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.008865999999999999,-24082,365243,-6739.0,-4305,21.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.7699787923819394,0.5298898341969072,,0.2609,0.9831,,,0.16,0.1379,0.3333,,,,0.1247,,,,0.2707,0.9831,,,0.1611,0.1379,0.3333,,,,0.1299,,,,0.2609,0.9831,,,0.16,0.1379,0.3333,,,,0.127,,,,block of flats,0.0981,Panel,No,0.0,0.0,0.0,0.0,-1681.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n406693,0,Cash loans,M,Y,Y,1,360000.0,646776.0,47196.0,585000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-20886,-4828,-6691.0,-3550,1.0,1,1,0,1,0,1,Drivers,3.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.26877929771350245,0.6748609164998034,0.05384435347241005,0.166,0.1126,0.9791,0.7144,0.025,0.2,0.1724,0.3333,0.375,,0.1345,0.1383,0.0039,0.1265,0.1691,0.1169,0.9791,0.7256,0.0252,0.2014,0.1724,0.3333,0.375,,0.1469,0.1441,0.0039,0.1339,0.1676,0.1126,0.9791,0.7182,0.0251,0.2,0.1724,0.3333,0.375,,0.1368,0.1408,0.0039,0.1291,reg oper account,block of flats,0.1296,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1829.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n301071,0,Cash loans,F,N,Y,0,112500.0,225000.0,16002.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.01885,-14832,-1752,-3974.0,-2517,,1,1,1,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.4100579586056324,0.4978862075324825,,0.0165,,0.9707,0.5988,0.0014,0.0,0.069,0.0417,0.0833,,0.0134,0.0315,0.0,0.0,0.0168,,0.9707,0.6145,0.0014,0.0,0.069,0.0417,0.0833,,0.0147,0.0328,0.0,0.0,0.0167,,0.9707,0.6042,0.0014,0.0,0.069,0.0417,0.0833,,0.0137,0.0321,0.0,0.0,,block of flats,0.0255,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1314.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n425563,0,Cash loans,F,N,N,0,225000.0,315000.0,24885.0,315000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.031329,-13942,-5201,-570.0,-4771,,1,1,1,1,0,0,Laborers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.7788669971534559,0.5942476398450505,0.29859498978739724,0.1887,0.0678,0.9985,0.9796,0.1024,0.24,0.1034,0.5833,0.4167,0.0323,0.1454,0.195,0.0386,0.049,0.1922,0.0703,0.9985,0.9804,0.1034,0.2417,0.1034,0.5833,0.4167,0.0331,0.1589,0.2032,0.0389,0.0518,0.1905,0.0678,0.9985,0.9799,0.1031,0.24,0.1034,0.5833,0.4167,0.0329,0.1479,0.1985,0.0388,0.05,reg oper account,block of flats,0.22,Monolithic,No,0.0,0.0,0.0,0.0,-166.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n258082,0,Cash loans,M,Y,Y,4,90000.0,604152.0,29196.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-12610,-733,-2914.0,-4643,14.0,1,1,1,1,1,0,Laborers,6.0,2,2,THURSDAY,15,0,0,0,1,1,1,School,0.5561391002914591,0.5547402928125048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,1.0,0.0,-1906.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n229843,0,Cash loans,F,N,Y,0,211500.0,108000.0,5643.0,108000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018209,-21746,365243,-13750.0,-4337,,1,0,0,1,0,0,,1.0,3,3,FRIDAY,12,0,0,0,0,0,0,XNA,,0.4174330301295451,0.520897599048938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1024.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n156745,0,Cash loans,F,N,N,0,315000.0,1890000.0,49855.5,1890000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.007114,-19427,365243,-2111.0,-2952,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,0.8331615112005137,0.529652290370198,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1295.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n150035,1,Cash loans,M,Y,Y,2,225000.0,518562.0,25078.5,463500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006008,-12758,-978,-503.0,-1232,9.0,1,1,0,1,1,0,Laborers,4.0,2,2,WEDNESDAY,18,0,0,0,0,1,1,Business Entity Type 3,,0.19091035652353708,0.3606125659189888,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n272476,0,Cash loans,M,N,Y,0,202500.0,886176.0,29416.5,765000.0,Family,Pensioner,Higher education,Civil marriage,House / apartment,0.008473999999999999,-23843,365243,-3795.0,-4581,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,XNA,,0.5237485956578649,0.6397075677637197,0.0588,0.0381,0.9791,0.7144,0.0248,0.0,0.1379,0.1667,0.2083,0.0,0.0458,0.0525,0.0097,0.043,0.0567,0.0272,0.9786,0.7190000000000001,0.025,0.0,0.1379,0.1667,0.2083,0.0,0.045,0.054000000000000006,0.0,0.0,0.0593,0.0381,0.9791,0.7182,0.0249,0.0,0.1379,0.1667,0.2083,0.0,0.0466,0.0535,0.0097,0.0439,reg oper account,block of flats,0.0575,Block,No,0.0,0.0,0.0,0.0,-112.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n268088,0,Cash loans,F,N,Y,0,144000.0,675000.0,21906.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.011703,-21693,365243,-12340.0,-4654,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.7520079092623827,0.6512602186973006,0.1835,0.0974,0.9831,0.7688,0.0429,0.2,0.1724,0.3333,0.375,0.1235,0.1496,0.1916,0.0,0.0043,0.187,0.10099999999999999,0.9831,0.7779,0.0433,0.2014,0.1724,0.3333,0.375,0.1264,0.1635,0.1996,0.0,0.0046,0.1853,0.0974,0.9831,0.7719,0.0431,0.2,0.1724,0.3333,0.375,0.1257,0.1522,0.195,0.0,0.0044,,block of flats,0.1885,Panel,No,0.0,0.0,0.0,0.0,-1941.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,2.0,0.0\n342628,0,Cash loans,M,Y,Y,1,328500.0,453514.5,27535.5,391500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.035792000000000004,-10442,-2644,-4959.0,-3119,12.0,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Self-employed,0.5003586097686461,0.7197941265738648,0.4614823912998385,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1800.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n397897,0,Revolving loans,F,N,N,2,135000.0,360000.0,18000.0,360000.0,Other_B,State servant,Secondary / secondary special,Married,Rented apartment,0.010006,-12575,-1790,-2287.0,-250,,1,1,0,1,0,0,Cleaning staff,4.0,2,1,TUESDAY,14,0,0,0,0,0,0,Other,,0.4688046140517762,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1780.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n394514,0,Cash loans,F,N,N,0,67500.0,152820.0,15241.5,135000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.019101,-16459,-1404,-4090.0,-8,,1,1,0,1,0,0,,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,Self-employed,0.514034066852216,0.690453789871016,0.7801436381572275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2159.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n395521,0,Cash loans,F,N,N,0,252000.0,254700.0,17149.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007273999999999998,-12859,-1843,-636.0,-1162,,1,1,0,1,0,0,Private service staff,2.0,2,2,FRIDAY,13,0,1,1,0,1,1,Other,0.5687535674503913,0.07189401495445227,0.08673126364696013,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-572.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n373453,0,Cash loans,M,N,Y,1,180000.0,227520.0,8707.5,180000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.020246,-22483,-2298,-1370.0,-4348,,1,1,0,1,0,0,Managers,2.0,3,3,WEDNESDAY,8,0,0,0,0,0,0,Trade: type 7,0.4612421244971271,0.1906571540361412,0.11538666200733562,0.0825,0.0796,0.9757,0.6668,0.0092,0.0,0.1379,0.1667,0.2083,0.0882,0.0672,0.0709,0.0,0.0,0.084,0.0826,0.9757,0.6798,0.0089,0.0,0.1379,0.1667,0.2083,0.0841,0.0735,0.0738,0.0,0.0,0.0833,0.0796,0.9757,0.6713,0.0093,0.0,0.1379,0.1667,0.2083,0.0898,0.0684,0.0722,0.0,0.0,reg oper account,block of flats,0.0605,Panel,No,0.0,0.0,0.0,0.0,-2380.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n359034,0,Cash loans,F,N,N,0,76500.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005144,-17563,-775,-6787.0,-1097,,1,1,1,1,0,0,Sales staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Other,,0.3927963771611295,0.05882590047568205,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-790.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n441847,0,Cash loans,M,Y,Y,2,157500.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-13072,-4087,-2082.0,-4583,3.0,1,1,0,1,0,0,Laborers,4.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6410832528993032,0.7454047857042924,,0.1369,0.1114,0.996,0.9796,0.0123,0.14,0.1207,0.3438,0.4167,0.0053,0.0862,0.1293,0.0097,0.1609,0.0693,0.0915,0.996,0.9804,0.0,0.0806,0.069,0.375,0.4167,0.0,0.055999999999999994,0.0737,0.0,0.0399,0.1494,0.1114,0.996,0.9799,0.0124,0.14,0.1207,0.3542,0.4167,0.0054,0.0876,0.1343,0.0097,0.1479,reg oper account,block of flats,0.1665,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1509.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n390225,0,Cash loans,F,N,Y,2,90000.0,675000.0,32602.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.026392,-13458,-854,-5438.0,-4459,,1,1,1,1,1,0,Laborers,4.0,2,2,MONDAY,17,0,0,0,0,1,1,Industry: type 3,0.4001822571897539,0.3165561439252505,,0.0124,,0.9811,,,0.0,,0.0833,,,,0.0097,,,0.0126,,0.9811,,,0.0,,0.0833,,,,0.0101,,,0.0125,,0.9811,,,0.0,,0.0833,,,,0.0099,,,,block of flats,0.0138,Panel,No,7.0,0.0,7.0,0.0,-1164.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n184254,0,Cash loans,F,N,Y,0,90000.0,1005120.0,29520.0,720000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-16071,-5875,-5970.0,-4180,,1,1,0,1,1,0,Cleaning staff,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Other,0.4420953846277832,0.4813793189798296,0.2822484337007223,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1618.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n306068,0,Cash loans,F,N,Y,0,135000.0,67765.5,7074.0,58500.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.016612000000000002,-15678,-138,-8898.0,-5170,,1,1,0,1,0,0,Cooking staff,2.0,2,2,SATURDAY,12,0,0,0,1,1,0,Business Entity Type 3,0.2731902846571908,0.47463541221384,0.6690566947824041,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n287598,0,Cash loans,M,N,Y,4,130500.0,436032.0,21339.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-12784,-1367,-454.0,-458,,1,1,1,1,1,0,Low-skill Laborers,6.0,2,2,THURSDAY,14,0,0,0,0,1,1,Self-employed,,0.2881846746704597,0.33285056416487313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2394.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,4.0\n305926,0,Cash loans,F,N,N,0,135000.0,675000.0,24246.0,675000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.031329,-11677,-2544,-2408.0,-3266,,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.3792736419228873,0.71112711654589,0.21275630545434146,0.1237,0.1241,0.9767,0.6804,0.0102,0.0,0.2069,0.1667,0.2083,0.1104,0.1009,0.1002,0.0,0.0,0.1261,0.1288,0.9767,0.6929,0.0103,0.0,0.2069,0.1667,0.2083,0.113,0.1102,0.1044,0.0,0.0,0.1249,0.1241,0.9767,0.6847,0.0103,0.0,0.2069,0.1667,0.2083,0.1124,0.1026,0.102,0.0,0.0,reg oper account,block of flats,0.0788,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1437.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n360536,0,Cash loans,F,N,Y,0,121500.0,614223.0,24489.0,549000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008625,-15571,-4808,-7111.0,-4588,,1,1,1,1,0,0,Core staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Postal,,0.6683452750617586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1699.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n345960,0,Cash loans,F,N,Y,0,148500.0,835380.0,33259.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.072508,-18710,-9014,-5575.0,-2250,,1,1,0,1,1,0,,2.0,1,1,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.7530423036013465,0.5370699579791587,0.0722,0.0768,0.9742,,,0.0,0.1207,0.1667,,,,0.0376,,0.026000000000000002,0.063,0.0547,0.9737,,,0.0,0.1034,0.1667,,,,0.0344,,0.0,0.0729,0.0768,0.9742,,,0.0,0.1207,0.1667,,,,0.0383,,0.0265,,block of flats,0.0383,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2695.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n342949,0,Cash loans,F,N,N,0,360000.0,328405.5,21361.5,283500.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.022625,-11637,-801,-959.0,-3420,,1,1,0,1,0,0,Managers,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.4917476206454071,0.3910549766342248,0.0825,0.08,0.9742,0.6464,0.0089,0.0,0.1379,0.1667,0.2083,0.0401,0.0672,0.0707,0.0,0.0,0.084,0.083,0.9742,0.6602,0.009000000000000001,0.0,0.1379,0.1667,0.2083,0.040999999999999995,0.0735,0.0737,0.0,0.0,0.0833,0.08,0.9742,0.6511,0.009000000000000001,0.0,0.1379,0.1667,0.2083,0.0408,0.0684,0.07200000000000001,0.0,0.0,reg oper account,block of flats,0.0605,Panel,No,0.0,0.0,0.0,0.0,-1034.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,0.0\n446527,0,Cash loans,F,Y,Y,0,112500.0,1264500.0,36972.0,1264500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-13325,-1059,-384.0,-1000,7.0,1,1,1,1,1,0,Drivers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Transport: type 3,,0.6029799190627586,0.4632753280912678,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-826.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n270603,0,Cash loans,M,Y,N,3,247500.0,1256400.0,44644.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-14445,-2751,-7322.0,-4526,10.0,1,1,0,1,0,0,Laborers,5.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6080413930588995,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,10.0,0.0,-1629.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n444599,0,Revolving loans,F,N,Y,1,67500.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.005084,-17070,-1128,-6440.0,-606,,1,1,0,1,0,0,,3.0,2,2,FRIDAY,18,0,0,0,0,0,0,Other,,0.6389365465705036,0.5726825047161584,0.0825,0.063,0.9801,,,,0.1379,0.1667,,,,0.0689,,,0.084,0.0654,0.9801,,,,0.1379,0.1667,,,,0.0718,,,0.0833,0.063,0.9801,,,,0.1379,0.1667,,,,0.0701,,,,block of flats,0.0542,Panel,No,10.0,0.0,9.0,0.0,-303.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n174972,0,Cash loans,F,N,Y,2,135000.0,68256.0,4558.5,54000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-15408,-4343,-8309.0,-4584,,1,1,0,1,1,0,Security staff,4.0,2,2,MONDAY,8,0,0,0,0,0,0,Government,0.6586932852611519,0.6023777392537794,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1951.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n141570,0,Cash loans,F,N,Y,0,135000.0,675000.0,24043.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-18812,-6523,-286.0,-2126,,1,1,0,1,0,0,Medicine staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Medicine,,0.05885168472852233,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1362.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n331001,0,Cash loans,F,N,Y,0,157500.0,703584.0,20700.0,504000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.01885,-19607,-3661,-7623.0,-3143,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,School,,0.5535462766295624,0.6986675550534175,0.0619,,0.9757,,,,0.1034,0.1667,,0.0,,0.051,,0.0298,0.063,,0.9757,,,,0.1034,0.1667,,0.0,,0.0531,,0.0315,0.0625,,0.9757,,,,0.1034,0.1667,,0.0,,0.0519,,0.0304,,block of flats,0.0489,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1164.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,5.0\n202064,0,Cash loans,F,Y,N,0,135000.0,485640.0,41674.5,450000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.035792000000000004,-10173,-673,-77.0,-1013,11.0,1,1,0,1,0,0,Accountants,2.0,2,2,TUESDAY,15,0,1,1,0,1,1,Culture,0.2704346692749849,0.4461710421516012,0.3046721837533529,0.0701,0.0895,0.9990000000000001,0.9864,0.013999999999999999,0.0,0.1034,0.125,0.1667,0.0508,0.0473,0.0518,0.0,0.0,0.0641,0.0832,0.9990000000000001,0.9804,0.0093,0.0,0.069,0.125,0.1667,0.0481,0.055999999999999994,0.0468,0.0,0.0,0.0682,0.0841,0.9990000000000001,0.9866,0.0105,0.0,0.1034,0.125,0.1667,0.0483,0.0539,0.0514,0.0,0.0,reg oper account,block of flats,0.0418,Block,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n345236,0,Cash loans,F,N,Y,2,157500.0,350181.0,36900.0,328500.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.035792000000000004,-10550,-143,-787.0,-3244,,1,1,0,1,0,0,Secretaries,4.0,2,2,THURSDAY,13,0,0,0,0,0,0,Government,0.2457705142392861,0.6623467244369105,,0.0464,0.0704,0.9896,0.8572,0.03,0.0,0.1379,0.1667,0.2083,0.0444,0.0378,0.0497,0.0,0.0,0.0473,0.073,0.9896,0.8628,0.0303,0.0,0.1379,0.1667,0.2083,0.0454,0.0413,0.0518,0.0,0.0,0.0468,0.0704,0.9896,0.8591,0.0302,0.0,0.1379,0.1667,0.2083,0.0451,0.0385,0.0506,0.0,0.0,reg oper account,block of flats,0.0555,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-484.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n170678,0,Cash loans,F,N,Y,0,135000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.072508,-22400,365243,-9247.0,-2904,,1,0,0,1,1,0,,1.0,1,1,WEDNESDAY,15,0,0,0,0,0,0,XNA,0.9047422416150004,0.70994874964605,0.6785676886853644,0.0684,0.0644,0.9752,0.66,0.0,0.0264,0.069,0.2775,0.3192,0.0,0.0558,0.0331,0.0,0.0908,0.0672,0.0514,0.9737,0.6537,0.0,0.0403,0.0345,0.3333,0.375,0.0,0.0588,0.0303,0.0,0.083,0.0666,0.0504,0.9757,0.6713,0.0,0.04,0.0345,0.3333,0.375,0.0,0.0547,0.0297,0.0,0.0932,reg oper account,block of flats,0.0552,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1568.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,0.0\n216046,0,Cash loans,M,N,Y,0,112500.0,152820.0,17370.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-10438,-1550,-4794.0,-3019,,1,1,1,1,0,0,Sales staff,2.0,2,2,THURSDAY,13,0,0,0,1,1,0,Self-employed,,0.4008251964831404,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n254696,0,Revolving loans,F,N,Y,0,202500.0,1800000.0,90000.0,1800000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-9143,-386,-3564.0,-779,,1,1,1,1,0,0,High skill tech staff,2.0,2,2,SATURDAY,10,1,1,0,1,1,0,University,,0.6593767088755507,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-469.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n238622,0,Cash loans,M,Y,Y,1,450000.0,1971072.0,62019.0,1800000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018801,-13596,-2907,-1175.0,-1181,3.0,1,1,1,1,0,0,Managers,3.0,2,2,WEDNESDAY,14,0,0,0,1,1,1,Transport: type 1,,0.5554855581115821,0.4014074137749511,0.4711,0.1875,0.9975,0.9592,0.1342,0.16,0.1379,0.375,,0.0355,0.3824,0.3734,0.0077,0.0094,0.48,0.1946,0.9975,0.9608,0.1355,0.1611,0.1379,0.375,,0.0363,0.4178,0.3891,0.0078,0.01,0.4757,0.1875,0.9975,0.9597,0.1351,0.16,0.1379,0.375,,0.0361,0.3891,0.3802,0.0078,0.0096,,block of flats,0.4057,Panel,No,0.0,0.0,0.0,0.0,-942.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n148678,1,Revolving loans,F,N,Y,0,157500.0,315000.0,15750.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.00963,-13561,-694,-7568.0,-4005,,1,1,0,1,0,0,Medicine staff,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Medicine,0.6146782723430666,0.08438095163835167,0.2608559142068693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1462.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n356912,0,Cash loans,M,Y,N,0,180000.0,630000.0,24543.0,630000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-18646,-3509,-8539.0,-2182,7.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.6203369133335482,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,10.0,0.0,-1479.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n182372,0,Cash loans,M,Y,N,1,270000.0,990000.0,32850.0,990000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-12630,-4716,-949.0,-4365,4.0,1,1,1,1,1,0,Laborers,3.0,2,2,FRIDAY,20,0,0,0,1,1,1,Business Entity Type 3,,0.4373380303961703,0.4471785780453068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-543.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,5.0\n380747,0,Cash loans,F,N,Y,0,180000.0,284400.0,18306.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-15041,-2344,-959.0,-1433,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Industry: type 11,0.6469661365168351,0.4584511061544818,0.4848514754962666,0.0515,0.0,0.9742,0.6464,0.0053,0.0,0.1034,0.1667,0.2083,0.0516,0.042,0.0504,0.0,0.0,0.0525,0.0,0.9742,0.6602,0.0053,0.0,0.1034,0.1667,0.2083,0.0528,0.0459,0.0525,0.0,0.0,0.052000000000000005,0.0,0.9742,0.6511,0.0053,0.0,0.1034,0.1667,0.2083,0.0525,0.0428,0.0513,0.0,0.0,reg oper account,block of flats,0.0425,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-396.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n124239,0,Cash loans,F,N,Y,0,121500.0,506889.0,19237.5,418500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-15048,-2597,-9178.0,-4350,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.2653117484731741,0.12373532211307872,0.5639,,0.9841,,,0.64,0.5517,0.3333,,0.4252,,0.6251,,0.0,0.5746,,0.9841,,,0.6445,0.5517,0.3333,,0.4349,,0.6513,,0.0,0.5694,,0.9841,,,0.64,0.5517,0.3333,,0.4326,,0.6364,,0.0,,block of flats,0.4917,Panel,No,10.0,2.0,10.0,2.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n391333,1,Cash loans,F,N,N,0,225000.0,906228.0,38524.5,810000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.030755,-10193,-1025,-4534.0,-2635,,1,1,0,1,0,0,Accountants,2.0,2,2,SUNDAY,14,0,0,0,1,1,0,Business Entity Type 1,,0.5833521433493902,0.2580842039460289,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-236.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,8.0,3.0,5.0\n342683,0,Cash loans,M,N,N,1,315000.0,536917.5,30109.5,463500.0,Unaccompanied,Working,Incomplete higher,Married,With parents,0.011657,-13462,-381,-183.0,-1492,,1,1,0,1,0,0,IT staff,3.0,1,1,WEDNESDAY,11,0,1,1,0,0,0,Self-employed,0.4032819175882281,0.6290319041058704,0.4561097392782771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1152.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n359342,0,Cash loans,M,N,Y,0,247500.0,675000.0,26154.0,675000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.025164,-8765,-1357,-8735.0,-472,,1,1,0,1,1,0,Core staff,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Hotel,,0.4971099279515841,0.2793353208976285,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1621.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n269519,0,Cash loans,F,N,Y,0,315000.0,622413.0,27544.5,495000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.009175,-18865,-10525,-7018.0,-2394,,1,1,0,1,0,0,Accountants,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,Advertising,0.8477657157934929,0.6715751814376653,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1738.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n263649,0,Cash loans,F,N,N,0,58500.0,217602.0,15142.5,198000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.031329,-24833,365243,-4938.0,-4387,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,XNA,,0.17137669062915853,0.7366226976503176,0.033,0.0301,0.9727,0.626,0.0229,0.0,0.069,0.125,0.1667,0.0322,0.0269,0.0252,0.0,0.0,0.0336,0.0312,0.9727,0.6406,0.0232,0.0,0.069,0.125,0.1667,0.0329,0.0294,0.0263,0.0,0.0,0.0333,0.0301,0.9727,0.631,0.0231,0.0,0.069,0.125,0.1667,0.0328,0.0274,0.0257,0.0,0.0,reg oper account,block of flats,0.0198,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-91.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n117928,0,Cash loans,F,N,Y,0,216000.0,576072.0,21847.5,405000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010032,-15982,-1122,-7772.0,-2084,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,WEDNESDAY,4,0,0,0,0,0,0,Government,0.6447137815741174,0.6237968293837545,0.5406544504453575,0.0742,,0.9861,,,0.12,0.1034,0.3333,,,,0.1135,,,0.0756,,0.9861,,,0.0806,0.069,0.3333,,,,0.0802,,,0.0749,,0.9861,,,0.12,0.1034,0.3333,,,,0.1155,,,,block of flats,0.0702,Panel,No,1.0,0.0,1.0,0.0,-1862.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n263897,0,Cash loans,F,N,Y,0,81000.0,284400.0,16456.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-24129,365243,-9847.0,-4316,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.5939839799905154,0.3077366963789207,0.1474,0.0965,0.9866,0.8164,0.0759,0.16,0.1379,0.3333,0.375,0.1165,0.1202,0.1408,0.0,0.0,0.1502,0.1001,0.9866,0.8236,0.0766,0.1611,0.1379,0.3333,0.375,0.1191,0.1313,0.1467,0.0,0.0,0.1489,0.0965,0.9866,0.8189,0.0764,0.16,0.1379,0.3333,0.375,0.1185,0.1223,0.1433,0.0,0.0,reg oper account,block of flats,0.1522,Panel,No,0.0,0.0,0.0,0.0,-529.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n242254,0,Cash loans,M,N,N,0,67500.0,808650.0,26217.0,675000.0,Children,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.011657,-22221,365243,-8631.0,-4705,,1,0,0,1,0,0,,2.0,1,1,TUESDAY,12,0,0,0,0,0,0,XNA,,0.6020791524578465,0.746300213050371,0.0082,0.0,0.9702,0.5920000000000001,,0.0,0.0345,0.0417,0.0417,0.0211,0.0067,0.0066,0.0,0.0,0.0084,0.0,0.9702,0.608,,0.0,0.0345,0.0417,0.0417,0.0216,0.0073,0.0068,0.0,0.0,0.0083,0.0,0.9702,0.5975,,0.0,0.0345,0.0417,0.0417,0.0215,0.0068,0.0067,0.0,0.0,reg oper spec account,block of flats,0.0083,\"Stone, brick\",No,3.0,0.0,3.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n260759,0,Cash loans,F,N,Y,0,67500.0,337500.0,14427.0,337500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.028663,-18638,365243,-8170.0,-2150,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,8,0,0,0,1,0,0,XNA,,0.2264532403490737,0.7136313997323308,0.3536,0.2215,0.9876,0.83,0.0878,0.36,0.3103,0.3333,0.375,0.2623,0.2849,0.3725,0.0154,0.0058,0.3603,0.2299,0.9876,0.8367,0.0886,0.3625,0.3103,0.3333,0.375,0.2683,0.3113,0.3881,0.0156,0.0061,0.35700000000000004,0.2215,0.9876,0.8323,0.0884,0.36,0.3103,0.3333,0.375,0.2669,0.2899,0.3792,0.0155,0.0059,reg oper spec account,block of flats,0.3422,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n365605,1,Cash loans,M,Y,Y,0,225000.0,592560.0,30384.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Rented apartment,0.02461,-15564,-3328,-224.0,-4852,13.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.1910000501687185,0.6033149102713584,,0.0041,0.0,0.9518,0.3404,0.0007,0.0,0.069,0.0417,0.0833,0.0152,0.0034,0.0032,0.0,0.0,0.0042,0.0,0.9518,0.3662,0.0007,0.0,0.069,0.0417,0.0833,0.0155,0.0037,0.0033,0.0,0.0,0.0042,0.0,0.9518,0.3492,0.0007,0.0,0.069,0.0417,0.0833,0.0154,0.0034,0.0032,0.0,0.0,,block of flats,0.0029,Wooden,No,9.0,0.0,9.0,0.0,-2956.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n303111,0,Cash loans,M,Y,Y,2,126000.0,458235.0,23526.0,382500.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.035792000000000004,-13018,-2018,-511.0,-5326,6.0,1,1,1,1,1,0,Laborers,4.0,2,2,THURSDAY,16,0,0,0,0,1,1,Agriculture,0.31200684737796897,0.5061135681157009,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n397926,0,Cash loans,F,N,Y,0,315000.0,905688.0,29214.0,756000.0,Children,State servant,Higher education,Married,House / apartment,0.010966,-17358,-5093,-4016.0,-778,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,University,0.8646790183604479,0.6847183766793794,0.18303516721781032,0.0608,0.0778,0.9886,0.8436,0.0155,0.0,0.2069,0.1667,0.2083,0.0135,0.0496,0.0655,0.0,0.0,0.062,0.0808,0.9886,0.8497,0.0157,0.0,0.2069,0.1667,0.2083,0.0138,0.0542,0.0682,0.0,0.0,0.0614,0.0778,0.9886,0.8457,0.0156,0.0,0.2069,0.1667,0.2083,0.0137,0.0504,0.0667,0.0,0.0,org spec account,block of flats,0.0603,Panel,No,1.0,1.0,1.0,1.0,-416.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n413957,0,Cash loans,F,N,N,0,162000.0,755190.0,28116.0,675000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.002506,-12648,-463,-564.0,-579,,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,3,0,0,0,0,0,0,University,0.6310963887673983,0.625780730426611,0.20559813854932085,0.0082,,0.9707,0.5988,0.0,0.0,0.0345,0.0417,0.0417,,0.0067,0.005,0.0,0.0,0.0084,,0.9707,0.6145,0.0,0.0,0.0345,0.0417,0.0417,,0.0073,0.0052,0.0,0.0,0.0083,,0.9707,0.6042,0.0,0.0,0.0345,0.0417,0.0417,,0.0068,0.0051,0.0,0.0,reg oper account,block of flats,0.0039,,No,0.0,0.0,0.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n248775,0,Cash loans,F,N,Y,0,202500.0,1051294.5,30870.0,918000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010032,-17699,-189,-10196.0,-1237,,1,1,0,1,0,0,Sales staff,1.0,2,2,SATURDAY,5,0,0,0,0,0,0,Business Entity Type 3,,0.5479138854857379,0.5726825047161584,0.0371,0.017,0.9727,0.626,0.0069,0.0,0.069,0.1667,0.0,0.0032,0.0277,0.0308,0.0116,0.0168,0.0378,0.0176,0.9727,0.6406,0.0069,0.0,0.069,0.1667,0.0,0.0032,0.0303,0.0321,0.0117,0.0178,0.0375,0.017,0.9727,0.631,0.0069,0.0,0.069,0.1667,0.0,0.0032,0.0282,0.0314,0.0116,0.0172,reg oper account,block of flats,0.0319,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1779.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n176758,1,Cash loans,M,Y,Y,0,121500.0,904500.0,30024.0,904500.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.025164,-20342,-1545,-1379.0,-2920,14.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,8,0,0,0,0,1,1,Business Entity Type 3,,0.1942664499695638,0.35233997269170386,0.0,,0.0,,,,,,,,,,,,0.0,,0.0005,,,,,,,,,,,,0.0,,0.0,,,,,,,,,,,,,block of flats,0.0,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n369124,1,Cash loans,F,N,Y,0,450000.0,758002.5,50989.5,715500.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.025164,-22370,365243,-2294.0,-5122,,1,0,0,1,0,0,,1.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.07957511946992166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1645.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n352912,0,Cash loans,F,N,Y,0,81000.0,284400.0,13963.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-9961,-273,-1244.0,-1898,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,13,0,0,0,1,1,0,Self-employed,,0.40562945558389935,0.5046813193144684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-120.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n382769,0,Cash loans,M,N,Y,0,247500.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-11618,-1176,-1309.0,-4277,,1,1,0,1,0,0,,2.0,3,1,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.3686434239050087,0.694862239652445,0.19747451156854226,0.0742,0.0546,0.9856,0.8028,0.0173,0.08,0.069,0.3333,0.0417,0.0246,0.0605,0.0717,0.0,0.0,0.0756,0.0567,0.9856,0.8105,0.0174,0.0806,0.069,0.3333,0.0417,0.0252,0.0661,0.0747,0.0,0.0,0.0749,0.0546,0.9856,0.8054,0.0174,0.08,0.069,0.3333,0.0417,0.025,0.0616,0.073,0.0,0.0,reg oper account,block of flats,0.0658,Panel,No,4.0,3.0,4.0,1.0,-22.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n127021,0,Cash loans,F,N,N,0,234000.0,1125000.0,32895.0,1125000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.018029,-13486,-4007,-7305.0,-4677,,1,1,1,1,0,0,Accountants,1.0,3,3,FRIDAY,4,0,0,0,0,0,0,Business Entity Type 3,0.8013148656643025,0.19421511211361847,0.35233997269170386,,,0.9767,,,,0.1034,0.1667,,,,,,,,,0.9767,,,,0.1034,0.1667,,,,,,,,,0.9767,,,,0.1034,0.1667,,,,,,,,block of flats,0.0402,Panel,No,0.0,0.0,0.0,0.0,-1547.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n392265,0,Cash loans,F,N,Y,0,42750.0,254700.0,14350.5,225000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-22370,365243,-5934.0,-4061,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.5379890569452419,0.6626377922738201,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n429926,0,Cash loans,F,N,Y,0,112500.0,808650.0,31464.0,675000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.008068,-10900,-120,-1092.0,-1457,,1,1,1,1,0,0,High skill tech staff,2.0,3,3,SATURDAY,11,0,0,0,0,0,0,School,0.34951189874927946,0.7024309001366358,0.5478104658520093,0.0165,,0.9757,,,0.0,0.069,0.0417,,,,0.0133,,0.0,0.0168,,0.9747,,,0.0,0.069,0.0417,,,,0.0129,,0.0,0.0167,,0.9757,,,0.0,0.069,0.0417,,,,0.0136,,0.0,,block of flats,0.0106,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-315.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n450144,0,Cash loans,M,Y,Y,0,202500.0,859500.0,30582.0,859500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-15385,-2450,-1889.0,-2350,6.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Other,,0.11107607235492248,0.4848514754962666,0.0289,0.0,0.9866,0.8164,0.0044,0.0,0.069,0.0417,0.0833,0.0111,0.0235,0.0208,0.0,0.0,0.0294,0.0,0.9866,0.8236,0.0045,0.0,0.069,0.0417,0.0833,0.0113,0.0257,0.0217,0.0,0.0,0.0291,0.0,0.9866,0.8189,0.0045,0.0,0.069,0.0417,0.0833,0.0112,0.0239,0.0212,0.0,0.0,reg oper account,block of flats,0.0188,Wooden,No,3.0,0.0,3.0,0.0,-1.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n419697,0,Revolving loans,F,N,Y,0,112500.0,202500.0,10125.0,202500.0,Family,Working,Higher education,Married,House / apartment,0.031329,-10194,-365,-7859.0,-1318,,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.565576696947391,0.6714352946756019,0.3296550543128238,0.1093,0.0491,0.9866,,,0.12,0.1034,0.3333,,0.0506,,0.1187,,0.0,0.1113,0.0509,0.9866,,,0.1208,0.1034,0.3333,,0.0517,,0.1236,,0.0,0.1103,0.0491,0.9866,,,0.12,0.1034,0.3333,,0.0515,,0.1208,,0.0,,block of flats,0.1032,Panel,No,3.0,1.0,3.0,1.0,-1406.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n438086,0,Cash loans,F,Y,Y,0,117000.0,1288350.0,37800.0,1125000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.000533,-19206,-3327,-6982.0,-2735,21.0,1,1,1,1,0,0,Core staff,2.0,3,3,SATURDAY,5,0,1,1,0,0,0,Kindergarten,,0.4241429916117221,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1976.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n104002,0,Cash loans,F,N,Y,0,81000.0,218016.0,17352.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006305,-19011,-1426,-12200.0,-1848,,1,1,0,1,0,0,Sales staff,2.0,3,3,MONDAY,7,0,0,0,0,1,1,Self-employed,,0.4545230708569028,0.43473324875017305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-723.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n286589,0,Cash loans,F,N,Y,0,90000.0,276277.5,17991.0,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-21433,365243,-8806.0,-4894,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,0.8616619353852827,0.7099127242761272,0.8347841592331774,1.0,0.1477,0.9811,0.7416,0.1576,0.84,0.7241,0.3333,0.375,0.2808,0.8388,0.8941,0.1583,0.7663,1.0,0.1532,0.9811,0.7517,0.1591,0.8459,0.7241,0.3333,0.375,0.2872,0.9164,0.9315,0.1595,0.8112,1.0,0.1477,0.9811,0.7451,0.1586,0.84,0.7241,0.3333,0.375,0.2857,0.8534,0.9101,0.1592,0.7824,reg oper account,block of flats,0.956,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2708.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n294063,0,Cash loans,M,N,Y,1,225000.0,314100.0,13437.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-18809,-167,-12206.0,-2354,,1,1,0,1,1,0,Laborers,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6747169520415612,0.475849908720221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-659.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n198526,0,Cash loans,F,N,Y,0,135000.0,752742.0,40230.0,697500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018801,-16814,-1052,-3905.0,-347,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.09702114899058767,0.5656079814115492,0.1485,0.1107,0.9781,0.7008,0.0697,0.16,0.1379,0.3333,0.0417,0.0895,0.121,0.0989,0.0,0.1752,0.1513,0.1149,0.9782,0.7125,0.0703,0.1611,0.1379,0.3333,0.0417,0.0915,0.1322,0.10300000000000001,0.0,0.1855,0.1499,0.1107,0.9781,0.7048,0.0701,0.16,0.1379,0.3333,0.0417,0.091,0.1231,0.1006,0.0,0.1788,org spec account,block of flats,0.11599999999999999,Panel,No,0.0,0.0,0.0,0.0,-1510.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n218492,0,Cash loans,M,N,N,0,202500.0,1255680.0,41499.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.030755,-17145,-10334,-3136.0,-701,,1,1,1,1,1,0,Laborers,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.6142203233807373,0.475849908720221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1008.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n114259,0,Cash loans,M,N,Y,0,115515.0,175599.0,11875.5,146578.5,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-21648,-209,-4067.0,-4070,,1,1,1,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,1,1,Trade: type 7,,0.019035390445853208,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n393300,0,Cash loans,M,N,N,0,112500.0,137538.0,16452.0,121500.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.010966,-12942,-1104,-728.0,-2373,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,14,0,0,0,0,1,1,Government,,0.4731524714205152,0.5316861425197883,0.0412,0.0659,0.9901,0.8640000000000001,,0.0,0.1379,0.1667,0.2083,0.0132,,0.0563,,0.0,0.042,0.0684,0.9901,0.8693,,0.0,0.1379,0.1667,0.2083,0.0135,,0.0586,,0.0,0.0416,0.0659,0.9901,0.8658,,0.0,0.1379,0.1667,0.2083,0.0134,,0.0573,,0.0,,block of flats,0.0442,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-504.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n131035,0,Cash loans,F,N,Y,0,180000.0,1227901.5,50139.0,1129500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-21156,-4281,-9710.0,-3959,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6124674690779462,0.5370699579791587,0.1227,0.1308,0.9767,0.6804,0.0182,0.0,0.2759,0.1667,0.2083,0.0672,0.1,0.1153,0.0,0.0,0.125,0.1358,0.9767,0.6929,0.0183,0.0,0.2759,0.1667,0.2083,0.0688,0.1093,0.1202,0.0,0.0,0.1239,0.1308,0.9767,0.6847,0.0183,0.0,0.2759,0.1667,0.2083,0.0684,0.1018,0.1174,0.0,0.0,reg oper account,block of flats,0.1007,Panel,No,7.0,0.0,6.0,0.0,-1431.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n332743,0,Cash loans,M,Y,Y,0,180000.0,207000.0,14130.0,207000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.015221,-12563,-2608,-1488.0,-3789,0.0,1,1,0,1,0,0,Managers,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6786686495409409,,0.0412,0.0315,0.9757,,,0.0,0.069,0.1667,,0.0148,,0.0311,,0.0,0.042,0.0327,0.9757,,,0.0,0.069,0.1667,,0.0151,,0.0324,,0.0,0.0416,0.0315,0.9757,,,0.0,0.069,0.1667,,0.015,,0.0317,,0.0,,block of flats,0.0319,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-487.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n423215,0,Cash loans,M,N,N,0,135000.0,148707.0,16015.5,139500.0,Family,Working,Secondary / secondary special,Married,With parents,0.002042,-8442,-259,-8442.0,-1114,,1,1,1,1,0,0,Laborers,2.0,3,3,SATURDAY,12,0,0,0,1,1,0,Trade: type 5,,0.10734082114994688,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-348.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n103085,0,Cash loans,M,Y,Y,1,391500.0,808650.0,26217.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.006629,-16790,-379,-1197.0,-346,9.0,1,1,1,1,0,0,Managers,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Other,,0.7103808476055092,0.475849908720221,0.0124,0.0,0.9881,,,0.0,0.2241,0.0208,,0.0,,0.0127,,0.0028,0.0126,0.0,0.9881,,,0.0,0.0345,0.0,,0.0,,0.0113,,0.0,0.0125,0.0,0.9881,,,0.0,0.2241,0.0208,,0.0,,0.0129,,0.0028,,block of flats,0.0114,Wooden,No,0.0,0.0,0.0,0.0,-1492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n124736,0,Cash loans,F,N,Y,0,202500.0,495000.0,17910.0,495000.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.010006,-17549,-10045,-1518.0,-1106,,1,1,1,1,1,0,,2.0,2,1,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.6921011760496,0.2580842039460289,0.3979,0.2445,0.9856,0.8028,0.2017,0.44,0.3793,0.3333,0.375,0.2121,0.3236,0.4176,0.0039,0.0345,0.4055,0.2537,0.9856,0.8105,0.2035,0.4431,0.3793,0.3333,0.375,0.2169,0.3535,0.4351,0.0039,0.0366,0.4018,0.2445,0.9856,0.8054,0.203,0.44,0.3793,0.3333,0.375,0.2158,0.3292,0.4251,0.0039,0.0353,reg oper account,block of flats,0.4463,Panel,No,0.0,0.0,0.0,0.0,-1008.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n262560,0,Cash loans,F,N,N,0,180000.0,1125000.0,36423.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.008019,-15786,-2317,-6147.0,-3786,,1,1,0,1,1,0,Laborers,1.0,2,2,WEDNESDAY,17,1,1,0,1,1,0,Telecom,0.6253280660163034,0.7168340471956267,0.6446794549585961,0.0928,0.1176,0.9851,0.7959999999999999,0.0031,0.0,0.2069,0.1667,0.2083,0.0702,0.0756,0.0922,0.0,0.0,0.0945,0.122,0.9851,0.804,0.0032,0.0,0.2069,0.1667,0.2083,0.0718,0.0826,0.0961,0.0,0.0,0.0937,0.1176,0.9851,0.7987,0.0032,0.0,0.2069,0.1667,0.2083,0.0715,0.077,0.0939,0.0,0.0,reg oper account,block of flats,0.0743,Panel,No,0.0,0.0,0.0,0.0,-905.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n236500,0,Cash loans,M,Y,Y,0,126000.0,599778.0,26419.5,477000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.008473999999999999,-21653,365243,-363.0,-4472,2.0,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,0.6445072291017719,0.558232216121926,0.8128226070575616,0.1423,0.1123,0.9856,0.8028,0.0282,0.0,0.069,0.1667,0.2083,0.0,0.11599999999999999,0.0964,0.0,0.0,0.145,0.1165,0.9856,0.8105,0.0284,0.0,0.069,0.1667,0.2083,0.0,0.1267,0.1004,0.0,0.0,0.1436,0.1123,0.9856,0.8054,0.0283,0.0,0.069,0.1667,0.2083,0.0,0.11800000000000001,0.0981,0.0,0.0,reg oper account,block of flats,0.0758,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-744.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n232338,0,Cash loans,F,Y,N,1,72000.0,247275.0,21150.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-14448,-4282,-7782.0,-4472,8.0,1,1,1,1,0,0,Sales staff,3.0,2,2,MONDAY,14,0,0,0,0,1,1,Government,,0.5430018591791096,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-613.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n419400,0,Revolving loans,M,Y,N,2,270000.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-15990,-1683,-340.0,-5085,21.0,1,1,0,1,0,0,Accountants,4.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.6683903493280252,0.591513515292287,0.7407990879702335,0.0928,0.0857,0.9831,,,,0.2069,0.1667,,0.0787,,0.0909,,0.0,0.0945,0.0889,0.9831,,,,0.2069,0.1667,,0.0805,,0.0947,,0.0,0.0937,0.0857,0.9831,,,,0.2069,0.1667,,0.0801,,0.0925,,0.0,,block of flats,0.0715,Panel,No,6.0,2.0,6.0,1.0,-2381.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n334652,0,Cash loans,M,Y,Y,0,202500.0,857655.0,36468.0,693000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-19620,-3309,-2463.0,-2686,11.0,1,1,0,1,1,0,Drivers,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.7161277545846301,0.19633396621345675,0.0134,0.0,0.9518,0.3404,0.0017,0.0,0.0345,0.0417,,0.0183,,0.0056,,0.0,0.0137,0.0,0.9518,0.3662,0.0017,0.0,0.0345,0.0417,,0.0188,,0.0058,,0.0,0.0135,0.0,0.9518,0.3492,0.0017,0.0,0.0345,0.0417,,0.0187,,0.0057,,0.0,reg oper spec account,block of flats,0.0052,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1852.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n111754,1,Cash loans,M,N,N,0,85500.0,234576.0,24165.0,202500.0,Children,Working,Secondary / secondary special,Single / not married,House / apartment,0.006305,-18515,-201,-305.0,-2032,,1,1,0,1,0,0,Laborers,1.0,3,3,SATURDAY,5,0,0,0,0,0,0,Other,,0.6826754610606239,,0.0278,0.0503,0.9821,0.7552,0.0035,0.0,0.069,0.0833,0.125,,0.0227,0.0252,0.0,0.0,0.0284,0.0522,0.9821,0.7648,0.0035,0.0,0.069,0.0833,0.125,,0.0248,0.0262,0.0,0.0,0.0281,0.0503,0.9821,0.7585,0.0035,0.0,0.069,0.0833,0.125,,0.0231,0.0256,0.0,0.0,reg oper account,block of flats,0.0217,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n378658,0,Cash loans,F,Y,N,1,90000.0,226152.0,9958.5,162000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-13117,-6300,-5080.0,-2666,29.0,1,1,0,1,0,0,Secretaries,3.0,2,2,MONDAY,11,0,0,0,0,0,0,School,,0.6486048124391643,0.5370699579791587,,0.0402,0.9727,0.626,0.0028,0.0,0.069,0.0833,0.125,0.0314,,0.0192,0.0039,0.0053,,0.0417,0.9727,0.6406,0.0028,0.0,0.069,0.0833,0.125,0.0322,,0.0201,0.0039,0.0056,,0.0402,0.9727,0.631,0.0028,0.0,0.069,0.0833,0.125,0.032,,0.0196,0.0039,0.0054,,block of flats,0.0285,\"Stone, brick\",No,13.0,0.0,13.0,0.0,-1229.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n235606,0,Cash loans,M,N,N,2,126000.0,675000.0,21906.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,Office apartment,0.018209,-13462,-4569,-1506.0,-4125,,1,1,0,1,0,0,Managers,4.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Government,0.5309919330138011,0.4234701586932922,0.4812493411434029,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2106.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n355768,0,Cash loans,F,N,Y,0,112500.0,360000.0,18508.5,360000.0,Unaccompanied,Working,Higher education,Widow,House / apartment,0.003069,-18563,-4836,-3957.0,-2120,,1,1,0,1,0,0,Core staff,1.0,3,3,THURSDAY,11,0,0,0,0,0,0,School,,0.6519367684419135,0.4382813743111921,0.1155,,0.9851,,,,0.3103,0.1667,,,,,,,0.1176,,0.9851,,,,0.3103,0.1667,,,,,,,0.1166,,0.9851,,,,0.3103,0.1667,,,,,,,,,0.0965,,No,6.0,0.0,6.0,0.0,-1453.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n417712,0,Cash loans,F,N,Y,0,360000.0,450000.0,30573.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-11089,-3902,-5348.0,-2459,,1,1,0,1,1,0,Core staff,2.0,1,1,THURSDAY,14,0,0,0,0,0,0,Bank,0.4615628440110714,0.5793871951941204,0.6512602186973006,0.2289,,0.9871,0.8232,0.0,0.32,0.1379,0.5417,0.5833,,0.1866,0.1532,0.0,0.3552,0.2332,,0.9871,0.8301,0.0,0.3222,0.1379,0.5417,0.5833,,0.2039,0.1596,0.0,0.376,0.2311,,0.9871,0.8256,0.0,0.32,0.1379,0.5417,0.5833,,0.1898,0.156,0.0,0.3626,reg oper account,block of flats,0.1977,Panel,No,2.0,0.0,2.0,0.0,-263.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n225262,0,Cash loans,M,Y,Y,2,270000.0,1350000.0,37255.5,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-11654,-623,-5423.0,-3044,7.0,1,1,1,1,1,0,Managers,4.0,2,2,MONDAY,16,0,0,0,0,1,1,Trade: type 2,0.4244640110637629,0.6081612541840066,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1399.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n398100,0,Cash loans,F,Y,N,0,157500.0,1260000.0,33367.5,1260000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-13672,-810,-965.0,-3642,18.0,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.34356494762030765,0.6560635995384274,0.6832688314232291,0.4433,0.2011,0.9985,,,0.48,0.2069,0.625,,0.1323,,0.4674,,0.0971,0.4517,0.2086,0.9985,,,0.4834,0.2069,0.625,,0.1353,,0.48700000000000004,,0.1028,0.4476,0.2011,0.9985,,,0.48,0.2069,0.625,,0.1346,,0.4758,,0.0991,,block of flats,0.5031,Panel,No,0.0,0.0,0.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n213672,0,Cash loans,F,Y,Y,0,450000.0,1264500.0,33354.0,1264500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.035792000000000004,-13788,-1660,-2821.0,-4559,1.0,1,1,1,1,0,0,Managers,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.3460591841324597,0.6670935208141663,0.3572932680336494,0.0825,0.0011,0.9757,0.6668,0.001,0.0,0.1379,0.1667,0.2083,0.0,0.0672,0.0649,0.0,0.0,0.084,0.0011,0.9757,0.6798,0.001,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.0676,0.0,0.0,0.0833,0.0011,0.9757,0.6713,0.001,0.0,0.1379,0.1667,0.2083,0.0,0.0684,0.066,0.0,0.0,reg oper account,block of flats,0.0577,Block,No,3.0,0.0,3.0,0.0,-1349.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n404147,0,Revolving loans,F,N,Y,0,112500.0,225000.0,11250.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.014464,-17681,-10106,-6438.0,-648,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,4,0,0,0,0,0,0,Business Entity Type 2,,0.4077717282011587,0.8106180215523969,0.1918,0.0682,0.9767,,,0.0,0.069,0.1667,,,,0.0606,,,0.1954,0.0708,0.9767,,,0.0,0.069,0.1667,,,,0.0631,,,0.1936,0.0682,0.9767,,,0.0,0.069,0.1667,,,,0.0617,,,,block of flats,0.0596,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-273.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n427816,0,Cash loans,F,N,N,0,135000.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-10691,-1905,-2000.0,-139,,1,1,1,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,1,1,0,Business Entity Type 3,,0.14954969285931624,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,6.0,2.0,-237.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n232532,0,Cash loans,M,Y,N,0,112500.0,900000.0,40801.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-18471,-450,-7086.0,-2011,6.0,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 2,0.6141421645404179,0.7427020986182267,0.5190973382084597,0.0866,0.1008,0.9856,,,0.1064,0.0917,0.3333,,,,0.0585,,0.0875,0.0294,0.022000000000000002,0.9861,,,0.0403,0.0345,0.3333,,,,0.0262,,0.0,0.0999,0.1014,0.9861,,,0.12,0.1034,0.3333,,,,0.0657,,0.0823,,block of flats,0.237,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n323092,0,Cash loans,F,N,Y,0,90000.0,657000.0,21321.0,657000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.026392,-22166,-115,-3619.0,-4341,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Trade: type 7,0.7930043401292307,0.2130669435767593,0.7338145369642702,0.368,0.2113,0.9851,,,0.44,0.3793,0.3333,,0.3117,,0.405,,0.0098,0.375,0.2193,0.9851,,,0.4431,0.3793,0.3333,,0.3188,,0.42200000000000004,,0.0103,0.3716,0.2113,0.9851,,,0.44,0.3793,0.3333,,0.3171,,0.4123,,0.01,,block of flats,0.3207,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n176125,0,Cash loans,F,N,N,0,157500.0,1303812.0,34524.0,1138500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.031329,-14697,-2711,-2226.0,-4370,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Industry: type 7,0.4549992131101117,0.5067093616839006,0.511891801533151,0.0701,0.1066,0.9846,0.7892,0.0128,0.0,0.1493,0.1667,0.2083,0.0473,0.0566,0.065,0.0025,0.0084,0.063,0.0786,0.9851,0.804,0.0099,0.0,0.1379,0.1667,0.2083,0.0354,0.0542,0.059000000000000004,0.0039,0.0,0.0625,0.076,0.9851,0.7987,0.0113,0.0,0.1379,0.1667,0.2083,0.0406,0.0513,0.0581,0.0039,0.0041,reg oper account,block of flats,0.0051,Block,No,12.0,1.0,12.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n430913,0,Cash loans,M,N,Y,2,180000.0,53910.0,4450.5,45000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-12128,-4758,-6225.0,-4375,,1,1,1,1,1,0,Laborers,4.0,3,3,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 2,0.21829996744990804,0.03901514414357673,0.5388627065779676,0.1309,0.1543,0.9757,0.6668,0.0165,0.0,0.2759,0.1667,0.2083,0.1259,0.1042,0.1142,0.0116,0.0178,0.1334,0.1602,0.9757,0.6798,0.0167,0.0,0.2759,0.1667,0.2083,0.1288,0.1139,0.11900000000000001,0.0117,0.0189,0.1322,0.1543,0.9757,0.6713,0.0166,0.0,0.2759,0.1667,0.2083,0.1281,0.106,0.1163,0.0116,0.0182,reg oper account,block of flats,0.1028,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1730.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n138761,0,Cash loans,M,Y,Y,0,166500.0,1249740.0,66712.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23133,-395,-45.0,-4960,6.0,1,1,0,1,0,0,,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Other,0.6294876726490245,0.5355205032105881,0.7091891096653581,0.0,0.0,0.9995,0.9932,0.0,0.08,0.0345,0.5,0.0417,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9995,0.9935,0.0,0.0806,0.0345,0.5,0.0417,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9995,0.9933,0.0,0.08,0.0345,0.5,0.0417,0.0,0.0,0.0,0.0,0.0,reg oper account,block of flats,0.2675,Others,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n435531,0,Cash loans,F,N,Y,0,98550.0,454500.0,19255.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-21244,365243,-8498.0,-4101,,1,0,0,1,1,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,0.6217362873671782,0.27397588439096165,0.6006575372857061,0.2258,0.1655,0.9881,0.8368,0.1388,0.24,0.2069,0.3333,0.0417,0.0528,0.1816,0.1362,0.0116,0.3491,0.23,0.1717,0.9881,0.8432,0.1401,0.2417,0.2069,0.3333,0.0417,0.054000000000000006,0.1983,0.1419,0.0117,0.3696,0.228,0.1655,0.9881,0.8390000000000001,0.1397,0.24,0.2069,0.3333,0.0417,0.0537,0.1847,0.1386,0.0116,0.3564,reg oper account,block of flats,0.183,Panel,No,2.0,0.0,2.0,0.0,-1519.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,1.0\n360843,0,Cash loans,M,Y,N,1,450000.0,1984500.0,52348.5,1984500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-11466,-283,-812.0,-3983,4.0,1,1,0,1,0,0,,3.0,1,1,WEDNESDAY,15,1,1,0,0,0,0,Realtor,0.433885955991305,0.7489490224422898,0.6446794549585961,0.4526,0.1495,0.9985,0.9796,0.2463,0.56,0.2414,0.6667,0.7083,0.1294,0.3631,0.4449,0.027000000000000003,0.0796,0.4611,0.1552,0.9985,0.9804,0.2485,0.5639,0.2414,0.6667,0.7083,0.1323,0.3967,0.4636,0.0272,0.0842,0.457,0.1495,0.9985,0.9799,0.2478,0.56,0.2414,0.6667,0.7083,0.1316,0.3694,0.4529,0.0272,0.0812,reg oper account,block of flats,0.5019,Panel,No,6.0,0.0,6.0,0.0,-6.0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n311398,0,Cash loans,F,N,Y,0,49500.0,354276.0,13486.5,292500.0,Unaccompanied,Pensioner,Lower secondary,Civil marriage,House / apartment,0.020713,-22780,365243,-4707.0,-5136,,1,0,0,1,0,0,,2.0,3,3,SUNDAY,6,0,0,0,0,0,0,XNA,,0.5867603091884612,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n324080,0,Cash loans,F,N,Y,0,90000.0,188478.0,17752.5,166500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-8064,-280,-2598.0,-745,,1,1,0,1,0,0,Accountants,1.0,3,3,SATURDAY,11,0,0,0,0,1,1,Bank,,0.2238362423082464,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,0.0,-594.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n379349,0,Cash loans,F,N,Y,0,90000.0,675000.0,26284.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-17135,-1321,-5871.0,-676,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,SUNDAY,10,0,0,0,0,0,0,Self-employed,,0.5951542139341136,0.11247434965561487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-712.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n402395,0,Cash loans,F,Y,N,0,270000.0,1363500.0,37624.5,1363500.0,Family,Working,Incomplete higher,Civil marriage,House / apartment,0.01885,-11621,-2067,-4876.0,-4138,18.0,1,1,0,1,0,0,Accountants,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Bank,0.23351371901683,0.5744446916948862,0.22383131747015547,0.0371,0.0,0.9752,0.66,,0.0,0.1034,0.125,,0.0258,0.0294,0.0385,0.0039,0.0368,0.0378,0.0,0.9752,0.6733,,0.0,0.1034,0.125,,0.0264,0.0321,0.0401,0.0039,0.039,0.0375,0.0,0.9752,0.6645,,0.0,0.1034,0.125,,0.0262,0.0299,0.0392,0.0039,0.0376,,block of flats,0.0406,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-2334.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n269332,0,Cash loans,F,N,N,0,130500.0,1066320.0,38430.0,900000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.028663,-17944,-1585,-5421.0,-1492,,1,1,0,1,1,0,,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 1,0.2597467542840177,0.568816974518449,0.5602843280409464,0.3773,,0.9826,0.762,,0.08,0.069,0.3333,0.375,,,0.1523,,0.1084,0.3845,,0.9826,0.7713,,0.0806,0.069,0.3333,0.375,,,0.1586,,0.1147,0.381,,0.9826,0.7652,,0.08,0.069,0.3333,0.375,,,0.155,,0.1106,,block of flats,0.1433,,No,2.0,0.0,2.0,0.0,-640.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n343156,0,Cash loans,F,Y,Y,4,81000.0,755190.0,36328.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.022625,-11978,-1148,-5076.0,-4143,2.0,1,1,1,1,0,0,Cooking staff,6.0,2,2,TUESDAY,9,0,0,0,0,1,1,School,0.4396549353482049,0.5733163623297463,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2026.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n341965,0,Cash loans,F,N,Y,0,166500.0,210456.0,11880.0,166500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.01885,-22783,365243,-159.0,-4040,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.23096248864559385,0.4578995512067301,0.0216,0.0542,0.9677,0.5579999999999999,0.0031,0.0,0.1034,0.0833,0.125,0.011000000000000001,0.0177,0.018000000000000002,0.0,0.0,0.0221,0.0562,0.9677,0.5753,0.0031,0.0,0.1034,0.0833,0.125,0.0112,0.0193,0.0187,0.0,0.0,0.0219,0.0542,0.9677,0.5639,0.0031,0.0,0.1034,0.0833,0.125,0.0112,0.018000000000000002,0.0183,0.0,0.0,reg oper account,block of flats,0.0204,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1304.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n314960,0,Revolving loans,F,N,Y,0,315000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.032561,-20139,-2079,-4822.0,-3569,,1,1,0,1,1,0,,1.0,1,1,MONDAY,17,0,0,0,0,0,0,Other,0.9155656608957754,0.7255570925469597,0.17352743046491467,0.1433,0.0046,0.9776,0.6940000000000001,0.0157,0.0,0.2414,0.1667,0.2083,0.0974,0.1168,0.138,0.0,0.0,0.146,0.0048,0.9777,0.706,0.0158,0.0,0.2414,0.1667,0.2083,0.0996,0.1276,0.1438,0.0,0.0,0.1447,0.0046,0.9776,0.6981,0.0158,0.0,0.2414,0.1667,0.2083,0.0991,0.1189,0.1405,0.0,0.0,reg oper account,block of flats,0.1171,Panel,No,0.0,0.0,0.0,0.0,-1481.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n403547,0,Cash loans,F,N,N,0,135000.0,629325.0,29290.5,562500.0,Family,Commercial associate,Higher education,Single / not married,House / apartment,0.00963,-17967,-310,-9789.0,-1511,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.5312186695179743,0.7728591560128978,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1669.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n316519,0,Cash loans,F,Y,Y,1,157500.0,1515415.5,60228.0,1354500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-12946,-821,-2597.0,-4333,9.0,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.814385868176396,0.7428357975308639,0.7366226976503176,,,0.9921,,,,,,,,,0.0363,,,,,0.9921,,,,,,,,,0.0378,,,,,0.9921,,,,,,,,,0.037000000000000005,,,,,0.0306,,No,1.0,0.0,1.0,0.0,-682.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n266476,0,Cash loans,F,Y,N,0,135000.0,271066.5,21546.0,234000.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.01885,-14962,-2250,-1276.0,-3492,13.0,1,1,0,1,0,1,HR staff,2.0,2,2,WEDNESDAY,9,0,0,0,1,1,0,Self-employed,0.7390490280724991,0.6412086096423265,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1796.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,8.0\n259390,0,Cash loans,F,N,Y,1,225000.0,675000.0,21906.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.026392,-14368,-5168,-3745.0,-4668,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,14,0,0,0,0,0,0,School,0.7060325942511179,0.6461237891828393,0.3774042489507649,0.034,0.0545,0.9945,,,0.0,0.1034,0.125,,,,0.0223,,0.0539,0.0347,0.0566,0.9945,,,0.0,0.1034,0.125,,,,0.0233,,0.0571,0.0344,0.0545,0.9945,,,0.0,0.1034,0.125,,,,0.0227,,0.0551,,block of flats,0.0293,\"Stone, brick\",No,9.0,1.0,9.0,1.0,-1137.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n226211,0,Cash loans,F,N,Y,0,121500.0,152820.0,15016.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.006008,-24336,365243,-9315.0,-4137,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,XNA,,0.7265270461267174,0.7826078370261895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-744.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n412970,1,Cash loans,M,N,N,0,112500.0,284400.0,19134.0,225000.0,Unaccompanied,Working,Lower secondary,Separated,With parents,0.019101,-13364,-403,-7207.0,-3330,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,14,0,0,0,0,1,1,Self-employed,,0.5565921934706924,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n166638,0,Revolving loans,F,N,Y,0,81000.0,225000.0,11250.0,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018209,-18740,-8897,-8840.0,-2284,,1,1,1,1,0,0,Cleaning staff,2.0,3,3,WEDNESDAY,8,0,0,0,0,0,0,Other,,0.5841651061058748,0.1766525794312139,0.1479,0.0,0.9791,0.7144,0.0079,0.0,0.1034,0.1667,0.2083,0.0374,0.1126,0.0508,0.0367,0.0287,0.1502,0.0,0.9791,0.7256,0.008,0.0,0.1034,0.1667,0.2083,0.0316,0.1212,0.0522,0.0311,0.0297,0.1494,0.0,0.9791,0.7182,0.008,0.0,0.1034,0.1667,0.2083,0.038,0.1146,0.0517,0.0369,0.0293,reg oper account,block of flats,0.0457,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-427.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n191344,0,Cash loans,M,N,Y,2,157500.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006305,-13087,-2587,-4071.0,-4085,,1,1,0,1,0,0,Laborers,4.0,3,3,FRIDAY,5,0,0,0,0,1,1,Trade: type 7,0.2516869567398023,0.16461344127576755,0.7610263695502636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-742.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n256945,0,Cash loans,F,Y,N,2,157500.0,415224.0,12028.5,328500.0,Unaccompanied,Working,Incomplete higher,Separated,House / apartment,0.031329,-17427,-829,-435.0,-977,14.0,1,1,0,1,0,0,,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Government,,0.6349670262835281,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1512.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n339816,0,Cash loans,F,N,Y,0,202500.0,270000.0,12717.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-12760,-2424,-2288.0,-4726,,1,1,0,1,1,0,Accountants,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,,0.5742789664769874,0.5298898341969072,0.067,0.0,0.9762,0.6736,0.0295,0.0,0.1379,0.1667,0.2083,0.1018,0.0538,0.051,0.0039,0.0428,0.0683,0.0,0.9762,0.6864,0.0298,0.0,0.1379,0.1667,0.2083,0.1041,0.0588,0.0531,0.0039,0.0453,0.0677,0.0,0.9762,0.6779999999999999,0.0297,0.0,0.1379,0.1667,0.2083,0.1036,0.0547,0.0519,0.0039,0.0437,reg oper account,block of flats,0.0655,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2183.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n291438,1,Cash loans,F,N,Y,1,90000.0,94230.0,5062.5,67500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-11566,-251,-4726.0,-2661,,1,1,0,1,0,0,High skill tech staff,3.0,1,1,SATURDAY,10,0,0,0,0,0,0,Industry: type 11,,0.6715269480605109,0.5585066276769286,0.0186,,0.9791,,,,0.1034,0.0417,,,,0.0053,,,0.0189,,0.9791,,,,0.1034,0.0417,,,,0.0055,,,0.0187,,0.9791,,,,0.1034,0.0417,,,,0.0054,,,,block of flats,0.0197,,No,0.0,0.0,0.0,0.0,-609.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n263834,0,Cash loans,F,N,Y,2,180000.0,364896.0,19926.0,315000.0,Family,Working,Higher education,Civil marriage,House / apartment,0.00496,-11689,-549,-656.0,-3919,,1,1,1,1,1,0,Managers,4.0,2,2,TUESDAY,13,0,0,0,0,1,1,Government,0.4251033320123621,0.6941152425565781,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-291.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n427802,0,Cash loans,M,Y,Y,1,243000.0,170640.0,11533.5,135000.0,Unaccompanied,State servant,Incomplete higher,Married,Rented apartment,0.01885,-12687,-4840,-373.0,-4149,3.0,1,1,0,1,0,1,High skill tech staff,3.0,2,2,SATURDAY,17,0,0,0,0,0,0,Other,0.26512429306098434,0.6186358670770137,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,8.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n374097,0,Cash loans,M,Y,Y,0,90000.0,254700.0,27558.0,225000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.014464,-9993,-200,-559.0,-1919,19.0,1,1,1,1,0,0,Core staff,1.0,2,2,THURSDAY,7,0,0,0,0,0,0,Emergency,0.10076458859968394,0.2693764739836195,,0.3649,0.1072,0.9821,0.7552,0.0,0.0,0.069,0.3333,0.0,0.1713,0.2942,0.1474,0.0154,0.0175,0.3718,0.1112,0.9821,0.7648,0.0,0.0,0.069,0.3333,0.0,0.1753,0.3214,0.1536,0.0156,0.0186,0.3685,0.1072,0.9821,0.7585,0.0,0.0,0.069,0.3333,0.0,0.1743,0.2993,0.1501,0.0155,0.0179,reg oper spec account,block of flats,0.11599999999999999,Panel,No,2.0,0.0,2.0,0.0,-464.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n402777,0,Cash loans,M,Y,Y,0,103500.0,239850.0,25578.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-11142,-937,-5221.0,-2915,25.0,1,1,1,1,1,0,,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.4603138747929933,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-357.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n290762,0,Cash loans,M,Y,Y,0,247500.0,708939.0,47506.5,612000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.04622,-8469,-912,-2119.0,-1095,8.0,1,1,0,1,0,0,Low-skill Laborers,1.0,1,1,SUNDAY,14,0,0,0,0,0,0,Self-employed,,0.4476412695304625,,0.3423,0.1332,0.9965,0.9456,0.0863,0.56,0.2414,0.5417,0.5833,0.0431,0.2765,0.3771,0.0116,0.4049,0.3487,0.1382,0.9965,0.9477,0.0871,0.5639,0.2414,0.5417,0.5833,0.0441,0.3021,0.3929,0.0117,0.4286,0.3456,0.1332,0.9965,0.9463,0.0868,0.56,0.2414,0.5417,0.5833,0.0438,0.2813,0.3839,0.0116,0.4134,reg oper account,block of flats,0.3847,Panel,No,0.0,0.0,0.0,0.0,-239.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n407268,0,Cash loans,F,N,Y,2,144000.0,592560.0,31153.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-11712,-696,-1309.0,-3444,,1,1,0,1,0,1,Laborers,4.0,1,1,THURSDAY,12,0,0,0,0,0,0,Trade: type 3,0.4891981974816166,0.6857513061874715,0.4561097392782771,0.1485,0.09699999999999999,0.9806,0.7348,0.0214,0.16,0.1379,0.3333,0.375,0.0814,0.121,0.0989,,,0.1513,0.1007,0.9806,0.7452,0.0216,0.1611,0.1379,0.3333,0.375,0.0832,0.1322,0.10300000000000001,,,0.1499,0.09699999999999999,0.9806,0.7383,0.0215,0.16,0.1379,0.3333,0.375,0.0828,0.1231,0.1006,,,reg oper account,block of flats,0.1212,Panel,No,8.0,0.0,8.0,0.0,-2706.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n138687,0,Cash loans,F,Y,Y,0,94500.0,783927.0,33345.0,688500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.019101,-19378,-6604,-11141.0,-2938,17.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Industry: type 3,,0.4625637038804749,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-535.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n251715,0,Cash loans,F,Y,Y,1,135000.0,592560.0,35806.5,450000.0,Other_B,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00823,-12727,-1665,-4558.0,-4590,10.0,1,1,0,1,0,0,Managers,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.7095810107919817,0.12995250625816995,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n430883,0,Cash loans,F,N,Y,1,180000.0,1000858.5,33205.5,864000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.003818,-15957,-2563,-2935.0,-3985,,1,1,0,1,0,0,Sales staff,3.0,2,2,FRIDAY,3,0,0,0,1,1,0,Trade: type 7,,0.5108956813077621,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1234.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n147310,0,Cash loans,F,Y,Y,0,108000.0,526491.0,28179.0,454500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.004849,-21444,365243,-7069.0,-4842,8.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.22697846146418746,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-360.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n289374,0,Cash loans,M,Y,Y,0,292500.0,965340.0,101497.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15899,-3235,-8547.0,-4129,2.0,1,1,0,1,0,1,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.7993145642750162,0.7205388926579414,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,0.0,-3234.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,6.0\n125258,0,Cash loans,F,N,Y,0,202500.0,1762110.0,48586.5,1575000.0,Family,Working,Secondary / secondary special,Married,Rented apartment,0.016612000000000002,-10847,-1256,-5558.0,-1532,,1,1,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,1,1,Self-employed,0.4740203938624397,0.6090623994637668,0.17982176508970435,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-619.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n257408,0,Cash loans,M,Y,Y,0,135000.0,352044.0,16542.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-21983,-1114,-4808.0,-4808,7.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Government,,0.7066188392542991,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n116411,0,Cash loans,F,N,Y,0,157500.0,50940.0,5877.0,45000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.00733,-12305,-1221,-3498.0,-3618,,1,1,0,1,0,0,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.67040691948204,0.5867400085415683,0.1938,0.1309,0.9940000000000001,,,0.2,0.1724,0.375,,0.0153,,0.2404,,0.0041,0.1975,0.1358,0.9940000000000001,,,0.2014,0.1724,0.375,,0.0156,,0.2505,,0.0044,0.1957,0.1309,0.9940000000000001,,,0.2,0.1724,0.375,,0.0155,,0.2447,,0.0042,,block of flats,0.215,Panel,No,0.0,0.0,0.0,0.0,-495.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n339459,0,Cash loans,F,N,Y,0,247500.0,545040.0,26509.5,450000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.010276,-17224,-2100,-484.0,-785,,1,1,1,1,0,0,Sales staff,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Trade: type 6,0.554827318544431,0.6819454889251202,0.3572932680336494,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-10.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n273872,0,Cash loans,F,N,Y,0,180000.0,319500.0,29871.0,319500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.007305,-9446,-148,-4033.0,-2120,,1,1,0,1,1,0,,1.0,3,3,WEDNESDAY,10,0,0,0,1,1,0,Business Entity Type 3,0.3750397219935421,0.4973012633185316,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,5.0,0.0,-554.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n302627,0,Cash loans,F,N,N,0,126000.0,948582.0,27864.0,679500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.020713,-23676,365243,-880.0,-4900,,1,0,0,1,1,0,,1.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,XNA,,0.6296848284583305,0.5989262182569273,0.0969,0.0816,0.9811,0.7416,0.0087,0.0,0.1379,0.1667,0.2083,0.0292,0.079,0.0639,0.0,0.0,0.0987,0.0847,0.9811,0.7517,0.0088,0.0,0.1379,0.1667,0.2083,0.0298,0.0863,0.0666,0.0,0.0,0.0978,0.0816,0.9811,0.7451,0.0087,0.0,0.1379,0.1667,0.2083,0.0297,0.0804,0.065,0.0,0.0,reg oper account,block of flats,0.055,Panel,No,0.0,0.0,0.0,0.0,-2002.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n160179,0,Cash loans,F,N,Y,0,90000.0,288873.0,16258.5,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.020713,-22960,365243,-5563.0,-5965,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,10,0,0,0,0,0,0,XNA,,0.5291292818450244,0.5460231970049609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-129.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n206033,0,Cash loans,F,N,N,0,135000.0,640080.0,29970.0,450000.0,Family,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.011703,-21764,365243,-9437.0,-4334,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.7333078668592322,0.6879328378491735,0.1856,0.1144,0.9776,0.8232,,0.2,0.1724,0.3333,0.375,0.1372,0.1513,0.2252,0.0,0.0025,0.1891,0.1187,0.9687,0.8301,,0.2014,0.1724,0.3333,0.375,0.1404,0.1653,0.2346,0.0,0.0026,0.1874,0.1144,0.9776,0.8256,,0.2,0.1724,0.3333,0.375,0.1396,0.1539,0.2292,0.0,0.0025,reg oper account,block of flats,0.2047,Panel,No,2.0,0.0,2.0,0.0,-1746.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n407351,0,Cash loans,M,N,Y,0,171000.0,148500.0,13747.5,148500.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.028663,-21854,-787,-1137.0,-4425,,1,1,0,1,1,0,Security staff,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Other,,0.5878256616213042,0.5656079814115492,0.1876,0.1276,0.9846,0.7892,0.0252,0.2,0.1724,0.3333,0.375,0.0961,0.1471,0.1883,0.027000000000000003,0.046,0.1912,0.1324,0.9846,0.7975,0.0255,0.2014,0.1724,0.3333,0.375,0.0983,0.1607,0.1961,0.0272,0.0487,0.1894,0.1276,0.9846,0.792,0.0254,0.2,0.1724,0.3333,0.375,0.0978,0.1496,0.1916,0.0272,0.047,reg oper account,block of flats,0.1719,\"Stone, brick\",No,17.0,0.0,16.0,0.0,-262.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n217167,0,Cash loans,F,N,Y,0,270000.0,539590.5,36189.0,445500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-17587,-1090,-782.0,-1133,,1,1,0,1,0,0,,2.0,1,1,THURSDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.7449771043239344,0.4311917977993083,0.0124,,0.9707,0.5988,0.0198,0.0,0.069,0.0417,0.0833,0.0718,0.0101,0.0165,0.0,0.0497,0.0126,,0.9707,0.6145,0.0199,0.0,0.069,0.0417,0.0833,0.0735,0.011000000000000001,0.0172,0.0,0.0526,0.0125,,0.9707,0.6042,0.0199,0.0,0.069,0.0417,0.0833,0.0731,0.0103,0.0168,0.0,0.0507,,block of flats,0.0238,Others,No,0.0,0.0,0.0,0.0,-59.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n393525,0,Cash loans,F,N,N,0,157500.0,1160973.0,34074.0,909000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-13127,-1981,-5663.0,-419,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Business Entity Type 2,,0.6200278506765836,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-888.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n338681,1,Cash loans,F,N,Y,1,90000.0,343800.0,16852.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-14617,-764,-5432.0,-5432,,1,1,1,1,1,0,Laborers,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Industry: type 3,0.4023675632639072,0.3011451584781604,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-557.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n401254,0,Cash loans,M,N,N,1,90000.0,326439.0,12433.5,229500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006207,-9947,-1557,-8834.0,-2230,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.2295832470273357,0.4646029117316596,0.8203829585116356,,,0.9851,,,0.04,0.0345,0.3333,,0.0566,,0.0922,,,,,0.9851,,,0.0403,0.0345,0.3333,,0.0579,,0.096,,,,,0.9851,,,0.04,0.0345,0.3333,,0.0576,,0.0938,,,,block of flats,0.0786,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1485.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n344203,0,Cash loans,M,Y,N,0,225000.0,1262583.0,36913.5,1102500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006670999999999999,-15011,-2036,-6476.0,-4338,17.0,1,1,1,1,0,0,Managers,2.0,2,2,SATURDAY,12,0,0,0,1,1,0,Agriculture,,0.627008940783644,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-700.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n236165,0,Cash loans,M,Y,Y,1,135000.0,315000.0,17716.5,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-10357,-15,-4379.0,-2951,0.0,1,1,0,1,0,0,,3.0,2,2,SUNDAY,12,0,0,0,0,0,0,Transport: type 4,,0.5936622415938175,0.5814837058057234,0.0825,,0.9811,,,0.0,0.2069,0.1667,,,,0.0995,,0.0671,0.084,,0.9811,,,0.0,0.2069,0.1667,,,,0.1037,,0.071,0.0833,,0.9811,,,0.0,0.2069,0.1667,,,,0.1013,,0.0685,,block of flats,0.0929,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1597.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n255085,1,Cash loans,F,Y,Y,0,135000.0,601470.0,29065.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18807,-1559,-2227.0,-2351,25.0,1,1,0,1,0,0,Medicine staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Other,,0.393041536404497,0.2445163919946749,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-850.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n440692,0,Cash loans,M,Y,Y,1,135000.0,143910.0,17208.0,135000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.009175,-14543,-383,-3429.0,-5259,11.0,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Other,,0.7347338107599877,0.2750003523983893,0.2485,0.1366,0.9871,0.8232,0.0489,0.16,0.1379,0.3333,0.375,0.1238,0.1967,0.1113,0.027000000000000003,0.0898,0.2532,0.1417,0.9871,0.8301,0.0494,0.1611,0.1379,0.3333,0.375,0.1266,0.2149,0.1159,0.0272,0.0951,0.2509,0.1366,0.9871,0.8256,0.0492,0.16,0.1379,0.3333,0.375,0.126,0.2001,0.1132,0.0272,0.0917,reg oper account,block of flats,0.1562,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-127.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n107366,0,Cash loans,F,Y,Y,0,180000.0,314100.0,16573.5,225000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-8743,-190,-286.0,-1431,13.0,1,1,0,1,0,0,Sales staff,2.0,3,3,SUNDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.4524029675971058,0.5421389240555138,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-286.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n345287,0,Cash loans,M,Y,Y,0,67500.0,454500.0,21996.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.02461,-20565,-11392,-11153.0,-3838,9.0,1,1,1,1,1,0,Laborers,2.0,2,2,SUNDAY,9,0,0,0,0,0,0,Business Entity Type 1,,0.6860245601868028,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-570.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n246664,0,Cash loans,F,N,Y,0,180000.0,729792.0,40878.0,630000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.019689,-18474,-888,-8176.0,-1992,,1,1,0,1,1,0,Sales staff,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,,0.19158681895876334,0.6347055309763198,0.2021,0.2497,0.9801,,,0.0,0.4138,0.1667,,0.2562,,0.2041,,0.1535,0.2059,0.2591,0.9801,,,0.0,0.4138,0.1667,,0.262,,0.2127,,0.1625,0.204,0.2497,0.9801,,,0.0,0.4138,0.1667,,0.2606,,0.2078,,0.1568,,block of flats,0.2078,Monolithic,No,1.0,0.0,1.0,0.0,-294.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n156635,0,Cash loans,F,N,Y,0,144000.0,653328.0,21204.0,468000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-19227,-2234,-2694.0,-2707,,1,1,0,1,0,0,,2.0,3,3,THURSDAY,10,0,0,0,0,1,1,Government,,0.4562205753614534,0.4578995512067301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-330.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n128682,1,Cash loans,M,N,Y,0,202500.0,780286.5,56911.5,666000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.006305,-8410,-733,-8404.0,-1081,,1,1,0,1,0,1,Drivers,1.0,3,3,SATURDAY,10,0,0,0,1,1,0,Self-employed,0.11091153130049253,0.32901683934059217,0.15380258630671767,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-824.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n191408,1,Cash loans,M,N,N,0,135000.0,1078200.0,29781.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-19287,-2954,-11346.0,-2849,,1,1,0,1,1,0,,2.0,2,2,SUNDAY,13,0,0,0,1,1,0,Business Entity Type 2,,0.6438806069503393,0.6863823354047934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,0.0,11.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n334696,0,Cash loans,M,Y,N,1,135000.0,450000.0,12001.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-16993,-941,-3580.0,-436,13.0,1,1,0,1,0,0,Core staff,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,,0.5834424950186524,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n415982,0,Cash loans,F,N,Y,0,225000.0,521280.0,27423.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.031329,-15474,-1262,-932.0,-4038,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,School,,0.6703972556232092,0.2263473957097693,0.1237,0.1191,0.9801,0.728,0.0158,0.0,0.2759,0.1667,0.2083,,0.0983,0.1017,0.0116,0.0187,0.1261,0.1236,0.9801,0.7387,0.016,0.0,0.2759,0.1667,0.2083,,0.1074,0.106,0.0117,0.0198,0.1249,0.1191,0.9801,0.7316,0.0159,0.0,0.2759,0.1667,0.2083,,0.1,0.1036,0.0116,0.0191,reg oper account,block of flats,0.0927,Panel,No,0.0,0.0,0.0,0.0,-1031.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n157438,0,Cash loans,F,N,Y,0,112500.0,808650.0,23305.5,675000.0,\"Spouse, partner\",Pensioner,Lower secondary,Married,House / apartment,0.031329,-22034,365243,-8995.0,-2772,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.2877766398568215,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2995.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n131801,0,Cash loans,M,N,Y,0,157500.0,270000.0,29079.0,270000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010276,-10886,-1089,-1791.0,-3285,,1,1,0,1,0,1,Sales staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Trade: type 2,0.5781350340953461,0.6979690524884818,0.6279908192952864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-475.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n112330,0,Cash loans,F,N,N,0,27000.0,225000.0,9531.0,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,With parents,0.00702,-18707,-161,-7309.0,-2243,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,12,0,0,0,1,1,0,Kindergarten,,0.7532333890418726,0.5762088360175724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1540.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n351162,1,Cash loans,M,Y,Y,1,81000.0,119925.0,12415.5,112500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-11266,-217,-4644.0,-3793,3.0,1,1,1,1,0,0,Low-skill Laborers,3.0,3,3,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.033759477827145404,0.3825018041447388,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1557.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n362495,0,Cash loans,M,Y,N,1,315000.0,1512000.0,41580.0,1512000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-11891,-621,-1206.0,-4456,1.0,1,1,1,1,0,0,Sales staff,3.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5488183140085487,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1327.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n341832,0,Cash loans,F,Y,Y,0,171000.0,312840.0,24844.5,247500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.030755,-21056,-594,-5398.0,-4222,17.0,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,School,0.8305589924708293,0.7693740481204534,0.16342569473134347,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-348.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,8.0\n107164,0,Cash loans,F,N,Y,0,252000.0,824823.0,29353.5,688500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.018634,-23053,365243,-13657.0,-4816,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,XNA,,0.7540089043317737,0.8193176922872417,0.0742,0.0424,0.9786,0.7076,0.0306,0.08,0.069,0.3333,0.0417,0.0489,0.0605,0.0765,0.0,0.0,0.0756,0.044000000000000004,0.9786,0.7190000000000001,0.0309,0.0806,0.069,0.3333,0.0417,0.05,0.0661,0.0797,0.0,0.0,0.0749,0.0424,0.9786,0.7115,0.0308,0.08,0.069,0.3333,0.0417,0.0498,0.0616,0.0779,0.0,0.0,org spec account,block of flats,0.0769,Panel,No,0.0,0.0,0.0,0.0,-423.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n139692,0,Cash loans,F,Y,Y,0,225000.0,942300.0,30528.0,675000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.022625,-18298,-1506,-5939.0,-1822,14.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.8325807478688457,0.5462124907203059,0.7338145369642702,0.2598,0.1527,0.9841,0.7824,0.0967,0.28,0.2414,0.3333,0.375,0.1304,0.2118,0.2655,0.0039,0.0,0.2647,0.1585,0.9841,0.7909,0.0975,0.282,0.2414,0.3333,0.375,0.1333,0.2314,0.2767,0.0039,0.0,0.2623,0.1527,0.9841,0.7853,0.0973,0.28,0.2414,0.3333,0.375,0.1326,0.2155,0.2703,0.0039,0.0,org spec account,block of flats,0.2617,Panel,No,0.0,0.0,0.0,0.0,-2835.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n101459,0,Cash loans,F,N,Y,0,112500.0,971280.0,54364.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00963,-17512,-2643,-8424.0,-1061,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Trade: type 6,,0.5464076106429163,,0.0918,0.1132,0.9866,0.8164,0.016,0.0,0.2069,0.1667,0.2083,0.1578,0.0748,0.0888,0.0,0.0,0.0935,0.1174,0.9866,0.8236,0.0161,0.0,0.2069,0.1667,0.2083,0.1614,0.0817,0.0925,0.0,0.0,0.0926,0.1132,0.9866,0.8189,0.0161,0.0,0.2069,0.1667,0.2083,0.1605,0.0761,0.0904,0.0,0.0,,block of flats,0.0786,Panel,No,0.0,0.0,0.0,0.0,-872.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n207105,0,Cash loans,F,N,Y,1,121500.0,152820.0,15786.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Office apartment,0.030755,-16311,-6558,-2814.0,-1418,,1,1,0,1,0,0,,3.0,2,2,FRIDAY,12,0,0,0,0,1,1,Emergency,0.6126286844322789,0.37583729069923816,0.7688075728291359,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n190208,0,Cash loans,M,Y,N,0,225000.0,582768.0,37242.0,540000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.072508,-11159,-548,-5271.0,-3835,4.0,1,1,1,1,0,0,Laborers,2.0,1,1,TUESDAY,12,0,0,0,0,0,0,Construction,0.5680856701790445,0.69083223184818,,0.2742,0.11699999999999999,0.9846,0.7892,0.0,0.4,0.2759,0.3542,0.0417,0.1214,0.2236,0.2564,0.0,0.1927,0.1555,0.0505,0.9786,0.7190000000000001,0.0,0.3222,0.1379,0.3333,0.0417,0.0,0.1359,0.1675,0.0,0.0528,0.2769,0.11699999999999999,0.9846,0.792,0.0,0.4,0.2759,0.3542,0.0417,0.1235,0.2275,0.261,0.0,0.1967,reg oper account,block of flats,0.3498,Panel,No,0.0,0.0,0.0,0.0,-436.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n242640,0,Revolving loans,F,N,Y,2,54000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.019101,-14173,-1481,-6986.0,-4493,,1,1,0,1,0,0,,4.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,,0.2509140398831516,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-533.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n339350,0,Cash loans,F,Y,Y,0,126000.0,1098000.0,46521.0,1098000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-23336,365243,-4754.0,-4769,8.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.37130335728417496,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n318407,0,Cash loans,F,N,Y,0,99000.0,497520.0,30568.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.02461,-23595,365243,-149.0,-4428,,1,0,0,1,1,0,,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,XNA,,0.6466236268106064,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1801.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n345619,0,Cash loans,F,Y,Y,0,180000.0,1476000.0,40720.5,1476000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-17254,-4246,-304.0,-792,6.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.7059519960751975,0.4848514754962666,0.1907,0.01,0.9965,0.9524,0.0916,0.24,0.1034,0.6667,0.7083,0.1149,0.1496,0.1845,0.027000000000000003,0.1517,0.1943,0.0104,0.9965,0.9543,0.0924,0.2417,0.1034,0.6667,0.7083,0.1175,0.1635,0.1922,0.0272,0.1606,0.1926,0.01,0.9965,0.953,0.0921,0.24,0.1034,0.6667,0.7083,0.1169,0.1522,0.1878,0.0272,0.1549,reg oper account,block of flats,0.2281,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1136.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n341979,0,Cash loans,F,N,Y,0,180000.0,1275921.0,37305.0,999000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-21931,365243,-3075.0,-4495,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.7447402232834737,0.7958031328748403,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-575.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n317376,0,Cash loans,M,N,Y,1,315000.0,450000.0,24412.5,450000.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.026392,-10115,-3231,-4216.0,-587,,1,1,1,1,1,0,Managers,3.0,2,2,THURSDAY,6,0,0,0,0,0,0,Other,0.2953628390979113,0.7056523095974689,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1262.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n324722,1,Cash loans,M,Y,N,0,90000.0,301464.0,19395.0,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-20531,-1911,-7301.0,-3984,12.0,1,1,0,1,1,0,Drivers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Construction,,0.022198263896671785,0.6577838002083306,0.1474,0.0936,0.9886,0.8436,0.0345,0.16,0.1379,0.3333,0.375,0.0971,0.11599999999999999,0.1573,0.0193,0.0219,0.1502,0.0971,0.9886,0.8497,0.0348,0.1611,0.1379,0.3333,0.375,0.0993,0.1267,0.1638,0.0195,0.0232,0.1489,0.0936,0.9886,0.8457,0.0347,0.16,0.1379,0.3333,0.375,0.0988,0.11800000000000001,0.1601,0.0194,0.0224,reg oper account,block of flats,0.1285,Panel,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n441974,0,Cash loans,M,N,Y,1,180000.0,314100.0,17167.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-10079,-862,-634.0,-661,,1,1,0,1,0,0,Sales staff,3.0,3,3,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.16897570671116913,0.6755994137316819,0.11033242816628903,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1010.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n304856,0,Cash loans,F,N,Y,1,405000.0,900000.0,46084.5,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.072508,-21207,365243,-4016.0,-4669,,1,0,0,1,0,0,,3.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.7680872210829129,0.35233997269170386,0.0701,0.1078,0.9727,0.626,0.0074,0.0,0.1217,0.1667,0.0158,0.0061,0.0572,0.0581,0.0015,0.0036,0.063,0.1122,0.9732,0.6472,0.0,0.0,0.1379,0.1667,0.0,0.0,0.0551,0.0507,0.0,0.0034,0.0625,0.1081,0.9732,0.6377,0.0083,0.0,0.1379,0.1667,0.0,0.0,0.0513,0.0519,0.0,0.0033,reg oper account,block of flats,0.0547,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1203.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n256902,0,Cash loans,M,Y,Y,1,202500.0,450000.0,17739.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.0228,-9771,-742,-924.0,-2310,2.0,1,1,0,1,0,0,,3.0,2,2,MONDAY,11,0,0,0,0,1,1,Self-employed,0.4119581629915701,0.4836726067062795,,0.0753,0.036000000000000004,0.9921,0.8708,0.0135,0.08,0.069,0.3333,0.375,0.0146,0.0622,0.0682,0.0,0.0253,0.0756,0.0059,0.9906,0.8759,0.0136,0.0806,0.069,0.3333,0.375,0.0,0.068,0.0503,0.0,0.0,0.076,0.036000000000000004,0.9921,0.8725,0.0136,0.08,0.069,0.3333,0.375,0.0149,0.0633,0.0694,0.0,0.0259,reg oper account,block of flats,0.0766,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1338.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,,,,,,\n161822,0,Cash loans,M,N,Y,0,306000.0,746280.0,54306.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.072508,-21807,365243,-9.0,-3897,,1,0,0,1,0,0,,2.0,1,1,SATURDAY,14,0,0,0,0,0,0,XNA,,0.2741238006930241,,0.0433,0.0106,0.9727,0.626,0.0339,0.0,0.1034,0.125,0.0417,0.0,0.0664,0.035,0.0,0.0238,0.0441,0.011000000000000001,0.9727,0.6406,0.0342,0.0,0.1034,0.125,0.0417,0.0,0.0725,0.0364,0.0,0.0252,0.0437,0.0106,0.9727,0.631,0.0341,0.0,0.1034,0.125,0.0417,0.0,0.0676,0.0356,0.0,0.0243,reg oper account,block of flats,0.0327,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-63.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n251860,0,Cash loans,M,Y,N,1,225000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.031329,-14897,-6083,-663.0,-4095,2.0,1,1,0,1,0,0,Managers,3.0,2,2,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 2,,0.2858978721410488,0.3556387169923543,0.2227,0.1415,0.9851,,,0.24,0.2069,0.3333,,0.1731,,0.233,,0.0,0.2269,0.1469,0.9851,,,0.2417,0.2069,0.3333,,0.177,,0.2427,,0.0,0.2248,0.1415,0.9851,,,0.24,0.2069,0.3333,,0.1761,,0.2372,,0.0,,block of flats,0.2123,Panel,No,0.0,0.0,0.0,0.0,-492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,6.0,1.0,2.0\n257440,0,Revolving loans,F,N,Y,1,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-11646,-1589,-3954.0,-1336,,1,1,0,1,0,0,Core staff,3.0,2,2,THURSDAY,13,0,0,0,0,0,0,Kindergarten,0.6938796542198093,0.3061805399238521,0.2314393514998941,0.1,0.1047,0.9940000000000001,0.9184,0.0361,0.0,0.069,0.1667,0.2083,0.0183,0.0815,0.0803,0.0,0.0,0.1019,0.1087,0.9940000000000001,0.9216,0.0365,0.0,0.069,0.1667,0.2083,0.0187,0.0891,0.0836,0.0,0.0,0.10099999999999999,0.1047,0.9940000000000001,0.9195,0.0364,0.0,0.069,0.1667,0.2083,0.0186,0.0829,0.0817,0.0,0.0,not specified,block of flats,0.0832,Others,No,2.0,2.0,2.0,1.0,-120.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n312270,0,Cash loans,F,N,N,0,90000.0,895500.0,29592.0,895500.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.005084,-15351,-721,-11880.0,-245,,1,1,0,1,1,0,Security staff,2.0,2,2,THURSDAY,18,0,1,1,0,1,1,Government,,0.6192032082091975,0.41534714488434,0.0515,0.0677,0.9836,0.7756,0.0323,0.0,0.1724,0.1667,0.2083,0.0433,0.042,0.055999999999999994,0.0,0.0,0.0525,0.0702,0.9836,0.7844,0.0326,0.0,0.1724,0.1667,0.2083,0.0443,0.0459,0.0583,0.0,0.0,0.052000000000000005,0.0677,0.9836,0.7786,0.0325,0.0,0.1724,0.1667,0.2083,0.0441,0.0428,0.057,0.0,0.0,reg oper account,block of flats,0.0617,Panel,No,0.0,0.0,0.0,0.0,-515.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n235461,0,Cash loans,M,N,N,1,90000.0,450000.0,30442.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.01885,-19714,-7917,-5417.0,-2694,,1,1,0,1,1,0,Drivers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Transport: type 3,0.5132589776672964,0.21387469250639587,,0.0866,0.0274,0.9846,0.7892,0.019,0.08,0.0345,0.5,0.5417,0.0747,0.0672,0.07400000000000001,0.0154,0.0334,0.0882,0.0284,0.9846,0.7975,0.0191,0.0806,0.0345,0.5,0.5417,0.0764,0.0735,0.0771,0.0156,0.0354,0.0874,0.0274,0.9846,0.792,0.0191,0.08,0.0345,0.5,0.5417,0.076,0.0684,0.0753,0.0155,0.0341,reg oper account,block of flats,0.0758,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1266.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n103064,0,Cash loans,M,N,Y,0,225000.0,379008.0,40945.5,360000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-10665,-1227,-10647.0,-3221,,1,1,0,1,1,0,Cooking staff,1.0,1,1,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.15102164461027484,0.7567605725114481,0.5190973382084597,0.2151,0.1077,0.9801,0.728,0.0,0.2664,0.1607,0.375,0.0417,0.0,0.2017,0.3097,0.0019,0.013000000000000001,0.1513,0.0981,0.9801,0.7387,0.0,0.1611,0.1379,0.3333,0.0417,0.0,0.1322,0.1496,0.0,0.0012,0.1509,0.095,0.9801,0.7316,0.0,0.16,0.1379,0.3333,0.0417,0.0,0.2052,0.3832,0.0019,0.0081,reg oper account,block of flats,0.2978,Panel,No,0.0,0.0,0.0,0.0,-2385.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n365348,0,Cash loans,F,N,N,0,76500.0,156384.0,18688.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-15977,-6236,-6965.0,-4145,,1,1,1,1,1,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.695372021532603,0.7449321846094795,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2218.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n118431,1,Cash loans,F,Y,Y,0,175500.0,675000.0,53460.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-18395,-8610,-8624.0,-1949,8.0,1,1,0,1,0,0,,2.0,3,3,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 1,,0.32599226134750164,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1730.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n122436,0,Cash loans,F,N,Y,0,202500.0,521280.0,28408.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010032,-18951,-11436,-7901.0,-2489,,1,1,0,1,0,0,Medicine staff,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Medicine,,0.7288885432718486,0.6848276586890367,0.1113,0.1954,0.9707,0.5988,0.0472,0.0,0.1724,0.1667,0.0417,0.0955,,0.1288,,0.1398,0.1134,0.2027,0.9707,0.6145,0.0476,0.0,0.1724,0.1667,0.0417,0.0977,,0.1342,,0.14800000000000002,0.1124,0.1954,0.9707,0.6042,0.0475,0.0,0.1724,0.1667,0.0417,0.0972,,0.1311,,0.1428,reg oper account,block of flats,0.1575,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1148.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n150340,0,Cash loans,F,N,N,0,180000.0,1575000.0,43312.5,1575000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-18145,-713,-7686.0,-1695,,1,1,0,1,0,0,Medicine staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Medicine,0.9067136105635419,0.5853969593313008,0.5478104658520093,0.1041,0.1296,0.9786,0.7076,0.0103,0.0,0.2069,0.1667,0.2083,0.063,0.0841,0.086,0.0039,0.0124,0.1061,0.1345,0.9786,0.7190000000000001,0.0104,0.0,0.2069,0.1667,0.2083,0.0645,0.0918,0.0896,0.0039,0.0131,0.1051,0.1296,0.9786,0.7115,0.0104,0.0,0.2069,0.1667,0.2083,0.0641,0.0855,0.0875,0.0039,0.0127,reg oper account,block of flats,0.076,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n269114,0,Cash loans,F,N,Y,2,270000.0,1012500.0,32791.5,1012500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-13107,-2849,-6824.0,-4288,,1,1,1,1,1,0,High skill tech staff,4.0,1,1,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.7238949673059027,,0.0979,,0.9821,,,,0.069,0.4583,,,,0.095,,0.0135,0.0998,,0.9821,,,,0.069,0.4583,,,,0.099,,0.0143,0.0989,,0.9821,,,,0.069,0.4583,,,,0.0967,,0.0138,,,0.0038,Panel,No,4.0,0.0,4.0,0.0,-176.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n133848,0,Cash loans,F,N,N,0,202500.0,1174500.0,38817.0,1174500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-18156,-1406,-11736.0,-1702,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 2,,0.5722836195568997,0.6894791426446275,0.134,0.0569,0.9776,0.6940000000000001,0.0151,0.0,0.2759,0.1667,,0.0951,0.1076,0.1193,0.0077,0.0043,0.1366,0.059000000000000004,0.9777,0.706,0.0152,0.0,0.2759,0.1667,,0.0973,0.1175,0.1243,0.0078,0.0045,0.1353,0.0569,0.9776,0.6981,0.0152,0.0,0.2759,0.1667,,0.0968,0.1095,0.1214,0.0078,0.0044,reg oper account,block of flats,0.10300000000000001,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-928.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n259563,0,Cash loans,F,N,Y,0,135000.0,240660.0,11835.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-9062,-619,-2460.0,-1732,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Transport: type 4,0.3030134462363797,0.0007719287043142205,,0.2845,0.3528,0.9811,,,0.0,0.1724,0.125,,,,0.2356,,0.9556,0.2899,0.3662,0.9811,,,0.0,0.1724,0.125,,,,0.2454,,1.0,0.2873,0.3528,0.9811,,,0.0,0.1724,0.125,,,,0.2398,,0.9756,,block of flats,0.335,\"Stone, brick\",No,3.0,1.0,3.0,0.0,-266.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n140901,0,Cash loans,F,N,N,0,67500.0,370107.0,15808.5,319500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.005144,-21076,365243,-449.0,-446,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,,0.6345969422162787,0.5064842396679806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1080.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n105742,0,Cash loans,M,N,Y,0,135000.0,389844.0,18882.0,315000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-22675,365243,-10690.0,-5374,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,7,0,0,0,0,0,0,XNA,,0.3148601605241635,,0.0722,0.0382,0.9806,,,0.0,0.1379,0.1667,,0.0,,0.067,,0.0,0.0735,0.0396,0.9806,,,0.0,0.1379,0.1667,,0.0,,0.0698,,0.0,0.0729,0.0382,0.9806,,,0.0,0.1379,0.1667,,0.0,,0.0682,,0.0,,block of flats,0.0563,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1956.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n363989,0,Cash loans,F,N,Y,0,90000.0,367389.0,16312.5,279000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-14077,-3586,-8204.0,-4850,,1,1,1,1,1,0,Laborers,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6252037956832454,0.6701797807308728,0.41184855592423975,0.0825,0.0593,0.9781,0.7008,0.0268,0.0,0.1379,0.1667,0.0417,0.0599,0.0672,0.0695,0.0,0.0,0.084,0.0615,0.9782,0.7125,0.027000000000000003,0.0,0.1379,0.1667,0.0417,0.0613,0.0735,0.0724,0.0,0.0,0.0833,0.0593,0.9781,0.7048,0.0269,0.0,0.1379,0.1667,0.0417,0.061,0.0684,0.0708,0.0,0.0,org spec account,block of flats,0.0693,Panel,No,0.0,0.0,0.0,0.0,-1368.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n177570,0,Cash loans,M,N,N,1,90000.0,241618.5,25767.0,229500.0,Unaccompanied,State servant,Incomplete higher,Married,With parents,0.00702,-9624,-2000,-9607.0,-2184,,1,1,0,1,0,0,Core staff,3.0,2,2,FRIDAY,9,0,0,0,0,1,1,Police,,0.5204585119225346,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1474.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n114146,0,Cash loans,M,N,N,2,108000.0,323194.5,14364.0,279000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-21237,-1112,-1424.0,-4799,,1,1,1,1,0,0,Laborers,4.0,2,2,FRIDAY,8,0,0,0,0,0,0,Housing,,0.13846732365801595,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n311896,0,Cash loans,M,Y,N,0,180000.0,1546020.0,42642.0,1350000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.025164,-21720,-3834,-1368.0,-4780,5.0,1,1,0,1,0,0,Managers,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,0.5131242689489529,0.6571236925572042,,0.23399999999999999,0.1284,0.993,0.8164,0.1456,0.2,0.1897,0.2708,0.4167,0.1797,0.3362,0.2279,0.0077,0.0644,0.0546,0.0582,0.9866,0.8236,0.147,0.0,0.0345,0.1667,0.4167,0.0111,0.3673,0.02,0.0078,0.0464,0.2363,0.1284,0.993,0.8189,0.1465,0.2,0.1897,0.2708,0.4167,0.1828,0.342,0.23199999999999998,0.0078,0.0658,not specified,block of flats,0.4415,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1569.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n297296,1,Cash loans,M,N,N,1,180000.0,545040.0,26509.5,450000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,With parents,0.011657,-17676,-2655,-1281.0,-1168,,1,1,1,1,1,1,IT staff,3.0,1,1,SUNDAY,19,1,1,0,1,1,0,Self-employed,,0.6970878405602434,0.21155076420525776,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2260.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n306889,0,Cash loans,M,N,Y,0,103500.0,454500.0,44406.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.005084,-14790,-318,-4585.0,-4955,,1,1,0,1,1,0,Laborers,1.0,2,2,WEDNESDAY,9,0,1,1,0,1,1,Industry: type 11,,0.3007265258610688,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n136933,0,Cash loans,F,N,Y,0,135000.0,314100.0,13963.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-19855,-9066,-10456.0,-3344,,1,1,0,1,0,0,Medicine staff,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 2,,0.5829003011256835,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-220.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n278790,0,Cash loans,F,N,N,0,81000.0,675000.0,21775.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-15118,-720,-7914.0,-4477,,1,1,1,1,0,0,Accountants,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Government,0.5516221747434311,0.3384346841106623,0.1684161714286957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-1310.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n382984,0,Cash loans,F,N,Y,0,135000.0,835380.0,33129.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.011703,-19169,-1913,-10320.0,-2660,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,9,0,0,0,0,0,0,School,0.5817871143558444,0.5839313596005724,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-580.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n288672,0,Revolving loans,F,Y,N,0,202500.0,180000.0,9000.0,180000.0,Unaccompanied,State servant,Higher education,Single / not married,With parents,0.010556,-10041,-1721,-777.0,-2717,7.0,1,1,0,1,0,0,High skill tech staff,1.0,3,3,THURSDAY,12,0,0,0,1,0,1,Security Ministries,0.3698663340752039,0.4842078097012266,0.375711009574066,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n134791,0,Cash loans,F,N,Y,0,112500.0,672174.0,19782.0,481500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-23171,-1319,-2376.0,-4117,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,School,,0.4064680013023311,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n159834,1,Cash loans,F,N,Y,0,103500.0,254700.0,13446.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.014464,-23113,365243,-11120.0,-1233,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,6,0,0,0,0,0,0,XNA,,0.5374074244526348,,0.0278,,0.9712,,,0.0,0.1034,0.125,,,,0.026000000000000002,,0.0312,0.0284,,0.9712,,,0.0,0.1034,0.125,,,,0.0271,,0.0331,0.0281,,0.9712,,,0.0,0.1034,0.125,,,,0.0265,,0.0319,,block of flats,0.0226,Block,No,0.0,0.0,0.0,0.0,-1180.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n430829,0,Cash loans,F,N,N,0,225000.0,497520.0,48595.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,With parents,0.035792000000000004,-14274,-5155,-8387.0,-4829,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 2,,0.7085147059148261,0.6109913280868294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1944.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n127891,0,Cash loans,F,N,Y,0,135000.0,781920.0,25101.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.010006,-19309,365243,-1089.0,-2794,,1,0,0,1,0,0,,2.0,2,1,FRIDAY,15,0,0,0,0,0,0,XNA,0.6678415362485207,0.4098593293456229,0.2340151665320674,0.1289,0.1497,0.996,0.9456,0.0841,0.0,0.2414,0.1667,0.2083,0.1631,0.1051,0.1458,0.0,0.0,0.1313,0.1553,0.996,0.9477,0.0849,0.0,0.2414,0.1667,0.2083,0.1668,0.1148,0.1519,0.0,0.0,0.1301,0.1497,0.996,0.9463,0.0847,0.0,0.2414,0.1667,0.2083,0.1659,0.1069,0.1484,0.0,0.0,reg oper account,block of flats,0.1323,Panel,No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n294638,1,Cash loans,F,N,N,0,405000.0,407520.0,32328.0,360000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-16943,-7245,-1401.0,-471,,1,1,0,1,0,1,,1.0,1,1,THURSDAY,15,0,0,0,0,0,0,Other,0.7432529113004392,0.7486158328715714,0.511891801533151,0.1392,0.0873,0.9762,0.6736,,0.16,0.1379,0.3333,0.0417,,0.1067,0.1094,0.0309,0.1091,0.1418,0.0906,0.9762,0.6864,,0.1611,0.1379,0.3333,0.0417,,0.1166,0.1139,0.0311,0.1155,0.1405,0.0873,0.9762,0.6779999999999999,,0.16,0.1379,0.3333,0.0417,,0.1086,0.1113,0.0311,0.1114,,block of flats,0.1097,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2660.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,1.0\n102121,0,Cash loans,F,Y,Y,2,135000.0,191880.0,19791.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-17421,-702,-667.0,-751,5.0,1,1,0,1,0,0,Cooking staff,4.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.5445328735097945,0.5701251449648249,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-599.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n136673,0,Cash loans,F,Y,Y,0,180000.0,1040985.0,30568.5,909000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-20711,365243,-320.0,-4226,13.0,1,0,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.3573422337147276,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-181.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n305850,0,Cash loans,F,Y,Y,0,225000.0,1024740.0,52452.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-21824,365243,-4411.0,-3334,5.0,1,0,0,1,0,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.4815212626285129,0.7992967832109371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3034.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n427864,0,Cash loans,M,N,Y,0,180000.0,472500.0,37462.5,472500.0,Unaccompanied,State servant,Incomplete higher,Single / not married,Co-op apartment,0.026392,-8951,-1149,-7351.0,-1561,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Other,0.08947373285048235,0.6112626359925103,0.5602843280409464,0.4351,0.2793,0.9911,0.7892,0.3901,0.46,0.4138,0.3542,0.375,0.3719,0.6438,0.488,0.1081,0.0785,0.0525,0.0282,0.9846,0.7975,0.3937,0.0,0.0345,0.3333,0.375,0.0474,0.7034,0.0548,0.1089,0.0227,0.4393,0.2793,0.9911,0.792,0.3926,0.46,0.4138,0.3542,0.375,0.3783,0.655,0.4967,0.1087,0.0801,reg oper account,block of flats,0.0603,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-800.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n295922,0,Cash loans,M,N,Y,0,225000.0,254700.0,25321.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-18552,-299,-1235.0,-2088,,1,1,1,1,0,0,,2.0,2,2,THURSDAY,14,0,1,1,0,1,1,School,,0.1285888959352062,0.7136313997323308,0.0557,0.0132,0.9722,0.6192,0.0204,0.0,0.069,0.0833,0.125,0.0304,0.0429,0.0368,0.0116,0.0529,0.0567,0.0137,0.9722,0.6341,0.0206,0.0,0.069,0.0833,0.125,0.0311,0.0468,0.0383,0.0117,0.055999999999999994,0.0562,0.0132,0.9722,0.6243,0.0206,0.0,0.069,0.0833,0.125,0.031,0.0436,0.0375,0.0116,0.054000000000000006,reg oper account,block of flats,0.0516,\"Stone, brick\",No,6.0,0.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n384809,0,Cash loans,F,N,Y,2,135000.0,544491.0,21226.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-13960,-1850,-6965.0,-4241,,1,1,0,1,0,0,Cooking staff,4.0,2,2,TUESDAY,16,0,0,0,0,0,0,Kindergarten,0.3974715569072088,0.5498793872800003,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-944.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,9.0\n298143,0,Cash loans,M,N,Y,0,81000.0,191880.0,19107.0,180000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.019101,-22098,365243,-2858.0,-5179,,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,0.7941872924274668,0.5756363489859897,0.6246146584503397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-610.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n319689,0,Cash loans,F,N,Y,0,112500.0,534141.0,20263.5,441000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-18134,-1317,-7408.0,-1678,,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Other,0.5648359450123142,0.6470282604438302,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1647.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n150182,0,Cash loans,F,N,Y,0,180000.0,1237684.5,47272.5,1138500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.025164,-17285,-527,-4956.0,-833,,1,1,0,1,0,0,HR staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.4991608567958203,0.536276484420012,0.8038850611746273,0.499,0.3432,0.9836,0.7756,0.3203,0.56,0.4828,0.3333,0.375,0.0,0.4068,0.5464,0.0,0.0,0.5084,0.3562,0.9836,0.7844,0.3233,0.5639,0.4828,0.3333,0.375,0.0,0.4444,0.5693,0.0,0.0,0.5038,0.3432,0.9836,0.7786,0.3224,0.56,0.4828,0.3333,0.375,0.0,0.4139,0.5562,0.0,0.0,reg oper account,block of flats,0.6049,Panel,No,8.0,1.0,8.0,0.0,-2087.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n186721,0,Cash loans,F,Y,N,2,67500.0,450000.0,32746.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.026392,-11352,-1160,-2742.0,-2917,3.0,1,1,1,1,0,0,Sales staff,4.0,2,2,SATURDAY,12,0,0,0,0,0,0,Trade: type 7,0.5486469663609839,0.6552440436598822,,0.0082,0.0145,0.9722,,,0.0,0.069,0.0417,,,,0.0088,,,0.0084,0.0151,0.9722,,,0.0,0.069,0.0417,,,,0.0092,,,0.0083,0.0145,0.9722,,,0.0,0.069,0.0417,,,,0.009000000000000001,,,,block of flats,0.0073,Wooden,Yes,7.0,0.0,7.0,0.0,-369.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n301714,0,Cash loans,M,N,Y,0,180000.0,403330.5,27081.0,333000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.032561,-9984,-1125,-76.0,-2557,,1,1,0,1,1,0,Cooking staff,2.0,1,1,THURSDAY,12,0,0,0,0,0,0,Restaurant,,0.3928120240107172,,0.1746,0.1356,0.9841,,,0.4,0.2297,0.2221,,,,0.1613,,0.0022,0.0788,0.0879,0.9846,,,0.4028,0.1724,0.1667,,,,0.0713,,0.0024,0.0781,0.0859,0.9846,,,0.4,0.1724,0.1667,,,,0.0708,,0.0023,,block of flats,0.3042,Panel,No,0.0,0.0,0.0,0.0,-62.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n328267,0,Cash loans,F,N,Y,0,135000.0,345510.0,16749.0,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-22442,365243,-10018.0,-4209,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,XNA,,0.34652008164475256,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,5.0,0.0,-745.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n276555,0,Cash loans,F,Y,Y,0,157500.0,783000.0,23022.0,783000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.015221,-21254,-3019,-9100.0,-4593,2.0,1,1,0,1,0,0,Drivers,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.5254119675464457,0.646329897706246,0.2557,0.5937,0.9876,,,0.28,0.2414,0.3333,,0.1389,,0.1897,,,0.2605,0.6161,0.9876,,,0.282,0.2414,0.3333,,0.1421,,0.1976,,,0.2581,0.5937,0.9876,,,0.28,0.2414,0.3333,,0.1413,,0.1931,,,,block of flats,0.2421,Panel,No,0.0,0.0,0.0,0.0,-1066.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n307977,0,Cash loans,F,N,Y,0,67500.0,328365.0,18981.0,297000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010643,-23703,365243,-13501.0,-4401,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,0.7350918797618129,0.5695408772364199,,0.0928,0.1106,0.9811,,,0.0,0.2069,0.1667,,0.0641,,0.092,,0.1209,0.0945,0.1148,0.9811,,,0.0,0.2069,0.1667,,0.0655,,0.0959,,0.128,0.0937,0.1106,0.9811,,,0.0,0.2069,0.1667,,0.0652,,0.0937,,0.1234,,block of flats,0.0986,Panel,No,5.0,0.0,5.0,0.0,-358.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n147883,0,Cash loans,M,N,Y,0,112500.0,227520.0,14940.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.009175,-10527,-900,-4794.0,-3202,,1,1,0,1,0,1,High skill tech staff,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4884140841905022,0.4055661913834919,0.5046813193144684,0.0825,0.085,0.9786,0.7076,0.0107,0.0,0.1379,0.1667,0.2083,0.0463,0.0672,0.0698,0.0,0.0,0.084,0.0882,0.9786,0.7190000000000001,0.0108,0.0,0.1379,0.1667,0.2083,0.0473,0.0735,0.0727,0.0,0.0,0.0833,0.085,0.9786,0.7115,0.0107,0.0,0.1379,0.1667,0.2083,0.0471,0.0684,0.071,0.0,0.0,reg oper account,block of flats,0.0607,Panel,No,1.0,0.0,1.0,0.0,-1751.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n182184,0,Cash loans,M,N,Y,1,135000.0,355500.0,28084.5,355500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.026392,-9095,-232,-706.0,-666,,1,1,0,1,0,1,Laborers,3.0,2,2,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.26402868307317384,0.5449059084646412,0.4223696523543468,0.4959,0.4347,0.9816,,,0.0,1.0,0.1667,,0.5531,,0.4418,,0.0104,0.5053,0.4511,0.9816,,,0.0,1.0,0.1667,,0.5658,,0.4603,,0.011000000000000001,0.5007,0.4347,0.9816,,,0.0,1.0,0.1667,,0.5628,,0.4497,,0.0106,,block of flats,0.3498,Panel,No,0.0,0.0,0.0,0.0,-338.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n442671,0,Cash loans,F,Y,N,0,103500.0,675000.0,21906.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-16337,-601,-3288.0,-4406,14.0,1,1,0,1,0,0,Sales staff,2.0,3,3,SUNDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.477521425006457,0.5530814690374567,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1796.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n422581,0,Cash loans,F,N,Y,0,180000.0,1223010.0,48627.0,1125000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-19810,-5752,-4636.0,-3340,,1,1,0,1,0,0,Accountants,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Bank,,0.6216390517233732,0.3376727217405312,0.0814,0.0551,0.995,0.932,0.069,0.08,0.0345,0.625,0.6667,0.0,0.0664,0.1132,0.0,0.0,0.083,0.0572,0.995,0.9347,0.0696,0.0806,0.0345,0.625,0.6667,0.0,0.0725,0.1179,0.0,0.0,0.0822,0.0551,0.995,0.9329,0.0694,0.08,0.0345,0.625,0.6667,0.0,0.0676,0.1152,0.0,0.0,org spec account,block of flats,0.1267,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2378.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n172082,0,Cash loans,M,Y,Y,1,225000.0,1483650.0,62991.0,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00702,-16403,-1483,-10512.0,-4765,2.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,10,0,0,0,1,1,0,Business Entity Type 3,0.3052040005207849,0.5661064577047057,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1731.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n172231,0,Cash loans,F,N,Y,0,189000.0,1354500.0,50328.0,1354500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.026392,-22268,-2474,-12275.0,-4053,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.8315270952628121,0.7103268562148948,0.7981372313187245,0.1546,0.1401,0.9801,,,0.0,0.3448,0.1667,,0.0576,,0.1535,,0.0,0.1576,0.1454,0.9801,,,0.0,0.3448,0.1667,,0.0589,,0.1599,,0.0,0.1561,0.1401,0.9801,,,0.0,0.3448,0.1667,,0.0586,,0.1562,,0.0,,block of flats,0.1207,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-30.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n389412,0,Cash loans,M,N,N,0,180000.0,417024.0,25200.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Co-op apartment,0.018634,-15974,-2352,-8535.0,-5051,,1,1,1,1,0,0,Laborers,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Construction,,0.2915516362512112,,0.0619,0.0511,0.9767,0.6804,0.0036,0.0,0.1034,0.1667,0.2083,0.0102,0.0504,0.0513,0.0,0.0091,0.063,0.053,0.9767,0.6929,0.0037,0.0,0.1034,0.1667,0.2083,0.0105,0.0551,0.0535,0.0,0.0097,0.0625,0.0511,0.9767,0.6847,0.0036,0.0,0.1034,0.1667,0.2083,0.0104,0.0513,0.0522,0.0,0.0093,org spec account,block of flats,0.0523,Panel,No,0.0,0.0,0.0,0.0,-533.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n176224,0,Cash loans,F,Y,Y,2,90000.0,180000.0,11632.5,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.015221,-15525,-3357,-4973.0,-4980,4.0,1,1,0,1,0,0,Accountants,4.0,2,2,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.7929666117918862,0.5092016298052255,0.44539624156083396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-808.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n284386,1,Cash loans,M,N,Y,0,157500.0,640458.0,30942.0,517500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-17812,-480,-9110.0,-1260,,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Agriculture,,0.2542454862229005,0.06337536230998861,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2141.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n378457,0,Cash loans,F,N,N,0,90000.0,450000.0,16294.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-19988,-3203,-8367.0,-3505,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Medicine,,0.6820355988768458,,0.0876,0.1493,0.9886,,,0.0,0.2414,0.1667,,0.1048,,0.0951,,,0.0893,0.1549,0.9886,,,0.0,0.2414,0.1667,,0.1072,,0.0991,,,0.0885,0.1493,0.9886,,,0.0,0.2414,0.1667,,0.1066,,0.0968,,,,block of flats,0.0748,\"Stone, brick\",No,2.0,1.0,2.0,0.0,-1699.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n433697,0,Cash loans,F,N,Y,0,108000.0,263686.5,15138.0,238500.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.00733,-21730,365243,-14427.0,-3453,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,17,0,0,0,0,0,0,XNA,,0.1763929834151091,0.2851799046358216,0.1031,0.0889,0.9776,,,,0.2069,0.1667,,0.0199,,0.0884,,,0.105,0.0922,0.9777,,,,0.2069,0.1667,,0.0203,,0.0921,,,0.1041,0.0889,0.9776,,,,0.2069,0.1667,,0.0202,,0.0899,,,,block of flats,0.0756,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-147.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n178985,0,Cash loans,F,Y,Y,2,76500.0,168102.0,17779.5,148500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-15021,-3275,-4779.0,-4793,32.0,1,1,0,1,0,0,,4.0,2,2,TUESDAY,10,0,0,0,1,1,0,Trade: type 3,,0.5709824315063338,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2496.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n411601,0,Cash loans,F,N,Y,0,76500.0,1107981.0,32526.0,967500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-18850,-5889,-8715.0,-1527,,1,1,0,1,0,1,,2.0,2,2,TUESDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.9256049189922172,0.6282201928354093,0.8038850611746273,0.0082,0.0,0.9717,0.6124,0.001,0.0,0.0345,0.0417,0.0833,0.0347,0.0067,0.0091,0.0,0.0,0.0084,0.0,0.9717,0.6276,0.001,0.0,0.0345,0.0417,0.0833,0.0354,0.0073,0.0095,0.0,0.0,0.0083,0.0,0.9717,0.6176,0.001,0.0,0.0345,0.0417,0.0833,0.0353,0.0068,0.0092,0.0,0.0,reg oper account,block of flats,0.0077,Wooden,No,0.0,0.0,0.0,0.0,-1983.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n381521,0,Cash loans,F,N,N,0,225000.0,490495.5,29205.0,454500.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,With parents,0.035792000000000004,-16146,-1621,-672.0,-792,,1,1,1,1,0,0,Managers,2.0,2,2,THURSDAY,14,0,0,0,0,1,1,Self-employed,0.4710857149882541,0.6899300913937738,0.3859146722745145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1023.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n173523,0,Cash loans,F,N,N,0,99000.0,161730.0,8388.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-16251,-1972,-9039.0,-4811,,1,1,1,1,0,0,Managers,2.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Kindergarten,,0.5319451166368208,0.12610055883623253,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-55.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n370353,0,Cash loans,F,N,N,0,135000.0,490495.5,29335.5,454500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-16885,-2533,-7562.0,-403,,1,1,0,1,1,0,Core staff,2.0,2,2,FRIDAY,13,0,0,0,0,1,1,School,,0.7169139378289102,0.2721336844147212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-333.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,0.0\n126406,0,Cash loans,M,N,Y,0,108000.0,101880.0,10075.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-24444,-13806,-11241.0,-4478,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Construction,0.8539876159131669,0.5556961314823932,0.6832688314232291,0.0691,0.0732,0.9876,0.7892,0.03,0.08,0.0517,0.5625,0.625,0.0494,0.0546,0.0687,0.0,0.0,0.0683,0.0755,0.9846,0.7975,0.0302,0.0806,0.0345,0.5417,0.625,0.0255,0.0597,0.0694,0.0,0.0,0.0697,0.0732,0.9876,0.792,0.0301,0.08,0.0517,0.5625,0.625,0.0503,0.0556,0.07,0.0,0.0,reg oper account,block of flats,0.0571,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n363788,0,Cash loans,F,N,Y,0,315000.0,1876608.0,65358.0,1620000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.04622,-13306,-201,-6015.0,-3638,,1,1,0,1,1,1,IT staff,2.0,1,1,MONDAY,19,0,1,1,0,0,0,Other,0.6217217351060962,0.6149506718855996,0.16146308000577247,0.0412,,0.9687,,,,0.1034,0.125,,,,0.0448,,,0.042,,0.9687,,,,0.1034,0.125,,,,0.0466,,,0.0416,,0.9687,,,,0.1034,0.125,,,,0.0456,,,,,0.0352,,No,0.0,0.0,0.0,0.0,-1892.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,7.0\n134225,0,Cash loans,M,Y,N,0,225000.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-16089,-302,-7686.0,-4103,29.0,1,1,0,1,0,0,Drivers,2.0,3,3,WEDNESDAY,9,0,0,0,0,1,1,Self-employed,,0.2285168733298301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1651.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n234093,0,Cash loans,F,Y,Y,1,112500.0,531706.5,19228.5,459000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-9628,-1477,-3658.0,-2290,3.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,16,0,0,0,1,1,0,Transport: type 3,0.4453223557934521,0.5244521866013889,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1223.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n322391,0,Cash loans,M,N,Y,2,270000.0,675000.0,36094.5,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-14557,-553,-5956.0,-4781,,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,10,0,1,1,0,1,1,Industry: type 9,0.3553885801395853,0.04536612647098641,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n375598,0,Cash loans,F,N,N,0,166500.0,755190.0,30078.0,675000.0,Children,Pensioner,Lower secondary,Single / not married,House / apartment,0.002042,-23377,365243,-9566.0,-4423,,1,0,0,1,0,0,,1.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.7552075018415387,0.6058362647264226,0.0835,0.0809,0.9871,0.8232,0.0255,0.12,0.1034,0.3333,0.0417,0.1515,0.0681,0.1024,0.0,0.042,0.0851,0.084,0.9871,0.8301,0.0257,0.1208,0.1034,0.3333,0.0417,0.1549,0.0744,0.1067,0.0,0.0445,0.0843,0.0809,0.9871,0.8256,0.0256,0.12,0.1034,0.3333,0.0417,0.1541,0.0693,0.1042,0.0,0.0429,reg oper account,block of flats,0.0878,Panel,No,0.0,0.0,0.0,0.0,-1261.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n184447,0,Cash loans,F,N,Y,0,90000.0,807984.0,26833.5,697500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00733,-20583,365243,-10235.0,-3175,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,XNA,,0.6172057672451009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-634.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n173576,0,Cash loans,M,Y,N,0,112500.0,454500.0,25506.0,454500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-18620,-1269,-4357.0,-2177,10.0,1,1,1,1,1,0,Security staff,2.0,2,2,FRIDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.6417623028405574,0.6348707183665275,0.4794489811780563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2281.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n423934,0,Cash loans,F,N,Y,2,157500.0,298512.0,20079.0,270000.0,Children,Working,Higher education,Married,House / apartment,0.014464,-13886,-2069,-4773.0,-2273,,1,1,1,1,1,1,Core staff,4.0,2,2,SUNDAY,7,0,0,0,0,0,0,Kindergarten,0.5929165015659339,0.2949461831236946,0.13937578009978951,0.1588,,0.9811,,,0.0,,,,,,0.051,,,0.1618,,0.9811,,,0.0,,,,,,0.0532,,,0.1603,,0.9811,,,0.0,,,,,,0.052000000000000005,,,,block of flats,0.0401,,No,1.0,0.0,1.0,0.0,-664.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n322122,0,Cash loans,F,Y,Y,0,135000.0,528633.0,24624.0,472500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-19983,-1255,-13174.0,-3159,25.0,1,1,1,1,0,0,Accountants,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Self-employed,0.6202008609067751,0.6666222882789957,0.8435435389318647,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-464.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n217484,0,Cash loans,M,N,N,2,247500.0,225000.0,15165.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008019,-14168,-423,-10287.0,-1501,,1,1,0,1,0,0,Laborers,4.0,2,2,MONDAY,18,0,0,0,0,1,1,Business Entity Type 3,0.20932182423269888,0.5344433568166397,,0.0928,0.0791,0.9856,0.8028,0.0118,0.0,0.2069,0.1667,0.2083,0.0463,0.0756,0.0776,0.0,0.0,0.0945,0.08199999999999999,0.9856,0.8105,0.0119,0.0,0.2069,0.1667,0.2083,0.0474,0.0826,0.0808,0.0,0.0,0.0937,0.0791,0.9856,0.8054,0.0119,0.0,0.2069,0.1667,0.2083,0.0471,0.077,0.079,0.0,0.0,reg oper account,block of flats,0.0822,Panel,No,0.0,0.0,0.0,0.0,-452.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n106322,0,Cash loans,M,N,N,0,90000.0,215640.0,18634.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.008068,-18778,-184,-7230.0,-2318,,1,1,0,1,0,0,Security staff,2.0,3,3,WEDNESDAY,5,0,0,0,0,0,0,Business Entity Type 3,,0.2697424554535296,,0.0041,,0.9717,,,,,0.0,,,,0.003,,,0.0042,,0.9717,,,,,0.0,,,,0.0031,,,0.0042,,0.9717,,,,,0.0,,,,0.003,,,,block of flats,,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-436.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n361381,0,Cash loans,M,Y,Y,2,135000.0,545040.0,25537.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.010006,-17547,-1008,-3967.0,-1060,8.0,1,1,0,1,0,0,Drivers,4.0,2,1,THURSDAY,14,0,0,0,0,0,0,Security,0.3877417413864461,0.7551549436964353,0.4884551844437485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,1.0,-577.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n336667,0,Cash loans,F,N,Y,2,270000.0,345505.5,13153.5,211500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.016612000000000002,-15438,-2197,-3001.0,-4852,,1,1,0,1,0,1,Secretaries,4.0,2,2,TUESDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.7873593449664236,0.5434793479058391,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-686.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n108258,0,Cash loans,M,N,Y,1,360000.0,942300.0,30528.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-12074,-2140,-6122.0,-4057,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.6816228785429582,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n193372,0,Cash loans,M,Y,N,0,292500.0,1183963.5,31360.5,927000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-10224,-2506,-4796.0,-2324,8.0,1,1,0,1,0,0,Managers,1.0,1,1,SATURDAY,17,0,0,0,0,0,0,Other,0.6581107767794376,0.6020686743470054,0.4507472818545589,0.0629,0.0603,0.9732,0.6328,0.0014,0.0,0.1034,0.1667,0.2083,0.0,0.0504,0.0509,0.0039,0.0035,0.0641,0.0625,0.9732,0.6472,0.0014,0.0,0.1034,0.1667,0.2083,0.0,0.0551,0.053,0.0039,0.0037,0.0635,0.0603,0.9732,0.6377,0.0014,0.0,0.1034,0.1667,0.2083,0.0,0.0513,0.0518,0.0039,0.0035,reg oper account,block of flats,0.0408,Block,No,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n165536,0,Revolving loans,M,N,Y,1,157500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-13946,-250,-6506.0,-4619,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,7,0,1,1,0,1,1,Industry: type 9,,0.4669263277982167,,0.0619,0.0729,0.9861,0.8096,0.0323,0.0,0.1379,0.1667,0.2083,0.0637,0.0504,0.0617,0.0,0.0,0.063,0.0757,0.9861,0.8171,0.0326,0.0,0.1379,0.1667,0.2083,0.0652,0.0551,0.0643,0.0,0.0,0.0625,0.0729,0.9861,0.8121,0.0325,0.0,0.1379,0.1667,0.2083,0.0648,0.0513,0.0628,0.0,0.0,reg oper account,block of flats,0.0493,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n216439,1,Cash loans,F,N,N,1,270000.0,976500.0,28552.5,976500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-15573,-2655,-3136.0,-3326,,1,1,0,1,0,0,Sales staff,3.0,1,1,WEDNESDAY,8,0,0,0,0,1,1,Business Entity Type 3,,0.3781022166255257,0.19182160241360605,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-221.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n245489,0,Cash loans,F,Y,Y,0,225000.0,917860.5,52821.0,850500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-13744,-1676,-5552.0,-5133,1.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 1,,0.5923375239104829,0.5989262182569273,0.2598,0.1449,0.9821,0.7552,0.0606,0.28,0.2414,0.3333,0.375,0.1636,0.2118,0.2792,0.0,0.0,0.2647,0.1504,0.9821,0.7648,0.0611,0.282,0.2414,0.3333,0.375,0.1674,0.2314,0.2909,0.0,0.0,0.2623,0.1449,0.9821,0.7585,0.061,0.28,0.2414,0.3333,0.375,0.1665,0.2155,0.2842,0.0,0.0,reg oper spec account,block of flats,0.2527,Panel,No,0.0,0.0,0.0,0.0,-511.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n165855,0,Cash loans,M,N,Y,0,270000.0,592560.0,35937.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019101,-13428,-247,-1019.0,-5954,,1,1,0,1,1,0,Laborers,1.0,2,2,TUESDAY,16,0,0,0,0,1,1,Self-employed,0.35273017488163994,0.7311899155014426,0.6161216908872079,0.0639,0.0528,0.9767,0.6804,0.0236,0.0,0.1379,0.625,0.1667,0.0506,0.0504,0.0425,0.0077,0.011000000000000001,0.0651,0.0547,0.9767,0.6929,0.0238,0.0,0.1379,0.625,0.1667,0.0518,0.0551,0.0442,0.0078,0.0116,0.0645,0.0528,0.9767,0.6847,0.0238,0.0,0.1379,0.625,0.1667,0.0515,0.0513,0.0432,0.0078,0.0112,reg oper spec account,block of flats,0.0487,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1135.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n191242,0,Cash loans,F,N,Y,0,270000.0,675000.0,39240.0,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-20325,365243,-11480.0,-3533,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.6682531724778021,0.7583930617144343,0.066,0.0356,0.9776,,,0.0,0.1379,0.1667,,0.0443,,0.0585,,0.022000000000000002,0.0672,0.0369,0.9777,,,0.0,0.1379,0.1667,,0.0453,,0.0609,,0.0233,0.0666,0.0356,0.9776,,,0.0,0.1379,0.1667,,0.0451,,0.0595,,0.0224,,block of flats,0.055,\"Stone, brick\",No,3.0,0.0,3.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n381383,0,Revolving loans,F,N,Y,2,135000.0,157500.0,7875.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-14343,-160,-7689.0,-4104,,1,1,0,1,1,0,Cleaning staff,3.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5955861898639844,0.5352762504724826,0.1113,0.0666,0.9841,0.7688,0.0377,0.12,0.1034,0.3333,0.375,0.0588,0.0908,0.07200000000000001,0.0,0.0,0.1134,0.0623,0.9861,0.7452,0.0266,0.1208,0.1034,0.3333,0.375,0.0559,0.0992,0.0105,0.0,0.0,0.1124,0.0666,0.9861,0.7719,0.0379,0.12,0.1034,0.3333,0.375,0.0598,0.0923,0.1047,0.0,0.0,reg oper spec account,block of flats,0.081,Panel,No,0.0,0.0,0.0,0.0,-480.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n161489,0,Cash loans,M,Y,N,0,180000.0,1067940.0,31356.0,765000.0,Unaccompanied,Working,Higher education,Civil marriage,With parents,0.020246,-11772,-2576,-2882.0,-3435,17.0,1,1,0,1,1,0,Drivers,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Self-employed,,0.21070433269952216,0.6940926425266661,0.0732,0.0646,0.9796,0.7212,0.0087,0.0,0.1379,0.1667,0.2083,0.0322,0.0597,0.0683,0.0,,0.0746,0.0671,0.9796,0.7321,0.0087,0.0,0.1379,0.1667,0.2083,0.0329,0.0652,0.0711,0.0,,0.0739,0.0646,0.9796,0.7249,0.0087,0.0,0.1379,0.1667,0.2083,0.0328,0.0607,0.0695,0.0,,reg oper account,block of flats,0.0584,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n322319,0,Cash loans,M,Y,N,1,360000.0,835380.0,40189.5,675000.0,Unaccompanied,Pensioner,Higher education,Married,With parents,0.072508,-23269,365243,-11247.0,-4531,31.0,1,0,0,1,1,0,,3.0,1,1,SUNDAY,16,1,0,0,0,0,0,XNA,0.6972246236018262,0.7658202950574501,0.5867400085415683,0.29,0.17800000000000002,0.9781,0.7008,0.129,0.2932,0.2297,0.4721,0.5138,0.0,0.2365,0.1798,0.0,0.0,0.1029,0.0433,0.9786,0.7190000000000001,0.0184,0.0806,0.0345,0.5417,0.5833,0.0,0.09,0.055,0.0,0.0,0.102,0.0462,0.9786,0.7115,0.0355,0.08,0.0345,0.5417,0.5833,0.0,0.0838,0.0538,0.0,0.0,reg oper account,block of flats,0.0666,Block,No,0.0,0.0,0.0,0.0,-3789.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n378214,0,Revolving loans,F,N,Y,0,54000.0,157500.0,7875.0,,,Working,Secondary / secondary special,Married,House / apartment,0.018634,-19827,-11367,-9770.0,-2794,,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,School,,0.4303341539056891,0.13259749523614614,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-236.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n349157,0,Cash loans,F,Y,N,2,112500.0,254700.0,20250.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-12450,-284,-2628.0,-4837,9.0,1,1,0,1,0,0,Sales staff,4.0,2,2,MONDAY,13,0,0,0,0,0,0,Self-employed,0.4796456298351636,0.6758677418958491,0.4471785780453068,0.0278,,0.9861,,,,0.1034,0.0833,,,,0.0287,,,0.0284,,0.9861,,,,0.1034,0.0833,,,,0.0299,,,0.0281,,0.9861,,,,0.1034,0.0833,,,,0.0292,,,,block of flats,0.0235,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-2469.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n107704,1,Cash loans,M,N,Y,0,112500.0,485640.0,36441.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.006670999999999999,-23125,365243,-10.0,-4882,,1,0,0,1,0,0,,1.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.5015599437172107,0.7874761977281463,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-616.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n298120,0,Cash loans,M,Y,Y,2,135000.0,679500.0,27076.5,679500.0,Family,Working,Secondary / secondary special,Married,With parents,0.008625,-13827,-1288,-2066.0,-2291,17.0,1,1,1,1,0,0,Drivers,4.0,2,2,WEDNESDAY,11,0,0,0,0,1,1,Agriculture,,0.0497429526617616,0.41534714488434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-638.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n229421,0,Cash loans,M,Y,Y,0,270000.0,450000.0,22977.0,450000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,Municipal apartment,0.010643,-8903,-135,-4887.0,-1589,7.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,10,1,1,0,1,1,0,Self-employed,0.4678669254580609,0.36266261164617203,0.2405414172860865,0.0862,0.0999,0.9781,0.7008,0.0095,0.0,0.1724,0.1667,0.2083,0.1073,0.0703,0.0775,0.0013,0.0089,0.0735,0.0856,0.9786,0.7190000000000001,0.0078,0.0,0.1379,0.1667,0.2083,0.0882,0.0643,0.0694,0.0,0.0,0.0864,0.1011,0.9781,0.7048,0.0095,0.0,0.1724,0.1667,0.2083,0.1078,0.071,0.0778,0.0,0.0,reg oper account,block of flats,0.0567,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n354933,0,Cash loans,F,Y,Y,1,450000.0,1099899.0,39636.0,949500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-16490,-1828,-8248.0,-28,5.0,1,1,0,1,0,1,Managers,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Other,,0.16884529337454238,0.4170996682522097,0.0392,0.0319,0.9737,0.6396,0.0029,0.0,0.069,0.1667,0.2083,0.0583,0.0303,0.0199,0.0077,0.0047,0.0399,0.0331,0.9737,0.6537,0.0029,0.0,0.069,0.1667,0.2083,0.0596,0.0331,0.0207,0.0078,0.0049,0.0396,0.0319,0.9737,0.6444,0.0029,0.0,0.069,0.1667,0.2083,0.0593,0.0308,0.0202,0.0078,0.0048,reg oper account,block of flats,0.0252,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n123847,0,Revolving loans,M,N,Y,0,225000.0,450000.0,22500.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.011657,-17817,-1284,-1276.0,-1357,,1,1,0,1,0,0,,1.0,1,1,THURSDAY,14,0,1,1,0,0,0,Business Entity Type 2,,0.784043210255602,0.3490552510751822,0.0928,0.1295,0.9891,0.8504,0.0135,0.0,0.1724,0.1667,0.2083,0.0905,0.0756,0.1054,0.0,0.0341,0.0945,0.1344,0.9891,0.8563,0.0137,0.0,0.1724,0.1667,0.2083,0.0926,0.0826,0.1098,0.0,0.036000000000000004,0.0937,0.1295,0.9891,0.8524,0.0136,0.0,0.1724,0.1667,0.2083,0.0921,0.077,0.1073,0.0,0.0348,reg oper account,block of flats,0.0829,Panel,No,1.0,0.0,1.0,0.0,-1678.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n400845,0,Cash loans,M,Y,Y,1,135000.0,237024.0,12231.0,180000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-12747,-596,-6622.0,-4661,10.0,1,1,1,1,0,0,,3.0,2,2,FRIDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.5164731426697139,0.5567274263630174,0.0928,,0.9831,,,0.1,0.0862,0.3333,,,,0.0909,,0.0614,0.0378,,0.9821,,,0.0403,0.0345,0.3333,,,,0.0348,,0.0287,0.0937,,0.9831,,,0.1,0.0862,0.3333,,,,0.0925,,0.0627,,block of flats,0.0322,Panel,No,0.0,0.0,0.0,0.0,-1100.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n347557,0,Cash loans,F,Y,Y,0,270000.0,675000.0,32602.5,675000.0,Unaccompanied,Commercial associate,Higher education,Married,Municipal apartment,0.010006,-11058,-2808,-842.0,-1514,4.0,1,1,0,1,0,1,,2.0,2,1,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.6628600966970634,0.6894059054714091,0.5262949398096192,0.2186,0.1333,0.998,0.9728,0.1222,0.16,0.1379,0.3333,0.375,0.1883,0.1782,0.2047,0.0,0.0,0.2227,0.1383,0.998,0.9739,0.1233,0.1611,0.1379,0.3333,0.375,0.1926,0.1947,0.2133,0.0,0.0,0.2207,0.1333,0.998,0.9732,0.1229,0.16,0.1379,0.3333,0.375,0.1916,0.1813,0.2084,0.0,0.0,reg oper account,specific housing,0.2325,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-80.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n137064,0,Cash loans,F,N,Y,0,90000.0,555273.0,17910.0,463500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.019689,-10377,-100,-3072.0,-501,,1,1,0,1,0,0,Accountants,1.0,2,2,TUESDAY,17,0,0,0,1,1,1,Construction,,0.4894250177872082,0.5028782772082183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1565.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n389980,0,Cash loans,M,N,Y,0,202500.0,848745.0,43465.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-12368,-3402,-1281.0,-4682,,1,1,0,1,0,0,Drivers,2.0,3,3,THURSDAY,7,0,0,0,0,1,1,Security,0.5532395010831344,0.6774274968014289,0.3656165070113335,0.0433,0.0,0.9861,0.8096,0.0014,0.0,0.0345,0.0833,0.0417,0.03,0.0353,0.0215,0.0,0.0,0.0441,0.0,0.9861,0.8171,0.0014,0.0,0.0345,0.0833,0.0417,0.0307,0.0386,0.0224,0.0,0.0,0.0437,0.0,0.9861,0.8121,0.0014,0.0,0.0345,0.0833,0.0417,0.0306,0.0359,0.0219,0.0,0.0,reg oper account,block of flats,0.0177,\"Stone, brick\",No,16.0,0.0,16.0,0.0,-1483.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n411810,1,Cash loans,F,N,N,0,90000.0,338832.0,27297.0,292500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.020713,-9425,-1773,-9419.0,-2100,,1,1,0,1,0,0,High skill tech staff,1.0,3,3,WEDNESDAY,13,0,0,0,1,1,0,Business Entity Type 3,,0.24400782092021314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n234398,0,Cash loans,F,N,Y,0,112500.0,514777.5,27297.0,477000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.031329,-20440,-1284,-2788.0,-3976,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Medicine,,0.26525634018619443,0.6313545365850379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-513.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n409619,0,Cash loans,F,N,Y,0,243000.0,755190.0,30078.0,675000.0,Family,Commercial associate,Secondary / secondary special,Married,With parents,0.028663,-16312,-4709,-3368.0,-4492,,1,1,0,1,0,0,Security staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Security,0.7844172934494621,0.6300264047511326,0.6528965519806539,0.066,0.001,0.9737,,,0.0,0.1379,0.125,,0.0139,,0.0511,,0.0,0.0672,0.001,0.9737,,,0.0,0.1379,0.125,,0.0142,,0.0532,,0.0,0.0666,0.001,0.9737,,,0.0,0.1379,0.125,,0.0142,,0.052000000000000005,,0.0,,block of flats,0.0438,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n450285,0,Cash loans,F,N,Y,0,67500.0,334152.0,14287.5,270000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.01885,-17401,-2665,-2882.0,-937,,1,1,1,1,0,0,Core staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Postal,0.3242837449724029,0.7083069204398761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n129615,0,Cash loans,M,Y,N,0,112500.0,254700.0,20250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-14739,-4465,-2624.0,-4668,13.0,1,1,0,1,0,0,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.3998854877912741,,0.0024,0.0151,0.9682,,,0.0,0.0345,0.0417,,0.0365,,0.0153,,0.006999999999999999,0.0021,0.0121,0.9682,,,0.0,0.0345,0.0417,,0.0282,,0.0155,,0.0021,0.0021,0.015,0.9682,,,0.0,0.0345,0.0417,,0.037000000000000005,,0.0156,,0.0021,,block of flats,0.0159,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-660.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n263975,0,Cash loans,F,N,N,0,135000.0,225000.0,25447.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-15892,-2296,-9798.0,-4217,,1,1,0,1,1,0,,2.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.6514030643746153,0.30885814568779363,0.5531646987710016,0.0619,0.063,0.9762,0.6736,,0.0,0.1034,0.1667,0.2083,0.2026,0.0488,0.0516,0.0039,0.0269,0.063,0.0654,0.9762,0.6864,,0.0,0.1034,0.1667,0.2083,0.2073,0.0533,0.0537,0.0039,0.0285,0.0625,0.063,0.9762,0.6779999999999999,,0.0,0.1034,0.1667,0.2083,0.2062,0.0496,0.0525,0.0039,0.0275,reg oper account,block of flats,0.0464,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-943.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,5.0\n185985,0,Cash loans,M,Y,N,1,112500.0,534672.0,15453.0,423000.0,Unaccompanied,Working,Higher education,Married,Co-op apartment,0.031329,-12146,-1459,-1814.0,-2107,11.0,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.24013076801190364,0.4822201051801953,0.4489622731076524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1429.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n167013,0,Cash loans,F,N,N,2,135000.0,384048.0,18031.5,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.018029,-15356,-315,-2335.0,-4889,,1,1,0,1,0,0,,4.0,3,3,TUESDAY,6,0,0,0,0,1,1,Hotel,,0.03528175162430538,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n160222,0,Cash loans,M,N,N,0,135000.0,290358.0,32836.5,256500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.030755,-9511,-803,-4250.0,-2074,,1,1,0,0,0,0,,1.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.09441817201867823,0.2382023194090317,0.4543210601605785,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-534.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,1.0,0.0,0.0\n223852,0,Cash loans,M,Y,Y,0,157500.0,1078200.0,31653.0,900000.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.030755,-18307,-5210,-10824.0,-1602,22.0,1,1,1,1,1,0,Security staff,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,School,0.4102418709954828,0.5379292676922953,0.4992720153045617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2394.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n201394,0,Cash loans,F,N,Y,0,135000.0,787086.0,22684.5,657000.0,Family,Pensioner,Secondary / secondary special,Separated,House / apartment,0.031329,-21401,365243,-9543.0,-3803,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,0.6338887249548286,0.7434744174049293,0.5797274227921155,0.2155,0.1313,0.9871,0.8232,0.0969,0.24,0.2069,0.3333,0.375,0.2058,0.174,0.2295,0.0077,0.1033,0.2195,0.1362,0.9871,0.8301,0.0978,0.2417,0.2069,0.3333,0.375,0.2105,0.1901,0.2391,0.0078,0.1094,0.2176,0.1313,0.9871,0.8256,0.0975,0.24,0.2069,0.3333,0.375,0.2094,0.177,0.2336,0.0078,0.1055,reg oper spec account,block of flats,0.2559,Panel,No,1.0,0.0,1.0,0.0,-338.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n346948,0,Cash loans,F,N,Y,0,67500.0,508495.5,19894.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009175,-20493,365243,-684.0,-4051,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.5647040144941721,0.2764406945454034,,,0.9816,,,0.28,0.2414,0.3333,,0.2143,,0.2647,,,,,0.9816,,,0.282,0.2414,0.3333,,0.2192,,0.2758,,,,,0.9816,,,0.28,0.2414,0.3333,,0.2181,,0.2695,,,,block of flats,0.233,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1068.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n358644,0,Cash loans,F,N,N,0,67500.0,135000.0,5944.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.00733,-23124,365243,-6373.0,-4340,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.1774789181720986,0.6925590674998008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1118.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n347312,0,Cash loans,M,Y,Y,0,189000.0,774000.0,32922.0,774000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-22854,365243,-8513.0,-4271,10.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.7055750887984773,,0.2103,0.1727,0.9881,0.8368,0.0579,0.24,0.2069,0.3333,0.375,0.1053,0.1656,0.2207,0.027000000000000003,0.1431,0.2143,0.1792,0.9881,0.8432,0.0584,0.2417,0.2069,0.3333,0.375,0.1077,0.1809,0.2299,0.0272,0.1515,0.2123,0.1727,0.9881,0.8390000000000001,0.0583,0.24,0.2069,0.3333,0.375,0.1071,0.1685,0.2247,0.0272,0.1461,reg oper account,block of flats,0.2363,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n440725,0,Cash loans,M,N,N,0,270000.0,135000.0,15228.0,135000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.0228,-14279,-2160,-1412.0,-5056,,1,1,0,1,1,0,Drivers,2.0,2,2,TUESDAY,9,0,0,0,1,1,0,Transport: type 3,,0.6485898603376894,0.3791004853998145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1133.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n258092,1,Cash loans,M,N,N,0,135000.0,900000.0,45954.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-16203,-4770,-9205.0,-4466,,1,1,1,1,0,0,Drivers,2.0,2,2,THURSDAY,17,0,1,1,0,1,1,Transport: type 4,0.4674576075263426,0.3949577101035641,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-902.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n334859,0,Cash loans,F,N,Y,1,135000.0,225000.0,26833.5,225000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-15689,-3119,-2457.0,-4532,,1,1,0,1,0,0,,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Self-employed,,0.4945461380443175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-413.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n152248,0,Cash loans,M,N,Y,0,144000.0,121500.0,11272.5,121500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.026392,-18256,-2382,-103.0,-1734,,1,1,0,1,1,0,Drivers,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,,0.7064102623228917,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2118.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n349623,0,Cash loans,F,Y,N,0,135000.0,1350000.0,39474.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15047,-1646,-6536.0,-4932,15.0,1,1,1,1,0,0,Waiters/barmen staff,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Government,,0.7542035398315264,0.4101025731788671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-840.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n276305,0,Revolving loans,F,Y,Y,0,180000.0,630000.0,31500.0,630000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.028663,-14463,-7220,-4981.0,-4478,9.0,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Medicine,,0.6513907395505351,0.7476633896301825,0.0742,0.0559,0.9791,0.7144,0.0,0.08,0.069,0.3333,0.375,0.0355,0.0605,0.076,0.0,0.0,0.0756,0.057999999999999996,0.9791,0.7256,0.0,0.0806,0.069,0.3333,0.375,0.0364,0.0661,0.0792,0.0,0.0,0.0749,0.0559,0.9791,0.7182,0.0,0.08,0.069,0.3333,0.375,0.0362,0.0616,0.0774,0.0,0.0,reg oper account,block of flats,0.0689,Panel,No,1.0,0.0,1.0,0.0,-648.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n332004,0,Cash loans,M,N,Y,0,112500.0,314055.0,17167.5,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-12558,-2604,-6179.0,-4183,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.4426383332452928,0.5549779405305426,0.44539624156083396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n217753,0,Cash loans,F,N,Y,0,517500.0,790830.0,57676.5,675000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.04622,-13976,-572,-8079.0,-5027,,1,1,0,1,0,0,Core staff,2.0,1,1,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 1,0.6222807998471864,0.7545074499104223,0.3046721837533529,0.0825,0.069,0.9742,0.6464,0.0084,0.0,0.1379,0.1667,0.2083,0.0,0.0672,0.07,0.0,0.0,0.084,0.0716,0.9742,0.6602,0.0085,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.0729,0.0,0.0,0.0833,0.069,0.9742,0.6511,0.0085,0.0,0.1379,0.1667,0.2083,0.0,0.0684,0.0712,0.0,0.0,reg oper account,block of flats,0.0596,Panel,No,1.0,0.0,1.0,0.0,-2687.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n237446,0,Cash loans,M,Y,Y,0,135000.0,495000.0,26982.0,495000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Co-op apartment,0.014519999999999996,-20791,365243,-13097.0,-4303,19.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,0.8173220586342372,0.6631678450966293,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,2.0,5.0,1.0,-2213.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n130868,0,Cash loans,F,N,N,0,157500.0,1288350.0,37669.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23111,365243,-4601.0,-4691,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,XNA,0.7493563761385604,0.471190488104388,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1313.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n187052,0,Cash loans,F,N,N,2,90000.0,450000.0,17095.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.018209,-13159,-1990,-2493.0,-3892,,1,1,0,1,0,0,Medicine staff,4.0,3,3,WEDNESDAY,12,0,0,0,1,1,0,Medicine,0.3459711717527785,0.6206819179703472,0.28371188263500075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n297422,0,Cash loans,F,Y,Y,1,337500.0,900000.0,38263.5,900000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.031329,-10291,-1011,-479.0,-2129,2.0,1,1,0,1,0,1,Core staff,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.5730303092396417,0.6256578199209609,0.5989262182569273,0.0701,0.0797,0.9771,0.6872,0.006,0.0,0.1379,0.1667,0.2083,0.0394,0.0538,0.0509,0.0154,0.0988,0.0714,0.0827,0.9772,0.6994,0.0061,0.0,0.1379,0.1667,0.2083,0.0403,0.0588,0.053,0.0156,0.1046,0.0708,0.0797,0.9771,0.6914,0.0061,0.0,0.1379,0.1667,0.2083,0.0401,0.0547,0.0518,0.0155,0.1008,not specified,block of flats,0.0648,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-262.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n188968,0,Cash loans,M,N,Y,1,171000.0,474183.0,22234.5,391500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-14552,-285,-5248.0,-3424,,1,1,0,1,0,0,Managers,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4781264036405794,0.4740512892789932,0.1031,,0.9906,,,0.08,0.069,0.375,,,,0.1362,,0.154,0.105,,0.9906,,,0.0806,0.069,0.375,,,,0.1419,,0.163,0.1041,,0.9906,,,0.08,0.069,0.375,,,,0.1386,,0.1572,,block of flats,0.1399,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-172.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n169276,0,Cash loans,F,N,N,0,171000.0,436032.0,13347.0,360000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.009334,-18438,365243,-7605.0,-1984,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.7615066508621019,0.3376727217405312,,,0.9791,,,,0.0862,0.0417,,,,0.0126,,,,,0.9712,,,,0.069,0.0417,,,,0.0093,,,,,0.9791,,,,0.0862,0.0417,,,,0.0129,,,,block of flats,0.006999999999999999,Wooden,No,0.0,0.0,0.0,0.0,-1514.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n292914,0,Cash loans,F,N,Y,0,67500.0,101880.0,10566.0,90000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-20174,365243,-5886.0,-3651,,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.6048577537397158,0.7435593141311444,0.1258,0.1149,0.9826,0.762,0.0151,0.0,0.2759,0.1667,0.2083,0.0112,0.0967,0.0947,0.027000000000000003,0.0358,0.1282,0.1192,0.9826,0.7713,0.0152,0.0,0.2759,0.1667,0.2083,0.0114,0.1056,0.0986,0.0272,0.0379,0.127,0.1149,0.9826,0.7652,0.0152,0.0,0.2759,0.1667,0.2083,0.0114,0.0983,0.0964,0.0272,0.0365,reg oper account,block of flats,0.0905,Block,No,2.0,0.0,2.0,0.0,-2626.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n442000,0,Cash loans,F,N,N,0,135000.0,521280.0,19854.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-11979,-615,-4.0,-4343,,1,1,0,1,0,0,Cooking staff,2.0,3,3,SATURDAY,8,0,0,0,0,0,0,Restaurant,0.2525953790565877,0.636693657318655,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-350.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,,,,,,\n334152,0,Cash loans,F,N,Y,1,157500.0,177903.0,14391.0,148500.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.04622,-10761,-658,-4266.0,-3417,,1,1,0,1,0,0,Cooking staff,2.0,1,1,FRIDAY,12,0,0,0,0,0,0,Kindergarten,0.33867563292168984,0.4668664555459796,,0.0629,0.0,0.9871,0.8232,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0504,0.055,0.0039,0.0076,0.0641,0.0,0.9871,0.8301,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0551,0.0573,0.0039,0.0081,0.0635,0.0,0.9871,0.8256,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0513,0.055999999999999994,0.0039,0.0078,reg oper account,block of flats,0.0449,Panel,No,0.0,0.0,0.0,0.0,-402.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n395336,0,Cash loans,F,N,N,0,202500.0,1125000.0,32895.0,1125000.0,Unaccompanied,Commercial associate,Higher education,Widow,House / apartment,0.072508,-19286,-870,-1650.0,-1650,,1,1,0,1,1,0,,1.0,1,1,SUNDAY,18,0,0,0,0,1,1,Business Entity Type 3,0.92945840876281,0.5329522323974801,0.4812493411434029,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1391.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n303845,0,Cash loans,F,N,N,0,130500.0,360000.0,17316.0,360000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.014464,-23502,365243,-10615.0,-4189,,1,0,0,1,0,0,,1.0,2,2,SUNDAY,10,0,0,0,0,0,0,XNA,,0.027189038868127586,0.520897599048938,0.0598,0.0699,0.9816,0.7484,0.0082,0.0,0.1034,0.1667,0.2083,0.0434,0.0488,0.0551,0.0,0.0,0.0609,0.0725,0.9816,0.7583,0.0083,0.0,0.1034,0.1667,0.2083,0.0444,0.0533,0.0574,0.0,0.0,0.0604,0.0699,0.9816,0.7518,0.0083,0.0,0.1034,0.1667,0.2083,0.0441,0.0496,0.0561,0.0,0.0,reg oper account,block of flats,0.0479,Block,No,1.0,1.0,1.0,1.0,-2317.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n413022,0,Cash loans,M,N,N,0,202500.0,1185282.0,38367.0,1035000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.005084,-22447,365243,-11892.0,-4105,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.5406458321648316,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-929.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n435331,0,Cash loans,F,Y,N,0,135000.0,835380.0,35523.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.014464,-16643,-1176,-5542.0,-180,17.0,1,1,0,1,0,0,Drivers,1.0,2,2,WEDNESDAY,6,0,0,0,0,0,0,Self-employed,,0.6017228442608689,0.5531646987710016,0.0647,0.0645,0.9767,0.7008,0.2158,0.0,0.1379,0.1354,0.2083,0.0526,0.067,0.0588,0.0,0.0,0.063,0.0509,0.9782,0.7125,0.2178,0.0,0.1034,0.1667,0.2083,0.0322,0.0551,0.0167,0.0,0.0,0.0625,0.0491,0.9781,0.7048,0.2172,0.0,0.1034,0.1667,0.2083,0.0471,0.0513,0.0536,0.0,0.0,reg oper account,block of flats,0.0526,Panel,No,0.0,0.0,0.0,0.0,-2042.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n296434,1,Cash loans,F,N,Y,1,90000.0,325908.0,17811.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-13077,-1841,-3154.0,-3934,,1,1,0,1,0,0,Cooking staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4411014287496432,0.5281157718065357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-288.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n412233,1,Cash loans,F,N,Y,0,153000.0,1800000.0,62698.5,1800000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-23672,365243,-4911.0,-4520,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,0.7334119103781155,0.6850959149400206,0.6738300778602003,0.0825,0.0793,0.9767,,,0.0,0.1379,0.1667,,,,,,0.0,0.084,0.0823,0.9767,,,0.0,0.1379,0.1667,,,,,,0.0,0.0833,0.0793,0.9767,,,0.0,0.1379,0.1667,,,,,,0.0,,,0.0739,Mixed,No,6.0,0.0,6.0,0.0,-1720.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n131105,0,Cash loans,F,N,Y,0,144000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-18497,-4220,-6518.0,-2048,,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Self-employed,0.8672918286492697,0.7481093169959899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-302.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n112008,0,Cash loans,M,N,N,0,76500.0,67500.0,7398.0,67500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.006296,-10083,-1292,-4202.0,-2036,,1,1,1,1,0,0,Sales staff,1.0,3,3,THURSDAY,12,0,0,0,0,0,0,Trade: type 2,,0.12080305904138715,,0.0619,0.0667,0.9771,,,0.0,0.1379,0.1667,,,,0.0345,,0.0612,0.063,0.0692,0.9772,,,0.0,0.1379,0.1667,,,,0.036000000000000004,,0.0648,0.0625,0.0667,0.9771,,,0.0,0.1379,0.1667,,,,0.0352,,0.0625,,block of flats,0.0405,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-743.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n181626,0,Cash loans,M,Y,Y,0,225000.0,675000.0,43267.5,675000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.04622,-9210,-762,-849.0,-1895,65.0,1,1,1,1,0,0,Drivers,1.0,1,1,THURSDAY,10,0,1,1,0,1,1,Business Entity Type 3,0.10327158861910668,0.2837908524172176,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-589.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n331642,0,Cash loans,M,N,N,0,67500.0,254700.0,24939.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.009657,-14483,-1564,-8262.0,-4096,,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.27441542824050064,0.8667312342599449,0.0649,0.0591,0.9712,0.6056,0.0083,0.0,0.1552,0.0833,0.125,0.0619,0.053,0.0475,0.0,0.0004,0.0053,0.0,0.9623,0.5034,0.0,0.0,0.0345,0.0,0.0417,0.0,0.0046,0.0022,0.0,0.0,0.0656,0.0591,0.9712,0.6109,0.0083,0.0,0.1552,0.0833,0.125,0.063,0.0539,0.0483,0.0,0.0004,org spec account,block of flats,0.0018,Wooden,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n186904,0,Cash loans,F,N,Y,0,90000.0,238500.0,11727.0,238500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-17506,-5405,-4491.0,-1031,,1,1,0,1,0,0,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Government,,0.6989868573182564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-326.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n383316,0,Cash loans,F,N,Y,0,157500.0,1125000.0,33025.5,1125000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-22547,365243,-14980.0,-4324,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.2319427311585574,0.3893387918468769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-528.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n401633,0,Cash loans,F,N,Y,0,202500.0,225000.0,10620.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007114,-19778,-1299,-2004.0,-3295,,1,1,0,1,0,0,Realty agents,2.0,2,2,SATURDAY,17,0,0,0,0,1,1,Realtor,,0.6401363333954535,0.4578995512067301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1260.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,5.0\n155395,1,Cash loans,F,N,Y,0,112500.0,202500.0,9976.5,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18942,-2248,-9849.0,-2465,,1,1,0,1,0,0,Medicine staff,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Medicine,,0.4241323095206088,0.12850437737240394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-276.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n102625,0,Cash loans,M,Y,Y,0,121500.0,178290.0,17631.0,157500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.01885,-22472,-6489,-2551.0,-4315,7.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Transport: type 4,,0.6223999660582944,0.6610235391308081,0.1237,0.1264,0.9771,0.6872,0.0158,0.0,0.2759,0.1667,0.2083,0.0121,0.1009,0.1079,0.0,0.0,0.1261,0.1311,0.9772,0.6994,0.0159,0.0,0.2759,0.1667,0.2083,0.0124,0.1102,0.1124,0.0,0.0,0.1249,0.1264,0.9771,0.6914,0.0159,0.0,0.2759,0.1667,0.2083,0.0123,0.1026,0.1098,0.0,0.0,,block of flats,0.0848,Panel,No,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n451068,0,Cash loans,F,N,N,0,112500.0,808650.0,26086.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.007114,-14891,-1113,-4325.0,-4639,,1,1,1,1,1,0,Medicine staff,2.0,2,2,SATURDAY,12,0,0,0,0,1,1,Other,0.41467391507908224,0.2620553359511834,0.6446794549585961,0.0711,,0.9771,,,0.0,0.1379,0.1667,,0.1419,,,,,0.0725,,0.9772,,,0.0,0.1379,0.1667,,0.1452,,,,,0.0718,,0.9771,,,0.0,0.1379,0.1667,,0.1444,,,,,,block of flats,0.0524,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1433.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n125625,0,Cash loans,F,N,Y,1,270000.0,1006920.0,39933.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-13749,-2877,-2505.0,-4788,,1,1,1,1,1,0,Managers,3.0,1,1,THURSDAY,7,0,0,0,0,0,0,Medicine,,0.6719416367739021,0.6594055320683344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1450.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n454280,0,Cash loans,M,Y,N,0,193500.0,66222.0,4815.0,58500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-13004,-2587,-2630.0,-3505,26.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,6,0,0,0,0,0,0,Transport: type 2,,0.4163698522210865,,0.0711,0.0799,0.9771,0.6872,0.0271,0.0,0.1379,0.1667,0.2083,0.1048,0.057999999999999996,0.0609,0.0097,0.0088,0.0725,0.0768,0.9772,0.6994,0.0245,0.0,0.1379,0.1667,0.2083,0.0777,0.0634,0.0592,0.0,0.0,0.0718,0.0799,0.9771,0.6914,0.0272,0.0,0.1379,0.1667,0.2083,0.1066,0.059000000000000004,0.062,0.0097,0.0089,reg oper account,block of flats,0.0579,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2496.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n229519,0,Cash loans,F,N,Y,0,225000.0,265851.0,13702.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-14821,-1161,-8913.0,-4184,,1,1,0,1,0,0,,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,Medicine,0.5512198578162822,0.25561615602133475,0.3606125659189888,0.0722,0.0493,0.9791,0.7144,0.0264,0.0,0.1379,0.1667,0.2083,0.0222,0.0572,0.066,0.0077,0.0376,0.0735,0.0511,0.9791,0.7256,0.0266,0.0,0.1379,0.1667,0.2083,0.0227,0.0624,0.0687,0.0078,0.0398,0.0729,0.0493,0.9791,0.7182,0.0265,0.0,0.1379,0.1667,0.2083,0.0226,0.0581,0.0672,0.0078,0.0383,org spec account,block of flats,0.0745,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1893.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n381556,0,Cash loans,F,N,N,1,135000.0,450000.0,32611.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020246,-14101,-1535,-1578.0,-331,,1,1,1,1,0,0,Managers,2.0,3,3,SATURDAY,6,0,0,0,0,0,0,Self-employed,0.7463360054255478,0.4751534578723418,,0.2918,0.2482,0.9881,0.8368,0.0418,0.28,0.2414,0.375,0.4167,0.2333,0.2345,0.3059,0.0154,0.0094,0.2973,0.2575,0.9881,0.8432,0.0421,0.282,0.2414,0.375,0.4167,0.2386,0.2562,0.3188,0.0156,0.01,0.2946,0.2482,0.9881,0.8390000000000001,0.042,0.28,0.2414,0.375,0.4167,0.2374,0.2386,0.3114,0.0155,0.0096,reg oper account,block of flats,0.2655,Panel,No,1.0,0.0,1.0,0.0,-833.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n227043,0,Cash loans,F,N,N,0,135000.0,675000.0,41427.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.011657,-15425,-1745,-12548.0,-4730,,1,1,0,1,1,0,Laborers,2.0,1,1,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.8085814155509505,0.5154953751603267,0.1845,0.1384,0.9821,0.7552,0.1215,0.2,0.1724,0.3333,0.375,0.0603,0.1505,0.1942,0.0,0.0,0.188,0.1436,0.9821,0.7648,0.1226,0.2014,0.1724,0.3333,0.375,0.0617,0.1644,0.2023,0.0,0.0,0.1863,0.1384,0.9821,0.7585,0.1223,0.2,0.1724,0.3333,0.375,0.0614,0.1531,0.1977,0.0,0.0,reg oper spec account,block of flats,0.2192,Panel,No,0.0,0.0,0.0,0.0,-818.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n193134,0,Cash loans,F,N,Y,0,90000.0,700830.0,20214.0,585000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-20500,365243,-10845.0,-1379,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.4148986368283817,0.30162489168411943,0.0175,0.0,0.9796,0.7212,0.0017,0.0,0.069,0.0417,0.0833,0.0235,0.0185,0.0143,0.0,0.0,0.0126,0.0,0.9772,0.6994,0.0014,0.0,0.069,0.0417,0.0833,0.0174,0.0202,0.0107,0.0,0.0,0.0177,0.0,0.9796,0.7249,0.0017,0.0,0.069,0.0417,0.0833,0.024,0.0188,0.0145,0.0,0.0,reg oper account,block of flats,0.0088,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-176.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n314263,0,Cash loans,F,Y,Y,0,90000.0,576837.0,16866.0,481500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-14037,-1564,-11679.0,-1454,10.0,1,1,1,1,1,1,Laborers,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.4086623313995873,0.7473668550122247,0.524496446363472,0.4052,0.1967,0.9811,0.7416,0.139,0.44,0.3793,0.3333,0.375,0.2262,0.3286,0.3728,0.0077,0.0086,0.4128,0.2041,0.9811,0.7517,0.1403,0.4431,0.3793,0.3333,0.375,0.2314,0.359,0.3884,0.0078,0.0092,0.4091,0.1967,0.9811,0.7451,0.1399,0.44,0.3793,0.3333,0.375,0.2302,0.3343,0.3795,0.0078,0.0088,reg oper account,block of flats,0.3332,Panel,No,4.0,3.0,4.0,1.0,-1554.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n151751,0,Cash loans,F,N,Y,2,121500.0,675000.0,32472.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-10738,-2423,-4627.0,-2984,,1,1,0,1,0,1,,4.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 2,0.5941192709847831,0.5477784646093653,0.3791004853998145,0.0825,0.1205,0.9836,0.7756,0.0141,0.0,0.2069,0.1667,0.2083,0.0951,0.0672,0.0863,0.0,0.0,0.084,0.1251,0.9836,0.7844,0.0142,0.0,0.2069,0.1667,0.2083,0.0973,0.0735,0.0899,0.0,0.0,0.0833,0.1205,0.9836,0.7786,0.0141,0.0,0.2069,0.1667,0.2083,0.0968,0.0684,0.0878,0.0,0.0,reg oper account,block of flats,0.0755,\"Stone, brick\",No,2.0,1.0,1.0,1.0,-301.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,2.0\n293753,1,Revolving loans,F,N,Y,0,90000.0,247500.0,12375.0,247500.0,Family,Commercial associate,Higher education,Single / not married,House / apartment,0.008575,-8246,-876,-8223.0,-914,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,11,0,0,0,0,1,1,Trade: type 7,0.20787277772962687,0.25090170964260017,0.18303516721781032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-334.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n202049,0,Cash loans,F,N,Y,0,121500.0,1170000.0,34209.0,1170000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-18187,-307,-5468.0,-1722,,1,1,0,1,0,0,Cooking staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Trade: type 7,,0.7079543895211422,0.4650692149562261,0.0933,0.0643,0.9821,0.6804,0.006999999999999999,0.04,0.0862,0.3958,0.2083,0.0647,0.0538,0.0792,0.0232,0.0539,0.0735,0.0516,0.9767,0.6929,0.0071,0.0,0.0345,0.1667,0.2083,0.0543,0.0588,0.0558,0.0233,0.0536,0.0942,0.0643,0.9821,0.6847,0.0071,0.04,0.0862,0.3958,0.2083,0.0658,0.0547,0.0806,0.0233,0.055,reg oper account,block of flats,0.057,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-2357.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n262533,0,Revolving loans,F,N,Y,1,193500.0,495000.0,24750.0,495000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-16397,-4317,-5060.0,-4394,,1,1,0,1,0,0,Accountants,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Transport: type 4,,0.6452583160310311,0.5316861425197883,0.1639,0.1392,0.9866,0.7416,0.015,0.1,0.2241,0.2708,0.2083,0.0839,0.1084,0.1717,0.0,0.0885,0.1355,0.1346,0.9811,0.7517,0.0152,0.0,0.1724,0.1667,0.2083,0.0858,0.1185,0.1112,0.0,0.04,0.1655,0.1392,0.9866,0.7451,0.0151,0.1,0.2241,0.2708,0.2083,0.0854,0.1103,0.1748,0.0,0.0903,reg oper account,block of flats,0.2164,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1408.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n312079,0,Cash loans,F,N,Y,2,112500.0,1546020.0,45333.0,1350000.0,Family,Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-8958,-1795,-901.0,-1550,,1,1,0,1,0,0,Sales staff,4.0,2,2,MONDAY,14,0,0,0,0,0,0,Self-employed,,0.6474975711077288,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n299528,0,Cash loans,F,Y,Y,3,202500.0,161730.0,12645.0,135000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.010643,-12041,-961,-5242.0,-4619,7.0,1,1,0,1,0,0,Sales staff,4.0,2,2,TUESDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.3908436015886106,0.5054029660682395,0.2032521136204725,0.0928,0.0593,0.9906,0.8436,0.015,0.08,0.0345,0.2708,0.375,0.0567,0.0756,0.075,0.0,0.0,0.0945,0.0616,0.9886,0.8497,0.0151,0.0806,0.0345,0.2083,0.375,0.057999999999999996,0.0826,0.0781,0.0,0.0,0.0937,0.0593,0.9906,0.8457,0.0151,0.08,0.0345,0.2708,0.375,0.0577,0.077,0.0763,0.0,0.0,reg oper account,block of flats,0.0652,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1231.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n186563,0,Cash loans,F,N,Y,0,292500.0,777024.0,41526.0,720000.0,Unaccompanied,Commercial associate,Higher education,Widow,Office apartment,0.072508,-16534,-1593,-8251.0,-54,,1,1,0,1,0,0,,1.0,1,1,WEDNESDAY,11,0,0,0,0,0,0,Bank,0.6118858208781931,0.6996535857066483,0.5262949398096192,0.2593,0.1584,0.9781,0.7008,0.0854,0.28,0.2414,0.3333,0.2083,0.0,0.21100000000000002,0.2209,0.0019,0.0053,0.1513,0.0838,0.9782,0.7125,0.0627,0.1611,0.1379,0.3333,0.0417,0.0,0.1322,0.10099999999999999,0.0,0.0,0.2618,0.1584,0.9781,0.7048,0.0859,0.28,0.2414,0.3333,0.2083,0.0,0.2146,0.2249,0.0019,0.0054,reg oper account,block of flats,0.2776,Panel,No,0.0,0.0,0.0,0.0,-2649.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,3.0\n193818,0,Cash loans,F,N,N,1,130500.0,835380.0,42781.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-16229,-8606,-6062.0,-4631,,1,1,0,1,0,0,Medicine staff,3.0,2,2,TUESDAY,14,0,0,0,0,0,0,Medicine,,0.5305292725134474,0.6971469077844458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n168934,0,Cash loans,F,N,N,0,112500.0,284400.0,13257.0,225000.0,Unaccompanied,Working,Higher education,Civil marriage,With parents,0.04622,-8944,-721,-3657.0,-1601,,1,1,0,1,0,0,High skill tech staff,2.0,1,1,SATURDAY,11,0,0,0,0,0,0,Self-employed,0.3702764042163504,0.6539140075299193,0.3774042489507649,0.0907,0.0799,0.9747,0.6532,0.0544,0.0,0.1379,0.1667,0.2083,0.0,0.07400000000000001,0.0734,0.0,0.0,0.0924,0.0829,0.9747,0.6668,0.0549,0.0,0.1379,0.1667,0.2083,0.0,0.0808,0.0765,0.0,0.0,0.0916,0.0799,0.9747,0.6578,0.0547,0.0,0.1379,0.1667,0.2083,0.0,0.0752,0.0747,0.0,0.0,reg oper account,block of flats,0.0875,Block,No,1.0,1.0,1.0,1.0,-1690.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n265847,0,Cash loans,M,Y,Y,0,108000.0,900000.0,46084.5,900000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.025164,-23620,-984,-8179.0,-4322,26.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Self-employed,,0.15767370804607614,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1096.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n372881,0,Cash loans,F,Y,Y,1,135000.0,405000.0,37867.5,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-18553,-2279,-8351.0,-2102,13.0,1,1,1,1,1,0,Core staff,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Insurance,0.7196930723324158,0.7562651244620316,0.4561097392782771,0.0,,0.9826,,,0.0,,,,,,0.3777,,,0.0,,0.9826,,,0.0,,,,,,0.3935,,,0.0,,0.9826,,,0.0,,,,,,0.3845,,,,,0.3797,,No,1.0,0.0,1.0,0.0,-834.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n339465,0,Cash loans,F,N,N,0,157500.0,625536.0,27553.5,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-12886,-3540,-2636.0,-4916,,1,1,1,1,1,1,Managers,2.0,2,2,FRIDAY,16,0,0,0,0,1,1,Government,0.4086922376088143,0.6807157912312328,0.21275630545434146,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2416.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n256361,0,Cash loans,F,Y,Y,1,126000.0,358344.0,24075.0,283500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.006670999999999999,-15053,-8019,-6826.0,-4681,30.0,1,1,0,1,0,0,Private service staff,3.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.5837028890363462,0.7946285827840042,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-2812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n216377,0,Cash loans,M,Y,Y,0,225000.0,855000.0,36355.5,855000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018029,-15427,-2583,-3108.0,-3995,6.0,1,1,0,1,0,0,Drivers,2.0,3,2,WEDNESDAY,6,0,0,0,0,0,0,Transport: type 3,0.4027544865864849,0.5335019503854916,0.3031463744186309,0.0722,0.0833,0.9856,0.8028,0.0141,0.0,0.1379,0.1667,0.2083,0.0756,0.0588,0.0738,0.0,0.0,0.0735,0.0865,0.9856,0.8105,0.0142,0.0,0.1379,0.1667,0.2083,0.0773,0.0643,0.0769,0.0,0.0,0.0729,0.0833,0.9856,0.8054,0.0142,0.0,0.1379,0.1667,0.2083,0.0769,0.0599,0.0751,0.0,0.0,reg oper account,block of flats,0.0713,Panel,No,1.0,0.0,1.0,0.0,-1695.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n336187,0,Cash loans,M,Y,Y,0,90000.0,314055.0,16186.5,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014464,-22555,365243,-7152.0,-4514,36.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,4,0,0,0,0,0,0,XNA,,0.34205735705705353,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-885.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n289829,0,Cash loans,F,N,Y,0,135000.0,337500.0,26500.5,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.030755,-13202,-1052,-6229.0,-2096,,1,1,0,1,1,0,,2.0,2,2,MONDAY,13,1,1,0,1,1,0,Self-employed,0.6238772213175467,0.6729338697859822,0.12850437737240394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-359.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n213330,1,Cash loans,F,Y,Y,3,225000.0,1183963.5,34749.0,927000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.072508,-16129,-2217,-10053.0,-4099,4.0,1,1,0,1,1,0,Laborers,5.0,1,1,WEDNESDAY,11,0,0,0,0,0,0,Other,,0.7184159941148175,0.6642482627052363,0.3948,0.2041,0.9801,0.728,0.5728,0.64,0.2759,0.4583,0.5,0.0,0.3219,0.3981,0.0,0.0145,0.4023,0.2118,0.9801,0.7387,0.578,0.6445,0.2759,0.4583,0.5,0.0,0.3517,0.4148,0.0,0.0154,0.3987,0.2041,0.9801,0.7316,0.5764,0.64,0.2759,0.4583,0.5,0.0,0.3275,0.4053,0.0,0.0148,reg oper account,block of flats,0.2607,Panel,No,0.0,0.0,0.0,0.0,-3200.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n423958,1,Cash loans,F,N,Y,0,121500.0,202500.0,11115.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-18936,-232,-6690.0,-1269,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Self-employed,,0.14274507352280744,0.5046813193144684,0.2227,0.1262,0.9816,,,0.24,0.2069,0.3333,,0.1252,,0.2321,,0.0,0.2269,0.1309,0.9816,,,0.2417,0.2069,0.3333,,0.1281,,0.2416,,0.0,0.2248,0.1262,0.9816,,,0.24,0.2069,0.3333,,0.1274,,0.2363,,0.0,,block of flats,0.1824,Panel,No,0.0,0.0,0.0,0.0,-746.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n432599,0,Revolving loans,F,N,Y,0,112500.0,292500.0,14625.0,292500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-16764,-2401,-9732.0,-304,,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Kindergarten,0.6650532395467648,0.4882174383719332,0.4722533429586386,0.034,0.052000000000000005,0.9881,0.8368,0.0215,0.0,0.1034,0.0833,0.0833,0.0136,0.0277,0.0193,0.0,0.0,0.0347,0.054000000000000006,0.9881,0.8432,0.0217,0.0,0.1034,0.0833,0.0833,0.0139,0.0303,0.0201,0.0,0.0,0.0344,0.052000000000000005,0.9881,0.8390000000000001,0.0217,0.0,0.1034,0.0833,0.0833,0.0139,0.0282,0.0197,0.0,0.0,reg oper account,block of flats,0.027000000000000003,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-867.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n122890,0,Cash loans,F,Y,Y,0,90000.0,149256.0,15291.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-17199,-10081,-7561.0,-749,10.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Transport: type 2,,0.6118029026677672,0.7726311345553628,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-425.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n245519,0,Cash loans,M,Y,Y,3,112500.0,247275.0,19953.0,225000.0,Other_B,Working,Secondary / secondary special,Married,House / apartment,0.006296,-14009,-624,-3037.0,-4136,24.0,1,1,0,1,0,0,Laborers,5.0,3,3,TUESDAY,12,0,1,1,0,1,1,Self-employed,,0.6225181622402651,0.35233997269170386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-342.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n321532,0,Cash loans,F,N,N,0,76500.0,284400.0,15880.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-19205,-213,-9072.0,-2721,,1,1,1,1,1,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Medicine,,0.16214456766623808,0.2866524828090694,0.0876,0.0917,0.9791,0.7144,0.0428,0.0,0.2069,0.1667,0.2083,0.0681,0.0714,0.0885,0.0,0.0,0.0893,0.0952,0.9791,0.7256,0.0432,0.0,0.2069,0.1667,0.2083,0.0697,0.0781,0.0922,0.0,0.0,0.0885,0.0917,0.9791,0.7182,0.0431,0.0,0.2069,0.1667,0.2083,0.0693,0.0727,0.0901,0.0,0.0,reg oper account,block of flats,0.0696,Panel,No,3.0,1.0,3.0,0.0,-1737.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n421354,0,Cash loans,M,Y,Y,0,225000.0,225000.0,23755.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019689,-18424,-5389,-4753.0,-1968,2.0,1,1,0,1,0,1,High skill tech staff,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Housing,,0.2670630304225067,,0.1801,0.1095,0.995,0.932,0.0655,0.1732,0.1493,0.3054,0.1804,0.1655,0.1468,0.1761,0.0051,0.0101,0.0609,0.0683,0.9995,0.9935,0.0276,0.0806,0.069,0.2083,0.25,0.2001,0.0533,0.0554,0.0,0.0,0.1499,0.1128,0.9995,0.9933,0.0349,0.16,0.1379,0.3333,0.25,0.19899999999999998,0.1231,0.1667,0.0,0.0,not specified,block of flats,0.157,Panel,No,0.0,0.0,0.0,0.0,-438.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n364929,1,Cash loans,M,N,N,0,168750.0,949500.0,29943.0,949500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-10063,-583,-2972.0,-2741,,1,1,0,1,0,0,Sales staff,1.0,2,2,SUNDAY,8,0,0,0,1,1,0,Trade: type 2,,0.5068733382926642,0.34578480246959553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1425.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n224468,0,Cash loans,M,Y,N,0,202500.0,161730.0,12964.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-17293,-394,-9756.0,-830,8.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,11,0,1,1,0,1,1,Construction,,0.600265054911633,0.34741822720026416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-333.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n362114,0,Cash loans,F,N,Y,1,180000.0,544491.0,17694.0,454500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.005144,-14804,-3058,-5444.0,-4002,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Trade: type 7,,0.21138885565636129,0.6023863442690867,0.1103,0.0767,0.9861,0.8096,,0.12,0.1034,0.3333,,0.077,,0.1154,,0.0,0.1124,0.0796,0.9861,0.8171,,0.1208,0.1034,0.3333,,0.0787,,0.1202,,0.0,0.1114,0.0767,0.9861,0.8121,,0.12,0.1034,0.3333,,0.0783,,0.1175,,0.0,,block of flats,0.1033,Panel,No,2.0,0.0,2.0,0.0,-266.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n102929,0,Cash loans,F,N,N,0,130500.0,384048.0,13923.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.022625,-20243,365243,-6631.0,-3166,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.7748720292508685,0.4722533429586386,0.0825,0.0771,0.9732,0.6328,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0765,0.0672,0.0544,0.0,0.0176,0.084,0.08,0.9732,0.6472,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0731,0.0735,0.0458,0.0,0.0186,0.0833,0.0771,0.9732,0.6377,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0778,0.0684,0.0554,0.0,0.0179,reg oper account,block of flats,0.051,Block,No,4.0,0.0,4.0,0.0,-1459.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,5.0\n261696,0,Cash loans,F,N,N,0,67500.0,1107981.0,32395.5,967500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-22211,-6660,-4057.0,-4086,,1,1,0,1,1,0,Security staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Kindergarten,,0.3542247319929012,0.28961123838200553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2119.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n329044,0,Cash loans,F,N,N,0,202500.0,450000.0,25834.5,450000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.001276,-16911,-2744,-4887.0,-441,,1,1,0,1,1,0,Accountants,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,School,0.8055197706142558,0.4288070038757747,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1681.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n367967,0,Cash loans,F,N,N,0,63000.0,808650.0,23175.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-21604,365243,-9305.0,-4559,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.43826378500301344,0.8224987619370829,0.8979,0.6162,0.9871,0.8232,0.3824,0.88,0.7586,0.375,0.0417,0.3806,,0.8888,,0.0608,0.9149,0.6395,0.9871,0.8301,0.3858,0.8862,0.7586,0.375,0.0417,0.3893,,0.9259999999999999,,0.0644,0.9066,0.6162,0.9871,0.8256,0.3848,0.88,0.7586,0.375,0.0417,0.3872,,0.9048,,0.0621,reg oper spec account,block of flats,0.9214,Panel,No,2.0,0.0,2.0,0.0,-2565.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n401433,0,Cash loans,F,N,N,1,292500.0,948096.0,31464.0,720000.0,Unaccompanied,State servant,Higher education,Married,Municipal apartment,0.072508,-13731,-5216,-4559.0,-5055,,1,1,0,1,1,1,,3.0,1,1,SATURDAY,13,0,0,0,0,0,0,School,0.7982959630233242,0.8060711267225175,0.5046813193144684,0.2443,0.092,0.993,0.9048,0.0993,0.24,0.0862,0.5833,0.625,0.0,0.1992,0.2305,0.0,0.0075,0.1481,0.0858,0.993,0.9085,0.0579,0.2417,0.069,0.4583,0.5,0.0,0.1295,0.1613,0.0,0.0056,0.2467,0.092,0.993,0.9061,0.0999,0.24,0.0862,0.5833,0.625,0.0,0.2027,0.2347,0.0,0.0076,reg oper account,block of flats,0.2429,Others,No,5.0,0.0,5.0,0.0,-1722.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n206228,0,Cash loans,M,Y,Y,0,450000.0,2410380.0,89455.5,2250000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-16581,-5098,-5202.0,-130,7.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Construction,,0.5900543958764853,0.5602843280409464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1459.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n376071,0,Cash loans,M,Y,N,0,135000.0,422505.0,32827.5,391500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14115,-5530,-3651.0,-3728,3.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,17,0,0,0,0,0,0,Self-employed,0.7394068664660539,0.7468090586871199,0.5849900404894085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2341.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n124419,0,Cash loans,F,Y,Y,1,157500.0,1179000.0,32553.0,1179000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.019101,-17298,-2440,-6279.0,-837,11.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Postal,,0.7029334431794494,0.7738956942145427,0.0977,0.1055,0.9796,0.7212,0.0516,0.0,0.2069,0.1667,0.1388,0.0207,0.0794,0.0898,0.0019,0.003,0.0735,0.0811,0.9796,0.7256,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0,0.0643,0.0652,0.0,0.0,0.0937,0.09699999999999999,0.9796,0.7249,0.052000000000000005,0.0,0.2069,0.1667,0.2083,0.021,0.0765,0.0896,0.0019,0.0018,reg oper account,block of flats,0.0527,\"Stone, brick\",No,2.0,0.0,1.0,0.0,-795.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n366129,0,Cash loans,M,N,Y,0,202500.0,1293502.5,41854.5,1129500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-18757,-877,-4258.0,-2311,,1,1,1,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,1,1,0,Industry: type 1,,0.6381390896799526,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1673.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n440217,0,Cash loans,F,N,Y,3,112500.0,1078200.0,38200.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.025164,-12991,-1026,-1997.0,-1987,,1,1,1,1,1,0,Core staff,4.0,2,2,THURSDAY,11,0,0,0,0,0,0,Kindergarten,,0.2653117484731741,,0.1155,0.1779,0.997,0.9592,0.0209,0.04,0.2069,0.25,0.2083,0.0354,0.0941,0.1338,0.0,0.0,0.1176,0.1847,0.997,0.9608,0.0211,0.0403,0.2069,0.25,0.2083,0.0362,0.1028,0.1394,0.0,0.0,0.1166,0.1779,0.997,0.9597,0.0211,0.04,0.2069,0.25,0.2083,0.036000000000000004,0.0958,0.1362,0.0,0.0,reg oper account,block of flats,0.1189,Panel,No,5.0,0.0,5.0,0.0,-663.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n389766,0,Cash loans,F,N,Y,0,157500.0,697500.0,44577.0,697500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-17795,-383,-10253.0,-1345,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.3727262713719129,0.6047479928325242,0.5082869913916046,0.0825,,0.9776,,,0.0,0.1379,0.1667,,,,0.0491,,0.0852,0.084,,0.9777,,,0.0,0.1379,0.1667,,,,0.0512,,0.0902,0.0833,,0.9776,,,0.0,0.1379,0.1667,,,,0.05,,0.087,,block of flats,0.0571,Panel,No,1.0,0.0,1.0,0.0,-2760.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n423909,0,Cash loans,F,N,N,1,180000.0,900000.0,26316.0,900000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.00963,-10275,-534,-4124.0,-2681,,1,1,0,1,1,0,,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.2566754923411777,0.4574143208201126,0.6722428897082422,0.0742,0.0621,0.9886,0.8436,0.0172,0.08,0.069,0.3333,0.375,0.0301,0.0605,0.0791,0.0,0.0,0.0756,0.0644,0.9886,0.8497,0.0174,0.0806,0.069,0.3333,0.375,0.0308,0.0661,0.0824,0.0,0.0,0.0749,0.0621,0.9886,0.8457,0.0173,0.08,0.069,0.3333,0.375,0.0307,0.0616,0.0805,0.0,0.0,reg oper account,block of flats,0.0716,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-434.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n289426,0,Cash loans,M,N,N,1,180000.0,1078200.0,31653.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,Municipal apartment,0.011703,-17578,-826,-4859.0,-1103,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,1,1,Self-employed,,0.6997684536604185,0.5136937663039473,,,0.9767,,,,0.069,0.0417,,,,0.0129,,,,,0.9767,,,,0.069,0.0417,,,,0.0134,,,,,0.9767,,,,0.069,0.0417,,,,0.0131,,,,,0.0101,,No,2.0,0.0,2.0,0.0,-540.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n397408,0,Cash loans,M,Y,Y,0,135000.0,222768.0,17991.0,180000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-15177,-1624,-8841.0,-4371,20.0,1,1,0,1,0,0,Security staff,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Government,,0.6213561265806405,0.4902575124990026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2358.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n183374,0,Cash loans,F,N,Y,0,279000.0,497520.0,32521.5,450000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.006629,-17709,-719,-5567.0,-1256,,1,1,0,1,1,0,Accountants,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,Business Entity Type 2,0.7168902532612899,0.2409751359547491,0.5154953751603267,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-754.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n246776,0,Cash loans,F,Y,N,0,180000.0,297000.0,7965.0,297000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.014519999999999996,-12291,-1420,-1060.0,-3697,1.0,1,1,0,1,0,0,Medicine staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Medicine,,0.3911598961399558,0.36896873825284665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-4.0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n131312,0,Cash loans,F,Y,Y,0,117000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.035792000000000004,-8769,-1148,-3440.0,-1461,1.0,1,1,0,1,0,0,,1.0,2,2,MONDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.5154427429780172,0.6955341244608098,0.3376727217405312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-974.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n380698,1,Cash loans,F,Y,Y,1,157500.0,499261.5,24412.5,351000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.018029,-13292,-1707,-2553.0,-537,1.0,1,1,0,1,0,0,Sales staff,3.0,3,3,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,0.4217411173753315,0.023145400954250533,,0.0186,,0.9682,,,0.0,0.069,0.0833,,0.0095,,0.0155,,,0.0189,,0.9682,,,0.0,0.069,0.0833,,0.0097,,0.0162,,,0.0187,,0.9682,,,0.0,0.069,0.0833,,0.0096,,0.0158,,,,block of flats,0.0386,Block,No,0.0,0.0,0.0,0.0,-658.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n165739,0,Revolving loans,F,N,N,0,67500.0,315000.0,15750.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.031329,-18941,-1774,-5251.0,-2379,,1,1,0,1,1,0,Core staff,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,0.7421126518071206,0.4771552424864728,0.4992720153045617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n413130,0,Cash loans,F,N,Y,0,112500.0,450000.0,35685.0,450000.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.015221,-8323,-277,-6385.0,-841,,1,1,0,1,0,0,,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4124400531286872,0.4543007914595353,0.08118594316672112,0.0742,0.0485,0.9906,0.8708,0.0469,0.08,0.069,0.375,0.4167,0.0519,0.0588,0.0767,0.0077,0.0257,0.0756,0.0504,0.9906,0.8759,0.0473,0.0806,0.069,0.375,0.4167,0.0531,0.0643,0.0799,0.0078,0.0272,0.0749,0.0485,0.9906,0.8725,0.0472,0.08,0.069,0.375,0.4167,0.0528,0.0599,0.078,0.0078,0.0262,,block of flats,0.0915,Panel,No,0.0,0.0,0.0,0.0,-808.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n130601,0,Cash loans,F,N,Y,0,112500.0,1193580.0,35028.0,855000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.006629,-18181,-1742,-6803.0,-685,,1,1,1,1,1,0,,1.0,2,2,THURSDAY,7,0,0,0,0,0,0,Other,,0.02796243283231829,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-91.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n358101,0,Cash loans,F,N,Y,0,135000.0,604152.0,30847.5,540000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,Office apartment,0.02461,-8149,-111,-871.0,-819,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.2310923493917717,0.6680398328491045,0.5495965024956946,0.0639,0.0395,0.9727,,,0.04,0.1379,0.125,,0.0191,,0.0495,,0.0168,0.0651,0.040999999999999995,0.9727,,,0.0403,0.1379,0.125,,0.0196,,0.0515,,0.0178,0.0645,0.0395,0.9727,,,0.04,0.1379,0.125,,0.0195,,0.0504,,0.0171,,block of flats,0.0456,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-896.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n410939,0,Cash loans,M,N,Y,0,130500.0,490495.5,25393.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-8412,-349,-2714.0,-171,,1,1,0,1,0,0,Laborers,1.0,3,3,SATURDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.17184921857353694,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-136.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n309904,0,Cash loans,M,N,N,1,540000.0,753925.5,54990.0,643500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.007305,-10372,-549,-4979.0,-30,,1,1,0,1,1,1,,3.0,3,3,FRIDAY,10,0,0,0,1,1,0,Business Entity Type 3,0.17813568782028316,0.530507486398446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-324.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n219152,0,Cash loans,M,N,N,0,450000.0,883863.0,23314.5,737784.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.003818,-12594,-686,-6731.0,-4316,,1,1,1,1,1,0,Laborers,1.0,2,2,THURSDAY,11,0,0,0,1,1,0,Industry: type 9,,0.1120383679328115,0.4884551844437485,0.0082,0.0,0.9737,0.6396,0.0007,0.0,0.0345,0.0417,0.0417,0.0093,0.0067,0.006999999999999999,0.0,0.0,0.0084,0.0,0.9737,0.6537,0.0007,0.0,0.0345,0.0417,0.0417,0.0095,0.0073,0.0073,0.0,0.0,0.0083,0.0,0.9737,0.6444,0.0007,0.0,0.0345,0.0417,0.0417,0.0094,0.0068,0.0072,0.0,0.0,reg oper spec account,block of flats,0.0059,Wooden,No,0.0,0.0,0.0,0.0,-5.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n243878,0,Cash loans,F,N,Y,0,90000.0,592560.0,30384.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-19521,-2143,-2295.0,-2162,,1,1,1,1,1,0,Laborers,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Self-employed,,0.2520913722536209,0.3441550073724169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n209279,0,Cash loans,F,N,Y,0,135000.0,810000.0,77040.0,810000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-18630,-1058,-3962.0,-2156,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Self-employed,,0.32527195705418105,,0.1113,0.0786,0.9836,0.7756,0.0,0.12,0.1034,0.3333,0.375,0.0761,0.0908,0.069,0.0,0.0,0.1134,0.0816,0.9836,0.7844,0.0,0.1208,0.1034,0.3333,0.375,0.0778,0.0992,0.0719,0.0,0.0,0.1124,0.0786,0.9836,0.7786,0.0,0.12,0.1034,0.3333,0.375,0.0774,0.0923,0.0702,0.0,0.0,reg oper account,block of flats,0.0916,Panel,No,9.0,0.0,9.0,0.0,-850.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n339582,0,Cash loans,F,Y,Y,0,225000.0,1223010.0,48631.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-18562,-2180,-6366.0,-2097,6.0,1,1,0,1,0,0,Cooking staff,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4007828775840127,0.4280412582501185,0.4920600938649263,0.2876,0.1062,0.9866,0.8164,0.0924,0.28,0.2414,0.375,0.4167,0.045,0.2328,0.2454,0.0077,0.1477,0.2931,0.1102,0.9866,0.8236,0.0933,0.282,0.2414,0.375,0.4167,0.046,0.2544,0.2557,0.0078,0.1563,0.2904,0.1062,0.9866,0.8189,0.09300000000000001,0.28,0.2414,0.375,0.4167,0.0458,0.2369,0.2498,0.0078,0.1508,reg oper spec account,block of flats,0.2756,Panel,No,0.0,0.0,0.0,0.0,-1961.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n180360,0,Revolving loans,F,N,Y,2,135000.0,202500.0,10125.0,202500.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.010006,-11251,-1640,-5730.0,-3945,,1,1,1,1,1,0,Medicine staff,3.0,2,2,SATURDAY,8,0,0,0,0,0,0,Other,0.34360083255797363,0.5515350504582153,0.3876253444214701,,,0.9866,,,,,,,,,0.0131,,,,,0.9866,,,,,,,,,0.0136,,,,,0.9866,,,,,,,,,0.0133,,,,,0.0103,,No,0.0,0.0,0.0,0.0,-1983.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n326281,0,Cash loans,F,N,N,0,247500.0,808650.0,31464.0,675000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.00496,-18970,365243,-3580.0,-2493,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,XNA,,0.6706484691228488,0.6971469077844458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-392.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n131107,0,Revolving loans,F,N,Y,0,126000.0,270000.0,13500.0,270000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.02461,-9437,-2198,-4178.0,-937,,1,1,0,1,1,0,Core staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,School,0.26990829404772515,0.5442496722276691,,0.0742,0.0452,0.9846,,,0.08,0.069,0.3333,,0.044000000000000004,,0.0767,,0.0,0.0756,0.0469,0.9846,,,0.0806,0.069,0.3333,,0.045,,0.0799,,0.0,0.0749,0.0452,0.9846,,,0.08,0.069,0.3333,,0.0448,,0.0781,,0.0,,block of flats,0.0699,Panel,No,0.0,0.0,0.0,0.0,-1860.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n118882,0,Cash loans,F,N,N,0,180000.0,1057500.0,31050.0,1057500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.003069,-17456,-2423,-914.0,-992,,1,1,0,1,0,0,Laborers,2.0,3,3,WEDNESDAY,11,0,0,0,0,1,1,Government,,0.3013384853481652,0.7435593141311444,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,2.0,5.0,0.0,-528.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n420205,0,Cash loans,F,N,Y,0,112500.0,450000.0,14130.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018209,-23251,365243,-3087.0,-3669,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,10,0,0,0,1,0,0,XNA,,0.3500590986250667,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-580.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n150864,0,Cash loans,M,Y,Y,2,90000.0,563877.0,28921.5,504000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14224,-2531,-8282.0,-5015,9.0,1,1,0,1,1,0,Laborers,4.0,2,2,SATURDAY,11,0,0,0,0,1,1,Industry: type 4,0.6114427929166716,0.14975840422166792,0.7838324335199619,0.0928,0.1026,0.9811,0.7416,0.0,0.0,0.2069,0.1667,0.2083,0.0839,0.0756,0.0778,0.0,0.0,0.0945,0.1065,0.9811,0.7517,0.0,0.0,0.2069,0.1667,0.2083,0.0858,0.0826,0.0811,0.0,0.0,0.0937,0.1026,0.9811,0.7451,0.0,0.0,0.2069,0.1667,0.2083,0.0853,0.077,0.0792,0.0,0.0,reg oper account,block of flats,0.0677,Panel,No,1.0,0.0,1.0,0.0,-2450.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n372567,0,Cash loans,F,N,Y,3,193500.0,1214145.0,64818.0,1147500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009175,-14043,-2302,-2425.0,-3861,,1,1,1,1,1,0,Sales staff,5.0,2,2,MONDAY,18,0,0,0,0,0,0,Trade: type 7,,0.7113337118085901,,0.1443,,0.9771,,,0.0,0.2414,0.1667,,,,0.1326,,0.0,0.1471,,0.9772,,,0.0,0.2414,0.1667,,,,0.1382,,0.0,0.1457,,0.9771,,,0.0,0.2414,0.1667,,,,0.135,,0.0,,block of flats,0.1118,Panel,No,3.0,0.0,2.0,0.0,-299.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n303912,0,Cash loans,F,N,Y,0,126000.0,243000.0,17410.5,243000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-23694,365243,-7598.0,-4447,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,XNA,0.7777841608254579,0.2860094974966144,0.5884877883422673,0.1464,0.1033,0.9861,0.8096,0.0372,0.16,0.1379,0.3333,0.375,0.0744,0.1177,0.1448,0.0077,0.0157,0.1492,0.1072,0.9861,0.8171,0.0375,0.1611,0.1379,0.3333,0.375,0.0761,0.1286,0.1509,0.0078,0.0167,0.1478,0.1033,0.9861,0.8121,0.0374,0.16,0.1379,0.3333,0.375,0.0757,0.1197,0.1474,0.0078,0.0161,reg oper account,block of flats,0.1377,Panel,No,0.0,0.0,0.0,0.0,-410.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n147636,0,Cash loans,F,N,Y,1,135000.0,538389.0,43294.5,477000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.02461,-10446,-529,-3588.0,-194,,1,1,0,1,1,1,Accountants,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.1886969611419745,0.6267583136583027,,0.0856,0.0096,0.9742,0.6464,0.0354,0.0,0.1379,0.1667,0.2083,0.0903,0.0672,0.0641,0.0116,0.0052,0.0872,0.0099,0.9742,0.6602,0.0357,0.0,0.1379,0.1667,0.2083,0.0924,0.0735,0.0668,0.0117,0.0055,0.0864,0.0096,0.9742,0.6511,0.0356,0.0,0.1379,0.1667,0.2083,0.0919,0.0684,0.0653,0.0116,0.0053,reg oper account,block of flats,0.0698,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1727.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n129641,0,Cash loans,F,Y,Y,0,225000.0,622413.0,31909.5,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.072508,-20063,-661,-2101.0,-3164,2.0,1,1,0,1,0,0,,1.0,1,1,THURSDAY,13,0,0,0,0,0,0,Bank,,0.7541427273912824,0.3001077565791181,0.2385,0.0789,0.9965,0.9524,0.1171,0.2664,0.1034,0.7637,0.7637,0.0,0.1827,0.2452,0.0541,0.0497,0.1849,0.0508,0.9965,0.9543,0.08800000000000001,0.3222,0.1379,0.6667,0.5833,0.0,0.1478,0.1853,0.0506,0.0354,0.2686,0.0931,0.9965,0.953,0.1315,0.32,0.1379,0.6667,0.7083,0.0,0.2086,0.2736,0.0543,0.0583,reg oper account,block of flats,0.2319,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n291803,0,Cash loans,F,N,Y,0,67500.0,267102.0,19125.0,247500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.020713,-22797,365243,-9621.0,-4147,,1,0,0,1,0,0,,2.0,3,2,FRIDAY,6,0,0,0,0,0,0,XNA,,0.5227175836414353,,0.267,0.20600000000000002,0.9871,0.8232,0.0656,0.32,0.2759,0.375,0.4167,0.1072,0.2169,0.3341,0.0039,0.0097,0.2721,0.2138,0.9871,0.8301,0.0662,0.3222,0.2759,0.375,0.4167,0.1096,0.2369,0.3481,0.0039,0.0103,0.2696,0.20600000000000002,0.9871,0.8256,0.066,0.32,0.2759,0.375,0.4167,0.109,0.2206,0.3401,0.0039,0.01,reg oper account,block of flats,0.3008,Panel,No,0.0,0.0,0.0,0.0,-589.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n226139,0,Cash loans,F,N,N,0,135000.0,814041.0,26388.0,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.072508,-15352,-73,-9308.0,-3314,,1,1,1,1,1,0,Laborers,2.0,1,1,THURSDAY,19,0,0,0,0,0,0,Business Entity Type 3,,0.6502925164908437,0.7106743858828587,0.3242,0.2016,0.9821,,,0.3,0.2241,0.4792,,0.2677,,0.2775,,0.0338,0.2679,0.1368,0.9821,,,0.1611,0.069,0.3333,,0.1192,,0.1696,,0.0026,0.3274,0.2016,0.9821,,,0.3,0.2241,0.4792,,0.2724,,0.2825,,0.0345,,block of flats,0.3226,Panel,No,0.0,0.0,0.0,0.0,-1125.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n343974,0,Cash loans,M,Y,Y,3,148500.0,490495.5,42097.5,454500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.030755,-12960,-2027,-2549.0,-4618,18.0,1,1,0,1,1,0,Drivers,5.0,2,2,SATURDAY,10,0,0,0,0,1,1,Police,,0.3486375112432884,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n185012,0,Cash loans,F,N,Y,0,256500.0,519633.0,33336.0,481500.0,Family,Working,Higher education,Married,House / apartment,0.006296,-13750,-809,-6608.0,-4297,,1,1,0,1,0,0,,2.0,3,3,WEDNESDAY,16,0,0,0,0,1,1,Transport: type 4,,0.5357163127097813,0.2067786544915716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-708.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n186144,0,Cash loans,F,N,N,1,135000.0,622188.0,22477.5,472500.0,Family,State servant,Secondary / secondary special,Civil marriage,Office apartment,0.04622,-14270,-4068,-7536.0,-4928,,1,1,0,1,0,0,Laborers,3.0,1,1,SATURDAY,12,0,0,0,1,1,0,Business Entity Type 3,0.5812127537066151,0.7424763787126663,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1922.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n300323,0,Cash loans,F,Y,Y,0,360000.0,1271929.5,48577.5,1170000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-17987,-5125,-9320.0,-1534,2.0,1,1,0,1,1,0,,2.0,1,1,SATURDAY,15,0,0,0,0,0,0,School,0.7038704395865261,0.681262107739152,0.8106180215523969,0.1216,0.1225,0.9876,0.83,0.0383,0.16,0.069,0.4583,0.5,0.0296,0.0992,0.1442,0.0,0.0,0.1239,0.1271,0.9876,0.8367,0.0387,0.1611,0.069,0.4583,0.5,0.0303,0.1084,0.1502,0.0,0.0,0.1228,0.1225,0.9876,0.8323,0.0386,0.16,0.069,0.4583,0.5,0.0301,0.1009,0.1468,0.0,0.0,reg oper spec account,block of flats,0.1139,Panel,No,1.0,1.0,1.0,0.0,-2260.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n416686,0,Cash loans,F,N,Y,0,180000.0,808650.0,23305.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-21686,365243,-3411.0,-4451,,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,0.6376530089660094,0.28354645942656703,0.7047064232963289,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-3.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n254686,0,Cash loans,F,N,Y,0,238500.0,1345500.0,43533.0,1345500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-20323,-2226,-3131.0,-3843,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Self-employed,0.5043605881493679,0.16214456766623808,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-619.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n228653,0,Revolving loans,F,N,Y,0,108000.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-14733,-2814,-6800.0,-3142,,1,1,0,1,0,0,Sales staff,1.0,3,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.6085470689997966,0.7387375888871007,0.5460231970049609,0.1052,0.0362,0.9771,0.6872,0.0114,0.0,0.2069,0.1667,0.2083,0.0615,0.0849,0.0926,0.0039,0.0079,0.1071,0.0375,0.9772,0.6994,0.0115,0.0,0.2069,0.1667,0.2083,0.0629,0.0927,0.0964,0.0039,0.0083,0.1062,0.0362,0.9771,0.6914,0.0114,0.0,0.2069,0.1667,0.2083,0.0625,0.0864,0.0942,0.0039,0.008,reg oper account,block of flats,0.0807,Panel,No,0.0,0.0,0.0,0.0,-1516.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n249123,1,Cash loans,M,Y,Y,2,112500.0,414792.0,28188.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.008019,-13777,-192,-7100.0,-4006,24.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.09069096474797674,0.40280673921569743,,0.0928,0.0902,0.9796,0.7212,0.0401,0.0,0.2069,0.1667,0.0417,0.0522,0.07400000000000001,0.0772,0.0077,0.021,0.0945,0.0936,0.9796,0.7321,0.0404,0.0,0.2069,0.1667,0.0417,0.0534,0.0808,0.0805,0.0078,0.0222,0.0937,0.0902,0.9796,0.7249,0.0403,0.0,0.2069,0.1667,0.0417,0.0531,0.0752,0.0786,0.0078,0.0214,reg oper account,block of flats,0.0872,Panel,No,2.0,0.0,2.0,0.0,-2774.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n280960,0,Revolving loans,F,N,N,1,67500.0,247500.0,12375.0,247500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018634,-9434,-1180,-9404.0,-250,,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,16,0,0,0,0,0,0,Trade: type 3,,0.08767142743139633,0.6986675550534175,0.10099999999999999,0.0819,0.9811,0.7416,0.0112,0.0,0.2069,0.1667,0.0417,0.0591,0.0731,0.0831,0.0425,0.0344,0.1029,0.0849,0.9811,0.7517,0.0113,0.0,0.2069,0.1667,0.0417,0.0604,0.0799,0.0866,0.0428,0.0365,0.102,0.0819,0.9811,0.7451,0.0113,0.0,0.2069,0.1667,0.0417,0.0601,0.0744,0.0846,0.0427,0.0352,reg oper account,block of flats,0.0704,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1100.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n237890,0,Cash loans,F,N,N,0,54000.0,562491.0,23962.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.008865999999999999,-21869,365243,-9460.0,-4512,,1,0,0,1,0,0,,1.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.7951542278717786,0.7610263695502636,0.0124,,0.9786,,,0.0,0.1034,0.0417,,0.0122,,0.0062,,0.0,0.0126,,0.9786,,,0.0,0.1034,0.0417,,0.0125,,0.0064,,0.0,0.0125,,0.9786,,,0.0,0.1034,0.0417,,0.0124,,0.0063,,0.0,,block of flats,0.0073,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1547.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n333215,0,Cash loans,F,N,Y,1,157500.0,513531.0,24835.5,459000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-13934,-442,-1091.0,-2155,,1,1,0,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.4674245999169155,0.5522759436268835,,0.1619,0.0727,0.9816,,,0.24,0.2069,0.2917,,,,0.1078,,0.177,0.1649,0.0755,0.9816,,,0.2417,0.2069,0.2917,,,,0.1123,,0.1874,0.1634,0.0727,0.9816,,,0.24,0.2069,0.2917,,,,0.1097,,0.1807,,block of flats,0.1671,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-602.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n286922,0,Cash loans,F,N,N,0,162000.0,594081.0,23242.5,531000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-22743,365243,-5482.0,-4061,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,0.6399230894210063,0.7051615297653359,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-264.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n293906,1,Cash loans,F,N,Y,1,175500.0,432661.5,29398.5,373500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-10574,-2088,-1713.0,-215,,1,1,1,1,1,0,Core staff,3.0,2,2,TUESDAY,9,0,0,0,0,1,1,Government,,0.6217830545573335,0.07153267628052741,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-430.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n151679,0,Cash loans,M,N,Y,0,67500.0,761067.0,27468.0,657000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-15463,-2343,-8022.0,-4946,,1,1,0,1,0,0,Security staff,1.0,3,2,FRIDAY,5,0,0,0,0,0,0,Self-employed,,0.6020791524578465,0.3296550543128238,0.0887,0.0912,0.9841,0.7824,0.0153,0.0,0.2069,0.1667,0.2083,,0.0714,0.052000000000000005,0.0039,0.0112,0.0903,0.0946,0.9841,0.7909,0.0154,0.0,0.2069,0.1667,0.2083,,0.0781,0.0542,0.0039,0.0118,0.0895,0.0912,0.9841,0.7853,0.0154,0.0,0.2069,0.1667,0.2083,,0.0727,0.0529,0.0039,0.0114,reg oper account,block of flats,0.0947,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-709.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n386584,0,Cash loans,F,Y,Y,0,157500.0,1078200.0,31653.0,900000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.01885,-8616,-706,-5644.0,-1206,14.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,School,0.6641606635235627,0.6479715853864009,0.35233997269170386,0.1031,,0.9786,,,0.0,0.2069,0.1667,,0.1553,,0.0627,,0.0,0.105,,0.9786,,,0.0,0.2069,0.1667,,0.1588,,0.0653,,0.0,0.1041,,0.9786,,,0.0,0.2069,0.1667,,0.158,,0.0638,,0.0,,block of flats,0.0734,Block,No,4.0,0.0,4.0,0.0,-1085.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n413105,1,Revolving loans,F,N,Y,0,119250.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.020246,-10639,-188,-5118.0,-3237,,1,1,0,1,0,0,Accountants,1.0,3,3,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.0760804954210417,0.8357765157975799,0.3026,0.1937,0.9921,0.8912,0.092,0.28,0.2414,0.375,0.2292,0.156,0.2467,0.3056,0.0,0.033,0.1891,0.1038,0.9891,0.8563,0.0929,0.1611,0.1379,0.375,0.0417,0.1303,0.1653,0.1738,0.0,0.0,0.3055,0.1937,0.9921,0.8927,0.0926,0.28,0.2414,0.375,0.2292,0.1587,0.251,0.3111,0.0,0.0337,reg oper spec account,block of flats,0.3998,Panel,No,0.0,0.0,0.0,0.0,-2605.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n337152,0,Cash loans,F,N,Y,0,126000.0,891387.0,34821.0,769500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-18533,-2473,-91.0,-2042,,1,1,0,1,0,0,,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Construction,,0.4093146511153141,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n253890,0,Cash loans,F,Y,Y,1,112500.0,171000.0,13639.5,171000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-12970,-686,-2125.0,-4323,14.0,1,1,0,1,0,1,,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Medicine,,0.6439658438075141,0.43473324875017305,0.1928,0.1465,0.9851,0.7959999999999999,0.0486,0.2,0.1724,0.3333,0.375,0.0859,0.1547,0.2089,0.0116,0.0095,0.1964,0.152,0.9851,0.804,0.049,0.2014,0.1724,0.3333,0.375,0.0879,0.16899999999999998,0.2176,0.0117,0.01,0.1947,0.1465,0.9851,0.7987,0.0489,0.2,0.1724,0.3333,0.375,0.0874,0.1573,0.2126,0.0116,0.0097,reg oper account,block of flats,0.1663,Panel,No,0.0,0.0,0.0,0.0,-1463.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n368413,0,Cash loans,M,N,Y,1,157500.0,900000.0,87678.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-16710,365243,-242.0,-242,,1,0,0,1,0,0,,3.0,2,2,SATURDAY,17,0,0,0,0,0,0,XNA,0.4967742326309568,0.601874812431812,0.7801436381572275,,,0.9856,,,,,,,,,0.1068,,,,,0.9856,,,,,,,,,0.1113,,,,,0.9856,,,,,,,,,0.1088,,,,,0.084,,No,10.0,0.0,10.0,0.0,-740.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n144571,0,Cash loans,F,N,N,0,315000.0,382500.0,26005.5,382500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.006670999999999999,-19382,-2009,-713.0,-2918,,1,1,1,1,1,0,Managers,1.0,2,2,SUNDAY,14,0,0,0,0,1,1,Construction,0.7464012657478449,0.6782106704442642,0.36227724703843145,0.2928,,0.9990000000000001,,,0.16,0.1379,0.2917,,,,0.2463,,0.0068,0.2983,,0.9990000000000001,,,0.1611,0.1379,0.2917,,,,0.2567,,0.0072,0.2956,,0.9990000000000001,,,0.16,0.1379,0.2917,,,,0.2508,,0.006999999999999999,,block of flats,0.2585,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1748.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n108414,0,Cash loans,F,N,Y,2,112500.0,562491.0,23962.5,454500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008473999999999999,-10113,-1687,-857.0,-2753,,1,1,0,1,0,0,Managers,4.0,2,2,SATURDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.44544168162419395,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n318915,0,Cash loans,F,N,Y,0,157500.0,630000.0,27882.0,630000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.028663,-22557,365243,-13571.0,-4258,,1,0,0,1,1,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.5302732783357412,0.4223696523543468,0.0825,0.02,0.9771,0.6872,0.0,0.0,0.1379,0.1667,0.2083,0.0647,0.0672,0.0549,0.0,0.0206,0.084,0.0208,0.9772,0.6994,0.0,0.0,0.1379,0.1667,0.2083,0.0661,0.0735,0.0572,0.0,0.0218,0.0833,0.02,0.9771,0.6914,0.0,0.0,0.1379,0.1667,0.2083,0.0658,0.0684,0.0559,0.0,0.021,reg oper account,block of flats,0.0476,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-214.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n326991,0,Revolving loans,M,Y,N,1,202500.0,360000.0,18000.0,360000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-16368,-561,-8002.0,-3766,10.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,THURSDAY,15,0,0,0,0,1,1,Business Entity Type 3,0.4646947850200338,0.6975540028931808,0.4632753280912678,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2178.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n353552,0,Cash loans,F,N,Y,0,112500.0,848745.0,37516.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018029,-21734,365243,-1058.0,-4783,,1,0,0,1,0,0,,1.0,3,3,FRIDAY,6,0,0,0,0,0,0,XNA,,0.4960275340239281,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1879.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n118370,0,Cash loans,F,Y,Y,1,67500.0,445500.0,16794.0,445500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-14370,-4590,-1228.0,-1682,6.0,1,1,1,1,0,0,,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Government,,0.4593687715816655,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-1163.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n227370,0,Cash loans,F,N,Y,0,247500.0,723996.0,30802.5,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-10008,-708,-4544.0,-2676,,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 1,,0.6400758817030231,0.5638350489514956,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-265.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n350333,0,Cash loans,F,N,Y,0,135000.0,291915.0,12987.0,252000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-23356,-3163,-10600.0,-4387,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6717825437089173,0.6512602186973006,0.0619,0.0461,0.9742,0.6464,0.0054,0.0,0.1034,0.1667,0.2083,0.0,0.0488,0.0487,0.0077,0.0052,0.063,0.0478,0.9742,0.6602,0.0054,0.0,0.1034,0.1667,0.2083,0.0,0.0533,0.0507,0.0078,0.0055,0.0625,0.0461,0.9742,0.6511,0.0054,0.0,0.1034,0.1667,0.2083,0.0,0.0496,0.0495,0.0078,0.0053,not specified,block of flats,0.0423,Block,No,0.0,0.0,0.0,0.0,-466.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n415401,0,Cash loans,F,N,N,2,135000.0,254700.0,28935.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Rented apartment,0.014464,-12012,-1509,-989.0,-1548,,1,1,0,1,0,0,Sales staff,4.0,2,2,FRIDAY,7,0,0,0,1,1,0,Self-employed,,0.4455875350351088,0.41885428862332175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-418.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n264762,0,Cash loans,F,N,Y,0,90000.0,630000.0,20452.5,630000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-18940,-681,-3407.0,-2420,,1,1,0,1,0,0,,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 2,,0.2755358466931539,0.6690566947824041,0.0928,0.1545,0.9762,0.6736,0.1275,0.0,0.2069,0.1667,0.2083,0.0984,0.0748,0.0885,0.0039,0.0042,0.0945,0.1603,0.9762,0.6864,0.1287,0.0,0.2069,0.1667,0.2083,0.1007,0.0817,0.0922,0.0039,0.0044,0.0937,0.1545,0.9762,0.6779999999999999,0.1283,0.0,0.2069,0.1667,0.2083,0.1001,0.0761,0.0901,0.0039,0.0043,org spec account,block of flats,0.0697,Panel,No,1.0,1.0,1.0,1.0,-1640.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,0.0\n240895,0,Cash loans,F,N,Y,0,135000.0,283500.0,12141.0,283500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-20279,365243,-7117.0,-3814,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,XNA,,0.5018551526276808,0.722392890081304,0.2608,0.0832,0.9826,0.762,0.1525,0.28,0.2414,0.3333,0.375,0.1131,0.2118,0.1638,0.0039,0.0012,0.2658,0.0864,0.9826,0.7713,0.1538,0.282,0.2414,0.3333,0.375,0.1157,0.2314,0.1706,0.0039,0.0013,0.2634,0.0832,0.9826,0.7652,0.1534,0.28,0.2414,0.3333,0.375,0.1151,0.2155,0.1667,0.0039,0.0012,reg oper account,block of flats,0.2125,Panel,No,0.0,0.0,0.0,0.0,-1116.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n356020,0,Cash loans,F,N,N,0,180000.0,1546020.0,42642.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-18467,-104,-2461.0,-1923,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.526311591827846,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-168.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n366183,0,Cash loans,F,N,Y,0,157500.0,414229.5,26599.5,342000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.04622,-15194,-1864,-9316.0,-2631,,1,1,0,1,0,0,,1.0,1,1,WEDNESDAY,14,0,1,1,0,0,0,Business Entity Type 3,,0.6542455065391289,0.816092360478441,0.0619,0.0736,0.9791,0.7144,0.0094,0.0,0.1379,0.1667,0.2083,0.0,0.0504,0.061,0.0,0.0,0.063,0.0764,0.9791,0.7256,0.0095,0.0,0.1379,0.1667,0.2083,0.0,0.0551,0.0636,0.0,0.0,0.0625,0.0736,0.9791,0.7182,0.0094,0.0,0.1379,0.1667,0.2083,0.0,0.0513,0.0621,0.0,0.0,reg oper spec account,block of flats,0.048,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n323321,0,Cash loans,F,N,Y,0,112500.0,518562.0,20695.5,463500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.015221,-20633,-13535,-13223.0,-4176,,1,1,1,1,1,0,,2.0,2,2,FRIDAY,8,0,0,0,0,1,1,School,0.8109977629125874,0.43289307510748176,0.3360615207658242,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n178685,0,Revolving loans,M,N,N,1,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.008625,-9303,-743,-3434.0,-1974,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Construction,,0.2685981940270387,0.25946765482111994,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1366.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n148426,1,Cash loans,F,N,N,0,67500.0,269550.0,18234.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.025164,-22281,365243,-1665.0,-4920,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.061686130935855565,0.2225807646753351,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-491.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n175844,0,Cash loans,M,N,Y,2,180000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010966,-14008,-397,-2363.0,-4590,,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.6933858204162269,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2830.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n306864,0,Revolving loans,F,N,Y,0,112500.0,337500.0,16875.0,337500.0,Family,Working,Higher education,Married,House / apartment,0.035792000000000004,-18055,-1696,-60.0,-1579,,1,1,0,1,0,0,Sales staff,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Trade: type 1,,0.4967655264617898,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1163.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n441712,0,Cash loans,F,N,Y,0,112500.0,278460.0,25668.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-9744,-209,-557.0,-570,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 1,,0.30919433983648537,0.13177013253138142,0.0619,0.0,0.9747,0.6532,0.0314,0.0,0.1034,0.1667,0.2083,0.0385,0.0504,0.0497,0.0,0.0,0.063,0.0,0.9747,0.6668,0.0317,0.0,0.1034,0.1667,0.2083,0.0394,0.0551,0.0518,0.0,0.0,0.0625,0.0,0.9747,0.6578,0.0316,0.0,0.1034,0.1667,0.2083,0.0392,0.0513,0.0506,0.0,0.0,reg oper account,block of flats,0.0562,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-87.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n403607,0,Revolving loans,M,Y,Y,0,315000.0,900000.0,45000.0,900000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.011657,-10166,-2578,-2615.0,-2634,6.0,1,1,0,1,0,0,Core staff,2.0,1,1,FRIDAY,15,1,1,0,0,0,0,Security Ministries,,0.5172103360878948,0.8038850611746273,0.0928,0.0895,0.9598,,,0.0,0.2069,0.0417,,0.0,,0.0795,,0.0,0.0945,0.0929,0.9598,,,0.0,0.2069,0.0417,,0.0,,0.0828,,0.0,0.0937,0.0895,0.9598,,,0.0,0.2069,0.0417,,0.0,,0.0809,,0.0,,block of flats,0.0694,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n336680,0,Revolving loans,F,N,N,1,112500.0,135000.0,6750.0,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010006,-10547,-2913,-1088.0,-123,,1,1,0,1,1,0,Core staff,3.0,2,2,MONDAY,16,0,0,0,0,0,0,Kindergarten,0.1690837246945464,0.3576988649216302,0.5937175866150576,0.2619,0.1409,0.9985,0.9796,0.1571,0.28,0.2414,0.3333,0.375,0.1383,0.2135,0.2498,0.0,0.0,0.2668,0.1462,0.9985,0.9804,0.1585,0.282,0.2414,0.3333,0.375,0.1414,0.2332,0.2603,0.0,0.0,0.2644,0.1409,0.9985,0.9799,0.1581,0.28,0.2414,0.3333,0.375,0.1407,0.2172,0.2543,0.0,0.0,reg oper account,block of flats,0.1965,Panel,No,2.0,0.0,2.0,0.0,-166.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n343254,0,Cash loans,F,N,Y,0,238500.0,781920.0,25969.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.011657,-22736,365243,-5739.0,-5058,,1,0,0,1,0,0,,2.0,1,1,SUNDAY,11,0,0,0,0,0,0,XNA,,0.2456290313570448,0.7281412993111438,0.0278,0.0877,0.9866,0.8164,0.0532,0.0,0.1034,0.0833,0.125,0.0099,0.0227,0.0307,0.0,0.0,0.0284,0.091,0.9866,0.8236,0.0537,0.0,0.1034,0.0833,0.125,0.0101,0.0248,0.032,0.0,0.0,0.0281,0.0877,0.9866,0.8189,0.0536,0.0,0.1034,0.0833,0.125,0.01,0.0231,0.0312,0.0,0.0,reg oper account,block of flats,0.0532,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1628.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n294632,0,Cash loans,F,N,Y,0,180000.0,659025.0,42246.0,562500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-14851,-1990,-8933.0,-4992,,1,1,0,1,1,0,Core staff,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Services,,0.7262749769929652,0.511891801533151,0.1485,0.0738,0.9801,0.728,0.0282,0.16,0.1379,0.3333,0.375,0.0951,0.1202,0.1425,0.0039,0.0259,0.1513,0.0766,0.9801,0.7387,0.0285,0.1611,0.1379,0.3333,0.375,0.0972,0.1313,0.1484,0.0039,0.0274,0.1499,0.0738,0.9801,0.7316,0.0284,0.16,0.1379,0.3333,0.375,0.0967,0.1223,0.145,0.0039,0.0264,reg oper account,block of flats,0.1331,Panel,No,1.0,0.0,1.0,0.0,-1963.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,1.0,1.0,5.0\n344235,0,Cash loans,M,Y,Y,0,315000.0,473760.0,51021.0,450000.0,Family,Working,Higher education,Married,House / apartment,0.002042,-9864,-942,-4604.0,-2532,14.0,1,1,1,1,0,0,,2.0,3,3,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.3970482425009413,0.544320184355964,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-800.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n313577,0,Cash loans,M,Y,Y,0,382500.0,1868814.0,123565.5,1804500.0,Unaccompanied,State servant,Higher education,Married,Office apartment,0.04622,-12058,-3749,-2754.0,-4071,3.0,1,1,0,1,0,0,Core staff,2.0,1,1,MONDAY,16,0,0,0,0,0,0,Security Ministries,,0.7671705751362599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-841.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n338278,0,Cash loans,F,Y,Y,0,72000.0,157500.0,16960.5,157500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-12495,-1625,-2337.0,-4809,29.0,1,1,1,1,1,0,Medicine staff,2.0,3,3,WEDNESDAY,11,0,0,0,0,0,0,Industry: type 3,0.3417431570123047,0.5053811010289818,0.7801436381572275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2184.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,5.0\n434959,0,Cash loans,F,N,Y,0,90000.0,422892.0,23742.0,382500.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010276,-23603,365243,-11270.0,-4394,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.5650909997991383,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1444.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,3.0\n175219,0,Cash loans,F,N,Y,0,40500.0,127350.0,12532.5,112500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.01885,-16394,-5873,-6856.0,-4532,,1,1,1,1,1,0,Cooking staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Kindergarten,0.30082994763890264,0.7318555987356303,0.8004513396487078,,,0.9767,0.6804,,,,0.1667,0.0417,0.0438,,0.0397,,,,,0.9767,0.6929,,,,0.1667,0.0417,0.0448,,0.0413,,,,,0.9767,0.6847,,,,0.1667,0.0417,0.0445,,0.0404,,,,block of flats,0.0391,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1078.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n286140,0,Cash loans,F,N,Y,0,405000.0,876154.5,44865.0,769500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.007120000000000001,-20505,365243,-8231.0,-4050,,1,0,0,1,0,0,,1.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.6469133858735353,0.11392239629221085,0.1175,0.1527,0.9861,0.8096,,0.24,0.2069,0.3333,0.0417,0.146,0.0941,0.2207,0.0077,0.1163,0.1197,0.1585,0.9861,0.8171,,0.2417,0.2069,0.3333,0.0417,0.1493,0.1028,0.2299,0.0078,0.1232,0.1187,0.1527,0.9861,0.8121,,0.24,0.2069,0.3333,0.0417,0.1485,0.0958,0.2246,0.0078,0.1188,org spec account,block of flats,0.1726,Panel,No,0.0,0.0,0.0,0.0,-1480.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,4.0\n124725,0,Cash loans,M,N,N,0,135000.0,251280.0,12960.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008625,-15243,-763,-10.0,-5271,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Construction,,0.4939120627188764,0.1096264336424546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n269892,0,Cash loans,F,Y,Y,0,112500.0,904500.0,29178.0,904500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20099,-4762,-12726.0,-3441,4.0,1,1,1,1,1,0,Laborers,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6786924932150971,0.3441550073724169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-2482.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n330389,0,Cash loans,F,N,N,2,270000.0,1546020.0,45333.0,1350000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.00496,-12989,-4504,-2412.0,-2672,,1,1,0,1,1,0,Core staff,4.0,2,2,SUNDAY,13,0,0,0,0,0,0,Police,0.5831070852188502,0.7004020730771615,,0.2526,,0.996,,,0.12,0.6552,0.2083,,,,0.27899999999999997,,1.0,0.2574,,0.996,,,0.1208,0.6552,0.2083,,,,0.2906,,1.0,0.255,,0.996,,,0.12,0.6552,0.2083,,,,0.284,,1.0,,block of flats,0.2299,,No,2.0,0.0,2.0,0.0,-1400.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n158776,0,Cash loans,F,N,Y,1,112500.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009334,-14546,-1097,-7737.0,-4192,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,18,0,0,0,0,0,0,Industry: type 3,,0.5734286949171314,0.7922644738669378,0.1485,0.0752,0.9796,0.7212,0.0,0.16,0.1379,0.3333,0.0417,0.027999999999999997,0.121,0.09,0.0,0.0,0.1513,0.0781,0.9796,0.7321,0.0,0.1611,0.1379,0.3333,0.0417,0.0286,0.1322,0.0938,0.0,0.0,0.1499,0.0752,0.9796,0.7249,0.0,0.16,0.1379,0.3333,0.0417,0.0285,0.1231,0.0916,0.0,0.0,reg oper account,block of flats,0.124,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-669.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n186208,0,Cash loans,F,N,N,0,225000.0,1118920.5,59746.5,1057500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.072508,-20447,-4637,-14563.0,-3989,,1,1,0,1,0,0,Laborers,2.0,1,1,MONDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.3768843433535922,0.656158373001177,0.0722,0.0685,0.9762,0.6736,0.0,0.0,0.1207,0.1667,0.2083,0.0576,0.0588,0.0578,0.0,0.0,0.063,0.0552,0.9762,0.6864,0.0,0.0,0.1034,0.1667,0.2083,0.0589,0.0551,0.0509,0.0,0.0,0.0729,0.0685,0.9762,0.6779999999999999,0.0,0.0,0.1207,0.1667,0.2083,0.0586,0.0599,0.0588,0.0,0.0,reg oper account,block of flats,0.0384,\"Stone, brick\",No,2.0,1.0,2.0,0.0,-2393.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n184900,0,Cash loans,F,N,Y,0,53550.0,785398.5,31275.0,702000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-23451,365243,-951.0,-4655,,1,0,0,1,1,0,,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,XNA,,0.5421117836026433,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1287.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n112336,0,Cash loans,M,N,Y,1,405000.0,814041.0,28971.0,679500.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.008625,-14860,-1285,-2277.0,-1668,,1,1,0,1,0,0,,3.0,2,2,THURSDAY,16,0,0,0,0,0,0,Self-employed,,0.7251432345307142,0.7032033049040319,0.033,,0.9821,,,0.0,0.0862,0.1458,,,,0.0517,,0.0,0.0336,,0.9737,,,0.0,0.069,0.125,,,,0.0266,,0.0,0.0333,,0.9821,,,0.0,0.0862,0.1458,,,,0.0526,,0.0,,block of flats,0.0201,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1218.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n334715,0,Cash loans,F,N,Y,0,112500.0,275040.0,10498.5,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.00702,-22325,365243,-2524.0,-4218,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.5762345188371857,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,3.0,4.0,1.0,-1453.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n410609,0,Cash loans,F,N,N,0,441000.0,338832.0,17428.5,292500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011657,-20194,-1881,-4756.0,-3681,,1,1,0,1,0,0,Sales staff,2.0,1,1,FRIDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.5575613127870954,0.4538617077334849,0.6263042766749393,0.001,0.0,0.9632,0.4968,0.0,0.0,0.0345,0.0,0.0417,0.0012,0.0008,0.0014,0.0,0.0006,0.0011,0.0,0.9633,0.5165,0.0,0.0,0.0345,0.0,0.0417,0.0013,0.0009,0.0014,0.0,0.0007,0.001,0.0,0.9632,0.5035,0.0,0.0,0.0345,0.0,0.0417,0.0013,0.0009,0.0014,0.0,0.0007,not specified,terraced house,0.0012,Others,No,6.0,0.0,6.0,0.0,-685.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n138943,0,Cash loans,F,N,N,0,67500.0,245268.0,19377.0,202500.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.022625,-10296,-195,-4431.0,-1686,,1,1,1,1,0,0,Sales staff,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Other,0.0984011414479755,0.2972204157344906,0.05533319953763991,0.3577,0.2178,0.993,0.9048,0.0811,0.28,0.2414,0.375,0.4167,0.1921,0.2917,0.3497,0.0,0.0,0.3645,0.226,0.993,0.9085,0.0819,0.282,0.2414,0.375,0.4167,0.1965,0.3186,0.3643,0.0,0.0,0.3612,0.2178,0.993,0.9061,0.0817,0.28,0.2414,0.375,0.4167,0.1954,0.2967,0.3559,0.0,0.0,reg oper account,block of flats,0.3194,Panel,No,0.0,0.0,0.0,0.0,-816.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n398531,0,Cash loans,F,N,Y,0,72000.0,100737.0,9940.5,94500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-24960,365243,-11151.0,-4701,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.7888832878959968,0.8327850252992314,0.0773,,0.9742,0.7212,,0.0,0.1379,0.1667,0.0417,0.0685,0.0588,0.07,,,0.0735,,0.9742,0.7321,,0.0,0.1379,0.1667,0.0417,0.0701,0.0643,0.0729,,,0.0781,,0.9742,0.7249,,0.0,0.1379,0.1667,0.0417,0.0697,0.0599,0.0712,,,reg oper spec account,block of flats,0.0523,Panel,No,1.0,1.0,1.0,1.0,-1492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n269728,0,Cash loans,M,N,Y,1,153000.0,135000.0,9954.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Municipal apartment,0.009175,-16005,-8598,-8698.0,-4234,,1,1,1,1,0,0,Core staff,3.0,2,2,TUESDAY,17,0,0,0,0,1,1,Police,0.4527840296990854,0.014884719927883781,0.3556387169923543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1784.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,3.0\n109989,0,Cash loans,M,Y,N,0,225000.0,260640.0,28066.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014464,-10683,-447,-1440.0,-2877,18.0,1,1,1,1,1,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Transport: type 4,,0.6550711276284358,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1100.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n300777,0,Cash loans,M,Y,Y,0,157500.0,562491.0,27189.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-14121,-2301,-137.0,-4410,22.0,1,1,0,1,1,0,Laborers,2.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Security,,0.6777141395996154,0.18303516721781032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1202.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n116657,0,Revolving loans,F,Y,Y,0,135000.0,202500.0,10125.0,202500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-16594,-1590,-11094.0,-138,2.0,1,1,0,1,0,0,,2.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Other,0.6312260307622581,0.6712471231399957,0.24318648044201235,0.1113,0.0631,0.9841,0.7824,0.0266,0.12,0.1034,0.3333,0.375,,0.0908,0.113,0.0,0.0,0.1134,0.0654,0.9841,0.7909,0.0268,0.1208,0.1034,0.3333,0.375,,0.0992,0.1177,0.0,0.0,0.1124,0.0631,0.9841,0.7853,0.0268,0.12,0.1034,0.3333,0.375,,0.0923,0.115,0.0,0.0,reg oper account,block of flats,0.0889,Panel,No,1.0,0.0,1.0,0.0,-1369.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n257046,0,Cash loans,M,Y,Y,0,450000.0,1096020.0,52857.0,900000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.007114,-16479,-1117,-2859.0,-12,13.0,1,1,1,1,1,0,Managers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,0.6096162551926441,0.5822887636337793,0.4632753280912678,0.1175,0.151,0.9816,,,,0.2069,0.1667,,,,0.1247,,0.0198,0.1197,0.1567,0.9816,,,,0.2069,0.1667,,,,0.1299,,0.0209,0.1187,0.151,0.9816,,,,0.2069,0.1667,,,,0.127,,0.0202,,block of flats,0.1024,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n295581,0,Revolving loans,F,Y,Y,0,90000.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.031329,-8072,-1184,-8018.0,-756,64.0,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Industry: type 5,,0.21336041280276405,,0.1485,0.0835,0.9831,0.7688,0.0131,0.2,0.1724,0.3333,0.375,0.1174,0.121,0.1511,0.0,0.0,0.1513,0.0866,0.9831,0.7779,0.0132,0.2014,0.1724,0.3333,0.375,0.1201,0.1322,0.1574,0.0,0.0,0.1499,0.0835,0.9831,0.7719,0.0132,0.2,0.1724,0.3333,0.375,0.1195,0.1231,0.1538,0.0,0.0,reg oper account,block of flats,0.1342,Panel,No,0.0,0.0,0.0,0.0,-268.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n396433,0,Cash loans,F,Y,Y,0,675000.0,225000.0,16132.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010276,-9202,-510,-9199.0,-790,64.0,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,13,0,1,1,0,1,1,Transport: type 2,0.3019466601771888,0.7041420999184886,0.1684161714286957,0.0371,0.0611,0.9737,0.6396,0.0424,0.0,0.0345,0.0833,0.125,0.0106,0.0303,0.0294,0.0,0.0,0.0378,0.0634,0.9737,0.6537,0.0427,0.0,0.0345,0.0833,0.125,0.0108,0.0331,0.0307,0.0,0.0,0.0375,0.0611,0.9737,0.6444,0.0426,0.0,0.0345,0.0833,0.125,0.0108,0.0308,0.03,0.0,0.0,reg oper account,block of flats,0.0232,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-423.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n273143,0,Cash loans,F,N,Y,0,72000.0,284400.0,16011.0,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.01885,-24360,365243,-10770.0,-4319,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,XNA,,0.6153855273118887,0.6940926425266661,0.1732,0.2452,0.9856,,,0.2,0.1724,0.3333,,,,0.1019,,0.1944,0.1765,0.2545,0.9856,,,0.2014,0.1724,0.3333,,,,0.1061,,0.2058,0.1749,0.2452,0.9856,,,0.2,0.1724,0.3333,,,,0.1037,,0.1985,,block of flats,0.2011,Panel,No,2.0,0.0,2.0,0.0,-334.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n367685,0,Cash loans,F,N,N,0,45000.0,545040.0,17536.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010556,-20858,365243,-9541.0,-4123,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,16,0,0,0,0,0,0,XNA,,0.5087917454064658,0.6479768603302221,0.1113,0.0784,0.9871,,,0.12,0.1034,0.3333,,0.0828,,0.1152,,0.1147,0.1134,0.0813,0.9871,,,0.1208,0.1034,0.3333,,0.0847,,0.12,,0.1214,0.1124,0.0784,0.9871,,,0.12,0.1034,0.3333,,0.0842,,0.1173,,0.1171,reg oper account,block of flats,0.1156,Panel,No,3.0,2.0,3.0,1.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n274473,0,Cash loans,M,Y,Y,0,360000.0,792346.5,26316.0,684000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.031329,-19680,-3762,-4834.0,-3241,40.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Self-employed,0.7983215818175724,0.4813247260678205,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.0,0.0,16.0,0.0,-1424.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n357581,0,Cash loans,M,Y,Y,0,171000.0,302206.5,16524.0,229500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.020713,-15189,-2254,-3479.0,-4349,3.0,1,1,1,1,0,0,Laborers,2.0,3,3,THURSDAY,9,0,0,0,0,1,1,Business Entity Type 1,,0.15824961073701505,0.7700870700124128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1873.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n409480,0,Cash loans,F,N,N,0,135000.0,1546020.0,44437.5,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-16955,-3612,-8986.0,-499,,1,1,0,1,1,0,Sales staff,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Housing,0.7660565370640586,0.4730925093377376,0.6754132910917112,0.5485,0.2588,0.9871,0.8232,0.1854,0.56,0.4828,0.3333,0.375,0.1076,0.4472,0.5284,0.0,0.0,0.5588,0.2685,0.9871,0.8301,0.1871,0.5639,0.4828,0.3333,0.375,0.11,0.4885,0.5505,0.0,0.0,0.5538,0.2588,0.9871,0.8256,0.1866,0.56,0.4828,0.3333,0.375,0.1095,0.4549,0.5379,0.0,0.0,not specified,block of flats,0.517,Panel,No,1.0,0.0,1.0,0.0,-2832.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n422989,0,Revolving loans,F,N,N,0,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.022625,-7778,-744,-6177.0,-416,,1,1,1,1,0,0,,1.0,2,2,MONDAY,12,0,0,0,0,1,1,Postal,,0.4803803403907297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-356.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n154070,0,Cash loans,F,N,Y,0,90000.0,216000.0,12528.0,216000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.016612000000000002,-24467,365243,-14568.0,-3965,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.5786089530784869,,0.0804,0.0779,0.9762,,,0.0,0.069,0.1667,,0.0,,0.0624,,0.0,0.0819,0.0809,0.9762,,,0.0,0.069,0.1667,,0.0,,0.065,,0.0,0.0812,0.0779,0.9762,,,0.0,0.069,0.1667,,0.0,,0.0635,,0.0,,block of flats,0.0723,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-413.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n247671,0,Cash loans,F,Y,Y,2,144000.0,269550.0,21163.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-11761,-926,-2958.0,-2695,4.0,1,1,0,1,0,0,Sales staff,4.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.698871819010661,0.4794489811780563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-328.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n415875,0,Cash loans,F,Y,Y,0,180000.0,679896.0,38092.5,630000.0,Family,State servant,Higher education,Civil marriage,House / apartment,0.032561,-9278,-2113,-9220.0,-1647,4.0,1,1,0,1,1,0,Core staff,2.0,1,1,SATURDAY,15,0,0,0,0,0,0,Police,0.3557997661839966,0.6416713170483551,0.6347055309763198,0.3309,0.2167,0.9781,0.7008,0.0687,0.36,0.3103,0.3333,0.375,0.1168,0.2698,0.3218,0.0,0.0,0.3372,0.2249,0.9782,0.7125,0.0693,0.3625,0.3103,0.3333,0.375,0.1195,0.2948,0.3353,0.0,0.0,0.3341,0.2167,0.9781,0.7048,0.0691,0.36,0.3103,0.3333,0.375,0.1189,0.2745,0.3276,0.0,0.0,reg oper account,block of flats,0.2906,Panel,No,3.0,0.0,3.0,0.0,-1182.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n175683,1,Cash loans,F,N,Y,0,81000.0,247275.0,17716.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.002042,-18696,-644,-543.0,-1812,,1,1,1,1,0,0,Sales staff,1.0,3,3,FRIDAY,14,0,0,0,0,0,0,Self-employed,0.6646139948665354,0.2636017782631885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-704.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n213176,0,Cash loans,F,N,Y,0,148500.0,855882.0,43830.0,765000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-10214,-3336,-1378.0,-2753,,1,1,0,1,0,0,Accountants,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.44052837438524217,0.4864527658721389,0.4083588531230431,0.2103,0.1371,0.9965,0.9524,0.0608,0.2,0.1724,0.375,0.0417,0.0318,0.1673,0.2146,0.0193,0.0642,0.2143,0.1423,0.9965,0.9543,0.0613,0.2014,0.1724,0.375,0.0417,0.0326,0.1827,0.2236,0.0195,0.068,0.2123,0.1371,0.9965,0.953,0.0612,0.2,0.1724,0.375,0.0417,0.0324,0.1702,0.2185,0.0194,0.0656,reg oper spec account,block of flats,0.21600000000000005,Panel,No,6.0,0.0,6.0,0.0,-1594.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n137703,0,Cash loans,M,N,Y,0,157500.0,252000.0,9630.0,252000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.032561,-20631,-4037,-6750.0,-3914,,1,1,0,1,0,0,Security staff,2.0,1,1,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6891108370159869,0.7309873696832169,0.0928,0.0716,0.9752,0.66,0.0169,0.0,0.2069,0.1667,0.2083,0.0467,0.0756,0.0817,0.0,0.0042,0.0945,0.0743,0.9752,0.6733,0.017,0.0,0.2069,0.1667,0.2083,0.0478,0.0826,0.0851,0.0,0.0044,0.0937,0.0716,0.9752,0.6645,0.017,0.0,0.2069,0.1667,0.2083,0.0475,0.077,0.0832,0.0,0.0043,reg oper account,block of flats,0.0652,Panel,No,0.0,0.0,0.0,0.0,-1140.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n269036,0,Cash loans,M,N,Y,0,360000.0,254700.0,27153.0,225000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.032561,-11253,-202,-3709.0,-478,,1,1,0,1,0,0,Managers,1.0,1,1,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.29418733176505474,0.5316861425197883,0.145,0.1232,0.9752,,,0.0464,0.1838,0.1942,,0.0686,,0.1054,,0.0193,0.105,0.1048,0.9752,,,0.0,0.1724,0.1667,,0.0,,0.0095,,0.0,0.1041,0.1011,0.9752,,,0.0,0.1724,0.1667,,0.0785,,0.0918,,0.0027,,block of flats,0.2654,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,1.0\n144075,0,Cash loans,M,Y,Y,2,180000.0,1024740.0,55719.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006305,-10876,-934,-2595.0,-3469,6.0,1,1,0,1,0,0,Core staff,4.0,3,3,TUESDAY,9,0,0,0,0,1,1,School,0.19669365131495686,0.5573530174834337,0.3842068130556564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-489.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n138630,0,Cash loans,F,N,Y,0,135000.0,758214.0,32256.0,603000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-22367,365243,-1134.0,-4996,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.6746305575225072,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-2001.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n203578,0,Cash loans,F,Y,N,0,270000.0,1305000.0,38286.0,1305000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-18676,-613,-8173.0,-1510,6.0,1,1,1,1,0,0,Sales staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,,0.03870396663726212,0.5726825047161584,0.1485,0.0551,0.9881,0.8368,0.0177,0.16,0.1379,0.3333,0.375,0.0445,0.121,0.1544,0.0,0.0,0.1513,0.0572,0.9881,0.8432,0.0179,0.1611,0.1379,0.3333,0.375,0.0455,0.1322,0.1608,0.0,0.0,0.1499,0.0551,0.9881,0.8390000000000001,0.0179,0.16,0.1379,0.3333,0.375,0.0453,0.1231,0.1572,0.0,0.0,reg oper spec account,block of flats,0.1214,Panel,No,8.0,0.0,8.0,0.0,-620.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n228195,0,Cash loans,F,Y,Y,0,76500.0,1350000.0,37125.0,1350000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.015221,-23097,365243,-12727.0,-4101,1.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.6649825342964949,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1592.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n443229,0,Revolving loans,F,N,Y,0,90000.0,202500.0,10125.0,202500.0,Family,Commercial associate,Secondary / secondary special,Married,Office apartment,0.010276,-10748,-1448,-530.0,-537,,1,1,0,0,1,0,Managers,2.0,2,2,MONDAY,18,0,0,0,0,1,1,Business Entity Type 3,,0.11119055970630357,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n282101,0,Cash loans,M,Y,Y,1,135000.0,675000.0,24799.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.009334,-13651,-1892,-6679.0,-3640,32.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Self-employed,,0.6132042329369451,0.18629294965553744,,,0.9712,,,,0.069,0.0417,,,,0.0109,,,,,0.9712,,,,0.069,0.0417,,,,0.0114,,,,,0.9712,,,,0.069,0.0417,,,,0.0111,,,,block of flats,0.0086,Wooden,No,10.0,2.0,10.0,2.0,-332.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n223733,0,Cash loans,F,N,Y,0,135000.0,999000.0,29340.0,999000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-20034,-129,-9118.0,-3560,,1,1,0,1,1,0,Sales staff,1.0,2,2,SUNDAY,7,0,0,0,0,0,0,Self-employed,,0.5433654089580903,,,,0.9722,,,,,0.0625,,,,,,,,,0.9722,,,,,0.0417,,,,,,,,,0.9722,,,,,0.0625,,,,,,,,,0.0158,,No,0.0,0.0,0.0,0.0,-2330.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n405606,0,Cash loans,F,N,Y,0,157500.0,510853.5,24705.0,441000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018634,-23385,365243,-49.0,-4305,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,0.6564481134833,0.09328798515848752,0.36896873825284665,0.0825,0.08,0.9771,0.6872,0.0316,0.0,0.1379,0.1667,0.2083,0.049,0.0672,0.0705,0.0,0.0,0.084,0.083,0.9772,0.6994,0.0318,0.0,0.1379,0.1667,0.2083,0.0501,0.0735,0.0735,0.0,0.0,0.0833,0.08,0.9771,0.6914,0.0318,0.0,0.1379,0.1667,0.2083,0.0498,0.0684,0.0718,0.0,0.0,,block of flats,0.0727,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n419979,0,Cash loans,F,Y,Y,0,121500.0,314100.0,16573.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009657,-14420,-314,-4179.0,-4287,6.0,1,1,0,1,0,0,Cooking staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Self-employed,,0.6067272815805814,0.4794489811780563,0.0082,0.0,0.9727,0.626,0.0014,0.0,0.069,0.0417,0.0833,0.0274,0.0067,0.0084,0.0,0.0,0.0084,0.0,0.9727,0.6406,0.0014,0.0,0.069,0.0417,0.0833,0.027999999999999997,0.0073,0.0087,0.0,0.0,0.0083,0.0,0.9727,0.631,0.0014,0.0,0.069,0.0417,0.0833,0.0279,0.0068,0.0085,0.0,0.0,reg oper account,block of flats,0.0074,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1077.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n300281,0,Cash loans,M,Y,Y,0,225000.0,746280.0,58963.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14445,-136,-384.0,-4816,16.0,1,1,1,1,0,0,Laborers,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,0.4205688378330081,0.594405809686534,0.838724852442103,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n358274,0,Cash loans,M,N,Y,0,157500.0,314100.0,16164.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-16699,-1783,-1040.0,-245,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,,0.6151111756629916,0.28961123838200553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1015.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n290421,0,Cash loans,F,N,Y,0,112500.0,891000.0,27013.5,891000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-22191,365243,-8142.0,-4138,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.7578617945268987,,0.0825,0.055999999999999994,0.9881,0.8368,0.0151,0.08,0.069,0.375,0.4167,0.0494,0.0671,0.0846,0.0013,0.0198,0.084,0.0527,0.9881,0.8432,0.0137,0.0806,0.069,0.375,0.4167,0.0307,0.0735,0.0869,0.0,0.0,0.0833,0.0568,0.9881,0.8390000000000001,0.0149,0.08,0.069,0.375,0.4167,0.0527,0.0684,0.0862,0.0,0.0,reg oper account,block of flats,0.0741,Panel,No,1.0,0.0,1.0,0.0,-655.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n115720,1,Cash loans,M,N,N,1,180000.0,808650.0,26086.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-15686,-2832,-9814.0,-4729,,1,1,1,1,0,0,Laborers,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5298102697760758,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-2202.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n331760,0,Cash loans,F,N,N,0,328500.0,545040.0,20547.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.019101,-13554,-146,-5908.0,-4227,,1,1,1,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,15,1,1,1,1,1,1,Business Entity Type 3,0.5571469607451934,0.6721826116470874,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2204.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n252464,0,Cash loans,F,N,Y,0,90000.0,275076.0,32773.5,243000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16612,-1863,-5191.0,-167,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,,0.5903558665758692,,0.1433,0.045,0.9851,,,0.04,0.0345,0.3333,,0.0689,,0.0858,,0.0905,0.146,0.0467,0.9851,,,0.0403,0.0345,0.3333,,0.0705,,0.0894,,0.0958,0.1447,0.045,0.9851,,,0.04,0.0345,0.3333,,0.0701,,0.0874,,0.0924,,block of flats,0.0872,Monolithic,No,4.0,0.0,4.0,0.0,-218.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n408981,0,Cash loans,F,Y,Y,0,427500.0,1546020.0,45202.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-19984,-3362,-12672.0,-3013,1.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.5563223337593032,0.7121551551910698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-783.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n371746,0,Cash loans,F,N,Y,0,112500.0,1102500.0,43852.5,1102500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-17247,-2208,-6253.0,-804,,1,1,1,1,1,0,Managers,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Self-employed,0.8223084797242097,0.6420684314862475,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1272.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n434682,0,Cash loans,M,N,Y,0,63000.0,490005.0,29740.5,423000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009657,-19922,-2797,-2864.0,-3084,,1,1,1,1,1,0,Laborers,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 2,,0.5718981857494104,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n312956,0,Cash loans,M,Y,Y,0,225000.0,1113840.0,53716.5,900000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-13724,-739,-7816.0,-2266,9.0,1,1,0,1,0,0,Drivers,1.0,1,1,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.369951621912985,0.4543210601605785,0.0742,0.0371,0.9752,,,0.04,0.0345,0.3333,,,,0.0503,,0.0022,0.0756,0.0385,0.9752,,,0.0403,0.0345,0.3333,,,,0.0523,,0.0024,0.0749,0.0371,0.9752,,,0.04,0.0345,0.3333,,,,0.0513,,0.0023,,block of flats,0.04,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-591.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n319223,0,Cash loans,M,Y,N,0,315000.0,360000.0,26194.5,360000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-17942,-3244,-11754.0,-1469,5.0,1,1,1,1,1,0,,2.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Medicine,0.8009923898479665,0.7202922405380732,0.2032521136204725,0.6474,0.4701,0.9806,,,0.72,0.6207,0.3333,,0.5435,,0.6231,,0.0303,0.6597,0.4878,0.9806,,,0.725,0.6207,0.3333,,0.5559,,0.6492,,0.0321,0.6537,0.4701,0.9806,,,0.72,0.6207,0.3333,,0.5529,,0.6343,,0.031,,block of flats,0.4967,Panel,No,0.0,0.0,0.0,0.0,-930.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n434623,0,Revolving loans,F,N,Y,0,270000.0,675000.0,33750.0,675000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.020713,-20177,-7219,-527.0,-3466,,1,1,0,1,0,0,Core staff,1.0,3,1,SATURDAY,9,0,0,0,0,0,0,Government,,0.5900755539258112,0.7992967832109371,0.0742,0.0516,0.9836,,,0.08,0.069,0.3333,,0.0215,,0.07200000000000001,,0.0,0.0756,0.0536,0.9836,,,0.0806,0.069,0.3333,,0.022000000000000002,,0.0751,,0.0,0.0749,0.0516,0.9836,,,0.08,0.069,0.3333,,0.0218,,0.0733,,0.0,,block of flats,0.0567,Panel,No,0.0,0.0,0.0,0.0,-658.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n323744,0,Cash loans,F,Y,Y,0,270000.0,1227901.5,46899.0,1129500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-20726,-5549,-9783.0,-4232,3.0,1,1,0,1,0,0,Core staff,2.0,1,1,MONDAY,16,0,0,0,0,0,0,Government,,0.28823850714833504,0.5832379256761245,,,0.9846,,,,0.1379,,,,,0.1076,,,,,0.9846,,,,0.1379,,,,,0.1121,,,,,0.9846,,,,0.1379,,,,,0.1095,,,,,0.1327,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,10.0,1.0,2.0\n258398,0,Cash loans,F,N,Y,1,360000.0,840996.0,24718.5,702000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006852,-13762,-704,-6332.0,-4278,,1,1,0,1,0,0,Sales staff,3.0,3,3,FRIDAY,7,0,1,1,0,1,1,Business Entity Type 3,0.5968043406079093,0.34667360282139803,0.7267112092725122,0.0093,0.0,0.9722,,,0.0,0.0345,0.0417,,0.0113,,0.0082,,0.021,0.0095,0.0,0.9722,,,0.0,0.0345,0.0417,,0.0116,,0.0085,,0.0222,0.0094,0.0,0.9722,,,0.0,0.0345,0.0417,,0.0115,,0.0083,,0.0214,,block of flats,0.0064,Wooden,No,5.0,1.0,5.0,0.0,-1751.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n292884,0,Cash loans,F,N,Y,0,135000.0,544491.0,16047.0,454500.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-18448,-1659,-7265.0,-2005,,1,1,1,1,0,0,,1.0,2,2,MONDAY,11,0,0,0,0,1,1,Government,,0.6993088314127529,0.6413682574954046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1550.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n105660,0,Revolving loans,F,N,Y,0,180000.0,247500.0,12375.0,247500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-9370,-1105,-4062.0,-1388,,1,1,1,1,1,0,Core staff,2.0,1,1,SUNDAY,11,0,1,1,0,1,1,Insurance,,0.5753532091006883,0.15759499866631024,0.10099999999999999,0.0959,0.9886,,,0.064,0.1241,0.2417,,0.0179,,0.0999,,0.0022,0.0599,0.087,0.9871,,,0.0,0.1034,0.1667,,0.0094,,0.0681,,0.0,0.0822,0.0839,0.9871,,,0.0,0.1034,0.1667,,0.0154,,0.0914,,0.0,,block of flats,0.2096,Panel,No,0.0,0.0,0.0,0.0,-679.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n421088,0,Cash loans,M,Y,N,0,157500.0,523152.0,48114.0,463500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.016612000000000002,-19686,-5536,-4581.0,-3239,31.0,1,1,0,1,0,1,Laborers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.6648314954324234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1926.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n207805,0,Cash loans,M,Y,Y,2,157500.0,473760.0,51151.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019689,-14321,-2514,-1263.0,-1927,6.0,1,1,0,1,0,0,Managers,4.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.4831279704270701,0.594553417197462,,0.0732,0.0804,0.9816,0.7484,0.0148,0.0,0.1379,0.1667,0.2083,0.0246,0.0588,0.0658,0.0039,0.0511,0.0746,0.0834,0.9816,0.7583,0.0149,0.0,0.1379,0.1667,0.2083,0.0252,0.0643,0.0686,0.0039,0.0541,0.0739,0.0804,0.9816,0.7518,0.0149,0.0,0.1379,0.1667,0.2083,0.0251,0.0599,0.067,0.0039,0.0522,reg oper account,block of flats,0.0629,Panel,No,4.0,1.0,4.0,0.0,-1132.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n437439,0,Cash loans,M,Y,N,0,45000.0,101880.0,10075.5,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23227,365243,-15297.0,-3788,36.0,1,0,0,1,0,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.6575423678725456,0.7981372313187245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-376.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,4.0\n302732,0,Cash loans,F,N,Y,0,315000.0,1260702.0,39712.5,1129500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-16079,-3162,-240.0,-4129,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,,0.5805058919729756,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-664.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n413101,0,Cash loans,F,Y,Y,0,270000.0,1762110.0,46611.0,1575000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.022625,-18315,-3541,-6988.0,-1855,1.0,1,1,0,1,1,0,High skill tech staff,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.7895344630266602,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-769.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n190668,0,Cash loans,M,N,N,0,90000.0,117162.0,11542.5,103500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-14301,-1114,-6297.0,-4606,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,15,0,0,0,0,1,1,Business Entity Type 2,0.11108598439310063,0.5154682605020935,0.5046813193144684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,1.0,-1499.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n412080,0,Cash loans,F,N,N,1,171000.0,463284.0,33700.5,382500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009549,-9933,-1102,-912.0,-2605,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Industry: type 7,0.6102723998248067,0.6635780367249186,0.6722428897082422,0.2216,0.0896,0.9791,0.7144,0.6381,0.16,0.1379,0.3333,0.375,0.1233,0.1807,0.1766,0.0154,0.1414,0.2258,0.09300000000000001,0.9791,0.7256,0.6439,0.1611,0.1379,0.3333,0.375,0.1261,0.1974,0.184,0.0156,0.1497,0.2238,0.0896,0.9791,0.7182,0.6421,0.16,0.1379,0.3333,0.375,0.1255,0.1838,0.1798,0.0155,0.1443,reg oper account,block of flats,0.1792,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2244.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n372444,0,Cash loans,F,N,Y,0,126000.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-19326,-1818,-2875.0,-2622,,1,1,0,1,1,0,Laborers,2.0,3,3,SUNDAY,8,0,0,0,0,0,0,Industry: type 3,0.7914314256869764,0.34950704566625745,0.3233112448967859,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,2.0,7.0,1.0,-829.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n149942,0,Cash loans,M,Y,Y,0,157500.0,234324.0,15786.0,207000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-21107,-988,-11917.0,-4010,6.0,1,1,0,1,0,0,Drivers,2.0,3,3,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.4132168331898188,0.8454379217710598,0.0722,0.0845,0.9781,0.7008,0.0086,0.0,0.1379,0.1667,0.2083,0.0544,0.0588,0.0669,0.0,0.0,0.0735,0.0877,0.9782,0.7125,0.0087,0.0,0.1379,0.1667,0.2083,0.0557,0.0643,0.0697,0.0,0.0,0.0729,0.0845,0.9781,0.7048,0.0087,0.0,0.1379,0.1667,0.2083,0.0554,0.0599,0.0681,0.0,0.0,reg oper account,block of flats,0.0573,\"Stone, brick\",No,10.0,0.0,10.0,0.0,-791.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n368304,0,Cash loans,F,Y,Y,0,225000.0,900000.0,35824.5,900000.0,Family,Working,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-17766,-2640,-6621.0,-1307,10.0,1,1,1,1,1,0,,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 2,,0.5507885179960961,0.3077366963789207,0.1371,0.0,0.9732,0.6328,0.0261,0.0,0.069,0.1667,0.2083,0.0358,0.1067,0.0576,0.0232,0.0182,0.1397,0.0,0.9732,0.6472,0.0264,0.0,0.069,0.1667,0.2083,0.0366,0.1166,0.06,0.0233,0.0193,0.1384,0.0,0.9732,0.6377,0.0263,0.0,0.069,0.1667,0.2083,0.0364,0.1086,0.0586,0.0233,0.0186,reg oper account,block of flats,0.0493,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1921.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n285292,0,Cash loans,F,N,Y,0,157500.0,630000.0,34308.0,630000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.011703,-14015,-941,-7062.0,-4176,,1,1,0,1,0,1,Sales staff,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,,0.6438856211355779,,0.0928,0.0881,0.9831,0.7688,0.018000000000000002,0.0,0.2069,0.1667,0.2083,0.0273,0.0756,0.0839,0.0,0.0,0.0945,0.0915,0.9831,0.7779,0.0181,0.0,0.2069,0.1667,0.2083,0.0279,0.0826,0.0874,0.0,0.0,0.0937,0.0881,0.9831,0.7719,0.0181,0.0,0.2069,0.1667,0.2083,0.0278,0.077,0.0854,0.0,0.0,not specified,block of flats,0.0758,Panel,No,1.0,0.0,1.0,0.0,-522.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n168590,0,Revolving loans,F,N,Y,0,135000.0,405000.0,20250.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-17173,-1354,-10177.0,-731,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,Other,0.7151665956672912,0.7702188304369981,0.3280631605201915,0.0928,0.131,0.9826,0.762,0.0154,0.0,0.2069,0.1667,0.2083,0.0926,0.0756,0.0894,0.0,0.0,0.0945,0.1359,0.9826,0.7713,0.0155,0.0,0.2069,0.1667,0.2083,0.0947,0.0826,0.0932,0.0,0.0,0.0937,0.131,0.9826,0.7652,0.0155,0.0,0.2069,0.1667,0.2083,0.0942,0.077,0.091,0.0,0.0,reg oper account,block of flats,0.0787,Mixed,No,1.0,1.0,1.0,0.0,-1930.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n371480,0,Cash loans,F,Y,N,0,180000.0,755190.0,31995.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.04622,-16417,-3026,-10441.0,-3621,8.0,1,1,1,1,0,0,Laborers,2.0,1,1,SATURDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.7537492288467954,0.5370699579791587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2000.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n406542,0,Cash loans,F,N,N,0,130500.0,263686.5,28107.0,238500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.02461,-10632,-298,-3222.0,-3222,,1,1,0,1,0,0,Drivers,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,Other,,0.25310696810787225,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2428.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n115630,0,Revolving loans,F,N,Y,0,90000.0,315000.0,15750.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.018029,-18984,-3162,-9930.0,-2498,,1,1,0,1,0,0,Cleaning staff,1.0,3,3,MONDAY,11,0,0,0,0,0,0,Other,,0.6427164650504279,0.6446794549585961,0.0289,0.0,0.9732,0.6192,0.001,0.0,0.069,0.0833,0.0417,0.0055,0.0067,0.0233,0.0,0.0011,0.0084,0.0,0.9722,0.6341,0.001,0.0,0.0345,0.0417,0.0417,0.0,0.0073,0.0086,0.0,0.0,0.0291,0.0,0.9732,0.6243,0.001,0.0,0.069,0.0833,0.0417,0.0056,0.0068,0.0237,0.0,0.0011,reg oper account,block of flats,0.0523,Wooden,No,6.0,2.0,6.0,2.0,-2146.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n283663,0,Cash loans,F,N,Y,1,40500.0,549882.0,16209.0,459000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17877,-5934,-3152.0,-1412,,1,1,0,1,0,0,Core staff,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Kindergarten,,0.7013831270094978,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1241.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n124748,0,Cash loans,F,N,Y,0,112500.0,781920.0,25969.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007305,-21538,365243,-5950.0,-4276,,1,0,0,1,1,0,,2.0,3,3,MONDAY,12,0,0,0,0,0,0,XNA,,0.6354686663170481,0.7062051096536562,0.0186,,,,,,,0.0417,,,,0.015,,,0.0189,,,,,,,0.0417,,,,0.0156,,,0.0187,,,,,,,0.0417,,,,0.0152,,,,block of flats,0.0118,,No,0.0,0.0,0.0,0.0,-2032.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n397830,0,Cash loans,F,N,N,0,157500.0,509400.0,28444.5,450000.0,Unaccompanied,Working,Incomplete higher,Married,Rented apartment,0.008865999999999999,-14643,-1574,-4194.0,-1767,,1,1,1,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,22,0,0,0,0,0,0,Self-employed,0.531612676026193,0.6548932283129316,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-851.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n127088,0,Cash loans,M,N,Y,0,90000.0,148140.0,11835.0,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003813,-21947,365243,-3086.0,-536,,1,0,0,1,0,1,,2.0,2,2,WEDNESDAY,6,0,0,0,0,0,0,XNA,0.3872361280631049,0.11700038883174155,0.36227724703843145,0.1144,0.0772,0.9826,,,0.0,0.1379,0.1667,,0.0196,,0.0596,,0.022000000000000002,0.1166,0.0801,0.9826,,,0.0,0.1379,0.1667,,0.02,,0.0621,,0.0233,0.1155,0.0772,0.9826,,,0.0,0.1379,0.1667,,0.0199,,0.0607,,0.0225,,specific housing,0.0609,Panel,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n432657,0,Revolving loans,M,N,Y,0,157500.0,450000.0,22500.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-9905,-2691,-4725.0,-2549,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,9,0,0,0,1,1,0,Business Entity Type 2,,0.4248161058463641,0.31547215492577346,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1523.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n265375,1,Cash loans,M,N,N,0,166500.0,225000.0,10944.0,225000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.006629,-9161,-313,-3774.0,-1829,,1,1,0,1,1,0,Drivers,1.0,2,2,SUNDAY,10,0,0,0,0,1,1,Transport: type 3,0.2455788534801089,0.36516825934527497,0.6363761710860439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1030.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n135073,0,Cash loans,F,N,Y,0,202500.0,852088.5,38632.5,688500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011657,-18887,-5335,-1230.0,-2395,,1,1,0,1,0,0,,2.0,1,1,WEDNESDAY,15,0,1,1,0,0,0,Business Entity Type 3,,0.6613728197657269,0.6801388218428291,0.0619,,0.9975,0.966,,0.0,0.2414,0.1667,0.2083,0.0655,0.0504,0.071,0.0,0.0,0.063,,0.9975,0.9673,,0.0,0.2414,0.1667,0.2083,0.067,0.0551,0.0739,0.0,0.0,0.0625,,0.9975,0.9665,,0.0,0.2414,0.1667,0.2083,0.0667,0.0513,0.0722,0.0,0.0,reg oper account,block of flats,0.0558,Monolithic,No,1.0,0.0,1.0,0.0,-690.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n312267,0,Cash loans,F,N,Y,1,292500.0,345024.0,29740.5,288000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.04622,-12948,-2006,-7054.0,-3699,,1,1,0,1,0,1,Core staff,3.0,1,1,MONDAY,15,0,1,1,1,0,0,Security Ministries,0.3131293561298022,0.7691955136950774,0.2032521136204725,,,,,,,,,,,,0.0552,,,,,,,,,,,,,,0.0575,,,,,,,,,,,,,,0.0562,,,,,0.0434,,No,3.0,0.0,3.0,0.0,-807.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n180129,0,Cash loans,F,N,N,0,112500.0,675000.0,19867.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018209,-19502,-1600,-9412.0,-3013,,1,1,0,1,0,0,,1.0,3,3,SATURDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.6065028933237117,0.4776491548517548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n105885,1,Revolving loans,F,N,Y,2,81000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-10328,-523,-2817.0,-381,,1,1,0,1,0,0,Sales staff,4.0,2,2,SATURDAY,13,0,0,0,0,0,0,Trade: type 3,0.2239893553320016,0.06589377625835477,0.6413682574954046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-613.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n273815,0,Revolving loans,M,Y,Y,0,180000.0,540000.0,27000.0,540000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.072508,-13674,-333,-7724.0,-2789,11.0,1,1,0,1,1,1,High skill tech staff,1.0,1,1,SATURDAY,15,0,0,0,0,0,0,Government,0.4974482227900331,0.7649878654844101,0.5585066276769286,0.1646,0.0689,0.9841,0.7076,0.1221,0.1264,0.0803,0.5275,0.5833,0.0517,0.0824,0.1835,0.0,0.0272,0.25,0.0397,0.9772,0.7190000000000001,0.1232,0.1611,0.0345,0.3333,0.5833,0.0,0.09,0.3373,0.0,0.0,0.1494,0.0718,0.9781,0.7115,0.1228,0.16,0.069,0.5417,0.5833,0.0379,0.0838,0.145,0.0,0.0,reg oper spec account,block of flats,0.2724,Block,No,0.0,0.0,0.0,0.0,-819.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n244929,0,Cash loans,F,N,Y,0,49500.0,47970.0,5089.5,45000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-15462,-395,-4020.0,-4567,,1,1,1,1,1,0,Sales staff,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Trade: type 7,,0.04771589312029672,,0.2227,,0.9881,,,0.24,0.2069,0.3333,,0.1533,,0.2206,,,0.2269,,0.9881,,,0.2417,0.2069,0.3333,,0.1568,,0.2298,,,0.2248,,0.9881,,,0.24,0.2069,0.3333,,0.156,,0.2246,,,,block of flats,0.1735,Panel,No,0.0,0.0,0.0,0.0,-298.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n309756,0,Cash loans,F,Y,Y,0,198000.0,517788.0,19647.0,427500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010556,-21119,365243,-3582.0,-4473,3.0,1,0,0,1,0,0,,2.0,3,3,WEDNESDAY,17,0,0,0,0,0,0,XNA,0.7932653969624505,0.2945189051511191,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1211.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n298237,0,Cash loans,F,N,N,2,67500.0,59256.0,4441.5,45000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.015221,-11728,-839,-5299.0,-2993,,1,1,1,1,0,0,,3.0,2,2,MONDAY,14,0,0,0,0,0,0,Government,,0.3191401660608097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n303329,0,Cash loans,F,N,N,2,180000.0,906228.0,46269.0,810000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-13791,-1353,-1081.0,-1577,,1,1,1,1,0,0,Accountants,4.0,2,2,THURSDAY,20,0,0,0,0,1,1,Industry: type 3,0.7925309172715816,0.4574034663646168,,0.2,0.1194,0.997,,0.0909,0.24,0.1034,0.5417,0.5,0.09300000000000001,0.158,0.2974,0.0232,0.0641,0.2038,0.1239,0.997,,0.0917,0.2417,0.1034,0.5417,0.5,0.0951,0.1726,0.3098,0.0233,0.0679,0.2019,0.1194,0.997,,0.0914,0.24,0.1034,0.5417,0.5,0.0946,0.1608,0.3027,0.0233,0.0655,org spec account,block of flats,0.318,Monolithic,No,1.0,0.0,1.0,0.0,-873.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n236216,1,Cash loans,M,Y,N,0,90000.0,490536.0,23859.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.014519999999999996,-13490,-1149,-4698.0,-2256,17.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,8,0,0,0,1,1,0,Government,0.1841628226867372,0.2319427311585574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-263.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n206801,0,Cash loans,F,N,N,0,225000.0,1097491.5,46629.0,931500.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,House / apartment,0.014464,-16657,-1133,-1448.0,-209,,1,1,0,1,1,0,Medicine staff,2.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Medicine,0.30417963861016634,0.14790515782069666,0.5154953751603267,0.0928,0.0482,0.9836,,,,0.2069,0.1667,,,,0.0828,,0.0353,0.0945,0.05,0.9836,,,,0.2069,0.1667,,,,0.0862,,0.0373,0.0937,0.0482,0.9836,,,,0.2069,0.1667,,,,0.0843,,0.036000000000000004,,block of flats,0.0728,Panel,No,0.0,0.0,0.0,0.0,-2515.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n208522,0,Cash loans,F,N,Y,0,112500.0,293535.0,29160.0,265500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.011657,-21670,-2629,-10954.0,-1901,,1,1,0,1,0,0,,1.0,1,1,TUESDAY,12,0,0,0,0,1,1,Medicine,0.6623265251016435,0.685213845537127,0.6577838002083306,0.0619,0.0779,0.9851,0.7959999999999999,0.0086,0.0,0.1034,0.1667,0.2083,0.0,0.0504,0.0658,0.0,0.0,0.063,0.0809,0.9851,0.804,0.0087,0.0,0.1034,0.1667,0.2083,0.0,0.0551,0.0685,0.0,0.0,0.0625,0.0779,0.9851,0.7987,0.0087,0.0,0.1034,0.1667,0.2083,0.0,0.0513,0.0669,0.0,0.0,reg oper account,block of flats,0.0564,Panel,No,0.0,0.0,0.0,0.0,-607.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n168305,0,Cash loans,F,N,Y,1,135000.0,545040.0,26509.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00496,-15616,-4255,-5002.0,-4801,,1,1,1,1,1,0,,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.4189132300954746,0.4594176491226248,0.4812493411434029,0.0825,,0.9796,,,0.0,0.0345,0.1667,0.2083,,,0.0464,,,0.084,,0.9796,,,0.0,0.0345,0.1667,0.2083,,,0.0475,,,0.0833,,0.9796,,,0.0,0.0345,0.1667,0.2083,,,0.0472,,,,specific housing,0.0313,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-968.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n409713,1,Cash loans,M,Y,N,0,180000.0,450000.0,35554.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Municipal apartment,0.011657,-10267,-2976,-8468.0,-2533,18.0,1,1,0,1,0,0,Drivers,1.0,1,1,WEDNESDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.6830069700068934,0.43473324875017305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1259.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n192895,1,Revolving loans,F,N,Y,1,76500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-11570,-102,-5248.0,-2553,,1,1,1,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.530817927520793,,0.0,,0.0,,,0.0,,,,,,,,,0.0,,0.0,,,0.0,,,,,,,,,0.0,,0.0,,,0.0,,,,,,,,,,block of flats,0.0,,No,0.0,0.0,0.0,0.0,-8.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,,,,,,\n130546,0,Cash loans,F,N,N,0,270000.0,900000.0,32017.5,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.0228,-20470,365243,-6240.0,-4010,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.5562413693572473,0.3280631605201915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1676.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n160794,0,Cash loans,F,N,Y,0,202500.0,1258650.0,53455.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.002134,-16101,-7141,-4413.0,-4452,,1,1,0,1,0,1,Core staff,2.0,3,3,MONDAY,14,0,0,0,0,1,1,School,,0.4852237017598399,0.6479768603302221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n203301,1,Cash loans,F,N,Y,0,112500.0,312768.0,19030.5,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.005084,-17313,-2386,-9344.0,-843,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.1214572174764951,0.7062051096536562,0.0825,0.0921,0.9767,,,,0.1379,0.1667,,0.0244,,0.0467,,,0.084,0.0955,0.9767,,,,0.1379,0.1667,,0.0249,,0.0486,,,0.0833,0.0921,0.9767,,,,0.1379,0.1667,,0.0248,,0.0475,,,,block of flats,0.0553,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1320.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n453209,0,Cash loans,F,Y,Y,1,180000.0,550980.0,43659.0,450000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.010966,-12756,-2632,-4947.0,-2940,4.0,1,1,0,1,0,1,Managers,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,0.37756868583119196,0.7055069429969123,0.6446794549585961,0.0082,0.0,0.9712,0.6056,0.0011,0.0,0.0345,0.0417,0.0833,0.0183,0.0067,0.0084,0.0,0.0,0.0084,0.0,0.9712,0.621,0.0011,0.0,0.0345,0.0417,0.0833,0.0187,0.0073,0.0087,0.0,0.0,0.0083,0.0,0.9712,0.6109,0.0011,0.0,0.0345,0.0417,0.0833,0.0186,0.0068,0.0085,0.0,0.0,reg oper account,block of flats,0.0072,Block,No,0.0,0.0,0.0,0.0,-330.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n441280,1,Cash loans,F,N,Y,0,193500.0,1546020.0,42642.0,1350000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.025164,-20980,-11800,-2040.0,-280,,1,1,0,1,0,0,Medicine staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Medicine,,0.697147864431653,0.7583930617144343,0.1526,,0.9881,,,0.0,0.4138,0.1667,,,,0.1042,,0.2232,0.1555,,0.9881,,,0.0,0.4138,0.1667,,,,0.1085,,0.2363,0.1541,,0.9881,,,0.0,0.4138,0.1667,,,,0.1061,,0.2279,,block of flats,0.1305,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-209.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n245597,1,Revolving loans,F,N,Y,0,81000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.01885,-10983,-1176,-2372.0,-2375,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Industry: type 7,0.18177634130399173,0.3568000554074159,0.13259749523614614,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1584.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n386078,0,Cash loans,M,N,Y,0,67500.0,665892.0,21609.0,477000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-19832,-554,-658.0,-1581,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.11837412641147645,0.722392890081304,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-416.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n318197,0,Cash loans,M,Y,Y,2,112500.0,229230.0,15052.5,202500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-11630,-978,-4715.0,-4156,8.0,1,1,1,1,0,0,Laborers,4.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.194141946406026,0.2622583692422573,0.5832379256761245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2528.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n266857,0,Cash loans,F,Y,N,0,270000.0,481500.0,47754.0,481500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-23274,365243,-12396.0,-4639,9.0,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.6951959691704224,,0.6021,0.5124,0.9826,,,0.68,0.5862,0.3333,,0.5795,,0.7059,,0.0,0.6134,0.5317,0.9826,,,0.6848,0.5862,0.3333,,0.5927,,0.7354,,0.0,0.6079,0.5124,0.9826,,,0.68,0.5862,0.3333,,0.5896,,0.7185,,0.0,,block of flats,0.655,Panel,No,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n307918,0,Cash loans,F,N,Y,2,157500.0,135000.0,13482.0,135000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.0228,-12114,-314,-10735.0,-1548,,1,1,0,1,0,0,,4.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.6918405560372151,0.5901337368421279,0.6195277080511546,0.2227,0.0565,0.9851,0.7959999999999999,0.0488,0.24,0.2069,0.3333,0.0417,0.0975,0.1799,0.23600000000000002,0.0077,0.0127,0.2269,0.0586,0.9851,0.804,0.0493,0.2417,0.2069,0.3333,0.0417,0.0997,0.1965,0.2459,0.0078,0.0134,0.2248,0.0565,0.9851,0.7987,0.0491,0.24,0.2069,0.3333,0.0417,0.0992,0.183,0.2402,0.0078,0.013000000000000001,not specified,block of flats,0.1884,Panel,No,2.0,0.0,2.0,0.0,-1779.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n406959,0,Revolving loans,M,N,N,0,225000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.008625,-13741,-1378,-7508.0,-4344,,1,1,0,1,0,1,Managers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.506925923932453,0.7859108405603399,0.7517237147741489,0.0175,0.0418,0.9717,,,0.0,0.069,0.0833,,,,0.0189,,0.0,0.0179,0.0434,0.9717,,,0.0,0.069,0.0833,,,,0.0197,,0.0,0.0177,0.0418,0.9717,,,0.0,0.069,0.0833,,,,0.0192,,0.0,,block of flats,0.0148,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-735.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n388156,0,Cash loans,F,N,N,2,67500.0,225000.0,16501.5,225000.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.004849,-11089,-1539,-4119.0,-3438,,1,1,1,1,0,0,Cooking staff,4.0,2,2,FRIDAY,12,1,1,0,1,1,0,Kindergarten,0.2254400332492441,0.4201742261448295,0.4436153084085652,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1146.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n289107,1,Cash loans,M,Y,N,1,225000.0,248760.0,26788.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.00702,-17854,-2280,-2180.0,-1391,8.0,1,1,1,1,1,1,Laborers,3.0,2,2,TUESDAY,15,1,1,0,1,1,0,Transport: type 4,0.15426978795346802,0.3761040767240886,0.25396280933631177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-69.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,5.0\n341378,0,Cash loans,M,Y,Y,0,405000.0,755190.0,29947.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.072508,-22581,-2691,-6346.0,-4016,5.0,1,1,0,1,1,0,High skill tech staff,2.0,1,1,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.7512322536263214,0.5744466170995097,0.2189,0.2204,0.9911,0.8776,0.0011,0.3732,0.1838,0.4442,0.0417,0.0,0.1779,0.2648,0.0025,0.0676,0.0578,0.1013,0.9911,0.8824,0.0,0.1611,0.1379,0.5417,0.0417,0.0,0.0496,0.0969,0.0039,0.0068,0.2207,0.2738,0.9911,0.8792,0.0011,0.32,0.1379,0.5417,0.0417,0.0,0.1813,0.2364,0.0039,0.0147,reg oper account,block of flats,0.2222,Panel,No,0.0,0.0,0.0,0.0,-788.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n194792,0,Cash loans,M,Y,Y,0,202500.0,1006920.0,42790.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-15438,-720,-4083.0,-4083,6.0,1,1,1,1,1,0,Laborers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Housing,,0.6042513236025229,0.2707073872651806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-541.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n437433,0,Cash loans,F,N,Y,0,135000.0,527377.5,27094.5,400500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-18995,-2088,-2668.0,-2431,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Self-employed,,0.472193172202221,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1001.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n273870,0,Cash loans,F,N,Y,0,193500.0,1288350.0,37800.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-15679,-793,-3743.0,-4002,,1,1,0,1,1,0,Sales staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,0.6789493733384766,0.15727332954329926,0.2301588968634147,0.0928,0.0858,0.9771,,,0.0,0.2069,0.1667,,0.0715,,0.0874,,0.0,0.0945,0.0891,0.9772,,,0.0,0.2069,0.1667,,0.0731,,0.091,,0.0,0.0937,0.0858,0.9771,,,0.0,0.2069,0.1667,,0.0727,,0.0889,,0.0,,block of flats,0.09,Panel,No,0.0,0.0,0.0,0.0,-1637.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n265662,0,Cash loans,M,Y,N,0,202500.0,814041.0,23800.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.030755,-8447,-1204,-4772.0,-1075,12.0,1,1,1,1,0,0,Drivers,2.0,2,2,SATURDAY,15,0,0,0,1,1,0,Business Entity Type 3,0.2301716463096717,0.21568177256242616,0.2622489709189549,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-806.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n311661,0,Cash loans,F,N,Y,0,135000.0,454500.0,19386.0,454500.0,Family,Working,Secondary / secondary special,Widow,House / apartment,0.004849,-19526,-3437,-13624.0,-3060,,1,1,0,1,1,0,Medicine staff,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Medicine,,0.2628807890522423,0.21885908222837447,0.1299,,0.9801,,,,0.2759,0.1667,,0.1349,,0.1166,,0.0,0.1324,,0.9801,,,,0.2759,0.1667,,0.138,,0.1214,,0.0,0.1312,,0.9801,,,,0.2759,0.1667,,0.1373,,0.1186,,0.0,,block of flats,0.1043,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2040.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n314110,1,Cash loans,F,Y,Y,0,180000.0,457717.5,33435.0,414000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.010006,-16231,-3205,-8995.0,-4724,3.0,1,1,1,1,1,0,Sales staff,2.0,2,1,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.3721817910017969,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-747.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n372688,0,Cash loans,F,N,Y,0,40950.0,225000.0,10039.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.028663,-21920,365243,-5055.0,-4739,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.5449655586060089,0.7898803468924867,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1176.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n431073,0,Cash loans,M,N,N,0,157500.0,194922.0,15214.5,157500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.00733,-19496,-1231,-8791.0,-3050,,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6388003264024186,0.7194907850918436,0.1485,0.1047,0.9876,,,0.16,0.1379,0.3333,,0.2051,,0.0927,,0.0011,0.1513,0.1086,0.9876,,,0.1611,0.1379,0.3333,,0.2097,,0.0966,,0.0012,0.1499,0.1047,0.9876,,,0.16,0.1379,0.3333,,0.2086,,0.0943,,0.0011,,block of flats,0.1221,Panel,No,1.0,1.0,1.0,1.0,-1383.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n317188,0,Cash loans,F,N,Y,0,112500.0,614223.0,31491.0,549000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16506,-161,-8536.0,-65,,1,1,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,1,1,0,Self-employed,0.6654862951205514,0.6452432994177021,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1620.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n177243,0,Cash loans,M,Y,Y,2,202500.0,1278045.0,35145.0,1116000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-12695,-1535,-1781.0,-5046,21.0,1,1,0,1,1,0,,4.0,3,3,THURSDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.277193299523475,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n398203,0,Cash loans,F,Y,Y,2,157500.0,835605.0,24561.0,697500.0,Family,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.025164,-12404,-621,-678.0,-692,8.0,1,1,0,1,0,0,Cooking staff,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Transport: type 2,0.4096068383341088,0.5336271223660763,0.34578480246959553,0.1021,0.0831,0.9762,0.6736,0.0257,0.0,0.2069,0.1667,0.2083,0.0493,0.079,0.0839,0.0193,0.0511,0.10400000000000001,0.0862,0.9762,0.6864,0.026000000000000002,0.0,0.2069,0.1667,0.2083,0.0504,0.0863,0.0874,0.0195,0.0541,0.1031,0.0831,0.9762,0.6779999999999999,0.0259,0.0,0.2069,0.1667,0.2083,0.0501,0.0804,0.0854,0.0194,0.0522,not specified,block of flats,0.0912,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n157943,0,Cash loans,M,Y,Y,2,180000.0,1350000.0,39474.0,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-15692,-2551,-3171.0,-4422,1.0,1,1,1,1,1,0,Managers,4.0,2,2,MONDAY,22,0,0,0,0,1,1,Transport: type 4,,0.7174107284936683,0.520897599048938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3615.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n391652,0,Cash loans,F,Y,Y,1,69750.0,339241.5,12312.0,238500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-18432,365243,-34.0,-1986,11.0,1,0,0,1,0,0,,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,XNA,,0.2178983844556452,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n159256,0,Cash loans,F,N,Y,1,108000.0,1078200.0,38331.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-12876,-1116,-2357.0,-2039,,1,1,0,1,1,0,Managers,3.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4991326480048802,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-810.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n451346,0,Cash loans,F,Y,Y,2,225000.0,709033.5,51592.5,657000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-16176,-2467,-4796.0,-4572,6.0,1,1,0,1,0,0,Accountants,4.0,1,1,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.5579123589452009,0.72068417886442,0.3774042489507649,0.1041,,0.9906,,,,0.1034,0.3333,,,,,,,0.1061,,0.9906,,,,0.1034,0.3333,,,,,,,0.1051,,0.9906,,,,0.1034,0.3333,,,,,,,,block of flats,0.1199,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,6.0\n447929,0,Cash loans,F,Y,Y,1,144000.0,781920.0,32998.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-16477,-2282,-6070.0,-17,14.0,1,1,0,1,1,0,,3.0,2,2,FRIDAY,16,0,0,0,0,0,0,Transport: type 4,,0.5155501850367096,0.5028782772082183,0.0423,0.0343,0.9886,0.8436,0.0027,0.0,0.0345,0.0833,0.0,0.0537,0.0345,0.0389,0.0077,0.0297,0.0431,0.0356,0.9886,0.8497,0.0028,0.0,0.0345,0.0833,0.0,0.0549,0.0376,0.0406,0.0078,0.0315,0.0427,0.0343,0.9886,0.8457,0.0027,0.0,0.0345,0.0833,0.0,0.0546,0.0351,0.0396,0.0078,0.0303,org spec account,block of flats,0.0386,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2210.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n161641,0,Cash loans,F,N,Y,1,189000.0,178290.0,17496.0,157500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.01885,-16552,-5074,-5249.0,-75,,1,1,0,1,0,0,,3.0,2,2,SUNDAY,10,0,0,0,0,0,0,Other,,0.7618996032845168,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1611.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n262597,0,Cash loans,M,Y,N,0,135000.0,1061599.5,31171.5,927000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-22176,365243,-3113.0,-3113,9.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.6546658493825528,0.2458512138252296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-1502.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n229552,0,Cash loans,F,N,Y,0,54000.0,1436850.0,42142.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-20529,365243,-1838.0,-3643,,1,0,0,1,0,0,,2.0,3,1,MONDAY,7,0,0,0,0,0,0,XNA,,0.7245675468144317,,0.1845,0.1504,0.9841,0.7824,0.0403,0.2,0.1724,0.3333,0.375,0.0606,0.1505,0.1933,,0.0,0.188,0.156,0.9841,0.7909,0.0407,0.2014,0.1724,0.3333,0.375,0.062,0.1644,0.2014,,0.0,0.1863,0.1504,0.9841,0.7853,0.0406,0.2,0.1724,0.3333,0.375,0.0617,0.1531,0.1968,,0.0,reg oper account,block of flats,0.1741,Panel,No,1.0,0.0,1.0,0.0,-341.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n393476,1,Cash loans,M,N,Y,0,135000.0,691020.0,22419.0,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-22599,-5566,-11622.0,-4376,,1,1,0,1,0,0,,2.0,1,1,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.672327153033642,0.5902333386185574,0.2216,0.1093,0.9841,0.7824,0.0716,0.24,0.2069,0.3333,0.375,0.1519,0.1807,0.2028,0.0,0.0,0.2258,0.1134,0.9841,0.7909,0.0723,0.2417,0.2069,0.3333,0.375,0.1554,0.1974,0.2113,0.0,0.0,0.2238,0.1093,0.9841,0.7853,0.0721,0.24,0.2069,0.3333,0.375,0.1546,0.1838,0.2065,0.0,0.0,reg oper spec account,block of flats,0.1987,Panel,No,1.0,0.0,1.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n104301,0,Cash loans,M,Y,Y,0,202500.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-12943,-894,-756.0,-2340,1.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,1,1,0,Business Entity Type 3,,0.62960323878573,0.2807895743848605,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-606.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n396155,0,Cash loans,F,N,N,0,202500.0,563877.0,42291.0,504000.0,Family,Working,Higher education,Married,House / apartment,0.009657,-10842,-2410,-156.0,-1626,,1,1,0,1,1,0,Core staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Police,0.6035685715500649,0.6328711732095873,0.5028782772082183,0.1856,0.1409,0.9841,0.7824,0.0,0.2,0.1724,0.3333,0.0417,0.1089,0.1513,0.1919,0.0,0.0019,0.1891,0.1462,0.9841,0.7909,0.0,0.2014,0.1724,0.3333,0.0417,0.1114,0.1653,0.1999,0.0,0.002,0.1874,0.1409,0.9841,0.7853,0.0,0.2,0.1724,0.3333,0.0417,0.1108,0.1539,0.1953,0.0,0.0019,reg oper account,block of flats,0.1797,Panel,No,4.0,1.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n221757,0,Cash loans,M,Y,N,0,382500.0,472500.0,44860.5,454500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.032561,-11784,-2960,-6072.0,-4106,10.0,1,1,1,1,1,0,IT staff,2.0,1,1,THURSDAY,15,0,1,1,0,1,1,Telecom,,0.7034354958361237,0.7826078370261895,0.0753,0.0826,0.9722,0.6192,0.0159,0.0,0.1724,0.1667,0.2083,0.0207,0.0614,0.0935,,0.0121,0.0767,0.0858,0.9722,0.6341,0.016,0.0,0.1724,0.1667,0.2083,0.0212,0.067,0.0974,,0.0128,0.076,0.0826,0.9722,0.6243,0.016,0.0,0.1724,0.1667,0.2083,0.0211,0.0624,0.0952,,0.0124,reg oper account,block of flats,0.0849,Panel,No,0.0,0.0,0.0,0.0,-2058.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n393047,0,Cash loans,F,Y,Y,0,202500.0,790434.0,33619.5,706500.0,Unaccompanied,Working,Higher education,Married,Co-op apartment,0.016612000000000002,-17917,-8011,-2851.0,-1470,65.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Other,0.7379513860583621,0.5461908098514419,0.4956658291397297,0.1977,0.1173,0.9940000000000001,0.932,0.079,0.16,0.1931,0.3417,0.4167,0.0876,0.1666,0.1733,0.0203,0.018000000000000002,0.1523,0.0868,0.995,0.9347,0.0531,0.1611,0.1379,0.375,0.4167,0.0192,0.1451,0.0865,0.0117,0.0,0.1686,0.1019,0.995,0.9329,0.0654,0.16,0.1379,0.375,0.4167,0.0877,0.1355,0.1562,0.0136,0.0111,reg oper account,block of flats,0.1579,Panel,No,0.0,0.0,0.0,0.0,-1070.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n249450,0,Cash loans,M,Y,N,0,202500.0,369000.0,9733.5,369000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.008575,-14723,-4897,-8716.0,-4962,3.0,1,1,0,1,1,0,Drivers,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Self-employed,,0.4559113682234132,0.7922644738669378,0.0763,0.057,0.9831,0.7688,0.046,0.04,0.069,0.25,0.2917,0.053,0.0622,0.0647,0.0,0.0,0.042,0.0451,0.9821,0.7648,0.0312,0.0,0.069,0.1667,0.2083,0.0429,0.0367,0.0382,0.0,0.0,0.077,0.057,0.9831,0.7719,0.0463,0.04,0.069,0.25,0.2917,0.054000000000000006,0.0633,0.0659,0.0,0.0,org spec account,block of flats,0.0289,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n281918,1,Cash loans,F,N,Y,2,157500.0,1078200.0,31653.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.014464,-13229,-2090,-7294.0,-3310,,1,1,0,1,1,0,Cooking staff,3.0,2,2,THURSDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.6321247710982906,0.5199727536163273,0.2445163919946749,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-526.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n160945,1,Cash loans,F,N,Y,0,202500.0,675000.0,70879.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-9939,-1885,-9910.0,-1500,,1,1,1,1,0,0,,2.0,1,1,WEDNESDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.7095027495261984,0.08559545132461807,0.0423,0.0155,0.9483,,,0.0,0.069,0.125,,0.0157,,0.0497,,0.0105,0.0431,0.016,0.9484,,,0.0,0.069,0.125,,0.0161,,0.0518,,0.0111,0.0427,0.0155,0.9483,,,0.0,0.069,0.125,,0.016,,0.0506,,0.0107,,block of flats,0.0492,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1032.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n134014,0,Cash loans,M,Y,N,0,112500.0,269550.0,11547.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-24161,365243,-14989.0,-4534,14.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,,,0.1227,0.1066,0.9796,,,0.0,0.2759,0.1667,,0.1177,,0.114,,0.0375,0.125,0.1107,0.9796,,,0.0,0.2759,0.1667,,0.1204,,0.1188,,0.0397,0.1239,0.1066,0.9796,,,0.0,0.2759,0.1667,,0.1198,,0.1161,,0.0383,,block of flats,0.0978,Panel,No,2.0,0.0,2.0,0.0,-1664.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n450850,0,Cash loans,F,Y,Y,1,202500.0,553806.0,28404.0,495000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.008625,-10853,-3933,-5382.0,-3184,9.0,1,1,0,1,0,1,Sales staff,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,0.2813308454189115,0.5380271040828426,0.4489622731076524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-909.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n140602,0,Cash loans,M,Y,N,1,315000.0,1190340.0,63549.0,1125000.0,Family,State servant,Higher education,Married,House / apartment,0.035792000000000004,-11440,-2178,-2412.0,-2735,6.0,1,1,0,1,0,0,,3.0,2,2,TUESDAY,15,0,0,0,1,1,0,Other,,0.6125453206158398,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1499.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n402938,0,Cash loans,F,N,N,0,90000.0,187704.0,12217.5,148500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,With parents,0.00702,-11624,-407,-5456.0,-3434,,1,1,0,1,0,0,Accountants,2.0,2,2,TUESDAY,9,1,1,0,1,1,0,Business Entity Type 3,,0.6821683676458394,0.1895952597360396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-185.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,2.0\n354345,0,Cash loans,F,N,N,1,135000.0,781920.0,25969.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-15062,-5394,-1474.0,-1570,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.7263523509070885,0.16318703546427088,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1758.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n117183,0,Cash loans,M,Y,N,2,180000.0,526491.0,29529.0,454500.0,Unaccompanied,Working,Higher education,Married,Office apartment,0.035792000000000004,-9535,-1634,-3664.0,-2215,19.0,1,1,0,1,0,0,Core staff,4.0,2,2,TUESDAY,10,0,0,0,1,1,0,Religion,,0.7116748506855284,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-858.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n328708,0,Revolving loans,M,Y,Y,2,247500.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-15860,-265,-56.0,-4147,23.0,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,19,0,0,0,0,1,1,Business Entity Type 3,0.4525212222996171,0.6874170873954591,0.41534714488434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2084.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n304039,0,Cash loans,F,N,Y,0,171000.0,582804.0,27135.0,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.00823,-21083,365243,-4319.0,-4426,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.42667127573527996,0.6413682574954046,0.033,0.0314,0.9737,0.6396,0.0128,0.0,0.069,0.125,0.1667,0.0363,0.0269,0.0254,0.0,0.0,0.0336,0.0325,0.9737,0.6537,0.0129,0.0,0.069,0.125,0.1667,0.0371,0.0294,0.0265,0.0,0.0,0.0333,0.0314,0.9737,0.6444,0.0129,0.0,0.069,0.125,0.1667,0.0369,0.0274,0.0259,0.0,0.0,reg oper account,block of flats,0.027000000000000003,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-517.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n451634,0,Cash loans,F,N,Y,1,112500.0,781920.0,25969.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018801,-16474,-2304,-7071.0,-27,,1,1,0,1,0,0,,3.0,2,2,FRIDAY,7,0,0,0,0,0,0,Hotel,0.549894364578032,0.5869882890694587,0.4048783643353997,0.0773,0.076,0.9771,0.6872,0.0367,0.0,0.1724,0.1667,0.0417,0.0526,0.063,0.0656,0.0,0.0,0.0788,0.0789,0.9772,0.6994,0.0371,0.0,0.1724,0.1667,0.0417,0.0538,0.0689,0.0683,0.0,0.0,0.0781,0.076,0.9771,0.6914,0.037000000000000005,0.0,0.1724,0.1667,0.0417,0.0535,0.0641,0.0668,0.0,0.0,reg oper spec account,block of flats,0.0717,Panel,No,1.0,0.0,1.0,0.0,-1459.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n307660,0,Cash loans,M,Y,Y,0,180000.0,1288350.0,41692.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010006,-15723,-229,-4340.0,-3826,6.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Construction,0.284105699938478,0.5393367395649199,0.4794489811780563,0.0371,0.1015,0.9940000000000001,0.9184,0.0107,0.0,0.1034,0.0833,0.125,0.0194,0.0303,0.0427,0.0039,0.012,0.0378,0.1053,0.9940000000000001,0.9216,0.0108,0.0,0.1034,0.0833,0.125,0.0199,0.0331,0.0445,0.0039,0.0127,0.0375,0.1015,0.9940000000000001,0.9195,0.0108,0.0,0.1034,0.0833,0.125,0.0198,0.0308,0.0434,0.0039,0.0123,reg oper account,block of flats,0.0411,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-1955.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n314111,0,Cash loans,F,Y,N,0,94500.0,545040.0,20677.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-21933,365243,-9847.0,-4064,25.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.3876710641273682,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1196.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n373086,0,Cash loans,M,Y,Y,0,202500.0,508495.5,24592.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.008625,-8620,-728,-3428.0,-1244,15.0,1,1,0,1,0,0,Drivers,1.0,2,2,TUESDAY,16,0,0,0,0,1,1,Self-employed,,0.5042167600616579,0.2340151665320674,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-562.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n185668,0,Cash loans,F,N,Y,0,202500.0,1281712.5,54306.0,1179000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003069,-18238,-3215,-6773.0,-1422,,1,1,1,1,1,0,Managers,2.0,3,3,FRIDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.6065081121660879,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,0.0,-1897.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n244061,0,Cash loans,M,N,Y,0,238500.0,888840.0,29506.5,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-21749,-2445,-4802.0,-3908,,1,1,0,1,1,0,Security staff,2.0,1,1,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.7864833804659372,0.7309835566410336,0.6058362647264226,,,0.9498,,,,,,,,,,,,,,0.9499,,,,,,,,,,,,,,0.9498,,,,,,,,,,,,,block of flats,0.0338,,No,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n212375,1,Cash loans,M,N,Y,0,83250.0,343800.0,10921.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.031329,-21460,-2614,-2189.0,-3676,,1,1,0,1,0,0,Security staff,1.0,2,2,TUESDAY,10,0,0,0,0,1,1,Security,,0.16990359924812706,0.17249546677733105,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,6.0\n172062,0,Cash loans,M,Y,Y,0,157500.0,500211.0,51385.5,463500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018801,-20817,-3018,-380.0,-4181,14.0,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6523485080249737,0.7838324335199619,0.0619,0.0541,0.9886,0.8436,0.0263,0.0,0.1379,0.1667,0.0417,0.0177,0.0504,0.0516,0.0,0.0,0.063,0.0561,0.9886,0.8497,0.0266,0.0,0.1379,0.1667,0.0417,0.0181,0.0551,0.0537,0.0,0.0,0.0625,0.0541,0.9886,0.8457,0.0265,0.0,0.1379,0.1667,0.0417,0.018000000000000002,0.0513,0.0525,0.0,0.0,reg oper spec account,block of flats,0.0549,Panel,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n353404,0,Cash loans,F,N,N,0,54000.0,646920.0,19044.0,540000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00702,-19920,-526,-9886.0,-3458,,1,1,0,1,0,0,Cooking staff,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,University,,0.7866714761316479,,0.1021,0.0748,0.9851,0.7824,0.0,0.18,0.1552,0.3333,0.375,0.0298,0.0832,0.0784,0.0,0.0,0.10400000000000001,0.0776,0.9841,0.7909,0.0,0.1611,0.1379,0.3333,0.375,0.0305,0.0909,0.0656,0.0,0.0,0.1031,0.0748,0.9851,0.7853,0.0,0.18,0.1552,0.3333,0.375,0.0303,0.0847,0.0798,0.0,0.0,org spec account,block of flats,0.0832,Panel,No,12.0,0.0,12.0,0.0,-1681.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n203848,0,Cash loans,F,N,Y,1,202500.0,328405.5,23485.5,283500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-10246,-1723,-174.0,-1407,,1,1,0,1,0,0,Sales staff,3.0,3,3,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.0877165194449513,0.5715769242239119,0.5064842396679806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,3.0,8.0,3.0,-756.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n137266,0,Cash loans,F,N,N,0,67500.0,441000.0,18814.5,441000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.010643,-22459,365243,-8724.0,-4609,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.0035524512965970736,0.7838324335199619,0.0082,0.0,0.9742,,,0.0,0.0345,0.0417,,0.0162,,0.0064,,0.0,0.0084,0.0,0.9742,,,0.0,0.0345,0.0417,,0.0166,,0.0067,,0.0,0.0083,0.0,0.9742,,,0.0,0.0345,0.0417,,0.0165,,0.0065,,0.0,,block of flats,0.0054,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n404488,0,Cash loans,F,N,Y,0,351000.0,1256400.0,36864.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-15745,-224,-1581.0,-1520,,1,1,0,1,0,0,Security staff,2.0,3,3,WEDNESDAY,5,0,0,0,1,1,0,Security,0.4134857950110698,0.4356383669954685,0.8357765157975799,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1241.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n190989,0,Revolving loans,F,N,Y,0,202500.0,585000.0,29250.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-17670,-6396,-5407.0,-1200,,1,1,0,1,0,0,Managers,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Trade: type 7,,0.6663938392261817,0.25533177083329,0.1371,0.0833,0.9801,0.728,0.0313,0.0,0.2759,0.1667,0.2083,0.1378,0.1084,0.1189,0.0154,0.0223,0.1397,0.0865,0.9801,0.7387,0.0316,0.0,0.2759,0.1667,0.2083,0.141,0.1185,0.1239,0.0156,0.0237,0.1384,0.0833,0.9801,0.7316,0.0315,0.0,0.2759,0.1667,0.2083,0.1402,0.1103,0.121,0.0155,0.0228,org spec account,block of flats,0.1155,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-299.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n181500,0,Revolving loans,M,Y,Y,0,180000.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-9089,-1807,-7322.0,-1758,6.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6539090586502363,0.20915469884100693,0.0825,0.0553,0.9896,0.8572,0.0114,0.08,0.069,0.375,0.4167,0.0454,0.0672,0.085,0.0,0.0,0.084,0.0574,0.9896,0.8628,0.0115,0.0806,0.069,0.375,0.4167,0.0464,0.0735,0.0886,0.0,0.0,0.0833,0.0553,0.9896,0.8591,0.0115,0.08,0.069,0.375,0.4167,0.0461,0.0684,0.0865,0.0,0.0,reg oper account,block of flats,0.0731,Others,No,1.0,1.0,1.0,0.0,-1133.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n305429,0,Cash loans,M,N,Y,0,216000.0,288000.0,13554.0,288000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-13351,-2791,-7157.0,-425,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,Self-employed,,0.5124638229042106,0.6296742509538716,0.0124,,0.9687,,,,,,,,,0.0135,,0.0,0.0126,,0.9687,,,,,,,,,0.0141,,0.0,0.0125,,0.9687,,,,,,,,,0.0137,,0.0,,block of flats,0.0115,,No,0.0,0.0,0.0,0.0,-291.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n152867,0,Cash loans,F,N,Y,0,180000.0,1117917.0,44464.5,958500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17571,-2010,-2760.0,-1102,,1,1,1,1,1,0,Cooking staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,,0.7165676466002339,0.8193176922872417,0.0722,0.0769,0.9776,0.6940000000000001,0.008,0.0,0.1379,0.1667,0.2083,0.0386,0.0588,0.0639,0.0,0.0,0.0735,0.0798,0.9777,0.706,0.0081,0.0,0.1379,0.1667,0.2083,0.0395,0.0643,0.0665,0.0,0.0,0.0729,0.0769,0.9776,0.6981,0.008,0.0,0.1379,0.1667,0.2083,0.0393,0.0599,0.065,0.0,0.0,reg oper account,block of flats,0.0502,\"Stone, brick\",No,8.0,0.0,8.0,0.0,-2157.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n396677,0,Cash loans,F,N,Y,1,135000.0,797557.5,42624.0,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003813,-14314,-1688,-6061.0,-3920,,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,0.5891196981349309,0.37997054745322745,,0.1649,0.11900000000000001,0.9876,,,0.2,0.1724,0.375,,,,0.2022,,,0.1681,0.1234,0.9876,,,0.2014,0.1724,0.375,,,,0.2107,,,0.1665,0.11900000000000001,0.9876,,,0.2,0.1724,0.375,,,,0.2059,,,,block of flats,0.1591,Panel,No,0.0,0.0,0.0,0.0,-640.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n225687,0,Cash loans,M,Y,Y,0,180000.0,550980.0,30766.5,450000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.011657,-22720,-3218,-10713.0,-4744,11.0,1,1,0,1,1,0,Drivers,2.0,1,1,TUESDAY,11,0,0,0,0,1,1,Government,,0.7552479259949072,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1551.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n428671,0,Cash loans,M,N,N,0,112500.0,630000.0,24543.0,630000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009334,-18308,-5053,-8017.0,-807,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,1,1,1,Business Entity Type 3,,0.16203170925663654,0.3556387169923543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-513.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n345579,0,Cash loans,M,N,Y,0,90000.0,98910.0,7164.0,90000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.00702,-19360,-864,-4056.0,-2867,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Government,,0.5691655571782801,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-2126.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n228267,1,Cash loans,F,N,N,0,78750.0,135000.0,12510.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.0228,-13249,-71,-6334.0,-774,,1,1,1,1,1,0,Cleaning staff,1.0,2,2,TUESDAY,15,0,0,0,0,1,1,Restaurant,,0.08096835236694662,0.048925866762844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-248.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n237623,1,Cash loans,F,N,N,0,270000.0,916470.0,24174.0,765000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.007273999999999998,-15359,-7384,-6032.0,-4930,,1,1,1,1,0,0,High skill tech staff,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Telecom,0.41486273349194,0.5406186782015922,0.19633396621345675,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n143412,0,Cash loans,F,N,Y,1,202500.0,808650.0,29709.0,675000.0,Unaccompanied,State servant,Higher education,Married,Office apartment,0.006852,-9907,-1714,-2757.0,-1930,,1,1,0,1,0,0,Core staff,3.0,3,3,MONDAY,12,0,0,0,0,0,0,School,0.3835887277913784,0.07780906116228566,0.16048893062734468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-534.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n440860,0,Cash loans,F,N,Y,0,157500.0,888840.0,35815.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-16977,-3723,-5109.0,-528,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Trade: type 5,0.560361509866771,,,0.2196,,0.9826,,,0.0,0.2069,0.3333,,,,0.2314,,,0.2237,,0.9826,,,0.0,0.2069,0.3333,,,,0.2411,,,0.2217,,0.9826,,,0.0,0.2069,0.3333,,,,0.2355,,,,block of flats,0.182,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1707.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n425121,0,Cash loans,F,N,Y,0,112500.0,135000.0,13279.5,135000.0,Family,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-24584,365243,-11175.0,-3996,,1,0,0,1,0,0,,2.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.26525634018619443,,0.0887,0.0805,0.9846,,,,0.2069,0.1667,,0.0217,,0.0834,,0.0157,0.0903,0.0836,0.9846,,,,0.2069,0.1667,,0.0222,,0.0869,,0.0166,0.0895,0.0805,0.9846,,,,0.2069,0.1667,,0.0221,,0.0849,,0.016,,block of flats,0.069,Panel,No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n346663,0,Cash loans,F,N,N,0,202500.0,900000.0,52753.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-15895,-2546,-6408.0,-3805,,1,1,0,1,0,0,Accountants,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.6097130194579602,0.6840807275718308,0.6075573001388961,0.10099999999999999,0.1073,0.9811,0.7416,0.062,0.0,0.2069,0.1667,0.2083,0.0687,0.0824,0.0905,0.0116,0.0262,0.1029,0.1113,0.9811,0.7517,0.0626,0.0,0.2069,0.1667,0.2083,0.0703,0.09,0.0943,0.0117,0.0277,0.102,0.1073,0.9811,0.7451,0.0624,0.0,0.2069,0.1667,0.2083,0.0699,0.0838,0.0921,0.0116,0.0267,reg oper account,block of flats,0.0861,Panel,No,1.0,0.0,1.0,0.0,-1591.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n155454,0,Cash loans,F,Y,N,0,202500.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-16112,-6337,-7275.0,-4214,10.0,1,1,1,1,1,0,Managers,2.0,3,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.5474805138065816,0.8193176922872417,0.0505,0.0709,0.9816,0.7484,0.0076,0.0,0.1379,0.1667,0.2083,0.031,0.0412,0.0646,0.0,0.0,0.0515,0.0736,0.9816,0.7583,0.0077,0.0,0.1379,0.1667,0.2083,0.0318,0.045,0.0673,0.0,0.0,0.051,0.0709,0.9816,0.7518,0.0077,0.0,0.1379,0.1667,0.2083,0.0316,0.0419,0.0658,0.0,0.0,reg oper account,block of flats,0.055,Panel,No,0.0,0.0,0.0,0.0,-56.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n332510,0,Cash loans,F,N,Y,0,90000.0,254700.0,16713.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.002042,-18587,-11571,-8527.0,-2133,,1,1,1,1,0,0,Low-skill Laborers,2.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Agriculture,,0.1295130428238426,0.6545292802242897,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1099.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n231122,0,Cash loans,F,N,Y,0,117000.0,675000.0,19867.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.014519999999999996,-13211,-233,-6734.0,-3987,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,16,0,0,0,0,1,1,Transport: type 4,0.4449773969499968,,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2191.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n409919,0,Cash loans,F,Y,Y,1,157500.0,206271.0,20529.0,193500.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-11566,-4411,-2782.0,-3234,4.0,1,1,0,1,1,0,Medicine staff,3.0,1,1,FRIDAY,13,0,1,1,0,0,0,Other,,0.7588836021510006,0.4048783643353997,,,0.9742,,,,0.1034,0.1667,,,,,,,,,0.9742,,,,0.1034,0.1667,,,,,,,,,0.9742,,,,0.1034,0.1667,,,,,,,,block of flats,0.0525,Panel,No,0.0,0.0,0.0,0.0,-707.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n112372,0,Cash loans,F,N,Y,0,112500.0,145557.0,11668.5,121500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00963,-22600,365243,-3989.0,-3965,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.3386305549153376,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-194.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n235947,0,Revolving loans,F,Y,N,0,360000.0,810000.0,40500.0,810000.0,Family,Commercial associate,Incomplete higher,Married,Municipal apartment,0.04622,-18073,-1652,-5329.0,-1598,6.0,1,1,0,1,0,0,Accountants,2.0,1,1,SATURDAY,13,0,1,1,0,0,0,Realtor,0.874989878082418,0.5708645797396434,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2871.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n220200,0,Cash loans,F,N,Y,0,157500.0,314100.0,16033.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.014464,-15663,-1613,-162.0,-2550,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.7347371331772237,0.3840281710502208,0.5513812618027899,0.1268,0.0425,0.9786,,0.0284,0.04,0.0345,0.3333,,0.0292,0.1009,0.0378,0.0116,0.0247,0.1292,0.0441,0.9786,,0.0287,0.0403,0.0345,0.3333,,0.0299,0.1102,0.0393,0.0117,0.0261,0.128,0.0425,0.9786,,0.0286,0.04,0.0345,0.3333,,0.0297,0.1026,0.0384,0.0116,0.0252,reg oper account,block of flats,0.0506,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1187.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n348293,0,Cash loans,M,N,N,0,135000.0,348264.0,27513.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-21209,-166,-9810.0,-4760,,1,1,1,1,1,0,,2.0,1,1,SATURDAY,12,0,0,0,0,0,0,Government,0.7555780558963521,0.7653257961126219,0.7165702448010511,0.4876,0.0,0.9851,0.7959999999999999,0.0,0.96,0.4138,0.4583,0.5,0.0991,0.3976,0.4859,0.0,0.3462,0.4968,0.0,0.9851,0.804,0.0,0.9667,0.4138,0.4583,0.5,0.1014,0.4343,0.5062,0.0,0.3665,0.4924,0.0,0.9851,0.7987,0.0,0.96,0.4138,0.4583,0.5,0.1008,0.4045,0.4946,0.0,0.3535,reg oper account,block of flats,0.4574,Panel,No,4.0,0.0,4.0,0.0,-2500.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n349387,0,Cash loans,M,Y,Y,0,472500.0,696528.0,55161.0,630000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-11358,-3696,-3331.0,-2442,9.0,1,1,0,1,1,0,,2.0,1,1,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.1843156042628638,0.6769925005149129,0.3740208032583212,0.0619,0.0547,0.9732,0.6328,0.0101,0.0,0.1262,0.1388,0.1804,0.0,0.0496,0.0552,0.0039,0.0253,0.042,0.0073,0.9722,0.6341,0.0002,0.0,0.1379,0.125,0.1667,0.0,0.0358,0.0529,0.0039,0.0005,0.0677,0.0731,0.9732,0.6377,0.0071,0.0,0.1379,0.125,0.1667,0.0,0.0547,0.0534,0.0039,0.0181,not specified,block of flats,0.04,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1699.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n339609,0,Cash loans,F,N,Y,0,157500.0,720000.0,38488.5,720000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-22430,365243,-2877.0,-2877,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.5389944384051514,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1711.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n450166,0,Cash loans,F,Y,Y,0,360000.0,808650.0,23773.5,675000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.019101,-19784,-1305,-7885.0,-3316,1.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,MONDAY,16,0,0,0,0,0,0,Construction,0.6772069831790739,0.7455209669288266,0.3108182544189319,0.1394,0.1247,0.9896,,,0.136,0.1172,0.3333,,0.091,,0.1332,,0.1619,0.0935,0.1736,0.9876,,,0.0806,0.069,0.3333,,0.0931,,0.047,,0.1515,0.0926,0.1673,0.9876,,,0.08,0.069,0.3333,,0.0926,,0.046,,0.1461,,block of flats,0.2254,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2439.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n387728,0,Cash loans,F,N,Y,0,67500.0,134775.0,10710.0,112500.0,Children,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010556,-17853,-936,-2267.0,-1398,,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,12,0,0,0,0,0,0,Business Entity Type 2,,0.5730702740547051,0.5190973382084597,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1725.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n355045,0,Cash loans,F,N,Y,0,135000.0,238500.0,9121.5,238500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.018634,-10470,-1922,-267.0,-2801,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.5833219519473335,0.3809241010246151,0.6363761710860439,,,0.9732,,,,0.1379,0.1667,,,,0.0564,,,,,0.9732,,,,0.1379,0.1667,,,,0.0587,,,,,0.9732,,,,0.1379,0.1667,,,,0.0574,,,,,0.0638,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1936.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n189331,0,Cash loans,F,N,Y,0,85500.0,675000.0,36747.0,675000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.026392,-17604,-1806,-4716.0,-1079,,1,1,1,1,1,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Insurance,,0.7099982802088525,0.5082869913916046,0.0557,0.0273,0.9791,,,0.0,0.0345,0.3333,,0.031,,0.0455,,0.0361,0.0567,0.0284,0.9791,,,0.0,0.0345,0.3333,,0.0317,,0.0474,,0.0382,0.0562,0.0273,0.9791,,,0.0,0.0345,0.3333,,0.0315,,0.0463,,0.0369,,block of flats,0.0405,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-371.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n132191,0,Cash loans,F,Y,N,0,112500.0,254700.0,25321.5,225000.0,\"Spouse, partner\",Working,Higher education,Married,Municipal apartment,0.006207,-10402,-1097,-361.0,-1163,16.0,1,1,1,1,0,0,Sales staff,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5404448871322305,0.3723336657058204,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1345.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n370826,0,Cash loans,M,N,Y,0,247500.0,204858.0,10588.5,171000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-14758,-3091,-815.0,-2933,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6967186872850359,0.7107226546159106,0.28812959991785075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1677.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,5.0\n251529,0,Cash loans,F,N,N,2,27900.0,360000.0,19530.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-12693,-3220,-2137.0,-3016,,1,1,1,1,0,0,Cleaning staff,4.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Other,0.4325815021050325,0.5625903417519946,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1861.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n228641,0,Cash loans,F,N,N,0,405000.0,80865.0,6480.0,67500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.030755,-22287,365243,-9700.0,-2659,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,18,0,0,0,0,0,0,XNA,,0.6292359937437814,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1899.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n395918,0,Cash loans,M,Y,Y,1,180000.0,495216.0,33628.5,427500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-16578,-1539,-4409.0,-124,64.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,THURSDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.4009985187382321,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-120.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n239009,0,Cash loans,F,N,Y,0,121500.0,675000.0,32602.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.006305,-14481,-1124,-3813.0,-4134,,1,1,0,1,0,0,Cooking staff,1.0,3,3,MONDAY,5,0,0,0,0,0,0,Self-employed,,0.6171799344319835,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-564.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n179248,0,Revolving loans,F,N,N,0,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Widow,Municipal apartment,0.008865999999999999,-16772,-9232,-11025.0,-310,,1,1,0,1,0,0,Security staff,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,School,,0.2953510656567823,0.1873887653772306,0.1402,0.1056,0.9806,0.7348,0.0207,0.0,0.2759,0.1667,0.2083,0.0927,0.1135,0.1054,0.0039,0.0,0.1429,0.1096,0.9806,0.7452,0.0209,0.0,0.2759,0.1667,0.2083,0.0948,0.124,0.1098,0.0039,0.0,0.1416,0.1056,0.9806,0.7383,0.0208,0.0,0.2759,0.1667,0.2083,0.0943,0.1154,0.1073,0.0039,0.0,reg oper account,block of flats,0.0942,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-347.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n447887,0,Revolving loans,F,Y,Y,1,135000.0,495000.0,24750.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-13958,-603,-17.0,-898,13.0,1,1,0,1,0,0,Managers,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.4194254355794794,0.5876773033942753,0.5280927512030451,0.0691,0.0613,0.9771,0.6872,0.0085,0.0,0.1379,0.1667,0.0417,0.0232,0.0538,0.0504,0.0116,0.0884,0.0704,0.0636,0.9772,0.6994,0.0086,0.0,0.1379,0.1667,0.0417,0.0237,0.0588,0.0525,0.0117,0.0936,0.0697,0.0613,0.9771,0.6914,0.0085,0.0,0.1379,0.1667,0.0417,0.0236,0.0547,0.0513,0.0116,0.0903,reg oper account,block of flats,0.0635,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2530.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n259026,0,Cash loans,F,N,Y,0,144000.0,1074222.0,31540.5,769500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-19926,-417,-9940.0,-3477,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,14,0,1,1,0,0,0,Self-employed,,0.7633168645764625,0.7309873696832169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-440.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,2.0\n350863,0,Cash loans,M,N,Y,2,112500.0,1345036.5,39325.5,1174500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006296,-13407,-3256,-448.0,-4633,,1,1,0,1,0,0,,4.0,3,3,MONDAY,13,0,0,0,0,1,1,Business Entity Type 2,,0.5584263459540235,0.5832379256761245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-534.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n273191,1,Cash loans,M,Y,Y,0,360000.0,521280.0,27423.0,450000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.04622,-16317,-442,-6560.0,-4702,7.0,1,1,1,1,1,0,Managers,2.0,1,1,SUNDAY,15,0,1,1,0,1,1,Business Entity Type 3,0.3719020328407964,0.7407877594832003,0.12850437737240394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3666.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n305198,0,Cash loans,F,Y,N,1,225000.0,755190.0,35122.5,675000.0,Unaccompanied,State servant,Higher education,Married,Office apartment,0.014464,-11271,-530,-1209.0,-327,4.0,1,1,0,1,0,1,Core staff,3.0,2,2,TUESDAY,9,0,0,0,1,1,0,School,0.2342724192676925,0.4941635041528233,,0.1753,0.0967,0.998,0.966,0.0676,0.16,0.0345,0.375,0.4167,0.017,0.1429,0.1914,0.0,0.0,0.1786,0.1004,0.998,0.9673,0.0682,0.1611,0.0345,0.375,0.4167,0.0174,0.1561,0.1994,0.0,0.0,0.177,0.0967,0.998,0.9665,0.068,0.16,0.0345,0.375,0.4167,0.0173,0.1454,0.1949,0.0,0.0,not specified,block of flats,0.1587,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n342521,1,Cash loans,M,Y,N,1,202500.0,355536.0,15790.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Rented apartment,0.020713,-17510,-1263,-92.0,-928,12.0,1,1,0,1,0,0,Security staff,3.0,3,2,WEDNESDAY,9,0,0,0,0,0,0,School,,0.7348744326908445,,0.1237,0.1165,0.9762,0.6736,0.013999999999999999,0.0,0.2069,0.1667,0.2083,0.0747,0.1009,0.1021,0.0,0.0,0.1261,0.1209,0.9762,0.6864,0.0141,0.0,0.2069,0.1667,0.2083,0.0764,0.1102,0.1064,0.0,0.0,0.1249,0.1165,0.9762,0.6779999999999999,0.013999999999999999,0.0,0.2069,0.1667,0.2083,0.076,0.1026,0.1039,0.0,0.0,not specified,block of flats,0.0879,Panel,No,4.0,0.0,3.0,0.0,-2218.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n445546,0,Cash loans,F,N,Y,1,135000.0,808650.0,26086.5,675000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.008473999999999999,-12038,-2902,-3164.0,-4180,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Other,0.3650873042115005,0.5321846707723369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1085.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n189302,0,Cash loans,F,N,Y,0,112500.0,283419.0,20668.5,234000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.035792000000000004,-12766,-2441,-6323.0,-2712,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.6194455185037637,0.6722428897082422,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-258.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n286839,0,Cash loans,F,Y,N,0,189000.0,646920.0,17194.5,540000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.020713,-10732,-1949,-8733.0,-3413,1.0,1,1,0,1,0,0,,1.0,3,1,SUNDAY,11,0,0,0,0,0,0,Other,,0.3427910210076641,,0.0976,0.0977,0.9881,0.83,0.0403,0.1332,0.1148,0.3333,0.0417,0.0674,0.1194,0.1089,0.0,0.0,0.0557,0.0567,0.9876,0.8367,0.0407,0.0806,0.069,0.3333,0.0417,0.069,0.1304,0.0525,0.0,0.0,0.0926,0.0919,0.9876,0.8323,0.0406,0.12,0.1034,0.3333,0.0417,0.0686,0.1214,0.0773,0.0,0.0,reg oper account,block of flats,0.1796,Panel,No,0.0,0.0,0.0,0.0,-694.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n421116,0,Cash loans,M,Y,Y,0,126000.0,193500.0,15102.0,193500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-12697,-1641,-6604.0,-4780,2.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,13,0,0,0,0,0,0,School,0.3073732953889063,0.6193836576964615,0.3910549766342248,0.0082,0.0045,0.9503,0.32,0.0014,0.0,0.0345,0.0,0.0417,0.0378,0.0067,0.0026,0.0,0.0,0.0084,0.0047,0.9503,0.3466,0.0014,0.0,0.0345,0.0,0.0417,0.0387,0.0073,0.0027,0.0,0.0,0.0083,0.0045,0.9503,0.3291,0.0014,0.0,0.0345,0.0,0.0417,0.0385,0.0068,0.0027,0.0,0.0,reg oper account,specific housing,0.0028,Wooden,No,4.0,1.0,4.0,1.0,-2689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n187611,0,Cash loans,M,Y,Y,0,180000.0,855882.0,38803.5,765000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006852,-19112,-1868,-11440.0,-2403,17.0,1,1,0,1,0,0,Managers,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,Self-employed,,0.4276237238122054,0.5046813193144684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1162.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n212311,0,Cash loans,F,N,Y,0,157500.0,1166724.0,34245.0,913500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-20580,-3145,-4306.0,-2495,,1,1,0,1,0,0,Cleaning staff,2.0,1,1,FRIDAY,9,0,0,0,0,0,0,Industry: type 4,,0.6804590885332561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n343756,0,Cash loans,F,N,Y,0,162000.0,1256400.0,36864.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010147,-17616,-2871,-216.0,-1162,,1,1,0,1,0,0,Secretaries,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,Government,,0.7776067205331959,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1377.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n170379,0,Cash loans,M,Y,N,0,202500.0,143910.0,15241.5,135000.0,Family,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.003818,-14284,-2467,-2690.0,-2797,13.0,1,1,1,1,0,0,Laborers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.1356252213060868,0.5919766183185521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,-687.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n452345,0,Cash loans,M,N,Y,0,202500.0,182448.0,21780.0,157500.0,Family,Working,Higher education,Married,With parents,0.035792000000000004,-10368,-2164,-1508.0,-2982,,1,1,1,1,1,0,High skill tech staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.21265035141548885,0.43721965126218737,0.6785676886853644,0.0732,,0.9940000000000001,0.9184,0.0283,0.08,0.0345,0.625,0.6667,0.0184,0.057999999999999996,0.0694,0.0077,0.0079,0.0746,,0.9940000000000001,0.9216,0.0285,0.0806,0.0345,0.625,0.6667,0.0188,0.0634,0.0723,0.0078,0.0084,0.0739,,0.9940000000000001,0.9195,0.0285,0.08,0.0345,0.625,0.6667,0.0187,0.059000000000000004,0.0706,0.0078,0.0081,reg oper account,block of flats,0.0718,,No,3.0,0.0,3.0,0.0,-1462.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,4.0\n268500,0,Cash loans,F,Y,Y,3,189000.0,841500.0,29943.0,841500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-12910,-3077,-1624.0,-973,0.0,1,1,0,1,0,0,,5.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.4761983389961624,0.0828578232376625,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,1.0,-163.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n100860,0,Cash loans,M,N,Y,1,180000.0,711747.0,56362.5,607500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.010643,-10241,-732,-2739.0,-1101,,1,1,0,1,0,1,Low-skill Laborers,3.0,2,2,FRIDAY,16,1,1,1,1,0,0,Self-employed,0.2268213898870803,0.4383768427638231,,0.1485,0.1076,0.9846,,,0.16,0.1379,0.3333,,0.1076,,0.1523,,0.0085,0.1513,0.1117,0.9846,,,0.1611,0.1379,0.3333,,0.1101,,0.1586,,0.009000000000000001,0.1499,0.1076,0.9846,,,0.16,0.1379,0.3333,,0.1095,,0.155,,0.0087,,block of flats,0.1216,Panel,No,0.0,0.0,0.0,0.0,-98.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n399785,0,Cash loans,F,N,Y,0,58500.0,571446.0,16839.0,477000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.031329,-23233,365243,-7236.0,-4387,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,8,0,0,0,0,0,0,XNA,0.7485785781719071,0.5265133025194553,0.7557400501752248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,-1549.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n358441,0,Cash loans,M,N,Y,0,135000.0,531000.0,29781.0,531000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-13602,-404,-4269.0,-4882,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Other,,0.5389020651108841,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2617.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n190326,0,Cash loans,M,N,Y,0,292500.0,1256400.0,36864.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.005313,-15792,-147,-7219.0,-4222,,1,1,0,1,0,0,Laborers,1.0,2,2,SUNDAY,12,0,0,0,0,0,0,Industry: type 4,0.2677745973996313,0.7605243138590143,0.3774042489507649,0.0928,0.1117,0.9781,,,0.0,0.2069,0.1667,,0.0914,,0.0782,,0.0,0.0945,0.1159,0.9782,,,0.0,0.2069,0.1667,,0.0935,,0.0815,,0.0,0.0937,0.1117,0.9781,,,0.0,0.2069,0.1667,,0.09300000000000001,,0.0796,,0.0,,block of flats,0.0874,Panel,No,0.0,0.0,0.0,0.0,-24.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n330062,0,Cash loans,F,N,Y,1,180000.0,506889.0,33871.5,418500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-18138,-372,-3482.0,-1678,,1,1,0,1,0,0,Managers,3.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.1745768496405899,0.6279908192952864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n436033,0,Cash loans,F,N,Y,0,81000.0,808650.0,23773.5,675000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.031329,-22048,365243,-10927.0,-3997,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.26525634018619443,0.5513812618027899,0.0041,0.0,0.9747,,0.0,0.0,0.0345,0.0,0.0417,0.0172,0.0034,0.0029,,0.0,0.0042,0.0,0.9747,,0.0,0.0,0.0345,0.0,0.0417,0.0176,0.0037,0.003,,0.0,0.0042,0.0,0.9747,,0.0,0.0,0.0345,0.0,0.0417,0.0175,0.0034,0.0029,,0.0,not specified,terraced house,0.0023,Wooden,No,3.0,0.0,3.0,0.0,-860.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n155983,0,Cash loans,M,Y,Y,1,315000.0,794173.5,38335.5,697500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014464,-13498,-3097,-1132.0,-2417,5.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,5,0,0,0,0,0,0,Self-employed,0.303977616880765,0.5299028756183823,0.5691487713619409,0.033,0.0407,0.9752,0.66,0.0028,0.0,0.069,0.125,0.1667,0.0255,0.0269,0.0255,0.0,0.0,0.0336,0.0422,0.9752,0.6733,0.0028,0.0,0.069,0.125,0.1667,0.0261,0.0294,0.0266,0.0,0.0,0.0333,0.0407,0.9752,0.6645,0.0028,0.0,0.069,0.125,0.1667,0.0259,0.0274,0.026000000000000002,0.0,0.0,reg oper spec account,block of flats,0.0216,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-293.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n166122,0,Cash loans,M,Y,N,0,117000.0,265536.0,13554.0,180000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-9502,-1789,-8583.0,-2079,1.0,1,1,0,1,0,1,Laborers,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,0.14161174818827485,0.5974546137015648,0.4722533429586386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-1544.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n244135,0,Cash loans,M,Y,N,1,337500.0,248760.0,26248.5,225000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.04622,-12794,-3574,-1774.0,-5150,14.0,1,1,0,1,0,0,,3.0,1,1,SUNDAY,11,0,0,0,0,0,0,Military,0.6571514827801375,0.6599314826650198,0.6263042766749393,0.2165,0.1088,0.9841,0.7824,0.0223,0.24,0.2069,0.3333,0.375,,0.1765,0.21,0.0,0.0,0.2206,0.1129,0.9841,0.7909,0.0225,0.2417,0.2069,0.3333,0.375,,0.1928,0.2188,0.0,0.0,0.2186,0.1088,0.9841,0.7853,0.0224,0.24,0.2069,0.3333,0.375,,0.1796,0.2137,0.0,0.0,reg oper account,block of flats,0.1652,Panel,No,5.0,0.0,5.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n172613,0,Cash loans,F,N,N,1,81000.0,67765.5,7425.0,58500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.008068,-12886,-1728,-6119.0,-4384,,1,1,1,1,0,0,Cleaning staff,2.0,3,3,THURSDAY,14,0,0,0,0,0,0,Other,0.5521118606444775,0.11700038883174155,,0.0165,,0.9757,,,0.0,0.069,0.0417,,,,0.0127,,,0.0168,,0.9757,,,0.0,0.069,0.0417,,,,0.0133,,,0.0167,,0.9757,,,0.0,0.069,0.0417,,,,0.013000000000000001,,,,block of flats,0.01,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1008.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n211428,0,Cash loans,F,N,Y,0,135000.0,900000.0,31887.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-12257,-257,-1798.0,-4057,,1,1,1,1,1,0,Accountants,2.0,2,2,SUNDAY,23,0,0,0,0,0,0,Kindergarten,,0.6548685166420565,0.7062051096536562,0.3268,0.2138,0.997,0.9592,0.2298,0.36,0.3103,0.3333,0.375,0.2193,0.2631,0.3633,0.0154,0.0497,0.33299999999999996,0.2219,0.997,0.9608,0.2319,0.3625,0.3103,0.3333,0.375,0.2243,0.2874,0.3785,0.0156,0.0526,0.33,0.2138,0.997,0.9597,0.2312,0.36,0.3103,0.3333,0.375,0.2231,0.2676,0.3698,0.0155,0.0507,reg oper account,block of flats,0.2857,Panel,No,0.0,0.0,0.0,0.0,-2210.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n326986,0,Revolving loans,M,N,N,0,675000.0,2250000.0,112500.0,2250000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.010147,-22465,365243,-3346.0,-4571,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,0.5482222729986302,0.7936756692773701,,0.0732,,0.9916,,,,0.069,0.3333,,,,0.1379,,,0.0746,,0.9916,,,,0.069,0.3333,,,,0.1437,,,0.0739,,0.9916,,,,0.069,0.3333,,,,0.1404,,,,block of flats,0.1084,\"Stone, brick\",No,,,,,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n137277,0,Revolving loans,F,N,N,0,130500.0,270000.0,13500.0,270000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.002042,-11699,-3371,-3735.0,-3798,,1,1,0,1,0,0,,2.0,3,3,MONDAY,14,0,0,0,0,0,0,University,,0.4075499496750167,0.32173528219668485,0.0825,0.0768,0.9771,0.6872,0.0096,0.0,0.1379,0.1667,0.2083,0.0859,0.0672,0.0757,0.0,0.0,0.084,0.0797,0.9772,0.6994,0.0096,0.0,0.1379,0.1667,0.2083,0.0879,0.0735,0.0789,0.0,0.0,0.0833,0.0768,0.9771,0.6914,0.0096,0.0,0.1379,0.1667,0.2083,0.0874,0.0684,0.0771,0.0,0.0,,block of flats,0.0648,Panel,No,4.0,0.0,4.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n297607,0,Cash loans,M,Y,Y,0,225000.0,1035000.0,33385.5,1035000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018029,-11377,-3596,-1605.0,-3965,12.0,1,1,1,1,1,0,Laborers,2.0,3,3,SUNDAY,11,0,0,0,0,0,0,Other,,0.4591569779490442,0.3979463219016906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1081.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n281152,1,Cash loans,F,N,Y,0,337500.0,351792.0,19215.0,252000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.04622,-11778,-3135,-2291.0,-582,,1,1,0,1,1,0,Managers,2.0,1,1,THURSDAY,14,0,0,0,0,0,0,School,0.6100747571212668,0.6993915930144177,0.6754132910917112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1268.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n191459,0,Cash loans,F,N,N,2,112500.0,601470.0,25614.0,450000.0,Family,Commercial associate,Higher education,Separated,House / apartment,0.006233,-14575,-130,-2870.0,-276,,1,1,0,1,0,0,Sales staff,3.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.7328842709505813,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2283.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n125946,0,Cash loans,F,N,N,0,112500.0,296280.0,14382.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010276,-23305,365243,-7755.0,-5384,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.6340389809759129,0.3077366963789207,0.0278,0.0,0.9677,0.5579999999999999,0.0063,0.0,0.1379,0.0833,0.125,0.0769,0.0227,0.0429,0.0,0.0,0.0284,0.0,0.9677,0.5753,0.0063,0.0,0.1379,0.0833,0.125,0.0786,0.0248,0.0447,0.0,0.0,0.0281,0.0,0.9677,0.5639,0.0063,0.0,0.1379,0.0833,0.125,0.0782,0.0231,0.0436,0.0,0.0,reg oper account,block of flats,0.0371,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1995.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,1.0,1.0\n251526,0,Cash loans,M,N,Y,0,315000.0,123768.0,6016.5,81000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010276,-18131,-1271,-1773.0,-1533,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Construction,,0.6819217735152221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-625.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,9.0\n396891,0,Revolving loans,M,Y,Y,2,450000.0,900000.0,45000.0,900000.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.003540999999999999,-18622,-2097,-5335.0,-2153,1.0,1,1,0,1,0,0,Laborers,4.0,1,1,SUNDAY,13,0,0,0,0,0,0,Trade: type 7,0.5815354554303364,0.401592203518616,0.3077366963789207,0.0165,0.0,0.9866,0.8164,0.0053,0.0,0.069,0.0417,0.0833,0.0101,0.0132,0.0096,0.001,0.0296,0.0168,0.0,0.9871,0.8301,0.0051,0.0,0.069,0.0417,0.0833,0.0099,0.0147,0.01,0.0,0.0308,0.0167,0.0,0.9871,0.8256,0.0051,0.0,0.069,0.0417,0.0833,0.0103,0.0137,0.0098,0.0,0.03,reg oper account,block of flats,0.0167,Wooden,Yes,0.0,0.0,0.0,0.0,-1638.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n255121,0,Cash loans,F,N,Y,0,175500.0,143910.0,17208.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-17808,-469,-1486.0,-1351,,1,1,1,1,0,0,Medicine staff,2.0,2,2,THURSDAY,12,0,0,0,1,1,0,Medicine,,0.30089209911203424,0.33285056416487313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-210.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n190693,0,Cash loans,F,N,Y,0,112500.0,110016.0,4081.5,72000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-23684,365243,-12864.0,-4078,,1,0,0,1,0,0,,1.0,2,2,SUNDAY,15,0,0,0,0,0,0,XNA,,0.5421497801666451,0.7490217048463391,0.0619,,0.9796,,,0.0,0.1379,0.1667,,,,0.0514,,,0.063,,0.9796,,,0.0,0.1379,0.1667,,,,0.0535,,,0.0625,,0.9796,,,0.0,0.1379,0.1667,,,,0.0523,,,,block of flats,0.044000000000000004,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-362.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n106965,0,Cash loans,F,N,N,0,216000.0,225000.0,12694.5,225000.0,Family,Pensioner,Incomplete higher,Widow,House / apartment,0.026392,-23931,365243,-15078.0,-4089,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.477537139595824,0.8128226070575616,0.0082,,0.9712,,,0.0,0.0345,0.0417,,0.0153,,0.0081,,0.0,0.0084,,0.9712,,,0.0,0.0345,0.0417,,0.0156,,0.0084,,0.0,0.0083,,0.9712,,,0.0,0.0345,0.0417,,0.0155,,0.0082,,0.0,,block of flats,0.0063,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n288169,0,Cash loans,F,N,Y,0,112500.0,598486.5,21627.0,454500.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-19528,-738,-9697.0,-1911,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Military,,0.5963023110394031,0.4489622731076524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n391686,0,Cash loans,F,Y,Y,1,135000.0,781695.0,22986.0,652500.0,Other_B,Commercial associate,Higher education,Separated,House / apartment,0.04622,-17133,-896,-8804.0,-657,22.0,1,1,0,1,0,0,High skill tech staff,2.0,1,1,TUESDAY,16,0,0,0,0,0,0,Transport: type 4,0.5414421814579817,0.7394881463795919,0.6212263380626669,0.2206,0.1348,0.9791,0.7144,0.0563,0.24,0.2069,0.3333,0.375,0.0,0.1799,0.2198,0.0,0.0,0.2248,0.1399,0.9791,0.7256,0.0568,0.2417,0.2069,0.3333,0.375,0.0,0.1965,0.22899999999999998,0.0,0.0,0.2228,0.1348,0.9791,0.7182,0.0566,0.24,0.2069,0.3333,0.375,0.0,0.183,0.2238,0.0,0.0,reg oper account,block of flats,0.2292,Panel,No,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n226762,0,Revolving loans,F,N,Y,3,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005084,-12778,-774,-1018.0,-2043,,1,1,0,1,0,0,Core staff,5.0,2,2,MONDAY,14,0,0,0,0,0,0,Kindergarten,0.5333709103350384,0.7441161614541483,0.746300213050371,0.0247,,0.9732,,,,0.069,0.0833,,,,0.019,,,0.0252,,0.9732,,,,0.069,0.0833,,,,0.0198,,,0.025,,0.9732,,,,0.069,0.0833,,,,0.0194,,,,block of flats,0.015,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-407.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n165857,0,Revolving loans,M,Y,Y,1,189000.0,270000.0,13500.0,270000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-12212,-1642,-1550.0,-4503,4.0,1,1,1,1,1,0,Laborers,3.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6880369923042703,0.2793353208976285,0.067,0.0372,0.9762,0.6736,0.0067,0.0,0.1379,0.1667,0.2083,0.0502,0.0538,0.0495,0.0039,0.0881,0.0683,0.0386,0.9762,0.6864,0.0068,0.0,0.1379,0.1667,0.2083,0.0514,0.0588,0.0516,0.0039,0.0933,0.0677,0.0372,0.9762,0.6779999999999999,0.0068,0.0,0.1379,0.1667,0.2083,0.0511,0.0547,0.0504,0.0039,0.09,reg oper account,block of flats,0.0618,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-596.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n145855,0,Cash loans,F,N,N,2,135000.0,269550.0,21163.5,225000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.007305,-11376,-181,-2101.0,-3609,,1,1,1,1,0,0,Sales staff,4.0,3,3,FRIDAY,12,0,0,0,0,0,0,Trade: type 7,,0.07612508393415691,0.3031463744186309,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-606.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n216631,0,Cash loans,F,N,N,0,180000.0,675000.0,34290.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.016612000000000002,-16873,-929,-10988.0,-418,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6997087254547529,0.7981372313187245,0.0371,0.0659,0.9727,0.626,0.0065,0.0,0.069,0.1667,0.0417,0.0254,,0.0425,,0.0688,0.0378,0.0684,0.9727,0.6406,0.0066,0.0,0.069,0.1667,0.0417,0.0259,,0.0443,,0.0729,0.0375,0.0659,0.9727,0.631,0.0065,0.0,0.069,0.1667,0.0417,0.0258,,0.0433,,0.0703,reg oper account,block of flats,0.0519,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-653.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n198057,0,Cash loans,M,Y,Y,0,67500.0,119925.0,11857.5,112500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.02461,-17636,-2353,-7117.0,-1179,9.0,1,1,1,1,1,0,Security staff,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Security,,0.6866036203873634,0.8106180215523969,0.0825,0.0786,0.9767,0.6804,0.0327,0.0,0.1379,0.1667,0.2083,0.0675,0.0672,0.0705,,0.0,0.084,0.0815,0.9767,0.6929,0.033,0.0,0.1379,0.1667,0.2083,0.069,0.0735,0.0734,,0.0,0.0833,0.0786,0.9767,0.6847,0.0329,0.0,0.1379,0.1667,0.2083,0.0687,0.0684,0.0717,,0.0,reg oper account,block of flats,0.0733,Panel,No,1.0,0.0,1.0,0.0,-2089.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n207937,1,Cash loans,F,N,Y,2,112500.0,534141.0,25029.0,441000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-13892,-1846,-6015.0,-1770,,1,1,0,1,0,0,,4.0,2,2,THURSDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.6534387632978403,0.0963186927645401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-323.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n266504,0,Cash loans,F,Y,Y,0,216000.0,1350000.0,43119.0,1350000.0,Unaccompanied,Commercial associate,Higher education,Widow,House / apartment,0.022625,-19515,-409,0.0,-2948,1.0,1,1,0,1,0,0,Accountants,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,Self-employed,0.8013233095268318,0.7674946146718669,,0.0928,,0.0,,,0.0,,0.1667,,,,,,,0.0945,,0.0005,,,0.0,,0.1667,,,,,,,0.0937,,0.0,,,0.0,,0.1667,,,,,,,,block of flats,0.0618,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-536.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n139214,1,Cash loans,M,Y,Y,2,225000.0,343800.0,16155.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-11503,-1174,-388.0,-2405,8.0,1,1,0,1,0,0,,4.0,2,2,MONDAY,6,0,0,0,0,1,1,Transport: type 1,,0.501401405136645,0.13094715293601816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n347552,0,Cash loans,M,Y,Y,0,225000.0,522396.0,38142.0,472500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.025164,-11741,-3199,-5496.0,-4016,16.0,1,1,0,1,0,1,Drivers,2.0,2,2,SATURDAY,8,0,0,0,1,1,0,Business Entity Type 3,0.1891547832703214,0.3651023602991135,0.7826078370261895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1282.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n164151,0,Cash loans,F,N,Y,0,202500.0,521280.0,28408.5,450000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.005313,-10118,-1316,-10118.0,-495,,1,1,0,1,0,0,Secretaries,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Housing,,0.03417493455999352,,0.0474,0.0531,0.9861,0.8096,0.0061,0.0,0.1034,0.1667,0.0,0.0,0.0378,0.0445,0.0039,0.0012,0.0483,0.0551,0.9861,0.8171,0.0061,0.0,0.1034,0.1667,0.0,0.0,0.0413,0.0464,0.0039,0.0013,0.0479,0.0531,0.9861,0.8121,0.0061,0.0,0.1034,0.1667,0.0,0.0,0.0385,0.0453,0.0039,0.0012,reg oper account,block of flats,0.0397,Panel,No,0.0,0.0,0.0,0.0,-513.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n448463,0,Cash loans,M,Y,N,2,135000.0,1007761.5,38515.5,927000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.009657,-19347,-229,-5547.0,-2889,11.0,1,1,0,1,0,0,High skill tech staff,4.0,2,2,SATURDAY,12,0,0,0,0,0,0,School,0.7089868076644725,0.5661547990713984,0.7076993447402619,0.2969,0.231,0.9856,0.8028,0.032,0.32,0.2759,0.3333,0.375,0.2283,0.2421,0.3253,0.0,0.1623,0.3025,0.2397,0.9856,0.8105,0.0323,0.3222,0.2759,0.3333,0.375,0.2335,0.2645,0.3389,0.0,0.1718,0.2998,0.231,0.9856,0.8054,0.0322,0.32,0.2759,0.3333,0.375,0.2323,0.2463,0.3312,0.0,0.1657,reg oper account,block of flats,0.3087,Panel,No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n182908,0,Cash loans,F,N,Y,0,112500.0,101880.0,10206.0,90000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.025164,-18733,-2689,-1828.0,-2221,,1,1,1,1,1,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,1,1,Restaurant,,0.16219210595922867,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-401.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n366814,0,Cash loans,M,N,Y,0,180000.0,341280.0,24961.5,270000.0,Unaccompanied,Commercial associate,Lower secondary,Single / not married,House / apartment,0.006670999999999999,-10463,-467,-3407.0,-2848,,1,1,0,1,0,1,Waiters/barmen staff,1.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.17602961199212086,0.34455685279052106,,0.0041,,0.9652,0.524,,,0.0345,0.0833,,,,0.004,,,0.0042,,0.9652,0.5426,,,0.0345,0.0833,,,,0.0041,,,0.0042,,0.9652,0.5304,,,0.0345,0.0833,,,,0.004,,,,block of flats,0.0054,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-207.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n316560,1,Cash loans,F,N,N,0,180000.0,263686.5,25816.5,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.008019,-19705,365243,-13816.0,-3227,,1,0,0,1,0,0,,1.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.5042987575489829,0.5154953751603267,0.0825,0.0825,0.9762,0.6736,0.0328,0.0,0.1379,0.1667,0.2083,0.0293,0.0672,0.0625,0.0,0.0,0.084,0.0856,0.9762,0.6864,0.0331,0.0,0.1379,0.1667,0.2083,0.03,0.0735,0.0652,0.0,0.0,0.0833,0.0825,0.9762,0.6779999999999999,0.033,0.0,0.1379,0.1667,0.2083,0.0298,0.0684,0.0637,0.0,0.0,reg oper account,block of flats,0.0676,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-919.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n330366,0,Cash loans,F,N,N,0,112500.0,213948.0,12073.5,189000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-22362,-3604,-11957.0,-4163,,1,1,0,1,0,0,,2.0,3,3,TUESDAY,13,0,0,0,0,0,0,University,,0.7146852309434051,0.5867400085415683,0.0639,0.0806,0.9752,0.66,0.0561,0.0,0.1379,0.125,0.1667,0.0478,0.0521,0.0487,0.0,0.0,0.0651,0.0836,0.9752,0.6733,0.0566,0.0,0.1379,0.125,0.1667,0.0488,0.0569,0.0507,0.0,0.0,0.0645,0.0806,0.9752,0.6645,0.0565,0.0,0.1379,0.125,0.1667,0.0486,0.053,0.0495,0.0,0.0,reg oper account,block of flats,0.0383,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n220525,0,Cash loans,M,Y,Y,0,112500.0,254700.0,27153.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-10060,-189,-4564.0,-2746,9.0,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.38997897370383655,0.5034678397340143,0.7700870700124128,0.0247,0.0292,0.9796,0.6192,0.0008,0.0,0.069,0.1042,0.0833,0.0116,0.0067,0.0254,0.0,0.0,0.0084,0.0,0.9722,0.6341,0.0009,0.0,0.0345,0.0417,0.0833,0.0043,0.0073,0.0059,0.0,0.0,0.025,0.0292,0.9796,0.6243,0.0008,0.0,0.069,0.1042,0.0833,0.0118,0.0068,0.0259,0.0,0.0,reg oper account,block of flats,0.0364,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2128.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n275228,0,Cash loans,F,Y,Y,0,328500.0,609898.5,34186.5,526500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-15807,-1120,-9813.0,-341,6.0,1,1,0,1,0,0,High skill tech staff,2.0,1,1,THURSDAY,13,0,0,0,0,0,0,Transport: type 3,0.6769982598328455,0.7316495633536951,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2803.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n303234,1,Cash loans,M,N,Y,0,67500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009175,-10669,-3025,-4217.0,-3021,,1,1,1,1,1,0,Laborers,1.0,2,2,SATURDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.2563700049190531,,0.0124,0.0,0.9781,,,0.0,0.069,0.0417,,0.0077,,0.0096,,0.0,0.0126,0.0,0.9782,,,0.0,0.069,0.0417,,0.0079,,0.01,,0.0,0.0125,0.0,0.9781,,,0.0,0.069,0.0417,,0.0078,,0.0098,,0.0,,block of flats,0.008,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n422586,0,Cash loans,M,Y,N,2,270000.0,572076.0,45999.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-13971,-305,-3894.0,-3894,23.0,1,1,0,1,1,0,Laborers,4.0,1,1,SUNDAY,16,0,1,1,0,1,1,Business Entity Type 2,0.5048997520654906,0.6430076591229831,0.6109913280868294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1241.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n425825,0,Cash loans,F,N,Y,0,225000.0,1345500.0,37129.5,1345500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020713,-21445,-2092,-6940.0,-4867,,1,1,0,1,0,0,Security staff,1.0,3,3,SATURDAY,8,0,0,0,0,1,1,Security,,0.6713146782234499,0.6161216908872079,0.0773,0.0,0.9846,0.7892,0.0227,0.0,0.1379,0.1667,0.2083,0.0458,0.0614,0.0769,0.0077,0.0394,0.0788,0.0,0.9846,0.7975,0.0229,0.0,0.1379,0.1667,0.2083,0.0469,0.067,0.0802,0.0078,0.0418,0.0781,0.0,0.9846,0.792,0.0228,0.0,0.1379,0.1667,0.2083,0.0466,0.0624,0.0783,0.0078,0.0403,reg oper spec account,block of flats,0.0815,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-2580.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n338054,0,Cash loans,M,N,Y,1,157500.0,324000.0,23607.0,324000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-14685,-567,-949.0,-3427,,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,11,0,0,0,0,1,1,School,,0.269204355920222,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-574.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n222036,0,Revolving loans,M,Y,Y,1,135000.0,202500.0,10125.0,202500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-17382,-2626,-5880.0,-910,13.0,1,1,0,1,1,0,Laborers,3.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.3849752295692485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1753.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n337234,0,Cash loans,F,Y,Y,1,67500.0,67500.0,8140.5,67500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-9834,-1801,-1202.0,-1321,16.0,1,1,1,1,0,0,Core staff,3.0,2,2,MONDAY,17,0,0,0,0,0,0,Government,0.7319454917348185,0.624386797869104,0.3723336657058204,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-520.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n322137,0,Cash loans,F,N,Y,0,202500.0,495000.0,17779.5,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.00733,-12688,-1487,-6688.0,-2911,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,0.244271055672119,0.7067910765579899,0.5638350489514956,0.067,0.0788,0.9742,,,0.0,0.1034,0.125,,0.0,,0.0567,,0.0361,0.0683,0.0818,0.9742,,,0.0,0.1034,0.125,,0.0,,0.0591,,0.0382,0.0677,0.0788,0.9742,,,0.0,0.1034,0.125,,0.0,,0.0578,,0.0368,,block of flats,0.0734,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n449380,0,Cash loans,F,N,Y,0,247500.0,850846.5,36180.0,760500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.00496,-19131,-5154,-6051.0,-2611,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,18,0,0,0,0,0,0,Medicine,,0.6820687938446653,0.622922000268356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-728.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n439970,0,Cash loans,M,N,Y,2,202500.0,835380.0,40320.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-11522,-2159,-110.0,-341,,1,1,1,1,1,0,Laborers,4.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Agriculture,,0.0436770154490711,0.5424451438613613,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-333.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n156724,0,Cash loans,M,N,Y,2,292500.0,450000.0,21109.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.007114,-14347,-6387,-1737.0,-3900,,1,1,0,1,0,0,,4.0,2,2,TUESDAY,12,0,0,0,1,1,0,Government,,0.6680495316023085,0.5064842396679806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-609.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n407741,0,Cash loans,M,Y,N,0,90000.0,243000.0,22617.0,243000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-13120,-936,-3201.0,-4021,3.0,1,1,0,1,0,0,Drivers,1.0,2,2,THURSDAY,8,0,0,0,0,0,0,Transport: type 3,,0.6076869448935412,0.6075573001388961,0.0031,0.0,0.9518,,,0.0,0.0345,0.0,,0.0159,,0.0028,,0.0,0.0021,0.0,0.9518,,,0.0,0.0345,0.0,,0.0163,,0.0017,,0.0,0.0031,0.0,0.9518,,,0.0,0.0345,0.0,,0.0162,,0.0028,,0.0,,block of flats,0.0031,Wooden,No,0.0,0.0,0.0,0.0,-195.0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n104253,0,Cash loans,F,N,N,1,270000.0,1061599.5,31171.5,927000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-17435,-2214,-1903.0,-997,,1,1,0,1,0,0,Managers,3.0,1,1,SUNDAY,15,0,0,0,0,0,0,School,0.6862401507974478,0.7025725754868668,0.2032521136204725,0.0835,0.0818,0.9737,0.6396,0.057999999999999996,0.0,0.1379,0.1667,0.2083,0.0,0.0672,0.0701,0.0039,0.0036,0.0851,0.0849,0.9737,0.6537,0.0585,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.073,0.0039,0.0038,0.0843,0.0818,0.9737,0.6444,0.0583,0.0,0.1379,0.1667,0.2083,0.0,0.0684,0.0714,0.0039,0.0037,reg oper account,block of flats,0.0559,Panel,No,0.0,0.0,0.0,0.0,-1830.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n220397,0,Cash loans,F,Y,N,0,189000.0,728460.0,38578.5,675000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.019101,-8740,-1104,-1294.0,-1328,10.0,1,1,0,1,1,0,Medicine staff,2.0,2,2,TUESDAY,17,0,1,1,0,1,1,Military,0.3461992255475836,0.6736505905305363,0.4902575124990026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-157.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n345240,0,Cash loans,M,N,Y,0,157500.0,234324.0,24993.0,207000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.01885,-21837,-839,-7437.0,-4451,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Other,,0.6622293413928331,0.3296550543128238,0.1485,,0.9896,,,0.16,0.1379,0.3333,,0.0901,,0.0912,,,0.1513,,0.9896,,,0.1611,0.1379,0.3333,,0.0922,,0.095,,,0.1499,,0.9896,,,0.16,0.1379,0.3333,,0.0917,,0.0928,,,,block of flats,0.1228,Panel,No,3.0,0.0,3.0,0.0,-1508.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n415858,1,Cash loans,F,N,Y,0,112500.0,284400.0,13833.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.008473999999999999,-12011,-1663,-811.0,-3912,,1,1,1,1,1,0,Cleaning staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.44658255567313904,0.4245596483563272,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-574.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n312786,0,Cash loans,M,Y,N,0,135000.0,288562.5,26977.5,261000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-17835,-8600,-4372.0,-1363,6.0,1,1,0,1,0,0,Laborers,2.0,3,3,TUESDAY,11,0,0,0,0,0,0,Construction,0.6800120431773989,0.07335348607742505,0.6397075677637197,0.0722,0.1296,0.9702,0.5920000000000001,0.0324,0.0,0.1379,0.1667,0.0417,0.0766,0.0588,0.0992,0.0,0.0,0.0735,0.1345,0.9702,0.608,0.0327,0.0,0.1379,0.1667,0.0417,0.0783,0.0643,0.1034,0.0,0.0,0.0729,0.1296,0.9702,0.5975,0.0326,0.0,0.1379,0.1667,0.0417,0.0779,0.0599,0.10099999999999999,0.0,0.0,reg oper account,block of flats,0.078,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n254997,0,Cash loans,M,Y,Y,0,180000.0,900000.0,50386.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.014519999999999996,-21816,-8271,-6055.0,-4171,8.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Medicine,,0.6649240716454545,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2595.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n116724,1,Cash loans,M,Y,Y,1,90000.0,254700.0,17149.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.006852,-10231,-986,-8188.0,-2086,17.0,1,1,1,1,0,0,Laborers,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,Other,0.13138563002666673,0.19721666419625533,0.5388627065779676,,,0.9781,,,0.0,0.069,0.0417,,,,,,,,,0.9782,,,0.0,0.069,0.0417,,,,,,,,,0.9781,,,0.0,0.069,0.0417,,,,,,,,block of flats,0.0171,Panel,No,9.0,0.0,9.0,0.0,-588.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n291524,0,Revolving loans,F,N,Y,0,54000.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-18566,-462,-10572.0,-2070,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Agriculture,,0.4851636186421564,0.6413682574954046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-541.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n433684,0,Cash loans,M,N,N,0,234000.0,1078200.0,31522.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.0105,-12300,-192,-4842.0,-4383,,1,1,0,1,0,0,Laborers,2.0,3,3,TUESDAY,12,0,0,0,1,1,0,Construction,0.3195160364179233,0.4847976671589439,,0.0124,,0.9617,,,0.0,0.069,0.0417,,0.0133,,0.0083,,0.0,0.0126,,0.9618,,,0.0,0.069,0.0417,,0.0136,,0.0086,,0.0,0.0125,,0.9617,,,0.0,0.069,0.0417,,0.0135,,0.0084,,0.0,,block of flats,0.0077,Wooden,No,0.0,0.0,0.0,0.0,-1761.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n212998,0,Cash loans,F,N,Y,0,112500.0,338314.5,19552.5,306000.0,Family,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.010276,-23494,365243,-4292.0,-4289,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.6153441204398488,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-510.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n108321,0,Cash loans,F,N,Y,0,202500.0,1293502.5,37948.5,1129500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.005313,-23649,365243,-8134.0,-4510,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.35192710848167025,0.6446794549585961,,,0.9672,,,0.0,0.0345,0.0417,,,,,,,,,0.9672,,,0.0,0.0345,0.0417,,,,,,,,,0.9672,,,0.0,0.0345,0.0417,,,,,,,,block of flats,0.0063,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-273.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n125480,0,Cash loans,M,Y,Y,0,202500.0,521280.0,27423.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006629,-13481,-2349,-4314.0,-327,10.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,6,0,0,0,0,0,0,Self-employed,0.5345213486168977,0.0026730910454076827,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,2.0,-655.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n268344,0,Cash loans,F,N,Y,0,67500.0,98910.0,7011.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.008068,-24339,365243,-886.0,-4054,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,6,0,0,0,0,0,0,XNA,,0.08713768303466135,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n201514,0,Revolving loans,F,N,N,1,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.010556,-10115,-1658,-4076.0,-1422,,1,1,0,1,0,0,,3.0,3,3,FRIDAY,12,1,1,0,0,0,0,Business Entity Type 3,0.40862601762587625,0.6683016491276186,0.1117563710719636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-466.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n292573,0,Cash loans,M,Y,Y,0,189715.5,1350000.0,37255.5,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Office apartment,0.018801,-10776,-1617,-4389.0,-2968,20.0,1,1,1,1,0,0,Sales staff,1.0,2,2,MONDAY,7,0,0,0,1,1,0,Trade: type 2,,0.22244883347939567,,0.0711,0.063,0.9806,0.7348,0.0287,0.0,0.1379,0.1667,0.2083,0.0,0.0572,0.061,0.0039,0.0057,0.0725,0.0654,0.9806,0.7452,0.028999999999999998,0.0,0.1379,0.1667,0.2083,0.0,0.0624,0.0635,0.0039,0.006,0.0718,0.063,0.9806,0.7383,0.0289,0.0,0.1379,0.1667,0.2083,0.0,0.0581,0.0621,0.0039,0.0058,reg oper account,block of flats,0.0649,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-402.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n106274,0,Cash loans,F,N,Y,0,56700.0,545040.0,19705.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-20898,365243,-7833.0,-4420,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.4516348842653883,0.5867400085415683,0.0928,0.0997,0.9891,0.8504,0.0156,0.0,0.2069,0.1667,0.2083,0.0416,0.0748,0.0807,0.0039,0.0031,0.0945,0.1035,0.9891,0.8563,0.0157,0.0,0.2069,0.1667,0.2083,0.0426,0.0817,0.0841,0.0039,0.0033,0.0937,0.0997,0.9891,0.8524,0.0157,0.0,0.2069,0.1667,0.2083,0.0424,0.0761,0.0822,0.0039,0.0032,reg oper account,block of flats,0.0857,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1486.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n421886,0,Cash loans,M,Y,Y,3,135000.0,571486.5,29178.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14433,-3241,-1957.0,-1963,35.0,1,1,0,1,0,0,Laborers,5.0,2,2,MONDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.4937381430474842,0.678578035083677,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-807.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n220717,0,Cash loans,F,N,Y,0,67500.0,142200.0,9630.0,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.028663,-24019,365243,-12989.0,-4593,,1,0,0,1,1,1,,1.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.5607706530702113,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n284371,0,Cash loans,F,Y,N,1,175500.0,706477.5,55948.5,639000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-7980,-597,-7964.0,-551,1.0,1,1,0,1,0,1,Core staff,3.0,2,2,TUESDAY,14,0,0,0,1,1,0,Bank,0.3226974754591653,0.20418295311876744,0.363945238612397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-392.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n424621,0,Cash loans,F,Y,Y,0,270000.0,1219500.0,48492.0,1219500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.026392,-13953,-1372,-8049.0,-4645,6.0,1,1,0,1,1,0,,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.7319628346149224,0.6376794490199357,0.4382813743111921,0.0825,,0.9781,,,0.0,0.1379,0.1667,,,,0.0484,,0.0893,0.084,,0.9782,,,0.0,0.1379,0.1667,,,,0.0505,,0.0946,0.0833,,0.9781,,,0.0,0.1379,0.1667,,,,0.0493,,0.0912,,block of flats,0.0575,Panel,No,1.0,1.0,1.0,1.0,-3348.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n292907,0,Cash loans,F,N,Y,0,90000.0,481500.0,21339.0,481500.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.014519999999999996,-22952,365243,-5548.0,-5151,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.3941793611097536,,0.1485,,0.9821,,,0.16,0.1379,0.3333,,,,0.091,,0.2253,0.1513,,0.9821,,,0.1611,0.1379,0.3333,,,,0.0948,,0.2385,0.1499,,0.9821,,,0.16,0.1379,0.3333,,,,0.0927,,0.23,,block of flats,0.1206,Panel,No,0.0,0.0,0.0,0.0,-775.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n320490,0,Cash loans,M,Y,Y,0,225000.0,284400.0,22599.0,225000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-13706,-2046,-844.0,-4715,20.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,14,0,0,0,1,1,0,Self-employed,,0.4555262630270393,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-300.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n356498,0,Cash loans,F,N,Y,1,252000.0,760225.5,51138.0,679500.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-16412,-809,-8623.0,-3108,,1,1,0,1,0,0,Core staff,3.0,1,1,WEDNESDAY,17,0,0,0,0,0,0,Kindergarten,0.4993155478885427,0.7532902888884158,0.42589289800515295,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2264.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n380349,0,Cash loans,F,N,Y,0,112500.0,191880.0,18819.0,180000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.011657,-16867,-9600,-8881.0,-396,,1,1,1,1,1,0,Core staff,1.0,1,1,MONDAY,15,0,0,0,0,0,0,Kindergarten,0.7918644521920679,0.4307040855061772,0.722392890081304,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1280.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n205749,0,Cash loans,F,Y,N,0,112500.0,675000.0,19476.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018209,-12825,-456,-3327.0,-4554,21.0,1,1,0,1,0,0,HR staff,2.0,3,3,WEDNESDAY,13,0,0,0,0,0,0,Government,0.6045136159978578,0.5670676763349843,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n152602,1,Cash loans,F,N,Y,0,166500.0,284400.0,16011.0,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.006852,-24776,365243,-4.0,-4899,,1,0,0,1,0,0,,2.0,3,3,MONDAY,11,0,0,0,0,0,0,XNA,,0.3870794589441369,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1254.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n183466,0,Revolving loans,F,N,Y,1,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010556,-8534,-871,-5071.0,-904,,1,1,0,1,0,0,,3.0,3,3,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.4646845051261207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-222.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n323590,1,Cash loans,M,Y,Y,1,135000.0,333000.0,26437.5,333000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-12965,-1184,-3083.0,-4477,11.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,8,0,0,0,0,0,0,Kindergarten,,0.6658540005274143,0.15855489979486306,0.0082,0.0,0.9707,,,0.0,0.069,0.0417,,,,,,0.0,0.0084,0.0,0.9707,,,0.0,0.069,0.0417,,,,,,0.0,0.0083,0.0,0.9707,,,0.0,0.069,0.0417,,,,,,0.0,,,0.0066,\"Stone, brick\",No,3.0,2.0,3.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n388599,0,Cash loans,F,N,N,2,90000.0,544491.0,17694.0,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00702,-12991,-972,-4450.0,-5194,,1,1,0,1,0,0,Core staff,4.0,2,2,SATURDAY,12,0,0,0,0,0,0,School,,0.1017532235321706,0.3859146722745145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n117714,0,Cash loans,M,Y,Y,0,157500.0,446931.0,21865.5,369000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006008,-15945,-283,-3416.0,-3431,21.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,Agriculture,,0.02629443332260249,0.41534714488434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n282875,0,Cash loans,F,Y,Y,3,135000.0,353241.0,23737.5,319500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-16416,-3240,-6039.0,-4216,28.0,1,1,0,1,0,0,Core staff,5.0,2,2,MONDAY,13,0,0,0,0,0,0,Kindergarten,,0.5918305028241804,0.44539624156083396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1648.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n215014,1,Cash loans,F,Y,Y,1,99000.0,189621.0,13927.5,144000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-15573,-5709,-3293.0,-2646,17.0,1,1,0,1,0,0,Laborers,3.0,3,3,THURSDAY,11,0,0,0,0,0,0,Industry: type 11,0.5618091432851539,0.7463953517571499,0.1997705696341145,0.0608,0.0575,0.9816,0.7484,0.0153,0.0,0.1379,0.1667,0.2083,0.0528,0.0488,0.054000000000000006,0.0039,0.0048,0.062,0.0597,0.9816,0.7583,0.0154,0.0,0.1379,0.1667,0.2083,0.054000000000000006,0.0533,0.0563,0.0039,0.0051,0.0614,0.0575,0.9816,0.7518,0.0154,0.0,0.1379,0.1667,0.2083,0.0537,0.0496,0.055,0.0039,0.0049,reg oper account,block of flats,0.0519,Panel,No,2.0,1.0,2.0,1.0,-1851.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n182839,0,Cash loans,M,Y,Y,0,112500.0,273636.0,21618.0,247500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00733,-19158,-164,-2450.0,-2719,10.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Housing,0.5795034344352185,0.6268503886840149,0.6144143775673561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1846.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n437995,0,Cash loans,M,N,N,1,180000.0,1293502.5,37944.0,1129500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.019689,-12409,-2606,-3228.0,-4362,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,11,0,0,0,1,1,0,Electricity,,0.4074179565217023,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-258.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n101289,0,Cash loans,F,N,Y,0,112500.0,1133748.0,33277.5,990000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020713,-19459,-843,-9561.0,-2895,,1,1,0,1,0,0,Security staff,2.0,3,3,MONDAY,6,0,0,0,0,1,1,Security,0.6052530393911487,0.6142773194688561,0.7180328113294772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1550.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n397916,0,Cash loans,F,N,Y,0,157500.0,472500.0,46863.0,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.026392,-22106,365243,-562.0,-4356,,1,0,0,1,0,0,,1.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.6611475010710917,0.6041125998015721,0.33399999999999996,,0.9846,,,0.36,0.3103,0.3333,,,,0.2111,,0.0,0.3403,,0.9846,,,0.3625,0.3103,0.3333,,,,0.2199,,0.0,0.3373,,0.9846,,,0.36,0.3103,0.3333,,,,0.2149,,0.0,,block of flats,0.166,Panel,No,2.0,1.0,2.0,1.0,-1251.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n185931,0,Revolving loans,F,N,Y,0,180000.0,135000.0,6750.0,135000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.0105,-22911,365243,-10883.0,-5169,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,10,0,0,0,0,0,0,XNA,,0.5902342286634823,0.524496446363472,0.0742,0.0475,0.9851,0.7959999999999999,0.0317,0.08,0.069,0.3333,0.0,0.0582,0.0605,0.0744,0.0,0.0,0.0756,0.0455,0.9851,0.804,0.032,0.0806,0.069,0.3333,0.0,0.0595,0.0661,0.0769,0.0,0.0,0.0749,0.0475,0.9851,0.7987,0.0319,0.08,0.069,0.3333,0.0,0.0592,0.0616,0.0757,0.0,0.0,org spec account,block of flats,0.0667,Panel,No,0.0,0.0,0.0,0.0,-416.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n165307,0,Cash loans,M,N,Y,0,292500.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019689,-15351,-2754,-2614.0,-4455,,1,1,1,1,1,0,Managers,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Industry: type 11,,0.6414701722732076,0.3108182544189319,0.0515,0.0,0.9866,0.8164,0.0014,0.0,0.1724,0.1667,0.2083,0.0226,0.042,0.0573,0.0,0.0495,0.0525,0.0,0.9866,0.8236,0.0014,0.0,0.1724,0.1667,0.2083,0.0231,0.0459,0.0597,0.0,0.0524,0.052000000000000005,0.0,0.9866,0.8189,0.0014,0.0,0.1724,0.1667,0.2083,0.023,0.0428,0.0583,0.0,0.0505,not specified,block of flats,0.0552,Panel,No,0.0,0.0,0.0,0.0,-1599.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n411431,0,Cash loans,F,N,N,0,157500.0,1530000.0,40491.0,1530000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-21971,365243,-10951.0,-4972,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,0.7057609935225946,0.5605659695385757,0.5172965813614878,0.0619,0.0546,0.9841,0.7824,0.0252,0.0,0.1379,0.1667,0.2083,0.028999999999999998,0.0504,0.052000000000000005,0.0,0.0,0.063,0.0567,0.9841,0.7909,0.0254,0.0,0.1379,0.1667,0.2083,0.0296,0.0551,0.0542,0.0,0.0,0.0625,0.0546,0.9841,0.7853,0.0253,0.0,0.1379,0.1667,0.2083,0.0295,0.0513,0.0529,0.0,0.0,reg oper account,block of flats,0.0547,Panel,No,2.0,0.0,2.0,0.0,-1819.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n354562,0,Cash loans,F,N,N,0,72000.0,302206.5,20443.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-9376,-327,-7180.0,-1244,,1,1,1,1,1,0,Sales staff,2.0,2,2,FRIDAY,9,0,0,0,1,1,0,Self-employed,,0.17092378602024555,0.06779391064125932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-330.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n327973,0,Cash loans,F,Y,Y,3,76500.0,378117.0,24745.5,342000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-13446,-2619,-3866.0,-2651,64.0,1,1,0,1,0,0,,5.0,2,2,THURSDAY,15,0,0,0,0,1,1,Business Entity Type 3,0.7525519451780459,0.4536774223284598,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n235894,0,Cash loans,M,Y,Y,1,166500.0,855000.0,34038.0,855000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.015221,-17195,365243,-3694.0,-744,1.0,1,0,0,1,1,0,,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.5738405161746867,0.6109913280868294,0.0619,0.0546,0.9806,,,,0.1379,0.1667,,0.0648,,0.0534,,0.3504,0.063,0.0567,0.9806,,,,0.1379,0.1667,,0.0663,,0.0557,,0.3709,0.0625,0.0546,0.9806,,,,0.1379,0.1667,,0.0659,,0.0544,,0.3577,,block of flats,0.042,Panel,No,6.0,0.0,6.0,0.0,-390.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n293982,0,Cash loans,F,N,N,0,85500.0,343800.0,10921.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-12263,-765,-512.0,-4771,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 2,,0.7519344956506224,0.4632753280912678,0.0629,0.057999999999999996,0.9841,0.7824,0.0084,0.0,0.1379,0.1667,0.2083,0.0475,0.0513,0.0557,0.0,0.0,0.0641,0.0602,0.9841,0.7909,0.0085,0.0,0.1379,0.1667,0.2083,0.0485,0.055999999999999994,0.0581,0.0,0.0,0.0635,0.057999999999999996,0.9841,0.7853,0.0085,0.0,0.1379,0.1667,0.2083,0.0483,0.0522,0.0567,0.0,0.0,reg oper account,block of flats,0.0484,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n418935,0,Revolving loans,M,N,Y,1,166500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009334,-18524,-1832,-6695.0,-1838,,1,1,0,1,1,0,Drivers,3.0,2,2,SATURDAY,14,0,0,0,0,0,0,Government,,0.5789927927716146,0.6925590674998008,0.1247,0.1728,0.9826,0.762,0.0162,0.0,0.2759,0.1667,0.0,0.132,0.0992,0.1128,0.0116,0.0308,0.1271,0.1793,0.9826,0.7713,0.0164,0.0,0.2759,0.1667,0.0,0.135,0.1084,0.1176,0.0117,0.0326,0.126,0.1728,0.9826,0.7652,0.0163,0.0,0.2759,0.1667,0.0,0.1342,0.1009,0.1149,0.0116,0.0315,reg oper account,block of flats,0.1043,Panel,No,7.0,0.0,7.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n237434,1,Cash loans,F,N,Y,0,157500.0,656361.0,26860.5,522000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-11738,-301,-2763.0,-2768,,1,1,0,1,0,0,,2.0,3,2,SUNDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.14540933557301028,,0.368,0.2459,0.9781,0.7008,0.0615,0.24,0.2069,0.4167,0.375,0.1633,0.2984,0.3371,0.0077,0.0026,0.375,0.2552,0.9782,0.7125,0.062,0.2417,0.2069,0.4167,0.375,0.16699999999999998,0.326,0.3513,0.0078,0.0027,0.3716,0.2459,0.9781,0.7048,0.0619,0.24,0.2069,0.4167,0.375,0.1661,0.3036,0.3432,0.0078,0.0026,org spec account,block of flats,0.2988,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-242.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n334470,0,Cash loans,M,Y,Y,0,103500.0,172512.0,13675.5,144000.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-20648,-1925,-4556.0,-3062,11.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,University,,0.5844731686445248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-677.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n216881,0,Revolving loans,F,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-7786,-801,-5451.0,-459,,1,1,0,1,1,0,Cooking staff,1.0,3,3,TUESDAY,5,0,0,0,1,1,0,Business Entity Type 3,,0.3750527615066985,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-408.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n320861,0,Cash loans,M,N,Y,0,225000.0,1113840.0,44172.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.030755,-16531,-4405,-8936.0,-67,,1,1,0,1,1,0,Laborers,1.0,2,2,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 2,,0.5289658285087285,0.7838324335199619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n213062,0,Cash loans,F,Y,Y,1,202500.0,990000.0,32067.0,990000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-15444,-676,-3466.0,-4273,9.0,1,1,0,1,0,0,Sales staff,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.26545669740917643,0.6296742509538716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-71.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n136419,1,Cash loans,M,N,Y,0,135000.0,284256.0,27819.0,270000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-25032,365243,-3478.0,-4109,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.6693091533439343,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n345449,0,Cash loans,F,N,Y,0,324805.5,900000.0,50724.0,900000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.026392,-9080,-962,-3610.0,-1722,,1,1,1,1,0,1,Core staff,2.0,2,2,WEDNESDAY,16,1,1,0,1,1,0,Trade: type 2,,0.5707306475239029,0.43473324875017305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-59.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n344747,0,Cash loans,M,Y,Y,0,225000.0,954207.0,33804.0,796500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-19040,-312,-3894.0,-2564,11.0,1,1,0,1,0,0,Drivers,1.0,3,2,SUNDAY,8,0,0,0,0,0,0,Transport: type 3,,0.5123818700918991,0.1510075350878296,0.032,0.0212,0.9940000000000001,0.9184,0.0112,0.08,0.069,0.3333,0.3333,0.0203,0.0219,0.0396,0.0193,0.0441,0.0326,0.022000000000000002,0.9940000000000001,0.9216,0.0113,0.0806,0.069,0.3333,0.3333,0.0208,0.0239,0.0412,0.0195,0.0466,0.0323,0.0212,0.9940000000000001,0.9195,0.0112,0.08,0.069,0.3333,0.3333,0.0206,0.0222,0.0403,0.0194,0.045,,block of flats,0.0468,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-816.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n218058,0,Cash loans,F,Y,Y,0,135000.0,810000.0,29092.5,810000.0,Family,Working,Higher education,Married,House / apartment,0.006852,-9977,-1447,-4451.0,-757,17.0,1,1,0,1,0,0,Core staff,2.0,3,3,WEDNESDAY,6,0,0,0,1,1,0,Bank,0.2321857756889924,0.16040826652682824,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-530.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n375497,0,Cash loans,F,Y,Y,0,157500.0,296280.0,23539.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-12251,-1359,-3676.0,-1310,8.0,1,1,0,1,0,0,Sales staff,2.0,3,3,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3108941163206833,0.5817567860628902,0.2340151665320674,0.1577,0.1118,0.9876,0.83,0.0659,0.2,0.1724,0.3333,0.375,0.1984,0.1278,0.1883,0.0039,0.0013,0.1607,0.11599999999999999,0.9876,0.8367,0.0665,0.2014,0.1724,0.3333,0.375,0.203,0.1396,0.1962,0.0039,0.0014,0.1593,0.1118,0.9876,0.8323,0.0664,0.2,0.1724,0.3333,0.375,0.2019,0.13,0.1917,0.0039,0.0014,reg oper spec account,block of flats,0.1844,Panel,No,9.0,2.0,9.0,1.0,-1535.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n333323,0,Cash loans,M,Y,Y,0,99000.0,315000.0,19269.0,315000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16813,-2798,-7213.0,-351,11.0,1,1,1,1,1,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.0285041833664655,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-2414.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n395272,0,Cash loans,F,N,N,0,135000.0,227520.0,13189.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008625,-19179,-970,-4427.0,-2670,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.6627622951165543,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1547.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n369532,0,Cash loans,M,N,Y,0,135000.0,269550.0,18364.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010032,-10657,-907,-4738.0,-3318,,1,1,0,1,0,0,Drivers,1.0,2,2,MONDAY,8,0,0,0,0,0,0,Self-employed,,0.5787262475312671,,0.1227,0.1332,0.9786,0.7076,,0.0,0.2759,0.1667,,0.0,,0.0767,,0.001,0.125,0.1379,0.9786,0.7190000000000001,,0.0,0.2759,0.1667,,0.0,,0.0794,,0.0011,0.1239,0.1332,0.9786,0.7115,,0.0,0.2759,0.1667,,0.0,,0.0781,,0.001,reg oper account,block of flats,0.0882,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-615.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n302902,0,Cash loans,M,Y,N,0,225000.0,1024740.0,52452.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006233,-12389,-1354,-5398.0,-3239,13.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.739589238225427,0.3774042489507649,0.1392,0.0,0.9831,,,,0.3103,0.1667,,0.0179,,0.0754,,0.0427,0.1418,0.0,0.9831,,,,0.3103,0.1667,,0.0183,,0.0786,,0.0452,0.1405,0.0,0.9831,,,,0.3103,0.1667,,0.0182,,0.0767,,0.0436,,block of flats,0.1101,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-532.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n335315,0,Cash loans,F,Y,Y,0,83250.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.010276,-19284,-3953,-1574.0,-2842,4.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.5024535543860726,0.1516644988610186,0.807273923277881,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-562.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n437100,0,Cash loans,F,N,Y,0,112500.0,281493.0,11925.0,243000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.031329,-18594,-5344,-2771.0,-2127,,1,1,0,1,1,0,Core staff,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,Agriculture,0.6439095441927164,0.40530789512610266,0.4632753280912678,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-15.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n126176,0,Cash loans,M,Y,N,3,81000.0,814041.0,23800.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-14272,-1861,-2714.0,-5348,17.0,1,1,1,1,0,0,Security staff,5.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.592707102947451,0.6496203111237195,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2196.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n397412,1,Cash loans,M,N,N,1,225000.0,363190.5,24399.0,328500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.007120000000000001,-9990,-585,-4168.0,-210,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,12,0,1,1,0,1,1,Business Entity Type 1,0.3340800463786738,0.3039321548865665,0.43473324875017305,0.0835,,0.9876,,,0.0,0.069,0.1667,,,,0.0501,,,0.0851,,0.9876,,,0.0,0.069,0.1667,,,,0.0522,,,0.0843,,0.9876,,,0.0,0.069,0.1667,,,,0.051,,,,block of flats,0.0493,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n291981,0,Cash loans,F,N,N,0,90000.0,145557.0,8923.5,121500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-18163,365243,-3610.0,-1705,,1,0,0,1,0,0,,2.0,3,3,SATURDAY,13,0,0,0,0,0,0,XNA,,0.4756988268495197,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-202.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n194206,0,Cash loans,M,Y,N,0,135000.0,900000.0,32017.5,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-17430,-251,-5396.0,-946,8.0,1,1,0,1,1,0,Drivers,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Security,,0.4226535289126135,0.6658549219640212,0.1526,0.129,0.9906,0.8708,0.0392,0.16,0.1379,0.375,0.0,0.0843,0.1244,0.1611,0.0077,0.157,0.1555,0.1339,0.9906,0.8759,0.0395,0.1611,0.1379,0.375,0.0,0.0862,0.1359,0.1679,0.0078,0.1662,0.1541,0.129,0.9906,0.8725,0.0394,0.16,0.1379,0.375,0.0,0.0858,0.1266,0.16399999999999998,0.0078,0.1603,org spec account,block of flats,0.1608,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1616.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n282493,0,Revolving loans,M,Y,Y,0,675000.0,1237500.0,61875.0,1237500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-10758,-862,-491.0,-3452,3.0,1,1,0,1,0,0,,2.0,1,1,WEDNESDAY,19,0,0,0,0,0,0,Business Entity Type 2,,0.7590556167879141,0.3910549766342248,0.3778,0.1304,0.9861,,,0.44,0.1897,0.6667,,,,0.3983,,0.0285,0.3435,0.1224,0.9861,,,0.4028,0.1724,0.6667,,,,0.37,,0.0048,0.3815,0.1304,0.9861,,,0.44,0.1897,0.6667,,,,0.4054,,0.0291,,block of flats,0.2907,Panel,No,0.0,0.0,0.0,0.0,-394.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n147904,0,Cash loans,M,N,Y,0,135000.0,56034.0,4423.5,49500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010643,-8581,-627,-4722.0,-1265,,1,1,1,1,1,0,Managers,1.0,2,2,TUESDAY,10,0,1,1,0,1,1,Security,0.10675731336941344,0.5771633748280915,,0.1536,0.0,0.9911,0.8776,0.0283,0.0,0.2069,0.1667,0.2083,0.1164,0.1244,0.168,0.0039,0.0048,0.1565,0.0,0.9911,0.8824,0.0286,0.0,0.2069,0.1667,0.2083,0.1191,0.1359,0.175,0.0039,0.0051,0.1551,0.0,0.9911,0.8792,0.0285,0.0,0.2069,0.1667,0.2083,0.1185,0.1266,0.171,0.0039,0.0049,reg oper account,block of flats,0.1486,Panel,No,0.0,0.0,0.0,0.0,-1127.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n205864,0,Cash loans,F,Y,Y,1,112500.0,327024.0,11745.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-14467,-812,-5736.0,-4715,9.0,1,1,1,1,1,0,High skill tech staff,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Medicine,,0.6733284068565731,0.6041125998015721,0.2227,,0.9841,,,0.24,0.2069,0.3333,,,,0.233,,0.0,0.2269,,0.9841,,,0.2417,0.2069,0.3333,,,,0.2427,,0.0,0.2248,,0.9841,,,0.24,0.2069,0.3333,,,,0.2371,,0.0,,block of flats,0.2091,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n132874,0,Cash loans,M,Y,N,1,225000.0,137538.0,10008.0,121500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008625,-12431,-282,-1050.0,-3141,12.0,1,1,1,1,1,0,Managers,3.0,2,2,MONDAY,16,0,0,0,0,0,0,Medicine,0.3385014302144057,0.2262885672678969,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-788.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n300662,0,Cash loans,F,N,Y,0,90000.0,101880.0,10053.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.030755,-20765,365243,-9685.0,-4291,,1,0,0,1,0,0,,1.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,0.6980934923178049,0.677193306153166,0.7713615919194317,0.2969,0.2551,0.9871,0.8232,0.0825,0.32,0.2759,0.3333,0.375,0.1088,0.2421,0.3503,0.0039,0.0079,0.3025,0.2647,0.9871,0.8301,0.0833,0.3222,0.2759,0.3333,0.375,0.1113,0.2645,0.365,0.0039,0.0084,0.2998,0.2551,0.9871,0.8256,0.0831,0.32,0.2759,0.3333,0.375,0.1107,0.2463,0.3566,0.0039,0.0081,reg oper account,block of flats,0.3061,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2026.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n295540,0,Cash loans,F,Y,Y,1,67500.0,544500.0,17694.0,544500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-14218,-7394,-7969.0,-1774,0.0,1,1,0,1,0,0,Medicine staff,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Medicine,0.6190987795282956,0.2453495532540649,0.4329616670974407,0.2227,0.1275,0.9816,0.7484,0.0388,0.24,0.2069,0.3333,0.0417,0.1215,0.1816,0.2355,0.0,0.0,0.2269,0.1324,0.9816,0.7583,0.0392,0.2417,0.2069,0.3333,0.0417,0.1243,0.1983,0.2454,0.0,0.0,0.2248,0.1275,0.9816,0.7518,0.0391,0.24,0.2069,0.3333,0.0417,0.1236,0.1847,0.2397,0.0,0.0,reg oper account,block of flats,0.1856,Panel,No,0.0,0.0,0.0,0.0,-1644.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n266437,0,Cash loans,F,Y,N,0,157500.0,755190.0,32125.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010643,-9500,-2231,-6069.0,-2155,12.0,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.3482651615534909,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1486.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n289440,0,Revolving loans,F,Y,Y,0,180000.0,405000.0,20250.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.02461,-15540,-8599,-7992.0,-4095,15.0,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6724812944942762,0.6263042766749393,0.0722,,0.9776,0.6940000000000001,0.0079,0.0,0.1379,0.1667,0.0417,0.0148,0.0588,0.0678,0.0,0.0,0.0735,,0.9777,0.706,0.008,0.0,0.1379,0.1667,0.0417,0.0151,0.0643,0.0707,0.0,0.0,0.0729,,0.9776,0.6981,0.008,0.0,0.1379,0.1667,0.0417,0.015,0.0599,0.0691,0.0,0.0,reg oper account,block of flats,0.057999999999999996,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1520.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n237945,0,Revolving loans,F,N,Y,1,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-13129,-1821,-8438.0,-365,,1,1,0,1,0,1,Laborers,3.0,2,2,FRIDAY,16,0,0,0,0,0,0,Self-employed,0.1993312575069948,0.5096388261812794,0.3842068130556564,0.0134,,0.9702,,,0.0,0.069,0.0417,,0.0,,0.0111,,0.0,0.0137,,0.9702,,,0.0,0.069,0.0417,,0.0,,0.0115,,0.0,0.0135,,0.9702,,,0.0,0.069,0.0417,,0.0,,0.0113,,0.0,,block of flats,0.0136,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-323.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,3.0\n184015,0,Cash loans,M,N,Y,2,180000.0,814666.5,41724.0,715500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-18742,-10856,-5839.0,-2289,,1,1,0,1,0,1,Laborers,4.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Electricity,0.3965362381168887,0.6461087891127391,0.7151031019926098,0.0825,0.0794,0.9742,0.6464,0.0088,0.0,0.1379,0.1667,0.2083,0.0,0.0672,0.063,0.0,0.0,0.084,0.0824,0.9742,0.6602,0.0089,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.0656,0.0,0.0,0.0833,0.0794,0.9742,0.6511,0.0088,0.0,0.1379,0.1667,0.2083,0.0,0.0684,0.0641,0.0,0.0,reg oper account,block of flats,0.0495,\"Stone, brick\",No,4.0,1.0,4.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n375368,0,Cash loans,F,N,Y,0,270000.0,1002856.5,44302.5,823500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-17413,-10574,-6487.0,-951,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.5931362398666787,0.6436398889696583,0.3490552510751822,0.0742,0.0438,0.9901,0.8640000000000001,0.0189,0.08,0.069,0.3333,0.375,0.0,0.0605,0.084,0.0,0.0,0.0756,0.0455,0.9901,0.8693,0.0191,0.0806,0.069,0.3333,0.375,0.0,0.0661,0.0875,0.0,0.0,0.0749,0.0438,0.9901,0.8658,0.019,0.08,0.069,0.3333,0.375,0.0,0.0616,0.0855,0.0,0.0,reg oper account,block of flats,0.0764,Block,No,14.0,1.0,14.0,1.0,-913.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n211617,0,Cash loans,F,N,N,1,76500.0,256167.0,20367.0,211500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-12393,-4624,-1323.0,-4095,,1,1,0,1,0,0,Medicine staff,3.0,2,2,MONDAY,7,0,0,0,0,0,0,Medicine,0.30677893915575466,0.6529384361715459,0.4956658291397297,0.0351,,0.9697,,,,0.0345,0.0417,,0.0187,,0.0128,,0.0,0.0357,,0.9697,,,,0.0345,0.0417,,0.0191,,0.0133,,0.0,0.0354,,0.9697,,,,0.0345,0.0417,,0.019,,0.013000000000000001,,0.0,,block of flats,0.0101,Block,No,0.0,0.0,0.0,0.0,-388.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n363271,0,Cash loans,F,Y,N,0,171000.0,364230.0,24210.0,337500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.018634,-24877,365243,-3745.0,-5702,8.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.632606930209662,,,,0.9881,0.8368,,0.16,0.1379,0.375,0.4167,0.0914,,0.1741,,0.0329,,,0.9881,0.8432,,0.1611,0.1379,0.375,0.4167,0.0935,,0.1814,,0.0349,,,0.9881,0.8390000000000001,,0.16,0.1379,0.375,0.4167,0.09300000000000001,,0.1772,,0.0336,,block of flats,0.1409,,No,0.0,0.0,0.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n221969,0,Cash loans,F,Y,N,0,81000.0,808650.0,22365.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-18042,-6004,-5029.0,-1592,19.0,1,1,0,1,0,0,Accountants,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Government,,0.19752497489483725,,0.0777,0.0815,0.9846,0.7892,0.0121,0.0264,0.1148,0.2358,0.2221,0.0167,0.0628,0.0761,0.0025,0.0025,0.0735,0.0806,0.9777,0.706,0.0079,0.0,0.1034,0.1667,0.2083,0.0155,0.0643,0.0666,0.0,0.0,0.0749,0.0818,0.9881,0.8390000000000001,0.0139,0.0,0.1034,0.2083,0.2083,0.0168,0.0616,0.0778,0.0,0.0,reg oper account,block of flats,0.0503,Block,No,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n441242,0,Cash loans,F,Y,N,2,247500.0,2156400.0,59301.0,1800000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-14257,-6834,-6053.0,-4824,9.0,1,1,1,1,1,0,Laborers,4.0,3,1,FRIDAY,13,0,0,0,0,0,0,Industry: type 11,,0.5440001535481273,0.501075160239048,0.0903,0.2708,0.9732,,,0.0,0.1793,0.1667,,0.1134,,0.0808,,0.0604,0.0504,0.281,0.9732,,,0.0,0.1034,0.1667,,0.11599999999999999,,0.0432,,0.0419,0.0614,0.2708,0.9732,,,0.0,0.1034,0.1667,,0.1154,,0.0487,,0.0415,,block of flats,0.1726,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n291811,0,Cash loans,M,Y,Y,0,180000.0,450000.0,36081.0,450000.0,Family,Working,Higher education,Married,House / apartment,0.030755,-13790,-3010,-3321.0,-4486,12.0,1,1,1,1,1,0,Drivers,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Agriculture,0.3937369518949087,0.0002341861324124313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,1.0,-407.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n250386,0,Cash loans,M,Y,N,1,112500.0,1762110.0,48586.5,1575000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006207,-14942,-2711,-7622.0,-4620,8.0,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,16,0,0,0,0,0,0,Electricity,,0.6857560185547517,0.6212263380626669,0.0784,,0.9762,,,0.0,0.1379,0.1667,,0.0608,,0.0525,,0.0111,0.0798,,0.9762,,,0.0,0.1379,0.1667,,0.0622,,0.0547,,0.0117,0.0791,,0.9762,,,0.0,0.1379,0.1667,,0.0618,,0.0534,,0.0113,,block of flats,0.0445,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-30.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n159955,1,Cash loans,M,N,Y,0,270000.0,450000.0,30073.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-10886,-308,-4340.0,-3011,,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,9,0,1,1,0,0,0,Construction,0.20391427524287825,0.4463547696193835,0.4525335592581747,0.2258,0.2448,0.9851,0.7959999999999999,0.0431,0.0,0.5172,0.1667,0.0417,0.1507,0.1807,0.1853,0.0154,0.0155,0.23,0.2541,0.9851,0.804,0.0435,0.0,0.5172,0.1667,0.0417,0.1541,0.1974,0.193,0.0156,0.0164,0.228,0.2448,0.9851,0.7987,0.0434,0.0,0.5172,0.1667,0.0417,0.1533,0.1838,0.1886,0.0155,0.0158,reg oper account,block of flats,0.1726,Panel,No,2.0,0.0,2.0,0.0,-1707.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n322619,0,Cash loans,F,Y,N,1,112500.0,270000.0,13131.0,270000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.035792000000000004,-10385,-598,-3524.0,-3043,7.0,1,1,1,1,0,0,Sales staff,3.0,2,2,SUNDAY,10,0,0,0,0,0,0,Self-employed,0.4828586772380719,0.6546856242737169,0.3706496323299817,0.0082,0.0,0.9702,,,,0.069,0.0417,,,,0.0054,,0.0085,0.0084,0.0,0.9702,,,,0.069,0.0417,,,,0.0056,,0.009000000000000001,0.0083,0.0,0.9702,,,,0.069,0.0417,,,,0.0055,,0.0087,,block of flats,0.0061,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-724.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,6.0\n358333,0,Cash loans,M,Y,Y,1,423000.0,1350000.0,39474.0,1350000.0,Family,Working,Higher education,Married,House / apartment,0.028663,-15934,-3492,-3864.0,-4537,6.0,1,1,1,1,1,0,Managers,3.0,2,2,MONDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.5191430570978969,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0755,,No,0.0,0.0,0.0,0.0,-1737.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n440072,0,Cash loans,M,N,Y,0,202500.0,735579.0,35518.5,585000.0,Unaccompanied,State servant,Incomplete higher,Married,House / apartment,0.04622,-8910,-431,-8909.0,-1567,,1,1,0,1,1,1,Core staff,2.0,1,1,WEDNESDAY,13,0,0,0,0,1,1,Police,,0.5015490100317049,0.2807895743848605,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0111,,No,4.0,2.0,4.0,2.0,-383.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n358351,0,Cash loans,F,Y,Y,0,112500.0,219042.0,12361.5,193500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-20968,365243,-1100.0,-4407,25.0,1,0,0,1,0,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.6345868006863699,0.5726825047161584,0.0619,0.0389,0.9836,0.7756,,0.0,0.0345,0.1667,0.0417,0.0443,0.0504,0.0384,0.0,0.0,0.063,0.0404,0.9836,0.7844,,0.0,0.0345,0.1667,0.0417,0.0453,0.0551,0.04,0.0,0.0,0.0625,0.0389,0.9836,0.7786,,0.0,0.0345,0.1667,0.0417,0.0451,0.0513,0.0391,0.0,0.0,reg oper account,block of flats,0.0302,Block,No,1.0,1.0,1.0,1.0,-564.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n197361,0,Cash loans,F,N,Y,0,112500.0,436032.0,29268.0,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.004849,-10858,-1119,-5259.0,-3545,,1,1,0,1,0,0,,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.27416296332641604,0.7407990879702335,0.2106,0.1162,0.9856,0.8028,0.0689,0.2264,0.1952,0.3333,0.375,0.1222,0.179,0.2247,0.0,0.0035,0.1964,0.0,0.9851,0.804,0.0409,0.2417,0.2069,0.3333,0.375,0.0,0.191,0.2248,0.0,0.0,0.2165,0.1162,0.9851,0.8054,0.0693,0.24,0.2069,0.3333,0.375,0.1773,0.1821,0.2322,0.0,0.0,org spec account,block of flats,0.1902,Panel,No,1.0,0.0,1.0,0.0,-237.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n429675,0,Cash loans,F,Y,Y,0,157500.0,351792.0,17055.0,252000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.003813,-21198,365243,-3948.0,-4409,13.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.2437497457884428,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n375329,0,Cash loans,M,Y,Y,1,247500.0,355536.0,18283.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-15385,-1597,-3642.0,-1304,10.0,1,1,0,1,0,1,Laborers,3.0,3,3,THURSDAY,7,0,0,0,0,0,0,Industry: type 9,0.4673497837249633,0.3854413164304908,0.42589289800515295,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n202877,0,Cash loans,M,Y,N,1,112500.0,180000.0,21361.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.010643,-14894,-167,-8832.0,-5119,14.0,1,1,1,1,0,0,Laborers,3.0,2,2,FRIDAY,15,1,1,0,1,1,0,Business Entity Type 2,0.4066132436009171,0.3215969798134054,0.7062051096536562,0.0289,0.0274,0.9697,,,0.0,0.1034,0.125,,0.0363,,0.0193,,,0.0294,0.0284,0.9697,,,0.0,0.1034,0.125,,0.0371,,0.0201,,,0.0291,0.0274,0.9697,,,0.0,0.1034,0.125,,0.0369,,0.0196,,,,block of flats,0.0256,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-76.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n424454,0,Revolving loans,F,N,Y,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.008575,-8673,-1147,-843.0,-1313,,1,1,0,1,1,0,Core staff,2.0,2,2,FRIDAY,16,0,1,1,1,0,0,Trade: type 3,0.3911698562116228,0.29659958200331354,0.4902575124990026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-328.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n312632,0,Cash loans,F,N,Y,0,144000.0,1024636.5,33993.0,918000.0,Group of people,Pensioner,Higher education,Married,House / apartment,0.04622,-20781,365243,-14279.0,-3315,,1,0,0,1,0,0,,2.0,1,1,MONDAY,13,0,0,0,0,0,0,XNA,,0.7352960124701695,,0.0619,0.0477,0.9767,0.6804,0.0065,0.0,0.1034,0.1667,0.2083,0.0603,0.0504,0.0508,0.0,0.0,0.063,0.0495,0.9767,0.6929,0.0066,0.0,0.1034,0.1667,0.2083,0.0617,0.0551,0.0529,0.0,0.0,0.0625,0.0477,0.9767,0.6847,0.0066,0.0,0.1034,0.1667,0.2083,0.0613,0.0513,0.0517,0.0,0.0,reg oper account,block of flats,0.0435,Panel,No,0.0,0.0,0.0,0.0,-417.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n146873,0,Cash loans,F,N,N,1,135000.0,594000.0,19161.0,594000.0,Family,State servant,Secondary / secondary special,Separated,Municipal apartment,0.019689,-14457,-2682,-4169.0,-4926,,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Military,0.6424022036153411,0.6119898541202536,,0.033,0.0196,0.9752,,,0.0,0.1379,0.125,,0.0266,,0.0251,,0.0399,0.0336,0.0204,0.9752,,,0.0,0.1379,0.125,,0.0273,,0.0261,,0.0423,0.0333,0.0196,0.9752,,,0.0,0.1379,0.125,,0.0271,,0.0255,,0.0407,,block of flats,0.031,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-3079.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n431910,0,Revolving loans,F,N,Y,0,94500.0,135000.0,6750.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-21871,365243,-4523.0,-5120,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,12,0,0,0,0,0,0,XNA,,0.6261647211113526,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1715.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n355740,1,Cash loans,M,N,Y,0,157500.0,314100.0,21375.0,225000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-18064,-1805,-4217.0,-1466,,1,1,0,1,1,0,Laborers,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Other,,0.4710106832401047,0.41184855592423975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-963.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n234798,0,Cash loans,F,N,Y,0,67500.0,110331.0,10876.5,103500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019101,-24809,365243,-5578.0,-4824,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,,0.5939154195458289,0.6127042441012546,0.1278,0.0925,0.9826,0.762,0.0245,0.0932,0.1724,0.2221,0.2638,0.1012,0.1042,0.1082,0.0,0.0615,0.063,0.0742,0.9821,0.7648,0.0078,0.0,0.1379,0.1667,0.2083,0.0385,0.0551,0.0571,0.0,0.0206,0.0625,0.0716,0.9826,0.7652,0.0085,0.0,0.1379,0.1667,0.2083,0.0578,0.0513,0.0613,0.0,0.0217,reg oper account,block of flats,0.1961,Panel,No,0.0,0.0,0.0,0.0,-1473.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n154887,0,Cash loans,F,N,Y,0,112500.0,1146816.0,41323.5,990000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-13507,-4475,-14.0,-4591,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Industry: type 2,0.5879102478403799,0.7138149258121882,,0.2381,0.1415,0.9995,,,0.26,0.1724,0.3542,,0.0522,,0.1995,,0.0,0.2206,0.1064,0.9995,,,0.2417,0.1034,0.3333,,0.0182,,0.1437,,0.0,0.2405,0.1415,0.9995,,,0.26,0.1724,0.3542,,0.0531,,0.2031,,0.0,,block of flats,0.2053,Panel,No,6.0,0.0,6.0,0.0,-2588.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n385029,0,Cash loans,F,Y,N,3,76500.0,508495.5,20164.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-14604,-718,-3300.0,-5719,9.0,1,1,1,1,0,0,Core staff,5.0,2,2,THURSDAY,18,0,0,0,0,1,1,Government,,0.5155174153229055,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1105.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n205192,0,Cash loans,F,N,N,0,135000.0,203760.0,19980.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.00702,-22653,-8251,-10559.0,-4549,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Self-employed,0.9132362155569388,0.5936833416597456,0.7047064232963289,0.1201,0.1201,0.9856,0.8028,0.0248,0.22,0.1897,0.3333,0.1525,0.0882,0.0979,0.1233,0.0174,0.0038,0.1134,0.0938,0.9856,0.8105,0.0077,0.2014,0.1724,0.3333,0.0417,0.0618,0.0992,0.1177,0.0,0.0,0.1213,0.1062,0.9856,0.8054,0.0288,0.22,0.1897,0.3333,0.0417,0.0996,0.0996,0.1247,0.0,0.0,org spec account,block of flats,0.1045,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n281490,0,Cash loans,F,N,N,0,112500.0,450000.0,24543.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.019101,-21414,365243,-9618.0,-4858,,1,0,0,1,1,0,,1.0,2,2,MONDAY,18,0,0,0,0,0,0,XNA,,0.6603485015885044,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2479.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n187670,0,Cash loans,F,Y,Y,0,337500.0,545040.0,43191.0,450000.0,Family,Working,Higher education,Married,House / apartment,0.04622,-13084,-2960,-7230.0,-5255,2.0,1,1,0,1,1,0,,2.0,1,1,WEDNESDAY,12,0,1,1,0,0,0,Business Entity Type 2,0.08360341090778557,0.4159447982212265,0.28812959991785075,0.0278,0.0562,0.9871,0.8232,0.0037,0.0,0.1034,0.0833,0.0417,0.0155,0.0227,0.0235,0.0,0.0,0.0284,0.0583,0.9871,0.8301,0.0038,0.0,0.1034,0.0833,0.0417,0.0159,0.0248,0.0245,0.0,0.0,0.0281,0.0562,0.9871,0.8256,0.0038,0.0,0.1034,0.0833,0.0417,0.0158,0.0231,0.0239,0.0,0.0,reg oper account,block of flats,0.0205,Panel,No,2.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n124332,0,Cash loans,F,Y,N,0,81000.0,450000.0,13774.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.015221,-21100,365243,-301.0,-4269,8.0,1,0,0,1,1,0,,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,XNA,0.8213364080045653,0.3802694000825025,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-229.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n201863,0,Cash loans,F,Y,N,1,216000.0,675000.0,25146.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-13481,-4910,-4812.0,-536,9.0,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Police,,0.7005810006866652,0.31703177858344445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3207.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n434490,0,Cash loans,M,N,Y,0,216000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-17398,-1795,-9574.0,-947,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Industry: type 3,,0.6547844907017011,0.8027454758994178,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1545.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n364640,0,Cash loans,F,Y,N,0,270000.0,448272.0,27553.5,396000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.032561,-10241,-1225,-10177.0,-862,3.0,1,1,1,1,1,0,Core staff,2.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Kindergarten,0.460148406354159,0.5185643901800869,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-2040.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n158999,0,Revolving loans,F,N,Y,0,180000.0,315000.0,15750.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.00733,-18098,-3796,-12239.0,-1645,,1,1,0,1,1,0,Sales staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Self-employed,,0.7551468571511556,,,,0.9771,,,,,,,,,0.0876,,,,,0.9772,,,,,,,,,0.0913,,,,,0.9771,,,,,,,,,0.0892,,,,,0.1081,,No,0.0,0.0,0.0,0.0,-2552.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n196407,0,Cash loans,M,N,Y,0,157500.0,622188.0,26284.5,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-19951,-4978,-4627.0,-3345,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,16,0,0,0,1,1,0,School,,0.5575741996624457,0.11761373170805695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1695.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n423103,0,Cash loans,F,N,Y,0,157500.0,219042.0,26122.5,193500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-20545,365243,-8079.0,-2861,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.41592354879719984,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-251.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n108350,0,Cash loans,M,Y,Y,2,180000.0,148500.0,17752.5,148500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.015221,-14989,-728,-1239.0,-5191,7.0,1,1,1,1,0,0,Drivers,4.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Trade: type 7,,0.5979593944418736,0.6626377922738201,0.0196,0.0,0.9389,0.1636,0.0089,0.0,0.069,0.0417,0.0417,,0.016,0.01,0.0,0.0,0.02,0.0,0.9389,0.1964,0.009000000000000001,0.0,0.069,0.0417,0.0417,,0.0174,0.0105,0.0,0.0,0.0198,0.0,0.9389,0.1748,0.009000000000000001,0.0,0.069,0.0417,0.0417,,0.0162,0.0102,0.0,0.0,reg oper spec account,block of flats,0.0079,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n424379,0,Revolving loans,M,Y,Y,2,126000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.019101,-13987,-836,-8084.0,-4409,15.0,1,1,0,1,0,0,Laborers,4.0,2,2,MONDAY,9,0,0,0,0,1,1,Electricity,0.42859154984513104,0.6557329264173517,0.6610235391308081,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-346.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n247987,0,Cash loans,M,N,Y,0,54000.0,436032.0,16564.5,360000.0,Unaccompanied,Pensioner,Lower secondary,Married,Municipal apartment,0.010032,-20166,365243,-3894.0,-2556,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,5,0,0,0,0,0,0,XNA,,0.6316509261453298,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,-223.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n402686,0,Cash loans,F,N,Y,0,202500.0,513000.0,21735.0,513000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.010966,-20296,365243,-686.0,-406,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.3111688661562623,0.3092753558842053,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2842.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n240037,0,Cash loans,F,N,Y,0,135000.0,495351.0,48384.0,459000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.01885,-16943,-1324,-7174.0,-473,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,12,0,1,1,0,0,0,Business Entity Type 3,,0.3239583333241273,0.6195277080511546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-313.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n364349,0,Cash loans,F,N,N,1,67500.0,254700.0,24808.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009549,-13233,-1288,-7257.0,-3906,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.6272562684723911,0.6480563782846739,,0.0825,0.0633,0.9786,,,0.0,0.1379,0.1667,,0.0564,,0.0481,,0.0,0.084,0.0657,0.9786,,,0.0,0.1379,0.1667,,0.0577,,0.0501,,0.0,0.0833,0.0633,0.9786,,,0.0,0.1379,0.1667,,0.0574,,0.0489,,0.0,,block of flats,0.0548,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n187979,0,Cash loans,F,N,Y,0,76500.0,675000.0,19737.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-18909,-11516,-12024.0,-2442,,1,1,1,1,1,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Other,0.7260342220026816,0.6569660102058966,0.190705947811054,0.2969,0.1571,0.9821,0.7552,0.0349,0.32,0.2759,0.3333,0.375,0.1891,0.2421,0.2912,0.0,0.0,0.3025,0.163,0.9821,0.7648,0.0352,0.3222,0.2759,0.3333,0.375,0.1934,0.2645,0.3034,0.0,0.0,0.2998,0.1571,0.9821,0.7585,0.0351,0.32,0.2759,0.3333,0.375,0.1924,0.2463,0.2965,0.0,0.0,reg oper account,block of flats,0.2291,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1577.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n129001,1,Cash loans,F,N,N,1,76500.0,263686.5,17298.0,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.005144,-18031,365243,-7217.0,-1327,,1,0,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,0.5646143147079749,,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-157.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n198996,1,Cash loans,M,Y,Y,2,135000.0,1123443.0,36369.0,981000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.031329,-13045,-560,-7028.0,-4326,64.0,1,1,0,1,1,0,Cooking staff,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Restaurant,0.040321829571029116,0.6103113111717916,0.5814837058057234,0.1351,0.1126,0.9781,0.7008,0.0162,0.0,0.2759,0.1667,,0.0,,0.121,,0.0506,0.1376,0.1168,0.9782,0.7125,0.0164,0.0,0.2759,0.1667,,0.0,,0.126,,0.0536,0.1364,0.1126,0.9781,0.7048,0.0163,0.0,0.2759,0.1667,,0.0,,0.1231,,0.0516,reg oper account,block of flats,0.115,\"Stone, brick\",No,10.0,3.0,10.0,0.0,-955.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n314338,0,Cash loans,F,Y,Y,1,112500.0,675000.0,25708.5,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.016612000000000002,-10544,-378,-10493.0,-1107,17.0,1,1,1,1,1,0,Sales staff,3.0,2,2,TUESDAY,13,0,0,0,0,0,0,Military,,0.10002158815181182,0.1455428133497032,0.1258,0.126,0.9742,0.6464,0.0928,0.0,0.2069,0.1667,0.2083,0.0383,0.1009,0.1004,0.0077,0.0053,0.1282,0.1307,0.9742,0.6602,0.0937,0.0,0.2069,0.1667,0.2083,0.0391,0.1102,0.1046,0.0078,0.0057,0.127,0.126,0.9742,0.6511,0.0934,0.0,0.2069,0.1667,0.2083,0.0389,0.1026,0.1022,0.0078,0.0055,reg oper account,block of flats,0.1309,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n386996,0,Cash loans,F,N,Y,0,180000.0,323194.5,17050.5,279000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.014464,-14823,-1067,-2864.0,-4232,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Self-employed,,0.6023253611108021,0.5919766183185521,0.0825,0.1314,0.9816,,,0.0,0.2069,0.1667,,0.1058,,0.0727,,0.0,0.084,0.1363,0.9816,,,0.0,0.2069,0.1667,,0.1082,,0.0757,,0.0,0.0833,0.1314,0.9816,,,0.0,0.2069,0.1667,,0.1077,,0.07400000000000001,,0.0,,block of flats,0.0633,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-973.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n162754,0,Cash loans,M,Y,N,0,450000.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.010032,-15955,-2383,-4046.0,-4578,18.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 2,0.2499462219547756,0.437671682677536,0.6479768603302221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,2.0,-1891.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n206784,0,Cash loans,F,N,N,0,67500.0,675000.0,21775.5,675000.0,Unaccompanied,Working,Higher education,Married,With parents,0.035792000000000004,-16167,-4112,-3574.0,-4836,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 1,0.5076572378264512,0.6906780826372135,0.5226973172821112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n204626,0,Cash loans,F,Y,N,0,405000.0,1006920.0,51543.0,900000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.006629,-12840,-3128,-1040.0,-1026,8.0,1,1,1,1,1,0,Cooking staff,2.0,2,2,SUNDAY,11,0,0,0,0,1,1,Self-employed,,0.690495851598947,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-567.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n281911,0,Cash loans,F,N,Y,0,112500.0,143910.0,13653.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006233,-19176,365243,-6145.0,-1669,,1,0,0,1,1,0,,2.0,2,2,MONDAY,16,0,0,0,0,0,0,XNA,0.5834830812284935,0.8031282440147328,0.4578995512067301,0.0474,0.0344,0.9816,0.7484,0.0088,0.04,0.0345,0.3333,0.375,0.0622,0.0378,0.0472,0.0039,0.0042,0.0483,0.0357,0.9816,0.7583,0.0088,0.0403,0.0345,0.3333,0.375,0.0637,0.0413,0.0492,0.0039,0.0045,0.0479,0.0344,0.9816,0.7518,0.0088,0.04,0.0345,0.3333,0.375,0.0633,0.0385,0.048,0.0039,0.0043,reg oper spec account,block of flats,0.0428,Panel,No,4.0,0.0,4.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n259835,0,Cash loans,M,Y,N,2,180000.0,326664.0,16807.5,234000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.01885,-15818,-523,-1447.0,-4471,3.0,1,1,0,1,0,0,Drivers,4.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Other,0.12483285567655135,0.609150911292172,0.7530673920730478,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-342.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n125708,0,Cash loans,F,N,Y,0,126000.0,490495.5,28156.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.022625,-17902,365243,-1050.0,-1464,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,9,0,0,0,0,0,0,XNA,,0.5520326099898029,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.0,0.0,12.0,0.0,-380.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n141244,0,Cash loans,F,N,N,0,135000.0,675000.0,35964.0,675000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.026392,-22381,-5298,-12453.0,-4065,,1,1,1,1,1,0,Laborers,1.0,2,2,MONDAY,16,0,0,0,0,0,0,School,,0.1489832106679998,,0.2216,0.1392,0.9826,,,0.24,0.2069,0.3333,,0.2214,,0.2319,,0.0,0.2258,0.1444,0.9826,,,0.2417,0.2069,0.3333,,0.2265,,0.2416,,0.0,0.2238,0.1392,0.9826,,,0.24,0.2069,0.3333,,0.2253,,0.2361,,0.0,,block of flats,0.1824,Panel,No,5.0,0.0,5.0,0.0,-1269.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n393243,0,Cash loans,F,N,Y,0,112500.0,807984.0,25497.0,697500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-9459,-2534,-892.0,-612,,1,1,1,1,0,0,Waiters/barmen staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,University,0.1511225404637936,0.3875672497284315,,0.032,0.0,0.9801,0.728,0.0032,0.0,0.1379,0.0417,0.0833,0.0,0.0252,0.0264,0.0039,0.0149,0.0326,0.0,0.9801,0.7387,0.0033,0.0,0.1379,0.0417,0.0833,0.0,0.0275,0.0275,0.0039,0.0158,0.0323,0.0,0.9801,0.7316,0.0033,0.0,0.1379,0.0417,0.0833,0.0,0.0257,0.0269,0.0039,0.0152,reg oper account,block of flats,0.024,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-630.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n142143,0,Cash loans,F,Y,Y,2,720000.0,675000.0,35626.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.018209,-9913,-199,-640.0,-2529,23.0,1,1,1,1,0,0,Sales staff,4.0,3,3,FRIDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.1650541496083926,0.4986788978748944,0.6610235391308081,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-797.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n185720,0,Cash loans,F,N,N,0,99000.0,276277.5,22279.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.0228,-10906,-449,-552.0,-1455,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Trade: type 1,0.5154074185830215,0.08825914244690647,0.29708661164720285,0.166,0.1429,0.9841,,,0.2,0.1724,0.3333,,0.0391,,0.1628,,0.0115,0.1691,0.1483,0.9841,,,0.2014,0.1724,0.3333,,0.04,,0.1697,,0.0122,0.1676,0.1429,0.9841,,,0.2,0.1724,0.3333,,0.0398,,0.1658,,0.0117,,block of flats,0.1592,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-331.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n453743,0,Cash loans,M,N,Y,0,180000.0,573408.0,35208.0,495000.0,Unaccompanied,State servant,Incomplete higher,Married,House / apartment,0.072508,-8843,-126,-4106.0,-1503,,1,1,0,1,0,0,Core staff,2.0,1,1,TUESDAY,12,1,1,0,0,0,1,Police,0.28577887366565435,0.7360274331241756,0.4992720153045617,0.1835,0.0911,0.9786,,,0.2,0.1552,0.4375,,,,0.1156,,0.036000000000000004,0.0998,0.0412,0.9777,,,0.0806,0.0345,0.3333,,,,0.0536,,0.0074,0.1853,0.0911,0.9786,,,0.2,0.1552,0.4375,,,,0.1177,,0.0368,,block of flats,0.2225,Block,No,0.0,0.0,0.0,0.0,-762.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n437352,0,Cash loans,M,Y,Y,0,175500.0,225000.0,24363.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-15345,-4439,-495.0,-4714,1.0,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Police,0.3181069906653894,0.3092924345348155,0.4920600938649263,0.1443,,0.9995,,,,0.1379,0.3333,,,,0.1865,,0.2252,0.1471,,0.9995,,,,0.1379,0.3333,,,,0.1943,,0.2385,0.1457,,0.9995,,,,0.1379,0.3333,,,,0.1898,,0.23,,block of flats,0.1956,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-321.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n127579,1,Cash loans,M,Y,Y,0,157500.0,485640.0,44671.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.003069,-10405,-1642,-712.0,-2881,4.0,1,1,0,1,0,0,Sales staff,1.0,3,3,WEDNESDAY,17,0,0,0,0,0,0,Self-employed,0.21805570210562705,0.6573404491745918,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-389.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n194138,0,Cash loans,F,N,Y,0,135000.0,592560.0,35937.0,450000.0,Family,Pensioner,Higher education,Single / not married,House / apartment,0.016612000000000002,-23099,365243,-8199.0,-4668,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.4789722126474361,,0.1113,0.0923,0.9881,0.8368,0.0682,0.12,0.1034,0.3333,0.375,0.0305,0.0908,0.1316,0.0,0.0,0.1134,0.0958,0.9881,0.8432,0.0689,0.1208,0.1034,0.3333,0.375,0.0312,0.0992,0.1371,0.0,0.0,0.1124,0.0923,0.9881,0.8390000000000001,0.0687,0.12,0.1034,0.3333,0.375,0.0311,0.0923,0.1339,0.0,0.0,reg oper account,block of flats,0.1408,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n273139,0,Revolving loans,F,Y,Y,1,139500.0,495000.0,24750.0,495000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-16885,-571,-570.0,-430,1.0,1,1,0,1,0,0,,3.0,1,1,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 2,,0.693046332108434,0.7324033228040929,,,0.9816,,,0.08,0.069,0.5417,,,,,,,,,0.9816,,,0.0806,0.069,0.5417,,,,,,,,,0.9816,,,0.08,0.069,0.5417,,,,,,,,block of flats,0.078,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-389.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n138134,0,Cash loans,M,N,N,0,112500.0,900000.0,45954.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-14553,-1732,-2573.0,-5181,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,THURSDAY,19,0,1,1,0,1,1,Other,0.5063315713297071,0.4786775297704238,0.7557400501752248,0.1227,0.1289,0.9816,,,0.0,0.2759,0.1667,,0.0,,0.1144,,0.0,0.125,0.1338,0.9816,,,0.0,0.2759,0.1667,,0.0,,0.1192,,0.0,0.1239,0.1289,0.9816,,,0.0,0.2759,0.1667,,0.0,,0.1165,,0.0,,block of flats,0.09,Panel,No,1.0,0.0,1.0,0.0,-1805.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n218412,0,Cash loans,M,Y,Y,0,225000.0,1125000.0,106947.0,1125000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010643,-18032,-2255,-7532.0,-1533,8.0,1,1,0,1,1,0,Laborers,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Trade: type 7,,0.7398334385664707,0.7862666146611379,0.1649,0.1174,0.9896,0.8572,0.0549,0.08,0.069,0.375,0.4167,0.0653,0.1345,0.1651,0.0,0.0,0.1681,0.1218,0.9896,0.8628,0.0554,0.0806,0.069,0.375,0.4167,0.0668,0.1469,0.172,0.0,0.0,0.1665,0.1174,0.9896,0.8591,0.0552,0.08,0.069,0.375,0.4167,0.0664,0.1368,0.1681,0.0,0.0,reg oper account,block of flats,0.1599,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1794.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n176923,0,Cash loans,M,Y,Y,0,315000.0,625536.0,33934.5,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-8440,-1206,-353.0,-571,3.0,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Self-employed,0.4432185020913238,0.24801121386248065,0.04263384305443076,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,2.0,0.0,0.0,0.0\n428064,0,Cash loans,F,N,N,0,112500.0,50940.0,5166.0,45000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-23832,365243,-642.0,-5248,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.20546512513334286,0.7252764347002191,0.0082,0.0,0.9677,0.5579999999999999,0.0002,0.0,0.0345,0.0417,0.0417,0.0,0.0067,0.0082,0.0,0.0,0.0084,0.0,0.9677,0.5753,0.0002,0.0,0.0345,0.0417,0.0417,0.0,0.0073,0.0085,0.0,0.0,0.0083,0.0,0.9677,0.5639,0.0002,0.0,0.0345,0.0417,0.0417,0.0,0.0068,0.0083,0.0,0.0,reg oper spec account,block of flats,0.0065,Block,No,0.0,0.0,0.0,0.0,-1202.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n193068,0,Cash loans,M,Y,Y,0,270000.0,360000.0,13711.5,360000.0,,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.011703,-11271,-800,-5165.0,-3877,1.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.7696765593361753,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,6.0,0.0,-267.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,8.0,4.0,9.0\n138293,0,Cash loans,F,N,Y,0,202500.0,364896.0,28957.5,315000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.011703,-11790,-722,-418.0,-3374,,1,1,0,1,1,1,Managers,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Electricity,0.2958653281719233,0.7078142115926982,0.21155076420525776,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1349.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n287856,0,Cash loans,M,N,Y,0,216000.0,560664.0,18216.0,468000.0,Family,Pensioner,Higher education,Married,House / apartment,0.010147,-22468,365243,-6894.0,-4407,,1,0,0,1,1,0,,2.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.5901231583176779,0.7238369900414456,0.1485,,0.9901,0.8640000000000001,,0.16,0.1379,0.3333,0.375,0.0641,0.1202,0.1493,0.0039,0.0765,0.1513,,0.9901,0.8693,,0.1611,0.1379,0.3333,0.375,0.0655,0.1313,0.1556,0.0039,0.081,0.1499,,0.9901,0.8658,,0.16,0.1379,0.3333,0.375,0.0652,0.1223,0.152,0.0039,0.0781,reg oper account,block of flats,0.1341,,No,2.0,0.0,2.0,0.0,-728.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n245282,0,Cash loans,F,N,N,0,171000.0,927000.0,28102.5,927000.0,Unaccompanied,State servant,Higher education,Single / not married,With parents,0.019689,-13416,-2360,-6018.0,-2679,,1,1,1,1,0,0,Core staff,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Government,0.5150850764109302,0.7739284000565521,0.3996756156233169,0.0928,,0.9841,,,,0.2069,0.1667,,,,0.0778,,,0.0945,,0.9841,,,,0.2069,0.1667,,,,0.0811,,,0.0937,,0.9841,,,,0.2069,0.1667,,,,0.0792,,,,block of flats,0.0612,Panel,No,6.0,0.0,6.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n440548,0,Cash loans,F,N,N,0,135000.0,781920.0,23836.5,675000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010006,-24033,365243,-8817.0,-4716,,1,0,0,1,1,0,,1.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.1620346784028127,0.7407990879702335,0.1959,0.0753,0.9876,0.83,0.1265,0.24,0.1034,0.625,0.6667,,0.1597,0.2094,0.0,0.0,0.1996,0.0782,0.9876,0.8367,0.1276,0.2417,0.1034,0.625,0.6667,,0.1745,0.2182,0.0,0.0,0.1978,0.0753,0.9876,0.8323,0.1273,0.24,0.1034,0.625,0.6667,,0.1625,0.2131,0.0,0.0,reg oper account,block of flats,0.2338,Panel,No,0.0,0.0,0.0,0.0,-1639.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n288745,0,Cash loans,F,N,Y,0,157500.0,337500.0,16546.5,337500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-17406,-774,-1189.0,-880,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Medicine,,0.6601768179818946,0.2955826421513093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-15.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n183071,0,Cash loans,F,Y,Y,0,117000.0,808650.0,26217.0,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018209,-14177,-6084,-751.0,-4666,3.0,1,1,0,1,1,0,Medicine staff,2.0,3,3,FRIDAY,7,0,0,0,0,0,0,Medicine,0.3492185406726452,0.5702966360936764,0.6925590674998008,0.1031,0.1065,0.9796,0.7212,0.0862,0.0,0.2069,0.1667,0.2083,0.0761,0.0841,0.0938,0.0,0.0,0.105,0.1106,0.9796,0.7321,0.0869,0.0,0.2069,0.1667,0.2083,0.0778,0.0918,0.0978,0.0,0.0,0.1041,0.1065,0.9796,0.7249,0.0867,0.0,0.2069,0.1667,0.2083,0.0774,0.0855,0.0955,0.0,0.0,reg oper account,block of flats,0.0738,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n335597,0,Cash loans,F,N,Y,0,225000.0,774117.0,39654.0,625500.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-14154,-690,-699.0,-900,,1,1,0,1,0,0,Managers,2.0,3,2,TUESDAY,14,0,0,0,0,0,0,Self-employed,0.3049697436683385,0.3398314683823481,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-419.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n200275,0,Cash loans,F,N,Y,0,157500.0,808650.0,23773.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-19169,-2907,-2439.0,-2336,,1,1,1,1,1,0,Medicine staff,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Medicine,0.6408045566356572,0.6161977982015999,0.5370699579791587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2191.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n395320,0,Cash loans,M,Y,Y,0,225000.0,592560.0,32274.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-15164,-874,-2713.0,-3815,17.0,1,1,1,1,0,0,Sales staff,2.0,3,3,MONDAY,10,0,0,0,0,0,0,Self-employed,0.4512710621986693,0.6036131802770575,0.5370699579791587,0.0515,0.08,0.9851,0.7959999999999999,0.0134,0.0,0.1034,0.1667,0.2083,0.0801,0.042,0.0865,0.0,0.0,0.0525,0.083,0.9851,0.804,0.0135,0.0,0.1034,0.1667,0.2083,0.08199999999999999,0.0459,0.0901,0.0,0.0,0.052000000000000005,0.08,0.9851,0.7987,0.0135,0.0,0.1034,0.1667,0.2083,0.0815,0.0428,0.08800000000000001,0.0,0.0,reg oper account,block of flats,0.0786,Panel,No,0.0,0.0,0.0,0.0,-716.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n181712,0,Revolving loans,M,N,Y,2,90000.0,180000.0,9000.0,180000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.005084,-14709,-180,-5036.0,-5041,,1,1,1,1,0,0,Laborers,4.0,2,2,THURSDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.5044791512009201,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2461.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n229215,0,Cash loans,F,N,Y,0,274500.0,1236816.0,32755.5,1080000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.010006,-19808,-390,-260.0,-3044,,1,1,1,1,0,0,,2.0,2,1,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.7636256467704929,0.7315593913574782,0.5744466170995097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n398250,0,Cash loans,M,N,Y,1,157500.0,521280.0,27292.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-9747,-143,-4364.0,-2414,,1,1,1,1,1,0,Core staff,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.18288332785125,0.5393910696789003,,0.0619,0.0749,0.9896,0.8572,0.0902,0.0,0.1379,0.1667,0.2083,0.0558,0.0504,0.0318,0.0,0.0205,0.063,0.0777,0.9896,0.8628,0.091,0.0,0.1379,0.1667,0.2083,0.0571,0.0551,0.0331,0.0,0.0217,0.0625,0.0749,0.9896,0.8591,0.0907,0.0,0.1379,0.1667,0.2083,0.0568,0.0513,0.0323,0.0,0.0209,reg oper account,block of flats,0.0493,Panel,No,0.0,0.0,0.0,0.0,-1229.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n328374,1,Revolving loans,M,Y,N,0,67500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.028663,-15570,-121,-4651.0,-631,64.0,1,1,1,1,0,0,Laborers,1.0,2,2,SATURDAY,9,1,1,0,1,1,0,Business Entity Type 3,,0.4656039203098717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1332.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n385669,0,Cash loans,M,N,N,0,135000.0,900000.0,45954.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-9635,-323,-4227.0,-1954,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.3044042385314227,0.5867400085415683,0.1216,,0.9791,,,0.0,0.069,0.1667,,0.0622,,0.0615,,0.0044,0.1239,,0.9791,,,0.0,0.069,0.1667,,0.0636,,0.0641,,0.0047,0.1228,,0.9791,,,0.0,0.069,0.1667,,0.0633,,0.0626,,0.0045,,block of flats,0.0493,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-344.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,6.0\n409600,0,Cash loans,M,N,N,0,270000.0,851778.0,22599.0,711000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-16677,-7869,-2491.0,-204,,1,1,1,1,1,0,Managers,2.0,2,2,MONDAY,17,0,0,0,0,0,0,University,0.6755429743409871,0.4972192623469297,0.3723336657058204,0.0557,0.0082,0.9940000000000001,0.9184,0.0172,0.08,0.069,0.3333,0.375,0.0118,0.0454,0.0776,0.0,0.0,0.0567,0.0085,0.9940000000000001,0.9216,0.0174,0.0806,0.069,0.3333,0.375,0.0121,0.0496,0.0809,0.0,0.0,0.0562,0.0082,0.9940000000000001,0.9195,0.0173,0.08,0.069,0.3333,0.375,0.012,0.0462,0.079,0.0,0.0,reg oper account,block of flats,0.0747,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-486.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n211850,0,Cash loans,M,Y,Y,2,135000.0,493497.0,49936.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.004849,-12783,-3577,-3178.0,-3158,11.0,1,1,0,1,0,0,Drivers,4.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,,0.3404891679357616,0.3280631605201915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1181.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n232263,0,Cash loans,F,N,Y,0,135000.0,450000.0,21109.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.0228,-13279,-2441,-7361.0,-5784,,1,1,0,1,1,0,Managers,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6846811784636658,0.08086102071082764,,0.132,0.0,0.9767,,,0.0,0.2759,0.1667,,0.1156,,0.1056,,0.0902,0.1345,0.0,0.9767,,,0.0,0.2759,0.1667,,0.1182,,0.11,,0.0955,0.1332,0.0,0.9767,,,0.0,0.2759,0.1667,,0.1176,,0.1074,,0.0921,,block of flats,0.1107,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-130.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n314885,0,Cash loans,M,Y,Y,0,202500.0,1147500.0,43834.5,1147500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-18406,-5961,-4123.0,-1952,4.0,1,1,1,1,1,0,Drivers,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,,0.7609780443453638,0.8245949709919925,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-698.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n104091,0,Cash loans,F,N,Y,1,333000.0,168147.0,15552.0,153000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-12460,-1297,-1843.0,-2487,,1,1,0,1,0,0,Accountants,3.0,2,2,FRIDAY,18,0,0,0,0,0,0,Other,,0.6874922631557376,0.19747451156854226,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1099.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n334931,0,Cash loans,M,Y,Y,0,112500.0,259794.0,26748.0,229500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006207,-20689,-1537,-9331.0,-2252,17.0,1,1,0,1,0,1,Laborers,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.4705475842540605,0.6006575372857061,0.2062,0.0,0.9871,0.8232,,0.2,0.1724,0.375,0.4167,0.1144,0.1681,0.2136,0.0,0.0,0.2101,0.0,0.9871,0.8301,,0.2014,0.1724,0.375,0.4167,0.11699999999999999,0.1837,0.2225,0.0,0.0,0.2082,0.0,0.9871,0.8256,,0.2,0.1724,0.375,0.4167,0.1164,0.171,0.2174,0.0,0.0,reg oper account,block of flats,0.1924,Panel,No,2.0,0.0,2.0,0.0,-1154.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n167863,0,Cash loans,F,N,N,1,157500.0,760225.5,30280.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.007114,-11328,-3714,-607.0,-3767,,1,1,0,1,0,0,Laborers,3.0,2,2,SUNDAY,16,0,0,0,0,0,0,Industry: type 9,0.5146081636802708,0.6793502105949143,0.8004513396487078,0.0526,0.0273,0.9796,0.7212,0.0281,0.04,0.0345,0.3333,0.375,0.0,0.0403,0.0406,0.0116,0.0634,0.0536,0.0283,0.9796,0.7321,0.0284,0.0403,0.0345,0.3333,0.375,0.0,0.0441,0.0423,0.0117,0.0672,0.0531,0.0273,0.9796,0.7249,0.0283,0.04,0.0345,0.3333,0.375,0.0,0.040999999999999995,0.0413,0.0116,0.0648,reg oper account,block of flats,0.0426,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1864.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n119092,0,Cash loans,F,Y,Y,0,360000.0,1024740.0,52452.0,900000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.003813,-14498,-6123,-1840.0,-4430,17.0,1,1,0,1,0,1,Core staff,2.0,2,2,MONDAY,6,0,0,0,0,1,1,Security Ministries,0.5252667021876389,0.4824275875767628,0.5673792367572691,0.1155,0.0963,0.9846,0.7892,0.2553,0.0,0.1379,0.3333,0.0417,0.0347,0.0925,0.1364,0.0077,0.0696,0.1176,0.1,0.9846,0.7975,0.2576,0.0,0.1379,0.3333,0.0417,0.0355,0.10099999999999999,0.1421,0.0078,0.0737,0.1166,0.0963,0.9846,0.792,0.2569,0.0,0.1379,0.3333,0.0417,0.0353,0.0941,0.1388,0.0078,0.0711,reg oper account,block of flats,0.1232,Panel,No,4.0,0.0,4.0,0.0,-1441.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n126306,0,Cash loans,F,N,Y,0,135000.0,450000.0,45549.0,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.030755,-22265,-1550,-43.0,-3968,,1,1,0,1,0,0,Realty agents,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Other,0.7613460558653865,0.6600885071602788,0.6313545365850379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n127609,0,Cash loans,F,N,N,0,207000.0,675000.0,26154.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010643,-15178,-694,-7456.0,-3313,,1,1,1,1,0,0,Laborers,2.0,2,2,MONDAY,18,0,0,0,0,0,0,Industry: type 11,0.6234311568583781,0.2594797631749833,,0.0639,0.0525,0.9727,0.626,,0.0,0.1379,0.125,0.1667,0.0461,0.0521,0.0426,0.0,0.0,0.0651,0.0545,0.9727,0.6406,,0.0,0.1379,0.125,0.1667,0.0472,0.0569,0.0444,0.0,0.0,0.0645,0.0525,0.9727,0.631,,0.0,0.1379,0.125,0.1667,0.0469,0.053,0.0434,0.0,0.0,reg oper spec account,block of flats,0.0335,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-434.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n214033,0,Cash loans,F,Y,N,1,112500.0,518562.0,25078.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-15914,-3939,-3359.0,-3632,11.0,1,1,0,1,1,0,Sales staff,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Trade: type 7,0.4264778532240311,0.5818951185806743,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2578.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n370988,0,Cash loans,F,N,N,1,81000.0,269550.0,14751.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.018801,-9926,-2021,-1064.0,-1249,,1,1,0,1,0,0,Sales staff,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.3214892336020016,0.6840996308552291,0.475849908720221,0.099,0.1222,0.9776,0.6940000000000001,0.052000000000000005,0.0,0.2069,0.1667,0.2083,0.0614,0.0807,0.0766,0.0,0.1437,0.1008,0.1268,0.9777,0.706,0.0525,0.0,0.2069,0.1667,0.2083,0.0628,0.0882,0.0798,0.0,0.1522,0.0999,0.1222,0.9776,0.6981,0.0523,0.0,0.2069,0.1667,0.2083,0.0624,0.0821,0.078,0.0,0.1467,reg oper account,block of flats,0.0915,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1458.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n159619,0,Cash loans,F,Y,N,2,225000.0,1078200.0,38331.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.035792000000000004,-11661,-958,-4959.0,-3365,2.0,1,1,0,1,0,0,Realty agents,4.0,2,2,SATURDAY,14,0,0,0,1,1,0,Services,,0.6074836071939805,0.21275630545434146,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-576.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n134152,0,Cash loans,F,N,Y,0,135000.0,203760.0,21748.5,180000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010276,-16640,-358,-5437.0,-196,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.10176521627603266,,0.0144,,0.9717,,,0.0,0.1034,0.0417,,0.045,,0.019,,0.0059,0.0147,,0.9717,,,0.0,0.1034,0.0417,,0.046,,0.0198,,0.0062,0.0146,,0.9717,,,0.0,0.1034,0.0417,,0.0458,,0.0193,,0.006,,block of flats,0.0161,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-241.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n396189,1,Cash loans,F,N,N,1,121500.0,444420.0,23269.5,337500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-13361,-1078,-1675.0,-4254,,1,1,0,1,0,0,Laborers,3.0,1,1,THURSDAY,17,0,0,0,0,1,1,Business Entity Type 3,0.11802386426818275,0.7397239878106661,0.14287252304131962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,6.0,0.0,6.0,0.0,-495.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n225179,0,Revolving loans,M,Y,Y,0,292500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006852,-10554,-1210,-1020.0,-3065,10.0,1,1,0,1,0,0,Managers,2.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.1943757716007711,0.041832751116950326,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-168.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n416186,0,Cash loans,F,N,N,1,135000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-12417,-2572,-6268.0,-2744,,1,1,0,1,0,1,,3.0,1,1,TUESDAY,15,0,0,0,0,1,1,Other,0.5143564489580104,0.5657089366352108,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-160.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n339806,0,Cash loans,F,N,Y,0,157500.0,412942.5,40356.0,373500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.02461,-23745,365243,-34.0,-4376,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.6531812114156426,0.6769925032909132,0.0247,0.0,0.9737,0.6396,,0.0,0.069,0.0833,0.0417,0.0182,0.0202,0.019,0.0,0.0,0.0252,0.0,0.9737,0.6537,,0.0,0.069,0.0833,0.0417,0.0186,0.022000000000000002,0.0198,0.0,0.0,0.025,0.0,0.9737,0.6444,,0.0,0.069,0.0833,0.0417,0.0185,0.0205,0.0193,0.0,0.0,reg oper account,block of flats,0.0235,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1883.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n380657,0,Cash loans,F,N,Y,0,225000.0,284400.0,18513.0,225000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010276,-16903,-1569,-6140.0,-448,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Other,0.6006312885391616,0.4131797182557985,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1936.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n434250,0,Cash loans,F,N,Y,0,315000.0,1391418.0,38394.0,1215000.0,Unaccompanied,Commercial associate,Higher education,Widow,House / apartment,0.04622,-20898,-2137,-3974.0,-3974,,1,1,0,1,0,0,Accountants,1.0,1,1,TUESDAY,11,0,0,0,0,1,1,Construction,,0.7341580431862685,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1153.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n423601,0,Cash loans,F,Y,Y,0,450000.0,1201500.0,39838.5,1201500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-19561,-9409,-10033.0,-3087,14.0,1,1,0,1,1,0,Medicine staff,2.0,3,2,THURSDAY,8,0,0,0,0,0,0,Medicine,0.8194203424945579,0.6063619753942738,0.5190973382084597,0.0897,0.1045,0.9846,0.7892,0.0171,0.0,0.2069,0.1667,0.2083,0.0648,0.0731,0.0816,0.0,0.0,0.0914,0.1085,0.9846,0.7975,0.0172,0.0,0.2069,0.1667,0.2083,0.0662,0.0799,0.085,0.0,0.0,0.0906,0.1045,0.9846,0.792,0.0172,0.0,0.2069,0.1667,0.2083,0.0659,0.0744,0.0831,0.0,0.0,reg oper account,block of flats,0.0735,Panel,No,3.0,0.0,3.0,0.0,-869.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n397284,0,Cash loans,F,N,N,0,135000.0,904500.0,38452.5,904500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-15822,-3859,-6616.0,-4191,,1,1,1,1,0,0,High skill tech staff,2.0,1,1,MONDAY,11,0,0,0,0,0,0,Other,,0.7194190822749299,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-494.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n214299,0,Cash loans,M,Y,Y,0,247500.0,1125000.0,32895.0,1125000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.032561,-16463,-46,-1241.0,-3,3.0,1,1,0,1,1,0,Core staff,2.0,1,1,SUNDAY,8,0,0,0,0,0,0,Government,0.5165840774655579,0.5896205825123418,0.4812493411434029,0.1443,0.0408,0.998,0.9728,0.0058,0.16,0.0345,0.7917,0.8333,0.0326,0.1765,0.1394,0.2915,0.0075,0.1471,0.0423,0.998,0.9739,0.0057,0.1611,0.0345,0.7917,0.8333,0.0128,0.1286,0.1057,0.0,0.0,0.1457,0.0408,0.998,0.9732,0.0059,0.16,0.0345,0.7917,0.8333,0.0332,0.1796,0.1419,0.2931,0.0077,reg oper account,block of flats,0.1425,Monolithic,No,0.0,0.0,0.0,0.0,-1590.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n112480,0,Cash loans,F,Y,N,2,112500.0,284400.0,14854.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-9935,-394,-4621.0,-2375,15.0,1,1,0,1,0,0,Sales staff,4.0,2,2,SATURDAY,10,0,0,0,0,1,1,Trade: type 3,,0.5059058560978968,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-343.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n234889,0,Cash loans,M,N,Y,1,297000.0,584766.0,24903.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-22230,-5412,-2214.0,-4569,,1,1,0,1,0,0,,3.0,1,1,THURSDAY,14,0,1,1,0,0,0,Business Entity Type 2,,0.6797502120363215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-213.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n135904,0,Cash loans,M,N,Y,0,112500.0,414792.0,18400.5,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-10869,-487,-5013.0,-3189,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,19,0,0,0,1,1,0,Self-employed,,0.7025954227066719,0.7435593141311444,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1471.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n175545,0,Cash loans,F,Y,Y,1,135000.0,1467000.0,38695.5,1467000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-16667,-4640,-3907.0,-209,9.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.5176303329967954,0.7691799843915925,0.7726311345553628,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2090.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n177769,0,Cash loans,F,N,N,0,94500.0,271066.5,10174.5,234000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.005313,-20486,365243,-11468.0,-4000,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.6469733225508181,0.5028782772082183,0.0773,0.0874,0.9841,0.7824,,0.0,0.1724,0.1667,0.2083,0.0881,0.063,0.0418,0.0,0.0,0.0788,0.0907,0.9841,0.7909,,0.0,0.1724,0.1667,0.2083,0.0901,0.0689,0.0435,0.0,0.0,0.0781,0.0874,0.9841,0.7853,,0.0,0.1724,0.1667,0.2083,0.0896,0.0641,0.0425,0.0,0.0,reg oper account,block of flats,0.0584,Panel,No,2.0,0.0,2.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n150022,0,Cash loans,M,Y,Y,0,225000.0,450000.0,44509.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-13598,-1978,-6832.0,-4681,16.0,1,1,1,1,1,0,Drivers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Self-employed,0.3644912564347241,0.17269112243888454,,0.1031,,0.9821,0.7552,,0.0,0.2069,0.1667,0.0417,,0.0841,0.0897,,,0.105,,0.9821,0.7648,,0.0,0.2069,0.1667,0.0417,,0.0918,0.0934,,,0.1041,,0.9821,0.7585,,0.0,0.2069,0.1667,0.0417,,0.0855,0.0913,,,org spec account,,,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2429.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n223348,0,Cash loans,F,N,N,0,112500.0,450000.0,21888.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008575,-20322,365243,-4994.0,-3288,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,7,0,0,0,0,0,0,XNA,0.813146710204194,0.70827529327452,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2042.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n407760,0,Cash loans,M,N,N,0,90000.0,225000.0,14377.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.019101,-10457,-1192,-4794.0,-2994,,1,1,1,1,0,0,Low-skill Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,0.2706544736749757,0.634845372383537,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-533.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n368272,0,Cash loans,M,Y,N,0,157500.0,675000.0,41296.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.035792000000000004,-10441,-2863,-961.0,-3118,11.0,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Self-employed,0.3983843204795514,0.15017933766014835,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n111617,0,Cash loans,F,N,Y,0,292500.0,1800000.0,62698.5,1800000.0,Children,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-14958,-1774,-3627.0,-5516,,1,1,0,1,0,0,,2.0,1,1,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.7777215153149133,0.7313875821189549,0.42765737003502935,0.1536,0.1545,0.9806,,,0.0,0.3448,0.1667,,,,0.0855,,0.1759,0.1565,0.1603,0.9806,,,0.0,0.3448,0.1667,,,,0.0891,,0.1862,0.1551,0.1545,0.9806,,,0.0,0.3448,0.1667,,,,0.0871,,0.1796,,block of flats,0.1181,Panel,No,0.0,0.0,0.0,0.0,-730.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n194420,0,Revolving loans,F,N,Y,0,202500.0,585000.0,29250.0,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006629,-17110,-5016,-9236.0,-642,,1,1,0,1,0,0,,2.0,2,2,MONDAY,6,0,0,0,0,0,0,University,0.5114766992568223,0.6965658113967738,0.6279908192952864,0.0196,,0.9886,,,,0.1034,0.0417,,,,0.0187,,,0.02,,0.9886,,,,0.1034,0.0417,,,,0.0195,,,0.0198,,0.9886,,,,0.1034,0.0417,,,,0.0191,,,,block of flats,0.0147,\"Stone, brick\",No,,,,,-538.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n398556,0,Cash loans,F,N,Y,0,225000.0,497520.0,52920.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-13905,-2309,-1713.0,-1713,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Trade: type 7,0.4196794506647025,0.6826470373757049,0.4525335592581747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-638.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n355662,0,Cash loans,F,N,Y,0,135000.0,265851.0,16195.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.04622,-19347,-1336,-4349.0,-2054,,1,1,0,1,0,0,Laborers,1.0,1,1,SATURDAY,15,0,0,0,0,1,1,Self-employed,,0.7715089132598344,0.4101025731788671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1512.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n126908,0,Cash loans,F,Y,Y,0,103500.0,1078200.0,31653.0,900000.0,Family,Working,Higher education,Married,House / apartment,0.028663,-16333,-335,-4378.0,-4187,11.0,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Kindergarten,0.7955392581683958,0.5800691653140029,0.3092753558842053,0.0082,0.0088,0.9692,0.5784,0.0012,0.0,0.0345,0.0417,0.0833,0.0163,0.0067,0.0085,0.0,0.0,0.0084,0.0091,0.9692,0.5949,0.0012,0.0,0.0345,0.0417,0.0833,0.0167,0.0073,0.0089,0.0,0.0,0.0083,0.0088,0.9692,0.584,0.0012,0.0,0.0345,0.0417,0.0833,0.0166,0.0068,0.0087,0.0,0.0,reg oper account,block of flats,0.0067,Block,No,11.0,0.0,11.0,0.0,-1479.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n327865,0,Cash loans,F,N,N,0,202500.0,675000.0,32602.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-10584,-1558,-74.0,-941,,1,1,1,1,0,0,Core staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Bank,,0.3171857044168269,0.22383131747015547,0.1629,0.1155,0.9856,0.8028,0.2811,0.16,0.1379,0.3333,0.375,0.1911,0.1328,0.1653,0.0039,0.0055,0.166,0.1198,0.9856,0.8105,0.2837,0.1611,0.1379,0.3333,0.375,0.1954,0.1451,0.1723,0.0039,0.0059,0.1645,0.1155,0.9856,0.8054,0.2829,0.16,0.1379,0.3333,0.375,0.1944,0.1351,0.1683,0.0039,0.0057,reg oper account,block of flats,0.1537,Panel,No,0.0,0.0,0.0,0.0,-617.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,6.0\n329114,0,Cash loans,M,N,Y,0,135000.0,755190.0,36459.0,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.010147,-16737,-924,-2772.0,-69,,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,,0.7581105049131155,0.6577838002083306,0.2557,,0.9866,,,0.08,0.0345,0.3333,,,,0.1418,,0.0259,0.2605,,0.9866,,,0.0806,0.0345,0.3333,,,,0.1477,,0.0274,0.2581,,0.9866,,,0.08,0.0345,0.3333,,,,0.1443,,0.0265,,block of flats,0.1193,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-754.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n213552,0,Cash loans,M,Y,Y,0,225000.0,239850.0,23850.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.006629,-21281,365243,-10185.0,-4802,18.0,1,0,0,1,0,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.5610668689208647,0.7435593141311444,0.1376,0.1245,0.9856,0.8028,0.0471,0.1,0.1379,0.25,0.125,0.1254,0.1072,0.1556,0.0232,0.0271,0.0914,0.1112,0.9851,0.804,0.022000000000000002,0.0,0.1034,0.1667,0.0417,0.0736,0.079,0.0897,0.0039,0.0014,0.139,0.1245,0.9856,0.8054,0.0474,0.1,0.1379,0.25,0.125,0.1275,0.109,0.1584,0.0233,0.0277,org spec account,block of flats,0.2281,Panel,No,0.0,0.0,0.0,0.0,-2401.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n406608,0,Cash loans,F,N,Y,0,135000.0,675000.0,35964.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-19934,-1147,-12375.0,-3473,,1,1,1,1,1,0,,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Self-employed,0.7020560791591449,0.6896025322581566,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2863.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n167002,0,Cash loans,F,N,N,1,81000.0,101880.0,6808.5,90000.0,Unaccompanied,Working,Lower secondary,Separated,Municipal apartment,0.005002,-13384,-946,-20.0,-5074,,1,1,1,1,0,1,Core staff,2.0,3,3,WEDNESDAY,13,0,0,0,0,0,0,Government,,0.3734447394694951,0.7407990879702335,0.0082,0.0,0.9776,0.6940000000000001,0.0,0.0,0.1034,0.0417,0.0417,0.0073,,0.0065,,0.0127,0.0084,0.0,0.9777,0.706,0.0,0.0,0.1034,0.0417,0.0417,0.0074,,0.0068,,0.0135,0.0083,0.0,0.9776,0.6981,0.0,0.0,0.1034,0.0417,0.0417,0.0074,,0.0066,,0.013000000000000001,reg oper account,block of flats,0.0079,Wooden,No,4.0,1.0,4.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n374043,0,Cash loans,F,N,N,0,90000.0,130320.0,13819.5,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.02461,-21771,365243,-1987.0,-5029,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.6759827043147885,,0.1062,0.0579,0.9771,,,0.0,0.069,0.1667,,0.0135,,0.0353,,0.0248,0.1082,0.0601,0.9772,,,0.0,0.069,0.1667,,0.0138,,0.0367,,0.0262,0.1072,0.0579,0.9771,,,0.0,0.069,0.1667,,0.0138,,0.0359,,0.0253,,block of flats,0.0526,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1477.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n328039,0,Cash loans,F,N,N,0,85500.0,1339884.0,36846.0,1170000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.028663,-22145,365243,-4936.0,-1223,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.28701082530043426,0.4311917977993083,0.0165,0.0,0.9727,,,0.0,0.069,0.0417,,0.0211,,0.01,,0.0,0.0168,0.0,0.9727,,,0.0,0.069,0.0417,,0.0215,,0.0105,,0.0,0.0167,0.0,0.9727,,,0.0,0.069,0.0417,,0.0214,,0.0102,,0.0,,block of flats,0.0086,Mixed,No,1.0,0.0,1.0,0.0,-1594.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n149328,0,Cash loans,M,N,Y,1,103500.0,691020.0,22419.0,495000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018029,-14106,-4287,-1709.0,-4801,,1,1,0,1,0,0,Laborers,3.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.15967923350263774,0.8482442999507556,0.0392,0.0712,0.9965,0.9524,0.0512,0.0,0.1034,0.1667,0.0,0.0522,0.0303,0.0448,0.0077,0.0412,0.0399,0.0739,0.9965,0.9543,0.0516,0.0,0.1034,0.1667,0.0,0.0534,0.0331,0.0467,0.0078,0.0437,0.0396,0.0712,0.9965,0.953,0.0515,0.0,0.1034,0.1667,0.0,0.0531,0.0308,0.0456,0.0078,0.0421,reg oper account,block of flats,0.0722,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1796.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n229269,0,Revolving loans,F,Y,N,0,112500.0,225000.0,11250.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-22959,365243,-9959.0,-4120,28.0,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,0.7706551785649473,0.603691659588871,0.5762088360175724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-81.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n295816,0,Cash loans,F,N,N,0,360000.0,1418868.0,72076.5,1354500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.002506,-21221,-2837,-9243.0,-4161,,1,1,1,1,1,0,Accountants,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 2,,0.6924505598364441,0.4048783643353997,0.1134,,0.9811,0.7416,0.0,0.04,0.1379,0.3333,0.0417,,0.0925,0.0683,0.0,0.0044,0.1155,,0.9811,0.7517,0.0,0.0403,0.1379,0.3333,0.0417,,0.10099999999999999,0.0712,0.0,0.0047,0.1145,,0.9811,0.7451,0.0,0.04,0.1379,0.3333,0.0417,,0.0941,0.0696,0.0,0.0045,reg oper account,block of flats,0.0547,,No,1.0,0.0,1.0,0.0,-2182.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n138466,0,Cash loans,F,N,N,0,112500.0,163008.0,17253.0,144000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.01885,-19167,-2061,-5823.0,-2694,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.6815756907847206,0.35617800654695353,0.6545292802242897,0.1,0.1091,0.9767,0.6804,0.055999999999999994,0.0,0.0345,0.1667,0.2083,0.1237,0.0807,0.0775,0.0039,0.0101,0.1019,0.1132,0.9767,0.6929,0.0565,0.0,0.0345,0.1667,0.2083,0.1265,0.0882,0.0808,0.0039,0.0107,0.10099999999999999,0.1091,0.9767,0.6847,0.0564,0.0,0.0345,0.1667,0.2083,0.1259,0.0821,0.0789,0.0039,0.0103,reg oper account,block of flats,0.0938,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-1631.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n102913,0,Cash loans,F,N,N,0,99000.0,814041.0,23800.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-20278,-10425,-11197.0,-3521,,1,1,0,1,1,0,Managers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Other,,0.6946860220299829,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-706.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n144547,0,Revolving loans,M,N,Y,1,135000.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020246,-12274,-3512,-2415.0,-4429,,1,1,0,1,0,0,Core staff,2.0,3,3,TUESDAY,14,0,0,0,0,0,0,Transport: type 2,,0.3785599598419497,0.41534714488434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n328553,0,Cash loans,M,Y,N,2,76500.0,225000.0,10944.0,225000.0,Unaccompanied,Working,Lower secondary,Married,Municipal apartment,0.0105,-14096,-4710,-4717.0,-1522,18.0,1,1,0,1,0,0,Laborers,4.0,3,3,FRIDAY,14,0,0,0,0,0,0,Other,,0.19671168255190088,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1312.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n167862,0,Cash loans,F,N,Y,0,202500.0,1029784.5,54994.5,927000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17093,-19,-3074.0,-618,,1,1,1,1,1,0,,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Industry: type 9,0.5403358520196024,0.6649191995294237,0.3441550073724169,0.1077,0.0467,0.9871,0.8232,0.0294,0.08,0.0345,0.4583,0.5,0.0469,0.0878,0.0841,0.0,0.0,0.1082,0.0485,0.9871,0.8301,0.0288,0.0806,0.0345,0.4583,0.5,0.0432,0.0946,0.0836,0.0,0.0,0.1088,0.0467,0.9871,0.8256,0.0296,0.08,0.0345,0.4583,0.5,0.0477,0.0894,0.0856,0.0,0.0,reg oper spec account,block of flats,0.0787,Panel,No,8.0,0.0,8.0,0.0,-1011.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,4.0\n260043,0,Cash loans,F,N,Y,3,112500.0,452385.0,29038.5,373500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.007120000000000001,-14566,-2256,-14.0,-5742,,1,1,0,1,0,1,Core staff,5.0,2,2,SATURDAY,8,0,0,0,0,0,0,School,0.5941320603627934,0.28188796474020505,0.6769925032909132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1826.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n247258,0,Cash loans,F,Y,Y,0,135000.0,142632.0,14022.0,126000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008575,-18213,-680,-10494.0,-1762,1.0,1,1,0,1,1,0,,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5800425315760338,0.2692857999073816,0.0619,0.0629,0.9811,,,0.0,0.1379,0.1667,,0.0238,,0.0541,,0.0,0.063,0.0653,0.9811,,,0.0,0.1379,0.1667,,0.0244,,0.0564,,0.0,0.0625,0.0629,0.9811,,,0.0,0.1379,0.1667,,0.0242,,0.0551,,0.0,,block of flats,0.0469,Panel,No,0.0,0.0,0.0,0.0,-1154.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n149647,0,Cash loans,M,Y,Y,2,162000.0,859500.0,33579.0,859500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-13066,-179,-2764.0,-2459,3.0,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,8,0,1,1,0,1,1,Construction,0.3209206804099716,0.12251115965433035,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-243.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n255089,0,Cash loans,M,Y,N,0,135000.0,239850.0,28593.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-18303,-561,-3006.0,-1735,17.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.1157389381899536,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n168417,0,Revolving loans,F,N,Y,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.015221,-19047,-5071,-9627.0,-2547,,1,1,0,1,0,0,Managers,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Other,,0.7253044674052986,0.7476633896301825,0.0598,0.0475,0.9762,0.6736,0.0226,0.0,0.1034,0.1667,0.0417,0.0337,0.0488,0.0495,0.0039,0.0065,0.0609,0.0493,0.9762,0.6864,0.0228,0.0,0.1034,0.1667,0.0417,0.0345,0.0533,0.0516,0.0039,0.0069,0.0604,0.0475,0.9762,0.6779999999999999,0.0227,0.0,0.1034,0.1667,0.0417,0.0343,0.0496,0.0504,0.0039,0.0066,reg oper account,block of flats,0.0513,Panel,No,0.0,0.0,0.0,0.0,-1966.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n131159,0,Cash loans,F,N,Y,0,40500.0,76410.0,7573.5,67500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.02461,-24576,365243,-4406.0,-4074,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.6143964829807141,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1985.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n361702,0,Cash loans,M,N,Y,0,315000.0,943425.0,27585.0,787500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.072508,-23099,365243,-9871.0,-4103,,1,0,0,1,0,0,,2.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.6595043947041416,0.2067786544915716,0.1454,0.11599999999999999,0.9737,0.6396,0.0,0.2,0.1724,0.2917,0.3333,0.0,0.1185,0.1288,0.0,0.14400000000000002,0.1481,0.1204,0.9737,0.6537,0.0,0.2014,0.1724,0.2917,0.3333,0.0,0.1295,0.1342,0.0,0.1525,0.1468,0.11599999999999999,0.9737,0.6444,0.0,0.2,0.1724,0.2917,0.3333,0.0,0.1206,0.1311,0.0,0.1471,reg oper account,block of flats,0.1326,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1758.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n130401,0,Cash loans,F,N,N,0,157500.0,354519.0,23053.5,328500.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.018634,-14357,-1093,-884.0,-4977,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,,0.5624181349133656,0.4382813743111921,0.0887,0.0943,0.9781,0.7008,0.013000000000000001,0.0,0.2069,0.1667,0.0417,0.0519,0.0672,0.0707,0.0232,0.0842,0.0903,0.0978,0.9782,0.7125,0.0131,0.0,0.2069,0.1667,0.0417,0.0531,0.0735,0.0736,0.0233,0.0892,0.0895,0.0943,0.9781,0.7048,0.013000000000000001,0.0,0.2069,0.1667,0.0417,0.0528,0.0684,0.0719,0.0233,0.086,reg oper spec account,block of flats,0.0714,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-458.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n122513,0,Cash loans,F,N,N,0,144000.0,204768.0,7848.0,162000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-18413,-3725,-3674.0,-1959,,1,1,0,1,1,0,Core staff,2.0,1,1,MONDAY,14,0,0,0,0,0,0,Kindergarten,0.8499533295204494,0.66735085735917,0.7407990879702335,0.1753,0.1917,0.9945,0.9252,0.0349,0.0,0.2759,0.1667,0.2083,0.0,0.1429,0.239,0.0,0.0,0.1786,0.19899999999999998,0.9945,0.9281,0.0352,0.0,0.2759,0.1667,0.2083,0.0,0.1561,0.2491,0.0,0.0,0.177,0.1917,0.9945,0.9262,0.0351,0.0,0.2759,0.1667,0.2083,0.0,0.1454,0.2433,0.0,0.0,not specified,block of flats,0.2071,Panel,No,0.0,0.0,0.0,0.0,-1544.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n319796,0,Cash loans,F,N,Y,0,162000.0,239850.0,23719.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.018029,-24955,-4258,-1924.0,-4075,,1,1,0,1,0,0,Accountants,1.0,3,3,FRIDAY,9,0,0,0,0,0,0,Other,,0.3731429058973288,0.4329616670974407,,,0.9856,,,,,,,,,,,,,,0.9856,,,,,,,,,,,,,,0.9856,,,,,,,,,,,,,block of flats,0.0676,Panel,No,0.0,0.0,0.0,0.0,-2156.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n143075,0,Cash loans,F,N,N,0,135000.0,180000.0,18441.0,180000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.011703,-19827,-6128,-986.0,-2370,,1,1,1,1,1,0,Sales staff,1.0,2,2,FRIDAY,20,0,0,0,0,0,0,Self-employed,,0.7399007780438213,0.4992720153045617,0.1014,0.0698,0.9856,0.8028,0.0178,0.0532,0.1607,0.2221,0.2638,0.0605,0.0815,0.1066,0.0051,0.0108,0.0599,0.0601,0.9826,0.7713,0.0095,0.0,0.1379,0.1667,0.2083,0.0497,0.0505,0.0723,0.0078,0.0,0.0791,0.0627,0.9841,0.7853,0.0148,0.0,0.1379,0.1667,0.2083,0.0561,0.065,0.0769,0.0078,0.006,reg oper account,block of flats,0.0655,Panel,No,0.0,0.0,0.0,0.0,-1802.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n444013,0,Cash loans,M,N,N,1,382500.0,481176.0,23278.5,360000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.020713,-15829,-2133,-298.0,-4447,,1,1,1,1,1,1,Laborers,3.0,3,2,FRIDAY,13,0,1,1,0,1,1,Other,0.13347671132824124,0.5569429611482866,,0.23199999999999998,0.1077,0.9985,0.9796,0.0676,0.12,0.1034,0.375,0.0417,0.0195,0.1816,0.1459,0.0347,0.0573,0.2363,0.1118,0.9985,0.9804,0.0682,0.1208,0.1034,0.375,0.0417,0.0199,0.1983,0.152,0.035,0.0607,0.2342,0.1077,0.9985,0.9799,0.068,0.12,0.1034,0.375,0.0417,0.0198,0.1847,0.1485,0.0349,0.0585,reg oper account,block of flats,0.1693,Panel,No,0.0,0.0,0.0,0.0,-1054.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n412590,0,Cash loans,F,N,N,0,78475.5,521280.0,22378.5,450000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.022625,-18816,-2172,-12801.0,-2343,,1,1,1,1,0,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3299005943422717,0.5526598190794112,,0.1485,0.0814,0.9781,0.7008,0.0476,0.16,0.1379,0.3333,0.375,0.0982,0.121,0.1431,0.0,0.0,0.1513,0.0844,0.9782,0.7125,0.0481,0.1611,0.1379,0.3333,0.375,0.1004,0.1322,0.1491,0.0,0.0,0.1499,0.0814,0.9781,0.7048,0.0479,0.16,0.1379,0.3333,0.375,0.0999,0.1231,0.1457,0.0,0.0,reg oper account,block of flats,0.1386,Panel,No,1.0,0.0,1.0,0.0,-144.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,,,,,,\n225855,0,Cash loans,M,N,Y,0,121500.0,824823.0,21888.0,688500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.031329,-14476,-3264,-8610.0,-4599,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,Government,0.055803853393853284,0.379558486257831,0.03414688565772187,0.0918,0.1077,0.9752,0.66,0.015,0.0,0.2414,0.1667,0.2083,0.1156,0.07400000000000001,0.085,0.0039,0.0143,0.0935,0.1118,0.9752,0.6733,0.0152,0.0,0.2414,0.1667,0.2083,0.1182,0.0808,0.0885,0.0039,0.0151,0.0926,0.1077,0.9752,0.6645,0.0151,0.0,0.2414,0.1667,0.2083,0.1176,0.0752,0.0865,0.0039,0.0146,reg oper account,block of flats,0.0782,Panel,No,0.0,0.0,0.0,0.0,-2005.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n126597,0,Cash loans,F,N,Y,0,270000.0,675000.0,32602.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.032561,-18296,-2471,-8623.0,-1829,,1,1,0,1,1,0,Laborers,1.0,1,1,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.5986725960058654,0.678897510343461,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-1418.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n403660,0,Cash loans,M,N,N,0,117000.0,225000.0,23872.5,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.020246,-16323,-7195,-6005.0,-4202,,1,1,0,1,1,0,Laborers,2.0,3,3,TUESDAY,13,0,0,0,0,0,0,Industry: type 9,,0.1776481697674297,0.7675231046555077,0.066,0.0626,0.9871,0.8232,0.0132,0.0,0.1379,0.1667,0.2083,0.0595,0.0504,0.0372,0.2471,0.1024,0.0672,0.065,0.9871,0.8301,0.0134,0.0,0.1379,0.1667,0.2083,0.0609,0.0551,0.0387,0.249,0.1084,0.0666,0.0626,0.9871,0.8256,0.0133,0.0,0.1379,0.1667,0.2083,0.0605,0.0513,0.0378,0.2484,0.1046,reg oper account,block of flats,0.0515,Panel,No,1.0,0.0,1.0,0.0,-1691.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n325069,0,Revolving loans,M,N,Y,1,112500.0,337500.0,16875.0,337500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-15457,-364,-6727.0,-4353,,1,1,0,1,1,0,Laborers,3.0,2,2,FRIDAY,10,0,0,0,0,1,1,Trade: type 3,,0.6290982382949823,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-317.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n443558,0,Cash loans,F,N,N,0,67500.0,254700.0,14220.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.003813,-22999,365243,-11537.0,-4877,,1,0,0,1,0,0,,1.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.4475493532215731,0.6313545365850379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1026.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n224616,0,Cash loans,M,Y,Y,0,202500.0,971280.0,51876.0,900000.0,\"Spouse, partner\",Pensioner,Higher education,Civil marriage,House / apartment,0.010556,-23723,365243,-537.0,-3992,7.0,1,0,0,1,0,0,,2.0,3,3,MONDAY,10,0,0,0,0,0,0,XNA,,0.5681464318489624,0.42589289800515295,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2326.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n131859,0,Cash loans,F,N,Y,0,270000.0,1035000.0,30393.0,1035000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-21090,-2569,-9276.0,-2479,,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Other,0.6421442727977661,0.6539090586502363,0.2764406945454034,0.1113,0.0955,0.9871,0.8232,0.0311,0.12,0.1034,0.3333,0.375,0.1527,0.0908,0.129,0.0,0.0,0.1134,0.0991,0.9871,0.8301,0.0314,0.1208,0.1034,0.3333,0.375,0.1562,0.0992,0.1344,0.0,0.0,0.1124,0.0955,0.9871,0.8256,0.0313,0.12,0.1034,0.3333,0.375,0.1553,0.0923,0.1313,0.0,0.0,reg oper account,block of flats,0.1513,Panel,No,0.0,0.0,0.0,0.0,-779.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,7.0\n321537,0,Cash loans,F,Y,Y,2,67500.0,640080.0,31131.0,450000.0,Family,Working,Higher education,Married,Municipal apartment,0.006852,-12262,-3038,-3581.0,-2750,17.0,1,1,0,1,0,0,Secretaries,4.0,3,3,TUESDAY,12,0,0,0,0,0,0,School,0.40466109680002654,0.1720111097319638,0.40314167665875134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-373.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n389066,0,Cash loans,F,N,Y,0,90000.0,74628.0,7965.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.010276,-15869,-476,-994.0,-4540,,1,1,0,1,0,1,Managers,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Other,0.5852392996078097,0.60013911887132,0.7981372313187245,0.0722,0.0,0.9762,0.6736,0.096,0.0,0.1379,0.1667,0.2083,0.0219,0.0588,0.0667,0.0,0.0,0.0735,0.0,0.9762,0.6864,0.0969,0.0,0.1379,0.1667,0.2083,0.0224,0.0643,0.0695,0.0,0.0,0.0729,0.0,0.9762,0.6779999999999999,0.0966,0.0,0.1379,0.1667,0.2083,0.0223,0.0599,0.0679,0.0,0.0,reg oper account,block of flats,0.0525,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n304329,0,Cash loans,F,N,N,0,76500.0,225000.0,10944.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-8721,-1407,-3468.0,-237,,1,1,0,1,1,0,Laborers,2.0,2,2,SUNDAY,18,0,0,0,0,0,0,Postal,0.16778743255655545,0.18759541008068148,,0.2268,0.121,0.9821,0.7552,0.0776,0.0,0.1379,0.3333,0.375,0.0577,0.1849,0.1716,0.0,0.0,0.2311,0.1256,0.9821,0.7648,0.0783,0.0,0.1379,0.3333,0.375,0.059000000000000004,0.20199999999999999,0.1788,0.0,0.0,0.22899999999999998,0.121,0.9821,0.7585,0.0781,0.0,0.1379,0.3333,0.375,0.0587,0.1881,0.1747,0.0,0.0,reg oper spec account,block of flats,0.2187,Panel,No,0.0,0.0,0.0,0.0,-1285.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n390530,0,Cash loans,F,N,N,0,90000.0,889515.0,26005.5,742500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.01885,-16108,-2243,-9108.0,-4022,,1,1,1,1,1,0,Low-skill Laborers,1.0,2,2,TUESDAY,14,0,0,0,0,1,1,Self-employed,,0.51225074420939,0.1694287272664794,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,-1088.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n394465,0,Cash loans,F,Y,Y,0,45000.0,76410.0,7686.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-17216,-524,-8398.0,-776,15.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6635389814652778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n218527,0,Cash loans,M,N,Y,0,112500.0,979992.0,28782.0,702000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-20201,-446,-5798.0,-3214,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.4707110252063426,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,2.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n173547,0,Cash loans,F,N,Y,0,180000.0,1223010.0,51948.0,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.072508,-20596,365243,-10764.0,-2721,,1,0,0,1,1,0,,1.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,XNA,0.6961281369945302,0.7579259945082681,0.5620604831738043,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-395.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n250385,0,Cash loans,F,N,N,0,180000.0,428854.5,16294.5,301500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009657,-20371,-540,-3013.0,-3869,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 1,,0.6832625786216977,0.4596904504249018,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1444.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n128390,0,Cash loans,M,N,Y,0,157500.0,790830.0,62613.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010032,-18616,-3620,-1474.0,-2174,,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Self-employed,,0.5453396981741323,,0.0742,0.0567,0.9806,0.7348,0.0179,0.08,0.069,0.3333,0.0417,0.0537,0.0605,0.0767,0.0,0.0,0.0756,0.0589,0.9806,0.7452,0.0181,0.0806,0.069,0.3333,0.0417,0.0549,0.0661,0.0799,0.0,0.0,0.0749,0.0567,0.9806,0.7383,0.018000000000000002,0.08,0.069,0.3333,0.0417,0.0546,0.0616,0.0781,0.0,0.0,reg oper account,block of flats,0.0701,Panel,No,0.0,0.0,0.0,0.0,-871.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n280578,0,Cash loans,F,Y,Y,1,90000.0,1006920.0,39933.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-18791,-277,-8245.0,-2239,14.0,1,1,1,1,1,0,Core staff,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Trade: type 7,,0.7263749527629855,0.4578995512067301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-519.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n182892,0,Cash loans,F,N,Y,0,202500.0,1051245.0,30865.5,877500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.019101,-22477,365243,-8078.0,-4601,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.6772124270923221,0.6863823354047934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1567.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,3.0\n290570,0,Cash loans,F,N,Y,0,205200.0,1762110.0,48586.5,1575000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.0228,-18702,-3138,-2556.0,-2253,,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Medicine,,0.3454216068856413,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,1.0,-1809.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n133605,0,Cash loans,F,N,N,1,225000.0,808650.0,23305.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010966,-18677,-2113,-6966.0,-2211,,1,1,0,1,0,0,Medicine staff,3.0,2,2,MONDAY,17,0,0,0,0,0,0,Government,0.08534318336863063,0.2861300822187729,0.3996756156233169,0.0722,0.0653,0.9891,0.8504,,0.0,0.2069,0.1667,0.2083,0.0802,,0.0826,,0.0779,0.0735,0.0677,0.9891,0.8563,,0.0,0.2069,0.1667,0.2083,0.0821,,0.0861,,0.0825,0.0729,0.0653,0.9891,0.8524,,0.0,0.2069,0.1667,0.2083,0.0816,,0.0841,,0.0795,org spec account,block of flats,0.0718,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2632.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n158058,1,Cash loans,F,Y,N,0,135000.0,521280.0,35392.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020713,-15301,-4415,-4726.0,-3729,0.0,1,1,1,1,0,0,,2.0,3,3,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.7849638946966557,0.4711686931762708,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1831.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n132649,0,Cash loans,F,N,N,0,90000.0,269550.0,11871.0,225000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.026392,-14679,-7361,-4705.0,-2783,,1,1,0,1,0,0,Medicine staff,1.0,2,2,TUESDAY,11,0,0,0,0,1,1,Medicine,0.7893386698165472,0.696940060838023,0.6363761710860439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-694.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n314330,0,Cash loans,F,N,Y,0,112500.0,239850.0,23494.5,225000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.072508,-25074,365243,-162.0,-4005,,1,0,0,1,0,0,,1.0,1,1,WEDNESDAY,16,0,0,0,0,0,0,XNA,,0.7421710333085877,0.7407990879702335,0.3979,0.2301,0.9921,0.8912,0.2281,0.56,0.2414,0.625,0.5,0.0,0.3219,0.4893,0.0116,0.0047,0.4055,0.2388,0.9921,0.8955,0.2301,0.5639,0.2414,0.625,0.5,0.0,0.3517,0.5098,0.0117,0.005,0.4018,0.2301,0.9921,0.8927,0.2295,0.56,0.2414,0.625,0.5,0.0,0.3275,0.4981,0.0116,0.0048,reg oper account,block of flats,0.3858,Panel,No,0.0,0.0,0.0,0.0,-380.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n393067,0,Cash loans,F,N,Y,1,90000.0,125136.0,8275.5,99000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.016612000000000002,-14431,-1816,-5174.0,-5191,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Industry: type 9,0.4741834860985305,0.634363656836067,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-290.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n285607,0,Cash loans,F,N,Y,0,189000.0,1078200.0,31653.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-23125,365243,-27.0,-2886,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.4863435088754162,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-393.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n184610,0,Cash loans,M,Y,Y,0,112500.0,675000.0,28597.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-9527,-185,-4410.0,-1673,9.0,1,1,1,1,1,0,Sales staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.3329438210249792,,0.3021,0.1656,0.9990000000000001,0.9864,0.2412,0.28,0.2414,0.375,0.4167,0.0,0.2463,0.3129,0.0,0.0,0.3078,0.1719,0.9990000000000001,0.9869,0.2434,0.282,0.2414,0.375,0.4167,0.0,0.2691,0.326,0.0,0.0,0.305,0.1656,0.9990000000000001,0.9866,0.2427,0.28,0.2414,0.375,0.4167,0.0,0.2505,0.3185,0.0,0.0,not specified,block of flats,0.37799999999999995,Panel,No,1.0,0.0,1.0,0.0,-371.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n321494,0,Cash loans,F,N,Y,0,315000.0,1223010.0,51948.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.02461,-20786,-2170,-9469.0,-4291,,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.7062325309237768,0.6701411108989919,0.5334816299804352,0.1851,0.0671,0.9916,0.8844,,0.24,0.1034,0.4583,0.0417,,0.1984,0.1584,0.0347,0.2528,0.1197,0.0696,0.9916,0.8889,,0.1611,0.069,0.4583,0.0417,,0.2167,0.0751,0.035,0.2198,0.1868,0.0671,0.9916,0.8859,,0.24,0.1034,0.4583,0.0417,,0.2018,0.1613,0.0349,0.2581,reg oper account,block of flats,0.2714,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1892.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n216391,1,Revolving loans,M,N,N,0,250200.0,585000.0,29250.0,585000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-11851,-616,-595.0,-3573,,1,1,0,1,0,0,Managers,2.0,3,3,MONDAY,12,0,0,0,0,1,1,Construction,0.33489665293689364,0.4703895973366315,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1875.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n372310,0,Revolving loans,M,Y,Y,0,211392.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-8911,-343,-3791.0,-1309,13.0,1,1,1,1,0,0,Sales staff,2.0,3,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.15002700162611607,0.18774876215032896,,0.1979,0.077,0.9806,0.7348,0.1252,0.04,0.0345,0.3333,0.0417,0.0496,0.132,0.0504,0.0,0.0,0.2017,0.0799,0.9806,0.7452,0.1264,0.0403,0.0345,0.3333,0.0417,0.0508,0.1442,0.0525,0.0,0.0,0.1999,0.077,0.9806,0.7383,0.126,0.04,0.0345,0.3333,0.0417,0.0505,0.1342,0.0513,0.0,0.0,not specified,specific housing,0.1081,\"Stone, brick\",Yes,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,,,,,,\n100547,1,Cash loans,M,Y,N,0,211500.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-10037,-148,-781.0,-2714,1.0,1,1,1,1,1,1,High skill tech staff,2.0,2,2,SATURDAY,11,0,1,1,0,1,1,Other,,0.22060476276693167,0.2735646775174348,0.0247,,0.9767,,,0.0,0.1034,0.0417,,,,0.0199,,0.0,0.0252,,0.9767,,,0.0,0.1034,0.0417,,,,0.0207,,0.0,0.025,,0.9767,,,0.0,0.1034,0.0417,,,,0.0202,,0.0,,block of flats,0.0156,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1401.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n267227,0,Cash loans,F,N,Y,0,157500.0,177903.0,9211.5,148500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.025164,-17253,-2087,-3482.0,-801,,1,1,0,1,0,0,Security staff,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 1,,0.5480547156713091,0.7062051096536562,0.0698,0.0706,0.9806,0.7348,0.0118,0.08,0.1207,0.2083,,0.0264,,0.0633,,0.0041,0.0588,0.0694,0.9791,0.7256,0.0089,0.0806,0.1379,0.1667,,0.0165,,0.0548,,0.0,0.0583,0.0669,0.9791,0.7182,0.0089,0.08,0.1379,0.1667,,0.0269,,0.0538,,0.0,reg oper account,block of flats,0.0414,Panel,No,1.0,0.0,1.0,0.0,-2045.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n325666,0,Revolving loans,M,Y,N,0,202500.0,585000.0,29250.0,585000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.02461,-21012,365243,-5563.0,-3281,6.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.6965057223857432,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1538.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n200001,0,Cash loans,M,Y,Y,0,202500.0,573628.5,24435.0,463500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-21874,-1915,-6484.0,-4204,9.0,1,1,0,1,0,0,Security staff,2.0,1,1,SATURDAY,13,0,0,0,0,1,1,Security,,0.6175931799190886,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-306.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n186016,0,Revolving loans,F,N,N,2,171000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.003818,-16845,-1333,-5387.0,-375,,1,1,0,1,0,0,Waiters/barmen staff,4.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.6992812413261389,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0426,,No,2.0,0.0,2.0,0.0,-57.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n331513,0,Cash loans,F,Y,Y,2,126000.0,269550.0,14242.5,225000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-11920,-194,-3982.0,-2645,46.0,1,1,1,1,0,0,Medicine staff,3.0,2,2,THURSDAY,11,0,0,0,0,1,1,Medicine,0.3981470607929723,0.2379722440237392,0.3506958432829587,0.0082,0.0,0.9682,0.5648,0.0005,0.0,0.0345,0.0417,0.0833,0.0066,0.0067,0.0103,0.0,0.0,0.0084,0.0,0.9682,0.5818,0.0005,0.0,0.0345,0.0417,0.0833,0.0067,0.0073,0.0108,0.0,0.0,0.0083,0.0,0.9682,0.5706,0.0005,0.0,0.0345,0.0417,0.0833,0.0067,0.0068,0.0105,0.0,0.0,not specified,block of flats,0.0081,Block,Yes,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n270004,0,Cash loans,M,N,N,0,90000.0,225000.0,17667.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.011703,-15582,-761,-8071.0,-784,,1,1,1,1,1,0,Security staff,2.0,2,2,THURSDAY,19,0,0,0,0,1,1,Business Entity Type 3,0.4924831713638338,0.4953551559004281,,0.1134,0.1043,0.9781,0.7008,0.0386,0.0,0.2069,0.1667,0.0417,0.1216,0.0925,0.0894,0.0,0.0,0.1155,0.1083,0.9782,0.7125,0.039,0.0,0.2069,0.1667,0.0417,0.1244,0.10099999999999999,0.0931,0.0,0.0,0.1145,0.1043,0.9781,0.7048,0.0388,0.0,0.2069,0.1667,0.0417,0.1237,0.0941,0.091,0.0,0.0,reg oper account,block of flats,0.0926,Panel,No,0.0,0.0,0.0,0.0,-358.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n325970,0,Cash loans,F,N,N,1,57150.0,327024.0,12456.0,270000.0,Children,Pensioner,Secondary / secondary special,Separated,House / apartment,0.001276,-15395,365243,-4410.0,-3213,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,,0.7199176035198134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n103723,0,Cash loans,F,Y,Y,0,67500.0,808650.0,23773.5,675000.0,\"Spouse, partner\",Pensioner,Lower secondary,Married,House / apartment,0.01885,-21659,365243,-4063.0,-4153,10.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.4220506681594633,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-716.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n411994,0,Cash loans,F,Y,N,2,126000.0,770292.0,32764.5,688500.0,Unaccompanied,State servant,Secondary / secondary special,Married,Office apartment,0.031329,-12335,-5040,-891.0,-3411,13.0,1,1,0,1,0,0,Medicine staff,4.0,2,2,THURSDAY,14,0,0,0,0,0,0,Medicine,,0.6155770123426783,0.7252764347002191,0.1392,0.0,0.9876,0.83,0.0214,0.0,0.3103,0.1667,0.2083,0.1024,0.1135,0.1447,0.0,0.0,0.1418,0.0,0.9876,0.8367,0.0216,0.0,0.3103,0.1667,0.2083,0.1047,0.124,0.1508,0.0,0.0,0.1405,0.0,0.9876,0.8323,0.0215,0.0,0.3103,0.1667,0.2083,0.1042,0.1154,0.1473,0.0,0.0,reg oper account,block of flats,0.1138,Panel,No,3.0,0.0,3.0,0.0,-1071.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n174352,0,Cash loans,F,N,Y,0,180000.0,675000.0,22437.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.014519999999999996,-23061,365243,-13129.0,-3918,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,XNA,,0.13001680361346202,0.28371188263500075,0.1294,0.1142,0.9801,0.728,0.04,0.0,0.2759,0.1667,0.2083,0.1257,0.1051,0.1186,0.0029,0.006,0.1208,0.0945,0.9801,0.7387,0.0276,0.0,0.2759,0.1667,0.2083,0.0692,0.1056,0.0981,0.0,0.0,0.1197,0.1055,0.9801,0.7316,0.0454,0.0,0.2759,0.1667,0.2083,0.1436,0.0983,0.1114,0.0019,0.0019,reg oper account,block of flats,0.1729,Panel,No,0.0,0.0,0.0,0.0,-907.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n343875,0,Cash loans,F,N,Y,0,67500.0,207000.0,14535.0,207000.0,Children,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-19423,-942,-7589.0,-2954,,1,1,0,1,0,0,Cooking staff,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Medicine,,0.5116005538193673,0.7490217048463391,0.16699999999999998,0.0626,0.9881,0.8368,0.0754,0.08,0.069,0.3333,0.0417,0.0379,0.1362,0.106,0.0039,0.0009,0.1702,0.065,0.9881,0.8432,0.0761,0.0806,0.069,0.3333,0.0417,0.0387,0.1488,0.1104,0.0039,0.001,0.1686,0.0626,0.9881,0.8390000000000001,0.0759,0.08,0.069,0.3333,0.0417,0.0385,0.1385,0.1079,0.0039,0.001,reg oper account,block of flats,0.10300000000000001,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n121451,0,Revolving loans,F,N,Y,0,54000.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005084,-14690,-2316,-5498.0,-4767,,1,1,1,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.6194403635845744,0.7091891096653581,,,0.9767,,,,,,,,,0.0066,,,,,0.9767,,,,,,,,,0.0069,,,,,0.9767,,,,,,,,,0.0067,,,,block of flats,0.0058,,Yes,0.0,0.0,0.0,0.0,-375.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n445510,0,Cash loans,M,Y,Y,0,202500.0,113211.0,10512.0,94500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.018029,-8658,-74,-5615.0,-1331,9.0,1,1,0,1,0,0,,2.0,3,3,FRIDAY,9,1,1,0,1,1,0,Industry: type 3,0.1741452081247346,0.616321910015568,0.2692857999073816,0.0835,0.0629,0.9901,0.8640000000000001,0.0679,0.12,0.1034,0.3333,0.375,0.0155,0.0681,0.1037,0.0,0.0,0.0851,0.0653,0.9901,0.8693,0.0685,0.1208,0.1034,0.3333,0.375,0.0159,0.0744,0.1081,0.0,0.0,0.0843,0.0629,0.9901,0.8658,0.0683,0.12,0.1034,0.3333,0.375,0.0158,0.0693,0.1056,0.0,0.0,reg oper account,block of flats,0.0816,Panel,No,4.0,0.0,4.0,0.0,-888.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n141214,0,Cash loans,F,N,N,0,207000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.019101,-9092,-1055,-3641.0,-1689,,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Bank,0.4624219132269844,0.5478218000604596,0.5154953751603267,0.0258,0.059000000000000004,0.9697,,,0.0,0.1379,0.0833,,0.0918,,0.0359,,,0.0263,0.0613,0.9697,,,0.0,0.1379,0.0833,,0.0939,,0.0374,,,0.026000000000000002,0.059000000000000004,0.9697,,,0.0,0.1379,0.0833,,0.0934,,0.0366,,,,block of flats,0.0283,Mixed,No,0.0,0.0,0.0,0.0,-422.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n314341,0,Cash loans,F,N,Y,1,202500.0,254700.0,27558.0,225000.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,House / apartment,0.04622,-9980,-1170,-474.0,-353,,1,1,1,1,0,0,Medicine staff,3.0,1,1,SUNDAY,15,0,0,0,0,0,0,Medicine,0.2710961661025508,0.6297970021683061,0.14375805349116522,0.018000000000000002,0.0,0.9707,0.5920000000000001,0.0167,0.0,0.0517,0.0417,0.0833,0.0393,0.0101,0.0123,0.0,0.0,0.0126,0.0,0.9702,0.608,0.0169,0.0,0.0345,0.0417,0.0833,0.0305,0.011000000000000001,0.0108,0.0,0.0,0.0182,0.0,0.9707,0.5975,0.0168,0.0,0.0517,0.0417,0.0833,0.04,0.0103,0.0125,0.0,0.0,reg oper account,block of flats,0.0203,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-303.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n388828,1,Cash loans,F,N,Y,0,252000.0,835380.0,40320.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17341,365243,-10584.0,-871,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.593598939321916,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n106626,0,Cash loans,F,N,N,0,135000.0,515529.0,26451.0,391500.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.0228,-8442,-640,-3029.0,-1114,,1,1,0,1,1,0,Waiters/barmen staff,1.0,2,2,TUESDAY,11,0,0,0,1,1,0,Business Entity Type 3,0.1615528769279425,0.507414450417913,0.1446481462546413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-544.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n394490,0,Cash loans,F,N,Y,0,360000.0,1671210.0,44217.0,1395000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-19850,-465,-13932.0,-3362,,1,1,0,1,0,0,HR staff,2.0,1,1,SUNDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.7254395091596776,0.5832379256761245,0.16699999999999998,0.0824,0.9757,0.6668,0.0828,0.0,0.2759,0.1667,0.2083,0.0,0.1345,0.0984,0.0077,0.2081,0.1702,0.0855,0.9757,0.6798,0.0835,0.0,0.2759,0.1667,0.2083,0.0,0.1469,0.1025,0.0078,0.2203,0.1686,0.0824,0.9757,0.6713,0.0833,0.0,0.2759,0.1667,0.2083,0.0,0.1368,0.1001,0.0078,0.2124,not specified,block of flats,0.1226,Panel,No,0.0,0.0,0.0,0.0,-2379.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n429492,1,Cash loans,M,Y,Y,0,90000.0,244584.0,11529.0,193500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-14474,-3775,-2761.0,-4653,1.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Self-employed,0.14674417898336778,0.7147521115964561,0.2340151665320674,0.0588,0.0395,0.9925,0.898,0.0298,0.0,0.069,0.2083,0.0417,0.0072,0.0479,0.0403,0.0,0.0,0.0599,0.040999999999999995,0.9926,0.902,0.0301,0.0,0.069,0.2083,0.0417,0.0074,0.0523,0.042,0.0,0.0,0.0593,0.0395,0.9925,0.8994,0.03,0.0,0.069,0.2083,0.0417,0.0073,0.0487,0.040999999999999995,0.0,0.0,reg oper account,block of flats,0.048,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1854.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n248270,0,Cash loans,F,N,Y,0,450000.0,988191.0,41998.5,909000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-15580,-3101,-2579.0,-2999,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Self-employed,0.4347698598814485,0.705979231471042,0.5902333386185574,0.0722,0.0625,0.9786,0.7076,0.0076,0.0,0.1379,0.1667,0.2083,0.0691,0.0588,0.0668,0.0,0.0,0.0735,0.0648,0.9786,0.7190000000000001,0.0077,0.0,0.1379,0.1667,0.2083,0.0707,0.0643,0.0696,0.0,0.0,0.0729,0.0625,0.9786,0.7115,0.0077,0.0,0.1379,0.1667,0.2083,0.0703,0.0599,0.068,0.0,0.0,reg oper account,block of flats,0.0525,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-553.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n230281,0,Cash loans,M,N,N,0,180000.0,147888.0,7321.5,117000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,With parents,0.016612000000000002,-13596,-1865,-5365.0,-5365,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.13466420770013826,0.4740512892789932,0.1474,0.0,0.9836,,,0.16,0.1379,0.3333,,0.0,,0.1396,,0.0,0.1502,0.0,0.9836,,,0.1611,0.1379,0.3333,,0.0,,0.1455,,0.0,0.1489,0.0,0.9836,,,0.16,0.1379,0.3333,,0.0,,0.1421,,0.0,,block of flats,0.1243,Panel,No,1.0,1.0,1.0,1.0,-702.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n413149,0,Cash loans,F,N,Y,0,382500.0,1005120.0,29520.0,720000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-19697,-2734,-9746.0,-3226,,1,1,0,1,1,0,Accountants,2.0,1,1,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.8206270731997233,0.4354071996344949,0.4418358231994413,0.18899999999999997,0.0802,0.9781,0.7008,0.0,0.1864,0.1148,0.4167,0.4583,0.0,0.1541,0.1061,0.0,0.0003,0.1765,0.0657,0.9777,0.706,0.0,0.1611,0.069,0.4583,0.5,0.0,0.1543,0.0954,0.0,0.0,0.1749,0.0639,0.9781,0.7048,0.0,0.16,0.069,0.4583,0.5,0.0,0.1437,0.0934,0.0,0.0,reg oper account,block of flats,0.1673,Block,No,0.0,0.0,0.0,0.0,-1877.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n136442,0,Cash loans,M,N,N,0,157500.0,231813.0,16263.0,193500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Civil marriage,With parents,0.035792000000000004,-19427,-1328,-9899.0,-2407,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,15,0,0,0,1,1,0,Government,,0.5493001835081851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n355939,0,Cash loans,F,N,Y,0,67500.0,1321020.0,35424.0,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-20511,365243,-2174.0,-3888,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.4780609268213007,0.7121551551910698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-675.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n320268,0,Cash loans,F,N,N,0,180000.0,862560.0,25348.5,720000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-17966,-2603,-1413.0,-1454,,1,1,0,1,0,0,Managers,2.0,1,1,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6238129179867825,0.6873747966972311,0.6279908192952864,0.1423,0.0622,0.9737,,,,0.1034,0.0833,,,,0.0178,,,0.145,0.0645,0.9737,,,,0.1034,0.0833,,,,0.0185,,,0.1436,0.0622,0.9737,,,,0.1034,0.0833,,,,0.0181,,,,block of flats,0.0232,,No,1.0,1.0,1.0,1.0,-2578.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n280521,1,Cash loans,F,N,Y,0,99000.0,276277.5,11380.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.031329,-23065,-352,-3947.0,-4168,,1,1,0,1,0,0,Sales staff,1.0,2,2,THURSDAY,7,0,0,0,0,0,0,Self-employed,,0.6731456030619017,0.7267112092725122,0.1,0.0537,0.9816,0.7484,0.024,0.08,0.0345,0.4583,0.5,0.0588,0.0807,0.0849,0.0039,0.0035,0.1019,0.0558,0.9816,0.7583,0.0242,0.0806,0.0345,0.4583,0.5,0.0602,0.0882,0.0884,0.0039,0.0037,0.10099999999999999,0.0537,0.9816,0.7518,0.0242,0.08,0.0345,0.4583,0.5,0.0599,0.0821,0.0864,0.0039,0.0035,reg oper account,block of flats,0.0806,Panel,No,6.0,0.0,6.0,0.0,-2482.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n215866,0,Cash loans,F,N,Y,0,104850.0,225000.0,22383.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.026392,-24817,365243,-16296.0,-1119,,1,0,0,1,0,0,,1.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.62127895083512,,0.0825,,0.9776,,,0.0,0.1379,0.1667,,,,0.0718,,0.0,0.084,,0.9777,,,0.0,0.1379,0.1667,,,,0.0748,,0.0,0.0833,,0.9776,,,0.0,0.1379,0.1667,,,,0.0731,,0.0,,block of flats,0.0564,Panel,No,3.0,0.0,3.0,0.0,-319.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n168641,0,Cash loans,M,Y,Y,0,225000.0,792162.0,40576.5,630000.0,Other_B,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.007273999999999998,-15642,-1918,-2751.0,-2808,23.0,1,1,0,1,0,0,Drivers,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.24898722033915802,0.6626377922738201,0.0722,0.0495,0.9762,0.6736,0.0237,0.0,0.1379,0.1667,0.0,0.0,0.0588,0.0353,0.0,0.0026,0.0735,0.0514,0.9762,0.6864,0.0239,0.0,0.1379,0.1667,0.0,0.0,0.0643,0.0368,0.0,0.0028,0.0729,0.0495,0.9762,0.6779999999999999,0.0239,0.0,0.1379,0.1667,0.0,0.0,0.0599,0.036000000000000004,0.0,0.0027,reg oper account,block of flats,0.0411,Panel,No,0.0,0.0,0.0,0.0,-639.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n266121,0,Revolving loans,M,N,N,1,180000.0,337500.0,16875.0,337500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.026392,-17055,-8129,-7507.0,-571,,1,1,0,1,1,0,IT staff,3.0,2,2,SUNDAY,12,0,0,0,0,0,0,Medicine,0.9269629375901528,0.6989362435455766,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1332.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n140896,0,Revolving loans,F,N,N,0,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.015221,-10447,-2048,-7816.0,-3105,,1,1,1,1,0,0,Accountants,2.0,2,2,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.5676670237701904,0.3724168643741293,0.14734591802757252,0.3371,0.2205,0.9891,0.8504,,0.32,0.2759,0.375,0.0417,,0.2681,0.3615,0.0309,0.0093,0.3435,0.2289,0.9891,0.8563,,0.3222,0.2759,0.375,0.0417,,0.2929,0.3766,0.0311,0.0099,0.3404,0.2205,0.9891,0.8524,,0.32,0.2759,0.375,0.0417,,0.2728,0.368,0.0311,0.0095,reg oper spec account,block of flats,0.3582,Panel,No,0.0,0.0,0.0,0.0,-211.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n135818,1,Cash loans,F,N,Y,0,90000.0,808650.0,26086.5,675000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-15599,-5662,-8419.0,-5103,,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.7794429428474675,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2958.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n159846,0,Cash loans,F,N,N,1,90000.0,373140.0,19084.5,337500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-12286,-4486,-1216.0,-3988,,1,1,0,1,0,0,Medicine staff,3.0,2,2,THURSDAY,13,0,0,0,0,0,0,Medicine,0.2819782664713397,0.5249811795795709,0.363945238612397,0.0351,,0.9702,,,,0.0345,0.0417,,0.0187,,0.0128,,0.0,0.0357,,0.9702,,,,0.0345,0.0417,,0.0191,,0.0133,,0.0,0.0354,,0.9702,,,,0.0345,0.0417,,0.019,,0.013000000000000001,,0.0,,block of flats,0.0101,Block,No,0.0,0.0,0.0,0.0,-281.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n340349,0,Cash loans,F,Y,Y,1,67500.0,325908.0,17064.0,247500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00963,-15694,-623,-8925.0,-4242,11.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5009421884612639,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-545.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n326675,0,Cash loans,M,Y,Y,0,180000.0,781920.0,43789.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18388,-566,-6929.0,-1951,3.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Transport: type 4,,0.7320958466281527,0.7764098512142026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,0.0,-1501.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n169900,0,Cash loans,F,N,Y,1,189000.0,808650.0,23305.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00823,-21367,-314,-2670.0,-4405,,1,1,0,1,0,0,Medicine staff,3.0,2,2,MONDAY,16,0,0,0,0,0,0,Other,0.8568323140540103,0.6782488484707472,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2515.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n167141,0,Cash loans,F,N,N,0,90000.0,1006920.0,42660.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-22590,365243,-10506.0,-4142,,1,0,0,1,1,0,,2.0,2,2,MONDAY,19,0,0,0,0,0,0,XNA,,0.5476972086864886,0.7922644738669378,0.134,0.1141,0.9856,0.8028,0.0709,0.16,0.1379,0.3333,0.0417,0.0509,0.1076,0.1053,0.0077,0.0702,0.1366,0.1184,0.9856,0.8105,0.0716,0.1611,0.1379,0.3333,0.0417,0.052000000000000005,0.1175,0.1097,0.0078,0.0743,0.1353,0.1141,0.9856,0.8054,0.0714,0.16,0.1379,0.3333,0.0417,0.0518,0.1095,0.1072,0.0078,0.0716,reg oper account,block of flats,0.0621,Panel,No,1.0,0.0,1.0,0.0,-1992.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,4.0\n382317,0,Cash loans,F,N,N,1,112500.0,202500.0,21483.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-13891,-2932,-4483.0,-4688,,1,1,1,1,0,0,Core staff,3.0,1,1,SUNDAY,18,0,0,0,0,0,0,University,,0.7416602029145435,0.3774042489507649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1340.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n422873,0,Cash loans,F,N,Y,1,193500.0,615109.5,31536.0,531000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00733,-13005,-416,-12975.0,-1358,,1,1,1,1,0,0,Laborers,3.0,2,2,MONDAY,21,0,0,0,0,0,0,Self-employed,,0.5171393503004331,0.0005272652387098817,0.1351,0.0723,0.9747,,,0.0,0.1034,0.125,0.0417,0.0,,0.0339,,0.0004,0.1376,0.0751,0.9747,,,0.0,0.1034,0.125,0.0417,0.0,,0.0353,,0.0004,0.1364,0.0723,0.9747,,,0.0,0.1034,0.125,0.0417,0.0,,0.0345,,0.0004,,specific housing,0.0759,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-673.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n211494,1,Cash loans,M,Y,N,0,112500.0,508495.5,24462.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-13275,-2071,-917.0,-1909,17.0,1,1,0,1,0,0,Laborers,1.0,2,2,SUNDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.17421162214020056,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-799.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n426237,0,Cash loans,F,N,Y,0,247500.0,526500.0,27013.5,526500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.026392,-16207,-5085,-1365.0,-4785,,1,1,0,1,0,1,Core staff,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Police,,0.4446531562127192,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1799.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n390406,0,Cash loans,M,Y,Y,1,112500.0,1024740.0,55588.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15541,-3263,-2625.0,-2633,14.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,17,0,0,0,0,1,1,Other,0.6627395852592373,0.6717873652906118,0.7490217048463391,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1883.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n156102,0,Revolving loans,M,N,Y,1,103500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.01885,-11515,-1411,-2969.0,-2033,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,1,1,0,Industry: type 6,0.16540772647442015,0.5123654794494128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n361433,1,Cash loans,F,N,Y,0,225000.0,585000.0,42700.5,585000.0,Family,Working,Higher education,Married,House / apartment,0.008865999999999999,-18981,-1154,-752.0,-793,,1,1,1,1,0,0,Private service staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Services,0.2559290767824932,0.2986200289723507,0.08011638046387602,0.0825,0.062,0.9747,0.6532,0.0069,0.0,0.1379,0.1667,0.2083,0.0487,0.0672,0.0628,0.0,0.0,0.084,0.0643,0.9747,0.6668,0.0069,0.0,0.1379,0.1667,0.2083,0.0498,0.0735,0.0654,0.0,0.0,0.0833,0.062,0.9747,0.6578,0.0069,0.0,0.1379,0.1667,0.2083,0.0496,0.0684,0.0639,0.0,0.0,reg oper account,block of flats,0.0494,\"Stone, brick\",No,3.0,2.0,3.0,1.0,-1004.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,1.0,3.0\n315014,0,Cash loans,M,Y,Y,1,306000.0,927252.0,26703.0,774000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018029,-16574,-275,-614.0,-108,16.0,1,1,0,1,0,0,Drivers,3.0,3,3,MONDAY,8,0,0,0,0,1,1,Business Entity Type 3,,0.5095513880642009,0.4920600938649263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n345162,0,Cash loans,F,N,Y,2,135000.0,299772.0,11430.0,247500.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.010643,-11651,-2372,-5268.0,-4026,,1,1,0,1,0,0,Accountants,4.0,2,2,SATURDAY,13,0,0,0,0,1,1,Medicine,0.7347233504895584,0.3408231613169002,0.4632753280912678,0.1165,0.10800000000000001,0.9846,0.7892,,0.0,0.2759,0.1667,0.2083,0.0877,0.0941,0.1031,0.0039,0.0312,0.1187,0.1121,0.9846,0.7975,,0.0,0.2759,0.1667,0.2083,0.0897,0.1028,0.1075,0.0039,0.033,0.1176,0.10800000000000001,0.9846,0.792,,0.0,0.2759,0.1667,0.2083,0.0892,0.0958,0.105,0.0039,0.0318,reg oper account,block of flats,0.0879,\"Stone, brick\",No,2.0,1.0,2.0,0.0,-333.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n245995,1,Cash loans,F,N,N,0,90000.0,687600.0,18135.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.004849,-15241,-8513,-9367.0,-4108,,1,1,0,1,0,0,Medicine staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Medicine,0.6275806876500618,0.1970816761914012,0.07495928089904688,0.0808,0.0824,0.9757,0.6668,0.0189,0.0,0.1379,0.1667,0.175,0.0792,0.0659,0.0608,0.0015,0.0184,0.084,0.0,0.9762,0.6864,0.0071,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.0436,0.0,0.0,0.0833,0.0885,0.9762,0.6779999999999999,0.0105,0.0,0.1379,0.1667,0.2083,0.0874,0.0684,0.0656,0.0,0.0,reg oper account,block of flats,0.0546,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n240420,0,Cash loans,M,Y,N,0,180000.0,616261.5,19480.5,468000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-14163,-157,-8281.0,-4836,8.0,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.6132892670662599,0.3347626718743028,0.19519840600440985,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n412073,0,Cash loans,M,Y,Y,2,135000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010147,-16817,-2191,-4253.0,-5,1.0,1,1,0,1,0,0,Laborers,4.0,2,2,MONDAY,15,0,0,0,0,0,0,Construction,,0.7381547350581545,0.8027454758994178,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2393.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n344347,0,Cash loans,F,N,Y,0,270000.0,1678239.0,46278.0,1314000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.006008,-18124,365243,-8240.0,-1653,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,0.8615639144282446,0.5522218716447042,0.5954562029091491,0.1495,0.0438,0.9861,0.8096,0.013999999999999999,0.04,0.0345,0.3333,0.375,0.0096,0.1219,0.0988,0.0,0.0,0.1523,0.0454,0.9861,0.8171,0.0141,0.0403,0.0345,0.3333,0.375,0.0098,0.1331,0.1029,0.0,0.0,0.1509,0.0438,0.9861,0.8121,0.0141,0.04,0.0345,0.3333,0.375,0.0097,0.124,0.1005,0.0,0.0,reg oper account,block of flats,0.0853,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-1506.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n366715,0,Cash loans,F,Y,N,0,405000.0,1042560.0,34587.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-15972,-1452,-10105.0,-1474,2.0,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,Self-employed,0.4777993137734473,0.6430026394297861,0.3723336657058204,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2073.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n355100,0,Cash loans,M,Y,Y,0,85500.0,378801.0,26937.0,351000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-17416,-1593,-8344.0,-940,2.0,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.4419754748328088,0.6582166578070671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2967.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n359762,1,Cash loans,M,Y,Y,1,315000.0,239850.0,28593.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.004849,-17276,-1884,-4019.0,-827,21.0,1,1,0,1,1,0,Laborers,3.0,2,2,THURSDAY,17,0,0,0,0,0,0,Self-employed,0.6287884973392257,0.06610541170290278,0.2735646775174348,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1209.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n393750,0,Cash loans,F,N,N,1,112500.0,900000.0,24880.5,900000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.019101,-9558,-187,-1995.0,-2162,,1,1,0,1,1,0,Core staff,3.0,2,2,WEDNESDAY,18,0,0,0,0,1,1,Electricity,0.5281071122561221,0.6725727983852061,0.36896873825284665,0.0402,0.0333,0.9871,0.8232,0.01,0.0,0.069,0.1667,0.2083,0.0353,0.0252,0.0177,0.0347,0.0178,0.040999999999999995,0.0345,0.9871,0.8301,0.0101,0.0,0.069,0.1667,0.2083,0.0361,0.0275,0.0185,0.035,0.0189,0.0406,0.0333,0.9871,0.8256,0.0101,0.0,0.069,0.1667,0.2083,0.0359,0.0257,0.018000000000000002,0.0349,0.0182,reg oper account,block of flats,0.0233,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1407.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n151985,0,Cash loans,F,Y,Y,0,270000.0,922500.0,30487.5,922500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.0105,-21221,365243,-3941.0,-4702,6.0,1,0,0,1,1,0,,2.0,3,3,WEDNESDAY,17,0,0,0,0,0,0,XNA,,0.7291563778453338,0.5971924268337128,0.0732,0.0906,0.9821,0.7552,0.0132,0.0,0.2069,0.1667,0.2083,0.0782,0.0588,0.0822,0.0039,0.0153,0.0746,0.094,0.9821,0.7648,0.0133,0.0,0.2069,0.1667,0.2083,0.0799,0.0643,0.0856,0.0039,0.0162,0.0739,0.0906,0.9821,0.7585,0.0133,0.0,0.2069,0.1667,0.2083,0.0795,0.0599,0.0837,0.0039,0.0156,reg oper account,block of flats,0.068,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-988.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n382260,0,Cash loans,M,N,Y,0,202500.0,550489.5,19903.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-21788,-3268,-7325.0,-4168,,1,1,0,1,1,0,Security staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.7861863510903367,0.6140648635892013,0.6263042766749393,0.0577,0.0742,0.9896,0.8572,0.0398,0.2,0.1724,0.375,0.4167,0.0125,0.0471,0.0504,0.0039,0.0416,0.0588,0.077,0.9896,0.8628,0.0401,0.2014,0.1724,0.375,0.4167,0.0127,0.0514,0.0526,0.0039,0.044000000000000004,0.0583,0.0742,0.9896,0.8591,0.04,0.2,0.1724,0.375,0.4167,0.0127,0.0479,0.0513,0.0039,0.0425,reg oper account,block of flats,0.0705,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1721.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n275000,0,Cash loans,M,Y,Y,3,450000.0,497520.0,52920.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-13177,-2805,-1905.0,-2247,3.0,1,1,0,1,1,0,Laborers,5.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,Other,,0.2275006993522185,0.2807895743848605,0.0649,0.0,0.9757,0.6668,0.0148,0.0,0.1379,0.1667,0.2083,0.0437,0.0521,0.0488,0.0039,0.0051,0.0662,0.0,0.9757,0.6798,0.0149,0.0,0.1379,0.1667,0.2083,0.0447,0.0569,0.0509,0.0039,0.0054,0.0656,0.0,0.9757,0.6713,0.0148,0.0,0.1379,0.1667,0.2083,0.0445,0.053,0.0497,0.0039,0.0052,reg oper account,block of flats,0.0476,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-346.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n190450,0,Cash loans,M,Y,N,0,157500.0,343377.0,23076.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-17935,-1105,-7390.0,-1478,11.0,1,1,0,1,0,0,Managers,2.0,3,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.031510462711261025,0.3656165070113335,0.0881,0.0751,0.9831,0.7688,0.0662,0.04,0.1207,0.3542,0.1042,0.0702,0.071,0.0852,0.0039,0.0166,0.0872,0.0477,0.9806,0.7452,0.0183,0.0,0.0345,0.1667,0.0,0.0584,0.0744,0.0857,0.0,0.0,0.08900000000000001,0.0751,0.9831,0.7719,0.0666,0.04,0.1207,0.3542,0.1042,0.0714,0.0723,0.0867,0.0039,0.017,reg oper account,block of flats,0.0746,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-903.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n245513,0,Cash loans,F,N,Y,0,225000.0,840951.0,35761.5,679500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006629,-20770,-5134,-7895.0,-1686,,1,1,0,1,1,0,Core staff,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Police,0.6723944448203586,0.6039322961751229,0.4489622731076524,0.0825,0.0,0.9851,0.7959999999999999,0.0164,0.0,0.1379,0.1667,0.2083,0.0186,0.0672,0.0849,0.0,0.0206,0.084,0.0,0.9851,0.804,0.0165,0.0,0.1379,0.1667,0.2083,0.0191,0.0735,0.0885,0.0,0.0,0.0833,0.0,0.9851,0.7987,0.0165,0.0,0.1379,0.1667,0.2083,0.019,0.0684,0.0864,0.0,0.021,reg oper account,block of flats,0.0757,Panel,No,0.0,0.0,0.0,0.0,-516.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n128515,0,Cash loans,F,N,Y,0,81000.0,508495.5,22527.0,454500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-21528,365243,-11964.0,-4717,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.6671129459196146,0.8027454758994178,0.0165,0.0394,0.9742,0.6464,0.0138,0.0,0.069,0.0417,0.0833,0.0336,0.0134,0.0128,0.0,0.0,0.0168,0.0409,0.9742,0.6602,0.0139,0.0,0.069,0.0417,0.0833,0.0343,0.0147,0.0133,0.0,0.0,0.0167,0.0394,0.9742,0.6511,0.0139,0.0,0.069,0.0417,0.0833,0.0342,0.0137,0.013000000000000001,0.0,0.0,reg oper account,block of flats,0.0176,\"Stone, brick\",No,5.0,3.0,5.0,1.0,-1175.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n197011,0,Cash loans,F,N,Y,2,225000.0,71955.0,8136.0,67500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-10019,-1054,-4.0,-2574,,1,1,0,1,0,0,Accountants,4.0,2,2,TUESDAY,9,0,0,0,0,0,0,Agriculture,,0.4518027778054603,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-623.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n127898,0,Cash loans,F,Y,N,0,45000.0,1288350.0,41692.5,1125000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.035792000000000004,-21471,365243,-7241.0,-4400,16.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,,0.3674728109606966,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n301068,0,Cash loans,M,Y,N,0,315000.0,1129500.0,33025.5,1129500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.003122,-12257,-1997,-2788.0,-1608,23.0,1,1,0,1,0,0,Managers,2.0,3,3,TUESDAY,12,1,1,0,1,1,0,Business Entity Type 3,0.4334343931304074,0.6407204703103776,0.3490552510751822,0.0165,0.0118,0.9831,,,0.0,0.069,0.0417,,0.0134,,0.0063,,0.0,0.0168,0.0123,0.9831,,,0.0,0.069,0.0417,,0.0137,,0.0065,,0.0,0.0167,0.0118,0.9831,,,0.0,0.069,0.0417,,0.0136,,0.0064,,0.0,,block of flats,0.0091,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-502.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n383555,0,Cash loans,F,N,Y,0,90000.0,544491.0,17694.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-22524,365243,-12503.0,-4976,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.08722817693199995,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1666.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n251637,0,Cash loans,M,N,N,0,157500.0,513531.0,38524.5,459000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.01885,-11036,-758,-841.0,-3459,,1,1,1,1,0,0,Laborers,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.7497458376253374,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1377.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n314999,0,Cash loans,F,N,N,0,67500.0,685386.0,39478.5,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17100,-5522,-9271.0,-628,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Trade: type 7,,0.4680967473390649,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2421.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,3.0\n112455,0,Cash loans,F,N,N,0,90000.0,203760.0,19849.5,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.02461,-17979,-6997,-4648.0,-1505,,1,1,0,1,1,0,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,School,,0.4287641559214899,0.7503751495159068,0.0072,0.0,0.9503,0.32,0.0011,0.0,0.0345,0.0,0.0417,0.019,0.0059,0.0065,0.0,0.0,0.0074,0.0,0.9503,0.3466,0.0011,0.0,0.0345,0.0,0.0417,0.0194,0.0064,0.0067,0.0,0.0,0.0073,0.0,0.9503,0.3291,0.0011,0.0,0.0345,0.0,0.0417,0.0193,0.006,0.0066,0.0,0.0,reg oper account,block of flats,0.0057,Wooden,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n258369,0,Cash loans,F,N,N,2,112500.0,452385.0,14332.5,373500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-12842,-1137,-294.0,-5326,,1,1,0,1,0,0,Sales staff,4.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Trade: type 7,0.5293031203743401,0.6504516342822646,0.29859498978739724,0.0722,,0.9841,0.7824,0.0976,0.0,0.1724,0.1667,0.0,0.0566,0.0572,0.0659,0.0077,0.0072,0.0735,,0.9841,0.7909,0.0985,0.0,0.1724,0.1667,0.0,0.0579,0.0624,0.0686,0.0078,0.0076,0.0729,,0.9841,0.7853,0.0982,0.0,0.1724,0.1667,0.0,0.0576,0.0581,0.067,0.0078,0.0073,org spec account,block of flats,0.0534,Block,No,1.0,1.0,1.0,1.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n386674,0,Revolving loans,M,N,Y,0,180000.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.04622,-12521,-1616,-6288.0,-4013,,1,1,0,1,0,0,Laborers,1.0,1,1,FRIDAY,17,0,0,0,0,0,0,Housing,0.6265036714438011,0.6198990458222706,,0.0381,0.0,0.9717,,,0.0,0.1034,0.125,,0.0,,0.0381,,0.0,0.0389,0.0,0.9717,,,0.0,0.1034,0.125,,0.0,,0.0397,,0.0,0.0385,0.0,0.9717,,,0.0,0.1034,0.125,,0.0,,0.0388,,0.0,,block of flats,0.034,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-168.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n431859,0,Cash loans,M,N,N,0,153000.0,1303812.0,35851.5,1138500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.031329,-10822,-2873,-10513.0,-3454,,1,1,0,1,1,0,Managers,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,Industry: type 5,0.23378279060740226,0.6440460583980713,0.3506958432829587,,0.0,0.9836,0.7756,0.0169,0.0,0.2759,0.1667,0.0417,0.0895,0.1067,0.133,,0.0652,,0.0,0.9836,0.7844,0.017,0.0,0.2759,0.1667,0.0417,0.0916,0.1166,0.1386,,0.069,,0.0,0.9836,0.7786,0.017,0.0,0.2759,0.1667,0.0417,0.0911,0.1086,0.1354,,0.0665,reg oper account,block of flats,0.1279,Block,No,0.0,0.0,0.0,0.0,-2750.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n448346,0,Cash loans,F,N,Y,1,157500.0,781920.0,42547.5,675000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.010643,-11663,-590,-9384.0,-1253,,1,1,0,1,0,1,Laborers,2.0,2,2,TUESDAY,15,0,1,1,0,1,1,Industry: type 11,0.2625695410613506,0.5699214773915657,0.3280631605201915,0.1309,0.1187,0.9762,,0.0564,0.0,0.2759,0.1667,,0.1016,,0.1191,,0.1419,0.1334,0.1232,0.9762,,0.0569,0.0,0.2759,0.1667,,0.1039,,0.1241,,0.1502,0.1322,0.1187,0.9762,,0.0568,0.0,0.2759,0.1667,,0.1034,,0.1212,,0.1448,,block of flats,0.1245,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n197504,1,Cash loans,F,Y,Y,0,126000.0,573628.5,27724.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.04622,-11653,-1097,-5108.0,-2488,18.0,1,1,0,1,1,0,Laborers,1.0,1,1,MONDAY,18,0,0,0,0,0,0,Self-employed,,0.29022099381431143,,,,,,,,,,,,,0.221,,,,,,,,,,,,,,0.2303,,,,,,,,,,,,,,0.225,,,,block of flats,0.1738,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n235371,0,Revolving loans,F,N,Y,1,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Married,With parents,0.015221,-9984,-162,-3988.0,-2619,,1,1,0,1,0,0,Accountants,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3418326447086064,0.4289141284296525,0.6279908192952864,0.1113,0.0804,0.9876,0.83,0.0,0.12,0.1034,0.3333,0.375,0.0,0.0908,0.0685,0.0,0.0011,0.1134,0.0835,0.9876,0.8367,0.0,0.1208,0.1034,0.3333,0.375,0.0,0.0992,0.0714,0.0,0.0011,0.1124,0.0804,0.9876,0.8323,0.0,0.12,0.1034,0.3333,0.375,0.0,0.0923,0.0698,0.0,0.0011,reg oper account,block of flats,0.0911,Panel,No,2.0,0.0,2.0,0.0,-388.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n189973,1,Cash loans,F,Y,Y,0,270000.0,509400.0,37197.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.010006,-19716,-2065,-767.0,-3277,15.0,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,9,0,0,0,0,1,1,School,0.7894341949434496,0.6077182243034684,0.23791607950711405,0.099,0.1039,0.9975,0.966,,0.0,0.1724,0.1667,0.2083,0.057999999999999996,,,,0.0255,0.1008,0.1078,0.9975,0.9673,,0.0,0.1724,0.1667,0.2083,0.0594,,,,0.027000000000000003,0.0999,0.1039,0.9975,0.9665,,0.0,0.1724,0.1667,0.2083,0.0591,,,,0.026000000000000002,reg oper account,block of flats,0.0741,Panel,No,0.0,0.0,0.0,0.0,-352.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n238521,0,Cash loans,F,N,N,0,135000.0,1211049.0,35541.0,1057500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-19085,-2697,-4401.0,-2573,,1,1,1,1,0,0,Cleaning staff,2.0,2,2,THURSDAY,17,1,1,0,1,1,0,Advertising,,0.2105552644493315,0.2807895743848605,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1204.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n269286,0,Cash loans,F,N,N,0,112500.0,816660.0,23535.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.019689,-19703,-524,-3396.0,-3240,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,SATURDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.7136462271698946,0.2813128647349483,,0.001,,0.9871,,,,0.069,0.0,,0.0192,,0.0006,,0.0009,0.0011,,0.9871,,,,0.069,0.0,,0.0196,,0.0007,,0.001,0.001,,0.9871,,,,0.069,0.0,,0.0195,,0.0007,,0.001,,terraced house,0.0007,Wooden,No,0.0,0.0,0.0,0.0,-125.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1.0,1.0,0.0,0.0,0.0,2.0\n208664,0,Cash loans,F,N,Y,0,225000.0,1288350.0,37800.0,1125000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.011657,-19244,-324,-245.0,-2353,,1,1,0,1,1,0,,2.0,1,1,THURSDAY,11,0,1,1,0,0,0,Business Entity Type 3,,0.3533098642929516,0.4048783643353997,0.1402,0.0877,0.9831,0.7688,0.0252,0.0,0.3103,0.1667,0.2083,0.0304,0.1118,0.1425,0.0116,0.022000000000000002,0.1429,0.091,0.9831,0.7779,0.0254,0.0,0.3103,0.1667,0.2083,0.0311,0.1221,0.1485,0.0117,0.0233,0.1416,0.0877,0.9831,0.7719,0.0254,0.0,0.3103,0.1667,0.2083,0.031,0.1137,0.1451,0.0116,0.0225,reg oper account,block of flats,0.1458,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n189741,0,Cash loans,F,N,Y,0,76500.0,343800.0,12478.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.008865999999999999,-22889,365243,-6070.0,-4336,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.3347675417286997,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-22.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n196670,0,Cash loans,M,Y,N,0,180000.0,781920.0,32868.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00496,-10519,-1519,-4545.0,-2740,7.0,1,1,0,1,1,0,Drivers,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6031892999035466,,0.1845,,,,,,,0.3333,,,,0.1232,,,0.188,,,,,,,0.3333,,,,0.1284,,,0.1863,,,,,,,0.3333,,,,0.1254,,,,,0.1526,,No,0.0,0.0,0.0,0.0,-636.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n386503,0,Cash loans,F,N,Y,0,112500.0,348264.0,17820.0,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-23675,365243,-12399.0,-4671,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.6598627733936087,0.7838324335199619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-631.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n299810,0,Revolving loans,F,N,Y,0,270000.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.007273999999999998,-10509,-749,-4403.0,-3182,,1,1,0,1,1,1,Accountants,1.0,2,2,MONDAY,18,0,0,0,0,0,0,Bank,0.2161161654922812,0.4745318095976594,0.5673792367572691,0.0711,0.0,0.9861,0.8096,0.0102,0.0,0.1379,0.1667,0.0,0.0317,0.057999999999999996,0.0453,0.0039,0.0036,0.0725,0.0,0.9861,0.8171,0.0103,0.0,0.1379,0.1667,0.0,0.0324,0.0634,0.0472,0.0039,0.0038,0.0718,0.0,0.9861,0.8121,0.0103,0.0,0.1379,0.1667,0.0,0.0322,0.059000000000000004,0.0461,0.0039,0.0036,reg oper account,block of flats,0.0517,Panel,No,0.0,0.0,0.0,0.0,-282.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n109712,0,Cash loans,M,Y,N,0,225000.0,412794.0,33097.5,382500.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.003069,-13606,-1839,-3712.0,-1971,6.0,1,1,0,1,0,0,Managers,2.0,3,3,FRIDAY,17,0,0,0,0,0,0,Other,,0.4535636053281183,0.09507039584133267,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1601.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n183967,0,Cash loans,F,Y,N,1,162000.0,1006920.0,40063.5,900000.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.006629,-14242,-984,-6124.0,-3324,20.0,1,1,0,1,0,0,Sales staff,3.0,2,2,THURSDAY,4,0,0,0,0,0,0,Business Entity Type 3,,0.5516919010582899,0.3979463219016906,0.0052,,0.9732,,,,0.0345,0.0,,0.0002,,0.0008,,,0.0053,,0.9732,,,,0.0345,0.0,,0.0002,,0.0009,,,0.0052,,0.9732,,,,0.0345,0.0,,0.0002,,0.0009,,,,block of flats,0.0018,Wooden,Yes,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n176571,0,Cash loans,F,N,Y,0,157500.0,447768.0,29920.5,405000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-19713,-2869,-10543.0,-3249,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,,0.2487296976618488,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n178877,0,Revolving loans,M,N,Y,2,202500.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-13883,-5515,-2093.0,-4836,,1,1,0,1,0,0,Managers,4.0,2,2,MONDAY,19,0,0,0,0,0,0,Trade: type 7,0.6831524783932686,0.5805058919729756,0.13680052191177486,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-621.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n368753,0,Cash loans,F,N,Y,0,135000.0,545040.0,25537.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.0228,-20635,365243,-291.0,-3345,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.3458370527429171,0.5406544504453575,0.0082,,0.9791,,,,,0.0417,,,,0.0077,,,0.0084,,0.9791,,,,,0.0417,,,,0.008,,,0.0083,,0.9791,,,,,0.0417,,,,0.0079,,,,block of flats,0.0067,Wooden,No,0.0,0.0,0.0,0.0,-1360.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n253319,0,Cash loans,F,N,Y,0,216000.0,996849.0,42363.0,891000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-15968,-1846,-1034.0,-707,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.7785745469987155,0.5090486077140198,,0.0546,0.0273,0.9841,0.7824,0.0221,0.04,0.0345,0.3333,0.375,0.0502,0.0445,0.0518,0.0,0.0,0.0557,0.0284,0.9841,0.7909,0.0223,0.0403,0.0345,0.3333,0.375,0.0513,0.0487,0.054000000000000006,0.0,0.0,0.0552,0.0273,0.9841,0.7853,0.0222,0.04,0.0345,0.3333,0.375,0.051,0.0453,0.0527,0.0,0.0,reg oper account,block of flats,0.0528,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1136.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n150215,0,Cash loans,F,N,Y,0,112500.0,261288.0,9981.0,171000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.009657,-21007,365243,-12514.0,-4178,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.6836410547104761,0.4992720153045617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-795.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n427097,0,Cash loans,M,Y,N,1,202500.0,706221.0,19548.0,589500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-9100,-1232,-9083.0,-1786,2.0,1,1,0,1,0,0,Security staff,3.0,2,2,WEDNESDAY,12,0,1,1,0,0,0,Security,0.20294739729391484,0.3933076212439476,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-127.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n438405,0,Revolving loans,F,Y,Y,1,225000.0,630000.0,31500.0,630000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-13794,-2847,-2891.0,-617,5.0,1,1,1,1,1,0,Accountants,3.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.7104145994798341,0.5474967665479525,0.8327850252992314,0.0619,0.0715,0.9881,0.8164,0.0353,0.04,0.0862,0.25,0.0417,0.0723,0.0504,0.0776,0.0039,0.0,0.063,0.0667,0.9866,0.8236,0.0357,0.0,0.0345,0.1667,0.0417,0.07400000000000001,0.0551,0.0693,0.0039,0.0,0.0625,0.0715,0.9881,0.8189,0.0356,0.04,0.0862,0.25,0.0417,0.0736,0.0513,0.079,0.0039,0.0,org spec account,terraced house,0.0523,Panel,No,0.0,0.0,0.0,0.0,-296.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n126855,0,Cash loans,F,Y,Y,0,351000.0,1054935.0,47056.5,904500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-16792,-5390,-10647.0,-327,9.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Military,0.7656095067085359,0.5870572058822118,0.30162489168411943,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-1839.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n188216,0,Cash loans,F,N,Y,1,90000.0,170640.0,11236.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16165,-1905,-7630.0,-5207,,1,1,1,1,1,0,Low-skill Laborers,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.537271514277896,0.8245949709919925,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n120801,1,Cash loans,M,N,N,0,157500.0,343683.0,18774.0,261000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.032561,-8332,-252,-8295.0,-997,,1,1,0,1,0,1,,1.0,1,1,TUESDAY,18,0,0,0,0,1,1,Business Entity Type 3,0.1989337182173309,0.1827520052564234,0.3139166772114369,0.1072,0.0563,0.9806,,,,0.2759,0.1667,,,,0.0993,,0.132,0.1092,0.0584,0.9806,,,,0.2759,0.1667,,,,0.1034,,0.1397,0.1083,0.0563,0.9806,,,,0.2759,0.1667,,,,0.10099999999999999,,0.1348,,,0.1167,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n124777,0,Cash loans,F,N,N,0,112500.0,450000.0,22977.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.005084,-17503,-5601,-6152.0,-1040,,1,1,1,1,1,0,Laborers,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Housing,,0.623006209394121,,0.0464,0.0439,0.9801,0.728,0.0299,0.0,0.1034,0.1667,0.125,0.0285,0.0378,0.0387,0.0,0.0,0.0315,0.0271,0.9801,0.7387,0.0122,0.0,0.069,0.1667,0.0417,0.0153,0.0275,0.0265,0.0,0.0,0.0468,0.0439,0.9801,0.7316,0.0301,0.0,0.1034,0.1667,0.125,0.028999999999999998,0.0385,0.0394,0.0,0.0,reg oper spec account,block of flats,0.0266,Panel,No,0.0,0.0,0.0,0.0,-2531.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n329766,0,Revolving loans,M,N,Y,0,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-22684,365243,-8690.0,-4059,,1,0,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.5877355888846567,0.6738300778602003,0.1108,0.0766,0.9886,0.8572,0.0638,0.12,0.1034,0.3333,0.0417,0.0302,0.1202,0.1102,0.0,0.0016,0.0756,0.0529,0.9881,0.8628,0.0644,0.0806,0.069,0.3333,0.0417,0.0309,0.1313,0.0763,0.0,0.0017,0.1119,0.0766,0.9886,0.8591,0.0642,0.12,0.1034,0.3333,0.0417,0.0307,0.1223,0.1122,0.0,0.0017,reg oper spec account,block of flats,0.0654,Panel,No,0.0,0.0,0.0,0.0,-200.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n447328,0,Cash loans,F,N,Y,3,121500.0,594121.5,30465.0,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-20416,365243,-5119.0,-3953,,1,0,0,1,0,0,,5.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,0.8154936821154579,0.11973184071634768,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n366654,0,Cash loans,M,Y,Y,0,180000.0,180000.0,19386.0,180000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006233,-16517,-1493,-1850.0,-67,7.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Other,,0.5615945637476663,0.4974688893052743,0.0124,,0.9697,0.5852,0.0018,0.0,0.069,0.0417,0.0833,0.0126,0.0101,0.0121,0.0,0.0046,0.0126,,0.9697,0.6014,0.0018,0.0,0.069,0.0417,0.0833,0.0129,0.011000000000000001,0.0126,0.0,0.0049,0.0125,,0.9697,0.5907,0.0018,0.0,0.069,0.0417,0.0833,0.0129,0.0103,0.0123,0.0,0.0047,,block of flats,0.0105,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-599.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n348164,0,Cash loans,M,Y,Y,0,225000.0,490495.5,50391.0,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-10213,-923,-4845.0,-2883,3.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Trade: type 7,0.21414303681440086,0.7783470438216825,0.6092756673894402,0.066,,0.9767,,,0.0,0.1379,0.1667,,,,0.0702,,,0.0672,,0.9757,,,0.0,0.1379,0.1667,,,,0.0731,,,0.0666,,0.9767,,,0.0,0.1379,0.1667,,,,0.0715,,,,block of flats,0.0717,Panel,No,1.0,0.0,1.0,0.0,-2472.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n442420,0,Cash loans,M,Y,Y,1,202500.0,1104637.5,67711.5,1044000.0,\"Spouse, partner\",Working,Higher education,Married,Municipal apartment,0.02461,-8621,-143,-8610.0,-1303,15.0,1,1,0,1,1,0,Laborers,3.0,2,2,FRIDAY,12,0,0,0,1,1,1,Business Entity Type 2,0.21971968456704014,0.4971153946648738,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-393.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n270925,0,Cash loans,F,N,N,1,234000.0,731403.0,54814.5,684000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.007273999999999998,-15242,-2066,-6131.0,-4562,,1,1,0,1,0,0,Sales staff,3.0,2,2,SUNDAY,15,0,0,0,0,0,0,Self-employed,0.6891239721189334,0.7880590279955877,0.813917469762627,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2260.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n153894,1,Cash loans,F,N,Y,0,135000.0,1288350.0,37795.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.01885,-20880,365243,-9363.0,-4211,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.6657274889293708,0.18848953379516767,0.3670000000000001,0.0725,0.9871,0.8232,0.0377,0.0,0.1379,0.1667,,0.0568,0.0975,0.0334,0.9266,0.0947,0.3739,0.0752,0.9871,0.8301,0.038,0.0,0.1379,0.1667,,0.0581,0.1065,0.0348,0.9339,0.1002,0.3706,0.0725,0.9871,0.8256,0.0379,0.0,0.1379,0.1667,,0.0578,0.0992,0.034,0.9317,0.0967,,block of flats,0.0468,Panel,No,3.0,0.0,3.0,0.0,-1765.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n156842,0,Cash loans,F,N,N,0,202500.0,521280.0,31630.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-20093,365243,-4992.0,-3488,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,19,0,0,0,0,0,0,XNA,,0.3527005485243914,0.4974688893052743,0.1608,,0.993,,,0.16,0.1379,0.375,,,,0.1762,,0.0091,0.1639,,0.993,,,0.1611,0.1379,0.375,,,,0.1835,,0.0096,0.1624,,0.993,,,0.16,0.1379,0.375,,,,0.1793,,0.0093,,block of flats,0.1405,Panel,No,0.0,0.0,0.0,0.0,-1330.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n276971,0,Cash loans,F,N,N,0,135000.0,808650.0,31333.5,675000.0,Family,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.032561,-20158,365243,-12045.0,-3461,,1,0,0,1,1,0,,1.0,1,1,SUNDAY,15,0,0,0,0,0,0,XNA,0.8601706082993471,0.5835222124251309,,,0.057999999999999996,0.9826,0.762,0.0202,0.1,0.069,0.4583,0.5,0.027000000000000003,,0.0957,,0.0,,0.0323,0.9821,0.7648,0.0194,0.0806,0.0345,0.3333,0.375,0.0146,,0.0736,,0.0,,0.057999999999999996,0.9826,0.7652,0.0203,0.1,0.069,0.4583,0.5,0.0275,,0.0974,,0.0,reg oper account,block of flats,0.0661,Panel,No,5.0,0.0,5.0,0.0,-1779.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n110940,0,Cash loans,F,N,Y,0,180000.0,369531.0,39798.0,351000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018801,-12961,-318,-454.0,-4052,,1,1,1,1,0,0,Private service staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,0.035546049200770806,0.673140791725087,0.4294236843421945,0.2,0.0,0.9990000000000001,0.9864,0.0,0.16,0.069,0.7083,0.75,0.0336,0.1631,0.2674,0.0,0.4008,0.2038,0.0,0.9990000000000001,0.9869,0.0,0.1611,0.069,0.7083,0.75,0.0343,0.1781,0.2786,0.0,0.4244,0.2019,0.0,0.9990000000000001,0.9866,0.0,0.16,0.069,0.7083,0.75,0.0342,0.1659,0.2723,0.0,0.4093,reg oper spec account,block of flats,0.4034,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-203.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n329301,0,Cash loans,F,Y,Y,1,270000.0,728460.0,43506.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.02461,-11515,-161,-345.0,-1764,22.0,1,1,0,1,0,1,Managers,3.0,2,2,MONDAY,10,0,0,0,1,1,1,Business Entity Type 3,0.5513641748861341,0.5915240827712676,0.2366108235287817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-432.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n190470,0,Cash loans,F,N,N,0,225000.0,1546020.0,40914.0,1350000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.010556,-19124,-4517,-5443.0,-2685,,1,1,0,1,0,0,Core staff,1.0,3,3,THURSDAY,13,0,0,0,0,0,0,Self-employed,0.7291929085617845,0.4919935659095884,0.4365064990977374,0.1464,0.1113,0.9806,0.7348,0.0721,0.0,0.2759,0.1667,0.2083,0.0814,0.1177,0.1071,0.0077,0.0264,0.1492,0.1155,0.9806,0.7452,0.0728,0.0,0.2759,0.1667,0.2083,0.0833,0.1286,0.1115,0.0078,0.027999999999999997,0.1478,0.1113,0.9806,0.7383,0.0726,0.0,0.2759,0.1667,0.2083,0.0828,0.1197,0.109,0.0078,0.027000000000000003,reg oper account,block of flats,0.1294,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-3631.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n350080,0,Cash loans,F,N,N,1,202500.0,584766.0,24772.5,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-17946,-1680,-3228.0,-1480,,1,1,0,1,0,0,Laborers,3.0,3,3,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.8424434222818592,0.5853120380240442,0.633031641417419,0.0124,0.0,0.9757,,,0.0,0.1034,0.0417,,0.0,,0.0067,,0.0127,0.0126,0.0,0.9757,,,0.0,0.1034,0.0417,,0.0,,0.006999999999999999,,0.0134,0.0125,0.0,0.9757,,,0.0,0.1034,0.0417,,0.0,,0.0069,,0.0129,,block of flats,0.0081,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1269.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n268105,0,Cash loans,F,Y,Y,0,121500.0,566055.0,16353.0,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-23457,365243,-4594.0,-4594,14.0,1,0,0,1,0,0,,2.0,3,3,MONDAY,10,0,0,0,0,0,0,XNA,0.7809623516632158,0.5788541949829243,0.6075573001388961,0.0918,0.1006,0.9801,0.728,0.0122,0.0,0.2069,0.1667,0.0417,,,0.0868,,0.0034,0.0935,0.1044,0.9801,0.7387,0.0123,0.0,0.2069,0.1667,0.0417,,,0.0905,,0.0036,0.0926,0.1006,0.9801,0.7316,0.0123,0.0,0.2069,0.1667,0.0417,,,0.0884,,0.0035,,block of flats,0.0757,Panel,No,1.0,0.0,1.0,0.0,-1777.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n280064,0,Cash loans,F,N,N,0,171000.0,189621.0,14980.5,144000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-12515,-190,-4794.0,-4640,,1,1,1,1,0,0,Sales staff,2.0,2,2,THURSDAY,7,0,0,0,0,0,0,Industry: type 3,,0.18483176632893286,0.3910549766342248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1302.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,9.0\n210362,0,Cash loans,F,N,Y,0,67500.0,270000.0,12717.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-15201,-1365,-3638.0,-3943,,1,1,0,1,0,0,Laborers,2.0,3,3,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.4827703865488402,0.11084956237790124,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-739.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n397896,0,Cash loans,M,Y,Y,0,171000.0,765261.0,32553.0,684000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-20545,-462,-9775.0,-4108,11.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,Industry: type 11,,0.7222573372424708,0.816092360478441,0.1031,0.0846,0.9841,0.7824,0.0474,0.0,0.2069,0.1667,0.0417,0.0631,0.0841,0.0881,0.0,0.0,0.105,0.0877,0.9841,0.7909,0.0478,0.0,0.2069,0.1667,0.0417,0.0645,0.0918,0.0918,0.0,0.0,0.1041,0.0846,0.9841,0.7853,0.0477,0.0,0.2069,0.1667,0.0417,0.0642,0.0855,0.0897,0.0,0.0,reg oper spec account,block of flats,0.0952,\"Stone, brick\",No,9.0,1.0,8.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n121856,0,Cash loans,F,N,Y,0,99000.0,254700.0,20250.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-18002,-5432,-8647.0,-1549,,1,1,1,1,1,0,Secretaries,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,School,,0.32635272217004463,0.4578995512067301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-291.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n388649,0,Cash loans,F,N,Y,1,135000.0,900000.0,50386.5,900000.0,Unaccompanied,State servant,Higher education,Widow,House / apartment,0.072508,-18104,-418,-9211.0,-329,,1,1,0,1,1,0,High skill tech staff,2.0,1,1,FRIDAY,18,0,0,0,0,0,0,Housing,,0.7864548774983015,0.7407990879702335,0.2014,0.0854,0.9881,0.8368,0.1091,0.24,0.0966,0.7083,0.75,0.0953,0.1634,0.2182,0.0039,0.0136,0.1418,0.0608,0.9871,0.8301,0.0904,0.2417,0.069,0.6667,0.7083,0.071,0.1827,0.1535,0.0,0.0,0.2082,0.0855,0.9871,0.8256,0.1156,0.24,0.1034,0.6667,0.7083,0.0969,0.1702,0.2209,0.0039,0.0169,reg oper account,block of flats,0.1743,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n304432,0,Cash loans,F,Y,N,0,207000.0,679500.0,64516.5,679500.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.010032,-17697,-1780,-2830.0,-1242,32.0,1,1,0,1,1,0,Accountants,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6295573413427599,0.6446794549585961,0.1753,0.094,0.9886,0.8436,0.0626,0.16,0.1379,0.375,0.4167,0.0,0.1429,0.187,0.0,0.0,0.1786,0.0975,0.9886,0.8497,0.0632,0.1611,0.1379,0.375,0.4167,0.0,0.1561,0.1948,0.0,0.0,0.177,0.094,0.9886,0.8457,0.063,0.16,0.1379,0.375,0.4167,0.0,0.1454,0.1903,0.0,0.0,reg oper account,block of flats,0.1813,Panel,No,0.0,0.0,0.0,0.0,-2014.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n210221,0,Cash loans,M,Y,Y,1,135000.0,135000.0,9729.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13618,-2946,-4561.0,-4561,12.0,1,1,1,1,0,0,Drivers,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Self-employed,0.5747898417274591,0.7344609536377956,0.8193176922872417,0.0144,0.0,0.9727,0.626,0.0014,0.0,0.069,0.0417,0.0417,0.0239,0.0101,0.0069,0.0077,0.0269,0.0147,0.0,0.9727,0.6406,0.0014,0.0,0.069,0.0417,0.0417,0.0244,0.011000000000000001,0.0072,0.0078,0.0285,0.0146,0.0,0.9727,0.631,0.0014,0.0,0.069,0.0417,0.0417,0.0243,0.0103,0.006999999999999999,0.0078,0.0275,,block of flats,0.0121,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n150385,0,Cash loans,M,N,N,3,103500.0,446940.0,11920.5,292500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-11743,-126,-977.0,-4116,,1,1,0,1,1,0,Cleaning staff,5.0,3,3,WEDNESDAY,4,0,0,0,0,0,0,Transport: type 2,0.19168118020831,0.3354252948132261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,-260.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,,,,,,\n394261,0,Cash loans,F,N,N,0,135000.0,315000.0,35626.5,315000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.02461,-15808,-1923,-285.0,-5198,,1,1,1,1,0,0,Laborers,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.8001880132775983,0.7769593855139382,0.4578995512067301,,,0.9727,0.626,0.006,0.0,0.0345,0.1667,0.2083,0.0532,0.1261,0.0855,0.0077,0.0025,,,0.9727,0.6406,0.006,0.0,0.0345,0.1667,0.2083,0.0544,0.1377,0.0891,0.0078,0.0026,,,0.9727,0.631,0.006,0.0,0.0345,0.1667,0.2083,0.0541,0.1283,0.087,0.0078,0.0025,,block of flats,0.0672,\"Stone, brick\",No,4.0,2.0,4.0,0.0,-296.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n270866,0,Cash loans,F,N,N,0,157500.0,517788.0,18729.0,427500.0,Family,Working,Secondary / secondary special,Married,Office apartment,0.008625,-19554,-3609,-9543.0,-3097,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 1,,0.6747313499729766,0.2822484337007223,,,0.9866,,,,0.069,0.0417,,0.0836,,0.0696,,0.0,,,0.9866,,,,0.069,0.0417,,0.0855,,0.0725,,0.0,,,0.9866,,,,0.069,0.0417,,0.0851,,0.0708,,0.0,,block of flats,0.0622,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2080.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n259930,1,Cash loans,M,N,N,0,90000.0,592560.0,32274.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-21383,-934,0.0,-4827,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,16,0,0,0,0,1,1,Trade: type 7,,0.2191044888331019,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n150121,0,Cash loans,F,N,Y,0,180000.0,722925.0,26095.5,549000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.032561,-20909,-1096,-2857.0,-408,,1,1,0,1,1,0,Accountants,1.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Construction,0.9178095233879566,0.6782202151740777,0.7380196196295241,0.033,0.0,0.9712,0.4764,0.0072,0.0,0.1379,0.125,0.0417,0.012,0.0269,0.0376,0.0,0.0,0.0336,0.0,0.9712,0.4969,0.0072,0.0,0.1379,0.125,0.0417,0.0123,0.0294,0.0364,0.0,0.0,0.0333,0.0,0.9712,0.4834,0.0072,0.0,0.1379,0.125,0.0417,0.0122,0.0274,0.0382,0.0,0.0,reg oper account,block of flats,0.0318,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-837.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n398610,0,Cash loans,F,N,N,0,135000.0,679500.0,19998.0,679500.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.026392,-19037,-1561,-82.0,-2584,,1,1,0,1,1,0,,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.7579763108248971,0.6949503270687732,0.7016957740576931,0.1485,,0.9826,,,0.16,0.1379,0.3333,,,,0.1552,,0.0,0.1513,,0.9826,,,0.1611,0.1379,0.3333,,,,0.1617,,0.0,0.1499,,0.9826,,,0.16,0.1379,0.3333,,,,0.158,,0.0,,block of flats,0.1221,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n175551,0,Cash loans,F,Y,Y,0,99000.0,594261.0,26307.0,513000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.010643,-15969,-9311,-9873.0,-4232,13.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5357815800989373,0.7194907850918436,0.032,0.0147,0.9816,0.7484,0.0051,0.0,0.1034,0.0833,0.125,0.0672,0.0261,0.0305,0.0,0.0,0.0326,0.0153,0.9816,0.7583,0.0052,0.0,0.1034,0.0833,0.125,0.0688,0.0285,0.0318,0.0,0.0,0.0323,0.0147,0.9816,0.7518,0.0051,0.0,0.1034,0.0833,0.125,0.0684,0.0265,0.031,0.0,0.0,reg oper spec account,block of flats,0.0265,\"Stone, brick\",No,7.0,0.0,7.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n273026,0,Revolving loans,M,N,N,0,164997.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,Rented apartment,0.011657,-10545,-794,-4800.0,-3155,,1,1,1,1,0,0,Managers,2.0,1,1,TUESDAY,12,1,1,0,1,1,0,Trade: type 2,0.35456684858089177,0.5527949715739855,0.3376727217405312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-749.0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n229390,0,Cash loans,F,N,Y,0,180000.0,139500.0,10557.0,139500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-19996,365243,-4603.0,-1496,,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.5913602773458688,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,1.0,-448.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n249522,0,Cash loans,F,N,Y,0,292500.0,254700.0,24939.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Rented apartment,0.002042,-24058,365243,-9523.0,-4330,,1,0,0,1,0,0,,2.0,3,3,MONDAY,12,0,0,0,0,0,0,XNA,,0.5332896926119624,0.2225807646753351,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1426.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n281971,0,Cash loans,M,N,N,0,225000.0,1133748.0,31176.0,990000.0,Family,Commercial associate,Secondary / secondary special,Married,With parents,0.002506,-9076,-698,-1887.0,-1648,,1,1,0,1,0,1,Laborers,2.0,2,2,SATURDAY,5,0,0,0,0,0,0,Trade: type 2,0.15149713233705572,0.28606755300474473,0.2458512138252296,0.1278,,0.9861,0.8096,0.0,0.0,0.3562,0.1667,0.0417,,0.1042,0.131,0.0,0.0018,0.1239,,0.9866,0.8236,0.0,0.0,0.3793,0.1667,0.0417,,0.1084,0.1317,0.0,0.0,0.128,,0.9866,0.8189,0.0,0.0,0.3793,0.1667,0.0417,,0.1052,0.1302,0.0,0.0,reg oper account,block of flats,0.0994,,No,1.0,0.0,1.0,0.0,-659.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,1.0,2.0,3.0\n268314,0,Cash loans,F,Y,Y,0,135000.0,191880.0,20794.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-16600,-3817,-10240.0,-131,6.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3824001658494501,0.5395866503301898,0.6925590674998008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2938.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n130702,0,Cash loans,F,N,Y,0,202500.0,337761.0,20538.0,256500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.010966,-9227,-970,-3101.0,-1594,,1,1,0,1,0,0,Accountants,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.2732066528072045,0.31703177858344445,0.2412,0.2099,0.9866,0.8164,0.0629,0.28,0.2414,0.3333,0.375,0.0924,,0.2728,,0.0774,0.2458,0.2178,0.9866,0.8236,0.0635,0.282,0.2414,0.3333,0.375,0.0945,,0.2842,,0.0819,0.2436,0.2099,0.9866,0.8189,0.0633,0.28,0.2414,0.3333,0.375,0.094,,0.2777,,0.079,org spec account,block of flats,0.2633,Panel,No,0.0,0.0,0.0,0.0,-409.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n384664,0,Cash loans,F,N,N,0,67500.0,770913.0,29871.0,643500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-22620,365243,-4565.0,-4678,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.5380488451054877,0.5744466170995097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1072.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n428120,0,Cash loans,F,N,N,0,112500.0,331920.0,17077.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15469,-2904,-7711.0,-4964,,1,1,0,1,0,0,Cooking staff,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Kindergarten,,0.5960390787145751,0.2793353208976285,0.2175,0.1112,0.9871,0.8232,0.0488,0.24,0.2069,0.3333,0.375,0.2062,0.1715,0.2062,0.027000000000000003,0.1009,0.2216,0.1154,0.9871,0.8301,0.0493,0.2417,0.2069,0.3333,0.375,0.2109,0.1873,0.2148,0.0272,0.1068,0.2196,0.1112,0.9871,0.8256,0.0491,0.24,0.2069,0.3333,0.375,0.2097,0.1744,0.2099,0.0272,0.10300000000000001,reg oper account,block of flats,0.1841,Panel,No,2.0,1.0,2.0,1.0,-1641.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n150584,1,Cash loans,M,N,Y,0,45000.0,99000.0,8064.0,99000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.007120000000000001,-19230,-1280,-10342.0,-2601,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.7917605066368941,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1098.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n439654,0,Cash loans,M,Y,N,0,135000.0,1078200.0,31522.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.007120000000000001,-20394,-651,-9825.0,-3948,4.0,1,1,1,1,1,0,Drivers,1.0,2,2,SUNDAY,9,0,0,0,0,1,1,Self-employed,,0.6377905940551429,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1968.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n198589,0,Cash loans,M,Y,Y,2,157500.0,1255680.0,41629.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-15427,-3926,-1836.0,-4602,24.0,1,1,0,1,0,0,Laborers,4.0,2,2,SATURDAY,15,0,0,0,0,0,0,Transport: type 4,,0.5387390469012872,0.7826078370261895,0.0732,0.0,0.9896,0.8572,0.0142,0.08,0.069,0.3333,0.375,0.0647,0.0588,0.0826,0.0039,0.0076,0.0746,0.0,0.9896,0.8628,0.0144,0.0806,0.069,0.3333,0.375,0.0662,0.0643,0.0861,0.0039,0.008,0.0739,0.0,0.9896,0.8591,0.0143,0.08,0.069,0.3333,0.375,0.0658,0.0599,0.0841,0.0039,0.0078,reg oper account,block of flats,0.0744,Panel,No,3.0,0.0,3.0,0.0,-2456.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n217998,0,Cash loans,F,Y,N,1,157500.0,1174500.0,31113.0,1174500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-12139,-313,-4487.0,-4515,12.0,1,1,0,1,1,0,Accountants,3.0,2,2,TUESDAY,16,0,0,0,1,1,0,Business Entity Type 3,0.7670878274624972,0.65544161166964,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n267937,0,Cash loans,F,N,Y,0,54000.0,755190.0,32125.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-21366,365243,-984.0,-3936,,1,0,0,1,0,0,,2.0,3,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.2578485882676631,0.4048783643353997,0.1258,0.1447,0.9647,0.5172,0.0182,0.0,0.2414,0.125,0.1667,0.0117,0.1017,0.1439,0.0039,0.005,0.1282,0.1501,0.9647,0.5361,0.0184,0.0,0.2414,0.125,0.1667,0.0119,0.1111,0.15,0.0039,0.0053,0.127,0.1447,0.9647,0.5237,0.0183,0.0,0.2414,0.125,0.1667,0.0119,0.1035,0.1465,0.0039,0.0052,reg oper account,block of flats,0.1243,\"Stone, brick\",No,1.0,1.0,1.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n143043,0,Cash loans,F,N,N,0,319500.0,1493086.5,45400.5,1363500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-18926,-2150,-4538.0,-2401,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Other,,0.6135776057008236,0.7394117535524816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1545.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n165246,0,Cash loans,F,N,Y,0,166500.0,517500.0,26550.0,517500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-19829,-12553,-10664.0,-3315,,1,1,1,1,0,0,Private service staff,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.614634768856793,0.6690566947824041,0.19899999999999998,0.1478,0.9851,0.7959999999999999,0.0448,0.2,0.1724,0.3333,0.375,0.1073,0.1605,0.2053,0.0116,0.0124,0.2027,0.1534,0.9851,0.804,0.0452,0.2014,0.1724,0.3333,0.375,0.1097,0.1754,0.2139,0.0117,0.0132,0.2009,0.1478,0.9851,0.7987,0.0451,0.2,0.1724,0.3333,0.375,0.1091,0.1633,0.209,0.0116,0.0127,reg oper account,block of flats,0.1642,Panel,No,5.0,0.0,5.0,0.0,-2012.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n254774,1,Cash loans,F,N,N,1,202500.0,512338.5,24012.0,423000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.035792000000000004,-14697,-1617,-7320.0,-4311,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,14,0,0,0,0,1,1,Self-employed,0.5393037973479722,0.5044846176572537,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1682.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,1.0\n229821,0,Cash loans,M,Y,Y,2,202500.0,454500.0,23206.5,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.003122,-16481,-1433,-9021.0,-13,2.0,1,1,0,1,0,0,Laborers,4.0,3,3,WEDNESDAY,9,1,1,0,1,1,0,Business Entity Type 3,,0.3960974619302415,0.5971924268337128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,3.0,3.0,2.0,-707.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n179376,0,Cash loans,M,Y,Y,0,261000.0,450000.0,24543.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-11876,-1201,-455.0,-4544,16.0,1,1,0,1,0,1,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.08599542089693014,0.571003858235917,0.4223696523543468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-275.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n446746,0,Revolving loans,M,N,Y,0,270000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.04622,-9368,-1142,-4089.0,-2035,,1,1,0,1,0,0,,1.0,1,1,WEDNESDAY,13,0,1,1,0,0,0,Transport: type 4,,0.7587755501470167,0.44539624156083396,0.1845,0.1182,0.9891,0.8504,0.0444,0.32,0.1379,0.4583,0.5,0.1962,0.1488,0.2148,0.0077,0.0054,0.188,0.1226,0.9891,0.8563,0.0448,0.3222,0.1379,0.4583,0.5,0.2007,0.1625,0.2238,0.0078,0.0058,0.1863,0.1182,0.9891,0.8524,0.0447,0.32,0.1379,0.4583,0.5,0.1996,0.1513,0.2187,0.0078,0.0055,reg oper account,block of flats,0.1944,Panel,No,3.0,0.0,3.0,0.0,-549.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n339630,0,Cash loans,F,Y,Y,0,144000.0,634482.0,20596.5,454500.0,Children,Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-15028,-626,-4527.0,-4554,9.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.4231904446323963,0.6015236838785534,0.6178261467332483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1107.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n385358,0,Cash loans,F,Y,N,2,112500.0,526491.0,18909.0,454500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-21283,365243,-6127.0,-4235,39.0,1,0,0,1,0,0,,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.609249827776616,0.7557400501752248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n323427,0,Cash loans,M,Y,Y,0,180000.0,518562.0,36220.5,463500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.032561,-20392,-1075,-7160.0,-3725,3.0,1,1,0,1,1,0,Security staff,2.0,1,1,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.5953017459858168,0.666481339590848,0.4329616670974407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-525.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n338788,0,Cash loans,F,N,N,0,225000.0,1350000.0,54985.5,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21120,365243,-6521.0,-4305,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,0.8364285487057339,0.6649971491683929,0.7194907850918436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3782.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n424094,0,Cash loans,M,Y,Y,2,225000.0,1269000.0,37102.5,1269000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.026392,-17225,-4277,-483.0,-763,10.0,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.43827475116244974,0.6571877411105737,,0.0247,,0.9945,,,0.04,0.0345,0.3333,,,,0.08199999999999999,,0.0478,0.0252,,0.9945,,,0.0403,0.0345,0.3333,,,,0.0854,,0.0506,0.025,,0.9945,,,0.04,0.0345,0.3333,,,,0.0835,,0.0488,,block of flats,0.0749,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-289.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n252065,0,Cash loans,F,Y,N,0,157500.0,774000.0,50175.0,774000.0,Family,Working,Incomplete higher,Married,House / apartment,0.030755,-8777,-620,-87.0,-774,1.0,1,1,0,1,0,0,,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.31225167436242485,0.4000061913957577,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-716.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n237222,0,Cash loans,F,N,N,1,112500.0,1546020.0,40914.0,1350000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,With parents,0.018801,-13074,-2705,-4311.0,-1232,,1,1,1,1,0,0,Core staff,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.5160719325797006,0.5783796701336551,0.5954562029091491,0.1577,0.2573,0.993,0.898,0.2935,0.0,0.3793,0.2083,0.0417,0.2066,0.1185,0.1238,0.0463,0.2902,0.1607,0.267,0.993,0.902,0.2962,0.0,0.3793,0.2083,0.0417,0.2113,0.1295,0.129,0.0467,0.3073,0.1593,0.2573,0.993,0.8994,0.2954,0.0,0.3793,0.2083,0.0417,0.2102,0.1206,0.126,0.0466,0.2963,reg oper spec account,block of flats,0.1605,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1889.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n443895,0,Cash loans,F,N,Y,0,247500.0,755190.0,31995.0,675000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.072508,-18501,-5409,-10135.0,-2057,,1,1,1,1,1,0,Managers,2.0,1,1,MONDAY,16,0,0,0,0,1,1,Government,,0.3250368363408654,0.4668640059537032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-62.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n209074,0,Cash loans,M,Y,Y,1,180000.0,509400.0,40374.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-17505,-459,-167.0,-1053,5.0,1,1,0,1,0,0,Laborers,3.0,2,2,SUNDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.16067953205202828,0.5562899483572256,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-354.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n199449,0,Cash loans,M,Y,Y,1,112500.0,360000.0,18976.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-9867,-1400,-3809.0,-2206,12.0,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6354889284248603,0.5849900404894085,0.3649,0.3273,0.9811,0.7416,,0.0,0.8621,0.1667,0.0,0.4072,,0.2183,,0.1655,0.3718,0.3396,0.9811,0.7517,,0.0,0.8621,0.1667,0.0,0.4165,,0.2275,,0.1752,0.3685,0.3273,0.9811,0.7451,,0.0,0.8621,0.1667,0.0,0.4143,,0.2223,,0.16899999999999998,,block of flats,0.3012,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n351323,0,Cash loans,M,Y,N,0,450000.0,1546020.0,42642.0,1350000.0,Family,Pensioner,Higher education,Married,House / apartment,0.016612000000000002,-19482,365243,-817.0,-3029,6.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,0.5428793587691326,0.671898251669939,0.7076993447402619,0.0515,,0.997,,,0.16,0.1379,0.2917,,0.0423,,0.1047,,0.1294,0.0525,,0.997,,,0.1611,0.1379,0.2917,,0.0432,,0.1091,,0.13699999999999998,0.052000000000000005,,0.997,,,0.16,0.1379,0.2917,,0.043,,0.1066,,0.1321,,block of flats,0.1452,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n175646,0,Cash loans,F,N,N,0,112500.0,544068.0,19669.5,382500.0,Family,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.010006,-18845,365243,-4380.0,-2312,,1,0,0,1,0,0,,2.0,2,1,MONDAY,14,0,0,0,0,0,0,XNA,0.7312390332885584,0.6008735586661283,0.5971924268337128,0.0289,0.0496,0.9935,0.9048,0.0128,0.0,0.069,0.125,0.0417,0.0351,0.0227,0.0356,0.0039,0.0321,0.0294,0.0514,0.9935,0.9085,0.0129,0.0,0.069,0.125,0.0417,0.0359,0.0248,0.0371,0.0039,0.034,0.0291,0.0496,0.9935,0.9061,0.0129,0.0,0.069,0.125,0.0417,0.0357,0.0231,0.0362,0.0039,0.0328,reg oper spec account,block of flats,0.027999999999999997,Panel,No,3.0,0.0,2.0,0.0,-1449.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n100570,0,Cash loans,F,Y,N,0,180000.0,1395000.0,36927.0,1395000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-13162,-215,-7286.0,-4794,5.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5695462383736847,0.6212263380626669,0.1495,0.0934,0.9876,0.83,0.0314,0.16,0.1379,0.3333,0.375,0.0772,0.121,0.1551,0.0039,0.0006,0.1523,0.0969,0.9876,0.8367,0.0317,0.1611,0.1379,0.3333,0.375,0.0789,0.1322,0.1616,0.0039,0.0007,0.1509,0.0934,0.9876,0.8323,0.0316,0.16,0.1379,0.3333,0.375,0.0785,0.1231,0.1579,0.0039,0.0006,reg oper account,block of flats,0.1393,Panel,No,0.0,0.0,0.0,0.0,-19.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n353508,0,Cash loans,M,Y,Y,1,90000.0,283500.0,20290.5,283500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Rented apartment,0.015221,-12658,-2221,-2068.0,-4073,10.0,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,9,0,0,0,0,1,1,Government,,0.4665017997726721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2045.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n343771,0,Cash loans,M,N,Y,0,90000.0,220500.0,25065.0,220500.0,Family,Working,Higher education,Civil marriage,House / apartment,0.020713,-17378,-379,-4899.0,-934,,1,1,0,1,1,0,Security staff,2.0,3,2,SUNDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.3662231906981334,0.7583870909156669,0.2851799046358216,0.1237,0.1309,0.9762,0.6736,0.016,0.0,0.2759,0.1667,0.2083,0.0753,0.1,0.115,0.0039,0.001,0.1261,0.1358,0.9762,0.6864,0.0161,0.0,0.2759,0.1667,0.2083,0.077,0.1093,0.1198,0.0039,0.001,0.1249,0.1309,0.9762,0.6779999999999999,0.0161,0.0,0.2759,0.1667,0.2083,0.0766,0.1018,0.1171,0.0039,0.001,reg oper account,block of flats,0.0991,Panel,No,8.0,0.0,8.0,0.0,-1332.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n301991,0,Cash loans,F,N,Y,0,225000.0,900000.0,48825.0,900000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.014519999999999996,-12122,-339,-6165.0,-4178,,1,1,0,1,0,0,,1.0,2,2,SUNDAY,8,0,0,0,0,0,0,Police,0.8112185165343161,0.6676760267486729,0.5424451438613613,0.1474,0.1032,0.9861,0.8096,0.0,0.16,0.1379,0.3333,0.375,0.0742,0.1202,0.1512,0.0,0.0,0.1502,0.1071,0.9861,0.8171,0.0,0.1611,0.1379,0.3333,0.375,0.0759,0.1313,0.1575,0.0,0.0,0.1489,0.1032,0.9861,0.8121,0.0,0.16,0.1379,0.3333,0.375,0.0755,0.1223,0.1539,0.0,0.0,reg oper account,block of flats,0.1189,Panel,No,0.0,0.0,0.0,0.0,-739.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n174642,0,Cash loans,F,N,Y,0,90000.0,679266.0,22041.0,567000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-16547,-832,-3972.0,-35,,1,1,0,1,0,0,Sales staff,1.0,2,2,THURSDAY,7,0,0,0,0,0,0,Government,,0.07987674975578653,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1477.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n405705,1,Revolving loans,F,Y,Y,0,180000.0,427500.0,21375.0,427500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.006852,-20004,-1843,-9737.0,-3383,28.0,1,1,0,1,0,0,Core staff,2.0,3,3,THURSDAY,12,0,0,0,0,0,0,University,0.5525140270854203,0.5993569703383165,0.2225807646753351,0.1082,0.0415,0.9866,0.8164,0.1702,0.12,0.1034,0.3333,0.0,0.0877,0.0866,0.07200000000000001,0.0077,0.0328,0.1103,0.043,0.9866,0.8236,0.1717,0.1208,0.1034,0.3333,0.0,0.0897,0.0946,0.075,0.0078,0.0347,0.1093,0.0415,0.9866,0.8189,0.1712,0.12,0.1034,0.3333,0.0,0.0892,0.0881,0.0733,0.0078,0.0335,reg oper spec account,block of flats,0.09300000000000001,Panel,No,2.0,1.0,2.0,1.0,-2542.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n196938,0,Cash loans,F,N,Y,2,202500.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-13420,-1617,-2251.0,-3412,,1,1,0,1,0,0,High skill tech staff,4.0,2,2,MONDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.1823924466828969,0.6251711402033557,0.34741822720026416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-772.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n327347,0,Cash loans,F,N,Y,0,202500.0,1546020.0,45333.0,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-22603,365243,-4613.0,-4613,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.629990723757851,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-360.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n320110,0,Cash loans,F,Y,Y,0,382500.0,1467612.0,58203.0,1350000.0,Family,State servant,Higher education,Married,House / apartment,0.002506,-19680,-3269,-6971.0,-3092,14.0,1,1,1,1,1,0,Accountants,2.0,2,2,THURSDAY,4,0,0,0,0,0,0,Police,0.8300894910568514,0.7474783166089,0.524496446363472,0.1072,,0.9836,0.7756,0.0,0.0,0.2759,0.1667,0.0417,,0.0874,0.1124,0.0,0.0,0.1092,,0.9836,0.7844,0.0,0.0,0.2759,0.1667,0.0417,,0.0955,0.1171,0.0,0.0,0.1083,,0.9836,0.7786,0.0,0.0,0.2759,0.1667,0.0417,,0.0889,0.1144,0.0,0.0,reg oper account,block of flats,0.0884,,No,0.0,0.0,0.0,0.0,-1021.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n270012,0,Cash loans,F,N,Y,0,202500.0,1122714.0,47695.5,1003500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018634,-17235,-1249,-3334.0,-777,,1,1,0,1,0,1,Sales staff,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Self-employed,0.5373375768749828,0.7568330197523747,0.5867400085415683,0.0247,,0.9737,,,0.0,0.1034,0.125,,0.0262,,0.0333,,0.0731,0.0252,,0.9737,,,0.0,0.1034,0.125,,0.0268,,0.0347,,0.0774,0.025,,0.9737,,,0.0,0.1034,0.125,,0.0266,,0.0339,,0.0747,,block of flats,0.0421,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n256096,0,Cash loans,F,N,N,0,112500.0,675000.0,29731.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008625,-22460,365243,-8090.0,-4380,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,XNA,,0.5963549508574566,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-672.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n132698,0,Cash loans,M,Y,N,2,360000.0,1157670.0,110047.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-11081,-2087,-4732.0,-3212,9.0,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,,0.5369996773322617,0.6041125998015721,0.0371,0.0591,0.9617,0.4764,0.0,0.0,0.1034,0.125,0.1667,0.0565,0.0303,0.0409,0.0,0.0,0.0378,0.0613,0.9618,0.4969,0.0,0.0,0.1034,0.125,0.1667,0.0578,0.0331,0.0426,0.0,0.0,0.0375,0.0591,0.9617,0.4834,0.0,0.0,0.1034,0.125,0.1667,0.0575,0.0308,0.0416,0.0,0.0,reg oper account,block of flats,0.0329,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-727.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n377073,0,Revolving loans,M,Y,Y,0,112500.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.020246,-19893,-3079,-8006.0,-3449,11.0,1,1,0,1,1,0,High skill tech staff,1.0,3,3,TUESDAY,11,0,0,0,0,0,0,Transport: type 4,,0.5183787694129388,0.6894791426446275,0.1649,0.1056,0.9861,,,0.16,0.1379,0.375,,0.0812,,0.1663,,0.0607,0.1681,0.1096,0.9861,,,0.1611,0.1379,0.375,,0.083,,0.1732,,0.0642,0.1665,0.1056,0.9861,,,0.16,0.1379,0.375,,0.0826,,0.1693,,0.062,,block of flats,0.1308,Panel,No,2.0,0.0,2.0,0.0,-1109.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n329794,0,Cash loans,F,Y,Y,0,270000.0,966645.0,38466.0,864000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.003813,-10559,-2259,-5140.0,-3238,64.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Construction,0.3679565338523425,0.5838091588946659,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2202.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n267674,0,Cash loans,M,Y,N,2,202500.0,859500.0,31981.5,859500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.026392,-14084,-1830,-2368.0,-4844,1.0,1,1,0,1,1,0,Drivers,4.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Government,,0.3262277391452403,0.2314393514998941,0.099,,0.997,,,0.08,0.0345,0.625,,,,0.0667,,0.2311,0.1008,,0.997,,,0.0806,0.0345,0.625,,,,0.0695,,0.2446,0.0999,,0.997,,,0.08,0.0345,0.625,,,,0.0679,,0.2359,,block of flats,0.1027,Panel,No,0.0,0.0,0.0,0.0,-55.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,4.0,2.0,2.0\n221864,0,Cash loans,F,N,Y,0,112500.0,508495.5,24592.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.00702,-15564,-2021,-1652.0,-4336,,1,1,1,1,0,0,,2.0,2,2,FRIDAY,8,1,1,0,1,1,0,Business Entity Type 3,,0.3685103189734264,0.13510601574017175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-175.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n211871,0,Revolving loans,M,Y,Y,2,112500.0,270000.0,13500.0,270000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-12375,-1463,-4509.0,-4555,29.0,1,1,0,1,0,0,Laborers,4.0,2,2,SUNDAY,15,0,0,0,0,0,0,Military,,0.3542247319929012,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-588.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n128816,0,Cash loans,F,Y,Y,4,135000.0,679500.0,22455.0,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-15015,-1078,-3615.0,-4744,10.0,1,1,1,1,0,0,Laborers,6.0,2,2,SUNDAY,11,0,0,0,0,1,1,Trade: type 7,,0.4243245979972841,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-899.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n141054,0,Cash loans,F,N,Y,0,81000.0,187929.0,15066.0,171000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-18377,-769,-3815.0,-1901,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,School,,0.4010773097712557,0.3672910183026313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-290.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n248937,0,Revolving loans,F,N,N,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.019101,-8364,-758,-3163.0,-1039,,1,1,0,1,0,0,,1.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.6482874226643085,0.6604465875912057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-288.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n291744,0,Cash loans,M,Y,N,0,337500.0,2250000.0,83385.0,2250000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.04622,-9964,-1387,-4669.0,-2616,3.0,1,1,0,1,1,0,IT staff,1.0,1,1,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3095171337912919,0.6279698966846026,0.4794489811780563,0.1649,0.1085,0.9871,,,0.16,0.1379,0.375,,0.0193,,0.1708,,0.0,0.1681,0.1126,0.9871,,,0.1611,0.1379,0.375,,0.0197,,0.17800000000000002,,0.0,0.1665,0.1085,0.9871,,,0.16,0.1379,0.375,,0.0196,,0.1739,,0.0,,block of flats,0.1542,Panel,No,0.0,0.0,0.0,0.0,-179.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n287071,0,Cash loans,F,N,Y,0,90000.0,143910.0,14148.0,135000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.019101,-24855,365243,-4716.0,-4725,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.5533733358596248,0.7969726818373722,,0.0348,0.9856,,,,0.069,0.3333,,,,0.0179,,,,0.0361,0.9856,,,,0.069,0.3333,,,,0.0187,,,,0.0348,0.9856,,,,0.069,0.3333,,,,0.0183,,,,block of flats,0.0244,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1467.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n272393,0,Cash loans,F,N,Y,0,137880.0,697500.0,20394.0,697500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-18458,-10432,-5997.0,-1981,,1,1,1,1,1,0,Core staff,2.0,3,3,MONDAY,12,0,0,0,0,0,0,Kindergarten,,0.350611553571315,0.746300213050371,0.033,0.0319,0.9776,0.6940000000000001,0.0028,0.0,0.069,0.125,0.1667,0.0069,0.0269,0.0253,0.0,0.0,0.0336,0.0331,0.9777,0.706,0.0028,0.0,0.069,0.125,0.1667,0.0071,0.0294,0.0264,0.0,0.0,0.0333,0.0319,0.9776,0.6981,0.0028,0.0,0.069,0.125,0.1667,0.006999999999999999,0.0274,0.0258,0.0,0.0,reg oper account,block of flats,0.0214,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-381.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n307622,0,Cash loans,M,Y,Y,0,112500.0,194076.0,8352.0,162000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.020246,-23883,365243,-134.0,-2611,3.0,1,0,0,1,0,0,,1.0,3,3,THURSDAY,11,0,0,0,0,0,0,XNA,,0.4207763579850186,0.4223696523543468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-733.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n256769,0,Cash loans,M,Y,Y,0,202500.0,906228.0,35937.0,810000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-20027,-7041,-10097.0,-3476,3.0,1,1,1,1,0,0,,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,Industry: type 9,,0.3356154315031817,0.4920600938649263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1759.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n367443,0,Cash loans,F,Y,Y,0,171000.0,647046.0,21001.5,463500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17901,-1167,-3849.0,-1459,64.0,1,1,0,1,0,0,Medicine staff,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Medicine,,0.5086660451303804,0.7981372313187245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1510.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n184719,0,Cash loans,F,Y,Y,1,157500.0,364896.0,18760.5,315000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.009657,-12631,-4508,-128.0,-3123,7.0,1,1,0,1,0,0,Core staff,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.32472940191934874,0.6038224465770997,0.3506958432829587,0.2399,0.1597,0.9940000000000001,0.9184,0.10400000000000001,0.2264,0.2297,0.3471,0.3333,0.1286,0.1956,0.2463,0.0,0.0,0.1964,0.1507,0.9871,0.8301,0.0601,0.1611,0.2414,0.3333,0.2083,0.1017,0.1717,0.2096,0.0,0.0,0.2415,0.1667,0.997,0.9597,0.0823,0.24,0.2414,0.3333,0.375,0.1186,0.1984,0.2634,0.0,0.0,reg oper account,block of flats,0.1966,Panel,No,1.0,0.0,1.0,0.0,-2410.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n100226,0,Revolving loans,F,N,N,2,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-13584,-680,-350.0,-2097,,1,1,0,1,0,0,Sales staff,4.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,0.394163265516274,0.6365722494978974,,0.1148,0.084,0.9945,0.9252,0.0034,0.0932,0.1262,0.3054,0.3471,0.0393,0.0936,0.1036,0.0,0.0,0.063,0.0955,0.9975,0.9673,0.0,0.0,0.1379,0.375,0.4167,0.0151,0.0551,0.0637,0.0,0.0,0.1218,0.0921,0.9975,0.9665,0.0,0.12,0.1379,0.375,0.4167,0.0206,0.1,0.1027,0.0,0.0,reg oper account,block of flats,0.0544,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n415264,0,Cash loans,F,N,Y,0,135000.0,800500.5,34047.0,715500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.009657,-19599,-4812,-3751.0,-3144,,1,1,0,1,0,1,Laborers,1.0,2,2,THURSDAY,10,0,1,1,0,1,1,Construction,,0.6392795205326256,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1086.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n129661,0,Cash loans,F,N,N,0,90000.0,810000.0,23683.5,810000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-19007,-740,-423.0,-2144,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,Industry: type 3,,0.7007369389222671,0.4489622731076524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1659.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n129774,1,Cash loans,F,N,N,0,81000.0,448056.0,21919.5,315000.0,Unaccompanied,Working,Higher education,Married,With parents,0.020246,-9365,-264,-3082.0,-42,,1,1,0,1,0,0,Core staff,2.0,3,3,THURSDAY,14,0,0,0,0,0,0,Kindergarten,0.5334215107750436,0.5865322921066672,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-648.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n126957,0,Cash loans,F,N,N,0,135000.0,625536.0,22599.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-20072,-2592,-7991.0,-2891,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Industry: type 3,,0.2533716311332267,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1403.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n104590,0,Revolving loans,F,N,Y,0,76500.0,225000.0,11250.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.072508,-20657,365243,-9450.0,-3887,,1,0,0,1,0,0,,2.0,1,1,FRIDAY,13,0,0,0,0,0,0,XNA,,0.7202613996620082,0.8224987619370829,0.1412,0.1001,0.9806,0.7348,0.0,0.1864,0.1434,0.4792,0.5208,0.0374,0.1152,0.1313,0.0,0.0085,0.1134,0.0974,0.9782,0.7125,0.0,0.1611,0.1379,0.3333,0.375,0.0554,0.0992,0.1068,0.0,0.0129,0.1286,0.0939,0.9806,0.7383,0.0,0.16,0.1379,0.4792,0.5208,0.0409,0.1056,0.1139,0.0,0.0124,reg oper account,block of flats,0.0832,Panel,No,0.0,0.0,0.0,0.0,-220.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n428171,0,Cash loans,M,Y,Y,0,270000.0,685386.0,50004.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-10492,-2822,-2846.0,-3022,4.0,1,1,0,1,0,0,Drivers,2.0,3,3,MONDAY,13,0,0,0,0,0,0,Self-employed,0.25433321940531706,0.4810080960122193,0.6363761710860439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1721.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n291620,0,Cash loans,F,N,Y,1,43969.5,202500.0,14400.0,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008068,-20797,365243,-12457.0,-3931,,1,0,0,1,0,0,,3.0,3,3,MONDAY,11,0,0,0,0,0,0,XNA,,0.5046267451417716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n276727,0,Cash loans,M,N,Y,0,112500.0,792477.0,25695.0,661500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.002042,-17992,-4682,-9082.0,-1531,,1,1,0,1,0,0,,2.0,3,3,MONDAY,14,0,0,0,0,0,0,Self-employed,,0.511409313782359,0.7751552674785404,0.1031,0.0836,0.9791,,,0.0,0.2069,0.1667,,0.0,,0.0609,,0.10400000000000001,0.105,0.0868,0.9791,,,0.0,0.2069,0.1667,,0.0,,0.0634,,0.1101,0.1041,0.0836,0.9791,,,0.0,0.2069,0.1667,,0.0,,0.062,,0.1062,,block of flats,0.0705,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-537.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n300077,0,Cash loans,F,Y,Y,0,202500.0,755190.0,30078.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-15419,-6399,-3974.0,-4409,16.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.564204041831652,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-989.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,8.0,1.0,3.0\n244297,0,Cash loans,F,N,Y,0,157500.0,1125000.0,47664.0,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.028663,-22334,365243,-8314.0,-4814,,1,0,0,1,0,0,,1.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.47311976473379,0.22383131747015547,0.1784,0.1036,0.9881,,,0.2,0.1724,0.3333,,,,0.1835,,0.0303,0.1817,0.1075,0.9881,,,0.2014,0.1724,0.3333,,,,0.1912,,0.0321,0.1801,0.1036,0.9881,,,0.2,0.1724,0.3333,,,,0.1868,,0.0309,,block of flats,0.1685,Panel,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n383162,0,Cash loans,F,Y,Y,2,90000.0,225000.0,14377.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010032,-10373,-87,-874.0,-2452,8.0,1,1,1,1,1,0,Managers,4.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.4235076621709632,0.28001048237888626,,,,0.9762,,,0.0,0.1034,0.1667,,,,0.0336,,,,,0.9762,,,0.0,0.1034,0.1667,,,,0.035,,,,,0.9762,,,0.0,0.1034,0.1667,,,,0.0342,,,,block of flats,0.0396,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-834.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n251801,0,Cash loans,M,Y,Y,0,166500.0,700830.0,27283.5,585000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.006305,-8454,-1003,-452.0,-1128,14.0,1,1,0,1,0,0,,1.0,3,3,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.1292159579410286,0.30788339549426275,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-409.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n328527,0,Cash loans,M,Y,N,0,180000.0,1125000.0,40410.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.01885,-13318,-1876,-5179.0,-3399,9.0,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,10,1,1,0,1,1,0,Agriculture,0.4665973030789791,0.6970139557924727,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-707.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n187830,0,Cash loans,F,N,Y,0,135000.0,296505.0,23904.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.022625,-11528,-201,-1299.0,-73,,1,1,0,1,1,1,Private service staff,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.35325911376286384,0.4288498528973769,,0.2918,0.13,0.9871,0.8232,0.0626,0.12,0.1034,0.3333,0.375,0.1091,0.237,0.2683,0.0039,0.0051,0.2973,0.1349,0.9871,0.8301,0.0632,0.1208,0.1034,0.3333,0.375,0.1116,0.259,0.2795,0.0039,0.0055,0.2946,0.13,0.9871,0.8256,0.063,0.12,0.1034,0.3333,0.375,0.111,0.2411,0.2731,0.0039,0.0053,reg oper account,block of flats,0.2121,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-908.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,1.0,0.0,0.0,0.0,0.0\n396391,0,Cash loans,F,N,Y,0,135000.0,1235691.0,36261.0,967500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00733,-17643,-2576,-5821.0,-1193,,1,1,0,1,0,0,Cooking staff,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,Self-employed,0.7209236349850796,0.7027279166193316,0.7713615919194317,0.1464,0.001,0.9831,,,0.16,0.1379,0.3333,,0.0,,0.1561,,0.0,0.1492,0.001,0.9831,,,0.1611,0.1379,0.3333,,0.0,,0.1626,,0.0,0.1478,0.001,0.9831,,,0.16,0.1379,0.3333,,0.0,,0.1589,,0.0,,block of flats,0.1383,Panel,No,0.0,0.0,0.0,0.0,-1566.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n230334,1,Cash loans,F,N,Y,0,270000.0,518562.0,24030.0,463500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.008575,-16951,-669,-1151.0,-476,,1,1,0,1,0,0,Managers,1.0,2,2,MONDAY,11,1,1,0,1,1,0,Trade: type 3,0.5222460132837252,0.5294452738842772,,0.0124,0.0041,0.9518,,,0.0,0.069,0.0417,,0.3332,,0.0063,,0.0,0.0126,0.0043,0.9518,,,0.0,0.069,0.0417,,0.3408,,0.0065,,0.0,0.0125,0.0041,0.9518,,,0.0,0.069,0.0417,,0.33899999999999997,,0.0064,,0.0,,block of flats,0.0049,Wooden,Yes,0.0,0.0,0.0,0.0,-29.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n410547,0,Cash loans,F,N,N,0,360000.0,738567.0,23953.5,616500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-12094,-695,-5963.0,-4448,,1,1,0,1,0,0,Sales staff,1.0,3,3,MONDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.4335429318319601,0.4795015870266161,0.20915469884100693,0.1361,0.1196,0.9846,0.7892,0.0259,0.0,0.3448,0.1667,0.2083,0.1334,0.111,0.1358,0.0,0.0,0.1387,0.1241,0.9846,0.7975,0.0262,0.0,0.3448,0.1667,0.2083,0.1364,0.1212,0.1415,0.0,0.0,0.1374,0.1196,0.9846,0.792,0.0261,0.0,0.3448,0.1667,0.2083,0.1357,0.1129,0.1383,0.0,0.0,reg oper spec account,block of flats,0.1068,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-574.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n393748,0,Cash loans,F,Y,Y,1,94500.0,191880.0,19107.0,180000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008575,-10493,-3596,-2760.0,-2817,13.0,1,1,1,1,1,0,Laborers,3.0,2,2,SUNDAY,12,0,0,0,1,1,1,Industry: type 1,,0.552265129329306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1445.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n160096,0,Cash loans,F,N,Y,0,90000.0,824823.0,24246.0,688500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00733,-17368,-10618,-6694.0,-912,,1,1,0,1,0,0,Secretaries,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Security Ministries,,0.5333767741865516,0.4668640059537032,0.0711,0.0661,0.9816,0.7484,0.0293,0.0,0.1379,0.1667,0.2083,0.0895,0.057999999999999996,0.0717,0.0,0.0,0.0725,0.0686,0.9816,0.7583,0.0295,0.0,0.1379,0.1667,0.2083,0.0916,0.0634,0.0747,0.0,0.0,0.0718,0.0661,0.9816,0.7518,0.0294,0.0,0.1379,0.1667,0.2083,0.0911,0.059000000000000004,0.073,0.0,0.0,reg oper account,block of flats,0.0724,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2006.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n365764,0,Cash loans,M,Y,Y,0,180000.0,365175.0,28980.0,301500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-13920,-713,-1871.0,-349,3.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Self-employed,0.2100469273290036,0.216137120757786,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-2068.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n214102,0,Cash loans,F,N,Y,0,360000.0,1293552.0,72360.0,1170000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-10216,-2808,-1072.0,-1318,,1,1,0,1,0,1,Accountants,2.0,1,1,THURSDAY,10,0,0,0,0,0,0,Trade: type 7,0.8048208927558976,0.5241958506061452,,0.1945,0.1478,0.9806,0.7348,0.0477,0.08,0.1838,0.2775,0.2638,0.0457,0.2,0.1871,0.0135,0.0804,0.0945,0.10400000000000001,0.9786,0.7190000000000001,0.0149,0.0,0.2069,0.1667,0.2083,0.0,0.0826,0.0884,0.0,0.0,0.0937,0.1167,0.9786,0.7115,0.015,0.0,0.2069,0.1667,0.2083,0.0514,0.2035,0.0878,0.0136,0.005,reg oper account,block of flats,0.3747,Panel,No,1.0,1.0,1.0,1.0,-1067.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n209201,0,Revolving loans,F,N,N,0,76500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.0228,-8073,-531,-2904.0,-762,,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,15,0,0,0,1,1,0,Trade: type 3,0.253993061421746,0.2850325480093777,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-490.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n431474,0,Cash loans,M,N,N,0,360000.0,2517300.0,66402.0,2250000.0,Group of people,Commercial associate,Higher education,Married,House / apartment,0.072508,-15546,-420,-599.0,-1883,,1,1,1,1,1,0,Laborers,2.0,1,1,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.5743360877405782,0.6674722078867837,0.22888341670067305,0.1706,0.1705,0.9767,0.6804,0.0,0.0,0.2931,0.1667,0.2083,0.0,0.1391,0.1437,0.0,0.0107,0.1628,0.1665,0.9767,0.6929,0.0,0.0,0.2759,0.1667,0.2083,0.0,0.1423,0.1419,0.0,0.0104,0.1723,0.1705,0.9767,0.6847,0.0,0.0,0.2931,0.1667,0.2083,0.0,0.1415,0.1463,0.0,0.011000000000000001,reg oper account,block of flats,0.1097,Panel,No,1.0,0.0,1.0,0.0,-480.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n105753,0,Cash loans,F,Y,N,2,135000.0,244584.0,9351.0,193500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010032,-10914,-1669,-5090.0,-266,17.0,1,1,0,1,0,0,,4.0,2,2,TUESDAY,10,0,0,0,0,0,0,Industry: type 12,0.3034261836738285,0.2113159567618281,0.15855489979486306,0.0082,0.0,0.9702,0.5920000000000001,0.0015,0.0,0.0345,0.0417,0.0417,0.0,0.0067,0.0083,0.0,0.0,0.0084,0.0,0.9702,0.608,0.0015,0.0,0.0345,0.0417,0.0417,0.0,0.0073,0.0086,0.0,0.0,0.0083,0.0,0.9702,0.5975,0.0015,0.0,0.0345,0.0417,0.0417,0.0,0.0068,0.0084,0.0,0.0,reg oper account,block of flats,0.0073,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n399514,0,Cash loans,M,Y,Y,0,225000.0,2250000.0,59355.0,2250000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.008575,-23544,-5794,-778.0,-4725,4.0,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Government,0.7725603351165521,0.6574488028805213,0.7776594425716818,0.5536,,0.9871,,,0.6,0.5172,0.3333,,0.4323,,0.64,,,0.5273,,0.9871,,,0.5639,0.4828,0.3333,,0.4405,,0.6222,,,0.5589999999999999,,0.9871,,,0.6,0.5172,0.3333,,0.4398,,0.6515,,,,block of flats,0.4697,Panel,No,0.0,0.0,0.0,0.0,-2967.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n102117,0,Cash loans,M,Y,N,0,315000.0,1113840.0,57001.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.035792000000000004,-15722,-1073,-1022.0,-4219,65.0,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,11,0,0,0,1,1,0,Services,,0.5836656927472312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-731.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n398575,0,Cash loans,F,Y,N,1,49500.0,309420.0,14553.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14433,-2260,-3593.0,-4542,8.0,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Trade: type 7,0.425658621443294,0.35728197361233577,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3058.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n209059,0,Cash loans,F,N,Y,0,81000.0,177489.0,19242.0,166500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.002042,-18085,-1350,-7234.0,-1642,,1,1,1,1,1,0,Sales staff,2.0,3,3,MONDAY,14,0,0,0,0,0,0,Trade: type 7,,0.13730018140517733,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,5.0,0.0,-356.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n270288,0,Cash loans,F,N,Y,0,112500.0,225000.0,18171.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011703,-16330,-2394,-4459.0,-2654,,1,1,1,1,0,0,Sales staff,2.0,2,2,THURSDAY,11,0,0,0,0,1,1,Self-employed,,0.7301398863064695,0.30620229831350426,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-387.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n376621,1,Cash loans,M,N,Y,0,94500.0,717003.0,21091.5,598500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.031329,-23137,365243,-10158.0,-4191,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.6374318458087861,0.40314167665875134,0.0825,0.0909,0.9861,0.8096,,0.0,0.2069,0.1667,0.2083,0.0783,0.0656,0.0519,0.0077,0.0041,0.084,0.0943,0.9861,0.8171,,0.0,0.2069,0.1667,0.2083,0.0801,0.0716,0.0541,0.0078,0.0044,0.0833,0.0909,0.9861,0.8121,,0.0,0.2069,0.1667,0.2083,0.0796,0.0667,0.0529,0.0078,0.0042,reg oper account,,0.0613,Panel,No,5.0,0.0,5.0,0.0,-1663.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n160277,0,Cash loans,M,Y,N,0,135000.0,1288350.0,37053.0,1125000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.022625,-11479,-2707,-3792.0,-3555,4.0,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Government,,0.2533137204767347,0.5585066276769286,0.0732,,0.9831,,,,0.0345,0.3333,,,,0.0374,,,0.0746,,0.9831,,,,0.0345,0.3333,,,,0.0389,,,0.0739,,0.9831,,,,0.0345,0.3333,,,,0.038,,,,block of flats,0.0502,,No,0.0,0.0,0.0,0.0,-526.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n182743,0,Cash loans,F,N,Y,0,180000.0,194076.0,13635.0,162000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-23583,365243,-1430.0,-4917,,1,0,0,1,0,0,,2.0,1,1,SATURDAY,11,0,0,0,0,0,0,XNA,,0.7512077328263553,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-405.0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n132662,0,Cash loans,F,N,Y,1,202500.0,1162170.0,37620.0,832500.0,Family,Working,Higher education,Married,House / apartment,0.008625,-14251,-2934,-2430.0,-3113,,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Industry: type 7,0.390826765286057,0.48071877498242,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-911.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n158732,0,Cash loans,F,N,Y,0,112500.0,157500.0,15237.0,157500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-10331,-1092,-7362.0,-355,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6264820334733799,,0.0887,0.1117,0.9896,,,0.0,0.2069,0.1667,,0.0634,,0.0753,,0.0252,0.0903,0.1159,0.9896,,,0.0,0.2069,0.1667,,0.0649,,0.0784,,0.0266,0.0895,0.1117,0.9896,,,0.0,0.2069,0.1667,,0.0645,,0.0766,,0.0257,,block of flats,0.0647,\"Stone, brick\",No,8.0,0.0,8.0,0.0,-833.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n174916,0,Cash loans,M,Y,Y,2,450000.0,1035000.0,30393.0,1035000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-14652,-998,-1441.0,-4431,6.0,1,1,0,1,1,0,Sales staff,4.0,2,2,THURSDAY,11,0,0,0,0,0,0,Trade: type 7,0.6834853088577217,0.7109069511376164,0.4206109640437848,0.1196,0.0602,0.9975,,,0.12,0.1034,0.375,,0.038,,0.1648,,0.019,0.1218,0.0624,0.9975,,,0.1208,0.1034,0.375,,0.0388,,0.1717,,0.0201,0.1207,0.0602,0.9975,,,0.12,0.1034,0.375,,0.0386,,0.1677,,0.0194,,block of flats,0.1734,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1370.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n215468,0,Cash loans,M,N,Y,1,157500.0,101880.0,10809.0,90000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-10879,-944,-9654.0,-3492,,1,1,1,1,1,0,Sales staff,3.0,2,2,WEDNESDAY,14,0,0,0,1,1,0,Self-employed,0.5207438688740765,0.5644137237414711,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-407.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n266584,0,Cash loans,F,N,Y,0,180000.0,2695500.0,71235.0,2250000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.072508,-21734,-11696,-10224.0,-5208,,1,1,0,1,1,0,,2.0,1,1,TUESDAY,16,0,0,0,0,0,0,Other,0.9217928793392026,0.7502747451047523,0.7295666907060153,0.27899999999999997,0.0943,0.9856,0.8028,0.0,0.32,0.1379,0.6667,0.7083,0.0,0.2274,0.295,0.0,0.0,0.2847,0.0978,0.9856,0.8105,0.0,0.3222,0.1379,0.6667,0.7083,0.0,0.2479,0.3064,0.0,0.0,0.2821,0.0943,0.9856,0.8054,0.0,0.32,0.1379,0.6667,0.7083,0.0,0.2313,0.3002,0.0,0.0,org spec account,block of flats,0.2313,Panel,No,2.0,0.0,2.0,0.0,-150.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n276499,0,Cash loans,F,N,Y,2,90000.0,942300.0,30528.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-18423,-610,-10095.0,-1954,,1,1,0,1,0,0,Security staff,4.0,2,2,WEDNESDAY,15,0,1,1,0,0,0,Security,0.6886522428157615,0.301587152142322,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n448202,0,Cash loans,F,Y,Y,0,90000.0,592560.0,31153.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014464,-11657,-3093,-2342.0,-4184,21.0,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,6,0,0,0,0,0,0,Self-employed,0.36312458299472455,0.7069632546784417,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-369.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n225821,0,Cash loans,F,N,Y,0,90000.0,328405.5,15930.0,283500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22285,365243,-2642.0,-4613,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.5372878237917023,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.0,0.0,11.0,0.0,-970.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n217333,0,Cash loans,F,Y,Y,0,293850.0,454500.0,16452.0,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.0228,-10040,-2214,-4299.0,-2722,10.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Other,0.4070996105853108,0.594764255417682,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1508.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n304706,0,Cash loans,F,N,N,2,157500.0,785250.0,20844.0,562500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018801,-10848,-94,-7118.0,-3508,,1,1,0,1,1,0,,4.0,2,2,TUESDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.4494133840177863,0.6236633942205788,0.42765737003502935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.0,1.0,12.0,0.0,-1152.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n158393,0,Cash loans,M,Y,Y,0,112500.0,675000.0,34465.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-18452,365243,-4845.0,-2003,8.0,1,0,0,1,1,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.5876614069246879,0.11761373170805695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-480.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n390413,0,Cash loans,M,N,Y,1,202500.0,521280.0,35392.5,450000.0,\"Spouse, partner\",Working,Lower secondary,Married,Rented apartment,0.018801,-11708,-3697,-4209.0,-3743,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 1,,0.6736794348071765,0.3139166772114369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,4.0\n382454,0,Cash loans,M,Y,Y,0,270000.0,688500.0,29299.5,688500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.003540999999999999,-16686,-2031,-1477.0,-54,24.0,1,1,0,1,0,0,Laborers,1.0,1,1,TUESDAY,11,0,0,0,0,0,0,Housing,0.3268457392277857,0.4557540653392477,,0.1093,0.0752,0.9841,0.7824,0.0393,0.12,0.1034,0.3333,,0.0138,0.0866,0.1065,0.0116,0.0139,0.1113,0.0781,0.9841,0.7909,0.0396,0.1208,0.1034,0.3333,,0.0141,0.0946,0.111,0.0117,0.0147,0.1103,0.0752,0.9841,0.7853,0.0395,0.12,0.1034,0.3333,,0.013999999999999999,0.0881,0.1084,0.0116,0.0142,reg oper account,block of flats,0.1083,Panel,No,11.0,0.0,10.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n347049,0,Cash loans,F,N,N,0,81000.0,454500.0,13288.5,454500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.072508,-22464,365243,-12317.0,-4279,,1,0,0,1,1,0,,2.0,1,1,FRIDAY,17,0,0,0,0,0,0,XNA,0.8073727839336726,0.6917748858203865,0.6545292802242897,0.201,0.1,0.9826,0.762,0.0,0.24,0.1034,0.625,0.6667,0.139,0.1605,0.2412,0.0154,0.0026,0.2048,0.1038,0.9826,0.7713,0.0,0.2417,0.1034,0.625,0.6667,0.1422,0.1754,0.2513,0.0156,0.0027,0.203,0.1,0.9826,0.7652,0.0,0.24,0.1034,0.625,0.6667,0.1414,0.1633,0.2455,0.0155,0.0026,reg oper account,block of flats,0.1903,Panel,No,0.0,0.0,0.0,0.0,-1451.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n308751,0,Cash loans,F,Y,Y,0,225000.0,1129500.0,31059.0,1129500.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.04622,-16119,-400,-8232.0,-2047,14.0,1,1,1,1,0,0,,2.0,1,1,FRIDAY,13,0,1,1,0,1,1,Other,0.7089576258749831,0.4270671716680711,0.6848276586890367,0.1113,0.0631,0.9871,0.8232,0.0156,0.12,0.1034,0.3333,0.375,,0.0908,0.114,0.0,0.0,0.1134,0.0654,0.9871,0.8301,0.0157,0.1208,0.1034,0.3333,0.375,,0.0992,0.1187,0.0,0.0,0.1124,0.0631,0.9871,0.8256,0.0157,0.12,0.1034,0.3333,0.375,,0.0923,0.11599999999999999,0.0,0.0,reg oper account,block of flats,0.0896,Panel,No,0.0,0.0,0.0,0.0,-7.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n213139,1,Cash loans,F,N,N,0,202500.0,346500.0,18796.5,346500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-10793,-1266,-2019.0,-1282,,1,1,1,1,0,0,Cleaning staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Industry: type 8,0.2633524766671139,0.2829826199416448,,0.0165,,,0.7008,,,0.0345,0.125,,,,0.0076,,,0.0168,,,0.7125,,,0.0345,0.125,,,,0.008,,,0.0167,,,0.7048,,,0.0345,0.125,,,,0.0078,,,reg oper spec account,block of flats,0.012,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-287.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n192449,0,Cash loans,M,Y,N,0,270000.0,1024740.0,52452.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17537,-1419,-2144.0,-1078,12.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.4889287213718935,0.5280830740298991,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n381508,0,Cash loans,F,N,Y,0,112500.0,808650.0,23305.5,675000.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.019101,-21879,365243,-4471.0,-4979,,1,0,0,1,1,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,0.8067189403724341,0.6019638880948592,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n361257,0,Cash loans,M,Y,Y,1,202500.0,1351111.5,44784.0,1210500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.030755,-13821,-393,-4561.0,-4623,6.0,1,1,0,1,0,0,Drivers,3.0,2,2,SATURDAY,11,0,0,0,0,1,1,Emergency,,0.6468584398594643,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-287.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n119374,1,Cash loans,F,Y,Y,0,292500.0,586633.5,30082.5,445500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.010032,-16018,-9115,-8866.0,-3920,20.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Construction,,0.03079047069595092,,0.0536,0.0734,0.9841,,,0.0,0.0345,0.0833,,0.0127,,0.025,,0.0091,0.0546,0.0761,0.9841,,,0.0,0.0345,0.0833,,0.013000000000000001,,0.026000000000000002,,0.0096,0.0541,0.0734,0.9841,,,0.0,0.0345,0.0833,,0.013000000000000001,,0.0254,,0.0092,,specific housing,0.0196,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n429849,0,Cash loans,M,Y,Y,1,675000.0,521280.0,26743.5,450000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.018801,-14608,-1943,-8654.0,-5092,7.0,1,1,0,1,0,0,Managers,3.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.4574685937038808,0.4382813743111921,0.0825,0.0791,0.9747,0.6532,0.0438,0.0,0.1379,0.1667,0.2083,0.0683,0.0672,0.0704,0.0,0.0,0.084,0.0821,0.9747,0.6668,0.0442,0.0,0.1379,0.1667,0.2083,0.0698,0.0735,0.0734,0.0,0.0,0.0833,0.0791,0.9747,0.6578,0.0441,0.0,0.1379,0.1667,0.2083,0.0694,0.0684,0.0717,0.0,0.0,not specified,block of flats,0.0793,Panel,No,10.0,0.0,10.0,0.0,-878.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n122754,0,Cash loans,F,Y,Y,0,247500.0,1365651.0,40059.0,1192500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-16098,-4449,-7041.0,-4409,18.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Self-employed,,0.4825968540230419,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-468.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n187246,0,Cash loans,F,N,Y,0,121500.0,781920.0,43789.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.007273999999999998,-20217,-148,-6237.0,-3770,,1,1,0,1,0,0,Cooking staff,1.0,2,2,FRIDAY,16,0,0,0,0,1,1,Restaurant,,0.3536296962400711,,0.0619,0.0539,0.9871,0.8232,0.0119,0.0,0.1379,0.1667,0.0417,0.0128,0.0504,0.0588,0.0,0.0,0.063,0.0559,0.9871,0.8301,0.012,0.0,0.1379,0.1667,0.0417,0.0131,0.0551,0.0612,0.0,0.0,0.0625,0.0539,0.9871,0.8256,0.012,0.0,0.1379,0.1667,0.0417,0.013000000000000001,0.0513,0.0598,0.0,0.0,reg oper account,block of flats,0.0527,Panel,No,0.0,0.0,0.0,0.0,-1536.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n203944,0,Revolving loans,M,Y,Y,1,315000.0,900000.0,45000.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15190,-5484,-6115.0,-1475,3.0,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,16,0,0,0,0,0,0,Trade: type 7,,0.5701948152681002,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-895.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n153573,0,Cash loans,M,Y,Y,2,157500.0,288562.5,22927.5,261000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-12659,-1194,-2632.0,-4430,24.0,1,1,0,1,0,0,High skill tech staff,4.0,2,2,MONDAY,13,0,0,0,1,1,1,Transport: type 4,0.3034392619888164,0.5010405921947695,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-2542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n381396,0,Cash loans,F,N,Y,2,292500.0,319981.5,17487.0,243000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018801,-17056,-817,-1967.0,-546,,1,1,0,1,0,0,Core staff,4.0,2,2,THURSDAY,8,0,0,0,1,1,0,Self-employed,,0.5683610328008506,0.4650692149562261,0.0041,0.0,0.9682,,,0.0,0.0345,0.0,,0.0145,,0.0024,,0.0,0.0042,0.0,0.9682,,,0.0,0.0345,0.0,,0.0148,,0.0025,,0.0,0.0042,0.0,0.9682,,,0.0,0.0345,0.0,,0.0148,,0.0025,,0.0,,block of flats,0.0019,Wooden,Yes,0.0,0.0,0.0,0.0,-1656.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n244413,0,Cash loans,F,N,Y,0,180000.0,781920.0,25969.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-19779,-9043,-5385.0,-3332,,1,1,0,1,0,0,Medicine staff,2.0,3,3,THURSDAY,9,0,0,0,0,1,1,Medicine,,0.2801824502452667,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1121.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n156070,0,Revolving loans,M,Y,N,1,360000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-20451,-5706,-3631.0,-3978,7.0,1,1,0,1,0,0,Managers,3.0,2,2,THURSDAY,18,0,0,0,0,0,0,Self-employed,0.7799900433223318,0.624981522710182,0.7713615919194317,0.0619,0.0484,0.9742,0.6464,0.0054,0.0,0.1034,0.1667,0.2083,0.0257,0.0504,0.0516,0.0,0.0,0.063,0.0502,0.9742,0.6602,0.0055,0.0,0.1034,0.1667,0.2083,0.0263,0.0551,0.0538,0.0,0.0,0.0625,0.0484,0.9742,0.6511,0.0054,0.0,0.1034,0.1667,0.2083,0.0261,0.0513,0.0526,0.0,0.0,reg oper account,block of flats,0.0436,Panel,No,,,,,-647.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,2.0\n347481,0,Cash loans,F,N,N,0,58500.0,286704.0,12145.5,247500.0,Unaccompanied,Pensioner,Lower secondary,Widow,House / apartment,0.008068,-20011,365243,-9782.0,-3428,,1,0,0,1,0,0,,1.0,3,3,MONDAY,9,0,0,0,0,0,0,XNA,,0.4820508477626507,0.7738956942145427,,,0.9662,,,,,,,,,0.0032,,,,,0.9662,,,,,,,,,0.0033,,,,,0.9662,,,,,,,,,0.0033,,,,block of flats,0.0028,,Yes,0.0,0.0,0.0,0.0,-851.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,3.0\n404837,0,Cash loans,F,N,Y,1,90000.0,351792.0,17055.0,252000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-13136,-782,-13103.0,-4633,,1,1,1,1,1,0,Core staff,3.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Government,0.5753449786840128,0.3119709357402773,0.5262949398096192,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2100.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n152261,0,Cash loans,F,N,Y,1,202500.0,1125000.0,32895.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.001276,-15173,-1503,-6717.0,-4165,,1,1,0,1,1,0,Medicine staff,3.0,2,2,TUESDAY,4,0,0,0,0,0,0,Other,,0.7246417303318243,0.8633633824101478,0.0838,0.0728,0.9876,0.8368,0.0051,0.0,0.0886,0.1667,0.0417,0.0424,0.0731,0.0573,0.0006,0.0677,0.0315,0.0348,0.9901,0.8693,0.0,0.0,0.069,0.1667,0.0417,0.028999999999999998,0.0275,0.0193,0.0,0.0519,0.0583,0.0706,0.9881,0.8658,0.0049,0.0,0.069,0.1667,0.0417,0.0408,0.0966,0.064,0.0,0.0774,reg oper account,block of flats,0.0252,Block,No,1.0,0.0,1.0,0.0,-1778.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n372810,0,Cash loans,M,Y,Y,2,180000.0,315000.0,20506.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.015221,-9870,-635,-4385.0,-2559,2.0,1,1,1,1,1,0,,4.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5979593944418736,,0.0041,,0.9722,,,,0.0345,0.0,,,,0.0026,,,0.0042,,0.9722,,,,0.0345,0.0,,,,0.0027,,,0.0042,,0.9722,,,,0.0345,0.0,,,,0.0027,,,,block of flats,0.0021,Others,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n334968,0,Cash loans,F,N,Y,0,90000.0,536917.5,22878.0,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.031329,-21405,365243,-13864.0,-4872,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,0.8669954858032738,0.7234664335961999,0.4956658291397297,0.066,0.0813,0.9722,0.6192,0.0063,0.0,0.1379,0.125,0.1667,0.0534,0.0538,0.0509,0.0,0.0,0.0672,0.0844,0.9722,0.6341,0.0063,0.0,0.1379,0.125,0.1667,0.0547,0.0588,0.053,0.0,0.0,0.0666,0.0813,0.9722,0.6243,0.0063,0.0,0.1379,0.125,0.1667,0.0544,0.0547,0.0518,0.0,0.0,reg oper account,block of flats,0.0435,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3051.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,8.0\n322597,0,Cash loans,M,Y,N,0,247500.0,900000.0,32017.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-13320,-1556,-2181.0,-4768,1.0,1,1,1,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,11,0,1,1,0,1,1,Construction,,0.02869067695579328,0.33125086459090186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-290.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n263899,0,Cash loans,M,N,Y,0,90000.0,490495.5,30136.5,454500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.00702,-10600,-371,-4799.0,-1315,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,,0.3341785450713292,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n231695,0,Cash loans,F,N,Y,1,135000.0,299772.0,23814.0,247500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010643,-9548,-664,-2468.0,-674,,1,1,0,1,0,0,,3.0,2,2,FRIDAY,10,1,1,0,1,1,1,Other,,0.480817033514484,0.6956219298394389,0.0526,0.0768,0.996,,,0.0,0.1034,0.0833,,0.0188,,0.0422,,0.0,0.0536,0.0797,0.996,,,0.0,0.1034,0.0833,,0.0193,,0.044000000000000004,,0.0,0.0531,0.0768,0.996,,,0.0,0.1034,0.0833,,0.0192,,0.043,,0.0,,block of flats,0.0332,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-290.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n157987,0,Cash loans,M,N,Y,0,202500.0,781920.0,42417.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21837,365243,-7546.0,-2610,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.7009295042510659,0.2276129150623945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1567.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n102452,0,Revolving loans,F,N,Y,2,364500.0,450000.0,22500.0,450000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.005084,-12866,-4521,-2125.0,-2185,,1,1,0,1,0,0,Core staff,4.0,2,2,TUESDAY,10,0,0,0,0,1,1,Government,0.7233022735830011,0.7346016679660659,0.6246146584503397,0.0928,0.1022,0.9806,0.7348,0.0763,0.0,0.2069,0.1667,0.0,0.0286,0.0756,0.0914,0.0,0.0,0.0945,0.1061,0.9806,0.7452,0.077,0.0,0.2069,0.1667,0.0,0.0293,0.0826,0.0952,0.0,0.0,0.0937,0.1022,0.9806,0.7383,0.0767,0.0,0.2069,0.1667,0.0,0.0291,0.077,0.09300000000000001,0.0,0.0,,block of flats,0.0719,Panel,No,4.0,0.0,4.0,0.0,-1523.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n380529,0,Revolving loans,F,N,Y,0,450000.0,765000.0,38250.0,765000.0,Family,Commercial associate,Higher education,Separated,House / apartment,0.030755,-20979,-10759,-12837.0,-3525,,1,1,0,1,1,0,Managers,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.7978857315532204,0.7729167329753772,0.622922000268356,0.0773,0.0756,0.9806,0.7348,0.0312,0.0,0.1724,0.1667,0.2083,0.0139,0.063,0.0691,0.0,0.0,0.0788,0.0784,0.9806,0.7452,0.0315,0.0,0.1724,0.1667,0.2083,0.0142,0.0689,0.07200000000000001,0.0,0.0,0.0781,0.0756,0.9806,0.7383,0.0314,0.0,0.1724,0.1667,0.2083,0.0141,0.0641,0.0703,0.0,0.0,reg oper spec account,block of flats,0.0714,Panel,No,2.0,0.0,2.0,0.0,-905.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n240632,0,Cash loans,M,Y,Y,1,135000.0,601470.0,32760.0,450000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Rented apartment,0.031329,-8483,-177,-4869.0,-1144,14.0,1,1,0,1,0,0,Managers,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Trade: type 1,0.2066846699076453,0.5794031823476867,0.20915469884100693,0.0907,0.0894,0.9801,0.728,0.0138,0.0,0.2069,0.1667,0.2083,0.0576,0.0731,0.0744,0.0039,0.0358,0.0924,0.0928,0.9801,0.7387,0.0139,0.0,0.2069,0.1667,0.2083,0.0589,0.0799,0.0775,0.0039,0.0379,0.0916,0.0894,0.9801,0.7316,0.0138,0.0,0.2069,0.1667,0.2083,0.0586,0.0744,0.0757,0.0039,0.0366,reg oper account,block of flats,0.0663,Panel,No,0.0,0.0,0.0,0.0,-1509.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,4.0\n388630,0,Revolving loans,F,Y,Y,1,202500.0,450000.0,22500.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.007305,-11906,-4123,-1480.0,-1475,12.0,1,1,1,1,1,1,Accountants,3.0,3,3,MONDAY,10,0,0,0,0,0,0,Medicine,0.4978548313308371,0.6639831030757417,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3163.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n167827,0,Cash loans,M,Y,Y,0,405000.0,514777.5,31621.5,477000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.010006,-10772,-546,-8231.0,-3431,5.0,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.672264522443698,0.4170996682522097,0.0711,0.0517,0.9881,0.8368,,0.08,0.069,0.3333,0.375,0.0366,,,,0.0089,0.0725,0.0537,0.9881,0.8432,,0.0806,0.069,0.3333,0.375,0.0374,,,,0.0095,0.0718,0.0517,0.9881,0.8390000000000001,,0.08,0.069,0.3333,0.375,0.0372,,,,0.0091,reg oper account,block of flats,0.0672,Panel,No,0.0,0.0,0.0,0.0,-539.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n158614,0,Cash loans,M,Y,N,2,247500.0,755190.0,28116.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-15288,-453,-3185.0,-4487,16.0,1,1,1,1,0,0,Laborers,4.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6328305256334706,0.363945238612397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2675.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n367803,0,Cash loans,M,N,N,0,180000.0,427500.0,20920.5,427500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-8494,-657,-246.0,-1078,,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,16,0,0,0,0,1,1,Industry: type 2,0.1876733407283728,0.1111019849823876,0.6940926425266661,0.0825,0.1182,0.9906,,,0.0,0.2759,0.1667,,0.0521,,0.0894,,0.0,0.084,0.1226,0.9906,,,0.0,0.2759,0.1667,,0.0532,,0.0932,,0.0,0.0833,0.1182,0.9906,,,0.0,0.2759,0.1667,,0.053,,0.091,,0.0,,block of flats,0.0703,\"Stone, brick\",No,8.0,0.0,8.0,0.0,-132.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n315232,0,Revolving loans,M,Y,Y,0,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.003069,-21578,-2724,-7409.0,-4375,16.0,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,11,0,0,0,0,1,1,Other,,0.5327726041611346,0.3344541255096772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-880.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,5.0\n260848,0,Cash loans,F,N,Y,0,270000.0,1159515.0,34033.5,1012500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.008575,-22099,365243,-167.0,-4039,,1,0,0,1,1,0,,1.0,2,2,MONDAY,15,1,0,0,0,0,0,XNA,0.7393830204616422,0.691378418658652,0.4812493411434029,0.6314,,0.9995,,,0.28,0.2241,0.375,,0.3787,,0.4,,0.0467,0.5662,,0.9995,,,0.282,0.2069,0.375,,0.3437,,0.3824,,0.0259,0.6376,,0.9995,,,0.28,0.2241,0.375,,0.3853,,0.4072,,0.0476,,block of flats,0.3778,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2213.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n109860,1,Cash loans,F,N,N,0,90000.0,171000.0,9945.0,171000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.014519999999999996,-24048,365243,-6263.0,-4001,,1,0,0,1,0,0,,1.0,2,2,MONDAY,7,0,0,0,0,0,0,XNA,,0.34112290759888264,0.6706517530862718,0.1361,,0.9742,0.6464,0.0401,0.0,0.1034,0.1667,0.2083,0.019,0.0967,0.0445,0.0116,0.0512,0.1387,,0.9742,0.6602,0.0404,0.0,0.1034,0.1667,0.2083,0.0194,0.1056,0.0464,0.0117,0.0542,0.1374,,0.9742,0.6511,0.0403,0.0,0.1034,0.1667,0.2083,0.0193,0.0983,0.0453,0.0116,0.0522,reg oper account,block of flats,0.0569,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-333.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n237848,0,Cash loans,M,N,N,0,247500.0,539100.0,29376.0,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Office apartment,0.014464,-10244,-383,-4477.0,-2844,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,4,0,0,0,1,1,0,Military,0.11010260532424444,0.2188800819720864,0.178760465484575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1515.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n267390,0,Revolving loans,F,N,Y,0,450000.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-14891,-1268,-4188.0,-3235,,1,1,0,1,0,0,Core staff,2.0,1,1,FRIDAY,11,0,0,0,0,0,0,Medicine,0.7416454617244282,0.7316066267000548,,0.10099999999999999,0.0369,0.9786,0.7076,0.0169,0.08,0.0345,0.5417,0.5833,0.0,0.0824,0.0849,0.0,0.0013,0.1029,0.0383,0.9786,0.7190000000000001,0.0171,0.0806,0.0345,0.5417,0.5833,0.0,0.09,0.0884,0.0,0.0014,0.102,0.0369,0.9786,0.7115,0.017,0.08,0.0345,0.5417,0.5833,0.0,0.0838,0.0864,0.0,0.0014,reg oper account,block of flats,0.0734,Block,No,0.0,0.0,0.0,0.0,-558.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,0.0\n219555,0,Revolving loans,M,N,Y,0,144000.0,1350000.0,67500.0,1350000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.00823,-22659,365243,-8343.0,-4675,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,0.7799566694967575,0.5833680881686577,,0.0598,0.0616,0.9881,0.8368,0.0292,0.0,0.1379,0.1667,0.2083,0.013000000000000001,0.0488,0.0605,0.0,0.0,0.0609,0.0639,0.9881,0.8432,0.0295,0.0,0.1379,0.1667,0.2083,0.0133,0.0533,0.063,0.0,0.0,0.0604,0.0616,0.9881,0.8390000000000001,0.0294,0.0,0.1379,0.1667,0.2083,0.0132,0.0496,0.0616,0.0,0.0,reg oper account,block of flats,0.0635,Panel,No,0.0,0.0,0.0,0.0,-128.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n349978,0,Cash loans,F,N,Y,0,112500.0,675000.0,19867.5,675000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.028663,-18172,-4918,-6871.0,-1720,,1,1,1,1,1,1,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Government,0.4979984709234545,0.5091579093824312,0.6413682574954046,0.0165,0.0529,0.9816,,,0.0,0.069,0.0417,,0.0117,,0.0109,,0.0697,0.0168,0.0549,0.9816,,,0.0,0.069,0.0417,,0.012,,0.0114,,0.0738,0.0167,0.0529,0.9816,,,0.0,0.069,0.0417,,0.0119,,0.0111,,0.0711,,block of flats,0.0237,Panel,No,0.0,0.0,0.0,0.0,-807.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n276049,0,Cash loans,M,Y,Y,0,112500.0,593010.0,17469.0,495000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-18338,365243,-2886.0,-1867,14.0,1,0,0,1,0,0,,2.0,3,3,SATURDAY,11,0,0,0,0,0,0,XNA,,0.7246242765010749,0.6971469077844458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1298.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n399368,0,Revolving loans,F,N,N,0,90000.0,135000.0,6750.0,135000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00496,-16831,-3239,-8892.0,-367,,1,1,1,1,1,0,Sales staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Trade: type 7,,0.6058137931589587,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-819.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n164589,0,Revolving loans,M,Y,Y,0,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.032561,-10381,-91,-5248.0,-3049,8.0,1,1,0,1,0,0,Laborers,1.0,1,1,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6640522133311447,0.32298688554289484,0.5082869913916046,0.2186,,0.9831,0.7688,0.0325,0.24,0.2069,0.3333,0.375,0.1218,,0.2284,,0.0,0.2227,,0.9831,0.7779,0.0328,0.2417,0.2069,0.3333,0.375,0.1245,,0.23800000000000002,,0.0,0.2207,,0.9831,0.7719,0.0327,0.24,0.2069,0.3333,0.375,0.1239,,0.2325,,0.0,reg oper account,block of flats,0.1974,Mixed,No,4.0,0.0,4.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n388146,0,Cash loans,F,N,N,0,135000.0,135000.0,5089.5,135000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018801,-11093,-2300,-5097.0,-866,,1,1,0,1,1,0,Accountants,2.0,2,2,MONDAY,13,0,0,0,0,0,0,School,,0.15829913646208862,0.7121551551910698,0.0711,0.063,0.9806,0.7348,0.0287,0.0,0.1379,0.1667,0.2083,0.0,0.0572,0.061,0.0039,0.0057,0.0725,0.0654,0.9806,0.7452,0.028999999999999998,0.0,0.1379,0.1667,0.2083,0.0,0.0624,0.0635,0.0039,0.006,0.0718,0.063,0.9806,0.7383,0.0289,0.0,0.1379,0.1667,0.2083,0.0,0.0581,0.0621,0.0039,0.0058,reg oper account,block of flats,0.0649,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1250.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n377378,0,Cash loans,F,N,Y,2,90000.0,57564.0,6025.5,54000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13443,-673,-4431.0,-4442,,1,1,1,1,1,0,Sales staff,4.0,2,2,THURSDAY,12,0,0,0,0,1,1,Self-employed,0.6402692749077593,0.4058719973626676,0.7776594425716818,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-472.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n101093,1,Cash loans,F,N,Y,0,166500.0,152820.0,17370.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-9082,-1247,-6104.0,-692,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,16,0,0,0,0,1,1,Industry: type 1,0.12743618889571018,0.2972843676473985,0.09141199381412644,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-82.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n281787,0,Cash loans,F,Y,Y,0,225000.0,523237.5,25578.0,432000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009657,-17717,-798,-812.0,-817,2.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5356020918362122,0.31547215492577346,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-653.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n272190,0,Cash loans,M,Y,Y,0,189000.0,634482.0,20466.0,454500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019101,-22456,-150,-7080.0,-4315,20.0,1,1,0,1,1,0,Security staff,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Security,,0.7457781010559201,0.6144143775673561,0.1543,0.0947,0.9935,0.9048,0.0465,0.16,0.1379,0.375,0.2292,0.0654,0.1236,0.1873,0.0154,0.057,0.0924,0.0071,0.9901,0.8693,0.0469,0.2014,0.1724,0.375,0.0417,0.0281,0.0808,0.105,0.0078,0.0107,0.1645,0.1163,0.9945,0.9195,0.0468,0.2,0.1724,0.375,0.2292,0.0642,0.13,0.2314,0.0155,0.0634,reg oper account,block of flats,0.18100000000000002,Panel,No,0.0,0.0,0.0,0.0,-2003.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n352128,0,Cash loans,M,N,N,0,101250.0,675000.0,32472.0,675000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.026392,-23772,365243,-11270.0,-4532,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.7069813752471475,0.5867400085415683,0.1804,,0.9841,,,0.2,0.1724,0.3333,,0.1025,,0.1394,,0.2676,0.1838,,0.9841,,,0.2014,0.1724,0.3333,,0.1048,,0.1192,,0.2832,0.1822,,0.9841,,,0.2,0.1724,0.3333,,0.1042,,0.1165,,0.2732,,block of flats,0.1482,Panel,No,3.0,0.0,3.0,0.0,-1147.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n422009,0,Revolving loans,F,N,Y,1,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009175,-14097,-151,-8001.0,-4101,,1,1,1,1,0,0,Sales staff,3.0,2,2,FRIDAY,17,0,0,0,0,0,0,Trade: type 7,,0.6059756652131411,,0.1237,0.0,0.9757,,,0.0,0.2069,0.1667,,0.0743,,0.0688,,0.1644,0.1261,0.0,0.9757,,,0.0,0.2069,0.1667,,0.076,,0.0717,,0.1741,0.1249,0.0,0.9757,,,0.0,0.2069,0.1667,,0.0756,,0.0701,,0.1679,,block of flats,0.0795,Panel,No,1.0,0.0,1.0,0.0,-740.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n326655,0,Cash loans,F,N,Y,0,225000.0,254700.0,14350.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-24500,365243,-3241.0,-4912,,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.4662732278128612,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1581.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n218076,0,Cash loans,F,Y,Y,3,90000.0,906228.0,46399.5,810000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-11637,-1127,-12.0,-4109,11.0,1,1,1,1,1,1,Sales staff,5.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,0.18139931742764173,0.5159434110919363,0.4489622731076524,0.1443,0.1586,0.9985,0.9796,0.0208,0.0,0.3103,0.1667,0.2083,0.1234,0.1177,0.1624,0.0,0.0,0.1471,0.1646,0.9985,0.9804,0.021,0.0,0.3103,0.1667,0.2083,0.1262,0.1286,0.1692,0.0,0.0,0.1457,0.1586,0.9985,0.9799,0.0209,0.0,0.3103,0.1667,0.2083,0.1255,0.1197,0.1653,0.0,0.0,reg oper account,block of flats,0.1397,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1274.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n174893,0,Cash loans,F,N,N,2,67500.0,219870.0,12055.5,157500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-10262,-120,-10232.0,-2936,,1,1,0,1,0,0,,4.0,2,2,MONDAY,11,0,0,0,0,0,0,Other,0.3107539948402298,0.2379643131162123,,0.0758,0.0823,0.9767,,0.0087,0.0,0.1207,0.1667,0.2083,0.0562,0.0546,0.0666,0.0077,0.0055,0.0704,0.0785,0.9747,,0.0088,0.0,0.1034,0.1667,0.2083,0.0561,0.0597,0.0661,0.0078,0.0,0.0765,0.0823,0.9767,,0.0087,0.0,0.1207,0.1667,0.2083,0.0572,0.0556,0.0678,0.0078,0.0056,reg oper spec account,block of flats,0.0523,Panel,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n419219,1,Cash loans,F,N,Y,0,135000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-19739,-351,-13846.0,-3133,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5997139870916456,0.656158373001177,0.1031,0.0773,0.9771,,,,0.2069,0.1667,,0.0612,,0.0606,,0.1044,0.105,0.0802,0.9772,,,,0.2069,0.1667,,0.0626,,0.0632,,0.1105,0.1041,0.0773,0.9771,,,,0.2069,0.1667,,0.0622,,0.0617,,0.1066,,block of flats,0.0704,\"Stone, brick\",No,3.0,0.0,2.0,0.0,-502.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n422379,0,Cash loans,F,N,N,0,103500.0,341280.0,9130.5,270000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.010276,-13197,-5905,-2417.0,-4562,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Medicine,0.2778305878706612,0.6488589535408882,0.16048893062734468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n422382,0,Cash loans,F,N,Y,0,81000.0,101880.0,5818.5,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.025164,-24349,365243,-4092.0,-4346,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.16219210595922867,0.7826078370261895,0.1485,,0.9886,,,0.16,0.1379,0.3333,,,,0.1534,,,0.1513,,0.9886,,,0.1611,0.1379,0.3333,,,,0.1598,,,0.1499,,0.9886,,,0.16,0.1379,0.3333,,,,0.1561,,,,block of flats,0.1206,Panel,No,3.0,0.0,3.0,0.0,-1545.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n426491,0,Revolving loans,F,N,Y,0,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-12082,-3181,-6178.0,-2945,,1,1,0,1,1,0,Core staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Transport: type 2,0.5067160246050888,0.6064767987472164,0.5460231970049609,0.0186,0.0738,0.9916,,,,0.1034,0.0417,,0.0594,,0.0227,,,0.0189,0.0765,0.9916,,,,0.1034,0.0417,,0.0607,,0.0236,,,0.0187,0.0738,0.9916,,,,0.1034,0.0417,,0.0604,,0.0231,,,,block of flats,0.0178,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-411.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,1.0\n445582,1,Cash loans,F,Y,N,0,202500.0,781920.0,32998.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006296,-16536,-1368,-2076.0,-84,7.0,1,1,1,1,0,0,,2.0,3,3,TUESDAY,12,0,0,0,0,0,0,Self-employed,,0.4565406674149159,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n233036,0,Cash loans,F,N,Y,0,54000.0,310500.0,11956.5,310500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-20002,-94,-10717.0,-3536,,1,1,1,1,1,0,Cleaning staff,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5766509695872251,0.3441550073724169,0.0856,0.0489,0.9851,0.7959999999999999,0.1564,0.08,0.0345,0.4583,,0.0318,0.0698,0.0769,,,0.0872,0.0508,0.9851,0.804,0.1578,0.0806,0.0345,0.4583,,0.0325,0.0762,0.0801,,,0.0864,0.0489,0.9851,0.7987,0.1574,0.08,0.0345,0.4583,,0.0323,0.071,0.0783,,,reg oper account,block of flats,0.0605,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-199.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n128783,0,Cash loans,M,N,N,2,360000.0,640080.0,24129.0,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,With parents,0.04622,-12043,-4638,-228.0,-3560,,1,1,0,1,0,0,Laborers,4.0,1,1,SATURDAY,12,0,1,1,0,0,0,Government,,0.702641114109698,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n318616,0,Cash loans,F,N,Y,0,112500.0,95940.0,9958.5,90000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.0105,-19383,365243,-665.0,-2421,,1,0,0,1,0,0,,2.0,3,3,MONDAY,16,0,0,0,0,0,0,XNA,,0.5761116985528713,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-813.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n330998,0,Cash loans,M,N,Y,1,180000.0,495801.0,34636.5,468000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-15052,-2988,-3582.0,-4096,,1,1,0,1,0,0,Drivers,3.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.3790613986220577,0.6285163728669966,,0.0825,0.0201,0.9747,0.6532,0.0068,0.0,0.1379,0.1667,0.2083,0.0694,0.0,0.064,0.0,0.0,0.084,0.0208,0.9747,0.6668,0.0068,0.0,0.1379,0.1667,0.2083,0.071,0.0,0.0667,0.0,0.0,0.0833,0.0201,0.9747,0.6578,0.0068,0.0,0.1379,0.1667,0.2083,0.0706,0.0,0.0652,0.0,0.0,reg oper spec account,block of flats,0.0541,Block,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n300790,1,Cash loans,F,N,Y,0,180000.0,253377.0,11290.5,211500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.006207,-19945,365243,-10540.0,-3466,,1,0,0,1,0,1,,1.0,2,2,SUNDAY,10,0,0,0,1,0,0,XNA,,0.3769716498026215,0.4101025731788671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,6.0,0.0,-1085.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n135519,0,Cash loans,F,N,Y,0,157500.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-15284,-1035,-3428.0,-4312,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.24987970511854585,0.4956658291397297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-577.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n222259,0,Cash loans,M,Y,N,2,157500.0,388512.0,35761.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-12578,-2342,-1132.0,-4099,7.0,1,1,0,1,0,0,Laborers,4.0,2,2,SUNDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.2529142567640196,0.5894247917621287,0.30162489168411943,0.0588,,0.9791,,,0.0,0.1379,0.1667,,0.061,,0.0539,,,0.0599,,0.9791,,,0.0,0.1379,0.1667,,0.0623,,0.0562,,,0.0593,,0.9791,,,0.0,0.1379,0.1667,,0.062,,0.0549,,,,block of flats,0.0583,\"Stone, brick\",No,8.0,0.0,8.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n202249,0,Cash loans,M,Y,Y,0,360000.0,2013840.0,59013.0,1800000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.009334,-15930,-1273,-6819.0,-4096,10.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Construction,,0.7300580133133316,0.7789040389824382,0.0619,0.0628,0.9821,0.7552,0.0087,0.0,0.1379,0.1667,0.0,0.0417,0.0504,0.0537,0.0,0.0,0.063,0.0652,0.9821,0.7648,0.0088,0.0,0.1379,0.1667,0.0,0.0427,0.0551,0.055999999999999994,0.0,0.0,0.0625,0.0628,0.9821,0.7585,0.0088,0.0,0.1379,0.1667,0.0,0.0424,0.0513,0.0547,0.0,0.0,reg oper account,block of flats,0.047,Panel,No,0.0,0.0,0.0,0.0,-1864.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n371900,0,Cash loans,M,N,Y,0,135000.0,192874.5,20380.5,166500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.008625,-16138,-966,-1867.0,-4110,,1,1,0,1,0,0,Drivers,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Transport: type 3,0.15388613091366246,0.3635324234213037,0.5028782772082183,,,0.9816,,,,0.069,0.1667,,0.0505,,0.0401,,0.0,,,0.9816,,,,0.069,0.1667,,0.0516,,0.0418,,0.0,,,0.9816,,,,0.069,0.1667,,0.0514,,0.0408,,0.0,,,0.0408,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n184485,0,Cash loans,F,Y,N,0,126000.0,315000.0,24885.0,315000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.035792000000000004,-13075,-5476,-6908.0,-420,7.0,1,1,1,1,1,0,Core staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,School,0.5553577992292292,0.6068472847260917,0.4365064990977374,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,-2431.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n244207,0,Revolving loans,M,Y,Y,0,157500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.030755,-12458,-2325,-5081.0,-4339,64.0,1,1,0,1,0,0,Security staff,1.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.4298517513528955,0.3092753558842053,0.0928,0.1012,0.9816,0.7484,0.042,0.0,0.2069,0.1667,0.2083,0.0378,0.0756,0.0877,0.0,0.0,0.0945,0.105,0.9816,0.7583,0.0424,0.0,0.2069,0.1667,0.2083,0.0386,0.0826,0.0914,0.0,0.0,0.0937,0.1012,0.9816,0.7518,0.0423,0.0,0.2069,0.1667,0.2083,0.0384,0.077,0.0893,0.0,0.0,reg oper account,block of flats,0.0919,Panel,No,2.0,0.0,2.0,0.0,-386.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n328590,0,Cash loans,F,N,Y,0,90000.0,675000.0,19737.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-22122,365243,-13060.0,-4661,,1,0,0,1,0,0,,2.0,3,3,SUNDAY,12,0,0,0,0,0,0,XNA,,0.22448275796907605,0.4830501881366946,0.0732,0.0842,0.9821,0.7552,0.0087,0.0,0.1379,0.1667,0.2083,0.0613,0.0588,0.0663,0.0039,0.009000000000000001,0.0746,0.0874,0.9821,0.7648,0.0088,0.0,0.1379,0.1667,0.2083,0.0627,0.0643,0.069,0.0039,0.0095,0.0739,0.0842,0.9821,0.7585,0.0088,0.0,0.1379,0.1667,0.2083,0.0623,0.0599,0.0674,0.0039,0.0091,reg oper account,block of flats,0.0588,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1624.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n236366,0,Cash loans,F,N,Y,0,189000.0,631332.0,35383.5,585000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-15795,-2561,-5289.0,-3855,,1,1,0,1,0,0,Private service staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6957914580879957,0.7096109079533709,0.8193176922872417,0.1227,0.1067,0.9801,0.728,0.0145,0.0,0.2759,0.1667,0.2083,0.1494,0.1,0.1152,0.0,0.0,0.125,0.1108,0.9801,0.7387,0.0146,0.0,0.2759,0.1667,0.2083,0.1528,0.1093,0.1201,0.0,0.0,0.1239,0.1067,0.9801,0.7316,0.0146,0.0,0.2759,0.1667,0.2083,0.152,0.1018,0.1173,0.0,0.0,reg oper account,block of flats,0.1058,Panel,No,2.0,0.0,2.0,0.0,-1560.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n355522,0,Cash loans,F,Y,N,0,270000.0,990000.0,65101.5,990000.0,Unaccompanied,Commercial associate,Higher education,Married,Rented apartment,0.014519999999999996,-16721,-2049,-2942.0,-265,13.0,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.7757594115740012,0.6488589535408882,0.7922644738669378,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2377.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n292824,0,Cash loans,M,Y,N,2,135000.0,1494486.0,39555.0,1305000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018029,-15675,-6833,-6635.0,-2300,18.0,1,1,0,1,0,0,Laborers,4.0,3,3,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.27263075002508835,0.3838678272245281,0.6479768603302221,0.1278,,0.9816,,,0.0,0.0345,0.1667,,,,0.0732,,0.0064,0.1303,,0.9816,,,0.0,0.0345,0.1667,,,,0.0763,,0.0068,0.1291,,0.9816,,,0.0,0.0345,0.1667,,,,0.0745,,0.0065,,block of flats,0.0576,Panel,No,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n114038,0,Revolving loans,F,N,N,0,112500.0,247500.0,12375.0,247500.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.01885,-16677,-501,-5898.0,-204,,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Self-employed,0.4684633745513683,0.341078674741716,0.722392890081304,0.1701,0.0545,0.9781,,,0.32,0.2759,0.2083,,,,0.1022,,0.1441,0.1733,0.0566,0.9782,,,0.3222,0.2759,0.2083,,,,0.1065,,0.1525,0.1718,0.0545,0.9781,,,0.32,0.2759,0.2083,,,,0.10400000000000001,,0.1471,,block of flats,0.1512,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-854.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n152853,0,Cash loans,F,N,Y,0,99000.0,381528.0,21433.5,315000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.020246,-23017,-3867,-9307.0,-4353,,1,1,1,1,1,0,,1.0,3,3,SUNDAY,9,0,0,0,0,1,1,Transport: type 4,,0.4653046763904744,0.6863823354047934,0.0845,0.1031,0.9861,0.8096,0.0306,0.0,0.069,0.1667,0.0417,0.0247,0.0689,0.0748,0.0,0.0,0.0861,0.107,0.9861,0.8171,0.0309,0.0,0.069,0.1667,0.0417,0.0253,0.0753,0.078,0.0,0.0,0.0854,0.1031,0.9861,0.8121,0.0308,0.0,0.069,0.1667,0.0417,0.0252,0.0701,0.0762,0.0,0.0,,block of flats,0.0935,Panel,No,1.0,1.0,1.0,1.0,-783.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n145474,0,Cash loans,F,N,N,0,157500.0,225000.0,11488.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.007114,-17394,-6506,-4044.0,-932,,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,Military,0.5993753750202251,0.7349255561223919,0.21155076420525776,0.1,0.0987,0.9816,,,,0.2069,0.1667,,,,0.0914,,0.0169,0.1008,0.0909,0.9816,,,,0.2069,0.1667,,,,0.0939,,0.0165,0.10099999999999999,0.0987,0.9816,,,,0.2069,0.1667,,,,0.0931,,0.0173,,block of flats,0.0743,Panel,No,0.0,0.0,0.0,0.0,-598.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n439102,0,Cash loans,F,Y,Y,0,130500.0,900000.0,26446.5,900000.0,\"Spouse, partner\",Working,Higher education,Married,With parents,0.015221,-9855,-824,-4380.0,-92,3.0,1,1,0,1,0,0,Accountants,2.0,2,2,THURSDAY,16,0,0,0,0,1,1,Self-employed,0.7188273876483773,0.5397333278223935,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1973.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n265928,0,Cash loans,F,N,Y,0,81000.0,648000.0,21415.5,648000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-15140,-1200,-4277.0,-2342,,1,1,1,1,0,1,Laborers,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.7747567853732167,0.5961496430331898,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-540.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n424098,0,Cash loans,F,N,Y,0,90000.0,269550.0,21294.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-10818,-602,-970.0,-3471,,1,1,0,1,1,0,Sales staff,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Self-employed,0.6165315532590431,0.5437885877952655,,0.0515,,0.9861,,,0.0,0.1379,0.1667,,,,0.0478,,,0.0525,,0.9861,,,0.0,0.1379,0.1667,,,,0.0498,,,0.052000000000000005,,0.9861,,,0.0,0.1379,0.1667,,,,0.0486,,,,block of flats,0.0376,,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n381496,0,Cash loans,F,N,Y,0,112500.0,234369.0,15790.5,193500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00702,-19320,-5570,-576.0,-2868,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Self-employed,0.7958740626893637,0.5631552892771103,0.6754132910917112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n395697,0,Cash loans,M,Y,N,0,180000.0,1035000.0,27301.5,1035000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006008,-16193,-1190,-2912.0,-4208,14.0,1,1,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,1,1,Other,,0.6643928043197165,0.4206109640437848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-480.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n196211,0,Cash loans,F,N,Y,0,202500.0,239850.0,22671.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.009175,-24942,365243,-2278.0,-4355,,1,0,0,1,1,0,,1.0,2,2,SUNDAY,11,0,0,0,0,0,0,XNA,,0.7240916053093676,0.7688075728291359,0.1423,0.1335,0.9856,,,0.16,0.1379,0.3333,,0.1413,,0.1445,,0.0906,0.145,0.1385,0.9856,,,0.1611,0.1379,0.3333,,0.1446,,0.1506,,0.0959,0.1436,0.1335,0.9856,,,0.16,0.1379,0.3333,,0.1438,,0.1471,,0.0925,,block of flats,0.1507,Panel,No,0.0,0.0,0.0,0.0,-158.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,5.0\n446747,0,Cash loans,F,N,Y,2,202500.0,753840.0,29340.0,540000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-14878,-6464,-9004.0,-5929,,1,1,0,1,0,0,Laborers,4.0,1,1,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.3768278552034466,0.7713615919194317,0.0825,0.0925,0.9608,,0.0172,0.0,0.2759,0.125,0.0833,,,0.0893,,0.0622,0.084,0.096,0.9608,,0.0173,0.0,0.2759,0.125,0.0833,,,0.09300000000000001,,0.0659,0.0833,0.0925,0.9608,,0.0173,0.0,0.2759,0.125,0.0833,,,0.0909,,0.0635,,block of flats,0.0931,\"Stone, brick\",No,5.0,1.0,5.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n273262,0,Cash loans,F,N,Y,0,54000.0,508495.5,20295.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-23021,365243,-4302.0,-4057,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.1698542589574682,0.5638350489514956,0.0619,0.2075,0.9841,0.7824,0.0015,0.0,0.1379,0.1667,0.0417,0.0514,0.0504,0.0567,0.0,0.0,0.063,0.2153,0.9841,0.7909,0.0015,0.0,0.1379,0.1667,0.0417,0.0526,0.0551,0.0591,0.0,0.0,0.0625,0.2075,0.9841,0.7853,0.0015,0.0,0.1379,0.1667,0.0417,0.0523,0.0513,0.0577,0.0,0.0,reg oper spec account,block of flats,0.0446,\"Stone, brick\",No,6.0,0.0,6.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n222329,0,Cash loans,F,N,Y,0,189000.0,1133748.0,33277.5,990000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.031329,-16561,-1659,-4243.0,-102,,1,1,0,1,0,0,Sales staff,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,Self-employed,,0.35006407390399724,0.6127042441012546,0.0722,0.0492,0.9811,0.7416,0.0093,0.0,0.1379,0.1667,0.2083,0.0626,0.0588,0.051,0.0,0.0,0.0735,0.0511,0.9811,0.7517,0.0094,0.0,0.1379,0.1667,0.2083,0.064,0.0643,0.0531,0.0,0.0,0.0729,0.0492,0.9811,0.7451,0.0094,0.0,0.1379,0.1667,0.2083,0.0637,0.0599,0.0519,0.0,0.0,reg oper account,block of flats,0.0452,Panel,No,0.0,0.0,0.0,0.0,-377.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n313430,0,Cash loans,M,N,N,0,99000.0,254700.0,24939.0,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-24594,365243,-8732.0,-5025,,1,0,0,1,1,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.3262565791904213,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-284.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n145244,0,Cash loans,F,N,Y,0,157500.0,817560.0,29362.5,675000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.003069,-23445,365243,-2767.0,-4425,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,13,0,0,0,0,0,0,XNA,0.6217861791692326,0.7459563354126734,0.5262949398096192,0.0536,,0.9881,,,,,,,,,,,,0.0546,,0.9881,,,,,,,,,,,,0.0541,,0.9881,,,,,,,,,,,,,,0.0387,,No,0.0,0.0,0.0,0.0,-1628.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,3.0\n155326,0,Cash loans,M,Y,N,0,360000.0,1546020.0,45202.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.030755,-19095,-617,-7009.0,-2629,8.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,15,0,1,1,0,1,1,Business Entity Type 3,,0.4142352259054224,0.2405414172860865,0.0175,0.0123,0.9662,0.5376,0.0025,0.0,0.069,0.0417,0.0833,0.0602,0.0143,0.0193,0.0,0.0,0.0179,0.0127,0.9662,0.5557,0.0025,0.0,0.069,0.0417,0.0833,0.0615,0.0156,0.0201,0.0,0.0,0.0177,0.0123,0.9662,0.5438,0.0025,0.0,0.069,0.0417,0.0833,0.0612,0.0145,0.0196,0.0,0.0,reg oper account,block of flats,0.0165,Block,No,0.0,0.0,0.0,0.0,-1382.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n250801,0,Cash loans,F,N,Y,0,126000.0,1350000.0,42390.0,1350000.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.026392,-22609,365243,-3390.0,-4662,,1,0,0,1,0,1,,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,,0.6503720795723372,,0.1649,0.0,0.9940000000000001,,,0.16,0.1379,0.375,,0.0637,,0.1812,,0.0079,0.1681,0.0,0.9940000000000001,,,0.1611,0.1379,0.375,,0.0652,,0.1888,,0.0083,0.1665,0.0,0.9940000000000001,,,0.16,0.1379,0.375,,0.0648,,0.1845,,0.008,,block of flats,0.1442,Panel,No,2.0,0.0,2.0,0.0,-1193.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n391103,0,Revolving loans,M,Y,Y,2,112500.0,270000.0,13500.0,270000.0,Family,Working,Higher education,Married,House / apartment,0.008473999999999999,-13836,-1960,-975.0,-4377,16.0,1,1,0,1,0,0,Managers,4.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.2933413589140679,0.4837709065256816,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-150.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n269555,0,Cash loans,F,N,Y,2,157500.0,1350000.0,39604.5,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018209,-14975,-187,-2719.0,-631,,1,1,0,1,0,0,,4.0,3,3,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.7818305606850529,0.5429367409374266,0.3606125659189888,0.1093,0.1161,0.993,0.9048,0.0,0.0,0.2069,0.2083,0.25,0.0961,0.0891,0.133,0.0,0.0,0.1113,0.1204,0.993,0.9085,0.0,0.0,0.2069,0.2083,0.25,0.0983,0.0973,0.1386,0.0,0.0,0.1103,0.1161,0.993,0.9061,0.0,0.0,0.2069,0.2083,0.25,0.0978,0.0906,0.1354,0.0,0.0,org spec account,block of flats,0.1241,\"Stone, brick\",No,3.0,2.0,3.0,2.0,-954.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n356935,0,Cash loans,M,N,Y,0,90000.0,225000.0,12564.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-19848,-299,-4606.0,-3102,,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Government,0.11092983760201527,0.4595860110675191,0.34090642641523844,0.1763,0.0517,0.9821,0.7552,0.0295,0.04,0.0345,0.3333,0.375,0.0437,0.1437,0.0868,0.0,0.0,0.1796,0.0537,0.9821,0.7648,0.0297,0.0403,0.0345,0.3333,0.375,0.0447,0.157,0.0904,0.0,0.0,0.17800000000000002,0.0517,0.9821,0.7585,0.0297,0.04,0.0345,0.3333,0.375,0.0444,0.1462,0.0884,0.0,0.0,reg oper account,block of flats,0.0844,Panel,No,0.0,0.0,0.0,0.0,-121.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n101853,0,Cash loans,F,N,N,0,247500.0,1223010.0,48631.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.00496,-20292,365243,-2004.0,-3761,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,0.5842884594636288,0.4813301853389775,0.2822484337007223,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n319570,0,Cash loans,M,Y,N,0,135000.0,1042560.0,32863.5,900000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.020713,-8699,-673,-5328.0,-1363,13.0,1,1,1,1,0,0,Managers,2.0,3,3,THURSDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.3500292476366663,0.3740208032583212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-639.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n182714,0,Cash loans,M,N,N,0,112500.0,755190.0,38686.5,675000.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.00733,-14771,-285,-7467.0,-1462,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,10,0,1,1,0,1,1,Business Entity Type 3,,0.5021995613740647,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-986.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n206162,1,Cash loans,F,N,Y,0,225000.0,398016.0,31576.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-17464,-2260,-7987.0,-999,,1,1,0,1,0,0,Medicine staff,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.5988743416005882,0.4563182267433249,0.15474363127259447,0.0495,0.0382,0.9901,,,0.08,0.069,0.3333,,0.0,,0.0674,,0.0,0.0504,0.0396,0.9901,,,0.0806,0.069,0.3333,,0.0,,0.0703,,0.0,0.05,0.0382,0.9901,,,0.08,0.069,0.3333,,0.0,,0.0686,,0.0,,block of flats,0.061,Panel,No,4.0,0.0,4.0,0.0,-1879.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n247898,0,Cash loans,M,Y,Y,1,180000.0,888840.0,32053.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-17528,-442,-499.0,-1075,6.0,1,1,0,1,0,0,,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5674595372515567,0.3723336657058204,0.0309,0.0306,0.9876,0.83,0.0046,0.0,0.069,0.1667,0.2083,0.0231,0.0244,0.03,0.0039,0.0014,0.0315,0.0318,0.9876,0.8367,0.0046,0.0,0.069,0.1667,0.2083,0.0237,0.0266,0.0313,0.0039,0.0015,0.0312,0.0306,0.9876,0.8323,0.0046,0.0,0.069,0.1667,0.2083,0.0235,0.0248,0.0306,0.0039,0.0015,reg oper account,block of flats,0.0239,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n307744,0,Cash loans,F,N,Y,0,216000.0,1800000.0,49630.5,1800000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.001276,-20134,-1515,-8198.0,-752,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.09406589318437536,0.4418358231994413,0.0619,0.0624,0.9806,0.7348,0.0099,0.0,0.1379,0.1667,0.0417,0.0579,0.0504,0.0604,0.0,0.0,0.063,0.0647,0.9806,0.7452,0.01,0.0,0.1379,0.1667,0.0417,0.0592,0.0551,0.063,0.0,0.0,0.0625,0.0624,0.9806,0.7383,0.01,0.0,0.1379,0.1667,0.0417,0.0589,0.0513,0.0615,0.0,0.0,reg oper account,block of flats,0.0529,Panel,No,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n388406,0,Revolving loans,M,Y,N,0,315000.0,450000.0,22500.0,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018209,-20740,-593,-10551.0,-4302,34.0,1,1,0,1,0,0,Security staff,2.0,3,3,WEDNESDAY,10,0,0,0,0,1,1,Security,0.4520044451015263,0.5985217699908095,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-208.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n375493,0,Cash loans,F,N,Y,1,112500.0,536917.5,28737.0,463500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.031329,-15490,-3195,-5148.0,-3028,,1,1,0,1,0,0,Core staff,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,Government,0.667357025985848,0.7011540739871743,0.7366226976503176,,,0.9836,,,,,,,,,,,,,,0.9836,,,,,,,,,,,,,,0.9836,,,,,,,,,,,,,,,,No,3.0,0.0,3.0,0.0,-1476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n314540,0,Cash loans,F,N,Y,1,67500.0,202500.0,14863.5,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-15028,-2584,-2856.0,-4986,,1,1,1,1,1,0,Laborers,3.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Industry: type 7,,0.3599628554030838,0.13510601574017175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-1337.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n401410,0,Cash loans,M,N,Y,0,405000.0,315000.0,8307.0,315000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-12603,-312,-6630.0,-4463,,1,1,0,1,1,0,IT staff,2.0,2,2,WEDNESDAY,13,0,1,1,0,1,1,Business Entity Type 3,,0.4623680047049849,0.6690566947824041,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-871.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n274824,0,Cash loans,F,Y,Y,1,90000.0,1125000.0,32895.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-13313,-6711,-989.0,-12,0.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,10,0,0,0,0,0,0,Housing,,0.6402723334458437,0.7421816117614419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2270.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n330651,0,Cash loans,F,Y,N,0,180000.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.007273999999999998,-13617,-1556,-3515.0,-2916,3.0,1,1,1,1,1,0,Managers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Government,0.8354016175982922,0.4689952151220929,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n265359,0,Cash loans,M,Y,Y,0,270000.0,521280.0,28408.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15689,-1798,-10.0,-2808,64.0,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Self-employed,,0.6419628884183587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-825.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n398252,0,Cash loans,F,N,Y,1,135000.0,272520.0,19827.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020246,-10440,-743,-4447.0,-2741,,1,1,1,1,1,0,Sales staff,2.0,3,3,SATURDAY,5,0,0,0,0,1,1,Self-employed,0.1321653672190259,0.34387072342413105,0.3185955240537633,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-101.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n237333,0,Cash loans,F,Y,Y,2,270000.0,1800000.0,62568.0,1800000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15463,-1791,-5607.0,-3234,5.0,1,1,1,1,1,0,Managers,4.0,2,2,THURSDAY,20,0,0,0,0,0,0,Business Entity Type 3,0.7981151390838687,0.4284963804269761,0.6263042766749393,0.0052,0.0,0.9518,0.3404,0.0003,0.0,0.0345,0.0417,0.0417,0.0131,0.0042,0.0026,0.0,0.0,0.0053,0.0,0.9518,0.3662,0.0003,0.0,0.0345,0.0417,0.0417,0.0134,0.0046,0.0027,0.0,0.0,0.0052,0.0,0.9518,0.3492,0.0003,0.0,0.0345,0.0417,0.0417,0.0134,0.0043,0.0027,0.0,0.0,not specified,terraced house,0.0021,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-565.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n239586,0,Revolving loans,F,Y,Y,0,675000.0,1350000.0,67500.0,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-15310,-5595,-3902.0,-4372,1.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6527302797599915,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-40.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n257210,0,Cash loans,M,N,Y,0,112500.0,222768.0,17370.0,180000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.019689,-12262,-2600,-1671.0,-4419,,1,1,1,1,0,0,Laborers,2.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.059903235486831234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-205.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n208007,0,Cash loans,F,Y,Y,2,112500.0,292500.0,18823.5,292500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-13295,-2912,-429.0,-4088,4.0,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,12,0,0,0,0,0,0,Industry: type 3,0.5285498718432899,0.4425320138637753,0.3280631605201915,0.0371,0.0,0.9727,0.626,0.0031,0.0,0.1034,0.0833,0.125,0.0199,0.0303,0.0303,0.0,0.0,0.0378,0.0,0.9727,0.6406,0.0032,0.0,0.1034,0.0833,0.125,0.0203,0.0331,0.0315,0.0,0.0,0.0375,0.0,0.9727,0.631,0.0032,0.0,0.1034,0.0833,0.125,0.0202,0.0308,0.0308,0.0,0.0,reg oper account,block of flats,0.0255,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n261285,0,Cash loans,F,Y,Y,0,225000.0,331632.0,32800.5,315000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-16091,-1962,-3923.0,-4625,3.0,1,1,1,1,1,0,,2.0,1,1,MONDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.8480962583302922,0.7380236887763918,,0.1691,0.0994,0.9856,0.8028,0.0273,0.2,0.1724,0.3333,0.375,,0.1378,0.1795,0.027000000000000003,0.0619,0.1723,0.1031,0.9856,0.8105,0.0276,0.2014,0.1724,0.3333,0.375,,0.1506,0.187,0.0272,0.0655,0.1707,0.0994,0.9856,0.8054,0.0275,0.2,0.1724,0.3333,0.375,,0.1402,0.1827,0.0272,0.0632,reg oper account,block of flats,0.1553,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-596.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n234443,0,Cash loans,F,N,N,0,157500.0,720000.0,27423.0,720000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.009334,-9131,-156,-9073.0,-1806,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.6393525842774933,0.5504584558706596,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1890.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n400891,0,Cash loans,F,N,Y,0,90000.0,675000.0,26901.0,675000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.028663,-21764,365243,-573.0,-1059,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.6246790803615423,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n290261,0,Cash loans,M,Y,N,0,193500.0,473760.0,43969.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-17162,-103,-3325.0,-713,23.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Trade: type 3,,0.3063942708205487,,0.2247,0.16899999999999998,0.9916,0.8844,0.0509,0.24,0.2069,0.3333,0.0417,0.0295,0.1816,0.2374,0.0077,0.0142,0.22899999999999998,0.1754,0.9916,0.8889,0.0514,0.2417,0.2069,0.3333,0.0417,0.0302,0.1983,0.2474,0.0078,0.015,0.2269,0.16899999999999998,0.9916,0.8859,0.0512,0.24,0.2069,0.3333,0.0417,0.03,0.1847,0.2417,0.0078,0.0145,reg oper spec account,block of flats,0.2317,Panel,No,0.0,0.0,0.0,0.0,-223.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n310500,0,Cash loans,F,N,Y,1,180000.0,1102171.5,43839.0,945000.0,Family,Working,Higher education,Married,House / apartment,0.020246,-16059,-1704,-4977.0,-4977,,1,1,0,1,0,0,High skill tech staff,3.0,3,3,THURSDAY,13,0,0,0,0,0,0,Other,0.8044790835636484,0.06670326122735093,0.2580842039460289,0.1113,0.6682,0.9831,0.7688,0.0259,0.16,0.1379,0.3333,0.375,0.0803,0.0899,0.1374,0.0039,0.0037,0.1134,0.6934,0.9831,0.7779,0.0262,0.1611,0.1379,0.3333,0.375,0.0821,0.0983,0.1431,0.0039,0.0039,0.1124,0.6682,0.9831,0.7719,0.0261,0.16,0.1379,0.3333,0.375,0.0817,0.0915,0.1398,0.0039,0.0038,org spec account,block of flats,0.12300000000000001,Panel,No,0.0,0.0,0.0,0.0,-260.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n276393,1,Cash loans,F,Y,Y,0,81000.0,138474.0,10854.0,126000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.015221,-11135,-873,-3783.0,-3771,11.0,1,1,1,1,1,0,Sales staff,1.0,2,2,MONDAY,8,0,0,0,0,1,1,Self-employed,0.08081054549011912,0.1172536531262561,0.4614823912998385,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1050.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n363032,0,Cash loans,M,Y,Y,0,180000.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-19511,-1074,-1122.0,-1991,6.0,1,1,1,1,0,0,Drivers,2.0,2,2,SUNDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.3616886866313773,0.624197022107929,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-923.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n380943,0,Cash loans,F,N,Y,0,157500.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.0228,-9844,-137,-18.0,-2524,,1,1,1,1,0,0,Core staff,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,Bank,0.6528488921534422,0.4402728498295361,0.6848276586890367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1725.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n160326,0,Cash loans,M,N,Y,2,135000.0,436032.0,23143.5,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020246,-15331,-227,-1225.0,-3943,,1,1,1,1,0,0,High skill tech staff,4.0,3,3,TUESDAY,7,0,0,0,0,1,1,Trade: type 7,0.3491723371321375,0.4277735961394977,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n411346,0,Cash loans,F,N,N,1,270000.0,1436850.0,37903.5,1125000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15070,-1538,-1210.0,-1036,,1,1,1,1,1,0,Core staff,3.0,2,2,FRIDAY,19,1,1,0,1,1,0,School,0.7455402788025048,0.4824439680249709,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-83.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n391024,1,Cash loans,M,N,Y,1,180000.0,1006920.0,42660.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-12313,-234,-6428.0,-4526,,1,1,0,1,0,0,Laborers,3.0,2,2,SUNDAY,17,0,0,0,0,0,0,Other,,0.046700856975900026,0.746300213050371,,,0.9682,0.5648,,,,0.0,,,,0.0064,,,,,0.9682,0.5818,,,,0.0,,,,0.0067,,,,,0.9682,0.5706,,,,0.0,,,,0.0065,,,,block of flats,0.005,Wooden,No,6.0,0.0,6.0,0.0,-553.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n399769,0,Cash loans,F,Y,Y,0,146250.0,286704.0,20520.0,247500.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.011703,-21287,365243,-1169.0,-4746,14.0,1,0,0,1,0,0,,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.7410857798926376,0.7910749129911773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,0.0,-1020.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n329154,0,Cash loans,M,Y,N,1,90000.0,225000.0,8613.0,225000.0,Unaccompanied,Working,Higher education,Married,Office apartment,0.018634,-18606,-2102,-2505.0,-2025,0.0,1,1,1,1,0,0,High skill tech staff,3.0,2,2,MONDAY,17,0,0,0,0,0,0,Medicine,,0.549034895952921,0.4083588531230431,0.0402,0.0387,0.9841,0.7824,0.0564,0.0,0.1034,0.1667,0.2083,0.0087,0.0328,0.0366,0.0,0.1447,0.040999999999999995,0.0402,0.9841,0.7909,0.0569,0.0,0.1034,0.1667,0.2083,0.0089,0.0358,0.0382,0.0,0.1531,0.0406,0.0387,0.9841,0.7853,0.0567,0.0,0.1034,0.1667,0.2083,0.0088,0.0333,0.0373,0.0,0.1477,not specified,block of flats,0.0389,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1747.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n146753,0,Cash loans,F,N,N,1,135000.0,900000.0,26316.0,900000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.026392,-14078,-3882,-4396.0,-4407,,1,1,1,1,1,0,Medicine staff,3.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.6612111855030209,0.7490217048463391,0.0021,,0.9876,,,0.0,,0.0,,,,,,,0.0021,,0.9876,,,0.0,,0.0,,,,,,,0.0021,,0.9876,,,0.0,,0.0,,,,,,,,terraced house,0.0023,\"Stone, brick\",No,2.0,1.0,2.0,0.0,-1871.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n394727,0,Cash loans,F,N,Y,0,337500.0,113760.0,5661.0,90000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.016612000000000002,-15124,-1175,-2413.0,-4409,,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.6416260638177881,0.6413682574954046,0.1464,0.1009,0.9856,0.8028,0.0603,0.16,0.1379,0.3333,0.375,0.091,0.1194,0.1391,0.0,0.0,0.1492,0.1047,0.9856,0.8105,0.0609,0.1611,0.1379,0.3333,0.375,0.0931,0.1304,0.145,0.0,0.0,0.1478,0.1009,0.9856,0.8054,0.0607,0.16,0.1379,0.3333,0.375,0.0926,0.1214,0.1416,0.0,0.0,reg oper account,block of flats,0.1424,Panel,No,0.0,0.0,0.0,0.0,-494.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n382975,0,Cash loans,M,N,Y,1,135000.0,521280.0,27423.0,450000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-9833,-238,-4304.0,-2511,,1,1,0,1,0,0,Laborers,3.0,1,1,WEDNESDAY,18,0,0,0,0,1,1,Business Entity Type 3,0.4562050620476945,0.4743137060042058,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2268.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n269948,0,Cash loans,F,N,Y,0,54000.0,255960.0,9778.5,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-20340,365243,-6636.0,-3581,,1,0,0,1,0,0,,2.0,3,3,MONDAY,5,0,0,0,0,0,0,XNA,,0.3003817484416115,0.6956219298394389,0.1454,0.1197,0.9901,0.8640000000000001,0.0358,0.16,0.1379,0.3333,0.0417,,,0.1709,,0.0,0.1481,0.1242,0.9901,0.8693,0.0361,0.1611,0.1379,0.3333,0.0417,,,0.17800000000000002,,0.0,0.1468,0.1197,0.9901,0.8658,0.036000000000000004,0.16,0.1379,0.3333,0.0417,,,0.174,,0.0,,block of flats,0.154,Panel,No,1.0,0.0,1.0,0.0,-413.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,0.0\n233251,0,Revolving loans,F,N,N,0,67500.0,135000.0,6750.0,135000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018634,-22335,365243,-12254.0,-3984,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,,0.1943383402683443,0.3441550073724169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-265.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n215072,0,Cash loans,F,N,N,0,157500.0,576000.0,29389.5,576000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.014519999999999996,-12983,-558,-6901.0,-4780,,1,1,0,1,0,0,Security staff,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Security,,0.6172574308849424,0.4740512892789932,0.1134,0.0584,0.9896,,,0.08,0.0345,0.625,,,,0.1443,,0.0116,0.1155,0.0606,0.9896,,,0.0806,0.0345,0.625,,,,0.1503,,0.0123,0.1145,0.0584,0.9896,,,0.08,0.0345,0.625,,,,0.1469,,0.0119,,block of flats,0.11599999999999999,Panel,No,1.0,1.0,1.0,1.0,-2553.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n384948,0,Cash loans,M,Y,N,0,112500.0,545040.0,25407.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-18071,-213,-484.0,-1613,18.0,1,1,0,1,0,0,Drivers,2.0,3,3,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.7084198581363365,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-232.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n447148,0,Cash loans,M,Y,Y,0,135000.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.005313,-16251,-128,-505.0,-4239,6.0,1,1,1,1,0,0,Drivers,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,,0.6658442697128529,,0.0515,0.0706,0.9876,0.83,,0.0,0.1379,0.1667,0.2083,0.0,0.042,0.0348,0.0,0.0,0.0525,0.0733,0.9876,0.8367,,0.0,0.1379,0.1667,0.2083,0.0,0.0459,0.0363,0.0,0.0,0.052000000000000005,0.0706,0.9876,0.8323,,0.0,0.1379,0.1667,0.2083,0.0,0.0428,0.0354,0.0,0.0,reg oper account,block of flats,0.0465,Panel,No,3.0,0.0,3.0,0.0,-2636.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n310161,0,Cash loans,M,Y,N,0,315000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.072508,-22344,-2013,-1290.0,-4557,9.0,1,1,0,1,0,0,Drivers,2.0,1,1,THURSDAY,10,0,0,0,0,0,0,Government,,0.7401700229047752,0.4525335592581747,0.1326,0.075,0.9806,0.7348,0.0168,0.128,0.0966,0.45,0.4917,0.0,0.1078,0.1194,0.0015,0.0086,0.1502,0.0471,0.9772,0.6994,0.0001,0.1611,0.1379,0.3333,0.375,0.0,0.0964,0.10400000000000001,0.0,0.0,0.1468,0.0936,0.9776,0.6981,0.0056,0.16,0.1379,0.3333,0.375,0.0,0.1197,0.1078,0.0,0.0065,reg oper account,block of flats,0.0833,Panel,No,7.0,0.0,7.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n277696,0,Cash loans,F,N,Y,0,76500.0,135000.0,7452.0,135000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.009334,-22220,365243,-12186.0,-4298,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.6432686398851757,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2314.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n193917,0,Cash loans,M,Y,Y,0,202500.0,351000.0,17199.0,351000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-11003,-1897,-4976.0,-1929,29.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.12200134460918345,0.5666811011743987,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1692.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n208931,0,Cash loans,M,Y,N,0,112500.0,728460.0,40806.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-21628,-2559,-6107.0,-4847,2.0,1,1,0,1,0,0,Drivers,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,Transport: type 3,,0.521353515132774,0.375711009574066,0.0289,0.0,0.9831,0.7688,0.0,0.0,0.1034,0.0417,0.0417,0.0355,0.0235,0.018000000000000002,0.0,0.0,0.0294,0.0,0.9831,0.7779,0.0,0.0,0.1034,0.0417,0.0417,0.0363,0.0257,0.0187,0.0,0.0,0.0291,0.0,0.9831,0.7719,0.0,0.0,0.1034,0.0417,0.0417,0.0361,0.0239,0.0183,0.0,0.0,reg oper spec account,block of flats,0.019,\"Stone, brick\",No,5.0,1.0,5.0,1.0,-1258.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n344693,0,Cash loans,F,Y,Y,0,157500.0,1506816.0,48127.5,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-18656,-3297,-4271.0,-2198,10.0,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.5211850674164867,0.5490078242242105,,0.1763,0.0638,0.9861,,,0.04,0.0345,0.3333,,0.0189,,0.08800000000000001,,0.0,0.1796,0.0663,0.9861,,,0.0403,0.0345,0.3333,,0.0194,,0.0917,,0.0,0.17800000000000002,0.0638,0.9861,,,0.04,0.0345,0.3333,,0.0193,,0.0896,,0.0,,block of flats,0.0696,Panel,No,1.0,0.0,1.0,0.0,-1603.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n188357,0,Cash loans,F,N,Y,0,157500.0,299772.0,11430.0,247500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-20463,-1602,-6300.0,-2938,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Industry: type 3,,0.5476105328996892,0.5334816299804352,0.1227,0.0988,0.9806,0.7348,0.017,0.0,0.2759,0.1667,0.2083,0.1184,0.1,0.1032,0.0,0.0,0.125,0.1025,0.9806,0.7452,0.0172,0.0,0.2759,0.1667,0.2083,0.1211,0.1093,0.1075,0.0,0.0,0.1239,0.0988,0.9806,0.7383,0.0172,0.0,0.2759,0.1667,0.2083,0.1205,0.1018,0.105,0.0,0.0,reg oper account,block of flats,0.0811,Panel,No,2.0,0.0,2.0,0.0,-679.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n366231,1,Cash loans,M,N,Y,0,180000.0,319500.0,15025.5,319500.0,Unaccompanied,Commercial associate,Lower secondary,Married,House / apartment,0.018209,-16899,-912,-5708.0,-429,,1,1,0,1,1,0,Drivers,2.0,3,3,FRIDAY,8,0,0,0,0,0,0,Transport: type 4,0.5517555366662424,0.5722836195568997,0.2392262794694045,0.1237,0.1262,0.9762,0.6736,0.0131,0.0,0.2069,0.1667,0.2083,0.0793,0.1009,0.096,0.0,0.0,0.1261,0.1309,0.9762,0.6864,0.0133,0.0,0.2069,0.1667,0.2083,0.0811,0.1102,0.1,0.0,0.0,0.1249,0.1262,0.9762,0.6779999999999999,0.0132,0.0,0.2069,0.1667,0.2083,0.0807,0.1026,0.0977,0.0,0.0,reg oper account,block of flats,0.0827,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-643.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n167659,0,Cash loans,F,N,Y,1,225000.0,338832.0,27297.0,292500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,Rented apartment,0.018029,-10450,-1020,-3857.0,-2516,,1,1,0,1,0,1,Laborers,2.0,3,3,FRIDAY,5,0,0,0,1,1,0,Self-employed,0.3721973512480922,0.2653117484731741,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,4.0,2.0\n295868,0,Cash loans,F,Y,Y,0,94500.0,808650.0,23643.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-19137,-966,-9315.0,-2640,1.0,1,1,1,1,1,0,Sales staff,2.0,3,3,WEDNESDAY,8,0,0,0,0,0,0,Trade: type 7,0.815724994339346,0.5278977820856664,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2002.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n437333,0,Revolving loans,F,N,Y,0,112500.0,292500.0,14625.0,292500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007114,-17097,-1709,-7065.0,-640,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Self-employed,,0.6431682727235835,0.15855489979486306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14.0,0.0,14.0,0.0,-1331.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,9.0\n225484,0,Cash loans,M,N,Y,0,45000.0,269550.0,11547.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006852,-21344,365243,-365.0,-4790,,1,0,0,1,0,0,,2.0,3,3,WEDNESDAY,5,0,0,0,1,0,0,XNA,0.6258581003250487,0.5262843329661053,0.13595104417329515,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,2.0\n253511,0,Cash loans,M,N,N,0,157500.0,413235.0,30199.5,337500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.035792000000000004,-8509,-201,-3374.0,-1192,,1,1,1,1,1,1,Drivers,1.0,2,2,SATURDAY,13,0,0,0,0,1,1,Other,0.1492194649893097,0.489069833926959,0.2925880073368475,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-576.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n192747,0,Cash loans,F,Y,N,0,63000.0,180000.0,17802.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-16295,-8748,-1589.0,-4752,16.0,1,1,1,1,0,0,,2.0,2,2,TUESDAY,19,0,0,0,0,1,1,Government,0.2192215007383224,0.6394559958991822,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1556.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n330502,0,Cash loans,F,N,Y,0,229500.0,1528200.0,53118.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-18314,-11558,-11995.0,-1783,,1,1,1,1,1,0,Laborers,2.0,1,1,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.553167951919478,0.6397075677637197,0.0227,0.0285,0.9722,,,,0.1034,0.0833,,0.0101,,0.0136,,0.0112,0.0231,0.0296,0.9722,,,,0.1034,0.0833,,0.0104,,0.0142,,0.0118,0.0229,0.0285,0.9722,,,,0.1034,0.0833,,0.0103,,0.0138,,0.0114,,block of flats,0.0165,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3117.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,1.0,0.0,0.0,9.0\n364140,0,Cash loans,F,N,Y,1,90000.0,299772.0,20160.0,247500.0,Family,Working,Higher education,Civil marriage,House / apartment,0.028663,-14119,-7072,-937.0,-4081,,1,1,0,1,1,0,Laborers,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Housing,,0.4547507904976845,0.7380196196295241,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n332069,0,Cash loans,F,N,Y,0,315000.0,512446.5,40617.0,463500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.019101,-10608,-3446,-5000.0,-3152,,1,1,0,1,1,1,Managers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Self-employed,,0.6594454656598676,0.3233112448967859,0.1237,0.1744,0.9851,0.7959999999999999,0.0787,0.0,0.3448,0.2083,0.2083,0.1281,0.2606,0.157,1.0,0.0614,0.1261,0.1809,0.9851,0.804,0.0794,0.0,0.3448,0.2083,0.2083,0.131,0.2847,0.1636,1.0,0.065,0.1249,0.1744,0.9851,0.7987,0.0792,0.0,0.3448,0.2083,0.2083,0.1303,0.2651,0.1598,1.0,0.0626,org spec account,block of flats,0.1665,Panel,No,2.0,0.0,2.0,0.0,-579.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,5.0\n179330,0,Cash loans,M,Y,Y,2,135000.0,810000.0,26770.5,810000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003122,-14425,-2214,-8172.0,-1293,14.0,1,1,1,1,1,0,,4.0,3,3,MONDAY,9,0,0,0,0,0,0,Medicine,,0.6819454889251202,0.6313545365850379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-778.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n178034,0,Cash loans,F,N,Y,2,112500.0,808650.0,23773.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-11984,-931,-9844.0,-3645,,1,1,0,1,0,0,Cooking staff,4.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.4582144817770238,0.2792352251632336,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-567.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n444232,0,Cash loans,F,N,N,0,171000.0,675000.0,28597.5,675000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.002042,-18617,-7114,-2383.0,-2151,,1,1,0,1,0,0,,2.0,3,3,FRIDAY,16,0,0,0,0,0,0,University,0.7308514575720405,0.5147636756462116,0.5971924268337128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1421.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n104777,1,Cash loans,F,N,Y,0,157500.0,829224.0,30595.5,594000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.001276,-15440,-1661,-2231.0,-4229,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.4129885411245684,0.4583696700847446,0.6246146584503397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-198.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n227656,0,Cash loans,M,Y,N,1,270000.0,1006920.0,39933.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-15107,-2334,-1158.0,-5726,14.0,1,1,0,1,0,0,Drivers,3.0,3,3,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.4033907725458226,0.8347841592331774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,6.0,1.0,-871.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n275149,0,Revolving loans,F,N,N,0,180000.0,360000.0,18000.0,360000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-15157,-8563,-9235.0,-3416,,1,1,0,1,0,0,Medicine staff,2.0,3,3,THURSDAY,12,0,0,0,0,0,0,Medicine,,0.4787430135790116,0.4561097392782771,0.1649,0.1597,0.9742,0.6464,0.0175,0.0,0.2759,0.1667,0.2083,0.1289,0.1345,0.1421,0.0,0.0,0.1681,0.1657,0.9742,0.6602,0.0177,0.0,0.2759,0.1667,0.2083,0.1319,0.1469,0.14800000000000002,0.0,0.0,0.1665,0.1597,0.9742,0.6511,0.0177,0.0,0.2759,0.1667,0.2083,0.1312,0.1368,0.1446,0.0,0.0,reg oper account,block of flats,0.1213,Panel,No,5.0,1.0,5.0,0.0,-1598.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n254500,0,Cash loans,F,N,Y,0,112500.0,747558.0,21856.5,535500.0,Family,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.007114,-21714,365243,-5.0,-4501,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,0.7186665661882332,0.3343050628049433,0.8038850611746273,0.066,0.0677,,0.6464,,,0.1379,0.125,0.1667,0.0792,,0.0563,,0.028999999999999998,0.0672,0.0703,,0.6602,,,0.1379,0.125,0.1667,0.0811,,0.0586,,0.0307,0.0666,0.0677,,0.6511,,,0.1379,0.125,0.1667,0.0806,,0.0573,,0.0296,,block of flats,0.0506,Panel,No,0.0,0.0,0.0,0.0,-1485.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n294670,0,Cash loans,F,N,Y,1,112500.0,125136.0,10017.0,99000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.015221,-15431,-1259,-926.0,-4292,,1,1,1,1,0,0,Core staff,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Government,0.6331273148179927,0.5137695110286533,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-609.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n187049,0,Cash loans,F,N,Y,0,90000.0,76410.0,7573.5,67500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.019101,-22594,365243,-10571.0,-3872,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.3548852999024324,0.7886807751817684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1478.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n447942,0,Cash loans,M,Y,Y,0,135000.0,942300.0,30528.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.007305,-19384,-576,-2181.0,-2219,11.0,1,1,0,1,1,0,Cleaning staff,2.0,3,3,FRIDAY,8,0,0,0,0,0,0,Other,,0.5847970942349758,0.4206109640437848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-409.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n133989,0,Cash loans,M,Y,N,0,157500.0,284400.0,17397.0,225000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.014464,-19394,-4305,-7614.0,-2653,23.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,3,0,0,0,0,0,0,Electricity,,0.5758873937793278,0.17352743046491467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1911.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n386969,0,Cash loans,F,Y,N,0,90000.0,288562.5,28669.5,261000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.0105,-15221,-231,-7289.0,-4208,3.0,1,1,0,1,0,0,Laborers,1.0,3,3,WEDNESDAY,18,0,0,0,0,0,0,Industry: type 11,0.5876061121918804,0.30626880997978656,0.6246146584503397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0805,,No,0.0,0.0,0.0,0.0,-1712.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n360236,0,Cash loans,F,Y,Y,0,112500.0,331920.0,16096.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-10452,-1724,-764.0,-1950,9.0,1,1,0,1,0,0,Managers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.5886210632879396,0.6963855242235735,0.6279908192952864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,4.0\n138681,1,Revolving loans,F,N,Y,4,144000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-9232,-892,-1606.0,-1859,,1,1,0,1,0,0,Cooking staff,6.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,0.2871954847417575,0.16481200855254566,0.7838324335199619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-272.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n169006,0,Cash loans,F,N,N,0,270000.0,1125000.0,32373.0,1125000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.007114,-11271,-349,-1707.0,-3920,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.5322377225994654,0.3271223912663729,0.6848276586890367,0.1649,0.2343,0.9836,0.7756,0.1019,0.0,0.5517,0.1667,0.2083,0.0623,0.1311,0.1592,0.0154,0.0345,0.1681,0.2431,0.9836,0.7844,0.1029,0.0,0.5517,0.1667,0.2083,0.0637,0.1433,0.1659,0.0156,0.0365,0.1665,0.2343,0.9836,0.7786,0.1026,0.0,0.5517,0.1667,0.2083,0.0634,0.1334,0.1621,0.0155,0.0352,not specified,block of flats,0.1468,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n345379,0,Cash loans,F,Y,Y,0,67500.0,152820.0,9949.5,135000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.015221,-9584,-2634,-4177.0,-2272,2.0,1,1,1,1,1,1,,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Government,0.2811164304464701,0.3060597723579207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-380.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n443629,0,Revolving loans,F,Y,Y,1,112500.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-8467,-927,-8401.0,-875,1.0,1,1,0,1,0,0,High skill tech staff,3.0,3,3,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5694872649671822,0.3441550073724169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-144.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n118547,0,Cash loans,M,N,N,0,157500.0,942300.0,30528.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.007114,-11191,-1506,-4941.0,-3704,,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,10,0,0,0,1,1,0,Security,0.2092896401850072,0.6641392088775145,0.8038850611746273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2014.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n455437,0,Cash loans,F,N,Y,0,90000.0,314055.0,17167.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-16496,-1702,-7952.0,-36,,1,1,0,1,0,0,Cooking staff,1.0,3,3,FRIDAY,7,0,0,0,0,0,0,Self-employed,,0.27883050078471583,0.4507472818545589,0.0165,0.0318,0.9737,,,,0.069,0.0417,,0.013000000000000001,,0.0107,,0.0,0.0168,0.033,0.9737,,,,0.069,0.0417,,0.0133,,0.0111,,0.0,0.0167,0.0318,0.9737,,,,0.069,0.0417,,0.0132,,0.0109,,0.0,,block of flats,0.0092,Wooden,No,0.0,0.0,0.0,0.0,-338.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n282474,0,Cash loans,F,N,N,0,90000.0,675000.0,22306.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-17355,-3273,-8401.0,-911,,1,1,1,1,1,0,Secretaries,2.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,Government,0.6431105806304457,0.6143602172758629,0.44539624156083396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-643.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n313909,0,Cash loans,F,N,Y,0,90000.0,225000.0,13045.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.014519999999999996,-24015,365243,-359.0,-4298,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,0.8334144744461838,0.344191494398425,0.4311917977993083,0.0825,0.0797,0.9712,0.6056,0.1007,0.0,0.1379,0.1667,0.0417,0.0138,0.0672,0.0629,0.0,0.0,0.084,0.0827,0.9712,0.621,0.1016,0.0,0.1379,0.1667,0.0417,0.0141,0.0735,0.0656,0.0,0.0,0.0833,0.0797,0.9712,0.6109,0.1013,0.0,0.1379,0.1667,0.0417,0.0141,0.0684,0.064,0.0,0.0,reg oper account,block of flats,0.055,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1887.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n119883,0,Cash loans,F,N,N,1,157500.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-11727,-2155,-368.0,-789,,1,1,0,1,0,0,Cooking staff,3.0,2,2,TUESDAY,17,0,1,1,0,1,1,Business Entity Type 3,0.4149700278270329,0.3552258086526689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n388723,0,Cash loans,F,N,Y,0,90000.0,86346.0,8541.0,81000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018634,-23320,365243,-13756.0,-4152,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.6230267532832134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n385012,0,Cash loans,F,N,N,0,90000.0,594000.0,19161.0,594000.0,Unaccompanied,State servant,Higher education,Civil marriage,Rented apartment,0.030755,-10524,-1557,-3357.0,-3215,,1,1,1,1,0,0,Core staff,2.0,2,2,SATURDAY,12,0,0,0,1,1,0,Security Ministries,0.22288810463436606,0.6176964647506341,0.28812959991785075,0.0804,0.0,0.9955,0.9388,0.026000000000000002,0.08,0.069,0.375,0.4167,0.0992,0.0656,0.0905,0.0,0.0,0.0819,0.0,0.9955,0.9412,0.0262,0.0806,0.069,0.375,0.4167,0.1015,0.0716,0.0943,0.0,0.0,0.0812,0.0,0.9955,0.9396,0.0262,0.08,0.069,0.375,0.4167,0.10099999999999999,0.0667,0.0921,0.0,0.0,reg oper account,block of flats,0.0945,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-498.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n272342,0,Cash loans,F,Y,N,0,180000.0,284400.0,10719.0,225000.0,Unaccompanied,Commercial associate,Higher education,Widow,House / apartment,0.0228,-18536,-282,-5235.0,-2079,11.0,1,1,1,1,1,0,Managers,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5930238015189391,,0.0124,0.0194,0.9925,0.898,0.0022,0.0,0.0345,0.125,0.0417,0.0075,0.0101,0.0154,0.0,0.0,0.0126,0.0202,0.9926,0.902,0.0022,0.0,0.0345,0.125,0.0417,0.0077,0.011000000000000001,0.0161,0.0,0.0,0.0125,0.0194,0.9925,0.8994,0.0022,0.0,0.0345,0.125,0.0417,0.0076,0.0103,0.0157,0.0,0.0,reg oper account,block of flats,0.0133,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-970.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n277960,0,Cash loans,M,Y,Y,0,270000.0,1574532.0,64260.0,1350000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.018801,-10432,-1980,-365.0,-2798,16.0,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Trade: type 2,,0.2858978721410488,0.6986675550534175,0.4845,0.3225,0.9866,0.8164,0.2371,0.56,0.4828,0.375,0.4167,0.3526,0.3942,0.5547,0.0039,0.0057,0.4937,0.3347,0.9866,0.8236,0.2393,0.5639,0.4828,0.375,0.4167,0.3606,0.4307,0.578,0.0039,0.0061,0.4892,0.3225,0.9866,0.8189,0.2386,0.56,0.4828,0.375,0.4167,0.3587,0.401,0.5647,0.0039,0.0058,reg oper spec account,block of flats,0.5672,Panel,No,10.0,0.0,10.0,0.0,-1677.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n326326,0,Cash loans,F,Y,Y,1,135000.0,1078200.0,31653.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-11073,-1197,-9766.0,-518,64.0,1,1,0,1,1,0,Laborers,3.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 1,0.4844900344932737,0.5907735820773713,0.511891801533151,0.368,0.0,0.9821,0.7552,,0.4,0.3448,0.3333,0.375,0.1642,,0.3989,,0.0047,0.375,0.0,0.9821,0.7648,,0.4028,0.3448,0.3333,0.375,0.1679,,0.4156,,0.005,0.3716,0.0,0.9821,0.7585,,0.4,0.3448,0.3333,0.375,0.16699999999999998,,0.406,,0.0048,not specified,block of flats,0.364,Panel,No,0.0,0.0,0.0,0.0,-1586.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n438627,1,Revolving loans,M,N,Y,0,90000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-21101,-887,-13657.0,-4279,,1,1,0,0,0,0,Laborers,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,Self-employed,,0.12494120183190865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n346978,0,Revolving loans,F,N,Y,0,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-19891,-498,-7819.0,-3411,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 1,0.5863117200035377,0.6114964388626948,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-652.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n330688,0,Cash loans,M,N,Y,0,90000.0,315000.0,15448.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-20494,-230,-2439.0,-2573,,1,1,1,1,1,0,Drivers,2.0,2,2,FRIDAY,8,0,0,0,0,1,1,Transport: type 4,,0.15766499536312614,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-778.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n428009,1,Cash loans,F,N,Y,0,90000.0,532494.0,38749.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-15734,-164,-3233.0,-4256,,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.4872457518958041,0.5426925344799808,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1408.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n343600,0,Revolving loans,M,Y,N,0,270000.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.032561,-15123,-113,-8827.0,-4185,8.0,1,1,0,1,0,0,Drivers,2.0,1,1,THURSDAY,16,0,0,0,0,0,0,Self-employed,,0.6629235666085151,0.19633396621345675,0.3649,0.1712,0.9831,0.7688,0.0803,0.32,0.1607,0.4167,0.4583,0.0518,,0.2865,,0.0181,0.3718,0.1193,0.9831,0.7779,0.0372,0.1611,0.1379,0.4583,0.5,0.0,,0.1816,,0.0,0.3685,0.1531,0.9831,0.7719,0.0865,0.32,0.1379,0.4583,0.5,0.0606,,0.2847,,0.0022,reg oper account,block of flats,0.1573,Panel,No,4.0,0.0,4.0,0.0,-2229.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n259485,0,Cash loans,F,Y,Y,0,360000.0,1312110.0,52038.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018634,-20082,-3747,-3184.0,-891,13.0,1,1,1,1,0,0,Managers,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,,0.7472264496444061,0.08730427071350706,0.0722,0.0842,0.9796,0.7212,0.0,0.0,0.1379,0.1667,0.2083,0.0442,0.0588,0.0676,0.0,0.0,0.0735,0.0874,0.9796,0.7321,0.0,0.0,0.1379,0.1667,0.2083,0.0452,0.0643,0.0704,0.0,0.0,0.0729,0.0842,0.9796,0.7249,0.0,0.0,0.1379,0.1667,0.2083,0.045,0.0599,0.0688,0.0,0.0,reg oper account,block of flats,0.0711,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1525.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n391219,0,Cash loans,F,N,N,1,202500.0,1078200.0,38200.5,900000.0,Children,Commercial associate,Higher education,Married,House / apartment,0.018029,-12737,-2606,-279.0,-1547,,1,1,0,1,1,0,Managers,3.0,3,2,SATURDAY,11,0,0,0,0,0,0,Realtor,0.50082931306249,0.2653117484731741,0.5954562029091491,0.0928,0.0864,0.9801,0.728,0.0137,0.0,0.2069,0.1667,0.2083,0.0666,0.0756,0.0856,0.0,0.0,0.0945,0.0896,0.9801,0.7387,0.0138,0.0,0.2069,0.1667,0.2083,0.0681,0.0826,0.0892,0.0,0.0,0.0937,0.0864,0.9801,0.7316,0.0138,0.0,0.2069,0.1667,0.2083,0.0677,0.077,0.0872,0.0,0.0,reg oper account,block of flats,0.0795,Panel,No,0.0,0.0,0.0,0.0,-851.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n393099,0,Cash loans,M,N,Y,1,180000.0,679266.0,26451.0,567000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.025164,-16464,-3759,-9066.0,-15,,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.3734188731058757,0.1593713799385472,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-421.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n272051,0,Cash loans,F,N,N,0,292500.0,1845000.0,50868.0,1845000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.007273999999999998,-16516,-1732,-1255.0,-45,,1,1,0,1,0,0,Managers,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,Trade: type 3,0.8650055257096703,0.6494317070220861,0.746300213050371,0.0412,0.0371,0.9781,0.7008,0.016,0.0,0.069,0.1667,0.0,0.008,0.0336,0.0394,0.0,0.0,0.042,0.0385,0.9782,0.7125,0.0162,0.0,0.069,0.1667,0.0,0.0081,0.0367,0.0411,0.0,0.0,0.0416,0.0371,0.9781,0.7048,0.0161,0.0,0.069,0.1667,0.0,0.0081,0.0342,0.0401,0.0,0.0,reg oper account,block of flats,0.0398,Panel,No,3.0,0.0,3.0,0.0,-366.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n108650,0,Cash loans,M,Y,N,0,135000.0,1120500.0,32890.5,1120500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.014519999999999996,-13315,-2752,-6469.0,-3855,10.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Government,,0.6890077610009161,0.3539876078507373,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1851.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,1.0\n414213,0,Cash loans,M,N,Y,2,135000.0,239850.0,25960.5,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.011703,-11850,-807,-5807.0,-3695,,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.6673702749361776,0.5814837058057234,0.0515,0.0485,0.9836,,,0.0,0.1379,0.1667,,0.0264,,0.0264,,0.0183,0.0525,0.0504,0.9836,,,0.0,0.1379,0.1667,,0.027000000000000003,,0.0275,,0.0193,0.052000000000000005,0.0485,0.9836,,,0.0,0.1379,0.1667,,0.0269,,0.0269,,0.0186,,block of flats,0.0325,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n174969,0,Cash loans,F,N,Y,0,171000.0,360000.0,18508.5,360000.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.019101,-8631,-423,-4785.0,-1295,,1,1,1,1,0,0,Accountants,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,0.35149000056272034,0.12380524344922435,0.3774042489507649,0.1072,0.0231,0.9841,0.7824,0.0188,0.12,0.1034,0.3333,0.375,0.0172,0.0849,0.11,0.0116,0.0,0.1092,0.024,0.9841,0.7909,0.019,0.1208,0.1034,0.3333,0.375,0.0176,0.0927,0.1146,0.0117,0.0,0.1083,0.0231,0.9841,0.7853,0.0189,0.12,0.1034,0.3333,0.375,0.0175,0.0864,0.1119,0.0116,0.0,reg oper account,block of flats,0.0108,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n448581,0,Cash loans,F,Y,Y,1,90000.0,284400.0,18643.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-9510,-245,-657.0,-1169,7.0,1,1,0,1,1,0,Managers,3.0,3,3,WEDNESDAY,14,0,0,0,1,1,0,Business Entity Type 3,,0.2607549531837377,0.2636468134452008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-799.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,4.0\n421620,0,Cash loans,M,Y,Y,0,90000.0,545040.0,26640.0,450000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.004849,-13955,-475,-1868.0,-2843,16.0,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,1,1,Self-employed,,0.4141397202940969,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-268.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n216852,1,Cash loans,M,N,Y,0,126000.0,266652.0,17950.5,202500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-19929,-356,-5581.0,-1960,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,,0.5614330392550523,0.4365064990977374,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-565.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n346042,0,Revolving loans,M,Y,Y,0,180000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.0105,-13982,-1049,-7190.0,-1789,18.0,1,1,1,1,1,0,Laborers,2.0,3,3,TUESDAY,15,0,0,0,0,0,0,Housing,0.3886129696797812,0.5719838452645221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-647.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n448058,0,Cash loans,F,N,Y,0,99000.0,886176.0,29286.0,765000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-17575,-1023,-6767.0,-1119,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.4754261350342943,0.4596904504249018,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-384.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n360912,0,Cash loans,M,Y,Y,1,157500.0,508495.5,24592.5,454500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.00702,-9292,-229,-3279.0,-1955,18.0,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.13547263232948326,0.008260762086093191,0.4938628816996244,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n301797,0,Cash loans,F,N,N,0,121500.0,1546020.0,45202.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-20440,-4818,-4984.0,-4002,,1,1,0,1,1,0,,2.0,2,2,FRIDAY,7,0,0,0,0,0,0,Business Entity Type 1,,0.5800052435597204,0.3910549766342248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,1.0,-1618.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,5.0\n290815,1,Cash loans,F,N,N,2,270000.0,1255680.0,41499.0,1125000.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.010556,-14437,-134,-3683.0,-2599,,1,1,1,1,1,0,Core staff,4.0,3,3,FRIDAY,15,1,1,0,1,1,0,Advertising,0.6076454488013321,0.034439367301656504,0.3046721837533529,0.0577,0.0803,0.9776,0.6940000000000001,0.0782,0.0,0.1379,0.1667,0.2083,0.1353,0.0471,0.0544,0.0,0.0,0.0588,0.0833,0.9777,0.706,0.0789,0.0,0.1379,0.1667,0.2083,0.1384,0.0514,0.0567,0.0,0.0,0.0583,0.0803,0.9776,0.6981,0.0787,0.0,0.1379,0.1667,0.2083,0.1376,0.0479,0.0554,0.0,0.0,reg oper account,block of flats,0.0428,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3335.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n370243,1,Cash loans,M,Y,Y,1,180000.0,651600.0,28831.5,562500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020246,-18226,-4524,-9184.0,-1758,10.0,1,1,0,1,1,1,Laborers,3.0,3,3,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.5655441186843594,0.6879712766717088,0.09885928035482196,0.0804,0.0974,0.9811,,,0.0,0.2069,0.1667,,0.0515,,0.0727,,0.1005,0.063,0.0649,0.9801,,,0.0,0.1379,0.1667,,0.023,,0.0559,,0.0,0.0812,0.0974,0.9811,,,0.0,0.2069,0.1667,,0.0524,,0.07400000000000001,,0.1026,,block of flats,0.0422,Panel,No,2.0,0.0,2.0,0.0,-1103.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n322953,0,Revolving loans,M,Y,N,0,166500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Incomplete higher,Married,With parents,0.028663,-10300,-1229,-4664.0,-2983,8.0,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.5043915240701111,0.5189519934353019,0.2622489709189549,0.0619,0.0578,0.9791,0.7144,0.0064,0.0,0.1034,0.1667,0.0417,0.0583,0.0504,0.0509,0.0,0.0,0.063,0.06,0.9791,0.7256,0.0065,0.0,0.1034,0.1667,0.0417,0.0596,0.0551,0.0531,0.0,0.0,0.0625,0.0578,0.9791,0.7182,0.0064,0.0,0.1034,0.1667,0.0417,0.0593,0.0513,0.0519,0.0,0.0,reg oper account,block of flats,0.0436,Panel,No,3.0,0.0,3.0,0.0,-30.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n304468,1,Cash loans,M,Y,Y,2,360000.0,1575000.0,49585.5,1575000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-10908,-800,-5200.0,-3567,2.0,1,1,0,1,0,0,Core staff,4.0,2,2,MONDAY,8,0,0,0,1,1,0,Self-employed,,0.7187255489028868,0.1455428133497032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n401458,0,Cash loans,F,N,Y,0,108000.0,156384.0,16560.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006296,-23417,-2046,-3982.0,-4957,,1,1,0,1,0,0,Secretaries,2.0,3,3,THURSDAY,11,0,0,0,0,0,0,Other,0.5510558506776208,0.58516872182435,0.816092360478441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-652.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n267829,0,Cash loans,M,N,Y,1,112500.0,592560.0,26230.5,450000.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.007305,-15102,-588,-3933.0,-4973,,1,1,1,1,1,0,Laborers,3.0,3,3,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,,0.1712245823653266,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-255.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n122676,0,Cash loans,M,N,Y,0,162000.0,937638.0,48006.0,823500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.01885,-14166,-5576,-6284.0,-4563,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Security Ministries,0.12350434523696455,0.6678555294591682,0.266456808245056,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1100.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n206608,0,Cash loans,F,N,N,0,144000.0,314100.0,19111.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.009657,-19315,-12636,-485.0,-2840,,1,1,1,1,1,0,Managers,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,Postal,0.34889316737209,0.38532218531696905,0.11761373170805695,0.2206,,0.9836,,,0.24,0.2069,0.3333,,0.0316,,0.1392,,0.294,0.2248,,0.9836,,,0.2417,0.2069,0.3333,,0.0323,,0.145,,0.3113,0.2228,,0.9836,,,0.24,0.2069,0.3333,,0.0321,,0.1417,,0.3002,,block of flats,0.1822,Panel,No,0.0,0.0,0.0,0.0,-1835.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n367228,0,Cash loans,F,N,Y,0,202500.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008575,-17583,-96,-6808.0,-698,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,17,0,1,1,0,1,1,Business Entity Type 3,,0.4631073730404519,0.3791004853998145,0.1485,0.1144,0.9801,0.728,,0.16,0.1379,0.3333,,,,0.0951,,,0.1513,0.1187,0.9801,0.7387,,0.1611,0.1379,0.3333,,,,0.0991,,,0.1499,0.1144,0.9801,0.7316,,0.16,0.1379,0.3333,,,,0.0968,,,,block of flats,0.12300000000000001,Panel,No,0.0,0.0,0.0,0.0,-515.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n206892,0,Cash loans,M,Y,Y,2,180000.0,1051245.0,30865.5,877500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018029,-11425,-2429,-5724.0,-3942,15.0,1,1,0,1,0,0,Laborers,4.0,3,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,,0.2500682999333556,0.190705947811054,0.0928,0.0859,0.9786,0.7076,0.0117,0.0,0.2069,0.1667,0.2083,,0.0756,0.0598,0.0,0.0,0.0945,0.0892,0.9786,0.7190000000000001,0.0118,0.0,0.2069,0.1667,0.2083,,0.0826,0.0624,0.0,0.0,0.0937,0.0859,0.9786,0.7115,0.0117,0.0,0.2069,0.1667,0.2083,,0.077,0.0609,0.0,0.0,reg oper account,block of flats,0.0896,Panel,No,1.0,0.0,1.0,0.0,-1469.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n388972,0,Cash loans,M,N,N,0,180000.0,892044.0,28903.5,639000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-15942,-5638,-8026.0,-4180,,1,1,0,1,0,0,Laborers,2.0,3,3,SUNDAY,8,0,0,0,0,0,0,Business Entity Type 1,0.5155950776594178,0.6174847194116728,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1723.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n397547,0,Cash loans,M,N,Y,0,90000.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-13927,-1588,-2519.0,-1637,,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.3329486776629225,,0.1979,0.0868,0.9876,,,0.1064,0.0917,0.3333,,,,0.0772,,0.0,0.2269,0.0646,0.9876,,,0.1208,0.1034,0.3333,,,,0.06,,0.0,0.2248,0.0986,0.9876,,,0.12,0.1034,0.3333,,,,0.0884,,0.0,,block of flats,0.1708,Mixed,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n187189,0,Cash loans,M,Y,N,0,180000.0,118512.0,6556.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.01885,-12321,-2407,-3235.0,-3238,19.0,1,1,0,1,0,0,,2.0,2,2,SUNDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.2874226911644714,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-259.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n328931,0,Cash loans,F,N,Y,0,157500.0,450000.0,35685.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.028663,-21473,365243,-3557.0,-4022,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.5342638009091816,0.34090642641523844,0.1263,0.0685,0.9757,0.6668,0.0,0.0,0.0862,0.1667,0.2083,0.0839,0.10300000000000001,0.0715,0.0,0.0237,0.084,0.0524,0.9722,0.6341,0.0,0.0,0.0345,0.1667,0.2083,0.066,0.0735,0.069,0.0,0.0222,0.1275,0.0685,0.9757,0.6713,0.0,0.0,0.0862,0.1667,0.2083,0.0854,0.1047,0.0727,0.0,0.0242,reg oper account,block of flats,0.0613,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-190.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n113421,0,Cash loans,F,N,N,0,67500.0,279000.0,10647.0,279000.0,\"Spouse, partner\",Commercial associate,Higher education,Civil marriage,House / apartment,0.015221,-16242,-2117,-9734.0,-4070,,1,1,0,1,0,0,Security staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,School,,0.4564755610298482,0.5100895276257282,0.0928,0.1,0.9791,,,0.0,0.2069,0.1667,,0.0578,,0.0877,,0.0,0.0945,0.1038,0.9791,,,0.0,0.2069,0.1667,,0.0591,,0.0914,,0.0,0.0937,0.1,0.9791,,,0.0,0.2069,0.1667,,0.0588,,0.0893,,0.0,,block of flats,0.0927,Panel,No,0.0,0.0,0.0,0.0,-1435.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n369185,0,Cash loans,F,N,Y,0,144000.0,338832.0,18508.5,292500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-12790,-2737,-6052.0,-4583,,1,1,0,1,1,0,Sales staff,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Industry: type 3,0.4833884436559464,0.6026134118597403,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1182.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n309152,0,Cash loans,M,Y,Y,2,112500.0,284400.0,13963.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-13463,-1193,-4171.0,-5820,1.0,1,1,0,1,0,0,Drivers,4.0,2,2,THURSDAY,13,0,0,0,0,1,1,Transport: type 3,0.3904396021494803,0.2023379151298627,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,0.0,-427.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n288286,0,Revolving loans,F,N,Y,0,76500.0,202500.0,10125.0,202500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.020713,-8523,-1339,-218.0,-1192,,1,1,0,1,0,0,Sales staff,1.0,3,2,FRIDAY,15,0,0,0,0,0,0,Self-employed,0.10934447138283987,0.3267422505231851,,0.0082,0.0004,0.9722,0.6192,0.0007,0.0,0.0345,0.0417,0.0833,0.1017,0.0067,0.0054,0.0,0.0,0.0084,0.0004,0.9722,0.6341,0.0007,0.0,0.0345,0.0417,0.0833,0.1041,0.0073,0.0057,0.0,0.0,0.0083,0.0004,0.9722,0.6243,0.0007,0.0,0.0345,0.0417,0.0833,0.1035,0.0068,0.0055,0.0,0.0,reg oper account,block of flats,0.0047,Block,No,0.0,0.0,0.0,0.0,-862.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n266684,0,Cash loans,M,N,N,0,180000.0,1288350.0,37669.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-13872,-2270,-722.0,-4480,,1,1,0,1,1,0,,2.0,2,2,MONDAY,13,0,1,1,0,1,1,Other,,0.5102891283122993,0.4418358231994413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-92.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n327214,0,Cash loans,M,Y,Y,2,54000.0,254700.0,27153.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-12190,-4407,-1123.0,-4343,17.0,1,1,1,1,1,0,,4.0,3,3,THURSDAY,6,0,0,0,0,0,0,Business Entity Type 2,,0.44786296474980347,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-635.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n380936,0,Cash loans,F,N,N,0,450000.0,1138500.0,76522.5,1138500.0,Unaccompanied,State servant,Higher education,Single / not married,With parents,0.006629,-12419,-812,-3407.0,-4125,,1,1,0,1,0,0,Managers,1.0,2,2,SATURDAY,5,0,0,0,0,0,0,Government,0.34820071220466825,0.5937255407541094,0.7267112092725122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-557.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n394013,0,Cash loans,F,N,Y,0,67500.0,437076.0,28899.0,405000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22504,365243,-15579.0,-4043,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.16318703546427088,0.7992967832109371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n232236,0,Cash loans,M,Y,N,0,180000.0,728460.0,57685.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Rented apartment,0.030755,-10062,-3137,-3422.0,-2729,7.0,1,1,0,1,0,1,Drivers,2.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Agriculture,,0.3506762817437804,0.3280631605201915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-885.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n294285,0,Revolving loans,M,N,Y,1,180000.0,450000.0,22500.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.01885,-20793,-459,-19.0,-4249,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6468884108939293,0.41885428862332175,0.066,0.0592,0.9742,0.6464,0.0056,0.0,0.1379,0.125,0.1667,0.0262,0.0538,0.0521,0.0,0.0,0.0672,0.0614,0.9742,0.6602,0.0056,0.0,0.1379,0.125,0.1667,0.0268,0.0588,0.0542,0.0,0.0,0.0666,0.0592,0.9742,0.6511,0.0056,0.0,0.1379,0.125,0.1667,0.0267,0.0547,0.053,0.0,0.0,reg oper account,block of flats,0.044000000000000004,Panel,No,0.0,0.0,0.0,0.0,-1321.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n254632,0,Cash loans,F,Y,Y,0,270000.0,1016496.0,30816.0,877500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010006,-17232,-6595,-4572.0,-710,4.0,1,1,0,1,0,0,Medicine staff,1.0,2,1,SATURDAY,11,0,0,0,0,0,0,Medicine,0.4950418061315803,0.7371983959493037,0.6109913280868294,0.1186,0.1443,0.9935,0.9116,0.0713,0.0,0.2759,0.1667,0.2083,0.1015,0.0958,0.1316,0.0039,0.022000000000000002,0.1208,0.1498,0.9935,0.9151,0.0719,0.0,0.2759,0.1667,0.2083,0.1039,0.1047,0.1372,0.0039,0.0233,0.1197,0.1443,0.9935,0.9128,0.0717,0.0,0.2759,0.1667,0.2083,0.1033,0.0975,0.134,0.0039,0.0224,reg oper account,block of flats,0.1492,Panel,No,3.0,0.0,3.0,0.0,-223.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n412924,0,Cash loans,F,Y,N,0,157500.0,1293502.5,37948.5,1129500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-22088,365243,-9324.0,-2358,5.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.5866012314213017,0.746300213050371,0.0619,0.0015,0.9771,0.6872,0.0095,0.0,0.1379,0.1667,0.2083,0.1038,0.0504,0.057,0.0,0.0,0.063,0.0016,0.9772,0.6994,0.0096,0.0,0.1379,0.1667,0.2083,0.1061,0.0551,0.0594,0.0,0.0,0.0625,0.0015,0.9771,0.6914,0.0095,0.0,0.1379,0.1667,0.2083,0.1056,0.0513,0.057999999999999996,0.0,0.0,not specified,block of flats,0.0448,Panel,No,0.0,0.0,0.0,0.0,-1883.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n427308,1,Cash loans,M,N,N,0,157500.0,270000.0,28647.0,270000.0,Family,Working,Secondary / secondary special,Single / not married,With parents,0.025164,-9485,-1100,-4048.0,-1982,,1,1,1,1,1,0,Sales staff,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4694605913281535,0.6291339546880055,0.4812493411434029,0.0928,0.0,0.9771,0.6872,0.0142,0.0,0.2069,0.1667,0.2083,0.0786,0.0748,0.0862,0.0039,0.0043,0.0945,0.0,0.9772,0.6994,0.0143,0.0,0.2069,0.1667,0.2083,0.0804,0.0817,0.0898,0.0039,0.0045,0.0937,0.0,0.9771,0.6914,0.0143,0.0,0.2069,0.1667,0.2083,0.08,0.0761,0.0878,0.0039,0.0043,org spec account,block of flats,0.0765,Panel,No,1.0,0.0,1.0,0.0,-1037.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n442649,0,Cash loans,M,N,Y,2,405000.0,900000.0,46084.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.007273999999999998,-11160,-1061,-5146.0,-3645,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,12,0,0,0,1,1,0,Government,,0.7917857435567505,0.7826078370261895,0.0082,,0.9722,,,0.0,0.069,0.0417,,0.0,,0.0059,,0.0,0.0084,,0.9722,,,0.0,0.069,0.0417,,0.0,,0.0061,,0.0,0.0083,,0.9722,,,0.0,0.069,0.0417,,0.0,,0.006,,0.0,,block of flats,0.0069,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3378.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n446843,0,Cash loans,F,N,Y,1,67500.0,225000.0,10620.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.014519999999999996,-10362,-1523,-4640.0,-2505,,1,1,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.2800779308894161,0.2318881971357951,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n337227,0,Revolving loans,F,N,Y,0,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-16082,-6152,-4664.0,-4673,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,School,0.5815784770612419,0.5951489450237417,0.7838324335199619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-522.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n186577,0,Cash loans,F,N,N,0,58500.0,544491.0,17563.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-18722,-10008,-7693.0,-2231,,1,1,1,1,1,0,Cooking staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Trade: type 3,,0.19421511211361847,0.4866531565147181,0.0732,0.0776,0.9781,0.7008,0.0077,0.0,0.1379,0.1667,0.2083,0.015,0.0588,0.0642,0.0039,0.0213,0.0746,0.0806,0.9782,0.7125,0.0078,0.0,0.1379,0.1667,0.2083,0.0153,0.0643,0.0669,0.0039,0.0226,0.0739,0.0776,0.9781,0.7048,0.0078,0.0,0.1379,0.1667,0.2083,0.0152,0.0599,0.0653,0.0039,0.0218,reg oper account,block of flats,0.0551,Block,No,0.0,0.0,0.0,0.0,-1139.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n203397,0,Cash loans,F,N,Y,0,81000.0,454500.0,19386.0,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-15902,-2327,-9645.0,-3463,,1,1,0,1,0,0,Sales staff,2.0,3,3,MONDAY,6,0,0,0,0,0,0,Business Entity Type 3,,0.5770406261524078,,0.1206,0.1561,0.9801,0.728,0.0568,0.0,0.2759,0.1667,0.2083,0.0903,0.0967,0.1143,0.0077,0.0087,0.1229,0.162,0.9801,0.7387,0.0573,0.0,0.2759,0.1667,0.2083,0.0924,0.1056,0.1191,0.0078,0.0092,0.1218,0.1561,0.9801,0.7316,0.0572,0.0,0.2759,0.1667,0.2083,0.0919,0.0983,0.1163,0.0078,0.0089,reg oper account,block of flats,0.1209,Panel,No,0.0,0.0,0.0,0.0,-1811.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n315220,0,Cash loans,M,Y,Y,0,189000.0,503676.0,26500.5,382500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Municipal apartment,0.04622,-9958,-510,-573.0,-2473,2.0,1,1,0,1,0,0,Laborers,1.0,1,1,FRIDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.21311544237769808,0.03991115717892625,0.3572932680336494,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-245.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n200227,0,Cash loans,F,N,Y,1,135000.0,900000.0,24750.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-9444,-1532,-2484.0,-2098,,1,1,0,1,0,0,Core staff,3.0,3,3,MONDAY,11,0,0,0,0,0,0,Kindergarten,,0.3468023871293304,,0.1526,0.0,0.9811,0.7416,0.0234,0.0264,0.1034,0.2221,0.2638,0.0302,0.1222,0.0797,0.0103,0.0339,0.0599,0.0,0.9806,0.7452,0.0074,0.0,0.069,0.1667,0.2083,0.0223,0.0523,0.053,0.0,0.0,0.0697,0.0,0.9806,0.7383,0.0183,0.0,0.1034,0.1667,0.2083,0.0283,0.0547,0.0626,0.0116,0.0517,reg oper spec account,block of flats,0.055,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n279469,0,Cash loans,M,N,N,0,157500.0,497520.0,36184.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018634,-16009,-4143,-2325.0,-2335,,1,1,1,1,0,0,Sales staff,2.0,2,2,MONDAY,18,0,0,0,1,1,0,Self-employed,,0.3976415831319524,0.4329616670974407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-489.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n262453,0,Cash loans,F,N,N,1,108000.0,364896.0,19926.0,315000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.020246,-12710,-2999,-648.0,-4065,,1,1,1,1,0,0,Core staff,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,Government,0.39265501247864704,0.4990342442151726,0.2778856891082046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-1310.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n165345,0,Cash loans,F,N,Y,0,108000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-17845,-2226,-1912.0,-1387,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,Transport: type 4,,0.7452761157592045,0.5656079814115492,0.3763,0.3719,0.9791,0.7144,0.1788,0.0,0.8966,0.1667,0.2083,0.3806,0.3068,0.3617,0.0,0.0,0.3834,0.3859,0.9791,0.7256,0.1804,0.0,0.8966,0.1667,0.2083,0.3893,0.3352,0.3769,0.0,0.0,0.3799,0.3719,0.9791,0.7182,0.1799,0.0,0.8966,0.1667,0.2083,0.3872,0.3121,0.3682,0.0,0.0,reg oper account,block of flats,0.3822,Panel,No,1.0,1.0,1.0,1.0,-1318.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n440658,0,Cash loans,F,Y,Y,0,157500.0,850500.0,24867.0,850500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.015221,-22188,365243,-8547.0,-4087,11.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.1586286567556069,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,0.0,-1700.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n322295,0,Cash loans,F,N,Y,0,135000.0,562932.0,29605.5,427500.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.00496,-16794,-1987,-3633.0,-229,,1,1,0,1,1,0,Laborers,1.0,2,2,SATURDAY,12,0,0,0,0,1,1,Industry: type 11,0.6888114242499848,0.3874323062737605,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,2.0,-215.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n119726,1,Cash loans,F,Y,N,0,135000.0,360000.0,18378.0,360000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-22142,365243,-7477.0,-4909,24.0,1,0,0,1,1,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.35003422271982604,0.1777040724853336,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-98.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n454282,0,Cash loans,F,N,Y,0,157500.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-20401,-7001,-117.0,-3960,,1,1,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Other,0.6626388135595517,0.6610789113110741,0.6512602186973006,0.1485,,0.9886,,,0.16,0.1379,0.3333,,0.1388,,0.0937,,,0.1513,,0.9886,,,0.1611,0.1379,0.3333,,0.142,,0.0977,,,0.1499,,0.9886,,,0.16,0.1379,0.3333,,0.1412,,0.0954,,,,block of flats,0.1219,Panel,No,3.0,0.0,3.0,0.0,-931.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n340097,0,Cash loans,F,N,Y,0,225000.0,640080.0,29970.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-13744,-885,-1681.0,-1723,,1,1,0,1,1,0,Cleaning staff,2.0,1,1,WEDNESDAY,16,0,0,0,0,0,0,Housing,,0.5367223809895243,0.1694287272664794,0.0577,0.0,0.9732,,,0.0,0.1034,0.1667,,0.0711,,0.0409,,0.013999999999999999,0.0588,0.0,0.9732,,,0.0,0.1034,0.1667,,0.0697,,0.0354,,0.0113,0.0583,0.0,0.9732,,,0.0,0.1034,0.1667,,0.0724,,0.0421,,0.0143,,block of flats,0.037000000000000005,\"Stone, brick\",No,7.0,1.0,7.0,0.0,-284.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n334623,0,Cash loans,M,Y,Y,0,180000.0,1762110.0,46480.5,1575000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-12736,-3462,-2446.0,-3329,1.0,1,1,0,1,1,0,,2.0,2,2,SUNDAY,8,0,0,0,0,0,0,Business Entity Type 2,,0.7843207764233556,0.4830501881366946,0.1031,0.0847,0.9781,0.7008,0.013999999999999999,0.0,0.2069,0.1667,0.2083,0.0847,0.0841,0.0905,0.0,0.0,0.105,0.0879,0.9782,0.7125,0.0142,0.0,0.2069,0.1667,0.2083,0.0866,0.0918,0.0943,0.0,0.0,0.1041,0.0847,0.9781,0.7048,0.0141,0.0,0.2069,0.1667,0.2083,0.0862,0.0855,0.0922,0.0,0.0,reg oper account,block of flats,0.0789,\"Stone, brick\",No,6.0,2.0,6.0,0.0,-1305.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n181493,0,Cash loans,M,Y,Y,0,99000.0,900000.0,26446.5,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-22805,365243,-10187.0,-4535,7.0,1,0,0,1,1,0,,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,XNA,0.6957502940487831,0.17070078585779758,0.7922644738669378,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-424.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n323897,0,Cash loans,F,N,Y,0,81000.0,619965.0,17896.5,517500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.00496,-20438,365243,-11132.0,-3006,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.6397181183052103,0.6785676886853644,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-304.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n195536,0,Cash loans,F,Y,N,0,832500.0,1386684.0,129654.0,1354500.0,Family,Working,Higher education,Married,House / apartment,0.025164,-13725,-4145,-104.0,-4342,1.0,1,1,1,1,0,0,Accountants,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 1,,0.5891866296961207,0.5867400085415683,0.3784,0.3231,0.9975,0.966,0.3642,0.44,0.1724,0.8333,0.625,0.3505,0.2917,0.7239,0.0772,0.6945,0.3855,0.3353,0.9975,0.9673,0.3675,0.4431,0.1724,0.8333,0.625,0.3585,0.3186,0.7542,0.0778,0.7352,0.382,0.3231,0.9975,0.9665,0.3665,0.44,0.1724,0.8333,0.625,0.3566,0.2967,0.7369,0.0776,0.7091,reg oper spec account,block of flats,0.9195,Mixed,No,0.0,0.0,0.0,0.0,-1035.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n155250,0,Cash loans,F,N,N,1,135000.0,450000.0,21109.5,450000.0,Family,Working,Secondary / secondary special,Single / not married,Rented apartment,0.015221,-12168,-307,-1959.0,-1958,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,9,0,0,0,1,1,0,Self-employed,,0.627591768327591,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-248.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n365440,0,Cash loans,F,Y,Y,1,180000.0,1267735.5,41026.5,1107000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-8335,-447,-8286.0,-914,6.0,1,1,0,1,1,0,,3.0,1,1,SUNDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.7305714189521554,0.6675061820534712,,0.0758,0.0027,0.9762,0.6736,0.0241,0.04,0.0345,0.3333,0.375,0.0,0.0605,0.0517,0.0058,0.0065,0.0756,0.0027,0.9762,0.6864,0.0157,0.0403,0.0345,0.3333,0.375,0.0,0.0661,0.0536,0.0,0.0034,0.0765,0.0027,0.9762,0.6779999999999999,0.0243,0.04,0.0345,0.3333,0.375,0.0,0.0616,0.0526,0.0058,0.0067,reg oper spec account,block of flats,0.0415,Block,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n230573,1,Cash loans,F,N,Y,0,157500.0,387000.0,26307.0,387000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,With parents,0.072508,-10073,-521,-4828.0,-798,,1,1,0,1,1,0,,1.0,1,1,THURSDAY,19,0,0,0,0,0,0,Business Entity Type 2,0.3962211083877642,0.7042787491941063,0.11033242816628903,0.8825,0.6268,0.9821,,,0.96,0.8276,0.3333,,0.4442,,0.8242,,0.0071,0.8992,0.6505,0.9821,,,0.9667,0.8276,0.3333,,0.4544,,0.8587,,0.0075,0.8909999999999999,0.6268,0.9821,,,0.96,0.8276,0.3333,,0.452,,0.8390000000000001,,0.0073,,block of flats,0.6498,Panel,No,1.0,0.0,1.0,0.0,-732.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n379212,0,Cash loans,F,N,Y,0,108000.0,1084500.0,31837.5,1084500.0,Other_B,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-20707,-3718,-9286.0,-3917,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 1,,0.09051242886929917,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-1069.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n140528,0,Cash loans,F,Y,Y,1,180000.0,715095.0,48109.5,675000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.014464,-17505,-6642,-6511.0,-1061,24.0,1,1,1,1,1,0,Core staff,3.0,2,2,SATURDAY,5,0,0,0,0,0,0,School,0.5221070648543878,0.363765199111832,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1672.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n300543,0,Cash loans,F,Y,Y,1,83250.0,107820.0,8473.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-9044,-1852,-1733.0,-1718,20.0,1,1,1,1,0,0,Sales staff,3.0,3,3,FRIDAY,8,0,0,0,0,0,0,Government,0.3781172702920772,0.20194996257099426,0.4776491548517548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1475.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n130437,0,Cash loans,F,N,N,0,135000.0,495985.5,17946.0,409500.0,Unaccompanied,Pensioner,Incomplete higher,Single / not married,House / apartment,0.072508,-22225,365243,-4578.0,-4360,,1,0,0,1,1,0,,1.0,1,1,FRIDAY,14,0,0,0,0,0,0,XNA,,0.4363481714081137,0.41534714488434,0.1964,0.1425,0.9801,0.728,0.0324,0.24,0.1724,0.3958,0.4375,0.0,0.1597,0.1788,0.0019,0.0221,0.0998,0.0481,0.9782,0.7125,0.0213,0.1611,0.069,0.3333,0.375,0.0,0.0872,0.0887,0.0,0.0061,0.1983,0.1425,0.9801,0.7316,0.0326,0.24,0.1724,0.3958,0.4375,0.0,0.1625,0.182,0.0019,0.0225,reg oper account,block of flats,0.2465,Panel,No,1.0,0.0,1.0,0.0,-1488.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n160316,0,Cash loans,F,N,N,0,99000.0,814041.0,28971.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00496,-18615,-5160,-2112.0,-1734,,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6990420668172441,0.8297501422395771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-570.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n231588,1,Cash loans,F,N,N,0,112500.0,450000.0,24412.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-12968,-416,-2944.0,-623,,1,1,1,1,1,0,Accountants,2.0,2,2,THURSDAY,20,0,0,0,0,1,1,Business Entity Type 3,0.3211788727529649,0.6150231605620977,0.09022093929076848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-460.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n409604,0,Cash loans,F,N,Y,0,76500.0,314100.0,13963.5,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.025164,-16432,-1722,-5540.0,-5775,,1,1,0,1,0,0,Cooking staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Kindergarten,,0.4661480634481962,0.43473324875017305,0.0701,0.0179,0.9737,0.6396,0.0144,0.0,0.1379,0.125,0.1667,0.0597,0.053,0.0501,0.0193,0.0317,0.0714,0.0186,0.9737,0.6537,0.0145,0.0,0.1379,0.125,0.1667,0.061,0.0579,0.0522,0.0195,0.0335,0.0708,0.0179,0.9737,0.6444,0.0145,0.0,0.1379,0.125,0.1667,0.0607,0.0539,0.051,0.0194,0.0323,not specified,block of flats,0.0541,Block,No,0.0,0.0,0.0,0.0,-1868.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n325199,0,Cash loans,M,N,Y,0,112500.0,123637.5,8923.5,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.022625,-24887,365243,-1785.0,-4751,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,0.8368320822283859,0.025873913962196567,0.6313545365850379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,2.0,1.0,2.0,1.0,-1389.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n291506,0,Revolving loans,M,Y,Y,0,126000.0,270000.0,13500.0,270000.0,Family,Commercial associate,Higher education,Single / not married,House / apartment,0.04622,-7949,-155,-7949.0,-628,10.0,1,1,0,1,0,0,,1.0,1,1,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.2663177886870393,0.7638379592140738,0.28961123838200553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-567.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.0,0.0,0.0,0.0,0.0,1.0\n328517,0,Revolving loans,M,Y,Y,0,283500.0,585000.0,29250.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-16110,-1322,-3194.0,-4186,12.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Construction,,0.6502527318118689,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2224.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n216756,0,Cash loans,M,N,Y,1,225000.0,1236816.0,36292.5,1080000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.018634,-12143,-3509,-5923.0,-4480,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,13,0,0,0,1,1,1,Other,,0.4882065106580768,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2315.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n186875,0,Cash loans,M,Y,Y,0,202500.0,640080.0,31261.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-12748,-871,-19.0,-4280,3.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Construction,,0.4671658263166037,0.3556387169923543,0.0278,0.0565,0.9866,0.8164,0.0039,0.0,0.1034,0.0833,0.0833,0.0,0.0227,0.018000000000000002,0.0,0.0,0.0284,0.0586,0.9866,0.8236,0.004,0.0,0.1034,0.0833,0.0833,0.0,0.0248,0.0187,0.0,0.0,0.0281,0.0565,0.9866,0.8189,0.004,0.0,0.1034,0.0833,0.0833,0.0,0.0231,0.0183,0.0,0.0,,block of flats,0.0239,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-77.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,7.0\n112969,1,Revolving loans,M,N,N,0,90000.0,157500.0,7875.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.030755,-13410,-729,-1755.0,-1768,,1,1,1,1,0,0,Laborers,1.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.2482861175489315,0.27233042145362546,0.4329616670974407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-558.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n321584,0,Cash loans,M,Y,Y,0,225000.0,971280.0,56920.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.032561,-14453,-4974,-4289.0,-4884,11.0,1,1,0,1,0,0,Laborers,1.0,1,1,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.7161722068279773,0.5406544504453575,0.2418,0.1239,0.9786,0.7144,,0.34,0.2672,0.25,0.375,,0.2824,0.2841,,0.0028,0.1239,0.0703,0.9791,0.7256,,0.3222,0.2759,0.1667,0.375,,0.3085,0.4481,,0.003,0.2519,0.1286,0.9791,0.7182,,0.34,0.2759,0.25,0.375,,0.2873,0.3433,,0.0029,reg oper account,block of flats,0.5308,Panel,No,3.0,0.0,3.0,0.0,-717.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n264618,0,Cash loans,F,N,Y,0,112500.0,130320.0,13815.0,112500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.008625,-14153,-2949,-5758.0,-4679,,1,1,1,1,0,1,Core staff,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6674722078867837,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2390.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n347157,0,Cash loans,F,Y,N,0,121500.0,990000.0,28944.0,990000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14255,-7702,-4899.0,-4899,4.0,1,1,1,1,1,0,Cooking staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Kindergarten,0.4566349076099461,0.5806496646560155,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2417.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n399703,0,Cash loans,F,N,Y,0,90000.0,296280.0,16200.0,225000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019689,-20782,365243,-8446.0,-4296,,1,0,0,1,1,1,,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,0.7402575827053057,0.26671646364621504,,0.0928,0.0842,0.9836,0.7756,0.0231,0.1,0.0862,0.3333,0.375,0.0796,0.075,0.0929,0.0029,0.0031,0.0378,0.0413,0.9836,0.7844,0.011000000000000001,0.0403,0.0345,0.3333,0.375,0.0418,0.0321,0.0386,0.0,0.0,0.0937,0.0821,0.9836,0.7786,0.0227,0.1,0.0862,0.3333,0.375,0.0876,0.077,0.0958,0.0019,0.0025,reg oper account,block of flats,0.0362,Panel,No,2.0,0.0,2.0,0.0,-529.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n135238,0,Cash loans,F,N,N,0,67500.0,450000.0,20979.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008473999999999999,-14542,-718,-1806.0,-3785,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,Self-employed,0.4808194467742054,0.7651450844434791,0.5136937663039473,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-560.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n109029,0,Cash loans,M,N,Y,0,135000.0,608076.0,28476.0,427500.0,Family,Working,Higher education,Married,House / apartment,0.072508,-10609,-849,-4990.0,-3240,,1,1,0,1,1,1,Core staff,2.0,1,1,THURSDAY,17,0,0,0,0,0,0,Other,0.6080921378950322,0.6704310784989117,0.5442347412142162,0.3242,0.1003,0.9791,0.7144,0.0496,0.12,0.0517,0.3333,0.375,0.0,0.2622,0.1674,0.0097,0.096,0.2206,0.1017,0.9786,0.7190000000000001,0.0001,0.0806,0.0345,0.3333,0.375,0.0,0.191,0.1156,0.0078,0.0743,0.3274,0.1003,0.9791,0.7182,0.0499,0.12,0.0517,0.3333,0.375,0.0,0.2668,0.1704,0.0097,0.098,reg oper account,block of flats,0.2027,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-437.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n113563,0,Cash loans,F,Y,N,0,135000.0,168102.0,19093.5,148500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019101,-18276,-4053,-2792.0,-1806,3.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,15,0,0,0,1,1,0,Business Entity Type 3,0.4603328623163481,0.7157986774168651,0.8027454758994178,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2642.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n440152,0,Cash loans,F,N,Y,0,157500.0,625536.0,22599.0,540000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.0228,-15413,-553,-5488.0,-2066,,1,1,0,1,1,0,Sales staff,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,Self-employed,0.6682669062142612,0.6347743996553797,0.5971924268337128,0.0041,,0.9717,,,,,0.0,,0.0155,,0.0026,,,0.0042,,0.9717,,,,,0.0,,0.0159,,0.0028,,,0.0042,,0.9717,,,,,0.0,,0.0158,,0.0027,,,,block of flats,0.0021,Wooden,No,5.0,0.0,5.0,0.0,-249.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n179193,0,Cash loans,F,N,Y,0,207000.0,2285784.0,60426.0,1908000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.001276,-16193,-1455,-6278.0,-2469,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.1112294656824407,0.7001838506835805,0.0722,0.0757,0.9836,0.7756,0.0107,0.0,0.1724,0.1667,0.0417,0.0399,0.0588,0.0693,0.0,0.0,0.063,0.0327,0.9747,0.6668,0.0053,0.0,0.1379,0.1667,0.0417,0.027999999999999997,0.0551,0.0517,0.0,0.0,0.0625,0.0783,0.9876,0.8323,0.0081,0.0,0.1724,0.1667,0.0417,0.0371,0.0513,0.0637,0.0,0.0,reg oper account,block of flats,0.0432,Panel,No,0.0,0.0,0.0,0.0,-26.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n316887,0,Cash loans,F,N,Y,1,135000.0,573408.0,19080.0,495000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.00733,-16545,-8090,-10650.0,-91,,1,1,0,1,0,0,Medicine staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Other,,0.6665979889053817,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-648.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n447653,0,Cash loans,M,Y,Y,0,450000.0,130500.0,16236.0,130500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.016612000000000002,-13646,-478,-11157.0,-4922,3.0,1,1,0,1,1,0,Managers,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Trade: type 7,,0.6723319705171924,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-353.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n227689,0,Cash loans,F,Y,Y,0,157500.0,225000.0,12334.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-19822,-5532,-9807.0,-3355,3.0,1,1,1,1,1,0,Core staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,0.6083975522264742,0.60665422987879,0.8367640773817324,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-505.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n258521,0,Cash loans,M,Y,N,0,225000.0,935640.0,98379.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008625,-15693,-1991,-3720.0,-4236,8.0,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,13,0,1,1,0,1,1,Transport: type 4,0.6562766502151862,0.7228447685177726,0.5814837058057234,0.0722,,0.9816,0.7484,,0.0,0.1379,0.1667,0.2083,,0.0588,0.0648,0.0,,0.0735,,0.9816,0.7583,,0.0,0.1379,0.1667,0.2083,,0.0643,0.0675,0.0,,0.0729,,0.9816,0.7518,,0.0,0.1379,0.1667,0.2083,,0.0599,0.0659,0.0,,reg oper account,block of flats,0.055,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2468.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n328485,0,Cash loans,F,N,Y,0,225000.0,1270746.0,38659.5,1138500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.022625,-18309,-4810,-4693.0,-1852,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Self-employed,,0.6573700018125913,,0.3505,0.0,0.9886,0.8436,0.3689,0.0,0.2414,0.3333,0.375,0.0,0.2858,0.2564,0.0,0.0,0.3571,0.0,0.9886,0.8497,0.3723,0.0,0.2414,0.3333,0.375,0.0,0.3122,0.2671,0.0,0.0,0.3539,0.0,0.9886,0.8457,0.3713,0.0,0.2414,0.3333,0.375,0.0,0.2907,0.261,0.0,0.0,reg oper account,block of flats,0.2017,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-755.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n283104,0,Cash loans,F,N,Y,1,112500.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-15023,-4221,-794.0,-4470,,1,1,1,1,1,0,Accountants,3.0,3,3,TUESDAY,13,0,0,0,0,0,0,Bank,,0.19597326943016166,0.2707073872651806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n231888,0,Cash loans,M,N,Y,1,157500.0,294322.5,19291.5,243000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007305,-12056,-199,-5044.0,-3966,,1,1,0,1,0,0,Sales staff,3.0,3,3,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6575669882034132,0.3876253444214701,0.1113,0.1121,0.9791,0.7076,0.0114,0.0,0.2241,0.1667,0.2083,0.0201,0.0908,0.0776,0.0039,0.0029,0.1124,0.1124,0.9786,0.7190000000000001,0.0115,0.0,0.2069,0.1667,0.2083,0.0206,0.0992,0.0625,0.0039,0.003,0.1124,0.1121,0.9791,0.7115,0.0115,0.0,0.2241,0.1667,0.2083,0.0204,0.0923,0.079,0.0039,0.0029,reg oper account,block of flats,0.0746,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2087.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n385420,0,Cash loans,F,N,Y,0,81000.0,495000.0,27769.5,495000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.026392,-14540,-778,-492.0,-515,,1,1,1,1,1,0,Cleaning staff,2.0,2,2,SUNDAY,16,0,0,0,0,0,0,Medicine,,0.6027705004978313,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1235.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n398044,0,Cash loans,F,Y,N,1,180000.0,604413.0,19107.0,459000.0,,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-11541,-744,-1520.0,-1452,4.0,1,1,0,1,1,0,Sales staff,3.0,1,1,TUESDAY,16,0,0,0,1,1,0,Business Entity Type 3,0.510336945348096,0.5872215322039792,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n312308,0,Cash loans,F,N,N,0,81000.0,467257.5,17743.5,328500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002134,-15735,-4586,-7514.0,-4230,,1,1,0,1,0,0,Core staff,2.0,3,3,SATURDAY,6,0,0,0,0,0,0,Kindergarten,,0.7317397161673316,0.622922000268356,,,0.9771,,,,,,,,,0.0103,,,,,0.9772,,,,,,,,,0.0107,,,,,0.9771,,,,,,,,,0.0104,,,,block of flats,0.0081,,Yes,8.0,0.0,8.0,0.0,-2057.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n236079,1,Cash loans,F,N,Y,0,112500.0,675000.0,29731.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.003122,-17482,-517,-2923.0,-1015,,1,1,1,1,0,0,Core staff,2.0,3,3,SATURDAY,13,0,0,0,0,0,0,Kindergarten,,0.5762505381802387,0.3280631605201915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1131.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n423511,0,Cash loans,F,N,Y,1,135000.0,987156.0,35586.0,765000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-17921,-8017,-8038.0,-1453,,1,1,0,1,1,0,,3.0,3,3,SATURDAY,6,0,0,0,0,1,1,Government,0.6088649876157386,0.5029047724145553,0.30620229831350426,0.0639,0.0851,0.9737,0.6396,0.0063,0.0,0.1379,0.125,0.1667,0.044000000000000004,0.0521,0.0484,0.0,0.0,0.0651,0.0883,0.9737,0.6537,0.0064,0.0,0.1379,0.125,0.1667,0.045,0.0569,0.0505,0.0,0.0,0.0645,0.0851,0.9737,0.6444,0.0063,0.0,0.1379,0.125,0.1667,0.0447,0.053,0.0493,0.0,0.0,reg oper account,block of flats,0.0415,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-569.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n341326,1,Cash loans,M,Y,Y,2,90000.0,284400.0,18643.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-17313,-189,-6766.0,-558,10.0,1,1,0,1,0,0,Drivers,4.0,2,2,SATURDAY,10,0,0,0,0,1,1,Advertising,,0.3760630278232229,0.4206109640437848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-141.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n379947,0,Cash loans,F,N,Y,0,90000.0,326664.0,15844.5,234000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006852,-22068,365243,-4918.0,-2291,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,XNA,,0.3203435220955649,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-967.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,12.0\n348294,0,Cash loans,F,N,Y,0,225000.0,296280.0,19062.0,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.018801,-8446,-1201,-49.0,-1116,,1,1,0,1,0,0,Sales staff,1.0,2,2,THURSDAY,11,0,1,1,0,1,1,Emergency,,0.6841279846388156,0.2445163919946749,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-458.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n353322,0,Cash loans,F,N,Y,0,112500.0,284400.0,17527.5,225000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.010966,-22998,365243,-8669.0,-4481,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,,0.5240758591557275,0.7503751495159068,0.1124,0.0902,0.9886,0.8436,0.0198,0.12,0.1034,0.3333,0.0417,0.0784,0.0908,0.1047,0.0039,0.0684,0.1145,0.0936,0.9886,0.8497,0.02,0.1208,0.1034,0.3333,0.0417,0.0802,0.0992,0.1091,0.0039,0.0724,0.1135,0.0902,0.9886,0.8457,0.02,0.12,0.1034,0.3333,0.0417,0.0797,0.0923,0.1066,0.0039,0.0698,org spec account,block of flats,0.1081,Panel,No,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n280801,0,Cash loans,M,N,Y,2,135000.0,814041.0,28971.0,679500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-12932,-583,-519.0,-4733,,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Industry: type 7,0.2471182502038805,0.4525990668678327,0.7380196196295241,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n283473,0,Cash loans,F,Y,Y,0,135000.0,1305000.0,38286.0,1305000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-13633,-6430,-4554.0,-264,2.0,1,1,0,1,0,0,Waiters/barmen staff,2.0,3,3,SATURDAY,8,0,0,0,0,0,0,Kindergarten,,0.5163147731368093,0.8214433128935934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1098.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n287767,0,Cash loans,F,Y,Y,0,178650.0,134775.0,9508.5,112500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.030755,-17291,365243,-6931.0,-847,1.0,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,XNA,0.905048053893758,0.5363145510360974,0.15474363127259447,0.2887,0.3727,0.9906,0.8708,0.0535,0.28,0.2414,0.375,0.4167,0.0489,0.2353,0.3312,0.0,0.0,0.2941,0.3868,0.9906,0.8759,0.054000000000000006,0.282,0.2414,0.375,0.4167,0.05,0.2571,0.3451,0.0,0.0,0.2915,0.3727,0.9906,0.8725,0.0539,0.28,0.2414,0.375,0.4167,0.0498,0.2394,0.3371,0.0,0.0,reg oper account,block of flats,0.2898,Panel,No,0.0,0.0,0.0,0.0,-652.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n340245,0,Revolving loans,F,N,Y,0,270000.0,585000.0,29250.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.001276,-8292,-888,-265.0,-336,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,,0.104632806801656,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n312069,0,Cash loans,M,Y,N,1,216000.0,781920.0,39924.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.002506,-12845,-1574,-1613.0,-1645,15.0,1,1,1,1,1,0,Core staff,3.0,2,2,FRIDAY,0,0,0,0,0,0,0,Trade: type 7,0.543947455446116,0.6683840512431313,0.4561097392782771,0.1227,,0.9791,0.7144,0.0,0.0,0.2759,0.1667,0.0417,,0.1,0.1031,0.0,0.0439,0.125,,0.9791,0.7256,0.0,0.0,0.2759,0.1667,0.0417,,0.1093,0.1074,0.0,0.0465,0.1239,,0.9791,0.7182,0.0,0.0,0.2759,0.1667,0.0417,,0.1018,0.1049,0.0,0.0449,reg oper account,block of flats,0.0906,,No,1.0,0.0,1.0,0.0,-1004.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n331730,0,Cash loans,F,N,Y,1,166500.0,518562.0,21969.0,463500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.030755,-14333,-1024,-5115.0,-5118,,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Transport: type 2,0.6907445497541165,0.6751535423635787,0.475849908720221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-945.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n111732,0,Cash loans,M,N,N,1,157500.0,545040.0,36553.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018029,-16030,-1542,-7150.0,-4638,,1,1,0,1,0,0,Drivers,2.0,3,3,THURSDAY,16,0,0,0,0,0,0,Trade: type 7,,0.08404198050058924,0.3672910183026313,0.0804,0.0923,0.9886,0.8436,0.083,0.0,0.2069,0.1667,0.0417,0.0344,0.0656,0.0908,0.0,0.0,0.0819,0.0958,0.9886,0.8497,0.0837,0.0,0.2069,0.1667,0.0417,0.0352,0.0716,0.0946,0.0,0.0,0.0812,0.0923,0.9886,0.8457,0.0835,0.0,0.2069,0.1667,0.0417,0.035,0.0667,0.0925,0.0,0.0,reg oper account,block of flats,0.1168,Panel,No,0.0,0.0,0.0,0.0,-2050.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n425053,0,Cash loans,F,N,N,0,117000.0,781920.0,25839.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.003813,-15206,-7823,-4566.0,-4620,,1,1,0,1,1,0,Medicine staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Medicine,0.7461887106413277,0.4334031457853185,0.6279908192952864,0.0608,0.0601,0.9856,,,0.0,0.1379,0.1667,,,,0.0546,,,0.062,0.0624,0.9856,,,0.0,0.1379,0.1667,,,,0.0569,,,0.0614,0.0601,0.9856,,,0.0,0.1379,0.1667,,,,0.0556,,,,block of flats,0.0517,Panel,No,2.0,0.0,2.0,0.0,-1687.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n194693,0,Cash loans,M,Y,Y,0,135000.0,835380.0,40320.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Rented apartment,0.018801,-8294,-709,-925.0,-960,64.0,1,1,0,1,0,1,Core staff,2.0,2,2,THURSDAY,11,0,0,0,0,1,1,Military,0.03718600156444428,0.2636527195631183,0.1895952597360396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-613.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n416574,0,Cash loans,M,N,N,0,135000.0,1078200.0,31522.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-16791,-1292,-7966.0,-321,,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Security,,0.7238468870550844,0.4686596550493113,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1699.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n392375,0,Cash loans,M,Y,N,1,121500.0,135000.0,14305.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-9923,-530,-953.0,-2577,12.0,1,1,1,1,0,0,Security staff,3.0,3,3,FRIDAY,13,0,0,0,0,1,1,Security,,0.6366481314263818,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1522.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n121854,0,Cash loans,M,N,N,0,112500.0,360000.0,18508.5,360000.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.02461,-11295,-4441,-3422.0,-3678,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,Self-employed,,0.6549228812131263,0.39449540531239935,0.0577,0.0616,0.9722,0.6192,,0.0,0.1379,0.125,0.1667,0.0276,0.0471,0.069,0.0,0.0,0.0588,0.064,0.9722,0.6341,,0.0,0.1379,0.125,0.1667,0.0282,0.0514,0.0719,0.0,0.0,0.0583,0.0616,0.9722,0.6243,,0.0,0.1379,0.125,0.1667,0.0281,0.0479,0.0702,0.0,0.0,,block of flats,0.0593,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1726.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,4.0\n225563,1,Cash loans,M,Y,Y,0,315000.0,640080.0,31261.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-18661,-1030,-1286.0,-1875,2.0,1,1,0,1,0,1,Laborers,2.0,2,2,MONDAY,8,0,0,0,0,1,1,Business Entity Type 3,0.8874678499888642,0.5373204425799044,0.16441417882990705,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-770.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,2.0\n387327,0,Cash loans,F,Y,Y,0,157500.0,684657.0,20146.5,571500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.04622,-19803,-5955,-6999.0,-3337,7.0,1,1,0,1,0,0,Private service staff,1.0,1,1,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6587870899965276,0.4884551844437485,0.0619,0.0618,0.9816,,,0.0,0.1379,0.1667,,0.0107,,0.0352,,0.0,0.063,0.0641,0.9816,,,0.0,0.1379,0.1667,,0.011000000000000001,,0.0367,,0.0,0.0625,0.0618,0.9816,,,0.0,0.1379,0.1667,,0.0109,,0.0358,,0.0,,block of flats,0.0412,Panel,No,0.0,0.0,0.0,0.0,-2632.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,8.0,0.0,0.0\n308905,0,Cash loans,F,N,Y,0,67500.0,405000.0,20808.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-9546,-501,-920.0,-2213,,1,1,1,1,0,0,Core staff,2.0,2,2,THURSDAY,14,0,0,0,1,1,0,Kindergarten,,0.6735832821070913,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1892.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n334264,0,Cash loans,M,Y,N,1,112500.0,450000.0,22018.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010643,-13939,-1096,-122.0,-4061,12.0,1,1,1,1,0,0,Drivers,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 2,,0.6590721339192476,,0.0165,0.0,0.9742,0.6464,0.0015,0.0,0.069,0.0417,0.0833,0.0455,0.0134,0.0128,0.0,0.0,0.0168,0.0,0.9742,0.6602,0.0015,0.0,0.069,0.0417,0.0833,0.0465,0.0147,0.0134,0.0,0.0,0.0167,0.0,0.9742,0.6511,0.0015,0.0,0.069,0.0417,0.0833,0.0462,0.0137,0.0131,0.0,0.0,reg oper account,block of flats,0.0109,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1927.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n448648,0,Cash loans,F,N,N,0,121500.0,360000.0,23134.5,360000.0,Unaccompanied,Working,Higher education,Married,With parents,0.0228,-10910,-441,-709.0,-1206,,1,1,0,1,0,1,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 2,0.18143738625399106,0.5469386980332311,,0.0443,0.0781,0.993,0.9048,,0.0,0.1034,0.1667,0.2083,0.0902,,0.0544,,0.0,0.0452,0.081,0.993,0.9085,,0.0,0.1034,0.1667,0.2083,0.0923,,0.0567,,0.0,0.0448,0.0781,0.993,0.9061,,0.0,0.1034,0.1667,0.2083,0.0918,,0.0554,,0.0,reg oper account,block of flats,0.0469,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-395.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n194361,0,Cash loans,F,N,Y,0,135000.0,808650.0,23773.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.007305,-16294,-7397,-2392.0,-4370,,1,1,0,1,0,0,Laborers,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.7169907095572138,0.2872614842678766,0.722392890081304,0.0876,0.09,0.9796,0.7212,0.01,0.0,0.1724,0.1667,0.0417,0.0607,0.0714,0.0788,0.0,0.0,0.084,0.0834,0.9767,0.6929,0.0082,0.0,0.1379,0.1667,0.0417,0.0565,0.0735,0.0735,0.0,0.0,0.0885,0.09,0.9796,0.7249,0.01,0.0,0.1724,0.1667,0.0417,0.0617,0.0727,0.0802,0.0,0.0,reg oper account,block of flats,0.0555,Panel,No,1.0,0.0,1.0,0.0,-209.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n126571,1,Cash loans,M,Y,N,0,292500.0,298512.0,19948.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.020713,-17715,-662,-9927.0,-1244,20.0,1,1,1,1,0,0,Drivers,2.0,3,3,MONDAY,14,0,0,0,0,0,0,Self-employed,0.3261323812984764,0.5199017969730215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-524.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n151563,1,Cash loans,F,N,Y,0,67500.0,229500.0,7713.0,229500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-16860,-6030,-7383.0,-411,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 2,,0.4015186339455688,0.6075573001388961,0.1814,0.1413,0.9891,,,0.2,0.1724,0.3333,,,,0.1869,,0.0091,0.1849,0.1466,0.9891,,,0.2014,0.1724,0.3333,,,,0.1947,,0.0097,0.1832,0.1413,0.9891,,,0.2,0.1724,0.3333,,,,0.1903,,0.0093,,block of flats,0.1662,Panel,No,0.0,0.0,0.0,0.0,-1716.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n307292,0,Cash loans,F,Y,Y,0,900000.0,1078200.0,31522.5,900000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.019101,-17336,-3424,-6971.0,-886,4.0,1,1,1,1,1,0,Managers,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.686071659919743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2697.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n265526,0,Cash loans,F,N,N,0,90000.0,592560.0,26230.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Rented apartment,0.006670999999999999,-15507,-2080,-382.0,-3913,,1,1,0,1,1,0,Sales staff,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Self-employed,0.6918556323057401,0.7322459322343059,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n298449,1,Cash loans,M,N,N,0,135000.0,269550.0,14751.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.0105,-10835,-516,-2887.0,-2878,,1,1,1,1,0,0,Laborers,2.0,3,3,THURSDAY,8,0,0,0,1,1,0,Construction,,0.17340460229791907,0.1595195404777181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-211.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,6.0\n136082,0,Revolving loans,F,N,Y,0,72000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.020246,-8429,-1494,-8416.0,-1103,,1,1,0,1,0,0,Cooking staff,1.0,3,3,SATURDAY,12,0,0,0,0,1,1,Self-employed,0.2253983607390879,0.23027184401340936,0.6023863442690867,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n212020,0,Cash loans,M,N,Y,1,112500.0,888840.0,32053.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16010,365243,-678.0,-4247,,1,0,0,1,1,0,,3.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.6489187391903537,0.4740512892789932,0.0619,0.0611,0.9836,0.7756,0.0083,0.0,0.1379,0.1667,0.2083,0.0417,0.0504,0.0524,0.0,0.0,0.063,0.0634,0.9836,0.7844,0.0084,0.0,0.1379,0.1667,0.2083,0.0426,0.0551,0.0546,0.0,0.0,0.0625,0.0611,0.9836,0.7786,0.0083,0.0,0.1379,0.1667,0.2083,0.0424,0.0513,0.0534,0.0,0.0,reg oper account,block of flats,0.0458,Panel,No,0.0,0.0,0.0,0.0,-3032.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n199973,0,Cash loans,M,Y,N,2,135000.0,450000.0,13027.5,450000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-12947,-6142,-7050.0,-4936,7.0,1,1,0,1,0,0,,4.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 2,0.2692607996775211,0.3114454758826744,0.1674084472266241,0.0082,,0.9757,,,0.0,0.0345,0.0417,,0.0371,,0.0043,,0.0038,0.0084,,0.9757,,,0.0,0.0345,0.0417,,0.0379,,0.0045,,0.004,0.0083,,0.9757,,,0.0,0.0345,0.0417,,0.0377,,0.0044,,0.0038,,block of flats,0.0042,Wooden,No,3.0,0.0,3.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n122788,0,Cash loans,M,Y,N,0,112500.0,254412.0,11988.0,166500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18937,365243,-5083.0,-2488,12.0,1,0,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.6965889207668875,0.6178261467332483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1243.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n327867,1,Cash loans,F,N,Y,0,67500.0,213322.5,16983.0,162000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-12637,-1837,-6686.0,-4221,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,,0.6556687484693159,0.3490552510751822,0.1021,0.09699999999999999,0.9781,0.7008,0.0113,0.0,0.2069,0.1667,0.2083,0.1094,0.0824,0.0906,0.0039,0.0092,0.10400000000000001,0.1007,0.9782,0.7125,0.0114,0.0,0.2069,0.1667,0.2083,0.1119,0.09,0.0944,0.0039,0.0097,0.1031,0.09699999999999999,0.9781,0.7048,0.0113,0.0,0.2069,0.1667,0.2083,0.1113,0.0838,0.0922,0.0039,0.0093,reg oper spec account,block of flats,0.0794,Panel,No,0.0,0.0,0.0,0.0,-1916.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n384151,0,Cash loans,F,N,Y,0,90000.0,571486.5,24340.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-18465,-5336,-6410.0,-1994,,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,9,0,0,0,0,0,0,Business Entity Type 2,,0.4646845051261207,0.4311917977993083,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14.0,0.0,14.0,0.0,-112.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n401872,0,Cash loans,F,N,Y,0,157500.0,247986.0,12060.0,207000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-20951,365243,-13169.0,-1247,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.5889272501252611,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1626.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n404899,0,Cash loans,M,N,Y,1,360000.0,787131.0,44082.0,679500.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.006629,-12911,-963,-5503.0,-1039,,1,1,0,1,0,0,Managers,3.0,2,2,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.5518976354413081,0.6515248017081812,0.4650692149562261,0.10099999999999999,0.0,0.9806,0.7348,0.0,0.0,0.2069,0.125,0.0417,,0.0824,0.0921,0.0,0.0,0.1029,0.0,0.9806,0.7452,0.0,0.0,0.2069,0.125,0.0417,,0.09,0.0959,0.0,0.0,0.102,0.0,0.9806,0.7383,0.0,0.0,0.2069,0.125,0.0417,,0.0838,0.0937,0.0,0.0,reg oper spec account,block of flats,0.0781,Panel,No,3.0,0.0,3.0,0.0,-843.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n360664,0,Cash loans,M,N,Y,0,202500.0,271867.5,21168.0,243000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018634,-20833,-3289,-6366.0,-4388,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5535354682106923,,,,0.9911,,,,,,,,,0.2028,,,,,0.9911,,,,,,,,,0.2113,,,,,0.9911,,,,,,,,,0.2065,,,,,0.1943,,No,1.0,0.0,1.0,0.0,-1735.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n336719,1,Cash loans,F,N,Y,0,81000.0,646920.0,20997.0,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-21749,365243,-8047.0,-4594,,1,0,0,1,1,0,,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,XNA,,0.033464701110636555,0.20559813854932085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-759.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n132040,0,Cash loans,M,N,Y,0,171000.0,187083.0,22333.5,175500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-9453,-346,-4189.0,-1828,,1,1,0,1,0,0,Laborers,2.0,1,1,FRIDAY,15,0,0,0,0,0,0,Construction,,0.6847278181120409,0.6313545365850379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-753.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n355487,0,Cash loans,F,N,N,0,175500.0,675000.0,48136.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-18643,-658,-2715.0,-1487,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Trade: type 7,,0.4499836213431203,0.5406544504453575,0.0495,0.0156,0.9732,0.6328,,0.0,0.1034,0.125,0.1667,0.0346,0.0387,0.0397,0.0077,0.0141,0.0504,0.0161,0.9732,0.6472,,0.0,0.1034,0.125,0.1667,0.0354,0.0422,0.0414,0.0078,0.0149,0.05,0.0156,0.9732,0.6377,,0.0,0.1034,0.125,0.1667,0.0352,0.0393,0.0405,0.0078,0.0144,reg oper spec account,block of flats,0.037000000000000005,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n228943,0,Cash loans,F,Y,N,0,306000.0,900000.0,31887.0,900000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.018634,-16033,-2450,-3461.0,-5194,3.0,1,1,0,1,1,0,Accountants,2.0,2,2,SUNDAY,13,0,0,0,0,0,0,Self-employed,,0.7167186255372394,0.4722533429586386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-806.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n105997,1,Cash loans,M,Y,Y,0,94500.0,521280.0,31630.5,450000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-22312,365243,-162.0,-4194,14.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.5936886166221982,0.4956658291397297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-834.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n111001,0,Cash loans,M,Y,N,0,135000.0,180000.0,19030.5,180000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.0228,-10721,-3450,-2407.0,-3373,16.0,1,1,1,1,1,0,Core staff,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,Transport: type 1,0.2363768290682944,0.6019534089554641,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1187.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n453980,0,Revolving loans,F,Y,Y,2,72000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-14981,-2205,-6053.0,-4253,1.0,1,1,0,1,0,0,Sales staff,4.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.4550233249654976,0.5675722487554948,,0.0928,0.1085,0.9826,,,0.0,0.2069,0.1667,,0.1343,,0.0505,,0.0038,0.0945,0.1126,0.9826,,,0.0,0.2069,0.1667,,0.1374,,0.0526,,0.004,0.0937,0.1085,0.9826,,,0.0,0.2069,0.1667,,0.1366,,0.0514,,0.0039,,block of flats,0.0704,Panel,No,,,,,-165.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n148406,0,Cash loans,F,N,Y,0,112500.0,183294.0,9724.5,153000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.022625,-10470,-581,-1878.0,-3003,,1,1,0,1,0,0,Accountants,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Transport: type 4,0.5245855470793792,0.5735303226355871,0.11104240226943142,0.1608,0.0743,0.9786,,,0.0,0.069,0.1667,,0.0641,,0.0468,,0.3396,0.1639,0.0771,0.9786,,,0.0,0.069,0.1667,,0.0656,,0.0488,,0.3595,0.1624,0.0743,0.9786,,,0.0,0.069,0.1667,,0.0653,,0.0477,,0.3467,,specific housing,0.0775,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-881.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n386794,0,Cash loans,M,N,Y,0,157500.0,247500.0,21303.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-18996,-4531,-3184.0,-2545,,1,1,1,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Industry: type 5,,0.5301044223849315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-412.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n315657,0,Cash loans,M,Y,N,1,112500.0,882000.0,47119.5,882000.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.003122,-10473,-1952,-4407.0,-3068,8.0,1,1,1,1,0,0,Core staff,3.0,3,3,FRIDAY,15,0,0,0,0,1,1,Other,,0.6575275952668811,0.8106180215523969,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-889.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n351637,0,Cash loans,F,Y,Y,1,103500.0,269550.0,19858.5,225000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.01885,-13429,-3899,-2724.0,-4285,16.0,1,1,0,1,0,0,,2.0,2,2,SATURDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.3697262526067023,0.7390455707822527,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-519.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n105442,0,Cash loans,M,N,Y,0,450000.0,1635120.0,56965.5,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-20193,-1169,-160.0,-3521,,1,1,0,1,1,1,High skill tech staff,2.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.7428065547753928,0.7776594425716818,0.0619,0.0718,0.9732,0.6328,0.0167,0.0,0.1379,0.1667,0.2083,0.0,0.0504,0.0587,0.0077,0.0269,0.063,0.0745,0.9732,0.6472,0.0169,0.0,0.1379,0.1667,0.2083,0.0,0.0551,0.0612,0.0078,0.0285,0.0625,0.0718,0.9732,0.6377,0.0168,0.0,0.1379,0.1667,0.2083,0.0,0.0513,0.0598,0.0078,0.0275,reg oper account,block of flats,0.0612,Panel,No,0.0,0.0,0.0,0.0,-1839.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n276385,0,Cash loans,F,N,Y,0,72000.0,646920.0,18670.5,540000.0,Family,Pensioner,Lower secondary,Married,House / apartment,0.018634,-21021,365243,-12959.0,-4523,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.7562167514678866,0.4329616670974407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1799.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n383382,0,Cash loans,M,Y,Y,1,112500.0,318528.0,20484.0,252000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.0228,-14862,-2283,-4189.0,-4866,28.0,1,1,0,1,0,0,Drivers,3.0,2,2,FRIDAY,16,0,0,0,0,1,1,Other,,0.3378474040248485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-809.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,4.0\n284414,0,Cash loans,F,N,Y,1,202500.0,1800000.0,62698.5,1800000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.005084,-13976,-3679,-2563.0,-4989,,1,1,0,1,0,0,Sales staff,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,0.5860394023342276,0.5564464733641881,0.6528965519806539,0.0928,0.1005,0.9786,,,,0.2069,0.1667,,,,0.087,,,0.0945,0.1043,0.9786,,,,0.2069,0.1667,,,,0.0907,,,0.0937,0.1005,0.9786,,,,0.2069,0.1667,,,,0.0886,,,,block of flats,0.0685,Panel,No,0.0,0.0,0.0,0.0,-601.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n243315,0,Revolving loans,F,N,N,0,130500.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00496,-16631,-1615,-6035.0,-165,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Self-employed,,0.05942231673421465,0.6594055320683344,0.0928,,0.9796,,,,0.1724,0.1667,,,,0.0536,,,0.0945,,0.9796,,,,0.1724,0.1667,,,,0.0559,,,0.0937,,0.9796,,,,0.1724,0.1667,,,,0.0546,,,,,0.064,,No,2.0,1.0,2.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n151029,0,Cash loans,M,Y,N,2,157500.0,957033.0,53437.5,904500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-14404,-5647,-1811.0,-4275,6.0,1,1,1,1,1,0,High skill tech staff,4.0,2,2,THURSDAY,17,0,0,0,0,0,0,Transport: type 2,,0.7118946682856718,0.6754132910917112,0.2351,0.2135,0.9856,0.8028,0.0816,0.24,0.2069,0.3333,0.375,0.0105,0.1891,0.3035,0.0116,0.1385,0.2395,0.2216,0.9856,0.8105,0.0824,0.2417,0.2069,0.3333,0.375,0.0107,0.2066,0.3162,0.0117,0.1466,0.2373,0.2135,0.9856,0.8054,0.0822,0.24,0.2069,0.3333,0.375,0.0107,0.1924,0.3089,0.0116,0.1414,,block of flats,0.2712,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-836.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n322980,1,Cash loans,M,N,N,0,216000.0,540000.0,42795.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008865999999999999,-10281,-411,-5073.0,-2865,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.7128652257314662,0.3588451673297107,0.3046721837533529,0.0309,0.0605,0.9742,0.6464,0.0055,0.0,0.1034,0.1667,0.0417,0.0317,0.0252,0.057,0.0,0.0,0.0315,0.0628,0.9742,0.6602,0.0055,0.0,0.1034,0.1667,0.0417,0.0324,0.0275,0.0594,0.0,0.0,0.0312,0.0605,0.9742,0.6511,0.0055,0.0,0.1034,0.1667,0.0417,0.0322,0.0257,0.0581,0.0,0.0,reg oper account,block of flats,0.0479,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-746.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n269609,0,Cash loans,F,N,N,2,67500.0,675000.0,32602.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-11565,-1733,-310.0,-2637,,1,1,0,1,0,0,Sales staff,4.0,2,2,MONDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.6888720530591066,0.3608197713127845,0.5280927512030451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-289.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n298385,0,Cash loans,F,Y,N,1,90000.0,270000.0,10282.5,270000.0,Children,Commercial associate,Higher education,Single / not married,House / apartment,0.010147,-14698,-7994,-4396.0,-2547,7.0,1,1,0,1,1,0,Accountants,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Bank,,0.13285009327713404,0.4241303111942548,0.0777,0.0362,0.993,0.9116,0.0087,0.0,0.1607,0.1875,0.2292,0.0216,0.0361,0.053,0.0,0.0,0.0315,0.0294,0.9886,0.8497,0.0084,0.0,0.069,0.1667,0.2083,0.0221,0.0275,0.0547,0.0,0.0,0.0583,0.0362,0.9925,0.9128,0.0088,0.0,0.069,0.1875,0.2292,0.022000000000000002,0.0368,0.054000000000000006,0.0,0.0,reg oper spec account,terraced house,0.0202,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n123890,0,Cash loans,F,N,Y,0,67500.0,675000.0,22437.0,675000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.008068,-23565,365243,-10830.0,-4832,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,8,0,0,0,0,0,0,XNA,,0.15824961073701505,0.4992720153045617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1158.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n285857,1,Cash loans,F,N,Y,0,292500.0,1206954.0,35419.5,945000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.04622,-20971,-2900,-1541.0,-4079,,1,1,0,1,0,0,Core staff,1.0,1,1,WEDNESDAY,11,0,0,0,0,1,1,Self-employed,,0.3047747886021344,0.1061556230153924,0.0495,0.0488,0.9657,0.5308,0.0085,0.0,0.1724,0.1458,0.1667,0.0917,0.0395,0.0656,0.0039,0.0142,0.042,0.0,0.9618,0.4969,0.0072,0.0,0.1379,0.125,0.1667,0.0763,0.0367,0.0405,0.0,0.0,0.05,0.0488,0.9657,0.5371,0.0085,0.0,0.1724,0.1458,0.1667,0.0933,0.0402,0.0668,0.0039,0.0145,reg oper account,block of flats,0.0306,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-825.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n133794,0,Cash loans,M,N,Y,1,157500.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-9259,-1893,-353.0,-1917,,1,1,0,1,1,0,Low-skill Laborers,3.0,3,3,SUNDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.3645449555046808,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1393.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n122244,0,Cash loans,M,N,N,0,180000.0,450000.0,32746.5,450000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.019689,-9380,-1713,-5103.0,-1726,,1,1,1,1,1,0,,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Transport: type 4,,0.287100331358414,0.1694287272664794,0.1144,0.0818,0.9771,0.6872,,0.0,0.2069,0.1667,0.0417,0.0593,,0.0877,,0.0294,0.1166,0.0849,0.9772,0.6994,,0.0,0.2069,0.1667,0.0417,0.0607,,0.0914,,0.0311,0.1155,0.0818,0.9771,0.6914,,0.0,0.2069,0.1667,0.0417,0.0603,,0.0893,,0.03,,block of flats,0.08199999999999999,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-718.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,2.0\n273273,1,Cash loans,F,Y,Y,0,180000.0,675000.0,23202.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.026392,-23801,365243,-4429.0,-1019,11.0,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.5955598541006532,,0.134,,0.9935,,,0.16,0.1379,0.375,,,,0.1519,,0.0105,0.1366,,0.9935,,,0.1611,0.1379,0.375,,,,0.1583,,0.0111,0.1353,,0.9935,,,0.16,0.1379,0.375,,,,0.1546,,0.0107,,block of flats,0.1218,Panel,No,3.0,0.0,3.0,0.0,-3088.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n381857,1,Cash loans,M,N,N,0,157500.0,143910.0,17077.5,135000.0,Unaccompanied,Working,Higher education,Married,Co-op apartment,0.009334,-9809,-184,-2897.0,-2496,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,17,1,1,0,1,1,0,Business Entity Type 2,,0.5508209807121868,,0.0144,0.0,0.9866,0.8164,0.0,0.0,0.0,0.0,0.0417,0.0,0.005,0.0015,0.0309,0.0034,0.0147,0.0,0.9866,0.8236,0.0,0.0,0.0,0.0,0.0417,0.0,0.0055,0.0016,0.0311,0.0036,0.0146,0.0,0.9866,0.8189,0.0,0.0,0.0,0.0,0.0417,0.0,0.0051,0.0016,0.0311,0.0035,org spec account,block of flats,0.002,Wooden,No,0.0,0.0,0.0,0.0,-551.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n260671,0,Cash loans,F,N,Y,0,225000.0,495000.0,47128.5,495000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.001417,-15841,-1418,-10445.0,-1439,,1,1,0,1,0,0,Managers,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Self-employed,0.7195396882149842,0.4970060605318525,0.7366226976503176,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n235449,0,Cash loans,F,Y,N,1,90000.0,298512.0,19948.5,270000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.028663,-10563,-439,-804.0,-853,11.0,1,1,1,1,1,0,,3.0,2,2,THURSDAY,19,0,0,0,0,1,1,Other,0.6452380205380869,0.5724227824528423,0.42765737003502935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-749.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n446916,0,Revolving loans,F,N,Y,0,90000.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-8421,-164,-8410.0,-689,,1,1,0,0,0,0,Core staff,2.0,2,2,MONDAY,10,0,0,0,1,1,0,Bank,0.1073538893705229,0.09986815298203704,0.05571142329500084,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n408643,0,Cash loans,F,N,Y,0,225000.0,504000.0,30964.5,504000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018209,-16079,-627,-3371.0,-4983,,1,1,0,1,0,0,Sales staff,2.0,3,3,FRIDAY,15,0,0,0,0,0,0,Trade: type 7,,0.4559439147469835,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n327641,0,Cash loans,F,N,Y,0,112500.0,113746.5,9247.5,103500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.020713,-10206,-624,-3959.0,-2254,,1,1,0,1,0,0,Core staff,2.0,3,2,SATURDAY,13,0,0,0,0,0,0,Kindergarten,0.49429053329059996,0.07464511197006125,0.6848276586890367,0.0608,0.0627,0.9781,0.7008,0.0489,0.0,0.1379,0.1667,0.2083,0.0339,0.0496,0.0529,0.0,0.0,0.062,0.065,0.9782,0.7125,0.0493,0.0,0.1379,0.1667,0.2083,0.0347,0.0542,0.0551,0.0,0.0,0.0614,0.0627,0.9781,0.7048,0.0492,0.0,0.1379,0.1667,0.2083,0.0345,0.0504,0.0538,0.0,0.0,reg oper account,block of flats,0.0416,Panel,No,0.0,0.0,0.0,0.0,-897.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n430091,0,Cash loans,F,N,Y,0,121500.0,1138900.5,33430.5,994500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-20026,-1550,-9913.0,-3569,,1,1,1,1,1,0,Medicine staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Medicine,,0.5002970958614138,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-434.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n316223,0,Cash loans,F,N,Y,0,54000.0,50940.0,5089.5,45000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.01885,-18691,-922,-1717.0,-2244,,1,1,1,1,1,1,,1.0,2,2,MONDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.7201336081644234,0.6801388218428291,0.0165,,0.9846,0.7892,0.0017,0.0,0.069,0.0417,0.0833,,0.0134,0.0147,0.0,0.0,0.0168,,0.9846,0.7975,0.0017,0.0,0.069,0.0417,0.0833,,0.0147,0.0153,0.0,0.0,0.0167,,0.9846,0.792,0.0017,0.0,0.069,0.0417,0.0833,,0.0137,0.0149,0.0,0.0,reg oper spec account,block of flats,0.012,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-760.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n330380,0,Cash loans,F,N,Y,1,112500.0,1042560.0,34587.0,900000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006296,-17052,-9748,-1501.0,-604,,1,1,0,1,0,0,Core staff,3.0,3,3,FRIDAY,10,0,0,0,0,0,0,School,0.7710673774584352,0.4738884322608505,0.6413682574954046,0.0928,0.0927,0.9786,0.7212,0.0,0.0,0.1552,0.1667,0.125,0.0212,0.0756,0.0513,0.0,0.0,0.0945,0.0962,0.9767,0.7517,0.0,0.0,0.1034,0.1667,0.0417,0.0216,0.0826,0.0534,0.0,0.0,0.0937,0.0927,0.9786,0.7451,0.0,0.0,0.1552,0.1667,0.125,0.0215,0.077,0.0522,0.0,0.0,reg oper account,block of flats,0.1699,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n385240,0,Cash loans,F,N,Y,0,225000.0,1724220.0,50544.0,1350000.0,Other_B,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-22161,-1619,-242.0,-2328,,1,1,0,1,0,0,Sales staff,2.0,1,1,MONDAY,13,0,1,1,0,0,0,Business Entity Type 3,,0.7638261249604399,0.2458512138252296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-491.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n354649,0,Cash loans,M,Y,Y,0,121500.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.001417,-14713,-730,-2358.0,-4785,9.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,15,0,0,0,1,0,1,Self-employed,,0.3795533365904105,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-527.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n449876,0,Cash loans,F,N,Y,0,90000.0,288000.0,20043.0,288000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-20387,-1385,-3535.0,-3580,,1,1,1,1,0,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,Agriculture,,0.3938138788262025,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1659.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n222289,0,Cash loans,F,N,Y,1,180000.0,700830.0,22738.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009657,-13987,-871,-343.0,-4330,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,0.4082116840992217,0.26344474904124376,0.501075160239048,0.1845,0.1482,0.9861,0.8096,0.3225,0.2,0.1724,0.3333,0.375,0.1576,0.1505,0.1945,0.0,0.0,0.188,0.1538,0.9861,0.8171,0.3255,0.2014,0.1724,0.3333,0.375,0.1612,0.1644,0.2027,0.0,0.0,0.1863,0.1482,0.9861,0.8121,0.3246,0.2,0.1724,0.3333,0.375,0.1604,0.1531,0.198,0.0,0.0,reg oper spec account,block of flats,0.1763,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n255289,1,Cash loans,M,N,N,0,202500.0,746896.5,40648.5,594000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Rented apartment,0.0105,-8221,-1238,-2767.0,-845,,1,1,0,1,0,0,Laborers,2.0,3,3,WEDNESDAY,15,0,0,0,0,0,0,Transport: type 2,0.6085997109031833,0.2622795243729244,0.3108182544189319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-827.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n450549,0,Cash loans,F,N,N,0,157500.0,728460.0,38938.5,675000.0,Family,State servant,Higher education,Civil marriage,House / apartment,0.020246,-14070,-209,-7984.0,-4487,,1,1,0,1,0,1,Core staff,2.0,3,3,MONDAY,14,0,0,0,0,1,1,Government,0.7676212059549029,0.5396355435906887,0.6279908192952864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2570.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n211821,0,Cash loans,F,N,Y,2,135000.0,345645.0,16942.5,243000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-10687,-1636,-4552.0,-2049,,1,1,0,1,0,0,Laborers,4.0,2,2,SUNDAY,12,0,0,0,0,0,0,Industry: type 6,0.4646925861609013,0.5515242327971844,0.16146308000577247,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-291.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n284656,0,Revolving loans,F,N,Y,0,126000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-16174,-242,-4685.0,-4818,,1,1,0,1,0,0,Sales staff,1.0,2,2,SUNDAY,15,0,0,0,0,0,0,Self-employed,0.6042938092952193,0.6673799835114839,0.5190973382084597,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1890.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n388049,0,Revolving loans,F,N,Y,2,270000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-9518,-586,-253.0,-2193,,1,1,0,1,0,0,Core staff,4.0,2,2,SATURDAY,16,0,0,0,0,0,0,Kindergarten,0.4608665280710411,0.3476003582419204,0.6626377922738201,0.0278,0.0432,0.9871,,,0.0,0.1034,0.0833,,0.0094,,0.0254,,0.0,0.0284,0.0449,0.9871,,,0.0,0.1034,0.0833,,0.0096,,0.0265,,0.0,0.0281,0.0432,0.9871,,,0.0,0.1034,0.0833,,0.0096,,0.0259,,0.0,,block of flats,0.0294,Panel,No,7.0,0.0,7.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n419299,1,Cash loans,M,Y,Y,0,67500.0,263686.5,14854.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-16484,-1148,-699.0,-43,18.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,6,0,0,0,0,1,1,Business Entity Type 3,,0.36452975863668335,0.3280631605201915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,3.0\n452949,0,Cash loans,F,N,Y,0,450000.0,463500.0,42642.0,463500.0,Family,Working,Higher education,Married,House / apartment,0.030755,-14976,-999,-490.0,-363,,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Other,0.20269299810943006,0.5236940497511178,0.392773860603134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-322.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n432779,0,Revolving loans,F,N,N,0,72000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-13079,-194,-3809.0,-1179,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Industry: type 7,0.2268120887281269,0.3368648297697268,,0.0619,0.0365,0.9801,0.728,0.0236,0.0,0.1379,0.1667,0.0417,0.1078,0.0504,0.0538,0.0,0.0094,0.063,0.0379,0.9801,0.7387,0.0238,0.0,0.1379,0.1667,0.0417,0.1102,0.0551,0.0561,0.0,0.01,0.0625,0.0365,0.9801,0.7316,0.0237,0.0,0.1379,0.1667,0.0417,0.1097,0.0513,0.0548,0.0,0.0096,reg oper account,block of flats,0.0572,Panel,No,2.0,1.0,2.0,0.0,-206.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n274306,0,Cash loans,M,Y,Y,0,135000.0,454500.0,19386.0,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.030755,-13238,-3123,-7118.0,-3343,17.0,1,1,1,1,1,0,Laborers,1.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.5344705614796641,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2293.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n237209,0,Revolving loans,F,Y,Y,1,90000.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020246,-15350,-384,-9404.0,-4498,7.0,1,1,0,1,1,0,Laborers,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,Self-employed,0.5544670443049416,0.5019590221324841,,0.2089,0.1573,0.9856,,,0.2264,0.1952,0.3333,,0.0706,,0.217,,0.0054,0.1492,0.1106,0.9856,,,0.1611,0.1379,0.3333,,0.0432,,0.1569,,0.0,0.2248,0.1695,0.9856,,,0.24,0.2069,0.3333,,0.0734,,0.2433,,0.0082,,block of flats,0.188,Panel,No,0.0,0.0,0.0,0.0,-902.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n264082,0,Cash loans,F,Y,Y,0,315000.0,500490.0,52686.0,450000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.011703,-8959,-293,-8956.0,-1502,27.0,1,1,0,1,0,1,,1.0,2,2,MONDAY,10,0,1,1,0,0,0,Business Entity Type 3,,0.5402439289477569,,0.1,0.1064,0.9826,,,0.0,0.2759,0.1667,,0.1214,,0.1034,,0.0199,0.1019,0.1105,0.9826,,,0.0,0.2759,0.1667,,0.1241,,0.1077,,0.021,0.10099999999999999,0.1064,0.9826,,,0.0,0.2759,0.1667,,0.1235,,0.1052,,0.0203,,block of flats,0.0948,\"Stone, brick\",No,1.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n398539,0,Cash loans,F,Y,Y,0,238500.0,1078200.0,34780.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-11477,-276,-1932.0,-4042,8.0,1,1,0,1,1,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3664140182787077,0.7216998859575542,0.36227724703843145,0.3113,0.0,0.997,0.9524,0.3611,0.24,0.1034,0.625,0.0417,0.1846,0.2152,0.2291,0.1776,0.0793,0.3172,0.0,0.997,0.9543,0.3644,0.2417,0.1034,0.625,0.0417,0.1889,0.2351,0.2387,0.179,0.0839,0.3144,0.0,0.997,0.953,0.3634,0.24,0.1034,0.625,0.0417,0.1879,0.2189,0.2332,0.1786,0.0809,reg oper account,block of flats,0.2449,Panel,No,0.0,0.0,0.0,0.0,-973.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n416702,0,Cash loans,F,N,Y,0,220500.0,916470.0,26928.0,765000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.04622,-23018,365243,-14976.0,-4403,,1,0,0,1,0,0,,1.0,1,1,TUESDAY,11,0,0,0,0,0,0,XNA,,0.607415819672833,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n449285,1,Cash loans,M,Y,Y,1,202500.0,521280.0,28408.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018801,-12153,-2304,-960.0,-4536,1.0,1,1,1,1,1,0,Sales staff,3.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.41858404734139376,0.6059495583894647,0.107532175802471,0.0773,0.0762,0.9767,0.6804,0.036000000000000004,0.0,0.1724,0.1667,0.2083,0.0575,0.063,0.0654,0.0,0.0,0.0788,0.0791,0.9767,0.6929,0.0363,0.0,0.1724,0.1667,0.2083,0.0588,0.0689,0.0682,0.0,0.0,0.0781,0.0762,0.9767,0.6847,0.0362,0.0,0.1724,0.1667,0.2083,0.0585,0.0641,0.0666,0.0,0.0,reg oper account,block of flats,0.0812,Panel,No,1.0,1.0,1.0,1.0,-1771.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,5.0\n387541,0,Cash loans,M,Y,N,0,126000.0,225000.0,22014.0,225000.0,Family,Working,Higher education,Single / not married,With parents,0.035792000000000004,-16515,-696,-1980.0,-74,10.0,1,1,1,1,1,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6295165416506708,,0.0866,0.0996,0.9955,0.9388,0.013000000000000001,0.06,0.1034,0.25,0.0417,0.0616,0.0706,0.1069,0.0,0.0,0.063,0.0858,0.9955,0.9412,0.0113,0.0,0.1034,0.1667,0.0417,0.0197,0.0551,0.0768,0.0,0.0,0.0874,0.0996,0.9955,0.9396,0.0131,0.06,0.1034,0.25,0.0417,0.0626,0.0718,0.1088,0.0,0.0,reg oper account,block of flats,0.0641,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1032.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n176524,0,Revolving loans,F,Y,Y,2,135000.0,405000.0,20250.0,405000.0,Family,Working,Higher education,Married,House / apartment,0.010147,-13256,-686,-2816.0,-4738,6.0,1,1,0,1,0,0,Accountants,4.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.7407043352747225,0.2556036735579329,0.7238369900414456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n358823,0,Cash loans,M,N,Y,1,112500.0,172021.5,15907.5,148500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-18406,-1324,-5592.0,-1948,,1,1,0,1,0,0,Drivers,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Transport: type 3,,0.6544631259079668,0.8106180215523969,0.0124,,0.9513,,,0.0,0.0345,0.0833,,,,0.0153,,,0.0126,,0.9513,,,0.0,0.0345,0.0833,,,,0.016,,,0.0125,,0.9513,,,0.0,0.0345,0.0833,,,,0.0156,,,,block of flats,0.0121,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-138.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n436143,0,Revolving loans,F,N,Y,0,247500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00496,-16933,-1355,-3396.0,-459,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,,0.6946535546681383,0.5388627065779676,0.0704,,0.9821,,,0.0,0.1379,0.1667,,0.0713,,,,,0.042,,0.9786,,,0.0,0.069,0.1667,,0.0182,,,,,0.0573,,0.9821,,,0.0,0.1379,0.1667,,0.0725,,,,,,block of flats,0.0261,,No,0.0,0.0,0.0,0.0,-766.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n432392,0,Cash loans,F,N,Y,0,121500.0,328405.5,25542.0,283500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009657,-21568,365243,-10228.0,-4158,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.3897910925622646,0.5744466170995097,,0.6753,0.9841,,,0.64,0.5517,0.3333,,,,0.8499,,1.0,,0.7008,0.9841,,,0.6445,0.5517,0.3333,,,,0.8855,,1.0,,0.6753,0.9841,,,0.64,0.5517,0.3333,,,,0.8651,,1.0,,,1.0,,No,0.0,0.0,0.0,0.0,-528.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n103883,0,Cash loans,F,N,Y,0,103500.0,45000.0,4738.5,45000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.022625,-17007,-1569,-1082.0,-543,,1,1,1,1,1,0,Sales staff,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,,0.7001083157393387,0.4920600938649263,0.2237,0.1416,0.9851,0.7959999999999999,0.11,0.24,0.2069,0.3333,0.375,0.1332,0.1807,0.2057,0.0077,0.0088,0.2279,0.147,0.9851,0.804,0.111,0.2417,0.2069,0.3333,0.375,0.1363,0.1974,0.2143,0.0078,0.0093,0.2259,0.1416,0.9851,0.7987,0.1107,0.24,0.2069,0.3333,0.375,0.1355,0.1838,0.2094,0.0078,0.0089,reg oper account,block of flats,0.2239,Panel,No,0.0,0.0,0.0,0.0,-2462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n379544,0,Cash loans,F,N,N,0,135000.0,1174090.5,46561.5,1080000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.026392,-17008,-4963,-7685.0,-548,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,WEDNESDAY,19,0,0,0,0,0,0,Business Entity Type 2,0.6812782583912543,0.6982271424319866,0.7688075728291359,0.2227,,0.9866,,,0.24,0.2069,0.3333,,,,0.2294,,0.0,0.2269,,0.9866,,,0.2417,0.2069,0.3333,,,,0.2391,,0.0,0.2248,,0.9866,,,0.24,0.2069,0.3333,,,,0.2336,,0.0,,block of flats,0.1805,Block,No,2.0,0.0,2.0,0.0,-2807.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,1.0,2.0\n379163,0,Cash loans,F,N,Y,0,180000.0,942300.0,27679.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-19254,-521,-3767.0,-2786,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,9,0,0,0,0,1,1,Business Entity Type 2,0.607455762095478,0.4984219559472609,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n125445,0,Cash loans,F,N,Y,1,72000.0,354276.0,20466.0,292500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009175,-15912,-1227,-6263.0,-3997,,1,1,0,1,0,0,Cooking staff,3.0,2,2,WEDNESDAY,13,0,0,0,1,1,0,Business Entity Type 3,0.7402694799281534,0.5587121156931707,0.5849900404894085,0.1557,0.1304,0.9722,,,0.0,0.0345,0.1667,,0.0293,,0.0988,,0.1676,0.1586,0.1353,0.9722,,,0.0,0.0345,0.1667,,0.03,,0.1029,,0.1774,0.1572,0.1304,0.9722,,,0.0,0.0345,0.1667,,0.0298,,0.1006,,0.1711,,block of flats,0.0829,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2443.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n169622,0,Cash loans,F,Y,Y,0,103500.0,263686.5,14175.0,238500.0,Children,Pensioner,Secondary / secondary special,Widow,House / apartment,0.025164,-22438,365243,-10447.0,-4992,4.0,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,0.6795406169239673,0.6823011068000024,,0.1103,0.0553,0.9856,0.8028,0.0401,0.12,0.1034,0.3333,0.375,0.0244,0.0899,0.1125,0.0,0.0,0.1124,0.0574,0.9856,0.8105,0.0405,0.1208,0.1034,0.3333,0.375,0.025,0.0983,0.1172,0.0,0.0,0.1114,0.0553,0.9856,0.8054,0.0404,0.12,0.1034,0.3333,0.375,0.0248,0.0915,0.1145,0.0,0.0,reg oper spec account,block of flats,0.1104,Panel,No,0.0,0.0,0.0,0.0,-1388.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n289155,0,Cash loans,F,Y,Y,0,306000.0,352422.0,26478.0,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.010006,-21714,365243,-1178.0,-1834,8.0,1,0,0,1,1,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.6913877505407457,0.44539624156083396,0.099,0.0577,0.9925,0.898,0.0618,0.08,0.069,0.4583,0.0417,0.0543,0.0799,0.1031,0.0039,0.0035,0.1008,0.0599,0.9926,0.902,0.0624,0.0806,0.069,0.4583,0.0417,0.0555,0.0872,0.1074,0.0039,0.0037,0.0999,0.0577,0.9925,0.8994,0.0622,0.08,0.069,0.4583,0.0417,0.0552,0.0812,0.1049,0.0039,0.0036,reg oper account,block of flats,0.1149,Panel,No,0.0,0.0,0.0,0.0,-1051.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n212352,0,Cash loans,F,N,Y,0,180000.0,679500.0,26946.0,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.019101,-14200,-1534,-253.0,-1947,,1,1,0,1,0,0,Cooking staff,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,Self-employed,0.488763063012866,0.2468507131726972,0.4686596550493113,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n286395,0,Cash loans,F,Y,N,0,112500.0,188460.0,11515.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-15702,-5303,-5656.0,-4168,11.0,1,1,0,1,0,0,Medicine staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Medicine,0.5290608618490942,0.5604097498199767,0.5567274263630174,,,0.9702,,,,,,,,,,,,,,0.9702,,,,,,,,,,,,,,0.9702,,,,,,,,,,,,,block of flats,0.0133,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2827.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n411004,0,Cash loans,F,N,Y,0,112500.0,628114.5,22689.0,477000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.020713,-17558,-8061,-6756.0,-1093,,1,1,0,1,0,0,Core staff,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Kindergarten,0.5430285214354796,0.5894406677441542,0.6161216908872079,0.0928,0.0829,0.9796,0.7212,,0.0,0.2069,0.1667,,0.0383,,0.08800000000000001,,0.0,0.0945,0.086,0.9796,0.7321,,0.0,0.2069,0.1667,,0.0392,,0.0916,,0.0,0.0937,0.0829,0.9796,0.7249,,0.0,0.2069,0.1667,,0.039,,0.0895,,0.0,reg oper account,block of flats,0.0692,Panel,No,1.0,1.0,1.0,1.0,-1326.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,0.0\n389234,0,Cash loans,M,N,Y,0,157500.0,1006920.0,39933.0,900000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-14189,-1324,-1931.0,-4527,,1,1,1,1,0,0,Drivers,2.0,1,1,TUESDAY,21,0,1,1,0,1,1,Transport: type 3,,0.6785255679354211,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-398.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n247181,0,Cash loans,F,N,Y,1,144000.0,633681.0,30951.0,445500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.006852,-13256,-230,-6344.0,-4370,,1,1,0,1,0,0,Sales staff,2.0,3,3,WEDNESDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.07824954765079309,0.1047946227497676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n261650,0,Cash loans,M,Y,Y,0,202500.0,254700.0,20250.0,225000.0,Unaccompanied,Working,Higher education,Civil marriage,Municipal apartment,0.032561,-10359,-130,-4568.0,-3004,7.0,1,1,0,1,0,0,Laborers,2.0,1,1,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.05457462227405728,0.544108642651863,0.28812959991785075,0.4794,0.0318,0.9791,0.7144,,0.52,0.4483,0.3333,0.375,0.4472,,0.3304,,0.0049,0.4884,0.033,0.9791,0.7256,,0.5236,0.4483,0.3333,0.375,0.4574,,0.3442,,0.0052,0.484,0.0318,0.9791,0.7182,,0.52,0.4483,0.3333,0.375,0.455,,0.3363,,0.005,reg oper account,block of flats,0.39299999999999996,Panel,No,0.0,0.0,0.0,0.0,-721.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n158397,0,Cash loans,M,Y,N,0,247500.0,1666746.0,48735.0,1305000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014464,-21911,-5059,-2867.0,-2879,7.0,1,1,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.6635585093744705,0.12530787842823918,0.1649,0.1215,0.9911,0.8776,0.1191,0.16,0.1379,0.375,0.4167,0.0825,0.1345,0.1895,0.0,0.0,0.1681,0.1261,0.9911,0.8824,0.1202,0.1611,0.1379,0.375,0.4167,0.0844,0.1469,0.1974,0.0,0.0,0.1665,0.1215,0.9911,0.8792,0.1198,0.16,0.1379,0.375,0.4167,0.084,0.1368,0.1929,0.0,0.0,reg oper spec account,block of flats,0.1494,Mixed,No,0.0,0.0,0.0,0.0,-1782.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n286281,0,Cash loans,F,N,Y,0,67500.0,900000.0,38263.5,900000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-21586,365243,-10198.0,-4618,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,0.4443793030556778,0.7140293022822672,0.4436153084085652,0.0619,0.0724,0.9856,0.8028,0.0092,0.0,0.1379,0.1667,0.2083,0.0601,0.0504,0.062,0.0,0.0,0.063,0.0751,0.9856,0.8105,0.0092,0.0,0.1379,0.1667,0.2083,0.0615,0.0551,0.0646,0.0,0.0,0.0625,0.0724,0.9856,0.8054,0.0092,0.0,0.1379,0.1667,0.2083,0.0612,0.0513,0.0631,0.0,0.0,reg oper account,block of flats,0.0496,Panel,No,1.0,0.0,1.0,0.0,-1110.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n317676,0,Cash loans,F,Y,N,2,90000.0,450000.0,24543.0,450000.0,Unaccompanied,State servant,Incomplete higher,Married,House / apartment,0.035792000000000004,-11429,-3106,-1350.0,-3802,9.0,1,1,1,1,0,0,Core staff,4.0,2,2,FRIDAY,16,0,0,0,0,0,0,Government,0.5761913581612689,0.7616932595478536,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-638.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n406509,0,Cash loans,F,N,Y,0,94500.0,540000.0,29425.5,540000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-17031,-3855,-5348.0,-573,,1,1,1,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,9,0,0,0,1,0,1,Self-employed,,0.4916109861059108,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2137.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n164109,0,Cash loans,M,N,N,0,135000.0,552555.0,18391.5,477000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,Rented apartment,0.072508,-23337,365243,-9797.0,-4897,,1,0,0,1,1,0,,1.0,1,1,SATURDAY,10,0,0,0,0,0,0,XNA,,0.4774662138478004,0.4812493411434029,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n206059,0,Cash loans,M,Y,N,0,202500.0,679500.0,19867.5,679500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009334,-11318,-2369,-5939.0,-3531,12.0,1,1,1,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Industry: type 3,0.451527175025557,0.6446323816299063,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1370.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n367428,0,Cash loans,F,N,Y,0,216000.0,781920.0,25969.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-21875,365243,-3021.0,-4949,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,0.8668027157575656,0.7245108098841091,0.7421816117614419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1942.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n302283,0,Cash loans,F,N,Y,0,90000.0,454500.0,25375.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15815,-777,-9921.0,-1653,,1,1,1,1,0,0,Cooking staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Self-employed,,0.5437560378134573,0.29708661164720285,0.0082,0.016,0.9697,0.5852,0.0016,0.0,0.0345,0.0417,0.0833,0.0458,0.0059,0.0057,0.0039,0.0082,0.0084,0.0166,0.9697,0.6014,0.0016,0.0,0.0345,0.0417,0.0833,0.0469,0.0064,0.006,0.0039,0.0087,0.0083,0.016,0.9697,0.5907,0.0016,0.0,0.0345,0.0417,0.0833,0.0466,0.006,0.0058,0.0039,0.0084,reg oper account,block of flats,0.0063,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n432982,0,Cash loans,F,N,Y,0,90000.0,521280.0,25209.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-20930,365243,-7783.0,-3402,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.5015872779244256,0.3425288720742255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-543.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n220876,0,Cash loans,F,N,N,0,247500.0,668983.5,63517.5,643500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.030755,-20278,-1256,-8579.0,-3785,,1,1,1,1,1,0,Sales staff,1.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.7263401811636915,,0.1351,0.0865,0.9801,,,0.0,0.2759,0.1667,,0.005,,0.1188,,0.011000000000000001,0.1376,0.0898,0.9801,,,0.0,0.2759,0.1667,,0.0051,,0.1238,,0.0116,0.1364,0.0865,0.9801,,,0.0,0.2759,0.1667,,0.0051,,0.121,,0.0112,,block of flats,0.0968,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1179.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n292862,0,Revolving loans,F,Y,Y,1,225000.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-12667,-5690,-11844.0,-1912,23.0,1,1,1,1,1,0,Laborers,3.0,1,1,FRIDAY,12,0,0,0,0,0,0,Construction,,0.6807823259065227,,0.1845,0.1216,0.9841,,,0.2,0.1724,0.3333,,,,0.1792,,0.0209,0.188,0.1262,0.9841,,,0.2014,0.1724,0.3333,,,,0.1867,,0.0221,0.1863,0.1216,0.9841,,,0.2,0.1724,0.3333,,,,0.1824,,0.0213,,block of flats,0.1454,Panel,No,0.0,0.0,0.0,0.0,-506.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n450476,1,Revolving loans,F,N,Y,0,135000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.002042,-17724,-198,-4922.0,-1108,,1,1,0,1,0,0,Laborers,1.0,3,3,SATURDAY,13,0,1,1,1,1,1,Business Entity Type 3,,0.21882400611293398,0.0576392903477425,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-247.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n372986,0,Cash loans,F,N,Y,0,171000.0,254700.0,24939.0,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.008625,-17769,-9359,-2006.0,-1301,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,10,0,0,0,0,1,1,Transport: type 2,,0.19006061419924847,0.8245949709919925,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n157097,0,Cash loans,M,Y,Y,0,486000.0,1078200.0,34911.0,900000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.019101,-16717,-7858,-1392.0,-204,15.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,14,0,1,1,0,1,1,Military,,0.3599175140812164,0.5334816299804352,0.1515,0.0522,0.998,0.9728,0.0423,0.12,0.1034,0.3125,0.3542,0.0095,0.1202,0.1403,0.0154,0.0335,0.1261,0.0,0.9975,0.9673,0.0,0.1208,0.1034,0.25,0.2917,0.0,0.1102,0.1277,0.0,0.0,0.153,0.0522,0.998,0.9732,0.0426,0.12,0.1034,0.3125,0.3542,0.0096,0.1223,0.1429,0.0155,0.0342,reg oper account,block of flats,0.1706,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n277982,0,Cash loans,F,N,Y,0,157500.0,735579.0,32530.5,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-17708,-1903,-6411.0,-1202,,1,1,0,1,0,1,Realty agents,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Services,,0.4492855476834583,0.326475210066026,0.0979,0.0488,0.9871,0.8232,0.0302,0.0,0.2069,0.1667,0.0417,0.077,0.0748,0.0819,0.0232,0.0386,0.0998,0.0506,0.9871,0.8301,0.0304,0.0,0.2069,0.1667,0.0417,0.0787,0.0817,0.0854,0.0233,0.0408,0.0989,0.0488,0.9871,0.8256,0.0304,0.0,0.2069,0.1667,0.0417,0.0783,0.0761,0.0834,0.0233,0.0394,,block of flats,0.0728,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-2159.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n210257,0,Cash loans,F,N,Y,0,211500.0,1327500.0,38812.5,1327500.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.032561,-10257,-2702,-10208.0,-535,,1,1,1,1,1,0,Managers,2.0,1,1,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.5202456562484125,0.20208660168203949,0.2464,0.1772,0.9846,,,0.36,0.2069,0.375,,0.1386,,0.2332,,0.0082,0.2511,0.1839,0.9846,,,0.3625,0.2069,0.375,,0.1417,,0.243,,0.0087,0.2488,0.1772,0.9846,,,0.36,0.2069,0.375,,0.141,,0.2374,,0.0084,,block of flats,0.2347,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-336.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n175118,0,Cash loans,F,N,N,0,90000.0,450000.0,16164.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010556,-16246,-507,-1915.0,-1832,,1,1,1,1,0,0,High skill tech staff,2.0,3,3,SUNDAY,11,0,0,0,0,0,0,Industry: type 11,0.5093296311208063,0.7324988129168228,0.6127042441012546,0.0247,0.0361,0.9801,0.728,0.0026,0.0,0.069,0.0833,0.125,0.0078,0.0202,0.0213,0.0,0.0,0.0252,0.0375,0.9801,0.7387,0.0027,0.0,0.069,0.0833,0.125,0.008,0.022000000000000002,0.0222,0.0,0.0,0.025,0.0361,0.9801,0.7316,0.0027,0.0,0.069,0.0833,0.125,0.008,0.0205,0.0217,0.0,0.0,reg oper account,block of flats,0.0168,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2641.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,2.0\n426826,0,Cash loans,F,N,Y,1,99000.0,1078200.0,38331.0,900000.0,\"Spouse, partner\",Commercial associate,Incomplete higher,Married,House / apartment,0.006233,-14920,-5325,-3534.0,-4724,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.6006165561666159,0.3201633668633456,0.1371,0.0906,0.9771,,,0.0,0.2759,0.2083,,0.0165,,0.1176,,0.1626,0.1397,0.0941,0.9772,,,0.0,0.2759,0.2083,,0.0169,,0.1225,,0.1722,0.1384,0.0906,0.9771,,,0.0,0.2759,0.2083,,0.0168,,0.1197,,0.166,,block of flats,0.1384,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n375097,0,Cash loans,F,Y,Y,0,135000.0,283500.0,29178.0,283500.0,Other_B,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.019101,-17742,-1417,-697.0,-1225,17.0,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,11,0,0,0,0,1,1,Industry: type 11,0.6136455990218453,0.5325167553496261,0.5046813193144684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n437880,0,Cash loans,M,N,Y,0,126000.0,755190.0,36328.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-11831,-120,-5175.0,-4221,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,13,0,0,0,1,1,0,Business Entity Type 2,,0.0036460048466754897,0.3441550073724169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n363028,0,Cash loans,M,Y,N,3,247500.0,1129500.0,44923.5,1129500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-14164,-694,-8080.0,-3943,28.0,1,1,0,1,0,0,Drivers,5.0,2,2,SUNDAY,16,0,0,0,1,1,0,Self-employed,0.6399678981047374,0.584250095454697,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,9.0\n308256,0,Cash loans,F,N,Y,0,135000.0,320382.0,15543.0,229500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009175,-15208,-5301,-1837.0,-5151,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,18,0,0,0,0,0,0,Other,,0.4569747478260112,0.30162489168411943,0.1711,0.0758,0.9791,,,0.0,0.0345,0.1667,,,,0.0744,,0.0143,0.1744,0.0787,0.9791,,,0.0,0.0345,0.1667,,,,0.0775,,0.0151,0.1728,0.0758,0.9791,,,0.0,0.0345,0.1667,,,,0.0757,,0.0146,,block of flats,0.0604,\"Stone, brick\",No,11.0,2.0,11.0,2.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n174319,0,Cash loans,F,N,Y,2,135000.0,254700.0,20250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.0105,-12917,-832,-6319.0,-4501,,1,1,1,1,0,0,Managers,4.0,3,3,WEDNESDAY,9,0,0,0,0,1,1,Self-employed,0.4930532005273141,0.4271527830935049,0.11392239629221085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1141.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n255036,0,Cash loans,F,N,Y,0,135000.0,439074.0,23949.0,328500.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.014464,-8575,-282,-4776.0,-182,,1,1,0,1,0,1,Core staff,1.0,2,2,THURSDAY,8,0,0,0,1,1,0,Self-employed,0.08015637303934876,0.5858267954783446,,0.0495,,0.9821,0.7552,,0.0,0.1034,0.125,0.1667,,0.0403,0.0398,,,0.0504,,0.9772,0.6994,,0.0,0.1034,0.125,0.1667,,0.0441,0.0412,,,0.05,,0.9821,0.7585,,0.0,0.1034,0.125,0.1667,,0.040999999999999995,0.0405,,,reg oper account,block of flats,0.0341,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n245432,0,Cash loans,M,N,N,0,112500.0,973710.0,28467.0,697500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.003818,-20620,365243,-6042.0,-4138,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,6,0,0,0,0,0,0,XNA,,0.6527253229428417,0.5814837058057234,0.0485,0.0688,0.9816,0.7484,0.0278,0.0,0.1034,0.125,0.1667,0.0521,0.0387,0.0423,0.0039,0.1157,0.0494,0.0713,0.9816,0.7583,0.027999999999999997,0.0,0.1034,0.125,0.1667,0.0533,0.0422,0.044000000000000004,0.0039,0.1224,0.0489,0.0688,0.9816,0.7518,0.0279,0.0,0.1034,0.125,0.1667,0.053,0.0393,0.043,0.0039,0.1181,reg oper account,block of flats,0.0584,Block,No,1.0,0.0,1.0,0.0,-175.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n167044,0,Cash loans,F,N,Y,0,94500.0,593010.0,21915.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-16630,-329,-1823.0,-172,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,1,1,0,Kindergarten,,0.5722943248014335,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-149.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n122304,0,Cash loans,M,Y,Y,2,360000.0,531265.5,16812.0,373500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-13550,-4636,-7679.0,-4404,23.0,1,1,0,1,0,0,Laborers,4.0,1,1,THURSDAY,15,0,0,0,0,0,0,Trade: type 7,,0.81650442516164,0.6940926425266661,0.2515,0.1917,0.9851,,,0.28,0.2414,0.3333,,0.0333,,0.17300000000000001,,0.0112,0.2563,0.19899999999999998,0.9851,,,0.282,0.2414,0.3333,,0.034,,0.1803,,0.0119,0.254,0.1917,0.9851,,,0.28,0.2414,0.3333,,0.0339,,0.1761,,0.0115,,block of flats,0.2246,Panel,No,1.0,0.0,1.0,0.0,-233.0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n225794,0,Cash loans,M,Y,Y,0,292500.0,935640.0,91278.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010643,-21328,-982,-5000.0,-4676,2.0,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Transport: type 4,0.7616398522313051,0.6075774599994973,0.7898803468924867,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-164.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n396805,0,Cash loans,F,N,Y,0,171000.0,553806.0,26770.5,495000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.016612000000000002,-22933,365243,-9621.0,-3993,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,18,0,0,0,0,0,0,XNA,,0.05854720197843692,0.4956658291397297,0.2784,0.2381,0.9871,0.8232,0.0523,0.32,0.2759,0.3333,0.0417,0.1724,0.2202,0.2984,0.0309,0.049,0.2836,0.2471,0.9871,0.8301,0.0528,0.3222,0.2759,0.3333,0.0417,0.1764,0.2406,0.3109,0.0311,0.0519,0.281,0.2381,0.9871,0.8256,0.0527,0.32,0.2759,0.3333,0.0417,0.1754,0.22399999999999998,0.3038,0.0311,0.05,reg oper spec account,block of flats,0.2454,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n137499,1,Cash loans,M,Y,Y,0,180000.0,474048.0,25843.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-11634,-756,-1505.0,-3829,25.0,1,1,0,1,0,0,Laborers,2.0,3,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.08720574146326386,0.44318486859788375,0.3876253444214701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-786.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,8.0\n282545,0,Cash loans,F,Y,Y,0,94500.0,454500.0,14661.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20640,365243,-2710.0,-4139,5.0,1,0,0,1,1,0,,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,XNA,,0.6502775974813615,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2536.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n447773,1,Cash loans,F,Y,N,0,135000.0,1350000.0,39595.5,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.014519999999999996,-17588,-5187,-6864.0,-1135,23.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,0.8148184052996754,0.3667513472500221,0.5424451438613613,0.1646,0.0971,0.9921,0.8708,0.0,0.26,0.1552,0.3192,0.2708,0.0476,0.1748,0.1835,0.0386,0.1544,0.0452,0.1003,0.9906,0.8759,0.0,0.2014,0.1379,0.125,0.0417,0.0242,0.1653,0.1332,0.0,0.0,0.2082,0.0971,0.9906,0.8725,0.0,0.26,0.1552,0.375,0.2708,0.0484,0.1779,0.1868,0.0388,0.1577,reg oper spec account,block of flats,0.1677,Panel,No,1.0,1.0,1.0,1.0,-1294.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n274873,0,Cash loans,F,N,Y,0,191700.0,247275.0,17716.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018801,-20885,365243,-337.0,-4399,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.6571631077288558,0.5849900404894085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n373896,0,Cash loans,M,Y,N,0,225000.0,532494.0,38880.0,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018029,-10761,-2701,-1132.0,-1049,11.0,1,1,0,1,1,0,Laborers,2.0,3,3,FRIDAY,7,0,0,0,0,0,0,Business Entity Type 2,0.41403040462194346,0.4812428375362657,0.5370699579791587,0.0773,0.0895,0.9846,,,0.0,0.1724,0.1667,,0.0194,,0.0729,,0.0,0.0788,0.0929,0.9846,,,0.0,0.1724,0.1667,,0.0199,,0.076,,0.0,0.0781,0.0895,0.9846,,,0.0,0.1724,0.1667,,0.0198,,0.0742,,0.0,,block of flats,0.0574,Panel,No,4.0,0.0,4.0,0.0,-1969.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n272727,0,Cash loans,F,Y,Y,2,67500.0,127350.0,13639.5,112500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-13121,-1260,-5741.0,-3980,25.0,1,1,0,1,0,0,Security staff,4.0,2,2,TUESDAY,14,0,0,0,0,0,0,Construction,,0.4742591816339269,,0.0825,0.0778,0.9757,0.6668,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.1583,0.0664,0.0621,0.0039,0.0036,0.084,0.0807,0.9757,0.6798,0.0071,0.0,0.1379,0.1667,0.2083,0.1619,0.0725,0.0647,0.0039,0.0038,0.0833,0.0778,0.9757,0.6713,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.161,0.0676,0.0633,0.0039,0.0037,reg oper account,block of flats,0.0535,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1125.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n164177,0,Cash loans,F,N,Y,0,112500.0,346968.0,27540.0,274500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.005313,-14732,-2549,-7088.0,-2611,,1,1,0,1,0,0,Secretaries,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Self-employed,0.6852667595764245,0.5886572311317612,0.4632753280912678,,,0.9896,,,0.04,0.0345,0.3333,,,,,,,,,0.9896,,,0.0403,0.0345,0.3333,,,,,,,,,0.9896,,,0.04,0.0345,0.3333,,,,,,,,block of flats,0.0406,Panel,No,0.0,0.0,0.0,0.0,-1651.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n354829,0,Cash loans,F,N,N,0,247500.0,760225.5,30150.0,679500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.022625,-14501,-2388,-2686.0,-4513,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,Bank,0.5524244198409731,0.6586002748069573,0.4311917977993083,0.1763,0.0655,0.9816,0.7484,0.0284,0.04,0.0345,0.3333,0.375,0.0681,0.1437,0.0859,0.0,0.0,0.1796,0.0679,0.9816,0.7583,0.0286,0.0403,0.0345,0.3333,0.375,0.0697,0.157,0.0895,0.0,0.0,0.17800000000000002,0.0655,0.9816,0.7518,0.0285,0.04,0.0345,0.3333,0.375,0.0693,0.1462,0.0875,0.0,0.0,reg oper account,block of flats,0.0944,Panel,No,0.0,0.0,0.0,0.0,-2334.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n296936,0,Cash loans,F,N,Y,0,225000.0,454500.0,21060.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-21427,365243,-12844.0,-364,,1,0,0,1,0,0,,2.0,3,2,WEDNESDAY,6,0,0,0,0,0,0,XNA,,0.15967923350263774,0.3791004853998145,0.1485,0.0802,0.9801,0.728,0.0272,0.16,0.1379,0.3333,0.375,0.1113,0.121,0.1397,0.0,0.0,0.1513,0.0832,0.9801,0.7387,0.0275,0.1611,0.1379,0.3333,0.375,0.1138,0.1322,0.1455,0.0,0.0,0.1499,0.0802,0.9801,0.7316,0.0274,0.16,0.1379,0.3333,0.375,0.1132,0.1231,0.1422,0.0,0.0,reg oper account,block of flats,0.1306,Panel,No,2.0,1.0,2.0,0.0,-1138.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,2.0\n367630,0,Cash loans,F,N,N,0,112500.0,454500.0,25506.0,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,Municipal apartment,0.032561,-21438,-414,-13115.0,-4220,,1,1,1,1,0,0,,1.0,1,1,SATURDAY,15,0,0,0,0,0,0,Other,,0.6636610217296472,0.5762088360175724,0.0351,0.0025,0.9116,0.0,0.0,0.0,0.1724,0.0833,0.125,0.02,0.0261,0.0338,0.0116,0.0852,0.0357,0.0026,0.9116,0.0,0.0,0.0,0.1724,0.0833,0.125,0.0205,0.0285,0.0352,0.0117,0.0902,0.0354,0.0025,0.9116,0.0,0.0,0.0,0.1724,0.0833,0.125,0.0204,0.0265,0.0344,0.0116,0.087,reg oper account,block of flats,0.0451,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-64.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n392737,0,Cash loans,F,N,N,0,270000.0,1354500.0,70132.5,1354500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.018801,-23804,365243,-1812.0,-4447,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.5912704399473007,0.7338145369642702,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1547.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n148932,0,Cash loans,F,N,Y,0,216000.0,835380.0,33259.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-19820,-7147,-5061.0,-2737,,1,1,1,1,1,0,Laborers,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.2955513515124641,0.3506958432829587,0.134,0.1551,0.9727,0.626,0.0254,0.0,0.2759,0.1667,0.2083,0.0782,0.1067,0.1601,0.0116,0.035,0.1366,0.1609,0.9727,0.6406,0.0257,0.0,0.2759,0.1667,0.2083,0.08,0.1166,0.1668,0.0117,0.037000000000000005,0.1353,0.1551,0.9727,0.631,0.0256,0.0,0.2759,0.1667,0.2083,0.0795,0.1086,0.163,0.0116,0.0357,reg oper account,block of flats,0.1474,Block,No,0.0,0.0,0.0,0.0,-2688.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n330146,0,Cash loans,F,Y,Y,1,112500.0,225000.0,12204.0,225000.0,Children,Working,Higher education,Civil marriage,House / apartment,0.003813,-10030,-264,-8217.0,-550,7.0,1,1,1,1,1,0,Core staff,3.0,2,2,SUNDAY,7,0,0,0,0,1,1,Bank,0.3040580424034732,0.12675746310756875,0.30162489168411943,0.0206,,0.9747,,,0.0,0.069,0.0417,,,,0.0053,,0.0209,0.021,,0.9747,,,0.0,0.069,0.0417,,,,0.0055,,0.0221,0.0208,,0.9747,,,0.0,0.069,0.0417,,,,0.0054,,0.0213,,block of flats,0.0087,Wooden,No,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,7.0\n323529,0,Cash loans,F,Y,N,0,180000.0,679500.0,67891.5,679500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.032561,-10807,-2263,-9789.0,-1792,1.0,1,1,0,1,1,0,,2.0,1,1,TUESDAY,11,0,0,0,0,1,1,Other,0.6230783065037182,0.7075337372482275,0.2103502286944494,0.1237,0.0872,0.9762,0.6736,0.0132,0.0,0.2069,0.1667,0.2083,0.0877,0.1009,0.106,0.0,0.0,0.1261,0.0905,0.9762,0.6864,0.0133,0.0,0.2069,0.1667,0.2083,0.0897,0.1102,0.1105,0.0,0.0,0.1249,0.0872,0.9762,0.6779999999999999,0.0133,0.0,0.2069,0.1667,0.2083,0.0893,0.1026,0.1079,0.0,0.0,reg oper account,block of flats,0.0906,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-361.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,1.0\n443684,0,Cash loans,M,Y,N,1,180000.0,454500.0,21865.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-13772,-1490,-1596.0,-4553,9.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4417901730065661,0.6471381245712653,0.44539624156083396,0.0825,,0.9801,,,0.0,0.2069,0.1667,,0.1829,,0.078,,0.0342,0.084,,0.9801,,,0.0,0.2069,0.1667,,0.1871,,0.0813,,0.0362,0.0833,,0.9801,,,0.0,0.2069,0.1667,,0.1861,,0.0794,,0.0349,,block of flats,0.0688,Panel,No,1.0,0.0,1.0,0.0,-3242.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n210967,0,Cash loans,M,Y,Y,0,90000.0,942300.0,27679.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.004849,-21648,-3535,-6253.0,-2115,9.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,1,1,Self-employed,,0.4963445955086408,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1115.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n189941,0,Cash loans,F,N,N,0,135000.0,327024.0,11875.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-19761,-3331,-455.0,-3311,,1,1,0,1,1,0,Cleaning staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Security Ministries,,0.6721585182404215,0.5442347412142162,0.0923,0.0517,0.997,0.9592,0.0154,0.08,0.069,0.3333,0.2083,0.0481,0.0744,0.0816,0.0039,0.0035,0.0756,0.0445,0.997,0.9608,0.0,0.0806,0.069,0.3333,0.0417,0.0427,0.0652,0.0802,0.0039,0.0033,0.0932,0.0517,0.997,0.9597,0.0155,0.08,0.069,0.3333,0.2083,0.049,0.0757,0.083,0.0039,0.0036,reg oper spec account,terraced house,0.0686,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1524.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n121271,0,Cash loans,M,Y,Y,1,175500.0,967500.0,28417.5,967500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.015221,-14484,-7252,-2283.0,-4571,6.0,1,1,0,1,1,0,Laborers,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Transport: type 2,0.5061680659167448,0.6812288679093877,0.7992967832109371,0.0907,0.1077,0.9816,,,,0.2069,0.1667,,0.0383,,0.0911,,0.0061,0.0924,0.1118,0.9816,,,,0.2069,0.1667,,0.0391,,0.0949,,0.0065,0.0916,0.1077,0.9816,,,,0.2069,0.1667,,0.0389,,0.0927,,0.0063,,block of flats,0.0716,Panel,No,1.0,0.0,1.0,0.0,-2048.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n157421,0,Revolving loans,M,Y,N,0,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.025164,-18388,-1739,-15.0,-1914,7.0,1,1,0,1,0,0,Managers,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,0.4926289909764956,0.4706129598790317,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n390813,0,Cash loans,F,N,Y,1,144000.0,582804.0,29884.5,463500.0,Family,Working,Higher education,Married,House / apartment,0.006207,-11249,-4017,-383.0,-1930,,1,1,0,1,0,1,Sales staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Services,0.3759517773711441,0.692618185754322,0.3046721837533529,0.0278,,0.9836,0.7756,,0.0,0.1034,0.0833,0.0,,0.0227,0.0305,,,0.0284,,0.9836,0.7844,,0.0,0.1034,0.0833,0.0,,0.0248,0.0318,,,0.0281,,0.9836,0.7786,,0.0,0.1034,0.0833,0.0,,0.0231,0.0311,,,reg oper spec account,block of flats,0.024,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n214110,0,Cash loans,M,Y,Y,0,360000.0,1470330.0,54616.5,1372500.0,Family,Pensioner,Higher education,Married,House / apartment,0.035792000000000004,-22248,365243,-9428.0,-4905,0.0,1,0,0,1,1,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,0.6837873715325749,0.3948270773270777,0.5064842396679806,0.1577,0.0823,0.9871,0.8232,0.0451,0.16,0.1379,0.375,0.4167,0.0748,0.1261,0.17300000000000001,0.0116,0.0332,0.1607,0.0854,0.9871,0.8301,0.0455,0.1611,0.1379,0.375,0.4167,0.0765,0.1377,0.1802,0.0117,0.0351,0.1593,0.0823,0.9871,0.8256,0.0453,0.16,0.1379,0.375,0.4167,0.0761,0.1283,0.1761,0.0116,0.0339,reg oper account,block of flats,0.1433,Panel,No,0.0,0.0,0.0,0.0,-551.0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n397838,0,Cash loans,F,N,Y,0,157500.0,288873.0,16258.5,238500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.025164,-21810,-6257,-7934.0,-5014,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,School,0.8178072431975868,0.6612209825899087,0.8327850252992314,0.1237,0.0637,0.9886,,,0.0,0.2759,0.1667,,0.0095,,0.0752,,0.0,0.1261,0.0661,0.9886,,,0.0,0.2759,0.1667,,0.0097,,0.0783,,0.0,0.1249,0.0637,0.9886,,,0.0,0.2759,0.1667,,0.0096,,0.0765,,0.0,,block of flats,0.1019,Panel,No,0.0,0.0,0.0,0.0,-1810.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n409369,0,Cash loans,F,Y,N,0,90000.0,225000.0,10944.0,225000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.019689,-10997,-1934,-2931.0,-3388,17.0,1,1,1,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6117301904341707,0.3859146722745145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1534.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n155045,0,Revolving loans,F,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-8342,-1254,-104.0,-1028,,1,1,1,1,1,0,Cooking staff,1.0,2,2,THURSDAY,10,0,0,0,1,1,0,Self-employed,0.4134986573705279,0.351852300573644,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1372.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n379608,0,Cash loans,F,N,Y,0,103500.0,284400.0,16011.0,225000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-24367,365243,-4326.0,-4492,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.672683548697527,0.7281412993111438,0.0825,0.0557,0.9757,0.5988,0.0249,0.0,0.1379,0.1667,0.2083,0.0409,0.0639,0.0594,0.0154,0.0774,0.084,0.0578,0.9757,0.6145,0.0251,0.0,0.1379,0.1667,0.2083,0.0418,0.0698,0.0619,0.0156,0.0819,0.0833,0.0557,0.9757,0.6042,0.025,0.0,0.1379,0.1667,0.2083,0.0416,0.065,0.0605,0.0155,0.079,reg oper account,block of flats,0.0636,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n308345,0,Revolving loans,M,Y,N,0,157500.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.015221,-15337,-1153,-3615.0,-4498,2.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,SATURDAY,8,0,0,0,1,1,0,Business Entity Type 3,0.189627039757536,0.5231976561847652,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n110830,0,Cash loans,M,Y,Y,0,180000.0,595903.5,28795.5,481500.0,Unaccompanied,Working,Incomplete higher,Separated,House / apartment,0.018634,-11478,-515,-6145.0,-4088,9.0,1,1,1,1,0,0,Managers,1.0,2,2,THURSDAY,13,0,0,0,0,1,1,Industry: type 4,0.2586638496629486,0.2835642291736645,0.4400578303966329,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-776.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n332043,0,Cash loans,M,Y,Y,0,225000.0,814041.0,23800.5,679500.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.00963,-19692,-1471,-7769.0,-2548,4.0,1,1,1,1,1,0,Security staff,1.0,2,2,TUESDAY,12,0,1,1,0,1,1,Security,,0.629353324398331,0.4848514754962666,0.0753,0.0844,0.9891,0.8504,0.0101,0.0,0.1724,0.1667,0.2083,0.0146,0.0614,0.0732,0.0,0.0,0.0767,0.0876,0.9891,0.8563,0.0102,0.0,0.1724,0.1667,0.2083,0.015,0.067,0.0762,0.0,0.0,0.076,0.0844,0.9891,0.8524,0.0102,0.0,0.1724,0.1667,0.2083,0.0149,0.0624,0.0745,0.0,0.0,reg oper account,block of flats,0.0631,Panel,No,0.0,0.0,0.0,0.0,-2053.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n260934,0,Cash loans,F,N,Y,0,72000.0,101880.0,10206.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010643,-23322,365243,-13125.0,-4541,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.6508194658180643,0.7517237147741489,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-588.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n355758,0,Revolving loans,F,N,Y,2,180000.0,225000.0,11250.0,225000.0,Family,Working,Higher education,Civil marriage,House / apartment,0.032561,-12862,-2366,-2552.0,-4092,,1,1,1,1,0,0,Accountants,4.0,1,1,SATURDAY,10,0,0,0,0,0,0,Trade: type 3,0.4403083461027867,0.2858978721410488,0.4992720153045617,0.3175,0.4359,0.9876,0.83,,0.56,0.2414,0.4583,,0.0767,,0.3423,,,0.3235,0.4524,0.9876,0.8367,,0.5639,0.2414,0.4583,,0.0784,,0.3566,,,0.3206,0.4359,0.9876,0.8323,,0.56,0.2414,0.4583,,0.078,,0.3484,,,reg oper account,block of flats,0.3851,Panel,No,2.0,0.0,2.0,0.0,-1562.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n157020,0,Cash loans,F,N,Y,1,180000.0,1293502.5,35568.0,1129500.0,Family,Working,Higher education,Married,House / apartment,0.006852,-13729,-1947,-3327.0,-1353,,1,1,0,1,0,0,Managers,3.0,3,3,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3278890318004643,0.30123260712885763,0.12769879437277135,0.1165,0.066,0.9886,0.8436,0.024,0.0,0.1724,0.1667,0.1667,0.092,0.095,0.10300000000000001,0.0,0.0101,0.1187,0.0685,0.9886,0.8497,0.0243,0.0,0.1724,0.1667,0.1667,0.0941,0.1038,0.1073,0.0,0.0106,0.1176,0.066,0.9886,0.8457,0.0242,0.0,0.1724,0.1667,0.1667,0.0936,0.0966,0.1049,0.0,0.0103,reg oper account,block of flats,0.0964,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1064.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n175165,0,Revolving loans,M,Y,Y,0,90000.0,225000.0,11250.0,225000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.028663,-16330,-5380,-2912.0,-4077,2.0,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,School,,0.6247508551565555,,0.166,0.2636,0.9801,,,0.2,0.1724,0.3333,,0.0,,0.1692,,0.2501,0.1691,0.2736,0.9801,,,0.2014,0.1724,0.3333,,0.0,,0.1763,,0.2648,0.1676,0.2636,0.9801,,,0.2,0.1724,0.3333,,0.0,,0.1723,,0.2553,,block of flats,0.1331,\"Stone, brick\",No,,,,,-688.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n245306,0,Cash loans,F,N,Y,0,67500.0,657000.0,19341.0,657000.0,Family,Pensioner,Lower secondary,Widow,House / apartment,0.028663,-23209,365243,-12691.0,-4368,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.355040513627405,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-138.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n389792,0,Cash loans,F,Y,Y,0,211500.0,381528.0,22032.0,315000.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.04622,-10531,-106,-253.0,-1842,7.0,1,1,0,1,0,0,Sales staff,2.0,1,1,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.3327205907862608,0.7753904041029387,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-705.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,11.0,0.0,0.0\n389852,0,Revolving loans,F,N,Y,1,144000.0,135000.0,6750.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.008625,-12857,-2379,-6476.0,-5000,,1,1,1,1,0,0,High skill tech staff,3.0,2,2,THURSDAY,11,0,0,0,0,1,1,School,0.4813424379365344,0.6386842693875221,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-499.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n343170,0,Cash loans,F,N,N,0,157500.0,472500.0,27256.5,472500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.028663,-19052,-6574,-8987.0,-2598,,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Industry: type 3,,0.5483526002953051,0.7776594425716818,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2043.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n337333,1,Cash loans,M,N,Y,0,180000.0,719860.5,48429.0,679500.0,Family,Working,Higher education,Married,House / apartment,0.030755,-17344,-3074,-2215.0,-746,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Self-employed,0.6450579158987814,0.6634462162678144,,0.0928,0.0886,0.9836,0.7756,0.012,0.0,0.2069,0.1667,0.2083,0.1166,0.0748,0.0851,0.0039,0.0044,0.0945,0.092,0.9836,0.7844,0.0121,0.0,0.2069,0.1667,0.2083,0.1193,0.0817,0.0887,0.0039,0.0046,0.0937,0.0886,0.9836,0.7786,0.012,0.0,0.2069,0.1667,0.2083,0.1186,0.0761,0.0866,0.0039,0.0045,reg oper account,block of flats,0.09,Panel,No,2.0,2.0,2.0,1.0,-738.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n454426,0,Cash loans,M,Y,N,2,270000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.009334,-22331,365243,-3667.0,-5761,21.0,1,0,0,1,1,0,,4.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.7116613892244099,0.7530673920730478,0.0835,0.1069,0.9786,0.7076,0.0108,0.0,0.2069,0.1667,0.2083,0.0644,0.0672,0.0746,0.0039,0.0832,0.0851,0.1109,0.9786,0.7190000000000001,0.0109,0.0,0.2069,0.1667,0.2083,0.0658,0.0735,0.0777,0.0039,0.0881,0.0843,0.1069,0.9786,0.7115,0.0108,0.0,0.2069,0.1667,0.2083,0.0655,0.0684,0.0759,0.0039,0.085,reg oper account,block of flats,0.0826,Panel,No,3.0,0.0,2.0,0.0,-1984.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n144444,0,Cash loans,F,N,N,0,67500.0,159264.0,10305.0,126000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018634,-24089,365243,-3697.0,-4420,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.0423715913114984,0.4365064990977374,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n165487,1,Cash loans,M,N,Y,1,157500.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-9795,-234,-705.0,-2456,,1,1,0,1,0,0,Drivers,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 2,,0.35291525281612857,0.5046813193144684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n208106,0,Revolving loans,M,Y,Y,1,675000.0,1350000.0,67500.0,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-16673,-2699,-6142.0,-221,3.0,1,1,0,1,0,0,Managers,3.0,1,1,SATURDAY,10,0,0,0,0,0,0,Bank,,0.7113022790628791,0.646329897706246,0.1921,0.1243,0.9891,0.8504,0.0577,0.24,0.1148,0.5971,0.7083,0.0,0.179,0.3018,0.0116,0.0228,0.1008,0.12300000000000001,0.9891,0.8563,0.0,0.3222,0.1379,0.6667,0.7083,0.0,0.1956,0.3045,0.0117,0.0053,0.2248,0.1243,0.9891,0.8524,0.0581,0.32,0.1379,0.6667,0.7083,0.0,0.1821,0.3072,0.0116,0.0233,org spec account,block of flats,0.2387,Panel,No,0.0,0.0,0.0,0.0,-566.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n233162,1,Cash loans,M,Y,Y,3,157500.0,900000.0,29875.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-15735,-178,-639.0,-4725,3.0,1,1,0,1,0,0,Drivers,5.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Transport: type 3,,0.5619606375534278,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-743.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n324141,0,Cash loans,F,N,Y,1,67500.0,1078200.0,31522.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.015221,-12226,-2713,-65.0,-4583,,1,1,0,1,0,0,,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.6587409780762977,0.7470281424148458,0.5744466170995097,0.0825,0.0812,0.9732,0.6328,,0.0,0.1379,0.1667,0.0417,0.0158,0.0664,0.0631,0.0039,0.0053,0.084,0.0843,0.9732,0.6472,,0.0,0.1379,0.1667,0.0417,0.0162,0.0725,0.0657,0.0039,0.0056,0.0833,0.0812,0.9732,0.6377,,0.0,0.1379,0.1667,0.0417,0.0161,0.0676,0.0642,0.0039,0.0054,reg oper account,block of flats,0.0546,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1503.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n171340,0,Cash loans,F,Y,N,0,180000.0,675000.0,34465.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014464,-17784,-746,-6902.0,-1320,10.0,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,4,0,0,0,0,0,0,Business Entity Type 3,,0.4454092710302832,0.17352743046491467,0.0186,0.0093,0.9697,0.5852,0.0161,0.0,0.1034,0.0417,0.0417,0.0142,0.0151,0.0195,0.0,0.0,0.0189,0.0096,0.9697,0.6014,0.0163,0.0,0.1034,0.0417,0.0417,0.0146,0.0165,0.0203,0.0,0.0,0.0187,0.0093,0.9697,0.5907,0.0162,0.0,0.1034,0.0417,0.0417,0.0145,0.0154,0.0199,0.0,0.0,reg oper account,block of flats,0.0242,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-892.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n426111,0,Cash loans,M,Y,Y,1,202500.0,153000.0,10881.0,153000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-11466,-2314,-1138.0,-4153,17.0,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,11,0,1,1,0,1,1,Industry: type 9,0.5562285612619601,0.6242021516843133,0.6413682574954046,0.1258,0.0022,0.9786,,,0.0,0.2759,0.1667,,0.0284,,0.1126,,0.0691,0.1282,0.0023,0.9786,,,0.0,0.2759,0.1667,,0.028999999999999998,,0.1173,,0.0732,0.127,0.0022,0.9786,,,0.0,0.2759,0.1667,,0.0289,,0.1146,,0.0706,,block of flats,0.1132,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1268.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n219973,0,Revolving loans,F,N,Y,0,108000.0,315000.0,15750.0,315000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-17202,-7302,-9936.0,-745,,1,1,1,1,1,0,Core staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,School,,0.7316452698840658,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-1084.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n380590,0,Cash loans,F,N,Y,0,180000.0,225000.0,23625.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.022625,-11107,-668,-974.0,-2456,,1,1,1,1,1,0,Accountants,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,School,0.56591113430041,0.14855951159489436,0.4365064990977374,0.0577,0.0356,0.9891,0.8368,0.0071,0.0,0.0345,0.3542,0.375,0.0537,0.0605,0.0494,0.0,0.0179,0.042,0.0342,0.9881,0.8432,0.0072,0.0,0.0345,0.3333,0.375,0.0445,0.0661,0.051,0.0,0.0189,0.0583,0.0356,0.9891,0.8390000000000001,0.0072,0.0,0.0345,0.3542,0.375,0.0547,0.0616,0.0503,0.0,0.0183,reg oper account,block of flats,0.0385,Panel,No,0.0,0.0,0.0,0.0,-567.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n291226,0,Cash loans,M,N,Y,0,202500.0,622413.0,31909.5,495000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.0228,-20733,365243,-6054.0,-3906,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.7958869993997368,,0.1763,0.1165,0.9856,,,0.04,0.0345,0.3333,,0.0237,,0.083,,0.0035,0.1796,0.1209,0.9856,,,0.0403,0.0345,0.3333,,0.0243,,0.0865,,0.0037,0.17800000000000002,0.1165,0.9856,,,0.04,0.0345,0.3333,,0.0241,,0.0845,,0.0036,,block of flats,0.0872,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-499.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n404719,0,Cash loans,F,N,Y,1,90000.0,728460.0,38938.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-18135,-4309,-11706.0,-1690,,1,1,1,1,1,0,Sales staff,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Self-employed,,0.5615084189738873,0.8297501422395771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2339.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n135050,0,Cash loans,F,N,Y,0,112500.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-16719,-4977,-2746.0,-271,,1,1,1,1,0,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,0.4128492560810661,0.6505858638622015,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1928.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n386891,0,Cash loans,M,Y,N,2,157500.0,787131.0,26145.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-13041,-4795,-4213.0,-4438,12.0,1,1,0,1,1,0,Laborers,4.0,3,3,MONDAY,9,0,0,0,0,0,0,Transport: type 2,0.2163213937113661,0.356067693646701,0.6144143775673561,0.0753,0.0845,0.9796,0.7212,0.0086,0.0,0.1379,0.1667,0.2083,0.0515,0.0597,0.0663,0.0077,0.0159,0.0767,0.0877,0.9796,0.7321,0.0087,0.0,0.1379,0.1667,0.2083,0.0527,0.0652,0.0691,0.0078,0.0169,0.076,0.0845,0.9796,0.7249,0.0086,0.0,0.1379,0.1667,0.2083,0.0524,0.0607,0.0675,0.0078,0.0163,reg oper account,block of flats,0.0603,\"Stone, brick\",No,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,1.0\n154111,0,Cash loans,F,N,Y,1,157500.0,279000.0,33241.5,279000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-10484,-3187,-8448.0,-2147,,1,1,0,1,0,0,Private service staff,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,0.3513570273326829,0.3656296940073164,0.7281412993111438,0.1361,0.049,0.9811,0.7416,,,0.3448,0.1667,,0.0315,0.111,0.1102,,0.0595,0.1387,0.0509,0.9811,0.7517,,,0.3448,0.1667,,0.0322,0.1212,0.1149,,0.063,0.1374,0.049,0.9811,0.7451,,,0.3448,0.1667,,0.032,0.1129,0.1122,,0.0607,reg oper account,block of flats,0.0996,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1477.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n134117,0,Cash loans,F,N,N,0,135000.0,675000.0,29731.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-21958,365243,-2582.0,-4619,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,14,0,0,0,0,0,0,XNA,0.7779048324647784,0.6058137931589587,0.6832688314232291,0.0082,,0.9677,,,0.0,0.069,0.0417,,0.0314,,0.0056,,0.0,0.0084,,0.9677,,,0.0,0.069,0.0417,,0.0322,,0.0058,,0.0,0.0083,,0.9677,,,0.0,0.069,0.0417,,0.032,,0.0057,,0.0,,block of flats,0.0063,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2170.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n211323,0,Cash loans,F,N,Y,0,157500.0,641173.5,27297.0,553500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-17053,-3098,-5883.0,-527,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Business Entity Type 2,0.3888503192843255,0.2499698908030095,0.4884551844437485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1920.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n295647,0,Cash loans,F,N,Y,0,112500.0,436032.0,21339.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.003069,-19376,-199,-9094.0,-2140,,1,1,0,1,0,0,High skill tech staff,1.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Transport: type 3,,0.520594953965652,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1109.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n453257,0,Cash loans,F,N,Y,0,126000.0,497520.0,32391.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006629,-16021,-730,-4448.0,-4448,,1,1,1,1,0,0,Cooking staff,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Other,,0.5536165301178255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-1221.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n113011,0,Cash loans,M,N,Y,0,112500.0,450000.0,21109.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006852,-13445,-1525,-3359.0,-3863,,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,5,0,0,0,0,1,1,Self-employed,,0.26974676298359124,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-544.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n225609,0,Revolving loans,F,N,Y,0,202500.0,270000.0,13500.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.010147,-15063,-802,-2332.0,-5232,,1,1,0,1,0,0,Sales staff,1.0,2,2,FRIDAY,18,0,0,0,0,0,0,Self-employed,0.6592217010119651,0.4845245796375478,0.2263473957097693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2526,,No,4.0,0.0,4.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n250179,0,Cash loans,M,Y,N,0,107100.0,288873.0,16713.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.006207,-9988,-2532,-9985.0,-2652,64.0,1,1,0,1,0,1,Laborers,1.0,2,2,THURSDAY,16,0,0,0,0,1,1,Business Entity Type 3,0.3173596654163808,0.33263792440161394,0.5919766183185521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1693.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n100759,0,Cash loans,M,Y,N,2,202500.0,938304.0,50121.0,810000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,Municipal apartment,0.04622,-16290,-4752,-8870.0,-3060,3.0,1,1,0,1,0,0,Drivers,4.0,1,1,WEDNESDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.3655238490112184,0.7772283250141669,0.5226973172821112,0.0773,0.073,0.9771,,,0.0,0.1724,0.1667,,0.013000000000000001,,0.0622,,0.0294,0.0788,0.0758,0.9772,,,0.0,0.1724,0.1667,,0.0133,,0.0648,,0.0312,0.0781,0.073,0.9771,,,0.0,0.1724,0.1667,,0.0132,,0.0633,,0.0301,,block of flats,0.0494,Panel,No,0.0,0.0,0.0,0.0,-363.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n338484,1,Cash loans,M,Y,N,0,108000.0,590071.5,16911.0,492547.5,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.00823,-18724,-4319,-3036.0,-1549,16.0,1,1,1,1,0,0,Drivers,1.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.6849873977091516,0.5673792367572691,0.0773,0.0561,0.9771,,,,0.1724,0.1667,,,,0.0619,,0.0032,0.0788,0.0582,0.9772,,,,0.1724,0.1667,,,,0.0645,,0.0034,0.0781,0.0561,0.9771,,,,0.1724,0.1667,,,,0.063,,0.0033,,block of flats,0.048,Panel,No,0.0,0.0,0.0,0.0,-477.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n261467,0,Cash loans,F,N,Y,2,121500.0,948582.0,27864.0,679500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-13132,-5554,-1271.0,-1365,,1,1,0,1,0,0,Core staff,4.0,3,2,SATURDAY,8,0,0,0,0,0,0,School,0.5214299183910984,0.6403478792974197,0.5298898341969072,0.033,0.0,0.9737,0.6396,0.0028,0.0,0.069,0.125,0.1667,0.0,0.0269,0.0256,0.0,0.0,0.0336,0.0,0.9737,0.6537,0.0028,0.0,0.069,0.125,0.1667,0.0,0.0294,0.0267,0.0,0.0,0.0333,0.0,0.9737,0.6444,0.0028,0.0,0.069,0.125,0.1667,0.0,0.0274,0.026000000000000002,0.0,0.0,reg oper account,block of flats,0.0201,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1891.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n152221,0,Cash loans,M,Y,Y,2,270000.0,818410.5,27175.5,706500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-12032,-4121,-5475.0,-4016,9.0,1,1,0,1,1,0,Laborers,4.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 2,0.4301249111607031,0.6544383996900622,0.34741822720026416,0.0825,0.1208,0.9851,0.7959999999999999,,0.08,0.0345,0.4167,0.0417,,0.0656,0.0734,0.0077,0.0104,0.084,0.1253,0.9851,0.804,,0.0806,0.0345,0.4167,0.0417,,0.0716,0.0764,0.0078,0.011000000000000001,0.0833,0.1208,0.9851,0.7987,,0.08,0.0345,0.4167,0.0417,,0.0667,0.0747,0.0078,0.0106,reg oper account,block of flats,0.0789,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1815.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n253746,0,Cash loans,F,Y,Y,1,382500.0,1812456.0,49842.0,1620000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006629,-11025,-1588,-826.0,-2475,16.0,1,1,1,1,0,0,Core staff,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,Government,0.630729996435114,0.6627720702252099,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,-850.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,2.0,0.0,1.0\n110225,0,Cash loans,M,Y,N,0,270000.0,305221.5,15934.5,252000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.032561,-11585,-1676,-5662.0,-3317,0.0,1,1,0,1,0,0,,1.0,1,1,FRIDAY,12,0,0,0,0,0,0,Medicine,0.2343279236749667,0.7541143447824014,0.5226973172821112,0.1423,,0.9702,,,,0.1034,0.375,,,,0.1954,,0.0618,0.145,,0.9702,,,,0.1034,0.375,,,,0.2036,,0.0654,0.1436,,0.9702,,,,0.1034,0.375,,,,0.1989,,0.0631,,,0.1671,\"Stone, brick\",No,2.0,2.0,2.0,1.0,-3.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n140706,0,Cash loans,F,N,Y,0,108000.0,733176.0,21568.5,612000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.019101,-20440,-1804,-3758.0,-1842,,1,1,0,1,1,0,Laborers,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Restaurant,,0.6435295356270064,0.4596904504249018,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,5.0,0.0,-961.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n312041,0,Cash loans,F,N,Y,0,225000.0,1170400.5,49716.0,1003500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.008019,-17251,-2856,-7958.0,-457,,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5780703512422153,0.22133520635446602,0.0247,0.0081,0.9742,0.6464,0.0041,0.0,0.069,0.0833,0.125,0.0189,0.0202,0.0191,0.0,0.0,0.0252,0.0084,0.9742,0.6602,0.0042,0.0,0.069,0.0833,0.125,0.0193,0.022000000000000002,0.0199,0.0,0.0,0.025,0.0081,0.9742,0.6511,0.0041,0.0,0.069,0.0833,0.125,0.0192,0.0205,0.0195,0.0,0.0,reg oper account,block of flats,0.0173,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1811.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n120818,0,Cash loans,M,N,N,0,135000.0,450000.0,16294.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-17613,-2641,-1736.0,-1147,,1,1,1,1,1,0,Drivers,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Self-employed,0.42322712642092747,0.4690224445861711,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,4.0\n341757,1,Cash loans,F,N,Y,1,126000.0,306306.0,15768.0,247500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.00823,-13569,-1416,-7211.0,-2358,,1,1,0,1,0,1,,3.0,2,2,FRIDAY,16,0,0,0,0,1,1,Self-employed,0.782957772284241,0.11011887402945084,0.1777040724853336,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-1214.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n116449,0,Cash loans,F,N,N,0,157500.0,292500.0,8379.0,292500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007120000000000001,-13206,-356,-793.0,-4318,,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,15,0,0,0,1,1,0,Business Entity Type 3,,0.6486596343168586,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-302.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n401070,1,Cash loans,F,N,Y,0,112500.0,835380.0,40320.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-11652,-2174,-5347.0,-4200,,1,1,1,1,1,0,Core staff,2.0,2,2,THURSDAY,10,0,0,0,1,0,1,Kindergarten,0.5372826375522718,0.153312084241807,0.7850520263728172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1488.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n280178,0,Cash loans,F,N,N,0,133200.0,462195.0,18954.0,351000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-17747,-679,-1541.0,-1254,,1,1,0,1,1,0,Medicine staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Medicine,0.5289265125763527,0.6533941934375115,0.5046813193144684,0.1474,0.096,0.9781,0.7008,0.019,0.16,0.1379,0.3333,0.375,0.3154,0.1202,0.1417,0.0,0.0062,0.1502,0.0996,0.9782,0.7125,0.0192,0.1611,0.1379,0.3333,0.375,0.3226,0.1313,0.1476,0.0,0.0066,0.1489,0.096,0.9781,0.7048,0.0191,0.16,0.1379,0.3333,0.375,0.3209,0.1223,0.1442,0.0,0.0064,reg oper account,block of flats,0.1128,Panel,No,1.0,0.0,1.0,0.0,-574.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n218050,0,Cash loans,M,Y,Y,1,112500.0,1089000.0,36121.5,1089000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-15138,-2836,-2550.0,-2935,7.0,1,1,0,1,0,0,Core staff,3.0,2,2,FRIDAY,9,0,0,0,0,0,0,Kindergarten,,0.16040532147836672,0.8128226070575616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2362.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n123161,0,Cash loans,F,Y,Y,0,135000.0,573628.5,24435.0,463500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.072508,-15950,-7757,-8589.0,-162,17.0,1,1,0,1,0,0,,1.0,1,1,WEDNESDAY,17,0,0,0,0,0,0,Telecom,,0.4745427150331085,0.4311917977993083,0.1351,0.0644,0.9891,0.8504,0.0665,0.16,0.069,0.6308,0.6725,0.0,0.1091,0.1569,0.0044,0.0032,0.1439,0.0589,0.9881,0.8432,0.0595,0.1611,0.069,0.6667,0.7083,0.0,0.1249,0.1251,0.0039,0.0014,0.1416,0.0663,0.9881,0.8390000000000001,0.0678,0.16,0.069,0.6667,0.7083,0.0,0.1154,0.1736,0.0039,0.0014,reg oper account,block of flats,0.0965,Panel,No,0.0,0.0,0.0,0.0,-148.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n303174,0,Cash loans,M,Y,Y,0,180000.0,1575000.0,43443.0,1575000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.003069,-15386,-7251,-1817.0,-1971,7.0,1,1,0,1,0,0,Core staff,2.0,3,3,TUESDAY,12,0,0,0,0,0,0,Police,,0.4970169939324425,0.5656079814115492,0.0722,0.0588,0.9821,,,0.0,0.1724,0.1667,,0.0957,,0.0436,,0.0,0.0735,0.061,0.9821,,,0.0,0.1724,0.1667,,0.0979,,0.0455,,0.0,0.0729,0.0588,0.9821,,,0.0,0.1724,0.1667,,0.0974,,0.0444,,0.0,,block of flats,0.0506,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1202.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n367506,0,Cash loans,F,N,Y,1,180000.0,1113840.0,44302.5,900000.0,\"Spouse, partner\",State servant,Higher education,Married,House / apartment,0.04622,-10147,-368,-3911.0,-2825,,1,1,0,1,1,0,Core staff,3.0,1,1,MONDAY,10,0,0,0,0,1,1,School,,0.6166114452962421,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n441129,0,Cash loans,F,N,Y,0,180000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008068,-15676,365243,-3647.0,-4957,,1,0,0,1,1,0,,2.0,3,3,FRIDAY,7,0,0,0,0,0,0,XNA,,0.3782050613800788,0.5867400085415683,0.0876,0.1022,0.9816,0.7484,0.0373,0.0,0.2069,0.1667,0.2083,,0.0714,0.10800000000000001,0.0,0.0,0.0893,0.1061,0.9816,0.7583,0.0376,0.0,0.2069,0.1667,0.2083,,0.0781,0.1125,0.0,0.0,0.0885,0.1022,0.9816,0.7518,0.0375,0.0,0.2069,0.1667,0.2083,,0.0727,0.11,0.0,0.0,reg oper spec account,block of flats,0.1053,Panel,No,1.0,0.0,1.0,0.0,-2046.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n421622,0,Cash loans,F,Y,N,2,67500.0,258709.5,17419.5,234000.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.006296,-12296,-1077,-3092.0,-1779,1.0,1,1,0,1,0,0,Core staff,3.0,3,3,SATURDAY,11,0,0,0,0,1,1,Insurance,0.31512143547231924,0.3254591568868179,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-305.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n240480,0,Revolving loans,F,N,Y,0,94500.0,270000.0,13500.0,270000.0,Unaccompanied,Pensioner,Lower secondary,Widow,House / apartment,0.032561,-21579,365243,-12368.0,-3685,,1,0,0,1,1,0,,1.0,1,1,MONDAY,13,0,0,0,0,0,0,XNA,,0.4992365187445348,0.7597121819739279,0.0845,0.0932,0.9752,0.66,0.0146,0.0,0.2069,0.1667,0.2083,0.0522,0.0685,0.065,0.0019,0.0024,0.0861,0.0965,0.9752,0.6733,0.0145,0.0,0.2069,0.1667,0.2083,0.0524,0.0744,0.0406,0.0,0.0011,0.0854,0.0932,0.9752,0.6645,0.0146,0.0,0.2069,0.1667,0.2083,0.0532,0.0697,0.0793,0.0019,0.0025,reg oper account,block of flats,0.0306,Panel,No,0.0,0.0,0.0,0.0,-232.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n260636,0,Cash loans,M,N,N,0,157500.0,582768.0,35653.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-10649,-2001,-5086.0,-3227,,1,1,1,1,1,1,Drivers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.5865004728173188,,0.0825,0.0591,0.9786,0.7076,0.0084,0.0,0.1379,0.1667,0.2083,0.0352,0.0672,0.0746,0.0,0.0,0.084,0.0613,0.9786,0.7190000000000001,0.0084,0.0,0.1379,0.1667,0.2083,0.036000000000000004,0.0735,0.0777,0.0,0.0,0.0833,0.0591,0.9786,0.7115,0.0084,0.0,0.1379,0.1667,0.2083,0.0358,0.0684,0.0759,0.0,0.0,reg oper account,block of flats,0.0633,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1915.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n317810,0,Cash loans,M,N,Y,0,85500.0,360000.0,17640.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005313,-14174,-449,-7895.0,-4241,,1,1,0,1,1,0,Laborers,2.0,2,2,MONDAY,7,0,0,0,1,1,0,Government,,0.14834389123156194,0.2925880073368475,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n317461,0,Cash loans,M,Y,N,2,112500.0,511920.0,13500.0,405000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.031329,-15710,-3393,-1905.0,-4172,3.0,1,1,1,1,1,0,Core staff,4.0,2,2,TUESDAY,17,0,0,0,0,1,1,Police,0.7051714110795831,0.5181385467795485,0.3842068130556564,0.0247,0.0436,0.9846,0.7892,0.0027,0.0,0.069,0.0833,0.0417,0.0086,0.0202,0.024,0.0,0.0,0.0252,0.0453,0.9846,0.7975,0.0028,0.0,0.069,0.0833,0.0417,0.0088,0.022000000000000002,0.025,0.0,0.0,0.025,0.0436,0.9846,0.792,0.0028,0.0,0.069,0.0833,0.0417,0.0088,0.0205,0.0244,0.0,0.0,reg oper spec account,block of flats,0.0204,Block,No,0.0,0.0,0.0,0.0,-1697.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n271190,0,Cash loans,F,N,N,0,180000.0,328405.5,25920.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018801,-15142,-1802,-1645.0,-4069,,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4698230725781852,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.0,1.0,11.0,1.0,-1696.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n259809,0,Cash loans,M,Y,N,0,337500.0,1019164.5,40549.5,823500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-15380,-2860,-385.0,-3738,9.0,1,1,0,1,0,0,Drivers,2.0,1,1,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5637362072352043,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1893.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n240542,0,Cash loans,F,N,N,0,62550.0,630000.0,19228.5,630000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.011703,-23968,365243,-12779.0,-4390,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.7522117595466962,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n455094,0,Cash loans,F,Y,Y,0,337500.0,1327500.0,38812.5,1327500.0,Family,Working,Higher education,Married,House / apartment,0.04622,-16687,-2396,-1225.0,-230,6.0,1,1,1,1,1,0,Managers,2.0,1,1,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.7907184809048371,0.7411361275101312,0.3185955240537633,0.2918,,0.998,,,0.52,0.3103,0.625,0.375,,,0.3888,,0.2282,0.2973,,0.998,,,0.5236,0.3103,0.625,0.375,,,0.4051,,0.2416,0.2946,,0.998,,,0.52,0.3103,0.625,0.375,,,0.3958,,0.233,,block of flats,0.4952,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1797.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,6.0,0.0,0.0\n417889,0,Cash loans,M,N,N,0,225000.0,746280.0,59094.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,Office apartment,0.010966,-10212,-684,-10142.0,-1767,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,13,0,1,1,0,1,1,Trade: type 6,,0.7068681109295358,,0.0722,0.0797,0.9811,0.7416,0.0077,0.0,0.1379,0.1667,0.2083,0.0,0.0588,0.0663,0.0,0.0,0.0735,0.0827,0.9811,0.7517,0.0078,0.0,0.1379,0.1667,0.2083,0.0,0.0643,0.0691,0.0,0.0,0.0729,0.0797,0.9811,0.7451,0.0077,0.0,0.1379,0.1667,0.2083,0.0,0.0599,0.0675,0.0,0.0,org spec account,block of flats,0.0564,Panel,No,0.0,0.0,0.0,0.0,-503.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n175978,0,Cash loans,F,N,Y,0,76500.0,81000.0,9742.5,81000.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.015221,-8300,-590,-4398.0,-961,,1,1,1,1,0,0,Sales staff,1.0,2,2,SUNDAY,12,0,0,0,0,0,0,Self-employed,0.28579872057170363,0.6166321232261023,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-389.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n257334,0,Cash loans,F,Y,N,0,315000.0,891000.0,48334.5,891000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-15239,-1467,-3000.0,-3000,3.0,1,1,1,1,1,0,Managers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,,0.5762718970550027,0.3344541255096772,0.1031,,0.9781,,,0.0,0.2069,0.1667,,0.0982,,0.0899,,0.0,0.105,,0.9782,,,0.0,0.2069,0.1667,,0.1004,,0.0937,,0.0,0.1041,,0.9781,,,0.0,0.2069,0.1667,,0.0999,,0.0916,,0.0,,block of flats,0.0707,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-316.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n125771,0,Cash loans,F,N,Y,0,405000.0,1800000.0,47614.5,1800000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-18317,-1537,-6584.0,-1855,,1,1,0,1,1,0,Accountants,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Transport: type 4,0.7305209576703977,0.614453469379269,,0.0495,0.0757,0.9881,,,0.0,0.1034,0.125,,0.0398,,0.048,,0.0132,0.0504,0.0786,0.9881,,,0.0,0.1034,0.125,,0.0407,,0.05,,0.0139,0.05,0.0757,0.9881,,,0.0,0.1034,0.125,,0.0405,,0.0489,,0.0134,,block of flats,0.0406,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1556.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,,,,,,\n271772,0,Cash loans,M,Y,Y,1,450000.0,454500.0,45999.0,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-14815,-3253,-722.0,-4214,0.0,1,1,0,1,1,0,Managers,3.0,2,2,THURSDAY,17,0,0,0,0,0,0,Medicine,0.5740378383095891,0.7527738030345192,0.6144143775673561,0.4938,,0.998,,,0.48,0.2069,0.7083,,,,0.6673,,0.1647,0.5032,,0.998,,,0.4834,0.2069,0.7083,,,,0.6953,,0.1743,0.4986,,0.998,,,0.48,0.2069,0.7083,,,,0.6793,,0.1681,,block of flats,0.5607,Mixed,No,0.0,0.0,0.0,0.0,-1626.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n143994,0,Cash loans,F,Y,N,0,67500.0,436032.0,15660.0,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008625,-19273,-2753,-9539.0,-2180,20.0,1,1,1,1,0,0,Managers,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Kindergarten,,0.5741292648365941,0.4956658291397297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2018.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n427994,0,Cash loans,F,N,N,2,112500.0,312768.0,16506.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.020246,-12405,-825,-589.0,-1687,,1,1,1,1,0,0,Accountants,3.0,3,3,THURSDAY,14,0,0,0,0,0,0,Self-employed,0.29972857905960426,0.3877645058082773,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n325201,0,Cash loans,M,N,Y,0,112500.0,543735.0,26289.0,486000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22267,365243,-4610.0,-4621,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.518558930818646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,0.0,-303.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n379438,0,Cash loans,F,N,N,0,157500.0,450000.0,35685.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21648,-4712,-13024.0,-4866,,1,1,1,1,1,0,Sales staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Trade: type 7,0.8504306892846941,0.5865641106735029,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1799.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n326243,0,Cash loans,F,N,Y,0,180000.0,450000.0,42075.0,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.018801,-19030,-679,-6115.0,-2575,,1,1,1,1,1,0,,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.7838981378697044,0.5890754730848642,0.8128226070575616,0.1485,0.0877,0.9781,0.7008,0.0779,0.16,0.1379,0.3333,0.375,0.1238,0.121,0.14800000000000002,0.0,0.0,0.1513,0.091,0.9782,0.7125,0.0786,0.1611,0.1379,0.3333,0.375,0.1266,0.1322,0.1542,0.0,0.0,0.1499,0.0877,0.9781,0.7048,0.0784,0.16,0.1379,0.3333,0.375,0.1259,0.1231,0.1507,0.0,0.0,reg oper account,block of flats,0.159,Panel,No,0.0,0.0,0.0,0.0,-3690.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n282280,0,Cash loans,M,N,N,0,135000.0,360000.0,15858.0,360000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.026392,-11667,-191,-3344.0,-3366,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Industry: type 9,,0.5887525380585614,0.4507472818545589,0.0021,,0.9886,,,0.0,0.069,0.0,,,,,,,0.0021,,0.9886,,,0.0,0.069,0.0,,,,,,,0.0021,,0.9886,,,0.0,0.069,0.0,,,,,,,,terraced house,0.0019,Panel,No,1.0,0.0,1.0,0.0,-167.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,7.0,0.0,2.0\n232039,0,Cash loans,F,N,Y,0,315000.0,536917.5,30109.5,463500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.032561,-19827,-1906,-2674.0,-3302,,1,1,0,1,0,0,Sales staff,1.0,1,1,FRIDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.7812691508167366,0.7231338186001914,0.4650692149562261,0.0515,0.1148,0.9836,0.7756,0.0286,0.04,0.1207,0.3125,0.3542,0.0295,0.0391,0.12,0.0135,0.0754,0.0105,0.0871,0.9806,0.7452,0.0151,0.0,0.069,0.2917,0.3333,0.0279,0.0037,0.0963,0.0039,0.0635,0.052000000000000005,0.1148,0.9836,0.7786,0.0288,0.04,0.1207,0.3125,0.3542,0.03,0.0398,0.1222,0.0136,0.077,reg oper account,terraced house,0.0857,\"Stone, brick\",No,8.0,0.0,8.0,0.0,-1520.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n401398,0,Cash loans,M,Y,N,0,135000.0,482593.5,27832.5,436500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.019101,-18645,-2905,-5895.0,-2192,14.0,1,1,0,1,0,0,Security staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,School,,0.2533716311332267,0.7969726818373722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1257.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n219040,0,Cash loans,M,Y,N,0,157500.0,1305000.0,51754.5,1305000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.04622,-23454,-452,-15453.0,-4716,21.0,1,1,1,1,1,0,Laborers,1.0,1,1,THURSDAY,15,0,0,0,0,1,1,Other,0.7530996795337072,0.4576802675850921,0.5762088360175724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,0.0,-2711.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n178575,0,Cash loans,F,N,Y,1,112500.0,468000.0,26131.5,468000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.0105,-15739,-4443,-8689.0,-4811,,1,1,0,1,1,0,Core staff,2.0,3,3,FRIDAY,11,0,0,0,0,1,1,University,,0.6190536679722553,0.722392890081304,0.0632,,0.9856,,,0.0664,0.0572,0.3333,,,,,,,0.0378,,0.9861,,,0.0806,0.069,0.3333,,,,,,,0.0749,,0.9861,,,0.08,0.069,0.3333,,,,,,,,block of flats,0.0341,Block,No,0.0,0.0,0.0,0.0,-738.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n245628,0,Cash loans,F,Y,N,0,112500.0,971280.0,54364.5,900000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Municipal apartment,0.010276,-17976,-6105,-9629.0,-1514,2.0,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.7724003160010792,0.6332826234094526,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1057.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n191269,0,Cash loans,M,Y,Y,0,112500.0,243000.0,19327.5,243000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-17600,-2477,-6910.0,-1136,11.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,Self-employed,,0.6551798224996243,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n370516,0,Cash loans,M,N,Y,0,225000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.011703,-20347,-8363,-1618.0,-3762,,1,1,1,1,1,0,Managers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.7469578839356928,0.7076993447402619,0.0923,0.0914,0.9781,0.7008,0.0128,0.0,0.1897,0.1667,0.2083,0.0712,0.0748,0.0855,0.0019,0.0065,0.0714,0.0799,0.9772,0.6994,0.0124,0.0,0.1724,0.1667,0.2083,0.0699,0.0624,0.0858,0.0,0.0069,0.0932,0.0914,0.9781,0.7048,0.0129,0.0,0.1897,0.1667,0.2083,0.0725,0.0761,0.087,0.0019,0.0066,reg oper account,block of flats,0.0711,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-3161.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n258164,0,Cash loans,F,N,N,0,85500.0,562500.0,16447.5,562500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16275,-2073,-6605.0,-5104,,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Medicine,0.6266401769279887,0.5835115836898781,0.5691487713619409,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2173.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n359361,0,Cash loans,F,Y,N,0,121500.0,755190.0,28894.5,675000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-14963,-2743,-4123.0,-1857,64.0,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,,0.4486039010683053,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1042.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n104069,0,Cash loans,M,Y,Y,1,180000.0,343800.0,16852.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-12199,-2353,-1264.0,-4311,13.0,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.513294239514084,0.5919766183185521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1855.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n352393,1,Cash loans,M,Y,Y,0,90000.0,143910.0,17208.0,135000.0,Family,Working,Incomplete higher,Married,House / apartment,0.005144,-18992,-3243,-5667.0,-2521,5.0,1,1,0,1,1,0,Drivers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,,0.6536318682465208,,0.0608,0.0623,0.9771,0.6872,0.0052,0.0,0.1034,0.1667,0.2083,0.0555,0.0496,0.0444,0.027000000000000003,0.0248,0.062,0.0646,0.9772,0.6994,0.0053,0.0,0.1034,0.1667,0.2083,0.0567,0.0542,0.0463,0.0272,0.0262,0.0614,0.0623,0.9771,0.6914,0.0053,0.0,0.1034,0.1667,0.2083,0.0564,0.0504,0.0452,0.0272,0.0253,org spec account,block of flats,0.0432,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-943.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n394136,0,Cash loans,F,N,Y,0,171000.0,272520.0,21658.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-19477,-698,-10535.0,-2011,,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.58069628648508,0.6037753649706784,0.5709165417729987,0.0165,0.054000000000000006,0.9851,0.7959999999999999,,0.0,0.069,0.0417,0.0417,,0.0134,0.0175,0.0,0.0016,0.0168,0.0561,0.9851,0.804,,0.0,0.069,0.0417,0.0417,,0.0147,0.0182,0.0,0.0017,0.0167,0.054000000000000006,0.9851,0.7987,,0.0,0.069,0.0417,0.0417,,0.0137,0.0178,0.0,0.0016,,block of flats,0.0141,Wooden,Yes,0.0,0.0,0.0,0.0,-258.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n365822,0,Cash loans,F,N,Y,0,171000.0,256500.0,14449.5,256500.0,Family,Working,Higher education,Civil marriage,House / apartment,0.006670999999999999,-17466,-1675,-7522.0,-1019,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Self-employed,0.6321227155289463,0.2210562785392201,0.6058362647264226,0.0021,0.0025,0.9652,,,,0.0345,0.0,,,,0.001,,,0.0021,0.0025,0.9652,,,,0.0345,0.0,,,,0.0011,,,0.0021,0.0025,0.9652,,,,0.0345,0.0,,,,0.001,,,,block of flats,0.0011,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1822.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n285804,0,Revolving loans,F,Y,N,0,135000.0,270000.0,13500.0,270000.0,Other_A,Working,Secondary / secondary special,Single / not married,With parents,0.030755,-9401,-1230,-3850.0,-2010,8.0,1,1,0,1,0,0,Private service staff,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,Self-employed,,0.6108052736110858,0.4066174366275036,0.0082,0.0,0.9722,0.6192,0.0006,0.0,0.0345,0.0417,0.0833,0.0071,0.0067,0.0056,0.0,0.0,0.0084,0.0,0.9722,0.6341,0.0006,0.0,0.0345,0.0417,0.0833,0.0072,0.0073,0.0058,0.0,0.0,0.0083,0.0,0.9722,0.6243,0.0006,0.0,0.0345,0.0417,0.0833,0.0072,0.0068,0.0057,0.0,0.0,reg oper account,block of flats,0.0047,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1708.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n184334,0,Cash loans,M,N,Y,0,202500.0,403249.5,18927.0,283500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-21343,-599,-4735.0,-4736,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.3511145860949029,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-139.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n341331,0,Revolving loans,M,N,Y,1,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006852,-15191,-1375,-6530.0,-2477,,1,1,1,1,1,0,Laborers,3.0,3,3,THURSDAY,6,0,0,0,0,0,0,Housing,0.3396603021029761,0.7421417412011779,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1221.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n371074,0,Cash loans,F,N,Y,0,171000.0,990000.0,29074.5,990000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.00702,-22304,365243,-483.0,-4779,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,0.5322465253544493,0.5316674331960082,0.3842068130556564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2002.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n362887,0,Cash loans,M,N,N,0,157500.0,178290.0,17230.5,157500.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.010643,-9545,-1034,-4239.0,-2202,,1,1,0,1,0,0,Sales staff,1.0,2,2,SUNDAY,12,0,0,0,0,0,0,Trade: type 2,0.5116688556352496,0.3660203315419941,0.4311917977993083,0.0928,0.1152,0.9866,,,0.0,0.2069,0.1667,,0.1319,,0.0541,,0.0,0.0945,0.1195,0.9866,,,0.0,0.2069,0.1667,,0.1349,,0.0564,,0.0,0.0937,0.1152,0.9866,,,0.0,0.2069,0.1667,,0.1342,,0.0551,,0.0,,block of flats,0.0746,Panel,No,0.0,0.0,0.0,0.0,-216.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n418890,0,Cash loans,M,Y,Y,0,157500.0,1325475.0,56290.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.011657,-23024,365243,-12240.0,-4988,4.0,1,0,0,1,0,0,,2.0,1,1,TUESDAY,13,0,0,0,0,0,0,XNA,,0.405777084233638,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n208081,0,Revolving loans,F,N,Y,0,81000.0,180000.0,9000.0,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.028663,-22215,365243,-9398.0,-4829,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.7372873539775368,0.7435593141311444,0.0835,0.0628,0.9871,0.8232,0.0274,0.0,0.2069,0.1667,0.0417,0.0,0.0681,0.0819,0.0,0.027000000000000003,0.0851,0.0651,0.9871,0.8301,0.0277,0.0,0.2069,0.1667,0.0417,0.0,0.0744,0.0854,0.0,0.0286,0.0843,0.0628,0.9871,0.8256,0.0276,0.0,0.2069,0.1667,0.0417,0.0,0.0693,0.0834,0.0,0.0276,reg oper account,block of flats,0.0794,\"Stone, brick\",No,,,,,-1093.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n339420,0,Cash loans,M,N,Y,0,112500.0,1006920.0,42790.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010556,-13380,-2910,-6538.0,-3485,,1,1,0,1,0,0,Managers,2.0,3,3,TUESDAY,11,0,0,0,0,0,0,Self-employed,0.6229163667493641,0.4156420234634833,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-787.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n103344,0,Cash loans,F,N,Y,0,54000.0,225000.0,9661.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-22606,365243,-3236.0,-4053,,1,0,0,1,1,1,,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,0.8977062380342862,0.24898722033915802,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n352247,0,Revolving loans,M,N,N,0,180000.0,225000.0,11250.0,225000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-12007,-235,-603.0,-3988,,1,1,0,1,1,0,Core staff,1.0,1,1,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 1,,0.7477960096730942,,0.2216,0.3276,0.9776,,,0.24,0.2069,0.3333,,0.0,,0.2125,,0.0,0.2258,0.34,0.9777,,,0.2417,0.2069,0.3333,,0.0,,0.2214,,0.0,0.2238,0.3276,0.9776,,,0.24,0.2069,0.3333,,0.0,,0.2164,,0.0,,block of flats,0.1672,Panel,No,0.0,0.0,0.0,0.0,-628.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n315439,0,Cash loans,M,Y,N,1,135000.0,454500.0,21865.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018209,-11447,-2246,-4927.0,-3251,21.0,1,1,0,1,0,0,Laborers,3.0,3,3,THURSDAY,7,0,0,0,0,0,0,Transport: type 2,,0.4813465631791898,0.2764406945454034,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1861.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n249905,0,Revolving loans,F,N,Y,0,180000.0,540000.0,27000.0,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.032561,-21565,-8208,-3919.0,-3919,,1,1,0,1,0,0,Laborers,1.0,1,1,THURSDAY,17,0,0,0,0,0,0,Government,,0.7067230952170506,,0.0763,,0.9776,,,0.08,0.1034,0.3958,,0.0853,,0.0705,,0.0197,0.0525,,0.9747,,,0.0806,0.0345,0.1667,,0.0645,,0.0505,,0.0046,0.077,,0.9776,,,0.08,0.1034,0.3958,,0.0868,,0.0718,,0.0201,,block of flats,0.0391,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-244.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n219139,0,Cash loans,F,N,Y,0,157500.0,472500.0,25303.5,472500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-13682,-3132,-1511.0,-1410,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5692781615601447,0.3723336657058204,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,3.0\n182128,0,Cash loans,M,Y,Y,2,180000.0,900000.0,26316.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.008625,-19159,-797,-9889.0,-2713,6.0,1,1,1,1,0,0,Sales staff,4.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 1,0.6228229274791807,0.6427114435378487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-386.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n270336,0,Cash loans,F,N,Y,0,135000.0,260640.0,26838.0,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006629,-11886,-474,-4544.0,-4067,,1,1,1,1,1,0,Managers,2.0,2,2,MONDAY,7,0,0,0,0,0,0,Postal,0.6760465171664793,0.4970443274463756,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2007.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n414468,0,Cash loans,F,N,Y,0,103500.0,706500.0,49302.0,706500.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.025164,-17236,-1759,-9474.0,-781,,1,1,0,1,0,0,Accountants,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Bank,,0.6371993356025242,0.5902333386185574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-804.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n262671,0,Cash loans,F,N,N,0,157500.0,452385.0,22131.0,373500.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.020246,-10977,-180,-5201.0,-3595,,1,1,0,1,1,0,,1.0,3,3,TUESDAY,14,0,0,0,0,0,0,Industry: type 10,0.6452966974320247,0.34971588321874497,0.4668640059537032,0.0619,0.1228,0.9916,0.8844,0.0194,0.0,0.2414,0.1667,0.0,0.0168,0.0496,0.0755,0.0039,0.0606,0.063,0.1275,0.9916,0.8889,0.0196,0.0,0.2414,0.1667,0.0,0.0172,0.0542,0.0786,0.0039,0.0641,0.0625,0.1228,0.9916,0.8859,0.0195,0.0,0.2414,0.1667,0.0,0.0171,0.0504,0.0768,0.0039,0.0619,reg oper account,block of flats,0.0831,\"Stone, brick\",No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n103501,0,Cash loans,F,N,N,0,112500.0,592560.0,32274.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-10088,-128,-4511.0,-1916,,1,1,0,1,1,0,Cooking staff,2.0,2,2,MONDAY,17,0,0,0,0,1,1,Self-employed,0.1459391584738049,0.5804366632122319,0.24988506275045536,0.0,,0.9806,,,0.0,0.1034,0.0417,,,,0.0175,,,0.0,,0.9806,,,0.0,0.1034,0.0417,,,,0.0182,,,0.0,,0.9806,,,0.0,0.1034,0.0417,,,,0.0178,,,,block of flats,0.0147,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-252.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n168197,0,Cash loans,F,N,N,0,247500.0,1575000.0,50940.0,1575000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-20391,365243,-7119.0,-3821,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,0.7919300044217892,0.7119888452823577,0.646329897706246,0.0784,,0.9762,,,0.0,0.1379,0.1667,,,,0.0598,,0.0052,0.0798,,0.9762,,,0.0,0.1379,0.1667,,,,0.0623,,0.0055,0.0791,,0.9762,,,0.0,0.1379,0.1667,,,,0.0608,,0.0053,,block of flats,0.0481,\"Stone, brick\",No,4.0,1.0,4.0,1.0,-317.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n283418,0,Cash loans,M,Y,Y,0,135000.0,545040.0,35617.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.019101,-14253,-279,-229.0,-897,13.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Transport: type 4,,0.5418023648135687,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-846.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n151996,0,Cash loans,M,Y,Y,0,135000.0,381096.0,30240.0,301500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003813,-18489,365243,-5067.0,-2021,18.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.07315900553525659,0.40314167665875134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-713.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n134035,0,Cash loans,M,Y,N,1,247500.0,508495.5,24592.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-11673,-1130,-1668.0,-4326,4.0,1,1,0,1,0,0,Drivers,3.0,2,2,FRIDAY,10,0,1,1,1,0,0,Business Entity Type 3,0.2372171204107134,0.1376813834253171,0.4920600938649263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1621.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n226892,0,Revolving loans,F,N,Y,0,90000.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.04622,-14118,-131,-3042.0,-4720,,1,1,0,1,0,0,,1.0,1,1,SUNDAY,12,1,1,0,0,0,0,Business Entity Type 2,0.7235180524498904,0.3737978563219818,0.7764098512142026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-784.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n205055,0,Revolving loans,M,Y,Y,0,162000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.025164,-15855,-5761,-6013.0,-5885,4.0,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,7,0,0,0,0,0,0,Business Entity Type 2,,0.6316458382432906,0.8327850252992314,0.1031,0.0977,0.9771,0.6872,0.0276,0.0,0.1724,0.1667,0.2083,0.0306,0.0756,0.0733,0.0,0.0131,0.0945,0.1045,0.9772,0.6994,0.0054,0.0,0.2069,0.1667,0.2083,0.019,0.0826,0.0538,0.0,0.0,0.0937,0.0978,0.9771,0.6914,0.0117,0.0,0.2069,0.1667,0.2083,0.0199,0.077,0.0731,0.0,0.0032,reg oper account,block of flats,0.0574,Panel,No,0.0,0.0,0.0,0.0,-233.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n373126,0,Cash loans,M,N,N,0,171000.0,675000.0,19737.0,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-22743,365243,-1995.0,-4648,,1,0,0,1,1,0,,2.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.5736747302888131,0.20092608771597087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1983.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n262008,0,Cash loans,F,Y,Y,1,90000.0,263686.5,25816.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-17789,-7253,-9189.0,-1342,25.0,1,1,0,1,0,0,Laborers,3.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.05661315376541491,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2334.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n161830,0,Cash loans,F,Y,N,1,126000.0,253737.0,16650.0,229500.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.018634,-11089,-1949,-1861.0,-1089,23.0,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.7096559669425453,0.6296742509538716,0.0804,0.0384,0.9747,0.6532,0.0072,0.0,0.1379,0.1667,0.2083,0.0225,0.0639,0.0607,0.0077,0.0221,0.0819,0.0399,0.9747,0.6668,0.0072,0.0,0.1379,0.1667,0.2083,0.023,0.0698,0.0633,0.0078,0.0234,0.0812,0.0384,0.9747,0.6578,0.0072,0.0,0.1379,0.1667,0.2083,0.0229,0.065,0.0618,0.0078,0.0225,,block of flats,0.0565,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-179.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n450862,0,Cash loans,F,N,N,1,103500.0,327024.0,18391.5,270000.0,Unaccompanied,State servant,Incomplete higher,Separated,House / apartment,0.018029,-11364,-1760,-2281.0,-2774,,1,1,0,1,0,0,Core staff,2.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,School,0.18077404290388924,0.28578180948779625,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1104.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n455124,0,Cash loans,M,N,N,0,445500.0,840951.0,38002.5,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-14726,-1278,-2420.0,-2420,,1,1,1,1,0,0,,2.0,1,1,SATURDAY,21,0,0,0,0,1,1,Business Entity Type 3,,0.5891919226410672,0.1940678276718812,0.1113,0.069,0.9801,0.728,0.0563,0.08,0.069,0.3333,0.375,0.0,0.0908,0.0994,0.0,0.0,0.1134,0.0716,0.9801,0.7387,0.0568,0.0806,0.069,0.3333,0.375,0.0,0.0992,0.1035,0.0,0.0,0.1124,0.069,0.9801,0.7316,0.0566,0.08,0.069,0.3333,0.375,0.0,0.0923,0.1012,0.0,0.0,reg oper account,block of flats,0.1089,Panel,No,0.0,0.0,0.0,0.0,-1337.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n322502,0,Cash loans,F,N,Y,0,135000.0,225000.0,11781.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-17955,-926,-11972.0,-1500,,1,1,1,1,1,0,Laborers,2.0,1,1,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.7570052764832514,0.7814438527899646,0.7713615919194317,0.1402,0.1017,0.9767,0.6804,0.0196,0.0,0.3103,0.1667,0.2083,0.2563,0.1126,0.1243,0.0077,0.0088,0.1429,0.1055,0.9767,0.6929,0.0198,0.0,0.3103,0.1667,0.2083,0.2621,0.12300000000000001,0.1295,0.0078,0.0093,0.1416,0.1017,0.9767,0.6847,0.0197,0.0,0.3103,0.1667,0.2083,0.2608,0.1146,0.1265,0.0078,0.0089,reg oper account,block of flats,0.0997,Panel,No,2.0,0.0,2.0,0.0,-2220.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n364153,0,Cash loans,F,N,Y,0,144000.0,1129500.0,31189.5,1129500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-21947,365243,-8956.0,-4654,,1,0,0,1,1,0,,2.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.6565174117745634,,0.1402,,0.9886,,,0.0,0.069,0.5417,,,,0.1377,,0.0142,0.1429,,0.9886,,,0.0,0.069,0.5417,,,,0.1434,,0.0151,0.1416,,0.9886,,,0.0,0.069,0.5417,,,,0.1401,,0.0145,,block of flats,0.1114,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1695.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n200673,0,Revolving loans,F,N,Y,0,99000.0,202500.0,10125.0,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-22891,365243,-10787.0,-4235,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,0.8062236534896685,0.16416286646629996,0.3123653692278984,0.0825,0.0739,0.9851,0.7959999999999999,0.0128,0.0,0.2069,0.1667,0.2083,0.0665,0.0672,0.0775,0.0,0.0,0.084,0.0767,0.9851,0.804,0.0129,0.0,0.2069,0.1667,0.2083,0.068,0.0735,0.0808,0.0,0.0,0.0833,0.0739,0.9851,0.7987,0.0129,0.0,0.2069,0.1667,0.2083,0.0677,0.0684,0.0789,0.0,0.0,reg oper account,block of flats,0.061,Panel,No,,,,,-1078.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n445583,0,Cash loans,F,N,Y,0,135000.0,405000.0,19827.0,405000.0,Unaccompanied,Commercial associate,Higher education,Widow,House / apartment,0.031329,-15273,-300,-5711.0,-4137,,1,1,0,1,1,0,Accountants,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,Construction,,0.6232681108430954,0.8193176922872417,0.068,0.0812,0.9757,0.6668,0.006,0.0,0.1379,0.1667,0.2083,0.0389,0.0538,0.0505,0.0077,0.0468,0.0693,0.0843,0.9757,0.6798,0.006,0.0,0.1379,0.1667,0.2083,0.0398,0.0588,0.0526,0.0078,0.0495,0.0687,0.0812,0.9757,0.6713,0.006,0.0,0.1379,0.1667,0.2083,0.0396,0.0547,0.0514,0.0078,0.0478,reg oper account,block of flats,0.0499,Block,No,2.0,0.0,2.0,0.0,-2838.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n347628,0,Cash loans,F,N,Y,0,103500.0,238500.0,24565.5,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-21714,365243,-9504.0,-4327,,1,0,0,1,1,0,,2.0,3,3,TUESDAY,9,0,0,0,0,0,0,XNA,,0.4785738485730471,0.8327850252992314,0.0825,0.1197,0.9866,0.8164,0.0152,0.0,0.2069,0.1667,0.2083,0.0854,0.0672,0.0814,0.0,0.0,0.084,0.1242,0.9866,0.8236,0.0153,0.0,0.2069,0.1667,0.2083,0.0873,0.0735,0.0848,0.0,0.0,0.0833,0.1197,0.9866,0.8189,0.0153,0.0,0.2069,0.1667,0.2083,0.0869,0.0684,0.0829,0.0,0.0,org spec account,block of flats,0.0723,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-546.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n214981,0,Cash loans,M,Y,Y,3,117000.0,1078200.0,31653.0,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-13792,-451,-3306.0,-4313,5.0,1,1,1,1,0,0,Laborers,5.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Medicine,,0.6544383996900622,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1807.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n170181,0,Cash loans,F,Y,Y,1,900000.0,1928304.0,79708.5,1800000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-11685,-4543,-5314.0,-1611,4.0,1,1,0,1,0,1,High skill tech staff,3.0,1,1,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.8266942118147291,0.5118901393925105,0.326475210066026,0.3381,0.1337,0.9881,0.8368,,0.48,0.2069,0.5417,,0.1417,,0.3779,,0.0046,0.3445,0.1387,0.9881,0.8432,,0.4834,0.2069,0.5417,,0.145,,0.3937,,0.0049,0.3414,0.1337,0.9881,0.8390000000000001,,0.48,0.2069,0.5417,,0.1442,,0.3847,,0.0047,reg oper spec account,block of flats,0.2982,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2726.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n454247,0,Cash loans,F,N,N,0,67500.0,254700.0,17149.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.007120000000000001,-10080,-360,-539.0,-1068,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.2642348553150661,0.6372650515191888,0.3979463219016906,0.1856,,0.9896,0.8572,,0.0,0.3448,0.2083,0.0417,0.0555,,0.1003,,0.2456,0.1891,,0.9896,0.8628,,0.0,0.3448,0.2083,0.0417,0.0568,,0.1045,,0.26,0.1874,,0.9896,0.8591,,0.0,0.3448,0.2083,0.0417,0.0565,,0.1021,,0.2508,not specified,block of flats,0.1323,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-177.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n290493,0,Cash loans,M,N,Y,0,157500.0,675000.0,32602.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.02461,-15755,-3392,-9809.0,-4803,,1,1,0,1,0,1,Laborers,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6525766030364993,0.375711009574066,0.0515,0.0747,0.9707,,,0.0,0.1379,0.125,,0.0789,,0.0548,,0.0439,0.0525,0.0775,0.9707,,,0.0,0.1379,0.125,,0.0807,,0.0571,,0.0464,0.052000000000000005,0.0747,0.9707,,,0.0,0.1379,0.125,,0.0803,,0.0558,,0.0448,,block of flats,0.0587,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-756.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n360711,0,Revolving loans,M,Y,Y,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.04622,-7851,-919,-7091.0,-528,65.0,1,1,0,1,0,0,Laborers,1.0,1,1,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5118464288692578,0.3842068130556564,0.2639,,0.9821,0.7552,0.1745,0.32,0.2759,0.3333,0.0417,0.0921,0.2152,0.2553,0.0193,0.1157,0.2689,,0.9821,0.7648,0.1761,0.3222,0.2759,0.3333,0.0417,0.0942,0.2351,0.266,0.0195,0.1225,0.2665,,0.9821,0.7585,0.1756,0.32,0.2759,0.3333,0.0417,0.0937,0.2189,0.2599,0.0194,0.1181,org spec account,block of flats,0.3214,Panel,No,0.0,0.0,0.0,0.0,-472.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n445321,0,Cash loans,F,Y,Y,1,99000.0,900000.0,26446.5,900000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.002042,-12647,-3700,-862.0,-2574,2.0,1,1,1,1,0,0,Core staff,3.0,3,3,TUESDAY,14,0,0,0,1,1,1,Medicine,0.39688530749881895,0.6750000507503456,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n153407,0,Cash loans,F,N,Y,0,166500.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-14255,-1028,-441.0,-4935,,1,1,1,1,1,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.3847836772346969,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2481.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n188250,1,Cash loans,F,Y,Y,2,112500.0,284400.0,22599.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-13691,-1738,-3227.0,-3779,3.0,1,1,1,1,0,0,Sales staff,4.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.3232547190235808,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-309.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n376376,0,Cash loans,F,N,Y,0,121500.0,167121.0,12015.0,139500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008625,-17794,-123,-1343.0,-1343,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Trade: type 7,,0.6069098899438852,0.4525335592581747,0.0619,0.0488,0.9742,0.6464,0.0059,0.0,0.1034,0.1667,0.2083,0.0556,0.0504,0.0518,0.0,0.0,0.063,0.0507,0.9742,0.6602,0.0059,0.0,0.1034,0.1667,0.2083,0.0569,0.0551,0.054000000000000006,0.0,0.0,0.0625,0.0488,0.9742,0.6511,0.0059,0.0,0.1034,0.1667,0.2083,0.0566,0.0513,0.0527,0.0,0.0,reg oper account,block of flats,0.0439,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n415427,0,Cash loans,F,N,Y,0,121500.0,824823.0,24246.0,688500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.008625,-21408,-2441,-6237.0,-4852,,1,1,1,1,1,0,,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Self-employed,,0.5246703347520485,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n281491,0,Cash loans,F,Y,N,1,135000.0,398016.0,22977.0,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010032,-12566,-1463,-1828.0,-4026,17.0,1,1,1,1,0,0,Sales staff,3.0,2,2,THURSDAY,13,0,0,0,0,0,0,Self-employed,0.28846203822403466,0.01676164756194995,0.4992720153045617,0.1216,0.1067,0.9771,0.6872,0.0496,0.0,0.2759,0.1667,0.2083,0.1488,0.0983,0.11199999999999999,0.0039,0.0043,0.1239,0.1107,0.9772,0.6994,0.0501,0.0,0.2759,0.1667,0.2083,0.1521,0.1074,0.1167,0.0039,0.0045,0.1228,0.1067,0.9771,0.6914,0.0499,0.0,0.2759,0.1667,0.2083,0.1513,0.1,0.114,0.0039,0.0044,org spec account,block of flats,0.1161,Panel,No,0.0,0.0,0.0,0.0,-666.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n406538,0,Revolving loans,M,N,N,2,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.019101,-16117,-4405,-2163.0,-4302,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Housing,,0.3666954840864055,0.7992967832109371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-1202.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n212070,0,Revolving loans,F,Y,Y,0,67500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.010643,-8222,-1510,-8214.0,-903,1.0,1,1,0,1,0,0,,1.0,2,2,THURSDAY,15,0,0,0,1,1,0,Self-employed,0.15809180955998836,0.5448679486127022,0.4436153084085652,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-264.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n379390,0,Cash loans,F,Y,N,0,157500.0,900000.0,29034.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-13734,-5400,-3343.0,-4329,11.0,1,1,1,1,1,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Government,,0.707334595152638,,0.2268,0.1475,0.9955,0.932,,0.2,0.1724,0.375,0.4167,,0.1832,0.2339,0.0077,0.0093,0.2311,0.1531,0.9955,0.9347,,0.2014,0.1724,0.375,0.4167,,0.2002,0.2437,0.0078,0.0099,0.22899999999999998,0.1475,0.9955,0.9329,,0.2,0.1724,0.375,0.4167,,0.1864,0.2381,0.0078,0.0095,org spec account,block of flats,0.2628,Panel,No,0.0,0.0,0.0,0.0,-1385.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n286177,0,Revolving loans,F,N,N,0,202500.0,382500.0,19125.0,382500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010006,-16962,-3656,-1910.0,-489,,1,1,0,1,0,0,,2.0,2,1,WEDNESDAY,16,0,0,0,0,0,0,Industry: type 9,0.6483962522002904,0.7911613704945349,0.4561097392782771,0.1082,0.0903,0.997,0.9592,0.0609,0.12,0.1034,0.3333,0.375,0.075,0.0883,0.1323,0.0,0.0,0.1103,0.0937,0.997,0.9608,0.0614,0.1208,0.1034,0.3333,0.375,0.0768,0.0964,0.1378,0.0,0.0,0.1093,0.0903,0.997,0.9597,0.0613,0.12,0.1034,0.3333,0.375,0.0764,0.0898,0.1346,0.0,0.0,reg oper account,block of flats,0.1204,Block,No,1.0,1.0,1.0,1.0,-1932.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n120497,0,Cash loans,M,Y,N,0,292500.0,552555.0,43785.0,477000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-10260,-1519,-4425.0,-2360,4.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,18,1,1,0,1,1,1,Business Entity Type 3,0.22452296083105253,0.5996772402487865,0.1245194708919845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-784.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n386818,0,Cash loans,F,Y,Y,1,180000.0,1078200.0,34911.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.00702,-13298,-1267,-232.0,-1525,23.0,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.2419499982046695,0.6196620007801766,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n370957,0,Cash loans,M,N,N,0,130500.0,427500.0,23188.5,427500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-9917,-564,-44.0,-2578,,1,1,1,1,0,0,Laborers,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 2,,0.28738686274528713,0.4920600938649263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-36.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n213349,0,Cash loans,F,N,Y,3,135000.0,307287.0,24768.0,256500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.019101,-15426,-2190,-424.0,-4805,,1,1,0,1,0,0,Security staff,4.0,2,2,TUESDAY,14,0,0,0,0,0,0,Transport: type 1,0.7790724604970886,0.6787783227270308,0.6479768603302221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-976.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n377412,0,Cash loans,M,N,Y,2,315000.0,78192.0,9409.5,67500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010032,-11070,-887,-7287.0,-1379,,1,1,1,1,1,0,,4.0,2,2,TUESDAY,6,0,0,0,0,1,1,Other,0.16890620698054304,0.35569675332718964,0.5352762504724826,0.0948,0.0957,0.9836,,,,0.3103,0.1667,,,,0.1178,,0.083,0.0966,0.0993,0.9836,,,,0.3103,0.1667,,,,0.1227,,0.0879,0.0958,0.0957,0.9836,,,,0.3103,0.1667,,,,0.1199,,0.0847,,block of flats,0.1376,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-515.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n350050,1,Cash loans,F,N,Y,0,69750.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-9089,-2228,-3900.0,-1625,,1,1,0,1,0,0,Cleaning staff,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,Government,0.1528222233392723,0.6593128572819061,0.3774042489507649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1146.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n284749,0,Cash loans,M,Y,N,3,252000.0,247675.5,19296.0,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006296,-13628,-2409,-1979.0,-4487,9.0,1,1,0,1,1,0,,5.0,3,3,WEDNESDAY,13,0,0,0,0,0,0,Security Ministries,0.3968260640754886,0.5345630557991047,0.2807895743848605,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1795.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n413527,0,Cash loans,F,N,Y,0,126000.0,675000.0,21906.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-20749,365243,-5543.0,-4029,,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.2610374728886161,0.4956658291397297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1604.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n260955,0,Cash loans,M,Y,N,1,157500.0,450000.0,32746.5,450000.0,Family,Working,Secondary / secondary special,Married,Office apartment,0.01885,-14477,-505,-2852.0,-4537,10.0,1,1,0,1,0,0,Drivers,3.0,2,2,WEDNESDAY,17,0,0,0,0,1,1,Business Entity Type 2,0.5988658477243419,0.6399952725334246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,6.0,2.0,-792.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n260278,0,Cash loans,M,Y,N,0,180000.0,549882.0,14634.0,459000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.019101,-10198,-220,-381.0,-856,3.0,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Self-employed,0.14454875039659368,0.5583184982482068,,0.2804,0.0979,0.998,0.9728,0.0348,0.16,0.0862,0.5208,0.4583,0.0456,0.2185,0.2421,0.0927,0.0933,0.2048,0.0869,0.997,0.9608,0.0351,0.0806,0.069,0.4167,0.4583,0.0,0.157,0.16699999999999998,0.0934,0.0938,0.2831,0.0979,0.998,0.9732,0.035,0.16,0.0862,0.5208,0.4583,0.0464,0.2223,0.2465,0.0932,0.0952,org spec account,block of flats,0.1664,Monolithic,No,0.0,0.0,0.0,0.0,-1647.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n107361,0,Cash loans,F,N,Y,3,81000.0,284400.0,13387.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-12661,-2269,-220.0,-43,,1,1,1,1,1,0,Laborers,5.0,2,2,SATURDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.6932323790364908,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1757.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n162891,0,Revolving loans,M,N,Y,0,360000.0,1350000.0,67500.0,1350000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.019101,-21719,365243,-1132.0,-3150,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,0.8461559335874929,0.5965496988878408,0.6413682574954046,0.0619,0.1139,0.9921,0.8776,0.0097,0.0,0.2069,0.1667,0.2083,0.0198,0.0504,0.0797,0.0,0.0,0.063,0.1182,0.9921,0.8824,0.0097,0.0,0.2069,0.1667,0.2083,0.0202,0.0551,0.0831,0.0,0.0,0.0625,0.1139,0.9921,0.8792,0.0097,0.0,0.2069,0.1667,0.2083,0.0201,0.0513,0.0812,0.0,0.0,reg oper account,block of flats,0.068,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-679.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n115629,0,Cash loans,M,N,N,0,180000.0,832500.0,37620.0,832500.0,Family,Working,Secondary / secondary special,Married,Rented apartment,0.028663,-10355,-579,-4658.0,-2849,,1,1,1,1,0,0,Drivers,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Self-employed,,0.5481630413480174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,2.0,5.0,2.0,-354.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n207136,0,Cash loans,F,N,N,0,94500.0,207306.0,8910.0,148500.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-23181,365243,-15232.0,-4405,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.3249408940282091,0.6594055320683344,0.0825,0.0801,0.9791,0.7144,0.0082,0.0,0.1379,0.1667,0.2083,0.0994,0.0672,0.0721,0.0,0.0,0.084,0.0831,0.9791,0.7256,0.0083,0.0,0.1379,0.1667,0.2083,0.1017,0.0735,0.0752,0.0,0.0,0.0833,0.0801,0.9791,0.7182,0.0082,0.0,0.1379,0.1667,0.2083,0.1012,0.0684,0.0734,0.0,0.0,reg oper account,block of flats,0.0645,Panel,No,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n266802,0,Cash loans,F,N,Y,0,94500.0,452385.0,17181.0,373500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.022625,-23782,-2111,-9534.0,-5236,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6587035208707981,,0.0825,0.0,0.9776,0.6940000000000001,0.0997,0.0,0.1379,0.1667,0.2083,0.0,0.0672,0.0693,0.0,0.0,0.084,0.0,0.9777,0.706,0.1006,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.0722,0.0,0.0,0.0833,0.0,0.9776,0.6981,0.1004,0.0,0.1379,0.1667,0.2083,0.0,0.0684,0.0706,0.0,0.0,reg oper account,block of flats,0.0545,Panel,No,0.0,0.0,0.0,0.0,-450.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n172926,0,Cash loans,F,N,Y,2,40500.0,149256.0,17010.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-13806,-5081,-7411.0,-4445,,1,1,1,1,1,0,,4.0,2,2,MONDAY,10,0,0,0,0,0,0,School,0.5058587277708309,0.5814800824651016,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-619.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n117056,0,Cash loans,M,N,N,0,225000.0,284400.0,22468.5,225000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.028663,-13251,-548,-610.0,-4613,,1,1,1,1,1,0,Sales staff,2.0,2,2,MONDAY,7,0,0,0,1,1,0,Business Entity Type 3,0.4540655901074106,0.6645146914701847,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-127.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n332461,1,Cash loans,M,N,N,0,202500.0,358443.0,13639.5,252000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-16500,-2941,-8989.0,-25,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Self-employed,,0.16214456766623808,0.6848276586890367,0.0392,0.0132,0.9737,0.6396,0.0019,0.0,0.069,0.1667,0.0417,0.0144,,0.03,,0.005,0.0399,0.0137,0.9737,0.6537,0.0019,0.0,0.069,0.1667,0.0417,0.0147,,0.0312,,0.0053,0.0396,0.0132,0.9737,0.6444,0.0019,0.0,0.069,0.1667,0.0417,0.0147,,0.0305,,0.0051,,block of flats,0.0257,Mixed,No,10.0,3.0,10.0,3.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n126049,1,Revolving loans,F,N,Y,0,67500.0,180000.0,9000.0,180000.0,Other_B,Working,Secondary / secondary special,Single / not married,Rented apartment,0.010966,-7863,-377,-7849.0,-489,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,12,0,0,0,1,1,0,Business Entity Type 3,,0.4875617958504421,0.3201633668633456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n218859,0,Cash loans,F,N,Y,0,67500.0,344043.0,14706.0,297000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-21709,365243,-5959.0,-4533,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.2007754035972499,0.6706517530862718,0.0742,0.0495,0.9816,0.7484,0.0141,0.08,0.069,0.3333,,0.0717,,0.0718,,0.0022,0.0756,0.0514,0.9816,0.7583,0.0142,0.0806,0.069,0.3333,,0.0734,,0.0749,,0.0023,0.0749,0.0495,0.9816,0.7518,0.0142,0.08,0.069,0.3333,,0.073,,0.0731,,0.0022,org spec account,block of flats,0.0569,Panel,No,0.0,0.0,0.0,0.0,-471.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n170519,0,Cash loans,F,N,Y,0,76500.0,573408.0,19080.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-21359,-5066,-5895.0,-2794,,1,1,0,1,0,0,Waiters/barmen staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Other,,0.6319052838762231,0.3774042489507649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-3107.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n272670,0,Cash loans,F,Y,Y,1,121500.0,384048.0,14607.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Rented apartment,0.008865999999999999,-11677,-157,-1913.0,-2590,5.0,1,1,1,1,0,0,Managers,3.0,2,2,FRIDAY,15,0,0,0,1,1,0,Business Entity Type 3,0.3585437111895524,0.3492088064522291,0.4561097392782771,0.0021,,0.9727,,,,,0.0,,,,0.0011,,0.0005,0.0021,,0.9727,,,,,0.0,,,,0.0011,,0.0005,0.0021,,0.9727,,,,,0.0,,,,0.0011,,0.0005,,terraced house,0.001,Wooden,No,2.0,0.0,2.0,0.0,-1204.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n403859,0,Cash loans,M,N,Y,1,225000.0,835380.0,40320.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020713,-9633,-2799,-1976.0,-2312,,1,1,0,1,0,0,Laborers,3.0,3,3,THURSDAY,11,0,0,0,0,1,1,Business Entity Type 2,,0.5625096216720853,0.5388627065779676,0.0722,0.0,0.9752,0.66,0.0072,0.0,0.1379,0.1667,0.2083,0.027999999999999997,0.0555,0.052000000000000005,0.0154,0.0382,0.0735,0.0,0.9752,0.6733,0.0073,0.0,0.1379,0.1667,0.2083,0.0287,0.0606,0.0542,0.0156,0.0404,0.0729,0.0,0.9752,0.6645,0.0073,0.0,0.1379,0.1667,0.2083,0.0285,0.0564,0.0529,0.0155,0.039,reg oper spec account,block of flats,0.0532,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n350704,0,Cash loans,M,Y,Y,2,112500.0,1467612.0,58333.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-13337,-2008,-2503.0,-4691,26.0,1,1,0,1,0,0,Laborers,4.0,2,2,SATURDAY,6,0,0,0,0,0,0,School,,0.5477838815803826,0.8435435389318647,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n214801,0,Cash loans,M,Y,Y,1,168750.0,1046142.0,30717.0,913500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.022625,-17646,-3323,-4069.0,-1204,9.0,1,1,0,1,0,0,Managers,3.0,2,2,THURSDAY,12,0,0,0,0,0,0,Security,0.8718391716052978,0.6862317703387476,0.6313545365850379,0.1031,,0.9935,,,0.2,0.1034,0.5417,,,,0.0916,,0.0151,0.105,,0.9935,,,0.2014,0.1034,0.5417,,,,0.0954,,0.0159,0.1041,,0.9935,,,0.2,0.1034,0.5417,,,,0.0932,,0.0154,,block of flats,0.1397,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1220.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n148096,0,Cash loans,M,N,Y,0,135000.0,1350000.0,37255.5,1350000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-23334,365243,-10664.0,-4686,,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,0.6016888786248866,0.6619064349812456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,10.0,0.0,-1600.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n384335,0,Revolving loans,M,N,Y,0,76500.0,157500.0,7875.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-18926,-282,-1270.0,-1692,,1,1,1,1,0,0,Laborers,2.0,3,3,SUNDAY,6,0,0,0,0,0,0,Industry: type 12,,0.3556015398229665,0.5262949398096192,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1302.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n386005,0,Cash loans,F,N,Y,2,121500.0,1009566.0,33493.5,904500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010556,-14126,-2478,-12051.0,-2139,,1,1,0,1,0,0,High skill tech staff,4.0,3,3,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.6286585025419419,0.5918357853033152,,0.6918,0.5682,0.9831,0.7688,0.07,0.8,0.6897,0.3333,0.375,0.0985,0.5589999999999999,0.7329,0.0232,0.17600000000000002,0.7048,0.5896,0.9831,0.7779,0.0706,0.8056,0.6897,0.3333,0.375,0.1008,0.6107,0.7636,0.0233,0.1863,0.6985,0.5682,0.9831,0.7719,0.0704,0.8,0.6897,0.3333,0.375,0.1002,0.5686,0.7461,0.0233,0.1797,reg oper account,block of flats,0.6553,Panel,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n231088,0,Cash loans,M,Y,Y,2,292500.0,450000.0,35685.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-12953,-1141,-486.0,-4100,15.0,1,1,1,1,0,0,Drivers,4.0,2,2,THURSDAY,12,0,0,0,0,1,1,Self-employed,,0.5658432437750581,0.4294236843421945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-428.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n441541,0,Cash loans,F,N,Y,0,121500.0,239850.0,23850.0,225000.0,Unaccompanied,Pensioner,Lower secondary,Widow,House / apartment,0.019101,-24750,365243,-3471.0,-4520,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.33175016802953683,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n404565,0,Cash loans,F,N,Y,1,180000.0,540000.0,23008.5,540000.0,Family,State servant,Secondary / secondary special,Separated,House / apartment,0.006629,-16665,-1524,-4890.0,-194,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Other,0.7537811498671404,0.5643653376504306,0.5531646987710016,0.1196,0.1495,0.9831,0.7688,0.0218,0.0,0.2069,0.1667,0.2083,0.1085,0.0941,0.1176,0.0154,0.0212,0.1197,0.1533,0.9831,0.7779,0.022000000000000002,0.0,0.2069,0.1667,0.2083,0.095,0.10099999999999999,0.121,0.0156,0.0217,0.1207,0.1495,0.9831,0.7719,0.022000000000000002,0.0,0.2069,0.1667,0.2083,0.1104,0.0958,0.1197,0.0155,0.0217,reg oper account,block of flats,0.1068,Panel,No,3.0,0.0,3.0,0.0,-2229.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n432674,0,Cash loans,F,N,Y,0,247500.0,1113840.0,57001.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-16995,-10297,-9623.0,-530,,1,1,0,1,0,0,,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 2,0.5079621265670912,0.5031726403981952,0.5779691187553125,0.1103,0.0814,0.9866,0.8164,0.0777,0.12,0.1034,0.3333,0.375,0.0431,0.0899,0.1161,0.0,0.0,0.1124,0.0845,0.9866,0.8236,0.0784,0.1208,0.1034,0.3333,0.375,0.0441,0.0983,0.1209,0.0,0.0,0.1114,0.0814,0.9866,0.8189,0.0782,0.12,0.1034,0.3333,0.375,0.0438,0.0915,0.1182,0.0,0.0,reg oper spec account,block of flats,0.1338,Panel,No,0.0,0.0,0.0,0.0,-2934.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n452286,0,Cash loans,M,Y,N,0,112500.0,135000.0,14305.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-14473,-1821,-1383.0,-3385,7.0,1,1,1,1,0,0,Drivers,2.0,2,2,MONDAY,20,0,0,0,0,0,0,Self-employed,,0.5033749068608014,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,1.0,9.0,1.0,-723.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n194824,0,Cash loans,F,N,Y,0,90000.0,225000.0,12564.0,225000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23891,365243,-8065.0,-4175,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.24786034604174184,0.6594055320683344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2390.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n209200,1,Cash loans,F,N,Y,2,90000.0,170640.0,13612.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-13196,-140,-886.0,-2983,,1,1,1,1,0,0,Laborers,4.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Industry: type 1,,0.05492741168762495,0.2392262794694045,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n211199,1,Cash loans,F,N,Y,1,90000.0,225000.0,12204.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-9551,-2176,-9538.0,-2238,,1,1,1,1,1,0,Laborers,3.0,2,2,TUESDAY,13,0,0,0,0,1,1,Industry: type 2,0.09564665439118117,0.6620532293488427,0.097581612687446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-228.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,7.0\n278173,0,Cash loans,F,N,Y,0,117000.0,607500.0,29353.5,607500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010147,-22333,365243,-8504.0,-4502,,1,0,0,1,0,1,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.721691101701255,0.6642482627052363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1723.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n357108,0,Cash loans,F,N,Y,0,180000.0,2250000.0,59355.0,2250000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.010006,-20422,365243,-2249.0,-3845,,1,0,0,1,0,0,,2.0,2,1,FRIDAY,12,0,0,0,0,0,0,XNA,0.857477371954036,0.586214066741229,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-604.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,,,,,,\n307458,0,Cash loans,F,N,Y,2,173250.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.00702,-13219,-5542,-7342.0,-2795,,1,1,0,1,0,0,Core staff,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Postal,,0.17626911891511768,0.15193454904964762,,,0.9781,,,,,,,,,0.0682,,,,,0.9782,,,,,,,,,0.0711,,,,,0.9781,,,,,,,,,0.0694,,,,,0.057999999999999996,,No,6.0,0.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n198678,0,Cash loans,F,Y,N,0,157500.0,1314117.0,38551.5,1147500.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.00496,-17873,-1145,-5740.0,-1388,6.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.6468484492579611,0.5442347412142162,0.0825,,0.9796,,,0.0,0.0345,0.1667,0.2083,,,0.0464,,,0.084,,0.9796,,,0.0,0.0345,0.1667,0.2083,,,0.0475,,,0.0833,,0.9796,,,0.0,0.0345,0.1667,0.2083,,,0.0472,,,,specific housing,0.0313,\"Stone, brick\",No,12.0,0.0,12.0,0.0,-725.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n421250,0,Cash loans,F,N,Y,0,144000.0,675000.0,26901.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.005002,-20665,365243,-6743.0,-4155,,1,0,0,1,1,0,,1.0,3,3,SATURDAY,11,0,0,0,0,0,0,XNA,0.5941938738893231,0.6168182056013152,0.7838324335199619,0.0124,0.024,0.9786,,,0.0,0.069,0.0417,,0.0057,0.0101,0.01,0.0,0.0,0.0126,0.0249,0.9786,,,0.0,0.069,0.0417,,0.0058,0.011000000000000001,0.0105,0.0,0.0,0.0125,0.024,0.9786,,,0.0,0.069,0.0417,,0.0058,0.0103,0.0102,0.0,0.0,,block of flats,0.012,Block,No,0.0,0.0,0.0,0.0,-794.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n407529,0,Cash loans,M,Y,Y,0,157500.0,1125000.0,39987.0,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-19597,-1595,-8240.0,-3082,10.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Other,,0.7025542969823059,0.656158373001177,0.2974,0.1638,0.9886,0.8504,0.0824,0.38,0.2586,0.4167,0.3333,0.2557,0.24,0.3428,0.0135,0.022000000000000002,0.2605,0.1368,0.9891,0.8563,0.0761,0.3625,0.2069,0.375,0.25,0.2092,0.2259,0.2942,0.0078,0.0018,0.2998,0.1638,0.9891,0.8524,0.0829,0.38,0.2586,0.4167,0.3333,0.2601,0.2441,0.3501,0.0136,0.0224,reg oper account,block of flats,0.2261,Panel,No,3.0,0.0,3.0,0.0,-2476.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n355690,0,Revolving loans,F,N,N,3,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Lower secondary,Civil marriage,House / apartment,0.010276,-13569,-6193,-4195.0,-4035,,1,1,0,1,0,0,Core staff,5.0,2,2,MONDAY,9,0,0,0,0,0,0,Agriculture,,0.3857417949552039,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n173542,0,Cash loans,F,Y,N,0,135000.0,1125000.0,32895.0,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.015221,-21214,365243,-762.0,-4719,2.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.6266866933965973,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1819.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n129653,0,Cash loans,M,N,N,1,135000.0,284400.0,13833.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.011703,-14213,-2488,-1034.0,-4211,,1,1,1,1,1,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Industry: type 1,,0.7396903046515381,0.2225807646753351,0.0825,0.057999999999999996,0.9732,0.6328,0.0083,0.0,0.1379,0.1667,0.0417,0.0844,0.0672,0.0634,0.0,0.0,0.084,0.0602,0.9732,0.6472,0.0084,0.0,0.1379,0.1667,0.0417,0.0863,0.0735,0.0661,0.0,0.0,0.0833,0.057999999999999996,0.9732,0.6377,0.0083,0.0,0.1379,0.1667,0.0417,0.0859,0.0684,0.0646,0.0,0.0,reg oper account,block of flats,0.0544,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-620.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n349883,0,Cash loans,M,N,Y,1,135000.0,284400.0,19134.0,225000.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.030755,-8777,-388,-3570.0,-1321,,1,1,0,1,0,0,Low-skill Laborers,3.0,2,2,WEDNESDAY,18,0,0,0,0,1,1,Industry: type 3,0.12350817283257548,0.3475259768594262,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-878.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n160399,0,Cash loans,M,N,N,0,90000.0,101880.0,6363.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-10903,-82,-5035.0,-3270,,1,1,1,1,0,0,Low-skill Laborers,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,Other,0.1794335060621888,0.7401363771905645,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1085.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n327383,0,Revolving loans,F,N,Y,1,54000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-11521,-285,-3076.0,-4165,,1,1,0,1,0,0,Sales staff,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Self-employed,0.12599827760212873,0.34435933891601034,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1910.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n222042,0,Cash loans,M,Y,N,0,112500.0,545040.0,25407.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-19337,-5365,-10343.0,-2882,13.0,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,14,0,0,0,0,1,1,Agriculture,,0.6526509667129935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n447368,0,Cash loans,F,N,N,0,157500.0,286704.0,20083.5,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018801,-22078,365243,-15629.0,-4993,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,XNA,,0.09298876893570553,0.6848276586890367,0.2227,0.1617,0.9786,0.7076,0.1129,0.24,0.2069,0.3333,0.375,0.121,0.1816,0.2215,0.0,0.0,0.2269,0.1678,0.9786,0.7190000000000001,0.114,0.2417,0.2069,0.3333,0.375,0.1238,0.1983,0.2308,0.0,0.0,0.2248,0.1617,0.9786,0.7115,0.1137,0.24,0.2069,0.3333,0.375,0.1231,0.1847,0.2255,0.0,0.0,reg oper spec account,block of flats,0.23600000000000002,Panel,No,0.0,0.0,0.0,0.0,-764.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n108361,0,Cash loans,M,N,Y,0,202500.0,333000.0,16150.5,333000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.072508,-18945,365243,-8144.0,-2485,,1,0,0,1,1,0,,1.0,1,1,MONDAY,11,0,0,0,0,0,0,XNA,,0.3747913973010998,,0.0659,0.0616,0.9727,0.626,0.018000000000000002,0.0,0.1172,0.1667,0.0417,0.033,0.0537,0.0463,0.0,0.0032,0.084,0.0877,0.9727,0.6406,0.0,0.0,0.1034,0.1667,0.0417,0.0,0.0735,0.0338,0.0,0.004,0.0625,0.0685,0.9727,0.631,0.0232,0.0,0.1034,0.1667,0.0417,0.0363,0.0513,0.0457,0.0,0.0037,reg oper account,block of flats,0.0404,Block,No,0.0,0.0,0.0,0.0,-119.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n123445,0,Cash loans,F,N,Y,0,126000.0,398016.0,22977.0,360000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.018209,-20705,365243,-9297.0,-4251,,1,0,0,1,1,0,,2.0,3,3,FRIDAY,6,0,0,0,0,0,0,XNA,0.7684605710074862,0.4345204235318307,0.520897599048938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-686.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n195083,0,Cash loans,F,N,Y,0,90000.0,225000.0,11632.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018209,-19464,365243,-9286.0,-2989,,1,0,0,1,0,0,,1.0,3,3,TUESDAY,7,0,0,0,0,0,0,XNA,,0.4466141745141018,0.1654074596555436,0.0825,0.0783,0.9776,0.6940000000000001,0.1327,0.0,0.1379,0.1667,0.0417,0.054000000000000006,0.0672,0.0687,0.0,0.0,0.084,0.0813,0.9777,0.706,0.1339,0.0,0.1379,0.1667,0.0417,0.0552,0.0735,0.0716,0.0,0.0,0.0833,0.0783,0.9776,0.6981,0.1336,0.0,0.1379,0.1667,0.0417,0.055,0.0684,0.07,0.0,0.0,reg oper account,block of flats,0.0726,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n137783,0,Cash loans,F,N,Y,2,126000.0,1216201.5,35689.5,1062000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.018029,-14030,-368,-3400.0,-4447,,1,1,1,1,0,0,,4.0,3,3,THURSDAY,6,0,0,0,0,0,0,Government,0.6286048484639363,0.31305152219090543,0.7151031019926098,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-36.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n247060,0,Cash loans,M,N,Y,1,135000.0,675000.0,32602.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.005084,-13770,-517,-2162.0,-5095,,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.4168853969892688,,0.134,,0.9871,0.8232,,0.16,0.069,0.5417,,0.0392,,0.0864,,,0.1366,,0.9871,0.8301,,0.1611,0.069,0.5417,,0.0401,,0.09,,,0.1353,,0.9871,0.8256,,0.16,0.069,0.5417,,0.0399,,0.08800000000000001,,,reg oper account,block of flats,0.1162,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1767.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n151115,0,Cash loans,F,N,Y,1,270000.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.018634,-15131,-1994,-3064.0,-3153,,1,1,0,1,1,0,Managers,3.0,2,2,TUESDAY,9,0,0,0,1,1,0,Business Entity Type 3,,0.5256845986546003,0.0005272652387098817,0.0619,0.0539,0.9767,0.6804,0.0,0.0,0.1034,0.1667,0.2083,0.0933,0.0504,0.0501,0.0,0.0599,0.063,0.055999999999999994,0.9767,0.6929,0.0,0.0,0.1034,0.1667,0.2083,0.0954,0.0551,0.0522,0.0,0.0634,0.0625,0.0539,0.9767,0.6847,0.0,0.0,0.1034,0.1667,0.2083,0.0949,0.0513,0.051,0.0,0.0612,reg oper spec account,block of flats,0.0524,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2148.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n191947,0,Cash loans,M,N,N,0,54000.0,95940.0,9616.5,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.010276,-22048,365243,-6317.0,-4058,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.6987337400200161,,,,0.9717,,,,,,,,,0.1125,,,,,0.9717,,,,,,,,,0.1172,,,,,0.9717,,,,,,,,,0.1145,,,,,0.0885,,No,0.0,0.0,0.0,0.0,-366.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n255130,0,Cash loans,M,Y,Y,1,112500.0,67500.0,5539.5,67500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-21691,-10457,-10480.0,-5021,9.0,1,1,0,1,0,0,,3.0,2,2,FRIDAY,10,0,0,0,0,1,1,Business Entity Type 1,,0.4812264599507319,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n266034,0,Cash loans,M,Y,N,2,135000.0,343683.0,13221.0,261000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-16525,-673,-7846.0,-80,18.0,1,1,0,1,0,0,Laborers,4.0,2,2,MONDAY,15,0,0,0,1,1,0,Business Entity Type 3,0.2430362733481508,0.70165327986064,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-869.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n338338,0,Cash loans,M,Y,Y,0,270000.0,592560.0,35937.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-15322,-4936,-9374.0,-1224,9.0,1,1,0,1,0,0,Laborers,2.0,1,1,THURSDAY,14,0,0,0,0,0,0,Government,,0.7419910617783192,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-575.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n389744,0,Cash loans,F,N,Y,1,238500.0,371979.0,32053.5,310500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.003818,-13842,-3483,-7796.0,-1104,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,5,0,0,0,0,0,0,Industry: type 3,0.1887186140076952,0.43006613609772665,0.13595104417329515,0.0454,0.0593,0.9886,,,0.0,0.1379,0.1667,,0.0,,0.0645,,0.0,0.0462,0.0615,0.9886,,,0.0,0.1379,0.1667,,0.0,,0.0672,,0.0,0.0458,0.0593,0.9886,,,0.0,0.1379,0.1667,,0.0,,0.0657,,0.0,,block of flats,0.0507,Block,No,0.0,0.0,0.0,0.0,-1749.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n388775,0,Cash loans,F,Y,N,1,180000.0,781920.0,43789.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018029,-15736,-2260,-9424.0,-4552,13.0,1,1,1,1,1,0,,3.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Industry: type 11,0.5453592651438203,0.14365730257793674,0.6006575372857061,0.2062,0.2318,0.9821,,,0.0,0.4138,0.1667,,0.0,,0.1202,,0.0,0.2101,0.2406,0.9821,,,0.0,0.4138,0.1667,,0.0,,0.1253,,0.0,0.2082,0.2318,0.9821,,,0.0,0.4138,0.1667,,0.0,,0.1224,,0.0,,block of flats,0.1424,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1933.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n330383,0,Cash loans,F,N,Y,0,54000.0,168102.0,9778.5,148500.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.030755,-23377,365243,-123.0,-4539,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,XNA,,0.5813417114262445,0.746300213050371,0.0412,0.0314,0.9975,0.966,0.0141,0.0,0.069,0.1667,0.2083,0.0252,0.0336,0.0294,0.0,0.0,0.042,0.0326,0.9975,0.9673,0.0143,0.0,0.069,0.1667,0.2083,0.0258,0.0367,0.0307,0.0,0.0,0.0416,0.0314,0.9975,0.9665,0.0142,0.0,0.069,0.1667,0.2083,0.0256,0.0342,0.03,0.0,0.0,reg oper account,block of flats,0.0309,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n170238,0,Cash loans,M,Y,Y,1,225000.0,735579.0,40032.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-15421,-244,-7965.0,-4880,19.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.5628533995512142,0.3034003987371119,0.5172965813614878,0.1144,0.0852,0.9836,0.7756,,0.16,0.1379,0.3333,0.375,1.0,0.0925,0.0807,0.0039,0.0714,0.1166,0.0884,0.9836,0.7844,,0.1611,0.1379,0.3333,0.375,1.0,0.10099999999999999,0.0841,0.0039,0.0756,0.1155,0.0852,0.9836,0.7786,,0.16,0.1379,0.3333,0.375,1.0,0.0941,0.0822,0.0039,0.0729,reg oper account,block of flats,0.1012,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1714.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n335205,0,Cash loans,F,N,Y,2,135000.0,162000.0,13986.0,162000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019689,-13445,-399,-566.0,-4610,,1,1,1,1,0,0,,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,School,0.4545718108022914,0.4662188080011887,0.18195910978627847,0.1649,0.0806,0.998,0.9728,0.0566,0.08,0.069,0.375,0.4167,0.0387,0.1311,0.138,0.0154,0.0124,0.1681,0.0837,0.998,0.9739,0.0572,0.0806,0.069,0.375,0.4167,0.0396,0.1433,0.1438,0.0156,0.0132,0.1665,0.0806,0.998,0.9732,0.057,0.08,0.069,0.375,0.4167,0.0394,0.1334,0.1405,0.0155,0.0127,reg oper account,block of flats,0.1477,Panel,No,2.0,1.0,2.0,1.0,-515.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n366798,0,Cash loans,M,Y,Y,0,135000.0,127350.0,12726.0,112500.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.018801,-18480,-1689,-6646.0,-2015,16.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,7,0,0,0,0,1,1,Business Entity Type 3,,0.4383230049361169,0.3979463219016906,0.0619,0.0549,0.9846,0.7892,,0.0,0.1379,0.1667,0.2083,0.0391,0.0504,0.0517,0.0,0.0,0.063,0.057,0.9846,0.7975,,0.0,0.1379,0.1667,0.2083,0.04,0.0551,0.0539,0.0,0.0,0.0625,0.0549,0.9846,0.792,,0.0,0.1379,0.1667,0.2083,0.0397,0.0513,0.0526,0.0,0.0,reg oper account,block of flats,0.045,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n246831,1,Cash loans,F,N,Y,0,135000.0,450000.0,21109.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-12113,-5399,-492.0,-2797,,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Medicine,0.4028055184882441,0.3464507599691285,0.11836432636740067,0.3701,0.1998,0.997,,,0.4,0.3103,0.375,,0.1461,0.3001,0.3106,0.0077,0.0923,0.3771,0.2073,0.997,,,0.4028,0.3103,0.375,,0.1495,0.3278,0.3236,0.0078,0.0977,0.3737,0.1998,0.997,,,0.4,0.3103,0.375,,0.1487,0.3053,0.3162,0.0078,0.0942,reg oper account,block of flats,0.3104,Panel,No,1.0,0.0,1.0,0.0,-1688.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n111120,0,Cash loans,M,Y,N,2,85500.0,327024.0,21420.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.020246,-12424,-5024,-5061.0,-4379,18.0,1,1,0,1,0,0,Laborers,3.0,3,3,MONDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.4212614321032485,0.6528965519806539,0.0619,0.0327,0.9796,,,0.0,0.1379,0.1667,,0.053,,0.0533,,0.0,0.063,0.034,0.9796,,,0.0,0.1379,0.1667,,0.0542,,0.0556,,0.0,0.0625,0.0327,0.9796,,,0.0,0.1379,0.1667,,0.0539,,0.0543,,0.0,,block of flats,0.0467,Panel,No,1.0,1.0,1.0,0.0,-2121.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n347716,0,Cash loans,F,N,Y,0,135000.0,852088.5,43636.5,688500.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,Municipal apartment,0.014464,-13819,-7097,-7680.0,-3376,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,6,0,0,0,0,0,0,Military,0.3376567731971004,0.6366987155999421,0.8463780633178121,0.1031,0.0813,0.9791,0.7144,0.0385,0.0,0.2069,0.1667,0.2083,0.0564,0.0815,0.096,0.0116,0.0162,0.105,0.0844,0.9791,0.7256,0.0389,0.0,0.2069,0.1667,0.2083,0.0577,0.0891,0.1,0.0117,0.0172,0.1041,0.0813,0.9791,0.7182,0.0388,0.0,0.2069,0.1667,0.2083,0.0574,0.0829,0.0977,0.0116,0.0166,reg oper account,block of flats,0.1001,Panel,No,0.0,0.0,0.0,0.0,-658.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n289257,0,Cash loans,M,Y,Y,0,202500.0,143910.0,15628.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-14181,-977,-2300.0,-602,8.0,1,1,1,1,1,0,Laborers,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Industry: type 4,,0.5786249482992923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-293.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n154492,0,Cash loans,F,Y,Y,0,49500.0,163512.0,10579.5,135000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.01885,-14957,-2317,-2533.0,-5401,13.0,1,1,1,1,1,0,Core staff,1.0,2,2,MONDAY,9,0,0,0,0,1,1,Government,0.7418266479612903,0.40932522527776943,0.5190973382084597,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-1449.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n344965,1,Cash loans,M,Y,N,0,112500.0,1272888.0,37345.5,1111500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-14838,-1314,-1303.0,-4327,14.0,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Medicine,,0.4058509049481616,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n370158,0,Cash loans,F,N,Y,2,94500.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.003813,-10434,-1801,-4049.0,-770,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,10,0,0,0,0,1,1,Medicine,0.23031421023316986,0.4139752535435992,0.5744466170995097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1536.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n437547,0,Revolving loans,F,N,Y,0,202500.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-17771,-2065,-9569.0,-1203,,1,1,1,1,1,0,Core staff,2.0,1,1,THURSDAY,19,0,0,0,0,0,0,School,0.854430152498866,0.7242968902088562,0.2622489709189549,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-810.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n196531,0,Cash loans,F,Y,N,2,202500.0,1045854.0,37183.5,873000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-10147,-2559,-9439.0,-372,6.0,1,1,0,1,0,0,Managers,4.0,2,2,FRIDAY,13,0,0,0,1,1,0,Business Entity Type 3,0.2810860630406332,0.665381893533202,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-36.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n351654,0,Cash loans,M,N,N,2,225000.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Higher education,Separated,Office apartment,0.011657,-15213,-1811,-5550.0,-3921,,1,1,1,1,0,0,,3.0,1,1,TUESDAY,14,0,0,0,0,1,1,Business Entity Type 2,,0.7428399748898993,0.4101025731788671,0.1289,0.177,0.9811,,,0.0,0.1724,0.1667,,0.0266,,0.1189,,0.0575,0.1313,0.1837,0.9811,,,0.0,0.1724,0.1667,,0.0272,,0.1239,,0.0609,0.1301,0.177,0.9811,,,0.0,0.1724,0.1667,,0.0271,,0.121,,0.0587,,block of flats,0.0935,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-599.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n142462,0,Cash loans,M,Y,Y,2,225000.0,265851.0,20718.0,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-14165,-356,-4602.0,-1992,2.0,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,,0.5422583390749162,,0.033,0.0,0.9732,0.6328,0.003,0.0,0.069,0.125,0.1667,0.0134,0.0269,0.0253,0.0,0.0,0.0336,0.0,0.9732,0.6472,0.003,0.0,0.069,0.125,0.1667,0.0137,0.0294,0.0263,0.0,0.0,0.0333,0.0,0.9732,0.6377,0.003,0.0,0.069,0.125,0.1667,0.0137,0.0274,0.0257,0.0,0.0,reg oper account,block of flats,0.0226,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-555.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n225638,0,Cash loans,F,N,Y,1,162000.0,544491.0,20002.5,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.003813,-14323,-4753,-2504.0,-3918,,1,1,0,1,0,0,Core staff,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Government,0.6906293537024579,0.6107064992827197,0.7238369900414456,0.0928,,0.9935,,,0.12,0.1034,0.3333,,,,,,,0.0945,,0.9935,,,0.1208,0.1034,0.3333,,,,,,,0.0937,,0.9935,,,0.12,0.1034,0.3333,,,,,,,,block of flats,0.0896,Panel,No,0.0,0.0,0.0,0.0,-542.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n105150,0,Cash loans,M,Y,N,0,180000.0,269982.0,30663.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.014464,-9556,-1659,-3005.0,-2146,12.0,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,6,0,0,0,1,1,0,Transport: type 4,,0.2667549569314385,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n314607,1,Cash loans,M,N,Y,0,112500.0,450000.0,23107.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-19625,-815,-2634.0,-2956,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,17,0,0,0,0,1,1,Government,,0.014793929725171481,0.2955826421513093,0.0649,0.1,0.9965,,,0.0,0.1034,0.1667,,,,0.065,,,0.0662,0.1038,0.9965,,,0.0,0.1034,0.1667,,,,0.0677,,,0.0656,0.1,0.9965,,,0.0,0.1034,0.1667,,,,0.0661,,,,block of flats,0.0749,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-584.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n396209,0,Cash loans,F,N,N,0,112500.0,254700.0,14220.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-23915,365243,-3771.0,-4513,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.6403881675744952,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-14.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n306734,0,Cash loans,F,Y,Y,2,225000.0,219042.0,21793.5,193500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.003122,-16199,-711,-2895.0,-4008,14.0,1,1,0,1,0,0,Sales staff,4.0,3,3,SATURDAY,14,0,0,0,0,1,1,Self-employed,,0.1517995979325104,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n243630,0,Cash loans,M,Y,Y,1,315000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.009657,-16770,365243,-5541.0,-274,8.0,1,0,0,1,0,0,,3.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.5584047768534631,0.8038850611746273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-362.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,0.0\n114616,0,Cash loans,F,N,Y,2,225000.0,381528.0,24511.5,315000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.010966,-15052,-644,-9036.0,-4720,,1,1,0,1,1,0,Core staff,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Trade: type 3,0.5811804797071072,0.4151003750432625,0.3046721837533529,0.0856,0.0457,0.9831,0.7688,0.0287,0.08,0.0345,0.5417,0.5833,0.0118,0.0698,0.0824,0.0,0.0,0.0872,0.0475,0.9831,0.7779,0.0289,0.0806,0.0345,0.5417,0.5833,0.0121,0.0762,0.0859,0.0,0.0,0.0864,0.0457,0.9831,0.7719,0.0289,0.08,0.0345,0.5417,0.5833,0.012,0.071,0.0839,0.0,0.0,org spec account,block of flats,0.0805,Block,No,1.0,0.0,1.0,0.0,-2466.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n400659,0,Cash loans,M,Y,N,1,76500.0,808650.0,22365.0,675000.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,Rented apartment,0.001333,-20051,-523,-8603.0,-1133,26.0,1,1,0,1,0,0,Laborers,3.0,3,3,TUESDAY,11,0,0,0,1,1,0,Government,,0.6283580817444725,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-284.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n244966,0,Cash loans,F,Y,Y,0,535500.0,314100.0,14323.5,225000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.04622,-15200,-4583,-7477.0,-4151,2.0,1,1,0,1,0,1,Accountants,1.0,1,1,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.7638457612045175,0.6231140598783591,0.31703177858344445,0.4,0.1395,0.9896,0.8572,,0.56,0.2414,0.5417,0.5833,,,0.3835,,0.0208,0.4076,0.1448,0.9896,0.8628,,0.5639,0.2414,0.5417,0.5833,,,0.3995,,0.022000000000000002,0.4039,0.1395,0.9896,0.8591,,0.56,0.2414,0.5417,0.5833,,,0.3904,,0.0212,reg oper account,block of flats,0.3447,Panel,No,0.0,0.0,0.0,0.0,-827.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n176739,0,Cash loans,F,N,Y,3,135000.0,594121.5,28710.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-9999,-1049,-1715.0,-772,,1,1,0,1,0,0,,5.0,2,2,MONDAY,16,0,0,0,0,1,1,Industry: type 3,0.1303109788280262,0.2444598974910602,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-661.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n220026,0,Cash loans,F,N,Y,0,135000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.020246,-16391,-7222,-2128.0,-2128,,1,1,0,1,0,0,Core staff,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,Transport: type 2,0.5202474818659916,0.4895616298270111,0.5172965813614878,0.1897,0.1049,0.9771,0.6872,0.0266,0.0,0.4138,0.1667,0.2083,0.1614,0.1538,0.1836,0.0039,0.1164,0.1933,0.1089,0.9772,0.6994,0.0269,0.0,0.4138,0.1667,0.2083,0.1651,0.168,0.1913,0.0039,0.1232,0.1915,0.1049,0.9771,0.6914,0.0268,0.0,0.4138,0.1667,0.2083,0.1642,0.1565,0.1869,0.0039,0.1189,reg oper account,block of flats,0.1444,Panel,No,1.0,0.0,1.0,0.0,-2729.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n220121,0,Cash loans,F,Y,N,1,220500.0,675000.0,53460.0,675000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.014519999999999996,-17575,-2442,-272.0,-1087,2.0,1,1,1,1,1,0,Accountants,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Industry: type 9,0.8522185546513821,0.6066855383353253,0.108924403541012,0.1172,0.0872,0.9841,0.6804,0.0921,0.1,0.069,0.3192,0.2083,0.0194,,0.0826,,0.0079,0.0567,0.0859,0.9767,0.6929,0.0929,0.0,0.0345,0.1667,0.2083,0.0,,0.0251,,0.0,0.1239,0.0872,0.9767,0.6847,0.0926,0.1,0.0345,0.1667,0.2083,0.0146,,0.0605,,0.0081,reg oper account,block of flats,0.1693,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-274.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n182771,0,Cash loans,M,Y,Y,0,270000.0,675000.0,69165.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.019689,-8755,-899,-1496.0,-910,10.0,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Self-employed,,0.14260063520423166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1381.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n394036,0,Cash loans,F,Y,N,1,112500.0,215640.0,13027.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-13973,-978,-6650.0,-3222,17.0,1,1,1,1,0,0,Sales staff,3.0,2,2,TUESDAY,15,0,0,0,1,1,0,Self-employed,0.4594831384121378,0.4604985836842889,0.17982176508970435,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-591.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n335158,0,Cash loans,M,Y,Y,0,90000.0,314100.0,16573.5,225000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-9417,-240,-4148.0,-2033,11.0,1,1,0,1,0,1,Core staff,1.0,2,2,FRIDAY,14,0,0,0,0,1,1,Police,0.11451993913627327,0.2641284763422555,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1027.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n176950,0,Cash loans,F,Y,Y,0,135000.0,254700.0,26874.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-10126,-102,-8359.0,-2351,2.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Telecom,0.3351290255684226,0.5415472057557755,0.4740512892789932,0.0825,0.08,0.9881,0.8368,0.0136,0.0,0.1379,0.1667,0.2083,0.0819,0.0672,0.0821,0.0,0.0,0.084,0.083,0.9881,0.8432,0.0137,0.0,0.1379,0.1667,0.2083,0.0837,0.0735,0.0855,0.0,0.0,0.0833,0.08,0.9881,0.8390000000000001,0.0137,0.0,0.1379,0.1667,0.2083,0.0833,0.0684,0.0835,0.0,0.0,reg oper account,block of flats,0.07200000000000001,Panel,No,1.0,1.0,1.0,1.0,-787.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n193991,0,Cash loans,M,Y,Y,0,225000.0,1699740.0,88141.5,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16056,-4337,-7916.0,-4830,5.0,1,1,1,1,1,0,Laborers,2.0,2,2,WEDNESDAY,17,0,1,1,0,1,1,Business Entity Type 3,,0.7219896708318009,0.7738956942145427,0.0557,0.0603,0.9801,0.728,0.0087,0.0,0.1379,0.1667,0.2083,0.0432,0.0437,0.0507,0.0077,0.129,0.0567,0.0626,0.9801,0.7387,0.0088,0.0,0.1379,0.1667,0.2083,0.0442,0.0478,0.0529,0.0078,0.1365,0.0562,0.0603,0.9801,0.7316,0.0088,0.0,0.1379,0.1667,0.2083,0.044000000000000004,0.0445,0.0516,0.0078,0.1317,reg oper account,block of flats,0.0679,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-687.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n443741,0,Cash loans,F,N,Y,0,90000.0,47970.0,5539.5,45000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.006296,-21033,365243,-4514.0,-4543,,1,0,0,1,1,0,,1.0,3,3,FRIDAY,11,0,0,0,0,0,0,XNA,,0.5755455359308875,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-648.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n225802,0,Cash loans,M,N,Y,0,225000.0,521280.0,31630.5,450000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.072508,-9125,-1030,-7129.0,-1749,,1,1,0,1,1,0,Managers,1.0,1,1,MONDAY,17,0,0,0,0,0,0,Government,,0.7329099555983389,0.3740208032583212,0.2546,0.0941,0.9891,0.8572,0.1374,0.32,0.1379,0.6042,0.5833,0.0,0.1236,0.2804,0.0347,0.0287,0.1639,0.0977,0.9891,0.8628,0.1387,0.2417,0.1034,0.5417,0.5833,0.0,0.135,0.1991,0.035,0.0054,0.2571,0.0941,0.9891,0.8591,0.1383,0.32,0.1379,0.6042,0.5833,0.0,0.1257,0.2854,0.0349,0.0293,reg oper account,block of flats,0.1616,Panel,No,0.0,0.0,0.0,0.0,-401.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n196644,0,Cash loans,F,N,Y,1,135000.0,373311.0,25384.5,283500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.009334,-18315,-2070,-7595.0,-1829,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Government,,0.8065424234470964,0.4578995512067301,0.0124,0.0,0.9742,0.6464,0.002,0.0,0.069,0.0417,0.0833,0.0116,0.0101,0.0097,0.0,0.0,0.0126,0.0,0.9742,0.6602,0.0021,0.0,0.069,0.0417,0.0833,0.0119,0.011000000000000001,0.0101,0.0,0.0,0.0125,0.0,0.9742,0.6511,0.002,0.0,0.069,0.0417,0.0833,0.0118,0.0103,0.0099,0.0,0.0,reg oper account,block of flats,0.0111,Wooden,No,0.0,0.0,0.0,0.0,-2441.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n210120,0,Cash loans,F,N,N,1,270000.0,1024740.0,55719.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018634,-11163,-1423,-6269.0,-516,,1,1,1,1,0,0,Secretaries,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5222702055183075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-578.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n408522,0,Cash loans,M,N,Y,0,112500.0,220662.0,21627.0,207000.0,,Pensioner,Incomplete higher,Married,House / apartment,0.022625,-25112,365243,-11009.0,-4051,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.7019644681350671,0.5549467685334323,0.2227,0.1446,0.9851,0.7959999999999999,0.0936,0.28,0.2414,0.3333,0.375,0.1656,0.1816,0.2513,0.0,0.0,0.2269,0.15,0.9851,0.804,0.0944,0.282,0.2414,0.3333,0.375,0.1693,0.1983,0.2619,0.0,0.0,0.2248,0.1446,0.9851,0.7987,0.0941,0.28,0.2414,0.3333,0.375,0.1685,0.1847,0.2558,0.0,0.0,,block of flats,0.2488,Panel,No,0.0,0.0,0.0,0.0,-1082.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n172977,1,Cash loans,M,N,Y,1,157500.0,239850.0,28593.0,225000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.006852,-10693,-533,-1426.0,-3347,,1,1,0,1,0,0,,3.0,3,3,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.2319557783830425,0.3208055234848897,0.3425288720742255,0.0742,0.0401,0.9886,0.8436,0.0386,0.08,0.069,0.3333,0.375,0.0102,0.0605,0.0817,0.0,0.0,0.0756,0.0416,0.9886,0.8497,0.039,0.0806,0.069,0.3333,0.375,0.0105,0.0661,0.0852,0.0,0.0,0.0749,0.0401,0.9886,0.8457,0.0389,0.08,0.069,0.3333,0.375,0.0104,0.0616,0.0832,0.0,0.0,reg oper account,block of flats,0.0854,Panel,No,3.0,1.0,3.0,1.0,-389.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n383040,0,Cash loans,F,N,Y,0,67500.0,630000.0,20322.0,630000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.015221,-23701,365243,-13571.0,-4764,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,XNA,,0.3224420119428691,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1593.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,8.0\n105560,0,Revolving loans,F,N,Y,2,67500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-14775,-91,-7461.0,-4429,,1,1,0,1,0,0,Laborers,4.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.665781015977692,0.7934490301648944,0.0773,0.1012,0.9896,0.8504,0.1894,0.0,0.2069,0.1667,0.2083,0.0414,0.063,0.0609,0.0,0.1377,0.0788,0.1051,0.9896,0.8563,0.1911,0.0,0.2069,0.1667,0.2083,0.0423,0.0689,0.0635,0.0,0.1458,0.0781,0.1012,0.9896,0.8524,0.1906,0.0,0.2069,0.1667,0.2083,0.0421,0.0641,0.062,0.0,0.1406,reg oper spec account,block of flats,0.1035,Panel,No,1.0,0.0,1.0,0.0,-1767.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n143745,1,Cash loans,F,N,N,0,54000.0,568800.0,17437.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-23258,365243,-7502.0,-4362,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.4856224459172129,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1632.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n288335,0,Cash loans,F,N,Y,0,157500.0,360000.0,15858.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.014519999999999996,-17594,-6770,-5295.0,-1127,,1,1,0,1,1,0,,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Self-employed,0.7393182884705735,0.3342855969861764,0.7121551551910698,,,0.9796,,,,,,,,,0.1111,,,,,0.9796,,,,,,,,,0.1157,,,,,0.9796,,,,,,,,,0.1131,,,,,0.0874,,No,0.0,0.0,0.0,0.0,-1742.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n171989,1,Cash loans,F,N,N,1,135000.0,781920.0,40054.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,With parents,0.008865999999999999,-12186,-4702,-4861.0,-2348,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Government,,0.3485630265006149,0.3606125659189888,0.1113,0.1619,0.9896,0.8572,0.02,0.0,0.2759,0.1667,0.2083,0.0841,0.0908,0.1152,0.0039,0.0007,0.1134,0.168,0.9896,0.8628,0.0202,0.0,0.2759,0.1667,0.2083,0.0861,0.0992,0.1201,0.0039,0.0008,0.1124,0.1619,0.9896,0.8591,0.0202,0.0,0.2759,0.1667,0.2083,0.0856,0.0923,0.1173,0.0039,0.0008,reg oper account,block of flats,0.1573,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n177726,0,Cash loans,M,Y,Y,0,112500.0,937638.0,45108.0,823500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-14010,-101,-2454.0,-4461,16.0,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.5096169667070907,,0.1464,,0.9921,,,0.08,0.069,0.3333,,,,0.1048,,0.0,0.1492,,0.9921,,,0.0806,0.069,0.3333,,,,0.1091,,0.0,0.1478,,0.9921,,,0.08,0.069,0.3333,,,,0.1066,,0.0,,,0.0824,Panel,No,2.0,0.0,2.0,0.0,-388.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n311754,0,Cash loans,F,N,Y,0,225000.0,1305000.0,38286.0,1305000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.030755,-12239,-744,-5983.0,-1228,,1,1,0,1,0,0,Accountants,2.0,2,2,SATURDAY,14,0,0,0,1,1,0,Business Entity Type 3,,0.051825640936604564,0.7910749129911773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n186397,0,Cash loans,F,N,N,0,135000.0,828000.0,24340.5,828000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-18112,-272,-8695.0,-1661,,1,1,1,1,1,0,Managers,2.0,3,3,THURSDAY,7,0,0,0,0,0,0,Government,0.706190349062929,0.3573974761054831,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1988.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,2.0\n146133,0,Cash loans,F,N,N,0,135000.0,269550.0,14242.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-13405,-998,-579.0,-2861,,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,18,0,0,0,0,0,0,Restaurant,,0.4492801370363177,0.1293142889822697,0.051,0.1639,0.9642,0.5172,0.0128,0.0,0.1207,0.0833,0.1667,0.0329,0.0731,0.055,0.0232,0.1381,0.0126,0.1701,0.9638,0.5361,0.0129,0.0,0.069,0.0417,0.1667,0.0337,0.0799,0.0164,0.0233,0.1462,0.0515,0.1639,0.9642,0.5237,0.0129,0.0,0.1207,0.0833,0.1667,0.0335,0.0744,0.055999999999999994,0.0233,0.141,reg oper account,block of flats,0.1677,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1903.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n125343,0,Cash loans,M,N,N,1,310500.0,1381113.0,38110.5,1206000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-12420,-2446,-1697.0,-3915,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,THURSDAY,18,0,0,0,0,0,0,Agriculture,0.35097636798542114,0.6039218347602312,0.4543210601605785,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2316.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n288042,0,Cash loans,M,Y,N,1,225000.0,562981.5,18423.0,486000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005313,-9187,-535,-7001.0,-1836,64.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.6674382319781625,0.4561097392782771,0.1031,0.1341,0.9901,0.8640000000000001,0.0167,0.0,0.1724,0.1667,0.2083,0.1944,0.0841,0.0641,0.0,0.0,0.105,0.1392,0.9901,0.8693,0.0169,0.0,0.1724,0.1667,0.2083,0.1988,0.0918,0.0667,0.0,0.0,0.1041,0.1341,0.9901,0.8658,0.0168,0.0,0.1724,0.1667,0.2083,0.1977,0.0855,0.0652,0.0,0.0,reg oper account,block of flats,0.0886,Panel,No,0.0,0.0,0.0,0.0,-1319.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n313629,0,Cash loans,M,N,N,0,157500.0,95940.0,9486.0,90000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.031329,-13398,-6228,-5685.0,-4636,,1,1,1,1,1,0,Laborers,1.0,2,2,MONDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.6779002872623571,0.2622583692422573,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2102.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n404986,0,Cash loans,M,Y,N,0,106200.0,1350000.0,36202.5,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-17251,365243,-3257.0,-786,21.0,1,0,0,1,1,0,,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,XNA,0.594225845231855,0.4578322480042208,0.5046813193144684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2257.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n316026,0,Cash loans,M,N,Y,0,360000.0,675000.0,19737.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.031329,-19564,-1739,-2195.0,-3097,,1,1,0,1,1,0,Managers,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Advertising,0.7478108483452546,0.6785828046009369,0.18088797776707397,0.3948,0.0534,0.9876,0.83,0.0308,0.24,0.1034,0.625,0.6667,0.0946,0.3202,0.3383,0.0077,0.0133,0.4023,0.0554,0.9876,0.8367,0.0311,0.2417,0.1034,0.625,0.6667,0.0967,0.3499,0.3524,0.0078,0.0141,0.3987,0.0534,0.9876,0.8323,0.031,0.24,0.1034,0.625,0.6667,0.0962,0.3258,0.3443,0.0078,0.0136,not specified,block of flats,0.2858,Panel,No,0.0,0.0,0.0,0.0,-597.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n206223,0,Cash loans,M,N,N,1,283500.0,450000.0,22018.5,450000.0,Family,Working,Higher education,Married,House / apartment,0.006670999999999999,-11453,-2323,-2087.0,-3962,,1,1,0,1,0,0,,3.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.26711823530405104,0.6210370937515888,0.33125086459090186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1117.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n325023,0,Cash loans,M,N,Y,1,225000.0,1350000.0,57199.5,1350000.0,Unaccompanied,State servant,Higher education,Married,Office apartment,0.030755,-13142,-5321,-5125.0,-4523,,1,1,0,1,0,0,,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Security Ministries,,0.7856237128756667,0.7295666907060153,0.1835,0.0,0.9861,0.8096,0.107,0.08,0.069,0.3333,0.375,0.1318,0.1471,0.0744,0.0116,0.0221,0.187,0.0,0.9861,0.8171,0.10800000000000001,0.0806,0.069,0.3333,0.375,0.1348,0.1607,0.0775,0.0117,0.0234,0.1853,0.0,0.9861,0.8121,0.1077,0.08,0.069,0.3333,0.375,0.1341,0.1496,0.0757,0.0116,0.0226,reg oper account,block of flats,0.0897,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1550.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n414304,0,Cash loans,M,Y,N,1,243000.0,728460.0,74772.0,675000.0,Unaccompanied,State servant,Higher education,Married,With parents,0.006670999999999999,-13099,-2891,-2710.0,-4578,13.0,1,1,0,1,0,0,,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Government,,0.561336118307776,,0.0278,,0.9861,,,,0.069,0.0833,,,,0.0145,,0.0401,0.0284,,0.9861,,,,0.069,0.0833,,,,0.0151,,0.0425,0.0281,,0.9861,,,,0.069,0.0833,,,,0.0148,,0.040999999999999995,,block of flats,0.0202,Panel,No,0.0,0.0,0.0,0.0,-1621.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n223208,0,Cash loans,F,N,Y,0,180000.0,597024.0,38286.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-16545,-2230,-3453.0,-75,,1,1,0,1,0,0,Cooking staff,2.0,1,1,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.7644882121733809,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-598.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n186046,0,Cash loans,F,N,N,0,202500.0,573408.0,29407.5,495000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.00496,-19932,365243,-7785.0,-3480,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.7700330098899035,,0.1037,,0.9791,,,0.0,0.2328,0.1667,,,,0.0775,,0.0,0.125,,0.9791,,,0.0,0.2759,0.1667,,,,0.0,,0.0,0.1239,,0.9791,,,0.0,0.2759,0.1667,,,,0.0805,,0.0,,block of flats,0.031,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-380.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n440940,0,Cash loans,M,N,Y,0,225000.0,360000.0,14661.0,360000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.006207,-14905,-1247,-108.0,-3195,,1,1,1,1,0,0,Sales staff,2.0,2,2,FRIDAY,16,1,1,1,1,1,1,Business Entity Type 3,0.29394456807251473,0.5849510659656155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-732.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n149073,0,Revolving loans,F,N,Y,0,103500.0,202500.0,10125.0,202500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.00733,-20578,365243,-503.0,-3859,,1,0,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.5252647404381612,,0.0485,0.0406,0.9762,0.6736,0.0039,0.0,0.1034,0.125,0.1667,0.0244,0.0395,0.0398,0.0,0.0,0.0494,0.0422,0.9762,0.6864,0.0039,0.0,0.1034,0.125,0.1667,0.0249,0.0432,0.0415,0.0,0.0,0.0489,0.0406,0.9762,0.6779999999999999,0.0039,0.0,0.1034,0.125,0.1667,0.0248,0.0402,0.0405,0.0,0.0,reg oper account,block of flats,0.0334,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-468.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n417694,0,Cash loans,F,N,Y,0,135000.0,254700.0,16713.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.002042,-9117,-185,-3515.0,-1706,,1,1,0,1,0,0,Core staff,1.0,3,3,TUESDAY,14,0,0,0,0,0,0,Kindergarten,,0.015829467684121926,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,5.0,0.0,-786.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n122040,0,Cash loans,F,N,Y,3,40500.0,284400.0,16456.5,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.025164,-10541,-1177,-1673.0,-1964,,1,1,0,1,0,0,Core staff,5.0,2,2,TUESDAY,9,0,0,0,0,0,0,Postal,,0.2505648391346581,,0.1206,0.1219,0.9767,,,0.0,0.2069,0.1667,,0.0237,,0.1122,,0.0,0.1229,0.1265,0.9767,,,0.0,0.2069,0.1667,,0.0243,,0.1169,,0.0,0.1218,0.1219,0.9767,,,0.0,0.2069,0.1667,,0.0241,,0.1143,,0.0,,block of flats,0.0883,Panel,No,4.0,1.0,4.0,0.0,-301.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n198194,0,Cash loans,F,Y,Y,0,180000.0,302206.5,14134.5,229500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-16701,-2457,-6160.0,-236,13.0,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.3210771727702696,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n139411,0,Cash loans,M,N,Y,0,288000.0,1223010.0,51948.0,1125000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.010006,-18683,-5509,-393.0,-1975,,1,1,1,1,1,0,High skill tech staff,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Transport: type 4,,0.7699206922484172,0.7700870700124128,0.0124,0.0164,0.9911,0.8776,0.0066,0.0,0.0345,0.0833,0.0417,0.0033,0.0101,0.0095,0.0,0.0036,0.0126,0.017,0.9911,0.8824,0.0066,0.0,0.0345,0.0833,0.0417,0.0034,0.011000000000000001,0.0099,0.0,0.0038,0.0125,0.0164,0.9911,0.8792,0.0066,0.0,0.0345,0.0833,0.0417,0.0034,0.0103,0.0096,0.0,0.0036,reg oper spec account,block of flats,0.0074,Panel,No,1.0,0.0,1.0,0.0,-1412.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n332244,0,Cash loans,F,N,Y,0,112500.0,1236816.0,36292.5,1080000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-20496,-7192,-8541.0,-4054,,1,1,0,1,1,0,Core staff,2.0,3,3,SATURDAY,11,0,0,0,0,0,0,Government,0.7379360013729357,0.446165638624928,0.8004513396487078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2516.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n252884,0,Cash loans,F,N,Y,0,157500.0,327024.0,25965.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009175,-10284,-2306,-3974.0,-2743,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,15,0,0,0,1,1,0,Business Entity Type 3,,0.7206181454142874,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-377.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n399899,0,Revolving loans,F,N,Y,0,250200.0,720000.0,36000.0,720000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.0228,-17905,-4330,-4224.0,-1458,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Other,0.7746889049628254,0.627254386937052,0.6058362647264226,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-804.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n371992,0,Cash loans,F,N,Y,0,135000.0,288873.0,16713.0,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-21420,365243,-1743.0,-4344,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.6131160560379634,0.6956219298394389,0.0247,0.0419,0.9732,0.6328,0.0166,0.0,0.069,0.0833,0.125,0.0,0.0202,0.0193,0.0,0.0,0.0252,0.0435,0.9732,0.6472,0.0168,0.0,0.069,0.0833,0.125,0.0,0.022000000000000002,0.0201,0.0,0.0,0.025,0.0419,0.9732,0.6377,0.0167,0.0,0.069,0.0833,0.125,0.0,0.0205,0.0197,0.0,0.0,reg oper spec account,block of flats,0.0243,\"Stone, brick\",No,4.0,1.0,4.0,1.0,-2191.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n152367,0,Cash loans,F,N,N,0,128250.0,1023966.0,27009.0,733500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.030755,-11737,-510,-718.0,-4391,,1,1,1,1,0,0,Accountants,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,0.3224270551958598,0.618455281454231,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n155518,0,Cash loans,M,Y,N,1,180000.0,164952.0,10867.5,130500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-13539,-1112,-5758.0,-4576,20.0,1,1,0,1,0,0,Laborers,3.0,3,3,SUNDAY,6,0,0,0,0,0,0,Industry: type 4,0.2580354888123325,0.5703662995479544,0.5709165417729987,0.0062,0.0,0.9518,0.3404,0.0,0.0,0.0,0.0,0.0417,0.0028,0.005,0.0027,0.0,0.0,0.0063,0.0,0.9518,0.3662,0.0,0.0,0.0,0.0,0.0417,0.0028,0.0055,0.0028,0.0,0.0,0.0062,0.0,0.9518,0.3492,0.0,0.0,0.0,0.0,0.0417,0.0028,0.0051,0.0027,0.0,0.0,reg oper account,block of flats,0.0029,Wooden,No,1.0,0.0,1.0,0.0,-1824.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n277624,0,Cash loans,F,Y,Y,0,135000.0,675000.0,19476.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-23695,365243,-2878.0,-4428,4.0,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.7237725710282209,0.7016957740576931,0.1113,0.0824,0.9831,,,0.12,0.1034,0.3333,,0.0839,,0.1137,,0.0652,0.1134,0.0855,0.9831,,,0.1208,0.1034,0.3333,,0.0858,,0.1184,,0.069,0.1124,0.0824,0.9831,,,0.12,0.1034,0.3333,,0.0853,,0.1157,,0.0666,,block of flats,0.1128,Panel,No,0.0,0.0,0.0,0.0,-1824.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n353092,0,Cash loans,M,Y,N,0,202500.0,500427.0,27274.5,432000.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.020713,-9830,-421,-1143.0,-2498,15.0,1,1,0,1,0,0,,1.0,3,2,MONDAY,10,0,0,0,0,0,0,Government,,0.4057876297893316,,0.0825,0.0777,0.9732,0.6328,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0,0.0672,0.0644,0.0,0.0,0.084,0.0806,0.9732,0.6472,0.0071,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.0671,0.0,0.0,0.0833,0.0777,0.9732,0.6377,0.0071,0.0,0.1379,0.1667,0.2083,0.0,0.0684,0.0656,0.0,0.0,reg oper account,block of flats,0.0545,Block,No,4.0,1.0,4.0,1.0,-268.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n326501,0,Cash loans,F,N,Y,0,45000.0,152820.0,8662.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-12175,-337,-5984.0,-3230,,1,1,1,1,1,0,Cooking staff,2.0,2,2,MONDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.5704895434457941,0.5620604831738043,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,3.0,8.0,3.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n256152,0,Revolving loans,F,N,Y,0,166500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-12221,-4508,-6219.0,-4056,,1,1,0,1,0,0,Core staff,1.0,3,1,MONDAY,9,0,0,0,0,0,0,Transport: type 1,0.5309060779828878,0.1799019010189699,,0.1021,0.0972,0.9881,,,0.12,0.1034,0.3333,,0.035,,0.1277,,0.0283,0.10400000000000001,0.1008,0.9881,,,0.1208,0.1034,0.3333,,0.0358,,0.133,,0.0299,0.1031,0.0972,0.9881,,,0.12,0.1034,0.3333,,0.0356,,0.13,,0.0289,,block of flats,0.1066,Panel,No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n286268,0,Cash loans,F,N,N,0,112500.0,1575000.0,41548.5,1575000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.00702,-21622,-5978,-1594.0,-4431,,1,1,1,1,1,0,Core staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,School,,0.5977017718118923,0.3740208032583212,0.0928,0.0849,0.993,0.9048,,0.2,0.1724,0.3333,0.375,,0.0756,,,,0.0945,0.0881,0.993,0.9085,,0.2014,0.1724,0.3333,0.375,,0.0826,,,,0.0937,0.0849,0.993,0.9061,,0.2,0.1724,0.3333,0.375,,0.077,,,,reg oper spec account,block of flats,0.1046,Panel,No,0.0,0.0,0.0,0.0,-2317.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n214130,0,Cash loans,F,Y,Y,0,90000.0,225000.0,24363.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-11275,-1051,-1678.0,-2602,7.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.2906355885216753,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-391.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n441015,0,Cash loans,F,N,Y,0,103500.0,255960.0,16663.5,202500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-19278,-6351,-2774.0,-2807,,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 1,,0.6305920133073026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-419.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n236231,0,Cash loans,F,N,Y,1,99000.0,592560.0,35937.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-16093,-1913,-5335.0,-3694,,1,1,0,1,0,0,Sales staff,3.0,3,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,,0.30115896519935303,0.19633396621345675,0.1196,0.1022,0.9771,0.6872,0.0478,0.0,0.2759,0.1667,0.2083,0.0649,0.0941,0.1016,0.0154,0.0239,0.1218,0.1061,0.9772,0.6994,0.0483,0.0,0.2759,0.1667,0.2083,0.0664,0.1028,0.1058,0.0156,0.0253,0.1207,0.1022,0.9771,0.6914,0.0481,0.0,0.2759,0.1667,0.2083,0.066,0.0958,0.1034,0.0155,0.0244,reg oper account,block of flats,0.1112,Panel,No,3.0,0.0,3.0,0.0,-274.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n328286,0,Cash loans,F,Y,N,0,180000.0,269550.0,18234.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003122,-22865,-4493,-4910.0,-4167,26.0,1,1,1,1,0,0,,2.0,3,3,FRIDAY,13,0,0,0,0,1,1,Restaurant,,0.25720467948669073,0.1206410299309233,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3091.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n318570,0,Cash loans,M,N,Y,0,162000.0,1129500.0,31059.0,1129500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-17570,-1045,-9472.0,-1099,,1,1,0,1,1,0,Drivers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,,0.4109175876659965,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-26.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n409047,0,Cash loans,F,Y,Y,1,81000.0,538389.0,41796.0,477000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.018634,-15305,-5354,-12164.0,-124,65.0,1,1,0,1,1,0,Sales staff,2.0,2,2,MONDAY,12,0,0,0,0,1,1,Self-employed,0.324035865020113,0.4862888808658189,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1193.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n427293,0,Cash loans,F,N,Y,0,90000.0,1125000.0,32895.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.011657,-19653,-2698,-393.0,-3189,,1,1,1,1,1,0,Security staff,2.0,1,1,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.7876211786530559,0.7445406323797391,0.5190973382084597,0.033,0.0384,0.9752,0.66,0.0135,0.0,0.069,0.125,0.1667,0.0376,0.0269,0.0251,0.0,0.0,0.0336,0.0399,0.9752,0.6733,0.0136,0.0,0.069,0.125,0.1667,0.0385,0.0294,0.0261,0.0,0.0,0.0333,0.0384,0.9752,0.6645,0.0135,0.0,0.069,0.125,0.1667,0.0382,0.0274,0.0255,0.0,0.0,reg oper account,block of flats,0.0271,Block,No,1.0,0.0,1.0,0.0,-952.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n340413,0,Revolving loans,F,Y,Y,1,99000.0,270000.0,13500.0,270000.0,Family,Working,Higher education,Married,House / apartment,0.019101,-10953,-2039,-5085.0,-1382,1.0,1,1,1,1,0,0,Sales staff,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Trade: type 3,,0.69083223184818,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-369.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n369404,0,Cash loans,F,N,N,0,158040.0,755190.0,33264.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018801,-20953,365243,-2757.0,-4214,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,0.7838681877746769,0.5070755740697709,0.646329897706246,0.1216,0.0126,0.9955,0.9388,0.0487,0.08,0.0345,0.625,0.6667,0.0448,0.0874,0.1301,0.0541,0.0928,0.1239,0.0131,0.9955,0.9412,0.0491,0.0806,0.0345,0.625,0.6667,0.0458,0.0955,0.1355,0.0545,0.0982,0.1228,0.0126,0.9955,0.9396,0.049,0.08,0.0345,0.625,0.6667,0.0456,0.0889,0.1324,0.0543,0.0947,reg oper account,block of flats,0.1491,Monolithic,No,0.0,0.0,0.0,0.0,-656.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n432632,0,Cash loans,M,Y,N,0,112500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Higher education,Single / not married,Rented apartment,0.00733,-9937,-621,-4136.0,-2476,10.0,1,1,0,1,0,0,Waiters/barmen staff,1.0,2,2,WEDNESDAY,12,0,0,0,1,1,0,Business Entity Type 3,,0.5781770198608477,0.41885428862332175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-785.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n159022,0,Cash loans,F,N,Y,0,157500.0,1762110.0,48586.5,1575000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-13700,-2244,-5040.0,-1348,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Other,0.3657780896387272,0.7021062713791384,0.3506958432829587,0.0825,0.0785,0.9742,0.6464,0.0081,0.0,0.1379,0.1667,0.2083,0.0706,0.0672,0.0716,0.0,0.0,0.084,0.0814,0.9742,0.6602,0.0081,0.0,0.1379,0.1667,0.2083,0.0722,0.0735,0.0746,0.0,0.0,0.0833,0.0785,0.9742,0.6511,0.0081,0.0,0.1379,0.1667,0.2083,0.0718,0.0684,0.0729,0.0,0.0,reg oper account,block of flats,0.0563,Panel,No,1.0,1.0,1.0,1.0,-1032.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n205723,0,Cash loans,F,Y,N,1,112500.0,1206954.0,35419.5,945000.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.009549,-16040,-4610,-2342.0,-2345,22.0,1,1,0,1,0,0,Cooking staff,3.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,,0.2135146020258022,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1122.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n119740,0,Cash loans,M,N,N,0,135000.0,260640.0,31059.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.009549,-12650,-1452,-11814.0,-2254,,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.5754386905123761,,0.0773,,0.9836,,,0.0,0.1724,0.1667,,,,0.0411,,0.0,0.0788,,0.9836,,,0.0,0.1724,0.1667,,,,0.0428,,0.0,0.0781,,0.9836,,,0.0,0.1724,0.1667,,,,0.0418,,0.0,,block of flats,0.0547,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1422.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n271301,0,Revolving loans,M,Y,N,1,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.001417,-21747,-7200,-14095.0,-4534,14.0,1,1,0,1,0,0,,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Government,,0.21908578194112024,0.42589289800515295,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-1695.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n404352,0,Cash loans,F,Y,Y,0,45000.0,808650.0,23773.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018029,-16536,-1200,-4310.0,-86,22.0,1,1,0,1,0,0,Medicine staff,2.0,3,3,WEDNESDAY,5,0,1,1,0,1,1,Other,,0.31530848063595945,0.6144143775673561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1171.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n236218,0,Cash loans,F,Y,Y,0,247500.0,1006920.0,44352.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16786,-3619,-10852.0,-343,2.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Services,,0.6892326299457957,0.33285056416487313,0.0928,0.0,0.9767,0.6804,0.0,,,0.1667,0.2083,0.0,0.0756,0.083,0.0,0.0,0.0945,0.0,0.9767,0.6929,0.0,,,0.1667,0.2083,0.0,0.0826,0.0865,0.0,0.0,0.0937,0.0,0.9767,0.6847,0.0,,,0.1667,0.2083,0.0,0.077,0.0845,0.0,0.0,reg oper account,block of flats,0.0653,Panel,No,0.0,0.0,0.0,0.0,-2091.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n445889,0,Cash loans,F,N,Y,0,157500.0,254700.0,25321.5,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.008575,-25170,365243,-2254.0,-3924,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.441302382514606,0.19747451156854226,0.0278,0.0583,0.9965,,,0.0,0.1034,0.0833,,0.0112,,0.0266,,0.0,0.0284,0.0605,0.9965,,,0.0,0.1034,0.0833,,0.0115,,0.0277,,0.0,0.0281,0.0583,0.9965,,,0.0,0.1034,0.0833,,0.0114,,0.0271,,0.0,,block of flats,0.0209,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-325.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n343925,0,Cash loans,M,N,N,0,135000.0,781920.0,23706.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.020246,-8449,-867,-817.0,-881,,1,1,0,1,0,0,Laborers,1.0,3,3,FRIDAY,10,0,0,0,0,0,0,Housing,0.28420459290152633,0.2551421038574631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-760.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n174217,0,Revolving loans,F,Y,N,1,81000.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-13427,-631,-7445.0,-5359,9.0,1,1,1,1,0,0,Core staff,3.0,2,2,SATURDAY,14,0,0,0,0,0,0,Bank,0.5208387293150393,0.5099448549116228,0.4866531565147181,0.0866,0.0581,0.9856,0.7959999999999999,0.0132,0.0,0.1724,0.1667,0.1042,0.0634,0.0706,0.0896,0.0,0.0,0.0872,0.0,0.9841,0.7844,0.0114,0.0,0.1379,0.1667,0.0,0.057,0.0762,0.0904,0.0,0.0,0.0874,0.0581,0.9856,0.7987,0.0133,0.0,0.1724,0.1667,0.1042,0.0645,0.0718,0.0912,0.0,0.0,reg oper spec account,block of flats,0.0764,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-672.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n414077,0,Cash loans,M,Y,Y,1,180000.0,1762110.0,48586.5,1575000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13323,-5917,-1362.0,-3936,14.0,1,1,0,1,0,0,,3.0,2,2,MONDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.6587182690802599,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2220.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n333883,1,Cash loans,F,N,Y,0,103500.0,521280.0,27423.0,450000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.031329,-12351,-1443,-11769.0,-699,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Government,,0.6856146308860278,0.6161216908872079,0.1278,0.1886,0.9722,0.6192,0.0248,0.0,0.2414,0.1667,0.2083,0.1086,0.1042,0.1396,0.0,0.1535,0.1303,0.1958,0.9722,0.6341,0.0251,0.0,0.2414,0.1667,0.2083,0.1111,0.1139,0.1455,0.0,0.1626,0.1291,0.1886,0.9722,0.6243,0.025,0.0,0.2414,0.1667,0.2083,0.1105,0.106,0.1421,0.0,0.1568,reg oper account,block of flats,0.1234,Block,No,4.0,1.0,4.0,0.0,-161.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n342431,0,Cash loans,F,N,Y,0,135000.0,709879.5,31396.5,634500.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.009657,-18382,-1264,-17.0,-1932,,1,1,0,1,0,1,Private service staff,2.0,2,2,MONDAY,16,0,1,1,0,0,0,Self-employed,0.5776196862632432,0.5948116892032305,0.4650692149562261,0.0948,0.0809,0.9940000000000001,0.9184,0.0294,0.0,0.2069,0.1667,0.2083,0.0325,0.0756,0.0848,0.0077,0.0375,0.0966,0.084,0.9940000000000001,0.9216,0.0297,0.0,0.2069,0.1667,0.2083,0.0332,0.0826,0.0884,0.0078,0.0396,0.0958,0.0809,0.9940000000000001,0.9195,0.0296,0.0,0.2069,0.1667,0.2083,0.0331,0.077,0.0863,0.0078,0.0382,org spec account,block of flats,0.0828,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n419407,1,Cash loans,M,N,Y,0,202500.0,948730.5,31482.0,819000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-18037,-4164,-8581.0,-1539,,1,1,0,1,1,0,Security staff,2.0,2,2,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.6621608587182319,,0.1866,,0.9876,,,0.2,0.1724,0.3333,,,,0.1923,,0.0,0.1901,,0.9876,,,0.2014,0.1724,0.3333,,,,0.2003,,0.0,0.1884,,0.9876,,,0.2,0.1724,0.3333,,,,0.1957,,0.0,,block of flats,0.1512,Panel,No,13.0,0.0,13.0,0.0,-1586.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n438414,0,Cash loans,F,Y,Y,0,225000.0,969579.0,32175.0,837000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.003813,-22306,365243,-13137.0,-4857,26.0,1,0,0,1,0,1,,1.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,0.7519654779780409,0.7788898305526355,0.7981372313187245,0.1216,0.1322,0.9816,0.7484,0.2054,0.0,0.3103,0.1667,0.0417,0.0395,0.0983,0.1111,0.0,0.01,0.1239,0.1372,0.9816,0.7583,0.2072,0.0,0.3103,0.1667,0.0417,0.0404,0.1074,0.1157,0.0,0.0106,0.1228,0.1322,0.9816,0.7518,0.2067,0.0,0.3103,0.1667,0.0417,0.0402,0.1,0.1131,0.0,0.0102,reg oper spec account,block of flats,0.0977,Panel,No,2.0,1.0,2.0,1.0,-1714.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n226039,0,Cash loans,F,N,N,2,121500.0,450000.0,21109.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-12701,-1712,-6671.0,-3969,,1,1,1,1,1,0,Core staff,4.0,1,1,SUNDAY,14,0,0,0,0,0,0,Kindergarten,,0.7336071210238672,0.1385128770585923,,,0.9762,,,,0.1379,0.1667,,,,,,,,,0.9762,,,,0.1379,0.1667,,,,,,,,,0.9762,,,,0.1379,0.1667,,,,,,,,block of flats,0.0495,,No,1.0,0.0,1.0,0.0,-1011.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n380633,0,Cash loans,M,Y,N,3,225000.0,253737.0,16519.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-13634,-6527,-1227.0,-4410,1.0,1,1,0,1,1,0,Managers,5.0,2,2,THURSDAY,18,0,0,0,0,0,0,Self-employed,,0.7373127669966824,0.4686596550493113,0.067,0.0586,0.9776,,,0.0,0.1379,0.1667,,0.0713,,0.0502,,0.0,0.0683,0.0608,0.9777,,,0.0,0.1379,0.1667,,0.0729,,0.0523,,0.0,0.0677,0.0586,0.9776,,,0.0,0.1379,0.1667,,0.0725,,0.0511,,0.0,,block of flats,0.0395,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-2992.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n299005,1,Cash loans,F,Y,N,0,180000.0,1128442.5,60115.5,1066500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-23131,-6136,-10887.0,-4891,1.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.9093313037728948,0.7006085225796337,0.7503751495159068,0.132,0.0701,0.9876,0.83,0.0398,0.16,0.1379,0.3333,0.375,0.0392,0.1067,0.12,0.0039,0.0558,0.1345,0.0727,0.9876,0.8367,0.0402,0.1611,0.1379,0.3333,0.375,0.0401,0.1166,0.125,0.0039,0.0591,0.1332,0.0701,0.9876,0.8323,0.0401,0.16,0.1379,0.3333,0.375,0.0399,0.1086,0.1222,0.0039,0.057,reg oper account,block of flats,0.1279,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n270400,0,Cash loans,M,Y,Y,1,184500.0,243000.0,12537.0,243000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-16142,-456,-6191.0,-4338,17.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,THURSDAY,7,0,0,0,0,0,0,Transport: type 4,0.06690779386253272,0.30625951772565096,0.30620229831350426,0.8979,0.6162,0.9866,0.8164,0.3824,0.88,0.7586,0.375,0.0417,0.3806,,0.8888,,0.0608,0.9149,0.6395,0.9866,0.8236,0.3858,0.8862,0.7586,0.375,0.0417,0.3893,,0.9259999999999999,,0.0644,0.9066,0.6162,0.9866,0.8189,0.3848,0.88,0.7586,0.375,0.0417,0.3872,,0.9048,,0.0621,reg oper spec account,block of flats,0.9214,Panel,No,0.0,0.0,0.0,0.0,-758.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n111244,0,Cash loans,M,Y,N,2,211500.0,521280.0,35262.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-13162,-3588,-2637.0,-4819,7.0,1,1,1,1,0,0,Laborers,4.0,2,2,FRIDAY,19,0,0,0,0,0,0,Industry: type 5,,0.3748733860641769,0.8406665596573005,0.0649,0.0474,0.9737,0.6396,0.0018,0.0,0.1379,0.125,0.0417,0.0178,0.0521,0.049,0.0039,0.0051,0.0662,0.0492,0.9737,0.6537,0.0018,0.0,0.1379,0.125,0.0417,0.0182,0.0569,0.051,0.0039,0.0054,0.0656,0.0474,0.9737,0.6444,0.0018,0.0,0.1379,0.125,0.0417,0.0181,0.053,0.0499,0.0039,0.0052,reg oper account,block of flats,0.0406,Others,No,6.0,0.0,6.0,0.0,-2547.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n289827,0,Cash loans,F,N,Y,0,211500.0,508495.5,24592.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-14233,-3417,-6990.0,-4413,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,0.4786970316215246,0.7125938308557312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-657.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n281820,0,Cash loans,M,N,Y,0,99000.0,260640.0,31059.0,225000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.035792000000000004,-11891,-917,-11.0,-564,,1,1,0,1,0,1,Core staff,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Government,0.2069079636119532,0.07887006268328314,0.6971469077844458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n306429,0,Cash loans,M,Y,N,1,135000.0,472500.0,22860.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-15468,-3184,-2597.0,-4489,13.0,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,8,0,0,0,0,0,0,Industry: type 1,,0.6472829217022493,0.5971924268337128,0.066,0.0643,0.9781,0.7008,0.0077,0.0,0.1379,0.1667,0.2083,0.098,0.0538,0.0599,0.0,0.0223,0.0672,0.0668,0.9782,0.7125,0.0078,0.0,0.1379,0.1667,0.2083,0.1003,0.0588,0.0624,0.0,0.0236,0.0666,0.0643,0.9781,0.7048,0.0078,0.0,0.1379,0.1667,0.2083,0.0998,0.0547,0.061,0.0,0.0227,reg oper account,block of flats,0.0562,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n431952,0,Cash loans,M,Y,Y,0,270000.0,1125000.0,44617.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006207,-14512,-2467,-4282.0,-4062,4.0,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,13,0,0,0,0,0,0,Self-employed,,0.6802260599024328,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1837.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n267706,0,Cash loans,F,N,Y,0,135000.0,573408.0,25389.0,495000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.00702,-18529,-628,-5246.0,-2070,,1,1,1,1,0,0,Secretaries,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Government,0.7416657858009943,0.7160477299578915,0.5495965024956946,0.0907,0.1015,0.9836,0.7756,0.0112,,0.2069,0.1667,0.2083,0.0028,0.0731,0.0583,0.0039,0.0077,0.0924,0.1053,0.9836,0.7844,0.0113,,0.2069,0.1667,0.2083,0.0028,0.0799,0.0608,0.0039,0.0081,0.0916,0.1015,0.9836,0.7786,0.0112,,0.2069,0.1667,0.2083,0.0028,0.0744,0.0594,0.0039,0.0078,reg oper spec account,block of flats,0.075,Panel,No,0.0,0.0,0.0,0.0,-1906.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n449038,0,Cash loans,F,N,Y,2,135000.0,612612.0,29601.0,495000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-11951,-1800,-4320.0,-3086,,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,16,0,0,0,0,0,0,Postal,,0.4741664916138628,0.3001077565791181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,0.0,11.0,0.0,-3074.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n262980,0,Cash loans,F,N,Y,1,85500.0,628114.5,22689.0,477000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-13534,-3027,-7305.0,-3892,,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,,0.7239736329093801,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n164915,0,Cash loans,M,N,N,0,31500.0,104256.0,7924.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.006296,-19968,-13207,-13638.0,-3484,,1,1,1,1,1,0,,1.0,3,3,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 2,0.5861916494156647,0.4210481939317294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-488.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n138813,0,Cash loans,M,Y,N,0,135000.0,948582.0,33736.5,679500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.002042,-13136,-1087,-1971.0,-477,9.0,1,1,1,1,0,0,,2.0,3,3,SATURDAY,13,0,0,0,1,0,0,Agriculture,,0.5215554178427467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-243.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n231020,1,Cash loans,M,N,N,2,72000.0,360000.0,16911.0,360000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-11481,365243,-749.0,-4172,,1,0,0,1,0,0,,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,0.0917137460863136,0.34112290759888264,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1709.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n131087,0,Cash loans,F,Y,Y,0,157500.0,900000.0,26446.5,900000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.00702,-12549,-2257,-3153.0,-3657,2.0,1,1,1,1,1,0,Core staff,2.0,2,2,MONDAY,10,0,0,0,0,1,1,School,0.7627021797738787,0.5517351683810154,0.5190973382084597,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,10.0,1.0,-1040.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n380150,0,Cash loans,F,N,Y,0,76500.0,314100.0,12091.5,225000.0,Family,Working,Higher education,Married,Rented apartment,0.030755,-12913,-147,-1282.0,-3374,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Trade: type 3,0.435176117341805,0.3153887426599552,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-135.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n157983,0,Cash loans,F,Y,N,1,117000.0,675000.0,21906.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-14850,-1465,-1099.0,-5439,13.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,15,0,0,0,0,1,1,Construction,0.6400880548030455,0.7223362904604564,0.4206109640437848,0.1247,0.0643,0.9871,0.8232,0.0,0.08,0.1148,0.2221,0.2638,0.0643,0.1017,0.1301,0.0,0.0095,0.0819,0.0,0.9861,0.8171,0.0,0.0,0.069,0.1667,0.2083,0.0303,0.0716,0.0708,0.0,0.0008,0.0812,0.0,0.9876,0.8323,0.0,0.0,0.069,0.1667,0.2083,0.0474,0.0667,0.0693,0.0,0.0012,reg oper spec account,block of flats,0.2462,Panel,No,4.0,0.0,4.0,0.0,-1033.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n418778,0,Cash loans,M,N,Y,0,180000.0,450000.0,24412.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006305,-10319,-2699,-4558.0,-2847,,1,1,0,1,0,0,Laborers,2.0,3,3,SATURDAY,7,0,0,0,0,0,0,Self-employed,0.21712869097879967,0.3915088775795769,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1099.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n270796,0,Cash loans,F,N,Y,1,90000.0,285264.0,30082.5,252000.0,Other_B,Working,Secondary / secondary special,Married,House / apartment,0.018029,-11016,-477,-154.0,-2676,,1,1,0,1,0,1,Laborers,3.0,3,2,THURSDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.2526003855489068,0.3506713024584564,0.41184855592423975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-180.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n288037,0,Cash loans,M,N,Y,0,189000.0,573628.5,27724.5,463500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018634,-17303,-3018,-155.0,-857,,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,11,0,1,1,0,0,0,Security,0.3229506190606755,0.4739047882467746,0.470456116119975,0.0412,0.0,0.9791,,,0.0,0.069,0.1667,,0.0082,,0.0356,,0.0,0.042,0.0,0.9791,,,0.0,0.069,0.1667,,0.0084,,0.0371,,0.0,0.0416,0.0,0.9791,,,0.0,0.069,0.1667,,0.0083,,0.0362,,0.0,,block of flats,0.0316,\"Stone, brick\",No,6.0,2.0,6.0,2.0,-1673.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n249707,0,Revolving loans,F,Y,N,1,202500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.026392,-11467,-2172,-929.0,-2975,7.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.33297296138486543,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n214143,0,Cash loans,F,Y,Y,2,225000.0,269550.0,21739.5,225000.0,Unaccompanied,Working,Higher education,Married,Office apartment,0.020713,-10564,-904,-1678.0,-2576,13.0,1,1,1,1,1,0,Realty agents,4.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,0.3886696767343689,0.4756551951810529,0.15193454904964762,0.0928,0.0893,0.9836,0.7756,0.0,0.0,0.2069,0.1667,0.2083,0.0772,0.0748,0.0855,0.0039,0.0028,0.0945,0.0926,0.9836,0.7844,0.0,0.0,0.2069,0.1667,0.2083,0.079,0.0817,0.08900000000000001,0.0039,0.003,0.0937,0.0893,0.9836,0.7786,0.0,0.0,0.2069,0.1667,0.2083,0.0786,0.0761,0.087,0.0039,0.0028,reg oper account,block of flats,0.0768,Panel,No,3.0,0.0,3.0,0.0,-1329.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n421487,0,Cash loans,F,N,Y,0,49500.0,152820.0,15016.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.020713,-21678,365243,-13413.0,-4785,,1,0,0,1,0,0,,1.0,3,3,SATURDAY,9,0,0,0,0,0,0,XNA,0.7884067410323542,0.5659829129437353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2751.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n438704,0,Cash loans,M,Y,Y,1,180000.0,364896.0,19102.5,315000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018029,-13869,-930,-1125.0,-4595,14.0,1,1,0,1,0,0,,3.0,3,3,TUESDAY,7,0,0,0,1,1,0,Business Entity Type 3,,0.4899168283765479,0.813917469762627,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-591.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n350654,0,Cash loans,M,Y,N,0,315000.0,568197.0,24016.5,490500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-20508,-6285,-9698.0,-3841,16.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.4885398109527638,,0.1278,0.044000000000000004,0.9866,,,0.08,0.0345,0.625,,0.0635,,0.1201,,0.0143,0.1303,0.0456,0.9866,,,0.0806,0.0345,0.625,,0.065,,0.1252,,0.0151,0.1291,0.044000000000000004,0.9866,,,0.08,0.0345,0.625,,0.0646,,0.1223,,0.0145,,block of flats,0.1281,Panel,No,3.0,0.0,3.0,0.0,-2036.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n332129,0,Cash loans,F,N,N,0,315000.0,241618.5,26019.0,229500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-14764,-1712,-8853.0,-5353,,1,1,1,1,1,0,Accountants,2.0,1,1,MONDAY,9,0,0,0,0,0,0,Advertising,0.8186197825285728,0.7662670751640575,0.7623356180684377,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2766.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n205756,0,Cash loans,F,N,N,0,90000.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-15763,-1581,-5558.0,-4051,,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.6966582429163355,0.5298898341969072,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2323.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n400758,1,Cash loans,F,N,Y,1,81000.0,288873.0,13594.5,238500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.008473999999999999,-10700,-513,-63.0,-3353,,1,1,0,1,0,0,Cooking staff,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Restaurant,0.27818544040663645,0.16718546961281622,0.7421816117614419,0.0546,,0.9742,0.6464,,0.0,0.0345,0.0833,,0.0127,0.0445,0.0312,,0.0,0.0557,,0.9742,0.6602,,0.0,0.0345,0.0833,,0.013000000000000001,0.0487,0.0325,,0.0,0.0552,,0.9742,0.6511,,0.0,0.0345,0.0833,,0.0129,0.0453,0.0317,,0.0,reg oper account,block of flats,0.0245,\"Stone, brick\",Yes,1.0,1.0,1.0,1.0,-855.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n225518,0,Revolving loans,F,N,N,0,180000.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.031329,-14699,-420,-4630.0,-1738,,1,1,0,0,0,0,Managers,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Medicine,,0.6730589934255541,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,2.0,5.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n319511,0,Cash loans,F,N,Y,2,207000.0,355536.0,18612.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.022625,-17818,-3380,-2634.0,-1233,,1,1,0,1,1,0,Managers,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Self-employed,,0.5665898146203655,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-321.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n208513,0,Cash loans,M,N,Y,1,360000.0,849870.0,47583.0,787500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-19727,-5454,-945.0,-3240,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,TUESDAY,13,0,0,0,0,0,0,Industry: type 5,,0.7646063067910273,,0.2227,0.1603,0.9796,0.7212,0.1027,0.24,0.2069,0.3333,0.375,0.1067,0.1816,0.2207,0.0,0.0,0.2269,0.1664,0.9796,0.7321,0.1036,0.2417,0.2069,0.3333,0.375,0.1092,0.1983,0.2299,0.0,0.0,0.2248,0.1603,0.9796,0.7249,0.1033,0.24,0.2069,0.3333,0.375,0.1086,0.1847,0.2247,0.0,0.0,reg oper account,block of flats,0.2297,Panel,No,0.0,0.0,0.0,0.0,-2503.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n370499,0,Cash loans,F,Y,N,0,180000.0,808650.0,26217.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003540999999999999,-23706,365243,-6396.0,-2520,11.0,1,0,0,1,0,0,,2.0,1,1,TUESDAY,10,1,0,0,1,0,0,XNA,0.8452562494463686,0.6340034621575944,0.5762088360175724,0.0667,0.0682,0.9881,0.8368,0.0128,0.0,0.1493,0.1667,0.2083,0.0579,0.0543,0.0641,0.0,0.0,0.0473,0.049,0.9881,0.8432,0.0089,0.0,0.1724,0.1667,0.2083,0.0426,0.0413,0.0462,0.0,0.0,0.0625,0.0628,0.9881,0.8390000000000001,0.0118,0.0,0.1724,0.1667,0.2083,0.0608,0.0513,0.0602,0.0,0.0,reg oper account,block of flats,0.0387,Panel,No,4.0,0.0,4.0,0.0,-507.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n288978,0,Cash loans,F,N,N,2,301500.0,1309500.0,51934.5,1309500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.014519999999999996,-11312,-2128,-727.0,-1315,,1,1,0,1,0,0,Sales staff,4.0,2,2,THURSDAY,13,0,0,0,0,1,1,Self-employed,,0.5796323132306783,0.1510075350878296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-892.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n342990,0,Cash loans,F,N,Y,0,67500.0,90000.0,5152.5,90000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.026392,-23000,365243,-16913.0,-4397,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.5915346501656484,0.501075160239048,0.0784,,0.9737,,,0.0,0.1379,0.1667,,,,0.0654,,0.0,0.0798,,0.9737,,,0.0,0.1379,0.1667,,,,0.0682,,0.0,0.0791,,0.9737,,,0.0,0.1379,0.1667,,,,0.0666,,0.0,,block of flats,0.0515,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,0.0\n207901,1,Cash loans,F,Y,N,0,180000.0,1125000.0,60070.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.004849,-11658,-190,-5916.0,-2760,12.0,1,1,1,1,0,0,Managers,2.0,2,2,WEDNESDAY,16,0,0,0,0,1,1,Construction,0.16900922364917084,0.030948788163545293,0.19294222771695085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-599.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n362279,0,Cash loans,F,N,Y,0,180000.0,859500.0,38970.0,859500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-16254,-1878,-5928.0,-4144,,1,1,0,1,1,0,Sales staff,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,,0.6603779288342263,0.6925590674998008,0.1485,0.1116,0.9806,,,0.16,0.1379,0.3333,,,,0.1487,,0.0,0.1513,0.1158,0.9806,,,0.1611,0.1379,0.3333,,,,0.1549,,0.0,0.1499,0.1116,0.9806,,,0.16,0.1379,0.3333,,,,0.1513,,0.0,,,0.1537,Mixed,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n205576,0,Cash loans,F,N,N,0,112500.0,352044.0,16542.0,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.003818,-23023,365243,-12534.0,-4494,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,6,0,0,0,0,0,0,XNA,,0.3034651061911348,0.3001077565791181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-817.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n401937,0,Cash loans,M,Y,Y,0,360000.0,1451047.5,59229.0,1354500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-15167,-1177,-2062.0,-4866,13.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,SUNDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.5261240741520631,0.6910096844137753,0.4014074137749511,0.0515,,0.9866,,,0.0,0.1379,0.1667,,,,0.0517,,0.0,0.0525,,0.9866,,,0.0,0.1379,0.1667,,,,0.0539,,0.0,0.052000000000000005,,0.9866,,,0.0,0.1379,0.1667,,,,0.0527,,0.0,,block of flats,0.0545,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-660.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n376016,0,Cash loans,M,Y,N,0,247500.0,533668.5,27378.0,477000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.006852,-10642,-1826,-1504.0,-2739,16.0,1,1,0,1,0,0,Managers,1.0,3,3,SUNDAY,10,0,0,0,0,1,1,Self-employed,0.20729988279814465,0.0946283075950524,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-806.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n427359,0,Cash loans,F,N,Y,0,112500.0,161595.0,16686.0,139500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15646,-3740,-9817.0,-4220,,1,1,0,1,0,1,Sales staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Other,,0.7436120226873186,0.7776594425716818,0.0536,0.0137,0.9757,0.6668,0.005,0.0,0.1034,0.1667,0.1667,0.0134,0.0437,0.0431,0.0309,0.0085,0.0546,0.0142,0.9757,0.6798,0.0051,0.0,0.1034,0.1667,0.1667,0.0137,0.0478,0.0449,0.0311,0.009000000000000001,0.0541,0.0137,0.9757,0.6713,0.005,0.0,0.1034,0.1667,0.1667,0.0136,0.0445,0.0439,0.0311,0.0087,reg oper account,block of flats,0.0385,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2881.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n448830,0,Cash loans,M,N,Y,1,202500.0,439740.0,23985.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-9011,-207,-3090.0,-1694,,1,1,0,1,0,0,Drivers,3.0,2,2,FRIDAY,16,0,0,0,0,1,1,Business Entity Type 3,0.13039916100664106,0.5985217699908095,0.08730427071350706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1299.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,4.0,2.0\n256254,0,Cash loans,F,N,N,0,112500.0,174411.0,13909.5,144000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.002134,-10448,-535,-2922.0,-3109,,1,1,0,1,0,0,Sales staff,1.0,3,3,FRIDAY,10,0,0,0,1,1,0,Trade: type 7,,0.07752396360926643,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-208.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n101019,0,Cash loans,F,N,Y,0,382500.0,720000.0,21051.0,720000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-21493,-5582,-3104.0,-415,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SATURDAY,13,0,0,0,0,1,1,Transport: type 2,,0.6840003818264704,0.1245194708919845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1649.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n142216,0,Cash loans,F,N,N,1,128250.0,545040.0,25407.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-8776,-1739,-2227.0,-1062,,1,1,1,1,1,0,Sales staff,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.16025797276381756,0.1915258625230554,0.4722533429586386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1057.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n166710,0,Revolving loans,M,N,Y,0,405000.0,1350000.0,67500.0,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.022625,-11561,-186,-232.0,-237,,1,1,0,1,0,0,Waiters/barmen staff,1.0,2,2,THURSDAY,13,1,1,0,1,1,0,Business Entity Type 3,,0.2487296976618488,0.0005272652387098817,0.2423,,0.9906,,,0.24,0.2069,0.375,,,,0.2566,,,0.2468,,0.9906,,,0.2417,0.2069,0.375,,,,0.2673,,,0.2446,,0.9906,,,0.24,0.2069,0.375,,,,0.2612,,,,block of flats,0.2177,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n155977,0,Cash loans,F,Y,Y,2,135000.0,1288350.0,37800.0,1125000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-16487,-4612,-9005.0,-21,64.0,1,1,0,1,0,0,Accountants,4.0,2,2,THURSDAY,14,0,0,0,0,0,0,Government,0.606321182468756,0.6434392351918439,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,2.0,5.0,1.0,-1675.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,2.0,0.0,1.0\n315176,0,Cash loans,M,N,N,1,90000.0,594000.0,28705.5,594000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-11251,-1780,-1578.0,-3449,,1,1,0,1,0,0,Drivers,3.0,3,3,MONDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.4921083416997172,0.6058362647264226,0.033,0.0415,0.9737,0.6396,0.0179,0.0,0.069,0.125,0.0417,0.0416,0.0269,0.0255,0.0,0.0,0.0336,0.043,0.9737,0.6537,0.0181,0.0,0.069,0.125,0.0417,0.0426,0.0294,0.0265,0.0,0.0,0.0333,0.0415,0.9737,0.6444,0.018000000000000002,0.0,0.069,0.125,0.0417,0.0424,0.0274,0.0259,0.0,0.0,reg oper spec account,block of flats,0.0298,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1945.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n135092,0,Cash loans,M,Y,Y,0,225000.0,640080.0,29970.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018801,-14209,-897,-4186.0,-606,15.0,1,1,0,1,0,0,Drivers,1.0,2,2,FRIDAY,11,0,0,0,0,1,1,Self-employed,0.3210536165288167,0.5899750502233863,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1041.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n430807,0,Cash loans,F,N,Y,0,157500.0,253737.0,24849.0,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00963,-21099,365243,-3901.0,-4628,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,,0.6529879886064041,0.813917469762627,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-813.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n270384,0,Cash loans,F,N,Y,0,225000.0,1323000.0,38682.0,1323000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.04622,-15110,-3593,-9250.0,-4572,,1,1,0,1,0,1,Laborers,1.0,1,1,SATURDAY,20,0,0,0,0,0,0,Business Entity Type 3,0.6331601656598871,0.6227185485572462,0.6446794549585961,0.0928,0.0811,0.9806,,,0.0,0.1724,0.1667,,0.0181,,0.0597,,,0.0945,0.0842,0.9806,,,0.0,0.1724,0.1667,,0.0185,,0.0622,,,0.0937,0.0811,0.9806,,,0.0,0.1724,0.1667,,0.0184,,0.0608,,,,block of flats,0.0677,Panel,No,0.0,0.0,0.0,0.0,-1468.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n166073,0,Cash loans,F,N,Y,0,135000.0,1164667.5,37701.0,1017000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006629,-18436,-4610,-1950.0,-1970,,1,1,0,1,1,0,,2.0,2,2,SATURDAY,6,0,0,0,0,0,0,Kindergarten,,0.5012319325500654,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n381840,0,Cash loans,F,Y,Y,0,202500.0,432567.0,22779.0,328500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-10630,-670,-8329.0,-177,8.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,17,0,1,1,0,1,1,Military,0.21714521974709566,0.6058399031264623,,0.0876,0.0592,0.993,,,0.0,0.1148,0.1667,,0.0652,,0.0684,,0.0,0.0872,0.0523,0.9886,,,0.0,0.069,0.1667,,0.0511,,0.0622,,0.0,0.0864,0.0504,0.995,,,0.0,0.069,0.1667,,0.0658,,0.0611,,0.0,,block of flats,0.0531,Panel,No,0.0,0.0,0.0,0.0,-407.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n209512,0,Cash loans,M,Y,N,0,135000.0,675000.0,20466.0,675000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-23675,365243,-9705.0,-4288,17.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.4263289549938089,0.6144143775673561,0.1113,0.1135,0.9866,0.8164,0.0239,0.12,0.1034,0.3333,0.0417,0.0,0.0908,0.1272,0.0,0.0,0.1134,0.1177,0.9866,0.8236,0.0242,0.1208,0.1034,0.3333,0.0417,0.0,0.0992,0.1325,0.0,0.0,0.1124,0.1135,0.9866,0.8189,0.0241,0.12,0.1034,0.3333,0.0417,0.0,0.0923,0.1295,0.0,0.0,reg oper spec account,block of flats,0.1,Panel,No,9.0,0.0,9.0,0.0,-6.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n353188,0,Cash loans,M,N,N,0,225000.0,916470.0,26262.0,765000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.018634,-20664,-7849,-7883.0,-3117,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Other,,0.6152302429268314,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n168946,0,Cash loans,F,N,Y,0,270000.0,1436850.0,42142.5,1125000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-23007,365243,-13146.0,-4078,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.4227389083949984,0.5620604831738043,0.0619,0.0616,0.9737,0.6396,0.0048,0.0,0.1034,0.1667,0.2083,0.0627,0.0504,0.0504,0.0,0.0,0.063,0.0639,0.9737,0.6537,0.0048,0.0,0.1034,0.1667,0.2083,0.0641,0.0551,0.0525,0.0,0.0,0.0625,0.0616,0.9737,0.6444,0.0048,0.0,0.1034,0.1667,0.2083,0.0638,0.0513,0.0513,0.0,0.0,reg oper spec account,block of flats,0.0422,Block,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n165824,0,Cash loans,F,N,Y,0,180000.0,108000.0,8487.0,108000.0,Unaccompanied,State servant,Higher education,Separated,House / apartment,0.009334,-17460,-1619,-8425.0,-1010,,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,19,0,0,0,0,0,0,School,0.588109398915163,0.5682751954952989,0.6479768603302221,0.033,0.0214,0.9816,0.7348,,0.04,0.0345,0.3333,0.375,0.0257,,0.0373,,0.046,0.0336,0.0222,0.9816,0.7452,,0.0403,0.0345,0.3333,0.375,0.0263,,0.0389,,0.0487,0.0333,0.0214,0.9816,0.7383,,0.04,0.0345,0.3333,0.375,0.0262,,0.038,,0.047,,block of flats,0.0396,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1764.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n416965,0,Cash loans,M,N,Y,0,112500.0,284400.0,13257.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-14222,-4766,-5334.0,-4841,,1,1,1,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Housing,0.553217652752369,0.7286508089008152,0.4920600938649263,0.2227,0.1335,0.9836,0.7756,0.0581,0.24,0.2069,0.3333,0.375,0.099,0.1807,0.2436,0.0039,0.0,0.2269,0.1385,0.9836,0.7844,0.0587,0.2417,0.2069,0.3333,0.375,0.1013,0.1974,0.2538,0.0039,0.0,0.2248,0.1335,0.9836,0.7786,0.0585,0.24,0.2069,0.3333,0.375,0.1008,0.1838,0.248,0.0039,0.0,reg oper spec account,block of flats,0.2234,Panel,No,0.0,0.0,0.0,0.0,-233.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n224261,0,Cash loans,F,N,N,0,112500.0,477000.0,24358.5,477000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.028663,-24610,365243,-4425.0,-4640,,1,0,0,1,1,0,,1.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.4006466479846057,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-5.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n377072,0,Cash loans,M,Y,Y,0,112500.0,180000.0,14220.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-16789,-941,-9384.0,-331,23.0,1,1,1,1,1,0,Drivers,1.0,2,2,SATURDAY,12,0,0,0,0,1,1,Self-employed,,0.5792006661066271,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-702.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n167127,0,Cash loans,F,Y,Y,0,112500.0,1535553.0,40635.0,1372500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-21360,365243,-5910.0,-956,16.0,1,0,0,1,0,0,,2.0,3,3,SUNDAY,8,0,0,0,0,0,0,XNA,,0.41508975653222396,0.44539624156083396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-561.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n221443,0,Cash loans,F,N,N,0,99000.0,742500.0,21838.5,742500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-19254,-1962,-5766.0,-2487,,1,1,0,1,0,0,Laborers,2.0,1,1,SUNDAY,16,0,0,0,0,0,0,Business Entity Type 1,0.6436926458024533,0.4774662138478004,0.6785676886853644,0.0247,0.01,0.9742,0.6464,,0.0,0.1034,0.125,0.1667,,0.0185,0.0407,0.0077,,0.0252,0.0104,0.9742,0.6602,,0.0,0.1034,0.125,0.1667,,0.0202,0.0424,0.0078,,0.025,0.01,0.9742,0.6511,,0.0,0.1034,0.125,0.1667,,0.0188,0.0414,0.0078,,reg oper account,block of flats,0.0373,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1786.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n397111,0,Cash loans,F,Y,N,2,270000.0,675000.0,21775.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-13144,-682,-3198.0,-4803,6.0,1,1,1,1,1,0,,4.0,2,2,TUESDAY,13,0,0,0,0,0,0,Bank,,0.4465493205593707,0.5884877883422673,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-184.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n113813,1,Cash loans,M,Y,Y,0,360000.0,1546020.0,45333.0,1350000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.032561,-10837,-1228,-303.0,-3509,25.0,1,1,0,1,0,0,Sales staff,1.0,1,1,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6786686495409409,0.28371188263500075,1.0,0.1643,0.9990000000000001,0.9864,0.4387,0.8,0.3103,1.0,0.0833,0.2997,0.7002,1.0,0.8417,1.0,1.0,0.1705,0.9990000000000001,0.9869,0.4427,0.8056,0.3103,1.0,0.0833,0.3065,0.7649,1.0,0.8482,1.0,1.0,0.1643,0.9990000000000001,0.9866,0.4414,0.8,0.3103,1.0,0.0833,0.3049,0.7123,1.0,0.8463,1.0,reg oper spec account,block of flats,1.0,Mixed,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n372521,0,Cash loans,F,Y,Y,2,112500.0,677664.0,24471.0,585000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Co-op apartment,0.010147,-10611,-3489,-3216.0,-2928,15.0,1,1,0,1,0,0,Laborers,4.0,2,2,FRIDAY,11,0,0,0,0,0,0,Other,0.4800935078580082,0.477046133912824,0.511891801533151,0.0124,,0.9702,,,,0.069,0.0417,,,,0.01,,,0.0126,,0.9702,,,,0.069,0.0417,,,,0.0104,,,0.0125,,0.9702,,,,0.069,0.0417,,,,0.0102,,,,block of flats,0.0134,\"Stone, brick\",No,7.0,0.0,7.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n152372,0,Cash loans,M,Y,N,0,99000.0,970380.0,26815.5,810000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,With parents,0.035792000000000004,-9187,-193,-953.0,-1603,3.0,1,1,1,1,0,0,Sales staff,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.4440584941652498,0.6489237211169223,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-6.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n260761,0,Revolving loans,M,Y,Y,1,225000.0,630000.0,31500.0,630000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-15285,-2498,-1077.0,-4345,4.0,1,1,0,1,0,0,Laborers,3.0,1,1,WEDNESDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.5978801563177872,0.6770068459408171,0.3360615207658242,0.0557,,0.9732,0.6328,,,0.069,0.1667,0.2083,,,0.0757,,0.0338,0.0567,,0.9732,0.6472,,,0.069,0.1667,0.2083,,,0.0789,,0.0358,0.0562,,0.9732,0.6377,,,0.069,0.1667,0.2083,,,0.0771,,0.0345,reg oper account,block of flats,0.0669,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1268.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n255991,0,Cash loans,F,Y,N,1,144000.0,578979.0,22959.0,517500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-17910,-1053,-1366.0,-1457,3.0,1,1,1,1,0,0,High skill tech staff,3.0,3,3,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.7633574902334038,0.5920523486671772,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3040.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n270908,0,Cash loans,F,N,N,0,99000.0,450000.0,22018.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-23519,365243,-4956.0,-4123,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,XNA,,0.316982088953995,0.108924403541012,0.3715,0.815,0.993,,,0.16,0.1638,0.6667,,0.34299999999999997,,0.6093,,0.4613,0.1155,0.8457,0.993,,,0.0,0.0345,0.6667,,0.3508,,0.1864,,0.4883,0.3175,0.815,0.993,,,0.06,0.0862,0.6667,,0.349,,0.425,,0.4709,,block of flats,0.1515,Monolithic,No,8.0,0.0,8.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n205290,0,Cash loans,M,Y,Y,0,270000.0,755190.0,36459.0,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-14186,-936,-7788.0,-4015,24.0,1,1,0,1,0,0,High skill tech staff,2.0,3,3,FRIDAY,8,0,0,0,0,1,1,Business Entity Type 3,0.19192097270000047,0.18939163525147767,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n168567,0,Cash loans,F,N,N,0,67500.0,191880.0,18819.0,180000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.006852,-23996,365243,-1429.0,-4108,,1,0,0,1,0,0,,1.0,3,3,MONDAY,11,0,0,0,0,0,0,XNA,0.8678696598436257,0.02207519039076778,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,7.0,1.0,7.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n122604,0,Cash loans,F,N,Y,0,144900.0,1057266.0,44923.5,945000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.020246,-21763,365243,-12947.0,-5301,,1,0,0,1,0,1,,1.0,3,3,MONDAY,8,0,0,0,0,0,0,XNA,,0.7086682304751696,0.5884877883422673,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-494.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n401265,0,Cash loans,M,N,Y,0,315000.0,1418692.5,56263.5,1305000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018634,-11458,-1322,-7229.0,-3453,,1,1,1,1,0,1,Sales staff,2.0,2,2,THURSDAY,16,1,1,1,1,1,1,Business Entity Type 3,,0.2858978721410488,0.0485913394924409,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,0.0,11.0,0.0,-595.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n157519,0,Cash loans,F,N,Y,0,67500.0,254700.0,14751.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-14869,-448,-7245.0,-4619,,1,1,1,1,0,0,Sales staff,2.0,2,2,MONDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.02021174019590861,0.6178261467332483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-2060.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n143945,0,Cash loans,F,Y,N,2,171000.0,675000.0,38880.0,675000.0,Family,Working,Higher education,Married,House / apartment,0.028663,-12287,-5633,-2373.0,-4798,19.0,1,1,0,1,0,0,,4.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.28245636354543663,0.5500146958387322,0.6246146584503397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1433.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n129234,0,Cash loans,F,Y,Y,0,90000.0,143910.0,15628.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14072,-4457,-1546.0,-1618,20.0,1,1,1,1,1,0,Accountants,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Other,0.512952015681613,0.35856849883534514,0.5262949398096192,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1808.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n326897,0,Cash loans,F,N,Y,0,180000.0,239850.0,23494.5,225000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.025164,-21170,-2870,-9019.0,-3901,,1,1,0,1,0,0,,1.0,2,2,FRIDAY,9,0,0,0,0,1,1,Other,,0.5831129501526859,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1666.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n289931,0,Cash loans,M,N,Y,3,135000.0,254799.0,20259.0,193500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003818,-11026,-2768,-149.0,-3576,,1,1,0,1,0,1,Waiters/barmen staff,5.0,2,2,THURSDAY,7,0,0,0,0,0,0,Business Entity Type 1,0.2143067143636765,0.1220839541309492,0.6545292802242897,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-556.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n134424,0,Cash loans,M,Y,Y,0,245250.0,1057500.0,35077.5,1057500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-14209,-2134,-600.0,-2146,5.0,1,1,0,1,1,0,Drivers,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Transport: type 4,,0.2943099431269529,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-718.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n234797,0,Cash loans,F,N,Y,0,157500.0,239850.0,23364.0,225000.0,Unaccompanied,Pensioner,Lower secondary,Widow,House / apartment,0.019101,-24723,365243,-10174.0,-4113,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,XNA,,0.6551106549131976,0.4902575124990026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1477.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n381360,0,Cash loans,F,N,Y,0,157500.0,1230219.0,44316.0,1062000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-10844,-206,-2550.0,-3461,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,18,0,0,0,0,1,1,Business Entity Type 3,0.7514160579979298,0.5013795377233887,,0.0093,0.0985,0.9796,,,0.0,0.2069,0.1667,,0.0,,0.0866,,0.0035,0.0095,0.1022,0.9796,,,0.0,0.2069,0.1667,,0.0,,0.0902,,0.0037,0.0094,0.0985,0.9796,,,0.0,0.2069,0.1667,,0.0,,0.0881,,0.0035,,block of flats,0.076,Panel,No,0.0,0.0,0.0,0.0,-1260.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n236867,0,Cash loans,F,N,N,1,78750.0,143910.0,14364.0,135000.0,Family,Working,Higher education,Married,House / apartment,0.00496,-15503,-111,-2717.0,-2686,,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.6558937015268104,0.2795125808752404,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-969.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n279893,0,Cash loans,F,N,Y,2,78750.0,450000.0,21109.5,450000.0,Family,State servant,Higher education,Married,House / apartment,0.02461,-11370,-2552,-155.0,-2477,,1,1,1,1,1,0,,4.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Other,0.7844516723570241,0.610696101456263,0.3296550543128238,0.0619,0.0318,0.9781,,,0.0,0.1379,0.1667,,0.0363,,0.0614,,0.0,0.063,0.033,0.9782,,,0.0,0.1379,0.1667,,0.0371,,0.0639,,0.0,0.0625,0.0318,0.9781,,,0.0,0.1379,0.1667,,0.0369,,0.0625,,0.0,,block of flats,0.0522,Panel,No,1.0,0.0,1.0,0.0,-2100.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n203560,0,Cash loans,M,N,Y,0,261000.0,909000.0,26707.5,909000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-21779,-4227,-9142.0,-4772,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Other,,0.5677064199962363,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1323.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n140758,0,Cash loans,F,N,Y,0,112500.0,728460.0,40806.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-18403,-870,-52.0,-1957,,1,1,1,1,1,0,Sales staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Self-employed,,0.768768191405841,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-429.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n231770,0,Revolving loans,F,Y,Y,0,90000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00733,-10537,-1563,-969.0,-539,26.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.31124855345049585,0.6195277080511546,0.0082,0.0,0.9722,0.6192,0.0011,0.0,0.0345,0.0417,0.0833,0.0062,0.0067,0.0076,0.0,0.0,0.0084,0.0,0.9722,0.6341,0.0012,0.0,0.0345,0.0417,0.0833,0.0063,0.0073,0.0079,0.0,0.0,0.0083,0.0,0.9722,0.6243,0.0011,0.0,0.0345,0.0417,0.0833,0.0063,0.0068,0.0077,0.0,0.0,reg oper account,block of flats,0.0066,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-422.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n123585,0,Cash loans,F,N,Y,0,135000.0,592560.0,30384.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-15199,-1132,-4070.0,-4934,,1,1,0,1,0,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Other,0.3159022232483132,0.4691967175172884,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-633.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n439803,0,Cash loans,M,N,N,0,315000.0,450000.0,27193.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-17470,-2441,-1059.0,-1031,,1,1,1,1,0,0,,2.0,3,2,THURSDAY,8,0,0,0,0,1,1,Business Entity Type 3,0.5881158226634113,0.571700081622158,0.6413682574954046,0.0619,0.0628,0.9796,0.7212,0.0278,0.0,0.1379,0.1667,0.2083,0.0372,0.0504,0.0538,0.0,0.0,0.063,0.0652,0.9796,0.7321,0.0281,0.0,0.1379,0.1667,0.2083,0.0381,0.0551,0.0561,0.0,0.0,0.0625,0.0628,0.9796,0.7249,0.027999999999999997,0.0,0.1379,0.1667,0.2083,0.0379,0.0513,0.0548,0.0,0.0,reg oper account,block of flats,0.0575,Panel,No,0.0,0.0,0.0,0.0,-916.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n122480,0,Cash loans,F,N,Y,0,54000.0,108801.0,7699.5,99000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.020713,-20131,365243,-8245.0,-3479,,1,0,0,1,1,0,,1.0,3,3,THURSDAY,12,0,0,0,0,0,0,XNA,0.5546853991862429,0.5071247662084978,0.7992967832109371,0.0928,0.0861,0.9866,0.8164,0.0121,0.0,0.2069,0.1667,0.2083,0.0744,0.0756,0.0881,0.0,0.0,0.0945,0.0893,0.9866,0.8236,0.0122,0.0,0.2069,0.1667,0.2083,0.0761,0.0826,0.0918,0.0,0.0,0.0937,0.0861,0.9866,0.8189,0.0122,0.0,0.2069,0.1667,0.2083,0.0757,0.077,0.0897,0.0,0.0,reg oper account,block of flats,0.0759,Panel,No,3.0,0.0,3.0,0.0,-2427.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n447589,0,Cash loans,M,N,Y,2,112500.0,257391.0,18432.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-14098,-1000,-263.0,-4583,,1,1,0,1,0,0,Security staff,4.0,2,2,WEDNESDAY,15,0,1,1,0,0,0,Security,0.2022376756708326,0.4678408577479515,0.8027454758994178,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-131.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n449094,0,Cash loans,F,N,Y,2,81000.0,679500.0,19998.0,679500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-14346,-2739,-3872.0,-4119,,1,1,0,1,1,0,Laborers,4.0,3,3,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.3927285766897967,0.3774042489507649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-136.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n383489,0,Cash loans,F,N,N,0,202500.0,305221.5,11506.5,252000.0,Unaccompanied,State servant,Secondary / secondary special,Widow,Municipal apartment,0.003818,-22555,-2234,-2959.0,-1024,,1,1,1,1,1,1,,1.0,2,2,THURSDAY,6,0,0,0,0,0,0,Government,,0.6611377029482235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1272.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n271578,0,Cash loans,F,N,N,0,81000.0,253737.0,13018.5,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.025164,-23827,365243,-3048.0,-4402,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.062346314822114925,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n373117,0,Revolving loans,M,Y,Y,0,180000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.0105,-15085,-385,-7534.0,-3928,0.0,1,1,0,1,0,0,Managers,1.0,3,3,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.4729998421916974,0.39449540531239935,0.0392,0.0812,0.9702,0.5920000000000001,0.0087,0.0,0.1034,0.125,0.1667,0.0235,0.0286,0.0487,0.0154,0.0491,0.0399,0.0843,0.9702,0.608,0.0088,0.0,0.1034,0.125,0.1667,0.024,0.0312,0.0508,0.0156,0.052000000000000005,0.0396,0.0812,0.9702,0.5975,0.0088,0.0,0.1034,0.125,0.1667,0.0239,0.0291,0.0496,0.0155,0.0501,reg oper account,block of flats,0.054000000000000006,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-254.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n287905,0,Cash loans,F,N,Y,0,67500.0,137538.0,13527.0,121500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-23254,365243,-12388.0,-5183,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.0043250080374837106,0.7886807751817684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-257.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n393556,0,Cash loans,F,Y,Y,1,180000.0,865179.0,51651.0,792000.0,Family,Working,Incomplete higher,Married,House / apartment,0.020246,-16477,-2638,-3323.0,-17,2.0,1,1,0,1,0,0,Sales staff,3.0,3,3,MONDAY,8,0,0,0,0,1,1,Business Entity Type 3,,0.298391075407022,0.8128226070575616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1848.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n215299,0,Cash loans,F,N,Y,0,157500.0,927000.0,51894.0,927000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-17858,-381,-3365.0,-1386,,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Restaurant,,0.44392428808809625,0.2608559142068693,0.1031,0.1945,0.9826,,,,0.3448,0.1667,,0.0335,,0.1319,,0.0,0.105,0.2019,0.9826,,,,0.3448,0.1667,,0.0342,,0.1374,,0.0,0.1041,0.1945,0.9826,,,,0.3448,0.1667,,0.034,,0.1342,,0.0,,block of flats,0.1194,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-452.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n102785,0,Cash loans,F,N,N,1,202500.0,808650.0,22365.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Co-op apartment,0.015221,-13631,-1748,-861.0,-1974,,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 2,0.6581008322159784,0.6654695260765288,0.13259749523614614,0.1598,0.1113,0.9836,0.8164,,0.1064,0.1034,0.2775,0.0417,0.0685,,0.1001,,0.0056,0.0725,0.0722,0.9727,0.8171,,0.0,0.0345,0.3333,0.0417,0.0359,,0.0014,,0.0,0.1478,0.0874,0.9866,0.8189,,0.04,0.0345,0.3333,0.0417,0.0608,,0.0731,,0.0015,reg oper spec account,block of flats,0.2451,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1037.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n293313,1,Cash loans,F,N,N,0,121500.0,225000.0,10944.0,225000.0,Unaccompanied,Working,Incomplete higher,Married,With parents,0.003122,-9361,-413,-2040.0,-2032,,1,1,0,1,0,0,Core staff,2.0,3,3,WEDNESDAY,15,0,0,0,0,0,0,Kindergarten,,0.44539846760214064,0.1873887653772306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,0.0,-151.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n136675,0,Cash loans,F,N,Y,0,117000.0,787131.0,42066.0,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-16600,-2269,-6543.0,-141,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.5926912659941826,0.5406544504453575,,,0.9657,,,,0.1034,0.1667,,0.0062,,0.0078,,,,,0.9657,,,,0.1034,0.1667,,0.0063,,0.0081,,,,,0.9657,,,,0.1034,0.1667,,0.0063,,0.0079,,,,block of flats,0.0061,,Yes,0.0,0.0,0.0,0.0,-2823.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n266592,0,Cash loans,F,Y,Y,0,112500.0,508495.5,21541.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-19578,-162,-5820.0,-3122,29.0,1,1,1,1,1,0,Sales staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Self-employed,0.8284449974548582,0.20831770271903874,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-7.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n122662,0,Cash loans,M,Y,N,0,76500.0,454500.0,13419.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.015221,-20022,-1038,-2582.0,-3542,13.0,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,11,0,0,0,1,1,1,Security,,0.6547844907017011,0.6363761710860439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1505.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n109724,0,Cash loans,F,N,N,0,90000.0,454500.0,14661.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.011657,-21001,-1192,-9646.0,-4165,,1,1,1,1,1,0,Security staff,2.0,1,1,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.7041785433051622,0.3077366963789207,0.1031,0.1431,0.9871,0.8232,0.1247,0.0,0.2759,0.1667,0.2083,0.0937,0.0841,0.0998,0.0,0.0,0.105,0.1485,0.9871,0.8301,0.1258,0.0,0.2759,0.1667,0.2083,0.0958,0.0918,0.10400000000000001,0.0,0.0,0.1041,0.1431,0.9871,0.8256,0.1255,0.0,0.2759,0.1667,0.2083,0.0953,0.0855,0.1016,0.0,0.0,reg oper account,block of flats,0.1467,Block,No,7.0,1.0,7.0,0.0,-2370.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n257842,0,Cash loans,F,Y,N,0,67500.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-14461,-1243,-3731.0,-4969,14.0,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,,0.4695234994800211,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-416.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n111868,1,Cash loans,F,N,N,0,256500.0,640080.0,22990.5,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.035792000000000004,-18309,-535,-10679.0,-1849,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6785112579310274,0.44539624156083396,0.1371,0.0,0.9732,0.6328,0.0261,0.0,0.069,0.1667,0.2083,0.0358,0.1067,0.0576,0.0232,0.0182,0.1397,0.0,0.9732,0.6472,0.0264,0.0,0.069,0.1667,0.2083,0.0366,0.1166,0.06,0.0233,0.0193,0.1384,0.0,0.9732,0.6377,0.0263,0.0,0.069,0.1667,0.2083,0.0364,0.1086,0.0586,0.0233,0.0186,reg oper account,block of flats,0.0493,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-638.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,3.0\n384963,0,Cash loans,M,Y,Y,0,315000.0,1006920.0,51543.0,900000.0,Family,Working,Higher education,Married,House / apartment,0.020246,-17449,-9134,-7015.0,-1006,16.0,1,1,0,1,0,1,Managers,2.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,Industry: type 9,0.6790957933891224,0.4446801559221649,0.5673792367572691,0.0825,0.1455,0.9896,0.8572,0.016,0.0,0.2414,0.1667,0.0417,0.0625,0.0672,0.1106,0.0,0.0,0.084,0.1509,0.9896,0.8628,0.0162,0.0,0.2414,0.1667,0.0417,0.0639,0.0735,0.1152,0.0,0.0,0.0833,0.1455,0.9896,0.8591,0.0161,0.0,0.2414,0.1667,0.0417,0.0636,0.0684,0.1125,0.0,0.0,,block of flats,0.087,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1792.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n214622,0,Cash loans,F,N,Y,0,135000.0,247500.0,12037.5,247500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-8319,-689,-8284.0,-204,,1,1,1,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.2290505326978649,0.397788250259294,0.18303516721781032,0.099,0.0645,0.9826,0.762,0.0543,0.16,0.069,0.4583,0.5,,0.0807,0.1099,0.0,0.0,0.1008,0.067,0.9826,0.7713,0.0548,0.1611,0.069,0.4583,0.5,,0.0882,0.1145,0.0,0.0,0.0999,0.0645,0.9826,0.7652,0.0547,0.16,0.069,0.4583,0.5,,0.0821,0.1118,0.0,0.0,reg oper account,block of flats,0.1161,Panel,No,0.0,0.0,0.0,0.0,-508.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n153528,0,Cash loans,M,Y,Y,2,112500.0,396171.0,20358.0,342000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-13326,-3699,-3773.0,-4806,1.0,1,1,1,1,0,0,Managers,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Trade: type 7,0.5720546338781503,0.631365958545172,,0.1021,0.0727,0.9856,0.8028,0.0116,0.0,0.2069,0.1667,0.0417,0.0212,0.0832,0.0893,0.0,0.0,0.10400000000000001,0.0755,0.9856,0.8105,0.0117,0.0,0.2069,0.1667,0.0417,0.0217,0.0909,0.09300000000000001,0.0,0.0,0.1031,0.0727,0.9856,0.8054,0.0117,0.0,0.2069,0.1667,0.0417,0.0216,0.0847,0.0909,0.0,0.0,reg oper account,block of flats,0.0766,Panel,No,0.0,0.0,0.0,0.0,-374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n445264,1,Cash loans,F,N,N,1,54000.0,360000.0,26325.0,360000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.007120000000000001,-12129,365243,-6090.0,-3687,,1,0,0,1,0,0,,3.0,2,2,SUNDAY,11,0,0,0,0,0,0,XNA,,0.005711537084798501,0.3996756156233169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-257.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n450041,0,Cash loans,F,N,Y,0,189000.0,1180129.5,34636.5,1030500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.015221,-21444,365243,-12418.0,-4985,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.4799218448843785,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-904.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n314652,0,Cash loans,F,N,Y,0,127350.0,646920.0,20866.5,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018029,-20958,365243,-2865.0,-3608,,1,0,0,1,0,0,,1.0,3,3,FRIDAY,7,0,0,0,0,0,0,XNA,,0.4234648198810527,0.1777040724853336,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-693.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n137291,0,Cash loans,F,Y,Y,1,540000.0,2064186.0,54450.0,1845000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-18782,-1440,-1699.0,-2317,9.0,1,1,0,1,0,0,Managers,3.0,3,3,MONDAY,5,0,0,0,0,0,0,Services,0.7103454910381716,0.3372655112784962,0.17249546677733105,0.0753,0.054000000000000006,0.9806,0.7348,0.0138,0.0,0.1034,0.1667,0.0417,0.0,0.0597,0.0726,0.0077,0.043,0.0767,0.055999999999999994,0.9806,0.7452,0.0139,0.0,0.1034,0.1667,0.0417,0.0,0.0652,0.0756,0.0078,0.0456,0.076,0.054000000000000006,0.9806,0.7383,0.0138,0.0,0.1034,0.1667,0.0417,0.0,0.0607,0.0739,0.0078,0.044000000000000004,reg oper account,block of flats,0.0664,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1876.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n114640,0,Cash loans,F,Y,N,0,193500.0,733315.5,69754.5,679500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008575,-15194,-6244,-3194.0,-4841,1.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.8660590089773005,0.6082394174583526,0.7435593141311444,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1755.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n422927,0,Cash loans,F,N,Y,1,81000.0,422451.0,16056.0,297000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-10835,-2079,-10828.0,-3287,,1,1,0,1,0,0,Core staff,3.0,2,2,MONDAY,9,0,0,0,1,0,1,Kindergarten,,0.5511834515710935,0.3046721837533529,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n151210,0,Cash loans,F,N,N,1,283500.0,720000.0,21181.5,720000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010147,-13858,-905,-74.0,-4501,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,1,1,Self-employed,0.380298051478415,0.5712663140501699,0.6785676886853644,0.5196,0.2164,0.9995,0.9932,0.0876,0.32,0.1379,0.4583,0.5,0.0607,0.4236,0.3375,0.3012,0.0924,0.5294,0.2246,0.9995,0.9935,0.0884,0.3222,0.1379,0.4583,0.5,0.0621,0.4628,0.3516,0.3035,0.0978,0.5246,0.2164,0.9995,0.9933,0.0881,0.32,0.1379,0.4583,0.5,0.0618,0.431,0.3435,0.3028,0.0943,reg oper account,block of flats,0.3441,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n376699,0,Cash loans,F,N,N,1,90000.0,119925.0,12915.0,112500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-11786,-895,-5568.0,-992,,1,1,1,1,0,0,Sales staff,3.0,3,3,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.26864381062247106,0.6435044531959764,0.4311917977993083,0.1103,0.0787,0.9866,0.8164,0.0342,0.12,0.1034,0.3333,0.2083,0.0139,0.0899,0.1147,0.0,0.0,0.1124,0.0817,0.9866,0.8236,0.0346,0.1208,0.1034,0.3333,0.2083,0.0143,0.0983,0.1195,0.0,0.0,0.1114,0.0787,0.9866,0.8189,0.0345,0.12,0.1034,0.3333,0.2083,0.0142,0.0915,0.1168,0.0,0.0,reg oper account,block of flats,0.10800000000000001,Panel,No,1.0,0.0,1.0,0.0,-330.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n333830,0,Cash loans,F,Y,N,0,315000.0,450000.0,16677.0,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.019101,-9867,-2440,-5102.0,-2280,2.0,1,1,0,1,1,0,Managers,1.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Self-employed,0.2945962586070702,0.6366127207060497,0.4048783643353997,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1588.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n425452,1,Cash loans,M,N,Y,0,99000.0,312768.0,16096.5,270000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.015221,-21859,365243,-2007.0,-135,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.4376878285755236,0.4902575124990026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,0.0,11.0,0.0,-671.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n411781,0,Cash loans,M,Y,Y,0,90000.0,526491.0,19039.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-15348,-4674,-9413.0,-4690,7.0,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Other,0.32510167193304385,0.5607221772573031,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-483.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n387296,0,Cash loans,F,N,N,0,94500.0,270000.0,16515.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.010556,-22515,365243,-12841.0,-5083,,1,0,0,1,1,0,,2.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,XNA,0.7166497881347214,0.5807668026460651,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-143.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n136587,0,Cash loans,F,N,Y,0,90000.0,603000.0,19579.5,603000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-15933,-3625,-9270.0,-4606,,1,1,1,1,0,0,Cooking staff,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,School,,0.2487419566054849,0.2910973802776635,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2121.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n384888,0,Revolving loans,F,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.0105,-9817,-1052,-437.0,-2493,,1,1,0,1,1,0,Sales staff,1.0,3,3,THURSDAY,14,0,0,0,0,0,0,Self-employed,,0.4647443414837396,0.6832688314232291,0.0464,,0.9881,,,,0.1034,0.1667,,,,,,,0.0315,,0.9876,,,,0.069,0.1667,,,,,,,0.0468,,0.9881,,,,0.1034,0.1667,,,,,,,,block of flats,0.0224,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-555.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n427452,0,Cash loans,F,N,Y,0,180000.0,814041.0,23931.0,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-18862,-4533,-3109.0,-2417,,1,1,0,1,1,0,Cooking staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Military,,0.4591786996951638,0.3233112448967859,0.0928,0.2082,0.9831,0.7688,0.0381,0.0,0.1724,0.1667,0.0417,0.0761,0.0756,0.0785,0.0,0.0,0.0945,0.21600000000000005,0.9831,0.7779,0.0384,0.0,0.1724,0.1667,0.0417,0.0779,0.0826,0.0818,0.0,0.0,0.0937,0.2082,0.9831,0.7719,0.0383,0.0,0.1724,0.1667,0.0417,0.0775,0.077,0.0799,0.0,0.0,reg oper spec account,block of flats,0.0825,Panel,No,2.0,0.0,2.0,0.0,-1961.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n263924,0,Cash loans,F,Y,Y,1,121500.0,132444.0,8977.5,117000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-10772,-3117,-4690.0,-3339,2.0,1,1,0,1,0,0,Medicine staff,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Medicine,0.1635950982212655,0.4513641114469136,0.248535557339522,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2888.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n244101,1,Cash loans,F,N,Y,0,103500.0,254700.0,24939.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-23196,365243,-11748.0,-3746,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,,0.6879008586718606,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-996.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n367156,0,Cash loans,F,N,Y,2,135000.0,315000.0,17217.0,315000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.014519999999999996,-11315,-110,-5688.0,-2677,,1,1,0,1,0,0,,4.0,2,2,FRIDAY,7,0,0,0,0,1,1,Trade: type 2,0.2677763305782716,0.4518136100239068,0.3490552510751822,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1735.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n298797,0,Cash loans,F,N,N,1,135000.0,296280.0,17136.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-15124,-2338,-5221.0,-4226,,1,1,0,1,0,0,,3.0,3,3,MONDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.4875328532681577,0.34449759298534816,0.7421816117614419,0.0062,,0.9707,,,0.0,0.069,0.0417,,0.0322,,0.0062,,0.004,0.0063,,0.9707,,,0.0,0.069,0.0417,,0.0329,,0.0064,,0.0042,0.0062,,0.9707,,,0.0,0.069,0.0417,,0.0327,,0.0063,,0.004,,block of flats,0.0057,\"Stone, brick\",No,3.0,1.0,3.0,0.0,-2391.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n262698,0,Cash loans,F,N,N,0,180000.0,679500.0,28786.5,679500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,Municipal apartment,0.072508,-21823,365243,-11706.0,-4743,,1,0,0,1,1,0,,2.0,1,1,SUNDAY,17,0,0,0,0,0,0,XNA,,0.7762917209176815,0.6144143775673561,0.0736,0.0704,0.9742,0.6464,0.0011,0.0024,0.151,0.18,0.2217,0.0018,0.057,0.0556,0.0002,0.0011,0.0788,0.0625,0.9732,0.6472,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0689,0.0381,0.0,0.0,0.0781,0.0747,0.9737,0.6444,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0641,0.0636,0.0,0.0,reg oper account,block of flats,0.0394,Panel,No,0.0,0.0,0.0,0.0,-1707.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n356392,0,Cash loans,F,Y,N,0,112500.0,675000.0,27490.5,675000.0,Family,Working,Secondary / secondary special,Married,Rented apartment,0.007273999999999998,-11193,-229,-5287.0,-2589,17.0,1,1,1,1,0,0,Sales staff,2.0,2,2,MONDAY,21,0,0,0,0,1,1,Trade: type 3,,0.4179063918147753,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-243.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n441769,0,Cash loans,M,Y,Y,2,180000.0,239850.0,25447.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-14454,-702,-4220.0,-4451,0.0,1,1,1,1,0,0,Laborers,4.0,2,2,MONDAY,14,0,1,1,0,1,1,Construction,,0.7543899680800699,0.6642482627052363,,,0.9771,,,,0.1034,0.0833,,,,0.0239,,,,,0.9772,,,,0.1034,0.0833,,,,0.0249,,,,,0.9771,,,,0.1034,0.0833,,,,0.0243,,,,,0.0104,,No,0.0,0.0,0.0,0.0,-583.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n168838,1,Cash loans,F,Y,Y,1,180000.0,942300.0,30528.0,675000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.018029,-14019,-4549,-153.0,-147,3.0,1,1,0,1,0,0,Laborers,3.0,3,3,THURSDAY,7,0,0,0,0,1,1,Transport: type 4,0.7634245490413488,0.42208800678700464,0.4992720153045617,0.0515,0.0521,0.9816,0.7484,0.0224,0.0,0.1379,0.1667,0.0417,0.0205,0.0403,0.043,0.0077,0.0359,0.0525,0.054000000000000006,0.9816,0.7583,0.0226,0.0,0.1379,0.1667,0.0417,0.021,0.0441,0.0449,0.0078,0.038,0.052000000000000005,0.0521,0.9816,0.7518,0.0225,0.0,0.1379,0.1667,0.0417,0.0208,0.040999999999999995,0.0438,0.0078,0.0367,reg oper account,block of flats,0.0539,Panel,No,2.0,0.0,2.0,0.0,-1844.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n434211,0,Cash loans,M,Y,N,1,112500.0,755190.0,36328.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-15801,-4262,-9709.0,-5021,15.0,1,1,1,1,0,0,Laborers,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.5897581482194955,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1200.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n378629,1,Cash loans,F,Y,Y,1,238500.0,545040.0,35617.5,450000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.006305,-10797,-2252,-4547.0,-1204,14.0,1,1,0,1,0,0,High skill tech staff,3.0,3,3,FRIDAY,5,0,0,0,0,0,0,Military,0.24803872902435325,0.4448367606730008,0.5442347412142162,0.0619,0.0764,0.9821,0.7552,0.0105,0.0,0.1379,0.1667,0.2083,0.0552,0.0504,0.0365,0.0,0.0,0.063,0.0793,0.9821,0.7648,0.0105,0.0,0.1379,0.1667,0.2083,0.0564,0.0551,0.0381,0.0,0.0,0.0625,0.0764,0.9821,0.7585,0.0105,0.0,0.1379,0.1667,0.2083,0.0561,0.0513,0.0372,0.0,0.0,reg oper account,block of flats,0.0547,Panel,No,0.0,0.0,0.0,0.0,-760.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n140830,0,Cash loans,F,N,Y,0,144000.0,284400.0,19134.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-13473,-1028,-391.0,-2567,,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Bank,0.5252138002714359,0.7324173933177648,0.107532175802471,0.1845,0.1034,0.9891,0.8436,0.0342,0.2,0.1724,0.3333,0.0417,0.1154,0.1496,0.1879,0.0039,0.0012,0.188,0.1073,0.9891,0.8497,0.0346,0.2014,0.1724,0.3333,0.0417,0.11800000000000001,0.1635,0.1958,0.0039,0.0012,0.1863,0.1034,0.9891,0.8457,0.0345,0.2,0.1724,0.3333,0.0417,0.1174,0.1522,0.1913,0.0039,0.0012,org spec account,block of flats,0.1668,Panel,No,0.0,0.0,0.0,0.0,-1128.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n214080,0,Cash loans,F,N,Y,0,135000.0,895500.0,26181.0,895500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018634,-16975,-7738,-5026.0,-506,,1,1,1,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Medicine,0.7812540448062109,0.7586954903347716,0.7688075728291359,0.0722,0.0809,0.9791,0.7144,0.0325,0.0,0.1379,0.1667,0.2083,0.0537,0.0588,0.0676,0.0,0.0,0.0735,0.084,0.9791,0.7256,0.0328,0.0,0.1379,0.1667,0.2083,0.0549,0.0643,0.0705,0.0,0.0,0.0729,0.0809,0.9791,0.7182,0.0327,0.0,0.1379,0.1667,0.2083,0.0546,0.0599,0.0689,0.0,0.0,reg oper account,block of flats,0.0709,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1163.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n348806,0,Cash loans,F,N,Y,0,76500.0,86346.0,8541.0,81000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-23707,365243,-7521.0,-3969,,1,0,0,1,0,0,,2.0,1,1,FRIDAY,14,0,0,0,0,0,0,XNA,,0.7177209541809176,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-444.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n143838,1,Cash loans,F,N,Y,1,225000.0,229500.0,11862.0,229500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018209,-13291,-3855,-4237.0,-2522,,1,1,0,1,0,0,Laborers,3.0,3,3,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.4108194332378939,0.291316821798346,0.248535557339522,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1855.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n320253,0,Cash loans,F,Y,N,0,202500.0,1312110.0,55723.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-17545,365243,-6735.0,-1096,13.0,1,0,0,1,1,0,,2.0,3,3,THURSDAY,9,0,0,0,0,0,0,XNA,,0.2614595121667814,0.4329616670974407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-949.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n279126,0,Cash loans,F,Y,Y,0,76500.0,1035000.0,30393.0,1035000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-20441,365243,-9323.0,-3998,14.0,1,0,0,1,1,0,,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,,0.5554261620158675,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2129.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n321324,1,Cash loans,M,N,N,0,112500.0,270000.0,16312.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-10860,-2308,-3133.0,-3545,,1,1,1,1,0,0,Laborers,2.0,3,3,SUNDAY,15,0,0,0,0,0,0,Industry: type 9,0.11634583697661187,0.5638760317979481,0.363945238612397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1260.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n110900,0,Cash loans,F,N,Y,0,67500.0,398160.0,10633.5,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.007120000000000001,-18312,-341,-1225.0,-1818,,1,1,1,1,0,0,Cooking staff,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.4139275087233121,0.11836432636740067,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n153308,0,Cash loans,F,Y,Y,0,360000.0,253737.0,27054.0,229500.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.011657,-10031,-397,-2198.0,-2233,3.0,1,1,0,1,0,1,Managers,2.0,1,1,TUESDAY,12,0,1,1,1,0,0,Business Entity Type 3,,0.6115899456525229,0.052036407096232806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1806.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n174885,0,Cash loans,M,Y,N,0,270000.0,1078200.0,38200.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.04622,-19220,-3448,-11622.0,-2449,22.0,1,1,0,1,1,0,Drivers,2.0,1,1,WEDNESDAY,19,0,0,0,0,0,0,Other,0.3752282791318474,0.7057703891585944,0.7106743858828587,0.1485,0.106,0.9801,,,0.16,0.1379,0.3333,,,,0.1463,,,0.1513,0.11,0.9801,,,0.1611,0.1379,0.3333,,,,0.1524,,,0.1499,0.106,0.9801,,,0.16,0.1379,0.3333,,,,0.1489,,,,,0.1151,,No,0.0,0.0,0.0,0.0,-2229.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,1.0\n378281,0,Cash loans,M,N,Y,0,180000.0,900000.0,62640.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-23741,365243,-3289.0,-4383,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,0.813041928298752,0.6492325371581589,0.5744466170995097,0.1948,0.127,0.995,0.9388,0.1053,0.22,0.1897,0.375,0.4167,0.0276,0.1572,0.2221,0.0,0.004,0.1964,0.1284,0.995,0.9412,0.1062,0.2014,0.1724,0.375,0.4167,0.0251,0.1717,0.2164,0.0,0.0,0.1967,0.127,0.995,0.9396,0.1059,0.22,0.1897,0.375,0.4167,0.0281,0.1599,0.2261,0.0,0.0041,reg oper spec account,block of flats,0.1955,Panel,No,0.0,0.0,0.0,0.0,-1528.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n191815,0,Cash loans,M,N,N,2,180000.0,545040.0,26640.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018209,-10877,-2692,-1205.0,-3508,,1,1,1,1,0,0,Laborers,4.0,3,3,TUESDAY,14,0,0,0,0,1,1,Transport: type 2,0.2506345784562787,0.4169757689503713,0.09261717137485452,0.0784,0.0322,0.9757,0.6668,0.0182,0.0,0.1379,0.1667,0.0417,0.0547,0.0546,0.0505,0.0425,0.0886,0.0798,0.0335,0.9757,0.6798,0.0183,0.0,0.1379,0.1667,0.0417,0.0559,0.0597,0.0526,0.0428,0.0938,0.0791,0.0322,0.9757,0.6713,0.0183,0.0,0.1379,0.1667,0.0417,0.0556,0.0556,0.0514,0.0427,0.0904,reg oper account,block of flats,0.0497,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-986.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n226409,0,Cash loans,M,N,Y,0,405000.0,900000.0,40671.0,900000.0,Children,Commercial associate,Higher education,Civil marriage,House / apartment,0.04622,-18128,-782,-601.0,-1636,,1,1,0,1,1,1,High skill tech staff,2.0,1,1,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.6552045236165573,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2293.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n288787,0,Cash loans,M,Y,Y,1,247500.0,1467612.0,58333.5,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006629,-16542,-1779,-129.0,-100,12.0,1,1,1,1,1,0,Laborers,3.0,2,2,FRIDAY,6,0,0,0,0,0,0,Industry: type 9,,0.7367448327123389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1697.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n189521,1,Cash loans,M,Y,Y,1,180000.0,90000.0,9580.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-10282,-1557,-4742.0,-2647,21.0,1,1,1,1,1,0,,3.0,1,1,SATURDAY,13,1,1,0,1,1,0,Business Entity Type 3,0.5685020509458061,0.4256659096921087,0.7476633896301825,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n276757,0,Cash loans,M,N,N,0,103500.0,105534.0,10566.0,99000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014464,-16521,-1320,-4233.0,-60,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,4,0,0,0,0,0,0,Business Entity Type 2,0.4530731482396894,0.6955896914413091,,0.0852,0.0484,0.9896,0.8572,0.0455,0.08,0.0345,0.5417,0.4025,0.0595,0.0689,0.0822,0.0,0.0224,0.0809,0.0292,0.9891,0.8563,0.0,0.0806,0.0345,0.5417,0.5833,0.0297,0.0707,0.0651,0.0,0.0,0.0874,0.0482,0.9891,0.8524,0.0302,0.08,0.0345,0.5417,0.5833,0.0739,0.0701,0.091,0.0,0.0276,reg oper account,block of flats,0.0743,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2668.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n287366,0,Cash loans,F,Y,N,0,90000.0,278460.0,23899.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.009175,-10598,-2632,-4607.0,-2177,9.0,1,1,1,1,0,0,High skill tech staff,2.0,2,2,SUNDAY,13,0,0,0,1,1,1,Other,0.7311782263774367,0.6263540980186242,0.4920600938649263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1699.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,1.0,1.0\n157700,0,Cash loans,F,N,Y,0,225000.0,1360498.5,39910.5,1188000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-17644,-2974,-9635.0,-1186,,1,1,0,1,0,0,Managers,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4820199596782781,0.22879456545103394,0.7136313997323308,0.0031,0.0114,0.9667,0.5308,,0.0,0.0345,0.0833,,0.0384,0.0017,0.0029,0.0039,0.0,0.0032,0.0118,0.9667,0.5492,,0.0,0.0345,0.0833,,0.0393,0.0018,0.003,0.0039,0.0,0.0031,0.0114,0.9667,0.5371,,0.0,0.0345,0.0833,,0.0391,0.0017,0.0029,0.0039,0.0,,block of flats,0.005,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1769.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n362271,0,Cash loans,F,N,N,1,283500.0,1116076.5,37012.5,913500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-12141,-1708,-1525.0,-802,,1,1,0,1,0,1,Sales staff,3.0,2,2,TUESDAY,16,0,0,0,0,1,1,Self-employed,0.5162904813566117,0.4683744337480135,0.11761373170805695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1332.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n346371,0,Revolving loans,F,N,N,0,81000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-19744,-1926,-4155.0,-3018,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5053209720651425,,0.1062,0.1653,0.9657,0.5308,,0.0,0.1724,0.2083,0.2083,0.0308,,0.1081,,0.0374,0.1082,0.1715,0.9657,0.5492,,0.0,0.1724,0.2083,0.2083,0.0315,,0.1126,,0.0396,0.1072,0.1653,0.9657,0.5371,,0.0,0.1724,0.2083,0.2083,0.0313,,0.1101,,0.0382,reg oper account,block of flats,0.11800000000000001,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n168341,0,Cash loans,M,Y,Y,1,135000.0,194742.0,11898.0,139500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-9583,-211,-4193.0,-2125,16.0,1,1,0,1,0,0,Security staff,3.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Security,,0.5233013026156281,0.3572932680336494,0.0526,0.0602,0.9940000000000001,0.9184,0.0052,0.04,0.069,0.25,0.0417,0.0391,0.0429,0.0481,0.0,0.0646,0.0315,0.0458,0.9916,0.8889,0.0052,0.0,0.069,0.1667,0.0417,0.0283,0.0275,0.0188,0.0,0.0,0.0531,0.0602,0.9940000000000001,0.9195,0.0052,0.04,0.069,0.25,0.0417,0.0398,0.0436,0.0489,0.0,0.0659,org spec account,block of flats,0.0305,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n234536,0,Cash loans,F,N,Y,0,112500.0,237024.0,17374.5,180000.0,Unaccompanied,Working,Higher education,Married,With parents,0.008473999999999999,-10172,-1626,-1331.0,-2843,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Kindergarten,0.4644595148932414,0.6039322961751229,0.4048783643353997,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-283.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n173151,0,Cash loans,M,N,Y,0,202500.0,364797.0,38704.5,346500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010006,-11346,-2243,-284.0,-1198,,1,1,1,1,1,0,Drivers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.27618772167079964,0.6653234654984913,0.3672910183026313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-181.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n107815,0,Cash loans,F,N,Y,0,90000.0,1256400.0,36864.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-19033,-5531,-6720.0,-2592,,1,1,0,1,1,0,Sales staff,2.0,3,3,FRIDAY,8,0,0,0,0,1,1,Self-employed,,0.2622583692422573,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1202.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n393457,0,Cash loans,M,N,Y,0,157500.0,963000.0,43645.5,963000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.026392,-21554,-4033,-6612.0,-4314,,1,1,0,1,1,0,Laborers,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6522939532032522,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-287.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n388083,0,Cash loans,M,N,N,0,112500.0,90000.0,9450.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018634,-11792,-2260,-5078.0,-4097,,1,1,1,1,0,0,Cooking staff,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.4050125182361281,0.4347729779308029,,0.1856,0.1713,0.9816,0.8504,0.0945,0.2,0.1724,0.3333,0.375,,0.1513,0.1262,0.0,0.0,0.1891,0.1778,0.9742,0.8563,0.0953,0.2014,0.1724,0.3333,0.375,,0.1653,0.0569,0.0,0.0,0.1874,0.1713,0.9816,0.8524,0.0951,0.2,0.1724,0.3333,0.375,,0.1539,0.1284,0.0,0.0,reg oper spec account,block of flats,0.2072,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1015.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n419213,0,Revolving loans,F,Y,Y,0,202500.0,292500.0,14625.0,292500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.010966,-21015,365243,-9142.0,-4551,9.0,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,XNA,,0.4488689630634861,0.35895122857839673,0.1763,0.1469,0.9876,0.83,0.0442,0.2,0.1724,0.3333,0.375,0.0806,,0.2042,,0.0,0.1796,0.1524,0.9876,0.8367,0.0446,0.2014,0.1724,0.3333,0.375,0.0825,,0.2128,,0.0,0.17800000000000002,0.1469,0.9876,0.8323,0.0444,0.2,0.1724,0.3333,0.375,0.08199999999999999,,0.2079,,0.0,org spec account,block of flats,0.1842,Panel,No,4.0,0.0,4.0,0.0,-1514.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n427779,0,Cash loans,M,N,Y,0,135000.0,208512.0,22023.0,180000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.002042,-16391,365243,-4404.0,-4583,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,XNA,,0.061194342837266436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-283.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n412161,0,Cash loans,F,N,Y,0,49500.0,284400.0,10345.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008019,-18686,-237,-8124.0,-2223,,1,1,0,1,0,0,Medicine staff,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Security Ministries,0.7701188540718884,0.5963444230712701,0.7958031328748403,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-162.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n413688,0,Cash loans,F,N,N,0,225000.0,197820.0,12919.5,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.072508,-20935,365243,-2781.0,-4054,,1,0,0,1,0,0,,1.0,1,1,WEDNESDAY,13,1,0,0,0,0,0,XNA,,0.6479416562535343,0.4206109640437848,0.3866,0.1725,0.996,0.9456,0.2436,0.48,0.2069,0.6667,0.625,0.0,0.3144,0.4225,0.0039,0.0405,0.3939,0.1791,0.996,0.9477,0.2459,0.4834,0.2069,0.6667,0.625,0.0,0.3434,0.4402,0.0039,0.0429,0.3903,0.1725,0.996,0.9463,0.2452,0.48,0.2069,0.6667,0.625,0.0,0.3198,0.4301,0.0039,0.0414,reg oper account,block of flats,0.3411,Panel,No,0.0,0.0,0.0,0.0,-14.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n217630,0,Cash loans,M,N,Y,0,81000.0,450000.0,21109.5,450000.0,Unaccompanied,Commercial associate,Lower secondary,Married,House / apartment,0.015221,-20207,-3023,-4333.0,-3598,,1,1,1,1,1,0,Drivers,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Government,0.6847517845343488,0.6453183796221642,0.2079641743053816,0.0165,,0.9796,,,0.0,0.069,0.0417,,0.0117,,0.0144,,0.0,0.0168,,0.9796,,,0.0,0.069,0.0417,,0.0119,,0.015,,0.0,0.0167,,0.9796,,,0.0,0.069,0.0417,,0.0119,,0.0147,,0.0,,block of flats,0.0118,Panel,No,0.0,0.0,0.0,0.0,-1903.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n288236,0,Cash loans,M,Y,N,2,225000.0,1575000.0,52272.0,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-11659,-984,-5935.0,-4121,1.0,1,1,1,1,1,0,Laborers,4.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4024929811596796,0.2858978721410488,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1226.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n242700,0,Cash loans,F,N,Y,0,247500.0,521280.0,31630.5,450000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.010006,-18565,365243,-6444.0,-2088,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.7481876034759857,0.28961123838200553,0.0794,0.0517,0.9901,0.8640000000000001,0.0757,0.08,0.069,0.375,0.4167,0.0674,0.0647,0.0943,,0.0151,0.0809,0.0537,0.9901,0.8693,0.0764,0.0806,0.069,0.375,0.4167,0.0689,0.0707,0.0983,,0.016,0.0802,0.0517,0.9901,0.8658,0.0761,0.08,0.069,0.375,0.4167,0.0686,0.0658,0.096,,0.0155,reg oper account,block of flats,0.1186,Panel,No,0.0,0.0,0.0,0.0,-585.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n192241,0,Cash loans,F,Y,Y,0,135000.0,927000.0,39276.0,927000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.019689,-17417,-9003,-6603.0,-954,3.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Electricity,,0.5615138031309291,0.6041125998015721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-937.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n294855,0,Revolving loans,M,N,N,0,216000.0,405000.0,20250.0,405000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-22893,365243,-13842.0,-4140,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.6876519788967059,0.7850520263728172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-512.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n173350,0,Cash loans,F,N,N,0,52650.0,104256.0,11074.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-12083,-770,-1823.0,-1523,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Government,0.25601325099894395,0.20676760287492726,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2431.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n183692,0,Cash loans,F,Y,Y,1,90000.0,454500.0,19386.0,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-17810,-2658,-3121.0,-1353,1.0,1,1,0,1,0,0,Sales staff,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Medicine,,0.6914344076077911,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n154491,0,Cash loans,F,N,N,0,90000.0,531706.5,24768.0,459000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-22145,365243,-13640.0,-4928,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.6068264155399048,0.4722533429586386,0.0773,0.0785,0.9816,0.7484,0.0133,0.0,0.1724,0.1667,0.2083,0.031,0.063,0.0439,0.0,0.1067,0.0788,0.0815,0.9816,0.7583,0.0134,0.0,0.1724,0.1667,0.2083,0.0317,0.0689,0.0457,0.0,0.113,0.0781,0.0785,0.9816,0.7518,0.0134,0.0,0.1724,0.1667,0.2083,0.0315,0.0641,0.0447,0.0,0.1089,not specified,block of flats,0.065,Others,No,0.0,0.0,0.0,0.0,-1988.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n172875,0,Cash loans,F,N,Y,1,157500.0,392472.0,31135.5,310500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-10473,-1721,-3479.0,-2738,,1,1,0,1,1,0,,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.26834866800238605,0.4622538522033768,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-382.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n173369,0,Cash loans,F,Y,N,0,130500.0,776304.0,22378.5,648000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.007273999999999998,-8416,-1034,-4622.0,-1060,1.0,1,1,0,1,1,0,Accountants,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.14416115222018058,0.4815649381984515,0.6347055309763198,0.0557,0.18,0.9786,0.8028,0.0392,0.0664,0.1262,0.0762,0.2917,0.0444,0.2068,0.0495,0.0309,0.0177,0.0189,0.1868,0.9717,0.8105,0.0396,0.0,0.069,0.0417,0.2917,0.0052,0.2259,0.0099,0.0311,0.0,0.0187,0.18,0.9801,0.8054,0.0394,0.0,0.0862,0.0417,0.2917,0.0118,0.2104,0.0175,0.0311,0.0057,reg oper account,block of flats,0.0087,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-423.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n174434,0,Cash loans,M,N,N,0,112500.0,508495.5,22396.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014464,-22561,365243,-8487.0,-4717,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,4,0,0,0,0,0,0,XNA,,0.1158038561094103,0.3344541255096772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-375.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n380969,1,Cash loans,M,Y,Y,0,135000.0,508495.5,35518.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010643,-20128,-606,-1780.0,-1841,10.0,1,1,0,1,0,0,Drivers,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Self-employed,,0.3705022730824529,0.7922644738669378,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-905.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n246505,0,Cash loans,F,N,N,0,157500.0,135000.0,7749.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.01885,-17494,-2615,-6800.0,-1023,,1,1,1,1,1,0,,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.6010171320932925,0.4051181598372448,0.6769925032909132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1436.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n239716,0,Cash loans,M,Y,Y,0,292500.0,364896.0,28782.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-14675,-578,-145.0,-4279,12.0,1,1,0,1,0,0,Laborers,2.0,3,3,TUESDAY,9,0,0,0,0,1,1,Other,,0.038814768790303975,0.3425288720742255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1446.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n370923,1,Cash loans,F,N,Y,0,180000.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-13795,-4060,-2495.0,-4786,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.3727366048808908,0.5573907819072018,0.25946765482111994,0.0928,0.1123,0.9871,0.8232,0.0178,0.0,0.2069,0.1667,0.2083,0.0206,0.0756,0.0918,0.0,0.0,0.0945,0.1165,0.9871,0.8301,0.018000000000000002,0.0,0.2069,0.1667,0.2083,0.0211,0.0826,0.0956,0.0,0.0,0.0937,0.1123,0.9871,0.8256,0.0179,0.0,0.2069,0.1667,0.2083,0.021,0.077,0.0934,0.0,0.0,reg oper account,block of flats,0.0819,Panel,No,0.0,0.0,0.0,0.0,-135.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n423585,0,Cash loans,F,N,Y,2,67500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-11254,-216,-1136.0,-2655,,1,1,0,1,0,0,Cooking staff,4.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Self-employed,,0.2604347227466167,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-312.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n283016,0,Revolving loans,F,N,Y,0,180000.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.032561,-16674,-1411,-10219.0,-227,,1,1,0,1,1,0,Managers,1.0,1,1,SUNDAY,14,0,0,0,0,0,0,Self-employed,,0.6261493645316187,0.3791004853998145,0.1,0.0479,0.9806,0.7348,0.0,0.04,0.0345,0.5417,0.0,,0.0815,0.0925,0.0,0.0045,0.1019,0.0497,0.9806,0.7452,0.0,0.0403,0.0345,0.5417,0.0,,0.0891,0.0963,0.0,0.0048,0.10099999999999999,0.0479,0.9806,0.7383,0.0,0.04,0.0345,0.5417,0.0,,0.0829,0.0941,0.0,0.0046,reg oper account,block of flats,0.0737,Panel,No,2.0,1.0,2.0,1.0,-60.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n387181,0,Cash loans,F,N,Y,0,121500.0,99000.0,4891.5,99000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.026392,-20035,365243,-9777.0,-3596,,1,0,0,1,1,0,,1.0,2,2,MONDAY,17,0,0,0,0,0,0,XNA,,0.6337040313919953,,0.0577,,0.9776,,,0.0,0.1379,0.1667,,,,0.0372,,0.1055,0.0588,,0.9777,,,0.0,0.1379,0.1667,,,,0.0388,,0.1117,0.0583,,0.9776,,,0.0,0.1379,0.1667,,,,0.0379,,0.1077,,block of flats,0.0522,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1098.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n331720,1,Cash loans,M,N,Y,0,180000.0,398016.0,45036.0,360000.0,Unaccompanied,Working,Lower secondary,Single / not married,House / apartment,0.018029,-8123,-696,-6156.0,-706,,1,1,0,1,0,0,,1.0,3,3,MONDAY,14,0,0,0,0,0,0,Business Entity Type 2,0.15490979655112475,0.026925958486345773,0.3280631605201915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-13.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n108157,0,Cash loans,F,N,Y,0,135000.0,942300.0,30528.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.005144,-18807,365243,-10429.0,-2323,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.4216879964947861,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1147.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n121734,0,Cash loans,M,Y,N,1,270000.0,225000.0,6066.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010966,-17778,-8873,-765.0,-1314,3.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Industry: type 7,0.6946076807882974,0.4509092780187599,0.5082869913916046,0.1835,,0.993,,,0.12,0.1034,0.375,,,,0.1693,,0.1874,0.187,,0.993,,,0.1208,0.1034,0.375,,,,0.1764,,0.1984,0.1853,,0.993,,,0.12,0.1034,0.375,,,,0.1723,,0.1914,,block of flats,0.1739,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1608.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,3.0,2.0\n222514,0,Cash loans,F,N,Y,2,126000.0,301500.0,22068.0,301500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.026392,-11724,-2058,-256.0,-4379,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,13,0,0,0,0,0,0,Other,0.6439500794012485,0.5551885618115047,0.3108182544189319,0.1299,0.1,0.9796,,,0.0,0.069,0.1667,,0.0189,,0.0571,,0.0,0.1324,0.1038,0.9796,,,0.0,0.069,0.1667,,0.0194,,0.0595,,0.0,0.1312,0.1,0.9796,,,0.0,0.069,0.1667,,0.0193,,0.0582,,0.0,,block of flats,0.0449,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-426.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n416000,1,Cash loans,M,N,Y,0,193500.0,1312110.0,52168.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.04622,-14434,-301,-8527.0,-122,,1,1,0,1,0,0,Laborers,1.0,1,1,FRIDAY,17,0,0,0,0,1,1,Trade: type 3,,0.4567468474206666,,0.0735,0.0682,0.9786,0.6940000000000001,0.0295,0.0132,0.1034,0.2221,0.2083,0.0528,0.0664,0.0565,0.0039,0.0087,0.084,0.0732,0.9782,0.7125,0.0305,0.0,0.1379,0.1667,0.2083,0.025,0.0735,0.0404,0.0,0.0,0.0833,0.0705,0.9781,0.7048,0.0304,0.0,0.1379,0.1667,0.2083,0.065,0.0684,0.0609,0.0,0.0,reg oper account,block of flats,0.0305,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n308575,0,Revolving loans,F,N,Y,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018801,-10370,-1019,-4980.0,-3015,,1,1,0,1,0,0,Sales staff,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.487496233915001,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-267.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n140447,0,Cash loans,M,N,Y,0,202500.0,760225.5,32337.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.004849,-8336,-541,-952.0,-995,,1,1,0,1,0,0,Drivers,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.12101532358792196,0.5406078165487538,0.36227724703843145,0.0278,,0.9985,,,,0.069,0.0833,,,,0.0126,,,0.0284,,0.9985,,,,0.069,0.0833,,,,0.0131,,,0.0281,,0.9985,,,,0.069,0.0833,,,,0.0128,,,,block of flats,0.0172,Others,No,1.0,0.0,1.0,0.0,-793.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n212509,0,Cash loans,F,N,N,0,180000.0,358074.0,18409.5,256500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.020713,-11342,-2389,-5369.0,-3923,,1,1,0,1,0,0,,1.0,3,3,FRIDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.1898150000597376,0.5673792367572691,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,1.0,0.0,2.0\n419357,0,Cash loans,F,N,Y,2,180000.0,454500.0,14661.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-20074,-604,-867.0,-3466,,1,1,1,1,1,0,Laborers,4.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 2,,0.27894484293408656,0.5334816299804352,0.0227,0.0,0.9722,,,0.0,0.0345,0.0417,,0.0047,,0.0098,,0.0,0.0231,0.0,0.9722,,,0.0,0.0345,0.0417,,0.0048,,0.0102,,0.0,0.0229,0.0,0.9722,,,0.0,0.0345,0.0417,,0.0048,,0.01,,0.0,,block of flats,0.0077,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1804.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n324122,0,Cash loans,F,N,Y,2,99000.0,632664.0,46165.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.030755,-14730,-1912,-2308.0,-2957,,1,1,0,1,0,1,Laborers,4.0,2,2,SATURDAY,10,0,0,0,0,0,0,Industry: type 3,0.31021277050080875,0.11130731336450432,0.36896873825284665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n142418,1,Cash loans,F,N,Y,0,180000.0,526491.0,23319.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.005084,-20951,365243,-9951.0,-4110,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,20,0,0,0,0,0,0,XNA,0.6589019176082765,0.4404830271023277,0.18411615593071512,0.0237,,0.9861,,,,,,,,,0.0167,,,0.0242,,0.9861,,,,,,,,,0.0174,,,0.0239,,0.9861,,,,,,,,,0.017,,,,,0.0231,,No,4.0,2.0,4.0,0.0,-1536.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n447524,0,Cash loans,M,Y,N,0,202500.0,2013840.0,53253.0,1800000.0,Group of people,Working,Secondary / secondary special,Married,House / apartment,0.010147,-16605,-2022,-3985.0,-85,6.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,15,0,0,0,0,1,1,Self-employed,0.6432809849961688,0.08702815996924708,0.5513812618027899,,,0.9742,,,,,0.0,,,,,,,,,0.9742,,,,,0.0,,,,,,,,,0.9742,,,,,0.0,,,,,,,,,0.0031,Mixed,Yes,1.0,0.0,1.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n300015,0,Cash loans,M,Y,Y,2,450000.0,1042560.0,66757.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-12431,-2322,-6089.0,-4279,24.0,1,1,0,1,0,0,Managers,4.0,1,1,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.8006653117927095,0.42765737003502935,0.0928,0.1099,0.9796,0.7212,0.013000000000000001,0.0,0.2069,0.1667,0.2083,0.1471,0.0756,0.0914,0.0,0.0,0.0945,0.1141,0.9796,0.7321,0.0131,0.0,0.2069,0.1667,0.2083,0.1504,0.0826,0.0952,0.0,0.0,0.0937,0.1099,0.9796,0.7249,0.0131,0.0,0.2069,0.1667,0.2083,0.1496,0.077,0.09300000000000001,0.0,0.0,reg oper account,block of flats,0.0719,Panel,No,0.0,0.0,0.0,0.0,-1988.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n367454,0,Cash loans,F,Y,N,2,252000.0,733315.5,43006.5,679500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.007114,-15479,-609,-4098.0,-4793,2.0,1,1,1,1,1,0,Managers,4.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.7257510520231001,0.5770939963115626,0.6738300778602003,0.1598,0.3335,0.9940000000000001,,,0.24,0.1034,0.4583,,0.1136,,0.2269,,0.1371,0.1628,0.3461,0.9940000000000001,,,0.2417,0.1034,0.4583,,0.1162,,0.2364,,0.1452,0.1613,0.3335,0.9940000000000001,,,0.24,0.1034,0.4583,,0.1155,,0.231,,0.14,,block of flats,0.254,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-427.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n282137,0,Revolving loans,F,N,Y,0,112500.0,270000.0,13500.0,270000.0,Unaccompanied,State servant,Higher education,Widow,House / apartment,0.006629,-17585,-1959,-4598.0,-957,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,7,0,0,0,1,1,0,Other,,0.7425558134250354,0.28812959991785075,0.0814,0.0,0.9911,,,0.0,0.1379,0.1667,,0.0428,,0.086,,0.0445,0.083,0.0,0.9911,,,0.0,0.1379,0.1667,,0.0438,,0.0896,,0.0471,0.0822,0.0,0.9911,,,0.0,0.1379,0.1667,,0.0436,,0.0875,,0.0454,,block of flats,0.0773,Panel,No,1.0,0.0,1.0,0.0,-909.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n108840,1,Cash loans,F,N,Y,0,90000.0,854352.0,27688.5,612000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-22799,365243,-7071.0,-5603,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,XNA,0.5229936285249266,0.3657209960609989,0.6594055320683344,0.1,0.1155,0.9786,0.7076,0.0639,0.0,0.2069,0.1667,0.2083,0.0582,0.0807,0.0859,0.0039,0.1194,0.1019,0.1198,0.9786,0.7190000000000001,0.0645,0.0,0.2069,0.1667,0.2083,0.0595,0.0882,0.0895,0.0039,0.1264,0.10099999999999999,0.1155,0.9786,0.7115,0.0643,0.0,0.2069,0.1667,0.2083,0.0592,0.0821,0.0874,0.0039,0.1219,reg oper account,block of flats,0.0935,Panel,No,0.0,0.0,0.0,0.0,-153.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n404523,0,Cash loans,F,N,N,0,247500.0,503266.5,59854.5,463500.0,Unaccompanied,Commercial associate,Higher education,Married,Rented apartment,0.003818,-11459,-1061,-892.0,-2722,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,6,0,0,0,0,0,0,Business Entity Type 3,,0.44134012372324105,0.1852020815902493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n165710,0,Cash loans,F,N,N,2,292500.0,338832.0,12555.0,292500.0,Unaccompanied,State servant,Higher education,Separated,House / apartment,0.072508,-23659,-2962,-12223.0,-4671,,1,1,1,1,1,0,,3.0,1,1,FRIDAY,10,0,0,0,0,0,0,Other,0.7448286006556208,0.7418989516405684,0.18303516721781032,0.1526,0.1106,0.9762,0.6736,0.1863,0.08,0.1838,0.2221,0.2638,0.0205,0.1244,0.1277,0.0,0.0,0.1155,0.0723,0.9762,0.6864,0.1005,0.0,0.2069,0.1667,0.2083,0.0148,0.10099999999999999,0.0709,0.0,0.0,0.1239,0.1205,0.9762,0.6779999999999999,0.1504,0.0,0.2069,0.1667,0.2083,0.0219,0.1018,0.1043,0.0,0.0,reg oper account,block of flats,0.1694,Panel,No,2.0,2.0,2.0,0.0,-1309.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n102726,0,Cash loans,F,N,Y,1,78930.0,225000.0,10489.5,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-10086,-3290,-2536.0,-657,,1,1,1,1,0,0,Laborers,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Industry: type 3,,0.19103536435640905,0.6479768603302221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n300886,0,Cash loans,F,N,Y,0,85500.0,254700.0,26226.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.020246,-19991,-11094,-10250.0,-3503,,1,1,0,1,0,0,Managers,2.0,3,3,SUNDAY,11,0,0,0,0,0,0,Industry: type 3,,0.4189547067807917,,0.1768,0.0,0.9846,0.7892,0.0,0.08,0.3103,0.25,0.2917,0.086,0.1425,0.1685,0.0077,0.0702,0.1397,0.0,0.9831,0.7779,0.0,0.0,0.1379,0.1667,0.2083,0.08800000000000001,0.1185,0.1386,0.0,0.0,0.1785,0.0,0.9846,0.792,0.0,0.08,0.3103,0.25,0.2917,0.0875,0.1449,0.1715,0.0078,0.0716,reg oper account,block of flats,0.1604,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n209061,0,Cash loans,F,N,Y,0,225000.0,572076.0,38236.5,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.04622,-21470,365243,-6957.0,-4872,,1,0,0,1,0,0,,1.0,1,1,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.7442327289518332,0.5937175866150576,0.0825,0.0798,0.9757,0.6668,0.0,0.0,0.1379,0.1667,0.2083,0.0731,0.0672,0.0476,0.0,0.0,0.084,0.0828,0.9757,0.6798,0.0,0.0,0.1379,0.1667,0.2083,0.0748,0.0735,0.0496,0.0,0.0,0.0833,0.0798,0.9757,0.6713,0.0,0.0,0.1379,0.1667,0.2083,0.0744,0.0684,0.0484,0.0,0.0,reg oper spec account,block of flats,0.0552,Panel,No,0.0,0.0,0.0,0.0,-2169.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n257109,0,Cash loans,F,Y,N,1,180000.0,328405.5,21361.5,283500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020713,-13525,-5090,-2064.0,-2695,15.0,1,1,0,1,0,0,Laborers,2.0,3,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.5128106773677678,0.3378376202520429,0.8528284668510541,0.1381,0.0589,0.9866,0.8164,0.0158,0.16,0.1379,0.3333,0.0417,0.1172,0.1118,0.1474,0.0039,0.0466,0.1408,0.0611,0.9866,0.8236,0.0159,0.1611,0.1379,0.3333,0.0417,0.1198,0.1221,0.1535,0.0039,0.0493,0.1395,0.0589,0.9866,0.8189,0.0159,0.16,0.1379,0.3333,0.0417,0.1192,0.1137,0.15,0.0039,0.0476,org spec account,block of flats,0.126,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1611.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n288758,0,Cash loans,F,N,Y,0,90000.0,270000.0,13914.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-22178,365243,-12231.0,-4657,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.716207765596905,0.19747451156854226,0.0186,0.0425,0.9806,0.7348,0.0007,0.0,0.1034,0.0417,0.0,0.0221,0.0151,0.0175,0.0,0.0019,0.0189,0.0442,0.9806,0.7452,0.0008,0.0,0.1034,0.0417,0.0,0.0226,0.0165,0.0182,0.0,0.002,0.0187,0.0425,0.9806,0.7383,0.0007,0.0,0.1034,0.0417,0.0,0.0225,0.0154,0.0178,0.0,0.0019,org spec account,block of flats,0.0142,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-468.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n304362,0,Revolving loans,F,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.031329,-7792,-1033,-2543.0,-467,,1,1,1,1,0,0,Core staff,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Bank,,0.6391534436592254,0.30162489168411943,0.0825,0.0894,0.9791,0.7144,0.0375,0.0,0.2069,0.1667,0.2083,0.08,0.0672,0.0776,0.0,0.0,0.084,0.0928,0.9791,0.7256,0.0378,0.0,0.2069,0.1667,0.2083,0.0819,0.0735,0.0808,0.0,0.0,0.0833,0.0894,0.9791,0.7182,0.0377,0.0,0.2069,0.1667,0.2083,0.0814,0.0684,0.079,0.0,0.0,reg oper spec account,block of flats,0.0815,Panel,No,0.0,0.0,0.0,0.0,-919.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n387247,0,Cash loans,F,N,Y,0,144000.0,552555.0,17644.5,477000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-19378,365243,-12513.0,-2933,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,0.5296863010801319,0.7175835940114162,0.5316861425197883,0.0165,,0.9806,,,0.0,0.069,0.0417,,,,0.0133,,0.0,0.0168,,0.9806,,,0.0,0.069,0.0417,,,,0.0139,,0.0,0.0167,,0.9806,,,0.0,0.069,0.0417,,,,0.0136,,0.0,,block of flats,0.0114,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-3122.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n308758,0,Cash loans,M,Y,N,0,292500.0,630000.0,40261.5,630000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-12758,-483,-6425.0,-4963,4.0,1,1,1,1,1,0,Sales staff,2.0,1,1,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.15306156271033988,0.742258896600639,0.6940926425266661,0.1856,0.0,0.9821,0.7552,0.037000000000000005,0.2,0.1724,0.3333,0.375,0.0236,0.1505,0.1914,0.0039,0.0012,0.1891,0.0,0.9821,0.7648,0.0374,0.2014,0.1724,0.3333,0.375,0.0242,0.1644,0.1994,0.0039,0.0013,0.1874,0.0,0.9821,0.7585,0.0373,0.2,0.1724,0.3333,0.375,0.024,0.1531,0.1949,0.0039,0.0012,reg oper account,block of flats,0.1711,Panel,No,0.0,0.0,0.0,0.0,-3070.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n223836,0,Cash loans,F,N,Y,0,135000.0,558841.5,23805.0,499500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.005084,-22830,365243,-12717.0,-4674,,1,0,0,1,0,0,,2.0,2,2,MONDAY,16,0,0,0,0,0,0,XNA,,0.4092036274753397,,,0.1002,0.9801,0.728,,,0.2069,0.1667,,,,0.085,,,,0.10400000000000001,0.9801,0.7387,,,0.2069,0.1667,,,,0.0886,,,,0.1002,0.9801,0.7316,,,0.2069,0.1667,,,,0.0865,,,reg oper account,block of flats,0.0669,Panel,No,1.0,0.0,0.0,0.0,-1020.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n137155,0,Cash loans,F,Y,Y,0,40500.0,276277.5,11835.0,238500.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-21006,365243,-6655.0,-665,18.0,1,0,0,1,0,0,,2.0,3,3,TUESDAY,7,0,0,0,0,0,0,XNA,,0.381393483951714,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1887.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n193993,1,Cash loans,F,N,N,0,292500.0,1051294.5,27859.5,918000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.031329,-10531,-860,-3940.0,-2156,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Trade: type 7,,0.2651242425749029,0.1061556230153924,0.1216,0.1238,0.9791,0.7144,0.0179,0.0,0.2759,0.1667,0.2083,0.1196,0.0992,0.1039,0.0,0.0,0.1239,0.1285,0.9791,0.7256,0.0181,0.0,0.2759,0.1667,0.2083,0.1223,0.1084,0.1082,0.0,0.0,0.1228,0.1238,0.9791,0.7182,0.018000000000000002,0.0,0.2759,0.1667,0.2083,0.1216,0.1009,0.1057,0.0,0.0,not specified,block of flats,0.0915,Panel,No,0.0,0.0,0.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n180013,0,Cash loans,F,N,N,0,108000.0,1227901.5,52155.0,1129500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-12340,-111,-4813.0,-4386,,1,1,0,1,0,0,Sales staff,2.0,2,2,SUNDAY,11,0,0,0,1,1,0,Trade: type 3,,0.4343699817406553,0.2392262794694045,0.0247,0.0,0.9861,0.8096,0.0042,0.0,0.1034,0.0833,0.125,0.0,0.0202,0.0167,0.0,0.0,0.0252,0.0,0.9861,0.8171,0.0042,0.0,0.1034,0.0833,0.125,0.0,0.022000000000000002,0.0174,0.0,0.0,0.025,0.0,0.9861,0.8121,0.0042,0.0,0.1034,0.0833,0.125,0.0,0.0205,0.017,0.0,0.0,reg oper spec account,block of flats,0.0214,Panel,No,2.0,0.0,2.0,0.0,-491.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n242965,0,Cash loans,F,Y,Y,0,135000.0,477000.0,46597.5,477000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.032561,-11378,-2942,-4529.0,-150,4.0,1,1,0,1,0,0,Core staff,1.0,1,1,TUESDAY,19,0,0,0,0,0,0,Security Ministries,,0.8059617160727983,,0.183,0.1147,0.9762,0.6872,0.0333,0.12,0.2241,0.25,0.375,0.0533,0.1816,0.1422,0.0,0.0,0.146,0.1141,0.9762,0.6994,0.0336,0.0,0.2069,0.1667,0.375,0.0385,0.1983,0.1045,0.0,0.0,0.1848,0.1147,0.9762,0.6914,0.0335,0.12,0.2241,0.25,0.375,0.0542,0.1847,0.1027,0.0,0.0,reg oper account,block of flats,0.1954,Panel,No,1.0,0.0,1.0,0.0,-2115.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n161221,0,Cash loans,F,N,Y,1,135000.0,598486.5,21627.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.02461,-15533,-8717,-2509.0,-5072,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Transport: type 4,,0.07386794747928141,0.6023863442690867,0.1072,0.0788,0.9796,0.7008,0.0249,0.0,0.1034,0.1667,0.2083,0.0325,0.111,0.0546,0.0347,0.033,0.0704,0.0757,0.9782,0.7125,0.0251,0.0,0.069,0.1667,0.2083,0.016,0.1212,0.0465,0.035,0.0,0.1083,0.0788,0.9796,0.7048,0.0251,0.0,0.1034,0.1667,0.2083,0.0331,0.1129,0.0555,0.0349,0.0337,reg oper account,block of flats,0.0507,\"Stone, brick\",No,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n171431,0,Cash loans,M,N,Y,1,225000.0,901813.5,48177.0,778500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-16189,-1261,-10047.0,-4063,,1,1,0,1,0,0,Drivers,3.0,1,1,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6823295470500789,,0.2467,0.1432,0.9762,0.6736,0.0317,0.0932,0.2297,0.2221,0.2638,0.1695,0.1919,0.1894,0.0425,0.0188,0.1082,0.075,0.9762,0.6864,0.011000000000000001,0.0,0.1724,0.1667,0.2083,0.0927,0.0918,0.0937,0.0117,0.0185,0.1697,0.12,0.9762,0.6779999999999999,0.0176,0.0,0.2414,0.1667,0.2083,0.1955,0.136,0.1496,0.0155,0.0188,reg oper account,block of flats,0.2652,Panel,No,12.0,0.0,12.0,0.0,-683.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n404332,0,Cash loans,M,N,Y,2,157500.0,697500.0,29682.0,697500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-10087,-1447,-9.0,-2737,,1,1,0,1,0,0,,4.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.08490277857992519,0.633031641417419,0.0722,0.0688,0.9786,0.7076,0.0078,0.0,0.1379,0.1667,0.2083,0.0,0.0588,0.0678,0.0,0.0,0.0735,0.0714,0.9786,0.7190000000000001,0.0079,0.0,0.1379,0.1667,0.2083,0.0,0.0643,0.0707,0.0,0.0,0.0729,0.0688,0.9786,0.7115,0.0079,0.0,0.1379,0.1667,0.2083,0.0,0.0599,0.0691,0.0,0.0,reg oper account,block of flats,0.0598,Block,No,5.0,2.0,5.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n278376,0,Cash loans,F,N,Y,0,112500.0,189000.0,13572.0,189000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.020713,-10251,-2287,-287.0,-2924,,1,1,1,1,1,0,Core staff,2.0,3,3,WEDNESDAY,11,0,0,0,0,1,1,School,0.06405750817173984,0.5151569397261223,0.7992967832109371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1739.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n132157,0,Cash loans,F,Y,N,0,135000.0,755190.0,36328.5,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.02461,-12649,-435,-6584.0,-5228,6.0,1,1,1,1,0,0,Accountants,2.0,2,2,SATURDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.4547887913408776,0.5424591624692978,0.5673792367572691,0.1485,0.1043,0.9796,,,0.16,0.1379,0.3333,,,,0.14400000000000002,0.0154,0.0336,0.1513,0.1082,0.9796,,,0.1611,0.1379,0.3333,,,,0.15,0.0156,0.0356,0.1499,0.1043,0.9796,,,0.16,0.1379,0.3333,,,,0.1466,0.0155,0.0343,org spec account,block of flats,0.1206,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1533.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n265163,0,Cash loans,F,N,Y,0,135000.0,258768.0,13212.0,216000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-19212,-2927,-10723.0,-2394,,1,1,0,1,0,1,,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Industry: type 5,,0.6851572418300498,0.3001077565791181,0.0495,0.0506,0.9757,0.6668,0.0203,0.0,0.1034,0.125,0.0417,0.0447,0.0403,0.0396,0.0,0.0,0.0504,0.0525,0.9757,0.6798,0.0204,0.0,0.1034,0.125,0.0417,0.0457,0.0441,0.0413,0.0,0.0,0.05,0.0506,0.9757,0.6713,0.0204,0.0,0.1034,0.125,0.0417,0.0455,0.040999999999999995,0.0403,0.0,0.0,reg oper account,block of flats,0.0422,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1287.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n402445,0,Cash loans,F,N,N,1,112500.0,521280.0,19449.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008068,-14403,-2111,-1141.0,-4390,,1,1,0,1,0,0,Cooking staff,3.0,3,3,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.3839247205327738,0.5190973382084597,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1441.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n287745,0,Revolving loans,F,Y,Y,1,112500.0,292500.0,14625.0,292500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-15447,365243,-5893.0,-4409,6.0,1,0,0,1,0,0,,3.0,3,3,THURSDAY,11,0,0,0,0,0,0,XNA,,0.6308517674017321,0.2225807646753351,0.1237,0.0965,0.9742,,,0.0,0.2069,0.1667,,,,0.0711,,0.0,0.1261,0.1001,0.9742,,,0.0,0.2069,0.1667,,,,0.0741,,0.0,0.1249,0.0965,0.9742,,,0.0,0.2069,0.1667,,,,0.0724,,0.0,,block of flats,0.0559,\"Stone, brick\",No,4.0,2.0,4.0,2.0,-155.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n245876,0,Cash loans,M,Y,N,0,193500.0,1575000.0,41548.5,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-21916,-1612,-9469.0,-1254,3.0,1,1,1,1,1,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Housing,,0.5644674846259945,0.7238369900414456,0.0082,0.0271,0.9866,0.8164,0.0,0.0,0.0345,0.0417,0.0417,0.0212,0.0067,0.0053,0.0,0.0,0.0084,0.0282,0.9866,0.8236,0.0,0.0,0.0345,0.0417,0.0417,0.0217,0.0073,0.0055,0.0,0.0,0.0083,0.0271,0.9866,0.8189,0.0,0.0,0.0345,0.0417,0.0417,0.0216,0.0068,0.0054,0.0,0.0,reg oper account,block of flats,0.0069,Panel,No,4.0,0.0,4.0,0.0,-1496.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n115421,0,Cash loans,M,Y,Y,1,202500.0,679500.0,32688.0,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.031329,-13620,-912,-679.0,-4457,8.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,8,0,0,0,0,0,0,Business Entity Type 1,0.23267780983877945,0.5761811198699562,0.511891801533151,0.1634,0.0268,0.9791,0.7144,0.0273,0.1,0.1552,0.25,0.2917,0.1451,0.1328,0.1505,0.0019,0.0402,0.0735,0.0,0.9791,0.7256,0.0088,0.0,0.1379,0.1667,0.2083,0.0174,0.0643,0.0697,0.0,0.0,0.165,0.0268,0.9791,0.7182,0.0275,0.1,0.1552,0.25,0.2917,0.1477,0.1351,0.1532,0.0019,0.0411,reg oper account,block of flats,0.2016,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-70.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n454646,0,Cash loans,M,N,N,0,157500.0,269550.0,21294.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006233,-13329,-922,-5676.0,-2841,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.4588800393185413,,0.0825,0.1104,0.9881,0.8368,0.0165,,0.2759,0.1667,,0.0636,0.0672,0.0895,,0.0027,0.084,0.1146,0.9881,0.8432,0.0167,,0.2759,0.1667,,0.065,0.0735,0.0932,,0.0028,0.0833,0.1104,0.9881,0.8390000000000001,0.0166,,0.2759,0.1667,,0.0647,0.0684,0.0911,,0.0027,reg oper account,block of flats,0.08,Panel,No,0.0,0.0,0.0,0.0,-1057.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n425285,0,Cash loans,F,N,Y,0,112500.0,247500.0,12703.5,247500.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.028663,-14225,-3170,-6209.0,-4696,,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Construction,0.6999309520347894,0.6313710480569467,0.2622489709189549,,0.0622,0.9752,0.66,0.0079,0.0,0.1379,0.1667,0.2083,0.0525,0.0538,0.0503,,0.0727,,0.0645,0.9752,0.6733,0.008,0.0,0.1379,0.1667,0.2083,0.0536,0.0588,0.0525,,0.077,,0.0622,0.9752,0.6645,0.0079,0.0,0.1379,0.1667,0.2083,0.0534,0.0547,0.0513,,0.0743,reg oper account,block of flats,0.0597,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n427921,0,Cash loans,F,Y,Y,0,157500.0,814041.0,23800.5,679500.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.002042,-19901,-1319,-8892.0,-3304,6.0,1,1,0,1,0,0,Sales staff,2.0,3,3,FRIDAY,13,0,0,0,0,0,0,Trade: type 7,,0.07376479027215728,0.5849900404894085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-291.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n419006,0,Cash loans,F,N,N,1,202500.0,276277.5,18594.0,238500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006629,-19783,-1629,-4543.0,-3334,,1,1,0,1,0,0,Core staff,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6825996615401576,0.1500851762342935,0.0165,0.0,0.9806,0.7348,0.0,0.0,0.069,0.0417,0.0,0.0,0.0134,0.0184,0.0,0.0,0.0168,0.0,0.9801,0.7387,0.0,0.0,0.069,0.0417,0.0,0.0,0.0147,0.0189,0.0,0.0,0.0167,0.0,0.9806,0.7383,0.0,0.0,0.069,0.0417,0.0,0.0,0.0137,0.0187,0.0,0.0,reg oper spec account,block of flats,0.0142,Wooden,No,0.0,0.0,0.0,0.0,-630.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n449198,1,Cash loans,F,N,Y,2,180000.0,170640.0,13612.5,135000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,Rented apartment,0.016612000000000002,-14980,-932,0.0,-453,,1,1,0,1,0,0,Medicine staff,3.0,2,2,MONDAY,15,0,0,0,0,0,0,Other,,0.5878468543518713,0.3859146722745145,0.0598,0.063,0.9786,0.7076,0.0281,0.0,0.1379,0.1667,0.2083,0.0944,0.0471,0.0539,0.0077,0.10300000000000001,0.0609,0.0654,0.9786,0.7190000000000001,0.0283,0.0,0.1379,0.1667,0.2083,0.0966,0.0514,0.0562,0.0078,0.1091,0.0604,0.063,0.9786,0.7115,0.0283,0.0,0.1379,0.1667,0.2083,0.0961,0.0479,0.0549,0.0078,0.1052,reg oper account,block of flats,0.0802,\"Stone, brick\",No,8.0,0.0,8.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n396861,0,Cash loans,M,Y,N,1,184500.0,675000.0,32472.0,675000.0,Family,State servant,Secondary / secondary special,Married,Office apartment,0.010032,-11038,-1216,-1149.0,-3359,4.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,8,0,0,0,0,0,0,Medicine,0.3585172826150285,0.5325494178550566,0.5656079814115492,0.0309,0.0519,0.9727,0.626,0.0037,0.0,0.1034,0.0833,0.125,0.0,0.0151,0.0239,0.0463,0.032,0.0315,0.0538,0.9727,0.6406,0.0037,0.0,0.1034,0.0833,0.125,0.0,0.0165,0.0249,0.0467,0.0338,0.0312,0.0519,0.9727,0.631,0.0037,0.0,0.1034,0.0833,0.125,0.0,0.0154,0.0243,0.0466,0.0326,reg oper account,block of flats,0.0278,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1002.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n202370,0,Revolving loans,F,N,N,2,94500.0,180000.0,9000.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-14227,-2796,-3955.0,-708,,1,1,0,1,1,1,,4.0,2,2,TUESDAY,9,0,0,0,0,1,1,Industry: type 2,0.6025507803957626,0.6652212042394722,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2610.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n365592,0,Revolving loans,M,Y,Y,0,202500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007114,-12918,-436,-4761.0,-4136,2.0,1,1,1,1,1,0,Drivers,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Transport: type 4,0.2234566525798509,0.2266793250289581,0.1008037063175332,,,0.9707,,,,,,,,,0.0065,,,,,0.9707,,,,,,,,,0.0067,,,,,0.9707,,,,,,,,,0.0066,,,,block of flats,0.0056,,No,8.0,0.0,8.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n169152,0,Cash loans,F,Y,N,0,99000.0,315000.0,14004.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-17918,-5372,-5231.0,-1455,7.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Postal,0.5020823025541072,0.4700954306347973,0.06210303783729682,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1344.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n175826,0,Revolving loans,F,N,N,0,90000.0,247500.0,12375.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002042,-18843,-2710,-9990.0,-2385,,1,1,1,1,0,0,Medicine staff,2.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,Medicine,,0.2908385066851533,0.5849900404894085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n418538,0,Cash loans,F,N,N,0,112500.0,544491.0,16047.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,Municipal apartment,0.007114,-23008,365243,-11148.0,-4640,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,0.7469129234605356,0.6561573450083625,0.5691487713619409,0.0701,,0.9851,,,,,,,,,,,,0.0599,,0.9851,,,,,,,,,,,,0.0708,,0.9851,,,,,,,,,,,,,,0.0568,,No,1.0,0.0,1.0,0.0,-981.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n337243,0,Cash loans,F,Y,Y,2,540000.0,760225.5,58963.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006629,-12917,-2376,-2933.0,-4737,1.0,1,1,0,1,0,0,Managers,4.0,2,2,THURSDAY,10,0,0,0,0,0,0,Industry: type 3,0.3771906774461786,0.6421940600402358,0.1206410299309233,0.0876,0.1107,0.9836,0.7756,0.0249,0.0,0.1207,0.1667,0.0417,0.0664,0.0714,0.0737,0.0,0.0148,0.084,0.1136,0.9831,0.7779,0.0206,0.0,0.1034,0.1667,0.0417,0.0587,0.0735,0.0743,0.0,0.013000000000000001,0.0885,0.1107,0.9836,0.7786,0.025,0.0,0.1207,0.1667,0.0417,0.0676,0.0727,0.075,0.0,0.0151,reg oper account,block of flats,0.0699,Panel,No,0.0,0.0,0.0,0.0,-1546.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n257877,0,Cash loans,M,Y,N,1,216000.0,1456587.0,52447.5,1305000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.003069,-12386,-1180,-5009.0,-4472,5.0,1,1,0,1,0,0,Laborers,3.0,3,3,FRIDAY,15,0,0,0,0,1,1,Construction,0.19271184771477348,0.5531084952834124,0.1654074596555436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-471.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n419434,0,Cash loans,F,N,N,1,171000.0,667237.5,24097.5,576000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.04622,-16617,-1469,-285.0,-168,,1,1,0,1,0,0,Sales staff,2.0,1,1,SATURDAY,15,1,0,1,1,0,1,Self-employed,0.6435568008974036,0.7101153325562791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-757.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n402752,0,Cash loans,M,N,Y,0,157500.0,269550.0,12001.5,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.016612000000000002,-22201,365243,-6910.0,-5096,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.5084692946783511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n361445,0,Cash loans,F,N,Y,0,81000.0,137538.0,10993.5,121500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010147,-15071,-2483,-4401.0,-4415,,1,1,1,1,1,0,Sales staff,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,,0.6542306665015101,0.6023863442690867,,,0.9727,,,,,,,,,0.0054,,,,,0.9727,,,,,,,,,0.0056,,,,,0.9727,,,,,,,,,0.0055,,,,,0.0043,,No,0.0,0.0,0.0,0.0,-690.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n450686,0,Cash loans,F,N,Y,0,247500.0,495000.0,23944.5,495000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-18699,365243,-10918.0,-2179,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,XNA,,0.7739360520106399,0.22888341670067305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,0.0,11.0,0.0,-904.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,14.0,0.0,1.0\n243344,0,Cash loans,F,N,Y,0,67500.0,225000.0,10039.5,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.018209,-19982,-10841,-9973.0,-3363,,1,1,0,1,0,0,Laborers,2.0,3,3,SATURDAY,9,0,0,0,0,0,0,Electricity,,0.2715381278281757,0.8528284668510541,0.2247,0.1705,0.9866,0.8164,0.0456,0.24,0.2069,0.3333,0.0417,0.126,0.1832,0.2386,0.0,0.0,0.22899999999999998,0.1769,0.9866,0.8236,0.046,0.2417,0.2069,0.3333,0.0417,0.1289,0.2002,0.2486,0.0,0.0,0.2269,0.1705,0.9866,0.8189,0.0459,0.24,0.2069,0.3333,0.0417,0.1282,0.1864,0.2429,0.0,0.0,reg oper account,block of flats,0.2126,Panel,No,0.0,0.0,0.0,0.0,-364.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n271425,0,Cash loans,F,N,Y,0,90000.0,194076.0,11920.5,162000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.016612000000000002,-21850,365243,-9058.0,-5155,,1,0,0,1,1,0,,1.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.6006480288407193,0.5884877883422673,0.0041,0.0118,0.9881,,,0.0,,0.0417,,0.0812,,0.0052,,0.0,0.0042,0.0122,0.9881,,,0.0,,0.0417,,0.0831,,0.0054,,0.0,0.0042,0.0118,0.9881,,,0.0,,0.0417,,0.0826,,0.0053,,0.0,,block of flats,0.0065,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1821.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n354914,0,Cash loans,F,N,Y,0,225000.0,790830.0,45531.0,675000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.006670999999999999,-22049,365243,-5251.0,-3935,,1,0,0,1,0,1,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,,0.7084288920404718,0.6594055320683344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-61.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n339551,0,Cash loans,F,N,Y,0,40500.0,153000.0,5764.5,153000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.030755,-17818,365243,-9855.0,-1348,,1,0,0,1,1,0,,1.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.09169845515254348,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-566.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n455421,0,Cash loans,F,Y,Y,0,180000.0,521280.0,23089.5,450000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.072508,-19203,-4311,-139.0,-2737,6.0,1,1,0,1,0,0,,2.0,1,1,THURSDAY,14,0,0,0,0,0,0,Other,0.8581619717826302,0.7075970839692735,0.4206109640437848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n375184,0,Cash loans,F,N,Y,0,90000.0,129519.0,15498.0,121500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-13556,-362,-533.0,-583,,1,1,0,1,0,0,Medicine staff,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Other,,0.7183761791878212,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-3310.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n125744,0,Cash loans,F,N,Y,0,157500.0,472500.0,46030.5,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006296,-17065,-6298,-1705.0,-603,,1,1,0,1,1,0,Sales staff,2.0,3,3,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.5139501572547027,0.44374615690520336,0.7449321846094795,0.0082,0.0191,0.9732,,,,0.0345,0.0417,,0.0126,,0.0053,,0.0043,0.0084,0.0198,0.9732,,,,0.0345,0.0417,,0.0129,,0.0055,,0.0046,0.0083,0.0191,0.9732,,,,0.0345,0.0417,,0.0128,,0.0053,,0.0044,,block of flats,0.0057,Block,No,1.0,0.0,1.0,0.0,-540.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n366573,0,Cash loans,M,Y,Y,0,270000.0,543037.5,39645.0,463500.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.010556,-10146,-1356,-1282.0,-2808,16.0,1,1,0,1,0,1,Managers,2.0,3,3,SATURDAY,11,0,0,0,0,0,0,Other,,0.4774989486956868,0.42765737003502935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-484.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n227362,0,Cash loans,M,Y,N,0,427500.0,1350000.0,46926.0,1350000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.016612000000000002,-14059,-828,-1281.0,-3580,8.0,1,1,1,1,1,1,,2.0,2,2,FRIDAY,13,0,1,1,1,1,1,Business Entity Type 3,0.49116643700094,0.6761263774772632,0.375711009574066,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1841.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n189360,0,Cash loans,M,N,Y,1,180000.0,288873.0,16713.0,238500.0,Family,Working,Higher education,Married,House / apartment,0.016612000000000002,-16402,-758,-1005.0,-2116,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,14,0,0,0,0,0,0,Self-employed,0.5339868686835028,0.5508426222824204,0.722392890081304,0.1113,0.1005,0.9985,,,0.12,0.1034,0.3333,,0.0175,,0.1223,,0.0046,0.1134,0.1043,0.9985,,,0.1208,0.1034,0.3333,,0.0179,,0.1274,,0.0049,0.1124,0.1005,0.9985,,,0.12,0.1034,0.3333,,0.0178,,0.1245,,0.0047,,block of flats,0.1257,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-559.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n386226,0,Cash loans,F,N,N,1,315000.0,1070982.0,45504.0,909000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.014464,-17533,-597,-1913.0,-1088,,1,1,0,1,0,0,Managers,2.0,2,2,SATURDAY,7,0,0,0,0,1,1,Business Entity Type 3,,0.6158822792383727,0.15855489979486306,0.0186,0.0578,0.9816,,,0.0,0.069,0.0417,,0.0451,,0.016,,0.0256,0.0189,0.06,0.9816,,,0.0,0.069,0.0417,,0.0461,,0.0167,,0.0271,0.0187,0.0578,0.9816,,,0.0,0.069,0.0417,,0.0459,,0.0163,,0.0262,,block of flats,0.0339,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2025.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n359725,0,Revolving loans,F,N,N,0,45000.0,135000.0,6750.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-23290,365243,-4132.0,-4152,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,0.1970920119506155,0.6304035168860764,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1822.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n361391,0,Cash loans,F,N,N,2,225000.0,265851.0,20704.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.020246,-13279,-2331,-2371.0,-5254,,1,1,0,1,0,0,Cleaning staff,3.0,3,3,MONDAY,13,0,0,0,0,0,0,Police,0.2521700673644883,0.2916148745537355,0.6363761710860439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n314700,0,Cash loans,M,Y,Y,0,270000.0,247275.0,22810.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.010276,-10414,-753,-4485.0,-3098,7.0,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.7518286116331844,0.5483634318119094,0.1940678276718812,0.0722,0.0774,0.9801,0.728,0.0078,0.0,0.1379,0.1667,0.2083,0.0539,0.0588,0.0676,0.0,0.0016,0.0735,0.0803,0.9801,0.7387,0.0078,0.0,0.1379,0.1667,0.2083,0.0552,0.0643,0.0704,0.0,0.0017,0.0729,0.0774,0.9801,0.7316,0.0078,0.0,0.1379,0.1667,0.2083,0.0549,0.0599,0.0688,0.0,0.0016,reg oper account,block of flats,0.0535,Block,No,0.0,0.0,0.0,0.0,-672.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n103845,1,Cash loans,F,Y,Y,3,135000.0,414792.0,21847.5,315000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-11339,-487,-4013.0,-2822,12.0,1,1,0,1,1,0,Managers,5.0,2,2,FRIDAY,9,0,0,0,0,0,0,Postal,0.451244795673693,0.05016533798781138,0.04825898346784866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2645.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n317014,0,Cash loans,M,Y,Y,0,292500.0,835380.0,40320.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00496,-11588,-2160,-4628.0,-3726,24.0,1,1,0,1,1,0,Drivers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Emergency,0.4644485213480968,0.4744554722303302,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-730.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n285947,0,Cash loans,F,N,Y,0,202500.0,254700.0,24939.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.072508,-24303,365243,-14370.0,-3744,,1,0,0,1,1,0,,1.0,1,1,MONDAY,14,0,0,0,0,0,0,XNA,,0.686283561278054,,0.4041,0.2055,0.9801,0.728,0.5689,0.64,0.2759,0.4583,0.5,0.0,0.3219,0.3955,0.0347,0.0134,0.4118,0.2133,0.9801,0.7387,0.5741,0.6445,0.2759,0.4583,0.5,0.0,0.3517,0.41200000000000003,0.035,0.0142,0.408,0.2055,0.9801,0.7316,0.5725,0.64,0.2759,0.4583,0.5,0.0,0.3275,0.4026,0.0349,0.0137,reg oper account,block of flats,0.1339,Panel,No,0.0,0.0,0.0,0.0,-1390.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n202777,0,Cash loans,F,N,Y,0,157500.0,1215000.0,43641.0,1215000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-15640,-1328,-577.0,-4897,,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,Kindergarten,0.6421239597863565,0.3342855969861764,0.7421816117614419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2345.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n242995,0,Cash loans,M,N,Y,0,180000.0,1419606.0,41638.5,1111500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-19328,365243,-11308.0,-2068,,1,0,0,1,1,0,,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,XNA,,0.07827005414351948,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1674.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n452660,0,Cash loans,F,N,Y,0,112500.0,1035000.0,37174.5,1035000.0,Unaccompanied,Pensioner,Higher education,Married,With parents,0.015221,-23735,365243,-7620.0,-4642,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,0.8163115710196613,0.4772534420671844,0.392773860603134,,,0.9891,,,,,,,,,0.0855,,,,,0.9891,,,,,,,,,0.0891,,,,,0.9891,,,,,,,,,0.0871,,,,,0.0673,,No,0.0,0.0,0.0,0.0,-1771.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n404284,0,Cash loans,F,N,N,2,94500.0,754740.0,22198.5,630000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009549,-15930,-6186,-9899.0,-3975,,1,1,0,1,0,0,,4.0,2,2,TUESDAY,14,0,0,0,0,1,1,Industry: type 4,,0.561083024899614,0.7490217048463391,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-46.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n394733,0,Cash loans,F,N,Y,0,157500.0,254700.0,24939.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-23997,365243,-3990.0,-2795,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.6367341227221472,0.4241303111942548,0.0619,0.0637,0.9757,0.6668,0.0079,0.0,0.1034,0.1667,0.2083,0.043,0.0504,0.0498,0.0,0.0,0.063,0.0662,0.9757,0.6798,0.008,0.0,0.1034,0.1667,0.2083,0.0439,0.0551,0.0519,0.0,0.0,0.0625,0.0637,0.9757,0.6713,0.008,0.0,0.1034,0.1667,0.2083,0.0437,0.0513,0.0507,0.0,0.0,reg oper spec account,block of flats,0.0435,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1089.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n450869,0,Revolving loans,F,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-11450,-2656,-780.0,-2285,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 1,0.4201682263981888,0.32857767807834976,0.18629294965553744,0.0887,0.048,0.9841,0.7824,0.1118,0.08,0.0345,0.4583,0.0417,0.0404,0.0723,0.0777,0.0,0.0232,0.0903,0.0498,0.9841,0.7909,0.1128,0.0806,0.0345,0.4583,0.0417,0.0414,0.079,0.081,0.0,0.0246,0.0895,0.048,0.9841,0.7853,0.1125,0.08,0.0345,0.4583,0.0417,0.0412,0.0735,0.0791,0.0,0.0237,reg oper account,block of flats,0.08,\"Stone, brick\",No,8.0,1.0,8.0,1.0,-315.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n351570,0,Cash loans,F,N,Y,0,171000.0,1255680.0,45234.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,Co-op apartment,0.006305,-9470,-1622,-3478.0,-2145,,1,1,0,1,0,0,Sales staff,2.0,3,3,THURSDAY,10,0,0,0,1,1,0,Industry: type 3,,0.10890760878349458,0.6863823354047934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n107751,0,Cash loans,F,N,N,1,90000.0,454500.0,19237.5,454500.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.025164,-15213,-2934,-6030.0,-6031,,1,1,0,1,0,0,Secretaries,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Medicine,0.5613194590177479,0.6733861234340149,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-1695.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n218279,0,Cash loans,F,N,Y,0,58500.0,143910.0,14148.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.020713,-23694,365243,-4976.0,-4267,,1,0,0,1,0,0,,1.0,3,3,MONDAY,9,0,0,0,0,0,0,XNA,,0.4598358554593737,,0.1175,0.1307,0.9796,0.7212,0.0524,0.0,0.2759,0.1667,0.2083,0.0494,0.095,0.1104,0.0039,0.0032,0.1197,0.1357,0.9796,0.7321,0.0529,0.0,0.2759,0.1667,0.2083,0.0505,0.1038,0.115,0.0039,0.0034,0.1187,0.1307,0.9796,0.7249,0.0527,0.0,0.2759,0.1667,0.2083,0.0503,0.0966,0.1124,0.0039,0.0033,reg oper account,block of flats,0.0875,Panel,No,2.0,0.0,2.0,0.0,-507.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n375425,0,Cash loans,F,N,N,0,112500.0,180000.0,17532.0,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.01885,-23643,365243,-2471.0,-3926,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,,0.5000073504298986,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2091.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n133235,0,Cash loans,F,N,Y,0,270000.0,787500.0,29313.0,787500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.028663,-17789,-3270,-1303.0,-1314,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,17,0,0,0,1,0,1,Self-employed,,0.7010303434475794,0.4686596550493113,0.0041,0.0,0.9697,0.5852,0.0006,0.0,0.0345,0.0417,0.0833,0.0181,0.0034,0.0044,0.0,0.0,0.0042,0.0,0.9697,0.6014,0.0006,0.0,0.0345,0.0417,0.0833,0.0186,0.0037,0.0046,0.0,0.0,0.0042,0.0,0.9697,0.5907,0.0006,0.0,0.0345,0.0417,0.0833,0.0185,0.0034,0.0045,0.0,0.0,reg oper account,block of flats,0.0038,Block,No,1.0,0.0,1.0,0.0,-644.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,6.0,0.0,6.0\n224346,0,Cash loans,F,N,Y,0,220500.0,1044000.0,44361.0,1044000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.00963,-19450,365243,-2574.0,-2974,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,,0.1752332517392446,0.22133520635446602,0.1474,0.1951,0.9831,,,,0.3793,0.1667,,0.1237,,0.0965,,,0.1502,0.2025,0.9831,,,,0.3793,0.1667,,0.1265,,0.1006,,,0.1489,0.1951,0.9831,,,,0.3793,0.1667,,0.1258,,0.0983,,,,block of flats,0.1194,Panel,No,0.0,0.0,0.0,0.0,-1036.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n342761,0,Cash loans,M,N,N,0,202500.0,544491.0,17694.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006629,-12867,-2783,-6620.0,-3938,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.25900103884533643,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n156928,0,Revolving loans,M,N,Y,1,180000.0,405000.0,20250.0,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.022625,-17131,-1470,-537.0,-647,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,14,0,0,0,0,0,0,Other,0.6812801777837559,0.4441456226663119,0.6041125998015721,0.1485,0.1182,0.9811,0.7416,0.0386,0.16,0.1379,0.3333,0.375,0.1015,0.121,0.149,0.0,0.0014,0.1513,0.1226,0.9811,0.7517,0.039,0.1611,0.1379,0.3333,0.375,0.1038,0.1322,0.1552,0.0,0.0015,0.1499,0.1182,0.9811,0.7451,0.0389,0.16,0.1379,0.3333,0.375,0.1033,0.1231,0.1517,0.0,0.0014,reg oper account,block of flats,0.1586,Panel,No,0.0,0.0,0.0,0.0,-1359.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,1.0,3.0\n288187,0,Cash loans,M,Y,Y,0,99000.0,157500.0,17091.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-9079,-2161,-2282.0,-1744,11.0,1,1,0,1,0,0,,2.0,2,2,SATURDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.2330939219046823,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-32.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n375956,0,Cash loans,F,N,N,0,270000.0,781920.0,34573.5,675000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.00496,-21861,365243,-2747.0,-1652,,1,0,0,1,0,0,,1.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.2804339038929443,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n333494,0,Cash loans,M,Y,Y,0,450000.0,675000.0,80239.5,675000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.026392,-12069,-1949,-5973.0,-4677,1.0,1,1,0,1,0,1,Managers,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,Security,0.5773134162147665,0.7228184820130735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-411.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n140856,0,Cash loans,F,N,N,0,81000.0,755190.0,30757.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22743,365243,-11668.0,-3979,,1,0,0,1,1,0,,2.0,2,2,MONDAY,21,0,0,0,0,0,0,XNA,,0.4580602333395085,0.5388627065779676,0.2969,0.1613,0.9806,0.7348,0.1147,0.32,0.2759,0.3333,0.375,0.2092,0.2412,0.3083,0.0039,0.0036,0.3025,0.1674,0.9806,0.7452,0.1157,0.3222,0.2759,0.3333,0.375,0.214,0.2635,0.3212,0.0039,0.0039,0.2998,0.1613,0.9806,0.7383,0.1154,0.32,0.2759,0.3333,0.375,0.2129,0.2454,0.3138,0.0039,0.0037,reg oper account,block of flats,0.306,Panel,No,0.0,0.0,0.0,0.0,-145.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,3.0\n308725,0,Cash loans,F,Y,Y,1,121500.0,479353.5,38002.5,391500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-11180,-1253,-2848.0,-3673,13.0,1,1,0,1,0,1,Cooking staff,3.0,2,2,TUESDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.6343645233841636,0.6739870252648011,0.6626377922738201,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1817.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n245200,0,Cash loans,M,Y,N,1,225000.0,901192.5,35869.5,805500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-14865,-2738,-7320.0,-4348,9.0,1,1,1,1,1,0,Drivers,3.0,1,1,MONDAY,15,0,1,1,0,1,1,Business Entity Type 3,,0.7181549228348258,,0.001,,0.9881,,,0.0,0.0345,0.0,,,,0.0014,,0.0031,0.0011,,0.9881,,,0.0,0.0345,0.0,,,,0.0015,,0.0033,0.001,,0.9881,,,0.0,0.0345,0.0,,,,0.0014,,0.0031,,terraced house,0.0018,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1560.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n361516,0,Revolving loans,F,N,Y,0,292500.0,585000.0,29250.0,585000.0,Family,State servant,Higher education,Single / not married,House / apartment,0.010006,-17524,-5331,-1477.0,-1044,,1,1,0,1,0,0,Medicine staff,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,Medicine,0.7220852516091532,0.7280320875950451,0.4014074137749511,0.0247,,0.9811,,,,0.0345,0.0833,,0.038,,,,0.0,0.0252,,0.9811,,,,0.0345,0.0833,,0.0389,,,,0.0,0.025,,0.9811,,,,0.0345,0.0833,,0.0387,,,,0.0,,block of flats,0.013000000000000001,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-2366.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n286115,0,Cash loans,M,Y,Y,1,202500.0,545040.0,39627.0,450000.0,Unaccompanied,Working,Higher education,Married,Municipal apartment,0.032561,-18362,-1006,-10351.0,-1861,5.0,1,1,0,1,0,0,Managers,3.0,1,1,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3834173550010864,0.6821683676458394,0.07647351918548448,0.0608,,0.9846,,,0.2,0.1724,0.5,0.5417,,,0.1223,,0.2736,0.062,,0.9846,,,0.2014,0.1724,0.5,0.5417,,,0.1274,,0.2896,0.0614,,0.9846,,,0.2,0.1724,0.5,0.5417,,,0.1245,,0.2793,,block of flats,0.2453,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1391.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n138782,0,Cash loans,F,N,Y,0,135000.0,378180.0,10836.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018634,-14567,-2605,-6092.0,-5089,,1,1,0,1,1,0,Waiters/barmen staff,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.5012602373449313,0.6650068922405558,0.25396280933631177,,,0.9414,,,,,,,,,0.002,,,,,0.9414,,,,,,,,,0.0021,,,,,0.9414,,,,,,,,,0.0021,,,,,0.0017,,No,0.0,0.0,0.0,0.0,-1374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n419669,0,Cash loans,M,Y,Y,0,202500.0,283585.5,29907.0,256500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.016612000000000002,-15042,-596,-1884.0,-5101,10.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.31189114724926403,0.6577838002083306,0.16699999999999998,0.083,0.9781,0.7008,0.0762,0.0,0.1034,0.1667,0.2083,0.0227,0.1353,0.0608,0.0039,0.0311,0.1702,0.0861,0.9782,0.7125,0.0769,0.0,0.1034,0.1667,0.2083,0.0232,0.1478,0.0633,0.0039,0.033,0.1686,0.083,0.9781,0.7048,0.0766,0.0,0.1034,0.1667,0.2083,0.0231,0.1377,0.0619,0.0039,0.0318,reg oper account,block of flats,0.0962,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1670.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n208559,0,Cash loans,M,N,Y,0,180000.0,446292.0,29821.5,364500.0,Children,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-18037,-4036,-8028.0,-1600,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,8,0,0,0,0,0,0,Medicine,,0.5851262549959867,0.722392890081304,0.0247,0.0054,0.9732,0.6328,0.0066,0.0,0.1034,0.0833,0.125,0.0171,0.0202,0.0259,0.0,0.0,0.0252,0.0056,0.9732,0.6472,0.0067,0.0,0.1034,0.0833,0.125,0.0175,0.022000000000000002,0.027000000000000003,0.0,0.0,0.025,0.0054,0.9732,0.6377,0.0066,0.0,0.1034,0.0833,0.125,0.0174,0.0205,0.0264,0.0,0.0,reg oper account,block of flats,0.0317,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1744.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n226857,0,Cash loans,F,N,Y,0,157500.0,225000.0,22050.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-24428,365243,-14968.0,-4302,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.1994138865557652,0.5082869913916046,,,0.9776,0.6940000000000001,,0.0,0.1552,0.1667,0.2083,0.0791,,0.0819,,0.0106,,,0.9772,0.6994,,0.0,0.1724,0.1667,0.2083,0.06,,0.053,,0.0,,,0.9776,0.6981,,0.0,0.1724,0.1667,0.2083,0.0752,,0.0901,,0.0015,,block of flats,0.05,Panel,No,0.0,0.0,0.0,0.0,-1493.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,8.0\n357282,0,Revolving loans,F,Y,Y,1,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007305,-11650,-3561,-3125.0,-2048,13.0,1,1,0,1,0,0,Accountants,3.0,3,3,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.4605722320772408,0.5835487839235232,0.6023863442690867,0.0825,0.0631,0.9846,0.7892,0.0171,0.0,0.1379,0.1667,0.2083,0.0356,0.0664,0.0617,0.0039,0.0026,0.084,0.0654,0.9846,0.7975,0.0173,0.0,0.1379,0.1667,0.2083,0.0364,0.0725,0.0643,0.0039,0.0027,0.0833,0.0631,0.9846,0.792,0.0172,0.0,0.1379,0.1667,0.2083,0.0362,0.0676,0.0628,0.0039,0.0027,reg oper account,block of flats,0.0585,\"Stone, brick\",No,12.0,1.0,12.0,1.0,-583.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n355926,1,Revolving loans,F,Y,Y,0,121500.0,315000.0,15750.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-16384,-114,-6876.0,-5705,35.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Business Entity Type 2,,0.05932827652566355,0.2925880073368475,0.0804,0.0,0.9831,0.7688,0.0128,0.0,0.1379,0.1667,0.2083,0.0,0.0656,0.0925,0.0,0.0,0.0819,0.0,0.9831,0.7779,0.0129,0.0,0.1379,0.1667,0.2083,0.0,0.0716,0.0964,0.0,0.0,0.0812,0.0,0.9831,0.7719,0.0128,0.0,0.1379,0.1667,0.2083,0.0,0.0667,0.0942,0.0,0.0,reg oper account,block of flats,0.0797,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1557.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n148691,0,Cash loans,M,N,N,0,128695.5,284256.0,26707.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.019689,-8935,-539,-8288.0,-1620,,1,1,1,1,1,0,Sales staff,1.0,2,2,THURSDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.5194378300322396,0.2445163919946749,0.1237,0.0027,0.9851,0.7959999999999999,0.0157,0.0,0.2759,0.1667,0.0417,0.0,0.1009,0.1098,0.0,0.0,0.1261,0.0028,0.9851,0.804,0.0159,0.0,0.2759,0.1667,0.0417,0.0,0.1102,0.1144,0.0,0.0,0.1249,0.0027,0.9851,0.7987,0.0158,0.0,0.2759,0.1667,0.0417,0.0,0.1026,0.1118,0.0,0.0,org spec account,block of flats,0.0864,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-369.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n106458,0,Cash loans,F,N,N,0,90000.0,900000.0,26316.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-20732,365243,-3891.0,-4109,,1,0,0,1,0,1,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,0.7045866614739739,0.7209834105884629,0.7435593141311444,0.1196,0.1564,0.9831,0.7688,0.0199,0.0,0.1724,0.1667,0.2083,0.0776,0.0916,0.1028,0.027000000000000003,0.0473,0.1218,0.1623,0.9831,0.7779,0.0201,0.0,0.1724,0.1667,0.2083,0.0794,0.1001,0.1071,0.0272,0.05,0.1207,0.1564,0.9831,0.7719,0.02,0.0,0.1724,0.1667,0.2083,0.079,0.0932,0.1047,0.0272,0.0483,reg oper account,block of flats,0.0911,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-1882.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,3.0\n440850,0,Cash loans,F,Y,Y,2,180000.0,157050.0,10750.5,112500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.02461,-11573,-1792,-1248.0,-2533,64.0,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,15,0,1,1,0,0,0,Industry: type 12,0.501052509936353,0.6325866005220675,0.08226850764912726,0.1649,0.0801,0.998,0.9728,0.0811,0.16,0.069,0.6667,0.7083,,0.1345,0.2105,0.0077,0.0521,0.1681,0.0831,0.998,0.9739,0.0819,0.1611,0.069,0.6667,0.7083,,0.1469,0.2193,0.0078,0.0552,0.1665,0.0801,0.998,0.9732,0.0816,0.16,0.069,0.6667,0.7083,,0.1368,0.2143,0.0078,0.0532,reg oper account,block of flats,0.2213,Others,No,0.0,0.0,0.0,0.0,-734.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n139104,0,Cash loans,F,Y,Y,1,121500.0,490495.5,26131.5,454500.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.019101,-14351,-4426,-4398.0,-4598,20.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,11,0,0,0,0,1,1,Housing,0.7442031271235828,0.6196413854691976,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2663.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n238235,0,Cash loans,F,N,Y,0,121500.0,180000.0,5080.5,180000.0,Unaccompanied,Working,Incomplete higher,Single / not married,Municipal apartment,0.018029,-7987,-915,-6241.0,-388,,1,1,1,1,0,0,Laborers,1.0,3,3,MONDAY,12,0,0,0,1,1,0,Trade: type 2,,0.6328457686959241,0.190705947811054,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-14.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n400352,0,Cash loans,M,Y,N,0,360000.0,755190.0,34254.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010643,-11077,-844,-3158.0,-3711,6.0,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Self-employed,,0.6465886485961535,,0.0093,0.0,0.9702,,,0.0,0.3103,0.0,,,,0.005,,0.0035,0.0095,0.0,0.9702,,,0.0,0.3103,0.0,,,,0.0052,,0.0037,0.0094,0.0,0.9702,,,0.0,0.3103,0.0,,,,0.0051,,0.0036,,terraced house,0.0047,Mixed,No,0.0,0.0,0.0,0.0,-188.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n384055,0,Cash loans,F,Y,Y,0,112500.0,219069.0,10669.5,148500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-16214,-2482,-6961.0,-4257,36.0,1,1,0,1,0,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,School,0.29984362258357744,0.3568151109709929,0.18629294965553744,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2046.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n290802,0,Cash loans,F,Y,Y,0,270000.0,835380.0,40320.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-20914,-151,-1052.0,-2067,8.0,1,1,0,1,0,0,Core staff,2.0,1,1,FRIDAY,10,0,0,0,0,0,0,School,0.7233128880892703,0.7575406208546568,0.3606125659189888,0.0186,0.0129,0.9901,0.8640000000000001,0.0082,0.0,0.0345,0.125,0.1667,0.0,0.0134,0.0096,0.0077,0.0206,0.0189,0.0134,0.9901,0.8693,0.0083,0.0,0.0345,0.125,0.1667,0.0,0.0147,0.01,0.0078,0.0218,0.0187,0.0129,0.9901,0.8658,0.0082,0.0,0.0345,0.125,0.1667,0.0,0.0137,0.0098,0.0078,0.021,reg oper account,block of flats,0.012,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1903.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n144073,0,Cash loans,F,N,N,1,180000.0,900000.0,25924.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010006,-9459,-2604,-838.0,-2122,,1,1,1,1,1,0,Private service staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.5698091147270139,0.6641392088775145,0.4311917977993083,0.1979,0.136,0.9856,0.8028,,0.24,0.2069,0.3333,0.375,0.0793,0.1605,0.1336,0.0039,0.0993,0.2017,0.1411,0.9856,0.8105,,0.2417,0.2069,0.3333,0.375,0.0811,0.1754,0.1392,0.0039,0.1051,0.1999,0.136,0.9856,0.8054,,0.24,0.2069,0.3333,0.375,0.0807,0.1633,0.136,0.0039,0.1013,reg oper account,block of flats,0.1582,Block,No,1.0,0.0,1.0,0.0,-1674.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n403746,0,Cash loans,M,Y,Y,0,270000.0,1125000.0,37309.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-13237,-2573,-7222.0,-4141,6.0,1,1,0,1,1,1,Managers,2.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.7488708932266656,0.36896873825284665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,1.0,-1590.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n340122,0,Cash loans,F,N,Y,0,157500.0,1078200.0,38331.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010643,-20617,365243,-13179.0,-3959,,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,1,0,0,XNA,,0.5546538692478965,0.2955826421513093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n268884,1,Cash loans,F,Y,N,1,90000.0,315000.0,25015.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-10171,-227,-10106.0,-1454,9.0,1,1,0,1,0,1,Sales staff,2.0,2,2,MONDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.3847200933516293,0.4427801833026214,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n309451,0,Cash loans,F,N,Y,0,225000.0,485640.0,36441.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.072508,-22846,365243,-9623.0,-5317,,1,0,0,1,1,0,,2.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.5593913363483417,0.5814837058057234,0.0866,0.0974,0.9737,,,0.0,0.1466,0.1667,,,,0.0475,,0.0152,0.084,0.0906,0.9742,,,0.0,0.1379,0.1667,,,,0.0438,,0.0161,0.0833,0.0898,0.9742,,,0.0,0.1379,0.1667,,,,0.0454,,0.0155,,block of flats,0.0531,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n274530,0,Cash loans,F,N,Y,0,126000.0,136512.0,4900.5,108000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.005002,-21172,365243,-7159.0,-4146,,1,0,0,1,1,0,,1.0,3,3,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.7686243314557059,0.7826078370261895,0.0588,0.0,0.9781,0.7008,,0.0,0.1379,0.1667,,0.0169,,0.0373,,0.0565,0.0599,0.0,0.9782,0.7125,,0.0,0.1379,0.1667,,0.0173,,0.0388,,0.0598,0.0593,0.0,0.9781,0.7048,,0.0,0.1379,0.1667,,0.0172,,0.0379,,0.0576,org spec account,block of flats,0.0599,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1560.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n228468,0,Cash loans,F,N,N,0,90000.0,384048.0,18810.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010147,-21115,365243,-3940.0,-2317,,1,0,0,1,1,1,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.2594209415185879,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1453.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n335767,0,Revolving loans,F,Y,Y,1,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.030755,-15161,-1453,-8243.0,-1839,13.0,1,1,0,1,0,0,Sales staff,3.0,2,2,SUNDAY,15,0,0,0,0,0,0,Self-employed,,0.5497494841207051,0.4294236843421945,0.1485,0.1095,0.9886,0.8436,0.0288,0.16,0.1379,0.3333,0.375,0.049,0.121,0.1575,0.0,0.0,0.1513,0.1136,0.9886,0.8497,0.0291,0.1611,0.1379,0.3333,0.375,0.0501,0.1322,0.1641,0.0,0.0,0.1499,0.1095,0.9886,0.8457,0.028999999999999998,0.16,0.1379,0.3333,0.375,0.0498,0.1231,0.1603,0.0,0.0,reg oper account,block of flats,0.1396,Panel,No,5.0,0.0,5.0,0.0,-402.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n396575,0,Cash loans,M,Y,N,1,540000.0,1125000.0,57150.0,1125000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-13176,-764,-869.0,-4032,6.0,1,1,0,1,0,1,Managers,3.0,2,2,TUESDAY,18,0,0,0,0,0,0,Self-employed,,0.7384716075025171,0.3606125659189888,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-846.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,8.0\n246647,0,Cash loans,F,N,Y,0,225000.0,819391.5,45882.0,715500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006629,-22013,365243,-3180.0,-3542,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,6,0,0,0,0,0,0,XNA,,0.7241833400411535,0.7922644738669378,0.0835,0.0,0.9876,0.83,,0.0,0.1379,0.1667,0.2083,,0.0681,0.0862,0.0,0.0,0.0851,0.0,0.9876,0.8367,,0.0,0.1379,0.1667,0.2083,,0.0744,0.0898,0.0,0.0,0.0843,0.0,0.9876,0.8323,,0.0,0.1379,0.1667,0.2083,,0.0693,0.0877,0.0,0.0,reg oper account,block of flats,0.0683,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-906.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n392357,1,Cash loans,M,N,Y,0,90000.0,247500.0,19971.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-16121,-582,-6226.0,-4200,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6286235866699611,0.4938628816996244,0.1041,0.1132,0.9762,0.6736,0.0116,0.0,0.2069,0.1667,0.2083,0.0939,0.0849,0.0893,0.0,0.0,0.1061,0.1175,0.9762,0.6864,0.0117,0.0,0.2069,0.1667,0.2083,0.096,0.0927,0.0931,0.0,0.0,0.1051,0.1132,0.9762,0.6779999999999999,0.0117,0.0,0.2069,0.1667,0.2083,0.0955,0.0864,0.0909,0.0,0.0,reg oper account,block of flats,0.0766,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-179.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n211380,0,Cash loans,M,Y,N,0,112500.0,135000.0,9261.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-10766,-294,-5029.0,-2820,23.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,7,0,0,0,1,1,0,Business Entity Type 3,,0.6432335128088796,0.3280631605201915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-974.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n314979,0,Cash loans,M,Y,N,0,225000.0,550980.0,35343.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-11742,-904,-26.0,-3703,16.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,10,0,1,1,0,1,1,Business Entity Type 3,0.1658132563477575,0.6695462726781943,0.7076993447402619,0.0825,0.1005,0.9776,,,0.0,0.1379,0.1667,,0.0605,,0.0481,,0.0583,0.084,0.1043,0.9777,,,0.0,0.1379,0.1667,,0.0618,,0.0501,,0.0617,0.0833,0.1005,0.9776,,,0.0,0.1379,0.1667,,0.0615,,0.0489,,0.0595,,block of flats,0.0549,Panel,No,1.0,0.0,1.0,0.0,-667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n237674,0,Cash loans,F,N,Y,0,90000.0,225000.0,21037.5,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.026392,-22180,365243,-1015.0,-4529,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.13414267676858252,0.1694287272664794,0.1485,0.0706,0.9851,,,0.04,0.0345,0.3333,,,,0.1107,,0.0,0.1513,0.0733,0.9851,,,0.0403,0.0345,0.3333,,,,0.1153,,0.0,0.1499,0.0706,0.9851,,,0.04,0.0345,0.3333,,,,0.1127,,0.0,,block of flats,0.0871,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-245.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n340946,0,Cash loans,F,N,Y,0,157500.0,521136.0,59067.0,495000.0,Family,State servant,Higher education,Married,House / apartment,0.00496,-11842,-596,-5642.0,-2150,,1,1,0,1,0,1,Core staff,2.0,2,2,FRIDAY,17,0,0,0,1,1,1,Medicine,0.6137503793951928,0.6883420014640765,0.5620604831738043,0.0742,0.0555,0.9856,0.8028,0.0694,0.08,0.069,0.3333,0.375,0.0105,0.0605,0.0513,0.0,0.0,0.0756,0.0576,0.9856,0.8105,0.07,0.0806,0.069,0.3333,0.375,0.0108,0.0661,0.0411,0.0,0.0,0.0749,0.0555,0.9856,0.8054,0.0698,0.08,0.069,0.3333,0.375,0.0107,0.0616,0.0523,0.0,0.0,reg oper spec account,block of flats,0.0689,Panel,No,0.0,0.0,0.0,0.0,-254.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n390182,0,Cash loans,F,N,Y,0,112500.0,526491.0,23319.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-20283,365243,-12487.0,-3525,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.24678567059563644,0.6594055320683344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1476.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n262773,0,Cash loans,F,Y,Y,2,135000.0,509400.0,40374.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009657,-13533,-4854,-4809.0,-2447,3.0,1,1,0,1,0,1,Laborers,4.0,2,2,FRIDAY,12,0,0,0,1,1,0,Business Entity Type 3,0.1689335077295282,0.6951032870869119,0.6195277080511546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1723.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n250305,0,Revolving loans,F,N,Y,1,112500.0,337500.0,16875.0,337500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-13489,-728,-2397.0,-2397,,1,1,0,1,0,0,Sales staff,3.0,1,1,SATURDAY,13,0,0,0,0,1,1,Business Entity Type 1,0.4164729909625104,0.308396209085737,0.7490217048463391,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-427.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n196953,0,Cash loans,M,Y,N,0,171000.0,417024.0,26964.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.02461,-18499,-575,-656.0,-2039,4.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.6926672610216957,0.6059077862135173,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n334322,1,Cash loans,F,N,Y,0,112500.0,265536.0,13684.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.003813,-8067,-777,-4862.0,-717,,1,1,0,1,0,1,,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.044119453667702715,0.1474455049737305,0.17982176508970435,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-434.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n180880,0,Revolving loans,F,Y,N,0,157500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-7707,-313,-2305.0,-382,2.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Trade: type 6,0.2401614149785062,0.4227175630914517,0.20092608771597087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-343.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n212524,0,Cash loans,M,Y,Y,0,495000.0,860112.0,28557.0,742500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-14121,-4123,-698.0,-4268,2.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Industry: type 5,,0.571523374762266,0.5567274263630174,0.2577,0.1588,0.9841,0.7824,0.158,0.28,0.2414,0.3333,0.375,0.0,0.2017,0.2284,0.0386,0.0337,0.2626,0.1648,0.9841,0.7909,0.1595,0.282,0.2414,0.3333,0.375,0.0,0.2204,0.2379,0.0389,0.0357,0.2602,0.1588,0.9841,0.7853,0.159,0.28,0.2414,0.3333,0.375,0.0,0.2052,0.2325,0.0388,0.0344,reg oper account,block of flats,0.2574,Panel,No,0.0,0.0,0.0,0.0,-3228.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n139247,0,Cash loans,F,Y,Y,0,162000.0,490500.0,46701.0,490500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-20624,365243,-9832.0,-3701,14.0,1,0,0,1,1,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.2372829352375439,0.7530673920730478,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n141643,0,Cash loans,F,N,N,0,180000.0,668304.0,24894.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-19471,-4328,-4668.0,-2949,,1,1,0,1,0,0,Sales staff,2.0,1,1,THURSDAY,9,0,0,0,0,0,0,Self-employed,,0.3266460484326216,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n163105,0,Cash loans,F,N,N,0,81000.0,477000.0,29313.0,477000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-14148,-2836,-2532.0,-475,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,7,0,0,0,1,1,0,Business Entity Type 3,0.4641670983848203,0.5101197251399429,0.5971924268337128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-719.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n371308,0,Cash loans,F,Y,N,0,427500.0,1125000.0,32890.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-13587,-3608,-7536.0,-4160,7.0,1,1,1,1,1,0,Laborers,2.0,2,2,SUNDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5160526370690087,0.2955826421513093,0.0722,0.0,0.9747,0.6532,,0.0,0.1379,0.1667,0.0417,0.0458,,0.071,,0.0,0.0735,0.0,0.9747,0.6668,,0.0,0.1379,0.1667,0.0417,0.0468,,0.07400000000000001,,0.0,0.0729,0.0,0.9747,0.6578,,0.0,0.1379,0.1667,0.0417,0.0465,,0.0723,,0.0,org spec account,block of flats,0.0559,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1239.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n282260,0,Cash loans,F,N,Y,0,180000.0,1118286.0,32697.0,976500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.011703,-20423,365243,-8863.0,-3914,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.6616323354029693,0.5656079814115492,0.0928,0.0847,0.9881,,,0.0,0.2069,0.1667,,0.0898,,0.0834,,0.0,0.0945,0.0879,0.9881,,,0.0,0.2069,0.1667,,0.0919,,0.0868,,0.0,0.0937,0.0847,0.9881,,,0.0,0.2069,0.1667,,0.0914,,0.0849,,0.0,,block of flats,0.076,Panel,No,2.0,0.0,2.0,0.0,-192.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n343041,0,Cash loans,F,N,Y,0,108000.0,195543.0,14359.5,148500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.031329,-22633,365243,-10446.0,-4608,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.5084856906504007,0.25946765482111994,0.1979,0.1005,0.9856,0.8028,0.2832,0.16,0.069,0.4583,0.5,0.1193,0.1614,0.1703,0.0,0.0,0.2017,0.1043,0.9856,0.8105,0.2858,0.1611,0.069,0.4583,0.5,0.1221,0.1763,0.1774,0.0,0.0,0.1999,0.1005,0.9856,0.8054,0.285,0.16,0.069,0.4583,0.5,0.1214,0.1642,0.1733,0.0,0.0,reg oper account,block of flats,0.1339,Panel,No,0.0,0.0,0.0,0.0,-288.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n420723,0,Cash loans,F,N,Y,0,180000.0,755190.0,53838.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-23375,365243,-11772.0,-4340,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,XNA,,0.5507614654021804,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-746.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n378765,0,Cash loans,M,Y,Y,0,225000.0,315000.0,17217.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-18401,-1468,-5892.0,-1959,9.0,1,1,0,1,1,0,Security staff,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Other,,0.7270611272851263,0.6801388218428291,0.0186,0.0462,0.9881,,,0.0,0.1034,0.0417,,0.0116,,0.0168,,0.0,0.0189,0.0479,0.9881,,,0.0,0.1034,0.0417,,0.0119,,0.0175,,0.0,0.0187,0.0462,0.9881,,,0.0,0.1034,0.0417,,0.0118,,0.0171,,0.0,,block of flats,0.0132,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1882.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n319200,0,Cash loans,M,Y,Y,0,180000.0,348264.0,36697.5,315000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.020713,-15423,-4363,-5165.0,-4140,28.0,1,1,0,1,0,0,,2.0,3,3,MONDAY,8,0,0,0,0,1,1,Other,0.2571750195787211,0.4264412712221446,0.7597121819739279,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n114093,0,Cash loans,F,N,N,1,450000.0,900000.0,26446.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-15028,-1828,-2656.0,-3486,,1,1,0,1,1,0,Accountants,3.0,1,1,FRIDAY,17,0,1,1,0,0,0,Transport: type 4,0.6470024768189861,0.7333506305481906,0.5226973172821112,0.1113,0.1408,0.9836,0.7756,0.0,0.04,0.0345,0.625,0.6667,0.0542,0.0908,0.1025,0.0,0.0122,0.1134,0.1461,0.9836,0.7844,0.0,0.0403,0.0345,0.625,0.6667,0.0554,0.0992,0.1068,0.0,0.0129,0.1124,0.1408,0.9836,0.7786,0.0,0.04,0.0345,0.625,0.6667,0.0551,0.0923,0.1043,0.0,0.0124,reg oper account,block of flats,0.0832,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n105786,0,Cash loans,F,N,Y,0,81000.0,549000.0,17707.5,549000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.010276,-17050,-281,-3962.0,-602,,1,1,1,1,1,0,Sales staff,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Trade: type 7,0.7346389215439951,0.7057567660035551,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2917.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n153335,0,Cash loans,F,N,Y,1,202500.0,835380.0,31950.0,675000.0,,Working,Higher education,Married,House / apartment,0.006852,-16568,-4168,-5607.0,-101,,1,1,0,1,0,0,Core staff,3.0,3,3,FRIDAY,12,0,0,0,0,0,0,School,,0.3455650079897602,0.6127042441012546,0.0825,0.0517,0.9916,0.8844,0.0238,0.08,0.069,0.375,0.4167,0.1048,0.0672,0.0913,0.0,0.0,0.084,0.0537,0.9916,0.8889,0.024,0.0806,0.069,0.375,0.4167,0.1072,0.0735,0.0951,0.0,0.0,0.0833,0.0517,0.9916,0.8859,0.024,0.08,0.069,0.375,0.4167,0.1066,0.0684,0.09300000000000001,0.0,0.0,,block of flats,0.0849,Panel,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n276402,0,Cash loans,F,N,Y,0,135000.0,1256400.0,34551.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.007114,-11115,-1379,-2650.0,-3779,,1,1,1,1,0,0,Managers,1.0,2,2,MONDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.2616719594507528,0.3210056743609241,0.6626377922738201,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2864.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n304824,1,Cash loans,M,Y,Y,1,135000.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-11551,-1155,-11505.0,-1136,16.0,1,1,0,1,0,0,Laborers,3.0,3,3,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.6903743313373848,,0.1021,0.0901,0.9826,0.762,0.0293,0.12,0.1034,0.3333,0.0417,,,0.1214,,0.0,0.10400000000000001,0.0935,0.9826,0.7713,0.0296,0.1208,0.1034,0.3333,0.0417,,,0.1265,,0.0,0.1031,0.0901,0.9826,0.7652,0.0295,0.12,0.1034,0.3333,0.0417,,,0.1236,,0.0,,block of flats,0.1115,Panel,No,8.0,0.0,8.0,0.0,-1636.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n183737,0,Cash loans,M,Y,N,1,90000.0,247675.5,19449.0,229500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.02461,-12534,-2165,-3206.0,-3206,26.0,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6270703084623448,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1506.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n329864,0,Cash loans,M,Y,Y,0,135000.0,675000.0,53460.0,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.026392,-11995,-289,-5807.0,-595,65.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,14,0,0,0,1,1,0,Business Entity Type 3,0.4744413593734854,0.7036954572604582,0.22888341670067305,0.0082,,0.9697,,,0.0,0.0345,0.0417,,0.0391,,0.0082,,0.0,0.0084,,0.9697,,,0.0,0.0345,0.0417,,0.04,,0.0085,,0.0,0.0083,,0.9697,,,0.0,0.0345,0.0417,,0.0398,,0.0083,,0.0,,block of flats,0.0064,Block,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n227947,0,Cash loans,M,Y,Y,0,405000.0,1480500.0,40842.0,1480500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.026392,-13002,-1443,-1397.0,-4080,4.0,1,1,0,1,0,0,Core staff,2.0,2,2,SUNDAY,16,0,0,0,0,0,0,Trade: type 7,,0.675987493954844,0.09445169198377182,0.1237,,0.998,,,0.12,0.1034,0.375,,,,0.0854,,0.2149,0.1261,,0.998,,,0.1208,0.1034,0.375,,,,0.08900000000000001,,0.2275,0.1249,,0.998,,,0.12,0.1034,0.375,,,,0.0869,,0.2194,,block of flats,0.1139,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-720.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n401546,0,Cash loans,F,Y,Y,0,112500.0,560664.0,18216.0,468000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-18681,-6311,-11899.0,-2231,13.0,1,1,0,1,0,0,,2.0,3,3,THURSDAY,8,0,0,0,0,0,0,Transport: type 4,0.6849387539742691,0.6035765547723474,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.0,0.0,13.0,0.0,-1252.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n313235,0,Cash loans,F,Y,N,0,135000.0,157914.0,17136.0,139500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00496,-10317,-2178,-39.0,-3000,4.0,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,13,0,0,0,1,1,1,School,0.4647255691919594,0.608406147222984,0.13595104417329515,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1022.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n407845,0,Revolving loans,F,N,Y,0,85500.0,247500.0,12375.0,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-21402,365243,-7413.0,-4228,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.0018976685436431426,0.7862666146611379,0.0247,0.0,0.9737,0.6396,0.0023,0.0,0.1034,0.0417,0.0833,0.0481,0.0202,0.0199,0.0,0.0,0.0252,0.0,0.9737,0.6537,0.0023,0.0,0.1034,0.0417,0.0833,0.0492,0.022000000000000002,0.0208,0.0,0.0,0.025,0.0,0.9737,0.6444,0.0023,0.0,0.1034,0.0417,0.0833,0.049,0.0205,0.0203,0.0,0.0,reg oper account,block of flats,0.0157,\"Stone, brick\",No,8.0,0.0,8.0,0.0,-398.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n189699,0,Cash loans,F,Y,N,1,360000.0,890379.0,32112.0,751500.0,Unaccompanied,Working,Higher education,Widow,House / apartment,0.019101,-16480,-4023,-4075.0,-13,6.0,1,1,0,1,0,1,Managers,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Kindergarten,0.6934363599134776,0.1831540666554108,0.3360615207658242,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,5.0,9.0,3.0,-1807.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n267074,0,Cash loans,F,Y,Y,0,301500.0,1054773.0,34987.5,945000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.009657,-20545,-4217,-12515.0,-3850,7.0,1,1,0,1,1,0,Accountants,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.7869920857237489,0.7453715846545876,0.4992720153045617,0.1031,0.1097,0.9776,0.6940000000000001,,0.0,0.1724,0.1667,0.2083,0.1247,0.0841,0.0953,0.0,0.0,0.105,0.1139,0.9777,0.706,,0.0,0.1724,0.1667,0.2083,0.1276,0.0918,0.0993,0.0,0.0,0.1041,0.1097,0.9776,0.6981,,0.0,0.1724,0.1667,0.2083,0.1269,0.0855,0.09699999999999999,0.0,0.0,,block of flats,0.0993,Panel,No,0.0,0.0,0.0,0.0,-2253.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n280336,1,Cash loans,M,Y,N,0,180000.0,1620000.0,44550.0,1620000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-23059,365243,-3670.0,-4147,3.0,1,0,0,1,1,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.5373041332257166,0.6690566947824041,0.0474,0.1207,0.9796,0.7212,,0.0,0.2759,0.1667,,0.1442,0.0336,0.1021,0.0232,0.0,0.0483,0.1253,0.9796,0.7321,,0.0,0.2759,0.1667,,0.1475,0.0367,0.1064,0.0233,0.0,0.0479,0.1207,0.9796,0.7249,,0.0,0.2759,0.1667,,0.1467,0.0342,0.1039,0.0233,0.0,reg oper spec account,block of flats,0.1323,Panel,No,8.0,0.0,8.0,0.0,-1149.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n131156,0,Cash loans,F,Y,Y,1,112500.0,900000.0,26446.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14560,-199,-5740.0,-4260,19.0,1,1,0,1,0,0,Waiters/barmen staff,3.0,2,2,FRIDAY,11,0,0,0,0,1,1,Restaurant,0.5708965097961929,0.6881824785644541,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n454316,0,Cash loans,F,N,Y,0,225000.0,358443.0,13639.5,252000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003813,-22195,365243,-8763.0,-4257,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,5,0,0,0,0,0,0,XNA,,0.3710532611712085,0.25533177083329,0.0876,0.0699,0.9781,0.7008,0.1526,0.0,0.1379,0.1667,0.0417,,0.0597,0.0694,0.0541,0.0221,0.0893,0.0726,0.9782,0.7125,0.154,0.0,0.1379,0.1667,0.0417,,0.0652,0.0723,0.0545,0.0233,0.0885,0.0699,0.9781,0.7048,0.1536,0.0,0.1379,0.1667,0.0417,,0.0607,0.0707,0.0543,0.0225,reg oper account,block of flats,0.059000000000000004,Panel,No,0.0,0.0,0.0,0.0,-1459.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n447656,0,Cash loans,M,Y,N,3,157500.0,631332.0,38754.0,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.015221,-13730,-582,-7273.0,-4272,1.0,1,1,1,1,1,0,Laborers,5.0,2,2,TUESDAY,11,0,1,1,1,1,1,Transport: type 4,0.4109242764969056,0.6386590374587405,0.2314393514998941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-512.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n227124,0,Cash loans,F,N,N,0,54000.0,544491.0,16047.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-22254,365243,-13098.0,-4240,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.7627831069134738,0.8016009030071296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1624.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n344124,0,Cash loans,M,N,Y,0,103500.0,781920.0,25542.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.032561,-22956,365243,-2509.0,-5207,,1,0,0,1,1,0,,2.0,1,1,THURSDAY,10,0,0,0,0,0,0,XNA,,0.6782631646168626,,0.0722,,0.9757,,,,0.2414,0.1667,,0.0577,,,,,0.0735,,0.9757,,,,0.2414,0.1667,,0.059000000000000004,,,,,0.0729,,0.9757,,,,0.2414,0.1667,,0.0587,,,,,,block of flats,0.0575,Panel,No,0.0,0.0,0.0,0.0,-713.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n420056,1,Cash loans,M,Y,Y,0,202500.0,808650.0,26086.5,675000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.010556,-14084,-231,-343.0,-332,15.0,1,1,1,1,1,0,Laborers,2.0,3,3,SATURDAY,14,0,0,0,0,1,1,Construction,,0.5243921942204522,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1836.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n369372,0,Cash loans,F,N,Y,1,108000.0,94230.0,5062.5,67500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.009334,-15420,-1080,-8439.0,-3453,,1,1,0,1,0,0,Waiters/barmen staff,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,School,,0.6278166202534953,0.6092756673894402,0.0144,,0.9588,,,0.0,0.1034,0.0417,,0.0075,,0.015,,,0.0147,,0.9588,,,0.0,0.1034,0.0417,,0.0076,,0.0157,,,0.0146,,0.9588,,,0.0,0.1034,0.0417,,0.0076,,0.0153,,,,block of flats,0.0131,Wooden,No,4.0,1.0,4.0,1.0,-808.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n241636,0,Cash loans,F,N,N,1,99000.0,1082214.0,31770.0,945000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00496,-16719,-2740,-6086.0,-231,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,16,0,0,0,0,1,1,Business Entity Type 3,0.4536624345859,0.6715510652014458,0.7209441499436497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1219.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n171953,0,Cash loans,F,N,N,0,81000.0,450000.0,35685.0,450000.0,Family,State servant,Incomplete higher,Married,With parents,0.028663,-10156,-969,-4257.0,-313,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,University,0.1823146859966597,0.4144474836744813,,0.1402,0.1374,0.9935,0.9116,0.0419,0.1732,0.1379,0.4025,0.4442,0.1266,0.1143,0.1794,0.0167,0.1087,0.042,0.0257,0.9945,0.9281,0.0114,0.2417,0.0345,0.375,0.4167,0.0253,0.0367,0.047,0.0,0.0,0.1301,0.1932,0.9945,0.9262,0.0405,0.24,0.1724,0.375,0.4167,0.1288,0.1069,0.2098,0.0078,0.1443,reg oper account,block of flats,0.2148,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-2537.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n442278,0,Cash loans,F,N,N,0,112500.0,299772.0,20160.0,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-21648,365243,-1091.0,-4277,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,0.5452891301345563,0.7469909483566023,0.21396685226179807,0.0732,0.0745,0.9776,0.6940000000000001,0.0078,0.0,0.1379,0.1667,0.2083,0.0645,0.0572,0.0632,0.0116,0.0162,0.0746,0.0774,0.9777,0.706,0.0079,0.0,0.1379,0.1667,0.2083,0.066,0.0624,0.0659,0.0117,0.0171,0.0739,0.0745,0.9776,0.6981,0.0079,0.0,0.1379,0.1667,0.2083,0.0656,0.0581,0.0644,0.0116,0.0165,reg oper account,block of flats,0.0533,Panel,No,1.0,1.0,1.0,1.0,-1179.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n318415,0,Cash loans,F,N,Y,0,189000.0,665892.0,21609.0,477000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.032561,-21365,365243,-1590.0,-4777,,1,0,0,1,0,0,,1.0,1,1,FRIDAY,12,0,0,0,0,0,0,XNA,,0.6554465501818523,0.5726825047161584,0.0887,,0.9742,0.6464,0.0159,,0.2069,0.1667,,,,0.0759,,0.0863,0.0903,,0.9742,0.6602,0.0161,,0.2069,0.1667,,,,0.0791,,0.0913,0.0895,,0.9742,0.6511,0.016,,0.2069,0.1667,,,,0.0773,,0.0881,,block of flats,0.0872,,No,7.0,0.0,7.0,0.0,-587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n404012,0,Revolving loans,M,N,Y,0,135000.0,180000.0,9000.0,,,Working,Secondary / secondary special,Single / not married,With parents,0.026392,-9525,-1277,-4330.0,-2211,,1,1,1,1,1,0,Laborers,1.0,2,2,FRIDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.39841179007754535,0.324891229465852,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1773.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.0,0.0,0.0,0.0,0.0,1.0\n314844,1,Cash loans,M,N,N,0,90000.0,180000.0,12028.5,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003069,-13664,-668,-119.0,-4444,,1,1,0,1,0,0,Laborers,2.0,3,3,TUESDAY,18,0,0,0,0,1,1,Construction,0.6260588548477886,0.6379623346696226,0.8106180215523969,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-456.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n409042,0,Cash loans,F,Y,Y,0,225000.0,1515415.5,40104.0,1354500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-19172,-3849,-9924.0,-2716,16.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.4690224898735902,0.72316446451291,0.6397075677637197,0.1227,0.1141,0.9811,,,0.0,0.2759,0.1667,,0.0899,,0.1136,,0.0,0.125,0.1184,0.9811,,,0.0,0.2759,0.1667,,0.0919,,0.1183,,0.0,0.1239,0.1141,0.9811,,,0.0,0.2759,0.1667,,0.0915,,0.1156,,0.0,,block of flats,0.0893,Panel,No,0.0,0.0,0.0,0.0,-936.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n392601,0,Cash loans,F,Y,Y,0,180000.0,497520.0,28561.5,450000.0,Unaccompanied,Pensioner,Higher education,Civil marriage,House / apartment,0.035792000000000004,-23148,365243,-4765.0,-4504,8.0,1,0,0,1,1,0,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,XNA,0.765153942091246,0.6215001715311724,0.1008037063175332,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n141727,0,Revolving loans,M,Y,Y,0,180000.0,270000.0,13500.0,270000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.015221,-18348,-247,-6457.0,-1881,7.0,1,1,0,1,1,0,High skill tech staff,2.0,2,2,MONDAY,8,0,1,1,0,1,1,Business Entity Type 3,,0.596249669004221,0.3360615207658242,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-667.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n318140,0,Cash loans,F,N,Y,1,247500.0,152820.0,16213.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006852,-17751,-834,-2722.0,-1282,,1,1,0,1,0,0,,3.0,3,3,WEDNESDAY,13,0,0,0,1,1,0,Self-employed,,0.3108877063219309,0.5370699579791587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-423.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n423656,0,Cash loans,F,N,N,0,180000.0,781920.0,25101.0,675000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.025164,-23916,365243,-1272.0,-4472,,1,0,0,1,1,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.16513433666694372,0.363945238612397,0.0,,0.0,,,,,,,,,,,,0.0,,0.0005,,,,,,,,,,,,0.0,,0.0,,,,,,,,,,,,,block of flats,0.0,,No,1.0,0.0,1.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n268057,0,Cash loans,M,Y,Y,0,900000.0,1130791.5,30451.5,963000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.031329,-16905,-2695,-3037.0,-424,2.0,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,0.3612847105086039,0.6216904837706708,0.5046813193144684,0.1258,0.0822,0.995,0.9252,0.0401,0.18,0.1034,0.4583,0.4583,0.0847,0.0975,0.1378,0.0232,0.0554,0.1155,0.0848,0.9940000000000001,0.9151,0.0271,0.1208,0.1034,0.375,0.4167,0.0795,0.0983,0.136,0.0117,0.0351,0.127,0.0822,0.995,0.9262,0.0404,0.18,0.1034,0.4583,0.4583,0.0862,0.0992,0.1403,0.0233,0.0566,reg oper account,block of flats,0.1098,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2364.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n274360,1,Cash loans,F,N,N,0,139500.0,755190.0,32125.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.016612000000000002,-22280,365243,0.0,-1708,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,,0.3515282170208541,0.10015182033723906,0.1649,,0.9752,,,0.0,0.2759,0.1667,,0.1406,,0.1289,,0.0596,0.1681,,0.9752,,,0.0,0.2759,0.1667,,0.1438,,0.1343,,0.0631,0.1665,,0.9752,,,0.0,0.2759,0.1667,,0.14300000000000002,,0.1312,,0.0608,,block of flats,0.1039,\"Stone, brick\",No,1.0,1.0,1.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n352019,1,Cash loans,M,Y,Y,0,225000.0,268659.0,17298.0,243000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-18191,-1281,-8413.0,-1703,9.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.6729491467782778,0.5040254317167355,0.6075573001388961,0.2907,0.22899999999999998,0.9886,0.8436,0.133,0.08,0.069,0.3333,0.375,0.1477,0.237,0.1946,0.0,0.0109,0.2962,0.2377,0.9886,0.8497,0.1342,0.0806,0.069,0.3333,0.375,0.151,0.259,0.2028,0.0,0.0115,0.2935,0.22899999999999998,0.9886,0.8457,0.1338,0.08,0.069,0.3333,0.375,0.1502,0.2411,0.1981,0.0,0.0111,not specified,block of flats,0.2282,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n100556,0,Cash loans,F,N,Y,0,58500.0,50940.0,4959.0,45000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.0105,-22735,365243,-1583.0,-4711,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,10,0,0,0,0,0,0,XNA,,0.7421584798141769,,0.0742,,0.9851,,,,0.069,0.3333,,,,,,,0.0756,,0.9851,,,,0.069,0.3333,,,,,,,0.0749,,0.9851,,,,0.069,0.3333,,,,,,,,block of flats,0.0602,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1086.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n323114,0,Revolving loans,M,N,Y,1,92700.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-17611,-1526,-11014.0,-1100,,1,1,0,1,1,0,Cleaning staff,3.0,3,3,THURSDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.5052225788840075,0.33928769990891394,0.066,0.0805,0.9747,0.6532,,0.0,0.1724,0.125,0.1667,0.0562,0.0538,0.0503,,0.0416,0.0672,0.0835,0.9747,0.6668,,0.0,0.1724,0.125,0.1667,0.0575,0.0588,0.0524,,0.044000000000000004,0.0666,0.0805,0.9747,0.6578,,0.0,0.1724,0.125,0.1667,0.0572,0.0547,0.0512,,0.0425,,block of flats,0.0501,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1259.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n426877,0,Cash loans,F,N,Y,0,202500.0,260568.0,25902.0,247500.0,Unaccompanied,State servant,Secondary / secondary special,Married,Municipal apartment,0.04622,-17620,-729,-2.0,-1154,,1,1,0,1,0,0,Secretaries,2.0,1,1,SUNDAY,10,0,0,0,0,0,0,Medicine,0.7710720585422339,0.7338719960514725,0.7934490301648944,0.10099999999999999,0.0833,0.9791,0.7144,,0.0,0.1379,0.1667,0.2083,0.0579,0.0824,0.0546,0.0,,0.1029,0.0864,0.9791,0.7256,,0.0,0.1379,0.1667,0.2083,0.0593,0.09,0.0569,0.0,,0.102,0.0833,0.9791,0.7182,,0.0,0.1379,0.1667,0.2083,0.0589,0.0838,0.0556,0.0,,reg oper account,block of flats,0.0429,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-4026.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n317023,0,Cash loans,F,N,Y,1,112500.0,225000.0,11074.5,225000.0,Children,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-15757,-8774,-3504.0,-3996,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 2,,0.6853458994555536,0.8327850252992314,0.1227,0.1208,0.9806,,,0.0,0.2759,0.1667,,0.106,,0.1221,,0.0,0.125,0.1253,0.9806,,,0.0,0.2759,0.1667,,0.1084,,0.1273,,0.0,0.1239,0.1208,0.9806,,,0.0,0.2759,0.1667,,0.1079,,0.1243,,0.0,,block of flats,0.0961,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n203180,0,Cash loans,F,Y,Y,0,225000.0,302076.0,23508.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.00702,-14239,-2389,-4291.0,-4917,3.0,1,1,0,1,1,0,Core staff,1.0,2,2,FRIDAY,17,0,0,0,0,1,1,Security Ministries,0.3587694055321588,0.13549453291812494,0.07105512466873665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,3.0,0.0,-2232.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n223566,0,Cash loans,F,Y,N,0,180000.0,679500.0,27076.5,679500.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.010032,-12581,-1478,-1843.0,-4041,17.0,1,1,1,1,0,0,Sales staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Self-employed,0.08029530534631266,0.01774125310693159,0.4992720153045617,0.1216,0.1067,0.9771,0.6872,0.0496,0.0,0.2759,0.1667,0.2083,0.1488,0.0983,0.11199999999999999,0.0039,0.0043,0.1239,0.1107,0.9772,0.6994,0.0501,0.0,0.2759,0.1667,0.2083,0.1521,0.1074,0.1167,0.0039,0.0045,0.1228,0.1067,0.9771,0.6914,0.0499,0.0,0.2759,0.1667,0.2083,0.1513,0.1,0.114,0.0039,0.0044,org spec account,block of flats,0.1161,Panel,No,0.0,0.0,0.0,0.0,-681.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n214000,0,Cash loans,F,Y,N,1,90000.0,188478.0,22495.5,166500.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,With parents,0.035792000000000004,-8390,-927,-3801.0,-1055,64.0,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,14,0,0,0,0,1,1,Industry: type 2,,0.24786034604174184,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-402.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n351032,0,Cash loans,F,N,N,0,81000.0,900000.0,38133.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.011703,-22388,365243,-918.0,-4956,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,18,0,0,0,1,0,0,XNA,,0.7015434041251672,0.3979463219016906,0.0701,0.06,0.9781,,,0.0,0.1379,0.1667,,0.0767,,0.0635,,0.0074,0.0714,0.0623,0.9782,,,0.0,0.1379,0.1667,,0.0784,,0.0662,,0.0079,0.0708,0.06,0.9781,,,0.0,0.1379,0.1667,,0.078,,0.0647,,0.0076,,block of flats,0.0559,Panel,No,5.0,0.0,5.0,0.0,-787.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n297711,0,Revolving loans,F,N,Y,0,540000.0,292500.0,14625.0,292500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010643,-15151,-2687,-4242.0,-2589,,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Other,0.5038324628335884,0.6566258897868186,0.12140828616312165,0.0041,,0.9518,,,0.0,0.0345,0.0417,,,,0.0029,,,0.0042,,0.9518,,,0.0,0.0345,0.0417,,,,0.003,,,0.0042,,0.9518,,,0.0,0.0345,0.0417,,,,0.0029,,,,block of flats,0.0028,Mixed,No,2.0,0.0,2.0,0.0,-348.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n104155,0,Cash loans,F,Y,Y,0,202500.0,752742.0,40234.5,697500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.016612000000000002,-12918,-2700,-519.0,-2058,12.0,1,1,0,1,0,0,Sales staff,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,0.5884947706033239,0.4193966023981064,0.42765737003502935,0.1093,0.0769,0.9866,,,0.12,0.1034,0.3333,,0.0,,0.113,,0.0316,0.1113,0.0798,0.9866,,,0.1208,0.1034,0.3333,,0.0,,0.1177,,0.0334,0.1103,0.0769,0.9866,,,0.12,0.1034,0.3333,,0.0,,0.115,,0.0323,,block of flats,0.1119,Panel,No,0.0,0.0,0.0,0.0,-356.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n180449,1,Cash loans,M,N,Y,0,175500.0,454500.0,36040.5,454500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.015221,-10182,-931,-4900.0,-2848,,1,1,1,1,0,0,Drivers,2.0,2,2,MONDAY,13,0,0,0,0,1,1,Self-employed,,0.5343073304311577,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-946.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n442786,0,Cash loans,M,N,Y,1,90000.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019101,-12341,-205,-6478.0,-4380,,1,1,0,1,0,0,Drivers,3.0,2,2,THURSDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.5399180230201399,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,10.0,0.0,-695.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n202702,1,Cash loans,F,N,N,0,180000.0,909000.0,25641.0,909000.0,Family,Pensioner,Higher education,Widow,Municipal apartment,0.032561,-22610,365243,-9198.0,-4056,,1,0,0,1,0,0,,1.0,1,1,THURSDAY,13,0,0,0,0,0,0,XNA,,0.6777427965316387,0.8193176922872417,0.46799999999999997,0.2899,0.9871,0.8232,0.1975,0.56,0.2414,0.625,0.5,0.1737,0.3799,0.5538,0.0077,0.0107,0.4769,0.3008,0.9871,0.8301,0.1993,0.5639,0.2414,0.625,0.5,0.1777,0.4151,0.5770000000000001,0.0078,0.0113,0.4726,0.2899,0.9871,0.8256,0.1987,0.56,0.2414,0.625,0.5,0.1767,0.3865,0.5638,0.0078,0.0109,reg oper spec account,block of flats,0.4379,Panel,No,2.0,1.0,2.0,1.0,-1114.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n223897,1,Cash loans,F,Y,Y,3,184500.0,297000.0,14575.5,297000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-12171,-1857,-188.0,-1638,64.0,1,1,0,1,0,0,Security staff,5.0,2,2,THURSDAY,16,0,0,0,0,1,1,Business Entity Type 1,,0.5242885693598159,0.6246146584503397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1089.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n124998,0,Cash loans,F,N,N,0,90000.0,298512.0,23584.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.008473999999999999,-10945,-1280,-3620.0,-3639,,1,1,0,1,1,0,Drivers,2.0,2,2,TUESDAY,18,0,0,0,1,1,0,Bank,0.3882748902512237,0.4945461380443175,0.6626377922738201,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2179.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n228041,0,Cash loans,M,Y,Y,2,180000.0,593010.0,23107.5,495000.0,Unaccompanied,Working,Higher education,Married,With parents,0.019101,-9969,-2772,-2330.0,-2644,2.0,1,1,0,1,0,0,Managers,4.0,2,2,THURSDAY,17,0,0,0,0,1,1,Other,,0.6595289470168008,0.4686596550493113,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2129.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n249071,0,Revolving loans,F,Y,Y,0,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-13121,-2374,-6448.0,-5265,1.0,1,1,0,1,1,0,Medicine staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Other,,0.6281435788628326,0.3077366963789207,0.0124,0.0,0.9702,0.5920000000000001,0.0,0.0,0.069,0.0417,0.0417,0.0177,0.0101,0.0148,0.0,0.0048,0.0126,0.0,0.9702,0.608,0.0,0.0,0.069,0.0417,0.0417,0.0181,0.011000000000000001,0.0155,0.0,0.0051,0.0125,0.0,0.9702,0.5975,0.0,0.0,0.069,0.0417,0.0417,0.018000000000000002,0.0103,0.0151,0.0,0.0049,reg oper account,block of flats,0.0127,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-356.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n384041,0,Cash loans,F,N,N,2,135000.0,497520.0,56133.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.072508,-11510,-1621,-5227.0,-774,,1,1,1,1,0,0,Sales staff,4.0,1,1,SATURDAY,19,0,1,1,0,1,1,Government,0.29188309567021764,0.6447475907809339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1629.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n379371,0,Cash loans,F,N,Y,0,112500.0,156384.0,12100.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008575,-15557,-2365,-1711.0,-2372,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,FRIDAY,13,0,1,1,0,0,0,Business Entity Type 3,0.6352846121228223,0.625499036524787,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-328.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n177866,0,Cash loans,M,N,Y,0,270000.0,783927.0,40153.5,688500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.022625,-10142,-624,-4379.0,-2770,,1,1,0,1,0,1,,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Transport: type 4,,0.3423231616357008,0.041759360068304566,0.4227,0.41600000000000004,0.9836,0.7756,0.3987,0.0,1.0,0.1667,0.2083,0.3992,0.3446,0.4028,0.0,0.0,0.4307,0.4317,0.9836,0.7844,0.4024,0.0,1.0,0.1667,0.2083,0.4083,0.3765,0.4196,0.0,0.0,0.4268,0.41600000000000004,0.9836,0.7786,0.4013,0.0,1.0,0.1667,0.2083,0.4061,0.3506,0.41,0.0,0.0,reg oper account,block of flats,0.5366,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1259.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n209271,0,Cash loans,F,Y,Y,0,270000.0,675000.0,28507.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-11376,-1196,-369.0,-2881,1.0,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Kindergarten,,0.6884170556085866,0.3344541255096772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-468.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n383919,0,Cash loans,F,N,Y,0,67500.0,484789.5,19224.0,418500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-20701,365243,-10957.0,-4122,,1,0,0,1,1,0,,2.0,3,3,THURSDAY,11,0,0,0,0,0,0,XNA,0.7122587131376926,0.0493136861442978,0.6754132910917112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n404941,0,Cash loans,M,N,N,0,157500.0,594121.5,32359.5,472500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.010006,-11501,-2266,-3906.0,-3977,,1,1,0,1,0,0,Security staff,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Security,0.3284854069424381,0.5267477121757137,,0.2227,0.1334,0.9846,0.7892,0.0,0.24,0.2069,0.3333,0.375,0.0475,0.1816,0.2294,0.0,0.0058,0.2269,0.1384,0.9846,0.7975,0.0,0.2417,0.2069,0.3333,0.375,0.0486,0.1983,0.239,0.0,0.0062,0.2248,0.1334,0.9846,0.792,0.0,0.24,0.2069,0.3333,0.375,0.0483,0.1847,0.2335,0.0,0.006,reg oper account,block of flats,0.2108,Panel,No,0.0,0.0,0.0,0.0,-2714.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n191592,0,Cash loans,M,Y,N,0,225000.0,509922.0,31194.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-11117,-1255,-1967.0,-2007,25.0,1,1,0,1,0,0,Laborers,2.0,3,2,MONDAY,6,0,0,0,1,1,0,Business Entity Type 3,,0.5486287896306978,0.4830501881366946,0.0918,0.1006,0.9781,0.7008,0.0436,0.0,0.2069,0.1667,0.2083,0.0701,0.07400000000000001,0.0857,0.0039,0.0077,0.0935,0.1044,0.9782,0.7125,0.044000000000000004,0.0,0.2069,0.1667,0.2083,0.0717,0.0808,0.0892,0.0039,0.0082,0.0926,0.1006,0.9781,0.7048,0.0439,0.0,0.2069,0.1667,0.2083,0.0713,0.0752,0.0872,0.0039,0.0079,reg oper account,block of flats,0.0929,Panel,No,1.0,0.0,1.0,0.0,-668.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n212219,0,Cash loans,F,N,Y,0,90000.0,225000.0,22050.0,225000.0,Unaccompanied,Pensioner,Lower secondary,Civil marriage,House / apartment,0.026392,-24649,365243,-16066.0,-4637,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.7239037086133272,,0.1031,,0.9762,,,0.0,0.1724,0.1667,,,,0.0435,,0.0866,0.105,,0.9762,,,0.0,0.1724,0.1667,,,,0.0454,,0.0917,0.1041,,0.9762,,,0.0,0.1724,0.1667,,,,0.0443,,0.0885,,block of flats,0.0531,Panel,No,2.0,0.0,2.0,0.0,-1280.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n394763,1,Revolving loans,F,N,Y,0,90000.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-17957,-450,-7381.0,-4239,,1,1,0,1,1,0,Laborers,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5407978898943085,0.4632753280912678,0.2598,0.1399,0.9871,0.8232,0.0505,0.2,0.1724,0.3333,0.375,0.0948,0.21100000000000002,0.1956,0.0039,0.0023,0.2647,0.1452,0.9871,0.8301,0.051,0.2014,0.1724,0.3333,0.375,0.0969,0.2305,0.2038,0.0039,0.0025,0.2623,0.1399,0.9871,0.8256,0.0508,0.2,0.1724,0.3333,0.375,0.0964,0.2146,0.1991,0.0039,0.0024,reg oper spec account,block of flats,0.182,Panel,No,1.0,0.0,1.0,0.0,-212.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n202031,0,Cash loans,F,N,Y,1,54000.0,221832.0,10917.0,175500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-10985,-1398,-2561.0,-3037,,1,1,0,1,0,0,Cooking staff,3.0,2,2,THURSDAY,11,0,0,0,0,1,1,Self-employed,0.1935534885348721,0.4600585598724561,,0.0608,0.0861,0.9871,,,0.0,0.0345,0.125,,0.0153,,0.0681,,0.0,0.062,0.0893,0.9871,,,0.0,0.0345,0.125,,0.0156,,0.071,,0.0,0.0614,0.0861,0.9871,,,0.0,0.0345,0.125,,0.0155,,0.0694,,0.0,,block of flats,0.0536,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-441.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n398331,0,Revolving loans,F,N,N,0,225000.0,450000.0,22500.0,450000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-19070,365243,-10732.0,-839,,1,0,0,1,0,1,,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,0.4942242456988465,0.5952911979041314,0.7309873696832169,0.0515,0.0733,0.9841,0.7824,0.0298,0.0,0.1379,0.1667,0.0417,0.0459,0.0412,0.05,0.0039,0.0035,0.0525,0.076,0.9841,0.7909,0.0301,0.0,0.1379,0.1667,0.0417,0.0469,0.045,0.0521,0.0039,0.0037,0.052000000000000005,0.0733,0.9841,0.7853,0.03,0.0,0.1379,0.1667,0.0417,0.0467,0.0419,0.0509,0.0039,0.0036,reg oper account,block of flats,0.0564,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1024.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n428953,0,Cash loans,F,N,Y,0,112500.0,253737.0,14296.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.019101,-17833,-3178,-6021.0,-1384,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.44343850985926014,0.5849900404894085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1779.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n404930,0,Cash loans,F,N,N,0,135000.0,988875.0,42025.5,868500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.007114,-16714,-8981,-5092.0,-171,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.4680870067749529,0.6204811091929442,,0.1619,0.040999999999999995,0.9816,0.7484,0.0776,0.0532,0.0459,0.2358,0.0417,0.0332,0.1929,0.0468,0.0,0.0201,0.0126,0.0295,0.9821,0.7583,0.0745,0.0806,0.0345,0.3333,0.0417,0.0,0.2057,0.0104,0.0,0.0,0.2332,0.040999999999999995,0.9821,0.7518,0.078,0.08,0.0345,0.3333,0.0417,0.0477,0.1962,0.066,0.0,0.0214,reg oper account,specific housing,0.0086,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-617.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n420068,0,Cash loans,M,Y,Y,0,112500.0,363190.5,22347.0,328500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Co-op apartment,0.025164,-11040,-732,-1170.0,-1073,11.0,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6101552759440199,,0.0928,0.092,0.9861,,,0.0,0.2069,0.1667,,0.0272,,0.0871,,0.0,0.0945,0.0955,0.9861,,,0.0,0.2069,0.1667,,0.0278,,0.0907,,0.0,0.0937,0.092,0.9861,,,0.0,0.2069,0.1667,,0.0277,,0.0886,,0.0,,block of flats,0.0906,Panel,No,1.0,1.0,1.0,1.0,-628.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n384160,1,Cash loans,F,N,Y,0,135000.0,401386.5,27283.5,346500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-8513,-1628,-8284.0,-444,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,8,0,0,0,0,0,0,Business Entity Type 2,0.20138476118151769,0.6772841253344123,0.07298315565566517,0.0814,0.0284,0.9747,0.6532,0.0178,0.0,0.1379,0.1667,0.2083,0.0751,0.0647,0.0674,0.0077,0.0218,0.083,0.0295,0.9747,0.6668,0.018000000000000002,0.0,0.1379,0.1667,0.2083,0.0768,0.0707,0.0702,0.0078,0.0231,0.0822,0.0284,0.9747,0.6578,0.0179,0.0,0.1379,0.1667,0.2083,0.0764,0.0658,0.0686,0.0078,0.0222,reg oper account,block of flats,0.0675,Panel,No,0.0,0.0,0.0,0.0,-373.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n260481,0,Cash loans,F,Y,Y,0,99000.0,900000.0,38133.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.02461,-19164,365243,-2108.0,-2690,5.0,1,0,0,1,1,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.07730842888226272,0.470456116119975,0.0093,,0.9727,,,0.0,0.0345,0.0417,,,,0.0076,,0.0024,0.0095,,0.9727,,,0.0,0.0345,0.0417,,,,0.0079,,0.0026,0.0094,,0.9727,,,0.0,0.0345,0.0417,,,,0.0077,,0.0025,,block of flats,0.0065,Wooden,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n330186,0,Cash loans,M,N,Y,1,171000.0,1091565.0,46377.0,882000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-16085,-1182,-6905.0,-4953,,1,1,0,1,1,0,,3.0,3,3,FRIDAY,10,0,0,0,0,0,0,Government,0.4547800241170255,0.4927204930018845,0.21885908222837447,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-675.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n363560,0,Cash loans,F,Y,N,1,180000.0,450000.0,16164.0,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.035792000000000004,-12883,-1066,-2246.0,-3824,5.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,22,0,0,0,0,0,0,Self-employed,0.4301335780570648,0.30874145891108634,,0.0928,0.0987,0.9831,0.7688,0.0,0.0,0.2069,0.1667,0.0417,0.059000000000000004,0.0756,0.0606,0.0,0.0,0.0945,0.1024,0.9831,0.7779,0.0,0.0,0.2069,0.1667,0.0417,0.0603,0.0826,0.0632,0.0,0.0,0.0937,0.0987,0.9831,0.7719,0.0,0.0,0.2069,0.1667,0.0417,0.06,0.077,0.0617,0.0,0.0,reg oper account,block of flats,0.0684,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1005.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n455657,0,Cash loans,M,N,N,0,270000.0,728460.0,44563.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006207,-14255,-762,-8291.0,-5125,,1,1,1,1,1,1,Drivers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,0.607917276414749,0.5318634470827679,0.41534714488434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-185.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n186112,0,Cash loans,F,N,Y,0,112500.0,203760.0,19980.0,180000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.018634,-23984,365243,-9039.0,-3757,,1,0,0,1,1,0,,1.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.3730508407356794,0.4471785780453068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-300.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n218894,1,Cash loans,M,Y,Y,1,157500.0,997335.0,29290.5,832500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014464,-16602,-899,-791.0,-146,21.0,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,2,0,0,0,0,1,1,Industry: type 1,,0.5582106450407892,0.4543210601605785,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-929.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n229003,0,Cash loans,M,N,Y,0,90000.0,453514.5,24601.5,391500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-10095,-2446,-4281.0,-2417,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Self-employed,,0.13274935898704404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-707.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n368524,0,Cash loans,F,N,Y,0,202500.0,693000.0,64129.5,693000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.008068,-8228,-639,-3105.0,-885,,1,1,0,1,0,0,,1.0,3,3,SUNDAY,6,1,1,0,1,1,0,Business Entity Type 3,0.2589266672619959,0.13079048418672032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-978.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n118787,0,Cash loans,F,Y,N,1,225000.0,485640.0,39069.0,450000.0,Family,Working,Higher education,Married,House / apartment,0.00496,-15758,-2480,-913.0,-4115,22.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.6732466327176344,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-1134.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n251393,0,Revolving loans,F,Y,Y,0,112500.0,225000.0,11250.0,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-22189,365243,-4873.0,-4906,4.0,1,0,0,1,0,0,,2.0,3,3,THURSDAY,8,0,0,0,0,0,0,XNA,0.6562626921089985,0.4571104113869031,0.2721336844147212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,0.0,11.0,0.0,-1745.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n279819,0,Cash loans,F,N,N,1,292500.0,1725120.0,47569.5,1440000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.04622,-16806,-895,-534.0,-353,,1,1,0,1,1,0,Core staff,2.0,1,1,SATURDAY,17,0,1,1,0,0,0,Business Entity Type 3,0.8078013387123223,0.6325408570009757,,0.2021,0.107,0.9965,0.9524,,0.28,0.1379,0.6667,0.2917,0.1479,,0.2255,,0.1042,0.2059,0.111,0.9965,0.9543,,0.282,0.1379,0.6667,0.2917,0.1513,,0.2349,,0.1103,0.204,0.107,0.9965,0.953,,0.28,0.1379,0.6667,0.2917,0.1505,,0.2295,,0.1063,reg oper account,block of flats,0.2848,Mixed,No,0.0,0.0,0.0,0.0,-1960.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n245292,0,Revolving loans,F,N,N,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.006233,-8454,-1201,-3540.0,-1116,,1,1,0,1,0,1,Sales staff,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Trade: type 7,0.10330106182562512,0.4983618207178506,0.501075160239048,0.0928,0.0959,0.9821,0.7552,0.0173,0.0,0.2759,0.1667,0.0417,0.0172,0.0756,0.1027,0.0,0.0,0.0945,0.0995,0.9821,0.7648,0.0175,0.0,0.2759,0.1667,0.0417,0.0176,0.0826,0.107,0.0,0.0,0.0937,0.0959,0.9821,0.7585,0.0174,0.0,0.2759,0.1667,0.0417,0.0175,0.077,0.1045,0.0,0.0,reg oper account,block of flats,0.0808,Panel,No,2.0,0.0,2.0,0.0,-423.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n294117,0,Cash loans,F,Y,Y,2,126000.0,592560.0,31153.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018801,-10693,-225,-79.0,-2079,14.0,1,1,0,1,0,0,Core staff,4.0,2,2,THURSDAY,13,0,0,0,0,0,0,Self-employed,0.1184542486876168,0.5464292897553733,,0.268,0.1246,0.998,0.9728,0.1002,0.2,0.1724,0.375,0.4167,0.0967,0.2185,0.2384,0.0,0.0,0.2731,0.1293,0.998,0.9739,0.1012,0.2014,0.1724,0.375,0.4167,0.0989,0.2388,0.2484,0.0,0.0,0.2706,0.1246,0.998,0.9732,0.1009,0.2,0.1724,0.375,0.4167,0.0984,0.2223,0.2427,0.0,0.0,reg oper account,block of flats,0.2618,Panel,No,0.0,0.0,0.0,0.0,-1328.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n238099,0,Cash loans,M,N,Y,0,144000.0,314100.0,17167.5,225000.0,Family,Working,Secondary / secondary special,Single / not married,With parents,0.011703,-9634,-1552,-9492.0,-2303,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.33607509320594164,0.5813523558069821,,0.1433,0.17800000000000002,0.9831,,,0.0,0.4138,0.1667,,0.2501,,0.1674,,0.0,0.146,0.1847,0.9831,,,0.0,0.4138,0.1667,,0.2558,,0.1744,,0.0,0.1447,0.17800000000000002,0.9831,,,0.0,0.4138,0.1667,,0.2545,,0.1704,,0.0,,block of flats,0.1456,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1294.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n231553,0,Cash loans,F,N,Y,0,67500.0,225000.0,12204.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-13668,-808,-6609.0,-4434,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.2631435910213423,0.470456116119975,0.1237,0.1182,0.9752,0.66,0.0123,,0.2069,0.1667,,0.0223,,0.1022,,0.0,0.1261,0.1227,0.9752,0.6733,0.0124,,0.2069,0.1667,,0.0228,,0.1062,,0.0,0.1249,0.1183,0.9752,0.6645,0.0124,,0.2069,0.1667,,0.0227,,0.1038,,0.0,reg oper account,block of flats,0.0802,Panel,No,0.0,0.0,0.0,0.0,-1408.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n339285,0,Cash loans,M,N,N,0,58500.0,302341.5,11331.0,261000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-24313,365243,-1155.0,-4129,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.4813465631791898,0.7490217048463391,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n203943,0,Cash loans,M,Y,Y,1,157500.0,508495.5,21541.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-15931,-2341,-4543.0,-4560,14.0,1,1,0,1,0,0,Drivers,3.0,3,3,FRIDAY,8,0,0,0,0,1,1,Industry: type 9,,0.37484263943649604,0.5620604831738043,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-529.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n447278,0,Cash loans,F,N,N,0,81000.0,646920.0,20997.0,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010276,-19718,365243,-6762.0,-3267,,1,0,0,1,1,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.4360147457650744,0.3910549766342248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-216.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n371772,0,Revolving loans,M,N,Y,0,211500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-11777,-828,-1217.0,-4224,,1,1,1,1,0,0,Managers,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.7239386987225365,0.7362015919557316,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1605.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n311541,0,Cash loans,M,N,Y,0,63000.0,755190.0,36459.0,675000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.006629,-20457,365243,-6660.0,-3974,,1,0,0,1,0,0,,2.0,2,2,MONDAY,6,0,0,0,0,0,0,XNA,,0.2301672098330137,,0.0553,0.0559,0.9866,0.8368,0.0179,0.0264,0.069,0.3054,0.0417,0.0417,0.0451,0.0527,0.0,0.0,0.042,0.0313,0.9811,0.7975,0.0137,0.0403,0.0345,0.375,0.0417,0.0261,0.0367,0.0023,0.0,0.0,0.0416,0.0305,0.9871,0.8658,0.0139,0.04,0.0345,0.375,0.0417,0.0268,0.0342,0.0573,0.0,0.0,reg oper spec account,block of flats,0.0017,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n246915,0,Cash loans,F,Y,Y,0,112500.0,539100.0,27652.5,450000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.019101,-20628,365243,-9864.0,-4181,15.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,0.4827063764715042,0.6736121294504197,0.6296742509538716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-148.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n173760,1,Cash loans,F,N,Y,0,90000.0,180000.0,14058.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.014519999999999996,-8543,-946,-8525.0,-1230,,1,1,0,1,0,0,Medicine staff,2.0,2,2,THURSDAY,11,0,0,0,1,1,0,Medicine,0.13760167077044747,0.5677708186650533,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-504.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n149674,1,Cash loans,M,N,Y,0,247500.0,873342.0,25663.5,729000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.009175,-10057,-3408,-4701.0,-2740,,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,16,0,0,0,0,0,0,Construction,,0.7358446995330913,0.2925880073368475,0.25,0.1305,0.9921,0.9592,0.0201,0.26,0.2241,0.3542,0.4167,0.0614,0.0403,0.2482,0.0154,0.0205,0.0546,0.0292,0.9871,0.9608,0.0203,0.0403,0.0345,0.3333,0.4167,0.0389,0.0441,0.0381,0.0156,0.002,0.2524,0.1305,0.9921,0.9597,0.0203,0.26,0.2241,0.3542,0.4167,0.0624,0.040999999999999995,0.2526,0.0155,0.021,reg oper account,block of flats,0.0373,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-434.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n206872,0,Cash loans,M,N,Y,0,112500.0,143910.0,14364.0,135000.0,Family,Pensioner,Secondary / secondary special,Separated,House / apartment,0.04622,-23296,365243,-7074.0,-4045,,1,0,0,1,0,0,,1.0,1,1,MONDAY,11,0,0,0,0,0,0,XNA,,0.4494370506711652,,0.0825,0.0829,0.9762,0.6736,0.0626,0.0,0.1379,0.1667,0.2083,0.0511,0.0672,0.0704,0.0,0.0,0.084,0.086,0.9762,0.6864,0.0631,0.0,0.1379,0.1667,0.2083,0.0522,0.0735,0.0733,0.0,0.0,0.0833,0.0829,0.9762,0.6779999999999999,0.063,0.0,0.1379,0.1667,0.2083,0.0519,0.0684,0.0717,0.0,0.0,reg oper account,block of flats,0.0896,Panel,No,0.0,0.0,0.0,0.0,-329.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n435215,0,Cash loans,F,Y,N,1,112500.0,448056.0,16222.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.018801,-19711,-5648,-10109.0,-3257,15.0,1,1,0,1,0,0,Security staff,3.0,2,2,FRIDAY,9,0,0,0,1,1,0,Business Entity Type 3,,0.3165561439252505,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n112486,0,Cash loans,F,Y,Y,0,202500.0,1113840.0,44302.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-18191,-288,-121.0,-1718,4.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.5688988237664921,0.6832720434839605,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1482.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n400323,0,Cash loans,F,N,Y,0,157500.0,225000.0,24363.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.026392,-8097,-156,-2880.0,-664,,1,1,0,1,0,0,Waiters/barmen staff,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.30794756893766784,0.6874452794611469,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-648.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n377318,0,Cash loans,F,N,N,0,90000.0,549882.0,17869.5,459000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.018801,-10496,-438,-3460.0,-134,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 1,0.08972321397950545,0.5540758269449539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1531.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n158156,1,Cash loans,F,N,Y,0,99000.0,276277.5,18261.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-14063,-1593,-5323.0,-4479,,1,1,1,1,0,0,Sales staff,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Trade: type 3,,0.640040616163284,0.08391700365753578,0.0928,0.0999,0.9771,0.6872,0.0119,0.0,0.2069,0.1667,0.2083,0.0934,0.0748,0.0864,0.0039,0.004,0.0945,0.1037,0.9772,0.6994,0.012,0.0,0.2069,0.1667,0.2083,0.0955,0.0817,0.09,0.0039,0.0042,0.0937,0.0999,0.9771,0.6914,0.012,0.0,0.2069,0.1667,0.2083,0.095,0.0761,0.0879,0.0039,0.0041,reg oper account,block of flats,0.0753,Block,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n322042,0,Cash loans,M,Y,Y,0,135000.0,274500.0,22135.5,274500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.014464,-16040,-3670,-4740.0,-4670,24.0,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Self-employed,,0.6599707420852289,0.7662336700704004,0.0082,0.0,0.9632,,,0.0,0.069,0.0417,,0.0408,,0.0071,,0.0048,0.0084,0.0,0.9633,,,0.0,0.069,0.0417,,0.0417,,0.0074,,0.0051,0.0083,0.0,0.9632,,,0.0,0.069,0.0417,,0.0415,,0.0072,,0.0049,,block of flats,0.0078,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2560.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n216308,0,Cash loans,F,Y,N,1,225000.0,1192500.0,34996.5,1192500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-9391,365243,-4255.0,-2078,16.0,1,0,0,1,0,0,,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,0.3857982461782388,0.2298612494363501,0.6446794549585961,0.0907,0.0636,0.9816,0.7484,,0.0,0.0345,0.1667,0.2083,0.0194,0.07400000000000001,0.0328,0.0,0.0,0.0924,0.066,0.9816,0.7583,,0.0,0.0345,0.1667,0.2083,0.0198,0.0808,0.0342,0.0,0.0,0.0916,0.0636,0.9816,0.7518,,0.0,0.0345,0.1667,0.2083,0.0197,0.0752,0.0334,0.0,0.0,reg oper account,block of flats,0.0467,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n421567,0,Cash loans,M,N,Y,3,90000.0,135000.0,13482.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-13284,-2464,-7098.0,-787,,1,1,0,1,0,0,,5.0,3,3,FRIDAY,6,0,0,0,1,0,1,University,,0.3369723067021109,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-922.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n424171,0,Cash loans,F,Y,Y,0,247500.0,193572.0,19273.5,171000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.014464,-14847,-370,-5403.0,-2702,17.0,1,1,0,1,0,0,Accountants,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,School,0.5808663098584719,0.2759026675714409,0.4489622731076524,0.0619,0.0573,0.9851,0.7552,0.0095,0.0,0.1379,0.1667,0.2083,0.0591,0.0504,0.0591,0.0,0.0,0.063,0.0595,0.9851,0.7648,0.0096,0.0,0.1379,0.1667,0.2083,0.0605,0.0551,0.0616,0.0,0.0,0.0625,0.0573,0.9851,0.7585,0.0095,0.0,0.1379,0.1667,0.2083,0.0602,0.0513,0.0602,0.0,0.0,reg oper account,block of flats,0.0517,Panel,No,0.0,0.0,0.0,0.0,-2128.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n307142,0,Cash loans,M,Y,Y,3,540000.0,1120275.0,44428.5,1030500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006629,-13241,-246,-1984.0,-4713,13.0,1,1,0,1,0,0,Managers,5.0,2,2,THURSDAY,5,0,0,0,0,0,0,Transport: type 4,,0.4288177160311384,,0.0624,0.0,0.9757,0.6668,0.0,0.0,0.1379,0.1667,0.0417,,0.0492,0.0485,0.0077,0.0209,0.0599,0.0,0.9752,0.6733,0.0,0.0,0.1379,0.1667,0.0417,,0.0496,0.0499,0.0039,0.0038,0.063,0.0,0.9757,0.6713,0.0,0.0,0.1379,0.1667,0.0417,,0.05,0.0493,0.0078,0.0213,reg oper spec account,block of flats,0.0394,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-599.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n449996,0,Cash loans,F,N,Y,1,171000.0,80865.0,6385.5,67500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-16359,-2003,-10445.0,-4458,,1,1,0,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.526464238550629,0.6347055309763198,0.0732,0.0797,0.9811,0.7416,0.0078,0.0,0.1379,0.1667,0.2083,0.0,0.0572,0.0643,0.0116,0.0081,0.0746,0.0827,0.9811,0.7517,0.0079,0.0,0.1379,0.1667,0.2083,0.0,0.0624,0.067,0.0117,0.0086,0.0739,0.0797,0.9811,0.7451,0.0079,0.0,0.1379,0.1667,0.2083,0.0,0.0581,0.0654,0.0116,0.0083,reg oper account,block of flats,0.0566,Panel,No,6.0,0.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n243146,0,Cash loans,F,N,Y,0,307350.0,1354500.0,50328.0,1354500.0,Unaccompanied,Pensioner,Lower secondary,Separated,House / apartment,0.0228,-20879,365243,-5992.0,-4286,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.5171666526083986,0.8528284668510541,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-347.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n365013,0,Cash loans,F,N,Y,0,112500.0,202500.0,6489.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-19194,-4427,-5834.0,-2635,,1,1,0,1,0,0,Laborers,2.0,1,1,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 1,,0.6112938126689971,,0.1098,0.0539,0.9762,0.6736,0.0,0.2,0.2328,0.2083,0.0971,0.081,0.0849,0.1022,0.084,0.0066,0.0735,0.0397,0.9762,0.6864,0.0,0.282,0.2414,0.1667,0.0417,0.0132,0.0597,0.0657,0.0039,0.0032,0.0729,0.0388,0.9762,0.6779999999999999,0.0,0.26,0.2414,0.1667,0.0417,0.0858,0.0594,0.0642,0.0408,0.0031,org spec account,block of flats,0.1927,Panel,No,6.0,0.0,5.0,0.0,-1190.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n366630,1,Cash loans,M,Y,Y,0,225000.0,927252.0,27243.0,774000.0,\"Spouse, partner\",Working,Lower secondary,Married,House / apartment,0.006207,-15839,-2292,-7946.0,-1939,27.0,1,1,0,1,0,1,Laborers,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6393753261174304,0.6706517530862718,0.0175,0.0068,0.9796,0.7212,0.0017,0.0,0.069,0.0417,0.0833,0.0366,0.0134,0.013999999999999999,0.0039,0.0044,0.0179,0.0071,0.9796,0.7321,0.0018,0.0,0.069,0.0417,0.0833,0.0375,0.0147,0.0146,0.0039,0.0046,0.0177,0.0068,0.9796,0.7249,0.0017,0.0,0.069,0.0417,0.0833,0.0373,0.0137,0.0142,0.0039,0.0045,reg oper account,block of flats,0.0119,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-540.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n236992,1,Cash loans,M,N,Y,1,94500.0,225000.0,12915.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00702,-8637,-804,-208.0,-1321,,1,1,1,1,0,0,Sales staff,3.0,2,2,SUNDAY,8,0,0,0,0,1,1,Self-employed,,0.0970077393847046,0.16342569473134347,,,0.9732,,,,,,,,,0.0103,,,,,0.9732,,,,,,,,,0.0108,,,,,0.9732,,,,,,,,,0.0105,,,,,0.015,,No,0.0,0.0,0.0,0.0,-544.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n350827,0,Revolving loans,F,N,Y,0,81000.0,247500.0,12375.0,247500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-19304,365243,-4424.0,-2791,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,0.6198030872480688,0.4521277641785778,0.7435593141311444,0.0608,0.0703,0.9925,0.898,0.013000000000000001,0.0,0.1034,0.1667,0.2083,0.0125,0.0496,0.0602,0.0039,0.0009,0.062,0.0729,0.9926,0.902,0.0131,0.0,0.1034,0.1667,0.2083,0.0128,0.0542,0.0628,0.0039,0.001,0.0614,0.0703,0.9925,0.8994,0.013000000000000001,0.0,0.1034,0.1667,0.2083,0.0127,0.0504,0.0613,0.0039,0.0009,reg oper account,block of flats,0.0547,Panel,No,1.0,1.0,1.0,1.0,-2750.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n246767,0,Revolving loans,F,N,N,0,180000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-10082,-1473,-4653.0,-1090,,1,1,0,1,0,1,Core staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Bank,0.6670979521477328,0.5904351896940501,,0.1089,0.1116,0.9767,0.6464,0.0087,0.0,0.1838,0.1667,0.2083,0.0814,0.0885,0.0862,0.0019,0.0072,0.105,0.1064,0.9767,0.6276,0.0,0.0,0.1724,0.1667,0.2083,0.0563,0.0918,0.0616,0.0,0.0,0.1041,0.1025,0.9767,0.6511,0.0118,0.0,0.1724,0.1667,0.2083,0.0957,0.0855,0.091,0.0019,0.0065,reg oper spec account,block of flats,0.068,Panel,No,1.0,1.0,1.0,1.0,-456.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n250896,0,Cash loans,F,N,N,0,180000.0,239850.0,22671.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.02461,-25049,365243,-8337.0,-4044,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,,0.7882014353254916,,0.1485,0.1135,0.9886,0.8436,0.0864,0.08,0.069,0.3333,0.375,0.0993,0.121,0.1485,0.0,0.0,0.1513,0.1178,0.9886,0.8497,0.0872,0.0806,0.069,0.3333,0.375,0.1016,0.1322,0.1548,0.0,0.0,0.1499,0.1135,0.9886,0.8457,0.087,0.08,0.069,0.3333,0.375,0.1011,0.1231,0.1512,0.0,0.0,reg oper account,block of flats,0.1641,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1526.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n285623,0,Cash loans,F,N,Y,1,112500.0,1006920.0,42790.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.004849,-18217,-7651,-8124.0,-1743,,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,Security Ministries,0.7990804998804144,0.463852341956096,0.511891801533151,0.0959,,,,,,,0.375,,,,,,,0.0977,,,,,,,0.375,,,,,,,0.0968,,,,,,,0.375,,,,,,,,block of flats,0.1043,,No,0.0,0.0,0.0,0.0,-696.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n228860,0,Cash loans,F,Y,Y,1,202500.0,1017000.0,29866.5,1017000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-17342,-958,-2420.0,-893,9.0,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Trade: type 7,0.6174506654965966,0.4924144145076061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-355.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n378242,0,Cash loans,M,Y,Y,1,315000.0,738486.0,72072.0,688500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.01885,-17224,-1854,-327.0,-529,3.0,1,1,0,1,0,0,Managers,3.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.457973379707031,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-501.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n394801,0,Cash loans,F,N,Y,0,270000.0,2156400.0,59431.5,1800000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-21368,-8158,-3042.0,-2453,,1,1,0,1,0,0,Managers,2.0,3,3,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.6364913012802964,0.6430255641096323,,,0.9906,,,,,,,,,,,,,,0.9906,,,,,,,,,,,,,,0.9906,,,,,,,,,,,,,block of flats,0.0786,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1500.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n311701,0,Cash loans,F,N,Y,0,166500.0,328500.0,10444.5,328500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.031329,-8740,-1146,-4569.0,-1275,,1,1,1,1,0,0,,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4530534346039271,0.6802545988586775,0.6817058776720116,0.0753,0.0514,0.9757,0.6668,0.0288,0.08,0.069,0.2083,0.2221,0.1069,0.0614,0.0837,0.0,0.0,0.0084,0.0,0.9677,0.5753,0.002,0.0,0.0345,0.0417,0.0833,0.1093,0.0073,0.0081,0.0,0.0,0.026000000000000002,0.0,0.9677,0.5639,0.004,0.0,0.069,0.0417,0.0833,0.1087,0.0214,0.0084,0.0,0.0,not specified,block of flats,0.2288,Panel,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n382356,0,Revolving loans,F,N,N,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Lower secondary,Married,House / apartment,0.010556,-9684,-692,-5217.0,-407,,1,1,0,1,0,0,Sales staff,2.0,3,3,FRIDAY,14,0,0,0,1,1,1,Self-employed,0.2134254201946056,0.2592907243989447,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-328.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n361563,0,Cash loans,M,Y,Y,0,135000.0,254700.0,17149.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-13348,-926,-4676.0,-978,11.0,1,1,0,1,0,0,Laborers,2.0,3,3,THURSDAY,6,0,0,0,1,1,1,Business Entity Type 3,,0.6561178747137842,0.09950368352887068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-709.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n403844,1,Revolving loans,F,N,Y,0,90000.0,157500.0,7875.0,157500.0,Family,State servant,Secondary / secondary special,Married,With parents,0.019101,-9586,-2177,-4112.0,-1233,,1,1,1,1,1,0,Sales staff,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Postal,0.4603570180694877,0.16683865926483332,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-836.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n121466,0,Cash loans,M,N,Y,0,247500.0,1003500.0,33165.0,1003500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-15302,-596,-847.0,-5019,,1,1,1,1,1,0,Laborers,2.0,1,1,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.7369907518483392,0.511891801533151,0.0608,,0.9767,0.6804,,0.0,0.1034,0.1667,,0.0378,,0.05,,0.0022,0.062,,0.9767,0.6929,,0.0,0.1034,0.1667,,0.0387,,0.0521,,0.0024,0.0614,,0.9767,0.6847,,0.0,0.1034,0.1667,,0.0385,,0.0509,,0.0023,reg oper account,block of flats,0.0398,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-501.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n104795,0,Cash loans,F,N,Y,0,135000.0,244584.0,17851.5,193500.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.016612000000000002,-10303,-669,-532.0,-1638,,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Bank,0.3767194147878713,0.6674139624056618,,0.0577,0.0031,0.9781,0.7008,0.0092,0.0,0.1379,0.1667,0.2083,0.0673,0.0471,0.0537,0.0,0.1044,0.0588,0.0032,0.9782,0.7125,0.0093,0.0,0.1379,0.1667,0.2083,0.0688,0.0514,0.0559,0.0,0.1106,0.0583,0.0031,0.9781,0.7048,0.0093,0.0,0.1379,0.1667,0.2083,0.0685,0.0479,0.0546,0.0,0.1066,reg oper account,block of flats,0.07,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1842.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n108216,0,Cash loans,F,Y,Y,0,720000.0,1255680.0,45234.0,1125000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.04622,-16503,-3710,-4457.0,-8,1.0,1,1,0,1,0,0,,2.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Government,,0.7414841907340338,0.1446481462546413,0.1093,,0.9861,,,0.12,0.1034,0.3333,,,,,,,0.1113,,0.9861,,,0.1208,0.1034,0.3333,,,,,,,0.1103,,0.9861,,,0.12,0.1034,0.3333,,,,,,,,block of flats,0.09300000000000001,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1814.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,6.0\n209972,0,Cash loans,F,N,Y,0,112500.0,246357.0,24493.5,234000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-12323,-852,-7141.0,-4303,,1,1,0,1,0,0,Private service staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,,0.3285149651920687,0.6528965519806539,0.0619,0.0486,0.9801,0.728,0.0083,0.0,0.1379,0.1667,0.2083,0.0526,0.0504,0.0518,,0.0,0.063,0.0504,0.9801,0.7387,0.0084,0.0,0.1379,0.1667,0.2083,0.0538,0.0551,0.054000000000000006,,0.0,0.0625,0.0486,0.9801,0.7316,0.0084,0.0,0.1379,0.1667,0.2083,0.0535,0.0513,0.0528,,0.0,reg oper account,block of flats,0.0408,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n236189,0,Cash loans,F,Y,N,0,360000.0,679500.0,22455.0,679500.0,Family,Working,Secondary / secondary special,Single / not married,Rented apartment,0.030755,-13198,-3145,-7167.0,-4344,1.0,1,1,1,1,0,0,,1.0,2,2,MONDAY,15,1,1,0,1,1,0,Business Entity Type 3,0.2960200398770834,0.6336786511515049,0.41184855592423975,0.1031,0.1865,0.9901,0.8640000000000001,0.0218,0.0,0.3103,0.1667,0.2083,0.1169,0.0832,0.1216,0.0039,0.0049,0.105,0.1935,0.9901,0.8693,0.022000000000000002,0.0,0.3103,0.1667,0.2083,0.1196,0.0909,0.1267,0.0039,0.0052,0.1041,0.1865,0.9901,0.8658,0.0219,0.0,0.3103,0.1667,0.2083,0.11900000000000001,0.0847,0.1238,0.0039,0.005,reg oper account,block of flats,0.1086,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-320.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n297222,1,Revolving loans,F,N,Y,0,103500.0,270000.0,13500.0,270000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.007120000000000001,-16095,-661,-2078.0,-25,,1,1,0,1,0,0,Medicine staff,2.0,2,2,MONDAY,8,0,0,0,0,0,0,Medicine,,0.0062846309550741615,0.06380484447151785,,,0.9781,,,0.0,0.0345,0.0417,,0.0,,,,,,,0.9782,,,0.0,0.0345,0.0417,,0.0,,,,,,,0.9781,,,0.0,0.0345,0.0417,,0.0,,,,,,block of flats,0.006999999999999999,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-598.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n218400,0,Cash loans,M,Y,Y,2,112500.0,619254.0,23715.0,553500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-14632,-1252,-2592.0,-4396,1.0,1,1,0,1,1,0,Sales staff,4.0,2,2,THURSDAY,7,0,0,0,0,0,0,Self-employed,0.5735341500704287,0.5748722923126679,0.7503751495159068,0.1,0.0927,0.9786,0.7076,,0.0,0.2069,0.1667,0.2083,0.0623,0.0807,0.0567,0.0039,0.0122,0.1019,0.0962,0.9786,0.7190000000000001,,0.0,0.2069,0.1667,0.2083,0.0637,0.0882,0.0591,0.0039,0.0129,0.10099999999999999,0.0927,0.9786,0.7115,,0.0,0.2069,0.1667,0.2083,0.0634,0.0821,0.0578,0.0039,0.0125,reg oper account,block of flats,0.0693,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2146.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n215286,0,Cash loans,M,Y,Y,0,180000.0,270000.0,30591.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-17601,-1636,-588.0,-1075,10.0,1,1,1,1,1,0,Drivers,2.0,2,2,TUESDAY,13,0,1,1,0,1,1,Industry: type 9,0.4173196298534187,0.2527598753666401,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n287949,0,Cash loans,F,N,N,0,171000.0,508495.5,21541.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019689,-23642,365243,-12061.0,-2910,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,,0.5450956998877616,0.633031641417419,,,0.9786,,,,,,,,,0.0122,,,,,0.9786,,,,,,,,,0.0127,,,,,0.9786,,,,,,,,,0.0124,,,,,0.0096,,No,0.0,0.0,0.0,0.0,-378.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n234921,0,Cash loans,F,N,N,0,157500.0,1546020.0,45202.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-16589,-930,-1422.0,-125,,1,1,0,1,1,0,,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.09548418538670353,0.4794489811780563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2045.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n197420,0,Cash loans,F,N,Y,0,112500.0,349258.5,23755.5,301500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.009549,-8918,-228,-4414.0,-1579,,1,1,0,1,0,0,Sales staff,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.12233503226819285,0.5496412263052889,0.2764406945454034,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-399.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n375703,1,Cash loans,M,Y,N,1,90000.0,254700.0,12937.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-16776,-1688,-354.0,-331,14.0,1,1,1,1,1,0,Laborers,3.0,2,2,SATURDAY,8,0,0,0,0,1,1,Business Entity Type 3,,0.4443399835040716,0.17146836689679945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-811.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n266286,0,Cash loans,F,N,Y,0,225000.0,1046142.0,33876.0,913500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020713,-12269,-992,-3664.0,-3680,,1,1,0,1,0,0,Laborers,1.0,3,3,MONDAY,7,0,0,0,1,1,1,Business Entity Type 3,,0.4650707394371367,0.5370699579791587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-703.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n394663,0,Cash loans,F,N,N,0,157500.0,165960.0,8113.5,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-21928,365243,-5016.0,-5016,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,XNA,,0.2432220753185349,0.34090642641523844,0.0938,0.1031,0.9856,0.8028,0.0259,0.08,0.0345,0.3333,0.0417,,,0.0786,,0.0,0.0956,0.107,0.9856,0.8105,0.0261,0.0806,0.0345,0.3333,0.0417,,,0.0819,,0.0,0.0947,0.1031,0.9856,0.8054,0.026000000000000002,0.08,0.0345,0.3333,0.0417,,,0.08,,0.0,,block of flats,0.076,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2695.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n451948,0,Cash loans,F,N,Y,1,180000.0,540000.0,15916.5,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-14238,-6130,-1691.0,-4991,,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,School,,0.792595729549555,0.8435435389318647,0.2938,0.1408,0.9945,0.9252,0.2246,0.48,0.2069,0.4583,0.5,,0.2396,0.3191,0.0,0.0,0.2994,0.1461,0.9945,0.9281,0.2266,0.4834,0.2069,0.4583,0.5,,0.2617,0.3325,0.0,0.0,0.2967,0.1408,0.9945,0.9262,0.226,0.48,0.2069,0.4583,0.5,,0.2437,0.3249,0.0,0.0,reg oper account,block of flats,0.3738,Panel,No,1.0,0.0,1.0,0.0,-1545.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n321218,1,Cash loans,F,N,Y,0,112500.0,544491.0,17694.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.008865999999999999,-20899,365243,-6404.0,-3713,,1,0,0,1,0,0,,1.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.4554449095633676,0.3139166772114369,0.0557,0.0171,0.9781,0.7008,0.0086,0.04,0.0345,0.3333,0.375,0.0279,0.0454,0.0447,0.0,0.0,0.0567,0.0177,0.9782,0.7125,0.0087,0.0403,0.0345,0.3333,0.375,0.0286,0.0496,0.0466,0.0,0.0,0.0562,0.0171,0.9781,0.7048,0.0087,0.04,0.0345,0.3333,0.375,0.0284,0.0462,0.0455,0.0,0.0,reg oper account,block of flats,0.0399,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-334.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n389966,0,Cash loans,F,N,N,0,76500.0,679500.0,22585.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-16876,-97,-5981.0,-428,,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Other,0.6179391245818553,0.4536991023064769,0.4992720153045617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1968.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n274577,0,Cash loans,F,N,N,1,180000.0,450000.0,28890.0,450000.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.009657,-11372,-1885,-1464.0,-4053,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,12,1,1,0,1,1,0,Business Entity Type 3,,0.3698853626045697,0.6848276586890367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-576.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n103055,0,Cash loans,F,Y,Y,1,90000.0,469147.5,23953.5,351000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-16018,-2841,-4327.0,-5016,10.0,1,1,0,1,1,0,Laborers,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Postal,0.4158544392854678,0.6058712342897806,0.2340151665320674,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2460.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n440779,0,Cash loans,M,Y,Y,0,112500.0,942300.0,27679.5,675000.0,Children,Working,Secondary / secondary special,Widow,House / apartment,0.010147,-20829,-198,-12616.0,-4371,0.0,1,1,1,1,1,0,Cooking staff,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Restaurant,,0.6154114057580812,0.7700870700124128,0.0186,,0.9831,0.7688,,0.0,0.1034,0.0417,,,0.0151,0.0191,0.5212,0.0097,0.0189,,0.9831,0.7779,,0.0,0.1034,0.0417,,,0.0165,0.0199,0.5253,0.0103,0.0187,,0.9831,0.7719,,0.0,0.1034,0.0417,,,0.0154,0.0194,0.5241,0.0099,reg oper account,block of flats,0.0171,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1395.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n346554,0,Cash loans,F,Y,Y,2,225000.0,569673.0,27837.0,400500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019689,-14796,-669,-5795.0,-4333,12.0,1,1,0,1,0,0,Core staff,3.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Self-employed,0.3652102521720368,0.5384999385483423,0.8546298013688485,0.0918,0.0537,0.9801,0.728,0.0349,0.0,0.2069,0.1667,0.0417,0.106,0.0748,0.0755,0.0,0.0,0.0935,0.0557,0.9801,0.7387,0.0352,0.0,0.2069,0.1667,0.0417,0.1084,0.0817,0.0787,0.0,0.0,0.0926,0.0537,0.9801,0.7316,0.0351,0.0,0.2069,0.1667,0.0417,0.1078,0.0761,0.0769,0.0,0.0,reg oper account,block of flats,0.0785,Panel,No,4.0,0.0,4.0,0.0,-1915.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n446122,1,Cash loans,F,N,Y,0,112500.0,273636.0,21748.5,247500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.028663,-10908,-623,-3404.0,-3554,,1,1,0,1,0,0,Sales staff,1.0,2,2,SATURDAY,9,0,0,0,0,1,1,Government,0.3154744745204073,0.5774568656113563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,6.0,1.0,-346.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n180391,0,Cash loans,M,Y,N,0,180000.0,1288350.0,37053.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00823,-15932,-7298,-1103.0,-4159,34.0,1,1,1,1,0,0,Core staff,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Security Ministries,,0.5346283444762738,0.7238369900414456,0.0619,,0.998,,,0.08,0.0345,0.375,,,,0.0549,,0.0,0.063,,0.998,,,0.0806,0.0345,0.375,,,,0.0572,,0.0,0.0625,,0.998,,,0.08,0.0345,0.375,,,,0.0559,,0.0,,block of flats,0.0432,\"Stone, brick\",No,9.0,0.0,9.0,0.0,-1524.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n128365,0,Cash loans,F,Y,Y,0,238500.0,1546020.0,42642.0,1350000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.011657,-19757,-7342,-5151.0,-3311,6.0,1,1,1,1,1,0,Core staff,2.0,1,1,WEDNESDAY,15,0,0,0,0,0,0,School,,0.7881649272975071,0.501075160239048,0.133,0.1501,0.9781,0.7008,0.1751,0.0,0.2759,0.1667,0.2083,0.1568,0.1084,0.1217,0.0,0.0,0.1355,0.1557,0.9782,0.7125,0.1767,0.0,0.2759,0.1667,0.2083,0.1604,0.1185,0.1268,0.0,0.0,0.1343,0.1501,0.9781,0.7048,0.1762,0.0,0.2759,0.1667,0.2083,0.1595,0.1103,0.1239,0.0,0.0,reg oper account,block of flats,0.0957,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1723.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n250162,0,Cash loans,F,N,Y,0,247500.0,1067940.0,31356.0,765000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.031329,-10537,-2648,-2724.0,-1936,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Industry: type 7,,0.7011953109705741,0.25670557243930026,0.1151,0.1132,0.9811,0.7416,0.013999999999999999,0.0,0.2528,0.1667,0.2083,0.0469,0.0936,0.0882,0.0013,0.0131,0.1261,0.1073,0.9811,0.7517,0.0109,0.0,0.2759,0.1667,0.2083,0.037000000000000005,0.1102,0.0665,0.0,0.0,0.1249,0.11,0.9811,0.7451,0.0128,0.0,0.2759,0.1667,0.2083,0.0446,0.1026,0.0901,0.0,0.0,reg oper account,block of flats,0.0572,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-556.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n202385,0,Cash loans,F,N,Y,0,112500.0,1676520.0,44356.5,1498500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010032,-22027,365243,-6246.0,-4460,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,XNA,0.796639844676698,0.43793003286732896,0.36227724703843145,,,0.9876,,,0.1064,0.0917,0.375,,,,,,,,,0.9881,,,0.0806,0.069,0.375,,,,,,,,,0.9881,,,0.08,0.069,0.375,,,,,,,,block of flats,0.0675,Panel,No,0.0,0.0,0.0,0.0,-1551.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,8.0\n172905,0,Cash loans,M,N,Y,0,90000.0,332842.5,16317.0,234000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16733,-1266,-2430.0,-275,,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.7001725892257031,0.4848514754962666,0.1253,0.0352,0.9846,0.7892,0.0108,0.02,0.069,0.25,0.0417,0.0802,0.1004,0.0637,0.0097,0.0295,0.0515,0.0272,0.9826,0.7713,0.0083,0.0,0.0345,0.1667,0.0417,0.0484,0.0441,0.0485,0.0078,0.0217,0.1265,0.0352,0.9846,0.792,0.0109,0.02,0.069,0.25,0.0417,0.0816,0.1022,0.0649,0.0097,0.0301,reg oper account,block of flats,0.0411,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-377.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n270159,0,Cash loans,F,N,Y,0,112018.5,135000.0,8856.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Office apartment,0.018801,-10048,-900,-3317.0,-2729,,1,1,1,1,0,0,Sales staff,1.0,2,2,TUESDAY,7,0,0,0,1,1,0,Business Entity Type 3,,0.4752788875365872,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-511.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n306431,0,Cash loans,M,Y,Y,0,225000.0,1305000.0,35887.5,1305000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.022625,-18523,-2334,-3057.0,-2072,7.0,1,1,0,1,1,0,Laborers,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,,0.6806254823959837,0.248535557339522,0.0,,0.0,,,,,,,,,,,,0.0,,0.0005,,,,,,,,,,,,0.0,,0.0,,,,,,,,,,,,,block of flats,0.0,,No,1.0,0.0,1.0,0.0,-3057.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,2.0\n181431,0,Cash loans,F,N,Y,1,67500.0,450000.0,21649.5,450000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.019689,-16587,-1550,-1237.0,-124,,1,1,1,1,0,0,Cleaning staff,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,Industry: type 3,0.5082714279321624,0.3506713024584564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n104693,0,Cash loans,F,N,Y,0,103500.0,142200.0,8068.5,112500.0,Unaccompanied,Pensioner,Incomplete higher,Married,House / apartment,0.030755,-22505,365243,-4754.0,-4759,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.6497651998864213,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-377.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n236513,0,Cash loans,F,N,N,1,112500.0,441657.0,16780.5,310500.0,Unaccompanied,Working,Secondary / secondary special,Separated,Rented apartment,0.010643,-14726,-3379,-4198.0,-4452,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Kindergarten,0.426320027718876,0.7285340550698832,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n297945,0,Cash loans,F,Y,N,1,225000.0,855882.0,36391.5,765000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.030755,-12131,-1663,-1493.0,-3565,3.0,1,1,0,1,0,0,Managers,3.0,2,2,SATURDAY,15,0,0,0,0,1,1,Other,0.7661563245781401,0.7500083345845173,0.4776491548517548,0.1381,0.1008,0.9906,0.898,0.0299,0.14,0.1034,0.3542,0.1875,0.0923,0.1126,0.1449,0.0039,0.0038,0.0945,0.0703,0.9856,0.8105,0.027999999999999997,0.0806,0.0345,0.3333,0.0,0.0192,0.0826,0.0937,0.0,0.0,0.1395,0.1008,0.9866,0.8994,0.0301,0.14,0.1034,0.3542,0.1875,0.0939,0.1146,0.157,0.0039,0.0039,reg oper account,block of flats,0.0707,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1892.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n139612,1,Cash loans,F,N,Y,2,78750.0,354469.5,19359.0,306000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018801,-12593,-191,-6641.0,-4285,,1,1,1,1,1,0,Sales staff,3.0,2,2,SATURDAY,7,0,0,0,1,1,0,Business Entity Type 3,0.1583790942076032,0.33131400448219506,0.2822484337007223,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-488.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n454484,0,Cash loans,F,Y,N,0,315000.0,260640.0,20898.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-13455,-2748,-3656.0,-468,14.0,1,1,1,1,1,0,Sales staff,2.0,1,1,THURSDAY,10,0,0,0,0,0,0,Other,,0.6894433634692566,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n316145,0,Cash loans,F,N,Y,0,225000.0,508495.5,22527.0,454500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-21467,365243,-10563.0,-4227,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,XNA,,0.5659936562921726,0.6894791426446275,0.0722,0.0522,0.9786,,,0.0,0.1379,0.1667,,0.0781,,0.0531,,0.0,0.0735,0.0542,0.9786,,,0.0,0.1379,0.1667,,0.0799,,0.0553,,0.0,0.0729,0.0522,0.9786,,,0.0,0.1379,0.1667,,0.0795,,0.054000000000000006,,0.0,,block of flats,0.0417,\"Stone, brick\",No,9.0,0.0,9.0,0.0,-887.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n248573,0,Cash loans,F,N,Y,0,202500.0,667237.5,35680.5,576000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.009175,-23735,365243,-5183.0,-4806,,1,0,0,1,1,0,,1.0,2,2,MONDAY,18,0,0,0,0,0,0,XNA,0.871900395808025,0.7877045319003423,0.5902333386185574,0.2402,,0.9811,,,0.0,0.4483,0.1667,,,,0.2492,,,0.2447,,0.9811,,,0.0,0.4483,0.1667,,,,0.2597,,,0.2425,,0.9811,,,0.0,0.4483,0.1667,,,,0.2537,,,,block of flats,0.2163,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1290.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n337437,0,Cash loans,M,N,N,0,180000.0,450000.0,35685.0,450000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.04622,-10323,-1605,-4080.0,-2452,,1,1,0,1,1,0,,2.0,1,1,TUESDAY,15,0,0,0,0,1,1,Business Entity Type 3,0.2278539383867812,0.6506057476965103,0.06825115445188665,0.0722,,0.9781,,,,0.1379,0.1667,,0.0941,,0.0676,,,0.0735,,0.9782,,,,0.1379,0.1667,,0.0963,,0.0705,,,0.0729,,0.9781,,,,0.1379,0.1667,,0.0958,,0.0688,,,,block of flats,0.0575,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-113.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n261139,0,Cash loans,F,N,N,2,270000.0,787131.0,42066.0,679500.0,Unaccompanied,Commercial associate,Higher education,Married,Municipal apartment,0.072508,-14110,-3962,-7536.0,-4484,,1,1,0,1,1,0,Managers,4.0,1,1,MONDAY,18,0,0,0,0,0,0,Medicine,0.8314713632762967,0.7527656635940116,0.6769925032909132,0.0619,0.0616,0.9752,0.66,0.0464,0.0,0.1034,0.1667,0.0417,0.0,0.0504,0.0496,0.0,0.0,0.063,0.064,0.9752,0.6733,0.0469,0.0,0.1034,0.1667,0.0417,0.0,0.0551,0.0516,0.0,0.0,0.0625,0.0616,0.9752,0.6645,0.0467,0.0,0.1034,0.1667,0.0417,0.0,0.0513,0.0505,0.0,0.0,reg oper account,block of flats,0.039,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3199.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n271456,0,Cash loans,F,N,N,0,112500.0,675000.0,26770.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-23561,365243,-1976.0,-4450,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,6,0,0,0,0,0,0,XNA,,0.6619700497041943,0.6058362647264226,0.0825,0.0185,0.9757,,,0.0,0.1379,0.1667,,0.0,,0.0644,,0.033,0.084,0.0119,0.9752,,,0.0,0.1379,0.1667,,0.0,,0.0658,,0.0248,0.0833,0.0185,0.9757,,,0.0,0.1379,0.1667,,0.0,,0.0656,,0.0337,,block of flats,0.0608,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1617.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n244531,0,Cash loans,M,Y,Y,0,202500.0,495000.0,19309.5,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-21052,-1199,-205.0,-4329,3.0,1,1,0,1,1,0,Drivers,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Transport: type 4,,0.6749040993981836,0.2822484337007223,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1003.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n151362,0,Cash loans,M,Y,N,0,225000.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Rented apartment,0.007114,-18180,-283,-52.0,-1719,5.0,1,1,1,1,1,0,Laborers,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,Self-employed,0.30461948389928417,0.6166011061731194,,0.1124,0.0772,0.9886,,,0.12,0.1034,0.3333,,0.0751,,0.1168,,0.0,0.1145,0.0801,0.9886,,,0.1208,0.1034,0.3333,,0.0768,,0.1217,,0.0,0.1135,0.0772,0.9886,,,0.12,0.1034,0.3333,,0.0764,,0.1189,,0.0,,block of flats,0.0918,Panel,No,4.0,0.0,4.0,0.0,-746.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n186369,0,Cash loans,F,Y,Y,0,162000.0,113760.0,6480.0,90000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.030755,-22193,365243,-5087.0,-5101,11.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.274724676833188,0.6626377922738201,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-272.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n231132,0,Cash loans,F,N,N,0,135000.0,900000.0,45954.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018801,-10014,-592,-4869.0,-1676,,1,1,1,1,1,0,Core staff,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Bank,0.3646653168601153,0.5737602992678397,0.1419915427739129,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-639.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n198010,0,Cash loans,F,Y,Y,0,135000.0,254700.0,17019.0,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.010147,-24216,365243,-11719.0,-3851,12.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,,0.1134932667744905,0.7338145369642702,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1022.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n410873,0,Cash loans,M,Y,N,1,315000.0,971280.0,54364.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.016612000000000002,-10798,-2544,-5266.0,-2572,6.0,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Industry: type 11,,0.6883513837731845,0.7992967832109371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1839.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n196916,0,Cash loans,F,N,Y,0,117000.0,161730.0,8388.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-14042,-756,-815.0,-4359,,1,1,1,1,0,0,High skill tech staff,2.0,2,2,SATURDAY,11,0,0,0,0,1,1,Other,,0.6331708906942928,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1895.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n250662,0,Revolving loans,F,Y,Y,0,90000.0,180000.0,9000.0,180000.0,Family,Commercial associate,Incomplete higher,Married,House / apartment,0.026392,-7733,-206,-3569.0,-410,64.0,1,1,0,1,0,0,Accountants,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.17564146940563086,0.3758988500246272,0.4170996682522097,0.133,0.1539,0.9791,,,0.0,0.2759,0.1667,,,,0.1202,,0.0,0.1355,0.1597,0.9791,,,0.0,0.2759,0.1667,,,,0.1253,,0.0,0.1343,0.1539,0.9791,,,0.0,0.2759,0.1667,,,,0.1224,,0.0,,block of flats,0.0946,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-360.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n158633,0,Cash loans,F,N,N,2,225000.0,553806.0,23593.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-13699,-804,-954.0,-2724,,1,1,0,1,0,0,Sales staff,4.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,,0.6720669550235008,0.722392890081304,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1757.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n270117,0,Cash loans,F,N,Y,0,58500.0,244512.0,13783.5,216000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-24071,365243,-7372.0,-4027,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,XNA,,0.5856623056878202,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1079.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n427339,0,Cash loans,M,Y,Y,2,157500.0,1288350.0,37669.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.005002,-14288,-247,-4949.0,-4992,3.0,1,1,1,1,1,1,Core staff,4.0,3,3,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.7567364201765777,0.25259869783397665,0.1289,0.0872,0.9786,0.7076,0.0023,0.0,0.2759,0.1667,0.2083,0.1124,0.1042,0.1163,0.0039,0.0194,0.1313,0.0905,0.9786,0.7190000000000001,0.0023,0.0,0.2759,0.1667,0.2083,0.115,0.1139,0.1211,0.0039,0.0206,0.1301,0.0872,0.9786,0.7115,0.0023,0.0,0.2759,0.1667,0.2083,0.1143,0.106,0.1183,0.0039,0.0198,org spec account,block of flats,0.0969,Block,No,2.0,0.0,2.0,0.0,-1406.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n279575,0,Cash loans,F,Y,N,0,234000.0,1451047.5,55269.0,1354500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018801,-13915,-3496,-2331.0,-3003,6.0,1,1,0,1,1,0,Core staff,2.0,2,2,THURSDAY,19,0,0,0,0,0,0,School,,0.6124415172886426,0.6479768603302221,0.099,0.0246,0.9816,0.7484,0.0193,0.0,0.069,0.1667,0.2083,,0.0807,0.0661,0.0,0.0,0.1008,0.0255,0.9816,0.7583,0.0195,0.0,0.069,0.1667,0.2083,,0.0882,0.0688,0.0,0.0,0.0999,0.0246,0.9816,0.7518,0.0194,0.0,0.069,0.1667,0.2083,,0.0821,0.0673,0.0,0.0,reg oper account,block of flats,0.0602,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2582.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,4.0,2.0\n111293,0,Cash loans,M,Y,N,2,315000.0,1711764.0,50179.5,1530000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-13620,-160,-1306.0,-4663,16.0,1,1,0,1,0,0,Laborers,4.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.2847461745469529,0.5736426407840481,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3101.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n444394,0,Cash loans,M,Y,Y,0,180000.0,225000.0,22050.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-24639,365243,-12668.0,-4471,3.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.6913970822666173,0.4471785780453068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n455118,1,Revolving loans,M,Y,Y,1,157500.0,270000.0,13500.0,270000.0,Family,Working,Higher education,Married,House / apartment,0.030755,-11639,-789,-4981.0,-4160,8.0,1,1,1,1,1,0,Drivers,3.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.1066957945446016,0.6440009386854882,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1214.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n141683,0,Cash loans,M,Y,Y,0,135000.0,358344.0,24075.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-17945,-1451,-10274.0,-1492,10.0,1,1,0,1,0,1,Laborers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Self-employed,,0.5543621653446762,0.8590531292026357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3391.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n384309,0,Cash loans,F,Y,N,0,292500.0,203760.0,21006.0,180000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.018634,-9927,-353,-8130.0,-2596,2.0,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.33567679558404234,0.40628864362640177,0.5100895276257282,0.0928,0.0,0.9762,,,0.0,0.2069,0.1667,,0.0931,,0.0794,,0.0,0.0945,0.0,0.9762,,,0.0,0.2069,0.1667,,0.0953,,0.0827,,0.0,0.0937,0.0,0.9762,,,0.0,0.2069,0.1667,,0.0947,,0.0808,,0.0,,block of flats,0.0693,Panel,No,1.0,1.0,1.0,0.0,-1183.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n156168,0,Cash loans,M,Y,Y,0,450000.0,675000.0,21775.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-15149,-2184,-7717.0,-4513,17.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,12,0,0,0,1,1,0,Business Entity Type 2,,0.1813225935644545,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1510.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n263665,1,Cash loans,F,N,Y,1,202500.0,586633.5,35577.0,445500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006305,-13495,-3987,-3347.0,-4217,,1,1,0,1,0,0,Sales staff,3.0,3,3,FRIDAY,3,0,0,0,0,0,0,Self-employed,0.4868924079333592,0.4507360296754906,,0.2206,0.2845,0.9856,0.8028,0.0323,0.0,0.5172,0.1667,0.2083,0.1429,0.179,0.2032,0.0039,0.0045,0.2248,0.2952,0.9856,0.8105,0.0325,0.0,0.5172,0.1667,0.2083,0.1461,0.1956,0.2117,0.0039,0.0048,0.2228,0.2845,0.9856,0.8054,0.0325,0.0,0.5172,0.1667,0.2083,0.1454,0.1821,0.2069,0.0039,0.0046,reg oper account,block of flats,0.1785,\"Stone, brick\",No,11.0,0.0,10.0,0.0,-667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n311600,0,Cash loans,F,N,N,0,112500.0,755190.0,32125.5,675000.0,Family,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.006008,-18535,-4611,-4527.0,-2031,,1,1,0,1,0,0,Private service staff,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.6279132529944496,0.6782345319898124,,0.0784,0.0609,0.9826,0.762,0.0117,0.08,0.069,0.3333,0.375,0.1302,0.0639,0.0891,0.0,0.0,0.0798,0.0632,0.9826,0.7713,0.0118,0.0806,0.069,0.3333,0.375,0.1332,0.0698,0.0929,0.0,0.0,0.0791,0.0609,0.9826,0.7652,0.0117,0.08,0.069,0.3333,0.375,0.1325,0.065,0.0907,0.0,0.0,reg oper account,block of flats,0.0765,Panel,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n400401,0,Cash loans,M,N,N,2,225000.0,675000.0,50467.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014464,-12019,365243,-4762.0,-4419,,1,0,0,1,0,1,,4.0,2,2,FRIDAY,3,0,0,0,0,0,0,XNA,0.2994596242568112,0.6127995955652454,0.7421816117614419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-861.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n207111,1,Cash loans,F,Y,N,0,126000.0,675000.0,19737.0,675000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.028663,-18915,-5857,-6600.0,-2442,20.0,1,1,1,1,1,0,Core staff,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Kindergarten,,0.2610585643850836,0.4920600938649263,0.0742,0.0845,0.9776,0.6940000000000001,0.01,0.0,0.1379,0.1667,0.2083,0.0153,0.0588,0.066,0.0077,0.0078,0.0756,0.0876,0.9777,0.706,0.0101,0.0,0.1379,0.1667,0.2083,0.0156,0.0643,0.0688,0.0078,0.0083,0.0749,0.0845,0.9776,0.6981,0.0101,0.0,0.1379,0.1667,0.2083,0.0156,0.0599,0.0672,0.0078,0.008,reg oper account,block of flats,0.0709,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2974.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n203078,0,Revolving loans,F,Y,N,1,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.04622,-12368,-1865,-5482.0,-3648,0.0,1,1,0,1,0,0,Sales staff,3.0,1,1,SATURDAY,14,0,0,0,0,0,0,Self-employed,0.6693516275991862,0.7311684244579206,0.7062051096536562,0.1062,0.0366,0.9896,0.8572,0.0063,0.08,0.0345,0.5833,0.625,,0.0866,0.0976,0.0,0.0,0.1082,0.0379,0.9896,0.8628,0.0063,0.0806,0.0345,0.5833,0.625,,0.0946,0.1016,0.0,0.0,0.1072,0.0366,0.9896,0.8591,0.0063,0.08,0.0345,0.5833,0.625,,0.0881,0.0993,0.0,0.0,reg oper account,block of flats,0.0767,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1339.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n428178,1,Cash loans,M,N,N,0,157500.0,332473.5,21375.0,274500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-10971,-4085,-3839.0,-3589,,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,9,0,0,0,0,0,0,Business Entity Type 1,0.4411145040700832,0.055456508330878826,0.1777040724853336,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,2.0,1.0,-296.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n189338,0,Cash loans,M,Y,Y,0,427500.0,900000.0,57649.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-17934,-2965,-3801.0,-1424,3.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,9,0,1,1,0,1,1,Construction,,0.18325223444431835,0.4294236843421945,,0.1221,0.9816,,,0.16,0.1379,0.3333,,,,0.2183,,0.0769,,0.1267,0.9816,,,0.1611,0.1379,0.3333,,,,0.2274,,0.0814,,0.1221,0.9816,,,0.16,0.1379,0.3333,,,,0.2222,,0.0785,,block of flats,0.1884,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n303957,0,Cash loans,F,N,Y,1,225000.0,368163.0,36544.5,333000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,Rented apartment,0.035792000000000004,-12013,-1212,-456.0,-4345,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,SATURDAY,15,0,0,0,0,0,0,Self-employed,0.7652492327950482,0.7367745204480496,0.807273923277881,0.0619,0.0555,0.9791,0.7484,0.0081,0.0,0.1379,0.1667,0.2083,0.0316,0.0504,0.0823,0.0,0.0,0.063,0.0575,0.9772,0.7583,0.0082,0.0,0.1379,0.1667,0.2083,0.0324,0.0551,0.055999999999999994,0.0,0.0,0.0625,0.0555,0.9791,0.7518,0.0081,0.0,0.1379,0.1667,0.2083,0.0322,0.0513,0.0838,0.0,0.0,reg oper account,block of flats,0.0423,Panel,No,2.0,1.0,2.0,1.0,-2572.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n164057,0,Cash loans,M,N,Y,0,211500.0,573628.5,27724.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.005313,-13693,-205,-2378.0,-3896,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 1,,0.581405576574319,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-553.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n364243,0,Cash loans,M,Y,Y,0,270000.0,720000.0,33363.0,720000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-15523,-717,-1204.0,-1221,4.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,0.5245039786341864,0.6278983708841968,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n257591,0,Cash loans,F,N,N,1,90000.0,193500.0,20529.0,193500.0,Unaccompanied,Working,Incomplete higher,Separated,House / apartment,0.015221,-11513,-2619,-2453.0,-2602,,1,1,0,1,1,0,Managers,2.0,2,2,WEDNESDAY,20,0,0,0,0,0,0,Business Entity Type 3,0.2875883225091015,0.6817130372355956,0.1500851762342935,0.0619,0.0614,0.9831,,,0.0,0.1379,0.1667,,0.0425,,0.0534,,0.0,0.063,0.0637,0.9831,,,0.0,0.1379,0.1667,,0.0435,,0.0557,,0.0,0.0625,0.0614,0.9831,,,0.0,0.1379,0.1667,,0.0432,,0.0544,,0.0,,block of flats,0.0463,Panel,No,0.0,0.0,0.0,0.0,-253.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n138672,0,Cash loans,F,N,Y,0,148500.0,615109.5,22090.5,531000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.004849,-20844,-11514,-2628.0,-4292,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Electricity,,0.5147800620179405,,0.1031,0.109,0.9762,0.6736,0.0116,0.0,0.1724,0.1667,0.2083,0.1314,0.0841,0.0638,0.0,0.0,0.105,0.1131,0.9762,0.6864,0.0117,0.0,0.1724,0.1667,0.2083,0.1344,0.0918,0.0665,0.0,0.0,0.1041,0.109,0.9762,0.6779999999999999,0.0117,0.0,0.1724,0.1667,0.2083,0.1337,0.0855,0.0649,0.0,0.0,reg oper account,block of flats,0.0762,Panel,No,3.0,0.0,3.0,0.0,-1047.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n417932,1,Revolving loans,F,N,N,1,67500.0,135000.0,6750.0,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-7769,-785,-5525.0,-432,,1,1,0,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,,0.532925016546011,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n308646,0,Cash loans,F,N,Y,0,135000.0,675000.0,26284.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.026392,-21242,-1884,-4208.0,-4752,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Electricity,0.7905077616540066,0.6537457269376291,0.6347055309763198,0.3175,0.2065,0.9851,0.7959999999999999,0.0796,0.36,0.3103,0.3333,0.375,0.562,0.2564,0.3667,0.0116,0.0273,0.3235,0.2143,0.9851,0.804,0.0803,0.3625,0.3103,0.3333,0.375,0.5749,0.2801,0.3821,0.0117,0.0289,0.3206,0.2065,0.9851,0.7987,0.0801,0.36,0.3103,0.3333,0.375,0.5718,0.2608,0.3733,0.0116,0.0279,reg oper account,block of flats,0.2944,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n283786,0,Cash loans,F,N,Y,0,189000.0,1208304.0,51322.5,1080000.0,Unaccompanied,Pensioner,Incomplete higher,Widow,House / apartment,0.035792000000000004,-21845,365243,-13.0,-3568,,1,0,0,1,0,0,,1.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,0.2686368637313992,0.562079058773964,0.08846056465419698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-175.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n104883,0,Cash loans,F,N,Y,0,63000.0,207711.0,16308.0,189000.0,Family,Pensioner,Secondary / secondary special,Separated,House / apartment,0.018209,-23706,365243,-1350.0,-4537,,1,0,0,1,1,0,,1.0,3,3,TUESDAY,6,0,0,0,0,0,0,XNA,,0.2670801522458581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-665.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n119910,0,Cash loans,F,Y,Y,1,99000.0,243000.0,12712.5,243000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.018209,-8169,-140,-4072.0,-835,16.0,1,1,0,1,0,0,Sales staff,2.0,3,3,TUESDAY,7,0,0,0,0,0,0,Trade: type 3,0.2296395373908269,0.0937012819708266,0.19294222771695085,0.1031,0.1061,0.9786,0.7076,0.0,0.0,0.2069,0.1667,0.2083,0.1028,0.0841,0.0936,0.0,0.0,0.105,0.1102,0.9786,0.7190000000000001,0.0,0.0,0.2069,0.1667,0.2083,0.1052,0.0918,0.0975,0.0,0.0,0.1041,0.1061,0.9786,0.7115,0.0,0.0,0.2069,0.1667,0.2083,0.1046,0.0855,0.0952,0.0,0.0,reg oper account,block of flats,0.0736,Panel,No,0.0,0.0,0.0,0.0,-111.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n144281,0,Cash loans,M,Y,Y,0,126000.0,751500.0,21973.5,751500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018801,-11743,-2977,-1502.0,-4093,15.0,1,1,1,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,10,0,0,0,1,1,0,Housing,,0.5416992180398127,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-334.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n212662,1,Cash loans,F,N,Y,0,90000.0,314055.0,17167.5,238500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.009549,-10464,-1518,-5031.0,-2960,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.15755176609322252,,0.1588,0.0128,0.9806,,,,0.0345,0.1667,,0.0733,,0.0708,,0.0062,0.1618,0.0133,0.9806,,,,0.0345,0.1667,,0.075,,0.0738,,0.0065,0.1603,0.0128,0.9806,,,,0.0345,0.1667,,0.0746,,0.0721,,0.0063,,,0.057,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-341.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n204042,0,Cash loans,F,N,Y,0,90000.0,339241.5,12919.5,238500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006008,-10319,-1382,-1907.0,-2255,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Industry: type 1,0.2525319691734491,0.6576162263201287,0.16342569473134347,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1295.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n373979,1,Cash loans,M,N,Y,0,166500.0,454500.0,17608.5,454500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019101,-17940,-1280,-1393.0,-1393,,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,10,1,1,0,1,1,0,Construction,,0.3543948252235246,0.2721336844147212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1027.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n219985,0,Cash loans,M,N,N,2,315000.0,254700.0,24192.0,225000.0,,Commercial associate,Higher education,Married,House / apartment,0.006629,-17368,-8886,-1310.0,-900,,1,1,0,1,0,1,,4.0,2,2,TUESDAY,3,0,0,0,0,0,0,Other,,0.6773797100195001,0.12140828616312165,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-203.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n102806,0,Cash loans,F,N,Y,0,162000.0,711072.0,25668.0,540000.0,Children,Working,Secondary / secondary special,Single / not married,House / apartment,0.006670999999999999,-21160,-1512,-1768.0,-4534,,1,1,0,1,0,1,Cleaning staff,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Housing,0.8209742203038926,0.6001181282438388,0.35895122857839673,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1516.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n348418,0,Cash loans,M,N,N,0,225000.0,808650.0,26086.5,675000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.035792000000000004,-11376,-646,-808.0,-18,,1,1,0,1,0,0,Drivers,1.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.33617152121937866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-597.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n122194,0,Cash loans,F,Y,Y,1,198000.0,295168.5,15201.0,238500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018634,-12177,-4257,-1289.0,-1312,17.0,1,1,0,1,0,0,Managers,3.0,2,2,SATURDAY,12,0,0,0,1,1,0,Postal,,0.6782106704442642,0.3910549766342248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-896.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n396072,0,Cash loans,M,Y,Y,1,472500.0,1350000.0,39474.0,1350000.0,Family,Working,Higher education,Married,House / apartment,0.026392,-16081,-3558,-6249.0,-1407,13.0,1,1,0,1,1,0,Managers,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Industry: type 11,0.37277173968089583,0.7218799256671774,0.6195277080511546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n107942,0,Cash loans,M,Y,N,0,135000.0,229500.0,24714.0,229500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.026392,-8203,-464,-3054.0,-774,17.0,1,1,1,1,0,0,Laborers,1.0,2,2,FRIDAY,16,0,0,0,1,1,0,Business Entity Type 3,,0.5277996832353102,0.4543210601605785,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-699.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n305222,1,Cash loans,F,N,Y,0,202500.0,1203529.5,51120.0,1021500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-22833,365243,-6655.0,-4842,,1,0,0,1,0,0,,2.0,3,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.3896818709626937,0.1624419982223248,0.2515,0.2849,0.9861,0.8096,0.0707,0.28,0.2414,0.3333,0.375,0.1285,0.2051,0.3216,0.0,0.0,0.2563,0.2957,0.9861,0.8171,0.0713,0.282,0.2414,0.3333,0.375,0.1315,0.2241,0.3351,0.0,0.0,0.254,0.2849,0.9861,0.8121,0.0711,0.28,0.2414,0.3333,0.375,0.1308,0.2086,0.3274,0.0,0.0,reg oper spec account,block of flats,0.2916,Panel,No,8.0,0.0,8.0,0.0,-1015.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n256530,0,Cash loans,F,N,Y,0,103500.0,854352.0,25110.0,612000.0,Family,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.010147,-23672,365243,-13080.0,-4237,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,,0.5414277621053647,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1752.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n165212,0,Cash loans,F,N,Y,0,202500.0,443088.0,19647.0,382500.0,Unaccompanied,Commercial associate,Higher education,Widow,House / apartment,0.072508,-20384,-3095,-1078.0,-118,,1,1,0,1,0,0,,1.0,1,1,THURSDAY,14,0,0,0,0,0,0,Other,0.8308784581487714,0.2656400875888361,,0.0729,0.0722,0.9652,0.524,0.0,0.16,0.1379,0.2396,0.2812,0.0,0.0595,0.0888,0.0,0.0394,0.0368,0.0585,0.9593,0.4642,0.0,0.0806,0.069,0.25,0.2917,0.0,0.0321,0.0518,0.0,0.0,0.0677,0.0663,0.9657,0.5371,0.0,0.1,0.0862,0.25,0.2917,0.0,0.0556,0.0857,0.0,0.043,reg oper account,block of flats,0.0391,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1942.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n122636,0,Cash loans,F,Y,Y,0,126000.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.002042,-10302,-734,-4110.0,-2576,19.0,1,1,0,1,0,0,Core staff,2.0,3,3,WEDNESDAY,17,0,0,0,0,0,0,Trade: type 7,0.6786506443909293,0.566552224725282,0.5190973382084597,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n235188,0,Revolving loans,F,N,Y,0,112500.0,180000.0,9000.0,,,Pensioner,Secondary / secondary special,Widow,House / apartment,0.025164,-23089,365243,-4693.0,-4692,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.4392976931466098,0.6832688314232291,0.0165,0.0,0.9707,0.5988,0.0,0.0,0.1034,0.0,0.0417,0.042,0.0134,0.0097,0.0,0.0,0.0168,0.0,0.9707,0.6145,0.0,0.0,0.1034,0.0,0.0417,0.043,0.0147,0.0101,0.0,0.0,0.0167,0.0,0.9707,0.6042,0.0,0.0,0.1034,0.0,0.0417,0.0427,0.0137,0.0099,0.0,0.0,reg oper account,terraced house,0.0076,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n236760,0,Revolving loans,F,Y,Y,0,90000.0,225000.0,11250.0,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15342,-2841,-4241.0,-4235,33.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Other,,0.6899815476661695,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1622.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n284171,0,Cash loans,F,Y,Y,0,157500.0,824823.0,24246.0,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-15925,-243,-1721.0,-4694,64.0,1,1,0,1,1,0,Medicine staff,2.0,2,2,MONDAY,10,0,0,0,0,1,1,Medicine,0.7063590552818456,0.5742415423424007,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1741.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n280933,0,Cash loans,F,N,N,0,135000.0,900000.0,45954.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.02461,-22809,365243,-784.0,-4895,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,0.7874939894457126,0.6308466749123585,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-365.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n101161,0,Cash loans,F,N,N,0,135000.0,1546020.0,47038.5,1350000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Municipal apartment,0.019689,-23192,-3934,-6606.0,-5310,,1,1,0,1,1,0,Core staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Medicine,,0.44956150657306,,0.1175,0.1197,0.9791,0.7144,0.0234,0.0,0.2759,0.1667,0.2083,0.0667,0.0958,0.1025,0.0,0.0,0.1197,0.1242,0.9791,0.7256,0.0236,0.0,0.2759,0.1667,0.2083,0.0682,0.1047,0.1068,0.0,0.0,0.1187,0.1197,0.9791,0.7182,0.0235,0.0,0.2759,0.1667,0.2083,0.0678,0.0975,0.1043,0.0,0.0,reg oper account,block of flats,0.0934,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n126404,0,Cash loans,F,N,Y,1,67500.0,136260.0,8464.5,112500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.006296,-16993,-7751,-4839.0,-547,,1,1,1,1,1,0,Core staff,2.0,3,3,THURSDAY,9,0,0,0,0,0,0,Postal,0.2970028138826792,0.4954098201938349,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-989.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n102135,1,Cash loans,F,N,N,0,360000.0,1236816.0,36162.0,1080000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-18228,-5388,-4617.0,-1775,,1,1,1,1,1,0,Managers,2.0,2,2,SATURDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.2975631078951108,,0.1773,0.0617,0.9841,0.7824,,0.2,0.1724,0.3333,0.375,0.0985,0.0,0.1872,0.0,0.0341,0.1807,0.064,0.9841,0.7909,,0.2014,0.1724,0.3333,0.375,0.1007,0.0,0.1951,0.0,0.0361,0.179,0.0617,0.9841,0.7853,,0.2,0.1724,0.3333,0.375,0.1002,0.0,0.1906,0.0,0.0348,reg oper account,block of flats,0.1805,Panel,No,10.0,0.0,10.0,0.0,-258.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n345486,0,Cash loans,F,N,Y,0,90000.0,922500.0,27103.5,922500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-20991,-327,-10836.0,-3256,,1,1,1,1,0,0,,2.0,1,1,FRIDAY,14,0,0,0,1,1,0,Other,0.8264078084849071,0.7046247634283553,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-313.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n408834,0,Cash loans,M,Y,Y,1,267750.0,781920.0,61906.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008068,-20698,-2606,-5641.0,-4159,6.0,1,1,0,1,0,0,Managers,3.0,3,3,THURSDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.41853954984410335,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-876.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n361665,0,Cash loans,F,N,Y,0,202500.0,654498.0,28957.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009175,-18064,-11197,-8310.0,-1601,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.035640766345234214,0.3842068130556564,0.1113,0.0529,0.9861,,,0.04,0.0345,0.3333,,0.0637,,0.0384,,0.1124,0.1134,0.0549,0.9861,,,0.0403,0.0345,0.3333,,0.0651,,0.04,,0.11900000000000001,0.1124,0.0529,0.9861,,,0.04,0.0345,0.3333,,0.0648,,0.0391,,0.1147,,block of flats,0.0575,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n199693,0,Cash loans,M,N,Y,0,112500.0,1288350.0,37669.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009334,-21080,365243,-10206.0,-4011,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,XNA,,0.06492727990240713,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n397618,0,Cash loans,M,Y,Y,0,450000.0,850311.0,47610.0,742500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-17058,-706,-4179.0,-601,4.0,1,1,0,1,1,0,High skill tech staff,2.0,1,1,TUESDAY,14,0,1,1,0,0,0,Other,0.5290564570215606,0.7409976562327414,,0.1392,0.1758,0.9940000000000001,0.9184,,0.0,0.2069,0.1667,0.2083,,,0.0932,,,0.1418,0.1824,0.9940000000000001,0.9216,,0.0,0.2069,0.1667,0.2083,,,0.0971,,,0.1405,0.1758,0.9940000000000001,0.9195,,0.0,0.2069,0.1667,0.2083,,,0.0948,,,reg oper account,block of flats,0.12300000000000001,Block,No,0.0,0.0,0.0,0.0,-1054.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n275963,0,Cash loans,F,N,Y,0,202500.0,1102500.0,58738.5,1102500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-13299,-4101,-947.0,-4619,,1,1,1,1,0,0,Medicine staff,2.0,3,3,THURSDAY,12,0,0,0,0,0,0,Medicine,0.4083889345745141,0.5611799580518604,0.6610235391308081,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n104088,0,Cash loans,M,N,N,0,90000.0,177903.0,14391.0,148500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010556,-13454,-153,-3158.0,-3159,,1,1,0,1,0,0,Low-skill Laborers,2.0,3,3,SATURDAY,16,0,0,0,0,0,0,Agriculture,,0.1472284763516864,,0.1021,0.025,0.9935,0.9116,0.0293,0.08,0.0345,0.6667,0.0417,0.0494,0.0832,0.1141,0.0,0.0026,0.10400000000000001,0.0259,0.9935,0.9151,0.0296,0.0806,0.0345,0.6667,0.0417,0.0505,0.0909,0.1189,0.0,0.0027,0.1031,0.025,0.9935,0.9128,0.0295,0.08,0.0345,0.6667,0.0417,0.0502,0.0847,0.1162,0.0,0.0026,reg oper account,block of flats,0.1064,Monolithic,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n220065,1,Cash loans,M,Y,Y,0,225000.0,428854.5,20119.5,301500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-16311,-782,-7458.0,-4452,9.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,1,1,Industry: type 11,,0.11723328397761315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-40.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n327663,0,Cash loans,F,N,N,0,94500.0,497520.0,26257.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.02461,-9338,-973,-3131.0,-2018,,1,1,0,1,1,0,,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.5931029640935155,,0.0814,0.0638,0.9737,0.6396,,,0.1379,0.1667,0.0417,0.0391,0.0622,0.0581,0.0193,0.017,0.083,0.0662,0.9737,0.6537,,,0.1379,0.1667,0.0417,0.04,0.068,0.0605,0.0195,0.018000000000000002,0.0822,0.0638,0.9737,0.6444,,,0.1379,0.1667,0.0417,0.0398,0.0633,0.0591,0.0194,0.0173,reg oper account,block of flats,0.0653,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-343.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,,,,,,\n102402,0,Cash loans,F,Y,Y,0,135000.0,835605.0,24561.0,697500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.025164,-16523,-223,-7622.0,-71,41.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Housing,0.4883875799269889,0.18778211230781566,0.6413682574954046,0.0134,,0.9796,,,0.0,0.069,0.0417,,,,0.0059,,0.0114,0.0137,,0.9796,,,0.0,0.069,0.0417,,,,0.0062,,0.0121,0.0135,,0.9796,,,0.0,0.069,0.0417,,,,0.006,,0.0116,,block of flats,0.0071,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2113.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n247573,0,Cash loans,M,Y,N,0,427500.0,112500.0,9018.0,112500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.0105,-9045,-1123,-2485.0,-1610,17.0,1,1,0,1,0,0,Laborers,1.0,3,3,TUESDAY,16,0,0,0,1,1,0,Business Entity Type 3,,0.3207007088349741,0.053114172843977035,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n243054,0,Revolving loans,F,Y,Y,1,675000.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-16658,-6646,-6297.0,-158,15.0,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,18,0,0,0,0,0,0,Self-employed,,0.5549023280695493,0.7850520263728172,0.133,0.1244,0.9781,0.7008,,0.0,0.2759,0.1667,0.2083,0.040999999999999995,0.1084,0.1192,,0.0012,0.1355,0.1291,0.9782,0.7125,,0.0,0.2759,0.1667,0.2083,0.042,0.1185,0.1242,,0.0012,0.1343,0.1244,0.9781,0.7048,,0.0,0.2759,0.1667,0.2083,0.0417,0.1103,0.1213,,0.0012,,block of flats,0.094,\"Stone, brick\",No,,,,,-1246.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n211717,0,Cash loans,F,N,Y,0,157500.0,1104997.5,36648.0,990000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-21874,365243,-1397.0,-1636,,1,0,0,1,1,0,,2.0,3,3,WEDNESDAY,8,0,0,0,0,0,0,XNA,,0.3937982179517937,0.3108182544189319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n307490,1,Cash loans,F,N,Y,0,112500.0,225000.0,9531.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.020713,-20476,365243,-10139.0,-3310,,1,0,0,1,1,0,,2.0,3,3,MONDAY,5,0,0,0,0,0,0,XNA,,0.5749310786524775,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1207.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n270584,0,Cash loans,F,N,Y,0,252000.0,625536.0,32067.0,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010966,-22440,365243,-10588.0,-4562,,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.5883500858174703,0.524496446363472,0.1031,0.0944,0.9851,0.7959999999999999,0.0112,0.0,0.2069,0.1667,0.2083,0.0,0.0841,0.0914,0.0,0.0,0.105,0.098,0.9851,0.804,0.0113,0.0,0.2069,0.1667,0.2083,0.0,0.0918,0.0952,0.0,0.0,0.1041,0.0944,0.9851,0.7987,0.0113,0.0,0.2069,0.1667,0.2083,0.0,0.0855,0.09300000000000001,0.0,0.0,org spec account,block of flats,0.078,Panel,No,0.0,0.0,0.0,0.0,-2124.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n415143,0,Cash loans,F,N,Y,0,90000.0,885501.0,37516.5,715500.0,Family,Working,Lower secondary,Married,House / apartment,0.02461,-15382,-4202,-1272.0,-4044,,1,1,0,1,0,1,Cooking staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Government,,0.651678695346579,,0.0247,,0.9752,0.66,,0.0,0.1034,0.125,0.0417,,,0.0482,,,0.0252,,0.9752,0.6733,,0.0,0.1034,0.125,0.0417,,,0.0502,,,0.025,,0.9752,0.6645,,0.0,0.1034,0.125,0.0417,,,0.049,,,reg oper account,block of flats,0.04,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n433608,0,Cash loans,M,N,N,0,234900.0,1078200.0,29650.5,900000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.018634,-14959,-214,-8436.0,-3942,,1,1,1,1,0,0,,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.3963522257443016,0.4612429612901786,,0.5082,0.1455,0.9826,,,0.16,0.1379,0.3333,,0.0481,,0.2184,,,0.5179,0.1509,0.9826,,,0.1611,0.1379,0.3333,,0.0492,,0.2276,,,0.5132,0.1455,0.9826,,,0.16,0.1379,0.3333,,0.049,,0.2223,,,,block of flats,0.1832,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,,,,,,\n409649,0,Cash loans,F,N,N,0,90000.0,225000.0,14377.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.003813,-18380,-7448,-7661.0,-1906,,1,1,1,1,0,0,Core staff,2.0,2,2,TUESDAY,7,0,0,0,0,1,1,Insurance,0.6223389738058673,0.46380883565334496,0.4794489811780563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1534.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n301417,0,Cash loans,F,N,Y,0,112500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010147,-10859,-608,-10831.0,-3457,,1,1,0,1,0,1,,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.15277988422697505,0.6746545571916593,0.43473324875017305,0.0928,0.0464,0.9811,0.7416,0.0101,0.08,0.069,0.3333,0.375,0.0535,0.0756,0.0722,0.0,0.0,0.0945,0.0482,0.9811,0.7517,0.0102,0.0806,0.069,0.3333,0.375,0.0547,0.0826,0.0752,0.0,0.0,0.0937,0.0464,0.9811,0.7451,0.0102,0.08,0.069,0.3333,0.375,0.0544,0.077,0.0735,0.0,0.0,org spec account,block of flats,0.0623,Panel,No,1.0,0.0,1.0,0.0,-770.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n392249,0,Cash loans,F,N,Y,3,112500.0,835380.0,40320.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-11884,-452,-978.0,-4533,,1,1,0,1,0,0,Core staff,5.0,2,2,TUESDAY,16,0,0,0,0,0,0,Kindergarten,0.5622747757503319,0.5402928118753005,0.1206410299309233,0.0619,0.0604,0.9742,0.6464,0.0456,0.0,0.1379,0.1667,0.2083,0.0913,0.0504,0.0519,0.0,0.0,0.063,0.0627,0.9742,0.6602,0.046,0.0,0.1379,0.1667,0.2083,0.0934,0.0551,0.0541,0.0,0.0,0.0625,0.0604,0.9742,0.6511,0.0459,0.0,0.1379,0.1667,0.2083,0.0929,0.0513,0.0529,0.0,0.0,reg oper account,block of flats,0.0409,Panel,No,0.0,0.0,0.0,0.0,-2433.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n219865,0,Cash loans,F,Y,Y,1,90000.0,284400.0,13387.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-15075,-241,-1597.0,-25,13.0,1,1,1,1,0,0,Sales staff,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Self-employed,0.4823311564666818,0.3513836700016429,,0.1082,0.1091,0.9791,0.7144,0.0537,0.0,0.2414,0.1667,0.2083,0.0272,0.0883,0.1025,0.0,0.0,0.1103,0.1132,0.9791,0.7256,0.0542,0.0,0.2414,0.1667,0.2083,0.0279,0.0964,0.1068,0.0,0.0,0.1093,0.1091,0.9791,0.7182,0.054000000000000006,0.0,0.2414,0.1667,0.2083,0.0277,0.0898,0.1043,0.0,0.0,reg oper account,block of flats,0.11,Panel,No,2.0,0.0,2.0,0.0,-1272.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n272319,0,Cash loans,F,N,N,0,112500.0,278460.0,20947.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.019101,-24490,365243,-6646.0,-4645,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,16,0,0,0,0,0,0,XNA,,0.3830148097951694,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n333345,0,Cash loans,F,N,Y,0,162000.0,1078200.0,31653.0,900000.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.014464,-14615,-1750,-856.0,-856,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,7,0,0,0,0,0,0,Self-employed,,0.6923853582589039,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-615.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n111991,1,Cash loans,F,Y,Y,3,180000.0,675000.0,49117.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-11662,-404,-6301.0,-2856,18.0,1,1,0,1,1,0,Sales staff,4.0,2,2,THURSDAY,9,0,0,0,0,1,1,Industry: type 3,0.2265719022096032,0.6199402652136964,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1836.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n400029,0,Cash loans,F,N,Y,0,157500.0,684000.0,36441.0,684000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.001417,-16349,-1226,-696.0,-739,,1,1,1,1,1,0,Core staff,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,0.5059250149020709,0.3689989799440522,0.746300213050371,0.1773,0.0779,0.9811,0.7416,0.0556,0.0,0.0345,0.1667,0.2083,0.0526,0.1362,0.0512,0.0386,0.0183,0.1807,0.0808,0.9811,0.7517,0.0561,0.0,0.0345,0.1667,0.2083,0.0538,0.1488,0.0533,0.0389,0.0194,0.179,0.0779,0.9811,0.7451,0.055999999999999994,0.0,0.0345,0.1667,0.2083,0.0535,0.1385,0.0521,0.0388,0.0187,reg oper account,specific housing,0.0596,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-474.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n373626,0,Cash loans,F,Y,Y,1,180000.0,1350000.0,43551.0,1350000.0,Family,Working,Higher education,Married,House / apartment,0.006305,-13919,-839,-4850.0,-4403,6.0,1,1,0,1,1,0,Accountants,3.0,3,3,MONDAY,4,0,0,0,0,0,0,Business Entity Type 3,0.4347198984254304,0.4382422509178734,0.13765446191826075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1333.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n205468,0,Cash loans,F,N,Y,0,189000.0,640080.0,24259.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-15329,-2578,-113.0,-4790,,1,1,1,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Self-employed,,0.24217708848360056,0.4614823912998385,0.0186,0.0,0.9826,0.762,0.0026,0.0,0.069,0.0417,0.0833,0.0111,0.0151,0.0122,0.0,0.0064,0.0189,0.0,0.9826,0.7713,0.0026,0.0,0.069,0.0417,0.0833,0.0114,0.0165,0.0127,0.0,0.0068,0.0187,0.0,0.9826,0.7652,0.0026,0.0,0.069,0.0417,0.0833,0.0113,0.0154,0.0125,0.0,0.0065,reg oper account,block of flats,0.011000000000000001,Panel,No,0.0,0.0,0.0,0.0,-736.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n136246,0,Cash loans,M,N,N,0,315000.0,225000.0,23872.5,225000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.030755,-10765,-1494,-4760.0,-3338,,1,1,1,1,1,1,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Self-employed,,0.2858978721410488,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-603.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n253289,0,Cash loans,M,Y,Y,0,81000.0,147888.0,9756.0,117000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010556,-15014,-434,-9169.0,-4372,18.0,1,1,1,1,1,0,Laborers,1.0,3,3,SATURDAY,16,0,0,0,0,1,1,Business Entity Type 1,,0.4225788256559992,0.4614823912998385,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-475.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n315768,0,Cash loans,F,Y,N,0,126000.0,360000.0,35068.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010147,-20750,-443,-7205.0,-4298,32.0,1,1,1,1,0,0,Accountants,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Housing,,0.4424618842169465,0.3123653692278984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1679.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n290132,0,Cash loans,F,N,Y,0,90000.0,352044.0,17253.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-13210,-986,-7229.0,-4426,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.460797400428608,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1071.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n119647,0,Cash loans,F,N,Y,0,45000.0,454500.0,17739.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-12178,-931,-2948.0,-4449,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,9,0,0,0,0,1,1,Self-employed,,0.142870887589952,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-496.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n210098,0,Cash loans,M,Y,Y,0,180000.0,521280.0,44725.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-13087,-486,-7079.0,-4460,25.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,12,0,1,1,0,0,0,Business Entity Type 2,,0.4433683508097339,0.6413682574954046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-453.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n400629,0,Cash loans,F,N,Y,0,202500.0,894766.5,29700.0,679500.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.025164,-12000,-1856,-1113.0,-3553,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.3962543971892784,0.41885428862332175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-537.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n232319,0,Cash loans,F,N,N,0,54000.0,225000.0,22050.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-22349,365243,-6246.0,-4186,,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,0.2860730533524906,0.4001216583787967,0.6279908192952864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2589.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n241335,0,Cash loans,M,N,N,0,81000.0,331834.5,17073.0,252000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010643,-16085,-1850,-6368.0,-4200,,1,1,1,1,0,0,Laborers,1.0,2,2,WEDNESDAY,16,0,0,0,0,1,1,Business Entity Type 2,,0.6135827904945149,0.5902333386185574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1283.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n380601,0,Cash loans,M,N,Y,0,117000.0,292500.0,14355.0,292500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.0228,-16024,-220,-7875.0,-4696,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5424591624692978,0.10211878786358386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1997.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n205706,0,Cash loans,F,N,N,1,157500.0,808650.0,26217.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.009657,-11961,-1548,-926.0,-3782,,1,1,0,1,1,0,Laborers,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.5291709810646752,0.7568732618909222,0.5064842396679806,0.0928,0.1099,0.9841,0.7824,0.0119,0.0,0.2069,0.1667,0.0417,0.1454,0.0756,0.0905,0.0,0.0,0.0945,0.1141,0.9841,0.7909,0.0121,0.0,0.2069,0.1667,0.0417,0.1487,0.0826,0.0943,0.0,0.0,0.0937,0.1099,0.9841,0.7853,0.012,0.0,0.2069,0.1667,0.0417,0.14800000000000002,0.077,0.0921,0.0,0.0,reg oper account,block of flats,0.0973,Panel,No,1.0,0.0,1.0,0.0,-1352.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n298813,0,Cash loans,F,N,Y,0,225000.0,312840.0,22891.5,247500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.04622,-21286,365243,-8685.0,-4172,,1,0,0,1,0,0,,1.0,1,1,THURSDAY,19,0,0,0,0,0,0,XNA,0.5403490249685204,0.6768251126024971,,0.0629,0.08,0.9757,0.6668,0.0573,0.0,0.1379,0.1667,0.2083,0.0577,0.0504,0.064,0.0039,0.0032,0.0641,0.083,0.9757,0.6798,0.0578,0.0,0.1379,0.1667,0.2083,0.059000000000000004,0.0551,0.0667,0.0039,0.0034,0.0635,0.08,0.9757,0.6713,0.0577,0.0,0.1379,0.1667,0.2083,0.0587,0.0513,0.0652,0.0039,0.0033,reg oper account,block of flats,0.0824,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3568.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n246844,0,Cash loans,F,N,Y,0,90000.0,808650.0,23773.5,675000.0,\"Spouse, partner\",State servant,Higher education,Civil marriage,House / apartment,0.006207,-14666,-3612,-4407.0,-4405,,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Medicine,,0.6800738293439564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-989.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n186942,0,Cash loans,F,N,N,0,90000.0,505665.0,18292.5,355500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019689,-23641,365243,-9701.0,-3894,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.6329321410305581,,0.1649,,0.9866,,,0.04,0.0345,0.3333,,,,,,,0.1681,,0.9866,,,0.0403,0.0345,0.3333,,,,,,,0.1665,,0.9866,,,0.04,0.0345,0.3333,,,,,,,,specific housing,0.0365,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n244550,0,Revolving loans,M,Y,Y,2,135000.0,405000.0,20250.0,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-13847,-1307,-5870.0,-3389,65.0,1,1,0,1,0,0,Drivers,4.0,2,2,SATURDAY,12,0,0,0,0,1,1,Self-employed,0.2726184798721629,0.5003134965420797,0.40314167665875134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-2055.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n136535,0,Cash loans,F,N,Y,2,112500.0,448056.0,21919.5,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-11622,-953,-2856.0,-465,,1,1,0,1,0,0,,4.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.17190271505378873,0.5742468886994294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n180237,0,Cash loans,M,Y,Y,3,144000.0,1546020.0,42642.0,1350000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-11952,-384,-50.0,-3541,15.0,1,1,1,1,0,0,Laborers,5.0,2,2,THURSDAY,12,0,0,0,0,1,1,Industry: type 11,,0.5612068830992819,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1757.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n124690,0,Cash loans,F,Y,Y,0,135000.0,1051245.0,30865.5,877500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-17977,-824,-3273.0,-1522,2.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,0.690682233519843,0.5070154501587019,0.3344541255096772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-519.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n254090,0,Cash loans,F,N,N,1,112500.0,545040.0,28350.0,450000.0,Children,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-14790,-97,-4187.0,-4543,,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,17,0,0,0,0,0,0,Trade: type 3,,0.1689036090018746,0.6910212267577837,,0.071,0.9786,0.7076,0.0137,0.0,0.2069,0.1667,0.2083,0.0634,0.0756,0.0769,,0.0,,0.0737,0.9786,0.7190000000000001,0.0138,0.0,0.2069,0.1667,0.2083,0.0649,0.0826,0.0801,,0.0,,0.071,0.9786,0.7115,0.0138,0.0,0.2069,0.1667,0.2083,0.0645,0.077,0.0783,,0.0,reg oper account,block of flats,0.0713,Panel,No,0.0,0.0,0.0,0.0,-161.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n362066,0,Cash loans,F,N,N,1,135000.0,389844.0,18751.5,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Rented apartment,0.010276,-11648,-593,-1701.0,-2188,,1,1,1,1,0,0,Core staff,3.0,2,2,MONDAY,12,0,0,0,1,1,0,Self-employed,0.49311285581166797,0.7120022976424517,0.3441550073724169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1913.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n293247,1,Cash loans,F,N,Y,2,85500.0,177489.0,21060.0,166500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.007273999999999998,-16104,-3337,-5003.0,-2638,,1,1,1,1,0,0,,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Hotel,,0.5152880236058021,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-266.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n390713,0,Cash loans,F,N,Y,0,135000.0,315000.0,32404.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-18799,-1901,-6877.0,-1775,,1,1,1,1,1,0,High skill tech staff,2.0,3,3,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.2423055372320472,0.6413682574954046,0.1546,,0.9896,0.8572,,0.2,0.1724,0.375,0.4167,,0.1261,0.1878,0.0,0.0,0.1576,,0.9896,0.8628,,0.2014,0.1724,0.375,0.4167,,0.1377,0.1957,0.0,0.0,0.1561,,0.9896,0.8591,,0.2,0.1724,0.375,0.4167,,0.1283,0.1912,0.0,0.0,reg oper account,,0.1477,Panel,No,1.0,0.0,1.0,0.0,-1748.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n152008,0,Cash loans,F,N,N,2,180000.0,1489333.5,43546.5,1300500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-13395,-3623,-1102.0,-1082,,1,1,1,1,1,0,Sales staff,4.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3837392253095744,0.4152012548297372,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1694.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n429927,0,Cash loans,M,Y,Y,1,225000.0,1045854.0,37183.5,873000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.001333,-19396,-1002,-1528.0,-2876,19.0,1,1,0,1,0,0,Managers,3.0,3,3,THURSDAY,3,0,0,0,0,1,1,Emergency,0.7685407743426441,0.6585855241413017,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-256.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n391734,0,Cash loans,F,N,Y,1,180000.0,900000.0,43429.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002042,-11342,-363,-1489.0,-1469,,1,1,0,1,0,1,Waiters/barmen staff,3.0,3,3,MONDAY,16,1,1,0,1,1,0,Restaurant,0.1913950990261767,0.6873466011654853,0.5154953751603267,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-877.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n174567,0,Cash loans,F,N,Y,0,99000.0,781695.0,21627.0,652500.0,Children,Working,Secondary / secondary special,Widow,House / apartment,0.010966,-20304,-11630,-4216.0,-3318,,1,1,1,1,0,0,Laborers,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.5610668689208647,0.5779691187553125,0.0371,0.0695,0.9881,0.8368,,0.0,0.1379,0.0833,0.125,0.0508,0.0303,0.0208,0.0,0.0541,0.0378,0.0721,0.9881,0.8432,,0.0,0.1379,0.0833,0.125,0.0519,0.0331,0.0216,0.0,0.0573,0.0375,0.0695,0.9881,0.8390000000000001,,0.0,0.1379,0.0833,0.125,0.0517,0.0308,0.0211,0.0,0.0552,reg oper account,block of flats,0.0281,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2184.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n110323,0,Cash loans,F,N,Y,0,45000.0,254700.0,14751.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-16573,-6609,-5072.0,-123,,1,1,1,1,1,0,,2.0,2,2,MONDAY,4,0,0,0,0,0,0,School,0.5654398644387364,0.2415996426615083,,0.0155,0.0424,0.9871,0.8232,0.0077,0.0,0.0345,0.0833,0.125,0.0151,0.0126,0.0111,0.0,0.0195,0.0158,0.044000000000000004,0.9871,0.8301,0.0078,0.0,0.0345,0.0833,0.125,0.0155,0.0138,0.0115,0.0,0.0206,0.0156,0.0424,0.9871,0.8256,0.0078,0.0,0.0345,0.0833,0.125,0.0154,0.0128,0.0113,0.0,0.0199,not specified,block of flats,0.0129,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-55.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n246569,0,Cash loans,F,N,N,0,67500.0,675000.0,19867.5,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-21268,365243,-13356.0,-4740,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.1730068945215277,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n349095,0,Cash loans,F,N,Y,0,135000.0,373311.0,15943.5,283500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.020713,-19375,-1052,-1033.0,-2909,,1,1,0,1,0,0,,1.0,3,2,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.6524942122779418,0.6193475704354542,0.8235493123400324,0.0247,0.0495,0.9657,0.5308,0.0023,0.0,0.1034,0.0417,0.0833,0.1362,0.0202,0.0152,0.0,0.0,0.0252,0.0513,0.9657,0.5492,0.0023,0.0,0.1034,0.0417,0.0833,0.1393,0.022000000000000002,0.0158,0.0,0.0,0.025,0.0495,0.9657,0.5371,0.0023,0.0,0.1034,0.0417,0.0833,0.1386,0.0205,0.0155,0.0,0.0,reg oper account,block of flats,0.0132,Block,No,0.0,0.0,0.0,0.0,-693.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n314866,0,Cash loans,M,N,N,0,225000.0,1256400.0,44644.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.003818,-11187,-1912,-4890.0,-64,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,8,0,0,0,0,1,1,Trade: type 3,0.24579509342286754,0.5579409880176804,,0.0464,0.0551,0.9816,0.7484,0.0855,0.0,0.1034,0.125,0.0417,0.0078,0.0361,0.0425,0.0077,0.0614,0.0473,0.0572,0.9816,0.7583,0.0863,0.0,0.1034,0.125,0.0417,0.0079,0.0395,0.0442,0.0078,0.065,0.0468,0.0551,0.9816,0.7518,0.0861,0.0,0.1034,0.125,0.0417,0.0079,0.0368,0.0432,0.0078,0.0627,reg oper account,block of flats,0.0468,Panel,No,0.0,0.0,0.0,0.0,-432.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n393404,0,Cash loans,F,N,N,0,112500.0,772686.0,20511.0,553500.0,,State servant,Higher education,Married,House / apartment,0.014464,-9659,-1423,-1587.0,-1476,,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,2,0,0,0,1,1,0,Kindergarten,,0.5794191693346971,0.3077366963789207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n270134,0,Cash loans,F,N,Y,0,112500.0,168102.0,17352.0,148500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.008575,-9729,-823,-4333.0,-2396,,1,1,0,1,1,1,Accountants,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Other,0.5383087531789736,0.6742704525556736,0.4884551844437485,0.0928,0.1117,0.9791,0.7144,0.0,0.0,0.2069,0.1667,0.2083,,0.0756,0.06,0.0,0.0988,0.0945,0.1159,0.9791,0.7256,0.0,0.0,0.2069,0.1667,0.2083,,0.0826,0.0625,0.0,0.1046,0.0937,0.1117,0.9791,0.7182,0.0,0.0,0.2069,0.1667,0.2083,,0.077,0.061,0.0,0.1009,,block of flats,0.0686,Panel,No,0.0,0.0,0.0,0.0,-283.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,0.0\n303240,0,Cash loans,F,N,Y,0,90000.0,225000.0,8212.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003540999999999999,-21408,365243,-8053.0,-4633,,1,0,0,1,0,0,,2.0,1,1,MONDAY,8,0,0,0,0,0,0,XNA,,0.5616160993611683,0.6642482627052363,0.1601,0.1199,0.9891,0.8504,,0.1732,0.1493,0.3333,0.375,0.0465,,0.1562,,0.3359,0.188,0.1521,0.9891,0.8563,,0.2014,0.1724,0.3333,0.375,0.0326,,0.1199,,0.3556,0.1863,0.1466,0.9891,0.8524,,0.2,0.1724,0.3333,0.375,0.0543,,0.159,,0.3429,not specified,block of flats,0.1945,Panel,No,1.0,0.0,1.0,0.0,-1558.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n180961,0,Cash loans,F,Y,N,0,180000.0,1255680.0,41499.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-15346,-3565,-9367.0,-4779,13.0,1,1,1,1,0,0,Laborers,2.0,1,1,MONDAY,19,0,0,0,0,0,0,Transport: type 4,0.7909801993016371,0.5701037073894579,0.6848276586890367,0.0619,,0.9851,,,0.0,0.1034,0.1667,,0.0,,0.0309,,0.0,0.063,,0.9851,,,0.0,0.1034,0.1667,,0.0,,0.0322,,0.0,0.0625,,0.9851,,,0.0,0.1034,0.1667,,0.0,,0.0315,,0.0,,block of flats,0.0378,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1841.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n449648,1,Cash loans,M,Y,Y,0,67500.0,187083.0,20146.5,175500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-11232,-1338,-5034.0,-2986,16.0,1,1,1,1,1,0,,2.0,2,2,SATURDAY,9,0,0,0,1,1,1,Other,,0.04076184177111313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,-829.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n447830,1,Cash loans,F,N,N,2,202500.0,961146.0,25483.5,688500.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.02461,-14867,-731,-410.0,-4708,,1,1,0,1,0,0,Core staff,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Kindergarten,,0.5314877452127239,0.22888341670067305,0.1474,0.0954,0.9816,,,0.16,0.1379,0.3333,,0.0374,,0.1515,,0.0942,0.1502,0.099,0.9816,,,0.1611,0.1379,0.3333,,0.0383,,0.1579,,0.0997,0.1489,0.0954,0.9816,,,0.16,0.1379,0.3333,,0.038,,0.1543,,0.0962,,block of flats,0.1396,Panel,No,1.0,0.0,1.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n212943,0,Cash loans,F,Y,Y,0,94500.0,675000.0,28728.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-15051,-838,-1657.0,-5113,18.0,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Business Entity Type 1,0.6228312335619091,0.7524562315203036,0.6879328378491735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-1511.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n208445,0,Cash loans,M,Y,N,1,157500.0,540000.0,25150.5,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Office apartment,0.018209,-12986,-2345,-615.0,-4059,19.0,1,1,0,1,0,0,Laborers,3.0,3,3,FRIDAY,11,0,0,0,0,1,1,Transport: type 4,0.348469589821392,0.4011666124566676,0.5602843280409464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-463.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n179819,1,Cash loans,F,N,Y,1,135000.0,345289.5,18729.0,279000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.019689,-12362,-1904,-10339.0,-4445,,1,1,0,1,1,1,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Self-employed,0.3480602921808231,0.7269222420556174,,0.1041,0.0,0.9851,0.7959999999999999,0.0113,0.0,0.2069,0.1667,0.2083,0.1035,0.0841,0.0914,0.0039,0.0049,0.1061,0.0,0.9851,0.804,0.0114,0.0,0.2069,0.1667,0.2083,0.1059,0.0918,0.0952,0.0039,0.0052,0.1051,0.0,0.9851,0.7987,0.0113,0.0,0.2069,0.1667,0.2083,0.1053,0.0855,0.0931,0.0039,0.0051,org spec account,block of flats,0.0719,Panel,No,2.0,0.0,2.0,0.0,-261.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n163913,0,Revolving loans,F,N,Y,0,157500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.04622,-17016,-625,-5081.0,-511,,1,1,0,1,0,0,High skill tech staff,1.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.7985506195443821,0.6326374238567664,0.4866531565147181,0.0928,0.10099999999999999,0.9767,0.6804,0.0133,0.0,0.2069,0.1667,0.2083,0.0602,0.0756,0.0858,0.0,0.0,0.0945,0.1048,0.9767,0.6929,0.0134,0.0,0.2069,0.1667,0.2083,0.0615,0.0826,0.0894,0.0,0.0,0.0937,0.10099999999999999,0.9767,0.6847,0.0134,0.0,0.2069,0.1667,0.2083,0.0612,0.077,0.0874,0.0,0.0,reg oper account,block of flats,0.09300000000000001,Panel,No,0.0,0.0,0.0,0.0,-489.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,0.0\n387220,0,Cash loans,F,N,Y,0,157500.0,931401.0,39591.0,832500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.011703,-21482,365243,-13537.0,-2884,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.7675141250832495,,0.0619,0.0608,0.9727,,,0.0,0.1379,0.1667,,0.0707,,0.0665,,0.0,0.063,0.0631,0.9727,,,0.0,0.1379,0.1667,,0.0723,,0.0693,,0.0,0.0625,0.0608,0.9727,,,0.0,0.1379,0.1667,,0.0719,,0.0677,,0.0,,block of flats,0.0571,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1393.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n229335,0,Cash loans,M,Y,Y,0,225000.0,1395000.0,36927.0,1395000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.025164,-13821,-1172,-3746.0,-384,4.0,1,1,0,1,1,0,High skill tech staff,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Transport: type 4,,0.5889113681360177,0.3092753558842053,0.1093,0.1195,0.9737,0.6396,0.0154,0.0,0.2069,0.1667,0.2083,0.0366,0.0874,0.0832,0.0077,0.0938,0.1113,0.124,0.9737,0.6537,0.0156,0.0,0.2069,0.1667,0.2083,0.0374,0.0955,0.0867,0.0078,0.0993,0.1103,0.1195,0.9737,0.6444,0.0155,0.0,0.2069,0.1667,0.2083,0.0372,0.0889,0.0847,0.0078,0.0957,reg oper account,block of flats,0.0943,Block,No,0.0,0.0,0.0,0.0,-1204.0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n329854,0,Cash loans,F,N,Y,0,85500.0,269550.0,19300.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-24120,365243,-16131.0,-4095,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.4645865932799924,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1325.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n120144,0,Cash loans,F,N,Y,0,67500.0,312840.0,18090.0,247500.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-14114,-1115,-1019.0,-4912,,1,1,0,1,0,1,Sales staff,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.17151172929461725,0.30676617501610026,0.4543210601605785,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-671.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,2.0\n118014,0,Cash loans,F,Y,Y,0,360000.0,473760.0,51021.0,450000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.014464,-9424,-2250,-4052.0,-199,12.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,3,0,0,0,0,0,0,Business Entity Type 3,0.4846931501798527,0.58574189990971,0.08118594316672112,0.1031,0.0533,0.9771,0.6872,0.0361,0.0,0.2069,0.1667,0.0417,0.0796,0.0807,0.0951,0.0154,0.025,0.105,0.0553,0.9772,0.6994,0.0364,0.0,0.2069,0.1667,0.0417,0.0814,0.0882,0.0991,0.0156,0.0264,0.1041,0.0533,0.9771,0.6914,0.0363,0.0,0.2069,0.1667,0.0417,0.081,0.0821,0.0968,0.0155,0.0255,reg oper account,block of flats,0.0988,Panel,No,1.0,0.0,1.0,0.0,-1133.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n248777,0,Cash loans,F,N,Y,0,157500.0,904500.0,29178.0,904500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-17693,-4731,-6391.0,-1243,,1,1,1,1,1,0,Laborers,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,Agriculture,,0.6553428343760912,0.6144143775673561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1094.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n188060,0,Revolving loans,F,Y,Y,0,270000.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-15573,-1135,-7603.0,-4332,5.0,1,1,1,1,1,0,Core staff,2.0,1,1,FRIDAY,12,0,0,0,0,1,1,Trade: type 2,0.5741523897648214,0.6663987006583071,0.5549467685334323,0.0309,0.0344,0.9861,0.8096,0.0161,0.0,0.069,0.1667,0.2083,0.0492,0.0252,0.0164,0.0,0.0,0.0315,0.0357,0.9861,0.8171,0.0162,0.0,0.069,0.1667,0.2083,0.0503,0.0275,0.017,0.0,0.0,0.0312,0.0344,0.9861,0.8121,0.0162,0.0,0.069,0.1667,0.2083,0.05,0.0257,0.0167,0.0,0.0,reg oper account,block of flats,0.0217,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-108.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n214345,0,Cash loans,F,N,N,0,157500.0,566055.0,18387.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006008,-19581,-2603,-4894.0,-2792,,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.6776570439066593,0.7401868447017034,0.8128226070575616,0.0237,0.0259,0.9816,0.7484,0.0108,0.0,0.0345,0.1667,0.2083,0.0145,0.0193,0.0284,0.0,0.0,0.0242,0.0269,0.9816,0.7583,0.0109,0.0,0.0345,0.1667,0.2083,0.0148,0.0211,0.0296,0.0,0.0,0.0239,0.0259,0.9816,0.7518,0.0109,0.0,0.0345,0.1667,0.2083,0.0148,0.0197,0.0289,0.0,0.0,reg oper account,block of flats,0.0283,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1995.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n286630,0,Cash loans,F,N,Y,2,90000.0,257391.0,18432.0,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-14138,-558,-903.0,-4766,,1,1,0,1,0,0,Core staff,4.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,Self-employed,0.4509208642485311,0.661289558329338,0.324891229465852,,,0.9876,,,,,,,,,0.0029,,,,,0.9876,,,,,,,,,0.003,,,,,0.9876,,,,,,,,,0.003,,,,block of flats,0.0023,,Yes,1.0,0.0,1.0,0.0,-1898.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n450582,0,Cash loans,F,N,Y,0,67500.0,571446.0,17514.0,477000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.072508,-21671,365243,-7133.0,-4983,,1,0,0,1,1,0,,1.0,1,1,TUESDAY,9,0,0,0,0,0,0,XNA,,0.7315765684545767,,0.2918,0.2069,0.9906,0.8708,0.3545,0.48,0.2069,0.4583,0.5,0.0,0.2353,0.2805,0.0116,0.0849,0.2973,0.2147,0.9906,0.8759,0.3577,0.4834,0.2069,0.4583,0.5,0.0,0.2571,0.2922,0.0117,0.0899,0.2946,0.2069,0.9906,0.8725,0.3567,0.48,0.2069,0.4583,0.5,0.0,0.2394,0.2855,0.0116,0.0867,reg oper account,block of flats,0.2382,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-836.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n442689,0,Cash loans,F,N,N,3,135000.0,450000.0,27346.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-12347,-1694,-5342.0,-1615,,1,1,1,1,0,0,Laborers,5.0,2,2,FRIDAY,11,0,0,0,0,1,1,School,0.479422806114709,0.16696636451192173,0.5280927512030451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1337.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n360537,0,Cash loans,F,N,Y,0,135000.0,334152.0,14845.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17449,-2248,-1780.0,-1005,,1,1,0,1,0,0,Cooking staff,2.0,2,2,MONDAY,10,0,0,0,0,1,1,Kindergarten,0.6667012950801462,0.5327780475660913,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-2226.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n188918,0,Cash loans,M,N,Y,0,135000.0,787131.0,30762.0,679500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.031329,-21081,-1852,-3653.0,-3970,,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Security,,0.17092378602024555,0.4507472818545589,0.1357,0.07200000000000001,0.9821,0.7552,0.0097,0.1464,0.1262,0.3333,0.375,0.0449,0.0891,0.0962,0.0058,0.0318,0.1124,0.0747,0.9821,0.7648,0.0,0.1208,0.1034,0.3333,0.375,0.0128,0.0955,0.0718,0.0,0.0,0.1124,0.07200000000000001,0.9821,0.7585,0.0098,0.12,0.1034,0.3333,0.375,0.0242,0.0906,0.106,0.0058,0.0118,reg oper account,block of flats,0.0849,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n312311,0,Cash loans,F,Y,N,2,337500.0,888840.0,28039.5,675000.0,Other_B,Commercial associate,Higher education,Civil marriage,House / apartment,0.04622,-14216,-1959,-7567.0,-4646,3.0,1,1,0,1,0,0,Realty agents,4.0,1,1,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.7219721133950912,0.501075160239048,0.0412,,0.9583,,,0.0,0.1724,0.125,,0.1196,,,,,0.042,,0.9583,,,0.0,0.1724,0.125,,0.1224,,,,,0.0416,,0.9583,,,0.0,0.1724,0.125,,0.1217,,,,,,block of flats,0.0426,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3447.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n103120,0,Cash loans,F,N,N,0,90000.0,284400.0,13257.0,225000.0,Family,Working,Lower secondary,Civil marriage,House / apartment,0.035792000000000004,-16123,-1522,-7773.0,-4560,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,8,0,0,0,0,1,1,Agriculture,,0.3542247319929012,0.7726311345553628,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1471.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n271812,0,Cash loans,M,N,Y,0,202500.0,1215000.0,40284.0,1215000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.025164,-17542,-9722,-3530.0,-1031,,1,1,1,1,0,0,Core staff,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Police,,0.5230558209908184,0.622922000268356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-905.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n208678,0,Cash loans,F,N,Y,0,315000.0,2085120.0,72607.5,1800000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-12984,-3478,-724.0,-1976,,1,1,0,1,1,1,Accountants,2.0,1,1,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.5402809641259795,0.7584071250034379,0.5513812618027899,0.1608,0.086,0.9955,0.9388,0.0497,0.24,0.1034,0.5417,0.5833,0.0178,0.1311,0.1742,0.0,0.043,0.1639,0.0886,0.995,0.9347,0.0461,0.2417,0.1034,0.5417,0.5833,0.0178,0.1433,0.1806,0.0,0.0448,0.1624,0.086,0.9955,0.9396,0.05,0.24,0.1034,0.5417,0.5833,0.0181,0.1334,0.1773,0.0,0.0439,reg oper account,block of flats,0.1455,Panel,No,0.0,0.0,0.0,0.0,-466.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n264923,0,Cash loans,F,N,Y,0,180000.0,675000.0,52366.5,675000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.026392,-10731,-683,-4487.0,-3069,,1,1,0,1,0,0,,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Other,,0.2557576519111089,0.6041125998015721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n265872,0,Cash loans,F,N,Y,0,40500.0,528633.0,22527.0,472500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.009334,-21999,365243,-11070.0,-5212,,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.1407604532180816,,0.1577,,0.9851,,,0.16,0.1379,0.3333,,,,0.1456,,,0.1229,,0.9851,,,0.1208,0.1034,0.3333,,,,0.1153,,,0.1218,,0.9851,,,0.12,0.1034,0.3333,,,,0.1129,,,,block of flats,0.1904,Panel,No,1.0,0.0,1.0,0.0,-465.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n153715,0,Cash loans,F,N,Y,0,135000.0,459000.0,14805.0,459000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.019689,-20597,365243,-7.0,-3774,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.7309319512856032,,0.034,0.0473,1.0,,,0.0,0.1034,0.0833,,0.0323,,0.0288,,0.0028,0.0347,0.0491,1.0,,,0.0,0.1034,0.0833,,0.0331,,0.03,,0.003,0.0344,0.0473,1.0,,,0.0,0.1034,0.0833,,0.0329,,0.0293,,0.0029,,block of flats,0.0226,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-363.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n215411,0,Cash loans,M,Y,Y,2,90000.0,646920.0,23895.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-13945,-822,-716.0,-4382,24.0,1,1,0,1,0,0,,4.0,2,2,MONDAY,13,0,0,0,0,0,0,Other,,0.6233040524844439,0.5424451438613613,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1645.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n191955,0,Cash loans,F,Y,N,0,67500.0,225000.0,8469.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-14553,-206,-1438.0,-4520,16.0,1,1,1,1,0,0,,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,Transport: type 2,,0.3526406416581002,0.0005272652387098817,0.1113,0.0462,0.9856,0.8028,0.0316,0.04,0.0345,0.3333,0.375,0.0877,0.0908,0.0646,0.0,0.0,0.1134,0.048,0.9856,0.8105,0.0319,0.0403,0.0345,0.3333,0.375,0.0897,0.0992,0.0673,0.0,0.0,0.1124,0.0462,0.9856,0.8054,0.0318,0.04,0.0345,0.3333,0.375,0.0892,0.0923,0.0658,0.0,0.0,reg oper account,block of flats,0.0681,Panel,No,0.0,0.0,0.0,0.0,-1736.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n311969,0,Cash loans,M,N,Y,0,157500.0,305640.0,36400.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010276,-9728,-684,-1861.0,-1909,,1,1,0,1,0,0,Drivers,1.0,2,2,THURSDAY,16,0,0,0,1,1,0,Business Entity Type 3,,0.2873510369912569,0.33125086459090186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-312.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n325638,0,Cash loans,F,N,N,0,90000.0,808650.0,23773.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-23613,365243,-2546.0,-4179,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.5413734679622315,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n120482,0,Cash loans,F,Y,Y,3,121500.0,117162.0,12559.5,103500.0,Unaccompanied,State servant,Incomplete higher,Married,House / apartment,0.025164,-11027,-1422,-1760.0,-3581,13.0,1,1,0,1,0,0,,5.0,2,2,SATURDAY,8,0,0,0,0,0,0,Kindergarten,0.451824911089308,0.17125561616631882,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n419744,0,Cash loans,M,Y,Y,0,189000.0,728460.0,53010.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-18636,-760,-1694.0,-2101,7.0,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,11,1,1,0,1,1,0,Self-employed,0.4373674835045097,0.6549080549137015,,0.0309,0.0,0.9717,0.6124,0.0009,0.0,0.1034,0.0833,0.0417,0.0183,0.0252,0.0056,0.0,0.0,0.0315,0.0,0.9717,0.6276,0.0009,0.0,0.1034,0.0833,0.0417,0.0188,0.0275,0.0058,0.0,0.0,0.0312,0.0,0.9717,0.6176,0.0009,0.0,0.1034,0.0833,0.0417,0.0187,0.0257,0.0057,0.0,0.0,reg oper account,block of flats,0.0049,Block,No,0.0,0.0,0.0,0.0,-437.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n138331,0,Cash loans,F,N,Y,2,112500.0,900000.0,31887.0,900000.0,Family,Working,Secondary / secondary special,Married,With parents,0.025164,-11988,-2336,-511.0,-1571,,1,1,0,1,0,0,Core staff,4.0,2,2,FRIDAY,9,0,0,0,0,0,0,Kindergarten,0.6125930221340766,0.16219210595922867,0.5691487713619409,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,0.0,11.0,0.0,-1005.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n271977,0,Cash loans,F,N,Y,1,67500.0,477000.0,15516.0,477000.0,Family,Working,Higher education,Civil marriage,With parents,0.020246,-9433,-2040,-7379.0,-2091,,1,1,0,1,0,0,,3.0,3,3,SUNDAY,10,0,0,0,0,0,0,Industry: type 3,0.4711583976965854,0.08828378097000948,0.07058051883159755,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1741.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n204126,0,Cash loans,F,N,Y,0,157500.0,1113399.0,45949.5,945000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006233,-19312,-1520,-5762.0,-2843,,1,1,0,1,0,0,Cooking staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5966970555550555,,0.16699999999999998,0.0465,0.9896,0.8572,0.038,0.0,0.3448,0.1667,0.0417,0.0478,0.1311,0.1437,0.0232,0.0162,0.1702,0.0483,0.9896,0.8628,0.0383,0.0,0.3448,0.1667,0.0417,0.0489,0.1433,0.1497,0.0233,0.0171,0.1686,0.0465,0.9896,0.8591,0.0382,0.0,0.3448,0.1667,0.0417,0.0486,0.1334,0.1462,0.0233,0.0165,reg oper account,block of flats,0.1165,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n231525,0,Cash loans,M,N,N,0,157500.0,225000.0,14647.5,225000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.026392,-10069,-198,-1480.0,-2757,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.379758645439067,0.6109820049696578,0.7238369900414456,0.0825,,0.9767,,,0.0,0.1379,0.1667,,,,0.0583,,0.0,0.084,,0.9767,,,0.0,0.1379,0.1667,,,,0.0608,,0.0,0.0833,,0.9767,,,0.0,0.1379,0.1667,,,,0.0594,,0.0,,block of flats,0.0459,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1789.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n416378,1,Cash loans,F,N,Y,0,81000.0,450000.0,17095.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-21343,-3173,-2297.0,-4113,,1,1,1,1,1,0,,2.0,3,3,FRIDAY,7,0,0,0,0,1,1,Self-employed,,0.5538002598782508,0.40314167665875134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-1306.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n257914,0,Cash loans,F,N,Y,0,157500.0,159264.0,6133.5,126000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-19236,-2451,-2105.0,-2435,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6244534667744231,0.6769925032909132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1243.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n209014,0,Cash loans,F,N,Y,0,76500.0,490495.5,27517.5,454500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.019101,-15715,-784,-7484.0,-4572,,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Medicine,0.712066642738612,0.6362990182827344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-727.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n439098,0,Cash loans,F,Y,Y,0,202500.0,640080.0,31261.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.032561,-12808,-3685,-806.0,-4636,1.0,1,1,0,1,1,0,Laborers,1.0,1,1,SATURDAY,12,0,0,0,0,0,0,Industry: type 3,,0.2848142360575716,0.5884877883422673,0.4098,0.2734,0.9801,0.7416,,0.44,0.3793,0.3333,0.375,0.1231,0.3295,0.3879,0.0,0.0242,0.4118,0.2782,0.9801,0.7517,,0.4431,0.3793,0.3333,0.375,0.1253,0.36,0.3515,0.0,0.0,0.4138,0.2734,0.9801,0.7451,,0.44,0.3793,0.3333,0.375,0.1252,0.3352,0.4108,0.0,0.0247,reg oper account,block of flats,0.3173,Panel,No,0.0,0.0,0.0,0.0,-2207.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n279041,1,Cash loans,M,N,Y,0,450000.0,513000.0,27958.5,513000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-11952,-2721,-1133.0,-4512,,1,1,0,1,1,0,Drivers,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Industry: type 9,0.2377197192577024,0.6187029333875085,0.09507039584133267,0.0664,0.0442,0.9975,,,0.08,0.0552,0.4083,0.45,0.0085,,0.0651,,0.016,0.0441,0.0306,0.9975,,,0.0806,0.069,0.375,0.4167,0.0078,,0.0487,,0.0168,0.0822,0.0408,0.9975,,,0.08,0.069,0.375,0.4167,0.0087,,0.0747,,0.0163,,block of flats,0.0403,Panel,No,1.0,0.0,1.0,0.0,-558.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n389829,0,Cash loans,F,N,Y,3,54000.0,781695.0,25344.0,652500.0,Other_B,Working,Secondary / secondary special,Married,House / apartment,0.010276,-13813,-4830,-6392.0,-3316,,1,1,0,1,1,0,Medicine staff,5.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Medicine,0.7670499220352028,0.6339577929643578,0.7544061731797895,0.1196,0.1527,0.9906,0.8708,0.0252,0.0,0.2759,0.1667,0.0417,0.0,0.0975,0.1294,0.0,0.0,0.1218,0.1585,0.9906,0.8759,0.0254,0.0,0.2759,0.1667,0.0417,0.0,0.1065,0.1348,0.0,0.0,0.1207,0.1527,0.9906,0.8725,0.0253,0.0,0.2759,0.1667,0.0417,0.0,0.0992,0.1317,0.0,0.0,reg oper spec account,block of flats,0.1156,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n203642,0,Cash loans,F,N,Y,0,157500.0,675000.0,21775.5,675000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.00702,-22512,365243,-13063.0,-4935,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.3127600330931377,0.3201633668633456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n131433,0,Cash loans,F,N,Y,2,238500.0,900000.0,29875.5,900000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.026392,-12899,-189,-4757.0,-1105,,1,1,1,1,1,0,Accountants,4.0,2,2,SUNDAY,14,0,0,0,0,0,0,Bank,0.6743172847539957,0.39500474157700494,0.7238369900414456,0.0825,,0.9742,,,0.0,0.1379,0.1667,,,,0.0661,,0.0,0.084,,0.9742,,,0.0,0.1379,0.1667,,,,0.0689,,0.0,0.0833,,0.9742,,,0.0,0.1379,0.1667,,,,0.0673,,0.0,,block of flats,0.052000000000000005,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-833.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n442095,0,Cash loans,F,N,Y,0,72000.0,397881.0,14418.0,328500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.031329,-23748,365243,-3864.0,-4286,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.6627280811403536,0.6863823354047934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,10.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n368810,0,Cash loans,F,N,Y,0,180000.0,1078200.0,31653.0,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-19615,-4782,-11426.0,-3110,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Government,0.7537302886515493,0.6555107477850478,0.7826078370261895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-2078.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n236184,0,Cash loans,F,N,Y,1,157500.0,1006920.0,45630.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.01885,-15962,-937,-4268.0,-5517,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.67663375664729,,0.0082,,0.9697,,,,0.069,0.0417,,0.0286,,0.0087,,,0.0084,,0.9697,,,,0.069,0.0417,,0.0293,,0.0091,,,0.0083,,0.9697,,,,0.069,0.0417,,0.0291,,0.0089,,,,block of flats,0.0069,Wooden,Yes,1.0,1.0,1.0,1.0,-2399.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n234861,0,Cash loans,F,N,N,0,225000.0,1113840.0,53586.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019689,-10109,-2136,-1626.0,-1402,,1,1,0,1,0,1,Sales staff,2.0,2,2,SATURDAY,13,0,0,0,0,1,1,Self-employed,,0.5697553099960082,0.6075573001388961,0.0392,0.0,0.9742,0.6464,0.0021,0.0,0.069,0.1667,0.2083,0.017,0.0319,0.0326,0.0,0.0053,0.0399,0.0,0.9742,0.6602,0.0021,0.0,0.069,0.1667,0.2083,0.0174,0.0349,0.034,0.0,0.0056,0.0396,0.0,0.9742,0.6511,0.0021,0.0,0.069,0.1667,0.2083,0.0173,0.0325,0.0332,0.0,0.0054,reg oper spec account,block of flats,0.0292,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1937.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n150636,0,Cash loans,M,Y,Y,0,191250.0,708939.0,51718.5,612000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.031329,-12224,-612,-603.0,-756,3.0,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Transport: type 4,,0.5078790278242765,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-438.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n286445,0,Cash loans,F,N,Y,0,225000.0,2250000.0,59485.5,2250000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.009175,-21888,365243,-8421.0,-4742,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.7444615993319383,,0.1,0.1743,0.9886,,,0.0,0.3448,0.1667,,0.0789,,0.1259,,0.0898,0.1019,0.1808,0.9886,,,0.0,0.3448,0.1667,,0.0807,,0.1312,,0.095,0.10099999999999999,0.1743,0.9886,,,0.0,0.3448,0.1667,,0.0803,,0.1282,,0.0916,,block of flats,0.1185,\"Stone, brick\",No,6.0,1.0,6.0,1.0,-264.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n192385,0,Revolving loans,F,N,Y,0,85500.0,270000.0,13500.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.00963,-20260,365243,-1135.0,-3508,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.466779370337753,0.5046813193144684,0.0113,,0.9901,,,0.0,0.0345,0.0417,,0.0376,,0.0108,,,0.0116,,0.9901,,,0.0,0.0345,0.0417,,0.0385,,0.0112,,,0.0115,,0.9901,,,0.0,0.0345,0.0417,,0.0383,,0.011000000000000001,,,,block of flats,0.0085,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-477.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n204077,0,Cash loans,F,Y,Y,0,135000.0,284400.0,18643.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-10296,-3360,-4873.0,-42,5.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,12,0,0,0,1,1,0,Self-employed,0.5402260752551674,0.5951331381610749,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2321.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n425822,1,Cash loans,F,Y,N,0,135000.0,797814.0,29439.0,571500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-16403,-996,-9496.0,-1464,7.0,1,1,0,1,0,1,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,1,1,Business Entity Type 2,0.6360870894819552,0.06456445927182725,0.3092753558842053,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-840.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n368677,0,Cash loans,F,N,Y,0,270000.0,1096020.0,59589.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-18661,-281,-11598.0,-2200,,1,1,0,1,0,1,,2.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,Construction,0.09003703343056169,0.7756188291871728,,0.2258,0.1352,0.9767,0.6872,0.0,0.24,0.2069,0.3333,0.0417,0.043,0.1765,0.0348,0.0425,0.1122,0.2269,0.1163,0.9767,0.6929,0.0,0.2417,0.2069,0.3333,0.0417,0.0277,0.1837,0.0,0.0623,0.0023,0.2248,0.15,0.9767,0.6847,0.0,0.24,0.2069,0.3333,0.0417,0.0277,0.171,0.0,0.0621,0.006,reg oper account,block of flats,0.1737,Panel,No,0.0,0.0,0.0,0.0,-2988.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n227678,0,Cash loans,F,N,N,0,112500.0,1293502.5,35568.0,1129500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006305,-22536,365243,-12307.0,-4465,,1,0,0,1,1,0,,2.0,3,3,THURSDAY,8,0,0,0,0,0,0,XNA,,0.5832298939441198,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-503.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n288334,0,Cash loans,F,N,N,0,99000.0,526491.0,17397.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.028663,-22094,365243,-9133.0,-4617,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,16,0,0,0,0,0,0,XNA,,0.6464887021918472,0.5937175866150576,0.0918,0.0514,0.9871,0.8232,0.0211,0.08,0.069,0.3333,0.375,0.0328,0.07400000000000001,0.0811,0.0039,0.0374,0.0935,0.0533,0.9871,0.8301,0.0213,0.0806,0.069,0.3333,0.375,0.0336,0.0808,0.0845,0.0039,0.0396,0.0926,0.0514,0.9871,0.8256,0.0212,0.08,0.069,0.3333,0.375,0.0334,0.0752,0.0825,0.0039,0.0382,reg oper account,block of flats,0.0638,Panel,No,0.0,0.0,0.0,0.0,-2299.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n352992,0,Cash loans,F,Y,Y,0,202500.0,1345500.0,53491.5,1345500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.026392,-22080,365243,-1840.0,-4656,5.0,1,0,0,1,1,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.6564828926167802,0.7121551551910698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,0.0,11.0,0.0,-1397.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n124729,0,Revolving loans,F,N,Y,0,67500.0,202500.0,10125.0,202500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.026392,-18106,-9716,-2726.0,-1639,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,19,0,0,0,0,0,0,Culture,,0.6514304641708444,0.8482442999507556,0.099,0.0874,0.9841,,,0.0,0.069,0.1667,,,,0.0512,,0.0541,0.1008,0.0907,0.9841,,,0.0,0.069,0.1667,,,,0.0533,,0.0573,0.0999,0.0874,0.9841,,,0.0,0.069,0.1667,,,,0.0521,,0.0552,,block of flats,0.052000000000000005,Panel,No,0.0,0.0,0.0,0.0,-2212.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n386863,0,Cash loans,M,Y,Y,0,202500.0,675000.0,21906.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-14519,-3052,-1188.0,-4434,13.0,1,1,1,1,0,0,Laborers,2.0,2,2,MONDAY,9,0,0,0,0,1,1,Construction,0.3539642510689581,0.8059240953395244,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2273.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n244452,0,Cash loans,F,N,N,0,47250.0,675000.0,21775.5,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.010276,-22375,365243,-6002.0,-4836,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.22054836981105155,0.7688075728291359,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2135.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n153749,0,Cash loans,F,N,Y,1,157500.0,269550.0,14751.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003813,-13321,-1813,-5283.0,-5311,,1,1,0,1,1,0,Sales staff,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.4576022953398056,0.3021908835119109,0.4135967602644276,0.0619,0.0627,0.9846,,,0.0,0.1379,0.1667,,,,0.0363,,0.0091,0.063,0.0651,0.9846,,,0.0,0.1379,0.1667,,,,0.0378,,0.0096,0.0625,0.0627,0.9846,,,0.0,0.1379,0.1667,,,,0.0369,,0.0093,,block of flats,0.0419,Panel,No,0.0,0.0,0.0,0.0,-1936.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n149697,1,Cash loans,F,N,Y,2,180000.0,792477.0,30838.5,661500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.010032,-12283,-1248,-5042.0,-3301,,1,1,0,1,0,0,Laborers,4.0,2,2,SUNDAY,3,0,0,0,0,0,0,Postal,,0.05520846988336525,0.4436153084085652,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-98.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,5.0\n367778,0,Cash loans,F,Y,Y,1,81000.0,225000.0,24003.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-12987,-1111,-2425.0,-4520,64.0,1,1,0,1,0,0,Sales staff,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Self-employed,,0.4597924028070583,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-351.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n291938,0,Cash loans,F,N,Y,0,143100.0,755190.0,35122.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.010966,-18361,-3527,-6335.0,-1898,,1,1,1,1,0,0,Sales staff,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,Self-employed,,0.5879316220165333,0.38079968264891495,0.0186,0.0288,0.9781,0.7008,0.0119,0.0,0.1034,0.0417,0.0833,0.0,0.0151,0.0122,0.0,0.0,0.0189,0.0299,0.9782,0.7125,0.012,0.0,0.1034,0.0417,0.0833,0.0,0.0165,0.0127,0.0,0.0,0.0187,0.0288,0.9781,0.7048,0.012,0.0,0.1034,0.0417,0.0833,0.0,0.0154,0.0124,0.0,0.0,org spec account,block of flats,0.0096,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n453402,0,Cash loans,F,N,N,0,297000.0,270000.0,20911.5,270000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.072508,-19313,365243,-4006.0,-2856,,1,0,0,1,1,0,,2.0,1,1,WEDNESDAY,20,0,0,0,0,0,0,XNA,,0.7404643022904498,0.2458512138252296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2959,,No,2.0,0.0,2.0,0.0,-321.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n169997,0,Cash loans,F,N,N,0,315000.0,1575000.0,43312.5,1575000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.022625,-14999,-369,-3111.0,-836,,1,1,0,1,1,0,Managers,1.0,2,2,MONDAY,21,1,1,0,1,1,0,Other,0.7212312021086429,0.5654779057929841,0.10547318648733764,0.0495,0.0323,0.9831,0.7688,0.0196,0.08,0.0345,0.4583,0.5,0.0972,0.0403,0.0555,0.0,0.0,0.0504,0.0336,0.9831,0.7779,0.0197,0.0806,0.0345,0.4583,0.5,0.0994,0.0441,0.0578,0.0,0.0,0.05,0.0323,0.9831,0.7719,0.0197,0.08,0.0345,0.4583,0.5,0.0989,0.040999999999999995,0.0565,0.0,0.0,,block of flats,0.0589,Panel,No,0.0,0.0,0.0,0.0,-366.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,1.0\n222703,0,Cash loans,F,N,Y,0,126000.0,270000.0,13914.0,270000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.009175,-14373,-196,-491.0,-4795,,1,1,0,1,1,0,Laborers,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.2559997819041669,0.7276335622716316,0.6075573001388961,0.2666,0.0909,0.9985,,,0.2,0.1724,0.35,,0.0457,,0.1932,,0.0,0.3151,0.0142,0.9990000000000001,,,0.2014,0.1724,0.375,,0.0278,,0.1693,,0.0,0.3123,0.1389,0.9990000000000001,,,0.2,0.1724,0.375,,0.0293,,0.1942,,0.0,,block of flats,0.1602,Panel,No,0.0,0.0,0.0,0.0,-1305.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n392276,0,Cash loans,F,N,N,0,202500.0,1042560.0,37575.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-9881,-747,-4712.0,-1159,,1,1,0,1,1,0,Core staff,2.0,1,1,MONDAY,13,0,0,0,0,0,0,Self-employed,,0.6790976930554045,0.38079968264891495,0.3531,0.133,0.9871,0.932,0.047,0.38,0.2414,0.4792,0.5417,0.1792,0.306,0.3315,0.0039,0.0616,0.3372,0.138,0.9796,0.9347,0.0474,0.3625,0.1724,0.3333,0.5417,0.1594,0.3343,0.3362,0.0039,0.0054,0.3565,0.133,0.9871,0.9329,0.0473,0.38,0.2414,0.4792,0.5417,0.1823,0.3113,0.3374,0.0039,0.0629,org spec account,block of flats,0.2549,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-771.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n201467,0,Cash loans,F,N,Y,0,72000.0,254700.0,14751.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-24370,365243,-685.0,-746,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.633876597341991,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-521.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n173272,0,Cash loans,M,Y,Y,0,230400.0,302076.0,27832.5,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.002042,-11494,-4607,-5035.0,-3485,2.0,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.20686445085868133,0.4365794520423539,0.7338145369642702,0.0696,0.0733,0.9911,0.8776,0.0,0.0464,0.1321,0.2221,0.0417,0.0,0.0567,0.0815,0.0,0.1297,0.042,0.0953,0.9916,0.8889,0.0,0.0,0.069,0.1667,0.0417,0.0,0.0367,0.031,0.0,0.0623,0.0703,0.0843,0.9911,0.8792,0.0,0.0,0.1207,0.1667,0.0417,0.0,0.0577,0.0841,0.0,0.1186,reg oper account,block of flats,0.0234,Panel,No,0.0,0.0,0.0,0.0,-1724.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n222734,0,Cash loans,M,Y,Y,0,180000.0,450000.0,32742.0,450000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.028663,-10884,-2775,-1085.0,-3536,2.0,1,1,1,1,1,0,Laborers,2.0,2,2,WEDNESDAY,11,0,0,0,0,1,1,Business Entity Type 2,,0.4456307526614687,0.2580842039460289,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,2.0,1.0,-1809.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n209577,0,Cash loans,M,N,Y,2,157500.0,696150.0,29623.5,562500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.025164,-14706,-2578,-1947.0,-4482,,1,1,0,1,0,0,High skill tech staff,4.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4863052692343611,0.5352762504724826,0.099,0.0884,0.997,0.9592,0.0568,0.08,0.069,0.375,0.0417,0.0153,0.0782,0.1173,0.0116,0.0727,0.1008,0.0917,0.997,0.9608,0.0573,0.0806,0.069,0.375,0.0417,0.0157,0.0854,0.1222,0.0117,0.077,0.0999,0.0884,0.997,0.9597,0.0571,0.08,0.069,0.375,0.0417,0.0156,0.0795,0.1194,0.0116,0.0743,reg oper account,block of flats,0.1279,Others,No,1.0,0.0,1.0,0.0,-1617.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,7.0\n287961,0,Cash loans,F,N,N,0,157500.0,314055.0,16164.0,238500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-15003,-1622,-5550.0,-5551,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,17,0,0,0,1,1,0,Self-employed,0.4505072555754451,0.7365921189414943,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-703.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n127239,0,Cash loans,M,N,Y,1,90000.0,576837.0,18738.0,481500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018801,-15760,-1664,-3101.0,-4045,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Industry: type 2,,0.7552843038748253,0.5779691187553125,0.0887,0.1452,0.9861,,,0.0,0.2759,0.125,,0.0989,,0.1075,,0.0073,0.0903,0.1507,0.9861,,,0.0,0.2759,0.125,,0.1012,,0.11199999999999999,,0.0077,0.0895,0.1452,0.9861,,,0.0,0.2759,0.125,,0.1007,,0.1094,,0.0075,,block of flats,0.1152,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2120.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n222415,0,Cash loans,M,Y,N,0,157500.0,284400.0,18643.5,225000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.02461,-10586,-1724,-572.0,-2836,3.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.6777905551147667,0.6058362647264226,0.0825,0.081,0.9747,0.6532,0.0098,0.0,0.1379,0.1667,0.2083,0.0555,0.0672,0.0723,0.0,0.0,0.084,0.0841,0.9747,0.6668,0.0099,0.0,0.1379,0.1667,0.2083,0.0568,0.0735,0.0753,0.0,0.0,0.0833,0.081,0.9747,0.6578,0.0099,0.0,0.1379,0.1667,0.2083,0.0564,0.0684,0.0736,0.0,0.0,,block of flats,0.0901,Panel,No,0.0,0.0,0.0,0.0,-1999.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n350943,0,Cash loans,M,Y,Y,0,135000.0,781920.0,25969.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-20619,365243,-3332.0,-181,15.0,1,0,0,1,1,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,0.2889993754389367,0.5868239352269761,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-171.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n133864,0,Cash loans,F,N,Y,0,108000.0,286704.0,12757.5,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.007120000000000001,-22146,365243,-1282.0,-4591,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.7197500196086986,0.8106180215523969,0.0062,,0.9543,,,,0.2069,0.0,,,,0.0036,,0.0,0.0063,,0.9543,,,,0.2069,0.0,,,,0.0037,,0.0,0.0062,,0.9543,,,,0.2069,0.0,,,,0.0036,,0.0,,block of flats,0.0028,Wooden,No,7.0,0.0,7.0,0.0,-364.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n275382,0,Revolving loans,F,N,Y,0,90000.0,270000.0,13500.0,270000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006852,-19861,-8543,-10037.0,-3389,,1,1,0,1,0,0,,2.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,School,,0.4459116872436525,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-752.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n430803,0,Cash loans,M,N,Y,1,135000.0,500211.0,30730.5,463500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-10347,-3211,-8571.0,-3030,,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6942591537985155,0.5779691187553125,0.2206,0.0823,0.9901,0.8640000000000001,,0.24,0.2069,0.3333,0.0417,0.0536,0.1673,0.2261,0.0579,0.1858,0.2248,0.0854,0.9901,0.8693,,0.2417,0.2069,0.3333,0.0417,0.0548,0.1827,0.2355,0.0584,0.1967,0.2228,0.0823,0.9901,0.8658,,0.24,0.2069,0.3333,0.0417,0.0545,0.1702,0.2301,0.0582,0.1897,reg oper account,block of flats,0.2759,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-477.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n359145,0,Revolving loans,F,N,Y,0,135000.0,360000.0,18000.0,360000.0,Family,Working,Secondary / secondary special,Widow,House / apartment,0.031329,-18568,-5402,-8216.0,-2125,,1,1,0,1,0,0,Cooking staff,1.0,2,2,MONDAY,7,0,0,0,0,0,0,Medicine,,0.5149985439240586,0.4848514754962666,0.0722,0.0826,0.9781,,,0.0,0.1379,0.1667,,,,0.0448,,0.0,0.0735,0.0857,0.9782,,,0.0,0.1379,0.1667,,,,0.0466,,0.0,0.0729,0.0826,0.9781,,,0.0,0.1379,0.1667,,,,0.0456,,0.0,,block of flats,0.0605,Others,No,0.0,0.0,0.0,0.0,-1462.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n323296,0,Cash loans,F,N,Y,0,90000.0,127350.0,12726.0,112500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.014464,-24568,365243,-6371.0,-4994,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,7,0,0,0,0,0,0,XNA,0.7667654944280774,0.7242532204939478,0.4686596550493113,0.0928,0.1096,0.9821,0.7552,0.0148,0.0,0.2069,0.1667,0.0417,0.0,0.0756,0.08800000000000001,0.0,0.0,0.0945,0.1138,0.9821,0.7648,0.015,0.0,0.2069,0.1667,0.0417,0.0,0.0826,0.0917,0.0,0.0,0.0937,0.1096,0.9821,0.7585,0.0149,0.0,0.2069,0.1667,0.0417,0.0,0.077,0.0896,0.0,0.0,reg oper account,block of flats,0.0773,Panel,No,0.0,0.0,0.0,0.0,-1088.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n184261,0,Cash loans,F,Y,N,0,247500.0,1305000.0,38155.5,1305000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-10424,-3160,-60.0,-2924,1.0,1,1,1,1,1,0,,2.0,2,2,MONDAY,18,0,0,0,1,1,0,Hotel,0.8071940048306631,0.7227615226468579,0.2263473957097693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-608.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n184761,1,Cash loans,F,N,Y,0,135000.0,381528.0,18684.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006008,-14904,-693,-8369.0,-1302,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 1,0.2942528688530457,0.3847992071754413,0.3859146722745145,0.0526,0.0364,0.9742,0.6464,0.0218,0.0,0.069,0.125,0.1667,0.0571,0.0395,0.0367,0.0154,0.0137,0.0536,0.0378,0.9742,0.6602,0.022000000000000002,0.0,0.069,0.125,0.1667,0.0584,0.0432,0.0383,0.0156,0.0145,0.0531,0.0364,0.9742,0.6511,0.0219,0.0,0.069,0.125,0.1667,0.0581,0.0402,0.0374,0.0155,0.013999999999999999,reg oper account,block of flats,0.0438,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1932.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n319706,0,Cash loans,M,Y,Y,0,81000.0,808650.0,23773.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-20144,-12597,-20136.0,-3652,29.0,1,1,1,1,1,0,,2.0,2,2,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.5258481699799986,0.31703177858344445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-456.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n325994,0,Cash loans,M,N,Y,0,202500.0,830709.0,33075.0,742500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.00733,-19910,-2739,-4436.0,-3463,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Trade: type 7,,0.6624983145982638,0.3123653692278984,0.1485,0.1172,0.9811,,0.048,0.16,0.1379,0.3333,,0.0982,0.121,0.161,0.0193,0.1196,0.1513,0.1216,0.9811,,0.0485,0.1611,0.1379,0.3333,,0.1004,0.1322,0.1677,0.0195,0.1267,0.1499,0.1172,0.9811,,0.0483,0.16,0.1379,0.3333,,0.0999,0.1231,0.1639,0.0194,0.1222,,block of flats,0.1654,Panel,No,0.0,0.0,0.0,0.0,-787.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n330504,0,Cash loans,F,N,Y,0,112500.0,143910.0,15628.5,135000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.006305,-18985,-9018,-9405.0,-2474,,1,1,1,1,0,0,,1.0,3,3,SATURDAY,2,0,0,0,0,0,0,Agriculture,,0.5737977343076688,0.4776491548517548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-651.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n419404,0,Revolving loans,F,Y,Y,2,67500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.014519999999999996,-10638,-1471,-1263.0,-2587,34.0,1,1,0,1,0,0,Managers,4.0,2,2,MONDAY,8,0,0,0,0,0,0,Government,0.6084944245708456,0.2376947704263744,0.2580842039460289,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-476.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n167074,1,Cash loans,F,N,N,1,159750.0,544068.0,25492.5,382500.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.019689,-14290,-522,-5922.0,-4384,,1,1,0,1,0,0,Sales staff,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,Trade: type 7,,0.30876012715601675,0.190705947811054,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-7.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n301544,0,Cash loans,M,Y,Y,0,180000.0,111384.0,8748.0,90000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-21969,-6110,-1635.0,-695,13.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.7695835085870733,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-138.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n401303,0,Cash loans,F,N,Y,2,135000.0,675000.0,26770.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.026392,-14054,-1582,-1222.0,-4620,,1,1,0,1,0,0,Medicine staff,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Other,0.7257404955859903,0.621819051911367,0.7850520263728172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n295362,0,Cash loans,F,Y,N,1,450000.0,755190.0,52690.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-13251,-3351,-6250.0,-3986,11.0,1,1,0,1,0,0,Core staff,3.0,3,3,MONDAY,8,0,0,0,0,0,0,Government,0.4236954328865128,0.24505006170241145,0.5370699579791587,0.066,0.2378,0.9757,0.6668,0.0781,0.0,0.1379,0.1667,0.0417,0.0468,0.0538,0.0543,0.0,0.0,0.0672,0.2467,0.9757,0.6798,0.0788,0.0,0.1379,0.1667,0.0417,0.0478,0.0588,0.0565,0.0,0.0,0.0666,0.2378,0.9757,0.6713,0.0785,0.0,0.1379,0.1667,0.0417,0.0476,0.0547,0.0552,0.0,0.0,reg oper spec account,block of flats,0.0427,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2103.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n327416,0,Cash loans,F,N,Y,0,148500.0,454500.0,16231.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-20019,-3587,-4780.0,-2078,,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.5602047115338431,0.7266877732041682,0.6512602186973006,0.0268,,0.9891,,,0.0,0.1034,0.0833,,0.0176,,0.0245,,,0.0273,,0.9891,,,0.0,0.1034,0.0833,,0.018000000000000002,,0.0256,,,0.0271,,0.9891,,,0.0,0.1034,0.0833,,0.0179,,0.025,,,,block of flats,0.0193,Panel,No,4.0,0.0,4.0,0.0,-1457.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,9.0,0.0,0.0\n355482,1,Cash loans,M,N,N,1,270000.0,1288350.0,34114.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008625,-12584,-859,-3114.0,-4057,,1,1,0,1,1,0,Laborers,3.0,2,2,THURSDAY,10,0,0,0,1,0,1,Business Entity Type 1,,0.565332825368456,0.3740208032583212,0.2598,0.0754,0.9826,,,0.08,0.0345,0.3333,,0.0198,,0.0884,,0.0,0.2647,0.0783,0.9826,,,0.0806,0.0345,0.3333,,0.0203,,0.0921,,0.0,0.2623,0.0754,0.9826,,,0.08,0.0345,0.3333,,0.0201,,0.0899,,0.0,,block of flats,0.0635,Panel,No,0.0,0.0,0.0,0.0,-1808.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n236067,0,Revolving loans,F,N,Y,1,135000.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008625,-10277,-799,-849.0,-1922,,1,1,1,1,0,0,,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.2564075271121721,0.4722533429586386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-244.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n256975,0,Cash loans,F,N,N,0,135000.0,1159515.0,30717.0,1012500.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.009657,-14594,-443,-6557.0,-4870,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,17,0,0,0,1,1,1,Industry: type 11,,0.6616274397809203,0.6674577419214722,,,0.9568,,,,,,,,,0.0025,,,,,0.9568,,,,,,,,,0.0027,,,,,0.9568,,,,,,,,,0.0026,,,,block of flats,0.0051,,Yes,8.0,0.0,8.0,0.0,-1403.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n374311,0,Cash loans,F,N,Y,2,112500.0,276277.5,21510.0,238500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-13797,-1428,-5311.0,-5311,,1,1,0,1,1,0,Core staff,4.0,3,3,TUESDAY,13,0,0,0,0,0,0,University,,0.5242776613579293,,0.1775,0.115,0.9886,0.9456,0.0389,0.1884,0.1576,0.4104,0.6667,0.4957,0.0656,0.1986,0.0,0.0065,0.084,0.0292,0.9871,0.9477,0.0392,0.0806,0.069,0.375,0.6667,0.7233,0.0716,0.0445,0.0,0.0,0.0833,0.0565,0.9876,0.9463,0.0391,0.08,0.069,0.375,0.6667,0.7194,0.0667,0.0868,0.0,0.0,reg oper account,block of flats,0.2384,Panel,No,0.0,0.0,0.0,0.0,-3167.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n142007,0,Cash loans,F,Y,Y,0,157500.0,477792.0,15543.0,378000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-20548,365243,-9303.0,-4015,7.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.591513515292287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n381822,0,Cash loans,F,N,Y,1,90000.0,101880.0,10827.0,90000.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.00963,-11663,-845,-170.0,-3663,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,14,0,0,0,0,1,1,School,0.23351846547197005,0.4717844418978477,0.5709165417729987,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-602.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n422117,0,Revolving loans,M,Y,Y,0,315000.0,405000.0,20250.0,405000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.011703,-18505,365243,-6576.0,-2055,7.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.7080086413368857,0.7421816117614419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-195.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,0.0\n389636,0,Revolving loans,F,N,Y,0,180000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.030755,-13507,-931,-7420.0,-3831,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.7087540028679055,0.5971924268337128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1500.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n349611,0,Revolving loans,F,N,N,1,121500.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-10101,-1235,-442.0,-1181,,1,1,0,1,0,0,Drivers,3.0,3,3,THURSDAY,13,0,0,0,0,0,0,Kindergarten,0.4471462651000752,0.2312928017312572,0.6430255641096323,0.0825,0.0807,0.9742,,,0.0,0.1379,0.1667,,0.0618,,0.0683,,0.0112,0.084,0.0837,0.9742,,,0.0,0.1379,0.1667,,0.0632,,0.0712,,0.0118,0.0833,0.0807,0.9742,,,0.0,0.1379,0.1667,,0.0629,,0.0695,,0.0114,,block of flats,0.0562,Panel,No,0.0,0.0,0.0,0.0,-740.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n363803,0,Cash loans,M,Y,Y,0,450000.0,1374480.0,45553.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.00823,-9214,-2654,-4007.0,-1899,2.0,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,13,1,1,0,1,1,1,Other,,0.0920962825231005,0.4776491548517548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-682.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n424601,0,Cash loans,M,Y,Y,2,247500.0,1024740.0,49428.0,900000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-9953,-1704,-1096.0,-2431,31.0,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.1906342641998585,0.612213114041938,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1781.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n131396,0,Cash loans,F,N,N,0,270000.0,594261.0,28719.0,513000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-21542,-4528,-9308.0,-4146,,1,1,0,1,0,0,Medicine staff,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,Medicine,,0.6521153822090628,0.7338145369642702,0.2629,0.2027,0.9861,0.8096,0.0498,0.28,0.2414,0.3333,0.375,0.1206,0.2118,0.272,0.0116,0.0507,0.2679,0.2103,0.9861,0.8171,0.0502,0.282,0.2414,0.3333,0.375,0.1234,0.2314,0.2834,0.0117,0.0537,0.2654,0.2027,0.9861,0.8121,0.0501,0.28,0.2414,0.3333,0.375,0.1227,0.2155,0.2769,0.0116,0.0518,reg oper spec account,block of flats,0.2262,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1769.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n362494,0,Revolving loans,F,N,N,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.035792000000000004,-8019,-1082,-7927.0,-684,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,Other,0.16121312031525342,0.6622831444367725,0.5136937663039473,0.0722,0.0769,0.9776,0.6940000000000001,0.008,0.0,0.1379,0.1667,0.2083,0.0386,0.0588,0.0639,0.0,0.0,0.0735,0.0798,0.9777,0.706,0.0081,0.0,0.1379,0.1667,0.2083,0.0395,0.0643,0.0665,0.0,0.0,0.0729,0.0769,0.9776,0.6981,0.008,0.0,0.1379,0.1667,0.2083,0.0393,0.0599,0.065,0.0,0.0,reg oper account,block of flats,0.0502,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-485.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n207688,0,Cash loans,M,Y,Y,1,540000.0,1515415.5,57852.0,1354500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-11198,-335,-805.0,-3811,4.0,1,1,0,1,0,0,Managers,3.0,1,1,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.5884605197296157,0.8032215812894914,0.5136937663039473,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-424.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n417834,0,Cash loans,F,N,Y,0,112500.0,225000.0,11619.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-11020,-239,-5625.0,-2749,,1,1,0,1,0,1,Sales staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,0.5354427547419149,0.7591076047200732,0.6161216908872079,0.1526,0.0718,0.9906,0.8708,0.0312,0.16,0.1379,0.375,0.375,0.1923,0.1244,0.156,0.0,0.0,0.1555,0.0746,0.9906,0.8759,0.0315,0.1611,0.1379,0.375,0.375,0.1967,0.1359,0.1625,0.0,0.0,0.1541,0.0718,0.9906,0.8725,0.0314,0.16,0.1379,0.375,0.375,0.1957,0.1266,0.1588,0.0,0.0,reg oper account,block of flats,0.1227,Panel,No,3.0,0.0,3.0,0.0,-2332.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n377917,0,Cash loans,F,N,Y,0,81000.0,298512.0,29209.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-15336,-1048,-5109.0,-4387,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Trade: type 7,,0.434300137874384,0.2032521136204725,0.0742,0.0581,0.9881,0.8368,0.0259,0.08,0.069,0.3333,0.375,0.0365,0.0605,0.0772,0.0,0.0,0.0378,0.0341,0.9881,0.8432,0.0139,0.0403,0.0345,0.3333,0.375,0.0159,0.0331,0.0399,0.0,0.0,0.0749,0.0547,0.9881,0.8390000000000001,0.0251,0.08,0.069,0.3333,0.375,0.0381,0.0616,0.0787,0.0,0.0,reg oper spec account,block of flats,0.0377,Panel,No,2.0,0.0,2.0,0.0,-2148.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n443101,0,Cash loans,F,N,Y,0,67500.0,417024.0,21964.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-14969,-434,-603.0,-1761,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Self-employed,0.3094963535535113,0.1915258625230554,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1778.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n241416,0,Cash loans,F,N,N,0,252000.0,2156400.0,59301.0,1800000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-19594,-1737,-1874.0,-1877,,1,1,1,1,1,0,Sales staff,2.0,1,1,TUESDAY,10,1,1,0,1,1,0,Business Entity Type 3,,0.7537411110146589,0.34578480246959553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1036.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n434395,0,Cash loans,F,N,Y,4,67500.0,528687.0,25843.5,436500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-11949,-535,-339.0,-4336,,1,1,0,1,0,0,Cooking staff,6.0,2,2,SATURDAY,9,0,0,0,0,1,1,Self-employed,0.35668768019501496,0.09603519512107572,0.178760465484575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-296.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n270276,1,Cash loans,F,N,Y,0,315000.0,577741.5,20880.0,477000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019689,-15694,-2741,-4245.0,-352,,1,1,1,1,1,1,Managers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Other,0.5800504538684671,0.6320629488368297,0.2707073872651806,0.068,0.0,0.9737,0.6396,0.0315,0.0,0.1379,0.1667,0.2083,0.0484,0.0555,0.0634,0.0,0.079,0.0693,0.0,0.9737,0.6537,0.0317,0.0,0.1379,0.1667,0.2083,0.0495,0.0606,0.0661,0.0,0.0837,0.0687,0.0,0.9737,0.6444,0.0317,0.0,0.1379,0.1667,0.2083,0.0492,0.0564,0.0646,0.0,0.0807,reg oper spec account,block of flats,0.059000000000000004,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1638.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n146282,0,Cash loans,M,N,Y,0,112500.0,270126.0,18400.5,193500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.018209,-9189,-1205,-1355.0,-1743,,1,1,0,1,0,0,Low-skill Laborers,1.0,3,3,MONDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.31870317793010666,0.34741822720026416,0.1464,0.1068,0.9866,0.8164,0.0718,0.16,0.1379,0.3333,0.0417,0.0203,0.1194,0.1257,0.0,0.0,0.1492,0.1108,0.9866,0.8236,0.0725,0.1611,0.1379,0.3333,0.0417,0.0208,0.1304,0.131,0.0,0.0,0.1478,0.1068,0.9866,0.8189,0.0723,0.16,0.1379,0.3333,0.0417,0.0207,0.1214,0.128,0.0,0.0,reg oper account,block of flats,0.1382,Panel,No,0.0,0.0,0.0,0.0,-464.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n192167,0,Cash loans,F,N,Y,0,135000.0,152820.0,8662.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-21990,365243,-9197.0,-4466,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.7507783572088331,0.44539624156083396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2544.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n330912,0,Cash loans,F,Y,Y,0,225000.0,229500.0,9850.5,229500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.030755,-23437,365243,-5605.0,-4729,0.0,1,0,0,1,1,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,0.8616092431895143,0.7109024569294536,0.7623356180684377,0.1113,0.0841,0.9836,0.7756,0.172,0.12,0.1034,0.3333,,0.0274,0.0908,0.0722,0.0,0.0,0.0756,0.0562,0.9826,0.7713,0.1109,0.0806,0.069,0.3333,,0.0302,0.0661,0.0461,0.0,0.0,0.1124,0.0861,0.9836,0.7786,0.172,0.12,0.1034,0.3333,,0.0301,0.0923,0.0691,0.0,0.0,reg oper account,block of flats,0.0601,Panel,No,0.0,0.0,0.0,0.0,-1574.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n383399,0,Cash loans,M,Y,Y,3,351000.0,1024740.0,52452.0,900000.0,Unaccompanied,Working,Higher education,Married,With parents,0.035792000000000004,-14937,-2727,-469.0,-1326,2.0,1,1,0,1,0,1,Laborers,5.0,2,2,FRIDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.5447762134395913,0.491463422211524,0.0920128093981064,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1807.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n104816,0,Cash loans,F,N,N,0,76500.0,431280.0,18270.0,360000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010556,-21593,365243,-636.0,-5156,,1,0,0,1,0,0,,2.0,3,3,WEDNESDAY,15,0,0,0,0,0,0,XNA,0.6564042546671143,0.4497833783973405,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-294.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n191530,0,Cash loans,M,Y,Y,0,171000.0,900000.0,90837.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-22792,-1702,-2425.0,-4808,3.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Other,,0.627254386937052,0.8528284668510541,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2977.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n447276,0,Cash loans,F,N,Y,0,112500.0,167121.0,13527.0,139500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.00496,-17546,-1496,-2882.0,-805,,1,1,0,1,1,0,Laborers,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Industry: type 3,,0.4748371709356027,0.6594055320683344,0.0598,,0.9737,0.6396,,,0.1034,0.1667,0.2083,,0.0488,0.0308,,,0.0609,,0.9737,0.6537,,,0.1034,0.1667,0.2083,,0.0533,0.0321,,,0.0604,,0.9737,0.6444,,,0.1034,0.1667,0.2083,,0.0496,0.0313,,,,block of flats,0.0404,\"Stone, brick\",No,5.0,0.0,4.0,0.0,-710.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n340323,0,Cash loans,F,Y,Y,0,270000.0,946764.0,48469.5,765000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.04622,-20534,365243,-9324.0,-3830,1.0,1,0,0,1,0,0,,1.0,1,1,THURSDAY,13,0,0,0,0,0,0,XNA,0.8778087313493278,0.6118963825110796,0.41184855592423975,,,0.9717,,,,,,,,,,,,,,0.9717,,,,,,,,,,,,,,0.9717,,,,,,,,,,,,,block of flats,0.0872,,No,1.0,1.0,1.0,1.0,-765.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n122498,1,Cash loans,M,Y,Y,1,135000.0,495000.0,26982.0,495000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-15791,-4442,-3204.0,-4425,12.0,1,1,0,1,0,0,Drivers,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Government,,0.4604714199303776,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-741.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n348706,0,Cash loans,F,N,N,1,225000.0,299772.0,20160.0,247500.0,Unaccompanied,Commercial associate,Incomplete higher,Married,Municipal apartment,0.011657,-10057,-1560,-4833.0,-2512,,1,1,0,1,0,0,Managers,3.0,1,1,MONDAY,12,0,0,0,0,1,1,Trade: type 3,,0.680744306993716,,0.0144,0.0,0.9518,0.3404,0.0,0.0,0.069,0.0417,0.0833,0.0,0.0118,0.0133,0.0,0.0,0.0147,0.0,0.9518,0.3662,0.0,0.0,0.069,0.0417,0.0833,0.0,0.0129,0.0139,0.0,0.0,0.0146,0.0,0.9518,0.3492,0.0,0.0,0.069,0.0417,0.0833,0.0,0.012,0.0136,0.0,0.0,reg oper account,block of flats,0.0105,Wooden,No,1.0,0.0,1.0,0.0,-1777.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n234891,0,Cash loans,F,N,Y,0,180000.0,961146.0,28233.0,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-14528,-7850,-8002.0,-4019,,1,1,0,1,1,0,Drivers,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Electricity,0.38917807435462026,0.6634510989608053,0.7910749129911773,0.0082,0.0096,0.9677,0.5579999999999999,,0.0,0.069,0.0417,,0.0208,0.0067,0.0092,0.0,0.0,0.0084,0.01,0.9677,0.5753,,0.0,0.069,0.0417,,0.0213,0.0073,0.0096,0.0,0.0,0.0083,0.0096,0.9677,0.5639,,0.0,0.069,0.0417,,0.0212,0.0068,0.0094,0.0,0.0,reg oper account,block of flats,0.0092,Mixed,No,0.0,0.0,0.0,0.0,-1569.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n271643,0,Cash loans,F,N,Y,0,202500.0,280170.0,29547.0,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-12942,-2430,-7019.0,-4312,,1,1,0,1,0,0,Medicine staff,2.0,1,1,TUESDAY,9,0,0,0,0,1,1,Business Entity Type 1,0.43161624279876415,0.7342945934874787,0.6832688314232291,0.0227,0.0,0.9846,0.7892,0.0134,0.0,0.1034,0.0833,0.125,0.0,0.0185,0.0103,0.0,0.0,0.0231,0.0,0.9846,0.7975,0.0135,0.0,0.1034,0.0833,0.125,0.0,0.0202,0.0107,0.0,0.0,0.0229,0.0,0.9846,0.792,0.0135,0.0,0.1034,0.0833,0.125,0.0,0.0188,0.0105,0.0,0.0,,block of flats,0.0151,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1949.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n385994,0,Revolving loans,F,N,N,0,112500.0,270000.0,13500.0,270000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,Rented apartment,0.00702,-8057,-237,-8036.0,-746,,1,1,0,1,0,0,,1.0,2,2,FRIDAY,19,0,0,0,1,1,0,Business Entity Type 3,0.2046700963078132,0.5808679599489264,0.1117563710719636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-703.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n257479,0,Cash loans,F,N,Y,0,99000.0,450000.0,48595.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-10192,-1371,-3940.0,-2167,,1,1,1,1,1,0,Sales staff,2.0,2,2,MONDAY,16,0,0,0,0,1,1,Trade: type 7,,0.5859541292700584,0.2458512138252296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,7.0,0.0,-1370.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n267652,0,Cash loans,F,Y,N,0,157500.0,810000.0,34317.0,810000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15806,-266,-1406.0,-481,3.0,1,1,1,1,0,0,High skill tech staff,2.0,2,2,SUNDAY,19,0,0,0,0,0,0,Self-employed,0.6372423895168623,0.6122754107321084,0.28371188263500075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1166.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n292653,0,Cash loans,F,Y,Y,0,135000.0,691020.0,22419.0,495000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018029,-18391,-3356,-8130.0,-1945,64.0,1,1,0,1,0,0,,2.0,3,2,MONDAY,9,0,0,0,0,0,0,Self-employed,,0.6531217636175851,,0.0619,0.0626,0.9791,0.7144,0.0275,0.0,0.1379,0.1667,0.2083,0.0575,0.0496,0.0528,0.0039,0.0033,0.063,0.0649,0.9791,0.7256,0.0277,0.0,0.1379,0.1667,0.2083,0.0588,0.0542,0.055,0.0039,0.0035,0.0625,0.0626,0.9791,0.7182,0.0277,0.0,0.1379,0.1667,0.2083,0.0585,0.0504,0.0538,0.0039,0.0034,reg oper account,block of flats,0.0573,Panel,No,2.0,2.0,2.0,1.0,-726.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n414883,0,Cash loans,F,N,Y,0,225000.0,571500.0,29178.0,571500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.009175,-22920,365243,-2671.0,-1471,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,XNA,0.6742473949437678,0.059149132310899875,0.524496446363472,0.0093,0.04,0.9682,,,0.0,0.069,0.0417,,0.0088,,0.0082,,0.0105,0.0095,0.0415,0.9682,,,0.0,0.069,0.0417,,0.009000000000000001,,0.0086,,0.0111,0.0094,0.04,0.9682,,,0.0,0.069,0.0417,,0.009000000000000001,,0.0084,,0.0107,,block of flats,0.0088,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-121.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n455589,0,Cash loans,F,N,N,0,180000.0,454500.0,25375.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-19305,-1102,-13153.0,-2741,,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,,0.4395454789104932,,0.2227,0.0,0.9801,0.728,0.0441,0.24,0.2069,0.3333,0.375,0.1624,0.1816,0.2259,0.0,0.0,0.2269,0.0,0.9801,0.7387,0.0445,0.2417,0.2069,0.3333,0.375,0.1661,0.1983,0.2354,0.0,0.0,0.2248,0.0,0.9801,0.7316,0.0444,0.24,0.2069,0.3333,0.375,0.1653,0.1847,0.23,0.0,0.0,,block of flats,0.2374,Panel,No,1.0,0.0,1.0,0.0,-251.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n163911,0,Cash loans,M,Y,Y,0,76500.0,152820.0,15241.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23747,365243,-595.0,-4230,10.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.6312234403471902,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-255.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n251528,1,Cash loans,F,N,Y,2,310500.0,792162.0,43101.0,630000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.019101,-13506,-797,-244.0,-2060,,1,1,0,1,0,1,Core staff,3.0,2,2,THURSDAY,12,0,0,0,0,0,0,Trade: type 3,0.3686475386267741,0.6637391160398994,0.11247434965561487,0.1119,0.0676,0.9901,0.8504,0.0345,0.0,0.0862,0.1667,0.2083,0.0782,0.0815,0.0999,0.0444,0.0289,0.0861,0.0092,0.9856,0.8105,0.0348,0.0,0.069,0.1667,0.2083,0.0738,0.0551,0.0976,0.0039,0.004,0.1129,0.0676,0.9901,0.8524,0.0347,0.0,0.0862,0.1667,0.2083,0.0795,0.0829,0.1017,0.0446,0.0295,org spec account,block of flats,0.0843,Block,No,2.0,0.0,2.0,0.0,-2033.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n190474,0,Cash loans,F,N,Y,0,112500.0,1006920.0,42790.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-14165,-1975,-4472.0,-4607,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.5898057638479994,0.7407990879702335,0.0371,0.0738,0.9866,0.8164,0.0038,0.0,0.1034,0.0833,0.0,0.0,0.0303,0.0365,0.0,0.0094,0.0378,0.0766,0.9866,0.8236,0.0038,0.0,0.1034,0.0833,0.0,0.0,0.0331,0.0381,0.0,0.01,0.0375,0.0738,0.9866,0.8189,0.0038,0.0,0.1034,0.0833,0.0,0.0,0.0308,0.0372,0.0,0.0096,reg oper account,block of flats,0.0308,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-1800.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n194226,0,Cash loans,F,N,Y,0,180000.0,239418.0,16128.0,211500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.01885,-22419,365243,-1374.0,-4062,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,0.7359396276006949,0.6253914582259202,0.4329616670974407,0.067,0.0897,0.9836,0.7756,0.0119,0.0,0.2069,0.1667,0.2083,0.0319,0.0538,0.0624,0.0039,0.0575,0.0683,0.0931,0.9836,0.7844,0.012,0.0,0.2069,0.1667,0.2083,0.0326,0.0588,0.065,0.0039,0.0608,0.0677,0.0897,0.9836,0.7786,0.0119,0.0,0.2069,0.1667,0.2083,0.0325,0.0547,0.0635,0.0039,0.0587,reg oper spec account,block of flats,0.0681,Panel,No,3.0,1.0,3.0,0.0,-2596.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n192797,0,Cash loans,F,Y,N,0,189000.0,1575000.0,43312.5,1575000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-10103,-1995,-4633.0,-2770,1.0,1,1,1,1,0,0,Private service staff,2.0,1,1,MONDAY,17,0,0,0,0,0,0,Services,,0.653305045644048,0.3280631605201915,0.0821,0.0312,0.9896,0.8572,0.0397,0.08,0.069,0.375,0.4167,0.0182,0.067,0.0618,0.0,0.011000000000000001,0.084,0.0054,0.9891,0.8563,0.0188,0.0806,0.069,0.375,0.4167,0.0,0.0735,0.0521,0.0,0.0,0.0833,0.0417,0.9901,0.8658,0.0501,0.08,0.069,0.375,0.4167,0.0,0.0684,0.0519,0.0,0.0,reg oper spec account,block of flats,0.0671,Panel,No,4.0,0.0,4.0,0.0,-527.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n166879,0,Cash loans,M,Y,Y,1,180000.0,521280.0,41926.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-15520,-1876,-2741.0,-3403,11.0,1,1,0,1,0,0,Cooking staff,3.0,3,3,SATURDAY,9,0,0,0,0,0,0,Self-employed,0.4116070248921318,0.263831063872486,0.5726825047161584,0.1526,0.2273,0.9712,,,0.0,0.3448,0.2083,,0.14300000000000002,,0.18899999999999997,,0.1004,0.1555,0.2358,0.9712,,,0.0,0.3448,0.2083,,0.1462,,0.19699999999999998,,0.1063,0.1541,0.2273,0.9712,,,0.0,0.3448,0.2083,,0.1455,,0.1924,,0.1025,,block of flats,0.1873,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-353.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n212741,0,Cash loans,M,N,Y,0,238500.0,331920.0,17077.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.007273999999999998,-15541,-418,-5418.0,-2135,,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,15,0,0,0,1,1,1,Trade: type 3,,0.6007896455844198,0.6894791426446275,0.0165,0.0436,0.9752,0.66,0.0,0.0,0.069,0.0417,0.0417,0.0,0.0134,0.0129,0.0,0.0042,0.0168,0.0453,0.9752,0.6733,0.0,0.0,0.069,0.0417,0.0417,0.0,0.0147,0.0134,0.0,0.0044,0.0167,0.0436,0.9752,0.6645,0.0,0.0,0.069,0.0417,0.0417,0.0,0.0137,0.0131,0.0,0.0043,reg oper account,block of flats,0.0111,Panel,No,0.0,0.0,0.0,0.0,-1420.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n289688,1,Cash loans,F,N,Y,0,225000.0,227520.0,15331.5,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.007273999999999998,-15942,-539,-5361.0,-5364,,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.5133324806517005,,0.0165,0.0338,0.9866,0.8164,,0.0,0.069,0.0417,,0.0,0.0134,0.013999999999999999,,0.0043,0.0168,0.0351,0.9866,0.8236,,0.0,0.069,0.0417,,0.0,0.0147,0.0146,,0.0046,0.0167,0.0338,0.9866,0.8189,,0.0,0.069,0.0417,,0.0,0.0137,0.0143,,0.0044,reg oper account,block of flats,0.012,Panel,No,1.0,1.0,1.0,1.0,-904.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n344989,0,Cash loans,F,N,Y,0,112500.0,571486.5,24340.5,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-17895,-10862,-11060.0,-1448,,1,1,0,1,0,0,Accountants,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,Government,0.7928780753637537,0.7139623199775584,0.4471785780453068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1543.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,0.0\n308855,0,Cash loans,F,N,Y,0,126000.0,450000.0,30204.0,450000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018209,-8087,-506,-1126.0,-735,,1,1,0,1,0,0,,1.0,3,3,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.08893796152869304,0.2709804953786667,0.2458512138252296,0.1763,0.0715,0.9816,0.7484,0.0034,0.04,0.0345,0.3333,0.0417,0.0136,0.1437,0.081,0.0,0.0084,0.1796,0.0742,0.9816,0.7583,0.0034,0.0403,0.0345,0.3333,0.0417,0.0139,0.157,0.0844,0.0,0.0089,0.17800000000000002,0.0715,0.9816,0.7518,0.0034,0.04,0.0345,0.3333,0.0417,0.0138,0.1462,0.0825,0.0,0.0086,reg oper account,block of flats,0.0655,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-158.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n311373,0,Cash loans,F,Y,Y,0,75150.0,76410.0,8748.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-19900,-5259,-2005.0,-3432,6.0,1,1,1,1,1,1,Medicine staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Other,,0.3051595019096213,0.12850437737240394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-2240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n278130,0,Cash loans,F,N,Y,0,112500.0,1002870.0,37296.0,922500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.015221,-18107,-2014,-8487.0,-1634,,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,15,0,0,0,0,1,1,Bank,,0.6432385310569521,0.6109913280868294,0.0247,0.0867,0.9712,0.6056,,0.0,0.1034,0.0833,0.0417,0.0601,,0.0414,,0.0,0.0252,0.09,0.9712,0.621,,0.0,0.1034,0.0833,0.0417,0.0614,,0.0431,,0.0,0.025,0.0867,0.9712,0.6109,,0.0,0.1034,0.0833,0.0417,0.0611,,0.0421,,0.0,reg oper account,block of flats,0.0341,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-145.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n146013,0,Cash loans,F,Y,Y,0,99000.0,220032.0,8032.5,144000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-17694,-3766,-6811.0,-1231,4.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Other,,0.5457896822614875,0.5779691187553125,0.1278,0.1069,0.9781,0.7008,0.0148,0.0,0.2759,0.1667,0.2083,0.0,0.1042,0.1196,0.0,0.0845,0.1303,0.1109,0.9782,0.7125,0.015,0.0,0.2759,0.1667,0.2083,0.0,0.1139,0.1246,0.0,0.0895,0.1291,0.1069,0.9781,0.7048,0.0149,0.0,0.2759,0.1667,0.2083,0.0,0.106,0.1217,0.0,0.0863,not specified,block of flats,0.1119,Block,No,0.0,0.0,0.0,0.0,-1955.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n391428,0,Cash loans,M,N,N,1,324000.0,1711764.0,47070.0,1530000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.035792000000000004,-13179,-3258,-467.0,-487,,1,1,1,1,1,0,Managers,2.0,2,2,SUNDAY,12,0,1,1,0,1,1,Business Entity Type 3,0.7183304475054584,0.18886512237790595,0.6347055309763198,0.099,,0.9742,,,0.0,0.1379,0.2083,,0.061,,0.0769,,0.0,0.1008,,0.9742,,,0.0,0.1379,0.2083,,0.0624,,0.0801,,0.0,0.0999,,0.9742,,,0.0,0.1379,0.2083,,0.0621,,0.0783,,0.0,,block of flats,0.0605,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-833.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n187282,0,Revolving loans,F,N,N,0,157500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-11110,-3332,-2988.0,-3677,,1,1,1,1,1,0,Medicine staff,2.0,2,2,THURSDAY,12,0,1,1,0,1,1,Medicine,0.3246867601123375,0.22202927726231209,0.7713615919194317,0.109,0.0954,0.9876,0.83,0.0424,0.02,0.2241,0.2292,0.25,0.0508,0.0889,0.0741,0.0,0.0,0.083,0.0558,0.9841,0.7909,0.0257,0.0,0.2069,0.1667,0.2083,0.0287,0.0725,0.0528,0.0,0.0,0.0885,0.0836,0.9841,0.7853,0.0408,0.0,0.2069,0.1667,0.2083,0.0492,0.0727,0.0715,0.0,0.0,reg oper account,block of flats,0.0644,\"Stone, brick\",No,,,,,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n250978,0,Cash loans,F,N,Y,0,90000.0,675000.0,17806.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-21074,365243,-12411.0,-4275,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.2631435910213423,0.4920600938649263,0.134,0.0292,0.9831,0.7688,0.0159,0.0,0.2759,0.1667,0.2083,0.0501,0.1034,0.125,0.027000000000000003,0.0321,0.1366,0.0303,0.9831,0.7779,0.016,0.0,0.2759,0.1667,0.2083,0.0512,0.1129,0.1302,0.0272,0.034,0.1353,0.0292,0.9831,0.7719,0.016,0.0,0.2759,0.1667,0.2083,0.0509,0.1052,0.1272,0.0272,0.0328,reg oper account,block of flats,0.1139,Block,No,5.0,0.0,4.0,0.0,-1222.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n417015,0,Cash loans,F,N,Y,0,270000.0,1113840.0,44172.0,900000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.072508,-10151,-1446,-4941.0,-2806,,1,1,0,1,0,0,Sales staff,2.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.6463824403049208,0.6838302016129715,0.6956219298394389,0.2773,0.1403,0.9806,0.7348,0.0854,0.4,0.1724,0.5417,0.5833,0.0,0.2253,0.2966,0.0039,0.0124,0.1176,0.0723,0.9806,0.7452,0.0,0.0806,0.0345,0.4583,0.5,0.0,0.1019,0.1105,0.0039,0.0005,0.28,0.1403,0.9806,0.7383,0.0859,0.4,0.1724,0.5417,0.5833,0.0,0.2292,0.3019,0.0039,0.0126,reg oper account,block of flats,0.3884,Block,No,0.0,0.0,0.0,0.0,-829.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n371454,0,Cash loans,F,Y,Y,1,126000.0,808650.0,26217.0,675000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.006852,-19355,365243,-632.0,-2795,17.0,1,0,0,1,0,0,,2.0,3,3,WEDNESDAY,8,0,0,0,0,0,0,XNA,,0.18124469954742206,0.6058362647264226,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-568.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n302804,0,Cash loans,F,N,N,0,45000.0,288873.0,16258.5,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.006852,-21647,365243,-7245.0,-4279,,1,0,0,1,0,0,,1.0,3,3,TUESDAY,5,0,0,0,0,0,0,XNA,,0.20540087500406773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n419483,0,Cash loans,F,N,Y,0,202500.0,481495.5,32436.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.016612000000000002,-19658,-2511,-10000.0,-634,,1,1,1,1,0,0,Cooking staff,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6170610949676,0.2458512138252296,0.0928,0.1014,0.9866,,,0.0,0.2069,0.1667,,0.1004,,0.0865,,0.0,0.0945,0.1052,0.9866,,,0.0,0.2069,0.1667,,0.1027,,0.0901,,0.0,0.0937,0.1014,0.9866,,,0.0,0.2069,0.1667,,0.1021,,0.08800000000000001,,0.0,,block of flats,0.0751,Panel,No,0.0,0.0,0.0,0.0,-2379.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n241735,0,Cash loans,F,N,Y,1,157500.0,1436850.0,42142.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006233,-10399,-1795,-7898.0,-986,,1,1,0,1,0,0,,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Self-employed,0.3858589911425429,0.6480713407685634,0.4294236843421945,0.0928,0.0028,0.9786,0.7076,0.0133,0.0,0.2069,0.1667,0.2083,0.0877,0.0756,0.087,0.0,0.01,0.0945,0.0029,0.9786,0.7190000000000001,0.0134,0.0,0.2069,0.1667,0.2083,0.0897,0.0826,0.0907,0.0,0.0106,0.0937,0.0028,0.9786,0.7115,0.0133,0.0,0.2069,0.1667,0.2083,0.0892,0.077,0.0886,0.0,0.0103,reg oper account,block of flats,0.0706,Panel,No,1.0,0.0,1.0,0.0,-1639.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n252207,0,Cash loans,F,N,Y,0,315000.0,517500.0,22054.5,517500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018209,-20558,-8117,-10657.0,-3313,,1,1,0,1,0,0,Managers,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,Self-employed,0.8959477800872342,0.5413191728366504,0.5762088360175724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1472.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n320458,0,Cash loans,F,Y,Y,0,180000.0,500211.0,48861.0,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-19968,-10069,-11164.0,-3523,10.0,1,1,0,1,0,0,Medicine staff,2.0,1,1,SATURDAY,12,0,0,0,0,0,0,Medicine,,0.7958869993997368,0.4206109640437848,0.3711,,0.9861,,,0.4,0.1724,0.25,,,0.3026,0.3565,,0.099,0.3782,,0.9861,,,0.4028,0.1724,0.0417,,,0.3306,0.3714,,0.1048,0.3747,,0.9861,,,0.4,0.1724,0.25,,,0.3078,0.3629,,0.1011,reg oper account,block of flats,0.0215,,No,0.0,0.0,0.0,0.0,-343.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n257015,0,Cash loans,M,Y,N,2,189000.0,900000.0,46084.5,900000.0,Family,State servant,Higher education,Married,House / apartment,0.010643,-14116,-4553,-3568.0,-4865,7.0,1,1,0,1,0,0,Core staff,4.0,2,2,SUNDAY,11,0,0,0,0,1,1,Police,0.4121380489509607,0.4989413073712361,0.7557400501752248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2915.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n366843,0,Cash loans,F,N,Y,1,163350.0,1379376.0,40459.5,1080000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.011703,-15440,-4526,-9537.0,-2233,,1,1,0,1,0,0,Cooking staff,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Government,,0.5242503912518938,0.4956658291397297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n294291,1,Cash loans,F,N,N,1,202500.0,1350000.0,53671.5,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-10330,-1371,-4639.0,-719,,1,1,0,1,0,0,Secretaries,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3615458444096493,0.7116075396416186,0.24988506275045536,0.1031,0.0434,0.9757,,,0.0,0.1724,0.1667,,0.0225,,0.0867,,0.0,0.105,0.045,0.9757,,,0.0,0.1724,0.1667,,0.023,,0.0903,,0.0,0.1041,0.0434,0.9757,,,0.0,0.1724,0.1667,,0.0229,,0.0883,,0.0,,block of flats,0.0868,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-696.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n202842,0,Cash loans,F,N,Y,1,202500.0,314055.0,21375.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-16331,-547,-6291.0,-2326,,1,1,0,1,0,0,Sales staff,2.0,2,2,SUNDAY,13,0,0,0,0,1,1,Trade: type 7,,0.11041064157015976,0.2340151665320674,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0911,,No,0.0,0.0,0.0,0.0,-347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n373345,0,Cash loans,F,N,N,0,180000.0,454500.0,21865.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.010006,-11512,-290,-6464.0,-1193,,1,1,1,1,0,0,Laborers,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Industry: type 9,0.3667157344422,0.6206355811494889,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-774.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n347146,0,Cash loans,F,N,Y,3,112500.0,112500.0,9018.0,112500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.0228,-11423,-1541,-465.0,-4026,,1,1,1,1,0,0,Sales staff,4.0,2,2,FRIDAY,15,0,0,0,0,0,0,Self-employed,,0.6247508551565555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-290.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n423804,0,Cash loans,F,N,Y,0,45000.0,135000.0,7879.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-20182,365243,-4002.0,-3499,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.4659249549439119,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-328.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n299165,0,Cash loans,F,N,Y,0,247500.0,1223010.0,51948.0,1125000.0,Family,Working,Higher education,Married,House / apartment,0.00823,-16228,-815,-4570.0,-2988,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,12,0,1,1,0,1,1,School,,0.27162897329674546,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-559.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n449696,0,Cash loans,F,Y,Y,0,247500.0,567000.0,27274.5,567000.0,Unaccompanied,State servant,Higher education,Widow,House / apartment,0.018209,-20007,-10607,-5730.0,-3474,17.0,1,1,0,1,1,0,Core staff,1.0,3,3,SATURDAY,7,0,0,0,0,0,0,School,0.8809245681568717,0.6275099902968575,0.4884551844437485,0.0608,0.044000000000000004,0.9776,0.6940000000000001,,0.0,0.1034,0.1667,0.0417,0.0768,,0.0344,,0.0022,0.062,0.0457,0.9777,0.706,,0.0,0.1034,0.1667,0.0417,0.0786,,0.0359,,0.0023,0.0614,0.044000000000000004,0.9776,0.6981,,0.0,0.1034,0.1667,0.0417,0.0782,,0.035,,0.0022,reg oper account,block of flats,0.0404,Panel,No,0.0,0.0,0.0,0.0,-1361.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n206468,0,Cash loans,F,N,N,0,112500.0,467257.5,17743.5,328500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-18887,-2080,-7867.0,-1934,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Self-employed,,0.2575934077239959,,0.1237,0.0801,0.9891,0.8504,0.028999999999999998,0.12,0.1034,0.375,0.3229,0.0436,0.1009,0.1157,0.0,0.0,0.084,0.0545,0.9891,0.8563,0.0208,0.0806,0.069,0.375,0.4167,0.0,0.0735,0.0876,0.0,0.0,0.1249,0.0801,0.9891,0.8524,0.0263,0.12,0.1034,0.375,0.4167,0.0486,0.1026,0.1002,0.0,0.0,reg oper account,block of flats,0.0661,Panel,No,2.0,0.0,2.0,0.0,-1808.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n315904,0,Cash loans,F,N,Y,0,315000.0,675000.0,24376.5,675000.0,Unaccompanied,Working,Higher education,Separated,With parents,0.015221,-12188,-3839,-7622.0,-4309,,1,1,0,1,1,1,,1.0,2,2,MONDAY,13,1,1,0,1,1,0,Business Entity Type 2,0.3520745497331925,0.7568974050041247,,0.1113,0.0763,0.9881,,,0.12,0.1034,0.3333,,0.0874,,0.1101,,0.0022,0.1134,0.0792,0.9881,,,0.1208,0.1034,0.3333,,0.0894,,0.1147,,0.0024,0.1124,0.0763,0.9881,,,0.12,0.1034,0.3333,,0.0889,,0.11199999999999999,,0.0023,,block of flats,0.0987,Panel,No,0.0,0.0,0.0,0.0,-689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n202304,0,Cash loans,M,Y,Y,0,270000.0,562491.0,27189.0,454500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010006,-16800,-1328,-6863.0,-342,15.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Industry: type 9,,0.6087239057430422,0.3123653692278984,0.0742,0.0456,0.9866,0.8164,0.0662,0.08,0.069,0.3333,0.375,0.0771,0.0605,0.0795,0.0,0.0,0.0756,0.0473,0.9866,0.8236,0.0669,0.0806,0.069,0.3333,0.375,0.0788,0.0661,0.0828,0.0,0.0,0.0749,0.0456,0.9866,0.8189,0.0667,0.08,0.069,0.3333,0.375,0.0784,0.0616,0.0809,0.0,0.0,reg oper account,block of flats,0.0987,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2484.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,3.0\n157059,0,Revolving loans,M,Y,N,2,810000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,Office apartment,0.019101,-18135,-3705,-123.0,-1656,10.0,1,1,0,1,0,0,Managers,4.0,2,2,SATURDAY,15,0,1,1,0,1,1,Military,,0.6233759316965827,0.7623356180684377,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1147.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n402591,1,Cash loans,F,Y,Y,0,112500.0,283419.0,13914.0,234000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-12851,-3514,-2108.0,-2662,13.0,1,1,1,1,1,0,Laborers,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3968916551878493,0.3822141460040689,0.5602843280409464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1728.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n324049,0,Cash loans,M,N,N,1,54000.0,331920.0,18135.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-19425,-4410,-689.0,-708,,1,1,1,1,1,0,Laborers,3.0,3,3,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.622245478225575,0.6452483049872764,,0.0825,0.0734,0.9757,0.6668,0.0,0.0,0.1379,0.1667,0.0417,0.1014,0.0672,0.0762,0.0,0.0,0.084,0.0762,0.9757,0.6798,0.0,0.0,0.1379,0.1667,0.0417,0.1037,0.0735,0.0794,0.0,0.0,0.0833,0.0734,0.9757,0.6713,0.0,0.0,0.1379,0.1667,0.0417,0.1031,0.0684,0.0775,0.0,0.0,reg oper account,block of flats,0.0599,Panel,No,1.0,0.0,1.0,0.0,-65.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n238234,0,Revolving loans,F,N,Y,0,67500.0,202500.0,10125.0,202500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.003069,-18963,-3045,-9138.0,-2475,,1,1,0,1,0,0,Cleaning staff,2.0,3,3,MONDAY,11,0,0,0,0,0,0,Bank,,0.2880321818156152,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1489.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n244338,0,Revolving loans,M,Y,Y,0,810000.0,765000.0,38250.0,765000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.072508,-13537,-2075,-1872.0,-2159,6.0,1,1,0,1,0,0,IT staff,2.0,1,1,SUNDAY,14,0,0,0,0,0,0,Business Entity Type 1,,0.7200057809824963,,0.1093,0.1926,0.9717,0.6124,0.2635,0.0,0.2069,0.1667,0.2083,0.0,0.0891,0.1346,0.0,0.1754,0.1113,0.1999,0.9717,0.6276,0.2659,0.0,0.2069,0.1667,0.2083,0.0,0.0973,0.1403,0.0,0.1857,0.1103,0.1926,0.9717,0.6176,0.2651,0.0,0.2069,0.1667,0.2083,0.0,0.0906,0.1371,0.0,0.1791,reg oper account,block of flats,0.14400000000000002,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-344.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n240069,0,Cash loans,F,N,Y,0,270000.0,225000.0,22252.5,225000.0,Unaccompanied,State servant,Higher education,Separated,House / apartment,0.006233,-13136,-989,-2540.0,-3835,,1,1,0,1,1,0,Accountants,1.0,2,2,TUESDAY,14,0,0,0,0,1,1,Security Ministries,0.7098286872411075,0.6273310754200647,0.5867400085415683,0.0433,0.0195,0.9757,0.6668,0.0035,0.0,0.0862,0.125,0.1667,0.017,0.0353,0.0331,0.0,0.0167,0.021,0.0202,0.9747,0.6668,0.0016,0.0,0.0345,0.125,0.1667,0.0073,0.0184,0.0169,0.0,0.0146,0.0437,0.0195,0.9757,0.6713,0.0036,0.0,0.0862,0.125,0.1667,0.0173,0.0359,0.0337,0.0,0.0171,,block of flats,0.017,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-467.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n201427,0,Cash loans,M,Y,Y,0,202500.0,490495.5,29335.5,454500.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.010643,-14550,-2497,-4770.0,-5035,64.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,14,0,1,1,0,0,0,Business Entity Type 2,0.5988276245464196,0.3858661563858976,0.7238369900414456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-505.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n228483,0,Cash loans,F,N,Y,0,99000.0,544491.0,15916.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-20194,365243,-2937.0,-3568,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.6583494717641379,0.838724852442103,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2094.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n127660,0,Cash loans,M,Y,Y,0,90000.0,675000.0,21906.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-20312,365243,-2661.0,-3557,7.0,1,0,0,1,1,0,,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,XNA,,0.7575004537855933,0.36896873825284665,0.0825,0.1492,0.9876,,,0.0,0.2759,0.1667,,,,0.1069,,0.0,0.084,0.1548,0.9876,,,0.0,0.2759,0.1667,,,,0.1114,,0.0,0.0833,0.1492,0.9876,,,0.0,0.2759,0.1667,,,,0.1088,,0.0,,block of flats,0.0841,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2037.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n106961,0,Cash loans,F,N,N,0,180000.0,1327500.0,42952.5,1327500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006233,-17170,-4220,-8590.0,-721,,1,1,0,1,1,0,Managers,2.0,2,2,THURSDAY,17,0,0,0,0,1,1,Services,0.5307321609088577,0.6700299223105279,0.1008037063175332,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1718.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n128635,0,Cash loans,M,Y,N,2,135000.0,485640.0,38133.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-17225,-977,-1914.0,-760,19.0,1,1,1,1,1,0,Drivers,4.0,2,2,SUNDAY,18,0,0,0,0,1,1,Business Entity Type 3,,0.5366081935308513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n325022,0,Cash loans,F,N,Y,0,67500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-18869,-2400,-11771.0,-2344,,1,1,1,1,0,0,Security staff,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Industry: type 13,,0.7287632087258094,,0.0082,,0.9712,,,0.0,0.0345,0.0417,,0.0453,,0.0101,,0.0,0.0084,,0.9712,,,0.0,0.0345,0.0417,,0.0463,,0.0105,,0.0,0.0083,,0.9712,,,0.0,0.0345,0.0417,,0.0461,,0.0103,,0.0,,block of flats,0.008,Block,No,0.0,0.0,0.0,0.0,-907.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n392600,0,Cash loans,F,N,N,0,135000.0,495000.0,23944.5,495000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.030755,-10196,-417,-2666.0,-2874,,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Kindergarten,,0.7811187555371918,0.8245949709919925,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1679.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n158163,0,Cash loans,F,N,Y,0,225000.0,301095.0,24268.5,279000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-18221,-7803,-5082.0,-1771,,1,1,0,1,1,0,Sales staff,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Trade: type 7,,0.5828418172331169,0.4014074137749511,0.0124,0.0,0.9518,0.3404,0.0005,0.0,0.0345,0.0417,0.0833,0.0234,0.0101,0.011000000000000001,0.0,0.0,0.0126,0.0,0.9518,0.3662,0.0005,0.0,0.0345,0.0417,0.0833,0.0239,0.011000000000000001,0.0115,0.0,0.0,0.0125,0.0,0.9518,0.3492,0.0005,0.0,0.0345,0.0417,0.0833,0.0238,0.0103,0.0112,0.0,0.0,reg oper account,terraced house,0.0087,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1764.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n188185,0,Cash loans,F,N,Y,0,90000.0,225000.0,13045.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-24287,365243,-381.0,-4498,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,,0.23366121727543,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n114936,1,Cash loans,M,N,Y,0,135000.0,314055.0,19107.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-8270,-605,-570.0,-573,,1,1,0,1,0,0,Drivers,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,Medicine,0.20912731253048966,0.4397340317529186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-364.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n344944,0,Cash loans,M,N,Y,2,99000.0,50940.0,5616.0,45000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-13584,-391,-2979.0,-4679,,1,1,1,1,0,0,Laborers,4.0,2,2,SATURDAY,9,0,0,0,0,0,0,Industry: type 9,,0.0343558406768591,0.838724852442103,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n223532,0,Revolving loans,F,N,Y,0,202500.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.00963,-17134,-752,-742.0,-697,,1,1,1,1,0,0,Managers,2.0,2,2,MONDAY,14,1,1,0,1,1,0,Business Entity Type 1,,0.3721766813819927,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-124.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n200889,0,Cash loans,F,N,Y,1,247500.0,1021995.0,36837.0,792000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-9929,-587,-211.0,-2346,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Industry: type 9,0.19516182278346328,0.7038869233411489,0.2692857999073816,0.2227,0.1155,0.9881,0.8368,0.1203,0.24,0.2069,0.3333,0.375,0.0608,0.1816,0.2248,0.0,0.0,0.2269,0.1198,0.9881,0.8432,0.1214,0.2417,0.2069,0.3333,0.375,0.0621,0.1983,0.2342,0.0,0.0,0.2248,0.1155,0.9881,0.8390000000000001,0.1211,0.24,0.2069,0.3333,0.375,0.0618,0.1847,0.2289,0.0,0.0,reg oper account,block of flats,0.2426,Block,No,2.0,0.0,2.0,0.0,-2034.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n265294,0,Revolving loans,M,Y,Y,1,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-17177,-3103,-9570.0,-730,11.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Industry: type 7,,0.7228841953927138,0.3441550073724169,0.133,0.1214,0.9821,0.7552,0.0207,0.12,0.1552,0.25,,0.021,,0.1125,,0.0233,0.10400000000000001,0.1057,0.9821,0.7648,0.0116,0.1208,0.1034,0.1667,,0.0207,,0.0964,,0.0,0.1343,0.1214,0.9821,0.7585,0.0209,0.12,0.1552,0.25,,0.0214,,0.1145,,0.0237,reg oper account,block of flats,0.0728,Block,No,0.0,0.0,0.0,0.0,-190.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n154852,0,Revolving loans,M,Y,Y,0,540000.0,585000.0,29250.0,585000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018209,-15482,-768,-3007.0,-1397,8.0,1,1,0,1,0,0,Managers,2.0,3,3,MONDAY,11,0,0,0,0,0,0,Industry: type 9,0.2698072760699702,0.5972652676111565,,0.267,0.0,0.995,,,0.28,0.2069,0.375,,0.0317,,0.2741,,0.2425,0.2721,0.0,0.995,,,0.282,0.2069,0.375,,0.0325,,0.2855,,0.2568,0.2696,0.0,0.995,,,0.28,0.2069,0.375,,0.0323,,0.27899999999999997,,0.2476,org spec account,block of flats,0.2156,Mixed,No,0.0,0.0,0.0,0.0,-590.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n197438,0,Cash loans,F,N,Y,0,72000.0,1006920.0,40063.5,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21272,365243,-4758.0,-4821,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.6717246819032971,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-140.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n309754,0,Cash loans,F,N,Y,0,67500.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Higher education,Married,With parents,0.030755,-10245,-1563,-5013.0,-2633,,1,1,0,1,0,1,Sales staff,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Self-employed,0.3669723780458906,0.6341962610756307,0.1556893746835917,0.0825,0.0813,0.9831,0.7688,0.0104,0.0,0.2069,0.1667,,0.0715,0.0672,0.0777,0.0,0.0,0.084,0.0844,0.9831,0.7779,0.0105,0.0,0.2069,0.1667,,0.0731,0.0735,0.0809,0.0,0.0,0.0833,0.0813,0.9831,0.7719,0.0104,0.0,0.2069,0.1667,,0.0727,0.0684,0.079,0.0,0.0,reg oper spec account,block of flats,0.0667,Panel,No,3.0,1.0,3.0,1.0,-1065.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n203430,0,Cash loans,F,N,Y,0,171000.0,675000.0,21906.0,675000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.028663,-14895,-5510,-716.0,-4488,,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Medicine,,0.7216779249974188,0.7121551551910698,0.0825,0.0,0.9742,,,0.0,0.1379,0.1667,,0.0697,,0.0398,,0.0931,0.084,0.0,0.9742,,,0.0,0.1379,0.1667,,0.0702,,0.0413,,0.0887,0.0833,0.0,0.9742,,,0.0,0.1379,0.1667,,0.0709,,0.0405,,0.0951,,block of flats,0.0494,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1953.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n386655,0,Cash loans,F,N,Y,1,117000.0,79632.0,6291.0,63000.0,Family,Working,Secondary / secondary special,Married,Rented apartment,0.018209,-10429,-1081,-3519.0,-3078,,1,1,1,1,1,0,Core staff,3.0,3,3,SUNDAY,7,0,0,0,0,0,0,Kindergarten,,0.07846906433431702,0.42765737003502935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-248.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n405655,0,Cash loans,F,Y,Y,2,193500.0,545040.0,39496.5,450000.0,Unaccompanied,Commercial associate,Lower secondary,Married,House / apartment,0.01885,-10880,-995,-4842.0,-3430,4.0,1,1,0,1,0,0,,4.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Business Entity Type 2,,0.4116376750778419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n148168,0,Cash loans,F,Y,N,0,139500.0,1097676.0,30186.0,958500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.00702,-15330,-4181,-1934.0,-3239,2.0,1,1,1,1,0,0,Managers,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Other,0.4318352788336888,0.4962407302032871,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-17.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n403942,1,Cash loans,F,N,N,2,171000.0,124438.5,6750.0,94500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-14622,-1160,-941.0,-5709,,1,1,1,1,1,0,Core staff,4.0,2,2,MONDAY,10,0,0,0,0,0,0,Realtor,0.5666384364789809,0.6197599176128432,0.1694287272664794,0.0247,0.0255,0.9702,0.5920000000000001,0.0528,0.0,0.069,0.0833,0.0417,0.0429,0.0202,0.0291,0.0,0.0,0.0252,0.0265,0.9702,0.608,0.0533,0.0,0.069,0.0833,0.0417,0.0439,0.022000000000000002,0.0304,0.0,0.0,0.025,0.0255,0.9702,0.5975,0.0531,0.0,0.069,0.0833,0.0417,0.0436,0.0205,0.0297,0.0,0.0,reg oper account,block of flats,0.0518,Block,No,0.0,0.0,0.0,0.0,-1838.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n430884,0,Cash loans,M,N,Y,1,180000.0,343800.0,13090.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-18520,-586,-436.0,-1900,,1,1,0,1,0,1,Core staff,3.0,1,1,SATURDAY,12,1,1,0,0,0,0,Medicine,0.8357052633425177,0.7011632380434533,,0.0082,0.0,0.9707,,,,0.069,0.0417,,,,0.0078,,,0.0084,0.0,0.9707,,,,0.069,0.0417,,,,0.0082,,,0.0083,0.0,0.9707,,,,0.069,0.0417,,,,0.008,,,,terraced house,0.0062,Mixed,No,0.0,0.0,0.0,0.0,-497.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n340225,0,Cash loans,M,N,Y,0,157500.0,835605.0,24561.0,697500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-21365,365243,-5808.0,-4057,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,0.5257427937706008,0.1915258625230554,0.5602843280409464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1628.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n212856,0,Cash loans,F,N,N,1,112500.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.019101,-13676,-2678,-7494.0,-4006,,1,1,1,1,1,0,Accountants,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Self-employed,0.8906835480867438,0.746618808916669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1709.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n169916,0,Cash loans,F,Y,Y,0,81000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.014519999999999996,-17356,-2597,-5767.0,-881,21.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Other,,0.6585216010437263,0.6690566947824041,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2501.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n102521,1,Cash loans,F,N,Y,0,112500.0,296280.0,23539.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.006852,-13675,-415,-724.0,-4477,,1,1,0,1,0,0,Cooking staff,1.0,3,3,MONDAY,13,0,0,0,0,0,0,Self-employed,0.3592494679682263,0.12170243627609265,0.4294236843421945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1510.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n435677,0,Cash loans,F,N,Y,0,90000.0,194076.0,11857.5,162000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010966,-16002,-9406,-409.0,-4876,,1,1,0,1,0,0,Cooking staff,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Other,,0.5733538073665714,0.4489622731076524,0.2557,0.1815,0.9881,0.8368,0.0523,0.28,0.2414,0.3333,0.0417,0.125,0.2085,0.2544,0.0,0.0,0.2605,0.1884,0.9881,0.8432,0.0528,0.282,0.2414,0.3333,0.0417,0.1279,0.2277,0.2651,0.0,0.0,0.2581,0.1815,0.9881,0.8390000000000001,0.0526,0.28,0.2414,0.3333,0.0417,0.1272,0.2121,0.259,0.0,0.0,org spec account,block of flats,0.2287,Panel,No,0.0,0.0,0.0,0.0,-1866.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n335897,0,Revolving loans,F,Y,N,0,135000.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-9391,-2415,-9391.0,-1626,8.0,1,1,0,1,0,0,Realty agents,2.0,2,2,THURSDAY,15,0,0,0,0,1,1,Self-employed,0.3119271589083389,0.524593983966003,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1613.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n204377,0,Cash loans,F,N,N,1,157500.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-12621,-318,-6539.0,-3154,,1,1,1,1,0,0,Drivers,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Industry: type 5,0.5216879934047229,0.7047112302367688,0.656158373001177,,,0.9796,,,,0.1034,0.0417,,,,0.0164,,,,,0.9796,,,,0.1034,0.0417,,,,0.0171,,,,,0.9796,,,,0.1034,0.0417,,,,0.0167,,,,,0.0129,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-488.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n149114,0,Cash loans,F,N,Y,0,150750.0,284256.0,33862.5,270000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.035792000000000004,-19123,-780,-7540.0,-2677,,1,1,0,1,1,0,Waiters/barmen staff,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Government,0.7685737936017699,0.4659576041195576,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n421337,0,Cash loans,M,Y,N,0,67500.0,239850.0,23850.0,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.006296,-24941,365243,-2974.0,-4206,11.0,1,0,0,1,0,0,,2.0,3,3,MONDAY,11,0,0,0,0,0,0,XNA,,0.6199969390524761,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-516.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n289560,0,Cash loans,F,Y,N,1,90000.0,328500.0,16816.5,328500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-10598,-190,-4953.0,-1327,12.0,1,1,0,1,0,0,Drivers,3.0,3,3,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.3361638546556308,0.3294997788734827,0.5478104658520093,0.0928,0.0998,0.9781,0.7008,0.053,0.0,0.2069,0.1667,0.0417,0.054000000000000006,0.0756,0.0875,0.0,0.0,0.0945,0.1036,0.9782,0.7125,0.0535,0.0,0.2069,0.1667,0.0417,0.0553,0.0826,0.0911,0.0,0.0,0.0937,0.0998,0.9781,0.7048,0.0534,0.0,0.2069,0.1667,0.0417,0.055,0.077,0.08900000000000001,0.0,0.0,reg oper spec account,block of flats,0.0978,Panel,No,2.0,0.0,1.0,0.0,-1451.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n309938,0,Revolving loans,M,Y,Y,2,225000.0,270000.0,13500.0,270000.0,Other_A,Working,Secondary / secondary special,Married,House / apartment,0.031329,-14220,-5903,-4736.0,-4736,17.0,1,1,0,1,0,0,Laborers,4.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 2,,0.2622583692422573,0.4920600938649263,0.0722,,0.9771,,,0.0,0.1379,0.1667,,,,0.0638,,0.0208,0.0735,,0.9772,,,0.0,0.1379,0.1667,,,,0.0665,,0.022000000000000002,0.0729,,0.9771,,,0.0,0.1379,0.1667,,,,0.065,,0.0212,,block of flats,0.0598,Block,No,1.0,1.0,1.0,0.0,-607.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n223647,0,Cash loans,M,Y,Y,0,135000.0,135000.0,9396.0,135000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.00702,-11198,-1070,-3188.0,-3816,14.0,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,10,0,1,1,0,1,1,Government,,0.0558749296477371,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n435250,0,Cash loans,F,Y,N,0,112500.0,364896.0,16069.5,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Rented apartment,0.035792000000000004,-23028,365243,-2826.0,-4529,9.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,19,0,0,0,0,0,0,XNA,,0.7197720736212139,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-139.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n183995,0,Cash loans,F,N,Y,0,90000.0,454500.0,20151.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-21518,365243,-1710.0,-4466,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,XNA,,0.6354737318890222,0.5971924268337128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-1336.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n228238,0,Cash loans,M,Y,Y,1,157500.0,1024290.0,30078.0,855000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-13937,-4651,-5005.0,-4112,12.0,1,1,0,1,1,0,,3.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.5128298705560256,0.5902333386185574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2105.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n178704,0,Cash loans,F,N,Y,0,225000.0,760225.5,32337.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.00733,-17692,-2224,-6346.0,-1251,,1,1,0,1,0,0,Managers,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,,0.6491777563263073,0.6785676886853644,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n117674,1,Cash loans,M,Y,Y,1,112500.0,225000.0,18040.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-11802,-3531,-5572.0,-4376,25.0,1,1,1,1,0,0,,3.0,2,2,FRIDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.19076177992691,0.7700870700124128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2516.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n140398,0,Cash loans,F,N,Y,0,247500.0,1724220.0,47542.5,1350000.0,Family,Working,Higher education,Married,House / apartment,0.032561,-21611,-981,-10451.0,-4361,,1,1,0,1,0,0,Core staff,2.0,1,1,THURSDAY,20,0,0,0,0,1,1,School,0.6117304671574373,0.775592188103472,0.4561097392782771,0.1144,0.1298,0.9851,0.7959999999999999,0.0105,0.0,0.2414,0.125,0.1667,0.0882,0.0933,0.1258,0.0,0.0013,0.1166,0.1347,0.9851,0.804,0.0106,0.0,0.2414,0.125,0.1667,0.0902,0.1019,0.1311,0.0,0.0013,0.1155,0.1298,0.9851,0.7987,0.0106,0.0,0.2414,0.125,0.1667,0.0897,0.0949,0.1281,0.0,0.0013,reg oper account,block of flats,0.105,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n245107,0,Cash loans,M,Y,N,0,112500.0,288873.0,15376.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-15308,-272,-9407.0,-4269,5.0,1,1,0,1,0,0,Laborers,2.0,3,2,SATURDAY,13,0,0,0,0,0,0,Construction,0.2574368497538149,0.5623266439088529,0.7421816117614419,0.1577,0.1062,0.9841,0.7824,0.0369,0.2,0.1724,0.3333,0.375,0.0379,0.1252,0.1712,0.0154,0.0136,0.1607,0.1102,0.9841,0.7909,0.0373,0.2014,0.1724,0.3333,0.375,0.0388,0.1368,0.1784,0.0156,0.0144,0.1593,0.1062,0.9841,0.7853,0.0372,0.2,0.1724,0.3333,0.375,0.0386,0.1274,0.1743,0.0155,0.0139,reg oper account,block of flats,0.1578,Panel,No,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n396836,1,Cash loans,F,N,Y,0,112500.0,665892.0,25933.5,477000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-13011,-947,-6192.0,-4311,,1,1,0,1,1,0,Cooking staff,2.0,2,2,SATURDAY,8,0,0,0,1,1,0,Business Entity Type 3,,0.2777281390176555,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n178727,0,Cash loans,F,N,Y,0,112500.0,462694.5,31050.0,418500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.002134,-17576,-951,-5163.0,-1084,,1,1,0,1,0,0,Sales staff,1.0,3,3,TUESDAY,6,0,0,0,0,0,0,Self-employed,,0.4241002636726356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1078.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n179815,0,Cash loans,F,N,Y,0,157500.0,814041.0,23800.5,679500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.0105,-22482,365243,-1954.0,-5067,,1,0,0,1,1,0,,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,XNA,,0.6190330399599243,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1777.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n443137,0,Cash loans,F,Y,Y,0,225000.0,292500.0,14202.0,292500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-19586,-1040,-10279.0,-3132,21.0,1,1,0,1,0,0,Cleaning staff,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.7082969271658706,0.22262665388869526,0.4382813743111921,0.0794,0.0,0.9742,,,0.0,0.1379,0.1667,,0.0399,,0.0572,,0.0204,0.0809,0.0,0.9742,,,0.0,0.1379,0.1667,,0.0409,,0.0596,,0.0216,0.0802,0.0,0.9742,,,0.0,0.1379,0.1667,,0.0406,,0.0582,,0.0208,,block of flats,0.0532,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-437.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n185346,0,Cash loans,F,N,Y,0,157500.0,229500.0,19827.0,229500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.015221,-20112,365243,-3643.0,-3639,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.596628642127949,0.7121551551910698,0.0928,0.0802,0.9826,0.762,0.1633,0.0,0.2069,0.1667,0.2083,0.0611,0.0756,0.0876,0.0,0.0,0.0945,0.0832,0.9826,0.7713,0.1647,0.0,0.2069,0.1667,0.2083,0.0625,0.0826,0.0913,0.0,0.0,0.0937,0.0802,0.9826,0.7652,0.1643,0.0,0.2069,0.1667,0.2083,0.0622,0.077,0.0892,0.0,0.0,,block of flats,0.0893,Panel,No,2.0,0.0,2.0,0.0,-352.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n245267,0,Cash loans,M,N,N,0,180000.0,348264.0,32355.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-19343,-2713,-8927.0,-2837,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Government,,0.5771206807164158,0.6817058776720116,0.0732,0.0814,0.9776,0.6940000000000001,0.0594,0.0,0.1379,0.1667,0.0417,0.0752,0.0597,0.0469,0.0,0.0811,0.0746,0.0844,0.9777,0.706,0.0599,0.0,0.1379,0.1667,0.0417,0.0769,0.0652,0.0489,0.0,0.0858,0.0739,0.0814,0.9776,0.6981,0.0597,0.0,0.1379,0.1667,0.0417,0.0765,0.0607,0.0478,0.0,0.0828,not specified,block of flats,0.0546,Panel,No,0.0,0.0,0.0,0.0,-14.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n289070,0,Cash loans,M,N,Y,0,103500.0,781920.0,25969.5,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.01885,-20137,-8126,-1497.0,-3217,,1,1,1,1,1,0,Laborers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Other,0.7261889211697705,0.5851846465587699,0.33285056416487313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-2085.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n320957,0,Cash loans,F,Y,Y,0,137250.0,852088.5,33921.0,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-14754,-2063,-2878.0,-4140,0.0,1,1,0,1,1,0,Laborers,2.0,1,1,FRIDAY,18,0,0,0,0,1,1,Business Entity Type 3,0.5715611739640211,0.5615837958538897,0.5172965813614878,0.1474,,0.9856,,,0.16,0.1379,0.3333,,,,,,0.0,0.1502,,0.9856,,,0.1611,0.1379,0.3333,,,,,,0.0,0.1489,,0.9856,,,0.16,0.1379,0.3333,,,,,,0.0,,block of flats,0.1236,Panel,No,0.0,0.0,0.0,0.0,-77.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n191135,0,Cash loans,M,N,N,2,225000.0,711454.5,56340.0,643500.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.019101,-13969,-263,-116.0,-2735,,1,1,0,1,0,0,,4.0,2,2,TUESDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.5759140983699714,0.6212263380626669,0.0124,,0.9727,,0.0,0.0,0.0345,0.0833,0.0417,,0.0101,0.0112,0.0,0.0,0.0126,,0.9727,,0.0,0.0,0.0345,0.0833,0.0417,,0.011000000000000001,0.0116,0.0,0.0,0.0125,,0.9727,,0.0,0.0,0.0345,0.0833,0.0417,,0.0103,0.0114,0.0,0.0,,,0.0088,,No,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n152665,0,Cash loans,F,N,N,0,135000.0,450000.0,30073.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-13050,-1236,-3879.0,-4966,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Trade: type 7,0.6206047170777076,0.5643975951807224,0.6738300778602003,0.0165,0.0,0.9776,0.6940000000000001,0.0017,0.0,0.069,0.0417,0.0833,0.0404,0.0134,0.0143,0.0,0.0,0.0168,0.0,0.9777,0.706,0.0017,0.0,0.069,0.0417,0.0833,0.0413,0.0147,0.0149,0.0,0.0,0.0167,0.0,0.9776,0.6981,0.0017,0.0,0.069,0.0417,0.0833,0.0411,0.0137,0.0146,0.0,0.0,reg oper account,block of flats,0.0122,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1099.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n352470,0,Cash loans,M,Y,Y,1,225000.0,781920.0,28084.5,675000.0,Family,Working,Secondary / secondary special,Civil marriage,With parents,0.025164,-14381,-3418,-1833.0,-4011,12.0,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3671161314318481,0.6536071140282623,0.3031463744186309,0.2474,0.1112,0.9752,0.66,0.0758,0.0,0.4138,0.1667,0.2083,0.2908,0.2017,0.207,0.0,0.063,0.2521,0.1154,0.9752,0.6733,0.0765,0.0,0.4138,0.1667,0.2083,0.2975,0.2204,0.2157,0.0,0.0667,0.2498,0.1112,0.9752,0.6645,0.0763,0.0,0.4138,0.1667,0.2083,0.2959,0.2052,0.2107,0.0,0.0643,reg oper account,block of flats,0.2073,Panel,No,0.0,0.0,0.0,0.0,-1557.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n204858,0,Cash loans,F,N,N,0,225000.0,828000.0,35082.0,828000.0,Unaccompanied,Working,Higher education,Single / not married,Rented apartment,0.031329,-13791,-1619,-11685.0,-2427,,1,1,1,1,0,0,Medicine staff,1.0,2,2,SUNDAY,17,0,0,0,1,1,0,Medicine,0.6506218378816087,0.7087946267035699,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,4.0,1.0,4.0,1.0,-1688.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n334677,0,Cash loans,F,N,N,0,180000.0,454500.0,21865.5,454500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019689,-16300,-2488,-884.0,-4531,,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Business Entity Type 2,,0.5474859313982899,,0.0186,0.0,0.9712,,,0.0,0.069,0.0833,,0.0379,,0.0176,,0.0,0.0189,0.0,0.9712,,,0.0,0.069,0.0833,,0.0387,,0.0183,,0.0,0.0187,0.0,0.9712,,,0.0,0.069,0.0833,,0.0385,,0.0179,,0.0,,block of flats,0.0151,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-993.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n322208,0,Cash loans,F,N,Y,1,279000.0,635962.5,32602.5,549000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.002042,-9093,-221,-86.0,-1696,,1,1,0,1,0,1,,3.0,3,3,FRIDAY,12,0,0,0,0,0,0,Self-employed,,0.5184552021108099,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-784.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n381848,0,Cash loans,F,Y,N,0,157500.0,540000.0,20565.0,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.022625,-17545,-588,-6688.0,-1090,8.0,1,1,1,1,0,0,Core staff,1.0,2,2,SATURDAY,17,0,0,0,0,0,0,Insurance,0.620667154164569,0.7771904602123315,0.501075160239048,0.16699999999999998,,0.9767,,,0.0,0.069,0.1667,,0.0826,,0.0531,,0.0875,0.1702,,0.9767,,,0.0,0.069,0.1667,,0.0845,,0.0553,,0.0927,0.1686,,0.9767,,,0.0,0.069,0.1667,,0.084,,0.0541,,0.0894,,specific housing,0.0629,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-3256.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n111564,0,Cash loans,F,N,Y,1,90000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-16404,-2134,-6453.0,-4227,,1,1,1,1,0,0,Private service staff,3.0,2,2,TUESDAY,12,0,0,0,0,1,1,Self-employed,0.6620180507079017,0.5158778747738668,0.6722428897082422,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1595.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n400443,0,Cash loans,M,Y,Y,0,202500.0,291915.0,17770.5,252000.0,Unaccompanied,Working,Lower secondary,Single / not married,House / apartment,0.025164,-8843,-1277,-601.0,-1515,7.0,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,Electricity,,0.4746245065723085,0.5832379256761245,0.134,0.0918,0.9801,0.728,0.0825,0.0,0.2759,0.1667,0.2083,0.1466,0.1084,0.11699999999999999,0.0039,0.0342,0.1366,0.0953,0.9801,0.7387,0.0833,0.0,0.2759,0.1667,0.2083,0.15,0.1185,0.1219,0.0039,0.0363,0.1353,0.0918,0.9801,0.7316,0.0831,0.0,0.2759,0.1667,0.2083,0.1492,0.1103,0.1191,0.0039,0.035,reg oper account,block of flats,0.1424,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-70.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n351315,0,Cash loans,F,N,N,0,67500.0,52128.0,5472.0,45000.0,Unaccompanied,State servant,Higher education,Single / not married,With parents,0.00496,-9686,-598,-4387.0,-2347,,1,1,0,1,0,0,Core staff,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Medicine,0.4760109283939015,0.5533895496062319,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-444.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n329886,0,Cash loans,F,N,Y,0,135000.0,730017.0,37404.0,652500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00496,-11840,-1789,-1024.0,-4193,,1,1,0,1,1,0,Cooking staff,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.317876944424201,0.6826944094269752,0.5797274227921155,0.0031,,,,,,,,,,,,,,0.0032,,,,,,,,,,,,,,0.0031,,,,,,,,,,,,,,,block of flats,0.0016,,No,0.0,0.0,0.0,0.0,-265.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n412086,0,Cash loans,M,Y,Y,0,270000.0,1762110.0,48586.5,1575000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-17813,-2993,-4710.0,-1362,6.0,1,1,0,1,0,0,Managers,2.0,1,1,MONDAY,19,0,0,0,0,0,0,Business Entity Type 1,0.7478191834618562,0.6443969021203142,0.35233997269170386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,1.0,-1568.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n407172,0,Cash loans,M,N,Y,0,157500.0,291384.0,26856.0,270000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.022625,-9084,-680,-3574.0,-1677,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Industry: type 9,,0.7863777444210334,0.4740512892789932,0.0691,0.0,0.9752,0.66,0.008,0.0,0.1379,0.1667,0.2083,0.0842,0.0538,0.0562,0.0116,0.0475,0.0704,0.0,0.9752,0.6733,0.0081,0.0,0.1379,0.1667,0.2083,0.0861,0.0588,0.0585,0.0117,0.0503,0.0697,0.0,0.9752,0.6645,0.0081,0.0,0.1379,0.1667,0.2083,0.0857,0.0547,0.0572,0.0116,0.0485,reg oper account,block of flats,0.0556,Panel,No,0.0,0.0,0.0,0.0,-1100.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n215055,0,Cash loans,F,N,Y,0,121500.0,573628.5,25398.0,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.019689,-21276,-1714,-12576.0,-4644,,1,1,0,1,1,0,Cleaning staff,1.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5964760140188905,0.6347055309763198,0.1021,0.0984,0.9781,0.7008,0.055999999999999994,0.0,0.2759,0.1667,0.0417,0.1151,0.0101,0.1083,0.3359,0.0676,0.10400000000000001,0.1022,0.9782,0.7125,0.0565,0.0,0.2759,0.1667,0.0417,0.1177,0.011000000000000001,0.1128,0.3385,0.0716,0.1031,0.0984,0.9781,0.7048,0.0564,0.0,0.2759,0.1667,0.0417,0.1171,0.0103,0.1102,0.3377,0.0691,reg oper account,block of flats,0.1158,Panel,No,2.0,0.0,2.0,0.0,-2326.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,4.0\n193059,0,Cash loans,F,N,N,0,202500.0,780363.0,33192.0,697500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.028663,-20064,-4462,-11716.0,-2631,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,Trade: type 7,,0.4209895585943468,0.520897599048938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-357.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n172326,0,Cash loans,F,N,N,0,157500.0,239850.0,23364.0,225000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.010276,-24734,365243,-7627.0,-3991,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.6392240490726923,0.5442347412142162,0.0412,0.0,0.9747,0.6532,0.0034,0.0,0.069,0.1667,0.2083,0.0151,0.0336,0.0315,0.0,0.0,0.042,0.0,0.9747,0.6668,0.0035,0.0,0.069,0.1667,0.2083,0.0155,0.0367,0.0328,0.0,0.0,0.0416,0.0,0.9747,0.6578,0.0035,0.0,0.069,0.1667,0.2083,0.0154,0.0342,0.0321,0.0,0.0,reg oper spec account,block of flats,0.0267,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1526.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n206161,0,Cash loans,F,N,Y,0,180000.0,599472.0,36805.5,517500.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.007273999999999998,-15320,-992,-9415.0,-4194,,1,1,0,1,0,0,,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Bank,,0.7095342981916021,0.6738300778602003,0.0711,0.0,0.9767,0.6804,0.0106,0.0,0.1379,0.1667,0.0417,,0.057999999999999996,0.0616,0.0,0.0,0.0725,0.0,0.9767,0.6929,0.0107,0.0,0.1379,0.1667,0.0417,,0.0634,0.0642,0.0,0.0,0.0718,0.0,0.9767,0.6847,0.0107,0.0,0.1379,0.1667,0.0417,,0.059000000000000004,0.0627,0.0,0.0,reg oper account,block of flats,0.0543,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-491.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n178341,0,Revolving loans,M,Y,Y,1,216000.0,270000.0,13500.0,270000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-11868,-1885,-5816.0,-3725,7.0,1,1,0,1,0,0,,3.0,2,2,FRIDAY,17,0,0,0,0,0,0,Industry: type 3,,0.6938644680070357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-627.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n146854,0,Cash loans,F,N,Y,0,157500.0,225000.0,17541.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.026392,-12486,-4384,-1430.0,-3741,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.4068948022330205,0.3944718322188997,0.5531646987710016,0.16699999999999998,,0.998,,,0.12,0.1034,0.375,,,,0.1334,,0.0299,0.1702,,0.998,,,0.1208,0.1034,0.375,,,,0.139,,0.0317,0.1686,,0.998,,,0.12,0.1034,0.375,,,,0.1358,,0.0305,,block of flats,0.1114,\"Stone, brick\",No,3.0,2.0,3.0,2.0,-1568.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n321331,1,Cash loans,F,N,Y,0,157500.0,260640.0,28197.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009549,-10849,-3321,-2860.0,-3257,,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,13,0,0,0,0,1,1,Government,,0.4954262195033154,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-459.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n354645,0,Cash loans,F,N,Y,2,81000.0,497520.0,32521.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006296,-14365,-2305,-563.0,-1561,,1,1,0,1,0,0,Laborers,4.0,3,3,SATURDAY,14,0,0,0,0,1,1,Advertising,,0.2208492516927808,0.4561097392782771,0.0124,,0.9846,,,0.0,0.069,0.1667,,,,0.0177,,0.0376,0.0126,,0.9846,,,0.0,0.069,0.1667,,,,0.0184,,0.0398,0.0125,,0.9846,,,0.0,0.069,0.1667,,,,0.018000000000000002,,0.0384,,block of flats,0.0221,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,4.0,0.0\n407271,0,Cash loans,F,Y,Y,0,292500.0,441000.0,32220.0,441000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-10342,-2771,-4983.0,-3008,3.0,1,1,0,1,0,0,Medicine staff,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Medicine,,0.6228572529811236,0.6041125998015721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-327.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n351518,0,Cash loans,M,Y,N,1,112500.0,238500.0,18585.0,238500.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.019689,-18679,-2685,-9086.0,-2220,6.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,12,0,0,0,1,1,0,Construction,,0.5134581290024823,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2394.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n132394,0,Cash loans,F,N,Y,1,54000.0,258709.5,20569.5,234000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.015221,-8556,-1108,-478.0,-624,,1,1,1,1,0,0,Medicine staff,3.0,2,2,FRIDAY,7,0,0,0,0,1,1,Medicine,0.13111349477723594,0.09746074791660232,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-465.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n230011,0,Cash loans,F,Y,Y,0,450000.0,1078200.0,31522.5,900000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.014464,-17825,-6951,-7368.0,-1362,12.0,1,1,0,1,0,0,,2.0,2,2,SUNDAY,6,0,0,0,0,0,0,Government,,0.6110131898302954,0.5762088360175724,,,0.9712,,,,0.069,0.0833,,,,,,,,,0.9712,,,,0.069,0.0833,,,,,,,,,0.9712,,,,0.069,0.0833,,,,,,,,block of flats,0.0187,,No,4.0,1.0,4.0,1.0,-790.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n170142,0,Cash loans,F,N,N,1,103500.0,691020.0,18355.5,495000.0,,Working,Secondary / secondary special,Married,House / apartment,0.009657,-14555,-344,-5481.0,-1406,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,13,0,0,0,0,1,1,Other,,0.3861304738787184,0.5478104658520093,0.0247,0.0374,0.9767,0.6804,0.0,0.0,0.069,0.0417,0.0833,0.0085,0.0202,0.0178,0.0,0.0053,0.0252,0.0388,0.9767,0.6929,0.0,0.0,0.069,0.0417,0.0833,0.0087,0.022000000000000002,0.0185,0.0,0.0056,0.025,0.0374,0.9767,0.6847,0.0,0.0,0.069,0.0417,0.0833,0.0087,0.0205,0.0181,0.0,0.0054,reg oper account,block of flats,0.0151,\"Stone, brick\",No,4.0,1.0,4.0,1.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n100025,0,Cash loans,F,Y,Y,1,202500.0,1132573.5,37561.5,927000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-14815,-1652,-2299.0,-2299,14.0,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Trade: type 7,0.43770901984754174,0.2337669575081463,0.5424451438613613,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n112637,0,Cash loans,M,Y,Y,1,202500.0,582768.0,35784.0,540000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.011703,-8705,-189,-8295.0,-1388,2.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,FRIDAY,17,0,0,0,1,1,0,Business Entity Type 3,,0.6422041094810387,0.1986200451074864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-226.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n185374,0,Cash loans,F,Y,Y,0,54000.0,450000.0,16294.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-23447,365243,-4471.0,-4001,17.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,,0.813917469762627,0.099,,0.0,,,0.0,,0.1667,,,,,,,0.1008,,0.0005,,,0.0,,0.1667,,,,,,,0.0999,,0.0,,,0.0,,0.1667,,,,,,,,block of flats,0.0694,\"Stone, brick\",No,5.0,2.0,5.0,1.0,-2885.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n134395,0,Cash loans,F,N,N,0,90000.0,159993.0,9063.0,121500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-20108,-4526,-12592.0,-3641,,1,1,0,1,0,0,Medicine staff,2.0,2,2,TUESDAY,14,0,0,0,0,1,1,Medicine,,0.3218307986912857,0.6594055320683344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-3094.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n192003,0,Revolving loans,F,Y,Y,2,225000.0,585000.0,29250.0,585000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.020246,-16480,-6026,-3812.0,-33,15.0,1,1,0,1,0,0,Laborers,3.0,3,3,SATURDAY,10,0,0,0,0,0,0,Transport: type 4,0.5570488130634514,0.4767570068147187,0.5919766183185521,0.1495,0.1186,0.9727,0.626,0.0144,,0.2759,0.1667,0.2083,0.1798,0.1135,0.1712,0.0386,0.0737,0.1523,0.1231,0.9727,0.6406,0.0146,,0.2759,0.1667,0.2083,0.1839,0.124,0.1784,0.0389,0.078,0.1509,0.1186,0.9727,0.631,0.0145,,0.2759,0.1667,0.2083,0.1829,0.1154,0.1743,0.0388,0.0752,reg oper account,block of flats,0.1507,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-692.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n135474,0,Revolving loans,M,Y,Y,0,169771.5,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-11278,-2104,-2302.0,-3731,14.0,1,1,1,1,1,0,Managers,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Trade: type 3,0.15777558069210496,0.5453505420379522,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-384.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,,,,,,\n386359,0,Cash loans,F,N,N,0,112500.0,1256400.0,36733.5,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20748,365243,-9844.0,-3899,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.7005351276459034,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n137995,0,Cash loans,F,N,N,0,112500.0,247500.0,12703.5,247500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-20972,365243,-7434.0,-4496,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.7474783166089,0.12610055883623253,0.4082,,0.9866,,,0.44,0.3793,0.3333,,,,0.4127,,0.06,0.41600000000000004,,0.9866,,,0.4431,0.3793,0.3333,,,,0.43,,0.0635,0.4122,,0.9866,,,0.44,0.3793,0.3333,,,,0.4202,,0.0612,,block of flats,0.3704,Panel,No,2.0,0.0,2.0,0.0,-1310.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n347960,0,Cash loans,M,N,Y,1,315000.0,485640.0,41674.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-14867,-178,-447.0,-1546,,1,1,0,1,0,0,Managers,3.0,3,3,MONDAY,9,0,0,0,0,1,1,Construction,0.3668738179707911,0.3421853246731103,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-629.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n387218,0,Cash loans,M,N,Y,0,157500.0,1236816.0,36292.5,1080000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,Municipal apartment,0.011703,-9949,-3183,-837.0,-2527,,1,1,0,1,0,0,,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Self-employed,,0.6967922403787596,0.7209441499436497,0.1278,0.0684,0.9906,,,0.08,0.069,0.3333,,0.2152,,0.1036,,0.0,0.1303,0.071,0.9906,,,0.0806,0.069,0.3333,,0.2201,,0.10800000000000001,,0.0,0.1291,0.0684,0.9906,,,0.08,0.069,0.3333,,0.2189,,0.1055,,0.0,,block of flats,0.1113,Panel,No,1.0,0.0,1.0,0.0,-701.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n437064,0,Cash loans,F,N,Y,0,81000.0,265851.0,11839.5,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-24428,365243,-14469.0,-5091,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,,0.7765460562751698,0.8182475079127519,0.1124,,0.9801,,,0.0,0.2759,0.1667,,,,0.0784,,0.0,0.1145,,0.9801,,,0.0,0.2759,0.1667,,,,0.0817,,0.0,0.1135,,0.9801,,,0.0,0.2759,0.1667,,,,0.0798,,0.0,,,0.0914,Panel,No,2.0,0.0,2.0,0.0,-240.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n188444,0,Cash loans,M,N,Y,0,135000.0,450000.0,35685.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-8340,-352,-7405.0,-1003,,1,1,1,1,1,0,,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Trade: type 7,0.22712692763388115,0.497137261524917,0.2340151665320674,0.1227,0.1213,0.9786,,,0.0,0.2759,0.1667,,0.1733,,0.1036,,0.0374,0.125,0.1258,0.9786,,,0.0,0.2759,0.1667,,0.1773,,0.1079,,0.0396,0.1239,0.1213,0.9786,,,0.0,0.2759,0.1667,,0.1763,,0.1054,,0.0382,,block of flats,0.0896,Panel,No,3.0,0.0,3.0,0.0,-504.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n439042,0,Cash loans,F,N,N,0,117000.0,900000.0,32458.5,900000.0,Family,Working,Higher education,Married,House / apartment,0.026392,-16471,-1333,-10527.0,-16,,1,1,1,1,1,0,Core staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Kindergarten,0.7342975849147673,0.6191413322309025,0.2721336844147212,0.133,0.1519,0.9801,,,0.0,0.2759,0.1667,,0.0605,,0.1199,,0.0174,0.1355,0.1577,0.9801,,,0.0,0.2759,0.1667,,0.0618,,0.1249,,0.0185,0.1343,0.1519,0.9801,,,0.0,0.2759,0.1667,,0.0615,,0.122,,0.0178,,block of flats,0.0981,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-980.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n242729,1,Cash loans,M,Y,Y,0,135000.0,711612.0,27702.0,594000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.00702,-8048,-144,-3718.0,-699,9.0,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,16,0,0,0,1,1,0,Industry: type 1,0.03264087614488597,0.2707386464645821,0.2955826421513093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,0.0,-262.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n258584,0,Cash loans,F,Y,N,0,225000.0,1257430.5,36895.5,1098000.0,Family,Working,Higher education,Married,House / apartment,0.010276,-16490,-2376,-4229.0,-28,21.0,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,,0.5286661479497122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-544.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n168836,0,Cash loans,M,N,Y,0,143248.5,450000.0,19318.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-9917,-1034,-4506.0,-2550,,1,1,1,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Business Entity Type 3,0.22090860071407573,0.5734714862917271,0.4596904504249018,0.0619,0.0653,0.9871,0.8232,0.0296,0.0,0.1379,0.1667,0.2083,0.0475,0.0504,0.0333,0.0,0.0,0.063,0.0678,0.9871,0.8301,0.0298,0.0,0.1379,0.1667,0.2083,0.0486,0.0551,0.0347,0.0,0.0,0.0625,0.0653,0.9871,0.8256,0.0298,0.0,0.1379,0.1667,0.2083,0.0483,0.0513,0.0339,0.0,0.0,reg oper account,block of flats,0.0441,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1729.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,1.0\n210808,0,Cash loans,F,N,Y,1,112500.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-14448,-152,-6991.0,-227,,1,1,0,1,0,0,Sales staff,3.0,1,1,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.36466122091384745,0.772617220217783,0.2418614865234661,0.0412,0.0515,0.9578,0.42200000000000004,0.0023,0.0,0.1379,0.1667,0.2083,0.0537,0.0328,0.0486,0.0039,0.0056,0.042,0.0535,0.9578,0.4446,0.0023,0.0,0.1379,0.1667,0.2083,0.0549,0.0358,0.0506,0.0039,0.006,0.0416,0.0515,0.9578,0.4297,0.0023,0.0,0.1379,0.1667,0.2083,0.0546,0.0333,0.0495,0.0039,0.0057,reg oper account,block of flats,0.0508,\"Stone, brick\",No,3.0,1.0,3.0,0.0,-341.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n255126,0,Cash loans,F,N,N,0,157500.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.02461,-10924,-408,-1148.0,-3508,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,14,0,0,0,1,1,0,Military,0.21945459197358708,0.2502405780387156,0.1595195404777181,0.0278,0.0554,0.9861,,,0.0,0.1034,0.0833,,0.0,,0.0275,,0.0,0.0284,0.0575,0.9861,,,0.0,0.1034,0.0833,,0.0,,0.0287,,0.0,0.0281,0.0554,0.9861,,,0.0,0.1034,0.0833,,0.0,,0.027999999999999997,,0.0,,block of flats,0.0236,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n240571,0,Cash loans,F,N,Y,0,85500.0,108072.0,7164.0,85500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-11832,-2922,-5897.0,-3560,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,15,0,0,0,0,1,1,School,0.4626702282201349,0.5717643341309147,0.7091891096653581,0.055,,0.9866,,,0.0,0.1379,0.1667,,,,0.0521,,,0.0315,,0.9806,,,0.0,0.069,0.1667,,,,0.0315,,,0.0625,,0.9891,,,0.0,0.1379,0.1667,,,,0.055,,,,block of flats,0.0238,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1259.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n100336,1,Cash loans,F,Y,Y,0,157500.0,497520.0,28692.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.02461,-13989,-1350,-8112.0,-4715,5.0,1,1,1,1,1,0,Laborers,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Industry: type 3,,0.6290421096825476,0.2608559142068693,0.1485,0.111,0.9801,0.728,0.0725,0.16,0.1379,0.3333,0.375,0.0582,0.121,0.1514,0.0,0.0,0.1513,0.1152,0.9801,0.7387,0.0732,0.1611,0.1379,0.3333,0.375,0.0595,0.1322,0.1578,0.0,0.0,0.1499,0.111,0.9801,0.7316,0.073,0.16,0.1379,0.3333,0.375,0.0592,0.1231,0.1541,0.0,0.0,reg oper account,block of flats,0.1588,Panel,No,1.0,1.0,1.0,0.0,-1781.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n258389,0,Cash loans,M,N,Y,1,225000.0,373140.0,24934.5,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-10591,-2503,-484.0,-2219,,1,1,1,1,1,0,Laborers,3.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Transport: type 2,0.20261158349145086,0.7426352321284636,0.4812493411434029,0.0619,0.0505,0.9861,0.8096,0.021,0.0,0.1379,0.1667,0.2083,0.0502,0.0504,0.0406,0.0,0.0,0.063,0.0524,0.9861,0.8171,0.0212,0.0,0.1379,0.1667,0.2083,0.0514,0.0551,0.0423,0.0,0.0,0.0625,0.0505,0.9861,0.8121,0.0211,0.0,0.1379,0.1667,0.2083,0.0511,0.0513,0.0413,0.0,0.0,reg oper account,block of flats,0.0434,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1574.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n164327,0,Cash loans,F,N,N,0,81000.0,288873.0,9355.5,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.01885,-20215,365243,-9905.0,-3316,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,0.6852667595764245,0.6552539233353317,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n288800,1,Cash loans,F,N,Y,0,202500.0,314100.0,15241.5,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-24119,365243,-7476.0,-4672,,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.481832457186557,0.6925590674998008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n313221,0,Cash loans,M,Y,Y,1,225000.0,755190.0,36459.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018801,-10518,-3716,-4621.0,-3212,27.0,1,1,1,1,0,0,High skill tech staff,3.0,2,2,TUESDAY,12,0,0,0,1,1,0,Government,0.14817659178588047,0.6326526702823334,0.3859146722745145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-682.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n306774,0,Cash loans,F,N,Y,0,225000.0,755190.0,32125.5,675000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.008068,-11504,-2570,-776.0,-1458,,1,1,0,1,0,0,Core staff,2.0,3,3,THURSDAY,7,0,0,0,0,1,1,Police,0.3523851458758208,0.35305509219764186,0.7751552674785404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,10.0,0.0,-2005.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n120269,0,Cash loans,F,N,Y,1,202500.0,1426500.0,39226.5,1426500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019689,-13177,-2503,-1767.0,-280,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,13,0,0,0,0,0,0,Other,0.8152794517853942,0.6524675228468452,0.3996756156233169,0.0,0.0548,0.9781,0.7008,0.0936,0.0,0.1379,0.1667,0.0417,0.0463,0.0588,0.0531,0.0,0.0434,0.0,0.0569,0.9782,0.7125,0.0945,0.0,0.1379,0.1667,0.0417,0.0474,0.0643,0.0553,0.0,0.0459,0.0,0.0548,0.9781,0.7048,0.0942,0.0,0.1379,0.1667,0.0417,0.0471,0.0599,0.054000000000000006,0.0,0.0443,org spec account,block of flats,0.0512,Block,No,0.0,0.0,0.0,0.0,-1911.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n256307,0,Cash loans,F,Y,Y,0,225000.0,609183.0,22509.0,508500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-15574,-1824,-4722.0,-2606,13.0,1,1,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,1,1,Other,,0.7069406030449362,0.5673792367572691,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1289.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n179880,0,Cash loans,F,N,Y,0,180000.0,1147257.0,41337.0,927000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-15505,-109,-6215.0,-1903,,1,1,0,1,0,0,Medicine staff,2.0,2,2,MONDAY,14,0,0,0,1,1,0,University,0.6339092389033331,0.6407859079793541,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2057.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n122046,0,Cash loans,M,Y,N,0,270000.0,942300.0,24984.0,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.02461,-22234,-12716,-9862.0,-4351,3.0,1,1,1,1,1,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Business Entity Type 2,0.5269195952851994,0.7464036302679987,0.7352209993926424,0.0619,0.0577,0.9866,,,0.0,0.1379,0.1667,,0.0124,,0.0587,,0.0,0.063,0.0599,0.9866,,,0.0,0.1379,0.1667,,0.0127,,0.0611,,0.0,0.0625,0.0577,0.9866,,,0.0,0.1379,0.1667,,0.0127,,0.0597,,0.0,,terraced house,0.0486,Panel,No,0.0,0.0,0.0,0.0,-1438.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n410596,0,Revolving loans,F,N,Y,1,81000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-10460,-837,-3115.0,-3136,,1,1,0,1,0,0,Sales staff,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,0.4893837420685928,0.686970528340499,,0.0041,0.0,0.9732,0.6328,0.0,0.0,0.0345,0.0,0.0417,0.0,0.0034,0.0038,0.0,0.0,0.0042,0.0,0.9732,0.6472,0.0,0.0,0.0345,0.0,0.0417,0.0,0.0037,0.0039,0.0,0.0,0.0042,0.0,0.9732,0.6377,0.0,0.0,0.0345,0.0,0.0417,0.0,0.0034,0.0039,0.0,0.0,reg oper account,block of flats,0.003,Wooden,No,6.0,0.0,6.0,0.0,-312.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n138725,0,Cash loans,F,N,Y,0,292500.0,540000.0,17550.0,540000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.015221,-19284,-1030,-7053.0,-2836,,1,1,0,1,1,0,Sales staff,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,Self-employed,,0.5632843971890981,0.14825437906109115,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1099.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n237820,0,Cash loans,M,Y,Y,1,157500.0,1046142.0,30717.0,913500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-9915,-1341,-511.0,-2546,6.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,12,0,1,1,0,0,0,Transport: type 4,0.1966405825280416,0.6245150031155494,0.6528965519806539,0.0918,0.0879,0.9816,0.7484,0.0134,0.0,0.2069,0.1667,0.2083,0.0358,0.07400000000000001,0.0898,0.3359,0.004,0.0935,0.0912,0.9816,0.7583,0.0135,0.0,0.2069,0.1667,0.2083,0.0366,0.0808,0.0936,0.3385,0.0043,0.0926,0.0879,0.9816,0.7518,0.0135,0.0,0.2069,0.1667,0.2083,0.0364,0.0752,0.0914,0.3377,0.0041,reg oper account,block of flats,0.09300000000000001,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-448.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n297551,0,Cash loans,M,N,Y,0,90000.0,536917.5,19413.0,463500.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.010966,-21038,-1376,-10776.0,-4216,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Transport: type 4,,0.2007754035972499,,0.0186,,0.9851,,,0.0,0.1034,0.0417,,,,0.0174,,0.0061,0.0189,,0.9851,,,0.0,0.1034,0.0417,,,,0.0182,,0.0065,0.0187,,0.9851,,,0.0,0.1034,0.0417,,,,0.0177,,0.0063,,block of flats,0.015,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-2449.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n232649,0,Cash loans,F,N,Y,0,157500.0,630000.0,18549.0,630000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.026392,-18032,-3751,-9412.0,-1587,,1,1,0,1,1,0,Laborers,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Industry: type 2,,0.6780770286069379,0.4014074137749511,0.3619,0.36,0.9871,,,0.0,0.7931,0.1667,,,,0.3176,,,0.3687,0.3736,0.9871,,,0.0,0.7931,0.1667,,,,0.3309,,,0.3654,0.36,0.9871,,,0.0,0.7931,0.1667,,,,0.3233,,,,block of flats,0.3359,Panel,No,0.0,0.0,0.0,0.0,-1080.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n217671,0,Cash loans,F,N,Y,2,180000.0,599778.0,30753.0,477000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.0228,-12321,-2878,-655.0,-2474,,1,1,0,1,0,0,Core staff,4.0,2,2,SATURDAY,8,0,0,0,0,1,1,Kindergarten,,0.4404776376746306,0.8616527500630958,0.0124,0.0,0.9796,,,0.0,0.069,0.0417,,0.0094,,0.0101,,0.0151,0.0126,0.0,0.9796,,,0.0,0.069,0.0417,,0.0096,,0.0105,,0.016,0.0125,0.0,0.9796,,,0.0,0.069,0.0417,,0.0096,,0.0103,,0.0154,,block of flats,0.0086,Wooden,No,3.0,0.0,3.0,0.0,-973.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n122107,0,Revolving loans,F,N,Y,0,135000.0,270000.0,13500.0,270000.0,Other_A,Working,Higher education,Single / not married,House / apartment,0.010966,-8908,-195,-8877.0,-1570,,1,1,0,1,0,0,,1.0,2,2,FRIDAY,19,0,0,0,0,0,0,Business Entity Type 3,,0.6567540699984526,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-582.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n135119,0,Cash loans,F,Y,Y,0,315000.0,1535553.0,42354.0,1372500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.003069,-20948,365243,-6678.0,-4252,2.0,1,0,0,1,1,0,,2.0,3,3,SATURDAY,12,0,0,0,0,0,0,XNA,0.6087849902955864,0.4004681258625184,0.5028782772082183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1668.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n287554,0,Cash loans,F,N,Y,0,180000.0,1236816.0,36292.5,1080000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.00823,-9969,-1345,-9969.0,-2630,,1,1,0,1,0,1,,1.0,2,2,WEDNESDAY,18,0,1,1,0,1,1,Business Entity Type 3,0.5376847723692638,0.6730301209242691,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2299.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n373295,0,Cash loans,F,N,Y,0,112500.0,277969.5,10606.5,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.020246,-20369,365243,-8299.0,-3694,,1,0,0,1,1,0,,1.0,3,3,MONDAY,7,0,0,0,1,0,0,XNA,0.4701035186859284,0.28469843793160393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-173.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n326006,0,Cash loans,F,Y,N,0,180000.0,545040.0,20547.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-21997,365243,-11474.0,-4149,3.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,0.7383580141067996,0.718950951204067,0.4400578303966329,0.0691,0.0638,0.9781,0.7008,0.0075,0.0,0.1379,0.1667,0.2083,0.0382,0.0555,0.0622,0.0039,0.0186,0.0704,0.0662,0.9782,0.7125,0.0076,0.0,0.1379,0.1667,0.2083,0.0391,0.0606,0.0649,0.0039,0.0197,0.0697,0.0638,0.9781,0.7048,0.0075,0.0,0.1379,0.1667,0.2083,0.0389,0.0564,0.0634,0.0039,0.019,reg oper account,block of flats,0.0571,Panel,No,1.0,0.0,1.0,0.0,-201.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n371929,0,Cash loans,M,N,N,1,135000.0,343800.0,16852.5,225000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.008625,-11488,-1340,-1032.0,-3591,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Industry: type 1,0.14365149884447714,0.33187622465115857,0.5531646987710016,0.1108,,0.9871,,,0.06,0.0517,0.3333,,0.0669,,0.0864,,0.0,0.0756,,0.9871,,,0.0403,0.0345,0.3333,,0.0446,,0.0785,,0.0,0.1119,,0.9871,,,0.06,0.0517,0.3333,,0.0681,,0.08800000000000001,,0.0,,block of flats,0.0842,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-94.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,9.0\n237342,0,Cash loans,M,N,Y,0,54000.0,358443.0,13639.5,252000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-21440,-797,-10383.0,-4745,,1,1,0,1,0,0,Cleaning staff,2.0,3,3,SUNDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.3787297398938643,0.4048783643353997,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n288074,0,Cash loans,M,N,N,1,157500.0,612612.0,28516.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-15325,-3389,-498.0,-4122,,1,1,0,1,0,0,,3.0,3,3,MONDAY,12,0,0,0,0,0,0,Industry: type 9,,0.4514074331668316,0.17352743046491467,0.1485,0.1115,0.9826,0.762,0.0358,0.16,0.1379,0.3333,0.375,0.1019,0.121,0.1504,0.0,0.0,0.1513,0.1157,0.9826,0.7713,0.0362,0.1611,0.1379,0.3333,0.375,0.1042,0.1322,0.1567,0.0,0.0,0.1499,0.1115,0.9826,0.7652,0.0361,0.16,0.1379,0.3333,0.375,0.1037,0.1231,0.1531,0.0,0.0,reg oper account,block of flats,0.1183,Panel,No,2.0,0.0,2.0,0.0,-1323.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n217260,0,Cash loans,F,N,Y,0,225000.0,1575000.0,43312.5,1575000.0,Family,Pensioner,Higher education,Married,House / apartment,0.030755,-22606,365243,-10185.0,-4686,,1,0,0,1,0,0,,2.0,2,2,MONDAY,17,0,0,0,0,0,0,XNA,0.7768262972038841,0.7806697746287429,0.6528965519806539,0.0124,0.0,0.9717,,,0.0,0.069,0.0417,,0.0278,,0.0146,,0.0,0.0126,0.0,0.9717,,,0.0,0.069,0.0417,,0.0284,,0.0152,,0.0,0.0125,0.0,0.9717,,,0.0,0.069,0.0417,,0.0283,,0.0149,,0.0,,block of flats,0.0125,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-122.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n366392,0,Cash loans,M,Y,N,1,112500.0,277969.5,18576.0,229500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.022625,-12757,-2233,-408.0,-4775,15.0,1,1,0,1,0,0,,3.0,2,2,FRIDAY,17,0,0,0,0,1,1,Transport: type 2,,0.7466312193886749,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1933.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n395927,0,Cash loans,M,N,Y,0,121500.0,494550.0,39649.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-13765,-3822,-2345.0,-5051,,1,1,1,1,1,0,Drivers,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Medicine,,0.6354686663170481,0.6971469077844458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2162.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n146698,0,Revolving loans,F,N,Y,0,67500.0,135000.0,6750.0,135000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.020246,-8276,-1263,-732.0,-770,,1,1,0,1,0,0,Security staff,2.0,3,3,MONDAY,7,0,0,0,0,0,0,School,0.2383578236169962,0.4456415571965404,0.36227724703843145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,-463.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n317880,0,Cash loans,F,N,Y,0,135000.0,755190.0,30078.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-17626,-4129,-11768.0,-1178,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.5718500003778979,0.7838324335199619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-900.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n173039,0,Cash loans,M,Y,N,0,112500.0,288873.0,17329.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008865999999999999,-8593,-1326,-8420.0,-1263,14.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6942870032104914,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n149129,0,Cash loans,F,N,N,0,225000.0,900000.0,45954.0,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010006,-18103,-748,-9731.0,-1654,,1,1,1,1,1,0,Sales staff,2.0,2,2,MONDAY,19,0,0,0,0,0,0,Self-employed,,0.4624495446224256,0.7981372313187245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-699.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n195472,0,Cash loans,F,N,N,0,135000.0,904500.0,26446.5,904500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010147,-13913,-2871,-4669.0,-4920,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Self-employed,0.8705347379653698,0.6996581809063575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-862.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n165779,0,Cash loans,F,N,Y,0,157500.0,284427.0,18306.0,216000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-23362,365243,-1609.0,-4325,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.5945007021898856,0.6754132910917112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-830.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n269319,0,Cash loans,F,Y,Y,0,162000.0,1125000.0,47794.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-20770,365243,-9237.0,-4099,4.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.4482847835394167,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n417518,0,Revolving loans,F,N,Y,0,157950.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-21112,-1839,-13233.0,-4657,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,,0.4858627937348871,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,6.0,2.0,-1219.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n187121,0,Cash loans,M,Y,N,0,247500.0,2250000.0,59355.0,2250000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-12427,-601,-425.0,-4170,2.0,1,1,1,1,1,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Industry: type 9,,0.5734500907418902,0.4686596550493113,0.1485,,0.9881,,,0.0,0.1379,0.3333,,,,0.0933,,0.2407,0.1513,,0.9881,,,0.0,0.1379,0.3333,,,,0.0972,,0.2548,0.1499,,0.9881,,,0.0,0.1379,0.3333,,,,0.095,,0.2457,,block of flats,0.1257,Block,No,0.0,0.0,0.0,0.0,-526.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n318179,0,Cash loans,F,N,N,0,58500.0,640080.0,23121.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,With parents,0.035792000000000004,-16409,-354,-8046.0,-4777,,1,1,0,1,1,0,Laborers,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.3260210906483239,0.8277026681625442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n165628,0,Cash loans,F,N,Y,4,121500.0,942300.0,34618.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.010276,-11265,-3019,-5105.0,-3220,,1,1,1,1,1,0,Cleaning staff,6.0,2,2,WEDNESDAY,15,1,1,0,1,1,0,Housing,0.41842486075758734,0.6345868006863699,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-201.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n413888,0,Revolving loans,F,N,Y,0,630000.0,1350000.0,67500.0,1350000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.032561,-15882,-1116,-9977.0,-4248,,1,1,0,1,1,0,Medicine staff,2.0,1,1,WEDNESDAY,11,0,0,0,0,0,0,Medicine,,0.6578131448382354,0.4848514754962666,0.0773,0.0,0.9712,,,0.0,0.1724,0.1667,,0.0743,,0.0937,,0.0,0.0788,0.0,0.9712,,,0.0,0.1724,0.1667,,0.076,,0.0976,,0.0,0.0781,0.0,0.9712,,,0.0,0.1724,0.1667,,0.0756,,0.0953,,0.0,,block of flats,0.0737,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-284.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n252826,0,Cash loans,M,N,N,0,135000.0,298512.0,19948.5,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Rented apartment,0.008068,-10323,-1729,-1508.0,-2965,,1,1,0,1,0,0,Laborers,2.0,3,3,SUNDAY,12,0,0,0,1,1,0,Transport: type 4,0.08520114862634627,0.4666541888989381,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-540.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n274761,1,Cash loans,M,Y,Y,2,270000.0,781920.0,47965.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009334,-14380,-200,-1150.0,-4284,24.0,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,17,0,0,0,0,0,0,Other,,0.2265988359266469,0.08281470424406495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1780.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n368617,0,Cash loans,F,N,Y,0,99000.0,599778.0,26550.0,477000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-20547,365243,-10936.0,-3953,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.6118029026677672,0.520897599048938,0.0577,0.095,0.9801,,0.0064,0.0,0.1379,0.1667,0.0,0.0412,0.037000000000000005,0.0529,0.0463,0.0497,0.0588,0.0986,0.9801,,0.0065,0.0,0.1379,0.1667,0.0,0.0422,0.0404,0.0551,0.0467,0.0526,0.0583,0.095,0.9801,,0.0065,0.0,0.1379,0.1667,0.0,0.0419,0.0376,0.0539,0.0466,0.0507,reg oper account,block of flats,0.0559,\"Stone, brick\",No,3.0,1.0,3.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n139602,0,Cash loans,F,N,N,0,225000.0,1223010.0,47983.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-18966,-394,-9851.0,-2455,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Self-employed,,0.6871773988702063,0.4436153084085652,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n417320,0,Cash loans,F,N,Y,0,89100.0,755190.0,31995.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-20359,365243,-1597.0,-1854,,1,0,0,1,1,1,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.13539722630997045,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-372.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n117655,0,Cash loans,F,Y,N,1,157500.0,239850.0,25830.0,225000.0,Family,Working,Higher education,Separated,House / apartment,0.019689,-13239,-1571,-4276.0,-4128,13.0,1,1,1,1,0,0,Managers,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Government,,0.5997244859835851,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2429.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n346806,0,Cash loans,M,Y,Y,0,180000.0,383089.5,23566.5,346500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006852,-11047,-860,-39.0,-3045,10.0,1,1,0,1,0,0,Laborers,2.0,3,3,TUESDAY,6,0,0,0,0,0,0,Self-employed,,0.2607296625694168,0.4382813743111921,0.0608,0.5438,0.9995,0.9932,0.012,0.0,0.1034,0.1667,0.2083,0.1027,0.0454,0.05,0.0193,0.0381,0.062,0.5643,0.9995,0.9935,0.0121,0.0,0.1034,0.1667,0.2083,0.105,0.0496,0.0521,0.0195,0.0403,0.0614,0.5438,0.9995,0.9933,0.0121,0.0,0.1034,0.1667,0.2083,0.1045,0.0462,0.0509,0.0194,0.0389,reg oper account,block of flats,0.0542,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-550.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n195847,0,Cash loans,M,Y,Y,0,270000.0,942300.0,36643.5,675000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.032561,-17124,365243,-1148.0,-636,10.0,1,0,0,1,1,0,,1.0,1,1,MONDAY,13,0,0,0,0,0,0,XNA,,0.6175105444039353,0.1986200451074864,0.2804,0.1945,0.9876,0.83,0.03,0.28,0.2414,0.375,0.0417,0.0638,0.2286,0.3114,0.0232,0.0135,0.2857,0.2019,0.9876,0.8367,0.0303,0.282,0.2414,0.375,0.0417,0.0653,0.2498,0.3245,0.0233,0.0143,0.2831,0.1945,0.9876,0.8323,0.0302,0.28,0.2414,0.375,0.0417,0.0649,0.2326,0.317,0.0233,0.0137,reg oper account,block of flats,0.2479,Panel,No,5.0,0.0,5.0,0.0,-522.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n415878,1,Cash loans,M,Y,Y,0,337500.0,904500.0,29178.0,904500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-19944,-4956,-1296.0,-3463,7.0,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.5311922542761378,0.6506156894172154,0.12530787842823918,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3228.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,9.0,0.0,0.0\n394498,1,Cash loans,F,N,N,0,171000.0,1260000.0,36841.5,1260000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-12080,-4081,-5942.0,-4255,,1,1,0,1,0,0,Accountants,2.0,2,2,THURSDAY,20,0,0,0,0,0,0,Medicine,0.5615806388848265,0.32608355864853344,0.22009464485041005,0.0289,0.0676,0.9722,0.6192,0.0052,0.0,0.1034,0.0833,0.125,0.027000000000000003,0.0219,0.0283,0.0077,0.0074,0.0294,0.0702,0.9722,0.6341,0.0052,0.0,0.1034,0.0833,0.125,0.0276,0.0239,0.0295,0.0078,0.0079,0.0291,0.0676,0.9722,0.6243,0.0052,0.0,0.1034,0.0833,0.125,0.0274,0.0222,0.0288,0.0078,0.0076,reg oper account,block of flats,0.0239,\"Stone, brick\",No,7.0,1.0,7.0,0.0,-1986.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n437125,0,Cash loans,F,N,Y,0,112500.0,417024.0,27499.5,360000.0,,Working,Secondary / secondary special,Married,House / apartment,0.018801,-14530,-384,-4307.0,-4597,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Trade: type 7,,0.15893242375006345,0.42765737003502935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n403573,0,Revolving loans,F,N,Y,0,130500.0,202500.0,10125.0,202500.0,Family,Working,Incomplete higher,Married,With parents,0.035792000000000004,-7933,-253,-6862.0,-460,,1,1,0,1,1,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6366734238911165,0.3774042489507649,0.1464,0.1148,0.9811,0.7416,0.0351,0.16,0.1379,0.3333,0.375,0.0635,0.1194,0.1669,0.0,0.0114,0.1492,0.1192,0.9811,0.7517,0.0355,0.1611,0.1379,0.3333,0.375,0.065,0.1304,0.1739,0.0,0.012,0.1478,0.1148,0.9811,0.7451,0.0354,0.16,0.1379,0.3333,0.375,0.0646,0.1214,0.1699,0.0,0.0116,reg oper account,block of flats,0.1337,Panel,No,0.0,0.0,0.0,0.0,-508.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n148084,0,Cash loans,F,N,N,0,67500.0,225000.0,11488.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.000938,-22759,365243,-9814.0,-4311,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,4,0,0,0,0,0,0,XNA,,0.2724734479258431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1685.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n344304,0,Cash loans,M,N,Y,0,135000.0,53910.0,5071.5,45000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-10343,-444,-381.0,-2711,,1,1,0,1,1,0,Sales staff,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Self-employed,0.17759925609141294,0.5739153817820374,,0.1186,0.1266,0.9806,,,0.0,0.2759,0.1667,,0.1503,,0.1086,,0.0,0.1208,0.1314,0.9806,,,0.0,0.2759,0.1667,,0.1538,,0.1132,,0.0,0.1197,0.1266,0.9806,,,0.0,0.2759,0.1667,,0.1529,,0.1106,,0.0,,block of flats,0.0855,Panel,No,2.0,0.0,2.0,0.0,-411.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n307547,0,Revolving loans,F,N,Y,1,67500.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010147,-7923,-794,-4438.0,-600,,1,1,1,1,1,0,,3.0,2,2,MONDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.20852560904660086,0.5873646392985845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,0.0,11.0,0.0,-587.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n321349,0,Cash loans,M,Y,N,0,135000.0,337923.0,26829.0,279000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.006296,-10037,-1227,-4451.0,-2662,9.0,1,1,0,1,0,0,Sales staff,1.0,3,3,MONDAY,14,0,0,0,0,0,0,Self-employed,0.3594651810542822,0.4894632690000096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-483.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n177298,0,Cash loans,F,N,N,0,112500.0,1350000.0,43551.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-12665,-2785,-4469.0,-4594,,1,1,1,1,1,0,Sales staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,0.5355351020255018,0.5732361201354506,0.7267112092725122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-2305.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n240062,0,Cash loans,M,Y,Y,0,360000.0,945000.0,25056.0,945000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.015221,-14023,-5618,-2944.0,-5129,3.0,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.466476304204681,0.5418403653538282,0.25533177083329,0.0742,,0.9896,,,0.08,0.069,0.3333,,0.0392,,0.0737,,,0.0756,,0.9896,,,0.0806,0.069,0.3333,,0.0401,,0.0768,,,0.0749,,0.9896,,,0.08,0.069,0.3333,,0.0399,,0.0751,,,,block of flats,0.057999999999999996,Panel,No,7.0,0.0,7.0,0.0,-2063.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,3.0,3.0\n450459,0,Cash loans,F,N,Y,0,112500.0,675000.0,28728.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008865999999999999,-17819,-7464,-7235.0,-1344,,1,1,1,1,1,0,,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Government,,0.7784375744162884,,0.0082,,0.9796,,,,,,,,,0.0067,,,0.0084,,0.9796,,,,,,,,,0.006999999999999999,,,0.0083,,0.9796,,,,,,,,,0.0068,,,,,0.0053,,No,0.0,0.0,0.0,0.0,-2175.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n450723,0,Cash loans,M,Y,N,2,180000.0,227520.0,12478.5,180000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010556,-12173,-1063,-2813.0,-4832,3.0,1,1,0,1,0,0,Laborers,4.0,3,3,WEDNESDAY,15,0,1,1,0,1,1,Trade: type 7,0.6785638892257673,0.6263285088060729,0.5709165417729987,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-402.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n292002,0,Cash loans,F,N,Y,0,193500.0,454500.0,51534.0,454500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.026392,-11194,-231,-2187.0,-3036,,1,1,0,1,0,1,Core staff,2.0,2,2,MONDAY,18,0,0,0,0,0,0,Bank,0.6994814769212186,0.6586150251656661,0.4902575124990026,0.2216,,0.9806,,,0.24,0.2069,0.3333,,,,0.2211,,0.0,0.2258,,0.9806,,,0.2417,0.2069,0.3333,,,,0.2303,,0.0,0.2238,,0.9806,,,0.24,0.2069,0.3333,,,,0.225,,0.0,,block of flats,0.1739,Panel,No,0.0,0.0,0.0,0.0,-706.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n215798,0,Cash loans,M,N,Y,0,202500.0,1125000.0,60070.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00496,-18560,365243,-6536.0,-1972,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.6607456648576654,0.5902333386185574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0277,,No,0.0,0.0,0.0,0.0,-390.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n261679,0,Cash loans,M,Y,Y,1,450000.0,1287000.0,54657.0,1287000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.001276,-16154,-1482,-1622.0,-4779,12.0,1,1,0,1,0,0,Managers,3.0,2,2,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.4924800022854934,,0.1021,0.098,0.9886,0.8436,0.0,0.08,0.069,0.3333,0.0417,0.0815,0.0,0.1305,0.0,0.0073,0.10400000000000001,0.1017,0.9886,0.8497,0.0,0.0806,0.069,0.3333,0.0417,0.0833,0.0,0.136,0.0,0.0077,0.1031,0.098,0.9886,0.8457,0.0,0.08,0.069,0.3333,0.0417,0.0829,0.0,0.1329,0.0,0.0074,reg oper account,block of flats,0.1261,Mixed,No,6.0,0.0,6.0,0.0,-1937.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n118236,0,Cash loans,F,N,Y,2,135000.0,1256400.0,36864.0,900000.0,Children,Working,Higher education,Single / not married,House / apartment,0.031329,-14812,-1283,-7015.0,-4248,,1,1,1,1,0,0,Core staff,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,School,0.8081949122263652,0.6527104522927423,0.6006575372857061,0.0619,0.0619,0.9896,0.8572,0.0553,0.0,0.1034,0.1667,0.2083,0.027999999999999997,0.0504,0.0668,0.0,0.0,0.063,0.0642,0.9896,0.8628,0.0559,0.0,0.1034,0.1667,0.2083,0.0286,0.0551,0.0696,0.0,0.0,0.0625,0.0619,0.9896,0.8591,0.0557,0.0,0.1034,0.1667,0.2083,0.0284,0.0513,0.068,0.0,0.0,reg oper account,block of flats,0.0828,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1146.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n357646,0,Cash loans,M,Y,Y,1,225000.0,450000.0,30204.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-17169,-756,-5534.0,-696,20.0,1,1,0,1,0,0,Managers,3.0,2,2,SATURDAY,9,0,0,0,0,0,0,Other,0.5623074094353594,,0.7352209993926424,0.0021,,0.9891,,,0.0,,0.0,,,,0.0028,,,0.0021,,0.9891,,,0.0,,0.0,,,,0.0029,,,0.0021,,0.9891,,,0.0,,0.0,,,,0.0028,,,,block of flats,0.0022,Panel,No,0.0,0.0,0.0,0.0,-33.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n376653,0,Cash loans,M,N,N,0,162000.0,150948.0,11808.0,126000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Rented apartment,0.020246,-10109,-1303,-42.0,-1927,,1,1,0,1,1,0,Laborers,2.0,3,3,THURSDAY,8,0,0,0,1,1,0,Industry: type 9,0.15740475678107296,0.3606584015738963,0.4578995512067301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-864.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n188603,1,Cash loans,F,Y,Y,2,180000.0,790830.0,57546.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.022625,-13782,-715,-5279.0,-1916,7.0,1,1,0,1,0,0,Managers,4.0,2,2,THURSDAY,12,0,0,0,0,0,0,Mobile,0.4157578144728599,0.6384571544954131,0.41534714488434,0.2443,,0.9901,,,0.16,0.2759,0.375,,,,0.1085,,,0.2489,,0.9901,,,0.1611,0.2759,0.375,,,,0.1131,,,0.2467,,0.9901,,,0.16,0.2759,0.375,,,,0.1105,,,,block of flats,0.1513,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1701.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n364509,0,Cash loans,M,Y,Y,1,270000.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00496,-15032,-2632,-6342.0,-4796,8.0,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Self-employed,0.3928236669288303,0.6328356066838077,0.3740208032583212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-216.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n182099,0,Cash loans,F,N,N,0,76500.0,526491.0,17397.0,454500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.010006,-21539,365243,-6469.0,-3983,,1,0,0,1,0,0,,2.0,2,1,TUESDAY,15,0,0,0,0,0,0,XNA,0.8776711857688229,0.5865216857571516,0.5567274263630174,0.1485,0.1125,0.9861,0.8096,0.0374,0.16,0.1379,0.3333,0.375,0.0203,0.121,0.1608,0.0,0.0,0.1513,0.1168,0.9861,0.8171,0.0378,0.1611,0.1379,0.3333,0.375,0.0207,0.1322,0.1675,0.0,0.0,0.1499,0.1125,0.9861,0.8121,0.0377,0.16,0.1379,0.3333,0.375,0.0206,0.1231,0.1636,0.0,0.0,reg oper account,block of flats,0.1296,Panel,No,0.0,0.0,0.0,0.0,-722.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n159453,0,Cash loans,F,N,Y,0,292500.0,1515415.5,41800.5,1354500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-16704,-1590,-223.0,-258,,1,1,0,1,0,1,Laborers,2.0,1,1,MONDAY,15,0,1,1,0,0,0,Business Entity Type 2,0.6964851610062527,0.6117925155104974,0.4974688893052743,0.0232,0.0098,0.9995,0.9932,0.011000000000000001,0.0,0.069,0.0833,0.125,0.0,0.0189,0.0167,0.0,0.0,0.0221,0.0,0.9995,0.9935,0.0136,0.0,0.069,0.0833,0.125,0.0,0.0193,0.015,0.0,0.0,0.0219,0.0,0.9995,0.9933,0.0135,0.0,0.069,0.0833,0.125,0.0,0.018000000000000002,0.0148,0.0,0.0,reg oper account,block of flats,0.0188,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-362.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n394971,0,Cash loans,F,N,N,0,126000.0,296280.0,16069.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010276,-12836,-1567,-5677.0,-2372,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,17,0,0,0,1,1,0,Business Entity Type 3,,0.3661573501084383,0.3842068130556564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n420979,0,Cash loans,M,Y,N,0,112500.0,397881.0,19480.5,328500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00496,-16600,-1317,-6047.0,-157,15.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.5959600982295324,0.6161216908872079,0.0309,,0.9786,,,0.0,0.069,0.1667,,,,0.0273,,,0.0315,,0.9786,,,0.0,0.069,0.1667,,,,0.0284,,,0.0312,,0.9786,,,0.0,0.069,0.1667,,,,0.0277,,,,block of flats,0.0214,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-962.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n439051,0,Cash loans,M,Y,N,0,157500.0,824823.0,24246.0,688500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.019101,-15360,-3458,-7895.0,-2519,24.0,1,1,0,1,0,0,Security staff,1.0,2,2,TUESDAY,14,0,0,0,1,1,0,Security,,0.6025034372264959,0.4223696523543468,0.1567,0.1951,0.9866,0.8164,0.026000000000000002,0.0,0.4138,0.1667,0.2083,0.1162,0.1278,0.1676,0.0,0.0676,0.1597,0.2025,0.9866,0.8236,0.0262,0.0,0.4138,0.1667,0.2083,0.1188,0.1396,0.1747,0.0,0.0715,0.1582,0.1951,0.9866,0.8189,0.0261,0.0,0.4138,0.1667,0.2083,0.1182,0.13,0.1707,0.0,0.069,reg oper account,block of flats,0.1465,Panel,No,0.0,0.0,0.0,0.0,-1929.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n373051,1,Cash loans,F,N,N,1,67500.0,161730.0,14962.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-12791,-1112,-5813.0,-3714,,1,1,0,1,0,0,Waiters/barmen staff,2.0,2,2,MONDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.6672216136366506,0.23115286412023,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n218027,0,Cash loans,F,N,Y,0,112500.0,835605.0,24561.0,697500.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.011703,-18606,-11769,-9894.0,-2165,,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Postal,,0.7331282117178525,0.7898803468924867,0.1237,0.12,0.9866,0.8164,0.0247,0.0,0.2759,0.1667,0.2083,0.0608,0.1009,0.1141,0.0,0.0,0.1261,0.1246,0.9866,0.8236,0.0249,0.0,0.2759,0.1667,0.2083,0.0621,0.1102,0.1189,0.0,0.0,0.1249,0.12,0.9866,0.8189,0.0248,0.0,0.2759,0.1667,0.2083,0.0618,0.1026,0.1161,0.0,0.0,not specified,block of flats,0.1032,Panel,No,5.0,0.0,5.0,0.0,-2466.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n126104,0,Cash loans,F,N,Y,1,135000.0,814041.0,23931.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-16814,-422,-1673.0,-341,,1,1,0,1,0,0,Private service staff,3.0,2,2,TUESDAY,11,0,0,0,0,1,1,Services,,0.6752015007785725,0.3046721837533529,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1582.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,0.0\n432656,0,Cash loans,F,N,Y,0,94500.0,545040.0,19705.5,450000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-22357,365243,-12461.0,-4934,,1,0,0,1,1,0,,2.0,3,3,THURSDAY,8,0,0,0,0,0,0,XNA,,0.039941333502009575,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1668.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n282662,0,Cash loans,F,N,N,0,93150.0,675000.0,26770.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-15074,-3227,-4183.0,-4218,,1,1,1,1,0,0,Cleaning staff,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.52732060977551,0.2298535073078563,0.5884877883422673,0.0827,0.1077,0.9831,0.7688,0.1111,0.0,0.1638,0.1667,0.2083,0.0513,0.13699999999999998,0.0756,0.1274,0.0284,0.0735,0.0981,0.9831,0.7779,0.1122,0.0,0.1379,0.1667,0.2083,0.0314,0.1497,0.0795,0.1284,0.0063,0.0729,0.0945,0.9831,0.7719,0.1118,0.0,0.1379,0.1667,0.2083,0.0441,0.1394,0.0777,0.1281,0.0266,reg oper account,block of flats,0.06,Panel,No,0.0,0.0,0.0,0.0,-2748.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n354512,0,Cash loans,F,N,Y,0,90000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.031329,-18386,-8808,-3947.0,-1910,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,10,0,0,0,0,0,0,School,,0.5546376644614049,0.5136937663039473,0.0082,0.0,0.9672,0.5512,0.0011,0.0,0.0345,0.0417,0.0833,0.015,0.0067,0.006999999999999999,0.0,0.0,0.0084,0.0,0.9672,0.5688,0.0011,0.0,0.0345,0.0417,0.0833,0.0153,0.0073,0.0073,0.0,0.0,0.0083,0.0,0.9672,0.5572,0.0011,0.0,0.0345,0.0417,0.0833,0.0153,0.0068,0.0072,0.0,0.0,reg oper account,block of flats,0.0062,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n442154,0,Revolving loans,F,N,N,0,180000.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.031329,-21650,-11765,-11689.0,-4668,,1,1,0,1,0,0,Managers,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Medicine,,0.7275858880568684,0.7610263695502636,0.1907,0.0972,0.9702,0.5920000000000001,0.0505,0.0,0.6897,0.2083,0.25,0.1848,0.1345,0.1974,0.0965,0.222,0.1943,0.1009,0.9702,0.608,0.051,0.0,0.6897,0.2083,0.25,0.18899999999999997,0.1469,0.2057,0.0973,0.235,0.1926,0.0972,0.9702,0.5975,0.0508,0.0,0.6897,0.2083,0.25,0.188,0.1368,0.2009,0.09699999999999999,0.2267,org spec account,block of flats,0.2312,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n419049,0,Cash loans,F,N,Y,0,382500.0,1024740.0,52452.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.022625,-20400,-4001,-5922.0,-3646,,1,1,0,1,0,0,Security staff,1.0,2,2,MONDAY,8,0,0,0,0,1,1,Security,,0.5520001636195467,0.5744466170995097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,2.0,-3107.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n356485,0,Cash loans,F,N,Y,0,103500.0,263686.5,27148.5,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003813,-20916,365243,-4514.0,-4459,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,6,0,0,0,0,0,0,XNA,,0.655278621936963,0.4920600938649263,0.0918,0.0848,0.9886,,,0.0,0.2069,0.1667,,0.0734,,0.0876,,0.0052,0.0935,0.08800000000000001,0.9886,,,0.0,0.2069,0.1667,,0.0751,,0.0912,,0.0055,0.0926,0.0848,0.9886,,,0.0,0.2069,0.1667,,0.0747,,0.0891,,0.0053,,block of flats,0.07,Panel,No,0.0,0.0,0.0,0.0,-1046.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n177593,0,Cash loans,F,N,Y,0,135000.0,148500.0,17752.5,148500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.014464,-9039,-226,-2411.0,-1658,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Security Ministries,0.20850372644214296,0.3589055438561177,0.6956219298394389,0.099,0.0645,0.9781,0.7008,0.0315,0.16,0.069,0.4583,0.0417,0.0255,0.0807,0.0962,0.0,0.0,0.1008,0.067,0.9782,0.7125,0.0317,0.1611,0.069,0.4583,0.0417,0.026000000000000002,0.0882,0.1002,0.0,0.0,0.0999,0.0645,0.9781,0.7048,0.0317,0.16,0.069,0.4583,0.0417,0.0259,0.0821,0.0979,0.0,0.0,reg oper account,block of flats,0.0928,Panel,No,1.0,0.0,1.0,0.0,-896.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n160272,0,Cash loans,F,N,N,0,180000.0,558841.5,23805.0,499500.0,Family,Working,Higher education,Married,House / apartment,0.00496,-12263,-3329,-4500.0,-1894,,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Other,0.4122879718496029,0.6655912190051221,0.5513812618027899,,,0.9826,,,0.0,0.069,0.1667,,,,,,,,,0.9826,,,0.0,0.069,0.1667,,,,,,,,,0.9826,,,0.0,0.069,0.1667,,,,,,,,,0.0377,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1469.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n238974,0,Cash loans,F,N,Y,2,216000.0,878733.0,25690.5,733500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-13177,-5086,-2548.0,-5074,,1,1,0,1,1,0,Medicine staff,4.0,2,2,TUESDAY,10,0,0,0,0,0,0,Medicine,,0.6031788317585347,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1809.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n332051,1,Cash loans,M,Y,Y,0,112500.0,422802.0,25996.5,373500.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.030755,-13503,-4014,-7070.0,-4511,15.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.2782899643497417,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-166.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n429799,0,Cash loans,F,N,Y,1,112500.0,630000.0,27751.5,630000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-13987,-7371,-225.0,-5022,,1,1,1,1,1,0,Accountants,3.0,2,2,THURSDAY,13,0,0,0,0,1,1,Business Entity Type 2,0.5869933544693012,0.6107948767415,0.21155076420525776,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1406.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n444585,0,Revolving loans,F,N,Y,0,49500.0,270000.0,13500.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-20941,365243,-7155.0,-3816,,1,0,0,1,0,0,,2.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.7272650349594268,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-350.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n283558,0,Cash loans,F,Y,Y,0,135000.0,314100.0,17167.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.01885,-10471,-1141,-1212.0,-791,8.0,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Self-employed,,0.5511077158716935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,-1685.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n131899,0,Cash loans,F,Y,Y,0,135000.0,1260000.0,34780.5,1260000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-13423,-202,-357.0,-4473,9.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Industry: type 3,0.2700825189489618,0.3269298865314316,0.746300213050371,0.0505,0.0455,0.9851,0.7959999999999999,0.0038,0.0,0.1034,0.0833,0.125,0.0227,0.0412,0.0332,0.0,0.0,0.0515,0.0472,0.9851,0.804,0.0038,0.0,0.1034,0.0833,0.125,0.0232,0.045,0.0346,0.0,0.0,0.051,0.0455,0.9851,0.7987,0.0038,0.0,0.1034,0.0833,0.125,0.0231,0.0419,0.0338,0.0,0.0,reg oper account,block of flats,0.0282,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1512.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n315954,0,Cash loans,F,Y,N,0,112500.0,225000.0,14548.5,225000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.026392,-19169,-312,-8421.0,-2717,2.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Construction,0.5691871332498247,0.6944772695191592,0.5262949398096192,0.1856,,0.9886,,,0.12,0.1034,0.375,,,,0.1216,,0.0,0.1891,,0.9886,,,0.1208,0.1034,0.375,,,,0.1266,,0.0,0.1874,,0.9886,,,0.12,0.1034,0.375,,,,0.1237,,0.0,,block of flats,0.1119,Block,No,4.0,0.0,4.0,0.0,-6.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,0.0\n348471,0,Cash loans,F,N,Y,0,112500.0,326439.0,12433.5,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-21591,365243,-681.0,-2827,,1,0,0,1,0,1,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.6563447994511425,0.4722533429586386,0.0031,0.0,0.9672,0.5512,0.002,0.0,0.0345,0.0,0.0417,0.0,0.0025,0.0014,0.0,0.0,0.0032,0.0,0.9672,0.5688,0.0021,0.0,0.0345,0.0,0.0417,0.0,0.0028,0.0014,0.0,0.0,0.0031,0.0,0.9672,0.5572,0.0021,0.0,0.0345,0.0,0.0417,0.0,0.0026,0.0014,0.0,0.0,not specified,block of flats,0.0022,\"Stone, brick\",Yes,0.0,0.0,0.0,0.0,-1458.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n226094,0,Cash loans,F,N,N,0,126000.0,1350000.0,37125.0,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-21990,365243,-1793.0,-4873,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.6181972451974415,0.6940926425266661,0.0619,0.0635,0.9831,0.7688,,0.0,0.1379,0.1667,0.2083,0.0439,,0.037000000000000005,,0.0075,0.063,0.0658,0.9831,0.7779,,0.0,0.1379,0.1667,0.2083,0.0449,,0.0385,,0.0079,0.0625,0.0635,0.9831,0.7719,,0.0,0.1379,0.1667,0.2083,0.0447,,0.0376,,0.0076,,block of flats,0.0421,Panel,No,3.0,0.0,3.0,0.0,-1719.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n287576,0,Cash loans,F,N,N,0,369000.0,508495.5,24462.0,454500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.006629,-20375,-1606,-3453.0,-3774,,1,1,1,1,0,1,Accountants,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5847917845694193,0.24318648044201235,0.0742,0.0,0.9901,0.8640000000000001,,0.08,0.069,0.3333,,0.0102,,0.0902,,0.0,0.0756,0.0,0.9901,0.8693,,0.0806,0.069,0.3333,,0.0105,,0.0939,,0.0,0.0749,0.0,0.9901,0.8658,,0.08,0.069,0.3333,,0.0104,,0.0918,,0.0,reg oper account,block of flats,0.0801,Panel,No,2.0,0.0,2.0,0.0,-2221.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n192665,1,Revolving loans,F,N,Y,0,130500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014464,-11518,-2528,-1486.0,-2658,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Housing,,0.6458087273554549,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-127.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n320304,1,Cash loans,M,N,Y,1,103500.0,308133.0,13702.5,234000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.010032,-15748,-3429,-6878.0,-4389,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,5,0,0,0,0,0,0,Military,,0.6407557066290093,0.09322509537747448,0.1546,0.1534,0.9846,0.7892,0.0211,0.0,0.3448,0.1667,0.2083,0.1623,0.1252,0.1308,0.0039,0.0805,0.1576,0.1592,0.9846,0.7975,0.0213,0.0,0.3448,0.1667,0.2083,0.166,0.1368,0.1363,0.0039,0.0853,0.1561,0.1534,0.9846,0.792,0.0213,0.0,0.3448,0.1667,0.2083,0.1652,0.1274,0.1331,0.0039,0.0822,reg oper account,block of flats,0.1319,Panel,No,0.0,0.0,0.0,0.0,-1076.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n349106,0,Cash loans,M,N,Y,0,67500.0,337761.0,16380.0,256500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-22270,365243,-7264.0,-5090,,1,0,0,1,0,0,,2.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,XNA,0.6114553934159174,0.2554206434172181,0.8027454758994178,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-259.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n374148,0,Cash loans,F,N,Y,0,108000.0,291915.0,20443.5,252000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.031329,-22472,365243,-14679.0,-4640,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.1698542589574682,0.7309873696832169,0.0619,,0.9752,,,0.0,,0.1667,,,,0.0504,,0.0042,0.063,,0.9752,,,0.0,,0.1667,,,,0.0525,,0.0044,0.0625,,0.9752,,,0.0,,0.1667,,,,0.0513,,0.0043,,block of flats,0.0485,Panel,No,0.0,0.0,0.0,0.0,-440.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n350683,0,Cash loans,F,Y,Y,0,225000.0,1282500.0,33961.5,1282500.0,Unaccompanied,Commercial associate,Higher education,Widow,House / apartment,0.025164,-21488,-1122,-4388.0,-4388,2.0,1,1,0,1,0,0,Sales staff,1.0,2,2,THURSDAY,12,0,0,0,0,1,1,Self-employed,,0.3565993415991741,0.5082869913916046,0.0619,0.0793,0.9826,,,0.0,0.1379,0.1667,,0.0684,,0.0657,,0.02,0.063,0.0823,0.9826,,,0.0,0.1379,0.1667,,0.0699,,0.0684,,0.0211,0.0625,0.0793,0.9826,,,0.0,0.1379,0.1667,,0.0695,,0.0669,,0.0204,,block of flats,0.0713,\"Stone, brick\",No,10.0,1.0,10.0,1.0,-22.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n343065,0,Cash loans,M,N,Y,0,83250.0,625536.0,25245.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-19688,-118,-2444.0,-3236,,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.25634082347862586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-46.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n411256,0,Cash loans,F,N,Y,0,112500.0,278811.0,14364.0,189000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-18706,-364,-9635.0,-2231,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Trade: type 7,,0.7106417219443224,0.36896873825284665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2660.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n144978,0,Cash loans,F,N,Y,0,171000.0,1971072.0,68643.0,1800000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-11616,-3080,-5747.0,-3925,,1,1,0,1,0,0,Sales staff,2.0,3,3,WEDNESDAY,6,0,0,0,1,1,0,Business Entity Type 3,0.21866220441899314,0.7326530384947737,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-550.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n397992,0,Revolving loans,F,N,N,1,225000.0,675000.0,33750.0,675000.0,Unaccompanied,State servant,Higher education,Separated,House / apartment,0.018634,-16744,-8425,-10540.0,-298,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,School,0.5594031108169086,0.4120773263897904,0.470456116119975,0.0619,0.0629,0.9742,,,,0.1379,0.1667,,,,0.0517,,0.0,0.063,0.0653,0.9742,,,,0.1379,0.1667,,,,0.0539,,0.0,0.0625,0.0629,0.9742,,,,0.1379,0.1667,,,,0.0526,,0.0,,block of flats,0.0563,Panel,Yes,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n149315,0,Cash loans,F,N,Y,0,81000.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010966,-18075,-574,-9299.0,-1612,,1,1,0,1,0,0,,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,Other,,0.3366157435918635,0.7557400501752248,0.0619,,0.9856,,,,0.1379,0.1667,,,,0.0509,,0.0322,0.063,,0.9856,,,,0.1379,0.1667,,,,0.053,,0.0341,0.0625,,0.9856,,,,0.1379,0.1667,,,,0.0518,,0.0329,,block of flats,0.047,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n305545,0,Cash loans,M,Y,Y,1,495000.0,187659.0,18688.5,162000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010032,-16812,-71,-6864.0,-360,18.0,1,1,0,1,1,1,Managers,3.0,2,2,MONDAY,3,0,0,0,0,0,0,Security,0.4819338864230176,0.5621382667326039,0.8038850611746273,0.0,0.0551,0.9901,0.8640000000000001,0.0557,0.08,0.069,0.375,0.4167,0.0523,0.0,0.0995,0.0,0.0,0.0,0.0572,0.9901,0.8693,0.0562,0.0806,0.069,0.375,0.4167,0.0535,0.0,0.1036,0.0,0.0,0.0,0.0551,0.9901,0.8658,0.0561,0.08,0.069,0.375,0.4167,0.0532,0.0,0.1012,0.0,0.0,reg oper account,block of flats,0.1087,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2606.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n192757,0,Cash loans,F,N,Y,0,90000.0,314100.0,13963.5,225000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.035792000000000004,-14593,-1086,-1732.0,-3641,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,School,0.4473407546040168,0.6543592701816392,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,-2362.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n448418,0,Cash loans,F,N,Y,1,202500.0,679500.0,22585.5,679500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.018209,-10905,-1233,-9408.0,-833,,1,1,0,1,1,1,Sales staff,3.0,3,3,TUESDAY,13,0,0,0,0,0,0,Trade: type 7,0.317503308851375,0.4965960601684599,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-557.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n137811,0,Cash loans,F,N,Y,1,270000.0,538704.0,27634.5,481500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-14646,-691,-3270.0,-4604,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,SATURDAY,8,0,0,0,1,1,0,Business Entity Type 3,,0.6364963607704553,0.3046721837533529,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1759.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n125702,0,Cash loans,F,N,Y,1,166500.0,1006920.0,40063.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003069,-13832,-1549,-7737.0,-161,,1,1,0,1,0,0,High skill tech staff,3.0,3,3,THURSDAY,12,0,0,0,0,0,0,Self-employed,0.621432723414582,0.491463422211524,,0.1443,0.1052,0.9851,,,0.16,0.1379,0.3333,,0.1016,,0.0885,,0.0396,0.1471,0.1092,0.9851,,,0.1611,0.1379,0.3333,,0.1039,,0.0922,,0.0419,0.1457,0.1052,0.9851,,,0.16,0.1379,0.3333,,0.1034,,0.0901,,0.0404,,block of flats,0.1221,Panel,No,0.0,0.0,0.0,0.0,-989.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n136101,0,Cash loans,F,N,N,2,112500.0,327024.0,15372.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-10416,-943,-1787.0,-2858,,1,1,0,1,0,0,,4.0,2,2,FRIDAY,15,0,0,0,0,0,0,Restaurant,,0.5915663518411061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-182.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n118881,0,Cash loans,F,N,N,0,157500.0,506889.0,40176.0,418500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.00496,-12113,-2282,-12113.0,-639,,1,1,0,1,0,0,Waiters/barmen staff,1.0,2,2,MONDAY,12,0,0,0,1,1,0,Business Entity Type 3,,0.4560415565741968,0.3425288720742255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n358862,0,Cash loans,M,N,N,0,157500.0,1126548.0,32283.0,983713.5,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-18674,-5774,-11269.0,-2225,,1,1,1,1,1,0,Laborers,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6708657867483543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2857.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,,,,,,\n411699,0,Cash loans,F,N,Y,0,67500.0,585000.0,25897.5,585000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-24131,365243,-13940.0,-4684,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.6496507337414871,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-324.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n344490,0,Cash loans,F,N,Y,0,193500.0,755190.0,38686.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.00496,-19904,365243,-11191.0,-3451,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,XNA,,0.28579073638069863,0.4311917977993083,0.0928,,0.9811,0.7959999999999999,,0.0,0.1897,0.1667,0.2083,0.0611,,0.086,,0.0213,0.0945,,0.9772,0.804,,0.0,0.1724,0.1667,0.2083,0.0625,,0.0896,,0.0225,0.0937,,0.9811,0.7987,,0.0,0.1897,0.1667,0.2083,0.0622,,0.0876,,0.0217,not specified,block of flats,0.0721,,No,5.0,0.0,5.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n438299,0,Cash loans,M,Y,Y,0,315000.0,277969.5,15957.0,229500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.072508,-15367,-1318,-7906.0,-2222,8.0,1,1,0,1,0,0,Drivers,2.0,1,1,MONDAY,9,0,1,1,0,0,0,Business Entity Type 3,,0.5876773033942753,0.4329616670974407,0.2959,0.1704,0.9811,0.7416,0.2551,0.48,0.2069,0.4583,0.5,0.0,0.2404,0.2006,0.0039,0.0144,0.3015,0.1768,0.9811,0.7517,0.2574,0.4834,0.2069,0.4583,0.5,0.0,0.2626,0.209,0.0039,0.0152,0.2987,0.1704,0.9811,0.7451,0.2567,0.48,0.2069,0.4583,0.5,0.0,0.2446,0.2042,0.0039,0.0147,reg oper account,block of flats,0.261,Panel,No,0.0,0.0,0.0,0.0,-1122.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n323107,0,Cash loans,M,N,N,0,112500.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006305,-13511,-166,-3057.0,-4780,,1,1,1,1,0,0,Drivers,2.0,3,3,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 1,,0.6500985464294192,0.8193176922872417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n285908,0,Revolving loans,F,N,N,1,315000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.0228,-16405,-394,-6723.0,-4509,,1,1,1,1,0,0,Laborers,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Services,,0.6461287891423001,0.3656165070113335,0.0825,0.0806,0.9771,0.6872,0.0186,0.0,0.1379,0.1667,0.0417,0.0364,0.0672,0.0715,0.0,0.0,0.084,0.0837,0.9772,0.6994,0.0188,0.0,0.1379,0.1667,0.0417,0.0373,0.0735,0.0745,0.0,0.0,0.0833,0.0806,0.9771,0.6914,0.0188,0.0,0.1379,0.1667,0.0417,0.0371,0.0684,0.0728,0.0,0.0,reg oper account,block of flats,0.0664,Panel,No,1.0,1.0,1.0,1.0,-966.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n278951,0,Cash loans,F,N,Y,1,81000.0,450000.0,21109.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-10604,-2552,-4802.0,-2586,,1,1,1,1,0,0,Laborers,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Postal,0.3833358585903508,0.28239287051869794,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,2.0,-214.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n285244,1,Cash loans,M,Y,Y,0,360000.0,547344.0,39960.0,472500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-20909,-3204,-7140.0,-4386,3.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Trade: type 7,,0.6784206201206396,0.12769879437277135,0.0784,0.0782,0.9722,0.6192,0.0534,0.0,0.1379,0.1667,0.2083,0.0554,0.0605,0.0662,0.0154,0.0123,0.0798,0.0811,0.9722,0.6341,0.0539,0.0,0.1379,0.1667,0.2083,0.0567,0.0661,0.069,0.0156,0.013000000000000001,0.0791,0.0782,0.9722,0.6243,0.0537,0.0,0.1379,0.1667,0.2083,0.0564,0.0616,0.0674,0.0155,0.0125,reg oper account,block of flats,0.0521,Panel,No,0.0,0.0,0.0,0.0,-1305.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n411525,0,Cash loans,F,Y,Y,0,135000.0,879480.0,25843.5,630000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00733,-15454,-3719,-173.0,-5024,14.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Housing,,0.20761172549561066,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n414757,0,Cash loans,F,N,Y,0,90000.0,679500.0,19867.5,679500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-19482,365243,-8691.0,-3004,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,XNA,,0.1843183352776148,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-373.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n255015,0,Cash loans,F,N,Y,0,112500.0,544491.0,21096.0,454500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010643,-14507,-5691,-4978.0,-4978,,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Housing,,0.6369515910874088,0.28961123838200553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-440.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n316792,0,Cash loans,F,N,Y,0,189000.0,675000.0,36747.0,675000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003069,-14912,-2009,-1644.0,-4649,,1,1,0,1,0,0,Sales staff,2.0,3,3,THURSDAY,10,0,0,0,0,1,1,Self-employed,,0.5750486449731727,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n341164,0,Revolving loans,F,Y,Y,0,135000.0,292500.0,14625.0,292500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020246,-15260,-739,-299.0,-4980,16.0,1,1,0,1,1,0,Managers,2.0,3,3,MONDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.33383820669529035,0.4614929372283058,0.6545292802242897,0.0186,0.0,0.9886,0.8436,0.0039,0.0,0.1034,0.0417,0.0833,0.0289,0.0151,0.0188,0.0,0.0,0.0189,0.0,0.9886,0.8497,0.0039,0.0,0.1034,0.0417,0.0833,0.0296,0.0165,0.0196,0.0,0.0,0.0187,0.0,0.9886,0.8457,0.0039,0.0,0.1034,0.0417,0.0833,0.0294,0.0154,0.0192,0.0,0.0,reg oper account,block of flats,0.0169,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-403.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n359176,0,Cash loans,F,N,Y,0,135000.0,481495.5,32436.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-12645,-5794,-6255.0,-4290,,1,1,0,1,0,0,Medicine staff,1.0,3,2,TUESDAY,9,0,0,0,0,0,0,Medicine,0.4198646071265578,0.6048420742570294,0.1766525794312139,0.133,0.1499,0.9831,0.7688,0.0739,0.0,0.2414,0.1667,0.2083,0.1207,0.1084,0.1302,0.0,0.0,0.1355,0.1556,0.9831,0.7779,0.0745,0.0,0.2414,0.1667,0.2083,0.1235,0.1185,0.1356,0.0,0.0,0.1343,0.1499,0.9831,0.7719,0.0743,0.0,0.2414,0.1667,0.2083,0.1228,0.1103,0.1325,0.0,0.0,reg oper account,block of flats,0.1428,Panel,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n233694,0,Revolving loans,F,N,Y,0,90000.0,315000.0,15750.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-14523,-3233,-1093.0,-276,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Medicine,0.40493370615900137,0.6169370736497631,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-132.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n110238,0,Cash loans,F,N,Y,0,180000.0,1125000.0,37309.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-18062,-4470,-8836.0,-1620,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Trade: type 7,0.8671910737970121,0.5597416412079451,0.6347055309763198,0.2237,0.0912,0.9846,0.7892,,0.08,0.0345,0.3333,,0.0656,,0.0986,,,0.2279,0.0946,0.9846,0.7975,,0.0806,0.0345,0.3333,,0.0671,,0.1027,,,0.2259,0.0912,0.9846,0.792,,0.08,0.0345,0.3333,,0.0668,,0.1003,,,,specific housing,0.1143,Panel,No,0.0,0.0,0.0,0.0,-1612.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n181874,0,Cash loans,F,Y,Y,0,135000.0,945000.0,27760.5,945000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-15904,-5266,-8790.0,-4574,12.0,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Other,0.4258682542274519,0.5739207291958087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1658.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n374948,0,Cash loans,M,Y,Y,0,256500.0,1350000.0,39604.5,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-20504,-1702,-2815.0,-3259,17.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.580686936821281,0.16048893062734468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2599.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n442531,1,Cash loans,M,Y,Y,0,405000.0,1312110.0,55723.5,1125000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.020713,-16271,-398,-528.0,-3055,13.0,1,1,0,1,0,0,Drivers,2.0,3,1,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.4424187286513265,0.7281412993111438,0.078,0.0292,0.9767,,,0.0,0.069,0.1667,,0.0181,,0.0351,,0.0344,0.042,0.0,0.9772,,,0.0,0.0345,0.1667,,0.0166,,0.0338,,0.0,0.0489,0.0,0.9771,,,0.0,0.069,0.1667,,0.0177,,0.0333,,0.0417,,block of flats,0.0255,Panel,No,4.0,1.0,4.0,1.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n240566,0,Cash loans,F,N,Y,0,247500.0,284400.0,16326.0,225000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.072508,-24478,365243,-4032.0,-4031,,1,0,0,1,0,0,,1.0,1,1,TUESDAY,10,0,0,0,0,0,0,XNA,,0.7673502021575271,0.7826078370261895,0.1938,0.1074,0.993,0.9048,0.1217,0.32,0.1724,0.4583,0.4583,0.0,0.158,0.2204,0.0,0.0198,0.0987,0.0536,0.993,0.9085,0.0481,0.1611,0.069,0.4583,0.5,0.0,0.0863,0.1243,0.0,0.0039,0.0999,0.07200000000000001,0.993,0.9061,0.0486,0.16,0.069,0.4583,0.5,0.0,0.0821,0.128,0.0,0.0039,reg oper account,block of flats,0.3388,Panel,No,2.0,0.0,2.0,0.0,-2355.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n273549,0,Cash loans,F,Y,Y,1,130500.0,593010.0,19260.0,495000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.04622,-18395,-811,-11804.0,-1829,6.0,1,1,0,1,0,0,Core staff,2.0,1,1,SATURDAY,13,0,0,0,0,0,0,Government,0.6826605409189368,0.7706520018807247,0.5262949398096192,0.2216,0.1071,0.9836,0.7756,0.0342,0.24,0.2069,0.3333,0.375,0.1269,0.1807,0.2186,0.0,0.0,0.2258,0.1112,0.9836,0.7844,0.0345,0.2417,0.2069,0.3333,0.375,0.1298,0.1974,0.2277,0.0,0.0,0.2238,0.1071,0.9836,0.7786,0.0344,0.24,0.2069,0.3333,0.375,0.1291,0.1838,0.2225,0.0,0.0,reg oper account,block of flats,0.209,Panel,No,0.0,0.0,0.0,0.0,-1211.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n221334,0,Cash loans,F,Y,Y,0,450000.0,1042560.0,58347.0,900000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.072508,-18855,-5310,-8580.0,-2401,7.0,1,1,0,1,0,1,Managers,1.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Other,0.8785048422746767,0.7222178554525029,0.7062051096536562,0.3062,0.2925,0.9712,0.6056,0.2732,0.64,0.2759,0.4167,0.4583,0.0,0.2496,0.4191,0.0,0.2658,0.312,0.3035,0.9712,0.621,0.2757,0.6445,0.2759,0.4167,0.4583,0.0,0.2727,0.4366,0.0,0.2813,0.3092,0.2925,0.9712,0.6109,0.2749,0.64,0.2759,0.4167,0.4583,0.0,0.254,0.4266,0.0,0.2713,reg oper account,block of flats,0.3874,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n115068,0,Cash loans,F,N,Y,0,112500.0,646920.0,20866.5,540000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.02461,-16797,-1849,-10056.0,-345,,1,1,1,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,1,1,Government,,0.5401896136756775,0.7421816117614419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1194.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n438032,0,Cash loans,F,N,Y,0,135000.0,450000.0,24543.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-21552,365243,-9094.0,-4392,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,XNA,,0.613888647698849,0.11612491427195998,0.0371,0.0278,0.9876,0.83,0.0,0.04,0.0345,0.3333,0.375,0.0389,0.0303,0.0385,0.0,0.0,0.0378,0.0289,0.9876,0.8367,0.0,0.0403,0.0345,0.3333,0.375,0.0398,0.0331,0.0401,0.0,0.0,0.0375,0.0278,0.9876,0.8323,0.0,0.04,0.0345,0.3333,0.375,0.0395,0.0308,0.0392,0.0,0.0,reg oper account,block of flats,0.0303,Panel,No,7.0,0.0,7.0,0.0,-1845.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n395532,0,Cash loans,F,Y,Y,0,67500.0,740700.0,26734.5,562500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.002134,-16723,-381,-8730.0,-193,23.0,1,1,0,1,0,0,Core staff,1.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Insurance,,0.536744130545375,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n155876,1,Cash loans,M,Y,N,0,247500.0,1854000.0,49036.5,1854000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.015221,-10133,-1182,-2319.0,-2807,8.0,1,1,1,1,1,0,Core staff,1.0,2,2,SUNDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5299954794018314,0.5762088360175724,0.0825,0.1136,0.9831,0.7688,0.0464,0.0,0.2069,0.1667,0.2083,0.0384,0.0672,0.0812,0.0,0.0,0.084,0.1178,0.9831,0.7779,0.0468,0.0,0.2069,0.1667,0.2083,0.0393,0.0735,0.0846,0.0,0.0,0.0833,0.1136,0.9831,0.7719,0.0466,0.0,0.2069,0.1667,0.2083,0.0391,0.0684,0.0826,0.0,0.0,reg oper spec account,block of flats,0.0892,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n448232,0,Revolving loans,F,N,Y,1,76500.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019689,-15048,-3001,-4177.0,-18,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Industry: type 2,0.7140633236868923,0.6618623905885438,,0.2515,0.1656,0.9826,0.762,0.1706,0.28,0.2414,0.3333,0.0417,0.1559,0.2051,0.2649,0.0347,0.0333,0.2563,0.1718,0.9826,0.7713,0.1722,0.282,0.2414,0.3333,0.0417,0.1595,0.2241,0.276,0.035,0.0353,0.254,0.1656,0.9826,0.7652,0.1717,0.28,0.2414,0.3333,0.0417,0.1586,0.2086,0.2697,0.0349,0.034,reg oper account,block of flats,0.3126,Panel,No,2.0,0.0,2.0,0.0,-3040.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n201735,0,Cash loans,M,Y,Y,1,202500.0,1024740.0,49428.0,900000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-15493,-2257,-4019.0,-4957,16.0,1,1,0,1,0,1,Managers,3.0,1,1,WEDNESDAY,15,0,0,0,0,0,0,Advertising,0.5864939503936021,0.7221037775495791,0.7922644738669378,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1373.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n351753,0,Cash loans,F,Y,Y,1,243675.0,1502941.5,52371.0,1372500.0,Family,Working,Higher education,Married,House / apartment,0.022625,-15801,-6907,-6831.0,-4153,31.0,1,1,0,1,0,1,Accountants,3.0,2,2,MONDAY,16,0,0,0,0,0,0,Industry: type 9,0.6345387805105039,0.5841704180868753,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2538.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n229591,0,Cash loans,F,N,Y,0,112500.0,270000.0,13117.5,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-23557,365243,-5978.0,-4943,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.3552308172290725,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-1201.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n415047,0,Cash loans,M,N,Y,0,157500.0,675000.0,45238.5,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.026392,-14361,-1603,-8419.0,-4147,,1,1,1,1,1,1,,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.13225867130240782,0.5946588406718386,,0.1165,0.1052,0.9816,,,0.0,0.2759,0.1667,,0.1578,,0.1089,,0.0549,0.1187,0.1091,0.9816,,,0.0,0.2759,0.1667,,0.1614,,0.1135,,0.0581,0.1176,0.1052,0.9816,,,0.0,0.2759,0.1667,,0.1605,,0.1109,,0.055999999999999994,,block of flats,0.1038,Panel,No,0.0,0.0,0.0,0.0,-701.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n249894,0,Cash loans,F,N,N,0,135000.0,729792.0,32143.5,630000.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.007114,-12752,-4315,-5283.0,-1672,,1,1,1,1,1,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,,0.6334654278514623,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-571.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n243185,0,Cash loans,M,N,N,0,175500.0,808650.0,26217.0,675000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-8586,-1775,-1128.0,-1130,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,Construction,0.13312336287378235,0.10920932417119636,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n275456,0,Cash loans,F,N,Y,0,247500.0,592560.0,35937.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-14526,-5259,-8643.0,-584,,1,1,0,1,0,0,Managers,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.4706100040409693,0.6709478655062604,0.1455428133497032,0.0247,0.0,0.9791,0.7144,0.0025,0.0,0.069,0.0833,0.125,0.0383,0.0202,0.0216,,0.0,0.0252,0.0,0.9791,0.7256,0.0025,0.0,0.069,0.0833,0.125,0.0392,0.022000000000000002,0.0225,,0.0,0.025,0.0,0.9791,0.7182,0.0025,0.0,0.069,0.0833,0.125,0.039,0.0205,0.022000000000000002,,0.0,,block of flats,0.017,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-271.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n187411,0,Cash loans,M,Y,Y,1,162000.0,521280.0,31630.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.01885,-11810,-1278,-568.0,-574,16.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,11,0,1,1,0,1,1,Construction,,0.4392492168586681,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n232049,0,Cash loans,F,N,Y,0,157500.0,263686.5,26208.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-16767,-458,-7551.0,-311,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SUNDAY,5,0,0,0,0,0,0,Medicine,,0.6257858513456438,,0.0794,0.0803,0.9747,0.6532,0.027999999999999997,0.0,0.1379,0.1667,0.0417,0.0852,,0.0631,,0.0085,0.0809,0.0834,0.9747,0.6668,0.0282,0.0,0.1379,0.1667,0.0417,0.0872,,0.0658,,0.009000000000000001,0.0802,0.0803,0.9747,0.6578,0.0282,0.0,0.1379,0.1667,0.0417,0.0867,,0.0643,,0.0087,reg oper account,block of flats,0.065,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-708.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n348994,0,Cash loans,F,N,N,0,112500.0,450000.0,30573.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-14821,-6260,-6830.0,-4750,,1,1,1,1,0,0,Sales staff,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.4315031867045448,0.09569272423026376,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-88.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n281284,0,Cash loans,F,N,N,0,103500.0,257391.0,16771.5,238500.0,Family,Pensioner,Higher education,Married,House / apartment,0.003122,-24841,365243,-7085.0,-1031,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,XNA,,0.4990069098420942,0.6722428897082422,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-99.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n168410,0,Cash loans,M,Y,Y,0,180000.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-12888,-474,-6768.0,-1783,10.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,15,0,1,1,1,0,0,Other,,0.7149170435876151,0.475849908720221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1739.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n203585,0,Cash loans,M,Y,Y,0,270000.0,537669.0,20398.5,378000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-19041,-1042,-8634.0,-2573,20.0,1,1,0,1,0,0,Drivers,2.0,1,1,MONDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.6171075990340423,,0.0649,0.0555,0.9851,0.7959999999999999,0.0166,0.04,0.0345,0.3333,0.0417,0.0627,0.053,0.0516,0.0,0.0,0.0662,0.0575,0.9851,0.804,0.0167,0.0403,0.0345,0.3333,0.0417,0.0641,0.0579,0.0538,0.0,0.0,0.0656,0.0555,0.9851,0.7987,0.0167,0.04,0.0345,0.3333,0.0417,0.0638,0.0539,0.0525,0.0,0.0,reg oper account,block of flats,0.0496,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-436.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n271502,0,Cash loans,F,N,Y,0,180000.0,545040.0,25407.0,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.006233,-9102,-1213,-1321.0,-1736,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.5315585323653548,0.4101025731788671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n418524,0,Cash loans,F,N,Y,0,112500.0,479637.0,18211.5,396000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.020246,-23710,365243,-3398.0,-4750,,1,0,0,1,0,0,,2.0,3,3,MONDAY,8,0,0,0,0,0,0,XNA,0.6433945677675081,0.47355587274926,0.3723336657058204,,,,,,,0.069,0.0417,,,,0.0124,,,,,,,,,0.069,0.0417,,,,0.013000000000000001,,,,,,,,,0.069,0.0417,,,,0.0127,,,,block of flats,0.0098,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2314.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n252615,0,Cash loans,M,Y,Y,0,202500.0,497520.0,25888.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-20898,-2170,-7700.0,-3591,2.0,1,1,0,1,0,0,,2.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Culture,,0.7528795993838544,0.5937175866150576,0.1112,0.0657,0.9891,0.8504,0.1076,0.2,0.1034,0.4304,0.3888,0.0901,0.1009,0.1145,0.009000000000000001,0.0191,0.040999999999999995,0.0213,0.9886,0.8497,0.073,0.1611,0.069,0.4583,0.5,0.0,0.0358,0.0642,0.0,0.0,0.1051,0.0626,0.9886,0.8457,0.1095,0.16,0.0862,0.4583,0.5,0.1069,0.1,0.1039,0.0078,0.0155,reg oper spec account,block of flats,0.1705,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n383665,0,Cash loans,M,Y,N,0,157500.0,1125000.0,32895.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018801,-21324,-4465,-7267.0,-4859,9.0,1,1,1,1,1,0,Drivers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.6460837883568993,0.6801388218428291,0.0433,0.0334,0.9722,0.6192,0.0193,0.0,0.1034,0.0833,0.125,0.0314,0.0353,0.0357,0.0,0.0,0.0441,0.0347,0.9722,0.6341,0.0195,0.0,0.1034,0.0833,0.125,0.0321,0.0386,0.0371,0.0,0.0,0.0437,0.0334,0.9722,0.6243,0.0194,0.0,0.1034,0.0833,0.125,0.0319,0.0359,0.0363,0.0,0.0,reg oper account,block of flats,0.027999999999999997,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2017.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n399380,0,Cash loans,F,Y,Y,0,157500.0,495216.0,26995.5,427500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-18301,-518,-7671.0,-1240,17.0,1,1,0,1,0,0,Sales staff,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,Self-employed,,0.31722833046933663,0.19633396621345675,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,-2014.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n343554,0,Cash loans,M,N,Y,0,292500.0,1394550.0,134604.0,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.072508,-23041,365243,-7208.0,-2041,,1,0,0,1,1,0,,2.0,1,1,MONDAY,18,0,0,0,0,0,0,XNA,,0.7533227991247823,0.5460231970049609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3123,,No,3.0,0.0,3.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n418686,0,Revolving loans,F,N,Y,0,157500.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21037,-2136,-1962.0,-4186,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.6419634690581943,0.6976416513746925,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-1859.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n350159,0,Cash loans,M,Y,Y,0,135000.0,480060.0,22378.5,337500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010556,-14796,-2033,-2402.0,-2779,13.0,1,1,0,1,0,0,Laborers,2.0,3,3,WEDNESDAY,10,0,0,0,0,1,1,Other,,0.42788600908591656,0.2807895743848605,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1046.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n422632,0,Cash loans,M,N,Y,0,135000.0,696528.0,42741.0,630000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23256,365243,-10616.0,-1551,,1,0,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.6934834426370499,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-292.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n297597,0,Cash loans,M,Y,Y,1,225000.0,1078200.0,31653.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-16655,-1487,-2169.0,-188,5.0,1,1,0,1,0,0,Drivers,3.0,2,2,THURSDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.6321239728578547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1818.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n238110,0,Cash loans,F,N,Y,1,193500.0,900000.0,35824.5,900000.0,Unaccompanied,Commercial associate,Incomplete higher,Separated,House / apartment,0.001276,-16993,-1134,-5538.0,-540,,1,1,0,1,0,0,Accountants,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.334173679480932,0.5779691187553125,0.0371,0.0409,0.9742,0.6464,0.0101,0.0,0.069,0.1667,0.0417,0.0162,0.0286,0.0253,0.0039,0.0122,0.0378,0.0425,0.9742,0.6602,0.0102,0.0,0.069,0.1667,0.0417,0.0166,0.0312,0.0263,0.0039,0.0129,0.0375,0.0409,0.9742,0.6511,0.0102,0.0,0.069,0.1667,0.0417,0.0165,0.0291,0.0257,0.0039,0.0124,reg oper account,block of flats,0.0281,\"Stone, brick\",No,4.0,1.0,4.0,0.0,-2470.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n154144,0,Cash loans,M,Y,Y,0,135000.0,780363.0,33192.0,697500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-18675,365243,-1637.0,-2232,14.0,1,0,0,1,0,0,,2.0,3,2,MONDAY,14,0,0,0,0,0,0,XNA,0.3960054316470976,0.5577683895815206,0.8193176922872417,0.0814,0.0528,0.997,0.9592,0.024,0.08,0.069,0.375,0.4167,0.0,0.0664,0.094,0.0,0.0,0.083,0.0548,0.997,0.9608,0.0242,0.0806,0.069,0.375,0.4167,0.0,0.0725,0.0979,0.0,0.0,0.0822,0.0528,0.997,0.9597,0.0241,0.08,0.069,0.375,0.4167,0.0,0.0676,0.0957,0.0,0.0,reg oper account,block of flats,0.0739,Panel,No,0.0,0.0,0.0,0.0,-2114.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n201091,0,Cash loans,M,Y,Y,1,180000.0,283419.0,19071.0,234000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.016612000000000002,-15797,-448,-1746.0,-5855,8.0,1,1,0,1,0,0,,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Other,0.6233460696182054,0.6216904837706708,0.6279908192952864,0.1649,0.1124,0.9975,0.9592,0.1268,0.16,0.1379,0.375,0.0417,0.0,0.1345,0.1931,0.0,0.0,0.1681,0.1166,0.9975,0.9608,0.128,0.1611,0.1379,0.375,0.0417,0.0,0.1469,0.2012,0.0,0.0,0.1665,0.1124,0.9975,0.9597,0.1276,0.16,0.1379,0.375,0.0417,0.0,0.1368,0.1966,0.0,0.0,reg oper account,block of flats,0.1732,Panel,No,1.0,0.0,1.0,0.0,-347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n317313,0,Cash loans,F,N,Y,0,180000.0,817560.0,29493.0,675000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.005084,-13282,-1331,-4127.0,-5190,,1,1,1,1,1,1,Laborers,2.0,2,2,TUESDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.5764158330210712,0.15780444525535936,0.633031641417419,0.0041,,0.9806,,,,0.1379,0.0,,,,0.0074,,,0.0042,,0.9806,,,,0.1379,0.0,,,,0.0077,,,0.0042,,0.9806,,,,0.1379,0.0,,,,0.0076,,,,block of flats,0.0058,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2040.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n104375,0,Cash loans,F,Y,N,0,157500.0,1515415.5,40104.0,1354500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-13814,-7164,-5323.0,-5230,13.0,1,1,0,1,1,0,,2.0,3,3,THURSDAY,7,0,0,0,0,1,1,Transport: type 4,0.4833288430478334,0.2356090067790517,0.41534714488434,0.0729,0.0652,0.9881,0.8368,0.0172,0.08,0.1034,0.2917,0.0417,0.0128,0.0588,0.0894,0.0029,0.0066,0.0557,0.0499,0.9881,0.8432,0.0137,0.0806,0.069,0.3333,0.0417,0.0085,0.0487,0.0711,0.0,0.0,0.0552,0.048,0.9881,0.8390000000000001,0.0137,0.08,0.069,0.3333,0.0417,0.0085,0.0453,0.0714,0.0,0.0,reg oper account,block of flats,0.0537,Panel,No,0.0,0.0,0.0,0.0,-2355.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n270068,0,Cash loans,M,Y,N,2,157500.0,254700.0,18585.0,225000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.031329,-11018,-194,-5288.0,-3475,25.0,1,1,0,1,0,0,Laborers,4.0,2,2,SUNDAY,8,0,0,0,1,1,0,Business Entity Type 3,,0.5190065836212262,0.6279908192952864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1921.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n160842,0,Cash loans,F,N,N,1,67500.0,276277.5,11835.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.010276,-17304,-5175,-4513.0,-613,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,School,,0.6279545701990613,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1895.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n247608,0,Cash loans,F,N,N,0,112500.0,781920.0,25411.5,675000.0,Family,Working,Secondary / secondary special,Widow,House / apartment,0.006008,-22525,-5166,-5771.0,-4355,,1,1,1,1,1,0,,1.0,2,2,SATURDAY,12,0,0,0,1,0,1,Security Ministries,,0.6692413891536313,,0.5897,0.4299,0.9806,0.7348,0.252,0.64,0.5517,0.3333,0.375,0.0784,0.4783,0.6165,0.0116,0.0141,0.6008,0.4461,0.9806,0.7452,0.2543,0.6445,0.5517,0.3333,0.375,0.0802,0.5225,0.6423,0.0117,0.0149,0.5954,0.4299,0.9806,0.7383,0.2536,0.64,0.5517,0.3333,0.375,0.0798,0.4865,0.6276,0.0116,0.0144,reg oper spec account,block of flats,0.6258,Panel,No,2.0,0.0,2.0,0.0,-3.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n445652,0,Revolving loans,F,Y,Y,1,225000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Widow,House / apartment,0.009175,-12573,-896,-2765.0,-4265,15.0,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,18,0,0,0,0,1,1,Trade: type 7,,0.3048535631209588,0.5779691187553125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-63.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n171844,0,Revolving loans,F,N,Y,0,225000.0,405000.0,20250.0,405000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.00733,-23155,365243,-6804.0,-4227,,1,0,0,1,0,1,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,0.8887817527911129,0.6317323285931947,0.6722428897082422,0.1227,0.1205,0.9776,0.6940000000000001,,0.32,0.2759,0.1667,0.2083,0.0233,0.1,0.1144,0.0,0.0,0.125,0.1251,0.9777,0.706,,0.3222,0.2759,0.1667,0.2083,0.0238,0.1093,0.1192,0.0,0.0,0.1239,0.1205,0.9776,0.6981,,0.32,0.2759,0.1667,0.2083,0.0237,0.1018,0.1164,0.0,0.0,,block of flats,0.09,Panel,No,2.0,0.0,2.0,0.0,-1303.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n147205,0,Cash loans,F,N,N,1,270000.0,1078200.0,28570.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.011703,-10473,-150,-3720.0,-872,,1,1,0,1,1,0,Laborers,3.0,2,2,WEDNESDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.7243187234599714,0.20092608771597087,0.0124,0.033,0.9637,,,,0.0862,0.0417,,0.0223,,0.0144,,0.0,0.0126,0.0343,0.9638,,,,0.069,0.0417,,0.0163,,0.015,,0.0,0.0125,0.033,0.9637,,,,0.0862,0.0417,,0.0227,,0.0147,,0.0,,block of flats,0.0114,Others,Yes,1.0,0.0,1.0,0.0,-456.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n330470,0,Cash loans,M,N,Y,0,112500.0,343800.0,16852.5,225000.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.006305,-12817,-857,-891.0,-4065,,1,1,0,1,0,0,,2.0,3,3,SATURDAY,3,0,0,0,0,1,1,Business Entity Type 2,,0.3385032325984414,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-876.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n142761,0,Revolving loans,F,N,N,1,112500.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019689,-14384,-3818,-4063.0,-4413,,1,1,1,1,1,0,Sales staff,2.0,2,2,SUNDAY,12,0,0,0,0,1,1,Self-employed,,0.17070078585779758,0.1047946227497676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1784.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n141153,0,Revolving loans,F,Y,Y,2,99000.0,247500.0,12375.0,247500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019689,-10514,-538,-10514.0,-1836,6.0,1,1,1,1,1,1,,4.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 1,0.31691172153330704,0.465914071950225,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-1020.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n374486,0,Cash loans,M,Y,N,0,225000.0,832500.0,24340.5,832500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006008,-13375,-2139,-2420.0,-3347,1.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Other,0.25367830599498264,0.4894851268913248,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n278274,0,Cash loans,F,N,Y,0,67500.0,99000.0,4815.0,99000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.00702,-20459,365243,-10454.0,-3517,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.22513822890249807,0.4329616670974407,0.0082,,0.9672,0.5512,0.0034,0.0,0.069,0.0417,0.0833,0.0219,0.0067,0.012,0.0,0.0086,0.0084,,0.9672,0.5688,0.0035,0.0,0.069,0.0417,0.0833,0.0224,0.0073,0.0125,0.0,0.0091,0.0083,,0.9672,0.5572,0.0035,0.0,0.069,0.0417,0.0833,0.0223,0.0068,0.0122,0.0,0.0088,not specified,block of flats,0.0113,Mixed,No,2.0,1.0,2.0,1.0,-343.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n257240,1,Cash loans,F,N,N,0,225000.0,346500.0,13059.0,346500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,Municipal apartment,0.035792000000000004,-16576,-4014,-5843.0,-127,,1,1,1,1,1,0,Laborers,1.0,2,2,WEDNESDAY,20,0,0,0,0,0,0,Business Entity Type 3,,0.6863494703063642,0.34090642641523844,0.0258,0.0225,0.9523,0.3472,0.0031,0.0,0.0345,0.0417,0.0417,0.0342,0.021,0.0183,0.0,0.0,0.0263,0.0234,0.9523,0.3728,0.0031,0.0,0.0345,0.0417,0.0417,0.035,0.023,0.0191,0.0,0.0,0.026000000000000002,0.0225,0.9523,0.3559,0.0031,0.0,0.0345,0.0417,0.0417,0.0348,0.0214,0.0186,0.0,0.0,reg oper account,terraced house,0.0144,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-682.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n395088,0,Revolving loans,F,Y,N,2,157500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.030755,-15092,-5416,-460.0,-6214,19.0,1,1,0,1,1,0,Medicine staff,3.0,2,2,TUESDAY,16,0,0,0,0,0,0,Police,0.4546704361861072,0.6890218179252049,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1729.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,4.0\n123144,0,Cash loans,M,Y,Y,0,360000.0,688500.0,29299.5,688500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-19725,-6170,-11780.0,-3272,9.0,1,1,0,1,1,0,Managers,2.0,1,1,MONDAY,15,0,0,0,0,0,0,Trade: type 7,0.7236541872203825,0.6674382319781625,0.475849908720221,0.1979,0.0921,0.9786,,,0.24,0.2069,0.3333,,0.0,,0.1973,,0.1278,0.2017,0.0956,0.9786,,,0.2417,0.2069,0.3333,,0.0,,0.2055,,0.1353,0.1999,0.0921,0.9786,,,0.24,0.2069,0.3333,,0.0,,0.2008,,0.1305,,block of flats,0.183,Panel,No,0.0,0.0,0.0,0.0,-1398.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n243672,0,Cash loans,F,N,Y,0,247500.0,1125000.0,59940.0,1125000.0,Family,Working,Higher education,Married,House / apartment,0.00496,-15920,-2741,-6096.0,-4344,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,17,0,0,0,0,0,0,University,0.7334948601495075,0.6867541750084997,0.7062051096536562,0.1037,,0.9796,,,0.0,0.2328,0.1667,,,,0.0775,,0.0,0.125,,0.9796,,,0.0,0.2759,0.1667,,,,0.0,,0.0,0.1239,,0.9796,,,0.0,0.2759,0.1667,,,,0.0805,,0.0,,block of flats,0.031,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-665.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n325984,1,Cash loans,M,Y,Y,0,135000.0,855000.0,25128.0,855000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-17078,-9423,-2700.0,-626,15.0,1,1,0,1,1,0,Drivers,2.0,2,2,MONDAY,7,0,0,0,0,0,0,Industry: type 9,0.06019529127946805,0.6708657867483543,0.3979463219016906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2116.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n229780,0,Cash loans,M,N,Y,1,162000.0,364500.0,28926.0,364500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-9890,-2142,-512.0,-2504,,1,1,0,1,0,0,Laborers,3.0,3,3,FRIDAY,11,0,0,0,0,1,1,Self-employed,,0.19314621096609635,0.06126811384928985,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-557.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n374500,0,Cash loans,F,N,N,0,135000.0,343800.0,12960.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.032561,-18963,-1383,-13067.0,-2515,,1,1,0,1,0,0,Cooking staff,1.0,1,1,FRIDAY,16,0,0,0,0,0,0,Self-employed,,0.7469454840412518,0.7557400501752248,0.0722,0.0,0.9747,,,0.0,0.1379,0.1667,,0.0863,,0.0623,,0.0339,0.063,0.0,0.9737,,,0.0,0.1379,0.1667,,0.0572,,0.0525,,0.0,0.0729,0.0,0.9752,,,0.0,0.1379,0.1667,,0.0878,,0.069,,0.0346,,block of flats,0.054000000000000006,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2144.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n109634,0,Cash loans,F,N,Y,1,90000.0,254700.0,25321.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.011703,-10428,-1100,-10295.0,-2677,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Self-employed,0.4139489141864473,0.7067774810274863,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,0.0,-628.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n110483,0,Cash loans,F,N,N,1,292500.0,1042560.0,34587.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-12609,-3289,-10384.0,-1579,,1,1,0,1,0,0,Medicine staff,3.0,2,2,MONDAY,12,0,0,0,0,1,1,Medicine,0.26043059545919905,0.12403077299682362,0.4578995512067301,0.0742,0.0618,0.9856,,0.0,0.08,0.069,0.3333,,0.0789,0.0605,0.079,0.0,0.0,0.0756,0.0641,0.9856,,0.0,0.0806,0.069,0.3333,,0.0807,0.0661,0.0823,0.0,0.0,0.0749,0.0618,0.9856,,0.0,0.08,0.069,0.3333,,0.0803,0.0616,0.0804,0.0,0.0,org spec account,block of flats,0.0782,Panel,No,5.0,2.0,5.0,2.0,-468.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n223263,0,Revolving loans,F,N,Y,0,46800.0,135000.0,6750.0,135000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.010147,-21868,365243,-11757.0,-4266,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,0.8617061838136815,0.07555493625039103,0.28371188263500075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1003.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n143960,0,Cash loans,M,Y,Y,0,112500.0,225000.0,17905.5,225000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-15172,-2374,-3816.0,-5173,6.0,1,1,0,1,1,0,Sales staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3969847586140601,0.7256267568153384,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-231.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n195591,0,Cash loans,M,N,Y,0,135000.0,485640.0,34668.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-24940,365243,-2710.0,-4047,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.677938583120532,,0.0825,0.0091,0.9732,,,0.0,0.1379,0.1667,,,,0.0429,,0.0,0.084,0.0094,0.9732,,,0.0,0.1379,0.1667,,,,0.0447,,0.0,0.0833,0.0091,0.9732,,,0.0,0.1379,0.1667,,,,0.0436,,0.0,,block of flats,0.0508,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-2023.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n426883,1,Cash loans,M,N,Y,0,450000.0,751500.0,26622.0,751500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.02461,-10743,-3174,-529.0,-2462,,1,1,1,1,1,0,Managers,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Construction,0.4333974914261777,0.6868341411492649,0.1556893746835917,0.3773,0.1095,0.9901,0.8504,0.0918,0.24,0.1552,0.5417,0.5833,0.0484,0.3001,0.3743,0.027000000000000003,0.2525,0.3845,0.068,0.9806,0.7452,0.0319,0.2417,0.1034,0.3333,0.375,0.0495,0.3278,0.2286,0.0156,0.0152,0.381,0.1095,0.9901,0.8524,0.0924,0.24,0.1552,0.5417,0.5833,0.0493,0.3053,0.381,0.0272,0.2578,reg oper account,block of flats,0.1965,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-523.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n310731,0,Revolving loans,F,Y,N,0,54000.0,180000.0,9000.0,180000.0,Family,Working,Higher education,Married,House / apartment,0.035792000000000004,-17995,-4988,-8346.0,-1549,10.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,School,,0.6766816011821557,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1454.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n314529,0,Cash loans,F,N,Y,0,180000.0,1762110.0,48586.5,1575000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.02461,-15515,-8489,-6799.0,-3810,,1,1,0,1,1,0,Laborers,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Government,,0.4413077740743269,,0.1227,0.115,0.9781,,,0.0,0.2759,0.1667,,0.102,,0.114,,0.0,0.125,0.1193,0.9782,,,0.0,0.2759,0.1667,,0.1043,,0.1188,,0.0,0.1239,0.115,0.9781,,,0.0,0.2759,0.1667,,0.1038,,0.11599999999999999,,0.0,,block of flats,0.0986,Panel,No,0.0,0.0,0.0,0.0,-948.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n453086,0,Cash loans,F,Y,Y,0,157500.0,848745.0,46044.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.010276,-15426,-2034,-157.0,-4274,4.0,1,1,0,1,0,0,Cooking staff,2.0,2,2,MONDAY,12,0,0,0,0,1,1,School,0.589673759807109,0.6566751925709232,0.1940678276718812,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-784.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n300508,0,Cash loans,M,Y,Y,1,225000.0,808650.0,26217.0,675000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010556,-15779,-828,-362.0,-5031,8.0,1,1,0,1,0,0,Drivers,3.0,3,3,TUESDAY,14,0,0,0,0,1,1,Construction,,0.5543513607759295,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2355.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n347823,0,Cash loans,F,N,Y,1,90000.0,301464.0,23949.0,238500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.00733,-8827,-223,-4150.0,-1499,,1,1,0,1,0,1,Sales staff,3.0,2,2,THURSDAY,13,0,0,0,0,0,0,Self-employed,0.4245979020335972,0.578203685876198,0.375711009574066,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-605.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n167754,0,Cash loans,M,Y,N,0,180000.0,254700.0,27022.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-9375,-115,-9375.0,-2000,0.0,1,1,1,1,1,0,Drivers,2.0,2,2,SATURDAY,18,0,0,0,0,0,0,Transport: type 3,0.5891817471953547,0.3056929881840761,,0.0928,0.0028,0.9826,0.762,0.0152,0.0,0.2069,0.1667,0.0417,0.0,0.0756,0.0813,0.0,0.0,0.0945,0.0029,0.9826,0.7713,0.0153,0.0,0.2069,0.1667,0.0417,0.0,0.0826,0.0847,0.0,0.0,0.0937,0.0028,0.9826,0.7652,0.0153,0.0,0.2069,0.1667,0.0417,0.0,0.077,0.0827,0.0,0.0,org spec account,block of flats,0.0639,Panel,No,0.0,0.0,0.0,0.0,-39.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n181956,0,Cash loans,M,Y,N,0,166500.0,1125000.0,29677.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-18224,-4583,-2880.0,-1682,0.0,1,1,1,1,0,0,Core staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Housing,0.7319142727584949,0.6253094854911246,0.7981372313187245,0.033,0.0,0.9722,,,0.0,0.1379,0.125,,0.0438,,0.0416,,0.0179,0.0336,0.0,0.9722,,,0.0,0.1379,0.125,,0.0448,,0.0433,,0.019,0.0333,0.0,0.9722,,,0.0,0.1379,0.125,,0.0446,,0.0423,,0.0183,,block of flats,0.0328,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1053.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n223798,0,Cash loans,F,N,N,0,135000.0,101880.0,10053.0,90000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.019101,-20913,-12704,-3766.0,-4314,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,FRIDAY,17,0,0,0,0,0,0,Other,,0.6146658455744086,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1776.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n425996,0,Revolving loans,F,N,Y,0,157500.0,360000.0,18000.0,360000.0,Family,Pensioner,Higher education,Married,House / apartment,0.019101,-18610,365243,-7094.0,-1099,,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.6901966774788431,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-683.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n369886,0,Cash loans,M,Y,N,0,270000.0,545040.0,25537.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.006629,-12404,-1594,-5535.0,-4323,11.0,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,5,0,0,0,0,1,1,Business Entity Type 3,0.20554633490656266,0.5375378929910287,0.12530787842823918,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1077.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,7.0\n181716,1,Cash loans,M,N,Y,0,180000.0,292500.0,34843.5,292500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-13898,-539,-2070.0,-258,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,8,0,1,1,0,0,0,Business Entity Type 1,,0.24160765631373984,0.7016957740576931,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n210508,0,Cash loans,F,Y,Y,2,180000.0,1256400.0,36864.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-16673,-636,-4292.0,-226,13.0,1,1,0,1,1,0,Sales staff,4.0,3,3,THURSDAY,11,0,0,0,0,0,0,Other,0.6919649230171174,0.4373703168538173,0.05882590047568205,0.0361,0.0332,0.9876,0.83,0.0319,0.04,0.0345,0.3333,0.375,0.0112,0.0294,0.0436,0.0,0.0,0.0368,0.0344,0.9876,0.8367,0.0322,0.0403,0.0345,0.3333,0.375,0.0115,0.0321,0.0455,0.0,0.0,0.0364,0.0332,0.9876,0.8323,0.0321,0.04,0.0345,0.3333,0.375,0.0114,0.0299,0.0444,0.0,0.0,reg oper account,block of flats,0.0518,Panel,No,6.0,0.0,6.0,0.0,-1873.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n318711,0,Cash loans,F,N,Y,1,144000.0,871335.0,37048.5,715500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.020713,-13055,-379,-7126.0,-4227,,1,1,0,1,0,0,,2.0,3,2,WEDNESDAY,5,0,0,0,0,0,0,Self-employed,0.5481872435980233,0.2845203423226661,0.5280927512030451,0.2144,0.0621,0.9841,0.7824,0.2446,0.16,0.1379,0.3333,0.375,0.084,0.1731,0.147,0.0077,0.0834,0.2185,0.0644,0.9841,0.7909,0.2469,0.1611,0.1379,0.3333,0.375,0.0859,0.1892,0.1531,0.0078,0.0883,0.2165,0.0621,0.9841,0.7853,0.2462,0.16,0.1379,0.3333,0.375,0.0854,0.1761,0.1496,0.0078,0.0852,,block of flats,0.1337,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n218242,0,Cash loans,M,Y,Y,2,135000.0,161730.0,14832.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-11601,-1389,-904.0,-3986,16.0,1,1,0,1,0,0,Drivers,4.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6163270810124146,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-113.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n232585,0,Cash loans,F,N,Y,0,99000.0,188460.0,9864.0,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-16835,-3448,-4121.0,-390,,1,1,1,1,1,0,Laborers,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,School,,0.5633435687837205,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1225.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n255099,0,Cash loans,F,N,N,0,360000.0,880501.5,23224.5,734976.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-18474,-905,-6596.0,-2011,,1,1,1,1,1,0,Cooking staff,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.4256124499110995,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-207.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n382696,0,Cash loans,M,N,Y,0,135000.0,234576.0,25393.5,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006629,-10190,-1752,-6246.0,-2870,,1,1,0,1,0,0,Medicine staff,2.0,2,2,THURSDAY,5,0,0,0,0,0,0,Medicine,0.20606213114875616,0.5586366331189252,0.7713615919194317,0.1938,0.1479,0.9891,0.8504,0.1113,0.2,0.1724,0.375,0.375,0.0605,0.158,0.2618,0.0,0.0,0.1975,0.1535,0.9891,0.8563,0.1123,0.2014,0.1724,0.375,0.375,0.0619,0.1726,0.2727,0.0,0.0,0.1957,0.1479,0.9891,0.8524,0.11199999999999999,0.2,0.1724,0.375,0.375,0.0616,0.1608,0.2665,0.0,0.0,reg oper account,block of flats,0.2413,Panel,No,4.0,0.0,4.0,0.0,-708.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n433507,0,Cash loans,F,N,Y,0,112500.0,1321902.0,38781.0,1035000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-16235,-986,-729.0,-3733,,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Other,,0.3578596516621369,,0.1485,0.0,0.998,0.966,0.0592,0.16,0.069,0.625,0.6667,0.0957,0.1076,0.1606,0.0618,0.0534,0.1513,0.0,0.998,0.9673,0.0597,0.1611,0.069,0.625,0.6667,0.0978,0.1175,0.1674,0.0623,0.0565,0.1499,0.0,0.998,0.9665,0.0595,0.16,0.069,0.625,0.6667,0.0973,0.1095,0.1635,0.0621,0.0545,reg oper account,block of flats,0.1703,Block,No,0.0,0.0,0.0,0.0,-421.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n350411,1,Cash loans,M,Y,N,0,112500.0,225000.0,25447.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-10363,-3342,-585.0,-2950,19.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,Self-employed,0.043325966641189,0.6317933755051752,0.20915469884100693,0.1268,0.0643,0.9871,0.8232,0.0114,0.08,0.069,0.3333,0.375,0.0559,0.1034,0.0783,0.0,0.0,0.1292,0.0667,0.9871,0.8301,0.0115,0.0806,0.069,0.3333,0.375,0.0572,0.1129,0.0816,0.0,0.0,0.128,0.0643,0.9871,0.8256,0.0115,0.08,0.069,0.3333,0.375,0.0569,0.1052,0.0797,0.0,0.0,reg oper account,block of flats,0.0678,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-395.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,1.0\n287244,0,Cash loans,M,N,Y,0,112500.0,1100709.0,43780.5,1012500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-22759,365243,-12095.0,-5067,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.1942664499695638,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-693.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n313155,0,Cash loans,M,N,Y,1,49500.0,239850.0,25447.5,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.009175,-16066,-652,-7653.0,-2565,,1,1,1,1,0,0,Laborers,3.0,2,2,FRIDAY,13,0,0,0,0,1,1,Business Entity Type 2,,0.7712312431823097,0.34578480246959553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1336.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n184190,0,Cash loans,F,N,Y,0,126000.0,900000.0,26446.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-10187,-1970,-4749.0,-407,,1,1,1,1,1,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.7342476593703394,0.4223696523543468,0.2103,0.1925,0.9881,,,0.24,0.2069,0.3333,,0.1471,,0.2302,,0.0943,0.2143,0.1998,0.9881,,,0.2417,0.2069,0.3333,,0.1505,,0.2399,,0.0999,0.2123,0.1925,0.9881,,,0.24,0.2069,0.3333,,0.1497,,0.2344,,0.0963,,block of flats,0.2016,Panel,No,0.0,0.0,0.0,0.0,-1029.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n392756,0,Cash loans,F,N,Y,0,117000.0,948582.0,27864.0,679500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-20430,365243,-6775.0,-3913,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.7118946682856718,0.10344905212675168,0.1825,0.0914,0.9771,0.6872,0.0515,0.0,0.069,0.1667,0.2083,0.0443,0.1471,0.0637,0.0077,0.0732,0.1859,0.0948,0.9772,0.6994,0.0519,0.0,0.069,0.1667,0.2083,0.0454,0.1607,0.0664,0.0078,0.0775,0.1842,0.0914,0.9771,0.6914,0.0518,0.0,0.069,0.1667,0.2083,0.0451,0.1496,0.0648,0.0078,0.0747,reg oper account,block of flats,0.0941,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3244.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n445887,0,Cash loans,F,N,Y,0,189000.0,312768.0,13774.5,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010643,-22057,365243,-6951.0,-4321,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,0.7398018230684404,0.5474480080201232,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1861.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n174292,0,Cash loans,F,Y,Y,0,90000.0,550980.0,35343.0,450000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-13209,-233,-2856.0,-3897,24.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.6447401030183771,0.29709253572959504,0.8193176922872417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1557.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n427825,0,Cash loans,F,Y,N,2,270000.0,1762110.0,48456.0,1575000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-11455,-1273,-6018.0,-1202,4.0,1,1,0,1,1,0,Laborers,4.0,1,1,MONDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.5419008377680636,0.7196750280391047,0.5424451438613613,0.0124,0.0172,0.9886,,0.0034,0.0,0.0345,0.0417,,,0.0101,0.0134,,0.0,0.0126,0.0179,0.9886,,0.0034,0.0,0.0345,0.0417,,,0.011000000000000001,0.0139,,0.0,0.0125,0.0172,0.9886,,0.0034,0.0,0.0345,0.0417,,,0.0103,0.0136,,0.0,,block of flats,0.0124,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-846.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n137612,0,Cash loans,F,N,Y,0,225000.0,634482.0,18679.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-21697,365243,-10990.0,-4421,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,0.7645879992999544,0.582006839288284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-389.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n221749,0,Cash loans,F,N,Y,0,117000.0,257391.0,19372.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.025164,-18066,-2818,-9240.0,-1622,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.7113810911648134,0.6574438780680834,0.4956658291397297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1009.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n341919,0,Cash loans,F,N,Y,0,49500.0,327024.0,10575.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-22045,365243,-8168.0,-4810,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,6,0,0,0,0,0,0,XNA,,0.16021692934465198,0.4596904504249018,0.0021,0.0,0.9682,,,0.0,0.0345,0.0,,0.0152,,0.0014,,0.0,0.0021,0.0,0.9682,,,0.0,0.0345,0.0,,0.0155,,0.0014,,0.0,0.0021,0.0,0.9682,,,0.0,0.0345,0.0,,0.0154,,0.0014,,0.0,,block of flats,0.0011,Wooden,Yes,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n192192,0,Cash loans,M,Y,Y,2,270000.0,497520.0,32521.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-13039,-6188,-6705.0,-4059,2.0,1,1,0,1,0,0,Laborers,4.0,1,1,MONDAY,18,0,1,1,0,0,0,Business Entity Type 3,0.6251768682684384,,0.6785676886853644,0.0773,0.0529,0.9767,0.6804,0.0256,0.0,0.1724,0.1667,0.2083,0.0757,0.063,0.0631,0.0,0.0,0.0788,0.0549,0.9767,0.6929,0.0259,0.0,0.1724,0.1667,0.2083,0.0774,0.0689,0.0657,0.0,0.0,0.0781,0.0529,0.9767,0.6847,0.0258,0.0,0.1724,0.1667,0.2083,0.077,0.0641,0.0642,0.0,0.0,reg oper account,block of flats,0.0636,Panel,No,0.0,0.0,0.0,0.0,-475.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n357179,0,Revolving loans,F,N,Y,1,112500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.018209,-13422,-530,-2935.0,-5051,,1,1,1,1,0,0,Accountants,2.0,3,3,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.5695512465913325,0.4025490102629117,0.6690566947824041,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-497.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n259566,0,Cash loans,M,Y,Y,0,180000.0,521280.0,31630.5,450000.0,Other_A,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-10078,-621,-1677.0,-2713,19.0,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.41596073549430784,0.4722533429586386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1030.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n166666,1,Cash loans,F,N,Y,0,112500.0,450000.0,30573.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-18502,-786,-4541.0,-2049,,1,1,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.4490258501807637,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-397.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n222138,0,Cash loans,M,Y,Y,0,112500.0,225000.0,12694.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010006,-18430,-4399,-6282.0,-1977,13.0,1,1,1,1,1,0,Laborers,1.0,2,2,MONDAY,10,0,0,0,0,0,0,Government,0.5668511453824877,0.5221392587883524,0.7091891096653581,0.0412,0.0598,0.9871,0.8232,0.0112,0.0,0.069,0.1667,0.0417,,0.0336,0.0429,0.0,0.0218,0.042,0.062,0.9871,0.8301,0.0113,0.0,0.069,0.1667,0.0417,,0.0367,0.0447,0.0,0.0231,0.0416,0.0598,0.9871,0.8256,0.0113,0.0,0.069,0.1667,0.0417,,0.0342,0.0436,0.0,0.0223,reg oper account,block of flats,0.0382,Panel,No,1.0,0.0,1.0,0.0,-1564.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n174544,0,Cash loans,M,Y,N,1,117000.0,104256.0,11227.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.02461,-10834,-3584,-2436.0,-3502,11.0,1,1,1,1,0,0,Laborers,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Transport: type 4,0.31197269381434684,0.5103383094375619,0.5370699579791587,0.0278,0.0223,0.9717,,,0.0,0.1034,0.0833,,0.0479,,0.0302,,0.0043,0.0284,0.0232,0.9717,,,0.0,0.1034,0.0833,,0.049,,0.0314,,0.0045,0.0281,0.0223,0.9717,,,0.0,0.1034,0.0833,,0.0487,,0.0307,,0.0044,,block of flats,0.0247,Block,No,6.0,0.0,6.0,0.0,-1070.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n437906,0,Cash loans,F,N,N,0,94500.0,265851.0,11263.5,229500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-14696,-1078,-6728.0,-4138,,1,1,1,1,1,0,Sales staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Self-employed,0.7634740364155527,0.6797692531408248,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-387.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,0.0\n120824,0,Cash loans,M,N,Y,0,135000.0,130320.0,13815.0,112500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.006305,-8061,-1182,-2882.0,-730,,1,1,0,1,0,0,,2.0,3,3,SATURDAY,6,0,0,0,0,0,0,Transport: type 4,0.5804918026233157,0.4902884315967824,,0.0577,0.085,0.9801,0.728,0.008,0.0,0.1379,0.1667,0.2083,0.0424,,0.0533,,0.0553,0.0588,0.0882,0.9801,0.7387,0.008,0.0,0.1379,0.1667,0.2083,0.0433,,0.0556,,0.0585,0.0583,0.085,0.9801,0.7316,0.008,0.0,0.1379,0.1667,0.2083,0.0431,,0.0543,,0.0565,reg oper account,block of flats,0.0583,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-720.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n149517,0,Cash loans,M,Y,N,0,144000.0,1006920.0,40059.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.019689,-14875,-6250,-290.0,-4189,6.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Industry: type 2,,0.6590279104992322,0.6446794549585961,0.0392,0.0114,0.9747,,,0.0,0.069,0.1667,,0.0332,,0.0306,,0.0081,0.0399,0.0118,0.9747,,,0.0,0.069,0.1667,,0.0339,,0.0319,,0.0086,0.0396,0.0114,0.9747,,,0.0,0.069,0.1667,,0.0338,,0.0312,,0.0083,,block of flats,0.0258,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-2492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n455766,0,Revolving loans,F,Y,Y,0,112500.0,315000.0,15750.0,315000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.008625,-21387,365243,-13564.0,-1249,64.0,1,0,0,1,0,0,,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,XNA,,0.13519767634300586,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-527.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n125265,0,Cash loans,F,N,Y,0,135000.0,101880.0,10971.0,90000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.026392,-13368,-1225,-5627.0,-3786,,1,1,1,1,0,0,Sales staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.486912283186814,0.4398633351873708,0.7295666907060153,0.0598,0.0955,0.9921,,,0.0,0.2069,0.1667,,,,0.0678,,0.0042,0.0609,0.0991,0.9921,,,0.0,0.2069,0.1667,,,,0.0706,,0.0045,0.0604,0.0955,0.9921,,,0.0,0.2069,0.1667,,,,0.069,,0.0043,,block of flats,0.0542,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1328.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n182008,0,Cash loans,M,Y,N,0,112500.0,397881.0,21127.5,328500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.010643,-12207,-501,-3340.0,-14,1.0,1,1,0,1,1,0,Laborers,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Government,0.2643500130252578,0.6117873218937727,0.30620229831350426,0.1206,0.1136,0.9921,0.8912,,0.0,0.3448,0.2083,,0.1343,,0.1461,,0.0954,0.1229,0.1179,0.9921,0.8955,,0.0,0.3448,0.2083,,0.1374,,0.1522,,0.10099999999999999,0.1218,0.1136,0.9921,0.8927,,0.0,0.3448,0.2083,,0.1367,,0.1487,,0.0974,reg oper account,block of flats,0.1342,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n275127,1,Cash loans,F,N,Y,0,67500.0,206280.0,10161.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003813,-19837,-1130,-5089.0,-3316,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6267429669272047,0.13259749523614614,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-821.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n361624,0,Cash loans,M,N,Y,0,315000.0,884178.0,104931.0,850500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-8676,-908,-2986.0,-1328,,1,1,0,1,0,0,Laborers,2.0,1,1,WEDNESDAY,17,0,1,1,0,1,1,Other,0.1338585215910411,0.6135361264242111,,0.0969,0.0445,0.9871,,,0.16,0.069,0.4583,,,,0.105,,,0.0987,0.0462,0.9871,,,0.1611,0.069,0.4583,,,,0.1094,,,0.0978,0.0445,0.9871,,,0.16,0.069,0.4583,,,,0.1069,,,,block of flats,0.1112,Panel,No,0.0,0.0,0.0,0.0,-333.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n287409,0,Cash loans,M,Y,N,0,180000.0,781920.0,23836.5,675000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23994,365243,-6815.0,-4081,15.0,1,0,0,1,0,0,,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,XNA,,0.31430849269104544,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-1445.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n138903,0,Cash loans,F,N,N,0,157500.0,562491.0,27189.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.009334,-12888,-584,-7013.0,-3972,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.4819007816100445,0.5202893195525753,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-409.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n265447,0,Cash loans,M,Y,Y,0,180000.0,1350000.0,57330.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-17929,-5566,-10204.0,-1168,8.0,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Self-employed,,0.4047071640104663,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1704.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n197395,0,Cash loans,M,Y,Y,0,157500.0,848745.0,36090.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20580,-4675,-9838.0,-4102,7.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,1,1,Transport: type 3,,0.4845573496553779,0.6925590674998008,0.0402,,0.9861,,,,0.1379,0.125,,,,0.0445,,0.0431,0.040999999999999995,,0.9861,,,,0.1379,0.125,,,,0.0464,,0.0456,0.0406,,0.9861,,,,0.1379,0.125,,,,0.0453,,0.044000000000000004,,block of flats,0.0444,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1001.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n147590,0,Cash loans,F,N,Y,0,112500.0,835380.0,35523.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.032561,-19385,365243,-13526.0,-2746,,1,0,0,1,0,0,,1.0,1,1,THURSDAY,18,0,0,0,0,0,0,XNA,,0.584430681178909,,0.4624,0.2171,0.9801,0.7348,0.0673,0.5,0.4021,0.3333,0.375,0.14400000000000002,0.3757,0.4225,0.0058,0.0036,0.3761,0.1868,0.9801,0.7387,0.053,0.4028,0.3448,0.3333,0.375,0.1226,0.3278,0.3768,0.0039,0.001,0.4669,0.2171,0.9801,0.7383,0.0677,0.5,0.3448,0.3333,0.375,0.1465,0.3822,0.3695,0.0058,0.0037,reg oper account,block of flats,0.2871,Panel,No,0.0,0.0,0.0,0.0,-386.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n361971,0,Cash loans,F,N,Y,1,90000.0,125640.0,6543.0,90000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.030755,-16154,-2178,-10275.0,-4804,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Kindergarten,0.7878975493158995,0.5420846428998397,0.7366226976503176,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-899.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,3.0\n451578,0,Cash loans,F,N,Y,1,135000.0,818410.5,27175.5,706500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-16832,-1709,-909.0,-385,,1,1,0,1,0,0,Waiters/barmen staff,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Government,,0.6146036912043304,0.5334816299804352,0.0928,0.0809,0.9771,0.6872,0.0145,0.0,0.2069,0.1667,0.0417,0.035,0.0756,0.0611,0.0,0.0,0.0945,0.084,0.9772,0.6994,0.0146,0.0,0.2069,0.1667,0.0417,0.0358,0.0826,0.0636,0.0,0.0,0.0937,0.0809,0.9771,0.6914,0.0146,0.0,0.2069,0.1667,0.0417,0.0356,0.077,0.0622,0.0,0.0,reg oper account,block of flats,0.0693,Panel,No,0.0,0.0,0.0,0.0,-2981.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n143226,0,Cash loans,F,N,Y,0,270000.0,675000.0,45423.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.026392,-20332,365243,-2530.0,-3888,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.41367289702087817,,0.132,0.1177,0.9801,,,0.16,0.1379,0.3333,,,,0.0806,,0.1213,0.1345,0.1222,0.9801,,,0.1611,0.1379,0.3333,,,,0.084,,0.1285,0.1332,0.1177,0.9801,,,0.16,0.1379,0.3333,,,,0.0821,,0.1239,,block of flats,0.1173,Panel,No,0.0,0.0,0.0,0.0,-1277.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n205435,0,Cash loans,F,N,N,0,90000.0,257391.0,27850.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-15915,-1171,-3696.0,-4577,,1,1,0,1,0,1,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.5210206333012515,0.6127042441012546,0.0825,0.1273,0.9945,0.9252,0.0528,0.0,0.2069,0.1667,0.2083,0.0706,0.0672,0.0769,0.0,0.0,0.084,0.1321,0.9945,0.9281,0.0533,0.0,0.2069,0.1667,0.2083,0.0722,0.0735,0.0801,0.0,0.0,0.0833,0.1273,0.9945,0.9262,0.0532,0.0,0.2069,0.1667,0.2083,0.0718,0.0684,0.0782,0.0,0.0,reg oper spec account,block of flats,0.0697,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-912.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n437292,0,Cash loans,M,N,N,0,360000.0,1724220.0,47542.5,1350000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,Rented apartment,0.001276,-15707,-3150,-1151.0,-4275,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,5,0,0,0,0,0,0,Military,0.337514450407595,0.6109924000241267,0.7623356180684377,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-994.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n103535,0,Cash loans,F,N,Y,0,117000.0,675000.0,24246.0,675000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-14552,-1517,-2282.0,-4847,,1,1,1,1,0,0,Sales staff,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,Trade: type 1,0.4389081253755348,0.3667970561385415,0.3859146722745145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-826.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n432565,0,Cash loans,F,Y,N,0,135000.0,1350000.0,53541.0,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018634,-14945,-1213,-5028.0,-4984,12.0,1,1,1,1,1,0,Managers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6974386540211783,0.4101025731788671,0.0495,0.0365,0.9752,0.66,0.0,0.0,0.1379,0.1667,0.2083,,0.0403,0.0394,0.0039,0.0689,0.0504,0.0379,0.9752,0.6733,0.0,0.0,0.1379,0.1667,0.2083,,0.0441,0.0411,0.0039,0.073,0.05,0.0365,0.9752,0.6645,0.0,0.0,0.1379,0.1667,0.2083,,0.040999999999999995,0.0401,0.0039,0.0704,reg oper account,block of flats,0.0461,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-971.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,1.0,0.0,1.0,1.0\n310647,0,Cash loans,M,N,Y,0,180000.0,467257.5,16911.0,328500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-15082,-1410,-922.0,-4220,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 2,0.3236216611877199,0.4465060856001189,0.3979463219016906,0.0969,0.0382,0.9767,,,0.0,0.0345,0.1667,,0.0297,,0.0331,,0.0252,0.0987,0.0396,0.9767,,,0.0,0.0345,0.1667,,0.0304,,0.0345,,0.0267,0.0978,0.0382,0.9767,,,0.0,0.0345,0.1667,,0.0303,,0.0337,,0.0258,,block of flats,0.0334,Block,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n174152,0,Cash loans,F,N,Y,0,45000.0,107820.0,7632.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018801,-21091,365243,-7375.0,-4585,,1,0,0,1,0,0,,1.0,2,2,MONDAY,6,0,0,0,0,0,0,XNA,,0.10086923189664204,0.7394117535524816,0.3412,,0.9901,0.8640000000000001,,0.44,0.3793,0.375,0.375,0.1019,0.2774,0.42,0.0039,0.0036,0.3477,,0.9901,0.8693,,0.4431,0.3793,0.375,0.375,0.1042,0.303,0.4376,0.0039,0.0038,0.3445,,0.9901,0.8658,,0.44,0.3793,0.375,0.375,0.1036,0.2822,0.4275,0.0039,0.0037,org spec account,block of flats,0.4065,Panel,No,0.0,0.0,0.0,0.0,-1677.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n371989,0,Cash loans,M,Y,N,2,225000.0,284400.0,20610.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-11285,-3166,-1934.0,-3885,0.0,1,1,1,1,0,0,Laborers,4.0,2,2,TUESDAY,20,0,0,0,1,1,0,Business Entity Type 2,,0.2755358466931539,0.3979463219016906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-1928.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,0.0\n431324,0,Cash loans,F,N,Y,0,90000.0,277969.5,16087.5,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.009175,-21120,365243,-6625.0,-4055,,1,0,0,1,0,0,,1.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.7326401885203889,0.4170996682522097,0.11800000000000001,,0.9747,,,0.0,0.1207,0.1458,,0.0466,,0.0418,,0.0458,0.0767,,0.9747,,,0.0,0.1034,0.125,,0.0476,,0.043,,0.0485,0.1192,,0.9747,,,0.0,0.1207,0.1458,,0.0474,,0.0426,,0.0468,,block of flats,0.0463,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-601.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n259367,0,Cash loans,M,Y,Y,1,202500.0,495000.0,25402.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-12617,-1301,-6704.0,-4076,2.0,1,1,0,1,0,0,Drivers,3.0,2,2,SUNDAY,16,0,0,0,0,0,0,Government,,0.41529152171725575,0.633031641417419,0.2227,0.1263,0.9801,0.728,,0.24,0.2069,0.3333,,0.037000000000000005,,0.2301,,0.0,0.2269,0.131,0.9801,0.7387,,0.2417,0.2069,0.3333,,0.0378,,0.2398,,0.0,0.2248,0.1263,0.9801,0.7316,,0.24,0.2069,0.3333,,0.0376,,0.2343,,0.0,,block of flats,0.2211,Panel,No,0.0,0.0,0.0,0.0,-880.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n222126,0,Cash loans,F,N,Y,0,112500.0,904500.0,38452.5,904500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-19495,-3038,-11721.0,-3052,,1,1,0,1,0,0,Medicine staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Medicine,,0.5304421273561337,0.4722533429586386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-613.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n159082,0,Cash loans,M,N,N,0,112500.0,291384.0,28818.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009334,-23027,365243,-3925.0,-6166,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,0.6517442193226318,0.6705953359310199,0.5902333386185574,0.0619,0.1179,0.9846,0.7892,0.0,0.0,0.1379,0.1667,0.2083,0.0205,0.0504,0.036000000000000004,0.0,0.0636,0.063,0.1223,0.9846,0.7975,0.0,0.0,0.1379,0.1667,0.2083,0.0209,0.0551,0.0375,0.0,0.0673,0.0625,0.1179,0.9846,0.792,0.0,0.0,0.1379,0.1667,0.2083,0.0208,0.0513,0.0367,0.0,0.0649,reg oper account,block of flats,0.0422,Panel,No,1.0,0.0,1.0,0.0,-1849.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n333366,0,Cash loans,F,N,N,2,157500.0,1886850.0,49905.0,1575000.0,\"Spouse, partner\",Working,Higher education,Married,Municipal apartment,0.005002,-14686,-4852,-4449.0,-1804,,1,1,0,1,0,0,Core staff,4.0,3,3,SATURDAY,14,0,0,0,0,0,0,Transport: type 2,,0.5833840328135151,0.6971469077844458,0.0082,0.0,0.9757,,,0.0,0.0345,0.0417,,0.0033,,0.0064,,0.0,0.0084,0.0,0.9757,,,0.0,0.0345,0.0417,,0.0034,,0.0067,,0.0,0.0083,0.0,0.9757,,,0.0,0.0345,0.0417,,0.0034,,0.0065,,0.0,,block of flats,0.0051,Wooden,No,1.0,0.0,1.0,0.0,-1698.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n372647,0,Cash loans,M,Y,Y,0,202500.0,640080.0,31261.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-17732,-3443,-380.0,-1266,13.0,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 1,,0.6585511047278804,0.14734591802757252,0.0351,0.0423,0.9762,0.6328,0.0126,0.0,0.1034,0.1042,0.0833,0.0093,0.0101,0.0296,0.0,0.0506,0.0126,0.0,0.9732,0.6472,0.0127,0.0,0.069,0.0417,0.0833,0.0,0.011000000000000001,0.0059,0.0,0.0,0.0354,0.0423,0.9762,0.6377,0.0126,0.0,0.1034,0.1042,0.0833,0.0095,0.0103,0.0301,0.0,0.0516,reg oper account,block of flats,0.064,Block,No,0.0,0.0,0.0,0.0,-247.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n106190,0,Cash loans,F,N,Y,0,112500.0,1078200.0,31653.0,900000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.02461,-22688,-9823,-13009.0,-4316,,1,1,0,1,1,0,Core staff,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,Legal Services,,0.6930788948583436,0.7338145369642702,0.4876,0.2966,0.9821,,,0.44,0.3793,0.3333,,0.2641,,0.4799,,0.0329,0.4968,0.3078,0.9821,,,0.4431,0.3793,0.3333,,0.2701,,0.5001,,0.0348,0.4924,0.2966,0.9821,,,0.44,0.3793,0.3333,,0.2687,,0.4886,,0.0336,,block of flats,0.4421,Panel,No,0.0,0.0,0.0,0.0,-709.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,6.0,0.0,0.0\n450559,0,Cash loans,M,N,Y,0,225000.0,641173.5,34911.0,553500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00823,-17925,-189,-1181.0,-1455,,1,1,0,1,0,1,Low-skill Laborers,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Trade: type 7,,0.2902119847692973,0.4048783643353997,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-815.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n235101,0,Cash loans,F,N,N,0,90000.0,152820.0,8532.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-20928,365243,-14642.0,-3971,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.3586540045103404,0.054212984425889184,0.0773,0.068,0.9771,0.728,0.0117,0.04,0.1034,0.25,0.0417,0.0566,0.0588,0.0751,0.0077,0.0042,0.0756,0.0583,0.9747,0.7387,0.0118,0.0,0.069,0.1667,0.0417,0.0579,0.0643,0.0783,0.0078,0.0024,0.0781,0.068,0.9771,0.7316,0.0118,0.04,0.1034,0.25,0.0417,0.0576,0.0599,0.0765,0.0078,0.0043,reg oper account,block of flats,0.066,Panel,No,1.0,1.0,1.0,1.0,-687.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n427383,0,Cash loans,F,N,Y,0,45000.0,273636.0,15835.5,247500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-21092,365243,-10087.0,-4630,,1,0,0,1,1,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.6760305990563302,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-653.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n252240,0,Cash loans,M,N,N,0,112500.0,237024.0,15840.0,180000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-12670,-276,-6317.0,-4201,,1,1,1,1,1,0,Managers,1.0,1,1,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.2794260556872481,0.6731504143622827,,0.0969,0.1771,0.9707,0.5988,,0.16,0.1379,0.25,,0.0,,0.1267,,0.1607,0.0987,0.1838,0.9707,0.6145,,0.1611,0.1379,0.25,,0.0,,0.132,,0.1701,0.0978,0.1771,0.9707,0.6042,,0.16,0.1379,0.25,,0.0,,0.129,,0.1641,,block of flats,0.1346,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1113.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n179108,0,Revolving loans,F,N,Y,0,76500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-8392,-746,-7658.0,-1073,,1,1,0,1,0,0,,2.0,3,3,WEDNESDAY,9,0,0,0,1,1,0,Industry: type 2,0.4217260272632992,0.4549134589781206,0.25670557243930026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-344.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n403879,0,Cash loans,F,N,N,0,180000.0,405000.0,19480.5,405000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.02461,-17742,-3398,-4870.0,-1251,,1,1,1,1,1,1,High skill tech staff,1.0,2,2,TUESDAY,14,0,0,0,0,1,1,Transport: type 3,0.6177554584730119,0.6632166903400173,0.7338145369642702,0.0206,,0.9796,0.7212,0.0022,0.0,0.1034,0.0417,,0.0181,0.0168,0.0146,0.0039,0.0055,0.021,,0.9796,0.7321,0.0022,0.0,0.1034,0.0417,,0.0186,0.0184,0.0153,0.0039,0.0059,0.0208,,0.9796,0.7249,0.0022,0.0,0.1034,0.0417,,0.0185,0.0171,0.0149,0.0039,0.0057,reg oper spec account,block of flats,0.0127,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-691.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n290228,0,Cash loans,F,N,Y,1,202500.0,1024740.0,52452.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-15746,-691,-5490.0,-1274,,1,1,0,1,0,1,,2.0,2,2,THURSDAY,14,0,0,0,0,1,1,Hotel,0.5368013305307875,0.2797372303820568,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2802.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n404301,0,Cash loans,F,Y,N,0,157500.0,625536.0,25474.5,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-16300,-929,-4888.0,-4909,6.0,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Trade: type 3,,0.41044126878998666,0.6879328378491735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-401.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n210506,0,Cash loans,M,N,N,2,183375.0,808650.0,29709.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-14348,-3019,-1209.0,-4534,,1,1,1,1,1,0,Security staff,4.0,2,2,MONDAY,12,0,1,1,0,1,1,Security,0.5463190560173113,0.7267094885587236,0.08391700365753578,0.1031,0.1091,0.9762,,,0.0,0.1724,0.1667,,0.1177,,0.0957,,0.0,0.105,0.1132,0.9762,,,0.0,0.1724,0.1667,,0.1204,,0.0997,,0.0,0.1041,0.1091,0.9762,,,0.0,0.1724,0.1667,,0.1198,,0.0974,,0.0,,block of flats,0.0812,Panel,No,0.0,0.0,0.0,0.0,-1511.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n175452,0,Cash loans,F,Y,N,0,180000.0,1467612.0,58333.5,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018029,-11945,-2150,-4302.0,-2625,12.0,1,1,1,1,0,0,High skill tech staff,2.0,3,3,THURSDAY,10,0,0,0,0,1,1,Other,0.597331734923572,0.5827567455925489,0.8193176922872417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2123.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n219733,0,Cash loans,M,Y,Y,0,90000.0,286704.0,19395.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006008,-16290,-1017,-4028.0,-3082,18.0,1,1,1,1,0,0,Drivers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,0.3341724797142812,0.6718693266363627,,0.1186,0.0723,0.9856,0.7212,0.0245,0.08,0.1379,0.2708,0.2083,0.0689,0.057999999999999996,0.1199,0.0039,0.0034,0.0735,0.0551,0.9796,0.7321,0.0248,0.0,0.1379,0.1667,0.2083,0.0588,0.0634,0.0635,0.0039,0.0,0.1197,0.0723,0.9856,0.7249,0.0247,0.08,0.1379,0.2708,0.2083,0.0701,0.059000000000000004,0.1221,0.0039,0.0035,reg oper account,block of flats,0.1638,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-735.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n330412,0,Revolving loans,F,N,Y,1,148500.0,337500.0,16875.0,337500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.025164,-17065,-3535,-3299.0,-593,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.452466474085017,0.6019534089554641,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1927.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n285543,0,Cash loans,F,N,N,0,162000.0,450000.0,35554.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009549,-17657,-2046,-7704.0,-1201,,1,1,0,1,1,0,Managers,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Other,0.7957692107220891,0.21148729835983893,0.2392262794694045,0.0845,0.1215,0.9851,,,0.0,0.2069,0.1667,,0.0789,,0.0798,,0.0267,0.0861,0.126,0.9851,,,0.0,0.2069,0.1667,,0.0807,,0.0831,,0.0283,0.0854,0.1215,0.9851,,,0.0,0.2069,0.1667,,0.0803,,0.0812,,0.0273,,block of flats,0.0782,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2084.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n262413,0,Cash loans,M,Y,N,0,337500.0,781920.0,42417.0,675000.0,Unaccompanied,State servant,Higher education,Married,With parents,0.019101,-10266,-1652,-8451.0,-2943,2.0,1,1,1,1,1,0,Managers,2.0,2,2,SUNDAY,12,0,1,1,0,1,1,Military,0.8136216903774741,0.4360093684053527,0.14111510044661266,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1031.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n196289,0,Revolving loans,M,Y,Y,0,315000.0,202500.0,10125.0,202500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-16191,-2080,-567.0,-4968,4.0,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.7046970431304344,0.6842177625816666,0.5478104658520093,0.0922,0.0423,0.9846,0.7892,0.0281,0.0916,0.0714,0.3696,0.4133,0.0695,0.0745,0.0906,0.0053,0.0214,0.0515,0.0199,0.9856,0.8105,0.0217,0.0806,0.069,0.3333,0.375,0.0266,0.0441,0.0571,0.0,0.0,0.0791,0.0355,0.9851,0.7987,0.0243,0.08,0.069,0.3333,0.375,0.0691,0.065,0.0816,0.0039,0.0011,reg oper account,block of flats,0.1593,Panel,No,,,,,-380.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n260979,0,Cash loans,F,N,Y,0,180000.0,808650.0,23305.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-21805,365243,-14306.0,-4342,,1,0,0,1,1,0,,2.0,3,3,SATURDAY,8,0,0,0,0,0,0,XNA,,0.5015927447647356,0.5495965024956946,,,,0.6056,,,0.1034,0.1667,0.2083,,,,,,,,,0.621,,,0.1034,0.1667,0.2083,,,,,,,,,0.6109,,,0.1034,0.1667,0.2083,,,,,,,,0.0566,,No,2.0,0.0,2.0,0.0,-10.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n304103,0,Cash loans,M,N,Y,1,135000.0,513531.0,24705.0,459000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-13619,-1955,-3237.0,-4296,,1,1,0,1,0,0,,3.0,3,3,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6168750572811045,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-974.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n189396,0,Cash loans,M,Y,Y,0,315000.0,107820.0,9351.0,90000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.002042,-10245,-1240,-1267.0,-2889,10.0,1,1,0,1,1,0,Managers,2.0,3,3,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.37345497284469065,0.5531646987710016,0.0696,0.0733,0.9916,0.8844,0.0,0.0464,0.1321,0.2221,0.0417,0.0,0.0567,0.0815,0.0,0.1297,0.042,0.0953,0.9921,0.8955,0.0,0.0,0.069,0.1667,0.0417,0.0,0.0367,0.031,0.0,0.0623,0.0703,0.0843,0.9916,0.8859,0.0,0.0,0.1207,0.1667,0.0417,0.0,0.0577,0.0841,0.0,0.1186,reg oper account,block of flats,0.0234,Panel,No,1.0,0.0,1.0,0.0,-897.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n226813,0,Cash loans,F,N,Y,0,157500.0,891072.0,37881.0,720000.0,Unaccompanied,State servant,Higher education,Separated,House / apartment,0.007305,-18209,-9878,-8927.0,-1345,,1,1,0,1,0,0,Core staff,1.0,3,3,SATURDAY,9,0,0,0,0,0,0,School,,0.6184140001408729,0.33125086459090186,0.2557,0.1313,0.9871,0.8232,0.1035,0.24,0.2069,0.375,0.4167,0.1012,0.2076,0.1885,0.0039,0.0047,0.2605,0.1362,0.9871,0.8301,0.1045,0.2417,0.2069,0.375,0.4167,0.1035,0.2268,0.1964,0.0039,0.005,0.2581,0.1313,0.9871,0.8256,0.1042,0.24,0.2069,0.375,0.4167,0.10300000000000001,0.2112,0.1919,0.0039,0.0048,reg oper account,block of flats,0.2059,Panel,No,0.0,0.0,0.0,0.0,-444.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n233955,0,Cash loans,F,Y,Y,0,157500.0,284400.0,22599.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-17573,-679,-84.0,-1128,1.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Transport: type 3,,0.1859976495251325,0.10547318648733764,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n158886,0,Cash loans,F,N,Y,0,54000.0,258709.5,14850.0,234000.0,Children,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.007120000000000001,-22258,365243,-12057.0,-4832,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,7,0,0,0,0,0,0,XNA,,0.2622583692422573,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-202.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n359477,0,Cash loans,F,N,Y,0,130500.0,187704.0,12348.0,148500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.008575,-12917,-3442,-6209.0,-4580,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Self-employed,0.3134279213653371,0.4070115016340655,0.722392890081304,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1303.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n259175,0,Cash loans,F,N,Y,0,81000.0,526491.0,19039.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009175,-21601,365243,-2052.0,-4572,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.6894152702036098,0.7209441499436497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-103.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n388914,0,Cash loans,F,N,Y,2,540000.0,942300.0,30528.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.072508,-16540,-375,-10484.0,-16,,1,1,0,1,0,0,Laborers,3.0,1,1,THURSDAY,17,0,0,0,0,0,0,Industry: type 11,,0.7417691236914227,0.3441550073724169,0.1108,0.1027,0.9776,0.6940000000000001,0.0,0.1,0.1379,0.2083,0.25,0.0,0.0899,0.098,0.0019,0.0022,0.0368,0.0965,0.9777,0.706,0.0,0.0,0.1034,0.0833,0.125,0.0,0.0312,0.0404,0.0,0.0,0.1119,0.1027,0.9776,0.6981,0.0,0.1,0.1379,0.2083,0.25,0.0,0.0915,0.0997,0.0019,0.0022,reg oper account,block of flats,0.0315,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n211064,0,Cash loans,F,N,Y,0,180000.0,900000.0,38133.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-18825,-3199,-6932.0,-2378,,1,1,0,1,1,0,High skill tech staff,2.0,1,1,MONDAY,17,0,0,0,0,0,0,Industry: type 12,,0.5126495801244039,0.4884551844437485,0.1856,0.09699999999999999,0.9901,0.8640000000000001,0.0721,0.2,0.1724,0.375,0.0417,0.1425,0.1513,0.207,0.0,0.0,0.1891,0.1007,0.9901,0.8693,0.0727,0.2014,0.1724,0.375,0.0417,0.1457,0.1653,0.2156,0.0,0.0,0.1874,0.09699999999999999,0.9901,0.8658,0.0725,0.2,0.1724,0.375,0.0417,0.145,0.1539,0.2107,0.0,0.0,reg oper account,block of flats,0.2022,Panel,No,3.0,0.0,3.0,0.0,-23.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n104212,0,Cash loans,F,N,N,0,202500.0,605448.0,40455.0,571500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.04622,-22281,365243,-13222.0,-4750,,1,0,0,1,0,0,,1.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.6643537958034986,0.7738956942145427,0.0928,0.0734,0.9811,0.7416,0.0097,0.0,0.1379,0.1667,,,0.0756,0.0596,0.0,0.0,0.0945,0.0762,0.9811,0.7517,0.0098,0.0,0.1379,0.1667,,,0.0826,0.0621,0.0,0.0,0.0937,0.0734,0.9811,0.7451,0.0098,0.0,0.1379,0.1667,,,0.077,0.0607,0.0,0.0,reg oper spec account,block of flats,0.0529,Panel,No,0.0,0.0,0.0,0.0,-1185.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n166804,0,Cash loans,F,Y,Y,0,148500.0,534204.0,42205.5,495000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018029,-16010,-486,-472.0,-585,14.0,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,12,0,0,0,1,1,0,Business Entity Type 3,,0.4413347320805008,0.1510075350878296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-303.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n229237,0,Cash loans,F,Y,Y,1,112500.0,753840.0,24448.5,540000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-14849,-4677,-2441.0,-4318,11.0,1,1,0,1,0,0,Managers,3.0,2,2,SUNDAY,11,0,0,0,0,0,0,Postal,0.42367169054340736,0.7576329879446164,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,3.0,0.0,-1091.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n361707,0,Cash loans,M,Y,N,0,945000.0,755190.0,27985.5,675000.0,,Commercial associate,Lower secondary,Married,House / apartment,0.04622,-16723,-3750,-141.0,-274,2.0,1,1,0,1,0,0,Managers,2.0,1,1,THURSDAY,15,0,1,1,0,0,0,Security,0.16131833525229736,0.08662764017987278,0.33285056416487313,0.8196,0.2513,0.997,,,0.8,0.3448,0.4583,,0.2445,,0.8753,,1.0,0.8351,0.2608,0.997,,,0.8056,0.3448,0.4583,,0.2501,,0.912,,1.0,0.8275,0.2513,0.997,,,0.8,0.3448,0.4583,,0.2488,,0.8909999999999999,,1.0,,block of flats,0.9790000000000001,Monolithic,No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n432877,0,Cash loans,M,N,N,1,270000.0,1128595.5,33129.0,985500.0,Unaccompanied,Working,Higher education,Separated,Office apartment,0.032561,-14654,-590,-8528.0,-4655,,1,1,1,1,1,0,,2.0,1,1,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6493520453205368,0.06423703753438333,0.5701,0.0596,0.9811,0.7416,0.0666,0.6,0.5172,0.3333,0.375,0.3571,0.4648,0.5788,,0.0172,0.5809,0.0619,0.9811,0.7517,0.0672,0.6042,0.5172,0.3333,0.375,0.3652,0.5078,0.6031,,0.0182,0.5756,0.0596,0.9811,0.7451,0.067,0.6,0.5172,0.3333,0.375,0.3633,0.4729,0.5892,,0.0175,reg oper account,block of flats,0.4954,Panel,No,6.0,0.0,6.0,0.0,-1454.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n105527,0,Cash loans,F,N,Y,0,135000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-19478,-1403,-10318.0,-2995,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,13,0,1,1,0,1,1,Other,,0.2785359823939209,0.5154953751603267,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-341.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n342677,0,Cash loans,F,N,Y,0,283500.0,1325475.0,59256.0,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00963,-21705,365243,-9640.0,-4646,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.6068942389686699,0.5549467685334323,,0.0,0.9702,,,0.0,0.0345,0.0417,,0.0263,,0.0089,,0.0,,0.0,0.9702,,,0.0,0.0345,0.0417,,0.0269,,0.0092,,0.0,,0.0,0.9702,,,0.0,0.0345,0.0417,,0.0268,,0.009000000000000001,,0.0,,specific housing,0.0108,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n149619,0,Cash loans,F,N,Y,1,216000.0,611577.0,39213.0,522000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-10341,-2026,-139.0,-1242,,1,1,0,1,0,1,Accountants,3.0,2,2,FRIDAY,13,0,0,0,0,1,1,Industry: type 11,0.2982778668055375,0.4587280053696462,0.5226973172821112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-760.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n246193,0,Cash loans,F,N,Y,2,82350.0,450000.0,22018.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13726,-2484,-390.0,-2561,,1,1,1,1,1,0,Managers,4.0,2,2,MONDAY,14,0,0,0,0,0,0,Self-employed,,0.6360408858052775,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1525.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n172563,0,Cash loans,F,N,Y,0,157500.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002042,-23644,-522,-408.0,-3351,,1,1,0,1,0,0,Medicine staff,2.0,3,3,THURSDAY,14,0,0,0,0,0,0,Medicine,,0.12769229645613653,0.5673792367572691,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n122610,1,Cash loans,M,N,Y,0,180000.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.025164,-12531,-1565,-3894.0,-4317,,1,1,0,1,0,0,Security staff,1.0,2,2,WEDNESDAY,11,0,0,0,0,1,1,Security,,0.05126651417382234,0.7267112092725122,0.1866,0.0094,0.9846,0.7892,0.0715,0.0,0.1034,0.1667,0.2083,0.1132,0.1513,0.0917,0.0039,0.0274,0.1901,0.0098,0.9846,0.7975,0.0721,0.0,0.1034,0.1667,0.2083,0.1158,0.1653,0.0956,0.0039,0.028999999999999998,0.1884,0.0094,0.9846,0.792,0.0719,0.0,0.1034,0.1667,0.2083,0.1152,0.1539,0.0934,0.0039,0.0279,reg oper account,block of flats,0.1172,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n299437,0,Cash loans,F,N,Y,0,202500.0,848745.0,46174.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-15871,-470,-876.0,-3845,,1,1,0,1,0,0,Medicine staff,2.0,2,1,THURSDAY,15,0,0,0,0,0,0,Medicine,0.2937373051883537,0.3986948498022471,,0.4691,0.0312,0.998,0.9728,0.2291,0.56,0.2414,0.6667,0.7083,0.1323,0.3766,0.5407,0.027000000000000003,0.1034,0.4779,0.0323,0.998,0.9739,0.2312,0.5639,0.2414,0.6667,0.7083,0.1353,0.4114,0.5633,0.0272,0.1095,0.4736,0.0312,0.998,0.9732,0.2305,0.56,0.2414,0.6667,0.7083,0.1346,0.3831,0.5504,0.0272,0.1056,reg oper account,block of flats,0.493,Panel,No,0.0,0.0,0.0,0.0,-286.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n235942,0,Cash loans,F,Y,Y,2,121500.0,521280.0,35392.5,450000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.008575,-12797,-249,-3558.0,-3558,13.0,1,1,0,1,0,0,High skill tech staff,4.0,2,2,SATURDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.5615392876756722,0.4801237971165258,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n341200,1,Cash loans,M,N,Y,0,180000.0,931617.0,47695.5,765000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,Municipal apartment,0.04622,-9895,-437,-4682.0,-2573,,1,1,0,1,0,0,Laborers,1.0,1,1,TUESDAY,17,0,1,1,0,0,0,Government,0.6409652763134692,0.534018937643461,0.2622489709189549,0.2227,0.1527,0.9781,0.7008,0.0521,0.16,0.1379,0.3333,0.375,0.2368,0.1816,0.2132,0.0,0.0,0.2269,0.1585,0.9782,0.7125,0.0526,0.1611,0.1379,0.3333,0.375,0.2422,0.1983,0.2221,0.0,0.0,0.2248,0.1527,0.9781,0.7048,0.0524,0.16,0.1379,0.3333,0.375,0.2409,0.1847,0.217,0.0,0.0,reg oper account,block of flats,0.2231,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n195538,0,Cash loans,M,Y,Y,0,202500.0,182016.0,6988.5,144000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-21973,-1003,-7078.0,-4440,27.0,1,1,0,1,1,0,Managers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.8029087597989428,0.6165183893926081,0.7281412993111438,0.2227,0.1332,0.9846,0.7892,0.0834,0.24,0.2069,0.3333,0.375,0.042,0.1816,0.2315,0.0,0.0,0.2269,0.1382,0.9846,0.7975,0.0842,0.2417,0.2069,0.3333,0.375,0.0429,0.1983,0.2412,0.0,0.0,0.2248,0.1332,0.9846,0.792,0.084,0.24,0.2069,0.3333,0.375,0.0427,0.1847,0.2356,0.0,0.0,reg oper account,block of flats,0.2277,Panel,No,0.0,0.0,0.0,0.0,-1372.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n223673,0,Cash loans,F,N,Y,0,126000.0,417024.0,17797.5,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-17104,-8612,-8527.0,-654,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Industry: type 11,,0.7187255489028868,0.4083588531230431,0.0186,,0.9841,,,,,0.0833,,,,0.0173,,,0.0189,,0.9841,,,,,0.0833,,,,0.018000000000000002,,,0.0187,,0.9841,,,,,0.0833,,,,0.0176,,,,,0.0136,,No,3.0,0.0,3.0,0.0,-604.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n266420,0,Cash loans,F,N,N,0,225000.0,454500.0,25375.5,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.014464,-18666,-3336,-6918.0,-2215,,1,1,1,1,1,0,Managers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Construction,0.7227464303548586,0.7482947061819523,0.42589289800515295,0.0619,0.0694,0.9891,0.8504,0.0103,0.0,0.1379,0.1667,0.2083,0.0537,0.0504,0.0573,0.0,0.0,0.063,0.07200000000000001,0.9891,0.8563,0.0104,0.0,0.1379,0.1667,0.2083,0.055,0.0551,0.0597,0.0,0.0,0.0625,0.0694,0.9891,0.8524,0.0104,0.0,0.1379,0.1667,0.2083,0.0547,0.0513,0.0584,0.0,0.0,reg oper account,block of flats,0.0507,Panel,No,0.0,0.0,0.0,0.0,-1712.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n162193,0,Cash loans,M,Y,N,0,292500.0,409500.0,10800.0,409500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.018209,-10884,-665,-2717.0,-3549,13.0,1,1,1,1,1,0,Managers,1.0,3,3,THURSDAY,19,0,0,0,1,1,0,Business Entity Type 3,0.5928823641223845,0.28649648915938514,0.5064842396679806,0.2897,0.1865,0.9871,0.8232,0.0667,0.32,0.2759,0.3333,0.375,0.1893,0.2353,0.3259,0.0039,0.0071,0.2952,0.1935,0.9871,0.8301,0.0674,0.3222,0.2759,0.3333,0.375,0.1936,0.2571,0.3395,0.0039,0.0075,0.2925,0.1865,0.9871,0.8256,0.0672,0.32,0.2759,0.3333,0.375,0.1926,0.2394,0.3317,0.0039,0.0073,,block of flats,0.2579,Panel,No,0.0,0.0,0.0,0.0,-272.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,3.0,1.0,2.0\n155451,0,Cash loans,F,N,Y,0,180000.0,484789.5,24880.5,418500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.02461,-17990,365243,-146.0,-1489,,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.4924144145076061,0.2340151665320674,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-293.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n318349,0,Cash loans,M,Y,N,0,135000.0,378000.0,18382.5,378000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-12644,-460,-3539.0,-4419,4.0,1,1,1,1,1,0,,2.0,2,2,THURSDAY,15,0,1,1,0,1,1,Industry: type 1,,0.4179436289371469,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-329.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n272259,0,Cash loans,F,N,Y,0,157500.0,1115383.5,45558.0,1026000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-18632,-1789,-5876.0,-1901,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,7,0,0,0,0,1,1,Trade: type 2,,0.5637200729792761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1170.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n134443,0,Cash loans,F,N,Y,0,58500.0,229500.0,17289.0,229500.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.001417,-18347,-973,-77.0,-1896,,1,1,0,1,1,0,,2.0,2,2,WEDNESDAY,6,0,0,0,1,1,0,Business Entity Type 3,,0.4742319196788891,0.7764098512142026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3180.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n269964,1,Cash loans,M,N,Y,0,112500.0,237024.0,12856.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.009549,-20220,-12348,-9448.0,-3252,,1,1,1,1,0,0,Laborers,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Other,,0.5991416592285129,,0.0928,0.0855,0.9816,,,,0.2069,0.1667,,0.0609,,0.0914,,0.0,0.0945,0.0887,0.9816,,,,0.2069,0.1667,,0.0623,,0.0953,,0.0,0.0937,0.0855,0.9816,,,,0.2069,0.1667,,0.062,,0.0931,,0.0,,block of flats,0.0719,Panel,No,0.0,0.0,0.0,0.0,-2492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n215359,0,Cash loans,F,N,N,0,225000.0,835380.0,40320.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Municipal apartment,0.011657,-19329,-1508,-9808.0,-2796,,1,1,1,1,1,0,Laborers,2.0,1,1,THURSDAY,16,0,0,0,0,0,0,Postal,0.6375284143133972,0.6984666852138073,0.656158373001177,0.0722,0.0516,0.9791,,,0.0,0.1724,0.1667,,0.0,,0.0638,,0.0,0.0735,0.0536,0.9791,,,0.0,0.1724,0.1667,,0.0,,0.0665,,0.0,0.0729,0.0516,0.9791,,,0.0,0.1724,0.1667,,0.0,,0.065,,0.0,,block of flats,0.0503,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2446.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,5.0\n196774,0,Cash loans,F,N,Y,0,67500.0,555273.0,16366.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-19587,-1278,-7918.0,-3005,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Other,0.8407583451891945,0.6412639472093596,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2359.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n123048,0,Cash loans,F,N,Y,0,247500.0,640080.0,24259.5,450000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.003069,-20230,365243,-8435.0,-3451,,1,0,0,1,1,0,,2.0,3,3,TUESDAY,14,0,0,0,0,0,0,XNA,0.7376144992543067,0.7176677873108329,0.5280927512030451,,0.0,0.9856,,,0.0,0.3448,0.1667,,,,0.0869,,0.2092,,0.0,0.9856,,,0.0,0.3448,0.1667,,,,0.0905,,0.2214,,0.0,0.9856,,,0.0,0.3448,0.1667,,,,0.0884,,0.2136,,,0.1138,Mixed,No,10.0,0.0,10.0,0.0,-770.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n441345,0,Cash loans,F,N,N,0,225000.0,1097676.0,32224.5,958500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-22889,365243,-9202.0,-3953,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.5410042417996026,0.3774042489507649,0.0629,0.0863,0.9821,,,0.0,0.1379,0.1667,,0.0869,,0.0452,,0.0578,0.0641,0.0895,0.9821,,,0.0,0.1379,0.1667,,0.0889,,0.0471,,0.0612,0.0635,0.0863,0.9821,,,0.0,0.1379,0.1667,,0.0884,,0.046,,0.059000000000000004,,block of flats,0.0522,\"Stone, brick\",No,4.0,1.0,4.0,0.0,-1385.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n161342,0,Cash loans,F,Y,Y,0,198000.0,1166724.0,37768.5,913500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.002134,-19577,-82,-3846.0,-2990,16.0,1,1,0,1,0,0,Core staff,2.0,3,3,FRIDAY,7,0,0,0,0,0,0,School,,0.2804956855009612,0.33928769990891394,0.133,0.1636,0.9821,0.7552,0.0663,0.0,0.2069,0.1667,0.0417,0.0287,0.1059,0.0853,0.0116,0.0152,0.1355,0.1698,0.9821,0.7648,0.0669,0.0,0.2069,0.1667,0.0417,0.0294,0.1157,0.0889,0.0117,0.0161,0.1343,0.1636,0.9821,0.7585,0.0667,0.0,0.2069,0.1667,0.0417,0.0292,0.1077,0.0868,0.0116,0.0155,reg oper account,block of flats,0.1066,Panel,No,0.0,0.0,0.0,0.0,-277.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n358269,0,Cash loans,F,N,Y,0,90000.0,675000.0,24043.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-19632,-11677,-1407.0,-3060,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.6595436779932847,0.7503751495159068,0.1082,0.0453,0.9886,0.8436,0.0251,0.0464,0.0345,0.3471,0.3888,0.0907,0.086,0.0867,0.0103,0.0209,0.0599,0.027999999999999997,0.9881,0.8432,0.0206,0.0403,0.0345,0.3333,0.375,0.0497,0.0496,0.076,0.0039,0.0023,0.1176,0.0484,0.9881,0.8390000000000001,0.026000000000000002,0.04,0.0345,0.3333,0.375,0.0977,0.0945,0.0898,0.0097,0.0193,reg oper account,block of flats,0.0816,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-1361.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n114877,0,Cash loans,M,N,Y,0,135000.0,521280.0,28408.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-14666,-1476,-2111.0,-2111,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,Construction,,0.5464401292455752,0.4992720153045617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-625.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n130243,0,Revolving loans,F,Y,Y,0,90000.0,202500.0,10125.0,202500.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.003813,-14704,365243,-7789.0,-3077,25.0,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,XNA,,0.7510237753056075,,0.0928,0.1004,0.9821,0.7552,0.0444,0.0,0.2069,0.1667,0.0417,0.0932,0.0731,0.0836,0.0116,0.0129,0.0945,0.1042,0.9821,0.7648,0.0448,0.0,0.2069,0.1667,0.0417,0.0953,0.0799,0.0871,0.0117,0.0136,0.0937,0.1004,0.9821,0.7585,0.0447,0.0,0.2069,0.1667,0.0417,0.0948,0.0744,0.0851,0.0116,0.0132,reg oper spec account,block of flats,0.0686,Panel,No,0.0,0.0,0.0,0.0,-799.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n233695,0,Cash loans,M,N,Y,0,76500.0,572148.0,45333.0,517500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-17137,-1579,-7698.0,-698,,1,1,0,1,0,0,Security staff,2.0,3,3,WEDNESDAY,5,0,0,0,0,0,0,Security,,0.4450797897268017,0.3092753558842053,0.0505,0.0487,0.9821,0.7552,0.0086,0.0,0.1379,0.1667,0.0417,0.0408,0.0403,0.0432,0.0039,0.0456,0.0515,0.0506,0.9821,0.7648,0.0087,0.0,0.1379,0.1667,0.0417,0.0417,0.0441,0.045,0.0039,0.0483,0.051,0.0487,0.9821,0.7585,0.0087,0.0,0.1379,0.1667,0.0417,0.0415,0.040999999999999995,0.0439,0.0039,0.0466,reg oper account,block of flats,0.0486,Panel,No,0.0,0.0,0.0,0.0,-646.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n272331,1,Cash loans,M,N,Y,1,135000.0,254700.0,20119.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-9029,-114,-7445.0,-223,,1,1,0,1,0,1,Laborers,3.0,1,1,SATURDAY,11,0,1,1,0,1,1,Construction,0.3350443381443961,0.6376743966229461,,0.1845,0.1989,0.9846,0.7892,0.0522,0.0,0.4483,0.1667,0.2083,0.1722,0.1505,0.1138,0.0077,0.1313,0.188,0.2065,0.9846,0.7975,0.0527,0.0,0.4483,0.1667,0.2083,0.1761,0.1644,0.1185,0.0078,0.139,0.1863,0.1989,0.9846,0.792,0.0525,0.0,0.4483,0.1667,0.2083,0.1752,0.1531,0.1158,0.0078,0.134,org spec account,block of flats,0.209,Panel,No,3.0,0.0,3.0,0.0,-649.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n219508,1,Revolving loans,F,N,Y,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.010032,-10358,-618,-4749.0,-3048,,1,1,1,1,1,0,,1.0,2,2,SUNDAY,7,0,0,0,0,1,1,Government,0.5196672099981374,0.4241910618987143,0.2580842039460289,0.0515,0.0561,0.9881,,,,0.1379,0.1667,,0.0147,,0.0546,,,0.0525,0.0582,0.9881,,,,0.1379,0.1667,,0.015,,0.0569,,,0.052000000000000005,0.0561,0.9881,,,,0.1379,0.1667,,0.0149,,0.0556,,,,block of flats,0.0429,\"Stone, brick\",No,4.0,2.0,4.0,2.0,-404.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n423287,0,Cash loans,F,Y,Y,1,135000.0,246357.0,26662.5,234000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-14943,-309,-7490.0,-5510,4.0,1,1,1,1,1,0,Drivers,3.0,2,2,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.5870147841230807,0.6865047975453018,0.6413682574954046,0.33299999999999996,0.2073,0.9871,0.8232,0.0776,0.32,0.2759,0.375,0.4167,0.2941,0.26899999999999996,0.3401,0.0116,0.0059,0.3393,0.2151,0.9871,0.8301,0.0783,0.3222,0.2759,0.375,0.4167,0.3009,0.2938,0.3543,0.0117,0.0062,0.3362,0.2073,0.9871,0.8256,0.078,0.32,0.2759,0.375,0.4167,0.2993,0.2736,0.3462,0.0116,0.006,reg oper account,block of flats,0.2687,Panel,No,1.0,0.0,1.0,0.0,-1868.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n245270,0,Cash loans,F,N,Y,0,103500.0,540000.0,51403.5,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.014464,-21019,365243,-8081.0,-4123,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,7,0,0,0,0,0,0,XNA,,0.6324391961885926,,0.1753,0.1307,0.9891,0.8504,0.1209,0.2,0.1724,0.3333,0.0417,0.0311,0.1429,0.1954,0.0,0.0,0.1786,0.1357,0.9891,0.8563,0.122,0.2014,0.1724,0.3333,0.0417,0.0318,0.1561,0.2036,0.0,0.0,0.177,0.1307,0.9891,0.8524,0.1216,0.2,0.1724,0.3333,0.0417,0.0317,0.1454,0.1989,0.0,0.0,reg oper account,block of flats,0.1767,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-388.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n390187,0,Cash loans,M,Y,N,0,135000.0,1314117.0,36265.5,1147500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006852,-20428,365243,-6160.0,-3234,37.0,1,0,0,1,0,0,,2.0,3,3,TUESDAY,7,0,0,0,0,0,0,XNA,,0.6359193845836366,0.4382813743111921,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n154944,0,Cash loans,F,N,N,0,157500.0,760225.5,30280.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-13508,-488,-11833.0,-4621,,1,1,0,1,1,0,Sales staff,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Self-employed,0.7272054133713821,0.641917651461615,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2364.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,1.0\n100680,0,Cash loans,F,N,Y,0,90000.0,254700.0,27153.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006008,-16794,-3059,-176.0,-291,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,17,0,0,0,1,1,0,Business Entity Type 2,,0.7219194369942555,0.5064842396679806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1536.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n439518,0,Revolving loans,F,N,N,1,90000.0,247500.0,12375.0,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-12978,-89,-6220.0,-3622,,1,1,0,1,1,0,Laborers,3.0,2,2,FRIDAY,7,0,0,0,0,1,1,Industry: type 3,,0.5168608609426841,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1259.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n355441,0,Cash loans,F,N,N,1,315000.0,675000.0,53329.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-13141,-740,-7223.0,-5433,,1,1,1,1,1,0,Accountants,3.0,1,1,TUESDAY,11,0,0,0,0,0,0,Self-employed,0.7607577176537236,0.6294910409041904,,0.1928,0.2148,0.9831,0.7756,0.0783,0.4264,0.2759,0.3471,0.3958,0.1093,,0.3014,,0.0646,0.1964,0.1477,0.9821,0.7648,0.0463,0.2014,0.1724,0.3333,0.375,0.10099999999999999,,0.134,,0.0,0.1947,0.228,0.9826,0.7786,0.0788,0.44,0.2759,0.3333,0.3958,0.1112,,0.3653,,0.0021,reg oper account,block of flats,0.1575,Panel,No,0.0,0.0,0.0,0.0,-1881.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n192997,0,Cash loans,F,N,N,0,225000.0,430582.5,16362.0,355500.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.010966,-20061,-6986,-3693.0,-3578,,1,1,0,1,0,0,Medicine staff,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,Government,0.812981456620287,0.7050114743217336,0.5531646987710016,0.1474,0.0798,0.9866,0.8164,0.0267,0.04,0.0345,0.3333,0.375,0.0689,0.1202,0.0969,0.0,0.0,0.1502,0.0828,0.9866,0.8236,0.0269,0.0403,0.0345,0.3333,0.375,0.0705,0.1313,0.1009,0.0,0.0,0.1489,0.0798,0.9866,0.8189,0.0268,0.04,0.0345,0.3333,0.375,0.0701,0.1223,0.0986,0.0,0.0,,block of flats,0.0908,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2620.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n227359,0,Cash loans,F,N,N,0,81000.0,808650.0,23773.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.003813,-14465,-7275,-4641.0,-4655,,1,1,1,1,0,0,Cooking staff,2.0,2,2,TUESDAY,15,0,0,0,0,1,1,Medicine,,0.5368474390250345,0.5691487713619409,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-965.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n172322,0,Cash loans,F,Y,Y,1,112500.0,1223010.0,48501.0,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018029,-12520,-1935,-2938.0,-3775,9.0,1,1,1,1,0,0,,3.0,3,3,SATURDAY,12,0,0,0,0,1,1,Industry: type 9,0.3133765647357413,0.1942664499695638,0.520897599048938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1935.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n403263,0,Cash loans,F,N,Y,0,112500.0,332946.0,14233.5,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.019689,-20511,365243,-9087.0,-4011,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.4886982687833042,0.3185955240537633,0.0124,,0.9692,,,,,0.0417,,,,0.0154,,,0.0126,,0.9692,,,,,0.0417,,,,0.016,,,0.0125,,0.9692,,,,,0.0417,,,,0.0157,,,,block of flats,0.0121,Block,No,0.0,0.0,0.0,0.0,-2257.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n354565,0,Cash loans,F,N,N,0,270000.0,969579.0,32044.5,837000.0,Family,State servant,Higher education,Married,House / apartment,0.003069,-18165,-1876,-875.0,-1686,,1,1,0,1,0,0,Core staff,2.0,3,3,TUESDAY,15,0,0,0,1,1,0,School,0.6353071409023742,0.7660241630002038,0.6479768603302221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1417.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,5.0\n313679,0,Cash loans,F,N,Y,1,157500.0,436032.0,34578.0,360000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.022625,-11174,-1320,-329.0,-3850,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.2422240961758685,0.7416769619057064,0.4776491548517548,0.0371,,0.9712,,,0.0,0.069,0.1667,,0.0071,,0.0276,,0.0,0.0378,,0.9712,,,0.0,0.069,0.1667,,0.0073,,0.0288,,0.0,0.0375,,0.9712,,,0.0,0.069,0.1667,,0.0072,,0.0281,,0.0,,block of flats,0.0234,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-956.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n437645,0,Cash loans,F,N,Y,2,216000.0,1379376.0,44626.5,1080000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.011657,-13031,-3967,-4432.0,-1940,,1,1,0,1,1,0,High skill tech staff,4.0,1,1,TUESDAY,15,0,0,0,1,1,1,Other,0.5485024909737033,0.3483594726328587,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-562.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n292857,1,Cash loans,F,N,Y,2,112500.0,81000.0,8851.5,81000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-11013,-127,-425.0,-2453,,1,1,0,1,0,0,Core staff,4.0,2,2,SATURDAY,12,0,0,0,0,0,0,Trade: type 3,,0.10305367926442464,0.1293142889822697,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n116378,0,Cash loans,M,N,Y,2,310500.0,450000.0,24412.5,450000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.022625,-13752,-476,-2809.0,-4882,,1,1,1,1,1,1,,3.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.1355347612486149,0.4943384216818905,0.6642482627052363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-566.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n300679,0,Cash loans,F,Y,Y,0,117000.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-18378,-885,-967.0,-1912,1.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 1,,0.4265482461136509,0.2822484337007223,0.133,0.1111,0.9806,0.7348,0.0518,0.0,0.2759,0.1667,0.2083,0.0622,0.1042,0.1159,0.0193,0.0191,0.1355,0.1153,0.9806,0.7452,0.0523,0.0,0.2759,0.1667,0.2083,0.0637,0.1139,0.1208,0.0195,0.0202,0.1343,0.1111,0.9806,0.7383,0.0521,0.0,0.2759,0.1667,0.2083,0.0633,0.106,0.11800000000000001,0.0194,0.0195,reg oper account,block of flats,0.1236,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,5.0\n339886,0,Cash loans,M,Y,N,1,180000.0,119925.0,12852.0,112500.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.010032,-11408,-2800,-5390.0,-3679,8.0,1,1,0,1,0,1,Managers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Transport: type 4,0.7599443085144625,0.4070115016340655,0.7062051096536562,0.2227,0.0162,0.9861,0.8096,0.0604,0.08,0.069,0.3333,0.375,0.0,0.1807,0.0983,0.0039,0.0886,0.2269,0.0168,0.9861,0.8171,0.061,0.0806,0.069,0.3333,0.375,0.0,0.1974,0.1025,0.0039,0.0938,0.2248,0.0162,0.9861,0.8121,0.0608,0.08,0.069,0.3333,0.375,0.0,0.1838,0.1001,0.0039,0.0904,,block of flats,0.1296,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-402.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n322591,0,Cash loans,F,N,Y,0,225000.0,906894.0,46435.5,796500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-16523,-938,-470.0,-25,,1,1,0,1,0,1,Private service staff,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Self-employed,0.7056288105463593,0.24160765631373984,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1026.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n176654,0,Revolving loans,F,Y,N,1,247500.0,720000.0,36000.0,720000.0,Unaccompanied,Commercial associate,Higher education,Married,Municipal apartment,0.04622,-14745,-3296,-6497.0,-4257,8.0,1,1,0,1,0,0,Accountants,3.0,1,1,FRIDAY,15,0,1,1,0,0,0,Business Entity Type 3,,0.6688298130385927,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-287.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n455522,0,Cash loans,F,Y,Y,0,112500.0,508495.5,21541.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.005313,-22289,365243,-4259.0,-4134,13.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.2608561315184187,0.7366226976503176,0.1299,,0.9896,,,0.04,0.0345,0.3333,,0.0912,,0.0883,,0.0415,0.1324,,0.9896,,,0.0403,0.0345,0.3333,,0.0933,,0.092,,0.0439,0.1312,,0.9896,,,0.04,0.0345,0.3333,,0.0928,,0.0898,,0.0424,,block of flats,0.0854,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-757.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n355867,0,Cash loans,M,N,Y,0,225000.0,136287.0,10894.5,103500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.019101,-15196,-235,-559.0,-2195,,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Transport: type 3,0.8198802968420317,0.4372573165752676,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-552.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,1.0,1.0\n400780,0,Cash loans,F,N,Y,1,337500.0,273636.0,15705.0,247500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-12212,-608,-6349.0,-4789,,1,1,0,1,1,1,Accountants,3.0,1,1,THURSDAY,14,0,1,1,0,1,1,Business Entity Type 3,0.8289832815233171,0.6891811053651814,0.35233997269170386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1629.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n222004,0,Cash loans,F,N,Y,0,900000.0,463131.0,23773.5,346500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.010276,-15803,-2335,-3154.0,-3167,,1,1,0,1,0,1,Managers,1.0,2,2,FRIDAY,14,0,0,0,1,1,0,Business Entity Type 3,0.4280093167702908,0.5896364561944446,0.3791004853998145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1828.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n157248,1,Cash loans,F,N,Y,0,112500.0,614475.0,28773.0,432000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018029,-19811,365243,-633.0,-3367,,1,0,0,1,1,0,,1.0,3,3,TUESDAY,3,0,0,0,0,0,0,XNA,,0.10208548540955292,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-185.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n233201,0,Revolving loans,F,N,Y,2,315000.0,810000.0,40500.0,810000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-15538,-3133,-2591.0,-4747,,1,1,0,1,1,0,Accountants,4.0,3,1,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.2728604408525577,0.6907948662397052,0.5460231970049609,0.0711,0.0657,0.9861,,,0.08,0.069,0.3333,,0.0274,,0.0892,,0.0068,0.0725,0.0682,0.9861,,,0.0806,0.069,0.3333,,0.027999999999999997,,0.09300000000000001,,0.0072,0.0718,0.0657,0.9861,,,0.08,0.069,0.3333,,0.0279,,0.0908,,0.0069,,block of flats,0.0716,Panel,No,9.0,1.0,9.0,0.0,-633.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n394863,0,Cash loans,M,N,N,0,135000.0,808650.0,31464.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-18605,-98,-6026.0,-1194,,1,1,1,1,1,0,Laborers,2.0,1,1,MONDAY,14,0,0,0,1,1,0,Self-employed,,0.5776702791221875,0.3077366963789207,0.0825,,0.9841,,,0.0,0.2069,0.1667,,0.1258,,,,,0.084,,0.9841,,,0.0,0.2069,0.1667,,0.1287,,,,,0.0833,,0.9841,,,0.0,0.2069,0.1667,,0.128,,,,,,block of flats,0.0724,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-681.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n317451,0,Cash loans,F,N,Y,1,135000.0,1129500.0,31059.0,1129500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.025164,-11067,-3536,-2644.0,-2648,,1,1,1,1,0,0,Core staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Government,0.5433750754416466,0.29743057325494376,0.5602843280409464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1097.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n266370,0,Cash loans,M,Y,N,1,112500.0,314100.0,16573.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-10605,-1091,-4999.0,-3215,14.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6358991326942871,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,1.0,0.0,-2442.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n424329,0,Cash loans,M,Y,Y,0,112500.0,90000.0,8253.0,90000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.003069,-10677,-1960,-5049.0,-3302,11.0,1,1,0,1,1,0,High skill tech staff,2.0,3,3,THURSDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.2935594573666247,0.6675353013811383,0.1766525794312139,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1618.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n389983,0,Cash loans,F,N,Y,1,135000.0,860112.0,28557.0,742500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.031329,-13993,-684,-1337.0,-4852,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.6746957197600444,0.2631435910213423,0.5460231970049609,0.0552,0.0557,0.9881,0.8232,0.0181,0.02,0.1207,0.25,0.2083,0.0531,0.0672,0.0611,0.0,0.0,0.0284,0.0305,0.9871,0.8301,0.0182,0.0,0.0345,0.1667,0.2083,0.0248,0.0735,0.0452,0.0,0.0,0.0557,0.0557,0.9881,0.8256,0.0182,0.02,0.1207,0.25,0.2083,0.0541,0.0684,0.0622,0.0,0.0,reg oper account,block of flats,0.043,Panel,No,0.0,0.0,0.0,0.0,-2684.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,1.0,3.0\n136796,0,Revolving loans,F,Y,Y,0,270000.0,450000.0,22500.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-9832,-1303,-7173.0,-711,8.0,1,1,0,1,0,0,,2.0,1,1,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.7193352069830086,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-138.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n414727,0,Revolving loans,F,N,N,2,157500.0,270000.0,13500.0,270000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.0228,-16664,-163,-557.0,-107,,1,1,0,1,0,0,,4.0,2,2,SATURDAY,12,0,0,0,0,0,0,Other,0.8043886924827196,0.5453993388926778,0.3185955240537633,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-405.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,0.0\n139698,0,Cash loans,F,N,Y,0,112500.0,269550.0,18891.0,225000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.020713,-24818,365243,-3559.0,-3987,,1,0,0,1,0,0,,1.0,3,3,TUESDAY,11,0,0,0,0,0,0,XNA,,0.3211725170378706,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1509.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n188249,0,Cash loans,M,Y,Y,0,243000.0,275040.0,10498.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-17701,-6839,-236.0,-1241,1.0,1,1,0,1,1,0,Medicine staff,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Medicine,,0.7393617458324185,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1850.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n422798,0,Cash loans,F,N,Y,0,180000.0,186133.5,8950.5,141354.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018801,-23138,365243,-1604.0,-3606,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,XNA,,0.5187991385116537,0.5136937663039473,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-202.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n156767,0,Cash loans,F,N,Y,0,292500.0,922500.0,39082.5,922500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-18400,-11783,-4564.0,-1938,,1,1,0,1,0,0,,2.0,3,3,FRIDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.5516919010582899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-2295.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n106774,0,Cash loans,F,N,N,0,90000.0,531265.5,16236.0,373500.0,Unaccompanied,State servant,Higher education,Married,Office apartment,0.010147,-16314,-1215,-2541.0,-2880,,1,1,1,1,0,0,Core staff,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,School,0.7864254833404944,0.4603301721986253,0.5478104658520093,0.3979,0.1833,0.9836,0.7756,0.0787,0.32,0.2759,0.3333,0.375,0.1648,0.3244,0.3201,0.0,0.0,0.4055,0.1902,0.9836,0.7844,0.0794,0.3222,0.2759,0.3333,0.375,0.1685,0.3545,0.3335,0.0,0.0,0.4018,0.1833,0.9836,0.7786,0.0792,0.32,0.2759,0.3333,0.375,0.1676,0.3301,0.3258,0.0,0.0,reg oper spec account,block of flats,0.2948,Panel,No,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n376199,0,Cash loans,F,N,Y,0,180000.0,619254.0,29920.5,553500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20905,365243,-230.0,-4288,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.7111765277474406,0.4382813743111921,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-143.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n141256,0,Cash loans,F,Y,N,0,180000.0,900000.0,45954.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-14262,-1781,-1428.0,-4784,4.0,1,1,1,1,1,0,Sales staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Trade: type 7,,0.2858978721410488,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1321.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n411185,0,Cash loans,F,N,Y,0,202500.0,180000.0,12307.5,180000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-10178,-2936,-2966.0,-2768,,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Police,,0.6768346788470393,0.35895122857839673,0.1629,0.1384,0.9871,,,0.0,0.3793,0.1667,,0.1748,,0.1483,,0.0,0.166,0.1436,0.9871,,,0.0,0.3793,0.1667,,0.1788,,0.1546,,0.0,0.1645,0.1384,0.9871,,,0.0,0.3793,0.1667,,0.1778,,0.151,,0.0,,block of flats,0.1167,Panel,No,1.0,0.0,1.0,0.0,-1279.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n337054,0,Cash loans,M,Y,N,0,270000.0,904500.0,48190.5,904500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.007305,-18311,-10311,-6465.0,-1485,3.0,1,1,1,1,1,1,,2.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Other,,0.6045702623123299,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,1.0,0.0,1.0,0.0,-240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n160570,0,Cash loans,F,N,Y,0,162000.0,819792.0,39568.5,720000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.04622,-18405,-1213,-3468.0,-1947,,1,1,0,1,0,0,High skill tech staff,2.0,1,1,SATURDAY,17,0,0,0,0,1,1,Business Entity Type 3,0.31924318341586577,0.6847986239939305,0.3842068130556564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n447215,0,Cash loans,M,Y,N,0,157500.0,247500.0,14337.0,247500.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.026392,-16752,-3855,-3822.0,-308,2.0,1,1,0,1,1,0,Managers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Construction,,0.7556034639453564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n192285,0,Cash loans,F,N,Y,0,157500.0,781920.0,24682.5,675000.0,Family,State servant,Higher education,Single / not married,House / apartment,0.025164,-12480,-2043,-2142.0,-7,,1,1,0,1,1,0,HR staff,1.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,School,0.7761128825982258,0.6783967658314269,,0.066,0.0,0.9732,0.6328,0.0329,0.0,0.1379,0.125,0.1667,0.0539,0.0538,0.0506,0.0,0.0,0.0672,0.0,0.9732,0.6472,0.0332,0.0,0.1379,0.125,0.1667,0.0552,0.0588,0.0527,0.0,0.0,0.0666,0.0,0.9732,0.6377,0.0331,0.0,0.1379,0.125,0.1667,0.0549,0.0547,0.0515,0.0,0.0,reg oper account,block of flats,0.0578,Others,No,10.0,0.0,10.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n438968,0,Cash loans,F,N,Y,0,72000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.0228,-17373,-1652,-8037.0,-855,,1,1,0,1,1,0,Core staff,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Culture,,0.5111579646631679,0.7380196196295241,0.2052,0.1644,0.9826,0.762,0.1502,0.24,0.2069,0.3333,0.375,0.1484,0.1547,0.2155,0.0579,0.0717,0.209,0.1706,0.9826,0.7713,0.1515,0.2417,0.2069,0.3333,0.375,0.1518,0.16899999999999998,0.2245,0.0584,0.076,0.2071,0.1644,0.9826,0.7652,0.1511,0.24,0.2069,0.3333,0.375,0.151,0.1573,0.2193,0.0582,0.0732,reg oper account,block of flats,0.2672,Panel,No,5.0,0.0,5.0,0.0,-1032.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,1.0\n449028,1,Cash loans,M,N,Y,1,202500.0,479637.0,23458.5,396000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010643,-19998,-119,-2080.0,-3489,,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Self-employed,,0.1742525227926009,0.4223696523543468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-164.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n270158,0,Revolving loans,F,N,Y,0,112500.0,292500.0,14625.0,292500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-20651,-5741,-10066.0,-4071,,1,1,1,1,1,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6012458514460314,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-575.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n171174,0,Cash loans,M,N,Y,0,139500.0,170640.0,11236.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-10355,-1253,-4775.0,-2934,,1,1,1,1,1,0,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.12849756404406554,0.6195486112310943,0.5226973172821112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n354277,0,Revolving loans,F,N,N,0,90000.0,270000.0,13500.0,270000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.00496,-11157,-345,-4879.0,-3088,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,17,0,0,0,1,1,0,Government,,0.7021199922121956,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-915.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n268176,0,Cash loans,M,Y,Y,1,225000.0,1560726.0,43047.0,1395000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006629,-12778,-2562,-5662.0,-4258,4.0,1,1,0,1,0,0,Managers,3.0,2,2,WEDNESDAY,5,0,0,0,0,0,0,Other,,0.6458587456279075,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1480.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n216690,0,Cash loans,M,Y,N,0,157500.0,855000.0,22684.5,855000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.014519999999999996,-9815,-1771,-3784.0,-2451,6.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,10,0,0,0,1,1,0,Other,,0.698517343876388,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n344605,0,Cash loans,F,Y,Y,2,135000.0,497520.0,49338.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-11609,-1474,-5850.0,-3845,13.0,1,1,0,1,0,0,Laborers,4.0,1,1,THURSDAY,11,0,0,0,0,0,0,Hotel,0.4374196890455238,0.502418232531289,,0.0619,0.0618,0.9791,0.7144,0.0069,0.0,0.1379,0.1667,0.2083,0.0082,0.0504,0.0527,0.0,0.0,0.063,0.0642,0.9791,0.7256,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0084,0.0551,0.0549,0.0,0.0,0.0625,0.0618,0.9791,0.7182,0.0069,0.0,0.1379,0.1667,0.2083,0.0083,0.0513,0.0537,0.0,0.0,reg oper account,block of flats,0.0415,Panel,No,0.0,0.0,0.0,0.0,-2135.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n282753,0,Cash loans,F,N,N,0,216000.0,1045854.0,28890.0,873000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010966,-19876,-2518,-4860.0,-3411,,1,1,1,1,1,0,High skill tech staff,2.0,2,2,SATURDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.7595991114716906,0.524496446363472,0.0412,0.0465,0.9747,0.6532,0.0049,0.0,0.1034,0.125,0.1667,0.0497,0.0336,0.0398,0.0,0.0,0.042,0.0482,0.9747,0.6668,0.005,0.0,0.1034,0.125,0.1667,0.0508,0.0367,0.0415,0.0,0.0,0.0416,0.0465,0.9747,0.6578,0.0049,0.0,0.1034,0.125,0.1667,0.0506,0.0342,0.0405,0.0,0.0,reg oper account,block of flats,0.0313,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-625.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n384918,1,Cash loans,M,N,N,0,270000.0,888840.0,37494.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-13130,-2387,-7080.0,-4215,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.16631270890845504,0.2166970769428163,0.17456426555726348,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1434.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n267077,0,Cash loans,F,N,Y,0,119250.0,277969.5,13086.0,229500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00702,-19560,-5249,-6631.0,-3112,,1,1,0,1,0,0,Security staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Housing,0.5460670891654839,0.6168182056013152,0.6690566947824041,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-705.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n264156,0,Cash loans,F,N,Y,0,157500.0,929088.0,33502.5,720000.0,Children,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17033,-1843,-4834.0,-587,,1,1,0,1,1,0,Medicine staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Medicine,0.4119774353631209,0.5679371712863535,0.3296550543128238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-2031.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n142225,0,Cash loans,M,N,N,0,135000.0,675000.0,24246.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.01885,-13155,-2927,-6709.0,-4014,,1,1,1,1,0,0,Managers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Transport: type 4,0.6008009055181558,0.6838632960692034,0.4329616670974407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-624.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,3.0,3.0\n442430,0,Cash loans,F,Y,Y,0,225000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.014519999999999996,-16679,-830,-6167.0,-217,22.0,1,1,0,1,0,0,Accountants,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,Industry: type 1,,0.639203876711444,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2158.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n314034,0,Cash loans,M,N,Y,0,117000.0,224136.0,17838.0,198000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.004849,-11038,-3569,-5844.0,-3630,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,16,0,0,0,0,0,0,Police,,0.5273473108798393,0.2079641743053816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-860.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n408781,0,Cash loans,F,N,Y,0,180000.0,751500.0,40167.0,751500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010006,-15090,-240,-1955.0,-2183,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,7,0,0,0,0,0,0,Trade: type 3,,0.3023569143963294,0.6642482627052363,0.0206,0.0708,0.9811,0.7416,,0.0,0.1379,0.1667,0.2083,0.0208,0.0134,0.040999999999999995,0.0154,0.039,0.021,0.0735,0.9811,0.7517,,0.0,0.1379,0.1667,0.2083,0.0212,0.0147,0.0428,0.0156,0.0413,0.0208,0.0708,0.9811,0.7451,,0.0,0.1379,0.1667,0.2083,0.0211,0.0137,0.0418,0.0155,0.0399,reg oper account,block of flats,0.046,Block,No,0.0,0.0,0.0,0.0,-146.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n296722,0,Cash loans,M,Y,Y,1,157500.0,521280.0,28408.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-12747,-2384,-3599.0,-4011,16.0,1,1,1,1,1,0,Laborers,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 2,,0.43612229611797376,0.5028782772082183,0.0619,0.0627,0.9846,,,0.0,0.1379,0.1667,,0.0387,,0.0539,,0.0,0.063,0.0651,0.9846,,,0.0,0.1379,0.1667,,0.0395,,0.0562,,0.0,0.0625,0.0627,0.9846,,,0.0,0.1379,0.1667,,0.0393,,0.0549,,0.0,,block of flats,0.0424,Panel,No,2.0,1.0,2.0,0.0,-426.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n146905,0,Cash loans,F,Y,N,0,202500.0,592560.0,28638.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-17683,-3454,-1371.0,-672,11.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,11,0,0,0,0,1,1,Industry: type 8,,0.5772487595929132,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n413389,0,Cash loans,M,Y,Y,1,130500.0,412942.5,30055.5,373500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.016612000000000002,-13047,-2462,-5705.0,-4627,35.0,1,1,0,1,1,0,Drivers,3.0,2,2,TUESDAY,17,0,0,0,0,1,1,Transport: type 4,0.2627972428920438,0.5795683731759963,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,1.0\n315402,0,Cash loans,F,N,N,0,112500.0,450000.0,23107.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.028663,-8480,-103,-8247.0,-1142,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.5312862671592056,0.7032033049040319,0.0959,0.0658,0.9737,,,0.0,0.1897,0.1667,,,,0.10099999999999999,,0.0374,0.0914,0.0037,0.9717,,,0.0,0.1724,0.1667,,,,0.0938,,0.0,0.0968,0.0658,0.9737,,,0.0,0.1897,0.1667,,,,0.1028,,0.0381,,,0.0775,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n376236,0,Cash loans,M,Y,Y,0,171000.0,760225.5,32337.0,679500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00823,-16236,-1243,-8763.0,-4985,14.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,1,1,Industry: type 3,,0.6155304381965824,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2714.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n266808,0,Cash loans,F,Y,Y,1,157500.0,855000.0,24997.5,855000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-11755,-886,-5638.0,-4177,15.0,1,1,0,1,0,0,,3.0,3,3,MONDAY,7,0,0,0,0,0,0,Construction,0.3814653442195032,0.4808934578434657,,0.1017,0.0948,0.9911,0.8776,0.0293,0.1464,0.1493,0.3333,0.2775,0.0905,0.0821,0.1126,0.0039,0.0237,0.0683,0.0475,0.9901,0.8693,0.0154,0.0403,0.069,0.25,0.125,0.0766,0.0588,0.0816,0.0039,0.0008,0.0822,0.0927,0.9901,0.8658,0.0325,0.16,0.1724,0.2917,0.25,0.0851,0.0667,0.0932,0.0039,0.0055,reg oper account,block of flats,0.0701,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1532.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n249079,0,Cash loans,M,N,N,0,112500.0,328365.0,18981.0,297000.0,Family,Commercial associate,Secondary / secondary special,Married,With parents,0.035792000000000004,-13816,-2980,-533.0,-4572,,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,10,0,0,0,0,1,1,Housing,,0.3313382282004527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-166.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n399250,0,Cash loans,M,N,N,0,247500.0,781920.0,46561.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.032561,-10141,-1038,-2478.0,-2794,,1,1,0,1,0,0,,2.0,1,1,THURSDAY,16,0,0,0,0,0,0,Self-employed,0.18165935933352584,0.4854913499032552,0.4830501881366946,0.0124,0.1225,0.9866,,,0.08,0.069,0.4583,,0.0207,,0.23800000000000002,,0.0129,0.0126,0.1271,0.9866,,,0.0806,0.069,0.4583,,0.0212,,0.2479,,0.0136,0.0125,0.1225,0.9866,,,0.08,0.069,0.4583,,0.0211,,0.2422,,0.0131,,block of flats,0.19,Panel,No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n396645,0,Cash loans,M,Y,Y,0,139500.0,315000.0,14814.0,315000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.014519999999999996,-10240,-314,-4639.0,-2791,12.0,1,1,0,1,0,0,Managers,1.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.08503709479256984,0.2707073872651806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1453.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n139408,0,Cash loans,F,N,Y,1,202500.0,696150.0,35671.5,562500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-14328,-2517,-8416.0,-2146,,1,1,0,1,0,0,Sales staff,3.0,2,2,SATURDAY,16,0,0,0,0,0,0,Self-employed,0.5401382510325948,0.6108676527121807,0.5046813193144684,0.2938,0.0,0.9846,0.7892,,0.32,0.2759,0.3333,0.375,0.2479,,0.3178,,0.0051,0.2994,0.0,0.9846,0.7975,,0.3222,0.2759,0.3333,0.375,0.2536,,0.3312,,0.0054,0.2967,0.0,0.9846,0.792,,0.32,0.2759,0.3333,0.375,0.2523,,0.3236,,0.0052,reg oper account,block of flats,0.2891,Panel,No,0.0,0.0,0.0,0.0,-884.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n276592,0,Cash loans,M,Y,N,0,270000.0,450000.0,30204.0,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.014519999999999996,-9280,-265,-4156.0,-25,19.0,1,1,0,1,1,0,Sales staff,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.40252797353894376,0.41885428862332175,,,0.9786,0.7076,,0.0,0.1379,0.2083,,0.034,,0.066,,0.0,,,0.9782,0.7125,,0.0,0.1379,0.1667,,0.0,,0.0686,,0.0,,,0.9786,0.7115,,0.0,0.1379,0.1667,,0.0346,,0.0672,,0.0,,block of flats,0.1737,Panel,No,1.0,0.0,1.0,0.0,-576.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,1.0\n244446,0,Cash loans,M,Y,Y,0,270000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-11374,-925,-5700.0,-3964,7.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.2547848682721272,0.2650494299443805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,2.0,7.0,2.0,-37.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,3.0\n277917,0,Cash loans,M,Y,Y,0,157500.0,244584.0,16474.5,193500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-10802,-2913,-3228.0,-3223,14.0,1,1,0,1,0,0,Laborers,2.0,3,3,THURSDAY,10,0,0,0,1,1,1,Transport: type 2,,0.2188127921822288,0.18195910978627847,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-208.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n395942,0,Cash loans,F,N,Y,0,157500.0,1042812.0,30618.0,747000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-12150,-730,-5735.0,-3229,,1,1,0,1,0,0,Managers,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Government,0.3831707962093104,0.6887171732100018,0.7838324335199619,0.0742,0.0453,0.9876,0.83,,0.08,0.069,0.3333,0.375,0.0921,0.0614,0.0775,0.0,0.0013,0.0746,0.047,0.9876,0.8367,,0.0806,0.069,0.3333,0.375,0.0942,0.067,0.0803,0.0,0.0,0.0749,0.0453,0.9876,0.8323,,0.08,0.069,0.3333,0.375,0.0937,0.0624,0.0789,0.0,0.0013,reg oper account,block of flats,0.0612,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-353.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n125087,0,Revolving loans,F,N,Y,0,135000.0,157500.0,7875.0,157500.0,Family,State servant,Secondary / secondary special,Single / not married,House / apartment,0.04622,-16366,-3272,-6401.0,-4464,,1,1,1,1,1,0,Medicine staff,1.0,1,1,SUNDAY,12,0,1,1,0,1,1,Other,,0.704064649079114,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n121644,0,Cash loans,M,N,Y,0,247500.0,942300.0,27679.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00823,-17481,-1582,-7391.0,-1022,,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 2,,0.6675595665096435,0.6397075677637197,,,0.9896,,,,,,,,,0.2165,,,,,0.9896,,,,,,,,,0.2255,,,,,0.9896,,,,,,,,,0.2204,,,,,0.1703,,No,1.0,1.0,1.0,1.0,-2119.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n358364,0,Cash loans,F,N,Y,0,405000.0,848745.0,46174.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-19707,-2670,-2501.0,-3223,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.581671651965913,0.3706496323299817,0.1268,0.1248,0.9866,0.8164,0.2032,0.0,0.3793,0.1667,0.2083,0.0803,0.1034,0.1413,0.0,0.0,0.1292,0.1295,0.9866,0.8236,0.2051,0.0,0.3793,0.1667,0.2083,0.0821,0.1129,0.1472,0.0,0.0,0.128,0.1248,0.9866,0.8189,0.2045,0.0,0.3793,0.1667,0.2083,0.0817,0.1052,0.1438,0.0,0.0,reg oper account,block of flats,0.1111,Panel,No,2.0,0.0,2.0,0.0,-248.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,3.0\n108570,0,Cash loans,F,N,Y,0,180000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.004849,-18231,-2482,-6264.0,-1726,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Business Entity Type 2,,0.5611207216797706,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1527.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n151048,0,Cash loans,F,N,N,0,157500.0,1125000.0,32895.0,1125000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.011657,-15548,-4090,-4593.0,-796,,1,1,0,1,1,0,Core staff,2.0,1,1,MONDAY,13,0,1,1,0,1,1,Government,,0.7289879206647862,0.10822632266971416,0.0495,0.0479,0.9747,0.6532,0.0043,0.0,0.1034,0.125,,0.0108,0.0403,0.0406,,0.0,0.0504,0.0497,0.9747,0.6668,0.0044,0.0,0.1034,0.125,,0.011000000000000001,0.0441,0.0423,,0.0,0.05,0.0479,0.9747,0.6578,0.0044,0.0,0.1034,0.125,,0.011000000000000001,0.040999999999999995,0.0413,,0.0,reg oper account,block of flats,0.0343,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1105.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,10.0,1.0,1.0\n401778,0,Cash loans,F,Y,Y,2,247500.0,1006920.0,51543.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-11681,-81,-1.0,-665,4.0,1,1,0,1,0,0,Accountants,4.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.33074140733784124,0.5010187247051328,0.2079641743053816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-366.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n455934,0,Revolving loans,F,Y,Y,0,166500.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-19069,-7966,-1837.0,-2200,4.0,1,1,0,1,1,0,Sales staff,1.0,2,2,FRIDAY,12,0,0,0,1,1,1,Trade: type 7,,0.4978260727865916,0.14644206667896328,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-466.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n104525,0,Cash loans,F,N,N,0,225000.0,679500.0,22455.0,679500.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.009334,-22151,365243,-1062.0,-4538,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,20,0,0,0,0,0,0,XNA,,0.5990891385138115,0.41534714488434,0.0619,0.0585,0.9806,0.7348,0.0,0.0,0.1379,0.1667,0.0417,0.0,0.0504,0.0364,0.0,0.0,0.063,0.0603,0.9801,0.7387,0.0,0.0,0.1379,0.1667,0.0417,0.0,0.0551,0.038,0.0,0.0,0.0625,0.0585,0.9806,0.7383,0.0,0.0,0.1379,0.1667,0.0417,0.0,0.0513,0.0371,0.0,0.0,reg oper account,block of flats,0.0424,Panel,No,0.0,0.0,0.0,0.0,-743.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n308481,0,Cash loans,F,Y,N,1,157500.0,313438.5,31131.0,283500.0,Family,State servant,Higher education,Married,House / apartment,0.020713,-17089,-5870,-1144.0,-617,6.0,1,1,0,1,0,1,Core staff,3.0,3,3,SATURDAY,11,0,0,0,0,0,0,School,0.7431719352716697,0.6767342259005626,0.8128226070575616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1763.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n357932,1,Cash loans,M,N,Y,0,256500.0,521280.0,28408.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-13363,-622,-770.0,-981,,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,16,0,1,1,0,1,1,Construction,,0.20539730598929795,0.2418614865234661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n366702,0,Cash loans,F,N,N,0,67500.0,299250.0,8356.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-11642,-2400,-4524.0,-2444,,1,1,0,1,0,0,Cooking staff,2.0,2,2,TUESDAY,13,0,0,0,1,1,0,Government,,0.4815703976645154,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n421703,0,Cash loans,F,N,N,0,81000.0,331632.0,31459.5,315000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.007120000000000001,-10082,-1705,-4729.0,-1740,,1,1,1,1,0,0,,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.4668547137389025,0.6154424590314042,0.5954562029091491,0.0825,0.0798,0.9786,0.7076,,0.0,0.1379,0.1667,0.2083,0.0385,0.0672,0.0485,0.0,0.0,0.084,0.0828,0.9786,0.7190000000000001,,0.0,0.1379,0.1667,0.2083,0.0394,0.0735,0.0505,0.0,0.0,0.0833,0.0798,0.9786,0.7115,,0.0,0.1379,0.1667,0.2083,0.0392,0.0684,0.0493,0.0,0.0,reg oper account,block of flats,0.0548,Panel,No,1.0,0.0,1.0,0.0,-948.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n211574,0,Cash loans,F,N,N,0,90000.0,531706.5,28975.5,459000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009175,-17493,-1457,-3212.0,-1041,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6171747677899205,0.7032033049040319,0.1108,0.0114,0.9871,,,0.08,0.0862,0.2708,,0.0317,,0.1229,,0.0713,0.0767,0.0115,0.9861,,,0.0,0.0345,0.1667,,0.016,,0.0596,,0.0,0.1119,0.0114,0.9871,,,0.08,0.0862,0.2708,,0.0323,,0.1251,,0.0728,,block of flats,0.16399999999999998,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1682.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n180931,0,Cash loans,F,N,Y,1,157500.0,625500.0,49549.5,625500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.014519999999999996,-10538,-1470,-1386.0,-700,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,Other,0.4312584692226912,0.331963508739294,0.6075573001388961,0.0082,,0.9578,,,0.0,0.069,0.0417,,0.0054,,0.01,,0.0,0.0084,,0.9578,,,0.0,0.069,0.0417,,0.0055,,0.0105,,0.0,0.0083,,0.9578,,,0.0,0.069,0.0417,,0.0055,,0.0102,,0.0,,block of flats,0.009000000000000001,Wooden,No,1.0,0.0,1.0,0.0,-1089.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n249112,0,Cash loans,F,Y,Y,3,180000.0,612612.0,31410.0,495000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00733,-10319,-1517,-195.0,-762,15.0,1,1,0,1,0,0,Laborers,5.0,2,2,THURSDAY,17,0,0,0,0,0,0,Industry: type 12,0.3533903947777582,0.5405154909429712,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n142555,1,Cash loans,F,N,Y,0,90000.0,225000.0,20767.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.003122,-14875,-8280,-2824.0,-4652,,1,1,1,1,0,0,,1.0,3,3,FRIDAY,17,0,0,0,0,0,0,Industry: type 11,0.5628229499168323,0.37283605781443785,0.2250868621163805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-628.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,2.0,1.0\n160669,0,Cash loans,M,N,N,1,135000.0,521280.0,31500.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010556,-11293,-387,-1172.0,-3292,,1,1,1,1,0,0,Laborers,3.0,3,3,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 1,,0.0439592071386073,0.29708661164720285,0.0186,0.0,0.9821,0.7552,0.0,0.0,0.1034,0.0417,0.0833,0.0,0.0151,0.0134,0.0,0.0,0.0189,0.0,0.9821,0.7648,0.0,0.0,0.1034,0.0417,0.0833,0.0,0.0165,0.013999999999999999,0.0,0.0,0.0187,0.0,0.9821,0.7585,0.0,0.0,0.1034,0.0417,0.0833,0.0,0.0154,0.0136,0.0,0.0,reg oper account,block of flats,0.0105,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-563.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n314605,0,Cash loans,M,Y,N,0,202500.0,473760.0,51021.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-15674,-6336,-7062.0,-4693,64.0,1,1,1,1,0,0,Drivers,2.0,1,1,THURSDAY,18,0,0,0,0,1,1,Construction,,0.41472346625959416,0.3233112448967859,0.1113,0.0896,0.9901,,,0.12,0.1034,0.3333,,0.1067,,0.1275,,0.1749,0.1134,0.09300000000000001,0.9901,,,0.1208,0.1034,0.3333,,0.1091,,0.1328,,0.1851,0.1124,0.0896,0.9901,,,0.12,0.1034,0.3333,,0.1085,,0.1297,,0.1785,,block of flats,0.1383,Panel,No,2.0,0.0,2.0,0.0,-728.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n232571,0,Cash loans,F,N,N,1,45000.0,157500.0,8671.5,157500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018209,-10164,-198,-8005.0,-2827,,1,1,0,1,0,0,Sales staff,2.0,3,3,MONDAY,8,0,0,0,0,0,0,Trade: type 7,0.13174720029425382,0.3631479775399892,,0.2536,0.1557,0.9891,,,0.2,0.1724,0.4583,,0.1172,,0.0486,,0.0107,0.2584,0.1616,0.9891,,,0.2014,0.1724,0.4583,,0.1198,,0.0506,,0.0114,0.2561,0.1557,0.9891,,,0.2,0.1724,0.4583,,0.1192,,0.0495,,0.011000000000000001,,block of flats,0.1841,Panel,No,0.0,0.0,0.0,0.0,-535.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n323091,1,Cash loans,M,Y,Y,0,180000.0,317979.0,19345.5,274500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-8140,-227,-1149.0,-809,5.0,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Other,0.04346026272565465,0.6595436779932847,0.3108182544189319,0.0773,0.0705,0.9801,,,0.0,0.2069,0.1667,,0.0194,,0.076,,0.0142,0.063,0.0732,0.9801,,,0.0,0.2069,0.1667,,0.0169,,0.0785,,0.0,0.0781,0.0705,0.9801,,,0.0,0.2069,0.1667,,0.0198,,0.0774,,0.0145,,block of flats,0.067,Panel,No,4.0,0.0,4.0,0.0,-354.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n266198,0,Cash loans,F,Y,Y,0,202500.0,1077061.5,31621.5,940500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.016612000000000002,-17260,-3518,-5561.0,-562,24.0,1,1,0,1,0,0,,2.0,2,2,SATURDAY,15,0,0,0,0,1,1,Government,0.6360195630983188,0.4720896236442054,0.6769925032909132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2195.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n258577,0,Cash loans,F,N,Y,1,157500.0,582768.0,45229.5,540000.0,Family,Working,Higher education,Married,House / apartment,0.025164,-14561,-582,-1141.0,-203,,1,1,0,1,0,0,,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.6685862432378058,0.4907256255659831,,0.0742,0.0344,0.9921,,,0.08,0.069,0.3333,,,,0.033,,0.1386,0.0756,0.0357,0.9921,,,0.0806,0.069,0.3333,,,,0.0343,,0.1468,0.0749,0.0344,0.9921,,,0.08,0.069,0.3333,,,,0.0335,,0.1415,,block of flats,0.0561,Panel,No,0.0,0.0,0.0,0.0,-259.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n235129,0,Cash loans,F,N,N,0,157500.0,900000.0,38263.5,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.020246,-20410,365243,-10192.0,-3916,,1,0,0,1,0,0,,1.0,3,3,SATURDAY,11,0,0,0,0,0,0,XNA,0.7278152262511068,0.4168907128324509,0.6347055309763198,0.1227,0.132,0.9776,0.6940000000000001,0.016,0.0,0.2759,0.1667,0.2083,0.0923,0.1,0.1146,0.0,0.0,0.125,0.13699999999999998,0.9777,0.706,0.0162,0.0,0.2759,0.1667,0.2083,0.0945,0.1093,0.1194,0.0,0.0,0.1239,0.132,0.9776,0.6981,0.0161,0.0,0.2759,0.1667,0.2083,0.094,0.1018,0.1167,0.0,0.0,reg oper spec account,block of flats,0.0989,Panel,No,2.0,1.0,2.0,1.0,-2731.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,0.0\n431727,0,Revolving loans,F,N,Y,0,76500.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-7823,-224,-666.0,-505,,1,1,0,1,1,0,Waiters/barmen staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Restaurant,,0.4356437433139224,0.2314393514998941,,,0.9786,,,,,,,,,0.0145,,,,,0.9786,,,,,,,,,0.0152,,,,,0.9786,,,,,,,,,0.0148,,,,,0.0125,,No,4.0,0.0,4.0,0.0,-687.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n188829,0,Cash loans,M,N,N,0,180000.0,450000.0,24412.5,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.04622,-11889,-1663,-5556.0,-4002,,1,1,1,1,0,0,Drivers,1.0,1,1,MONDAY,19,0,1,1,0,1,1,Other,0.2521633995910517,0.7154159475381864,0.5531646987710016,0.0082,,0.9697,,,0.0,0.069,0.0417,,0.0045,,0.0074,,0.0,0.0084,,0.9697,,,0.0,0.069,0.0417,,0.0046,,0.0078,,0.0,0.0083,,0.9697,,,0.0,0.069,0.0417,,0.0046,,0.0076,,0.0,,block of flats,0.0059,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2327.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n216947,0,Cash loans,M,N,Y,2,49500.0,101880.0,10939.5,90000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.009657,-15905,-593,-9948.0,-4060,,1,1,1,1,0,0,Laborers,4.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.4015974586577861,0.816092360478441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-337.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n264478,0,Cash loans,F,N,Y,0,90000.0,545040.0,19705.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-21312,365243,-4733.0,-4739,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,,0.5222865736446969,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1496.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n101468,0,Cash loans,M,Y,N,1,247891.5,2254500.0,65988.0,2254500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.031329,-11361,-1252,-5403.0,-3572,11.0,1,1,1,1,0,0,Sales staff,3.0,2,2,TUESDAY,8,0,0,0,0,0,0,Trade: type 7,0.23661145625810265,0.5403254000630133,,0.0629,0.0301,0.9742,0.6464,0.0163,0.0,0.1379,0.1667,0.2083,0.0511,0.0471,0.055999999999999994,0.0232,0.031,0.0641,0.0312,0.9742,0.6602,0.0165,0.0,0.1379,0.1667,0.2083,0.0522,0.0514,0.0583,0.0233,0.0328,0.0635,0.0301,0.9742,0.6511,0.0164,0.0,0.1379,0.1667,0.2083,0.052000000000000005,0.0479,0.057,0.0233,0.0316,reg oper account,block of flats,0.0597,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2531.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n401131,0,Cash loans,F,Y,Y,0,292500.0,1550655.0,41035.5,1386000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,Municipal apartment,0.04622,-16055,-6324,-8532.0,-4259,5.0,1,1,0,1,0,0,Laborers,1.0,1,1,MONDAY,15,0,0,0,0,0,0,Other,,0.7344737478175832,0.8050196619153701,0.0619,0.0632,0.9771,0.6872,,0.0,0.1034,0.1667,0.2083,0.0545,0.0504,0.0493,0.0,0.0,0.063,0.0655,0.9772,0.6994,,0.0,0.1034,0.1667,0.2083,0.0558,0.0551,0.0514,0.0,0.0,0.0625,0.0632,0.9771,0.6914,,0.0,0.1034,0.1667,0.2083,0.0555,0.0513,0.0502,0.0,0.0,reg oper account,block of flats,0.0393,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n210322,0,Cash loans,F,N,Y,0,135000.0,229230.0,12928.5,202500.0,Family,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.005084,-22936,365243,-11529.0,-4343,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.5988685266696441,0.2764406945454034,0.0124,0.0,0.9732,,,0.0,0.069,0.0417,,,,0.0131,,,0.0126,0.0,0.9732,,,0.0,0.069,0.0417,,,,0.0136,,,0.0125,0.0,0.9732,,,0.0,0.069,0.0417,,,,0.0133,,,,block of flats,0.0103,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-64.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,0.0\n455688,0,Cash loans,F,N,N,0,90000.0,315000.0,15151.5,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.010006,-21306,365243,-5447.0,-3869,,1,0,0,1,0,0,,1.0,2,2,MONDAY,19,0,0,0,0,0,0,XNA,,0.360734039722964,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-3021.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n410750,1,Cash loans,M,Y,N,0,121500.0,668304.0,28444.5,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.031329,-9215,-108,-316.0,-1721,8.0,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3312562058386259,0.5229903573380453,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.062,,No,1.0,0.0,1.0,0.0,-1614.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n187682,0,Revolving loans,F,N,N,0,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.006852,-9085,-559,-89.0,-1749,,1,1,0,1,0,0,Sales staff,1.0,3,3,TUESDAY,5,0,0,0,0,0,0,Self-employed,0.2883894706644647,0.18269648972697644,0.2366108235287817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n218689,0,Cash loans,M,Y,Y,0,112500.0,207711.0,14899.5,189000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-24018,365243,-8784.0,-4050,41.0,1,0,0,1,0,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.09893243442124962,0.6658549219640212,0.168,0.0866,0.9861,0.8096,0.0422,0.16,0.1379,0.375,0.0417,0.0273,0.1345,0.1649,0.0116,0.0599,0.1712,0.0899,0.9861,0.8171,0.0426,0.1611,0.1379,0.375,0.0417,0.0279,0.1469,0.1718,0.0117,0.0635,0.1697,0.0866,0.9861,0.8121,0.0425,0.16,0.1379,0.375,0.0417,0.0278,0.1368,0.1679,0.0116,0.0612,reg oper account,block of flats,0.1658,Panel,No,0.0,0.0,0.0,0.0,-1783.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n271819,0,Cash loans,M,N,Y,0,99000.0,675000.0,24376.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-20005,-5142,-2869.0,-3381,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Construction,,0.6809581320926831,0.7180328113294772,0.0557,,0.9776,,,0.04,0.0345,0.3333,,,,,,0.0,0.0567,,0.9777,,,0.0403,0.0345,0.3333,,,,,,0.0,0.0562,,0.9776,,,0.04,0.0345,0.3333,,,,,,0.0,,,0.442,,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n409762,0,Cash loans,F,N,Y,1,270000.0,1006920.0,51543.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-16756,-543,-1619.0,-289,,1,1,0,1,1,0,Core staff,3.0,2,2,THURSDAY,16,0,0,0,0,0,0,Self-employed,,0.7164343895071114,,0.0082,0.0,0.9712,,,0.0,0.0345,0.0417,,0.0121,,0.0054,,0.0,0.0084,0.0,0.9712,,,0.0,0.0345,0.0417,,0.0123,,0.0056,,0.0,0.0083,0.0,0.9712,,,0.0,0.0345,0.0417,,0.0123,,0.0054,,0.0,,block of flats,0.0077,\"Stone, brick\",No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n348281,0,Cash loans,M,N,N,1,270000.0,756000.0,22234.5,756000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008019,-12675,-1347,-864.0,-4731,,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Bank,0.2748696584006491,0.6779194847386771,0.5190973382084597,0.0959,0.1254,0.9717,0.6124,0.0114,0.0,0.2069,0.1667,0.2083,0.061,0.0748,0.0968,0.0154,0.0472,0.0977,0.1301,0.9717,0.6276,0.0115,0.0,0.2069,0.1667,0.2083,0.0624,0.0817,0.1009,0.0156,0.0499,0.0968,0.1254,0.9717,0.6176,0.0115,0.0,0.2069,0.1667,0.2083,0.0621,0.0761,0.0986,0.0155,0.0482,reg oper account,block of flats,0.0872,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-559.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n327122,0,Cash loans,F,N,Y,0,270000.0,906615.0,30091.5,688500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.072508,-17604,-98,-114.0,-1142,,1,1,0,1,1,0,Core staff,2.0,1,1,THURSDAY,16,0,0,0,0,0,0,Trade: type 3,0.5841574818048921,0.7934069711271426,0.7281412993111438,0.2072,0.0934,0.9801,0.728,,0.08,0.0345,0.3333,0.375,0.0,0.1689,0.1125,,0.0738,0.2111,0.0969,0.9801,0.7387,,0.0806,0.0345,0.3333,0.375,0.0,0.1846,0.1172,,0.0782,0.2092,0.0934,0.9801,0.7316,,0.08,0.0345,0.3333,0.375,0.0,0.1719,0.1145,,0.0754,reg oper account,block of flats,0.1045,Block,No,0.0,0.0,0.0,0.0,-3154.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n176221,0,Cash loans,F,N,Y,0,180000.0,612612.0,27112.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.011657,-17436,-8075,-8981.0,-991,,1,1,0,1,1,0,Medicine staff,1.0,1,1,TUESDAY,10,0,0,0,0,0,0,Other,0.4962902926875186,0.6591802239547134,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-981.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n303944,0,Cash loans,F,N,Y,0,157500.0,814041.0,26257.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-20148,-4680,-7041.0,-2800,,1,1,0,1,1,0,Laborers,2.0,3,3,MONDAY,17,0,0,0,0,0,0,Industry: type 2,,0.17080606268438298,0.7194907850918436,0.1041,0.0547,0.9881,,,0.04,0.0345,0.25,,0.0618,,0.0747,,0.016,0.0714,0.0567,0.9861,,,0.0403,0.0345,0.1667,,0.0633,,0.0778,,0.0169,0.1051,0.0547,0.9881,,,0.04,0.0345,0.25,,0.0629,,0.076,,0.0163,,block of flats,0.0765,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1233.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n330844,0,Cash loans,F,N,Y,3,94500.0,348264.0,23404.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-11466,-3236,-760.0,-2725,,1,1,1,1,0,0,Low-skill Laborers,5.0,2,2,FRIDAY,10,0,0,0,0,1,1,Self-employed,0.4683445176725315,0.1676701407166605,0.3774042489507649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-1955.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n390507,0,Cash loans,M,N,Y,0,112500.0,225000.0,22050.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.026392,-14680,-1318,-8511.0,-4552,,1,1,0,1,1,0,Laborers,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.3987487764919218,0.6871350905026973,0.7252764347002191,0.0464,,0.9886,,,0.0,0.0345,0.3333,,,,0.0683,,0.0024,0.0473,,0.9886,,,0.0,0.0345,0.3333,,,,0.0712,,0.0025,0.0468,,0.9886,,,0.0,0.0345,0.3333,,,,0.0696,,0.0024,,block of flats,0.0543,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2704.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n177831,0,Cash loans,M,N,Y,0,225000.0,521280.0,28408.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.014464,-19282,-258,-627.0,-2701,,1,1,0,1,0,0,Drivers,1.0,2,2,MONDAY,8,0,0,0,0,0,0,Electricity,,0.4551195195301488,0.3108182544189319,0.1247,0.1422,0.9836,0.7756,0.0,0.0,0.2759,0.1667,0.0,0.0789,0.1009,0.1047,0.0039,0.0045,0.1271,0.1475,0.9836,0.7844,0.0,0.0,0.2759,0.1667,0.0,0.0807,0.1102,0.1091,0.0039,0.0047,0.126,0.1422,0.9836,0.7786,0.0,0.0,0.2759,0.1667,0.0,0.0803,0.1026,0.1066,0.0039,0.0046,reg oper account,block of flats,0.0833,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-409.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n195763,0,Cash loans,F,N,Y,0,135000.0,808650.0,26217.0,675000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.035792000000000004,-17416,365243,-11194.0,-965,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,0.8090976612691321,0.6753693262883778,0.31703177858344445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2213.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n250985,0,Cash loans,F,N,Y,0,202500.0,760225.5,30280.5,679500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.008019,-19183,-3943,-8177.0,-2725,,1,1,0,1,0,0,,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.609130085625611,,0.0773,0.0856,0.9851,0.7959999999999999,0.0184,0.0,0.1724,0.1667,0.0417,0.053,0.063,0.0414,0.0,0.0,0.0788,0.0889,0.9851,0.804,0.0186,0.0,0.1724,0.1667,0.0417,0.0542,0.0689,0.0431,0.0,0.0,0.0781,0.0856,0.9851,0.7987,0.0185,0.0,0.1724,0.1667,0.0417,0.0539,0.0641,0.0421,0.0,0.0,reg oper account,block of flats,0.0548,Panel,No,2.0,1.0,2.0,0.0,-2293.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n363353,0,Cash loans,M,N,Y,0,202500.0,315000.0,17217.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006670999999999999,-9666,-1505,-1281.0,-2345,,1,1,1,1,0,0,Sales staff,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,Trade: type 2,0.2134402599015177,0.6509585985282039,0.5298898341969072,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-377.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n264690,0,Cash loans,F,N,Y,0,292500.0,521280.0,31630.5,450000.0,Other_B,Pensioner,Higher education,Single / not married,House / apartment,0.072508,-21462,365243,-15594.0,-4547,,1,0,0,1,0,0,,1.0,1,1,MONDAY,13,0,0,0,0,0,0,XNA,,0.7732082978271198,,,,0.9742,,,,,,,,,0.0674,,0.0018,,,0.9742,,,,,,,,,0.0702,,0.0019,,,0.9742,,,,,,,,,0.0686,,0.0018,,block of flats,0.0589,,No,6.0,2.0,6.0,2.0,-1190.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n281610,0,Cash loans,F,N,N,1,90000.0,117162.0,13378.5,103500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-10442,-309,-1911.0,-3126,,1,1,0,1,0,1,Sales staff,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Self-employed,0.2552395333521732,0.5949065514629996,,0.0495,0.0,0.9737,0.6396,0.0057,0.0,0.1034,0.125,0.0417,0.0364,,0.0403,,0.0,0.0504,0.0,0.9737,0.6537,0.0057,0.0,0.1034,0.125,0.0417,0.0372,,0.042,,0.0,0.05,0.0,0.9737,0.6444,0.0057,0.0,0.1034,0.125,0.0417,0.037000000000000005,,0.040999999999999995,,0.0,,block of flats,0.0317,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1484.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n110327,0,Cash loans,F,Y,Y,0,157500.0,149256.0,16879.5,135000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.009334,-16407,-8116,-2474.0,-1256,2.0,1,1,1,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Transport: type 2,0.8863676419646592,0.6061061902538124,0.1510075350878296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-267.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n130982,0,Revolving loans,M,Y,N,0,117000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.018801,-11145,-246,-3651.0,-2396,14.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.33006044000903195,0.44539624156083396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-285.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n224056,0,Cash loans,F,N,Y,0,207000.0,1066320.0,38299.5,900000.0,Unaccompanied,Working,Lower secondary,Widow,House / apartment,0.007273999999999998,-23250,-3990,-263.0,-4750,,1,1,1,1,1,0,Laborers,1.0,2,2,SATURDAY,12,1,1,0,1,1,1,Business Entity Type 2,,0.735223650501813,0.4632753280912678,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1343.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n280244,0,Cash loans,M,Y,Y,0,90000.0,101880.0,6763.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.011703,-13192,-1895,-6420.0,-4034,7.0,1,1,1,1,0,0,Laborers,1.0,2,2,SATURDAY,11,0,0,0,0,1,1,Agriculture,0.08891576034312527,0.6599412977260477,0.6785676886853644,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-569.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n258608,0,Cash loans,F,N,Y,1,211500.0,640080.0,24259.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.072508,-12605,-2308,-6571.0,-3855,,1,1,0,1,1,0,High skill tech staff,2.0,1,1,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.4006555126040965,0.6317018035543432,0.656158373001177,0.1343,0.1001,0.9801,0.7348,0.0,0.1384,0.1355,0.3388,0.3442,0.0,0.1083,0.1122,0.0062,0.0652,0.084,0.0153,0.9732,0.6472,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.0456,0.0,0.0015,0.1197,0.0843,0.9791,0.7182,0.0,0.16,0.1379,0.3333,0.2083,0.0,0.0941,0.0866,0.0039,0.0107,reg oper account,block of flats,0.1667,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1756.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n136771,0,Cash loans,F,Y,Y,0,157500.0,582804.0,24822.0,463500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-19430,-478,-1855.0,-2984,8.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,10,0,1,1,0,1,1,Business Entity Type 3,0.6656869809227879,0.620542900907562,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1092.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n286450,0,Cash loans,M,Y,Y,0,202500.0,1147500.0,31554.0,1147500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003069,-17120,365243,-10553.0,-656,18.0,1,0,0,1,1,0,,2.0,3,3,SATURDAY,12,0,0,0,0,0,0,XNA,,0.6122234970784659,0.4686596550493113,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1093.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n163196,0,Cash loans,M,Y,Y,0,225000.0,1125000.0,36423.0,1125000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.014519999999999996,-15792,-2836,-1231.0,-4619,14.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Other,,0.6697155908978699,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n454301,0,Cash loans,F,N,N,0,180000.0,675000.0,53460.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018029,-8282,-95,-6904.0,-960,,1,1,0,1,1,0,Sales staff,2.0,3,3,THURSDAY,16,0,0,0,0,0,0,Trade: type 1,0.031245940701962464,0.3391547810043076,0.053114172843977035,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-527.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,0.0\n307116,0,Cash loans,F,N,Y,1,72450.0,683613.0,19984.5,683613.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-15717,-1941,-3659.0,-4431,,1,1,1,1,1,0,Core staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Government,0.7692381946801915,0.7652315237942942,,0.0485,,0.9876,,,0.08,0.0345,0.4583,,,,,,,0.0494,,0.9876,,,0.0806,0.0345,0.4583,,,,,,,0.0489,,0.9876,,,0.08,0.0345,0.4583,,,,,,,,,0.0398,Panel,No,0.0,0.0,0.0,0.0,-930.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n332721,0,Cash loans,F,N,N,0,135000.0,254700.0,14220.0,225000.0,Unaccompanied,Commercial associate,Higher education,Widow,House / apartment,0.02461,-24326,-7711,-3518.0,-3969,,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,18,0,0,0,0,0,0,School,,0.7346229841635166,0.7324033228040929,,0.3054,0.9856,0.8028,0.1077,0.44,0.3793,0.3333,0.375,0.244,0.3312,0.4137,0.0039,0.0011,,0.317,0.9856,0.8105,0.1087,0.4431,0.3793,0.3333,0.375,0.2496,0.3618,0.431,0.0039,0.0012,,0.3054,0.9856,0.8054,0.1084,0.44,0.3793,0.3333,0.375,0.2483,0.3369,0.4211,0.0039,0.0012,reg oper account,block of flats,0.3845,Panel,No,0.0,0.0,0.0,0.0,-1489.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,4.0\n355421,0,Cash loans,M,N,N,1,180000.0,373140.0,24421.5,337500.0,Unaccompanied,State servant,Secondary / secondary special,Married,Office apartment,0.015221,-11118,-2681,-809.0,-3713,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Military,,0.04042713826702963,0.5971924268337128,0.1041,0.0755,0.9901,0.8640000000000001,0.0096,0.0,0.1034,0.2083,0.25,0.0569,0.0849,0.0409,0.0,0.0,0.1061,0.0783,0.9901,0.8693,0.0097,0.0,0.1034,0.2083,0.25,0.0582,0.0927,0.0426,0.0,0.0,0.1051,0.0755,0.9901,0.8658,0.0097,0.0,0.1034,0.2083,0.25,0.0578,0.0864,0.0416,0.0,0.0,not specified,block of flats,0.0641,Panel,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n280176,0,Cash loans,M,N,Y,1,315000.0,343800.0,16024.5,225000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.028663,-14950,-5302,-2856.0,-984,,1,1,0,1,0,0,Security staff,3.0,2,2,FRIDAY,12,0,1,1,0,1,1,Business Entity Type 3,,0.5569807327485597,0.7700870700124128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-3133.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n390945,0,Cash loans,F,N,Y,0,247500.0,497520.0,27909.0,450000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.008575,-23238,365243,-9310.0,-4332,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.4870427757622936,0.8245949709919925,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,2.0,0.0,-541.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n328177,0,Cash loans,F,N,N,0,126000.0,454500.0,16695.0,454500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22097,365243,-1408.0,-4695,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,0.8603246998544807,0.658408492219652,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-330.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n320433,1,Revolving loans,F,N,Y,1,112500.0,180000.0,9000.0,,,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-8329,-1287,-608.0,-20,,1,1,1,1,0,0,Private service staff,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Services,,0.5313352762757125,,0.1588,,0.9771,,,0.0,0.069,0.1667,,,,0.0488,,0.0,0.1618,,0.9772,,,0.0,0.069,0.1667,,,,0.0509,,0.0,0.1603,,0.9771,,,0.0,0.069,0.1667,,,,0.0497,,0.0,,block of flats,0.0384,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-524.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n251104,0,Cash loans,F,N,Y,0,225000.0,343800.0,13090.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-22212,365243,-680.0,-4626,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.6717439697513481,0.5334816299804352,0.2555,0.1214,0.9985,0.9796,0.0871,0.35,0.1507,0.4583,0.0,0.0,0.1994,0.214,0.040999999999999995,0.0941,0.1943,0.0923,0.9985,0.9804,0.0762,0.4028,0.1724,0.4583,0.0,0.0,0.1506,0.1807,0.0156,0.0134,0.2727,0.1234,0.9985,0.9799,0.0871,0.4,0.1724,0.4583,0.0,0.0,0.2185,0.2203,0.033,0.0809,reg oper account,block of flats,0.1964,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1082.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n305007,0,Cash loans,F,Y,Y,0,112500.0,269550.0,11871.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.004849,-8456,-1280,-6066.0,-1017,2.0,1,1,1,1,0,0,Sales staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.278329493958807,0.10211878786358386,1.0,0.0356,0.9876,0.8776,0.0199,0.1332,0.1207,0.2358,0.2638,0.0688,1.0,0.0738,0.0283,0.0007,0.0504,0.0111,0.9906,0.8759,0.0014,0.0403,0.1034,0.1667,0.25,0.0413,0.0441,0.0352,0.0,0.0,0.0807,0.0255,0.9906,0.8725,0.0095,0.12,0.1034,0.2292,0.25,0.0518,0.0633,0.0525,0.0155,0.001,org spec account,block of flats,0.1777,\"Stone, brick\",No,5.0,1.0,5.0,1.0,-1001.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n420092,0,Cash loans,F,N,Y,1,112500.0,540000.0,17550.0,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018209,-10241,-2665,-4473.0,-2301,,1,1,0,1,0,0,Sales staff,2.0,3,3,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.3370113934036521,0.7898803468924867,0.0928,0.099,0.9806,0.7348,0.1706,0.0,0.2069,0.1667,0.0417,0.0647,0.0756,0.0874,0.0,0.0,0.0945,0.1027,0.9806,0.7452,0.1721,0.0,0.2069,0.1667,0.0417,0.0662,0.0826,0.091,0.0,0.0,0.0937,0.099,0.9806,0.7383,0.1717,0.0,0.2069,0.1667,0.0417,0.0658,0.077,0.0889,0.0,0.0,reg oper account,block of flats,0.0933,Panel,No,1.0,0.0,1.0,0.0,-719.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n434492,1,Cash loans,F,N,Y,1,157500.0,450000.0,35554.5,450000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.026392,-9174,-769,-5594.0,-748,,1,1,0,1,0,0,Core staff,3.0,2,2,THURSDAY,18,0,0,0,0,0,0,School,0.3684685659020721,0.2858978721410488,,0.0825,0.085,0.9762,,,0.0,0.1379,0.1667,,0.0776,,0.0662,,0.0,0.084,0.0882,0.9762,,,0.0,0.1379,0.1667,,0.0794,,0.069,,0.0,0.0833,0.085,0.9762,,,0.0,0.1379,0.1667,,0.079,,0.0674,,0.0,,block of flats,0.0521,Panel,No,2.0,0.0,2.0,0.0,-303.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n431570,0,Cash loans,F,N,Y,0,405000.0,1021500.0,29866.5,1021500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-23411,365243,-8827.0,-4469,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.6017018815944609,0.2636468134452008,0.1,0.111,0.9826,0.762,0.0471,0.0,0.2069,0.1667,0.2083,0.0273,0.0807,0.08800000000000001,0.0039,0.013000000000000001,0.1019,0.1152,0.9826,0.7713,0.0475,0.0,0.2069,0.1667,0.2083,0.027999999999999997,0.0882,0.0917,0.0039,0.0138,0.10099999999999999,0.111,0.9826,0.7652,0.0474,0.0,0.2069,0.1667,0.2083,0.0278,0.0821,0.0896,0.0039,0.0133,reg oper spec account,block of flats,0.0949,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2784.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n313745,0,Cash loans,M,Y,Y,0,90000.0,254700.0,14751.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-24470,365243,-13382.0,-4161,29.0,1,0,0,1,0,0,,2.0,2,2,SUNDAY,15,0,0,0,0,0,0,XNA,,0.655273682283725,0.5028782772082183,0.133,,0.9806,,,0.0,0.2759,0.1667,,,,0.0795,,,0.1355,,0.9806,,,0.0,0.2759,0.1667,,,,0.0829,,,0.1343,,0.9806,,,0.0,0.2759,0.1667,,,,0.081,,,,block of flats,0.1034,\"Stone, brick\",No,7.0,2.0,5.0,2.0,-1320.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n337556,0,Cash loans,M,N,N,0,315000.0,414229.5,13590.0,342000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-17819,-2394,-1302.0,-1347,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6682531724778021,0.5460231970049609,0.0,,0.0,,,0.0,,,,,,,,,0.0,,0.0005,,,0.0,,,,,,,,,0.0,,0.0,,,0.0,,,,,,,,,,block of flats,0.0,,No,0.0,0.0,0.0,0.0,-541.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n156682,0,Cash loans,F,Y,Y,0,135000.0,900000.0,38263.5,900000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.028663,-16499,-3872,-4375.0,-58,4.0,1,1,0,1,0,0,Private service staff,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,Services,0.4910581871980794,0.6962468006796975,0.6690566947824041,,0.1301,0.9796,,,0.16,0.1379,0.3333,,,,0.2132,,0.0208,,0.135,0.9796,,,0.1611,0.1379,0.3333,,,,0.2221,,0.022000000000000002,,0.1301,0.9796,,,0.16,0.1379,0.3333,,,,0.217,,0.0212,,,0.2178,Mixed,No,1.0,1.0,1.0,1.0,-1660.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n263829,0,Cash loans,F,N,Y,0,135000.0,283585.5,29907.0,256500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006852,-18069,-4580,-1604.0,-1621,,1,1,0,1,0,0,Secretaries,2.0,3,3,SATURDAY,9,0,0,0,0,0,0,Military,,0.2167973120763193,0.7922644738669378,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1302.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n138746,0,Cash loans,M,N,Y,0,90000.0,647046.0,21001.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20235,-2056,-10570.0,-3416,,1,1,0,1,0,0,Security staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Kindergarten,,0.3360788080121793,0.2750003523983893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-180.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n223436,0,Cash loans,M,Y,N,0,202500.0,646920.0,20997.0,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.072508,-14143,365243,-4284.0,-4321,20.0,1,0,0,1,1,0,,1.0,1,1,WEDNESDAY,16,1,0,0,1,0,0,XNA,,0.1691339589707663,0.6577838002083306,0.0799,0.0616,0.9742,0.6464,0.0,0.01,0.1293,0.1771,0.2188,0.0,0.0649,0.0673,0.001,0.0018,0.084,0.0479,0.9737,0.6537,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.0622,0.0,0.0,0.0822,0.0665,0.9737,0.6444,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0671,0.071,0.0,0.0,not specified,block of flats,0.047,Block,No,0.0,0.0,0.0,0.0,-120.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n299205,0,Cash loans,M,Y,N,0,270000.0,808650.0,23773.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.019101,-15838,-802,-8341.0,-4309,17.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.7184071466490513,0.5691487713619409,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-576.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n302374,0,Cash loans,F,N,Y,1,130500.0,555273.0,16366.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.007120000000000001,-19936,-4926,-1698.0,-3479,,1,1,0,1,0,0,Medicine staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Medicine,0.8152488317527857,0.4352728129079888,0.6690566947824041,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1647.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n382007,0,Revolving loans,F,N,Y,0,112500.0,337500.0,16875.0,337500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-21905,365243,-654.0,-4394,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,XNA,0.8109923431577958,0.7588515901853621,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-2674.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n206452,0,Cash loans,F,N,N,0,139500.0,422235.0,18018.0,364500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.022625,-21982,365243,-11967.0,-4466,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.6754987609051931,0.6512602186973006,0.1485,0.1164,0.9836,0.7756,0.0377,0.16,0.1379,0.3333,0.375,0.1645,0.121,0.1564,0.0,0.0,0.1513,0.1208,0.9836,0.7844,0.0381,0.1611,0.1379,0.3333,0.375,0.1683,0.1322,0.1629,0.0,0.0,0.1499,0.1164,0.9836,0.7786,0.038,0.16,0.1379,0.3333,0.375,0.1674,0.1231,0.1592,0.0,0.0,reg oper account,block of flats,0.163,Panel,No,2.0,0.0,2.0,0.0,-2307.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n236102,0,Cash loans,F,N,Y,1,450000.0,922500.0,49279.5,922500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-13638,-4512,-714.0,-4536,,1,1,0,1,0,0,Managers,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Self-employed,,0.6381845349294564,0.20092608771597087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2275.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n240545,0,Cash loans,F,Y,N,3,202500.0,364896.0,19926.0,315000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-12426,-511,-1944.0,-2267,9.0,1,1,0,1,0,0,Laborers,5.0,1,1,MONDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.544243468623302,0.7504304050326365,0.622922000268356,0.066,0.0,0.9752,0.66,0.0054,0.0,0.1379,0.125,0.1667,,0.0538,0.0505,0.0,0.0,0.0672,0.0,0.9752,0.6733,0.0055,0.0,0.1379,0.125,0.1667,,0.0588,0.0527,0.0,0.0,0.0666,0.0,0.9752,0.6645,0.0054,0.0,0.1379,0.125,0.1667,,0.0547,0.0515,0.0,0.0,reg oper account,block of flats,0.0398,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-303.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n415464,0,Revolving loans,M,N,Y,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010147,-8431,-784,-8254.0,-833,,1,1,0,1,1,0,,1.0,2,2,SATURDAY,17,0,0,0,0,0,0,Insurance,,0.6914810607686772,0.4740512892789932,0.1649,0.9132,0.9886,0.8436,0.0331,0.16,0.1379,0.375,0.4167,0.1278,0.1345,0.1705,0.0,0.0833,0.1681,0.9476,0.9886,0.8497,0.0334,0.1611,0.1379,0.375,0.4167,0.1307,0.1469,0.1776,0.0,0.0882,0.1665,0.9132,0.9886,0.8457,0.0333,0.16,0.1379,0.375,0.4167,0.13,0.1368,0.1735,0.0,0.085,,block of flats,0.157,Panel,No,0.0,0.0,0.0,0.0,-247.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n103610,0,Cash loans,F,N,N,0,162000.0,754740.0,22198.5,630000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-17730,-2081,-5667.0,-1204,,1,1,1,1,1,0,Medicine staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Medicine,0.6039703632190384,0.5870306998193092,0.5262949398096192,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1184.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n322288,0,Cash loans,F,Y,Y,0,121500.0,522396.0,32089.5,472500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010147,-17663,-1709,-7467.0,-1208,13.0,1,1,1,1,1,0,Core staff,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Kindergarten,,0.5053209720651425,0.7194907850918436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1507.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n172473,0,Cash loans,F,Y,Y,1,225000.0,135000.0,15358.5,135000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018209,-9979,-938,-3051.0,-1388,6.0,1,1,0,1,0,0,Core staff,3.0,3,3,SUNDAY,12,0,0,0,0,0,0,Bank,0.3324243153834601,0.2695443559685189,0.2314393514998941,0.1237,0.1185,0.9791,,,0.12,0.2759,0.1667,,0.10099999999999999,,0.0776,,0.001,0.1261,0.12300000000000001,0.9791,,,0.1208,0.2759,0.1667,,0.1034,,0.0809,,0.0011,0.1249,0.1185,0.9791,,,0.12,0.2759,0.1667,,0.1028,,0.079,,0.001,,block of flats,0.0904,Panel,No,6.0,0.0,6.0,0.0,-873.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n307275,0,Revolving loans,M,N,Y,0,292500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.072508,-8237,-701,-372.0,-887,,1,1,0,1,1,1,Sales staff,2.0,1,1,TUESDAY,12,0,0,0,0,0,0,Trade: type 2,0.5182129911666564,0.7389991775834137,0.3201633668633456,0.0775,0.0553,0.9747,0.6532,0.0,0.016,0.0966,0.2333,0.275,0.0729,0.0632,0.0609,0.0,0.0019,0.0756,0.0873,0.9747,0.6668,0.0,0.0,0.1379,0.1667,0.2083,0.0745,0.0661,0.0527,0.0,0.0,0.0749,0.0408,0.9747,0.6578,0.0,0.0,0.1379,0.1667,0.2083,0.0741,0.0616,0.0679,0.0,0.0,reg oper account,block of flats,0.0354,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-343.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n185132,0,Cash loans,F,N,N,2,135000.0,312768.0,16506.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16174,-911,-6962.0,-3959,,1,1,0,1,0,0,Managers,4.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6659485694096903,0.7169716281361828,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1084.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n432250,0,Cash loans,F,N,Y,0,162000.0,239850.0,23719.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.009334,-15227,-1929,-3771.0,-5324,,1,1,1,1,0,0,Sales staff,2.0,2,2,SUNDAY,16,0,0,0,0,0,0,Trade: type 7,0.3036579037685877,0.5298538492520493,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1375.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n135957,0,Cash loans,F,N,N,1,180000.0,1629000.0,56623.5,1629000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-16194,-4250,-2580.0,-4991,,1,1,0,1,1,0,Laborers,3.0,1,1,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.923581734037429,0.6509635670780287,,0.0825,,0.9702,,,0.0,0.2069,0.1667,,0.0,,0.0761,,0.1455,0.084,,0.9702,,,0.0,0.2069,0.1667,,0.0,,0.0793,,0.1541,0.0833,,0.9702,,,0.0,0.2069,0.1667,,0.0,,0.0775,,0.1486,,block of flats,0.0915,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1619.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n420237,0,Revolving loans,M,N,N,1,121500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-11662,-97,-4476.0,-2561,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,6,0,0,0,1,1,0,Business Entity Type 3,0.19748257541183664,0.2405793862805541,0.6754132910917112,0.0124,0.0334,0.9771,0.6872,0.012,0.0,0.069,0.0417,0.0833,0.0162,0.0101,0.0133,0.0,0.0,0.0126,0.0346,0.9772,0.6994,0.0121,0.0,0.069,0.0417,0.0833,0.0166,0.011000000000000001,0.0138,0.0,0.0,0.0125,0.0334,0.9771,0.6914,0.0121,0.0,0.069,0.0417,0.0833,0.0165,0.0103,0.0135,0.0,0.0,reg oper account,block of flats,0.017,Panel,No,0.0,0.0,0.0,0.0,-213.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n382021,0,Cash loans,F,N,Y,0,162000.0,207000.0,19116.0,207000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-21066,365243,-3605.0,-4235,,1,0,0,1,1,0,,2.0,3,3,THURSDAY,7,0,0,0,0,0,0,XNA,0.3045015328650029,0.08378646587128898,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-363.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n265947,0,Cash loans,M,N,Y,0,283500.0,1032133.5,34240.5,891000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-23654,-1528,-9929.0,-4203,,1,1,0,1,0,0,Drivers,2.0,1,1,SUNDAY,15,0,0,0,0,1,1,Housing,,0.7273604483853695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-932.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n371608,0,Cash loans,F,N,Y,0,180000.0,1197000.0,35127.0,1197000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018209,-20875,-85,-5261.0,-4196,,1,1,0,1,0,0,,2.0,3,3,MONDAY,8,0,0,0,0,0,0,Industry: type 1,,0.2359360420919137,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-273.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,4.0\n259860,0,Cash loans,F,N,Y,0,126000.0,256500.0,16519.5,256500.0,Family,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-24454,365243,-2375.0,-4538,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,0.5056068348134658,0.5793978533150027,0.6263042766749393,0.2227,,0.9861,,,0.24,0.2069,0.3333,,0.1215,,0.22,,,0.2269,,0.9861,,,0.2417,0.2069,0.3333,,0.1243,,0.2292,,,0.2248,,0.9861,,,0.24,0.2069,0.3333,,0.1236,,0.2239,,,,block of flats,0.2183,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n155558,0,Cash loans,M,Y,Y,1,270000.0,679500.0,26442.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00963,-14869,-868,-9010.0,-4364,6.0,1,1,1,1,1,0,Laborers,3.0,2,2,WEDNESDAY,13,0,0,0,1,0,1,Self-employed,0.5775247920548634,0.4536069636136631,0.2750003523983893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2168.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n276254,0,Cash loans,F,Y,Y,0,283500.0,193500.0,13086.0,193500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-12123,-196,-3517.0,-4502,5.0,1,1,0,1,0,0,Accountants,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Medicine,0.4397790673689884,0.19714743086787145,0.646329897706246,0.099,,0.9836,,,0.08,0.0345,0.5417,,,,0.0912,,0.0,0.1008,,0.9836,,,0.0806,0.0345,0.5417,,,,0.095,,0.0,0.0999,,0.9836,,,0.08,0.0345,0.5417,,,,0.0928,,0.0,,block of flats,0.0717,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n169315,0,Cash loans,F,N,Y,0,225000.0,1024785.0,57352.5,922500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.010556,-21150,365243,-11986.0,-4409,,1,0,0,1,0,0,,1.0,3,3,WEDNESDAY,16,0,0,0,0,0,0,XNA,,0.6650166351720957,0.6848276586890367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1368.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,0.0\n234741,0,Cash loans,M,Y,Y,2,225000.0,521280.0,24286.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-15468,-229,-2677.0,-4216,25.0,1,1,1,1,1,0,Security staff,4.0,2,2,FRIDAY,9,0,0,0,0,0,0,Kindergarten,0.3151710388887854,0.3075153943433496,0.4938628816996244,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-353.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n207298,1,Cash loans,F,N,N,0,157500.0,436032.0,21339.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020246,-16048,-2737,-4933.0,-4868,,1,1,0,1,1,0,Managers,1.0,3,3,SUNDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.8062208915326484,0.3104334596933041,0.2622489709189549,0.1041,0.1165,0.9767,0.6804,0.0124,0.0,0.2069,0.1667,0.2083,0.0921,0.0824,0.0885,0.0116,0.0247,0.1061,0.1209,0.9767,0.6929,0.0125,0.0,0.2069,0.1667,0.2083,0.0943,0.09,0.0922,0.0117,0.0261,0.1051,0.1165,0.9767,0.6847,0.0125,0.0,0.2069,0.1667,0.2083,0.0938,0.0838,0.0901,0.0116,0.0252,reg oper account,block of flats,0.0818,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1788.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n298756,0,Cash loans,F,N,Y,0,126000.0,1395000.0,38493.0,1395000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-17933,-5832,-8890.0,-1492,,1,1,0,1,1,0,Accountants,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Transport: type 2,,0.7739819598848224,0.7490217048463391,0.0082,,0.9622,,,0.0,0.069,0.0417,,,,0.0097,,0.0,0.0084,,0.9623,,,0.0,0.069,0.0417,,,,0.0101,,0.0,0.0083,,0.9622,,,0.0,0.069,0.0417,,,,0.0099,,0.0,,block of flats,0.0087,,No,2.0,0.0,2.0,0.0,-156.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n135697,1,Revolving loans,F,Y,Y,1,157500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.015221,-7962,-1392,-561.0,-644,2.0,1,1,1,1,1,1,Managers,3.0,2,2,THURSDAY,10,0,0,0,1,1,0,Business Entity Type 3,0.7276208096858694,0.7106597081424562,0.4686596550493113,0.3134,0.1104,0.9985,0.9796,0.1687,0.32,0.1379,0.6667,0.5417,0.1383,0.2538,0.3059,0.0077,0.0113,0.3193,0.1146,0.9985,0.9804,0.1703,0.3222,0.1379,0.6667,0.5417,0.1414,0.2773,0.3187,0.0078,0.012,0.3164,0.1104,0.9985,0.9799,0.1698,0.32,0.1379,0.6667,0.5417,0.1407,0.2582,0.3114,0.0078,0.0116,reg oper account,block of flats,0.3353,Panel,No,0.0,0.0,0.0,0.0,-1115.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n442663,0,Cash loans,F,N,Y,0,90000.0,310671.0,19134.0,256500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-20288,365243,-12449.0,-3576,,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,0.7930493172755181,0.6420583803586494,,0.0814,0.0728,0.9762,0.6736,0.0062,0.0,0.1379,0.1667,0.2083,0.0479,0.0622,0.0667,0.0193,0.0215,0.083,0.0677,0.9762,0.6864,0.0062,0.0,0.1379,0.1667,0.2083,0.049,0.068,0.0685,0.0195,0.0218,0.0822,0.0728,0.9762,0.6779999999999999,0.0062,0.0,0.1379,0.1667,0.2083,0.0487,0.0633,0.0679,0.0194,0.022000000000000002,reg oper account,block of flats,0.0581,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1680.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n390563,0,Cash loans,M,N,Y,2,103500.0,152820.0,16587.0,135000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-12796,-154,-5451.0,-4369,,1,1,1,1,1,0,Drivers,4.0,2,2,SUNDAY,15,0,0,0,0,0,0,Self-employed,,0.6586740235305923,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n197036,0,Cash loans,F,N,Y,1,112500.0,1125000.0,33025.5,1125000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.031329,-17094,-9090,-5469.0,-617,,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,12,0,0,0,0,0,0,Industry: type 3,,0.5989000451024897,0.5867400085415683,0.066,0.0805,0.9767,0.6804,0.0498,0.0,0.1379,0.1667,0.0417,0.0645,0.0538,0.0504,0.0,0.047,0.0672,0.0836,0.9767,0.6929,0.0503,0.0,0.1379,0.1667,0.0417,0.0659,0.0588,0.0525,0.0,0.0498,0.0666,0.0805,0.9767,0.6847,0.0502,0.0,0.1379,0.1667,0.0417,0.0656,0.0547,0.0513,0.0,0.048,reg oper account,block of flats,0.0499,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-598.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n357181,0,Cash loans,F,N,N,0,166500.0,526500.0,15525.0,526500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-15453,-1313,-7827.0,-3915,,1,1,0,1,0,0,Sales staff,2.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.6914861460138225,0.5483309371246726,0.5832379256761245,,0.1774,0.9786,,,0.28,0.2414,0.3333,,,,0.3886,,,,0.1841,0.9786,,,0.282,0.2414,0.3333,,,,0.4049,,,,0.1774,0.9786,,,0.28,0.2414,0.3333,,,,0.3956,,,,,0.3056,Panel,No,1.0,0.0,1.0,0.0,-2876.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n397622,0,Cash loans,F,N,Y,0,279000.0,1641280.5,45265.5,1467000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002506,-19589,-828,-9604.0,-3124,,1,1,0,1,1,0,Core staff,2.0,2,2,FRIDAY,5,0,0,0,0,0,0,Government,,0.4067634683071194,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-343.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n208492,0,Cash loans,F,N,Y,0,112500.0,390960.0,20601.0,337500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-15749,-877,-1286.0,-614,,1,1,0,1,0,0,Laborers,2.0,1,1,TUESDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.7073572289393115,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-302.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n237292,0,Revolving loans,F,N,Y,1,112500.0,225000.0,11250.0,225000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,With parents,0.009175,-9607,-812,-4195.0,-787,,1,1,0,1,0,0,,3.0,2,2,SATURDAY,15,0,0,0,0,0,0,Self-employed,0.4257969328628023,0.6960340218672595,0.4974688893052743,0.2278,0.2894,0.9851,,,0.24,0.2069,0.3333,,,,0.2287,,0.0044,0.2321,0.3004,0.9851,,,0.2417,0.2069,0.3333,,,,0.2383,,0.0047,0.23,0.2894,0.9851,,,0.24,0.2069,0.3333,,,,0.2328,,0.0045,,block of flats,0.1808,Panel,No,0.0,0.0,0.0,0.0,-445.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n398505,0,Cash loans,M,N,Y,0,144000.0,94810.5,6466.5,72000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-19131,-265,-8283.0,-2661,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.5452692120114947,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-600.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n147564,0,Cash loans,F,N,Y,0,54000.0,225000.0,21276.0,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-25084,365243,-10555.0,-4439,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.653319904356954,,0.0825,,0.9776,0.6940000000000001,,,0.1379,0.1667,0.2083,,,0.071,,0.1505,0.084,,0.9777,0.706,,,0.1379,0.1667,0.2083,,,0.07400000000000001,,0.1593,0.0833,,0.9776,0.6981,,,0.1379,0.1667,0.2083,,,0.0723,,0.1536,,block of flats,0.0886,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-596.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n152412,0,Cash loans,M,Y,Y,0,202500.0,452385.0,16380.0,373500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.032561,-21967,365243,-2340.0,-3928,5.0,1,0,0,1,1,0,,2.0,1,1,SATURDAY,11,0,0,0,0,0,0,XNA,,0.58032482545015,0.6041125998015721,0.1735,0.1338,0.9851,0.7959999999999999,,0.16,0.1148,0.5275,0.375,0.059000000000000004,,0.1808,,,0.1155,0.0508,0.9861,0.7909,,0.0806,0.0345,0.625,0.375,0.0193,,0.1181,,,0.1145,0.1338,0.9861,0.7987,,0.08,0.0345,0.625,0.375,0.06,,0.11800000000000001,,,reg oper account,block of flats,0.2502,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2437.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n243897,0,Cash loans,F,N,Y,2,126000.0,835380.0,35392.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.007305,-13813,-3991,-7924.0,-2166,,1,1,0,1,1,0,Laborers,4.0,3,3,THURSDAY,15,0,0,0,0,0,0,Postal,,0.458440247883504,0.6610235391308081,0.2268,0.1474,0.9886,0.8436,0.0975,0.24,0.2069,0.375,0.375,0.2083,0.1849,0.2527,0.0,0.0,0.2311,0.1529,0.9886,0.8497,0.0984,0.2417,0.2069,0.375,0.375,0.213,0.20199999999999999,0.2633,0.0,0.0,0.22899999999999998,0.1474,0.9886,0.8457,0.0981,0.24,0.2069,0.375,0.375,0.2119,0.1881,0.2573,0.0,0.0,reg oper account,block of flats,0.2521,Panel,No,0.0,0.0,0.0,0.0,-1508.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n243577,0,Cash loans,M,Y,Y,1,112500.0,76410.0,9198.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018801,-10497,-1084,-5000.0,-3168,10.0,1,1,1,1,0,0,Laborers,3.0,2,2,FRIDAY,8,0,0,0,1,1,0,Business Entity Type 3,,0.5632897764994405,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-291.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n383097,1,Cash loans,F,N,Y,3,171000.0,1546020.0,45202.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-14469,-2225,-1421.0,-5155,,1,1,1,1,0,0,Core staff,5.0,3,3,FRIDAY,5,0,0,0,0,1,1,Kindergarten,,0.1888785227674091,0.6496203111237195,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1916.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,4.0\n361263,0,Cash loans,F,N,Y,1,157500.0,269982.0,27792.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-14386,-265,-3980.0,-1508,,1,1,0,1,1,1,Managers,3.0,1,1,MONDAY,17,0,0,0,0,0,0,Trade: type 3,0.390065195176457,0.502489300462258,0.6658549219640212,,,0.9478,,,,,,,,,0.1439,,,,,0.9479,,,,,,,,,0.1499,,,,,0.9478,,,,,,,,,0.1465,,,,,0.1208,,No,1.0,0.0,1.0,0.0,-1712.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n224350,0,Cash loans,M,Y,N,0,180000.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-14219,-3989,-4135.0,-4133,14.0,1,1,0,1,1,0,Laborers,2.0,1,1,FRIDAY,13,0,1,1,0,1,1,Other,0.3647902526342063,0.7433242473944505,0.2636468134452008,0.0804,0.0793,0.9737,0.6396,0.0086,0.0,0.1379,0.1667,0.0417,0.0,0.0647,0.0612,0.0039,0.0084,0.0819,0.0823,0.9737,0.6537,0.0087,0.0,0.1379,0.1667,0.0417,0.0,0.0707,0.0637,0.0039,0.0089,0.0812,0.0793,0.9737,0.6444,0.0087,0.0,0.1379,0.1667,0.0417,0.0,0.0658,0.0623,0.0039,0.0086,reg oper account,block of flats,0.0499,\"Stone, brick\",No,4.0,1.0,4.0,1.0,-2255.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n220802,1,Revolving loans,M,N,Y,0,112500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-14833,-1261,-3269.0,-4699,,1,1,0,0,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Self-employed,,0.3947016839932945,0.3296550543128238,0.0619,0.0623,0.9816,0.7484,0.0095,0.0,0.1379,0.1667,0.2083,0.0,0.0504,0.06,0.0,0.0,0.063,0.0647,0.9816,0.7583,0.0096,0.0,0.1379,0.1667,0.2083,0.0,0.0551,0.0625,0.0,0.0,0.0625,0.0623,0.9816,0.7518,0.0096,0.0,0.1379,0.1667,0.2083,0.0,0.0513,0.0611,0.0,0.0,reg oper account,block of flats,0.0524,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n136408,0,Cash loans,F,N,Y,0,153000.0,704844.0,28084.5,630000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-18090,-3372,-9366.0,-1638,,1,1,1,1,1,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Trade: type 7,,0.7605083827801206,0.5100895276257282,0.0082,0.0,0.9627,,,0.0,0.069,0.0417,,0.0282,,0.0105,,0.0,0.0084,0.0,0.9628,,,0.0,0.069,0.0417,,0.0288,,0.011000000000000001,,0.0,0.0083,0.0,0.9627,,,0.0,0.069,0.0417,,0.0287,,0.0107,,0.0,,block of flats,0.0091,Block,No,5.0,1.0,5.0,0.0,-1862.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n316008,0,Cash loans,F,N,Y,2,225000.0,526491.0,22261.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-11291,-1080,-2420.0,-3284,,1,1,0,1,0,0,Core staff,4.0,2,2,THURSDAY,15,0,0,0,0,0,0,Kindergarten,0.5425195819308146,0.6939620050947495,0.4561097392782771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1515.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n328226,1,Cash loans,F,N,Y,1,157500.0,497520.0,33376.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-11048,-1559,-4825.0,-3727,,1,1,0,1,0,0,Sales staff,2.0,3,3,FRIDAY,6,0,0,0,0,0,0,Business Entity Type 3,,0.07522112103457601,0.2405414172860865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-197.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n325272,1,Cash loans,F,Y,Y,0,315000.0,350581.5,37327.5,333000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003813,-16483,-1063,-3859.0,-31,14.0,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,5,0,0,0,0,0,0,Self-employed,0.4256953592802919,0.3773980252926297,0.15663982703141147,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-568.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n121849,0,Cash loans,F,Y,Y,0,180000.0,381528.0,17914.5,315000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-12701,-732,-164.0,-4047,8.0,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Trade: type 7,0.28170988831581906,0.5587768128829501,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-616.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n183002,0,Cash loans,F,N,Y,1,157500.0,1006920.0,67563.0,900000.0,Unaccompanied,State servant,Secondary / secondary special,Married,With parents,0.020713,-14038,-2168,-2220.0,-4515,,1,1,0,1,0,0,High skill tech staff,3.0,3,2,SATURDAY,6,0,0,0,0,0,0,Medicine,0.36289565571547583,0.12654219301336614,0.7623356180684377,0.0619,0.0583,0.9737,0.6396,0.0064,0.0,0.1034,0.1667,0.2083,0.1192,0.0504,0.051,0.0,0.0,0.063,0.0605,0.9737,0.6537,0.0065,0.0,0.1034,0.1667,0.2083,0.1219,0.0551,0.0531,0.0,0.0,0.0625,0.0583,0.9737,0.6444,0.0065,0.0,0.1034,0.1667,0.2083,0.1212,0.0513,0.0519,0.0,0.0,reg oper account,block of flats,0.0436,Panel,No,0.0,0.0,0.0,0.0,-1935.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n248805,0,Cash loans,F,N,Y,0,225000.0,1024636.5,33993.0,918000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17533,-2115,-783.0,-1087,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,1,1,Self-employed,,0.7087088613576156,0.30162489168411943,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2176.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n127935,0,Cash loans,M,Y,Y,0,54000.0,348264.0,19993.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-19239,-6972,-6498.0,-2762,6.0,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.5163964421621263,0.7005534773450529,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1874.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n229377,0,Cash loans,F,N,Y,0,112500.0,573628.5,29416.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-17670,-442,-5374.0,-1222,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.5004775033058118,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-848.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n438529,0,Cash loans,F,Y,Y,0,135000.0,640080.0,31131.0,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.018029,-8157,-1173,-2734.0,-824,1.0,1,1,0,1,0,0,Laborers,1.0,3,3,MONDAY,10,0,0,0,0,1,1,Transport: type 2,,0.08683202581837039,0.02788174064882383,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n199357,0,Cash loans,F,Y,N,0,166500.0,315000.0,19165.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-10404,-1101,-842.0,-3082,14.0,1,1,1,1,1,1,Laborers,2.0,2,2,TUESDAY,11,0,0,0,1,1,0,Agriculture,0.4121787406984991,0.5235904109721272,0.3280631605201915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-805.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n282467,0,Cash loans,M,N,N,0,180000.0,1350000.0,37125.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-14595,-3161,-8251.0,-4054,,1,1,0,1,1,0,Drivers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,,0.6760066521464073,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2747.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n404612,0,Cash loans,F,N,N,0,90000.0,508495.5,26127.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00823,-20100,365243,-10983.0,-3436,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.0849163714584476,0.5602843280409464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-837.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n336259,0,Cash loans,M,Y,Y,1,225000.0,999886.5,78997.5,945000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.006207,-8676,-629,-261.0,-1288,8.0,1,1,0,1,0,0,,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,,0.6960849113950746,,0.0186,,0.9851,0.7959999999999999,,0.0,0.1034,0.0417,0.0417,,0.0151,0.0167,,,0.0189,,0.9851,0.804,,0.0,0.1034,0.0417,0.0417,,0.0165,0.0174,,,0.0187,,0.9851,0.7987,,0.0,0.1034,0.0417,0.0417,,0.0154,0.017,,,reg oper account,block of flats,0.0132,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-541.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n283932,0,Cash loans,F,N,Y,0,126000.0,454500.0,14791.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.026392,-22949,365243,-14005.0,-4749,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.5644782366210463,0.5567274263630174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1174.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n244455,0,Cash loans,F,N,N,1,315000.0,746280.0,54436.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Rented apartment,0.025164,-16748,-1823,-1078.0,-242,,1,1,0,1,0,1,Managers,3.0,2,2,TUESDAY,14,0,0,0,0,1,1,Other,0.750235279972741,0.4454092710302832,0.1008037063175332,0.0567,,0.9891,,,0.0,0.1724,0.1667,,,,0.0425,,0.0,0.0578,,0.9891,,,0.0,0.1724,0.1667,,,,0.0443,,0.0,0.0573,,0.9891,,,0.0,0.1724,0.1667,,,,0.0433,,0.0,,block of flats,0.0335,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1312.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n372130,0,Cash loans,F,N,Y,0,216000.0,675000.0,26284.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-12694,-1485,-4503.0,-4260,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.34224931709439793,0.3046721837533529,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n216848,0,Cash loans,F,N,N,0,135000.0,210456.0,11340.0,166500.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.025164,-22968,365243,-9436.0,-5031,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.2970605706921691,0.6910212267577837,0.1505,0.0778,0.9846,0.7892,0.0554,0.16,0.1379,0.3333,0.375,0.2017,0.1227,0.1573,0.0,0.0,0.1534,0.0808,0.9846,0.7975,0.0559,0.1611,0.1379,0.3333,0.375,0.2063,0.1341,0.1639,0.0,0.0,0.152,0.0778,0.9846,0.792,0.0558,0.16,0.1379,0.3333,0.375,0.2052,0.1248,0.1601,0.0,0.0,not specified,block of flats,0.154,Panel,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n375278,0,Cash loans,F,N,N,0,135000.0,983299.5,37453.5,904500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.0105,-8778,-1112,-8568.0,-474,,1,1,0,1,0,0,,2.0,3,3,WEDNESDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.4424618842169465,,0.0742,,0.9886,,,0.08,0.069,0.3333,,,,,,,0.0756,,0.9886,,,0.0806,0.069,0.3333,,,,,,,0.0749,,0.9886,,,0.08,0.069,0.3333,,,,,,,,block of flats,0.0676,Block,No,0.0,0.0,0.0,0.0,-466.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n239545,0,Cash loans,F,Y,Y,0,157500.0,746280.0,59094.0,675000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.032561,-22694,365243,-187.0,-4156,65.0,1,0,0,1,1,0,,2.0,1,1,WEDNESDAY,14,1,0,0,1,0,0,XNA,,0.4868570279356818,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1983.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n444147,0,Cash loans,F,Y,Y,1,360000.0,454500.0,34110.0,454500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.014464,-11301,-1123,-222.0,-3177,64.0,1,1,0,1,0,0,Managers,3.0,2,2,SUNDAY,8,0,0,0,0,1,1,Construction,0.5220497200595957,0.6681465112923016,0.6817058776720116,0.1649,0.0707,0.9995,0.9932,0.1063,0.16,0.0345,0.9583,0.0417,,0.1345,0.2082,0.0347,0.391,0.1681,0.0734,0.9995,0.9935,0.1073,0.1611,0.0345,0.9583,0.0417,,0.1469,0.217,0.035,0.414,0.1665,0.0707,0.9995,0.9933,0.107,0.16,0.0345,0.9583,0.0417,,0.1368,0.212,0.0349,0.3992,reg oper account,block of flats,0.2631,Monolithic,No,1.0,0.0,1.0,0.0,-2828.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n147780,1,Cash loans,M,N,Y,1,225000.0,1256400.0,36864.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.004849,-15306,-839,-6592.0,-4069,,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,9,0,0,0,0,1,1,Construction,,0.5388368588235773,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1709.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n163464,0,Cash loans,M,N,N,2,112500.0,188460.0,10350.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-12682,-442,-1252.0,-4388,,1,1,0,1,0,0,Laborers,4.0,3,3,MONDAY,7,0,0,0,0,0,0,Business Entity Type 2,,0.4776680818160271,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-704.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n140670,0,Cash loans,M,Y,Y,0,202500.0,1350000.0,43681.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.025164,-10001,-2663,-4887.0,-2644,7.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 2,,0.29698294958375937,0.6971469077844458,0.0608,0.0411,0.9717,0.6124,0.052000000000000005,0.0,0.1034,0.1667,0.2083,0.1313,0.0496,0.0684,0.0,0.0,0.062,0.0427,0.9717,0.6276,0.0525,0.0,0.1034,0.1667,0.2083,0.1343,0.0542,0.0713,0.0,0.0,0.0614,0.0411,0.9717,0.6176,0.0524,0.0,0.1034,0.1667,0.2083,0.1336,0.0504,0.0696,0.0,0.0,reg oper account,block of flats,0.0823,Mixed,No,2.0,0.0,2.0,0.0,-2418.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n188455,0,Revolving loans,F,N,Y,0,135000.0,225000.0,11250.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Office apartment,0.010032,-17718,-367,-9338.0,-1261,,1,1,0,1,1,0,Laborers,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,0.7546283406703693,0.5917935248762586,0.4686596550493113,0.0715,0.0988,0.9742,0.6464,,0.0,0.1379,0.1667,,,,0.0442,,,0.0609,0.066,0.9717,0.6276,,0.0,0.1034,0.1667,,,,0.0346,,,0.0666,0.0805,0.9752,0.6645,,0.0,0.1379,0.1667,,,,0.0341,,,reg oper account,block of flats,0.0395,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-517.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n303349,0,Cash loans,F,N,Y,0,100300.5,225000.0,22158.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.030755,-15858,-156,-9970.0,-4818,,1,1,1,1,1,0,Sales staff,1.0,2,2,MONDAY,16,0,0,0,0,0,0,Trade: type 3,,0.6886937322275167,0.1986200451074864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-127.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n316627,0,Cash loans,F,N,N,0,225000.0,814041.0,28971.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.04622,-14387,-329,-4546.0,-3090,,1,1,0,1,0,0,Cleaning staff,1.0,1,1,FRIDAY,17,0,0,0,0,1,1,Industry: type 7,,0.7757367841085786,0.324891229465852,0.3289,0.1191,0.9935,0.9116,,0.4,0.1724,0.6667,,0.1143,,0.3588,,0.0776,0.3351,0.1236,0.9935,0.9151,,0.4028,0.1724,0.6667,,0.1169,,0.3739,,0.0822,0.3321,0.1191,0.9935,0.9128,,0.4,0.1724,0.6667,,0.1163,,0.3653,,0.0793,reg oper account,block of flats,0.299,Mixed,No,0.0,0.0,0.0,0.0,-1850.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n260938,0,Revolving loans,M,N,Y,0,202500.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-18680,-1906,-2217.0,-2205,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Self-employed,,0.6500537770524697,0.07801576652252579,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1523.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n187324,0,Cash loans,F,N,N,0,126000.0,1040985.0,27589.5,909000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.006207,-18085,-6121,-4858.0,-1603,,1,1,1,1,0,0,Medicine staff,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Medicine,,0.6902808363097918,0.6817058776720116,0.0959,,0.9886,,,0.0,0.2759,0.1667,,,,0.1022,,0.0211,0.0977,,0.9886,,,0.0,0.2759,0.1667,,,,0.1064,,0.0223,0.0968,,0.9886,,,0.0,0.2759,0.1667,,,,0.10400000000000001,,0.0215,,block of flats,0.0897,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-447.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n410231,0,Cash loans,F,Y,Y,1,135000.0,454500.0,14791.5,454500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.028663,-13713,-1108,-2794.0,-4045,10.0,1,1,0,1,1,0,Core staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,,0.6761551081267114,0.4101025731788671,0.0227,0.0,0.9747,,,0.0,0.1034,0.0417,,,,,,0.0076,0.0231,0.0,0.9747,,,0.0,0.1034,0.0417,,,,,,0.008,0.0229,0.0,0.9747,,,0.0,0.1034,0.0417,,,,,,0.0077,,,0.0174,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-558.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n394390,0,Revolving loans,M,Y,Y,3,81000.0,180000.0,9000.0,180000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.019689,-13776,-4306,-7584.0,-4562,14.0,1,1,0,1,0,0,Laborers,5.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.6550760686563266,0.6545292802242897,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-2190.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n251039,0,Cash loans,F,N,N,1,193500.0,288873.0,14805.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-11388,-3873,-5394.0,-3644,,1,1,0,1,1,0,Laborers,3.0,3,2,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.3512192186030195,0.8193176922872417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1783.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n108376,0,Cash loans,M,N,N,1,220500.0,348264.0,22387.5,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13124,-1766,-1823.0,-1844,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3207993293123973,0.44081180867328906,0.7338145369642702,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1389.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n174640,0,Cash loans,F,N,Y,0,175500.0,414792.0,18400.5,315000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00496,-15657,-8900,-4413.0,-4684,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Medicine,0.5877881732716369,0.7098857035122578,0.5226973172821112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-493.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n287086,0,Cash loans,M,Y,Y,0,238500.0,545040.0,25407.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.016612000000000002,-13872,-1402,-2612.0,-4631,22.0,1,1,1,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,0.6604260392614207,0.5840482375789345,0.4830501881366946,0.0619,0.0505,0.9836,0.7756,0.0,0.0,0.1379,0.1667,0.0,0.0603,0.0504,0.0523,0.0,0.0,0.063,0.0524,0.9836,0.7844,0.0,0.0,0.1379,0.1667,0.0,0.0202,0.0551,0.0526,0.0,0.0,0.0625,0.0505,0.9836,0.7786,0.0,0.0,0.1379,0.1667,0.0,0.0613,0.0513,0.0532,0.0,0.0,reg oper account,block of flats,0.0434,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2405.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n251655,0,Cash loans,F,N,Y,2,99000.0,612000.0,42592.5,612000.0,Family,State servant,Higher education,Married,House / apartment,0.00496,-14083,-5831,-4346.0,-4361,,1,1,1,1,1,0,Core staff,4.0,2,2,TUESDAY,15,0,0,0,0,1,1,University,0.8413326684988284,0.6490930873078619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-583.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n285095,0,Cash loans,F,N,Y,1,45000.0,57564.0,5823.0,54000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-17375,-815,-6998.0,-868,,1,1,1,1,0,0,Cleaning staff,3.0,2,2,TUESDAY,8,0,0,0,0,0,0,Self-employed,,0.6820782777813582,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,3.0,6.0,3.0,-788.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n241661,0,Revolving loans,F,N,Y,0,90000.0,247500.0,12375.0,247500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.030755,-23073,365243,-13293.0,-4599,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,0.7060968027563072,0.7364351038315075,,0.0722,0.0794,0.9811,0.7416,0.0079,0.0,0.1379,0.1667,0.2083,0.0542,0.0588,0.063,0.0,0.0,0.0735,0.0823,0.9811,0.7517,0.008,0.0,0.1379,0.1667,0.2083,0.0554,0.0643,0.0657,0.0,0.0,0.0729,0.0794,0.9811,0.7451,0.008,0.0,0.1379,0.1667,0.2083,0.0551,0.0599,0.0642,0.0,0.0,reg oper account,block of flats,0.0539,\"Stone, brick\",No,7.0,4.0,7.0,4.0,-1831.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n406162,0,Cash loans,M,Y,N,0,261000.0,269550.0,19300.5,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.002042,-24358,365243,-5405.0,-4480,11.0,1,0,0,1,0,0,,2.0,3,3,THURSDAY,11,0,0,0,0,0,0,XNA,0.8295152239231878,0.7014518232541704,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1060.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n197522,0,Cash loans,M,Y,Y,0,135000.0,239850.0,23850.0,225000.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.01885,-19704,-8448,-8391.0,-3238,20.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Industry: type 9,,0.2113341797599376,0.7992967832109371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1992.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n177453,0,Revolving loans,F,N,N,4,63000.0,135000.0,6750.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,Municipal apartment,0.020246,-13970,-4428,-2586.0,-1198,,1,1,1,1,0,0,,5.0,3,3,SATURDAY,6,0,0,0,0,1,1,Kindergarten,,0.4595805798928399,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-813.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n176894,0,Revolving loans,F,N,Y,2,112500.0,157500.0,7875.0,157500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-9999,-1365,-1652.0,-2660,,1,1,0,1,0,0,Core staff,4.0,2,2,TUESDAY,17,0,0,0,0,1,1,School,,0.5333713316476699,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1206.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n394114,0,Cash loans,M,N,N,0,202500.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-19826,-3091,-9875.0,-3371,,1,1,0,1,1,0,Laborers,2.0,2,2,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.4783064687626085,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n309116,0,Cash loans,M,Y,Y,0,441000.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-18915,-582,-3709.0,-2464,2.0,1,1,0,1,0,0,,2.0,1,1,TUESDAY,10,0,0,0,0,0,0,Self-employed,,0.7826961336682041,0.4206109640437848,0.0866,,0.9429,0.218,0.0599,0.0,0.1034,0.125,0.1667,0.028999999999999998,0.0706,0.09699999999999999,0.0,0.0,0.0882,,0.9429,0.2486,0.0604,0.0,0.1034,0.125,0.1667,0.0297,0.0771,0.1011,0.0,0.0,0.0874,,0.9429,0.2285,0.0603,0.0,0.1034,0.125,0.1667,0.0295,0.0718,0.0988,0.0,0.0,not specified,block of flats,0.1091,\"Stone, brick\",Yes,1.0,0.0,1.0,0.0,-1831.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n354012,0,Cash loans,F,Y,Y,0,256500.0,985851.0,55179.0,913500.0,Family,Working,Higher education,Married,House / apartment,0.00496,-20453,-3581,-9844.0,-3635,22.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,SUNDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.6219630278992776,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1331.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n348052,0,Cash loans,F,N,N,0,157500.0,1125000.0,44617.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-16423,-8719,-7362.0,-4256,,1,1,0,1,1,0,Managers,2.0,2,2,SUNDAY,8,0,0,0,0,0,0,Business Entity Type 1,0.5901442127376834,0.6362990182827344,0.4776491548517548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-721.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n323814,1,Revolving loans,F,N,Y,0,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.020246,-7772,-938,-3984.0,-341,,1,1,0,1,0,0,Sales staff,2.0,3,3,THURSDAY,8,0,1,1,0,1,1,Industry: type 3,0.19635726666799344,0.6605348532249744,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-545.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n145437,1,Cash loans,M,N,Y,2,144000.0,269550.0,18364.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-14071,-815,-2547.0,-4821,,1,1,0,1,0,0,Laborers,4.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 2,0.15473282684041664,0.13329409336946396,0.15663982703141147,0.0928,0.071,0.9791,0.7144,0.0027,0.0,0.2069,0.1667,0.2083,0.0297,0.0756,0.0772,0.0,0.0,0.0945,0.0737,0.9791,0.7256,0.0028,0.0,0.2069,0.1667,0.2083,0.0304,0.0826,0.0804,0.0,0.0,0.0937,0.071,0.9791,0.7182,0.0028,0.0,0.2069,0.1667,0.2083,0.0302,0.077,0.0786,0.0,0.0,reg oper account,block of flats,0.0607,Panel,No,3.0,2.0,3.0,2.0,-842.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n311424,0,Cash loans,F,N,Y,0,40500.0,584766.0,23319.0,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003813,-21592,365243,-11649.0,-5043,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.7806697746287429,0.5867400085415683,0.0928,0.1004,0.9821,0.7552,0.0444,0.0,0.2069,0.1667,0.0417,0.0932,0.0731,0.0836,0.0116,0.0129,0.0945,0.1042,0.9821,0.7648,0.0448,0.0,0.2069,0.1667,0.0417,0.0953,0.0799,0.0871,0.0117,0.0136,0.0937,0.1004,0.9821,0.7585,0.0447,0.0,0.2069,0.1667,0.0417,0.0948,0.0744,0.0851,0.0116,0.0132,reg oper spec account,block of flats,0.0686,Panel,No,3.0,0.0,2.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n444931,0,Cash loans,F,N,N,0,72000.0,518463.0,16407.0,364500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009334,-11053,-1493,-168.0,-361,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Self-employed,,0.5880163844654945,0.190705947811054,0.0928,,0.9796,0.7212,0.004,0.0,0.069,0.1667,0.2083,0.0115,0.0756,0.0334,0.0232,0.0586,0.0945,,0.9796,0.7321,0.0041,0.0,0.069,0.1667,0.2083,0.0118,0.0826,0.0348,0.0233,0.062,0.0937,,0.9796,0.7249,0.0041,0.0,0.069,0.1667,0.2083,0.0117,0.077,0.034,0.0233,0.0598,reg oper account,block of flats,0.0419,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n360732,0,Cash loans,M,Y,Y,2,765000.0,1056681.0,44901.0,972000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-13477,-646,-724.0,-1789,3.0,1,1,0,1,0,1,Managers,4.0,1,1,SUNDAY,11,0,0,0,0,0,0,Self-employed,0.6100789626618452,0.7139399904530994,0.41184855592423975,0.1918,0.09,0.9975,0.966,0.0682,0.24,0.1034,0.4583,0.5,0.0565,0.1555,0.2195,0.0039,0.5675,0.1954,0.0934,0.9975,0.9673,0.0688,0.2417,0.1034,0.4583,0.5,0.0578,0.1699,0.2287,0.0039,0.6007,0.1936,0.09,0.9975,0.9665,0.0686,0.24,0.1034,0.4583,0.5,0.0575,0.1582,0.2235,0.0039,0.5794,reg oper account,block of flats,0.29600000000000004,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-262.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n218483,0,Cash loans,M,N,N,0,414000.0,675000.0,41296.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-17310,-1920,-3799.0,-861,,1,1,0,1,1,0,Managers,2.0,1,1,MONDAY,16,0,1,1,0,1,1,Trade: type 6,,0.7353215489663362,0.6246146584503397,0.2072,0.0698,0.9945,0.9252,,0.24,0.1034,0.6667,0.7083,0.0,0.1689,0.2243,0.0,0.0,0.2111,0.0724,0.9945,0.9281,,0.2417,0.1034,0.6667,0.7083,0.0,0.1846,0.2337,0.0,0.0,0.2092,0.0698,0.9945,0.9262,,0.24,0.1034,0.6667,0.7083,0.0,0.1719,0.2283,0.0,0.0,reg oper account,block of flats,0.2169,Panel,No,0.0,0.0,0.0,0.0,-554.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n451321,0,Cash loans,F,N,N,0,81000.0,269550.0,11871.0,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.010276,-21164,365243,-8638.0,-4717,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,0.5839470337980307,0.685798428137435,0.3046721837533529,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1789.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n149649,0,Cash loans,F,N,N,0,90000.0,270000.0,15075.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.028663,-22723,365243,-7272.0,-4449,,1,0,0,1,0,0,,1.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.6839389335452055,0.4794489811780563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n255310,0,Cash loans,M,N,N,1,112500.0,781695.0,25213.5,652500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005144,-14095,-6397,-8176.0,-4592,,1,1,1,1,1,0,,3.0,2,2,FRIDAY,17,0,0,0,0,0,0,Electricity,,0.4904523776302875,0.16640554618393638,0.0742,0.0391,0.9851,0.7959999999999999,0.0256,0.08,0.069,0.3333,0.375,0.0384,0.0605,0.048,0.0,0.0,0.0756,0.0406,0.9851,0.804,0.0259,0.0806,0.069,0.3333,0.375,0.0393,0.0661,0.05,0.0,0.0,0.0749,0.0391,0.9851,0.7987,0.0258,0.08,0.069,0.3333,0.375,0.0391,0.0616,0.0489,0.0,0.0,reg oper spec account,block of flats,0.0615,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-850.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n344513,0,Cash loans,F,N,N,0,76500.0,450000.0,16164.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.015221,-19432,-12619,-13318.0,-2868,,1,1,0,1,1,0,Laborers,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 2,,0.5217736831186115,0.7801436381572275,0.0814,0.0,0.9747,0.6532,0.0068,0.0,0.1379,0.1667,0.2083,0.0,0.0664,0.061,0.0,0.047,0.083,0.0,0.9747,0.6668,0.0069,0.0,0.1379,0.1667,0.2083,0.0,0.0725,0.0636,0.0,0.0498,0.0822,0.0,0.9747,0.6578,0.0068,0.0,0.1379,0.1667,0.2083,0.0,0.0676,0.0621,0.0,0.048,reg oper account,block of flats,0.0612,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-926.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n430633,0,Cash loans,F,N,N,0,76500.0,225000.0,10039.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.0228,-13450,-383,-3523.0,-4495,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Transport: type 4,,0.6142773194688561,0.878739766981187,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-196.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n110475,0,Cash loans,F,N,Y,1,225000.0,440784.0,34956.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008625,-13783,-2700,-5424.0,-4833,,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,15,0,0,0,0,1,1,Government,0.3050334515414692,0.5615837958538897,0.11836432636740067,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,6.0,0.0,-1358.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n265560,0,Cash loans,F,N,N,2,112500.0,314100.0,16573.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.016612000000000002,-10046,-701,-1316.0,-2204,,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Industry: type 3,0.3252374503853502,0.2407192470222029,0.23272477626794336,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n318219,0,Revolving loans,M,Y,Y,3,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.0228,-14093,-536,-7218.0,-4846,9.0,1,1,1,1,1,0,,5.0,2,2,FRIDAY,9,0,0,0,0,1,1,Other,,0.42568729409122297,0.7610263695502636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-978.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n369832,0,Cash loans,F,N,Y,0,90000.0,284400.0,16011.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.04622,-23873,365243,-9402.0,-3996,,1,0,0,1,0,0,,1.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,XNA,0.768018318187397,0.741580588031106,0.746300213050371,0.2227,0.132,0.9831,0.7688,0.0822,0.24,0.2069,0.3333,0.375,0.1013,0.1816,0.2317,0.0,0.0,0.2269,0.1369,0.9831,0.7779,0.0829,0.2417,0.2069,0.3333,0.375,0.1036,0.1983,0.2414,0.0,0.0,0.2248,0.132,0.9831,0.7719,0.0827,0.24,0.2069,0.3333,0.375,0.10300000000000001,0.1847,0.2358,0.0,0.0,reg oper account,block of flats,0.235,Panel,No,1.0,1.0,1.0,1.0,-116.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n335603,0,Cash loans,F,N,N,3,121500.0,450000.0,24412.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.010032,-15103,-1690,-4818.0,-4810,,1,1,0,1,1,0,Cleaning staff,5.0,2,2,FRIDAY,12,0,0,0,0,0,0,School,0.5897250899344201,0.4528862240210471,0.3539876078507373,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1910.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n289878,0,Cash loans,F,N,N,0,126679.5,765000.0,39609.0,765000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-9359,-1780,-3968.0,-2036,,1,1,1,1,0,0,,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6522195698056616,0.16219210595922867,,0.1485,0.0953,0.9801,0.728,0.0275,0.16,0.1379,0.3333,0.375,0.2081,0.121,0.1439,0.0,0.0,0.1513,0.0989,0.9801,0.7387,0.0277,0.1611,0.1379,0.3333,0.375,0.2129,0.1322,0.15,0.0,0.0,0.1499,0.0953,0.9801,0.7316,0.0277,0.16,0.1379,0.3333,0.375,0.2118,0.1231,0.1465,0.0,0.0,reg oper account,block of flats,0.1282,Panel,No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n383913,0,Cash loans,F,N,Y,0,112500.0,603000.0,18409.5,603000.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.026392,-22287,365243,-3771.0,-3736,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,XNA,,0.71922482146448,0.3791004853998145,0.2227,,0.9846,,,0.24,0.2069,0.3333,,0.1573,,0.2314,,0.0,0.2269,,0.9846,,,0.2417,0.2069,0.3333,,0.1609,,0.2411,,0.0,0.2248,,0.9846,,,0.24,0.2069,0.3333,,0.16,,0.2356,,0.0,,block of flats,0.182,Panel,No,4.0,0.0,4.0,0.0,-1834.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n333912,0,Cash loans,F,Y,Y,0,225000.0,1305000.0,36018.0,1305000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-14308,-1124,-4927.0,-4389,3.0,1,1,0,1,0,0,Private service staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Services,0.8046153057243761,0.6447075199346778,0.6041125998015721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n331629,1,Cash loans,F,N,Y,3,135000.0,545040.0,26509.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-11552,-346,-8989.0,-3257,,1,1,1,1,1,0,Cooking staff,5.0,3,3,WEDNESDAY,8,0,0,0,0,0,0,Trade: type 3,0.30905448368536903,0.24058338153118725,0.2276129150623945,,,0.9861,0.8096,0.0,0.16,0.1379,0.3333,,,,0.1732,,0.0,,,0.9861,0.8171,0.0,0.1611,0.1379,0.3333,,,,0.1805,,0.0,,,0.9861,0.8121,0.0,0.16,0.1379,0.3333,,,,0.1763,,0.0,,block of flats,0.1362,,No,3.0,1.0,3.0,0.0,-764.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,6.0\n134641,0,Cash loans,F,N,N,0,135000.0,755190.0,29677.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-20309,365243,-5005.0,-3414,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,XNA,,0.7008836618069889,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1555.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n213920,0,Revolving loans,M,Y,Y,1,157500.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-14642,-569,-4996.0,-4088,25.0,1,1,0,1,0,0,Drivers,3.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.18129039618452608,0.5869564801599411,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-435.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n169615,0,Cash loans,F,N,N,1,180000.0,225000.0,23625.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-13200,-1002,-1006.0,-1220,,1,1,1,1,1,0,Accountants,3.0,1,1,TUESDAY,17,0,1,1,0,1,1,Business Entity Type 3,0.8331148145303001,0.5737442554205374,,0.4541,0.12300000000000001,0.9975,0.9796,0.2433,0.54,0.1724,0.8333,1.0,0.1795,0.5724,0.5003,0.0309,0.0405,0.2017,0.0714,0.9965,0.9804,0.2455,0.2417,0.1034,0.6667,1.0,0.1229,0.6253,0.2239,0.0311,0.0374,0.4585,0.12300000000000001,0.9975,0.9799,0.2448,0.54,0.1724,0.8333,1.0,0.1826,0.5823,0.5093,0.0311,0.0414,reg oper account,block of flats,0.2361,Panel,No,2.0,1.0,2.0,0.0,-711.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n103895,0,Cash loans,F,N,Y,1,67500.0,886176.0,31828.5,765000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-14573,-182,-2779.0,-5334,,1,1,1,1,1,0,,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Government,,0.4054607580178961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2448.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n339576,0,Cash loans,F,N,N,0,180000.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018209,-19759,-1119,-7128.0,-3250,,1,1,1,1,1,0,High skill tech staff,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,Trade: type 7,,0.6510877701599931,0.4400578303966329,0.2371,,0.9901,,,0.24,0.2069,0.375,,,,0.2707,,0.1619,0.2416,,0.9901,,,0.2417,0.2069,0.375,,,,0.282,,0.1714,0.2394,,0.9901,,,0.24,0.2069,0.375,,,,0.2756,,0.1653,,block of flats,0.2481,Panel,No,0.0,0.0,0.0,0.0,-2606.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n287670,0,Cash loans,M,Y,Y,0,135000.0,797818.5,38511.0,634500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-17179,-1923,-1414.0,-175,9.0,1,1,0,1,0,1,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,,0.18483176632893286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n386179,0,Cash loans,F,N,N,0,180000.0,577147.5,24583.5,459000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.01885,-18579,-8954,-10703.0,-2134,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Transport: type 2,0.7218865314961239,0.5820015194453029,0.6363761710860439,0.067,0.0777,0.9757,0.6668,0.0313,0.0,0.1379,0.1667,0.2083,0.015,0.0538,0.0502,0.0039,0.0519,0.0683,0.0806,0.9757,0.6798,0.0316,0.0,0.1379,0.1667,0.2083,0.0154,0.0588,0.0523,0.0039,0.055,0.0677,0.0777,0.9757,0.6713,0.0315,0.0,0.1379,0.1667,0.2083,0.0153,0.0547,0.0511,0.0039,0.053,reg oper account,block of flats,0.0679,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3251.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n204297,0,Cash loans,M,Y,N,0,135000.0,924858.0,64368.0,873000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-11298,-635,-4905.0,-3435,10.0,1,1,1,1,0,0,,1.0,2,2,TUESDAY,10,0,0,0,0,1,1,Industry: type 9,,0.7212121022071782,0.3656165070113335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1880.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n263718,0,Cash loans,F,N,N,1,157500.0,831771.0,45252.0,661500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15717,-2877,-6012.0,-4684,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Hotel,,0.6942266610235679,0.3723336657058204,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1736.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n338436,0,Cash loans,F,N,Y,0,112500.0,320382.0,15543.0,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.008625,-22591,365243,-5017.0,-3178,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.12964129443925151,0.746300213050371,,,0.9851,,,,0.0345,0.1667,,0.0836,,0.0509,,0.0,,,0.9851,,,,0.0345,0.1667,,0.0855,,0.053,,0.0,,,0.9851,,,,0.0345,0.1667,,0.0851,,0.0518,,0.0,,,0.0436,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-518.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n375210,0,Cash loans,F,Y,N,3,166500.0,385056.0,10287.0,252000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.020246,-14459,-627,-2311.0,-5178,15.0,1,1,0,1,0,0,Core staff,5.0,3,3,WEDNESDAY,8,0,0,0,0,0,0,Security,0.6091954407974245,0.4443291852323493,0.33285056416487313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n210439,0,Cash loans,F,Y,Y,3,180000.0,1255680.0,41629.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-12643,-77,-3462.0,-3210,24.0,1,1,0,1,0,0,Sales staff,5.0,2,2,WEDNESDAY,19,0,0,0,0,0,0,Trade: type 7,,0.6023829769391104,0.5352762504724826,0.1103,0.0581,0.9846,0.7892,0.0089,0.12,0.1034,0.3333,0.0417,0.023,,0.1487,,0.005,0.1124,0.0603,0.9846,0.7975,0.009000000000000001,0.1208,0.1034,0.3333,0.0417,0.0236,,0.155,,0.0053,0.1114,0.0581,0.9846,0.792,0.009000000000000001,0.12,0.1034,0.3333,0.0417,0.0234,,0.1514,,0.0051,reg oper account,block of flats,0.1229,Panel,No,0.0,0.0,0.0,0.0,-664.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n448889,0,Cash loans,F,N,N,0,49500.0,143910.0,14148.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018634,-24693,365243,-12239.0,-4029,,1,0,0,1,0,0,,1.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.515282561819354,0.6722428897082422,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-79.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n447645,0,Cash loans,F,N,Y,0,202500.0,462825.0,29709.0,378000.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.020713,-18006,-1033,-8857.0,-1541,,1,1,0,1,0,0,,2.0,3,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,0.3971688714345566,0.4688862991135395,0.622922000268356,0.1021,0.1084,0.9796,0.7212,0.015,0.0,0.2069,0.1667,0.2083,0.0358,0.0832,0.0915,0.0,0.0,0.10400000000000001,0.1124,0.9796,0.7321,0.0151,0.0,0.2069,0.1667,0.2083,0.0366,0.0909,0.0954,0.0,0.0,0.1031,0.1084,0.9796,0.7249,0.0151,0.0,0.2069,0.1667,0.2083,0.0364,0.0847,0.0932,0.0,0.0,reg oper account,block of flats,0.0802,Panel,No,0.0,0.0,0.0,0.0,-1751.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n104964,1,Cash loans,M,N,Y,2,270000.0,857169.0,25191.0,715500.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.001333,-17735,-531,-1500.0,-65,,1,1,0,1,0,0,Laborers,4.0,3,3,FRIDAY,8,0,0,0,0,0,0,Trade: type 7,,0.4221520179346397,0.29708661164720285,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-942.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n105662,1,Cash loans,M,Y,Y,0,90000.0,314100.0,13963.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.008865999999999999,-20114,-195,-6411.0,-3464,17.0,1,1,0,1,1,0,Security staff,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Security,,0.49938412464365994,0.23791607950711405,0.0557,0.0362,0.9786,0.7076,0.0092,0.04,0.0345,0.3333,0.0417,0.0496,0.0454,0.0464,0.0,0.0,0.0567,0.0376,0.9786,0.7190000000000001,0.0093,0.0403,0.0345,0.3333,0.0417,0.0507,0.0496,0.0484,0.0,0.0,0.0562,0.0362,0.9786,0.7115,0.0093,0.04,0.0345,0.3333,0.0417,0.0505,0.0462,0.0473,0.0,0.0,reg oper account,block of flats,0.0365,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-411.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n280355,0,Cash loans,F,N,Y,0,270000.0,1258650.0,53455.5,1125000.0,Family,Working,Higher education,Separated,House / apartment,0.00733,-19331,-9579,-7908.0,-2858,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,School,,0.6577786880200154,0.2471909382666521,0.0619,0.0796,0.9866,0.8164,,0.0,0.1034,0.1667,0.0417,,,0.0646,,,0.063,0.0826,0.9866,0.8236,,0.0,0.1034,0.1667,0.0417,,,0.0673,,,0.0625,0.0796,0.9866,0.8189,,0.0,0.1034,0.1667,0.0417,,,0.0658,,,reg oper account,block of flats,0.0508,Panel,No,0.0,0.0,0.0,0.0,-3231.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n288657,0,Cash loans,F,N,N,0,157500.0,900000.0,38263.5,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-18523,365243,-1143.0,-2073,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,XNA,,0.3486921380889981,0.5744466170995097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2463.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n434595,0,Cash loans,F,N,Y,1,202500.0,326664.0,17851.5,234000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-10627,-256,-542.0,-2237,,1,1,0,1,0,1,Sales staff,3.0,2,2,SATURDAY,15,0,0,0,0,0,0,Bank,0.2365108820739761,0.6467934986560049,0.3360615207658242,0.166,0.073,0.9767,,,0.0,0.1034,0.1667,,,,0.0625,,0.0038,0.1691,0.0757,0.9767,,,0.0,0.1034,0.1667,,,,0.0651,,0.004,0.1676,0.073,0.9767,,,0.0,0.1034,0.1667,,,,0.0636,,0.0038,,block of flats,0.0538,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-2435.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n191050,0,Cash loans,F,N,Y,0,76500.0,284400.0,17527.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-21718,365243,-1614.0,-4851,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.7077056596153634,0.8357765157975799,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1583.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n313277,0,Cash loans,M,N,Y,0,112500.0,967500.0,34879.5,967500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-16897,-1824,-8001.0,-442,,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,8,0,0,0,0,1,1,Restaurant,0.35816769319649944,0.6573158210441619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-545.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n320145,0,Cash loans,F,Y,Y,0,135000.0,463941.0,23818.5,400500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.018029,-18719,-626,-2954.0,-2263,14.0,1,1,1,1,0,0,High skill tech staff,2.0,3,3,TUESDAY,9,0,0,0,0,1,1,Security Ministries,0.4640571737277288,0.5175324935819776,0.4101025731788671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1107.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n450300,0,Cash loans,F,N,N,0,675000.0,1575000.0,43443.0,1575000.0,Family,Working,Higher education,Married,House / apartment,0.025164,-13034,-4814,-2534.0,-3941,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Government,0.6579257851570735,0.6880088293880456,0.11104240226943142,0.2546,0.1408,0.9881,0.8368,0.1527,0.28,0.2414,0.3333,0.375,0.0446,0.2059,0.2613,0.0077,0.0128,0.2595,0.1461,0.9881,0.8432,0.1541,0.282,0.2414,0.3333,0.375,0.0456,0.225,0.2722,0.0078,0.0135,0.2571,0.1408,0.9881,0.8390000000000001,0.1536,0.28,0.2414,0.3333,0.375,0.0454,0.2095,0.2659,0.0078,0.013000000000000001,reg oper account,block of flats,0.2917,Panel,No,6.0,1.0,6.0,1.0,-626.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n133461,0,Cash loans,M,N,Y,0,112500.0,152820.0,17370.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.008865999999999999,-8940,-1767,-8919.0,-1579,,1,1,0,1,0,0,Laborers,1.0,2,2,SUNDAY,16,0,0,0,0,0,0,Transport: type 2,0.6471478210247743,0.4570507186351354,0.6246146584503397,0.1237,0.0859,0.9866,0.8164,0.0336,0.12,0.1034,0.375,0.4167,0.0868,0.1009,0.1333,0.0,0.0,0.1261,0.0892,0.9866,0.8236,0.034,0.1208,0.1034,0.375,0.4167,0.0887,0.1102,0.1389,0.0,0.0,0.1249,0.0859,0.9866,0.8189,0.0339,0.12,0.1034,0.375,0.4167,0.0883,0.1026,0.1357,0.0,0.0,reg oper spec account,block of flats,0.1048,Panel,No,0.0,0.0,0.0,0.0,-1292.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n165774,0,Cash loans,M,Y,Y,2,360000.0,948582.0,33736.5,679500.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.072508,-14266,-1919,-5279.0,-4509,6.0,1,1,0,1,1,0,Laborers,4.0,1,1,WEDNESDAY,16,1,1,0,0,0,1,Medicine,,0.6501731561796822,0.0005272652387098817,0.0866,0.0275,0.9786,0.7076,0.0687,0.08,0.0345,0.4583,0.5,0.0533,0.0698,0.0722,0.0039,0.0051,0.0882,0.0285,0.9786,0.7190000000000001,0.0693,0.0806,0.0345,0.4583,0.5,0.0545,0.0762,0.0752,0.0039,0.0054,0.0874,0.0275,0.9786,0.7115,0.0691,0.08,0.0345,0.4583,0.5,0.0542,0.071,0.0735,0.0039,0.0052,reg oper account,block of flats,0.0579,Block,No,0.0,0.0,0.0,0.0,-389.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n314956,0,Cash loans,F,N,N,0,90000.0,113760.0,6660.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.018029,-20933,365243,-11114.0,-815,,1,0,0,1,0,0,,1.0,3,3,MONDAY,8,0,0,0,0,0,0,XNA,,0.6442265116601843,0.5460231970049609,,,0.9613,,,,0.069,0.0417,,0.0043,,0.0109,,,,,0.9613,,,,0.069,0.0417,,0.0044,,0.0114,,,,,0.9613,,,,0.069,0.0417,,0.0044,,0.0111,,,,block of flats,0.0086,Wooden,Yes,2.0,0.0,1.0,0.0,-774.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n115687,0,Cash loans,F,N,Y,0,63000.0,156384.0,16155.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.015221,-23204,365243,-12733.0,-4349,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,0.82226326903876,0.641802034426104,0.8016009030071296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-175.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n271362,0,Cash loans,F,N,N,0,99000.0,213156.0,10494.0,139500.0,Family,Commercial associate,Higher education,Single / not married,With parents,0.00963,-10393,-2234,-4426.0,-2741,,1,1,0,1,0,0,,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,Industry: type 11,0.2252625771364505,0.5584586991914776,0.25396280933631177,0.0784,,0.9916,,,0.0,0.1379,0.1667,,0.0285,,0.0284,,0.0113,0.0798,,0.9916,,,0.0,0.1379,0.1667,,0.0291,,0.0295,,0.0119,0.0791,,0.9916,,,0.0,0.1379,0.1667,,0.028999999999999998,,0.0289,,0.0115,,specific housing,0.0455,Panel,No,0.0,0.0,0.0,0.0,-1385.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,3.0\n402605,0,Cash loans,F,N,Y,0,135000.0,675000.0,28728.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018209,-22186,365243,-12361.0,-3921,,1,0,0,1,0,0,,1.0,3,3,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.3851357468181984,0.17456426555726348,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n417306,0,Revolving loans,F,N,Y,0,360000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.04622,-10773,-224,-4947.0,-2582,,1,1,0,1,1,0,Core staff,1.0,1,1,TUESDAY,15,0,1,1,0,0,0,Bank,,0.7511586863771055,,0.1144,,0.9876,0.83,,0.08,0.0345,0.625,0.6667,,,0.1041,,0.0703,0.1166,,0.9876,0.8367,,0.0806,0.0345,0.625,0.6667,,,0.1084,,0.0744,0.1155,,0.9876,0.8323,,0.08,0.0345,0.625,0.6667,,,0.1059,,0.0718,reg oper account,block of flats,0.0971,Panel,No,0.0,0.0,0.0,0.0,-1757.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n392839,0,Cash loans,M,N,Y,0,112500.0,254700.0,25321.5,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.008625,-24190,365243,-8271.0,-4647,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,XNA,,0.5528922765405544,0.8193176922872417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n409974,0,Cash loans,F,Y,N,0,270000.0,755190.0,38686.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.04622,-12802,-1320,-6632.0,-75,17.0,1,1,0,1,0,0,Laborers,2.0,1,1,TUESDAY,13,0,1,1,1,0,0,Business Entity Type 3,0.5718144146565063,0.6579263483757197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n151344,0,Cash loans,F,Y,N,0,157500.0,760225.5,30150.0,679500.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.04622,-15398,-1582,-7347.0,-4453,6.0,1,1,1,1,1,0,Accountants,2.0,1,1,TUESDAY,13,0,0,0,0,1,1,Industry: type 4,0.7469697321669528,0.7369526016681198,0.5709165417729987,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-1007.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n322307,0,Revolving loans,F,Y,Y,1,99000.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.026392,-10988,-442,-4832.0,-3132,3.0,1,1,1,1,1,0,Sales staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Trade: type 7,0.37735266277657015,0.702787299385755,0.4329616670974407,0.0974,,0.9871,,,0.08,0.0345,0.5417,,,,0.0911,,0.0193,0.0987,,0.9871,,,0.0806,0.0345,0.5417,,,,0.0949,,0.0049,0.0984,,0.9871,,,0.08,0.0345,0.5417,,,,0.0927,,0.0197,,block of flats,0.0726,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-184.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n313057,1,Cash loans,M,Y,Y,2,225000.0,254700.0,27153.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-11356,-552,-788.0,-3825,13.0,1,1,0,1,0,0,Managers,4.0,2,2,THURSDAY,10,0,0,0,0,0,0,Industry: type 1,0.27941715672231976,0.2632538485511991,0.07495928089904688,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n189766,0,Cash loans,F,N,N,1,67500.0,491823.0,18225.0,373500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.00733,-11102,-222,-2454.0,-3450,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Government,0.33272255331630723,0.765058623037823,0.5779691187553125,0.1278,0.11,0.9851,,,0.0,0.2759,0.1667,0.0417,0.0968,,0.1181,,0.0155,0.1303,0.1142,0.9851,,,0.0,0.2759,0.1667,0.0417,0.099,,0.1231,,0.0164,0.1291,0.11,0.9851,,,0.0,0.2759,0.1667,0.0417,0.0985,,0.1202,,0.0158,,block of flats,0.1054,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n349383,0,Cash loans,F,N,N,0,45000.0,225000.0,11488.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-10035,-113,-3141.0,-1273,,1,1,0,1,0,0,Sales staff,2.0,3,3,THURSDAY,5,0,0,0,0,0,0,Self-employed,0.5420368848703063,0.3603508776958648,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1154.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n284322,0,Cash loans,M,N,N,0,135000.0,315000.0,15448.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.018209,-10456,-1962,-271.0,-2994,,1,1,0,1,0,0,Laborers,2.0,3,3,SUNDAY,11,0,0,0,0,0,0,Self-employed,0.2440247383228717,0.4719206811565693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-688.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n395047,0,Cash loans,F,N,Y,0,135000.0,257391.0,26374.5,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-22445,365243,-5912.0,-4497,,1,0,0,1,0,1,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,0.5981160298179039,0.6735255834784984,0.5744466170995097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1665.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n131626,0,Cash loans,F,N,N,2,157500.0,735052.5,35491.5,657000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-10955,-1397,-2878.0,-3235,,1,1,0,1,0,1,High skill tech staff,4.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 2,0.3318948818311184,0.6775756122577494,0.2622489709189549,0.0515,,0.9871,0.8232,,,0.1379,0.1667,0.2083,,0.042,0.0298,,,0.0525,,0.9871,0.8301,,,0.1379,0.1667,0.2083,,0.0459,0.031,,,0.052000000000000005,,0.9871,0.8256,,,0.1379,0.1667,0.2083,,0.0428,0.0303,,,,block of flats,0.0396,\"Stone, brick\",No,8.0,1.0,8.0,1.0,-1057.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n448032,0,Revolving loans,M,Y,Y,2,315000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-16051,-262,-252.0,-4253,7.0,1,1,0,1,0,0,Managers,4.0,1,1,TUESDAY,14,0,1,1,0,0,0,Business Entity Type 3,0.6578840062050738,0.7361803570218137,0.6144143775673561,0.0619,0.0,0.9732,0.6328,0.0309,0.0,0.1034,0.1667,0.0417,0.0,0.0967,0.0508,0.0,0.0,0.063,0.0,0.9732,0.6472,0.0312,0.0,0.1034,0.1667,0.0417,0.0,0.1056,0.0529,0.0,0.0,0.0625,0.0,0.9732,0.6377,0.0311,0.0,0.1034,0.1667,0.0417,0.0,0.0983,0.0517,0.0,0.0,reg oper account,block of flats,0.04,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-318.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n127507,0,Cash loans,F,N,N,0,135000.0,540000.0,28768.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.016612000000000002,-20756,-3593,-8711.0,-4135,,1,1,0,1,1,0,,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,Electricity,,0.4765606274689239,0.656158373001177,0.1113,0.071,0.9881,0.7688,0.021,0.12,0.1034,0.3333,0.375,0.0613,0.0908,0.1319,0.0039,0.0014,0.1134,0.0737,0.9881,0.7779,0.0212,0.1208,0.1034,0.3333,0.375,0.0627,0.0992,0.1374,0.0039,0.0015,0.1124,0.071,0.9881,0.7719,0.0211,0.12,0.1034,0.3333,0.375,0.0623,0.0923,0.1343,0.0039,0.0015,reg oper account,block of flats,0.1278,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1511.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n105794,0,Revolving loans,F,N,Y,0,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-22255,365243,-9717.0,-4440,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,,0.1380270464882295,0.6940926425266661,0.0124,0.0,0.9806,0.7348,0.0015,0.0,0.069,0.0417,0.0833,0.0067,0.0101,0.0083,0.0,0.0122,0.0126,0.0,0.9806,0.7452,0.0015,0.0,0.069,0.0417,0.0833,0.0069,0.011000000000000001,0.0086,0.0,0.0129,0.0125,0.0,0.9806,0.7383,0.0015,0.0,0.069,0.0417,0.0833,0.0068,0.0103,0.0084,0.0,0.0125,reg oper account,block of flats,0.0092,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-742.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n205495,0,Cash loans,F,Y,Y,0,238500.0,510853.5,26878.5,441000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.003813,-11026,-1056,-2647.0,-917,9.0,1,1,0,1,0,1,Accountants,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Industry: type 4,0.4014517632880648,0.28249037158925183,0.6313545365850379,0.0521,0.0643,0.9796,0.7212,0.1213,0.0,0.1034,0.1667,0.0417,0.0621,0.042,0.0619,0.0039,0.0235,0.0525,0.0667,0.9796,0.7321,0.1224,0.0,0.069,0.1667,0.0417,0.0635,0.0459,0.0644,0.0039,0.0174,0.0526,0.0643,0.9796,0.7249,0.1221,0.0,0.1034,0.1667,0.0417,0.0632,0.0428,0.063,0.0039,0.024,reg oper spec account,block of flats,0.0553,Panel,No,1.0,0.0,1.0,0.0,-1836.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n269706,0,Cash loans,F,N,N,0,157500.0,454500.0,19255.5,454500.0,Unaccompanied,Working,Higher education,Married,With parents,0.030755,-10513,-1955,-2216.0,-2237,,1,1,0,1,1,0,Core staff,2.0,2,2,FRIDAY,10,0,0,0,1,1,0,Hotel,0.5597277095362259,0.5775849171931334,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1759.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n261751,0,Cash loans,F,N,Y,0,135000.0,497520.0,33376.5,450000.0,Family,State servant,Higher education,Married,House / apartment,0.032561,-8553,-375,-202.0,-1058,,1,1,0,1,0,0,High skill tech staff,2.0,1,1,THURSDAY,13,0,0,0,0,0,0,Medicine,0.4616111743742091,0.7410689959377265,,0.0196,0.0136,0.9324,,,0.1,0.1034,0.2083,,,,0.0174,,0.0035,0.02,0.0142,0.9325,,,0.0,0.0345,0.125,,,,0.0181,,0.0037,0.0198,0.0136,0.9324,,,0.1,0.1034,0.2083,,,,0.0177,,0.0036,,block of flats,0.0144,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-458.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n282672,0,Cash loans,M,N,N,0,450000.0,1096020.0,56092.5,900000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.072508,-16632,-5653,-8946.0,-157,,1,1,0,1,1,0,High skill tech staff,2.0,1,1,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 2,,0.5319723394413813,0.5814837058057234,0.1753,0.0533,0.9856,0.8028,0.0485,0.16,0.0345,1.0,1.0,0.0094,0.1429,0.1985,0.0,0.0615,0.1786,0.0553,0.9856,0.8105,0.0489,0.1611,0.0345,1.0,1.0,0.0096,0.1561,0.2069,0.0,0.0651,0.177,0.0533,0.9856,0.8054,0.0488,0.16,0.0345,1.0,1.0,0.0095,0.1454,0.2021,0.0,0.0628,reg oper account,block of flats,0.1695,Panel,No,0.0,0.0,0.0,0.0,-476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n122037,0,Cash loans,F,N,N,0,112500.0,646920.0,17919.0,540000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.011703,-11269,-1364,-1509.0,-655,,1,1,0,1,1,0,Sales staff,1.0,2,2,FRIDAY,14,0,0,0,0,1,1,Self-employed,0.3239855264600172,0.7105517810122959,0.5136937663039473,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n151515,1,Cash loans,F,N,Y,0,157500.0,675000.0,49248.0,675000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.031329,-10155,-1884,-7316.0,-2831,,1,1,0,1,1,0,Sales staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Self-employed,0.2413198891336924,0.3804188604518256,0.097581612687446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,1.0,-544.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n415641,0,Cash loans,M,N,Y,2,180000.0,135000.0,14670.0,135000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010276,-13900,-2301,-769.0,-763,,1,1,0,1,0,0,,4.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Agriculture,,0.5317055477746226,0.20092608771597087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-696.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n185065,0,Cash loans,M,Y,Y,0,270000.0,601470.0,30708.0,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Co-op apartment,0.072508,-8344,-1359,-8292.0,-1016,65.0,1,1,0,1,0,0,Drivers,1.0,1,1,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 2,,0.6446223626505175,0.15193454904964762,0.0485,0.0475,0.9722,0.6192,0.0,0.12,0.1034,0.2083,0.0417,0.0,0.0395,0.049,0.0,0.0566,0.0494,0.0493,0.9722,0.6341,0.0,0.1208,0.1034,0.2083,0.0417,0.0,0.0432,0.051,0.0,0.0599,0.0489,0.0475,0.9722,0.6243,0.0,0.12,0.1034,0.2083,0.0417,0.0,0.0402,0.0499,0.0,0.0577,reg oper account,block of flats,0.0508,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-956.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n206464,0,Cash loans,F,N,Y,1,202500.0,1012500.0,43029.0,1012500.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.026392,-15452,-8422,-593.0,-4736,,1,1,0,1,0,0,Secretaries,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.5488746078957605,0.5367332557849201,0.4365064990977374,0.0691,,0.9801,,,0.0,0.1379,0.1667,,,,0.059000000000000004,,0.0085,0.0704,,0.9801,,,0.0,0.1379,0.1667,,,,0.0614,,0.009000000000000001,0.0697,,0.9801,,,0.0,0.1379,0.1667,,,,0.06,,0.0087,,block of flats,0.0482,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-940.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,2.0\n318526,0,Cash loans,M,Y,Y,1,202500.0,485640.0,38263.5,450000.0,Family,Commercial associate,Secondary / secondary special,Married,Rented apartment,0.00963,-15984,-1500,-263.0,-267,26.0,1,1,0,1,0,0,Drivers,3.0,2,2,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6155666627155097,,0.1423,0.0913,0.9806,0.7348,0.0428,0.0,0.0345,0.1667,0.2083,0.0674,0.1135,0.044000000000000004,0.0116,0.0132,0.145,0.0948,0.9806,0.7452,0.0432,0.0,0.0345,0.1667,0.2083,0.069,0.124,0.0458,0.0117,0.013999999999999999,0.1436,0.0913,0.9806,0.7383,0.0431,0.0,0.0345,0.1667,0.2083,0.0686,0.1154,0.0448,0.0116,0.0135,,block of flats,0.0609,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1093.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n308074,1,Cash loans,F,N,Y,2,171000.0,711081.0,55161.0,630000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-12139,-1170,-6107.0,-3128,,1,1,0,1,1,0,Core staff,4.0,3,2,MONDAY,14,0,0,0,0,0,0,Other,,0.5533463126963111,0.3001077565791181,0.0825,0.0798,0.9752,0.66,0.0088,0.0,0.1379,0.1667,0.2083,0.1611,0.0672,0.0706,0.0,0.0,0.084,0.0828,0.9752,0.6733,0.0089,0.0,0.1379,0.1667,0.2083,0.1648,0.0735,0.0735,0.0,0.0,0.0833,0.0798,0.9752,0.6645,0.0088,0.0,0.1379,0.1667,0.2083,0.1639,0.0684,0.0719,0.0,0.0,reg oper spec account,block of flats,0.0603,Panel,No,1.0,0.0,0.0,0.0,-645.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n441461,0,Revolving loans,M,N,Y,0,405000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.010006,-15762,-7132,-8124.0,-3955,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Industry: type 9,0.5789777672428773,0.7558658527402933,0.5971924268337128,0.0247,0.0,0.9811,0.7416,0.0,0.0,0.1034,0.0417,0.0833,0.0,0.0202,0.0193,0.0,0.0052,0.0252,0.0,0.9811,0.7517,0.0,0.0,0.1034,0.0417,0.0833,0.0,0.022000000000000002,0.0197,0.0,0.0054,0.025,0.0,0.9811,0.7451,0.0,0.0,0.1034,0.0417,0.0833,0.0,0.0205,0.0198,0.0,0.0053,reg oper account,block of flats,0.016,Block,No,0.0,0.0,0.0,0.0,-2472.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n241594,0,Cash loans,F,N,Y,0,135000.0,225000.0,23755.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-13122,-2572,-7971.0,-2224,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Trade: type 7,,0.7152111041929715,0.6610235391308081,0.1649,,0.9886,,,0.16,0.1379,0.375,,,,0.1722,,0.0,0.1681,,0.9886,,,0.1611,0.1379,0.375,,,,0.1795,,0.0,0.1665,,0.9886,,,0.16,0.1379,0.375,,,,0.1753,,0.0,,block of flats,0.1355,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n228258,0,Cash loans,F,N,Y,1,180000.0,225000.0,18040.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.006629,-17780,-3377,-2611.0,-336,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Industry: type 3,0.7015306997291961,0.4493450655937873,,0.0124,,0.9826,,,,0.0345,0.0417,,,,,,,0.0126,,0.9826,,,,0.0345,0.0417,,,,,,,0.0125,,0.9826,,,,0.0345,0.0417,,,,,,,,block of flats,0.0089,Wooden,No,3.0,0.0,3.0,0.0,-437.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n301514,0,Cash loans,F,Y,Y,2,450000.0,1931652.0,67270.5,1764000.0,Family,Working,Higher education,Married,House / apartment,0.006207,-15333,-1103,-5119.0,-5382,7.0,1,1,0,1,0,1,Accountants,4.0,2,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,0.8482305862221928,0.7027964345883526,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1109.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n448228,0,Revolving loans,F,Y,Y,2,225000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.006305,-12924,-4390,-97.0,-2630,17.0,1,1,0,1,0,0,Sales staff,3.0,3,3,MONDAY,8,0,0,0,0,1,1,Self-employed,,0.5905092203586265,0.31703177858344445,0.0619,0.0566,0.9816,,,,0.1034,0.1667,,,,0.0608,,,0.063,0.0587,0.9816,,,,0.1034,0.1667,,,,0.0633,,,0.0625,0.0566,0.9816,,,,0.1034,0.1667,,,,0.0619,,,,block of flats,0.0613,Panel,No,3.0,0.0,3.0,0.0,-154.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n253879,0,Cash loans,M,Y,N,0,112500.0,263686.5,27819.0,238500.0,Family,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.02461,-9115,-209,-3902.0,-1795,14.0,1,1,0,1,1,0,Security staff,1.0,2,2,SATURDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.5569483571315073,0.511891801533151,0.0711,0.111,0.9786,0.7076,0.0427,0.0,0.1379,0.1667,0.2083,0.0996,0.0546,0.0629,0.0154,0.0357,0.0725,0.1152,0.9786,0.7190000000000001,0.0431,0.0,0.1379,0.1667,0.2083,0.1019,0.0597,0.0655,0.0156,0.0378,0.0718,0.111,0.9786,0.7115,0.043,0.0,0.1379,0.1667,0.2083,0.1013,0.0556,0.064,0.0155,0.0365,reg oper account,block of flats,0.0828,\"Stone, brick\",No,5.0,1.0,5.0,1.0,-736.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,1.0\n391526,0,Cash loans,F,N,N,0,166500.0,618840.0,17145.0,405000.0,Unaccompanied,Working,Higher education,Civil marriage,With parents,0.018801,-8852,-453,-112.0,-1527,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.2805647701898136,0.44111907772754055,0.4471785780453068,0.067,0.0301,0.9767,0.6804,0.0156,0.0,0.1034,0.1667,0.2083,0.0052,0.0504,0.0481,0.0193,0.0187,0.0683,0.0312,0.9767,0.6929,0.0157,0.0,0.1034,0.1667,0.2083,0.0053,0.0551,0.0502,0.0195,0.0198,0.0677,0.0301,0.9767,0.6847,0.0157,0.0,0.1034,0.1667,0.2083,0.0053,0.0513,0.049,0.0194,0.0191,not specified,block of flats,0.0505,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-400.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n268791,1,Cash loans,M,Y,N,0,180000.0,601470.0,32760.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.006629,-12099,-508,-5554.0,-92,65.0,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,7,0,0,0,1,1,0,Business Entity Type 3,,0.2785711387266622,0.7490217048463391,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n371203,0,Revolving loans,F,N,Y,1,90000.0,180000.0,9000.0,180000.0,Children,State servant,Secondary / secondary special,Single / not married,House / apartment,0.009334,-10194,-1698,-2068.0,-2068,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,15,0,0,0,0,0,0,School,,0.5719088934245911,0.21396685226179807,0.0412,0.0303,0.9762,,,0.0,0.069,0.1667,,,,0.0316,,0.0,0.042,0.0315,0.9762,,,0.0,0.069,0.1667,,,,0.0329,,0.0,0.0416,0.0303,0.9762,,,0.0,0.069,0.1667,,,,0.0322,,0.0,,block of flats,0.0249,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-311.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n146078,0,Cash loans,F,Y,N,0,324000.0,509400.0,40243.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-16867,-515,-8386.0,-425,7.0,1,1,1,1,1,1,Laborers,2.0,1,1,SUNDAY,11,0,1,1,0,1,1,Government,0.7774417486553772,0.7542724483383454,0.4920600938649263,0.033,0.0826,0.9588,,,0.0,0.1379,0.125,,0.0129,,0.0394,,0.0566,0.0336,0.0857,0.9588,,,0.0,0.1379,0.125,,0.0132,,0.0411,,0.0599,0.0333,0.0826,0.9588,,,0.0,0.1379,0.125,,0.0131,,0.0401,,0.0577,,block of flats,0.0433,\"Stone, brick\",Yes,0.0,0.0,0.0,0.0,-137.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n102272,0,Revolving loans,M,Y,Y,0,450000.0,540000.0,27000.0,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-18641,-6044,-11268.0,-2171,4.0,1,1,0,1,0,0,Laborers,2.0,1,1,WEDNESDAY,15,0,0,0,0,1,1,Business Entity Type 3,0.6892584093784915,0.755106421717656,0.6479768603302221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-745.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n265696,0,Revolving loans,M,Y,N,1,130500.0,270000.0,13500.0,270000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-16387,-2284,-5303.0,-4417,1.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,16,0,0,0,0,1,1,Construction,,0.03246376024384475,0.633031641417419,0.0619,0.0513,0.9796,0.7212,0.0,0.0,0.1379,0.1667,0.2083,0.0041,0.0504,0.0543,0.0,0.0166,0.063,0.0532,0.9796,0.7321,0.0,0.0,0.1379,0.1667,0.2083,0.0042,0.0551,0.0565,0.0,0.0176,0.0625,0.0513,0.9796,0.7249,0.0,0.0,0.1379,0.1667,0.2083,0.0041,0.0513,0.0553,0.0,0.017,reg oper account,block of flats,0.0463,Panel,No,,,,,-246.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n313754,0,Cash loans,M,N,Y,0,67500.0,227520.0,11196.0,180000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.010966,-16677,-824,-4896.0,-161,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Industry: type 4,,0.6808773626671605,0.28812959991785075,0.0031,0.0,0.9737,,,0.0,0.0345,0.0,,0.0171,,0.0022,,0.0,0.0032,0.0,0.9737,,,0.0,0.0345,0.0,,0.0175,,0.0023,,0.0,0.0031,0.0,0.9737,,,0.0,0.0345,0.0,,0.0174,,0.0022,,0.0,,block of flats,0.0041,Wooden,No,2.0,0.0,2.0,0.0,-889.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,0.0\n415242,0,Cash loans,F,Y,Y,0,405000.0,810000.0,54351.0,810000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.032561,-10021,-513,-1168.0,-2682,7.0,1,1,1,1,1,0,Accountants,2.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,Construction,,0.8028272632545214,0.7751552674785404,0.3711,,0.9861,0.8096,0.0,0.28,0.2414,0.3333,0.0,,0.3026,0.3414,0.0,0.0879,0.3782,,0.9861,0.8171,0.0,0.282,0.2414,0.3333,0.0,,0.3306,0.3557,0.0,0.0931,0.3747,,0.9861,0.8121,0.0,0.28,0.2414,0.3333,0.0,,0.3078,0.3476,0.0,0.0898,reg oper account,block of flats,0.2877,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-426.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n226714,0,Cash loans,F,N,Y,0,90000.0,697500.0,22500.0,697500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-17310,-2935,-11149.0,-778,,1,1,0,1,0,0,Cooking staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.38607345853964736,,,,0.9806,,,,,,,,,0.0606,,,,,0.9806,,,,,,,,,0.0631,,,,,0.9806,,,,,,,,,0.0617,,,,,0.0476,,No,0.0,0.0,0.0,0.0,0.0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n415328,0,Cash loans,F,N,N,0,450000.0,1350000.0,37125.0,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.010006,-22823,365243,-3440.0,-4567,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,,0.594358360789718,0.5406544504453575,0.0309,,0.9955,,,0.0,0.0459,0.1667,,,,0.0467,,,0.0315,,0.9955,,,0.0,0.0345,0.1667,,,,0.0486,,,0.0312,,0.9955,,,0.0,0.0345,0.1667,,,,0.0475,,,,block of flats,0.0371,Others,No,0.0,0.0,0.0,0.0,-1789.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n366199,0,Cash loans,F,N,Y,0,67500.0,555273.0,18040.5,463500.0,Family,Working,Secondary / secondary special,Widow,House / apartment,0.006852,-14366,-637,-746.0,-4804,,1,1,0,1,0,0,Laborers,1.0,3,3,FRIDAY,7,0,0,0,0,0,0,Postal,0.528415506733778,0.35611783398095337,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-1178.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n431391,0,Revolving loans,F,Y,Y,0,247500.0,1125000.0,56250.0,1125000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.0228,-19033,-2904,-3883.0,-2572,1.0,1,1,0,1,0,0,HR staff,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.6375076504613296,0.28961123838200553,0.0423,0.0376,0.993,0.9048,0.0088,0.08,0.0345,0.4583,0.5,0.0406,0.0336,0.0922,0.0039,0.0285,0.0431,0.0391,0.993,0.9085,0.0089,0.0806,0.0345,0.4583,0.5,0.0416,0.0367,0.096,0.0039,0.0302,0.0427,0.0376,0.993,0.9061,0.0089,0.08,0.0345,0.4583,0.5,0.0413,0.0342,0.0938,0.0039,0.0291,reg oper account,block of flats,0.0787,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-630.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n377293,0,Cash loans,M,N,Y,1,202500.0,450000.0,23692.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.014519999999999996,-15938,-1037,-5899.0,-3345,,1,1,0,1,0,0,,3.0,2,2,SUNDAY,13,0,0,0,1,1,1,Business Entity Type 3,,0.4942345642291083,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-446.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n385845,0,Cash loans,F,N,Y,0,283500.0,1125000.0,46557.0,1125000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.026392,-19856,-3471,-1087.0,-279,,1,1,0,1,0,0,Accountants,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,School,0.5967617990374187,0.7253131810216837,,0.1031,,0.9762,,,0.0,0.1724,0.1667,,,,0.0672,,0.0,0.105,,0.9762,,,0.0,0.1724,0.1667,,,,0.07,,0.0,0.1041,,0.9762,,,0.0,0.1724,0.1667,,,,0.0684,,0.0,,block of flats,0.0529,Panel,No,0.0,0.0,0.0,0.0,-2789.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n111489,0,Revolving loans,M,Y,Y,0,450000.0,765000.0,38250.0,765000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-17465,-187,-4822.0,-1005,1.0,1,1,0,1,0,0,Sales staff,2.0,1,1,MONDAY,18,0,1,1,0,0,0,Business Entity Type 3,0.5533465549298012,0.6869564208201039,0.5656079814115492,0.0412,0.0671,0.9712,0.6056,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0336,0.0534,0.0,0.0566,0.042,0.0696,0.9712,0.621,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0367,0.0556,0.0,0.06,0.0416,0.0671,0.9712,0.6109,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0342,0.0544,0.0,0.0578,reg oper account,block of flats,0.0543,\"Stone, brick\",No,,,,,-19.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n104052,0,Cash loans,F,N,Y,0,81000.0,755190.0,36459.0,675000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.006233,-20493,365243,-12005.0,-4056,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.7889415534962428,0.4241303111942548,0.0742,0.0015,0.9781,0.7008,0.0161,0.08,0.069,0.3333,0.375,0.0648,0.0605,0.0769,0.0,0.0012,0.0756,0.0016,0.9782,0.7125,0.0162,0.0806,0.069,0.3333,0.375,0.0663,0.0661,0.0801,0.0,0.0013,0.0749,0.0015,0.9781,0.7048,0.0162,0.08,0.069,0.3333,0.375,0.066,0.0616,0.0782,0.0,0.0012,reg oper account,block of flats,0.0607,Panel,No,7.0,0.0,7.0,0.0,-1000.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n390294,0,Cash loans,F,N,N,0,201600.0,742500.0,20547.0,742500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.035792000000000004,-12888,-3480,-6329.0,-4516,,1,1,0,1,0,0,Accountants,1.0,2,2,MONDAY,18,0,0,0,0,1,1,Industry: type 7,,0.4305003454628451,0.3376727217405312,0.0495,0.0644,0.9856,0.8028,0.0238,0.0,0.1379,0.1667,0.2083,0.034,0.0395,0.0527,0.0039,0.0046,0.0504,0.0668,0.9856,0.8105,0.024,0.0,0.1379,0.1667,0.2083,0.0348,0.0432,0.0549,0.0039,0.0049,0.05,0.0644,0.9856,0.8054,0.024,0.0,0.1379,0.1667,0.2083,0.0346,0.0402,0.0536,0.0039,0.0047,reg oper account,block of flats,0.0555,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n302505,0,Revolving loans,M,Y,Y,0,225000.0,337500.0,16875.0,337500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-18639,-120,-10727.0,-1506,3.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.7039097121474709,0.8027454758994178,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-2245.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n393321,0,Cash loans,M,Y,Y,0,225000.0,1575000.0,41679.0,1575000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-21127,-402,-511.0,-1398,2.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Government,,0.3797593444355409,0.14375805349116522,0.2268,0.1418,0.996,0.9456,0.4127,0.24,0.2069,0.375,0.0417,0.1452,0.174,0.2573,0.0502,0.1067,0.2311,0.1471,0.996,0.9477,0.4164,0.2417,0.2069,0.375,0.0417,0.1485,0.1901,0.2681,0.0506,0.113,0.22899999999999998,0.1418,0.996,0.9463,0.4153,0.24,0.2069,0.375,0.0417,0.1477,0.177,0.262,0.0505,0.109,reg oper account,block of flats,0.2735,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n453143,0,Cash loans,F,N,Y,0,180000.0,553806.0,28404.0,495000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.019689,-14941,-620,-187.0,-4964,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,0.6223618268972978,0.5475455241642251,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-747.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n446529,0,Cash loans,M,Y,Y,1,225000.0,754740.0,29245.5,630000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-14309,-294,-1140.0,-4450,17.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Construction,,0.5906308332570137,0.8193176922872417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-368.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,0.0\n393401,0,Cash loans,F,Y,N,0,270000.0,1687266.0,62658.0,1575000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-19791,-7185,-505.0,-3323,2.0,1,1,1,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,1,1,1,Government,0.7336002517703013,0.7296095891364849,0.5136937663039473,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n153313,0,Cash loans,F,N,Y,0,121500.0,497520.0,53581.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.031329,-12504,-168,-4323.0,-4292,,1,1,1,1,1,0,Core staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Insurance,0.5176722683487438,0.5424428800045198,0.3876253444214701,0.0737,0.008,0.9806,0.7348,0.0082,0.0,0.1552,0.1667,0.0417,0.0,0.0601,0.0555,0.0,0.0233,0.0735,0.0,0.9767,0.6929,0.0083,0.0,0.1379,0.1667,0.0417,0.0,0.0643,0.0457,0.0,0.0037,0.0744,0.008,0.9806,0.7383,0.0082,0.0,0.1552,0.1667,0.0417,0.0,0.0611,0.0565,0.0,0.0238,org spec account,block of flats,0.0535,Block,No,7.0,1.0,7.0,1.0,-721.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n319080,0,Cash loans,F,Y,Y,0,306000.0,1185282.0,34785.0,1035000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-11222,-2977,-1746.0,-3899,12.0,1,1,0,1,0,0,Accountants,2.0,2,2,TUESDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.5663481519415552,0.4223696523543468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2799.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n319431,0,Cash loans,F,N,Y,0,261000.0,765000.0,48888.0,765000.0,Family,Working,Higher education,Single / not married,House / apartment,0.032561,-9923,-1304,-363.0,-2556,,1,1,1,1,1,0,,1.0,1,1,WEDNESDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.18350896593203173,0.6504764934117311,,,0.0613,,,,,,,,,,,,,,0.0636,,,,,,,,,,,,,,0.0613,,,,,,,,,,,,,,,0.0427,Panel,No,8.0,0.0,8.0,0.0,-812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n403202,0,Revolving loans,F,N,Y,0,58500.0,405000.0,20250.0,405000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.006305,-21212,365243,-1120.0,-4116,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,9,0,0,0,0,0,0,XNA,0.7036824720981005,0.6914204108977959,0.5744466170995097,0.0206,0.0362,0.9896,0.8572,0.0041,0.0,0.069,0.1667,0.2083,0.034,0.0168,0.0261,0.0,0.0,0.021,0.0376,0.9896,0.8628,0.0041,0.0,0.069,0.1667,0.2083,0.0348,0.0184,0.0272,0.0,0.0,0.0208,0.0362,0.9896,0.8591,0.0041,0.0,0.069,0.1667,0.2083,0.0346,0.0171,0.0266,0.0,0.0,reg oper spec account,block of flats,0.028999999999999998,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1324.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n411986,0,Cash loans,M,Y,Y,0,364500.0,592560.0,31153.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-11023,-1302,-4875.0,-830,1.0,1,1,0,1,0,0,,2.0,1,1,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.4915945900449878,0.5797274227921155,0.0825,0.1124,0.9831,0.7688,0.0834,0.0,0.2069,0.1667,0.2083,0.0656,0.0672,0.0828,0.0,0.0,0.084,0.1166,0.9831,0.7779,0.0841,0.0,0.2069,0.1667,0.2083,0.0671,0.0735,0.0863,0.0,0.0,0.0833,0.1124,0.9831,0.7719,0.0839,0.0,0.2069,0.1667,0.2083,0.0668,0.0684,0.0843,0.0,0.0,reg oper account,block of flats,0.1107,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1032.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n450359,1,Revolving loans,F,N,Y,2,112500.0,247500.0,12375.0,247500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Office apartment,0.01885,-13706,-5818,-5930.0,-4589,,1,1,0,1,0,0,Laborers,4.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.309184742397831,0.5461420272560602,0.4329616670974407,,,0.9806,,,,,,,,,0.1513,,,,,0.9806,,,,,,,,,0.1576,,,,,0.9806,,,,,,,,,0.154,,,,,0.1217,,No,7.0,2.0,7.0,2.0,-1509.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n105873,0,Cash loans,F,Y,Y,0,157500.0,354276.0,20466.0,292500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.001276,-9747,-385,-4203.0,-2429,16.0,1,1,0,1,0,0,Sales staff,1.0,2,2,FRIDAY,4,0,0,0,0,0,0,Insurance,0.2367791454712805,0.6138419973187353,,0.0487,0.0595,0.9856,0.8028,0.0347,0.0,0.1034,0.1667,0.0833,0.0459,0.0387,0.0512,0.0,0.0009,0.0305,0.0369,0.9856,0.8105,0.0351,0.0,0.069,0.1667,0.0417,0.0222,0.0422,0.0305,0.0,0.0,0.0396,0.0541,0.9856,0.8054,0.035,0.0,0.069,0.1667,0.0417,0.0472,0.0393,0.0441,0.0,0.0,reg oper account,block of flats,0.0257,Panel,No,0.0,0.0,0.0,0.0,-493.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n334266,0,Cash loans,F,N,Y,1,202500.0,497520.0,32521.5,450000.0,Unaccompanied,State servant,Incomplete higher,Married,House / apartment,0.004849,-10001,-246,-4376.0,-2663,,1,1,0,1,0,0,Accountants,3.0,2,2,THURSDAY,12,0,0,0,1,1,0,School,0.6596762985487691,0.3943255872023558,0.20915469884100693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1728.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n154682,0,Cash loans,F,Y,Y,0,135000.0,269550.0,21739.5,225000.0,Family,Working,Higher education,Married,House / apartment,0.006670999999999999,-13001,-2272,-183.0,-2533,4.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.7224062128077645,0.7250560571620154,,0.0309,,0.9955,,,,0.069,0.1667,,0.0263,,0.0365,,0.0003,0.0315,,0.9955,,,,0.069,0.1667,,0.0269,,0.0381,,0.0003,0.0312,,0.9955,,,,0.069,0.1667,,0.0267,,0.0372,,0.0003,,block of flats,0.0379,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n133625,0,Revolving loans,F,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.016612000000000002,-7892,-233,-2761.0,-547,,1,1,1,1,1,0,Core staff,1.0,2,2,FRIDAY,14,0,0,0,1,1,0,Bank,0.2735449685106028,0.3973692501136424,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-314.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n294012,0,Cash loans,M,Y,Y,0,270000.0,677664.0,34600.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-9815,-1341,-2872.0,-2491,1.0,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6560586652294871,0.29859498978739724,0.0814,0.0585,0.9945,0.9252,0.0,0.08,0.069,0.375,,0.0,0.0664,0.08900000000000001,0.0,0.0017,0.083,0.0608,0.9945,0.9281,0.0,0.0806,0.069,0.375,,0.0,0.0725,0.0927,0.0,0.0018,0.0822,0.0585,0.9945,0.9262,0.0,0.08,0.069,0.375,,0.0,0.0676,0.0905,0.0,0.0018,reg oper account,block of flats,0.1014,Block,No,0.0,0.0,0.0,0.0,-488.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n358913,0,Cash loans,F,N,Y,0,90000.0,229500.0,11848.5,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-22998,365243,-2316.0,-4716,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.2245284444663105,0.8193176922872417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-136.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n144935,0,Cash loans,F,N,N,0,132750.0,1125000.0,32242.5,1125000.0,Unaccompanied,State servant,Incomplete higher,Single / not married,With parents,0.0105,-10100,-2916,-3877.0,-2597,,1,1,1,1,0,0,Core staff,1.0,3,3,WEDNESDAY,19,0,0,0,0,1,1,Government,0.6213703354671983,0.57972289040008,0.5028782772082183,,,0.9831,,,,0.1034,0.0417,,,,0.0172,,,,,0.9831,,,,0.1034,0.0417,,,,0.0179,,,,,0.9831,,,,0.1034,0.0417,,,,0.0175,,,,,0.0147,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2224.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n177269,1,Cash loans,M,Y,N,1,180000.0,418500.0,20484.0,418500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-15207,-920,-8824.0,-4198,6.0,1,1,1,1,1,0,,3.0,2,2,TUESDAY,12,0,0,0,0,1,1,Transport: type 2,,0.09508078155031027,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n370247,0,Cash loans,F,N,Y,0,270000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010147,-16017,-4328,-8222.0,-4344,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Self-employed,,0.7532862249062812,,0.3701,0.2414,0.9806,0.7348,0.0681,0.4,0.3448,0.3333,0.375,0.2465,0.3009,0.3531,0.0039,0.001,0.3771,0.2505,0.9806,0.7452,0.0687,0.4028,0.3448,0.3333,0.375,0.2521,0.3287,0.3679,0.0039,0.0011,0.3737,0.2414,0.9806,0.7383,0.0685,0.4,0.3448,0.3333,0.375,0.2508,0.3061,0.3594,0.0039,0.0011,org spec account,block of flats,0.3154,Panel,No,0.0,0.0,0.0,0.0,-731.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n207539,0,Cash loans,F,N,Y,1,157500.0,1078200.0,31522.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010147,-14317,-1356,-2053.0,-5085,,1,1,1,1,1,0,Core staff,2.0,2,2,WEDNESDAY,21,0,0,0,0,0,0,Kindergarten,,0.2384484310766664,0.2866524828090694,0.1907,0.0,0.9757,0.6668,0.0225,0.0,0.0345,0.1667,0.2083,0.0368,0.1555,0.0801,0.0,0.0,0.1943,0.0,0.9757,0.6798,0.0227,0.0,0.0345,0.1667,0.2083,0.0377,0.1699,0.0834,0.0,0.0,0.1926,0.0,0.9757,0.6713,0.0226,0.0,0.0345,0.1667,0.2083,0.0375,0.1582,0.0815,0.0,0.0,reg oper spec account,block of flats,0.0753,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1763.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n194706,0,Cash loans,M,N,N,2,135000.0,247275.0,19417.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-13584,-1675,-4365.0,-4591,,1,1,1,1,1,0,Laborers,4.0,2,2,SATURDAY,16,0,0,0,0,0,0,Self-employed,,0.7582828954116584,,0.2845,0.2283,0.9801,,,0.36,0.2759,0.3333,,0.2521,,0.1864,,,0.2899,0.2369,0.9801,,,0.3625,0.2759,0.3333,,0.2578,,0.1942,,,0.2873,0.2283,0.9801,,,0.36,0.2759,0.3333,,0.2565,,0.1897,,,,block of flats,0.2733,Panel,No,0.0,0.0,0.0,0.0,-3237.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n218610,0,Cash loans,F,N,Y,0,202500.0,943515.0,31180.5,814500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-20459,365243,-8368.0,-4017,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,XNA,,0.6178616983619296,0.6925590674998008,0.0433,0.0419,0.9831,,,0.0,0.0345,0.1667,,0.023,,0.0304,,0.0784,0.0441,0.0435,0.9831,,,0.0,0.0345,0.1667,,0.0235,,0.0317,,0.083,0.0437,0.0419,0.9831,,,0.0,0.0345,0.1667,,0.0234,,0.031,,0.0801,,block of flats,0.040999999999999995,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2350.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n263351,0,Cash loans,F,N,Y,0,135000.0,983299.5,41791.5,904500.0,Family,Pensioner,Higher education,Married,House / apartment,0.025164,-21766,365243,-10184.0,-5123,,1,0,0,1,0,0,,2.0,2,2,MONDAY,8,0,0,0,0,0,0,XNA,,0.5388151230990156,0.31547215492577346,0.1495,0.1041,0.9861,0.8028,0.0347,0.16,0.1379,0.3333,0.0417,0.0304,0.121,0.1532,0.0039,0.0012,0.1523,0.10800000000000001,0.9861,0.8105,0.035,0.1611,0.1379,0.3333,0.0417,0.031,0.1322,0.1597,0.0039,0.0013,0.1509,0.1041,0.9861,0.8054,0.0349,0.16,0.1379,0.3333,0.0417,0.0309,0.1231,0.156,0.0039,0.0012,org spec account,block of flats,0.1208,Panel,No,6.0,0.0,6.0,0.0,-1686.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n123561,0,Cash loans,F,Y,Y,0,90000.0,808650.0,23305.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-22631,365243,-1631.0,-4211,26.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,0.8206374822509364,0.16332743413915107,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1631.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n226535,0,Cash loans,F,Y,Y,1,135000.0,296280.0,23539.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-11789,-903,-948.0,-4413,9.0,1,1,0,1,0,1,Managers,3.0,2,2,THURSDAY,16,0,0,0,0,0,0,Trade: type 3,0.3418923094292436,0.571202042857366,0.3425288720742255,0.1113,0.298,0.998,,,0.12,0.1034,0.4167,,,,0.1524,,,0.1134,0.3092,0.998,,,0.1208,0.1034,0.4167,,,,0.1588,,,0.1124,0.298,0.998,,,0.12,0.1034,0.4167,,,,0.1551,,,,block of flats,0.1465,Panel,No,0.0,0.0,0.0,0.0,-2845.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n186582,0,Cash loans,F,N,Y,2,94500.0,521280.0,31630.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007114,-12102,-1873,-67.0,-877,,1,1,0,1,0,0,Laborers,4.0,2,2,SATURDAY,11,0,0,0,0,1,1,Self-employed,0.4967499249934748,0.2630333631834562,0.4365064990977374,0.0732,0.0848,0.9796,0.7212,0.0085,0.0,0.1379,0.1667,0.2083,0.0446,0.0572,0.0637,0.0116,0.01,0.0746,0.08800000000000001,0.9796,0.7321,0.0086,0.0,0.1379,0.1667,0.2083,0.0456,0.0624,0.0664,0.0117,0.0106,0.0739,0.0848,0.9796,0.7249,0.0085,0.0,0.1379,0.1667,0.2083,0.0454,0.0581,0.0649,0.0116,0.0102,reg oper account,block of flats,0.0523,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1728.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n262119,0,Cash loans,F,N,Y,1,225000.0,1546020.0,45333.0,1350000.0,Unaccompanied,Working,Higher education,Married,Municipal apartment,0.011703,-17066,-1829,-1714.0,-621,,1,1,0,1,1,0,Laborers,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Self-employed,,0.7027233484306864,0.2067786544915716,0.1031,0.0952,0.9786,0.7076,0.0,0.0,0.2069,0.1667,0.0417,0.0477,0.0841,0.1009,0.0,0.0,0.105,0.0988,0.9786,0.7190000000000001,0.0,0.0,0.2069,0.1667,0.0417,0.0488,0.0918,0.1051,0.0,0.0,0.1041,0.0952,0.9786,0.7115,0.0,0.0,0.2069,0.1667,0.0417,0.0485,0.0855,0.1027,0.0,0.0,reg oper spec account,block of flats,0.0794,Panel,No,0.0,0.0,0.0,0.0,-1767.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n185807,0,Cash loans,M,N,N,0,135000.0,454500.0,26091.0,454500.0,Family,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.00963,-22878,365243,-9148.0,-4238,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,XNA,,0.3536296962400711,,0.1196,0.1598,0.9871,0.8232,0.0178,0.0,0.2759,0.1667,0.2083,0.0387,0.0975,0.11199999999999999,0.0,0.0,0.1218,0.1659,0.9871,0.8301,0.018000000000000002,0.0,0.2759,0.1667,0.2083,0.0396,0.1065,0.1167,0.0,0.0,0.1207,0.1598,0.9871,0.8256,0.0179,0.0,0.2759,0.1667,0.2083,0.0394,0.0992,0.114,0.0,0.0,reg oper account,block of flats,0.0978,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3024.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n391364,0,Cash loans,M,Y,Y,1,247500.0,225000.0,12204.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-10995,-3496,-1746.0,-3631,14.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Industry: type 9,0.6080689650752548,0.6405090205305645,0.25670557243930026,0.1969,0.0721,0.9851,0.7959999999999999,,0.24,0.1034,0.625,0.6667,0.1105,0.1605,0.1224,0.0,0.0,0.2006,0.0749,0.9851,0.804,,0.2417,0.1034,0.625,0.6667,0.113,0.1754,0.1276,0.0,0.0,0.1988,0.0721,0.9851,0.7987,,0.24,0.1034,0.625,0.6667,0.1124,0.1633,0.1246,0.0,0.0,reg oper account,block of flats,0.1643,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n363056,0,Cash loans,F,N,Y,0,112500.0,545040.0,20677.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.014464,-23722,-2993,-8364.0,-4505,,1,1,1,1,1,0,Sales staff,1.0,2,2,MONDAY,4,0,0,0,0,0,0,Self-employed,,0.4283785724637826,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-851.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n421292,0,Cash loans,F,N,Y,0,90000.0,558000.0,17838.0,558000.0,Family,Pensioner,Secondary / secondary special,Separated,House / apartment,0.01885,-22711,365243,-3873.0,-4442,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,0.7540664976597741,0.6117769345841487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,3.0,3.0,0.0,-1322.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n210473,0,Cash loans,F,N,N,3,72000.0,619965.0,18126.0,517500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008865999999999999,-13258,-1106,-7101.0,-3560,,1,1,1,1,0,0,Sales staff,4.0,2,2,THURSDAY,13,0,0,0,0,1,1,Other,,0.4901791353994955,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1425.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n394146,0,Revolving loans,M,Y,Y,0,85500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-18132,-2954,-10700.0,-1670,27.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Kindergarten,,0.655155120544468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-675.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n116220,0,Cash loans,M,Y,N,0,247500.0,1288350.0,37800.0,1125000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.028663,-21892,-4577,-5561.0,-4131,9.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.4015940816492183,0.25482639037637056,0.6347055309763198,0.0619,0.051,0.9771,0.6872,0.0082,0.0,0.1379,0.1667,0.2083,0.0663,0.0504,0.0522,0.0,0.0,0.063,0.0529,0.9772,0.6994,0.0083,0.0,0.1379,0.1667,0.2083,0.0678,0.0551,0.0544,0.0,0.0,0.0625,0.051,0.9771,0.6914,0.0083,0.0,0.1379,0.1667,0.2083,0.0674,0.0513,0.0532,0.0,0.0,reg oper account,block of flats,0.0456,\"Stone, brick\",No,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n265825,0,Cash loans,F,N,Y,0,270000.0,1889739.0,75073.5,1764000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007114,-21171,365243,-8738.0,-4635,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,XNA,0.7150225227112833,0.2629358786975408,0.6212263380626669,0.0619,0.0714,0.9841,0.7824,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0504,0.0578,0.0,0.0,0.063,0.0741,0.9841,0.7909,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0551,0.0602,0.0,0.0,0.0625,0.0714,0.9841,0.7853,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0513,0.0588,0.0,0.0,reg oper account,block of flats,0.0455,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n123896,0,Cash loans,F,N,Y,2,67500.0,225000.0,10039.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018209,-13868,-3008,-2638.0,-5396,,1,1,1,1,0,0,Sales staff,3.0,3,3,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3989183291773772,0.6586740235305923,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2318.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n393722,0,Cash loans,F,N,N,0,135000.0,225000.0,6579.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.0228,-15615,-2165,-2020.0,-4632,,1,1,0,1,1,0,High skill tech staff,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Trade: type 7,0.8327839523517983,0.636567190460889,,0.1237,0.16,0.9826,0.762,0.0813,0.0,0.2759,0.1667,0.2083,0.0712,0.1009,0.10300000000000001,0.0,0.0,0.1261,0.166,0.9826,0.7713,0.08199999999999999,0.0,0.2759,0.1667,0.2083,0.0729,0.1102,0.1073,0.0,0.0,0.1249,0.16,0.9826,0.7652,0.0818,0.0,0.2759,0.1667,0.2083,0.0725,0.1026,0.1048,0.0,0.0,reg oper account,block of flats,0.1254,Panel,No,2.0,0.0,2.0,0.0,-1118.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,,,,,,\n152347,0,Cash loans,F,N,Y,0,225000.0,562491.0,27058.5,454500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.00823,-9693,-1375,-4112.0,-496,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Trade: type 7,0.6432728713243778,0.4828316495685313,0.7121551551910698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2113.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n254091,0,Cash loans,F,N,Y,0,135000.0,970380.0,28503.0,810000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-19989,-1747,-262.0,-254,,1,1,0,1,0,0,Sales staff,2.0,3,3,THURSDAY,6,0,0,0,1,1,0,Industry: type 3,0.6529851082709542,0.5152880236058021,0.4956658291397297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1785.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,6.0\n400896,0,Cash loans,F,N,N,1,202500.0,543735.0,26289.0,486000.0,Unaccompanied,Working,Higher education,Married,Municipal apartment,0.018029,-13933,-3657,-5589.0,-4357,,1,1,1,1,0,0,Core staff,3.0,3,3,TUESDAY,11,0,0,0,0,0,0,University,0.6612541821373957,0.4577671125762546,0.7490217048463391,0.1711,0.113,0.9816,,,,0.1724,0.1667,,0.1112,,0.0698,,0.1136,0.1744,0.1172,0.9816,,,,0.1724,0.1667,,0.1137,,0.0727,,0.1202,0.1728,0.113,0.9816,,,,0.1724,0.1667,,0.1131,,0.0711,,0.11599999999999999,,block of flats,0.0796,Mixed,No,0.0,0.0,0.0,0.0,-710.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n340930,0,Cash loans,F,N,N,0,135000.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.026392,-18200,-11445,-2971.0,-1719,,1,1,1,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 2,,0.4976292686126128,0.5190973382084597,0.1567,0.0709,0.9791,,,0.0,0.0345,0.1667,,,,0.0729,,0.0,0.1597,0.0736,0.9791,,,0.0,0.0345,0.1667,,,,0.076,,0.0,0.1582,0.0709,0.9791,,,0.0,0.0345,0.1667,,,,0.0742,,0.0,,block of flats,0.0574,\"Stone, brick\",No,9.0,3.0,9.0,1.0,-2910.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n399433,0,Cash loans,M,Y,Y,2,225000.0,513531.0,24835.5,459000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-16645,-5204,-8226.0,-193,9.0,1,1,0,1,0,0,Managers,4.0,2,2,SATURDAY,9,0,0,0,0,0,0,Construction,0.7347095673443352,0.6564434401257764,0.3077366963789207,0.0928,0.1004,0.9762,0.6736,0.0117,0.0,0.2069,0.1667,,0.0196,,0.0875,,0.0008,0.0945,0.1042,0.9767,0.6864,0.0118,0.0,0.2069,0.1667,,0.0199,,0.0906,,0.0,0.0937,0.1004,0.9767,0.6779999999999999,0.0118,0.0,0.2069,0.1667,,0.0199,,0.0891,,0.0,reg oper account,block of flats,0.0684,Panel,No,0.0,0.0,0.0,0.0,-988.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n166766,0,Cash loans,F,N,Y,0,72000.0,509400.0,28575.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-21621,365243,-9254.0,-4734,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,0.8844230982434571,0.7149304139385761,0.6769925032909132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2090.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n278094,0,Cash loans,F,N,Y,0,135000.0,314055.0,13963.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.011657,-17866,-1527,-10388.0,-1386,,1,1,0,1,0,0,Cleaning staff,2.0,1,1,THURSDAY,10,0,0,0,0,0,0,Other,,0.5895994173125247,0.7597121819739279,0.0918,0.1105,0.9836,,,0.0,0.2069,0.1667,,0.0192,,0.091,,0.0,0.0935,0.1146,0.9836,,,0.0,0.2069,0.1667,,0.0196,,0.0948,,0.0,0.0926,0.1105,0.9836,,,0.0,0.2069,0.1667,,0.0195,,0.0926,,0.0,,block of flats,0.0793,Panel,No,0.0,0.0,0.0,0.0,-1358.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n239648,0,Revolving loans,M,Y,Y,2,135000.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-14531,-2750,-4823.0,-4814,12.0,1,1,0,1,1,0,,4.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 1,0.8060316262890312,0.7113067695796232,0.5531646987710016,0.0124,0.0429,0.9911,,,0.0,0.069,0.0417,,0.0097,,0.0122,,0.0,0.0126,0.0446,0.9911,,,0.0,0.069,0.0417,,0.0099,,0.0127,,0.0,0.0125,0.0429,0.9911,,,0.0,0.069,0.0417,,0.0099,,0.0124,,0.0,,block of flats,0.0096,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-3013.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n305445,0,Cash loans,F,N,Y,0,67500.0,229230.0,12928.5,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.018801,-23976,365243,-12781.0,-4460,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,0.7406992420546814,0.2344766670794813,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n209615,0,Cash loans,M,N,Y,0,157500.0,1258681.5,36931.5,985500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.022625,-19309,-1209,-1529.0,-1258,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,MONDAY,11,0,0,0,0,1,1,Trade: type 3,,0.5683556680894969,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-962.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n348143,0,Cash loans,F,N,Y,1,135000.0,313438.5,19305.0,283500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-9929,-2954,-9274.0,-1528,,1,1,0,1,0,1,Laborers,3.0,2,2,THURSDAY,15,0,0,0,0,0,0,Industry: type 7,0.2643242286551221,0.2102319434595473,0.0005272652387098817,,,0.9647,,,,,,,,,0.021,,,,,0.9647,,,,,,,,,0.0219,,,,,0.9647,,,,,,,,,0.0214,,,,,0.0165,,No,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n362754,0,Cash loans,M,N,Y,0,54000.0,64692.0,5314.5,54000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.018634,-10253,-1508,-9044.0,-2471,,1,1,1,1,0,0,Laborers,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Government,0.2411111798999113,0.1753249235630957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-894.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n259117,0,Cash loans,F,Y,Y,0,180000.0,302206.5,13441.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-11972,-1361,-393.0,-2808,29.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.5921161699755355,0.6085780608005347,0.8337869986815019,0.0825,0.0619,0.9742,0.6464,0.0266,0.0,0.1379,0.1667,0.2083,0.0611,0.0672,0.0628,0.0,0.0,0.084,0.0643,0.9742,0.6602,0.0268,0.0,0.1379,0.1667,0.2083,0.0625,0.0735,0.0655,0.0,0.0,0.0833,0.0619,0.9742,0.6511,0.0267,0.0,0.1379,0.1667,0.2083,0.0621,0.0684,0.064,0.0,0.0,reg oper account,block of flats,0.0639,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2324.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n216347,0,Cash loans,F,Y,Y,1,225000.0,185877.0,9490.5,126000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.072508,-15717,-729,-2941.0,-2983,3.0,1,1,0,1,1,1,Core staff,2.0,1,1,THURSDAY,15,0,0,0,0,0,0,Government,0.742286860416219,0.7207017861700585,0.03790057351909254,0.0742,0.0602,0.9702,0.5920000000000001,0.0012,0.12,0.1379,0.1667,0.2083,0.1089,0.0605,0.1019,0.0,0.1358,0.0756,0.0625,0.9702,0.608,0.0012,0.1208,0.1379,0.1667,0.2083,0.1113,0.0661,0.1061,0.0,0.1438,0.0749,0.0602,0.9702,0.5975,0.0012,0.12,0.1379,0.1667,0.2083,0.1107,0.0616,0.1037,0.0,0.1387,reg oper spec account,block of flats,0.1103,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-616.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n434733,0,Cash loans,F,N,Y,0,180000.0,674842.5,49234.5,576000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.011657,-14419,-3579,-1004.0,-4889,,1,1,0,1,0,1,Core staff,2.0,1,1,FRIDAY,11,0,1,1,0,0,0,Kindergarten,0.681817365523288,0.6896680593074973,,0.0124,0.0,0.9692,0.5784,0.0015,0.0,0.069,0.0417,,0.0059,0.0101,0.0106,,0.0,0.0126,0.0,0.9692,0.5949,0.0015,0.0,0.069,0.0417,,0.006,0.011000000000000001,0.0111,,0.0,0.0125,0.0,0.9692,0.584,0.0015,0.0,0.069,0.0417,,0.006,0.0103,0.0108,,0.0,reg oper account,block of flats,0.0092,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1763.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n317310,0,Cash loans,F,N,Y,0,202500.0,607500.0,23670.0,607500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.008473999999999999,-14278,-2263,-1588.0,-380,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Transport: type 2,0.7928200035066042,0.5650856255453044,0.08673126364696013,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-535.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n200102,0,Cash loans,F,N,N,2,112500.0,501435.0,24385.5,414000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.010006,-14579,-3368,-7014.0,-3201,,1,1,0,1,0,0,Medicine staff,4.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.7448233559043099,,0.1196,0.0,0.9876,0.83,0.0,0.0,0.2414,0.1667,0.2083,0.0,0.0975,0.1327,0.0,0.1129,0.1218,0.0,0.9876,0.8367,0.0,0.0,0.2414,0.1667,0.2083,0.0,0.1065,0.1383,0.0,0.1195,0.1207,0.0,0.9876,0.8323,0.0,0.0,0.2414,0.1667,0.2083,0.0,0.0992,0.1351,0.0,0.1153,reg oper account,block of flats,0.1428,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1183.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n388235,0,Cash loans,F,N,Y,0,135000.0,747000.0,19836.0,747000.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.026392,-21075,365243,-10411.0,-2549,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,18,0,0,0,0,0,0,XNA,0.8627237623180998,0.7068318609362197,,0.0722,,0.9786,,,0.0,0.1379,0.1667,,0.0822,,0.0621,,,0.0735,,0.9786,,,0.0,0.1379,0.1667,,0.0841,,0.0647,,,0.0729,,0.9786,,,0.0,0.1379,0.1667,,0.0836,,0.0632,,,,block of flats,0.0494,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-126.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n126993,0,Cash loans,F,N,N,0,202500.0,1123443.0,32976.0,981000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00702,-16234,-410,-8857.0,-4598,,1,1,0,1,0,0,Medicine staff,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Medicine,,0.22121069804632568,0.3791004853998145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-86.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n153136,0,Cash loans,F,N,Y,0,45000.0,161730.0,7254.0,135000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-20004,365243,-3442.0,-3509,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,XNA,0.7805356441481484,0.5290475559555677,0.8633633824101478,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n204761,1,Cash loans,F,N,Y,2,117000.0,1017000.0,40149.0,1017000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-15161,-6707,-6062.0,-4950,,1,1,0,1,0,0,Laborers,4.0,3,3,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.4125685855521528,0.5445642510623193,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1093.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n127807,0,Cash loans,M,Y,Y,1,202500.0,1546020.0,45333.0,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-13626,-4570,-5819.0,-4596,12.0,1,1,1,1,1,0,Laborers,3.0,3,3,FRIDAY,11,0,0,0,0,0,0,Government,0.4139339033041241,0.6663014652948542,0.4294236843421945,0.0619,0.0472,0.9742,0.6464,0.0062,0.0,0.1034,0.1667,0.2083,0.0345,0.0504,0.0508,0.0,0.0,0.063,0.049,0.9742,0.6602,0.0063,0.0,0.1034,0.1667,0.2083,0.0352,0.0551,0.0529,0.0,0.0,0.0625,0.0472,0.9742,0.6511,0.0063,0.0,0.1034,0.1667,0.2083,0.0351,0.0513,0.0517,0.0,0.0,reg oper account,block of flats,0.0433,Panel,No,0.0,0.0,0.0,0.0,-1819.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n320384,0,Cash loans,M,N,Y,0,58050.0,254700.0,14751.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-24502,365243,-5713.0,-4330,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,,0.641409819129489,0.4722533429586386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n407426,0,Cash loans,F,N,Y,0,72000.0,168102.0,10413.0,148500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018634,-23504,365243,-13707.0,-4198,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.25369443629822275,,0.0722,0.0595,0.9776,0.6940000000000001,0.0085,0.0,0.1379,0.1667,0.2083,0.0,0.0588,0.0633,0.0,0.0209,0.0735,0.0618,0.9777,0.706,0.0085,0.0,0.1379,0.1667,0.2083,0.0,0.0643,0.066,0.0,0.0221,0.0729,0.0595,0.9776,0.6981,0.0085,0.0,0.1379,0.1667,0.2083,0.0,0.0599,0.0644,0.0,0.0213,reg oper account,block of flats,0.0543,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-435.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n195133,1,Revolving loans,M,N,N,1,99000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-13361,-400,-753.0,-4205,,1,1,0,1,1,0,Laborers,3.0,2,2,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 2,,0.009661209861925698,,0.1124,0.05,0.9821,0.7552,0.0217,0.12,0.1034,0.3333,0.375,0.08,0.0908,0.1045,0.0039,0.0137,0.1145,0.0519,0.9821,0.7648,0.0219,0.1208,0.1034,0.3333,0.375,0.0818,0.0992,0.1089,0.0039,0.0145,0.1135,0.05,0.9821,0.7585,0.0219,0.12,0.1034,0.3333,0.375,0.0814,0.0923,0.1064,0.0039,0.013999999999999999,not specified,block of flats,0.0971,Panel,No,0.0,0.0,0.0,0.0,-331.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n161235,0,Cash loans,F,N,Y,0,45000.0,262246.5,18252.0,243000.0,Children,Working,Secondary / secondary special,Civil marriage,House / apartment,0.01885,-16218,-9593,-7416.0,-4877,,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Postal,0.4570319142114493,0.2942599867538939,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-496.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n336910,0,Cash loans,F,N,Y,0,112500.0,787500.0,23157.0,787500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-19588,365243,-6735.0,-3128,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.6190124115180362,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1210.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n111801,1,Cash loans,F,N,Y,1,90000.0,942300.0,27679.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-17563,-4823,-6663.0,-1102,,1,1,0,1,0,0,,3.0,3,3,MONDAY,7,0,0,0,0,0,0,Housing,0.7056912345979021,0.4970060605318525,0.07698446914647789,0.1361,0.1196,0.9846,0.7892,0.0259,0.0,0.3448,0.1667,0.2083,0.1334,0.111,0.1358,0.0,0.0,0.1387,0.1241,0.9846,0.7975,0.0262,0.0,0.3448,0.1667,0.2083,0.1364,0.1212,0.1415,0.0,0.0,0.1374,0.1196,0.9846,0.792,0.0261,0.0,0.3448,0.1667,0.2083,0.1357,0.1129,0.1383,0.0,0.0,reg oper spec account,block of flats,0.1068,\"Stone, brick\",No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n136911,0,Cash loans,F,N,Y,0,83250.0,225000.0,21919.5,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-22802,365243,-5296.0,-4711,,1,0,0,1,1,0,,2.0,3,3,MONDAY,13,0,0,0,0,0,0,XNA,0.8060316262890312,0.5446835647799196,,,,0.9826,,,0.28,0.2414,0.3333,,,,0.2545,,0.0244,,,0.9826,,,0.282,0.2414,0.3333,,,,0.2652,,0.0258,,,0.9826,,,0.28,0.2414,0.3333,,,,0.2591,,0.0249,,,0.2055,,No,3.0,0.0,3.0,0.0,-2488.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n450425,0,Cash loans,F,N,Y,0,315000.0,1762110.0,48586.5,1575000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.072508,-17263,-283,-10862.0,-766,,1,1,0,1,0,0,Realty agents,2.0,1,1,MONDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.8090580633035738,0.6685633613606587,0.6528965519806539,0.6639,0.4567,0.9776,0.6940000000000001,0.4283,0.72,0.6207,0.3333,0.375,0.0,0.5405,0.6365,0.0039,0.0,0.6765,0.474,0.9777,0.706,0.4322,0.725,0.6207,0.3333,0.375,0.0,0.5904,0.6632,0.0039,0.0,0.6703,0.4567,0.9776,0.6981,0.431,0.72,0.6207,0.3333,0.375,0.0,0.5498,0.648,0.0039,0.0,reg oper account,block of flats,0.7348,Panel,No,1.0,0.0,1.0,0.0,-596.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n292312,0,Revolving loans,M,N,N,0,202500.0,180000.0,9000.0,180000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.028663,-8190,-586,-2996.0,-595,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,11,0,0,0,1,1,0,Medicine,,0.5049273969438829,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-271.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n181992,0,Cash loans,M,Y,Y,0,387000.0,622413.0,33894.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-18648,-1431,-2182.0,-2191,5.0,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Trade: type 7,0.34088464906241217,0.5404557493627058,0.1766525794312139,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-1334.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n334673,0,Cash loans,M,Y,Y,2,135000.0,1024290.0,29947.5,855000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018029,-15111,-6556,-2696.0,-4876,64.0,1,1,0,1,0,1,Laborers,4.0,3,3,SATURDAY,6,0,0,0,0,0,0,Business Entity Type 2,0.3981301155020726,0.4300232570478878,0.7530673920730478,0.0165,,0.9742,,,0.0,0.069,0.0417,,0.0,,0.0079,,0.0,0.0168,,0.9742,,,0.0,0.069,0.0417,,0.0,,0.0082,,0.0,0.0167,,0.9742,,,0.0,0.069,0.0417,,0.0,,0.008,,0.0,,block of flats,0.0099,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1767.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n125205,0,Cash loans,F,N,Y,0,157500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010966,-15710,-1056,-6404.0,-602,,1,1,0,1,0,1,Sales staff,1.0,2,2,MONDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.2035228320289013,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,1.0,1.0,1.0,1.0,-194.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n426784,0,Cash loans,F,Y,N,0,180000.0,519633.0,26527.5,481500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-21497,365243,-14691.0,-3935,18.0,1,0,0,1,0,0,,2.0,3,2,MONDAY,7,0,0,0,0,0,0,XNA,,0.7846276480374984,0.6817058776720116,0.0247,0.0,0.9742,0.6464,0.0021,0.0,0.069,0.0417,0.0833,0.0367,0.0202,0.0168,0.0,0.0,0.0252,0.0,0.9742,0.6602,0.0021,0.0,0.069,0.0417,0.0833,0.0376,0.022000000000000002,0.0175,0.0,0.0,0.025,0.0,0.9742,0.6511,0.0021,0.0,0.069,0.0417,0.0833,0.0374,0.0205,0.0171,0.0,0.0,reg oper account,block of flats,0.0144,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2528.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n149425,0,Cash loans,F,N,Y,0,115200.0,64692.0,5314.5,54000.0,Family,Working,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-19336,-3025,-4912.0,-1709,,1,1,0,1,0,0,Cooking staff,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,School,,0.5337631740639611,0.3376727217405312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-609.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n400547,0,Cash loans,F,N,N,0,90000.0,512064.0,23868.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-11529,-3172,-2905.0,-2905,,1,1,0,1,1,0,Medicine staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Medicine,,0.7066641705968951,0.5954562029091491,0.0,,0.9846,,,0.0,0.069,,,,,0.0922,,,0.0,,0.9846,,,0.0,0.069,,,,,0.096,,,0.0,,0.9846,,,0.0,0.069,,,,,0.0938,,,,,0.1145,,No,0.0,0.0,0.0,0.0,-1504.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n266786,0,Cash loans,M,N,N,1,126000.0,1223010.0,51948.0,1125000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.009549,-13211,-5594,-9029.0,-3710,,1,1,0,1,1,0,Laborers,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.6364811822093713,0.5867400085415683,0.0784,0.0557,0.9876,0.83,0.0114,0.08,0.069,0.3333,0.375,0.0623,0.0605,0.0804,0.0154,0.0042,0.0798,0.0578,0.9876,0.8367,0.0115,0.0806,0.069,0.3333,0.375,0.0637,0.0661,0.0837,0.0156,0.0044,0.0791,0.0557,0.9876,0.8323,0.0115,0.08,0.069,0.3333,0.375,0.0633,0.0616,0.0818,0.0155,0.0042,reg oper spec account,block of flats,0.0703,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n363211,0,Revolving loans,F,Y,Y,2,270000.0,675000.0,33750.0,675000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.010643,-13163,-2613,-3551.0,-4906,8.0,1,1,0,1,0,0,Accountants,4.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.4735708170467318,0.6475923972339399,0.5691487713619409,0.0376,0.032,0.9737,0.6396,0.0,0.0,0.069,0.1667,0.2083,0.0188,0.0303,0.0285,0.0019,0.0113,0.0347,0.0332,0.9737,0.6537,0.0,0.0,0.069,0.1667,0.2083,0.0138,0.0294,0.0266,0.0,0.0,0.038,0.032,0.9737,0.6444,0.0,0.0,0.069,0.1667,0.2083,0.0191,0.0308,0.028999999999999998,0.0019,0.0116,reg oper spec account,block of flats,0.0248,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-2805.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n285686,0,Cash loans,F,Y,N,0,135000.0,904500.0,29178.0,904500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Rented apartment,0.014464,-12990,-2665,-4045.0,-257,17.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,1,1,0,Trade: type 7,0.5054455334342673,0.4153977253337627,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-498.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n383478,0,Revolving loans,F,Y,N,1,135000.0,337500.0,16875.0,337500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-15740,-5870,-4540.0,-4820,21.0,1,1,0,1,0,0,Core staff,3.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,School,,0.5871049155237489,0.6528965519806539,0.1113,0.0,0.9767,,,0.0,0.0345,0.1667,,,,,,,0.1134,0.0,0.9767,,,0.0,0.0345,0.1667,,,,,,,0.1124,0.0,0.9767,,,0.0,0.0345,0.1667,,,,,,,,block of flats,0.0561,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2968.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n451166,0,Cash loans,F,N,Y,0,157500.0,697500.0,23179.5,697500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.028663,-18189,-5079,-6876.0,-1719,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Housing,0.7099779612091088,0.6002703019591401,0.7922644738669378,0.0763,0.07,0.9866,0.8164,0.0134,0.0,0.1724,0.1667,0.2083,0.0606,0.0622,0.068,0.0,0.0,0.0777,0.0726,0.9866,0.8236,0.0135,0.0,0.1724,0.1667,0.2083,0.0619,0.068,0.0708,0.0,0.0,0.077,0.07,0.9866,0.8189,0.0135,0.0,0.1724,0.1667,0.2083,0.0616,0.0633,0.0692,0.0,0.0,reg oper account,block of flats,0.0615,Panel,No,0.0,0.0,0.0,0.0,-1687.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n437225,0,Cash loans,F,Y,Y,0,225000.0,1198548.0,50913.0,1102500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.035792000000000004,-23475,365243,-1608.0,-3990,22.0,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,0.7361731827142817,0.4818269975107381,0.43473324875017305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n428185,0,Cash loans,M,Y,N,1,225000.0,675000.0,32602.5,675000.0,Family,Working,Higher education,Married,House / apartment,0.014519999999999996,-11427,-1718,-5373.0,-4105,10.0,1,1,1,1,0,0,Managers,3.0,2,2,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.4271313798278096,0.7281412993111438,0.1541,0.0871,0.9925,0.898,0.0189,0.16,0.1379,0.3542,0.3958,0.0775,0.1244,0.1554,0.0174,0.0332,0.10400000000000001,0.0641,0.9891,0.8563,0.0,0.1208,0.1034,0.3333,0.375,0.0498,0.0882,0.1046,0.0117,0.0215,0.1556,0.0871,0.9925,0.8994,0.0191,0.16,0.1379,0.3542,0.3958,0.0789,0.1266,0.1582,0.0175,0.0339,reg oper spec account,block of flats,0.1834,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1242.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n330891,0,Cash loans,F,N,Y,0,94500.0,450000.0,16294.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-19928,-2552,-7031.0,-3469,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Cleaning,,0.6729819971755705,0.4543210601605785,0.1134,0.0975,0.9791,,,0.0,0.2759,0.1667,,0.1252,,0.1044,,0.0336,0.1155,0.1012,0.9791,,,0.0,0.2759,0.1667,,0.1281,,0.1087,,0.0356,0.1145,0.0975,0.9791,,,0.0,0.2759,0.1667,,0.1274,,0.1063,,0.0343,,block of flats,0.0977,Panel,No,4.0,0.0,4.0,0.0,-1130.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n279855,0,Cash loans,F,N,N,0,112500.0,247275.0,19264.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-15212,-3495,-8393.0,-4284,,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,16,0,0,0,0,1,0,Self-employed,,0.17543561225508586,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-704.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n179265,0,Cash loans,M,Y,N,0,108000.0,203760.0,20281.5,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-22380,-1070,-2955.0,-4088,7.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Housing,0.6665696784205123,0.7668462211836569,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-2062.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n222080,0,Cash loans,F,N,N,0,81000.0,640080.0,29970.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.010032,-8858,-1294,-909.0,-1187,,1,1,0,1,1,0,Sales staff,2.0,2,2,MONDAY,7,0,0,0,0,0,0,Trade: type 7,,0.2598791389322457,,0.0093,0.0,0.9697,0.5852,0.001,0.0,0.0345,0.0417,0.0833,0.0424,0.0076,0.0077,0.0,0.0,0.0095,0.0,0.9697,0.6014,0.001,0.0,0.0345,0.0417,0.0833,0.0434,0.0083,0.008,0.0,0.0,0.0094,0.0,0.9697,0.5907,0.001,0.0,0.0345,0.0417,0.0833,0.0432,0.0077,0.0079,0.0,0.0,reg oper account,block of flats,0.0066,Wooden,No,1.0,1.0,1.0,1.0,-714.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n192610,0,Cash loans,F,N,Y,0,67500.0,389844.0,16645.5,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.007120000000000001,-23720,365243,-814.0,-4512,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.580686936821281,0.7544061731797895,0.1577,0.1296,0.9791,,,0.0,0.1724,0.1667,,0.0766,,0.0408,,0.0212,0.1607,0.1345,0.9791,,,0.0,0.1724,0.1667,,0.0783,,0.0425,,0.0224,0.1593,0.1296,0.9791,,,0.0,0.1724,0.1667,,0.0779,,0.0415,,0.0216,,block of flats,0.0459,\"Stone, brick\",No,6.0,3.0,6.0,2.0,-1395.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n174330,0,Cash loans,M,N,Y,0,99000.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010276,-17836,-1262,-10025.0,-1247,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,School,,0.6224873298574359,0.07153267628052741,0.0907,0.0,0.9796,,,0.0,0.2069,0.1667,,,,0.0833,,0.0137,0.0924,0.0,0.9796,,,0.0,0.2069,0.1667,,,,0.0868,,0.0145,0.0916,0.0,0.9796,,,0.0,0.2069,0.1667,,,,0.0848,,0.0139,,block of flats,0.0685,Panel,No,0.0,0.0,0.0,0.0,-1941.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n346691,0,Cash loans,M,Y,Y,0,135000.0,1154565.0,38286.0,945000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-23729,-1431,-5626.0,-4661,9.0,1,1,0,1,1,0,,2.0,1,1,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6748705129563277,,0.0423,0.0,0.9116,0.0,0.0105,0.0,0.1724,0.125,0.1667,0.0407,0.0303,0.0706,0.0193,0.0288,0.0431,0.0,0.9116,0.0,0.0106,0.0,0.1724,0.125,0.1667,0.0416,0.0331,0.0735,0.0195,0.0305,0.0427,0.0,0.9116,0.0,0.0106,0.0,0.1724,0.125,0.1667,0.0414,0.0308,0.0718,0.0194,0.0294,not specified,block of flats,0.0675,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-465.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n315850,0,Revolving loans,M,N,Y,0,99000.0,135000.0,6750.0,135000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.031329,-9163,365243,-9063.0,-1844,,1,0,0,1,1,0,,1.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,0.138583280872738,0.6297154226769748,,0.2082,0.1739,0.9811,0.7416,0.031,0.0,0.3793,0.1667,0.2083,0.1403,0.1681,0.2002,0.0077,0.0916,0.2122,0.1805,0.9811,0.7517,0.0312,0.0,0.3793,0.1667,0.2083,0.1435,0.1837,0.2086,0.0078,0.09699999999999999,0.2103,0.1739,0.9811,0.7451,0.0311,0.0,0.3793,0.1667,0.2083,0.1428,0.171,0.2038,0.0078,0.0936,reg oper account,block of flats,0.2058,Block,No,0.0,0.0,0.0,0.0,-1363.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n433333,0,Revolving loans,F,N,Y,0,157500.0,585000.0,29250.0,585000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.072508,-20602,-2350,-14359.0,-3854,,1,1,0,1,1,0,,1.0,1,1,SUNDAY,17,0,0,0,0,0,0,Business Entity Type 1,,0.17514161766798286,,0.0268,0.0818,0.9697,,,0.03,0.0776,0.1979,,0.0,,0.0743,,0.0732,0.0042,0.0841,0.9692,,,0.0,0.069,0.1667,,0.0,,0.0556,,0.044000000000000004,0.0057,0.0817,0.9692,,,0.0,0.069,0.1667,,0.0,,0.0625,,0.0836,,block of flats,0.0596,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1366.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n451545,0,Cash loans,M,N,Y,0,35550.0,284400.0,16456.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-23189,365243,-376.0,-3464,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.19783364880065307,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n420518,0,Cash loans,F,N,N,1,112500.0,521280.0,22216.5,450000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,With parents,0.018801,-12355,-1722,-3465.0,-3583,,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,7,0,0,0,0,0,0,Trade: type 2,0.6323714045426188,0.7265140116185768,0.4471785780453068,0.0825,0.0,0.9906,0.8708,0.051,0.0,0.0345,0.1667,0.2083,0.0144,0.0672,0.0497,0.0,0.0,0.084,0.0,0.9906,0.8759,0.0514,0.0,0.0345,0.1667,0.2083,0.0148,0.0735,0.0518,0.0,0.0,0.0833,0.0,0.9906,0.8725,0.0513,0.0,0.0345,0.1667,0.2083,0.0147,0.0684,0.0506,0.0,0.0,not specified,block of flats,0.0669,Panel,No,2.0,1.0,2.0,1.0,-1248.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n455667,0,Cash loans,M,N,Y,0,90000.0,152820.0,15241.5,135000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.035792000000000004,-24779,365243,-2575.0,-3052,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.3573422337147276,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2016.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n399014,0,Cash loans,F,N,Y,0,135000.0,1185282.0,34785.0,1035000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.031329,-16562,-8106,-8512.0,-106,,1,1,1,1,1,0,Core staff,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,School,0.7266948287100322,0.698517343876388,0.5406544504453575,0.1124,0.0766,0.9881,0.8368,0.0255,0.12,0.1034,0.3333,0.375,,0.0908,0.1167,0.0039,0.0094,0.1145,0.0795,0.9881,0.8432,0.0257,0.1208,0.1034,0.3333,0.375,,0.0992,0.1216,0.0039,0.0099,0.1135,0.0766,0.9881,0.8390000000000001,0.0256,0.12,0.1034,0.3333,0.375,,0.0923,0.1188,0.0039,0.0096,reg oper account,block of flats,0.1078,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2621.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,2.0\n396195,1,Revolving loans,F,N,N,0,117000.0,225000.0,11250.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.009549,-16182,-1158,-5844.0,-3860,,1,1,0,0,0,0,Sales staff,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.06336129382310868,0.10278201441992896,0.2375,0.0857,0.9841,0.7824,0.0365,0.0532,0.0459,0.3333,0.375,0.0792,0.1914,0.1155,0.0103,0.0029,0.1817,0.0666,0.9831,0.7779,0.0278,0.0403,0.0345,0.3333,0.375,0.0563,0.157,0.0901,0.0078,0.001,0.1801,0.0642,0.9841,0.7853,0.0278,0.04,0.0345,0.3333,0.375,0.0838,0.1462,0.0886,0.0078,0.001,reg oper spec account,block of flats,0.0704,Panel,No,1.0,0.0,1.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n323783,0,Cash loans,F,N,Y,0,112500.0,784858.5,28318.5,648000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-18199,-2561,-908.0,-1740,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,8,0,0,0,0,0,0,Agriculture,,0.6334755825279912,0.4489622731076524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1405.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n241119,0,Cash loans,F,N,Y,1,139500.0,369720.0,27031.5,292500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.006296,-13263,-1019,-6696.0,-4605,,1,1,0,1,0,0,High skill tech staff,2.0,3,3,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.5054256468836851,0.6243355108871944,0.4614823912998385,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2093.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,1.0,5.0\n175611,0,Cash loans,F,Y,Y,0,270000.0,942300.0,34749.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-9715,-2209,-4366.0,-698,6.0,1,1,0,1,0,0,,2.0,1,1,TUESDAY,18,1,0,1,0,0,0,Business Entity Type 3,,0.6582609318842191,0.5226973172821112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1096.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n193498,1,Cash loans,M,Y,Y,0,180000.0,450000.0,27193.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-12801,-1122,-990.0,-4655,9.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.3550154770914494,0.2822484337007223,0.1113,0.065,0.9861,0.8096,0.0356,0.12,0.1034,0.3333,0.375,0.0321,0.0908,0.1151,0.0,0.0,0.1134,0.0675,0.9861,0.8171,0.0359,0.1208,0.1034,0.3333,0.375,0.0328,0.0992,0.12,0.0,0.0,0.1124,0.065,0.9861,0.8121,0.0358,0.12,0.1034,0.3333,0.375,0.0326,0.0923,0.1172,0.0,0.0,reg oper account,block of flats,0.11,Panel,No,9.0,0.0,9.0,0.0,-1224.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n170726,0,Cash loans,M,Y,N,0,164898.0,239850.0,22554.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.030755,-9214,-173,-3800.0,-1764,17.0,1,1,1,1,1,0,,1.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6374975435693357,0.7136313997323308,0.1701,0.0647,0.9901,,,0.04,0.0345,0.3333,,0.0562,,0.0863,,0.0,0.1733,0.0672,0.9901,,,0.0403,0.0345,0.3333,,0.0575,,0.09,,0.0,0.1718,0.0647,0.9901,,,0.04,0.0345,0.3333,,0.0572,,0.0879,,0.0,,block of flats,0.0798,Panel,No,0.0,0.0,0.0,0.0,-5.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n131985,0,Cash loans,F,N,Y,0,94500.0,288873.0,15376.5,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.022625,-18698,-783,-9114.0,-2228,,1,1,1,1,1,0,Cooking staff,1.0,2,2,FRIDAY,9,0,0,0,1,1,0,School,0.4796015056803044,0.6202493554782303,0.34741822720026416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-405.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n434456,0,Cash loans,F,Y,Y,1,112500.0,521280.0,34339.5,450000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-12851,-996,-2844.0,-1997,4.0,1,1,1,1,1,0,Sales staff,3.0,2,2,SATURDAY,8,0,0,0,0,0,0,Trade: type 3,,0.6680495316023085,0.4614823912998385,0.0371,0.0409,0.9682,,,0.0,0.1034,0.0833,,0.0393,,0.0254,,0.0,0.0378,0.0424,0.9682,,,0.0,0.1034,0.0833,,0.0402,,0.0264,,0.0,0.0375,0.0409,0.9682,,,0.0,0.1034,0.0833,,0.04,,0.0258,,0.0,,block of flats,0.0372,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-670.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n336735,0,Cash loans,M,Y,N,0,292500.0,373140.0,39721.5,337500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.010032,-17862,365243,-11060.0,-1299,0.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,,0.4992419856274731,0.5726825047161584,0.1485,0.1121,0.9846,0.7892,0.0354,0.16,0.1379,0.3333,0.0417,0.1164,0.121,0.1517,0.0,0.0,0.0756,0.059000000000000004,0.9846,0.7975,0.0176,0.0806,0.069,0.3333,0.0417,0.0597,0.0661,0.0791,0.0,0.0,0.1499,0.1121,0.9846,0.792,0.0357,0.16,0.1379,0.3333,0.0417,0.1184,0.1231,0.1544,0.0,0.0,reg oper account,block of flats,0.2081,Panel,No,0.0,0.0,0.0,0.0,-258.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n323589,0,Cash loans,F,N,N,2,112500.0,52128.0,5895.0,45000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.018634,-11266,-3456,-9533.0,-3775,,1,1,0,1,1,0,,3.0,2,2,WEDNESDAY,19,0,0,0,0,0,0,Bank,0.5575286036889245,0.6602160636620369,0.3233112448967859,0.1299,0.1058,0.9806,0.7348,0.1011,0.16,0.1379,0.3333,0.375,0.0771,0.1042,0.1422,0.0077,0.0108,0.1324,0.1098,0.9806,0.7452,0.102,0.1611,0.1379,0.3333,0.375,0.0788,0.1139,0.1482,0.0078,0.0114,0.1312,0.1058,0.9806,0.7383,0.1017,0.16,0.1379,0.3333,0.375,0.0784,0.106,0.1448,0.0078,0.011000000000000001,reg oper account,block of flats,0.1695,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2210.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n119858,0,Cash loans,M,Y,N,0,99000.0,765000.0,24151.5,765000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.005313,-10238,-156,-6824.0,-2657,23.0,1,1,0,1,1,0,Laborers,2.0,2,2,WEDNESDAY,13,0,0,0,1,1,0,Industry: type 3,,0.5364124346759702,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-955.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n350109,0,Cash loans,F,Y,Y,0,292500.0,2085120.0,72477.0,1800000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-19439,-3126,-13425.0,-2731,5.0,1,1,0,1,1,0,Laborers,2.0,1,1,TUESDAY,18,0,1,1,0,0,0,Business Entity Type 3,,0.7237856865197726,,0.10099999999999999,0.0441,0.9801,0.728,0.0239,0.08,0.0345,0.5417,0.5833,0.0,0.0824,0.0843,0.0,0.0,0.1029,0.0458,0.9801,0.7387,0.0241,0.0806,0.0345,0.5417,0.5833,0.0,0.09,0.0879,0.0,0.0,0.102,0.0441,0.9801,0.7316,0.0241,0.08,0.0345,0.5417,0.5833,0.0,0.0838,0.0859,0.0,0.0,reg oper account,block of flats,0.0794,Block,No,0.0,0.0,0.0,0.0,-794.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n174932,0,Cash loans,M,Y,Y,0,135000.0,203760.0,19980.0,180000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-19204,365243,-2793.0,-2755,11.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.7179955528847454,0.7862666146611379,,,0.9826,,,,,,,,,0.0658,,,,,0.9826,,,,,,,,,0.0685,,,,,0.9826,,,,,,,,,0.0669,,,,,0.0905,,No,1.0,1.0,1.0,1.0,-2728.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n219117,0,Cash loans,M,Y,N,0,80302.5,1147500.0,29007.0,1147500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018801,-20597,-153,-4525.0,-3980,14.0,1,1,1,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Trade: type 3,,0.4201209515551245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-125.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,,,,,,\n166754,0,Cash loans,M,Y,N,0,225000.0,1762110.0,46611.0,1575000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.020713,-12166,-3083,-1754.0,-4723,9.0,1,1,0,1,0,0,Laborers,2.0,3,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.23063246038054394,0.1025635384298422,0.7136313997323308,0.2969,0.0794,0.9866,0.8164,0.0845,0.08,0.0345,0.3333,0.0417,0.0556,0.2446,0.0758,0.0,0.0,0.3025,0.0824,0.9866,0.8236,0.0853,0.0806,0.0345,0.3333,0.0417,0.0569,0.2672,0.079,0.0,0.0,0.2998,0.0794,0.9866,0.8189,0.0851,0.08,0.0345,0.3333,0.0417,0.0566,0.2488,0.0772,0.0,0.0,reg oper account,block of flats,0.1059,Panel,No,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n222772,0,Cash loans,F,Y,N,0,90000.0,719860.5,48298.5,679500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-11376,-1191,-10728.0,-1178,1.0,1,1,1,1,1,0,,2.0,2,2,SUNDAY,16,0,0,0,0,0,0,Self-employed,,0.6851666761632537,0.5172965813614878,0.0371,0.0256,0.9841,,,0.04,0.0345,0.3333,,0.0414,,0.0411,,0.0,0.0378,0.0265,0.9841,,,0.0403,0.0345,0.3333,,0.0423,,0.0428,,0.0,0.0375,0.0256,0.9841,,,0.04,0.0345,0.3333,,0.0421,,0.0418,,0.0,,block of flats,0.0323,Panel,No,3.0,0.0,3.0,0.0,-1034.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n402539,0,Revolving loans,F,N,N,0,180000.0,450000.0,22500.0,450000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.018801,-23597,365243,-8472.0,-1293,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.6336634226510744,0.622922000268356,0.2227,0.1206,0.9786,,,0.24,0.2069,0.3333,,0.1337,,0.2252,,0.0009,0.2269,0.1251,0.9786,,,0.2417,0.2069,0.3333,,0.1367,,0.2346,,0.001,0.2248,0.1206,0.9786,,,0.24,0.2069,0.3333,,0.136,,0.2292,,0.001,,block of flats,0.1771,Panel,No,13.0,0.0,13.0,0.0,-192.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n282370,0,Cash loans,M,Y,Y,2,157500.0,675000.0,32602.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-12990,-2023,-12960.0,-2645,7.0,1,1,0,1,1,0,Laborers,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 2,,0.7025588666691702,0.39449540531239935,1.0,0.6077,0.9816,0.7484,,1.0,0.9309999999999999,0.3333,0.0,0.8698,,0.6451,,0.0036,1.0,0.6307,0.9816,0.7583,,1.0,0.9309999999999999,0.3333,0.0,0.8896,,0.6721,,0.0038,1.0,0.6077,0.9816,0.7518,,1.0,0.9309999999999999,0.3333,0.0,0.8849,,0.6567,,0.0036,,block of flats,1.0,Panel,No,1.0,0.0,1.0,0.0,-1637.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n322385,0,Cash loans,F,N,Y,3,135000.0,288873.0,14179.5,238500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.014464,-11091,-3368,-5134.0,-2205,,1,1,0,1,0,0,Core staff,5.0,2,2,FRIDAY,8,0,0,0,0,0,0,School,,0.5822408934736459,0.6545292802242897,0.1113,0.0971,0.9901,0.8640000000000001,,0.0,0.1034,0.2083,0.25,0.0478,0.0908,0.0878,0.0,0.0473,0.1134,0.1008,0.9901,0.8693,,0.0,0.1034,0.2083,0.25,0.0489,0.0992,0.0915,0.0,0.0501,0.1124,0.0971,0.9901,0.8658,,0.0,0.1034,0.2083,0.25,0.0486,0.0923,0.0894,0.0,0.0483,reg oper account,block of flats,0.0793,Panel,No,4.0,0.0,4.0,0.0,-696.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n302959,0,Cash loans,F,N,Y,0,45000.0,177903.0,7965.0,148500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010556,-21154,365243,-109.0,-4204,,1,0,0,1,0,0,,1.0,3,3,MONDAY,9,0,0,0,0,0,0,XNA,,0.5102672699704127,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1780.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,6.0\n310442,0,Cash loans,F,N,N,0,157500.0,708939.0,25591.5,612000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006305,-12253,-2593,-1045.0,-3114,,1,1,0,1,1,0,,2.0,3,3,MONDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.3779842514086237,0.4818761347491694,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-435.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n438268,0,Cash loans,F,N,Y,0,171000.0,888840.0,29506.5,675000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.005002,-18432,-3502,-9411.0,-1967,,1,1,0,1,1,0,Core staff,2.0,3,3,MONDAY,18,0,0,0,0,0,0,Kindergarten,,0.4039329584845779,0.7801436381572275,0.1237,0.1128,0.9871,0.8232,0.0094,0.1,0.1552,0.25,0.2083,0.1,0.0504,0.092,0.0,0.004,0.063,0.0666,0.9871,0.8301,0.0095,0.0,0.1379,0.1667,0.2083,0.077,0.0551,0.0642,0.0,0.0,0.1249,0.1128,0.9871,0.8256,0.0095,0.1,0.1552,0.25,0.2083,0.1017,0.0513,0.0937,0.0,0.004,reg oper account,block of flats,0.16,Panel,No,0.0,0.0,0.0,0.0,-2793.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n248610,0,Cash loans,F,N,Y,0,99000.0,119925.0,11812.5,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.007273999999999998,-24116,365243,-6279.0,-4375,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.5993097102070701,0.5316861425197883,0.1134,0.0627,0.9831,0.7688,0.0214,0.12,0.1034,0.3333,,,0.0925,0.1255,0.0,0.0,0.1155,0.0651,0.9831,0.7779,0.0216,0.1208,0.1034,0.3333,,,0.10099999999999999,0.1308,0.0,0.0,0.1145,0.0627,0.9831,0.7719,0.0215,0.12,0.1034,0.3333,,,0.0941,0.1278,0.0,0.0,reg oper account,block of flats,0.0987,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1517.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n327236,0,Revolving loans,M,N,Y,0,450000.0,1350000.0,67500.0,1350000.0,Family,Commercial associate,Higher education,Married,Office apartment,0.010643,-18165,-5433,-5006.0,-1618,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Industry: type 1,,0.6010623414371019,,0.0619,0.0632,0.9762,0.6736,0.0053,0.0,0.1034,0.1667,0.2083,0.0366,0.0504,0.0502,0.0,0.0,0.063,0.0656,0.9762,0.6864,0.0053,0.0,0.1034,0.1667,0.2083,0.0374,0.0551,0.0523,0.0,0.0,0.0625,0.0632,0.9762,0.6779999999999999,0.0053,0.0,0.1034,0.1667,0.2083,0.0372,0.0513,0.0511,0.0,0.0,reg oper account,block of flats,0.0423,\"Stone, brick\",No,,,,,-370.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,8.0,0.0\n183853,0,Cash loans,F,Y,Y,0,67500.0,152820.0,8770.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-13962,-767,-7722.0,-4630,8.0,1,1,1,1,1,0,Sales staff,2.0,2,2,THURSDAY,14,0,0,0,0,1,1,Self-employed,0.3788824843464826,0.4689298651618411,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-428.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n307926,0,Cash loans,F,N,N,0,117000.0,152820.0,10210.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17087,-7310,-9527.0,-625,,1,1,1,1,1,0,Medicine staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Medicine,,0.7609899766549443,0.25259869783397665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-692.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n259608,0,Cash loans,M,N,Y,1,135000.0,566055.0,18387.0,472500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-22847,365243,-10369.0,-4518,,1,0,0,1,0,0,,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.6110599654178636,0.33125086459090186,0.0825,,0.9861,0.8096,,0.0,0.2069,0.1667,0.2083,,,,,,0.084,,0.9861,0.8171,,0.0,0.2069,0.1667,0.2083,,,,,,0.0833,,0.9861,0.8121,,0.0,0.2069,0.1667,0.2083,,,,,,reg oper account,block of flats,0.0654,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n117456,0,Cash loans,F,N,Y,1,193500.0,1082214.0,31770.0,945000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-15254,-1660,-4989.0,-4483,,1,1,0,1,0,0,Sales staff,2.0,3,3,SUNDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.3682526125554973,0.6452132653325579,0.5902333386185574,0.0082,0.0,0.9687,0.5716,0.0013,0.0,0.0345,0.0417,0.0833,0.0306,0.0067,0.0107,0.0,0.0,0.0084,0.0,0.9687,0.5884,0.0013,0.0,0.0345,0.0417,0.0833,0.0313,0.0073,0.0111,0.0,0.0,0.0083,0.0,0.9687,0.5773,0.0013,0.0,0.0345,0.0417,0.0833,0.0311,0.0068,0.0109,0.0,0.0,reg oper spec account,block of flats,0.0084,Block,Yes,5.0,0.0,5.0,0.0,-1735.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n114184,0,Cash loans,F,Y,Y,1,157500.0,652500.0,30366.0,652500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-13829,-3248,-822.0,-4318,9.0,1,1,0,1,1,0,High skill tech staff,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Other,,0.5644782366210463,0.6313545365850379,0.132,,0.9841,,,0.0,0.069,0.1667,,,,0.057999999999999996,,0.0,0.1345,,0.9841,,,0.0,0.069,0.1667,,,,0.0605,,0.0,0.1332,,0.9841,,,0.0,0.069,0.1667,,,,0.0591,,0.0,,block of flats,0.0456,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-747.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,6.0\n117542,0,Cash loans,F,N,Y,0,135000.0,502497.0,30051.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018634,-20739,-1210,-1533.0,-4161,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,15,0,0,0,0,1,1,Industry: type 4,,0.4002318872145721,0.6738300778602003,,,0.9757,,,,,,,,,0.0558,,,,,0.9757,,,,,,,,,0.0582,,,,,0.9757,,,,,,,,,0.0568,,,,,0.0484,,No,1.0,0.0,1.0,0.0,-901.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,4.0\n157729,0,Cash loans,F,N,Y,0,135000.0,417708.0,26694.0,369000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019689,-19003,-7015,-11459.0,-2274,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,Transport: type 2,,0.6223537114160583,0.5709165417729987,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n263060,0,Cash loans,F,Y,Y,1,423000.0,1443946.5,61308.0,1246500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-15676,-4436,-4237.0,-2067,3.0,1,1,0,1,1,0,Core staff,3.0,1,1,THURSDAY,15,0,0,0,0,0,0,Security Ministries,0.8311850409917517,0.6997133201729184,0.6785676886853644,0.1381,0.0292,0.9886,0.8436,0.0321,0.1332,0.1034,0.4304,0.4721,0.0463,0.1121,0.1413,0.0025,0.0161,0.084,0.0,0.9876,0.8367,0.0155,0.0806,0.0345,0.375,0.4167,0.0244,0.0735,0.0857,0.0039,0.0,0.0854,0.0342,0.9891,0.8524,0.0315,0.08,0.069,0.375,0.4167,0.053,0.0693,0.0878,0.0039,0.002,reg oper account,block of flats,0.2286,Panel,No,1.0,0.0,1.0,0.0,-642.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n377425,0,Cash loans,F,Y,Y,0,157500.0,495972.0,23989.5,414000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-19240,-3239,-1352.0,-2783,13.0,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Trade: type 7,,0.5189792885849057,0.7209441499436497,0.0845,0.0195,0.9965,0.9524,0.0246,0.08,0.069,0.375,0.0,0.0686,0.0689,0.0873,0.0,0.0,0.0861,0.0203,0.9965,0.9543,0.0248,0.0806,0.069,0.375,0.0,0.0701,0.0753,0.0909,0.0,0.0,0.0854,0.0195,0.9965,0.953,0.0248,0.08,0.069,0.375,0.0,0.0698,0.0701,0.0888,0.0,0.0,not specified,block of flats,0.0821,Panel,No,1.0,0.0,1.0,0.0,-2431.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,5.0\n334752,0,Cash loans,F,Y,Y,0,112500.0,1223010.0,48631.5,1125000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.018209,-15805,-1280,-4654.0,-2330,17.0,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,9,0,0,0,0,1,1,School,0.4762799198260174,0.4985476933905983,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,1.0,-1560.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n181260,0,Revolving loans,M,Y,Y,2,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-9519,-27,-255.0,-2188,10.0,1,1,1,1,1,0,Core staff,4.0,1,1,SUNDAY,16,0,1,1,0,1,1,Bank,0.1574024120529572,0.7282658344315223,0.7850520263728172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-674.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n128477,0,Cash loans,F,N,Y,0,180000.0,519633.0,38979.0,481500.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.00702,-16337,-2142,-4431.0,-4867,,1,1,0,1,0,0,Sales staff,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,Self-employed,0.4475680436519045,0.7551549436964353,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3167.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n261459,0,Cash loans,F,N,N,0,49500.0,454500.0,13419.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-22444,365243,-11760.0,-4618,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,XNA,0.7590692004116899,0.331963508739294,0.6986675550534175,0.2866,0.2038,0.9841,0.7824,0.1409,0.32,0.2759,0.3333,0.375,0.1043,0.2303,0.2821,0.0154,0.0027,0.292,0.2115,0.9841,0.7909,0.1422,0.3222,0.2759,0.3333,0.375,0.1067,0.2516,0.2939,0.0156,0.0028,0.2894,0.2038,0.9841,0.7853,0.1418,0.32,0.2759,0.3333,0.375,0.1062,0.2343,0.2872,0.0155,0.0027,reg oper account,block of flats,0.2989,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n179338,0,Revolving loans,F,N,Y,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.028663,-8072,-939,-2301.0,-733,,1,1,0,1,0,0,Core staff,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,,0.40616732760234403,,0.1856,0.1377,0.9861,,,0.2,0.1724,0.3333,,0.0268,,0.1977,,0.0,0.1891,0.1429,0.9861,,,0.2014,0.1724,0.3333,,0.0274,,0.20600000000000002,,0.0,0.1874,0.1377,0.9861,,,0.2,0.1724,0.3333,,0.0273,,0.2013,,0.0,,block of flats,0.1792,Panel,No,1.0,0.0,1.0,0.0,-374.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n147506,0,Revolving loans,F,N,Y,0,94500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-19995,-4079,-6895.0,-3224,,1,1,1,1,0,0,Sales staff,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Government,,0.3022692805440713,0.07905969599457675,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2134.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n442974,1,Cash loans,M,N,Y,1,180000.0,183384.0,12384.0,162000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-15452,-719,-2722.0,-4671,,1,1,1,1,0,0,Laborers,3.0,3,3,THURSDAY,8,0,1,1,0,1,1,Business Entity Type 2,,0.4189759999792775,0.28812959991785075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-753.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n162047,0,Cash loans,F,N,N,1,54000.0,170640.0,12366.0,135000.0,Unaccompanied,Pensioner,Lower secondary,Married,Municipal apartment,0.007120000000000001,-21941,365243,-5047.0,-5070,,1,0,0,1,0,0,,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.21597785487637366,0.28371188263500075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-277.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n222104,0,Cash loans,M,N,Y,2,225000.0,805536.0,32076.0,720000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-13158,-1471,-1812.0,-1815,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.19336728849544832,0.5970495881356092,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1054.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,5.0\n343377,0,Cash loans,F,N,Y,0,180000.0,619749.0,46467.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.007114,-22281,-1585,-2401.0,-4699,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Medicine,0.7171090767747121,0.5265023994594884,0.5513812618027899,,,0.9921,0.8912,,,,,,,,0.0791,,0.1675,,,0.9921,0.8955,,,,,,,,0.0824,,0.1774,,,0.9921,0.8927,,,,,,,,0.0805,,0.171,reg oper account,block of flats,0.0986,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-62.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n268710,0,Cash loans,F,N,N,0,112500.0,526491.0,29529.0,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.008625,-17342,-3006,-12013.0,-712,,1,1,0,1,0,0,Managers,1.0,2,2,TUESDAY,18,0,0,0,0,0,0,Self-employed,0.6736938272188653,0.6445672559985497,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3216.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n165568,0,Cash loans,M,Y,Y,1,229500.0,1024740.0,55588.5,900000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.031329,-20540,365243,-7324.0,-167,4.0,1,0,0,1,0,0,,3.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.6467235559137112,0.2807895743848605,,,0.9896,,,,,,,,,0.2104,,,,,0.9896,,,,,,,,,0.2192,,,,,0.9896,,,,,,,,,0.2142,,,,,0.2028,,No,1.0,0.0,1.0,0.0,-3232.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n117390,0,Revolving loans,M,Y,Y,1,202500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Married,With parents,0.019101,-10098,-546,-2928.0,-2763,18.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Transport: type 4,0.4382116423815096,0.7604884679104944,0.5989262182569273,0.1175,,0.9811,,,,0.3103,0.125,,,,,,,0.1197,,0.9811,,,,0.3103,0.125,,,,,,,0.1187,,0.9811,,,,0.3103,0.125,,,,,,,,block of flats,0.0939,,No,0.0,0.0,0.0,0.0,-396.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n430353,0,Cash loans,F,N,N,2,126000.0,499500.0,16240.5,499500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010032,-13861,-1393,-2854.0,-4376,,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,5,0,0,0,0,0,0,Trade: type 7,0.5040622758067904,0.6133546352220471,0.6263042766749393,0.1546,0.1196,0.9891,0.8436,0.0325,0.2,0.1724,0.375,0.4167,0.0014,0.1261,0.1886,0.0,0.0,0.1576,0.1242,0.9891,0.8497,0.0328,0.2014,0.1724,0.375,0.4167,0.0014,0.1377,0.1965,0.0,0.0,0.1561,0.1196,0.9891,0.8457,0.0327,0.2,0.1724,0.375,0.4167,0.0014,0.1283,0.192,0.0,0.0,reg oper account,block of flats,0.1661,Panel,No,0.0,0.0,0.0,0.0,-753.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n310705,1,Cash loans,M,N,N,0,54000.0,95940.0,10309.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-13822,-669,-8159.0,-3396,,1,1,1,1,1,0,Medicine staff,1.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Medicine,,0.25537074105223,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-412.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n443813,0,Cash loans,F,N,Y,1,90000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-11513,-175,-2147.0,-29,,1,1,1,1,1,0,Sales staff,3.0,2,2,TUESDAY,14,0,0,0,0,0,0,Self-employed,0.26210768014354324,0.4618027168739223,,0.0763,0.0422,0.9881,0.8368,,0.08,0.069,0.3333,0.375,0.0097,0.0605,0.0787,0.0077,0.0681,0.0777,0.0438,0.9881,0.8432,,0.0806,0.069,0.3333,0.375,0.0099,0.0661,0.08199999999999999,0.0078,0.0721,0.077,0.0422,0.9881,0.8390000000000001,,0.08,0.069,0.3333,0.375,0.0098,0.0616,0.0801,0.0078,0.0695,reg oper spec account,block of flats,0.0767,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n385725,0,Cash loans,F,N,N,0,85500.0,454500.0,26091.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.001417,-21749,365243,-3085.0,-3526,,1,0,0,1,0,1,,2.0,2,2,MONDAY,6,0,0,0,0,0,0,XNA,,0.5095568529636952,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1452.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n302698,0,Cash loans,F,N,Y,0,427500.0,1508899.5,61587.0,1408500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-19301,365243,-1506.0,-2853,,1,0,0,1,0,1,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.05482647038240991,0.2314393514998941,0.3062,0.0681,0.9826,0.762,0.0964,0.16,0.069,0.4583,0.5,0.0,0.2429,0.2287,0.0309,0.1633,0.312,0.0707,0.9826,0.7713,0.0973,0.1611,0.069,0.4583,0.5,0.0,0.2654,0.2383,0.0311,0.1729,0.3092,0.0681,0.9826,0.7652,0.09699999999999999,0.16,0.069,0.4583,0.5,0.0,0.2471,0.2328,0.0311,0.1667,reg oper account,block of flats,0.2681,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-15.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,2.0\n439864,1,Cash loans,F,N,Y,1,207000.0,970380.0,28503.0,810000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.010032,-12903,-385,-3715.0,-5161,,1,1,0,1,0,0,Accountants,2.0,2,2,SUNDAY,8,0,0,0,0,0,0,Bank,0.3076594162835093,0.7558698880009765,0.3425288720742255,0.0856,0.0225,0.9767,0.6804,0.0373,0.0,0.069,0.125,0.1667,0.0165,0.0698,0.0293,0.0,0.0,0.0252,0.0,0.9727,0.6406,0.0376,0.0,0.0345,0.0833,0.125,0.0015,0.022000000000000002,0.0271,0.0,0.0,0.0864,0.0225,0.9767,0.6847,0.0375,0.0,0.069,0.125,0.1667,0.0168,0.071,0.0298,0.0,0.0,reg oper account,block of flats,0.0232,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1672.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,5.0\n399707,0,Cash loans,F,Y,Y,0,247500.0,664569.0,32098.5,594000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.04622,-16872,-3870,-3212.0,-342,65.0,1,1,0,1,1,0,,1.0,1,1,MONDAY,11,0,0,0,0,0,0,Transport: type 4,,0.6199917870204851,0.2405414172860865,0.0825,0.0796,0.9752,0.66,0.008,0.0,0.1379,0.1667,0.2083,0.0315,0.0672,0.0703,0.0,0.0,0.084,0.0826,0.9752,0.6733,0.0081,0.0,0.1379,0.1667,0.2083,0.0322,0.0735,0.0732,0.0,0.0,0.0833,0.0796,0.9752,0.6645,0.008,0.0,0.1379,0.1667,0.2083,0.032,0.0684,0.0715,0.0,0.0,reg oper account,block of flats,0.0597,Panel,No,1.0,0.0,1.0,0.0,-1697.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n116502,0,Cash loans,M,N,Y,1,450000.0,1206954.0,39064.5,945000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-12283,-3345,-1165.0,-2745,,1,1,0,1,0,1,Managers,3.0,1,1,FRIDAY,10,0,1,1,0,0,0,Business Entity Type 3,0.5798200404400222,0.7471231774666682,,0.2753,0.121,0.997,0.9592,0.1167,0.32,0.1379,0.6667,0.7083,0.0227,0.2244,0.2971,0.0,0.0,0.2805,0.1256,0.997,0.9608,0.1177,0.3222,0.1379,0.6667,0.7083,0.0232,0.2452,0.3095,0.0,0.0,0.2779,0.121,0.997,0.9597,0.1174,0.32,0.1379,0.6667,0.7083,0.0231,0.2283,0.3024,0.0,0.0,reg oper account,block of flats,0.2974,Panel,No,0.0,0.0,0.0,0.0,-519.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n156571,0,Cash loans,F,N,Y,0,67500.0,315000.0,31284.0,315000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.009549,-19751,365243,-5648.0,-3233,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.5936886166221982,0.7992967832109371,0.0103,0.0445,0.9921,0.8912,0.0051,0.0,0.0345,0.0417,0.0417,0.0219,0.0084,0.0135,0.0,0.0,0.0105,0.0462,0.9921,0.8955,0.0051,0.0,0.0345,0.0417,0.0417,0.0224,0.0092,0.013999999999999999,0.0,0.0,0.0104,0.0445,0.9921,0.8927,0.0051,0.0,0.0345,0.0417,0.0417,0.0223,0.0086,0.0137,0.0,0.0,reg oper account,block of flats,0.0134,Block,No,1.0,0.0,1.0,0.0,-2356.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n328324,0,Cash loans,M,Y,Y,1,225000.0,296280.0,21690.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019101,-12620,-172,-1958.0,-4410,4.0,1,1,0,1,0,1,High skill tech staff,3.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.8271395473052894,0.6997271040866192,0.25396280933631177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3064.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n294877,0,Cash loans,F,Y,Y,0,54000.0,240660.0,8775.0,157500.0,Family,State servant,Higher education,Married,House / apartment,0.030755,-18002,-1654,-9224.0,-1546,25.0,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Government,,0.5259190491669743,0.7180328113294772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1734.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n330327,0,Cash loans,F,N,N,0,180000.0,790830.0,44158.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010006,-13848,-3091,-2970.0,-3096,,1,1,1,1,1,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.4801023333645441,0.6486795685731173,0.656158373001177,0.1969,0.0706,0.9876,0.762,0.1245,0.24,0.1034,0.625,0.6667,,0.1605,0.2093,0.0,0.0,0.2006,0.0732,0.9876,0.7713,0.1256,0.2417,0.1034,0.625,0.6667,,0.1754,0.2181,0.0,0.0,0.1988,0.0706,0.9876,0.7652,0.1253,0.24,0.1034,0.625,0.6667,,0.1633,0.2131,0.0,0.0,reg oper account,block of flats,0.2327,Panel,No,6.0,3.0,6.0,3.0,-1172.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n290754,0,Cash loans,M,Y,N,0,180000.0,1082214.0,35041.5,945000.0,\"Spouse, partner\",Working,Higher education,Single / not married,With parents,0.035792000000000004,-10060,-1894,-4021.0,-2610,10.0,1,1,0,1,0,0,Core staff,1.0,2,2,WEDNESDAY,13,0,1,1,0,1,1,Police,,0.13863697462502467,0.6195277080511546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,1.0\n102647,0,Cash loans,F,Y,Y,0,63000.0,609187.5,26964.0,544500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21715,365243,-11444.0,-4673,10.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,14,0,0,0,1,0,0,XNA,,0.6580985801226856,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-893.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n219357,0,Cash loans,F,Y,Y,1,157500.0,270000.0,18171.0,270000.0,Other_B,Working,Higher education,Single / not married,With parents,0.0228,-11995,-224,-853.0,-3556,8.0,1,1,0,1,1,0,Accountants,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.5595861123316737,0.6526856672137916,0.6610235391308081,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,-1173.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n108531,0,Cash loans,M,Y,Y,0,261000.0,675000.0,34596.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-13478,-4896,-3258.0,-4728,8.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Business Entity Type 2,,0.4045491242329184,0.7352209993926424,0.1031,0.0284,0.9767,0.6804,0.0116,0.0,0.2069,0.1667,0.2083,0.075,0.0807,0.0858,0.0154,0.0116,0.105,0.0294,0.9767,0.6929,0.0117,0.0,0.2069,0.1667,0.2083,0.0767,0.0882,0.0894,0.0156,0.0123,0.1041,0.0284,0.9767,0.6847,0.0117,0.0,0.2069,0.1667,0.2083,0.0763,0.0821,0.0873,0.0155,0.0119,reg oper account,block of flats,0.0764,\"Stone, brick\",No,4.0,1.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n311713,0,Cash loans,F,N,Y,0,90000.0,390960.0,16695.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010643,-16002,-1820,-8978.0,-4352,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.6372934725052222,0.5479138854857379,0.4206109640437848,0.4454,0.3036,0.9876,0.83,0.0,0.48,0.4138,0.3333,0.375,0.2719,0.3631,0.4665,0.0,0.0,0.4538,0.3151,0.9876,0.8367,0.0,0.4834,0.4138,0.3333,0.375,0.2781,0.3967,0.4861,0.0,0.0,0.4497,0.3036,0.9876,0.8323,0.0,0.48,0.4138,0.3333,0.375,0.2766,0.3694,0.4749,0.0,0.0,reg oper spec account,block of flats,0.3669,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n210494,0,Cash loans,M,Y,Y,0,427500.0,728460.0,40675.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-20421,-2035,-9298.0,-3664,13.0,1,1,0,1,0,0,Security staff,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 1,0.763909536012761,0.6379168773456432,0.4329616670974407,0.0773,,0.9871,,,,,0.1667,,,,0.0678,,,0.0788,,0.9871,,,,,0.1667,,,,0.0707,,,0.0781,,0.9871,,,,,0.1667,,,,0.0691,,,,block of flats,0.0531,Panel,No,0.0,0.0,0.0,0.0,-1660.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n296564,0,Cash loans,M,N,N,0,135000.0,332946.0,18189.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.02461,-8917,-904,-457.0,-1610,,1,1,0,1,0,0,Drivers,1.0,2,2,FRIDAY,16,0,0,0,1,1,0,Business Entity Type 2,0.14561333809979324,0.15920446095815635,0.22383131747015547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-803.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n329860,0,Cash loans,F,N,Y,0,315000.0,747558.0,21555.0,535500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-21685,365243,-1060.0,-4854,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,,0.3436635317123039,0.324891229465852,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-695.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n172985,0,Cash loans,F,N,Y,4,90000.0,625356.0,18414.0,522000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.002134,-13496,-4832,-3492.0,-1902,,1,1,0,1,1,0,Medicine staff,6.0,3,3,MONDAY,9,0,0,0,0,0,0,Medicine,,0.6222252146124853,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n306762,0,Cash loans,F,N,N,1,81000.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-12035,-1480,-2559.0,-2559,,1,1,0,1,0,0,Cooking staff,3.0,2,2,SATURDAY,18,0,0,0,1,1,0,Self-employed,,0.1958182626607685,0.7850520263728172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n252353,0,Cash loans,M,N,Y,0,112500.0,381528.0,27778.5,315000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.014464,-8928,-1701,-8928.0,-1584,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,5,0,0,0,0,0,0,Self-employed,0.13465580965487736,0.4283196715256857,0.43473324875017305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-28.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n301577,0,Cash loans,F,N,N,0,135000.0,848745.0,37386.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21415,-9085,-12113.0,-4471,,1,1,0,0,0,0,Core staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Kindergarten,,0.3950831316851284,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n134804,0,Cash loans,F,Y,Y,1,238500.0,1078200.0,38331.0,900000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.018029,-15292,-732,-854.0,-4296,18.0,1,1,0,1,0,0,Accountants,3.0,3,3,MONDAY,9,0,0,0,0,0,0,Business Entity Type 1,0.5674609187558194,0.6470332546050739,0.3910549766342248,0.0722,0.0405,0.9727,,,0.0,0.1724,0.125,,,,0.0581,,0.0202,0.0735,0.042,0.9727,,,0.0,0.1724,0.125,,,,0.0605,,0.0213,0.0729,0.0405,0.9727,,,0.0,0.1724,0.125,,,,0.0591,,0.0206,,block of flats,0.0501,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1570.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n106305,0,Cash loans,M,N,Y,2,225000.0,814041.0,23931.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.007114,-11370,-1188,-5169.0,-3847,,1,1,0,1,0,0,Drivers,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Self-employed,,0.5729258140795367,0.2650494299443805,0.0608,0.0674,0.9881,,,0.0,0.1379,0.2083,,0.0333,,0.0706,,0.0073,0.062,0.0699,0.9881,,,0.0,0.1379,0.2083,,0.034,,0.0736,,0.0078,0.0614,0.0674,0.9881,,,0.0,0.1379,0.2083,,0.0339,,0.0719,,0.0075,,block of flats,0.0571,Panel,No,5.0,1.0,5.0,1.0,-410.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n101959,0,Cash loans,F,N,Y,0,378000.0,1288350.0,37800.0,1125000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.010276,-20093,-6266,-5943.0,-242,,1,1,0,1,1,0,Secretaries,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,School,,0.5998137228666071,0.4596904504249018,0.2103,,0.9901,,,0.2,0.2069,0.375,,,,0.2431,,0.0061,0.2143,,0.9901,,,0.2014,0.2069,0.375,,,,0.2533,,0.0064,0.2123,,0.9901,,,0.2,0.2069,0.375,,,,0.2475,,0.0062,,block of flats,0.1925,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1151.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,11.0,0.0,0.0\n393038,0,Cash loans,M,Y,Y,0,157500.0,592560.0,31153.5,450000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.01885,-10780,-1685,-4352.0,-3115,18.0,1,1,0,1,0,0,Core staff,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4738339127126645,0.5334816299804352,0.1866,0.1562,0.9891,,,0.24,0.2069,0.3333,,0.0763,,0.1987,,0.2202,0.1901,0.1621,0.9891,,,0.2417,0.2069,0.3333,,0.0781,,0.207,,0.2332,0.1884,0.1562,0.9891,,,0.24,0.2069,0.3333,,0.0777,,0.2023,,0.2249,,block of flats,0.231,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-968.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n116174,0,Cash loans,M,Y,Y,0,270000.0,1035832.5,30415.5,904500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00733,-18063,-3228,-3838.0,-1609,7.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Self-employed,,0.5557393235499353,,0.1216,0.0855,0.9846,0.7892,,0.0,0.069,0.1667,0.0417,0.0,0.0992,0.063,,0.0082,0.1239,0.0887,0.9846,0.7975,,0.0,0.069,0.1667,0.0417,0.0,0.1084,0.0657,,0.0086,0.1228,0.0855,0.9846,0.792,,0.0,0.069,0.1667,0.0417,0.0,0.1009,0.0642,,0.0083,,block of flats,0.0683,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-596.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n105324,0,Cash loans,F,N,N,1,135000.0,364896.0,15588.0,315000.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.010643,-16534,-3876,-6855.0,-85,,1,1,0,1,0,0,Cooking staff,3.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5362166645978854,0.4794489811780563,0.0825,0.0694,0.9906,0.8708,0.0171,0.08,0.069,0.375,0.4167,0.0248,0.0672,0.0852,0.0,0.0,0.084,0.07200000000000001,0.9906,0.8759,0.0172,0.0806,0.069,0.375,0.4167,0.0254,0.0735,0.0888,0.0,0.0,0.0833,0.0694,0.9906,0.8725,0.0172,0.08,0.069,0.375,0.4167,0.0252,0.0684,0.0868,0.0,0.0,reg oper spec account,block of flats,0.0764,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2252.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n426624,0,Cash loans,F,N,N,3,63000.0,225000.0,17797.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.007114,-14641,-5728,-1387.0,-5382,,1,1,1,1,1,0,Core staff,5.0,2,2,THURSDAY,12,0,0,0,0,0,0,School,,0.4131426043082684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-985.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n302690,0,Cash loans,M,N,Y,0,180000.0,293733.0,31635.0,279000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003818,-14050,-3840,-4483.0,-4474,,1,1,0,1,1,0,Drivers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Industry: type 3,,0.6253965812830798,0.7623356180684377,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2572.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n341861,0,Cash loans,F,N,Y,0,90000.0,555273.0,18040.5,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-17505,365243,-4369.0,-1065,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.5350200515446843,0.5937175866150576,0.1031,,0.9796,0.7212,,0.0,0.2069,0.1667,,,,0.0909,,,0.105,,0.9796,0.7321,,0.0,0.2069,0.1667,,,,0.0947,,,0.1041,,0.9796,0.7249,,0.0,0.2069,0.1667,,,,0.0926,,,,block of flats,0.0996,,No,5.0,0.0,5.0,0.0,-366.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n134556,0,Cash loans,M,Y,Y,0,180000.0,729792.0,35239.5,630000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-17176,-132,-9768.0,-725,2.0,1,1,0,1,0,0,Laborers,2.0,1,1,SUNDAY,11,0,1,1,0,0,0,Industry: type 1,0.37281927713692775,0.6692559106427993,0.5849900404894085,0.1227,0.08900000000000001,0.9727,0.626,,0.0,0.2069,0.1667,,0.0426,,0.0943,,,0.125,0.0924,0.9727,0.6406,,0.0,0.2069,0.1667,,0.0435,,0.0983,,,0.1239,0.08900000000000001,0.9727,0.631,,0.0,0.2069,0.1667,,0.0433,,0.096,,,reg oper account,block of flats,0.0742,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3545.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n188251,0,Cash loans,F,Y,Y,0,90000.0,148365.0,10678.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.010643,-10669,-199,-8413.0,-2203,6.0,1,1,1,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Business Entity Type 3,0.2423880070288868,0.539505159870132,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1401.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n376626,0,Cash loans,F,N,Y,0,112500.0,206271.0,21267.0,193500.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010032,-22785,365243,-9517.0,-4731,,1,0,0,1,0,0,,1.0,2,2,MONDAY,5,0,0,0,0,0,0,XNA,,0.3826479750488737,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-764.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,3.0\n295291,0,Cash loans,F,Y,Y,0,67500.0,450000.0,20979.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-11698,-1583,-2879.0,-2899,10.0,1,1,1,1,0,0,Sales staff,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,Self-employed,0.3509280438418282,0.6180682020474201,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1427.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n208427,0,Cash loans,F,N,Y,2,76500.0,355536.0,15660.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-10169,-1627,-1949.0,-2620,,1,1,1,1,0,0,Cooking staff,4.0,2,2,FRIDAY,8,0,0,0,0,0,0,Industry: type 11,,0.26651977539251576,0.4101025731788671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,1.0,-1900.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n327913,0,Cash loans,F,N,Y,0,117000.0,528633.0,25560.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-17463,-3504,-3257.0,-842,,1,1,1,1,1,1,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6891155218438558,0.2692857999073816,0.0289,,0.9737,0.6396,,0.0,0.1034,0.0833,0.0417,,0.0235,,0.0,,0.0294,,0.9737,0.6537,,0.0,0.1034,0.0833,0.0417,,0.0257,,0.0,,0.0291,,0.9737,0.6444,,0.0,0.1034,0.0833,0.0417,,0.0239,,0.0,,org spec account,block of flats,0.0607,\"Stone, brick\",No,3.0,2.0,3.0,0.0,-1081.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n210885,0,Cash loans,F,N,Y,0,72000.0,276277.5,19359.0,238500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.02461,-21589,-1376,-11250.0,-5089,,1,1,0,1,0,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Other,,0.7448649155395819,0.7570690154522959,0.0674,0.0725,0.9836,0.7756,0.008,0.0,0.1379,0.1667,0.2083,0.0267,0.0546,0.0517,0.0013,0.0019,0.063,0.0717,0.9831,0.7779,0.0072,0.0,0.1379,0.1667,0.2083,0.0188,0.0542,0.0519,0.0,0.0,0.0687,0.0691,0.9836,0.7786,0.0079,0.0,0.1379,0.1667,0.2083,0.0294,0.0564,0.0532,0.0,0.0,reg oper account,block of flats,0.044000000000000004,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-775.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n251334,0,Cash loans,F,N,Y,0,192150.0,900000.0,25794.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.020713,-21079,-5940,-7560.0,-4566,,1,1,0,1,0,0,Laborers,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,Postal,0.8366509538227346,0.6178978395827104,0.7407990879702335,0.0883,0.0,0.9821,0.7552,0.0176,0.0,0.2069,0.1667,0.2083,0.0433,0.07200000000000001,0.0807,0.0,0.0,0.0903,0.0,0.9816,0.7583,0.0176,0.0,0.2069,0.1667,0.2083,0.0388,0.079,0.0831,0.0,0.0,0.0895,0.0,0.9821,0.7585,0.0177,0.0,0.2069,0.1667,0.2083,0.0435,0.0735,0.0822,0.0,0.0,reg oper spec account,block of flats,0.0724,Panel,No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n410522,0,Cash loans,F,N,Y,0,130500.0,215865.0,23247.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008625,-22465,-3866,-8544.0,-2722,,1,1,0,1,1,0,Managers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.7333591827623045,0.511891801533151,0.1124,,0.9776,,,0.0,0.2759,0.1667,,0.1342,,0.1027,,0.0503,0.10400000000000001,,0.9777,,,0.0,0.2759,0.1667,,0.1372,,0.0961,,0.0,0.1135,,0.9776,,,0.0,0.2759,0.1667,,0.1365,,0.1045,,0.0513,,block of flats,0.121,Panel,No,0.0,0.0,0.0,0.0,-2271.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n159933,0,Cash loans,F,N,Y,0,67500.0,508495.5,20295.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-22943,365243,-14560.0,-4318,,1,0,0,1,0,0,,2.0,1,1,THURSDAY,16,0,0,0,0,0,0,XNA,,0.720032230912855,0.6246146584503397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1296.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n421722,0,Cash loans,F,N,Y,0,67500.0,125361.0,9121.5,103500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007305,-20333,365243,-6474.0,-3646,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,9,0,0,0,0,0,0,XNA,0.8065783161663953,0.2143567280477449,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n346609,0,Cash loans,M,Y,Y,0,360000.0,180000.0,17532.0,180000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-17792,-6466,-11936.0,-1352,5.0,1,1,0,1,1,1,High skill tech staff,2.0,1,1,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.7525458306810284,,,,0.9796,,,,,,,,,0.2105,,,,,0.9796,,,,,,,,,0.2193,,,,,0.9796,,,,,,,,,0.2143,,,,,0.1656,,No,0.0,0.0,0.0,0.0,-618.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n314392,0,Revolving loans,M,Y,Y,0,135000.0,180000.0,9000.0,180000.0,Family,Working,Higher education,Single / not married,House / apartment,0.025164,-8282,-290,-7551.0,-972,7.0,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,10,0,0,0,1,1,1,Industry: type 4,0.333930606672301,0.5087534889176487,0.722392890081304,0.1031,0.0991,0.9821,0.7552,0.0737,0.0,0.2069,0.1667,0.2083,0.0423,0.0841,0.0607,0.0,0.0284,0.105,0.1029,0.9821,0.7648,0.0743,0.0,0.2069,0.1667,0.2083,0.0432,0.0918,0.0632,0.0,0.0301,0.1041,0.0991,0.9821,0.7585,0.0741,0.0,0.2069,0.1667,0.2083,0.043,0.0855,0.0618,0.0,0.028999999999999998,reg oper spec account,block of flats,0.071,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1052.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n263136,0,Cash loans,F,N,Y,0,112500.0,166810.5,19926.0,144000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-13180,-1106,-7322.0,-1872,,1,1,0,1,0,0,Sales staff,2.0,1,1,THURSDAY,15,0,0,0,0,1,1,Self-employed,,0.3952294743204838,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n382363,1,Cash loans,F,N,Y,0,315000.0,254700.0,20250.0,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.020246,-9246,-1342,-4125.0,-1937,,1,1,0,1,0,0,Accountants,1.0,3,3,SUNDAY,9,1,1,0,1,1,0,Business Entity Type 3,0.3654623504357936,0.5577737834940921,0.12769879437277135,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-488.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n257742,0,Cash loans,F,N,N,0,157500.0,225000.0,18040.5,225000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-19188,-8486,-12333.0,-2727,,1,1,1,1,1,0,Accountants,2.0,1,1,WEDNESDAY,19,0,0,0,0,0,0,Government,,0.6748801092660659,,0.1191,0.0802,0.9752,0.66,0.0137,0.02,0.1552,0.25,0.2917,0.1384,0.0605,0.09699999999999999,0.0,0.002,0.0756,0.0204,0.9742,0.6602,0.0094,0.0,0.0345,0.1667,0.2083,0.138,0.0661,0.0633,0.0,0.0,0.1202,0.0802,0.9752,0.6645,0.0138,0.02,0.1552,0.25,0.2917,0.1409,0.0616,0.0987,0.0,0.0021,reg oper account,block of flats,0.0478,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-2243.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n254443,0,Cash loans,M,Y,Y,2,157500.0,781920.0,42547.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020713,-10792,-1975,-3058.0,-3444,19.0,1,1,0,1,0,0,Laborers,4.0,3,3,WEDNESDAY,7,0,0,0,0,1,1,Business Entity Type 1,,0.5365375041316693,0.4471785780453068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,4.0,0.0,-559.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n365808,0,Cash loans,M,Y,N,2,180000.0,1583392.5,41769.0,1415259.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14675,-3829,-470.0,-4085,2.0,1,1,1,1,0,0,Laborers,4.0,2,2,THURSDAY,10,0,0,0,0,0,0,Transport: type 4,,0.703116064559405,0.7421816117614419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-628.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n136780,0,Cash loans,M,Y,N,0,112500.0,511249.5,26230.5,382500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-10617,-557,-5039.0,-2900,12.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 1,,0.6570842752186578,0.29708661164720285,0.1522,0.059000000000000004,0.9886,0.8640000000000001,0.0029,0.1464,0.0803,0.5417,0.5417,0.1202,0.1038,0.1777,0.0232,0.0332,0.0998,0.0472,0.9866,0.8236,0.0029,0.1611,0.069,0.625,0.4167,0.0965,0.0817,0.1665,0.0233,0.0,0.1645,0.059000000000000004,0.9866,0.8658,0.0029,0.16,0.069,0.625,0.5417,0.1152,0.1056,0.1709,0.0233,0.0339,org spec account,block of flats,0.1615,Panel,No,0.0,0.0,0.0,0.0,-2404.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n398951,0,Revolving loans,M,Y,Y,2,135000.0,135000.0,6750.0,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14886,-1117,-7464.0,-4629,6.0,1,1,0,1,0,0,Managers,4.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.715202195947929,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1368.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n377713,1,Cash loans,M,N,Y,0,135000.0,545040.0,26509.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-9938,-961,-7334.0,-855,,1,1,0,1,1,0,Laborers,2.0,3,2,SATURDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.14289632705537614,0.381001456549598,0.41885428862332175,0.1804,0.0556,0.9821,0.7552,0.057,0.08,0.069,0.3333,0.375,0.0803,,0.073,,,0.1838,0.0577,0.9821,0.7648,0.0575,0.0806,0.069,0.3333,0.375,0.0822,,0.0628,,,0.1822,0.0556,0.9821,0.7585,0.0573,0.08,0.069,0.3333,0.375,0.0817,,0.0744,,,reg oper account,block of flats,0.0675,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-565.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n302976,0,Cash loans,F,Y,Y,0,112500.0,544491.0,21096.0,454500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.005313,-17378,-505,-9498.0,-937,16.0,1,1,1,1,1,0,Laborers,2.0,2,2,SUNDAY,13,0,0,0,0,1,1,Agriculture,,0.4108593619369958,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-274.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n244649,0,Cash loans,M,N,Y,2,247500.0,622413.0,33894.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-11701,-2487,-5062.0,-3764,,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,11,0,1,1,0,0,0,Transport: type 2,0.19442976159514724,0.643133141247054,0.2910973802776635,0.0845,0.1309,0.9886,0.8436,0.0566,0.0,0.2069,0.1667,0.2083,0.0813,0.0689,0.0844,0.0,0.0,0.0861,0.1358,0.9886,0.8497,0.0571,0.0,0.2069,0.1667,0.2083,0.0832,0.0753,0.0879,0.0,0.0,0.0854,0.1309,0.9886,0.8457,0.057,0.0,0.2069,0.1667,0.2083,0.0827,0.0701,0.0859,0.0,0.0,reg oper spec account,block of flats,0.0797,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1506.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n117369,0,Cash loans,M,N,Y,0,256500.0,1724220.0,50544.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-20406,-2806,-2422.0,-3053,,1,1,0,1,1,0,Laborers,2.0,1,1,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.7865887535669214,0.7515100430377213,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,1.0,0.0,1.0,0.0,-1126.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,5.0,5.0,0.0,0.0,1.0\n255266,0,Cash loans,F,Y,Y,0,90000.0,239850.0,25578.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002042,-13122,-638,-4215.0,-2149,3.0,1,1,1,1,0,0,Sales staff,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,Self-employed,,0.40342234973374375,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-537.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n275474,1,Cash loans,M,Y,Y,0,112500.0,808650.0,29839.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.007120000000000001,-22592,365243,-4984.0,-4234,10.0,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,10,0,0,0,1,0,0,XNA,0.5867897563312761,0.2850815706429428,0.5620604831738043,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1251.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n445779,0,Cash loans,F,N,N,1,99000.0,71955.0,7749.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-10971,-932,-1807.0,-2228,,1,1,1,1,1,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.4127185150945686,0.07905969599457675,0.068,0.0533,0.9811,,,0.12,0.1034,0.25,,,,0.0447,,0.0,0.063,0.0454,0.9811,,,0.0806,0.069,0.1667,,,,0.0386,,0.0,0.0687,0.0533,0.9811,,,0.12,0.1034,0.25,,,,0.0455,,0.0,,,0.0568,Mixed,No,0.0,0.0,0.0,0.0,-1158.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n284700,0,Cash loans,F,Y,Y,0,225000.0,961146.0,28233.0,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-17223,-10074,-8875.0,-757,1.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Industry: type 7,,0.16990359924812706,0.7324033228040929,0.1876,0.2746,0.9881,0.8368,0.0399,0.2,0.1724,0.3333,0.375,0.1156,0.153,0.1151,0.0039,0.0045,0.1912,0.285,0.9881,0.8432,0.0403,0.2014,0.1724,0.3333,0.375,0.1182,0.1671,0.1199,0.0039,0.0047,0.1894,0.2746,0.9881,0.8390000000000001,0.0402,0.2,0.1724,0.3333,0.375,0.1176,0.1556,0.1172,0.0039,0.0046,reg oper spec account,block of flats,0.1512,Panel,No,2.0,1.0,2.0,1.0,-747.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n397319,0,Cash loans,F,Y,N,2,202500.0,1080423.0,42849.0,873000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-14117,-1156,-5712.0,-925,21.0,1,1,1,1,1,0,Sales staff,4.0,2,2,FRIDAY,9,0,0,0,0,0,0,Self-employed,0.4291371669556264,0.68953231626321,0.4525335592581747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2138.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n328596,0,Cash loans,F,N,Y,0,180000.0,755190.0,30078.0,675000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.030755,-20309,365243,-77.0,-3247,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.5552803648877036,0.3859146722745145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n201799,1,Cash loans,F,N,Y,0,225000.0,568908.0,27369.0,508500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.002042,-19830,-938,-874.0,-3211,,1,1,0,1,0,0,Sales staff,2.0,3,3,TUESDAY,12,0,1,1,0,1,1,Trade: type 7,0.5021463878671567,0.5414549088081161,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-151.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n165244,0,Cash loans,F,N,Y,0,270000.0,973710.0,34627.5,697500.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.018801,-13158,-1998,-3910.0,-3910,,1,1,0,1,0,0,Medicine staff,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Medicine,,0.5802129794371108,0.5919766183185521,0.2227,0.1609,0.9796,0.7212,0.1082,0.24,0.2069,0.3333,0.375,0.0924,0.1816,0.2227,0.0,0.0,0.2269,0.1669,0.9796,0.7321,0.1092,0.2417,0.2069,0.3333,0.375,0.0945,0.1983,0.2321,0.0,0.0,0.2248,0.1609,0.9796,0.7249,0.1089,0.24,0.2069,0.3333,0.375,0.094,0.1847,0.2267,0.0,0.0,reg oper account,block of flats,0.2343,Panel,No,0.0,0.0,0.0,0.0,-594.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n445361,0,Cash loans,F,Y,Y,0,126000.0,1002856.5,40972.5,823500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-18563,-4045,-10738.0,-2086,6.0,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Medicine,,0.7045610416342121,0.7764098512142026,0.3093,,0.9851,,,0.32,0.2759,0.3333,,0.1601,,0.3287,,,0.3151,,0.9851,,,0.3222,0.2759,0.3333,,0.1638,,0.3425,,,0.3123,,0.9851,,,0.32,0.2759,0.3333,,0.1629,,0.3346,,,,block of flats,0.2585,Panel,No,3.0,0.0,3.0,0.0,-2708.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n297891,0,Cash loans,F,N,Y,0,114750.0,1468719.0,42943.5,1282500.0,Family,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.072508,-21376,365243,-10857.0,-4887,,1,0,0,1,1,0,,2.0,1,1,FRIDAY,11,0,0,0,0,0,0,XNA,,0.7222134683737619,0.8128226070575616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-304.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n203738,0,Cash loans,F,Y,Y,0,211500.0,1661418.0,43825.5,1485000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.005084,-14610,-968,-3878.0,-4032,6.0,1,1,1,1,0,0,Accountants,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6575472920065787,0.4365064990977374,0.3856,,0.9901,0.8640000000000001,,0.32,0.2759,0.4167,,0.0438,,0.2524,,,0.3929,,0.9901,0.8693,,0.3222,0.2759,0.4167,,0.0448,,0.263,,,0.3893,,0.9901,0.8658,,0.32,0.2759,0.4167,,0.0446,,0.2569,,,reg oper account,block of flats,0.361,Panel,No,0.0,0.0,0.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n138207,0,Cash loans,F,N,N,1,90000.0,585000.0,29997.0,585000.0,Family,Commercial associate,Secondary / secondary special,Separated,With parents,0.030755,-9427,-300,-4049.0,-724,,1,1,0,1,0,1,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,1,1,Trade: type 3,0.6003619720155373,0.4684125491015286,,0.033,0.0306,0.9578,0.42200000000000004,0.0,0.0,0.1379,0.125,0.1667,0.0201,,0.0388,,0.0045,0.0336,0.0317,0.9578,0.4446,0.0,0.0,0.1379,0.125,0.1667,0.0206,,0.0404,,0.0048,0.0333,0.0306,0.9578,0.4297,0.0,0.0,0.1379,0.125,0.1667,0.0205,,0.0395,,0.0046,reg oper account,block of flats,0.035,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1238.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n269672,0,Cash loans,F,N,Y,2,90000.0,156384.0,16024.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-11255,-1733,-2519.0,-3900,,1,1,1,1,0,0,Cooking staff,4.0,2,2,THURSDAY,5,0,0,0,0,0,0,School,,0.392791161593506,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-623.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n144335,1,Cash loans,M,N,Y,3,121500.0,367389.0,20061.0,279000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-12310,-823,-3957.0,-4522,,1,1,0,1,0,0,Laborers,5.0,2,2,THURSDAY,17,0,0,0,0,1,1,Industry: type 9,0.15494567329187933,0.4478034835065557,0.3233112448967859,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2880.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,6.0,0.0,1.0\n381664,0,Cash loans,F,N,N,1,135000.0,545040.0,35617.5,450000.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.014464,-9932,-1127,-1155.0,-158,,1,1,0,1,0,0,,3.0,2,2,FRIDAY,7,0,0,0,1,1,0,Business Entity Type 3,0.5264965093908546,0.05222034629695047,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-396.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n362526,0,Cash loans,M,N,Y,0,135000.0,605439.0,30910.5,481500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-10430,-802,-4873.0,-3118,,1,1,0,1,0,0,Low-skill Laborers,1.0,2,2,SATURDAY,11,0,0,0,0,1,1,Self-employed,0.14555506237944768,0.028869769996223024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n152761,0,Cash loans,F,Y,Y,0,112500.0,1288350.0,37800.0,1125000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.030755,-18229,-3671,-9323.0,-1765,7.0,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Medicine,,0.6724331292832124,0.8482442999507556,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1150.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n333767,0,Cash loans,M,Y,Y,0,202500.0,360000.0,28440.0,360000.0,Unaccompanied,Working,Incomplete higher,Married,Municipal apartment,0.011657,-10437,-2370,-1438.0,-3004,30.0,1,1,1,1,1,0,Drivers,2.0,1,1,FRIDAY,11,0,0,0,0,0,0,Housing,0.2503856293731838,0.7094937352501174,0.09507039584133267,0.0619,0.0915,0.9891,0.8504,0.0717,0.0,0.1034,0.1667,0.2083,0.0159,0.0504,0.0705,0.0,0.0,0.063,0.0949,0.9891,0.8563,0.0723,0.0,0.1034,0.1667,0.2083,0.0162,0.0551,0.0734,0.0,0.0,0.0625,0.0915,0.9891,0.8524,0.0721,0.0,0.1034,0.1667,0.2083,0.0161,0.0513,0.0718,0.0,0.0,not specified,block of flats,0.0558,Panel,No,0.0,0.0,0.0,0.0,-2951.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n162039,0,Revolving loans,F,N,Y,0,675000.0,450000.0,22500.0,450000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.011657,-20933,-8473,-8777.0,-4492,,1,1,0,1,0,0,Core staff,2.0,1,1,TUESDAY,16,0,0,0,0,0,0,Medicine,,0.7690634913057925,0.501075160239048,0.0825,0.0692,0.9752,0.66,0.0069,0.0,0.1379,0.1667,0.2083,0.0506,,0.0702,,,0.084,0.0718,0.9752,0.6733,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0518,,0.0731,,,0.0833,0.0692,0.9752,0.6645,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0515,,0.0714,,,reg oper account,block of flats,0.0709,Panel,No,0.0,0.0,0.0,0.0,-1586.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n190261,1,Cash loans,M,N,N,0,81000.0,545040.0,20547.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-18154,-946,-13481.0,-1553,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.11020461780944862,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-922.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n303096,0,Cash loans,M,Y,Y,0,202500.0,770292.0,32764.5,688500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010147,-13346,-570,-7473.0,-4805,12.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Housing,,0.4447017559248959,0.7623356180684377,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-849.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n379293,0,Cash loans,F,Y,N,2,103500.0,808650.0,23643.0,675000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.04622,-12434,-4668,-6506.0,-5099,2.0,1,1,1,1,1,0,Core staff,4.0,1,1,THURSDAY,20,0,0,0,0,1,1,Security Ministries,0.3382421868049864,0.7398334385664707,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-736.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n369892,1,Cash loans,M,Y,Y,1,135000.0,254700.0,20119.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-17706,-1743,-3960.0,-1111,13.0,1,1,0,1,1,0,Managers,3.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.3547901840005182,0.1895952597360396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1572.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n345204,1,Cash loans,M,Y,Y,0,225000.0,285723.0,26334.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018634,-8891,-558,-652.0,-1548,14.0,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 1,0.1100792229206924,0.25420402648302953,0.1510075350878296,0.0227,,0.9747,,,0.0,0.069,0.0833,,,,0.0139,,0.0186,0.0231,,0.9747,,,0.0,0.069,0.0833,,,,0.0145,,0.0197,0.0229,,0.9747,,,0.0,0.069,0.0833,,,,0.0141,,0.019,,block of flats,0.023,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-319.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n351416,0,Cash loans,M,Y,Y,0,225000.0,781920.0,61776.0,675000.0,Other_B,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.072508,-16842,-3817,-9159.0,-190,15.0,1,1,0,1,1,0,Drivers,1.0,1,1,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6051347223379406,,0.5876,0.2762,0.9836,0.7756,0.3167,0.96,0.4138,0.4583,0.5,0.4216,0.4791,0.5179,0.0,0.0,0.5987,0.2866,0.9836,0.7844,0.3196,0.9667,0.4138,0.4583,0.5,0.4313,0.5234,0.5396,0.0,0.0,0.5933,0.2762,0.9836,0.7786,0.3187,0.96,0.4138,0.4583,0.5,0.429,0.4874,0.5272,0.0,0.0,reg oper account,block of flats,0.4074,Panel,No,3.0,0.0,3.0,0.0,-1667.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n304146,0,Cash loans,F,N,Y,0,81000.0,539590.5,19512.0,445500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.010643,-21631,365243,-12317.0,-4318,,1,0,0,1,0,0,,1.0,2,2,SUNDAY,10,0,0,0,0,0,0,XNA,,0.5446401785663166,0.7076993447402619,0.0082,0.0,0.9727,,,0.0,0.0345,0.0417,,0.023,,0.0076,,0.0,0.0084,0.0,0.9727,,,0.0,0.0345,0.0417,,0.0235,,0.0079,,0.0,0.0083,0.0,0.9727,,,0.0,0.0345,0.0417,,0.0234,,0.0077,,0.0,,block of flats,0.0059,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1380.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n451789,0,Cash loans,F,N,Y,0,171000.0,1129500.0,43150.5,1129500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-19460,-2239,-69.0,-2335,,1,1,1,1,0,0,Managers,1.0,2,2,TUESDAY,18,0,0,0,0,1,1,Trade: type 7,,0.2747987543868697,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n415203,0,Cash loans,F,Y,Y,2,360000.0,503676.0,25848.0,382500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.003813,-14756,-7854,-3200.0,-5364,14.0,1,1,0,1,0,0,,4.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Other,,0.26263086016264764,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n100411,0,Cash loans,M,Y,N,1,450000.0,1035832.5,33412.5,904500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009334,-19410,-5545,-1006.0,-1822,9.0,1,1,1,1,0,0,Managers,3.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Self-employed,,0.6097806994342824,0.22383131747015547,0.0103,0.2148,0.9752,0.66,0.0,0.0,0.1034,0.125,0.0417,0.0468,0.0084,0.0236,0.0,0.0844,0.0105,0.2229,0.9752,0.6733,0.0,0.0,0.1034,0.125,0.0417,0.0479,0.0092,0.0246,0.0,0.0893,0.0104,0.2148,0.9752,0.6645,0.0,0.0,0.1034,0.125,0.0417,0.0477,0.0086,0.0241,0.0,0.0862,reg oper account,block of flats,0.0369,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-792.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n308193,0,Cash loans,F,N,Y,1,180000.0,965340.0,94041.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.008068,-13667,-416,-862.0,-4596,,1,1,0,1,1,0,Core staff,3.0,3,3,TUESDAY,11,0,0,0,0,0,0,Transport: type 2,0.7429964298825913,0.3575431332993905,0.5028782772082183,0.0619,0.08900000000000001,0.995,0.932,0.0127,0.0,0.1034,0.1667,0.2083,0.0215,0.0504,0.0883,0.0,0.0,0.063,0.0924,0.995,0.9347,0.0128,0.0,0.1034,0.1667,0.2083,0.022000000000000002,0.0551,0.092,0.0,0.0,0.0625,0.08900000000000001,0.995,0.9329,0.0127,0.0,0.1034,0.1667,0.2083,0.0219,0.0513,0.0899,0.0,0.0,,block of flats,0.0917,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n230945,0,Cash loans,F,N,N,0,90000.0,150948.0,7389.0,126000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.025164,-22510,365243,-9205.0,-4788,,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,0.5657504381088688,0.19383553200375644,0.3425288720742255,0.2041,0.1089,0.9871,,,0.2,0.1724,0.375,,0.0206,,0.0932,,0.0,0.20800000000000002,0.113,0.9871,,,0.2014,0.1724,0.375,,0.0211,,0.0971,,0.0,0.2061,0.1089,0.9871,,,0.2,0.1724,0.375,,0.021,,0.0949,,0.0,,block of flats,0.1441,Panel,No,5.0,0.0,5.0,0.0,-1578.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n444426,0,Cash loans,M,Y,Y,1,630000.0,675000.0,37692.0,675000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.04622,-11870,-1778,-6017.0,-4301,3.0,1,1,1,1,1,0,,2.0,1,1,MONDAY,17,0,1,1,0,1,1,Business Entity Type 3,,0.656048796510573,,0.066,0.0624,0.9737,0.6396,0.0497,0.0,0.1379,0.125,0.1667,0.0526,0.0538,0.0331,0.0,0.0,0.0672,0.0648,0.9737,0.6537,0.0502,0.0,0.1379,0.125,0.1667,0.0538,0.0588,0.0345,0.0,0.0,0.0666,0.0624,0.9737,0.6444,0.05,0.0,0.1379,0.125,0.1667,0.0535,0.0547,0.0337,0.0,0.0,reg oper account,block of flats,0.0402,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-652.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n209829,0,Cash loans,F,N,Y,0,144000.0,536917.5,27544.5,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-14131,-188,-4074.0,-4088,,1,1,0,1,1,0,Private service staff,2.0,1,1,TUESDAY,17,0,1,1,0,0,0,Services,0.618448204221412,0.6579263483757197,0.5100895276257282,0.1567,0.0955,0.9940000000000001,0.9184,0.0382,0.16,0.1379,0.375,0.4167,0.0978,0.1278,0.1747,0.0,0.0,0.1597,0.0991,0.9940000000000001,0.9216,0.0385,0.1611,0.1379,0.375,0.4167,0.1,0.1396,0.182,0.0,0.0,0.1582,0.0955,0.9940000000000001,0.9195,0.0384,0.16,0.1379,0.375,0.4167,0.0995,0.13,0.1779,0.0,0.0,reg oper account,block of flats,0.1612,Panel,No,0.0,0.0,0.0,0.0,-680.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n313266,0,Cash loans,F,N,Y,0,67500.0,382500.0,22086.0,382500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-20997,365243,-185.0,-2895,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,7,0,0,0,0,0,0,XNA,,0.6289553586097927,0.5100895276257282,,0.0387,0.9906,,,,0.0345,0.0417,,,,0.0112,,0.0511,,0.0401,0.9906,,,,0.0345,0.0417,,,,0.0117,,0.0541,,0.0387,0.9906,,,,0.0345,0.0417,,,,0.0114,,0.0522,,,0.0199,\"Stone, brick\",No,2.0,1.0,2.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n163006,0,Revolving loans,F,N,Y,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018801,-23100,365243,-7360.0,-4137,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,,0.6145259929847031,,0.2227,0.1614,0.9816,0.7484,0.0926,0.24,0.2069,0.3333,0.375,0.1485,0.1816,0.2309,0.0,0.0,0.2269,0.1675,0.9816,0.7583,0.0934,0.2417,0.2069,0.3333,0.375,0.1518,0.1983,0.2406,0.0,0.0,0.2248,0.1614,0.9816,0.7518,0.0932,0.24,0.2069,0.3333,0.375,0.151,0.1847,0.235,0.0,0.0,reg oper spec account,block of flats,0.233,Panel,No,1.0,1.0,1.0,1.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n293649,0,Cash loans,M,Y,Y,0,180000.0,509922.0,49806.0,472500.0,Other_B,State servant,Secondary / secondary special,Single / not married,With parents,0.035792000000000004,-9453,-1548,-3563.0,-1956,1.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,MONDAY,13,0,0,0,0,1,1,Other,0.20820774820331409,0.6202545061570619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1184.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,5.0\n385494,0,Cash loans,F,N,Y,0,202500.0,117000.0,7911.0,117000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-10393,-535,-897.0,-512,,1,1,0,1,1,0,,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.620115488528001,0.4057876297893316,0.2822484337007223,0.2773,0.1507,0.9841,,,0.32,0.2759,0.3333,,0.3135,,0.2952,,0.0,0.2826,0.1564,0.9841,,,0.3222,0.2759,0.3333,,0.3207,,0.3076,,0.0,0.28,0.1507,0.9841,,,0.32,0.2759,0.3333,,0.319,,0.3005,,0.0,,block of flats,0.2322,Panel,No,4.0,1.0,4.0,1.0,-381.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n402337,0,Cash loans,F,Y,Y,0,225000.0,1350000.0,47056.5,1350000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.010032,-22906,365243,-21.0,-5064,13.0,1,0,0,1,1,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,0.8540250875140297,0.4796980676450317,0.5442347412142162,0.1237,0.0647,0.9891,0.8504,0.0664,0.12,0.1034,0.375,0.4167,0.0438,0.1009,0.1331,0.0,0.0,0.1261,0.0671,0.9891,0.8563,0.067,0.1208,0.1034,0.375,0.4167,0.0448,0.1102,0.1387,0.0,0.0,0.1249,0.0647,0.9891,0.8524,0.0668,0.12,0.1034,0.375,0.4167,0.0446,0.1026,0.1355,0.0,0.0,reg oper spec account,block of flats,0.141,Panel,No,1.0,0.0,1.0,0.0,-1016.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,0.0\n283677,0,Cash loans,F,N,Y,0,351000.0,1288350.0,34114.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-13835,-2596,-1748.0,-4649,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,,0.7666584995308802,0.4489622731076524,0.0021,0.0,0.9767,,,0.0,0.069,0.0,,,,0.0012,,0.0,0.0021,0.0,0.9767,,,0.0,0.069,0.0,,,,0.0012,,0.0,0.0021,0.0,0.9767,,,0.0,0.069,0.0,,,,0.0012,,0.0,,block of flats,0.0027,Others,Yes,0.0,0.0,0.0,0.0,-1114.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n142030,0,Cash loans,F,Y,Y,0,135000.0,849870.0,45409.5,787500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.022625,-16790,-4575,-4403.0,-307,7.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,,0.6337446382332618,0.29708661164720285,0.2639,0.0774,0.9801,0.728,0.1218,0.16,0.069,0.625,0.6667,0.1991,0.2152,0.2356,0.0,0.0,0.2689,0.0803,0.9801,0.7387,0.1229,0.1611,0.069,0.625,0.6667,0.2036,0.2351,0.2455,0.0,0.0,0.2665,0.0774,0.9801,0.7316,0.1226,0.16,0.069,0.625,0.6667,0.2025,0.2189,0.2398,0.0,0.0,reg oper account,block of flats,0.2519,Panel,No,0.0,0.0,0.0,0.0,-1539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n208739,1,Cash loans,F,N,Y,0,112500.0,521280.0,28408.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.031329,-9616,-2677,-2898.0,-1993,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,16,0,0,0,1,1,0,Trade: type 7,,0.05130694609076999,0.7136313997323308,0.1021,0.1079,0.9841,0.7824,0.006,0.0,0.2759,0.1667,0.2083,0.1185,0.0824,0.1111,0.0039,0.0035,0.10400000000000001,0.11199999999999999,0.9841,0.7909,0.0061,0.0,0.2759,0.1667,0.2083,0.1212,0.09,0.1157,0.0039,0.0037,0.1031,0.1079,0.9841,0.7853,0.006,0.0,0.2759,0.1667,0.2083,0.1206,0.0838,0.1131,0.0039,0.0036,reg oper account,block of flats,0.0873,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n138821,0,Cash loans,F,Y,Y,0,135000.0,711072.0,25668.0,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00823,-16257,-1697,-3168.0,-4486,2.0,1,1,0,1,0,0,Managers,2.0,2,2,SUNDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.056772198602472214,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-31.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n243937,0,Cash loans,F,Y,Y,0,81000.0,328405.5,16897.5,283500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.008865999999999999,-18180,365243,-8324.0,-828,10.0,1,0,0,1,0,0,,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,0.4237645035238566,0.4341550858759688,0.7688075728291359,0.0825,,0.9722,0.6192,,0.0,0.1379,0.1667,0.0,0.0653,,0.0418,,0.0552,0.084,,0.9722,0.6341,,0.0,0.1379,0.1667,0.0,0.0667,,0.0435,,0.0585,0.0833,,0.9722,0.6243,,0.0,0.1379,0.1667,0.0,0.0664,,0.0425,,0.0564,,specific housing,0.0494,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n398681,0,Cash loans,M,N,Y,1,225000.0,343377.0,25011.0,283500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-13799,-1172,-1353.0,-4298,,1,1,0,1,0,0,Security staff,3.0,1,1,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.5117483676138904,0.6627427444817454,0.7435593141311444,0.3784,,0.9831,,,0.4,0.1724,0.4583,,0.1007,,0.4048,,0.2471,0.3855,,0.9831,,,0.4028,0.1724,0.4583,,0.10300000000000001,,0.4218,,0.2616,0.382,,0.9831,,,0.4,0.1724,0.4583,,0.1025,,0.4121,,0.2523,reg oper spec account,block of flats,0.3757,Panel,No,1.0,0.0,1.0,0.0,-1829.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n300501,0,Cash loans,M,N,N,0,112500.0,269550.0,14242.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-11317,-2034,-131.0,-3035,,1,1,0,1,0,0,Managers,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Trade: type 7,,0.6893918580822029,0.4956658291397297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2143.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n225019,0,Cash loans,F,Y,Y,2,144000.0,783927.0,34659.0,688500.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.025164,-12083,-4979,-1866.0,-4525,18.0,1,1,0,1,0,0,Core staff,4.0,2,2,FRIDAY,16,0,0,0,0,0,0,Kindergarten,0.5556284486581312,0.576715029283536,0.3376727217405312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1750.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,1.0,0.0,2.0,1.0\n216153,0,Cash loans,M,Y,N,0,112500.0,270000.0,12024.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-21031,-5141,-8949.0,-3993,3.0,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Transport: type 4,,0.5937255407541094,,0.099,0.0613,0.9876,,,0.12,0.069,0.3958,,,,0.0926,,0.0,0.0756,0.0603,0.9876,,,0.0806,0.069,0.3333,,,,0.0799,,0.0,0.0999,0.0613,0.9876,,,0.12,0.069,0.3958,,,,0.0943,,0.0,,block of flats,0.0673,Panel,No,2.0,0.0,2.0,0.0,-500.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n280690,0,Cash loans,M,Y,N,0,157500.0,1575000.0,41548.5,1575000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-23608,365243,-5400.0,-3972,10.0,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,XNA,,0.3098158963203317,0.5388627065779676,0.0093,0.0508,0.9886,0.8436,0.0019,0.0,0.0345,0.0417,0.0833,0.0147,0.0076,0.0144,0.0,0.0,0.0095,0.0527,0.9886,0.8497,0.0019,0.0,0.0345,0.0417,0.0833,0.015,0.0083,0.015,0.0,0.0,0.0094,0.0508,0.9886,0.8457,0.0019,0.0,0.0345,0.0417,0.0833,0.0149,0.0077,0.0146,0.0,0.0,reg oper spec account,block of flats,0.0124,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-8.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n142377,0,Cash loans,F,N,Y,0,170100.0,630000.0,43978.5,630000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-17633,365243,-5680.0,-1169,,1,0,0,1,1,0,,2.0,3,3,THURSDAY,10,0,0,0,0,0,0,XNA,0.6197801739114527,0.5709985015784695,0.7826078370261895,0.1485,0.0975,0.9886,0.8436,0.0379,0.16,0.1379,0.3333,0.375,0.0795,0.121,0.1498,0.0,0.0,0.1513,0.1011,0.9886,0.8497,0.0383,0.1611,0.1379,0.3333,0.375,0.0813,0.1322,0.1561,0.0,0.0,0.1499,0.0975,0.9886,0.8457,0.0382,0.16,0.1379,0.3333,0.375,0.0809,0.1231,0.1525,0.0,0.0,reg oper account,block of flats,0.1386,Panel,No,4.0,0.0,4.0,0.0,-1468.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,6.0,0.0,3.0\n255424,0,Cash loans,F,N,Y,2,40500.0,450000.0,21109.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-12532,-2366,-4469.0,-4525,,1,1,0,1,0,0,Laborers,4.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4342616183852859,0.7307857021466792,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n211714,0,Cash loans,F,N,Y,0,112500.0,278460.0,24030.0,225000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.008068,-21215,-808,-4484.0,-4580,,1,1,0,1,0,0,Medicine staff,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,Medicine,,0.3593987860327892,0.07249667675378216,0.0433,0.0718,0.9871,,,0.0,0.1034,0.1667,,0.0123,,0.0445,,,0.0441,0.0745,0.9871,,,0.0,0.1034,0.1667,,0.0126,,0.0464,,,0.0437,0.0718,0.9871,,,0.0,0.1034,0.1667,,0.0125,,0.0453,,,,block of flats,0.0385,Panel,No,5.0,0.0,5.0,0.0,-376.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n331120,0,Revolving loans,F,N,Y,1,157500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010966,-13883,-6297,-6081.0,-4248,,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,14,0,0,0,0,0,0,School,0.7462054515269207,0.6704793938314525,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-318.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n345887,1,Cash loans,M,Y,N,0,180000.0,497520.0,33376.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-19792,-11264,-12357.0,-3327,9.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.6655827104848681,0.5339046625017316,0.11104240226943142,0.1031,0.1091,0.9781,0.7008,0.1282,0.0,0.2069,0.1667,0.2083,0.0198,0.0841,0.0597,0.0,0.0,0.105,0.1132,0.9782,0.7125,0.1293,0.0,0.2069,0.1667,0.2083,0.0203,0.0918,0.0622,0.0,0.0,0.1041,0.1091,0.9781,0.7048,0.129,0.0,0.2069,0.1667,0.2083,0.0202,0.0855,0.0608,0.0,0.0,reg oper account,block of flats,0.0701,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-1382.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,9.0\n253475,0,Cash loans,F,N,Y,0,67500.0,622575.0,22491.0,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-21185,365243,-11138.0,-4471,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.13872578458306675,,0.2948,0.1856,0.9846,,,0.32,0.2759,0.3333,,0.2366,,0.3109,,0.0072,0.3004,0.1926,0.9846,,,0.3222,0.2759,0.3333,,0.242,,0.324,,0.0076,0.2977,0.1856,0.9846,,,0.32,0.2759,0.3333,,0.2408,,0.3165,,0.0074,,block of flats,0.2927,Panel,No,2.0,0.0,2.0,0.0,-533.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n170896,0,Cash loans,F,N,N,0,112500.0,270000.0,32040.0,270000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.008068,-8044,-291,-2894.0,-612,,1,1,0,1,0,0,Core staff,1.0,3,3,WEDNESDAY,12,0,0,0,1,1,0,Medicine,0.14048512426716453,0.16236750055415092,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-376.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n378091,0,Cash loans,F,N,Y,0,220500.0,788103.0,28435.5,598500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.008865999999999999,-17624,-3362,-7351.0,-1148,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,14,0,0,0,0,1,1,Agriculture,,0.4254948445165657,0.40314167665875134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n164366,0,Cash loans,F,N,Y,0,67500.0,206280.0,10161.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-9901,-2647,-3710.0,-2551,,1,1,0,1,0,0,Sales staff,1.0,3,3,MONDAY,5,0,0,0,0,1,1,Self-employed,,0.5833627732482791,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n344308,0,Cash loans,F,N,Y,0,99000.0,545040.0,20677.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.006670999999999999,-21047,-296,-6391.0,-4326,,1,1,1,1,1,0,Sales staff,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.9032679172217292,0.6259599458242607,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-2483.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n398071,0,Cash loans,M,Y,Y,1,270000.0,675000.0,32602.5,675000.0,\"Spouse, partner\",State servant,Higher education,Married,With parents,0.018209,-12594,-3998,-2142.0,-4736,14.0,1,1,0,1,1,0,Core staff,3.0,3,3,MONDAY,14,0,0,0,0,0,0,Police,0.4109777710730279,0.4180606659813069,0.5082869913916046,0.0825,0.0694,0.9757,0.6668,0.008,0.0,0.2069,0.1667,0.2083,0.068,0.0672,0.0704,0.0,0.0,0.084,0.07200000000000001,0.9757,0.6798,0.0081,0.0,0.2069,0.1667,0.2083,0.0695,0.0735,0.0734,0.0,0.0,0.0833,0.0694,0.9757,0.6713,0.0081,0.0,0.2069,0.1667,0.2083,0.0692,0.0684,0.0717,0.0,0.0,reg oper account,block of flats,0.0598,Panel,No,0.0,0.0,0.0,0.0,-2536.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n444301,0,Cash loans,M,N,N,0,112500.0,447768.0,29920.5,405000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.007120000000000001,-8728,-700,-2659.0,-1316,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,TUESDAY,15,0,0,0,1,1,0,Business Entity Type 3,0.10046462668592243,0.29398758450226137,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n161247,0,Cash loans,F,N,Y,0,112500.0,254700.0,30357.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-17024,-182,-10944.0,-571,,1,1,0,1,0,0,Accountants,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6398944999665165,,0.0598,0.0767,0.5852,,,0.0,0.2297,0.1667,,,,0.0816,,0.0001,0.0,0.0614,0.9757,,,0.0,0.2069,0.1667,,,,0.0587,,0.0001,0.0833,0.0595,0.9757,,,0.0,0.2069,0.1667,,,,0.0663,,0.0001,,block of flats,0.0,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n403186,0,Cash loans,F,Y,Y,0,315000.0,512446.5,40617.0,463500.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.007120000000000001,-15980,-417,-992.0,-1593,12.0,1,1,0,1,0,1,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.2708708987787028,0.5819004388050107,0.44539624156083396,0.0948,1.0,0.9806,0.8572,0.0872,0.16,0.0862,0.1875,,,0.121,0.0861,,0.0063,0.042,1.0,0.9722,0.8628,0.08800000000000001,0.1611,0.0345,0.0417,,,0.1322,0.0897,,0.0067,0.0958,1.0,0.9806,0.8591,0.0878,0.16,0.0862,0.1875,,,0.1231,0.0877,,0.0065,,block of flats,0.0068,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-641.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,9.0\n409943,1,Cash loans,M,N,N,0,202500.0,675000.0,36747.0,675000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.035792000000000004,-9625,-925,-485.0,-2293,,1,1,0,1,1,0,Realty agents,1.0,2,2,SATURDAY,16,0,0,0,0,0,0,Self-employed,,0.5454806650456794,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-573.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n394750,0,Cash loans,F,N,N,0,202500.0,337500.0,16546.5,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-12433,-1063,-6205.0,-3363,,1,1,0,1,0,0,,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.4297197816426169,0.4206218060146373,0.5064842396679806,0.0113,,0.9518,,,0.0,0.0345,0.0417,,,,0.0041,,0.0,0.0116,,0.9518,,,0.0,0.0345,0.0417,,,,0.0043,,0.0,0.0115,,0.9518,,,0.0,0.0345,0.0417,,,,0.0042,,0.0,,block of flats,0.0044,Wooden,Yes,0.0,0.0,0.0,0.0,-578.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n441902,0,Cash loans,F,N,Y,0,112500.0,755190.0,33394.5,675000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.04622,-18494,-11471,-9673.0,-2030,,1,1,1,1,0,0,,2.0,1,1,THURSDAY,18,0,0,0,0,1,1,Transport: type 2,0.4966770021736687,0.7657928418805818,0.2650494299443805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-3002.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n406218,0,Cash loans,M,Y,Y,0,171000.0,625536.0,32386.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Rented apartment,0.002506,-11395,-261,-3281.0,-3957,16.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,5,0,0,0,0,0,0,Business Entity Type 3,,0.6268145828217786,0.43473324875017305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-192.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n103137,0,Cash loans,F,N,Y,0,225000.0,1546020.0,45333.0,1350000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-23305,-2521,-11251.0,-4211,,1,1,0,1,0,0,Accountants,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.6572665622416514,0.7136313997323308,0.068,,0.9836,,,0.0,0.1724,0.1667,,,,0.044000000000000004,,0.0999,0.0693,,0.9836,,,0.0,0.1724,0.1667,,,,0.0459,,0.1058,0.0687,,0.9836,,,0.0,0.1724,0.1667,,,,0.0448,,0.102,,block of flats,0.0564,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1459.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n401936,0,Cash loans,F,Y,Y,0,103500.0,545040.0,26640.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.006852,-22710,365243,-7657.0,-4479,38.0,1,0,0,1,0,0,,1.0,3,3,THURSDAY,5,0,0,0,0,0,0,XNA,,0.4245810182644848,0.6528965519806539,0.0082,0.0218,0.9757,,,,0.0345,0.0417,,,,0.0076,,0.0022,0.0084,0.0226,0.9757,,,,0.0345,0.0417,,,,0.0079,,0.0023,0.0083,0.0218,0.9757,,,,0.0345,0.0417,,,,0.0077,,0.0022,,block of flats,0.0064,Panel,No,2.0,1.0,2.0,0.0,-573.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n188671,0,Cash loans,F,N,N,1,180000.0,509400.0,28575.0,450000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.0228,-10483,-1704,-4690.0,-3169,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,14,0,0,0,0,0,0,Medicine,0.5307365639650357,0.640040616163284,0.6246146584503397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2336.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n137209,0,Cash loans,M,Y,Y,0,157500.0,1078200.0,34911.0,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-19456,-2315,-11850.0,-3003,1.0,1,1,0,1,0,0,Drivers,2.0,2,2,SUNDAY,13,0,0,0,0,0,0,Self-employed,,0.5985795696282931,0.7194907850918436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-222.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,2.0\n387500,0,Cash loans,F,Y,Y,2,67500.0,472500.0,15372.0,472500.0,Family,State servant,Higher education,Married,House / apartment,0.031329,-15522,-4500,-6653.0,-5063,10.0,1,1,0,1,0,0,Core staff,4.0,2,2,FRIDAY,10,0,0,0,0,1,1,Government,0.4206700841869451,0.6309077828611711,0.524496446363472,0.0608,0.1053,0.9876,0.83,0.0094,0.0,0.2414,0.0833,0.125,0.0732,0.0479,0.035,0.0077,0.0098,0.062,0.1093,0.9876,0.8367,0.0095,0.0,0.2414,0.0833,0.125,0.0749,0.0523,0.0365,0.0078,0.0104,0.0614,0.1053,0.9876,0.8323,0.0095,0.0,0.2414,0.0833,0.125,0.0745,0.0487,0.0357,0.0078,0.01,reg oper account,block of flats,0.0472,Block,No,1.0,0.0,1.0,0.0,-1927.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n373828,0,Cash loans,M,N,N,0,238500.0,369085.5,29722.5,342000.0,Other_B,State servant,Higher education,Single / not married,House / apartment,0.00496,-13170,-4897,-6959.0,-4963,,1,1,1,1,0,0,Core staff,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,Police,0.21827885031635727,0.6329524626924492,0.6263042766749393,0.0704,,0.9821,,,0.0,0.1379,0.1667,,0.0713,,,,,0.042,,0.9786,,,0.0,0.069,0.1667,,0.0182,,,,,0.0573,,0.9821,,,0.0,0.1379,0.1667,,0.0725,,,,,,block of flats,0.0261,,No,5.0,1.0,5.0,0.0,-638.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n176664,0,Cash loans,F,N,Y,0,72000.0,675000.0,19867.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00702,-17652,-321,-8539.0,-1187,,1,1,0,1,0,0,Medicine staff,2.0,2,2,MONDAY,10,0,0,0,0,1,1,Medicine,,0.02328175686265157,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,1.0,-841.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n446350,0,Cash loans,F,N,Y,0,166500.0,562981.5,27211.5,486000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-17714,365243,-1268.0,-1268,,1,0,0,1,0,1,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.5664824128884873,0.6109913280868294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-20.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n326687,0,Cash loans,F,N,Y,0,202500.0,1288350.0,41692.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0105,-20889,-1129,-10461.0,-4445,,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,13,0,0,0,0,0,0,Industry: type 3,0.4156783722554475,0.7553287607641699,0.520897599048938,0.0041,0.0,0.9856,0.8028,0.0024,0.0,0.0,0.0,0.0417,0.0,0.0034,0.0033,0.0,0.0,0.0042,0.0,0.9856,0.8105,0.0024,0.0,0.0,0.0,0.0417,0.0,0.0037,0.0035,0.0,0.0,0.0042,0.0,0.9856,0.8054,0.0024,0.0,0.0,0.0,0.0417,0.0,0.0034,0.0034,0.0,0.0,not specified,terraced house,0.004,Wooden,No,1.0,1.0,1.0,1.0,-715.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n158034,0,Cash loans,F,N,Y,0,180000.0,573408.0,29277.0,495000.0,Family,Working,Higher education,Married,House / apartment,0.010966,-10700,-1215,-5279.0,-3132,,1,1,0,1,0,0,Medicine staff,2.0,2,2,THURSDAY,12,0,0,0,1,1,0,Other,0.6352907563942121,0.30254606806197104,0.722392890081304,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,-2444.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n154837,0,Cash loans,F,Y,Y,0,180000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006233,-13434,-473,-13286.0,-2857,64.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Military,,0.6560389276569535,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-194.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,2.0\n250136,0,Cash loans,F,N,Y,1,90000.0,254700.0,20250.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018801,-11588,-1516,-2033.0,-3335,,1,1,1,1,1,0,,3.0,2,2,THURSDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.03217106801177168,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-526.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n445291,0,Cash loans,F,N,Y,0,288000.0,924394.5,36103.5,702000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.006852,-14900,-5044,-6382.0,-4654,,1,1,0,1,0,0,,2.0,3,3,TUESDAY,13,0,0,0,0,0,0,Security Ministries,,0.31911165713219164,0.18411615593071512,0.0825,0.0993,0.9796,0.7212,0.0729,0.0,0.1379,0.1667,0.0,0.0423,0.0672,0.0768,0.0,0.0,0.084,0.10300000000000001,0.9796,0.7321,0.0735,0.0,0.1379,0.1667,0.0,0.0432,0.0735,0.08,0.0,0.0,0.0833,0.0993,0.9796,0.7249,0.0733,0.0,0.1379,0.1667,0.0,0.043,0.0684,0.0782,0.0,0.0,reg oper account,block of flats,0.1026,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-153.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n204906,0,Cash loans,M,N,Y,2,247500.0,521280.0,31630.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-12057,-1960,-4628.0,-3585,,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.2302795960194378,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-144.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n363575,0,Cash loans,F,N,Y,0,360000.0,521280.0,38151.0,450000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006233,-16182,-3163,-5052.0,-5040,,1,1,0,1,1,0,Managers,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Medicine,0.8495380009373512,0.8166550874148094,0.6956219298394389,0.0742,0.0628,0.9826,0.762,0.019,0.08,0.069,0.3333,0.375,0.0391,0.0597,0.0765,0.0039,0.0167,0.0756,0.0651,0.9826,0.7713,0.0192,0.0806,0.069,0.3333,0.375,0.04,0.0652,0.0797,0.0039,0.0177,0.0749,0.0628,0.9826,0.7652,0.0191,0.08,0.069,0.3333,0.375,0.0398,0.0607,0.0779,0.0039,0.017,reg oper account,block of flats,0.0742,Panel,No,2.0,0.0,2.0,0.0,-1746.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n204463,0,Revolving loans,F,N,Y,1,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.009334,-12450,-1431,-6434.0,-3222,,1,1,1,1,1,0,Sales staff,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.10831656026075097,0.3850839643274924,0.3556387169923543,0.0103,0.0,0.9747,0.6464,0.0014,0.0,0.0459,0.0275,0.0833,0.0097,0.0084,0.0104,0.0,0.0,0.0084,0.0,0.9742,0.6602,0.0008,0.0,0.0345,0.0417,0.0833,0.0069,0.0073,0.0067,0.0,0.0,0.0104,0.0,0.9747,0.6511,0.0014,0.0,0.0345,0.0417,0.0833,0.0099,0.0086,0.0105,0.0,0.0,reg oper account,block of flats,0.0005,Wooden,No,0.0,0.0,0.0,0.0,-1341.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n284588,0,Cash loans,F,N,Y,1,90000.0,755190.0,36328.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-15125,-2075,-8037.0,-4849,,1,1,0,1,1,0,Sales staff,3.0,2,2,THURSDAY,11,0,0,0,0,1,1,Self-employed,0.4390104416563826,0.7112124600139569,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2575.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n199211,1,Cash loans,M,N,Y,0,103500.0,89388.0,4477.5,58500.0,Unaccompanied,Working,Lower secondary,Single / not married,House / apartment,0.025164,-15182,-1644,-5443.0,-5448,,1,1,0,1,0,0,Low-skill Laborers,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Self-employed,,0.25062643928239525,0.7886807751817684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-29.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n370938,0,Cash loans,M,N,Y,0,72000.0,71955.0,7879.5,67500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.020246,-10161,-1924,-4570.0,-2638,,1,1,1,1,0,0,Sales staff,2.0,3,3,MONDAY,12,0,0,0,0,0,0,Self-employed,0.12816631185823396,0.06748205414490929,0.4614823912998385,0.1134,0.0869,0.9876,0.83,0.0184,0.0,0.3448,0.1667,0.2083,0.1032,0.0916,0.1105,0.0039,0.0917,0.1155,0.0902,0.9876,0.8367,0.0186,0.0,0.3448,0.1667,0.2083,0.1056,0.1001,0.1152,0.0039,0.0971,0.1145,0.0869,0.9876,0.8323,0.0185,0.0,0.3448,0.1667,0.2083,0.105,0.0932,0.1125,0.0039,0.0936,reg oper account,block of flats,0.1169,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-294.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n125610,0,Cash loans,F,N,Y,0,117000.0,258709.5,14980.5,234000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.020713,-18976,-2679,-5665.0,-2359,,1,1,1,1,0,0,Sales staff,1.0,3,3,FRIDAY,13,0,0,0,0,0,0,Self-employed,0.5046036551797874,0.1141262440645194,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n376499,0,Cash loans,F,N,Y,0,90000.0,284400.0,20740.5,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.010556,-20864,365243,-299.0,-2843,,1,0,0,1,1,0,,2.0,3,3,WEDNESDAY,15,0,0,0,0,0,0,XNA,0.6845303964144579,0.3943308099129433,0.5298898341969072,,,0.9786,,,,,,,,,0.0356,,,,,0.9786,,,,,,,,,0.037000000000000005,,,,,0.9786,,,,,,,,,0.0362,,,,,0.0293,,No,4.0,0.0,4.0,0.0,-556.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n410947,0,Revolving loans,F,Y,Y,1,40500.0,135000.0,6750.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.02461,-13856,-7164,-6704.0,-3918,15.0,1,1,1,1,1,0,Medicine staff,3.0,2,2,WEDNESDAY,8,0,0,0,0,1,1,Medicine,0.6234996361812705,0.6527104522927423,0.6910212267577837,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-82.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n276887,0,Cash loans,F,N,Y,0,157500.0,922500.0,27103.5,922500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-18880,-5754,-5660.0,-2420,,1,1,0,1,1,0,,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Other,,0.6434894033595319,0.4489622731076524,0.0371,0.0827,0.9916,,,0.0,0.1379,0.0833,,0.0743,,0.0456,,0.0125,0.0378,0.0858,0.9916,,,0.0,0.1379,0.0833,,0.076,,0.0475,,0.0132,0.0375,0.0827,0.9916,,,0.0,0.1379,0.0833,,0.0756,,0.0464,,0.0128,,block of flats,0.0359,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-48.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n102150,0,Cash loans,M,N,Y,0,171000.0,545040.0,26509.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Rented apartment,0.00963,-17851,-486,-2713.0,-1367,,1,1,1,1,0,0,Laborers,2.0,2,2,SUNDAY,11,0,1,1,0,1,1,Business Entity Type 3,,0.7416434432148603,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-300.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n363918,0,Cash loans,F,N,Y,0,126000.0,254700.0,14350.5,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-23925,365243,-9145.0,-4090,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,XNA,0.8357622984602808,0.7333677348018516,0.375711009574066,0.0619,0.085,0.9816,0.7484,0.008,0.0,0.1379,0.1667,0.0417,,,0.053,,0.0,0.063,0.0882,0.9816,0.7583,0.008,0.0,0.1379,0.1667,0.0417,,,0.0552,,0.0,0.0625,0.085,0.9816,0.7518,0.008,0.0,0.1379,0.1667,0.0417,,,0.054000000000000006,,0.0,,block of flats,0.0461,Panel,No,1.0,0.0,0.0,0.0,-371.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,6.0\n263976,0,Revolving loans,F,Y,Y,0,135000.0,360000.0,18000.0,360000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.031329,-19908,-2541,-5253.0,-3398,7.0,1,1,1,1,1,0,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,0.8934217221089202,0.6183623960940494,,0.1008,0.0377,0.9921,0.8912,0.034,0.16,0.069,0.5,0.5417,0.0536,0.0799,0.1195,0.0106,0.0328,0.0441,0.0185,0.9916,0.8889,0.0175,0.0806,0.0345,0.4583,0.5,0.0691,0.0367,0.0678,0.0,0.0,0.1192,0.0332,0.9921,0.8927,0.0397,0.16,0.069,0.4583,0.5,0.0598,0.0962,0.1349,0.0116,0.0365,reg oper account,block of flats,0.0553,Panel,No,0.0,0.0,0.0,0.0,-428.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n139740,0,Cash loans,M,N,N,0,270000.0,1134000.0,31315.5,1134000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.030755,-10997,-1682,-1196.0,-3403,,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Trade: type 3,0.1590755092133794,0.5528814650775455,0.2445163919946749,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-239.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n188356,0,Revolving loans,F,N,N,0,126000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Single / not married,Rented apartment,0.031329,-9026,-229,-929.0,-1694,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,16,0,0,0,1,1,0,Trade: type 7,,0.4753061552681833,0.5136937663039473,0.0082,0.0044,0.9677,0.5579999999999999,0.0011,0.0,0.0345,0.0417,0.0833,0.0138,0.0067,0.0085,0.0,0.0,0.0084,0.0046,0.9677,0.5753,0.0011,0.0,0.0345,0.0417,0.0833,0.0141,0.0073,0.0088,0.0,0.0,0.0083,0.0044,0.9677,0.5639,0.0011,0.0,0.0345,0.0417,0.0833,0.013999999999999999,0.0068,0.0086,0.0,0.0,reg oper account,block of flats,0.0073,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-922.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n100979,0,Revolving loans,F,N,Y,0,157500.0,450000.0,22500.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17924,-1285,-4360.0,-1484,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Trade: type 7,,0.6147538911956172,0.6642482627052363,0.1082,0.0455,0.9811,0.7416,0.0225,0.0,0.0517,0.1667,0.2083,0.0699,0.0883,0.0314,0.0,0.0567,0.1092,0.0472,0.9811,0.7517,0.0,0.0,0.0345,0.1667,0.2083,0.0408,0.0955,0.0326,0.0,0.0,0.1093,0.0455,0.9811,0.7451,0.0227,0.0,0.0517,0.1667,0.2083,0.0711,0.0898,0.0319,0.0,0.0578,not specified,block of flats,0.0246,Panel,No,1.0,1.0,1.0,1.0,-614.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n157029,0,Cash loans,F,N,N,0,45000.0,50940.0,5346.0,45000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.010276,-19790,-4170,-9413.0,-3268,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,11,0,0,0,1,1,0,Self-employed,,0.2393908218494237,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-719.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n133391,1,Cash loans,M,Y,N,2,180000.0,435861.0,51858.0,414000.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.019101,-19182,-101,-4606.0,-2666,13.0,1,1,0,1,0,0,Managers,4.0,2,2,MONDAY,13,0,0,0,0,1,1,Government,,0.21212616621415425,0.3556387169923543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1412.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n344399,0,Cash loans,F,Y,Y,0,202500.0,1157958.0,49189.5,1035000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-16563,-6195,-5279.0,-120,2.0,1,1,1,1,0,1,Medicine staff,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Other,0.6341984338493938,0.4419872209183588,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1783.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n165858,0,Cash loans,M,N,Y,0,157500.0,125136.0,7312.5,99000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-22663,-4554,-6975.0,-4543,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,13,0,0,0,0,1,1,School,,0.6784969475914651,,0.1649,,0.9916,,,0.2,0.1724,0.375,,,,0.2385,,,0.1681,,0.9916,,,0.2014,0.1724,0.375,,,,0.2485,,,0.1665,,0.9916,,,0.2,0.1724,0.375,,,,0.2428,,,,block of flats,0.1876,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-857.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n447721,0,Cash loans,F,N,Y,0,157500.0,257391.0,18040.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-20549,-7296,-14423.0,-4059,,1,1,0,1,0,0,Laborers,1.0,3,2,MONDAY,13,0,0,0,0,0,0,Industry: type 3,,0.6140234037139616,0.4776491548517548,0.2186,0.1547,0.9776,0.6940000000000001,0.0172,0.16,0.1379,0.3333,0.375,0.0757,0.1765,0.21,0.0077,0.0188,0.2227,0.1606,0.9777,0.706,0.0174,0.1611,0.1379,0.3333,0.375,0.0775,0.1928,0.2188,0.0078,0.0199,0.2207,0.1547,0.9776,0.6981,0.0174,0.16,0.1379,0.3333,0.375,0.077,0.1796,0.2138,0.0078,0.0192,not specified,block of flats,0.1787,Panel,No,0.0,0.0,0.0,0.0,-1124.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n437861,0,Cash loans,F,Y,Y,1,247500.0,808650.0,23773.5,675000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.005002,-16183,-9137,-7297.0,-4867,1.0,1,1,0,1,1,0,Core staff,3.0,3,3,THURSDAY,9,0,0,0,0,0,0,School,,0.7264618697122244,0.5884877883422673,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3184.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n150615,0,Cash loans,F,N,Y,0,72000.0,260640.0,26838.0,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15817,-600,-5482.0,-5198,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6924133027327579,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-477.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n203104,0,Cash loans,F,N,N,1,360000.0,1190340.0,66465.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-15404,-2383,-2785.0,-4640,,1,1,0,1,0,0,Managers,3.0,1,1,THURSDAY,9,0,0,0,0,1,1,Self-employed,,0.73827306307868,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1005.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n312907,0,Cash loans,F,N,Y,0,225000.0,450000.0,19822.5,450000.0,Family,Working,Higher education,Married,House / apartment,0.007120000000000001,-9731,-1969,-908.0,-741,,1,1,1,1,1,0,Managers,2.0,2,2,WEDNESDAY,7,0,0,0,1,1,0,Trade: type 2,,0.5659130796679341,0.36227724703843145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1142.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n264772,0,Cash loans,M,Y,Y,2,90000.0,152820.0,11205.0,135000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.028663,-13232,-1743,-1100.0,-4445,11.0,1,1,1,1,1,0,Drivers,4.0,2,2,SATURDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.3694267788603541,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,3.0,5.0,2.0,-413.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n150445,0,Cash loans,M,N,Y,0,108000.0,528687.0,24777.0,436500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018634,-14753,-151,-3434.0,-4929,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.4432250462354502,0.07008419595422463,,,,0.9831,,,,,,,,,0.0158,,,,,0.9831,,,,,,,,,0.0165,,,,,0.9831,,,,,,,,,0.0161,,,,,0.0141,,No,0.0,0.0,0.0,0.0,-255.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n387054,0,Cash loans,M,Y,N,0,225000.0,808650.0,26086.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008575,-23545,-4687,-6011.0,-4160,3.0,1,1,0,1,1,0,High skill tech staff,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.7057204357921129,0.6971469077844458,0.134,0.0809,0.9796,,,0.2,0.1552,0.25,,,,0.0715,,,0.084,0.084,0.9752,,,0.2014,0.1379,0.1667,,,,0.0745,,,0.1353,0.0809,0.9796,,,0.2,0.1552,0.25,,,,0.0728,,,,block of flats,0.0562,Block,No,0.0,0.0,0.0,0.0,-897.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n258569,0,Cash loans,F,Y,Y,0,112500.0,694152.0,29407.5,694152.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.028663,-23125,365243,-74.0,-4699,5.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.5861185850798718,0.41184855592423975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1867.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n407428,0,Cash loans,F,N,Y,0,90000.0,553581.0,31909.5,472500.0,Family,Working,Higher education,Married,House / apartment,0.018634,-16290,-8260,-5858.0,-4213,,1,1,0,1,0,0,Medicine staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Medicine,,0.2822732380125532,0.23791607950711405,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1355.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n294893,0,Cash loans,M,N,Y,2,202500.0,481495.5,35239.5,454500.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.030755,-13887,-6267,-6497.0,-4411,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,15,0,0,0,0,0,0,Military,,0.6709720043323416,,0.0072,0.006,0.9518,,,0.0,0.0345,0.0417,,0.0029,,0.0035,,0.0,0.0074,0.0062,0.9518,,,0.0,0.0345,0.0417,,0.003,,0.0037,,0.0,0.0073,0.006,0.9518,,,0.0,0.0345,0.0417,,0.003,,0.0036,,0.0,,block of flats,0.0044,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-568.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n239556,0,Revolving loans,F,Y,N,5,135000.0,1350000.0,67500.0,1350000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.00702,-13776,-929,-841.0,-4234,3.0,1,1,0,1,0,1,Core staff,7.0,2,2,THURSDAY,16,0,0,0,0,0,0,Military,0.7386669835931096,0.584388192458164,,0.1299,,0.9965,,,0.22,0.1897,0.3333,,,,0.1481,,0.0,0.1134,,0.996,,,0.2014,0.1724,0.3333,,,,0.1415,,0.0,0.1312,,0.9965,,,0.22,0.1897,0.3333,,,,0.1507,,0.0,,block of flats,0.1068,Block,No,0.0,0.0,0.0,0.0,-341.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n127078,0,Cash loans,F,N,N,1,112500.0,497520.0,25272.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-12042,-2894,-3095.0,-4215,,1,1,0,1,1,0,,3.0,3,2,FRIDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.4075797565896245,0.4899987987315613,0.5531646987710016,0.3247,0.0787,0.9851,0.7959999999999999,0.0693,0.08,0.0345,0.3333,0.0417,0.048,0.2648,0.0808,0.0,0.0,0.3309,0.0817,0.9851,0.804,0.0699,0.0806,0.0345,0.3333,0.0417,0.0491,0.2893,0.0841,0.0,0.0,0.3279,0.0787,0.9851,0.7987,0.0697,0.08,0.0345,0.3333,0.0417,0.0488,0.2694,0.0822,0.0,0.0,reg oper account,block of flats,0.1014,Panel,No,1.0,0.0,1.0,0.0,-564.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n122551,0,Cash loans,F,N,Y,0,216000.0,468000.0,13810.5,468000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018209,-19862,-5862,-5807.0,-3375,,1,1,1,1,1,0,Core staff,2.0,3,3,FRIDAY,10,0,0,0,0,1,1,School,,0.4951856973023813,0.4938628816996244,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n226112,0,Cash loans,M,Y,N,0,180000.0,540000.0,39294.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.019101,-10596,-1812,-632.0,-2944,4.0,1,1,1,1,0,0,High skill tech staff,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.3403545645222823,0.651544660729078,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1943.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n336435,0,Cash loans,M,N,N,0,180000.0,409500.0,20047.5,409500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-9779,-336,-406.0,-2429,,1,1,1,1,0,0,Laborers,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.25259057548404523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-105.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n133870,1,Cash loans,M,N,Y,1,225000.0,500490.0,59526.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.032561,-9964,-86,-4785.0,-1783,,1,1,0,1,1,1,,3.0,1,1,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.1893785854783259,0.5079282175905123,,0.0821,0.0415,0.9911,,,0.08,0.069,0.375,,,,0.0936,,0.0048,0.084,0.0413,0.9916,,,0.0806,0.069,0.375,,,,0.0911,,0.0038,0.0833,0.0415,0.9916,,,0.08,0.069,0.375,,,,0.0941,,0.0042,,block of flats,0.0696,Panel,No,2.0,0.0,2.0,0.0,-443.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n130176,0,Cash loans,F,N,Y,0,112500.0,273636.0,20043.0,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,With parents,0.007120000000000001,-8739,-643,-3319.0,-1409,,1,1,0,1,0,1,Sales staff,2.0,2,2,FRIDAY,13,0,0,0,1,1,0,Business Entity Type 3,,0.5081850949651039,0.3360615207658242,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-1485.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n237794,0,Cash loans,F,N,Y,0,135000.0,958122.0,34542.0,742500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-20678,-205,-430.0,-2908,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Other,0.7817792921947266,0.4731306669370791,0.3825018041447388,0.0082,0.0,0.9727,0.626,0.0076,0.0,0.0345,0.0417,0.0833,0.0211,0.0067,0.0081,0.0,0.0,0.0084,0.0,0.9727,0.6406,0.0076,0.0,0.0345,0.0417,0.0833,0.0216,0.0073,0.0084,0.0,0.0,0.0083,0.0,0.9727,0.631,0.0076,0.0,0.0345,0.0417,0.0833,0.0215,0.0068,0.0082,0.0,0.0,reg oper account,block of flats,0.0064,Wooden,No,0.0,0.0,0.0,0.0,-424.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n323057,0,Revolving loans,F,N,Y,1,99000.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-13645,-3082,-7795.0,-1222,,1,1,0,1,0,1,Security staff,3.0,3,3,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4227546435904058,0.4076080308905629,0.4938628816996244,0.0619,,0.9831,,,0.0,0.1034,0.1667,,0.0044,,0.0643,,0.0142,0.063,,0.9831,,,0.0,0.1034,0.1667,,0.0045,,0.067,,0.0151,0.0625,,0.9831,,,0.0,0.1034,0.1667,,0.0045,,0.0655,,0.0145,,block of flats,0.0506,Panel,No,2.0,0.0,2.0,0.0,-1936.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n289520,0,Revolving loans,F,N,Y,0,157500.0,405000.0,20250.0,405000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-15508,-2112,-3163.0,-4545,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Legal Services,,0.553205787168901,,0.033,0.0628,0.9911,,,0.0,0.1034,0.1667,,0.0296,,0.0434,,0.0,0.0336,0.0652,0.9911,,,0.0,0.1034,0.1667,,0.0302,,0.0452,,0.0,0.0333,0.0628,0.9911,,,0.0,0.1034,0.1667,,0.0301,,0.0441,,0.0,,block of flats,0.0341,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-628.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n271282,0,Cash loans,F,N,Y,0,315000.0,254700.0,24939.0,225000.0,Unaccompanied,State servant,Higher education,Widow,House / apartment,0.032561,-24194,-6637,-4095.0,-4392,,1,1,0,1,0,0,Managers,1.0,1,1,TUESDAY,17,0,0,0,0,0,0,Other,,0.8029380091625028,0.6738300778602003,0.0825,0.0,0.9752,,,0.0,0.1379,0.1667,,0.0,,0.1687,,0.0,0.084,0.0,0.9757,,,0.0,0.1379,0.1667,,0.0,,0.28600000000000003,,0.0,0.0833,0.0,0.9757,,,0.0,0.1379,0.1667,,0.0,,0.1747,,0.0,,block of flats,0.264,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n154762,0,Cash loans,F,Y,Y,0,157500.0,1125000.0,32895.0,1125000.0,Unaccompanied,State servant,Incomplete higher,Married,House / apartment,0.028663,-13958,-2440,-2090.0,-4705,9.0,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Government,0.2669279259905281,0.6798596902089087,0.646329897706246,0.0825,0.0512,0.9871,0.8232,0.0084,0.0,0.2069,0.1667,0.0417,0.0228,0.0672,0.0739,0.0,0.0,0.084,0.0531,0.9871,0.8301,0.0085,0.0,0.2069,0.1667,0.0417,0.0234,0.0735,0.077,0.0,0.0,0.0833,0.0512,0.9871,0.8256,0.0085,0.0,0.2069,0.1667,0.0417,0.0232,0.0684,0.0752,0.0,0.0,reg oper account,block of flats,0.0581,Panel,No,0.0,0.0,0.0,0.0,-984.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n221734,0,Cash loans,M,Y,Y,0,157500.0,314055.0,16573.5,238500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-20270,-525,-3949.0,-3477,26.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,15,0,1,1,0,0,0,Construction,,0.6965242120273543,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-2218.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n437489,0,Cash loans,F,N,Y,0,157500.0,654498.0,28957.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-18847,-3013,-7554.0,-2384,,1,1,0,1,1,0,Sales staff,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,Self-employed,0.7127928464666559,0.5774995506295669,0.3672910183026313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2011.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n290089,0,Cash loans,F,N,Y,0,63000.0,630747.0,22783.5,544500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.003818,-20927,365243,-6339.0,-1281,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,5,0,0,0,0,0,0,XNA,,0.3621926802413197,,0.0144,0.0,0.9776,0.6940000000000001,0.0016,0.0,0.0345,0.0417,0.0833,0.0194,0.0118,0.0121,0.0,0.0,0.0147,0.0,0.9777,0.706,0.0016,0.0,0.0345,0.0417,0.0833,0.0199,0.0129,0.0126,0.0,0.0,0.0146,0.0,0.9776,0.6981,0.0016,0.0,0.0345,0.0417,0.0833,0.0198,0.012,0.0123,0.0,0.0,reg oper account,block of flats,0.0095,Block,No,0.0,0.0,0.0,0.0,-791.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n170070,0,Cash loans,F,N,Y,0,180000.0,107820.0,7798.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.022625,-20227,-2375,-14349.0,-3368,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.2729028091344847,0.6610235391308081,0.1036,0.0789,0.9767,0.6804,0.0107,0.0,0.1724,0.1667,0.2083,0.0507,0.0849,0.0674,0.0,0.0,0.105,0.0819,0.9767,0.6929,0.0108,0.0,0.1724,0.1667,0.2083,0.0518,0.0927,0.0703,0.0,0.0,0.1046,0.0789,0.9767,0.6847,0.0108,0.0,0.1724,0.1667,0.2083,0.0515,0.0864,0.0687,0.0,0.0,reg oper account,block of flats,0.0862,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n421244,0,Cash loans,F,Y,Y,2,135000.0,406597.5,29047.5,351000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-10559,-2992,-3069.0,-2761,12.0,1,1,0,1,0,0,Sales staff,4.0,2,2,SUNDAY,11,0,0,0,1,1,0,Self-employed,,0.6072228633067226,0.5620604831738043,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-167.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n114378,1,Cash loans,F,N,Y,2,157500.0,485640.0,36441.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-16560,-3989,-2363.0,-115,,1,1,0,1,0,0,Sales staff,4.0,2,2,SATURDAY,16,0,0,0,0,0,0,Self-employed,,0.2587870587895701,0.25533177083329,,,0.9781,,,,,,,,,0.0441,,,,,0.9782,,,,,,,,,0.0459,,,,,0.9781,,,,,,,,,0.0449,,,,,0.0516,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n301887,0,Cash loans,F,N,Y,0,270000.0,254700.0,20250.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.01885,-8544,-1201,-8488.0,-1138,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Bank,,0.5539029259928321,,0.0825,,0.9762,,,,0.1379,0.1667,,,,0.0477,,,0.084,,0.9762,,,,0.1379,0.1667,,,,0.0497,,,0.0833,,0.9762,,,,0.1379,0.1667,,,,0.0485,,,,block of flats,0.055,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-317.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n287660,0,Cash loans,F,Y,Y,0,225000.0,473760.0,46282.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.011703,-18267,-7728,-8443.0,-1814,9.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,Other,,0.7382857390334994,0.746300213050371,0.0124,0.0305,0.9622,,,,0.069,0.0417,,0.048,,0.0134,,0.0247,0.0126,0.0317,0.9623,,,,0.069,0.0417,,0.0491,,0.0139,,0.0261,0.0125,0.0305,0.9622,,,,0.069,0.0417,,0.0488,,0.0136,,0.0252,,block of flats,0.0159,Block,Yes,3.0,0.0,3.0,0.0,-1018.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n110750,0,Cash loans,F,N,Y,0,94500.0,392427.0,19215.0,324000.0,Children,Working,Secondary / secondary special,Widow,House / apartment,0.025164,-17121,-382,-2793.0,-643,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,Trade: type 7,,0.16318703546427088,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1954.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n248309,0,Cash loans,F,N,Y,0,225000.0,700830.0,22738.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003818,-16569,-5865,-2291.0,-86,,1,1,0,1,0,0,Accountants,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Self-employed,,0.5739367713333913,0.41534714488434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0269,,No,2.0,1.0,2.0,0.0,-683.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n438951,0,Cash loans,F,Y,Y,0,121500.0,364896.0,26077.5,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.006008,-14430,-2613,-2344.0,-1655,11.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,0.8129545754850511,0.6328813348083813,0.7713615919194317,0.2588,,0.9955,,,0.24,0.2069,0.3333,0.375,,,0.2448,,0.0137,0.2637,,0.9955,,,0.2417,0.2069,0.3333,0.375,,,0.2551,,0.0145,0.2613,,0.9955,,,0.24,0.2069,0.3333,0.375,,,0.2492,,0.013999999999999999,reg oper account,block of flats,0.1955,Panel,No,0.0,0.0,0.0,0.0,-691.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n402215,0,Cash loans,F,N,Y,0,171000.0,646920.0,20997.0,540000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.002042,-15668,-3479,-502.0,-3714,,1,1,1,1,0,0,Core staff,2.0,3,3,TUESDAY,14,0,0,0,0,0,0,Kindergarten,,0.5425025819318211,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-578.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n284416,0,Cash loans,F,Y,Y,2,117000.0,1125000.0,50314.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-13400,-4465,-2055.0,-4265,12.0,1,1,0,1,1,1,Core staff,4.0,2,2,FRIDAY,14,0,0,0,0,0,0,School,0.6768745386292034,0.6109040388451604,0.5280927512030451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-845.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n347750,0,Cash loans,F,N,Y,2,180000.0,204768.0,11884.5,162000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.032561,-13023,-493,-6614.0,-4719,,1,1,0,1,1,0,Core staff,3.0,1,1,SATURDAY,10,0,0,0,0,0,0,Restaurant,0.6188027372083377,0.5914184041760053,0.5902333386185574,0.1433,,0.9757,0.6668,0.0151,0.0,0.2414,0.1667,0.2083,0.0294,0.1168,0.1127,0.0,0.0,0.146,,0.9757,0.6798,0.0152,0.0,0.2414,0.1667,0.2083,0.03,0.1276,0.1046,0.0,0.0,0.1447,,0.9757,0.6713,0.0152,0.0,0.2414,0.1667,0.2083,0.0299,0.1189,0.1022,0.0,0.0,reg oper account,block of flats,0.08,Panel,No,2.0,0.0,2.0,0.0,-546.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n305410,0,Cash loans,M,Y,N,0,112500.0,403249.5,19741.5,283500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.006629,-11744,-638,-4985.0,-565,10.0,1,1,0,1,0,0,Drivers,1.0,2,2,SATURDAY,7,0,0,0,0,0,0,Agriculture,,0.4069165045386122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-554.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n359808,0,Cash loans,F,N,Y,0,112500.0,1624500.0,56466.0,1624500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-22843,365243,-3559.0,-4140,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.2587954479962963,0.7544061731797895,0.1237,0.0896,0.9727,0.626,0.0,0.0,0.2069,0.1667,0.0417,0.1047,0.1009,0.0787,0.0,0.0,0.1261,0.09300000000000001,0.9727,0.6406,0.0,0.0,0.2069,0.1667,0.0417,0.1071,0.1102,0.08199999999999999,0.0,0.0,0.1249,0.0896,0.9727,0.631,0.0,0.0,0.2069,0.1667,0.0417,0.1065,0.1026,0.0801,0.0,0.0,reg oper account,block of flats,0.0909,Panel,No,0.0,0.0,0.0,0.0,-22.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n108465,0,Cash loans,F,N,N,0,225000.0,675000.0,45108.0,675000.0,Family,Working,Higher education,Married,House / apartment,0.035792000000000004,-12254,-516,-4011.0,-2920,,1,1,1,1,1,1,Sales staff,2.0,2,2,MONDAY,13,0,0,0,1,1,0,Business Entity Type 3,0.4714953970938498,0.4740083774566379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1773.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n357784,0,Cash loans,F,N,Y,0,76500.0,172512.0,15952.5,144000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018634,-10396,-1524,-582.0,-730,,1,1,1,1,1,0,Core staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Medicine,0.6029508088413884,0.6005588541060239,0.2103502286944494,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-653.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n300317,0,Cash loans,F,Y,N,0,112500.0,653121.0,48960.0,616500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006852,-16886,-1795,-2085.0,-440,22.0,1,1,0,1,0,0,Cooking staff,2.0,3,3,SUNDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.48588545070647704,0.0005435869763768923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n124572,0,Cash loans,F,N,Y,1,135000.0,780363.0,31077.0,697500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-15962,-3276,-6712.0,-4357,,1,1,0,1,1,0,Core staff,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Kindergarten,,0.16995295093717294,0.4992720153045617,0.0082,0.0,0.9757,0.6668,0.0008,0.0,0.0345,0.0417,0.0833,0.0084,0.0067,0.0063,0.0,0.0019,0.0084,0.0,0.9757,0.6798,0.0008,0.0,0.0345,0.0417,0.0833,0.0086,0.0073,0.0066,0.0,0.002,0.0083,0.0,0.9757,0.6713,0.0008,0.0,0.0345,0.0417,0.0833,0.0085,0.0068,0.0064,0.0,0.002,reg oper spec account,block of flats,0.0054,Wooden,Yes,0.0,0.0,0.0,0.0,-276.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n280046,0,Cash loans,F,N,Y,2,81000.0,296280.0,14382.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018801,-12109,-4556,-5222.0,-4002,,1,1,1,1,1,0,,4.0,2,2,MONDAY,8,0,0,0,0,0,0,School,,0.3078181622771944,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n217688,0,Cash loans,M,Y,Y,0,112500.0,314055.0,16164.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.030755,-13637,-2027,-4109.0,-3619,16.0,1,1,0,1,1,0,Laborers,1.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Construction,,0.517488812058077,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3015.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n162014,0,Cash loans,F,N,Y,0,135000.0,450000.0,27324.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003069,-10142,-1815,-3794.0,-1750,,1,1,0,1,0,0,Waiters/barmen staff,2.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.3376844504891209,0.5749203903846312,0.3723336657058204,,,0.9856,,,,,,,,,0.0437,,,,,0.9856,,,,,,,,,0.0455,,,,,0.9856,,,,,,,,,0.0445,,,,,0.067,,No,5.0,1.0,5.0,1.0,-1164.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n119297,0,Cash loans,M,Y,N,0,126000.0,540000.0,25020.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-20136,-1795,-3270.0,-3682,8.0,1,1,0,1,1,0,Security staff,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,Security,,0.5754867718237853,0.3876253444214701,0.066,,0.9727,,,0.0,0.1379,0.1667,,,,0.0502,,0.0531,0.0672,,0.9727,,,0.0,0.1379,0.1667,,,,0.0523,,0.0562,0.0666,,0.9727,,,0.0,0.1379,0.1667,,,,0.0511,,0.0542,,block of flats,0.0511,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n317842,0,Revolving loans,M,N,Y,0,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.005144,-17162,-2920,-9582.0,-686,,1,1,1,1,1,0,,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.10461641865760003,0.6195277080511546,0.0577,0.0694,0.9871,0.8232,0.0093,0.0,0.1379,0.1667,0.2083,0.0,0.0471,0.0516,0.0,0.0,0.0588,0.07200000000000001,0.9871,0.8301,0.0094,0.0,0.1379,0.1667,0.2083,0.0,0.0514,0.0538,0.0,0.0,0.0583,0.0694,0.9871,0.8256,0.0094,0.0,0.1379,0.1667,0.2083,0.0,0.0479,0.0525,0.0,0.0,reg oper spec account,block of flats,0.0406,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-617.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n210892,0,Cash loans,F,N,Y,0,90000.0,135000.0,10953.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009334,-17137,-450,-2441.0,-670,,1,1,1,1,1,0,Sales staff,2.0,2,2,TUESDAY,9,0,0,0,1,1,1,Business Entity Type 3,,0.6625618747291121,0.6178261467332483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1708.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n306900,0,Cash loans,M,Y,Y,2,270000.0,166810.5,18094.5,144000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.014464,-13497,-363,-90.0,-360,9.0,1,1,0,1,0,0,Drivers,4.0,2,2,MONDAY,3,0,0,0,0,0,0,Transport: type 3,0.26224105280951354,0.5507019486387515,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-285.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n406904,0,Cash loans,F,N,N,0,135000.0,225000.0,17775.0,225000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.026392,-10377,-1429,-1926.0,-3032,,1,1,1,1,0,0,Core staff,1.0,2,2,THURSDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.7191629940150022,0.1766525794312139,0.1526,,0.9742,,,0.0,0.0345,0.1667,,0.0879,,0.0955,,0.0,0.1555,,0.9742,,,0.0,0.0345,0.1667,,0.0899,,0.0995,,0.0,0.1541,,0.9742,,,0.0,0.0345,0.1667,,0.0894,,0.0972,,0.0,,block of flats,0.077,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1160.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,2.0\n293575,0,Cash loans,F,N,Y,0,270000.0,673875.0,21865.5,562500.0,Other_A,Commercial associate,Higher education,Separated,House / apartment,0.072508,-17404,-6033,-780.0,-962,,1,1,0,1,0,0,Managers,1.0,1,1,SUNDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5430018591791096,,0.1728,0.1061,0.9791,0.7144,0.1354,0.1864,0.1493,0.3333,0.1525,0.0057,0.1409,0.1898,0.0,0.0448,0.1513,0.0787,0.9791,0.7256,0.053,0.1611,0.1034,0.3333,0.0417,0.0,0.1322,0.1484,0.0,0.0,0.1499,0.0943,0.9791,0.7182,0.0542,0.16,0.1379,0.3333,0.0417,0.0,0.1231,0.146,0.0,0.0,org spec account,block of flats,0.1639,Panel,No,0.0,0.0,0.0,0.0,-1930.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n399505,0,Cash loans,F,N,Y,0,315000.0,1885536.0,95913.0,1800000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.010276,-23122,365243,-7127.0,-4521,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,17,0,0,0,0,0,0,XNA,,0.6104985236342949,0.3962195720630885,0.1485,0.0847,0.9816,,,0.16,0.1379,0.3333,,0.0965,,0.0925,,0.1458,0.1513,0.0879,0.9816,,,0.1611,0.1379,0.3333,,0.0987,,0.0963,,0.1544,0.1499,0.0847,0.9816,,,0.16,0.1379,0.3333,,0.0981,,0.0941,,0.1489,,block of flats,0.1214,Panel,No,2.0,2.0,2.0,1.0,-1586.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n319381,0,Cash loans,F,N,N,0,360000.0,1797354.0,49423.5,1606500.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.02461,-16168,-191,-6950.0,-4276,,1,1,1,1,1,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 1,0.4620395885803262,0.7732811476897242,,0.0918,0.086,0.9816,,,0.0,0.2069,0.1667,,0.0939,,0.0821,,0.0338,0.0935,0.0892,0.9816,,,0.0,0.2069,0.1667,,0.096,,0.0856,,0.0358,0.0926,0.086,0.9816,,,0.0,0.2069,0.1667,,0.0955,,0.0836,,0.0345,,block of flats,0.0786,Panel,No,0.0,0.0,0.0,0.0,-2880.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n312816,0,Cash loans,F,N,N,1,148500.0,292500.0,19917.0,292500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Rented apartment,0.018209,-13345,-655,-1102.0,-1496,,1,1,0,1,0,0,,3.0,3,3,TUESDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.16724637290962238,0.3672910183026313,0.2144,0.3105,0.9911,0.8776,0.1449,0.0,0.4483,0.1667,0.0417,0.1754,0.1748,0.20800000000000002,0.0,0.0,0.2185,0.3223,0.9911,0.8824,0.1462,0.0,0.4483,0.1667,0.0417,0.1794,0.191,0.2168,0.0,0.0,0.2165,0.3105,0.9911,0.8792,0.1458,0.0,0.4483,0.1667,0.0417,0.1785,0.1779,0.2118,0.0,0.0,reg oper account,block of flats,0.1636,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n246170,0,Cash loans,F,N,N,0,225000.0,900000.0,50256.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.072508,-10823,-304,-5390.0,-3450,,1,1,1,1,1,0,Sales staff,1.0,1,1,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.6257380408325581,0.6957146962919043,0.6212263380626669,0.0825,0.0822,0.9732,0.6328,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0664,0.0696,0.0039,0.0524,0.084,0.0849,0.9732,0.6472,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0725,0.0724,0.0039,0.0545,0.0833,0.0822,0.9732,0.6377,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0676,0.0709,0.0039,0.0535,not specified,block of flats,0.066,Block,No,0.0,0.0,0.0,0.0,-631.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n379216,0,Cash loans,F,N,N,0,270000.0,900000.0,35694.0,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020713,-17714,-4039,-6968.0,-1260,,1,1,1,1,1,0,,2.0,3,3,THURSDAY,9,0,0,0,0,0,0,School,0.6557879560172652,0.5023252973976471,0.7121551551910698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-410.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n391805,0,Cash loans,M,Y,Y,1,225000.0,1671210.0,48996.0,1395000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-16605,-646,-624.0,-128,16.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,13,0,1,1,0,0,0,Business Entity Type 3,0.12334558986722488,0.39903041568689,0.44539624156083396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-126.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n329123,1,Cash loans,F,N,Y,0,225000.0,1142725.5,69907.5,1080000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-19681,-501,-12617.0,-2444,,1,1,0,1,0,0,Medicine staff,2.0,1,1,FRIDAY,9,0,0,0,0,1,1,Medicine,,0.5457842612101165,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-304.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n122979,0,Cash loans,F,N,Y,2,292500.0,640080.0,24259.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-10854,-1261,-8096.0,-878,,1,1,0,1,0,1,High skill tech staff,4.0,1,1,SATURDAY,15,0,0,0,1,1,0,Transport: type 3,0.6402326268754328,0.6396021898119644,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-893.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n435351,0,Cash loans,M,N,Y,0,135000.0,450000.0,27193.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-10418,-462,-568.0,-3074,,1,1,1,1,0,0,Laborers,2.0,3,3,FRIDAY,7,0,0,0,0,1,1,Construction,0.06640679865782825,0.28897481695744304,0.190705947811054,0.0515,,0.9990000000000001,0.9864,,0.0,0.069,0.1667,,,,0.0563,,0.0,0.0525,,0.9990000000000001,0.9869,,0.0,0.069,0.1667,,,,0.0587,,0.0,0.052000000000000005,,0.9990000000000001,0.9866,,0.0,0.069,0.1667,,,,0.0573,,0.0,,block of flats,0.0443,Mixed,No,0.0,0.0,0.0,0.0,-335.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n223445,0,Cash loans,F,N,Y,0,162000.0,1049746.5,32958.0,940500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.007120000000000001,-23043,365243,-1006.0,-3980,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.2061120240839004,0.7106743858828587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-199.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n156114,1,Cash loans,F,Y,Y,0,90000.0,640080.0,29970.0,450000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.011657,-14787,-1984,-8937.0,-4020,9.0,1,1,0,1,0,0,,2.0,1,1,WEDNESDAY,14,0,0,0,0,1,1,Business Entity Type 1,,0.11890002643682705,0.8128226070575616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1687.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n372512,0,Cash loans,F,N,Y,0,126000.0,239850.0,23494.5,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010643,-20929,-3458,-2683.0,-4370,,1,1,1,1,1,0,Sales staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,,0.5420032192934199,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,1.0\n117618,0,Cash loans,M,Y,Y,2,90000.0,495000.0,21100.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-11388,-2049,-2040.0,-130,9.0,1,1,0,1,0,0,Drivers,4.0,2,2,MONDAY,13,0,0,0,0,0,0,Self-employed,,0.6523137917827871,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1579.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n209612,0,Cash loans,F,N,Y,0,225000.0,1185282.0,34785.0,1035000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-10287,-289,-14.0,-1776,,1,1,0,1,0,0,,2.0,1,1,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.4517482845334529,0.648081315596332,0.20208660168203949,1.0,1.0,0.9995,0.9932,1.0,1.0,0.3103,1.0,1.0,0.5797,1.0,1.0,0.0579,1.0,1.0,1.0,0.9995,0.9935,1.0,1.0,0.3103,1.0,1.0,0.5929,1.0,1.0,0.0584,1.0,1.0,1.0,0.9995,0.9933,1.0,1.0,0.3103,1.0,1.0,0.5898,1.0,1.0,0.0582,1.0,reg oper account,block of flats,1.0,Monolithic,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n110631,0,Cash loans,F,N,Y,0,90000.0,254700.0,14350.5,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-23803,365243,-9166.0,-4772,,1,0,0,1,0,0,,2.0,2,2,MONDAY,16,0,0,0,0,0,0,XNA,0.8193418499049804,0.3441470714744494,0.7267112092725122,0.1639,0.1159,0.9747,0.6532,0.0207,0.12,0.2414,0.25,0.2083,0.1051,0.1278,0.1254,0.027000000000000003,0.0883,0.16699999999999998,0.1203,0.9747,0.6668,0.0209,0.1208,0.2414,0.25,0.2083,0.1075,0.1396,0.1307,0.0272,0.0935,0.1655,0.1159,0.9747,0.6578,0.0209,0.12,0.2414,0.25,0.2083,0.1069,0.13,0.1277,0.0272,0.0901,not specified,block of flats,0.1292,\"Stone, brick\",No,9.0,0.0,9.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n377299,0,Cash loans,F,Y,Y,0,360000.0,1530000.0,53311.5,1530000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00963,-18356,-1820,-7648.0,-1906,7.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Housing,0.6382593774538784,0.23376304061031475,0.6642482627052363,0.1082,0.0804,0.9901,,,0.12,0.1034,0.3333,,0.0586,,0.115,,0.0012,0.1103,0.0834,0.9901,,,0.1208,0.1034,0.3333,,0.0599,,0.1199,,0.0013,0.1093,0.0804,0.9901,,,0.12,0.1034,0.3333,,0.0596,,0.1171,,0.0012,,block of flats,0.10400000000000001,Panel,No,0.0,0.0,0.0,0.0,-733.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n403010,0,Cash loans,M,Y,Y,1,225000.0,592560.0,32274.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-9419,-1845,-1864.0,-2084,9.0,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.4585596910330012,0.3572932680336494,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-693.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n350698,0,Cash loans,M,N,Y,0,135000.0,254700.0,14751.0,225000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.019101,-24210,365243,-2363.0,-2611,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.6931626188052318,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1566.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n324951,0,Revolving loans,M,N,Y,1,108000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-18542,-115,-3822.0,-2006,,1,1,0,1,0,0,Security staff,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.2443952804499169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-212.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n134960,0,Cash loans,F,N,Y,0,112500.0,985500.0,50193.0,985500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-8427,-216,-7007.0,-1061,,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,19,0,0,0,0,0,0,Business Entity Type 1,,0.6477171507340563,,0.4577,0.38,0.9826,,,0.0,1.0,0.1667,,0.4109,,0.4101,,0.0061,0.4664,0.3943,0.9826,,,0.0,1.0,0.1667,,0.4203,,0.4273,,0.0065,0.4622,0.38,0.9826,,,0.0,1.0,0.1667,,0.418,,0.4175,,0.0063,,block of flats,0.3714,Panel,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n193973,0,Cash loans,F,N,Y,0,193500.0,1072809.0,31495.5,895500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-13381,-1444,-703.0,-4578,,1,1,1,1,1,0,Core staff,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Agriculture,,0.06928042341407474,0.5136937663039473,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-910.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n130767,0,Cash loans,F,N,N,0,157500.0,414792.0,20083.5,315000.0,Unaccompanied,Working,Incomplete higher,Separated,With parents,0.020246,-10024,-740,-10001.0,-2686,,1,1,0,1,0,0,Sales staff,1.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,,0.07273606373648435,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n106661,0,Cash loans,M,Y,N,0,202500.0,545040.0,25537.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.018801,-10481,-2769,-10481.0,-2831,11.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,15,0,0,0,0,1,1,Business Entity Type 2,,0.2747333911956145,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1453.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n119986,0,Cash loans,F,N,N,0,135000.0,1467612.0,56029.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.04622,-21907,-11531,-10663.0,-4731,,1,1,0,1,0,0,Secretaries,1.0,1,1,THURSDAY,12,0,0,0,1,1,1,Emergency,0.8870186116736379,0.7649485493537842,0.6195277080511546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2162.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n325561,1,Cash loans,F,N,Y,2,117000.0,314100.0,17167.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-13842,-563,-6456.0,-407,,1,1,0,1,0,0,,4.0,3,3,THURSDAY,7,0,0,0,0,1,1,Business Entity Type 3,,0.2785799282354501,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n215435,0,Cash loans,M,Y,Y,2,225000.0,1047195.0,44496.0,936000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-17069,-2453,-5326.0,-509,7.0,1,1,0,1,0,0,Drivers,4.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,0.6690620220581733,0.4760097131863237,0.4223696523543468,0.0598,0.0783,0.9752,0.66,0.0055,0.0,0.1379,0.125,0.1667,0.0221,0.0462,0.0434,0.0116,0.0251,0.0609,0.0813,0.9752,0.6733,0.0056,0.0,0.1379,0.125,0.1667,0.0226,0.0505,0.0453,0.0117,0.0266,0.0604,0.0783,0.9752,0.6645,0.0056,0.0,0.1379,0.125,0.1667,0.0225,0.047,0.0442,0.0116,0.0256,reg oper account,block of flats,0.0427,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-297.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n149627,0,Cash loans,F,N,Y,0,135000.0,512064.0,25033.5,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Municipal apartment,0.032561,-19959,-1004,-14873.0,-3008,,1,1,0,1,0,0,,1.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6561376101306043,0.107532175802471,0.1103,0.0764,0.9801,0.728,,0.12,0.1034,0.3333,0.375,0.038,0.0899,0.3055,0.0,0.0,0.1124,0.0793,0.9801,0.7387,,0.1208,0.1034,0.3333,0.375,0.0389,0.0983,0.1209,0.0,0.0,0.1114,0.0764,0.9801,0.7316,,0.12,0.1034,0.3333,0.375,0.0387,0.0915,0.311,0.0,0.0,reg oper account,block of flats,0.3893,Panel,No,1.0,1.0,1.0,0.0,-363.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n379902,0,Cash loans,F,N,Y,0,112500.0,1288350.0,35428.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-21041,365243,-3678.0,-4497,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.5161072494830723,,,0.1221,0.9821,,,0.16,0.1379,0.3333,,,,0.2183,,0.0769,,0.1267,0.9821,,,0.1611,0.1379,0.3333,,,,0.2274,,0.0814,,0.1221,0.9821,,,0.16,0.1379,0.3333,,,,0.2222,,0.0785,,block of flats,0.1884,Panel,No,5.0,0.0,5.0,0.0,-1463.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n150760,0,Cash loans,F,N,N,1,90000.0,194076.0,16600.5,162000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.006207,-9067,-170,-3343.0,-1070,,1,1,1,1,1,0,Laborers,3.0,2,2,SUNDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.3880189166407811,0.6413682574954046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1172.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n396088,0,Cash loans,F,N,Y,0,81000.0,553806.0,21222.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-18843,-3763,-8525.0,-2260,,1,1,0,1,1,0,High skill tech staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.7333506305481906,0.6397075677637197,0.267,0.3465,0.9886,0.8436,0.0976,0.52,0.4483,0.3333,0.375,0.3731,0.2177,0.5865,0.0,0.2672,0.2721,0.3595,0.9886,0.8497,0.0985,0.5236,0.4483,0.3333,0.375,0.3816,0.2378,0.6111,0.0,0.2829,0.2696,0.3465,0.9886,0.8457,0.0982,0.52,0.4483,0.3333,0.375,0.3796,0.2215,0.5971,0.0,0.2728,reg oper spec account,block of flats,0.5147,Panel,No,1.0,1.0,1.0,1.0,-476.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n280756,0,Cash loans,F,N,Y,0,90000.0,99000.0,10273.5,99000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.026392,-22251,365243,-10197.0,-4338,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.7740278611583059,0.4632753280912678,0.066,,0.9732,,,0.0,0.1379,0.1667,,,,0.0503,,0.0526,0.0672,,0.9732,,,0.0,0.1379,0.1667,,,,0.0524,,0.0557,0.0666,,0.9732,,,0.0,0.1379,0.1667,,,,0.0512,,0.0537,,block of flats,0.051,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1430.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n417948,0,Cash loans,M,Y,Y,0,135000.0,1046142.0,30717.0,913500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-20022,-2602,-7002.0,-3173,3.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.2622583692422573,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1346.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,0.0\n253045,1,Cash loans,M,Y,Y,2,292500.0,746280.0,59094.0,675000.0,Group of people,Working,Higher education,Married,House / apartment,0.030755,-13401,-1924,-3240.0,-3247,2.0,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.09918578772210612,0.6325459397322956,0.2225807646753351,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1745.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n230706,0,Cash loans,F,N,Y,0,495000.0,1269000.0,85279.5,1269000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-21527,365243,-8618.0,-4974,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,0.7112249837286025,0.6447826511067507,0.4418358231994413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-2596.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,1.0,4.0\n350519,0,Cash loans,M,N,N,0,99000.0,225000.0,11619.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-8686,-564,-4484.0,-1374,,1,1,0,1,1,0,Laborers,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Postal,,0.5748829808956363,0.42765737003502935,0.1495,0.0909,0.9871,0.8232,,0.16,0.1379,0.3333,0.375,0.1062,0.121,0.1637,0.0039,0.0031,0.1523,0.0943,0.9871,0.8301,,0.1611,0.1379,0.3333,0.375,0.1086,0.1322,0.1705,0.0039,0.0033,0.1509,0.0909,0.9871,0.8256,,0.16,0.1379,0.3333,0.375,0.10800000000000001,0.1231,0.1666,0.0039,0.0032,reg oper account,block of flats,0.1294,Panel,No,2.0,1.0,2.0,1.0,-205.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n213581,1,Cash loans,M,N,N,0,112500.0,1339884.0,39307.5,1170000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.003813,-17729,-83,-2803.0,-1050,,1,1,1,1,0,0,Laborers,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.4105177866274705,0.4638360270280475,,0.1227,0.1157,0.9801,0.728,0.2162,0.0,0.2759,0.1667,0.0417,0.0467,0.1,0.1124,0.0,0.0454,0.125,0.1201,0.9801,0.7387,0.2182,0.0,0.2759,0.1667,0.0417,0.0478,0.1093,0.1171,0.0,0.0481,0.1239,0.1157,0.9801,0.7316,0.2176,0.0,0.2759,0.1667,0.0417,0.0475,0.1018,0.1144,0.0,0.0463,reg oper account,block of flats,0.0983,Panel,No,4.0,0.0,4.0,0.0,-314.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n332125,0,Cash loans,M,Y,Y,1,202500.0,420588.0,29272.5,360000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.02461,-15299,-4304,-5169.0,-1915,5.0,1,1,0,1,0,0,Managers,3.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5945165169209793,0.41534714488434,,0.0,0.9613,0.4696,0.0163,0.0,0.2414,0.1667,0.2083,0.068,0.0471,0.1128,0.0193,0.1198,,0.0,0.9613,0.4904,0.0164,0.0,0.2414,0.1667,0.2083,0.0696,0.0514,0.1175,0.0195,0.1269,,0.0,0.9613,0.4767,0.0164,0.0,0.2414,0.1667,0.2083,0.0692,0.0479,0.1148,0.0194,0.1223,reg oper account,block of flats,0.1108,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1632.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n114487,0,Cash loans,F,Y,N,1,166500.0,640080.0,29970.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,Office apartment,0.04622,-11642,-524,-2851.0,-692,7.0,1,1,0,1,0,0,Core staff,3.0,1,1,THURSDAY,18,0,0,0,0,0,0,Government,0.3371113624070206,0.5396681386739741,0.2067786544915716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1657.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n281354,0,Cash loans,F,N,N,2,90000.0,156384.0,13419.0,135000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.007120000000000001,-10386,-1559,-609.0,-2781,,1,1,0,1,1,0,Core staff,4.0,2,2,TUESDAY,18,0,0,0,0,0,0,Security Ministries,0.4313538685104555,0.5712341787541174,0.14111510044661266,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-157.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n381021,0,Cash loans,M,Y,N,1,157500.0,746280.0,54436.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-10783,-3128,-5349.0,-3428,14.0,1,1,0,1,0,1,Laborers,3.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.7178472970769024,0.6451632059807737,0.4365064990977374,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n160540,0,Cash loans,F,Y,Y,0,247500.0,225000.0,21919.5,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006629,-14008,-2405,-8114.0,-4481,0.0,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Police,0.5048643973900813,0.7255309656112119,0.6413682574954046,0.0216,,0.9876,0.83,0.0,0.0,0.1034,0.0833,0.0417,0.003,0.0034,0.0238,0.0,0.0,0.0042,,0.9856,0.8105,0.0,0.0,0.069,0.0,0.0417,0.0031,0.0037,0.004,0.0,0.0,0.0219,,0.9876,0.8323,0.0,0.0,0.1034,0.0833,0.0417,0.0031,0.0034,0.0243,0.0,0.0,org spec account,terraced house,0.003,Wooden,Yes,2.0,0.0,2.0,0.0,-783.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n264118,0,Cash loans,F,N,Y,0,90000.0,675000.0,28728.0,675000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.014519999999999996,-21010,365243,-9544.0,-3992,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.344191494398425,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-278.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n154874,0,Cash loans,F,N,Y,0,157500.0,201469.5,16047.0,153000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-14518,-2823,-22.0,-5572,,1,1,0,1,0,0,,2.0,1,1,FRIDAY,13,0,0,0,0,1,1,School,,0.7313360257819522,0.34090642641523844,0.1155,0.0,0.9965,0.9524,,0.08,0.0345,0.7083,0.75,0.2431,0.0941,0.1461,,0.048,0.1176,0.0,0.9965,0.9543,,0.0806,0.0345,0.7083,0.75,0.2487,0.1028,0.1523,,0.0508,0.1166,0.0,0.9965,0.953,,0.08,0.0345,0.7083,0.75,0.2473,0.0958,0.1488,,0.049,,block of flats,0.0,Monolithic,No,0.0,0.0,0.0,0.0,-1003.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n129620,0,Cash loans,M,Y,N,1,180000.0,675000.0,26901.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-14456,-5529,-7195.0,-4289,1.0,1,1,1,1,0,0,Core staff,3.0,2,2,MONDAY,13,0,0,0,1,1,1,Religion,,0.604183341581245,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2734.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n174880,0,Cash loans,M,Y,Y,0,315000.0,1288350.0,41562.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-15165,-2430,-1281.0,-397,2.0,1,1,1,1,0,0,Drivers,2.0,2,2,WEDNESDAY,12,0,1,1,0,1,1,Business Entity Type 3,0.3901072569249801,0.6327543063368241,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-633.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n279398,1,Cash loans,F,N,N,0,121500.0,315000.0,12960.0,315000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008865999999999999,-9783,-1688,-9455.0,-913,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Trade: type 2,,0.2212860526551216,0.2079641743053816,0.0814,,0.9826,,,,0.3793,0.1667,,,,,,,0.083,,0.9826,,,,0.3793,0.1667,,,,,,,0.0822,,0.9826,,,,0.3793,0.1667,,,,,,,,block of flats,0.1226,,No,3.0,0.0,3.0,0.0,-1654.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,9.0\n396032,0,Cash loans,F,Y,N,0,234000.0,810000.0,34317.0,810000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Office apartment,0.04622,-13600,-4512,-872.0,-6115,6.0,1,1,0,1,0,0,Medicine staff,2.0,1,1,THURSDAY,13,0,0,0,0,0,0,Military,0.7557380034886241,0.6416109788469503,0.6313545365850379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-840.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n298265,0,Cash loans,M,Y,N,0,315000.0,706410.0,68944.5,679500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.011703,-10190,-3009,-4803.0,-2631,11.0,1,1,0,1,0,1,Sales staff,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.4410077246780973,0.7294326777315829,0.6594055320683344,0.5144,0.2937,0.9841,0.7824,,0.56,0.4828,0.3333,0.375,0.37,,0.5775,,0.0088,0.5242,0.3048,0.9841,0.7909,,0.5639,0.4828,0.3333,0.375,0.3784,,0.6017,,0.0093,0.5194,0.2937,0.9841,0.7853,,0.56,0.4828,0.3333,0.375,0.3764,,0.5879,,0.0089,not specified,block of flats,0.5249,Panel,No,0.0,0.0,0.0,0.0,-1083.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n220034,0,Cash loans,M,Y,N,2,256500.0,327024.0,15372.0,270000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.009657,-12494,-5198,-836.0,-3506,19.0,1,1,1,1,0,0,Managers,4.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Military,,0.7661809005914354,0.2636468134452008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2125.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n246090,0,Cash loans,M,N,N,1,99000.0,247500.0,10210.5,247500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.014519999999999996,-12905,-5275,-7020.0,-4642,,1,1,0,1,1,0,Laborers,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Industry: type 11,0.2127998698658935,0.5644513565197372,0.3996756156233169,,0.0,0.9786,0.7076,,0.0,0.1838,0.1667,,0.051,,0.0826,,0.0106,,0.0,0.9786,0.7190000000000001,,0.0,0.2069,0.1667,,0.0288,,0.0699,,0.0,,0.0,0.9786,0.7115,,0.0,0.2069,0.1667,,0.0518,,0.0917,,0.0045,,block of flats,0.0527,Panel,No,0.0,0.0,0.0,0.0,-336.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n410147,0,Cash loans,F,N,Y,1,112500.0,1288350.0,37669.5,1125000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00963,-16335,-8390,-7442.0,-4185,,1,1,1,1,1,0,Cooking staff,3.0,2,2,THURSDAY,10,0,0,0,0,0,0,Industry: type 12,,0.13183564718411914,0.622922000268356,0.0701,,0.9762,,,0.0,0.1379,0.1667,,0.0515,,0.0536,,0.0,0.0714,,0.9762,,,0.0,0.1379,0.1667,,0.0526,,0.0559,,0.0,0.0708,,0.9762,,,0.0,0.1379,0.1667,,0.0524,,0.0546,,0.0,,block of flats,0.0496,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1501.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n249725,1,Cash loans,M,N,Y,0,126000.0,225000.0,15349.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-9167,-433,-2923.0,-1837,,1,1,1,1,0,0,Laborers,2.0,2,2,MONDAY,11,0,0,0,0,1,1,Self-employed,,0.3109345566111512,0.04567680960674469,,,0.9722,,,,,,,,,0.001,,,,,0.9722,,,,,,,,,0.001,,,,,0.9722,,,,,,,,,0.001,,,,block of flats,0.0016,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n302227,0,Cash loans,F,N,N,0,121500.0,270000.0,14778.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.020246,-8292,-890,-758.0,-953,,1,1,1,1,1,0,Sales staff,1.0,3,3,MONDAY,11,0,0,0,0,0,0,Self-employed,0.29590216003962866,0.3014351752462574,0.29859498978739724,0.0619,0.0746,0.9836,,,0.0,0.1379,0.1667,,0.0372,,0.0565,,0.0,0.063,0.0774,0.9836,,,0.0,0.1379,0.1667,,0.038,,0.0588,,0.0,0.0625,0.0746,0.9836,,,0.0,0.1379,0.1667,,0.0378,,0.0575,,0.0,,block of flats,0.0497,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-443.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n151890,0,Revolving loans,F,N,Y,0,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.018634,-21698,365243,-10547.0,-4261,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.3152943178932899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-245.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n304681,0,Cash loans,F,N,N,2,67500.0,314100.0,12420.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14852,-553,-854.0,-4970,,1,1,0,1,0,0,Sales staff,4.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,0.2854217707792417,0.6259497058747622,0.4382813743111921,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n442642,0,Cash loans,F,N,Y,2,180000.0,557770.5,33345.0,481500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-11825,-1049,-1911.0,-1911,,1,1,0,1,0,0,Laborers,4.0,1,1,FRIDAY,13,0,0,0,1,1,0,Business Entity Type 3,0.3867475294772613,0.5985217699908095,,0.0412,0.0551,0.9762,,,0.04,0.0517,0.1875,,0.0248,,0.0425,,,0.0084,0.0572,0.9643,,,0.0,0.0345,0.0417,,0.0228,,0.0077,,,0.0416,0.0551,0.9762,,,0.04,0.0517,0.1875,,0.0252,,0.0433,,,,block of flats,0.0063,Others,No,2.0,1.0,2.0,1.0,-706.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n414512,0,Cash loans,M,Y,Y,3,157500.0,900000.0,26446.5,900000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.006296,-15282,-527,-3389.0,-3394,7.0,1,1,0,1,0,0,Drivers,5.0,3,3,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 2,0.32065296974289514,0.6827370410370003,0.2925880073368475,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-520.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n109594,0,Revolving loans,F,N,Y,1,180000.0,495000.0,24750.0,495000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.072508,-13954,-2715,-7956.0,-5595,,1,1,0,1,0,0,Core staff,3.0,1,1,WEDNESDAY,17,0,0,0,0,0,0,School,0.7603778251462847,0.7617369194296673,0.4902575124990026,0.1237,0.1023,0.9752,0.66,0.0464,0.0,0.2069,0.1667,0.2083,0.0,0.1,0.0931,0.0039,0.0079,0.084,0.086,0.9752,0.6733,0.0368,0.0,0.1379,0.1667,0.2083,0.0,0.0725,0.048,0.0039,0.0045,0.1249,0.1023,0.9752,0.6645,0.0466,0.0,0.2069,0.1667,0.2083,0.0,0.1018,0.0947,0.0039,0.0081,reg oper account,block of flats,0.0553,Block,No,0.0,0.0,0.0,0.0,-1542.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n413905,0,Cash loans,M,N,Y,1,180000.0,215865.0,25749.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.014464,-18186,-2554,-8704.0,-1743,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,6,0,0,0,0,1,1,Business Entity Type 1,0.411915336465888,0.5248939268350317,0.7490217048463391,0.1443,0.13,0.9871,0.8232,0.0266,0.0,0.3448,0.1667,0.2083,0.1134,0.1177,0.1422,0.0,0.0,0.1471,0.1349,0.9871,0.8301,0.0268,0.0,0.3448,0.1667,0.2083,0.11599999999999999,0.1286,0.1482,0.0,0.0,0.1457,0.13,0.9871,0.8256,0.0268,0.0,0.3448,0.1667,0.2083,0.1154,0.1197,0.1448,0.0,0.0,reg oper account,block of flats,0.131,Panel,No,0.0,0.0,0.0,0.0,-296.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n298755,0,Cash loans,M,Y,Y,2,270000.0,675000.0,25015.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-13833,-5349,-967.0,-4639,3.0,1,1,0,1,0,0,Drivers,4.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Government,0.4823245351148518,0.6887031087365862,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2059.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n454577,0,Cash loans,F,N,N,0,157500.0,558855.0,34317.0,477000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.032561,-10009,-1078,-2118.0,-601,,1,1,0,1,1,1,Sales staff,2.0,1,1,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.484271470077628,0.6504914084967827,0.3046721837533529,0.1034,0.0787,0.9846,0.7892,0.035,0.16,0.1148,0.375,0.375,0.075,0.1505,0.1809,0.0,0.0,0.0158,0.0237,0.9846,0.7975,0.0354,0.1208,0.069,0.3333,0.375,0.0453,0.1644,0.1254,0.0,0.0,0.1114,0.0818,0.9846,0.792,0.0353,0.16,0.1034,0.3333,0.375,0.06,0.1531,0.1936,0.0,0.0,reg oper account,block of flats,0.1692,Panel,No,0.0,0.0,0.0,0.0,-1617.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n122688,0,Cash loans,M,Y,Y,0,180000.0,536917.5,30109.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.031329,-15844,-6224,-5856.0,-4806,7.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Industry: type 9,,0.6845814587656789,0.8193176922872417,0.0861,0.1294,0.9896,0.8572,0.0177,0.02,0.1724,0.25,0.2917,0.0607,0.0702,0.0955,0.0,0.0,0.0777,0.1317,0.9896,0.8628,0.0154,0.0,0.1724,0.1667,0.2083,0.0414,0.068,0.0871,0.0,0.0,0.0869,0.1294,0.9896,0.8591,0.0178,0.02,0.1724,0.25,0.2917,0.0617,0.0714,0.0972,0.0,0.0,reg oper spec account,block of flats,0.0741,Panel,No,0.0,0.0,0.0,0.0,-331.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n436316,1,Revolving loans,M,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.035792000000000004,-7751,-135,-2091.0,-324,,1,1,1,1,1,0,Sales staff,1.0,2,2,SUNDAY,10,0,0,0,0,0,0,Trade: type 7,0.15045697303762165,0.4760588022084772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-118.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n436899,0,Cash loans,F,N,Y,0,81000.0,630000.0,32296.5,630000.0,Group of people,Working,Incomplete higher,Civil marriage,House / apartment,0.026392,-15827,-468,-3975.0,-4185,,1,1,0,1,1,0,Cleaning staff,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.5746157453430992,,0.1485,,0.9861,,,0.16,0.1379,0.3333,,,,0.1529,,0.0,0.1513,,0.9861,,,0.1611,0.1379,0.3333,,,,0.1593,,0.0,0.1499,,0.9861,,,0.16,0.1379,0.3333,,,,0.1557,,0.0,,block of flats,0.1203,Panel,No,2.0,0.0,2.0,0.0,-46.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n372397,0,Cash loans,F,N,Y,2,99000.0,814041.0,23800.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-14379,-1722,-4210.0,-4335,,1,1,0,1,0,0,Laborers,4.0,2,2,FRIDAY,8,0,0,0,0,0,0,Housing,0.4655370481993565,0.5053209720651425,0.6690566947824041,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-678.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n237147,0,Cash loans,M,Y,N,0,135000.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-14950,-3745,-3344.0,-4387,21.0,1,1,0,1,0,0,,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Industry: type 4,,0.6867823996204151,0.7713615919194317,0.0722,0.0655,0.9841,,,0.0,0.1379,0.1667,,0.0,,0.0681,,0.0,0.0735,0.0679,0.9841,,,0.0,0.1379,0.1667,,0.0,,0.0709,,0.0,0.0729,0.0655,0.9841,,,0.0,0.1379,0.1667,,0.0,,0.0693,,0.0,,block of flats,0.0535,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1869.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n116773,0,Cash loans,F,Y,N,0,225000.0,1223010.0,51948.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21188,-2088,-4814.0,-4751,17.0,1,1,0,1,0,0,Cooking staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 1,,0.7936792501716066,0.7121551551910698,0.0928,0.0567,0.9856,0.8028,0.0416,0.12,0.1034,0.3333,0.375,0.059000000000000004,0.0756,0.0875,0.0,0.0,0.0945,0.0589,0.9856,0.8105,0.042,0.1208,0.1034,0.3333,0.375,0.0603,0.0826,0.0912,0.0,0.0,0.0937,0.0567,0.9856,0.8054,0.0419,0.12,0.1034,0.3333,0.375,0.06,0.077,0.0891,0.0,0.0,reg oper account,block of flats,0.0688,Panel,No,0.0,0.0,0.0,0.0,-1853.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n213950,1,Cash loans,M,N,Y,0,121500.0,352044.0,17253.0,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.010643,-17540,365243,-3.0,-1092,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.5487154311001134,0.17456426555726348,0.1454,0.0472,0.9831,0.7688,0.0354,0.0,0.0517,0.1667,0.2083,0.0761,0.1387,0.0583,0.1042,0.0445,0.0945,0.0176,0.9831,0.7779,0.0357,0.0,0.0345,0.1667,0.2083,0.0659,0.1515,0.0533,0.1051,0.0,0.1468,0.0472,0.9831,0.7719,0.0356,0.0,0.0517,0.1667,0.2083,0.0774,0.1411,0.0594,0.1048,0.0455,reg oper account,block of flats,0.0596,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-945.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n419560,0,Cash loans,M,N,Y,2,67500.0,178290.0,19048.5,157500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-16398,-6953,-8619.0,-4240,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,12,0,0,0,0,0,0,Industry: type 3,0.2824384484304749,0.6181920837915079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n286006,0,Cash loans,F,Y,Y,1,270000.0,398223.0,30946.5,369000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.031329,-13411,-3403,-11329.0,-2359,6.0,1,1,0,1,0,0,Managers,3.0,2,2,FRIDAY,9,0,0,0,0,1,1,School,0.6503947487160401,0.652036003590788,0.1624419982223248,0.0113,0.0,0.9727,0.626,0.0014,0.0,0.0345,0.0417,0.0833,0.0428,0.0092,0.0089,0.0,0.0,0.0116,0.0,0.9727,0.6406,0.0014,0.0,0.0345,0.0417,0.0833,0.0438,0.0101,0.0093,0.0,0.0,0.0115,0.0,0.9727,0.631,0.0014,0.0,0.0345,0.0417,0.0833,0.0436,0.0094,0.0091,0.0,0.0,reg oper account,block of flats,0.0078,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1684.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,1.0\n303696,0,Cash loans,M,N,Y,0,247500.0,1800000.0,73444.5,1800000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-9974,-324,-3546.0,-1411,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,MONDAY,15,0,1,1,0,1,1,Industry: type 3,,0.28670215741580984,0.19747451156854226,0.0381,0.0237,0.9727,0.626,0.0054,0.0,0.1034,0.0833,0.125,0.0268,0.0303,0.0305,0.0039,0.0235,0.0389,0.0246,0.9727,0.6406,0.0054,0.0,0.1034,0.0833,0.125,0.0274,0.0331,0.0317,0.0039,0.0249,0.0385,0.0237,0.9727,0.631,0.0054,0.0,0.1034,0.0833,0.125,0.0273,0.0308,0.031,0.0039,0.024,reg oper account,block of flats,0.0308,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-96.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n359070,0,Revolving loans,F,Y,Y,1,112500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006305,-15077,-3957,-2379.0,-5277,24.0,1,1,0,1,0,0,Sales staff,3.0,3,3,MONDAY,10,0,0,0,0,0,0,Self-employed,0.38765360876983385,0.3190261384661553,0.7688075728291359,0.1031,0.0993,0.9871,0.8232,0.0158,0.0,0.2759,0.1667,0.2083,0.0798,,0.1029,,0.0259,0.105,0.1031,0.9871,0.8301,0.016,0.0,0.2759,0.1667,0.2083,0.0816,,0.1072,,0.0275,0.1041,0.0993,0.9871,0.8256,0.0159,0.0,0.2759,0.1667,0.2083,0.0812,,0.1047,,0.0265,reg oper account,block of flats,0.0952,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-642.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n313442,0,Cash loans,M,Y,Y,0,112500.0,514777.5,52879.5,477000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006670999999999999,-10964,-172,-86.0,-3646,7.0,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,,0.6499244284451773,0.6658549219640212,0.1113,0.0108,1.0,,,0.08,0.069,0.375,,0.0327,,0.087,,0.0327,0.1134,0.0112,1.0,,,0.0806,0.069,0.375,,0.0334,,0.0906,,0.0347,0.1124,0.0108,1.0,,,0.08,0.069,0.375,,0.0333,,0.0885,,0.0334,,block of flats,0.0894,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-122.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n275453,0,Cash loans,F,N,Y,0,337500.0,485640.0,33930.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-23783,365243,-11716.0,-4631,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.4955683472606134,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14.0,1.0,13.0,1.0,-378.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n229841,0,Cash loans,F,N,N,0,85500.0,247275.0,19548.0,225000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006008,-11397,-1048,-5105.0,-413,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Government,0.5232097342370243,0.4660229033455504,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n245009,0,Revolving loans,M,Y,Y,1,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010556,-13284,-221,-4427.0,-4436,24.0,1,1,0,1,0,0,Drivers,3.0,3,3,MONDAY,17,0,0,0,0,0,0,Other,,0.5426871074363241,0.25396280933631177,0.066,0.0,0.9747,0.6532,,0.0,0.1379,0.125,0.1667,0.0149,,0.0496,,0.004,0.0672,0.0,0.9747,0.6668,,0.0,0.1379,0.125,0.1667,0.0153,,0.0517,,0.0042,0.0666,0.0,0.9747,0.6578,,0.0,0.1379,0.125,0.1667,0.0152,,0.0505,,0.0041,reg oper spec account,block of flats,0.0399,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-562.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n402390,0,Cash loans,F,N,Y,0,180000.0,271066.5,11614.5,234000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009175,-17644,-3826,-4639.0,-1187,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.736239812072847,0.7570690154522959,0.0464,0.0295,0.9851,,,0.04,0.0345,0.3333,,,,0.0496,,0.0,0.0473,0.0306,0.9851,,,0.0403,0.0345,0.3333,,,,0.0516,,0.0,0.0468,0.0295,0.9851,,,0.04,0.0345,0.3333,,,,0.0505,,0.0,,block of flats,0.039,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-577.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n242548,0,Revolving loans,M,Y,Y,1,171000.0,270000.0,13500.0,270000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.009549,-11272,-4304,-5846.0,-3916,2.0,1,1,0,1,0,0,Core staff,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Police,0.39369053145533306,0.6528938361461942,0.34578480246959553,0.0881,0.1208,0.9921,,,0.1,0.069,0.3542,,0.0757,,0.1291,,0.0606,0.0536,0.1254,0.9921,,,0.0806,0.0345,0.3333,,0.0532,,0.0921,,0.0642,0.08900000000000001,0.1208,0.9921,,,0.1,0.069,0.3542,,0.077,,0.1315,,0.0619,,block of flats,0.0827,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1089.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n132483,0,Cash loans,F,Y,Y,0,171000.0,755190.0,36459.0,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.030755,-16365,-2456,-3199.0,-4451,24.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 1,,0.5407272926058109,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,1.0,-40.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n291690,0,Cash loans,F,N,N,0,157500.0,1350000.0,46926.0,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.032561,-20019,365243,-2864.0,-3446,,1,0,0,1,1,0,,2.0,1,1,FRIDAY,14,0,0,0,0,0,0,XNA,,0.7787052391959006,0.6769925032909132,0.0515,0.1148,0.9831,0.7688,0.0286,0.04,0.1207,0.3125,0.3542,0.0295,0.0391,0.12,0.0135,0.0754,0.0105,0.0871,0.9801,0.7387,0.0151,0.0,0.069,0.2917,0.3333,0.0279,0.0037,0.0963,0.0039,0.0635,0.052000000000000005,0.1148,0.9831,0.7719,0.0288,0.04,0.1207,0.3125,0.3542,0.03,0.0398,0.1222,0.0136,0.077,reg oper account,terraced house,0.0857,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1868.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,5.0\n228532,0,Cash loans,F,N,N,1,112500.0,412614.0,32728.5,364500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.0105,-15337,-97,-1414.0,-4838,,1,1,0,1,0,0,Sales staff,2.0,3,3,WEDNESDAY,15,0,0,0,0,0,0,Trade: type 7,,0.22493229539122267,0.3893387918468769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n254004,0,Cash loans,M,N,N,2,360000.0,900000.0,38263.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-13625,-2246,-3669.0,-4201,,1,1,1,1,1,0,Managers,4.0,1,1,THURSDAY,16,0,1,1,0,1,1,Transport: type 4,,0.661891753828851,0.5513812618027899,0.5701,0.198,0.9935,0.9116,0.1712,0.64,0.2759,0.6667,0.7083,0.0625,0.44799999999999995,0.6169,0.0772,0.0464,0.5809,0.2055,0.9935,0.9151,0.1727,0.6445,0.2759,0.6667,0.7083,0.0639,0.4894,0.6427,0.0778,0.0491,0.5756,0.198,0.9935,0.9128,0.1723,0.64,0.2759,0.6667,0.7083,0.0636,0.4558,0.628,0.0776,0.0473,org spec account,block of flats,0.5788,Panel,No,0.0,0.0,0.0,0.0,-728.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n294786,0,Cash loans,F,N,N,1,90000.0,271066.5,10737.0,234000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.020246,-11212,-1057,-2823.0,-3618,,1,1,0,1,0,0,Accountants,2.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Government,0.5299109080134755,0.4853384070781742,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0038,,No,0.0,0.0,0.0,0.0,-1871.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n377867,0,Cash loans,F,N,Y,0,135000.0,625356.0,23103.0,522000.0,Family,Working,Secondary / secondary special,Widow,House / apartment,0.025164,-21997,-3323,-10582.0,-4299,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,Other,,0.07532919553904542,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n294919,0,Cash loans,M,Y,Y,1,157500.0,528633.0,28804.5,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-8625,-185,-7793.0,-1308,8.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,9,0,0,0,0,1,1,Business Entity Type 1,0.21252753722883647,0.558021888719344,0.6738300778602003,0.0753,0.0584,0.9831,0.7688,0.0157,0.08,0.069,0.3333,0.375,0.04,0.0614,0.0767,0.0,0.0037,0.0767,0.0606,0.9831,0.7779,0.0159,0.0806,0.069,0.3333,0.375,0.0409,0.067,0.0799,0.0,0.0039,0.076,0.0584,0.9831,0.7719,0.0158,0.08,0.069,0.3333,0.375,0.0407,0.0624,0.0781,0.0,0.0037,reg oper spec account,block of flats,0.0603,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-669.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.0,2.0,0.0,0.0,0.0,2.0\n237625,0,Cash loans,F,N,Y,0,112500.0,593010.0,19129.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Office apartment,0.018634,-21298,-723,-4226.0,-4298,,1,1,1,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Other,,0.6071133326332142,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-888.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n341351,0,Cash loans,M,N,N,0,112500.0,284400.0,13257.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.010643,-8920,-1850,-8846.0,-1589,,1,1,1,1,0,0,Laborers,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Housing,,0.7167097457697379,0.20092608771597087,0.0186,0.0447,0.9806,,,0.0,0.1034,0.0417,,0.0133,,0.0158,,0.0036,0.0189,0.0464,0.9806,,,0.0,0.1034,0.0417,,0.0136,,0.0165,,0.0038,0.0187,0.0447,0.9806,,,0.0,0.1034,0.0417,,0.0136,,0.0161,,0.0037,,block of flats,0.0132,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-177.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n104540,0,Cash loans,M,N,N,0,65250.0,239850.0,23494.5,225000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,Municipal apartment,0.010556,-24839,365243,-14421.0,-4100,,1,0,0,1,1,0,,2.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.43686993655270456,0.4956658291397297,0.2227,0.1177,0.9791,0.7144,0.0,0.24,0.2069,0.3333,0.0417,0.0278,0.1816,0.2201,0.0,0.213,0.2269,0.1221,0.9791,0.7256,0.0,0.2417,0.2069,0.3333,0.0417,0.0285,0.1983,0.2293,0.0,0.2255,0.2248,0.1177,0.9791,0.7182,0.0,0.24,0.2069,0.3333,0.0417,0.0283,0.1847,0.22399999999999998,0.0,0.2175,reg oper account,block of flats,0.2194,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-225.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n333571,0,Revolving loans,F,N,Y,0,157500.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.002506,-18236,-857,-1723.0,-1773,,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,6,0,0,0,0,0,0,Business Entity Type 1,,0.739589238225427,0.4974688893052743,0.0309,0.0538,0.9836,0.7756,0.0057,0.0,0.069,0.1667,0.2083,0.0264,0.0252,0.0299,0.0,0.0,0.0315,0.0559,0.9836,0.7844,0.0058,0.0,0.069,0.1667,0.2083,0.027000000000000003,0.0275,0.0312,0.0,0.0,0.0312,0.0538,0.9836,0.7786,0.0058,0.0,0.069,0.1667,0.2083,0.0269,0.0257,0.0305,0.0,0.0,reg oper account,block of flats,0.0292,Panel,No,0.0,0.0,0.0,0.0,-168.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n168937,0,Cash loans,F,N,Y,0,247500.0,2025000.0,53550.0,2025000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-16424,-4989,-5746.0,-3419,,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 1,0.5406541824650801,0.6038956808069772,0.7281412993111438,0.0598,,0.9816,0.7484,,0.0,0.1379,0.1667,,,,0.0579,,,0.0588,,0.9816,0.7583,,0.0,0.1379,0.1667,,,,0.0573,,,0.0604,,0.9816,0.7518,,0.0,0.1379,0.1667,,,,0.0589,,,,block of flats,0.0986,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1531.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n177771,0,Cash loans,M,N,N,0,67500.0,225000.0,24232.5,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-19182,365243,-1809.0,-2732,,1,0,0,1,0,0,,2.0,3,3,SUNDAY,11,0,0,0,0,0,0,XNA,,0.3983069703653537,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-273.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n378679,0,Cash loans,F,N,Y,1,81000.0,454500.0,17608.5,454500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,With parents,0.019101,-8966,-747,-3458.0,-1594,,1,1,1,1,0,1,Sales staff,3.0,2,2,TUESDAY,14,0,0,0,0,0,0,Trade: type 2,,0.2575934077239959,0.29708661164720285,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1599.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n289774,0,Cash loans,F,N,Y,0,112500.0,101880.0,10206.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008625,-17384,-6956,-9331.0,-937,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,School,,0.5620736761445608,0.8171723992791566,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n392975,0,Cash loans,F,N,N,1,157500.0,675000.0,21775.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.031329,-11618,-4053,-4039.0,-2845,,1,1,1,1,0,0,Core staff,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Medicine,0.4054492683709889,0.6138057122444669,0.31547215492577346,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-1531.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n103713,0,Cash loans,F,N,N,1,126000.0,478498.5,50377.5,454500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.019101,-16024,-8097,-3995.0,-4722,,1,1,0,1,0,0,Core staff,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Other,0.6563962800502279,0.2002880998338482,0.6413682574954046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1855.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n302663,0,Cash loans,F,Y,Y,0,157500.0,1125000.0,47794.5,1125000.0,Unaccompanied,Pensioner,Academic degree,Married,House / apartment,0.028663,-21843,365243,-5044.0,-4665,5.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.6413394014672911,0.5280927512030451,0.0727,0.0768,0.9806,0.7348,0.0089,0.06,0.0862,0.2083,0.25,0.0529,0.057999999999999996,0.0725,0.0058,0.0026,0.0252,0.042,0.9732,0.6472,0.0021,0.0,0.069,0.0833,0.125,0.0361,0.0193,0.0184,0.0,0.0,0.0734,0.0768,0.9806,0.7383,0.0089,0.06,0.0862,0.2083,0.25,0.0538,0.059000000000000004,0.0738,0.0058,0.0026,reg oper account,block of flats,0.0162,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3749.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n188445,1,Cash loans,F,N,Y,0,63000.0,446931.0,21865.5,369000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-16312,-1671,-5303.0,-5275,,1,1,1,1,1,0,Laborers,2.0,2,2,TUESDAY,6,0,0,0,0,0,0,Other,,0.07146911479462101,0.3962195720630885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-249.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n432808,0,Cash loans,F,Y,Y,2,315000.0,1035000.0,43983.0,1035000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-13945,-2349,-2223.0,-4017,10.0,1,1,1,1,0,0,Realty agents,4.0,1,1,MONDAY,15,0,1,1,0,1,1,Business Entity Type 3,0.7086638881373908,0.7027598928057368,0.5370699579791587,0.0825,0.0704,0.9757,0.6668,0.0305,0.0,0.1379,0.1667,0.2083,0.1795,0.0672,0.0669,0.0,0.0,0.084,0.073,0.9757,0.6798,0.0308,0.0,0.1379,0.1667,0.2083,0.1836,0.0735,0.0697,0.0,0.0,0.0833,0.0704,0.9757,0.6713,0.0307,0.0,0.1379,0.1667,0.2083,0.1826,0.0684,0.0681,0.0,0.0,reg oper account,block of flats,0.0693,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-829.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n154029,0,Cash loans,F,N,N,0,67500.0,385164.0,17095.5,292500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.022625,-23816,365243,-4970.0,-4970,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.5852005711152046,0.7969726818373722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1092.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n382385,0,Revolving loans,F,N,Y,0,157500.0,450000.0,22500.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-19283,-1839,-5354.0,-2837,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,7,0,0,0,0,0,0,Trade: type 7,,0.5545620405889784,,0.1649,0.1273,0.9916,0.8844,0.0,0.16,0.1379,0.375,0.4167,0.0,0.1345,0.2454,0.0,0.0,0.1681,0.1321,0.9916,0.8889,0.0,0.1611,0.1379,0.375,0.4167,0.0,0.1469,0.2556,0.0,0.0,0.1665,0.1273,0.9916,0.8859,0.0,0.16,0.1379,0.375,0.4167,0.0,0.1368,0.2498,0.0,0.0,org spec account,block of flats,0.193,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-2513.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n439520,0,Revolving loans,F,Y,Y,1,81000.0,180000.0,9000.0,180000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.015221,-8157,-654,-1189.0,-846,12.0,1,1,1,1,1,0,Sales staff,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Trade: type 2,0.2512393794742389,0.309371857114328,0.02847424788475705,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-461.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n223346,0,Cash loans,M,Y,Y,0,148500.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20052,-3189,-3097.0,-3532,12.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 2,,0.7061607631516438,0.6971469077844458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-954.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n342956,0,Cash loans,F,N,N,0,81000.0,225000.0,12694.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.025164,-24253,365243,-4184.0,-3448,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,8,0,0,0,0,0,0,XNA,,0.19421511211361847,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n177991,0,Cash loans,M,Y,Y,1,450000.0,1546020.0,45333.0,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,With parents,0.032561,-17237,-1048,-9474.0,-716,24.0,1,1,0,1,1,1,Laborers,2.0,1,1,MONDAY,17,0,0,0,0,0,0,Construction,0.6036172169361953,0.7844095429515531,0.6722428897082422,0.1113,0.0794,0.9826,0.762,0.0005,0.12,0.1034,0.3333,0.375,0.129,0.1051,0.1539,0.0039,0.0032,0.0756,0.0545,0.9826,0.7713,0.0005,0.0806,0.069,0.3333,0.375,0.1938,0.0652,0.2502,0.0039,0.0014,0.0749,0.0533,0.9826,0.7652,0.0005,0.08,0.069,0.3333,0.375,0.1927,0.1069,0.1441,0.0039,0.0013,org spec account,block of flats,0.0636,Panel,No,0.0,0.0,0.0,0.0,-631.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n383114,0,Cash loans,F,Y,Y,0,126000.0,675000.0,22437.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-18458,-2104,-16166.0,-1985,10.0,1,1,0,1,0,0,Core staff,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,School,0.593539352000385,0.5854341108077996,0.09821859526287233,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-505.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n230057,0,Cash loans,F,N,N,0,67500.0,90000.0,8766.0,90000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.018029,-23196,365243,-11006.0,-4806,,1,0,0,1,1,0,,1.0,3,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.03637167728813178,,0.0928,0.0871,0.9771,0.6872,0.0117,0.0,0.2069,0.1667,0.2083,0.0602,0.0756,0.0875,0.0,0.0,0.0945,0.0904,0.9772,0.6994,0.0118,0.0,0.2069,0.1667,0.2083,0.0616,0.0826,0.0912,0.0,0.0,0.0937,0.0871,0.9771,0.6914,0.0118,0.0,0.2069,0.1667,0.2083,0.0612,0.077,0.0891,0.0,0.0,reg oper account,block of flats,0.0791,Panel,No,0.0,0.0,0.0,0.0,-1090.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n289734,0,Cash loans,F,Y,N,1,90000.0,152820.0,16344.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-12264,-3290,-3053.0,-2197,8.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,THURSDAY,18,0,0,0,0,0,0,Business Entity Type 1,0.3961936179296504,0.6653185962667371,0.0005272652387098817,0.0722,,0.9776,,,,0.1379,0.1667,,,,0.0462,,0.0,0.0735,,0.9777,,,,0.1379,0.1667,,,,0.0481,,0.0,0.0729,,0.9776,,,,0.1379,0.1667,,,,0.047,,0.0,,block of flats,0.053,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-1567.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,2.0\n330642,0,Cash loans,F,N,Y,0,135000.0,1255680.0,45103.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.014464,-16184,-3414,-938.0,-4030,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,5,0,0,0,0,0,0,Trade: type 7,,0.6501582348175377,0.8245949709919925,0.1402,0.0403,0.9801,0.728,0.0577,0.08,0.0345,0.3333,0.0417,0.049,0.1143,0.0953,0.0,0.0,0.1429,0.0418,0.9801,0.7387,0.0583,0.0806,0.0345,0.3333,0.0417,0.0501,0.1249,0.0993,0.0,0.0,0.1416,0.0403,0.9801,0.7316,0.0581,0.08,0.0345,0.3333,0.0417,0.0498,0.1163,0.09699999999999999,0.0,0.0,reg oper account,block of flats,0.1065,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,2.0\n118142,0,Revolving loans,F,Y,Y,1,292500.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-15788,-1649,-7394.0,-4768,64.0,1,1,0,1,0,1,Accountants,2.0,1,1,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 1,0.8325524067342172,0.7520813083111397,0.3962195720630885,0.3691,0.2321,0.9806,0.7348,0.2174,0.4,0.3448,0.3333,0.375,0.0,0.3009,0.2358,0.0,0.0,0.3761,0.2408,0.9806,0.7452,0.2194,0.4028,0.3448,0.3333,0.375,0.0,0.3287,0.2457,0.0,0.0,0.3726,0.2321,0.9806,0.7383,0.2188,0.4,0.3448,0.3333,0.375,0.0,0.3061,0.2401,0.0,0.0,reg oper account,block of flats,0.2762,Panel,No,0.0,0.0,0.0,0.0,-366.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n434546,0,Cash loans,F,N,Y,0,216000.0,1185282.0,34785.0,1035000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-21438,-10694,-10040.0,-4711,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 2,,0.2264532403490737,0.4794489811780563,0.1629,0.1714,0.9856,0.8028,0.0249,0.0,0.3793,0.1667,0.2083,0.051,0.132,0.1567,0.0039,0.0213,0.166,0.1779,0.9856,0.8105,0.0251,0.0,0.3793,0.1667,0.2083,0.0522,0.1442,0.1632,0.0039,0.0225,0.1645,0.1714,0.9856,0.8054,0.0251,0.0,0.3793,0.1667,0.2083,0.0519,0.1342,0.1595,0.0039,0.0217,reg oper account,block of flats,0.1415,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-529.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n440343,0,Cash loans,F,N,Y,1,76500.0,526491.0,23319.0,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.018634,-17181,-2293,-1013.0,-719,,1,1,1,1,0,0,Sales staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Trade: type 7,,0.17178387368888307,0.4101025731788671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1249.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n128096,0,Cash loans,F,Y,Y,0,112500.0,409653.0,15570.0,288000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010032,-22256,365243,-1818.0,-898,25.0,1,0,0,1,0,0,,2.0,2,2,SATURDAY,5,0,0,0,0,0,0,XNA,,0.1164885318112526,,0.1082,0.0335,0.9821,,,0.0,0.0345,0.125,,0.0214,,0.0276,,0.0,0.1103,0.0348,0.9821,,,0.0,0.0345,0.125,,0.0219,,0.0288,,0.0,0.1093,0.0335,0.9821,,,0.0,0.0345,0.125,,0.0218,,0.0281,,0.0,,block of flats,0.0296,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1230.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n251418,0,Cash loans,F,N,N,1,90000.0,675000.0,32472.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.028663,-10253,-1462,-4232.0,-1904,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6668069345778603,0.2925880073368475,0.0753,0.0787,0.9717,,,0.0,0.1379,0.1667,,0.0,,0.0876,,0.0,0.0767,0.0817,0.9717,,,0.0,0.1379,0.1667,,0.0,,0.0913,,0.0,0.076,0.0787,0.9717,,,0.0,0.1379,0.1667,,0.0,,0.0892,,0.0,,block of flats,0.1229,\"Stone, brick\",No,10.0,3.0,10.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n205051,0,Revolving loans,F,N,Y,0,90000.0,247500.0,12375.0,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.031329,-16241,-4381,-9046.0,-4742,,1,1,0,1,0,0,,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 2,0.4956759863587339,0.3851978892408207,0.7121551551910698,0.0825,0.0748,0.9816,0.7484,0.0136,0.0,0.2069,0.1667,0.2083,0.0838,0.0664,0.0768,0.0039,0.004,0.084,0.0776,0.9816,0.7583,0.0137,0.0,0.2069,0.1667,0.2083,0.0857,0.0725,0.08,0.0039,0.0043,0.0833,0.0748,0.9816,0.7518,0.0137,0.0,0.2069,0.1667,0.2083,0.0852,0.0676,0.0782,0.0039,0.0041,reg oper account,block of flats,0.0613,Panel,No,5.0,0.0,5.0,0.0,-190.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,0.0\n427860,0,Cash loans,F,N,N,0,225000.0,675000.0,19737.0,675000.0,Unaccompanied,State servant,Higher education,Separated,Municipal apartment,0.019101,-13635,-6190,-6908.0,-3503,,1,1,0,1,1,0,Core staff,1.0,2,2,THURSDAY,18,0,0,0,0,0,0,Police,,0.7104798163389784,0.7238369900414456,0.0742,0.0783,0.9767,0.6804,0.0078,0.0,0.1379,0.1667,0.2083,0.0699,0.0588,0.0636,0.0077,0.0113,0.0756,0.0813,0.9767,0.6929,0.0079,0.0,0.1379,0.1667,0.2083,0.0715,0.0643,0.0662,0.0078,0.012,0.0749,0.0783,0.9767,0.6847,0.0079,0.0,0.1379,0.1667,0.2083,0.0711,0.0599,0.0647,0.0078,0.0115,reg oper account,block of flats,0.0525,Panel,No,3.0,0.0,3.0,0.0,-1640.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n356492,0,Cash loans,M,N,N,0,225000.0,1215000.0,43641.0,1215000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-18877,-11358,-4515.0,-2422,,1,1,1,1,1,0,Laborers,2.0,1,1,SUNDAY,14,0,0,0,0,0,0,Construction,0.6111424369719539,0.7147476531793548,0.7407990879702335,0.1784,0.0603,0.9935,0.9116,0.0,0.24,0.069,0.875,0.0,0.0,0.1454,0.2563,0.0,0.0092,0.1817,0.0625,0.9935,0.9151,0.0,0.2417,0.069,0.875,0.0,0.0,0.1589,0.267,0.0,0.0097,0.1801,0.0603,0.9935,0.9128,0.0,0.24,0.069,0.875,0.0,0.0,0.1479,0.2609,0.0,0.0094,,block of flats,0.2036,Panel,No,0.0,0.0,0.0,0.0,-640.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n439433,0,Cash loans,F,N,Y,1,135000.0,675000.0,21906.0,675000.0,Family,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.011703,-16517,-800,-10079.0,-60,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.7083792035296911,0.6706517530862718,0.0165,0.0371,0.9891,0.8504,0.0018,0.0,0.069,0.0417,0.0417,0.0236,0.0134,0.0139,0.0,0.0,0.0168,0.0385,0.9891,0.8563,0.0018,0.0,0.069,0.0417,0.0417,0.0241,0.0147,0.0145,0.0,0.0,0.0167,0.0371,0.9891,0.8524,0.0018,0.0,0.069,0.0417,0.0417,0.024,0.0137,0.0141,0.0,0.0,reg oper account,block of flats,0.0119,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-980.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n455115,0,Cash loans,F,N,Y,0,90000.0,425313.0,34231.5,387000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00496,-15466,-586,-4375.0,-4634,,1,1,0,1,0,0,Private service staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Self-employed,0.5041042606866234,0.3572568665683311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-493.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n336079,0,Cash loans,M,Y,Y,0,40500.0,85320.0,4891.5,67500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-23011,365243,-8569.0,-3964,39.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,0.7695048352339615,0.7207237943461339,0.39449540531239935,0.1856,,0.9876,0.83,,0.2,0.1724,0.3333,0.375,,,0.1896,,0.0021,0.1891,,0.9876,0.8367,,0.2014,0.1724,0.3333,0.375,,,0.1975,,0.0022,0.1874,,0.9876,0.8323,,0.2,0.1724,0.3333,0.375,,,0.193,,0.0021,reg oper account,block of flats,0.1887,Panel,No,4.0,0.0,4.0,0.0,-1040.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n203759,0,Cash loans,M,Y,Y,0,270000.0,301896.0,23490.0,252000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.035792000000000004,-10745,-241,-653.0,-3345,16.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,18,0,0,0,1,1,0,Construction,0.19040387611928364,0.3630721207093156,0.17982176508970435,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-988.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n123062,0,Cash loans,M,Y,Y,0,157500.0,463284.0,24115.5,382500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010643,-17639,-2173,-10109.0,-1176,8.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5078079756706403,0.7922644738669378,0.0314,0.0574,0.9642,0.6192,0.0302,0.0,0.1207,0.0833,0.1667,0.0301,0.0395,0.0357,0.0232,0.0142,0.0084,0.0596,0.9568,0.6341,0.0305,0.0,0.069,0.0417,0.1667,0.0172,0.0432,0.0071,0.0233,0.0036,0.0317,0.0574,0.9642,0.6243,0.0304,0.0,0.1207,0.0833,0.1667,0.0306,0.0402,0.0364,0.0233,0.0144,reg oper account,block of flats,0.0563,Wooden,No,1.0,0.0,1.0,0.0,-1497.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,5.0\n251454,0,Cash loans,F,N,N,1,63000.0,72000.0,4941.0,72000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.030755,-9922,-960,-2162.0,-2208,,1,1,1,1,1,0,,3.0,2,2,MONDAY,13,0,0,0,0,1,1,Self-employed,,0.6747409484105439,0.6144143775673561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,4.0,0.0,-908.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n155073,0,Cash loans,F,N,N,0,225000.0,700830.0,22738.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010556,-15098,-1670,-9209.0,-4058,,1,1,0,1,0,0,,1.0,3,3,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 1,0.5795249743353315,0.6120262019654392,0.6879328378491735,0.1227,,0.9871,0.8164,0.0898,0.0,0.2759,0.1667,0.0417,,0.1,0.0638,0.0,0.2258,0.125,,0.9871,0.8236,0.0906,0.0,0.2759,0.1667,0.0417,,0.1093,0.0665,0.0,0.239,0.1239,,0.9871,0.8189,0.0904,0.0,0.2759,0.1667,0.0417,,0.1018,0.0649,0.0,0.2305,reg oper account,block of flats,0.0993,Panel,No,1.0,0.0,1.0,0.0,-2602.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n195331,1,Cash loans,F,N,N,0,117000.0,180000.0,19516.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.010032,-16363,-660,-8933.0,-489,,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.4546353689750935,0.5988002339268601,0.3876253444214701,0.0351,0.0413,0.9737,0.6396,,0.0,0.069,0.125,0.1667,0.0264,0.0286,0.0324,,0.0327,0.0357,0.0429,0.9737,0.6537,,0.0,0.069,0.125,0.1667,0.027000000000000003,0.0312,0.0337,,0.0346,0.0354,0.0413,0.9737,0.6444,,0.0,0.069,0.125,0.1667,0.0269,0.0291,0.033,,0.0334,reg oper account,block of flats,0.0363,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n318294,0,Cash loans,F,Y,N,2,67500.0,299772.0,14103.0,247500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006305,-15591,-3561,-3399.0,-4414,14.0,1,1,0,1,0,0,High skill tech staff,4.0,3,3,MONDAY,10,0,0,0,0,0,0,Transport: type 2,0.5198812328780483,0.18755542103959802,0.5744466170995097,0.0619,,0.9876,,,0.0,0.1379,0.1667,,,,0.0675,,0.1408,0.063,,0.9876,,,0.0,0.1379,0.1667,,,,0.0704,,0.1491,0.0625,,0.9876,,,0.0,0.1379,0.1667,,,,0.0688,,0.1438,,block of flats,0.0531,Panel,No,0.0,0.0,0.0,0.0,-1838.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n152796,0,Cash loans,M,N,N,0,112500.0,254700.0,27022.5,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.025164,-8643,-706,-2801.0,-974,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,THURSDAY,15,0,0,0,1,1,0,Business Entity Type 3,0.2049940385032044,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,3.0,0.0,3.0,0.0,-474.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n288384,0,Cash loans,F,N,N,0,225000.0,1800000.0,74286.0,1800000.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,House / apartment,0.04622,-18363,-2785,-316.0,-1906,,1,1,1,1,0,0,Core staff,2.0,1,1,SUNDAY,15,0,1,1,0,1,1,Medicine,0.7452198498351358,0.7041967640222564,0.11911906455945008,0.0165,0.0,0.9757,0.6668,0.002,0.0,0.069,0.0417,0.0833,0.0,,0.0125,,0.0,0.0168,0.0,0.9757,0.6798,0.002,0.0,0.069,0.0417,0.0833,0.0,,0.013000000000000001,,0.0,0.0167,0.0,0.9757,0.6713,0.002,0.0,0.069,0.0417,0.0833,0.0,,0.0127,,0.0,,block of flats,0.0109,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-162.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n446532,0,Cash loans,F,N,Y,1,90000.0,242595.0,12829.5,202500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008473999999999999,-12405,-946,-370.0,-3024,,1,1,1,1,1,0,Cleaning staff,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4096495920357498,0.00977082790295456,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n170613,0,Cash loans,M,N,Y,0,225000.0,360000.0,42723.0,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.002042,-9026,-305,-813.0,-828,,1,1,0,1,1,0,Managers,2.0,3,3,THURSDAY,12,0,1,1,0,1,1,Trade: type 3,0.23044429501273864,0.3128352419279635,0.3910549766342248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-808.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n369078,0,Cash loans,F,N,Y,2,180000.0,976077.0,52132.5,922500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14432,-3328,-7328.0,-4433,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,13,0,0,0,0,1,1,Agriculture,,0.6404888796085584,0.8193176922872417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1900.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n214963,0,Cash loans,F,N,Y,1,103500.0,675000.0,21906.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-18370,365243,-8816.0,-1917,,1,0,0,1,0,0,,3.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,0.5458282466231429,0.6435546172708011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n442184,0,Cash loans,F,N,Y,2,157500.0,675000.0,47110.5,675000.0,Family,Working,Higher education,Married,House / apartment,0.04622,-12109,-2126,-6112.0,-1708,,1,1,1,1,0,1,Private service staff,4.0,1,1,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.7394375909256328,0.7281412993111438,0.1649,0.0514,0.9916,,,0.16,0.1379,0.375,,,,0.1652,,,0.1681,0.0534,0.9916,,,0.1611,0.1379,0.375,,,,0.1721,,,0.1665,0.0514,0.9916,,,0.16,0.1379,0.375,,,,0.1682,,,,block of flats,0.1829,Panel,No,0.0,0.0,0.0,0.0,-791.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n272198,0,Cash loans,M,Y,N,1,315000.0,99504.0,10444.5,90000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.072508,-18274,-2550,-7970.0,-1781,3.0,1,1,1,1,0,0,High skill tech staff,3.0,1,1,WEDNESDAY,18,1,0,1,1,0,1,Business Entity Type 3,0.4042310048483856,0.7273474386924942,0.501075160239048,0.1254,0.0594,0.9776,0.6940000000000001,0.0728,0.11199999999999999,0.069,0.5,0.5417,0.0075,0.1002,0.1017,0.0093,0.039,0.1029,0.0402,0.9777,0.706,0.0533,0.0806,0.0345,0.5417,0.5833,0.0076,0.09,0.055,0.0,0.0,0.102,0.0391,0.9776,0.6981,0.0531,0.08,0.0345,0.5417,0.5833,0.0076,0.0838,0.0863,0.0,0.0,reg oper account,block of flats,0.2007,Panel,No,3.0,0.0,3.0,0.0,-906.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n405840,0,Revolving loans,F,N,Y,1,270000.0,810000.0,40500.0,810000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.028663,-18367,-362,-1786.0,-1919,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 1,,0.5348514051572913,0.5989262182569273,0.1412,0.1601,0.9757,,,0.0,0.2759,0.1667,,,,0.1035,,0.0708,0.1439,0.1661,0.9757,,,0.0,0.2759,0.1667,,,,0.1078,,0.075,0.1426,0.1601,0.9757,,,0.0,0.2759,0.1667,,,,0.1054,,0.0723,,,0.1594,\"Stone, brick\",No,,,,,-1630.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n397548,0,Cash loans,F,N,N,0,202500.0,799299.0,33993.0,702000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-14890,-5968,-6493.0,-4200,,1,1,0,1,0,1,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Security Ministries,0.7724764515008967,0.5468899291385799,0.7352209993926424,0.0052,0.0,0.9767,0.6804,0.0007,0.0,0.0345,0.0417,0.0833,0.0,0.0042,0.0185,0.0,0.0018,0.0053,0.0,0.9767,0.6929,0.0007,0.0,0.0345,0.0417,0.0833,0.0,0.0046,0.0192,0.0,0.0019,0.0052,0.0,0.9767,0.6847,0.0007,0.0,0.0345,0.0417,0.0833,0.0,0.0043,0.0188,0.0,0.0018,reg oper account,block of flats,0.0149,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1373.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n417615,0,Cash loans,M,Y,N,0,252000.0,1024740.0,52452.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-21400,-3921,-3906.0,-1446,15.0,1,1,0,1,0,1,Drivers,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.8213325165643622,0.5718714162668649,0.2822484337007223,0.0619,,0.9737,0.6396,,,0.1034,0.1667,0.2083,,0.0504,0.0511,,,0.063,,0.9737,0.6537,,,0.1034,0.1667,0.2083,,0.0551,0.0532,,,0.0625,,0.9737,0.6444,,,0.1034,0.1667,0.2083,,0.0513,0.052000000000000005,,,,block of flats,0.0431,Panel,No,0.0,0.0,0.0,0.0,-1853.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n238060,0,Cash loans,F,N,Y,0,45000.0,135000.0,6624.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.031329,-23028,365243,-9562.0,-4868,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.5990943906877257,0.5046813193144684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n236074,0,Revolving loans,F,Y,Y,0,225000.0,405000.0,20250.0,405000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-17551,-1433,-9565.0,-1099,13.0,1,1,0,1,0,0,,2.0,2,2,SATURDAY,17,0,0,0,0,0,0,Other,0.5309567106586617,0.7155717471508417,,0.0773,0.0736,0.9791,0.7144,0.0643,0.0,0.1724,0.1667,0.2083,0.0688,0.063,0.073,0.0,0.0,0.0788,0.0764,0.9791,0.7256,0.0649,0.0,0.1724,0.1667,0.2083,0.0704,0.0689,0.076,0.0,0.0,0.0781,0.0736,0.9791,0.7182,0.0647,0.0,0.1724,0.1667,0.2083,0.07,0.0641,0.0743,0.0,0.0,reg oper account,block of flats,0.0925,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-259.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n353224,0,Cash loans,M,Y,N,1,76500.0,513531.0,26347.5,459000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-11735,-3816,-303.0,-4333,14.0,1,1,0,1,0,0,Laborers,3.0,3,3,THURSDAY,7,0,0,0,0,0,0,Electricity,,0.458662850313521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n111867,0,Cash loans,M,Y,Y,0,90000.0,67500.0,4005.0,67500.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.026392,-21746,365243,-10983.0,-4057,17.0,1,0,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.4121250078693111,,0.0986,,0.9851,,,0.08,0.0345,0.5417,,,,0.0789,,0.0424,0.1008,,0.9851,,,0.0806,0.0345,0.5417,,,,0.0566,,0.0,0.0999,,0.9851,,,0.08,0.0345,0.5417,,,,0.0923,,0.0006,,block of flats,0.0702,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n240573,0,Cash loans,F,N,Y,3,135000.0,1247121.0,36463.5,1089000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-14347,-3906,-2645.0,-2644,,1,1,1,1,1,0,Accountants,5.0,3,3,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.4778544520728973,0.17092378602024555,0.5298898341969072,0.0619,,0.9752,,,0.0,0.1034,0.1667,,,,0.0503,,0.0,0.063,,0.9752,,,0.0,0.1034,0.1667,,,,0.0524,,0.0,0.0625,,0.9752,,,0.0,0.1034,0.1667,,,,0.0512,,0.0,,block of flats,0.0396,Panel,No,4.0,0.0,4.0,0.0,-362.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,10.0\n252873,0,Cash loans,F,N,Y,2,157500.0,863226.0,36702.0,697500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-14436,-144,-6846.0,-396,,1,1,0,1,0,0,Sales staff,4.0,2,1,TUESDAY,13,0,0,0,0,0,0,Other,,0.6681077211274685,0.7490217048463391,0.1495,0.2169,0.9876,0.83,0.039,0.0,0.3448,0.1667,0.2083,0.0393,0.121,0.1714,0.0039,0.0058,0.1523,0.2251,0.9876,0.8367,0.0393,0.0,0.3448,0.1667,0.2083,0.0402,0.1322,0.1786,0.0039,0.0061,0.1509,0.2169,0.9876,0.8323,0.0392,0.0,0.3448,0.1667,0.2083,0.04,0.1231,0.1745,0.0039,0.0059,reg oper account,block of flats,0.1386,Panel,No,0.0,0.0,0.0,0.0,-1041.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n443143,0,Cash loans,F,N,Y,0,90000.0,267102.0,21546.0,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-17753,-938,-5432.0,-1299,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,TUESDAY,15,0,0,0,1,1,0,Hotel,,0.7188890870187586,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,-609.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n156651,1,Cash loans,M,N,Y,2,135000.0,413235.0,32778.0,337500.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.008865999999999999,-11814,-747,-5192.0,-2257,,1,1,0,1,0,1,Laborers,4.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,,0.04416552908390673,0.7209441499436497,0.0082,0.0,0.9796,0.7212,0.0008,0.0,0.0345,0.0417,0.0833,0.0424,0.0067,0.0066,0.0,0.0,0.0084,0.0,0.9796,0.7321,0.0008,0.0,0.0345,0.0417,0.0833,0.0434,0.0073,0.0068,0.0,0.0,0.0083,0.0,0.9796,0.7249,0.0008,0.0,0.0345,0.0417,0.0833,0.0432,0.0068,0.0067,0.0,0.0,reg oper account,block of flats,0.0056,Wooden,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,3.0\n101952,0,Cash loans,F,N,Y,1,112500.0,491031.0,36841.5,463500.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.006008,-10280,-1624,-1011.0,-1504,,1,1,0,1,1,0,,3.0,2,2,SUNDAY,12,0,0,0,1,1,0,Services,0.30950390991454463,0.6974248104828312,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1144.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n315286,0,Cash loans,F,N,Y,0,157500.0,582228.0,17154.0,486000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.020246,-16586,-5268,-45.0,-63,,1,1,0,1,0,0,Core staff,2.0,3,3,FRIDAY,12,0,0,0,1,1,0,Kindergarten,0.6089512939521801,0.4464574472382398,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1702.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n235764,0,Cash loans,F,N,Y,0,202500.0,508495.5,26091.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-20006,-4621,-643.0,-3541,,1,1,0,1,0,0,Accountants,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6696865680400219,0.5620604831738043,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2363.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n160382,0,Cash loans,M,Y,Y,0,180000.0,1024740.0,52452.0,900000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.026392,-13026,-2642,-6718.0,-4367,3.0,1,1,0,1,1,0,IT staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Medicine,0.7877290979507879,0.6875345451869934,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2245.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n430537,0,Cash loans,F,N,N,0,112500.0,1129500.0,29794.5,1129500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-13043,-3195,-3183.0,-4750,,1,1,0,1,1,0,,2.0,2,2,SUNDAY,14,0,0,0,0,0,0,Medicine,0.6366925407115407,0.6404083109709828,0.6479768603302221,0.1134,0.087,0.9896,0.8572,0.0245,0.12,0.1034,0.3333,0.375,0.0668,0.0916,0.1197,0.0039,0.0006,0.1155,0.0903,0.9896,0.8628,0.0247,0.1208,0.1034,0.3333,0.375,0.0684,0.1001,0.1247,0.0039,0.0006,0.1145,0.087,0.9896,0.8591,0.0246,0.12,0.1034,0.3333,0.375,0.068,0.0932,0.1218,0.0039,0.0006,reg oper account,block of flats,0.1076,Panel,No,1.0,0.0,1.0,0.0,-2529.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n377561,0,Cash loans,M,Y,Y,0,225000.0,497520.0,53712.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008625,-18882,-3966,-493.0,-2442,13.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6654354481102308,0.7151031019926098,0.0258,0.0,0.9722,0.6192,0.0005,0.0,0.0345,0.0417,0.0417,0.0177,0.021,0.0017,0.0,0.0,0.0263,0.0,0.9722,0.6341,0.0005,0.0,0.0345,0.0417,0.0417,0.0181,0.023,0.0018,0.0,0.0,0.026000000000000002,0.0,0.9722,0.6243,0.0005,0.0,0.0345,0.0417,0.0417,0.018000000000000002,0.0214,0.0017,0.0,0.0,reg oper account,block of flats,0.0016,\"Stone, brick\",No,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n284313,0,Cash loans,F,N,Y,0,207000.0,1971072.0,68643.0,1800000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-23356,365243,-14245.0,-4246,,1,0,0,1,1,1,,2.0,3,3,TUESDAY,11,0,0,0,0,0,0,XNA,0.7690859559980701,0.04254851691665856,0.6161216908872079,0.3155,0.3533,0.9786,0.7076,0.0672,0.0,0.7931,0.1667,0.2083,0.2212,0.2522,0.1978,0.0232,0.1566,0.3214,0.3666,0.9786,0.7190000000000001,0.0678,0.0,0.7931,0.1667,0.2083,0.2263,0.2755,0.2061,0.0233,0.1657,0.3185,0.3533,0.9786,0.7115,0.0677,0.0,0.7931,0.1667,0.2083,0.2251,0.2565,0.2014,0.0233,0.1598,reg oper account,block of flats,0.2264,Panel,No,2.0,1.0,2.0,0.0,-972.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n138888,0,Cash loans,F,N,Y,2,45000.0,123637.5,8923.5,112500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-10407,-1464,-10390.0,-2948,,1,1,1,1,0,0,Sales staff,4.0,2,2,THURSDAY,10,0,0,0,0,1,1,Self-employed,,0.5306817720558981,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-1455.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n221956,0,Cash loans,F,N,Y,1,112500.0,730017.0,31059.0,652500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-15275,-4225,-4349.0,-4061,,1,1,0,1,0,0,Medicine staff,3.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Kindergarten,,0.6991754659623944,0.7047064232963289,0.034,0.0059,0.9598,0.4492,0.0029,0.0,0.1034,0.0833,0.125,0.0618,0.0277,0.0256,0.0,0.0,0.0347,0.0062,0.9598,0.4708,0.0029,0.0,0.1034,0.0833,0.125,0.0633,0.0303,0.0267,0.0,0.0,0.0344,0.0059,0.9598,0.4566,0.0029,0.0,0.1034,0.0833,0.125,0.0629,0.0282,0.0261,0.0,0.0,reg oper account,block of flats,0.0217,\"Stone, brick\",No,3.0,1.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n397746,1,Cash loans,M,N,Y,0,153000.0,112500.0,5373.0,112500.0,Other_B,Commercial associate,Secondary / secondary special,Single / not married,Rented apartment,0.018209,-9870,-670,-4754.0,-2540,,1,1,0,1,0,0,Laborers,1.0,3,3,TUESDAY,16,0,0,0,1,1,0,Industry: type 11,0.17157705398738593,0.006014044765305327,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-269.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n329047,0,Cash loans,F,N,Y,0,119475.0,135000.0,12622.5,135000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.016612000000000002,-7866,-335,-44.0,-532,,1,1,1,1,0,0,Managers,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,Trade: type 2,,0.08657055945105932,0.3123653692278984,0.1629,0.0,0.9752,0.66,,0.0,0.1034,0.1667,0.2083,0.1247,0.1303,0.057999999999999996,0.0116,0.0404,0.166,0.0,0.9752,0.6733,,0.0,0.1034,0.1667,0.2083,0.1276,0.1423,0.0605,0.0117,0.0428,0.1645,0.0,0.9752,0.6645,,0.0,0.1034,0.1667,0.2083,0.1269,0.1325,0.0591,0.0116,0.0413,reg oper account,block of flats,0.0586,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-8.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n250931,0,Cash loans,F,Y,N,2,127926.0,607500.0,16704.0,607500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-13313,-3895,-3867.0,-2246,4.0,1,1,1,1,0,0,Accountants,4.0,2,2,FRIDAY,10,0,0,0,0,0,0,Industry: type 3,0.6326837074781246,0.6547152856071019,0.4206109640437848,0.1485,0.113,0.9881,0.8368,0.03,0.16,0.1379,0.3333,0.375,0.0804,0.121,0.1544,0.0,0.0,0.1513,0.1172,0.9881,0.8432,0.0303,0.1611,0.1379,0.3333,0.375,0.0822,0.1322,0.1609,0.0,0.0,0.1499,0.113,0.9881,0.8390000000000001,0.0302,0.16,0.1379,0.3333,0.375,0.0818,0.1231,0.1572,0.0,0.0,reg oper account,block of flats,0.1379,Panel,No,8.0,0.0,8.0,0.0,-5.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n192822,1,Revolving loans,M,N,N,0,81000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-9433,-714,-3740.0,-1986,,1,1,1,1,1,0,Laborers,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,Advertising,,0.4955628807950189,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-310.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n323080,0,Cash loans,F,N,Y,1,337500.0,1822158.0,50238.0,1521000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16947,-7071,-6440.0,-509,,1,1,0,1,0,0,Medicine staff,3.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Medicine,0.7525420686144495,0.7293204522902837,0.2276129150623945,0.0624,0.0204,0.9841,0.8096,0.015,0.0,0.0517,0.1667,0.2083,0.035,0.0252,0.0317,0.0,0.0167,0.0315,0.0,0.9826,0.8171,0.0152,0.0,0.0345,0.1667,0.2083,0.0255,0.0275,0.0218,0.0,0.0,0.063,0.0204,0.9841,0.8121,0.0151,0.0,0.0517,0.1667,0.2083,0.0356,0.0257,0.0322,0.0,0.017,reg oper account,block of flats,0.0247,Block,No,0.0,0.0,0.0,0.0,-751.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n266702,0,Cash loans,M,N,Y,0,157500.0,183784.5,14350.5,148500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.019101,-8249,-128,-629.0,-855,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,16,1,1,0,1,1,0,Self-employed,,0.08465674957394656,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n139137,0,Cash loans,F,Y,Y,0,216000.0,629325.0,26793.0,562500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010643,-17051,-928,-4116.0,-602,1.0,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Trade: type 3,0.5489490247345088,0.6569758663680708,0.4650692149562261,0.068,0.0464,0.9806,0.7348,0.0087,0.04,0.0345,0.3333,0.375,0.0347,0.0538,0.0525,0.0077,0.0247,0.0693,0.0482,0.9806,0.7452,0.0088,0.0403,0.0345,0.3333,0.375,0.0355,0.0588,0.0547,0.0078,0.0261,0.0687,0.0464,0.9806,0.7383,0.0087,0.04,0.0345,0.3333,0.375,0.0353,0.0547,0.0534,0.0078,0.0252,reg oper account,block of flats,0.0514,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-780.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n320588,1,Cash loans,F,Y,N,0,225000.0,225000.0,17410.5,225000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.022625,-13821,-690,-7963.0,-2806,1.0,1,1,0,1,1,0,Managers,1.0,2,2,TUESDAY,18,0,0,0,0,0,0,Trade: type 7,0.3759538512292542,0.7795820052017239,0.3606125659189888,0.4845,0.4749,0.9776,0.6940000000000001,0.2281,0.0,1.0,0.1667,0.2083,0.5479999999999999,0.395,0.4617,0.0,0.0,0.4937,0.4929,0.9777,0.706,0.2302,0.0,1.0,0.1667,0.2083,0.5605,0.4316,0.48100000000000004,0.0,0.0,0.4892,0.4749,0.9776,0.6981,0.2295,0.0,1.0,0.1667,0.2083,0.5576,0.4019,0.47,0.0,0.0,reg oper account,block of flats,0.4895,Panel,No,0.0,0.0,0.0,0.0,-604.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,3.0\n119195,0,Cash loans,F,N,Y,0,171000.0,675000.0,21906.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-12053,-2959,-4716.0,-2603,,1,1,0,1,0,0,Core staff,2.0,2,2,SUNDAY,15,0,0,0,0,0,0,Kindergarten,,0.4395400919425487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1499.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n194757,0,Cash loans,F,N,Y,0,67500.0,284400.0,16011.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.01885,-21068,365243,-10660.0,-4151,,1,0,0,1,0,0,,1.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.6990972705492573,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-373.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n224527,0,Cash loans,M,N,N,0,112500.0,508495.5,24592.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.022625,-19192,-404,-7534.0,-2725,,1,1,0,1,0,0,Drivers,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,Transport: type 3,,0.7783357256373088,0.7180328113294772,0.0856,0.0964,0.9632,0.4968,0.0126,0.0,0.1724,0.125,0.1667,0.0418,0.0698,0.0911,0.0193,0.1622,0.0872,0.1,0.9633,0.5165,0.0127,0.0,0.1724,0.125,0.1667,0.0427,0.0762,0.0949,0.0195,0.1717,0.0864,0.0964,0.9632,0.5035,0.0127,0.0,0.1724,0.125,0.1667,0.0425,0.071,0.0928,0.0194,0.1656,reg oper account,block of flats,0.1368,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3114.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n349800,0,Cash loans,M,Y,Y,0,247500.0,582768.0,37372.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-10857,-2089,-4647.0,-3374,1.0,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Self-employed,0.1672672017657333,0.3762118385538861,,,,,,,,,,,,,0.0054,,,,,,,,,,,,,,0.0056,,,,,,,,,,,,,,0.0055,,,,,0.0042,,No,0.0,0.0,0.0,0.0,-717.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n113895,0,Cash loans,F,N,Y,1,157500.0,521280.0,35392.5,450000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-10219,-1303,-5071.0,-367,,1,1,0,1,1,0,Laborers,3.0,1,1,SATURDAY,16,0,0,0,0,1,1,Postal,0.07223653749243605,0.4758788113960288,0.3791004853998145,0.033,0.0789,0.9672,,,0.0,0.1379,0.125,,,,0.0467,,,0.0336,0.0819,0.9672,,,0.0,0.1379,0.125,,,,0.0486,,,0.0333,0.0789,0.9672,,,0.0,0.1379,0.125,,,,0.0475,,,,block of flats,0.0633,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-424.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n274255,0,Cash loans,F,N,Y,0,405000.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008575,-11657,-1446,-2799.0,-2862,,1,1,0,1,1,0,Managers,2.0,2,2,SUNDAY,16,0,0,0,0,0,0,Self-employed,0.1972487272214536,0.6962745482469104,0.34741822720026416,0.0082,0.0,0.9518,,,0.0,0.0345,0.0,,,,0.0038,,,0.0084,0.0,0.9518,,,0.0,0.0345,0.0,,,,0.0039,,,0.0083,0.0,0.9518,,,0.0,0.0345,0.0,,,,0.0038,,,,block of flats,0.0042,Wooden,No,1.0,0.0,1.0,0.0,-1518.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,2.0\n195432,1,Cash loans,M,N,N,1,90000.0,239418.0,19044.0,211500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-10138,-2391,-2582.0,-2568,,1,1,1,1,0,0,Low-skill Laborers,2.0,3,3,THURSDAY,5,0,0,0,0,0,0,Self-employed,0.1887362083649953,0.5623051159972904,,0.0619,0.0555,0.9866,,,0.0,0.1379,0.1667,,0.0304,,0.0535,,0.0,0.063,0.0575,0.9866,,,0.0,0.1379,0.1667,,0.031,,0.0558,,0.0,0.0625,0.0555,0.9866,,,0.0,0.1379,0.1667,,0.0309,,0.0545,,0.0,,block of flats,0.0421,Panel,No,0.0,0.0,0.0,0.0,-528.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n173018,1,Cash loans,F,N,N,0,90000.0,440784.0,34956.0,360000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Rented apartment,0.019101,-17213,-1143,-3675.0,-771,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Postal,,0.6209341575843875,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1002.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,2.0\n106637,0,Cash loans,M,Y,Y,3,967500.0,450000.0,30073.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-11901,-546,-2932.0,-215,64.0,1,1,0,1,0,0,Managers,5.0,1,1,MONDAY,14,0,0,0,1,1,0,Business Entity Type 3,0.6979071590674906,0.4920864795799473,0.4543210601605785,0.2165,0.1088,0.9846,0.7892,0.0223,0.24,0.2069,0.3333,0.375,,0.1765,0.21,0.0,0.0,0.2206,0.1129,0.9846,0.7975,0.0225,0.2417,0.2069,0.3333,0.375,,0.1928,0.2188,0.0,0.0,0.2186,0.1088,0.9846,0.792,0.0224,0.24,0.2069,0.3333,0.375,,0.1796,0.2137,0.0,0.0,reg oper account,block of flats,0.1652,Panel,No,0.0,0.0,0.0,0.0,-88.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n234138,0,Cash loans,F,Y,Y,0,171000.0,675000.0,24799.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.005002,-16887,-3625,-4095.0,-438,5.0,1,1,1,1,1,1,Core staff,2.0,3,3,TUESDAY,12,0,0,0,0,0,0,School,0.342819742993731,0.5372986967565796,0.5744466170995097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-582.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n283427,0,Cash loans,F,N,N,0,157500.0,403249.5,19741.5,283500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.003122,-18093,-7107,-7837.0,-1635,,1,1,0,1,0,0,Laborers,1.0,3,3,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6692365485855833,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1572.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n294442,0,Cash loans,M,Y,Y,0,166500.0,745429.5,30055.5,643500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.016612000000000002,-9000,-618,-3813.0,-500,0.0,1,1,0,1,0,1,Security staff,1.0,2,2,MONDAY,12,0,1,1,0,0,0,Business Entity Type 3,0.4789132105031186,0.016388813852687124,0.4294236843421945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n261086,0,Cash loans,M,Y,Y,0,135000.0,457834.5,22396.5,378000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.025164,-19331,365243,-3191.0,-2873,19.0,1,0,0,1,0,0,,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.2653117484731741,0.7992967832109371,0.0701,0.0103,0.9801,,,0.0,0.1379,0.1667,,0.0109,,0.0662,,0.0,0.0714,0.0107,0.9801,,,0.0,0.1379,0.1667,,0.0111,,0.069,,0.0,0.0708,0.0103,0.9801,,,0.0,0.1379,0.1667,,0.0111,,0.0674,,0.0,,block of flats,0.0521,\"Stone, brick\",No,14.0,0.0,14.0,0.0,-17.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n385362,0,Cash loans,M,Y,Y,0,155002.5,1170000.0,32305.5,1170000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.030755,-12273,-2030,-6351.0,-4509,14.0,1,1,1,1,1,0,Laborers,1.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Trade: type 3,,0.6107168970084892,0.6971469077844458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-258.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n395478,0,Revolving loans,F,N,Y,0,180000.0,135000.0,6750.0,135000.0,Children,Working,Higher education,Widow,House / apartment,0.026392,-19729,-623,-7379.0,-3268,,1,1,0,1,0,1,Accountants,1.0,2,2,THURSDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.8451914916551201,0.72678331271146,0.03923771507209612,0.066,,0.9742,,,0.0,0.1034,0.1667,,,,0.0506,,,0.0672,,0.9742,,,0.0,0.1034,0.1667,,,,0.0528,,,0.0666,,0.9742,,,0.0,0.1034,0.1667,,,,0.0516,,,,block of flats,0.0612,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-488.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n202242,1,Cash loans,M,N,Y,0,292500.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.008019,-15577,-1330,-7263.0,-1036,,1,1,0,1,0,0,Security staff,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.4638153517903907,0.5701733785477298,0.19633396621345675,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n153610,1,Cash loans,M,Y,Y,0,247500.0,1260702.0,39712.5,1129500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-23721,365243,-5926.0,-4015,21.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.2962028246127759,0.08281470424406495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1684.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n200577,0,Revolving loans,F,N,Y,0,67500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.019689,-11717,-1537,-11687.0,-4036,,1,1,0,1,0,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Other,0.6631899131361331,0.7559425149713397,0.656158373001177,0.1237,0.109,0.9771,0.6872,0.0198,0.0,0.2759,0.1667,0.0417,0.0,0.1009,0.10300000000000001,0.0,0.0,0.1261,0.1131,0.9772,0.6994,0.02,0.0,0.2759,0.1667,0.0417,0.0,0.1102,0.1073,0.0,0.0,0.1249,0.109,0.9771,0.6914,0.0199,0.0,0.2759,0.1667,0.0417,0.0,0.1026,0.1049,0.0,0.0,org spec account,block of flats,0.0919,Block,No,0.0,0.0,0.0,0.0,-981.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n274404,1,Revolving loans,F,N,N,0,45000.0,337500.0,16875.0,337500.0,Family,Working,Higher education,Married,Rented apartment,0.019101,-10135,-721,-4745.0,-1560,,1,1,1,1,1,0,Core staff,2.0,2,2,TUESDAY,13,0,0,0,1,1,0,Kindergarten,,0.5660097711985461,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-506.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n426483,0,Cash loans,F,N,Y,0,202500.0,781920.0,28215.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.00702,-21114,-1395,-12137.0,-4056,,1,1,0,1,0,0,Sales staff,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Self-employed,0.8257410481621374,0.6106025164886261,0.5673792367572691,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-742.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n426819,0,Cash loans,F,N,Y,0,139500.0,253737.0,25227.0,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.019101,-23153,365243,-4520.0,-4696,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.2563991885360997,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-404.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n230673,0,Revolving loans,M,Y,Y,0,225000.0,630000.0,31500.0,630000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.072508,-13997,-872,-8006.0,-3586,4.0,1,1,0,1,0,0,Drivers,1.0,1,1,THURSDAY,18,0,0,0,0,0,0,Other,0.2554782125457921,0.7583189665498894,0.5352762504724826,0.1784,0.0978,0.9791,0.7144,0.0,0.24,0.1552,0.3125,0.3542,0.092,0.1454,0.1539,0.0,0.0006,0.063,0.0612,0.9742,0.6602,0.0,0.0,0.1034,0.1667,0.2083,0.0564,0.0551,0.0515,0.0,0.0005,0.1801,0.0978,0.9791,0.7182,0.0,0.24,0.1552,0.3125,0.3542,0.0936,0.1479,0.1567,0.0,0.0006,reg oper account,block of flats,0.2038,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-953.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n266698,0,Cash loans,F,Y,N,0,90000.0,225000.0,10489.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.019689,-9439,-1527,-366.0,-489,1.0,1,1,1,1,1,0,Sales staff,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Self-employed,0.3077196711150018,0.6232989180473089,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,1.0,-1715.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n353803,0,Cash loans,F,Y,N,0,211500.0,774000.0,20547.0,774000.0,,Commercial associate,Higher education,Single / not married,House / apartment,0.035792000000000004,-9320,-131,-38.0,-410,10.0,1,1,0,1,0,0,Waiters/barmen staff,1.0,2,2,SATURDAY,15,0,0,0,0,1,1,Business Entity Type 3,0.3150336859079634,0.6373054895343601,0.19633396621345675,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n437285,0,Cash loans,M,Y,Y,0,252000.0,1159515.0,34033.5,1012500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-11562,-4207,-4184.0,-3636,18.0,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,16,0,0,0,0,0,0,Transport: type 2,,0.5858798526199412,0.6722428897082422,0.0825,0.0705,0.9757,0.6668,0.0325,0.0,0.1379,0.1667,0.0417,0.0657,0.0672,0.0711,,0.0,0.084,0.0732,0.9757,0.6798,0.0328,0.0,0.1379,0.1667,0.0417,0.0672,0.0735,0.0741,,0.0,0.0833,0.0705,0.9757,0.6713,0.0327,0.0,0.1379,0.1667,0.0417,0.0669,0.0684,0.0724,,0.0,reg oper spec account,block of flats,0.0737,Panel,No,0.0,0.0,0.0,0.0,-531.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n337307,0,Cash loans,F,N,Y,0,67500.0,571446.0,16839.0,477000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-19303,365243,-9469.0,-2850,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.16040532147836672,0.7136313997323308,0.1144,0.0933,0.9871,0.8232,0.0124,0.0,0.2069,0.1667,0.0417,0.0854,0.0925,0.0986,0.0039,0.0038,0.1166,0.0968,0.9871,0.8301,0.0125,0.0,0.2069,0.1667,0.0417,0.0873,0.10099999999999999,0.1028,0.0039,0.004,0.1155,0.0933,0.9871,0.8256,0.0125,0.0,0.2069,0.1667,0.0417,0.0869,0.0941,0.1004,0.0039,0.0039,reg oper account,block of flats,0.1013,Block,No,3.0,1.0,3.0,1.0,-411.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n165638,1,Cash loans,F,N,N,0,405000.0,1305000.0,38155.5,1305000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-19799,-739,-931.0,-3355,,1,1,0,1,1,0,Accountants,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.7945222958838101,0.21300828506817712,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1269.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n396218,0,Cash loans,M,Y,Y,0,180000.0,383089.5,40356.0,346500.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.010147,-20305,-1765,-4280.0,-3068,17.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Self-employed,,0.639350115196884,,0.1856,,0.9851,,,0.2,0.1724,0.1667,,0.1216,,0.1132,,,0.1891,,0.9851,,,0.2014,0.1724,0.1667,,0.1244,,0.1179,,,0.1874,,0.9851,,,0.2,0.1724,0.1667,,0.1237,,0.1152,,,,block of flats,0.1499,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2448.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n105588,0,Cash loans,M,Y,Y,0,225000.0,539100.0,26064.0,450000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-22579,365243,-9665.0,-4743,4.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.6151111756629916,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n305338,0,Cash loans,F,N,Y,0,202500.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003069,-16985,-8386,-7093.0,-542,,1,1,0,1,1,0,,2.0,3,3,SUNDAY,11,0,0,0,0,0,0,Other,0.5834014446196237,0.7269309236750182,0.0005272652387098817,0.1361,0.1016,0.9856,,,0.0,0.1724,0.1667,,0.0406,,0.0451,,0.0017,0.1387,0.1055,0.9856,,,0.0,0.1724,0.1667,,0.0416,,0.047,,0.0018,0.1374,0.1016,0.9856,,,0.0,0.1724,0.1667,,0.0413,,0.0459,,0.0018,,block of flats,0.0802,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-974.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n281231,0,Cash loans,F,N,Y,0,180000.0,555273.0,20529.0,463500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019689,-13386,-3720,-1219.0,-4071,,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Self-employed,,0.6524675228468452,0.746300213050371,0.2227,0.1693,0.9821,0.7552,,0.24,0.2069,0.3333,0.0417,0.1356,,0.2302,,0.0,0.2269,0.1757,0.9821,0.7648,,0.2417,0.2069,0.3333,0.0417,0.1387,,0.2398,,0.0,0.2248,0.1693,0.9821,0.7585,,0.24,0.2069,0.3333,0.0417,0.138,,0.2343,,0.0,reg oper spec account,block of flats,0.2179,Panel,No,1.0,0.0,1.0,0.0,-477.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n311520,0,Cash loans,M,Y,Y,2,247500.0,964989.0,31257.0,805500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.003818,-16632,-5215,-4255.0,-65,16.0,1,1,0,1,0,0,,4.0,2,2,MONDAY,5,0,0,0,0,0,0,Other,0.4414980830637476,0.5966391675152887,0.5971924268337128,0.0928,0.1005,0.9776,0.6940000000000001,0.0309,0.0,0.2069,0.1667,0.0,0.0328,0.0756,0.0576,0.0,0.0777,0.0945,0.1042,0.9777,0.706,0.0312,0.0,0.2069,0.1667,0.0,0.0336,0.0826,0.06,0.0,0.0822,0.0937,0.1005,0.9776,0.6981,0.0311,0.0,0.2069,0.1667,0.0,0.0334,0.077,0.0586,0.0,0.0793,reg oper spec account,block of flats,0.0667,Panel,No,2.0,1.0,2.0,0.0,-1390.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n290909,1,Cash loans,M,N,N,1,220500.0,521136.0,54855.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.001333,-12323,-402,-214.0,-4618,,1,1,1,1,1,0,Laborers,3.0,3,3,MONDAY,9,0,0,0,1,1,1,Transport: type 2,0.1427740327554346,0.3859335250494535,0.7922644738669378,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-631.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n287377,0,Cash loans,F,Y,Y,0,157500.0,840996.0,24718.5,702000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.0228,-20361,-1374,-9294.0,-3906,12.0,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6822347409257103,0.7421816117614419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n338680,0,Cash loans,F,N,Y,1,180000.0,239850.0,25317.0,225000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010147,-16860,-3659,-4237.0,-412,,1,1,1,1,0,0,,3.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,,0.6163477647367122,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n179069,0,Cash loans,F,N,N,0,157500.0,315000.0,11313.0,315000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.015221,-12193,-3388,-3150.0,-4255,,1,1,1,1,1,0,Core staff,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Medicine,0.6695061618316418,0.5869352738170059,0.5919766183185521,0.033,,0.9747,0.6532,,,0.069,0.125,0.0417,,,0.0164,,,0.0336,,0.9747,0.6668,,,0.069,0.125,0.0417,,,0.0171,,,0.0333,,0.9747,0.6578,,,0.069,0.125,0.0417,,,0.0167,,,reg oper account,block of flats,0.0198,,No,1.0,0.0,1.0,0.0,-2426.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,0.0\n148925,0,Cash loans,M,Y,N,0,175500.0,724500.0,36990.0,724500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-10004,-2997,-4056.0,-2680,4.0,1,1,1,1,1,0,Drivers,1.0,3,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,,0.309928130207,0.2301588968634147,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1168.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n110320,0,Cash loans,F,N,Y,0,112500.0,797557.5,26487.0,688500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16004,-125,-56.0,-4157,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,16,0,0,0,1,1,0,Services,0.5093693950145469,0.3621623711206497,0.7490217048463391,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n141330,0,Revolving loans,F,N,Y,1,90000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.00823,-12828,-125,-2896.0,-4834,,1,1,0,1,0,0,Sales staff,3.0,2,2,SATURDAY,17,0,0,0,0,1,1,Trade: type 7,,0.5327453870193479,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-400.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n254572,0,Cash loans,F,Y,N,0,126000.0,675000.0,28597.5,675000.0,Family,State servant,Secondary / secondary special,Married,Municipal apartment,0.006670999999999999,-16472,-9031,-22.0,-19,8.0,1,1,1,1,0,0,Cooking staff,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Kindergarten,0.3418266785117883,0.575235664800713,0.6706517530862718,0.0072,0.0021,0.9652,,,,0.0345,0.0417,,,,0.0062,,0.0009,0.0074,0.0022,0.9652,,,,0.0345,0.0417,,,,0.0064,,0.001,0.0073,0.0021,0.9652,,,,0.0345,0.0417,,,,0.0063,,0.0009,,block of flats,0.0049,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-2533.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n191538,0,Cash loans,F,N,N,1,135000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-13957,-737,-2790.0,-4248,,1,1,0,1,0,0,Sales staff,3.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.32867899642154497,0.6144143775673561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1501.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n344722,0,Cash loans,F,N,Y,0,112500.0,202500.0,15813.0,202500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-12355,-751,-1215.0,-4763,,1,1,0,1,0,0,Accountants,2.0,2,2,MONDAY,9,0,0,0,0,1,1,Other,,0.5632951557948928,0.5226973172821112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n406229,0,Cash loans,M,Y,N,0,135000.0,500211.0,26028.0,463500.0,Family,Commercial associate,Higher education,Separated,With parents,0.030755,-10845,-526,-4883.0,-3092,11.0,1,1,0,1,0,0,Laborers,1.0,2,2,SUNDAY,16,0,0,0,0,0,0,Trade: type 3,0.3879201274888012,0.6708368152851013,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-271.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n321546,0,Cash loans,F,Y,N,0,270000.0,850311.0,54342.0,742500.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.04622,-17804,-4180,-1820.0,-1329,6.0,1,1,1,1,1,0,Managers,2.0,1,1,THURSDAY,12,0,0,0,0,0,0,Self-employed,0.8231734905403315,0.5982117070353347,0.25670557243930026,0.1021,0.1062,0.9821,0.7552,0.0072,0.0,0.069,0.1667,0.2083,,0.0824,0.0517,0.0039,0.0639,0.10400000000000001,0.1103,0.9821,0.7648,0.0072,0.0,0.069,0.1667,0.2083,,0.09,0.0539,0.0039,0.0676,0.1031,0.1062,0.9821,0.7585,0.0072,0.0,0.069,0.1667,0.2083,,0.0838,0.0526,0.0039,0.0652,reg oper spec account,block of flats,0.0624,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1892.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n421995,0,Cash loans,F,N,Y,0,112500.0,152820.0,9477.0,135000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.035792000000000004,-22501,365243,-6901.0,-5061,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.3552308172290725,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-461.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n232804,0,Cash loans,F,N,Y,0,90000.0,225000.0,15790.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-19436,-4138,-12558.0,-2978,,1,1,1,1,1,0,Managers,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Trade: type 7,0.6542879068769514,0.7209966074611664,,0.0108,0.001,0.9613,0.4288,0.0009,0.0,0.069,0.0208,0.0417,0.0093,0.0092,0.0054,0.0,0.0,0.0032,0.0,0.9762,0.2225,0.0006,0.0,0.0345,0.0,0.0417,0.0004,0.0037,0.0015,0.0,0.0,0.0109,0.001,0.9642,0.4364,0.0009,0.0,0.0517,0.0208,0.0417,0.0004,0.0094,0.0054,0.0,0.0,not specified,block of flats,0.0074,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2355.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n414978,0,Cash loans,M,Y,Y,0,180000.0,1223010.0,48631.5,1125000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00733,-16920,-1663,-3403.0,-465,9.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Self-employed,,0.6648266226488371,0.6430255641096323,0.0124,0.0,0.9717,0.6124,0.0014,0.0,0.069,0.0417,0.0417,0.0021,,0.0085,,0.0,0.0126,0.0,0.9717,0.6276,0.0014,0.0,0.069,0.0417,0.0417,0.0021,,0.0089,,0.0,0.0125,0.0,0.9717,0.6176,0.0014,0.0,0.069,0.0417,0.0417,0.0021,,0.0087,,0.0,reg oper account,block of flats,0.0157,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3565.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n414781,0,Cash loans,F,N,N,0,112500.0,436032.0,21339.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-8836,-842,-6730.0,-1501,,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,Government,,0.3823070935916513,0.4223696523543468,0.0825,0.0778,0.9737,0.6396,0.0532,0.0,0.1379,0.1667,0.2083,0.1634,0.0672,0.0664,0.0,0.0,0.084,0.0807,0.9737,0.6537,0.0537,0.0,0.1379,0.1667,0.2083,0.1672,0.0735,0.0692,0.0,0.0,0.0833,0.0778,0.9737,0.6444,0.0536,0.0,0.1379,0.1667,0.2083,0.1663,0.0684,0.0676,0.0,0.0,reg oper account,block of flats,0.0522,Panel,No,0.0,0.0,0.0,0.0,-276.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n255945,0,Cash loans,M,Y,Y,0,126000.0,1147500.0,31554.0,1147500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-21663,365243,-6251.0,-4166,2.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.7295491885505266,0.5100895276257282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-619.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n121913,0,Cash loans,M,Y,Y,0,112500.0,417024.0,21964.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.015221,-14749,-1901,-7224.0,-4194,27.0,1,1,1,1,0,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Industry: type 7,,0.5046923421915086,0.6545292802242897,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n409036,0,Cash loans,F,N,N,0,112500.0,675000.0,21775.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-16071,-910,-4542.0,-4542,,1,1,0,1,1,0,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 2,0.3984499990117078,0.6916163328401422,0.501075160239048,0.2495,0.2688,0.9831,0.7688,0.1225,0.0,0.4138,0.1667,,0.2404,0.2034,0.2307,0.0,0.0,0.2542,0.2789,0.9831,0.7779,0.1236,0.0,0.4138,0.1667,,0.2459,0.2222,0.2404,0.0,0.0,0.2519,0.2688,0.9831,0.7719,0.1233,0.0,0.4138,0.1667,,0.2446,0.2069,0.2348,0.0,0.0,reg oper spec account,block of flats,0.2469,Panel,No,7.0,1.0,7.0,0.0,-2189.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n371933,0,Cash loans,F,N,Y,0,202500.0,315000.0,14004.0,315000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018209,-19772,365243,-4710.0,-3304,,1,0,0,1,0,1,,1.0,3,3,TUESDAY,8,0,0,0,0,0,0,XNA,0.6502761529155114,0.12578519670608693,0.35233997269170386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1301.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n430122,0,Cash loans,F,N,Y,0,67500.0,1078200.0,31522.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005084,-13491,-6073,-2015.0,-1489,,1,1,1,1,0,0,Sales staff,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,,0.6591556594908279,0.5709165417729987,0.0928,0.0923,0.9806,0.7348,0.0117,0.0,0.2069,0.1667,0.0,0.016,0.0756,0.0791,0.0,0.0,0.0945,0.0958,0.9806,0.7452,0.0118,0.0,0.2069,0.1667,0.0,0.0164,0.0826,0.0824,0.0,0.0,0.0937,0.0923,0.9806,0.7383,0.0118,0.0,0.2069,0.1667,0.0,0.0163,0.077,0.0805,0.0,0.0,reg oper account,block of flats,0.0686,Panel,No,3.0,0.0,3.0,0.0,-1033.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n131683,0,Cash loans,F,Y,N,0,216000.0,225000.0,6066.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-17064,-3181,-3510.0,-597,1.0,1,1,1,1,0,0,Private service staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Other,,0.6599363902126975,0.5797274227921155,1.0,0.6077,0.9821,0.7552,,1.0,0.9309999999999999,0.3333,0.0,0.8698,,0.6451,,0.0036,1.0,0.6307,0.9821,0.7648,,1.0,0.9309999999999999,0.3333,0.0,0.8896,,0.6721,,0.0038,1.0,0.6077,0.9821,0.7585,,1.0,0.9309999999999999,0.3333,0.0,0.8849,,0.6567,,0.0036,,block of flats,1.0,Panel,No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n382285,0,Cash loans,F,Y,Y,1,292500.0,584460.0,16879.5,382500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.022625,-12975,-2078,-4397.0,-4883,4.0,1,1,0,1,1,0,Sales staff,3.0,2,2,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.7544760898564049,0.7598466028546851,0.7076993447402619,0.0,,0.0,,,0.0,,,,,,,,,0.0,,0.0005,,,0.0,,,,,,,,,0.0,,0.0,,,0.0,,,,,,,,,,block of flats,0.0,,No,0.0,0.0,0.0,0.0,-241.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n147673,0,Cash loans,F,N,Y,0,67500.0,417024.0,22752.0,360000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22459,365243,-9339.0,-4249,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,0.8433949616430549,0.1953537989258013,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-726.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n211533,0,Cash loans,M,Y,Y,0,99000.0,99576.0,5530.5,67500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-8206,-820,-2904.0,-875,10.0,1,1,0,1,0,1,Laborers,2.0,2,2,WEDNESDAY,14,0,0,0,1,1,0,Business Entity Type 2,0.1174389932037245,0.4719697283147101,0.2580842039460289,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-768.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n319283,0,Cash loans,F,Y,Y,0,103500.0,135000.0,8856.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-7820,-135,-3465.0,-453,20.0,1,1,1,1,0,0,Core staff,2.0,2,2,TUESDAY,14,0,0,0,0,1,1,Bank,,0.6080309697365297,,0.0577,,0.9851,,,,0.1379,0.1667,,0.0151,,0.0529,,0.0348,0.0588,,0.9851,,,,0.1379,0.1667,,0.0154,,0.0551,,0.0368,0.0583,,0.9851,,,,0.1379,0.1667,,0.0153,,0.0538,,0.0355,,block of flats,0.054000000000000006,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-231.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n442236,0,Revolving loans,M,N,Y,0,171000.0,157500.0,7875.0,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22247,365243,-11756.0,-279,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.6403478792974197,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1135.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n385730,0,Cash loans,F,N,N,2,112500.0,104481.0,11380.5,94500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00702,-13301,-1977,-1488.0,-3886,,1,1,0,1,1,0,Core staff,4.0,2,2,WEDNESDAY,11,0,0,0,0,1,1,School,,0.1453631461138042,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n155659,0,Cash loans,F,N,Y,0,180000.0,518562.0,20695.5,463500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-20438,-5950,-9993.0,-3971,,1,1,0,1,1,0,Accountants,2.0,1,1,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.8129451663849524,0.7208910217663109,,0.2196,0.1503,0.9767,,,0.24,0.2069,0.3333,,0.0261,,0.1433,,,0.2237,0.156,0.9767,,,0.2417,0.2069,0.3333,,0.0267,,0.1493,,,0.2217,0.1503,0.9767,,,0.24,0.2069,0.3333,,0.0265,,0.1459,,,,block of flats,0.1907,Panel,No,0.0,0.0,0.0,0.0,-1669.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n447066,0,Cash loans,M,Y,Y,2,157500.0,573628.5,24435.0,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.00823,-15715,-4148,-451.0,-4912,4.0,1,1,0,1,0,0,Managers,3.0,2,2,THURSDAY,14,0,0,0,0,1,1,Trade: type 7,0.5803411126104353,0.566229994250585,0.8128226070575616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-922.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n397434,0,Cash loans,F,N,Y,0,135000.0,584766.0,24903.0,472500.0,Unaccompanied,State servant,Secondary / secondary special,Separated,Municipal apartment,0.006629,-17559,-9477,-9511.0,-942,,1,1,0,1,1,0,Core staff,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,School,,0.4911956248870282,0.4722533429586386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n177885,1,Cash loans,M,Y,Y,1,135000.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-15266,-1410,-2124.0,-3967,30.0,1,1,0,1,0,0,,3.0,2,2,SATURDAY,12,0,0,0,0,1,1,Business Entity Type 2,,0.16219210595922867,0.3672910183026313,0.1959,0.1135,0.9965,0.9524,0.0346,0.2,0.1724,0.375,0.4167,0.1256,0.1572,0.2041,0.0116,0.035,0.1996,0.1177,0.9965,0.9543,0.0349,0.2014,0.1724,0.375,0.4167,0.1285,0.1717,0.2126,0.0117,0.037000000000000005,0.1978,0.1135,0.9965,0.953,0.0348,0.2,0.1724,0.375,0.4167,0.1278,0.1599,0.2078,0.0116,0.0357,reg oper account,block of flats,0.1883,Panel,No,6.0,0.0,6.0,0.0,-144.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n154059,0,Cash loans,F,N,Y,0,157500.0,366588.0,33750.0,306000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-18127,-3105,-2400.0,-1654,,1,1,0,1,0,0,Sales staff,2.0,1,1,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.7107091100674796,0.7156652022464628,0.5028782772082183,0.1017,0.0899,0.9821,0.7552,0.0,0.1064,0.0459,0.5275,0.5692,0.0,0.1076,0.0393,0.0,0.033,0.0882,0.0438,0.9752,0.6733,0.0,0.0806,0.0345,0.4583,0.5,0.0,0.1175,0.0,0.0,0.0,0.0874,0.0422,0.9757,0.6713,0.0,0.08,0.0345,0.4583,0.5,0.0,0.1095,0.0462,0.0,0.0,reg oper account,block of flats,0.057,Block,No,0.0,0.0,0.0,0.0,-668.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n212152,0,Cash loans,F,N,N,0,157500.0,675000.0,35964.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.025164,-21421,-6919,-9329.0,-4975,,1,1,0,1,0,0,Managers,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Self-employed,,0.3990985891066013,0.7862666146611379,0.0825,0.0795,0.9762,0.6736,0.008,0.0,0.1379,0.1667,0.2083,0.0658,0.0672,0.0697,0.0,0.0,0.084,0.0825,0.9762,0.6864,0.008,0.0,0.1379,0.1667,0.2083,0.0673,0.0735,0.0726,0.0,0.0,0.0833,0.0795,0.9762,0.6779999999999999,0.008,0.0,0.1379,0.1667,0.2083,0.067,0.0684,0.071,0.0,0.0,org spec account,block of flats,0.0592,Panel,No,9.0,1.0,9.0,1.0,-1478.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n274282,1,Cash loans,M,N,N,0,103500.0,94500.0,4891.5,94500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-8388,-501,-8377.0,-1047,,1,1,1,1,1,0,Core staff,1.0,3,3,MONDAY,12,0,0,0,0,0,0,Trade: type 2,0.29140647374706224,0.4492043892371392,,0.0711,0.0774,0.9781,0.7008,0.0322,0.0,0.1379,0.1667,0.2083,,0.0572,0.0657,0.0039,0.0035,0.0725,0.0803,0.9782,0.7125,0.0325,0.0,0.1379,0.1667,0.2083,,0.0624,0.0685,0.0039,0.0037,0.0718,0.0774,0.9781,0.7048,0.0324,0.0,0.1379,0.1667,0.2083,,0.0581,0.0669,0.0039,0.0036,reg oper account,block of flats,0.0525,Panel,No,7.0,2.0,7.0,1.0,-736.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n358867,0,Revolving loans,M,N,Y,0,103500.0,270000.0,13500.0,,,Commercial associate,Higher education,Single / not married,House / apartment,0.025164,-8402,-402,-7149.0,-1060,,1,1,0,1,1,0,Laborers,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.14914541477007182,0.521691834609975,0.470456116119975,0.0268,0.0421,0.9702,0.5920000000000001,0.0479,0.0,0.1034,0.0833,0.125,0.0,0.021,0.0381,0.0039,0.0042,0.0273,0.0437,0.9702,0.608,0.0484,0.0,0.1034,0.0833,0.125,0.0,0.023,0.0397,0.0039,0.0044,0.0271,0.0421,0.9702,0.5975,0.0482,0.0,0.1034,0.0833,0.125,0.0,0.0214,0.0387,0.0039,0.0042,reg oper account,block of flats,0.057,Block,No,3.0,0.0,3.0,0.0,-1719.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n320063,0,Revolving loans,F,Y,Y,1,65250.0,157500.0,7875.0,157500.0,Unaccompanied,State servant,Secondary / secondary special,Married,With parents,0.035792000000000004,-8447,-1135,-3290.0,-1106,0.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Postal,0.57335685004637,0.6778908360513103,0.11247434965561487,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-645.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n258287,0,Cash loans,M,N,N,0,225000.0,728460.0,57555.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.030755,-8156,-841,-2968.0,-819,,1,1,1,1,1,0,Core staff,1.0,2,2,WEDNESDAY,11,0,0,0,1,1,0,Self-employed,0.1605412953021602,0.3048442949371834,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-970.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n276237,0,Cash loans,M,N,N,0,360000.0,270000.0,14778.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.0228,-20337,-1670,-413.0,-1833,,1,1,0,1,1,0,Laborers,2.0,2,2,MONDAY,7,0,0,0,0,0,0,Other,,0.5211461483313072,0.13680052191177486,0.1479,0.0628,0.997,0.9592,0.0693,0.12,0.1034,0.3333,0.375,0.0714,0.1202,0.1107,0.0019,0.004,0.1502,0.0649,0.997,0.9608,0.0696,0.1208,0.1034,0.3333,0.375,0.0591,0.1313,0.1151,0.0,0.0032,0.1494,0.0628,0.997,0.9597,0.0697,0.12,0.1034,0.3333,0.375,0.0726,0.1223,0.1127,0.0019,0.0041,reg oper account,block of flats,0.1141,Panel,No,6.0,1.0,5.0,0.0,-1578.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n184625,0,Cash loans,F,N,Y,0,135000.0,495000.0,47128.5,495000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.026392,-18359,-7156,-8781.0,-1913,,1,1,0,1,1,0,Core staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Kindergarten,,0.6815042279525211,0.6195277080511546,0.066,,0.9712,,,0.0,0.1379,0.1667,,,,0.0452,,0.1848,0.0672,,0.9712,,,0.0,0.1379,0.1667,,,,0.0471,,0.1956,0.0666,,0.9712,,,0.0,0.1379,0.1667,,,,0.046,,0.1886,,block of flats,0.0757,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-167.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n450002,0,Cash loans,F,Y,Y,0,130500.0,1252363.5,47830.5,1152000.0,Family,Pensioner,Lower secondary,Married,House / apartment,0.003813,-23256,365243,-7573.0,-5166,21.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.08600334121315173,0.3859146722745145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n418818,0,Cash loans,F,Y,Y,1,360000.0,539100.0,29376.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-13932,-3340,-5735.0,-4643,10.0,1,1,0,1,0,0,Managers,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.7515141266265988,0.3108182544189319,,0.1113,0.9836,,,,0.2069,0.1667,,,,0.0523,,,,0.1155,0.9836,,,,0.2069,0.1667,,,,0.0545,,,,0.1113,0.9836,,,,0.2069,0.1667,,,,0.0532,,,,,0.0647,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-3096.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n227435,0,Cash loans,M,N,N,0,180000.0,302076.0,25924.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-14162,-741,-6330.0,-4415,,1,1,1,1,0,0,Laborers,2.0,1,1,THURSDAY,10,0,0,0,0,0,0,Construction,0.2356006749262437,0.7049341557013291,0.7969726818373722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1323.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n259171,0,Cash loans,F,N,Y,0,54000.0,291384.0,20853.0,270000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-22708,365243,-7068.0,-4249,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.534878606727517,0.5849900404894085,0.1113,0.0184,0.9717,0.6124,0.0236,0.0,0.3448,0.1667,0.2083,0.0757,0.0841,0.1194,0.0309,0.0672,0.1134,0.0191,0.9717,0.6276,0.0239,0.0,0.3448,0.1667,0.2083,0.0774,0.0918,0.1244,0.0311,0.0712,0.1124,0.0184,0.9717,0.6176,0.0238,0.0,0.3448,0.1667,0.2083,0.077,0.0855,0.1215,0.0311,0.0686,not specified,block of flats,0.1215,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n210565,0,Cash loans,F,Y,N,1,67500.0,254700.0,20250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.002134,-7841,-219,-318.0,-460,14.0,1,1,0,1,0,0,Sales staff,3.0,3,3,MONDAY,6,0,0,0,0,0,0,Self-employed,,0.04690963752948007,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-124.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n164772,0,Cash loans,F,N,N,0,270000.0,781920.0,42547.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.005144,-15844,-1500,-892.0,-4286,,1,1,0,1,1,0,Sales staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,0.4354194790443801,0.5168881642867668,0.2263473957097693,0.066,0.1016,0.9945,0.9252,,0.0,0.1724,0.125,,0.0,,0.0601,,0.0,0.0672,0.1055,0.9945,0.9281,,0.0,0.1724,0.125,,0.0,,0.0626,,0.0,0.0666,0.1016,0.9945,0.9262,,0.0,0.1724,0.125,,0.0,,0.0611,,0.0,,block of flats,0.0665,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1780.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n238003,0,Cash loans,M,Y,Y,1,180000.0,239850.0,25447.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007305,-15089,-2383,-6297.0,-4989,18.0,1,1,1,1,1,0,Drivers,3.0,3,3,MONDAY,7,0,0,0,0,1,1,Self-employed,,0.561944488657868,,0.1979,0.1833,0.9940000000000001,0.9184,,0.04,0.3793,0.3333,0.25,0.0379,0.1614,0.2102,0.0039,0.0117,0.2017,0.1902,0.9940000000000001,0.9216,,0.0403,0.3793,0.3333,0.25,0.0387,0.1763,0.21899999999999997,0.0039,0.0124,0.1999,0.1833,0.9940000000000001,0.9195,,0.04,0.3793,0.3333,0.25,0.0385,0.1642,0.214,0.0039,0.012,,block of flats,0.1679,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-1184.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n402722,0,Cash loans,F,N,Y,0,112500.0,675000.0,34596.0,675000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.014519999999999996,-18871,-618,-2173.0,-2371,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.7126209020619777,0.4017708909315234,0.8106180215523969,0.0577,,0.9801,0.728,,0.0,0.1379,0.1667,0.2083,,,0.0361,,0.0477,0.0588,,0.9801,0.7387,,0.0,0.1379,0.1667,0.2083,,,0.0376,,0.0505,0.0583,,0.9801,0.7316,,0.0,0.1379,0.1667,0.2083,,,0.0367,,0.0487,org spec account,block of flats,0.0526,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n377328,0,Revolving loans,F,N,Y,1,135000.0,405000.0,20250.0,405000.0,Children,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.032561,-13453,-338,-7364.0,-4912,,1,1,0,1,0,0,Core staff,2.0,1,1,TUESDAY,15,0,0,0,0,0,0,Kindergarten,,0.6543493783920105,0.3556387169923543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-220.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n142735,0,Cash loans,F,N,Y,0,112500.0,1048500.0,30784.5,1048500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-19675,-3150,-7284.0,-3173,,1,1,0,1,1,0,Laborers,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Postal,,0.6762604424780145,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1592.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,8.0\n357525,0,Cash loans,M,Y,Y,0,179100.0,254700.0,25321.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018801,-24190,-826,-8941.0,-4545,14.0,1,1,0,1,0,0,Security staff,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Security Ministries,,0.6090311582809871,0.2793353208976285,0.1567,0.0943,0.9876,,,0.16,0.1379,0.3333,,,,0.1607,,0.0558,0.1597,0.0978,0.9876,,,0.1611,0.1379,0.3333,,,,0.1674,,0.0591,0.1582,0.0943,0.9876,,,0.16,0.1379,0.3333,,,,0.1636,,0.057,,block of flats,0.1685,\"Stone, brick\",No,2.0,1.0,2.0,0.0,-63.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n265731,0,Cash loans,F,N,Y,0,270000.0,824823.0,24246.0,688500.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.0105,-19103,-971,-7864.0,-2650,,1,1,0,1,0,0,Accountants,1.0,3,3,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.7157052487644403,0.5954562029091491,0.0464,0.0484,0.9896,0.8572,0.0063,0.0,0.1034,0.1667,0.2083,0.0382,0.0378,0.0417,0.0,0.0,0.0473,0.0502,0.9896,0.8628,0.0064,0.0,0.1034,0.1667,0.2083,0.039,0.0413,0.0434,0.0,0.0,0.0468,0.0484,0.9896,0.8591,0.0064,0.0,0.1034,0.1667,0.2083,0.0388,0.0385,0.0424,0.0,0.0,reg oper account,block of flats,0.0362,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2534.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n365547,0,Cash loans,F,N,Y,0,126000.0,807984.0,26833.5,697500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-20318,-1209,-3215.0,-3398,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Trade: type 7,,0.4049021069479557,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n199071,0,Cash loans,F,N,Y,0,180000.0,381528.0,14512.5,315000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.00496,-20385,365243,-7646.0,-3699,,1,0,0,1,1,0,,2.0,2,2,SUNDAY,13,0,0,0,0,0,0,XNA,,0.4617701070969076,0.4400578303966329,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-874.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n241046,0,Cash loans,F,N,Y,3,112500.0,675000.0,32602.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-15092,-2512,-8719.0,-4581,,1,1,0,1,1,0,Core staff,5.0,2,2,FRIDAY,8,0,0,0,0,0,0,Agriculture,,0.2245284444663105,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-447.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n455833,0,Cash loans,F,Y,N,0,112500.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.002134,-15887,-8434,-4555.0,-4586,17.0,1,1,1,1,0,0,Sales staff,2.0,3,3,SATURDAY,11,0,0,0,0,1,1,Other,,0.2517163695921742,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1383.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n341007,0,Cash loans,M,N,Y,1,202500.0,592560.0,28638.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-14647,-7484,-2754.0,-4146,,1,1,0,1,1,0,Laborers,3.0,3,3,FRIDAY,10,0,0,0,0,0,0,Industry: type 5,0.33676971345942736,0.15706766007824707,0.3123653692278984,0.368,0.1323,0.9796,,,,0.8276,0.1667,,0.0721,,0.3488,,0.1352,0.375,0.1373,0.9796,,,,0.8276,0.1667,,0.0737,,0.3634,,0.1431,0.3716,0.1323,0.9796,,,,0.8276,0.1667,,0.0734,,0.355,,0.138,,block of flats,0.3292,Panel,No,0.0,0.0,0.0,0.0,-1546.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n284132,0,Cash loans,F,Y,Y,1,135000.0,495000.0,19179.0,495000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.031329,-12158,-5502,-5508.0,-2470,0.0,1,1,0,1,0,0,HR staff,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Industry: type 9,0.4775853823057196,0.6923574123742647,0.2940831009471255,0.0753,0.0363,0.9767,0.6804,0.0078,0.0,0.1379,0.1667,0.2083,0.0352,0.0588,0.0662,0.0116,0.0226,0.0767,0.0377,0.9767,0.6929,0.0079,0.0,0.1379,0.1667,0.2083,0.036000000000000004,0.0643,0.069,0.0117,0.0239,0.076,0.0363,0.9767,0.6847,0.0079,0.0,0.1379,0.1667,0.2083,0.0358,0.0599,0.0674,0.0116,0.0231,reg oper spec account,block of flats,0.0613,Block,No,2.0,1.0,2.0,0.0,-2198.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n307007,0,Cash loans,F,N,Y,0,126000.0,263686.5,17298.0,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009657,-20820,365243,-9370.0,-3840,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.5026642364732409,0.07058051883159755,0.0897,0.0706,0.9856,0.8028,0.0264,0.08,0.1034,0.25,0.2917,0.0764,0.0731,0.0901,0.0,0.0484,0.0315,0.0321,0.9841,0.7909,0.0144,0.0,0.069,0.1667,0.2083,0.0617,0.0275,0.0283,0.0,0.0,0.0906,0.0706,0.9856,0.8054,0.0266,0.08,0.1034,0.25,0.2917,0.0777,0.0744,0.0917,0.0,0.0494,reg oper spec account,block of flats,0.1604,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-333.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n322257,0,Cash loans,M,N,N,0,135000.0,450000.0,30073.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.005084,-8540,-588,-3222.0,-1234,,1,1,1,1,0,0,Laborers,1.0,2,2,THURSDAY,14,0,0,0,1,1,0,Business Entity Type 3,0.12749714238973436,0.6847042142443579,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-375.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n362127,0,Cash loans,F,N,N,0,270000.0,454500.0,20020.5,454500.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.007114,-14579,-6171,-8504.0,-4375,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Government,,0.596996977688406,0.7091891096653581,0.0093,0.0,0.9717,0.6124,0.0,0.0,0.0345,0.0417,0.0417,0.0362,0.0076,0.0078,0.0,0.0,0.0095,0.0,0.9717,0.6276,0.0,0.0,0.0345,0.0417,0.0417,0.037000000000000005,0.0083,0.0082,0.0,0.0,0.0094,0.0,0.9717,0.6176,0.0,0.0,0.0345,0.0417,0.0417,0.0368,0.0077,0.008,0.0,0.0,reg oper spec account,block of flats,0.0062,Wooden,No,0.0,0.0,0.0,0.0,-1354.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n262330,0,Cash loans,M,Y,Y,1,157500.0,315000.0,20637.0,315000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.020713,-11136,-2731,-3038.0,-3026,9.0,1,1,1,1,1,0,Laborers,3.0,3,3,FRIDAY,9,0,0,0,0,0,0,Government,,0.5209333169314682,0.3672910183026313,0.0742,0.0565,0.9925,0.898,0.02,0.08,0.069,0.3333,0.375,0.0491,0.0605,0.0926,0.0,0.0,0.0756,0.0587,0.9926,0.902,0.0202,0.0806,0.069,0.3333,0.375,0.0502,0.0661,0.0965,0.0,0.0,0.0749,0.0565,0.9925,0.8994,0.0201,0.08,0.069,0.3333,0.375,0.05,0.0616,0.0943,0.0,0.0,reg oper account,block of flats,0.0838,Panel,No,0.0,0.0,0.0,0.0,-1355.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n106955,0,Revolving loans,F,N,N,0,157500.0,450000.0,22500.0,,,Working,Secondary / secondary special,Married,House / apartment,0.010006,-11993,-2921,-1289.0,-1948,,1,1,1,1,1,1,Private service staff,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.5056090444133728,0.464613790741544,0.5370699579791587,0.2598,0.1406,0.998,,,0.28,0.2414,0.3333,,0.0189,,,,0.0188,0.2647,0.1459,0.998,,,0.282,0.2414,0.3333,,0.0194,,,,0.0199,0.2623,0.1406,0.998,,,0.28,0.2414,0.3333,,0.0193,,,,0.0192,,block of flats,0.1971,Panel,No,0.0,0.0,0.0,0.0,-476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n334987,0,Cash loans,F,N,Y,0,135000.0,337923.0,18999.0,279000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008625,-18801,365243,-6078.0,-2363,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.4175074814576742,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n206545,0,Cash loans,F,N,Y,0,67500.0,144000.0,7812.0,144000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.028663,-23719,365243,-6230.0,-4284,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,0.712070267415179,0.6294655394356058,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2488.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n129078,0,Cash loans,F,N,Y,0,90000.0,283419.0,16398.0,234000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006207,-14227,-2780,-1645.0,-2573,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,,0.3693911211028864,0.8396980847302156,0.1031,0.0782,0.9762,0.6736,,0.0,0.1724,0.1667,0.2083,0.05,0.0841,0.0675,0.0,0.0,0.105,0.0812,0.9762,0.6864,,0.0,0.1724,0.1667,0.2083,0.0511,0.0918,0.0703,0.0,0.0,0.1041,0.0782,0.9762,0.6779999999999999,,0.0,0.1724,0.1667,0.2083,0.0509,0.0855,0.0687,0.0,0.0,reg oper account,block of flats,0.0586,Panel,No,0.0,0.0,0.0,0.0,-667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n344318,0,Cash loans,F,N,Y,0,56250.0,105534.0,10408.5,99000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.020713,-25120,365243,-14784.0,-4595,,1,0,0,1,0,0,,1.0,3,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.4885070268605387,0.7801436381572275,0.1021,0.1204,0.9786,0.7076,0.0113,0.0,0.2069,0.1667,0.2083,0.0298,0.0824,0.0884,0.0039,0.0059,0.10400000000000001,0.125,0.9786,0.7190000000000001,0.0114,0.0,0.2069,0.1667,0.2083,0.0305,0.09,0.0921,0.0039,0.0062,0.1031,0.1204,0.9786,0.7115,0.0114,0.0,0.2069,0.1667,0.2083,0.0303,0.0838,0.09,0.0039,0.006,reg oper account,block of flats,0.077,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-322.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n337882,1,Cash loans,F,N,Y,1,292500.0,545040.0,43191.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-12587,-1352,-4112.0,-1907,,1,1,0,1,0,0,Security staff,3.0,2,2,THURSDAY,5,0,0,0,0,0,0,Government,0.03949343219683184,0.08717769870317994,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1289.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n416471,0,Cash loans,F,N,N,0,112500.0,592560.0,26230.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Office apartment,0.005313,-15200,-3169,-6821.0,-4836,,1,1,0,1,0,0,,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 1,,0.15853236766559792,0.6178261467332483,0.0577,,0.9757,,,0.0,0.1034,0.1667,,0.0377,,0.0496,,0.0137,0.0588,,0.9757,,,0.0,0.1034,0.1667,,0.0385,,0.0517,,0.0145,0.0583,,0.9757,,,0.0,0.1034,0.1667,,0.0383,,0.0505,,0.0139,,block of flats,0.045,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1403.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n397222,0,Cash loans,F,Y,N,0,108000.0,728460.0,37134.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-16352,-1420,-2632.0,-4468,5.0,1,1,1,1,0,0,Sales staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,,0.7850486178090921,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n361628,0,Cash loans,F,N,Y,0,90000.0,308133.0,14953.5,234000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-15873,-8564,-4492.0,-4708,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.4619222888506978,0.6610235391308081,0.0588,,0.9945,,,,0.2414,0.0833,,,,0.0607,,,0.0599,,0.9945,,,,0.2414,0.0833,,,,0.0632,,,0.0593,,0.9945,,,,0.2414,0.0833,,,,0.0618,,,,block of flats,0.0477,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n443563,0,Cash loans,M,Y,Y,2,137250.0,225000.0,24363.0,225000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.008068,-9797,-1590,-1149.0,-2346,10.0,1,1,1,1,1,0,Medicine staff,4.0,3,3,TUESDAY,4,0,0,0,0,0,0,Other,,0.17346416397316045,0.3572932680336494,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1098.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n182359,0,Cash loans,F,N,N,1,117000.0,254700.0,17149.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-14285,-1752,-3179.0,-5189,,1,1,0,1,1,0,High skill tech staff,3.0,3,3,TUESDAY,10,0,0,0,0,0,0,Medicine,,0.6141374143891783,0.1455428133497032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1883.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n453705,0,Cash loans,F,N,Y,2,81000.0,314100.0,16573.5,225000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.002134,-11955,-1723,-1855.0,-3449,,1,1,0,1,0,0,High skill tech staff,4.0,3,3,WEDNESDAY,9,0,0,0,0,1,1,Emergency,,0.3284088496217509,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-284.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n439348,0,Cash loans,M,Y,N,1,180000.0,679500.0,27076.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-13997,-2274,-7975.0,-4245,14.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,12,0,0,0,0,1,1,Construction,,0.6916862882555666,0.7121551551910698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1002.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n270366,0,Cash loans,F,N,Y,0,202500.0,781920.0,31522.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-17609,-514,-671.0,-209,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,15,0,0,0,1,1,1,Business Entity Type 3,,0.6754220621467759,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-202.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n205136,0,Cash loans,M,Y,Y,1,184500.0,598486.5,21627.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14149,-331,-7800.0,-4880,13.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,8,0,0,0,0,0,0,Self-employed,0.43988796130930974,0.2533716311332267,0.8277026681625442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n449784,0,Cash loans,F,N,Y,0,225000.0,225000.0,22050.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-25033,365243,-306.0,-4635,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,,0.6489835017045373,0.6971469077844458,0.0412,,0.993,,,0.0,0.1379,0.1667,,0.187,,0.1271,,0.0386,0.042,,0.993,,,0.0,0.1379,0.1667,,0.1912,,0.1324,,0.0408,0.0416,,0.993,,,0.0,0.1379,0.1667,,0.1902,,0.1294,,0.0394,,block of flats,0.1175,Panel,No,2.0,0.0,2.0,0.0,-1650.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n323646,0,Cash loans,M,Y,Y,0,135000.0,720000.0,46143.0,720000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-9977,-530,-6881.0,-2620,12.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Industry: type 11,0.4938243149531919,0.5095787125388178,0.5460231970049609,0.1113,0.0873,0.9901,0.8640000000000001,0.0242,0.12,0.1034,0.3333,0.375,0.0674,0.0908,0.1251,0.0,0.0,0.1134,0.0906,0.9901,0.8693,0.0244,0.1208,0.1034,0.3333,0.375,0.0689,0.0992,0.1304,0.0,0.0,0.1124,0.0873,0.9901,0.8658,0.0244,0.12,0.1034,0.3333,0.375,0.0686,0.0923,0.1274,0.0,0.0,reg oper account,block of flats,0.1117,Panel,No,0.0,0.0,0.0,0.0,-2524.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n198121,0,Cash loans,M,Y,Y,1,157500.0,509400.0,22171.5,450000.0,Family,Working,Higher education,Civil marriage,House / apartment,0.009549,-12591,-1026,-6325.0,-4477,3.0,1,1,0,1,1,0,,3.0,2,2,SUNDAY,11,0,0,0,1,1,0,Business Entity Type 3,0.1616930151202395,0.6618477085006735,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,1.0,0.0,1.0,0.0,-650.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n175632,1,Cash loans,F,N,N,0,112500.0,380533.5,15219.0,328500.0,Unaccompanied,State servant,Higher education,Widow,House / apartment,0.016612000000000002,-22396,-9477,-3261.0,-686,,1,1,0,1,0,0,Core staff,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Medicine,,0.6052862426371514,0.41885428862332175,0.1856,0.1012,0.995,,,0.08,0.069,0.375,,0.0194,,0.1349,,0.0017,0.1891,0.105,0.995,,,0.0806,0.069,0.375,,0.0199,,0.1405,,0.0018,0.1874,0.1012,0.995,,,0.08,0.069,0.375,,0.0198,,0.1373,,0.0018,,block of flats,0.1295,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-560.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n265494,0,Revolving loans,F,N,Y,1,193500.0,405000.0,20250.0,405000.0,Unaccompanied,State servant,Secondary / secondary special,Married,With parents,0.04622,-11498,-1522,-4600.0,-1571,,1,1,0,1,0,0,Medicine staff,3.0,1,1,FRIDAY,12,0,0,0,0,0,0,Medicine,,0.4294927158805639,0.08011638046387602,,,0.9781,,,,,,,,,,,,,,0.9782,,,,,,,,,,,,,,0.9781,,,,,,,,,,,,,block of flats,0.242,,No,0.0,0.0,0.0,0.0,-872.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n356719,0,Cash loans,F,N,Y,0,103500.0,220662.0,21627.0,207000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018029,-24018,365243,-7410.0,-2562,,1,0,0,1,1,0,,1.0,3,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.13039323750909848,,0.0371,0.0257,0.9816,0.7484,0.0181,0.04,0.0345,0.3333,0.375,0.0301,0.0303,0.0406,0.0,0.0,0.0378,0.0266,0.9816,0.7583,0.0182,0.0403,0.0345,0.3333,0.375,0.0308,0.0331,0.0423,0.0,0.0,0.0375,0.0257,0.9816,0.7518,0.0182,0.04,0.0345,0.3333,0.375,0.0306,0.0308,0.0414,0.0,0.0,reg oper account,block of flats,0.0418,Panel,No,0.0,0.0,0.0,0.0,-482.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n437396,0,Cash loans,F,N,Y,0,112500.0,454500.0,16452.0,454500.0,Unaccompanied,State servant,Higher education,Married,With parents,0.003069,-20461,-3774,-5925.0,-3516,,1,1,0,1,0,0,Core staff,2.0,3,3,FRIDAY,16,0,0,0,0,0,0,Government,0.9045187936670848,0.6250481494456427,0.6879328378491735,0.0608,0.0694,0.9791,,,0.0,0.1379,0.1667,,0.0374,,0.0359,,0.002,0.062,0.07200000000000001,0.9791,,,0.0,0.1379,0.1667,,0.0382,,0.0374,,0.0021,0.0614,0.0694,0.9791,,,0.0,0.1379,0.1667,,0.038,,0.0366,,0.002,,block of flats,0.0417,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1498.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n327161,0,Cash loans,M,N,Y,0,112500.0,441000.0,24052.5,441000.0,Family,Working,Secondary / secondary special,Married,Rented apartment,0.014519999999999996,-8423,-604,-2224.0,-1107,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,8,0,0,0,0,1,1,Transport: type 4,,0.5744821112047114,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-406.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n118474,0,Cash loans,M,Y,Y,0,135000.0,640080.0,29970.0,450000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.025164,-12804,-842,-715.0,-4017,7.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Government,,0.4664582611692362,0.524496446363472,0.0907,0.0396,0.9767,0.6804,0.0217,0.0,0.1379,0.1667,0.2083,0.0634,0.0656,0.0614,0.0386,0.0451,0.0924,0.0411,0.9767,0.6929,0.0219,0.0,0.1379,0.1667,0.2083,0.0649,0.0716,0.064,0.0389,0.0477,0.0916,0.0396,0.9767,0.6847,0.0219,0.0,0.1379,0.1667,0.2083,0.0645,0.0667,0.0625,0.0388,0.046,not specified,block of flats,0.07,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n326767,0,Cash loans,F,N,Y,0,225000.0,682609.5,64813.5,682609.5,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-23032,-2231,-13568.0,-4304,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Self-employed,,0.2755358466931539,0.7407990879702335,0.0371,0.0,0.9742,0.6464,0.0,0.0,0.1034,0.0833,0.125,0.0514,0.0303,0.03,0.0,0.0,0.0378,0.0,0.9742,0.6602,0.0,0.0,0.1034,0.0833,0.125,0.0526,0.0331,0.0313,0.0,0.0,0.0375,0.0,0.9742,0.6511,0.0,0.0,0.1034,0.0833,0.125,0.0523,0.0308,0.0306,0.0,0.0,reg oper account,block of flats,0.0253,Mixed,No,11.0,1.0,11.0,1.0,-388.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n250777,0,Cash loans,F,N,Y,1,202500.0,327024.0,21028.5,270000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.019689,-17213,-3558,-5365.0,-765,,1,1,0,1,0,0,Secretaries,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,Other,,0.5593158783173714,0.40314167665875134,0.0656,0.0172,0.9776,0.6192,0.0021,0.032,0.1241,0.175,0.0833,0.0579,0.0134,0.0781,0.0,0.0493,0.0168,0.0055,0.9722,0.6341,0.0021,0.0,0.1379,0.1667,0.0833,0.0307,0.0147,0.0083,0.0,0.0,0.0781,0.0174,0.9737,0.6243,0.0021,0.0,0.1379,0.1667,0.0833,0.043,0.0137,0.0647,0.0,0.0304,reg oper account,block of flats,0.1948,\"Stone, brick\",No,8.0,1.0,8.0,1.0,-147.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n283708,0,Cash loans,M,Y,Y,2,360000.0,790830.0,52978.5,675000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.025164,-14894,-1954,-1148.0,-4507,3.0,1,1,0,1,0,1,Managers,4.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6353849630337661,0.6646755489387347,0.4776491548517548,0.0691,0.1091,0.9965,0.9524,0.0057,0.08,0.069,0.3333,0.375,0.0225,0.0563,0.0906,0.0039,0.0218,0.0704,0.1132,0.9965,0.9543,0.0058,0.0806,0.069,0.3333,0.375,0.023,0.0615,0.0944,0.0039,0.0231,0.0697,0.1091,0.9965,0.953,0.0058,0.08,0.069,0.3333,0.375,0.0229,0.0573,0.0922,0.0039,0.0222,reg oper spec account,block of flats,0.076,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n103435,0,Cash loans,F,N,Y,1,67500.0,284400.0,19134.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-9344,-920,-881.0,-993,,1,1,0,1,0,0,Sales staff,3.0,3,3,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.28257103659592536,0.7085734121048038,0.3774042489507649,0.0722,0.0643,0.9762,0.6736,0.0103,0.0,0.1379,0.1667,0.2083,0.0202,0.0588,0.0445,0.0,0.1552,0.0735,0.0667,0.9762,0.6864,0.0104,0.0,0.1379,0.1667,0.2083,0.0207,0.0643,0.0464,0.0,0.1643,0.0729,0.0643,0.9762,0.6779999999999999,0.0104,0.0,0.1379,0.1667,0.2083,0.0205,0.0599,0.0453,0.0,0.1585,reg oper account,block of flats,0.0688,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1457.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n281509,0,Cash loans,F,N,Y,0,225000.0,545040.0,20677.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.014464,-17205,-2630,-3307.0,-46,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,5,0,0,0,0,0,0,Housing,0.6708191993780046,0.4140813589312042,,0.0825,0.0815,0.9821,,,0.0,0.1724,0.1667,,0.0141,,0.0736,,0.0,0.0735,0.0845,0.9796,,,0.0,0.1379,0.1667,,0.0145,,0.0671,,0.0,0.0833,0.0815,0.9821,,,0.0,0.1724,0.1667,,0.0144,,0.0749,,0.0,,block of flats,0.0549,Block,No,0.0,0.0,0.0,0.0,-580.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n207281,0,Cash loans,F,N,Y,2,337500.0,1223010.0,51948.0,1125000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-14218,-6382,-4650.0,-4705,,1,1,0,1,0,0,Medicine staff,4.0,1,1,SATURDAY,10,0,0,0,0,0,0,Medicine,0.4459119641992051,0.6670061008134399,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1726.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n242997,1,Cash loans,F,N,Y,0,112500.0,490495.5,31347.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-11846,-3240,-3262.0,-1656,,1,1,1,1,0,0,Cooking staff,2.0,2,2,FRIDAY,14,0,0,0,1,1,0,Business Entity Type 3,0.4758235245474429,0.6972124980000384,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,2.0,5.0,1.0,-924.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n273267,0,Cash loans,F,N,Y,0,81000.0,835380.0,40189.5,675000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.001333,-16226,-2632,-2799.0,-1928,,1,1,0,1,0,0,,2.0,3,3,THURSDAY,6,0,0,0,0,0,0,Self-employed,,0.5599356306312757,0.6706517530862718,0.1062,0.0,0.9861,0.8096,,0.0,0.069,0.1667,,0.053,,0.076,,0.0454,0.1082,0.0,0.9861,0.8171,,0.0,0.069,0.1667,,0.0542,,0.0792,,0.0481,0.1072,0.0,0.9861,0.8121,,0.0,0.069,0.1667,,0.0539,,0.0773,,0.0464,reg oper account,block of flats,0.0696,Panel,No,0.0,0.0,0.0,0.0,-401.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n168351,0,Cash loans,F,N,Y,1,90000.0,592560.0,35937.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-17659,-7137,-9164.0,-1202,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Industry: type 3,,0.5119393135137794,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1632.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n101673,0,Cash loans,M,N,Y,0,112500.0,252000.0,13324.5,252000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-16970,-9516,-2200.0,-519,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 2,0.6128740921938612,0.6709382097231565,0.3910549766342248,0.1485,,0.9866,,,0.06,0.0345,0.3958,,0.1305,,0.0377,,0.1187,0.1261,,0.9866,,,0.0403,0.0345,0.3333,,0.1334,,0.0283,,0.1401,0.1499,,0.9866,,,0.06,0.0345,0.3958,,0.1327,,0.0406,,0.1351,,specific housing,0.0548,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-598.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,1.0\n176056,0,Cash loans,F,N,Y,0,180000.0,862560.0,25348.5,720000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-21036,365243,-8979.0,-4404,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,0.834836857026744,0.6971432474496118,0.5028782772082183,0.3412,0.3235,0.9871,0.8232,0.0617,0.4,0.3448,0.3333,0.375,0.2718,0.26899999999999996,0.3471,0.0425,0.1438,0.3477,0.3357,0.9871,0.8301,0.0622,0.4028,0.3448,0.3333,0.375,0.278,0.2938,0.3616,0.0428,0.1522,0.3445,0.3235,0.9871,0.8256,0.0621,0.4,0.3448,0.3333,0.375,0.2765,0.2736,0.3533,0.0427,0.1468,reg oper account,block of flats,0.33799999999999997,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-887.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n167136,0,Cash loans,F,Y,Y,1,67500.0,112500.0,9018.0,112500.0,Other_B,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-9555,-1184,-2612.0,-2244,1.0,1,1,0,1,0,0,Cooking staff,2.0,2,2,MONDAY,10,0,0,0,1,1,0,Restaurant,0.1742774615467067,0.3579501082629542,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-285.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n388469,0,Cash loans,F,Y,Y,0,202500.0,1236816.0,36162.0,1080000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.030755,-14780,-3489,-810.0,-3974,64.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,14,0,0,0,0,1,1,Self-employed,,0.5176744566806448,0.6296742509538716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1643.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n311750,1,Cash loans,M,Y,Y,1,202500.0,540000.0,29425.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-11483,-1438,-5344.0,-3282,17.0,1,1,1,1,0,0,Laborers,3.0,3,3,WEDNESDAY,5,0,0,0,0,1,1,Business Entity Type 3,,0.1388564741599787,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-625.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n295458,0,Cash loans,M,Y,Y,0,135000.0,242595.0,22248.0,202500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00733,-17305,-3441,-993.0,-832,9.0,1,1,1,1,1,0,Drivers,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,Emergency,,0.2822643775687258,0.4884551844437485,0.0825,0.0799,0.9801,,,0.0,0.1379,0.1667,,0.0243,,0.0564,,0.0733,0.084,0.0829,0.9801,,,0.0,0.1379,0.1667,,0.0248,,0.0588,,0.0776,0.0833,0.0799,0.9801,,,0.0,0.1379,0.1667,,0.0247,,0.0574,,0.0748,,block of flats,0.0603,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-356.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n263559,1,Revolving loans,F,N,Y,0,270000.0,157500.0,7875.0,157500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-11243,-3216,-3361.0,-3400,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 2,,0.6767820626711141,,0.0237,,0.9737,,,0.0,0.069,0.0833,,0.0086,,0.0183,,0.0022,0.0242,,0.9737,,,0.0,0.069,0.0833,,0.0088,,0.019,,0.0023,0.0239,,0.9737,,,0.0,0.069,0.0833,,0.0087,,0.0186,,0.0023,,block of flats,0.0149,\"Stone, brick\",No,13.0,1.0,13.0,0.0,-91.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n163540,0,Cash loans,F,N,N,0,166500.0,1024740.0,43546.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-23685,-1790,-7327.0,-4762,,1,1,0,1,1,0,Private service staff,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Self-employed,0.8497886655069486,0.616652800734061,0.5226973172821112,0.0722,0.068,0.9781,,,0.0,0.1379,0.1667,,,,0.068,,0.0734,0.0735,0.0706,0.9782,,,0.0,0.1379,0.1667,,,,0.0709,,0.0777,0.0729,0.068,0.9781,,,0.0,0.1379,0.1667,,,,0.0693,,0.0749,,block of flats,0.0695,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-824.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n239194,0,Cash loans,F,N,Y,0,130500.0,1223010.0,48631.5,1125000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-20708,-4453,-5157.0,-4124,,1,1,1,1,1,0,Medicine staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Medicine,,0.6636219712202527,0.8128226070575616,0.1866,0.0952,0.9871,,,0.2,0.1724,0.3333,,0.1061,,0.1762,,0.0512,0.1901,0.0988,0.9871,,,0.2014,0.1724,0.3333,,0.1085,,0.1836,,0.0542,0.1884,0.0952,0.9871,,,0.2,0.1724,0.3333,,0.10800000000000001,,0.1794,,0.0523,,block of flats,0.1731,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n228165,0,Cash loans,M,Y,Y,1,153000.0,436032.0,21208.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018801,-13738,-2534,-1659.0,-4242,22.0,1,1,1,1,0,0,,3.0,2,2,TUESDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.31229020122402684,0.41885428862332175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n443580,0,Cash loans,F,N,Y,0,157500.0,684652.5,37269.0,544500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.006852,-21603,365243,-4256.0,-2369,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,8,0,0,0,0,0,0,XNA,,0.046432893228716805,0.6058362647264226,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n404240,0,Cash loans,F,Y,N,0,270000.0,679500.0,64516.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-15019,-1970,-3129.0,-3268,2.0,1,1,1,1,1,0,,2.0,3,3,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.07761628037302502,0.6801388218428291,0.0722,0.1296,0.9702,0.5920000000000001,0.0324,0.0,0.1379,0.1667,0.0417,0.0766,0.0588,0.0992,0.0,0.0,0.0735,0.1345,0.9702,0.608,0.0327,0.0,0.1379,0.1667,0.0417,0.0783,0.0643,0.1034,0.0,0.0,0.0729,0.1296,0.9702,0.5975,0.0326,0.0,0.1379,0.1667,0.0417,0.0779,0.0599,0.10099999999999999,0.0,0.0,reg oper account,block of flats,0.078,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2903.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n307045,0,Cash loans,F,Y,Y,0,81000.0,78192.0,8208.0,67500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.005002,-20311,365243,-3417.0,-3534,4.0,1,0,0,1,1,0,,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,XNA,,0.7805386971898397,0.4365064990977374,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2821.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n438744,0,Cash loans,F,N,Y,0,157500.0,1237032.0,41013.0,1012500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-15684,-607,-2703.0,-4926,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Kindergarten,0.4960383814378291,0.6155925365872476,0.5082869913916046,0.0464,0.0278,0.9916,0.8844,0.0116,0.048,0.0414,0.3333,0.375,0.0345,0.0403,0.0485,0.0,0.0067,0.0378,0.0246,0.993,0.8759,0.0069,0.0403,0.0345,0.3333,0.375,0.0274,0.0331,0.0385,0.0,0.0,0.0375,0.0237,0.9921,0.8792,0.013999999999999999,0.04,0.0345,0.3333,0.375,0.0294,0.0308,0.0381,0.0,0.0,reg oper account,block of flats,0.0331,Panel,No,4.0,0.0,4.0,0.0,-1698.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n390667,0,Cash loans,M,Y,N,1,126000.0,450000.0,12001.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008473999999999999,-15210,-98,-3878.0,-4009,0.0,1,1,0,1,0,0,Sales staff,3.0,2,2,THURSDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.6765763383265497,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n369214,0,Cash loans,M,Y,N,0,540000.0,221031.0,15849.0,184500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-18898,-3518,-1659.0,-2436,14.0,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,18,0,0,0,0,1,1,Business Entity Type 3,,0.6518027800708549,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1787.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n325679,0,Cash loans,M,N,Y,1,292500.0,2013840.0,53122.5,1800000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-14151,-4242,-6.0,-4696,,1,1,0,1,0,0,Core staff,3.0,2,2,THURSDAY,14,0,0,0,0,1,1,Police,0.5748200875222487,0.7718864717764855,0.6363761710860439,0.066,0.0523,0.9762,,,0.0,0.1379,0.1667,,0.0,,0.0496,,0.1003,0.0672,0.0542,0.9762,,,0.0,0.1379,0.1667,,0.0,,0.0517,,0.1061,0.0666,0.0523,0.9762,,,0.0,0.1379,0.1667,,0.0,,0.0505,,0.1024,,block of flats,0.0723,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-2556.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n295076,0,Cash loans,F,N,Y,0,166500.0,991318.5,39442.5,801000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.01885,-16677,-9482,-5121.0,-207,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.7067366922228941,0.6023863442690867,0.6928,0.5105,0.9826,0.762,0.1044,0.56,0.4828,0.3333,0.375,0.3078,0.5648,0.5848,0.0,0.5712,0.7059,0.5298,0.9826,0.7713,0.1053,0.5639,0.4828,0.3333,0.375,0.3148,0.6171,0.6093,0.0,0.6047,0.6995,0.5105,0.9826,0.7652,0.105,0.56,0.4828,0.3333,0.375,0.3131,0.5746,0.5953,0.0,0.5832,reg oper account,block of flats,0.5646,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1846.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n250841,0,Cash loans,M,Y,Y,1,202500.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-16886,-5390,-2345.0,-418,14.0,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.5437451877364533,0.2366108235287817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1603.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n342492,0,Cash loans,F,N,N,1,252000.0,729000.0,30883.5,729000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-13369,-329,-4391.0,-4421,,1,1,0,1,0,0,Core staff,3.0,1,1,FRIDAY,13,0,0,0,0,0,0,Trade: type 3,,0.7271045198703463,,0.2515,0.1947,0.9871,0.8232,0.0617,0.28,0.2414,0.3333,0.375,0.0,0.2051,0.2811,0.0,0.0,0.2563,0.2021,0.9871,0.8301,0.0623,0.282,0.2414,0.3333,0.375,0.0,0.2241,0.2929,0.0,0.0,0.254,0.1947,0.9871,0.8256,0.0621,0.28,0.2414,0.3333,0.375,0.0,0.2086,0.2861,0.0,0.0,reg oper spec account,block of flats,0.2224,Panel,No,4.0,0.0,4.0,0.0,-1000.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n173418,0,Cash loans,F,Y,Y,1,112500.0,328365.0,21114.0,297000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-9909,-1057,-1037.0,-1779,0.0,1,1,0,1,0,0,Cleaning staff,3.0,2,2,WEDNESDAY,8,0,0,0,1,0,1,Business Entity Type 3,,0.011596249384033685,0.4525335592581747,0.0923,0.0808,0.9831,0.7688,0.0057,0.0,0.1034,0.1667,0.2083,0.0195,0.07400000000000001,0.06,0.0058,0.0295,0.062,0.0827,0.9831,0.7779,0.0035,0.0,0.069,0.1667,0.2083,0.0183,0.0514,0.0566,0.0,0.0,0.0932,0.0808,0.9831,0.7719,0.0057,0.0,0.1034,0.1667,0.2083,0.0198,0.0752,0.061,0.0058,0.0302,reg oper account,block of flats,0.0516,Panel,No,2.0,0.0,2.0,0.0,-651.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n372269,0,Cash loans,F,N,N,0,1125000.0,755190.0,31995.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.009549,-13278,-1870,-11688.0,-4333,,1,1,1,1,0,0,Accountants,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.5474953155357677,0.7265096666964431,,0.0928,0.0855,0.9841,,,,0.2069,0.1667,,0.0742,,0.0909,,0.0,0.0945,0.0887,0.9841,,,,0.2069,0.1667,,0.0759,,0.0947,,0.0,0.0937,0.0855,0.9841,,,,0.2069,0.1667,,0.0755,,0.0925,,0.0,,block of flats,0.0715,Panel,No,1.0,0.0,1.0,0.0,-1570.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n181006,0,Cash loans,F,Y,Y,0,135000.0,486459.0,18468.0,342000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-20238,-3050,-6080.0,-3534,10.0,1,1,0,1,0,0,Accountants,2.0,3,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6507524334451046,0.489337586909405,,0.0928,0.1003,0.9801,0.728,0.0433,0.0,0.2069,0.1667,0.2083,0.0674,0.0756,0.0872,0.0,0.0,0.0945,0.10400000000000001,0.9801,0.7387,0.0437,0.0,0.2069,0.1667,0.2083,0.0689,0.0826,0.0908,0.0,0.0,0.0937,0.1003,0.9801,0.7316,0.0436,0.0,0.2069,0.1667,0.2083,0.0685,0.077,0.0887,0.0,0.0,reg oper account,block of flats,0.0922,Panel,No,1.0,0.0,1.0,0.0,-2278.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n149509,0,Cash loans,F,N,Y,1,202500.0,253377.0,11290.5,211500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Municipal apartment,0.072508,-16728,-3628,-10732.0,-276,,1,1,0,1,1,0,,2.0,1,1,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.7238119163475945,0.36896873825284665,0.1732,0.094,0.9826,0.762,0.0591,0.24,0.1034,0.4583,0.5,0.1428,0.1412,0.1188,0.0,0.0028,0.1765,0.0976,0.9826,0.7713,0.0597,0.2417,0.1034,0.4583,0.5,0.146,0.1543,0.1238,0.0,0.003,0.1749,0.094,0.9826,0.7652,0.0595,0.24,0.1034,0.4583,0.5,0.1453,0.1437,0.1209,0.0,0.0029,reg oper account,block of flats,0.1458,Panel,No,0.0,0.0,0.0,0.0,-405.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n124547,0,Cash loans,F,N,Y,1,103500.0,610335.0,20299.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13501,-4064,-5065.0,-2160,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,10,0,0,0,0,1,1,Government,,0.4686358035596955,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2274.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n221333,0,Cash loans,F,N,Y,0,90000.0,360000.0,17640.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-10708,-1279,-2780.0,-2850,,1,1,1,1,1,0,Sales staff,2.0,2,2,WEDNESDAY,11,0,0,0,1,1,0,Self-employed,0.2989239819283601,0.624289350218873,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n226283,1,Revolving loans,M,N,Y,0,225000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.020713,-19889,-292,-533.0,-1800,,1,1,0,1,0,1,Security staff,1.0,3,2,FRIDAY,5,0,0,0,0,0,0,Business Entity Type 3,,0.021507463824929282,0.10015182033723906,0.2959,0.0784,0.9866,0.8164,0.0732,0.08,0.0345,0.3333,0.0417,0.0432,0.2404,0.0752,0.0039,0.0103,0.3015,0.0814,0.9866,0.8236,0.0739,0.0806,0.0345,0.3333,0.0417,0.0442,0.2626,0.0784,0.0039,0.0109,0.2987,0.0784,0.9866,0.8189,0.0737,0.08,0.0345,0.3333,0.0417,0.044000000000000004,0.2446,0.0766,0.0039,0.0105,reg oper account,block of flats,0.1014,Panel,No,,,,,-512.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n440427,0,Cash loans,F,N,N,0,360000.0,1525482.0,43848.0,1363500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.010032,-15379,-244,-2300.0,-2299,,1,1,0,1,0,0,Accountants,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,Restaurant,,0.4958854040354333,,0.1381,0.0679,0.9801,0.728,0.0253,0.0,0.1034,0.1667,0.2083,0.0155,0.1126,0.0684,0.0,0.0,0.1408,0.0704,0.9801,0.7387,0.0255,0.0,0.1034,0.1667,0.2083,0.0159,0.12300000000000001,0.0712,0.0,0.0,0.1395,0.0679,0.9801,0.7316,0.0255,0.0,0.1034,0.1667,0.2083,0.0158,0.1146,0.0696,0.0,0.0,org spec account,specific housing,0.0676,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n209422,0,Cash loans,M,Y,N,2,225000.0,1467612.0,58333.5,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-15351,-3405,-3302.0,-5433,6.0,1,1,0,1,0,0,Core staff,4.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Self-employed,0.4742782584440135,0.6029380383654671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1760.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n288381,0,Cash loans,M,N,N,0,112500.0,539100.0,27652.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.072508,-13905,-342,-8030.0,-630,,1,1,0,1,1,1,Laborers,2.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.08616649255258413,0.7182168852581624,0.7517237147741489,0.3423,0.2098,0.9806,0.7348,0.23,0.56,0.2414,0.4583,0.5,0.0,0.2765,0.3802,0.0116,0.01,0.3487,0.2177,0.9806,0.7452,0.2321,0.5639,0.2414,0.4583,0.5,0.0,0.3021,0.3961,0.0117,0.0106,0.3456,0.2098,0.9806,0.7383,0.2315,0.56,0.2414,0.4583,0.5,0.0,0.2813,0.387,0.0116,0.0103,reg oper account,block of flats,0.3012,Panel,No,1.0,0.0,1.0,0.0,-1772.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n229136,0,Cash loans,F,N,N,0,292500.0,505642.5,27427.5,436500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-14772,-1042,-1518.0,-2812,,1,1,1,1,1,0,Accountants,2.0,1,1,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.6113197925372308,0.4135967602644276,0.1948,0.0488,0.9975,0.966,0.0123,0.12,0.0517,0.7083,0.75,0.0087,0.158,0.1572,0.0039,0.0092,0.1155,0.0252,0.997,0.9608,0.0069,0.0806,0.0345,0.6667,0.7083,0.0089,0.0992,0.1017,0.0,0.0,0.1967,0.0488,0.9975,0.9665,0.0124,0.12,0.0517,0.7083,0.75,0.0089,0.1608,0.1601,0.0039,0.0094,reg oper account,block of flats,0.1743,Monolithic,No,0.0,0.0,0.0,0.0,-689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,2.0\n150843,0,Cash loans,F,N,Y,0,180000.0,284400.0,8928.0,225000.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.035792000000000004,-8184,-563,-3047.0,-792,,1,1,1,1,0,0,Sales staff,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Self-employed,,0.5949645193554565,0.1510075350878296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-261.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n260319,0,Cash loans,F,Y,Y,0,157500.0,539100.0,26064.0,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010643,-19398,-12629,-9287.0,-2954,5.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6086376115091727,0.5649942608905311,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1832.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n406467,0,Cash loans,F,N,Y,1,166500.0,271066.5,19408.5,234000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.002134,-13402,-2894,-296.0,-2293,,1,1,0,1,0,0,Core staff,2.0,3,3,MONDAY,13,0,0,0,1,1,0,School,,0.2800589796050773,0.33928769990891394,,,0.9771,,,,,,,,,0.0102,,,,,0.9772,,,,,,,,,0.0107,,,,,0.9771,,,,,,,,,0.0104,,,,block of flats,0.008,,Yes,4.0,3.0,4.0,3.0,-461.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n372096,0,Cash loans,F,N,Y,0,225000.0,472500.0,44860.5,454500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.002506,-22319,-2826,-10701.0,-4337,,1,1,0,1,0,0,Cooking staff,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,Other,,0.7467139456055475,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n141381,0,Cash loans,F,N,N,1,360000.0,1113840.0,47322.0,900000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.031329,-11356,-359,-3989.0,-3774,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,TUESDAY,8,0,0,0,1,1,0,Business Entity Type 3,0.5431908367458087,0.6799168013938083,0.42589289800515295,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1505.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n184793,0,Cash loans,M,Y,Y,0,81000.0,518562.0,20695.5,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00823,-22880,365243,-4598.0,-4699,20.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.4676666447183202,0.8435435389318647,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n339906,0,Cash loans,F,N,Y,0,121500.0,416052.0,15813.0,292500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-19816,-6554,-11731.0,-3305,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Other,,0.6685972793963697,,0.0082,,0.9742,,,0.0,0.0345,0.0417,,0.0139,,0.006,,0.0,0.0084,,0.9742,,,0.0,0.0345,0.0417,,0.0142,,0.0062,,0.0,0.0083,,0.9742,,,0.0,0.0345,0.0417,,0.0141,,0.0061,,0.0,,block of flats,0.0047,\"Stone, brick\",No,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n426371,1,Revolving loans,M,N,Y,0,94500.0,135000.0,6750.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-20837,365243,-8342.0,-1014,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,7,0,0,0,0,0,0,XNA,,0.4137895883316755,0.25670557243930026,0.1485,0.0977,0.9886,,,0.16,0.1379,0.3333,,0.0261,,0.1502,,0.0,0.1513,0.1014,0.9886,,,0.1611,0.1379,0.3333,,0.0266,,0.1565,,0.0,0.1499,0.0977,0.9886,,,0.16,0.1379,0.3333,,0.0265,,0.1529,,0.0,,block of flats,0.1405,Panel,No,0.0,0.0,0.0,0.0,-1005.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n240807,0,Cash loans,F,N,Y,0,112500.0,284400.0,16011.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.022625,-24373,365243,-9780.0,-4627,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,9,0,0,0,0,0,0,XNA,,0.6152354194514281,0.7688075728291359,0.1856,0.1097,0.9866,0.8164,0.0698,0.2,0.1724,0.3333,0.375,,0.1513,0.19,0.0,0.0,0.1891,0.1139,0.9866,0.8236,0.0704,0.2014,0.1724,0.3333,0.375,,0.1653,0.1979,0.0,0.0,0.1874,0.1097,0.9866,0.8189,0.0702,0.2,0.1724,0.3333,0.375,,0.1539,0.1934,0.0,0.0,,block of flats,0.1876,Panel,No,0.0,0.0,0.0,0.0,-659.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n136671,0,Revolving loans,F,N,Y,0,135000.0,292500.0,14625.0,292500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-23552,365243,-11853.0,-4159,,1,0,0,1,1,0,,2.0,3,3,FRIDAY,8,0,0,0,0,0,0,XNA,0.7174138239406584,0.5866065343049431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-368.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n132903,0,Cash loans,F,N,N,0,90000.0,149256.0,14760.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.0228,-21900,365243,-9789.0,-4493,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,0.4428607855924851,0.6563349346415885,0.4083588531230431,0.0639,0.0374,0.9836,0.7756,0.0067,0.04,0.0345,0.3333,0.0417,0.0387,0.0513,0.0621,0.0039,0.0053,0.0651,0.0388,0.9836,0.7844,0.0067,0.0403,0.0345,0.3333,0.0417,0.0396,0.055999999999999994,0.0647,0.0039,0.0056,0.0645,0.0374,0.9836,0.7786,0.0067,0.04,0.0345,0.3333,0.0417,0.0394,0.0522,0.0632,0.0039,0.0054,not specified,block of flats,0.05,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-548.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n130030,0,Cash loans,F,Y,Y,0,157500.0,733500.0,37579.5,733500.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.028663,-21739,365243,-4458.0,-4465,3.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.16354868437086004,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n240534,0,Cash loans,F,N,Y,0,148500.0,765000.0,23323.5,765000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.011703,-8361,-411,-6265.0,-1037,,1,1,1,1,1,0,Core staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Police,0.2586079173795305,0.5767310438047093,0.5638350489514956,0.0928,0.0824,0.9911,,,0.0,0.2069,0.1667,,,,0.0832,,0.0,0.0945,0.0855,0.9911,,,0.0,0.2069,0.1667,,,,0.0867,,0.0,0.0937,0.0824,0.9911,,,0.0,0.2069,0.1667,,,,0.0847,,0.0,,block of flats,0.0721,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-156.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n252519,0,Cash loans,M,N,Y,0,90000.0,220500.0,13324.5,220500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.030755,-12709,-543,-6762.0,-4466,,1,1,0,1,0,0,,1.0,2,2,FRIDAY,18,0,0,0,0,0,0,Postal,0.229531656037255,0.6630896854570649,0.34741822720026416,0.0165,0.0,0.9429,0.218,,0.0,0.0345,0.0417,,0.0039,,0.0102,,0.0,0.0168,0.0,0.9429,0.2486,,0.0,0.0345,0.0417,,0.004,,0.0106,,0.0,0.0167,0.0,0.9429,0.2285,,0.0,0.0345,0.0417,,0.004,,0.0103,,0.0,not specified,terraced house,0.008,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-586.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n122601,0,Cash loans,F,Y,Y,0,81000.0,219042.0,12703.5,193500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-19113,-4731,-258.0,-2598,6.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,9,0,0,0,1,1,0,Military,,0.2653117484731741,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1608.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n355152,0,Cash loans,F,N,Y,1,135000.0,398160.0,31585.5,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-13923,-2118,-7786.0,-4221,,1,1,1,1,1,0,Sales staff,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6795454823560807,0.6956219298394389,0.0701,,0.9831,,,0.0,0.2069,0.1667,,,,0.0631,,0.0698,0.0714,,0.9831,,,0.0,0.2069,0.1667,,,,0.0657,,0.0739,0.0708,,0.9831,,,0.0,0.2069,0.1667,,,,0.0642,,0.0713,,block of flats,0.065,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-670.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,1.0\n432896,0,Cash loans,F,N,Y,0,112500.0,247275.0,17716.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-20716,-1295,-2022.0,-2022,,1,1,0,1,0,0,Cooking staff,2.0,1,1,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.7063883897154215,0.7114953338030588,0.5298898341969072,0.132,,0.9866,,,,0.3448,0.1667,,,,,,,0.1345,,0.9866,,,,0.3448,0.1667,,,,,,,0.1332,,0.9866,,,,0.3448,0.1667,,,,,,,,block of flats,0.1095,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1292.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,0.0\n402230,0,Cash loans,F,N,Y,0,126000.0,781920.0,36364.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00733,-21765,365243,-9195.0,-5043,,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.8010698516538737,0.8327850252992314,0.0887,0.10800000000000001,0.9876,,,0.0,0.2069,0.1667,0.0417,0.0,,0.0826,,0.0,0.0903,0.1121,0.9876,,,0.0,0.2069,0.1667,0.0417,0.0,,0.0861,,0.0,0.0895,0.10800000000000001,0.9876,,,0.0,0.2069,0.1667,0.0417,0.0,,0.0841,,0.0,,block of flats,0.073,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-738.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n183168,0,Cash loans,M,Y,Y,2,225000.0,445500.0,24165.0,445500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-13261,-909,-5176.0,-4721,0.0,1,1,0,1,1,0,High skill tech staff,4.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.7102953584845626,0.8128226070575616,0.0825,0.0809,0.9747,,,0.0,0.1379,0.1667,,,,0.0727,,0.0,0.084,0.084,0.9747,,,0.0,0.1379,0.1667,,,,0.0758,,0.0,0.0833,0.0809,0.9747,,,0.0,0.1379,0.1667,,,,0.07400000000000001,,0.0,,block of flats,0.0572,Panel,No,2.0,1.0,2.0,0.0,-1589.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n230094,0,Cash loans,F,N,Y,2,65250.0,269550.0,13761.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-13092,-2673,-3522.0,-4836,,1,1,1,1,0,0,,4.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,School,0.8262098961239616,0.0004679200973223923,0.09569272423026376,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-636.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n135391,0,Cash loans,M,Y,N,0,81000.0,107820.0,10017.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.035792000000000004,-10635,-2940,-4932.0,-2973,11.0,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Trade: type 7,0.1679615414223644,0.4835470032211937,0.5971924268337128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,0.0,-1489.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n442635,1,Cash loans,F,N,Y,1,112500.0,177903.0,13968.0,148500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.006852,-10901,-424,-557.0,-3443,,1,1,0,1,0,0,High skill tech staff,2.0,3,3,FRIDAY,5,0,0,0,0,0,0,Business Entity Type 3,0.3910014545610214,0.7618837350511866,0.36896873825284665,0.3196,0.0001,0.997,,,0.36,0.3103,0.375,,,,0.0003,,0.0004,0.3256,0.0001,0.997,,,0.3625,0.3103,0.375,,,,0.0003,,0.0004,0.3227,0.0001,0.997,,,0.36,0.3103,0.375,,,,0.0003,,0.0004,,block of flats,0.0003,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-517.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n290613,0,Cash loans,F,N,Y,0,112500.0,1256400.0,36864.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018029,-17710,-927,-9859.0,-1268,,1,1,1,1,1,0,Core staff,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,Trade: type 7,0.8032791840552743,0.5416177838511975,0.470456116119975,0.1031,0.0833,0.9831,0.7688,0.0115,0.0,0.2069,0.1667,0.0417,0.0565,0.0714,0.0789,,0.0485,0.105,0.0865,0.9831,0.7779,0.0116,0.0,0.2069,0.1667,0.0417,0.0578,0.0781,0.0822,,0.0514,0.1041,0.0833,0.9831,0.7719,0.0116,0.0,0.2069,0.1667,0.0417,0.0575,0.0727,0.0804,,0.0496,reg oper account,block of flats,0.0789,Panel,No,1.0,0.0,1.0,0.0,-756.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n453242,0,Cash loans,M,N,Y,0,117000.0,835605.0,24561.0,697500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-19277,365243,-1368.0,-2808,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.6920172911617859,0.5726825047161584,0.0588,,0.9975,,,0.08,0.069,0.3333,,,,0.0714,,0.0683,0.0599,,0.9975,,,0.0806,0.069,0.3333,,,,0.0744,,0.0723,0.0593,,0.9975,,,0.08,0.069,0.3333,,,,0.0727,,0.0697,,block of flats,0.071,\"Stone, brick\",No,4.0,1.0,4.0,0.0,-497.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n422311,0,Revolving loans,M,Y,N,1,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.018634,-14437,365243,-697.0,-249,2.0,1,0,0,1,1,0,,3.0,2,2,TUESDAY,18,0,0,0,0,0,0,XNA,,0.5007727151978785,0.7530673920730478,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-243.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n111743,0,Cash loans,F,Y,Y,1,112500.0,360000.0,23134.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Co-op apartment,0.015221,-15231,-390,-3344.0,-3341,65.0,1,1,0,1,1,1,Sales staff,3.0,2,2,SUNDAY,17,0,0,0,1,1,0,Business Entity Type 3,0.4318873309238786,0.7223582194577336,0.746300213050371,0.0165,0.0313,0.9737,,,0.0,0.069,0.0417,,0.0279,,0.0124,,0.0,0.0168,0.0325,0.9737,,,0.0,0.069,0.0417,,0.0285,,0.0129,,0.0,0.0167,0.0313,0.9737,,,0.0,0.069,0.0417,,0.0284,,0.0126,,0.0,,block of flats,0.0098,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-356.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n304073,0,Cash loans,M,Y,N,0,157500.0,299772.0,20160.0,247500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.025164,-10213,-419,-8750.0,-2863,3.0,1,1,0,1,0,0,Core staff,1.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Medicine,,0.16214456766623808,0.6785676886853644,0.1247,0.0563,0.9732,0.6328,0.1041,0.0,0.2069,0.1667,0.2083,0.0,0.0925,0.1388,0.0425,0.0869,0.1271,0.0584,0.9732,0.6472,0.105,0.0,0.2069,0.1667,0.2083,0.0,0.10099999999999999,0.1446,0.0428,0.092,0.126,0.0563,0.9732,0.6377,0.1047,0.0,0.2069,0.1667,0.2083,0.0,0.0941,0.1413,0.0427,0.0887,reg oper account,block of flats,0.185,Block,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n191874,0,Cash loans,F,N,Y,0,180000.0,350860.5,17010.0,283500.0,Other_A,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.030755,-14025,-349,-1995.0,-5970,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3423001521885151,0.15357057289804046,0.7826078370261895,0.1557,0.0831,0.9781,0.7008,0.0138,0.0,0.1034,0.1667,0.2083,0.0742,0.1261,0.0604,0.0039,0.003,0.1586,0.0863,0.9782,0.7125,0.0139,0.0,0.1034,0.1667,0.2083,0.0758,0.1377,0.0629,0.0039,0.0032,0.1572,0.0831,0.9781,0.7048,0.0138,0.0,0.1034,0.1667,0.2083,0.0754,0.1283,0.0615,0.0039,0.0031,reg oper account,block of flats,0.0557,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n162562,0,Cash loans,F,N,N,0,180000.0,269550.0,11547.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.011657,-23569,365243,-13143.0,-4559,,1,0,0,1,0,0,,2.0,1,1,SATURDAY,11,0,0,0,0,0,0,XNA,,0.6660729065222493,0.7583930617144343,0.0619,0.0809,0.9821,,0.0111,0.0,0.1379,0.1667,0.2083,0.0836,0.0504,0.0375,,,0.063,0.084,0.9821,,0.0112,0.0,0.1379,0.1667,0.2083,0.0856,0.0551,0.039,,,0.0625,0.0809,0.9821,,0.0112,0.0,0.1379,0.1667,0.2083,0.0851,0.0513,0.0382,,,reg oper account,block of flats,0.0517,Panel,No,7.0,1.0,7.0,0.0,-1600.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n154236,0,Cash loans,M,Y,N,2,180000.0,988191.0,39316.5,909000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.035792000000000004,-14103,-6912,-8074.0,-4536,2.0,1,1,0,1,0,0,,4.0,2,2,MONDAY,12,0,0,0,0,0,0,Other,,0.7155361403419012,0.6430255641096323,0.0773,0.0245,0.9975,0.966,0.0237,0.08,0.0345,0.625,0.6667,0.0898,0.0538,0.0742,0.0425,0.013999999999999999,0.0788,0.0254,0.9975,0.9673,0.0239,0.0806,0.0345,0.625,0.6667,0.0919,0.0588,0.0773,0.0428,0.0148,0.0781,0.0245,0.9975,0.9665,0.0238,0.08,0.0345,0.625,0.6667,0.0914,0.0547,0.0755,0.0427,0.0143,reg oper account,block of flats,0.0743,Monolithic,No,4.0,0.0,4.0,0.0,-3003.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n290767,1,Cash loans,M,N,N,1,283500.0,938034.0,30258.0,783000.0,Unaccompanied,State servant,Secondary / secondary special,Married,With parents,0.020713,-12332,-3262,-3877.0,-4029,,1,1,0,1,0,0,,3.0,3,3,TUESDAY,7,0,0,0,0,0,0,Security Ministries,,0.06947526146219961,,0.0907,0.0766,0.9881,,,0.0,0.2069,0.1667,,0.0788,,0.0827,,0.0,0.0924,0.0795,0.9881,,,0.0,0.2069,0.1667,,0.0806,,0.0862,,0.0,0.0916,0.0766,0.9881,,,0.0,0.2069,0.1667,,0.0801,,0.0842,,0.0,,block of flats,0.0732,Panel,No,3.0,0.0,3.0,0.0,-1379.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n334650,0,Cash loans,M,N,Y,0,270000.0,640080.0,24259.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009334,-11701,-3821,-2441.0,-3891,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.229334747134464,0.6886609132270598,0.4400578303966329,0.0835,0.0,0.9712,0.6056,0.0048,0.0,0.1034,0.125,0.1667,0.0421,0.0681,0.0343,0.0,0.0,0.0851,0.0,0.9712,0.621,0.0048,0.0,0.1034,0.125,0.1667,0.0431,0.0744,0.0358,0.0,0.0,0.0843,0.0,0.9712,0.6109,0.0048,0.0,0.1034,0.125,0.1667,0.0429,0.0693,0.0349,0.0,0.0,reg oper account,block of flats,0.0338,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1953.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n251252,0,Cash loans,F,N,Y,0,67500.0,95940.0,9472.5,90000.0,Unaccompanied,Pensioner,Lower secondary,Civil marriage,House / apartment,0.018209,-20794,365243,-1925.0,-4094,,1,0,0,1,1,0,,2.0,3,3,MONDAY,6,0,0,0,0,0,0,XNA,,0.3702167081191991,0.5280927512030451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1526.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n311694,1,Cash loans,M,Y,N,0,450000.0,1256400.0,40657.5,900000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.006233,-12804,-791,-969.0,-1795,24.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,Other,,0.3124076230712269,0.5262949398096192,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,3.0,0.0,3.0,0.0,-1132.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n191536,0,Cash loans,F,N,N,0,67500.0,760225.5,30280.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00963,-17712,-1604,-8574.0,-1259,,1,1,1,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.5494501868322045,0.17485432131742842,0.7801436381572275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-733.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n427513,0,Cash loans,M,Y,Y,1,180000.0,679500.0,22050.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-8951,-1160,-3477.0,-1617,3.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 2,,0.1777504206599131,0.6041125998015721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n306745,0,Cash loans,M,Y,Y,0,157500.0,526500.0,25456.5,526500.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.015221,-8278,-1077,-3135.0,-956,65.0,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,12,0,0,0,1,1,0,Construction,,0.5758500066079526,0.3656165070113335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-757.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n382302,0,Cash loans,M,N,Y,0,67500.0,1327500.0,36634.5,1327500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.0228,-21285,-4568,0.0,-4610,,1,1,0,1,0,0,Security staff,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.7525091448249661,0.5620252318225031,0.6347055309763198,0.1113,0.0823,0.9886,0.8436,0.0265,0.16,0.1379,0.3333,0.0417,0.0736,0.0899,0.1399,0.0039,0.0059,0.1134,0.0854,0.9886,0.8497,0.0267,0.1611,0.1379,0.3333,0.0417,0.0753,0.0983,0.1457,0.0039,0.0063,0.1124,0.0823,0.9886,0.8457,0.0266,0.16,0.1379,0.3333,0.0417,0.0749,0.0915,0.1424,0.0039,0.0061,not specified,block of flats,0.1113,Panel,No,7.0,0.0,6.0,0.0,-1800.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n326770,0,Cash loans,M,Y,Y,0,67500.0,225000.0,11488.5,225000.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.018209,-19289,-1057,-2872.0,-2842,14.0,1,1,1,1,1,0,,2.0,3,3,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4616614102044035,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-480.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n294158,0,Cash loans,F,N,N,3,202500.0,1288350.0,41692.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-12376,-620,-3710.0,-4294,,1,1,0,1,0,1,Secretaries,5.0,1,1,THURSDAY,15,0,0,0,0,0,0,Construction,0.4129413969341911,0.15152106430399534,0.12610055883623253,0.0722,,0.9752,,,0.0,0.2414,0.1667,,0.013000000000000001,,0.063,,0.0063,0.0735,,0.9752,,,0.0,0.2414,0.1667,,0.0132,,0.0656,,0.0066,0.0729,,0.9752,,,0.0,0.2414,0.1667,,0.0132,,0.0641,,0.0064,,block of flats,0.0509,Panel,No,0.0,0.0,0.0,0.0,-310.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,4.0\n213771,0,Cash loans,F,N,N,0,270000.0,1125000.0,44617.5,1125000.0,Unaccompanied,Working,Higher education,Separated,With parents,0.018209,-17888,-3010,-9098.0,-1366,,1,1,0,1,1,0,Managers,1.0,3,3,TUESDAY,8,0,0,0,0,1,1,Business Entity Type 3,0.8587248323801091,0.5687794307736778,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-823.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n373281,0,Cash loans,F,Y,Y,2,450000.0,1192500.0,113355.0,1192500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.014464,-13818,-6241,-326.0,-4187,10.0,1,1,0,1,0,0,Core staff,4.0,2,2,TUESDAY,7,0,0,0,0,1,1,Medicine,0.6842439944471724,0.6728568583914314,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2697.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n311867,0,Revolving loans,M,N,Y,0,292500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-17534,-2294,-6235.0,-1026,,1,1,0,1,1,0,Laborers,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Housing,,0.3359763502119845,0.722392890081304,0.0928,,0.9796,,,0.0,0.2069,0.1667,,,,0.0781,,0.0,0.0945,,0.9796,,,0.0,0.2069,0.1667,,,,0.0813,,0.0,0.0937,,0.9796,,,0.0,0.2069,0.1667,,,,0.0795,,0.0,,block of flats,0.0614,Panel,No,0.0,0.0,0.0,0.0,-249.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n380775,0,Cash loans,F,N,Y,0,189000.0,458235.0,22176.0,382500.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.005313,-22655,365243,-11245.0,-3924,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.6634999239708613,0.3425288720742255,0.0773,0.07,0.9841,0.7824,,0.0,0.1724,0.1667,0.2083,0.0469,0.063,0.0417,0.0,0.0,0.0788,0.0726,0.9841,0.7909,,0.0,0.1724,0.1667,0.2083,0.048,0.0689,0.0435,0.0,0.0,0.0781,0.07,0.9841,0.7853,,0.0,0.1724,0.1667,0.2083,0.0477,0.0641,0.0425,0.0,0.0,reg oper account,block of flats,0.0653,Panel,No,0.0,0.0,0.0,0.0,-913.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,6.0\n226048,0,Cash loans,M,Y,Y,0,157500.0,302076.0,22716.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.018029,-9643,-938,-2929.0,-1879,3.0,1,1,0,1,0,0,Managers,1.0,3,3,SATURDAY,11,0,0,0,0,0,0,Construction,0.1767513183865034,0.5031945070940433,0.2735646775174348,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-939.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n364402,1,Cash loans,F,N,N,0,90000.0,454500.0,21865.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-16273,-3503,-3471.0,-4153,,1,1,1,1,0,0,Sales staff,2.0,3,3,SATURDAY,15,0,0,0,0,0,0,Self-employed,,0.368474697983803,0.4489622731076524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1113.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n416692,0,Cash loans,F,N,Y,0,144000.0,272520.0,17563.5,225000.0,Family,State servant,Higher education,Married,House / apartment,0.018634,-16164,-3720,-9019.0,-4381,,1,1,1,1,0,0,Managers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Other,,0.7976151016208565,0.4223696523543468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3428.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n118729,0,Cash loans,M,N,Y,0,135000.0,225000.0,15219.0,225000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.035792000000000004,-20066,-2352,-4886.0,-663,,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Industry: type 3,,0.6916116688330696,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2316.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n279946,0,Cash loans,F,N,N,0,76500.0,284400.0,16456.5,225000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.00496,-24148,365243,-13773.0,-4571,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.3026845156233697,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n387778,1,Cash loans,M,Y,Y,0,225000.0,677664.0,36760.5,585000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.018029,-11367,-2785,-5587.0,-3759,17.0,1,1,0,1,0,1,Laborers,1.0,3,3,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.26158316402753434,0.5263224953286364,,0.0619,0.0768,0.9856,0.8028,0.0111,0.0,0.1034,0.1667,0.0417,,,0.063,,0.0,0.063,0.0797,0.9856,0.8105,0.0112,0.0,0.1034,0.1667,0.0417,,,0.0657,,0.0,0.0625,0.0768,0.9856,0.8054,0.0112,0.0,0.1034,0.1667,0.0417,,,0.0642,,0.0,,block of flats,0.0557,Panel,No,0.0,0.0,0.0,0.0,-99.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n213406,0,Cash loans,M,Y,Y,1,270000.0,848745.0,43465.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-11397,-2095,-1243.0,-3734,25.0,1,1,0,1,0,0,,3.0,2,2,MONDAY,5,0,0,0,0,0,0,Security Ministries,0.4820949987716068,0.23850005728142085,0.11987796089553485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1379.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n347517,0,Cash loans,F,Y,Y,0,112500.0,414792.0,21847.5,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-17912,-1452,-5531.0,-1453,4.0,1,1,1,1,0,0,Sales staff,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.7314004702250294,0.7838324335199619,0.1495,0.11599999999999999,0.9861,0.8096,0.0345,0.16,0.1379,0.3333,0.375,0.0476,0.121,0.1629,0.0039,0.0038,0.1523,0.1203,0.9861,0.8171,0.0348,0.1611,0.1379,0.3333,0.375,0.0487,0.1322,0.1698,0.0039,0.004,0.1509,0.11599999999999999,0.9861,0.8121,0.0347,0.16,0.1379,0.3333,0.375,0.0485,0.1231,0.1659,0.0039,0.0039,reg oper account,block of flats,0.147,Panel,No,0.0,0.0,0.0,0.0,-1748.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n427118,0,Cash loans,F,N,Y,0,157500.0,1426500.0,49711.5,1426500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.015221,-19156,-521,-5731.0,-2688,,1,1,0,1,1,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 1,0.6269751502938395,0.6101188644779725,0.2314393514998941,0.0825,0.1112,0.9921,0.8912,0.0218,0.0,0.1379,0.1667,0.2083,0.0741,0.0672,0.0877,0.0,0.0,0.084,0.1154,0.9921,0.8955,0.022000000000000002,0.0,0.1379,0.1667,0.2083,0.0758,0.0735,0.0913,0.0,0.0,0.0833,0.1112,0.9921,0.8927,0.0219,0.0,0.1379,0.1667,0.2083,0.0754,0.0684,0.0892,0.0,0.0,reg oper account,block of flats,0.1127,Panel,No,3.0,0.0,3.0,0.0,-1387.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n262529,0,Cash loans,M,N,N,0,157500.0,651816.0,20596.5,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-17830,-1563,-2457.0,-1037,,1,1,0,1,0,0,Drivers,2.0,1,1,TUESDAY,16,0,0,0,0,0,0,Postal,,0.7314606095461631,,0.4887,0.2382,0.9871,,,0.64,0.2759,0.625,,0.0,,0.2728,,0.2179,0.4979,0.2472,0.9871,,,0.6445,0.2759,0.625,,0.0,,0.2842,,0.2307,0.4934,0.2382,0.9871,,,0.64,0.2759,0.625,,0.0,,0.2777,,0.2225,,block of flats,0.3766,Panel,No,2.0,0.0,2.0,0.0,-379.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n123484,0,Cash loans,F,Y,Y,0,306000.0,1219500.0,33534.0,1219500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.00496,-12476,-161,-3597.0,-1390,64.0,1,1,1,1,0,0,,1.0,2,2,TUESDAY,14,1,1,0,1,1,0,Business Entity Type 3,0.43685420308805856,0.7211769263663861,0.06423703753438333,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-414.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n142319,0,Cash loans,F,N,Y,0,202500.0,585000.0,29997.0,585000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018209,-9415,-294,-1212.0,-600,,1,1,0,1,0,0,High skill tech staff,2.0,3,3,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.7844083243848761,0.5101415837452213,0.4902575124990026,0.3268,0.1939,0.997,0.9524,0.1389,0.28,0.2069,0.625,0.2917,0.1988,0.2614,0.3212,0.0232,0.0791,0.33299999999999996,0.2012,0.997,0.9543,0.1402,0.282,0.2069,0.625,0.2917,0.2033,0.2856,0.3346,0.0233,0.0838,0.33,0.1939,0.997,0.953,0.1398,0.28,0.2069,0.625,0.2917,0.2022,0.2659,0.327,0.0233,0.0808,reg oper account,block of flats,0.3237,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-704.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n305130,1,Cash loans,F,N,N,0,117000.0,437076.0,35172.0,405000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.003069,-14010,-282,-325.0,-4155,,1,1,0,1,0,0,,1.0,3,3,MONDAY,14,0,0,0,0,1,1,Government,,0.007953938743981231,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-223.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n174756,0,Revolving loans,F,Y,Y,0,180000.0,585000.0,29250.0,585000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007273999999999998,-20818,-14226,-10211.0,-4275,8.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Industry: type 7,,0.6054272950738095,0.28961123838200553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-162.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n229265,0,Cash loans,M,Y,N,0,112500.0,201474.0,19755.0,189000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-24538,365243,-3287.0,-4857,21.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.7552317568750389,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1664.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n368103,0,Cash loans,F,N,N,1,216000.0,900000.0,48825.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-14154,-2087,-3481.0,-3481,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,10,0,0,0,0,1,1,Construction,0.4103873078662504,0.5925804019301055,0.2032521136204725,0.0041,0.0155,0.9652,0.524,0.0075,0.0,0.0345,0.0417,0.0,0.015,0.0034,0.005,0.0,0.0,0.0042,0.016,0.9652,0.5426,0.0076,0.0,0.0345,0.0417,0.0,0.0154,0.0037,0.0052,0.0,0.0,0.0042,0.0155,0.9652,0.5304,0.0076,0.0,0.0345,0.0417,0.0,0.0153,0.0034,0.005,0.0,0.0,reg oper account,block of flats,0.008,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-225.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n247356,0,Cash loans,F,N,Y,1,162000.0,345843.0,36823.5,328500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006852,-10049,-2092,-1949.0,-2518,,1,1,0,1,0,0,,3.0,3,3,SUNDAY,8,0,0,0,0,0,0,Military,0.2958634866483176,0.3890111814196861,0.3296550543128238,0.0825,0.068,0.9781,0.7008,0.0372,0.0,0.1379,0.1667,0.2083,0.0125,0.0672,0.053,0.0,0.0,0.084,0.0706,0.9782,0.7125,0.0375,0.0,0.1379,0.1667,0.2083,0.0128,0.0735,0.0552,0.0,0.0,0.0833,0.068,0.9781,0.7048,0.0374,0.0,0.1379,0.1667,0.2083,0.0127,0.0684,0.054000000000000006,0.0,0.0,not specified,block of flats,0.062,Others,No,0.0,0.0,0.0,0.0,-699.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,11.0\n244022,0,Cash loans,F,N,Y,0,202500.0,674635.5,26883.0,603000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.011657,-23253,365243,-1400.0,-4580,,1,0,0,1,1,0,,1.0,1,1,THURSDAY,12,0,0,0,0,0,0,XNA,,0.7120022976424517,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-590.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n454737,0,Cash loans,M,N,Y,0,247500.0,1236816.0,36292.5,1080000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-17622,-551,-7534.0,-1123,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,7,0,0,0,1,0,1,Industry: type 7,,0.4343914726855013,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n355306,1,Cash loans,F,N,Y,0,94500.0,265851.0,11394.0,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008575,-19426,-1183,-10881.0,-2970,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,School,,0.3351962266384302,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,2.0,5.0,0.0,-1024.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n297359,0,Cash loans,M,Y,N,0,177750.0,1575000.0,39946.5,1575000.0,Unaccompanied,State servant,Higher education,Single / not married,Rented apartment,0.010147,-9168,-1125,-249.0,-1854,6.0,1,1,1,1,0,0,Core staff,1.0,2,2,MONDAY,11,0,0,0,1,1,0,Police,,0.7288669364625668,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-5.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n163642,0,Cash loans,F,N,Y,0,225000.0,407727.0,32341.5,333000.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.0228,-8373,-1689,-2966.0,-1036,,1,1,0,1,0,0,Waiters/barmen staff,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.5598017718965242,0.3158752715773922,0.4066174366275036,0.0371,0.0225,0.9796,0.7212,0.0088,0.04,0.0345,0.3333,0.0417,0.0352,0.0303,0.0389,0.0,0.0,0.0378,0.0233,0.9796,0.7321,0.0089,0.0403,0.0345,0.3333,0.0417,0.036000000000000004,0.0331,0.0405,0.0,0.0,0.0375,0.0225,0.9796,0.7249,0.0089,0.04,0.0345,0.3333,0.0417,0.0358,0.0308,0.0396,0.0,0.0,reg oper account,block of flats,0.0354,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-519.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n419778,0,Cash loans,M,Y,Y,0,90000.0,288873.0,16713.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-14123,-192,-4546.0,-4599,14.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5225811917013218,0.1624419982223248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1912.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n160278,0,Cash loans,F,N,Y,0,126900.0,808650.0,23643.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-19853,-4599,-3090.0,-3228,,1,1,0,1,1,0,Sales staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Self-employed,,0.2493799784294735,,0.0619,0.0554,0.9826,0.762,0.016,0.0,0.1379,0.1667,0.2083,0.0443,0.0504,0.0582,0.0,0.0,0.063,0.055999999999999994,0.9826,0.7713,0.0084,0.0,0.1379,0.1667,0.2083,0.0451,0.0551,0.0601,0.0,0.0,0.0625,0.0554,0.9826,0.7652,0.0161,0.0,0.1379,0.1667,0.2083,0.045,0.0513,0.0592,0.0,0.0,reg oper account,block of flats,0.0454,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-14.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n445607,1,Revolving loans,M,N,Y,0,135000.0,157500.0,7875.0,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-21519,365243,-8984.0,-3871,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.4742591816339269,,0.1292,,,0.8844,,0.0932,0.0803,0.3333,0.375,0.0,0.121,0.1092,0.0,0.0,0.0662,,,0.8432,,0.0403,0.0345,0.3333,0.375,0.0,0.1322,0.0859,0.0,0.0,0.1499,,,0.8792,,0.08,0.069,0.3333,0.375,0.0,0.1231,0.1089,0.0,0.0,reg oper account,block of flats,0.0818,Panel,No,,,,,-568.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n323644,0,Cash loans,M,Y,Y,0,202500.0,225000.0,21919.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.01885,-18973,-1074,-10150.0,-2510,13.0,1,1,1,1,0,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.6663063273990301,0.4902575124990026,0.1701,0.0545,0.9776,,,0.32,0.2759,0.2083,,,,0.1022,,0.1441,0.1733,0.0566,0.9777,,,0.3222,0.2759,0.2083,,,,0.1065,,0.1525,0.1718,0.0545,0.9776,,,0.32,0.2759,0.2083,,,,0.10400000000000001,,0.1471,,block of flats,0.1512,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2979.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n103626,0,Cash loans,M,N,Y,0,81000.0,284400.0,15880.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006233,-24427,365243,-2742.0,-4243,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.6695898158478278,0.6986675550534175,0.0546,0.0311,0.9826,0.762,0.0234,0.04,0.0345,0.3333,0.375,0.0145,0.0445,0.0563,0.0,0.0,0.0557,0.0323,0.9826,0.7713,0.0236,0.0403,0.0345,0.3333,0.375,0.0148,0.0487,0.0587,0.0,0.0,0.0552,0.0311,0.9826,0.7652,0.0235,0.04,0.0345,0.3333,0.375,0.0147,0.0453,0.0573,0.0,0.0,reg oper account,block of flats,0.0571,Panel,No,7.0,1.0,7.0,0.0,-1098.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n433777,0,Cash loans,M,Y,Y,0,135000.0,463284.0,17595.0,382500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.018634,-8217,-517,-8216.0,-908,20.0,1,1,0,1,0,0,Core staff,1.0,2,2,SUNDAY,15,0,0,0,1,1,0,Security Ministries,,0.4855896717275426,0.2851799046358216,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-858.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n213282,0,Cash loans,F,Y,Y,0,292500.0,1327855.5,54211.5,1138500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-19635,-2731,-1715.0,-3127,31.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Other,,0.211885056254826,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,1.0,11.0,1.0,-2415.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n346030,0,Cash loans,F,N,Y,0,135000.0,513531.0,26347.5,459000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Rented apartment,0.019101,-10876,-4005,-4820.0,-1614,,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,9,0,0,0,0,1,1,Business Entity Type 2,0.7734831630244549,0.5835594123869595,0.6848276586890367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-1021.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n395169,0,Cash loans,M,N,Y,0,292500.0,956574.0,40657.5,855000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.005313,-22314,365243,-462.0,-491,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.26164112451502924,0.3556387169923543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n391089,0,Cash loans,F,N,N,0,270000.0,270000.0,13261.5,270000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Rented apartment,0.035792000000000004,-17326,-615,-5319.0,-856,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,14,0,0,0,1,1,0,Business Entity Type 3,0.3808877791514356,0.4633574903905171,0.324891229465852,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,1.0\n444361,0,Cash loans,M,N,N,0,270000.0,787131.0,41935.5,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-13708,-767,-4777.0,-1170,,1,1,1,1,1,0,Laborers,2.0,1,1,THURSDAY,18,0,0,0,0,0,0,Advertising,0.0921128910628265,0.4813465631791898,0.8327850252992314,0.0619,0.0745,0.9727,0.626,0.0618,0.0,0.1379,0.1667,0.2083,0.0,0.0504,0.0639,0.0039,0.0451,0.063,0.0774,0.9727,0.6406,0.0624,0.0,0.1379,0.1667,0.2083,0.0,0.0551,0.0666,0.0039,0.0477,0.0625,0.0745,0.9727,0.631,0.0622,0.0,0.1379,0.1667,0.2083,0.0,0.0513,0.065,0.0039,0.046,reg oper spec account,block of flats,0.0601,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-523.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n104033,0,Cash loans,F,N,Y,0,112500.0,787086.0,23143.5,657000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.022625,-21513,365243,-3647.0,-4983,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,0.5757142447763463,,0.7165702448010511,0.1881,0.2348,0.4973,0.932,0.1276,0.16,0.2759,0.375,0.375,0.24,0.3051,0.4008,0.0077,0.0343,0.0,0.2437,0.0005,0.9347,0.1288,0.0,0.2759,0.375,0.375,0.2454,0.3333,0.4176,0.0078,0.0364,0.19,0.2348,0.4973,0.9329,0.1284,0.16,0.2759,0.375,0.375,0.2441,0.3104,0.408,0.0078,0.0351,reg oper account,block of flats,0.0,Mixed,No,0.0,0.0,0.0,0.0,-1471.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n382774,0,Cash loans,M,N,N,0,90000.0,426645.0,23274.0,324000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-12519,-2454,-1364.0,-4926,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.27228381325321144,0.6358890065690538,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-342.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n249044,0,Cash loans,F,N,Y,1,67500.0,170640.0,11533.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006852,-11159,-618,-4802.0,-1241,,1,1,1,1,0,0,,3.0,3,3,THURSDAY,11,0,0,0,0,0,0,Self-employed,,0.043380215653422455,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n391638,0,Cash loans,F,N,N,0,180000.0,588465.0,25920.0,468000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.007305,-22951,365243,-1340.0,-2192,,1,0,0,1,0,0,,1.0,3,3,FRIDAY,13,0,0,0,0,0,0,XNA,0.8592687236517609,0.6200639130075088,0.7850520263728172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1971.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n250285,0,Cash loans,M,Y,N,0,157500.0,625536.0,35059.5,540000.0,Family,Working,Secondary / secondary special,Married,With parents,0.003069,-12032,-1905,-5787.0,-3989,10.0,1,1,0,1,0,0,Laborers,2.0,3,3,WEDNESDAY,13,0,0,0,0,1,1,Other,0.4198280046400169,0.6120677408334932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-1451.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n369523,0,Revolving loans,M,Y,Y,0,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-9811,-1400,-4349.0,-2384,18.0,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Self-employed,0.13601084272086275,0.6778574108924426,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-982.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n213781,0,Cash loans,F,Y,Y,2,202500.0,805536.0,32076.0,720000.0,Family,State servant,Higher education,Married,House / apartment,0.04622,-11560,-3289,-1090.0,-4176,7.0,1,1,0,1,0,1,Core staff,4.0,1,1,SUNDAY,12,0,0,0,0,0,0,Government,0.5504675206606638,0.7306781339891885,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-802.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n226055,0,Cash loans,F,N,N,2,225000.0,976077.0,54634.5,922500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006629,-12479,-1786,-1287.0,-316,,1,1,1,1,1,0,Laborers,4.0,2,2,FRIDAY,9,0,0,0,1,1,1,Other,0.5270848527463045,0.32490731768899983,0.4436153084085652,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-1513.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n438613,0,Cash loans,F,N,Y,0,202500.0,808650.0,22365.0,675000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.020713,-19070,-1198,-191.0,-2096,,1,1,0,1,0,0,Managers,1.0,3,2,MONDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.8810404235809084,0.3977306288398629,0.2103502286944494,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-337.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n454954,0,Cash loans,M,N,Y,0,135000.0,360000.0,42853.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.014519999999999996,-9492,-659,-9492.0,-2156,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,8,0,0,0,1,1,0,Business Entity Type 3,0.12356081260699606,0.6603190731045404,0.4812493411434029,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1112.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n141136,0,Cash loans,F,N,Y,0,112500.0,286704.0,14769.0,247500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.010966,-13286,-3902,-7371.0,-4549,,1,1,0,1,0,1,,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6714259698967022,0.3112673050275249,0.7076993447402619,0.1031,0.0673,0.9786,0.7008,0.0133,0.0,0.2069,0.1667,0.2083,0.0861,0.0841,0.0895,0.0,0.0,0.105,0.0698,0.9786,0.7125,0.0135,0.0,0.2069,0.1667,0.2083,0.08800000000000001,0.0918,0.0933,0.0,0.0,0.1041,0.0673,0.9786,0.7048,0.0134,0.0,0.2069,0.1667,0.2083,0.0876,0.0855,0.0911,0.0,0.0,org spec account,block of flats,0.0704,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-346.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n327764,0,Cash loans,M,Y,Y,1,225000.0,900000.0,35694.0,900000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.007305,-12158,-3486,-979.0,-4797,1.0,1,1,1,1,1,0,Core staff,3.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Police,0.5155023525444933,0.6231859534271725,0.5919766183185521,0.1485,0.1285,0.9856,0.8028,,0.16,0.1379,0.3333,,0.0632,,0.1519,,0.0224,0.1513,0.1333,0.9856,0.8105,,0.1611,0.1379,0.3333,,0.0647,,0.1583,,0.0237,0.1499,0.1285,0.9856,0.8054,,0.16,0.1379,0.3333,,0.0643,,0.1547,,0.0229,reg oper account,block of flats,0.1559,Panel,No,2.0,1.0,2.0,0.0,-1451.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,1.0,2.0\n238672,0,Cash loans,F,N,Y,0,67500.0,509922.0,26788.5,472500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010276,-19796,-2799,-1582.0,-3322,,1,1,0,1,0,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,Electricity,,0.40165001129320416,,0.0722,0.057999999999999996,0.9806,,,,0.1379,0.1667,,,,0.0743,,0.0657,0.0735,0.0602,0.9806,,,,0.1379,0.1667,,,,0.0774,,0.0695,0.0729,0.057999999999999996,0.9806,,,,0.1379,0.1667,,,,0.0756,,0.067,,block of flats,0.0584,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n298518,0,Cash loans,F,N,Y,0,60750.0,296280.0,15255.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-18332,365243,-11263.0,-1890,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.6082967336657287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1030.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n244969,0,Cash loans,M,Y,Y,0,202500.0,1155226.5,38308.5,1035000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.025164,-18864,-2222,-376.0,-1538,13.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,1,1,1,Self-employed,0.5544714116089058,0.5507290018931074,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n255152,0,Cash loans,F,N,N,0,45000.0,76410.0,4266.0,67500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.015221,-24087,365243,-7379.0,-4422,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,0.8264382407330014,0.6280260915964064,0.7726311345553628,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n143777,0,Cash loans,F,Y,Y,0,99000.0,178290.0,17761.5,157500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.008625,-21968,365243,-3377.0,-5190,2.0,1,0,0,1,0,0,,1.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.05275868917027357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-646.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n168221,0,Cash loans,F,N,Y,1,112500.0,188460.0,8428.5,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007305,-13917,-2687,-7709.0,-4719,,1,1,0,1,0,0,Core staff,3.0,3,3,SATURDAY,8,0,0,0,0,0,0,Self-employed,0.7185128400606291,0.6690574223871188,0.6690566947824041,0.1526,0.1087,0.9831,0.7688,0.0523,0.16,0.1379,0.3333,0.375,0.0795,0.1236,0.1576,0.0039,0.0052,0.1555,0.1128,0.9831,0.7779,0.0527,0.1611,0.1379,0.3333,0.375,0.0813,0.135,0.1642,0.0039,0.0055,0.1541,0.1087,0.9831,0.7719,0.0526,0.16,0.1379,0.3333,0.375,0.0809,0.1257,0.1604,0.0039,0.0053,reg oper account,block of flats,0.1537,Panel,No,0.0,0.0,0.0,0.0,-1479.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n294017,0,Cash loans,F,Y,Y,0,126000.0,341280.0,21937.5,270000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-16749,-7481,-9632.0,-288,11.0,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.6587687974017874,0.5381901582216221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-976.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n126558,0,Cash loans,F,N,Y,0,45000.0,137538.0,10863.0,121500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.004849,-18495,-2770,-11779.0,-2048,,1,1,0,1,0,0,Waiters/barmen staff,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,School,,0.699400787946153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1432.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n414107,0,Revolving loans,F,Y,Y,3,225000.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.001276,-12335,-1708,-2305.0,-3517,21.0,1,1,0,1,0,0,Core staff,5.0,2,2,FRIDAY,5,0,0,0,0,0,0,Kindergarten,,0.3992034789269836,0.2940831009471255,0.1082,0.1088,0.9692,0.8096,0.0189,0.0,0.2414,0.2083,0.0417,0.1057,0.1004,0.1055,0.0019,0.0,0.0788,0.0793,0.9861,0.8171,0.0108,0.0,0.1724,0.1667,0.0417,0.0848,0.0689,0.078,0.0,0.0,0.0781,0.0775,0.9861,0.8121,0.019,0.0,0.1724,0.1667,0.0417,0.1106,0.1022,0.0769,0.0019,0.0,reg oper account,block of flats,0.1796,Panel,No,7.0,0.0,7.0,0.0,-348.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n169846,0,Cash loans,F,N,Y,0,90000.0,891072.0,35469.0,720000.0,Family,Commercial associate,Secondary / secondary special,Widow,Municipal apartment,0.04622,-23683,-1954,-9914.0,-4209,,1,1,0,1,0,0,Medicine staff,1.0,1,1,THURSDAY,13,0,1,1,0,0,0,Business Entity Type 3,,0.6556391259619215,,0.1052,0.0,0.9861,0.8096,0.0,0.0,0.2414,0.1667,0.2083,0.0,0.0832,0.0912,0.0116,0.0,0.1071,0.0,0.9861,0.8171,0.0,0.0,0.2414,0.1667,0.2083,0.0,0.0909,0.095,0.0117,0.0,0.1062,0.0,0.9861,0.8121,0.0,0.0,0.2414,0.1667,0.2083,0.0,0.0847,0.0928,0.0116,0.0,reg oper account,block of flats,0.0717,Panel,No,0.0,0.0,0.0,0.0,-1004.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n155976,0,Cash loans,F,N,Y,0,292500.0,495000.0,21933.0,495000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-20231,365243,-465.0,-3668,,1,0,0,1,0,1,,2.0,3,3,FRIDAY,7,0,0,0,0,0,0,XNA,0.8695151652761454,0.2309702569252431,0.6610235391308081,0.1381,0.2091,0.998,,,0.0,0.2414,0.1667,,0.1456,,0.1518,,0.0,0.1408,0.217,0.998,,,0.0,0.2414,0.1667,,0.1489,,0.1581,,0.0,0.1395,0.2091,0.998,,,0.0,0.2414,0.1667,,0.1481,,0.1545,,0.0,,block of flats,0.1979,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-596.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n373113,0,Cash loans,F,N,Y,0,202500.0,1546020.0,42642.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-23007,-1697,-8914.0,-4436,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Trade: type 6,0.916664753837362,0.6017909703500439,0.6545292802242897,0.1196,0.0,0.9876,0.83,0.0,0.0,0.2414,0.1667,0.2083,0.0,0.0975,0.1327,0.0,0.1129,0.1218,0.0,0.9876,0.8367,0.0,0.0,0.2414,0.1667,0.2083,0.0,0.1065,0.1383,0.0,0.1195,0.1207,0.0,0.9876,0.8323,0.0,0.0,0.2414,0.1667,0.2083,0.0,0.0992,0.1351,0.0,0.1153,reg oper account,block of flats,0.1428,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1489.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n111266,0,Cash loans,F,N,Y,0,58500.0,276277.5,19777.5,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22423,365243,-1541.0,-4944,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,12,1,0,0,1,0,0,XNA,0.7986074931551744,0.7611291567415328,0.6610235391308081,0.1237,0.073,0.9891,0.8504,0.0527,0.12,0.1034,0.375,0.4167,0.1972,0.1009,0.1383,0.0,0.0024,0.1261,0.0758,0.9891,0.8563,0.0532,0.1208,0.1034,0.375,0.4167,0.2017,0.1102,0.14400000000000002,0.0,0.0026,0.1249,0.073,0.9891,0.8524,0.0531,0.12,0.1034,0.375,0.4167,0.2006,0.1026,0.1407,0.0,0.0025,reg oper account,block of flats,0.1381,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n346888,1,Revolving loans,F,N,N,1,81000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-8922,-649,-1565.0,-707,,1,1,1,1,1,0,Core staff,3.0,2,2,TUESDAY,18,0,0,0,0,0,0,Self-employed,,0.4866712837392048,0.5388627065779676,0.032,0.0861,0.9762,0.6736,0.0016,0.0,0.1379,0.0417,0.0833,0.0237,0.0261,0.0177,0.0,0.0,0.0326,0.0894,0.9762,0.6864,0.0016,0.0,0.1379,0.0417,0.0833,0.0242,0.0285,0.0185,0.0,0.0,0.0323,0.0861,0.9762,0.6779999999999999,0.0016,0.0,0.1379,0.0417,0.0833,0.0241,0.0265,0.0181,0.0,0.0,reg oper account,block of flats,0.0148,Wooden,No,0.0,0.0,0.0,0.0,-466.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n402668,0,Cash loans,F,N,Y,0,67500.0,608076.0,28476.0,427500.0,Children,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16399,-2738,-3003.0,-4705,,1,1,0,1,1,0,Cooking staff,2.0,2,2,FRIDAY,10,0,0,0,0,1,1,School,,0.6754316500065565,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-447.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n233826,0,Cash loans,M,N,Y,0,112500.0,238896.0,16092.0,189000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.01885,-22545,365243,-8846.0,-5044,,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.6284295714710261,0.5190973382084597,0.1856,0.1435,0.9876,,,0.2,0.1724,0.3333,,0.1005,,0.2025,,0.0055,0.1513,0.1173,0.9876,,,0.1611,0.1379,0.3333,,0.0,,0.1697,,0.0045,0.1874,0.1435,0.9876,,,0.2,0.1724,0.3333,,0.1022,,0.2061,,0.0056,,block of flats,0.1919,Panel,No,6.0,0.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n321383,0,Cash loans,F,Y,Y,3,126000.0,536917.5,19413.0,463500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018801,-14663,-1167,-7222.0,-4433,8.0,1,1,0,1,1,0,Waiters/barmen staff,5.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,Restaurant,0.4937005810508697,0.6315084538182761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1535.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n243100,0,Cash loans,M,Y,Y,1,427500.0,285264.0,30852.0,252000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00733,-14833,-2091,-1620.0,-4572,14.0,1,1,0,1,0,1,Core staff,3.0,2,2,THURSDAY,17,0,0,0,0,0,0,Self-employed,0.5174736250147439,0.5942265490543082,0.3425288720742255,0.2273,0.1264,0.9965,0.9524,0.026000000000000002,0.24,0.2069,0.375,0.4167,0.168,0.1816,0.2635,0.0347,0.1113,0.2269,0.0566,0.9965,0.9543,0.0263,0.2417,0.2069,0.375,0.4167,0.0968,0.1983,0.2693,0.035,0.1135,0.2295,0.1264,0.9965,0.953,0.0262,0.24,0.2069,0.375,0.4167,0.1709,0.1847,0.2682,0.0349,0.1136,reg oper account,block of flats,0.2266,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-875.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n327773,0,Cash loans,F,N,Y,0,67500.0,159264.0,6003.0,126000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-22270,365243,-12778.0,-4630,,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.5404774737083331,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1077.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n250759,0,Cash loans,F,N,N,0,175500.0,170640.0,6561.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020713,-14868,-526,-1078.0,-5047,,1,1,1,1,0,0,,1.0,3,3,SUNDAY,17,0,0,0,0,0,0,Other,0.3742237696972657,0.5987792200033096,0.4525335592581747,0.1402,0.1,0.9801,0.7756,0.0081,0.0,0.1379,0.1667,0.2083,0.0674,0.0572,0.0736,0.0077,0.054000000000000006,0.0735,0.0801,0.9772,0.7844,0.0082,0.0,0.1379,0.1667,0.2083,0.0603,0.0624,0.0685,0.0078,0.0082,0.1416,0.1,0.9801,0.7786,0.0082,0.0,0.1379,0.1667,0.2083,0.0686,0.0581,0.075,0.0078,0.0551,reg oper account,block of flats,0.0578,Panel,No,0.0,0.0,0.0,0.0,-899.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n177716,0,Cash loans,F,N,N,0,108000.0,518562.0,25078.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.008865999999999999,-19123,-3103,-6579.0,-2631,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,School,,0.7515100430377213,0.6363761710860439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1818.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n318671,0,Cash loans,M,N,N,0,315000.0,942300.0,36643.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.072508,-8763,-666,-3847.0,-1125,,1,1,0,1,1,0,,1.0,1,1,FRIDAY,19,0,0,0,0,0,0,Trade: type 3,0.1537584188136215,0.4696269858965164,0.5989262182569273,0.2082,0.0799,0.995,0.932,0.11699999999999999,0.24,0.1034,0.6667,0.7083,0.0,0.1689,0.0779,0.0039,0.0088,0.2122,0.0829,0.995,0.9347,0.1181,0.2417,0.1034,0.6667,0.7083,0.0,0.1846,0.0811,0.0039,0.0093,0.2103,0.0799,0.995,0.9329,0.1178,0.24,0.1034,0.6667,0.7083,0.0,0.1719,0.0793,0.0039,0.009000000000000001,reg oper account,block of flats,0.1476,Panel,No,5.0,2.0,5.0,2.0,-352.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n384472,0,Cash loans,M,N,Y,1,76500.0,132768.0,6907.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-11273,-899,-5735.0,-3720,,1,1,0,1,0,0,Laborers,3.0,3,3,SUNDAY,6,0,0,0,0,0,0,Business Entity Type 3,,0.1000432432959458,0.3539876078507373,0.1216,0.1331,0.9826,,,,0.2759,0.1667,,0.0231,,0.1141,,0.0443,0.1239,0.1381,0.9826,,,,0.2759,0.1667,,0.0236,,0.1188,,0.0469,0.1228,0.1331,0.9826,,,,0.2759,0.1667,,0.0235,,0.1161,,0.0452,,block of flats,0.0993,Panel,No,8.0,1.0,8.0,0.0,-778.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n118452,0,Cash loans,F,Y,Y,0,90000.0,231813.0,9571.5,193500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-15434,-851,-4707.0,-4669,10.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Trade: type 3,,0.5185043369607307,0.6161216908872079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-353.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n110641,1,Revolving loans,F,Y,Y,0,225000.0,270000.0,13500.0,270000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.003818,-17663,-2584,-7697.0,-1210,19.0,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,5,0,0,0,0,0,0,Government,0.5546373629206552,0.4303234324083616,0.6195277080511546,0.0608,0.0785,0.9866,0.8164,0.0563,0.0,0.1207,0.1667,0.0417,0.0405,0.0496,0.0631,0.0,0.0,0.0609,0.0741,0.9861,0.8171,0.0524,0.0,0.1034,0.1667,0.0417,0.0302,0.0533,0.0578,0.0,0.0,0.0614,0.0785,0.9866,0.8189,0.0567,0.0,0.1207,0.1667,0.0417,0.0412,0.0504,0.0642,0.0,0.0,reg oper account,block of flats,0.07200000000000001,Panel,No,2.0,1.0,2.0,1.0,-602.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n236342,0,Cash loans,M,Y,N,2,180000.0,584766.0,22401.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-15691,-861,-912.0,-3028,12.0,1,1,0,1,1,0,Laborers,4.0,3,3,SATURDAY,9,0,0,0,1,1,0,Self-employed,,0.6622782534245124,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n436030,0,Revolving loans,F,N,Y,0,45000.0,135000.0,6750.0,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-15642,-743,-9734.0,-4295,,1,1,0,1,0,0,Core staff,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Kindergarten,,0.6614707616076161,0.6279908192952864,0.0722,0.0773,0.9801,,,0.0,0.1379,0.1667,,0.0062,,0.0635,,0.0,0.0735,0.0802,0.9801,,,0.0,0.1379,0.1667,,0.0064,,0.0662,,0.0,0.0729,0.0773,0.9801,,,0.0,0.1379,0.1667,,0.0064,,0.0647,,0.0,,block of flats,0.05,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-939.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n363385,0,Cash loans,F,Y,Y,0,247500.0,1312110.0,55723.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019689,-21478,-1272,-9377.0,-5033,4.0,1,1,0,1,0,0,Private service staff,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,,0.6445522262464077,0.6956219298394389,0.0619,0.039,0.9866,0.8164,0.0169,0.04,0.0345,0.375,0.4167,0.0325,0.0504,0.0617,0.0,0.0,0.063,0.0405,0.9866,0.8236,0.0171,0.0403,0.0345,0.375,0.4167,0.0333,0.0551,0.0642,0.0,0.0,0.0625,0.039,0.9866,0.8189,0.017,0.04,0.0345,0.375,0.4167,0.0331,0.0513,0.0628,0.0,0.0,reg oper spec account,block of flats,0.0577,\"Stone, brick\",No,2.0,0.0,1.0,0.0,-1781.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n166830,0,Cash loans,F,Y,Y,1,211500.0,1113840.0,57001.5,900000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.018801,-11066,-1052,-4914.0,-3602,10.0,1,1,0,1,0,0,Secretaries,3.0,2,2,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.2839553337845483,0.5884877883422673,0.1113,0.0823,0.9841,0.7824,0.0538,0.12,0.1034,0.3333,0.0417,0.0692,0.0908,0.1106,0.0,0.0,0.1134,0.0854,0.9841,0.7909,0.0543,0.1208,0.1034,0.3333,0.0417,0.0708,0.0992,0.1152,0.0,0.0,0.1124,0.0823,0.9841,0.7853,0.0541,0.12,0.1034,0.3333,0.0417,0.0704,0.0923,0.1126,0.0,0.0,reg oper account,block of flats,0.087,Panel,No,2.0,0.0,2.0,0.0,-680.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n130948,0,Revolving loans,F,N,Y,0,112500.0,270000.0,13500.0,270000.0,Family,Pensioner,Higher education,Married,House / apartment,0.019101,-20656,365243,-3102.0,-4135,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.728789142995527,0.16640554618393638,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-573.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n422941,0,Cash loans,M,Y,N,0,270000.0,760225.5,30150.0,679500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-11505,-2364,-5082.0,-3122,8.0,1,1,1,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Construction,0.4213552876412698,0.5339862879667728,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-572.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n400900,0,Cash loans,M,N,Y,0,202500.0,661500.0,52393.5,661500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-20794,-1805,-2740.0,-4119,,1,1,0,1,0,1,Drivers,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Other,,0.4852892474661919,0.14916746132334885,0.1412,,0.9955,,,0.48,0.4138,0.375,,,,0.2457,,0.69,0.1439,,0.9955,,,0.4834,0.4138,0.375,,,,0.256,,0.7305,0.1426,,0.9955,,,0.48,0.4138,0.375,,,,0.2501,,0.7045,,block of flats,0.3433,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1395.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n204802,0,Cash loans,F,N,Y,0,112500.0,675000.0,19737.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.00702,-22893,365243,-5573.0,-4115,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.4859938953076072,0.5406544504453575,0.0082,0.0,0.9707,0.5988,0.0,0.0,0.069,0.0417,0.0833,0.0139,0.0067,0.0084,0.0,0.0,0.0084,0.0,0.9707,0.6145,0.0,0.0,0.069,0.0417,0.0833,0.0142,0.0073,0.0088,0.0,0.0,0.0083,0.0,0.9707,0.6042,0.0,0.0,0.069,0.0417,0.0833,0.0141,0.0068,0.0086,0.0,0.0,reg oper account,block of flats,0.0066,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1792.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n433742,0,Cash loans,M,Y,Y,0,202500.0,755190.0,35122.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.030755,-11791,-1758,-5851.0,-4337,5.0,1,1,0,1,0,0,,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,0.4419384131733147,0.3564187422159076,0.8004513396487078,0.0742,0.0535,0.9831,0.7688,0.0298,0.08,0.069,0.3333,0.375,1.0,0.0588,0.0749,0.0077,0.0097,0.0756,0.0556,0.9831,0.7779,0.0301,0.0806,0.069,0.3333,0.375,1.0,0.0643,0.0781,0.0078,0.0103,0.0749,0.0535,0.9831,0.7719,0.03,0.08,0.069,0.3333,0.375,1.0,0.0599,0.0763,0.0078,0.01,reg oper account,block of flats,0.0774,Panel,No,0.0,0.0,0.0,0.0,-105.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n233402,1,Cash loans,F,N,N,1,180000.0,297000.0,27238.5,297000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Municipal apartment,0.006305,-13773,-1271,-1993.0,-2288,,1,1,1,1,0,0,,2.0,3,3,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.19073111801053186,0.39896748984986863,0.11911906455945008,0.033,,0.9866,0.8164,0.006,0.0,0.0345,0.125,0.0,0.0732,0.0261,0.0319,0.0039,0.0072,0.0336,,0.9866,0.8236,0.0061,0.0,0.0345,0.125,0.0,0.0749,0.0285,0.0332,0.0039,0.0076,0.0333,,0.9866,0.8189,0.0061,0.0,0.0345,0.125,0.0,0.0745,0.0265,0.0324,0.0039,0.0073,reg oper account,block of flats,0.0299,Panel,No,1.0,0.0,1.0,0.0,-575.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n178215,0,Cash loans,M,Y,Y,0,180000.0,1303812.0,42187.5,1138500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007305,-20317,-4183,-524.0,-2454,1.0,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.2235514363673728,0.3556387169923543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-500.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n359940,0,Revolving loans,F,N,Y,2,94500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-8193,-462,-1351.0,-869,,1,1,1,1,1,0,Accountants,4.0,2,2,WEDNESDAY,10,0,0,0,1,0,1,Restaurant,0.21211508367906304,0.5943372718253973,0.5849900404894085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,3.0,7.0,0.0,-1093.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n293439,0,Cash loans,F,N,N,0,67500.0,225000.0,23184.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-14794,-158,-3328.0,-5056,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Business Entity Type 1,0.4543438902323561,0.35171766466196475,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1420.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n259948,0,Revolving loans,M,N,Y,2,180000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-13417,-874,-69.0,-926,,1,1,0,1,0,0,Drivers,4.0,2,2,THURSDAY,9,0,0,0,0,0,0,Transport: type 4,,0.1077984577271493,0.2276129150623945,0.0392,,0.9747,,,0.0,0.069,0.1667,,0.0236,,0.028999999999999998,,0.0053,0.0399,,0.9747,,,0.0,0.069,0.1667,,0.0241,,0.0302,,0.0056,0.0396,,0.9747,,,0.0,0.069,0.1667,,0.024,,0.0295,,0.0054,,block of flats,0.0258,Block,No,0.0,0.0,0.0,0.0,-648.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n303102,0,Cash loans,M,Y,Y,0,135000.0,297000.0,21739.5,297000.0,Family,State servant,Incomplete higher,Civil marriage,House / apartment,0.015221,-8517,-249,-7200.0,-1201,16.0,1,1,0,1,1,1,Core staff,2.0,2,2,TUESDAY,13,0,0,0,0,1,1,Security Ministries,,0.029154368032244662,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n384708,0,Revolving loans,F,Y,N,0,270000.0,225000.0,11250.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.009175,-13610,-947,-1333.0,-3590,2.0,1,1,1,1,1,0,Core staff,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Legal Services,,0.6778908360513103,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n173826,0,Cash loans,F,Y,Y,0,112500.0,1267038.0,50377.5,1165500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009657,-13246,-2530,-937.0,-2924,18.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.5888054834744145,0.3506958432829587,0.33399999999999996,0.2691,0.9891,0.8504,0.1075,0.36,0.3103,0.3333,0.0417,0.0,0.2723,0.4011,0.0039,0.0022,0.3403,0.2792,0.9891,0.8563,0.1084,0.3625,0.3103,0.3333,0.0417,0.0,0.2975,0.4179,0.0039,0.0023,0.3373,0.2691,0.9891,0.8524,0.1081,0.36,0.3103,0.3333,0.0417,0.0,0.27699999999999997,0.4083,0.0039,0.0022,reg oper spec account,block of flats,0.3159,Panel,No,0.0,0.0,0.0,0.0,-560.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n184923,0,Cash loans,F,N,Y,1,90000.0,263686.5,26208.0,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020713,-14470,-2664,-1734.0,-5138,,1,1,0,1,1,0,Laborers,3.0,3,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.5643223267625584,,0.2773,0.0376,0.9955,0.9388,0.067,0.24,0.2069,0.375,0.4167,0.0,0.2261,0.2704,0.0,0.0,0.2826,0.039,0.9955,0.9412,0.0676,0.2417,0.2069,0.375,0.4167,0.0,0.247,0.2817,0.0,0.0,0.28,0.0376,0.9955,0.9396,0.0674,0.24,0.2069,0.375,0.4167,0.0,0.23,0.2753,0.0,0.0,not specified,block of flats,0.2493,Panel,No,0.0,0.0,0.0,0.0,-3452.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n292107,0,Cash loans,F,N,Y,0,202500.0,678996.0,34798.5,540000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.032561,-13853,-226,-7964.0,-3736,,1,1,0,1,1,1,,1.0,1,1,WEDNESDAY,18,0,0,0,0,0,0,Construction,0.7306984157719888,0.7294369935101229,0.190705947811054,,0.2858,0.9851,0.7959999999999999,0.0701,0.44,0.3793,0.3333,0.375,0.2159,,0.4414,,0.0,,0.2966,0.9851,0.804,0.0707,0.4431,0.3793,0.3333,0.375,0.2208,,0.4599,,0.0,,0.2858,0.9851,0.7987,0.0705,0.44,0.3793,0.3333,0.375,0.2197,,0.4493,,0.0,reg oper account,block of flats,0.3854,Panel,No,4.0,1.0,4.0,0.0,-979.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n107375,0,Cash loans,M,Y,Y,0,252000.0,454500.0,35172.0,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00733,-10596,-1485,-2818.0,-3213,1.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.6671517944268149,0.23272477626794336,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1551.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n370083,0,Cash loans,F,Y,N,0,225000.0,700830.0,19269.0,585000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.018634,-16671,-1709,-10591.0,-209,3.0,1,1,0,1,1,1,,1.0,2,2,TUESDAY,19,0,0,0,0,0,0,Other,0.5371947327534621,0.3111829278071476,0.3774042489507649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-144.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n376603,0,Cash loans,F,N,Y,0,67500.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.035792000000000004,-13610,-1481,-1359.0,-2613,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Self-employed,0.5962809782815486,0.4016342452651734,0.8106180215523969,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1266.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n280741,0,Cash loans,F,Y,N,2,130500.0,508495.5,24462.0,454500.0,Family,Working,Secondary / secondary special,Married,With parents,0.019101,-10119,-1364,-3811.0,-516,9.0,1,1,0,1,1,0,,4.0,2,2,SUNDAY,16,0,0,0,1,1,1,Business Entity Type 3,0.4338012795697174,0.4787702987149992,0.11104240226943142,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-245.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n267907,0,Cash loans,M,Y,Y,0,436500.0,445500.0,24295.5,445500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-22413,-667,-2379.0,-4249,4.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.5998974175729043,0.5820759955103322,0.23791607950711405,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1917.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,5.0\n298537,0,Cash loans,M,Y,N,0,157500.0,544491.0,17694.0,454500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.016612000000000002,-17702,-198,-2181.0,-1252,2.0,1,1,0,1,0,0,Security staff,2.0,2,2,MONDAY,15,0,0,0,1,1,0,Security,0.3160760853074221,0.4432766077692771,0.5762088360175724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-656.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n298969,0,Cash loans,F,N,Y,0,54000.0,755190.0,29677.5,675000.0,Family,Pensioner,Secondary / secondary special,Married,Co-op apartment,0.00496,-22169,365243,-3370.0,-3604,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.29134391008893584,,,,0.9742,,,,0.1034,0.0833,,,,0.0252,,,,,0.9742,,,,0.1034,0.0833,,,,0.0262,,,,,0.9742,,,,0.1034,0.0833,,,,0.0256,,,,,0.0232,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1043.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n326555,0,Cash loans,F,Y,N,0,157500.0,1305000.0,38286.0,1305000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.035792000000000004,-15014,-1369,-7479.0,-4359,8.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,9,0,0,0,1,1,1,Construction,0.6826164954629079,0.6097859027736124,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-584.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n373078,1,Cash loans,M,N,N,0,270000.0,450000.0,35554.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.032561,-19286,-1083,-1014.0,-1201,,1,1,1,1,0,0,Drivers,1.0,1,1,SATURDAY,12,0,0,0,0,0,0,Transport: type 3,,0.7412032477558415,0.1674084472266241,,,0.9786,,,,,,,,,0.0223,,,,,0.9786,,,,,,,,,0.0232,,,,,0.9786,,,,,,,,,0.0227,,,,,0.028999999999999998,,No,0.0,0.0,0.0,0.0,-469.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n180884,0,Cash loans,F,N,Y,2,90000.0,555273.0,18040.5,463500.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17961,-3446,-9597.0,-1519,,1,1,0,1,1,0,Sales staff,4.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6258882638089798,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n166983,0,Revolving loans,F,N,N,1,99000.0,180000.0,9000.0,180000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.015221,-11665,-3693,-4562.0,-409,,1,1,0,1,0,0,Medicine staff,3.0,2,2,MONDAY,12,0,0,0,1,1,0,Medicine,,0.38783718837968334,0.25670557243930026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-1446.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n211963,0,Cash loans,F,N,N,2,121500.0,539590.5,26244.0,445500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-12431,-2617,-1705.0,-2586,,1,1,1,1,0,0,Medicine staff,4.0,3,3,THURSDAY,10,0,0,0,0,1,1,Medicine,,0.5077697166878518,0.3893387918468769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1481.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n312971,0,Cash loans,F,N,Y,2,157500.0,582804.0,28165.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-14965,-7668,-6093.0,-4493,,1,1,0,1,0,0,Accountants,4.0,2,2,MONDAY,11,0,0,0,0,0,0,Other,,0.33921359735049983,0.5046813193144684,0.0216,0.0757,0.9593,0.4424,0.0063,0.0,0.2414,0.0833,0.125,0.038,0.0168,0.0317,0.0039,0.0052,0.0221,0.0786,0.9593,0.4642,0.0063,0.0,0.2414,0.0833,0.125,0.0388,0.0184,0.0331,0.0039,0.0055,0.0219,0.0757,0.9593,0.4499,0.0063,0.0,0.2414,0.0833,0.125,0.0386,0.0171,0.0323,0.0039,0.0053,reg oper account,block of flats,0.0295,\"Stone, brick\",No,9.0,0.0,9.0,0.0,-1096.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n224709,0,Cash loans,F,N,Y,0,112500.0,607500.0,16834.5,607500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.014519999999999996,-16899,-8387,-5947.0,-431,,1,1,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Other,0.5155178068046071,0.2376947704263744,0.28371188263500075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-151.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n216283,0,Cash loans,M,N,N,0,112500.0,544491.0,17694.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.022625,-13024,-1827,-4271.0,-4894,,1,1,0,1,0,0,Drivers,1.0,2,2,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.30135264843262,0.5055560207596904,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1747.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n304369,0,Cash loans,M,Y,Y,0,225000.0,904500.0,29178.0,904500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-20017,-2020,-7369.0,-2769,5.0,1,1,0,1,0,0,Drivers,2.0,1,1,TUESDAY,11,0,0,0,0,1,1,Self-employed,,0.3598923255381595,0.5656079814115492,0.0619,0.0636,0.9896,0.8572,0.0101,0.0,0.1379,0.1667,0.2083,0.0448,0.0504,0.0545,0.0,0.0,0.063,0.066,0.9896,0.8628,0.0102,0.0,0.1379,0.1667,0.2083,0.0458,0.0551,0.0567,0.0,0.0,0.0625,0.0636,0.9896,0.8591,0.0102,0.0,0.1379,0.1667,0.2083,0.0456,0.0513,0.0554,0.0,0.0,reg oper account,block of flats,0.0428,Panel,No,9.0,0.0,9.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n205319,0,Cash loans,F,N,Y,0,135000.0,942300.0,36643.5,675000.0,Other_B,Working,Secondary / secondary special,Single / not married,House / apartment,0.009334,-17210,-807,-4613.0,-33,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,0.2484924008410124,0.6362990182827344,,0.0082,0.0,0.9742,0.6464,0.0,0.0,0.0345,0.0417,0.0417,0.0387,0.0067,0.0051,0.0,0.0,0.0084,0.0,0.9742,0.6602,0.0,0.0,0.0345,0.0417,0.0417,0.0395,0.0073,0.0043,0.0,0.0,0.0083,0.0,0.9742,0.6511,0.0,0.0,0.0345,0.0417,0.0417,0.0393,0.0068,0.0052,0.0,0.0,reg oper spec account,block of flats,0.0048,Block,No,3.0,0.0,3.0,0.0,-1665.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n419624,0,Cash loans,F,Y,Y,0,135000.0,755190.0,36459.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-19943,-1377,-9852.0,-3475,9.0,1,1,0,1,0,0,Cleaning staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Other,,0.6793740275880861,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-819.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n347873,0,Cash loans,F,N,Y,0,202500.0,1800000.0,49630.5,1800000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.026392,-23355,-420,-5879.0,-4573,,1,1,0,1,1,0,Core staff,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,School,0.7969647250260241,0.7520772309682862,0.5352762504724826,0.0402,,0.9722,,,0.0,0.1034,0.0833,,,,0.0303,,0.0,0.040999999999999995,,0.9722,,,0.0,0.1034,0.0833,,,,0.0315,,0.0,0.0406,,0.9722,,,0.0,0.1034,0.0833,,,,0.0308,,0.0,,block of flats,0.0238,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1557.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n256494,0,Cash loans,M,N,N,0,135000.0,983160.0,62833.5,900000.0,Other_A,Working,Secondary / secondary special,Married,House / apartment,0.00496,-19983,-676,-6508.0,-3512,,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.5432412842323583,0.626620183893151,0.6626377922738201,0.1108,,0.9896,,,,0.1034,0.3542,,,,,,,0.0662,,0.9881,,,,0.0345,0.3333,,,,,,,0.1119,,0.9896,,,,0.1034,0.3542,,,,,,,,block of flats,0.0551,,No,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n293306,0,Cash loans,M,N,Y,0,315000.0,1149210.0,33732.0,1003500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.02461,-20658,-323,-8737.0,-3363,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Kindergarten,,0.2858978721410488,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,10.0,0.0,-1060.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n330027,0,Cash loans,F,N,Y,0,256500.0,402214.5,27009.0,328500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010147,-15948,-6538,-10009.0,-4468,,1,1,0,1,0,1,,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.4280573193177195,0.7490217048463391,0.0742,0.0464,0.9786,,,0.08,0.069,0.3333,,0.0341,,0.0462,,0.0,0.0756,0.0481,0.9786,,,0.0806,0.069,0.3333,,0.0349,,0.0482,,0.0,0.0749,0.0464,0.9786,,,0.08,0.069,0.3333,,0.0347,,0.0471,,0.0,,block of flats,0.055,Panel,No,5.0,2.0,5.0,1.0,-687.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n394591,0,Cash loans,M,Y,Y,0,90000.0,1016496.0,33723.0,877500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-17036,-1432,-2743.0,-578,7.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Housing,,0.5918410677612339,0.6545292802242897,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n445751,1,Cash loans,M,Y,Y,0,135000.0,447768.0,29920.5,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.005144,-9332,-184,-1469.0,-1462,12.0,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Self-employed,0.06131252521683046,0.2605737388713479,,0.1227,0.1432,0.9816,0.7484,0.0181,0.0,0.2759,0.1667,0.2083,0.0538,0.1,0.1208,0.0,0.0,0.125,0.1486,0.9816,0.7583,0.0182,0.0,0.2759,0.1667,0.2083,0.055,0.1093,0.1259,0.0,0.0,0.1239,0.1432,0.9816,0.7518,0.0182,0.0,0.2759,0.1667,0.2083,0.0547,0.1018,0.12300000000000001,0.0,0.0,org spec account,block of flats,0.1072,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n188726,0,Cash loans,M,Y,Y,0,270000.0,1467612.0,58333.5,1350000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.00496,-16181,365243,-4535.0,-4553,20.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,XNA,,0.13999783491273796,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n178794,0,Cash loans,M,Y,Y,2,189000.0,450000.0,20983.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.005313,-12597,-886,-336.0,-4380,19.0,1,1,0,1,1,1,Drivers,4.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.15406866904762562,0.3842068130556564,0.0619,0.0471,0.9762,0.6736,0.0252,0.0,0.1034,0.1667,0.2083,0.0286,0.0504,0.0512,0.0,0.0,0.063,0.0489,0.9762,0.6864,0.0255,0.0,0.1034,0.1667,0.2083,0.0293,0.0551,0.0533,0.0,0.0,0.0625,0.0471,0.9762,0.6779999999999999,0.0254,0.0,0.1034,0.1667,0.2083,0.0291,0.0513,0.0521,0.0,0.0,reg oper account,block of flats,0.0444,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n293262,0,Cash loans,M,N,N,0,94500.0,437076.0,28260.0,405000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.005313,-24736,365243,-1126.0,-4272,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.3397382623810007,0.7597121819739279,0.1773,0.1371,0.9921,0.8912,,0.2,0.1724,0.375,0.2917,0.18600000000000005,0.1446,0.1078,0.0,0.0068,0.1807,0.1423,0.9921,0.8955,,0.2014,0.1724,0.375,0.2917,0.1902,0.1579,0.1124,0.0,0.0072,0.179,0.1371,0.9921,0.8927,,0.2,0.1724,0.375,0.2917,0.1892,0.1471,0.1098,0.0,0.0069,reg oper account,block of flats,0.147,Panel,No,0.0,0.0,0.0,0.0,-3.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n225082,0,Cash loans,F,N,Y,0,108000.0,276408.0,14242.5,198000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.010966,-17808,-2090,-2583.0,-1334,,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Postal,0.6861982772098824,0.4872886239685123,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n307464,0,Cash loans,F,N,Y,1,225000.0,1527579.0,53226.0,1395000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-17272,-608,-15252.0,-828,,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Other,0.4271850083628783,0.4952075628666062,0.656158373001177,0.2216,0.1454,0.9791,0.7144,0.0406,0.24,0.2069,0.3333,0.375,0.0252,0.1807,0.2084,0.0,0.0,0.2258,0.1508,0.9791,0.7256,0.040999999999999995,0.2417,0.2069,0.3333,0.375,0.0258,0.1974,0.2172,0.0,0.0,0.2238,0.1454,0.9791,0.7182,0.0409,0.24,0.2069,0.3333,0.375,0.0257,0.1838,0.2122,0.0,0.0,reg oper spec account,block of flats,0.1862,Panel,No,1.0,0.0,1.0,0.0,-2877.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n304709,0,Cash loans,F,N,Y,0,225000.0,675000.0,19737.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-15545,-355,-4230.0,-1837,,1,1,1,1,1,0,Sales staff,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 1,,0.5043916877548364,0.5424451438613613,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1310.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n309796,1,Cash loans,F,Y,N,0,112500.0,796500.0,23418.0,796500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-14003,-1308,-6435.0,-3911,17.0,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Self-employed,0.4153048379431025,0.3442507292017128,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1605.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n361208,0,Cash loans,M,N,Y,0,171000.0,373140.0,19449.0,337500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Rented apartment,0.030755,-9491,-2562,-4049.0,-2179,,1,1,0,1,0,1,Managers,1.0,2,2,SATURDAY,11,0,0,0,1,1,0,Trade: type 3,0.4257623539184192,0.464358142933744,0.6380435278721609,0.1474,0.1727,0.9826,0.762,0.027000000000000003,0.0,0.3448,0.1667,0.0417,0.1094,0.1143,0.1434,0.027000000000000003,0.0433,0.1502,0.1792,0.9826,0.7713,0.0273,0.0,0.3448,0.1667,0.0417,0.1119,0.1249,0.1495,0.0272,0.0459,0.1489,0.1727,0.9826,0.7652,0.0272,0.0,0.3448,0.1667,0.0417,0.1113,0.1163,0.146,0.0272,0.0442,reg oper account,block of flats,0.1285,Panel,No,0.0,0.0,0.0,0.0,-211.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n143724,0,Cash loans,F,N,N,0,180000.0,528633.0,19588.5,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00733,-23451,365243,-10628.0,-4482,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,XNA,,0.5741399582619129,0.7267112092725122,0.0619,0.07400000000000001,0.9856,0.8028,0.0128,0.0,0.2069,0.1667,0.2083,0.0423,0.0496,0.0675,0.0039,0.0082,0.063,0.0768,0.9856,0.8105,0.0129,0.0,0.2069,0.1667,0.2083,0.0433,0.0542,0.0703,0.0039,0.0087,0.0625,0.07400000000000001,0.9856,0.8054,0.0129,0.0,0.2069,0.1667,0.2083,0.043,0.0504,0.0687,0.0039,0.0084,reg oper account,block of flats,0.0619,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-378.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n243361,0,Cash loans,M,Y,N,0,225000.0,891072.0,37750.5,720000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.002506,-16322,-5730,-3072.0,-4675,20.0,1,1,1,1,1,0,Core staff,2.0,2,2,MONDAY,4,0,0,0,0,0,0,Police,,0.27488156162595234,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-620.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n305618,0,Cash loans,F,Y,N,0,135000.0,2013840.0,53253.0,1800000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018634,-16180,-1196,-3444.0,-4226,2.0,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,17,0,0,0,0,1,1,Business Entity Type 3,0.883546475676671,0.6411683617776793,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-381.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n432976,0,Cash loans,F,N,Y,0,112500.0,344043.0,25852.5,297000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005002,-21335,-3498,-3437.0,-4485,,1,1,0,1,0,0,Medicine staff,2.0,3,3,SATURDAY,11,0,0,0,0,1,1,Medicine,0.7260131224456537,0.6663500847451339,0.5902333386185574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1274.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n243297,0,Revolving loans,F,N,Y,0,225000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Higher education,Married,With parents,0.04622,-11778,-3105,-1262.0,-1507,,1,1,0,1,0,0,,2.0,1,1,THURSDAY,13,0,1,1,0,0,0,Business Entity Type 1,0.8113700853005068,0.4553147489692502,0.470456116119975,0.066,,,,,0.16,0.1379,0.6667,,,,0.1362,,,0.0672,,,,,0.1611,0.1379,0.6667,,,,0.1419,,,0.0666,,,,,0.16,0.1379,0.6667,,,,0.1386,,,,,0.1136,,No,0.0,0.0,0.0,0.0,-1245.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n345448,0,Cash loans,F,Y,Y,0,90000.0,720000.0,25861.5,720000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-23582,365243,-3010.0,-4304,13.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,XNA,,0.5487804102642285,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n370086,0,Cash loans,M,N,Y,0,180000.0,1528200.0,53248.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-13025,-3626,-5640.0,-4905,,1,1,1,1,0,0,Security staff,2.0,2,2,SUNDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.3937812641042712,0.6973140492678999,0.5638350489514956,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-740.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n442830,0,Cash loans,M,Y,Y,0,162000.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003818,-20066,-3651,-6672.0,-3424,31.0,1,1,1,1,1,0,Drivers,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.33865583495665713,0.402322884289871,0.6496203111237195,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n121392,0,Cash loans,F,N,N,0,63000.0,101880.0,5976.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.01885,-16568,-4419,-1292.0,-124,,1,1,1,1,1,0,Security staff,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,Housing,,0.3331672630833481,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1085.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n442022,1,Cash loans,F,N,N,0,135000.0,254700.0,14220.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010032,-15755,-5975,-3075.0,-1663,,1,1,1,1,1,0,Core staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Bank,,0.01373572868724255,0.4740512892789932,,,0.9881,,,0.2132,0.1838,0.3608,,,,,,,,,0.9871,,,0.1208,0.1034,0.375,,,,,,,,,0.9881,,,0.24,0.2069,0.375,,,,,,,,block of flats,0.2136,Panel,No,0.0,0.0,0.0,0.0,-408.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n100289,0,Cash loans,M,Y,N,0,202500.0,526491.0,26878.5,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-14811,-7668,-1761.0,-4268,0.0,1,1,1,1,1,0,Managers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Transport: type 4,0.6451510088256213,0.6587035208707981,0.6971469077844458,0.4072,0.2893,0.9816,0.7484,0.0089,0.44,0.3793,0.3333,0.375,0.1754,0.3261,0.4133,0.027000000000000003,0.0083,0.4149,0.3002,0.9816,0.7583,0.009000000000000001,0.4431,0.3793,0.3333,0.375,0.1794,0.3563,0.4306,0.0272,0.0087,0.4112,0.2893,0.9816,0.7518,0.009000000000000001,0.44,0.3793,0.3333,0.375,0.1784,0.3318,0.4207,0.0272,0.0084,,block of flats,0.3288,Panel,No,0.0,0.0,0.0,0.0,-669.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n253362,0,Cash loans,M,N,N,0,135000.0,717003.0,19845.0,598500.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.072508,-14205,-975,-8351.0,-1874,,1,1,0,1,0,0,Sales staff,2.0,1,1,SUNDAY,14,0,0,0,0,0,0,Trade: type 7,,0.6706049968357451,0.6263042766749393,0.1921,0.1083,0.9791,0.7144,0.1272,0.2132,0.0917,0.5417,0.5554,0.0,0.1541,0.2082,0.0116,0.0104,0.0882,0.033,0.9767,0.6929,0.0229,0.0806,0.0345,0.4583,0.5,0.0,0.0771,0.0747,0.0,0.0,0.0999,0.1083,0.9781,0.7048,0.128,0.08,0.0345,0.5417,0.5,0.0,0.0812,0.0742,0.0039,0.0106,reg oper account,block of flats,0.3821,Block,No,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n184080,0,Cash loans,F,N,Y,0,135000.0,436032.0,21339.0,360000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-16283,365243,-9172.0,-4644,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,0.678041169881885,0.618011417782674,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-1757.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n406080,0,Cash loans,M,Y,Y,0,450000.0,1532506.5,48118.5,1399500.0,Family,Commercial associate,Incomplete higher,Married,House / apartment,0.04622,-15867,-370,-7106.0,-4936,3.0,1,1,0,1,0,0,Sales staff,2.0,1,1,WEDNESDAY,13,0,1,1,0,1,1,Business Entity Type 3,0.4015346033000906,0.7573718887656244,0.6195277080511546,0.2598,0.2101,0.9851,0.7959999999999999,0.0619,0.28,0.2414,0.3333,0.375,,0.2118,0.2867,0.0,0.0,0.2647,0.2181,0.9851,0.804,0.0625,0.282,0.2414,0.3333,0.375,,0.2314,0.2987,0.0,0.0,0.2623,0.2101,0.9851,0.7987,0.0623,0.28,0.2414,0.3333,0.375,,0.2155,0.2918,0.0,0.0,reg oper account,block of flats,0.2374,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n359792,0,Cash loans,F,N,Y,0,135000.0,640080.0,23121.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.030755,-18901,-899,-7099.0,-2451,,1,1,1,1,1,0,Sales staff,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Trade: type 7,0.5887066788079751,0.6698510138086485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n362701,0,Cash loans,F,N,N,0,135000.0,527377.5,19674.0,400500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007305,-13468,-1618,-150.0,-5609,,1,1,0,1,0,0,,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Other,0.6303449162664949,0.6010780719795731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n179420,0,Cash loans,F,Y,Y,1,135000.0,518562.0,25078.5,463500.0,Unaccompanied,Working,Lower secondary,Civil marriage,House / apartment,0.025164,-13959,-2868,-186.0,-4403,3.0,1,1,0,1,1,0,Core staff,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Police,,0.3736136046401485,0.6212263380626669,0.4227,0.2587,0.9846,,,0.48,0.4138,0.3333,,0.069,,0.4347,,0.023,0.4307,0.2685,0.9846,,,0.4834,0.4138,0.3333,,0.0706,,0.4529,,0.0243,0.4268,0.2587,0.9846,,,0.48,0.4138,0.3333,,0.0702,,0.4425,,0.0234,,block of flats,0.4959,Panel,No,0.0,0.0,0.0,0.0,-989.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n414697,0,Cash loans,M,Y,N,0,247500.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-18386,-1221,-10410.0,-1936,20.0,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,Business Entity Type 1,,0.6978860684195336,,0.0237,0.0215,0.9677,0.5579999999999999,0.0033,0.0,0.1724,0.0417,0.0833,0.0347,0.0185,0.0228,0.0039,0.0077,0.0242,0.0224,0.9677,0.5753,0.0033,0.0,0.1724,0.0417,0.0833,0.0355,0.0202,0.0237,0.0039,0.0081,0.0239,0.0215,0.9677,0.5639,0.0033,0.0,0.1724,0.0417,0.0833,0.0353,0.0188,0.0232,0.0039,0.0078,reg oper account,block of flats,0.0162,\"Stone, brick\",No,7.0,1.0,7.0,1.0,-1201.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n320059,0,Cash loans,F,N,N,0,63000.0,239850.0,23364.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-22575,365243,-7136.0,-4081,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.6391635305152503,,0.1175,0.1555,0.9896,,,0.0,0.2759,0.1667,,0.1676,,0.1294,,0.0609,0.1197,0.1613,0.9896,,,0.0,0.2759,0.1667,,0.1714,,0.1348,,0.0644,0.1187,0.1555,0.9896,,,0.0,0.2759,0.1667,,0.1705,,0.1317,,0.0621,,block of flats,0.1352,Panel,No,0.0,0.0,0.0,0.0,-1102.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n233139,0,Cash loans,F,N,Y,0,112500.0,256500.0,12469.5,256500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.028663,-22097,365243,-6029.0,-4304,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.7700949617803731,0.21155076420525776,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1584.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n416060,0,Revolving loans,F,N,Y,0,171000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-19720,-4849,-4347.0,-2917,,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,17,0,0,0,0,1,1,Police,0.7990634690464282,0.660270022880093,0.8277026681625442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-270.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n138002,0,Cash loans,M,N,Y,0,900000.0,1306107.0,124141.5,1237500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-20022,-3036,-8679.0,-3431,,1,1,0,1,1,0,Managers,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.8733642602535912,0.710826049048614,0.4920600938649263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2594.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n446740,0,Cash loans,F,N,Y,0,157500.0,101880.0,10206.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.0228,-15873,-1816,-3844.0,-2584,,1,1,1,1,1,0,Sales staff,1.0,2,2,FRIDAY,18,0,0,0,0,0,0,Business Entity Type 1,0.6131236361923085,0.7280147680047832,0.4866531565147181,,,0.9821,,,,0.0862,0.0417,,0.0135,,0.01,,,,,0.9806,,,,0.069,0.0417,,0.0082,,0.0104,,,,,0.9821,,,,0.0862,0.0417,,0.0137,,0.0102,,,,block of flats,0.0079,Wooden,Yes,0.0,0.0,0.0,0.0,-1449.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n430165,0,Cash loans,F,N,Y,2,225000.0,247275.0,19953.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-15961,-3634,-3541.0,-2596,,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,15,0,0,0,0,0,0,Industry: type 11,,0.6744049158461981,0.6690566947824041,0.0722,0.049,0.9786,,,0.0,0.1379,0.1667,,0.064,,0.0512,,0.0,0.0735,0.0508,0.9786,,,0.0,0.1379,0.1667,,0.0654,,0.0534,,0.0,0.0729,0.049,0.9786,,,0.0,0.1379,0.1667,,0.0651,,0.0521,,0.0,,block of flats,0.0446,Panel,No,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n452622,0,Cash loans,F,N,Y,0,126000.0,553806.0,26770.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011703,-13260,-6594,-12901.0,-50,,1,1,0,1,1,0,Medicine staff,2.0,2,2,WEDNESDAY,13,0,0,0,1,0,1,Other,0.399123944320528,0.7078458681266127,0.3774042489507649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n165737,0,Revolving loans,F,N,Y,0,112500.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-20813,-9400,-8948.0,-4305,,1,1,0,1,1,0,Medicine staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Medicine,,0.5892977772026421,0.746300213050371,0.0773,0.0847,0.9841,,,0.0,0.1724,0.1667,,,,0.0679,,0.067,0.0788,0.0879,0.9841,,,0.0,0.1724,0.1667,,,,0.0707,,0.0709,0.0781,0.0847,0.9841,,,0.0,0.1724,0.1667,,,,0.0691,,0.0684,,block of flats,0.0755,Panel,No,2.0,2.0,2.0,2.0,-2114.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n314880,0,Cash loans,M,Y,Y,1,180000.0,1354500.0,50197.5,1354500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-17630,-4400,-4932.0,-1181,11.0,1,1,0,1,0,0,Managers,3.0,1,1,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.807618343778443,,0.2505,0.1917,0.9846,0.7892,,0.28,0.2414,0.3333,,0.0086,,0.2809,,,0.2553,0.19899999999999998,0.9846,0.7975,,0.282,0.2414,0.3333,,0.0088,,0.2926,,,0.2529,0.1917,0.9846,0.792,,0.28,0.2414,0.3333,,0.0087,,0.2859,,,reg oper account,block of flats,0.2224,Panel,No,0.0,0.0,0.0,0.0,-780.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n451329,0,Cash loans,M,N,N,0,157500.0,1079883.0,35689.5,967500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Office apartment,0.005313,-20793,365243,-3703.0,-4170,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.7161677617927457,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2295.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n321955,0,Cash loans,F,N,Y,0,112500.0,254700.0,14751.0,225000.0,Unaccompanied,State servant,Secondary / secondary special,Widow,House / apartment,0.010276,-24460,-2066,-4711.0,-3915,,1,1,0,1,0,0,Security staff,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Other,,0.6648412408942129,,0.1649,0.1455,0.9871,0.8232,0.0507,0.16,0.1379,0.375,0.4167,0.0481,0.2353,0.1737,0.0,0.0,0.042,0.1509,0.9871,0.8301,0.0512,0.0403,0.0345,0.375,0.4167,0.0086,0.2571,0.0443,0.0,0.0,0.1665,0.1455,0.9871,0.8256,0.051,0.16,0.1379,0.375,0.4167,0.0489,0.2394,0.1769,0.0,0.0,reg oper account,block of flats,0.2676,Panel,No,0.0,0.0,0.0,0.0,-2439.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n145258,0,Cash loans,M,Y,N,2,135000.0,74628.0,8568.0,67500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-12608,-2275,-1146.0,-4401,30.0,1,1,1,1,1,0,Drivers,4.0,2,2,FRIDAY,20,0,0,0,0,0,0,Self-employed,,0.5427956463827064,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1871.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n380080,0,Cash loans,F,Y,N,1,148500.0,417024.0,20191.5,360000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.030755,-11708,-91,-6480.0,-2319,7.0,1,1,0,1,0,0,HR staff,3.0,2,2,MONDAY,15,0,0,0,0,0,0,Security Ministries,0.5537747207482421,0.6837923770965908,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2624.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n165682,0,Cash loans,F,Y,Y,0,211500.0,707287.5,31284.0,562500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-23647,-852,-6120.0,-4683,7.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Housing,0.7329225184090691,0.6423849780333304,0.4365064990977374,0.0495,0.0163,0.9916,,,0.0,0.069,0.0833,,0.0853,,0.0593,,0.1332,0.0504,0.0169,0.9916,,,0.0,0.069,0.0833,,0.0872,,0.0618,,0.1411,0.05,0.0163,0.9916,,,0.0,0.069,0.0833,,0.0868,,0.0604,,0.136,,block of flats,0.0756,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-643.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n437552,0,Cash loans,F,N,Y,0,202500.0,1678522.5,58473.0,1449000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-19400,-5959,-5798.0,-2946,,1,1,1,1,1,0,Medicine staff,2.0,3,3,THURSDAY,5,0,0,0,0,0,0,Medicine,0.6689347899301498,0.5394942943157925,0.4830501881366946,0.201,0.1617,0.9916,0.8844,0.0512,0.2,0.1724,0.375,0.4167,0.1913,0.1639,0.2415,0.0,0.0,0.2048,0.1678,0.9916,0.8889,0.0516,0.2014,0.1724,0.375,0.4167,0.1957,0.1791,0.2516,0.0,0.0,0.203,0.1617,0.9916,0.8859,0.0515,0.2,0.1724,0.375,0.4167,0.1946,0.1667,0.2458,0.0,0.0,reg oper account,block of flats,0.1907,Panel,No,0.0,0.0,0.0,0.0,-1758.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n389093,0,Cash loans,F,Y,Y,2,157500.0,332946.0,18189.0,238500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008865999999999999,-13543,-2719,-8674.0,-1603,4.0,1,1,0,1,0,0,Core staff,4.0,2,2,THURSDAY,15,0,0,0,0,0,0,Insurance,0.628868960966182,0.7549284522143299,,0.0814,0.1354,0.9851,,,0.0,0.2069,0.1667,,0.0981,,0.0844,,0.1623,0.083,0.1405,0.9851,,,0.0,0.2069,0.1667,,0.1003,,0.08800000000000001,,0.1718,0.0822,0.1354,0.9851,,,0.0,0.2069,0.1667,,0.0998,,0.0859,,0.1657,,block of flats,0.1016,Panel,No,0.0,0.0,0.0,0.0,-2327.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n308015,0,Cash loans,F,N,Y,0,315000.0,971280.0,49338.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010032,-13589,-200,-2303.0,-1295,,1,1,0,1,0,1,Accountants,2.0,2,2,THURSDAY,7,0,0,0,0,0,0,Advertising,0.7316436117977206,0.7530097685181036,0.8050196619153701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-725.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n102718,0,Revolving loans,M,Y,Y,0,157500.0,450000.0,22500.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-21512,-1658,-5223.0,-4670,3.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.7006489418450896,0.7283869869025309,0.6674577419214722,0.1216,0.2132,0.9836,0.7756,0.0,0.0,0.2759,0.1667,0.0417,0.0949,0.0975,0.1117,0.0077,0.0118,0.1239,0.2212,0.9836,0.7844,0.0,0.0,0.2759,0.1667,0.0417,0.0971,0.1065,0.1164,0.0078,0.0125,0.1228,0.2132,0.9836,0.7786,0.0,0.0,0.2759,0.1667,0.0417,0.0966,0.0992,0.1137,0.0078,0.012,reg oper account,block of flats,0.0904,Panel,No,0.0,0.0,0.0,0.0,-2280.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n205810,0,Cash loans,F,N,N,0,36000.0,269550.0,11547.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.004849,-19566,365243,-12006.0,-2799,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,0.8014808787565659,0.6246636993353376,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1516.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n235580,0,Cash loans,F,Y,N,0,202500.0,450000.0,22860.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.011703,-9912,-416,-4146.0,-1639,4.0,1,1,0,1,1,0,Core staff,2.0,2,2,MONDAY,18,0,0,0,0,0,0,Advertising,0.3466074982164611,0.7309147481104606,0.5884877883422673,0.0619,0.0453,0.9742,,,0.0,0.1034,0.1667,,0.0545,,0.0495,,0.0,0.063,0.047,0.9742,,,0.0,0.1034,0.1667,,0.0557,,0.0516,,0.0,0.0625,0.0453,0.9742,,,0.0,0.1034,0.1667,,0.0554,,0.0504,,0.0,,block of flats,0.0417,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1223.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n367490,0,Cash loans,F,Y,N,0,108000.0,225000.0,9531.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.032561,-22190,365243,-14014.0,-4286,13.0,1,0,0,1,1,0,,2.0,1,1,WEDNESDAY,20,0,0,0,0,0,0,XNA,,0.7076830416407058,0.7544061731797895,0.0175,0.022000000000000002,0.9742,0.6464,,0.0,0.069,0.0833,,0.0286,,0.0152,,0.0174,0.0179,0.0228,0.9742,0.6602,,0.0,0.069,0.0833,,0.0293,,0.0158,,0.0184,0.0177,0.022000000000000002,0.9742,0.6511,,0.0,0.069,0.0833,,0.0291,,0.0154,,0.0178,,block of flats,0.0157,Block,No,0.0,0.0,0.0,0.0,-1788.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n253699,0,Cash loans,M,N,Y,0,180000.0,450000.0,47749.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.020713,-9416,-1981,-3596.0,-2077,,1,1,1,1,1,0,Laborers,1.0,3,3,SUNDAY,12,0,0,0,1,1,0,Self-employed,0.3385093475701456,0.6954785517777008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-940.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n110448,0,Cash loans,F,N,Y,1,135000.0,395766.0,21600.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-16377,-2971,-8913.0,-4094,,1,1,0,1,0,0,Laborers,3.0,2,2,SUNDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.724208905007129,0.581102192854995,0.20208660168203949,0.0825,0.0627,0.9747,0.6532,0.0278,0.0,0.1379,0.1667,0.2083,0.0,0.063,0.0658,0.0193,0.0173,0.084,0.0651,0.9747,0.6668,0.0281,0.0,0.1379,0.1667,0.2083,0.0,0.0689,0.0686,0.0195,0.0183,0.0833,0.0627,0.9747,0.6578,0.027999999999999997,0.0,0.1379,0.1667,0.2083,0.0,0.0641,0.067,0.0194,0.0177,reg oper account,block of flats,0.0707,Panel,No,0.0,0.0,0.0,0.0,-2078.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n340393,0,Cash loans,F,Y,Y,0,247500.0,1006920.0,40063.5,900000.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.00733,-11915,-1963,-5261.0,-3271,9.0,1,1,1,1,0,0,Sales staff,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.7189827951766631,0.6355497118665108,,0.16699999999999998,0.0749,0.9841,0.7824,0.0162,0.08,0.069,0.3333,0.0417,0.1219,0.1362,0.1057,0.0,0.0,0.1702,0.0777,0.9841,0.7909,0.0163,0.0806,0.069,0.3333,0.0417,0.1247,0.1488,0.1101,0.0,0.0,0.1686,0.0749,0.9841,0.7853,0.0163,0.08,0.069,0.3333,0.0417,0.124,0.1385,0.1076,0.0,0.0,reg oper spec account,block of flats,0.1007,Panel,No,2.0,0.0,2.0,0.0,-1448.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n212320,0,Cash loans,F,N,Y,0,90000.0,254700.0,23814.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-13242,-3365,-4174.0,-2044,,1,1,1,1,0,0,Core staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Government,0.5631274249179732,0.7188669906572951,0.7407990879702335,0.0072,0.0,0.9523,0.3472,0.0,0.0,0.0345,0.0,0.0417,0.0104,0.0059,0.0053,0.0,0.0,0.0074,0.0,0.9523,0.3728,0.0,0.0,0.0345,0.0,0.0417,0.0106,0.0064,0.0055,0.0,0.0,0.0073,0.0,0.9523,0.3559,0.0,0.0,0.0345,0.0,0.0417,0.0106,0.006,0.0054,0.0,0.0,not specified,terraced house,0.0042,Wooden,No,1.0,0.0,1.0,0.0,-1193.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n338134,0,Cash loans,M,N,Y,0,292500.0,900000.0,67423.5,900000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Municipal apartment,0.028663,-9641,-385,-4382.0,-2275,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,10,0,1,1,0,1,1,Trade: type 3,,0.7162433216760185,0.6722428897082422,0.0376,0.0856,0.9786,0.8096,0.011000000000000001,0.0,0.1034,0.1042,0.2083,0.0301,0.0546,0.0386,0.0,0.0326,0.0084,0.0888,0.9717,0.8171,0.0111,0.0,0.0345,0.0417,0.2083,0.0308,0.0597,0.0083,0.0,0.0345,0.038,0.0856,0.9786,0.8121,0.0111,0.0,0.1034,0.1042,0.2083,0.0306,0.0556,0.0393,0.0,0.0332,reg oper account,block of flats,0.0676,Wooden,No,1.0,0.0,1.0,0.0,-843.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n191616,0,Cash loans,M,N,Y,0,202500.0,733315.5,39199.5,679500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-20805,-462,-2966.0,-2560,,1,1,0,1,1,0,High skill tech staff,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6917981986227693,0.6785676886853644,0.0144,0.0102,0.9722,0.6192,0.0021,0.0,0.0345,0.125,0.1667,0.0138,0.0101,0.0115,0.0077,0.0095,0.0147,0.0106,0.9722,0.6341,0.0021,0.0,0.0345,0.125,0.1667,0.0142,0.011000000000000001,0.0119,0.0078,0.0101,0.0146,0.0102,0.9722,0.6243,0.0021,0.0,0.0345,0.125,0.1667,0.0141,0.0103,0.0117,0.0078,0.0097,reg oper account,block of flats,0.0111,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-394.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n176012,0,Cash loans,F,N,Y,0,157500.0,225000.0,22050.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.030755,-23890,365243,-13809.0,-4182,,1,0,0,1,0,0,,1.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.6332115224296526,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1396.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n261266,0,Cash loans,F,N,Y,0,157500.0,629320.5,25087.5,508500.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.011657,-19600,-4408,-9429.0,-2811,,1,1,0,1,1,0,Sales staff,1.0,1,1,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.7121099030514665,0.7394117535524816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1765.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n363516,0,Cash loans,F,N,Y,1,90000.0,130320.0,13018.5,112500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010032,-13698,-1195,-7442.0,-72,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,7,0,0,0,0,1,1,Self-employed,0.4615013336848882,0.1655930925067656,0.807273923277881,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1207.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n148795,0,Cash loans,M,N,Y,0,112500.0,808650.0,23773.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00823,-21679,365243,-7038.0,-4610,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,0.300893165102514,0.2877138958370263,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n114839,0,Cash loans,F,Y,N,0,58500.0,675000.0,34465.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-20315,365243,-10279.0,-3748,7.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.5867072852764784,0.656158373001177,0.1227,0.0814,0.9856,0.8028,0.0159,0.0,0.2759,0.1667,0.2083,0.1056,0.0992,0.1083,0.0039,0.0187,0.125,0.0845,0.9856,0.8105,0.0161,0.0,0.2759,0.1667,0.2083,0.10800000000000001,0.1084,0.1129,0.0039,0.0198,0.1239,0.0814,0.9856,0.8054,0.016,0.0,0.2759,0.1667,0.2083,0.1074,0.1009,0.1103,0.0039,0.0191,reg oper account,block of flats,0.098,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1059.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n138549,0,Cash loans,F,Y,N,2,135000.0,896157.0,45886.5,801000.0,Family,Working,Secondary / secondary special,Married,Office apartment,0.008625,-12047,-3782,-2787.0,-3136,5.0,1,1,0,1,0,0,High skill tech staff,4.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Military,0.8002092122994382,0.7421166319521791,0.5352762504724826,0.1237,0.0956,0.9955,,,0.12,0.1034,0.375,,0.0196,,0.1886,,0.0,0.1261,0.0992,0.9955,,,0.1208,0.1034,0.375,,0.02,,0.1965,,0.0,0.1249,0.0956,0.9955,,,0.12,0.1034,0.375,,0.0199,,0.192,,0.0,,block of flats,0.1483,Panel,No,9.0,0.0,9.0,0.0,-2056.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n409351,0,Cash loans,M,N,Y,0,76500.0,45000.0,5206.5,45000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-23320,365243,-1137.0,-4212,,1,0,0,1,1,0,,2.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.6997362931617497,0.22383131747015547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n199512,0,Cash loans,F,N,N,0,121500.0,1546020.0,40783.5,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.01885,-9002,-785,-766.0,-1461,,1,1,1,1,0,0,Sales staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.4043542357673389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-95.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n244332,0,Cash loans,M,Y,N,0,292500.0,263686.5,29880.0,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-11577,365243,-1239.0,-1441,18.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.12235843879996595,0.4596904504249018,0.2227,0.1572,0.9791,0.7144,0.0872,0.24,0.2069,0.3333,0.375,0.0975,0.1816,0.2266,0.0,0.0,0.2269,0.1631,0.9791,0.7256,0.08800000000000001,0.2417,0.2069,0.3333,0.375,0.0997,0.1983,0.2361,0.0,0.0,0.2248,0.1572,0.9791,0.7182,0.0877,0.24,0.2069,0.3333,0.375,0.0992,0.1847,0.2307,0.0,0.0,reg oper account,block of flats,0.2259,Panel,No,0.0,0.0,0.0,0.0,-891.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n416620,0,Cash loans,F,N,Y,0,31500.0,50940.0,5166.0,45000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.01885,-15202,-8087,-905.0,-4346,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,School,0.4006491447072673,0.4488148664149661,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2218.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n245258,0,Cash loans,F,Y,Y,0,135000.0,1185120.0,39298.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-14254,-1414,-1484.0,-5564,6.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Insurance,0.7949750616184221,0.7002598049289216,,0.4948,0.1943,0.9975,0.966,0.7781,0.48,0.2069,0.625,0.6667,0.0,0.4035,0.4951,0.0386,0.1657,0.5042,0.2016,0.9975,0.9673,0.7852,0.4834,0.2069,0.625,0.6667,0.0,0.4408,0.5158,0.0389,0.1754,0.4996,0.1943,0.9975,0.9665,0.7831,0.48,0.2069,0.625,0.6667,0.0,0.4104,0.504,0.0388,0.1692,reg oper account,block of flats,0.5329999999999999,Block,No,0.0,0.0,0.0,0.0,-753.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n385850,0,Cash loans,M,Y,Y,0,202500.0,298512.0,31473.0,270000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018634,-18061,-1051,-9396.0,-1623,18.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,13,0,1,1,0,0,0,Business Entity Type 3,,0.6326577523652218,0.5190973382084597,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n262445,0,Cash loans,F,N,Y,2,40500.0,232344.0,12735.0,157500.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14192,-107,-7144.0,-4396,,1,1,0,1,1,0,Sales staff,4.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.2972963512531248,0.6056989021263005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-646.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n389287,0,Cash loans,F,N,N,0,90000.0,402939.0,15061.5,306000.0,,Working,Secondary / secondary special,Single / not married,House / apartment,0.02461,-10471,-335,-4383.0,-3134,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.5903505782007629,0.3539876078507373,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n101439,0,Cash loans,F,N,Y,0,202500.0,1350000.0,51552.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.026392,-19029,-3592,-8570.0,-1065,,1,1,0,1,0,0,Core staff,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Kindergarten,0.7354394432542947,0.6292666032117074,0.5460231970049609,0.0165,0.0088,0.9702,,,0.0,0.069,0.0417,,0.0409,,0.0146,,0.0,0.0168,0.0091,0.9702,,,0.0,0.069,0.0417,,0.0418,,0.0153,,0.0,0.0167,0.0088,0.9702,,,0.0,0.069,0.0417,,0.0416,,0.0149,,0.0,,block of flats,0.0115,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-653.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,4.0\n206185,0,Cash loans,F,N,Y,0,159750.0,273636.0,13900.5,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-23165,365243,-15957.0,-4327,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.6583347158837795,,0.0186,,0.9722,0.6124,0.0073,0.0,0.0862,0.0625,0.125,0.0227,0.0202,0.0203,0.0,0.005,0.0126,,0.9717,0.6276,0.0073,0.0,0.069,0.0417,0.125,0.0228,0.022000000000000002,0.0151,0.0,0.0047,0.0187,,0.9722,0.6176,0.0073,0.0,0.0862,0.0625,0.125,0.0231,0.0205,0.0207,0.0,0.0051,org spec account,block of flats,0.0213,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1164.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n328345,0,Cash loans,F,N,N,0,135000.0,755190.0,28116.0,675000.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.072508,-22616,365243,-11632.0,-4575,,1,0,0,1,1,0,,1.0,1,1,THURSDAY,13,0,0,0,0,0,0,XNA,,0.7632457447877967,0.6296742509538716,0.2557,0.1373,0.9841,0.7824,0.3868,0.4,0.1724,0.4583,0.5,0.0,0.1975,0.2689,0.0502,0.0864,0.2605,0.1425,0.9841,0.7909,0.3903,0.4028,0.1724,0.4583,0.5,0.0,0.2158,0.2801,0.0506,0.0915,0.2581,0.1373,0.9841,0.7853,0.3892,0.4,0.1724,0.4583,0.5,0.0,0.2009,0.2737,0.0505,0.0882,reg oper account,block of flats,0.0845,Block,No,1.0,1.0,1.0,1.0,-845.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n355058,0,Cash loans,F,N,Y,0,45000.0,835380.0,35523.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-19222,-1727,-8060.0,-2331,,1,1,0,1,0,0,Cleaning staff,2.0,3,3,TUESDAY,5,0,0,0,0,0,0,Other,,0.32980421579989266,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n414643,0,Cash loans,F,N,Y,0,108000.0,381528.0,27778.5,315000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,With parents,0.019689,-9909,-1222,-4696.0,-2396,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Industry: type 11,,0.5893718704769024,0.2405414172860865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-56.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n448531,1,Cash loans,M,Y,Y,0,270000.0,900000.0,45954.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-13928,-190,-8014.0,-4641,7.0,1,1,0,1,1,0,Drivers,2.0,2,2,TUESDAY,10,0,1,1,0,1,1,Trade: type 3,0.06253093964378789,0.6503124080458572,0.2608559142068693,0.0247,0.0456,0.9816,0.7484,0.0144,0.0,0.069,0.0833,0.125,0.0081,0.0202,0.0215,0.0,0.0,0.0252,0.0474,0.9816,0.7583,0.0145,0.0,0.069,0.0833,0.125,0.0083,0.022000000000000002,0.0224,0.0,0.0,0.025,0.0456,0.9816,0.7518,0.0144,0.0,0.069,0.0833,0.125,0.0082,0.0205,0.0219,0.0,0.0,reg oper spec account,block of flats,0.0169,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1988.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n157355,0,Cash loans,M,N,Y,0,202500.0,819792.0,36238.5,720000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-22960,365243,-7332.0,-4762,,1,0,0,1,0,0,,2.0,1,1,FRIDAY,14,0,0,0,0,0,0,XNA,,0.6050511162804846,0.7981372313187245,0.5649,0.3592,0.9846,0.7892,0.099,0.6,0.5172,0.3333,0.375,0.1761,0.4539,0.5832,0.0309,0.026000000000000002,0.5756,0.3727,0.9846,0.7975,0.0999,0.6042,0.5172,0.3333,0.375,0.1801,0.4959,0.6076,0.0311,0.0275,0.5704,0.3592,0.9846,0.792,0.0997,0.6,0.5172,0.3333,0.375,0.1792,0.4617,0.5936,0.0311,0.0266,reg oper account,block of flats,0.5185,Panel,No,3.0,0.0,3.0,0.0,-1145.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n285298,0,Cash loans,F,N,N,0,166500.0,2250000.0,83385.0,2250000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-15610,-5348,-588.0,-4757,,1,1,1,1,1,0,,2.0,2,2,WEDNESDAY,22,0,1,1,0,0,0,Self-employed,0.5649076433909598,0.6701121070143387,0.7380196196295241,0.0619,0.0469,0.9851,0.7959999999999999,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0504,0.0704,0.0,0.0,0.063,0.0487,0.9851,0.804,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0551,0.0734,0.0,0.0,0.0625,0.0469,0.9851,0.7987,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0513,0.0717,0.0,0.0,reg oper account,block of flats,0.0554,Panel,No,0.0,0.0,0.0,0.0,-207.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n353858,0,Cash loans,M,Y,Y,1,135000.0,264888.0,28656.0,234000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-17033,-2795,-3290.0,-566,4.0,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.3715242473954101,0.5036865043614434,0.7503751495159068,0.1897,0.0687,0.995,0.932,0.0,0.16,0.1379,0.375,0.0417,0.107,0.1437,0.1924,0.0502,0.1082,0.1933,0.0713,0.995,0.9347,0.0,0.1611,0.1379,0.375,0.0417,0.1094,0.157,0.2005,0.0506,0.1146,0.1915,0.0687,0.995,0.9329,0.0,0.16,0.1379,0.375,0.0417,0.1088,0.1462,0.1959,0.0505,0.1105,reg oper account,block of flats,0.1749,Panel,No,0.0,0.0,0.0,0.0,-1136.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n167728,0,Cash loans,M,Y,Y,0,157500.0,325908.0,16767.0,247500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.025164,-13638,-3979,-1494.0,-3530,17.0,1,1,0,1,0,1,Laborers,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.4588854692441904,0.5531646987710016,0.1495,0.1056,0.9806,0.7348,0.0395,0.16,0.1379,0.3333,0.375,0.0324,0.1219,0.1522,0.0,0.0,0.1523,0.1096,0.9806,0.7452,0.0398,0.1611,0.1379,0.3333,0.375,0.0331,0.1331,0.1586,0.0,0.0,0.1509,0.1056,0.9806,0.7383,0.0397,0.16,0.1379,0.3333,0.375,0.033,0.124,0.155,0.0,0.0,reg oper account,block of flats,0.1413,Panel,No,0.0,0.0,0.0,0.0,-1880.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n235322,1,Cash loans,M,N,N,0,67500.0,225000.0,14647.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.005002,-9147,-780,-3729.0,-1811,,1,1,1,1,0,0,Drivers,1.0,3,3,TUESDAY,10,0,0,0,0,0,0,Self-employed,,0.2491876389366461,,,,0.9747,,,,,,,,,0.0065,,,,,0.9747,,,,,,,,,0.0067,,,,,0.9747,,,,,,,,,0.0066,,,,block of flats,0.0051,,No,0.0,0.0,0.0,0.0,-366.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n194531,0,Cash loans,F,Y,Y,0,202500.0,1501794.0,47155.5,1345500.0,Family,Pensioner,Higher education,Civil marriage,House / apartment,0.006852,-20310,365243,-1378.0,-1605,16.0,1,0,0,1,0,0,,2.0,3,3,THURSDAY,11,0,0,0,0,0,0,XNA,,0.1851383760236112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-583.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n343460,0,Cash loans,F,N,Y,2,157500.0,253737.0,17086.5,229500.0,Family,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.035792000000000004,-12662,-116,-11.0,-1963,,1,1,0,1,0,0,Sales staff,4.0,2,2,TUESDAY,16,0,0,0,0,0,0,Trade: type 7,,0.38596461975256907,,0.0742,0.0182,0.9911,0.8776,0.0,0.08,0.069,0.3333,0.0417,0.0622,0.0605,0.0475,0.0,0.1115,0.0756,0.0189,0.9911,0.8824,0.0,0.0806,0.069,0.3333,0.0417,0.0636,0.0661,0.0495,0.0,0.11800000000000001,0.0749,0.0182,0.9911,0.8792,0.0,0.08,0.069,0.3333,0.0417,0.0632,0.0616,0.0484,0.0,0.1138,reg oper spec account,block of flats,0.0616,Block,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n364407,0,Cash loans,F,N,Y,0,90000.0,781920.0,25969.5,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-21744,365243,-6573.0,-4585,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,12,0,0,0,0,0,0,XNA,,0.5015435431883959,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2231.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n238285,0,Cash loans,F,N,N,0,90000.0,343800.0,12478.5,225000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.025164,-21828,365243,-6599.0,-919,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,8,0,0,0,0,0,0,XNA,,0.04683636596097917,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,4.0,6.0,4.0,-1555.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n425568,0,Cash loans,F,Y,N,2,238500.0,354469.5,15147.0,306000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010032,-11583,-4221,-1970.0,-3341,3.0,1,1,0,1,0,0,,4.0,2,2,THURSDAY,3,0,0,0,0,1,1,Police,0.6464208282235644,0.5167298034926385,0.6397075677637197,0.0928,0.0951,0.9796,0.7212,0.0719,0.0,0.2069,0.1667,0.2083,0.019,0.0756,0.0601,0.0,0.0751,0.0945,0.0987,0.9796,0.7321,0.0725,0.0,0.2069,0.1667,0.2083,0.0194,0.0826,0.0626,0.0,0.0795,0.0937,0.0951,0.9796,0.7249,0.0723,0.0,0.2069,0.1667,0.2083,0.0193,0.077,0.0612,0.0,0.0766,not specified,block of flats,0.0866,Panel,No,0.0,0.0,0.0,0.0,-1131.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n444674,0,Cash loans,M,Y,Y,0,418500.0,1174005.0,81837.0,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.02461,-19038,-73,-4913.0,-2562,4.0,1,1,0,1,1,0,,2.0,2,2,THURSDAY,19,1,1,0,1,1,0,Business Entity Type 3,,0.7595751514431967,0.5495965024956946,0.1031,0.0698,0.993,0.8640000000000001,0.1033,0.1,0.0862,0.375,0.4167,0.0407,0.0836,0.1285,0.0019,0.0005,0.084,0.0551,0.9926,0.8432,0.0563,0.0806,0.069,0.375,0.4167,0.0,0.0735,0.1092,0.0,0.0,0.1041,0.0698,0.993,0.8658,0.1039,0.1,0.0862,0.375,0.4167,0.0414,0.0851,0.1309,0.0019,0.0006,org spec account,block of flats,0.0824,Panel,No,0.0,0.0,0.0,0.0,-5.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n227877,0,Cash loans,F,N,Y,0,112500.0,537669.0,20398.5,378000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-17424,-226,-9177.0,-971,,1,1,1,1,1,0,Cooking staff,2.0,2,2,MONDAY,15,0,0,0,0,1,1,Self-employed,,0.624197022107929,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-434.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n302647,0,Cash loans,M,Y,Y,2,90000.0,52128.0,6025.5,45000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-15155,-2065,-889.0,-5780,16.0,1,1,1,1,1,0,High skill tech staff,4.0,3,3,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.4930465721745659,0.6017176036292368,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1621.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n178038,0,Cash loans,F,N,Y,0,202500.0,225000.0,8212.5,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Office apartment,0.010006,-13936,-6599,-3425.0,-1223,,1,1,0,1,0,0,Core staff,2.0,2,2,SUNDAY,9,0,0,0,0,0,0,Industry: type 9,,0.09374586006709633,,0.2206,0.1475,0.9891,0.8504,,0.24,0.2069,0.3333,0.375,0.1161,0.1799,0.2653,,0.033,0.2248,0.1531,0.9891,0.8563,,0.2417,0.2069,0.3333,0.375,0.1188,0.1965,0.2764,,0.0349,0.2228,0.1475,0.9891,0.8524,,0.24,0.2069,0.3333,0.375,0.1182,0.183,0.2701,,0.0337,reg oper account,block of flats,0.3323,Panel,No,0.0,0.0,0.0,0.0,-28.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n369142,0,Cash loans,F,N,Y,1,99000.0,127350.0,13711.5,112500.0,Unaccompanied,Working,Secondary / secondary special,Separated,Rented apartment,0.010643,-10713,-1600,-754.0,-3370,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.7683013910744924,0.5814837058057234,0.1515,0.0727,0.9821,,,0.0,0.0345,0.1667,,0.0076,,0.0449,,0.1361,0.1544,0.0755,0.9821,,,0.0,0.0345,0.1667,,0.0077,,0.0468,,0.1441,0.153,0.0727,0.9821,,,0.0,0.0345,0.1667,,0.0077,,0.0457,,0.139,,block of flats,0.0659,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1739.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n235573,0,Cash loans,F,Y,N,0,135000.0,171000.0,18085.5,171000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Co-op apartment,0.028663,-15198,-3991,-5940.0,-4214,15.0,1,1,0,1,0,0,Sales staff,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Self-employed,0.4338056218563375,0.4584945396329119,0.4920600938649263,0.0082,0.0049,0.9687,0.5716,0.0011,0.0,0.0345,0.0417,0.0833,0.0143,0.0067,0.0084,0.0,0.0,0.0084,0.005,0.9687,0.5884,0.0011,0.0,0.0345,0.0417,0.0833,0.0146,0.0073,0.0088,0.0,0.0,0.0083,0.0049,0.9687,0.5773,0.0011,0.0,0.0345,0.0417,0.0833,0.0145,0.0068,0.0086,0.0,0.0,reg oper account,block of flats,0.0078,Block,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n295208,0,Cash loans,F,N,Y,0,250200.0,135000.0,12492.0,135000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010147,-19696,-12938,-5089.0,-3191,,1,1,1,1,1,0,High skill tech staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Transport: type 2,,0.5644406043731274,0.520897599048938,0.0722,0.0503,0.993,0.898,,0.04,0.0345,0.375,0.0417,,0.0588,0.0775,,,0.0735,0.0522,0.993,0.902,,0.0403,0.0345,0.375,0.0417,,0.0643,0.0807,,,0.0729,0.0503,0.993,0.8994,,0.04,0.0345,0.375,0.0417,,0.0599,0.0788,,,reg oper spec account,block of flats,0.0706,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3161.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n267310,0,Cash loans,F,N,N,0,211500.0,248760.0,26919.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-19450,-12654,-7193.0,-3000,,1,1,1,1,1,0,High skill tech staff,2.0,1,1,FRIDAY,20,0,0,0,0,0,0,Business Entity Type 2,0.8288491497973217,0.7236720061339758,0.2721336844147212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1525.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n276520,0,Cash loans,M,N,N,2,157500.0,1180129.5,31261.5,1030500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-13106,-2280,-2763.0,-4253,,1,1,0,1,0,0,Managers,4.0,3,3,FRIDAY,15,0,0,0,0,0,0,Housing,0.2324790163256361,0.586861049077376,0.6832688314232291,0.0825,0.0807,0.9762,0.6736,0.0598,0.0,0.1379,0.1667,0.2083,0.018000000000000002,0.0672,0.0692,0.0,0.0,0.084,0.0838,0.9762,0.6864,0.0603,0.0,0.1379,0.1667,0.2083,0.0184,0.0735,0.07200000000000001,0.0,0.0,0.0833,0.0807,0.9762,0.6779999999999999,0.0602,0.0,0.1379,0.1667,0.2083,0.0183,0.0684,0.0704,0.0,0.0,reg oper account,block of flats,0.0544,Panel,No,2.0,0.0,2.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n378929,0,Cash loans,F,N,Y,1,148500.0,808650.0,23773.5,675000.0,Family,State servant,Secondary / secondary special,Married,Municipal apartment,0.00963,-13258,-1143,-7395.0,-4140,,1,1,0,1,0,0,Medicine staff,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Medicine,,0.05793311532493884,0.3893387918468769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-25.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n187857,0,Cash loans,F,N,Y,0,225000.0,1515415.5,40104.0,1354500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014464,-22487,365243,-414.0,-4735,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,3,0,0,0,0,0,0,XNA,0.8711461975307466,0.693348626437239,0.5495965024956946,0.0629,0.0345,0.9821,0.7552,0.0188,0.0,0.1379,0.2083,0.2083,0.0514,0.0504,0.052000000000000005,0.0039,0.0204,0.0641,0.0358,0.9821,0.7648,0.0189,0.0,0.1379,0.2083,0.2083,0.0526,0.0551,0.0541,0.0039,0.0216,0.0635,0.0345,0.9821,0.7585,0.0189,0.0,0.1379,0.2083,0.2083,0.0523,0.0513,0.0529,0.0039,0.0208,reg oper spec account,block of flats,0.0556,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-514.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n420307,0,Cash loans,M,Y,Y,0,112500.0,278460.0,21676.5,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20506,-3314,-12687.0,-3949,19.0,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,9,0,0,0,0,1,1,Agriculture,,0.3474069821233893,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1966.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n287659,0,Cash loans,F,N,Y,0,247500.0,728847.0,26307.0,553500.0,Unaccompanied,State servant,Secondary / secondary special,Widow,House / apartment,0.018801,-19710,-684,-3567.0,-3262,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Industry: type 10,,0.3880916166706583,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n215557,1,Cash loans,F,N,Y,2,135000.0,541323.0,29493.0,405000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.020246,-10750,-484,-871.0,-3356,,1,1,0,1,0,1,Laborers,4.0,3,3,MONDAY,14,0,0,0,0,0,0,Industry: type 11,0.12072789753560227,0.25462712488797346,0.11987796089553485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-311.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,3.0\n230029,0,Cash loans,M,Y,Y,2,180000.0,942300.0,30528.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16038,-2158,-3444.0,-4497,17.0,1,1,0,1,0,0,Laborers,4.0,2,2,FRIDAY,15,0,0,0,0,1,1,Emergency,0.2114304354607741,0.6532307475968048,0.4471785780453068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2795.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n346516,0,Cash loans,F,N,Y,0,162000.0,380533.5,27193.5,328500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018634,-12573,-905,-3333.0,-3951,,1,1,1,1,1,0,,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.7410946218552468,0.675776714928381,,0.0722,0.0825,0.9767,0.6804,,0.0,0.1379,0.1667,0.2083,0.0365,0.0588,0.0673,0.0,0.0,0.0735,0.0856,0.9767,0.6929,,0.0,0.1379,0.1667,0.2083,0.0374,0.0643,0.0701,0.0,0.0,0.0729,0.0825,0.9767,0.6847,,0.0,0.1379,0.1667,0.2083,0.0372,0.0599,0.0685,0.0,0.0,reg oper account,block of flats,0.0606,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1082.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n240060,0,Cash loans,M,N,Y,0,99900.0,254700.0,24192.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006008,-21002,-209,-10181.0,-4235,,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.3820386018572229,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n205830,0,Cash loans,F,Y,Y,0,256500.0,1641280.5,43425.0,1467000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.032561,-21760,-6292,-15858.0,-4047,14.0,1,1,0,1,1,0,High skill tech staff,2.0,1,1,SUNDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6697301018421121,0.8327850252992314,0.3309,0.2167,0.9781,0.7008,0.0687,0.36,0.3103,0.3333,0.375,0.1168,0.2698,0.3218,0.0,0.0,0.3372,0.2249,0.9782,0.7125,0.0693,0.3625,0.3103,0.3333,0.375,0.1195,0.2948,0.3353,0.0,0.0,0.3341,0.2167,0.9781,0.7048,0.0691,0.36,0.3103,0.3333,0.375,0.1189,0.2745,0.3276,0.0,0.0,reg oper account,block of flats,0.2906,Panel,No,3.0,0.0,3.0,0.0,-1089.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,0.0\n349167,0,Cash loans,F,N,Y,1,180000.0,343800.0,16852.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-12826,-1288,-550.0,-2999,,1,1,0,1,1,0,Laborers,3.0,2,2,SUNDAY,14,0,0,0,0,1,1,Self-employed,,0.25205839014269443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n274448,0,Cash loans,F,N,Y,0,90000.0,808650.0,21460.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-22416,-5365,-1663.0,-4682,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Self-employed,,0.4432927974366237,0.3723336657058204,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1070.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n427591,0,Cash loans,M,Y,N,1,225000.0,203760.0,24309.0,180000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-10121,-451,-4709.0,-2735,7.0,1,1,0,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.7312500846431583,0.08503261489695353,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n324792,0,Cash loans,M,Y,Y,1,270000.0,1141254.0,63855.0,1057500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-17496,-1427,-10007.0,-1029,8.0,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,Transport: type 4,0.4967035195464948,0.4612755657446646,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-925.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n322962,0,Cash loans,M,N,N,0,315000.0,1125000.0,41823.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-13573,-317,-7643.0,-4931,,1,1,0,1,0,0,Drivers,2.0,1,1,WEDNESDAY,12,0,1,1,0,0,0,Other,0.44407595195488747,0.6616029611515984,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n301934,0,Cash loans,M,N,Y,0,270000.0,1096020.0,55962.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-14549,-774,-1641.0,-5318,,1,1,1,1,0,0,,2.0,2,2,TUESDAY,15,0,1,1,1,1,1,Industry: type 9,,0.4160829270904863,0.3441550073724169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-583.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n323101,0,Cash loans,M,N,Y,0,76500.0,117162.0,9387.0,103500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.00702,-22107,365243,-5256.0,-4277,,1,0,0,1,0,0,,1.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.6172522646401636,0.6594055320683344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-625.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n327421,0,Revolving loans,M,N,N,1,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018634,-11413,-290,-738.0,-3750,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Other,0.30616081291653285,0.6721488806237733,0.5460231970049609,0.0722,0.0543,0.9796,0.7212,0.0079,0.0,0.1379,0.1667,0.0417,0.0486,0.0538,0.0532,0.0232,0.0128,0.0735,0.0564,0.9796,0.7321,0.0079,0.0,0.1379,0.1667,0.0417,0.0497,0.0588,0.0555,0.0233,0.0136,0.0729,0.0543,0.9796,0.7249,0.0079,0.0,0.1379,0.1667,0.0417,0.0494,0.0547,0.0542,0.0233,0.0131,reg oper account,block of flats,0.0419,\"Stone, brick\",No,8.0,0.0,8.0,0.0,-977.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n404281,0,Cash loans,F,N,Y,0,112500.0,288873.0,13464.0,238500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Office apartment,0.025164,-11576,-741,-1027.0,-2890,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,8,0,0,0,1,1,0,Business Entity Type 3,,0.1320234746308537,0.16146308000577247,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,2.0,-283.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n137611,0,Cash loans,F,N,N,2,81000.0,462195.0,31396.5,351000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.02461,-10253,-572,-4592.0,-1966,,1,1,0,1,0,0,Sales staff,4.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.4273347218570122,,0.0448,0.0566,0.9866,0.8096,0.0188,0.0,0.1034,0.125,0.0833,0.0206,0.0668,0.0459,0.6834,0.0857,0.0284,0.0478,0.9841,0.7779,0.0035,0.0,0.1034,0.0833,0.0417,0.021,0.0248,0.0288,0.0,0.0908,0.0453,0.0566,0.9866,0.8121,0.0189,0.0,0.1034,0.125,0.0833,0.0209,0.068,0.0467,0.6871,0.0875,org spec account,block of flats,0.0236,Panel,No,0.0,0.0,0.0,0.0,-264.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n342430,0,Cash loans,F,N,Y,0,270000.0,781920.0,61906.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-13990,-1440,-104.0,-4713,,1,1,0,1,0,0,Laborers,2.0,1,1,WEDNESDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.77232127402972,0.5262949398096192,0.2778,0.0641,0.9955,0.9388,0.0979,0.16,0.069,0.6667,0.7083,0.0168,0.2265,0.2316,0.0058,0.0004,0.2826,0.0662,0.9955,0.9412,0.09699999999999999,0.1611,0.069,0.6667,0.7083,0.0169,0.247,0.2406,0.0039,0.0,0.2805,0.0641,0.9955,0.9396,0.0985,0.16,0.069,0.6667,0.7083,0.0171,0.2304,0.2358,0.0058,0.0004,reg oper account,block of flats,0.185,Block,No,0.0,0.0,0.0,0.0,-3191.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n392397,0,Cash loans,M,N,N,0,90000.0,814041.0,23800.5,679500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010966,-15334,-285,-5144.0,-4760,,1,1,1,1,1,0,,2.0,2,2,SUNDAY,15,0,0,0,0,0,0,Other,0.3921492028927828,0.7286897198404929,0.4206109640437848,0.1031,0.0838,0.9836,0.7756,0.0115,0.0,0.2069,0.1667,0.2083,0.0,0.0841,0.0943,0.0,0.0,0.105,0.087,0.9836,0.7844,0.0116,0.0,0.2069,0.1667,0.2083,0.0,0.0918,0.0983,0.0,0.0,0.1041,0.0838,0.9836,0.7786,0.0115,0.0,0.2069,0.1667,0.2083,0.0,0.0855,0.096,0.0,0.0,reg oper account,block of flats,0.0805,Panel,No,0.0,0.0,0.0,0.0,-1374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n269881,0,Cash loans,F,N,Y,2,270000.0,1354500.0,47209.5,1354500.0,\"Spouse, partner\",Commercial associate,Higher education,Civil marriage,House / apartment,0.006008,-11008,-3697,-958.0,-3686,,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Business Entity Type 1,0.8157382813215199,0.6715655350517489,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1505.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,6.0\n194077,1,Cash loans,F,N,N,2,112500.0,239850.0,25960.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-10580,-799,-1116.0,-1665,,1,1,1,1,0,0,Laborers,4.0,3,3,FRIDAY,14,0,0,0,0,0,0,Industry: type 3,,0.04757895739367896,0.178760465484575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-726.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n172637,1,Cash loans,M,N,Y,0,157500.0,101880.0,10206.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.007305,-14432,-946,-5356.0,-4907,,1,1,1,1,1,0,Sales staff,1.0,3,3,FRIDAY,11,0,0,0,0,0,0,Construction,,,0.3139166772114369,0.2474,0.2036,0.9891,0.8504,0.1065,0.24,0.2069,0.375,0.4167,0.1116,0.2009,0.2732,0.0039,0.0051,0.2521,0.2113,0.9891,0.8563,0.1075,0.2417,0.2069,0.375,0.4167,0.1142,0.2195,0.2846,0.0039,0.0054,0.2498,0.2036,0.9891,0.8524,0.1072,0.24,0.2069,0.375,0.4167,0.1136,0.2044,0.2781,0.0039,0.0052,reg oper account,block of flats,0.2743,Panel,No,10.0,0.0,10.0,0.0,-1373.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n341842,0,Cash loans,F,Y,Y,0,270000.0,269550.0,10116.0,225000.0,Unaccompanied,Pensioner,Higher education,Civil marriage,House / apartment,0.00702,-23918,365243,-6224.0,-4473,24.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.5007945827265349,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-587.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n305189,0,Cash loans,F,N,N,0,67500.0,339241.5,12312.0,238500.0,Other_B,Working,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-19406,-532,-3985.0,-2936,,1,1,0,1,1,0,Laborers,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,Postal,,,0.7136313997323308,0.0124,,0.9727,0.626,,,0.069,0.0,0.0833,,0.0101,,,,0.0126,,0.9727,0.6406,,,0.069,0.0,0.0833,,0.011000000000000001,,,,0.0125,,0.9727,0.631,,,0.069,0.0,0.0833,,0.0103,,,,reg oper account,block of flats,0.0073,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n146802,0,Cash loans,F,N,N,0,130500.0,704844.0,27342.0,630000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.030755,-23854,365243,-2315.0,-4462,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,0.7333773431236069,0.5168936249434889,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n181476,0,Cash loans,F,N,Y,1,135000.0,338832.0,20857.5,292500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.01885,-20406,365243,-928.0,-3966,,1,0,0,1,0,0,,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.3064779273184876,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n121456,0,Cash loans,M,Y,N,0,148500.0,636187.5,35653.5,589500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Municipal apartment,0.003818,-15282,-2566,-6442.0,-4106,15.0,1,1,1,1,1,0,Drivers,2.0,2,2,FRIDAY,8,0,0,0,0,1,1,Business Entity Type 3,,0.21145448039821613,0.7530673920730478,0.0021,,0.9861,,,,0.069,0.0,,0.0289,,0.002,,,0.0021,,0.9861,,,,0.069,0.0,,0.0296,,0.0021,,,0.0021,,0.9861,,,,0.069,0.0,,0.0295,,0.0021,,,,block of flats,0.0016,Wooden,No,1.0,0.0,1.0,0.0,-1549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n419047,0,Cash loans,F,N,N,0,157500.0,276277.5,21510.0,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006207,-12833,-615,-6412.0,-3797,,1,1,1,1,1,0,,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.5960969945746594,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1296.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n442411,0,Cash loans,F,Y,N,0,112500.0,225000.0,16371.0,225000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.018801,-10031,-1975,-4874.0,-2337,14.0,1,1,0,1,1,0,Cooking staff,2.0,2,2,WEDNESDAY,7,0,0,0,1,1,0,Business Entity Type 3,,0.17194571607005818,0.10344905212675168,0.1227,0.12,0.9762,0.6736,0.059000000000000004,0.0,0.2759,0.1667,0.2083,0.1204,0.1,0.1053,0.0,0.0,0.125,0.1245,0.9762,0.6864,0.0595,0.0,0.2759,0.1667,0.2083,0.1232,0.1093,0.1097,0.0,0.0,0.1239,0.12,0.9762,0.6779999999999999,0.0594,0.0,0.2759,0.1667,0.2083,0.1225,0.1018,0.1072,0.0,0.0,reg oper account,block of flats,0.1151,Panel,No,5.0,3.0,5.0,3.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n121512,0,Cash loans,F,Y,Y,0,360000.0,1339884.0,39307.5,1170000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010966,-15890,-1867,-283.0,-2935,16.0,1,1,0,1,0,0,Accountants,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.6234221368944864,0.1674084472266241,0.2021,,0.9806,,,0.24,0.2069,0.3333,,0.1312,,0.1909,,0.1894,0.2059,,0.9806,,,0.2417,0.2069,0.3333,,0.1342,,0.1989,,0.2005,0.204,,0.9806,,,0.24,0.2069,0.3333,,0.1335,,0.1943,,0.1934,,block of flats,0.1901,Panel,No,3.0,0.0,3.0,0.0,-1384.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n347303,0,Cash loans,M,Y,Y,0,135000.0,770292.0,32764.5,688500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.0228,-14890,365243,-5998.0,-5690,3.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,0.3560409052690488,0.5729739687880481,0.475849908720221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,8.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n123818,0,Cash loans,F,Y,Y,2,252000.0,622188.0,26284.5,472500.0,Other_B,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006852,-9654,-2639,-3616.0,-2332,17.0,1,1,0,1,0,0,Core staff,4.0,3,3,SATURDAY,6,0,0,0,0,0,0,Security Ministries,,0.04196090941958281,0.5028782772082183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n361590,0,Cash loans,F,N,N,1,90000.0,99000.0,7789.5,99000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020713,-12955,-4202,-336.0,-4802,,1,1,1,1,1,0,Sales staff,3.0,3,3,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.418306557408021,0.4162370094557444,0.3556387169923543,0.2742,0.1825,0.9985,0.9796,0.0949,0.28,0.2414,0.375,0.4167,0.1704,0.2236,0.3102,0.0116,0.0132,0.2794,0.1894,0.9985,0.9804,0.0957,0.282,0.2414,0.375,0.4167,0.1743,0.2443,0.3232,0.0117,0.013999999999999999,0.2769,0.1825,0.9985,0.9799,0.0955,0.28,0.2414,0.375,0.4167,0.1734,0.2275,0.3158,0.0116,0.0135,,block of flats,0.2987,Panel,No,0.0,0.0,0.0,0.0,-1598.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n286914,0,Cash loans,F,N,N,0,180000.0,746280.0,54436.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010147,-18385,-9762,-5388.0,-1919,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Medicine,0.6533615753550172,0.7582588459487526,0.4578995512067301,0.2196,0.1964,0.9906,0.8708,0.1134,0.24,0.2069,0.3333,0.375,0.2919,0.1782,0.22699999999999998,0.0039,0.0262,0.2237,0.2038,0.9906,0.8759,0.1144,0.2417,0.2069,0.3333,0.375,0.2985,0.1947,0.2365,0.0039,0.0278,0.2217,0.1964,0.9906,0.8725,0.1141,0.24,0.2069,0.3333,0.375,0.2969,0.1813,0.2311,0.0039,0.0268,org spec account,block of flats,0.2462,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1753.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n237125,0,Revolving loans,F,N,Y,2,180000.0,225000.0,11250.0,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.009549,-14620,-2810,-816.0,-1047,,1,1,1,1,1,0,Sales staff,4.0,2,2,MONDAY,18,0,0,0,0,0,0,Self-employed,0.5804465971942552,0.4971536616771407,0.1940678276718812,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-898.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n137793,0,Cash loans,F,N,Y,1,45000.0,261288.0,9981.0,171000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-15189,-540,-5088.0,-4571,,1,1,0,1,1,0,Sales staff,3.0,2,2,THURSDAY,12,0,0,0,0,0,0,Trade: type 7,0.3825630141811015,0.497399664675701,0.722392890081304,0.1206,,0.9901,,,0.16,0.1724,0.3333,,,,,,,0.1229,,0.9901,,,0.1611,0.1724,0.3333,,,,,,,0.1218,,0.9901,,,0.16,0.1724,0.3333,,,,,,,,block of flats,0.1207,Monolithic,No,0.0,0.0,0.0,0.0,-1741.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n255148,0,Cash loans,F,N,Y,0,90000.0,1247184.0,40360.5,976500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-14923,-6965,-3755.0,-4278,,1,1,0,1,0,0,Medicine staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,School,,0.6721681557119399,0.5884877883422673,,0.0,0.9742,0.6464,0.0126,0.0,0.0345,0.0417,0.0833,0.0046,0.0067,0.0053,,0.0127,,0.0,0.9742,0.6602,0.0127,0.0,0.0345,0.0417,0.0833,0.0047,0.0073,0.0055,,0.0134,,0.0,0.9742,0.6511,0.0127,0.0,0.0345,0.0417,0.0833,0.0047,0.0068,0.0054,,0.0129,reg oper account,block of flats,0.0062,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-786.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n258578,0,Cash loans,M,N,N,0,99000.0,573628.5,27724.5,463500.0,Unaccompanied,Working,Higher education,Separated,With parents,0.002134,-14497,-1178,-1894.0,-3523,,1,1,0,1,0,0,Managers,1.0,3,3,MONDAY,11,0,0,0,0,0,0,Mobile,,0.5091688395012891,,0.0825,,0.9856,,,,0.1379,0.1667,,,,,,,0.084,,0.9856,,,,0.1379,0.1667,,,,,,,0.0833,,0.9856,,,,0.1379,0.1667,,,,,,,,,0.0651,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n283452,0,Cash loans,M,Y,Y,0,360000.0,545040.0,26640.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.04622,-14290,-1691,-8277.0,-2138,2.0,1,1,0,1,0,1,Sales staff,1.0,1,1,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6498099862934397,,0.0732,0.0649,0.9786,0.7076,0.0104,0.0,0.1379,0.1667,0.2083,0.10800000000000001,0.0588,0.063,0.0039,0.0072,0.0746,0.0674,0.9786,0.7190000000000001,0.0105,0.0,0.1379,0.1667,0.2083,0.1104,0.0643,0.0656,0.0039,0.0076,0.0739,0.0649,0.9786,0.7115,0.0105,0.0,0.1379,0.1667,0.2083,0.1098,0.0599,0.0641,0.0039,0.0073,reg oper account,block of flats,0.0568,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-486.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n417721,0,Cash loans,F,N,Y,0,112500.0,540000.0,15916.5,540000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.030755,-16124,-1226,-1241.0,-2387,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.5945902620112257,0.6993318220493521,0.8193176922872417,,,0.998,,,,,,,,,0.0,,0.0,,,0.998,,,,,,,,,0.0,,0.0,,,0.998,,,,,,,,,0.0,,0.0,,block of flats,0.0977,,No,0.0,0.0,0.0,0.0,-864.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n373617,0,Revolving loans,F,Y,Y,2,135000.0,135000.0,6750.0,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020713,-12880,-5818,-4781.0,-5468,1.0,1,1,0,1,0,0,Medicine staff,4.0,3,3,WEDNESDAY,11,0,0,0,0,1,1,Medicine,0.5206688618846476,0.3992244580011302,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1719.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,6.0\n293727,0,Cash loans,M,Y,Y,2,148500.0,106794.0,5922.0,76500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011703,-11492,-456,-5113.0,-4056,13.0,1,1,0,1,0,0,Drivers,4.0,2,2,MONDAY,15,0,1,1,0,0,0,Transport: type 4,,0.4771334205969894,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1930.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n292788,0,Cash loans,M,Y,Y,0,135000.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-19533,-1429,-4380.0,-3074,0.0,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.322081476677902,0.7070266737982958,0.7194907850918436,0.0227,0.0424,0.9806,,,0.0,0.069,0.0417,,,,,,0.0,0.0231,0.044000000000000004,0.9806,,,0.0,0.069,0.0417,,,,,,0.0,0.0229,0.0424,0.9806,,,0.0,0.069,0.0417,,,,,,0.0,,,0.0207,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n278392,0,Cash loans,F,N,Y,0,157500.0,727785.0,23607.0,607500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-18569,-2775,-5411.0,-1981,,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Security,,0.3333470426512535,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,1.0,0.0,-162.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n273668,0,Cash loans,F,N,Y,3,166500.0,380533.5,20772.0,328500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.031329,-10285,-2663,-4491.0,-2851,,1,1,0,1,0,0,Core staff,5.0,2,2,SATURDAY,8,0,0,0,0,0,0,Kindergarten,,0.2038881864671201,0.3962195720630885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,5.0\n218842,0,Revolving loans,F,N,Y,2,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-13345,-1588,-1087.0,-5069,,1,1,0,1,0,0,Laborers,4.0,3,3,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.61556768025186,0.5053920335511887,0.4048783643353997,0.10099999999999999,0.0939,0.9826,0.762,0.0,0.0,0.2069,0.1667,0.2083,0.0204,0.0824,0.0889,0.0,0.0,0.1029,0.0974,0.9826,0.7713,0.0,0.0,0.2069,0.1667,0.2083,0.0209,0.09,0.0926,0.0,0.0,0.102,0.0939,0.9826,0.7652,0.0,0.0,0.2069,0.1667,0.2083,0.0208,0.0838,0.0905,0.0,0.0,reg oper account,block of flats,0.0699,Panel,No,1.0,1.0,1.0,1.0,-139.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n283059,0,Cash loans,M,N,Y,0,90000.0,486000.0,15390.0,486000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-20363,-1811,-12453.0,-3850,,1,1,0,1,0,0,Security staff,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Security,,0.3420278293317465,,0.1031,,0.9786,0.7076,,0.0,0.1724,0.1667,0.2083,0.0737,,0.0899,,0.0,0.105,,0.9786,0.7190000000000001,,0.0,0.1724,0.1667,0.2083,0.0754,,0.0936,,0.0,0.1041,,0.9786,0.7115,,0.0,0.1724,0.1667,0.2083,0.075,,0.0915,,0.0,,block of flats,0.0707,Panel,No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n151923,0,Cash loans,M,Y,Y,0,113400.0,495000.0,25272.0,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-9295,-1242,-9135.0,-1976,4.0,1,1,0,1,0,0,Security staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Services,,0.5427468043590125,0.633031641417419,0.1072,,0.9876,,,0.12,0.1034,0.3333,,,,0.1133,,,0.1092,,0.9876,,,0.1208,0.1034,0.3333,,,,0.1181,,,0.1083,,0.9876,,,0.12,0.1034,0.3333,,,,0.1153,,,,block of flats,0.1243,Panel,No,4.0,0.0,4.0,0.0,-27.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,5.0\n303573,0,Cash loans,F,N,N,0,292500.0,1350000.0,57199.5,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-9019,-2104,-2616.0,-188,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,Transport: type 4,0.5381286019431494,0.38262214664914296,,0.033,0.0011,0.9722,,,0.0,0.069,0.125,,0.0,,0.0329,,0.0,0.0336,0.0011,0.9722,,,0.0,0.069,0.125,,0.0,,0.0342,,0.0,0.0333,0.0011,0.9722,,,0.0,0.069,0.125,,0.0,,0.0335,,0.0,reg oper spec account,block of flats,0.0332,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-842.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n166120,0,Cash loans,M,Y,Y,0,180000.0,646920.0,25195.5,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.001276,-18802,-1290,-5802.0,-1660,36.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Agriculture,,0.5042003605368319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1215.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n301455,0,Cash loans,F,N,Y,2,135000.0,1080000.0,31576.5,1080000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.015221,-11728,-2943,-1369.0,-23,,1,1,0,1,1,0,Accountants,4.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.5431886433567235,0.5082342837607312,0.6706517530862718,0.1103,0.0765,0.9891,0.8504,,0.12,0.1034,0.3333,0.0417,0.0739,,0.1093,,0.0029,0.1124,0.0794,0.9891,0.8563,,0.1208,0.1034,0.3333,0.0417,0.0756,,0.1139,,0.003,0.1114,0.0765,0.9891,0.8524,,0.12,0.1034,0.3333,0.0417,0.0752,,0.1113,,0.0029,reg oper account,block of flats,0.0968,Panel,No,0.0,0.0,0.0,0.0,-2239.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n343606,0,Cash loans,F,N,Y,2,67500.0,900000.0,38133.0,900000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.02461,-13207,-5470,-7227.0,-3549,,1,1,0,1,1,0,Core staff,4.0,2,2,TUESDAY,9,0,0,0,0,0,0,Bank,,0.7212516717738992,0.6626377922738201,0.0928,,,,,,0.2069,0.1667,,,,,,,0.0945,,,,,,0.2069,0.1667,,,,,,,0.0937,,,,,,0.2069,0.1667,,,,,,,,block of flats,0.0611,,No,0.0,0.0,0.0,0.0,-400.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,0.0\n166178,0,Cash loans,M,N,Y,0,216000.0,675000.0,19867.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.025164,-19721,-4305,-6325.0,-3280,,1,1,0,1,1,0,Laborers,1.0,2,2,SUNDAY,8,0,0,0,0,0,0,Hotel,0.5219504680806027,0.26525634018619443,0.6594055320683344,0.0948,0.0317,0.9856,0.8028,0.0141,0.0,0.2069,0.1667,0.2083,0.0198,0.0756,0.0958,0.0077,0.0475,0.0966,0.0329,0.9856,0.8105,0.0142,0.0,0.2069,0.1667,0.2083,0.0202,0.0826,0.0998,0.0078,0.0503,0.0958,0.0317,0.9856,0.8054,0.0142,0.0,0.2069,0.1667,0.2083,0.0201,0.077,0.0975,0.0078,0.0485,reg oper account,block of flats,0.0934,Panel,No,0.0,0.0,0.0,0.0,-1890.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n226274,0,Cash loans,F,Y,N,2,135000.0,391090.5,23760.0,297000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.018029,-10625,-1701,-5386.0,-23,14.0,1,1,0,1,0,0,Security staff,3.0,3,3,SATURDAY,12,0,0,0,0,0,0,Transport: type 2,0.3950142631994169,0.4594122182461667,0.38079968264891495,0.033,0.002,0.9732,,,,0.069,0.125,,0.0062,,0.0245,,0.0048,0.0336,0.0021,0.9732,,,,0.069,0.125,,0.0063,,0.0255,,0.005,0.0333,0.002,0.9732,,,,0.069,0.125,,0.0063,,0.0249,,0.0049,,block of flats,0.0193,Mixed,No,0.0,0.0,0.0,0.0,-930.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n330783,0,Cash loans,F,N,N,0,112500.0,397881.0,20340.0,328500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-16519,-2446,-1544.0,-65,,1,1,1,1,1,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5623427896884889,0.8224987619370829,0.0742,0.0293,0.9767,0.6804,0.0169,0.04,0.0345,0.3333,0.375,0.0068,0.0605,0.0492,0.0,0.0,0.0756,0.0304,0.9767,0.6929,0.0171,0.0403,0.0345,0.3333,0.375,0.006999999999999999,0.0661,0.0512,0.0,0.0,0.0749,0.0293,0.9767,0.6847,0.017,0.04,0.0345,0.3333,0.375,0.006999999999999999,0.0616,0.05,0.0,0.0,reg oper account,block of flats,0.0462,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,3.0\n360289,0,Cash loans,M,Y,N,0,270000.0,986274.0,28966.5,706500.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.010643,-14098,-1991,-1081.0,-475,7.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,WEDNESDAY,20,1,1,0,1,1,0,Business Entity Type 3,,0.6157270701746601,0.2405414172860865,,0.0,0.9707,,,0.0,0.1379,0.0,,,,0.0026,,0.0,,0.0,0.9707,,,0.0,0.1379,0.0,,,,0.0027,,0.0,,0.0,0.9707,,,0.0,0.1379,0.0,,,,0.0027,,0.0,,,0.0021,Mixed,No,0.0,0.0,0.0,0.0,-2306.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n132566,0,Cash loans,F,Y,Y,0,202500.0,1288350.0,37800.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.019101,-19270,-1068,-8527.0,-2807,5.0,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.21716926321495167,0.3630266098813311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2105.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n294137,0,Cash loans,M,Y,Y,2,135000.0,450000.0,21109.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-17082,-6947,-3285.0,-625,13.0,1,1,0,1,1,0,Core staff,4.0,2,2,FRIDAY,17,0,1,1,0,0,0,Transport: type 2,,0.5828311835476353,0.3572932680336494,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1809.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n412571,0,Cash loans,F,N,N,0,112500.0,447768.0,22873.5,405000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.02461,-17225,-3953,-8571.0,-773,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,,0.5984482028537585,0.6006575372857061,0.0928,0.1005,0.9836,0.7688,0.0119,0.0,0.2069,0.1667,0.2083,0.0396,0.0748,0.0864,0.0039,0.0043,0.0945,0.1043,0.9836,0.7779,0.012,0.0,0.2069,0.1667,0.2083,0.0405,0.0817,0.09,0.0039,0.0045,0.0937,0.1005,0.9836,0.7719,0.012,0.0,0.2069,0.1667,0.2083,0.0402,0.0761,0.0879,0.0039,0.0044,reg oper account,block of flats,0.0754,Panel,No,1.0,0.0,1.0,0.0,-153.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n274738,1,Revolving loans,F,Y,Y,0,126000.0,360000.0,18000.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14640,-2234,-5756.0,-2543,10.0,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.3551256437854799,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1598.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n131082,0,Cash loans,M,Y,Y,2,225000.0,328405.5,19975.5,283500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.072508,-20290,365243,-877.0,-3730,7.0,1,0,0,1,1,0,,4.0,1,1,THURSDAY,10,0,0,0,0,0,0,XNA,,0.7461759062135301,0.5352762504724826,0.0619,0.0673,0.9762,,,0.0,0.1034,0.1667,,0.0109,,0.0494,,0.0,0.063,0.0698,0.9762,,,0.0,0.1034,0.1667,,0.0111,,0.0515,,0.0,0.0625,0.0673,0.9762,,,0.0,0.1034,0.1667,,0.0111,,0.0503,,0.0,,block of flats,0.0388,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1068.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n188674,0,Revolving loans,M,N,Y,0,90000.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-22346,-4355,-3613.0,-3800,,1,1,0,1,0,0,Low-skill Laborers,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Other,,0.2809460592146216,0.3441550073724169,0.0784,0.0,0.9727,0.626,0.1027,0.0,0.1724,0.1667,0.2083,0.0172,,0.0634,0.0695,0.0752,0.0798,0.0,0.9727,0.6406,0.1037,0.0,0.1724,0.1667,0.2083,0.0176,,0.0661,0.07,0.0796,0.0791,0.0,0.9727,0.631,0.1034,0.0,0.1724,0.1667,0.2083,0.0175,,0.0645,0.0699,0.0768,reg oper account,block of flats,0.0562,Block,No,0.0,0.0,0.0,0.0,-178.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n205654,0,Cash loans,M,N,Y,0,225000.0,569398.5,45117.0,486000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-13687,-867,-825.0,-4138,,1,1,0,1,0,1,Drivers,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Construction,0.1545259935313212,0.19775037512433769,0.1245194708919845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-389.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n280238,0,Cash loans,F,Y,Y,2,369000.0,781920.0,42417.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.014519999999999996,-15802,-1011,-1586.0,-2066,6.0,1,1,1,1,0,0,Managers,4.0,2,2,WEDNESDAY,7,0,0,0,1,1,0,Other,,0.6320731198007741,0.3490552510751822,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-790.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n339239,1,Cash loans,F,N,Y,1,112500.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.006296,-15034,-1708,-1156.0,-4263,,1,1,0,1,0,0,Core staff,2.0,3,3,FRIDAY,12,0,0,0,0,1,1,Trade: type 7,,0.3539696612828425,0.6910212267577837,0.1237,0.1318,0.9801,0.728,0.0,0.0,0.2759,0.1667,0.0417,0.0338,0.1009,0.0774,0.0,0.0,0.1261,0.1368,0.9801,0.7387,0.0,0.0,0.2759,0.1667,0.0417,0.0346,0.1102,0.0807,0.0,0.0,0.1249,0.1318,0.9801,0.7316,0.0,0.0,0.2759,0.1667,0.0417,0.0344,0.1026,0.0788,0.0,0.0,reg oper account,block of flats,0.0893,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1012.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n413400,0,Cash loans,F,N,Y,0,76500.0,117162.0,11542.5,103500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.022625,-17031,-1472,-6132.0,-568,,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,School,,0.7773418918752674,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1362.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n135587,0,Cash loans,F,Y,Y,2,135000.0,1095111.0,61281.0,1035000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Municipal apartment,0.002506,-13259,-832,-524.0,-3345,15.0,1,1,1,1,0,0,Drivers,4.0,2,2,MONDAY,7,0,0,0,0,0,0,Military,,0.6897054999193545,,0.0814,0.1002,0.9876,0.83,,,0.2069,0.1667,,0.0536,,0.0495,,0.1265,0.083,0.10400000000000001,0.9876,0.8367,,,0.2069,0.1667,,0.0549,,0.0515,,0.1339,0.0822,0.1002,0.9876,0.8323,,,0.2069,0.1667,,0.0546,,0.0503,,0.1292,reg oper spec account,block of flats,0.0664,Panel,No,0.0,0.0,0.0,0.0,-597.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n312729,0,Cash loans,F,N,Y,0,112500.0,254700.0,24939.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010147,-24436,365243,-11392.0,-4144,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.2465946746738157,0.6006575372857061,0.0722,0.1361,0.9826,0.762,0.0156,0.0,0.2759,0.1667,0.2083,0.0693,0.057999999999999996,0.1029,0.0039,0.0063,0.0735,0.1412,0.9826,0.7713,0.0157,0.0,0.2759,0.1667,0.2083,0.0709,0.0634,0.1072,0.0039,0.0067,0.0729,0.1361,0.9826,0.7652,0.0157,0.0,0.2759,0.1667,0.2083,0.0705,0.059000000000000004,0.1047,0.0039,0.0064,org spec account,block of flats,0.0908,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n433567,0,Cash loans,M,N,N,0,180000.0,746280.0,59094.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.018801,-12810,-1115,-6854.0,-4453,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,15,0,0,0,1,1,0,Advertising,0.31534850098568257,0.4672311467253443,0.4686596550493113,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1342.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n158785,0,Cash loans,M,N,Y,2,90000.0,187704.0,14958.0,148500.0,Family,Working,Lower secondary,Married,House / apartment,0.020246,-14538,-1643,-353.0,-4957,,1,1,0,1,0,0,Laborers,4.0,3,3,SATURDAY,13,0,0,0,0,1,1,Transport: type 4,0.35215319538071344,0.4753007017100794,0.6178261467332483,0.0381,0.0,0.9737,0.6396,0.0034,0.0,0.1034,0.0833,0.0417,0.038,0.0303,0.0275,0.0039,0.0087,0.0389,0.0,0.9737,0.6537,0.0034,0.0,0.1034,0.0833,0.0417,0.0388,0.0331,0.0286,0.0039,0.0092,0.0385,0.0,0.9737,0.6444,0.0034,0.0,0.1034,0.0833,0.0417,0.0386,0.0308,0.0279,0.0039,0.0088,reg oper spec account,block of flats,0.0235,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1368.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,3.0\n372888,0,Cash loans,F,N,N,2,135000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.02461,-13656,-329,-1784.0,-4943,,1,1,0,1,0,0,,4.0,2,2,TUESDAY,17,0,0,0,0,0,0,Kindergarten,,0.6639147955190343,0.2707073872651806,0.1031,0.0939,0.9816,,,0.0,0.2069,0.1667,,0.0977,,0.0911,,0.0,0.105,0.0974,0.9816,,,0.0,0.2069,0.1667,,0.1,,0.0949,,0.0,0.1041,0.0939,0.9816,,,0.0,0.2069,0.1667,,0.0994,,0.0927,,0.0,,block of flats,0.0776,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2226.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,0.0\n177704,1,Cash loans,F,N,Y,1,157500.0,545040.0,20677.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-14989,-1793,-84.0,-3346,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,9,0,0,0,0,0,0,Trade: type 7,0.6729958364810534,0.14370304114225504,0.6479768603302221,0.1237,0.1018,0.9786,0.7076,0.0417,0.0,0.2069,0.1667,0.2083,0.0938,0.1009,0.1053,0.0,0.0,0.1261,0.1056,0.9786,0.7190000000000001,0.042,0.0,0.2069,0.1667,0.2083,0.0959,0.1102,0.1097,0.0,0.0,0.1249,0.1018,0.9786,0.7115,0.0419,0.0,0.2069,0.1667,0.2083,0.0954,0.1026,0.1072,0.0,0.0,reg oper account,block of flats,0.0881,\"Stone, brick\",No,7.0,0.0,6.0,0.0,-1217.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n196331,0,Cash loans,F,N,Y,0,90000.0,99000.0,10143.0,99000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-23648,365243,-7609.0,-33,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.16318703546427088,,0.099,0.0896,0.9767,,,0.0,0.2069,0.1667,,0.0452,,0.0758,,0.146,0.1008,0.09300000000000001,0.9767,,,0.0,0.2069,0.1667,,0.0462,,0.079,,0.1546,0.0999,0.0896,0.9767,,,0.0,0.2069,0.1667,,0.046,,0.0772,,0.1491,,block of flats,0.0977,\"Stone, brick\",No,6.0,0.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n424688,0,Cash loans,F,N,Y,0,135000.0,526491.0,19039.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.04622,-19565,-6046,-7834.0,-3103,,1,1,0,1,1,0,Laborers,1.0,1,1,FRIDAY,11,0,0,0,0,0,0,Electricity,,0.6357725474797173,0.6545292802242897,0.0124,,0.9821,,,,0.069,0.0417,,,,0.0065,,0.0403,0.0126,,0.9821,,,,0.069,0.0417,,,,0.0068,,0.0427,0.0125,,0.9821,,,,0.069,0.0417,,,,0.0067,,0.0411,,block of flats,0.0147,,No,0.0,0.0,0.0,0.0,-359.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n305128,0,Cash loans,M,N,Y,1,225000.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-11373,-974,-768.0,-2317,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,9,1,1,0,0,0,0,Business Entity Type 3,0.17149163335737067,0.563838387721513,0.4578995512067301,0.0165,,0.0,,,0.0,0.069,0.0417,,,,0.0097,,,0.0168,,0.0005,,,0.0,0.069,0.0417,,,,0.0101,,,0.0167,,0.0,,,0.0,0.069,0.0417,,,,0.0099,,,,block of flats,0.0132,Wooden,No,0.0,0.0,0.0,0.0,-441.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n393837,0,Cash loans,M,Y,N,2,157500.0,720000.0,39060.0,720000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006305,-13619,-2294,-2926.0,-4422,24.0,1,1,1,1,0,0,Laborers,4.0,3,3,MONDAY,5,0,0,0,0,0,0,Business Entity Type 3,,0.4660011368073969,0.7751552674785404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-772.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n342264,0,Cash loans,F,Y,N,0,202500.0,900000.0,48082.5,900000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.014519999999999996,-13164,-192,-4671.0,-3422,1.0,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,10,1,1,0,1,1,0,Kindergarten,0.3672804499637982,0.5101907654651776,,,,0.9886,,,0.16,0.1379,0.3333,0.375,0.1583,0.121,0.1519,,,,,0.9886,,,0.1611,0.1379,0.3333,0.375,0.1619,0.1322,0.1583,,,,,0.9886,,,0.16,0.1379,0.3333,0.375,0.1611,0.1231,0.1546,,,,block of flats,0.1195,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-184.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n272731,0,Cash loans,F,N,N,0,94500.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-14741,-3458,-6921.0,-4816,,1,1,1,1,0,0,,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3094151291020128,0.4898020707914436,0.4614823912998385,0.3134,0.4703,0.9851,0.7959999999999999,0.0504,0.0,0.1379,0.125,0.1667,0.098,0.2555,0.2467,0.0,0.1267,0.3193,0.488,0.9851,0.804,0.0508,0.0,0.1379,0.125,0.1667,0.1002,0.2792,0.257,0.0,0.1341,0.3164,0.4703,0.9851,0.7987,0.0507,0.0,0.1379,0.125,0.1667,0.0997,0.2599,0.2511,0.0,0.1293,reg oper account,block of flats,0.2777,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-2018.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,1.0\n262287,0,Cash loans,F,Y,Y,1,360000.0,1350000.0,51552.0,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-12005,-2666,-2916.0,-3090,4.0,1,1,0,1,0,0,Managers,3.0,2,2,SATURDAY,8,0,0,0,0,0,0,Other,0.517197722346622,0.6665688284885032,0.4812493411434029,0.0969,0.0969,0.9836,,,,0.2069,0.1667,,0.0628,,,,0.0225,0.0987,0.1005,0.9836,,,,0.2069,0.1667,,0.0642,,,,0.0238,0.0978,0.0969,0.9836,,,,0.2069,0.1667,,0.0639,,,,0.0229,,block of flats,0.0682,Panel,No,0.0,0.0,0.0,0.0,-1304.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n379637,0,Cash loans,F,N,Y,0,90000.0,277969.5,10606.5,229500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.031329,-23344,-466,-244.0,-5001,,1,1,0,1,0,0,Medicine staff,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,Government,0.6662493682798052,0.4589071890444127,0.33125086459090186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-408.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n335016,0,Cash loans,F,N,N,0,135000.0,239850.0,23719.5,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,With parents,0.006629,-13844,-1901,-2836.0,-4672,,1,1,1,1,1,0,Core staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Kindergarten,,0.4888075513710618,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-211.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n213890,0,Revolving loans,M,N,Y,0,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.031329,-11361,-2977,-5886.0,-2863,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,TUESDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.5945323314559543,0.7001838506835805,0.0546,,0.9752,,,,0.1379,0.125,,,,0.0518,,0.0133,0.0557,,0.9752,,,,0.1379,0.125,,,,0.054000000000000006,,0.0141,0.0552,,0.9752,,,,0.1379,0.125,,,,0.0528,,0.0136,,block of flats,0.0437,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2804.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n417326,0,Cash loans,F,N,Y,0,112500.0,781920.0,25542.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006207,-21212,365243,-13119.0,-2194,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.6954137097746577,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-952.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n372051,0,Cash loans,M,Y,Y,0,144000.0,127350.0,15241.5,112500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.002042,-9560,-412,-2177.0,-2203,7.0,1,1,0,1,0,0,Core staff,2.0,3,3,MONDAY,9,0,0,0,0,1,1,Government,0.26127935779857586,0.35925782931914896,0.13342925446731707,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-281.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n215160,0,Cash loans,F,N,N,0,135000.0,254700.0,16582.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-17249,-7978,-9136.0,-803,,1,1,1,1,0,0,,2.0,3,3,MONDAY,15,0,0,0,0,1,1,Business Entity Type 1,,0.3359519577721808,0.3280631605201915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-783.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n417551,0,Cash loans,F,N,Y,0,103500.0,1303812.0,35982.0,1138500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008068,-16122,-9302,-8532.0,-4776,,1,1,0,1,1,0,Cooking staff,2.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.5056106828986154,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-916.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,0.0\n445210,0,Cash loans,F,N,N,0,90000.0,675000.0,22306.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00733,-13130,-705,-6999.0,-4064,,1,1,1,1,1,0,Sales staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.4888580401724874,0.6526360945733339,0.6658549219640212,,,0.9876,0.83,,0.0,0.1379,0.1667,0.0417,,0.0336,0.0532,,,,,0.9876,0.8367,,0.0,0.1379,0.1667,0.0417,,0.0367,0.0554,,,,,0.9876,0.8323,,0.0,0.1379,0.1667,0.0417,,0.0342,0.0541,,,reg oper account,block of flats,0.078,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1084.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n205357,0,Revolving loans,F,N,Y,0,157500.0,157500.0,7875.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-19572,-1641,-8185.0,-3104,,1,1,0,1,0,0,Cooking staff,2.0,2,2,THURSDAY,8,0,1,1,0,1,1,Business Entity Type 3,,0.4294444928778424,0.6594055320683344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-224.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n318554,0,Cash loans,F,Y,Y,0,126000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-15872,-6301,-5435.0,-2164,13.0,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 2,,0.2622583692422573,0.4311917977993083,0.0199,0.0,0.9811,0.7416,0.0,0.0,0.0572,0.0417,0.0833,0.0052,0.0162,0.0126,0.0,0.0095,0.0126,0.0,0.9806,0.7452,0.0,0.0,0.069,0.0417,0.0833,0.0,0.011000000000000001,0.0103,0.0,0.0032,0.0229,0.0,0.9811,0.7451,0.0,0.0,0.069,0.0417,0.0833,0.0,0.0188,0.0139,0.0,0.0121,reg oper account,block of flats,0.0084,Wooden,Yes,0.0,0.0,0.0,0.0,-402.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n231541,0,Cash loans,M,N,Y,1,121500.0,302206.5,16524.0,229500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009334,-16175,-987,-5577.0,-4038,,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Other,,0.6572911920664802,0.3185955240537633,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n252179,0,Cash loans,M,Y,Y,0,211500.0,746280.0,59094.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.02461,-19854,365243,-2307.0,-3273,17.0,1,0,0,1,0,1,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.5766563079937084,0.633031641417419,0.099,0.0599,0.9965,0.9524,0.0247,0.08,0.069,0.375,0.4167,0.0814,0.0807,0.0975,0.0,0.0,0.084,0.0423,0.995,0.9347,0.0183,0.0403,0.0345,0.375,0.4167,0.0503,0.0735,0.1157,0.0,0.0,0.0916,0.0556,0.995,0.9329,0.0225,0.08,0.069,0.375,0.4167,0.0522,0.0752,0.1131,0.0,0.0,not specified,block of flats,0.0768,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n293534,0,Revolving loans,F,N,Y,1,90000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-12297,-5231,-3739.0,-4058,,1,1,1,1,0,0,High skill tech staff,3.0,2,2,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 2,,0.5075456265471127,0.6925590674998008,0.0165,0.0324,0.9732,,,0.0,0.069,0.0417,,0.0323,,0.0128,,0.0,0.0168,0.0337,0.9732,,,0.0,0.069,0.0417,,0.0331,,0.0134,,0.0,0.0167,0.0324,0.9732,,,0.0,0.069,0.0417,,0.0329,,0.0131,,0.0,,block of flats,0.0109,Mixed,No,2.0,0.0,2.0,0.0,-1653.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n165972,0,Cash loans,M,Y,N,1,180000.0,284400.0,18643.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-12534,-1552,-4101.0,-4149,11.0,1,1,0,1,0,0,Drivers,3.0,2,2,SUNDAY,13,0,0,0,1,0,1,Business Entity Type 2,0.4491531142440453,0.469174932989163,0.7688075728291359,0.23199999999999998,0.1982,0.9846,0.7892,0.0384,0.24,0.2069,0.3333,0.375,0.157,0.1891,0.2392,0.0,0.0,0.2363,0.2056,0.9846,0.7975,0.0388,0.2417,0.2069,0.3333,0.375,0.1606,0.2066,0.2493,0.0,0.0,0.2342,0.1982,0.9846,0.792,0.0387,0.24,0.2069,0.3333,0.375,0.1597,0.1924,0.2435,0.0,0.0,reg oper account,block of flats,0.2092,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,5.0\n213457,0,Cash loans,F,N,Y,1,157500.0,1436850.0,42142.5,1125000.0,Unaccompanied,Commercial associate,Higher education,Married,Municipal apartment,0.032561,-15382,-791,-9522.0,-3968,,1,1,0,1,1,0,,3.0,1,1,WEDNESDAY,18,0,0,0,0,0,0,Other,,0.7835243945078931,0.5762088360175724,0.4392,0.2291,0.9791,,,0.46,0.3966,0.3333,,,,0.3295,,0.0053,0.188,0.1066,0.9791,,,0.2014,0.1724,0.3333,,,,0.4792,,0.0011,0.4434,0.2291,0.9791,,,0.46,0.3966,0.3333,,,,0.4682,,0.0054,,block of flats,0.5691,,No,0.0,0.0,0.0,0.0,-3299.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,7.0,0.0,4.0\n235717,0,Revolving loans,F,N,Y,0,157500.0,202500.0,10125.0,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-22798,365243,-5001.0,-5001,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,0.7138629461504199,0.6544927962670168,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-964.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n339998,0,Cash loans,M,N,Y,0,67500.0,291384.0,20853.0,270000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.025164,-21428,-1996,-2067.0,-2071,,1,1,1,1,1,0,,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Agriculture,0.6427310980419981,0.5516486329530234,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n218921,0,Cash loans,M,N,Y,0,202500.0,553806.0,22090.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-16666,-392,-5273.0,-217,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Medicine,,0.5628378626456231,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n196950,0,Cash loans,M,Y,Y,0,112500.0,134316.0,14233.5,126000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.035792000000000004,-8407,-632,-3128.0,-1074,13.0,1,1,0,1,0,0,Drivers,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Military,,0.05083640846593279,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n137322,0,Cash loans,F,N,N,0,180000.0,573408.0,19080.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-17361,-3503,-8008.0,-900,,1,1,0,1,0,0,Medicine staff,1.0,2,2,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.5856898151815452,0.5755775881801538,0.7435593141311444,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-321.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n214269,0,Cash loans,F,N,Y,0,270000.0,1350000.0,51552.0,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-18777,365243,-827.0,-2338,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,0.8111426971158142,0.6271981446469054,0.7047064232963289,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1597.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n133708,0,Cash loans,F,N,N,0,247500.0,528633.0,22527.0,472500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-19515,-710,-4994.0,-3028,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,1,1,Hotel,,0.47560065611817204,0.7180328113294772,0.0619,0.0773,0.9821,0.7552,,0.0,0.2069,0.1667,0.2083,0.0622,,0.0603,,0.0079,0.063,0.0802,0.9821,0.7648,,0.0,0.2069,0.1667,0.2083,0.0636,,0.0628,,0.0084,0.0625,0.0773,0.9821,0.7585,,0.0,0.2069,0.1667,0.2083,0.0633,,0.0614,,0.0081,,block of flats,0.0684,Panel,No,0.0,0.0,0.0,0.0,-2505.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n425894,0,Cash loans,M,Y,Y,0,157500.0,239787.0,25308.0,207000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-14023,-2782,-2954.0,-4659,7.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Other,0.4274727133695214,0.5659775412462638,0.21885908222837447,0.134,0.1056,0.9935,0.9048,0.0202,0.0,0.3017,0.1875,0.2221,0.0814,0.0916,0.1454,0.0656,0.0821,0.042,0.0,0.9916,0.8889,0.0049,0.0,0.0345,0.1667,0.2083,0.0,0.0331,0.0442,0.0156,0.0228,0.0994,0.1023,0.9940000000000001,0.9195,0.0055,0.0,0.2414,0.1875,0.2083,0.0792,0.0385,0.1329,0.0155,0.0688,reg oper account,block of flats,0.1945,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1834.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n363431,0,Cash loans,F,N,N,1,202500.0,1035832.5,33543.0,904500.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.006670999999999999,-10791,-201,-100.0,-3445,,1,1,0,1,0,0,Core staff,3.0,2,2,THURSDAY,13,0,0,0,0,0,0,Military,0.5529510865902993,0.2192392123766496,0.6545292802242897,0.1031,0.0795,0.9985,0.9796,,0.12,0.1034,0.3333,,0.1385,0.0841,0.1325,,0.0,0.105,0.0825,0.9985,0.9804,,0.1208,0.1034,0.3333,,0.1416,0.0918,0.138,,0.0,0.1041,0.0795,0.9985,0.9799,,0.12,0.1034,0.3333,,0.1409,0.0855,0.1349,,0.0,,block of flats,0.1042,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n386524,0,Cash loans,M,N,Y,0,56250.0,191880.0,19107.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-23660,-4960,-5803.0,-4700,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,1,1,Housing,,0.5223956932624529,0.32173528219668485,0.0742,0.0503,0.9806,0.7348,0.1033,0.08,0.069,0.3333,0.375,0.016,0.0605,0.0718,0.0,0.0,0.0756,0.0522,0.9806,0.7452,0.1042,0.0806,0.069,0.3333,0.375,0.0164,0.0661,0.0748,0.0,0.0,0.0749,0.0503,0.9806,0.7383,0.1039,0.08,0.069,0.3333,0.375,0.0163,0.0616,0.0731,0.0,0.0,reg oper account,block of flats,0.0565,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n155798,0,Cash loans,F,N,N,0,135000.0,474048.0,22806.0,360000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.031329,-22765,365243,-10249.0,-4179,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,XNA,,0.6445872952246072,0.7610263695502636,0.1412,0.0484,0.9856,0.8028,0.0334,0.08,0.0345,0.5417,0.5833,0.2073,0.1152,0.1334,0.0,0.0,0.1439,0.0502,0.9856,0.8105,0.0337,0.0806,0.0345,0.5417,0.5833,0.2121,0.1258,0.139,0.0,0.0,0.1426,0.0484,0.9856,0.8054,0.0336,0.08,0.0345,0.5417,0.5833,0.21100000000000002,0.1171,0.1358,0.0,0.0,reg oper account,block of flats,0.1232,Block,No,0.0,0.0,0.0,0.0,-1758.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n140162,0,Cash loans,F,N,Y,1,94500.0,720000.0,29452.5,720000.0,Other_B,Working,Higher education,Single / not married,House / apartment,0.026392,-10487,-3472,-3485.0,-3022,,1,1,0,1,1,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Other,0.8319279317878782,0.5998609632417345,0.6674577419214722,0.1031,0.1488,0.9826,,,0.0,0.2759,0.1667,,0.0876,,0.1074,,0.0,0.105,0.1544,0.9826,,,0.0,0.2759,0.1667,,0.0896,,0.1119,,0.0,0.1041,0.1488,0.9826,,,0.0,0.2759,0.1667,,0.0891,,0.1093,,0.0,,block of flats,0.0845,\"Stone, brick\",No,1.0,1.0,1.0,0.0,-2073.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n357682,0,Cash loans,F,N,Y,0,76500.0,526491.0,17527.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-17572,-4601,-129.0,-1115,,1,1,1,1,0,0,Cooking staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.6519963111143796,0.5691487713619409,0.1938,0.0,0.9831,0.7688,0.0223,0.0,0.4138,0.2083,0.2083,0.0,0.158,0.1764,0.0,0.0,0.1975,0.0,0.9831,0.7779,0.0225,0.0,0.4138,0.2083,0.2083,0.0,0.1726,0.1838,0.0,0.0,0.1957,0.0,0.9831,0.7719,0.0225,0.0,0.4138,0.2083,0.2083,0.0,0.1608,0.1795,0.0,0.0,reg oper account,block of flats,0.1509,Block,No,2.0,0.0,2.0,0.0,-3113.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n283741,0,Cash loans,F,Y,Y,0,225000.0,755190.0,30757.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.032561,-13204,-3298,-3084.0,-4047,16.0,1,1,1,1,1,0,Laborers,2.0,1,1,FRIDAY,10,0,0,0,0,0,0,Other,,0.7377487770045652,0.4543210601605785,0.0828,0.0892,0.9697,,,0.04,0.1131,0.1308,,0.08,,0.0942,,0.0441,0.0126,0.0,0.9762,,,0.0,0.1034,0.0833,,0.0416,,0.1934,,0.0052,0.0281,0.0696,0.9697,,,0.0,0.1034,0.0833,,0.0702,,0.0426,,0.0121,,block of flats,0.2589,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2864.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n332820,0,Cash loans,F,N,N,1,135000.0,288873.0,16713.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.010032,-16039,-4266,-10164.0,-2482,,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,4,0,0,0,0,0,0,Trade: type 7,,0.5634134965252379,0.6195277080511546,0.1606,0.1211,0.9836,,,0.1732,0.1493,0.3333,,0.1035,,0.1628,,0.0933,0.1513,0.0874,0.9841,,,0.1611,0.1379,0.3333,,0.0781,,0.1173,,0.0522,0.1499,0.1132,0.9841,,,0.16,0.1379,0.3333,,0.0981,,0.1554,,0.0911,,block of flats,0.1687,Panel,No,0.0,0.0,0.0,0.0,-1152.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n264496,0,Cash loans,F,N,N,0,90000.0,808650.0,26086.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.032561,-15958,-3302,-10015.0,-4156,,1,1,0,1,0,0,Managers,1.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Trade: type 7,,0.6614266894901198,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2179.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n435904,0,Cash loans,F,N,Y,0,171000.0,796500.0,26451.0,796500.0,Family,Commercial associate,Higher education,Single / not married,House / apartment,0.018209,-17015,-3091,-8263.0,-550,,1,1,0,1,0,0,High skill tech staff,1.0,3,3,MONDAY,14,0,0,0,0,0,0,Business Entity Type 2,0.65264852886502,0.4497346731116392,0.3859146722745145,0.2016,0.1008,0.9771,0.6872,0.0042,0.0,0.1241,0.1667,0.2083,0.0462,0.1378,0.0751,0.0,0.0,0.1712,0.0871,0.9772,0.6994,0.0039,0.0,0.1034,0.1667,0.2083,0.0516,0.1497,0.0662,0.0,0.0,0.1718,0.084,0.9771,0.6914,0.0042,0.0,0.1034,0.1667,0.2083,0.0513,0.1402,0.0647,0.0,0.0,reg oper spec account,specific housing,0.05,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1486.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n254099,0,Revolving loans,M,N,N,1,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019689,-12891,-1466,-1035.0,-4213,,1,1,0,1,0,0,,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3928215585903315,0.5797069065836626,0.475849908720221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-974.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n391488,0,Cash loans,M,Y,N,0,202500.0,1102171.5,46827.0,945000.0,Family,Commercial associate,Incomplete higher,Married,House / apartment,0.035792000000000004,-11677,-689,-5760.0,-4273,3.0,1,1,0,1,0,1,Laborers,2.0,2,2,MONDAY,17,0,0,0,1,0,1,Military,,0.7030658501461273,0.622922000268356,,,0.9896,,,0.2,0.2414,0.375,,,,,,,,,0.9896,,,0.2014,0.2414,0.375,,,,,,,,,0.9896,,,0.2,0.2414,0.375,,,,,,,,block of flats,0.1903,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,2.0,3.0\n354872,0,Cash loans,M,N,Y,1,157500.0,573408.0,31234.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-11950,-1811,-2226.0,-4532,,1,1,0,1,0,0,Drivers,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,,0.3128023368504581,0.7421816117614419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-463.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n115243,0,Cash loans,M,N,Y,2,99000.0,247500.0,17599.5,247500.0,\"Spouse, partner\",Working,Incomplete higher,Married,House / apartment,0.028663,-11454,-2196,-4189.0,-3552,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,14,0,0,0,1,1,0,Business Entity Type 3,0.15190778635387653,0.7763221001487723,0.7946285827840042,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2119.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n236857,0,Cash loans,F,N,N,0,45000.0,117000.0,6246.0,117000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018209,-21836,365243,-7033.0,-1890,,1,0,0,1,0,0,,1.0,3,3,FRIDAY,9,0,0,0,0,0,0,XNA,0.7354824382288107,0.0925691090373304,0.375711009574066,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n198914,1,Cash loans,F,N,N,0,157500.0,454500.0,30622.5,454500.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-20370,-9344,-1283.0,-3216,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Kindergarten,,0.2834842706107392,0.3656165070113335,0.1577,0.1215,0.9901,,,0.16,0.1379,0.3333,,,,,,0.0052,0.1607,0.1261,0.9901,,,0.1611,0.1379,0.3333,,,,,,0.0055,0.1593,0.1215,0.9901,,,0.16,0.1379,0.3333,,,,,,0.0053,,,0.1901,Mixed,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n239028,0,Cash loans,M,N,Y,0,90000.0,360000.0,9495.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007114,-11178,-2782,-4917.0,-3596,,1,1,0,1,1,1,Laborers,2.0,2,2,THURSDAY,7,0,0,0,0,1,1,Business Entity Type 2,,0.2908881216122062,0.4382813743111921,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-751.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n120356,0,Cash loans,F,N,N,0,382500.0,327024.0,23926.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006207,-17960,-342,-1594.0,-1336,,1,1,0,1,0,1,Sales staff,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Agriculture,0.7562141619039635,0.6456286351698771,0.7922644738669378,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n291735,0,Cash loans,F,N,Y,0,270000.0,1787283.0,49149.0,1597500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-17992,-2959,-5961.0,-1525,,1,1,0,1,0,0,,2.0,1,1,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.3746940449750675,,0.0825,0.0821,0.9757,0.6668,0.0889,0.0,0.1379,0.1667,0.2083,0.0,0.0672,0.0469,0.0,0.0547,0.084,0.0853,0.9762,0.6864,0.0896,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.0475,0.0,0.0,0.0833,0.0822,0.9762,0.6779999999999999,0.0894,0.0,0.1379,0.1667,0.2083,0.0,0.0684,0.0484,0.0,0.0836,reg oper account,block of flats,0.0552,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n328888,0,Cash loans,F,Y,Y,0,157500.0,1314117.0,36265.5,1147500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-14953,-5188,-2424.0,-4043,0.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.2628177936667717,0.7465733004169502,0.5136937663039473,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,4.0,0.0,-1058.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n218168,0,Cash loans,M,Y,N,0,270000.0,679500.0,36202.5,679500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.02461,-16318,-1512,-972.0,-4153,7.0,1,1,0,1,0,1,Managers,2.0,2,2,THURSDAY,15,0,0,0,1,0,1,Self-employed,0.7094079274447873,0.5880958445202938,0.4543210601605785,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1620.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n137314,0,Cash loans,F,N,Y,1,72000.0,244512.0,26073.0,216000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.02461,-14938,-411,-2724.0,-4833,,1,1,0,1,0,0,Managers,2.0,2,2,SATURDAY,12,0,0,0,0,1,1,Postal,,0.5108301095258806,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-408.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n184685,0,Cash loans,F,N,N,0,144000.0,857169.0,30496.5,715500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-19948,-4314,-276.0,-3434,,1,1,0,1,1,0,Sales staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6590279104992322,0.7165702448010511,0.0928,0.0997,0.9881,,,0.0,0.2069,0.1667,,0.0437,,0.0872,,0.0,0.0945,0.1034,0.9881,,,0.0,0.2069,0.1667,,0.0447,,0.0908,,0.0,0.0937,0.0997,0.9881,,,0.0,0.2069,0.1667,,0.0445,,0.0887,,0.0,,block of flats,0.0899,Panel,No,0.0,0.0,0.0,0.0,-1203.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n193174,0,Cash loans,F,N,Y,0,90000.0,555273.0,18040.5,463500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.011703,-18694,-5247,-12823.0,-2226,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Other,0.8012261888132587,0.7228666727641787,0.5919766183185521,0.0629,0.0625,0.9757,,,0.0,0.1379,0.125,,0.0535,,0.049,,0.0052,0.0641,0.0649,0.9757,,,0.0,0.1379,0.125,,0.0547,,0.051,,0.0055,0.0635,0.0625,0.9757,,,0.0,0.1379,0.125,,0.0545,,0.0499,,0.0053,,block of flats,0.0427,\"Stone, brick\",No,9.0,0.0,9.0,0.0,-2792.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n389785,0,Cash loans,F,Y,N,1,270000.0,1696500.0,64615.5,1696500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.015221,-12789,-1538,-2774.0,-4245,1.0,1,1,1,1,0,0,Managers,3.0,2,2,SUNDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.5674756393228018,0.20915469884100693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2182.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n260325,0,Cash loans,M,Y,Y,0,121500.0,90000.0,6961.5,90000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.008625,-10359,-742,-4958.0,-2988,5.0,1,1,1,1,0,0,Laborers,1.0,2,2,FRIDAY,17,0,0,0,0,0,0,Self-employed,,0.6155407881898891,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-600.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n356065,0,Cash loans,F,N,Y,0,37350.0,52767.0,5269.5,49500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-20304,365243,-2049.0,-3171,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,0.7909670460274212,0.3534597691847088,0.5673792367572691,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-21.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n343158,0,Cash loans,M,Y,Y,0,112500.0,477000.0,15516.0,477000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.00733,-10365,-565,-10249.0,-2648,12.0,1,1,0,1,0,0,Drivers,1.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.31394935463214113,0.5526165686366717,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1049.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n327511,0,Cash loans,F,Y,Y,1,112500.0,178213.5,14418.0,144000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010032,-9094,-141,-9094.0,-1440,65.0,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,7,0,0,0,0,0,0,Business Entity Type 2,0.26805026216628186,0.05226148902628385,,0.3701,0.0852,0.9836,0.7756,0.0921,0.4,0.3448,0.3333,0.375,0.2836,0.3009,0.3961,0.0039,0.0077,0.3771,0.0884,0.9836,0.7844,0.0929,0.4028,0.3448,0.3333,0.375,0.29,0.3287,0.4127,0.0039,0.0081,0.3737,0.0852,0.9836,0.7786,0.0927,0.4,0.3448,0.3333,0.375,0.2885,0.3061,0.4032,0.0039,0.0078,,block of flats,0.3636,Panel,No,2.0,0.0,2.0,0.0,-908.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n354061,0,Cash loans,F,N,Y,0,351000.0,878247.0,61254.0,792000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23212,365243,-5265.0,-2826,,1,0,0,1,0,0,,2.0,2,2,MONDAY,15,0,0,0,1,0,0,XNA,,0.3106956606536522,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1526.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n104389,0,Cash loans,M,N,Y,2,202500.0,1006920.0,51543.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-12172,-421,-6279.0,-4508,,1,1,0,1,0,0,Drivers,4.0,3,3,THURSDAY,13,0,1,1,0,1,1,Industry: type 7,0.3680901681792246,0.2689291115461607,0.7238369900414456,0.132,0.0704,0.9781,,,0.0,0.069,0.1667,,0.0,,0.0549,,0.0406,0.1345,0.073,0.9782,,,0.0,0.069,0.1667,,0.0,,0.0572,,0.0429,0.1332,0.0704,0.9781,,,0.0,0.069,0.1667,,0.0,,0.0559,,0.0414,,block of flats,0.0716,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n167213,1,Cash loans,M,Y,Y,1,247500.0,364896.0,19233.0,315000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.014464,-12168,-4493,-6228.0,-1412,13.0,1,1,0,1,0,0,Accountants,3.0,2,2,TUESDAY,9,0,0,0,1,1,0,Business Entity Type 3,0.35503832886789993,0.34459636201328764,0.4311917977993083,0.0124,0.0,0.9632,0.4968,0.0,0.0,0.1034,0.0417,0.0,0.0,0.0,0.01,0.0,0.018000000000000002,0.0126,0.0,0.9633,0.5165,0.0,0.0,0.1034,0.0417,0.0,0.0,0.0,0.0104,0.0,0.0191,0.0125,0.0,0.9632,0.5035,0.0,0.0,0.1034,0.0417,0.0,0.0,0.0,0.0101,0.0,0.0184,org spec account,block of flats,0.0136,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1315.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n180749,0,Cash loans,M,N,Y,0,157500.0,761949.0,46746.0,697500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006233,-18657,-11033,-7810.0,-2195,,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Industry: type 9,,0.5898745390063802,0.8625103025687644,0.0742,0.0062,0.9891,0.8504,0.0166,0.04,0.0345,0.3333,0.375,0.0099,0.0605,0.0731,0.0,0.0,0.0756,0.0065,0.9891,0.8563,0.0167,0.0403,0.0345,0.3333,0.375,0.0101,0.0661,0.0761,0.0,0.0,0.0749,0.0062,0.9891,0.8524,0.0167,0.04,0.0345,0.3333,0.375,0.01,0.0616,0.0744,0.0,0.0,reg oper account,block of flats,0.0575,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n111329,0,Cash loans,F,N,Y,0,96750.0,239850.0,23494.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-23704,365243,-6842.0,-4785,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.6824622502232589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-3240.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,4.0\n192111,0,Cash loans,F,N,Y,0,90000.0,473661.0,23166.0,333000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.009175,-12491,-4722,-6602.0,-4439,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.7253741715278126,0.363945238612397,0.0753,0.0539,0.9856,,,0.08,0.069,0.3333,,,,0.0768,,0.0,0.0767,0.0559,0.9856,,,0.0806,0.069,0.3333,,,,0.08,,0.0,0.076,0.0539,0.9856,,,0.08,0.069,0.3333,,,,0.0782,,0.0,,block of flats,0.0604,Panel,No,0.0,0.0,0.0,0.0,-828.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n170479,0,Cash loans,F,N,Y,0,112500.0,177903.0,12780.0,148500.0,Family,Working,Higher education,Separated,House / apartment,0.018029,-20535,-153,-6945.0,-3872,,1,1,0,1,0,0,,1.0,3,3,FRIDAY,12,0,0,0,0,1,1,Government,0.6855355101091855,0.5146817433121449,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-706.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n369450,0,Cash loans,F,N,N,0,135000.0,407520.0,32197.5,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.04622,-14838,-1681,-56.0,-4992,,1,1,0,1,0,0,,1.0,1,1,THURSDAY,13,0,0,0,0,0,0,Kindergarten,0.6075211022919826,0.6994099827175155,0.6986675550534175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1327.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n364003,0,Cash loans,F,N,Y,0,157500.0,634360.5,28071.0,567000.0,Family,State servant,Secondary / secondary special,Widow,House / apartment,0.007305,-22486,-1115,-1161.0,-4745,,1,1,0,1,1,0,Cleaning staff,1.0,3,3,TUESDAY,11,0,0,0,0,0,0,Other,,0.4192315418816256,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-145.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n442713,1,Cash loans,M,N,N,1,90000.0,254700.0,17149.5,225000.0,Family,Working,Secondary / secondary special,Married,With parents,0.00702,-10488,-1663,-732.0,-3024,,1,1,1,1,1,0,,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 2,,0.17150094008285136,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-691.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n194841,0,Cash loans,M,Y,Y,2,135000.0,284400.0,19003.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-13771,-525,-2557.0,-4204,11.0,1,1,1,1,0,0,Drivers,4.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.5964549603995615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-256.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n147064,1,Cash loans,M,Y,Y,0,180000.0,499261.5,24282.0,351000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.008068,-8728,-576,-852.0,-1381,22.0,1,1,1,1,0,0,Drivers,2.0,3,3,WEDNESDAY,7,0,0,0,0,1,1,Security,,0.3793164812299557,0.2392262794694045,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-314.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n191558,0,Cash loans,F,N,N,0,56250.0,518562.0,22972.5,463500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-21959,365243,-14218.0,-4698,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,0.7397235435596874,0.714462227408255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-474.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n354254,0,Cash loans,F,N,Y,0,135000.0,521280.0,23089.5,450000.0,Family,Working,Secondary / secondary special,Widow,House / apartment,0.00823,-23653,-115,-11997.0,-5171,,1,1,0,1,0,0,Core staff,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Postal,0.8269144624795763,0.3504622018504321,0.4400578303966329,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1078.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n121343,0,Cash loans,F,N,N,0,135000.0,152820.0,16587.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.007120000000000001,-21386,365243,-3790.0,-4934,,1,0,0,1,0,0,,1.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.5165823611113012,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-844.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n395879,0,Cash loans,M,N,Y,0,112500.0,343800.0,16852.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-17047,-6705,-6219.0,-541,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,1,1,Business Entity Type 2,,0.3681745209517913,0.6658549219640212,0.0928,0.1081,0.9836,0.7756,0.0,0.0,0.2069,0.1667,0.2083,0.0,0.0756,0.0845,0.0,0.0,0.0945,0.1122,0.9836,0.7844,0.0,0.0,0.2069,0.1667,0.2083,0.0,0.0826,0.08800000000000001,0.0,0.0,0.0937,0.1081,0.9836,0.7786,0.0,0.0,0.2069,0.1667,0.2083,0.0,0.077,0.086,0.0,0.0,reg oper account,block of flats,0.0664,Panel,No,1.0,0.0,1.0,0.0,-2524.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n188272,0,Revolving loans,F,N,N,2,81000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-16338,-564,-4983.0,-5072,,1,1,0,0,1,0,Core staff,4.0,2,2,MONDAY,15,0,0,0,0,0,0,Trade: type 7,,0.7395302710709482,0.7136313997323308,0.099,0.0961,0.9925,,,0.12,0.1034,0.3333,,0.0616,,0.0669,,0.2149,0.1008,0.0997,0.9926,,,0.1208,0.1034,0.3333,,0.063,,0.0697,,0.2275,0.0999,0.0961,0.9925,,,0.12,0.1034,0.3333,,0.0627,,0.0681,,0.2194,,block of flats,0.0993,\"Stone, brick\",No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n216332,0,Cash loans,F,N,Y,0,112500.0,284400.0,19134.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.006629,-23822,365243,-4688.0,-2674,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,5,0,0,0,0,0,0,XNA,,0.7829526560199854,0.7490217048463391,,,0.9846,,,,,,,,,0.0551,,,,,0.9846,,,,,,,,,0.0574,,,,,0.9846,,,,,,,,,0.0561,,,,,0.0433,,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n307582,0,Cash loans,F,N,N,0,112500.0,254700.0,15579.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-15609,-3796,-7141.0,-4889,,1,1,0,1,1,0,Core staff,2.0,2,2,SATURDAY,16,0,0,0,0,1,1,School,,0.4715446710230465,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-836.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n437596,0,Cash loans,F,N,Y,0,67500.0,810000.0,23814.0,810000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-13519,-3000,-4914.0,-4931,,1,1,0,1,0,0,Medicine staff,2.0,2,2,MONDAY,10,0,0,0,0,1,1,Medicine,,0.34112290759888264,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1813.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n319617,1,Cash loans,F,N,Y,0,135000.0,382500.0,18733.5,382500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-13672,-2575,-2505.0,-4892,,1,1,0,1,1,0,,2.0,3,3,SUNDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.6071205696816279,0.4762224356180166,0.2636468134452008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1827.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,8.0\n442205,0,Cash loans,F,N,Y,0,292500.0,370629.0,13441.5,306000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.04622,-23274,-2241,-12661.0,-3862,,1,1,0,1,0,0,,1.0,1,1,TUESDAY,15,0,0,0,0,0,0,School,,0.7558497112459993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1552.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n158214,0,Cash loans,F,N,N,1,119250.0,943425.0,26073.0,787500.0,Unaccompanied,Working,Secondary / secondary special,Widow,Municipal apartment,0.007114,-16045,-345,-8798.0,-3555,,1,1,0,1,1,0,Laborers,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,School,,0.2377621363958828,0.36896873825284665,0.0742,0.0522,0.9851,,,0.08,0.069,0.3333,,,,0.0723,,0.0,0.0756,0.0542,0.9851,,,0.0806,0.069,0.3333,,,,0.0753,,0.0,0.0749,0.0522,0.9851,,,0.08,0.069,0.3333,,,,0.0736,,0.0,,block of flats,0.0569,Panel,No,3.0,0.0,3.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n448034,0,Cash loans,M,N,N,0,135000.0,450000.0,32877.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.016612000000000002,-19156,-141,-841.0,-721,,1,1,0,1,0,1,High skill tech staff,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Security,0.4464252658635807,0.5123053801997169,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,2.0,5.0\n333018,0,Cash loans,M,Y,Y,0,247500.0,675000.0,21906.0,675000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Civil marriage,With parents,0.022625,-14083,-2233,-5765.0,-4507,2.0,1,1,0,1,1,0,Drivers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3227593024755941,0.7599423591733702,0.524496446363472,0.0,,0.9742,0.6464,,,0.0345,0.125,,,,,,,0.0,,0.9742,0.6602,,,0.0345,0.125,,,,,,,0.0,,0.9742,0.6511,,,0.0345,0.125,,,,,,,reg oper account,block of flats,0.0,,No,9.0,0.0,9.0,0.0,-1534.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n430458,0,Cash loans,M,Y,Y,2,67500.0,156384.0,16551.0,135000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.006296,-13248,-995,-782.0,-4834,8.0,1,1,0,1,0,0,Drivers,4.0,3,3,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.7498976160691879,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-749.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n440150,0,Cash loans,F,N,Y,0,225000.0,1129500.0,44923.5,1129500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018801,-13115,-2543,-3768.0,-27,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,,0.4822747052193835,,0.0211,0.0205,0.9672,0.5512,0.0114,0.0,0.069,0.0625,0.1042,0.0262,0.0172,0.0197,0.0,0.0,0.0084,0.0,0.9657,0.5492,0.0009,0.0,0.0345,0.0417,0.0833,0.0226,0.0073,0.0078,0.0,0.0,0.0213,0.0205,0.9672,0.5572,0.0115,0.0,0.069,0.0625,0.1042,0.0267,0.0175,0.0201,0.0,0.0,reg oper account,block of flats,0.0251,Wooden,Yes,1.0,1.0,1.0,1.0,-964.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n311611,0,Cash loans,M,N,N,0,90000.0,328405.5,21361.5,283500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.022625,-8907,-289,-1648.0,-1572,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.3717926034429808,0.6825143755019625,0.20092608771597087,0.1485,0.0876,0.9866,0.8164,0.0561,0.16,0.1379,0.3333,0.375,,0.121,0.1512,0.0,0.0,0.1513,0.0909,0.9866,0.8236,0.0566,0.1611,0.1379,0.3333,0.375,,0.1322,0.1575,0.0,0.0,0.1499,0.0876,0.9866,0.8189,0.0565,0.16,0.1379,0.3333,0.375,,0.1231,0.1539,0.0,0.0,,block of flats,0.1496,Panel,No,0.0,0.0,0.0,0.0,-479.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n377364,0,Cash loans,F,N,Y,1,67500.0,167895.0,16069.5,157500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020246,-18468,-3538,-2334.0,-2021,,1,1,0,1,0,0,Sales staff,3.0,3,3,MONDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.7669614593346636,0.3287948083359833,0.6986675550534175,0.0856,0.071,0.9861,0.8096,0.066,0.12,0.1034,0.3333,0.375,0.0197,0.0698,0.1058,0.0,0.0,0.0872,0.0737,0.9861,0.8171,0.0666,0.1208,0.1034,0.3333,0.375,0.0202,0.0762,0.1102,0.0,0.0,0.0864,0.071,0.9861,0.8121,0.0664,0.12,0.1034,0.3333,0.375,0.0201,0.071,0.1077,0.0,0.0,reg oper account,block of flats,0.1193,Panel,No,1.0,0.0,1.0,0.0,-2352.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n171831,0,Cash loans,F,N,Y,1,112500.0,608076.0,28476.0,427500.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-15807,-354,-1211.0,-1205,,1,1,0,1,0,0,Cooking staff,3.0,3,3,THURSDAY,9,0,0,0,0,0,0,Restaurant,,0.3542097255361397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-322.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n306249,0,Cash loans,F,N,Y,0,67500.0,286704.0,20520.0,247500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.008019,-13818,-5796,-7913.0,-4844,,1,1,0,1,1,0,Laborers,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Industry: type 11,0.4163827690783163,0.4543170551891342,0.5638350489514956,0.0464,0.1237,0.9826,0.762,0.053,0.0,0.2069,0.1667,0.2083,0.0456,0.0756,0.0921,0.0,0.0,0.0,0.1284,0.9826,0.7713,0.0535,0.0,0.2069,0.1667,0.2083,0.0467,0.0826,0.0959,0.0,0.0,0.0468,0.1237,0.9826,0.7652,0.0533,0.0,0.2069,0.1667,0.2083,0.0464,0.077,0.0937,0.0,0.0,reg oper account,block of flats,0.07400000000000001,Panel,No,0.0,0.0,0.0,0.0,-342.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n331728,0,Cash loans,M,Y,Y,0,202500.0,1432647.0,41188.5,1251000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.005313,-23303,-551,-642.0,-4634,1.0,1,1,0,1,1,0,Security staff,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Security,,0.7022480357725048,0.6894791426446275,0.0887,0.1107,0.9841,0.7824,0.0118,0.0,0.2069,0.1667,0.2083,0.1074,0.0723,0.0514,0.0,0.0,0.0903,0.1149,0.9841,0.7909,0.0119,0.0,0.2069,0.1667,0.2083,0.1099,0.079,0.0536,0.0,0.0,0.0895,0.1107,0.9841,0.7853,0.0119,0.0,0.2069,0.1667,0.2083,0.1093,0.0735,0.0524,0.0,0.0,reg oper spec account,block of flats,0.0711,Panel,No,2.0,0.0,2.0,0.0,-631.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n168357,0,Cash loans,M,N,N,0,135000.0,773680.5,37350.0,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018634,-12739,-1362,-1588.0,-1824,,1,1,1,1,1,0,Drivers,2.0,2,2,FRIDAY,14,0,0,0,1,1,0,Business Entity Type 1,,0.3679405554386685,0.5744466170995097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-449.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n431429,0,Cash loans,F,N,Y,0,135000.0,208854.0,19656.0,184500.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.031329,-22376,-10118,-8550.0,-4693,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Security Ministries,,0.3321381115522129,0.8633633824101478,0.0918,0.0741,0.9881,,,0.0,0.2069,0.1667,,0.0947,,0.081,,0.0264,0.0935,0.0769,0.9881,,,0.0,0.2069,0.1667,,0.0968,,0.0844,,0.0279,0.0926,0.0741,0.9881,,,0.0,0.2069,0.1667,,0.0963,,0.0825,,0.0269,,block of flats,0.0637,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n395136,0,Cash loans,M,Y,N,0,112500.0,278613.0,25911.0,252000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-17015,-2505,-1010.0,-568,6.0,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Industry: type 4,,0.26607969974891643,0.5902333386185574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-330.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n394259,0,Cash loans,M,N,N,1,112500.0,302341.5,11200.5,261000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-9131,-216,-8480.0,-1804,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.5251938517438115,0.0969483170508572,0.0619,0.0511,0.9737,0.6396,0.0893,0.0,0.1034,0.1667,0.2083,0.034,0.0496,0.0447,0.0039,0.0029,0.063,0.0531,0.9737,0.6537,0.0902,0.0,0.1034,0.1667,0.2083,0.0348,0.0542,0.0466,0.0039,0.0031,0.0625,0.0511,0.9737,0.6444,0.0899,0.0,0.1034,0.1667,0.2083,0.0346,0.0504,0.0455,0.0039,0.003,reg oper account,block of flats,0.0488,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-358.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n205310,0,Cash loans,F,N,Y,0,85500.0,521280.0,25209.0,450000.0,Unaccompanied,Pensioner,Lower secondary,Separated,Municipal apartment,0.020713,-22732,365243,-8580.0,-4103,,1,0,0,1,0,0,,1.0,3,1,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.1769814776064333,,0.1608,0.0,0.9712,0.6056,0.0307,0.0,0.2759,0.1667,0.0,0.1106,0.1294,0.2048,0.0077,0.0455,0.1639,0.0,0.9712,0.621,0.031,0.0,0.2759,0.1667,0.0,0.1131,0.1414,0.2134,0.0078,0.0481,0.1624,0.0,0.9712,0.6109,0.0309,0.0,0.2759,0.1667,0.0,0.1125,0.1317,0.2085,0.0078,0.0464,reg oper account,block of flats,0.1878,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1588.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n273238,1,Cash loans,M,N,Y,1,157500.0,189000.0,14971.5,189000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018209,-13334,-794,-7182.0,-1452,,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,11,0,0,0,0,1,1,Construction,0.2335469456115669,0.3452040864864309,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-302.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n391491,1,Cash loans,M,Y,N,0,117000.0,450000.0,22018.5,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.026392,-8522,-1175,-3354.0,-1188,8.0,1,1,0,1,1,0,Drivers,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6210319472019753,0.22888341670067305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-473.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n425805,0,Cash loans,M,Y,Y,1,90000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018634,-14384,-3412,-1129.0,-4104,1.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.7498648043075455,0.7530673920730478,0.0505,0.0647,0.9727,0.626,,0.0,0.1034,0.125,0.1667,0.0338,0.0412,0.0402,0.0039,0.0122,0.0515,0.0672,0.9727,0.6406,,0.0,0.1034,0.125,0.1667,0.0346,0.045,0.0418,0.0039,0.0129,0.051,0.0647,0.9727,0.631,,0.0,0.1034,0.125,0.1667,0.0344,0.0419,0.0409,0.0039,0.0124,reg oper account,block of flats,0.0495,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1863.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n264305,1,Cash loans,M,N,N,0,40500.0,58500.0,5913.0,58500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.030755,-14771,-752,-8654.0,-3737,,1,1,1,1,1,0,Laborers,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,Industry: type 3,,0.4348804581663195,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n307819,0,Cash loans,M,Y,Y,0,157500.0,270000.0,16209.0,270000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.026392,-9670,-1109,-9596.0,-2024,4.0,1,1,0,1,0,0,Security staff,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Kindergarten,,0.5858533242981672,0.5298898341969072,0.5907,0.4832,0.9866,,,0.0,1.0,0.1667,,0.9829,,0.5219,,0.0006,0.6019,0.5015,0.9866,,,0.0,1.0,0.1667,,1.0,,0.5437,,0.0007,0.5964,0.4832,0.9866,,,0.0,1.0,0.1667,,1.0,,0.5312,,0.0007,,block of flats,0.539,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n223747,0,Revolving loans,F,N,Y,0,103500.0,270000.0,13500.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-20966,365243,-7825.0,-4446,,1,0,0,1,0,0,,2.0,1,1,THURSDAY,15,0,0,0,0,0,0,XNA,0.6738414927152839,0.7426561291262426,0.7476633896301825,0.0732,0.0646,0.9771,0.6872,0.0079,0.0,0.1379,0.1667,0.2083,0.0931,0.0588,0.0662,0.0039,0.0045,0.0746,0.0671,0.9772,0.6994,0.0079,0.0,0.1379,0.1667,0.2083,0.0952,0.0643,0.069,0.0039,0.0047,0.0739,0.0646,0.9771,0.6914,0.0079,0.0,0.1379,0.1667,0.2083,0.0947,0.0599,0.0674,0.0039,0.0045,reg oper account,block of flats,0.0573,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1867.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n393011,0,Cash loans,M,Y,Y,0,90000.0,675000.0,19737.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-18560,-9951,-9847.0,-2092,6.0,1,1,1,1,1,0,Managers,2.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,School,0.5853980690215786,0.530327746520748,0.5971924268337128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1754.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n315417,0,Cash loans,F,Y,Y,0,225000.0,1062643.5,38169.0,823500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16893,365243,-2184.0,-429,13.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,0.8089351331101561,0.6434342182019581,0.4668640059537032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-785.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n220809,0,Cash loans,F,N,Y,2,315000.0,539100.0,27522.0,450000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.072508,-12172,-1197,-4718.0,-1240,,1,1,0,1,1,1,High skill tech staff,4.0,1,1,THURSDAY,10,0,0,0,0,0,0,Government,0.6491636664003364,0.6991110705810547,0.6848276586890367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.512,,No,0.0,0.0,0.0,0.0,-938.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n134237,0,Cash loans,M,Y,N,0,270000.0,724500.0,20340.0,724500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.028663,-18601,-1616,-12606.0,-2156,7.0,1,1,1,1,1,0,,1.0,2,2,THURSDAY,16,0,1,1,0,1,1,Self-employed,0.4877382462315673,0.30691035552620755,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-722.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n162849,1,Cash loans,M,Y,Y,1,202500.0,781920.0,42547.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-12552,-682,-5989.0,-4522,9.0,1,1,0,1,0,0,,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6221540515324818,0.20504063672888448,0.09821859526287233,0.1485,0.109,0.9786,0.7076,0.0702,0.16,0.1379,0.3333,0.375,0.0415,0.121,0.1493,0.0,0.0,0.1513,0.1131,0.9786,0.7190000000000001,0.0709,0.1611,0.1379,0.3333,0.375,0.0424,0.1322,0.1556,0.0,0.0,0.1499,0.109,0.9786,0.7115,0.0707,0.16,0.1379,0.3333,0.375,0.0422,0.1231,0.152,0.0,0.0,reg oper spec account,block of flats,0.1176,Panel,No,1.0,0.0,1.0,0.0,-517.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n252821,0,Cash loans,F,N,Y,0,180000.0,879480.0,25843.5,630000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.011657,-22357,-6064,-2328.0,-4010,,1,1,0,1,0,0,,1.0,1,1,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 1,,0.7253915958409962,,0.0175,0.1049,0.9831,0.7688,,0.08,0.0345,0.3333,0.375,0.13,0.0143,0.0931,,0.0607,0.0179,0.1088,0.9831,0.7779,,0.0806,0.0345,0.3333,0.375,0.133,0.0156,0.09699999999999999,,0.0642,0.0177,0.1049,0.9831,0.7719,,0.08,0.0345,0.3333,0.375,0.1323,0.0145,0.0948,,0.062,reg oper account,block of flats,0.0951,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-2514.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n350588,0,Cash loans,M,Y,Y,1,225000.0,1125000.0,44748.0,1125000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-12384,-1009,-6482.0,-2957,3.0,1,1,0,1,1,0,Drivers,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Industry: type 9,,0.624468851236594,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3773.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n261712,0,Cash loans,F,N,Y,0,36900.0,97038.0,7029.0,81000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.020246,-21093,365243,-10624.0,-3864,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,11,0,0,0,0,0,0,XNA,,0.034333358255672485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n171732,0,Cash loans,F,N,Y,0,90000.0,318411.0,21406.5,288000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-20373,-3441,-3807.0,-3807,,1,1,0,1,0,0,Cooking staff,2.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,School,,0.5122015715677707,0.6109913280868294,0.0691,0.0843,0.9821,0.7552,0.0087,0.0,0.1379,0.1667,0.2083,0.0,0.0538,0.0629,0.0116,0.0223,0.0704,0.0875,0.9821,0.7648,0.0088,0.0,0.1379,0.1667,0.2083,0.0,0.0588,0.0656,0.0117,0.0236,0.0697,0.0843,0.9821,0.7585,0.0087,0.0,0.1379,0.1667,0.2083,0.0,0.0547,0.0641,0.0116,0.0228,reg oper account,block of flats,0.0591,\"Stone, brick\",No,2.0,2.0,2.0,1.0,-237.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n311018,0,Cash loans,F,N,N,0,292500.0,371862.0,29511.0,328500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.006207,-19282,-303,-2087.0,-2823,,1,1,0,1,0,1,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Other,0.5923403122726978,0.5864421355778132,0.5549467685334323,0.0784,,0.9742,,,0.0,0.1379,0.1667,,0.0383,,0.0579,,,0.0798,,0.9742,,,0.0,0.1379,0.1667,,0.0391,,0.0603,,,0.0791,,0.9742,,,0.0,0.1379,0.1667,,0.0389,,0.0589,,,,block of flats,0.0512,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n444621,0,Cash loans,M,N,Y,0,130500.0,486000.0,39096.0,486000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-20989,365243,-11297.0,-4528,,1,0,0,1,0,0,,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,XNA,,0.3978615907091013,0.5046813193144684,0.066,0.0,0.9727,0.626,0.0056,0.0,0.1379,0.125,0.1667,0.0142,0.0513,0.0483,0.0116,0.0083,0.0672,0.0,0.9727,0.6406,0.0057,0.0,0.1379,0.125,0.1667,0.0145,0.055999999999999994,0.0503,0.0117,0.0088,0.0666,0.0,0.9727,0.631,0.0056,0.0,0.1379,0.125,0.1667,0.0144,0.0522,0.0491,0.0116,0.0085,reg oper account,block of flats,0.0428,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2161.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n353335,0,Cash loans,F,N,Y,0,117000.0,188685.0,9760.5,157500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006008,-16998,-4490,-3952.0,-530,,1,1,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.5929499120905004,0.3296550543128238,0.1237,0.0853,0.9826,0.762,,0.0,0.069,0.1667,0.2083,0.0664,0.1009,0.0608,0.0,0.0,0.1261,0.0885,0.9826,0.7713,,0.0,0.069,0.1667,0.2083,0.0679,0.1102,0.0633,0.0,0.0,0.1249,0.0853,0.9826,0.7652,,0.0,0.069,0.1667,0.2083,0.0676,0.1026,0.0619,0.0,0.0,reg oper account,block of flats,0.0683,\"Stone, brick\",No,4.0,1.0,4.0,1.0,-1808.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n410145,0,Revolving loans,F,N,N,0,180000.0,1125000.0,56250.0,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.004849,-10080,-802,-4613.0,-2582,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Bank,0.5454074819188793,0.32747867849592993,0.3108182544189319,0.1113,0.1021,0.9811,0.6056,0.0091,0.08,0.1034,0.3542,0.1667,0.023,0.0328,0.1249,0.0347,0.064,0.0504,0.0987,0.9712,0.621,0.0092,0.0,0.069,0.125,0.1667,0.0115,0.0358,0.0608,0.035,0.0287,0.1124,0.1021,0.9811,0.6109,0.0092,0.08,0.1034,0.3542,0.1667,0.0234,0.0333,0.1271,0.0349,0.0654,reg oper spec account,block of flats,0.1725,\"Stone, brick\",No,,,,,-617.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n402598,0,Cash loans,F,N,Y,0,135000.0,450000.0,13774.5,450000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.015221,-22375,365243,-12328.0,-4000,,1,0,0,1,1,0,,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.07292950842931681,0.2866524828090694,0.0619,0.0621,0.9836,0.7756,,0.0,0.1379,0.1667,0.2083,0.0448,0.0504,0.054000000000000006,0.0,0.0,0.063,0.0644,0.9836,0.7844,,0.0,0.1379,0.1667,0.2083,0.0458,0.0551,0.0563,0.0,0.0,0.0625,0.0621,0.9836,0.7786,,0.0,0.1379,0.1667,0.2083,0.0456,0.0513,0.055,0.0,0.0,reg oper spec account,block of flats,0.0468,Panel,No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n292396,0,Cash loans,F,N,Y,2,67500.0,76410.0,8154.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-14695,-1105,-4898.0,-4904,,1,1,1,1,0,0,,4.0,2,2,TUESDAY,8,0,0,0,0,1,1,Self-employed,,0.34942253258743233,0.13342925446731707,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-785.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n177541,0,Cash loans,M,Y,Y,1,225000.0,743031.0,39717.0,688500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.00963,-16057,-2964,-123.0,-4655,6.0,1,1,0,1,1,0,Core staff,3.0,2,2,TUESDAY,13,0,1,1,0,0,0,Trade: type 7,0.5315004211825118,0.6173090918753992,0.7981372313187245,0.1691,0.1496,0.9965,0.966,0.0527,0.14,0.1207,0.3542,0.375,0.0836,0.158,0.1772,0.0193,0.0491,0.1471,0.1408,0.9955,0.9673,0.0532,0.1208,0.1034,0.3333,0.375,0.0855,0.1726,0.1501,0.0195,0.0427,0.1707,0.1496,0.9965,0.9665,0.053,0.14,0.1207,0.3542,0.375,0.0851,0.1608,0.1804,0.0194,0.0502,not specified,terraced house,0.2031,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n106919,0,Cash loans,M,N,Y,2,112500.0,704844.0,31176.0,630000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-15820,-568,-1259.0,-4991,,1,1,0,1,0,1,,4.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6429022389742077,0.7106743858828587,0.1041,0.0093,0.9856,0.8028,0.0209,0.0,0.2414,0.1667,0.0417,0.0,0.0849,0.0985,0.0,0.0136,0.1061,0.0096,0.9856,0.8105,0.0211,0.0,0.2414,0.1667,0.0417,0.0,0.0927,0.1026,0.0,0.0144,0.1051,0.0093,0.9856,0.8054,0.0211,0.0,0.2414,0.1667,0.0417,0.0,0.0864,0.1002,0.0,0.0139,reg oper spec account,block of flats,0.0804,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1595.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n129907,0,Cash loans,F,N,Y,0,115650.0,454500.0,16452.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.008473999999999999,-22969,365243,-12183.0,-4673,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.5911541707354161,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-872.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n153589,0,Cash loans,M,Y,N,0,126000.0,225000.0,16371.0,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-10576,-3094,-5101.0,-3261,19.0,1,1,0,1,1,1,Laborers,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.08228224412539219,0.2848721457479015,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-147.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n377325,0,Cash loans,F,N,N,0,40500.0,808650.0,23773.5,675000.0,Children,Pensioner,Lower secondary,Married,House / apartment,0.007120000000000001,-23681,365243,-5770.0,-4079,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.26525634018619443,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n302486,0,Cash loans,M,Y,N,0,135000.0,201474.0,20056.5,189000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-17854,-2898,-4493.0,-1401,14.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Construction,,0.4065102067356057,0.7209441499436497,0.1227,0.0979,0.9846,0.7892,0.0218,0.0,0.2069,0.1667,0.2083,0.0871,0.1,0.1034,0.0,0.0,0.125,0.1016,0.9846,0.7975,0.022000000000000002,0.0,0.2069,0.1667,0.2083,0.0891,0.1093,0.1077,0.0,0.0,0.1239,0.0979,0.9846,0.792,0.0219,0.0,0.2069,0.1667,0.2083,0.0887,0.1018,0.1052,0.0,0.0,reg oper spec account,block of flats,0.0932,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n229611,0,Cash loans,F,N,Y,2,202500.0,382500.0,27846.0,382500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.015221,-12899,-1338,-216.0,-5572,,1,1,0,1,0,0,Cooking staff,3.0,2,2,THURSDAY,13,0,0,0,0,0,0,Self-employed,0.2831358540430479,0.4781427729629338,0.38079968264891495,0.1495,0.067,0.9811,,,0.0,0.1034,0.1667,,0.1205,,0.0606,,0.0,0.1481,0.0677,0.9801,,,0.0,0.1034,0.1667,,0.1233,,0.063,,0.0,0.1509,0.067,0.9811,,,0.0,0.1034,0.1667,,0.1226,,0.0617,,0.0,,block of flats,0.0617,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-499.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n387244,0,Cash loans,F,Y,Y,0,157500.0,508495.5,20295.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.010147,-22252,365243,-273.0,-4356,26.0,1,0,0,1,1,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,0.7863987577335271,0.5900649749428191,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-273.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n366371,0,Cash loans,F,Y,Y,0,135000.0,247500.0,13554.0,247500.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.028663,-9983,-2453,-4036.0,-2643,7.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.4387796931419543,0.5865322921066672,0.3825018041447388,0.0907,0.0663,0.9771,,,0.0532,0.1148,0.2221,,,,0.0585,,0.0,0.063,0.0553,0.9767,,,0.0,0.1034,0.1667,,,,0.0381,,0.0,0.0625,0.0663,0.9767,,,0.0,0.1034,0.1667,,,,0.0374,,0.0,,block of flats,0.0541,\"Stone, brick\",No,3.0,2.0,3.0,2.0,-1560.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n261623,0,Cash loans,F,N,Y,0,112500.0,278613.0,30136.5,252000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-11958,-1340,-4460.0,-4476,,1,1,0,1,0,0,Secretaries,1.0,3,3,FRIDAY,14,0,0,0,1,1,0,Medicine,,0.5634403911392738,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,2.0,5.0,2.0,-293.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n351145,0,Cash loans,F,N,Y,0,108000.0,1042560.0,34587.0,900000.0,Other_B,Working,Higher education,Separated,House / apartment,0.00496,-18601,-1695,-5810.0,-2137,,1,1,0,1,0,0,Realty agents,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Services,,0.6523931408142849,0.807273923277881,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-291.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n150481,0,Revolving loans,M,Y,N,1,135000.0,405000.0,20250.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.011657,-15900,-4359,-8123.0,-4453,6.0,1,1,0,1,0,0,Laborers,2.0,1,1,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.6688830903958629,0.1895952597360396,0.0928,0.0903,0.9791,0.7144,0.0434,0.0,0.2069,0.1667,0.2083,0.0973,0.0756,0.0776,0.0,0.0,0.0945,0.0937,0.9791,0.7256,0.0438,0.0,0.2069,0.1667,0.2083,0.0996,0.0826,0.0809,0.0,0.0,0.0937,0.0903,0.9791,0.7182,0.0437,0.0,0.2069,0.1667,0.2083,0.099,0.077,0.079,0.0,0.0,reg oper account,block of flats,0.0611,Panel,No,0.0,0.0,0.0,0.0,-1996.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n343019,0,Cash loans,F,N,Y,0,81000.0,545040.0,20677.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-18040,-1079,-11341.0,-1576,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.7023278093327765,0.6572764142732339,0.4974688893052743,0.0124,,0.9816,,,,0.069,0.0417,,,,,,,0.0126,,0.9816,,,,0.069,0.0417,,,,,,,0.0125,,0.9816,,,,0.069,0.0417,,,,,,,,block of flats,0.0086,Panel,No,1.0,0.0,1.0,0.0,-80.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n136056,1,Cash loans,F,N,N,0,99000.0,450000.0,12001.5,450000.0,Family,Working,Secondary / secondary special,Married,Rented apartment,0.015221,-13940,-396,-12.0,-1302,,1,1,0,1,1,0,Sales staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Trade: type 3,0.3148391578732745,0.6159236643379488,0.1245194708919845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,5.0,0.0,-159.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n415213,1,Revolving loans,F,N,N,1,112500.0,225000.0,11250.0,225000.0,Other_B,Working,Secondary / secondary special,Married,With parents,0.020246,-7825,-1236,-2567.0,-501,,1,1,1,1,1,0,Sales staff,3.0,3,3,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.3439557908269681,0.017586441459487234,0.2103502286944494,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-487.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n391819,0,Cash loans,F,N,Y,0,225000.0,348826.5,17095.5,288000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.003813,-17296,-581,-6590.0,-838,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.7684064785058452,0.5726825047161584,0.0938,0.0858,0.9791,0.7144,0.1594,0.0,0.2069,0.1667,0.0417,0.0418,0.07400000000000001,0.0831,0.0116,0.0324,0.0956,0.08900000000000001,0.9791,0.7256,0.1609,0.0,0.2069,0.1667,0.0417,0.0428,0.0808,0.0866,0.0117,0.0343,0.0947,0.0858,0.9791,0.7182,0.1604,0.0,0.2069,0.1667,0.0417,0.0425,0.0752,0.0846,0.0116,0.0331,reg oper account,block of flats,0.0738,Panel,No,4.0,1.0,4.0,1.0,-2920.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n186203,0,Cash loans,F,Y,N,0,180000.0,450000.0,23107.5,450000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.032561,-12478,-2793,-6610.0,-3174,7.0,1,1,0,1,1,0,Laborers,2.0,1,1,MONDAY,20,0,0,0,0,0,0,Business Entity Type 3,,0.6733765043691148,0.7001838506835805,0.4619,,0.9801,,,0.48,0.4138,0.3333,,,,0.2339,,0.0027,0.4706,,0.9801,,,0.4834,0.4138,0.3333,,,,0.2437,,0.0029,0.4663,,0.9801,,,0.48,0.4138,0.3333,,,,0.2381,,0.0028,,block of flats,0.31,,No,1.0,0.0,1.0,0.0,-985.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n384624,0,Cash loans,M,N,Y,0,202500.0,152820.0,12204.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.007273999999999998,-16482,-622,-2816.0,-21,,1,1,0,1,0,0,Security staff,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Other,0.3786724061592062,0.4279395416780212,0.4170996682522097,0.0186,0.0,0.9861,0.8096,0.0025,0.0,0.1034,0.0417,0.0417,,0.0151,0.0154,0.0,0.0,0.0189,0.0,0.9861,0.8171,0.0025,0.0,0.1034,0.0417,0.0417,,0.0165,0.0161,0.0,0.0,0.0187,0.0,0.9861,0.8121,0.0025,0.0,0.1034,0.0417,0.0417,,0.0154,0.0157,0.0,0.0,reg oper account,block of flats,0.0121,Panel,No,0.0,0.0,0.0,0.0,-433.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n163562,0,Cash loans,M,Y,Y,0,112500.0,314100.0,16573.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018209,-10330,-645,-4484.0,-3016,29.0,1,1,0,1,1,0,Core staff,1.0,3,3,FRIDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.3441313402359605,0.4461980599757596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n356359,0,Revolving loans,M,Y,Y,0,135000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.015221,-10239,-328,-4880.0,-2912,7.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,8,0,0,0,0,1,1,Self-employed,0.2827448917225625,0.589181336730529,0.08064954432143595,0.0067,0.0042,0.9523,,,0.0,0.1207,0.0208,,0.0391,,0.0057,,0.0,0.0021,0.0,0.9518,,,0.0,0.069,0.0,,0.04,,0.0013,,0.0,0.0068,0.0042,0.9523,,,0.0,0.1207,0.0208,,0.0398,,0.0058,,0.0,,block of flats,0.0011,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n275110,1,Cash loans,F,N,Y,1,112500.0,835605.0,29736.0,697500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010643,-10942,-1466,-989.0,-3516,,1,1,0,1,1,0,Core staff,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Self-employed,,0.5471175095977449,0.7801436381572275,0.066,0.0027,0.9737,,,,0.1379,0.125,,0.0343,,0.0499,,0.016,0.0672,0.0028,0.9737,,,,0.1379,0.125,,0.035,,0.052000000000000005,,0.0169,0.0666,0.0027,0.9737,,,,0.1379,0.125,,0.0349,,0.0508,,0.0163,,block of flats,0.0427,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1448.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n126476,0,Cash loans,F,N,Y,1,225000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.072508,-12725,-1071,-6760.0,-3128,,1,1,0,1,0,1,Accountants,3.0,1,1,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.7736612190626379,0.7100748176000623,0.1766525794312139,0.1732,,0.9821,,,0.24,0.1034,0.4583,,,,,,,0.1765,,0.9821,,,0.2417,0.1034,0.4583,,,,,,,0.1749,,0.9821,,,0.24,0.1034,0.4583,,,,,,,,block of flats,0.1467,Panel,No,2.0,1.0,2.0,1.0,-1837.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,7.0,2.0,5.0\n450562,1,Cash loans,M,Y,Y,0,225000.0,1042560.0,34587.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-21373,-4038,-399.0,-4475,5.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,,0.465582156278288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n414135,0,Cash loans,F,Y,Y,0,94500.0,652311.0,21172.5,544500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-14316,-2788,-7567.0,-4805,9.0,1,1,0,1,0,0,Cooking staff,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,School,,0.16318703546427088,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1707.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n340578,0,Cash loans,M,Y,N,1,180000.0,601474.5,27999.0,486000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.011657,-13778,-274,-806.0,-4238,3.0,1,1,0,1,0,0,Drivers,3.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,,0.7831049786449708,0.3046721837533529,0.0825,0.0849,0.9771,0.6872,0.063,0.0,0.1379,0.1667,0.2083,0.0147,0.0672,0.0691,0.0,0.0,0.084,0.0881,0.9772,0.6994,0.0636,0.0,0.1379,0.1667,0.2083,0.0151,0.0735,0.07200000000000001,0.0,0.0,0.0833,0.0849,0.9771,0.6914,0.0634,0.0,0.1379,0.1667,0.2083,0.015,0.0684,0.0703,0.0,0.0,not specified,block of flats,0.0543,\"Stone, brick\",No,2.0,1.0,2.0,0.0,-2878.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n170002,0,Cash loans,F,N,Y,0,382500.0,1161360.0,41845.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.04622,-18303,-2127,-8543.0,-1846,,1,1,0,1,0,0,Medicine staff,1.0,1,1,TUESDAY,15,0,1,1,0,0,0,Medicine,0.7700499907958274,0.7724788895670589,,0.2804,0.1107,0.9891,0.8504,0.14400000000000002,0.32,0.1379,0.6667,0.7083,0.1254,0.2278,0.2946,0.0039,0.0024,0.2857,0.1149,0.9891,0.8563,0.1453,0.3222,0.1379,0.6667,0.7083,0.1283,0.2489,0.3069,0.0039,0.0025,0.2831,0.1107,0.9891,0.8524,0.1449,0.32,0.1379,0.6667,0.7083,0.1276,0.2317,0.2999,0.0039,0.0024,reg oper account,block of flats,0.3109,Panel,No,0.0,0.0,0.0,0.0,-634.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n288248,0,Cash loans,M,Y,Y,3,171000.0,728460.0,44694.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-14321,-3404,-47.0,-4927,10.0,1,1,0,1,1,0,Drivers,5.0,2,2,MONDAY,11,0,0,0,0,0,0,Other,,0.5102618053788035,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1649.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n113409,0,Cash loans,F,N,Y,1,225000.0,1928304.0,71590.5,1800000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006233,-15521,-1128,-5994.0,-1130,,1,1,0,1,0,0,,3.0,2,2,SUNDAY,15,0,0,0,0,0,0,Transport: type 4,,0.7012273819222192,0.3656165070113335,0.0742,0.0619,0.9811,0.7416,0.019,0.08,0.069,0.3333,0.375,0.0439,0.0597,0.0746,0.0039,0.0082,0.0756,0.0643,0.9811,0.7517,0.0192,0.0806,0.069,0.3333,0.375,0.0449,0.0652,0.0777,0.0039,0.0087,0.0749,0.0619,0.9811,0.7451,0.0191,0.08,0.069,0.3333,0.375,0.0447,0.0607,0.0759,0.0039,0.0084,reg oper account,block of flats,0.0708,Panel,No,6.0,0.0,6.0,0.0,-2297.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n407431,0,Cash loans,F,N,Y,1,90000.0,675000.0,21906.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14525,-3225,-1452.0,-109,,1,1,1,1,1,0,Accountants,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.7178848471188273,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2972.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n267354,0,Cash loans,F,Y,Y,0,418500.0,568800.0,16429.5,450000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.022625,-16374,-2081,-2254.0,-4557,2.0,1,1,0,1,1,0,Accountants,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,School,,0.7960574620302691,0.6092756673894402,0.0,,0.0,,,0.0,,,,,,,,,0.0,,0.0005,,,0.0,,,,,,,,,0.0,,0.0,,,0.0,,,,,,,,,,block of flats,0.0,,No,0.0,0.0,0.0,0.0,-393.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n355200,0,Cash loans,M,Y,N,0,180000.0,539590.5,20470.5,445500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.009657,-19680,365243,-617.0,-3218,7.0,1,0,0,1,0,0,,1.0,2,2,SUNDAY,12,0,0,0,0,0,0,XNA,0.3990285514246606,0.35314500223324025,,0.0753,0.0681,0.9796,0.7212,0.0071,0.0,0.1379,0.1667,0.2083,0.0647,0.0605,0.0656,0.0039,0.0142,0.0767,0.0707,0.9796,0.7321,0.0071,0.0,0.1379,0.1667,0.2083,0.0662,0.0661,0.0683,0.0039,0.015,0.076,0.0681,0.9796,0.7249,0.0071,0.0,0.1379,0.1667,0.2083,0.0659,0.0616,0.0668,0.0039,0.0145,reg oper spec account,block of flats,0.0547,Block,No,0.0,0.0,0.0,0.0,-400.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n428124,1,Cash loans,F,N,Y,2,189000.0,321574.5,25537.5,265500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-10614,-291,-9050.0,-1196,,1,1,1,1,1,0,Accountants,4.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Transport: type 4,0.0587549860768164,0.18403904613391728,,0.3753,0.18,0.9876,0.83,0.0811,0.36,0.3103,0.375,0.4167,0.2035,0.3051,0.3945,0.0039,0.0026,0.3824,0.1868,0.9876,0.8367,0.0819,0.3625,0.3103,0.375,0.4167,0.2081,0.3333,0.41100000000000003,0.0039,0.0027,0.3789,0.18,0.9876,0.8323,0.0816,0.36,0.3103,0.375,0.4167,0.207,0.3104,0.4016,0.0039,0.0026,reg oper account,block of flats,0.3109,Panel,No,1.0,0.0,1.0,0.0,-348.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n342965,0,Cash loans,F,N,Y,1,87750.0,540000.0,23917.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-17418,-7459,-8515.0,-973,,1,1,1,1,1,1,Waiters/barmen staff,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Medicine,0.7054653698801439,0.5228648831309883,0.32173528219668485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.0,0.0,12.0,0.0,-2252.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n400397,0,Cash loans,M,Y,Y,0,225000.0,808650.0,29839.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-16540,-2109,-46.0,-2,25.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Construction,,0.4738611724088498,0.520897599048938,0.0278,0.0412,0.9707,0.5988,0.0158,0.0,0.1034,0.0833,0.125,,0.0227,0.0251,0.0,0.0,0.0284,0.0427,0.9707,0.6145,0.016,0.0,0.1034,0.0833,0.125,,0.0248,0.0261,0.0,0.0,0.0281,0.0412,0.9707,0.6042,0.0159,0.0,0.1034,0.0833,0.125,,0.0231,0.0255,0.0,0.0,reg oper account,block of flats,0.0284,\"Stone, brick\",No,9.0,0.0,9.0,0.0,-935.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n283107,0,Cash loans,M,N,Y,2,270000.0,331920.0,17077.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-12488,-3608,-6600.0,-5096,,1,1,0,1,0,0,Security staff,4.0,1,1,WEDNESDAY,13,0,1,1,0,0,0,Security,0.5631796158623167,0.5447323734594194,0.5709165417729987,0.0928,0.085,0.9866,0.8164,0.1457,0.0,0.2069,0.1667,0.2083,0.0223,0.0756,0.0927,0.0,0.0309,0.0945,0.0882,0.9866,0.8236,0.147,0.0,0.2069,0.1667,0.2083,0.0228,0.0826,0.0966,0.0,0.0327,0.0937,0.085,0.9866,0.8189,0.1466,0.0,0.2069,0.1667,0.2083,0.0227,0.077,0.0944,0.0,0.0315,reg oper spec account,block of flats,0.0796,Panel,No,0.0,0.0,0.0,0.0,-2099.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n105381,0,Cash loans,F,N,Y,0,36000.0,123993.0,8946.0,103500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-23977,365243,-3736.0,-4149,,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.6050040851719234,,0.1979,0.0493,0.9836,0.7756,0.0371,0.16,0.1379,0.3333,0.0417,0.0415,0.1614,0.1743,0.0,0.0,0.2017,0.0511,0.9836,0.7844,0.0374,0.1611,0.1379,0.3333,0.0417,0.0424,0.1763,0.1815,0.0,0.0,0.1999,0.0493,0.9836,0.7786,0.0373,0.16,0.1379,0.3333,0.0417,0.0422,0.1642,0.1774,0.0,0.0,reg oper spec account,block of flats,0.1978,Block,No,9.0,0.0,9.0,0.0,-357.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n264038,0,Cash loans,F,Y,Y,0,90000.0,378000.0,18513.0,378000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.028663,-19045,-1669,-6791.0,-2252,5.0,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,,0.243234150697984,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-124.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n233851,0,Cash loans,F,N,Y,0,157500.0,257562.0,11043.0,184500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-21522,365243,-3119.0,-4908,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,,0.3941271420959317,0.3859146722745145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-656.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n138184,1,Cash loans,F,N,Y,0,103500.0,101880.0,5845.5,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.020246,-23610,365243,-9766.0,-4786,,1,0,0,1,1,0,,1.0,3,3,SATURDAY,10,0,0,0,0,0,0,XNA,,0.03822517860385693,0.5082869913916046,0.0928,0.099,0.9776,0.6940000000000001,0.0119,0.0,0.1724,0.2083,0.0417,0.054000000000000006,0.0748,0.0876,0.0039,0.0696,0.0945,0.1027,0.9777,0.706,0.012,0.0,0.1724,0.2083,0.0417,0.0552,0.0817,0.0912,0.0039,0.0737,0.0937,0.099,0.9776,0.6981,0.012,0.0,0.1724,0.2083,0.0417,0.0549,0.0761,0.0891,0.0039,0.0711,reg oper spec account,block of flats,0.0905,Panel,No,4.0,0.0,4.0,0.0,-1512.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n173989,0,Cash loans,F,N,Y,0,135000.0,225000.0,22383.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-17843,-4713,-4647.0,-1364,,1,1,0,1,0,0,Laborers,2.0,3,1,MONDAY,11,0,0,0,0,0,0,Other,,0.4874142821009783,0.6263042766749393,0.0742,0.0876,0.9836,0.7756,0.0167,0.08,0.069,0.3333,0.0417,0.027000000000000003,0.0605,0.0722,0.0,0.0,0.0756,0.0909,0.9836,0.7844,0.0169,0.0806,0.069,0.3333,0.0417,0.0276,0.0661,0.0752,0.0,0.0,0.0749,0.0876,0.9836,0.7786,0.0168,0.08,0.069,0.3333,0.0417,0.0274,0.0616,0.0735,0.0,0.0,reg oper account,block of flats,0.0659,Panel,No,2.0,0.0,2.0,0.0,-753.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n304832,0,Cash loans,M,Y,N,1,202500.0,193572.0,11241.0,171000.0,Unaccompanied,Working,Higher education,Separated,Rented apartment,0.016612000000000002,-13275,-827,-7236.0,-4074,3.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Transport: type 3,0.3395750544459469,0.4938246055441985,,0.3577,1.0,0.9841,0.7824,0.1508,0.4,0.3448,0.3333,0.375,0.2724,0.2908,0.3838,0.0039,0.0041,0.3645,1.0,0.9841,0.7909,0.1522,0.4028,0.3448,0.3333,0.375,0.2787,0.3177,0.3993,0.0039,0.0043,0.3612,1.0,0.9841,0.7853,0.1518,0.4,0.3448,0.3333,0.375,0.2772,0.2959,0.3907,0.0039,0.0042,reg oper account,block of flats,0.3389,Panel,No,0.0,0.0,0.0,0.0,-1643.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n431182,0,Cash loans,F,Y,Y,1,81000.0,119925.0,11812.5,112500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-14754,-7749,-6999.0,-4331,21.0,1,1,0,1,1,0,Core staff,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,School,,0.15829913646208862,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n294764,1,Cash loans,F,N,N,0,180000.0,511920.0,13500.0,405000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.010966,-15454,-3176,-678.0,-5677,,1,1,0,1,1,0,Core staff,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Police,,0.7204111787185121,0.1510075350878296,0.1031,0.0986,0.9767,0.6804,0.0125,0.0,0.2069,0.1667,0.2083,0.0637,0.0841,0.0907,0.0,0.0,0.105,0.1023,0.9767,0.6929,0.0126,0.0,0.2069,0.1667,0.2083,0.0652,0.0918,0.0945,0.0,0.0,0.1041,0.0986,0.9767,0.6847,0.0126,0.0,0.2069,0.1667,0.2083,0.0648,0.0855,0.0923,0.0,0.0,reg oper account,block of flats,0.0782,Block,No,0.0,0.0,0.0,0.0,-931.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n168476,0,Revolving loans,F,N,Y,0,112500.0,337500.0,16875.0,337500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.00496,-17115,-344,-4646.0,-653,,1,1,0,1,1,0,Sales staff,1.0,2,2,MONDAY,10,0,0,0,0,1,1,Trade: type 7,0.7220089676100997,0.6663841162557979,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1103.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n448845,0,Cash loans,F,N,N,1,382500.0,1125000.0,47664.0,1125000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.011703,-17988,-236,-4320.0,-1534,,1,1,1,1,1,0,Managers,3.0,2,2,TUESDAY,19,0,0,0,0,1,1,Industry: type 3,,0.2858978721410488,0.21885908222837447,0.0833,0.079,0.9811,,,0.0,0.1655,0.1667,,,,0.0748,,0.0849,0.0714,0.0695,0.9806,,,0.0,0.1379,0.1667,,,,0.0667,,0.0793,0.0729,0.079,0.9806,,,0.0,0.1379,0.1667,,,,0.0654,,0.0765,,block of flats,0.0683,Panel,No,1.0,0.0,1.0,0.0,-817.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n163804,0,Cash loans,F,N,Y,0,90000.0,450000.0,22977.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-18942,-390,-7258.0,-2469,,1,1,0,1,1,0,Cleaning staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Other,0.4812365123370259,0.6594356436730071,0.15474363127259447,0.1845,0.1908,0.9891,0.8504,0.0988,0.0,0.4138,0.1667,0.2083,0.3068,0.1488,0.1941,0.0077,0.002,0.188,0.198,0.9891,0.8563,0.0997,0.0,0.4138,0.1667,0.2083,0.3138,0.1625,0.2022,0.0078,0.0022,0.1863,0.1908,0.9891,0.8524,0.0994,0.0,0.4138,0.1667,0.2083,0.3122,0.1513,0.1976,0.0078,0.0021,reg oper account,block of flats,0.1781,Panel,No,0.0,0.0,0.0,0.0,-2246.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n118121,0,Cash loans,F,Y,Y,0,225000.0,675000.0,41296.5,675000.0,Unaccompanied,Working,Higher education,Separated,Rented apartment,0.008019,-11031,-137,-4379.0,-1662,8.0,1,1,1,1,0,0,,1.0,2,2,FRIDAY,10,1,1,0,1,1,0,Business Entity Type 3,0.18986894513892144,0.7437995882958672,0.16441417882990705,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-743.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n359693,0,Cash loans,F,N,N,1,90000.0,67500.0,7218.0,67500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.003069,-10379,-186,-4825.0,-2877,,1,1,0,1,1,0,Core staff,2.0,3,3,FRIDAY,18,0,0,0,0,0,0,Self-employed,0.11885173483214148,0.4666378611944033,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1585.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n395777,0,Cash loans,F,N,Y,0,90000.0,900000.0,26316.0,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008625,-21777,-2902,-6720.0,-4732,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,School,,0.6338562972476668,0.3296550543128238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1098.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n124594,0,Cash loans,F,N,Y,0,108000.0,550980.0,35343.0,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20969,-2205,-9224.0,-3542,,1,1,1,1,0,0,Laborers,2.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Other,,0.4937808770982484,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-117.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n109455,0,Cash loans,F,Y,N,0,90000.0,755190.0,35122.5,675000.0,,Working,Secondary / secondary special,Married,House / apartment,0.018029,-21177,-6325,-6512.0,-4355,64.0,1,1,0,1,0,0,,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,School,0.7909465841682403,0.05978143611389593,0.5779691187553125,0.1588,0.1215,0.9906,0.8708,0.0427,0.2,0.1724,0.3333,0.0417,,,0.1849,,0.0,0.1618,0.1261,0.9906,0.8759,0.043,0.2014,0.1724,0.3333,0.0417,,,0.1927,,0.0,0.1603,0.1215,0.9906,0.8725,0.0429,0.2,0.1724,0.3333,0.0417,,,0.1883,,0.0,,block of flats,0.1688,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n273076,0,Cash loans,M,N,N,0,247500.0,163008.0,16249.5,144000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-17299,-1700,-1891.0,-838,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.8208533635575485,0.6080153345682971,0.6479768603302221,0.0979,0.055999999999999994,0.9742,0.6396,0.0096,0.0,0.1379,0.2083,0.25,0.0474,0.0773,0.0776,0.0116,0.0183,0.0998,0.0581,0.9742,0.6537,0.0097,0.0,0.1379,0.2083,0.25,0.0485,0.0845,0.0809,0.0117,0.0194,0.0989,0.055999999999999994,0.9742,0.6444,0.0097,0.0,0.1379,0.2083,0.25,0.0482,0.0787,0.079,0.0116,0.0187,reg oper spec account,block of flats,0.0759,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2896.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n420892,0,Cash loans,M,Y,Y,0,157500.0,337500.0,40185.0,337500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-12894,-1912,-5216.0,-2646,4.0,1,1,0,1,0,1,Drivers,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Self-employed,0.2034282599289815,0.2934341540579812,0.7898803468924867,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n293582,0,Cash loans,F,N,Y,0,270000.0,316125.0,11353.5,261000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,With parents,0.030755,-23432,365243,-12165.0,-4018,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.7675219289282381,0.7338145369642702,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-812.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n147717,1,Cash loans,F,N,Y,0,90000.0,416052.0,15813.0,292500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.020246,-14637,-717,-6041.0,-1819,,1,1,0,1,1,0,,2.0,3,3,TUESDAY,12,0,0,0,0,0,0,Government,0.4779603191195351,0.5239013211786432,0.4311917977993083,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-322.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n411685,0,Cash loans,F,N,N,0,90000.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Widow,Municipal apartment,0.018029,-17810,-371,-9468.0,-1348,,1,1,0,1,0,0,Cooking staff,1.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.4155836005691278,0.4956658291397297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-669.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n268879,0,Cash loans,F,N,Y,1,135000.0,314100.0,16164.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008575,-16148,-5566,-3322.0,-4261,,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,Other,,0.7529894326284462,,0.1742,0.0,0.995,0.932,0.0331,0.16,0.1379,0.375,0.4167,0.0,0.142,0.1146,0.0,0.0,0.1775,0.0,0.995,0.9347,0.0334,0.1611,0.1379,0.375,0.4167,0.0,0.1552,0.1193,0.0,0.0,0.1759,0.0,0.995,0.9329,0.0333,0.16,0.1379,0.375,0.4167,0.0,0.1445,0.1166,0.0,0.0,reg oper spec account,block of flats,0.1534,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1810.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n175462,1,Cash loans,M,N,N,0,112500.0,210249.0,12703.5,175500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.019689,-10540,-1403,-1572.0,-2966,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,16,0,0,0,1,1,0,Business Entity Type 2,,0.36010393327364965,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-516.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n232509,0,Cash loans,M,Y,N,0,225000.0,540000.0,16371.0,540000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-18026,-2479,-10106.0,-1579,24.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Self-employed,,0.6611279046872527,0.4329616670974407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2087.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n387392,0,Cash loans,M,Y,N,0,135000.0,157500.0,18688.5,157500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.003069,-12082,-1008,-622.0,-665,10.0,1,1,0,1,0,0,Drivers,1.0,3,3,THURSDAY,15,0,0,0,0,1,1,Security,0.2845913735590862,0.02860669990264033,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-264.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n438537,0,Cash loans,F,Y,Y,0,225000.0,271066.5,21861.0,234000.0,Unaccompanied,State servant,Higher education,Widow,House / apartment,0.010032,-14565,-7093,-8615.0,-4642,12.0,1,1,0,1,0,0,Drivers,1.0,2,2,SATURDAY,8,0,0,0,0,0,0,Government,,0.6378259552950253,0.3876253444214701,0.2522,0.2454,0.9791,0.7144,0.0988,0.2264,0.3331,0.2221,0.2638,0.1604,0.2009,0.2688,0.0219,0.0947,0.105,0.1189,0.9777,0.706,0.0339,0.0,0.2069,0.1667,0.2083,0.0556,0.0918,0.0931,0.0,0.0,0.1041,0.1147,0.9776,0.6981,0.0401,0.0,0.2069,0.1667,0.2083,0.0845,0.0855,0.091,0.0,0.0,reg oper account,block of flats,0.5554,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1615.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n400591,0,Cash loans,F,N,Y,0,117000.0,113760.0,4216.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00702,-20806,-1592,-9674.0,-4117,,1,1,1,1,0,0,Laborers,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 1,,0.6385076298234658,0.807273923277881,,,0.9856,,,,,,,,,0.0079,,,,,0.9856,,,,,,,,,0.0082,,,,,0.9856,,,,,,,,,0.008,,,,,0.0242,,No,3.0,0.0,3.0,0.0,-2087.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n100100,0,Cash loans,M,Y,Y,2,202500.0,796396.5,38443.5,643500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008019,-15909,-1094,-3134.0,-4180,65.0,1,1,0,1,0,1,Managers,4.0,2,2,FRIDAY,17,0,0,0,0,1,1,Industry: type 11,0.4476751393608083,0.4957651407174961,0.8245949709919925,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1876.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n273745,0,Cash loans,F,N,Y,0,211500.0,450000.0,19197.0,450000.0,Family,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.008865999999999999,-20225,365243,-4541.0,-3221,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,,0.5088136062110159,0.5797274227921155,0.0247,,0.9871,,,0.0,0.1034,0.0833,,0.0115,,0.0271,,0.0,0.0252,,0.9871,,,0.0,0.1034,0.0833,,0.0117,,0.0282,,0.0,0.025,,0.9871,,,0.0,0.1034,0.0833,,0.0117,,0.0276,,0.0,,block of flats,0.0236,Panel,No,0.0,0.0,0.0,0.0,-2918.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n359300,0,Revolving loans,M,Y,Y,0,360000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.072508,-15760,-4108,-4048.0,-4045,17.0,1,1,0,1,1,0,Laborers,2.0,1,1,THURSDAY,9,0,1,1,0,0,0,Transport: type 4,,0.7339232427928302,,0.3052,0.1544,0.9811,0.7416,0.4317,0.48,0.2069,0.4583,0.5,0.0,0.2421,0.3,0.0309,0.0,0.3109,0.1602,0.9811,0.7517,0.4356,0.4834,0.2069,0.4583,0.5,0.0,0.2645,0.3126,0.0311,0.0,0.3081,0.1544,0.9811,0.7451,0.4344,0.48,0.2069,0.4583,0.5,0.0,0.2463,0.3054,0.0311,0.0,reg oper account,block of flats,0.2199,Panel,No,1.0,0.0,1.0,0.0,-1418.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n379567,0,Cash loans,F,Y,Y,0,180000.0,1528200.0,53248.5,1350000.0,\"Spouse, partner\",Commercial associate,Higher education,Single / not married,House / apartment,0.025164,-9422,-833,-4201.0,-2087,7.0,1,1,0,1,0,0,Managers,1.0,2,2,SATURDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.17717488920042018,0.15154074480067842,0.5638350489514956,0.0646,,0.9891,,,0.0,0.0345,0.1804,,,,0.0459,,0.0019,0.063,,0.9881,,,0.0,0.0345,0.1667,,,,0.0436,,0.0,0.0625,,0.9886,,,0.0,0.0345,0.1667,,,,0.0481,,0.0,,block of flats,0.0329,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-839.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n114233,0,Cash loans,F,Y,N,0,436500.0,1971072.0,68512.5,1800000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-12289,-1429,-5235.0,-557,3.0,1,1,0,1,1,0,Managers,2.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 2,0.5434255196838057,0.7111765277474406,0.3791004853998145,0.3948,0.1945,0.9811,0.7416,0.2907,0.48,0.2069,0.625,0.6667,0.0,0.3219,0.2971,0.0,0.0,0.4023,0.2019,0.9811,0.7517,0.2933,0.4834,0.2069,0.625,0.6667,0.0,0.3517,0.3095,0.0,0.0,0.3987,0.1945,0.9811,0.7451,0.2925,0.48,0.2069,0.625,0.6667,0.0,0.3275,0.3024,0.0,0.0,reg oper account,block of flats,0.3926,Panel,No,4.0,1.0,4.0,0.0,-446.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n205984,0,Cash loans,F,N,N,0,180000.0,225000.0,23755.5,225000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009549,-18693,-906,-1169.0,-2249,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.6041733204259537,0.18065161433618326,0.5691487713619409,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,1.0,0.0,2.0\n280060,0,Cash loans,M,N,Y,0,234000.0,891000.0,37881.0,891000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.026392,-17103,-2366,-5633.0,-645,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,18,0,0,0,0,0,0,Self-employed,,0.6745297489049006,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1377.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n398156,0,Cash loans,F,N,N,0,112500.0,119925.0,14229.0,112500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-13492,-2149,-3126.0,-3191,,1,1,0,1,0,0,,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,Agriculture,,0.3785496710977705,0.34578480246959553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-478.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n250061,0,Revolving loans,F,Y,Y,1,157500.0,360000.0,18000.0,360000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.032561,-11035,-330,-5068.0,-2204,3.0,1,1,0,1,1,0,High skill tech staff,2.0,1,1,WEDNESDAY,11,0,0,0,0,0,0,Trade: type 7,,0.5488128992226043,,0.1027,0.1402,0.9747,0.6532,0.0171,0.0,0.2483,0.1667,0.2083,0.0552,0.0835,0.0925,0.0008,0.0041,0.083,0.1409,0.9747,0.6668,0.0144,0.0,0.2069,0.1667,0.2083,0.0165,0.0725,0.0794,0.0,0.004,0.0843,0.1373,0.9747,0.6578,0.0148,0.0,0.2069,0.1667,0.2083,0.0496,0.0693,0.0817,0.0,0.0041,reg oper account,block of flats,0.0608,Panel,No,3.0,0.0,3.0,0.0,-444.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n100072,0,Cash loans,M,N,N,0,180000.0,1080000.0,44118.0,1080000.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.010006,-7907,-1324,-4557.0,-586,,1,1,0,1,0,1,Sales staff,1.0,2,1,TUESDAY,9,0,0,0,0,0,0,Trade: type 2,,0.0265407750366898,0.43473324875017305,0.0928,0.0955,0.9856,0.8028,0.013000000000000001,0.0,0.2069,0.1667,0.2083,0.1147,0.0756,0.0913,0.0,0.0,0.0945,0.0991,0.9856,0.8105,0.0131,0.0,0.2069,0.1667,0.2083,0.1173,0.0826,0.0951,0.0,0.0,0.0937,0.0955,0.9856,0.8054,0.0131,0.0,0.2069,0.1667,0.2083,0.1167,0.077,0.0929,0.0,0.0,reg oper account,block of flats,0.0804,Panel,No,0.0,0.0,0.0,0.0,-725.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n395706,0,Cash loans,F,N,N,3,112500.0,417024.0,21964.5,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020246,-10578,-3290,-1400.0,-1095,,1,1,0,1,0,0,Core staff,5.0,3,3,MONDAY,14,0,0,0,0,0,0,Restaurant,0.3085337523418773,0.5136547925655118,0.2471909382666521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n141399,0,Cash loans,F,N,Y,0,157500.0,640080.0,23121.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.025164,-21583,-3064,-9672.0,-4497,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.6501532609648264,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-833.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n358868,0,Cash loans,M,Y,Y,0,202500.0,298512.0,21762.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-10291,-1017,-2113.0,-2834,22.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,Restaurant,,0.2749905421216322,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1022.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n345272,0,Cash loans,F,N,Y,1,450000.0,1100709.0,43780.5,1012500.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.020246,-17406,-5848,-480.0,-955,,1,1,0,1,0,0,Accountants,3.0,3,3,FRIDAY,10,0,0,0,0,1,1,Self-employed,,0.4275487926279365,0.34741822720026416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1090.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n241771,0,Revolving loans,F,N,N,0,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.005313,-8563,-1602,-8563.0,-1219,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Self-employed,0.3898149615963805,0.3333762006648302,,0.266,0.1331,0.9836,0.7756,0.0609,0.2,0.1724,0.3333,0.375,,0.2085,0.2231,0.0386,0.2545,0.271,0.1382,0.9836,0.7844,0.0615,0.2014,0.1724,0.3333,0.375,,0.2277,0.2324,0.0389,0.2694,0.2686,0.1331,0.9836,0.7786,0.0613,0.2,0.1724,0.3333,0.375,,0.2121,0.2271,0.0388,0.2598,reg oper account,block of flats,0.2696,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-140.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n330958,0,Cash loans,F,Y,Y,1,207000.0,1044783.0,55665.0,940500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-12367,-2376,-973.0,-4717,7.0,1,1,0,1,0,0,Managers,3.0,1,1,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.64731287635335,,,0.2858,0.9856,0.8028,0.0701,0.44,0.3793,0.3333,0.375,0.2159,,0.4414,,0.0,,0.2966,0.9856,0.8105,0.0707,0.4431,0.3793,0.3333,0.375,0.2208,,0.4599,,0.0,,0.2858,0.9856,0.8054,0.0705,0.44,0.3793,0.3333,0.375,0.2197,,0.4493,,0.0,reg oper account,block of flats,0.3854,Panel,No,0.0,0.0,0.0,0.0,-726.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n220194,0,Revolving loans,M,N,N,1,202500.0,225000.0,11250.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-10893,-1304,-8002.0,-3387,,1,1,0,1,0,0,Managers,3.0,1,1,TUESDAY,17,0,0,0,0,0,0,Transport: type 4,0.5370914416607561,0.6422593791708723,0.5334816299804352,0.6351,0.4064,0.9866,0.8164,0.1275,0.68,0.5862,0.3333,0.0417,0.0,0.5169,0.4422,0.0039,0.0,0.6471,0.4217,0.9866,0.8236,0.1287,0.6848,0.5862,0.3333,0.0417,0.0,0.5647,0.4607,0.0039,0.0,0.6412,0.4064,0.9866,0.8189,0.1283,0.68,0.5862,0.3333,0.0417,0.0,0.5259,0.4501,0.0039,0.0,reg oper account,block of flats,0.5339,Panel,No,1.0,0.0,1.0,0.0,-1108.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n389395,0,Cash loans,F,N,Y,0,135000.0,774000.0,27549.0,774000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-23429,365243,-8112.0,-4381,,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.09211822625042301,0.4794489811780563,0.0804,0.0807,0.9752,0.66,0.0084,0.0,0.1379,0.1667,0.2083,0.0412,0.0639,0.0604,0.0077,0.0115,0.0819,0.0837,0.9752,0.6733,0.0085,0.0,0.1379,0.1667,0.2083,0.0421,0.0698,0.063,0.0078,0.0122,0.0812,0.0807,0.9752,0.6645,0.0085,0.0,0.1379,0.1667,0.2083,0.0419,0.065,0.0615,0.0078,0.0118,reg oper account,block of flats,0.0546,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n275639,0,Cash loans,M,N,Y,1,270000.0,743031.0,41620.5,688500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.011657,-11297,-1075,-1194.0,-3792,,1,1,0,1,1,0,Laborers,3.0,1,1,MONDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.5502815876854287,0.6732754953814531,0.6347055309763198,0.0392,0.0624,0.9747,0.6532,0.0049,0.0,0.1034,0.125,0.1667,0.0,0.0303,0.031,0.0077,0.0375,0.0399,0.0647,0.9747,0.6668,0.005,0.0,0.1034,0.125,0.1667,0.0,0.0331,0.0323,0.0078,0.0397,0.0396,0.0624,0.9747,0.6578,0.005,0.0,0.1034,0.125,0.1667,0.0,0.0308,0.0316,0.0078,0.0383,reg oper account,block of flats,0.0349,\"Stone, brick\",No,4.0,1.0,4.0,0.0,-1134.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n235319,0,Cash loans,F,N,Y,1,180000.0,1436850.0,46480.5,1125000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-12553,-115,-3689.0,-1204,,1,1,0,1,1,1,Accountants,3.0,1,1,FRIDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.521653638240409,0.04994260189624306,0.4845,,0.9861,,,0.72,0.3103,0.5417,,,,,,,0.4937,,0.9861,,,0.725,0.3103,0.5417,,,,,,,0.4892,,0.9861,,,0.72,0.3103,0.5417,,,,,,,,block of flats,0.3497,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n393868,0,Cash loans,F,Y,Y,0,103500.0,738000.0,21708.0,738000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-13683,-1808,-6850.0,-1194,9.0,1,1,0,1,1,0,Cooking staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.3426087657105523,0.6754132910917112,0.0041,0.0,0.9642,,,0.0,,0.0,,,,0.0035,,0.0,0.0042,0.0,0.9643,,,0.0,,0.0,,,,0.0036,,0.0,0.0042,0.0,0.9642,,,0.0,,0.0,,,,0.0036,,0.0,,block of flats,0.0028,Wooden,No,0.0,0.0,0.0,0.0,-751.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n370993,0,Cash loans,F,Y,N,0,180000.0,855000.0,24997.5,855000.0,Unaccompanied,Commercial associate,Higher education,Separated,Municipal apartment,0.010032,-11203,-898,-6086.0,-686,12.0,1,1,0,1,1,0,Core staff,1.0,2,2,THURSDAY,5,0,0,0,0,0,0,Bank,,0.6947370382603053,0.2851799046358216,0.0742,0.0564,0.9856,0.8028,0.0183,0.08,0.069,0.3333,0.375,0.0236,0.0605,0.076,0.0,0.0,0.0756,0.0585,0.9856,0.8105,0.0185,0.0806,0.069,0.3333,0.375,0.0242,0.0661,0.0792,0.0,0.0,0.0749,0.0564,0.9856,0.8054,0.0184,0.08,0.069,0.3333,0.375,0.024,0.0616,0.0774,0.0,0.0,reg oper account,block of flats,0.0598,Panel,No,2.0,0.0,2.0,0.0,-1551.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,1.0,0.0,1.0\n162612,0,Cash loans,F,N,Y,1,135000.0,1006920.0,42790.5,900000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00963,-15614,-8002,-4338.0,-4680,,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,14,0,0,0,0,0,0,Medicine,,0.6101448727932867,,0.0825,0.0722,0.9786,0.7076,,0.0,0.1379,0.1667,0.2083,,,0.0559,,0.0,0.084,0.0749,0.9786,0.7190000000000001,,0.0,0.1379,0.1667,0.2083,,,0.0582,,0.0,0.0833,0.0722,0.9786,0.7115,,0.0,0.1379,0.1667,0.2083,,,0.0569,,0.0,reg oper account,block of flats,0.0485,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-534.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n152573,0,Cash loans,F,N,Y,2,135000.0,889515.0,26136.0,742500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-11447,-1387,-494.0,-3413,,1,1,0,1,0,0,Cooking staff,4.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Restaurant,,0.5680015617379708,0.3606125659189888,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-742.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n104893,0,Cash loans,F,N,Y,0,103500.0,339241.5,15943.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018634,-15446,-440,-437.0,-4468,,1,1,0,1,0,0,Sales staff,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.769474916096668,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-399.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n149562,0,Cash loans,F,N,Y,1,202500.0,1191825.0,83079.0,1125000.0,Family,Working,Higher education,Married,House / apartment,0.04622,-13224,-574,-7348.0,-4202,,1,1,1,1,0,0,Managers,3.0,1,1,SUNDAY,11,0,0,0,0,1,1,Medicine,0.6878252993744353,0.736583633174682,0.3280631605201915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-600.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n376892,0,Cash loans,F,N,Y,2,112500.0,225000.0,11074.5,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.02461,-11586,-863,-5126.0,-681,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,13,0,0,0,0,0,0,Self-employed,,0.5802768924542486,0.4135967602644276,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1511.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n364124,0,Cash loans,F,N,Y,0,90000.0,1570797.0,41436.0,1404000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006852,-22881,365243,-7766.0,-4631,,1,0,0,1,1,0,,2.0,3,3,SUNDAY,5,0,0,0,0,0,0,XNA,,0.08131072699755099,,0.1237,0.1363,0.9826,0.762,0.2144,0.0,0.2069,0.1667,0.0417,0.0,0.0983,0.1108,0.0116,0.008,0.1261,0.1414,0.9826,0.7713,0.2163,0.0,0.2069,0.1667,0.0417,0.0,0.1074,0.1155,0.0117,0.0085,0.1249,0.1363,0.9826,0.7652,0.2157,0.0,0.2069,0.1667,0.0417,0.0,0.1,0.1128,0.0116,0.0082,reg oper account,block of flats,0.1172,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-667.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,,,,,,\n234770,0,Cash loans,F,N,Y,1,67500.0,225000.0,12694.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-18039,-554,-4251.0,-1593,,1,1,1,1,1,0,Cooking staff,3.0,2,2,SUNDAY,12,0,0,0,0,0,0,Restaurant,0.7585450341903124,0.7247333516895333,0.6545292802242897,0.1155,0.0942,0.9831,,,0.0,0.0345,0.1667,,0.0598,,0.0617,,0.019,0.1176,0.0977,0.9831,,,0.0,0.0345,0.1667,,0.0612,,0.0643,,0.0201,0.1166,0.0942,0.9831,,,0.0,0.0345,0.1667,,0.0609,,0.0628,,0.0194,,block of flats,0.0526,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2914.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n375731,0,Cash loans,F,N,Y,0,180000.0,544491.0,20133.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-17581,-1339,-7866.0,-1109,,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Services,0.8907566836596461,0.5637684753422596,0.2650494299443805,0.1206,,0.9786,,,0.0,0.2759,0.1667,,0.1116,,0.1125,,0.0077,0.1229,,0.9786,,,0.0,0.2759,0.1667,,0.1141,,0.1172,,0.0082,0.1218,,0.9786,,,0.0,0.2759,0.1667,,0.1135,,0.1145,,0.0079,,block of flats,0.0902,Panel,No,0.0,0.0,0.0,0.0,-3075.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n293522,0,Cash loans,M,Y,Y,0,270000.0,521280.0,27423.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-19903,-7427,-155.0,-294,2.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6586694376492547,0.5816131194515125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-150.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n448365,0,Cash loans,M,Y,Y,0,450000.0,545040.0,36553.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-17125,-776,-357.0,-547,7.0,1,1,0,1,0,0,Managers,2.0,1,1,TUESDAY,16,0,0,0,0,1,1,Business Entity Type 3,0.5508940194186751,0.626123769667239,0.3876253444214701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1155.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n151382,0,Cash loans,M,N,Y,0,81000.0,325908.0,13936.5,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.010966,-18672,-493,-8972.0,-1859,,1,1,0,1,0,0,Medicine staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Other,,0.7405105265072752,,0.0742,0.0493,0.9806,0.7348,0.0303,0.08,0.069,0.3333,0.0417,0.042,0.0605,0.0696,0.0,0.0,0.0756,0.0511,0.9806,0.7452,0.0306,0.0806,0.069,0.3333,0.0417,0.0429,0.0661,0.0726,0.0,0.0,0.0749,0.0493,0.9806,0.7383,0.0305,0.08,0.069,0.3333,0.0417,0.0427,0.0616,0.0709,0.0,0.0,org spec account,block of flats,0.0714,Block,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n232475,0,Cash loans,M,N,Y,1,202500.0,1024740.0,52452.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-15085,-4033,-7822.0,-4496,,1,1,0,1,0,1,,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Self-employed,,0.4819907896670771,0.6430255641096323,0.0876,0.1032,0.9801,0.728,0.0469,0.0,0.2069,0.1667,0.2083,0.0,0.0698,0.0906,0.0077,0.0076,0.0893,0.1071,0.9801,0.7387,0.0474,0.0,0.2069,0.1667,0.2083,0.0,0.0762,0.0943,0.0078,0.008,0.0885,0.1032,0.9801,0.7316,0.0472,0.0,0.2069,0.1667,0.2083,0.0,0.071,0.0922,0.0078,0.0078,reg oper account,block of flats,0.0985,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n437840,0,Cash loans,F,N,N,0,46350.0,74182.5,5868.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002042,-20868,-4462,-2021.0,-2390,,1,1,1,1,1,0,Cleaning staff,2.0,3,3,MONDAY,13,0,0,0,0,0,0,School,0.7324290911324519,0.13307445931733858,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-234.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n423500,0,Revolving loans,F,Y,Y,0,225000.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.011703,-21067,-10751,-9281.0,-4485,4.0,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Medicine,0.7997197919978364,0.7014930366435816,0.7934490301648944,0.1227,0.1325,0.9871,,,0.0,0.2759,0.1667,,0.06,,0.1169,,0.0,0.125,0.1375,0.9871,,,0.0,0.2759,0.1667,,0.0613,,0.1218,,0.0,0.1239,0.1325,0.9871,,,0.0,0.2759,0.1667,,0.061,,0.11900000000000001,,0.0,,block of flats,0.092,Mixed,No,0.0,0.0,0.0,0.0,-156.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n327193,0,Cash loans,F,N,N,0,90000.0,177903.0,14391.0,148500.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.020246,-9335,-115,-6822.0,-1999,,1,1,0,1,0,0,Core staff,1.0,3,3,MONDAY,14,0,0,0,0,0,0,Self-employed,0.2390324751697334,0.1294662086035108,,0.0186,0.0408,0.9896,0.8572,0.003,0.0,0.069,0.0833,0.125,0.0316,0.0151,0.0178,0.0,0.0,0.0189,0.0424,0.9896,0.8628,0.003,0.0,0.069,0.0833,0.125,0.0323,0.0165,0.0186,0.0,0.0,0.0187,0.0408,0.9896,0.8591,0.003,0.0,0.069,0.0833,0.125,0.0321,0.0154,0.0181,0.0,0.0,reg oper account,block of flats,0.0157,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n113982,0,Cash loans,F,N,Y,1,126000.0,359775.0,38875.5,337500.0,Other_B,Working,Secondary / secondary special,Widow,House / apartment,0.030755,-14375,-3483,-6838.0,-4349,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Industry: type 3,0.3846092018462822,0.6538744155597626,0.6178261467332483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-290.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n387203,0,Cash loans,F,N,N,1,202500.0,552555.0,28341.0,477000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018801,-10110,-550,-218.0,-2033,,1,1,0,1,0,0,Sales staff,3.0,2,2,SATURDAY,13,0,0,0,1,1,0,Business Entity Type 3,0.3705505755880072,0.5817674274826601,0.4066174366275036,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,2.0,-1009.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n172592,0,Cash loans,F,N,Y,0,135000.0,302341.5,21172.5,261000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-21773,-13148,-690.0,-4757,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Government,,0.6846947724303312,0.4489622731076524,0.0773,,0.9856,,,0.0,0.1724,0.1667,,0.0169,,0.0413,,0.0117,0.0788,,0.9856,,,0.0,0.1724,0.1667,,0.0173,,0.0431,,0.0124,0.0781,,0.9856,,,0.0,0.1724,0.1667,,0.0172,,0.0421,,0.0119,,block of flats,0.0573,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2397.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n268567,0,Cash loans,M,N,Y,0,126000.0,103500.0,10732.5,103500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008575,-17224,365243,-9616.0,-772,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,XNA,,0.4419278954396067,,0.0124,0.0466,0.9707,,,0.0,0.069,0.0417,,0.0,,0.0129,,0.0032,0.0126,0.0484,0.9707,,,0.0,0.069,0.0417,,0.0,,0.0134,,0.0034,0.0125,0.0466,0.9707,,,0.0,0.069,0.0417,,0.0,,0.0131,,0.0032,,,0.011000000000000001,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-863.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n208964,1,Cash loans,M,Y,Y,3,135000.0,312768.0,21285.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-10477,-1413,-816.0,-590,8.0,1,1,1,1,1,1,Laborers,5.0,2,2,FRIDAY,12,0,0,0,0,1,1,Self-employed,,0.035386102816991775,0.17982176508970435,0.0247,0.0475,0.9886,0.8436,0.0046,0.0,0.1034,0.0833,0.0417,0.012,0.0202,0.0255,0.0039,0.0115,0.0252,0.0493,0.9886,0.8497,0.0046,0.0,0.1034,0.0833,0.0417,0.0123,0.022000000000000002,0.0265,0.0039,0.0122,0.025,0.0475,0.9886,0.8457,0.0046,0.0,0.1034,0.0833,0.0417,0.0122,0.0205,0.0259,0.0039,0.0118,reg oper account,block of flats,0.0225,Panel,No,0.0,0.0,0.0,0.0,-525.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n200685,0,Cash loans,F,N,N,0,180000.0,1125000.0,32895.0,1125000.0,Family,State servant,Higher education,Married,House / apartment,0.031329,-14601,-565,-909.0,-5172,,1,1,0,1,0,0,Accountants,2.0,2,2,SATURDAY,14,0,0,0,0,1,1,Other,0.6981735955583203,0.6590967012895258,0.7165702448010511,,,0.9856,,,,,,,,,,,,,,0.9856,,,,,,,,,,,,,,0.9856,,,,,,,,,,,,,,0.0006,,No,1.0,0.0,1.0,0.0,-455.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n166668,0,Cash loans,M,N,Y,2,202500.0,225000.0,12204.0,225000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.018209,-15150,-2863,-7786.0,-4032,,1,1,1,1,1,0,Laborers,4.0,3,3,THURSDAY,8,0,0,0,0,0,0,School,,0.5654779057929841,0.2955826421513093,0.0113,0.0,0.9657,0.5308,0.0,0.0,0.069,0.0417,0.0833,0.0389,0.0092,0.0131,0.0,0.005,0.0116,0.0,0.9657,0.5492,0.0,0.0,0.069,0.0417,0.0833,0.0397,0.0101,0.0137,0.0,0.0052,0.0115,0.0,0.9657,0.5371,0.0,0.0,0.069,0.0417,0.0833,0.0395,0.0094,0.0134,0.0,0.0051,reg oper account,block of flats,0.0112,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-2874.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n188182,0,Revolving loans,F,N,N,0,112500.0,450000.0,22500.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-12306,-1018,-272.0,-2660,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Trade: type 7,0.5096455298676347,0.7585313110795242,0.4686596550493113,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1226.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,6.0\n281545,0,Cash loans,F,N,Y,2,135000.0,848745.0,37516.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-13501,-1158,-7053.0,-4253,,1,1,1,1,1,0,Core staff,4.0,2,2,SATURDAY,9,0,0,0,0,0,0,Kindergarten,,0.2452928737486281,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n299151,0,Cash loans,F,N,Y,0,157500.0,585000.0,25767.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010276,-15572,-3149,-8619.0,-3121,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Self-employed,0.2892082983757215,0.3269298865314316,0.266456808245056,0.0722,0.0576,0.9767,0.6804,0.0078,0.0,0.2069,0.1667,0.0417,0.0511,0.0588,0.0628,0.0,0.0,0.0735,0.0598,0.9767,0.6929,0.0079,0.0,0.2069,0.1667,0.0417,0.0523,0.0643,0.0655,0.0,0.0,0.0729,0.0576,0.9767,0.6847,0.0078,0.0,0.2069,0.1667,0.0417,0.052000000000000005,0.0599,0.064,0.0,0.0,reg oper account,block of flats,0.0537,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-155.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n269393,0,Revolving loans,F,N,Y,1,67500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019689,-12094,-803,-5162.0,-2994,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 2,0.166549388819558,0.5493922155068142,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-207.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n453877,0,Cash loans,F,Y,Y,0,450000.0,1113840.0,57001.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.014464,-15469,-2148,-1727.0,-1813,6.0,1,1,1,1,1,1,Managers,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,Trade: type 7,0.29206583080171944,0.6214898833165752,0.501075160239048,,,,,,,0.069,0.0833,,,,0.0213,,,,,,,,,0.069,0.0833,,,,0.0222,,,,,,,,,0.069,0.0833,,,,0.0217,,,,block of flats,0.0173,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1554.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n290577,0,Cash loans,M,N,Y,2,270000.0,270000.0,24894.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-14156,-1016,-546.0,-1834,,1,1,0,1,0,0,,4.0,3,1,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 2,,0.3111219965028397,,0.1165,0.12,0.9781,,,0.0,0.2069,0.1667,,0.0586,,0.0925,,0.0,0.1187,0.1245,0.9782,,,0.0,0.2069,0.1667,,0.0599,,0.0964,,0.0,0.1176,0.12,0.9781,,,0.0,0.2069,0.1667,,0.0596,,0.0942,,0.0,,block of flats,0.0728,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n207666,0,Cash loans,F,N,Y,0,135000.0,173196.0,17257.5,153000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.018801,-24773,365243,-5211.0,-4444,,1,0,0,1,0,0,,1.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,0.8909414820414618,0.6090259513302816,0.7862666146611379,0.1979,0.006,0.9806,,,0.24,0.2069,0.3333,,,,0.2051,,0.1923,0.2017,0.0062,0.9806,,,0.2417,0.2069,0.3333,,,,0.2137,,0.2036,0.1999,0.006,0.9806,,,0.24,0.2069,0.3333,,,,0.2088,,0.1964,,block of flats,0.2187,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n341267,0,Cash loans,F,N,Y,2,135000.0,328405.5,15930.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-13924,-6734,-5585.0,-4493,,1,1,0,1,0,0,,4.0,3,3,TUESDAY,6,0,0,0,0,0,0,Business Entity Type 1,,0.5101251897948935,0.7530673920730478,0.1454,0.1906,0.9906,0.8708,0.024,0.0,0.3103,0.1667,0.2083,0.1374,0.1185,0.1548,0.0,0.0,0.1481,0.1978,0.9906,0.8759,0.0242,0.0,0.3103,0.1667,0.2083,0.1405,0.1295,0.1613,0.0,0.0,0.1468,0.1906,0.9906,0.8725,0.0241,0.0,0.3103,0.1667,0.2083,0.1398,0.1206,0.1576,0.0,0.0,,block of flats,0.1348,Panel,No,5.0,1.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n114278,0,Cash loans,M,N,Y,0,135000.0,524493.0,29416.5,486000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.014464,-19773,-5645,-11300.0,-3289,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,5,0,0,0,0,0,0,Medicine,,0.33087328902414753,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-688.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n214465,0,Cash loans,F,Y,N,0,90000.0,521280.0,27292.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.04622,-12769,-2083,-2159.0,-2514,7.0,1,1,1,1,1,0,Sales staff,2.0,1,1,THURSDAY,14,0,1,1,0,1,1,Business Entity Type 3,0.4707377360324363,0.7047248815463723,,0.2113,0.1263,0.9965,0.9524,0.3835,0.24,0.2069,0.3333,0.2917,0.0,0.1723,0.2132,0.0,0.1811,0.2153,0.1311,0.9965,0.9543,0.387,0.2417,0.2069,0.3333,0.2917,0.0,0.1882,0.2221,0.0,0.1917,0.2134,0.1263,0.9965,0.953,0.3859,0.24,0.2069,0.3333,0.2917,0.0,0.1753,0.217,0.0,0.1849,reg oper account,block of flats,0.2097,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1954.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n338356,0,Cash loans,F,N,N,1,81000.0,436032.0,25159.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006008,-12180,-5510,-889.0,-4674,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 2,,0.7210449927205346,0.4848514754962666,0.0918,0.0453,0.9737,0.6396,0.0013,0.0,0.069,0.1667,0.2083,0.0328,0.0748,0.0393,0.0,0.0,0.0935,0.047,0.9737,0.6537,0.0013,0.0,0.069,0.1667,0.2083,0.0336,0.0817,0.0409,0.0,0.0,0.0926,0.0453,0.9737,0.6444,0.0013,0.0,0.069,0.1667,0.2083,0.0334,0.0761,0.04,0.0,0.0,reg oper account,block of flats,0.0316,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1596.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n165883,0,Cash loans,F,N,Y,0,94500.0,1078200.0,31653.0,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.001276,-22553,365243,-1081.0,-4505,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,XNA,,0.7455624517594224,0.5370699579791587,0.0361,0.0596,0.9831,0.7688,0.0017,0.0,0.1034,0.1667,0.0417,0.0532,0.0084,0.0404,0.0039,0.0049,0.0231,0.0318,0.9831,0.7779,0.0,0.0,0.069,0.1667,0.0417,0.026000000000000002,0.0,0.0304,0.0,0.005,0.0364,0.0596,0.9831,0.7719,0.0017,0.0,0.1034,0.1667,0.0417,0.0541,0.0086,0.0411,0.0039,0.005,reg oper spec account,block of flats,0.0258,Block,No,0.0,0.0,0.0,0.0,-593.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n346787,0,Cash loans,F,N,Y,0,72000.0,254700.0,24939.0,225000.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.018209,-25058,365243,-4094.0,-4711,,1,0,0,1,0,0,,1.0,3,3,SUNDAY,8,0,0,0,0,0,0,XNA,,0.18008910135236536,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n219734,0,Cash loans,M,Y,N,1,202500.0,1125000.0,34110.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.0228,-14752,-1779,-4723.0,-4266,35.0,1,1,1,1,0,0,Security staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Security,,0.5192194809984382,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,10.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n105419,0,Cash loans,F,Y,Y,1,180000.0,545040.0,43191.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-19038,-5275,-748.0,-1796,4.0,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,0.609092316464896,0.5692674376418004,,0.0804,0.0,0.9732,0.6328,0.0084,0.0,0.1379,0.1667,0.2083,0.0407,0.0647,0.0624,0.0039,0.0052,0.0819,0.0,0.9732,0.6472,0.0085,0.0,0.1379,0.1667,0.2083,0.0417,0.0707,0.065,0.0039,0.0055,0.0812,0.0,0.9732,0.6377,0.0084,0.0,0.1379,0.1667,0.2083,0.0415,0.0658,0.0635,0.0039,0.0053,reg oper account,block of flats,0.0649,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n377560,0,Cash loans,M,N,N,2,112500.0,552555.0,29569.5,477000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.00733,-14877,-2762,-753.0,-4508,,1,1,0,1,0,0,Laborers,4.0,2,2,SATURDAY,10,0,0,0,0,0,0,Industry: type 11,,0.6383309528046931,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1830.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n328540,0,Cash loans,F,N,Y,1,81000.0,272520.0,17545.5,225000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.015221,-17798,-1477,-2636.0,-1349,,1,1,1,1,1,0,Core staff,3.0,2,2,TUESDAY,8,0,0,0,0,0,0,School,0.7264155977066407,0.3419392535005741,0.14111510044661266,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1455.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n405499,0,Cash loans,M,Y,Y,1,180000.0,298512.0,31491.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-17132,-417,-3343.0,-616,9.0,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,,0.5725940441289267,0.6722428897082422,0.1124,0.1025,0.9866,0.8164,0.0344,0.12,0.1034,0.3333,0.375,0.0555,0.0899,0.0769,0.0077,0.0036,0.1145,0.1064,0.9866,0.8236,0.0348,0.1208,0.1034,0.3333,0.375,0.0567,0.0983,0.0802,0.0078,0.0038,0.1135,0.1025,0.9866,0.8189,0.0347,0.12,0.1034,0.3333,0.375,0.0564,0.0915,0.0783,0.0078,0.0037,reg oper account,block of flats,0.1008,Panel,No,1.0,0.0,1.0,0.0,-1132.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n319754,0,Cash loans,F,N,Y,0,112500.0,508495.5,20295.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-19054,-1296,-7297.0,-2612,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Bank,,0.4638795336747786,0.7726311345553628,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-479.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n174971,0,Cash loans,M,Y,Y,0,225000.0,450000.0,23562.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018801,-9326,-998,-4136.0,-1998,3.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.4924089488712034,0.4561097392782771,0.0222,0.0168,0.9682,0.5579999999999999,0.021,0.0,0.0862,0.0833,0.1667,0.0267,0.0294,0.0238,0.0039,0.0202,0.0084,0.0,0.9677,0.5753,0.0212,0.0,0.069,0.0417,0.1667,0.0273,0.0321,0.0064,0.0039,0.0214,0.0224,0.0168,0.9682,0.5639,0.0211,0.0,0.0862,0.0833,0.1667,0.0271,0.0299,0.0243,0.0039,0.0207,reg oper account,block of flats,0.0371,\"Stone, brick\",Yes,8.0,0.0,8.0,0.0,-843.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n341756,0,Cash loans,F,N,N,1,90000.0,345645.0,16942.5,243000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Municipal apartment,0.04622,-14790,-262,-4253.0,-2434,,1,1,0,1,0,0,,2.0,1,1,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.7040828738633856,0.520897599048938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-669.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n260822,0,Cash loans,F,N,Y,2,135000.0,694773.0,29565.0,621000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-11637,-2955,-3204.0,-3310,,1,1,0,1,0,0,Laborers,3.0,3,3,MONDAY,8,0,0,0,0,0,0,Postal,0.4176936817541578,0.2327735312555421,0.7910749129911773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1125.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n150996,0,Cash loans,F,N,N,2,121500.0,269550.0,17820.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-13507,-817,-6504.0,-4442,,1,1,0,1,1,0,Laborers,4.0,2,2,FRIDAY,11,0,1,1,0,1,1,Trade: type 2,0.6041458387365043,0.7397913456535982,0.3979463219016906,0.1237,0.12,0.9866,0.8164,0.0247,0.0,0.2759,0.1667,0.2083,0.0608,0.1009,0.1141,0.0,0.0,0.1261,0.1246,0.9866,0.8236,0.0249,0.0,0.2759,0.1667,0.2083,0.0621,0.1102,0.1189,0.0,0.0,0.1249,0.12,0.9866,0.8189,0.0248,0.0,0.2759,0.1667,0.2083,0.0618,0.1026,0.1161,0.0,0.0,not specified,block of flats,0.1032,Panel,No,3.0,0.0,3.0,0.0,-1778.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n298674,0,Cash loans,F,Y,Y,1,135000.0,454500.0,16695.0,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-15732,-220,-7435.0,-4569,16.0,1,1,1,1,1,0,Laborers,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Industry: type 3,0.5053549390096785,0.7649170931689228,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-228.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n353919,0,Cash loans,M,Y,Y,0,270000.0,1762110.0,48586.5,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.008865999999999999,-13672,-5584,-5525.0,-4193,3.0,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Industry: type 11,,0.483514237435881,0.7309873696832169,0.001,,0.9677,,,0.0,0.0345,0.0,,0.0,,0.0005,,0.0,0.0011,,0.9677,,,0.0,0.0345,0.0,,0.0,,0.0005,,0.0,0.001,,0.9677,,,0.0,0.0345,0.0,,0.0,,0.0005,,0.0,,block of flats,0.0006,Wooden,No,2.0,0.0,2.0,0.0,-701.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n427726,0,Cash loans,F,N,N,0,126000.0,1005120.0,29520.0,720000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-18267,-11220,-3360.0,-1798,,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Industry: type 9,,0.1936647341464891,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1906.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n382663,0,Cash loans,M,Y,Y,0,270000.0,601470.0,30838.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-10819,-1768,-3958.0,-3502,65.0,1,1,0,1,0,0,Laborers,2.0,3,1,SATURDAY,11,0,0,0,0,0,0,Construction,0.6240825478743706,0.5304693604174611,0.34090642641523844,0.0691,0.0592,0.9826,0.7416,0.0571,0.08,0.069,0.3333,,0.0314,0.0563,0.0711,,0.0225,0.0347,0.0352,0.9811,0.7517,0.0364,0.0403,0.0345,0.3333,,0.0212,0.0303,0.0458,,0.0089,0.0749,0.0512,0.9811,0.7451,0.0513,0.08,0.069,0.3333,,0.0354,0.0616,0.0723,,0.023,reg oper account,block of flats,0.0364,Panel,No,1.0,1.0,1.0,1.0,-920.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n195168,0,Cash loans,F,Y,Y,0,225000.0,1315957.5,52317.0,1210500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-22171,-7224,-3765.0,-4499,24.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Government,,0.6658442697128529,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1631.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,9.0,0.0,3.0\n169548,0,Cash loans,F,Y,N,0,337500.0,1762110.0,48586.5,1575000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-15999,-464,-6286.0,-961,3.0,1,1,1,1,0,0,Accountants,2.0,2,2,THURSDAY,17,0,1,1,0,1,1,Business Entity Type 3,,0.5835328410827336,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1478.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n328139,0,Cash loans,F,Y,Y,1,135000.0,328365.0,26073.0,297000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009657,-14679,-1305,-5027.0,-5027,18.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,17,0,0,0,0,0,0,Business Entity Type 2,,0.5947062783881626,0.7776594425716818,0.1564,0.166,0.9851,0.7959999999999999,0.0,0.1864,0.1379,0.4167,0.0417,0.0555,0.3018,0.1549,0.0,0.0208,0.0504,0.0509,0.9856,0.804,0.0,0.0806,0.0345,0.4583,0.0417,0.0064,0.3297,0.0349,0.0,0.0,0.05,0.166,0.9856,0.7987,0.0,0.08,0.0345,0.4583,0.0417,0.0064,0.307,0.0342,0.0,0.0297,reg oper account,block of flats,0.3587,Panel,No,0.0,0.0,0.0,0.0,-1933.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n119692,0,Cash loans,F,N,Y,1,166500.0,728847.0,26307.0,553500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-14790,-520,-1496.0,-4203,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,SUNDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.7246906307903385,0.687656675744049,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2535.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n364602,0,Cash loans,F,N,N,0,135000.0,536917.5,27274.5,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.031329,-19220,-3320,-8570.0,-2767,,1,1,0,1,0,0,Managers,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 1,,0.6194197436385451,0.7309873696832169,0.0412,0.0479,0.9891,0.8504,0.0182,0.0,0.0862,0.1667,0.2083,0.05,0.0336,0.0422,0.0,0.0,0.0315,0.0274,0.9876,0.8367,0.0048,0.0,0.069,0.1667,0.2083,0.0466,0.0275,0.0299,0.0,0.0,0.0416,0.0479,0.9891,0.8524,0.0183,0.0,0.0862,0.1667,0.2083,0.0509,0.0342,0.0429,0.0,0.0,reg oper account,block of flats,0.0252,Panel,No,10.0,0.0,10.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n327842,0,Cash loans,F,Y,Y,0,337500.0,953460.0,68922.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008575,-12225,-1505,-158.0,-1424,64.0,1,1,0,1,0,0,Secretaries,2.0,2,2,MONDAY,13,0,0,0,0,1,1,Self-employed,0.43459391455773866,0.476353346022947,0.3031463744186309,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-455.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n440251,0,Cash loans,F,N,Y,0,135000.0,239850.0,23494.5,225000.0,Children,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.010006,-23815,365243,-7041.0,-3934,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.6478119499202895,0.7407990879702335,0.1804,0.151,0.9906,0.8708,0.1976,0.2,0.1724,0.3333,0.375,0.1607,0.1471,0.2212,,0.0022,0.1838,0.1567,0.9906,0.8759,0.1994,0.2014,0.1724,0.3333,0.375,0.1644,0.1607,0.2305,,0.0024,0.1822,0.151,0.9906,0.8725,0.1988,0.2,0.1724,0.3333,0.375,0.1635,0.1496,0.2252,,0.0023,reg oper account,block of flats,0.2825,Panel,No,0.0,0.0,0.0,0.0,-1641.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n264591,0,Cash loans,F,N,N,0,99000.0,239850.0,22554.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.022625,-13461,-1146,-6788.0,-3431,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6837166492315329,0.7268831726358661,,0.1237,0.1244,0.9781,0.7008,0.0166,0.0,0.2759,0.1667,0.2083,0.1288,0.1009,0.1047,0.0,0.0419,0.1261,0.1291,0.9782,0.7125,0.0168,0.0,0.2759,0.1667,0.2083,0.1317,0.1102,0.1091,0.0,0.0443,0.1249,0.1244,0.9781,0.7048,0.0168,0.0,0.2759,0.1667,0.2083,0.131,0.1026,0.1066,0.0,0.0427,reg oper account,block of flats,0.0914,Block,No,4.0,0.0,4.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n372208,0,Cash loans,F,N,N,0,157500.0,270000.0,26703.0,270000.0,Unaccompanied,Commercial associate,Incomplete higher,Separated,House / apartment,0.035792000000000004,-17211,-2132,-739.0,-739,,1,1,0,1,1,0,Core staff,1.0,2,2,SUNDAY,11,0,0,0,0,0,0,Security Ministries,0.6355283010614301,0.6872197041207146,0.7366226976503176,0.0124,0.0,0.9727,0.626,0.0012,0.0,0.069,0.0417,0.0833,0.0215,0.0101,0.0094,0.0,0.0,0.0126,0.0,0.9727,0.6406,0.0012,0.0,0.069,0.0417,0.0833,0.0219,0.011000000000000001,0.0098,0.0,0.0,0.0125,0.0,0.9727,0.631,0.0012,0.0,0.069,0.0417,0.0833,0.0218,0.0103,0.0096,0.0,0.0,reg oper account,block of flats,0.0074,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1563.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n199733,0,Revolving loans,F,Y,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Lower secondary,Married,House / apartment,0.015221,-8806,-1701,-6215.0,-947,6.0,1,1,0,1,1,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 1,0.1241920713689631,0.53709210436963,0.3046721837533529,0.0495,,0.9747,,,,0.1034,0.125,,0.0235,,0.038,,0.005,0.0504,,0.9747,,,,0.1034,0.125,,0.024,,0.0396,,0.0053,0.05,,0.9747,,,,0.1034,0.125,,0.0239,,0.0386,,0.0051,,block of flats,0.0433,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-488.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n320584,0,Cash loans,M,N,N,0,112500.0,254700.0,23814.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.015221,-10957,-3161,-4400.0,-3290,,1,1,0,1,1,0,Laborers,1.0,2,2,THURSDAY,10,0,0,0,1,1,0,Trade: type 7,0.20228759536416188,0.6235094072545607,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-299.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n117740,0,Cash loans,M,Y,N,0,90000.0,127350.0,12726.0,112500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.010643,-8574,-1012,-8574.0,-1110,7.0,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,12,0,0,0,0,1,1,Police,,0.2732109949603715,0.5762088360175724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-77.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n132985,0,Cash loans,F,Y,Y,0,81000.0,239850.0,25578.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-13942,-1930,-4088.0,-5004,5.0,1,1,1,1,0,1,Sales staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.5421882847583813,0.6734726884526796,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1267.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n164674,0,Cash loans,F,N,Y,0,202500.0,313438.5,17631.0,283500.0,Unaccompanied,Pensioner,Higher education,Civil marriage,House / apartment,0.01885,-21909,365243,-20.0,-5009,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,XNA,,0.6922316384385054,0.4992720153045617,0.0825,0.0582,0.9762,,,,0.069,0.1667,,,,0.0512,,,0.084,0.0604,0.9762,,,,0.069,0.1667,,,,0.0534,,,0.0833,0.0582,0.9762,,,,0.069,0.1667,,,,0.0521,,,,block of flats,0.0403,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n260821,0,Cash loans,F,N,Y,1,72000.0,225000.0,15790.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.02461,-14884,-7305,-7520.0,-2428,,1,1,1,1,0,0,Core staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,School,,0.6801023754970458,0.8050196619153701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1384.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n290892,0,Cash loans,F,N,Y,0,112500.0,450000.0,17095.5,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.0228,-9245,-908,-8449.0,-1912,,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Kindergarten,0.2738682966136419,0.5201965346615604,0.6738300778602003,0.066,,0.9786,,,0.0,0.1379,0.1667,,0.0401,,0.0503,,0.0029,0.0672,,0.9786,,,0.0,0.1379,0.1667,,0.040999999999999995,,0.0524,,0.003,0.0666,,0.9786,,,0.0,0.1379,0.1667,,0.0408,,0.0512,,0.0029,,block of flats,0.044000000000000004,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n242350,0,Cash loans,F,Y,Y,0,126000.0,904500.0,38322.0,904500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16761,-1977,-1381.0,-312,15.0,1,1,1,1,0,0,Medicine staff,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Medicine,0.7702487158640791,0.2742412990372422,0.5280927512030451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1084.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n359486,0,Cash loans,F,N,Y,0,103500.0,225000.0,19242.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-12439,-2514,-2048.0,-2000,,1,1,1,1,1,1,Medicine staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Electricity,0.3713034291601103,0.6206046887131392,0.34741822720026416,0.0557,0.0495,0.9841,0.7824,0.0262,0.08,0.069,0.3333,0.375,0.0558,0.0454,0.044000000000000004,0.0,0.0936,0.0567,0.0513,0.9841,0.7909,0.0264,0.0806,0.069,0.3333,0.375,0.0571,0.0496,0.0459,0.0,0.0991,0.0562,0.0495,0.9841,0.7853,0.0264,0.08,0.069,0.3333,0.375,0.0568,0.0462,0.0448,0.0,0.0956,reg oper account,block of flats,0.0553,Panel,No,15.0,0.0,15.0,0.0,-394.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n185135,0,Cash loans,F,Y,Y,0,90000.0,188685.0,10008.0,157500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010276,-18790,-1915,-5464.0,-2348,64.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.5882635605832559,0.2289759646847516,0.5082869913916046,0.0464,,,,,,0.069,0.1667,,0.0271,,0.062,,0.0042,0.0473,,,,,,0.069,0.1667,,0.0278,,0.0646,,0.0044,0.0468,,,,,,0.069,0.1667,,0.0276,,0.0631,,0.0043,,block of flats,0.0487,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-428.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n207650,0,Cash loans,F,N,Y,0,211500.0,604152.0,23139.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-20364,-1414,-6149.0,-177,,1,1,0,1,0,0,Medicine staff,2.0,1,1,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 1,,0.7630322969849866,0.3280631605201915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n290353,0,Cash loans,F,N,Y,0,45000.0,47970.0,5296.5,45000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.006852,-24341,365243,-2738.0,-4248,,1,0,0,1,0,0,,1.0,3,3,SUNDAY,7,0,0,0,0,0,0,XNA,,0.2285747062880121,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n453416,0,Cash loans,F,Y,N,1,135000.0,269550.0,14112.0,225000.0,Children,State servant,Higher education,Married,House / apartment,0.020713,-19954,-3284,-11024.0,-3175,15.0,1,1,1,1,1,0,Core staff,3.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,Government,0.7558195821071275,0.2991699288481161,0.36227724703843145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-712.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n174237,0,Cash loans,F,N,N,1,189000.0,1125000.0,32895.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-19092,-829,-4521.0,-2608,,1,1,0,1,0,0,Accountants,3.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,0.7120521437605591,0.6334095750116939,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1974.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,6.0\n341854,0,Cash loans,F,N,N,0,112500.0,781920.0,28084.5,675000.0,Unaccompanied,Commercial associate,Higher education,Married,Rented apartment,0.030755,-10545,-3656,-983.0,-983,,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,1,1,0,University,,0.65793619131192,0.6940926425266661,0.0196,,0.9518,,,0.0,0.1034,0.0417,,,,0.0138,,0.0557,0.02,,0.9518,,,0.0,0.1034,0.0417,,,,0.0144,,0.059000000000000004,0.0198,,0.9518,,,0.0,0.1034,0.0417,,,,0.0141,,0.0569,,block of flats,0.023,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1505.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n423274,0,Cash loans,F,Y,N,1,135000.0,95940.0,11515.5,90000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.005144,-10819,-202,-1155.0,-2570,4.0,1,1,0,1,0,0,Core staff,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Self-employed,0.7496169450844461,0.5924431286003118,0.09322509537747448,0.0928,0.1164,0.9846,0.7892,0.0119,0.0,0.2069,0.1667,0.2083,0.0619,0.0756,0.0906,0.0,0.0,0.0945,0.1208,0.9846,0.7975,0.012,0.0,0.2069,0.1667,0.2083,0.0634,0.0826,0.0944,0.0,0.0,0.0937,0.1164,0.9846,0.792,0.0119,0.0,0.2069,0.1667,0.2083,0.063,0.077,0.0923,0.0,0.0,reg oper spec account,block of flats,0.0778,Panel,No,0.0,0.0,0.0,0.0,-2039.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n333400,0,Cash loans,M,N,Y,1,135000.0,288873.0,18936.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-11965,-3381,-6038.0,-4148,,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.502747460187871,0.6854213453829273,0.4596904504249018,0.3144,0.3386,0.9776,0.6940000000000001,0.1651,0.0,0.6897,0.1667,0.2083,0.3198,0.2564,0.3178,0.0,0.0,0.3204,0.3513,0.9777,0.706,0.1666,0.0,0.6897,0.1667,0.2083,0.3271,0.2801,0.3311,0.0,0.0,0.3175,0.3386,0.9776,0.6981,0.1661,0.0,0.6897,0.1667,0.2083,0.3254,0.2608,0.3235,0.0,0.0,reg oper account,block of flats,0.3402,Panel,No,0.0,0.0,0.0,0.0,-1355.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n323677,0,Cash loans,F,Y,N,0,225000.0,967500.0,28417.5,967500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.028663,-18046,-7504,-477.0,-1533,23.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Trade: type 7,0.5690939255305131,0.6096089752274453,,0.0773,0.0724,0.9831,0.7688,0.0139,0.0,0.1724,0.1667,0.2083,0.0662,0.063,0.069,0.0,0.0,0.0788,0.0751,0.9831,0.7779,0.013999999999999999,0.0,0.1724,0.1667,0.2083,0.0677,0.0689,0.0718,0.0,0.0,0.0781,0.0724,0.9831,0.7719,0.013999999999999999,0.0,0.1724,0.1667,0.2083,0.0673,0.0641,0.0702,0.0,0.0,reg oper account,block of flats,0.0626,Panel,No,0.0,0.0,0.0,0.0,-3240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n171536,0,Cash loans,F,N,N,0,112500.0,183294.0,14314.5,153000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.018634,-17239,-3037,-2728.0,-766,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,16,0,0,0,1,1,1,Business Entity Type 3,,0.3974006699134879,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1999.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n179217,0,Cash loans,F,Y,Y,0,99000.0,284400.0,16011.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-24195,365243,-2603.0,-3888,7.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,17,0,0,0,0,0,0,XNA,,0.4045859984119182,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,7.0,0.0,7.0,0.0,-657.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n254157,0,Cash loans,M,Y,N,3,180000.0,1312110.0,55593.0,1125000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010032,-14691,-3626,-3102.0,-4241,20.0,1,1,0,1,1,0,Drivers,5.0,2,2,WEDNESDAY,3,0,0,0,0,0,0,Business Entity Type 3,,0.6249558957818837,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1831.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n107863,0,Cash loans,M,Y,N,1,112500.0,808650.0,31333.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010556,-17503,-800,-9982.0,-1047,14.0,1,1,1,1,0,0,Core staff,3.0,3,3,SUNDAY,11,0,0,0,1,1,0,Trade: type 7,,0.2858978721410488,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-507.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n137156,0,Cash loans,F,N,N,0,157500.0,755190.0,28894.5,675000.0,,Commercial associate,Higher education,Widow,Rented apartment,0.025164,-18375,-6375,-3576.0,-1912,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,School,0.7980752560996274,0.6914950559551482,0.6545292802242897,0.0825,0.0698,0.9901,0.8640000000000001,0.0174,0.04,0.1379,0.25,0.2917,0.0164,0.0672,0.0836,0.0,0.0,0.0735,0.0533,0.9901,0.8693,0.0164,0.0,0.069,0.1667,0.2083,0.0147,0.0643,0.087,0.0,0.0,0.0833,0.0698,0.9901,0.8658,0.0175,0.04,0.1379,0.25,0.2917,0.0167,0.0684,0.0851,0.0,0.0,reg oper account,block of flats,0.0963,Block,No,4.0,0.0,4.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n251062,0,Cash loans,F,Y,Y,0,157500.0,855000.0,24997.5,855000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-13889,-865,-2461.0,-4855,3.0,1,1,0,1,1,1,,2.0,2,2,SUNDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.4773317628747693,0.6725824296105448,0.5971924268337128,0.0928,,0.9771,,,0.0,0.2069,0.1667,,,,0.1012,,0.0,0.0945,,0.9772,,,0.0,0.2069,0.1667,,,,0.1054,,0.0,0.0937,,0.9771,,,0.0,0.2069,0.1667,,,,0.10300000000000001,,0.0,,block of flats,0.0796,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-267.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n250131,0,Cash loans,F,N,Y,0,157500.0,1024740.0,49428.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-9964,-1631,-3419.0,-1291,,1,1,0,1,0,1,Core staff,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Trade: type 3,0.343834128172884,0.2920668112970868,0.392773860603134,0.1113,0.0606,0.9821,0.7552,0.0292,0.08,0.069,0.3333,0.375,0.085,0.0908,0.0797,0.0,0.0,0.1134,0.0629,0.9821,0.7648,0.0295,0.0806,0.069,0.3333,0.375,0.0869,0.0992,0.083,0.0,0.0,0.1124,0.0606,0.9821,0.7585,0.0294,0.08,0.069,0.3333,0.375,0.0864,0.0923,0.0811,0.0,0.0,reg oper account,block of flats,0.0842,Panel,No,0.0,0.0,0.0,0.0,-888.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n221210,0,Cash loans,F,N,Y,1,180000.0,521280.0,31630.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-10078,-293,-4102.0,-2320,,1,1,0,1,0,0,,3.0,2,2,MONDAY,14,0,0,0,0,1,1,Government,,0.6846192324272472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1625.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n212927,0,Cash loans,F,N,Y,0,157500.0,536917.5,23778.0,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.025164,-23251,365243,-12048.0,-5363,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.15357057289804046,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2577.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n283194,0,Revolving loans,F,N,Y,1,76500.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-13077,-350,-1175.0,-3654,,1,1,0,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Trade: type 3,,0.6179288167337234,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2753.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n172988,0,Cash loans,M,N,N,0,90000.0,467257.5,21910.5,328500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.007120000000000001,-13796,-90,-7634.0,-1205,,1,1,0,1,1,0,Laborers,2.0,2,2,SATURDAY,11,0,0,0,1,1,0,Business Entity Type 3,,0.153709906803129,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-165.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n157774,0,Cash loans,F,N,N,0,180000.0,900000.0,43299.0,900000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.016612000000000002,-22012,365243,-13827.0,-4888,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.21799902061590407,0.6363761710860439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2254.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n243535,0,Cash loans,M,N,N,0,391500.0,1467612.0,54517.5,1350000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.031329,-16742,-8458,-2556.0,-287,,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Medicine,,0.6372448317832702,0.392773860603134,0.0454,0.0374,0.9613,0.4696,0.0029,0.0,0.1379,0.1042,0.0208,0.002,0.037000000000000005,0.0415,0.0,0.0,0.0084,0.0,0.9598,0.4708,0.0023,0.0,0.069,0.0417,0.0,0.0,0.0073,0.0054,0.0,0.0,0.0458,0.0374,0.9613,0.4767,0.0029,0.0,0.1379,0.1042,0.0208,0.002,0.0376,0.0423,0.0,0.0,reg oper account,block of flats,0.006,Wooden,Yes,0.0,0.0,0.0,0.0,-1945.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n350000,0,Cash loans,F,Y,N,1,90000.0,450000.0,21649.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-14662,-2061,-2151.0,-1184,11.0,1,1,1,1,0,0,High skill tech staff,3.0,2,2,TUESDAY,14,0,0,0,0,1,1,Self-employed,0.35668362354871336,0.5102891283122993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-667.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n272177,0,Cash loans,M,N,N,0,157500.0,313438.5,29133.0,283500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.018634,-11161,-173,-4339.0,-3791,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.22182428793687808,0.683390341458391,,,,0.9916,,,,,,,,,0.353,,,,,0.9916,,,,,,,,,0.3678,,,,,0.9916,,,,,,,,,0.3593,,,,,0.3133,,No,1.0,0.0,1.0,0.0,-22.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n118999,0,Cash loans,F,N,Y,0,87300.0,408780.0,14679.0,337500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.0228,-20985,-199,-755.0,-4401,,1,1,1,1,0,0,Waiters/barmen staff,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Medicine,,0.5431646483490534,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-547.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n213352,0,Revolving loans,M,Y,Y,0,225000.0,675000.0,33750.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-20280,365243,-5567.0,-3421,4.0,1,0,0,1,0,0,,2.0,2,2,SUNDAY,15,0,0,0,0,0,0,XNA,,0.4236303313643529,0.4596904504249018,0.0082,,0.9732,,,0.0,0.0345,0.0417,0.0417,0.0029,0.0067,0.0074,0.0,,0.0084,,0.9732,,,0.0,0.0345,0.0417,0.0417,0.003,0.0073,0.0077,0.0,,0.0083,,0.9732,,,0.0,0.0345,0.0417,0.0417,0.003,0.0068,0.0075,0.0,,reg oper account,block of flats,0.0089,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2583.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n305158,0,Cash loans,F,N,Y,1,90000.0,71109.0,5746.5,54000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.014464,-11249,-226,-938.0,-3903,,1,1,0,1,0,1,Core staff,2.0,2,2,WEDNESDAY,7,0,0,0,0,1,1,School,0.5151822219607016,0.4221093435454996,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-385.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n447003,0,Revolving loans,F,N,Y,0,99000.0,270000.0,13500.0,270000.0,\"Spouse, partner\",State servant,Higher education,Married,House / apartment,0.009175,-9791,-699,-554.0,-2212,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,School,0.4930045926640205,0.7252783255258051,0.10822632266971416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-451.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n426743,0,Cash loans,M,N,Y,0,90000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003813,-18712,-3678,-3361.0,-2271,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.1666624328389173,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-282.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n371063,0,Cash loans,F,Y,N,1,180000.0,1006920.0,42660.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-10686,-1514,-4831.0,-3115,8.0,1,1,0,1,1,0,Sales staff,3.0,1,1,THURSDAY,16,0,0,0,0,0,0,Self-employed,0.3763728637244878,0.6826280874955613,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-160.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n180053,1,Cash loans,M,N,Y,1,135000.0,728460.0,57685.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.002134,-10303,-353,-4739.0,-2938,,1,1,0,1,0,0,Secretaries,3.0,3,3,TUESDAY,8,0,0,0,1,1,1,Security Ministries,,0.3460844537170605,0.190705947811054,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-209.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n149640,0,Cash loans,F,N,Y,1,382500.0,1030680.0,65866.5,900000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.04622,-11049,-693,-5189.0,-3651,,1,1,0,1,0,1,,2.0,1,1,SATURDAY,16,0,1,1,0,0,0,Business Entity Type 3,0.4988536849881756,0.7642203792972919,,0.1286,0.0719,0.9876,0.83,0.0249,0.18,0.0776,0.5625,0.6042,0.09300000000000001,0.1049,0.1481,0.0,0.019,0.1166,0.0517,0.9876,0.8367,0.027000000000000003,0.1611,0.069,0.5417,0.5833,0.054000000000000006,0.1019,0.091,0.0,0.0,0.1155,0.0715,0.9876,0.8323,0.0267,0.16,0.069,0.5417,0.5833,0.0707,0.0949,0.1282,0.0,0.0006,reg oper account,block of flats,0.1994,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1844.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n358240,0,Cash loans,F,N,Y,0,360000.0,679500.0,28917.0,679500.0,Unaccompanied,State servant,Higher education,Separated,House / apartment,0.04622,-18320,-187,-834.0,-1842,,1,1,1,1,0,0,Core staff,1.0,1,1,WEDNESDAY,13,0,0,0,0,1,1,Other,0.4769502530934603,0.6064507035631299,0.3001077565791181,0.0742,0.0593,0.995,0.932,0.0653,0.08,0.069,0.3333,0.375,0.0663,0.0605,0.0761,0.0,0.0,0.0756,0.0615,0.995,0.9347,0.0659,0.0806,0.069,0.3333,0.375,0.0679,0.0661,0.0793,0.0,0.0,0.0749,0.0593,0.995,0.9329,0.0657,0.08,0.069,0.3333,0.375,0.0675,0.0616,0.0775,0.0,0.0,reg oper spec account,block of flats,0.0881,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n187389,0,Revolving loans,F,N,Y,0,225000.0,450000.0,22500.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-19237,-11880,-612.0,-2783,,1,1,0,1,0,0,,1.0,3,2,SATURDAY,13,0,0,0,0,0,0,Industry: type 7,,0.5411562815779336,0.7076993447402619,0.1052,0.0362,0.9767,0.6804,0.0114,0.0,0.2069,0.1667,0.2083,0.0615,0.0849,0.0926,0.0039,0.0079,0.1071,0.0375,0.9767,0.6929,0.0115,0.0,0.2069,0.1667,0.2083,0.0629,0.0927,0.0964,0.0039,0.0083,0.1062,0.0362,0.9767,0.6847,0.0114,0.0,0.2069,0.1667,0.2083,0.0625,0.0864,0.0942,0.0039,0.008,reg oper account,block of flats,0.0807,Panel,No,0.0,0.0,0.0,0.0,-1564.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n205256,0,Revolving loans,F,N,N,0,180000.0,1350000.0,67500.0,1350000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,With parents,0.006629,-8550,-1154,-8550.0,-1207,,1,1,0,1,0,0,Accountants,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3651385303910656,0.22680584873122345,,0.2053,0.0,0.9866,0.8232,0.0191,0.0,0.1666,0.1667,0.2083,0.0242,0.4547,0.1056,0.0039,0.001,0.1261,0.0,0.9856,0.8105,0.0193,0.0,0.2069,0.1667,0.2083,0.0189,0.4968,0.0597,0.0039,0.0,0.1249,0.0,0.9856,0.8323,0.0192,0.0,0.2069,0.1667,0.2083,0.026000000000000002,0.4626,0.1245,0.0039,0.0,reg oper account,block of flats,0.0734,Panel,No,0.0,0.0,0.0,0.0,-210.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n281594,0,Cash loans,M,Y,Y,1,180000.0,225000.0,15349.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-15529,-1401,-6183.0,-4487,0.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,Self-employed,,0.6259189853494215,,0.1237,0.0967,0.9771,,,0.0,0.2069,0.1667,,0.0423,,0.1047,,0.0039,0.1261,0.1004,0.9772,,,0.0,0.2069,0.1667,,0.0433,,0.1091,,0.0041,0.1249,0.0967,0.9771,,,0.0,0.2069,0.1667,,0.0431,,0.1066,,0.004,,block of flats,0.091,Panel,No,0.0,0.0,0.0,0.0,-1863.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n191252,0,Cash loans,F,N,Y,0,202500.0,307944.0,30456.0,292500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.008473999999999999,-10871,-1591,-4830.0,-270,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Hotel,,0.4561880256483439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-262.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n420268,0,Cash loans,M,Y,Y,1,216000.0,691020.0,26905.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-11720,-1893,-954.0,-3593,5.0,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,10,0,0,0,0,1,1,Industry: type 1,0.20633848264412533,0.4267943144321565,0.5814837058057234,0.034,,0.9742,,,,0.0345,0.0417,,0.0069,,0.0087,,,0.0347,,0.9742,,,,0.0345,0.0417,,0.006999999999999999,,0.009000000000000001,,,0.0344,,0.9742,,,,0.0345,0.0417,,0.006999999999999999,,0.0088,,,,block of flats,0.0068,,Yes,1.0,0.0,1.0,0.0,-1641.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n123060,0,Revolving loans,M,N,Y,0,126000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-8204,-437,-291.0,-851,,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Industry: type 3,,0.4105735627845491,0.2608559142068693,0.1139,0.10400000000000001,0.9727,0.6668,0.0277,0.0,0.1952,0.125,0.1458,0.0625,0.0534,0.0734,0.0039,0.0843,0.1061,0.0943,0.9662,0.6406,0.0007,0.0,0.2759,0.1667,0.0833,0.013000000000000001,0.0073,0.0059,0.0039,0.0028,0.115,0.10400000000000001,0.9727,0.6713,0.0279,0.0,0.2759,0.1667,0.1458,0.0863,0.0543,0.1027,0.0039,0.1148,org spec account,block of flats,0.006,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.0,0.0,0.0,0.0,0.0,0.0\n199224,0,Cash loans,M,Y,Y,0,360000.0,1971072.0,68643.0,1800000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-16756,-989,-7591.0,-310,18.0,1,1,0,1,0,1,Medicine staff,2.0,1,1,FRIDAY,15,0,0,0,0,0,0,Medicine,0.6088228844920626,0.6829690926635971,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n175491,0,Cash loans,F,Y,Y,0,202500.0,544491.0,16047.0,454500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.025164,-19412,-375,-2351.0,-2893,18.0,1,1,0,1,0,0,Medicine staff,2.0,2,2,SATURDAY,8,0,0,0,1,1,1,Medicine,,0.16990359924812706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n263025,0,Revolving loans,M,N,Y,0,202500.0,157500.0,7875.0,157500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-16345,-3488,-1241.0,-4355,,1,1,1,1,1,0,Laborers,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.3788554420305337,0.3946076480867301,0.4938628816996244,0.0825,0.0374,0.9737,0.6396,0.0239,0.0,0.1379,0.1667,0.2083,0.0581,0.0672,0.0528,0.0,0.0184,0.084,0.0388,0.9737,0.6537,0.0241,0.0,0.1379,0.1667,0.2083,0.0594,0.0735,0.055,0.0,0.0194,0.0833,0.0374,0.9737,0.6444,0.0241,0.0,0.1379,0.1667,0.2083,0.0591,0.0684,0.0537,0.0,0.0187,reg oper account,block of flats,0.0654,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-2725.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n137819,0,Cash loans,F,Y,N,1,202500.0,675000.0,34465.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-15974,-3486,-3735.0,-4390,13.0,1,1,0,1,1,0,Realty agents,3.0,2,2,FRIDAY,14,0,0,0,0,0,0,Services,0.6327453330673422,0.6226517577753016,0.7076993447402619,0.0639,0.0586,0.9925,0.898,0.0089,0.0,0.1379,0.1667,0.2083,0.0237,0.0504,0.0561,0.0077,0.0065,0.0651,0.0609,0.9926,0.902,0.009000000000000001,0.0,0.1379,0.1667,0.2083,0.0242,0.0551,0.0585,0.0078,0.0069,0.0645,0.0586,0.9925,0.8994,0.0089,0.0,0.1379,0.1667,0.2083,0.0241,0.0513,0.0571,0.0078,0.0067,reg oper account,block of flats,0.0481,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-145.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n343117,0,Cash loans,F,Y,Y,2,72000.0,270000.0,12024.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-13139,-283,-4150.0,-4152,2.0,1,1,0,1,0,0,,4.0,2,2,TUESDAY,12,0,0,0,1,1,0,Other,,0.6158771059829309,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1180.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n127089,0,Cash loans,F,N,Y,0,126000.0,237024.0,18855.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-11935,-976,-2787.0,-4526,,1,1,0,1,1,0,Core staff,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,Self-employed,0.5497674647354829,0.15357057289804046,0.7801436381572275,0.0619,0.0628,0.9722,0.6192,0.006999999999999999,0.0,0.1034,0.1667,0.2083,0.0369,0.0471,0.0461,0.0154,0.0101,0.063,0.0652,0.9722,0.6341,0.0071,0.0,0.1034,0.1667,0.2083,0.0378,0.0514,0.048,0.0156,0.0106,0.0625,0.0628,0.9722,0.6243,0.0071,0.0,0.1034,0.1667,0.2083,0.0376,0.0479,0.0469,0.0155,0.0103,reg oper account,block of flats,0.0418,Block,No,11.0,0.0,10.0,0.0,-2058.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n171951,0,Cash loans,M,Y,Y,0,180000.0,512064.0,23998.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-19200,-2372,-4526.0,-2726,14.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.5888478383266021,0.1997705696341145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-1912.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n100869,0,Cash loans,F,N,N,0,90000.0,242950.5,14994.0,184500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020713,-16645,-300,-244.0,-208,,1,1,0,1,0,0,High skill tech staff,1.0,3,2,MONDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.08051491301159555,0.3643524818084565,0.3859146722745145,0.0825,0.0797,0.9757,0.6668,0.008,0.0,0.1379,0.1667,0.2083,0.0345,0.0672,0.0712,0.0,0.0,0.084,0.0828,0.9757,0.6798,0.0081,0.0,0.1379,0.1667,0.2083,0.0353,0.0735,0.0742,0.0,0.0,0.0833,0.0797,0.9757,0.6713,0.008,0.0,0.1379,0.1667,0.2083,0.0351,0.0684,0.0725,0.0,0.0,reg oper account,block of flats,0.055999999999999994,Panel,No,5.0,0.0,5.0,0.0,-18.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n158648,0,Cash loans,F,N,Y,0,103500.0,315000.0,12006.0,315000.0,Family,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.032561,-21634,-1428,-1153.0,-4680,,1,1,0,1,1,0,,1.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.3146620657019056,,0.1052,0.0745,0.9757,0.6668,0.0107,0.0,0.1724,0.1667,0.2083,0.1477,0.0841,0.0901,0.0077,0.0109,0.1071,0.0774,0.9757,0.6798,0.0108,0.0,0.1724,0.1667,0.2083,0.1511,0.0918,0.0939,0.0078,0.0116,0.1062,0.0745,0.9757,0.6713,0.0108,0.0,0.1724,0.1667,0.2083,0.1503,0.0855,0.0917,0.0078,0.0112,reg oper account,block of flats,0.0733,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n401050,0,Cash loans,F,N,N,0,90000.0,135000.0,14539.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009175,-20411,365243,-7767.0,-3478,,1,0,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,0.7693338964166758,0.7477795126617671,0.4722533429586386,0.1438,0.1295,0.9921,,,0.16,0.1552,0.3333,,0.003,,0.14800000000000002,,0.0112,0.0326,0.0302,0.9891,,,0.0403,0.0345,0.3333,,0.0,,0.0335,,0.0,0.1452,0.1295,0.9921,,,0.16,0.1552,0.3333,,0.003,,0.1507,,0.0115,,block of flats,0.2076,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-909.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n295708,0,Cash loans,M,Y,Y,0,225000.0,436032.0,31860.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.005084,-20658,-1796,-11563.0,-3275,14.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Self-employed,,0.698871819010661,,0.0619,,0.9747,,,,0.1034,0.1667,,,,0.0497,,0.0,0.063,,0.9747,,,,0.1034,0.1667,,,,0.0518,,0.0,0.0625,,0.9747,,,,0.1034,0.1667,,,,0.0506,,0.0,,block of flats,0.0391,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1105.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n416527,0,Cash loans,F,N,N,2,90000.0,385164.0,20295.0,292500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.018029,-11043,365243,-3630.0,-156,,1,0,0,1,0,0,,3.0,3,3,FRIDAY,10,0,0,0,0,0,0,XNA,0.27060736327469503,0.4852400881388628,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-659.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n354380,0,Cash loans,F,N,Y,0,90000.0,562491.0,22437.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.009657,-18258,-3964,-2001.0,-1765,,1,1,0,1,1,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5513619620368974,0.746300213050371,0.0186,0.0496,0.9816,0.7484,0.0025,0.0,0.1034,0.0417,0.0417,0.0185,0.0151,0.018000000000000002,0.0,0.0,0.0189,0.0515,0.9816,0.7583,0.0025,0.0,0.1034,0.0417,0.0417,0.0189,0.0165,0.0187,0.0,0.0,0.0187,0.0496,0.9816,0.7518,0.0025,0.0,0.1034,0.0417,0.0417,0.0188,0.0154,0.0183,0.0,0.0,reg oper account,block of flats,0.0141,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n316668,0,Cash loans,M,N,Y,0,162000.0,604152.0,42178.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.004849,-22382,-8103,-7175.0,-4862,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Religion,0.879002118842072,0.5541676752563026,,0.146,0.0925,0.9816,0.7484,0.2163,0.1064,0.1838,0.2221,0.2638,0.1072,0.1471,0.1376,0.0097,0.034,0.0704,0.0,0.9796,0.7321,0.0099,0.0,0.1379,0.1667,0.2083,0.0632,0.0597,0.0638,0.0078,0.0196,0.0729,0.0925,0.9826,0.7652,0.2177,0.0,0.1379,0.1667,0.2083,0.0819,0.1496,0.0698,0.0097,0.0347,org spec account,block of flats,0.2313,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n140274,0,Cash loans,M,N,Y,1,90000.0,269550.0,14751.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-15352,-3125,-3171.0,-4138,,1,1,1,1,1,0,,3.0,3,3,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.5227885061127214,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n289413,0,Cash loans,M,N,N,1,157500.0,2250000.0,59485.5,2250000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.008575,-14170,-3801,-3305.0,-4452,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,17,0,0,0,1,0,1,Transport: type 2,0.6839937555613325,0.520829627081515,0.4048783643353997,0.0778,,0.9851,0.5920000000000001,,0.12,0.0862,0.2083,,0.0244,0.0269,0.0724,,0.0087,0.0336,,0.9702,0.608,,0.1208,0.069,0.0417,,0.025,0.0294,0.0116,,0.0,0.0786,,0.9851,0.5975,,0.12,0.0862,0.2083,,0.0249,0.0274,0.0737,,0.0089,reg oper account,block of flats,0.0087,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-13.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0.0,0.0,0.0,0.0,0.0,0.0\n219490,0,Cash loans,F,N,Y,0,126000.0,1080000.0,31576.5,1080000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.004849,-15328,-3152,-6185.0,-4700,,1,1,0,1,1,0,Sales staff,2.0,2,2,FRIDAY,16,0,0,0,0,1,1,Business Entity Type 3,0.5135526801352981,0.6832578461336695,0.31547215492577346,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1324.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n342998,0,Cash loans,F,N,Y,1,202500.0,945000.0,40167.0,945000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.003069,-16981,-2005,-7577.0,-539,,1,1,0,1,0,0,Laborers,2.0,3,3,FRIDAY,15,0,0,0,0,0,0,University,0.749372978266848,0.6497701762842617,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-608.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n230095,0,Cash loans,M,N,Y,2,112500.0,225000.0,22383.0,225000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.0228,-24773,365243,-9303.0,-3506,,1,0,0,1,0,0,,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,0.7355615372904901,0.6321392282020271,0.6380435278721609,0.1062,0.0635,0.9851,,,0.08,0.0345,0.2917,,0.0623,,0.0709,,0.1216,0.1082,0.0658,0.9851,,,0.0806,0.0345,0.2917,,0.0637,,0.0739,,0.1287,0.1072,0.0635,0.9851,,,0.08,0.0345,0.2917,,0.0634,,0.0722,,0.1241,,block of flats,0.0825,Panel,No,3.0,1.0,3.0,0.0,-1277.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n380661,0,Cash loans,F,N,Y,0,202500.0,777024.0,41526.0,720000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-19192,-1534,-5891.0,-2728,,1,1,0,1,1,0,Core staff,2.0,1,1,SATURDAY,15,0,0,0,0,0,0,Industry: type 9,0.8469906276515118,0.6722500683594065,0.7338145369642702,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3307,,No,0.0,0.0,0.0,0.0,-726.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,1.0\n333996,0,Cash loans,F,N,Y,0,85500.0,497520.0,49207.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018634,-17993,-4901,-5369.0,-1542,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Agriculture,0.4901502483581599,0.3305006075453485,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1383.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n436777,0,Cash loans,F,N,N,0,144000.0,1078200.0,28570.5,900000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.02461,-23390,-9589,-7383.0,-4304,,1,1,0,1,1,0,,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Postal,,0.524283115361769,0.6910212267577837,0.0206,0.0142,0.9841,0.7824,0.0027,0.0,0.0917,0.0554,0.0971,0.0278,0.0168,0.0202,0.0,0.0,0.0189,0.0,0.9806,0.7452,0.0025,0.0,0.1034,0.0417,0.0833,0.0209,0.0165,0.018000000000000002,0.0,0.0,0.0187,0.0,0.9836,0.7786,0.0025,0.0,0.1034,0.0417,0.0833,0.0258,0.0154,0.0179,0.0,0.0,reg oper account,block of flats,0.0219,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1750.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n275983,0,Cash loans,F,N,N,0,157500.0,700830.0,22608.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.003540999999999999,-14373,-1254,-4216.0,-5326,,1,1,1,1,0,0,Sales staff,1.0,1,1,THURSDAY,9,0,0,0,0,0,0,Self-employed,0.5272346810489775,0.7013327439948316,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-427.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n204045,0,Cash loans,M,Y,Y,2,265500.0,1762110.0,48586.5,1575000.0,Family,Pensioner,Higher education,Married,House / apartment,0.019101,-19333,365243,-13.0,-1035,9.0,1,0,0,1,0,0,,4.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,0.6432485298157853,0.6936554014841687,0.18195910978627847,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-472.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n171553,0,Cash loans,F,N,Y,0,103500.0,808650.0,23305.5,675000.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.0228,-20201,365243,-13142.0,-3672,,1,0,0,1,0,0,,1.0,2,2,MONDAY,8,0,0,0,0,0,0,XNA,0.6586436018304777,0.5379944922777148,0.7121551551910698,0.1031,0.0938,0.9796,0.7212,0.0115,0.0,0.2069,0.1667,0.2083,0.0982,0.0841,0.0907,0.0,0.0,0.105,0.0973,0.9796,0.7321,0.0116,0.0,0.2069,0.1667,0.2083,0.1004,0.0918,0.0945,0.0,0.0,0.1041,0.0938,0.9796,0.7249,0.0116,0.0,0.2069,0.1667,0.2083,0.0999,0.0855,0.0923,0.0,0.0,reg oper account,block of flats,0.0776,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n305666,0,Cash loans,M,Y,Y,0,180000.0,988191.0,39321.0,909000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-12151,-1070,-6252.0,-4557,0.0,1,1,0,1,1,0,Sales staff,2.0,2,2,SATURDAY,16,0,0,0,0,1,1,Other,0.3791862420220473,0.6828222950576277,0.1301285429480269,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-820.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n422493,0,Revolving loans,M,Y,Y,1,180000.0,180000.0,9000.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-13789,-3518,-5000.0,-3424,13.0,1,1,0,1,0,1,Laborers,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Industry: type 5,0.5288163870952078,0.629990723757851,0.1654074596555436,0.0619,0.0475,0.9737,,,0.0,0.1379,0.1667,,0.0614,,0.0361,,0.0,0.063,0.0493,0.9737,,,0.0,0.1379,0.1667,,0.0628,,0.0376,,0.0,0.0625,0.0475,0.9737,,,0.0,0.1379,0.1667,,0.0625,,0.0367,,0.0,,block of flats,0.0414,Panel,No,0.0,0.0,0.0,0.0,-2074.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n140745,0,Cash loans,F,N,N,1,157500.0,942300.0,30528.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.003818,-15690,-1463,-9028.0,-3203,,1,1,0,1,0,0,Cleaning staff,3.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Self-employed,0.1582200933945909,0.587179127270098,0.7862666146611379,,,0.9757,,,,0.0345,0.0417,,0.0048,,0.0048,,,,,0.9757,,,,0.0345,0.0417,,0.0049,,0.005,,,,,0.9757,,,,0.0345,0.0417,,0.0049,,0.0049,,,,block of flats,0.0048,Wooden,Yes,0.0,0.0,0.0,0.0,-2084.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n368498,0,Cash loans,F,Y,Y,1,225000.0,1006920.0,42790.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.007273999999999998,-12881,-5324,-7028.0,-3884,24.0,1,1,0,1,0,1,Core staff,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Medicine,0.6257546016831919,0.6196259237027962,0.3441550073724169,0.0082,0.0,0.9518,0.3404,0.0008,0.0,0.0345,0.0417,,0.0,,0.0051,,0.0033,0.0084,0.0,0.9518,0.3662,0.0008,0.0,0.0345,0.0417,,0.0,,0.0053,,0.0035,0.0083,0.0,0.9518,0.3492,0.0008,0.0,0.0345,0.0417,,0.0,,0.0052,,0.0033,reg oper account,block of flats,0.0052,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1066.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n319767,0,Cash loans,M,Y,N,0,157500.0,148500.0,11862.0,148500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.028663,-19372,-1089,-1155.0,-2928,7.0,1,1,0,1,0,0,Drivers,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,Bank,,0.23845240205064844,0.470456116119975,0.0876,0.0478,0.9841,0.7824,0.1112,0.08,0.0345,0.4583,0.0417,0.0104,0.0714,0.0773,0.0,0.0097,0.0893,0.0496,0.9841,0.7909,0.1122,0.0806,0.0345,0.4583,0.0417,0.0106,0.0781,0.0805,0.0,0.0103,0.0885,0.0478,0.9841,0.7853,0.1119,0.08,0.0345,0.4583,0.0417,0.0106,0.0727,0.0787,0.0,0.0099,reg oper account,block of flats,0.079,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1288.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n189813,0,Revolving loans,F,Y,Y,0,315000.0,900000.0,45000.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-12907,-321,-9684.0,-120,4.0,1,1,0,1,0,0,Managers,2.0,2,1,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.5388886840916869,0.6397634771944356,,0.2289,0.2897,0.9856,0.8028,0.0652,0.0,0.5517,0.1667,0.2083,0.1888,0.1832,0.266,0.0154,0.0692,0.2332,0.3007,0.9856,0.8105,0.0658,0.0,0.5517,0.1667,0.2083,0.1931,0.2002,0.2771,0.0156,0.0733,0.2311,0.2897,0.9856,0.8054,0.0656,0.0,0.5517,0.1667,0.2083,0.1921,0.1864,0.2708,0.0155,0.0707,reg oper account,block of flats,0.2599,Panel,No,0.0,0.0,0.0,0.0,-357.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n161652,0,Cash loans,F,N,Y,0,90000.0,573408.0,25389.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-20009,-6468,-10156.0,-3502,,1,1,1,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Other,,0.6305257893711189,0.7238369900414456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n251738,0,Cash loans,M,N,Y,1,450000.0,497520.0,36184.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-16396,-2248,-530.0,-286,,1,1,0,1,1,0,Core staff,3.0,1,1,TUESDAY,15,0,1,1,0,0,0,Trade: type 7,0.2865101454810316,0.7439995591044637,,0.2227,0.1368,0.9796,0.7212,0.1011,0.24,0.2069,0.3333,0.375,0.1512,0.1816,0.1532,0.0,0.0021,0.2269,0.142,0.9796,0.7321,0.102,0.2417,0.2069,0.3333,0.375,0.1546,0.1983,0.1597,0.0,0.0023,0.2248,0.1368,0.9796,0.7249,0.1017,0.24,0.2069,0.3333,0.375,0.1538,0.1847,0.156,0.0,0.0022,reg oper account,block of flats,0.1763,Panel,No,1.0,0.0,1.0,0.0,-550.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n209688,0,Revolving loans,F,Y,N,0,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-9799,-1056,-3961.0,-2491,9.0,1,1,0,1,0,0,Accountants,1.0,2,2,MONDAY,12,0,0,0,0,1,1,Transport: type 4,,0.6840003818264704,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1928.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n373771,0,Cash loans,M,N,Y,1,180000.0,295168.5,15201.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-17314,-10439,-3267.0,-860,,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Transport: type 4,,0.7319671593749096,0.5585066276769286,0.1278,0.0428,0.9846,0.7892,0.0541,0.08,0.0345,0.625,0.6667,0.0482,0.1042,0.1215,0.0154,0.0148,0.1303,0.0444,0.9846,0.7975,0.0546,0.0806,0.0345,0.625,0.6667,0.0493,0.1139,0.1266,0.0156,0.0157,0.1291,0.0428,0.9846,0.792,0.0545,0.08,0.0345,0.625,0.6667,0.0491,0.106,0.1237,0.0155,0.0151,reg oper account,block of flats,0.0985,Panel,No,0.0,0.0,0.0,0.0,-683.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n364976,0,Revolving loans,F,N,Y,1,270000.0,450000.0,22500.0,450000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-16008,-1113,-8182.0,-4502,,1,1,0,1,0,0,High skill tech staff,3.0,1,1,THURSDAY,16,0,0,0,0,0,0,Government,0.5601045285281595,0.5740651028289282,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-929.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n320984,0,Cash loans,F,Y,N,1,292500.0,2250000.0,59485.5,2250000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.032561,-15787,-5548,-905.0,-2956,7.0,1,1,0,1,0,0,Laborers,3.0,1,1,THURSDAY,17,0,0,0,0,0,0,Security Ministries,0.7965925831574957,0.7755883820508742,0.7992967832109371,0.2505,0.4103,0.9717,,,0.16,0.5172,0.25,,,,0.3427,,0.0111,0.2553,0.4258,0.9717,,,0.1611,0.5172,0.25,,,,0.3571,,0.0118,0.2529,0.4103,0.9717,,,0.16,0.5172,0.25,,,,0.3489,,0.0114,,block of flats,0.2904,\"Stone, brick\",No,5.0,0.0,4.0,0.0,-584.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n211870,0,Cash loans,F,N,N,1,225000.0,779688.0,34474.5,630000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.072508,-15192,-6817,-2459.0,-5639,,1,1,0,1,0,1,Laborers,3.0,1,1,SUNDAY,13,0,1,1,0,0,0,Business Entity Type 1,0.7594650446058644,0.7112573716130981,0.7407990879702335,0.12300000000000001,0.0827,0.9856,,,0.1464,0.0917,0.5833,,0.1043,,0.1353,,0.0117,0.0578,0.0407,0.9772,,,0.0806,0.0345,0.3333,,0.0961,,0.07,,0.0,0.0906,0.0694,0.9856,,,0.12,0.0345,0.5417,,0.1052,,0.1308,,0.0041,,block of flats,0.1653,Panel,No,1.0,0.0,1.0,0.0,-3479.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n265779,0,Cash loans,F,N,Y,0,270000.0,566055.0,16681.5,472500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.0228,-13777,-1128,-322.0,-5132,,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Government,0.5729978679302647,0.5235849562431928,0.656158373001177,0.3134,0.1228,0.9995,0.9932,0.1298,0.32,0.1379,0.5833,0.625,0.1086,0.2114,0.3123,0.2027,0.0128,0.2468,0.0868,0.9995,0.9935,0.1002,0.2417,0.1034,0.5833,0.625,0.0666,0.18,0.2579,0.1518,0.0077,0.3164,0.1022,0.9995,0.9933,0.1299,0.32,0.1379,0.5833,0.625,0.10400000000000001,0.2151,0.3179,0.2038,0.013000000000000001,reg oper account,block of flats,0.2506,Panel,No,0.0,0.0,0.0,0.0,-766.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n171928,0,Cash loans,F,N,N,1,157500.0,592560.0,22090.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-11510,-2398,-5470.0,-4191,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 2,,0.06689956230578079,0.2692857999073816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n117834,0,Cash loans,F,N,Y,0,270000.0,2013840.0,76837.5,1800000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.072508,-20739,365243,-420.0,-2991,,1,0,0,1,0,0,,2.0,1,1,THURSDAY,11,0,0,0,0,0,0,XNA,,0.7340001011089077,0.7490217048463391,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-682.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n257553,0,Cash loans,M,N,Y,0,225000.0,814041.0,26257.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.00823,-17228,-7330,-9160.0,-769,,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,15,0,0,0,0,1,1,Trade: type 7,,0.4846993545968824,0.44539624156083396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-344.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n445650,0,Cash loans,F,N,Y,0,193500.0,343377.0,19840.5,283500.0,Family,Pensioner,Higher education,Married,House / apartment,0.02461,-22977,365243,-4145.0,-4123,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,,0.642836971947398,0.3723336657058204,0.1227,0.131,0.9811,0.7416,0.0511,0.0,0.2759,0.1667,0.2083,0.113,0.1,0.114,0.0,0.0,0.125,0.136,0.9811,0.7517,0.0516,0.0,0.2759,0.1667,0.2083,0.1156,0.1093,0.1188,0.0,0.0,0.1239,0.131,0.9811,0.7451,0.0514,0.0,0.2759,0.1667,0.2083,0.115,0.1018,0.1161,0.0,0.0,reg oper account,block of flats,0.0897,Panel,No,0.0,0.0,0.0,0.0,-460.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n375612,0,Cash loans,M,N,Y,0,180000.0,198000.0,19584.0,198000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-21506,-1006,-3141.0,-602,,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,13,0,0,0,0,1,1,Self-employed,,0.7444948783618192,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n341273,0,Revolving loans,F,N,Y,0,450000.0,855000.0,42750.0,855000.0,Group of people,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-18304,-8106,-6768.0,-1853,,1,1,0,1,1,0,Accountants,1.0,1,1,TUESDAY,10,0,0,0,0,0,0,Industry: type 5,,0.6671517944268149,0.7252764347002191,0.2876,0.1277,0.9916,,,0.35200000000000004,0.1517,0.6083,,0.1443,,0.3127,,0.0333,0.062,0.0349,0.9926,,,0.5639,0.2414,0.6667,,0.0663,,0.0789,,0.0012,0.3373,0.1408,0.9921,,,0.4,0.1724,0.6667,,0.1302,,0.3589,,0.0052,,block of flats,0.2773,Panel,No,,,,,-303.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n174715,0,Cash loans,F,N,Y,0,67500.0,579195.0,24669.0,468000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-21667,365243,-3792.0,-4089,,1,0,0,1,0,0,,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,XNA,,0.414909253918436,,0.1031,,0.9801,,,0.0,0.2069,0.1667,,,,0.0937,,0.0,0.105,,0.9801,,,0.0,0.2069,0.1667,,,,0.0976,,0.0,0.1041,,0.9801,,,0.0,0.2069,0.1667,,,,0.0954,,0.0,,block of flats,0.0737,Panel,No,0.0,0.0,0.0,0.0,-687.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n334942,0,Cash loans,M,Y,Y,1,391500.0,835380.0,40320.0,675000.0,Children,Working,Higher education,Civil marriage,House / apartment,0.014464,-13342,-523,-235.0,-4825,8.0,1,1,0,1,0,0,Accountants,3.0,2,2,FRIDAY,6,0,0,0,0,0,0,Security Ministries,0.5403797616300424,0.6827701968360167,0.646329897706246,0.3794,0.1091,0.9826,0.762,0.2668,0.08,0.069,0.3333,0.375,0.0841,0.3093,0.1551,0.0,0.1099,0.3876,0.1132,0.9826,0.7713,0.2654,0.0806,0.069,0.3333,0.375,0.0705,0.3388,0.1592,0.0,0.115,0.3836,0.1091,0.9826,0.7652,0.2696,0.08,0.069,0.3333,0.375,0.0824,0.3151,0.1583,0.0,0.1111,reg oper account,block of flats,0.1438,Panel,No,0.0,0.0,0.0,0.0,-663.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n273316,0,Cash loans,F,N,Y,0,117000.0,123768.0,4572.0,81000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.04622,-22419,-3856,-5062.0,-4231,,1,1,0,1,0,0,Sales staff,1.0,1,1,FRIDAY,16,0,0,0,0,0,0,Government,,0.3580003663015249,0.6986675550534175,0.0608,0.0,0.9737,0.6396,0.0064,0.0,0.1034,0.1667,0.2083,0.0,0.0488,0.0486,0.0039,0.0053,0.062,0.0,0.9737,0.6537,0.0064,0.0,0.1034,0.1667,0.2083,0.0,0.0533,0.0507,0.0039,0.0057,0.0614,0.0,0.9737,0.6444,0.0064,0.0,0.1034,0.1667,0.2083,0.0,0.0496,0.0495,0.0039,0.0055,reg oper account,block of flats,0.0526,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n375819,0,Cash loans,F,Y,Y,0,180000.0,1223010.0,51817.5,1125000.0,Family,Commercial associate,Higher education,Widow,House / apartment,0.02461,-19890,-3618,-8016.0,-3201,3.0,1,1,0,1,0,1,Managers,1.0,2,2,THURSDAY,17,0,0,0,0,0,0,University,0.8515705760680686,0.6092290039995516,0.807273923277881,0.0866,0.0863,0.9732,0.6328,0.0616,0.0,0.1379,0.2083,0.2083,0.0647,0.0681,0.0701,0.0116,0.004,0.0882,0.0895,0.9732,0.6472,0.0621,0.0,0.1379,0.2083,0.2083,0.0662,0.0744,0.0731,0.0117,0.0043,0.0874,0.0863,0.9732,0.6377,0.062,0.0,0.1379,0.2083,0.2083,0.0659,0.0693,0.0714,0.0116,0.0041,reg oper account,block of flats,0.0914,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1118.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n135003,0,Cash loans,F,Y,Y,0,225000.0,755190.0,36459.0,675000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003069,-15286,-3322,-2214.0,-5599,3.0,1,1,0,1,0,0,Managers,2.0,3,3,THURSDAY,16,0,0,0,0,1,1,Self-employed,0.8412890034704422,0.4928407396303668,0.4418358231994413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-290.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n299660,0,Cash loans,F,N,Y,0,126000.0,630747.0,18441.0,526500.0,Family,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.006852,-20041,-2650,-123.0,-3442,,1,1,0,1,0,0,,2.0,3,3,SATURDAY,8,0,0,0,0,0,0,Military,,0.2525327829653766,,0.0814,0.0811,0.9781,0.7008,0.0342,0.0,0.1379,0.1667,0.0417,0.1552,0.0656,0.0686,0.0039,0.0051,0.083,0.0841,0.9782,0.7125,0.0345,0.0,0.1379,0.1667,0.0417,0.1588,0.0716,0.0715,0.0039,0.0055,0.0822,0.0811,0.9781,0.7048,0.0344,0.0,0.1379,0.1667,0.0417,0.1579,0.0667,0.0698,0.0039,0.0053,reg oper account,block of flats,0.0712,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1339.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n367246,0,Cash loans,F,N,Y,2,180000.0,679500.0,27076.5,679500.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.020713,-15519,-2434,-1400.0,-4746,,1,1,0,1,0,0,Sales staff,3.0,3,2,WEDNESDAY,13,0,0,0,0,0,0,Trade: type 7,,0.7167319448728764,0.7922644738669378,0.3124,0.0745,0.9846,0.7892,,0.08,0.0345,0.3333,0.375,0.0624,0.2538,0.0753,0.0039,0.0184,0.3183,0.0773,0.9846,0.7975,,0.0806,0.0345,0.3333,0.375,0.0638,0.2773,0.0784,0.0039,0.0195,0.3154,0.0745,0.9846,0.792,,0.08,0.0345,0.3333,0.375,0.0634,0.2582,0.0766,0.0039,0.0188,,specific housing,0.1047,Panel,No,0.0,0.0,0.0,0.0,-1776.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n369101,0,Cash loans,F,Y,N,0,67500.0,312768.0,13774.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008575,-12151,-784,-2216.0,-1905,11.0,1,1,1,1,0,0,Cooking staff,2.0,2,2,FRIDAY,14,0,0,0,1,1,0,Kindergarten,0.2762513414288601,0.6830779834885324,0.11761373170805695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1877.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n134977,0,Cash loans,M,Y,Y,0,180000.0,284400.0,18513.0,225000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.018634,-10254,-117,-3644.0,-2930,10.0,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,21,0,0,0,0,1,1,Business Entity Type 3,0.2355624706500796,0.6450029947017184,,0.1655,0.1447,0.9781,0.6940000000000001,0.0249,0.0,0.2759,0.1667,0.0417,0.1552,0.1345,0.139,0.0039,0.0065,0.1681,0.1502,0.9782,0.706,0.0251,0.0,0.2759,0.1667,0.0417,0.1588,0.1469,0.1448,0.0039,0.0069,0.1671,0.1447,0.9781,0.6981,0.0251,0.0,0.2759,0.1667,0.0417,0.1579,0.1368,0.1415,0.0039,0.0066,reg oper account,block of flats,0.1243,Panel,No,0.0,0.0,0.0,0.0,-1512.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n351401,0,Cash loans,F,N,Y,3,67500.0,163008.0,17424.0,144000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.018801,-12651,-4724,-1583.0,-4476,,1,1,1,1,0,0,Core staff,5.0,2,2,FRIDAY,11,0,0,0,1,1,0,School,0.4677678929391757,0.5260989685594651,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2661.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n274366,0,Cash loans,M,Y,N,2,261000.0,746280.0,54436.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.005084,-15203,-965,-5185.0,-5185,1.0,1,1,1,1,1,0,Laborers,4.0,2,2,SATURDAY,11,0,0,0,0,0,0,School,,0.2871092828811117,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-719.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n155678,0,Cash loans,M,N,N,1,135000.0,391500.0,36036.0,391500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-13923,-694,-452.0,-4469,,1,1,0,1,0,0,Drivers,3.0,2,2,TUESDAY,11,0,0,0,1,1,0,Transport: type 4,0.26134760899215354,0.7655103366929608,0.7610263695502636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1256.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n420460,0,Cash loans,F,N,Y,0,99000.0,967500.0,28287.0,967500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-20229,365243,-3984.0,-3705,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.4494586947215733,0.8193176922872417,0.3715,0.815,0.993,,,0.16,0.1638,0.6667,,0.34299999999999997,,0.6093,,0.4613,0.1155,0.8457,0.993,,,0.0,0.0345,0.6667,,0.3508,,0.1864,,0.4883,0.3175,0.815,0.993,,,0.06,0.0862,0.6667,,0.349,,0.425,,0.4709,,block of flats,0.1515,Monolithic,No,0.0,0.0,0.0,0.0,-1122.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n317987,0,Cash loans,M,Y,Y,1,180000.0,675000.0,49117.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20847,-2587,-7524.0,-3694,17.0,1,1,0,1,0,0,Drivers,3.0,2,2,TUESDAY,14,0,0,0,0,1,1,Self-employed,,0.3713646158322359,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,0.0,-457.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n438760,0,Cash loans,F,N,Y,0,144900.0,225000.0,22050.0,225000.0,Unaccompanied,Pensioner,Incomplete higher,Civil marriage,House / apartment,0.072508,-24889,365243,-6491.0,-4908,,1,0,0,1,1,0,,2.0,1,1,FRIDAY,18,0,0,0,0,0,0,XNA,,0.7540089043317737,0.7352209993926424,0.0575,0.0749,0.9747,0.5988,0.0433,0.0376,0.0869,0.2546,0.2083,0.0,0.0289,0.0648,0.0008,0.0472,0.0294,0.0858,0.9702,0.6145,0.0437,0.0,0.069,0.1667,0.2083,0.0,0.0257,0.0547,0.0,0.003,0.05,0.0826,0.9717,0.6042,0.0436,0.0,0.069,0.1667,0.2083,0.0,0.0239,0.0566,0.0,0.0495,reg oper spec account,block of flats,0.0518,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1011.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n216709,0,Cash loans,M,N,Y,0,247500.0,553500.0,47637.0,553500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.022625,-17946,-4577,-561.0,-1435,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,12,0,0,0,1,1,0,Trade: type 7,0.3678331970847603,0.6237249820133042,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1176.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n304972,0,Cash loans,F,N,Y,0,180000.0,640080.0,24259.5,450000.0,Other_A,Working,Secondary / secondary special,Civil marriage,House / apartment,0.032561,-19184,-375,-9370.0,-2707,,1,1,0,1,0,0,Sales staff,2.0,1,1,TUESDAY,15,0,0,0,0,0,0,Self-employed,,0.7674594930553464,0.14734591802757252,0.0423,0.0272,0.9309,,,0.04,0.1379,0.2083,,,,0.1004,,0.0786,0.0431,0.0282,0.9309999999999999,,,0.0403,0.1379,0.2083,,,,0.1046,,0.0832,0.0427,0.0272,0.9309,,,0.04,0.1379,0.2083,,,,0.1022,,0.0802,,block of flats,0.096,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2997.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,0.0\n290436,0,Cash loans,M,Y,N,2,216000.0,824823.0,29353.5,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-16305,-3527,-3123.0,-4315,14.0,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 2,,0.6898926656968928,0.36896873825284665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-253.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n159137,0,Cash loans,M,N,Y,0,94500.0,450000.0,19953.0,450000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.003540999999999999,-21512,365243,-9171.0,-4711,,1,0,0,1,0,0,,2.0,1,1,WEDNESDAY,8,0,0,0,0,0,0,XNA,,0.2666352116513647,0.7281412993111438,0.1103,0.0783,0.9836,0.7756,0.043,0.12,0.1034,0.3333,,0.0141,0.0899,0.1122,0.0,0.0,0.1124,0.0812,0.9836,0.7844,0.0434,0.1208,0.1034,0.3333,,0.0144,0.0983,0.1169,0.0,0.0,0.1114,0.0783,0.9836,0.7786,0.0432,0.12,0.1034,0.3333,,0.0143,0.0915,0.1142,0.0,0.0,reg oper account,block of flats,0.1118,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n319857,0,Cash loans,F,N,Y,2,139950.0,1125000.0,33025.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.018209,-14555,-4500,-8310.0,-4637,,1,1,0,1,0,0,,3.0,3,3,SUNDAY,13,0,0,0,0,0,0,Industry: type 2,0.21814011721163648,0.5845846922657391,0.6848276586890367,0.1113,0.095,0.9851,,,0.12,0.1034,0.3333,,0.1128,,0.1242,,0.0,0.1134,0.0986,0.9851,,,0.1208,0.1034,0.3333,,0.1154,,0.1294,,0.0,0.1124,0.095,0.9851,,,0.12,0.1034,0.3333,,0.1148,,0.1264,,0.0,,block of flats,0.113,Panel,No,0.0,0.0,0.0,0.0,-2052.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n374488,0,Cash loans,M,Y,Y,0,90000.0,675000.0,41296.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00733,-11703,-3070,-5167.0,-3925,10.0,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Police,,0.3113516947853137,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1788.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n280454,0,Cash loans,M,Y,N,1,67500.0,67500.0,7087.5,67500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Rented apartment,0.018801,-13203,-1731,-3738.0,-4130,32.0,1,1,1,1,1,0,Drivers,3.0,2,2,SATURDAY,18,0,0,0,1,1,0,Construction,0.32766115261034595,0.15967629929343666,0.6313545365850379,0.0021,,,,,,,0.0,,,,0.002,,,0.0021,,,,,,,0.0,,,,0.0021,,,0.0021,,,,,,,0.0,,,,0.002,,,,terraced house,0.0016,,No,0.0,0.0,0.0,0.0,-1853.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n336489,0,Cash loans,F,N,N,0,81000.0,270000.0,9828.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-23482,365243,-6139.0,-4914,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.6663743931439072,0.722392890081304,0.1351,0.1885,0.9856,0.8028,0.0217,0.0,0.3103,0.1667,0.2083,0.2005,0.1101,0.1487,0.0,0.0,0.1376,0.1956,0.9856,0.8105,0.0219,0.0,0.3103,0.1667,0.2083,0.2051,0.1203,0.1549,0.0,0.0,0.1364,0.1885,0.9856,0.8054,0.0218,0.0,0.3103,0.1667,0.2083,0.204,0.11199999999999999,0.1514,0.0,0.0,reg oper account,block of flats,0.1288,Panel,No,0.0,0.0,0.0,0.0,-1504.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n437111,0,Cash loans,M,Y,Y,0,180000.0,604413.0,25537.5,459000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-18209,-441,-895.0,-1741,23.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.5432894471376978,0.30620229831350426,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-187.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n452013,0,Cash loans,M,Y,N,0,540000.0,1206000.0,111730.5,1206000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-10271,-1479,-1324.0,-2939,1.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Construction,0.5097935357367017,0.7325330901478799,0.5673792367572691,0.0722,0.0673,0.9796,0.7212,0.0079,0.0,0.1379,0.1667,0.2083,0.0441,0.0588,0.0629,0.0,0.0194,0.0735,0.0698,0.9796,0.7321,0.008,0.0,0.1379,0.1667,0.2083,0.0451,0.0643,0.0656,0.0,0.0206,0.0729,0.0673,0.9796,0.7249,0.0079,0.0,0.1379,0.1667,0.2083,0.0449,0.0599,0.0641,0.0,0.0198,reg oper account,block of flats,0.0537,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n201583,0,Cash loans,F,N,N,0,157500.0,1288350.0,37669.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.030755,-21854,365243,-3242.0,-3251,,1,0,0,1,1,0,,1.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.6463537542316603,0.4066174366275036,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1694.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n443733,0,Cash loans,M,N,N,0,112500.0,398016.0,31446.0,360000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.031329,-13950,-2188,-3782.0,-3781,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Self-employed,0.25514376674118444,0.4543983752986866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-643.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n448914,0,Revolving loans,F,N,Y,0,112500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019101,-17198,-1247,-1660.0,-379,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SATURDAY,17,0,0,0,0,0,0,Medicine,0.6387470110956199,0.4248267924545127,0.4812493411434029,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-297.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n311890,0,Cash loans,F,N,Y,1,135000.0,117162.0,12559.5,103500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-12205,-1127,-4434.0,-4510,,1,1,0,1,0,0,Sales staff,3.0,2,2,FRIDAY,10,0,0,0,1,1,0,Self-employed,,0.6244996194071845,0.22009464485041005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-511.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n238844,0,Cash loans,M,Y,Y,1,130500.0,601470.0,29065.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-16159,-1368,-2771.0,-4112,27.0,1,1,1,1,0,0,Security staff,3.0,2,2,MONDAY,14,0,1,1,0,1,1,Security,,0.3845197041839431,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-898.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n159459,0,Cash loans,F,N,Y,1,157500.0,314100.0,17167.5,225000.0,Unaccompanied,Working,Lower secondary,Single / not married,House / apartment,0.019689,-9397,-1698,-1389.0,-2015,,1,1,0,1,0,0,Medicine staff,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Medicine,,0.2328438346228017,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1246.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n146769,0,Cash loans,F,N,Y,0,81000.0,810000.0,23814.0,810000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-16414,-7703,-10314.0,-4379,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6255144405374307,0.2653117484731741,0.39449540531239935,0.0134,0.0,0.9707,0.5988,0.0241,0.0,0.069,0.0417,0.0833,0.0,0.0076,0.0116,0.0154,0.0206,0.0137,0.0,0.9707,0.6145,0.0243,0.0,0.069,0.0417,0.0833,0.0,0.0083,0.0121,0.0156,0.0218,0.0135,0.0,0.9707,0.6042,0.0242,0.0,0.069,0.0417,0.0833,0.0,0.0077,0.0118,0.0155,0.021,reg oper account,block of flats,0.0132,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1551.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n424061,0,Cash loans,M,Y,Y,0,126000.0,1100709.0,44959.5,1012500.0,Unaccompanied,Working,Incomplete higher,Married,With parents,0.030755,-8886,-1959,-389.0,-1570,25.0,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,17,0,0,0,1,1,0,Trade: type 3,,0.6520905772982926,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-190.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n105831,1,Cash loans,F,Y,Y,2,135000.0,1024740.0,52321.5,900000.0,Unaccompanied,State servant,Incomplete higher,Married,House / apartment,0.018801,-11164,-2066,-4982.0,-1258,23.0,1,1,0,1,0,1,Core staff,4.0,2,2,SUNDAY,10,0,0,0,0,0,0,Police,0.5511804971174702,0.5609322307337177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-31.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n299959,0,Cash loans,M,Y,Y,3,292500.0,862560.0,23850.0,720000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-12934,-355,-340.0,-3734,0.0,1,1,0,1,0,0,Sales staff,5.0,2,2,FRIDAY,13,0,0,0,0,0,0,Trade: type 3,,0.5483742632826928,0.19182160241360605,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-331.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n119058,0,Cash loans,F,N,Y,2,81000.0,339948.0,23787.0,315000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.006852,-11734,365243,-4066.0,-3276,,1,0,0,1,1,0,,4.0,3,3,MONDAY,4,0,0,0,0,0,0,XNA,,0.3171809683780949,0.6610235391308081,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,1.0,9.0,1.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n148308,1,Cash loans,M,Y,Y,1,1890000.0,781920.0,61906.5,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-13203,-2404,-2054.0,-4732,3.0,1,1,0,1,0,1,Managers,3.0,1,1,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.5979375351270506,0.6401161833252154,,0.0294,0.0258,0.9717,0.6124,0.0005,0.0,0.0517,0.1042,0.1458,0.0269,0.024,0.0346,0.0,0.0075,0.0074,0.0268,0.9479,0.314,0.0,0.0,0.0345,0.0417,0.0833,0.0,0.0064,0.0055,0.0,0.0024,0.0297,0.0258,0.9717,0.6176,0.0005,0.0,0.0517,0.1042,0.1458,0.0274,0.0244,0.0352,0.0,0.0077,reg oper account,block of flats,0.0508,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2010.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n142941,0,Cash loans,F,N,Y,0,810000.0,675000.0,21906.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-13460,-4594,-925.0,-1016,,1,1,0,1,0,0,Laborers,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,Electricity,0.4571810799964627,0.4525719781960022,0.7570690154522959,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1590.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n409503,0,Cash loans,F,N,Y,0,135000.0,647046.0,21001.5,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-17749,-345,-1745.0,-1290,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Industry: type 11,,0.210300936442862,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1863.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n426008,0,Cash loans,F,N,Y,0,135000.0,340587.0,35028.0,319500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009657,-22568,365243,-8586.0,-4079,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.4701989313751287,0.6940926425266661,0.0732,0.0442,0.9846,0.7892,0.0311,0.08,0.069,0.3333,0.375,0.0679,0.0597,0.0715,0.0,0.0,0.0746,0.0458,0.9846,0.7975,0.0314,0.0806,0.069,0.3333,0.375,0.0694,0.0652,0.0745,0.0,0.0,0.0739,0.0442,0.9846,0.792,0.0313,0.08,0.069,0.3333,0.375,0.069,0.0607,0.0728,0.0,0.0,reg oper account,block of flats,0.0562,Panel,No,0.0,0.0,0.0,0.0,-655.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n401028,0,Cash loans,F,N,Y,1,157500.0,276277.5,20232.0,238500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.008865999999999999,-16406,-2926,-9419.0,-4619,,1,1,0,1,0,1,,3.0,2,2,SUNDAY,14,0,0,0,0,0,0,Construction,0.8983731687465821,0.7328799900233459,0.6479768603302221,0.1052,0.0904,0.9767,0.6804,0.0186,0.0,0.2069,0.1667,0.2083,0.1117,0.0807,0.0749,0.0232,0.1295,0.1071,0.0938,0.9767,0.6929,0.0188,0.0,0.2069,0.1667,0.2083,0.1142,0.0882,0.078,0.0233,0.1371,0.1062,0.0904,0.9767,0.6847,0.0187,0.0,0.2069,0.1667,0.2083,0.1136,0.0821,0.0762,0.0233,0.1323,reg oper account,block of flats,0.0973,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1701.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n195453,0,Cash loans,F,N,Y,0,261000.0,815607.0,32476.5,729000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.005002,-18311,-11406,-10078.0,-1817,,1,1,0,1,0,0,Laborers,1.0,3,3,MONDAY,16,0,0,0,0,0,0,Industry: type 5,,0.5893771626985295,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2107.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n270485,0,Cash loans,F,N,Y,0,405000.0,588465.0,28440.0,468000.0,Unaccompanied,Commercial associate,Higher education,Widow,House / apartment,0.020713,-15799,-2727,-117.0,-3267,,1,1,0,1,0,0,,1.0,3,2,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.7264331646448001,0.02649166613224646,0.3962195720630885,0.0825,0.0828,0.9742,0.6464,0.0095,0.0,0.1724,0.1667,0.2083,0.0321,0.0672,0.0706,0.0,0.0,0.084,0.0859,0.9742,0.6602,0.0096,0.0,0.1724,0.1667,0.2083,0.0329,0.0735,0.0735,0.0,0.0,0.0833,0.0828,0.9742,0.6511,0.0096,0.0,0.1724,0.1667,0.2083,0.0327,0.0684,0.0718,0.0,0.0,reg oper account,block of flats,0.0607,Panel,No,1.0,0.0,1.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n328075,0,Cash loans,F,Y,Y,0,90000.0,296280.0,19930.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-12037,-4010,-6179.0,-3449,6.0,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Agriculture,,0.7243056236378143,0.2721336844147212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2040.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n183842,0,Cash loans,F,N,N,0,72000.0,454500.0,16452.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-20529,365243,-2730.0,-3953,,1,0,0,1,0,0,,2.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.4353050648557213,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1990.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n305979,0,Cash loans,M,Y,N,0,90000.0,942300.0,30528.0,675000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-13726,-598,-2713.0,-4554,18.0,1,1,0,1,0,0,Security staff,2.0,2,2,MONDAY,7,0,0,0,0,1,1,Security,,0.30876012715601675,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n380268,0,Cash loans,F,N,Y,0,112500.0,1147500.0,38052.0,1147500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.015221,-18746,-11994,-10684.0,-2257,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Medicine,0.6818269537725611,0.20961175408429736,0.21885908222837447,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-418.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n324201,0,Cash loans,M,Y,N,1,180000.0,248760.0,26919.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-10545,-2152,-3214.0,-3214,9.0,1,1,1,1,0,0,Drivers,3.0,1,1,TUESDAY,9,0,1,1,0,1,1,Business Entity Type 3,0.2370428226712188,0.6670352420910267,0.5620604831738043,0.1232,0.0895,0.9901,0.8640000000000001,0.0153,0.12,0.1034,0.375,0.4167,0.0371,0.1004,0.1378,0.0,0.0386,0.125,0.0928,0.9901,0.8693,0.0047,0.1208,0.1034,0.375,0.4167,0.0371,0.1093,0.1368,0.0,0.0125,0.1244,0.0895,0.9901,0.8658,0.0154,0.12,0.1034,0.375,0.4167,0.0377,0.1022,0.1403,0.0,0.0394,reg oper account,block of flats,0.1173,Panel,No,3.0,0.0,3.0,0.0,-2777.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,4.0\n385661,0,Cash loans,F,Y,Y,0,112500.0,585000.0,17104.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005313,-16354,-132,-8799.0,-648,21.0,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,16,0,0,0,0,1,1,Postal,0.5239307583368416,0.6365975442296551,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1873.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n233286,0,Revolving loans,M,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-9125,-348,-5743.0,-1775,,1,1,1,1,1,0,Sales staff,1.0,2,2,MONDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.393730356684729,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.0,0.0,11.0,0.0,-1182.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n386071,0,Revolving loans,M,Y,N,0,270000.0,225000.0,11250.0,225000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.032561,-11557,-2001,-5615.0,-4011,5.0,1,1,0,1,0,1,High skill tech staff,1.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,Industry: type 9,0.6557460525826172,0.6620532293488427,0.5172965813614878,0.6072,0.2925,0.9821,,,0.8,0.3448,0.4583,,0.069,,0.6599,,0.0536,0.6187,0.3036,0.9821,,,0.8056,0.3448,0.4583,,0.0706,,0.6875,,0.0568,0.6131,0.2925,0.9821,,,0.8,0.3448,0.4583,,0.0702,,0.6717,,0.0548,,block of flats,0.5307,Panel,No,0.0,0.0,0.0,0.0,-736.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n226783,0,Cash loans,M,N,Y,2,202500.0,1546020.0,42642.0,1350000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-16567,-3961,-9046.0,-93,,1,1,0,1,0,0,Laborers,4.0,2,2,SUNDAY,12,0,0,0,1,0,1,Other,,0.6663452229592627,0.7380196196295241,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1432.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n141829,0,Cash loans,F,Y,Y,0,148500.0,900000.0,60390.0,900000.0,Unaccompanied,Working,Higher education,Married,Co-op apartment,0.026392,-11378,-202,-1435.0,-2185,7.0,1,1,1,1,1,0,High skill tech staff,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Industry: type 7,0.6859317378921214,0.6999935220690742,0.4206109640437848,0.5454,0.3778,0.9846,0.7892,0.2709,0.64,0.5172,0.3333,,0.5338,0.4404,0.6726,0.0193,0.0192,0.5557,0.392,0.9846,0.7975,0.2733,0.6445,0.5172,0.3333,,0.5459,0.4812,0.7007,0.0195,0.0204,0.5506,0.3778,0.9846,0.792,0.2726,0.64,0.5172,0.3333,,0.5429999999999999,0.4481,0.6847,0.0194,0.0196,reg oper spec account,block of flats,0.629,Panel,No,4.0,0.0,4.0,0.0,-1462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n209185,0,Cash loans,F,N,Y,0,81000.0,808650.0,23305.5,675000.0,Unaccompanied,Pensioner,Lower secondary,Civil marriage,Municipal apartment,0.020713,-21799,365243,-5021.0,-5026,,1,0,0,1,1,0,,2.0,3,3,THURSDAY,6,0,0,0,0,0,0,XNA,,0.20654890546620752,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1515.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n329009,0,Revolving loans,F,Y,Y,2,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-11036,-884,-2783.0,-3469,3.0,1,1,0,1,0,0,,4.0,3,3,TUESDAY,8,0,0,0,0,0,0,Government,0.4192919871274169,0.4500485684849606,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,2.0,-1046.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n258230,0,Cash loans,F,N,Y,0,157500.0,562491.0,28890.0,454500.0,Unaccompanied,Working,Higher education,Married,Municipal apartment,0.018634,-19536,-2030,-7516.0,-3057,,1,1,1,1,1,0,Laborers,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5987266836058771,0.23272477626794336,0.0495,0.0606,0.9752,0.66,0.0432,0.0,0.1034,0.125,0.1667,0.0,0.0403,0.0405,0.0,0.0,0.0504,0.0629,0.9752,0.6733,0.0436,0.0,0.1034,0.125,0.1667,0.0,0.0441,0.0422,0.0,0.0,0.05,0.0606,0.9752,0.6645,0.0435,0.0,0.1034,0.125,0.1667,0.0,0.040999999999999995,0.0413,0.0,0.0,reg oper account,block of flats,0.0555,Panel,No,0.0,0.0,0.0,0.0,-990.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n258325,0,Cash loans,F,N,Y,1,135000.0,454500.0,35302.5,454500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.026392,-14392,-544,-4053.0,-4531,,1,1,0,1,0,0,Cooking staff,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Government,,0.647667251750929,0.7295666907060153,0.1278,,0.9896,,,0.08,0.069,0.3333,,,,0.1077,,0.0,0.1303,,0.9896,,,0.0806,0.069,0.3333,,,,0.1122,,0.0,0.1291,,0.9896,,,0.08,0.069,0.3333,,,,0.1096,,0.0,,block of flats,0.0847,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n372645,0,Cash loans,F,Y,Y,1,225000.0,1092519.0,31941.0,954000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.020246,-14342,-854,-1753.0,-1193,1.0,1,1,0,1,0,0,Core staff,3.0,3,3,SUNDAY,9,0,0,0,1,1,0,Government,0.5424011091004501,0.3808312823711053,0.2622489709189549,0.0454,0.0698,0.997,0.9592,0.0282,0.0,0.069,0.1667,0.2083,0.0325,0.0361,0.0446,0.0039,0.0647,0.0462,0.0725,0.997,0.9608,0.0285,0.0,0.069,0.1667,0.2083,0.0333,0.0395,0.0464,0.0039,0.0685,0.0458,0.0698,0.997,0.9597,0.0284,0.0,0.069,0.1667,0.2083,0.0331,0.0368,0.0454,0.0039,0.066,reg oper account,block of flats,0.055,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1114.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n448881,0,Cash loans,F,Y,Y,0,157500.0,780363.0,30946.5,697500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-19506,-3399,-4413.0,-2996,15.0,1,1,1,1,1,0,,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Government,,0.40550820195405995,0.4489622731076524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-262.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n147306,0,Cash loans,F,Y,N,2,193500.0,225000.0,21037.5,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.0105,-13911,-140,-823.0,-4590,8.0,1,1,0,1,0,0,Core staff,4.0,3,3,MONDAY,18,0,0,0,0,0,0,Mobile,0.4117119286941301,0.5541244529819497,0.5460231970049609,0.1773,0.1037,0.9846,0.7892,0.0327,0.2,0.1724,0.3333,0.375,0.075,0.1378,0.1708,0.0309,0.151,0.1807,0.1076,0.9846,0.7975,0.033,0.2014,0.1724,0.3333,0.375,0.0767,0.1506,0.17800000000000002,0.0311,0.1598,0.179,0.1037,0.9846,0.792,0.0329,0.2,0.1724,0.3333,0.375,0.0763,0.1402,0.1739,0.0311,0.1541,reg oper account,block of flats,0.1672,\"Stone, brick\",No,8.0,2.0,8.0,2.0,-380.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n417207,0,Cash loans,F,N,Y,0,135000.0,454500.0,21865.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-17451,-5972,-3844.0,-997,,1,1,1,1,0,0,Laborers,2.0,3,3,FRIDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.7455251860162421,0.6298224942400503,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1376.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n130527,0,Cash loans,M,N,N,0,135000.0,545040.0,43060.5,450000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-10575,-1062,-8057.0,-3130,,1,1,1,1,1,0,Sales staff,1.0,3,3,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4203340610689473,0.3296550543128238,0.1649,0.1698,0.9891,0.8504,0.1405,0.0,0.2759,0.1667,0.0417,0.1811,0.1345,0.1692,0.0,0.0,0.1681,0.1762,0.9891,0.8563,0.1418,0.0,0.2759,0.1667,0.0417,0.1853,0.1469,0.1762,0.0,0.0,0.1665,0.1698,0.9891,0.8524,0.1414,0.0,0.2759,0.1667,0.0417,0.1843,0.1368,0.1722,0.0,0.0,,block of flats,0.2099,Panel,No,0.0,0.0,0.0,0.0,-791.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n124105,0,Cash loans,F,N,Y,1,103500.0,526491.0,19039.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-18544,-1880,-10111.0,-1830,,1,1,1,1,1,0,Core staff,3.0,3,3,MONDAY,7,0,0,0,0,0,0,Postal,0.6012417964096051,0.5161017882589568,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-364.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n196324,1,Cash loans,F,Y,N,0,157500.0,1024740.0,52452.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.01885,-12901,-791,-5182.0,-5288,7.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Business Entity Type 2,0.6094269088608351,0.6454284843078232,0.2418614865234661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n128761,0,Cash loans,F,N,N,0,157500.0,900000.0,26316.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.005144,-19305,-1652,-8924.0,-2527,,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,14,0,1,1,0,1,1,Business Entity Type 3,,0.7264053756532713,0.2405414172860865,0.0825,0.1065,0.9757,0.6668,0.0077,0.0,0.1379,0.1667,0.2083,0.0717,0.0639,0.0602,0.0154,0.033,0.084,0.1105,0.9757,0.6798,0.0077,0.0,0.1379,0.1667,0.2083,0.0734,0.0698,0.0628,0.0156,0.0349,0.0833,0.1065,0.9757,0.6713,0.0077,0.0,0.1379,0.1667,0.2083,0.073,0.065,0.0613,0.0155,0.0337,reg oper spec account,block of flats,0.0587,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1342.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n267973,0,Revolving loans,F,N,Y,0,225000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-17860,-4762,-1188.0,-1399,,1,1,0,1,1,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.5330937516601588,0.3233112448967859,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-614.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n360870,0,Cash loans,M,Y,N,1,180000.0,701721.0,33889.5,567000.0,\"Spouse, partner\",State servant,Secondary / secondary special,Civil marriage,House / apartment,0.001276,-12605,-1544,-3696.0,-3696,16.0,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,5,0,0,0,0,0,0,Emergency,0.1739241150978318,0.38398678958341936,0.6894791426446275,0.0495,0.0623,0.9811,0.7416,0.0,0.0,0.1034,0.1667,0.0417,0.0224,0.0,0.0402,0.0,0.0492,0.0504,0.0646,0.9811,0.7517,0.0,0.0,0.1034,0.1667,0.0417,0.0229,0.0,0.0419,0.0,0.0521,0.05,0.0623,0.9811,0.7451,0.0,0.0,0.1034,0.1667,0.0417,0.0228,0.0,0.0409,0.0,0.0502,reg oper spec account,block of flats,0.0428,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n187916,0,Cash loans,F,N,Y,0,135000.0,729792.0,39721.5,630000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.035792000000000004,-8695,-1056,-3383.0,-668,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,14,0,0,0,0,0,0,Self-employed,0.24656115392603736,0.2418882482390061,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n440211,0,Cash loans,F,Y,Y,0,157500.0,555273.0,16236.0,463500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.010147,-21592,365243,-7762.0,-4885,15.0,1,0,0,1,1,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.4402890165528481,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1139.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n391163,0,Revolving loans,M,N,Y,0,157500.0,315000.0,15750.0,315000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.022625,-11976,-4556,-4531.0,-4528,,1,1,0,1,1,0,Laborers,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,Transport: type 4,0.3501110242417053,0.6463437571584076,,0.3711,0.3644,0.9781,0.7008,0.1804,0.0,0.8276,0.1667,0.2083,0.3726,0.3026,0.3637,0.0,0.0,0.3782,0.3781,0.9782,0.7125,0.1821,0.0,0.8276,0.1667,0.2083,0.3811,0.3306,0.3789,0.0,0.0,0.3747,0.3644,0.9781,0.7048,0.1816,0.0,0.8276,0.1667,0.2083,0.3791,0.3078,0.3702,0.0,0.0,reg oper account,block of flats,0.3847,Panel,No,1.0,0.0,1.0,0.0,-511.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n248506,0,Cash loans,M,N,Y,0,157500.0,1600330.5,55755.0,1381500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-17803,-199,-8891.0,-1347,,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Transport: type 3,0.6188402685910406,0.5642201720255343,0.7726311345553628,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1927.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n281326,0,Cash loans,M,N,Y,0,225000.0,1288350.0,37800.0,1125000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-21809,-10574,-1274.0,-4267,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Transport: type 2,0.5622747757503319,0.1915258625230554,0.6658549219640212,0.0825,0.0796,0.9776,0.6940000000000001,,,0.1379,0.1667,,,,0.0491,,,0.084,0.0826,0.9777,0.706,,,0.1379,0.1667,,,,0.0512,,,0.0833,0.0796,0.9776,0.6981,,,0.1379,0.1667,,,,0.05,,,reg oper spec account,block of flats,0.0555,Panel,No,2.0,0.0,2.0,0.0,-1252.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n424727,0,Cash loans,F,N,Y,0,112500.0,157500.0,10525.5,157500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Office apartment,0.015221,-11237,-2501,-4685.0,-128,,1,1,0,1,1,0,Sales staff,1.0,2,2,SUNDAY,11,0,0,0,1,1,0,Business Entity Type 3,0.32059713130665873,0.5785982895062953,0.3979463219016906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-330.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n237738,0,Cash loans,M,Y,Y,0,135000.0,521280.0,35392.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-20882,-1130,-5484.0,-3431,16.0,1,1,0,1,0,0,Security staff,2.0,2,2,FRIDAY,9,0,0,0,1,1,0,Business Entity Type 3,0.551230791229896,0.5394508317231762,0.3556387169923543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1932.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n221641,0,Cash loans,M,Y,Y,0,540000.0,986782.5,44721.0,882000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-15238,-2410,-5285.0,-3863,2.0,1,1,0,1,1,0,,1.0,1,1,THURSDAY,15,0,0,0,0,0,0,Other,,0.551037387742896,0.4066174366275036,0.2588,0.1071,0.9925,0.898,0.1154,0.32,0.1379,0.6667,0.7083,0.0,0.2064,0.2697,0.0212,0.0378,0.2216,0.0968,0.9926,0.902,0.0843,0.2417,0.1034,0.6667,0.7083,0.0,0.1864,0.2336,0.0117,0.0053,0.2613,0.1071,0.9925,0.8994,0.1161,0.32,0.1379,0.6667,0.7083,0.0,0.2099,0.2745,0.0214,0.0386,not specified,block of flats,0.1752,Panel,No,0.0,0.0,0.0,0.0,-17.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n287112,0,Cash loans,F,N,Y,0,207000.0,568057.5,22657.5,459000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.01885,-21652,-3645,-9629.0,-4029,,1,1,0,1,0,0,Accountants,1.0,2,2,SATURDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.7136272674469057,0.4578995512067301,0.0165,,0.9781,,,0.0,0.069,0.0417,,,,0.0145,,,0.0168,,0.9782,,,0.0,0.069,0.0417,,,,0.0151,,,0.0167,,0.9781,,,0.0,0.069,0.0417,,,,0.0148,,,,block of flats,0.0114,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1847.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,2.0\n224652,0,Cash loans,F,N,Y,0,81000.0,610335.0,22050.0,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-21345,365243,-5552.0,-3893,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.4514994942664185,,0.1856,0.1189,0.9851,0.7959999999999999,0.0287,0.2,0.1724,0.3333,0.375,0.0907,0.1513,0.1958,0.0,0.0,0.1891,0.1234,0.9851,0.804,0.028999999999999998,0.2014,0.1724,0.3333,0.375,0.0928,0.1653,0.204,0.0,0.0,0.1874,0.1189,0.9851,0.7987,0.0289,0.2,0.1724,0.3333,0.375,0.0923,0.1539,0.1993,0.0,0.0,reg oper account,block of flats,0.179,Panel,No,1.0,1.0,1.0,0.0,-683.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n292504,0,Revolving loans,M,Y,Y,0,157500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-19150,-667,-639.0,-2703,21.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Industry: type 7,,0.4692838568028818,0.7394117535524816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-822.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n100410,0,Cash loans,F,Y,N,0,180000.0,1258650.0,56277.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-14239,-4073,-8317.0,-5695,10.0,1,1,1,1,1,0,Managers,2.0,1,1,THURSDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.7763107082812815,,0.1335,0.1111,0.9776,0.6940000000000001,0.0247,0.1,0.1552,0.25,0.2917,0.043,0.1088,0.1269,0.0,0.0,0.084,0.1017,0.9777,0.706,0.0116,0.0,0.1379,0.1667,0.2083,0.0321,0.0735,0.0727,0.0,0.0,0.1348,0.1111,0.9776,0.6981,0.0248,0.1,0.1552,0.25,0.2917,0.0438,0.1107,0.1291,0.0,0.0,reg oper spec account,block of flats,0.1654,Panel,No,5.0,0.0,5.0,0.0,-2461.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n357327,0,Cash loans,F,N,Y,0,54000.0,178290.0,10363.5,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.015221,-20335,365243,-9496.0,-2024,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,0.6371422578719381,0.3537996602844203,0.4471785780453068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2000.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n406495,0,Cash loans,F,N,N,0,255150.0,450000.0,46111.5,450000.0,Unaccompanied,Pensioner,Incomplete higher,Civil marriage,Rented apartment,0.072508,-18682,365243,-1218.0,-2072,,1,0,0,1,0,0,,2.0,1,1,THURSDAY,17,0,0,0,0,0,0,XNA,,0.5974966866671982,0.4794489811780563,0.1309,0.0484,0.9821,0.7552,0.0,0.08,0.0345,0.625,0.6667,0.0,0.1067,0.0,0.0,0.0,0.1334,0.0502,0.9821,0.7648,0.0,0.0806,0.0345,0.625,0.6667,0.0,0.1166,0.0,0.0,0.0,0.1322,0.0484,0.9821,0.7585,0.0,0.08,0.0345,0.625,0.6667,0.0,0.1086,0.0,0.0,0.0,reg oper account,block of flats,0.0918,Panel,No,0.0,0.0,0.0,0.0,-1015.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n425788,0,Cash loans,F,N,N,1,207000.0,1078200.0,31653.0,900000.0,Unaccompanied,State servant,Higher education,Single / not married,With parents,0.0228,-12212,-3662,-2000.0,-4213,,1,1,0,1,1,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Other,0.4299147520473493,0.3189786333600748,0.6832688314232291,0.1216,0.1291,0.995,0.8912,0.0416,0.16,0.1379,0.375,0.0417,0.0087,0.0941,0.1682,0.0232,0.0459,0.1239,0.134,0.995,0.8955,0.042,0.1611,0.1379,0.375,0.0417,0.0089,0.1028,0.1753,0.0233,0.0486,0.1228,0.1291,0.995,0.8927,0.0419,0.16,0.1379,0.375,0.0417,0.0088,0.0958,0.1713,0.0233,0.0469,reg oper account,block of flats,0.1423,Panel,No,0.0,0.0,0.0,0.0,-1271.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,0.0\n317720,0,Cash loans,F,Y,Y,0,198000.0,172021.5,18657.0,148500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008575,-16286,-2030,-4617.0,-4407,11.0,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Self-employed,,0.4777499222634693,0.7047064232963289,0.1289,0.1714,0.9871,0.8232,0.0225,0.0,0.3448,0.1667,0.2083,0.196,0.1042,0.1235,0.0039,0.0313,0.1313,0.1778,0.9871,0.8301,0.0227,0.0,0.3448,0.1667,0.2083,0.2005,0.1139,0.1287,0.0039,0.0331,0.1301,0.1714,0.9871,0.8256,0.0226,0.0,0.3448,0.1667,0.2083,0.1994,0.106,0.1257,0.0039,0.0319,reg oper account,block of flats,0.1039,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1343.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n422653,0,Cash loans,F,N,Y,0,112500.0,219042.0,14769.0,193500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-20566,-3145,-5356.0,-3208,,1,1,0,1,1,0,,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Other,,0.3154501269358706,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n247377,0,Cash loans,M,Y,Y,0,166500.0,808650.0,29839.5,675000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.020713,-18157,-1718,-6267.0,-1708,26.0,1,1,0,1,0,0,,1.0,3,3,THURSDAY,6,0,0,0,0,0,0,Housing,0.6111172284357702,0.6163425938451033,0.3572932680336494,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1391.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n421169,0,Cash loans,F,N,N,0,162000.0,755190.0,30078.0,675000.0,Family,State servant,Higher education,Single / not married,House / apartment,0.00496,-18411,-3858,-7136.0,-1955,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,17,0,0,0,0,0,0,Medicine,0.7779979772615268,0.6958072738545288,0.7838324335199619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-169.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n242024,0,Cash loans,F,N,Y,0,135000.0,521280.0,23089.5,450000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.022625,-23205,365243,-3065.0,-4116,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.31682587344297364,0.8128226070575616,0.0825,0.0644,0.9757,0.6668,0.0272,0.0,0.1379,0.1667,0.2083,0.1109,0.0672,0.0652,0.0,0.0,0.084,0.0669,0.9757,0.6798,0.0275,0.0,0.1379,0.1667,0.2083,0.1134,0.0735,0.068,0.0,0.0,0.0833,0.0644,0.9757,0.6713,0.0274,0.0,0.1379,0.1667,0.2083,0.1128,0.0684,0.0664,0.0,0.0,,block of flats,0.0662,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-415.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n334778,1,Revolving loans,F,N,Y,2,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-11892,-260,-5818.0,-596,,1,1,0,1,0,0,,4.0,2,2,THURSDAY,12,0,0,0,1,1,0,Business Entity Type 3,,0.5421769202693426,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-596.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n258493,0,Cash loans,M,Y,Y,1,540000.0,728460.0,44694.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-15522,-2681,-224.0,-2961,6.0,1,1,0,1,0,1,,3.0,1,1,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.647196263045369,0.7008103055189862,0.4776491548517548,0.1871,0.1292,0.9816,0.7484,0.0077,0.2,0.1724,0.3333,0.375,0.0666,0.15,0.1484,0.0116,0.1962,0.1124,0.0711,0.9816,0.7583,0.0,0.1208,0.1034,0.3333,0.375,0.0163,0.0983,0.1268,0.0,0.040999999999999995,0.1889,0.1292,0.9816,0.7518,0.0078,0.2,0.1724,0.3333,0.375,0.0678,0.1526,0.1511,0.0116,0.2003,reg oper account,block of flats,0.2147,Panel,No,3.0,0.0,3.0,0.0,-615.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n244513,0,Cash loans,F,N,N,0,90000.0,101880.0,11493.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-15432,-2636,-817.0,-4205,,1,1,1,1,1,0,Managers,2.0,2,2,MONDAY,14,0,0,0,0,1,1,Self-employed,0.6449182559181975,0.4970880611053474,0.6296742509538716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-844.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n367210,1,Cash loans,M,Y,Y,2,90000.0,101880.0,10206.0,90000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.006852,-15234,-7489,-4775.0,-4802,29.0,1,1,1,1,1,0,Laborers,4.0,3,3,TUESDAY,5,0,0,0,0,1,1,Other,,0.006906724782584811,0.3979463219016906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1912.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n252691,1,Cash loans,M,N,Y,0,180000.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-21542,-3449,-9080.0,-4093,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,6,0,0,0,0,0,0,Transport: type 4,0.8068966772787844,0.6907434844551577,,0.1433,0.1511,0.9816,,,,0.2759,0.1667,,,,0.1316,,,0.146,0.1568,0.9816,,,,0.2759,0.1667,,,,0.1371,,,0.1447,0.1511,0.9816,,,,0.2759,0.1667,,,,0.134,,,,block of flats,0.1416,Panel,No,3.0,0.0,3.0,0.0,-480.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n410633,0,Cash loans,F,N,N,0,135000.0,675000.0,19737.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018801,-13906,-1427,-3661.0,-4693,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,7,0,0,0,1,1,0,Business Entity Type 3,0.3641657562329139,0.5397550572365598,0.375711009574066,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1316.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n243045,0,Cash loans,F,N,N,0,135000.0,450000.0,19066.5,450000.0,Family,Working,Higher education,Married,House / apartment,0.010032,-15574,-1172,-4181.0,-4323,,1,1,1,1,1,0,Core staff,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Transport: type 2,0.6623917616145083,0.5305619514677582,0.6863823354047934,0.4093,0.1919,0.9861,,,0.4,0.3448,0.375,,0.1313,,0.4159,,0.0031,0.41700000000000004,0.1991,0.9861,,,0.4028,0.3448,0.375,,0.1343,,0.4333,,0.0033,0.4132,0.1919,0.9861,,,0.4,0.3448,0.375,,0.1336,,0.4234,,0.0031,,block of flats,0.3278,Panel,No,0.0,0.0,0.0,0.0,-1683.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n305036,0,Cash loans,F,N,Y,0,76500.0,76410.0,7686.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-15616,-6930,-8781.0,-4881,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Medicine,,0.2035228320289013,0.3740208032583212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-548.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n107609,0,Cash loans,M,N,Y,0,157500.0,1314117.0,38551.5,1147500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-16759,-338,-3502.0,-303,,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.612036586835116,0.3001077565791181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-537.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n167865,0,Revolving loans,F,N,Y,1,58500.0,157500.0,7875.0,157500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Municipal apartment,0.031329,-9611,-75,-764.0,-2287,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.3027596117692213,0.26886892555881203,0.8435435389318647,0.0454,0.0206,0.9985,,0.006999999999999999,0.0,0.1034,0.1667,0.2083,,0.037000000000000005,0.0488,,0.023,0.0462,0.0214,0.9985,,0.0071,0.0,0.1034,0.1667,0.2083,,0.0404,0.0509,,0.0244,0.0458,0.0206,0.9985,,0.006999999999999999,0.0,0.1034,0.1667,0.2083,,0.0376,0.0497,,0.0235,reg oper account,block of flats,0.0508,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1867.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n288289,0,Cash loans,F,N,N,0,135000.0,500211.0,47623.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.04622,-17005,-7198,-10930.0,-516,,1,1,0,1,0,0,Medicine staff,1.0,1,1,MONDAY,16,0,0,0,0,1,1,Medicine,,0.7577052585373267,,0.0619,,0.9776,,,0.16,0.1379,0.1667,,,,0.0499,,0.0476,0.063,,0.9777,,,0.1611,0.1379,0.1667,,,,0.052000000000000005,,0.0504,0.0625,,0.9776,,,0.16,0.1379,0.1667,,,,0.0508,,0.0486,,block of flats,0.0392,Panel,No,0.0,0.0,0.0,0.0,-1421.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n455375,0,Cash loans,M,Y,Y,1,112500.0,675000.0,32602.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-10030,-1399,-926.0,-2688,12.0,1,1,1,1,1,0,Drivers,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Emergency,0.17051296940299848,0.6013402169515356,0.17249546677733105,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1961.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n278440,0,Cash loans,M,N,Y,0,67500.0,540000.0,17550.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.025164,-18242,-7470,-6254.0,-1803,,1,1,0,1,0,0,,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.16219210595922867,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1551.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n145050,0,Revolving loans,F,Y,Y,0,180000.0,450000.0,22500.0,450000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.030755,-21498,365243,-5745.0,-4489,4.0,1,0,0,1,0,0,,1.0,2,2,SUNDAY,10,0,0,0,0,0,0,XNA,0.7449193115838421,0.5072887390025607,0.8599241760145402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-472.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n276379,1,Cash loans,F,Y,Y,2,225000.0,1078200.0,38331.0,900000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.072508,-20397,365243,-4071.0,-3934,64.0,1,0,0,1,1,0,,4.0,1,1,TUESDAY,15,0,0,0,0,0,0,XNA,,0.3746940449750675,0.24318648044201235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2429,,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n111845,0,Cash loans,M,N,Y,3,315000.0,1215000.0,39195.0,1215000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-11222,-381,-203.0,-2222,,1,1,1,1,0,0,Laborers,5.0,2,2,FRIDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.3308509883236657,0.4509309348994578,0.6479768603302221,0.1232,0.084,0.9901,0.7484,,0.18,0.1552,0.3125,0.2083,0.057,,0.2197,,0.0797,0.0525,0.0655,0.9816,0.7583,,0.1611,0.1379,0.1667,0.2083,0.0583,,0.2289,,0.0807,0.1244,0.084,0.9901,0.7518,,0.18,0.1552,0.3125,0.2083,0.0579,,0.2237,,0.0813,reg oper account,block of flats,0.1909,Panel,No,1.0,0.0,1.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n333115,0,Revolving loans,F,Y,Y,1,135000.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-7788,-777,-1802.0,-473,12.0,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,0.5551133134437322,0.1828108006796379,,0.1237,0.4964,0.9826,0.762,0.0248,0.0,0.2759,0.1667,0.0417,0.0227,0.1009,0.0733,0.0,0.1273,0.1261,0.5151,0.9826,0.7713,0.025,0.0,0.2759,0.1667,0.0417,0.0232,0.1102,0.0764,0.0,0.1348,0.1249,0.4964,0.9826,0.7652,0.025,0.0,0.2759,0.1667,0.0417,0.0231,0.1026,0.0746,0.0,0.13,reg oper account,block of flats,0.0854,Panel,No,0.0,0.0,0.0,0.0,-145.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n426093,0,Cash loans,M,Y,Y,0,360000.0,487971.0,49999.5,463500.0,Family,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.04622,-13063,-2452,-842.0,-4698,1.0,1,1,0,1,0,1,Security staff,2.0,1,1,THURSDAY,12,0,0,0,1,1,0,Construction,,0.7186946027461893,0.7557400501752248,0.3278,0.1028,0.995,0.932,0.0,0.4,0.1724,0.6667,0.7083,0.003,0.2673,0.3194,0.0,0.3018,0.33399999999999996,0.1066,0.995,0.9347,0.0,0.4028,0.1724,0.6667,0.7083,0.003,0.292,0.3328,0.0,0.3195,0.331,0.1028,0.995,0.9329,0.0,0.4,0.1724,0.6667,0.7083,0.003,0.2719,0.3252,0.0,0.3082,not specified,block of flats,0.3162,Panel,No,0.0,0.0,0.0,0.0,-1055.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,2.0\n242757,0,Cash loans,F,N,Y,0,90000.0,229230.0,11866.5,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018634,-21988,365243,-7524.0,-4220,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,0.7820717075740452,0.78418017523448,0.722392890081304,0.1113,,0.9896,,,0.12,0.1034,0.3333,,0.0144,,0.1147,,0.053,0.1134,,0.9896,,,0.1208,0.1034,0.3333,,0.0148,,0.1195,,0.0561,0.1124,,0.9896,,,0.12,0.1034,0.3333,,0.0147,,0.1167,,0.0541,,block of flats,0.0902,Panel,No,1.0,0.0,1.0,0.0,-784.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n100112,1,Cash loans,M,Y,Y,0,315000.0,953460.0,64107.0,900000.0,Family,Commercial associate,Incomplete higher,Single / not married,With parents,0.030755,-10199,-2015,-4802.0,-1038,2.0,1,1,0,1,0,0,,1.0,2,2,SUNDAY,13,0,0,0,0,1,1,Industry: type 4,,0.4323402118056743,0.07749854649006513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-591.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n447478,1,Cash loans,F,N,Y,0,135000.0,247275.0,19953.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-24366,365243,-3833.0,-4488,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.2782811804606177,0.622922000268356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-293.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n293765,0,Cash loans,F,N,Y,0,180000.0,1078200.0,31653.0,900000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21480,365243,-6035.0,-4957,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.5584533070196935,,0.2794,0.1578,0.9985,0.9796,0.1108,0.32,0.1379,0.625,0.6667,,0.2278,0.2712,0.0386,0.0694,0.2847,0.1638,0.9985,0.9804,0.1118,0.3222,0.1379,0.625,0.6667,,0.2489,0.2826,0.0389,0.0735,0.2821,0.1578,0.9985,0.9799,0.1115,0.32,0.1379,0.625,0.6667,,0.2317,0.2761,0.0388,0.0709,reg oper account,block of flats,0.289,Block,No,0.0,0.0,0.0,0.0,-1190.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n173156,0,Cash loans,F,N,Y,0,216000.0,625536.0,22599.0,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.009657,-20024,365243,-9861.0,-3547,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.7235408005258867,0.646329897706246,0.0598,0.0773,0.9821,0.7552,0.0271,0.0,0.1379,0.1667,0.2083,0.0632,0.0471,0.0494,0.0077,0.0154,0.0609,0.0802,0.9821,0.7648,0.0274,0.0,0.1379,0.1667,0.2083,0.0647,0.0514,0.0514,0.0078,0.0163,0.0604,0.0773,0.9821,0.7585,0.0273,0.0,0.1379,0.1667,0.2083,0.0643,0.0479,0.0503,0.0078,0.0157,reg oper account,block of flats,0.0422,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1110.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n396771,0,Cash loans,F,N,Y,0,112500.0,450000.0,24412.5,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.025164,-8999,-578,-6961.0,-1681,,1,1,1,1,0,1,Medicine staff,1.0,2,2,FRIDAY,15,0,0,0,1,1,0,Business Entity Type 3,0.4286629898567098,0.4909223675205631,0.09507039584133267,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-768.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n140952,0,Cash loans,F,N,Y,0,126000.0,420718.5,21609.0,319500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-19992,-1557,-7088.0,-3539,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.3888344803499241,,0.0404,0.0411,0.9841,0.7824,0.0061,0.0,0.0897,0.0917,0.0833,0.0397,0.0329,0.035,0.0,0.0031,0.0168,0.0,0.9811,0.7517,0.0017,0.0,0.069,0.0417,0.0417,0.0,0.0147,0.015,0.0,0.0,0.0167,0.04,0.9826,0.7652,0.002,0.0,0.069,0.0417,0.0417,0.0107,0.0137,0.0151,0.0,0.0044,reg oper account,block of flats,0.0447,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n262315,0,Cash loans,M,N,N,0,180000.0,398016.0,22558.5,360000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Municipal apartment,0.016612000000000002,-15771,-277,-2695.0,-2694,,1,1,1,1,0,0,Sales staff,1.0,2,2,THURSDAY,16,1,1,0,1,1,0,Business Entity Type 3,0.1905279047684632,0.4580710902229022,0.17352743046491467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-49.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n292186,0,Cash loans,M,Y,Y,2,202500.0,1257430.5,36895.5,1098000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14259,-1529,-7089.0,-344,14.0,1,1,1,1,0,0,Managers,4.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,,0.2508688309712892,0.4135967602644276,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1437.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n401957,0,Cash loans,F,Y,N,0,301500.0,1350000.0,47056.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17772,-4379,-9019.0,-1313,4.0,1,1,1,1,1,0,Accountants,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Transport: type 2,0.1557773621125136,0.6781343072499425,0.1997705696341145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1988.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n250700,0,Cash loans,F,N,Y,0,92250.0,143910.0,14364.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.003122,-24325,365243,-6701.0,-3766,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,14,0,0,0,0,0,0,XNA,,0.7292859154966966,0.6380435278721609,0.0763,0.1099,0.9911,,,0.0,0.2069,0.1667,,0.0672,,0.0793,,0.0,0.0777,0.1141,0.9911,,,0.0,0.2069,0.1667,,0.0687,,0.0826,,0.0,0.077,0.1099,0.9911,,,0.0,0.2069,0.1667,,0.0683,,0.0807,,0.0,,block of flats,0.0945,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-432.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n128532,0,Cash loans,M,Y,N,0,180000.0,700830.0,19399.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-20028,-460,-8575.0,-3393,7.0,1,1,0,1,0,0,Security staff,2.0,2,2,FRIDAY,12,0,1,1,0,1,1,Security,,0.4471709121361219,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-425.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,,,,,,\n150803,1,Revolving loans,M,Y,Y,0,135000.0,337500.0,16875.0,337500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-18696,-1255,-4771.0,-2238,9.0,1,1,0,1,0,0,Security staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,,0.559445233262107,,0.033,0.0,0.9747,0.6532,0.0029,0.0,0.069,0.125,0.1667,0.0,0.0269,0.0296,0.0,0.0,0.0336,0.0,0.9747,0.6668,0.0029,0.0,0.069,0.125,0.1667,0.0,0.0294,0.0308,0.0,0.0,0.0333,0.0,0.9747,0.6578,0.0029,0.0,0.069,0.125,0.1667,0.0,0.0274,0.0301,0.0,0.0,org spec account,block of flats,0.0248,Panel,No,2.0,0.0,2.0,0.0,-2418.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n181011,0,Revolving loans,F,N,Y,0,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-18406,-3068,-2239.0,-1931,,1,1,0,1,0,0,Sales staff,2.0,1,1,WEDNESDAY,12,0,1,1,0,0,0,Business Entity Type 3,0.7178043260367243,0.7020605326412545,0.5352762504724826,0.145,0.0682,0.9940000000000001,0.9116,0.07,0.24,0.1034,0.4858,0.5275,0.0991,0.1171,0.1676,0.0051,0.0051,0.1176,0.0511,0.996,0.9412,0.0658,0.1611,0.069,0.5417,0.5833,0.0731,0.1019,0.1422,0.0039,0.0028,0.1166,0.0532,0.996,0.9396,0.0662,0.16,0.069,0.5417,0.5833,0.0764,0.0949,0.1459,0.0039,0.0061,reg oper account,block of flats,0.22,Panel,No,2.0,0.0,1.0,0.0,-388.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n111178,0,Cash loans,F,N,N,2,81000.0,254700.0,14751.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.031329,-11213,-2292,-2848.0,-2854,,1,1,1,1,1,0,Sales staff,4.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Self-employed,,0.5844891011204217,0.5762088360175724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-146.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n436760,0,Cash loans,M,N,Y,0,81000.0,234000.0,23274.0,234000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.00702,-20191,-816,-7403.0,-3019,,1,1,1,1,0,0,Laborers,2.0,2,2,SUNDAY,11,0,0,0,0,1,1,Housing,0.8891307824960011,0.2547225934648657,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n190665,0,Cash loans,F,N,N,0,135000.0,417024.0,21964.5,360000.0,Family,Working,Higher education,Married,House / apartment,0.00496,-13522,-1113,-5407.0,-5045,,1,1,0,1,0,0,Cooking staff,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.4401758521513319,0.7557400501752248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-722.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n451647,0,Cash loans,F,Y,N,0,360000.0,918000.0,26838.0,918000.0,Unaccompanied,Working,Higher education,Separated,Rented apartment,0.010006,-19840,-1213,-1538.0,-3360,0.0,1,1,1,1,1,0,Accountants,1.0,2,2,SUNDAY,8,0,0,0,0,0,0,Construction,0.7371882889803965,0.6674333781347481,0.3842068130556564,0.044000000000000004,0.0242,0.9831,0.7552,0.0,0.0,0.0803,0.1667,0.2083,0.0147,0.0359,0.0315,0.0,0.0104,0.0315,0.0,0.9811,0.7517,0.0,0.0,0.0345,0.1667,0.2083,0.0105,0.0275,0.016,0.0,0.005,0.0416,0.0,0.9826,0.7585,0.0,0.0,0.069,0.1667,0.2083,0.0115,0.0342,0.0292,0.0,0.0113,reg oper account,block of flats,0.0236,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2125.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n102427,0,Cash loans,M,Y,N,1,180000.0,473661.0,23166.0,333000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-11459,-1536,-10435.0,-3596,6.0,1,1,0,1,0,0,Laborers,3.0,1,1,WEDNESDAY,17,0,0,0,0,1,1,Business Entity Type 3,0.22028699621196965,0.6473278532446918,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-605.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n212233,0,Cash loans,F,N,Y,0,157500.0,343683.0,15268.5,261000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.005313,-14201,-855,-4080.0,-5510,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Self-employed,,0.5551831615131809,0.7252764347002191,0.1237,0.0922,0.9786,0.7076,0.0115,0.0,0.2069,0.1667,0.2083,0.0203,0.1009,0.0676,0.0,0.0,0.1261,0.0957,0.9786,0.7190000000000001,0.0116,0.0,0.2069,0.1667,0.2083,0.0208,0.1102,0.0705,0.0,0.0,0.1249,0.0922,0.9786,0.7115,0.0116,0.0,0.2069,0.1667,0.2083,0.0207,0.1026,0.0689,0.0,0.0,reg oper account,block of flats,0.0791,Panel,No,0.0,0.0,0.0,0.0,-856.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n404356,0,Cash loans,F,N,Y,1,90000.0,381528.0,24970.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-12726,-1351,-781.0,-921,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5304040007657447,0.4974688893052743,0.1062,0.0,0.9906,0.8708,0.1486,0.0,0.2414,0.1667,0.2083,0.0,0.0866,0.1019,0.0,0.0,0.1082,0.0,0.9906,0.8759,0.1499,0.0,0.2414,0.1667,0.2083,0.0,0.0946,0.1062,0.0,0.0,0.1072,0.0,0.9906,0.8725,0.1495,0.0,0.2414,0.1667,0.2083,0.0,0.0881,0.1037,0.0,0.0,reg oper account,block of flats,0.0812,Panel,No,4.0,2.0,4.0,0.0,-592.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,14.0,0.0,1.0\n145665,0,Cash loans,F,N,N,1,135000.0,331632.0,35316.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,With parents,0.010006,-17772,-826,-735.0,-1321,,1,1,1,1,0,0,Sales staff,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Trade: type 7,0.7159546176572211,0.5583562455715563,,0.0835,0.0591,0.9940000000000001,0.9184,0.0209,0.12,0.1034,0.3333,0.375,0.0297,0.0672,0.09300000000000001,0.0039,0.0048,0.0851,0.0613,0.9940000000000001,0.9216,0.0211,0.1208,0.1034,0.3333,0.375,0.0303,0.0735,0.0969,0.0039,0.0051,0.0843,0.0591,0.9940000000000001,0.9195,0.0211,0.12,0.1034,0.3333,0.375,0.0302,0.0684,0.0947,0.0039,0.0049,reg oper account,block of flats,0.0742,Panel,No,0.0,0.0,0.0,0.0,-415.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n437381,0,Cash loans,M,N,Y,0,180000.0,143910.0,17208.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14998,-676,-810.0,-207,,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Transport: type 3,,0.5610022437117262,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-231.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n336412,0,Cash loans,M,N,Y,2,171000.0,373311.0,20380.5,283500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00733,-12035,-1439,-3863.0,-3526,,1,1,0,1,0,0,,4.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Other,0.2395409328355015,0.6172677632950178,0.190705947811054,0.0124,0.0483,0.9687,,0.0023,0.0,0.1034,0.0417,,0.0,,0.013999999999999999,,0.0007,0.0126,0.0501,0.9687,,0.0023,0.0,0.1034,0.0417,,0.0,,0.0145,,0.0007,0.0125,0.0483,0.9687,,0.0023,0.0,0.1034,0.0417,,0.0,,0.0142,,0.0007,,block of flats,0.0288,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-411.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n135012,0,Cash loans,F,N,N,0,81000.0,781920.0,23836.5,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.00733,-20869,365243,-4376.0,-2410,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.38801372397220396,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-930.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n378189,0,Cash loans,F,N,Y,0,99000.0,284400.0,10345.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007305,-22403,365243,-4525.0,-5018,,1,0,0,1,1,0,,2.0,3,3,THURSDAY,7,0,0,0,0,0,0,XNA,,0.3814915149730375,0.28961123838200553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1440.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n142832,0,Cash loans,F,N,Y,0,166500.0,278613.0,17932.5,252000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.025164,-23960,365243,-5115.0,-4065,,1,0,0,1,1,0,,1.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.4728581195686769,0.2650494299443805,0.2639,0.2807,0.9801,0.728,0.2121,0.24,0.2069,0.3333,0.375,0.0,0.2127,0.2519,0.0116,0.1637,0.2689,0.2913,0.9801,0.7387,0.214,0.2417,0.2069,0.3333,0.375,0.0,0.2323,0.2625,0.0117,0.1733,0.2665,0.2807,0.9801,0.7316,0.2134,0.24,0.2069,0.3333,0.375,0.0,0.2163,0.2564,0.0116,0.1671,reg oper account,block of flats,0.3497,Panel,No,3.0,0.0,3.0,0.0,-955.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,7.0\n406536,0,Cash loans,F,Y,Y,2,90000.0,337761.0,17244.0,256500.0,Unaccompanied,Working,Secondary / secondary special,Married,Co-op apartment,0.035792000000000004,-13385,-1692,-2225.0,-4688,22.0,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,13,0,0,0,0,0,0,Other,0.5055626427694455,0.6760018626538167,0.4365064990977374,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-955.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n111700,0,Cash loans,M,Y,Y,1,157500.0,255960.0,14823.0,202500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010556,-15283,-1821,-8434.0,-4800,6.0,1,1,0,1,0,1,Laborers,3.0,3,3,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.5745676383082016,0.7380196196295241,0.0619,0.0508,0.9796,0.7212,0.024,0.0,0.1379,0.1667,0.2083,0.0591,0.0504,0.0548,0.0,0.0,0.063,0.0527,0.9796,0.7321,0.0243,0.0,0.1379,0.1667,0.2083,0.0604,0.0551,0.0571,0.0,0.0,0.0625,0.0508,0.9796,0.7249,0.0242,0.0,0.1379,0.1667,0.2083,0.0601,0.0513,0.0558,0.0,0.0,reg oper account,block of flats,0.0563,Panel,No,0.0,0.0,0.0,0.0,-1125.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,2.0\n170245,0,Cash loans,M,Y,Y,1,202500.0,781920.0,33259.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-19862,-11888,-6690.0,-3399,16.0,1,1,0,1,1,1,Laborers,3.0,3,3,FRIDAY,7,0,0,0,0,0,0,Industry: type 5,,0.4958198058021017,0.7801436381572275,0.0825,0.0685,0.9737,0.6396,0.0072,0.0,0.1379,0.1667,0.2083,0.0719,0.0672,0.0645,0.0,0.0,0.084,0.071,0.9737,0.6537,0.0072,0.0,0.1379,0.1667,0.2083,0.0735,0.0735,0.0672,0.0,0.0,0.0833,0.0685,0.9737,0.6444,0.0072,0.0,0.1379,0.1667,0.2083,0.0732,0.0684,0.0657,0.0,0.0,reg oper account,block of flats,0.0507,Block,No,0.0,0.0,0.0,0.0,-2512.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n113429,0,Cash loans,M,Y,N,2,315000.0,1575000.0,58500.0,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-13399,-992,-7234.0,-4014,14.0,1,1,0,1,0,0,Laborers,4.0,2,2,MONDAY,10,0,1,1,0,1,1,Industry: type 9,0.6116758780249448,0.5969548877236388,0.6109913280868294,0.0928,0.027999999999999997,0.9846,0.7892,0.0101,0.0,0.1379,0.1667,0.0417,0.0202,0.0752,0.0556,0.0019,0.0538,0.063,0.0254,0.9846,0.7975,0.0102,0.0,0.1379,0.1667,0.0417,0.0186,0.0551,0.0575,0.0,0.0491,0.0937,0.027999999999999997,0.9846,0.792,0.0102,0.0,0.1379,0.1667,0.0417,0.0206,0.0765,0.0566,0.0019,0.055,reg oper account,block of flats,0.0434,\"Stone, brick\",No,3.0,2.0,3.0,2.0,-1136.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n360574,0,Cash loans,F,N,Y,1,67500.0,225000.0,15660.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-13979,-2607,-2158.0,-5058,,1,1,0,1,0,0,,3.0,2,2,SATURDAY,15,0,0,0,0,0,0,Hotel,,0.7459687672922295,0.6246146584503397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,3.0,6.0,0.0,-1520.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n153321,1,Cash loans,F,N,Y,0,171000.0,543037.5,39645.0,463500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.00963,-19382,-592,-8227.0,-2522,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,11,0,0,0,0,0,0,Housing,0.8299036470819444,0.5192740689484122,0.5513812618027899,0.1773,0.1496,0.9886,,,0.2,0.1724,0.3542,,0.109,,0.1928,,0.0998,0.1261,0.1064,0.9881,,,0.1208,0.1034,0.3333,,0.0945,,0.1352,,0.1057,0.179,0.1496,0.9886,,,0.2,0.1724,0.3542,,0.1109,,0.1962,,0.1019,,block of flats,0.26899999999999996,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-521.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n342480,0,Cash loans,F,Y,Y,0,135000.0,1057500.0,38115.0,1057500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-15634,-1108,-1004.0,-1067,16.0,1,1,0,1,0,0,Security staff,2.0,2,2,SATURDAY,13,0,0,0,0,1,1,Security,0.64488384314567,0.5896152912434939,0.41885428862332175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-959.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n131403,1,Cash loans,F,Y,Y,0,328500.0,1096020.0,55962.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-13270,-2290,-7395.0,-5518,2.0,1,1,1,1,0,0,Core staff,2.0,1,1,FRIDAY,15,0,0,0,1,1,0,Business Entity Type 1,0.6451064874920575,0.6521600289691367,0.03869751293326445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2503.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,1.0\n354336,0,Cash loans,F,N,Y,0,90000.0,246357.0,23521.5,234000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.01885,-23555,365243,-6624.0,-4336,,1,0,0,1,0,1,,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.4157801233636881,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-819.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n407936,0,Cash loans,F,N,N,0,315000.0,528633.0,35338.5,472500.0,Other_A,Working,Higher education,Single / not married,House / apartment,0.032561,-8518,-598,-6394.0,-1069,,1,1,1,1,1,0,Sales staff,1.0,1,1,SUNDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6683743574119358,0.31547215492577346,0.2096,0.0208,0.9876,0.83,0.0387,0.2264,0.1952,0.3471,0.3608,0.0,0.1701,0.2044,0.0039,0.0028,0.0945,0.0047,0.9777,0.706,0.0047,0.1208,0.1034,0.3333,0.2917,0.0,0.0817,0.0877,0.0,0.0,0.2061,0.0048,0.9896,0.8591,0.042,0.2,0.1724,0.3333,0.375,0.0,0.1693,0.2114,0.0039,0.0037,reg oper account,block of flats,0.1861,Panel,No,4.0,0.0,4.0,0.0,-286.0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n361184,0,Cash loans,F,N,Y,1,75600.0,112068.0,12199.5,99000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.002134,-15287,-8295,-6699.0,-4384,,1,1,0,1,0,0,Managers,3.0,3,3,SATURDAY,8,0,0,0,0,0,0,Postal,0.43715650025578295,0.675407680081258,0.5100895276257282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,3.0,10.0,2.0,-1523.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,5.0\n258774,1,Cash loans,M,N,Y,0,90000.0,364896.0,16200.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-13793,-306,-682.0,-2477,,1,1,0,1,0,0,Drivers,1.0,3,3,THURSDAY,9,0,0,0,1,1,0,Business Entity Type 3,,0.06984656709936808,0.6971469077844458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2227.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n274328,0,Revolving loans,M,N,N,0,157500.0,180000.0,9000.0,180000.0,Family,Commercial associate,Secondary / secondary special,Married,Rented apartment,0.006670999999999999,-13951,-1923,-996.0,-1387,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,13,0,0,0,0,1,1,Self-employed,0.3337556472959225,0.3699974966120733,0.4938628816996244,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-285.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n241868,0,Cash loans,M,Y,Y,1,157500.0,675000.0,32602.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-11050,-443,-5178.0,-3660,8.0,1,1,1,1,0,0,Drivers,3.0,2,2,THURSDAY,10,0,0,0,0,0,0,Agriculture,,0.5892713143370044,0.5334816299804352,,,0.9682,,,,0.0345,0.0833,,0.0,,0.0047,,,,,0.9682,,,,0.0345,0.0833,,0.0,,0.0049,,,,,0.9682,,,,0.0345,0.0833,,0.0,,0.0048,,,,block of flats,0.0049,Mixed,Yes,2.0,0.0,2.0,0.0,-326.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n273860,0,Cash loans,F,N,N,0,63000.0,284400.0,8752.5,225000.0,Unaccompanied,Pensioner,Lower secondary,Widow,Municipal apartment,0.003818,-19751,365243,-12779.0,-3292,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,6,0,0,0,0,0,0,XNA,,0.5184224453458428,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,1.0,0.0,1.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n433229,0,Cash loans,M,Y,Y,0,85050.0,76410.0,7686.0,67500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.0105,-14775,-7115,-1020.0,-4181,12.0,1,1,1,1,0,0,Core staff,2.0,3,3,SATURDAY,9,0,0,0,0,0,0,Agriculture,,0.6394862450380776,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1760.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n166142,0,Cash loans,F,N,Y,0,76500.0,96696.0,4833.0,76500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-17226,-4187,-1947.0,-753,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.39104355254146,0.6222920350945638,0.1940678276718812,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-547.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n421229,0,Cash loans,F,N,Y,1,135000.0,364896.0,26077.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018634,-16645,-9841,-4378.0,-182,,1,1,0,1,0,0,Medicine staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Medicine,,0.4845191179808397,0.7922644738669378,0.0206,,0.9518,,,0.0,0.0345,0.0417,,,,0.0141,,,0.021,,0.9518,,,0.0,0.0345,0.0417,,,,0.0147,,,0.0208,,0.9518,,,0.0,0.0345,0.0417,,,,0.0144,,,,block of flats,0.0111,Mixed,No,0.0,0.0,0.0,0.0,-632.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,1.0\n350269,0,Cash loans,F,N,Y,0,144000.0,976077.0,52132.5,922500.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.030755,-23672,365243,-13781.0,-4734,,1,0,0,1,1,0,,1.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,0.8760764567857371,0.6673751292415977,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1349.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n130510,0,Cash loans,M,Y,Y,1,225000.0,1255680.0,41629.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-9779,-1382,-4204.0,-2437,6.0,1,1,0,1,1,0,Laborers,3.0,2,2,FRIDAY,18,0,0,0,0,0,0,Industry: type 5,,0.4111716927566945,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-175.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n436716,0,Cash loans,M,N,Y,0,202500.0,225000.0,10620.0,225000.0,Family,Working,Higher education,Single / not married,House / apartment,0.026392,-11910,-183,-1157.0,-4251,,1,1,0,1,0,0,,1.0,2,2,SUNDAY,12,0,1,1,0,1,1,Other,0.26975155221840946,0.7092773430785244,0.3077366963789207,0.0711,,0.9901,,,0.0,0.1724,0.1667,,,,0.0676,,0.0,0.0725,,0.9901,,,0.0,0.1724,0.1667,,,,0.0704,,0.0,0.0718,,0.9901,,,0.0,0.1724,0.1667,,,,0.0688,,0.0,,block of flats,0.0531,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-285.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n148959,0,Cash loans,M,Y,Y,1,225000.0,1042560.0,40702.5,900000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.010032,-13525,-1062,-4495.0,-4387,15.0,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,4,0,0,0,0,0,0,Housing,0.4390169726589578,0.7649170931689228,,0.0825,,0.9757,,,0.0,0.1379,0.1667,,,,0.0478,,0.0,0.084,,0.9757,,,0.0,0.1379,0.1667,,,,0.0498,,0.0,0.0833,,0.9757,,,0.0,0.1379,0.1667,,,,0.0487,,0.0,,block of flats,0.0558,Panel,No,0.0,0.0,0.0,0.0,-759.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n228555,0,Cash loans,M,Y,N,0,292500.0,373311.0,22689.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Separated,Rented apartment,0.0228,-17398,-1962,-1048.0,-933,3.0,1,1,0,1,0,0,Drivers,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Transport: type 4,,0.5670086215128732,0.1895952597360396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-832.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n232193,0,Cash loans,M,Y,Y,0,157500.0,678996.0,28899.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17478,-1117,-100.0,-1038,2.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Government,0.5173191208223761,0.6767055220632967,0.6397075677637197,0.0619,0.0547,0.9752,,,0.0,0.1034,0.1667,,0.0495,,0.0488,,0.0093,0.063,0.0568,0.9752,,,0.0,0.1034,0.1667,,0.0506,,0.0509,,0.0098,0.0625,0.0547,0.9752,,,0.0,0.1034,0.1667,,0.0504,,0.0497,,0.0095,,block of flats,0.0491,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-3761.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n221917,0,Cash loans,F,Y,Y,0,135000.0,280332.0,25839.0,234000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-16804,-935,-7385.0,-323,19.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Transport: type 4,,0.724336189289773,0.266456808245056,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2671.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n135074,0,Cash loans,F,N,Y,1,135000.0,286704.0,14638.5,247500.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.0105,-16352,-3772,-2911.0,-3618,,1,1,0,1,0,0,Accountants,2.0,3,3,THURSDAY,12,0,0,0,0,0,0,Industry: type 9,,0.6792120536166231,0.633031641417419,0.1856,,0.9876,,,,0.4138,0.1667,,,,,,,0.1891,,0.9876,,,,0.4138,0.1667,,,,,,,0.1874,,0.9876,,,,0.4138,0.1667,,,,,,,,block of flats,0.1325,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-696.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n194974,0,Cash loans,F,N,N,0,166500.0,241618.5,22486.5,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.00702,-21957,365243,-3566.0,-4672,,1,0,0,1,1,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.6038799881497954,0.7862666146611379,0.0763,0.06,0.9945,0.9116,0.0199,0.1332,0.1148,0.3333,0.1525,0.0343,0.0524,0.0898,0.0013,0.0628,0.0368,0.044000000000000004,0.9916,0.9347,0.0104,0.0806,0.069,0.3333,0.0417,0.0,0.0321,0.0606,0.0,0.0275,0.0729,0.0588,0.995,0.9329,0.0104,0.08,0.069,0.3333,0.0417,0.0302,0.0299,0.0921,0.0,0.0565,reg oper account,block of flats,0.0514,Panel,No,0.0,0.0,0.0,0.0,-1786.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n177561,0,Cash loans,F,Y,Y,0,247500.0,746280.0,54436.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-17074,-4198,-5585.0,-633,25.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.7925238251007836,0.7597121819739279,0.1845,0.1237,0.9866,0.8164,0.0407,0.24,0.2069,0.3333,0.375,0.1227,0.1505,0.2108,0.0,0.0,0.188,0.1284,0.9866,0.8236,0.0411,0.2417,0.2069,0.3333,0.375,0.1255,0.1644,0.2196,0.0,0.0,0.1863,0.1237,0.9866,0.8189,0.040999999999999995,0.24,0.2069,0.3333,0.375,0.1248,0.1531,0.2146,0.0,0.0,reg oper account,block of flats,0.1879,Panel,No,0.0,0.0,0.0,0.0,-2162.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n327475,0,Cash loans,M,N,Y,0,135000.0,126000.0,10233.0,126000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.014519999999999996,-9149,-90,-3983.0,-1582,,1,1,0,1,1,0,Laborers,1.0,2,2,THURSDAY,6,0,0,0,0,0,0,Business Entity Type 3,,0.6860386905090856,,0.0433,0.0509,0.9717,,,0.0,0.1379,0.125,,0.066,,0.0555,,0.065,0.0441,0.0528,0.9717,,,0.0,0.1379,0.125,,0.0675,,0.0578,,0.0689,0.0437,0.0509,0.9717,,,0.0,0.1379,0.125,,0.0671,,0.0565,,0.0664,,block of flats,0.0612,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-778.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n218109,0,Revolving loans,F,Y,Y,1,94500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-10336,-1794,-450.0,-492,4.0,1,1,1,1,0,0,Sales staff,3.0,2,2,SUNDAY,12,0,0,0,0,0,0,Trade: type 3,0.4147983607659677,0.02034119298340929,0.5691487713619409,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-327.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n370974,0,Cash loans,M,N,Y,0,315000.0,1377000.0,56083.5,1377000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-16427,-1180,-3359.0,-4185,,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.5765000029677564,0.6657518199350144,0.3606125659189888,0.1392,0.1247,0.9901,,,0.12,0.1034,0.3333,,0.1174,,0.1493,,0.0024,0.1418,0.1294,0.9901,,,0.1208,0.1034,0.3333,,0.1201,,0.1556,,0.0026,0.1405,0.1247,0.9901,,,0.12,0.1034,0.3333,,0.1194,,0.152,,0.0025,,block of flats,0.1432,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n205938,0,Cash loans,M,Y,N,0,157500.0,225000.0,14647.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-10783,-1702,-4269.0,-3240,15.0,1,1,1,1,0,0,Laborers,2.0,3,3,TUESDAY,8,0,0,0,0,0,0,Transport: type 4,0.21180797223087927,0.6301691145176477,0.6363761710860439,0.0773,,0.9841,,,0.0,0.1379,0.1667,,,,0.062,,0.0,0.0315,,0.9826,,,0.0,0.069,0.1667,,,,0.0323,,0.0,0.0781,,0.9841,,,0.0,0.1379,0.1667,,,,0.0632,,0.0,,block of flats,0.0244,Panel,No,0.0,0.0,0.0,0.0,-391.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n445627,0,Cash loans,M,Y,Y,0,189000.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010032,-17289,-353,-3772.0,-786,18.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,5,0,0,0,0,0,0,Business Entity Type 3,0.3818950866613308,0.5880852501115592,0.6706517530862718,0.0103,0.0144,0.9667,0.5444,0.0014,0.0,0.069,0.0417,0.0833,0.0456,0.0084,0.0107,0.0,0.0,0.0105,0.0,0.9608,0.4838,0.0013,0.0,0.069,0.0417,0.0833,0.0299,0.0092,0.0086,0.0,0.0,0.0104,0.0144,0.9667,0.5505,0.0014,0.0,0.069,0.0417,0.0833,0.0464,0.0086,0.0109,0.0,0.0,not specified,block of flats,0.0072,Wooden,No,0.0,0.0,0.0,0.0,-1450.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n139177,0,Cash loans,M,N,Y,2,450000.0,225000.0,18040.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-15080,-2236,-8033.0,-4099,,1,1,1,1,1,0,Managers,4.0,1,1,TUESDAY,12,0,1,1,0,1,1,Business Entity Type 3,,0.6295981391854537,0.7713615919194317,0.1402,,0.9831,,,0.16,0.1379,0.3333,,,,0.1455,,,0.1429,,0.9831,,,0.1611,0.1379,0.3333,,,,0.1516,,,0.1416,,0.9831,,,0.16,0.1379,0.3333,,,,0.1481,,,,block of flats,0.1145,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1049.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n304662,0,Cash loans,F,N,N,0,99000.0,284256.0,33732.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003818,-12561,-2162,-644.0,-2178,,1,1,0,1,0,0,Core staff,2.0,2,2,SUNDAY,15,0,0,0,0,0,0,Trade: type 7,0.19301181843551465,0.6060122137498566,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-371.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n184734,0,Cash loans,F,N,Y,0,171000.0,808650.0,23773.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-21453,365243,-8024.0,-4621,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.3630266098813311,0.7165702448010511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n156966,0,Cash loans,F,N,Y,0,90000.0,256500.0,20695.5,256500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-18146,-826,-17480.0,-1687,,1,1,0,1,1,1,Sales staff,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Self-employed,0.6383001945529339,0.6200742162871823,,0.1237,0.1043,0.9747,0.6532,0.0169,0.0,0.2759,0.1667,0.2083,0.0899,0.1,0.1132,0.0039,0.0044,0.1261,0.1082,0.9747,0.6668,0.0171,0.0,0.2759,0.1667,0.2083,0.0919,0.1093,0.11800000000000001,0.0039,0.0046,0.1249,0.1043,0.9747,0.6578,0.017,0.0,0.2759,0.1667,0.2083,0.0915,0.1018,0.1153,0.0039,0.0045,reg oper account,block of flats,0.0993,Panel,No,4.0,0.0,4.0,0.0,-1473.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n142017,0,Cash loans,M,N,Y,1,279000.0,1223010.0,51948.0,1125000.0,Family,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.010643,-16694,-3465,-4831.0,-245,,1,1,0,1,1,0,,3.0,2,2,THURSDAY,10,0,0,0,0,1,1,School,0.3173481754438951,0.6731071113472946,0.4956658291397297,0.0722,0.086,0.9771,,0.0294,0.0,0.1379,0.1667,,0.049,,0.068,,0.0739,0.0735,0.0893,0.9772,,0.0297,0.0,0.1379,0.1667,,0.0501,,0.0709,,0.0782,0.0729,0.086,0.9771,,0.0296,0.0,0.1379,0.1667,,0.0499,,0.0692,,0.0755,,block of flats,0.0696,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1573.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n249331,0,Cash loans,M,Y,Y,0,135000.0,521280.0,28408.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-18516,-10948,-8746.0,-2039,4.0,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4833722540077567,,0.0928,0.0827,0.9796,0.7212,0.012,0.0,0.2069,0.1667,0.2083,0.0626,0.0756,0.0881,0.0039,0.0,0.0945,0.0858,0.9796,0.7321,0.0121,0.0,0.2069,0.1667,0.2083,0.064,0.0826,0.0918,0.0039,0.0,0.0937,0.0827,0.9796,0.7249,0.0121,0.0,0.2069,0.1667,0.2083,0.0636,0.077,0.0897,0.0039,0.0,reg oper spec account,block of flats,0.0693,Panel,No,2.0,1.0,2.0,1.0,-916.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n198741,0,Cash loans,F,N,Y,0,382500.0,900000.0,45954.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.020713,-16203,-1835,-2216.0,-4491,,1,1,0,1,1,1,Managers,2.0,3,2,TUESDAY,12,0,0,0,0,0,0,Agriculture,0.5967362733964452,0.4773189094317138,0.6722428897082422,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-537.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n435283,0,Cash loans,F,N,Y,0,148500.0,215865.0,21478.5,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019689,-21554,365243,-10252.0,-4897,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.7062106728894305,0.4614823912998385,0.0619,0.0751,0.9856,0.8028,0.0,0.0,0.1379,0.1667,0.2083,0.0464,0.0504,0.0568,0.0,0.0816,0.063,0.0779,0.9856,0.8105,0.0,0.0,0.1379,0.1667,0.2083,0.0474,0.0551,0.0592,0.0,0.0864,0.0625,0.0751,0.9856,0.8054,0.0,0.0,0.1379,0.1667,0.2083,0.0472,0.0513,0.0579,0.0,0.0833,reg oper account,block of flats,0.0624,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1893.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n328511,0,Cash loans,F,N,Y,0,180000.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-11868,-629,-8532.0,-4032,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,16,0,0,0,0,1,1,Self-employed,,0.5236995043672498,0.12689752616027333,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,2.0,-240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n343940,0,Cash loans,F,N,N,0,450000.0,269550.0,21739.5,225000.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.04622,-15133,-3735,-9186.0,-3698,,1,1,0,1,0,0,Laborers,2.0,1,1,THURSDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.7098721925722244,0.3672910183026313,0.0928,0.0828,0.9776,0.6940000000000001,0.0386,0.0,0.2069,0.1667,0.2083,0.0991,0.0756,0.0771,0.0,0.0,0.0945,0.0859,0.9777,0.706,0.039,0.0,0.2069,0.1667,0.2083,0.1013,0.0826,0.0803,0.0,0.0,0.0937,0.0828,0.9776,0.6981,0.0389,0.0,0.2069,0.1667,0.2083,0.1008,0.077,0.0785,0.0,0.0,reg oper account,block of flats,0.0817,Panel,No,0.0,0.0,0.0,0.0,-2374.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,3.0\n141617,0,Cash loans,F,N,Y,0,135000.0,808650.0,23773.5,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.005084,-22480,365243,-6565.0,-4203,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,XNA,,0.3342320688944385,,0.066,0.0798,0.9752,0.66,0.0307,0.0,0.1379,0.125,0.1667,,0.053,0.0405,0.0039,0.0021,0.0672,0.0828,0.9752,0.6733,0.0309,0.0,0.1379,0.125,0.1667,,0.0579,0.0333,0.0039,0.0023,0.0666,0.0798,0.9752,0.6645,0.0309,0.0,0.1379,0.125,0.1667,,0.0539,0.0413,0.0039,0.0022,reg oper account,block of flats,0.0391,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n373359,0,Cash loans,F,Y,Y,1,540000.0,1254739.5,66982.5,1129500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007305,-12808,-1240,-655.0,-4788,5.0,1,1,0,1,0,0,Managers,3.0,3,3,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.5187117425864713,0.6694978879703507,0.1940678276718812,0.2381,0.1992,0.9841,0.7824,0.0931,0.24,0.2069,0.3333,0.375,0.0345,0.1883,0.228,0.027000000000000003,0.0233,0.2426,0.2067,0.9841,0.7909,0.094,0.2417,0.2069,0.3333,0.375,0.0353,0.2057,0.2376,0.0272,0.0247,0.2405,0.1992,0.9841,0.7853,0.0937,0.24,0.2069,0.3333,0.375,0.0351,0.1915,0.2321,0.0272,0.0238,reg oper account,block of flats,0.2353,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3356.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n256248,0,Cash loans,F,N,N,2,72000.0,450000.0,16294.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.031329,-13892,-1092,-3084.0,-2713,,1,1,0,1,0,0,Laborers,4.0,2,2,MONDAY,7,0,0,0,0,0,0,Self-employed,0.3078364335028036,0.4891572627901142,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2105.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n239675,0,Cash loans,F,N,Y,0,67500.0,284400.0,16011.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.007120000000000001,-21632,365243,-882.0,-1987,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,0.5902275940260221,0.2622583692422573,,0.1237,0.3665,0.9851,0.7959999999999999,,0.0,0.2759,0.1667,0.2083,,,0.0658,,,0.1261,0.3803,0.9851,0.804,,0.0,0.2759,0.1667,0.2083,,,0.0686,,,0.1249,0.3665,0.9851,0.7987,,0.0,0.2759,0.1667,0.2083,,,0.067,,,reg oper account,block of flats,0.087,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n104887,0,Cash loans,F,N,N,0,225000.0,557770.5,20164.5,481500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-9768,-95,-108.0,-190,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,19,0,0,0,1,1,0,Business Entity Type 3,,0.6021682126217236,0.22383131747015547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-777.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n433125,0,Cash loans,F,N,Y,0,247500.0,717003.0,23260.5,598500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.032561,-19276,-4782,-5558.0,-2813,,1,1,0,1,1,0,Laborers,1.0,1,1,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6696236807481002,0.5136937663039473,0.0825,0.0635,0.9762,0.6736,0.0087,0.0,0.1379,0.1667,0.2083,0.0,0.0672,0.0686,0.0,0.0,0.084,0.0658,0.9762,0.6864,0.0088,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.0715,0.0,0.0,0.0833,0.0635,0.9762,0.6779999999999999,0.0087,0.0,0.1379,0.1667,0.2083,0.0,0.0684,0.0698,0.0,0.0,reg oper account,block of flats,0.0587,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1059.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,7.0\n227371,0,Cash loans,F,N,N,0,81000.0,916470.0,26797.5,765000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-21822,365243,-7041.0,-2462,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.6398944999665165,0.3606125659189888,0.6876,0.1359,0.9856,0.8028,0.0197,0.0,0.2759,0.1667,,,0.1782,0.0986,1.0,0.1068,0.7006,0.141,0.9856,0.8105,0.0199,0.0,0.2759,0.1667,,,0.1947,0.1027,1.0,0.1131,0.6943,0.1359,0.9856,0.8054,0.0199,0.0,0.2759,0.1667,,,0.1813,0.1003,1.0,0.1091,,block of flats,0.1115,Panel,No,8.0,0.0,8.0,0.0,-685.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n423974,0,Cash loans,F,Y,Y,1,202500.0,234576.0,23197.5,202500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.002506,-15061,-131,-4383.0,-1834,3.0,1,1,0,1,0,0,HR staff,2.0,2,2,SATURDAY,2,0,0,0,0,0,0,Military,0.6330123271310032,0.6683016491276186,0.15663982703141147,0.066,,0.9771,,,0.0,0.1379,0.125,,0.0,,,,,0.0672,,0.9772,,,0.0,0.1379,0.125,,0.0,,,,,0.0666,,0.9771,,,0.0,0.1379,0.125,,0.0,,,,,,block of flats,0.0399,Block,No,0.0,0.0,0.0,0.0,-475.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,1.0,0.0,0.0\n329927,1,Revolving loans,F,N,Y,1,96750.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-14244,-1401,-593.0,-1268,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,9,0,0,0,0,0,0,Cleaning,,0.3073617447707364,,0.1144,0.0644,0.9846,,,0.0532,0.1379,0.2221,,0.0246,,0.0841,,0.0467,0.0735,0.0578,0.9841,,,0.0,0.1379,0.1667,,0.0162,,0.0455,,0.0037,0.1364,0.0644,0.9841,,,0.0,0.1379,0.1667,,0.025,,0.064,,0.0376,,block of flats,0.1792,Block,No,3.0,1.0,3.0,1.0,-585.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n335925,1,Cash loans,M,Y,Y,0,126000.0,555273.0,21645.0,463500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010147,-20775,-2774,-2580.0,-3578,13.0,1,1,0,1,0,0,Sales staff,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,,0.1934838132931632,0.4543210601605785,0.132,,0.9781,,,0.0,0.2759,0.1667,,,,0.1037,,,0.1345,,0.9782,,,0.0,0.2759,0.1667,,,,0.1081,,,0.1332,,0.9781,,,0.0,0.2759,0.1667,,,,0.1056,,,,block of flats,0.0816,Panel,No,0.0,0.0,0.0,0.0,-18.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n411887,0,Revolving loans,M,N,N,2,202500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-8832,-44,-3388.0,-1469,,1,1,0,1,1,1,Laborers,4.0,3,3,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.3683474918744791,0.2925880073368475,0.0381,0.0253,0.9881,0.8368,0.0102,0.04,0.0345,0.3333,0.0417,0.0255,0.0286,0.0391,0.0116,0.0088,0.0389,0.0262,0.9881,0.8432,0.0103,0.0403,0.0345,0.3333,0.0417,0.0261,0.0312,0.0408,0.0117,0.0093,0.0385,0.0253,0.9881,0.8390000000000001,0.0103,0.04,0.0345,0.3333,0.0417,0.026000000000000002,0.0291,0.0398,0.0116,0.009000000000000001,reg oper account,block of flats,0.0383,Panel,No,2.0,0.0,2.0,0.0,-1797.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n387076,0,Cash loans,M,N,Y,0,90000.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.035792000000000004,-8149,-968,-2950.0,-815,,1,1,1,1,0,0,Laborers,1.0,2,2,TUESDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.4616288018469776,0.34578480246959553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-638.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n449654,0,Revolving loans,F,N,N,0,157500.0,360000.0,18000.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-11987,-2143,-6114.0,-4115,,1,1,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,,0.7441119976846097,0.633031641417419,0.2464,,0.9871,,,,0.2069,0.375,,,,,,,0.2511,,0.9871,,,,0.2069,0.375,,,,,,,0.2488,,0.9871,,,,0.2069,0.375,,,,,,,,block of flats,0.2201,,No,1.0,0.0,1.0,0.0,-1515.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n177827,0,Cash loans,F,N,Y,0,105750.0,521280.0,23089.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-18454,-3412,-8893.0,-1980,,1,1,1,1,1,0,Sales staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Trade: type 7,,0.6952098701183281,0.6479768603302221,0.034,0.0651,0.9702,,,0.0,0.1034,0.0833,,0.0274,,0.027000000000000003,,0.0,0.0347,0.0676,0.9702,,,0.0,0.1034,0.0833,,0.027999999999999997,,0.0281,,0.0,0.0344,0.0651,0.9702,,,0.0,0.1034,0.0833,,0.0279,,0.0275,,0.0,,block of flats,0.0212,\"Stone, brick\",No,1.0,0.0,0.0,0.0,-1443.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n337020,0,Cash loans,F,N,N,1,157500.0,808650.0,29839.5,675000.0,Family,Working,Higher education,Married,House / apartment,0.035792000000000004,-10327,-182,-297.0,-675,,1,1,0,1,0,1,Private service staff,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,0.4080814308942744,0.3935737694129049,,0.0711,0.15,0.9975,0.966,0.016,0.0,0.1034,0.0833,0.125,0.0523,0.057999999999999996,0.065,0.0,0.0885,0.0725,0.1556,0.9975,0.9673,0.0162,0.0,0.1034,0.0833,0.125,0.0535,0.0634,0.0677,0.0,0.0937,0.0718,0.15,0.9975,0.9665,0.0161,0.0,0.1034,0.0833,0.125,0.0532,0.059000000000000004,0.0662,0.0,0.0904,reg oper account,block of flats,0.0791,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n284530,0,Cash loans,F,N,Y,0,90000.0,100737.0,9810.0,94500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.011657,-22762,-1289,-1256.0,-4025,,1,1,1,1,1,0,Cleaning staff,1.0,1,1,MONDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.6714594152536033,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-1521.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n280999,0,Cash loans,F,N,N,0,112500.0,625356.0,20173.5,522000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010032,-12113,-2654,-726.0,-4500,,1,1,1,1,1,0,Accountants,2.0,2,2,SUNDAY,14,0,0,0,0,0,0,Self-employed,0.3970694045669934,0.4508172383516911,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2082.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n350406,0,Cash loans,F,N,Y,0,135000.0,653328.0,19233.0,468000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-20887,-1963,-13020.0,-3738,,1,1,1,1,0,0,Cleaning staff,2.0,2,2,TUESDAY,12,0,0,0,1,1,0,Business Entity Type 2,,0.7014243458455739,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-1694.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n142204,0,Cash loans,M,N,N,0,67500.0,168102.0,16506.0,148500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-19491,-4925,-9509.0,-3036,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.4662895539129526,0.7922644738669378,0.1216,0.0585,0.9791,0.7144,0.1702,0.0,0.2759,0.1667,0.2083,0.0463,0.0992,0.106,0.0,0.0068,0.1239,0.0607,0.9791,0.7256,0.1717,0.0,0.2759,0.1667,0.2083,0.0473,0.1084,0.1104,0.0,0.0072,0.1228,0.0585,0.9791,0.7182,0.1713,0.0,0.2759,0.1667,0.2083,0.0471,0.1009,0.1079,0.0,0.006999999999999999,reg oper account,block of flats,0.0849,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1501.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n293177,1,Cash loans,M,Y,N,0,135000.0,675000.0,22306.5,675000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.035792000000000004,-20240,365243,-10526.0,-3522,4.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,0.8556342946993836,0.6418120887362477,0.7252764347002191,0.0371,,0.9841,,,0.04,0.0345,0.3333,,,,0.0346,,,0.0378,,0.9841,,,0.0403,0.0345,0.3333,,,,0.0361,,,0.0375,,0.9841,,,0.04,0.0345,0.3333,,,,0.0353,,,,block of flats,0.0272,Panel,No,0.0,0.0,0.0,0.0,-2784.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n244970,1,Cash loans,M,Y,Y,2,202500.0,580500.0,22491.0,580500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-11928,-1320,-342.0,-3624,16.0,1,1,1,1,0,0,Drivers,4.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.14955803670912882,,0.1113,0.0835,0.9851,,,0.12,0.1034,0.3333,,0.0502,,0.1138,,0.0666,0.0756,0.059000000000000004,0.9851,,,0.0806,0.069,0.3333,,0.0325,,0.0802,,0.0467,0.1124,0.0835,0.9851,,,0.12,0.1034,0.3333,,0.0511,,0.1159,,0.068,,block of flats,0.0701,Panel,No,0.0,0.0,0.0,0.0,-442.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n141396,0,Cash loans,M,N,Y,0,117000.0,161730.0,8388.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.011657,-22933,365243,-7150.0,-1218,,1,0,0,1,0,0,,2.0,1,1,FRIDAY,13,0,0,0,0,0,0,XNA,0.4930951801513371,0.7370077063417347,0.5832379256761245,0.0041,0.0307,0.9513,,,0.0,0.0345,0.0417,,0.0,,0.0076,,0.0094,0.0042,0.0319,0.9513,,,0.0,0.0345,0.0417,,0.0,,0.008,,0.0099,0.0042,0.0307,0.9513,,,0.0,0.0345,0.0417,,0.0,,0.0078,,0.0096,,block of flats,0.0081,Wooden,No,0.0,0.0,0.0,0.0,-1653.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n232358,1,Cash loans,F,Y,Y,1,135000.0,691020.0,25384.5,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006233,-14624,-2161,-4547.0,-2744,10.0,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,14,0,0,0,0,0,0,Medicine,,0.5072942047355521,0.1245194708919845,0.0825,0.0027,0.9752,0.66,0.0087,0.0,0.1379,0.1667,0.2083,0.0598,0.0672,0.0702,0.0,0.0022,0.084,0.0028,0.9752,0.6733,0.0088,0.0,0.1379,0.1667,0.2083,0.0611,0.0735,0.0732,0.0,0.0023,0.0833,0.0027,0.9752,0.6645,0.0088,0.0,0.1379,0.1667,0.2083,0.0608,0.0684,0.0715,0.0,0.0022,reg oper account,block of flats,0.0557,Panel,No,0.0,0.0,0.0,0.0,-543.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n292934,0,Cash loans,F,N,Y,0,180000.0,302341.5,12937.5,261000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.006233,-23734,365243,-6834.0,-4407,,1,0,0,1,0,0,,1.0,2,2,SUNDAY,10,0,0,0,0,0,0,XNA,,0.4453822625566749,0.8528284668510541,0.0619,0.0497,0.9771,,,0.0,0.1379,0.1667,,0.0126,,0.0542,,0.0022,0.063,0.0516,0.9772,,,0.0,0.1379,0.1667,,0.0129,,0.0565,,0.0024,0.0625,0.0497,0.9771,,,0.0,0.1379,0.1667,,0.0128,,0.0552,,0.0023,,block of flats,0.0477,Panel,No,5.0,0.0,5.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n221938,0,Cash loans,F,N,Y,3,162000.0,781920.0,25969.5,675000.0,Unaccompanied,Pensioner,Higher education,Civil marriage,House / apartment,0.006008,-20816,365243,-8988.0,-4249,,1,0,0,1,0,0,,5.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.012932991598556737,0.1674084472266241,0.0186,0.034,0.9871,0.1432,0.0025,0.0,0.069,0.0833,0.125,0.0359,0.0151,0.0172,0.0,0.0,0.0189,0.0353,0.9871,0.1768,0.0025,0.0,0.069,0.0833,0.125,0.0367,0.0165,0.0179,0.0,0.0,0.0187,0.034,0.9871,0.1547,0.0025,0.0,0.069,0.0833,0.125,0.0365,0.0154,0.0175,0.0,0.0,reg oper account,block of flats,0.0149,Panel,No,0.0,0.0,0.0,0.0,-318.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n126294,0,Cash loans,F,N,N,0,495000.0,315000.0,12456.0,315000.0,,Commercial associate,Higher education,Separated,House / apartment,0.003540999999999999,-13526,-216,-759.0,-3520,,1,1,0,1,0,0,Accountants,1.0,1,1,THURSDAY,10,0,0,0,0,0,0,Industry: type 9,0.3717967326156232,0.5083545223658293,0.5989262182569273,0.1103,0.0765,0.9841,0.7824,0.0412,0.12,0.1034,0.3333,,0.0138,0.0899,0.11199999999999999,0.0,0.0,0.1124,0.0794,0.9841,0.7909,0.0416,0.1208,0.1034,0.3333,,0.0141,0.0983,0.1167,0.0,0.0,0.1114,0.0765,0.9841,0.7853,0.0415,0.12,0.1034,0.3333,,0.013999999999999999,0.0915,0.1141,0.0,0.0,reg oper account,block of flats,0.1107,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n158064,0,Cash loans,M,Y,Y,0,144000.0,239850.0,23850.0,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.015221,-24504,365243,-4572.0,-4579,9.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.4696487728436336,0.6296742509538716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1626.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n359667,0,Revolving loans,M,Y,Y,0,112500.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-20446,-1489,-1128.0,-3835,7.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Other,,0.6661944900574546,0.8224987619370829,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1072.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n312194,0,Cash loans,M,Y,Y,1,180000.0,1350000.0,57199.5,1350000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.015221,-11352,-874,-207.0,-3619,1.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,TUESDAY,17,0,0,0,0,0,0,Transport: type 4,0.2456476431776762,0.14108608019314553,0.5460231970049609,0.0577,0.0553,0.9911,,,0.0,0.1379,0.1667,,0.0121,,0.0533,,0.0,0.0588,0.0574,0.9911,,,0.0,0.1379,0.1667,,0.0124,,0.0555,,0.0,0.0583,0.0553,0.9911,,,0.0,0.1379,0.1667,,0.0123,,0.0542,,0.0,,block of flats,0.0571,Panel,No,5.0,0.0,5.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n122101,0,Cash loans,M,N,N,0,135000.0,1154362.5,37368.0,1008000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,Office apartment,0.019101,-8100,-1493,-386.0,-722,,1,1,0,1,0,0,Drivers,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Military,,0.4432604182224205,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,4.0,6.0,4.0,-704.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n140918,0,Cash loans,F,N,N,1,225000.0,630000.0,64683.0,630000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008019,-15509,-8102,-1281.0,-3066,,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Postal,,0.4400842474273349,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-528.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n276310,0,Cash loans,F,Y,Y,0,313650.0,746280.0,54436.5,675000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.072508,-13414,-1334,-7493.0,-4865,8.0,1,1,0,1,0,0,Managers,1.0,1,1,FRIDAY,19,0,0,0,0,0,0,Bank,0.7180477760761841,0.6905192178628659,0.248535557339522,0.1089,0.0663,0.9891,,,0.12,0.1148,0.4721,,0.0,,0.066,,0.0186,0.0746,0.0333,0.996,,,0.0,0.0345,0.1667,,0.0,,0.0477,,0.0,0.0937,0.0826,0.996,,,0.12,0.1034,0.5417,,0.0,,0.0544,,0.0071,,block of flats,0.0634,Panel,No,0.0,0.0,0.0,0.0,-1196.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n321340,0,Cash loans,F,N,Y,0,112500.0,405000.0,22099.5,405000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.014519999999999996,-14363,-1043,-8331.0,-5045,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.4156154673083873,0.4561097392782771,0.1082,0.0714,0.9901,0.8572,0.0464,0.12,0.0862,0.3958,0.4375,0.0633,0.0878,0.1148,0.0097,0.0633,0.0704,0.0379,0.9811,0.7517,0.0152,0.0806,0.0345,0.3333,0.375,0.0193,0.0606,0.0855,0.0039,0.036000000000000004,0.1093,0.0714,0.9901,0.8591,0.0467,0.12,0.0862,0.3958,0.4375,0.0644,0.0894,0.1169,0.0097,0.0647,reg oper spec account,block of flats,0.1043,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-307.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n235141,0,Cash loans,F,N,Y,0,81000.0,239850.0,23850.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010966,-24034,365243,-5.0,-4237,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.3702116095691348,0.7252764347002191,0.3113,0.2504,0.9821,0.7552,0.1331,0.36,0.3103,0.3333,0.0417,0.2327,0.2505,0.3042,0.0154,0.0416,0.3172,0.2598,0.9821,0.7648,0.1343,0.3625,0.3103,0.3333,0.0417,0.23800000000000002,0.2736,0.3169,0.0156,0.044000000000000004,0.3144,0.2504,0.9821,0.7585,0.1339,0.36,0.3103,0.3333,0.0417,0.2367,0.2548,0.3096,0.0155,0.0425,org spec account,block of flats,0.321,Block,No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n434111,0,Cash loans,F,N,Y,0,180000.0,495000.0,21933.0,495000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.004849,-22955,365243,-6624.0,-4631,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.06958136425368078,0.3376727217405312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1869.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n337071,0,Cash loans,F,N,Y,0,90000.0,258709.5,25717.5,234000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-20594,365243,-11055.0,-162,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.6308975984945305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-141.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n265125,0,Cash loans,F,N,Y,0,135000.0,675000.0,34596.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-14014,-2730,-6177.0,-3385,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Trade: type 7,,0.5765548752114255,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n265809,0,Cash loans,M,N,Y,0,135000.0,349659.0,27454.5,324000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.014464,-15243,-2253,-2282.0,-4533,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,8,0,1,1,0,1,1,Business Entity Type 3,0.26825843010306544,0.08350989712402906,0.4830501881366946,0.0856,0.0374,0.9762,0.6736,0.0145,0.0,0.069,0.125,0.1667,0.0323,0.0698,0.0297,0.0,0.0,0.0872,0.0388,0.9762,0.6864,0.0146,0.0,0.069,0.125,0.1667,0.033,0.0762,0.0309,0.0,0.0,0.0864,0.0374,0.9762,0.6779999999999999,0.0146,0.0,0.069,0.125,0.1667,0.0329,0.071,0.0302,0.0,0.0,reg oper spec account,block of flats,0.0313,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1373.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n289284,0,Cash loans,F,N,N,0,225000.0,555939.0,19966.5,459000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-14446,-1536,-7411.0,-4581,,1,1,0,1,1,0,Medicine staff,2.0,1,1,FRIDAY,15,0,1,1,0,0,0,Medicine,0.8038094406640488,0.7389527790500081,0.4543210601605785,0.0082,,0.9697,,,0.0,0.069,0.0417,,,,0.0077,,,0.0084,,0.9697,,,0.0,0.069,0.0417,,,,0.008,,,0.0083,,0.9697,,,0.0,0.069,0.0417,,,,0.0078,,,,block of flats,0.0068,\"Stone, brick\",No,4.0,1.0,4.0,0.0,-1183.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n422038,0,Cash loans,M,Y,Y,3,157500.0,213322.5,13761.0,162000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-11776,-4864,-5106.0,-4405,8.0,1,1,0,1,0,0,,5.0,2,2,FRIDAY,11,0,0,0,0,0,0,Agriculture,,0.490752950667041,0.5673792367572691,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2780.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n287129,0,Cash loans,F,N,N,1,162000.0,1288350.0,37669.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.011703,-18487,-1119,-7182.0,-2049,,1,1,1,1,0,0,Waiters/barmen staff,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.7587315194924902,0.5460231970049609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2429.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n254158,0,Cash loans,F,N,Y,0,216000.0,227520.0,18103.5,180000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.025164,-10921,-129,-3115.0,-3582,,1,1,0,1,1,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.1714326123404297,0.503866901621124,0.3344541255096772,0.1258,0.4204,0.9856,0.8028,0.0683,0.08,0.069,0.3333,0.375,0.0783,0.0008,0.0919,0.0,0.0202,0.1282,0.4363,0.9856,0.8105,0.0689,0.0806,0.069,0.3333,0.375,0.0801,0.0009,0.0958,0.0,0.0214,0.127,0.4204,0.9856,0.8054,0.0687,0.08,0.069,0.3333,0.375,0.0797,0.0009,0.0936,0.0,0.0206,reg oper account,block of flats,0.114,Panel,No,2.0,0.0,2.0,0.0,-1575.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n105464,0,Cash loans,F,N,Y,0,135000.0,500211.0,26779.5,463500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-17506,-2431,-5618.0,-1061,,1,1,0,1,0,0,Realty agents,2.0,3,2,MONDAY,10,0,0,0,0,0,0,Self-employed,0.6758180366209535,0.6794692862179239,0.2822484337007223,0.1711,0.1049,0.9945,0.9252,0.0462,0.16,0.1379,0.375,0.2292,0.0442,0.1378,0.1284,0.0077,0.1053,0.1544,0.0969,0.9921,0.8955,0.0416,0.1611,0.1379,0.375,0.0417,0.0,0.1322,0.1032,0.0039,0.0062,0.1728,0.1049,0.9945,0.9262,0.0465,0.16,0.1379,0.375,0.2292,0.045,0.1402,0.1307,0.0078,0.1075,reg oper account,block of flats,0.1729,Panel,No,1.0,0.0,1.0,0.0,-924.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n145079,0,Cash loans,F,Y,N,0,202500.0,810000.0,29092.5,810000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.022625,-15822,-1592,-581.0,-4658,13.0,1,1,1,1,0,0,Accountants,1.0,2,2,FRIDAY,18,0,0,0,1,1,0,Business Entity Type 1,0.6389591139521729,0.6967598992249242,0.4884551844437485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1723.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n349652,0,Cash loans,F,Y,N,1,67500.0,265851.0,16195.5,229500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.00702,-12035,-3012,-870.0,-4434,9.0,1,1,0,1,0,0,Private service staff,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.20431491769307927,0.639360199657299,0.7610263695502636,0.0804,,0.9742,0.6464,,0.0,0.1379,0.1667,0.2083,,0.0656,0.062,0.0077,,0.0819,,0.9742,0.6602,,0.0,0.1379,0.1667,0.2083,,0.0716,0.0646,0.0078,,0.0812,,0.9742,0.6511,,0.0,0.1379,0.1667,0.2083,,0.0667,0.0631,0.0078,,reg oper account,block of flats,0.0499,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1538.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n368563,0,Cash loans,M,Y,Y,0,225000.0,973503.0,52942.5,855000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.01885,-11125,-630,-5693.0,-804,7.0,1,1,0,1,0,1,Core staff,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Military,0.13498472208272114,0.6327797135067842,0.4632753280912678,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-629.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n259530,0,Cash loans,F,N,N,0,112500.0,568800.0,15772.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-16452,-985,-3690.0,-7,,1,1,0,1,0,0,Managers,2.0,3,3,SATURDAY,6,0,0,0,0,0,0,Medicine,0.5818043202502448,0.5972337071086856,0.7394117535524816,0.0742,0.1048,0.9856,0.8028,0.0365,0.08,0.069,0.3333,0.0417,0.0889,0.0538,0.14800000000000002,0.0309,0.0507,0.0756,0.1088,0.9856,0.8105,0.0368,0.0806,0.069,0.3333,0.0417,0.091,0.0588,0.1542,0.0311,0.0537,0.0749,0.1048,0.9856,0.8054,0.0367,0.08,0.069,0.3333,0.0417,0.0905,0.0547,0.1506,0.0311,0.0518,reg oper account,block of flats,0.1474,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,3.0\n408793,0,Revolving loans,F,N,Y,2,157500.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-15448,-4218,-8601.0,-3068,,1,1,0,1,0,0,High skill tech staff,4.0,2,2,WEDNESDAY,15,0,0,0,1,1,0,Business Entity Type 2,,0.6756233762981922,0.6577838002083306,0.1237,0.0,0.9762,0.6736,0.0141,0.0,0.2069,0.1667,0.2083,0.0675,0.0908,0.083,0.0463,0.0387,0.1261,0.0,0.9762,0.6864,0.0143,0.0,0.2069,0.1667,0.2083,0.069,0.0992,0.0864,0.0467,0.0409,0.1249,0.0,0.9762,0.6779999999999999,0.0142,0.0,0.2069,0.1667,0.2083,0.0687,0.0923,0.0845,0.0466,0.0395,reg oper account,block of flats,0.0737,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1779.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n332106,0,Cash loans,F,N,Y,0,118350.0,743958.0,24129.0,621000.0,Unaccompanied,Pensioner,Lower secondary,Widow,Municipal apartment,0.019689,-22142,365243,-2152.0,-4829,,1,0,0,1,0,0,,1.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.05050191495140127,0.4830501881366946,0.1299,0.0725,0.9742,,,0.0,0.1034,0.125,,0.0256,,0.0336,,0.0,0.1324,0.0752,0.9742,,,0.0,0.1034,0.125,,0.0261,,0.035,,0.0,0.1312,0.0725,0.9742,,,0.0,0.1034,0.125,,0.026000000000000002,,0.0342,,0.0,,block of flats,0.0264,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-88.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n312175,0,Cash loans,F,N,N,0,180000.0,661500.0,28156.5,661500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-14289,-477,-3890.0,-4898,,1,1,0,1,0,0,,2.0,2,2,SUNDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.4260606211565786,0.6632508799359089,0.5442347412142162,0.033,0.0011,0.9722,,,0.0,0.069,0.125,,0.0,,0.0329,,0.0,0.0336,0.0011,0.9722,,,0.0,0.069,0.125,,0.0,,0.0342,,0.0,0.0333,0.0011,0.9722,,,0.0,0.069,0.125,,0.0,,0.0335,,0.0,reg oper spec account,block of flats,0.0332,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-1851.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n338910,0,Cash loans,F,N,N,0,225000.0,922716.0,48829.5,855000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Rented apartment,0.011657,-9764,-1583,-4628.0,-2444,,1,1,0,1,1,0,Private service staff,1.0,1,1,THURSDAY,17,1,1,0,0,0,0,Business Entity Type 2,,0.7918073734081105,,0.2309,0.4909,0.9801,0.728,0.0,0.0,0.5517,0.1667,0.2083,0.0,0.1883,0.21899999999999997,0.0,0.0,0.2353,0.5094,0.9801,0.7387,0.0,0.0,0.5517,0.1667,0.2083,0.0,0.2057,0.2282,0.0,0.0,0.2332,0.4909,0.9801,0.7316,0.0,0.0,0.5517,0.1667,0.2083,0.0,0.1915,0.223,0.0,0.0,,block of flats,0.1723,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2518.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,,,,,,\n192975,0,Cash loans,F,N,Y,1,135000.0,1102500.0,39190.5,1102500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-15342,-1050,-4392.0,-4435,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Other,,0.34112290759888264,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,4.0,1.0,4.0,0.0,-724.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n445229,0,Cash loans,F,N,N,0,166500.0,1258650.0,53325.0,1125000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-16158,365243,-8220.0,-4756,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,0.5577880821213814,0.6269168815960124,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1763.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n201106,0,Cash loans,M,Y,Y,1,315000.0,542133.0,33295.5,468000.0,Unaccompanied,Working,Higher education,Civil marriage,Municipal apartment,0.011657,-13288,-453,-15.0,-4478,16.0,1,1,0,1,0,0,,3.0,1,1,TUESDAY,13,0,1,1,0,0,0,Other,0.4368607269929001,0.649526294473761,0.3001077565791181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3053.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n380568,0,Cash loans,M,Y,Y,1,270000.0,497520.0,32521.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-14177,-974,-3258.0,-4968,20.0,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,11,0,0,0,0,1,1,Transport: type 4,0.30814352289624125,0.5082397491726437,0.5064842396679806,0.0928,0.1105,0.9796,0.7212,0.04,0.0,0.2069,0.1667,0.2083,0.0262,0.0748,0.0882,0.0039,0.0016,0.0945,0.1137,0.9796,0.7321,0.0404,0.0,0.2069,0.1667,0.2083,0.0268,0.0817,0.0917,0.0039,0.0,0.0937,0.1105,0.9796,0.7249,0.0402,0.0,0.2069,0.1667,0.2083,0.0266,0.0761,0.0898,0.0039,0.0016,reg oper account,block of flats,0.0906,Panel,No,0.0,0.0,0.0,0.0,-1122.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n266419,0,Cash loans,F,N,N,0,180000.0,611905.5,40891.5,567000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018801,-20827,365243,-11760.0,-1571,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.7880188491718513,0.4938628816996244,0.1474,0.109,0.9786,,,0.16,0.1379,0.3333,,0.0807,0.1202,0.1492,0.0039,0.1909,0.1502,0.1131,0.9786,,,0.1611,0.1379,0.3333,,0.0825,0.1313,0.1555,0.0039,0.2021,0.1489,0.109,0.9786,,,0.16,0.1379,0.3333,,0.0821,0.1223,0.1519,0.0039,0.1949,,block of flats,0.1589,Panel,No,0.0,0.0,0.0,0.0,-167.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n256158,0,Revolving loans,M,Y,Y,0,270000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011657,-18741,-1796,-2806.0,-2296,16.0,1,1,0,1,0,0,Managers,2.0,1,1,THURSDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.8704908967187451,0.7558779583868745,0.13680052191177486,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,1.0,-954.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n426182,0,Cash loans,M,Y,N,0,450000.0,450000.0,21888.0,450000.0,Family,Working,Incomplete higher,Married,With parents,0.007305,-9227,-1960,-4048.0,-1885,22.0,1,1,0,1,1,0,Laborers,2.0,3,3,THURSDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.2413553132837609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-2007.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n403088,0,Cash loans,F,N,N,0,112500.0,414229.5,12685.5,342000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-22921,365243,-6224.0,-4386,,1,0,0,1,0,0,,2.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,XNA,0.6895726028429987,0.5108847527034267,0.42765737003502935,0.1536,0.1311,0.9762,0.6736,0.0264,0.0,0.3448,0.1667,0.2083,0.2504,0.1252,0.1399,0.0,0.0,0.1565,0.136,0.9762,0.6864,0.0266,0.0,0.3448,0.1667,0.2083,0.2561,0.1368,0.1457,0.0,0.0,0.1551,0.1311,0.9762,0.6779999999999999,0.0266,0.0,0.3448,0.1667,0.2083,0.2548,0.1274,0.1424,0.0,0.0,reg oper account,block of flats,0.11,Panel,No,3.0,0.0,3.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,4.0\n376752,0,Cash loans,M,Y,Y,0,135000.0,545040.0,20677.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.007273999999999998,-13600,-81,-5145.0,-4908,9.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,12,0,0,0,1,1,0,Business Entity Type 3,0.3456672101490947,0.6931626188052318,0.722392890081304,0.0856,0.0831,0.9806,0.7348,0.0497,0.04,0.0345,0.3333,0.375,0.0,0.0689,0.0354,0.0039,0.0078,0.0872,0.0863,0.9806,0.7452,0.0502,0.0403,0.0345,0.3333,0.375,0.0,0.0753,0.0369,0.0039,0.0082,0.0864,0.0831,0.9806,0.7383,0.05,0.04,0.0345,0.3333,0.375,0.0,0.0701,0.036000000000000004,0.0039,0.0079,org spec account,block of flats,0.0295,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-955.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,3.0\n277289,1,Cash loans,F,Y,Y,0,360000.0,384048.0,14607.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-15393,-1566,-9516.0,-4050,1.0,1,1,0,1,0,0,Accountants,2.0,3,3,FRIDAY,6,0,0,0,0,0,0,Business Entity Type 3,,0.4346278893415984,0.25533177083329,0.0701,0.0695,0.9781,,0.0086,0.0,0.1379,0.1667,,0.0149,,0.0641,,0.0084,0.0714,0.0721,0.9782,,0.0087,0.0,0.1379,0.1667,,0.0153,,0.0668,,0.0088,0.0708,0.0695,0.9781,,0.0086,0.0,0.1379,0.1667,,0.0152,,0.0653,,0.0085,,block of flats,0.0522,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-972.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n299198,0,Revolving loans,F,Y,N,0,81000.0,225000.0,11250.0,225000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.0105,-21098,-6196,-6377.0,-4536,19.0,1,1,0,1,0,0,,2.0,3,3,TUESDAY,13,0,0,0,0,0,0,School,,0.5570886479743676,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-340.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n196899,0,Revolving loans,F,N,N,0,157500.0,202500.0,10125.0,202500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-11900,-2655,-1038.0,-3775,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Transport: type 2,0.6018731712501775,0.5520380176751151,0.6894791426446275,0.1443,0.0267,0.9985,0.9796,0.0857,0.16,0.069,0.6667,0.7083,0.0747,0.1143,0.1711,0.0154,0.025,0.1471,0.0277,0.9985,0.9804,0.0865,0.1611,0.069,0.6667,0.7083,0.0764,0.1249,0.1783,0.0156,0.0265,0.1457,0.0267,0.9985,0.9799,0.0863,0.16,0.069,0.6667,0.7083,0.076,0.1163,0.1742,0.0155,0.0255,reg oper account,block of flats,0.1869,Mixed,No,3.0,0.0,3.0,0.0,-1816.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n329329,0,Cash loans,F,N,Y,1,135000.0,715095.0,46237.5,675000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.02461,-15389,-499,-114.0,-4357,,1,1,0,1,1,0,Sales staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Government,0.4797096104235897,0.6957609870547558,0.6925590674998008,0.0804,0.08,0.9836,0.7959999999999999,0.0123,0.0,0.2069,0.1667,0.2083,0.0,0.0656,0.0755,0.0,0.0,0.0819,0.083,0.9826,0.804,0.0124,0.0,0.2069,0.1667,0.2083,0.0,0.0716,0.0786,0.0,0.0,0.0812,0.08,0.9836,0.7987,0.0123,0.0,0.2069,0.1667,0.2083,0.0,0.0667,0.0768,0.0,0.0,reg oper account,block of flats,0.0373,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2336.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n267358,0,Cash loans,F,N,Y,0,90000.0,163008.0,17244.0,144000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.01885,-24185,365243,-11904.0,-4923,,1,0,0,1,0,0,,1.0,2,2,SUNDAY,13,0,0,0,0,0,0,XNA,,0.7570824481330662,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-2367.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n165241,0,Cash loans,F,N,Y,0,112500.0,239850.0,23494.5,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.006233,-25097,365243,-14471.0,-4042,,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.5331645092958596,0.7862666146611379,0.0639,0.0624,0.9801,0.728,0.0082,0.0,0.1379,0.1667,0.2083,0.0301,0.0521,0.0532,0.0,0.0,0.0651,0.0648,0.9801,0.7387,0.0082,0.0,0.1379,0.1667,0.2083,0.0308,0.0569,0.0554,0.0,0.0,0.0645,0.0624,0.9801,0.7316,0.0082,0.0,0.1379,0.1667,0.2083,0.0306,0.053,0.0541,0.0,0.0,reg oper spec account,block of flats,0.0463,Panel,No,0.0,0.0,0.0,0.0,-592.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n246797,0,Cash loans,F,N,N,0,157500.0,755190.0,28116.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.011703,-24048,365243,-14700.0,-4450,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,XNA,,0.7177253844792282,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1134.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n398809,0,Cash loans,F,Y,Y,1,130500.0,420894.0,20376.0,301500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002134,-14152,-5560,-6219.0,-4867,16.0,1,1,0,1,0,0,Cooking staff,3.0,3,3,MONDAY,12,0,0,0,0,0,0,Kindergarten,,0.5667992299960753,0.6041125998015721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-608.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n119000,0,Cash loans,F,N,Y,0,135000.0,545040.0,20677.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.031329,-22325,365243,-3951.0,-3784,,1,0,0,1,0,0,,1.0,2,2,SUNDAY,11,0,0,0,0,0,0,XNA,0.43424858836485786,0.5411454218475505,0.6263042766749393,0.0309,0.0731,0.9742,0.6464,0.0703,0.0,0.1034,0.1667,0.2083,0.0684,0.0227,0.0329,0.0116,0.0256,0.0315,0.0758,0.9742,0.6602,0.071,0.0,0.1034,0.1667,0.2083,0.07,0.0248,0.0343,0.0117,0.0272,0.0312,0.0731,0.9742,0.6511,0.0708,0.0,0.1034,0.1667,0.2083,0.0696,0.0231,0.0335,0.0116,0.0262,reg oper account,block of flats,0.0699,Block,No,7.0,4.0,7.0,2.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n200055,0,Cash loans,F,N,N,1,108000.0,233784.0,11497.5,153000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.003813,-9790,-777,-778.0,-2466,,1,1,0,1,0,0,Core staff,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,School,0.4257709985864535,0.4894960558520653,0.4902575124990026,0.0619,0.0611,0.9921,,,,0.1379,0.1667,,,,0.0542,,,0.063,0.0634,0.9921,,,,0.1379,0.1667,,,,0.0565,,,0.0625,0.0611,0.9921,,,,0.1379,0.1667,,,,0.0552,,,,block of flats,0.0426,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-295.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,6.0\n291237,0,Cash loans,F,N,Y,0,112500.0,142632.0,15489.0,126000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17441,-1222,-9472.0,-979,,1,1,0,1,1,0,Cooking staff,2.0,2,2,TUESDAY,14,0,0,0,0,1,1,Other,,0.2361963181266436,0.0720131886405828,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,1.0\n190936,0,Cash loans,F,N,N,1,157500.0,942300.0,26041.5,675000.0,Family,Working,Higher education,Married,With parents,0.025164,-11358,-1371,-629.0,-3289,,1,1,1,1,0,0,Accountants,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Other,,0.6160167746740753,0.6041125998015721,0.1247,0.0934,0.9796,0.7212,0.0479,0.0,0.2759,0.1667,0.2083,0.0701,0.0983,0.1051,0.0154,0.0222,0.1271,0.09699999999999999,0.9796,0.7321,0.0483,0.0,0.2759,0.1667,0.2083,0.0717,0.1074,0.1095,0.0156,0.0235,0.126,0.0934,0.9796,0.7249,0.0482,0.0,0.2759,0.1667,0.2083,0.0713,0.1,0.1069,0.0155,0.0226,reg oper account,block of flats,0.1136,Panel,No,11.0,0.0,11.0,0.0,-2605.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n285274,0,Cash loans,F,N,Y,0,112500.0,781920.0,28215.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.022625,-18612,-2298,-5877.0,-2146,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,Industry: type 11,0.6227793193613543,0.6299193575116371,0.5656079814115492,0.0722,,0.9791,,,0.0,0.1379,0.1667,,,,0.0653,,,0.0735,,0.9791,,,0.0,0.1379,0.1667,,,,0.0681,,,0.0729,,0.9791,,,0.0,0.1379,0.1667,,,,0.0665,,,,block of flats,0.0711,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n454299,0,Cash loans,F,N,Y,0,225000.0,1312110.0,55723.5,1125000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-21158,-140,-8744.0,-3277,,1,1,0,1,1,0,Accountants,2.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,Bank,,0.7448150434432234,0.7194907850918436,0.1828,0.1036,0.9876,0.8096,0.109,0.24,0.1493,0.5138,0.3958,0.0026,0.2021,0.2,0.0212,0.0267,0.042,0.0391,0.9762,0.6864,0.096,0.3222,0.0345,0.3333,0.375,0.0,0.1837,0.1052,0.0039,0.0069,0.2186,0.1185,0.9916,0.8121,0.1097,0.32,0.1379,0.5417,0.3958,0.0,0.2056,0.2268,0.0214,0.0072,reg oper account,block of flats,0.1768,Monolithic,No,0.0,0.0,0.0,0.0,-1883.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n183916,0,Cash loans,F,N,Y,0,103500.0,754740.0,22198.5,630000.0,Other_B,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018801,-20868,365243,-12598.0,-4172,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.550144585094241,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-680.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n255313,0,Cash loans,F,N,Y,0,112500.0,1170000.0,43357.5,1170000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.011703,-21948,365243,-13907.0,-4710,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.7409808685618832,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-125.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n378462,0,Cash loans,F,N,Y,1,202500.0,283419.0,16398.0,234000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.014464,-14599,-4566,-4648.0,-4208,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,Medicine,,0.5877514845005953,0.6446794549585961,0.0701,0.0595,0.9841,0.7824,0.0292,0.0,0.1379,0.1667,0.2083,0.039,0.0572,0.0576,0.0,0.0,0.0714,0.0618,0.9841,0.7909,0.0295,0.0,0.1379,0.1667,0.2083,0.0399,0.0624,0.06,0.0,0.0,0.0708,0.0595,0.9841,0.7853,0.0294,0.0,0.1379,0.1667,0.2083,0.0397,0.0581,0.0586,0.0,0.0,reg oper account,block of flats,0.0613,Panel,No,0.0,0.0,0.0,0.0,-2620.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n365347,0,Cash loans,F,N,Y,0,81000.0,143910.0,14872.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.015221,-23188,365243,-11779.0,-4636,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.4990069098420942,0.8128226070575616,0.0124,,0.9846,,,0.0,0.069,0.0417,,,0.0101,0.0114,,0.0049,0.0126,,0.9846,,,0.0,0.069,0.0417,,,0.011000000000000001,0.0119,,0.0052,0.0125,,0.9846,,,0.0,0.069,0.0417,,,0.0103,0.0116,,0.005,reg oper account,block of flats,0.01,Panel,No,9.0,0.0,9.0,0.0,-1244.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n334804,0,Revolving loans,F,N,Y,0,90000.0,157500.0,7875.0,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.02461,-22553,365243,-5856.0,-2783,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.6479266912513697,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-2173.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n404759,0,Cash loans,F,N,N,1,76500.0,163512.0,12915.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0105,-15587,-9030,-4345.0,-5243,,1,1,1,1,1,0,Medicine staff,3.0,3,3,TUESDAY,12,0,0,0,0,1,1,Medicine,,0.1908360571759764,0.4578995512067301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2127.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n255265,0,Cash loans,F,N,N,1,72000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018801,-12460,-200,-5096.0,-2539,,1,1,0,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.15727332954329926,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2014.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n407865,0,Cash loans,F,Y,Y,1,135000.0,783927.0,40153.5,688500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-13399,-1386,-7540.0,-1693,4.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,FRIDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.3909046353640032,0.3499546253055273,0.6279908192952864,0.0247,,0.9846,,,0.0,0.069,0.0833,,0.0093,,0.024,,,0.0252,,0.9846,,,0.0,0.069,0.0833,,0.0095,,0.025,,,0.025,,0.9846,,,0.0,0.069,0.0833,,0.0095,,0.0244,,,,block of flats,0.0188,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-628.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n196117,0,Cash loans,F,N,N,0,157500.0,609898.5,31270.5,526500.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.030755,-22972,365243,-7802.0,-4143,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.7037592872508034,0.5602843280409464,0.1082,0.0385,0.9826,0.762,0.0396,0.08,0.069,0.3333,0.375,0.04,0.0883,0.0979,0.0,0.045,0.1103,0.0399,0.9826,0.7713,0.04,0.0806,0.069,0.3333,0.375,0.040999999999999995,0.0964,0.1019,0.0,0.0477,0.1093,0.0385,0.9826,0.7652,0.0399,0.08,0.069,0.3333,0.375,0.0407,0.0898,0.0996,0.0,0.046,reg oper account,block of flats,0.1084,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1460.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n127264,0,Cash loans,M,N,N,0,135000.0,225000.0,11488.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003818,-11978,-960,-1038.0,-4594,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.0027077083791101288,0.1455428133497032,0.0598,0.0748,0.9901,0.8640000000000001,0.0171,0.0,0.1379,0.1667,0.0,0.0362,0.0488,0.0547,0.0,0.0429,0.0609,0.0776,0.9901,0.8693,0.0172,0.0,0.1379,0.1667,0.0,0.037000000000000005,0.0533,0.057,0.0,0.0454,0.0604,0.0748,0.9901,0.8658,0.0172,0.0,0.1379,0.1667,0.0,0.0368,0.0496,0.0557,0.0,0.0438,reg oper account,block of flats,0.0714,Panel,No,0.0,0.0,0.0,0.0,-955.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n181014,0,Cash loans,M,N,Y,1,234000.0,1075932.0,45715.5,922500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006629,-11534,-327,-3138.0,-4046,,1,1,0,1,0,0,Managers,3.0,2,2,THURSDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.7184381120397262,0.3774042489507649,0.1124,,0.9975,0.966,0.0519,0.08,0.069,0.3333,0.0417,0.0204,0.0857,0.0929,0.027000000000000003,0.0051,0.1145,,0.9975,0.9673,0.0524,0.0806,0.069,0.3333,0.0417,0.0209,0.0937,0.0968,0.0272,0.0054,0.1135,,0.9975,0.9665,0.0522,0.08,0.069,0.3333,0.0417,0.0208,0.0872,0.0946,0.0272,0.0052,,block of flats,0.1026,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n432678,0,Cash loans,M,N,Y,0,97650.0,315000.0,9027.0,315000.0,,Commercial associate,Higher education,Single / not married,House / apartment,0.035792000000000004,-7804,-226,-2575.0,-472,,1,1,1,1,0,0,,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.498662497304037,0.520897599048938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n278386,0,Cash loans,F,N,Y,0,67500.0,684657.0,20016.0,571500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.005144,-20587,365243,-7953.0,-2695,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.5972442273656641,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-487.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n336945,0,Cash loans,F,N,Y,2,67500.0,314100.0,13963.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-14354,-1716,-1723.0,-2350,,1,1,0,1,0,0,Managers,4.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,,0.6500090050311997,0.12140828616312165,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n271618,0,Cash loans,F,Y,Y,1,180000.0,473760.0,51151.5,450000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008575,-10706,-1565,-4532.0,-2735,19.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.6324188626125605,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1065.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n352014,1,Cash loans,M,Y,Y,0,153000.0,64692.0,4630.5,54000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.002042,-19877,365243,-6436.0,-3084,8.0,1,0,0,1,0,0,,2.0,3,3,MONDAY,12,0,0,0,0,0,0,XNA,,0.5268948936145829,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-1106.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n118348,0,Cash loans,M,N,Y,0,157500.0,463131.0,22410.0,346500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-11084,-1405,-5163.0,-2519,,1,1,0,1,0,1,Laborers,2.0,2,2,MONDAY,11,0,0,0,1,1,0,Agriculture,0.18719810085505825,0.38505807405799547,0.7165702448010511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-669.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n363459,0,Cash loans,M,Y,Y,0,157500.0,392427.0,12663.0,324000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018634,-23376,365243,-7588.0,-4224,4.0,1,0,0,1,0,0,,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.5787475728412418,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-45.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n167269,0,Cash loans,M,Y,N,1,135000.0,508495.5,24592.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.019101,-14597,-193,-2614.0,-4332,9.0,1,1,0,1,0,1,Drivers,3.0,2,2,THURSDAY,9,0,0,0,1,1,0,Self-employed,,0.7116389526251191,0.41534714488434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-416.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n184790,0,Cash loans,F,N,Y,1,67500.0,156384.0,16551.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003122,-17487,-1657,-7304.0,-1040,,1,1,0,1,0,0,,3.0,3,3,MONDAY,9,0,0,0,0,0,0,School,,0.5124255783415866,0.2340151665320674,0.0598,0.0449,0.9752,,,0.0,0.1034,0.1667,,0.045,,0.044000000000000004,,0.0055,0.0609,0.0466,0.9752,,,0.0,0.1034,0.1667,,0.046,,0.0459,,0.0058,0.0604,0.0449,0.9752,,,0.0,0.1034,0.1667,,0.0457,,0.0448,,0.0056,,block of flats,0.0388,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-471.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n309745,0,Cash loans,F,N,Y,0,202500.0,219069.0,11313.0,148500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-19386,-3163,-330.0,-2911,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.6561474776368696,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1230.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n210450,0,Cash loans,M,N,N,0,315000.0,1033870.5,30208.5,877500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.032561,-17807,-3726,-7031.0,-1361,,1,1,0,1,1,0,Drivers,1.0,1,1,WEDNESDAY,18,0,1,1,0,1,1,Transport: type 4,,0.6621461829746235,0.17146836689679945,0.0371,0.0527,0.9409,0.1568,,,0.1379,0.0833,0.125,0.0264,,0.0268,,0.0483,0.0378,0.0547,0.9384,0.1898,,,0.1379,0.0833,0.125,0.027000000000000003,,0.0058,,0.0512,0.0375,0.0527,0.9409,0.1681,,,0.1379,0.0833,0.125,0.0269,,0.0273,,0.0493,reg oper account,block of flats,0.0552,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-3240.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n189285,0,Cash loans,F,N,Y,0,135000.0,634500.0,28080.0,634500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-20730,-995,-5382.0,-4287,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.7974234768586357,0.6335517388224342,0.4101025731788671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-575.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n188097,1,Revolving loans,F,N,Y,0,99000.0,292500.0,14625.0,292500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.025164,-19281,-9978,-9166.0,-2835,,1,1,0,1,1,0,Cleaning staff,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.6113902892333293,0.656916727365286,0.6894791426446275,0.0186,0.1537,0.9821,0.7552,0.0379,0.08,0.069,0.3333,0.375,0.0077,0.0151,0.0939,0.0,0.0,0.0189,0.1595,0.9821,0.7648,0.0382,0.0806,0.069,0.3333,0.375,0.0079,0.0165,0.0978,0.0,0.0,0.0187,0.1537,0.9821,0.7585,0.0381,0.08,0.069,0.3333,0.375,0.0079,0.0154,0.0955,0.0,0.0,reg oper account,block of flats,0.10400000000000001,Panel,No,0.0,0.0,0.0,0.0,-1787.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n352163,0,Cash loans,F,N,Y,2,121500.0,604152.0,25726.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.006296,-10249,-2258,-4379.0,-919,,1,1,0,1,0,0,Sales staff,4.0,3,3,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.6251250205620009,0.4101025731788671,0.0619,0.0617,0.9831,0.7688,,0.0,0.1379,0.1667,0.2083,0.0577,0.0504,0.0542,0.0,0.0,0.063,0.0641,0.9831,0.7779,,0.0,0.1379,0.1667,0.2083,0.059000000000000004,0.0551,0.0565,0.0,0.0,0.0625,0.0617,0.9831,0.7719,,0.0,0.1379,0.1667,0.2083,0.0587,0.0513,0.0552,0.0,0.0,reg oper account,block of flats,0.047,Block,No,1.0,0.0,0.0,0.0,-1199.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n425484,1,Cash loans,F,Y,Y,0,135000.0,1282500.0,37498.5,1282500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.015221,-17777,-7281,-10423.0,-1331,6.0,1,1,0,1,1,0,Core staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,School,0.7207795581052464,0.2622583692422573,0.5937175866150576,0.0897,0.0794,0.9767,0.6804,0.0,0.0,0.1379,0.1667,0.2083,0.0603,0.0664,0.0638,0.0309,0.0273,0.0914,0.0824,0.9767,0.6929,0.0,0.0,0.1379,0.1667,0.2083,0.0617,0.0725,0.0665,0.0311,0.0289,0.0906,0.0794,0.9767,0.6847,0.0,0.0,0.1379,0.1667,0.2083,0.0614,0.0676,0.0649,0.0311,0.0279,reg oper account,block of flats,0.0605,Panel,No,2.0,0.0,2.0,0.0,-1329.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n323741,0,Cash loans,F,Y,Y,2,108000.0,231813.0,16614.0,193500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-14276,-1905,-3297.0,-4747,13.0,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Industry: type 3,,0.5798400999431373,0.1997705696341145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,5.0,0.0,-2561.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,8.0\n423973,1,Cash loans,F,N,Y,0,180000.0,550980.0,35343.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.007120000000000001,-17533,-1075,-4150.0,-1072,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,1,1,0,Transport: type 4,0.7734506377503635,0.6301130526780967,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-324.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n430769,1,Cash loans,M,Y,N,1,67500.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-15856,-4910,-45.0,-4274,25.0,1,1,1,1,0,0,,3.0,2,2,THURSDAY,15,0,0,0,0,1,1,Business Entity Type 2,,0.3655891184163961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2075.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n419473,0,Cash loans,M,Y,Y,0,382500.0,2013840.0,59013.0,1800000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-18707,-3003,-12.0,-2196,1.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,12,0,1,1,0,1,1,Construction,0.5523151381058619,0.3625716366648183,0.4083588531230431,0.0412,0.0237,0.9742,0.6464,0.0074,0.0,0.1034,0.125,0.1667,0.0452,0.0336,0.0398,0.0,0.0,0.042,0.0246,0.9742,0.6602,0.0074,0.0,0.1034,0.125,0.1667,0.0462,0.0367,0.0415,0.0,0.0,0.0416,0.0237,0.9742,0.6511,0.0074,0.0,0.1034,0.125,0.1667,0.046,0.0342,0.0405,0.0,0.0,reg oper spec account,block of flats,0.0448,Block,No,0.0,0.0,0.0,0.0,-302.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n446574,0,Cash loans,M,Y,Y,0,180000.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-16729,-7690,-2424.0,-276,40.0,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,8,0,0,0,0,1,1,Industry: type 1,0.7574050521099975,0.5880163844654945,0.7047064232963289,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n305798,0,Cash loans,F,Y,Y,1,67500.0,284427.0,19138.5,216000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019101,-13155,-150,-7307.0,-1030,18.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,11,0,0,0,0,1,1,Business Entity Type 2,0.3797565633648908,0.6476023782550502,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-495.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n428728,0,Cash loans,F,Y,Y,0,157500.0,337500.0,16416.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-11509,-1590,-1732.0,-3378,0.0,1,1,0,1,0,0,Accountants,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4283188062921089,0.7490271354970962,0.6528965519806539,0.0825,0.0797,0.9752,,,0.0,0.1379,0.1667,,0.0,,0.0706,,0.0,0.084,0.0827,0.9752,,,0.0,0.1379,0.1667,,0.0,,0.0735,,0.0,0.0833,0.0797,0.9752,,,0.0,0.1379,0.1667,,0.0,,0.0718,,0.0,,block of flats,0.0874,Mixed,No,4.0,0.0,4.0,0.0,-301.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n343723,0,Cash loans,M,Y,N,0,157500.0,533668.5,22738.5,477000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.0228,-10963,-1063,-5648.0,-507,15.0,1,1,0,1,1,0,Drivers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.265222246454188,,0.0392,0.0112,0.9762,0.6736,0.0081,0.0,0.1034,0.1667,,0.018000000000000002,0.0303,0.0267,0.0077,0.0533,0.0399,0.0116,0.9762,0.6864,0.0082,0.0,0.1034,0.1667,,0.0184,0.0331,0.0278,0.0078,0.0565,0.0396,0.0112,0.9762,0.6779999999999999,0.0082,0.0,0.1034,0.1667,,0.0183,0.0308,0.0272,0.0078,0.0544,reg oper account,block of flats,0.0371,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-777.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n400018,0,Cash loans,F,Y,Y,0,202500.0,1321902.0,38781.0,1035000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15301,-1936,-8996.0,-5036,9.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Industry: type 9,0.6474384277520331,0.6913924164232091,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1270.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n188869,1,Cash loans,F,N,Y,0,112500.0,229230.0,18238.5,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-22401,365243,-8889.0,-4834,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,0.7051971392894251,0.464776979924861,0.25670557243930026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-355.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n125925,0,Cash loans,F,N,N,1,270000.0,463500.0,45153.0,463500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00733,-10019,-145,-1124.0,-2145,,1,1,0,1,0,0,Waiters/barmen staff,3.0,2,2,SUNDAY,14,0,1,1,0,0,0,Business Entity Type 3,0.36609394424282776,0.6572468577719117,0.746300213050371,0.0804,0.0,0.9861,0.8096,0.0108,0.0,0.2069,0.1667,0.2083,0.0735,0.0656,0.0764,0.0,0.0,0.0819,0.0,0.9861,0.8171,0.0109,0.0,0.2069,0.1667,0.2083,0.0752,0.0716,0.0796,0.0,0.0,0.0812,0.0,0.9861,0.8121,0.0109,0.0,0.2069,0.1667,0.2083,0.0748,0.0667,0.0777,0.0,0.0,reg oper account,block of flats,0.066,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1224.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n452026,1,Revolving loans,F,N,Y,0,58500.0,180000.0,9000.0,180000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010276,-20492,365243,-2761.0,-2785,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,17,0,0,0,0,0,0,XNA,0.7595441600966932,0.6750000507503456,,0.0722,0.0785,0.9856,,,0.0,0.0345,0.1667,,,,0.0511,,0.059000000000000004,0.0735,0.0814,0.9856,,,0.0,0.0345,0.1667,,,,0.0532,,0.0624,0.0729,0.0785,0.9856,,,0.0,0.0345,0.1667,,,,0.052000000000000005,,0.0602,,specific housing,0.071,Others,No,0.0,0.0,0.0,0.0,-335.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n379802,0,Cash loans,M,Y,Y,0,225000.0,781920.0,37746.0,675000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.030755,-10936,-757,-540.0,-3350,4.0,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 2,0.31388081830493497,0.7350788885976849,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-521.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n236340,0,Cash loans,F,N,N,0,238500.0,1339884.0,36846.0,1170000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018801,-17748,-3396,-880.0,-1291,,1,1,1,1,1,0,Accountants,2.0,2,2,FRIDAY,8,0,0,0,0,1,1,Bank,0.4423243220638329,0.5419815059525984,0.2622489709189549,0.0742,0.0537,0.9821,,,0.08,0.069,0.3333,,0.0892,,0.0738,,0.0,0.0756,0.0558,0.9821,,,0.0806,0.069,0.3333,,0.0912,,0.0768,,0.0,0.0749,0.0537,0.9821,,,0.08,0.069,0.3333,,0.0907,,0.0751,,0.0,,block of flats,0.0759,Panel,No,0.0,0.0,0.0,0.0,-2794.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n377585,0,Revolving loans,F,N,N,0,121500.0,382500.0,19125.0,382500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.032561,-22092,365243,-12118.0,-3978,,1,0,0,1,1,0,,1.0,1,1,THURSDAY,13,0,0,0,0,0,0,XNA,,0.5125403115912169,0.3876253444214701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1505.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n238449,0,Revolving loans,F,N,Y,2,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-13515,-626,-6527.0,-4433,,1,1,0,1,1,0,,4.0,2,2,FRIDAY,12,0,0,0,0,0,0,Industry: type 11,,0.5223138537489672,0.8633633824101478,0.0577,0.0661,0.9816,0.7484,0.0321,0.0,0.1379,0.1667,0.2083,0.0709,0.0462,0.0536,0.0039,0.062,0.0588,0.0686,0.9816,0.7583,0.0324,0.0,0.1379,0.1667,0.2083,0.0725,0.0505,0.0558,0.0039,0.0657,0.0583,0.0661,0.9816,0.7518,0.0323,0.0,0.1379,0.1667,0.2083,0.0721,0.047,0.0545,0.0039,0.0633,reg oper account,block of flats,0.0711,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-359.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n328591,0,Cash loans,M,Y,Y,1,144000.0,409716.0,21046.5,342000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.005313,-15325,-2447,-4088.0,-4094,14.0,1,1,0,1,0,0,Low-skill Laborers,3.0,2,2,TUESDAY,14,0,0,0,0,0,0,Trade: type 7,0.3101711593484369,0.5356836785562789,,0.1387,0.0542,0.9881,0.8368,0.0234,0.0,0.3103,0.1667,0.2083,0.1229,0.1131,0.1378,0.0,0.0,0.0945,0.0,0.9866,0.8236,0.0176,0.0,0.2069,0.1667,0.2083,0.0645,0.0826,0.0929,0.0,0.0,0.14,0.0542,0.9881,0.8390000000000001,0.0236,0.0,0.3103,0.1667,0.2083,0.125,0.115,0.1403,0.0,0.0,reg oper account,block of flats,0.1628,Panel,No,8.0,0.0,8.0,0.0,-2500.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n111124,0,Cash loans,F,N,N,0,90000.0,225000.0,12204.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.030755,-8735,-929,-936.0,-936,,1,1,0,1,1,0,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Industry: type 11,0.3233933865719945,0.6361370619694947,0.14024318370802974,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n123569,0,Cash loans,F,N,Y,0,90000.0,990000.0,29074.5,990000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-22986,365243,-2923.0,-3943,,1,0,0,1,1,0,,2.0,3,3,FRIDAY,7,0,0,0,0,0,0,XNA,,0.5418946509954324,0.2067786544915716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1736.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n163441,0,Cash loans,M,N,N,0,135000.0,545040.0,19705.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-19149,-1908,-9819.0,-2640,,1,1,1,1,1,0,Drivers,2.0,2,2,FRIDAY,18,0,0,0,1,1,0,Self-employed,,0.7554701781732556,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1493.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n222814,0,Cash loans,F,N,N,0,153000.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-16969,-1388,-4042.0,-516,,1,1,0,1,0,0,Laborers,2.0,1,1,MONDAY,14,0,0,0,0,0,0,Trade: type 3,,0.5830916866399999,0.13177013253138142,0.0402,0.04,0.9836,0.7756,,0.12,0.0345,0.5833,0.625,0.0292,0.0361,0.1278,0.0116,0.0142,0.040999999999999995,0.0415,0.9836,0.7844,,0.1208,0.0345,0.5833,0.625,0.0298,0.0395,0.1332,0.0117,0.0151,0.0406,0.04,0.9836,0.7786,,0.12,0.0345,0.5833,0.625,0.0297,0.0368,0.1301,0.0116,0.0145,reg oper account,block of flats,0.0892,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1284.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n306828,0,Cash loans,F,N,N,0,157500.0,808650.0,23643.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.004849,-16206,-439,-722.0,-4901,,1,1,0,1,1,0,Sales staff,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Self-employed,,0.5655477553315796,0.29708661164720285,0.1474,,0.9866,,,,,,,,,,,,0.1502,,0.9866,,,,,,,,,,,,0.1489,,0.9866,,,,,,,,,,,,,block of flats,0.1059,\"Stone, brick\",No,2.0,1.0,2.0,0.0,-390.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n234631,0,Cash loans,M,Y,Y,0,135000.0,225000.0,26833.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-19140,-389,-4289.0,-2679,3.0,1,1,1,1,1,0,Laborers,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6173504187595154,0.6817058776720116,0.1093,,0.9935,,,0.12,0.1034,0.3333,,,,0.1164,,0.0,0.1113,,0.9935,,,0.1208,0.1034,0.3333,,,,0.1213,,0.0,0.1103,,0.9935,,,0.12,0.1034,0.3333,,,,0.1185,,0.0,,block of flats,0.0916,Panel,No,3.0,0.0,3.0,0.0,-471.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n300802,0,Cash loans,M,N,Y,0,135000.0,807984.0,26833.5,697500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018209,-17440,-746,-1090.0,-985,,1,1,0,1,0,0,Security staff,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.541818650818969,0.5100895276257282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2403.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n285858,0,Cash loans,F,N,N,0,90000.0,254700.0,14350.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.020246,-24412,365243,-9130.0,-4867,,1,0,0,1,1,0,,1.0,3,3,SATURDAY,9,0,0,0,0,0,0,XNA,,0.019062359227900203,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n283901,1,Cash loans,F,N,N,0,180000.0,427450.5,16411.5,369000.0,Family,State servant,Secondary / secondary special,Married,With parents,0.006852,-13933,-1073,-4185.0,-1600,,1,1,0,1,0,0,Laborers,2.0,3,3,FRIDAY,9,0,0,0,1,1,0,Medicine,,0.21983495386386145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-659.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n210475,0,Cash loans,F,N,Y,3,90000.0,582804.0,28035.0,463500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-16327,-2434,-4072.0,-4557,,1,1,0,1,0,1,Laborers,5.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Housing,,0.7015662968206807,0.7091891096653581,0.10099999999999999,0.1025,0.9762,,,0.0,0.2069,0.1667,,0.0985,,0.08800000000000001,,0.0148,0.1029,0.1064,0.9762,,,0.0,0.2069,0.1667,,0.1008,,0.0917,,0.0157,0.102,0.1025,0.9762,,,0.0,0.2069,0.1667,,0.1002,,0.0896,,0.0152,reg oper account,block of flats,0.0982,\"Stone, brick\",No,10.0,0.0,10.0,0.0,-1048.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n303472,0,Revolving loans,F,N,Y,0,202500.0,585000.0,29250.0,585000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.031329,-11389,-1649,-5309.0,-3936,,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.5476902126973897,0.6832673110717948,0.1061556230153924,0.5629,0.3819,0.9841,0.7824,0.1161,0.64,0.5517,0.3333,0.375,0.2451,0.4589,0.5816,0.0,0.0,0.5735,0.3963,0.9841,0.7909,0.1171,0.6445,0.5517,0.3333,0.375,0.2507,0.5014,0.606,0.0,0.0,0.5683,0.3819,0.9841,0.7853,0.1168,0.64,0.5517,0.3333,0.375,0.2493,0.4669,0.5921,0.0,0.0,reg oper account,block of flats,0.5209,Panel,No,1.0,1.0,1.0,0.0,-269.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n353902,0,Cash loans,F,N,N,0,135000.0,1125000.0,34110.0,1125000.0,Family,State servant,Secondary / secondary special,Married,Municipal apartment,0.072508,-16193,-463,-4587.0,-2249,,1,1,1,1,1,0,Sales staff,2.0,1,1,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.5701465822772029,0.34741822720026416,0.3701,0.2249,0.9776,0.6940000000000001,0.4633,0.4,0.3448,0.3333,0.375,0.0397,0.2917,0.3055,0.0463,0.0479,0.3771,0.2334,0.9777,0.706,0.4676,0.4028,0.3448,0.3333,0.375,0.0406,0.3186,0.3183,0.0467,0.0507,0.3737,0.2249,0.9776,0.6981,0.4663,0.4,0.3448,0.3333,0.375,0.0404,0.2967,0.311,0.0466,0.0489,reg oper account,block of flats,0.2533,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-637.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n393305,0,Cash loans,F,N,Y,1,112500.0,675000.0,34465.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.007305,-12272,-1518,-1089.0,-707,,1,1,0,1,1,0,Sales staff,3.0,3,3,FRIDAY,7,0,0,0,0,0,0,Bank,0.5935734721188289,0.5598170837107317,0.7121551551910698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n331975,0,Cash loans,M,Y,Y,0,135000.0,497520.0,32521.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-8472,-1364,-3222.0,-1164,14.0,1,1,1,1,1,0,Laborers,1.0,3,3,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.17242176403994555,0.23210638693366065,0.2636468134452008,0.0928,0.0827,0.9801,0.728,0.012,0.0,0.2069,0.1667,0.2083,0.0354,0.0748,0.0863,0.0039,0.0214,0.0945,0.0858,0.9801,0.7387,0.0121,0.0,0.2069,0.1667,0.2083,0.0362,0.0817,0.0899,0.0039,0.0227,0.0937,0.0827,0.9801,0.7316,0.0121,0.0,0.2069,0.1667,0.2083,0.036000000000000004,0.0761,0.0878,0.0039,0.0218,reg oper account,block of flats,0.0791,Panel,No,7.0,0.0,7.0,0.0,-994.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n322306,0,Cash loans,M,Y,Y,0,405000.0,239850.0,25447.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-10452,-1963,-3614.0,-444,0.0,1,1,1,1,0,0,Laborers,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,Construction,,0.564575001846526,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-422.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n219529,0,Cash loans,F,N,Y,1,112500.0,513531.0,35869.5,459000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-18592,-3724,-1054.0,-2145,,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Trade: type 7,0.5841209777155529,0.4397717444104921,0.6690566947824041,0.2474,0.1555,0.9881,0.8368,0.0657,0.24,0.2069,0.375,0.4167,0.1531,0.2017,0.218,0.0,0.1653,0.2521,0.1614,0.9881,0.8432,0.0663,0.2417,0.2069,0.375,0.4167,0.1566,0.2204,0.2271,0.0,0.175,0.2498,0.1555,0.9881,0.8390000000000001,0.0661,0.24,0.2069,0.375,0.4167,0.1558,0.2052,0.2219,0.0,0.1687,reg oper account,block of flats,0.2074,Panel,No,3.0,0.0,3.0,0.0,-184.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n266026,0,Cash loans,M,Y,Y,0,337500.0,1256400.0,44644.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-11647,-2421,-3127.0,-4114,2.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,1,1,Government,0.2919141559010444,0.6133701928455019,0.34741822720026416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,0.0,11.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n273498,0,Cash loans,F,Y,N,2,135000.0,371304.0,10341.0,243000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Municipal apartment,0.003818,-13922,-1349,-7841.0,-3507,17.0,1,1,0,1,0,0,Sales staff,4.0,2,2,WEDNESDAY,5,0,0,0,0,0,0,Self-employed,,0.7193263771154967,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-483.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n229489,0,Cash loans,M,Y,Y,0,315000.0,906615.0,30091.5,688500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-15783,-2250,-2230.0,-5525,14.0,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,10,0,0,0,1,1,0,Self-employed,,0.7191718270158112,0.4170996682522097,,,0.9692,,,,,0.0,,,,,,,,,0.9692,,,,,0.0,,,,,,,,,0.9692,,,,,0.0,,,,,,,,block of flats,0.0015,Wooden,No,7.0,3.0,7.0,0.0,-569.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n134108,0,Cash loans,F,N,Y,0,90000.0,521280.0,28408.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008019,-14843,-473,-7079.0,-3969,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,10,0,1,1,1,1,1,Industry: type 11,,0.3845197041839431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-457.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n115431,0,Cash loans,M,Y,Y,0,540000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-16583,-3742,-198.0,-129,9.0,1,1,0,1,0,0,Managers,2.0,1,1,MONDAY,13,1,1,0,0,0,0,Industry: type 12,0.8161829667066614,0.7158209196052271,0.3539876078507373,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1530.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n243404,0,Cash loans,M,Y,Y,0,292500.0,675000.0,53460.0,675000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-15787,-1038,-4023.0,-4688,31.0,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,11,0,1,1,0,1,1,Industry: type 9,,0.3474168976236582,0.0969483170508572,0.0309,,0.993,,,0.0,0.069,0.1667,,,,0.0186,,,0.0315,,0.993,,,0.0,0.069,0.1667,,,,0.0193,,,0.0312,,0.993,,,0.0,0.069,0.1667,,,,0.0189,,,,block of flats,0.0301,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-152.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n410793,1,Cash loans,M,N,Y,0,225000.0,1546020.0,45333.0,1350000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.003540999999999999,-8357,-678,-686.0,-1051,,1,1,0,1,0,0,,1.0,1,1,THURSDAY,9,0,0,0,0,0,0,Other,,0.27362369584685675,0.18195910978627847,0.1082,0.0479,0.9925,,,0.08,0.1379,0.1875,,0.0,,0.0772,,0.0011,0.0714,0.0,0.9866,,,0.0,0.1379,0.1667,,0.0,,0.0524,,0.0,0.1093,0.0479,0.9925,,,0.08,0.1379,0.1875,,0.0,,0.0786,,0.0011,,block of flats,0.0452,Panel,No,1.0,0.0,1.0,0.0,-640.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n227349,0,Cash loans,F,N,N,1,90000.0,729792.0,26343.0,630000.0,Family,State servant,Higher education,Separated,House / apartment,0.00496,-10040,-1239,-1479.0,-2722,,1,1,0,1,1,0,Core staff,2.0,2,2,FRIDAY,14,0,0,0,1,1,0,Government,0.5627859747242947,0.4271527830935049,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-509.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n288665,0,Cash loans,F,Y,Y,1,103500.0,327024.0,18904.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020246,-11241,-123,-4273.0,-493,14.0,1,1,0,1,0,0,Cooking staff,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,Kindergarten,0.2934109934979379,0.4893703734124229,0.4974688893052743,0.0608,0.0607,0.9811,0.7416,0.0075,0.0,0.1379,0.1667,0.2083,0.0592,0.0471,0.0526,0.0116,0.0302,0.062,0.063,0.9811,0.7517,0.0076,0.0,0.1379,0.1667,0.2083,0.0606,0.0514,0.0548,0.0117,0.032,0.0614,0.0607,0.9811,0.7451,0.0076,0.0,0.1379,0.1667,0.2083,0.0603,0.0479,0.0535,0.0116,0.0308,reg oper account,block of flats,0.0521,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n383684,0,Cash loans,M,Y,N,0,108000.0,535500.0,35914.5,535500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.001417,-10196,-979,-7598.0,-2332,17.0,1,1,0,1,0,1,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.30098986526984245,0.3439348662391216,0.7610263695502636,0.0608,0.0,0.9856,0.8028,0.0,0.0,0.1379,0.1667,0.0,0.0,0.0496,0.0517,0.0,0.0,0.062,0.0,0.9856,0.8105,0.0,0.0,0.1379,0.1667,0.0,0.0,0.0542,0.0539,0.0,0.0,0.0614,0.0,0.9856,0.8054,0.0,0.0,0.1379,0.1667,0.0,0.0,0.0504,0.0526,0.0,0.0,reg oper spec account,block of flats,0.0469,Block,No,0.0,0.0,0.0,0.0,-565.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n113636,0,Cash loans,F,N,N,0,117000.0,675000.0,37822.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-11804,-851,-5453.0,-3052,,1,1,0,1,0,0,Secretaries,2.0,2,2,THURSDAY,12,0,0,0,1,0,1,Business Entity Type 3,0.3495742017828408,0.5165714393382579,,0.0722,0.0633,0.9771,0.6872,0.0079,0.0,0.1379,0.1667,0.2083,0.0573,0.0588,0.0676,0.0,0.0,0.0735,0.0656,0.9772,0.6994,0.008,0.0,0.1379,0.1667,0.2083,0.0586,0.0643,0.0705,0.0,0.0,0.0729,0.0633,0.9771,0.6914,0.008,0.0,0.1379,0.1667,0.2083,0.0583,0.0599,0.0688,0.0,0.0,reg oper account,block of flats,0.0575,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-416.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n346603,0,Cash loans,F,N,Y,0,180000.0,366588.0,27540.0,306000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.006629,-22022,365243,-7276.0,-4413,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,7,0,0,0,0,0,0,XNA,,0.5397061658422937,0.8027454758994178,,,0.9776,,,0.08,0.069,0.0417,,,,,,,,,0.9772,,,0.0806,0.069,0.0417,,,,,,,,,0.9776,,,0.08,0.069,0.0417,,,,,,,,block of flats,0.0071,,No,1.0,0.0,1.0,0.0,-485.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,1.0,0.0,2.0,4.0\n216071,0,Cash loans,F,N,Y,0,180000.0,743697.0,49900.5,702000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.02461,-20008,-2711,-10755.0,-3251,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6290829308372451,,0.0722,0.0,0.9776,0.6940000000000001,0.0091,0.0,0.1379,0.1667,0.2083,0.0788,0.0588,0.063,0.0,0.0,0.0735,0.0,0.9777,0.706,0.0091,0.0,0.1379,0.1667,0.2083,0.0806,0.0643,0.0656,0.0,0.0,0.0729,0.0,0.9776,0.6981,0.0091,0.0,0.1379,0.1667,0.2083,0.0802,0.0599,0.0641,0.0,0.0,,block of flats,0.0594,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1021.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n405835,0,Cash loans,F,Y,Y,0,256500.0,539100.0,27652.5,450000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.011703,-17845,-263,-273.0,-1383,1.0,1,1,0,1,0,1,Core staff,2.0,2,2,TUESDAY,12,1,1,0,0,0,0,Bank,0.4221615434270263,0.6601866296081499,0.4884551844437485,0.1237,0.0,0.9742,0.6464,,0.0,0.2069,0.1667,0.2083,0.0789,,0.0951,,,0.1261,0.0,0.9742,0.6602,,0.0,0.2069,0.1667,0.2083,0.0807,,0.099,,,0.1249,0.0,0.9742,0.6511,,0.0,0.2069,0.1667,0.2083,0.0803,,0.0968,,,reg oper account,block of flats,0.0811,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n246785,0,Cash loans,F,Y,Y,0,135000.0,808650.0,26217.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-17520,-1086,-1197.0,-1072,7.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,School,,0.2467572180918347,0.524496446363472,0.1031,0.0262,0.9727,0.626,0.0404,0.0,0.1724,0.1667,0.2083,0.0163,0.0841,0.0695,0.0,0.0096,0.105,0.0272,0.9727,0.6406,0.0408,0.0,0.1724,0.1667,0.2083,0.0167,0.0918,0.0725,0.0,0.0102,0.1041,0.0262,0.9727,0.631,0.0407,0.0,0.1724,0.1667,0.2083,0.0166,0.0855,0.0708,0.0,0.0098,reg oper account,block of flats,0.0789,Panel,No,1.0,1.0,1.0,1.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n324601,0,Cash loans,F,N,N,0,45000.0,445500.0,11880.0,445500.0,Unaccompanied,Working,Higher education,Married,Co-op apartment,0.014519999999999996,-9921,-575,-6708.0,-407,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.51726835443494,0.34748630983062473,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,10.0,0.0,-2269.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n138222,0,Cash loans,F,N,Y,1,180000.0,576000.0,27841.5,576000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.019689,-14536,-6751,-8686.0,-1731,,1,1,0,1,0,0,Medicine staff,3.0,2,2,THURSDAY,15,0,0,0,0,0,0,Medicine,,0.6768394619138232,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2716.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n395944,0,Cash loans,M,Y,Y,0,112500.0,1170000.0,46530.0,1170000.0,Children,Pensioner,Secondary / secondary special,Married,Rented apartment,0.035792000000000004,-19828,365243,-2011.0,-2786,14.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,1,0,0,XNA,,0.7812421092465432,0.7886807751817684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-726.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n289911,1,Cash loans,M,Y,Y,2,157500.0,284400.0,22599.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018029,-12942,-295,-6345.0,-4055,7.0,1,1,1,1,0,0,,4.0,3,3,SATURDAY,8,0,0,0,1,1,0,Agriculture,0.1729471103845302,0.5525084390477213,0.25946765482111994,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-1062.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n305530,0,Cash loans,F,N,Y,0,72000.0,271066.5,12069.0,234000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.019101,-15844,-1102,-7255.0,-2538,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 2,0.5582786062855517,0.4792068780075268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-421.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n180870,1,Cash loans,F,N,Y,1,225000.0,301464.0,20277.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.002042,-8449,-1461,-2986.0,-796,,1,1,0,1,0,0,Sales staff,2.0,3,3,WEDNESDAY,17,0,0,0,0,0,0,Self-employed,,0.5285190244632134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-307.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n406537,0,Cash loans,F,N,Y,0,65250.0,273636.0,15408.0,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.007120000000000001,-23060,365243,-11092.0,-4267,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,0.7497181365704227,0.6973509722223975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n365747,0,Cash loans,F,Y,Y,0,225000.0,1724220.0,50544.0,1350000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.010276,-12115,-572,-5035.0,-3959,10.0,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,19,1,1,0,1,1,1,Business Entity Type 3,0.5790833459658027,0.6642952788232553,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-298.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n331144,0,Cash loans,F,N,Y,0,90000.0,177903.0,12780.0,148500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.0228,-22318,365243,-6263.0,-4750,,1,0,0,1,0,0,,1.0,2,2,SUNDAY,10,0,0,0,0,0,0,XNA,,0.6745777550244052,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-192.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n400732,0,Cash loans,F,N,Y,0,153000.0,938304.0,31140.0,810000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010966,-22824,365243,-3644.0,-4843,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.5252265697290917,0.5867400085415683,0.1155,0.099,0.9831,0.7688,0.0249,0.16,0.2414,0.2083,0.25,0.1143,0.0925,0.11900000000000001,0.0077,0.1507,0.1176,0.1028,0.9831,0.7779,0.0251,0.1611,0.2414,0.2083,0.25,0.1169,0.10099999999999999,0.124,0.0078,0.1596,0.1166,0.099,0.9831,0.7719,0.0251,0.16,0.2414,0.2083,0.25,0.1163,0.0941,0.1212,0.0078,0.1539,reg oper spec account,block of flats,0.14,\"Stone, brick\",No,4.0,1.0,4.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n398670,1,Cash loans,F,N,Y,0,130500.0,112500.0,6237.0,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.01885,-22363,365243,-2240.0,-4388,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.729618217098502,0.41184855592423975,0.1247,0.0696,0.996,0.9388,0.0509,0.12,0.1034,0.375,0.4167,0.0315,0.1009,0.1362,0.0039,0.0038,0.1271,0.0722,0.996,0.9412,0.0513,0.1208,0.1034,0.375,0.4167,0.0322,0.1102,0.1419,0.0039,0.004,0.126,0.0696,0.996,0.9396,0.0512,0.12,0.1034,0.375,0.4167,0.032,0.1026,0.1386,0.0039,0.0039,reg oper account,block of flats,0.1357,Panel,No,0.0,0.0,0.0,0.0,-1881.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n443124,0,Cash loans,F,Y,N,0,121500.0,370629.0,18157.5,306000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.020713,-9114,-235,-2895.0,-1789,15.0,1,1,0,1,0,1,Core staff,1.0,3,3,SATURDAY,9,0,0,0,1,1,0,Security,0.20057280546510248,0.006148059001027425,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n233718,0,Cash loans,F,N,Y,0,270000.0,1537434.0,53568.0,1404000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Municipal apartment,0.04622,-15450,-770,-4581.0,-3244,,1,1,0,1,0,1,Security staff,2.0,1,1,FRIDAY,10,0,0,0,0,0,0,Security,0.5962533148154224,0.6918308349034564,0.6092756673894402,0.0082,0.0,0.9717,0.6124,0.0006,0.0,0.0345,0.0417,0.0833,0.0042,0.0067,0.0058,0.0,0.0,0.0084,0.0,0.9717,0.6276,0.0006,0.0,0.0345,0.0417,0.0833,0.0043,0.0073,0.006,0.0,0.0,0.0083,0.0,0.9717,0.6176,0.0006,0.0,0.0345,0.0417,0.0833,0.0042,0.0068,0.0059,0.0,0.0,reg oper account,block of flats,0.0046,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2583.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n112044,0,Cash loans,M,Y,Y,0,234000.0,1024740.0,52321.5,900000.0,\"Spouse, partner\",Pensioner,Higher education,Married,Municipal apartment,0.04622,-20238,365243,-8638.0,-3275,7.0,1,0,0,1,1,0,,2.0,1,1,THURSDAY,11,0,0,0,0,0,0,XNA,,0.7508683626054357,0.7738956942145427,0.0619,0.0866,0.9881,,0.0181,0.0,0.2069,0.1667,,,,0.0836,,0.031,0.063,0.0899,0.9881,,0.0183,0.0,0.2069,0.1667,,,,0.0871,,0.0328,0.0625,0.0866,0.9881,,0.0182,0.0,0.2069,0.1667,,,,0.0851,,0.0316,,block of flats,0.0824,Panel,No,0.0,0.0,0.0,0.0,-1718.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n420151,0,Cash loans,F,N,Y,0,112500.0,593010.0,19260.0,495000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.020246,-12050,-2358,-3574.0,-3574,,1,1,0,1,1,0,Core staff,1.0,3,3,MONDAY,12,0,0,0,0,1,1,Self-employed,0.6049024012676304,0.38311816760240497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1749.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n379427,0,Cash loans,F,N,Y,0,157500.0,447453.0,22977.0,373500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.019101,-18477,-11222,-3512.0,-2006,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Industry: type 2,,0.4937590129109132,0.6801388218428291,0.1443,0.0763,0.9747,,,0.0,0.1379,0.1667,,0.07200000000000001,,0.0542,,0.1118,0.1471,0.0792,0.9747,,,0.0,0.1379,0.1667,,0.0736,,0.0565,,0.1184,0.1457,0.0763,0.9747,,,0.0,0.1379,0.1667,,0.0732,,0.0552,,0.1142,,block of flats,0.067,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2480.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n150408,0,Cash loans,F,Y,Y,2,171000.0,462195.0,20488.5,351000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00823,-15337,-3289,-8962.0,-5006,6.0,1,1,0,1,0,0,Sales staff,4.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Trade: type 7,,0.5523894907640341,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1900.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n240298,0,Cash loans,F,N,Y,0,225000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-19463,-156,-3149.0,-617,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,9,0,1,1,1,1,1,Business Entity Type 3,,0.6579066620954157,0.8327850252992314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1175.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n355507,0,Revolving loans,F,N,Y,1,540000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.018209,-10511,-1494,-4894.0,-2126,,1,1,0,1,0,0,Sales staff,2.0,3,3,TUESDAY,6,0,0,0,0,0,0,Self-employed,,0.08309743268259194,0.4992720153045617,0.1113,0.0955,0.9786,0.7076,0.0,0.0,0.2759,0.1667,0.2083,0.0794,0.0908,0.1079,0.0,0.0,0.1134,0.0991,0.9786,0.7190000000000001,0.0,0.0,0.2759,0.1667,0.2083,0.0812,0.0992,0.1124,0.0,0.0,0.1124,0.0955,0.9786,0.7115,0.0,0.0,0.2759,0.1667,0.2083,0.0808,0.0923,0.1098,0.0,0.0,reg oper account,block of flats,0.0849,Panel,No,4.0,0.0,4.0,0.0,-447.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n176304,0,Cash loans,M,Y,N,0,126000.0,652500.0,18072.0,652500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.005144,-9676,-2229,-4210.0,-2371,7.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,1,1,Trade: type 2,,0.5771206807164158,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n103987,0,Cash loans,F,N,Y,1,99000.0,552555.0,29569.5,477000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.008068,-15349,-4450,-6785.0,-4443,,1,1,0,1,0,0,Core staff,3.0,3,3,SATURDAY,7,0,0,0,0,0,0,Government,0.5649663036814737,0.11885879625332922,0.6296742509538716,0.0443,0.0835,0.9901,,,0.0,0.1379,0.1667,,,,0.0581,,,0.0452,0.0866,0.9901,,,0.0,0.1379,0.1667,,,,0.0605,,,0.0448,0.0835,0.9901,,,0.0,0.1379,0.1667,,,,0.0591,,,,block of flats,0.0611,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2478.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n119960,0,Cash loans,F,N,Y,2,63000.0,450000.0,22018.5,450000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.01885,-9914,-1238,-692.0,-2589,,1,1,1,1,1,0,,4.0,2,2,THURSDAY,11,0,0,0,0,0,0,School,,0.4897802123252981,0.5064842396679806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-450.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n129883,0,Cash loans,F,N,N,0,126000.0,225000.0,10944.0,225000.0,Unaccompanied,State servant,Incomplete higher,Single / not married,With parents,0.008068,-8166,-715,-2959.0,-819,,1,1,1,1,0,0,Core staff,1.0,3,3,SATURDAY,8,0,0,0,0,0,0,Government,,0.0047132565008855,0.0368624823151365,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-457.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n180107,0,Cash loans,F,N,Y,2,157500.0,521280.0,27423.0,450000.0,Family,Working,Secondary / secondary special,Married,Rented apartment,0.002042,-11013,-210,-5021.0,-3054,,1,1,0,1,0,0,Cooking staff,4.0,3,3,SATURDAY,10,0,1,1,0,0,0,Restaurant,,0.4915344713099084,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-665.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n182561,1,Cash loans,M,N,Y,0,112500.0,640080.0,31131.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.006305,-9692,-1043,-4045.0,-2062,,1,1,0,1,0,0,Low-skill Laborers,1.0,3,3,SUNDAY,3,0,0,0,0,0,0,Agriculture,,0.01702128494449796,,0.0577,0.067,0.9786,0.7076,0.0274,0.0,0.1034,0.1667,0.2083,0.044000000000000004,0.0471,0.0368,0.0,0.0,0.0588,0.0695,0.9786,0.7190000000000001,0.0276,0.0,0.1034,0.1667,0.2083,0.045,0.0514,0.0383,0.0,0.0,0.0583,0.067,0.9786,0.7115,0.0276,0.0,0.1034,0.1667,0.2083,0.0447,0.0479,0.0374,0.0,0.0,reg oper account,block of flats,0.0436,Panel,No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n439186,0,Cash loans,F,N,Y,0,112500.0,544491.0,17694.0,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.005084,-16864,-1177,-9117.0,-404,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.4931686891501111,0.6380435278721609,0.0753,0.0557,0.9846,0.7892,0.0385,0.0,0.0345,0.1667,0.2083,,0.0614,0.0433,0.0,0.006999999999999999,0.0767,0.0578,0.9846,0.7975,0.0388,0.0,0.0345,0.1667,0.2083,,0.067,0.0451,0.0,0.0074,0.076,0.0557,0.9846,0.792,0.0387,0.0,0.0345,0.1667,0.2083,,0.0624,0.044000000000000004,0.0,0.0071,reg oper account,block of flats,0.034,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n293581,0,Cash loans,M,N,N,1,247500.0,1223010.0,45454.5,1125000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010276,-12511,-3147,-4441.0,-4441,,1,1,0,1,1,0,Drivers,3.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.2298554068508813,0.3623745579756088,0.5388627065779676,0.0165,0.0,0.9806,0.7348,0.0,0.0,0.069,0.0417,0.0417,0.0,0.0134,0.0154,0.0,0.0,0.0168,0.0,0.9806,0.7452,0.0,0.0,0.069,0.0417,0.0417,0.0,0.0147,0.016,0.0,0.0,0.0167,0.0,0.9806,0.7383,0.0,0.0,0.069,0.0417,0.0417,0.0,0.0137,0.0156,0.0,0.0,reg oper spec account,block of flats,0.0121,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n422719,0,Cash loans,F,Y,Y,3,81000.0,521280.0,26613.0,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-11802,-1838,-4755.0,-2388,18.0,1,1,1,1,1,0,Laborers,5.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.4646793930349312,0.0748795717415263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1107.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n221368,0,Cash loans,F,N,N,1,90000.0,180000.0,8752.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-13016,-1122,-515.0,-473,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,14,0,0,0,0,0,0,Trade: type 3,,0.6971155447214119,0.2263473957097693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,1.0,-531.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n434611,0,Cash loans,M,N,Y,1,202500.0,1546020.0,45333.0,1350000.0,Unaccompanied,Working,Higher education,Married,Co-op apartment,0.007120000000000001,-10440,-421,-5241.0,-3107,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,WEDNESDAY,11,0,1,1,1,1,1,Industry: type 9,0.3829410073774379,0.6223639904179873,0.5620604831738043,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n364770,0,Cash loans,F,N,Y,1,81000.0,490495.5,31477.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006296,-14189,-2477,-1729.0,-4933,,1,1,0,1,0,0,Laborers,3.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.5111299257528661,0.22488655055000373,0.5832379256761245,0.0928,,0.9851,,,,0.2069,0.1667,,,,0.0559,,0.0292,0.0945,,0.9851,,,,0.2069,0.1667,,,,0.0582,,0.0309,0.0937,,0.9851,,,,0.2069,0.1667,,,,0.0569,,0.0298,,,0.07400000000000001,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n391998,0,Cash loans,F,Y,Y,0,184500.0,571486.5,27621.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010032,-21782,365243,-6765.0,-4147,20.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,6,0,0,0,0,0,0,XNA,,0.4387752877045254,,0.1742,0.0865,0.9836,,,0.04,0.0345,0.3333,,0.0383,,0.0831,,0.0681,0.1775,0.0897,0.9836,,,0.0403,0.0345,0.3333,,0.0392,,0.0866,,0.0721,0.1759,0.0865,0.9836,,,0.04,0.0345,0.3333,,0.039,,0.0846,,0.0695,,block of flats,0.0802,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-966.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n225238,0,Cash loans,M,N,N,0,405000.0,497520.0,52789.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-13169,-4520,-1912.0,-4582,,1,1,1,1,0,1,Laborers,2.0,1,1,THURSDAY,14,0,0,0,0,0,0,Government,0.6525844011885675,0.3267181986333973,0.6109913280868294,0.2649,0.2005,0.9945,0.9252,0.0,0.56,0.3103,0.4583,0.5,0.0,0.21600000000000005,0.2743,0.0,0.0925,0.27,0.2081,0.9945,0.9281,0.0,0.5639,0.3103,0.4583,0.5,0.0,0.23600000000000002,0.2858,0.0,0.0979,0.2675,0.2005,0.9945,0.9262,0.0,0.56,0.3103,0.4583,0.5,0.0,0.2198,0.2792,0.0,0.0944,reg oper account,block of flats,0.2358,Panel,No,1.0,0.0,1.0,0.0,-141.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n210557,0,Cash loans,M,Y,Y,2,247500.0,1329579.0,43020.0,1161000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006629,-13218,-1390,-45.0,-4104,13.0,1,1,0,1,0,0,Drivers,4.0,2,2,SATURDAY,6,0,0,0,0,0,0,Electricity,,0.6298887702394366,0.475849908720221,0.0814,0.0,0.9871,0.8232,0.0166,0.0,0.1379,0.1667,0.2083,0.0289,0.0664,0.084,0.0,0.0208,0.083,0.0,0.9871,0.8301,0.0167,0.0,0.1379,0.1667,0.2083,0.0296,0.0725,0.0875,0.0,0.0,0.0822,0.0,0.9871,0.8256,0.0167,0.0,0.1379,0.1667,0.2083,0.0294,0.0676,0.0855,0.0,0.0213,reg oper account,block of flats,0.0751,Panel,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n379063,1,Cash loans,M,N,Y,0,202500.0,331920.0,18135.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.025164,-16735,-4099,-207.0,-280,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,15,0,0,0,0,0,0,Self-employed,,0.605777238189489,0.3139166772114369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-545.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n171248,0,Cash loans,F,N,N,0,157500.0,521280.0,26613.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00963,-13958,-3689,-3800.0,-4429,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,13,0,1,1,0,1,1,Industry: type 3,,0.34817086236475714,0.6363761710860439,0.0186,,0.9881,,,0.0,0.069,0.0833,,,,,,,0.0189,,0.9881,,,0.0,0.069,0.0833,,,,,,,0.0187,,0.9881,,,0.0,0.069,0.0833,,,,,,,,block of flats,0.0152,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n183966,0,Cash loans,M,N,Y,0,202500.0,156384.0,16551.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.02461,-14708,-1547,-1446.0,-199,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,13,0,1,1,0,1,1,Business Entity Type 3,,0.21512741170495134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-9.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n347568,0,Cash loans,F,N,Y,0,180000.0,814041.0,23800.5,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.02461,-17893,-2274,-7638.0,-1416,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,,0.7426979197952767,0.8528284668510541,0.0742,0.0424,0.9841,0.7824,,0.08,0.069,0.3333,0.0417,0.0445,0.0605,0.07400000000000001,0.0,0.0,0.0756,0.044000000000000004,0.9841,0.7909,,0.0806,0.069,0.3333,0.0417,0.0455,0.0661,0.0771,0.0,0.0,0.0749,0.0424,0.9841,0.7853,,0.08,0.069,0.3333,0.0417,0.0452,0.0616,0.0753,0.0,0.0,reg oper account,block of flats,0.0749,Panel,No,10.0,3.0,10.0,2.0,-1860.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,10.0,0.0,1.0\n392467,0,Cash loans,M,Y,Y,2,180000.0,675000.0,53329.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-10380,-1035,-395.0,-929,13.0,1,1,1,1,1,1,Laborers,4.0,2,2,MONDAY,15,0,1,1,0,1,1,Construction,0.2906805538892268,0.2290145733087117,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-329.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n231769,0,Cash loans,F,N,Y,0,157500.0,702000.0,38214.0,702000.0,Unaccompanied,Pensioner,Incomplete higher,Civil marriage,House / apartment,0.026392,-22448,365243,-11908.0,-4563,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.6053280377810284,,0.0825,,0.9762,,,0.0,0.1379,0.1667,,,,0.0725,,0.0,0.084,,0.9762,,,0.0,0.1379,0.1667,,,,0.0755,,0.0,0.0833,,0.9762,,,0.0,0.1379,0.1667,,,,0.0738,,0.0,,block of flats,0.057,Panel,No,0.0,0.0,0.0,0.0,-504.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n393497,0,Cash loans,F,N,Y,0,180000.0,904500.0,38452.5,904500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.014519999999999996,-21581,-10353,-9056.0,-4980,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,University,0.6449587396140487,0.3382731323655561,0.3842068130556564,,,0.9876,0.83,,0.36,0.3103,0.3333,0.375,0.1762,,0.3226,,0.0159,,,0.9876,0.8367,,0.3625,0.3103,0.3333,0.375,0.1802,,0.3361,,0.0168,,,0.9876,0.8323,,0.36,0.3103,0.3333,0.375,0.1793,,0.3284,,0.0162,,block of flats,0.2592,Panel,No,0.0,0.0,0.0,0.0,-1033.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n275525,0,Revolving loans,F,N,Y,0,81000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-10808,-370,-2465.0,-1743,,1,1,1,1,1,0,Sales staff,2.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.387341007158664,0.5980540177521385,0.3344541255096772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1688.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n356992,0,Cash loans,F,N,Y,0,103500.0,536917.5,28737.0,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-19872,-11303,-730.0,-3340,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,14,0,0,0,0,1,1,Business Entity Type 2,,0.7172023181923226,0.6610235391308081,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n117140,0,Cash loans,M,N,Y,1,135000.0,1017000.0,29736.0,1017000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00496,-16555,-1598,-8728.0,-10,,1,1,1,1,1,0,Laborers,3.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.4084689906570571,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n101104,0,Cash loans,F,N,Y,0,124200.0,538704.0,23859.0,481500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-22925,365243,-6477.0,-4635,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.5823047200093102,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-359.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n302667,0,Cash loans,M,N,Y,0,90000.0,405000.0,19827.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-18257,-737,-356.0,-1812,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.6026971951007203,0.4400578303966329,0.1629,,0.9995,,,0.08,0.069,0.375,,,,0.1214,,0.0,0.166,,0.9995,,,0.0806,0.069,0.375,,,,0.1265,,0.0,0.1645,,0.9995,,,0.08,0.069,0.375,,,,0.1236,,0.0,,block of flats,0.0955,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n330151,0,Cash loans,M,N,N,0,112500.0,1067940.0,28170.0,765000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.02461,-9302,-206,-1959.0,-1962,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,18,0,0,0,0,1,1,Transport: type 2,,0.3410983334477937,0.22888341670067305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-158.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n311925,1,Revolving loans,F,N,Y,2,90000.0,180000.0,9000.0,180000.0,Other_B,Working,Secondary / secondary special,Single / not married,House / apartment,0.010643,-10150,-282,-7902.0,-2236,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.4079671356561111,,,,0.9891,,,0.04,0.0345,0.3333,,,,0.0967,,,,,0.9891,,,0.0403,0.0345,0.3333,,,,0.1008,,,,,0.9891,,,0.04,0.0345,0.3333,,,,0.0984,,,,block of flats,0.0761,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-238.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n290537,0,Cash loans,F,Y,Y,0,67500.0,251280.0,13284.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-11360,-2368,-11329.0,-51,24.0,1,1,1,1,0,0,Laborers,2.0,3,3,THURSDAY,14,0,0,0,0,0,0,Self-employed,,0.44179307097254616,,0.1485,0.1384,0.9811,0.7416,0.0548,0.24,0.2069,0.3333,0.375,0.1439,0.1202,0.2332,0.0039,0.0038,0.1513,0.1436,0.9811,0.7517,0.0553,0.2417,0.2069,0.3333,0.375,0.1472,0.1313,0.243,0.0039,0.0041,0.1499,0.1384,0.9811,0.7451,0.0552,0.24,0.2069,0.3333,0.375,0.1464,0.1223,0.2374,0.0039,0.0039,reg oper account,terraced house,0.2142,Panel,No,4.0,2.0,4.0,2.0,-286.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n431707,0,Cash loans,F,N,Y,0,72000.0,254700.0,14350.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.025164,-24422,365243,-4744.0,-4898,,1,0,0,1,0,0,,1.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.2267790062004907,0.7281412993111438,0.2052,0.3427,0.9836,0.7756,0.0,0.24,0.2069,0.3333,0.375,0.0,0.1673,0.2456,0.0,0.33399999999999996,0.209,0.3556,0.9836,0.7844,0.0,0.2417,0.2069,0.3333,0.375,0.0,0.1827,0.2559,0.0,0.3536,0.2071,0.3427,0.9836,0.7786,0.0,0.24,0.2069,0.3333,0.375,0.0,0.1702,0.25,0.0,0.341,reg oper account,block of flats,0.2658,Panel,No,0.0,0.0,0.0,0.0,-1575.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n181082,0,Cash loans,F,N,N,0,126000.0,1200429.0,39802.5,1075500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019101,-21296,365243,-9791.0,-4679,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.6203626641533607,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1182.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n246484,0,Cash loans,F,N,Y,0,112500.0,114430.5,11853.0,103500.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.031329,-22615,365243,-16729.0,-4332,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,0.7660311894940242,0.493463848752391,0.8027454758994178,0.0825,0.0273,0.9727,0.626,0.0,0.0,0.1379,0.1667,0.2083,0.0616,0.0672,0.0637,0.0,0.0238,0.084,0.0284,0.9727,0.6406,0.0,0.0,0.1379,0.1667,0.2083,0.063,0.0735,0.0664,0.0,0.0252,0.0833,0.0273,0.9727,0.631,0.0,0.0,0.1379,0.1667,0.2083,0.0627,0.0684,0.0649,0.0,0.0243,reg oper account,block of flats,0.054000000000000006,Mixed,No,1.0,1.0,1.0,1.0,-884.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n305891,1,Cash loans,F,N,Y,0,112500.0,355536.0,17235.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-17112,-9317,-415.0,-646,,1,1,0,1,1,0,Core staff,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Kindergarten,0.4464798791667852,0.0008266770740602543,0.04006159995185312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1114.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n201938,0,Cash loans,F,N,Y,0,135000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.019101,-19463,-1808,-9696.0,-3005,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Government,,0.546505165261773,0.4400578303966329,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1706.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n275742,0,Cash loans,F,N,N,0,225000.0,675000.0,34596.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.0228,-19802,-270,-3649.0,-3306,,1,1,1,1,1,0,Security staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.14428225193281558,0.6230010733527698,0.7421816117614419,0.0835,0.0818,0.993,,,0.12,0.1034,0.3333,,0.0562,,0.1244,,0.0,0.0851,0.0849,0.993,,,0.1208,0.1034,0.3333,,0.0574,,0.1296,,0.0,0.0843,0.0818,0.993,,,0.12,0.1034,0.3333,,0.0571,,0.1267,,0.0,,block of flats,0.1098,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2714.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n316359,1,Cash loans,M,Y,Y,0,90000.0,225000.0,12204.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-17861,-1843,-6243.0,-1401,10.0,1,1,0,1,1,0,Drivers,2.0,2,2,WEDNESDAY,21,0,0,0,0,0,0,Self-employed,,0.2433146636237752,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-28.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n271621,0,Cash loans,F,N,N,0,112500.0,562491.0,22140.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-22870,365243,-2391.0,-4388,,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.6337750921172697,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1299.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n259287,0,Cash loans,F,N,Y,0,180000.0,675000.0,41296.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.003818,-15318,-1675,-3410.0,-3453,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,4,0,0,0,0,0,0,Business Entity Type 1,0.4188228590002197,0.7451764709262029,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-517.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n252005,0,Cash loans,M,Y,Y,1,171000.0,76410.0,8770.5,67500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010643,-8500,-1073,-8496.0,-964,10.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.28594251914835256,0.4668640059537032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-308.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n315313,0,Cash loans,M,N,Y,2,157500.0,231813.0,21388.5,193500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-9827,-1502,-4528.0,-2512,,1,1,0,1,0,0,,4.0,3,3,TUESDAY,6,0,0,0,0,1,1,Business Entity Type 3,,0.20199930747774705,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-326.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n179797,0,Cash loans,F,N,Y,1,112500.0,801000.0,26599.5,801000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-12236,-484,-5258.0,-3087,,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,14,0,0,0,0,1,1,Industry: type 9,0.5489599682026757,0.4759551703720799,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1875.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n402710,0,Cash loans,F,N,N,0,202500.0,626476.5,49626.0,580500.0,Other_B,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.002506,-16140,-4658,-5544.0,-4382,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,3,0,0,0,0,0,0,Self-employed,0.5042788287701615,0.5181112482203918,0.6144143775673561,0.0371,0.0623,0.9821,,,0.0,0.1034,0.0833,,0.0,,0.0501,,0.0,0.0378,0.0647,0.9821,,,0.0,0.1034,0.0833,,0.0,,0.0522,,0.0,0.0375,0.0623,0.9821,,,0.0,0.1034,0.0833,,0.0,,0.051,,0.0,,block of flats,0.0394,Block,No,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n237032,0,Cash loans,M,N,Y,0,126000.0,824823.0,24246.0,688500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-19029,-287,-224.0,-2584,,1,1,0,1,0,0,Security staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6675207418773126,0.6363761710860439,0.1457,0.0952,0.9990000000000001,0.9932,0.0219,0.2264,0.0976,0.4583,0.5,0.0811,0.0504,0.1314,0.0,0.0,0.1492,0.0983,0.9995,0.9935,0.0221,0.2417,0.1034,0.4583,0.5,0.0162,0.0551,0.1355,0.0,0.0,0.1541,0.0955,0.9995,0.9933,0.022000000000000002,0.24,0.1034,0.4583,0.5,0.0169,0.0513,0.1419,0.0,0.0,reg oper spec account,block of flats,0.1589,Panel,No,1.0,0.0,1.0,0.0,-256.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n273762,0,Revolving loans,F,N,Y,1,117000.0,180000.0,9000.0,180000.0,Family,Commercial associate,Incomplete higher,Married,Co-op apartment,0.01885,-8569,-1502,-6048.0,-849,,1,1,0,1,0,0,Cleaning staff,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.16488976044268847,0.32092465297934986,0.03869751293326445,0.1072,0.1038,0.9781,0.7008,0.0519,0.0,0.2414,0.1667,0.2083,0.043,0.0874,0.0886,0.0,0.0,0.1092,0.1078,0.9782,0.7125,0.0524,0.0,0.2414,0.1667,0.2083,0.0439,0.0955,0.0924,0.0,0.0,0.1083,0.1038,0.9781,0.7048,0.0522,0.0,0.2414,0.1667,0.2083,0.0437,0.0889,0.0902,0.0,0.0,reg oper account,block of flats,0.0802,Panel,No,0.0,0.0,0.0,0.0,-655.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n262318,0,Cash loans,M,Y,Y,0,157500.0,810000.0,23814.0,810000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-17961,-878,-9456.0,-1489,6.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,10,0,1,1,0,1,1,Transport: type 2,,0.2653117484731741,0.4812493411434029,0.0825,0.0776,0.9757,0.6668,0.0069,0.0,0.1379,0.1667,0.2083,0.0,0.0656,0.0616,0.0077,0.0052,0.084,0.0805,0.9757,0.6798,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0,0.0716,0.0641,0.0078,0.0055,0.0833,0.0776,0.9757,0.6713,0.0069,0.0,0.1379,0.1667,0.2083,0.0,0.0667,0.0627,0.0078,0.0053,reg oper account,block of flats,0.0533,\"Stone, brick\",No,5.0,1.0,5.0,0.0,-2287.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n361275,0,Cash loans,M,N,Y,0,202500.0,1159515.0,34033.5,1012500.0,Unaccompanied,State servant,Higher education,Married,Co-op apartment,0.00496,-13745,-3656,-7364.0,-3815,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Other,0.5889313939838222,0.6400758817030231,0.4578995512067301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-463.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n173789,0,Cash loans,M,N,Y,0,135000.0,593010.0,19260.0,495000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16751,-1373,-6918.0,-297,,1,1,1,1,0,0,Laborers,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Industry: type 3,0.2534490985875673,0.7059156799243899,0.7016957740576931,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2265.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n405331,0,Cash loans,M,N,N,1,135000.0,469152.0,24691.5,405000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-12810,-1593,-6715.0,-961,,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,14,0,0,0,0,1,1,Trade: type 7,,0.3744635141218918,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-1484.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n211523,0,Cash loans,M,Y,Y,0,112500.0,269550.0,23188.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.004849,-8699,-491,-8699.0,-1288,16.0,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 2,,0.2785887179144719,,0.1223,0.0891,0.9821,0.7552,0.0264,0.08,0.1607,0.2221,0.2638,0.0977,0.0997,0.0758,0.0,0.0,0.0735,0.0311,0.9811,0.7517,0.0087,0.0,0.1379,0.1667,0.2083,0.0533,0.0643,0.047,0.0,0.0,0.0729,0.0759,0.9821,0.7585,0.0301,0.0,0.1379,0.1667,0.2083,0.1073,0.0599,0.0464,0.0,0.0,reg oper spec account,block of flats,0.1811,Panel,No,0.0,0.0,0.0,0.0,-995.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n216232,0,Cash loans,M,Y,Y,0,112500.0,900000.0,26446.5,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-14781,-1771,-1002.0,-4092,3.0,1,1,1,1,1,0,Security staff,2.0,2,2,MONDAY,18,0,0,0,1,0,1,Agriculture,,0.7107900882540976,0.5673792367572691,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-868.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n136265,0,Cash loans,M,N,Y,0,148500.0,127350.0,13176.0,112500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-11963,-3246,-4843.0,-3119,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Industry: type 11,,0.5268948936145829,0.6801388218428291,0.4845,0.3076,0.9866,0.8164,0.2314,0.56,0.4828,0.375,0.4167,0.3106,0.395,0.5552,0.0,0.0,0.4937,0.3192,0.9866,0.8236,0.2336,0.5639,0.4828,0.375,0.4167,0.3176,0.4316,0.5785,0.0,0.0,0.4892,0.3076,0.9866,0.8189,0.2329,0.56,0.4828,0.375,0.4167,0.316,0.4019,0.5652,0.0,0.0,reg oper spec account,block of flats,0.5632,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n344179,0,Cash loans,F,N,N,1,112500.0,170640.0,6430.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-15278,-1061,-8198.0,-4359,,1,1,1,1,1,0,Laborers,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.6034691593914409,0.4492693157780438,0.5316861425197883,0.0928,0.1152,0.9851,0.7959999999999999,0.0111,0.0,0.2069,0.1667,0.2083,0.033,0.0756,0.0951,0.0,0.0,0.0945,0.1196,0.9851,0.804,0.0112,0.0,0.2069,0.1667,0.2083,0.0338,0.0826,0.0991,0.0,0.0,0.0937,0.1152,0.9851,0.7987,0.0112,0.0,0.2069,0.1667,0.2083,0.0336,0.077,0.0968,0.0,0.0,reg oper account,block of flats,0.0748,Panel,No,11.0,0.0,11.0,0.0,-464.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n111827,0,Cash loans,M,Y,Y,0,171000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.025164,-19916,-4170,-10406.0,-3448,16.0,1,1,0,1,0,0,Drivers,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Trade: type 6,0.5339384756828364,0.6856240578021886,0.15474363127259447,0.0,,0.9523,,,0.0,,,,,,0.0066,,,0.0,,0.9523,,,0.0,,,,,,0.0069,,,0.0,,0.9523,,,0.0,,,,,,0.0067,,,,block of flats,0.0055,,No,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n100101,0,Cash loans,F,Y,Y,0,202500.0,343377.0,22072.5,283500.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.072508,-18138,-969,-7421.0,-1681,5.0,1,1,0,1,0,0,High skill tech staff,1.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 1,,0.7162122102538987,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1585.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n222996,0,Cash loans,F,N,Y,0,81000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-13848,-4591,-828.0,-4668,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Transport: type 2,0.4842758854611026,0.4854148781492562,0.4812493411434029,0.0,,0.0,,,0.0,,,,,,,,,0.0,,0.0005,,,0.0,,,,,,,,,0.0,,0.0,,,0.0,,,,,,,,,,block of flats,0.0,,No,0.0,0.0,0.0,0.0,-1526.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n124747,0,Cash loans,F,N,Y,1,135000.0,659533.5,26284.5,589500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.004849,-13420,-3029,-2976.0,-4495,,1,1,0,1,0,0,Managers,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Kindergarten,,0.2014499730401305,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n131327,0,Cash loans,F,N,Y,2,189000.0,598486.5,24156.0,454500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006629,-11879,-788,-5809.0,-3971,,1,1,0,1,0,1,High skill tech staff,4.0,2,2,THURSDAY,4,0,0,0,0,0,0,Business Entity Type 2,0.32392744856664124,0.6774513888019286,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-530.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n392294,0,Cash loans,F,N,N,0,247500.0,1067418.0,31338.0,891000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-22024,365243,-4009.0,-4042,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,XNA,,0.5727438838243716,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1262.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n315246,0,Cash loans,F,N,Y,0,157500.0,586764.0,22248.0,405000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.016612000000000002,-16883,-5987,-3182.0,-419,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,School,0.6004552854065002,0.5785023140586426,0.3123653692278984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2362.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n217494,0,Cash loans,F,N,Y,0,76500.0,225000.0,16132.5,225000.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,House / apartment,0.028663,-18456,-3088,-1482.0,-1994,,1,1,1,1,1,1,Core staff,2.0,2,2,SUNDAY,13,0,0,0,0,1,1,Government,,0.522772139470035,0.7435593141311444,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n406769,0,Cash loans,F,N,Y,0,202500.0,983299.5,41791.5,904500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-16257,-297,-7475.0,-4208,,1,1,0,1,0,1,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Transport: type 4,0.4529176335621375,0.6302302688841624,0.5779691187553125,0.1567,0.0,0.9767,0.6804,0.032,0.0,0.1034,0.1667,0.2083,0.0284,0.1278,0.0538,0.0,0.0,0.1597,0.0,0.9767,0.6929,0.0323,0.0,0.1034,0.1667,0.2083,0.028999999999999998,0.1396,0.055999999999999994,0.0,0.0,0.1582,0.0,0.9767,0.6847,0.0322,0.0,0.1034,0.1667,0.2083,0.0289,0.13,0.0548,0.0,0.0,reg oper account,block of flats,0.0464,Panel,No,2.0,0.0,2.0,0.0,-2625.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n370099,0,Cash loans,M,N,N,1,180000.0,1215000.0,48316.5,1215000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.006207,-14102,-2514,-4905.0,-4677,,1,1,1,1,1,0,Drivers,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.7589196121465718,0.5585066276769286,,,0.9737,,,,0.069,0.125,,,,0.0241,,,,,0.9737,,,,0.069,0.125,,,,0.0251,,,,,0.9737,,,,0.069,0.125,,,,0.0245,,,,,0.0205,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1787.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n284032,0,Cash loans,M,N,N,0,157500.0,104256.0,11920.5,90000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.030755,-12219,-224,-6309.0,-4279,,1,1,0,1,0,1,,1.0,2,2,FRIDAY,15,0,0,0,0,1,1,Other,0.13695364969118332,0.4423378143096715,,0.0995,0.0812,0.9776,0.6940000000000001,0.0502,0.0,0.069,0.1667,0.2083,0.0473,0.0807,0.0509,0.0019,0.0068,0.1008,0.084,0.9777,0.706,0.0333,0.0,0.069,0.1667,0.2083,0.0145,0.0882,0.0408,0.0,0.0,0.1004,0.0812,0.9776,0.6981,0.0505,0.0,0.069,0.1667,0.2083,0.0481,0.0821,0.0518,0.0019,0.0069,reg oper spec account,block of flats,0.0489,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-2602.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n119486,0,Cash loans,M,Y,N,0,112500.0,269550.0,12001.5,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-18688,-5709,-511.0,-2240,4.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Other,,0.2631435910213423,0.7530673920730478,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1660.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n122495,0,Cash loans,M,Y,Y,0,270000.0,254700.0,20250.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-8440,-1107,-928.0,-1097,15.0,1,1,0,1,0,0,Sales staff,2.0,3,3,SUNDAY,8,0,0,0,0,0,0,Trade: type 7,0.16290442573738026,0.4998706780429344,0.11465249430297055,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n438854,0,Cash loans,M,N,Y,0,126000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.026392,-13881,-2532,-6058.0,-1470,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.5090977935724867,0.6910212267577837,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1516.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n135631,0,Cash loans,M,Y,N,0,157500.0,849415.5,43501.5,697500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-14681,-2753,-3412.0,-1091,20.0,1,1,0,1,0,0,Drivers,2.0,3,3,MONDAY,12,1,1,0,1,1,0,Trade: type 7,0.7008695211713276,0.6415405786108015,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-806.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n235575,0,Cash loans,F,N,N,0,96750.0,225000.0,13594.5,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.0105,-19902,-2617,-8328.0,-2200,,1,1,1,1,0,0,Accountants,2.0,3,3,MONDAY,23,0,0,0,0,0,0,Other,0.8812699921353346,0.7733079829430964,0.6161216908872079,0.0928,,0.9876,0.83,0.0129,0.0,0.2069,0.1667,0.0417,0.0456,0.0756,0.0825,0.0,0.0,0.0945,,0.9876,0.8367,0.013000000000000001,0.0,0.2069,0.1667,0.0417,0.0466,0.0826,0.086,0.0,0.0,0.0937,,0.9876,0.8323,0.013000000000000001,0.0,0.2069,0.1667,0.0417,0.0464,0.077,0.084,0.0,0.0,reg oper account,block of flats,0.07200000000000001,\"Stone, brick\",No,2.0,2.0,2.0,1.0,-1992.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n385498,0,Cash loans,M,Y,Y,0,292500.0,1374480.0,45553.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-18386,-5276,-6890.0,-1469,1.0,1,1,0,1,0,0,Drivers,2.0,1,1,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 1,0.8380582699535354,0.4016289899677121,0.7267112092725122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n333733,0,Revolving loans,F,N,Y,0,67500.0,135000.0,6750.0,135000.0,Unaccompanied,State servant,Incomplete higher,Married,House / apartment,0.035792000000000004,-8372,-519,-167.0,-365,,1,1,0,1,0,0,Accountants,2.0,2,2,THURSDAY,13,0,0,0,0,1,1,Trade: type 7,0.4923439811080959,0.6046748130271035,,0.0278,0.0523,0.9881,0.8368,0.0,0.0,0.1034,0.0833,0.0417,0.026000000000000002,0.0227,0.0252,0.0,0.0,0.0284,0.0542,0.9881,0.8432,0.0,0.0,0.1034,0.0833,0.0417,0.0266,0.0248,0.0263,0.0,0.0,0.0281,0.0523,0.9881,0.8390000000000001,0.0,0.0,0.1034,0.0833,0.0417,0.0265,0.0231,0.0257,0.0,0.0,reg oper account,block of flats,0.0198,Panel,No,,,,,-761.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n385424,0,Cash loans,M,Y,Y,0,315000.0,1515415.5,41800.5,1354500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-13332,-1559,-4786.0,-4801,18.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,13,0,1,1,0,1,1,Business Entity Type 3,,0.2841065295328942,0.3791004853998145,0.0856,0.0,0.9632,,,0.0,0.069,0.125,,0.0198,,0.0344,,0.0,0.0872,0.0,0.9633,,,0.0,0.069,0.125,,0.0203,,0.0358,,0.0,0.0864,0.0,0.9632,,,0.0,0.069,0.125,,0.0202,,0.035,,0.0,,block of flats,0.0615,Mixed,Yes,8.0,0.0,8.0,0.0,-1189.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n357948,0,Cash loans,M,Y,N,0,180000.0,781920.0,40054.5,675000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-20144,-665,-459.0,-3692,4.0,1,1,0,1,0,0,Managers,2.0,1,1,SATURDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.6894105878569036,0.2822484337007223,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-32.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n191271,0,Cash loans,F,N,Y,0,225000.0,675000.0,49248.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-11788,-1074,-2571.0,-2809,,1,1,0,1,1,0,Managers,2.0,2,2,FRIDAY,12,0,0,0,1,1,0,Business Entity Type 3,0.6346802103928066,0.7291434219375909,0.4436153084085652,0.9247,,0.996,,,0.88,0.7586,0.375,,,,0.7006,,0.0819,0.9422,,0.996,,,0.8862,0.7586,0.375,,,,0.7299,,0.0867,0.9337,,0.996,,,0.88,0.7586,0.375,,,,0.7132,,0.0836,,block of flats,0.5688,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2650.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n379545,0,Revolving loans,M,Y,Y,1,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.025164,-9229,-2259,-3392.0,-1920,1.0,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.18034244686191966,0.516019869436448,0.4938628816996244,0.2474,0.1606,0.9871,0.8232,0.0898,0.24,0.2069,0.375,0.4167,0.0,0.2,0.1812,0.0077,0.0063,0.2521,0.1667,0.9871,0.8301,0.0906,0.2417,0.2069,0.375,0.4167,0.0,0.2185,0.1888,0.0078,0.0066,0.2498,0.1606,0.9871,0.8256,0.0903,0.24,0.2069,0.375,0.4167,0.0,0.2035,0.1844,0.0078,0.0064,reg oper account,block of flats,0.1929,Panel,No,1.0,0.0,1.0,0.0,-1148.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n211080,0,Cash loans,F,N,Y,0,202500.0,152820.0,16587.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Rented apartment,0.014464,-10197,-1128,-4540.0,-2842,,1,1,0,1,0,0,Cooking staff,1.0,2,2,WEDNESDAY,5,0,0,0,1,1,0,Business Entity Type 3,0.2440671385913297,0.34958659602631603,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1673.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n202616,0,Cash loans,M,N,Y,3,202500.0,539590.5,26244.0,445500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.009175,-14374,-458,-8237.0,-4464,,1,1,1,1,0,0,Low-skill Laborers,5.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.6932835309195376,0.190705947811054,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-369.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n111748,0,Cash loans,M,Y,N,0,112500.0,808650.0,22365.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-17193,-1669,-1926.0,-639,7.0,1,1,1,1,0,0,Laborers,2.0,2,2,WEDNESDAY,16,0,0,0,1,1,1,Business Entity Type 3,0.3092867051514329,0.38366614020768064,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-1.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n376906,0,Cash loans,M,N,Y,0,157500.0,573408.0,29407.5,495000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-10033,-241,-9911.0,-2697,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Industry: type 11,0.4635691505740696,0.19067740078455767,0.7850520263728172,0.0371,0.0209,0.9861,0.8096,0.0072,0.04,0.0345,0.3333,0.375,0.0451,0.0303,0.0407,0.0,0.0,0.0378,0.0216,0.9861,0.8171,0.0072,0.0403,0.0345,0.3333,0.375,0.0461,0.0331,0.0424,0.0,0.0,0.0375,0.0209,0.9861,0.8121,0.0072,0.04,0.0345,0.3333,0.375,0.0459,0.0308,0.0414,0.0,0.0,reg oper spec account,block of flats,0.0359,Block,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n254353,0,Cash loans,F,N,Y,0,337500.0,679500.0,28786.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-20656,-7470,-4368.0,-4220,,1,1,0,1,1,0,Medicine staff,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,Medicine,0.6589813804160205,0.6693817500384173,0.5849900404894085,0.1124,0.0959,0.9791,,,0.0,0.2759,0.1667,,0.156,,0.0664,,0.2887,0.1145,0.0995,0.9791,,,0.0,0.2759,0.1667,,0.1596,,0.0692,,0.3056,0.1135,0.0959,0.9791,,,0.0,0.2759,0.1667,,0.1587,,0.0676,,0.2947,,block of flats,0.115,Panel,No,0.0,0.0,0.0,0.0,-875.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n204130,0,Cash loans,F,N,Y,0,81000.0,781920.0,28215.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-18879,-2951,-10291.0,-2437,,1,1,0,1,1,0,High skill tech staff,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Industry: type 11,0.6975604058170273,0.7081306862678526,0.31703177858344445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n232284,0,Cash loans,M,Y,Y,5,180000.0,1035832.5,33543.0,904500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.022625,-14886,-7588,-4134.0,-4841,1.0,1,1,1,1,1,0,Laborers,7.0,2,2,SATURDAY,13,0,0,0,0,0,0,Industry: type 9,,0.6025977018321407,0.7338145369642702,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n210313,0,Cash loans,F,N,Y,0,157500.0,1125000.0,47794.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-20453,-1588,-7258.0,-3996,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Other,,0.4603247397206545,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n199607,0,Cash loans,M,N,N,0,153990.0,654498.0,25056.0,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.009549,-12040,-241,-2070.0,-2706,,1,1,1,1,0,0,Low-skill Laborers,2.0,2,2,FRIDAY,10,1,1,0,1,1,0,Business Entity Type 3,,0.3694777209864797,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,,,,,,\n418774,0,Revolving loans,F,N,Y,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-7931,-1004,-4694.0,-607,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,17,0,0,0,1,1,1,Self-employed,,0.3709665092717953,0.2353105173615833,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-307.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n325637,0,Cash loans,M,Y,Y,3,220500.0,454500.0,25506.0,454500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.025164,-17228,-5343,-3057.0,-766,1.0,1,1,1,1,0,0,Drivers,5.0,2,2,THURSDAY,11,0,1,1,0,1,1,Construction,0.1967495248791895,0.5957020606811154,0.520897599048938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1912.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n444848,0,Cash loans,F,N,Y,0,112500.0,331834.5,14746.5,252000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.010276,-21319,365243,-11154.0,-4797,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.3152046284941569,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-138.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n337254,0,Cash loans,F,N,Y,0,67500.0,526491.0,16942.5,454500.0,Unaccompanied,Pensioner,Higher education,Civil marriage,Municipal apartment,0.010032,-19155,365243,-13265.0,-2696,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,0.6554826041033057,0.5739314239714868,,0.0742,0.0569,0.9801,,,0.08,0.069,0.3333,,,,0.052000000000000005,,,0.0756,0.059000000000000004,0.9801,,,0.0806,0.069,0.3333,,,,0.0541,,,0.0749,0.0569,0.9801,,,0.08,0.069,0.3333,,,,0.0529,,,,block of flats,0.0597,Panel,No,0.0,0.0,0.0,0.0,-1392.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n395872,0,Revolving loans,F,Y,N,2,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.0228,-11473,-619,-30.0,-3814,4.0,1,1,0,1,0,0,Sales staff,4.0,2,2,THURSDAY,12,0,0,0,0,0,0,Trade: type 7,0.44375955098899,0.3665786908675875,0.1206410299309233,0.1536,0.1127,1.0,1.0,0.1478,0.16,0.1379,0.4167,0.0,0.0874,0.1252,0.1508,0.0,0.0,0.1565,0.11699999999999999,1.0,1.0,0.1491,0.1611,0.1379,0.4167,0.0,0.0894,0.1368,0.1571,0.0,0.0,0.1551,0.1127,1.0,1.0,0.1487,0.16,0.1379,0.4167,0.0,0.08900000000000001,0.1274,0.1535,0.0,0.0,reg oper account,block of flats,0.2048,Panel,No,4.0,0.0,4.0,0.0,-559.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n415658,0,Cash loans,F,N,Y,0,112500.0,553806.0,21960.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-20844,-10312,-1776.0,-4053,,1,1,0,1,1,0,,2.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Industry: type 3,0.8561902683452194,0.7118363590541427,0.7801436381572275,0.0619,0.0585,0.9851,0.7959999999999999,0.0078,0.0,0.1379,0.1667,0.0417,0.0482,0.0504,0.0353,0.0,0.0951,0.063,0.0607,0.9851,0.804,0.0079,0.0,0.1379,0.1667,0.0417,0.0493,0.0551,0.0368,0.0,0.1007,0.0625,0.0585,0.9851,0.7987,0.0079,0.0,0.1379,0.1667,0.0417,0.049,0.0513,0.036000000000000004,0.0,0.0971,reg oper account,block of flats,0.0485,Panel,No,0.0,0.0,0.0,0.0,-1719.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n116940,0,Cash loans,F,N,Y,1,90000.0,270000.0,21024.0,270000.0,Children,Working,Secondary / secondary special,Widow,With parents,0.008473999999999999,-13453,-2361,-5819.0,-4930,,1,1,1,1,1,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Government,,0.5666327735621003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-453.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n348868,0,Cash loans,M,Y,N,0,126000.0,973710.0,25816.5,697500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-10878,-2388,-1046.0,-3128,27.0,1,1,1,1,0,0,Laborers,2.0,2,2,MONDAY,13,0,0,0,0,1,1,Transport: type 4,,0.6873043052688281,0.4206109640437848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n135501,1,Cash loans,M,Y,Y,2,180000.0,225000.0,15219.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-14770,-2192,-2420.0,-35,16.0,1,1,0,1,0,0,,4.0,3,3,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.485953902594354,0.4752679804852136,,0.3289,0.1579,0.9826,,,0.36,0.3103,0.3333,,0.2188,,0.3454,,0.031,0.3351,0.1638,0.9826,,,0.3625,0.3103,0.3333,,0.2238,,0.3598,,0.0328,0.3321,0.1579,0.9826,,,0.36,0.3103,0.3333,,0.2226,,0.3516,,0.0316,,block of flats,0.309,Panel,No,0.0,0.0,0.0,0.0,-815.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n201098,0,Cash loans,F,N,N,0,90000.0,178290.0,20245.5,157500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014464,-10307,-2139,-4973.0,-1057,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,3,0,0,0,0,0,0,Self-employed,0.19423047046446154,0.5597793628045108,0.5478104658520093,0.0856,,0.9871,,,0.08,0.0345,0.4583,,,,0.0848,,,0.0872,,0.9871,,,0.0806,0.0345,0.4583,,,,0.0883,,,0.0864,,0.9871,,,0.08,0.0345,0.4583,,,,0.0863,,,,block of flats,0.0916,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1862.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n285602,0,Cash loans,F,N,Y,0,157500.0,1293502.5,37948.5,1129500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.02461,-21337,365243,-6824.0,-4383,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,XNA,,0.6073271682064187,0.5814837058057234,0.0742,0.0532,0.9906,0.8708,0.0172,0.08,0.069,0.3333,0.375,0.0126,0.0605,0.0868,0.0,0.0,0.0756,0.0552,0.9906,0.8759,0.0173,0.0806,0.069,0.3333,0.375,0.0129,0.0661,0.0904,0.0,0.0,0.0749,0.0532,0.9906,0.8725,0.0173,0.08,0.069,0.3333,0.375,0.0128,0.0616,0.0883,0.0,0.0,reg oper account,block of flats,0.0682,Panel,No,0.0,0.0,0.0,0.0,-1870.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n452015,0,Cash loans,M,N,N,0,157500.0,781920.0,25411.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-11289,-2760,-5067.0,-3239,,1,1,1,1,0,0,Laborers,2.0,3,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.1937812513715612,0.5375705093231067,0.4525335592581747,0.0742,0.0825,0.9866,0.8164,0.0172,0.12,0.1034,0.3333,0.375,0.0863,0.0588,0.0933,0.0077,0.1318,0.0756,0.0856,0.9866,0.8236,0.0173,0.1208,0.1034,0.3333,0.375,0.0883,0.0643,0.0972,0.0078,0.1396,0.0749,0.0825,0.9866,0.8189,0.0173,0.12,0.1034,0.3333,0.375,0.0878,0.0599,0.095,0.0078,0.1346,reg oper account,block of flats,0.102,Panel,No,0.0,0.0,0.0,0.0,-1183.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n334340,0,Cash loans,F,N,Y,1,229500.0,1014588.0,33660.0,909000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.031329,-15320,-7046,-2559.0,-4615,,1,1,0,1,1,0,Managers,3.0,2,2,SATURDAY,14,0,0,0,0,0,0,School,0.5105887611087406,0.6045702623123299,0.3001077565791181,0.0928,0.0705,0.9816,0.7484,0.0127,0.0,0.2069,0.1667,0.2083,0.0678,0.0756,0.0772,0.0,0.0,0.0945,0.0731,0.9816,0.7583,0.0128,0.0,0.2069,0.1667,0.2083,0.0694,0.0826,0.0805,0.0,0.0,0.0937,0.0705,0.9816,0.7518,0.0128,0.0,0.2069,0.1667,0.2083,0.069,0.077,0.0786,0.0,0.0,reg oper account,block of flats,0.0608,Panel,No,4.0,0.0,4.0,0.0,-1982.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,1.0\n132047,0,Revolving loans,M,Y,Y,0,135000.0,225000.0,11250.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23233,365243,-12339.0,-4630,21.0,1,0,0,1,0,0,,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,XNA,0.7772948853507049,0.7029471420326784,0.8038850611746273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-3069.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n169242,0,Cash loans,M,N,N,1,180000.0,891000.0,31698.0,891000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.009549,-14361,-3370,-4641.0,-4692,,1,1,1,1,0,0,Laborers,3.0,2,2,WEDNESDAY,16,0,0,0,1,1,0,Industry: type 11,0.4757000622009474,0.7117825289144373,0.15855489979486306,0.0124,0.0109,0.9831,0.7688,0.0014,0.0,0.069,0.0417,0.0833,0.0137,0.0101,0.0064,0.0,0.0,0.0126,0.0113,0.9831,0.7779,0.0014,0.0,0.069,0.0417,0.0833,0.013999999999999999,0.011000000000000001,0.0067,0.0,0.0,0.0125,0.0109,0.9831,0.7719,0.0014,0.0,0.069,0.0417,0.0833,0.013999999999999999,0.0103,0.0065,0.0,0.0,reg oper spec account,block of flats,0.0088,\"Stone, brick\",No,2.0,2.0,2.0,1.0,-857.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n269544,0,Revolving loans,M,Y,Y,0,270000.0,585000.0,29250.0,585000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.022625,-13470,-904,-2534.0,-4312,1.0,1,1,0,0,0,1,Accountants,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Government,,0.6216493383517616,0.6363761710860439,0.0247,,0.9727,,,0.0,0.0345,0.2083,,0.011000000000000001,,0.0211,,0.0,0.0252,,0.9727,,,0.0,0.0345,0.2083,,0.0113,,0.022000000000000002,,0.0,0.025,,0.9727,,,0.0,0.0345,0.2083,,0.0112,,0.0215,,0.0,,block of flats,0.0219,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-621.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n340278,0,Cash loans,F,N,Y,4,112500.0,117000.0,12600.0,117000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.028663,-13022,-876,-4631.0,-4162,,1,1,0,1,0,0,Laborers,6.0,2,2,SATURDAY,12,0,0,0,0,0,0,Construction,0.6646435494244993,0.22650687312623816,0.4920600938649263,0.0227,0.0018,0.9722,,,0.0,0.069,0.0417,,,,,,0.0,0.0231,0.0018,0.9722,,,0.0,0.069,0.0417,,,,,,0.0,0.0229,0.0018,0.9722,,,0.0,0.069,0.0417,,,,,,0.0,,,0.0128,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,7.0\n241411,0,Cash loans,F,N,N,0,90000.0,225000.0,11074.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010966,-12185,-1286,-2115.0,-1905,,1,1,1,1,1,0,Sales staff,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.17207652309586705,0.31703177858344445,0.033,0.0321,0.9732,0.6328,0.0033,0.0,0.069,0.125,0.1667,0.028999999999999998,0.0269,0.0254,0.0,0.0,0.0336,0.0333,0.9732,0.6472,0.0033,0.0,0.069,0.125,0.1667,0.0297,0.0294,0.0265,0.0,0.0,0.0333,0.0321,0.9732,0.6377,0.0033,0.0,0.069,0.125,0.1667,0.0295,0.0274,0.0258,0.0,0.0,reg oper account,block of flats,0.02,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n385333,0,Cash loans,F,N,Y,0,67500.0,45000.0,4977.0,45000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.028663,-14650,-987,-7778.0,-4478,,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5706449254687117,0.4489622731076524,0.1196,0.1564,0.9826,0.762,0.0199,0.0,0.1724,0.1667,0.2083,0.0776,0.0916,0.1028,0.027000000000000003,0.0473,0.1218,0.1623,0.9826,0.7713,0.0201,0.0,0.1724,0.1667,0.2083,0.0794,0.1001,0.1071,0.0272,0.05,0.1207,0.1564,0.9826,0.7652,0.02,0.0,0.1724,0.1667,0.2083,0.079,0.0932,0.1047,0.0272,0.0483,reg oper account,block of flats,0.0911,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2481.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n267156,0,Cash loans,F,N,Y,0,72000.0,225000.0,16821.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019689,-16990,-4472,-11113.0,-516,,1,1,0,1,1,1,Sales staff,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Self-employed,0.7486750589022716,0.4984438233148203,0.5867400085415683,0.1227,0.1164,0.9767,0.6804,0.0193,0.0,0.2759,0.1667,0.2083,0.0487,0.1,0.1143,0.0116,0.0111,0.125,0.1208,0.9767,0.6929,0.0195,0.0,0.2759,0.1667,0.2083,0.0499,0.1093,0.1191,0.0117,0.0118,0.1239,0.1164,0.9767,0.6847,0.0194,0.0,0.2759,0.1667,0.2083,0.0496,0.1018,0.1163,0.0116,0.0113,org spec account,block of flats,0.1028,Panel,No,0.0,0.0,0.0,0.0,-737.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n341053,0,Cash loans,M,N,Y,2,153000.0,94230.0,5823.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-15259,-1843,-7894.0,-2774,,1,1,0,1,0,0,,4.0,1,1,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.7508356358835535,0.5726825047161584,0.1443,0.0629,0.9816,0.7484,0.0844,0.1,0.0459,0.625,0.6667,0.0,0.1165,0.1385,0.0051,0.0116,0.1166,0.0462,0.9816,0.7583,0.0687,0.0806,0.0345,0.625,0.6667,0.0,0.10099999999999999,0.1098,0.0,0.0,0.1155,0.0445,0.9816,0.7518,0.0704,0.08,0.0345,0.625,0.6667,0.0,0.0949,0.1077,0.0039,0.0004,reg oper account,block of flats,0.1682,Block,No,0.0,0.0,0.0,0.0,-2591.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n240537,0,Cash loans,F,N,Y,1,72000.0,284256.0,30289.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-18602,-1051,-4966.0,-2135,,1,1,1,1,0,0,Laborers,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Industry: type 3,,0.6250276494160637,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3005.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n399398,0,Cash loans,F,N,Y,1,202500.0,534204.0,32809.5,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.010276,-18018,-3038,-8142.0,-1570,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Government,,0.2488645682685085,,0.0649,0.0254,0.9752,0.66,0.0094,0.0,0.1262,0.1525,0.1875,0.0368,0.0538,0.053,0.0019,0.0149,0.063,0.0059,0.9752,0.6733,0.0093,0.0,0.1379,0.1667,0.1667,0.0345,0.0588,0.0528,0.0,0.0,0.0666,0.0074,0.9752,0.6645,0.0094,0.0,0.1379,0.1667,0.1875,0.0374,0.0547,0.0525,0.0019,0.0,reg oper account,block of flats,0.0404,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1506.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n336872,1,Cash loans,F,N,N,0,270000.0,634482.0,20596.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23476,365243,-1718.0,-4108,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.3658021615450904,0.20915469884100693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-205.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n117574,0,Cash loans,M,N,N,2,337500.0,900000.0,45954.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-13520,-605,-7643.0,-4695,,1,1,1,1,1,0,Laborers,4.0,1,1,WEDNESDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.8112306996193767,0.7217525879180021,0.6545292802242897,0.0866,0.0936,0.9776,,,0.08,0.0345,0.4583,,,,0.1368,,0.0,0.0882,0.0972,0.9777,,,0.0806,0.0345,0.4583,,,,0.1425,,0.0,0.0874,0.0936,0.9776,,,0.08,0.0345,0.4583,,,,0.1392,,0.0,,block of flats,0.0568,Block,No,0.0,0.0,0.0,0.0,-1817.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n266683,0,Cash loans,F,N,Y,1,450000.0,1096020.0,52857.0,900000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.020713,-16937,-4851,-2799.0,-483,,1,1,0,1,0,0,Core staff,3.0,3,2,TUESDAY,8,0,0,0,0,0,0,Police,,0.5826397640434509,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n315236,0,Cash loans,M,N,N,2,144000.0,1053000.0,32053.5,1053000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018209,-13551,-132,-7660.0,-4566,,1,1,0,1,1,0,High skill tech staff,4.0,3,3,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4556678468921287,0.6529235697947089,0.2778856891082046,0.0825,0.0646,0.9771,,,0.0,0.1379,0.1667,,0.0655,,0.0702,,0.0,0.084,0.0671,0.9772,,,0.0,0.1379,0.1667,,0.067,,0.0732,,0.0,0.0833,0.0646,0.9771,,,0.0,0.1379,0.1667,,0.0667,,0.0715,,0.0,,block of flats,0.0699,Panel,No,4.0,0.0,4.0,0.0,-20.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n398568,0,Cash loans,F,N,Y,0,315000.0,1314117.0,38551.5,1147500.0,Family,Commercial associate,Higher education,Married,Rented apartment,0.0105,-18470,-957,-8427.0,-2014,,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,10,0,1,1,0,0,0,Construction,,0.17968906476919208,0.5190973382084597,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-537.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n275020,1,Cash loans,F,Y,N,0,135000.0,1046142.0,40536.0,913500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-21610,365243,-7308.0,-4374,13.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.30490454095950426,0.2750003523983893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-896.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n392264,0,Cash loans,M,N,Y,0,112500.0,1125000.0,60070.5,1125000.0,Unaccompanied,Working,Academic degree,Married,House / apartment,0.006296,-18019,-4843,-6864.0,-1539,,1,1,0,1,0,0,Laborers,2.0,3,3,MONDAY,12,0,0,0,0,1,1,Industry: type 3,,0.615716722065435,0.6246146584503397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-562.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n174078,0,Cash loans,F,Y,Y,2,441000.0,1860813.0,76923.0,1737000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-13756,-6388,-201.0,-5209,4.0,1,1,0,1,0,0,Accountants,4.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Transport: type 4,0.3375539815642446,0.5690743960102874,0.8327850252992314,0.033,0.1025,0.9747,0.6532,0.0,0.0,0.069,0.125,0.1667,0.0205,0.0219,0.0211,0.0193,0.0185,0.0336,0.1064,0.9747,0.6668,0.0,0.0,0.069,0.125,0.1667,0.021,0.0239,0.022000000000000002,0.0195,0.0196,0.0333,0.1025,0.9747,0.6578,0.0,0.0,0.069,0.125,0.1667,0.0208,0.0222,0.0215,0.0194,0.0189,reg oper account,block of flats,0.0206,Block,No,6.0,0.0,6.0,0.0,-734.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n292729,0,Cash loans,F,N,N,1,135000.0,450000.0,11871.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018029,-15086,-3229,-9162.0,-3933,,1,1,1,1,1,0,Managers,3.0,3,3,WEDNESDAY,12,0,0,0,1,0,1,Postal,,0.3797387416997311,0.520897599048938,0.0608,0.0732,0.9801,,,0.0,0.1034,0.1667,,,,0.0556,,0.0,0.062,0.076,0.9801,,,0.0,0.1034,0.1667,,,,0.0579,,0.0,0.0614,0.0732,0.9801,,,0.0,0.1034,0.1667,,,,0.0566,,0.0,,block of flats,0.0437,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-23.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n201661,0,Cash loans,M,Y,N,1,180000.0,277969.5,13086.0,229500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.003818,-9896,-185,-4580.0,-2563,11.0,1,1,0,1,0,0,Drivers,3.0,2,2,FRIDAY,7,0,0,0,0,1,1,Business Entity Type 3,,0.4627322318604872,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n302490,0,Cash loans,F,N,Y,0,100350.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-16772,-3453,-9832.0,-327,,1,1,0,1,1,0,Accountants,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,,0.6011619646820522,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1268.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n171092,0,Revolving loans,F,N,Y,0,72000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.035792000000000004,-8777,-2141,-1435.0,-1457,,1,1,0,1,1,0,Waiters/barmen staff,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Restaurant,,0.17764497517020056,0.5832379256761245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n294507,0,Revolving loans,M,N,N,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.018801,-8464,-1667,-7316.0,-1136,,1,1,0,1,0,0,Low-skill Laborers,1.0,2,2,FRIDAY,8,0,0,0,0,0,0,Construction,,0.608692654889135,0.25670557243930026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-876.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n305388,0,Cash loans,M,Y,Y,0,225000.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.006629,-15221,-3953,-9186.0,-4505,16.0,1,1,0,1,0,0,Drivers,1.0,2,2,SATURDAY,7,0,0,0,0,0,0,Construction,,0.18922047915959064,0.30620229831350426,,,0.9727,,,,,,,,,,,,,,0.9717,,,,,,,,,,,,,,0.9727,,,,,,,,,,,,,,0.0082,,No,0.0,0.0,0.0,0.0,-1069.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n427698,0,Cash loans,F,N,Y,0,90000.0,900000.0,26316.0,900000.0,Family,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.005084,-18152,-697,-9564.0,-1683,,1,1,1,1,1,0,Cleaning staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,School,,0.5735142765663288,0.5334816299804352,0.0928,0.1066,0.9836,,,0.0,0.2069,0.1667,,,,0.0926,,0.0092,0.0945,0.1107,0.9836,,,0.0,0.2069,0.1667,,,,0.0965,,0.0098,0.0937,0.1066,0.9836,,,0.0,0.2069,0.1667,,,,0.0943,,0.0094,,block of flats,0.0748,Panel,No,0.0,0.0,0.0,0.0,-1553.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n252010,0,Cash loans,F,N,Y,0,135000.0,672453.0,21982.5,580500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.011657,-21983,-2359,-9717.0,-4498,,1,1,0,1,0,0,Cleaning staff,1.0,1,1,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 1,,0.6906126731831149,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1098.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n185626,0,Cash loans,F,N,Y,0,45000.0,545040.0,19575.0,450000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.019101,-23410,365243,-12383.0,-3075,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.6146036912043304,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1004.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n409566,0,Cash loans,M,Y,N,0,81000.0,555273.0,17910.0,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-10761,-174,-415.0,-3432,7.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.20701877800953009,0.5638350489514956,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n203494,0,Cash loans,F,N,Y,0,157500.0,284400.0,16456.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.003069,-22409,365243,-8719.0,-1086,,1,0,0,1,0,0,,1.0,3,3,SATURDAY,11,0,0,0,0,0,0,XNA,,0.6188834740281831,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,1.0,0.0,-765.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n380888,0,Cash loans,F,N,Y,0,450000.0,254700.0,17149.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-24258,-5823,-14002.0,-4335,,1,1,0,1,0,0,Managers,2.0,1,1,WEDNESDAY,18,0,0,0,0,0,0,Business Entity Type 1,,0.743345107781633,0.4596904504249018,0.0784,,0.9806,,,0.16,0.069,0.375,,0.0656,,0.0476,,,0.0798,,0.9806,,,0.1611,0.069,0.375,,0.0671,,0.0495,,,0.0791,,0.9806,,,0.16,0.069,0.375,,0.0667,,0.0484,,,,block of flats,0.068,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-961.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n254963,0,Cash loans,F,N,N,0,175500.0,700830.0,20619.0,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.00702,-15947,-2791,-3922.0,-4023,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,0.5004845727187647,0.581208650279924,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1568.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n290853,0,Cash loans,M,N,Y,0,112500.0,339948.0,27387.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-9387,-940,-1776.0,-2074,,1,1,0,1,0,0,Laborers,2.0,3,3,THURSDAY,12,0,0,0,1,1,0,Business Entity Type 3,,0.4943821512823873,0.35233997269170386,0.0804,0.0808,0.9851,,,0.0,0.2069,0.1667,,0.0557,,0.0639,,0.0,0.0819,0.0838,0.9851,,,0.0,0.2069,0.1667,,0.057,,0.0666,,0.0,0.0812,0.0808,0.9851,,,0.0,0.2069,0.1667,,0.0567,,0.0651,,0.0,,block of flats,0.0573,\"Stone, brick\",No,2.0,0.0,1.0,0.0,-320.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n191390,0,Cash loans,M,Y,Y,0,135000.0,545040.0,35617.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-13711,-135,-2673.0,-5993,4.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Transport: type 4,0.17463647050313566,0.4323777797187303,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1337.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n233443,0,Cash loans,F,N,Y,3,128250.0,270000.0,13261.5,270000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.016612000000000002,-12855,-677,-1603.0,-4320,,1,1,1,1,0,0,Security staff,5.0,2,2,WEDNESDAY,11,0,0,0,0,1,1,Security,,0.4653427605977337,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-134.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n209026,0,Cash loans,F,N,Y,1,81000.0,207396.0,11380.5,157500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.011703,-9322,-1291,-4742.0,-1972,,1,1,0,1,0,1,,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3120504910159994,0.7141096686341447,0.048925866762844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-559.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n172587,0,Cash loans,M,N,N,0,27000.0,229230.0,13158.0,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,Municipal apartment,0.007114,-24728,365243,-1764.0,-570,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.11002462339461616,,0.0871,0.0944,0.9816,0.7484,0.0096,0.0,0.1724,0.1667,0.2083,0.0398,0.0706,0.0794,0.0019,0.0039,0.0725,0.0808,0.9816,0.7583,0.0079,0.0,0.1379,0.1667,0.2083,0.0407,0.0624,0.0671,0.0,0.0,0.08800000000000001,0.0944,0.9816,0.7518,0.0096,0.0,0.1724,0.1667,0.2083,0.0405,0.0718,0.0808,0.0019,0.004,,block of flats,0.0566,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n422956,1,Cash loans,F,Y,Y,1,153000.0,426645.0,23274.0,324000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-13016,-505,-4913.0,-5271,30.0,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,7,0,0,0,0,0,0,Other,,0.3090962623270829,0.17044612304522816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n329109,0,Cash loans,M,N,Y,1,157500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-14741,-907,-8683.0,-4859,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.4853930292019626,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-690.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n213022,0,Cash loans,F,N,Y,0,202500.0,262246.5,31252.5,243000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-14442,-1169,-4396.0,-5094,,1,1,0,1,1,1,Managers,2.0,1,1,WEDNESDAY,19,0,0,0,0,0,0,Business Entity Type 3,,0.6973140492678999,0.3556387169923543,0.1,0.0315,0.9866,0.8164,,0.08,0.0345,0.625,0.6667,0.0245,0.0925,0.1088,0.0,0.0,0.1019,0.0327,0.9866,0.8236,,0.0806,0.0345,0.625,0.6667,0.0251,0.10099999999999999,0.1134,0.0,0.0,0.10099999999999999,0.0315,0.9866,0.8189,,0.08,0.0345,0.625,0.6667,0.025,0.0941,0.1108,0.0,0.0,reg oper account,block of flats,0.0863,\"Stone, brick\",No,6.0,1.0,6.0,0.0,-378.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n314869,0,Cash loans,F,N,Y,0,99000.0,76752.0,7722.0,72000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-18976,-3730,-10750.0,-2523,,1,1,1,1,0,0,Sales staff,2.0,3,3,THURSDAY,7,0,0,0,0,0,0,Self-employed,,0.6033986429503106,0.3506958432829587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1864.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n213799,1,Cash loans,M,N,N,1,90000.0,256500.0,23656.5,256500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-13981,-733,-180.0,-4639,,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.29167812092551443,0.1245194708919845,0.0381,0.0745,0.9836,,,,1.0,0.0417,,,,0.0248,,,0.0389,0.0773,0.9836,,,,1.0,0.0417,,,,0.0259,,,0.0385,0.0745,0.9836,,,,1.0,0.0417,,,,0.0253,,,,block of flats,0.0195,\"Stone, brick\",No,3.0,1.0,3.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n190505,0,Cash loans,F,N,Y,0,112500.0,269550.0,20988.0,225000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.032561,-8168,-150,-8141.0,-850,,1,1,0,1,0,0,Laborers,2.0,1,1,MONDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.2958413888834401,0.1886273868274379,0.6832688314232291,0.1845,0.1027,0.9811,,,0.2,0.1724,0.3333,,,,0.1202,,0.0009,0.188,0.1066,0.9796,,,0.2014,0.1724,0.3333,,,,0.1118,,0.001,0.1863,0.1027,0.9811,,,0.2,0.1724,0.3333,,,,0.1223,,0.001,,block of flats,0.0961,,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n455303,0,Cash loans,M,N,Y,1,144000.0,358443.0,16839.0,252000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-12031,-243,-5935.0,-2625,,1,1,0,1,0,1,Low-skill Laborers,3.0,2,2,MONDAY,16,0,0,0,0,0,0,Trade: type 7,,0.26525634018619443,0.5971924268337128,0.2031,0.2074,0.9796,0.7212,0.1049,0.0,0.4828,0.1667,0.2083,0.0,0.1513,0.1796,0.0656,0.0853,0.2069,0.2152,0.9796,0.7321,0.1058,0.0,0.4828,0.1667,0.2083,0.0,0.1653,0.1872,0.0661,0.0903,0.2051,0.2074,0.9796,0.7249,0.1056,0.0,0.4828,0.1667,0.2083,0.0,0.1539,0.1829,0.066,0.0871,reg oper account,block of flats,0.2172,Panel,No,0.0,0.0,0.0,0.0,-799.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n134562,0,Cash loans,F,N,Y,0,225000.0,225000.0,12915.0,225000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.0105,-24060,365243,-3103.0,-4518,,1,0,0,1,0,0,,1.0,3,3,FRIDAY,9,0,0,0,0,0,0,XNA,,0.19220738303280588,0.24318648044201235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-266.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n241223,0,Cash loans,F,N,Y,0,112500.0,315000.0,33075.0,315000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006670999999999999,-21391,-9372,-11160.0,-4870,,1,1,1,1,0,0,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Kindergarten,,0.5743805419798349,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-190.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n224698,0,Cash loans,M,N,Y,2,112500.0,296280.0,21690.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-13795,-3677,-3907.0,-4586,,1,1,0,1,0,0,High skill tech staff,4.0,2,2,SUNDAY,13,0,0,0,0,0,0,Self-employed,,0.5961180542236658,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n433572,1,Cash loans,M,Y,N,0,135000.0,337761.0,17374.5,256500.0,Other_B,Working,Higher education,Single / not married,With parents,0.0228,-10788,-1398,-5206.0,-3470,13.0,1,1,0,1,1,0,High skill tech staff,1.0,2,2,SUNDAY,8,0,1,1,0,1,1,Business Entity Type 2,0.20622993496803804,0.428132272970562,0.4686596550493113,0.0711,0.0733,0.9846,0.7892,0.0123,0.0,0.2069,0.1667,0.2083,0.07200000000000001,0.057999999999999996,0.0716,0.0,0.0,0.0725,0.076,0.9846,0.7975,0.0124,0.0,0.2069,0.1667,0.2083,0.0736,0.0634,0.0746,0.0,0.0,0.0718,0.0733,0.9846,0.792,0.0123,0.0,0.2069,0.1667,0.2083,0.0732,0.059000000000000004,0.0729,0.0,0.0,reg oper account,block of flats,0.063,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n177808,0,Cash loans,M,Y,Y,2,166500.0,450000.0,22018.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.0228,-14732,-660,-1582.0,-4920,7.0,1,1,0,1,1,0,Security staff,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Security,0.457106496142481,0.6468484492579611,,0.1113,0.0678,0.9851,0.7959999999999999,,0.08,0.1034,0.3333,0.375,0.0352,,0.1175,,0.0,0.1134,0.0704,0.9851,0.804,,0.0806,0.1034,0.3333,0.375,0.036000000000000004,,0.1225,,0.0,0.1124,0.0678,0.9851,0.7987,,0.08,0.1034,0.3333,0.375,0.0358,,0.1197,,0.0,reg oper account,block of flats,0.1171,Panel,No,1.0,1.0,1.0,1.0,-496.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n201545,0,Cash loans,M,Y,Y,0,126000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-19633,-6626,-2874.0,-3196,4.0,1,1,0,1,0,0,Managers,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Self-employed,0.8785510650776833,0.6390323918125206,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-714.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n319770,1,Cash loans,F,Y,Y,1,90000.0,544068.0,26590.5,382500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-11562,-486,-11057.0,-2252,15.0,1,1,0,1,0,1,Sales staff,3.0,2,2,SUNDAY,12,0,0,0,0,0,0,Trade: type 7,0.1054786313523592,0.6110599654178636,,0.0619,0.0611,0.9851,0.7959999999999999,0.0286,0.0,0.1379,0.1667,0.2083,0.059000000000000004,0.0504,0.0525,0.0,0.0,0.063,0.0634,0.9851,0.804,0.0289,0.0,0.1379,0.1667,0.2083,0.0603,0.0551,0.0547,0.0,0.0,0.0625,0.0611,0.9851,0.7987,0.0288,0.0,0.1379,0.1667,0.2083,0.06,0.0513,0.0534,0.0,0.0,reg oper account,block of flats,0.0569,Panel,No,3.0,1.0,3.0,0.0,-677.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,9.0\n379147,0,Cash loans,F,N,Y,0,135000.0,136512.0,7618.5,108000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.006008,-19174,-5679,-9213.0,-2571,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Self-employed,,0.6229445750745948,0.4614823912998385,0.0825,0.0788,0.9767,0.6804,0.0079,0.0,0.1379,0.1667,0.2083,0.07,0.0672,0.0702,0.0,0.0,0.084,0.0818,0.9767,0.6929,0.008,0.0,0.1379,0.1667,0.2083,0.0715,0.0735,0.0731,0.0,0.0,0.0833,0.0788,0.9767,0.6847,0.008,0.0,0.1379,0.1667,0.2083,0.0712,0.0684,0.0714,0.0,0.0,reg oper account,block of flats,0.0595,Panel,No,0.0,0.0,0.0,0.0,-1479.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n282131,0,Cash loans,F,N,Y,1,229500.0,805072.5,29047.5,679500.0,Children,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.009334,-15532,-3069,-613.0,-6163,,1,1,0,1,0,1,Accountants,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,0.8719813314302746,0.7615741592042597,0.4329616670974407,0.0912,0.0683,0.9791,0.7144,0.0074,0.12,0.1034,0.3542,0.0208,0.0236,0.0735,0.1315,0.0039,0.0205,0.0336,0.0289,0.9782,0.7125,0.0,0.0806,0.069,0.3333,0.0,0.019,0.0294,0.122,0.0,0.0042,0.0921,0.0683,0.9791,0.7182,0.0074,0.12,0.1034,0.3542,0.0208,0.024,0.0748,0.1339,0.0039,0.021,reg oper spec account,block of flats,0.1082,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1626.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n424537,0,Cash loans,F,N,N,0,135000.0,755190.0,30078.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.010556,-21242,365243,-10356.0,-4397,,1,0,0,1,0,0,,1.0,3,3,SATURDAY,10,0,0,0,0,0,0,XNA,,0.4640318112639875,0.622922000268356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1234.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n354361,0,Cash loans,F,N,Y,0,94500.0,675000.0,24376.5,675000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.028663,-19279,-7210,-257.0,-2529,,1,1,0,1,0,0,Security staff,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,University,,0.646113789168058,0.20915469884100693,0.0722,0.0678,0.9821,0.7552,0.0078,0.0,0.1379,0.1667,0.2083,0.0365,0.0588,0.0532,0.0,0.0,0.0735,0.0704,0.9821,0.7648,0.0078,0.0,0.1379,0.1667,0.2083,0.0373,0.0643,0.0555,0.0,0.0,0.0729,0.0678,0.9821,0.7585,0.0078,0.0,0.1379,0.1667,0.2083,0.0371,0.0599,0.0542,0.0,0.0,reg oper account,block of flats,0.0419,Block,No,9.0,3.0,9.0,2.0,-547.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n224820,0,Cash loans,M,N,Y,2,202500.0,113760.0,9117.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-14641,-183,-439.0,-2269,,1,1,0,1,0,0,Cooking staff,4.0,2,2,TUESDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.21664675188212987,0.3274497829251655,0.3672910183026313,0.0155,,0.9786,,,,0.069,0.0417,,0.0143,,0.0124,,,0.0158,,0.9786,,,,0.069,0.0417,,0.0146,,0.013000000000000001,,,0.0156,,0.9786,,,,0.069,0.0417,,0.0145,,0.0127,,,,,0.0098,\"Stone, brick\",No,3.0,0.0,2.0,0.0,-389.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n299764,0,Cash loans,F,N,Y,0,112500.0,675000.0,21775.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-18324,-438,-101.0,-1816,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.7966326843661624,0.7541021801311991,0.5531646987710016,0.0928,,0.9806,,,0.0,0.2069,0.1667,,0.1843,,0.0882,,0.0,0.0945,,0.9806,,,0.0,0.2069,0.1667,,0.1885,,0.0919,,0.0,0.0937,,0.9806,,,0.0,0.2069,0.1667,,0.1875,,0.0898,,0.0,,block of flats,0.0694,Panel,No,2.0,1.0,2.0,1.0,-3103.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n417722,0,Cash loans,F,N,Y,2,112500.0,720000.0,36891.0,720000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-10366,-1886,-413.0,-3054,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,17,0,0,0,0,0,0,Industry: type 9,0.7248140661418493,0.5353899574568186,0.2636468134452008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,5.0,1.0,5.0,1.0,-1703.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n224052,0,Cash loans,F,Y,Y,1,135000.0,640080.0,29970.0,450000.0,Family,State servant,Higher education,Married,House / apartment,0.025164,-10032,-1212,-362.0,-2604,4.0,1,1,0,1,0,0,,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Legal Services,0.4925870124735102,0.6035713224625721,,0.1768,0.1133,0.9921,0.8912,0.0908,0.18,0.1897,0.25,0.2917,0.0,0.1412,0.1876,0.0135,0.0257,0.0242,0.0683,0.9896,0.8628,0.0072,0.0,0.069,0.125,0.1667,0.0,0.0184,0.0475,0.0117,0.0151,0.1785,0.1133,0.9921,0.8927,0.0914,0.18,0.1897,0.25,0.2917,0.0,0.1437,0.191,0.0136,0.0263,reg oper account,block of flats,0.3578,Panel,No,0.0,0.0,0.0,0.0,-549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n178319,0,Cash loans,M,N,N,0,81000.0,102384.0,5107.5,81000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.009657,-15202,-1770,-435.0,-4469,,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.2843214931584229,0.32193580764623714,0.4135967602644276,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,3.0,4.0,2.0,-159.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n282610,0,Cash loans,F,N,Y,0,157500.0,1104997.5,36648.0,990000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-23793,365243,-2116.0,-3914,,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.6784587850475126,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2336.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n403191,0,Cash loans,F,N,Y,0,72000.0,331834.5,14746.5,252000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007305,-19312,-2114,-1405.0,-628,,1,1,1,1,1,0,Sales staff,2.0,3,3,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.3677219047478381,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n239637,0,Cash loans,F,N,N,0,135000.0,628114.5,20542.5,477000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.006852,-15730,-4660,-9808.0,-4681,,1,1,0,1,0,0,,1.0,3,3,FRIDAY,14,0,0,0,0,0,0,Telecom,,0.3908578826565884,0.5334816299804352,0.0562,0.055999999999999994,0.9816,0.7756,0.001,0.0,0.1034,0.1042,0.0,0.0156,0.0849,0.0542,0.0,0.0066,0.0084,0.0,0.9796,0.7844,0.001,0.0,0.0345,0.0417,0.0,0.0044,0.0927,0.0077,0.0,0.0026,0.0567,0.055999999999999994,0.9816,0.7786,0.001,0.0,0.1034,0.1042,0.0,0.0159,0.0864,0.0551,0.0,0.0067,reg oper spec account,block of flats,0.0064,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n452446,0,Cash loans,M,Y,N,0,157500.0,781920.0,47965.5,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-19474,-1584,-11307.0,-3019,3.0,1,1,0,1,0,0,Drivers,2.0,1,1,THURSDAY,11,0,0,0,0,0,0,Transport: type 4,,0.7145291394553633,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1520.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n377798,0,Revolving loans,M,Y,Y,1,247500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011703,-13261,-243,-7240.0,-3996,13.0,1,1,0,1,1,0,Laborers,3.0,2,2,THURSDAY,18,0,0,0,1,1,1,Other,0.3255556748781361,0.6749616719715295,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1308.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n218429,0,Cash loans,F,Y,Y,0,99000.0,675000.0,20466.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006008,-22198,365243,-4164.0,-4534,1.0,1,0,0,1,1,0,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.4191889485518691,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n327662,0,Cash loans,F,N,Y,0,157500.0,1170000.0,36868.5,1170000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.019689,-18441,-11737,-11505.0,-1987,,1,1,0,1,1,0,Laborers,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Industry: type 5,0.6195739298743954,0.5797974793322076,0.3556387169923543,0.133,,0.9811,0.7416,0.0177,0.0,0.2759,0.1667,0.2083,0.0936,0.1025,0.1071,0.027000000000000003,0.0435,0.1355,,0.9811,0.7517,0.0179,0.0,0.2759,0.1667,0.2083,0.0957,0.11199999999999999,0.1116,0.0272,0.046,0.1343,,0.9811,0.7451,0.0178,0.0,0.2759,0.1667,0.2083,0.0952,0.1043,0.109,0.0272,0.0444,reg oper account,block of flats,0.109,Block,No,0.0,0.0,0.0,0.0,-966.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n168819,0,Cash loans,M,Y,Y,2,157500.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13981,-702,-2957.0,-4490,15.0,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,,0.6935996369492246,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1596.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n257772,0,Cash loans,M,Y,Y,0,202500.0,315000.0,33075.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-15413,-6628,-8179.0,-4587,4.0,1,1,0,1,0,0,Laborers,2.0,1,1,FRIDAY,19,0,0,0,0,1,1,Business Entity Type 3,0.5888736136207745,0.6115016338997369,0.633031641417419,0.0701,,0.9801,,,0.0,0.1379,0.1667,,0.0,,0.0645,,0.0115,0.0714,,0.9801,,,0.0,0.1379,0.1667,,0.0,,0.0672,,0.0121,0.0708,,0.9801,,,0.0,0.1379,0.1667,,0.0,,0.0656,,0.0117,,block of flats,0.0507,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1125.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n315100,0,Cash loans,F,N,Y,0,225000.0,1494486.0,41224.5,1305000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.032561,-21137,365243,-6782.0,-4627,,1,0,0,1,0,0,,2.0,1,1,MONDAY,11,0,0,0,0,0,0,XNA,0.7776542719564817,0.6663257754620836,0.20442262537632874,0.0464,,0.9712,,,,0.1724,0.125,,,,0.0637,,0.0161,0.0473,,0.9712,,,,0.1724,0.125,,,,0.0663,,0.0171,0.0468,,0.9712,,,,0.1724,0.125,,,,0.0648,,0.0165,,,0.0597,\"Stone, brick\",No,5.0,2.0,5.0,0.0,-830.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n289189,0,Revolving loans,F,N,Y,0,90000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-7866,-925,-406.0,-540,,1,1,1,1,0,0,Secretaries,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Business Entity Type 2,0.2504204723290335,0.15268580946625526,,0.0088,0.0,0.9657,,,0.0,0.0345,0.0208,,0.0,,0.0045,,0.0078,0.0042,0.0,0.9618,,,0.0,0.0345,0.0,,0.0,,0.0031,,0.0083,0.0088,0.0,0.9657,,,0.0,0.0345,0.0208,,0.0,,0.0046,,0.008,,terraced house,0.0023,Wooden,No,2.0,0.0,2.0,0.0,-469.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n195108,0,Revolving loans,F,Y,Y,0,135000.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-17564,-9149,-3010.0,-1106,3.0,1,1,1,1,1,0,High skill tech staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Postal,0.8240159646087105,0.5359121111981101,,0.1289,,0.9836,,,0.08,0.069,0.3333,0.375,,,,,,0.1313,,0.9836,,,0.0806,0.069,0.3333,0.375,,,,,,0.1301,,0.9836,,,0.08,0.069,0.3333,0.375,,,,,,reg oper account,block of flats,0.1072,Panel,No,4.0,0.0,4.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n326937,0,Cash loans,F,N,Y,2,135000.0,405000.0,19008.0,405000.0,Family,Working,Higher education,Married,House / apartment,0.026392,-11009,-3697,-4896.0,-2594,,1,1,0,1,0,0,Core staff,4.0,2,2,MONDAY,10,0,0,0,0,0,0,Transport: type 2,,0.6582461741649085,0.28812959991785075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-311.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n441544,0,Cash loans,M,N,Y,0,135000.0,770292.0,30676.5,688500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.02461,-16806,-1159,-9072.0,-294,,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 2,0.6009132634532061,0.7184911907489413,0.7503751495159068,0.134,0.1574,0.9821,0.7552,0.0155,0.0,0.2759,0.1667,0.2083,0.0,0.1067,0.1211,0.0116,0.0078,0.1366,0.1634,0.9821,0.7648,0.0156,0.0,0.2759,0.1667,0.2083,0.0,0.1166,0.1262,0.0117,0.0083,0.1353,0.1574,0.9821,0.7585,0.0156,0.0,0.2759,0.1667,0.2083,0.0,0.1086,0.1233,0.0116,0.008,reg oper account,block of flats,0.09699999999999999,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2847.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n315411,1,Cash loans,F,Y,Y,1,396000.0,1125000.0,36423.0,1125000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.026392,-11484,-1572,-278.0,-3815,7.0,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Other,0.2734483677365161,0.6166786470256079,0.2580842039460289,0.1062,0.1411,0.9866,0.8164,0.0154,0.0,0.2414,0.1667,0.2083,0.032,0.0866,0.0892,0.0,0.0133,0.1082,0.1464,0.9866,0.8236,0.0156,0.0,0.2414,0.1667,0.2083,0.0328,0.0946,0.0929,0.0,0.013999999999999999,0.1072,0.1411,0.9866,0.8189,0.0155,0.0,0.2414,0.1667,0.2083,0.0326,0.0881,0.0908,0.0,0.0135,reg oper account,block of flats,0.073,\"Stone, brick\",No,1.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,4.0\n110049,0,Cash loans,F,N,Y,0,202500.0,259794.0,27409.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-19002,-4951,-13047.0,-2485,,1,1,1,1,1,0,,2.0,1,1,MONDAY,11,0,0,0,0,0,0,Culture,0.7647438866459155,0.6197908366938767,,0.2381,0.0,0.9747,0.6532,0.0388,0.16,0.1379,0.3333,0.375,0.0133,0.1942,0.218,0.0,0.0,0.2426,0.0,0.9747,0.6668,0.0392,0.1611,0.1379,0.3333,0.375,0.0136,0.2121,0.2271,0.0,0.0,0.2405,0.0,0.9747,0.6578,0.039,0.16,0.1379,0.3333,0.375,0.0135,0.1975,0.2219,0.0,0.0,reg oper account,block of flats,0.1927,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-637.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n242635,0,Cash loans,F,Y,Y,0,112500.0,178290.0,12973.5,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-20712,365243,-9430.0,-391,17.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.5674058626516054,0.13259749523614614,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-350.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n132470,0,Cash loans,F,N,Y,0,103500.0,526491.0,19039.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-22722,365243,-1392.0,-5248,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,0.7686854057623533,0.2595259862682829,,0.1474,0.075,0.9776,0.6940000000000001,0.0443,0.16,0.1379,0.3333,0.375,0.0,0.1202,0.1432,0.0,0.0,0.1502,0.0779,0.9777,0.706,0.0447,0.1611,0.1379,0.3333,0.375,0.0,0.1313,0.1492,0.0,0.0,0.1489,0.075,0.9776,0.6981,0.0446,0.16,0.1379,0.3333,0.375,0.0,0.1223,0.1458,0.0,0.0,not specified,block of flats,0.1369,Panel,No,0.0,0.0,0.0,0.0,-728.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n237697,0,Cash loans,F,N,Y,0,99000.0,339948.0,24174.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-17132,-3251,-5835.0,-685,,1,1,1,1,0,0,Laborers,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 2,,0.7429068066004871,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-766.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n337329,0,Cash loans,F,N,Y,0,121500.0,630000.0,18549.0,630000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-23415,365243,-4501.0,-4627,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.27059619192277024,0.5744466170995097,0.1227,,0.9821,,,,0.2759,0.1667,,0.0992,,0.1148,,,0.125,,0.9821,,,,0.2759,0.1667,,0.1014,,0.1196,,,0.1239,,0.9821,,,,0.2759,0.1667,,0.1009,,0.1168,,,,block of flats,0.0903,Panel,No,0.0,0.0,0.0,0.0,-1581.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n204915,0,Cash loans,F,N,Y,2,202500.0,623749.5,31977.0,504000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.011657,-14285,-1998,-85.0,-392,,1,1,0,1,0,0,Cooking staff,4.0,1,1,FRIDAY,10,0,0,0,0,0,0,Government,0.3296387954197057,0.5779210029403269,0.6817058776720116,0.1474,0.1043,0.9816,0.7484,0.0159,0.16,0.1379,0.3333,0.375,0.1089,0.1202,0.1661,0.0,0.0,0.1502,0.1082,0.9816,0.7583,0.0161,0.1611,0.1379,0.3333,0.375,0.1113,0.1313,0.1731,0.0,0.0,0.1489,0.1043,0.9816,0.7518,0.016,0.16,0.1379,0.3333,0.375,0.1107,0.1223,0.1691,0.0,0.0,reg oper account,block of flats,0.1307,Panel,No,0.0,0.0,0.0,0.0,-1700.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n331570,0,Cash loans,F,N,N,0,130500.0,900000.0,32017.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-10380,-1696,-1062.0,-2611,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,0.5685605967402555,0.5338502445161224,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n162536,0,Cash loans,M,Y,N,0,157500.0,497520.0,39438.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15074,-2393,-9107.0,-4763,24.0,1,1,0,1,0,1,Laborers,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Business Entity Type 2,0.4009336101257399,0.6794311845600058,0.7435593141311444,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n438732,0,Cash loans,M,N,N,0,157500.0,675000.0,17806.5,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-12466,-256,-6018.0,-3857,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Other,0.4024738487913523,0.5391031094398469,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0.0,0.0,0.0,0.0,0.0,0.0\n160796,0,Cash loans,F,N,Y,0,166500.0,528633.0,24624.0,472500.0,Unaccompanied,Commercial associate,Lower secondary,Civil marriage,House / apartment,0.072508,-10131,-131,-4550.0,-189,,1,1,1,1,0,0,Core staff,2.0,1,1,MONDAY,11,0,0,0,0,0,0,Bank,,0.5058566608450309,0.2721336844147212,0.1546,0.2027,0.9707,0.5988,0.0605,0.2,0.1724,0.25,0.2917,0.0,0.1261,0.1878,0.0,0.2311,0.1576,0.2103,0.9707,0.6145,0.061,0.2014,0.1724,0.25,0.2917,0.0,0.1377,0.1957,0.0,0.2446,0.1561,0.2027,0.9707,0.6042,0.0609,0.2,0.1724,0.25,0.2917,0.0,0.1283,0.1912,0.0,0.2359,reg oper account,block of flats,0.198,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n112910,0,Cash loans,M,N,N,1,315000.0,450000.0,20979.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-12716,-893,-6754.0,-2257,,1,1,1,1,1,1,Drivers,3.0,2,2,FRIDAY,15,1,1,1,1,1,1,Construction,0.3754852750564417,0.7443034850691039,0.33125086459090186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-636.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n154358,0,Cash loans,F,N,N,4,202500.0,302206.5,13311.0,229500.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.008068,-12925,-2118,-3664.0,-4372,,1,1,1,1,0,0,,6.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Trade: type 7,0.2466022088510428,0.0936864268295058,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-15.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n112168,0,Cash loans,M,N,N,0,135000.0,119925.0,11857.5,112500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-13708,-5309,-5547.0,-4387,,1,1,1,1,1,0,Laborers,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.4514921495304768,0.6566801226635451,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2222.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n211601,0,Cash loans,F,Y,Y,2,225000.0,1303812.0,35982.0,1138500.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.035792000000000004,-13453,-1420,-2167.0,-1719,4.0,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,18,0,0,0,0,1,1,Business Entity Type 3,,0.7219765028181887,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1677.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n449278,0,Cash loans,F,N,Y,3,112500.0,101880.0,12217.5,90000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003069,-11957,-2003,-5958.0,-4165,,1,1,0,1,1,0,Sales staff,5.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Trade: type 7,,0.19587680987012893,0.5262949398096192,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1183.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n340491,0,Cash loans,F,N,Y,1,112500.0,270000.0,11439.0,270000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.020713,-16896,-9879,-8694.0,-438,,1,1,1,1,1,0,Core staff,3.0,3,3,MONDAY,5,0,0,0,0,0,0,School,0.7378624887258278,0.4547562126249663,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1875.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n275673,0,Revolving loans,F,N,Y,0,67500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-12503,-2901,-10184.0,-4069,,1,1,1,1,1,0,Laborers,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,0.3270713816555903,0.5921526951838642,0.3791004853998145,0.0928,,0.9861,,,0.0,0.2069,0.1667,,0.0176,,0.06,,0.0044,0.0945,,0.9861,,,0.0,0.2069,0.1667,,0.018000000000000002,,0.0625,,0.0047,0.0937,,0.9861,,,0.0,0.2069,0.1667,,0.0179,,0.0611,,0.0045,,block of flats,0.0676,Panel,No,1.0,0.0,1.0,0.0,-1051.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n177160,0,Cash loans,M,Y,Y,1,211500.0,497520.0,56133.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-8286,-1398,-3033.0,-972,7.0,1,1,1,1,1,0,Core staff,3.0,2,2,WEDNESDAY,11,0,0,0,1,1,0,Business Entity Type 1,,0.6899300913937738,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-495.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n149298,0,Cash loans,M,Y,Y,0,225000.0,732915.0,79065.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.072508,-12380,-1188,-4902.0,-16,4.0,1,1,0,1,1,0,Drivers,1.0,1,1,FRIDAY,12,0,0,0,0,0,0,Construction,0.29605504069215305,0.6989408449978258,0.4223696523543468,0.1804,0.1502,0.9916,0.8844,0.1358,0.32,0.1379,0.4583,0.4167,0.1178,0.1471,0.1343,0.0,0.005,0.1838,0.1558,0.9916,0.8889,0.13699999999999998,0.3222,0.1379,0.4583,0.4167,0.1205,0.1607,0.1399,0.0,0.0053,0.1822,0.1502,0.9916,0.8859,0.1367,0.32,0.1379,0.4583,0.4167,0.1199,0.1496,0.1367,0.0,0.0051,reg oper account,block of flats,0.1766,Panel,No,0.0,0.0,0.0,0.0,-1826.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,5.0\n419208,0,Cash loans,M,Y,Y,0,135000.0,1056447.0,31018.5,922500.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.009549,-19832,-747,-738.0,-3365,9.0,1,1,0,1,0,0,Security staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4670787341895963,0.6894791426446275,0.0567,0.0658,0.9781,,,,0.1379,0.1667,,0.0426,,0.0552,,,0.0578,0.0683,0.9782,,,,0.1379,0.1667,,0.0436,,0.0575,,,0.0573,0.0658,0.9781,,,,0.1379,0.1667,,0.0434,,0.0562,,,,block of flats,0.0471,Block,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n380659,0,Cash loans,F,N,Y,0,99000.0,90000.0,5152.5,90000.0,Family,Working,Secondary / secondary special,Widow,House / apartment,0.028663,-17352,-489,-4931.0,-743,,1,1,0,1,0,0,Managers,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Industry: type 11,0.6635985069945259,0.7360316817778053,0.8224987619370829,0.1031,0.0,0.9806,0.7348,0.0112,0.0,0.2069,0.1667,0.2083,0.0889,0.0841,0.0895,0.0,0.0,0.105,0.0,0.9806,0.7452,0.0113,0.0,0.2069,0.1667,0.2083,0.0909,0.0918,0.0933,0.0,0.0,0.1041,0.0,0.9806,0.7383,0.0113,0.0,0.2069,0.1667,0.2083,0.0904,0.0855,0.0911,0.0,0.0,reg oper account,block of flats,0.0704,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1228.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n204924,1,Cash loans,M,N,Y,0,202500.0,675000.0,28728.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-19550,-1815,-6670.0,-2952,,1,1,1,1,1,0,Drivers,2.0,1,1,TUESDAY,6,0,0,0,0,0,0,Self-employed,,0.4787757557574022,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-940.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n433552,0,Cash loans,F,N,Y,0,180000.0,1256400.0,40657.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-17832,-3500,-1676.0,-1328,,1,1,0,1,0,0,Laborers,2.0,1,1,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.3768278552034466,0.8245949709919925,0.1309,0.27699999999999997,0.9975,0.966,0.0077,0.24,0.1034,0.4583,0.0,0.0,0.1067,0.131,0.0,0.2545,0.1334,0.2875,0.9975,0.9673,0.0078,0.2417,0.1034,0.4583,0.0,0.0,0.1166,0.1365,0.0,0.2694,0.1322,0.27699999999999997,0.9975,0.9665,0.0077,0.24,0.1034,0.4583,0.0,0.0,0.1086,0.1334,0.0,0.2598,reg oper account,block of flats,0.1626,Panel,No,6.0,0.0,6.0,0.0,-1016.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n393979,0,Cash loans,F,Y,N,0,225000.0,521280.0,31500.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-15279,-5693,-1164.0,-3474,5.0,1,1,0,1,1,0,Managers,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Self-employed,0.6865598126899147,0.52303399986113,0.470456116119975,,,0.9717,,,,,,,,,0.0394,,,,,0.9717,,,,,,,,,0.0411,,,,,0.9717,,,,,,,,,0.0401,,,,,0.0454,,No,2.0,0.0,2.0,0.0,-2693.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n151690,0,Cash loans,F,N,Y,2,90000.0,702000.0,24871.5,702000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-13724,-5125,-7114.0,-2383,,1,1,0,1,0,0,Laborers,4.0,2,2,SATURDAY,17,0,0,0,0,0,0,Military,,0.6352356175782646,0.8633633824101478,0.5072,0.3066,0.9891,0.8504,0.3656,0.52,0.4483,0.375,0.4167,0.4502,0.41100000000000003,0.611,0.0116,0.0109,0.5168,0.3182,0.9891,0.8563,0.369,0.5236,0.4483,0.375,0.4167,0.4605,0.449,0.6366,0.0117,0.0115,0.5121,0.3066,0.9891,0.8524,0.368,0.52,0.4483,0.375,0.4167,0.45799999999999996,0.4181,0.622,0.0116,0.0111,reg oper account,block of flats,0.5812,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n164374,0,Cash loans,F,N,Y,0,58500.0,125136.0,7785.0,99000.0,Unaccompanied,Pensioner,Lower secondary,Widow,House / apartment,0.018801,-21400,365243,-4686.0,-4960,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.048125944454277585,0.6626377922738201,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n203158,0,Cash loans,F,N,Y,2,49500.0,254700.0,14751.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-15090,-7079,-6739.0,-4942,,1,1,1,1,0,0,Laborers,4.0,2,2,MONDAY,9,0,0,0,0,1,1,Agriculture,,0.736583633174682,0.7898803468924867,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1010.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n363282,0,Cash loans,M,N,N,1,121500.0,490500.0,20106.0,490500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.022625,-11531,-212,-5287.0,-4151,,1,1,0,1,0,1,Sales staff,3.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Trade: type 2,0.8027520449909988,0.4587823023279226,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-111.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n158113,1,Cash loans,F,N,Y,0,351000.0,679500.0,64647.0,679500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.026392,-12056,-2915,-6188.0,-4661,,1,1,0,1,1,0,Accountants,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Medicine,0.29203841624797633,0.7130414791042483,0.7826078370261895,0.1485,0.1128,0.9811,,,0.16,0.1379,0.3333,,,,0.1579,,0.0,0.1513,0.11699999999999999,0.9811,,,0.1611,0.1379,0.3333,,,,0.1646,,0.0,0.1499,0.1128,0.9811,,,0.16,0.1379,0.3333,,,,0.1608,,0.0,,block of flats,0.1242,Panel,No,0.0,0.0,0.0,0.0,-1636.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n385210,0,Cash loans,F,N,Y,0,144000.0,545040.0,17244.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-20032,-2367,-8233.0,-3547,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,7,0,0,0,0,0,0,Trade: type 2,0.6934025346338651,0.5812246182416896,0.4066174366275036,0.1639,0.0934,0.9891,0.8436,0.0587,0.16,0.1379,0.375,0.4167,0.0,0.1336,0.1864,0.0,0.0,0.16699999999999998,0.09699999999999999,0.9891,0.8497,0.0592,0.1611,0.1379,0.375,0.4167,0.0,0.146,0.1943,0.0,0.0,0.1655,0.0934,0.9891,0.8457,0.0591,0.16,0.1379,0.375,0.4167,0.0,0.136,0.1898,0.0,0.0,,block of flats,0.1466,Panel,No,7.0,0.0,7.0,0.0,-991.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n335626,0,Cash loans,F,N,N,0,315000.0,962370.0,67108.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-14896,-859,-8930.0,-5126,,1,1,1,1,1,0,Laborers,2.0,3,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.6481765619284178,0.3573824096229961,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1226.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n316180,0,Cash loans,M,Y,Y,2,117000.0,808650.0,31333.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-10698,-3363,-4968.0,-3362,17.0,1,1,1,1,1,0,Laborers,4.0,2,2,MONDAY,12,0,0,0,0,1,1,Business Entity Type 2,,0.5750379574764374,0.4048783643353997,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1909.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n441408,1,Revolving loans,F,Y,Y,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.003122,-9993,-947,-4.0,-2674,2.0,1,1,0,1,0,0,Core staff,2.0,3,3,TUESDAY,11,0,1,1,0,1,1,Government,0.5046655264446325,0.5786142848370875,0.10822632266971416,0.0879,,0.9975,0.9592,,0.0,0.1379,0.1146,0.1667,,0.0845,0.0733,0.0077,0.0017,0.0725,,0.998,0.9608,,0.0,0.1034,0.125,0.1667,,0.0882,0.057,0.0078,0.0,0.0869,,0.998,0.9597,,0.0,0.1379,0.125,0.1667,,0.0859,0.0749,0.0078,0.0018,reg oper account,block of flats,0.5528,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-505.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n301219,0,Cash loans,M,Y,N,1,112500.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006305,-9091,-390,-3682.0,-1776,21.0,1,1,0,1,0,1,Laborers,3.0,3,3,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 2,0.12939210689958247,0.4023071096081088,0.08281470424406495,0.0041,0.0,0.9518,0.3404,0.0,0.0,0.069,0.0,0.0417,0.0,0.0034,0.0035,0.0,0.0,0.0042,0.0,0.9518,0.3662,0.0,0.0,0.069,0.0,0.0417,0.0,0.0037,0.0036,0.0,0.0,0.0042,0.0,0.9518,0.3492,0.0,0.0,0.069,0.0,0.0417,0.0,0.0034,0.0035,0.0,0.0,reg oper account,block of flats,0.0042,Wooden,No,2.0,1.0,2.0,1.0,-1223.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n254349,0,Cash loans,F,N,N,0,112500.0,673875.0,19440.0,562500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.010643,-20602,365243,-12038.0,-4116,,1,0,0,1,0,0,,1.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.4050180331913005,0.7180328113294772,0.0082,0.0054,0.9677,,,0.0,0.0345,0.0417,,0.0156,,0.0081,,0.0,0.0084,0.0056,0.9677,,,0.0,0.0345,0.0417,,0.0159,,0.0084,,0.0,0.0083,0.0054,0.9677,,,0.0,0.0345,0.0417,,0.0158,,0.0082,,0.0,,block of flats,0.0063,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-251.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n226711,0,Cash loans,F,N,Y,0,112500.0,481176.0,23148.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.02461,-18067,-3208,-12111.0,-1612,,1,1,1,1,1,0,,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,University,,0.5774995506295669,,0.1227,0.085,0.9806,0.7348,0.1108,0.0,0.2759,0.1667,0.2083,0.1308,0.1,0.1142,0.0309,0.1265,0.125,0.0882,0.9806,0.7452,0.1118,0.0,0.2759,0.1667,0.2083,0.1338,0.1093,0.11900000000000001,0.0311,0.1339,0.1239,0.085,0.9806,0.7383,0.1115,0.0,0.2759,0.1667,0.2083,0.1331,0.1018,0.1163,0.0311,0.1292,reg oper account,block of flats,0.1797,\"Stone, brick\",No,7.0,2.0,7.0,0.0,-1716.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n109124,0,Cash loans,F,N,Y,0,180000.0,432000.0,34258.5,432000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-11635,-2100,-655.0,-846,,1,1,1,1,1,0,Core staff,2.0,3,3,MONDAY,14,0,0,0,0,0,0,Government,0.29415375161333884,0.2469442320646544,0.21155076420525776,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n227197,0,Cash loans,F,N,Y,0,135000.0,406597.5,17356.5,351000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.007120000000000001,-20586,365243,-4078.0,-4086,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.2631435910213423,0.4311917977993083,0.0165,,0.9682,0.5648,,0.0,0.069,0.0417,0.0417,,0.0134,0.0133,,,0.0168,,0.9682,0.5818,,0.0,0.069,0.0417,0.0417,,0.0147,0.0138,,,0.0167,,0.9682,0.5706,,0.0,0.069,0.0417,0.0417,,0.0137,0.0135,,,not specified,block of flats,0.0107,Others,No,0.0,0.0,0.0,0.0,-263.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,2.0\n175333,0,Cash loans,F,N,Y,0,225000.0,156384.0,16551.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.020713,-19127,-1083,-11324.0,-2679,,1,1,0,1,0,0,Laborers,1.0,3,2,SUNDAY,9,0,0,0,0,0,0,Transport: type 2,0.7656396443878615,0.6343382959276261,0.5585066276769286,0.0825,0.0806,0.9762,0.6736,0.0084,0.0,0.1379,0.1667,0.2083,0.0522,0.0672,0.0697,0.0,0.0,0.084,0.0836,0.9762,0.6864,0.0085,0.0,0.1379,0.1667,0.2083,0.0534,0.0735,0.0726,0.0,0.0,0.0833,0.0806,0.9762,0.6779999999999999,0.0085,0.0,0.1379,0.1667,0.2083,0.0531,0.0684,0.071,0.0,0.0,not specified,block of flats,0.0594,Panel,No,0.0,0.0,0.0,0.0,-778.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n211433,1,Cash loans,M,Y,N,0,225000.0,722430.0,26671.5,517500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.006852,-10118,-188,-963.0,-1659,17.0,1,1,0,1,0,0,,2.0,3,3,FRIDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.20225336372631825,0.004089970726894853,0.7557400501752248,0.0825,0.0715,0.9831,0.7688,0.0557,0.0,0.1379,0.1667,0.2083,0.0561,0.0664,0.0723,0.0039,0.0129,0.084,0.0742,0.9831,0.7779,0.0562,0.0,0.1379,0.1667,0.2083,0.0573,0.0725,0.0753,0.0039,0.0137,0.0833,0.0715,0.9831,0.7719,0.055999999999999994,0.0,0.1379,0.1667,0.2083,0.057,0.0676,0.0736,0.0039,0.0132,not specified,block of flats,0.0901,Panel,No,0.0,0.0,0.0,0.0,-1056.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n121915,0,Cash loans,F,N,Y,2,94500.0,177903.0,12510.0,148500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.025164,-14666,-3974,-7342.0,-5285,,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Trade: type 1,,0.5116770488864558,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1732.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n218792,0,Revolving loans,F,Y,Y,2,72000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-10158,-633,-496.0,-2851,18.0,1,1,0,1,0,0,Sales staff,4.0,2,2,MONDAY,11,0,0,0,0,1,1,Self-employed,0.5527434934870316,0.6187648367338338,0.33285056416487313,0.0165,0.0361,0.9821,,,0.0,0.069,0.0417,,0.025,,0.0091,,,0.0168,0.0375,0.9821,,,0.0,0.069,0.0417,,0.0256,,0.0094,,,0.0167,0.0361,0.9821,,,0.0,0.069,0.0417,,0.0255,,0.0092,,,,block of flats,0.0116,\"Stone, brick\",No,9.0,1.0,9.0,0.0,-1317.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n228695,0,Cash loans,F,N,Y,0,56250.0,284400.0,16326.0,225000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-20405,365243,-12732.0,-3125,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,XNA,0.4670043280524818,0.4053237076778611,0.3842068130556564,0.133,0.1519,0.9771,0.6872,0.0138,0.0,0.2759,0.1667,0.2083,0.1226,0.1076,0.1186,0.0039,0.0045,0.1355,0.1576,0.9772,0.6994,0.0139,0.0,0.2759,0.1667,0.2083,0.1254,0.1175,0.1236,0.0039,0.0047,0.1343,0.1519,0.9771,0.6914,0.0139,0.0,0.2759,0.1667,0.2083,0.1248,0.1095,0.1207,0.0039,0.0046,reg oper account,block of flats,0.1018,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n197983,0,Cash loans,F,N,Y,0,180000.0,827496.0,42381.0,679500.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.018634,-16254,-680,-7233.0,-4206,,1,1,0,1,0,1,Sales staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Trade: type 3,0.735632025857648,0.3788532349633629,0.32173528219668485,0.1474,0.0645,0.9781,0.7008,0.0144,0.0,0.069,0.1667,0.2083,0.049,0.11599999999999999,0.062,0.0193,0.0299,0.1502,0.067,0.9782,0.7125,0.0145,0.0,0.069,0.1667,0.2083,0.0501,0.1267,0.0646,0.0195,0.0317,0.1489,0.0645,0.9781,0.7048,0.0145,0.0,0.069,0.1667,0.2083,0.0498,0.11800000000000001,0.0632,0.0194,0.0305,reg oper account,block of flats,0.0553,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1675.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n309726,0,Cash loans,M,N,Y,0,202500.0,320922.0,36423.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.018801,-12294,-364,-874.0,-1251,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Construction,0.15408995945119194,0.4794360948932911,0.1895952597360396,0.1371,0.0597,0.9747,,0.0584,0.0,0.069,0.1667,,0.0431,,0.0508,,0.004,0.1397,0.062,0.9747,,0.0589,0.0,0.069,0.1667,,0.0441,,0.0529,,0.0042,0.1384,0.0597,0.9747,,0.0587,0.0,0.069,0.1667,,0.0439,,0.0517,,0.0041,,specific housing,0.0624,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-869.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n142839,0,Cash loans,F,N,Y,0,180000.0,162000.0,11916.0,162000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-18593,-1798,-7096.0,-2128,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,8,0,0,0,0,1,1,Construction,,0.6563349346415885,0.6769925032909132,0.033,0.0,0.9776,,,0.0,0.1034,0.0417,,0.0181,,0.0147,,0.0,0.0336,0.0,0.9777,,,0.0,0.1034,0.0417,,0.0185,,0.0153,,0.0,0.0333,0.0,0.9776,,,0.0,0.1034,0.0417,,0.0184,,0.015,,0.0,,block of flats,0.0105,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1884.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n401705,0,Cash loans,F,N,Y,1,247500.0,1254739.5,66982.5,1129500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009334,-16193,-411,-3994.0,-4035,,1,1,0,1,0,0,Cleaning staff,3.0,2,2,FRIDAY,13,0,0,0,1,1,0,Construction,,0.6560043856085771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1919.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n182875,0,Cash loans,M,N,Y,0,112500.0,152820.0,16344.0,135000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.008019,-9788,-587,-4499.0,-2464,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Self-employed,,0.3754064878340146,,0.1113,0.0685,0.9861,0.8096,0.0632,0.12,0.1034,0.3333,0.375,0.0511,0.0908,0.1152,0.0,0.0,0.1134,0.071,0.9861,0.8171,0.0637,0.1208,0.1034,0.3333,0.375,0.0522,0.0992,0.12,0.0,0.0,0.1124,0.0685,0.9861,0.8121,0.0636,0.12,0.1034,0.3333,0.375,0.0519,0.0923,0.1172,0.0,0.0,reg oper account,block of flats,0.0906,Panel,No,0.0,0.0,0.0,0.0,-369.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n223166,0,Cash loans,F,N,Y,0,67500.0,152820.0,16587.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-16184,-592,-298.0,-3595,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Trade: type 2,,0.6079319432627506,0.1895952597360396,,,0.9667,,,,0.069,0.0417,,,,0.0079,,0.0049,,,0.9667,,,,0.069,0.0417,,,,0.0083,,0.0052,,,0.9667,,,,0.069,0.0417,,,,0.0081,,0.005,,,0.0073,Mixed,No,1.0,1.0,1.0,1.0,-462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n410714,0,Cash loans,F,N,Y,0,234000.0,1002726.0,29317.5,837000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019101,-19929,365243,-7182.0,-3416,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,0.5143078715903221,0.5062502196125132,0.41534714488434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1246.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n276691,0,Revolving loans,F,N,Y,1,81000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-8866,-1225,-689.0,-101,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 1,,0.4681403046891375,,0.0722,0.0554,0.9871,0.8232,0.0148,0.0,0.0345,0.1667,0.2083,0.0639,0.0588,0.0482,0.0,0.0,0.0735,0.0575,0.9871,0.8301,0.0149,0.0,0.0345,0.1667,0.2083,0.0654,0.0643,0.0503,0.0,0.0,0.0729,0.0554,0.9871,0.8256,0.0149,0.0,0.0345,0.1667,0.2083,0.0651,0.0599,0.0491,0.0,0.0,reg oper account,block of flats,0.0379,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-324.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n283204,0,Revolving loans,F,N,Y,0,135000.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-15857,-3067,-3276.0,-4701,,1,1,0,1,0,0,Private service staff,1.0,2,2,SATURDAY,10,0,1,1,0,1,1,Self-employed,,0.6429273402649994,0.4812493411434029,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-903.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n356353,0,Cash loans,M,N,Y,1,180000.0,422235.0,22104.0,364500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.008068,-21628,-4006,-6945.0,-4789,,1,1,0,1,0,0,High skill tech staff,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Business Entity Type 1,,0.4600694239110964,0.7421816117614419,0.1082,0.126,0.9831,0.7688,0.0087,0.0,0.2414,0.1667,0.2083,,0.0883,0.1021,0.0,0.0,0.1103,0.1307,0.9831,0.7779,0.0088,0.0,0.2414,0.1667,0.2083,,0.0964,0.1064,0.0,0.0,0.1093,0.126,0.9831,0.7719,0.0087,0.0,0.2414,0.1667,0.2083,,0.0898,0.10400000000000001,0.0,0.0,reg oper spec account,block of flats,0.1069,Panel,No,0.0,0.0,0.0,0.0,-128.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n129056,0,Cash loans,F,N,Y,0,135000.0,135000.0,9729.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-16712,-4109,-1965.0,-243,,1,1,0,1,0,0,Sales staff,2.0,3,3,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.4632541788080211,0.6894791426446275,0.0082,0.0,0.9757,0.6668,0.0,0.0,0.0345,0.0,0.0417,0.0033,0.0067,0.0058,0.0,0.0,0.0084,0.0,0.9757,0.6798,0.0,0.0,0.0345,0.0,0.0417,0.0034,0.0073,0.006,0.0,0.0,0.0083,0.0,0.9757,0.6713,0.0,0.0,0.0345,0.0,0.0417,0.0034,0.0068,0.0059,0.0,0.0,reg oper account,block of flats,0.0045,\"Stone, brick\",No,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n255602,0,Cash loans,F,N,N,0,112500.0,135000.0,9018.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Married,With parents,0.025164,-10178,-1608,-2561.0,-2866,,1,1,1,1,0,0,Laborers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Medicine,0.34583317357456644,0.6080101227963106,,0.3361,0.1735,0.9846,0.7892,0.1269,0.36,0.3103,0.3333,0.375,0.0763,0.2723,0.3557,0.0077,0.0037,0.3424,0.1801,0.9846,0.7975,0.1281,0.3625,0.3103,0.3333,0.375,0.078,0.2975,0.3705,0.0078,0.004,0.3393,0.1735,0.9846,0.792,0.1277,0.36,0.3103,0.3333,0.375,0.0776,0.27699999999999997,0.36200000000000004,0.0078,0.0038,reg oper account,block of flats,0.3499,Panel,No,1.0,0.0,1.0,0.0,-1536.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n158231,1,Cash loans,M,Y,Y,1,360000.0,1754253.0,61105.5,1602000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007273999999999998,-16230,-639,-29.0,-4674,2.0,1,1,0,1,0,1,Managers,3.0,2,2,WEDNESDAY,16,0,0,0,0,1,1,Business Entity Type 3,0.21271843941669125,0.39176938380897935,0.2955826421513093,0.1474,0.1112,0.9791,0.7144,0.0572,0.16,0.1379,0.3333,0.375,0.1085,0.1202,0.1498,0.0,0.0,0.1502,0.1154,0.9791,0.7256,0.0577,0.1611,0.1379,0.3333,0.375,0.111,0.1313,0.1561,0.0,0.0,0.1489,0.1112,0.9791,0.7182,0.0576,0.16,0.1379,0.3333,0.375,0.1104,0.1223,0.1525,0.0,0.0,reg oper account,block of flats,0.1298,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-723.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n153673,0,Cash loans,M,N,Y,1,135000.0,72000.0,7249.5,72000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.0105,-10414,-2742,-1771.0,-2946,,1,1,1,1,1,0,Laborers,3.0,3,3,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 2,0.18338582372793416,0.534769799203404,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n450071,0,Cash loans,F,N,N,0,135000.0,858114.0,57577.5,810000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-21414,365243,-2609.0,-4818,,1,0,0,1,0,0,,2.0,3,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.11144582692558973,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1479.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n192534,0,Cash loans,F,N,N,0,67500.0,284400.0,8752.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.022625,-23951,365243,-14186.0,-4203,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.6904771578869712,0.7862666146611379,0.0,,0.0,,,0.0,,,,,,,,,0.0,,0.0005,,,0.0,,,,,,,,,0.0,,0.0,,,0.0,,,,,,,,,,block of flats,0.0,,No,4.0,0.0,4.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n372093,0,Cash loans,F,N,Y,0,121500.0,1350000.0,37125.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-16702,-9553,-9913.0,-249,,1,1,1,1,1,0,Core staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Kindergarten,0.6912975380992239,0.10919443370778197,0.33125086459090186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-745.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,6.0,4.0\n438544,0,Cash loans,F,N,Y,0,121500.0,454500.0,19255.5,454500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.010556,-11195,-3736,-1384.0,-3577,,1,1,1,1,1,0,Accountants,1.0,3,3,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.4273580581148742,0.7176234769458963,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-547.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n144375,0,Cash loans,F,N,Y,0,180000.0,450000.0,21109.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-19210,-736,-3628.0,-2754,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.6649429627942823,,0.6545292802242897,0.2598,0.1646,0.9831,,,0.28,0.2414,0.3333,,0.0,,0.2929,,0.0,0.2647,0.1708,0.9831,,,0.282,0.2414,0.3333,,0.0,,0.3052,,0.0,0.2623,0.1646,0.9831,,,0.28,0.2414,0.3333,,0.0,,0.2982,,0.0,,block of flats,0.3228,Panel,No,7.0,0.0,7.0,0.0,-259.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n318063,0,Cash loans,M,Y,N,0,171900.0,1006920.0,42790.5,900000.0,Family,Working,Higher education,Married,House / apartment,0.008625,-22215,-5515,-5180.0,-3667,14.0,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,0.7217179058059873,0.1930712491029848,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1504.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n367121,0,Cash loans,F,N,N,0,130500.0,288873.0,18936.0,238500.0,Other_B,Pensioner,Secondary / secondary special,Widow,House / apartment,0.00496,-19206,365243,-1302.0,-1351,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,XNA,0.6706766914682788,0.6335821994722224,0.5316861425197883,0.0402,,0.9757,,,,0.0517,0.25,,,,,,,0.0252,,0.9757,,,,0.0345,0.1667,,,,,,,0.0406,,0.9757,,,,0.0517,0.25,,,,,,,,block of flats,0.0257,,No,0.0,0.0,0.0,0.0,-1285.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n276277,1,Cash loans,F,N,Y,0,121500.0,497520.0,36184.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.003069,-19491,-455,-249.0,-269,,1,1,0,1,1,0,Laborers,1.0,3,3,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.2095969999592212,0.10467173773052428,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n292599,0,Cash loans,F,N,Y,0,180000.0,373500.0,13545.0,373500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.026392,-22013,365243,-5781.0,-4235,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,0.7003095499102769,0.6870880775503007,0.4902575124990026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-153.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,5.0,8.0\n271551,0,Cash loans,F,N,Y,0,76500.0,700830.0,22738.5,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.005313,-16635,-9877,-6851.0,-192,,1,1,1,1,1,0,Sales staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Government,,0.6231756832521206,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n133836,0,Cash loans,F,Y,Y,0,135000.0,450000.0,23107.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-15915,-1125,-3279.0,-4568,7.0,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,10,0,0,0,0,1,1,Other,,0.6554465501818523,,0.0165,,0.9796,,,0.0,0.069,0.0417,,0.0415,,0.0094,,0.018000000000000002,0.0168,,0.9796,,,0.0,0.069,0.0417,,0.0424,,0.0098,,0.019,0.0167,,0.9796,,,0.0,0.069,0.0417,,0.0422,,0.0096,,0.0184,,block of flats,0.0113,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3230.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n206457,0,Cash loans,F,N,N,0,157500.0,229500.0,8649.0,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.026392,-10479,-1196,-4615.0,-2813,,1,1,1,1,1,0,Core staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Trade: type 3,,0.7548839461927792,0.5797274227921155,0.0773,,0.9771,,,0.0,0.069,0.1667,,,,0.026000000000000002,,0.0216,0.0788,,0.9772,,,0.0,0.069,0.1667,,,,0.0271,,0.0229,0.0781,,0.9771,,,0.0,0.069,0.1667,,,,0.0264,,0.0221,,block of flats,0.0251,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1802.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n257609,0,Cash loans,M,N,N,0,117000.0,808650.0,31333.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-9586,-1623,-1555.0,-2259,,1,1,1,1,0,0,Managers,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,0.25111967160336685,0.7312586795394347,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1824.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n205856,0,Cash loans,M,Y,Y,1,225000.0,1266048.0,53640.0,1152000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.04622,-20182,-771,-12166.0,-3614,3.0,1,1,0,1,0,0,Drivers,2.0,1,1,SATURDAY,17,0,0,0,0,0,0,Other,0.4704007961715557,0.7542319155000429,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1682.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n123495,0,Cash loans,M,N,Y,0,112500.0,184500.0,12460.5,184500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018209,-21587,-1238,-11831.0,-4543,,1,1,0,1,0,0,Security staff,2.0,3,3,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.13475087085558424,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-63.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n230083,0,Cash loans,M,N,Y,1,202500.0,1006920.0,45630.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.006629,-18443,-453,-9918.0,-1919,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 2,,0.27753955754853066,,0.1433,0.0966,0.9791,0.7144,,0.0,0.069,0.1667,0.2083,0.0176,0.1042,0.0492,0.0579,0.0275,0.146,0.1002,0.9791,0.7256,,0.0,0.069,0.1667,0.2083,0.018000000000000002,0.1139,0.0512,0.0584,0.0291,0.1447,0.0966,0.9791,0.7182,,0.0,0.069,0.1667,0.2083,0.0179,0.106,0.0501,0.0582,0.0281,,specific housing,0.0627,Block,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n448198,0,Cash loans,F,N,Y,0,135000.0,961146.0,28233.0,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-13342,-1930,-2187.0,-2198,,1,1,0,1,0,0,Medicine staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Medicine,0.38181788627357,0.5450523201440938,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-500.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n188259,0,Cash loans,F,N,Y,1,112500.0,604152.0,26739.0,540000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.019101,-15469,-544,-8346.0,-819,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Kindergarten,0.6599183659775851,0.6261493645316187,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-2683.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n298081,0,Cash loans,M,Y,N,1,135000.0,472500.0,46030.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-18226,-7301,-7190.0,-1783,1.0,1,1,1,1,1,0,Laborers,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Other,,0.7378968295217635,0.7726311345553628,0.1423,0.1132,0.9925,,,0.18,0.1552,0.3333,,,,0.1662,,,0.1303,0.1175,0.9901,,,0.1611,0.1379,0.3333,,,,0.1732,,,0.1436,0.1132,0.9925,,,0.18,0.1552,0.3333,,,,0.1692,,,,block of flats,0.1601,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1817.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n300798,0,Cash loans,M,Y,Y,0,112500.0,100737.0,7461.0,76500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.008473999999999999,-9952,-1613,-9136.0,-572,5.0,1,1,0,1,0,0,,1.0,2,2,THURSDAY,11,0,0,0,0,1,1,Industry: type 5,,0.6282406221398813,0.1245194708919845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,9.0\n254077,0,Cash loans,M,Y,N,0,202500.0,942300.0,36643.5,675000.0,Family,Working,Higher education,Single / not married,House / apartment,0.00496,-10490,-1414,-517.0,-2733,3.0,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,15,0,0,0,0,1,1,Business Entity Type 2,0.278958205687453,0.5509941078798735,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-877.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n300490,0,Revolving loans,M,Y,Y,1,540000.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.072508,-12634,-1951,-6737.0,-3975,8.0,1,1,0,0,1,0,Managers,2.0,1,1,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4114629130914287,0.524496446363472,0.2804,0.1071,0.9856,0.8028,0.0,0.32,0.1379,0.6667,0.7083,0.0,0.2286,0.3432,0.0,0.0,0.2857,0.1107,0.9856,0.8105,0.0,0.3222,0.1379,0.6667,0.7083,0.0,0.2498,0.3569,0.0,0.0,0.2831,0.1069,0.9856,0.8054,0.0,0.32,0.1379,0.6667,0.7083,0.0,0.2326,0.3489,0.0,0.0,reg oper account,block of flats,0.2695,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n445631,0,Cash loans,F,Y,Y,1,112500.0,568057.5,22657.5,459000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.025164,-16356,-5358,-2374.0,-4740,6.0,1,1,0,1,0,0,,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Transport: type 2,,0.5736265957991026,0.4848514754962666,0.0227,0.0,0.9732,0.6328,0.002,0.0,0.069,0.0833,0.125,0.0711,0.0185,0.0168,0.0039,0.0081,0.0231,0.0,0.9732,0.6472,0.002,0.0,0.069,0.0833,0.125,0.0727,0.0202,0.0175,0.0039,0.0086,0.0229,0.0,0.9732,0.6377,0.002,0.0,0.069,0.0833,0.125,0.0724,0.0188,0.0171,0.0039,0.0083,reg oper account,block of flats,0.0143,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-396.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n313474,0,Cash loans,F,Y,Y,2,112500.0,331920.0,16096.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.0228,-13465,-2694,-1424.0,-1663,25.0,1,1,0,1,0,0,Core staff,4.0,2,2,FRIDAY,8,0,0,0,0,1,1,Police,0.6615986209679174,0.6339070465920231,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2165.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n404262,0,Cash loans,F,N,Y,0,67500.0,600678.0,40432.5,567000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-23777,365243,-10164.0,-3415,,1,0,0,1,1,0,,2.0,3,3,TUESDAY,8,0,0,0,0,0,0,XNA,,0.5932032297466489,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-12.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n254770,0,Cash loans,F,N,Y,0,175500.0,1065433.5,42385.5,913500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018634,-16074,-2068,-3186.0,-4340,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.4763151634850632,,,,0.9747,,,,,,,,,0.0793,,,,,0.9747,,,,,,,,,0.0826,,,,,0.9747,,,,,,,,,0.0807,,,,,0.0995,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n292931,0,Cash loans,F,Y,Y,0,112500.0,225000.0,15034.5,225000.0,Other_A,Working,Higher education,Single / not married,House / apartment,0.010006,-9300,-212,-3533.0,-1974,1.0,1,1,1,1,1,0,Core staff,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Bank,0.2653982183494744,0.5360861450068656,0.39449540531239935,0.0124,0.0,0.9767,0.6804,,0.0,0.069,0.0417,0.0,0.0188,0.0101,0.0059,0.0,0.0,0.0126,0.0,0.9767,0.6929,,0.0,0.069,0.0417,0.0,0.0193,0.011000000000000001,0.0061,0.0,0.0,0.0125,0.0,0.9767,0.6847,,0.0,0.069,0.0417,0.0,0.0192,0.0103,0.006,0.0,0.0,,terraced house,0.0078,Wooden,No,0.0,0.0,0.0,0.0,-304.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n194295,0,Cash loans,F,N,N,0,202500.0,711072.0,21690.0,540000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.025164,-16743,-502,-925.0,-273,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.7621578042099105,0.5535408724264547,0.5046813193144684,0.2969,0.2149,0.9881,0.8368,0.001,0.32,0.2759,0.3333,0.375,0.1494,0.2412,0.3298,0.0039,0.1448,0.3025,0.223,0.9881,0.8432,0.001,0.3222,0.2759,0.3333,0.375,0.1528,0.2635,0.3436,0.0039,0.1533,0.2998,0.2149,0.9881,0.8390000000000001,0.001,0.32,0.2759,0.3333,0.375,0.152,0.2454,0.3357,0.0039,0.1478,reg oper account,block of flats,0.2914,Panel,No,4.0,0.0,3.0,0.0,-2324.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n455208,0,Cash loans,M,N,N,0,112500.0,226908.0,10705.5,148500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.010276,-8881,-521,-3705.0,-1453,,1,1,0,1,0,0,Low-skill Laborers,1.0,2,2,MONDAY,12,0,0,0,1,1,0,Self-employed,,0.5766936763378981,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-332.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n218390,0,Cash loans,M,Y,Y,1,270000.0,1436850.0,46480.5,1125000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-17950,-1331,-671.0,-1513,7.0,1,1,0,1,0,1,Managers,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6878022588887691,0.6577838002083306,0.0258,0.0382,0.9722,0.6192,0.0119,0.0,0.1034,0.0833,0.125,0.0543,0.0193,0.0258,0.0077,0.0107,0.0263,0.0396,0.9722,0.6341,0.012,0.0,0.1034,0.0833,0.125,0.0556,0.0211,0.0268,0.0078,0.0113,0.026000000000000002,0.0382,0.9722,0.6243,0.012,0.0,0.1034,0.0833,0.125,0.0553,0.0197,0.0262,0.0078,0.0109,reg oper account,block of flats,0.0326,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1585.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n427753,0,Cash loans,F,N,Y,0,90000.0,568197.0,30402.0,526500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-19025,365243,-9242.0,-2544,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,XNA,,0.4691858252385467,0.3425288720742255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14.0,0.0,14.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n411545,1,Cash loans,F,Y,Y,0,270000.0,835380.0,40320.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-19267,-6717,-3147.0,-2808,12.0,1,1,1,1,0,0,Managers,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Self-employed,,0.3442507292017128,0.12530787842823918,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-272.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n133704,0,Cash loans,F,N,Y,0,54000.0,339241.5,15943.5,238500.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.006207,-19833,-590,-7523.0,-3080,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 1,,0.6750384271793303,,0.0619,0.0,0.9886,0.8436,0.0,0.0,0.1379,0.1667,0.0417,0.0135,0.0504,0.0619,0.0,0.0,0.063,0.0,0.9886,0.8497,0.0,0.0,0.1379,0.1667,0.0417,0.0138,0.0551,0.0645,0.0,0.0,0.0625,0.0,0.9886,0.8457,0.0,0.0,0.1379,0.1667,0.0417,0.0138,0.0513,0.0631,0.0,0.0,reg oper account,block of flats,0.0548,Panel,No,2.0,0.0,1.0,0.0,-225.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n431491,0,Cash loans,F,N,Y,0,105750.0,495000.0,21100.5,495000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018209,-20397,365243,-3091.0,-3764,,1,0,0,1,0,0,,1.0,3,3,MONDAY,9,0,0,0,0,0,0,XNA,,0.5212825746831389,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n235823,0,Cash loans,F,N,Y,0,157500.0,1125000.0,36423.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-9923,-611,-4237.0,-592,,1,1,0,1,0,0,Sales staff,2.0,3,3,SUNDAY,8,0,0,0,0,0,0,Self-employed,0.3899747679541836,0.3780610817935009,0.2750003523983893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0055,,No,1.0,0.0,1.0,0.0,-396.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n442346,0,Cash loans,M,Y,Y,1,126000.0,359725.5,23548.5,297000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-10109,-1435,-4773.0,-2738,14.0,1,1,0,1,0,0,Core staff,3.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Agriculture,,0.4239987893659434,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-383.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n373302,0,Cash loans,M,N,Y,0,135000.0,1886850.0,55170.0,1575000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.010032,-8904,-468,-5.0,-1580,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,6,0,0,0,1,1,0,Business Entity Type 3,0.3051477696745099,0.5500309323689437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n134573,0,Cash loans,M,Y,Y,0,162000.0,586633.5,39816.0,445500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006207,-10161,-692,-2149.0,-2830,9.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.2371195667596173,0.3075992211961217,0.646329897706246,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-698.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n304339,0,Cash loans,F,N,N,0,180000.0,1542645.0,62959.5,1440000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.003818,-21486,365243,-2931.0,-4807,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,4,0,0,0,1,0,0,XNA,0.7799430155206684,0.2548305428316889,0.33928769990891394,0.0165,,0.9811,,0.0,0.0,0.069,0.0417,0.0417,,,0.0099,,,0.0168,,0.9811,,0.0,0.0,0.069,0.0417,0.0417,,,0.0103,,,0.0167,,0.9811,,0.0,0.0,0.069,0.0417,0.0417,,,0.0101,,,reg oper spec account,block of flats,0.0078,Wooden,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n279759,1,Cash loans,M,Y,Y,2,112500.0,545040.0,26640.0,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00496,-12173,-3704,-4890.0,-3047,8.0,1,1,0,1,1,0,,4.0,2,2,THURSDAY,13,0,0,0,0,1,1,Culture,,0.6914344076077911,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n275258,0,Cash loans,F,N,Y,0,211500.0,1453257.0,42619.5,1269000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17977,-7359,-8416.0,-1399,,1,1,0,1,0,0,Core staff,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Kindergarten,0.523106092781093,0.5483634318119094,0.5832379256761245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n306236,0,Revolving loans,M,Y,Y,1,126000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-18033,-5430,-3795.0,-1587,9.0,1,1,1,1,0,0,Security staff,3.0,1,1,WEDNESDAY,14,0,1,1,0,1,1,Security,0.3996476814881573,0.7018775372734987,0.3506958432829587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-387.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n233684,0,Cash loans,F,Y,Y,0,270000.0,1113840.0,50463.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-12829,-2424,-6820.0,-243,10.0,1,1,0,1,0,0,,2.0,3,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 1,,0.4817287240892847,0.14111510044661266,0.0598,0.0724,0.9866,0.8164,0.0117,0.0,0.1379,0.1667,0.2083,0.0528,0.0488,0.057,0.0,0.0,0.0609,0.0752,0.9866,0.8236,0.0118,0.0,0.1379,0.1667,0.2083,0.054000000000000006,0.0533,0.0594,0.0,0.0,0.0604,0.0724,0.9866,0.8189,0.0117,0.0,0.1379,0.1667,0.2083,0.0537,0.0496,0.057999999999999996,0.0,0.0,not specified,block of flats,0.0512,Panel,No,1.0,0.0,1.0,0.0,-610.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n119460,0,Cash loans,F,N,Y,0,225000.0,760225.5,30280.5,679500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.04622,-21203,365243,-2174.0,-4602,,1,0,0,1,0,0,,2.0,1,1,MONDAY,11,0,0,0,0,0,0,XNA,0.7232279649995638,0.7311383351718141,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-819.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n269528,0,Cash loans,F,N,Y,0,126000.0,840780.0,35622.0,751500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.002506,-17438,-903,-1496.0,-986,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,6,0,0,0,0,0,0,Self-employed,,0.7225906012648662,,0.0515,0.0652,0.9836,0.7756,0.0,0.0,0.1379,0.125,0.0417,,0.042,0.054000000000000006,0.0,0.0,0.0525,0.0677,0.9836,0.7844,0.0,0.0,0.1379,0.125,0.0417,,0.0459,0.0563,0.0,0.0,0.052000000000000005,0.0652,0.9836,0.7786,0.0,0.0,0.1379,0.125,0.0417,,0.0428,0.055,0.0,0.0,reg oper account,block of flats,0.0425,Panel,No,4.0,0.0,4.0,0.0,-1030.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n446985,0,Cash loans,M,N,N,1,67500.0,269550.0,14242.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018029,-11190,-4373,-5701.0,-3545,,1,1,0,1,0,0,,3.0,3,3,THURSDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.2160556252632609,0.097581612687446,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,0.0,11.0,0.0,-1186.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n238138,0,Cash loans,F,N,Y,2,108000.0,80865.0,6385.5,67500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-12013,-1733,-4849.0,-2406,,1,1,0,1,0,0,Sales staff,4.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,0.3318125636424122,0.4265696419170419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-595.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n185100,0,Cash loans,F,N,Y,0,135000.0,302341.5,23872.5,261000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018029,-17017,-3092,-1235.0,-572,,1,1,0,1,0,0,Sales staff,1.0,3,2,MONDAY,6,0,0,0,0,0,0,Business Entity Type 3,,0.5925434451466494,,0.0928,0.10099999999999999,0.9762,0.6736,0.0437,0.0,0.2069,0.1667,0.2083,0.0997,0.0756,0.0886,0.0,0.0,0.0945,0.1048,0.9762,0.6864,0.0441,0.0,0.2069,0.1667,0.2083,0.102,0.0826,0.0924,0.0,0.0,0.0937,0.10099999999999999,0.9762,0.6779999999999999,0.044000000000000004,0.0,0.2069,0.1667,0.2083,0.1014,0.077,0.0902,0.0,0.0,reg oper account,block of flats,0.0936,Panel,No,1.0,0.0,1.0,0.0,-1036.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n208828,0,Cash loans,F,N,Y,0,157500.0,675000.0,34465.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006008,-22093,365243,-400.0,-4731,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,9,0,0,0,0,0,0,XNA,,0.6617791879685126,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-862.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n308400,0,Cash loans,F,N,Y,1,315000.0,1267038.0,50377.5,1165500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14837,-581,-6826.0,-4460,,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,11,0,0,0,1,1,0,Trade: type 7,0.4317029865494307,0.5277942332361606,0.4992720153045617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,3.0,4.0,3.0,-234.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,5.0\n112743,1,Cash loans,M,N,N,2,225000.0,855000.0,38637.0,855000.0,Family,State servant,Higher education,Single / not married,With parents,0.019101,-10348,-2753,-4656.0,-2619,,1,1,1,1,0,0,High skill tech staff,3.0,2,2,WEDNESDAY,17,0,1,1,0,1,1,Military,0.15290693041344305,0.4812865112946441,0.16146308000577247,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-547.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n118861,0,Cash loans,M,N,Y,0,157500.0,360000.0,28993.5,360000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.014519999999999996,-8987,-521,-722.0,-883,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3664304355131488,0.5055942842709624,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-721.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n379607,0,Cash loans,F,N,N,0,180000.0,450000.0,22977.0,450000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.00963,-10223,-3641,-4781.0,-2898,,1,1,0,1,1,0,Laborers,1.0,2,2,SUNDAY,20,0,0,0,0,0,0,Postal,0.2417943916588192,0.5440055781025662,0.3108182544189319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-432.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n287247,0,Revolving loans,M,Y,Y,3,495000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018209,-15087,-3262,-1323.0,-4483,14.0,1,1,0,1,0,0,Managers,5.0,3,3,THURSDAY,8,0,0,0,0,1,1,Business Entity Type 3,0.6894193133933696,0.4901354171828849,0.7435593141311444,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-290.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n354545,0,Cash loans,F,Y,Y,0,157500.0,900000.0,46084.5,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-10636,-1632,-4.0,-1828,9.0,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Trade: type 7,0.771084541103345,0.2438142472259874,,0.0825,0.0793,0.9742,0.6464,,0.0,0.1379,0.1667,0.0417,0.0472,0.0664,0.0606,0.0039,0.0038,0.084,0.0823,0.9742,0.6602,,0.0,0.1379,0.1667,0.0417,0.0483,0.0725,0.0631,0.0039,0.0041,0.0833,0.0793,0.9742,0.6511,,0.0,0.1379,0.1667,0.0417,0.048,0.0676,0.0617,0.0039,0.0039,reg oper account,block of flats,0.0523,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-50.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n413816,0,Cash loans,M,Y,Y,0,135000.0,283500.0,22068.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-20447,-5594,-8672.0,-3481,26.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,School,,0.4293266200664829,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n200744,0,Cash loans,M,Y,N,1,180000.0,790830.0,52978.5,675000.0,Unaccompanied,State servant,Incomplete higher,Married,With parents,0.003818,-10591,-1195,-4777.0,-3241,17.0,1,1,0,1,0,0,Drivers,3.0,2,2,FRIDAY,8,0,0,0,0,0,0,Police,,0.5119611686046663,0.4400578303966329,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1091.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n405947,1,Cash loans,F,N,Y,0,112500.0,180000.0,9895.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.022625,-14147,-2981,-8233.0,-4487,,1,1,1,1,1,0,Laborers,1.0,2,2,SATURDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.6576153687739964,0.3738746386651294,0.7309873696832169,0.1227,0.09699999999999999,0.9821,0.7552,0.0,0.0,0.2759,0.1667,0.2083,0.0,0.1,0.0725,0.0,0.0,0.125,0.1007,0.9821,0.7648,0.0,0.0,0.2759,0.1667,0.2083,0.0,0.1093,0.0755,0.0,0.0,0.1239,0.09699999999999999,0.9821,0.7585,0.0,0.0,0.2759,0.1667,0.2083,0.0,0.1018,0.0738,0.0,0.0,reg oper account,block of flats,0.0834,Panel,No,2.0,0.0,2.0,0.0,-422.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n199099,0,Cash loans,M,Y,Y,1,112500.0,1078200.0,34911.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-15964,-340,-1761.0,-1772,10.0,1,1,1,1,0,1,Sales staff,3.0,2,2,TUESDAY,12,0,0,0,0,1,1,Self-employed,,0.0006956419646162159,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-186.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n222101,0,Cash loans,F,N,Y,0,90000.0,265851.0,20875.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-10293,-3684,-4970.0,-2974,,1,1,1,1,1,0,Private service staff,2.0,2,2,TUESDAY,10,0,0,0,1,0,1,Trade: type 7,0.30106612210761874,0.42816439697452463,0.8425892767430772,0.0758,0.0236,0.9851,,,0.08,0.069,0.3542,,,,0.05,,0.1764,0.042,0.0245,0.9826,,,0.0403,0.0345,0.3333,,,,0.0285,,0.1868,0.0765,0.0236,0.9851,,,0.08,0.069,0.3542,,,,0.0509,,0.1801,,block of flats,0.0352,Panel,No,0.0,0.0,0.0,0.0,-1620.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n305131,0,Cash loans,M,Y,Y,0,270000.0,714001.5,25645.5,589500.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.00733,-11372,-3897,-1844.0,-3993,14.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Police,,0.5586581998453966,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-2257.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n451236,0,Cash loans,F,N,Y,0,180000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.028663,-20012,-1577,-7196.0,-3549,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.8771330079452248,0.46199294707885996,0.4614823912998385,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1453.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n172646,1,Cash loans,F,N,Y,0,225000.0,521280.0,25209.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.025164,-19093,-2330,-7980.0,-2621,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,,0.17543877559735482,0.5424451438613613,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-18.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n396459,0,Cash loans,F,Y,Y,2,270000.0,152820.0,15241.5,135000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-14753,-4031,-862.0,-3689,6.0,1,1,0,1,0,0,Medicine staff,4.0,1,1,THURSDAY,19,0,1,1,0,0,0,Kindergarten,0.8604786491444544,0.6799310783475129,0.7001838506835805,0.1526,0.0771,0.9896,0.8572,0.1134,0.16,0.1379,0.3333,0.375,0.0,0.121,0.0775,0.0154,0.2853,0.1555,0.08,0.9896,0.8628,0.1145,0.1611,0.1379,0.3333,0.375,0.0,0.1322,0.0807,0.0156,0.302,0.1541,0.0771,0.9896,0.8591,0.1142,0.16,0.1379,0.3333,0.375,0.0,0.1231,0.0788,0.0155,0.2912,reg oper account,block of flats,0.1229,Panel,No,1.0,0.0,1.0,0.0,-945.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n283574,0,Cash loans,F,N,Y,0,67500.0,239850.0,23850.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-24112,365243,-663.0,-4364,,1,0,0,1,1,0,,2.0,3,3,MONDAY,7,0,0,0,0,0,0,XNA,,0.3340715102351488,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-220.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n344127,0,Cash loans,F,N,Y,0,108000.0,273636.0,17617.5,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.01885,-20131,365243,-11353.0,-3500,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.6326526702823334,0.7267112092725122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2219.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n314505,1,Cash loans,M,Y,Y,0,450000.0,490495.5,31477.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20614,-3159,-6540.0,-3274,18.0,1,1,0,1,1,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,,0.23283602240729706,0.4525335592581747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-727.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n446789,0,Cash loans,F,Y,Y,0,202500.0,203760.0,24309.0,180000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-12850,-718,-681.0,-5028,2.0,1,1,0,1,0,0,Sales staff,2.0,1,1,FRIDAY,17,0,0,0,0,0,0,Other,,0.6008525809508071,0.41885428862332175,0.1423,0.0637,0.9985,0.9796,0.0155,0.1148,0.0559,0.6042,0.6042,0.0,0.11599999999999999,0.187,0.0,0.0,0.1061,0.0301,0.9985,0.9804,0.0089,0.0806,0.0345,0.6667,0.7083,0.0,0.0927,0.1215,0.0,0.0,0.141,0.069,0.9985,0.9799,0.0149,0.08,0.0345,0.6667,0.6458,0.0,0.1159,0.1943,0.0,0.0,reg oper spec account,block of flats,0.1735,Panel,No,0.0,0.0,0.0,0.0,-438.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n372367,0,Cash loans,F,N,Y,0,81000.0,448299.0,19120.5,387000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.031329,-20430,365243,-11079.0,-3782,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,XNA,0.5882828295746019,0.2622583692422573,0.7958031328748403,0.1402,1.0,0.9846,0.7892,0.0195,0.0,0.3103,0.1667,0.2083,0.0938,0.1143,0.1172,0.0,0.0,0.1429,1.0,0.9846,0.7975,0.0197,0.0,0.3103,0.1667,0.2083,0.096,0.1249,0.1221,0.0,0.0,0.1416,1.0,0.9846,0.792,0.0196,0.0,0.3103,0.1667,0.2083,0.0955,0.1163,0.1193,0.0,0.0,reg oper account,block of flats,0.0922,Panel,No,4.0,0.0,4.0,0.0,-968.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n201204,0,Cash loans,F,N,N,0,90000.0,753840.0,24448.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-15772,-1047,-4390.0,-4299,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 1,0.3559253925532852,0.4172096987407732,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3224.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n280067,0,Cash loans,F,Y,Y,1,90000.0,294322.5,15367.5,243000.0,Children,Working,Secondary / secondary special,Civil marriage,House / apartment,0.016612000000000002,-10717,-1564,-4968.0,-2905,21.0,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,0.4481582230452815,0.5007344470156673,0.4311917977993083,0.1474,0.0993,0.9871,0.8232,0.0295,0.16,0.1379,0.3333,0.375,0.0596,0.1194,0.1382,0.0039,0.0021,0.1502,0.1031,0.9871,0.8301,0.0298,0.1611,0.1379,0.3333,0.375,0.0609,0.1304,0.14400000000000002,0.0039,0.0022,0.1489,0.0993,0.9871,0.8256,0.0297,0.16,0.1379,0.3333,0.375,0.0606,0.1214,0.1406,0.0039,0.0021,reg oper account,block of flats,0.1253,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,0.0\n184336,0,Revolving loans,F,N,Y,0,67500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-18089,-4502,-7808.0,-1633,,1,1,0,1,1,0,,2.0,3,3,MONDAY,10,0,0,0,0,0,0,Other,,0.14706382113398098,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2597.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n349938,0,Cash loans,M,N,Y,0,189000.0,1125000.0,47794.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-15111,-5866,-3008.0,-4266,,1,1,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 1,,0.4663113221587283,0.524496446363472,,,,,,0.0,0.0345,0.1667,,,,,,0.0,,,,,,0.0,0.0345,0.1667,,,,,,0.0,,,,,,0.0,0.0345,0.1667,,,,,,0.0,,,0.0818,,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n129159,0,Revolving loans,M,Y,Y,1,225000.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Married,Co-op apartment,0.030755,-8230,-744,-4809.0,-906,10.0,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,12,0,0,0,1,1,0,Business Entity Type 3,0.29887952424230296,0.5213262304462215,0.31703177858344445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n148603,0,Revolving loans,F,N,Y,0,54000.0,157500.0,7875.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Widow,Rented apartment,0.019101,-22077,-553,-10188.0,-4273,,1,1,0,1,0,0,,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.6951568099368618,0.5124365053743444,0.7992967832109371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-353.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n340933,0,Cash loans,F,N,Y,0,67500.0,98910.0,7011.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-20204,365243,-2904.0,-3438,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,,0.5454860867029858,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1035.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n244057,0,Cash loans,F,N,N,0,81000.0,225000.0,12564.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.018801,-20347,-5217,-10127.0,-3864,,1,1,0,1,1,0,Laborers,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,Housing,,0.7602693294768582,0.4400578303966329,0.0108,0.0,0.9697,0.5716,0.0022,0.0,0.0603,0.0521,0.0692,0.0045,0.0095,0.0098,0.0,0.0,0.0084,0.0,0.9732,0.4969,0.0007,0.0,0.0345,0.0417,0.0833,0.0,0.0073,0.0053,0.0,0.0,0.0083,0.0,0.9722,0.6176,0.0013,0.0,0.0517,0.0417,0.0833,0.0052,0.0068,0.0068,0.0,0.0,reg oper account,block of flats,0.0188,\"Stone, brick\",No,4.0,2.0,4.0,1.0,-828.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n195558,1,Cash loans,F,Y,N,0,112500.0,195543.0,14359.5,148500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.025164,-16829,-1535,-1255.0,-380,3.0,1,1,0,1,0,0,Cleaning staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,,0.2878842200026013,0.18303516721781032,0.0928,0.1144,0.9831,0.7688,0.0142,0.0,0.2069,0.1667,0.2083,0.08,0.0756,0.0973,0.0,0.0,0.0945,0.1187,0.9831,0.7779,0.0143,0.0,0.2069,0.1667,0.2083,0.0818,0.0826,0.1014,0.0,0.0,0.0937,0.1144,0.9831,0.7719,0.0143,0.0,0.2069,0.1667,0.2083,0.0813,0.077,0.0991,0.0,0.0,reg oper account,block of flats,0.0843,Panel,No,3.0,0.0,3.0,0.0,-500.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n280157,0,Revolving loans,F,N,N,1,67500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-12976,-4883,-4632.0,-3059,,1,1,0,1,0,1,Laborers,3.0,2,2,MONDAY,18,0,0,0,0,0,0,Industry: type 3,0.31218713614043675,0.2724994577862616,0.30620229831350426,0.0619,0.0619,0.9767,0.6804,0.0236,0.0,0.1379,0.1667,0.0417,0.0618,0.0504,0.0527,0.0,0.0,0.063,0.0642,0.9767,0.6929,0.0238,0.0,0.1379,0.1667,0.0417,0.0632,0.0551,0.0549,0.0,0.0,0.0625,0.0619,0.9767,0.6847,0.0237,0.0,0.1379,0.1667,0.0417,0.0629,0.0513,0.0537,0.0,0.0,reg oper account,block of flats,0.0563,Panel,No,0.0,0.0,0.0,0.0,-1387.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n419947,1,Cash loans,M,Y,Y,0,180000.0,450000.0,35554.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.035792000000000004,-13392,-3186,-7378.0,-5035,17.0,1,1,1,1,1,0,Managers,2.0,2,2,THURSDAY,12,0,0,0,1,1,0,Business Entity Type 3,,0.6884311271591667,0.7047064232963289,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1396.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n315605,0,Cash loans,F,N,Y,0,157500.0,269982.0,27792.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-19957,-394,-8700.0,-3176,,1,1,1,1,0,1,Low-skill Laborers,2.0,2,2,MONDAY,9,1,1,0,1,1,1,Self-employed,0.5857648871976298,0.4491611058379752,0.5064842396679806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-640.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n437384,0,Cash loans,F,N,Y,1,247500.0,900000.0,46084.5,900000.0,Unaccompanied,Working,Incomplete higher,Married,With parents,0.018634,-12634,-1521,-6109.0,-4612,,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,0.2923236014834176,0.3629861578718441,0.2458512138252296,,,0.9916,,,,,,,,,0.1279,,,,,0.9916,,,,,,,,,0.1333,,,,,0.9916,,,,,,,,,0.1302,,,,,0.1049,,No,1.0,1.0,1.0,1.0,-778.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n148427,0,Cash loans,F,N,Y,0,31500.0,251091.0,24588.0,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-20682,365243,-8369.0,-4186,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.7383617864398141,,0.1031,0.0965,0.9776,0.6940000000000001,0.0124,0.0,0.2069,0.1667,0.2083,0.0931,0.0841,0.0946,0.0,0.0,0.105,0.1001,0.9777,0.706,0.0126,0.0,0.2069,0.1667,0.2083,0.0953,0.0918,0.0986,0.0,0.0,0.1041,0.0965,0.9776,0.6981,0.0125,0.0,0.2069,0.1667,0.2083,0.0948,0.0855,0.0963,0.0,0.0,reg oper account,block of flats,0.0744,Panel,No,0.0,0.0,0.0,0.0,-796.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n342325,0,Cash loans,M,N,Y,1,139500.0,1067418.0,34564.5,891000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005144,-13963,-168,-1445.0,-4888,,1,1,0,1,0,0,Managers,3.0,2,2,THURSDAY,15,0,0,0,1,1,0,Business Entity Type 3,0.2780008823270742,0.6348504416399308,0.5762088360175724,0.0619,0.07,0.9866,0.8164,0.0065,0.0,0.1379,0.1667,0.0417,0.0199,0.0504,0.0613,0.0,0.0,0.063,0.0726,0.9866,0.8236,0.0066,0.0,0.1379,0.1667,0.0417,0.0203,0.0551,0.0638,0.0,0.0,0.0625,0.07,0.9866,0.8189,0.0066,0.0,0.1379,0.1667,0.0417,0.0202,0.0513,0.0624,0.0,0.0,reg oper account,block of flats,0.0517,Panel,No,3.0,1.0,3.0,1.0,-361.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n152959,0,Cash loans,F,Y,Y,0,112500.0,808650.0,23773.5,675000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.01885,-20048,-12603,-7840.0,-2808,14.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Agriculture,,0.7217877191510523,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2255.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n368301,0,Cash loans,F,N,Y,1,157500.0,1546020.0,45333.0,1350000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.019689,-12755,-1388,-1078.0,-392,,1,1,0,1,0,0,,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Other,,0.6299550413445746,0.2176285202779586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-206.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n324311,0,Cash loans,M,Y,Y,0,450000.0,1305000.0,36018.0,1305000.0,Unaccompanied,State servant,Academic degree,Married,House / apartment,0.032561,-16256,-5413,-854.0,-4598,1.0,1,1,1,1,1,1,Managers,2.0,1,1,MONDAY,13,0,0,0,0,0,0,University,0.3803688835217298,0.7012731941401965,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1283.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n254872,0,Cash loans,F,N,Y,1,225000.0,149256.0,15799.5,135000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.003813,-17302,-5105,-4207.0,-853,,1,1,0,1,0,1,Core staff,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Government,0.8074098991441155,0.4768497440076378,0.2955826421513093,0.0052,0.0,0.9588,,,0.0,0.0345,0.0,,0.0271,,0.0028,,0.0,0.0053,0.0,0.9588,,,0.0,0.0345,0.0,,0.0277,,0.0029,,0.0,0.0052,0.0,0.9588,,,0.0,0.0345,0.0,,0.0275,,0.0028,,0.0,,block of flats,0.0022,Wooden,No,0.0,0.0,0.0,0.0,-1756.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n342983,0,Cash loans,M,N,N,0,90000.0,677664.0,34600.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.031329,-12395,-3515,-288.0,-4652,,1,1,1,1,0,0,Core staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Agriculture,0.2458655629322825,0.580686936821281,0.4418358231994413,0.0124,0.0149,0.9767,0.6804,0.0,0.0,0.069,0.0417,,0.0131,0.0101,0.0082,0.0,0.0,0.0126,0.0155,0.9767,0.6929,0.0,0.0,0.069,0.0417,,0.0134,0.011000000000000001,0.0086,0.0,0.0,0.0125,0.0149,0.9767,0.6847,0.0,0.0,0.069,0.0417,,0.0133,0.0103,0.0084,0.0,0.0,not specified,block of flats,0.0065,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1117.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n368993,0,Cash loans,F,Y,N,1,135000.0,1078200.0,31522.5,900000.0,Family,Working,Higher education,Married,House / apartment,0.020713,-17623,-8832,-4205.0,-744,10.0,1,1,1,1,0,0,Core staff,3.0,3,2,SUNDAY,17,0,0,0,0,0,0,Medicine,,0.7183275118514675,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-670.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,2.0,1.0\n230657,0,Cash loans,M,Y,N,0,180000.0,284400.0,18513.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.015221,-9313,-1546,-9285.0,-1991,1.0,1,1,1,1,1,0,Managers,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Construction,,0.3908110259830001,0.2445163919946749,0.0928,0.0799,0.9811,0.7416,0.0372,0.0,0.2069,0.1667,0.2083,0.0896,0.0756,0.0868,0.0,0.0,0.0945,0.0829,0.9811,0.7517,0.0375,0.0,0.2069,0.1667,0.2083,0.0916,0.0826,0.0904,0.0,0.0,0.0937,0.0799,0.9811,0.7451,0.0374,0.0,0.2069,0.1667,0.2083,0.0911,0.077,0.0883,0.0,0.0,reg oper account,block of flats,0.0782,Panel,No,0.0,0.0,0.0,0.0,-1059.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n107000,0,Cash loans,F,N,Y,2,135000.0,177768.0,14175.0,135000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008473999999999999,-13352,-2120,-6131.0,-3368,,1,1,0,1,0,1,Managers,4.0,2,2,TUESDAY,12,0,0,0,0,0,0,Government,0.5358868788256528,0.7443451003116258,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1393.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n312733,0,Revolving loans,F,N,N,0,67500.0,135000.0,6750.0,135000.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.04622,-18044,-1382,-8243.0,-1583,,1,1,1,1,0,0,Laborers,2.0,1,1,SATURDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.6260009044939694,,0.0268,0.0,0.9568,0.4084,0.0016,0.0,0.069,0.0417,0.0833,0.045,0.0219,0.0172,0.0,0.0,0.0273,0.0,0.9568,0.4316,0.0016,0.0,0.069,0.0417,0.0833,0.046,0.0239,0.018000000000000002,0.0,0.0,0.0271,0.0,0.9568,0.4163,0.0016,0.0,0.069,0.0417,0.0833,0.0457,0.0222,0.0175,0.0,0.0,not specified,block of flats,0.0136,Wooden,No,1.0,0.0,1.0,0.0,-518.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n164411,0,Revolving loans,F,Y,Y,3,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005084,-14595,-161,-2378.0,-2498,15.0,1,1,0,1,0,0,Security staff,5.0,2,2,TUESDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.3973430675934734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1206.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n331668,0,Cash loans,F,N,Y,0,112500.0,277969.5,12955.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-8519,-1126,-8517.0,-415,,1,1,0,1,1,0,Sales staff,2.0,2,2,SUNDAY,13,1,1,0,1,1,0,Trade: type 2,,0.5512591848970323,0.5100895276257282,0.1732,0.1094,0.9762,0.6736,0.003,0.0,0.0345,0.1667,0.2083,0.0335,0.1404,0.0579,0.0039,0.1459,0.1765,0.1135,0.9762,0.6864,0.003,0.0,0.0345,0.1667,0.2083,0.0343,0.1534,0.0603,0.0039,0.1544,0.1749,0.1094,0.9762,0.6779999999999999,0.003,0.0,0.0345,0.1667,0.2083,0.0341,0.1428,0.059000000000000004,0.0039,0.1489,reg oper account,specific housing,0.0773,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-217.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n126077,0,Cash loans,M,Y,N,0,135000.0,808650.0,26086.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.008575,-10038,-894,-4384.0,-2562,11.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.4775807866604082,0.1061556230153924,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-966.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n203953,0,Cash loans,M,N,N,0,135000.0,835380.0,40320.0,675000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.018801,-18967,-148,-1809.0,-1807,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,SATURDAY,7,0,0,0,0,1,1,Housing,,0.1860042712303933,,0.033,0.0908,0.9682,0.5648,0.0554,0.0,0.1379,0.0833,0.0833,0.0306,0.0252,0.025,0.0077,0.0464,0.0336,0.0942,0.9682,0.5818,0.0559,0.0,0.1379,0.0833,0.0833,0.0313,0.0275,0.0261,0.0078,0.0492,0.0333,0.0908,0.9682,0.5706,0.0558,0.0,0.1379,0.0833,0.0833,0.0312,0.0257,0.0255,0.0078,0.0474,reg oper account,block of flats,0.0601,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-279.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n212357,0,Cash loans,F,Y,N,0,247500.0,900000.0,57649.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-13277,-4739,-3998.0,-3223,9.0,1,1,0,1,1,0,Laborers,2.0,1,1,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.7742488737776433,0.7126744382856682,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1556,,No,3.0,0.0,3.0,0.0,-2713.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n294335,1,Cash loans,M,N,N,1,112500.0,137520.0,6538.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-12688,-2189,-1456.0,-4576,,1,1,0,1,0,0,Drivers,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.1813717542280142,0.6947973240071235,0.6785676886853644,0.1216,0.087,0.997,0.9524,0.0778,0.1,0.0862,0.4375,0.0417,0.0146,0.1042,0.1637,0.0154,0.0847,0.1134,0.0903,0.9965,0.9543,0.0785,0.0806,0.069,0.4167,0.0417,0.015,0.1139,0.1342,0.0156,0.0897,0.1228,0.087,0.997,0.953,0.0783,0.1,0.0862,0.4375,0.0417,0.0149,0.106,0.1667,0.0155,0.0865,reg oper account,block of flats,0.1773,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1499.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n191216,0,Cash loans,F,N,N,0,265500.0,1129500.0,31059.0,1129500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.031329,-19266,-2862,-5766.0,-2809,,1,1,1,1,1,0,Managers,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Bank,,0.6358890065690538,0.7801436381572275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-809.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n237402,0,Cash loans,F,N,Y,1,157500.0,276277.5,20349.0,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010032,-9923,-1900,-1606.0,-753,,1,1,0,1,0,0,Core staff,3.0,2,2,FRIDAY,3,0,0,0,0,0,0,Business Entity Type 3,0.2402533719279185,0.22613163232189784,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-452.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n392815,0,Cash loans,F,Y,Y,1,225000.0,819792.0,39568.5,720000.0,Family,Working,Higher education,Married,House / apartment,0.030755,-12786,-229,-5508.0,-5167,4.0,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.731466547643007,0.7957697449019256,0.8038850611746273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-908.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n411122,0,Cash loans,M,Y,N,0,135000.0,521280.0,19984.5,450000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.002134,-12786,-3636,-3464.0,-4195,17.0,1,1,0,1,0,1,,2.0,3,3,SATURDAY,6,0,0,0,1,0,1,Medicine,0.5399735739004784,0.09132394700279696,0.392773860603134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n415843,0,Cash loans,F,N,Y,0,49500.0,239850.0,23494.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.031329,-24583,365243,-1513.0,-998,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.4898348585637536,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,1.0,9.0,0.0,-971.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n370526,0,Cash loans,F,N,Y,1,135000.0,711000.0,20916.0,711000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.018209,-14832,-2424,-3247.0,-3860,,1,1,0,1,0,0,,3.0,3,3,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.7851399066799922,0.34691633041259523,0.6863823354047934,0.0742,0.1818,0.9707,0.5988,0.0091,0.0,0.2414,0.125,0.125,0.1013,0.0067,0.069,0.0309,0.0868,0.0756,0.1887,0.9707,0.6145,0.0091,0.0,0.2414,0.125,0.125,0.1036,0.0073,0.0719,0.0311,0.0919,0.0749,0.1818,0.9707,0.6042,0.0091,0.0,0.2414,0.125,0.125,0.10300000000000001,0.0068,0.0702,0.0311,0.0887,reg oper account,block of flats,0.0781,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2227.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n390828,0,Cash loans,M,Y,N,1,60750.0,284400.0,13257.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.0228,-11061,-1433,-5092.0,-3681,17.0,1,1,1,1,0,0,Drivers,3.0,2,2,SUNDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.3984222725612148,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-800.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n372695,0,Cash loans,F,N,Y,0,135000.0,454455.0,22234.5,319500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-15804,-99,-3875.0,-4044,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Other,,0.5156048009290936,0.6848276586890367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-208.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n279610,0,Cash loans,F,N,Y,2,225000.0,521280.0,31630.5,450000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-13809,-1101,-7541.0,-5169,,1,1,0,1,0,0,Managers,4.0,1,1,FRIDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.7392228749867441,0.6976047485050194,0.7801436381572275,0.0649,0.0455,0.9831,0.7688,0.0329,0.04,0.0345,0.3333,0.375,0.0079,0.053,0.0644,0.0,0.0,0.0662,0.0473,0.9831,0.7779,0.0332,0.0403,0.0345,0.3333,0.375,0.0081,0.0579,0.0671,0.0,0.0,0.0656,0.0455,0.9831,0.7719,0.0331,0.04,0.0345,0.3333,0.375,0.008,0.0539,0.0656,0.0,0.0,reg oper account,block of flats,0.0507,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-398.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n118970,0,Cash loans,F,N,Y,0,72000.0,675000.0,21906.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.01885,-20251,365243,-5066.0,-3724,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.7409724744610511,0.6161216908872079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1407.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n221006,0,Cash loans,F,N,Y,0,157500.0,585000.0,19143.0,585000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-22596,365243,-5475.0,-3974,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.7320100594789639,0.5478104658520093,,,0.9781,,,,,,,,,0.0684,,,,,0.9782,,,,,,,,,0.0712,,,,,0.9781,,,,,,,,,0.0696,,,,,0.055999999999999994,,No,6.0,0.0,6.0,0.0,-818.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n332715,0,Cash loans,F,Y,Y,1,288000.0,1113840.0,47322.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-15277,-4375,-3123.0,-3141,11.0,1,1,1,1,1,0,Managers,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Kindergarten,0.5399933356178943,0.4463817896019935,0.4902575124990026,,0.0704,0.9846,,,0.0,0.1379,0.1667,,,,0.0556,,,,0.073,0.9846,,,0.0,0.1379,0.1667,,,,0.057999999999999996,,,,0.0704,0.9846,,,0.0,0.1379,0.1667,,,,0.0566,,,,,0.0489,Panel,No,0.0,0.0,0.0,0.0,-769.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n247361,0,Cash loans,F,N,Y,0,81000.0,288873.0,16713.0,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.04622,-18085,-739,-12006.0,-1638,,1,1,0,1,1,0,,1.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,Other,0.6316168997564539,0.7464036302679987,0.7649392739475195,0.1149,0.0926,0.9871,0.8232,0.0,0.06,0.1897,0.25,0.2917,0.0728,0.0937,0.1228,0.0,0.0678,0.1134,0.0839,0.9782,0.7125,0.0,0.0,0.1034,0.1667,0.2083,0.0293,0.0992,0.1078,0.0,0.0,0.1161,0.0926,0.9871,0.8256,0.0,0.06,0.1897,0.25,0.2917,0.0741,0.0953,0.1251,0.0,0.0692,reg oper account,block of flats,0.1624,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n112899,0,Cash loans,M,N,Y,0,130050.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.018634,-19793,-3079,-4338.0,-3312,,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.7301743544621029,0.7764098512142026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,4.0,0.0,-2058.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n163331,0,Cash loans,M,Y,Y,0,202500.0,373140.0,29610.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-10624,-1305,-6008.0,-3247,3.0,1,1,0,1,1,0,,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.2419684581042129,,0.0825,,0.9841,,,0.0,0.2069,0.1667,,0.0199,,0.0827,,0.0,0.084,,0.9841,,,0.0,0.2069,0.1667,,0.0204,,0.0862,,0.0,0.0833,,0.9841,,,0.0,0.2069,0.1667,,0.0203,,0.0842,,0.0,,block of flats,0.0651,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-223.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n105866,0,Cash loans,F,N,Y,0,99000.0,808650.0,23773.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00733,-19138,-11543,-7593.0,-1751,,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Medicine,,0.5513240972847206,,0.0825,0.0831,0.9806,0.7348,0.1339,0.0,0.1379,0.1667,0.2083,0.0739,0.0672,0.0691,0.0,0.0865,0.084,0.0862,0.9806,0.7452,0.1351,0.0,0.1379,0.1667,0.2083,0.0756,0.0735,0.07200000000000001,0.0,0.0916,0.0833,0.0831,0.9806,0.7383,0.1348,0.0,0.1379,0.1667,0.2083,0.0752,0.0684,0.0704,0.0,0.0883,reg oper account,block of flats,0.0732,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-537.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n102215,0,Cash loans,F,N,N,0,112500.0,961609.5,38263.5,859500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-21409,-3300,-813.0,-4850,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Self-employed,,0.7166831054573073,0.7801436381572275,0.2959,0.0,0.9821,0.7552,0.0744,0.32,0.2759,0.3333,0.375,0.1728,0.2387,0.3137,0.0116,0.0061,0.3015,0.0,0.9821,0.7648,0.0751,0.3222,0.2759,0.3333,0.375,0.1768,0.2608,0.3268,0.0117,0.0065,0.2987,0.0,0.9821,0.7585,0.0749,0.32,0.2759,0.3333,0.375,0.1759,0.2428,0.3193,0.0116,0.0062,reg oper account,block of flats,0.2887,Panel,No,2.0,0.0,2.0,0.0,-1234.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n135511,1,Cash loans,M,N,N,0,117000.0,582804.0,29884.5,463500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-20895,-1304,-6501.0,-4413,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.7112739996036315,0.6709526933436697,0.7490217048463391,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1883.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n436408,0,Cash loans,M,N,Y,1,157500.0,364896.0,22450.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.04622,-11698,-99,-4847.0,-4061,,1,1,0,1,0,0,,2.0,1,1,FRIDAY,13,0,1,1,0,1,1,Business Entity Type 3,,0.6907668403963522,,0.0825,0.0,0.9752,0.66,0.099,0.0,0.1379,0.1667,0.2083,0.0554,0.0656,0.0612,0.0077,0.0239,0.084,0.0,0.9752,0.6733,0.0999,0.0,0.1379,0.1667,0.2083,0.0566,0.0716,0.0638,0.0078,0.0253,0.0833,0.0,0.9752,0.6645,0.0996,0.0,0.1379,0.1667,0.2083,0.0563,0.0667,0.0623,0.0078,0.0244,reg oper spec account,block of flats,0.0541,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-789.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n276572,0,Cash loans,M,Y,N,0,186273.9,1350000.0,34128.0,1350000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.019101,-15637,-341,-6305.0,-4312,4.0,1,1,1,1,0,0,Drivers,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Medicine,,0.6011409920629912,0.6626377922738201,0.0619,0.078,0.9831,0.7688,,0.0,0.1379,0.1667,0.2083,0.0213,0.0504,0.0552,0.0,0.0,0.063,0.0809,0.9831,0.7779,,0.0,0.1379,0.1667,0.2083,0.0218,0.0551,0.0575,0.0,0.0,0.0625,0.078,0.9831,0.7719,,0.0,0.1379,0.1667,0.2083,0.0217,0.0513,0.0562,0.0,0.0,reg oper account,block of flats,0.0434,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n281526,1,Cash loans,F,Y,Y,0,202500.0,500427.0,33984.0,432000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.020713,-19946,-6351,-2914.0,-2357,25.0,1,1,1,1,1,0,Core staff,1.0,3,2,THURSDAY,13,0,0,0,0,0,0,Medicine,,0.12031154359891665,0.16640554618393638,0.0825,0.0795,0.9752,0.66,0.0094,0.0,0.1379,0.1667,0.2083,0.061,0.0672,0.0696,0.0,0.0,0.084,0.0825,0.9752,0.6733,0.0095,0.0,0.1379,0.1667,0.2083,0.0624,0.0735,0.0725,0.0,0.0,0.0833,0.0795,0.9752,0.6645,0.0095,0.0,0.1379,0.1667,0.2083,0.0621,0.0684,0.0708,0.0,0.0,not specified,block of flats,0.0599,Panel,No,0.0,0.0,0.0,0.0,-1405.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n323567,0,Cash loans,F,N,Y,1,202500.0,1528200.0,53248.5,1350000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.008019,-11669,-632,-2248.0,-3999,,1,1,0,1,0,0,Drivers,3.0,2,2,WEDNESDAY,18,0,0,0,0,1,1,Business Entity Type 3,0.6079699484514873,0.5507127699764964,0.5762088360175724,0.0021,0.0,0.4774,0.3948,,0.0,0.0,0.0,0.0417,0.0,0.0034,0.0028,0.0,0.0024,0.0,0.0,0.0,0.4185,,0.0,0.0,0.0,0.0417,0.0,0.0037,0.003,0.0,0.0025,0.0021,0.0,0.4774,0.4029,,0.0,0.0,0.0,0.0417,0.0,0.0034,0.0029,0.0,0.0024,reg oper account,block of flats,0.0,Wooden,No,6.0,0.0,6.0,0.0,-633.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n431017,0,Cash loans,F,N,Y,1,90000.0,573408.0,32017.5,495000.0,Unaccompanied,Commercial associate,Higher education,Married,Municipal apartment,0.008068,-16162,-1291,-6141.0,-4396,,1,1,0,1,0,0,Private service staff,3.0,3,3,WEDNESDAY,5,0,0,0,0,1,1,Medicine,,0.35876970296147703,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-928.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n128296,0,Cash loans,M,Y,N,1,121500.0,679500.0,30834.0,679500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.014519999999999996,-13761,-2896,-7777.0,-4611,4.0,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Trade: type 7,0.4351174544567079,0.5800105704751206,0.5884877883422673,,,0.9791,0.7144,,0.0,0.1724,0.1667,,,,0.054000000000000006,,0.0492,,,0.9782,0.7125,,0.0,0.1379,0.1667,,,,0.0563,,0.0521,,,0.9791,0.7182,,0.0,0.1724,0.1667,,,,0.055,,0.0502,,block of flats,0.0532,Panel,No,0.0,0.0,0.0,0.0,-1837.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n293032,0,Cash loans,M,Y,Y,2,135000.0,284400.0,22599.0,225000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.016612000000000002,-15350,-6493,-3128.0,-4219,10.0,1,1,0,1,0,0,Core staff,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Military,,0.6317526780329022,0.6910212267577837,0.1485,0.1471,0.9881,0.8368,0.0576,0.16,0.1379,0.3333,0.375,0.0877,0.1202,0.1764,0.0039,0.0049,0.1513,0.1527,0.9881,0.8432,0.0581,0.1611,0.1379,0.3333,0.375,0.0897,0.1313,0.1838,0.0039,0.0052,0.1499,0.1471,0.9881,0.8390000000000001,0.057999999999999996,0.16,0.1379,0.3333,0.375,0.0892,0.1223,0.1796,0.0039,0.005,reg oper account,block of flats,0.1713,Panel,No,1.0,0.0,1.0,0.0,-1698.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n125008,0,Cash loans,F,N,Y,0,67500.0,703584.0,20700.0,504000.0,Other_B,State servant,Secondary / secondary special,Married,House / apartment,0.020246,-19854,-10342,-13944.0,-3343,,1,1,0,1,0,0,Core staff,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,Postal,,0.18012139263104054,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-2585.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n264730,0,Cash loans,F,N,N,0,247500.0,808650.0,24732.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018801,-18343,-2836,-1486.0,-1241,,1,1,1,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Other,,0.6499940804365926,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2688.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n264672,0,Cash loans,M,N,Y,0,180000.0,1442952.0,42318.0,1260000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-18855,-552,-6144.0,-1820,,1,1,0,1,0,0,Security staff,2.0,1,1,THURSDAY,11,0,0,0,0,0,0,Security,,0.6023934522394122,0.6363761710860439,0.0082,,0.9677,,,,0.0345,0.0417,,,,,,,0.0084,,0.9677,,,,0.0345,0.0417,,,,,,,0.0083,,0.9677,,,,0.0345,0.0417,,,,,,,,block of flats,0.0083,Wooden,No,2.0,0.0,2.0,0.0,-1670.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n144695,0,Cash loans,F,Y,Y,2,67500.0,794173.5,40680.0,697500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-15044,-1080,-4232.0,-4936,10.0,1,1,0,1,1,0,Accountants,4.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,0.5596492876683475,0.15426542413276212,0.5744466170995097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.0,2.0,12.0,2.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n281518,0,Revolving loans,M,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009549,-14975,-1300,-7597.0,-4733,,1,1,0,1,0,0,Laborers,1.0,2,2,SUNDAY,12,0,0,0,0,0,0,Housing,,0.7449895677414688,0.6279908192952864,0.0082,0.0,0.9573,0.4152,0.0023,0.0,0.069,0.0417,0.0833,0.0534,0.0067,0.0067,0.0,0.0,0.0084,0.0,0.9573,0.4381,0.0023,0.0,0.069,0.0417,0.0833,0.0546,0.0073,0.006999999999999999,0.0,0.0,0.0083,0.0,0.9573,0.423,0.0023,0.0,0.069,0.0417,0.0833,0.0543,0.0068,0.0068,0.0,0.0,,block of flats,0.0065,Wooden,Yes,0.0,0.0,0.0,0.0,-156.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n110920,0,Cash loans,M,N,Y,0,166500.0,1130760.0,33192.0,810000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-13849,-1973,-6119.0,-3987,,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Industry: type 7,0.401165052890953,0.5489590943815228,0.5136937663039473,0.1031,0.0,0.9781,0.7008,0.0123,0.0,0.2069,0.1667,0.2083,0.0198,0.0841,0.0901,0.0,0.0,0.105,0.0,0.9782,0.7125,0.0124,0.0,0.2069,0.1667,0.2083,0.0203,0.0918,0.0938,0.0,0.0,0.1041,0.0,0.9781,0.7048,0.0123,0.0,0.2069,0.1667,0.2083,0.0202,0.0855,0.0917,0.0,0.0,reg oper account,block of flats,0.0708,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1403.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n401171,1,Cash loans,M,Y,Y,0,315000.0,440784.0,32071.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.020713,-11238,-769,-384.0,-3927,9.0,1,1,1,1,0,0,Laborers,1.0,3,1,THURSDAY,11,1,1,0,1,1,0,Business Entity Type 2,0.19611745727781832,0.5971495418324797,0.2866524828090694,0.1052,0.1002,0.9891,0.8504,0.0299,0.12,0.1034,0.3333,0.0417,0.0617,0.0849,0.131,0.0039,0.0068,0.1071,0.10400000000000001,0.9891,0.8563,0.0302,0.1208,0.1034,0.3333,0.0417,0.0632,0.0927,0.1364,0.0039,0.0072,0.1062,0.1002,0.9891,0.8524,0.0301,0.12,0.1034,0.3333,0.0417,0.0628,0.0864,0.1333,0.0039,0.006999999999999999,reg oper account,block of flats,0.1045,Panel,No,0.0,0.0,0.0,0.0,-2521.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n271718,0,Cash loans,F,N,Y,0,94500.0,544491.0,17694.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010032,-21557,365243,-2049.0,-1937,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,7,0,0,0,0,0,0,XNA,,0.4265321994415247,0.36896873825284665,0.1227,0.1079,0.9776,0.6940000000000001,0.05,0.0,0.2759,0.1667,0.2083,0.1243,0.1,0.1129,0.0,0.0,0.125,0.11199999999999999,0.9777,0.706,0.0505,0.0,0.2759,0.1667,0.2083,0.1271,0.1093,0.1177,0.0,0.0,0.1239,0.1079,0.9776,0.6981,0.0503,0.0,0.2759,0.1667,0.2083,0.1264,0.1018,0.115,0.0,0.0,,block of flats,0.1162,Panel,No,2.0,1.0,2.0,0.0,-537.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n376566,0,Revolving loans,M,Y,N,0,180000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-8684,-89,-74.0,-1367,1.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,SATURDAY,15,0,0,0,0,1,1,Industry: type 9,0.2909266633606077,0.5752249785331207,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-874.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n437941,0,Cash loans,F,Y,Y,2,112500.0,508500.0,40306.5,508500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-10594,-800,-3101.0,-1728,18.0,1,1,0,1,0,0,Cooking staff,4.0,2,2,MONDAY,13,0,0,0,1,1,0,Kindergarten,0.4586555976246641,0.7562973695106953,0.25670557243930026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-449.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n346140,0,Revolving loans,M,Y,Y,0,90000.0,247500.0,12375.0,247500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.031329,-14351,-6199,-1282.0,-3401,3.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Business Entity Type 1,0.4898542431243904,0.6291594655328508,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1317.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n171645,0,Cash loans,F,N,Y,0,225000.0,2085120.0,72607.5,1800000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.04622,-13904,-328,-2212.0,-4898,,1,1,0,1,0,0,Sales staff,2.0,1,1,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.43876227923282624,0.7757367841085786,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-938.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n345815,0,Cash loans,F,N,Y,2,180000.0,545040.0,25537.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018029,-12003,-5427,-5641.0,-4398,,1,1,0,1,0,0,,4.0,3,2,SUNDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.4282340010601636,0.4992720153045617,0.0814,0.0764,0.9826,0.762,0.0128,0.0,0.2069,0.1667,0.2083,,0.0656,0.05,0.0039,0.0058,0.083,0.0792,0.9826,0.7713,0.0129,0.0,0.2069,0.1667,0.2083,,0.0716,0.0521,0.0039,0.0061,0.0822,0.0764,0.9826,0.7652,0.0129,0.0,0.2069,0.1667,0.2083,,0.0667,0.0509,0.0039,0.0059,reg oper account,block of flats,0.085,\"Stone, brick\",No,6.0,0.0,5.0,0.0,-1358.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n115690,0,Cash loans,M,N,N,0,405000.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-14293,-1496,-8208.0,-4917,,1,1,0,1,1,0,,2.0,2,2,SUNDAY,20,0,0,0,0,1,1,Business Entity Type 3,,0.5649082665939099,,0.3856,0.2682,0.9826,0.762,0.0802,0.44,0.3793,0.3333,0.375,0.3409,0.3026,0.2554,0.0541,0.0555,0.3929,0.2783,0.9826,0.7713,0.081,0.4431,0.3793,0.3333,0.375,0.3487,0.3306,0.2661,0.0545,0.0587,0.3893,0.2682,0.9826,0.7652,0.0807,0.44,0.3793,0.3333,0.375,0.3469,0.3078,0.2599,0.0543,0.0566,reg oper spec account,block of flats,0.3355,Panel,No,4.0,0.0,4.0,0.0,-2747.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n104476,0,Cash loans,M,Y,Y,0,270000.0,508495.5,34249.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.022625,-21996,-1953,-1209.0,-4850,15.0,1,1,0,1,0,0,Managers,1.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.6823105870345411,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2506.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n345039,0,Cash loans,F,N,N,1,166500.0,900000.0,50256.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Co-op apartment,0.018029,-17594,-3463,-3382.0,-1130,,1,1,0,1,0,0,Private service staff,3.0,3,2,WEDNESDAY,8,0,0,0,0,1,1,Self-employed,0.6558498090388621,0.590884598360732,0.5762088360175724,0.1268,0.1,0.9881,0.8368,0.0411,0.16,0.1379,0.3333,0.375,0.1343,0.1009,0.1472,0.0116,0.0569,0.1292,0.1038,0.9881,0.8432,0.0415,0.1611,0.1379,0.3333,0.375,0.1374,0.1102,0.1534,0.0117,0.0603,0.128,0.1,0.9881,0.8390000000000001,0.0414,0.16,0.1379,0.3333,0.375,0.1367,0.1026,0.1499,0.0116,0.0581,reg oper account,block of flats,0.1507,Panel,No,13.0,0.0,13.0,0.0,-1792.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n218868,0,Cash loans,M,Y,Y,0,175500.0,225000.0,14647.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,Co-op apartment,0.025164,-10704,-214,-5344.0,-3386,4.0,1,1,1,1,0,0,Drivers,2.0,2,2,SUNDAY,10,0,1,1,0,1,1,Business Entity Type 3,,0.07487654214834204,0.375711009574066,0.0082,0.0326,0.9732,,,0.0,0.069,0.0417,,0.0185,,0.013000000000000001,,0.0,0.0,0.0338,0.9732,,,0.0,0.069,0.0417,,0.0145,,0.0134,,0.0,0.0083,0.0326,0.9732,,,0.0,0.069,0.0417,,0.0188,,0.0132,,0.0,,block of flats,0.0109,Mixed,No,5.0,1.0,5.0,1.0,-396.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n228993,1,Cash loans,M,Y,Y,0,225000.0,276277.5,22279.5,238500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-19531,-978,-1030.0,-2852,2.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6116159183033476,0.7032033049040319,0.2031,0.2674,0.9965,0.9524,0.1831,0.24,0.1034,0.625,0.625,0.0847,0.1513,0.3348,0.0656,0.1455,0.2069,0.2775,0.9965,0.9543,0.1848,0.2417,0.1034,0.625,0.625,0.0867,0.1653,0.3489,0.0661,0.154,0.2051,0.2674,0.9965,0.953,0.1843,0.24,0.1034,0.625,0.625,0.0862,0.1539,0.3408,0.066,0.1485,not specified,block of flats,0.3995,Monolithic,No,0.0,0.0,0.0,0.0,-381.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n422153,0,Cash loans,M,N,Y,2,121500.0,500211.0,52654.5,463500.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,With parents,0.006629,-10938,-93,-4093.0,-777,,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,7,0,0,0,1,1,0,Industry: type 9,,0.7638142902971514,0.20559813854932085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-723.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n310149,1,Cash loans,F,N,Y,0,180000.0,545040.0,26509.5,450000.0,Unaccompanied,State servant,Incomplete higher,Single / not married,House / apartment,0.072508,-12129,-2986,-5805.0,-3814,,1,1,1,1,0,0,High skill tech staff,1.0,1,1,THURSDAY,12,0,0,0,0,0,0,University,,0.764409459656549,0.3108182544189319,0.5588,0.2307,0.9856,0.8028,0.2166,0.64,0.2759,0.6667,0.0417,0.0,0.4522,0.5875,0.0154,0.0204,0.5693,0.2394,0.9856,0.8105,0.2186,0.6445,0.2759,0.6667,0.0417,0.0,0.494,0.6121,0.0156,0.0216,0.5642,0.2307,0.9856,0.8054,0.218,0.64,0.2759,0.6667,0.0417,0.0,0.46,0.5981,0.0155,0.0208,reg oper account,block of flats,0.4665,Panel,No,0.0,0.0,0.0,0.0,-694.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n214907,0,Cash loans,F,N,Y,0,270000.0,790434.0,31477.5,706500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-23264,365243,-3962.0,-4862,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.6075096782146193,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-1599.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n337606,0,Cash loans,F,N,Y,0,157500.0,755190.0,36459.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.0105,-10078,-590,-3056.0,-2741,,1,1,0,1,0,0,Sales staff,2.0,3,3,MONDAY,14,0,0,0,1,1,0,Trade: type 3,,0.4596403233420845,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1197.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n301492,1,Cash loans,M,N,Y,0,126000.0,251091.0,23607.0,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-18371,-724,-10631.0,-1202,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.759681359066308,0.5681410664970301,,0.0124,0.0233,0.9727,,,0.0,0.069,0.0417,,0.0122,,0.0073,,0.0036,0.0126,0.0242,0.9727,,,0.0,0.069,0.0417,,0.0124,,0.0076,,0.0038,0.0125,0.0233,0.9727,,,0.0,0.069,0.0417,,0.0124,,0.0074,,0.0036,,block of flats,0.0075,\"Stone, brick\",No,2.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n303228,0,Cash loans,M,N,Y,0,121500.0,579942.0,42331.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-11157,-1993,-5145.0,-3479,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Kindergarten,,0.6747025537800861,0.3606125659189888,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2233.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n450452,0,Cash loans,F,N,N,0,130500.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Higher education,Widow,House / apartment,0.0228,-22890,-2589,-7267.0,-4439,,1,1,0,1,1,0,Laborers,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,Medicine,0.7585029377925523,0.3522463705038036,0.6801388218428291,0.1031,0.0713,0.9896,0.8572,0.0261,0.16,0.1379,0.3333,0.0417,0.0164,0.0807,0.1346,0.0154,0.0194,0.105,0.07400000000000001,0.9896,0.8628,0.0264,0.1611,0.1379,0.3333,0.0417,0.0168,0.0882,0.1402,0.0156,0.0205,0.1041,0.0713,0.9896,0.8591,0.0263,0.16,0.1379,0.3333,0.0417,0.0167,0.0821,0.13699999999999998,0.0155,0.0198,not specified,block of flats,0.1101,Panel,No,0.0,0.0,0.0,0.0,-695.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n382098,0,Cash loans,F,N,Y,1,135000.0,799956.0,28863.0,607500.0,Other_B,Working,Secondary / secondary special,Married,House / apartment,0.010966,-14565,-4048,-4536.0,-4463,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,13,0,0,0,1,1,0,Business Entity Type 3,,0.26281723330508383,0.6769925032909132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-2168.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n445971,0,Cash loans,F,Y,Y,0,135000.0,536917.5,19413.0,463500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-20423,365243,-5739.0,-3907,6.0,1,0,0,1,1,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,0.6535637470944905,0.5738084298780091,0.5226973172821112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,8.0\n104498,0,Cash loans,F,N,Y,1,72000.0,98910.0,7164.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-12494,-2959,-2444.0,-3665,,1,1,1,1,1,0,Core staff,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Government,0.4675984431901658,0.5651823597725373,0.3706496323299817,0.0041,,0.9717,,,0.0,,0.0,,0.0155,,0.0009,,0.0026,0.0042,,0.9717,,,0.0,,0.0,,0.0158,,0.0009,,0.0027,0.0042,,0.9717,,,0.0,,0.0,,0.0157,,0.0009,,0.0026,,block of flats,0.0013,Wooden,No,0.0,0.0,0.0,0.0,-1785.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,1.0,0.0,0.0,0.0\n405870,0,Cash loans,F,N,Y,0,108000.0,314100.0,15241.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-18383,-4752,-7461.0,-1923,,1,1,0,1,0,0,Cooking staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,School,,0.2823441276377031,0.6380435278721609,0.0825,0.0457,0.9791,0.7144,0.0423,0.04,0.0345,0.3333,0.6667,0.0541,0.0399,0.0572,0.0,0.0,0.084,0.0475,0.9692,0.5949,0.0427,0.0,0.0345,0.0417,0.6667,0.0478,0.0138,0.0084,0.0,0.0,0.0833,0.0457,0.9791,0.7182,0.0426,0.04,0.0345,0.3333,0.6667,0.0551,0.0406,0.0583,0.0,0.0,reg oper account,block of flats,0.0092,Wooden,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n264359,0,Cash loans,M,Y,Y,1,126000.0,299772.0,17338.5,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-18878,-5216,-732.0,-2336,10.0,1,1,0,1,0,0,Managers,3.0,2,2,SATURDAY,14,0,0,0,0,0,0,Trade: type 7,0.5803626406730833,0.7267398882465174,0.5513812618027899,0.0825,0.0808,0.9767,0.6804,0.0098,0.0,0.1379,0.1667,0.2083,0.0925,0.0672,0.0698,0.0,0.0,0.084,0.0839,0.9767,0.6929,0.0099,0.0,0.1379,0.1667,0.2083,0.0946,0.0735,0.0727,0.0,0.0,0.0833,0.0808,0.9767,0.6847,0.0099,0.0,0.1379,0.1667,0.2083,0.0941,0.0684,0.071,0.0,0.0,reg oper account,block of flats,0.0602,Panel,No,1.0,0.0,1.0,0.0,-994.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n361617,0,Cash loans,F,N,Y,1,180000.0,728460.0,40806.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-17195,-669,-6692.0,-709,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 1,,0.3056929881840761,0.3791004853998145,0.0742,0.0,0.9821,0.7552,0.0152,0.08,0.069,0.3333,0.375,0.0227,0.0605,0.0771,0.0,0.0,0.0756,0.0,0.9821,0.7648,0.0153,0.0806,0.069,0.3333,0.375,0.0232,0.0661,0.0803,0.0,0.0,0.0749,0.0,0.9821,0.7585,0.0153,0.08,0.069,0.3333,0.375,0.0231,0.0616,0.0785,0.0,0.0,reg oper account,block of flats,0.0606,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n194638,0,Cash loans,F,N,Y,0,202500.0,566055.0,15061.5,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.005084,-21183,365243,-10453.0,-4672,,1,0,0,1,0,1,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,0.6373527248884254,0.7438995864986833,0.6769925032909132,0.1155,0.1357,0.9861,0.8096,,0.12,0.1034,0.3333,0.375,0.0484,,0.122,,0.0745,0.1176,0.1408,0.9861,0.8171,,0.1208,0.1034,0.3333,0.375,0.0495,,0.1271,,0.0789,0.1166,0.1357,0.9861,0.8121,,0.12,0.1034,0.3333,0.375,0.0493,,0.1242,,0.0761,reg oper spec account,block of flats,0.1122,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1497.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,9.0\n103807,0,Cash loans,M,N,Y,0,67500.0,327024.0,21982.5,270000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-14867,-651,-296.0,-405,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,16,0,1,1,1,1,1,Business Entity Type 3,0.3272951578934734,0.022342538109866108,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n174212,0,Cash loans,M,Y,Y,0,135000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.020713,-13626,-801,-2995.0,-4563,2.0,1,1,0,1,0,0,Security staff,1.0,3,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5599356306312757,0.3876253444214701,0.0825,0.0811,0.9727,0.626,0.0602,0.0,0.1379,0.1667,0.2083,0.0357,0.0639,0.057999999999999996,0.0154,0.0119,0.084,0.0841,0.9727,0.6406,0.0607,0.0,0.1379,0.1667,0.2083,0.0365,0.0698,0.0605,0.0156,0.0126,0.0833,0.0811,0.9727,0.631,0.0606,0.0,0.1379,0.1667,0.2083,0.0363,0.065,0.0591,0.0155,0.0122,reg oper account,block of flats,0.0811,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-431.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n190235,0,Cash loans,F,N,Y,0,225000.0,1223010.0,51948.0,1125000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.011703,-23570,365243,-10408.0,-4261,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,18,0,0,0,0,0,0,XNA,,0.7494544152872933,0.3233112448967859,0.0412,0.0,0.9707,,,0.08,0.2069,0.0833,,0.0443,,0.0475,,0.0212,0.0021,0.0,0.9692,,,0.0806,0.2069,0.0,,0.0186,,0.0017,,0.0211,0.0416,0.0,0.9707,,,0.08,0.2069,0.0833,,0.045,,0.0484,,0.0217,,block of flats,0.0062,\"Stone, brick\",No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n315723,0,Cash loans,M,N,N,0,135000.0,237024.0,15970.5,180000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006670999999999999,-20576,-1513,-5250.0,-3730,,1,1,1,1,0,0,Drivers,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,,0.5516378158045098,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1220.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n113836,0,Cash loans,M,Y,Y,1,315000.0,728460.0,46683.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008625,-13903,-2184,-7701.0,-3694,12.0,1,1,0,1,0,0,Drivers,3.0,2,2,THURSDAY,13,0,0,0,0,1,1,Hotel,,0.5290802464984798,0.6041125998015721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2209.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n167016,0,Cash loans,F,Y,Y,0,111600.0,254700.0,14751.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-23969,365243,-2320.0,-3194,9.0,1,0,0,1,0,0,,2.0,3,3,THURSDAY,6,0,0,0,0,0,0,XNA,0.7622283011826546,0.21225776521816764,0.4489622731076524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1723.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,8.0\n345784,0,Cash loans,M,N,Y,0,112500.0,450000.0,22018.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-19348,-6590,-4715.0,-2873,,1,1,0,1,0,0,Security staff,2.0,2,2,FRIDAY,10,0,0,0,0,1,1,Business Entity Type 2,0.7346130725171464,0.6611817934134729,0.3108182544189319,0.0742,,0.9916,,,0.0,0.2759,0.125,,,,0.098,,0.0047,0.0756,,0.9916,,,0.0,0.2759,0.125,,,,0.1022,,0.005,0.0749,,0.9916,,,0.0,0.2759,0.125,,,,0.0998,,0.0048,,block of flats,0.0781,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1510.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n160643,0,Cash loans,F,N,Y,2,270000.0,1006920.0,40063.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.016612000000000002,-12543,-2474,-2839.0,-5192,,1,1,1,1,1,0,Laborers,4.0,2,2,TUESDAY,12,0,0,0,0,1,1,Self-employed,,0.6090884330997792,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1963.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n286282,0,Cash loans,F,N,Y,3,405000.0,1078200.0,34780.5,900000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.010006,-15469,-5350,-2705.0,-4514,,1,1,1,1,1,0,,5.0,2,1,MONDAY,10,0,0,0,0,0,0,Other,0.6775624232615091,0.7493271037179199,0.5352762504724826,0.1835,0.19,0.9806,0.7348,,0.0,0.4138,0.1667,0.2083,,,0.1585,,,0.187,0.1972,0.9806,0.7452,,0.0,0.4138,0.1667,0.2083,,,0.1652,,,0.1853,0.19,0.9806,0.7383,,0.0,0.4138,0.1667,0.2083,,,0.1614,,,reg oper account,block of flats,0.1306,Panel,No,0.0,0.0,0.0,0.0,-1754.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n132913,0,Cash loans,M,N,N,0,159880.5,585000.0,22279.5,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.005084,-9196,-653,-2488.0,-1816,,1,1,1,1,0,0,Sales staff,1.0,2,2,THURSDAY,16,1,1,0,1,1,0,Trade: type 2,,0.5892130942143725,0.20092608771597087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-625.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n344673,0,Cash loans,F,N,N,0,63000.0,528633.0,35599.5,472500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.003813,-15183,-5299,-4594.0,-4614,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,7,0,0,0,0,0,0,School,0.4400164636005116,0.4374295100707914,0.7530673920730478,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1804.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n334043,0,Revolving loans,F,Y,Y,1,99000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-11791,-3724,-262.0,-481,4.0,1,1,0,1,0,0,Waiters/barmen staff,3.0,3,3,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4975410353312427,0.27416296332641604,0.4014074137749511,0.0629,0.0776,0.9886,0.8436,0.0098,0.0,0.1379,0.1667,0.2083,0.0536,0.0513,0.0649,0.0,0.0,0.0641,0.0805,0.9886,0.8497,0.0099,0.0,0.1379,0.1667,0.2083,0.0548,0.055999999999999994,0.0677,0.0,0.0,0.0635,0.0776,0.9886,0.8457,0.0098,0.0,0.1379,0.1667,0.2083,0.0546,0.0522,0.0661,0.0,0.0,,block of flats,0.0564,Panel,No,0.0,0.0,0.0,0.0,-444.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n260905,0,Cash loans,F,N,Y,0,135000.0,225000.0,23755.5,225000.0,Other_A,State servant,Higher education,Civil marriage,House / apartment,0.010556,-9776,-745,-4744.0,-2449,,1,1,0,1,0,1,Core staff,2.0,3,3,MONDAY,17,0,0,0,0,0,0,Insurance,0.5837387030763058,0.3957365920687258,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n426012,1,Cash loans,M,Y,Y,0,225000.0,971280.0,62203.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-11683,-1643,-5459.0,-4073,5.0,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Self-employed,,0.3966258956919609,,0.0737,,0.9841,,,,0.0728,0.1617,,,,0.0091,,,0.063,,0.9851,,,,0.069,0.1667,,,,0.0095,,,0.0625,,0.9851,,,,0.069,0.1667,,,,0.0092,,,,block of flats,0.0292,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-877.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n420549,0,Cash loans,F,N,Y,0,157500.0,1042560.0,37575.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010032,-11180,-2531,-2709.0,-766,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,7,0,0,0,0,0,0,Insurance,,0.6298938681905423,0.7001838506835805,,,0.9737,,,0.0,0.069,0.125,,,,0.0168,,,,,0.9737,,,0.0,0.069,0.125,,,,0.0175,,,,,0.9737,,,0.0,0.069,0.125,,,,0.0171,,,,block of flats,0.0248,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1317.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n154156,0,Cash loans,M,Y,N,0,157500.0,545040.0,39627.0,450000.0,Other_B,Working,Secondary / secondary special,Married,House / apartment,0.018634,-16588,-3001,-2424.0,-129,1.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Transport: type 2,,0.4838200569068172,0.4014074137749511,0.0371,0.0,0.9856,0.7959999999999999,0.0086,0.04,0.0345,0.3333,0.375,0.0637,0.0303,0.0382,,0.0,0.0378,0.0,0.9856,0.804,0.0087,0.0403,0.0345,0.3333,0.375,0.0652,0.0331,0.0399,,0.0,0.0375,0.0,0.9856,0.7987,0.0087,0.04,0.0345,0.3333,0.375,0.0648,0.0308,0.0389,,0.0,,block of flats,0.0301,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1588.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n182786,0,Cash loans,M,Y,Y,0,270000.0,770292.0,29340.0,688500.0,Family,Working,Higher education,Single / not married,House / apartment,0.02461,-8278,-1395,-3142.0,-955,1.0,1,1,1,1,1,0,Sales staff,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,Trade: type 2,0.2316724390131956,0.5315421969812226,0.6738300778602003,0.134,0.0761,0.9791,,,0.0,0.069,0.1667,,0.0738,,0.068,,0.0161,0.1366,0.079,0.9791,,,0.0,0.069,0.1667,,0.0755,,0.0708,,0.0171,0.1353,0.0761,0.9791,,,0.0,0.069,0.1667,,0.0751,,0.0692,,0.0165,,block of flats,0.07,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-619.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n364643,0,Cash loans,F,N,N,0,112500.0,521280.0,23089.5,450000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.032561,-9906,-1113,-9743.0,-2037,,1,1,0,1,1,1,Core staff,2.0,1,1,WEDNESDAY,18,0,0,0,0,0,0,Advertising,,0.5925487247512586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-687.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n150186,0,Revolving loans,F,Y,Y,0,225000.0,900000.0,45000.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.011703,-15119,-112,-3972.0,-3733,5.0,1,1,0,1,0,1,Accountants,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Industry: type 11,0.5555760674685387,0.6627183054054923,0.22133520635446602,0.3021,,0.9940000000000001,,,0.44,0.3448,0.375,,,,0.4921,,0.1499,0.3078,,0.9940000000000001,,,0.4431,0.3448,0.375,,,,0.5127,,0.1587,0.305,,0.9940000000000001,,,0.44,0.3448,0.375,,,,0.501,,0.153,,block of flats,0.4197,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1340.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n154052,0,Cash loans,F,N,N,0,157500.0,1006920.0,40063.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.009657,-18432,-1503,-3349.0,-1957,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,17,0,0,0,1,1,0,Business Entity Type 3,,0.729078636556229,0.8327850252992314,0.0619,0.0,0.9826,0.762,0.0785,0.0,0.1379,0.1667,0.0417,0.0,0.0504,0.0,0.0,0.0785,0.063,0.0,0.9826,0.7713,0.0792,0.0,0.1379,0.1667,0.0417,0.0,0.0551,0.0,0.0,0.0831,0.0625,0.0,0.9826,0.7652,0.079,0.0,0.1379,0.1667,0.0417,0.0,0.0513,0.0,0.0,0.0801,reg oper account,block of flats,0.0429,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-233.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n375262,0,Cash loans,F,N,N,2,112500.0,808650.0,29839.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00496,-11414,-3656,-5145.0,-3646,,1,1,0,1,0,0,Medicine staff,4.0,2,2,TUESDAY,15,0,0,0,0,1,1,Medicine,,0.6223382927068994,0.6910212267577837,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-442.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n194973,0,Cash loans,M,Y,N,2,360000.0,900000.0,43299.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-15155,-2582,-1634.0,-2677,6.0,1,1,0,1,1,0,Drivers,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Other,0.3916099277560658,0.7434327092858661,0.4614823912998385,0.0165,,0.9846,0.7892,0.0048,0.0,0.069,0.0417,,0.0287,0.0134,0.0184,0.0,0.0,0.0168,,0.9846,0.7975,0.0049,0.0,0.069,0.0417,,0.0294,0.0147,0.0191,0.0,0.0,0.0167,,0.9846,0.792,0.0048,0.0,0.069,0.0417,,0.0292,0.0137,0.0187,0.0,0.0,,block of flats,0.0171,Wooden,No,0.0,0.0,0.0,0.0,-1761.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n433898,0,Cash loans,F,N,Y,0,180000.0,1241968.5,40194.0,1084500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.016612000000000002,-14746,-2714,-8855.0,-4650,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.5770352890375677,0.13595104417329515,0.0619,0.0,0.9841,0.7824,0.0118,0.0,0.1379,0.1667,,0.064,,0.0544,,0.0,0.063,0.0,0.9841,0.7909,0.0119,0.0,0.1379,0.1667,,0.0654,,0.0566,,0.0,0.0625,0.0,0.9841,0.7853,0.0119,0.0,0.1379,0.1667,,0.0651,,0.0553,,0.0,reg oper account,block of flats,0.0492,Panel,No,0.0,0.0,0.0,0.0,-765.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n104492,0,Cash loans,F,N,N,0,144000.0,508495.5,20295.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-23141,365243,-3221.0,-4416,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.6275048789276079,0.8347841592331774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-590.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n248186,0,Cash loans,F,N,Y,0,67500.0,544491.0,17694.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-14910,-634,-6252.0,-4269,,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,9,0,0,0,0,1,1,Industry: type 3,,0.6490980680982238,0.3723336657058204,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-2841.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n207008,0,Cash loans,F,N,N,0,135000.0,495351.0,25290.0,459000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-22061,365243,-3874.0,-3957,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.3476449906413137,0.7076993447402619,0.0773,0.0761,0.9776,0.6940000000000001,,0.0,0.1724,0.1667,0.0417,0.0799,0.063,0.065,0.0,0.0,0.0788,0.079,0.9777,0.706,,0.0,0.1724,0.1667,0.0417,0.0817,0.0689,0.0677,0.0,0.0,0.0781,0.0761,0.9776,0.6981,,0.0,0.1724,0.1667,0.0417,0.0812,0.0641,0.0662,0.0,0.0,reg oper spec account,block of flats,0.0698,Panel,No,6.0,0.0,6.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n351514,0,Cash loans,M,N,Y,0,180000.0,1223010.0,48631.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-14196,-3374,-8287.0,-3966,,1,1,0,1,0,0,Drivers,2.0,1,1,MONDAY,12,0,0,0,0,0,0,Restaurant,,0.7544912477741699,0.4206109640437848,0.0082,,0.9707,,,0.0,0.0345,0.0417,,,,,,,0.0084,,0.9707,,,0.0,0.0345,0.0417,,,,,,,0.0083,,0.9707,,,0.0,0.0345,0.0417,,,,,,,,block of flats,0.006,,No,1.0,0.0,1.0,0.0,-1862.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n238523,0,Cash loans,F,N,Y,0,135000.0,675000.0,19476.0,675000.0,\"Spouse, partner\",Pensioner,Higher education,Married,House / apartment,0.022625,-21072,365243,-9336.0,-4457,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,XNA,0.8666137965484345,0.7441286524960381,0.3201633668633456,0.1072,,0.9722,,,0.0,0.2069,0.2083,,,,0.1238,,0.1512,0.1092,,0.9722,,,0.0,0.2069,0.2083,,,,0.1289,,0.16,0.1083,,0.9722,,,0.0,0.2069,0.2083,,,,0.126,,0.1543,,block of flats,0.2048,\"Stone, brick\",No,3.0,0.0,3.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n374070,0,Cash loans,M,Y,Y,2,180000.0,538704.0,26046.0,481500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-10462,-1457,-4506.0,-2553,6.0,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Transport: type 2,0.2680450592798113,0.2632411250133655,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-950.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n339614,0,Cash loans,M,Y,N,0,180000.0,477175.5,46485.0,459000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.010147,-10794,-1849,-1930.0,-3405,12.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,12,0,0,0,0,1,1,Self-employed,0.256732838165579,0.2716765666238624,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1715.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n195850,0,Cash loans,F,N,N,0,225000.0,1140156.0,37809.0,1021500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Municipal apartment,0.032561,-16495,-6312,-6470.0,-11,,1,1,0,1,1,0,Cleaning staff,2.0,1,1,SATURDAY,12,0,0,0,0,0,0,Kindergarten,,0.7295017249912474,0.8193176922872417,0.0619,0.0,0.9737,0.6396,0.0075,0.0,0.1034,0.1667,0.2083,0.0,0.0504,0.0507,0.0,0.0,0.063,0.0,0.9737,0.6537,0.0076,0.0,0.1034,0.1667,0.2083,0.0,0.0551,0.0528,0.0,0.0,0.0625,0.0,0.9737,0.6444,0.0076,0.0,0.1034,0.1667,0.2083,0.0,0.0513,0.0516,0.0,0.0,reg oper account,block of flats,0.044000000000000004,\"Stone, brick\",No,8.0,0.0,7.0,0.0,-1134.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n425834,0,Cash loans,M,Y,Y,1,225000.0,463500.0,19638.0,463500.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.018801,-19080,-1224,-7304.0,-2621,14.0,1,1,0,1,0,0,Managers,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.631890024479856,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-2764.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n378152,0,Cash loans,F,N,Y,1,180000.0,239850.0,28593.0,225000.0,Unaccompanied,State servant,Higher education,Married,Office apartment,0.072508,-11141,-3250,-2719.0,-3513,,1,1,0,1,0,0,Core staff,3.0,1,1,MONDAY,13,0,0,0,0,0,0,Security Ministries,0.5107632618683845,0.5181494661729024,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1363.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n313289,0,Cash loans,M,Y,N,0,225000.0,485640.0,38263.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.02461,-16569,-5450,-1349.0,-103,7.0,1,1,0,1,0,0,Drivers,1.0,2,2,TUESDAY,9,0,0,0,0,1,1,Self-employed,,0.29922036536667024,0.7394117535524816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n276589,0,Cash loans,F,N,N,1,112500.0,381528.0,22032.0,315000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.003069,-14204,-865,-3050.0,-2564,,1,1,0,1,0,0,Sales staff,3.0,3,3,THURSDAY,12,0,0,0,0,0,0,Trade: type 7,0.55532287432638,0.6269271108489849,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n121751,0,Cash loans,F,N,Y,0,112500.0,225000.0,23872.5,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.02461,-11062,-3640,-5131.0,-3681,,1,1,0,1,1,0,Core staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Culture,0.7704191790477263,0.639425745653306,0.5832379256761245,0.0928,0.0901,0.9811,0.7416,0.0361,0.0,0.2069,0.1667,0.2083,0.0996,0.0756,0.0778,0.0,0.0,0.0945,0.0935,0.9811,0.7517,0.0364,0.0,0.2069,0.1667,0.2083,0.1019,0.0826,0.081,0.0,0.0,0.0937,0.0901,0.9811,0.7451,0.0363,0.0,0.2069,0.1667,0.2083,0.1014,0.077,0.0792,0.0,0.0,reg oper account,block of flats,0.0612,Panel,No,2.0,1.0,2.0,0.0,-1247.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n294022,0,Cash loans,F,N,N,0,225000.0,781920.0,28084.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.018029,-22668,365243,-4893.0,-4893,,1,0,0,1,0,0,,1.0,3,3,MONDAY,10,0,0,0,0,0,0,XNA,,0.4666052059997817,0.6512602186973006,0.066,,0.9816,,,,0.1379,0.1667,,,,0.0504,,0.0486,0.0672,,0.9816,,,,0.1379,0.1667,,,,0.0525,,0.0514,0.0666,,0.9816,,,,0.1379,0.1667,,,,0.0513,,0.0496,,block of flats,0.0424,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-1821.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n234607,0,Cash loans,F,Y,Y,0,157500.0,552555.0,33034.5,477000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.006852,-16729,-6944,-276.0,-290,13.0,1,1,0,1,0,0,,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,Culture,0.5858206524022591,0.2274622706496508,0.4902575124990026,0.0474,0.0,0.9995,,,0.08,0.069,0.2917,,0.0,,0.057,,0.0167,0.0483,0.0,0.9995,,,0.0806,0.069,0.2917,,0.0,,0.0594,,0.0177,0.0479,0.0,0.9995,,,0.08,0.069,0.2917,,0.0,,0.057999999999999996,,0.0171,,block of flats,0.0485,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-435.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n128815,0,Cash loans,F,N,N,0,202500.0,1206954.0,31968.0,945000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-20028,-4076,-9107.0,-3574,,1,1,0,1,0,0,Accountants,2.0,3,3,FRIDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.7745376646426481,0.5839419852615619,0.5513812618027899,0.0691,0.0803,0.9821,0.7552,0.0909,0.0,0.1379,0.1667,0.2083,0.0868,0.0538,0.0572,0.0116,0.0179,0.0704,0.0833,0.9821,0.7648,0.0917,0.0,0.1379,0.1667,0.2083,0.0887,0.0588,0.0596,0.0117,0.0189,0.0697,0.0803,0.9821,0.7585,0.0914,0.0,0.1379,0.1667,0.2083,0.0883,0.0547,0.0582,0.0116,0.0182,reg oper account,block of flats,0.0489,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2161.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n271377,0,Revolving loans,F,N,Y,1,90000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010147,-10970,-821,-6645.0,-3224,,1,1,0,1,0,0,Sales staff,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,0.3357733908846158,0.5031781070732985,0.6397075677637197,0.134,0.0844,0.9901,0.8640000000000001,0.0266,0.04,0.0345,0.3333,0.375,0.073,0.1084,0.0896,0.0039,0.0849,0.1366,0.0876,0.9901,0.8693,0.0268,0.0403,0.0345,0.3333,0.375,0.0746,0.1185,0.0933,0.0039,0.0899,0.1353,0.0844,0.9901,0.8658,0.0267,0.04,0.0345,0.3333,0.375,0.0743,0.1103,0.0912,0.0039,0.0867,reg oper account,block of flats,0.1034,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1117.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n271778,0,Cash loans,M,Y,N,0,90000.0,135000.0,12753.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.015221,-18421,-4728,-5482.0,-1966,3.0,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Self-employed,0.7164181789618889,0.7737676666685314,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-397.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n381916,0,Cash loans,M,N,Y,0,202500.0,760225.5,30280.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-20425,-4492,-12053.0,-3097,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.3330458178721503,0.6785676886853644,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,10.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n281172,0,Cash loans,F,N,Y,0,337500.0,1123681.5,59998.5,1062000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-22493,-1374,-1638.0,-4570,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Self-employed,,0.5584479148341248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1715.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n207408,0,Cash loans,F,N,N,0,135000.0,1185282.0,32724.0,1035000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.008625,-9123,-625,-3686.0,-1138,,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.7141048328326618,0.6018538524715134,0.5656079814115492,0.33299999999999996,0.3273,0.9831,0.7688,0.0735,0.36,0.3103,0.3333,0.375,0.2332,0.195,0.3367,0.0077,0.0015,0.3393,0.3396,0.9831,0.7779,0.0742,0.3625,0.3103,0.3333,0.375,0.2385,0.213,0.3508,0.0078,0.0016,0.3362,0.3273,0.9831,0.7719,0.07400000000000001,0.36,0.3103,0.3333,0.375,0.2372,0.1984,0.3428,0.0078,0.0015,reg oper account,block of flats,0.305,Panel,No,1.0,0.0,1.0,0.0,-1939.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n275430,1,Cash loans,M,N,Y,0,157500.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-11241,-1119,-315.0,-3821,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,20,0,0,0,0,1,1,Business Entity Type 3,,0.6108364636152029,0.2250868621163805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-925.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n286482,0,Cash loans,F,N,Y,0,202500.0,1220521.5,40338.0,1093500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.004849,-20029,-7385,-10999.0,-3566,,1,1,0,1,1,0,Managers,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Other,,0.7127460780058618,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2153.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n303084,0,Cash loans,M,Y,Y,2,202500.0,958500.0,31806.0,958500.0,\"Spouse, partner\",Commercial associate,Incomplete higher,Married,House / apartment,0.026392,-12387,-1281,-122.0,-3945,3.0,1,1,1,1,1,0,Drivers,4.0,2,2,SUNDAY,14,0,0,0,0,0,0,Transport: type 4,,0.7734651157659522,0.3910549766342248,0.2474,0.1041,1.0,1.0,0.1367,0.24,0.2069,0.375,0.0417,0.2291,0.2017,0.2044,0.0,0.0,0.2521,0.10800000000000001,1.0,1.0,0.138,0.2417,0.2069,0.375,0.0417,0.2343,0.2204,0.213,0.0,0.0,0.2498,0.1041,1.0,1.0,0.1376,0.24,0.2069,0.375,0.0417,0.2331,0.2052,0.2081,0.0,0.0,reg oper account,block of flats,0.2355,Panel,No,0.0,0.0,0.0,0.0,-2059.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n117506,0,Cash loans,F,N,Y,0,74700.0,49455.0,3568.5,45000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010966,-23489,365243,-13153.0,-4209,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.20468086750589826,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1533.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,1.0,3.0\n420969,0,Revolving loans,F,Y,Y,0,337500.0,585000.0,29250.0,585000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.04622,-7706,-792,-1138.0,-375,4.0,1,1,0,1,1,1,,2.0,1,1,SATURDAY,15,0,1,1,0,0,0,Business Entity Type 3,0.5461218665799896,0.7323016647303034,,0.0526,0.0718,0.9836,0.7756,0.0128,0.04,0.0345,0.3333,0.0417,0.0351,0.042,0.051,0.0039,0.0305,0.0536,0.0745,0.9836,0.7844,0.0129,0.0403,0.0345,0.3333,0.0417,0.0359,0.0459,0.0532,0.0039,0.0322,0.0531,0.0718,0.9836,0.7786,0.0129,0.04,0.0345,0.3333,0.0417,0.0357,0.0428,0.0519,0.0039,0.0311,reg oper account,block of flats,0.0467,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-525.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n234938,0,Cash loans,F,N,Y,0,270000.0,835380.0,40320.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14995,-3353,-6504.0,-4593,,1,1,0,1,1,0,Accountants,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.7100362021416697,0.6688346566135439,,0.1866,0.0658,0.9762,0.6736,0.0225,0.0,0.0345,0.1667,0.2083,0.0414,0.1521,0.0659,0.0,0.0,0.1901,0.0683,0.9762,0.6864,0.0227,0.0,0.0345,0.1667,0.2083,0.0424,0.1662,0.0687,0.0,0.0,0.1884,0.0658,0.9762,0.6779999999999999,0.0226,0.0,0.0345,0.1667,0.2083,0.0421,0.1548,0.0671,0.0,0.0,reg oper account,block of flats,0.0518,\"Stone, brick\",No,4.0,1.0,4.0,0.0,-932.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n407535,0,Cash loans,M,N,N,0,157500.0,755190.0,32125.5,675000.0,\"Spouse, partner\",Working,Lower secondary,Single / not married,House / apartment,0.00733,-22437,-4765,-7525.0,-4171,,1,1,0,1,0,0,,1.0,2,2,MONDAY,16,0,0,0,0,0,0,Construction,,0.72068417886442,,0.1784,0.0817,0.9826,0.762,0.0274,0.0,0.069,0.1667,0.0417,0.0,,0.0622,,0.0,0.1817,0.0847,0.9826,0.7713,0.0276,0.0,0.069,0.1667,0.0417,0.0,,0.0648,,0.0,0.1801,0.0817,0.9826,0.7652,0.0275,0.0,0.069,0.1667,0.0417,0.0,,0.0633,,0.0,reg oper account,block of flats,0.0561,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-2840.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n124692,0,Cash loans,F,N,Y,1,135000.0,1096020.0,52857.0,900000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.032561,-16301,-974,-13624.0,-266,,1,1,0,1,0,1,Core staff,3.0,1,1,THURSDAY,16,0,0,0,0,0,0,Kindergarten,0.6549753964860784,0.646138788965357,0.6041125998015721,0.1399,,0.9806,,,0.08,0.0345,0.3333,,0.0175,,0.09300000000000001,,,0.0882,,0.9806,,,0.0806,0.0345,0.3333,,0.0179,,0.0969,,,0.0874,,0.9806,,,0.08,0.0345,0.3333,,0.0178,,0.0947,,,,block of flats,0.0732,Mixed,No,0.0,0.0,0.0,0.0,-913.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n405263,0,Cash loans,F,N,N,0,112500.0,545040.0,26640.0,450000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,Municipal apartment,0.02461,-13732,-4690,-7840.0,-4582,,1,1,0,1,1,0,Laborers,1.0,2,2,FRIDAY,8,0,0,0,0,0,0,Government,,0.32054828344077185,0.0938365970374978,,0.0,0.9742,0.6464,0.002,0.0,0.069,0.0833,0.125,,,0.0199,0.0,0.0,,0.0,0.9742,0.6602,0.002,0.0,0.069,0.0833,0.125,,,0.0207,0.0,0.0,,0.0,0.9742,0.6511,0.002,0.0,0.069,0.0833,0.125,,,0.0203,0.0,0.0,,block of flats,0.0302,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-3231.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n449026,0,Cash loans,F,N,Y,0,58500.0,64692.0,4630.5,54000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-20491,-4469,-4305.0,-4047,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,0.8590462400098506,0.6666125686357883,0.4400578303966329,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-3051.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n149973,0,Cash loans,F,Y,Y,1,112500.0,675000.0,21906.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-13589,-5640,-3652.0,-5643,10.0,1,1,1,1,1,0,Medicine staff,3.0,3,3,TUESDAY,8,0,0,0,0,0,0,Medicine,0.4208575152556139,0.5232849375228935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1090.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n418943,0,Cash loans,M,N,Y,0,202500.0,772686.0,22275.0,553500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.014464,-22889,365243,-9737.0,-4509,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,6,0,0,0,0,0,0,XNA,,0.2858978721410488,,0.0918,0.1064,0.9866,0.8164,0.0405,0.0,0.2069,0.1667,0.2083,0.06,0.0748,0.0825,0.0,0.0,0.0935,0.1105,0.9866,0.8236,0.0409,0.0,0.2069,0.1667,0.2083,0.0614,0.0817,0.0859,0.0,0.0,0.0926,0.1064,0.9866,0.8189,0.0408,0.0,0.2069,0.1667,0.2083,0.061,0.0761,0.084,0.0,0.0,reg oper spec account,block of flats,0.087,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-718.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n180778,0,Cash loans,F,N,Y,0,67500.0,646920.0,20997.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.007120000000000001,-19723,-998,-7227.0,-3280,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,10,0,0,0,0,1,1,Industry: type 3,,0.2622583692422573,0.8347841592331774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-518.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n382465,0,Cash loans,F,N,N,0,67500.0,450000.0,19953.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-16754,-2550,-2914.0,-291,,1,1,1,1,1,0,Cleaning staff,2.0,2,2,MONDAY,8,0,0,0,0,0,0,Self-employed,0.4726784025249144,0.34112290759888264,0.7180328113294772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1187.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n297987,0,Cash loans,M,Y,Y,0,405000.0,840780.0,59926.5,751500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-20938,-2711,-930.0,-4149,13.0,1,1,0,1,1,1,Managers,2.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.7633010614528637,0.646329897706246,0.1814,0.1071,0.9896,0.8640000000000001,0.0,0.22,0.1207,0.4167,0.4167,0.0265,0.0975,0.1525,0.0,0.0023,0.1218,0.0574,0.9891,0.8693,0.0,0.1208,0.1034,0.375,0.4167,0.0271,0.1065,0.1409,0.0,0.0,0.1832,0.1071,0.9896,0.8658,0.0,0.22,0.1207,0.4167,0.4167,0.0269,0.0992,0.1552,0.0,0.0023,org spec account,block of flats,0.2298,Panel,No,0.0,0.0,0.0,0.0,-2628.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n256046,0,Cash loans,F,N,N,0,315000.0,835380.0,40189.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.072508,-17080,-1521,-4428.0,-612,,1,1,0,1,0,0,,2.0,1,1,FRIDAY,13,0,0,0,0,0,0,School,,0.7163455304182007,0.7623356180684377,0.2876,0.1815,0.9935,0.9116,0.185,0.4,0.1724,0.5417,0.5833,0.0,0.2345,0.2131,0.0,0.0104,0.2931,0.1884,0.9935,0.9151,0.1867,0.4028,0.1724,0.5417,0.5833,0.0,0.2562,0.222,0.0,0.011000000000000001,0.2904,0.1815,0.9935,0.9128,0.1862,0.4,0.1724,0.5417,0.5833,0.0,0.2386,0.2169,0.0,0.0106,reg oper account,block of flats,0.2787,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n293608,1,Cash loans,F,N,N,2,184500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-14994,-398,-4038.0,-5669,,1,1,0,1,0,0,,4.0,2,2,THURSDAY,15,0,0,0,1,1,0,Business Entity Type 3,,0.11061269997308908,0.41534714488434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n444892,0,Cash loans,F,N,N,0,90000.0,123637.5,8302.5,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.020713,-22382,365243,-8675.0,-4389,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,13,0,0,0,0,0,0,XNA,,0.5462287512569326,0.5478104658520093,0.0852,0.0,0.9861,0.8096,0.0224,0.0932,0.0803,0.3333,0.375,0.0272,0.0689,0.0889,0.0025,0.0053,0.0714,0.0,0.9856,0.8105,0.0168,0.0806,0.069,0.3333,0.375,0.0203,0.0606,0.0735,0.0,0.0,0.0749,0.0,0.9856,0.8054,0.0236,0.08,0.069,0.3333,0.375,0.0302,0.0616,0.0876,0.0,0.0,reg oper spec account,block of flats,0.0646,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n226232,0,Cash loans,M,Y,Y,1,202500.0,288873.0,17671.5,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006852,-18627,-10757,-6759.0,-2173,14.0,1,1,0,1,0,0,Laborers,3.0,3,3,TUESDAY,5,0,0,0,0,0,0,Transport: type 2,,0.11283927067030215,0.6195277080511546,0.0722,0.1142,0.9896,0.8572,0.0811,0.0,0.1379,0.1667,0.0,0.0543,0.0588,0.0869,0.0,0.0,0.0735,0.1185,0.9896,0.8628,0.0819,0.0,0.1379,0.1667,0.0,0.0555,0.0643,0.0906,0.0,0.0,0.0729,0.1142,0.9896,0.8591,0.0816,0.0,0.1379,0.1667,0.0,0.0552,0.0599,0.0885,0.0,0.0,reg oper account,block of flats,0.1149,\"Stone, brick\",No,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n358942,0,Cash loans,M,Y,Y,0,247500.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-19667,-1974,-4199.0,-3149,2.0,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.8194504242702415,0.7130056825797436,0.6848276586890367,,,0.9752,,,,,,,,,0.0643,,,,,0.9752,,,,,,,,,0.067,,,,,0.9752,,,,,,,,,0.0655,,,,,0.0506,,No,0.0,0.0,0.0,0.0,-487.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n396793,0,Cash loans,F,N,Y,5,112500.0,254700.0,16713.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-12648,-3512,-3836.0,-4898,,1,1,1,1,1,0,Core staff,7.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Agriculture,,0.4595316997529216,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-18.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n171714,0,Cash loans,F,N,Y,0,90000.0,781920.0,26856.0,675000.0,Family,Pensioner,Lower secondary,Separated,House / apartment,0.025164,-22301,365243,-7085.0,-4320,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,0.6876753349792,0.1763135752268863,0.1997705696341145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1143.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n305605,0,Cash loans,F,N,Y,0,76500.0,781920.0,25101.0,675000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.006852,-23533,365243,-872.0,-4086,,1,0,0,1,0,0,,2.0,3,3,SUNDAY,7,0,0,0,0,0,0,XNA,,0.35836734448521873,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-522.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n293287,0,Cash loans,F,N,Y,0,63000.0,254700.0,14350.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-24166,365243,-11785.0,-4395,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.16318703546427088,0.4740512892789932,0.0722,,0.9841,0.7824,0.0976,0.0,0.1724,0.1667,0.0,0.0566,0.0572,0.0659,0.0077,0.0072,0.0735,,0.9841,0.7909,0.0985,0.0,0.1724,0.1667,0.0,0.0579,0.0624,0.0686,0.0078,0.0076,0.0729,,0.9841,0.7853,0.0982,0.0,0.1724,0.1667,0.0,0.0576,0.0581,0.067,0.0078,0.0073,org spec account,block of flats,0.0534,Block,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n324452,1,Cash loans,M,N,Y,0,225000.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.009334,-13812,-3057,-3065.0,-3714,,1,1,0,1,1,0,Laborers,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,Military,0.24774372830506944,0.5731879729680599,0.3376727217405312,0.033,0.0,0.9747,0.6532,0.0028,0.0,0.069,0.125,0.0417,0.0319,0.0269,0.0253,0.0,0.0,0.0336,0.0,0.9747,0.6668,0.0028,0.0,0.069,0.125,0.0417,0.0327,0.0294,0.0264,0.0,0.0,0.0333,0.0,0.9747,0.6578,0.0028,0.0,0.069,0.125,0.0417,0.0325,0.0274,0.0258,0.0,0.0,reg oper account,block of flats,0.0214,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1967.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n262771,0,Cash loans,M,Y,Y,1,112500.0,314100.0,16573.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-16182,-1867,-263.0,-4738,64.0,1,1,0,1,0,0,High skill tech staff,3.0,1,1,MONDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.6750863944105435,0.05347808736117268,0.0485,,0.9573,,,0.0,0.2069,0.125,,0.0,,0.0565,,0.0747,0.0494,,0.9573,,,0.0,0.2069,0.125,,0.0,,0.0589,,0.0791,0.0489,,0.9573,,,0.0,0.2069,0.125,,0.0,,0.0576,,0.0763,,block of flats,0.0607,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-535.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n274194,0,Cash loans,F,N,N,1,135000.0,260640.0,27369.0,225000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.04622,-11776,-4624,-5101.0,-4072,,1,1,0,1,0,0,Medicine staff,2.0,1,1,MONDAY,12,0,0,0,0,1,1,Medicine,0.3624808834640646,0.5422311997236371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1469.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n442062,0,Cash loans,F,Y,Y,1,67500.0,630000.0,16749.0,630000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-19787,-3606,-10055.0,-3332,12.0,1,1,0,1,0,0,Security staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Security,,0.7214011257029428,,,,0.9871,,,,0.1724,0.3333,,,,0.196,,,,,0.9871,,,,0.1724,0.3333,,,,0.2042,,,,,0.9871,,,,0.1724,0.3333,,,,0.1995,,,,,0.1877,Panel,No,4.0,0.0,4.0,0.0,-974.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n112388,0,Cash loans,F,N,Y,0,126000.0,592560.0,27589.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.006670999999999999,-9264,-2019,-3632.0,-545,,1,1,0,1,0,0,Cooking staff,2.0,2,2,TUESDAY,17,0,0,0,1,1,0,Industry: type 4,0.3755350243107837,0.5558742933290207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-225.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n353541,0,Cash loans,F,Y,Y,1,81000.0,163201.5,11488.5,148500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-14168,-1104,-1696.0,-5742,64.0,1,1,0,1,0,0,,3.0,2,2,THURSDAY,11,0,0,0,1,1,0,Business Entity Type 2,0.5578186069246179,0.6036288765663111,0.7324033228040929,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1083.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n289001,0,Cash loans,M,Y,Y,0,1350000.0,900000.0,48825.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.072508,-17345,-4819,-4185.0,-892,2.0,1,1,0,1,1,1,Core staff,1.0,1,1,MONDAY,16,0,0,0,0,0,0,Insurance,0.6786275108855799,0.7085011573440548,0.6706517530862718,0.4485,0.1524,0.9935,0.9116,0.2683,0.52,0.1724,0.875,0.5833,0.0,0.3623,0.5278,0.0154,0.0767,0.4569,0.1582,0.9935,0.9151,0.2707,0.5236,0.1724,0.875,0.5833,0.0,0.3958,0.5499,0.0156,0.0812,0.4528,0.1524,0.9935,0.9128,0.27,0.52,0.1724,0.875,0.5833,0.0,0.3685,0.5373,0.0155,0.0783,reg oper account,block of flats,0.4318,Panel,No,0.0,0.0,0.0,0.0,-823.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n195334,0,Cash loans,F,N,Y,0,292500.0,1215000.0,46278.0,1215000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.026392,-20255,-2015,-13317.0,-3575,,1,1,1,1,1,0,Sales staff,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Trade: type 7,0.5748848980533275,0.7166253795838614,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2200.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n203881,0,Cash loans,F,N,N,1,135000.0,143910.0,15498.0,135000.0,Other_B,State servant,Secondary / secondary special,Married,House / apartment,0.006305,-9521,-277,-828.0,-1614,,1,1,0,1,0,0,Laborers,3.0,3,3,SUNDAY,6,0,0,0,1,1,0,Industry: type 3,,0.4723239737996672,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-60.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n133783,0,Cash loans,F,N,Y,2,67500.0,73944.0,4257.0,58500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18496,365243,-4220.0,-406,,1,0,0,1,1,0,,4.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,0.749140481873877,0.6525220649426486,0.05384435347241005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-305.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n360621,0,Cash loans,F,Y,Y,0,202500.0,729792.0,37390.5,630000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.018029,-8409,-330,-3213.0,-1068,1.0,1,1,0,1,1,1,Core staff,1.0,3,3,FRIDAY,13,0,0,0,0,0,0,Realtor,0.2433795663583936,0.4271902394647641,0.6690566947824041,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,2.0,0.0,2.0,0.0,-827.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n385422,0,Cash loans,F,Y,Y,0,121500.0,1042164.0,44284.5,931500.0,Children,State servant,Higher education,Married,House / apartment,0.018029,-19606,-6357,-3135.0,-3154,12.0,1,1,0,1,0,1,Managers,2.0,3,3,SUNDAY,5,0,0,0,0,0,0,Postal,0.8885955025072919,0.013529603950614266,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-498.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n247379,0,Revolving loans,F,N,Y,0,94500.0,180000.0,9000.0,180000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.008068,-12287,-3710,-2948.0,-2948,,1,1,0,1,0,0,Medicine staff,2.0,3,3,THURSDAY,11,0,0,0,0,0,0,Medicine,0.2954934753711071,0.20646649021899505,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-303.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.0,2.0,0.0,0.0,0.0,0.0\n225962,0,Cash loans,F,N,Y,0,135000.0,1350000.0,57330.0,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-21309,-630,-6088.0,-264,,1,1,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Other,0.7188095213615403,0.5829853644416975,0.6075573001388961,0.0794,0.0,0.9916,0.8844,0.003,0.0,0.2069,0.2083,0.25,0.0174,0.0647,0.0894,0.0,0.0516,0.0809,0.0,0.9916,0.8889,0.003,0.0,0.2069,0.2083,0.25,0.0177,0.0707,0.0932,0.0,0.0547,0.0802,0.0,0.9916,0.8859,0.003,0.0,0.2069,0.2083,0.25,0.0177,0.0658,0.091,0.0,0.0527,reg oper account,block of flats,0.1023,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n379088,0,Cash loans,F,N,Y,0,135000.0,194742.0,9369.0,139500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,Municipal apartment,0.02461,-20755,365243,-10465.0,-4009,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.36497057737107097,0.5884877883422673,,0.1073,0.9846,0.7892,0.0378,0.16,0.1379,0.3333,0.375,0.0935,0.121,0.1535,,0.0,,0.1113,0.9846,0.7975,0.0382,0.1611,0.1379,0.3333,0.375,0.0957,0.1322,0.16,,0.0,,0.1073,0.9846,0.792,0.0381,0.16,0.1379,0.3333,0.375,0.0951,0.1231,0.1563,,0.0,reg oper account,block of flats,0.1201,Panel,No,0.0,0.0,0.0,0.0,-382.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n318493,1,Cash loans,F,N,Y,0,112500.0,807984.0,26833.5,697500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-8551,-1660,-1968.0,-14,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,1,1,0,Trade: type 2,0.6002347147430179,0.5247303238464694,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-720.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n214619,0,Cash loans,F,N,N,0,135000.0,270000.0,21330.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.007114,-12124,-2636,-1854.0,-2127,,1,1,1,1,1,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Other,,0.2596016355924837,0.1595195404777181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1884.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n357960,0,Cash loans,M,Y,Y,0,112500.0,780363.0,37539.0,697500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.0105,-8781,-1498,-3769.0,-1472,16.0,1,1,1,1,0,0,Laborers,1.0,3,3,MONDAY,14,0,0,0,0,0,0,Self-employed,,0.015553197404432359,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n221439,0,Cash loans,F,N,Y,3,76500.0,547344.0,23139.0,472500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.031329,-14200,-1855,-1413.0,-931,,1,1,0,1,0,0,Accountants,5.0,2,2,TUESDAY,9,0,0,0,0,1,1,Business Entity Type 2,0.5596601797725644,0.6061009695427974,0.30162489168411943,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-425.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n216798,0,Cash loans,M,N,Y,2,90000.0,221031.0,20403.0,184500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006852,-10021,-2214,-2884.0,-2182,,1,1,0,1,0,1,Medicine staff,4.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,Medicine,,0.22113159548166167,0.6023863442690867,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-818.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n445385,0,Cash loans,F,N,Y,0,90000.0,206271.0,20529.0,193500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-11022,-3650,-3368.0,-2377,,1,1,0,1,0,0,Core staff,1.0,3,3,SATURDAY,9,0,0,0,0,1,1,Transport: type 2,,0.5385325452784266,0.7910749129911773,0.1753,0.1722,0.9801,,,0.0,0.3448,0.1667,,,,0.1587,,0.0,0.1786,0.1787,0.9801,,,0.0,0.3448,0.1667,,,,0.1653,,0.0,0.177,0.1722,0.9801,,,0.0,0.3448,0.1667,,,,0.1615,,0.0,,block of flats,0.1311,Panel,No,2.0,0.0,2.0,0.0,-1038.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n337439,0,Cash loans,F,Y,Y,0,135000.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-19381,-1126,-6600.0,-2742,8.0,1,1,1,1,0,0,Core staff,2.0,2,2,MONDAY,14,0,0,0,0,1,1,Medicine,,0.6674916219096344,0.7981372313187245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2101.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n185738,1,Cash loans,M,Y,Y,1,135000.0,405000.0,19827.0,405000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.02461,-10311,-1475,-845.0,-2659,28.0,1,1,1,1,1,0,Laborers,3.0,2,2,MONDAY,15,0,0,0,0,1,1,Construction,,0.10396478652119398,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-646.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n137245,0,Cash loans,M,Y,Y,1,90000.0,432661.5,22653.0,373500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-13517,-1694,-235.0,-4196,22.0,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.29457343119520163,0.7557400501752248,0.3015,0.1583,0.9826,0.762,0.1029,0.28,0.3448,0.25,0.2917,0.138,0.2438,0.2843,0.0116,0.011000000000000001,0.105,0.1147,0.9826,0.7713,0.0441,0.0,0.2069,0.1667,0.2083,0.1067,0.0918,0.0629,0.0039,0.0031,0.3045,0.1583,0.9826,0.7652,0.1036,0.28,0.3448,0.25,0.2917,0.1404,0.248,0.2894,0.0116,0.0112,org spec account,block of flats,0.4925,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n280137,0,Cash loans,M,N,Y,0,180000.0,1138068.0,37741.5,931500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.020246,-22191,365243,-6083.0,-4021,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,13,0,0,0,1,0,0,XNA,,0.33397908480891264,0.6246146584503397,0.0825,0.0316,0.9916,0.8844,0.0103,0.08,0.069,0.375,0.4167,0.0477,0.0672,0.0864,0.0,0.0,0.084,0.0327,0.9916,0.8889,0.0104,0.0806,0.069,0.375,0.4167,0.0488,0.0735,0.0901,0.0,0.0,0.0833,0.0316,0.9916,0.8859,0.0104,0.08,0.069,0.375,0.4167,0.0485,0.0684,0.08800000000000001,0.0,0.0,reg oper account,block of flats,0.0736,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n209131,0,Cash loans,F,N,Y,0,144000.0,1066320.0,38430.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-17873,-2383,-3109.0,-1402,,1,1,0,1,1,1,High skill tech staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.24160969743469,0.5030359732774922,0.7032033049040319,0.0082,0.0,0.9667,,,0.0,0.069,0.0417,,0.0203,,0.0117,,0.0,0.0084,0.0,0.9667,,,0.0,0.069,0.0417,,0.0207,,0.0122,,0.0,0.0083,0.0,0.9667,,,0.0,0.069,0.0417,,0.0206,,0.012,,0.0,,block of flats,0.0163,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1245.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n451722,0,Cash loans,F,Y,Y,1,229500.0,675000.0,32602.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-12795,-972,-6922.0,-2659,13.0,1,1,0,1,0,0,Core staff,3.0,2,2,THURSDAY,13,0,0,0,0,0,0,Self-employed,0.33298558481750296,0.7092097010215302,0.2910973802776635,0.1825,,0.9886,,,0.2,0.1724,0.3333,,,,0.0943,,,0.1859,,0.9886,,,0.2014,0.1724,0.3333,,,,0.0982,,,0.1842,,0.9886,,,0.2,0.1724,0.3333,,,,0.096,,,,block of flats,0.1371,Panel,No,3.0,0.0,3.0,0.0,-675.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n430486,0,Cash loans,F,N,N,2,135000.0,225000.0,10426.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-12734,-776,-1420.0,-4046,,1,1,0,1,0,0,,4.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3381432645356896,0.15412282749834694,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-123.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n196297,0,Cash loans,M,N,Y,0,90000.0,840996.0,24714.0,702000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.025164,-21581,365243,-8600.0,-4300,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,7,0,0,0,0,0,0,XNA,,0.26525634018619443,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2106.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n350088,0,Cash loans,F,N,Y,0,135000.0,1061599.5,31171.5,927000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-15389,-1690,-2646.0,-2932,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 1,,0.6950893834099449,0.28371188263500075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1402,,No,0.0,0.0,0.0,0.0,-842.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n308508,0,Cash loans,F,N,N,1,112500.0,547344.0,29821.5,472500.0,Unaccompanied,State servant,Higher education,Married,With parents,0.002134,-10047,-1687,-4825.0,-2598,,1,1,0,1,0,0,Core staff,3.0,3,3,WEDNESDAY,10,0,0,0,0,1,1,School,,0.3457133835201288,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n177398,0,Cash loans,M,Y,Y,0,135000.0,427450.5,18238.5,369000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.031329,-19756,-3663,-5493.0,-3303,4.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.7937222173149874,0.7180328113294772,0.0773,0.06,0.9786,,,0.0,0.1724,0.1667,,0.0529,,0.0352,,0.0,0.063,0.0497,0.9786,,,0.0,0.1379,0.1667,,0.0517,,0.0366,,0.0,0.0781,0.06,0.9786,,,0.0,0.1724,0.1667,,0.0538,,0.0358,,0.0,,block of flats,0.0428,Panel,No,0.0,0.0,0.0,0.0,-2067.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n424319,0,Cash loans,F,Y,N,0,270000.0,417024.0,20061.0,360000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00733,-22004,365243,-6348.0,-4076,12.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,0.8148530812877258,0.5509562373662606,0.29708661164720285,0.0619,0.0744,0.9906,,,,0.1379,0.1667,,0.0447,,0.0663,,0.10800000000000001,0.063,0.0773,0.9906,,,,0.1379,0.1667,,0.0457,,0.0691,,0.1143,0.0625,0.0744,0.9906,,,,0.1379,0.1667,,0.0455,,0.0675,,0.1103,,block of flats,0.0521,Monolithic,No,0.0,0.0,0.0,0.0,-1202.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n376140,0,Cash loans,F,N,Y,0,67500.0,112585.5,6286.5,85500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018801,-17040,-5894,-3322.0,-595,,1,1,0,1,0,0,,1.0,2,2,MONDAY,7,0,0,0,0,1,1,Transport: type 4,,0.3355520466798725,0.6178261467332483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2593.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n119636,0,Cash loans,F,N,N,0,159597.0,1485000.0,39172.5,1485000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.025164,-7962,-411,-2791.0,-602,,1,1,1,1,1,0,Core staff,2.0,2,2,MONDAY,8,0,0,0,1,1,0,Business Entity Type 1,0.22605185750735154,0.3112907453694597,0.3825018041447388,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-8.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n344112,0,Cash loans,F,N,N,0,153000.0,1288350.0,37800.0,1125000.0,Other_B,Working,Higher education,Married,House / apartment,0.00496,-10448,-2081,-4327.0,-2131,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,17,0,0,0,0,1,1,School,,0.5070373134226605,0.3031463744186309,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n146280,0,Cash loans,F,N,Y,0,112500.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-14884,-1113,-4291.0,-4291,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Government,,0.6623173805505627,0.21396685226179807,0.0722,0.0657,0.9776,,,0.0,0.1379,0.1667,,0.1085,,0.0436,,0.0,0.0735,0.0682,0.9777,,,0.0,0.1379,0.1667,,0.111,,0.0454,,0.0,0.0729,0.0657,0.9776,,,0.0,0.1379,0.1667,,0.1104,,0.0443,,0.0,,block of flats,0.0504,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-978.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n217657,0,Cash loans,M,Y,Y,1,180000.0,609183.0,19782.0,508500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018801,-16938,-5178,-8815.0,-461,4.0,1,1,0,1,0,0,Managers,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6657372214374481,0.7091891096653581,0.0082,0.0,0.9717,0.6124,0.001,0.0,0.0345,0.0417,0.0833,0.0178,0.0067,0.0099,0.0,0.0,0.0084,0.0,0.9717,0.6276,0.001,0.0,0.0345,0.0417,0.0833,0.0182,0.0073,0.0104,0.0,0.0,0.0083,0.0,0.9717,0.6176,0.001,0.0,0.0345,0.0417,0.0833,0.0181,0.0068,0.0101,0.0,0.0,reg oper account,block of flats,0.0084,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-783.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n266534,1,Revolving loans,M,Y,Y,0,67500.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010556,-15424,-563,-5184.0,-4627,7.0,1,1,1,1,1,0,Laborers,2.0,3,3,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 2,,0.10054444049407983,0.4507472818545589,0.1216,0.1329,0.9811,0.7416,0.0678,0.0,0.2759,0.1667,0.2083,0.0242,0.0983,0.11199999999999999,0.0039,0.0057,0.1239,0.138,0.9811,0.7517,0.0684,0.0,0.2759,0.1667,0.2083,0.0248,0.1074,0.1167,0.0039,0.006,0.1228,0.1329,0.9811,0.7451,0.0683,0.0,0.2759,0.1667,0.2083,0.0247,0.1,0.114,0.0039,0.0058,reg oper account,block of flats,0.0895,Panel,No,1.0,0.0,1.0,0.0,-584.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n423117,0,Cash loans,M,Y,Y,0,360000.0,1066320.0,38430.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-15653,-1163,-7968.0,-3164,7.0,1,1,0,1,0,1,Managers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5907365745922172,0.4241303111942548,0.0722,0.0853,0.9786,0.7076,0.0679,0.0,0.1379,0.1667,0.2083,0.0539,0.0588,0.0681,0.0,0.0,0.0735,0.0886,0.9786,0.7190000000000001,0.0685,0.0,0.1379,0.1667,0.2083,0.0551,0.0643,0.071,0.0,0.0,0.0729,0.0853,0.9786,0.7115,0.0683,0.0,0.1379,0.1667,0.2083,0.0548,0.0599,0.0693,0.0,0.0,reg oper account,block of flats,0.0907,\"Stone, brick\",No,1.0,1.0,1.0,0.0,-1061.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n335386,0,Cash loans,F,N,Y,2,225000.0,808650.0,31464.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-11877,-343,-4309.0,-1355,,1,1,0,1,0,0,,4.0,2,2,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.12699243977136795,0.5136937663039473,0.0619,0.0,0.9921,0.8912,0.0097,0.0,0.1034,0.1667,0.2083,0.0,0.0504,0.0613,0.0,0.0,0.063,0.0,0.9921,0.8955,0.0097,0.0,0.1034,0.1667,0.2083,0.0,0.0551,0.0638,0.0,0.0,0.0625,0.0,0.9921,0.8927,0.0097,0.0,0.1034,0.1667,0.2083,0.0,0.0513,0.0624,0.0,0.0,reg oper account,block of flats,0.0535,Panel,No,2.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,3.0\n372342,0,Cash loans,F,N,N,1,103500.0,781920.0,28215.0,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-20090,365243,-2661.0,-3251,,1,0,0,1,0,0,,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,0.7821500389025257,0.5547348914366487,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n294378,0,Cash loans,F,N,Y,0,112500.0,508495.5,22527.0,454500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-23367,365243,-12334.0,-4168,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.5509941078798735,,0.1052,,0.9831,0.762,0.1315,0.0,0.2414,0.1667,0.2083,0.0811,0.0857,0.0617,0.0,0.0,0.1071,,0.9831,0.7713,0.1327,0.0,0.2414,0.1667,0.2083,0.083,0.0937,0.0643,0.0,0.0,0.1062,,0.9831,0.7652,0.1323,0.0,0.2414,0.1667,0.2083,0.0825,0.0872,0.0628,0.0,0.0,reg oper account,block of flats,0.0744,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-541.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n168624,0,Cash loans,F,N,Y,1,72000.0,297130.5,15556.5,256500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.015221,-11045,-3663,-3606.0,-3616,,1,1,1,1,1,0,High skill tech staff,3.0,2,2,TUESDAY,7,0,0,0,0,0,0,Business Entity Type 2,0.2462935885575088,0.5290584528309059,0.5442347412142162,0.0742,0.0463,0.9742,,,0.0,0.069,0.125,,0.0594,,0.02,,0.0,0.0756,0.0481,0.9742,,,0.0,0.069,0.125,,0.0607,,0.0209,,0.0,0.0749,0.0463,0.9742,,,0.0,0.069,0.125,,0.0604,,0.0204,,0.0,,specific housing,0.0172,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-299.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n236541,0,Revolving loans,M,N,Y,2,112500.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-14793,-4564,-6003.0,-5055,,1,1,0,1,0,0,Laborers,4.0,3,3,FRIDAY,6,0,0,0,0,0,0,Transport: type 2,,0.4901791353994955,0.41885428862332175,0.0701,0.0993,0.9742,0.6464,0.0206,0.0,0.069,0.1667,0.2083,0.0644,0.0546,0.0747,0.0116,0.0239,0.0714,0.10300000000000001,0.9742,0.6602,0.0208,0.0,0.069,0.1667,0.2083,0.0658,0.0597,0.0778,0.0117,0.0253,0.0708,0.0993,0.9742,0.6511,0.0208,0.0,0.069,0.1667,0.2083,0.0655,0.0556,0.076,0.0116,0.0244,reg oper account,block of flats,0.0639,\"Stone, brick\",No,4.0,2.0,4.0,2.0,-2915.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n328366,0,Cash loans,M,Y,N,0,157500.0,521280.0,28408.5,450000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-19717,365243,-1727.0,-3239,14.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.06981247814898194,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-900.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n246467,0,Cash loans,F,Y,Y,0,157500.0,862560.0,25348.5,720000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15427,-5554,-6111.0,-4980,14.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Advertising,0.4697446119330539,0.6403529154402576,0.6956219298394389,0.0351,,0.9861,,,0.04,0.0345,0.3333,,0.03,,0.0368,,0.0126,0.0357,,0.9861,,,0.0403,0.0345,0.3333,,0.0306,,0.0384,,0.0134,0.0354,,0.9861,,,0.04,0.0345,0.3333,,0.0305,,0.0375,,0.0129,,block of flats,0.0317,Block,No,2.0,0.0,2.0,0.0,-1798.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n252510,0,Cash loans,F,N,Y,0,495000.0,2250000.0,114430.5,2250000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.028663,-14110,-4310,-6666.0,-4528,,1,1,0,1,0,0,Accountants,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Government,,0.7610973490538383,0.5832379256761245,0.0515,0.0861,0.9727,,,0.0,0.2069,0.125,,0.0,,0.0651,,0.0438,0.0525,0.0893,0.9727,,,0.0,0.2069,0.125,,0.0,,0.0678,,0.0463,0.052000000000000005,0.0861,0.9727,,,0.0,0.2069,0.125,,0.0,,0.0662,,0.0447,,block of flats,0.1021,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-493.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n437002,0,Cash loans,M,N,N,0,360000.0,781920.0,28084.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-8562,-444,-3384.0,-1249,,1,1,1,1,0,0,Laborers,1.0,2,2,WEDNESDAY,11,1,1,0,1,1,0,Business Entity Type 3,,0.3872091684729097,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-915.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n415324,0,Revolving loans,M,Y,Y,0,270000.0,810000.0,40500.0,810000.0,Family,Working,Higher education,Married,House / apartment,0.019689,-21500,-3605,-9093.0,-4030,3.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Construction,,0.6675886834902461,0.3441550073724169,,,0.9866,,,,,,,,,0.1315,,,,,0.9866,,,,,,,,,0.13699999999999998,,,,,0.9866,,,,,,,,,0.1339,,,,,0.1167,,No,2.0,0.0,2.0,0.0,-172.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n156607,1,Cash loans,M,N,Y,2,225000.0,1078200.0,31653.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-14183,-6268,-5095.0,-5099,,1,1,1,1,0,0,Laborers,4.0,3,3,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 2,0.5494720693611143,0.5112071421127015,0.5298898341969072,0.0309,0.0979,0.9906,0.8708,0.0443,0.0,0.2069,0.1667,0.2083,0.0938,0.0252,0.0684,0.0,0.0,0.0315,0.1015,0.9906,0.8759,0.0448,0.0,0.2069,0.1667,0.2083,0.096,0.0275,0.0712,0.0,0.0,0.0312,0.0979,0.9906,0.8725,0.0446,0.0,0.2069,0.1667,0.2083,0.0955,0.0257,0.0696,0.0,0.0,reg oper account,block of flats,0.078,Panel,No,0.0,0.0,0.0,0.0,-278.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,1.0\n155128,0,Cash loans,F,Y,Y,2,135000.0,337500.0,22684.5,337500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-11133,-163,-1.0,-3364,3.0,1,1,0,1,0,0,Core staff,4.0,2,2,SATURDAY,8,0,0,0,0,0,0,Self-employed,0.44960368281757934,0.7351895934313545,0.4884551844437485,0.16699999999999998,0.0,0.998,,,0.12,0.1034,0.3333,,0.0261,,0.1461,,0.0072,0.1702,0.0,0.998,,,0.1208,0.1034,0.3333,,0.0267,,0.1522,,0.0076,0.1686,0.0,0.998,,,0.12,0.1034,0.3333,,0.0266,,0.1487,,0.0073,,block of flats,0.1482,\"Stone, brick\",No,5.0,2.0,5.0,2.0,-794.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n160858,0,Cash loans,F,N,Y,0,67500.0,76410.0,7686.0,67500.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-22838,365243,-3301.0,-4440,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.32560319468953675,0.6594055320683344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n102821,0,Cash loans,M,N,Y,0,202500.0,677664.0,24471.0,585000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.009334,-20055,365243,-7456.0,-3473,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,XNA,,0.5695676827596661,0.5937175866150576,0.0608,0.055,0.9866,0.8164,0.0133,0.0,0.1379,0.1667,0.2083,0.0794,0.0496,0.0701,0.0,0.0,0.062,0.0571,0.9866,0.8236,0.0135,0.0,0.1379,0.1667,0.2083,0.0812,0.0542,0.0731,0.0,0.0,0.0614,0.055,0.9866,0.8189,0.0134,0.0,0.1379,0.1667,0.2083,0.0808,0.0504,0.0714,0.0,0.0,reg oper account,block of flats,0.0625,Panel,No,8.0,0.0,7.0,0.0,-1018.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n420998,0,Cash loans,F,N,N,0,135000.0,808650.0,24601.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.032561,-19189,-12455,-13201.0,-2462,,1,1,1,1,1,0,Core staff,1.0,1,1,TUESDAY,20,0,0,0,0,0,0,School,0.8095328248046253,0.7920415906403073,,0.1409,0.0732,0.9786,0.7008,0.0272,0.1332,0.0917,0.4721,0.375,0.0606,0.1315,0.1347,0.0,0.0013,0.1019,0.0479,0.9796,0.6994,0.0214,0.0806,0.0345,0.5417,0.375,0.0412,0.0891,0.0947,0.0,0.0,0.10099999999999999,0.0662,0.9796,0.7048,0.0274,0.08,0.0345,0.5417,0.375,0.0616,0.1338,0.0937,0.0,0.0013,reg oper account,block of flats,0.192,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1390.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n185331,0,Cash loans,F,Y,Y,0,135000.0,738171.0,37629.0,684000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.01885,-17727,-1678,-2578.0,-1255,1.0,1,1,0,1,1,0,Managers,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,School,,0.624771361234023,0.7016957740576931,0.2969,0.2124,0.9801,0.728,0.0351,0.32,0.2759,0.3333,0.375,0.3257,0.2421,0.2926,0.0,0.0,0.3025,0.2204,0.9801,0.7387,0.0354,0.3222,0.2759,0.3333,0.375,0.3331,0.2645,0.3048,0.0,0.0,0.2998,0.2124,0.9801,0.7316,0.0353,0.32,0.2759,0.3333,0.375,0.3313,0.2463,0.2978,0.0,0.0,reg oper account,block of flats,0.2301,\"Stone, brick\",No,5.0,0.0,4.0,0.0,-726.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n116911,0,Cash loans,F,N,N,0,220500.0,592560.0,22090.5,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.007114,-16670,-4768,-987.0,-220,,1,1,1,1,1,0,High skill tech staff,1.0,2,2,MONDAY,18,0,0,0,0,0,0,Other,0.7816133640523155,0.499269320044857,0.5046813193144684,0.0495,0.036000000000000004,0.9801,0.728,,0.04,0.0345,0.3333,0.375,0.0471,,0.0406,,0.0179,0.0504,0.0374,0.9801,0.7387,,0.0403,0.0345,0.3333,0.375,0.0482,,0.0423,,0.0189,0.05,0.036000000000000004,0.9801,0.7316,,0.04,0.0345,0.3333,0.375,0.0479,,0.0414,,0.0183,reg oper account,block of flats,0.0363,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-910.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n203572,0,Cash loans,F,N,Y,0,112500.0,675000.0,29862.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.01885,-22433,365243,-9003.0,-4524,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,0.7601200360221271,0.39139948338313707,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-940.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n431028,0,Cash loans,F,N,Y,0,180000.0,278613.0,28678.5,252000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-17445,-2493,-9304.0,-974,,1,1,0,1,0,0,HR staff,2.0,1,1,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.8003160748903394,0.6848276586890367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1128.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n402154,1,Revolving loans,F,N,Y,0,99000.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-16300,-1472,-2864.0,-2199,,1,1,0,1,1,0,Sales staff,2.0,1,1,TUESDAY,9,0,0,0,0,0,0,Trade: type 1,,0.09241309256848736,,0.1567,0.0942,0.9816,0.7484,,0.18,0.1379,0.3958,0.4375,0.0555,0.1341,0.1669,0.0019,0.0021,0.0557,0.0423,0.9816,0.7583,,0.0806,0.0345,0.3333,0.375,0.0568,0.0542,0.0661,0.0,0.0,0.1582,0.0942,0.9816,0.7518,,0.18,0.1379,0.3958,0.4375,0.0564,0.1364,0.1699,0.0019,0.0021,reg oper account,block of flats,0.2143,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n205719,0,Cash loans,F,Y,Y,2,180000.0,247500.0,22828.5,247500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-11011,-231,-2078.0,-2688,13.0,1,1,0,1,0,0,Managers,4.0,2,2,THURSDAY,11,0,0,0,0,0,0,Other,,0.6452282825182031,0.6023863442690867,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-233.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n376200,0,Cash loans,M,Y,N,1,270000.0,331632.0,34821.0,315000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.006629,-13281,-304,-2650.0,-4576,6.0,1,1,0,1,0,0,Drivers,3.0,2,2,TUESDAY,6,0,0,0,0,0,0,Emergency,,0.613064183809246,0.2721336844147212,0.0825,0.0,0.9851,0.7959999999999999,,0.0,0.1379,0.1667,,0.0179,,0.086,,0.0,0.084,0.0,0.9851,0.804,,0.0,0.1379,0.1667,,0.0183,,0.0896,,0.0,0.0833,0.0,0.9851,0.7987,,0.0,0.1379,0.1667,,0.0182,,0.0875,,0.0,reg oper account,block of flats,0.0763,Panel,No,0.0,0.0,0.0,0.0,-2190.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n308361,0,Cash loans,F,Y,N,3,135000.0,755190.0,36459.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-10659,-187,-243.0,-3074,10.0,1,1,0,1,0,0,Core staff,5.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Kindergarten,0.3004080721074895,0.3552558605867515,0.8435435389318647,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n144041,0,Cash loans,F,N,Y,1,175500.0,592560.0,32143.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.00496,-10825,-813,-2907.0,-3424,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.4096731071538785,0.6467285520322946,0.375711009574066,0.0663,,0.9846,,,,0.0345,0.1667,,,,,,,0.084,,0.9836,,,,0.0345,0.1667,,,,,,,0.0833,,0.9836,,,,0.0345,0.1667,,,,,,,,block of flats,0.0298,\"Stone, brick\",No,6.0,3.0,6.0,0.0,-258.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n345080,0,Cash loans,M,Y,N,2,135000.0,254700.0,25321.5,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14279,-5329,-204.0,-4099,24.0,1,1,0,1,0,0,Sales staff,4.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.6296746301540699,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n103172,1,Cash loans,F,Y,N,2,270000.0,343800.0,10552.5,225000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-17417,-269,-3381.0,-844,21.0,1,1,0,1,0,0,Core staff,4.0,2,2,FRIDAY,16,0,0,0,0,0,0,Other,,0.5321628937350742,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-510.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,,,,,,\n103944,0,Cash loans,M,Y,N,0,135000.0,417024.0,18369.0,360000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.035792000000000004,-14680,-746,-8027.0,-4382,29.0,1,1,1,1,0,0,Managers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Other,0.6338866735332704,0.6958489285853987,0.6848276586890367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3063.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n357341,0,Cash loans,F,Y,Y,2,135000.0,252000.0,18049.5,252000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.015221,-12564,-1738,-4988.0,-2592,14.0,1,1,0,1,1,0,Managers,4.0,2,2,SUNDAY,12,0,0,0,0,0,0,Other,0.4228538748654232,0.5470199775215556,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1547.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n182048,0,Cash loans,F,N,Y,0,135000.0,755190.0,32125.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.072508,-18825,-542,-12725.0,-2355,,1,1,0,1,1,0,,1.0,1,1,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 1,0.9005046203344947,0.7043060746552384,0.8193176922872417,0.2153,0.1396,0.9796,0.7212,0.0008,0.2664,0.1607,0.4167,0.4583,0.0,0.1747,0.2183,0.0045,0.002,0.1765,0.11,0.9796,0.7321,0.0,0.2417,0.1034,0.4583,0.5,0.0,0.1543,0.1916,0.0,0.0,0.1764,0.1085,0.9796,0.7249,0.0001,0.24,0.1034,0.4583,0.5,0.0,0.1437,0.1895,0.0039,0.0003,reg oper account,block of flats,0.2228,Panel,No,1.0,0.0,1.0,0.0,-1534.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n200471,0,Cash loans,F,N,Y,0,180000.0,835380.0,40320.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007305,-15246,-6254,-2681.0,-3059,,1,1,0,1,0,0,Managers,2.0,3,3,THURSDAY,9,0,0,0,0,0,0,Government,0.6421259911104668,0.6441613527063975,0.5691487713619409,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2036.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n309262,0,Cash loans,F,N,Y,0,112500.0,117162.0,12559.5,103500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-15569,-991,-2443.0,-4361,,1,1,0,1,0,1,Sales staff,2.0,3,3,THURSDAY,10,0,0,0,0,0,0,Self-employed,0.4449926788167902,0.1788075745823939,0.470456116119975,0.0495,0.0511,0.9752,0.66,0.004,0.0,0.1034,0.125,0.1667,0.0389,0.0403,0.0429,0.0,0.0101,0.0504,0.053,0.9752,0.6733,0.0041,0.0,0.1034,0.125,0.1667,0.0398,0.0441,0.0447,0.0,0.0107,0.05,0.0511,0.9752,0.6645,0.004,0.0,0.1034,0.125,0.1667,0.0396,0.040999999999999995,0.0436,0.0,0.0103,reg oper account,block of flats,0.0337,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n145198,0,Cash loans,F,Y,Y,0,135000.0,834048.0,30087.0,720000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.035792000000000004,-10960,-1400,-5053.0,-3614,17.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Other,0.6148038824129848,0.1173940573360721,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1470.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n391472,0,Cash loans,M,Y,Y,1,135000.0,178290.0,19048.5,157500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.004849,-10863,-1199,-5160.0,-3429,64.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,16,0,0,0,1,1,0,Self-employed,,,0.7252764347002191,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1526.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n246226,0,Cash loans,F,N,Y,0,202500.0,675000.0,64219.5,675000.0,Family,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-22510,365243,-90.0,-4230,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,,0.7176943715051698,0.3706496323299817,0.2577,,1.0,,,,0.1379,0.9583,,,,1.0,,0.0,0.2626,,1.0,,,,0.1379,0.9583,,,,1.0,,0.0,0.2602,,1.0,,,,0.1379,0.9583,,,,1.0,,0.0,,block of flats,0.8856,Monolithic,No,0.0,0.0,0.0,0.0,-737.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n429725,0,Cash loans,M,Y,Y,0,112500.0,180000.0,8883.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-11867,-761,-2803.0,-4364,4.0,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,Construction,,0.7618718333990907,0.6706517530862718,0.10099999999999999,0.0928,0.9796,,,0.0,0.2069,0.1667,,0.0787,,0.0873,,0.0248,0.1029,0.0963,0.9796,,,0.0,0.2069,0.1667,,0.0805,,0.091,,0.0263,0.102,0.0928,0.9796,,,0.0,0.2069,0.1667,,0.08,,0.0889,,0.0254,,block of flats,0.0741,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1563.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n284634,0,Cash loans,F,Y,Y,1,108000.0,675000.0,26901.0,675000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.018209,-14798,-6333,-8885.0,-5130,16.0,1,1,0,1,0,0,,2.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Government,,0.5596985299670921,0.6863823354047934,,,0.9866,,,0.36,0.3103,0.375,,,,0.3315,,0.0031,,,0.9866,,,0.3625,0.3103,0.375,,,,0.3453,,0.0033,,,0.9866,,,0.36,0.3103,0.375,,,,0.3374,,0.0032,,,0.2607,Panel,No,0.0,0.0,0.0,0.0,-795.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n351980,0,Cash loans,F,Y,Y,0,225000.0,1082214.0,31770.0,945000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.072508,-11040,-1387,-5137.0,-28,9.0,1,1,0,1,1,0,Accountants,1.0,1,1,WEDNESDAY,17,0,1,1,0,0,0,Transport: type 2,0.6746860191429939,0.6887921778530354,,0.1144,0.0448,0.9881,0.8368,0.0,0.08,0.0345,0.625,0.6667,0.0,0.0933,0.1057,0.0,0.065,0.1166,0.0465,0.9881,0.8432,0.0,0.0806,0.0345,0.625,0.6667,0.0,0.1019,0.1101,0.0,0.0688,0.1155,0.0448,0.9881,0.8390000000000001,0.0,0.08,0.0345,0.625,0.6667,0.0,0.0949,0.1076,0.0,0.0664,reg oper account,block of flats,0.0834,Panel,No,2.0,0.0,2.0,0.0,-889.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n378236,0,Cash loans,F,N,Y,0,94500.0,1345500.0,39469.5,1345500.0,Unaccompanied,Pensioner,Incomplete higher,Married,House / apartment,0.026392,-21556,365243,-1062.0,-1508,,1,0,0,1,0,0,,2.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.6480314401656243,0.22888341670067305,0.0722,0.0801,0.9781,,,0.0,0.1379,0.1667,,,,0.0632,,0.0079,0.0735,0.0831,0.9782,,,0.0,0.1379,0.1667,,,,0.0658,,0.0083,0.0729,0.0801,0.9781,,,0.0,0.1379,0.1667,,,,0.0643,,0.008,,block of flats,0.0514,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n288947,0,Cash loans,F,N,N,0,180000.0,1230565.5,40797.0,1102500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.002042,-22240,365243,-224.0,-4724,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,10,0,0,0,1,0,0,XNA,,0.31148299265309315,0.4650692149562261,0.0773,0.0018,0.9757,0.6668,0.0121,0.0,0.1379,0.1667,0.2083,0.0806,0.0538,0.0342,0.0425,0.0663,0.0788,0.0019,0.9757,0.6798,0.0122,0.0,0.1379,0.1667,0.2083,0.0825,0.0588,0.0357,0.0428,0.0702,0.0781,0.0018,0.9757,0.6713,0.0121,0.0,0.1379,0.1667,0.2083,0.0821,0.0547,0.0348,0.0427,0.0677,reg oper account,block of flats,0.0413,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-643.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n358440,0,Cash loans,M,N,N,0,90000.0,225000.0,24232.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.072508,-11467,-121,-5689.0,-4130,,1,1,0,1,0,0,Accountants,2.0,1,1,TUESDAY,20,0,0,0,0,0,0,Bank,0.28430350661725,0.4100285948648047,,0.0196,0.068,0.9498,,,0.0664,0.0572,0.1388,,0.0,,0.0308,,0.037000000000000005,0.0074,0.0167,0.9494,,,0.0806,0.069,0.125,,0.0,,0.0143,,0.0176,0.0219,0.0931,0.9493,,,0.08,0.069,0.125,,0.0,,0.0367,,0.0399,,block of flats,0.0404,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-95.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n230286,0,Cash loans,F,N,N,0,76500.0,163512.0,9387.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-14234,-2896,-6227.0,-4745,,1,1,1,1,0,0,Laborers,2.0,2,2,SATURDAY,15,0,0,0,0,1,1,Postal,0.2204510142518108,0.3973797232862504,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1694.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n355064,0,Cash loans,F,N,Y,0,90000.0,314100.0,15241.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.019689,-14549,365243,-1302.0,-4794,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,,0.6160943601253089,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,1.0,1.0,1.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n377383,0,Cash loans,M,Y,Y,0,175500.0,986553.0,28975.5,823500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003122,-16124,-1740,-2395.0,-4002,6.0,1,1,0,1,1,0,,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,Industry: type 1,,0.5769872541949774,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-540.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n447782,0,Cash loans,F,N,Y,0,67500.0,538704.0,23859.0,481500.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.020713,-23099,365243,-14021.0,-4625,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,XNA,,0.5578492968384864,0.5884877883422673,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-866.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n354245,0,Cash loans,F,N,Y,2,135000.0,1971072.0,68643.0,1800000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13946,-122,-1016.0,-5539,,1,1,0,1,0,0,,4.0,2,2,THURSDAY,14,0,0,0,0,0,0,Other,0.2865336369450755,0.6523088321874584,,0.2907,0.214,0.9975,0.966,0.1353,0.28,0.2414,0.375,0.0417,0.1689,0.2353,0.3659,0.0077,0.1693,0.2962,0.2221,0.9975,0.9673,0.1365,0.282,0.2414,0.375,0.0417,0.1727,0.2571,0.3812,0.0078,0.1792,0.2935,0.214,0.9975,0.9665,0.1361,0.28,0.2414,0.375,0.0417,0.1718,0.2394,0.3725,0.0078,0.1728,reg oper account,block of flats,0.3985,Panel,No,3.0,0.0,3.0,0.0,-953.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n127515,0,Cash loans,F,N,N,0,202500.0,1525482.0,56664.0,1363500.0,Unaccompanied,Working,Higher education,Civil marriage,Municipal apartment,0.010006,-21208,-1796,-6922.0,-4770,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,9,0,0,0,0,1,1,Self-employed,0.5432895375199567,0.668989632123973,0.6801388218428291,0.0031,0.0,0.9851,,,0.0,,0.0,,,,0.0036,,0.0,0.0032,0.0,0.9851,,,0.0,,0.0,,,,0.0038,,0.0,0.0031,0.0,0.9851,,,0.0,,0.0,,,,0.0037,,0.0,,block of flats,0.0029,Wooden,No,1.0,0.0,1.0,0.0,-1757.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,2.0,4.0\n237638,0,Cash loans,M,N,Y,0,135000.0,753840.0,24448.5,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-20984,365243,-12546.0,-4286,,1,0,0,1,1,0,,2.0,3,3,THURSDAY,12,0,0,0,0,0,0,XNA,,0.07653055165540942,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-957.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n168865,0,Cash loans,M,N,Y,2,315000.0,481500.0,23292.0,481500.0,Unaccompanied,Commercial associate,Higher education,Married,Office apartment,0.072508,-17704,-2386,-1224.0,-1218,,1,1,0,1,0,0,Laborers,4.0,1,1,WEDNESDAY,9,0,0,0,0,0,0,Construction,,0.12482171072407976,0.2314393514998941,0.0564,0.1629,0.9593,0.5920000000000001,0.0,0.08,0.0459,0.2221,0.25,0.0179,0.0168,0.053,0.0,0.125,0.021,0.2392,0.9518,0.608,0.0,0.0806,0.0345,0.2083,0.25,0.0061,0.0184,0.03,0.0,0.0284,0.05,0.2305,0.9568,0.5975,0.0,0.08,0.0345,0.2083,0.25,0.0179,0.0171,0.0412,0.0,0.1221,reg oper account,block of flats,0.1574,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-544.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n242364,1,Cash loans,F,N,Y,1,90000.0,509602.5,34605.0,387000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-8555,-155,-10.0,-30,,1,1,0,1,0,0,,3.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.21307061012464354,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-517.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n430968,0,Cash loans,F,N,Y,0,207000.0,450000.0,21780.0,450000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-22820,365243,-4724.0,-4733,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.7309964570024958,0.6195277080511546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n166676,0,Cash loans,F,N,Y,0,180000.0,684000.0,20128.5,684000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.030755,-22685,365243,-3951.0,-3910,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.6019376900711507,0.7062051096536562,0.1495,0.1069,0.9796,0.7212,0.0752,0.16,0.1379,0.3333,0.375,0.0411,0.121,0.1465,0.0039,0.0041,0.1523,0.111,0.9796,0.7321,0.0759,0.1611,0.1379,0.3333,0.375,0.042,0.1322,0.1526,0.0039,0.0044,0.1509,0.1069,0.9796,0.7249,0.0757,0.16,0.1379,0.3333,0.375,0.0418,0.1231,0.1491,0.0039,0.0042,reg oper spec account,block of flats,0.1572,Panel,No,4.0,1.0,4.0,0.0,-1646.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n423669,0,Revolving loans,M,Y,Y,0,225000.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00823,-18358,-3846,-4624.0,-1879,6.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.5405698004600661,0.21396685226179807,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1148.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n419948,0,Cash loans,F,N,N,2,90000.0,288873.0,10890.0,238500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.014464,-18998,-3000,-9556.0,-2535,,1,1,0,1,0,0,Accountants,4.0,2,2,WEDNESDAY,4,0,0,0,0,0,0,Other,0.5624183599295816,0.6353622823742088,0.6178261467332483,0.2412,0.1711,0.9796,0.7212,,0.16,0.1379,0.3333,0.0417,0.3092,,0.2294,,0.0,0.2458,0.1776,0.9796,0.7321,,0.1611,0.1379,0.3333,0.0417,0.3163,,0.239,,0.0,0.2436,0.1711,0.9796,0.7249,,0.16,0.1379,0.3333,0.0417,0.3146,,0.2335,,0.0,,block of flats,0.2656,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2741.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n130747,0,Cash loans,F,N,N,0,270000.0,500490.0,48757.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010032,-17573,-5792,-11451.0,-1108,,1,1,1,1,1,0,Managers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Government,0.6368540576100821,0.5317436619832779,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1395.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n319840,0,Cash loans,M,Y,Y,0,121500.0,1195587.0,33007.5,1044000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.001333,-19518,-1831,-3064.0,-2016,12.0,1,1,0,1,1,0,Drivers,2.0,3,3,WEDNESDAY,4,0,0,0,0,1,1,Industry: type 9,,0.4718116894140007,0.7636399214572418,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-7.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n288118,0,Cash loans,M,Y,Y,0,225000.0,450000.0,32746.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-14343,-3372,-6218.0,-4441,15.0,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,1,1,0,Construction,,0.6578623659764453,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2363.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n311575,0,Cash loans,F,N,Y,0,135000.0,281493.0,22698.0,243000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Co-op apartment,0.006670999999999999,-9307,-1326,-1095.0,-1316,,1,1,0,1,0,0,,1.0,2,2,FRIDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.6572813402382002,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n210669,1,Cash loans,F,N,N,2,900000.0,790830.0,57546.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-19992,-1426,-444.0,-3195,,1,1,1,1,0,0,Managers,4.0,2,2,WEDNESDAY,11,0,0,0,0,1,1,Self-employed,,0.5221010654181106,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-436.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n384093,0,Cash loans,M,Y,N,1,225000.0,832500.0,29623.5,832500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-14618,-3444,-2541.0,-4637,7.0,1,1,1,1,1,0,,3.0,2,2,THURSDAY,9,0,1,1,0,1,1,Construction,,0.5719303085728937,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3177.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n161694,0,Cash loans,F,N,N,0,202500.0,711612.0,18900.0,594000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.031329,-8849,-1175,-1763.0,-1322,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.29163103158690484,0.6482558541617309,0.5814837058057234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-524.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n437543,0,Cash loans,F,N,Y,0,157500.0,254700.0,14751.0,225000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.018801,-24559,365243,-2446.0,-4398,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.5230940077559416,0.5478104658520093,0.1361,,0.9851,0.7959999999999999,0.0535,0.16,0.1379,0.3333,0.375,0.0573,0.1076,0.1333,0.0309,0.0869,0.1345,,0.9851,0.804,0.054000000000000006,0.1611,0.1379,0.3333,0.375,0.0586,0.1175,0.1388,0.0311,0.092,0.1374,,0.9851,0.7987,0.0538,0.16,0.1379,0.3333,0.375,0.0583,0.1095,0.1357,0.0311,0.0888,org spec account,block of flats,0.1338,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-938.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,13.0\n141965,0,Revolving loans,F,Y,Y,1,261000.0,585000.0,29250.0,585000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.016612000000000002,-14530,-1442,-3915.0,-3655,3.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Self-employed,0.6525904133934284,0.7389696518611699,0.4830501881366946,0.1151,0.1482,0.9940000000000001,0.9184,0.0279,0.1332,0.069,0.4442,0.0417,0.0199,0.0913,0.1027,0.0116,0.0407,0.084,0.1112,0.9945,0.9281,0.0104,0.1611,0.069,0.375,0.0417,0.0175,0.067,0.0542,0.0039,0.0039,0.1187,0.1596,0.9945,0.9262,0.032,0.16,0.069,0.4583,0.0417,0.018000000000000002,0.0966,0.0783,0.0039,0.0072,reg oper account,block of flats,0.1073,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-262.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n310069,0,Cash loans,M,N,Y,0,144000.0,810000.0,37665.0,810000.0,Family,Working,Higher education,Married,House / apartment,0.00733,-11610,-2851,-2233.0,-4023,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Government,,0.7164477169197413,0.5954562029091491,0.0474,0.0279,0.9513,0.3336,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0387,0.0464,0.0116,0.0209,0.0483,0.0289,0.9513,0.3597,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0422,0.0483,0.0117,0.0221,0.0479,0.0279,0.9513,0.3425,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0393,0.0472,0.0116,0.0213,reg oper account,block of flats,0.0596,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-234.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n357656,0,Cash loans,M,N,N,0,90000.0,508495.5,20295.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-23702,365243,-8271.0,-4062,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,0.826741065262395,0.7922468211782742,0.5352762504724826,0.3670000000000001,0.22699999999999998,0.9886,0.8436,0.1505,0.44,0.3793,0.3333,0.375,0.3093,0.2992,0.4033,0.0,0.0,0.3739,0.2355,0.9886,0.8497,0.1518,0.4431,0.3793,0.3333,0.375,0.3163,0.3269,0.4202,0.0,0.0,0.3706,0.22699999999999998,0.9886,0.8457,0.1514,0.44,0.3793,0.3333,0.375,0.3146,0.3044,0.4106,0.0,0.0,not specified,block of flats,0.3995,Panel,No,0.0,0.0,0.0,0.0,-570.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n181455,0,Cash loans,F,N,N,0,112500.0,675000.0,28597.5,675000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.025164,-11532,-2483,-4835.0,-3648,,1,1,1,1,0,0,Accountants,2.0,2,2,THURSDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.6002493136310622,,0.0598,0.0,0.9697,0.6940000000000001,0.0,0.0,0.1379,0.1042,0.2083,0.0,0.0857,0.033,0.0,0.0064,0.0147,0.0,0.9618,0.706,0.0,0.0,0.069,0.0417,0.2083,0.0,0.0937,0.0079,0.0,0.0,0.0604,0.0,0.9697,0.6981,0.0,0.0,0.1379,0.1042,0.2083,0.0,0.0872,0.0336,0.0,0.0065,reg oper account,block of flats,0.0699,\"Stone, brick\",No,10.0,1.0,9.0,1.0,-1109.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n249868,0,Cash loans,F,N,Y,1,135000.0,1024290.0,30078.0,855000.0,Other_B,Working,Secondary / secondary special,Single / not married,House / apartment,0.010643,-14815,-1766,-2444.0,-258,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,11,0,1,1,0,1,1,Business Entity Type 2,0.482430477487421,0.5830013132636902,,0.0144,0.0575,0.9796,,0.0124,0.0,0.0345,0.1042,,0.0334,,0.0215,,0.0312,0.0084,0.0597,0.9732,,0.0007,0.0,0.0345,0.0417,,0.0234,,0.0057,,0.0017,0.0146,0.0575,0.9796,,0.0125,0.0,0.0345,0.1042,,0.034,,0.0219,,0.0319,,block of flats,0.0427,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1556.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n314889,0,Cash loans,F,N,Y,1,337500.0,651816.0,23539.5,495000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.030755,-11953,-846,-3874.0,-2343,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Kindergarten,0.3239855264600172,0.6389920372703354,0.5334816299804352,0.0124,0.0,0.9518,0.3404,0.0124,0.0,0.0345,0.0417,0.0833,0.0148,0.0101,0.0086,0.0,0.0,0.0126,0.0,0.9518,0.3662,0.0125,0.0,0.0345,0.0417,0.0833,0.0151,0.011000000000000001,0.0089,0.0,0.0,0.0125,0.0,0.9518,0.3492,0.0124,0.0,0.0345,0.0417,0.0833,0.0151,0.0103,0.0087,0.0,0.0,reg oper account,terraced house,0.0068,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-628.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n405779,0,Cash loans,M,Y,Y,1,135000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-11597,-3121,-2225.0,-3830,13.0,1,1,1,1,1,0,Managers,3.0,3,3,TUESDAY,7,0,0,0,0,0,0,Industry: type 11,0.5682895353226936,0.7643070541519681,0.6545292802242897,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-832.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n259440,0,Cash loans,F,N,N,1,90000.0,343800.0,16852.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.002134,-11630,-1890,-3693.0,-649,,1,1,0,1,0,0,Sales staff,3.0,3,3,TUESDAY,5,0,0,0,0,0,0,Self-employed,0.3744370082042929,0.024007642224771725,0.4938628816996244,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1481.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n409102,0,Cash loans,F,Y,N,0,270000.0,521280.0,28278.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-10569,-565,-5042.0,-3214,1.0,1,1,0,1,1,0,Sales staff,2.0,1,1,MONDAY,15,0,0,0,0,0,0,Business Entity Type 1,0.7079077615479744,0.6750480209193714,0.13259749523614614,0.1474,0.0817,0.9861,,,0.16,0.1379,0.3333,,,,0.13699999999999998,,,0.1502,0.0848,0.9861,,,0.1611,0.1379,0.3333,,,,0.1427,,,0.1489,0.0817,0.9861,,,0.16,0.1379,0.3333,,,,0.1395,,,,block of flats,0.1376,Panel,No,0.0,0.0,0.0,0.0,-237.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n123941,0,Cash loans,F,N,N,0,112500.0,199152.0,9711.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-19244,-1442,-9311.0,-2472,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Bank,,0.7120695538298979,0.7180328113294772,0.1856,0.1362,0.9871,0.8232,0.0,0.2,0.1724,0.3333,0.375,0.0577,0.1513,0.1931,,0.0022,0.1891,0.1413,0.9871,0.8301,0.0,0.2014,0.1724,0.3333,0.375,0.0591,0.1653,0.2012,,0.0023,0.1874,0.1362,0.9871,0.8256,0.0,0.2,0.1724,0.3333,0.375,0.0587,0.1539,0.1966,,0.0022,reg oper account,block of flats,0.1955,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1685.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n256722,0,Cash loans,F,Y,Y,0,128700.0,296280.0,19062.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-21319,365243,-4399.0,-2775,20.0,1,0,0,1,1,1,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,0.6999532300607217,0.649909502186469,,,,0.9796,,,,,,,,,0.0353,,,,,0.9796,,,,,,,,,0.0368,,,,,0.9796,,,,,,,,,0.0359,,,,,0.0455,,No,0.0,0.0,0.0,0.0,-454.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n266298,0,Cash loans,F,N,Y,0,90000.0,254700.0,14350.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.019101,-21606,365243,-8521.0,-3996,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.2533716311332267,0.5280927512030451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-479.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n452143,0,Cash loans,F,N,N,0,31500.0,314100.0,8941.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.031329,-19951,365243,-8780.0,-3503,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,17,0,0,0,0,0,0,XNA,0.736101069547411,0.6224308012484793,,0.2959,0.1407,0.9836,,,0.32,0.2759,0.3333,,0.192,,0.2764,,0.0,0.3015,0.146,0.9836,,,0.3222,0.2759,0.3333,,0.1964,,0.2879,,0.0,0.2987,0.1407,0.9836,,,0.32,0.2759,0.3333,,0.1954,,0.2813,,0.0,,block of flats,0.2183,Panel,No,1.0,1.0,1.0,0.0,-2244.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n386862,1,Cash loans,F,N,Y,0,67500.0,215640.0,9495.0,180000.0,Other_B,Pensioner,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-24297,365243,-9013.0,-4262,,1,0,0,1,1,0,,1.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.6244842354475232,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-748.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n404824,0,Revolving loans,F,N,Y,0,81000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-7981,-397,-4015.0,-34,,1,1,0,1,0,0,Sales staff,2.0,2,2,SUNDAY,14,0,0,0,0,0,0,Self-employed,0.28206955040632264,0.13848036754402834,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-18.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n330060,0,Cash loans,F,N,N,0,90000.0,264888.0,27945.0,234000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.019101,-9357,-1443,-4181.0,-2021,,1,1,0,1,0,0,Medicine staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Trade: type 3,0.5255929180938071,0.7774213640938942,0.3539876078507373,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1365.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,5.0,4.0\n277102,0,Revolving loans,M,N,Y,0,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21648,-2545,-237.0,-4248,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Business Entity Type 2,,0.5270093648367693,0.17456426555726348,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-575.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n118767,1,Cash loans,M,Y,Y,1,225000.0,685386.0,54279.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-13618,-1778,-2878.0,-5135,4.0,1,1,0,1,0,0,Drivers,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,,0.6656204220302323,,0.0928,0.0805,0.9821,0.7552,,0.0,0.1724,0.1667,0.2083,0.0899,0.0756,0.0793,0.0,0.0,0.0945,0.0836,0.9821,0.7648,,0.0,0.1724,0.1667,0.2083,0.092,0.0826,0.0826,0.0,0.0,0.0937,0.0805,0.9821,0.7585,,0.0,0.1724,0.1667,0.2083,0.0915,0.077,0.0807,0.0,0.0,reg oper account,block of flats,0.0624,Block,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n235910,0,Cash loans,F,Y,Y,0,270000.0,612000.0,27085.5,612000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.014519999999999996,-22332,365243,-7070.0,-5089,9.0,1,0,0,1,0,1,,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,0.6869515373538013,0.578550302524328,,,0.0588,0.9747,0.6532,,0.0,0.1034,0.1667,,,,0.0511,,0.0,,0.061,0.9747,0.6668,,0.0,0.1034,0.1667,,,,0.0533,,0.0,,0.0588,0.9747,0.6578,,0.0,0.1034,0.1667,,,,0.0521,,0.0,,block of flats,0.0402,Panel,No,5.0,0.0,5.0,0.0,-1109.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,4.0\n326547,0,Cash loans,M,N,Y,0,180000.0,477000.0,54076.5,477000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-19794,-4107,-8917.0,-3318,,1,1,1,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6409721249777194,0.22383131747015547,0.2557,,0.9796,,,0.2,0.1724,0.3333,,,,0.2207,,0.0852,0.2605,,0.9796,,,0.2014,0.1724,0.3333,,,,0.2299,,0.0902,0.2581,,0.9796,,,0.2,0.1724,0.3333,,,,0.2246,,0.087,,block of flats,0.1921,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1818.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n122421,0,Cash loans,F,N,Y,1,180000.0,646920.0,19044.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.006207,-13620,-1084,-162.0,-4543,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,17,0,0,0,0,1,1,Self-employed,0.5297061198239393,0.4940377830588482,0.4578995512067301,0.1268,0.0,0.9995,0.9864,0.0,0.08,0.069,0.625,0.6667,0.0,0.1009,0.0626,0.0116,0.3617,0.1292,0.0,0.9995,0.9869,0.0,0.0806,0.069,0.625,0.6667,0.0,0.1102,0.0652,0.0117,0.3829,0.128,0.0,0.9995,0.9866,0.0,0.08,0.069,0.625,0.6667,0.0,0.1026,0.0637,0.0116,0.3692,reg oper account,block of flats,0.1279,Monolithic,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n263328,0,Revolving loans,M,Y,Y,2,270000.0,180000.0,9000.0,180000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.002506,-8772,-694,-3560.0,-1412,12.0,1,1,0,1,0,0,Core staff,4.0,2,2,WEDNESDAY,5,0,0,0,0,1,1,Military,,0.2792484287545193,0.6058362647264226,0.066,0.0943,0.9876,0.83,0.0214,0.0,0.2069,0.125,0.1667,0.0566,0.0538,0.0713,0.0,0.0605,0.0672,0.0978,0.9876,0.8367,0.0216,0.0,0.2069,0.125,0.1667,0.0579,0.0588,0.0743,0.0,0.064,0.0666,0.0943,0.9876,0.8323,0.0216,0.0,0.2069,0.125,0.1667,0.0576,0.0547,0.0726,0.0,0.0617,reg oper account,block of flats,0.0674,Block,No,1.0,0.0,1.0,0.0,-106.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n374659,0,Cash loans,M,Y,N,0,315000.0,1024740.0,49428.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-15042,-7097,-3498.0,-3881,14.0,1,1,1,1,1,0,Laborers,2.0,3,1,MONDAY,14,0,0,0,0,0,0,Industry: type 11,0.2923601755122304,0.6402219656822471,0.40314167665875134,0.4175,0.371,0.9841,0.7824,0.1259,0.44,0.3793,0.3333,0.0417,0.1494,0.3379,0.4973,0.0116,0.0075,0.4254,0.385,0.9841,0.7909,0.1271,0.4431,0.3793,0.3333,0.0417,0.1528,0.3691,0.5181,0.0117,0.0079,0.4216,0.371,0.9841,0.7853,0.1267,0.44,0.3793,0.3333,0.0417,0.152,0.3437,0.5062,0.0116,0.0076,reg oper account,block of flats,0.3928,Mixed,No,6.0,0.0,6.0,0.0,-780.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n185670,0,Cash loans,M,N,Y,1,135000.0,298512.0,20079.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002134,-11573,-532,-738.0,-4176,,1,1,0,1,0,0,Drivers,3.0,3,3,SATURDAY,6,0,0,0,0,0,0,Self-employed,,0.3595296961432897,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,-487.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n222651,0,Cash loans,F,N,Y,0,315000.0,900000.0,31887.0,900000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.072508,-12283,-1664,-2042.0,-2053,,1,1,0,1,1,1,,2.0,1,1,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.4494370506711652,0.4329616670974407,0.2639,0.1305,0.998,0.9728,0.0,0.32,0.1379,0.6667,0.7083,0.0,0.2152,0.2847,0.0,0.057999999999999996,0.2689,0.1354,0.998,0.9739,0.0,0.3222,0.1379,0.6667,0.7083,0.0,0.2351,0.2966,0.0,0.0614,0.2665,0.1305,0.998,0.9732,0.0,0.32,0.1379,0.6667,0.7083,0.0,0.2189,0.2898,0.0,0.0592,reg oper account,block of flats,0.2365,Panel,No,3.0,1.0,3.0,0.0,-1051.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n332548,0,Cash loans,M,Y,N,0,315000.0,568800.0,15133.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.00823,-9130,-1514,-3028.0,-1815,7.0,1,1,1,1,0,0,Drivers,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6171747677899205,0.6296742509538716,0.0825,0.0703,0.9762,0.6736,0.0301,0.0,0.1379,0.1667,0.0417,0.0232,,0.0702,,0.0,0.084,0.0729,0.9762,0.6864,0.0304,0.0,0.1379,0.1667,0.0417,0.0237,,0.0731,,0.0,0.0833,0.0703,0.9762,0.6779999999999999,0.0303,0.0,0.1379,0.1667,0.0417,0.0236,,0.0714,,0.0,reg oper account,block of flats,0.0717,Panel,No,1.0,0.0,1.0,0.0,-4.0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n356505,0,Cash loans,F,Y,Y,0,315000.0,790830.0,62613.0,675000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.072508,-14517,-272,-8364.0,-5125,6.0,1,1,0,1,1,1,Managers,2.0,1,1,THURSDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.20246738241045706,0.7321944803211005,0.2692857999073816,0.1309,0.0465,0.9831,,,0.16,0.069,0.625,,0.0,,0.138,,0.0,0.1334,0.0483,0.9831,,,0.1611,0.069,0.625,,0.0,,0.1438,,0.0,0.1322,0.0465,0.9831,,,0.16,0.069,0.625,,0.0,,0.1405,,0.0,,block of flats,0.1086,Panel,No,0.0,0.0,0.0,0.0,-334.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n371043,0,Cash loans,M,Y,Y,0,157500.0,675000.0,31410.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-20135,-733,-1475.0,-3329,3.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Transport: type 3,,0.4747935467298175,0.2512394458905693,0.0784,,0.9762,,,0.0,0.1379,0.1667,,,,0.0618,,0.0085,0.0798,,0.9762,,,0.0,0.1379,0.1667,,,,0.0644,,0.009000000000000001,0.0791,,0.9762,,,0.0,0.1379,0.1667,,,,0.0629,,0.0086,,block of flats,0.0505,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1435.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,9.0,0.0,2.0\n178532,0,Cash loans,F,N,Y,3,234000.0,579942.0,45949.5,495000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.00496,-14244,-1512,-3427.0,-4695,,1,1,0,1,0,0,Medicine staff,5.0,2,2,TUESDAY,16,0,0,0,0,0,0,Medicine,0.4960936249652096,0.3995077116732952,,0.0268,,0.9513,,,,0.069,0.0417,,,,0.0137,,,0.0273,,0.9513,,,,0.069,0.0417,,,,0.0143,,,0.0271,,0.9513,,,,0.069,0.0417,,,,0.013999999999999999,,,,block of flats,0.0181,,No,2.0,0.0,2.0,0.0,-196.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n226263,0,Cash loans,F,Y,N,0,72000.0,219042.0,21793.5,193500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.035792000000000004,-8157,-831,-2540.0,-840,64.0,1,1,0,1,0,0,Cooking staff,1.0,2,2,THURSDAY,10,0,0,0,1,1,0,Kindergarten,0.298207564650201,0.1414017166127166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n305192,0,Cash loans,F,N,Y,0,112500.0,444420.0,24237.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.006233,-18903,-865,-1745.0,-223,,1,1,0,1,0,0,Sales staff,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Trade: type 7,0.6763445774369419,0.5855561731012029,0.04926257783871888,0.0753,0.0847,0.9871,0.8232,0.0141,0.0,0.1724,0.1667,0.2083,0.0243,0.0614,0.0677,0.0,0.009000000000000001,0.0767,0.0879,0.9871,0.8301,0.0142,0.0,0.1724,0.1667,0.2083,0.0249,0.067,0.0705,0.0,0.0095,0.076,0.0847,0.9871,0.8256,0.0142,0.0,0.1724,0.1667,0.2083,0.0248,0.0624,0.0689,0.0,0.0092,reg oper spec account,block of flats,0.0629,Panel,No,3.0,1.0,3.0,1.0,-373.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n415009,0,Cash loans,F,N,Y,0,81000.0,225000.0,17667.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.020246,-10195,-896,-6676.0,-2476,,1,1,1,1,1,0,,1.0,3,3,SUNDAY,7,0,0,0,0,0,0,Construction,,0.7076242300993474,0.470456116119975,0.1897,0.0505,0.9871,0.8232,0.0436,0.08,0.069,0.3333,0.375,0.0656,0.1505,0.1218,0.0193,0.057,0.1933,0.0524,0.9871,0.8301,0.0439,0.0806,0.069,0.3333,0.375,0.0671,0.1644,0.1269,0.0195,0.0603,0.1915,0.0505,0.9871,0.8256,0.0438,0.08,0.069,0.3333,0.375,0.0668,0.1531,0.124,0.0194,0.0582,reg oper account,block of flats,0.1334,Panel,No,2.0,0.0,2.0,0.0,-365.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n383349,0,Cash loans,F,N,Y,0,112500.0,314055.0,13437.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.003122,-17356,-1757,-8136.0,-907,,1,1,0,1,0,0,Laborers,1.0,3,3,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.7697385785381079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n199742,0,Revolving loans,M,Y,Y,1,202500.0,675000.0,33750.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.01885,-11185,-1151,-3802.0,-3807,2.0,1,1,0,1,0,0,Security staff,3.0,2,2,TUESDAY,11,0,1,1,0,0,0,Security,0.1842611230589636,0.2712268023721289,0.6479768603302221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1814.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n117505,0,Cash loans,F,N,Y,1,94500.0,76410.0,7686.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-11598,-170,-5349.0,-1021,,1,1,1,1,1,0,Laborers,3.0,1,1,THURSDAY,9,0,0,0,0,1,1,Industry: type 3,0.4498399363262886,0.6396525955057968,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-774.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n366816,0,Cash loans,M,N,Y,0,126000.0,675000.0,21906.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.011703,-20802,365243,-6011.0,-4310,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.2453495532540649,0.5478104658520093,0.1021,0.1006,0.9776,0.6940000000000001,0.011000000000000001,0.0,0.2069,0.1667,0.2083,0.0841,0.0824,0.0889,0.0039,0.0082,0.10400000000000001,0.1044,0.9777,0.706,0.0111,0.0,0.2069,0.1667,0.2083,0.086,0.09,0.0927,0.0039,0.0087,0.1031,0.1006,0.9776,0.6981,0.0111,0.0,0.2069,0.1667,0.2083,0.0855,0.0838,0.0905,0.0039,0.0084,reg oper account,block of flats,0.0777,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-967.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n369805,0,Cash loans,F,N,Y,1,135000.0,1099350.0,32274.0,787500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018801,-16922,-254,-6411.0,-467,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Trade: type 7,,0.6317374159970895,0.8224987619370829,0.0619,0.0559,0.9866,0.8164,0.0162,0.0,0.1379,0.1667,0.2083,0.0505,0.0504,0.0512,0.0,0.0,0.063,0.057999999999999996,0.9866,0.8236,0.0163,0.0,0.1379,0.1667,0.2083,0.0516,0.0551,0.0534,0.0,0.0,0.0625,0.0559,0.9866,0.8189,0.0163,0.0,0.1379,0.1667,0.2083,0.0514,0.0513,0.0522,0.0,0.0,reg oper account,block of flats,0.0492,Panel,No,4.0,0.0,4.0,0.0,-1632.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,3.0\n284385,0,Cash loans,M,N,Y,0,42300.0,263686.5,14854.5,238500.0,\"Spouse, partner\",Pensioner,Lower secondary,Married,House / apartment,0.020713,-22600,365243,0.0,-3897,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,13,0,0,0,0,0,0,XNA,,0.2026416107263967,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-671.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n140616,0,Cash loans,F,N,Y,0,67500.0,269550.0,14242.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010556,-18378,-10661,-1495.0,-1908,,1,1,1,1,1,0,Accountants,1.0,3,3,SATURDAY,11,0,0,0,0,1,1,Telecom,0.7973263603749811,0.4035960385656987,,0.0804,0.044000000000000004,0.9975,0.966,0.0568,0.08,0.069,0.375,0.4167,0.0124,0.0656,0.0983,0.0,0.0714,0.0819,0.0457,0.9975,0.9673,0.0573,0.0806,0.069,0.375,0.4167,0.0127,0.0716,0.1025,0.0,0.0756,0.0812,0.044000000000000004,0.9975,0.9665,0.0571,0.08,0.069,0.375,0.4167,0.0126,0.0667,0.1001,0.0,0.0729,reg oper account,block of flats,0.0945,Panel,No,0.0,0.0,0.0,0.0,-1216.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n378431,0,Cash loans,F,N,Y,0,103500.0,225000.0,21919.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-18277,-4432,-9042.0,-1743,,1,1,0,1,0,0,Laborers,2.0,3,2,WEDNESDAY,10,0,0,0,0,0,0,Transport: type 4,,0.6184604414981151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0991,,No,1.0,0.0,0.0,0.0,-1026.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n411927,0,Revolving loans,F,N,Y,2,112500.0,337500.0,16875.0,337500.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.04622,-18438,-3846,-4302.0,-1975,,1,1,0,1,0,0,Medicine staff,3.0,1,1,WEDNESDAY,17,0,0,0,0,0,0,Government,,0.7422212432974444,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-980.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n175191,1,Cash loans,F,N,Y,0,90000.0,517500.0,15259.5,517500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-17833,-2167,-6965.0,-1350,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.3231535953821115,0.26326657248426244,0.7583930617144343,0.0619,0.0725,0.9891,0.8504,0.0107,0.0,0.1034,0.1667,0.2083,0.083,0.0504,0.0324,0.0077,0.0934,0.063,0.0753,0.9891,0.8563,0.0107,0.0,0.1034,0.1667,0.2083,0.0849,0.0551,0.0337,0.0078,0.0989,0.0625,0.0725,0.9891,0.8524,0.0107,0.0,0.1034,0.1667,0.2083,0.0844,0.0513,0.033,0.0078,0.0954,not specified,block of flats,0.0458,Panel,No,0.0,0.0,0.0,0.0,-1564.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n380159,0,Revolving loans,M,N,Y,0,225000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00963,-16539,-2051,-262.0,-96,,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.43439192358402623,0.2857684194620716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-254.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n126031,0,Cash loans,F,N,N,0,157500.0,942300.0,27679.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.02461,-16953,-8054,-10463.0,-505,,1,1,0,1,1,0,Cleaning staff,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Trade: type 7,0.7453272482797042,0.6418422509183533,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-1824.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,8.0,0.0,1.0\n388059,0,Cash loans,M,N,Y,0,405000.0,1159411.5,46111.5,1066500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18195,-1122,-2415.0,-1750,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.6444707730756575,0.5416937891631629,0.392773860603134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1589.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n180824,0,Cash loans,F,Y,Y,1,90000.0,545040.0,31419.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008068,-14310,-4308,-3550.0,-4486,39.0,1,1,0,1,0,0,Sales staff,3.0,3,3,TUESDAY,4,0,0,0,0,0,0,Self-employed,0.3214641675526755,0.031178493927302837,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1226.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n132578,1,Cash loans,F,Y,Y,0,121500.0,360058.5,35073.0,342000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.005002,-22275,365243,-232.0,-4267,29.0,1,0,0,1,0,0,,1.0,3,3,TUESDAY,10,0,0,0,0,0,0,XNA,,0.6689460489647753,0.3556387169923543,0.0021,0.0,0.9727,0.626,0.0023,0.0,0.069,0.0,0.0417,0.0,0.0017,0.0012,0.0,0.0,0.0021,0.0,0.9727,0.6406,0.0023,0.0,0.069,0.0,0.0417,0.0,0.0018,0.0013,0.0,0.0,0.0021,0.0,0.9727,0.631,0.0023,0.0,0.069,0.0,0.0417,0.0,0.0017,0.0012,0.0,0.0,,block of flats,0.0012,Wooden,No,0.0,0.0,0.0,0.0,-2813.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n341635,0,Cash loans,F,N,Y,0,112500.0,807984.0,25803.0,697500.0,Children,Working,Higher education,Married,House / apartment,0.020246,-19869,-185,-17763.0,-3340,,1,1,1,1,0,0,Cleaning staff,2.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Trade: type 7,,0.4690278905011266,,0.0825,0.0792,0.9742,0.6464,0.0087,0.0,0.1379,0.1667,0.2083,0.0537,0.0672,0.0695,0.0,0.0,0.084,0.0822,0.9742,0.6602,0.0088,0.0,0.1379,0.1667,0.2083,0.0549,0.0735,0.0724,0.0,0.0,0.0833,0.0792,0.9742,0.6511,0.0087,0.0,0.1379,0.1667,0.2083,0.0547,0.0684,0.0708,0.0,0.0,reg oper account,block of flats,0.0547,Panel,No,4.0,0.0,4.0,0.0,-758.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n181119,0,Cash loans,F,Y,N,1,157500.0,382500.0,12429.0,382500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.02461,-11270,-186,-585.0,-1517,6.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.5221313259383927,0.6629528846250625,0.6801388218428291,0.0371,0.0,0.9990000000000001,0.9864,0.0491,0.0,0.069,0.0833,0.0417,0.0286,0.0303,0.027999999999999997,0.0,0.0222,0.0378,0.0,0.9990000000000001,0.9869,0.0496,0.0,0.069,0.0833,0.0417,0.0293,0.0331,0.0292,0.0,0.0236,0.0375,0.0,0.9990000000000001,0.9866,0.0494,0.0,0.069,0.0833,0.0417,0.0291,0.0308,0.0285,0.0,0.0227,reg oper spec account,block of flats,0.0269,Monolithic,No,0.0,0.0,0.0,0.0,-981.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n277542,1,Cash loans,F,N,N,0,99000.0,474048.0,25713.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010276,-8169,-464,-6090.0,-825,,1,1,1,1,0,0,,1.0,2,2,TUESDAY,8,0,0,0,0,1,1,Business Entity Type 3,0.17081198609989345,0.6268299283615502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-554.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n196406,0,Cash loans,F,N,N,1,139500.0,454500.0,27805.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.010032,-9810,-2434,-9809.0,-1683,,1,1,0,1,1,0,Sales staff,3.0,2,2,TUESDAY,5,0,0,0,0,0,0,Business Entity Type 3,0.3632084022510305,0.16284987880366525,0.3825018041447388,0.0402,0.0246,0.9732,0.6328,0.0129,0.0,0.1034,0.125,0.1667,0.0631,0.0328,0.0326,0.0039,0.0344,0.040999999999999995,0.0256,0.9732,0.6472,0.0131,0.0,0.1034,0.125,0.1667,0.0645,0.0358,0.034,0.0039,0.0364,0.0406,0.0246,0.9732,0.6377,0.013000000000000001,0.0,0.1034,0.125,0.1667,0.0642,0.0333,0.0332,0.0039,0.0351,reg oper account,block of flats,0.0402,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-238.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n318492,0,Cash loans,F,N,Y,1,135000.0,269982.0,29205.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-10953,-1578,-4842.0,-739,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,13,0,0,0,0,0,0,Other,,0.5058621269898559,,0.0505,0.0445,0.9781,0.7008,0.0056,0.08,0.0345,0.4583,0.5,0.0375,0.0412,0.057999999999999996,0.0,0.0,0.0515,0.0461,0.9782,0.7125,0.0057,0.0806,0.0345,0.4583,0.5,0.0384,0.045,0.0604,0.0,0.0,0.051,0.0445,0.9781,0.7048,0.0057,0.08,0.0345,0.4583,0.5,0.0382,0.0419,0.059000000000000004,0.0,0.0,reg oper account,block of flats,0.0456,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-2089.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n201500,0,Cash loans,M,Y,Y,0,270000.0,1288350.0,37800.0,1125000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.025164,-13374,-396,-7427.0,-4543,7.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Police,,0.6198475191513511,0.5744466170995097,0.1649,0.1119,0.9891,0.8504,0.0585,0.16,0.1379,0.375,0.4167,0.02,0.1345,0.1815,0.0,0.0,0.1681,0.1161,0.9891,0.8563,0.0591,0.1611,0.1379,0.375,0.4167,0.0205,0.1469,0.1891,0.0,0.0,0.1665,0.1119,0.9891,0.8524,0.0589,0.16,0.1379,0.375,0.4167,0.0204,0.1368,0.1847,0.0,0.0,reg oper account,block of flats,0.1748,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n455947,0,Cash loans,F,N,Y,0,90000.0,251091.0,23841.0,238500.0,Unaccompanied,State servant,Secondary / secondary special,Widow,House / apartment,0.014464,-21701,-626,-7573.0,-4283,,1,1,0,1,0,0,Security staff,1.0,2,2,MONDAY,2,0,0,0,0,1,1,Military,,0.5393204403484005,,0.0278,0.0708,0.9866,,,0.0,0.1034,0.0833,,0.0,,0.0276,,0.0,0.0284,0.0734,0.9866,,,0.0,0.1034,0.0833,,0.0,,0.0288,,0.0,0.0281,0.0708,0.9866,,,0.0,0.1034,0.0833,,0.0,,0.0281,,0.0,,block of flats,0.0217,Panel,No,3.0,0.0,3.0,0.0,-880.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n401324,0,Cash loans,M,Y,Y,1,90000.0,269550.0,14751.0,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.007114,-18166,365243,-4969.0,-1713,2.0,1,0,0,1,0,0,,3.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.6539981333869399,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-173.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n374714,0,Cash loans,F,N,Y,0,157500.0,894906.0,31833.0,747000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-17622,-2849,-7139.0,-1161,,1,1,0,1,0,0,Laborers,2.0,1,1,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6407355717756933,0.6754132910917112,0.0082,0.0,0.9662,0.5376,0.0014,0.0,0.069,0.0417,0.0833,,0.0067,0.0086,0.0,0.0,0.0084,0.0,0.9662,0.5557,0.0014,0.0,0.069,0.0417,0.0833,,0.0073,0.0089,0.0,0.0,0.0083,0.0,0.9662,0.5438,0.0014,0.0,0.069,0.0417,0.0833,,0.0068,0.0087,0.0,0.0,not specified,block of flats,0.0087,Others,No,1.0,0.0,1.0,0.0,-532.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,13.0,0.0,6.0\n417638,0,Cash loans,M,N,Y,1,270000.0,588874.5,28773.0,414000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-13750,-468,-316.0,-4148,,1,1,0,1,0,0,Laborers,3.0,1,1,FRIDAY,16,0,1,1,0,0,0,Business Entity Type 3,0.41882931389788014,0.4602649830854005,,0.1227,0.0391,0.9940000000000001,0.9184,0.0,0.08,0.0345,0.4583,0.5,,0.1,0.0745,0.0,0.0,0.125,0.0406,0.9940000000000001,0.9216,0.0,0.0806,0.0345,0.4583,0.5,,0.1093,0.0776,0.0,0.0,0.1239,0.0391,0.9940000000000001,0.9195,0.0,0.08,0.0345,0.4583,0.5,,0.1018,0.0758,0.0,0.0,reg oper account,block of flats,0.0784,Panel,No,0.0,0.0,0.0,0.0,-355.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n343791,0,Cash loans,F,N,Y,1,135000.0,76410.0,9198.0,67500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-11457,-2951,-2359.0,-3510,,1,1,0,1,0,0,Core staff,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Kindergarten,,0.6761120116545714,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1157.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n369384,0,Cash loans,F,N,Y,0,360000.0,1375380.0,49531.5,1215000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-21256,365243,-8706.0,-4659,,1,0,0,1,0,0,,2.0,1,1,TUESDAY,11,0,0,0,0,0,0,XNA,,0.709520777582793,0.326475210066026,0.0701,0.0521,0.9851,0.7959999999999999,0.0266,0.08,0.0345,0.3333,0.0417,,0.0572,0.0632,0.0,0.0,0.0714,0.054000000000000006,0.9851,0.804,0.0268,0.0806,0.0345,0.3333,0.0417,,0.0624,0.0658,0.0,0.0,0.0708,0.0521,0.9851,0.7987,0.0267,0.08,0.0345,0.3333,0.0417,,0.0581,0.0643,0.0,0.0,reg oper spec account,block of flats,0.0642,Panel,No,1.0,1.0,1.0,1.0,-1422.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n275692,0,Cash loans,F,N,Y,0,157500.0,99000.0,5368.5,99000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.007305,-17560,-1027,-7787.0,-1112,,1,1,1,1,1,0,,2.0,3,3,MONDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.1438349436356874,,0.168,0.1525,0.9816,0.7484,0.0196,0.16,0.1379,0.375,0.375,0.081,0.1362,0.1786,0.0039,0.0552,0.1712,0.1582,0.9816,0.7583,0.0198,0.1611,0.1379,0.375,0.375,0.0828,0.1488,0.1861,0.0039,0.0584,0.1697,0.1525,0.9816,0.7518,0.0197,0.16,0.1379,0.375,0.375,0.0824,0.1385,0.1818,0.0039,0.0563,reg oper account,block of flats,0.1632,\"Stone, brick\",No,11.0,0.0,10.0,0.0,-614.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n178326,1,Cash loans,M,N,N,0,157500.0,254700.0,21780.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.01885,-9259,-510,-241.0,-1894,,1,1,1,1,1,1,Security staff,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,Security,0.0715875906844329,0.1579905343360023,0.1510075350878296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-60.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n159255,0,Cash loans,F,N,N,0,54000.0,238896.0,9135.0,189000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.0228,-21330,365243,-4737.0,-4744,,1,0,0,1,0,0,,1.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.3532099437240507,0.6626377922738201,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1255.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n421179,0,Cash loans,M,N,N,0,112500.0,203760.0,21748.5,180000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.028663,-8874,-402,-8822.0,-1528,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 2,0.17419225074451514,0.6038276777479504,,0.1237,0.0603,0.9757,0.6668,0.0101,0.0,0.2069,0.1667,0.2083,0.0976,0.1009,0.0943,0.0,0.0,0.1261,0.0625,0.9757,0.6798,0.0102,0.0,0.2069,0.1667,0.2083,0.0998,0.1102,0.0983,0.0,0.0,0.1249,0.0603,0.9757,0.6713,0.0102,0.0,0.2069,0.1667,0.2083,0.0993,0.1026,0.096,0.0,0.0,reg oper account,block of flats,0.0797,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-300.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n345488,0,Cash loans,M,Y,Y,1,270000.0,802773.0,47065.5,693000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-13635,-935,-2285.0,-150,15.0,1,1,0,1,0,0,Drivers,3.0,1,1,SATURDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.7091194971702166,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-658.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n386496,0,Cash loans,M,N,Y,0,216000.0,327024.0,20137.5,270000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018634,-13254,-5330,-2814.0,-3389,,1,1,0,1,0,0,Medicine staff,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Medicine,0.3029107781040972,0.5869405754329791,0.6674577419214722,0.0093,,0.9712,,,,,0.0417,,,,,,,0.0095,,0.9712,,,,,0.0417,,,,,,,0.0094,,0.9712,,,,,0.0417,,,,,,,,,0.0062,,No,1.0,0.0,1.0,0.0,-829.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n325109,1,Cash loans,M,Y,Y,1,225000.0,755190.0,38686.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008068,-16477,-117,-104.0,-17,14.0,1,1,0,1,0,0,Managers,3.0,3,3,THURSDAY,8,0,0,0,0,0,0,Construction,0.5921268442881162,0.22343379197704188,,0.0649,0.0847,0.9786,,,0.0,0.1379,0.1667,,0.0531,,0.0599,,0.0242,0.0662,0.0879,0.9786,,,0.0,0.1379,0.1667,,0.0543,,0.0623,,0.0256,0.0656,0.0847,0.9786,,,0.0,0.1379,0.1667,,0.054000000000000006,,0.061,,0.0247,,block of flats,0.0472,\"Stone, brick\",No,1.0,0.0,0.0,0.0,-1853.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n441759,0,Cash loans,F,N,Y,0,112500.0,397881.0,22347.0,328500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008625,-21486,365243,-10570.0,-4679,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.6213304020146254,0.7503751495159068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1396.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n119045,0,Cash loans,M,Y,Y,0,202500.0,111384.0,8748.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-10584,-2530,-212.0,-3264,8.0,1,1,0,1,0,0,Drivers,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Self-employed,,0.5780170142006292,0.5064842396679806,0.0165,,0.9737,,,0.0,0.069,0.0417,,,,0.0126,,0.0027,0.0168,,0.9737,,,0.0,0.069,0.0417,,,,0.0131,,0.0029,0.0167,,0.9737,,,0.0,0.069,0.0417,,,,0.0128,,0.0028,,block of flats,0.0105,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1883.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n249658,1,Cash loans,M,N,Y,1,126000.0,152820.0,18265.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-9766,-2173,-9763.0,-2140,,1,1,0,1,0,0,Drivers,3.0,2,2,SUNDAY,16,1,1,0,1,1,0,Industry: type 1,0.13278098488593704,0.5359447432048026,0.07546094030670009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1037.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,4.0\n169500,0,Cash loans,F,N,Y,2,180000.0,1436850.0,42012.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.072508,-15608,-909,-9671.0,-4503,,1,1,0,1,1,0,,3.0,1,1,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.7671432321229185,0.326475210066026,0.6546,0.355,0.9816,,,0.78,0.4655,0.4792,,0.5492,,0.7296,,0.0366,0.5599,0.3684,0.9816,,,0.6042,0.4138,0.3333,,0.4611,,0.5473,,0.0027,0.6609999999999999,0.355,0.9816,,,0.78,0.4655,0.4792,,0.5587,,0.7428,,0.0374,,block of flats,0.4137,Panel,No,0.0,0.0,0.0,0.0,-513.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n176172,0,Cash loans,F,Y,Y,0,270000.0,314055.0,13437.0,238500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.01885,-17481,-8507,-9317.0,-1009,1.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Kindergarten,0.8162399854720489,0.6271418989149989,0.2793353208976285,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1762.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n203833,0,Cash loans,F,N,N,1,90000.0,203760.0,13396.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-10390,-503,-166.0,-2878,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 2,0.2257195344376613,0.6003070306473487,,0.0938,0.0,0.9935,,,0.0,0.2069,0.1667,,0.1349,,0.0861,,0.0356,0.0956,0.0,0.9935,,,0.0,0.2069,0.1667,,0.138,,0.0897,,0.0377,0.0947,0.0,0.9935,,,0.0,0.2069,0.1667,,0.1372,,0.0876,,0.0364,,block of flats,0.0754,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-65.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n212455,0,Cash loans,F,N,N,0,76500.0,808650.0,26217.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-18271,-901,-11102.0,-1800,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Other,,0.7744024744708417,,0.3959,0.4912,0.9796,0.7212,0.196,0.0,0.8966,0.1667,0.2083,0.3574,0.3202,0.391,0.0116,0.0162,0.4034,0.5097,0.9796,0.7321,0.1977,0.0,0.8966,0.1667,0.2083,0.3656,0.3499,0.4073,0.0117,0.0172,0.3997,0.4912,0.9796,0.7249,0.1972,0.0,0.8966,0.1667,0.2083,0.3637,0.3258,0.39799999999999996,0.0116,0.0166,reg oper account,block of flats,0.4182,Panel,No,0.0,0.0,0.0,0.0,-2615.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n185845,0,Cash loans,F,N,Y,0,180000.0,900000.0,26446.5,900000.0,Family,Pensioner,Higher education,Widow,House / apartment,0.026392,-23685,365243,-9875.0,-4047,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,XNA,,0.5240704049327407,0.5298898341969072,0.1856,,0.9866,,,0.2,0.1724,0.3333,,,,0.2058,,0.0079,0.1891,,0.9866,,,0.2014,0.1724,0.3333,,,,0.2144,,0.0084,0.1874,,0.9866,,,0.2,0.1724,0.3333,,,,0.2095,,0.0081,,block of flats,0.1908,Block,No,1.0,0.0,1.0,0.0,-1789.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n400106,0,Cash loans,F,N,N,1,103500.0,967428.0,34402.5,693000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.003813,-12565,-407,-6053.0,-3906,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,7,0,0,0,0,0,0,Self-employed,0.18527838286948226,0.3232595028185681,0.6738300778602003,0.1124,0.1275,0.9786,,,0.0,0.2759,0.1667,,,,0.1037,,0.0406,0.1145,0.1323,0.9786,,,0.0,0.2759,0.1667,,,,0.10800000000000001,,0.043,0.1135,0.1275,0.9786,,,0.0,0.2759,0.1667,,,,0.1055,,0.0415,,block of flats,0.0904,Panel,No,0.0,0.0,0.0,0.0,-1797.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n173525,0,Cash loans,M,N,N,0,225000.0,1113840.0,57001.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.003818,-16085,-3018,-10206.0,-4586,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,4,0,0,0,0,0,0,Business Entity Type 3,,0.5162000897033562,0.6006575372857061,0.1773,0.1573,0.9811,0.7484,0.07200000000000001,0.0,0.4138,0.1667,0.2083,0.0,0.1437,0.1695,0.0039,0.0088,0.1807,0.1633,0.9811,0.7583,0.0727,0.0,0.4138,0.1667,0.2083,0.0,0.157,0.1754,0.0039,0.0093,0.179,0.1573,0.9811,0.7518,0.0725,0.0,0.4138,0.1667,0.2083,0.0,0.1462,0.1726,0.0039,0.009000000000000001,reg oper account,block of flats,0.1673,Panel,No,0.0,0.0,0.0,0.0,-1736.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n160414,0,Revolving loans,M,Y,Y,2,180000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.006852,-15111,-2831,-9250.0,-124,15.0,1,1,0,1,0,0,Managers,3.0,3,3,MONDAY,5,0,0,0,0,0,0,Self-employed,0.17573620110475235,0.3821263700845271,,0.0825,0.0807,0.9791,0.7144,0.035,0.0,0.1379,0.1667,0.0417,0.0606,0.0008,0.0695,0.305,0.0031,0.084,0.0837,0.9791,0.7256,0.0354,0.0,0.1379,0.1667,0.0417,0.0619,0.0009,0.0724,0.3074,0.0033,0.0833,0.0807,0.9791,0.7182,0.0353,0.0,0.1379,0.1667,0.0417,0.0616,0.0009,0.0707,0.3067,0.0031,reg oper account,block of flats,0.0553,Panel,No,5.0,0.0,5.0,0.0,-964.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n228819,0,Cash loans,M,Y,Y,0,90000.0,373311.0,19188.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018634,-12196,-2552,-6237.0,-4625,6.0,1,1,0,1,0,1,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Other,,0.5112617834688211,,0.1258,0.1355,0.9901,0.8640000000000001,0.022000000000000002,0.0,0.2759,0.1667,0.0417,0.1036,0.1025,0.1338,0.0,0.0,0.1282,0.1406,0.9901,0.8693,0.0222,0.0,0.2759,0.1667,0.0417,0.1059,0.11199999999999999,0.1394,0.0,0.0,0.127,0.1355,0.9901,0.8658,0.0221,0.0,0.2759,0.1667,0.0417,0.1054,0.1043,0.1362,0.0,0.0,reg oper account,block of flats,0.1052,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-662.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n102133,1,Cash loans,F,N,Y,0,90000.0,540000.0,25978.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-16996,-3195,-2386.0,-544,,1,1,1,1,1,0,Sales staff,2.0,3,3,TUESDAY,14,0,0,0,0,1,1,Self-employed,,0.3369039103392858,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1880.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n394907,0,Cash loans,F,N,Y,0,157500.0,970380.0,28503.0,810000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-14046,-6583,-1366.0,-3979,,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,13,0,0,0,0,0,0,Industry: type 2,,0.5728080967523365,0.15286622915504153,0.1186,0.1143,0.9816,0.7484,0.0491,0.0,0.2759,0.1667,0.2083,0.0493,0.095,0.1088,0.0077,0.0162,0.1208,0.1186,0.9816,0.7583,0.0495,0.0,0.2759,0.1667,0.2083,0.0504,0.1038,0.1134,0.0078,0.0172,0.1197,0.1143,0.9816,0.7518,0.0494,0.0,0.2759,0.1667,0.2083,0.0501,0.0966,0.1108,0.0078,0.0166,reg oper account,block of flats,0.1159,Panel,No,3.0,0.0,3.0,0.0,-1744.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n429470,0,Cash loans,F,N,N,0,90000.0,203760.0,19980.0,180000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.022625,-24510,-120,-11499.0,-4243,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Culture,0.9089933040560796,0.7958230486070504,0.375711009574066,0.1495,0.1291,0.9841,0.7824,0.0815,0.16,0.1379,0.3333,0.375,0.1442,0.121,0.1522,0.0039,0.0023,0.1523,0.134,0.9841,0.7909,0.0822,0.1611,0.1379,0.3333,0.375,0.1475,0.1322,0.1585,0.0039,0.0025,0.1509,0.1291,0.9841,0.7853,0.08199999999999999,0.16,0.1379,0.3333,0.375,0.1467,0.1231,0.1549,0.0039,0.0024,reg oper spec account,block of flats,0.1871,Panel,No,4.0,0.0,4.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n280096,0,Cash loans,F,N,Y,0,112500.0,688500.0,27432.0,688500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Office apartment,0.018634,-13850,-1169,-7626.0,-4400,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.7566197039386223,0.5731879729680599,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-493.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n309031,0,Cash loans,F,N,Y,0,180000.0,835380.0,40320.0,675000.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.020713,-14141,-1724,-7111.0,-4783,,1,1,0,1,0,0,Sales staff,1.0,3,2,MONDAY,8,0,0,0,0,0,0,Self-employed,0.5281027064647323,0.4339509577301556,,0.301,0.077,0.9811,0.7416,0.0887,0.08,0.0345,0.3333,0.0417,0.0414,0.2454,0.0752,0.0,0.0,0.3067,0.08,0.9811,0.7517,0.0895,0.0806,0.0345,0.3333,0.0417,0.0424,0.2681,0.0783,0.0,0.0,0.3039,0.077,0.9811,0.7451,0.0892,0.08,0.0345,0.3333,0.0417,0.0422,0.2497,0.0765,0.0,0.0,reg oper account,block of flats,0.1076,Panel,No,0.0,0.0,0.0,0.0,-510.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n335264,0,Cash loans,F,N,Y,0,121500.0,562491.0,27189.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003069,-22645,365243,-12736.0,-4834,,1,0,0,1,0,0,,2.0,3,3,MONDAY,10,0,0,0,0,0,0,XNA,0.8758738233224898,0.2611851365060636,0.7751552674785404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n354524,0,Revolving loans,M,N,Y,0,135000.0,270000.0,13500.0,270000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.015221,-9303,-2114,-4187.0,-1980,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,12,0,0,0,1,1,0,Police,,0.2512100865337719,0.2925880073368475,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1086.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n104890,0,Cash loans,M,Y,N,0,112500.0,454500.0,21865.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-18670,-5455,-490.0,-2027,18.0,1,1,1,1,1,0,High skill tech staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Transport: type 4,,0.16962928849481174,,0.0021,,0.9861,,,0.0,,0.0,,,,0.0025,,,0.0021,,0.9861,,,0.0,,0.0,,,,0.0027,,,0.0021,,0.9861,,,0.0,,0.0,,,,0.0026,,,,terraced house,0.002,Panel,No,1.0,0.0,1.0,0.0,-824.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n277250,0,Cash loans,F,N,Y,0,405000.0,912019.5,40302.0,801000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.018801,-20681,-3548,-8609.0,-4222,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.4488202760253442,0.32173528219668485,0.0082,0.0,0.9622,0.4832,0.0014,0.0,0.0345,0.0417,0.0833,0.0082,0.0067,0.0102,0.0,0.0,0.0084,0.0,0.9623,0.5034,0.0014,0.0,0.0345,0.0417,0.0833,0.0083,0.0073,0.0107,0.0,0.0,0.0083,0.0,0.9622,0.4901,0.0014,0.0,0.0345,0.0417,0.0833,0.0083,0.0068,0.0104,0.0,0.0,reg oper account,block of flats,0.0088,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1315.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n188263,0,Cash loans,F,N,Y,0,225000.0,1006920.0,72778.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-17582,-1153,-3560.0,-1117,,1,1,0,1,0,0,,2.0,1,1,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.6096982950178074,0.7224546943778921,,0.2222,0.1219,0.9786,0.7076,0.02,0.24,0.2069,0.3333,0.375,0.018000000000000002,0.1811,0.0974,0.0,0.0786,0.1513,0.0974,0.9782,0.7125,0.0,0.1611,0.1379,0.3333,0.375,0.0,0.1322,0.0,0.0,0.0,0.1499,0.0939,0.9786,0.7115,0.0,0.16,0.1379,0.3333,0.375,0.009000000000000001,0.1231,0.1236,0.0,0.0,reg oper account,block of flats,0.3344,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n440406,0,Cash loans,F,N,Y,0,225000.0,1575000.0,47884.5,1575000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010966,-17356,-10365,-6586.0,-855,,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Industry: type 3,,0.6676372089457175,0.5902333386185574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2174.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n282689,0,Cash loans,F,N,Y,0,81000.0,152820.0,16177.5,135000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-12581,-2322,-4084.0,-2749,,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Government,,0.23065190737191546,,0.0268,0.0786,0.9647,0.5172,0.0044,0.0,0.1034,0.0417,0.0833,0.0268,0.0202,0.0235,0.0077,0.0134,0.0273,0.0816,0.9647,0.5361,0.0044,0.0,0.1034,0.0417,0.0833,0.0274,0.022000000000000002,0.0245,0.0078,0.0141,0.0271,0.0786,0.9647,0.5237,0.0044,0.0,0.1034,0.0417,0.0833,0.0273,0.0205,0.0239,0.0078,0.0136,reg oper account,block of flats,0.0222,\"Stone, brick\",No,3.0,0.0,3.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n292611,0,Cash loans,F,N,N,0,85500.0,278613.0,14283.0,252000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010556,-23031,365243,-13264.0,-3368,,1,0,0,1,0,0,,2.0,3,3,MONDAY,15,0,0,0,0,0,0,XNA,,0.5107208223956294,0.4471785780453068,0.1113,0.0804,0.9816,,,0.12,0.1034,0.3333,,0.0804,,0.1143,,0.0035,0.1134,0.0834,0.9816,,,0.1208,0.1034,0.3333,,0.0822,,0.1191,,0.0037,0.1124,0.0804,0.9816,,,0.12,0.1034,0.3333,,0.0818,,0.1164,,0.0035,,block of flats,0.0899,Panel,No,0.0,0.0,0.0,0.0,-476.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n119333,0,Cash loans,F,Y,N,0,112500.0,634482.0,18679.5,454500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.018801,-13566,-5536,-2321.0,-4690,16.0,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,Other,0.4437137311762323,0.5291892131716356,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3075.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n408216,1,Cash loans,M,N,Y,0,144000.0,381528.0,25627.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-10276,-550,-92.0,-2122,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Construction,,0.3882422254335544,0.6212263380626669,0.0361,0.0666,0.9965,0.9524,0.0143,0.0,0.0345,0.125,0.1667,0.0602,0.0294,0.0379,0.0,0.0436,0.0368,0.0691,0.9965,0.9543,0.0144,0.0,0.0345,0.125,0.1667,0.0616,0.0321,0.0395,0.0,0.0461,0.0364,0.0666,0.9965,0.953,0.0144,0.0,0.0345,0.125,0.1667,0.0612,0.0299,0.0386,0.0,0.0445,reg oper account,block of flats,0.0471,Mixed,No,0.0,0.0,0.0,0.0,-726.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n177628,0,Cash loans,F,Y,Y,0,202500.0,1258650.0,53455.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009175,-19639,-5539,-5879.0,-3142,5.0,1,1,0,1,0,0,Drivers,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,Government,0.5761525035482551,0.6207436969741632,0.6296742509538716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1121.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n164636,0,Cash loans,F,Y,N,0,67500.0,229500.0,11731.5,229500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.019101,-19033,-4621,-8634.0,-2572,13.0,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,13,0,0,0,0,1,1,Government,,0.6421186852505577,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1284.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n450015,0,Cash loans,F,N,Y,1,81000.0,152820.0,16177.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-15039,-1456,-1437.0,-4259,,1,1,0,1,0,0,Core staff,3.0,3,3,SATURDAY,12,0,0,0,0,0,0,Self-employed,,0.5553127650728906,0.8337869986815019,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n302239,0,Cash loans,F,Y,Y,1,202500.0,545040.0,20677.5,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.006305,-12912,-1374,-91.0,-4081,15.0,1,1,0,1,0,0,Sales staff,2.0,3,3,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.3115402589684739,0.6234529391127784,0.6658549219640212,0.1701,0.1384,0.9995,0.9932,0.0252,0.0,0.1034,0.1667,0.2083,0.0192,0.1387,0.1013,0.0,0.0,0.1733,0.1436,0.9995,0.9935,0.0255,0.0,0.1034,0.1667,0.2083,0.0197,0.1515,0.1055,0.0,0.0,0.1718,0.1384,0.9995,0.9933,0.0254,0.0,0.1034,0.1667,0.2083,0.0196,0.1411,0.1031,0.0,0.0,reg oper account,block of flats,0.0935,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-578.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n365733,0,Cash loans,M,Y,N,0,180000.0,592560.0,35937.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.019101,-10708,-523,-1658.0,-718,15.0,1,1,0,1,0,0,Drivers,1.0,2,2,TUESDAY,12,0,0,0,0,1,1,Self-employed,,0.5919203020832245,0.7194907850918436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n234432,0,Cash loans,M,N,Y,1,112500.0,614574.0,16339.5,513000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010966,-17456,-809,-9302.0,-994,,1,1,1,1,1,0,Laborers,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Industry: type 11,0.35826523788376885,0.3166271134260281,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-536.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n337986,0,Cash loans,F,Y,N,0,157500.0,942300.0,27549.0,675000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.025164,-18387,-4656,-6261.0,-1924,10.0,1,1,1,1,0,0,Laborers,2.0,2,2,MONDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.2653117484731741,0.3893387918468769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-773.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n262305,0,Cash loans,F,Y,Y,1,112500.0,118512.0,7380.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14037,-1133,-936.0,-5331,21.0,1,1,1,1,1,0,Sales staff,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3957665428724416,0.6714063487883749,0.3296550543128238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-879.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n224532,1,Cash loans,F,N,Y,1,58500.0,227520.0,11065.5,180000.0,Unaccompanied,Pensioner,Lower secondary,Single / not married,House / apartment,0.020246,-10301,365243,-2649.0,-2977,,1,0,0,1,1,0,,2.0,3,3,FRIDAY,5,0,0,0,0,0,0,XNA,,0.034333358255672485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-382.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n213071,0,Cash loans,M,Y,Y,2,279000.0,1185120.0,39298.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-11911,-1165,-1929.0,-4408,20.0,1,1,0,1,0,0,Drivers,4.0,3,2,SUNDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.3575431332993905,,0.10099999999999999,0.0649,0.9945,0.9252,0.0119,0.0,0.1379,0.1667,0.0417,0.0125,0.0756,0.0505,0.0309,0.0462,0.1029,0.0673,0.9945,0.9281,0.012,0.0,0.1379,0.1667,0.0417,0.0127,0.0826,0.0526,0.0311,0.0489,0.102,0.0649,0.9945,0.9262,0.0119,0.0,0.1379,0.1667,0.0417,0.0127,0.077,0.0514,0.0311,0.0471,reg oper account,block of flats,0.0562,Panel,No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n455114,0,Revolving loans,M,Y,Y,2,202500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009334,-12438,-2271,-790.0,-4248,1.0,1,1,0,1,0,0,Drivers,4.0,2,2,MONDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.55787626524203,0.6910212267577837,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1069.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n289483,1,Cash loans,M,N,Y,0,112500.0,343800.0,16852.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-13439,-3231,-5418.0,-5130,,1,1,1,1,1,0,Core staff,2.0,2,2,THURSDAY,10,0,1,1,0,1,1,Security,,0.26429001880403,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1360.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n140409,0,Cash loans,M,N,Y,0,67500.0,431280.0,23526.0,360000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.005313,-22830,365243,-10232.0,-3583,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.5297666898455635,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1028.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n373729,0,Cash loans,F,Y,Y,0,157500.0,180000.0,18900.0,180000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-15715,-1652,-6133.0,-4365,14.0,1,1,1,1,0,0,Cooking staff,2.0,3,3,FRIDAY,12,0,0,0,0,0,0,Self-employed,,0.6005483625223181,0.7946285827840042,0.0134,0.0283,0.9667,0.5444,0.0019,0.0,0.069,0.0417,0.0,0.028999999999999998,0.0101,0.0098,0.0039,0.0136,0.0137,0.0293,0.9667,0.5622,0.0019,0.0,0.069,0.0417,0.0,0.0297,0.011000000000000001,0.0102,0.0039,0.0144,0.0135,0.0283,0.9667,0.5505,0.0019,0.0,0.069,0.0417,0.0,0.0295,0.0103,0.01,0.0039,0.0139,reg oper account,block of flats,0.0107,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1064.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n108293,0,Revolving loans,F,N,Y,1,121500.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.0228,-10946,-726,-1718.0,-2201,,1,1,1,1,1,0,,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Trade: type 7,0.199431441155814,0.5228103282268427,0.4722533429586386,0.1031,0.1159,0.9811,0.7076,0.0121,0.0,0.2069,0.1667,0.2083,0.044000000000000004,0.0824,0.0701,0.0077,0.0096,0.105,0.1203,0.9786,0.7190000000000001,0.0122,0.0,0.2069,0.1667,0.2083,0.045,0.09,0.0557,0.0078,0.0101,0.1041,0.1159,0.9811,0.7115,0.0122,0.0,0.2069,0.1667,0.2083,0.0448,0.0838,0.0714,0.0078,0.0098,reg oper account,block of flats,0.0621,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2234.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n115599,0,Cash loans,F,N,Y,1,90000.0,45000.0,4450.5,45000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-13707,-2072,-1647.0,-4137,,1,1,1,1,1,1,Sales staff,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.6152538594546245,0.612462278771141,0.7801436381572275,0.1124,0.0482,0.9861,,,0.04,0.0345,0.3333,,0.0794,,0.0781,,0.0605,0.1145,0.0501,0.9861,,,0.0403,0.0345,0.3333,,0.0812,,0.0814,,0.064,0.1135,0.0482,0.9861,,,0.04,0.0345,0.3333,,0.0808,,0.0795,,0.0618,,block of flats,0.0656,Panel,No,0.0,0.0,0.0,0.0,-719.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n398192,0,Cash loans,M,N,Y,0,121500.0,432661.5,22783.5,373500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-9401,-1930,-3905.0,-1925,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 2,0.30609134074324884,0.6460687875200307,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-435.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n123010,0,Cash loans,F,N,N,2,180000.0,360000.0,26194.5,360000.0,Unaccompanied,Working,Higher education,Widow,With parents,0.025164,-12890,-4661,-5082.0,-5076,,1,1,1,1,0,0,Core staff,3.0,2,2,THURSDAY,7,0,0,0,0,0,0,Medicine,0.3005901619847399,0.5402765176522151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-370.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n241682,0,Cash loans,F,Y,Y,0,67500.0,178213.5,13455.0,144000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-14771,-2451,-7812.0,-5201,65.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Postal,0.5832553466427308,0.5879528131179297,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1786.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n110745,0,Cash loans,F,N,Y,1,126000.0,384048.0,18810.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.002134,-12596,-242,-895.0,-3302,,1,1,0,1,0,0,,3.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,Other,0.4995962020421856,0.5329467892428702,0.6626377922738201,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,1.0,11.0,0.0,-443.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n361467,0,Cash loans,F,N,N,0,135000.0,1190340.0,63418.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020246,-19638,-1042,-2981.0,-3057,,1,1,1,1,0,0,Laborers,2.0,3,3,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6035765547723474,0.28371188263500075,0.0309,0.0823,0.9727,0.626,0.0054,0.0,0.1034,0.0833,0.125,0.0203,0.0235,0.0339,0.0077,0.0137,0.0315,0.0854,0.9727,0.6406,0.0054,0.0,0.1034,0.0833,0.125,0.0208,0.0257,0.0353,0.0078,0.0145,0.0312,0.0823,0.9727,0.631,0.0054,0.0,0.1034,0.0833,0.125,0.0207,0.0239,0.0345,0.0078,0.013999999999999999,reg oper account,block of flats,0.0325,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1331.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n277437,0,Cash loans,F,N,Y,0,180000.0,679500.0,24201.0,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.0228,-17676,-2535,-472.0,-1229,,1,1,0,1,1,0,Private service staff,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Services,,0.1815076963017465,0.746300213050371,0.1907,0.1166,0.9985,0.9796,,0.2,0.1724,0.375,0.4167,0.0904,0.1555,0.2344,,0.0107,0.1943,0.121,0.9985,0.9804,,0.2014,0.1724,0.375,0.4167,0.0925,0.1699,0.2442,,0.0113,0.1926,0.1166,0.9985,0.9799,,0.2,0.1724,0.375,0.4167,0.092,0.1582,0.2386,,0.0109,reg oper account,block of flats,0.1867,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n453325,0,Cash loans,M,Y,Y,0,135000.0,765000.0,29902.5,765000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-20792,-2356,-3432.0,-4139,12.0,1,1,0,1,0,0,Drivers,2.0,3,3,SATURDAY,9,0,0,0,0,0,0,Self-employed,,0.4343108829148362,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-386.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n372641,0,Cash loans,F,Y,N,0,157500.0,675000.0,21906.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018209,-8764,-487,-725.0,-1414,13.0,1,1,1,1,1,0,,1.0,3,3,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.3284281420590284,0.3296550543128238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n295102,1,Cash loans,M,N,Y,0,126000.0,301464.0,23949.0,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.025164,-8290,-269,-3867.0,-956,,1,1,0,1,0,1,Low-skill Laborers,1.0,2,2,FRIDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.414809089351683,0.5913866989491036,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n432000,0,Cash loans,M,Y,Y,1,130500.0,807984.0,26833.5,697500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,With parents,0.022625,-15242,-3035,-4635.0,-4679,12.0,1,1,1,1,0,0,Drivers,3.0,2,2,SATURDAY,13,0,0,0,0,1,1,Transport: type 4,,0.08686324162134341,0.8245949709919925,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-337.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n263571,0,Revolving loans,F,N,Y,0,180000.0,337500.0,16875.0,337500.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.003818,-20906,-1621,-4456.0,-3981,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,MONDAY,8,0,0,0,0,1,1,Emergency,0.7929593557598902,0.2934930971516768,0.6801388218428291,0.0082,,0.9771,,,,0.0345,0.0417,,,,,,,0.0084,,0.9772,,,,0.0345,0.0417,,,,,,,0.0083,,0.9771,,,,0.0345,0.0417,,,,,,,,block of flats,0.0061,Mixed,No,0.0,0.0,0.0,0.0,-458.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n431596,0,Cash loans,F,N,Y,0,135000.0,312768.0,15043.5,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.072508,-11409,-2989,-5356.0,-2581,,1,1,0,1,1,0,,2.0,1,1,MONDAY,14,0,0,0,0,0,0,Business Entity Type 2,0.4142534551320493,0.7102863587605557,0.2707073872651806,0.1711,0.0574,0.9871,0.8232,0.2176,0.24,0.1034,0.5417,0.5833,0.0,0.1387,0.1502,0.0039,0.004,0.1744,0.0596,0.9871,0.8301,0.2196,0.2417,0.1034,0.5417,0.5833,0.0,0.1515,0.1565,0.0039,0.0042,0.1728,0.0574,0.9871,0.8256,0.21899999999999997,0.24,0.1034,0.5417,0.5833,0.0,0.1411,0.1529,0.0039,0.004,reg oper account,block of flats,0.11900000000000001,Panel,No,0.0,0.0,0.0,0.0,-1339.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n334790,0,Revolving loans,F,Y,Y,1,270000.0,630000.0,31500.0,630000.0,Unaccompanied,Working,Higher education,Married,With parents,0.032561,-13338,-4247,-5010.0,-3465,7.0,1,1,1,1,1,0,Core staff,3.0,1,1,TUESDAY,20,0,0,0,0,0,0,Police,,0.6851006326222622,0.3774042489507649,0.111,0.1128,0.9767,0.6736,0.2171,0.12,0.1724,0.2221,0.2083,0.0717,0.1168,0.0911,0.0039,0.057999999999999996,0.1471,0.1748,0.9762,0.6864,0.2191,0.0,0.2414,0.1667,0.2083,0.0194,0.1276,0.0466,0.0039,0.001,0.1457,0.1684,0.9762,0.6779999999999999,0.2185,0.0,0.2414,0.1667,0.2083,0.0557,0.1189,0.0941,0.0039,0.0029,org spec account,block of flats,0.0389,Panel,No,0.0,0.0,0.0,0.0,-915.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n285735,0,Cash loans,M,Y,N,0,135000.0,640080.0,31131.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Rented apartment,0.018801,-9615,-2243,-4004.0,-2271,22.0,1,1,0,1,0,0,,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.7006314563837495,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,3.0,3.0,2.0,-56.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,3.0\n105421,0,Cash loans,F,Y,Y,2,157500.0,755190.0,33394.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-13350,-459,-2535.0,-4494,16.0,1,1,0,1,1,0,High skill tech staff,4.0,3,3,THURSDAY,10,0,0,0,0,0,0,Trade: type 3,0.4312996637442984,0.40616732760234403,0.7490217048463391,0.201,0.1491,0.9861,0.8096,0.035,0.24,0.2069,0.3333,0.375,0.1137,0.1631,0.2206,0.0039,0.016,0.2048,0.1547,0.9861,0.8171,0.0353,0.2417,0.2069,0.3333,0.375,0.1163,0.1781,0.2299,0.0039,0.017,0.203,0.1491,0.9861,0.8121,0.0353,0.24,0.2069,0.3333,0.375,0.1157,0.1659,0.2246,0.0039,0.0164,not specified,block of flats,0.177,Panel,No,4.0,1.0,4.0,0.0,-1943.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n383669,0,Cash loans,M,Y,Y,2,225000.0,231813.0,16483.5,193500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010276,-12345,-4431,-578.0,-4654,6.0,1,1,0,1,0,0,Laborers,4.0,2,2,FRIDAY,16,0,0,0,0,1,1,Self-employed,,0.6525270231165675,0.6690566947824041,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-365.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n235565,0,Cash loans,F,N,Y,1,121500.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-13950,-869,-7251.0,-2625,,1,1,1,1,0,0,,3.0,2,2,MONDAY,8,0,0,0,0,0,0,Trade: type 7,,0.6133701928455019,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1883.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n443171,0,Cash loans,F,N,Y,0,225000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-14995,-936,-488.0,-1315,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5840641747530614,0.12610055883623253,0.0639,0.1576,0.9613,0.4696,0.0733,0.0,0.2414,0.2083,0.25,0.0584,0.0471,0.0913,0.0232,0.0754,0.0651,0.1636,0.9613,0.4904,0.07400000000000001,0.0,0.2414,0.2083,0.25,0.0597,0.0514,0.0951,0.0233,0.0798,0.0645,0.1576,0.9613,0.4767,0.0737,0.0,0.2414,0.2083,0.25,0.0594,0.0479,0.09300000000000001,0.0233,0.0769,reg oper account,block of flats,0.1031,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-432.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n411134,0,Cash loans,F,N,Y,0,157500.0,675000.0,32602.5,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-23610,365243,-2803.0,-4635,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.7048386281984397,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2996.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n333075,0,Cash loans,F,N,Y,0,40500.0,101880.0,10053.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.007120000000000001,-23718,365243,-5930.0,-4705,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,XNA,,0.6544433450004522,0.8435435389318647,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1111.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n185174,0,Cash loans,F,N,Y,0,126000.0,117162.0,7483.5,103500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007305,-24059,365243,-4154.0,-4349,,1,0,0,1,1,0,,2.0,3,3,FRIDAY,8,0,0,0,0,0,0,XNA,,0.22462364588441489,0.5064842396679806,0.0907,,0.993,,,0.0,0.2069,0.1667,,0.0652,,0.1178,,0.036000000000000004,0.0924,,0.993,,,0.0,0.2069,0.1667,,0.0667,,0.1227,,0.0382,0.0916,,0.993,,,0.0,0.2069,0.1667,,0.0664,,0.1199,,0.0368,,block of flats,0.1005,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n248062,0,Cash loans,F,Y,Y,1,90000.0,269550.0,12001.5,225000.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,House / apartment,0.00702,-15565,-2755,-2398.0,-3560,15.0,1,1,1,1,1,0,Core staff,3.0,2,2,MONDAY,16,0,0,0,0,1,1,Government,0.5142438373745775,0.6128359155438415,0.475849908720221,,,0.9767,,,,,,,,,0.0107,,,,,0.9767,,,,,,,,,0.0112,,,,,0.9767,,,,,,,,,0.0109,,,,,0.0165,,No,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n404567,0,Cash loans,F,N,N,0,292500.0,473760.0,44293.5,450000.0,Unaccompanied,Working,Incomplete higher,Separated,House / apartment,0.072508,-20435,-5366,-12888.0,-3971,,1,1,0,1,0,0,Medicine staff,1.0,1,1,TUESDAY,16,0,0,0,0,0,0,Medicine,0.9131955833990688,0.6667437718646944,0.34741822720026416,0.2959,0.1956,0.9821,0.7552,0.0002,0.32,0.2759,0.3333,0.375,0.0,0.2404,0.2827,0.0039,0.0043,0.3015,0.203,0.9821,0.7648,0.0002,0.3222,0.2759,0.3333,0.375,0.0,0.2626,0.2946,0.0039,0.0045,0.2987,0.1956,0.9821,0.7585,0.0002,0.32,0.2759,0.3333,0.375,0.0,0.2446,0.2878,0.0039,0.0044,reg oper account,block of flats,0.2234,Panel,No,0.0,0.0,0.0,0.0,-860.0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n138078,0,Cash loans,F,N,Y,0,121500.0,545040.0,26640.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.025164,-21983,365243,-6104.0,-4116,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.16318703546427088,,0.0247,0.0422,0.9866,0.8164,0.0032,0.0,0.1034,0.0833,0.0417,0.0402,0.0202,0.0256,0.0,0.008,0.0252,0.0438,0.9866,0.8236,0.0032,0.0,0.1034,0.0833,0.0417,0.0411,0.022000000000000002,0.0267,0.0,0.0084,0.025,0.0422,0.9866,0.8189,0.0032,0.0,0.1034,0.0833,0.0417,0.0409,0.0205,0.0261,0.0,0.0081,reg oper account,block of flats,0.0202,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n411721,0,Cash loans,F,N,N,0,67500.0,364896.0,14026.5,315000.0,Unaccompanied,State servant,Higher education,Separated,House / apartment,0.010643,-11471,-2602,-747.0,-4157,,1,1,0,1,0,0,Laborers,1.0,2,2,SUNDAY,14,0,0,0,0,0,0,Medicine,,0.29630768460737394,0.5082869913916046,0.1119,0.081,0.9801,0.728,0.013000000000000001,0.0,0.2414,0.1667,0.2083,0.113,0.087,0.0997,0.0193,0.0204,0.105,0.0719,0.9796,0.7321,0.0114,0.0,0.2069,0.1667,0.2083,0.1057,0.0918,0.0934,0.0,0.0,0.1129,0.081,0.9801,0.7316,0.0131,0.0,0.2414,0.1667,0.2083,0.115,0.0885,0.1015,0.0194,0.0208,reg oper spec account,block of flats,0.0767,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n285981,0,Cash loans,F,Y,N,0,225000.0,1231533.0,40828.5,1008000.0,Unaccompanied,Commercial associate,Higher education,Widow,House / apartment,0.04622,-19916,-3563,-11008.0,-2803,9.0,1,1,0,1,0,0,,1.0,1,1,MONDAY,19,0,0,0,0,1,1,Medicine,,0.6604073548415303,0.6161216908872079,0.0691,,0.9757,,,,0.1379,0.1458,,,,0.0528,,,0.0672,,0.9752,,,,0.1379,0.125,,,,0.0544,,,0.0697,,0.9757,,,,0.1379,0.1458,,,,0.0538,,,,block of flats,0.0452,\"Stone, brick\",No,3.0,0.0,2.0,0.0,-1503.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n174781,0,Revolving loans,M,N,N,0,117000.0,360000.0,18000.0,360000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.025164,-8909,-719,-5482.0,-1583,,1,1,0,1,0,0,IT staff,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.12022865652957085,0.5918305028241804,0.501075160239048,0.1758,0.1856,0.9871,0.8232,0.1682,0.16,0.1897,0.25,0.0417,0.0576,0.2345,0.1737,0.0077,0.0217,0.063,0.1927,0.9871,0.8301,0.1697,0.0,0.1034,0.1667,0.0417,0.059000000000000004,0.2562,0.053,0.0078,0.0,0.1775,0.1856,0.9871,0.8256,0.1692,0.16,0.1897,0.25,0.0417,0.0586,0.2386,0.1768,0.0078,0.0221,reg oper account,block of flats,0.3345,Panel,No,0.0,0.0,0.0,0.0,-283.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n440214,1,Cash loans,M,Y,Y,1,315000.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-12951,-2093,-616.0,-605,1.0,1,1,0,1,0,0,High skill tech staff,2.0,1,1,FRIDAY,9,0,0,0,0,0,0,School,,0.6468384585281295,,0.0825,0.0982,0.9722,,,0.0,0.1379,0.1667,,,,0.0675,,0.0004,0.084,0.1019,0.9722,,,0.0,0.1379,0.1667,,,,0.0703,,0.0004,0.0833,0.0982,0.9722,,,0.0,0.1379,0.1667,,,,0.0687,,0.0004,,block of flats,0.0531,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-440.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n115787,0,Cash loans,M,Y,Y,0,270000.0,1575000.0,64278.0,1575000.0,Unaccompanied,Working,Higher education,Married,Office apartment,0.028663,-17266,-8181,-5060.0,-790,13.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Industry: type 9,,0.6100304315220022,0.6092756673894402,0.1856,0.1165,0.9896,0.8572,0.0438,0.2,0.1724,0.3333,0.375,0.1945,0.1513,0.1976,0.0,0.0,0.1891,0.1209,0.9896,0.8628,0.0442,0.2014,0.1724,0.3333,0.375,0.19899999999999998,0.1653,0.2059,0.0,0.0,0.1874,0.1165,0.9896,0.8591,0.0441,0.2,0.1724,0.3333,0.375,0.1979,0.1539,0.2011,0.0,0.0,reg oper account,block of flats,0.1911,Panel,No,9.0,2.0,9.0,2.0,-1669.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n367139,0,Cash loans,F,Y,Y,2,202500.0,684657.0,18958.5,571500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-9868,-583,-196.0,-2072,23.0,1,1,0,1,0,0,Sales staff,4.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Trade: type 3,,0.5513349158460719,0.5832379256761245,0.3825,0.182,0.9990000000000001,0.9864,0.1475,0.64,0.2759,0.5417,0.5833,0.1483,0.3102,0.4474,0.0077,0.0105,0.3897,0.1889,0.9990000000000001,0.9869,0.1489,0.6445,0.2759,0.5417,0.5833,0.1517,0.3388,0.4662,0.0078,0.0111,0.3862,0.182,0.9990000000000001,0.9866,0.1485,0.64,0.2759,0.5417,0.5833,0.1509,0.3155,0.4555,0.0078,0.0107,reg oper account,block of flats,0.4368,Monolithic,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n288563,0,Cash loans,M,N,Y,0,90000.0,254700.0,25321.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010556,-16231,-4472,-7627.0,-4966,,1,1,1,1,0,0,Laborers,2.0,3,3,SATURDAY,10,0,0,0,0,0,0,Industry: type 11,,0.5302787251867157,0.8193176922872417,0.0619,0.0643,0.9806,0.7348,0.0073,0.0,0.1379,0.1667,0.2083,0.0935,0.0504,0.0538,0.0,0.0,0.063,0.0667,0.9806,0.7452,0.0074,0.0,0.1379,0.1667,0.2083,0.0956,0.0551,0.055999999999999994,0.0,0.0,0.0625,0.0643,0.9806,0.7383,0.0074,0.0,0.1379,0.1667,0.2083,0.0951,0.0513,0.0548,0.0,0.0,reg oper account,block of flats,0.0463,Panel,No,1.0,1.0,1.0,1.0,-689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n414466,0,Cash loans,F,N,Y,0,202500.0,835380.0,40320.0,675000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-19919,-1460,-7268.0,-2973,,1,1,0,1,0,0,Cleaning staff,2.0,1,1,SATURDAY,12,0,0,0,0,0,0,School,,0.6048995646388593,0.7544061731797895,0.0928,0.0915,0.9737,0.6396,0.0134,0.0,0.2069,0.1667,0.2083,0.0637,0.0756,0.0824,0.0,0.0,0.0945,0.095,0.9737,0.6537,0.0135,0.0,0.2069,0.1667,0.2083,0.0651,0.0826,0.0859,0.0,0.0,0.0937,0.0915,0.9737,0.6444,0.0134,0.0,0.2069,0.1667,0.2083,0.0648,0.077,0.0839,0.0,0.0,reg oper spec account,block of flats,0.0657,Panel,No,5.0,0.0,5.0,0.0,-466.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n141928,0,Cash loans,F,N,Y,0,135000.0,521568.0,18864.0,360000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.009657,-21428,365243,-11348.0,-4875,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.6658977874459105,0.4223696523543468,0.2227,0.1537,0.9841,0.7824,0.1499,0.24,0.2069,0.3333,0.375,0.0691,0.1816,0.2259,0.0,0.0,0.2269,0.1595,0.9841,0.7909,0.1513,0.2417,0.2069,0.3333,0.375,0.0706,0.1983,0.2354,0.0,0.0,0.2248,0.1537,0.9841,0.7853,0.1509,0.24,0.2069,0.3333,0.375,0.0703,0.1847,0.23,0.0,0.0,reg oper account,block of flats,0.2597,Panel,No,1.0,0.0,1.0,0.0,-2521.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n326030,0,Cash loans,F,N,N,0,54000.0,1260000.0,34650.0,1260000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-23334,365243,-11712.0,-4198,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.5971495418324797,0.8061492814355136,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1869.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,5.0,2.0,3.0\n386602,0,Revolving loans,F,Y,Y,0,202500.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.011657,-19140,-3999,-9782.0,-1842,2.0,1,1,0,1,0,0,Managers,2.0,1,1,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.8976550877090482,0.6585019312391047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-178.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n330116,0,Cash loans,F,N,Y,0,130500.0,646920.0,20997.0,540000.0,Unaccompanied,Working,Lower secondary,Civil marriage,House / apartment,0.014519999999999996,-11556,-458,-5683.0,-3590,,1,1,1,1,1,0,Core staff,2.0,2,2,WEDNESDAY,9,0,0,0,1,1,1,Government,0.2320943878536089,0.2663445441482756,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-679.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n443075,0,Cash loans,F,N,N,0,76500.0,170640.0,8298.0,135000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-20398,365243,-13359.0,-3875,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,18,0,0,0,0,0,0,XNA,0.476156446630898,0.6074783929163146,0.4578995512067301,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-208.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n183047,0,Cash loans,F,N,Y,0,135000.0,742500.0,21694.5,742500.0,Unaccompanied,Working,Incomplete higher,Married,With parents,0.01885,-12333,-2695,-5857.0,-3402,,1,1,1,1,0,1,High skill tech staff,2.0,2,2,WEDNESDAY,15,0,0,0,1,1,0,Government,0.5895176186693638,0.6068264155399048,0.501075160239048,,,0.9841,0.7824,,,,0.1667,0.0417,0.0618,0.0756,0.0889,,,,,0.9841,0.7909,,,,0.1667,0.0417,0.0632,0.0826,0.0927,,,,,0.9841,0.7853,,,,0.1667,0.0417,0.0628,0.077,0.0905,,,,block of flats,0.0765,Panel,No,2.0,1.0,2.0,1.0,-791.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n169020,0,Cash loans,M,Y,Y,0,112500.0,450000.0,12375.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-15428,-416,-2294.0,-4514,34.0,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Trade: type 3,0.2199425400685732,0.7613517291838071,0.42765737003502935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-258.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n230873,0,Cash loans,F,N,Y,0,171000.0,414229.5,15745.5,342000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.009657,-14208,-2925,-4878.0,-4970,,1,1,0,1,1,0,Sales staff,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5535138512211221,0.4848514754962666,0.0216,0.0,0.9717,,,0.0,0.069,0.0417,,0.0195,,0.0121,,0.0,0.0221,0.0,0.9717,,,0.0,0.069,0.0417,,0.0199,,0.0126,,0.0,0.0219,0.0,0.9717,,,0.0,0.069,0.0417,,0.0198,,0.0123,,0.0,,block of flats,0.0095,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-124.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n157021,0,Cash loans,M,Y,N,0,135000.0,239850.0,23850.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-23956,-15300,-7255.0,-4657,1.0,1,1,0,1,0,0,Laborers,2.0,3,2,FRIDAY,8,0,0,0,0,0,0,Electricity,,0.510196230088568,,0.1464,0.0975,0.9896,0.8572,0.0336,0.16,0.1379,0.375,0.4167,0.0776,0.1194,0.1819,0.0,0.0,0.1492,0.1012,0.9896,0.8628,0.0339,0.1611,0.1379,0.375,0.4167,0.0794,0.1304,0.1896,0.0,0.0,0.1478,0.0975,0.9896,0.8591,0.0338,0.16,0.1379,0.375,0.4167,0.079,0.1214,0.1852,0.0,0.0,reg oper account,block of flats,0.1615,Panel,No,0.0,0.0,0.0,0.0,-1549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n240464,0,Cash loans,F,N,N,1,126000.0,540000.0,22878.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.01885,-16936,-3001,-2107.0,-411,,1,1,1,1,0,0,Sales staff,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6323731103443364,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-142.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n403036,0,Cash loans,M,Y,Y,0,90000.0,156384.0,16551.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-19824,-2243,-3068.0,-3361,16.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.6445171557162276,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1482.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n253477,0,Cash loans,F,N,Y,0,135000.0,256500.0,20394.0,256500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-13061,-182,-887.0,-4889,,1,1,1,1,0,0,Cooking staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,School,0.3598703052499992,0.4725147329357295,,0.0619,,0.9732,,,,0.069,0.0833,,,,0.0,,0.1385,0.063,,0.9732,,,,0.069,0.0833,,,,0.0,,0.1466,0.0625,,0.9732,,,,0.069,0.0833,,,,0.0,,0.1414,,block of flats,0.0301,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-144.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n100800,0,Cash loans,F,N,Y,0,126000.0,698517.0,23215.5,603000.0,Unaccompanied,Pensioner,Lower secondary,Widow,House / apartment,0.018634,-21314,365243,-2486.0,-4613,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.2997892593212273,0.6894791426446275,0.0619,0.0791,0.9871,,,0.0,0.1379,0.1667,,0.0211,,0.0541,,0.0316,0.063,0.0821,0.9871,,,0.0,0.1379,0.1667,,0.0215,,0.0563,,0.0335,0.0625,0.0791,0.9871,,,0.0,0.1379,0.1667,,0.0214,,0.055,,0.0323,,block of flats,0.0494,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-807.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n237718,0,Cash loans,F,N,Y,0,166500.0,886176.0,29416.5,765000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.016612000000000002,-18765,365243,-9324.0,-2295,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.3113938943645677,0.7106743858828587,0.0928,0.1006,0.9826,0.762,0.0429,0.0,0.2069,0.1667,0.0417,0.0812,0.0756,0.0874,0.0,0.0,0.0945,0.1044,0.9826,0.7713,0.0433,0.0,0.2069,0.1667,0.0417,0.0831,0.0826,0.091,0.0,0.0,0.0937,0.1006,0.9826,0.7652,0.0432,0.0,0.2069,0.1667,0.0417,0.0826,0.077,0.0889,0.0,0.0,reg oper account,block of flats,0.0922,Panel,No,0.0,0.0,0.0,0.0,-20.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n188224,0,Cash loans,F,Y,Y,1,360000.0,640080.0,29970.0,450000.0,Family,Commercial associate,Higher education,Separated,House / apartment,0.009549,-14928,-212,-1319.0,-3867,2.0,1,1,0,1,0,0,Accountants,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.6218568548088629,0.5325494178550566,0.5424451438613613,0.0722,0.0714,0.9841,,,0.0,0.18100000000000002,0.1667,,,,0.0735,,0.0,0.0735,0.0691,0.9811,,,0.0,0.2069,0.1667,,,,0.0634,,0.0,0.0729,0.0707,0.9831,,,0.0,0.1897,0.1667,,,,0.0705,,0.0,not specified,block of flats,0.0479,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-462.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n329729,1,Cash loans,M,Y,Y,1,148500.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-14962,-5176,-3516.0,-3996,14.0,1,1,0,1,0,0,Laborers,3.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,Housing,,0.5515566856339768,0.14024318370802974,,,0.9806,,,,,,,,,,,,,,0.9806,,,,,,,,,,,,,,0.9806,,,,,,,,,,,,,,0.0014,Wooden,No,0.0,0.0,0.0,0.0,-32.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n405538,0,Cash loans,F,N,Y,0,135000.0,450000.0,21888.0,450000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.006008,-20890,365243,-5831.0,-4427,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,,0.7265531139807863,0.17456426555726348,0.2577,0.152,0.9866,,,0.24,0.2069,0.3333,,,,0.2626,,0.0425,0.2626,0.1577,0.9866,,,0.2417,0.2069,0.3333,,,,0.2736,,0.045,0.2602,0.152,0.9866,,,0.24,0.2069,0.3333,,,,0.2673,,0.0434,reg oper account,block of flats,0.2158,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-209.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n200355,0,Cash loans,F,Y,Y,1,225000.0,1506816.0,49927.5,1350000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.072508,-16762,-3730,-2379.0,-292,5.0,1,1,0,1,0,0,Laborers,3.0,1,1,WEDNESDAY,11,0,1,1,0,1,1,Government,,0.7495242130322628,0.1385128770585923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1753.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n323558,0,Cash loans,F,N,N,0,90000.0,284400.0,9211.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22550,365243,-11195.0,-43,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.16513433666694372,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,0.0,-2406.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n249614,0,Cash loans,F,N,Y,0,202500.0,1211049.0,39195.0,1057500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018634,-21968,365243,-3036.0,-4808,,1,0,0,1,0,0,,1.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.2502241671938069,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n377352,0,Cash loans,F,N,Y,0,67500.0,545040.0,25537.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.008068,-13673,-2514,-3282.0,-4882,,1,1,0,1,0,0,Security staff,2.0,3,3,TUESDAY,11,0,0,0,0,0,0,Government,0.3934036113733352,0.3296302343445041,0.7121551551910698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1583.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n414373,0,Cash loans,F,N,Y,2,180000.0,1096020.0,56092.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-11381,-2639,-4922.0,-919,,1,1,0,1,0,0,Core staff,4.0,2,2,FRIDAY,16,0,1,1,0,1,1,Trade: type 3,0.4932056533098456,0.5469874660268447,0.4489622731076524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-848.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,1.0\n410478,0,Cash loans,F,Y,Y,1,112500.0,129519.0,13599.0,121500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-15406,-1215,-5024.0,-5118,11.0,1,1,0,1,0,0,Security staff,3.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 2,,0.5511455840178614,0.8193176922872417,0.0619,0.0657,0.9851,0.7959999999999999,0.0091,0.0,0.1379,0.1667,0.2083,0.0134,0.0504,0.0637,0.0,0.0,0.063,0.0682,0.9851,0.804,0.0092,0.0,0.1379,0.1667,0.2083,0.0137,0.0551,0.0664,0.0,0.0,0.0625,0.0657,0.9851,0.7987,0.0091,0.0,0.1379,0.1667,0.2083,0.0136,0.0513,0.0648,0.0,0.0,reg oper spec account,block of flats,0.0551,Panel,No,0.0,0.0,0.0,0.0,-528.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n358310,0,Cash loans,F,N,Y,1,135000.0,450000.0,16294.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.0228,-16215,-4717,-61.0,-1561,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Security Ministries,,0.7122040381343394,0.8128226070575616,0.0928,0.0844,0.9846,,,0.0,0.2069,0.1667,,0.0729,,0.0768,,0.0,0.0945,0.0875,0.9846,,,0.0,0.2069,0.1667,,0.0745,,0.08,,0.0,0.0937,0.0844,0.9846,,,0.0,0.2069,0.1667,,0.0742,,0.0782,,0.0,,block of flats,0.067,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1384.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n119287,0,Cash loans,M,Y,Y,2,135000.0,526491.0,22261.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003069,-18331,-1176,-898.0,-1841,11.0,1,1,0,1,0,0,Security staff,4.0,3,3,THURSDAY,11,0,1,1,0,1,1,Security,,0.5845581398079928,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,2.0,-973.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n111668,1,Cash loans,F,Y,Y,0,135000.0,787131.0,42066.0,679500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.005144,-16883,-6045,-5272.0,-407,64.0,1,1,0,1,0,0,Core staff,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Government,0.7441997616447588,0.6144016636920214,,0.1608,0.1096,0.9906,0.8708,,0.16,0.1379,0.375,,0.0568,,0.1618,,0.038,0.1639,0.1138,0.9906,0.8759,,0.1611,0.1379,0.375,,0.0581,,0.1685,,0.0403,0.1624,0.1096,0.9906,0.8725,,0.16,0.1379,0.375,,0.0578,,0.1647,,0.0388,,block of flats,0.1533,Panel,No,0.0,0.0,0.0,0.0,-1086.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n205786,0,Cash loans,F,N,N,1,90000.0,180000.0,21492.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-10783,-254,-1442.0,-1442,,1,1,0,1,1,0,Accountants,3.0,2,2,TUESDAY,15,0,0,0,1,1,0,Government,0.7572084703886663,0.6184398011621871,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n272604,0,Cash loans,M,Y,N,0,270000.0,1339884.0,43222.5,1170000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13152,-1777,-7237.0,-4157,14.0,1,1,0,1,0,0,Security staff,2.0,2,2,WEDNESDAY,17,0,0,0,1,1,0,Security,,0.6369768744734986,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2318.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n266803,0,Cash loans,M,Y,Y,1,225000.0,825588.0,78516.0,765000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-16338,-6019,-2595.0,-4152,14.0,1,1,0,1,0,0,Laborers,3.0,3,1,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6428975242445126,0.5540920357389999,,0.2216,0.2262,0.9732,,,0.0,0.3793,0.1667,,0.1152,,0.1757,,0.012,0.2258,0.2347,0.9732,,,0.0,0.3793,0.1667,,0.1179,,0.183,,0.0127,0.2238,0.2262,0.9732,,,0.0,0.3793,0.1667,,0.1172,,0.1788,,0.0122,,block of flats,0.1408,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2133.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n184055,0,Cash loans,F,Y,Y,0,112500.0,369720.0,23755.5,292500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-15118,-5979,-2478.0,-923,12.0,1,1,0,1,0,0,Medicine staff,2.0,3,3,WEDNESDAY,11,0,0,0,0,0,0,Medicine,0.35607941323227804,0.4098275944367488,0.11319635306222815,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n149790,0,Cash loans,F,N,N,0,292500.0,900000.0,46084.5,900000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.035792000000000004,-11452,-2019,-5495.0,-3765,,1,1,0,1,0,0,Managers,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Bank,,0.5638760317979481,0.34578480246959553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1000.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n444445,0,Cash loans,M,N,N,1,262647.0,568800.0,15772.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-12312,-1837,-5984.0,-2830,,1,1,1,1,0,0,,3.0,2,2,THURSDAY,17,0,0,0,1,1,0,School,0.323919705257255,0.7539683422015692,0.6594055320683344,0.0186,,0.9911,0.8776,,0.0,0.069,0.0417,0.0833,,0.0151,,,,0.0189,,0.9911,0.8824,,0.0,0.069,0.0417,0.0833,,0.0165,,,,0.0187,,0.9911,0.8792,,0.0,0.069,0.0417,0.0833,,0.0154,,,,reg oper account,block of flats,0.0085,,No,2.0,0.0,2.0,0.0,-942.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n284165,0,Cash loans,M,Y,Y,0,196200.0,808650.0,31464.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-16335,-195,-1859.0,-3797,0.0,1,1,0,1,0,0,Cleaning staff,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Medicine,,0.16214456766623808,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,10.0,0.0,-2229.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n157019,0,Cash loans,F,Y,Y,1,90000.0,244512.0,25803.0,216000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-11666,-1284,-5697.0,-4305,16.0,1,1,0,1,0,0,Sales staff,3.0,3,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.7547199878412065,0.3462378841526181,0.5954562029091491,0.2062,0.1595,0.9886,0.8436,0.1448,0.24,0.2069,0.375,0.4167,0.0864,0.1681,0.2631,0.0,0.0,0.2101,0.1655,0.9886,0.8497,0.1461,0.2417,0.2069,0.375,0.4167,0.0884,0.1837,0.2741,0.0,0.0,0.2082,0.1595,0.9886,0.8457,0.1457,0.24,0.2069,0.375,0.4167,0.0879,0.171,0.2678,0.0,0.0,reg oper account,block of flats,0.2069,Panel,No,0.0,0.0,0.0,0.0,-1165.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n314440,1,Cash loans,F,N,Y,2,135000.0,315000.0,25015.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-13499,-3830,-2017.0,-2028,,1,1,1,1,0,0,Laborers,4.0,2,2,TUESDAY,12,0,0,0,0,1,1,Business Entity Type 2,0.23294463150237635,0.6182591799872186,0.190705947811054,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n399105,0,Cash loans,M,N,Y,1,247500.0,900000.0,38263.5,900000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-16738,-582,-3258.0,-295,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6407679325396131,0.5423723216098111,0.6161216908872079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3171.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n128518,0,Cash loans,F,N,Y,0,67500.0,355536.0,15790.5,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.02461,-20489,365243,-5065.0,-3887,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,,0.36700531583671786,0.5531646987710016,0.1,0.1137,0.9767,,,0.0,0.2069,0.1667,,0.1217,,0.087,,0.0095,0.1019,0.11800000000000001,0.9767,,,0.0,0.2069,0.1667,,0.1244,,0.0907,,0.0101,0.10099999999999999,0.1137,0.9767,,,0.0,0.2069,0.1667,,0.1238,,0.0886,,0.0097,reg oper account,terraced house,0.0963,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n210279,0,Cash loans,M,Y,N,0,225000.0,1125000.0,32895.0,1125000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.014464,-14985,-6381,-2757.0,-4150,6.0,1,1,0,1,1,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Military,0.5627011466468521,0.2497772477284343,0.3185955240537633,0.066,0.0818,0.9757,0.6668,0.0266,0.0,0.1379,0.125,0.1667,0.0196,0.0538,0.0328,0.0,0.0,0.0672,0.0849,0.9757,0.6798,0.0268,0.0,0.1379,0.125,0.1667,0.02,0.0588,0.0341,0.0,0.0,0.0666,0.0818,0.9757,0.6713,0.0267,0.0,0.1379,0.125,0.1667,0.0199,0.0547,0.0334,0.0,0.0,not specified,block of flats,0.0403,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1509.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n247751,0,Revolving loans,M,Y,Y,1,585000.0,1350000.0,67500.0,1350000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.04622,-13077,-109,-918.0,-4542,16.0,1,1,0,1,1,0,Laborers,2.0,1,1,THURSDAY,14,0,0,0,0,0,0,Construction,0.24799256813289994,0.7368635707432372,0.7544061731797895,0.1113,,0.9801,,,0.08,0.069,0.3333,,,,0.0765,,,0.1134,,0.9801,,,0.0806,0.069,0.3333,,,,0.0797,,,0.1124,,0.9801,,,0.08,0.069,0.3333,,,,0.0779,,,,block of flats,0.0642,\"Stone, brick\",No,,,,,-160.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n420762,0,Cash loans,M,N,N,0,145350.0,269550.0,19300.5,225000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,Municipal apartment,0.009334,-24405,365243,-16738.0,-4271,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.7970956981007935,0.470456116119975,0.0082,0.0,0.9732,0.6328,0.0009,0.0,0.0345,0.0417,0.0417,0.0044,0.0067,0.0064,0.0,0.0,0.0084,0.0,0.9732,0.6472,0.0009,0.0,0.0345,0.0417,0.0417,0.0045,0.0073,0.0067,0.0,0.0,0.0083,0.0,0.9732,0.6377,0.0009,0.0,0.0345,0.0417,0.0417,0.0045,0.0068,0.0066,0.0,0.0,not specified,block of flats,0.0055,Wooden,No,2.0,0.0,2.0,0.0,-1301.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n237559,0,Cash loans,F,N,Y,0,171000.0,509400.0,37066.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009549,-11221,-440,-857.0,-1215,,1,1,1,1,1,1,Sales staff,2.0,2,2,FRIDAY,11,0,1,1,0,1,1,Trade: type 3,0.2401759328963443,0.36943696705288703,0.09885928035482196,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-604.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n133668,0,Cash loans,F,Y,Y,0,202500.0,964368.0,34767.0,832500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006305,-16577,-4800,-8226.0,-109,18.0,1,1,0,1,1,0,Sales staff,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,Self-employed,,0.5759087574873025,0.2418614865234661,0.1113,0.0773,0.9886,,,0.12,0.1034,0.3333,,,0.0891,0.1224,0.0077,0.0024,0.1134,0.0802,0.9886,,,0.1208,0.1034,0.3333,,,0.0973,0.1275,0.0078,0.0025,0.1124,0.0773,0.9886,,,0.12,0.1034,0.3333,,,0.0906,0.1245,0.0078,0.0024,reg oper spec account,block of flats,0.1376,Panel,No,0.0,0.0,0.0,0.0,-870.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n356790,0,Cash loans,F,Y,Y,0,450000.0,454500.0,44406.0,454500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.028663,-15637,-2125,-5235.0,-4066,11.0,1,1,0,1,1,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Other,0.6490670266595268,0.6404183824836883,0.7649392739475195,0.0876,0.1044,0.9921,0.8844,0.0591,0.04,0.2297,0.375,0.2638,0.0774,0.0678,0.1335,0.0232,0.105,0.0483,0.0527,0.9901,0.8693,0.0171,0.0,0.069,0.1667,0.2083,0.0153,0.0413,0.1239,0.0195,0.0651,0.0843,0.0652,0.9921,0.8927,0.0719,0.04,0.2759,0.3333,0.2083,0.0891,0.065,0.1424,0.0233,0.1077,reg oper account,block of flats,0.172,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2281.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n366195,0,Cash loans,F,N,Y,1,99000.0,774117.0,34227.0,625500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14537,-1236,-2944.0,-5758,,1,1,0,1,1,0,Core staff,3.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,School,0.7022317034158676,0.6736025138153233,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-3405.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n260719,0,Cash loans,F,N,Y,0,58500.0,139230.0,10030.5,112500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-18619,-576,-5547.0,-2151,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Industry: type 11,,0.7151309239511124,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,1.0,0.0,1.0,0.0,-2140.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n360620,0,Cash loans,F,N,N,1,94500.0,135000.0,13351.5,135000.0,Unaccompanied,Working,Higher education,Widow,With parents,0.035792000000000004,-14677,-2245,-2615.0,-2629,,1,1,1,1,1,0,Accountants,2.0,2,2,SATURDAY,10,0,0,0,0,1,1,Agriculture,0.5987341850516469,0.6308874140112812,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1232.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n179106,0,Cash loans,F,N,Y,0,112500.0,149256.0,15799.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.018634,-20413,365243,-5535.0,-3560,,1,0,0,1,0,0,,1.0,2,2,MONDAY,15,0,0,0,1,0,0,XNA,,0.6882997791603791,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-521.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n395117,1,Cash loans,F,N,N,0,112500.0,450000.0,11871.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.028663,-10717,-474,-174.0,-987,,1,1,0,1,1,0,Sales staff,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Trade: type 7,0.10295277221213286,0.5805910922514066,0.18303516721781032,0.0423,,0.9846,,,0.0,0.0345,0.0833,,0.0632,,0.0217,,0.0,0.0431,,0.9846,,,0.0,0.0345,0.0833,,0.0646,,0.0226,,0.0,0.0427,,0.9846,,,0.0,0.0345,0.0833,,0.0643,,0.022000000000000002,,0.0,,block of flats,0.0213,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-119.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n258516,0,Cash loans,M,N,Y,0,135000.0,675000.0,27621.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-20440,365243,-7417.0,-3996,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,,0.3252143683262086,0.7597121819739279,0.0773,0.0867,0.9762,0.6736,0.1373,0.0,0.1724,0.1667,0.2083,0.0439,0.1025,0.0683,0.0135,0.0854,0.063,0.084,0.9747,0.6668,0.1386,0.0,0.1379,0.1667,0.2083,0.0,0.0496,0.0561,0.0039,0.0615,0.0781,0.0867,0.9762,0.6779999999999999,0.1382,0.0,0.1724,0.1667,0.2083,0.0447,0.1043,0.0695,0.0136,0.0872,reg oper account,block of flats,0.0751,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1513.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n251414,0,Cash loans,M,N,Y,0,144000.0,163008.0,16249.5,144000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.006305,-10279,-1628,-4394.0,-2561,,1,1,0,1,1,1,Core staff,1.0,3,3,MONDAY,4,0,0,0,0,0,0,Self-employed,0.67885495226787,0.5222101886459577,0.4507472818545589,0.1237,0.0841,0.9916,0.8844,0.0852,0.12,0.1034,0.375,0.4167,0.0606,0.1009,0.1373,0.0,0.0,0.1261,0.0873,0.9916,0.8889,0.086,0.1208,0.1034,0.375,0.4167,0.0619,0.1102,0.14300000000000002,0.0,0.0,0.1249,0.0841,0.9916,0.8859,0.0858,0.12,0.1034,0.375,0.4167,0.0616,0.1026,0.1397,0.0,0.0,reg oper spec account,block of flats,0.10800000000000001,Panel,No,0.0,0.0,0.0,0.0,-911.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n326855,0,Cash loans,F,N,N,0,112500.0,284400.0,19134.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-18927,-1430,-4368.0,-2491,,1,1,0,1,1,0,Sales staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Self-employed,0.7293011186796582,0.6752062964179772,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-553.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n223358,0,Cash loans,F,N,Y,2,90000.0,868500.0,25524.0,868500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.028663,-12782,-146,-1058.0,-4811,,1,1,0,1,0,0,Accountants,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Industry: type 4,0.4709381508401424,0.6401464082453323,0.5028782772082183,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-7.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n195743,0,Cash loans,F,N,Y,0,166500.0,315000.0,17217.0,315000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.031329,-8933,-1317,-4157.0,-1330,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,Industry: type 5,,0.3896298645659261,0.5656079814115492,0.0371,0.07,0.9662,0.5376,0.0304,0.0,0.1034,0.0833,0.125,0.085,0.0294,0.0189,0.0039,0.0021,0.0378,0.0727,0.9662,0.5557,0.0307,0.0,0.1034,0.0833,0.125,0.087,0.0321,0.0197,0.0039,0.0022,0.0375,0.07,0.9662,0.5438,0.0306,0.0,0.1034,0.0833,0.125,0.0865,0.0299,0.0192,0.0039,0.0022,reg oper account,block of flats,0.0319,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-310.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n221175,0,Cash loans,F,N,Y,0,207000.0,544491.0,17694.0,454500.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.002042,-14962,-1223,-1169.0,-2048,,1,1,0,1,0,0,Managers,1.0,3,3,MONDAY,13,0,0,0,0,0,0,Housing,,0.7344438941191666,0.7001838506835805,0.0381,0.0519,0.9742,,,0.0,0.1034,0.125,,0.0329,,0.032,,0.0517,0.0389,0.0539,0.9742,,,0.0,0.1034,0.125,,0.0337,,0.0333,,0.0548,0.0385,0.0519,0.9742,,,0.0,0.1034,0.125,,0.0335,,0.0325,,0.0528,,block of flats,0.0364,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1019.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n160792,0,Cash loans,F,N,Y,2,67500.0,283585.5,22536.0,256500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14709,-2120,-6171.0,-4790,,1,1,0,1,0,0,,4.0,2,2,THURSDAY,10,0,0,0,0,0,0,Government,,0.4473330937036185,,0.0165,0.0,0.9806,0.7348,0.0018,0.0,0.069,0.0417,0.0833,0.0174,0.0134,0.0119,0.0,0.0,0.0168,0.0,0.9806,0.7452,0.0018,0.0,0.069,0.0417,0.0833,0.0177,0.0147,0.0124,0.0,0.0,0.0167,0.0,0.9806,0.7383,0.0018,0.0,0.069,0.0417,0.0833,0.0177,0.0137,0.0122,0.0,0.0,reg oper account,,0.0136,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2105.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n126133,0,Cash loans,F,N,N,0,112500.0,810000.0,23814.0,810000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008068,-14012,-709,-8072.0,-1050,,1,1,0,1,0,0,High skill tech staff,1.0,3,3,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 2,0.3658355091717262,0.3605827703387404,0.11761373170805695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1041.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n341032,0,Cash loans,F,N,N,0,157500.0,1258650.0,56277.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.04622,-14290,-1173,-8220.0,-4716,,1,1,0,1,0,0,Sales staff,2.0,1,1,SUNDAY,18,0,0,0,0,0,0,Self-employed,0.5830770013556832,0.579008784023478,0.4848514754962666,0.0021,0.0,0.9722,0.6192,0.0,0.0,0.0,0.0,0.0417,0.0221,0.0017,0.0018,0.0,0.0,0.0021,0.0,0.9722,0.6341,0.0,0.0,0.0,0.0,0.0417,0.0226,0.0018,0.0019,0.0,0.0,0.0021,0.0,0.9722,0.6243,0.0,0.0,0.0,0.0,0.0417,0.0224,0.0017,0.0019,0.0,0.0,reg oper account,block of flats,0.0015,Others,No,0.0,0.0,0.0,0.0,-1340.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n297003,1,Cash loans,F,Y,Y,1,225000.0,1076287.5,45729.0,913500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006629,-10198,-519,-4537.0,-530,23.0,1,1,0,1,0,0,,3.0,2,2,SATURDAY,5,0,0,0,0,0,0,Government,,0.26526060209959595,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-12.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n443994,0,Cash loans,F,N,Y,0,292500.0,508495.5,36292.5,454500.0,Children,Pensioner,Higher education,Single / not married,House / apartment,0.01885,-23589,365243,-13456.0,-4398,,1,0,0,1,1,1,,1.0,2,2,SUNDAY,13,0,0,0,0,0,0,XNA,0.8639034915866335,0.4688590705741456,0.8027454758994178,0.0082,,0.9667,,,0.0,0.0345,0.125,,0.0088,,0.0146,,0.0001,0.0084,,0.9667,,,0.0,0.0345,0.125,,0.009000000000000001,,0.0152,,0.0001,0.0083,,0.9667,,,0.0,0.0345,0.125,,0.0089,,0.0149,,0.0001,,block of flats,0.0115,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1937.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n366826,0,Cash loans,M,N,Y,2,157500.0,397881.0,26716.5,328500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006629,-13082,-1073,-4932.0,-2132,,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.5119884874039542,0.2721336844147212,0.0918,0.1148,0.9841,0.7688,0.0212,0.04,0.1379,0.2083,0.0417,0.0686,0.0698,0.0962,0.0154,0.0471,0.0756,0.1117,0.9831,0.7779,0.0214,0.0,0.1034,0.125,0.0417,0.0583,0.0762,0.0759,0.0156,0.0,0.0906,0.1083,0.9841,0.7719,0.0214,0.0,0.1034,0.1667,0.0417,0.0648,0.071,0.0825,0.0155,0.0196,reg oper account,block of flats,0.0795,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n161420,0,Cash loans,F,Y,N,0,112500.0,1223010.0,51948.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-14000,-2387,-6827.0,-2717,16.0,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Self-employed,,0.4068690086008408,0.6313545365850379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-573.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n212982,0,Cash loans,F,N,Y,0,225000.0,1480500.0,51592.5,1480500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.014519999999999996,-18901,-4213,-4878.0,-2443,,1,1,0,1,1,0,Managers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Services,,0.2584977395655245,0.4740512892789932,0.0918,0.0831,0.9826,0.762,0.0,0.0,0.2069,0.1667,0.2083,0.0982,0.0748,0.0782,0.0,0.0,0.0935,0.0862,0.9826,0.7713,0.0,0.0,0.2069,0.1667,0.2083,0.1004,0.0817,0.0815,0.0,0.0,0.0926,0.0831,0.9826,0.7652,0.0,0.0,0.2069,0.1667,0.2083,0.0999,0.0761,0.0796,0.0,0.0,reg oper spec account,block of flats,0.0615,Panel,No,0.0,0.0,0.0,0.0,-509.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n362981,0,Cash loans,M,N,Y,0,112500.0,93829.5,10233.0,81000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.005144,-11860,-269,-11823.0,-4041,,1,1,0,1,0,1,Laborers,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Medicine,0.20768798618264936,0.7663962956325011,,0.0928,0.0993,0.9781,0.7008,0.012,0.0,0.2069,0.1667,0.2083,0.053,0.0756,0.0872,0.0,0.0,0.0945,0.10300000000000001,0.9782,0.7125,0.0121,0.0,0.2069,0.1667,0.2083,0.0542,0.0826,0.0908,0.0,0.0,0.0937,0.0993,0.9781,0.7048,0.0121,0.0,0.2069,0.1667,0.2083,0.0539,0.077,0.0888,0.0,0.0,reg oper spec account,block of flats,0.0751,Panel,No,0.0,0.0,0.0,0.0,-243.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n111970,0,Cash loans,F,N,N,0,135000.0,526491.0,19039.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-15700,-7956,-6864.0,-4627,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Transport: type 2,,0.7137568489527384,0.06733950871403091,0.1619,0.07400000000000001,0.9841,,,0.0,0.0345,0.1667,,,,0.1668,,0.0,0.1649,0.0768,0.9841,,,0.0,0.0345,0.1667,,,,0.1738,,0.0,0.1634,0.07400000000000001,0.9841,,,0.0,0.0345,0.1667,,,,0.1698,,0.0,,block of flats,0.1312,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2186.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n336246,0,Cash loans,F,N,N,1,112500.0,604683.0,25551.0,522000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.022625,-12833,-2989,-2143.0,-4760,,1,1,0,1,0,0,Sales staff,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5973546846595864,0.8106180215523969,0.1278,0.0437,0.9881,,,0.08,0.0345,0.625,,0.0519,,0.1209,,0.0148,0.1303,0.0453,0.9881,,,0.0806,0.0345,0.625,,0.053,,0.1259,,0.0157,0.1291,0.0437,0.9881,,,0.08,0.0345,0.625,,0.0528,,0.12300000000000001,,0.0151,,block of flats,0.1282,Panel,No,0.0,0.0,0.0,0.0,-2447.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n375374,0,Cash loans,F,N,Y,0,135000.0,76410.0,7573.5,67500.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.003122,-22217,365243,-7360.0,-3958,,1,0,0,1,0,0,,1.0,3,3,FRIDAY,11,0,0,0,0,0,0,XNA,,0.6756952584758334,0.722392890081304,0.0701,0.0664,0.9781,0.7008,0.0078,0.0,0.1379,0.1667,0.2083,0.0526,0.0563,0.0652,0.0039,0.0083,0.0714,0.0689,0.9782,0.7125,0.0078,0.0,0.1379,0.1667,0.2083,0.0537,0.0615,0.0679,0.0039,0.0088,0.0708,0.0664,0.9781,0.7048,0.0078,0.0,0.1379,0.1667,0.2083,0.0535,0.0573,0.0664,0.0039,0.0085,reg oper account,block of flats,0.0573,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-713.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n305156,0,Cash loans,M,N,Y,0,180000.0,518562.0,38083.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003122,-18624,-2394,-9772.0,-2186,,1,1,0,1,1,0,Drivers,2.0,3,3,FRIDAY,14,0,1,1,0,0,0,Transport: type 4,0.2950336374432641,0.6165028792452706,0.7503751495159068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-522.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n108442,0,Cash loans,F,N,Y,2,49500.0,95940.0,10309.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005144,-12342,-297,-3515.0,-4354,,1,1,1,1,1,0,Core staff,4.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Postal,,0.484267885790774,,0.0041,0.01,0.9747,0.6532,0.0,0.0,0.1379,0.0,0.0417,0.009000000000000001,0.0034,0.0021,0.0,0.0,0.0042,0.0104,0.9747,0.6668,0.0,0.0,0.1379,0.0,0.0417,0.0092,0.0037,0.0022,0.0,0.0,0.0042,0.01,0.9747,0.6578,0.0,0.0,0.1379,0.0,0.0417,0.0092,0.0034,0.0021,0.0,0.0,reg oper account,terraced house,0.0016,Wooden,Yes,0.0,0.0,0.0,0.0,-197.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n237217,0,Cash loans,F,N,Y,0,202500.0,99504.0,11938.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00702,-13243,-3578,-1484.0,-665,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,1,1,Industry: type 3,0.38951854142982334,0.0015866823618056866,0.07698446914647789,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-323.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n226009,0,Cash loans,F,N,Y,2,135000.0,203760.0,20281.5,180000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.032561,-15811,-7789,-9935.0,-4564,,1,1,0,1,0,0,Core staff,4.0,1,1,FRIDAY,19,0,0,0,0,0,0,School,,0.7182168852581624,0.8203829585116356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-342.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n170876,0,Cash loans,M,Y,N,0,180000.0,675000.0,36616.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.031329,-9919,-2769,-1656.0,-2440,16.0,1,1,0,1,1,0,Core staff,2.0,2,2,SUNDAY,13,0,0,0,0,1,1,Medicine,0.061744336433676376,0.6705470292450948,0.3842068130556564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1827.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n284021,0,Cash loans,F,N,Y,0,126000.0,254700.0,14350.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.018634,-24316,365243,-3765.0,-4368,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.7656673131949595,0.6512602186973006,0.1062,0.1228,0.9767,0.6804,,0.0,0.2069,0.1667,0.2083,0.0388,0.0841,0.0892,0.0116,0.0119,0.1082,0.1275,0.9767,0.6929,,0.0,0.2069,0.1667,0.2083,0.0397,0.0918,0.0929,0.0117,0.0126,0.1072,0.1228,0.9767,0.6847,,0.0,0.2069,0.1667,0.2083,0.0395,0.0855,0.0908,0.0116,0.0121,,block of flats,0.0815,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1645.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n408079,0,Cash loans,F,Y,N,0,112500.0,787131.0,30762.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-11149,-2937,-3554.0,-3631,12.0,1,1,1,1,1,0,Laborers,2.0,2,2,SUNDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.2947432345693359,0.3241355591278777,0.2793353208976285,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-796.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n323758,0,Cash loans,M,N,Y,0,135000.0,277969.5,16087.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-10152,-2584,-647.0,-2837,,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.33892315511034765,0.7416685824986909,0.40314167665875134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-304.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n339520,0,Cash loans,F,N,Y,0,78750.0,528633.0,22396.5,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.016612000000000002,-20790,365243,-1943.0,-1944,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.04386739629294322,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1469.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n138182,0,Cash loans,F,N,Y,0,94500.0,225000.0,16132.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.032561,-22846,365243,-2484.0,-1050,,1,0,0,1,1,0,,2.0,1,1,WEDNESDAY,18,0,0,0,0,0,0,XNA,,0.2940874482399952,0.5567274263630174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n422744,1,Cash loans,M,Y,N,0,270000.0,967500.0,28417.5,967500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-16677,-444,-8107.0,-232,3.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,8,0,0,0,0,1,1,Business Entity Type 3,,0.626835043484729,0.4489622731076524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-662.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n155785,0,Cash loans,F,N,Y,0,76500.0,270000.0,15628.5,270000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Municipal apartment,0.015221,-20875,-1836,-1718.0,-3886,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Other,,0.6117198025660773,0.3774042489507649,,,0.9851,,,,,,,,,0.0912,,,,,0.9851,,,,,,,,,0.095,,,,,0.9851,,,,,,,,,0.0929,,,,,0.0717,,No,0.0,0.0,0.0,0.0,-342.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n288688,0,Cash loans,M,Y,Y,0,333000.0,1800000.0,49630.5,1800000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-10708,-1947,-554.0,-3318,1.0,1,1,0,1,0,0,Sales staff,2.0,2,1,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.1501194558689223,0.7514692046784376,0.8463780633178121,0.6639,0.0873,0.9965,,,0.48,0.3793,0.625,,,,0.7641,,0.6811,0.6765,0.0906,0.9965,,,0.4834,0.3793,0.625,,,,0.7961,,0.7211,0.6703,0.0873,0.9965,,,0.48,0.3793,0.625,,,,0.7778,,0.6954,,block of flats,0.7628,Mixed,No,4.0,0.0,4.0,0.0,-505.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n244333,1,Cash loans,M,Y,Y,0,270000.0,423000.0,16074.0,423000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.003818,-13879,-2178,-8010.0,-3669,17.0,1,1,0,1,1,1,High skill tech staff,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.16642917571871782,0.6828980661093552,0.5136937663039473,0.0735,0.0452,0.9786,0.7076,0.0517,0.0,0.1552,0.1667,0.2083,0.0344,0.059000000000000004,0.062,0.0039,0.0068,0.063,0.0339,0.9782,0.7125,0.009000000000000001,0.0,0.1379,0.1667,0.2083,0.0125,0.0551,0.0541,0.0,0.0,0.0625,0.0489,0.9781,0.7048,0.0556,0.0,0.1379,0.1667,0.2083,0.0377,0.0513,0.0547,0.0,0.0,reg oper account,block of flats,0.0408,Panel,No,1.0,0.0,1.0,0.0,-798.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n441788,0,Cash loans,M,N,N,0,180000.0,531072.0,15646.5,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14594,-6347,-8727.0,-5271,,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Government,,0.6232835145697827,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-324.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,4.0\n178366,0,Cash loans,F,N,Y,0,157500.0,641173.5,28372.5,553500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008865999999999999,-19052,-10856,-2252.0,-2463,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Industry: type 3,,0.3095213889773423,0.4436153084085652,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n263809,0,Cash loans,M,N,Y,0,135000.0,207000.0,16726.5,207000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.014519999999999996,-16052,-1694,-8373.0,-5565,,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.3454858866640793,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-206.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n189301,0,Cash loans,F,N,N,0,36000.0,113760.0,7398.0,90000.0,Unaccompanied,Working,Lower secondary,Single / not married,With parents,0.035792000000000004,-14464,-1450,-8288.0,-4471,,1,1,1,1,0,0,Sales staff,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,,0.7031023702059879,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-896.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n385080,0,Cash loans,F,N,Y,0,112500.0,495000.0,21933.0,495000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.002042,-17064,-1682,-1602.0,-607,,1,1,0,1,0,0,Core staff,2.0,3,3,TUESDAY,9,0,0,0,0,0,0,School,0.6860745420776693,0.5579194139777031,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-639.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n131919,1,Revolving loans,M,Y,Y,0,112500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-7926,-726,-5126.0,-488,1.0,1,1,0,1,0,0,,1.0,3,3,THURSDAY,12,0,0,0,1,1,0,Business Entity Type 3,0.08617623760189214,0.0009664057381070043,0.16146308000577247,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-126.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,2.0\n383325,0,Cash loans,M,Y,Y,1,252000.0,1255680.0,45234.0,1125000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-12777,-743,-6761.0,-4019,1.0,1,1,0,1,0,0,Drivers,3.0,1,1,THURSDAY,13,0,1,1,0,0,0,Business Entity Type 3,,0.7295405592023069,0.5424451438613613,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-337.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n184111,1,Cash loans,F,N,Y,0,112500.0,545040.0,19705.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-20741,-1420,-18417.0,-4156,,1,1,0,1,1,0,Core staff,2.0,1,1,THURSDAY,14,0,0,0,0,0,0,School,,0.7174373273626072,0.2636468134452008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1772.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n311267,1,Cash loans,M,N,N,0,112500.0,679500.0,24070.5,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010032,-8854,-176,-1862.0,-1527,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,4,1,1,0,1,1,0,Business Entity Type 3,0.15212998762728827,0.5547997070931854,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-257.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n126754,0,Cash loans,M,N,Y,0,180000.0,254700.0,27558.0,225000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.010276,-10284,-2915,-4805.0,-2661,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.22555431687808505,0.6785676886853644,0.1113,0.0824,0.9861,,,0.12,0.1034,0.3333,,,,0.109,,0.1892,0.1134,0.0856,0.9861,,,0.1208,0.1034,0.3333,,,,0.1136,,0.2003,0.1124,0.0824,0.9861,,,0.12,0.1034,0.3333,,,,0.111,,0.1931,,block of flats,0.1269,Panel,No,1.0,1.0,1.0,1.0,-1756.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n393417,0,Cash loans,F,N,Y,0,292500.0,545040.0,34960.5,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.022625,-9948,-947,-9618.0,-2594,,1,1,0,1,0,0,Sales staff,1.0,2,2,FRIDAY,14,0,0,0,1,1,0,Self-employed,,0.3065058156503732,0.0963186927645401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n218740,0,Cash loans,F,N,N,0,112500.0,573628.5,27724.5,463500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-12259,-3108,-5348.0,-3164,,1,1,0,1,0,1,,2.0,1,1,SATURDAY,15,0,0,0,0,1,1,Business Entity Type 3,0.4944363666957402,0.5672663012893442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-958.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n394225,1,Cash loans,M,Y,Y,0,180000.0,487570.5,38650.5,441000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.01885,-15949,-760,-4523.0,-4541,11.0,1,1,0,1,1,0,Drivers,2.0,2,2,TUESDAY,9,0,0,0,0,1,1,Self-employed,,0.6964687411956794,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-325.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n235163,1,Cash loans,M,Y,Y,2,180000.0,619254.0,29920.5,553500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010556,-12109,-1067,-2476.0,-2464,10.0,1,1,0,1,0,0,,4.0,3,3,WEDNESDAY,11,0,0,0,0,1,1,Self-employed,,0.06023412174652468,0.05803230877897029,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-782.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n418191,0,Cash loans,F,N,Y,0,202500.0,697500.0,29682.0,697500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.018634,-22632,365243,-10759.0,-4276,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.5802396101880218,0.31547215492577346,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1529.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n110614,1,Cash loans,F,N,N,1,67500.0,167121.0,12015.0,139500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.020713,-9257,-1065,-3605.0,-1921,,1,1,0,1,0,0,Medicine staff,3.0,3,3,FRIDAY,5,0,0,0,1,1,0,Medicine,,0.6018119314305592,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1010.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n186988,0,Cash loans,M,N,Y,0,360000.0,755190.0,36459.0,675000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.019101,-21490,365243,-912.0,-5013,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,XNA,,0.5663750049107609,0.5638350489514956,0.0608,0.049,0.9791,0.7144,0.0177,0.0,0.1379,0.1667,0.2083,0.0219,0.0496,0.0537,0.0,0.0,0.062,0.0509,0.9791,0.7256,0.0179,0.0,0.1379,0.1667,0.2083,0.0224,0.0542,0.0559,0.0,0.0,0.0614,0.049,0.9791,0.7182,0.0178,0.0,0.1379,0.1667,0.2083,0.0223,0.0504,0.0547,0.0,0.0,reg oper spec account,block of flats,0.0422,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1721.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n204125,0,Cash loans,F,N,Y,0,90000.0,538704.0,23859.0,481500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-19358,-1094,-3565.0,-2776,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Medicine,,0.7432449678003322,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1130.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n312339,0,Cash loans,F,N,Y,0,180000.0,254700.0,26757.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.006207,-18550,-780,-11810.0,-2067,,1,1,0,1,0,0,Sales staff,1.0,2,2,SUNDAY,11,1,1,1,1,1,1,Trade: type 7,,0.7420245510572904,0.7850520263728172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n173478,1,Cash loans,M,N,N,0,157500.0,490536.0,38754.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.018029,-10276,-497,-9687.0,-1838,,1,1,1,1,1,0,Laborers,1.0,3,3,MONDAY,15,0,0,0,0,0,0,Industry: type 4,,0.5051679158303132,,0.1052,0.1767,0.9791,0.762,0.2368,0.0,0.3448,0.1388,0.0417,0.141,0.2135,0.1498,0.0425,0.0173,0.0189,0.068,0.9702,0.7713,0.2389,0.0,0.069,0.1667,0.0417,0.0,0.2332,0.0535,0.0428,0.0,0.0239,0.1767,0.9826,0.7652,0.2383,0.0,0.3448,0.1667,0.0417,0.1434,0.2172,0.1525,0.0427,0.0176,reg oper account,block of flats,0.3322,Panel,No,1.0,1.0,1.0,1.0,-600.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n139161,0,Cash loans,F,N,N,1,81000.0,408780.0,19183.5,337500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,With parents,0.003069,-11834,-2063,-273.0,-981,,1,1,0,1,0,0,Laborers,3.0,3,3,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.4858920750596227,0.7118318734377224,0.6610235391308081,0.1649,0.1027,0.9896,,,0.16,0.1379,0.375,,0.0552,,0.0907,,0.0014,0.1681,0.1066,0.9896,,,0.1611,0.1379,0.375,,0.0565,,0.0945,,0.0015,0.1665,0.1027,0.9896,,,0.16,0.1379,0.375,,0.0562,,0.0924,,0.0015,,block of flats,0.1376,Panel,No,4.0,0.0,3.0,0.0,-1076.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n298199,0,Cash loans,F,Y,Y,0,157500.0,254700.0,14350.5,225000.0,Family,Pensioner,Higher education,Separated,House / apartment,0.014464,-23836,365243,-13702.0,-4122,15.0,1,0,0,1,1,0,,1.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.7975303688684523,0.4938628816996244,0.0845,0.0827,0.9801,0.728,0.0,0.0,0.1379,0.1667,0.2083,0.016,0.0656,0.0772,0.0154,0.0249,0.0861,0.0858,0.9801,0.7387,0.0,0.0,0.1379,0.1667,0.2083,0.0164,0.0716,0.0805,0.0156,0.0263,0.0854,0.0827,0.9801,0.7316,0.0,0.0,0.1379,0.1667,0.2083,0.0163,0.0667,0.0786,0.0155,0.0254,reg oper account,block of flats,0.0699,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1760.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n414522,0,Cash loans,F,Y,Y,2,180000.0,1540305.0,42484.5,1206000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.007273999999999998,-12507,-2002,-3879.0,-1041,5.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,SUNDAY,10,0,0,0,0,0,0,Transport: type 3,0.5186278843088988,0.18795560654417487,,0.0124,0.0,0.9762,,,,0.069,0.0417,,,,0.0095,,,0.0126,0.0,0.9762,,,,0.069,0.0417,,,,0.0099,,,0.0125,0.0,0.9762,,,,0.069,0.0417,,,,0.0096,,,,block of flats,0.0074,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-6.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n205352,0,Cash loans,F,N,Y,0,81000.0,225000.0,13045.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-24227,365243,-6269.0,-4619,,1,0,0,1,1,0,,2.0,3,3,TUESDAY,11,0,0,0,0,0,0,XNA,,0.19524726213895907,0.11247434965561487,0.1464,0.0682,0.9856,0.8028,0.0235,0.08,0.069,0.3333,0.375,0.016,0.1185,0.0916,0.0039,0.0038,0.1492,0.0707,0.9856,0.8105,0.0237,0.0806,0.069,0.3333,0.375,0.0163,0.1295,0.0954,0.0039,0.004,0.1478,0.0682,0.9856,0.8054,0.0236,0.08,0.069,0.3333,0.375,0.0162,0.1206,0.0932,0.0039,0.0039,reg oper account,block of flats,0.0857,Panel,No,0.0,0.0,0.0,0.0,-466.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n197262,0,Revolving loans,M,N,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009175,-8867,-1414,-3510.0,-1524,,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.5402330659697788,0.17456426555726348,0.1588,0.2055,0.995,,,0.24,0.2069,0.375,,,,0.2442,,0.3933,0.1618,0.2133,0.995,,,0.2417,0.2069,0.375,,,,0.2545,,0.4164,0.1603,0.2055,0.995,,,0.24,0.2069,0.375,,,,0.2486,,0.4016,,block of flats,0.2776,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-241.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n222958,1,Cash loans,F,N,N,2,202500.0,1321020.0,35554.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-12084,-261,-4850.0,-610,,1,1,0,1,0,0,Secretaries,4.0,2,2,FRIDAY,16,0,0,0,1,1,0,Business Entity Type 3,0.5137160894991871,0.1598406822727738,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1184.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n154940,1,Cash loans,M,N,Y,2,157500.0,254700.0,17019.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.005084,-14025,-1400,-404.0,-2392,,1,1,1,1,0,0,Laborers,3.0,2,2,TUESDAY,18,0,0,0,0,1,1,Business Entity Type 3,0.21258819800422152,0.6853034568208178,0.3296550543128238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1274.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n399735,0,Revolving loans,F,Y,Y,0,135000.0,405000.0,20250.0,405000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-16568,-896,-579.0,-102,0.0,1,1,0,1,0,0,Accountants,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Restaurant,0.7287091216876713,0.6835369980057333,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-404.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n243877,0,Cash loans,F,N,Y,0,56250.0,545040.0,20677.5,450000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.015221,-21745,365243,-12118.0,-4456,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.6273055133039771,0.0720131886405828,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1471.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n183032,0,Cash loans,F,N,Y,0,112500.0,794173.5,40680.0,697500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008473999999999999,-15894,-2779,-8523.0,-5684,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.4382314839625433,0.6496203111237195,0.1474,0.0742,0.9811,0.7416,0.0979,0.0,0.069,0.1667,0.0417,0.0503,0.1202,0.0446,0.0,0.0,0.1502,0.077,0.9811,0.7517,0.0988,0.0,0.069,0.1667,0.0417,0.0514,0.1313,0.0465,0.0,0.0,0.1489,0.0742,0.9811,0.7451,0.0986,0.0,0.069,0.1667,0.0417,0.0512,0.1223,0.0454,0.0,0.0,reg oper account,,0.0886,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1948.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n151619,0,Cash loans,F,N,Y,0,112500.0,675000.0,32602.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-18874,-2923,-8153.0,-2419,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6913737526590238,0.7826078370261895,0.0082,,0.9722,,,0.0,0.0345,0.0417,,,,0.0065,,0.0,0.0084,,0.9722,,,0.0,0.0345,0.0417,,,,0.0068,,0.0,0.0083,,0.9722,,,0.0,0.0345,0.0417,,,,0.0067,,0.0,,block of flats,0.0055,Wooden,No,1.0,0.0,1.0,0.0,-1473.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n364850,0,Cash loans,M,N,Y,0,117000.0,422892.0,27531.0,382500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.011703,-10920,-160,-10910.0,-3097,,1,1,1,1,0,0,Laborers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.352966335904152,0.558221430608821,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1348.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n101539,0,Cash loans,F,N,N,0,180000.0,247500.0,12766.5,247500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00496,-14386,-4690,-8341.0,-4572,,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Kindergarten,0.2994021415060613,0.3674118191788127,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-695.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n184130,0,Revolving loans,F,Y,N,0,90000.0,180000.0,9000.0,180000.0,Other_B,Working,Secondary / secondary special,Separated,House / apartment,0.028663,-10441,-204,-223.0,-2789,64.0,1,1,0,1,0,0,Laborers,1.0,2,2,SUNDAY,15,1,1,0,1,1,0,Business Entity Type 3,,0.3657209960609989,0.7610263695502636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,2.0,-337.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n233460,0,Cash loans,M,N,N,1,135000.0,900000.0,32017.5,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-13086,-1858,-1884.0,-3533,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,12,0,0,0,1,1,0,Construction,0.07910815540352435,0.6926461184664364,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1516.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n439346,0,Cash loans,M,N,N,0,126000.0,231813.0,12703.5,193500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.0105,-10453,-1173,-4573.0,-3122,,1,1,0,1,0,0,Cooking staff,1.0,3,3,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 2,,0.0927382404289292,,0.2227,0.1745,0.9871,0.8232,0.0324,0.24,0.2069,,0.375,0.0569,0.179,0.2237,0.0116,0.0723,0.2269,0.1811,0.9871,0.8301,0.0327,0.2417,0.2069,,0.375,0.0582,0.1956,0.2331,0.0117,0.0766,0.2248,0.1745,0.9871,0.8256,0.0326,0.24,0.2069,,0.375,0.0578,0.1821,0.2278,0.0116,0.0739,org spec account,block of flats,0.2094,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-497.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n197915,0,Cash loans,F,N,N,0,900000.0,900000.0,57519.0,900000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-17180,-589,-11255.0,-620,,1,1,0,1,1,0,High skill tech staff,1.0,1,1,MONDAY,19,0,0,0,0,0,0,Bank,,0.7506760519842481,0.5460231970049609,0.1108,0.037000000000000005,0.9771,0.6872,0.0238,0.08,0.0345,0.625,0.6667,0.0,0.0908,0.1097,0.0077,0.0142,0.1124,0.0381,0.9772,0.6994,0.0241,0.0806,0.0345,0.625,0.6667,0.0,0.0992,0.1143,0.0078,0.0151,0.1119,0.037000000000000005,0.9771,0.6914,0.024,0.08,0.0345,0.625,0.6667,0.0,0.0923,0.1117,0.0078,0.0145,reg oper spec account,block of flats,0.0894,Panel,No,0.0,0.0,0.0,0.0,-1725.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n161787,0,Cash loans,F,N,Y,2,180000.0,900000.0,29740.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019689,-13979,-882,-8113.0,-2685,,1,1,0,1,0,0,,4.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.841408174183169,0.6711361244113311,0.5531646987710016,0.1144,0.0818,0.9776,0.6940000000000001,,0.0,0.2069,0.1667,0.0417,0.0593,,0.0877,,0.0294,0.1166,0.0849,0.9777,0.706,,0.0,0.2069,0.1667,0.0417,0.0607,,0.0914,,0.0311,0.1155,0.0818,0.9776,0.6981,,0.0,0.2069,0.1667,0.0417,0.0603,,0.0893,,0.03,,block of flats,0.08199999999999999,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1686.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n356930,0,Cash loans,F,N,Y,0,202500.0,716148.0,23229.0,513000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.020713,-18297,365243,-10053.0,-1844,,1,0,0,1,0,0,,1.0,3,3,TUESDAY,8,0,0,0,0,0,0,XNA,,0.1350290202215568,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-726.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n394744,0,Cash loans,M,Y,Y,2,112500.0,254700.0,16276.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-12336,-2504,-2034.0,-4696,18.0,1,1,1,1,1,0,,4.0,2,2,SUNDAY,10,0,0,0,0,1,1,Bank,,0.6851289379136728,0.7688075728291359,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1982.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n317147,0,Cash loans,F,Y,N,0,135000.0,824544.0,52695.0,720000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-18722,-1541,-2389.0,-2190,7.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,13,0,1,1,0,1,1,Business Entity Type 3,0.3923241025814461,0.4445883582435916,0.3506958432829587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1759.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n139429,0,Cash loans,F,N,Y,0,157500.0,247500.0,9018.0,247500.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018209,-21712,365243,-11459.0,-4329,,1,0,0,1,0,0,,1.0,3,3,SUNDAY,7,0,0,0,0,0,0,XNA,,0.5002096255545935,0.6626377922738201,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,4.0,1.0,-825.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n208016,0,Cash loans,F,N,Y,0,144000.0,533668.5,21163.5,477000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.01885,-22868,365243,-9647.0,-4496,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,14,0,0,0,1,0,0,XNA,0.7499303842249879,0.6537457269376291,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-767.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,1.0\n250825,0,Cash loans,M,Y,Y,0,112500.0,207306.0,10984.5,148500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003122,-16927,-5315,-608.0,-471,8.0,1,1,0,1,0,0,Managers,2.0,3,3,THURSDAY,11,0,1,1,0,1,1,Business Entity Type 3,0.4139574919052613,0.08245656157894563,0.6127042441012546,0.0619,0.0,0.9806,0.7348,0.0071,0.0,0.1379,0.1667,0.2083,0.0742,0.0504,0.0605,0.0,0.0,0.063,0.0,0.9806,0.7452,0.0072,0.0,0.1379,0.1667,0.2083,0.0759,0.0551,0.063,0.0,0.0,0.0625,0.0,0.9806,0.7383,0.0072,0.0,0.1379,0.1667,0.2083,0.0755,0.0513,0.0616,0.0,0.0,reg oper account,block of flats,0.0515,Others,No,0.0,0.0,0.0,0.0,-415.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n315500,0,Cash loans,F,N,N,0,337500.0,539100.0,27652.5,450000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.002134,-17357,-8185,-927.0,-846,,1,1,0,1,0,0,,2.0,3,3,TUESDAY,13,0,0,0,0,0,0,Security Ministries,,0.5425948461450151,0.375711009574066,0.0206,0.0235,0.9871,0.8232,,0.0,0.0345,0.1667,0.0417,,,0.0092,,0.0,0.021,0.0244,0.9871,0.8301,,0.0,0.0345,0.1667,0.0417,,,0.0096,,0.0,0.0208,0.0235,0.9871,0.8256,,0.0,0.0345,0.1667,0.0417,,,0.0094,,0.0,reg oper account,block of flats,0.0127,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-189.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n351555,0,Cash loans,M,N,Y,2,90000.0,697500.0,29682.0,697500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.010966,-12623,-1729,-2852.0,-4192,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.5669710401802045,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-513.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n332874,1,Cash loans,M,N,N,0,112500.0,352422.0,28386.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010966,-13843,-159,-5369.0,-4206,,1,1,0,1,0,1,Laborers,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,Construction,0.23363240051997225,0.6487194355301235,0.6528965519806539,0.0742,0.05,0.9856,0.8028,0.0139,0.08,0.069,0.3333,,0.061,,0.0693,,0.0108,0.0756,0.0519,0.9856,0.8105,0.0141,0.0806,0.069,0.3333,,0.0624,,0.0722,,0.0114,0.0749,0.05,0.9856,0.8054,0.013999999999999999,0.08,0.069,0.3333,,0.0621,,0.0706,,0.011000000000000001,org spec account,block of flats,0.0621,Panel,No,0.0,0.0,0.0,0.0,-281.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n236436,0,Cash loans,F,N,Y,0,72000.0,45000.0,4450.5,45000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.019101,-24628,-4992,-2636.0,-3986,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Construction,0.8485966458087245,0.5514106444147755,0.7281412993111438,,,0.9866,,0.0,0.08,0.0345,0.4583,0.0417,,0.0588,0.0076,,0.0231,,,0.9866,,0.0,0.0806,0.0345,0.4583,0.0417,,0.0643,0.0079,,0.0244,,,0.9866,,0.0,0.08,0.0345,0.4583,0.0417,,0.0599,0.0077,,0.0236,,,0.0645,,No,0.0,0.0,0.0,0.0,-1466.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n311203,0,Cash loans,F,N,N,0,90000.0,521280.0,24286.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-14645,-553,-918.0,-2862,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Trade: type 3,,0.3789458665724961,0.7352209993926424,0.0619,0.0536,0.9791,,,0.0,0.1379,0.1667,,0.0156,,0.0527,,0.0,0.063,0.0556,0.9791,,,0.0,0.1379,0.1667,,0.016,,0.0549,,0.0,0.0625,0.0536,0.9791,,,0.0,0.1379,0.1667,,0.0159,,0.0536,,0.0,reg oper account,block of flats,0.0559,Panel,No,0.0,0.0,0.0,0.0,-451.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n191755,0,Cash loans,F,Y,Y,1,157500.0,1223010.0,48631.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-15877,-1478,-1100.0,-4075,10.0,1,1,0,1,0,0,Managers,3.0,3,3,SATURDAY,7,0,0,0,0,1,1,Trade: type 7,,0.5227612283477409,0.6594055320683344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-484.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n433788,0,Cash loans,F,Y,Y,0,121500.0,1086426.0,46030.5,931500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17750,-2112,-4410.0,-1278,11.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Self-employed,,0.5070701082681047,0.622922000268356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,0.0,-354.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n245019,0,Cash loans,F,N,Y,0,144000.0,122521.5,9612.0,99000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020246,-15497,-4839,-8322.0,-4670,,1,1,1,1,1,0,Laborers,1.0,3,3,THURSDAY,5,0,0,0,0,0,0,Business Entity Type 3,0.4521445778907629,0.07231097535242999,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1591.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n351806,0,Revolving loans,F,N,Y,0,225000.0,382500.0,19125.0,382500.0,Unaccompanied,Working,Secondary / secondary special,Married,Co-op apartment,0.015221,-15119,-2275,-4011.0,-4830,,1,1,0,1,1,0,Private service staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 1,,0.7554459391154043,0.324891229465852,0.1629,0.0,,,,0.0,0.069,0.1667,,0.0,,0.0358,,0.0,0.166,0.0,,,,0.0,0.069,0.1667,,0.0,,0.0373,,0.0,0.1645,0.0,,,,0.0,0.069,0.1667,,0.0,,0.0365,,0.0,,block of flats,0.0327,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1591.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n426511,1,Cash loans,M,N,Y,0,144000.0,521280.0,35392.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019101,-17032,-2443,-2706.0,-558,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Industry: type 2,,0.329581914442152,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-33.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n319456,1,Revolving loans,F,N,N,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-7860,-582,-4882.0,-479,,1,1,1,1,1,0,,2.0,2,2,THURSDAY,16,0,0,0,1,1,1,Business Entity Type 2,0.15824364088878354,0.0070638633219244126,0.3506958432829587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,1.0,-709.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n292708,0,Cash loans,F,Y,Y,0,157500.0,360000.0,19543.5,360000.0,Family,Working,Incomplete higher,Married,House / apartment,0.015221,-9520,-1106,-513.0,-1959,11.0,1,1,1,1,0,0,Core staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Trade: type 2,,0.4419710410780596,0.3185955240537633,0.1216,0.1174,0.9791,0.7144,0.0529,0.0,0.2759,0.1667,0.2083,0.1277,0.0992,0.1147,0.0,0.0,0.1239,0.1218,0.9791,0.7256,0.0534,0.0,0.2759,0.1667,0.2083,0.1306,0.1084,0.1195,0.0,0.0,0.1228,0.1174,0.9791,0.7182,0.0533,0.0,0.2759,0.1667,0.2083,0.1299,0.1009,0.1168,0.0,0.0,reg oper account,block of flats,0.1035,Panel,No,10.0,0.0,10.0,0.0,-1092.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n208997,0,Cash loans,F,N,N,0,81000.0,203760.0,10480.5,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.01885,-24565,365243,-15520.0,-4229,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.7172111886120683,0.28812959991785075,,,0.9662,,,,,,,,,,,,,,0.9662,,,,,,,,,,,,,,0.9662,,,,,,,,,,,,,block of flats,0.0016,,No,0.0,0.0,0.0,0.0,-526.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n190621,0,Cash loans,F,N,Y,1,157500.0,675000.0,49117.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-11934,-290,-2782.0,-4007,,1,1,0,1,0,0,,3.0,2,2,FRIDAY,8,0,0,0,1,1,0,Business Entity Type 3,0.369094099352186,0.3015825461422213,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1520.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n387123,0,Cash loans,F,N,Y,0,69750.0,234000.0,13563.0,234000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.031329,-20509,365243,-8800.0,-4015,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.19040757926007795,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n305248,0,Cash loans,F,N,Y,0,117000.0,327024.0,23827.5,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-19131,-9614,-3379.0,-2607,,1,1,0,1,1,0,Laborers,2.0,3,3,TUESDAY,13,0,0,0,0,0,0,Self-employed,,0.4571918125784838,0.7165702448010511,0.2515,0.1754,0.9836,,,,0.1724,0.1667,,,,0.111,,0.2133,0.2563,0.182,0.9836,,,,0.1724,0.1667,,,,0.1156,,0.2259,0.254,0.1754,0.9836,,,,0.1724,0.1667,,,,0.113,,0.2178,,block of flats,0.1569,Mixed,No,5.0,0.0,5.0,0.0,-1018.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n437351,0,Cash loans,F,Y,Y,1,157500.0,1575000.0,41679.0,1575000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.0228,-16652,-2638,-2587.0,-194,8.0,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,14,0,0,0,0,0,0,School,0.8025392102983152,0.6697736327358679,0.24318648044201235,0.0881,0.1194,0.9940000000000001,0.9184,0.0207,0.0968,0.0738,0.3808,0.4225,0.0055,0.0693,0.1209,0.0116,0.0321,0.0504,0.0347,0.993,0.9085,0.0045,0.1208,0.1034,0.3333,0.375,0.0043,0.0404,0.0449,0.0039,0.0036,0.0802,0.0784,0.993,0.9061,0.0191,0.12,0.069,0.375,0.4167,0.0052,0.0641,0.1214,0.0078,0.0327,reg oper account,block of flats,0.1769,Panel,No,0.0,0.0,0.0,0.0,-1273.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n401329,0,Cash loans,F,N,Y,1,112500.0,76410.0,8154.0,67500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.003813,-15012,-6027,-4639.0,-4103,,1,1,0,1,0,0,Core staff,3.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,Kindergarten,,0.6349366144236278,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1400.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n418227,1,Cash loans,M,Y,Y,1,200250.0,157500.0,12442.5,157500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018634,-13257,-220,-2886.0,-491,20.0,1,1,1,1,1,0,Drivers,2.0,2,2,THURSDAY,10,0,1,1,0,1,1,Business Entity Type 3,0.1930462414435156,0.5549455355015832,0.2225807646753351,0.1485,,0.9826,0.762,,0.16,0.1379,0.3333,0.0417,0.0964,0.121,0.1542,0.0,,0.1513,,0.9826,0.7713,,0.1611,0.1379,0.3333,0.0417,0.0986,0.1322,0.1606,0.0,,0.1499,,0.9826,0.7652,,0.16,0.1379,0.3333,0.0417,0.0981,0.1231,0.1569,0.0,,reg oper account,block of flats,0.1213,Panel,No,0.0,0.0,0.0,0.0,-2045.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n414106,0,Cash loans,M,N,N,0,135000.0,355536.0,13306.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-18123,-4628,-34.0,-1681,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,16,0,1,1,0,0,0,Transport: type 2,,0.541628641871568,0.6738300778602003,0.066,0.0567,0.9771,0.6872,0.0354,0.0,0.1379,0.1667,0.2083,0.0382,0.0513,0.0509,0.0116,0.022000000000000002,0.0672,0.0589,0.9772,0.6994,0.0357,0.0,0.1379,0.1667,0.2083,0.039,0.055999999999999994,0.053,0.0117,0.0233,0.0666,0.0567,0.9771,0.6914,0.0356,0.0,0.1379,0.1667,0.2083,0.0388,0.0522,0.0518,0.0116,0.0224,reg oper account,block of flats,0.0642,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1679.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n316736,0,Cash loans,F,Y,Y,0,112500.0,485640.0,41679.0,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018634,-10063,-244,-1262.0,-1713,3.0,1,1,0,1,1,0,Core staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Trade: type 3,0.4950815800535465,0.5955756556247008,0.7570690154522959,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-630.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n448878,0,Cash loans,F,N,Y,0,252000.0,675000.0,26901.0,675000.0,Family,Pensioner,Higher education,Married,House / apartment,0.018634,-19307,365243,-3623.0,-2743,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,0.7512509080988697,0.691406413836263,0.6940926425266661,0.0495,0.0667,0.9742,0.6464,0.0485,0.0,0.1034,0.1667,0.2083,0.0257,0.0403,0.0395,,0.0246,0.0504,0.0692,0.9742,0.6602,0.049,0.0,0.1034,0.1667,0.2083,0.0263,0.0441,0.0412,,0.0261,0.05,0.0667,0.9742,0.6511,0.0488,0.0,0.1034,0.1667,0.2083,0.0262,0.040999999999999995,0.0402,,0.0252,reg oper account,block of flats,0.063,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2017.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n156363,0,Cash loans,M,N,N,0,315000.0,1258650.0,51264.0,1125000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-11073,-193,-1942.0,-3696,,1,1,1,1,1,0,Managers,2.0,1,1,THURSDAY,15,0,0,0,0,0,0,Insurance,0.5154294963479573,0.6834849627613659,0.22383131747015547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-640.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,6.0\n394307,0,Cash loans,M,N,N,0,180000.0,531733.5,38691.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-11108,-2152,-1099.0,-3243,,1,1,1,1,1,0,Laborers,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Construction,,0.581618440684176,,0.0825,0.0661,0.9742,0.6464,0.0288,0.0,0.1379,0.1667,0.2083,0.0639,0.0672,0.0706,0.0,0.0,0.084,0.0686,0.9742,0.6602,0.028999999999999998,0.0,0.1379,0.1667,0.2083,0.0654,0.0735,0.0735,0.0,0.0,0.0833,0.0661,0.9742,0.6511,0.028999999999999998,0.0,0.1379,0.1667,0.2083,0.065,0.0684,0.0719,0.0,0.0,,block of flats,0.0713,Panel,No,3.0,0.0,3.0,0.0,-432.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n177539,0,Cash loans,M,Y,N,0,315000.0,601470.0,32760.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014464,-10554,-2127,-52.0,-2708,21.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,5,0,0,0,1,1,0,Business Entity Type 3,,0.3657412867172147,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-849.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n177189,0,Cash loans,F,Y,Y,0,751500.0,2156400.0,59431.5,1800000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-18897,-3351,-11881.0,-2338,8.0,1,1,0,1,0,0,Accountants,2.0,1,1,THURSDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.739370173770864,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-695.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n423033,0,Cash loans,F,N,N,0,180000.0,760225.5,34483.5,679500.0,Unaccompanied,Working,Higher education,Married,Office apartment,0.028663,-18225,-2979,-6154.0,-541,,1,1,1,1,0,0,Core staff,2.0,2,2,FRIDAY,20,0,0,0,1,1,0,Self-employed,0.5646034498126771,0.5900543958764853,0.25670557243930026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-276.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n241810,0,Cash loans,F,N,N,1,270000.0,878733.0,25821.0,733500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-12175,-1955,-3846.0,-1383,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.5199891281119983,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1717.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n227065,0,Cash loans,F,N,Y,0,157500.0,1024636.5,33862.5,918000.0,Family,Working,Higher education,Married,House / apartment,0.006852,-15136,-1067,-732.0,-4544,,1,1,0,1,0,0,Laborers,2.0,3,3,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.4814947089216011,0.2833243943705199,0.8016009030071296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-210.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n203681,0,Revolving loans,F,N,Y,1,135000.0,180000.0,9000.0,180000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.020713,-9929,-1646,-423.0,-2605,,1,1,0,1,0,0,,3.0,3,3,FRIDAY,9,0,0,0,0,0,0,Kindergarten,0.6624451323464176,0.5135018323787599,0.10822632266971416,0.3361,0.0,0.9821,0.7552,0.0739,0.08,0.069,0.3333,0.375,0.0406,0.2681,0.1226,0.027000000000000003,0.0091,0.3424,0.0,0.9821,0.7648,0.0746,0.0806,0.069,0.3333,0.375,0.0415,0.2929,0.1277,0.0272,0.0097,0.3393,0.0,0.9821,0.7585,0.0744,0.08,0.069,0.3333,0.375,0.0413,0.2728,0.1248,0.0272,0.0093,reg oper spec account,specific housing,0.1393,Panel,No,0.0,0.0,0.0,0.0,-434.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n156210,0,Cash loans,M,Y,Y,2,171000.0,139500.0,9045.0,139500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-11579,-1564,-567.0,-3233,2.0,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.6643099085414216,0.8357765157975799,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1727.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n414866,0,Cash loans,M,N,Y,0,67500.0,101880.0,11101.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-17362,-373,-9058.0,-907,,1,1,1,1,0,1,Laborers,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Police,0.3881006428304232,0.6343687289283418,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-266.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n275911,0,Cash loans,F,N,Y,0,225000.0,1285816.5,41067.0,1152000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.072508,-20683,-1730,-11162.0,-3449,,1,1,0,1,1,0,Sales staff,2.0,1,1,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 1,,0.7234358083275407,,0.0356,0.0508,0.9722,0.6192,0.0486,0.0,0.1034,0.1667,0.2083,0.0579,0.028999999999999998,0.0352,0.0,0.0252,0.0305,0.0132,0.9722,0.6341,0.046,0.0,0.069,0.1667,0.2083,0.0529,0.0266,0.0205,0.0,0.0052,0.0359,0.0508,0.9722,0.6243,0.0489,0.0,0.1034,0.1667,0.2083,0.0589,0.0295,0.0358,0.0,0.0257,reg oper account,block of flats,0.0244,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-26.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n298372,0,Cash loans,M,Y,Y,0,238500.0,1007352.0,36180.0,765000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.006670999999999999,-22731,-8142,-10151.0,-4208,26.0,1,1,0,1,1,0,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,1,1,Construction,,0.6430829507529855,0.6006575372857061,0.0093,,0.9831,,,0.0,0.1034,0.0833,,0.0,,0.0056,,0.0147,0.0095,,0.9831,,,0.0,0.1034,0.0833,,0.0,,0.0058,,0.0155,0.0094,,0.9831,,,0.0,0.1034,0.0833,,0.0,,0.0057,,0.015,,block of flats,0.0076,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-198.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n417504,0,Cash loans,F,Y,Y,0,202500.0,1125000.0,33025.5,1125000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-13116,-2583,-2863.0,-2213,9.0,1,1,0,1,1,0,,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3404657102443006,0.7313961742331475,0.34741822720026416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,0.0,-2545.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n234637,0,Cash loans,F,N,Y,0,216000.0,941472.0,40018.5,841500.0,Children,Working,Secondary / secondary special,Single / not married,House / apartment,0.018634,-20410,-3222,-169.0,-1358,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5507344125079706,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1867.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n423662,0,Cash loans,M,N,Y,1,157500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-14719,-1952,-2077.0,-4898,,1,1,0,1,0,0,Drivers,3.0,2,2,SUNDAY,12,0,0,0,0,1,1,Business Entity Type 1,0.3422364735591757,0.6157063738514678,0.392773860603134,0.0948,0.1033,0.9851,0.7959999999999999,0.0128,0.0,0.2069,0.1667,0.2083,0.1094,0.0756,0.0873,0.0077,0.0163,0.0966,0.1072,0.9851,0.804,0.013000000000000001,0.0,0.2069,0.1667,0.2083,0.1119,0.0826,0.091,0.0078,0.0173,0.0958,0.1033,0.9851,0.7987,0.0129,0.0,0.2069,0.1667,0.2083,0.1113,0.077,0.0889,0.0078,0.0166,not specified,block of flats,0.0792,Panel,No,3.0,1.0,3.0,1.0,-1305.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n399470,0,Cash loans,F,Y,Y,0,180000.0,808650.0,31464.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-10457,-373,-719.0,-3114,64.0,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,0.4661749241871809,0.3714514058545122,0.7062051096536562,0.0722,0.077,0.9811,0.7416,0.0296,0.0,0.1379,0.1667,0.2083,0.0579,0.0588,0.062,0.0,0.0,0.0735,0.0799,0.9811,0.7517,0.0299,0.0,0.1379,0.1667,0.2083,0.0593,0.0643,0.0646,0.0,0.0,0.0729,0.077,0.9811,0.7451,0.0298,0.0,0.1379,0.1667,0.2083,0.0589,0.0599,0.0631,0.0,0.0,reg oper account,block of flats,0.065,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1011.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n243337,0,Cash loans,M,N,N,0,157500.0,384048.0,14607.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.005144,-15515,-713,-8120.0,-4327,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Self-employed,0.4684369616019046,0.6321646531878883,0.7544061731797895,0.066,0.0651,0.9742,0.6464,,0.0,0.1379,0.125,,0.0,,0.0494,,0.0,0.0672,0.0675,0.9742,0.6602,,0.0,0.1379,0.125,,0.0,,0.0515,,0.0,0.0666,0.0651,0.9742,0.6511,,0.0,0.1379,0.125,,0.0,,0.0503,,0.0,,block of flats,0.0504,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1643.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n298811,0,Cash loans,M,Y,Y,0,99000.0,640080.0,31261.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008068,-12623,-2041,-717.0,-2071,40.0,1,1,1,1,0,0,Low-skill Laborers,2.0,3,3,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4132009266687551,0.6313545365850379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-548.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n169253,0,Cash loans,F,N,Y,0,157500.0,740700.0,26734.5,562500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20582,365243,-9303.0,-3463,,1,0,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.7206313528689867,0.8256357449717892,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-297.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n176193,0,Cash loans,F,N,N,0,270000.0,1546020.0,49873.5,1350000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-14642,-1491,-4067.0,-2492,,1,1,1,1,0,1,Core staff,2.0,1,1,SUNDAY,14,0,0,0,0,1,1,Restaurant,0.5592309875170857,0.6750624112540059,0.248535557339522,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-778.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n214182,0,Cash loans,M,Y,Y,1,135000.0,274500.0,14499.0,274500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-13728,-581,-1152.0,-4105,11.0,1,1,0,1,0,0,Drivers,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Transport: type 3,0.4595072875422382,0.6972032651108929,0.7958031328748403,0.1095,0.1061,0.9826,,,0.0,0.1897,0.1667,,,,0.0875,,0.0084,0.0735,0.1296,0.9831,,,0.0,0.2759,0.1667,,,,0.0676,,0.0,0.1218,0.1069,0.9831,,,0.0,0.2069,0.1667,,,,0.0895,,0.0,,block of flats,0.0586,Panel,No,0.0,0.0,0.0,0.0,-1626.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n360564,0,Cash loans,F,N,N,0,157500.0,526491.0,32206.5,454500.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.018029,-12279,-777,-6421.0,-187,,1,1,0,1,1,0,,2.0,3,3,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.5361462913572967,0.4167950306125768,0.4974688893052743,0.0825,0.0803,0.9776,0.6940000000000001,0.0642,0.0,0.1379,0.1667,0.2083,0.0739,0.0672,0.0765,0.0,0.0,0.084,0.0833,0.9777,0.706,0.0648,0.0,0.1379,0.1667,0.2083,0.0756,0.0735,0.0797,0.0,0.0,0.0833,0.0803,0.9776,0.6981,0.0646,0.0,0.1379,0.1667,0.2083,0.0752,0.0684,0.0779,0.0,0.0,reg oper account,block of flats,0.0953,Panel,No,0.0,0.0,0.0,0.0,-437.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n351590,1,Cash loans,F,N,N,0,112500.0,781920.0,23836.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.008019,-21844,365243,-4708.0,-4716,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.39026451379202254,0.1008037063175332,0.0165,,0.0,,,0.0,0.069,0.0417,,,,,,,0.0168,,0.0005,,,0.0,0.069,0.0417,,,,,,,0.0167,,0.0,,,0.0,0.069,0.0417,,,,,,,,block of flats,0.0129,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n401387,0,Cash loans,M,Y,Y,0,99000.0,640080.0,19534.5,450000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.028663,-22936,365243,-6632.0,-4270,6.0,1,0,0,1,1,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.6035765547723474,0.7047064232963289,0.0665,0.0,0.9742,0.6464,0.005,0.0,0.1379,0.125,0.1667,0.0283,0.05,0.0475,0.0193,0.0259,0.0672,0.0,0.9742,0.6602,0.0049,0.0,0.1379,0.125,0.1667,0.0141,0.0523,0.0466,0.0078,0.0131,0.0671,0.0,0.9742,0.6511,0.0051,0.0,0.1379,0.125,0.1667,0.0288,0.0509,0.0484,0.0194,0.0264,reg oper account,block of flats,0.0451,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-705.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n264151,0,Cash loans,F,Y,Y,1,337500.0,540000.0,33165.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-10306,-3712,-4038.0,-2890,64.0,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.2318881971357951,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1138.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n202925,0,Cash loans,F,N,Y,0,202500.0,179865.0,11133.0,148500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.00702,-20661,365243,-7811.0,-4209,,1,0,0,1,1,0,,1.0,2,2,MONDAY,17,0,0,0,0,0,0,XNA,,0.7377191600338235,0.7776594425716818,0.1309,0.1066,0.993,0.9388,,0.22,0.1897,0.3333,,0.1104,0.1059,0.1479,0.0039,,0.1334,0.1106,0.9901,0.9412,,0.2014,0.1724,0.3333,,0.113,0.1157,0.1439,0.0039,,0.1322,0.1066,0.993,0.9396,,0.22,0.1897,0.3333,,0.1124,0.1077,0.1525,0.0039,,reg oper account,block of flats,0.1567,Panel,No,0.0,0.0,0.0,0.0,-1735.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n235650,0,Cash loans,M,Y,N,1,243000.0,540000.0,27571.5,540000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006305,-13430,-1676,-3424.0,-4478,7.0,1,1,1,1,0,0,,3.0,3,3,WEDNESDAY,15,0,0,0,0,0,0,Other,0.2099149533795979,0.3422197814173928,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-2136.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n418894,0,Revolving loans,F,N,N,1,90000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.0228,-10954,-3485,-4635.0,-2845,,1,1,1,1,1,0,Sales staff,3.0,2,2,TUESDAY,17,0,0,0,0,0,0,Self-employed,,0.6013978810549383,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,1.0,0.0,1.0,0.0,-1053.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n340127,0,Cash loans,F,N,Y,0,54000.0,270000.0,13914.0,270000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,With parents,0.018209,-8627,-124,-967.0,-1317,,1,1,0,1,0,0,Core staff,2.0,3,3,THURSDAY,15,0,0,0,0,0,0,Bank,0.18079891686382202,0.4016973105978592,,0.1031,0.1065,0.9791,0.7144,0.0862,0.0,0.2069,0.1667,0.2083,0.0761,0.0841,0.0938,0.0,0.0,0.105,0.1106,0.9791,0.7256,0.0869,0.0,0.2069,0.1667,0.2083,0.0778,0.0918,0.0978,0.0,0.0,0.1041,0.1065,0.9791,0.7182,0.0867,0.0,0.2069,0.1667,0.2083,0.0774,0.0855,0.0955,0.0,0.0,reg oper account,block of flats,0.0738,Panel,No,0.0,0.0,0.0,0.0,-565.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n332602,0,Cash loans,M,N,Y,0,247500.0,437877.0,34515.0,378000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.032561,-14202,-4466,-8304.0,-4221,,1,1,0,1,0,0,High skill tech staff,1.0,1,1,MONDAY,18,0,0,0,0,0,0,Business Entity Type 1,,0.7105877593732496,0.6092756673894402,0.2969,0.0991,0.9757,,0.0684,0.16,0.1379,0.3333,0.375,0.0721,0.2412,0.1652,0.0039,0.1721,0.3025,0.1028,0.9757,,0.0691,0.1611,0.1379,0.3333,0.375,0.0737,0.2635,0.1721,0.0039,0.1822,0.2998,0.0991,0.9757,,0.0689,0.16,0.1379,0.3333,0.375,0.0733,0.2454,0.1682,0.0039,0.1757,reg oper spec account,block of flats,0.1674,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-564.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n101617,1,Cash loans,F,N,Y,1,112500.0,107820.0,8775.0,90000.0,Other_B,Commercial associate,Secondary / secondary special,Civil marriage,With parents,0.020713,-11238,-382,-214.0,-3708,,1,1,0,1,0,0,Sales staff,3.0,3,3,SATURDAY,13,0,0,0,0,0,0,Trade: type 2,0.19002399738072684,0.2653117484731741,,0.3608,0.2806,0.9876,0.83,0.0985,0.4,0.3448,0.3333,0.375,0.1822,0.2942,0.4276,0.0,0.0234,0.3676,0.2912,0.9876,0.8367,0.0994,0.4028,0.3448,0.3333,0.375,0.1864,0.3214,0.4455,0.0,0.0248,0.3643,0.2806,0.9876,0.8323,0.0992,0.4,0.3448,0.3333,0.375,0.1854,0.2993,0.4353,0.0,0.0239,reg oper account,block of flats,0.3953,Panel,No,0.0,0.0,0.0,0.0,-1062.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n283516,0,Revolving loans,M,N,Y,1,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-9765,-1445,-4324.0,-2361,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,13,0,0,0,0,1,1,Transport: type 4,,0.4664854727365485,0.6178261467332483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-165.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n327251,0,Cash loans,F,Y,Y,0,337500.0,487192.5,23566.5,364500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.020713,-21383,365243,-14843.0,-4934,7.0,1,0,0,1,0,0,,1.0,3,2,MONDAY,8,0,0,0,0,0,0,XNA,0.6917595140860457,0.3340520511913895,0.5226973172821112,0.0082,0.0,0.9727,0.626,0.001,0.0,0.0345,0.0417,0.0833,0.0869,0.0067,0.0081,,0.0,0.0084,0.0,0.9727,0.6406,0.001,0.0,0.0345,0.0417,0.0833,0.0888,0.0073,0.0084,,0.0,0.0083,0.0,0.9727,0.631,0.001,0.0,0.0345,0.0417,0.0833,0.0884,0.0068,0.0082,,0.0,reg oper account,block of flats,0.0063,Wooden,Yes,1.0,0.0,1.0,0.0,-619.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n212614,0,Cash loans,M,Y,Y,0,292500.0,521280.0,28408.5,450000.0,Unaccompanied,State servant,Higher education,Single / not married,With parents,0.00496,-11531,-3597,-5696.0,-35,10.0,1,1,1,1,0,0,Core staff,1.0,2,2,TUESDAY,16,0,1,1,0,1,1,Police,0.21859425202932709,0.2858978721410488,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-345.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n388225,0,Revolving loans,M,N,N,0,112500.0,180000.0,9000.0,,,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010147,-9584,-1477,-9393.0,-2247,,1,1,1,1,0,0,Laborers,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Self-employed,0.17882780575101825,0.7223143603969757,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1742.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n209783,0,Cash loans,M,N,N,0,67500.0,454500.0,14661.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.026392,-19652,-2152,-11806.0,-3170,,1,1,1,1,1,0,,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Security,,0.5898110543695066,0.05097933339282625,0.0825,,0.9767,,,0.0,0.1379,0.1667,,,,0.0637,,0.0,0.084,,0.9767,,,0.0,0.1379,0.1667,,,,0.0664,,0.0,0.0833,,0.9767,,,0.0,0.1379,0.1667,,,,0.0648,,0.0,,block of flats,0.0501,\"Stone, brick\",No,8.0,0.0,8.0,0.0,-1043.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n208244,0,Cash loans,M,N,Y,1,54000.0,900000.0,26446.5,900000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.006296,-13327,-2718,-1014.0,-4169,,1,1,0,1,0,0,,3.0,3,3,TUESDAY,14,0,0,0,0,0,0,University,,0.6069411912356742,0.7194907850918436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n185960,0,Revolving loans,M,N,Y,0,270000.0,382500.0,19125.0,382500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.032561,-10729,-916,-5196.0,-3395,,1,1,0,1,1,0,Laborers,2.0,1,1,FRIDAY,17,0,0,0,0,0,0,Self-employed,0.17954416057109548,0.612036586835116,0.08616166238092926,0.1304,0.0897,0.9771,,,0.0,0.2966,0.1667,,,,0.075,,0.0057,0.1408,0.0931,0.9772,,,0.0,0.3103,0.1667,,,,0.0448,,0.0046,0.139,0.0897,0.9776,,,0.0,0.3103,0.1667,,,,0.0832,,0.0057,,block of flats,0.0593,Panel,No,1.0,0.0,1.0,0.0,-1681.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,2.0,0.0,0.0,3.0\n268037,0,Cash loans,F,N,Y,2,135000.0,199008.0,20893.5,180000.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.04622,-14368,-1675,-225.0,-223,,1,1,1,1,0,0,Sales staff,4.0,1,1,FRIDAY,17,0,0,0,0,0,0,Trade: type 7,,0.6785923435237022,0.4382813743111921,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-2515.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n448517,0,Cash loans,F,N,Y,2,99000.0,170640.0,11533.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-12622,-2172,-243.0,-4745,,1,1,1,1,1,0,Core staff,4.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Agriculture,0.32378421295318016,0.2880321818156152,0.3962195720630885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,6.0,0.0,-531.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n262156,0,Cash loans,F,Y,Y,0,157500.0,526491.0,29529.0,454500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.019101,-15095,-7697,-1128.0,-4143,5.0,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Government,,0.4243726736986547,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2305.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,0.0\n197527,0,Cash loans,F,Y,Y,0,157500.0,332473.5,19215.0,274500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-16873,-2078,-4014.0,-423,2.0,1,1,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Agriculture,0.5342266245513031,0.5430941075053155,0.3723336657058204,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-296.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n250610,0,Cash loans,M,N,N,0,450000.0,432990.0,34339.5,382500.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.04622,-19589,-712,-5215.0,-3083,,1,1,0,1,0,0,Laborers,2.0,1,1,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.7478702373741766,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-285.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n174543,0,Cash loans,F,N,Y,0,315000.0,1258650.0,53455.5,1125000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.011703,-21514,365243,-13231.0,-3118,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,0.3645219704062089,0.7406197624863837,0.6690566947824041,0.2237,0.1715,0.9811,0.7416,0.1033,0.24,0.2069,0.3333,0.375,0.1913,0.1816,0.2323,0.0039,0.0008,0.2279,0.1779,0.9811,0.7517,0.1042,0.2417,0.2069,0.3333,0.375,0.1956,0.1983,0.242,0.0039,0.0008,0.2259,0.1715,0.9811,0.7451,0.1039,0.24,0.2069,0.3333,0.375,0.1946,0.1847,0.2365,0.0039,0.0008,reg oper account,block of flats,0.2099,Panel,No,0.0,0.0,0.0,0.0,-2587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n413854,0,Revolving loans,F,N,Y,0,225000.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010006,-18540,-10437,-7619.0,-2078,,1,0,0,1,0,0,High skill tech staff,2.0,2,1,FRIDAY,10,0,0,0,0,0,0,Industry: type 9,0.8381002539601222,0.594764255417682,0.7801436381572275,0.1082,0.1497,0.9891,0.8504,0.1349,0.12,0.2069,0.25,0.2083,0.1193,0.0874,0.1584,0.0039,0.0541,0.1103,0.1554,0.9891,0.8563,0.1362,0.1208,0.2069,0.25,0.2083,0.1221,0.0955,0.1651,0.0039,0.0573,0.1093,0.1497,0.9891,0.8524,0.1358,0.12,0.2069,0.25,0.2083,0.1214,0.0889,0.1613,0.0039,0.0553,reg oper account,block of flats,0.2181,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-320.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n188855,0,Cash loans,M,Y,Y,3,99000.0,420718.5,17955.0,319500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-16512,365243,-5968.0,-74,13.0,1,0,0,1,0,0,,5.0,3,3,TUESDAY,8,0,0,0,0,0,0,XNA,,0.27129596643695464,0.3672910183026313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1886.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n334705,0,Cash loans,M,Y,Y,0,135000.0,246357.0,24993.0,234000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-20554,-143,-1122.0,-3900,11.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Self-employed,,0.7373085316033855,0.8128226070575616,0.2474,0.1555,0.9876,0.83,0.0657,0.24,0.2069,0.375,0.4167,0.1531,0.2017,0.218,0.0,0.1653,0.2521,0.1614,0.9876,0.8367,0.0663,0.2417,0.2069,0.375,0.4167,0.1566,0.2204,0.2271,0.0,0.175,0.2498,0.1555,0.9876,0.8323,0.0661,0.24,0.2069,0.375,0.4167,0.1558,0.2052,0.2219,0.0,0.1687,reg oper account,block of flats,0.2074,Panel,No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n394978,1,Cash loans,F,N,Y,1,135000.0,373500.0,23998.5,373500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-15166,-2497,-9252.0,-5159,,1,1,0,0,1,0,Sales staff,3.0,3,3,TUESDAY,9,0,0,0,0,0,0,Self-employed,,0.2298573783492356,0.2580842039460289,0.1237,0.1297,0.9776,0.6940000000000001,0.0191,0.0,0.2759,0.1667,0.0417,0.1105,0.1,0.1141,0.0039,0.001,0.1261,0.1346,0.9777,0.706,0.0192,0.0,0.2759,0.1667,0.0417,0.113,0.1093,0.1189,0.0039,0.0011,0.1249,0.1297,0.9776,0.6981,0.0192,0.0,0.2759,0.1667,0.0417,0.1124,0.1018,0.1162,0.0039,0.001,reg oper account,block of flats,0.1004,Panel,No,2.0,2.0,2.0,2.0,-1953.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n129870,0,Cash loans,F,N,Y,0,441000.0,900000.0,45954.0,900000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.005002,-16206,-9365,-8891.0,-4538,,1,1,1,1,1,0,Core staff,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,Transport: type 2,0.4796345987666508,0.7550578932420546,0.3233112448967859,0.0186,0.0502,0.9707,0.5988,0.0413,0.0,0.1034,0.0417,0.0833,0.015,0.0151,0.0177,0.0,0.0041,0.0189,0.0521,0.9707,0.6145,0.0417,0.0,0.1034,0.0417,0.0833,0.0153,0.0165,0.0185,0.0,0.0043,0.0187,0.0502,0.9707,0.6042,0.0416,0.0,0.1034,0.0417,0.0833,0.0152,0.0154,0.0181,0.0,0.0042,org spec account,block of flats,0.0226,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2475.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n154512,0,Cash loans,F,N,Y,0,166500.0,239850.0,23494.5,225000.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.004849,-24534,365243,-2000.0,-3955,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,,0.2765322061176028,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-998.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n148920,0,Cash loans,F,N,Y,1,157500.0,780363.0,31077.0,697500.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.031329,-14572,-1599,-2380.0,-4921,,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6521798711153507,0.08904388274986882,0.1918,0.0161,0.9811,0.7416,0.0355,0.0,0.069,0.1667,0.2083,0.0518,0.1429,0.0518,0.0618,0.0146,0.1954,0.0167,0.9811,0.7517,0.0358,0.0,0.069,0.1667,0.2083,0.053,0.1561,0.054000000000000006,0.0623,0.0154,0.1936,0.0161,0.9811,0.7451,0.0357,0.0,0.069,0.1667,0.2083,0.0527,0.1454,0.0527,0.0621,0.0149,reg oper account,block of flats,0.0439,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2758.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n370429,0,Cash loans,F,N,Y,0,58500.0,162000.0,12109.5,162000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-23513,365243,-2239.0,-5040,,1,0,0,1,0,1,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.5796802665181847,0.7238369900414456,0.1289,0.0524,0.9811,0.7416,0.015,0.0,0.2759,0.1667,0.0417,0.11,0.1051,0.1178,0.0154,0.034,0.1313,0.0544,0.9811,0.7517,0.0152,0.0,0.2759,0.1667,0.0417,0.1125,0.1148,0.1228,0.0156,0.036000000000000004,0.1301,0.0524,0.9811,0.7451,0.0151,0.0,0.2759,0.1667,0.0417,0.1119,0.1069,0.12,0.0155,0.0347,reg oper spec account,block of flats,0.1093,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-207.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n349260,0,Cash loans,M,Y,Y,0,180000.0,1800000.0,47484.0,1800000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-23614,365243,-3250.0,-4610,11.0,1,0,0,1,1,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.6734678795950501,0.6706517530862718,0.1773,0.0685,0.9955,0.9388,0.3183,0.16,0.1379,0.375,0.0417,0.0254,0.1412,0.1715,0.0154,0.0374,0.1807,0.0711,0.9955,0.9412,0.3212,0.1611,0.1379,0.375,0.0417,0.026000000000000002,0.1543,0.1787,0.0156,0.0395,0.179,0.0685,0.9955,0.9396,0.3204,0.16,0.1379,0.375,0.0417,0.0259,0.1437,0.1746,0.0155,0.0381,reg oper account,block of flats,0.174,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-438.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n156513,0,Cash loans,F,N,N,0,67500.0,675000.0,21906.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-17831,-4579,-10811.0,-1384,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.3068684927787245,0.3740208032583212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-541.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n385800,0,Cash loans,F,N,Y,1,270000.0,1078200.0,31653.0,900000.0,Family,Working,Higher education,Separated,House / apartment,0.011703,-22393,-779,-844.0,-806,,1,1,0,1,0,1,,2.0,2,2,FRIDAY,11,0,1,1,0,1,1,Self-employed,0.8933644734799707,0.5318580023850834,0.4436153084085652,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1004.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n185750,0,Cash loans,F,N,Y,0,157500.0,810000.0,43285.5,810000.0,\"Spouse, partner\",State servant,Higher education,Married,House / apartment,0.009334,-16485,-296,-5732.0,-40,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,Other,,0.8076659055189593,0.7295666907060153,0.0918,0.0999,0.9851,0.7959999999999999,0.0113,0.0,0.2069,0.1667,0.0,0.0713,0.07400000000000001,0.0849,0.0039,0.0077,0.0935,0.1037,0.9851,0.804,0.0114,0.0,0.2069,0.1667,0.0,0.073,0.0808,0.0885,0.0039,0.0081,0.0926,0.0999,0.9851,0.7987,0.0114,0.0,0.2069,0.1667,0.0,0.0726,0.0752,0.0865,0.0039,0.0078,reg oper account,block of flats,0.0684,Panel,No,0.0,0.0,0.0,0.0,-1208.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n153317,1,Cash loans,M,N,Y,0,112500.0,398016.0,26725.5,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.072508,-11939,-921,-2564.0,-928,,1,1,1,1,1,0,,1.0,1,1,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.41472346625959416,,0.366,0.2388,0.9821,,,0.4,0.3448,0.3333,,0.4897,,0.3534,,0.0025,0.3729,0.2478,0.9821,,,0.4028,0.3448,0.3333,,0.5009,,0.3683,,0.0027,0.3695,0.2388,0.9821,,,0.4,0.3448,0.3333,,0.4982,,0.3598,,0.0026,,block of flats,0.2785,Panel,No,0.0,0.0,0.0,0.0,-219.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n355882,0,Cash loans,F,N,N,1,225000.0,1223010.0,51948.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-15228,-2528,-8813.0,-4283,,1,1,0,1,0,0,Managers,3.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,,0.3481460487657955,0.4135967602644276,0.2086,,0.9856,,,0.12,0.1607,0.2917,,,,0.1503,,0.0069,0.2616,,0.9806,,,0.0,0.069,0.3333,,,,0.076,,0.0,0.2592,,0.9881,,,0.08,0.1724,0.3333,,,,0.0945,,0.0,,block of flats,0.2287,Panel,No,0.0,0.0,0.0,0.0,-1033.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n308541,0,Cash loans,F,N,N,1,112500.0,301500.0,16353.0,301500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.028663,-10052,-3130,-5967.0,-2241,,1,1,0,1,1,0,High skill tech staff,3.0,2,2,THURSDAY,12,0,0,0,0,0,0,Police,0.3930387384279538,0.5528382187255192,,0.0206,0.0268,0.9891,,,0.0,0.0345,0.1667,,0.0276,,0.0196,,0.0,0.021,0.0278,0.9891,,,0.0,0.0345,0.1667,,0.0283,,0.0205,,0.0,0.0208,0.0268,0.9891,,,0.0,0.0345,0.1667,,0.0281,,0.02,,0.0,,block of flats,0.0154,Block,No,0.0,0.0,0.0,0.0,-1439.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n213359,0,Cash loans,F,Y,N,1,103500.0,675000.0,34465.5,675000.0,Unaccompanied,Working,Higher education,Married,With parents,0.019101,-11201,-749,-6061.0,-374,6.0,1,1,1,1,0,0,Sales staff,3.0,2,2,THURSDAY,19,0,0,0,0,0,0,Self-employed,0.18397165186981912,0.6684131318800383,,0.0928,0.0784,0.9861,0.8096,0.0,0.0,0.1724,0.2083,0.25,0.0538,0.0756,0.0996,0.0,0.0,0.0945,0.0814,0.9861,0.8171,0.0,0.0,0.1724,0.2083,0.25,0.055,0.0826,0.1038,0.0,0.0,0.0937,0.0784,0.9861,0.8121,0.0,0.0,0.1724,0.2083,0.25,0.0547,0.077,0.1014,0.0,0.0,reg oper spec account,block of flats,0.0784,Panel,No,2.0,2.0,2.0,2.0,-338.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n111928,0,Cash loans,F,N,Y,0,202500.0,592560.0,31153.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010643,-14162,-5820,-1761.0,-4534,,1,1,0,1,0,0,,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Industry: type 11,0.6502178532489167,0.5365646925360635,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n432225,0,Cash loans,F,Y,Y,0,126000.0,1078200.0,31653.0,900000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.019101,-19922,-4556,-9064.0,-3414,64.0,1,1,0,1,0,0,Medicine staff,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Medicine,,0.3734907905498509,0.7503751495159068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n313607,0,Cash loans,F,N,Y,0,144000.0,652500.0,31518.0,652500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-21006,365243,-13172.0,-4123,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.35435479997203984,0.2458512138252296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-765.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n277670,0,Cash loans,M,N,Y,0,135000.0,167895.0,17757.0,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.04622,-23543,365243,-15960.0,-4174,,1,0,0,1,0,0,,1.0,1,1,SUNDAY,10,0,0,0,0,0,0,XNA,,0.4725528857278206,0.41184855592423975,0.0247,0.0,0.9737,0.6396,0.002,0.0,0.069,0.0833,0.125,,0.0202,0.0184,0.0,0.0,0.0252,0.0,0.9737,0.6537,0.002,0.0,0.069,0.0833,0.125,,0.022000000000000002,0.0192,0.0,0.0,0.025,0.0,0.9737,0.6444,0.002,0.0,0.069,0.0833,0.125,,0.0205,0.0187,0.0,0.0,reg oper account,block of flats,0.0145,Panel,No,0.0,0.0,0.0,0.0,-870.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n220762,0,Cash loans,F,Y,Y,0,360000.0,971280.0,54364.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006629,-20415,-4241,-5731.0,-3730,4.0,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Trade: type 7,,0.672341605375365,0.3490552510751822,0.0825,0.1064,0.9871,0.8232,0.0271,0.0,0.1379,0.1667,0.0417,0.1098,0.0664,0.0944,0.0039,0.0056,0.084,0.1104,0.9871,0.8301,0.0273,0.0,0.1379,0.1667,0.0417,0.1123,0.0725,0.0983,0.0039,0.0059,0.0833,0.1064,0.9871,0.8256,0.0273,0.0,0.1379,0.1667,0.0417,0.1117,0.0676,0.0961,0.0039,0.0057,reg oper account,block of flats,0.2669,Panel,No,0.0,0.0,0.0,0.0,-652.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n391725,0,Cash loans,F,N,Y,0,202500.0,354276.0,17361.0,292500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17854,-7347,-8995.0,-1389,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Transport: type 2,0.8476059324452808,0.35728197361233577,0.36896873825284665,0.1113,,0.9851,,,0.12,0.1034,0.3333,,0.1029,,0.1161,,0.0011,0.1134,,0.9851,,,0.1208,0.1034,0.3333,,0.1053,,0.1209,,0.0011,0.1124,,0.9851,,,0.12,0.1034,0.3333,,0.1047,,0.1182,,0.0011,,block of flats,0.0915,Panel,No,6.0,0.0,6.0,0.0,-2763.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n393125,0,Cash loans,F,N,Y,0,157500.0,450000.0,22018.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.014519999999999996,-22970,365243,-7818.0,-5063,,1,0,0,1,1,0,,1.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.269729533123615,0.6496203111237195,,,0.9906,0.8708,,0.0,0.3103,0.1667,,,,0.1905,,0.0,,,0.9906,0.8759,,0.0,0.3103,0.1667,,,,0.1985,,0.0,,,0.9906,0.8725,,0.0,0.3103,0.1667,,,,0.1939,,0.0,,block of flats,0.1498,\"Stone, brick\",No,5.0,1.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n324418,0,Cash loans,F,Y,N,0,135000.0,292500.0,13765.5,292500.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.026392,-9889,-2254,-4329.0,-2545,2.0,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,18,0,0,0,0,0,0,Bank,,0.5037302371203811,0.3077366963789207,0.2216,,0.9796,,,0.0,,0.3333,,0.1255,,0.2077,,0.0,0.2258,,0.9796,,,0.0,,0.3333,,0.1284,,0.2164,,0.0,0.2238,,0.9796,,,0.0,,0.3333,,0.1277,,0.2114,,0.0,,block of flats,0.1879,Panel,No,12.0,0.0,12.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n157724,0,Cash loans,M,Y,Y,0,270000.0,1436850.0,46480.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.001276,-17667,-579,-2204.0,-1167,64.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,4,0,0,0,0,0,0,Business Entity Type 3,0.7076335183306792,0.6115276087049188,0.4596904504249018,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-529.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n334197,0,Cash loans,F,N,Y,0,202500.0,900000.0,45954.0,900000.0,Family,Working,Higher education,Married,House / apartment,0.018801,-14176,-3274,-7081.0,-5220,,1,1,0,1,1,0,Core staff,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,School,0.3645854494438273,0.4847648961738161,0.031185979941655943,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3076.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n290924,0,Cash loans,F,N,Y,0,202500.0,938304.0,31140.0,810000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.04622,-22737,365243,-4354.0,-4669,,1,0,0,1,0,0,,2.0,1,1,TUESDAY,13,0,0,0,0,0,0,XNA,,0.7260010137883246,,0.066,0.0353,0.9747,0.6532,0.0357,0.0,0.1379,0.125,0.1667,0.0378,0.0538,0.0559,0.0,0.0,0.0672,0.0366,0.9747,0.6668,0.0361,0.0,0.1379,0.125,0.1667,0.0387,0.0588,0.0583,0.0,0.0,0.0666,0.0353,0.9747,0.6578,0.036000000000000004,0.0,0.1379,0.125,0.1667,0.0385,0.0547,0.0569,0.0,0.0,reg oper account,block of flats,0.0635,Others,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,1.0,0.0,0.0,2.0\n424970,0,Cash loans,F,N,N,1,67500.0,225000.0,16002.0,225000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.008019,-9876,-676,-229.0,-338,,1,1,0,1,0,0,Core staff,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Bank,0.5938421364680202,0.4336662901173017,,0.0825,0.0581,0.9821,,,0.0,0.1379,0.1667,,0.0448,,0.0575,,0.0213,0.084,0.0603,0.9821,,,0.0,0.1379,0.1667,,0.0458,,0.0599,,0.0225,0.0833,0.0581,0.9821,,,0.0,0.1379,0.1667,,0.0456,,0.0586,,0.0217,,block of flats,0.0499,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-411.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n111656,1,Cash loans,F,N,Y,0,202500.0,611950.5,48478.5,553500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.002134,-12915,-1905,-311.0,-25,,1,1,0,1,0,0,Accountants,2.0,3,3,TUESDAY,6,0,0,0,0,0,0,Restaurant,0.2802312384912854,0.3789252810073439,0.107532175802471,0.0825,0.1093,0.9831,0.7688,0.1195,0.04,0.1379,0.1667,0.0417,0.0,0.0672,0.0498,0.0,0.0,0.084,0.1134,0.9831,0.7779,0.1206,0.0403,0.1379,0.1667,0.0417,0.0,0.0735,0.0519,0.0,0.0,0.0833,0.1093,0.9831,0.7719,0.1203,0.04,0.1379,0.1667,0.0417,0.0,0.0684,0.0507,0.0,0.0,reg oper account,block of flats,0.0653,Panel,No,0.0,0.0,0.0,0.0,-1217.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n393780,0,Cash loans,M,Y,Y,0,135000.0,325908.0,17811.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.020246,-14152,-1177,-5183.0,-5183,64.0,1,1,0,1,0,0,Low-skill Laborers,1.0,3,3,FRIDAY,8,0,0,0,0,1,1,Business Entity Type 2,,0.5667133188779704,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1127.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n143144,0,Cash loans,M,Y,Y,2,270000.0,443088.0,22752.0,382500.0,Family,State servant,Higher education,Married,House / apartment,0.00496,-18471,-1448,-703.0,-2023,6.0,1,1,0,1,0,0,,4.0,2,2,MONDAY,13,0,0,0,0,0,0,Other,,0.6063463167535748,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-700.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n150447,0,Cash loans,F,Y,Y,0,126000.0,804096.0,21339.0,576000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.025164,-23345,365243,-1933.0,-5640,64.0,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,XNA,,0.20935102422726354,0.4436153084085652,0.0495,0.0466,0.9702,0.5920000000000001,0.0169,0.0,0.1034,0.125,0.1667,0.0507,0.0403,0.0587,0.0,0.0,0.0504,0.0484,0.9702,0.608,0.0171,0.0,0.1034,0.125,0.1667,0.0519,0.0441,0.0612,0.0,0.0,0.05,0.0466,0.9702,0.5975,0.017,0.0,0.1034,0.125,0.1667,0.0516,0.040999999999999995,0.0597,0.0,0.0,reg oper account,block of flats,0.0462,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-16.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n230693,0,Cash loans,F,N,Y,0,135000.0,808650.0,26217.0,675000.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.01885,-17246,-4727,-9436.0,-775,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,11,0,1,1,0,0,0,Industry: type 3,,0.3703441812737522,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n221755,0,Cash loans,F,N,Y,1,180000.0,750645.0,24808.5,648000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00733,-17794,-3533,-8217.0,-1342,,1,1,0,1,0,0,Accountants,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.7202261503998962,0.6075573001388961,0.0907,0.1028,0.9886,0.8436,0.0179,0.0,0.2759,0.1667,0.2083,0.0557,0.07400000000000001,0.1011,0.0,0.0,0.0924,0.1066,0.9886,0.8497,0.018000000000000002,0.0,0.2759,0.1667,0.2083,0.0569,0.0808,0.1054,0.0,0.0,0.0916,0.1028,0.9886,0.8457,0.018000000000000002,0.0,0.2759,0.1667,0.2083,0.0566,0.0752,0.10300000000000001,0.0,0.0,reg oper account,block of flats,0.0893,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2421.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n164791,0,Cash loans,F,N,Y,0,76500.0,297130.5,14422.5,256500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.031329,-20069,-1156,-4043.0,-3578,,1,1,0,1,0,0,Cooking staff,1.0,2,2,FRIDAY,15,0,0,0,0,1,1,Self-employed,,0.573963507883518,0.8193176922872417,0.0186,0.0434,0.9866,0.8164,0.0023,0.0,0.1034,0.0417,0.0833,0.0166,0.0151,0.0165,0.0,0.0,0.0189,0.045,0.9866,0.8236,0.0023,0.0,0.1034,0.0417,0.0833,0.017,0.0165,0.0171,0.0,0.0,0.0187,0.0434,0.9866,0.8189,0.0023,0.0,0.1034,0.0417,0.0833,0.0169,0.0154,0.0167,0.0,0.0,reg oper spec account,block of flats,0.0142,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-625.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n124599,1,Cash loans,F,N,N,0,81000.0,450000.0,20979.0,450000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.002042,-10359,-1405,-2004.0,-3047,,1,1,1,1,0,0,,1.0,3,3,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.6864152256598034,0.4754534035590896,0.3108182544189319,0.1113,0.0764,0.9876,,,0.16,0.1379,0.3333,,0.1399,,0.1305,,0.0654,0.1134,0.0792,0.9876,,,0.1611,0.1379,0.3333,,0.1431,,0.136,,0.0693,0.1124,0.0764,0.9876,,,0.16,0.1379,0.3333,,0.1424,,0.1329,,0.0668,,block of flats,0.1136,Panel,No,0.0,0.0,0.0,0.0,-923.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n431692,0,Cash loans,F,Y,Y,0,135000.0,490495.5,46701.0,454500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-19748,365243,-4425.0,-3137,4.0,1,0,0,1,0,0,,2.0,2,2,SATURDAY,8,0,0,0,0,0,0,XNA,,0.6426411390712514,0.5989262182569273,,,0.9806,,,,0.1379,0.1667,,,,0.0677,,,,,0.9806,,,,0.1379,0.1667,,,,0.0705,,,,,0.9806,,,,0.1379,0.1667,,,,0.0689,,,,,0.0706,Panel,No,0.0,0.0,0.0,0.0,-891.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n205488,0,Cash loans,M,Y,Y,1,360000.0,980838.0,52389.0,927000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-15725,-3746,-8382.0,-3911,6.0,1,1,0,1,1,0,Drivers,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6539486487548862,0.5154953751603267,0.1649,,0.9871,0.8232,,0.16,0.1379,0.375,0.0417,,0.1345,0.1651,0.0,0.0,0.1681,,0.9871,0.8301,,0.1611,0.1379,0.375,0.0417,,0.1469,0.172,0.0,0.0,0.1665,,0.9871,0.8256,,0.16,0.1379,0.375,0.0417,,0.1368,0.168,0.0,0.0,reg oper account,block of flats,0.1613,Panel,No,0.0,0.0,0.0,0.0,-1404.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n405875,0,Cash loans,M,Y,N,1,225000.0,2013840.0,55377.0,1800000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-13705,-759,-1346.0,-4929,6.0,1,1,1,1,0,0,Drivers,3.0,1,1,THURSDAY,21,0,0,0,0,0,0,Business Entity Type 3,,0.7509010864548842,0.7838324335199619,0.0835,0.1455,0.9836,0.7756,0.0186,0.0,0.3103,0.1667,0.2083,0.1336,0.0672,0.0901,0.0039,0.08,0.0851,0.1509,0.9836,0.7844,0.0188,0.0,0.3103,0.1667,0.2083,0.1366,0.0735,0.0939,0.0039,0.0847,0.0843,0.1455,0.9836,0.7786,0.0187,0.0,0.3103,0.1667,0.2083,0.1359,0.0684,0.0917,0.0039,0.0817,reg oper account,block of flats,0.0984,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-579.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n226908,0,Cash loans,M,Y,N,4,225000.0,1305000.0,36018.0,1305000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15037,-6729,-7339.0,-4079,12.0,1,1,0,1,1,0,Laborers,6.0,2,2,SATURDAY,12,0,0,0,0,1,0,Other,,0.7234226825710777,0.5460231970049609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1724.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n385263,1,Cash loans,F,N,Y,0,135000.0,225000.0,8482.5,225000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-20732,-992,-11797.0,-3928,,1,1,1,1,0,0,,2.0,2,2,FRIDAY,6,0,0,0,0,1,1,Housing,,0.3746018258056704,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,0.0,-2158.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n305384,0,Cash loans,M,N,Y,0,202500.0,545040.0,43191.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.04622,-12626,-2390,-6221.0,-4260,,1,1,0,1,0,0,Drivers,1.0,1,1,MONDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.1903367995166346,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-517.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n415248,0,Cash loans,F,N,Y,0,135000.0,225000.0,15034.5,225000.0,Unaccompanied,Commercial associate,Lower secondary,Single / not married,With parents,0.007305,-7931,-572,-790.0,-589,,1,1,0,1,0,0,Sales staff,1.0,3,3,WEDNESDAY,13,0,0,0,1,0,1,Bank,0.16916819083214185,0.3185560036792005,0.3962195720630885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-733.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n199136,0,Cash loans,F,N,Y,1,130500.0,380533.5,27193.5,328500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.025164,-18109,-597,-1160.0,-1668,,1,1,0,1,0,0,Accountants,2.0,2,2,SUNDAY,14,0,0,0,0,0,0,Kindergarten,0.6275910175850954,0.4146969271439306,0.6279908192952864,0.1237,0.0742,0.9757,0.6668,0.0053,0.0,0.2069,0.1667,0.2083,0.0686,0.0967,0.10099999999999999,0.0193,0.0469,0.1261,0.077,0.9757,0.6798,0.0054,0.0,0.2069,0.1667,0.2083,0.0702,0.1056,0.1052,0.0195,0.0496,0.1249,0.0742,0.9757,0.6713,0.0054,0.0,0.2069,0.1667,0.2083,0.0698,0.0983,0.1028,0.0194,0.0479,not specified,block of flats,0.0895,Panel,No,0.0,0.0,0.0,0.0,-1156.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n398964,0,Cash loans,M,Y,Y,1,193500.0,755190.0,36459.0,675000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.032561,-13674,-1960,-5276.0,-1210,8.0,1,1,0,1,1,0,High skill tech staff,2.0,1,1,TUESDAY,15,0,0,0,0,0,0,Security Ministries,,0.6817035474878581,0.4471785780453068,0.0825,0.0,0.9732,,,0.0,0.1379,0.1667,,0.0768,,0.0707,,0.0017,0.084,0.0,0.9732,,,0.0,0.1379,0.1667,,0.0785,,0.0734,,0.0018,0.0833,0.0,0.9732,,,0.0,0.1379,0.1667,,0.0781,,0.0719,,0.0017,,block of flats,0.0558,Panel,No,0.0,0.0,0.0,0.0,-1476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n327108,0,Cash loans,F,Y,N,0,112500.0,545040.0,16654.5,450000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.008019,-9937,-831,-4444.0,-2613,15.0,1,1,0,1,1,0,Core staff,1.0,2,2,SATURDAY,16,0,0,0,0,0,0,Self-employed,0.5966086373363222,0.7525621190987422,0.4686596550493113,0.1845,0.1793,0.9816,0.7484,0.0799,0.0,0.4138,0.1667,0.0,0.0876,0.1505,0.0984,0.0,0.0078,0.188,0.1861,0.9816,0.7583,0.0807,0.0,0.4138,0.1667,0.0,0.0896,0.1644,0.1025,0.0,0.0082,0.1863,0.1793,0.9816,0.7518,0.0805,0.0,0.4138,0.1667,0.0,0.0891,0.1531,0.1002,0.0,0.0079,reg oper account,block of flats,0.1211,Panel,No,6.0,0.0,6.0,0.0,-572.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n190322,0,Cash loans,M,N,Y,0,112500.0,787500.0,23157.0,787500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-14960,-127,-6697.0,-4896,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Trade: type 3,,0.28296043542779176,,0.1186,0.1705,0.9831,,,,0.3103,0.1667,,0.0305,,0.1222,,0.0168,0.1208,0.1769,0.9831,,,,0.3103,0.1667,,0.0312,,0.1273,,0.0178,0.1197,0.1705,0.9831,,,,0.3103,0.1667,,0.031,,0.1244,,0.0172,,block of flats,0.0997,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n417227,0,Cash loans,F,N,Y,0,270000.0,481855.5,49504.5,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-19828,-1184,-10563.0,-3388,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.5970180221351749,0.6817058776720116,0.1485,0.0,0.9851,0.7959999999999999,0.0,0.16,0.1379,0.3333,0.0,0.0,0.0,0.0885,0.0,0.0,0.1513,0.0,0.9851,0.804,0.0,0.1611,0.1379,0.3333,0.0,0.0,0.0,0.0923,0.0,0.0,0.1499,0.0,0.9851,0.7987,0.0,0.16,0.1379,0.3333,0.0,0.0,0.0,0.0901,0.0,0.0,reg oper account,block of flats,0.1217,Panel,No,2.0,0.0,2.0,0.0,-57.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n284461,0,Revolving loans,M,Y,N,0,180000.0,360000.0,18000.0,360000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-22415,365243,-2569.0,-3031,20.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,1,0,0,XNA,,0.4682001968381503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-725.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n129885,0,Cash loans,M,Y,N,0,270000.0,679500.0,19998.0,679500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.030755,-20151,-4950,-11876.0,-3587,1.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 1,0.5877903150238515,0.6280924993026441,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1516.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n430896,0,Cash loans,F,N,Y,0,67500.0,518562.0,20695.5,463500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-22767,365243,-11311.0,-3992,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.19421511211361847,0.5442347412142162,0.0247,,0.9816,0.7484,,,0.069,0.0833,,,,0.0217,,,0.0252,,0.9816,0.7583,,,0.069,0.0833,,,,0.0226,,,0.025,,0.9816,0.7518,,,0.069,0.0833,,,,0.0221,,,,block of flats,0.0171,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1295.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n245103,0,Cash loans,F,N,Y,0,270000.0,679500.0,30703.5,679500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.006629,-11031,-2795,-4943.0,-2656,,1,1,0,1,0,0,,1.0,2,2,SATURDAY,5,0,0,0,0,0,0,Business Entity Type 3,0.5391346836805804,0.4302751864791524,0.7597121819739279,0.0856,0.1555,0.9801,0.7144,0.0149,0.0,0.1724,0.1042,0.2083,0.0349,0.1093,0.1222,0.0772,,0.0168,0.1613,0.9791,0.7256,0.015,0.0,0.069,0.0417,0.2083,0.0357,0.1194,0.1274,0.0778,,0.0864,0.1555,0.9801,0.7182,0.015,0.0,0.1724,0.1042,0.2083,0.0355,0.1112,0.1244,0.0776,,,block of flats,0.0076,Block,No,4.0,0.0,4.0,0.0,-1481.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n113306,0,Cash loans,M,N,Y,0,67500.0,450000.0,22018.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-10855,-138,-4807.0,-3023,,1,1,1,1,1,0,Security staff,2.0,2,2,TUESDAY,11,0,1,1,1,1,1,Self-employed,,0.10404629870899693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n406795,0,Cash loans,F,N,N,0,90000.0,148500.0,17622.0,148500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.015221,-8549,-745,-371.0,-1226,,1,1,0,1,0,0,Waiters/barmen staff,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,0.2927718183031052,0.30040013087568274,,0.0928,0.08,0.9816,,,,0.2069,0.1667,,0.081,,0.0884,,,0.0945,0.083,0.9816,,,,0.2069,0.1667,,0.0828,,0.0921,,,0.0937,0.08,0.9816,,,,0.2069,0.1667,,0.0824,,0.09,,,,block of flats,0.0695,Panel,No,2.0,0.0,2.0,0.0,-522.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n453870,0,Cash loans,F,N,Y,0,70200.0,239850.0,23850.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-23477,365243,-7625.0,-5025,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.3588501985361047,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n446519,0,Cash loans,F,Y,Y,1,225000.0,665892.0,24592.5,477000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.020246,-14482,-1193,-3508.0,-4065,6.0,1,1,0,1,0,0,Managers,2.0,3,3,SATURDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.31693277113208834,0.06099114095658058,0.3791004853998145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1043.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n279003,0,Cash loans,M,Y,Y,0,135000.0,225000.0,17667.0,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.008019,-10039,-305,-4449.0,-2047,21.0,1,1,0,1,1,0,Drivers,2.0,2,2,SATURDAY,13,0,1,1,0,0,0,Business Entity Type 2,0.2559442268014356,0.5868875583663025,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-447.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n188018,0,Cash loans,M,N,Y,1,180000.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-13605,-625,-1219.0,-3278,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.2830425672754865,0.5142010578674685,,0.0825,0.0474,0.9732,0.6328,0.02,0.0,0.1379,0.1667,0.2083,0.076,0.0672,0.0652,0.0039,0.0054,0.084,0.0492,0.9732,0.6472,0.0201,0.0,0.1379,0.1667,0.2083,0.0778,0.0735,0.0679,0.0039,0.0057,0.0833,0.0474,0.9732,0.6377,0.0201,0.0,0.1379,0.1667,0.2083,0.0773,0.0684,0.0664,0.0039,0.0055,reg oper account,block of flats,0.065,Block,No,3.0,0.0,3.0,0.0,-950.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n387102,0,Cash loans,F,N,Y,0,94500.0,612612.0,28516.5,495000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.0228,-21258,-352,-3652.0,-4469,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,School,,0.5568890005771815,0.7992967832109371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-419.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n382579,0,Cash loans,F,N,Y,0,45000.0,152820.0,14886.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.031329,-25095,365243,-7041.0,-3933,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.6595387677020513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,0.0,-1306.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n186472,0,Cash loans,F,Y,Y,0,247500.0,1146816.0,41323.5,990000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-11640,-2413,-774.0,-3557,65.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Industry: type 4,0.6246816802119761,0.6318340711008479,0.8038850611746273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n436826,0,Cash loans,M,Y,Y,0,180000.0,381528.0,22032.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.030755,-18385,-1528,-3075.0,-1926,21.0,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,12,0,1,1,0,1,1,Business Entity Type 3,,0.2654822818822062,0.6986675550534175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-703.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,9.0\n299844,0,Cash loans,F,N,Y,1,135000.0,490495.5,30006.0,454500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.002506,-11588,-259,-5998.0,-1909,,1,1,0,1,0,0,Accountants,3.0,2,2,TUESDAY,1,0,0,0,0,0,0,Government,,0.7094396461249227,,0.1361,0.1508,0.9856,0.8028,0.0336,0.0,0.3448,0.125,0.1667,0.1275,0.1101,0.132,0.0039,0.0918,0.1387,0.1565,0.9856,0.8105,0.0339,0.0,0.3448,0.125,0.1667,0.1305,0.1203,0.1375,0.0039,0.0972,0.1374,0.1508,0.9856,0.8054,0.0339,0.0,0.3448,0.125,0.1667,0.1298,0.11199999999999999,0.1343,0.0039,0.0937,reg oper account,block of flats,0.1237,Block,No,0.0,0.0,0.0,0.0,-481.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n429901,0,Cash loans,F,N,Y,0,71100.0,560664.0,20727.0,468000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-21285,365243,-9959.0,-4819,,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.6857136058711085,0.8050196619153701,0.2443,0.138,0.9866,,,0.28,0.2414,0.3333,,0.0351,,0.2507,,0.0335,0.2489,0.1432,0.9866,,,0.282,0.2414,0.3333,,0.0359,,0.2612,,0.0354,0.2467,0.138,0.9866,,,0.28,0.2414,0.3333,,0.0357,,0.2552,,0.0342,,block of flats,0.2868,Panel,No,0.0,0.0,0.0,0.0,-1203.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n239349,0,Cash loans,F,N,Y,2,112500.0,454500.0,31761.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-9889,-1752,-351.0,-1927,,1,1,0,1,0,1,Laborers,4.0,2,2,THURSDAY,16,0,0,0,0,0,0,Transport: type 4,0.4053384687765302,0.5136711810057546,,0.1186,0.0856,0.9786,0.7076,0.0,0.0,0.1379,0.1667,0.2083,0.015,0.0967,0.0667,0.0,0.0,0.1208,0.0888,0.9786,0.7190000000000001,0.0,0.0,0.1379,0.1667,0.2083,0.0153,0.1056,0.0695,0.0,0.0,0.1197,0.0856,0.9786,0.7115,0.0,0.0,0.1379,0.1667,0.2083,0.0152,0.0983,0.0679,0.0,0.0,reg oper account,block of flats,0.064,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1230.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n174471,0,Cash loans,M,N,N,0,157500.0,284400.0,13963.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-14123,-915,-2567.0,-4022,,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.5506911272530148,0.7136313997323308,0.0619,0.0557,0.9851,0.7959999999999999,0.0259,0.0,0.1379,0.1667,0.2083,0.0143,0.0504,0.053,0.0,0.0,0.063,0.0578,0.9851,0.804,0.0262,0.0,0.1379,0.1667,0.2083,0.0147,0.0551,0.0552,0.0,0.0,0.0625,0.0557,0.9851,0.7987,0.0261,0.0,0.1379,0.1667,0.2083,0.0146,0.0513,0.0539,0.0,0.0,reg oper account,block of flats,0.0565,Panel,No,4.0,0.0,4.0,0.0,-2502.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n348640,0,Cash loans,F,N,Y,0,112500.0,312768.0,22374.0,270000.0,Family,Commercial associate,Higher education,Civil marriage,House / apartment,0.030755,-17536,-170,-16.0,-1061,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Trade: type 7,0.4479243195745352,0.6623418338759913,0.7295666907060153,0.0907,0.0,0.9821,0.7552,0.0,0.0,0.1379,0.2083,0.25,0.0271,0.0731,0.062,0.0,0.0,0.0924,0.0,0.9821,0.7648,0.0,0.0,0.1379,0.2083,0.25,0.0277,0.0799,0.0646,0.0,0.0,0.0916,0.0,0.9821,0.7585,0.0,0.0,0.1379,0.2083,0.25,0.0275,0.0744,0.0631,0.0,0.0,reg oper spec account,block of flats,0.0488,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n257722,0,Cash loans,F,N,Y,0,126000.0,518562.0,22099.5,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.010032,-22891,365243,-1410.0,-3970,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,3,0,0,0,0,0,0,XNA,,0.2293313366222277,0.7688075728291359,0.0103,0.0,0.9702,0.5920000000000001,0.002,0.0,0.069,0.0417,0.0833,0.0098,0.0067,0.0143,0.0077,0.0095,0.0105,0.0,0.9702,0.608,0.002,0.0,0.069,0.0417,0.0833,0.01,0.0073,0.0149,0.0078,0.01,0.0104,0.0,0.9702,0.5975,0.002,0.0,0.069,0.0417,0.0833,0.01,0.0068,0.0145,0.0078,0.0097,reg oper account,block of flats,0.0144,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-316.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,0.0\n134552,0,Cash loans,F,N,N,1,144000.0,166500.0,11254.5,166500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.00496,-18262,365243,-8551.0,-1713,,1,0,0,1,0,0,,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,0.5462182721166907,0.6555650644007379,0.2851799046358216,,,0.9876,,,,0.0345,0.375,,,,,,,,,0.9876,,,,0.0345,0.375,,,,,,,,,0.9876,,,,0.0345,0.375,,,,,,,,block of flats,0.0344,,No,0.0,0.0,0.0,0.0,-1699.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n268647,0,Cash loans,F,N,N,0,173367.0,1076247.0,40990.5,990000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.020713,-8794,-844,-4705.0,-1464,,1,1,1,1,0,0,Core staff,1.0,3,2,TUESDAY,14,0,0,0,0,0,0,Trade: type 2,,0.601801450936964,0.4206109640437848,0.0825,0.0631,0.9767,0.6804,0.1027,0.0,0.1379,0.1667,0.2083,0.0431,0.0672,0.0714,0.0,0.0,0.084,0.0655,0.9767,0.6929,0.1037,0.0,0.1379,0.1667,0.2083,0.0441,0.0735,0.0744,0.0,0.0,0.0833,0.0631,0.9767,0.6847,0.1034,0.0,0.1379,0.1667,0.2083,0.0439,0.0684,0.0727,0.0,0.0,reg oper account,block of flats,0.0562,Panel,No,8.0,1.0,8.0,1.0,-1099.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n247042,0,Cash loans,F,N,Y,0,292500.0,298728.0,15403.5,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.020713,-22386,365243,-1540.0,-5150,,1,0,0,1,0,0,,1.0,3,2,FRIDAY,5,0,0,0,0,0,0,XNA,,0.5786569382458528,0.41184855592423975,0.4711,0.3305,0.9826,0.762,0.1044,0.48,0.4138,0.3333,0.375,0.2632,0.3841,0.4867,0.0,0.0,0.48,0.34299999999999997,0.9826,0.7713,0.1054,0.4834,0.4138,0.3333,0.375,0.2692,0.4197,0.5071,0.0,0.0,0.4757,0.3305,0.9826,0.7652,0.1051,0.48,0.4138,0.3333,0.375,0.2678,0.3908,0.4955,0.0,0.0,not specified,block of flats,0.4399,Panel,No,2.0,0.0,2.0,0.0,-2559.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n107819,0,Revolving loans,M,N,N,2,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.022625,-12841,-781,-447.0,-626,,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.2858474392097992,0.4185182628494634,0.7713615919194317,0.1464,,0.9921,,,0.08,0.069,0.3333,,,,0.1077,,,0.1492,,0.9921,,,0.0806,0.069,0.3333,,,,0.1123,,,0.1478,,0.9921,,,0.08,0.069,0.3333,,,,0.1097,,,,block of flats,0.0847,,No,0.0,0.0,0.0,0.0,-1517.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n417564,0,Cash loans,F,N,Y,1,81000.0,675000.0,26284.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-12756,-2245,-342.0,-2979,,1,1,1,1,1,1,,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Government,0.5077058436653619,0.6367391807614797,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1766.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n237067,0,Cash loans,F,N,N,0,135000.0,499261.5,15133.5,351000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-18712,-4950,-12790.0,-2223,,1,1,0,1,1,0,Cleaning staff,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Housing,,0.7032392973156757,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,2.0,0.0\n126106,0,Cash loans,F,N,Y,0,126000.0,270000.0,13783.5,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.015221,-14956,-3496,-1884.0,-4824,,1,1,0,1,1,0,Core staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Insurance,0.5667968852447399,0.5350962118070013,,0.0619,,0.9826,,,,0.1379,0.1667,,0.0661,,0.0538,,,0.063,,0.9826,,,,0.1379,0.1667,,0.0676,,0.055999999999999994,,,0.0625,,0.9826,,,,0.1379,0.1667,,0.0672,,0.0547,,,,block of flats,0.0423,Panel,No,0.0,0.0,0.0,0.0,-2415.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n241306,0,Cash loans,F,N,Y,0,112500.0,675000.0,37822.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.01885,-16113,-1562,-1014.0,-4545,,1,1,1,1,1,0,Accountants,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Self-employed,0.7614680998338025,0.4210535245274077,0.6144143775673561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1719.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n322725,0,Cash loans,F,N,Y,1,180000.0,648000.0,34654.5,648000.0,Children,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-14719,-5180,-7317.0,-4820,,1,1,0,1,1,0,Managers,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.7343700130630095,0.7134931806617161,0.520897599048938,0.1485,0.11199999999999999,0.9851,,,0.16,0.1379,0.3333,,0.1557,,0.1535,,0.0001,0.1513,0.1162,0.9851,,,0.1611,0.1379,0.3333,,0.1592,,0.1599,,0.0001,0.1499,0.11199999999999999,0.9851,,,0.16,0.1379,0.3333,,0.1584,,0.1562,,0.0001,,block of flats,0.1431,Panel,No,1.0,0.0,1.0,0.0,-3149.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n165982,0,Cash loans,F,Y,Y,0,337500.0,1762110.0,48586.5,1575000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.018801,-9771,-887,-4614.0,-2440,13.0,1,1,0,1,0,0,Managers,1.0,2,2,FRIDAY,11,0,0,0,0,0,0,Other,0.1969829264662069,0.5032491737800177,0.6658549219640212,0.2474,0.1616,0.9876,0.83,0.1493,0.24,0.2069,0.375,0.4167,0.0969,0.2017,0.2557,0.0,0.0,0.2521,0.1677,0.9876,0.8367,0.1507,0.2417,0.2069,0.375,0.4167,0.0991,0.2204,0.2664,0.0,0.0,0.2498,0.1616,0.9876,0.8323,0.1503,0.24,0.2069,0.375,0.4167,0.0985,0.2052,0.2603,0.0,0.0,,block of flats,0.2827,Panel,No,0.0,0.0,0.0,0.0,-30.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n391017,0,Cash loans,M,N,N,0,225000.0,787131.0,44082.0,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.04622,-17977,-2405,-10631.0,-1524,,1,1,0,1,0,0,Drivers,1.0,1,1,TUESDAY,17,0,1,1,0,0,0,Business Entity Type 1,,0.7436828913793704,0.41885428862332175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1360.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n344823,0,Cash loans,F,Y,Y,0,112500.0,889515.0,26136.0,742500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008625,-13350,-1752,-308.0,-4299,64.0,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.6063064126911569,0.5547564968624812,0.12689752616027333,0.0165,0.0358,0.9861,0.8096,0.0067,0.0,0.069,0.0417,0.0833,0.006999999999999999,0.0134,0.0128,,,0.0168,0.033,0.9861,0.8171,0.001,0.0,0.069,0.0417,0.0833,0.0071,0.0147,0.0133,,,0.0167,0.0358,0.9861,0.8121,0.0067,0.0,0.069,0.0417,0.0833,0.0071,0.0137,0.013000000000000001,,,reg oper account,block of flats,0.0179,Panel,No,0.0,0.0,0.0,0.0,-1478.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n226648,0,Cash loans,F,N,Y,1,283500.0,156384.0,16969.5,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.001276,-20767,-692,-10595.0,-3926,,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,3,0,0,0,0,0,0,Trade: type 7,0.5481259409982544,0.3445222839832043,0.5406544504453575,0.0487,0.0595,0.9851,0.7959999999999999,0.0347,0.0,0.1034,0.1667,0.0833,0.0459,0.0387,0.0512,0.0,0.0009,0.0305,0.0369,0.9851,0.804,0.0351,0.0,0.069,0.1667,0.0417,0.0222,0.0422,0.0305,0.0,0.0,0.0396,0.0541,0.9851,0.7987,0.035,0.0,0.069,0.1667,0.0417,0.0472,0.0393,0.0441,0.0,0.0,reg oper account,block of flats,0.0257,Panel,No,0.0,0.0,0.0,0.0,-197.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n250645,1,Cash loans,F,N,Y,1,135000.0,566055.0,22063.5,472500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019689,-11389,-3760,-275.0,-1557,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Restaurant,,0.30465433204213305,0.5136937663039473,0.1227,0.1536,0.9836,0.7756,0.0193,0.0,0.2759,0.1667,0.0417,0.0643,0.1,0.1138,0.0,0.0,0.125,0.1594,0.9836,0.7844,0.0194,0.0,0.2759,0.1667,0.0417,0.0658,0.1093,0.1185,0.0,0.0,0.1239,0.1536,0.9836,0.7786,0.0194,0.0,0.2759,0.1667,0.0417,0.0654,0.1018,0.1158,0.0,0.0,reg oper account,block of flats,0.0895,Panel,No,4.0,1.0,4.0,1.0,-285.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n138234,1,Cash loans,F,N,Y,0,144000.0,261648.0,9994.5,207000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-23808,365243,-2253.0,-2602,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,0.7783169009956077,0.2609826401557945,0.2471909382666521,0.1979,0.1393,0.9836,0.7756,0.0603,0.32,0.1379,0.4583,0.5,0.2369,0.1614,0.2236,0.0,0.0,0.2017,0.1445,0.9836,0.7844,0.0609,0.3222,0.1379,0.4583,0.5,0.2423,0.1763,0.2329,0.0,0.0,0.1999,0.1393,0.9836,0.7786,0.0607,0.32,0.1379,0.4583,0.5,0.24100000000000002,0.1642,0.2276,0.0,0.0,reg oper account,block of flats,0.2348,Panel,No,0.0,0.0,0.0,0.0,-1033.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,6.0\n186933,0,Cash loans,M,Y,N,0,292500.0,971280.0,56790.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-12426,-424,-6092.0,-2234,14.0,1,1,1,1,1,0,,2.0,1,1,FRIDAY,11,0,1,1,0,0,0,Business Entity Type 3,,0.7127998007976447,0.5424451438613613,0.0619,0.0614,0.9742,0.6464,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0504,0.0324,0.0,0.0,0.063,0.0637,0.9742,0.6602,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0551,0.0338,0.0,0.0,0.0625,0.0614,0.9742,0.6511,0.0,0.0,0.1034,0.1667,0.2083,0.0,0.0513,0.033,0.0,0.0,reg oper account,block of flats,0.0467,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-1127.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n194415,0,Cash loans,M,Y,Y,0,135000.0,454500.0,36040.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-15009,-717,-3313.0,-4320,7.0,1,1,0,1,0,0,Drivers,2.0,3,3,THURSDAY,9,0,0,0,0,0,0,Transport: type 4,0.2446612660107978,0.4172469182828564,0.15663982703141147,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1630.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,5.0\n415922,1,Cash loans,F,N,Y,1,67500.0,284400.0,13387.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-11792,-557,-889.0,-2893,,1,1,1,1,0,0,Laborers,3.0,3,3,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.057601306346742674,0.3342077393547029,,0.066,0.0793,0.9752,0.66,0.0268,0.0,0.1379,0.125,0.1667,0.0578,0.0538,0.0502,0.0,0.0,0.0672,0.0823,0.9752,0.6733,0.0271,0.0,0.1379,0.125,0.1667,0.0591,0.0588,0.0523,0.0,0.0,0.0666,0.0793,0.9752,0.6645,0.027000000000000003,0.0,0.1379,0.125,0.1667,0.0588,0.0547,0.0511,0.0,0.0,reg oper account,block of flats,0.0395,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-287.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n126408,0,Cash loans,F,N,Y,1,225000.0,450000.0,23971.5,450000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.032561,-17257,-5406,-5663.0,-789,,1,1,0,1,0,0,Core staff,2.0,1,1,MONDAY,13,0,0,0,0,0,0,Government,,0.7095072566022936,0.6706517530862718,0.1454,0.1264,0.9752,,,0.1064,0.2069,0.1942,,0.0853,,0.1273,,0.0074,0.105,0.1055,0.9752,,,0.0,0.1724,0.1667,,0.0801,,0.0942,,0.0024,0.1041,0.1016,0.9752,,,0.0,0.1724,0.1667,,0.0809,,0.0927,,0.0026,,block of flats,0.1614,Panel,No,2.0,0.0,2.0,0.0,-1507.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n296308,0,Cash loans,M,Y,Y,0,292500.0,239850.0,25960.5,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.072508,-19617,-2081,-8728.0,-2758,12.0,1,1,1,1,0,0,,1.0,1,1,TUESDAY,11,0,1,1,0,1,1,Transport: type 4,0.7178490874507212,0.6208312105278675,0.3774042489507649,0.10099999999999999,,0.9781,,,0.08,0.0345,0.5417,,,,,,,0.1029,,0.9782,,,0.0806,0.0345,0.5417,,,,,,,0.102,,0.9781,,,0.08,0.0345,0.5417,,,,,,,,block of flats,0.0665,Panel,No,1.0,0.0,1.0,0.0,-334.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n377898,0,Cash loans,F,Y,Y,0,157500.0,1575000.0,43443.0,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-10400,-3099,-245.0,-1568,7.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4381137189214227,0.6633583218262745,0.3876253444214701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n190373,0,Revolving loans,F,N,Y,0,270000.0,270000.0,13500.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.032561,-21303,365243,-3795.0,-4535,,1,0,0,1,0,0,,1.0,1,1,WEDNESDAY,17,0,0,0,0,0,0,XNA,0.8639803814025013,0.6969169665760743,0.5620604831738043,0.3969,0.1785,0.9911,,,0.0,0.6207,0.1667,,0.10400000000000001,,0.4329,,0.0546,0.4044,0.1852,0.9911,,,0.0,0.6207,0.1667,,0.1064,,0.451,,0.0579,0.4008,0.1785,0.9911,,,0.0,0.6207,0.1667,,0.1058,,0.4407,,0.0558,,block of flats,0.3524,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-150.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n428491,0,Cash loans,F,N,Y,1,126000.0,1288350.0,41562.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008068,-16088,-1205,-1298.0,-4329,,1,1,0,1,0,0,,3.0,3,3,SATURDAY,10,0,0,0,0,0,0,Other,,0.4885288829110538,0.4048783643353997,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-450.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n178481,0,Cash loans,F,N,N,0,126000.0,454500.0,16321.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-20033,-1023,-2535.0,-3437,,1,1,1,1,1,0,Cooking staff,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Self-employed,,0.5137258089273916,0.045363437942628114,0.1546,0.1505,0.9846,0.7892,0.0254,0.0,0.3448,0.1667,0.2083,0.0,0.1236,0.1428,0.0116,0.0085,0.1576,0.1562,0.9846,0.7975,0.0256,0.0,0.3448,0.1667,0.2083,0.0,0.135,0.1488,0.0117,0.009000000000000001,0.1561,0.1505,0.9846,0.792,0.0256,0.0,0.3448,0.1667,0.2083,0.0,0.1257,0.1454,0.0116,0.0087,reg oper account,block of flats,0.1156,\"Stone, brick\",No,1.0,1.0,1.0,0.0,-1322.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n200495,0,Cash loans,M,N,Y,0,180000.0,634041.0,22905.0,481500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.022625,-20409,-1150,-610.0,-3894,,1,1,0,1,1,0,Cooking staff,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Industry: type 3,,0.7057340599278709,,0.0108,0.0001,0.4759,0.3404,0.0137,0.0,0.0345,0.0833,0.125,0.0,0.0084,0.0095,0.0039,0.0,0.0105,0.0001,0.0005,0.3662,0.0138,0.0,0.0345,0.0833,0.125,0.0,0.0092,0.0099,0.0039,0.0,0.0109,0.0001,0.4759,0.3492,0.0138,0.0,0.0345,0.0833,0.125,0.0,0.0086,0.0097,0.0039,0.0,not specified,block of flats,0.0075,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-858.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n245883,0,Cash loans,F,N,Y,1,180000.0,315000.0,9157.5,315000.0,Unaccompanied,Working,Higher education,Married,With parents,0.010032,-11543,-1114,-6107.0,-2739,,1,1,0,1,0,0,Laborers,3.0,2,2,SUNDAY,9,0,0,0,0,0,0,Services,0.6835942987002471,0.6907061129229015,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n373489,0,Cash loans,F,N,Y,1,234000.0,263686.5,20961.0,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.006629,-13865,-1677,-8002.0,-4277,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,7,0,0,0,0,0,0,Business Entity Type 1,,0.00036481651740081215,,0.0557,,0.9871,0.8232,0.0122,0.0,0.0345,0.125,,0.0136,0.0454,0.0581,0.0193,0.0531,0.0567,,0.9871,0.8301,0.0123,0.0,0.0345,0.125,,0.013999999999999999,0.0496,0.0605,0.0195,0.0563,0.0562,,0.9871,0.8256,0.0123,0.0,0.0345,0.125,,0.0139,0.0462,0.0591,0.0194,0.0543,reg oper account,block of flats,0.0473,Block,No,4.0,0.0,4.0,0.0,-2128.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n211246,0,Cash loans,F,N,Y,0,135000.0,157500.0,10656.0,157500.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.026392,-16284,-250,-5590.0,-4225,,1,1,0,1,1,0,Managers,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Security Ministries,,0.7224327700725479,,0.032,,0.9578,,,0.0,0.1034,0.125,,,,,,,0.0326,,0.9578,,,0.0,0.1034,0.125,,,,,,,0.0323,,0.9578,,,0.0,0.1034,0.125,,,,,,,,block of flats,0.0245,\"Stone, brick\",Yes,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n263418,1,Cash loans,M,N,N,1,112500.0,273636.0,25321.5,247500.0,Other_B,Working,Secondary / secondary special,Married,House / apartment,0.02461,-10018,-1015,-1178.0,-1178,,1,1,0,1,0,1,Laborers,3.0,2,2,TUESDAY,15,0,0,0,0,0,0,Transport: type 4,,0.2992111947281659,0.3859146722745145,0.0825,0.0448,0.9891,0.8504,,0.08,0.069,0.375,,0.0651,,0.0977,,0.0,0.084,0.0465,0.9891,0.8563,,0.0806,0.069,0.375,,0.0666,,0.1018,,0.0,0.0833,0.0448,0.9891,0.8524,,0.08,0.069,0.375,,0.0663,,0.0994,,0.0,,block of flats,0.0897,Monolithic,No,3.0,1.0,3.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n189719,0,Cash loans,M,Y,Y,0,157500.0,215640.0,11695.5,180000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.00702,-10062,-1139,-3553.0,-2558,2.0,1,1,0,1,1,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.40473776224503294,0.5140699572399979,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17.0,0.0,17.0,0.0,-412.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n145514,0,Cash loans,M,Y,Y,0,90000.0,225000.0,10620.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018209,-9080,-323,-3943.0,-1766,24.0,1,1,0,1,0,0,,2.0,3,3,WEDNESDAY,8,0,0,0,1,1,0,Construction,0.049504789633331665,0.452588231365397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-953.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n228983,0,Revolving loans,F,N,Y,1,157500.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.010147,-9733,-1048,-2183.0,-2189,,1,1,0,1,0,0,,3.0,2,2,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.4665562237444889,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-587.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n291031,0,Cash loans,F,N,Y,1,450000.0,1004791.5,56236.5,904500.0,Unaccompanied,Working,Higher education,Married,Municipal apartment,0.04622,-11997,-3254,-275.0,-4686,,1,1,0,1,0,0,Accountants,3.0,1,1,SATURDAY,11,0,0,0,0,0,0,Medicine,0.6408126951008297,0.7374948473192959,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,9.0\n314439,0,Cash loans,F,Y,Y,0,135000.0,544491.0,20133.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-21709,-230,-2999.0,-3003,13.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,10,0,1,1,0,1,1,Industry: type 4,0.8329156219899442,0.6907528269485467,0.2418614865234661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-214.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n271710,0,Cash loans,F,N,Y,1,139500.0,360000.0,23134.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-11637,-1782,-2651.0,-4282,,1,1,0,1,0,1,Core staff,3.0,3,3,SATURDAY,7,0,0,0,0,0,0,Kindergarten,,0.6656058206763109,0.4206109640437848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n346504,0,Cash loans,F,N,Y,0,126000.0,1288350.0,37669.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.072508,-18374,-3822,-12194.0,-1797,,1,1,1,1,1,0,Sales staff,1.0,1,1,TUESDAY,11,0,0,0,0,0,0,Industry: type 3,,0.7291952432313181,0.3791004853998145,0.2959,0.1967,0.9791,0.7144,0.1698,0.32,0.2759,0.3333,0.375,0.0,0.2412,0.28600000000000003,0.0,0.0,0.3015,0.2042,0.9791,0.7256,0.1713,0.3222,0.2759,0.3333,0.375,0.0,0.2635,0.298,0.0,0.0,0.2987,0.1967,0.9791,0.7182,0.1709,0.32,0.2759,0.3333,0.375,0.0,0.2454,0.2911,0.0,0.0,reg oper account,block of flats,0.2249,Panel,No,0.0,0.0,0.0,0.0,-1973.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n108888,0,Cash loans,F,N,Y,0,112500.0,630000.0,18184.5,630000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-22691,-874,-11376.0,-4196,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Medicine,,0.7564907790795842,0.5902333386185574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1346.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n232393,0,Revolving loans,F,N,Y,0,157500.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.006305,-8843,-887,-927.0,-1516,,1,1,0,1,0,0,,1.0,3,3,FRIDAY,3,0,0,0,0,0,0,Business Entity Type 3,0.2929731894610381,0.6466586034549839,0.4471785780453068,0.0701,,0.9811,0.7416,0.0075,0.0,0.1379,0.1667,0.0417,,0.0555,0.0611,0.0077,0.0159,0.0714,,0.9811,0.7517,0.0076,0.0,0.1379,0.1667,0.0417,,0.0606,0.0637,0.0078,0.0168,0.0708,,0.9811,0.7451,0.0075,0.0,0.1379,0.1667,0.0417,,0.0564,0.0622,0.0078,0.0162,reg oper account,block of flats,0.0556,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1267.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n281640,0,Cash loans,F,N,Y,2,112500.0,1154362.5,33880.5,1008000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.025164,-15229,-3455,-3340.0,-4516,,1,1,0,1,0,0,Core staff,4.0,2,2,SATURDAY,10,0,0,0,0,1,1,Medicine,0.519029511716662,0.15967923350263774,0.4014074137749511,0.066,0.0667,0.9762,,,0.0,0.1379,0.125,,0.0142,,0.0557,,0.0,0.0672,0.0692,0.9762,,,0.0,0.1379,0.125,,0.0145,,0.0581,,0.0,0.0666,0.0667,0.9762,,,0.0,0.1379,0.125,,0.0145,,0.0567,,0.0,,block of flats,0.0438,Panel,No,3.0,0.0,3.0,0.0,-406.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n131774,0,Revolving loans,F,N,Y,0,72000.0,202500.0,10125.0,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-19892,365243,-1300.0,-3423,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,0.7393966469228879,0.6524625642760948,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-139.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n131237,0,Cash loans,M,N,N,0,135000.0,691020.0,22288.5,495000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010556,-14014,-1483,-1795.0,-4226,,1,1,0,1,0,0,Laborers,1.0,3,3,SATURDAY,14,0,0,0,0,0,0,Self-employed,,0.31531792265507536,0.6144143775673561,0.1959,0.0045,0.9786,,,0.0,0.1034,0.1667,,0.015,,0.040999999999999995,,0.0061,0.1996,0.0047,0.9786,,,0.0,0.1034,0.1667,,0.0154,,0.0427,,0.0065,0.1978,0.0045,0.9786,,,0.0,0.1034,0.1667,,0.0153,,0.0417,,0.0063,,block of flats,0.065,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-834.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n315428,0,Cash loans,M,Y,N,1,243000.0,900000.0,35824.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-9721,-1868,-3179.0,-1999,64.0,1,1,0,1,0,0,Drivers,3.0,2,2,WEDNESDAY,8,0,1,1,0,1,1,Construction,0.1792890845269396,0.4198333009608081,0.06555002632575951,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-789.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n446144,0,Cash loans,M,N,Y,1,135000.0,208512.0,23710.5,180000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.030755,-14152,-453,-6436.0,-3933,,1,1,1,1,1,1,Drivers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Transport: type 3,,0.6663111894678413,0.324891229465852,0.0165,0.0407,0.9722,0.6192,0.0,0.0,0.0345,0.0417,0.0833,0.0115,0.0134,0.0117,0.0,0.0,0.0168,0.0423,0.9722,0.6341,0.0,0.0,0.0345,0.0417,0.0833,0.0117,0.0147,0.0121,0.0,0.0,0.0167,0.0407,0.9722,0.6243,0.0,0.0,0.0345,0.0417,0.0833,0.0117,0.0137,0.0119,0.0,0.0,reg oper account,block of flats,0.0092,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2851.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n275301,0,Cash loans,F,N,Y,1,81000.0,206280.0,10161.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019101,-15119,-949,-5769.0,-2382,,1,1,0,1,0,0,Cooking staff,2.0,2,2,SATURDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.6321341431166888,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2014.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n333790,0,Revolving loans,F,N,Y,0,135000.0,382500.0,19125.0,382500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.0105,-18772,-201,-10257.0,-2326,,1,1,0,1,0,0,Sales staff,1.0,3,3,THURSDAY,11,0,0,0,0,0,0,Trade: type 7,,0.6807395544605513,0.4561097392782771,0.1103,,0.9861,,,0.12,0.1034,0.3333,,,,,,,0.1124,,0.9861,,,0.1208,0.1034,0.3333,,,,,,,0.1114,,0.9861,,,0.12,0.1034,0.3333,,,,,,,,block of flats,0.0893,Panel,No,0.0,0.0,0.0,0.0,-144.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n330320,1,Cash loans,F,N,Y,0,135000.0,808650.0,23305.5,675000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.02461,-20546,365243,-2591.0,-4010,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.29359286276864643,0.0005272652387098817,0.1454,0.0845,0.9866,0.8164,0.0935,0.16,0.1379,0.3333,0.375,0.1022,0.1202,0.1469,0.0039,0.0045,0.1481,0.0877,0.9866,0.8236,0.0944,0.1611,0.1379,0.3333,0.375,0.1045,0.1313,0.1531,0.0039,0.0048,0.1468,0.0845,0.9866,0.8189,0.0941,0.16,0.1379,0.3333,0.375,0.1039,0.1223,0.1496,0.0039,0.0046,reg oper account,block of flats,0.1166,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n303680,0,Cash loans,M,Y,Y,0,56700.0,450000.0,16294.5,450000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.014519999999999996,-23767,365243,-3180.0,-4070,2.0,1,0,0,1,1,0,,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.6414148487292374,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-785.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n283570,0,Cash loans,F,Y,Y,2,157500.0,808650.0,26217.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-15634,-8149,-4517.0,-4539,1.0,1,1,0,1,0,0,Medicine staff,4.0,2,2,SATURDAY,13,0,0,0,0,1,1,Medicine,,0.6388860972610292,0.5513812618027899,0.0124,,0.9747,,,0.0,0.1034,0.0417,,0.0065,,0.0104,,0.0138,0.0126,,0.9747,,,0.0,0.1034,0.0417,,0.0067,,0.0108,,0.0146,0.0125,,0.9747,,,0.0,0.1034,0.0417,,0.0066,,0.0106,,0.0141,,block of flats,0.0091,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1949.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n199918,0,Cash loans,F,N,Y,0,135000.0,927000.0,33295.5,927000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00733,-19224,-669,-1648.0,-2727,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,11,0,1,1,0,1,1,Medicine,0.7783641775575297,0.2990048979514391,0.24988506275045536,0.0041,,0.9752,0.66,0.0015,,,0.0,0.0417,,0.0034,0.0031,,,0.0042,,0.9752,0.6733,0.0015,,,0.0,0.0417,,0.0037,0.0032,,,0.0042,,0.9752,0.6645,0.0015,,,0.0,0.0417,,0.0034,0.0032,,,not specified,block of flats,0.0024,Wooden,No,2.0,0.0,2.0,0.0,-388.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n364450,0,Cash loans,M,N,Y,0,135000.0,396000.0,28944.0,396000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.015221,-11387,-1174,-1685.0,-1693,,1,1,0,1,1,0,Security staff,2.0,2,2,MONDAY,12,0,0,0,1,1,0,Other,0.15856063127118006,0.2830758063959869,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-75.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n378549,0,Cash loans,F,N,N,1,135000.0,1170000.0,34209.0,1170000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018209,-11524,-1892,-688.0,-2964,,1,1,1,1,1,0,Sales staff,3.0,3,3,FRIDAY,18,0,0,0,0,0,0,Business Entity Type 3,,0.4963172625025938,0.5709165417729987,0.2598,0.1548,0.9886,0.8436,0.4871,0.28,0.2414,0.3333,0.375,0.033,0.2118,0.2687,0.0,0.1223,0.2647,0.1606,0.9886,0.8497,0.4916,0.282,0.2414,0.3333,0.375,0.0337,0.2314,0.2799,0.0,0.1295,0.2623,0.1548,0.9886,0.8457,0.4902,0.28,0.2414,0.3333,0.375,0.0336,0.2155,0.2735,0.0,0.1249,reg oper account,block of flats,0.2663,Panel,No,1.0,0.0,1.0,0.0,-431.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n394086,0,Cash loans,M,Y,Y,1,270000.0,450000.0,22018.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-9615,-2132,-3908.0,-1896,4.0,1,1,0,1,1,0,Sales staff,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Trade: type 7,0.2320140503244495,0.6634559816188926,0.5779691187553125,0.0804,0.046,0.9945,,,0.08,0.069,0.375,,,,0.0854,,0.0,0.0819,0.0478,0.9945,,,0.0806,0.069,0.375,,,,0.0889,,0.0,0.0812,0.046,0.9945,,,0.08,0.069,0.375,,,,0.0869,,0.0,,block of flats,0.0671,Panel,No,0.0,0.0,0.0,0.0,-360.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,6.0,0.0,4.0\n215964,0,Revolving loans,F,N,Y,0,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-8521,-376,-1516.0,-53,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,Self-employed,,0.6412337635480023,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-700.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n301110,1,Cash loans,M,Y,N,0,112500.0,167076.0,13059.0,135000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,With parents,0.01885,-11782,-412,-10742.0,-4365,9.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,13,0,0,0,1,1,0,Transport: type 2,0.15692117285711654,0.09013869693430764,0.3962195720630885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1483.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,10.0\n357350,0,Cash loans,F,Y,Y,0,112500.0,271066.5,21861.0,234000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.010147,-17080,-1130,-13178.0,-631,15.0,1,1,1,1,1,0,Low-skill Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,0.4992580911800843,0.3811510276857106,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n321420,0,Cash loans,F,N,Y,0,157500.0,1080000.0,38394.0,1080000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-18140,-2557,-381.0,-209,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,12,0,0,0,1,1,0,Self-employed,,0.08912874523505389,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1516.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n377603,0,Cash loans,M,Y,N,1,225000.0,687600.0,19039.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.010276,-10818,-1803,-4835.0,-3461,9.0,1,1,0,1,0,1,Core staff,2.0,2,2,FRIDAY,13,0,1,1,0,0,0,Police,0.6210812189269356,0.6665591080662874,,0.1113,0.0824,0.9861,,,0.12,0.1034,0.3333,,,,0.109,,0.1892,0.1134,0.0856,0.9861,,,0.1208,0.1034,0.3333,,,,0.1136,,0.2003,0.1124,0.0824,0.9861,,,0.12,0.1034,0.3333,,,,0.111,,0.1931,,block of flats,0.1269,Panel,No,1.0,0.0,1.0,0.0,-2013.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n139671,0,Revolving loans,F,N,Y,0,90000.0,180000.0,9000.0,180000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.020713,-8716,-238,-2376.0,-1367,,1,1,0,1,0,0,,1.0,3,3,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5697177861497342,,0.0619,0.0532,0.9886,0.8436,0.0084,,0.1379,0.1667,0.2083,0.0552,0.0504,0.0532,,0.0,0.063,0.0552,0.9886,0.8497,0.0084,,0.1379,0.1667,0.2083,0.0564,0.0551,0.0554,,0.0,0.0625,0.0532,0.9886,0.8457,0.0084,,0.1379,0.1667,0.2083,0.0561,0.0513,0.0542,,0.0,reg oper spec account,block of flats,0.0464,Panel,No,0.0,0.0,0.0,0.0,-283.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n262994,0,Cash loans,F,N,Y,0,90000.0,176328.0,11911.5,139500.0,Unaccompanied,State servant,Incomplete higher,Civil marriage,With parents,0.008068,-8582,-1214,-3208.0,-1233,,1,1,0,1,0,0,Core staff,2.0,3,3,THURSDAY,8,0,0,0,0,0,0,Kindergarten,,0.005866077196215112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n225013,0,Revolving loans,M,Y,N,0,202500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.028663,-9938,-1403,-1512.0,-2588,10.0,1,1,0,1,0,0,Managers,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Trade: type 7,0.3370422289314138,0.4007306675244545,0.6380435278721609,0.0495,0.0118,0.9786,0.7076,0.0,0.04,0.0345,0.3333,0.375,0.0,,0.0415,,0.0566,0.0504,0.0123,0.9786,0.7190000000000001,0.0,0.0403,0.0345,0.3333,0.375,0.0,,0.0433,,0.06,0.05,0.0118,0.9786,0.7115,0.0,0.04,0.0345,0.3333,0.375,0.0,,0.0423,,0.0578,,block of flats,0.0559,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-170.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n192394,0,Cash loans,F,N,Y,0,135000.0,755190.0,33394.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.007273999999999998,-24245,365243,-12.0,-4347,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.6480414155101238,,0.0495,0.0699,0.9806,0.7348,0.0073,0.0,0.1148,0.125,0.0417,0.0097,0.0398,0.0288,0.0025,0.0306,0.0168,0.0461,0.9752,0.6733,0.0019,0.0,0.1379,0.1667,0.0417,0.0,0.0147,0.009000000000000001,0.0039,0.0,0.0625,0.0812,0.9786,0.7115,0.01,0.0,0.1379,0.1667,0.0417,0.0,0.0504,0.0329,0.0039,0.0081,reg oper account,block of flats,0.0434,Panel,No,0.0,0.0,0.0,0.0,-63.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n444595,0,Cash loans,M,Y,Y,0,135000.0,592560.0,40216.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-22265,365243,-8700.0,-3950,20.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.3967462658724373,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n134570,0,Cash loans,F,N,Y,0,112500.0,675000.0,21906.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.026392,-19617,-2540,-7874.0,-3008,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,MONDAY,10,0,0,0,0,0,0,Industry: type 9,,0.6927857608438686,0.6528965519806539,0.0619,0.0636,0.9752,,,0.0,0.1034,0.1667,,0.0215,,0.0501,,0.0,0.063,0.066,0.9752,,,0.0,0.1034,0.1667,,0.022000000000000002,,0.0522,,0.0,0.0625,0.0636,0.9752,,,0.0,0.1034,0.1667,,0.0219,,0.051,,0.0,,block of flats,0.0394,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-3347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n191329,0,Revolving loans,M,Y,Y,0,900000.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006305,-21573,-7139,-4131.0,-3255,0.0,1,1,0,0,0,0,Managers,2.0,3,3,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.7979370445375347,0.5627194869842227,0.4294236843421945,0.0237,0.0278,0.9935,0.9116,0.0215,0.0,0.0345,0.1667,0.2083,0.026000000000000002,0.0193,0.0272,0.0039,0.0053,0.0242,0.0289,0.9935,0.9151,0.0217,0.0,0.0345,0.1667,0.2083,0.0266,0.0211,0.0283,0.0039,0.0056,0.0239,0.0278,0.9935,0.9128,0.0216,0.0,0.0345,0.1667,0.2083,0.0265,0.0197,0.0277,0.0039,0.0054,reg oper account,block of flats,0.0225,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-493.0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n450949,0,Cash loans,M,Y,N,1,135000.0,717003.0,23260.5,598500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-10848,-679,-439.0,-3520,15.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.2832273649012273,0.4622701594615979,0.511891801533151,0.0866,0.1073,0.9891,0.8504,0.0476,0.0,0.2069,0.1667,0.2083,0.0204,0.0706,0.0846,0.0,0.0,0.0882,0.1113,0.9891,0.8563,0.048,0.0,0.2069,0.1667,0.2083,0.0209,0.0771,0.0881,0.0,0.0,0.0874,0.1073,0.9891,0.8524,0.0479,0.0,0.2069,0.1667,0.2083,0.0207,0.0718,0.0861,0.0,0.0,reg oper account,block of flats,0.0926,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-491.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n140058,0,Cash loans,F,N,N,0,87750.0,119448.0,7434.0,94500.0,Unaccompanied,Working,Secondary / secondary special,Widow,Office apartment,0.035792000000000004,-16705,-1216,-5374.0,-259,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,13,0,0,0,1,1,0,Other,0.4323580367294567,0.6729675593409539,,,0.0831,0.9776,,,,0.1379,0.1667,,,,0.0683,,,,0.0862,0.9777,,,,0.1379,0.1667,,,,0.0712,,,,0.0831,0.9776,,,,0.1379,0.1667,,,,0.0696,,,,,0.0692,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1464.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n280809,1,Cash loans,F,N,N,0,117000.0,139500.0,11313.0,139500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00702,-15851,-2506,-7758.0,-4127,,1,1,0,1,1,0,Laborers,1.0,2,2,FRIDAY,8,0,0,0,0,1,1,Industry: type 11,,0.41104992814626856,0.13595104417329515,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,0.0,-1881.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n147025,0,Cash loans,F,N,Y,0,90000.0,508495.5,21672.0,454500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.0105,-21127,365243,-13571.0,-3995,,1,0,0,1,0,0,,2.0,3,3,MONDAY,13,0,0,0,0,0,0,XNA,,0.051238867598708944,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n146311,0,Cash loans,F,N,Y,0,112500.0,301500.0,21847.5,301500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-19987,-88,-2854.0,-2833,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Self-employed,,0.4346869981569854,0.7801436381572275,0.0041,0.0,0.9722,0.6192,0.0,0.0,0.1379,0.0,0.0417,,0.0034,0.004,0.0,0.0,0.0042,0.0,0.9722,0.6341,0.0,0.0,0.1379,0.0,0.0417,,0.0037,0.0042,0.0,0.0,0.0042,0.0,0.9722,0.6243,0.0,0.0,0.1379,0.0,0.0417,,0.0034,0.0041,0.0,0.0,,terraced house,0.0032,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1344.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n174644,0,Revolving loans,M,N,Y,0,112500.0,675000.0,33750.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-16562,-600,-8275.0,-106,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3491743459228093,0.6989960593018631,0.722392890081304,0.0867,0.0153,0.9881,0.8368,0.0003,0.0856,0.0738,0.3687,0.4104,0.0013,0.0702,0.0564,0.0022,0.1117,0.084,0.0156,0.9886,0.8497,0.0,0.0806,0.069,0.375,0.4167,0.0,0.0735,0.0562,0.0,0.113,0.0833,0.0151,0.9886,0.8457,0.0,0.08,0.069,0.375,0.4167,0.0,0.0684,0.0543,0.0,0.1089,reg oper account,block of flats,0.0657,Panel,No,0.0,0.0,0.0,0.0,-202.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n224937,0,Cash loans,F,N,Y,0,112500.0,185652.0,9157.5,121500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-18419,-9485,-8158.0,-1920,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.5756256653620986,0.20915469884100693,0.0825,0.0964,0.9891,0.8504,0.0139,0.0,0.2069,0.1667,0.2083,0.1003,0.0672,0.0884,0.0,0.0,0.084,0.1,0.9891,0.8563,0.013999999999999999,0.0,0.2069,0.1667,0.2083,0.1025,0.0735,0.0921,0.0,0.0,0.0833,0.0964,0.9891,0.8524,0.0139,0.0,0.2069,0.1667,0.2083,0.102,0.0684,0.09,0.0,0.0,reg oper account,block of flats,0.0767,Panel,No,7.0,0.0,7.0,0.0,-1135.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n341983,0,Cash loans,F,Y,N,0,180000.0,298512.0,21721.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-14609,-1207,-2630.0,-4413,1.0,1,1,1,1,1,0,,2.0,3,3,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.592549477090954,0.1392858609449651,0.6894791426446275,0.0887,0.0409,0.9866,0.8164,0.0275,0.04,0.0345,0.3333,0.375,0.0542,0.0723,0.052000000000000005,0.0,0.0,0.0672,0.0425,0.9866,0.8236,0.0229,0.0403,0.0345,0.3333,0.375,0.0555,0.0588,0.040999999999999995,0.0,0.0,0.0895,0.0409,0.9866,0.8189,0.0277,0.04,0.0345,0.3333,0.375,0.0552,0.0735,0.053,0.0,0.0,reg oper account,block of flats,0.0434,Panel,No,2.0,2.0,2.0,2.0,-2191.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n123233,0,Revolving loans,F,N,N,0,90000.0,180000.0,9000.0,,,Working,Secondary / secondary special,Single / not married,House / apartment,0.00496,-12920,-100,-5986.0,-4363,,1,1,0,1,1,0,Security staff,1.0,2,2,SATURDAY,18,0,0,0,0,0,0,Security,0.09693428125269744,0.6038956808069772,0.3740208032583212,0.0748,,0.9851,,,,0.0728,0.1667,,,,,,,0.063,,0.9856,,,,0.069,0.1667,,,,,,,0.0625,,0.9856,,,,0.069,0.1667,,,,,,,,block of flats,0.0292,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-564.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,1.0\n329735,0,Cash loans,M,Y,Y,0,157500.0,76410.0,8104.5,67500.0,Family,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.030755,-13291,-191,-4122.0,-4122,8.0,1,1,0,1,0,1,Drivers,1.0,2,2,MONDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.20286447704671265,0.6478019716067698,0.7380196196295241,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2699.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n407267,0,Cash loans,F,N,Y,0,67500.0,47970.0,4873.5,45000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.019101,-24477,365243,-9011.0,-4590,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.6573650764576678,0.6363761710860439,0.0433,0.0378,0.9871,0.8232,0.006999999999999999,0.0,0.1034,0.1667,0.0417,0.0204,0.0353,0.0383,0.0,0.0,0.0441,0.0392,0.9871,0.8301,0.0071,0.0,0.1034,0.1667,0.0417,0.0209,0.0386,0.0399,0.0,0.0,0.0437,0.0378,0.9871,0.8256,0.0071,0.0,0.1034,0.1667,0.0417,0.0208,0.0359,0.039,0.0,0.0,reg oper account,block of flats,0.0339,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-639.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n395917,0,Cash loans,M,N,Y,0,67500.0,239850.0,23850.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00496,-24924,365243,-10627.0,-4111,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.18732894031673186,0.622922000268356,0.0515,0.0771,0.9851,,,0.0,0.0862,0.3125,,0.0623,,0.034,,0.0006,0.0525,0.08,0.9831,,,0.0,0.0345,0.1667,,0.0637,,0.0355,,0.0006,0.052000000000000005,0.0771,0.9851,,,0.0,0.0862,0.3125,,0.0633,,0.0347,,0.0006,,block of flats,0.0405,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-455.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n360976,0,Revolving loans,F,N,Y,1,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.028663,-10643,-427,-2760.0,-2940,,1,1,0,1,0,0,,3.0,2,2,THURSDAY,16,0,0,0,0,0,0,Security,0.5168268926740176,0.5170082978006568,0.36227724703843145,0.0103,0.0,0.9702,,,0.0,0.0517,0.0417,,0.0,,0.0123,,0.0,0.0084,0.0,0.9702,,,0.0,0.0345,0.0417,,0.0,,0.008,,0.0,0.0104,0.0,0.9702,,,0.0,0.0517,0.0417,,0.0,,0.0126,,0.0,,block of flats,0.0242,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-2450.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n388577,0,Revolving loans,F,N,Y,0,180000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-13480,-2404,-7571.0,-4670,,1,1,0,1,1,0,Sales staff,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Self-employed,0.4048144323383421,0.1072444742709135,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.0,0.0,12.0,0.0,-622.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n277431,0,Cash loans,F,N,N,0,103500.0,314100.0,16164.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-18197,-5383,-2853.0,-1749,,1,1,0,1,1,0,,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 2,0.5508021644490857,0.4773952890084541,0.5779691187553125,0.0577,0.0738,0.9781,,,0.0,0.1379,0.1667,,0.0647,,0.0529,,0.1033,0.0588,0.0766,0.9782,,,0.0,0.1379,0.1667,,0.0662,,0.0551,,0.1093,0.0583,0.0738,0.9781,,,0.0,0.1379,0.1667,,0.0659,,0.0538,,0.1054,,block of flats,0.0688,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n346158,0,Cash loans,F,N,Y,0,76500.0,450000.0,22018.5,450000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-22046,365243,-2296.0,-4558,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.6795454823560807,,0.0531,0.0359,0.9801,0.728,0.0106,0.0,0.069,0.125,0.1667,0.3619,0.0433,0.0339,0.0,0.0,0.0473,0.0373,0.9737,0.6537,0.0066,0.0,0.0345,0.0833,0.125,0.3702,0.0413,0.0263,0.0,0.0,0.0536,0.0359,0.9801,0.7316,0.0107,0.0,0.069,0.125,0.1667,0.3682,0.044000000000000004,0.0345,0.0,0.0,reg oper account,block of flats,0.028999999999999998,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n407706,0,Cash loans,F,N,N,1,360000.0,2013840.0,53253.0,1800000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-13946,-245,-732.0,-5787,,1,1,0,1,0,0,Private service staff,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Services,,0.6870692712921708,0.7380196196295241,0.1402,0.1595,0.9771,0.6872,0.006999999999999999,0.0,0.2759,0.1667,0.2083,0.0901,0.1126,0.1098,0.0077,0.0565,0.1429,0.1655,0.9772,0.6994,0.0071,0.0,0.2759,0.1667,0.2083,0.0921,0.12300000000000001,0.1144,0.0078,0.0599,0.1416,0.1595,0.9771,0.6914,0.0071,0.0,0.2759,0.1667,0.2083,0.0916,0.1146,0.1118,0.0078,0.0577,reg oper account,block of flats,0.1025,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-403.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n397774,0,Revolving loans,F,Y,Y,0,540000.0,450000.0,22500.0,450000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.072508,-8886,-1158,-363.0,-364,1.0,1,1,0,1,0,0,Accountants,2.0,1,1,THURSDAY,13,0,0,0,0,0,0,Construction,,0.7152333740719159,0.2955826421513093,0.4454,0.2251,0.9836,0.7756,0.158,0.8,0.3448,0.4583,0.0417,0.0436,0.3572,0.4252,0.027000000000000003,0.0134,0.4538,0.2336,0.9836,0.7844,0.1595,0.8056,0.3448,0.4583,0.0417,0.0446,0.3903,0.44299999999999995,0.0272,0.0142,0.4497,0.2251,0.9836,0.7786,0.159,0.8,0.3448,0.4583,0.0417,0.0443,0.3634,0.4329,0.0272,0.0136,reg oper account,block of flats,0.3374,Panel,No,0.0,0.0,0.0,0.0,-320.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n333945,0,Cash loans,M,Y,Y,1,405000.0,754740.0,24345.0,630000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.035792000000000004,-17374,-1068,-615.0,-927,13.0,1,1,1,1,0,0,,3.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6897148596840477,0.07905969599457675,0.0412,,0.9916,,,,0.1379,0.1667,,,,0.0511,,,0.042,,0.9916,,,,0.1379,0.1667,,,,0.0533,,,0.0416,,0.9916,,,,0.1379,0.1667,,,,0.0521,,,,block of flats,0.0451,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-190.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,4.0\n165788,0,Cash loans,F,N,N,0,117000.0,227520.0,15331.5,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-14543,-2493,-3139.0,-4593,,1,1,1,1,1,0,Laborers,1.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Business Entity Type 3,0.3711424927267198,0.3056419364868849,0.6479768603302221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-825.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n324326,0,Cash loans,F,Y,Y,1,135000.0,1056447.0,31018.5,922500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-17118,-333,-9734.0,-647,25.0,1,1,0,1,0,0,Cooking staff,3.0,2,2,TUESDAY,11,0,0,0,0,1,1,Restaurant,,0.5820600366685019,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1632.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n260305,0,Cash loans,F,N,Y,1,112500.0,1724220.0,50544.0,1350000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.020246,-8825,-818,-7413.0,-1509,,1,1,0,1,0,0,Core staff,3.0,3,3,FRIDAY,11,0,0,0,0,0,0,Self-employed,0.19049382481049407,0.4625854489464515,0.4848514754962666,0.0742,0.085,0.9806,0.7348,0.0096,0.0,0.1379,0.1667,0.2083,0.0627,0.0588,0.0671,0.0077,0.0109,0.0746,0.0878,0.9801,0.7387,0.0095,0.0,0.1379,0.1667,0.2083,0.0631,0.0643,0.0698,0.0039,0.0082,0.0749,0.085,0.9806,0.7383,0.0097,0.0,0.1379,0.1667,0.2083,0.0638,0.0599,0.0683,0.0078,0.0111,reg oper account,block of flats,0.0546,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-724.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n426951,0,Cash loans,F,N,Y,2,225000.0,508495.5,24462.0,454500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.018029,-13373,-1923,-4093.0,-1866,,1,1,0,1,0,0,Laborers,4.0,3,3,MONDAY,8,0,0,0,1,0,1,Business Entity Type 3,0.43747189597274977,0.6135413114239103,0.3108182544189319,0.1186,,0.9886,,,0.0,0.1034,0.1667,,,,0.1174,,0.0,0.1208,,0.9886,,,0.0,0.1034,0.1667,,,,0.1223,,0.0,0.1197,,0.9886,,,0.0,0.1034,0.1667,,,,0.1195,,0.0,,block of flats,0.0923,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-562.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n185779,0,Cash loans,M,Y,Y,0,81000.0,50940.0,5089.5,45000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.025164,-24350,365243,-3599.0,-1406,13.0,1,0,0,1,0,0,,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.3687444354203567,0.4543210601605785,0.1031,0.0605,0.9757,0.6668,0.129,0.0,0.1724,0.1667,0.0,0.0141,0.0773,0.0603,0.0309,0.0278,0.105,0.0628,0.9757,0.6798,0.1302,0.0,0.1724,0.1667,0.0,0.0145,0.0845,0.0628,0.0311,0.0295,0.1041,0.0605,0.9757,0.6713,0.1299,0.0,0.1724,0.1667,0.0,0.0144,0.0787,0.0613,0.0311,0.0284,,block of flats,0.0705,Panel,No,1.0,1.0,1.0,1.0,-1830.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n222949,0,Cash loans,M,N,Y,0,112500.0,101880.0,12217.5,90000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.025164,-9285,-1480,-1478.0,-1930,,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.4310580055236188,0.2471909382666521,0.099,0.0539,0.9901,0.8640000000000001,0.0837,0.08,0.0345,0.625,0.6667,0.0104,0.0799,0.1119,0.0039,0.0064,0.1008,0.0559,0.9901,0.8693,0.0845,0.0806,0.0345,0.625,0.6667,0.0107,0.0872,0.1166,0.0039,0.0068,0.0999,0.0539,0.9901,0.8658,0.0843,0.08,0.0345,0.625,0.6667,0.0106,0.0812,0.1139,0.0039,0.0066,reg oper account,block of flats,0.1352,Panel,No,0.0,0.0,0.0,0.0,-1313.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n348144,0,Cash loans,F,Y,N,0,112500.0,225000.0,11074.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-15264,-6806,-4148.0,-4146,12.0,1,1,1,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Hotel,,0.6330286647144852,0.28961123838200553,0.0165,0.0,0.9742,0.6464,0.0067,0.0,0.1379,0.0417,0.0833,,0.0134,0.0112,0.0618,0.0168,0.0168,0.0,0.9742,0.6602,0.0067,0.0,0.1379,0.0417,0.0833,,0.0147,0.0117,0.0623,0.0178,0.0167,0.0,0.9742,0.6511,0.0067,0.0,0.1379,0.0417,0.0833,,0.0137,0.0114,0.0621,0.0171,reg oper account,terraced house,0.0125,Panel,No,0.0,0.0,0.0,0.0,-2355.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n363660,0,Cash loans,M,Y,Y,0,180000.0,1322608.5,52582.5,1134000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23616,365243,-5519.0,-4473,26.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.7046793757551191,0.7324033228040929,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n416741,0,Cash loans,F,N,N,0,112500.0,1178217.0,34578.0,922500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21617,365243,-10341.0,-4378,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.7352619613558565,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1748.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n433222,0,Revolving loans,F,N,N,2,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-9552,-1333,-4215.0,-2227,,1,1,0,1,0,0,,4.0,2,2,FRIDAY,13,0,0,0,1,1,0,Business Entity Type 2,,0.1421813861896045,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1669.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n235794,0,Cash loans,M,N,N,0,126000.0,454500.0,19255.5,454500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.003069,-12752,-3792,-6491.0,-4319,,1,1,0,1,0,0,Security staff,1.0,3,3,THURSDAY,11,0,0,0,0,1,1,Security,,0.5989840902571293,0.2608559142068693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-760.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n370388,0,Cash loans,M,Y,N,0,90000.0,247275.0,19417.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.010006,-24097,365243,-80.0,-4854,17.0,1,0,0,1,1,0,,2.0,2,1,MONDAY,18,0,0,0,0,0,0,XNA,,0.047020239192837474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3519.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n414340,0,Cash loans,M,Y,N,2,157500.0,1350000.0,39604.5,1350000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006629,-10283,-1855,-4555.0,-2801,12.0,1,1,0,1,0,0,Core staff,4.0,2,2,TUESDAY,10,0,0,0,1,1,0,Government,0.3323223174323499,0.2101956380349501,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1370.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n383556,0,Cash loans,M,Y,N,0,180000.0,1006920.0,40063.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006305,-17745,-1790,-3313.0,-1287,13.0,1,1,0,1,0,0,Laborers,2.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,Self-employed,0.4879127230795669,0.457886528628765,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1258.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n238677,0,Cash loans,F,N,Y,0,31500.0,217602.0,15475.5,198000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-24938,365243,-3604.0,-3933,,1,0,0,1,1,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,0.7501839290421528,0.6380986918280921,,0.0835,,0.9846,,,0.08,0.0345,0.4583,,0.0098,,0.0733,,0.0151,0.0851,,0.9846,,,0.0806,0.0345,0.4583,,0.01,,0.0764,,0.016,0.0843,,0.9846,,,0.08,0.0345,0.4583,,0.01,,0.0747,,0.0154,,block of flats,0.0729,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n126379,1,Cash loans,F,N,Y,1,168750.0,460858.5,22414.5,324000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020713,-14967,-5827,-2408.0,-4721,,1,1,1,1,0,0,Medicine staff,2.0,3,3,FRIDAY,7,0,0,0,0,0,0,Other,,0.06536810799857785,0.2955826421513093,0.0603,0.0477,0.9786,0.7076,0.0073,0.0,0.1379,0.1042,0.1458,0.0743,0.0475,0.0504,0.0077,0.011000000000000001,0.0168,0.0,0.9757,0.6798,0.0012,0.0,0.069,0.0417,0.0833,0.024,0.0147,0.0104,0.0,0.0,0.0609,0.0477,0.9786,0.7115,0.0073,0.0,0.1379,0.1042,0.1458,0.0756,0.0483,0.0513,0.0078,0.0112,reg oper spec account,block of flats,0.0835,Wooden,No,0.0,0.0,0.0,0.0,-458.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n167923,0,Cash loans,F,N,Y,0,81000.0,143910.0,14148.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.0228,-21094,365243,-9226.0,-4454,,1,0,0,1,1,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.612462278771141,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n179626,0,Cash loans,F,N,Y,2,126000.0,298512.0,31801.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006852,-12735,-1417,-4290.0,-4310,,1,1,1,1,1,0,,4.0,3,3,MONDAY,6,0,0,0,0,0,0,Hotel,0.29800226293731685,0.6665882689080561,0.5762088360175724,,,0.9752,,,,0.069,0.0417,,,,0.0123,,,,,0.9752,,,,0.069,0.0417,,,,0.0128,,,,,0.9752,,,,0.069,0.0417,,,,0.0125,,,,block of flats,0.0192,Block,No,4.0,1.0,4.0,1.0,-947.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n421277,0,Cash loans,F,Y,N,0,108000.0,315000.0,20497.5,315000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.014519999999999996,-9329,-327,-4113.0,-1839,7.0,1,1,0,1,0,0,Accountants,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,Medicine,,0.6297613120523038,0.4311917977993083,0.1046,0.0704,0.9921,,,0.08,0.069,0.375,,0.0259,,0.0559,,0.0192,0.0819,0.0564,0.9906,,,0.0806,0.069,0.375,,0.0263,,0.0525,,0.019,0.1057,0.0704,0.9921,,,0.08,0.069,0.375,,0.0263,,0.0569,,0.0196,,block of flats,0.0832,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1821.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n309794,1,Cash loans,M,N,Y,0,202500.0,670185.0,44784.0,621000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009175,-11052,-382,-5620.0,-3277,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,15,0,0,0,1,1,0,Business Entity Type 2,,0.05880083471490069,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,3.0,1.0,-947.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n372079,0,Cash loans,F,N,Y,0,81000.0,794070.0,24930.0,794070.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.031329,-21902,-9096,-3431.0,-3649,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,8,0,0,0,0,1,1,Government,,0.5875660242686778,0.178760465484575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-650.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n302721,0,Cash loans,M,N,Y,0,135000.0,443088.0,16551.0,382500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019101,-17015,-310,-9619.0,-560,,1,1,0,1,0,0,Security staff,1.0,2,2,FRIDAY,13,0,1,1,0,0,0,University,,0.6915603621757781,0.18303516721781032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-24.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n446920,0,Cash loans,M,Y,Y,0,270000.0,271066.5,21105.0,234000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.010032,-19385,-1968,-13505.0,-2932,17.0,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,4,0,0,0,0,0,0,Transport: type 4,0.3891759730412325,0.6188422104907426,0.4206109640437848,0.0722,0.0428,0.9781,0.7008,0.0213,0.0,0.1379,0.1667,0.0417,0.0047,0.0807,0.0741,0.0039,0.0128,0.0735,0.0444,0.9782,0.7125,0.0215,0.0,0.1379,0.1667,0.0417,0.0048,0.0882,0.0772,0.0039,0.0135,0.0729,0.0428,0.9781,0.7048,0.0214,0.0,0.1379,0.1667,0.0417,0.0048,0.0821,0.0754,0.0039,0.013000000000000001,reg oper account,block of flats,0.0625,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n446733,0,Cash loans,M,Y,N,0,315000.0,1303200.0,46939.5,1125000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006233,-20282,-11427,-8815.0,-3758,2.0,1,1,0,1,1,1,,2.0,2,2,FRIDAY,10,0,0,0,0,1,1,Business Entity Type 2,0.6981139849928706,0.5346283444762738,0.21640296051521946,0.1227,0.1752,0.9876,0.83,0.0247,0.0,0.4138,0.1667,0.2083,0.1196,0.0975,0.1358,0.0116,0.0209,0.125,0.1818,0.9876,0.8367,0.0249,0.0,0.4138,0.1667,0.2083,0.1223,0.1065,0.1414,0.0117,0.0221,0.1239,0.1752,0.9876,0.8323,0.0248,0.0,0.4138,0.1667,0.2083,0.1217,0.0992,0.1382,0.0116,0.0214,reg oper account,block of flats,0.1113,Panel,No,2.0,0.0,2.0,0.0,-1742.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n370191,0,Cash loans,F,Y,Y,1,360000.0,502497.0,39829.5,454500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.006008,-13010,-2613,-499.0,-2315,7.0,1,1,0,1,0,1,High skill tech staff,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.7291422848189111,0.5196015869396391,0.5779691187553125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-962.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n356644,0,Cash loans,F,N,N,0,229500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.011703,-14600,-6972,-4449.0,-4554,,1,1,0,1,1,0,,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 1,,0.012837590190681678,0.5388627065779676,0.0454,0.0,0.9757,,,0.0,0.1034,0.125,,0.0109,,0.0255,,0.0119,0.0462,0.0,0.9757,,,0.0,0.1034,0.125,,0.0111,,0.0265,,0.0126,0.0458,0.0,0.9757,,,0.0,0.1034,0.125,,0.011000000000000001,,0.0259,,0.0121,,block of flats,0.0289,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1194.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n340409,0,Cash loans,M,N,N,0,225000.0,1418868.0,73597.5,1354500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-12301,-1265,-4011.0,-4415,,1,1,1,1,0,1,Core staff,2.0,1,1,MONDAY,14,0,1,1,0,1,1,Insurance,0.3390875585338268,0.7389612155437577,0.3740208032583212,0.0845,0.0475,0.9752,0.66,0.0322,0.0,0.1379,0.1667,0.2083,0.0,0.0672,0.0412,0.0077,0.081,0.0861,0.0493,0.9752,0.6733,0.0325,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.0429,0.0078,0.0857,0.0854,0.0475,0.9752,0.6645,0.0324,0.0,0.1379,0.1667,0.2083,0.0,0.0684,0.0419,0.0078,0.0827,reg oper account,block of flats,0.0501,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-733.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n411696,0,Cash loans,F,N,N,2,157500.0,728460.0,44563.5,675000.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.018029,-9937,-232,-4757.0,-217,,1,1,0,1,0,0,Sales staff,4.0,3,3,MONDAY,11,0,0,0,1,1,0,Trade: type 2,0.3793069391843891,0.2826410952002709,0.266456808245056,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n338072,0,Cash loans,F,N,N,0,202500.0,1546020.0,45333.0,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.072508,-13925,-668,-7926.0,-1943,,1,1,0,1,1,0,HR staff,1.0,1,1,FRIDAY,20,0,0,0,0,0,0,Business Entity Type 3,0.8524644173129897,0.7030064995147489,0.190705947811054,0.2144,0.1214,0.9871,0.8232,0.0,0.32,0.1379,0.5417,0.5833,0.0,0.1748,0.2536,0.0,0.0623,0.2185,0.126,0.9871,0.8301,0.0,0.3222,0.1379,0.5417,0.5833,0.0,0.191,0.2642,0.0,0.0659,0.2165,0.1214,0.9871,0.8256,0.0,0.32,0.1379,0.5417,0.5833,0.0,0.1779,0.2582,0.0,0.0636,reg oper account,block of flats,0.213,Panel,No,0.0,0.0,0.0,0.0,-455.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,2.0\n440805,0,Cash loans,M,Y,N,1,157500.0,1042560.0,40702.5,900000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.010276,-10189,-973,-42.0,-2589,64.0,1,1,0,1,0,0,,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Military,,0.6189814680499297,0.5424451438613613,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1832.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n356496,1,Cash loans,M,Y,Y,2,180000.0,810000.0,31653.0,810000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-11502,-1656,-5460.0,-3494,1.0,1,1,0,1,0,0,,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Other,,0.5489969954514825,0.2353105173615833,0.166,0.0974,0.9881,,,0.2,0.1724,0.3333,,0.1101,,0.1839,,0.0,0.1691,0.10099999999999999,0.9881,,,0.2014,0.1724,0.3333,,0.1126,,0.1916,,0.0,0.1676,0.0974,0.9881,,,0.2,0.1724,0.3333,,0.11199999999999999,,0.1872,,0.0,,block of flats,0.1447,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n134680,0,Cash loans,F,N,N,0,40500.0,339241.5,12312.0,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-20720,365243,-7502.0,-4022,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,7,0,0,0,0,0,0,XNA,0.7950917376774002,0.2453495532540649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n379842,1,Cash loans,M,N,N,0,270000.0,900000.0,45954.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-13583,-2284,-2601.0,-4287,,1,1,0,1,0,0,Managers,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,Construction,0.24636743675938144,0.4513262055466902,0.248535557339522,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1236.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n431983,0,Cash loans,F,Y,N,2,135000.0,299250.0,8023.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.031329,-15564,-5146,-7289.0,-4278,6.0,1,1,0,1,0,0,,4.0,2,2,MONDAY,16,0,0,0,0,0,0,Government,0.5057791829416621,0.5845103441468734,0.24318648044201235,0.0742,0.0541,0.9836,0.7756,0.0104,0.08,0.069,0.3333,0.375,0.0491,0.0605,0.079,0.0,0.0,0.0756,0.0561,0.9836,0.7844,0.0105,0.0806,0.069,0.3333,0.375,0.0502,0.0661,0.0824,0.0,0.0,0.0749,0.0541,0.9836,0.7786,0.0104,0.08,0.069,0.3333,0.375,0.0499,0.0616,0.0805,0.0,0.0,reg oper account,block of flats,0.0678,Block,No,4.0,0.0,4.0,0.0,-1966.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n368252,0,Cash loans,F,N,Y,0,180000.0,473760.0,50400.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.010276,-16009,-1011,-8724.0,-4999,,1,1,0,1,0,0,,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6189866253617129,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-481.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n272552,1,Cash loans,F,N,Y,0,157500.0,675000.0,24246.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009549,-11674,-760,-18.0,-3946,,1,1,1,1,0,0,Sales staff,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.1076286526804982,0.4416420777568383,0.41184855592423975,0.0124,,0.9727,0.626,0.0019,0.0,0.069,0.0417,0.0833,0.0123,0.0101,0.0089,0.0,0.0,0.0126,,0.9727,0.6406,0.0019,0.0,0.069,0.0417,0.0833,0.0126,0.011000000000000001,0.0093,0.0,0.0,0.0125,,0.9727,0.631,0.0019,0.0,0.069,0.0417,0.0833,0.0125,0.0103,0.0091,0.0,0.0,reg oper account,block of flats,0.006999999999999999,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-452.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n329331,0,Cash loans,F,N,N,0,112500.0,555273.0,16366.5,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-23153,365243,-13255.0,-4404,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.5684415015457652,,0.0629,0.0927,0.9816,0.7484,0.008,0.0,0.1379,0.1667,0.2083,0.0342,0.0471,0.0566,0.0193,0.0533,0.0641,0.0962,0.9816,0.7583,0.0081,0.0,0.1379,0.1667,0.2083,0.035,0.0514,0.0589,0.0195,0.0564,0.0635,0.0927,0.9816,0.7518,0.008,0.0,0.1379,0.1667,0.2083,0.0348,0.0479,0.0576,0.0194,0.0544,reg oper spec account,block of flats,0.0609,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1504.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n242282,0,Cash loans,F,N,Y,0,135000.0,198000.0,20416.5,198000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Rented apartment,0.028663,-19901,-4271,-6953.0,-2599,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.3920333207527142,0.5292654881881145,0.5100895276257282,0.1237,0.1364,0.9752,0.66,0.0131,0.0,0.2069,0.1667,0.2083,0.1271,0.0992,0.1134,0.0077,0.0213,0.1261,0.1415,0.9752,0.6733,0.0132,0.0,0.2069,0.1667,0.2083,0.13,0.1084,0.1182,0.0078,0.0225,0.1249,0.1364,0.9752,0.6645,0.0132,0.0,0.2069,0.1667,0.2083,0.1293,0.1009,0.1155,0.0078,0.0217,org spec account,block of flats,0.1035,Panel,No,0.0,0.0,0.0,0.0,-181.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n410244,0,Cash loans,F,N,N,2,112500.0,526491.0,21136.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008865999999999999,-13037,-2954,-3688.0,-4467,,1,1,1,1,0,0,,4.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 2,,0.5129992312546628,0.6528965519806539,0.067,0.0801,0.9737,0.6396,0.0097,0.0,0.1379,0.1667,0.2083,0.027000000000000003,0.0538,0.05,0.0039,0.0541,0.0683,0.0832,0.9737,0.6537,0.0098,0.0,0.1379,0.1667,0.2083,0.0276,0.0588,0.0521,0.0039,0.0572,0.0677,0.0801,0.9737,0.6444,0.0098,0.0,0.1379,0.1667,0.2083,0.0275,0.0547,0.0509,0.0039,0.0552,reg oper account,block of flats,0.0564,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1066.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n332040,0,Cash loans,F,N,Y,0,225000.0,1272888.0,37341.0,1111500.0,Children,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-20818,-1027,-7731.0,-4378,,1,1,0,1,0,0,Security staff,2.0,3,2,FRIDAY,14,0,0,0,0,0,0,Construction,0.6666659379327761,0.494343887877229,0.520897599048938,0.1577,0.1062,0.9841,0.7824,0.0369,0.2,0.1724,0.3333,0.375,0.0379,0.1252,0.1712,0.0154,0.0136,0.1607,0.1102,0.9841,0.7909,0.0373,0.2014,0.1724,0.3333,0.375,0.0388,0.1368,0.1784,0.0156,0.0144,0.1593,0.1062,0.9841,0.7853,0.0372,0.2,0.1724,0.3333,0.375,0.0386,0.1274,0.1743,0.0155,0.0139,reg oper account,block of flats,0.1578,Panel,No,3.0,0.0,3.0,0.0,-11.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n119603,1,Cash loans,M,N,Y,1,157500.0,298512.0,21762.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-10748,-144,-5074.0,-3375,,1,1,1,1,0,0,Laborers,3.0,2,2,FRIDAY,16,0,1,1,0,1,1,Business Entity Type 2,,0.6101136627397501,0.4812493411434029,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-583.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n302006,0,Revolving loans,F,N,N,0,315000.0,900000.0,45000.0,900000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.030755,-22966,365243,-2877.0,-434,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,,0.7148011514207694,0.622922000268356,0.0742,0.0517,0.9826,0.7688,0.0467,0.08,0.069,0.3333,0.0417,0.0498,0.0605,0.0408,0.0,0.0016,0.0756,0.0537,0.9826,0.7779,0.0471,0.0806,0.069,0.3333,0.0417,0.051,0.0661,0.0424,0.0,0.0,0.0749,0.0517,0.9826,0.7719,0.047,0.08,0.069,0.3333,0.0417,0.0507,0.0616,0.0415,0.0,0.0016,reg oper account,block of flats,0.0575,Panel,No,0.0,0.0,0.0,0.0,-1852.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n242310,0,Cash loans,M,N,N,0,90000.0,143910.0,15111.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008625,-16319,-1029,-2106.0,-4612,,1,1,1,1,0,0,Security staff,2.0,2,2,SUNDAY,14,0,0,0,0,0,0,Security,,0.3175647101457025,0.5478104658520093,0.0742,0.0446,0.9821,,,0.08,0.069,0.3333,,0.0135,,0.075,,0.0,0.0756,0.0463,0.9821,,,0.0806,0.069,0.3333,,0.0138,,0.0782,,0.0,0.0749,0.0446,0.9821,,,0.08,0.069,0.3333,,0.0138,,0.0764,,0.0,,block of flats,0.0767,Panel,No,0.0,0.0,0.0,0.0,-1162.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n125358,0,Cash loans,F,N,Y,0,157500.0,599778.0,30753.0,477000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007305,-18819,365243,-2009.0,-2368,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,12,0,0,0,1,0,0,XNA,0.5369200153792575,0.6229291658732355,0.13342925446731707,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,0.0,-128.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n191409,0,Cash loans,F,N,N,0,225000.0,740088.0,29479.5,661500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.072508,-21448,365243,-394.0,-4810,,1,0,0,1,1,0,,1.0,1,1,THURSDAY,19,0,0,0,0,0,0,XNA,0.7249480427884603,0.6976001354669028,0.4206109640437848,0.2753,0.13699999999999998,0.9965,0.9524,0.5357,0.4,0.1724,0.5417,0.5833,0.024,0.21600000000000005,0.2543,0.0386,0.0182,0.2805,0.1421,0.9965,0.9543,0.5405,0.4028,0.1724,0.5417,0.5833,0.0245,0.23600000000000002,0.2649,0.0389,0.0193,0.2779,0.13699999999999998,0.9965,0.953,0.539,0.4,0.1724,0.5417,0.5833,0.0244,0.2198,0.2588,0.0388,0.0186,reg oper account,block of flats,0.2039,Panel,No,0.0,0.0,0.0,0.0,-2487.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n198403,0,Cash loans,M,N,Y,0,112500.0,315000.0,14683.5,315000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-9067,-1096,-8251.0,-1726,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,11,0,0,0,1,1,0,Self-employed,0.2082893664305448,0.7036042577496636,0.3556387169923543,0.0186,,0.9886,,,0.0,0.1034,0.0417,,,,0.019,,0.0,0.0189,,0.9886,,,0.0,0.1034,0.0417,,,,0.0198,,0.0,0.0187,,0.9886,,,0.0,0.1034,0.0417,,,,0.0194,,0.0,,block of flats,0.015,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-397.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n173337,0,Cash loans,F,N,Y,0,202500.0,261000.0,8482.5,261000.0,Family,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.072508,-20327,-3204,-10300.0,-3620,,1,1,1,1,0,0,High skill tech staff,1.0,1,1,FRIDAY,17,0,0,0,0,0,0,Medicine,,0.6121767726115808,0.6512602186973006,0.0711,0.066,0.9722,0.6192,0.0471,0.0,0.1379,0.1667,0.2083,0.0,0.0563,0.0567,0.0077,0.0519,0.0609,0.0531,0.9722,0.6341,0.0416,0.0,0.1379,0.1667,0.2083,0.0,0.0514,0.0532,0.0078,0.0527,0.0718,0.066,0.9722,0.6243,0.0474,0.0,0.1379,0.1667,0.2083,0.0,0.0573,0.0577,0.0078,0.053,reg oper account,block of flats,0.0508,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n197357,0,Revolving loans,F,Y,Y,0,135000.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-17896,-7804,-5117.0,-1422,13.0,1,1,0,1,1,0,High skill tech staff,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 2,0.6235660356613856,0.6296491338873488,0.7238369900414456,0.132,0.0518,0.9846,0.7892,0.0308,0.08,0.0345,0.625,0.6667,,0.1051,0.1104,0.0116,0.0086,0.1345,0.0537,0.9846,0.7975,0.0311,0.0806,0.0345,0.625,0.6667,,0.1148,0.115,0.0117,0.0091,0.1332,0.0518,0.9846,0.792,0.031,0.08,0.0345,0.625,0.6667,,0.1069,0.1124,0.0116,0.0087,reg oper spec account,block of flats,0.1056,Panel,No,0.0,0.0,0.0,0.0,-282.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n219209,0,Cash loans,M,Y,Y,0,180000.0,473841.0,34609.5,387000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-16468,-750,-5367.0,-25,28.0,1,1,0,1,0,0,Laborers,1.0,3,3,SATURDAY,7,0,0,0,0,1,1,Self-employed,0.4519716025790116,0.5738512114687663,0.0969483170508572,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-526.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n447443,0,Cash loans,F,N,Y,0,135000.0,450000.0,17095.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.014519999999999996,-15992,-2329,-9073.0,-2043,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Trade: type 7,,0.2034696657504089,0.13342925446731707,0.2067,0.1161,0.9881,0.8368,0.0243,0.11,0.0948,0.3438,0.3854,0.1339,0.1683,0.1572,0.0154,0.0327,0.1744,0.1087,0.9871,0.8301,0.0,0.0806,0.069,0.3333,0.375,0.0867,0.1524,0.1253,0.0,0.0072,0.1999,0.1075,0.9876,0.8323,0.0345,0.08,0.069,0.3333,0.375,0.1372,0.1637,0.1521,0.0097,0.0337,reg oper account,block of flats,0.1664,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-723.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n123080,0,Revolving loans,F,N,Y,0,56250.0,157500.0,7875.0,157500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.007273999999999998,-21860,365243,-10093.0,-4242,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.4710542716090835,0.7726311345553628,0.2969,0.2486,0.9831,0.7688,0.0702,0.32,0.2759,0.3333,0.375,0.1142,0.2421,0.3113,0.0,0.0,0.3025,0.258,0.9831,0.7779,0.0708,0.3222,0.2759,0.3333,0.375,0.1168,0.2645,0.3243,0.0,0.0,0.2998,0.2486,0.9831,0.7719,0.0706,0.32,0.2759,0.3333,0.375,0.1162,0.2463,0.3169,0.0,0.0,reg oper account,block of flats,0.2832,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-543.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n168145,0,Cash loans,F,Y,Y,0,225000.0,1078200.0,31653.0,900000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.007305,-22362,365243,-228.0,-5456,6.0,1,0,0,1,0,0,,1.0,3,3,FRIDAY,7,0,0,0,0,0,0,XNA,,0.12944156480523994,0.7352209993926424,0.0206,0.0004,0.9831,0.7688,0.0028,0.0,0.0345,0.1667,0.2083,0.0055,0.0168,0.0179,0.0,0.0,0.021,0.0004,0.9831,0.7779,0.0028,0.0,0.0345,0.1667,0.2083,0.0056,0.0184,0.0186,0.0,0.0,0.0208,0.0004,0.9831,0.7719,0.0028,0.0,0.0345,0.1667,0.2083,0.0056,0.0171,0.0182,0.0,0.0,reg oper account,block of flats,0.0156,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-336.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n390327,0,Revolving loans,F,N,Y,0,360000.0,495000.0,24750.0,495000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-13788,-4595,-7885.0,-4906,,1,1,0,1,0,0,,1.0,1,1,THURSDAY,19,0,0,0,0,0,0,Business Entity Type 3,0.7621738275117058,0.7820408243872593,0.8050196619153701,0.4216,0.1617,0.9851,0.7959999999999999,0.2655,0.48,0.2069,0.6667,0.7083,0.0,0.3421,0.257,0.0077,0.0059,0.4296,0.1678,0.9851,0.804,0.2679,0.4834,0.2069,0.6667,0.7083,0.0,0.3737,0.2677,0.0078,0.0062,0.4257,0.1617,0.9851,0.7987,0.2672,0.48,0.2069,0.6667,0.7083,0.0,0.348,0.2616,0.0078,0.006,reg oper account,block of flats,0.3485,Panel,No,,,,,-537.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n196708,0,Revolving loans,XNA,N,Y,1,135000.0,405000.0,20250.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-10647,-1228,-183.0,-1671,,1,1,1,1,1,0,Core staff,3.0,2,2,THURSDAY,15,0,0,0,0,0,0,Kindergarten,0.40496991650501296,0.6591851367448883,0.07698446914647789,0.0773,0.1353,0.9921,0.8912,,0.0,0.1379,0.1667,0.0417,,0.063,0.0621,0.0,,0.0788,0.1404,0.9921,0.8955,,0.0,0.1379,0.1667,0.0417,,0.0689,0.0647,0.0,,0.0781,0.1353,0.9921,0.8927,,0.0,0.1379,0.1667,0.0417,,0.0641,0.0632,0.0,,reg oper spec account,block of flats,0.0769,Panel,No,7.0,1.0,7.0,1.0,-851.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n237747,0,Cash loans,F,Y,N,0,72000.0,755190.0,28116.0,675000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.00963,-20454,365243,-11511.0,-4014,12.0,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,0.5543949825757202,0.6981948880376612,0.807273923277881,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n266070,0,Cash loans,F,Y,N,1,157500.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-13313,-400,-5592.0,-4155,25.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,11,0,0,0,1,1,0,Business Entity Type 3,,0.7148100672080484,0.8038850611746273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1691.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n198152,0,Cash loans,M,Y,N,1,135000.0,284400.0,13257.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-9311,-128,-1689.0,-1951,15.0,1,1,0,1,1,0,,3.0,3,3,FRIDAY,9,0,0,0,0,1,1,Industry: type 9,,0.5270529721751399,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,4.0,0.0,-934.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n129022,0,Cash loans,F,N,Y,0,243000.0,574785.0,29475.0,436500.0,Group of people,Commercial associate,Higher education,Married,House / apartment,0.018634,-16793,-3078,-6538.0,-338,,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Bank,,0.3888084973608622,0.3996756156233169,0.0247,0.0,0.9444,0.2384,0.0,0.0,0.1034,0.0833,0.0417,0.0249,0.0202,0.0175,0.0,0.0,0.0252,0.0,0.9444,0.2682,0.0,0.0,0.1034,0.0833,0.0417,0.0255,0.022000000000000002,0.0183,0.0,0.0,0.025,0.0,0.9444,0.2486,0.0,0.0,0.1034,0.0833,0.0417,0.0254,0.0205,0.0178,0.0,0.0,not specified,block of flats,0.0164,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1695.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n229137,0,Cash loans,M,N,Y,2,135000.0,414792.0,22630.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010032,-12264,-768,-6373.0,-4031,,1,1,1,1,1,0,Laborers,4.0,2,2,TUESDAY,5,0,0,0,0,0,0,Transport: type 2,0.1798360230704703,0.6501283912113393,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-385.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n289104,0,Cash loans,M,N,Y,0,292500.0,646920.0,20997.0,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-12660,-3498,-6727.0,-4372,,1,1,0,1,1,0,Laborers,2.0,1,1,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.2439953872131233,0.7282052454940173,,0.1098,0.0446,0.9826,0.762,0.0947,0.08,0.0345,0.625,0.0417,0.0,0.1664,0.10099999999999999,0.0,0.0888,0.1103,0.0462,0.9826,0.7713,0.0709,0.0806,0.0345,0.625,0.0417,0.0,0.1791,0.1038,0.0,0.0258,0.1109,0.0446,0.9826,0.7652,0.0953,0.08,0.0345,0.625,0.0417,0.0,0.1693,0.1028,0.0,0.0906,reg oper account,block of flats,0.0836,Block,No,0.0,0.0,0.0,0.0,-2460.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n409606,0,Cash loans,F,N,Y,0,207000.0,331632.0,32931.0,315000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.032561,-25047,365243,-7957.0,-4592,,1,0,0,1,0,0,,1.0,1,1,TUESDAY,12,0,0,0,0,0,0,XNA,,0.4729998421916974,0.2353105173615833,0.0814,,0.9722,0.6192,,0.0,0.1379,0.1667,0.2083,0.0046,,0.0782,,0.0033,0.083,,0.9722,0.6341,,0.0,0.1379,0.1667,0.2083,0.0047,,0.0815,,0.0035,0.0822,,0.9722,0.6243,,0.0,0.1379,0.1667,0.2083,0.0047,,0.0796,,0.0034,reg oper account,block of flats,0.0622,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1533.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n130172,0,Cash loans,F,N,Y,0,45000.0,145557.0,7132.5,121500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-22154,365243,-7913.0,-4943,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.6095673410238347,0.3506958432829587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1996.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n186589,0,Cash loans,F,N,Y,0,135000.0,641173.5,21316.5,553500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.020713,-20949,365243,-609.0,-4003,,1,0,0,1,0,1,,1.0,3,2,TUESDAY,7,0,0,0,0,0,0,XNA,0.3158716594161747,0.5511834515710935,0.5442347412142162,0.0412,0.0241,0.9940000000000001,0.9184,0.0085,0.04,0.0345,0.375,0.0417,0.0372,0.0336,0.05,0.0,0.0,0.042,0.025,0.9896,0.8628,0.0085,0.0403,0.0345,0.375,0.0417,0.0041,0.0367,0.0516,0.0,0.0,0.0416,0.0241,0.9940000000000001,0.9195,0.0085,0.04,0.0345,0.375,0.0417,0.0378,0.0342,0.0509,0.0,0.0,reg oper account,block of flats,0.0398,Panel,No,6.0,0.0,6.0,0.0,-750.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n110593,0,Cash loans,F,Y,Y,0,135000.0,512064.0,23998.5,360000.0,Unaccompanied,Working,Higher education,Married,With parents,0.025164,-9126,-462,-9108.0,-1771,17.0,1,1,0,1,0,1,Sales staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.521019621798979,0.5583670303972396,0.15286622915504153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-586.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n244175,0,Cash loans,M,Y,N,1,112500.0,178290.0,19332.0,157500.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.018029,-12252,-1661,-6331.0,-4074,22.0,1,1,0,1,0,0,Laborers,3.0,3,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.23549562348072936,0.4854148781492562,0.646329897706246,0.133,0.1188,0.9811,0.7416,0.0778,0.16,0.1379,0.3333,0.375,0.1391,0.1076,0.1242,0.0039,0.0646,0.1355,0.1232,0.9811,0.7517,0.0785,0.1611,0.1379,0.3333,0.375,0.1423,0.1175,0.1294,0.0039,0.0684,0.1343,0.1188,0.9811,0.7451,0.0782,0.16,0.1379,0.3333,0.375,0.1415,0.1095,0.1264,0.0039,0.0659,reg oper account,block of flats,0.1542,Panel,No,0.0,0.0,0.0,0.0,-1782.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n245051,0,Cash loans,M,Y,Y,0,270000.0,1078200.0,31653.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003122,-19536,-3410,-7988.0,-3078,13.0,1,1,0,1,1,0,Core staff,2.0,3,3,TUESDAY,12,0,0,0,0,1,1,Agriculture,,0.3243703412001071,0.4776491548517548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-517.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n422834,0,Cash loans,F,N,Y,0,157500.0,1066752.0,31320.0,931500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.025164,-15249,-1374,-9358.0,-4759,,1,1,0,1,0,0,Sales staff,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Trade: type 7,,0.4332259472860289,0.4329616670974407,0.1093,0.1228,0.9742,0.6464,0.023,0.0,0.2069,0.1667,0.2083,0.0935,0.0815,0.0818,0.0347,0.068,0.1113,0.1275,0.9742,0.6602,0.0232,0.0,0.2069,0.1667,0.2083,0.0956,0.0891,0.0852,0.035,0.07200000000000001,0.1103,0.1228,0.9742,0.6511,0.0232,0.0,0.2069,0.1667,0.2083,0.0951,0.0829,0.0833,0.0349,0.0695,reg oper account,block of flats,0.0917,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-782.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n425987,0,Cash loans,M,Y,Y,2,225000.0,1824480.0,57415.5,1575000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.011703,-13619,-700,-31.0,-2351,8.0,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,13,0,0,0,0,0,0,Construction,0.33392864058256044,0.7129475075374108,0.7238369900414456,0.0577,0.0738,0.9786,,,0.0,0.1379,0.1667,,0.0647,,0.0529,,0.1033,0.0588,0.0766,0.9786,,,0.0,0.1379,0.1667,,0.0662,,0.0551,,0.1093,0.0583,0.0738,0.9786,,,0.0,0.1379,0.1667,,0.0659,,0.0538,,0.1054,,block of flats,0.0688,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1275.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n151876,0,Cash loans,M,N,N,0,202500.0,180000.0,14220.0,180000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-14702,-3304,-4728.0,-4735,,1,1,0,1,1,0,Managers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4499361942488848,0.6905192178628659,0.4101025731788671,0.0155,0.0,0.9702,0.5920000000000001,0.0021,0.0,0.0862,0.0417,0.0833,0.0326,0.0126,0.016,0.0,0.0,0.0126,0.0,0.9692,0.5949,0.0015,0.0,0.069,0.0417,0.0833,0.0261,0.011000000000000001,0.0121,0.0,0.0,0.0156,0.0,0.9702,0.5975,0.0021,0.0,0.0862,0.0417,0.0833,0.0332,0.0128,0.0163,0.0,0.0,reg oper account,block of flats,0.016,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-63.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n188599,0,Cash loans,F,N,Y,2,157500.0,220500.0,10723.5,220500.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.01885,-12905,-5714,-12817.0,-2891,,1,1,1,1,1,0,High skill tech staff,4.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.7463443727530358,0.6176913007615118,0.622922000268356,0.1464,0.1064,0.9791,0.7144,0.0298,0.16,0.1379,0.3333,0.375,0.7324,0.1143,0.1316,0.0232,0.0409,0.1492,0.1104,0.9791,0.7256,0.0301,0.1611,0.1379,0.3333,0.375,0.7491,0.1249,0.1371,0.0233,0.0433,0.1478,0.1064,0.9791,0.7182,0.03,0.16,0.1379,0.3333,0.375,0.7451,0.1163,0.1339,0.0233,0.0418,reg oper spec account,block of flats,0.1287,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-614.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n198274,0,Revolving loans,F,N,Y,0,67500.0,135000.0,6750.0,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006008,-19879,-8314,-6881.0,-3291,,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Other,,0.6520062344313987,0.7151031019926098,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,3.0,0.0,3.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n324743,0,Cash loans,F,N,Y,0,112500.0,942300.0,27135.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-21173,-2329,-11373.0,-271,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Industry: type 11,,0.5872374337205918,,0.0629,0.061,0.9767,,,0.0,0.1552,0.1667,,0.03,,0.0574,,0.0293,0.0494,0.048,0.9767,,,0.0,0.1379,0.1667,,0.0162,,0.0517,,0.0,0.0635,0.061,0.9767,,,0.0,0.1552,0.1667,,0.0305,,0.0584,,0.0299,,block of flats,0.040999999999999995,Panel,No,1.0,0.0,1.0,0.0,-1135.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n449046,0,Cash loans,M,N,Y,0,58500.0,277969.5,10606.5,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.002134,-22309,365243,-13929.0,-4983,,1,0,0,1,0,0,,1.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,XNA,,0.3683220528492734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-907.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n427389,0,Revolving loans,F,N,Y,0,180000.0,450000.0,22500.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.022625,-14600,-1323,-8428.0,-3962,,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,Self-employed,,0.5632306032670022,,0.0825,,0.9776,,,0.0,0.1379,0.1667,,,,0.0687,,,0.084,,0.9777,,,0.0,0.1379,0.1667,,,,0.0716,,,0.0833,,0.9776,,,0.0,0.1379,0.1667,,,,0.07,,,,block of flats,0.054000000000000006,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n395503,1,Cash loans,M,Y,N,0,198000.0,360000.0,18508.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-21826,-1771,-4494.0,-4495,10.0,1,1,1,1,0,0,Drivers,2.0,2,2,FRIDAY,17,0,0,0,0,1,1,Government,,0.0011024948367169027,0.24988506275045536,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-2082.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n405500,0,Cash loans,F,N,Y,0,135000.0,983299.5,41791.5,904500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-21393,365243,-9913.0,-4852,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,6,0,0,0,0,0,0,XNA,,0.26499218702237104,0.7001838506835805,0.0825,0.0807,0.9762,0.6736,0.0097,0.0,0.1379,0.1667,0.2083,0.0394,0.0672,0.0637,0.0,0.0,0.084,0.0837,0.9762,0.6864,0.0098,0.0,0.1379,0.1667,0.2083,0.0403,0.0735,0.0663,0.0,0.0,0.0833,0.0807,0.9762,0.6779999999999999,0.0097,0.0,0.1379,0.1667,0.2083,0.04,0.0684,0.0648,0.0,0.0,reg oper account,block of flats,0.0554,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-879.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n409132,0,Cash loans,F,N,Y,0,103500.0,567000.0,24151.5,567000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00733,-15157,-644,-1351.0,-1368,,1,1,0,1,1,0,Sales staff,2.0,2,2,THURSDAY,16,0,0,0,0,1,1,Self-employed,,0.6675061820534712,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,0.0,9.0,0.0,-1063.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n100790,0,Cash loans,M,Y,N,0,157500.0,339948.0,27387.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005144,-9365,-1172,-1451.0,-1969,13.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.14819332845230235,0.5147636756462116,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1450.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n353837,0,Cash loans,F,N,Y,0,90000.0,123993.0,5251.5,103500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.006629,-24054,365243,-3918.0,-4516,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.7495939978295069,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-113.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n266904,0,Cash loans,F,N,N,0,175500.0,1845000.0,48798.0,1845000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-21092,-2785,-11653.0,-4560,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.6369161930748168,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2281.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n110877,0,Cash loans,M,Y,N,0,315000.0,1125171.0,47803.5,1035000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.006629,-17626,-1071,-5972.0,-1142,17.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Construction,,0.7797022246214447,0.31547215492577346,0.16699999999999998,0.1364,0.9856,0.8028,0.0806,0.2,0.1724,0.3333,0.0417,0.1788,0.1336,0.2033,0.0116,0.0146,0.1702,0.1415,0.9856,0.8105,0.0814,0.2014,0.1724,0.3333,0.0417,0.1829,0.146,0.2118,0.0117,0.0155,0.1686,0.1364,0.9856,0.8054,0.0812,0.2,0.1724,0.3333,0.0417,0.1819,0.136,0.207,0.0116,0.0149,org spec account,block of flats,0.2072,Panel,No,1.0,0.0,1.0,0.0,-600.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n367629,0,Cash loans,M,N,Y,1,157500.0,755190.0,36328.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.022625,-11268,-2722,-5151.0,-3736,,1,1,1,1,1,0,Drivers,3.0,2,2,TUESDAY,16,0,0,0,1,1,0,Business Entity Type 3,0.1373312621000302,0.7734995980262623,0.7194907850918436,0.0021,,0.9841,,,0.0,,0.0,,,,0.0023,,,0.0021,,0.9841,,,0.0,,0.0,,,,0.0024,,,0.0021,,0.9841,,,0.0,,0.0,,,,0.0023,,,,block of flats,0.0018,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1563.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n218648,0,Cash loans,F,N,Y,1,112500.0,314100.0,14683.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-10782,-269,-4966.0,-1105,,1,1,0,1,0,0,,3.0,1,1,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.37344162407357495,0.7151888332659498,0.4794489811780563,0.1464,0.1156,0.9856,0.8028,0.0392,0.32,0.1379,0.3333,0.375,,0.1194,0.1594,0.0,0.0,0.1492,0.12,0.9856,0.8105,0.0395,0.3222,0.1379,0.3333,0.375,,0.1304,0.1661,0.0,0.0,0.1478,0.1156,0.9856,0.8054,0.0394,0.32,0.1379,0.3333,0.375,,0.1214,0.1623,0.0,0.0,reg oper account,block of flats,0.1468,Panel,No,1.0,0.0,1.0,0.0,-646.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n365259,0,Revolving loans,F,N,Y,0,202500.0,585000.0,29250.0,585000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-23591,365243,-8930.0,-4419,,1,0,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,0.7045701021590317,0.5953228074824473,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2234.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n138375,0,Cash loans,F,N,N,2,202500.0,792346.5,24151.5,684000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.025164,-14680,-428,-2046.0,-4947,,1,1,0,1,0,0,Medicine staff,3.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6687959070125811,0.35895122857839673,0.0619,0.0474,0.9752,0.66,0.0209,0.0,0.1034,0.1667,0.2083,0.0797,0.0504,0.0506,0.0,0.0,0.063,0.0492,0.9752,0.6733,0.0211,0.0,0.1034,0.1667,0.2083,0.0815,0.0551,0.0527,0.0,0.0,0.0625,0.0474,0.9752,0.6645,0.021,0.0,0.1034,0.1667,0.2083,0.081,0.0513,0.0515,0.0,0.0,reg oper account,block of flats,0.0512,Panel,No,0.0,0.0,0.0,0.0,-2665.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n177690,0,Cash loans,M,N,Y,0,270000.0,545040.0,36553.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010032,-15870,-392,-225.0,-66,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.4184377671454709,0.2966999601330481,0.5136937663039473,0.0722,0.0643,0.9786,0.7076,0.0309,0.0,0.1379,0.1667,0.2083,0.0151,0.0588,0.0446,0.0,0.0777,0.0735,0.0667,0.9786,0.7190000000000001,0.0312,0.0,0.1379,0.1667,0.2083,0.0154,0.0643,0.0465,0.0,0.0823,0.0729,0.0643,0.9786,0.7115,0.0311,0.0,0.1379,0.1667,0.2083,0.0153,0.0599,0.0454,0.0,0.0793,not specified,block of flats,0.0525,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-207.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n180276,0,Cash loans,F,N,N,0,202500.0,547272.0,33610.5,495000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.009549,-12231,-2404,-4201.0,-443,,1,1,1,1,1,1,Sales staff,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.31080132929926474,0.7136585489568714,0.3344541255096772,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2228.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n378975,1,Cash loans,F,N,Y,0,90000.0,509922.0,27292.5,472500.0,Children,Pensioner,Secondary / secondary special,Single / not married,Municipal apartment,0.031329,-21247,365243,-15365.0,-4499,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.1374634449156962,,0.1557,0.1576,0.9767,0.6804,0.1146,0.0,0.2759,0.1667,0.2083,0.1539,0.1252,0.1254,0.0077,0.0244,0.1586,0.1636,0.9767,0.6929,0.1157,0.0,0.2759,0.1667,0.2083,0.1574,0.1368,0.1306,0.0078,0.0258,0.1572,0.1576,0.9767,0.6847,0.1154,0.0,0.2759,0.1667,0.2083,0.1566,0.1274,0.1276,0.0078,0.0249,reg oper account,block of flats,0.1039,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-371.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n312941,0,Cash loans,M,Y,N,0,157500.0,450000.0,12757.5,450000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-19489,-1332,-1685.0,-3029,9.0,1,1,0,1,0,0,Managers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Insurance,0.5173411930532903,0.5404991979001275,0.4311917977993083,0.1804,,0.9861,,,0.2,0.1724,0.3333,,,,0.1886,,0.0074,0.1838,,0.9861,,,0.2014,0.1724,0.3333,,,,0.1965,,0.0078,0.1822,,0.9861,,,0.2,0.1724,0.3333,,,,0.192,,0.0075,,block of flats,0.15,Panel,No,3.0,0.0,3.0,0.0,-2545.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,7.0,0.0,1.0\n274134,0,Cash loans,F,N,Y,0,99000.0,675000.0,21775.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.0105,-20893,365243,-2657.0,-4256,,1,0,0,1,1,0,,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,XNA,,0.3421262598417933,0.4382813743111921,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n410841,0,Cash loans,F,N,Y,2,135000.0,770292.0,29470.5,688500.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-10697,-244,-2408.0,-3259,,1,1,0,1,0,0,Private service staff,4.0,2,2,TUESDAY,14,0,0,0,0,0,0,Services,,0.2669988297990393,0.3893387918468769,0.0371,0.0,0.9752,0.66,0.0199,0.0,0.1034,0.0833,0.0417,0.0308,0.0303,0.0238,0.0,0.0,0.0378,0.0,0.9752,0.6733,0.0201,0.0,0.1034,0.0833,0.0417,0.0315,0.0331,0.0248,0.0,0.0,0.0375,0.0,0.9752,0.6645,0.0201,0.0,0.1034,0.0833,0.0417,0.0313,0.0308,0.0242,0.0,0.0,reg oper account,,0.0296,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n373385,0,Cash loans,F,Y,N,0,234000.0,1190340.0,68967.0,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-19682,-4697,-4676.0,-2521,3.0,1,1,0,1,0,0,Managers,2.0,3,2,MONDAY,7,0,0,0,0,0,0,Other,0.6938909197439702,0.4195616809535988,0.2636468134452008,0.033,0.0635,0.9627,0.49,0.0055,0.0,0.1034,0.125,0.1667,0.0708,0.0269,0.0363,0.0,0.0,0.0336,0.0659,0.9628,0.51,0.0055,0.0,0.1034,0.125,0.1667,0.0724,0.0294,0.0378,0.0,0.0,0.0333,0.0635,0.9627,0.4968,0.0055,0.0,0.1034,0.125,0.1667,0.07200000000000001,0.0274,0.0369,0.0,0.0,reg oper account,block of flats,0.0315,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2020.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n185608,0,Cash loans,M,N,Y,0,247500.0,900000.0,26316.0,900000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.011703,-8290,-417,-251.0,-969,,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Transport: type 4,0.4157084309577561,0.6747457475743129,0.14825437906109115,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1011.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n418974,0,Cash loans,F,N,Y,0,202500.0,273636.0,15408.0,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.00702,-20814,-7871,-4662.0,-3790,,1,1,0,1,1,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Industry: type 3,,0.7723058927823164,0.7850520263728172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2345.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n221813,0,Cash loans,M,N,N,1,180000.0,1350000.0,44617.5,1350000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.01885,-16218,-7989,-1413.0,-4279,,1,1,0,1,0,0,Core staff,3.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Police,0.5064530947476775,0.7894763174301571,0.7700870700124128,0.2041,0.1144,0.9866,0.8164,0.0457,0.22,0.1897,0.3333,0.375,0.1086,0.1664,0.2208,0.0,0.0,0.1891,0.1058,0.9866,0.8236,0.0428,0.2014,0.1724,0.3333,0.375,0.0582,0.1653,0.20800000000000002,0.0,0.0,0.2061,0.1144,0.9866,0.8189,0.046,0.22,0.1897,0.3333,0.375,0.1105,0.1693,0.2248,0.0,0.0,reg oper spec account,block of flats,0.1802,Panel,No,0.0,0.0,0.0,0.0,-1395.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n430340,0,Cash loans,M,N,N,2,157500.0,490536.0,15003.0,405000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-13603,-1133,-996.0,-1283,,1,1,0,1,0,0,,4.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.2213744854099183,0.3824568605949457,0.4436153084085652,0.0423,0.0171,0.9796,0.8164,0.0128,0.0,0.1379,0.1042,0.2083,0.0391,0.0588,0.0416,0.0,0.0,0.0126,0.0,0.9727,0.8236,0.013000000000000001,0.0,0.069,0.0417,0.2083,0.0297,0.0643,0.0134,0.0,0.0,0.0427,0.0171,0.9796,0.8189,0.0129,0.0,0.1379,0.1042,0.2083,0.0397,0.0599,0.0423,0.0,0.0,reg oper account,block of flats,0.0623,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1535.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n374000,0,Cash loans,F,N,Y,0,180000.0,837000.0,27130.5,837000.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.026392,-8823,-1897,-3684.0,-1493,,1,1,0,1,1,0,Sales staff,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3504892396059532,0.6413645513320907,0.20092608771597087,0.1237,,0.9771,,,0.0,0.1724,0.1667,,,,0.0715,,0.1158,0.1261,,0.9772,,,0.0,0.1724,0.1667,,,,0.0745,,0.1226,0.1249,,0.9771,,,0.0,0.1724,0.1667,,,,0.0728,,0.1182,,block of flats,0.0814,Panel,No,0.0,0.0,0.0,0.0,-1760.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,2.0\n190955,0,Cash loans,F,N,Y,0,112500.0,314055.0,16573.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-18297,-869,-6583.0,-1798,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Other,,0.4010825626886416,0.4329616670974407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-1140.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,7.0\n176130,0,Cash loans,M,Y,Y,0,202500.0,755190.0,36459.0,675000.0,Other_B,Working,Higher education,Married,House / apartment,0.008575,-14478,-2836,-5350.0,-4738,3.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Self-employed,0.17345597878133745,0.5760155717701834,0.7826078370261895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-528.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n281887,0,Cash loans,F,N,Y,0,112500.0,675000.0,21775.5,675000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.04622,-15526,-3433,-9223.0,-4785,,1,1,0,1,1,0,Sales staff,2.0,1,1,SUNDAY,23,0,1,1,0,1,1,Business Entity Type 3,,0.7465153727002523,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n450890,0,Cash loans,F,N,Y,0,90000.0,156384.0,11821.5,135000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.010276,-20594,365243,-10527.0,-4143,,1,0,0,1,1,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,0.7703832171441262,0.7448856936883097,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-297.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n360913,0,Cash loans,M,N,Y,0,173160.0,808650.0,22365.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018801,-15662,-1290,-204.0,-4645,,1,1,1,1,0,1,Managers,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Trade: type 3,,0.26258428017472896,0.6940926425266661,0.1237,,0.9881,,,0.12,0.069,0.375,,0.0146,,0.1234,,,0.1261,,0.9881,,,0.1208,0.069,0.375,,0.015,,0.1286,,,0.1249,,0.9881,,,0.12,0.069,0.375,,0.0149,,0.1256,,,,block of flats,0.09699999999999999,Panel,No,1.0,0.0,1.0,0.0,-167.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n423617,0,Cash loans,M,N,Y,1,135000.0,343377.0,21136.5,283500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009334,-18223,-10382,-10492.0,-1743,,1,1,0,1,1,0,Laborers,3.0,2,2,TUESDAY,17,0,0,0,0,0,0,Industry: type 7,0.5902746273365715,0.7005121896166963,0.7252764347002191,0.0722,0.0631,0.9811,0.7416,,0.0,0.1379,0.1667,0.2083,0.0727,,0.0664,,0.0,0.0735,0.0655,0.9811,0.7517,,0.0,0.1379,0.1667,0.2083,0.0744,,0.0691,,0.0,0.0729,0.0631,0.9811,0.7451,,0.0,0.1379,0.1667,0.2083,0.07400000000000001,,0.0675,,0.0,reg oper account,block of flats,0.0522,Mixed,No,1.0,0.0,1.0,0.0,-2406.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n148765,0,Cash loans,M,Y,Y,0,162000.0,168102.0,11362.5,148500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.00963,-18089,-4003,-7006.0,-1623,9.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.2268229401064432,0.25684555181983004,0.34578480246959553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n298230,0,Cash loans,M,Y,Y,2,351000.0,1215000.0,39325.5,1215000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-13563,-762,-3401.0,-4196,3.0,1,1,0,1,0,0,Managers,4.0,2,2,FRIDAY,12,0,0,0,0,1,1,Self-employed,,0.6209804802076668,0.6642482627052363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-2660.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,4.0\n409005,0,Cash loans,F,N,N,2,67500.0,225000.0,11074.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-14898,-638,-4573.0,-4229,,1,1,1,1,0,0,Core staff,4.0,2,2,MONDAY,10,0,0,0,0,1,1,Postal,,0.20357246321933267,0.36896873825284665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1162.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n112243,0,Cash loans,F,Y,Y,0,171000.0,922500.0,30618.0,922500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.030755,-16247,-7717,-1304.0,-5257,8.0,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Medicine,,0.6194661379112218,0.5779691187553125,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1606.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n360034,0,Cash loans,F,N,Y,2,157500.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006207,-11271,-550,-2487.0,-168,,1,1,1,1,1,0,Accountants,4.0,2,2,FRIDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.35990695950896645,0.4203820148056481,0.8016009030071296,,,,,,,,,,,,0.0442,,,,,,,,,,,,,,0.0461,,,,,,,,,,,,,,0.045,,,,,0.0348,,No,9.0,0.0,9.0,0.0,-1138.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n270266,0,Cash loans,F,N,Y,0,180000.0,738171.0,39325.5,684000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-14626,-2112,-8774.0,-4859,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Self-employed,,0.6377653351133437,,0.0412,0.0292,0.9732,,,0.0,0.069,0.1667,,0.0316,,0.0297,,0.0052,0.042,0.0303,0.9732,,,0.0,0.069,0.1667,,0.0323,,0.0309,,0.0055,0.0416,0.0292,0.9732,,,0.0,0.069,0.1667,,0.0321,,0.0302,,0.0053,,block of flats,0.0262,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-844.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n314781,0,Cash loans,F,N,Y,2,135000.0,755856.0,25110.0,652500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.030755,-15063,-7907,-577.0,-4077,,1,1,0,1,0,0,Medicine staff,4.0,2,2,FRIDAY,13,0,0,0,0,0,0,Medicine,0.4628855960449456,0.6610250146106955,0.43473324875017305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n436479,0,Cash loans,F,Y,N,0,270000.0,700830.0,27153.0,585000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.011703,-10658,-3005,-4691.0,-2557,4.0,1,1,0,1,1,0,Managers,2.0,2,2,SATURDAY,19,0,0,0,0,0,0,Self-employed,,0.7191320771787014,0.12295541790885495,0.1979,0.1043,0.9896,0.8572,0.0504,0.2,0.1724,0.375,0.4167,0.1044,0.1614,0.2281,0.0,0.0036,0.2017,0.1083,0.9896,0.8628,0.0508,0.2014,0.1724,0.375,0.4167,0.1068,0.1763,0.2376,0.0,0.0038,0.1999,0.1043,0.9896,0.8591,0.0507,0.2,0.1724,0.375,0.4167,0.1063,0.1642,0.2322,0.0,0.0037,reg oper account,block of flats,0.2209,Panel,No,0.0,0.0,0.0,0.0,-284.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n353199,0,Cash loans,F,Y,Y,0,675000.0,2156400.0,63180.0,1800000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.072508,-14696,-1081,-7221.0,-4484,9.0,1,1,0,1,1,0,,2.0,1,1,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.7881279033673962,0.7218579734387636,0.6279908192952864,0.1573,0.1802,0.9762,0.6736,0.0982,0.22399999999999998,0.1793,0.3417,0.3333,0.0247,0.0658,0.1618,0.0013,0.1008,0.0011,0.0612,0.9752,0.6733,0.0006,0.0,0.0345,0.1667,0.2083,0.0,0.0009,0.075,0.0,0.0,0.1197,0.1252,0.9771,0.6779999999999999,0.1478,0.08,0.2069,0.375,0.2083,0.0,0.0975,0.1041,0.0,0.0,org spec account,block of flats,0.4433,Block,No,0.0,0.0,0.0,0.0,-1512.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n223786,0,Cash loans,M,Y,Y,1,225000.0,381528.0,23472.0,315000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.009334,-17520,-4811,-183.0,-1044,1.0,1,1,0,1,0,0,Managers,3.0,2,2,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 1,,0.7696067737432244,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-863.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n189383,0,Cash loans,M,Y,N,0,225000.0,1530000.0,40360.5,1530000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-10054,-2015,-1013.0,-2739,13.0,1,1,0,1,0,0,Drivers,2.0,3,3,WEDNESDAY,17,0,0,0,0,0,0,Industry: type 3,0.12492559366542715,0.4800146329369862,0.4418358231994413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-6.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n253238,0,Cash loans,M,Y,Y,0,157500.0,135000.0,9913.5,135000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.015221,-14075,-401,-6412.0,-4151,9.0,1,1,0,1,0,0,Security staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Police,,0.5237376865217971,0.6863823354047934,0.0495,0.026000000000000002,0.9786,0.7076,0.11599999999999999,0.04,0.0345,0.3333,0.375,0.0394,0.0403,0.0405,0.0,0.1263,0.0504,0.027000000000000003,0.9786,0.7190000000000001,0.1171,0.0403,0.0345,0.3333,0.375,0.0403,0.0441,0.0422,0.0,0.1337,0.05,0.026000000000000002,0.9786,0.7115,0.1168,0.04,0.0345,0.3333,0.375,0.04,0.040999999999999995,0.0412,0.0,0.1289,reg oper account,block of flats,0.0634,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n166610,0,Cash loans,M,Y,Y,0,292500.0,345843.0,39150.0,328500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-17320,-4294,-8287.0,-873,2.0,1,1,0,1,0,0,Sales staff,2.0,3,3,SUNDAY,7,0,0,0,0,0,0,Self-employed,,0.5725940441289267,0.8027454758994178,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2315.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n403049,0,Cash loans,F,N,Y,2,121500.0,781920.0,28215.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-12130,-2181,-4438.0,-4608,,1,1,0,1,0,0,Sales staff,3.0,3,3,WEDNESDAY,5,0,0,0,0,0,0,Self-employed,0.5479048016414213,0.6041205853719229,0.23272477626794336,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1774.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,0.0\n157251,0,Cash loans,F,N,Y,0,112500.0,247500.0,15948.0,247500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-12279,-906,-1762.0,-3848,,1,1,0,1,1,1,Accountants,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Transport: type 4,0.6106780845640857,0.5960864646173853,0.6092756673894402,0.0773,0.0731,0.993,0.9048,0.0407,0.0,0.1724,0.1667,0.0417,0.011000000000000001,0.063,0.0691,0.0,0.0,0.0788,0.0759,0.993,0.9085,0.040999999999999995,0.0,0.1724,0.1667,0.0417,0.0112,0.0689,0.07200000000000001,0.0,0.0,0.0781,0.0731,0.993,0.9061,0.0409,0.0,0.1724,0.1667,0.0417,0.0112,0.0641,0.0703,0.0,0.0,reg oper account,block of flats,0.0766,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-780.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n139471,1,Cash loans,M,Y,Y,0,225000.0,497520.0,39438.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.018029,-19092,-1653,-840.0,-2617,16.0,1,1,1,1,0,0,Laborers,2.0,3,3,MONDAY,5,0,0,0,0,1,1,Construction,,0.5535624891629454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-420.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n425907,0,Cash loans,F,N,Y,0,121500.0,225000.0,17905.5,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-21968,365243,-14294.0,-362,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,0.4155302349630898,0.5923428043476328,0.3441550073724169,0.0165,0.0,0.9697,0.5852,,0.0,0.5517,0.0417,0.0417,0.0207,0.0134,0.0092,0.0,0.03,0.0168,0.0,0.9697,0.6014,,0.0,0.5517,0.0417,0.0417,0.0211,0.0147,0.0096,0.0,0.0317,0.0167,0.0,0.9697,0.5907,,0.0,0.5517,0.0417,0.0417,0.021,0.0137,0.0094,0.0,0.0306,reg oper account,block of flats,0.0117,Block,No,10.0,0.0,9.0,0.0,-2082.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,1.0,0.0,0.0,0.0,2.0\n173203,0,Revolving loans,F,N,Y,0,112500.0,675000.0,33750.0,675000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.032561,-21055,365243,-2644.0,-368,,1,0,0,1,0,0,,2.0,1,1,MONDAY,10,0,0,0,0,0,0,XNA,,0.5603235545046527,,0.2684,0.1485,0.9806,0.728,0.0088,0.29,0.2183,0.5833,0.2083,0.0532,0.2188,0.1535,0.0,0.1805,0.1513,0.054000000000000006,0.9811,0.7321,0.0,0.1208,0.1034,0.5833,0.0,0.0127,0.1322,0.0,0.0,0.0,0.1624,0.0902,0.9806,0.7383,0.0014,0.26,0.1034,0.5833,0.0,0.0376,0.1334,0.1476,0.0,0.0035,reg oper account,block of flats,0.0,Panel,No,0.0,0.0,0.0,0.0,-134.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,0.0\n424373,0,Cash loans,F,N,Y,0,207000.0,544500.0,17563.5,544500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.026392,-15179,-1113,-4172.0,-4543,,1,1,0,1,0,0,Accountants,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Other,0.8914223854753713,0.7024674652409583,0.19519840600440985,0.0495,0.0734,0.9722,,,0.0,0.1034,0.1667,,0.0634,,0.0403,,0.0388,0.0504,0.0762,0.9722,,,0.0,0.1034,0.1667,,0.0648,,0.042,,0.0411,0.05,0.0734,0.9722,,,0.0,0.1034,0.1667,,0.0645,,0.040999999999999995,,0.0396,,block of flats,0.0401,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1062.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n272684,1,Cash loans,M,N,N,1,157500.0,452385.0,29583.0,373500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-16212,-2344,-5229.0,-4466,,1,1,0,1,0,0,Drivers,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.4171777970269465,,0.0825,0.0685,0.9801,0.728,0.0121,0.0,0.2069,0.1667,0.2083,0.0354,0.0672,0.0789,0.0,0.0,0.084,0.0711,0.9801,0.7387,0.0122,0.0,0.2069,0.1667,0.2083,0.0362,0.0735,0.0822,0.0,0.0,0.0833,0.0685,0.9801,0.7316,0.0122,0.0,0.2069,0.1667,0.2083,0.036000000000000004,0.0684,0.0803,0.0,0.0,reg oper spec account,block of flats,0.0686,Panel,No,0.0,0.0,0.0,0.0,-122.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n290670,0,Cash loans,M,Y,Y,0,67500.0,129519.0,12807.0,121500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.02461,-24537,365243,-7450.0,-5289,4.0,1,0,0,1,0,0,,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.6139923077220247,0.6971469077844458,0.0186,0.0,0.9757,0.6668,0.0315,0.0,0.1034,0.0417,0.0833,0.0493,0.0143,0.0267,0.0039,0.0058,0.0189,0.0,0.9757,0.6798,0.0318,0.0,0.1034,0.0417,0.0833,0.0504,0.0156,0.0278,0.0039,0.0062,0.0187,0.0,0.9757,0.6713,0.0317,0.0,0.1034,0.0417,0.0833,0.0501,0.0145,0.0272,0.0039,0.006,reg oper account,block of flats,0.0395,\"Stone, brick\",No,6.0,0.0,6.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n413380,0,Cash loans,F,N,N,0,76500.0,501948.0,13369.5,328500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.003122,-19739,-3152,-2722.0,-3132,,1,1,0,1,0,0,Medicine staff,1.0,3,3,THURSDAY,9,0,0,0,0,0,0,Other,,0.3176547595818635,0.15663982703141147,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n260810,0,Revolving loans,F,N,Y,0,67500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-12247,-2361,-4169.0,-1234,,1,1,1,1,0,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3400708257017498,0.06253578067139869,0.6006575372857061,0.1309,,0.6538,,,0.1064,0.069,0.4792,,,,0.0733,,0.4466,0.0,,0.0005,,,0.0,0.0345,0.3333,,,,0.0763,,0.2005,0.1291,,0.9776,,,0.08,0.069,0.4792,,,,0.0746,,0.4559,,block of flats,0.0,Panel,No,4.0,0.0,4.0,0.0,-835.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n450936,0,Cash loans,M,Y,N,0,157500.0,269550.0,21739.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16776,-4291,-621.0,-309,28.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,,0.5837825921600501,0.2692857999073816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1615.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n191093,0,Cash loans,F,N,Y,0,135000.0,136512.0,9121.5,108000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.002042,-23752,365243,-1849.0,-4773,,1,0,0,1,0,0,,1.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.5981749146855198,0.5691487713619409,0.0165,,0.9801,,,0.0,0.1034,0.0417,,0.0126,,0.0086,,0.019,0.0168,,0.9801,,,0.0,0.1034,0.0417,,0.0129,,0.009000000000000001,,0.0201,0.0167,,0.9801,,,0.0,0.1034,0.0417,,0.0128,,0.0088,,0.0194,,block of flats,0.0109,Block,No,0.0,0.0,0.0,0.0,-426.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n184522,0,Cash loans,F,N,Y,3,90000.0,276277.5,21825.0,238500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-13704,-4103,-2025.0,-4602,,1,1,0,1,0,0,Medicine staff,5.0,1,1,MONDAY,12,0,0,0,0,0,0,Kindergarten,,0.7307512832888281,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-229.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n379227,0,Cash loans,M,N,Y,2,225000.0,431280.0,23395.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.04622,-9960,-1987,-4364.0,-2633,,1,1,1,1,1,0,Sales staff,3.0,1,1,SATURDAY,10,0,1,1,0,1,1,Business Entity Type 2,,0.6079267310969783,0.14825437906109115,0.0144,0.0,0.9622,0.4832,0.0044,0.0,0.1034,0.0417,0.0833,0.011000000000000001,0.0118,0.0181,0.0,0.0,0.0147,0.0,0.9623,0.5034,0.0044,0.0,0.1034,0.0417,0.0833,0.0112,0.0129,0.0189,0.0,0.0,0.0146,0.0,0.9622,0.4901,0.0044,0.0,0.1034,0.0417,0.0833,0.0112,0.012,0.0184,0.0,0.0,reg oper account,block of flats,0.0166,Block,No,0.0,0.0,0.0,0.0,-1955.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n308104,1,Cash loans,F,N,Y,0,135000.0,269550.0,12001.5,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.022625,-23666,365243,-843.0,-2284,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,15,0,0,0,0,0,0,XNA,0.8528708485354595,0.5631230109611207,0.622922000268356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n144573,0,Cash loans,M,N,Y,2,202500.0,254700.0,27153.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-17431,-745,-1643.0,-976,,1,1,0,1,0,0,Laborers,4.0,2,2,SATURDAY,10,0,0,0,1,1,0,Business Entity Type 3,0.25505642131892664,0.19100157229110468,0.6925590674998008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-463.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n439028,0,Cash loans,F,N,N,0,99000.0,585000.0,29866.5,585000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.008019,-21949,365243,-10449.0,-3648,,1,0,0,1,0,0,,1.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,0.5471449020258221,0.5658593599824819,0.4614823912998385,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-244.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n347706,0,Revolving loans,F,N,Y,0,67500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018209,-16984,-1884,-9928.0,-532,,1,1,1,1,0,0,,1.0,3,3,THURSDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.6845036714903376,0.32908442915978314,0.7675231046555077,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-278.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n121768,0,Cash loans,F,N,Y,0,112500.0,792477.0,25695.0,661500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-14316,-1953,-7470.0,-4437,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.33116613585449384,0.6846428397267359,0.8435435389318647,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1410.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n405116,0,Cash loans,M,Y,Y,0,58500.0,305955.0,23805.0,283500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00733,-11004,-619,-2350.0,-3420,13.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.18934737663881696,0.5840057375887959,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,6.0,0.0,-437.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n332889,0,Cash loans,F,Y,Y,0,99000.0,814041.0,23931.0,679500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006008,-10463,-412,-1386.0,-1610,14.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,15,0,0,0,1,1,0,Self-employed,0.32902574030421433,0.6255861146381101,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-474.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n217009,0,Cash loans,F,N,N,0,126000.0,266652.0,19534.5,202500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.020713,-8209,-951,-8202.0,-883,,1,1,0,1,0,0,Sales staff,1.0,3,3,FRIDAY,8,0,0,0,0,0,0,Trade: type 7,0.1725239558965638,0.6284295714710261,0.17044612304522816,0.1351,0.0736,0.9752,0.66,0.0266,0.0,0.1034,0.1667,0.2083,0.0352,0.0008,0.0338,0.5019,0.0361,0.1376,0.0764,0.9752,0.6733,0.0269,0.0,0.1034,0.1667,0.2083,0.036000000000000004,0.0009,0.0353,0.5058,0.0383,0.1364,0.0736,0.9752,0.6645,0.0268,0.0,0.1034,0.1667,0.2083,0.0358,0.0009,0.0344,0.5046,0.0369,reg oper account,block of flats,0.049,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-49.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,4.0\n266528,1,Cash loans,F,N,Y,1,81000.0,668304.0,31099.5,540000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010276,-8882,-1373,-439.0,-1111,,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Trade: type 3,0.2737364765327541,0.07245486566386689,,0.0722,,0.9806,0.7348,0.0079,,0.1379,0.1667,0.0,0.0659,0.0588,0.0683,0.0,,0.0735,,0.9806,0.7452,0.008,,0.1379,0.1667,0.0,0.0674,0.0643,0.0712,0.0,,0.0729,,0.9806,0.7383,0.008,,0.1379,0.1667,0.0,0.067,0.0599,0.0695,0.0,,org spec account,block of flats,0.0581,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-529.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n362000,1,Cash loans,M,Y,Y,0,202500.0,206271.0,22342.5,193500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.011703,-11609,-720,-11350.0,-13,3.0,1,1,0,1,0,0,Waiters/barmen staff,1.0,2,2,FRIDAY,16,0,1,1,0,0,0,Business Entity Type 2,,0.5991836741607169,,0.067,0.0725,0.9722,0.6192,0.0108,0.0,0.1034,0.1667,0.0417,0.0859,0.0513,0.0715,0.0154,0.0193,0.0683,0.0752,0.9722,0.6341,0.0109,0.0,0.1034,0.1667,0.0417,0.0879,0.055999999999999994,0.0745,0.0156,0.0205,0.0677,0.0725,0.9722,0.6243,0.0109,0.0,0.1034,0.1667,0.0417,0.0874,0.0522,0.0728,0.0155,0.0197,reg oper spec account,block of flats,0.0663,\"Stone, brick\",No,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n339911,0,Cash loans,M,Y,N,0,135000.0,728460.0,37134.0,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Municipal apartment,0.009334,-9460,-1248,-3891.0,-2067,3.0,1,1,1,1,1,0,Laborers,2.0,2,2,WEDNESDAY,18,0,0,0,0,1,1,Self-employed,,0.6641489642998615,0.6279908192952864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,6.0,0.0,-1580.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n273614,0,Cash loans,M,N,Y,2,315000.0,971280.0,51876.0,900000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00496,-13760,-4619,-4058.0,-188,,1,1,0,1,0,0,,4.0,2,2,THURSDAY,13,0,0,0,0,0,0,Other,0.610266092638102,0.6148522862357263,0.8016009030071296,0.0412,,0.9826,,,,0.069,0.1667,,,,0.0448,,,0.042,,0.9826,,,,0.069,0.1667,,,,0.0467,,,0.0416,,0.9826,,,,0.069,0.1667,,,,0.0456,,,,,0.0385,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1231.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n379379,0,Cash loans,F,N,Y,0,135000.0,994891.5,69372.0,913500.0,Unaccompanied,Pensioner,Lower secondary,Married,Municipal apartment,0.018801,-22359,365243,-11277.0,-4325,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.4853329448850378,0.4956658291397297,0.2227,0.1663,0.9841,0.7824,0.1478,0.24,0.2069,0.3333,0.375,0.1375,0.1816,0.2271,0.0,0.0,0.2269,0.1725,0.9841,0.7909,0.1491,0.2417,0.2069,0.3333,0.375,0.1406,0.1983,0.2367,0.0,0.0,0.2248,0.1663,0.9841,0.7853,0.1487,0.24,0.2069,0.3333,0.375,0.1399,0.1847,0.2312,0.0,0.0,reg oper account,block of flats,0.2595,Panel,No,4.0,0.0,4.0,0.0,-1452.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n109937,0,Cash loans,F,N,Y,0,252000.0,1288350.0,37800.0,1125000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.00963,-23029,-836,-12011.0,-4688,,1,1,0,1,1,0,Managers,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,Business Entity Type 2,0.8716147990042409,0.6179133282780278,0.6041125998015721,0.1653,0.1295,0.9846,0.7824,0.0384,0.1864,0.1607,0.3471,0.375,0.1585,0.1467,0.157,0.0,0.0673,0.1124,0.0913,0.9836,0.7844,0.0371,0.1208,0.1034,0.3333,0.375,0.0583,0.0983,0.0784,0.0,0.0,0.1374,0.1274,0.9851,0.7853,0.0387,0.16,0.1379,0.3333,0.375,0.1612,0.1492,0.1432,0.0,0.0727,reg oper spec account,block of flats,0.2206,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1452.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n319250,0,Cash loans,F,N,Y,1,135000.0,265500.0,9666.0,265500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-15882,-1360,-4686.0,-4000,,1,1,0,1,0,0,,3.0,3,3,THURSDAY,10,0,0,0,0,0,0,Industry: type 3,,0.5235795015086321,,0.0928,0.0998,0.9781,0.7008,0.053,0.0,0.2069,0.1667,0.0417,0.054000000000000006,0.0756,0.0875,0.0,0.0,0.0945,0.1036,0.9782,0.7125,0.0535,0.0,0.2069,0.1667,0.0417,0.0553,0.0826,0.0911,0.0,0.0,0.0937,0.0998,0.9781,0.7048,0.0534,0.0,0.2069,0.1667,0.0417,0.055,0.077,0.08900000000000001,0.0,0.0,reg oper spec account,block of flats,0.0978,Panel,No,4.0,0.0,4.0,0.0,-1154.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n244436,0,Revolving loans,F,N,Y,0,225000.0,1350000.0,67500.0,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-13229,-1287,-7327.0,-4459,,1,1,0,1,0,0,Laborers,2.0,1,1,MONDAY,9,0,0,0,0,0,0,Industry: type 6,0.33605931460430394,0.6709140696337718,,0.3429,0.218,0.9776,0.6940000000000001,0.0987,0.3732,0.3217,0.3333,0.375,0.0,0.2788,0.3267,0.0039,0.0147,0.2269,0.1402,0.9782,0.7125,0.0616,0.2417,0.2069,0.3333,0.375,0.0,0.1974,0.2184,0.0,0.0,0.2248,0.1423,0.9781,0.7048,0.0645,0.24,0.2069,0.3333,0.375,0.0,0.1847,0.215,0.0039,0.0053,reg oper account,block of flats,0.1692,Panel,No,0.0,0.0,0.0,0.0,-149.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n116626,0,Cash loans,F,N,N,1,74700.0,254700.0,13185.0,225000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.010006,-11526,-256,-1457.0,-3535,,1,1,1,1,1,0,High skill tech staff,2.0,2,2,MONDAY,11,1,1,0,1,1,0,Trade: type 6,0.37583150138465377,0.4886709483047579,,0.1443,0.0896,0.9925,0.898,0.0,0.0,0.2759,0.1667,0.2083,0.0369,0.1177,0.1587,0.0,0.0055,0.1471,0.09300000000000001,0.9926,0.902,0.0,0.0,0.2759,0.1667,0.2083,0.0377,0.1286,0.1654,0.0,0.0059,0.1457,0.0896,0.9925,0.8994,0.0,0.0,0.2759,0.1667,0.2083,0.0375,0.1197,0.1616,0.0,0.0057,reg oper spec account,block of flats,0.1482,Panel,No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n350955,1,Cash loans,F,N,Y,0,157500.0,1125000.0,47794.5,1125000.0,Family,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.025164,-17960,-1038,-1538.0,-1496,,1,1,0,1,0,0,Cooking staff,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Government,,0.4203873430913985,0.7407990879702335,0.0928,0.1084,0.9831,0.7688,0.0148,0.0,0.2069,0.1667,0.2083,0.1137,0.0756,0.0973,0.0,0.0,0.0945,0.1124,0.9831,0.7779,0.0149,0.0,0.2069,0.1667,0.2083,0.1163,0.0826,0.1013,0.0,0.0,0.0937,0.1084,0.9831,0.7719,0.0149,0.0,0.2069,0.1667,0.2083,0.1157,0.077,0.099,0.0,0.0,org spec account,block of flats,0.0846,Panel,No,15.0,0.0,15.0,0.0,-1031.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n352611,0,Cash loans,M,Y,Y,2,180000.0,339241.5,12789.0,238500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-15197,-2664,-4089.0,-1993,16.0,1,1,0,1,0,0,Managers,4.0,3,2,THURSDAY,7,0,0,0,0,0,0,Bank,0.14337334908676008,0.6870739729144045,0.42765737003502935,0.1918,0.1727,0.9940000000000001,,,0.2,0.1724,0.375,,0.0303,,0.2344,,0.1319,0.1954,0.1792,0.9940000000000001,,,0.2014,0.1724,0.375,,0.031,,0.2443,,0.1396,0.1936,0.1727,0.9940000000000001,,,0.2,0.1724,0.375,,0.0308,,0.2386,,0.1347,,block of flats,0.2134,Panel,No,0.0,0.0,0.0,0.0,-1286.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,1.0\n265802,0,Cash loans,F,Y,Y,1,180000.0,180000.0,15390.0,180000.0,Family,Working,Higher education,Married,House / apartment,0.030755,-11556,-743,-1008.0,-4179,1.0,1,1,1,1,0,0,,3.0,2,2,TUESDAY,10,0,0,0,1,1,0,Business Entity Type 3,0.5736098211575301,0.24549534113584925,0.25946765482111994,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-928.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n345411,0,Cash loans,F,N,Y,0,90000.0,590337.0,23409.0,477000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-17910,-9152,-11876.0,-697,,1,1,0,1,1,0,,2.0,3,3,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.4631617448026084,0.7557400501752248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1848.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n439367,0,Cash loans,F,N,N,0,135000.0,652311.0,21172.5,544500.0,Family,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.003818,-13375,-445,-7411.0,-3922,,1,1,0,1,1,0,Cleaning staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Kindergarten,,0.7460930640205381,0.31547215492577346,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-702.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n178664,0,Cash loans,F,N,Y,0,144000.0,754740.0,24345.0,630000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.0105,-15305,-4333,-1476.0,-4872,,1,1,0,1,1,0,Managers,1.0,3,3,TUESDAY,10,0,0,0,0,0,0,Police,,0.642178985644672,0.6594055320683344,0.1763,0.0625,0.9786,0.7076,0.0656,0.04,0.0345,0.3333,0.375,0.0602,0.1437,0.0826,0.0,0.0,0.1796,0.0648,0.9786,0.7190000000000001,0.0662,0.0403,0.0345,0.3333,0.375,0.0616,0.157,0.086,0.0,0.0,0.17800000000000002,0.0625,0.9786,0.7115,0.066,0.04,0.0345,0.3333,0.375,0.0613,0.1462,0.0841,0.0,0.0,reg oper account,block of flats,0.1008,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1831.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n165200,0,Cash loans,M,N,N,3,270000.0,1312110.0,50107.5,1125000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006008,-13161,-857,-3577.0,-4987,,1,1,0,1,1,0,,5.0,2,2,THURSDAY,17,0,0,0,0,1,1,Industry: type 3,,0.4113411224855197,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n269791,0,Cash loans,F,Y,Y,0,166500.0,1116076.5,37012.5,913500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-21964,365243,-4989.0,-5003,44.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.5138459891988415,,,,0.9866,,,,,,,,,,,,,,0.9866,,,,,,,,,,,,,,0.9866,,,,,,,,,,,,,,0.0883,,No,5.0,0.0,4.0,0.0,-1563.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n248982,0,Cash loans,F,Y,Y,0,166500.0,436032.0,24475.5,360000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.006008,-17047,-3308,-9689.0,-578,28.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,School,0.5381967090713695,0.6931672697623786,0.7530673920730478,0.2062,0.1449,0.9876,0.83,0.1412,0.24,0.2069,0.3333,0.375,0.1936,0.1681,0.217,0.027000000000000003,0.2309,0.2101,0.1504,0.9876,0.8367,0.1425,0.2417,0.2069,0.3333,0.375,0.1981,0.1837,0.2261,0.0272,0.2444,0.2082,0.1449,0.9876,0.8323,0.1421,0.24,0.2069,0.3333,0.375,0.19699999999999998,0.171,0.2209,0.0272,0.2357,reg oper account,block of flats,0.2489,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-137.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n215543,0,Cash loans,M,Y,Y,0,180000.0,284400.0,18643.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,Office apartment,0.018634,-21760,-470,-1001.0,-4466,12.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,9,0,1,1,0,0,0,Business Entity Type 2,,0.4394592891333869,0.07298315565566517,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n134259,0,Cash loans,F,N,N,0,112500.0,327024.0,21982.5,270000.0,Other_A,Working,Secondary / secondary special,Separated,House / apartment,0.009657,-17009,-117,-6466.0,-555,,1,1,0,1,1,0,,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6095881583251869,0.6738300778602003,0.33399999999999996,,0.9911,0.8776,,0.36,0.3103,0.3333,0.0417,0.1837,,,,,0.3403,,0.9911,0.8824,,0.3625,0.3103,0.3333,0.0417,0.1879,,,,,0.3373,,0.9911,0.8792,,0.36,0.3103,0.3333,0.0417,0.1869,,,,,,block of flats,0.3622,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n263311,1,Cash loans,F,N,N,0,67500.0,319981.5,19467.0,243000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-14630,-546,-486.0,-5253,,1,1,1,1,0,0,Laborers,2.0,3,3,MONDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.02878283899599064,0.15474363127259447,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,7.0,1.0,-423.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n277189,0,Cash loans,F,N,Y,0,81000.0,100737.0,10975.5,94500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-15255,-4168,-60.0,-4055,,1,1,0,1,0,0,Sales staff,2.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3896656901876008,0.7211901176257038,,0.08900000000000001,0.1011,0.997,0.9524,0.0158,0.0,0.2183,0.1667,0.0971,0.0663,0.0726,0.0846,0.0,0.0,0.0788,0.0799,0.9921,0.8955,0.0146,0.0,0.2414,0.1667,0.0417,0.0482,0.0689,0.0764,0.0,0.0,0.0822,0.0905,0.9990000000000001,0.9866,0.0151,0.0,0.2414,0.1667,0.0417,0.0651,0.0676,0.0898,0.0,0.0,reg oper account,block of flats,0.0659,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n152067,0,Cash loans,F,N,Y,0,382500.0,533304.0,22725.0,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.032561,-13438,-4007,-727.0,-4441,,1,1,0,1,0,0,,1.0,1,1,SUNDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.6725439038369067,0.6738300778602003,0.2575,0.1478,0.9821,0.7552,0.6757,0.4,0.131,0.4583,0.0417,0.0602,0.3471,0.2535,0.0,0.0123,0.1239,0.1225,0.9821,0.7648,0.6819,0.3222,0.069,0.4583,0.0417,0.0291,0.3792,0.1398,0.0,0.005,0.2436,0.1183,0.9821,0.7585,0.68,0.32,0.1379,0.4583,0.0417,0.0289,0.3531,0.2702,0.0,0.0072,reg oper account,block of flats,0.1065,Panel,No,0.0,0.0,0.0,0.0,-1276.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n185244,0,Cash loans,M,Y,Y,1,225000.0,584766.0,27225.0,472500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.020713,-12923,-321,-272.0,-2712,2.0,1,1,0,1,0,0,Laborers,3.0,3,3,MONDAY,6,0,0,0,0,1,1,Self-employed,0.19695775936471294,0.10759041970225693,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n281149,1,Cash loans,F,N,Y,0,306000.0,734166.0,49261.5,693000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-18927,-2978,-12634.0,-2435,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6557033065319188,0.31547215492577346,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1511.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,2.0\n284053,0,Cash loans,M,Y,Y,0,337500.0,1125000.0,36292.5,1125000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,With parents,0.01885,-9866,-2205,-7507.0,-2558,1.0,1,1,0,1,1,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Self-employed,0.5583396413565906,0.6139871249662611,0.2807895743848605,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2336.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,1.0,0.0,0.0,1.0,0.0\n193527,0,Cash loans,F,N,Y,2,126000.0,568057.5,27459.0,459000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13338,-2707,-1321.0,-2289,,1,1,1,1,0,0,Laborers,4.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6761215889065841,0.3360615207658242,0.0371,0.1316,0.9975,0.966,0.0137,0.0,0.1034,0.0833,,0.0353,0.0303,0.0309,,0.0345,0.0378,0.1366,0.9975,0.9673,0.0139,0.0,0.1034,0.0833,,0.0361,0.0331,0.0322,,0.0365,0.0375,0.1316,0.9975,0.9665,0.0138,0.0,0.1034,0.0833,,0.0359,0.0308,0.0315,,0.0352,,block of flats,0.0355,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-2125.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n263434,1,Cash loans,F,N,Y,1,135000.0,261648.0,12325.5,207000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.018634,-17370,-144,-861.0,-921,,1,1,1,1,0,0,Realty agents,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Realtor,0.7091126343695566,0.04746315224072852,0.09022093929076848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-391.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n437173,0,Cash loans,F,N,N,0,94500.0,349659.0,24997.5,324000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-16799,-1546,-5036.0,-339,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.4232299298511135,0.8357765157975799,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1230.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n257802,0,Cash loans,M,N,Y,1,202500.0,318528.0,23305.5,252000.0,Family,Working,Higher education,Married,House / apartment,0.010643,-11836,-2755,-34.0,-4244,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Electricity,,0.7458278491664929,0.20208660168203949,0.1402,0.1773,0.9871,0.8164,0.0191,0.0,0.3448,0.1667,0.2083,0.1341,0.1143,0.1419,0.0,0.0481,0.1429,0.184,0.9871,0.8236,0.0193,0.0,0.3448,0.1667,0.2083,0.1372,0.1249,0.1478,0.0,0.0509,0.1416,0.1773,0.9871,0.8189,0.0192,0.0,0.3448,0.1667,0.2083,0.1364,0.1163,0.1444,0.0,0.0491,org spec account,block of flats,0.122,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-921.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n392677,0,Revolving loans,M,N,Y,0,225000.0,630000.0,31500.0,630000.0,Unaccompanied,Commercial associate,Lower secondary,Civil marriage,Municipal apartment,0.072508,-9628,-188,-4029.0,-1962,,1,1,0,1,0,0,,2.0,1,1,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.7179291325907925,0.3740208032583212,0.159,0.111,0.9841,,,0.17600000000000002,0.1448,0.375,,0.0,,0.0977,,0.048,0.0641,0.0289,0.9777,,,0.0806,0.0345,0.3333,,0.0,,0.0284,,0.0,0.1499,0.0956,0.9776,,,0.16,0.1379,0.3333,,0.0,,0.1,,0.0015,,block of flats,0.1586,Panel,No,0.0,0.0,0.0,0.0,-1597.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n354170,0,Cash loans,F,N,Y,1,157500.0,999000.0,29209.5,999000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-15667,-4451,-4154.0,-5037,,1,1,1,1,0,0,Laborers,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Housing,,0.7019187168682417,0.4543210601605785,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2116.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n365124,0,Cash loans,F,N,N,2,67500.0,315000.0,14814.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-9766,-526,-2534.0,-2430,,1,1,0,1,0,0,,3.0,2,2,THURSDAY,13,0,0,0,1,1,0,Business Entity Type 3,0.3163933727851757,0.55315173662403,0.19182160241360605,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-924.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n432184,0,Cash loans,F,N,Y,0,225000.0,743031.0,41620.5,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-16694,-133,-5511.0,-229,,1,1,0,1,0,0,Sales staff,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Trade: type 7,0.683997576804319,0.6044238752469526,0.5495965024956946,0.0773,0.0755,0.9752,0.66,0.0298,0.0,0.1724,0.1667,0.2083,0.1409,0.063,0.0649,0.0,0.0,0.0788,0.0783,0.9752,0.6733,0.0301,0.0,0.1724,0.1667,0.2083,0.1441,0.0689,0.0676,0.0,0.0,0.0781,0.0755,0.9752,0.6645,0.03,0.0,0.1724,0.1667,0.2083,0.1433,0.0641,0.066,0.0,0.0,reg oper account,block of flats,0.0521,Panel,No,0.0,0.0,0.0,0.0,-1721.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,1.0,2.0\n421789,0,Cash loans,F,N,N,0,135000.0,675000.0,24246.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.00496,-17698,-1143,-6589.0,-1249,,1,1,1,1,0,0,Cooking staff,1.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Self-employed,0.4899094674478173,0.7314348365948964,0.4561097392782771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1107.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n183265,0,Cash loans,M,N,N,0,157500.0,927252.0,26703.0,774000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-18950,-661,-6469.0,-2465,,1,1,0,1,0,0,Managers,2.0,1,1,WEDNESDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.6798549307001304,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n279091,0,Cash loans,F,Y,Y,2,135000.0,1125000.0,33025.5,1125000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.022625,-11187,-497,-1132.0,-3792,8.0,1,1,0,1,1,0,Drivers,4.0,2,2,MONDAY,13,0,0,0,0,0,0,Trade: type 3,0.6456547340164988,0.5090376774994256,,0.0928,0.0,0.9796,0.7212,0.1358,0.0,0.1724,0.1667,0.2083,0.0,0.0756,0.0943,0.0,0.0,0.0945,0.0,0.9796,0.7321,0.13699999999999998,0.0,0.1724,0.1667,0.2083,0.0,0.0826,0.0982,0.0,0.0,0.0937,0.0,0.9796,0.7249,0.1366,0.0,0.1724,0.1667,0.2083,0.0,0.077,0.096,0.0,0.0,reg oper account,block of flats,0.0742,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-472.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n288660,0,Cash loans,F,N,N,0,112500.0,835380.0,33129.0,675000.0,Family,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.006852,-15447,-5065,-8462.0,-4202,,1,1,0,1,0,0,Waiters/barmen staff,1.0,3,3,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.4155195009694795,0.2841332161755037,0.470456116119975,0.1237,0.1197,0.9816,0.7484,0.0149,0.0,0.2069,0.1667,0.2083,0.069,0.1,0.1153,0.0039,0.004,0.1261,0.1243,0.9816,0.7583,0.0151,0.0,0.2069,0.1667,0.2083,0.0706,0.1093,0.1202,0.0039,0.0043,0.1249,0.1197,0.9816,0.7518,0.015,0.0,0.2069,0.1667,0.2083,0.0702,0.1018,0.1174,0.0039,0.0041,reg oper account,block of flats,0.0998,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n272484,1,Cash loans,F,N,Y,1,126000.0,251280.0,17127.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-11979,-857,-162.0,-1744,,1,1,0,1,1,0,Laborers,3.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Trade: type 3,,0.535253967140149,0.1654074596555436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1131.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n311349,0,Cash loans,M,N,Y,1,148500.0,895500.0,26311.5,895500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-9073,-1122,-3982.0,-1747,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Transport: type 2,,0.6412035787679794,0.3001077565791181,0.0567,0.0334,0.9940000000000001,,,0.0,0.1379,0.1667,,0.0131,,0.0635,,0.0,0.0578,0.0347,0.9940000000000001,,,0.0,0.1379,0.1667,,0.0134,,0.0662,,0.0,0.0573,0.0334,0.9940000000000001,,,0.0,0.1379,0.1667,,0.0133,,0.0647,,0.0,,block of flats,0.05,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n409687,0,Cash loans,F,N,Y,0,202500.0,1214100.0,67923.0,1125000.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.006629,-20767,-175,-2868.0,-2898,,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Construction,,0.4397232568356788,0.8327850252992314,0.0124,,0.9801,,,0.0,0.069,0.0417,,0.0293,,0.006,,,0.0126,,0.9801,,,0.0,0.069,0.0417,,0.0299,,0.0062,,,0.0125,,0.9801,,,0.0,0.069,0.0417,,0.0298,,0.0061,,,,block of flats,0.0078,Wooden,Yes,0.0,0.0,0.0,0.0,-800.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n361822,1,Cash loans,F,N,Y,0,225000.0,397881.0,22347.0,328500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006305,-16419,-2552,-5509.0,-3743,,1,1,0,1,0,0,,2.0,3,3,MONDAY,5,0,0,0,0,0,0,Business Entity Type 3,,0.38127998641845817,,0.0536,,0.9811,0.7416,0.013999999999999999,0.0,0.0345,0.1667,0.0417,,0.0437,0.0383,0.0,0.0,0.0546,,0.9811,0.7517,0.0142,0.0,0.0345,0.1667,0.0417,,0.0478,0.0399,0.0,0.0,0.0541,,0.9811,0.7451,0.0141,0.0,0.0345,0.1667,0.0417,,0.0445,0.039,0.0,0.0,org spec account,specific housing,0.0378,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-276.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n378150,0,Revolving loans,F,N,Y,0,184500.0,450000.0,22500.0,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.025164,-11371,-2442,-2254.0,-3542,,1,1,0,1,0,0,Managers,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Bank,,0.6227545108944115,0.5262949398096192,0.0412,0.0345,0.9742,0.6464,0.0041,0.0,0.069,0.1667,0.2083,0.0079,0.0336,0.0326,0.0,0.0,0.042,0.0358,0.9742,0.6602,0.0042,0.0,0.069,0.1667,0.2083,0.008,0.0367,0.034,0.0,0.0,0.0416,0.0345,0.9742,0.6511,0.0042,0.0,0.069,0.1667,0.2083,0.008,0.0342,0.0332,0.0,0.0,reg oper account,block of flats,0.0256,Block,No,6.0,0.0,6.0,0.0,-1426.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n358204,0,Cash loans,M,Y,Y,1,94500.0,900000.0,26446.5,900000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.015221,-14459,-5966,-6782.0,-4576,14.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,11,0,0,0,1,1,1,Housing,,0.5436475351763773,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-846.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n450733,0,Cash loans,F,N,Y,2,247500.0,315000.0,17086.5,315000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010643,-11732,-1064,-5133.0,-3766,,1,1,0,1,0,0,Accountants,4.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4245072004889312,0.7006681483774961,0.5902333386185574,,0.1536,0.9806,,,,0.2759,,,,,0.0773,,0.0207,,0.1594,0.9806,,,,0.2759,,,,,0.0806,,0.0219,,0.1536,0.9806,,,,0.2759,,,,,0.0787,,0.0211,,,0.1237,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-519.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n135867,0,Cash loans,M,Y,N,1,495000.0,1006920.0,45630.0,900000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.030755,-16030,-1889,-10088.0,-4879,,1,1,1,1,0,0,Managers,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Construction,0.3392856865096504,0.6631922681540987,0.1455428133497032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1477.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n325726,0,Cash loans,M,Y,Y,1,180000.0,457312.5,36261.0,373500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-12987,-5596,-6366.0,-4205,4.0,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.6438705784852047,0.5064842396679806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n235756,0,Cash loans,F,N,Y,0,112500.0,237024.0,18855.0,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-20609,365243,-4212.0,-3692,,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.7996617701211519,0.3606125659189888,0.0825,,0.9757,,,0.0,0.1379,0.1667,,0.0642,,0.0635,,0.0,0.084,,0.9757,,,0.0,0.1379,0.1667,,0.0657,,0.0662,,0.0,0.0833,,0.9757,,,0.0,0.1379,0.1667,,0.0654,,0.0647,,0.0,,block of flats,0.0538,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1202.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n121005,0,Cash loans,F,N,Y,0,67500.0,384048.0,18031.5,270000.0,Family,Working,Secondary / secondary special,Married,Rented apartment,0.019101,-13957,-1012,-894.0,-4801,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Trade: type 7,0.4909388924685798,0.7132964520962972,,0.0711,0.0546,0.9811,0.7416,0.0045,0.0,0.1379,0.1667,0.2083,0.0061,0.0546,0.057,0.0154,0.0151,0.0725,0.0567,0.9811,0.7517,0.0046,0.0,0.1379,0.1667,0.2083,0.0062,0.0597,0.0594,0.0156,0.016,0.0718,0.0546,0.9811,0.7451,0.0045,0.0,0.1379,0.1667,0.2083,0.0062,0.0556,0.057999999999999996,0.0155,0.0154,reg oper account,block of flats,0.0506,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-402.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n261370,1,Cash loans,M,Y,Y,0,270000.0,1078461.0,35770.5,819000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-15366,-1140,-2764.0,-2764,8.0,1,1,1,1,1,0,Sales staff,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Transport: type 4,,0.6400204637474272,0.3233112448967859,0.2031,0.1344,0.9911,0.8776,0.3182,0.2,0.1724,0.375,0.4167,0.1447,0.1631,0.2179,0.0116,0.0162,0.2069,0.1394,0.9911,0.8824,0.3211,0.2014,0.1724,0.375,0.4167,0.14800000000000002,0.1781,0.22699999999999998,0.0117,0.0172,0.2051,0.1344,0.9911,0.8792,0.3202,0.2,0.1724,0.375,0.4167,0.1472,0.1659,0.2218,0.0116,0.0165,reg oper spec account,block of flats,0.174,Panel,No,0.0,0.0,0.0,0.0,-867.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n202516,0,Cash loans,M,Y,Y,0,157500.0,526491.0,32337.0,454500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.00823,-14683,-2042,-7389.0,-1167,30.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,10,0,0,0,1,0,1,Transport: type 4,,0.3965369344717199,0.4974688893052743,0.2021,0.0632,0.9945,1.0,0.0514,0.0,0.3793,0.1667,0.2083,0.0877,0.1009,0.098,0.0,0.1292,0.1261,0.0656,0.9886,1.0,0.0518,0.0,0.1379,0.1667,0.2083,0.0897,0.1102,0.1021,0.0,0.1367,0.204,0.0632,0.9945,1.0,0.0517,0.0,0.3793,0.1667,0.2083,0.0892,0.1026,0.0998,0.0,0.1319,not specified,block of flats,0.26,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1046.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n192658,1,Cash loans,F,N,Y,2,135000.0,1024740.0,52452.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-12630,-1083,-10.0,-3432,,1,1,0,1,0,0,,4.0,3,3,FRIDAY,9,0,0,0,0,0,0,Government,0.38494191228817376,0.04937728694063541,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n198208,0,Cash loans,F,N,Y,0,157500.0,497520.0,33376.5,450000.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.006852,-21143,365243,-6802.0,-4095,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,4,0,0,0,0,0,0,XNA,,0.4180606659813069,,0.067,0.0868,0.9906,0.8708,0.0731,0.0,0.1379,0.1667,0.0,0.051,0.0546,0.0822,0.0,0.0,0.0683,0.0901,0.9906,0.8759,0.0738,0.0,0.1379,0.1667,0.0,0.0521,0.0597,0.0857,0.0,0.0,0.0677,0.0868,0.9906,0.8725,0.0736,0.0,0.1379,0.1667,0.0,0.0519,0.0556,0.0837,0.0,0.0,reg oper account,block of flats,0.1144,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-41.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n352201,0,Cash loans,M,Y,Y,0,135000.0,152820.0,16587.0,135000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.04622,-8583,-389,-634.0,-1248,14.0,1,1,0,1,0,0,IT staff,1.0,1,1,SATURDAY,10,0,0,0,1,1,0,Self-employed,0.3652020550942515,0.5049656614676064,0.6075573001388961,0.3613,,0.998,0.9728,0.2158,0.48,0.2069,0.625,0.625,,0.2879,0.4535,0.0309,0.0881,0.1996,,0.998,0.9739,0.1137,0.2417,0.1034,0.625,0.5833,,0.1745,0.2649,0.0,0.0,0.3648,,0.998,0.9732,0.2172,0.48,0.2069,0.625,0.625,,0.2929,0.4617,0.0311,0.09,reg oper account,block of flats,0.2616,Monolithic,No,0.0,0.0,0.0,0.0,-848.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n112197,0,Revolving loans,F,N,N,0,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.006670999999999999,-7751,-509,-7751.0,-425,,1,1,0,1,0,0,Sales staff,2.0,2,2,SUNDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.13769696156703315,,0.0082,0.0,0.9752,0.66,0.0014,0.0,0.069,0.0417,0.0833,0.0207,0.0067,0.0076,0.0,0.0,0.0084,0.0,0.9752,0.6733,0.0015,0.0,0.069,0.0417,0.0833,0.0212,0.0073,0.0079,0.0,0.0,0.0083,0.0,0.9752,0.6645,0.0015,0.0,0.069,0.0417,0.0833,0.0211,0.0068,0.0077,0.0,0.0,not specified,block of flats,0.006,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n270462,0,Cash loans,F,N,Y,0,180000.0,225000.0,17775.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-13571,-860,-6870.0,-5509,,1,1,0,1,0,0,Accountants,2.0,1,1,FRIDAY,12,0,0,0,1,1,0,Business Entity Type 3,0.8233856909553493,0.7961603985454752,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1612.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n442632,0,Cash loans,F,N,Y,0,274500.0,685687.5,50314.5,607500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.032561,-21792,365243,-9213.0,-5307,,1,0,0,1,0,1,,2.0,1,1,SATURDAY,12,0,0,0,0,0,0,XNA,,0.8002426768269301,0.511891801533151,0.0742,0.0807,0.93,,,0.0,0.2414,0.1667,,0.0349,,0.1054,,0.0196,0.0756,0.0837,0.93,,,0.0,0.2414,0.1667,,0.0357,,0.1099,,0.0208,0.0749,0.0807,0.93,,,0.0,0.2414,0.1667,,0.0355,,0.1073,,0.02,,block of flats,0.10400000000000001,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2668.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n387147,1,Cash loans,F,N,Y,1,202500.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-12098,-1297,-4379.0,-4468,,1,1,0,1,0,0,,3.0,2,2,SUNDAY,10,0,0,0,0,0,0,Other,0.2452530979977368,0.7087359467583026,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1550.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n152455,0,Cash loans,F,N,Y,0,103500.0,552555.0,19975.5,477000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.0228,-20721,365243,-9646.0,-4104,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,XNA,,0.6726498441166548,0.7700870700124128,0.0742,0.0425,0.9881,0.8368,0.0187,0.06,0.0345,0.3333,0.375,0.0087,0.0605,0.0759,0.0,0.0124,0.0189,0.0057,0.9841,0.7909,0.0,0.0403,0.0345,0.3333,0.375,0.0,0.0165,0.0627,0.0,0.0,0.0749,0.0425,0.9881,0.8390000000000001,0.0188,0.06,0.0345,0.3333,0.375,0.0088,0.0616,0.0772,0.0,0.0127,reg oper account,block of flats,0.0527,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-274.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,0.0\n196493,0,Cash loans,M,Y,Y,2,247500.0,900000.0,32017.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.015221,-14664,-2562,-477.0,-4566,64.0,1,1,0,1,1,0,Managers,4.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5301643398035135,0.22888341670067305,0.0825,0.0538,0.9762,0.6736,0.0058,0.0,0.2069,0.1667,0.2083,0.0344,0.0672,0.0632,0.0,0.0,0.084,0.0558,0.9762,0.6864,0.0059,0.0,0.2069,0.1667,0.2083,0.0352,0.0735,0.0659,0.0,0.0,0.0833,0.0538,0.9762,0.6779999999999999,0.0058,0.0,0.2069,0.1667,0.2083,0.035,0.0684,0.0643,0.0,0.0,reg oper account,block of flats,0.0497,Panel,No,1.0,0.0,1.0,0.0,-709.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n136241,0,Cash loans,M,N,Y,2,112500.0,1249740.0,69912.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006296,-16444,-4513,-5012.0,-1,,1,1,0,1,0,0,Security staff,4.0,3,3,WEDNESDAY,13,0,0,0,0,0,0,Transport: type 4,,0.4573329133901418,0.6363761710860439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1319.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,2.0\n361248,0,Cash loans,F,N,Y,0,337500.0,835605.0,24561.0,697500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-16974,-4294,-8901.0,-467,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,9,0,0,0,0,1,1,Trade: type 7,,0.4406285469489222,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1566.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n258298,0,Cash loans,F,N,Y,0,99000.0,573628.5,22878.0,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-17178,-2279,-8515.0,-728,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.7253959518119454,0.722392890081304,0.1564,0.0346,0.9821,0.7552,0.0138,0.0264,0.0572,0.2221,0.2638,0.0902,0.1264,0.08,0.0051,0.0222,0.1261,0.0242,0.9826,0.7713,0.0123,0.0,0.069,0.1667,0.2083,0.0137,0.1093,0.0577,0.0,0.0,0.1249,0.0273,0.9826,0.7652,0.0134,0.0,0.069,0.1667,0.2083,0.0859,0.1026,0.0748,0.0039,0.032,reg oper account,block of flats,0.0502,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-605.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n275746,0,Cash loans,F,Y,Y,0,202500.0,1333669.5,89617.5,1278000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018029,-15792,-2446,-2744.0,-4289,16.0,1,1,0,1,0,0,Core staff,2.0,3,3,FRIDAY,10,0,0,0,0,0,0,Legal Services,0.6170810322931108,0.503134373651308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-2347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n172324,1,Cash loans,F,N,Y,1,135000.0,177768.0,13063.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-13312,-1259,-6478.0,-5953,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,15,0,0,0,0,0,0,Self-employed,0.34683574902291764,0.3698904593004563,0.6006575372857061,0.0825,0.06,0.9727,0.626,0.0492,0.0,0.1379,0.1667,0.2083,0.0153,0.0672,0.063,0.0,0.0,0.084,0.0623,0.9727,0.6406,0.0497,0.0,0.1379,0.1667,0.2083,0.0157,0.0735,0.0656,0.0,0.0,0.0833,0.06,0.9727,0.631,0.0495,0.0,0.1379,0.1667,0.2083,0.0156,0.0684,0.0641,0.0,0.0,reg oper account,block of flats,0.0764,Mixed,No,3.0,1.0,3.0,1.0,-864.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n240893,0,Cash loans,M,N,Y,0,202500.0,631332.0,38623.5,585000.0,Unaccompanied,Working,Higher education,Married,With parents,0.005084,-9672,-2654,-4510.0,-2367,,1,1,0,1,1,1,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Self-employed,0.09745629437227453,0.6676566181318264,0.1986200451074864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,2.0,-723.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n397779,0,Cash loans,F,Y,Y,2,117000.0,526491.0,29529.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-19201,365243,-3670.0,-2622,9.0,1,0,0,1,0,0,,4.0,3,3,SATURDAY,9,0,0,0,0,0,0,XNA,,0.2388139508045465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n366833,0,Cash loans,F,Y,Y,2,315000.0,945000.0,31230.0,945000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-15210,-3035,-2362.0,-4308,0.0,1,1,0,1,0,0,Accountants,4.0,1,1,FRIDAY,11,0,1,1,0,0,0,Self-employed,0.6256635133606174,0.5778783293781498,0.33285056416487313,0.1443,0.1261,0.9781,0.7008,0.0547,0.0,0.2414,0.1667,0.2083,0.0527,0.1177,0.1296,0.0,0.0,0.1471,0.1308,0.9782,0.7125,0.0552,0.0,0.2414,0.1667,0.2083,0.0539,0.1286,0.135,0.0,0.0,0.1457,0.1261,0.9781,0.7048,0.055,0.0,0.2414,0.1667,0.2083,0.0536,0.1197,0.1319,0.0,0.0,reg oper account,block of flats,0.1318,Panel,No,0.0,0.0,0.0,0.0,-1809.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n164573,0,Cash loans,M,Y,N,0,675000.0,675000.0,49117.5,675000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,Rented apartment,0.04622,-16179,-420,-8688.0,-4315,8.0,1,1,1,1,0,1,Managers,2.0,1,1,SATURDAY,11,0,1,1,0,1,1,Other,0.6709675304009863,0.7235757924517778,0.097581612687446,0.1113,0.0852,0.9821,,,0.12,0.1034,0.3333,,,,0.1102,,,0.1134,0.0884,0.9821,,,0.1208,0.1034,0.3333,,,,0.1148,,,0.1124,0.0852,0.9821,,,0.12,0.1034,0.3333,,,,0.1122,,,,block of flats,0.1119,Panel,No,0.0,0.0,0.0,0.0,-1065.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n446347,0,Cash loans,M,N,Y,1,225000.0,183784.5,14350.5,148500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008865999999999999,-17090,-5802,-6421.0,-642,,1,1,0,1,0,1,Managers,3.0,2,2,MONDAY,18,0,0,0,0,0,0,Military,,0.4951802309141991,0.5849900404894085,0.0588,0.0605,0.9906,0.6668,0.0328,0.0,0.1724,0.1667,0.2083,0.0631,0.0479,0.0555,0.0,0.0,0.0599,0.0628,0.9906,0.6798,0.0331,0.0,0.1724,0.1667,0.2083,0.0645,0.0523,0.0578,0.0,0.0,0.0593,0.0605,0.9906,0.6713,0.033,0.0,0.1724,0.1667,0.2083,0.0642,0.0487,0.0565,0.0,0.0,reg oper account,block of flats,0.0616,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-484.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n184129,0,Cash loans,M,Y,N,0,189000.0,540000.0,16501.5,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.026392,-19523,-2170,-1709.0,-2689,1.0,1,1,0,1,0,0,Drivers,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Other,,0.5551507594495814,0.511891801533151,0.1485,,0.995,,,0.08,0.069,0.3333,,,,0.0928,,0.0,0.1513,,0.995,,,0.0806,0.069,0.3333,,,,0.0967,,0.0,0.1499,,0.995,,,0.08,0.069,0.3333,,,,0.0945,,0.0,,block of flats,0.073,Others,No,2.0,0.0,2.0,0.0,-7.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n371307,0,Cash loans,F,Y,N,0,90000.0,675000.0,21775.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.0228,-20187,365243,-5073.0,-3704,10.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,0.484860945216281,0.5831926855711411,0.6313545365850379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-433.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n444880,0,Cash loans,M,N,Y,0,202500.0,1530000.0,42205.5,1530000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.006233,-9211,-402,-941.0,-1883,,1,1,0,1,0,0,Core staff,1.0,2,2,WEDNESDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.21887260459329047,0.1455428133497032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n234042,0,Revolving loans,F,Y,Y,0,54000.0,180000.0,9000.0,180000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.04622,-23293,365243,-12289.0,-4825,4.0,1,0,0,1,0,0,,1.0,1,1,THURSDAY,12,0,0,0,0,0,0,XNA,,0.6756473379444479,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n127282,0,Cash loans,F,N,Y,0,112500.0,723996.0,34960.5,585000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009549,-10686,-459,-448.0,-910,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Bank,0.5012248795168991,0.4545339142187277,0.3296550543128238,0.0722,0.0849,0.9767,,,,0.1379,0.1667,,0.0839,,0.0669,,,0.0735,0.0881,0.9767,,,,0.1379,0.1667,,0.0858,,0.0697,,,0.0729,0.0849,0.9767,,,,0.1379,0.1667,,0.0853,,0.0681,,,,block of flats,0.0556,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-266.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n237302,1,Cash loans,M,N,Y,2,202500.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-14254,-1010,-114.0,-4453,,1,1,0,1,1,0,Laborers,4.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.3805631885264189,0.1595195404777181,0.0809,0.0326,0.9945,0.9184,,0.08,0.069,0.375,0.4167,,0.0645,0.0812,0.0068,0.0147,0.042,0.0509,0.9940000000000001,0.9151,,0.0403,0.0345,0.375,0.4167,,0.1056,0.1263,0.0078,0.0122,0.0817,0.0336,0.9940000000000001,0.9128,,0.08,0.069,0.375,0.4167,,0.0663,0.0864,0.0078,0.0117,reg oper account,block of flats,0.11,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n336325,0,Cash loans,M,Y,Y,1,157500.0,1024290.0,30078.0,855000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008068,-15461,-7851,-7870.0,-4746,23.0,1,1,1,1,1,0,Laborers,3.0,3,3,FRIDAY,6,0,0,0,0,1,1,Emergency,,0.4270671716680711,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1526.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n360463,1,Cash loans,M,N,Y,0,180000.0,675000.0,53329.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006233,-10352,-1743,-10337.0,-2851,,1,1,1,1,0,0,Laborers,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.16822730410214162,0.5999606868238181,,0.066,0.0,0.9767,0.6804,0.011000000000000001,0.0,0.1379,0.1667,0.2083,0.046,0.0538,0.0574,0.0,0.0105,0.0672,0.0,0.9767,0.6929,0.0111,0.0,0.1379,0.1667,0.2083,0.047,0.0588,0.0598,0.0,0.0111,0.0666,0.0,0.9767,0.6847,0.0111,0.0,0.1379,0.1667,0.2083,0.0468,0.0547,0.0584,0.0,0.0107,,block of flats,0.0474,Panel,No,3.0,0.0,3.0,0.0,-2808.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n386258,0,Cash loans,F,N,Y,1,135000.0,450000.0,32742.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-13991,-2169,-7974.0,-1297,,1,1,0,1,1,0,Core staff,3.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,0.454326358672067,0.4835906911549514,0.17352743046491467,0.0948,0.1111,0.9866,0.8164,0.0146,0.04,0.1207,0.3542,0.3125,0.1009,0.0773,0.0872,0.0,0.0157,0.0945,0.1153,0.9866,0.8236,0.0148,0.0,0.0345,0.1667,0.0417,0.1032,0.0826,0.0846,0.0,0.0,0.0958,0.1111,0.9866,0.8189,0.0147,0.04,0.1207,0.3542,0.3125,0.1026,0.0787,0.0887,0.0,0.0161,org spec account,block of flats,0.0707,Panel,No,0.0,0.0,0.0,0.0,-27.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n160584,0,Cash loans,F,N,Y,0,45000.0,755190.0,33394.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010006,-21899,365243,-1399.0,-4708,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.16284093539236028,0.4329616670974407,0.2165,0.2412,0.9876,0.83,0.0364,0.0,0.4828,0.1667,0.0417,0.0483,0.1765,0.1446,0.0,0.0,0.2206,0.2503,0.9876,0.8367,0.0367,0.0,0.4828,0.1667,0.0417,0.0494,0.1928,0.0669,0.0,0.0,0.2186,0.2412,0.9876,0.8323,0.0366,0.0,0.4828,0.1667,0.0417,0.0491,0.1796,0.1472,0.0,0.0,reg oper spec account,block of flats,0.2392,Block,No,0.0,0.0,0.0,0.0,-338.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n241413,0,Revolving loans,F,N,Y,0,171000.0,135000.0,6750.0,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-18437,-1183,-6428.0,-1993,,1,1,0,0,0,0,Sales staff,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.5872639358438011,,0.0082,0.0,0.9742,0.6464,0.0014,0.0,0.069,0.0417,0.0417,0.0,0.0059,0.0078,0.0039,0.0035,0.0084,0.0,0.9742,0.6602,0.0014,0.0,0.069,0.0417,0.0417,0.0,0.0064,0.0081,0.0039,0.0037,0.0083,0.0,0.9742,0.6511,0.0014,0.0,0.069,0.0417,0.0417,0.0,0.006,0.008,0.0039,0.0035,reg oper account,block of flats,0.0069,\"Stone, brick\",No,11.0,1.0,11.0,1.0,-1321.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,4.0\n381929,0,Cash loans,F,N,Y,0,90000.0,93829.5,9742.5,81000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.005313,-21388,365243,-8895.0,-4866,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,0.8804350482816081,0.6845767368864439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2297.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n240553,0,Cash loans,F,N,Y,0,180000.0,781920.0,25969.5,675000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.025164,-19715,365243,-763.0,-3205,,1,0,0,1,0,0,,1.0,2,2,MONDAY,7,0,0,0,0,0,0,XNA,0.6281321429869462,0.3561027915493316,0.4920600938649263,0.0495,0.0703,0.9717,0.6124,0.0112,0.0,0.1034,0.125,0.1667,0.0514,0.0403,0.0599,0.0,0.0,0.0504,0.0729,0.9717,0.6276,0.0113,0.0,0.1034,0.125,0.1667,0.0526,0.0441,0.0624,0.0,0.0,0.05,0.0703,0.9717,0.6176,0.0113,0.0,0.1034,0.125,0.1667,0.0523,0.040999999999999995,0.061,0.0,0.0,reg oper account,block of flats,0.0513,Block,No,4.0,1.0,4.0,1.0,-1767.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n100683,0,Revolving loans,M,Y,Y,0,157500.0,270000.0,13500.0,270000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.030755,-17327,-2135,-4582.0,-878,36.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,16,0,0,0,0,1,1,Self-employed,,0.597917337294572,0.7992967832109371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-440.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n209698,0,Cash loans,F,N,Y,0,112500.0,1129500.0,33025.5,1129500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-15458,-3054,-6497.0,-4669,,1,1,0,1,1,0,Core staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.5693475257509344,0.5725512303395108,,0.067,0.0575,0.9752,0.66,,0.0,0.1379,0.1667,0.2083,0.0525,0.0538,,0.0039,,0.0683,0.0597,0.9752,0.6733,,0.0,0.1379,0.1667,0.2083,0.0537,0.0588,,0.0039,,0.0677,0.0575,0.9752,0.6645,,0.0,0.1379,0.1667,0.2083,0.0534,0.0547,,0.0039,,not specified,block of flats,0.0607,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1753.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n217093,0,Cash loans,F,Y,Y,0,67500.0,422892.0,24408.0,382500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010556,-23803,365243,-4439.0,-4456,2.0,1,0,0,1,1,0,,2.0,3,3,TUESDAY,16,0,0,0,0,0,0,XNA,,0.3105973210381363,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-605.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n208463,0,Cash loans,F,N,N,2,112500.0,182016.0,11979.0,144000.0,Children,Working,Secondary / secondary special,Married,Municipal apartment,0.020246,-14129,-768,-3157.0,-4378,,1,1,0,1,0,0,Cleaning staff,4.0,3,3,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.033521331548010694,0.1385128770585923,0.0041,0.0,0.9732,0.6328,0.0,0.0,0.0,0.0,0.0,0.0,0.0034,0.0028,0.0,0.0,0.0042,0.0,0.9732,0.6472,0.0,0.0,0.0,0.0,0.0,0.0,0.0037,0.0029,0.0,0.0,0.0042,0.0,0.9732,0.6377,0.0,0.0,0.0,0.0,0.0,0.0,0.0034,0.0029,0.0,0.0,not specified,block of flats,0.0022,Wooden,No,4.0,0.0,4.0,0.0,-446.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n439056,0,Cash loans,F,Y,Y,0,180000.0,364896.0,16069.5,315000.0,Family,State servant,Higher education,Civil marriage,House / apartment,0.035792000000000004,-12507,-2065,-717.0,-4065,2.0,1,1,1,1,0,0,Managers,2.0,2,2,SUNDAY,9,0,0,0,0,0,0,Government,0.6225508634242716,0.7218491922491845,0.7490217048463391,0.0814,0.0869,0.9856,0.8028,0.04,0.0,0.2069,0.1667,0.2083,0.0609,0.0664,0.0815,0.0,0.0,0.083,0.0902,0.9856,0.8105,0.0404,0.0,0.2069,0.1667,0.2083,0.0623,0.0725,0.0849,0.0,0.0,0.0822,0.0869,0.9856,0.8054,0.0403,0.0,0.2069,0.1667,0.2083,0.062,0.0676,0.0829,0.0,0.0,reg oper account,block of flats,0.086,\"Stone, brick\",No,1.0,1.0,1.0,0.0,-1439.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,3.0\n390584,0,Cash loans,F,N,Y,2,148500.0,216144.0,15790.5,171000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-11473,-4602,-6131.0,-3464,,1,1,0,1,1,0,High skill tech staff,4.0,2,2,THURSDAY,13,0,0,0,1,0,1,Security Ministries,0.6833437832073419,0.21674162175195927,0.8083935912119442,0.0639,0.0165,0.9866,0.8164,0.0123,0.04,0.0345,0.3333,0.375,0.0539,0.0521,0.0642,0.0,0.0,0.0651,0.0171,0.9866,0.8236,0.0124,0.0403,0.0345,0.3333,0.375,0.0551,0.0569,0.0669,0.0,0.0,0.0645,0.0165,0.9866,0.8189,0.0124,0.04,0.0345,0.3333,0.375,0.0548,0.053,0.0653,0.0,0.0,reg oper spec account,block of flats,0.0574,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-433.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n376748,0,Cash loans,F,N,Y,1,112500.0,675000.0,19867.5,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-9753,365243,-1158.0,-2438,,1,0,0,1,1,0,,3.0,3,3,FRIDAY,12,0,0,0,0,0,0,XNA,0.4498902524995936,0.4165771111846197,,0.2041,0.1597,0.9776,,,0.0,0.3448,0.1667,,0.0366,,0.1823,,0.0079,0.20800000000000002,0.1658,0.9777,,,0.0,0.3448,0.1667,,0.0374,,0.19,,0.0084,0.2061,0.1597,0.9776,,,0.0,0.3448,0.1667,,0.0372,,0.1856,,0.0081,,terraced house,0.1592,Panel,No,2.0,1.0,2.0,1.0,-571.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n121906,0,Cash loans,F,Y,Y,0,360000.0,983299.5,41661.0,904500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.04622,-10031,-1009,-3699.0,-2700,0.0,1,1,1,1,0,1,,1.0,1,1,MONDAY,15,0,1,1,0,1,1,Business Entity Type 3,,0.6241303350797809,0.09261717137485452,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-767.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,5.0\n174892,0,Cash loans,M,Y,Y,0,180000.0,602194.5,44203.5,558000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-22261,365243,-7064.0,-4181,15.0,1,0,0,1,0,0,,2.0,3,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.3942524717890785,0.5585066276769286,0.1649,0.1605,0.9816,0.7484,0.0406,0.2,0.1724,0.3333,0.375,0.08800000000000001,0.1219,0.1628,0.0579,0.1224,0.1681,0.1666,0.9816,0.7583,0.0409,0.2014,0.1724,0.3333,0.375,0.09,0.1331,0.1696,0.0584,0.1295,0.1665,0.1605,0.9816,0.7518,0.0408,0.2,0.1724,0.3333,0.375,0.0895,0.124,0.1657,0.0582,0.1249,reg oper account,block of flats,0.1547,Panel,No,0.0,0.0,0.0,0.0,-692.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n320865,0,Cash loans,F,N,Y,1,108000.0,553806.0,21798.0,495000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-20994,365243,-4170.0,-4163,,1,0,0,1,0,0,,3.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.23807139570472124,0.7062051096536562,0.3186,0.168,0.9901,0.8640000000000001,0.0844,0.34,0.2931,0.375,0.4167,0.0,0.2589,0.28,0.0039,0.0018,0.2668,0.1518,0.9896,0.8628,0.0513,0.3222,0.2759,0.375,0.4167,0.0,0.2332,0.2885,0.0,0.0,0.3216,0.168,0.9901,0.8658,0.0849,0.34,0.2931,0.375,0.4167,0.0,0.2634,0.285,0.0039,0.0018,reg oper account,block of flats,0.2227,Panel,No,1.0,1.0,1.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n156321,0,Revolving loans,M,N,Y,0,148500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.020713,-9739,-519,-104.0,-1608,,1,1,0,1,0,0,Laborers,1.0,3,3,THURSDAY,12,0,0,0,0,1,1,Other,0.3452954286290552,0.0481139248747356,,0.0021,,0.9737,,,,,0.0,,0.0029,,,,,0.0021,,0.9737,,,,,0.0,,0.003,,,,,0.0021,,0.9737,,,,,0.0,,0.003,,,,,,block of flats,0.0012,Wooden,No,2.0,0.0,2.0,0.0,-142.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.0,0.0,0.0,0.0,0.0,4.0\n401558,0,Cash loans,M,N,Y,0,270000.0,990000.0,69034.5,990000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-9977,-3107,-904.0,-1276,,1,1,1,1,1,0,Sales staff,2.0,2,2,SUNDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.4099439594385263,0.3108182544189319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-96.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n293775,0,Cash loans,F,N,Y,2,144000.0,1125000.0,33025.5,1125000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.011657,-13985,-881,-537.0,-4518,,1,1,1,1,1,0,Core staff,4.0,1,1,SATURDAY,14,0,0,0,0,0,0,Trade: type 3,0.5665776579923135,0.1630288301211717,0.6577838002083306,0.0722,0.0831,0.9796,,,0.0,0.1379,0.1667,,0.0245,,0.0669,,0.0889,0.0735,0.0863,0.9796,,,0.0,0.1379,0.1667,,0.025,,0.0698,,0.0941,0.0729,0.0831,0.9796,,,0.0,0.1379,0.1667,,0.0249,,0.0682,,0.0908,,block of flats,0.07200000000000001,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2100.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n101886,0,Cash loans,M,N,Y,0,90000.0,562491.0,23962.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007114,-13832,-6228,-743.0,-4235,,1,1,0,1,1,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.3739002341915357,0.7557400501752248,0.0469,0.0408,0.9732,0.6328,0.0013,0.0,0.1034,0.1042,0.1458,0.0191,0.0378,0.0375,0.0019,0.0391,0.0168,0.0005,0.9722,0.6341,0.0,0.0,0.069,0.0417,0.0833,0.0,0.0147,0.016,0.0,0.0,0.0474,0.0408,0.9732,0.6377,0.0013,0.0,0.1034,0.1042,0.1458,0.0194,0.0385,0.0382,0.0019,0.0399,reg oper account,block of flats,0.0505,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n337922,0,Cash loans,F,Y,Y,0,225000.0,315000.0,21181.5,315000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.026392,-11419,-1336,-1437.0,-3751,8.0,1,1,0,1,1,1,Sales staff,1.0,2,2,MONDAY,18,0,0,0,0,1,1,Other,0.3958426436368401,0.7532577757708148,0.14375805349116522,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1344.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n348324,0,Cash loans,F,N,Y,0,67500.0,288873.0,11020.5,238500.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.00702,-23665,365243,-8562.0,-4794,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n351536,0,Cash loans,M,Y,N,2,270000.0,675000.0,36747.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-11817,-2810,-5887.0,-4101,15.0,1,1,0,1,0,0,,4.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6551254770919729,0.4436153084085652,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-339.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n140424,0,Revolving loans,F,N,Y,1,135000.0,270000.0,13500.0,270000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.011657,-15239,-113,-984.0,-4781,,1,1,0,1,1,0,Core staff,3.0,1,1,TUESDAY,13,0,0,0,0,0,0,Trade: type 6,,0.7363714319391351,0.8327850252992314,0.1474,0.0956,0.9811,,,0.16,0.1379,0.3333,,0.0,,0.1651,,0.0,0.1502,0.0992,0.9811,,,0.1611,0.1379,0.3333,,0.0,,0.172,,0.0,0.1489,0.0956,0.9811,,,0.16,0.1379,0.3333,,0.0,,0.1681,,0.0,,block of flats,0.1584,Panel,No,0.0,0.0,0.0,0.0,-2220.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n119970,0,Cash loans,F,Y,N,1,45000.0,239850.0,23364.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-16275,-3603,-6951.0,-4326,64.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,TUESDAY,12,0,0,0,0,0,0,Housing,,0.7532415181315868,0.20092608771597087,0.0753,0.0189,0.9771,0.6872,0.012,0.0,0.0345,0.125,0.1667,0.0214,0.0597,0.0229,0.0077,0.0025,0.0767,0.0196,0.9772,0.6994,0.0121,0.0,0.0345,0.125,0.1667,0.0219,0.0652,0.0238,0.0078,0.0027,0.076,0.0189,0.9771,0.6914,0.0121,0.0,0.0345,0.125,0.1667,0.0218,0.0607,0.0233,0.0078,0.0026,reg oper account,block of flats,0.0251,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-3070.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n173521,0,Cash loans,F,N,Y,0,112500.0,849415.5,40864.5,697500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.019101,-19638,-235,-23.0,-3170,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,11,0,0,0,0,1,1,Self-employed,,0.7206753749566852,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-689.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n380168,0,Cash loans,M,N,Y,0,112500.0,254700.0,16582.5,225000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.009334,-8772,-806,-5254.0,-1444,,1,1,0,1,0,0,Core staff,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,Military,0.2140716423906905,0.4798836384414236,0.3740208032583212,0.0773,0.0,0.9816,0.7484,0.0,0.0,0.0345,0.1667,0.2083,0.0118,0.0,0.0,0.0,0.0,0.0788,0.0,0.9816,0.7583,0.0,0.0,0.0345,0.1667,0.2083,0.0121,0.0,0.0,0.0,0.0,0.0781,0.0,0.9816,0.7518,0.0,0.0,0.0345,0.1667,0.2083,0.012,0.0,0.0,0.0,0.0,not specified,block of flats,0.0543,Others,No,2.0,0.0,2.0,0.0,-669.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n234683,0,Cash loans,F,N,Y,0,90000.0,709866.0,23026.5,508500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-23025,365243,-11193.0,-4477,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,XNA,,0.3246723337221858,0.6785676886853644,0.0979,,0.9846,,,0.0,0.2759,0.1667,,0.0498,,0.1023,,0.2735,0.0998,,0.9846,,,0.0,0.2759,0.1667,,0.051,,0.1065,,0.2896,0.0989,,0.9846,,,0.0,0.2759,0.1667,,0.0507,,0.1041,,0.2793,,block of flats,0.0804,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-2400.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n413161,0,Revolving loans,M,N,N,2,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.014519999999999996,-13979,-1611,-4822.0,-5438,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,9,0,0,0,0,0,0,Industry: type 11,0.2618735289246624,0.5940367163013718,0.5620604831738043,0.1485,,0.9856,0.7959999999999999,0.0089,0.16,0.1379,0.3333,0.375,0.0587,0.121,0.1035,0.0,0.1686,0.1513,,0.9856,0.804,0.0089,0.1611,0.1379,0.3333,0.375,0.0601,0.1322,0.1078,0.0,0.1785,0.1499,,0.9856,0.7987,0.0089,0.16,0.1379,0.3333,0.375,0.0597,0.1231,0.1053,0.0,0.1721,org spec account,block of flats,0.11800000000000001,Panel,No,0.0,0.0,0.0,0.0,-452.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n225350,0,Cash loans,F,N,N,0,135000.0,942300.0,30528.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-19969,-276,-2939.0,-3367,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.4162795178178845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1756.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n363305,0,Revolving loans,F,N,N,0,90000.0,247500.0,12375.0,247500.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.007305,-15838,-3183,-4873.0,-5123,,1,1,0,1,0,0,Sales staff,2.0,3,3,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.6509834409492751,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2147.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n354207,0,Cash loans,F,Y,Y,0,157500.0,1046142.0,33876.0,913500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.032561,-13177,-713,-12717.0,-48,1.0,1,1,0,1,0,0,,1.0,1,1,MONDAY,12,1,0,1,0,0,0,Business Entity Type 3,,0.7048158809055612,,0.0142,0.006999999999999999,0.9478,0.28600000000000003,0.0246,0.016,0.069,0.1,0.0417,0.0194,0.0129,0.0261,0.0039,0.0227,0.0273,0.0023,0.9518,0.3662,0.0006,0.0,0.0345,0.1667,0.0417,0.0138,0.023,0.0006,0.0039,0.0024,0.0151,0.0022,0.9518,0.3492,0.0133,0.0,0.069,0.1667,0.0417,0.018000000000000002,0.0214,0.0252,0.0039,0.0183,reg oper spec account,block of flats,0.0523,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-759.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n335860,0,Cash loans,F,N,Y,0,157500.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-11844,-5209,-1635.0,-4498,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.3785268341103837,0.7155940000427014,0.33125086459090186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1882.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n306487,0,Cash loans,F,N,Y,0,180000.0,306306.0,14863.5,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014464,-15495,-1748,-4416.0,-3756,,1,1,0,1,0,0,,2.0,2,2,SUNDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.3875466004764912,0.6561030727974422,0.4135967602644276,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-272.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n307991,0,Cash loans,F,N,Y,1,90000.0,269550.0,16416.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-15241,-540,-3611.0,-5650,,1,1,0,1,0,0,Sales staff,3.0,3,3,FRIDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.5222756615657531,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-527.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n167742,1,Cash loans,M,Y,Y,0,225000.0,891126.0,35469.0,796500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.002042,-15601,-1631,-6658.0,-4656,12.0,1,1,0,1,0,0,,2.0,3,3,THURSDAY,10,0,0,0,0,0,0,Self-employed,,0.20070523330623075,0.3046721837533529,0.0619,0.0721,0.9831,0.7688,0.0131,0.0,0.2069,0.1667,0.2083,0.1234,0.0504,0.0728,0.0,0.0,0.063,0.0749,0.9831,0.7779,0.0132,0.0,0.2069,0.1667,0.2083,0.1263,0.0551,0.0758,0.0,0.0,0.0625,0.0721,0.9831,0.7719,0.0131,0.0,0.2069,0.1667,0.2083,0.1256,0.0513,0.0741,0.0,0.0,reg oper account,block of flats,0.0644,Panel,No,8.0,1.0,8.0,0.0,-898.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n394429,1,Cash loans,F,N,Y,1,90000.0,165960.0,8604.0,112500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-11424,-3168,-4224.0,-2126,,1,1,0,1,0,1,Cooking staff,3.0,2,2,SATURDAY,11,0,0,0,0,1,1,Kindergarten,0.5300364183820404,0.535574895839186,0.11033242816628903,0.1021,0.2272,0.9806,,,,0.069,0.1667,,0.0301,,0.0598,,0.0008,0.10400000000000001,0.2358,0.9806,,,,0.069,0.1667,,0.0308,,0.0623,,0.0009,0.1031,0.2272,0.9806,,,,0.069,0.1667,,0.0306,,0.0609,,0.0009,,block of flats,0.0488,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1237.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n334369,0,Cash loans,M,N,N,0,202500.0,450000.0,30073.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.032561,-11177,-1270,-5242.0,-3471,,1,1,0,1,1,0,Drivers,2.0,1,1,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6675207418773126,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-812.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n385515,0,Cash loans,M,Y,N,0,180000.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Incomplete higher,Separated,House / apartment,0.035792000000000004,-18466,-598,-8857.0,-1777,5.0,1,1,0,1,0,0,Drivers,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5930185238421518,0.2079641743053816,0.1227,0.1062,0.9781,0.7008,0.0165,0.0,0.2759,0.1667,0.2083,0.0519,0.1,0.1158,0.0,0.0,0.125,0.1102,0.9782,0.7125,0.0167,0.0,0.2759,0.1667,0.2083,0.053,0.1093,0.1207,0.0,0.0,0.1239,0.1062,0.9781,0.7048,0.0166,0.0,0.2759,0.1667,0.2083,0.0528,0.1018,0.1179,0.0,0.0,reg oper account,block of flats,0.0911,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-2309.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n294338,0,Revolving loans,M,N,Y,2,112500.0,270000.0,13500.0,270000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014464,-16721,-1528,-360.0,-271,,1,1,0,1,0,0,Drivers,4.0,2,2,SUNDAY,6,0,0,0,0,0,0,Transport: type 4,,0.4898293939289541,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1106.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n328981,0,Cash loans,M,N,N,0,171000.0,1546020.0,40914.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20687,-4098,-2847.0,-3887,,1,1,1,1,1,0,Drivers,2.0,2,2,MONDAY,12,0,0,0,0,1,1,Industry: type 9,0.7598410880336679,0.6516638037964121,0.7826078370261895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n141321,1,Cash loans,M,Y,Y,2,90000.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-13845,-490,-650.0,-5402,7.0,1,1,0,1,0,0,Laborers,4.0,2,2,MONDAY,14,0,0,0,0,1,1,Industry: type 11,0.2926894625754761,0.27332390605860496,0.7394117535524816,0.0082,0.0,0.9781,0.7008,0.0011,0.0,0.0345,0.0417,0.0833,0.011000000000000001,0.0067,0.0065,0.0,0.0,0.0084,0.0,0.9782,0.7125,0.0011,0.0,0.0345,0.0417,0.0833,0.0112,0.0073,0.0068,0.0,0.0,0.0083,0.0,0.9781,0.7048,0.0011,0.0,0.0345,0.0417,0.0833,0.0112,0.0068,0.0067,0.0,0.0,reg oper account,block of flats,0.0058,Wooden,No,0.0,0.0,0.0,0.0,-1043.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n119436,0,Cash loans,F,N,Y,0,112500.0,239850.0,23494.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.035792000000000004,-24596,365243,-7747.0,-4128,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,0.7664428511293965,0.6911077261441376,0.08904388274986882,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-188.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n322910,0,Cash loans,F,N,Y,0,270000.0,479578.5,25240.5,414000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-18987,-1309,-6098.0,-2536,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Trade: type 3,,0.5559930599147517,0.21396685226179807,0.2227,0.1628,0.9901,0.8640000000000001,0.0397,0.24,0.2069,0.3333,0.375,0.1224,0.1816,0.2296,0.0,0.0,0.2269,0.16899999999999998,0.9901,0.8693,0.04,0.2417,0.2069,0.3333,0.375,0.1252,0.1983,0.2392,0.0,0.0,0.2248,0.1628,0.9901,0.8658,0.0399,0.24,0.2069,0.3333,0.375,0.1245,0.1847,0.2337,0.0,0.0,reg oper account,block of flats,0.2038,Panel,No,1.0,0.0,1.0,0.0,-556.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n151355,0,Cash loans,M,Y,Y,0,301500.0,900000.0,32017.5,900000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.002042,-10827,-1461,-4733.0,-3499,2.0,1,1,0,1,0,0,Core staff,1.0,3,3,WEDNESDAY,15,0,0,0,0,0,0,Security Ministries,0.4943678690678466,0.31433205753016685,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n170710,0,Cash loans,M,N,N,0,135000.0,697500.0,20524.5,697500.0,Family,Working,Higher education,Married,House / apartment,0.014519999999999996,-19082,-1550,-1712.0,-2606,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.269729533123615,,0.0952,0.0496,0.9771,0.6872,0.0104,0.04,0.1034,0.1667,0.1525,0.0457,0.0776,0.0557,0.0025,0.0089,0.0504,0.0,0.9747,0.6668,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0441,0.0428,0.0,0.0,0.0666,0.0365,0.9771,0.6914,0.0056,0.02,0.1207,0.1667,0.2083,0.0572,0.0547,0.0587,0.0,0.0,reg oper account,block of flats,0.0464,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-270.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n216570,0,Cash loans,F,N,Y,0,54000.0,490495.5,26262.0,454500.0,\"Spouse, partner\",Pensioner,Lower secondary,Married,House / apartment,0.022625,-23426,365243,-7341.0,-4628,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.5307852506268651,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1079.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n374376,0,Revolving loans,F,Y,Y,0,81000.0,135000.0,6750.0,135000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.010556,-21527,365243,-11827.0,-4123,6.0,1,0,0,1,1,0,,1.0,3,3,SATURDAY,11,0,0,0,0,0,0,XNA,,0.3790642420303027,0.28961123838200553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,2.0,7.0,1.0,-246.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n290719,0,Cash loans,F,N,Y,0,112500.0,143910.0,14017.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.001333,-21879,365243,-753.0,-4474,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,XNA,,0.5499551609833053,0.5549467685334323,0.0711,,0.9791,,,0.0,0.1379,0.1667,,0.0796,,0.0433,,0.0044,0.0725,,0.9791,,,0.0,0.1379,0.1667,,0.0814,,0.0451,,0.0046,0.0718,,0.9791,,,0.0,0.1379,0.1667,,0.081,,0.0441,,0.0044,,block of flats,0.0517,,No,4.0,0.0,3.0,0.0,-1812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n120653,0,Cash loans,F,N,Y,0,216000.0,409716.0,19840.5,342000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.00702,-23769,365243,-4038.0,-4641,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.24484784237726506,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-529.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n409073,0,Cash loans,F,Y,N,0,405000.0,473760.0,43969.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014464,-13044,-1793,-6809.0,-2838,5.0,1,1,0,1,0,1,,2.0,2,2,THURSDAY,8,0,0,0,0,1,1,Business Entity Type 3,0.5301333009815897,0.6626645363768122,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1832.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n445896,0,Cash loans,M,Y,Y,0,180000.0,296280.0,23539.5,225000.0,Family,Working,Incomplete higher,Married,House / apartment,0.02461,-9888,-1210,-4421.0,-2537,7.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 2,,0.5633812224961142,0.6397075677637197,0.0082,0.0,0.9682,0.5648,0.0009,0.0,0.0345,0.0417,0.0833,0.017,0.0067,0.0076,0.0,0.0,0.0084,0.0,0.9682,0.5818,0.0009,0.0,0.0345,0.0417,0.0833,0.0174,0.0073,0.0079,0.0,0.0,0.0083,0.0,0.9682,0.5706,0.0009,0.0,0.0345,0.0417,0.0833,0.0173,0.0068,0.0077,0.0,0.0,reg oper account,block of flats,0.0064,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n287975,0,Cash loans,F,N,Y,1,135000.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010147,-13370,-239,-3636.0,-4487,,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,9,0,0,0,1,1,0,Agriculture,,0.5268458336530635,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n395238,0,Cash loans,M,N,N,0,202500.0,302341.5,20268.0,261000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.00963,-9872,-2511,-7510.0,-2382,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,13,0,1,1,0,0,0,Business Entity Type 3,,0.4568282383489451,0.5919766183185521,0.0825,0.0556,0.9896,,,0.08,0.069,0.375,,0.0158,,0.0911,,0.0,0.084,0.0577,0.9896,,,0.0806,0.069,0.375,,0.0162,,0.0949,,0.0,0.0833,0.0556,0.9896,,,0.08,0.069,0.375,,0.0161,,0.0928,,0.0,,block of flats,0.0789,Panel,No,1.0,0.0,1.0,0.0,-2768.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n390831,0,Cash loans,F,Y,Y,0,315000.0,568057.5,27459.0,459000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17222,-1424,-5742.0,-765,22.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Transport: type 4,,0.6775851668694926,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,1.0,8.0,0.0,-2542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n140394,0,Cash loans,F,Y,Y,0,135000.0,1660428.0,45792.0,1386000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.022625,-16743,-4081,-8855.0,-295,6.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6552094637393479,,0.0619,0.0583,0.0,,,0.0,0.1034,0.1667,,0.0491,,0.0512,,,0.063,0.0605,0.0005,,,0.0,0.1034,0.1667,,0.0502,,0.0534,,,0.0625,0.0583,0.0,,,0.0,0.1034,0.1667,,0.0499,,0.0521,,,,block of flats,0.0403,Panel,No,0.0,0.0,0.0,0.0,-151.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n167069,0,Cash loans,M,N,Y,0,90000.0,354276.0,18472.5,292500.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.019101,-7790,-222,-7790.0,-475,,1,1,0,1,0,0,Core staff,1.0,2,2,THURSDAY,12,0,0,0,0,1,1,Trade: type 7,,0.4715283234857087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-166.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n251601,0,Cash loans,M,Y,Y,0,27000.0,182016.0,10579.5,144000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-22982,365243,-6132.0,-4013,12.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,XNA,,0.3428402871577623,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-730.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n322058,0,Cash loans,M,Y,Y,0,135000.0,284256.0,29979.0,270000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-21085,-1838,-12166.0,-4614,4.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Trade: type 3,,0.7453176268693755,0.7338145369642702,0.1227,0.1275,0.9831,0.7688,,0.0,0.2759,0.1667,0.2083,0.0455,,0.1148,,0.0,0.125,0.1323,0.9831,0.7779,,0.0,0.2759,0.1667,0.2083,0.0465,,0.1196,,0.0,0.1239,0.1275,0.9831,0.7719,,0.0,0.2759,0.1667,0.2083,0.0463,,0.1168,,0.0,reg oper account,block of flats,0.0903,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n177163,0,Cash loans,F,N,Y,0,90000.0,225000.0,17667.0,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.028663,-9452,-1024,-4115.0,-2085,,1,1,0,1,0,0,Secretaries,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Security Ministries,,0.4785574780261886,0.2103502286944494,0.2227,0.1705,0.9801,,,0.16,0.1379,0.3333,,,,0.1521,,0.0009,0.2269,0.1769,0.9801,,,0.1611,0.1379,0.3333,,,,0.1585,,0.0009,0.2248,0.1705,0.9801,,,0.16,0.1379,0.3333,,,,0.1549,,0.0009,,,0.2329,Mixed,No,4.0,2.0,4.0,2.0,-225.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n225202,1,Cash loans,M,N,Y,1,225000.0,1078200.0,31027.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.00702,-10729,-407,-292.0,-3399,,1,1,0,1,0,0,,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,0.19287554918826416,0.6665445271674332,0.6754132910917112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-115.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n383329,0,Cash loans,M,Y,Y,1,225000.0,900000.0,94509.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-17586,-2545,-3463.0,-1147,18.0,1,1,0,1,0,0,Managers,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,0.3594712869687398,0.5274236178195154,0.35895122857839673,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2879.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n407214,0,Cash loans,F,N,Y,0,135000.0,760225.5,30280.5,679500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.018801,-22177,365243,-6378.0,-4824,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,,0.6865800926750989,,0.1907,0.1343,0.9856,0.8028,0.0933,0.2,0.1724,0.3333,0.375,0.0521,0.1555,0.1869,0.0,0.0,0.1943,0.1393,0.9856,0.8105,0.0941,0.2014,0.1724,0.3333,0.375,0.0532,0.1699,0.1948,0.0,0.0,0.1926,0.1343,0.9856,0.8054,0.0939,0.2,0.1724,0.3333,0.375,0.053,0.1582,0.1903,0.0,0.0,reg oper account,block of flats,0.147,Panel,No,0.0,0.0,0.0,0.0,-1527.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n171605,0,Cash loans,M,Y,N,0,180000.0,900000.0,35694.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-10406,-3784,-5067.0,-3072,11.0,1,1,1,1,1,0,,2.0,2,2,SUNDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.5816290830925179,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1965.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n289661,0,Cash loans,F,N,Y,0,292500.0,810000.0,26901.0,810000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.028663,-23223,365243,-11585.0,-4112,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.6212017689490736,,0.0247,0.0158,0.9722,0.6192,0.0033,0.0,0.069,0.125,0.1667,0.0531,0.0202,0.0238,0.0,0.0,0.0252,0.0164,0.9722,0.6341,0.0033,0.0,0.069,0.125,0.1667,0.0543,0.022000000000000002,0.0248,0.0,0.0,0.025,0.0158,0.9722,0.6243,0.0033,0.0,0.069,0.125,0.1667,0.054000000000000006,0.0205,0.0242,0.0,0.0,reg oper account,block of flats,0.0205,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-377.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n432584,0,Cash loans,F,N,Y,1,135000.0,781920.0,50103.0,675000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-14998,-357,-3734.0,-4560,,1,1,0,1,1,0,Sales staff,3.0,1,1,SATURDAY,12,0,0,0,0,0,0,Transport: type 4,0.3584339365865549,0.7148858446358459,0.33125086459090186,0.2948,0.1431,0.9945,0.9252,0.1562,0.4,0.1724,0.6667,0.5833,0.0,0.2505,0.1857,0.0463,0.0622,0.3004,0.1485,0.9945,0.9281,0.1576,0.4028,0.1724,0.6667,0.5833,0.0,0.2736,0.1934,0.0467,0.0659,0.2977,0.1431,0.9945,0.9262,0.1572,0.4,0.1724,0.6667,0.5833,0.0,0.2548,0.18899999999999997,0.0466,0.0635,,block of flats,0.254,Panel,No,4.0,1.0,4.0,0.0,-2045.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n126820,0,Cash loans,M,Y,N,0,157500.0,1535553.0,40635.0,1372500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-9635,-2692,-3808.0,-2203,2.0,1,1,0,1,1,0,Managers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Trade: type 3,0.18601338587253244,0.7738748312442472,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n338007,0,Cash loans,F,N,Y,0,202500.0,247500.0,10404.0,247500.0,Children,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-18868,-1044,-1041.0,-2393,,1,1,0,1,0,0,Security staff,1.0,2,2,THURSDAY,11,0,1,1,0,1,1,Security,0.5663931396091583,0.6969631541054145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-611.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n155576,0,Revolving loans,F,N,N,4,112500.0,180000.0,9000.0,180000.0,Children,Commercial associate,Secondary / secondary special,Married,With parents,0.025164,-14746,-2644,-3661.0,-4124,,1,1,0,1,0,0,Core staff,6.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.516458251951771,0.5171775735028961,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,3.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n357328,0,Revolving loans,F,Y,Y,0,112500.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008625,-18127,-661,-1739.0,-1668,0.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Agriculture,,0.5959916910204219,0.13510601574017175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-444.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n152643,0,Cash loans,F,N,N,0,157500.0,135000.0,9526.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.028663,-21722,365243,-10982.0,-4727,,1,0,0,1,0,0,,1.0,2,2,SUNDAY,12,0,0,0,0,0,0,XNA,,0.7127281690755165,0.7700870700124128,0.1031,,0.9791,,,,0.2069,0.2083,,,,0.0889,,0.1633,0.105,,0.9791,,,,0.2069,0.2083,,,,0.0926,,0.1729,0.1041,,0.9791,,,,0.2069,0.2083,,,,0.0905,,0.1667,,block of flats,0.1054,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2227.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n249550,0,Revolving loans,F,N,N,0,94500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.0105,-8035,-199,-2811.0,-676,,1,1,0,1,0,0,Core staff,1.0,3,3,FRIDAY,12,0,0,0,0,1,1,Postal,,0.24289217249538395,0.25533177083329,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-473.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n279044,0,Cash loans,F,N,N,0,315000.0,792162.0,40576.5,630000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.006629,-15734,-4802,-7107.0,-4330,,1,1,0,1,0,0,Managers,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Other,0.6832366601467972,0.5273800140106505,0.6430255641096323,0.0732,0.0491,0.9876,0.83,0.0199,0.04,0.0345,0.3333,0.0417,0.0487,0.0597,0.0665,0.0,0.0,0.0746,0.051,0.9876,0.8367,0.02,0.0403,0.0345,0.3333,0.0417,0.0498,0.0652,0.0692,0.0,0.0,0.0739,0.0491,0.9876,0.8323,0.02,0.04,0.0345,0.3333,0.0417,0.0496,0.0607,0.0676,0.0,0.0,reg oper account,block of flats,0.0631,Panel,No,2.0,0.0,2.0,0.0,-1777.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n144487,1,Cash loans,M,Y,N,0,103500.0,855000.0,24997.5,855000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-13324,-3900,-5037.0,-3938,12.0,1,1,1,1,0,0,,1.0,2,2,MONDAY,16,0,0,0,0,0,0,Industry: type 3,0.3131787893621612,0.06406571212493566,0.5100895276257282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1102.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n172686,1,Cash loans,M,N,Y,0,189000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.020713,-10076,-248,-4882.0,-2746,,1,1,0,1,0,0,,1.0,3,3,FRIDAY,11,0,0,0,0,1,1,Military,,0.0318525927390967,0.3979463219016906,0.0928,0.096,0.9871,0.8232,0.013000000000000001,0.0,0.2069,0.1667,,0.09,0.0756,0.0873,0.0,0.0,0.0945,0.0997,0.9871,0.8301,0.0131,0.0,0.2069,0.1667,,0.092,0.0826,0.091,0.0,0.0,0.0937,0.096,0.9871,0.8256,0.013000000000000001,0.0,0.2069,0.1667,,0.0915,0.077,0.0889,0.0,0.0,reg oper account,block of flats,0.0758,Panel,No,0.0,0.0,0.0,0.0,-366.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n198439,1,Cash loans,M,N,Y,0,225000.0,450000.0,30573.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-16831,-806,-438.0,-386,,1,1,0,1,0,0,Laborers,2.0,3,3,TUESDAY,15,0,0,0,0,1,1,Business Entity Type 1,,0.02938924339650944,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-771.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n282578,0,Cash loans,F,N,Y,2,135000.0,808650.0,23773.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020713,-18884,-3167,-7400.0,-2445,,1,1,0,1,0,0,Managers,4.0,3,3,SATURDAY,11,0,0,0,0,0,0,Government,0.8070068392428341,0.5299573486819247,0.4938628816996244,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n297708,0,Cash loans,F,N,Y,0,112500.0,679500.0,19867.5,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-20791,-1823,-11781.0,-4308,,1,1,0,1,0,0,Accountants,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Trade: type 7,,0.628536795519749,0.34578480246959553,0.0732,0.0854,0.9781,0.7008,0.0615,0.0,0.1379,0.1667,0.2083,0.0592,0.0588,0.0654,0.0039,0.0015,0.0746,0.0886,0.9782,0.7125,0.0621,0.0,0.1379,0.1667,0.2083,0.0605,0.0643,0.0681,0.0039,0.0015,0.0739,0.0854,0.9781,0.7048,0.0619,0.0,0.1379,0.1667,0.2083,0.0602,0.0599,0.0665,0.0039,0.0015,reg oper account,block of flats,0.0853,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2521.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n134531,0,Cash loans,F,N,Y,0,112500.0,508495.5,20295.0,454500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.008625,-23595,365243,-10501.0,-4785,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,0.8395486876483534,0.4467276733059977,0.3893387918468769,0.1835,0.1147,0.9851,0.7959999999999999,0.0466,0.2,0.1724,0.3333,0.375,0.1654,0.1496,0.1924,0.0,0.0,0.187,0.1191,0.9851,0.804,0.047,0.2014,0.1724,0.3333,0.375,0.1691,0.1635,0.2004,0.0,0.0,0.1853,0.1147,0.9851,0.7987,0.0469,0.2,0.1724,0.3333,0.375,0.1682,0.1522,0.1958,0.0,0.0,reg oper account,block of flats,0.1768,Panel,No,1.0,1.0,1.0,1.0,-120.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n277544,0,Cash loans,F,N,Y,0,126000.0,251280.0,11065.5,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-21169,365243,-179.0,-3072,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.36310751971588334,0.4920600938649263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n155102,0,Cash loans,F,N,Y,0,135000.0,601470.0,32760.0,450000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-18346,-1562,-8391.0,-1880,,1,1,0,1,0,0,Laborers,2.0,1,1,SUNDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.4823456859029246,,0.0495,0.0611,0.9742,0.6464,0.0053,0.0,0.1034,0.125,0.1667,0.0719,0.0403,0.039,0.0,0.0,0.0504,0.0634,0.9742,0.6602,0.0053,0.0,0.1034,0.125,0.1667,0.0735,0.0441,0.0407,0.0,0.0,0.05,0.0611,0.9742,0.6511,0.0053,0.0,0.1034,0.125,0.1667,0.0731,0.040999999999999995,0.0397,0.0,0.0,reg oper account,block of flats,0.0307,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-519.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n422225,0,Revolving loans,F,Y,Y,0,112500.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-19845,-2681,-2882.0,-3170,6.0,1,1,0,1,0,0,Drivers,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Self-employed,,0.5745783288824574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n418632,0,Cash loans,M,Y,Y,0,180000.0,239850.0,25830.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-12200,-1127,-5958.0,-4476,18.0,1,1,1,1,1,0,Cooking staff,2.0,1,1,THURSDAY,16,0,1,1,0,1,1,Trade: type 3,,0.7316238018834627,,0.0866,0.0427,0.9762,0.6736,0.1041,0.08,0.0345,0.4583,0.5,0.0,0.0706,0.0723,0.0,0.0,0.0882,0.0443,0.9762,0.6864,0.105,0.0806,0.0345,0.4583,0.5,0.0,0.0771,0.0754,0.0,0.0,0.0874,0.0427,0.9762,0.6779999999999999,0.1047,0.08,0.0345,0.4583,0.5,0.0,0.0718,0.0736,0.0,0.0,reg oper account,block of flats,0.0572,Panel,No,0.0,0.0,0.0,0.0,-310.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n374764,0,Cash loans,F,Y,N,0,162000.0,268659.0,27243.0,243000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-17966,-2213,-5141.0,-1396,7.0,1,1,0,1,1,0,Managers,2.0,1,1,THURSDAY,9,0,0,0,0,0,0,School,0.8323095017365504,0.6794454729620667,0.4794489811780563,0.0557,,0.9757,,,0.04,0.0345,0.3333,,0.078,,0.0387,,0.0034,0.0567,,0.9757,,,0.0403,0.0345,0.3333,,0.0798,,0.0403,,0.0036,0.0562,,0.9757,,,0.04,0.0345,0.3333,,0.0793,,0.0394,,0.0034,,block of flats,0.0312,Panel,No,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n225905,1,Cash loans,F,N,Y,1,337500.0,997335.0,29290.5,832500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-15523,-2677,-1473.0,-1553,,1,1,0,1,0,0,Managers,3.0,1,1,FRIDAY,10,0,0,0,0,0,0,Other,,0.6615393126671433,0.1895952597360396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-801.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n289268,0,Cash loans,F,N,N,0,67500.0,450000.0,23139.0,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-19215,-92,-11340.0,-2739,,1,1,0,1,0,1,Cleaning staff,2.0,2,2,FRIDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.5868292372660139,0.4632753280912678,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1645.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n328330,0,Cash loans,F,Y,Y,1,126000.0,547344.0,19791.0,472500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.0105,-19664,-806,-9737.0,-2872,10.0,1,1,0,1,0,0,Security staff,3.0,3,3,THURSDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.5245067245171547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n305499,0,Cash loans,M,Y,Y,0,90000.0,518562.0,25078.5,463500.0,Family,Working,Higher education,Married,House / apartment,0.00733,-22096,-903,-9228.0,-4652,14.0,1,1,0,1,0,0,Security staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,,0.2802265549588228,0.5779691187553125,0.1856,0.1492,0.9871,,,0.2,0.1724,0.3333,,0.0888,,0.1993,,0.0191,0.1891,0.1548,0.9871,,,0.2014,0.1724,0.3333,,0.0909,,0.2076,,0.0202,0.1874,0.1492,0.9871,,,0.2,0.1724,0.3333,,0.0904,,0.2029,,0.0195,,block of flats,0.2403,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-433.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,4.0\n141703,0,Cash loans,F,N,Y,3,49500.0,389844.0,20034.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-12741,-1567,-1575.0,-4971,,1,1,1,1,1,0,Medicine staff,5.0,2,2,TUESDAY,8,0,0,0,0,0,0,Medicine,,0.7262358498441407,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1757.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n329198,0,Cash loans,M,Y,Y,2,337500.0,1113133.5,32674.5,972000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-12424,-4322,-110.0,-118,1.0,1,1,0,1,0,0,Sales staff,4.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,0.2534825509944305,0.6794502356879975,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1760.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n408866,0,Cash loans,F,N,Y,0,99000.0,312768.0,17095.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009549,-13982,-3684,-8123.0,-3998,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.6514900471549304,,0.0619,0.0,0.9856,,,0.0,0.1379,0.1667,,0.0402,,0.0346,,0.0,0.063,0.0,0.9856,,,0.0,0.1379,0.1667,,0.0411,,0.0361,,0.0,0.0625,0.0,0.9856,,,0.0,0.1379,0.1667,,0.0409,,0.0352,,0.0,,block of flats,0.0475,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n364077,0,Cash loans,M,Y,Y,2,225000.0,521280.0,26743.5,450000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.005084,-13543,-2107,-1118.0,-3709,2.0,1,1,1,1,0,0,Security staff,4.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.3271336419304697,0.6377905940551429,,0.0928,0.1005,0.9786,,,,0.2069,0.1667,,0.0085,,0.0874,,,0.0945,0.1042,0.9786,,,,0.2069,0.1667,,0.0087,,0.091,,,0.0937,0.1005,0.9786,,,,0.2069,0.1667,,0.0086,,0.0889,,,,block of flats,0.0687,Panel,No,0.0,0.0,0.0,0.0,-1262.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n447162,0,Cash loans,F,N,N,0,135000.0,343377.0,12460.5,283500.0,Unaccompanied,Pensioner,Higher education,Married,Municipal apartment,0.011657,-22170,365243,-5303.0,-4126,,1,0,0,1,0,0,,2.0,1,1,MONDAY,14,0,0,0,0,0,0,XNA,,0.7521546927955419,0.5673792367572691,0.0557,0.0538,0.9786,,,0.0,0.1034,0.1667,,,,0.0462,,0.0,0.0567,0.0559,0.9786,,,0.0,0.1034,0.1667,,,,0.0482,,0.0,0.0562,0.0538,0.9786,,,0.0,0.1034,0.1667,,,,0.047,,0.0,,block of flats,0.0364,Panel,No,2.0,1.0,2.0,1.0,-627.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n141065,0,Cash loans,F,N,Y,0,270000.0,1017000.0,32935.5,1017000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.032561,-10271,-240,-10242.0,-2662,,1,1,0,1,0,0,,1.0,1,1,THURSDAY,12,0,0,0,0,0,0,Self-employed,,0.7006222829828382,0.4294236843421945,0.2309,0.1795,0.9747,0.6532,0.027999999999999997,0.32,0.2759,0.25,0.0417,0.0536,0.1883,0.2008,0.0,0.0,0.2353,0.1862,0.9747,0.6668,0.0282,0.3222,0.2759,0.25,0.0417,0.0548,0.2057,0.2092,0.0,0.0,0.2332,0.1795,0.9747,0.6578,0.0282,0.32,0.2759,0.25,0.0417,0.0545,0.1915,0.2044,0.0,0.0,reg oper account,block of flats,0.1601,Panel,No,0.0,0.0,0.0,0.0,-434.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n104863,1,Cash loans,M,Y,N,0,265500.0,521280.0,28408.5,450000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.002042,-13936,-3411,-7969.0,-4607,3.0,1,1,0,1,0,0,High skill tech staff,1.0,3,3,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.12539501698079875,0.5573422275262607,0.6738300778602003,0.0825,0.0767,0.9796,0.7212,0.0344,0.0,0.1379,0.1667,0.0417,0.0312,0.0672,0.0759,0.0,0.002,0.084,0.0796,0.9796,0.7321,0.0347,0.0,0.1379,0.1667,0.0417,0.0319,0.0735,0.079,0.0,0.0021,0.0833,0.0767,0.9796,0.7249,0.0346,0.0,0.1379,0.1667,0.0417,0.0318,0.0684,0.0772,0.0,0.002,reg oper account,block of flats,0.0785,Panel,No,0.0,0.0,0.0,0.0,-199.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n223267,0,Cash loans,F,N,Y,1,90000.0,1546020.0,42642.0,1350000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00733,-19831,-5131,-7110.0,-3347,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,11,0,0,0,0,1,1,Business Entity Type 2,,0.2792352251632336,0.7421816117614419,0.0186,,0.9861,,,0.0,0.1034,0.0417,,,,0.0194,,0.0405,0.0189,,0.9861,,,0.0,0.1034,0.0417,,,,0.0202,,0.0429,0.0187,,0.9861,,,0.0,0.1034,0.0417,,,,0.0197,,0.0414,,block of flats,0.0241,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1696.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n107957,1,Cash loans,F,N,N,0,135000.0,909000.0,25641.0,909000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-22629,-1721,-1899.0,-1908,,1,1,1,1,1,0,Cooking staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Restaurant,,0.5845315868592648,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1532.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n450541,0,Cash loans,F,N,Y,0,315000.0,127350.0,10890.0,112500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018209,-12707,-2052,-512.0,-4838,,1,1,1,1,0,1,Managers,1.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.6006418903080339,0.30490917554574243,0.2471909382666521,0.1113,0.0461,0.9831,0.7688,0.0325,0.04,0.0345,0.3333,0.375,0.0762,0.0908,0.0647,0.0,0.0,0.1134,0.0478,0.9831,0.7779,0.0328,0.0403,0.0345,0.3333,0.375,0.0779,0.0992,0.0675,0.0,0.0,0.1124,0.0461,0.9831,0.7719,0.0327,0.04,0.0345,0.3333,0.375,0.0775,0.0923,0.0659,0.0,0.0,reg oper account,block of flats,0.0687,Panel,No,0.0,0.0,0.0,0.0,-941.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n251391,0,Cash loans,M,Y,Y,0,675000.0,1096020.0,55962.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.072508,-10113,-770,-4468.0,-2503,7.0,1,1,0,1,1,0,,2.0,1,1,FRIDAY,13,0,1,1,0,0,0,Business Entity Type 3,0.3298576051866905,0.5495708562038348,0.09950368352887068,0.0665,0.0942,0.9747,,,0.01,0.0948,0.2083,,0.0119,,0.0357,,0.0229,0.084,0.0771,0.9757,,,0.0,0.1379,0.1667,,0.0074,,0.0212,,0.0,0.077,0.0822,0.9757,,,0.0,0.1034,0.1667,,0.0141,,0.0389,,0.0033,,block of flats,0.026000000000000002,Block,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n238847,0,Cash loans,M,Y,Y,1,189000.0,555273.0,20529.0,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003813,-14905,-5939,-3502.0,-4339,22.0,1,1,0,1,0,0,Core staff,3.0,2,2,THURSDAY,8,0,0,0,0,1,1,Security Ministries,0.5436207085675829,0.3705532763630072,0.36896873825284665,0.0,,0.9682,,,0.0,0.0345,,,,,0.0044,,,0.0,,0.9682,,,0.0,0.0345,,,,,0.0046,,,0.0,,0.9682,,,0.0,0.0345,,,,,0.0045,,,,,0.0038,,No,2.0,1.0,2.0,1.0,-2372.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n331078,0,Cash loans,F,N,Y,0,99000.0,999000.0,30289.5,999000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.010032,-23291,365243,-14441.0,-3988,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,0.7704019803697558,0.7341964524826804,,0.1485,0.0849,0.9801,0.728,0.0529,0.16,0.1379,0.3333,0.375,0.1175,0.121,0.1543,0.0,0.0,0.1513,0.0881,0.9801,0.7387,0.0534,0.1611,0.1379,0.3333,0.375,0.1202,0.1322,0.1607,0.0,0.0,0.1499,0.0849,0.9801,0.7316,0.0533,0.16,0.1379,0.3333,0.375,0.1195,0.1231,0.157,0.0,0.0,,block of flats,0.1503,Panel,No,1.0,0.0,1.0,0.0,-179.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n187089,0,Cash loans,M,Y,Y,0,225000.0,1120500.0,47605.5,1120500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-16964,-9337,-10476.0,-497,3.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.19531942741162825,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n153140,0,Cash loans,F,N,N,0,157500.0,675000.0,21906.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-15270,-2801,-6034.0,-4091,,1,1,1,1,0,0,Managers,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Government,0.1863053361837765,0.4506548236237922,0.13426542355494275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2341.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n226687,0,Cash loans,F,N,N,0,101250.0,441481.5,15988.5,364500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.011703,-21776,365243,-13648.0,-4325,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,XNA,,0.6692510701822402,0.6879328378491735,,,0.9707,,,,,,,,,0.002,,,,,0.9707,,,,,,,,,0.0021,,,,,0.9707,,,,,,,,,0.0021,,,,block of flats,0.0016,,No,0.0,0.0,0.0,0.0,-247.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n317937,0,Cash loans,F,N,Y,2,189000.0,314055.0,21375.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13371,-4668,-3141.0,-3891,,1,1,1,1,1,0,Sales staff,4.0,2,2,THURSDAY,17,0,0,0,0,0,0,Trade: type 7,,0.6289349453836629,0.3046721837533529,0.0701,0.0955,0.9881,0.8368,0.0109,0.0,0.2069,0.1667,0.2083,0.0825,0.0572,0.0748,0.0,0.0063,0.0714,0.0991,0.9881,0.8432,0.011000000000000001,0.0,0.2069,0.1667,0.2083,0.0844,0.0624,0.078,0.0,0.0067,0.0708,0.0955,0.9881,0.8390000000000001,0.0109,0.0,0.2069,0.1667,0.2083,0.084,0.0581,0.0762,0.0,0.0065,reg oper account,block of flats,0.0662,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1684.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n345900,0,Cash loans,F,Y,Y,0,247500.0,840996.0,24718.5,702000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.011657,-20424,365243,-6909.0,-2638,11.0,1,0,0,1,0,0,,1.0,1,1,TUESDAY,14,0,0,0,0,0,0,XNA,,0.5571911624477737,0.0646719558983517,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1111.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n232548,0,Revolving loans,F,N,Y,0,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-14691,-804,-5309.0,-4470,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Other,0.18076487948569625,0.7676350641592861,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-356.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n169752,1,Cash loans,M,N,Y,0,180000.0,247275.0,19417.5,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.010006,-10196,-1983,-5045.0,-2847,,1,1,1,1,1,0,Core staff,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,School,,0.2347711832657629,0.2405414172860865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n439521,0,Cash loans,F,N,N,0,94500.0,167076.0,15453.0,135000.0,Family,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.005002,-17614,-1209,-7567.0,-1139,,1,1,1,1,0,0,Laborers,2.0,3,3,TUESDAY,14,0,0,0,0,1,1,Business Entity Type 2,,0.38147603574870864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-328.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n217166,0,Cash loans,F,N,Y,0,99000.0,79632.0,4698.0,63000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-9678,-1819,-1898.0,-2338,,1,1,0,1,0,0,Medicine staff,2.0,3,3,WEDNESDAY,8,0,0,0,1,1,0,Other,,0.2797592607865801,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n153124,0,Cash loans,M,Y,Y,1,135000.0,1506816.0,49927.5,1350000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.011657,-18425,-10927,-109.0,-1984,6.0,1,1,0,1,0,0,Drivers,3.0,1,1,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6331048600866256,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-891.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n237630,0,Revolving loans,F,Y,N,0,202500.0,630000.0,31500.0,630000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006629,-15815,-3514,-65.0,-4181,6.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,7,0,0,0,0,0,0,Security Ministries,,0.6467435401956746,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-41.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,0.0\n349808,0,Cash loans,F,N,Y,0,292500.0,1024290.0,30078.0,855000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010032,-19935,-2753,-10129.0,-3151,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Trade: type 7,,0.30021174136264195,0.520897599048938,0.157,0.0783,0.9881,0.8368,0.0243,0.1064,0.0917,0.3471,0.3888,0.0401,0.128,0.1102,0.0,0.0,0.083,0.0608,0.9856,0.8105,0.0237,0.0806,0.069,0.3333,0.375,0.018000000000000002,0.0725,0.0838,0.0,0.0,0.1312,0.0598,0.9866,0.8189,0.0248,0.08,0.069,0.3333,0.375,0.0463,0.1077,0.0901,0.0,0.0,reg oper account,block of flats,0.0633,Panel,No,0.0,0.0,0.0,0.0,-25.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n108064,0,Revolving loans,M,Y,Y,1,270000.0,337500.0,16875.0,337500.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.009657,-11605,-441,-2522.0,-4099,14.0,1,1,0,1,0,0,Core staff,3.0,2,2,SUNDAY,12,0,0,0,1,1,0,Bank,0.33029349053241996,0.3827357965650669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n236294,0,Revolving loans,F,N,Y,1,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00963,-17424,-10666,-6222.0,-980,,1,1,0,1,1,0,Medicine staff,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Medicine,0.4783066063641228,0.448501129773769,0.4525335592581747,0.032,0.0225,0.9752,0.66,0.0027,0.0,0.069,0.125,0.0417,0.0279,0.0252,0.0239,0.0039,0.0054,0.0326,0.0234,0.9752,0.6733,0.0028,0.0,0.069,0.125,0.0417,0.0285,0.0275,0.0249,0.0039,0.0057,0.0323,0.0225,0.9752,0.6645,0.0027,0.0,0.069,0.125,0.0417,0.0283,0.0257,0.0243,0.0039,0.0055,reg oper account,block of flats,0.0215,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-424.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n439281,0,Cash loans,M,N,Y,1,157500.0,640080.0,31261.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-13543,-361,-127.0,-4857,,1,1,0,1,0,0,Drivers,3.0,2,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,0.2772988315947801,0.7071716015986317,0.7121551551910698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,3.0,0.0,-347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n294705,0,Cash loans,M,N,Y,0,81000.0,254700.0,16276.5,225000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009334,-11391,-2971,-5536.0,-1477,,1,1,1,1,1,0,Low-skill Laborers,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.3870846470179362,0.5082869913916046,0.0938,0.10099999999999999,0.9762,0.6736,0.012,0.0,0.2069,0.1667,0.0,0.0662,0.0756,0.0865,0.0039,0.0034,0.0956,0.1048,0.9762,0.6864,0.0121,0.0,0.2069,0.1667,0.0,0.0677,0.0826,0.0896,0.0039,0.0036,0.0947,0.10099999999999999,0.9762,0.6779999999999999,0.0121,0.0,0.2069,0.1667,0.0,0.0673,0.077,0.0881,0.0039,0.0034,reg oper account,block of flats,0.0684,Panel,No,0.0,0.0,0.0,0.0,-761.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n160196,0,Cash loans,M,N,Y,0,180000.0,450000.0,30073.5,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.026392,-11256,-1034,-3411.0,-2145,,1,1,0,1,1,0,Managers,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.23729070374620026,0.6262107893252284,0.5460231970049609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-198.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n317344,0,Cash loans,F,N,N,0,225000.0,781920.0,25969.5,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.00496,-19926,365243,-3551.0,-2741,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,XNA,0.8297963078936421,0.7363247329424465,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-825.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n103957,0,Cash loans,M,Y,Y,0,225450.0,1602000.0,42390.0,1602000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.014519999999999996,-11995,-193,-3918.0,-4352,2.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,15,0,0,0,1,1,0,Construction,0.4136916069155566,0.42665522787835336,0.4206109640437848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n171979,0,Cash loans,F,N,N,0,315000.0,679500.0,64516.5,679500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.04622,-17414,-2325,-11031.0,-967,,1,1,1,1,1,0,,2.0,1,1,MONDAY,10,0,0,0,0,0,0,Other,0.8381614149334567,0.7349127758553716,0.5602843280409464,0.0716,0.0976,0.9757,0.6736,0.0104,0.04,0.1283,0.1667,0.2083,,0.0462,0.0708,0.0039,0.0475,0.084,0.0866,0.9762,0.6864,0.0105,0.0,0.1379,0.1667,0.2083,,0.0505,0.0553,0.0039,0.0502,0.0833,0.0976,0.9762,0.6779999999999999,0.0105,0.04,0.1379,0.1667,0.2083,,0.047,0.0721,0.0039,0.0485,reg oper account,block of flats,0.0289,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1027.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,5.0\n104483,0,Cash loans,F,N,Y,0,180000.0,790830.0,62613.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014464,-16183,-783,-6120.0,-451,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,4,0,0,0,0,0,0,Trade: type 5,,0.4513478659919669,0.3825018041447388,0.0722,0.0764,0.9831,0.7688,0.1117,0.0,0.1379,0.1667,0.0417,0.0507,0.0588,0.0638,0.0,0.0,0.0735,0.0793,0.9831,0.7779,0.1127,0.0,0.1379,0.1667,0.0417,0.0519,0.0643,0.0665,0.0,0.0,0.0729,0.0764,0.9831,0.7719,0.1124,0.0,0.1379,0.1667,0.0417,0.0516,0.0599,0.0649,0.0,0.0,reg oper account,block of flats,0.061,Block,No,1.0,0.0,1.0,0.0,-692.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n360094,0,Cash loans,F,N,Y,0,193500.0,257391.0,18432.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-21643,-391,-50.0,-2164,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Trade: type 7,0.7494958112671022,0.6485749079447093,0.363945238612397,0.0082,,0.9712,0.6056,,0.0,0.069,0.0417,0.0417,,0.0067,0.0055,0.0,0.0,0.0084,,0.9712,0.621,,0.0,0.069,0.0417,0.0417,,0.0073,0.0057,0.0,0.0,0.0083,,0.9712,0.6109,,0.0,0.069,0.0417,0.0417,,0.0068,0.0056,0.0,0.0,not specified,block of flats,0.0064,Mixed,Yes,0.0,0.0,0.0,0.0,-1503.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n430215,0,Cash loans,M,Y,Y,0,153000.0,315000.0,16623.0,315000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.026392,-10533,-572,-4720.0,-3192,64.0,1,1,0,1,0,0,Drivers,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5643277031764917,0.6940926425266661,0.16699999999999998,,0.9861,,,0.2,0.1724,0.3333,,,,0.1877,,0.0,0.1702,,0.9861,,,0.2014,0.1724,0.3333,,,,0.1955,,0.0,0.1686,,0.9861,,,0.2,0.1724,0.3333,,,,0.191,,0.0,,block of flats,0.1476,Panel,No,3.0,1.0,3.0,1.0,-1356.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n254746,0,Cash loans,F,N,Y,0,135000.0,818410.5,47979.0,706500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010643,-21263,-1492,-6720.0,-4242,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.8102921951594232,0.4931522915321545,0.5154953751603267,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-798.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n426888,0,Cash loans,F,N,Y,1,144000.0,733315.5,39199.5,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.009657,-14885,-699,-8791.0,-5736,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Self-employed,,0.3016746737377368,0.8050196619153701,0.0835,0.0512,0.9836,0.7756,0.0298,0.08,0.0345,0.4583,0.5,0.0269,0.0681,0.0751,0.0,0.0,0.0851,0.0531,0.9836,0.7844,0.0301,0.0806,0.0345,0.4583,0.5,0.0276,0.0744,0.0782,0.0,0.0,0.0843,0.0512,0.9836,0.7786,0.03,0.08,0.0345,0.4583,0.5,0.0274,0.0693,0.0764,0.0,0.0,reg oper account,block of flats,0.0754,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-806.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n173287,0,Cash loans,F,Y,Y,0,67500.0,101880.0,10566.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010643,-21862,365243,-13957.0,-4174,8.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.6790595680899584,,0.0825,0.0807,0.9762,0.6736,0.0081,0.0,0.1379,0.1667,0.2083,0.059000000000000004,0.0672,0.0643,0.0,0.0,0.084,0.0838,0.9762,0.6864,0.0082,0.0,0.1379,0.1667,0.2083,0.0603,0.0735,0.067,0.0,0.0,0.0833,0.0807,0.9762,0.6779999999999999,0.0082,0.0,0.1379,0.1667,0.2083,0.06,0.0684,0.0654,0.0,0.0,reg oper account,block of flats,0.055,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-486.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n294239,0,Cash loans,F,N,Y,2,202500.0,1078200.0,31653.0,900000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-16151,-2939,-10158.0,-3761,,1,1,0,1,0,0,Sales staff,4.0,2,2,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.5918215246765947,0.6704552366154141,0.43473324875017305,0.1227,0.1294,0.9786,0.7076,0.0162,0.0,0.2759,0.1667,0.2083,0.1399,0.1,0.1167,0.0,0.0,0.125,0.1342,0.9786,0.7190000000000001,0.0164,0.0,0.2759,0.1667,0.2083,0.1431,0.1093,0.1216,0.0,0.0,0.1239,0.1294,0.9786,0.7115,0.0163,0.0,0.2759,0.1667,0.2083,0.1423,0.1018,0.1188,0.0,0.0,reg oper account,block of flats,0.0918,Panel,No,2.0,0.0,2.0,0.0,-243.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n189903,1,Revolving loans,F,N,Y,0,135000.0,135000.0,6750.0,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-10395,-829,-3740.0,-1295,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.1550086659949527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,1.0,-767.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n304846,0,Cash loans,F,N,Y,0,45000.0,923454.0,27130.5,661500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018029,-21589,365243,-9361.0,-3356,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,5,0,0,0,0,0,0,XNA,,0.5441411885672448,0.7338145369642702,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2152.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n415640,0,Cash loans,F,N,Y,0,157500.0,359685.0,19642.5,310500.0,Family,Pensioner,Secondary / secondary special,Single / not married,Rented apartment,0.01885,-19635,365243,-4363.0,-2304,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,0.6301554041204571,0.13461834626188388,0.07058051883159755,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-1362.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n244745,0,Revolving loans,F,N,Y,1,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-9551,-668,-6133.0,-806,,1,1,1,1,1,0,Managers,3.0,2,2,TUESDAY,17,0,0,0,0,0,0,Self-employed,0.4473932039730241,0.04979983419786562,0.0969483170508572,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-280.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n188485,0,Cash loans,F,N,Y,1,135450.0,247986.0,20011.5,207000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008068,-14429,-2052,-2680.0,-5087,,1,1,0,1,0,0,High skill tech staff,3.0,3,3,MONDAY,9,0,0,0,0,0,0,Business Entity Type 2,,0.4591026742629393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-719.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n403402,0,Cash loans,F,N,N,0,202500.0,900000.0,29164.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010006,-14985,-3134,-2243.0,-4682,,1,1,0,1,0,0,,1.0,2,1,THURSDAY,8,0,0,0,0,0,0,Other,0.7352123556181099,0.5695087100704637,0.35233997269170386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-392.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n322635,0,Cash loans,F,N,N,0,103500.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-17040,-2286,-7786.0,-549,,1,1,0,1,1,0,Core staff,2.0,2,2,TUESDAY,9,0,0,0,0,1,1,Trade: type 3,0.6787316047546937,0.2884404322205102,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0117,,No,5.0,0.0,5.0,0.0,-1605.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n245314,0,Cash loans,M,Y,Y,1,135000.0,197820.0,18274.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-16428,-1640,-1011.0,-5715,4.0,1,1,0,1,0,0,Drivers,3.0,2,2,FRIDAY,9,0,0,0,0,0,0,Transport: type 3,,0.6668943807530323,0.7407990879702335,0.0082,0.0,0.9613,0.4696,0.0,0.0,0.069,0.0417,0.0833,,0.0067,0.0095,0.0,0.0,0.0084,0.0,0.9613,0.4904,0.0,0.0,0.069,0.0417,0.0833,,0.0073,0.0099,0.0,0.0,0.0083,0.0,0.9613,0.4767,0.0,0.0,0.069,0.0417,0.0833,,0.0068,0.0097,0.0,0.0,reg oper spec account,block of flats,0.0075,Wooden,No,2.0,0.0,2.0,0.0,-227.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n230767,0,Cash loans,M,Y,Y,1,225000.0,533304.0,36207.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-14035,-456,-1716.0,-1716,64.0,1,1,0,1,0,0,Laborers,3.0,1,1,FRIDAY,12,0,0,0,0,0,0,Other,,0.3367036460026513,0.17352743046491467,0.0825,,0.9762,,,0.0,0.1379,0.1667,,,,0.0664,,0.0,0.084,,0.9762,,,0.0,0.1379,0.1667,,,,0.0692,,0.0,0.0833,,0.9762,,,0.0,0.1379,0.1667,,,,0.0676,,0.0,,block of flats,0.0522,Panel,No,1.0,0.0,1.0,0.0,-731.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n107215,0,Cash loans,F,N,Y,1,180000.0,288873.0,16713.0,238500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018029,-13426,-1206,-778.0,-4807,,1,1,0,1,0,0,Sales staff,3.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Other,,0.3314981271440056,0.5388627065779676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n261375,0,Cash loans,M,Y,Y,0,180000.0,900000.0,38263.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-17128,-544,-6907.0,-664,7.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Construction,,0.7157853316000204,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1602.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n307612,0,Cash loans,M,Y,Y,0,126000.0,536917.5,27544.5,463500.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.008625,-19389,-716,-10493.0,-2929,65.0,1,1,0,1,0,0,Security staff,2.0,2,2,FRIDAY,11,0,1,1,0,0,0,Business Entity Type 3,0.4084337848591815,0.35742258753606143,0.520897599048938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,4.0,0.0,-156.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n199624,0,Cash loans,M,Y,N,0,135000.0,552555.0,28341.0,477000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-18991,-1911,-10559.0,-2517,17.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Government,,0.6613483321447229,0.5226973172821112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2246.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,1.0\n364550,0,Cash loans,F,N,Y,0,292500.0,472500.0,46863.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.04622,-22653,365243,-3653.0,-4150,,1,0,0,1,0,0,,1.0,1,1,MONDAY,16,0,0,0,0,0,0,XNA,,0.7238468870550844,0.4668640059537032,0.0082,0.0,0.9627,0.49,0.0015,0.0,0.069,0.0417,0.0833,,0.0067,0.0085,0.0,0.0,0.0084,0.0,0.9628,0.51,0.0016,0.0,0.069,0.0417,0.0833,,0.0073,0.0088,0.0,0.0,0.0083,0.0,0.9627,0.4968,0.0016,0.0,0.069,0.0417,0.0833,,0.0068,0.0086,0.0,0.0,reg oper account,block of flats,0.0067,Wooden,No,0.0,0.0,0.0,0.0,-1066.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n170384,0,Revolving loans,M,Y,N,1,180000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-11750,-1518,-9148.0,-416,4.0,1,1,0,1,1,0,Laborers,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,Self-employed,0.5988467362856705,0.4264252255818233,0.5744466170995097,0.4454,0.3448,0.9876,0.83,0.1395,0.48,0.4138,0.3333,0.375,0.2599,0.3631,0.5357,0.0116,0.0034,0.4538,0.3578,0.9876,0.8367,0.1408,0.4834,0.4138,0.3333,0.375,0.2659,0.3967,0.5581,0.0117,0.0036,0.4497,0.3448,0.9876,0.8323,0.1404,0.48,0.4138,0.3333,0.375,0.2644,0.3694,0.5453,0.0116,0.0035,reg oper account,block of flats,0.4984,Panel,No,0.0,0.0,0.0,0.0,-1620.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n405972,0,Cash loans,F,N,Y,0,67500.0,254700.0,14350.5,225000.0,Family,Pensioner,Higher education,Separated,House / apartment,0.030755,-24257,365243,-10.0,-4055,,1,0,0,1,0,0,,1.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.6299244552889619,0.807273923277881,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n203877,0,Cash loans,F,N,Y,1,337500.0,1042560.0,34456.5,900000.0,Children,Working,Higher education,Separated,House / apartment,0.010006,-12408,-287,-926.0,-1317,,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,13,0,0,0,1,1,0,Business Entity Type 3,0.6306496993162747,0.7200410472169465,0.4400578303966329,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-947.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n325998,0,Cash loans,M,N,Y,0,180000.0,787500.0,28030.5,787500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-18462,-4287,-5510.0,-2003,,1,1,1,1,1,0,Security staff,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.6962745482469104,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2188.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n154896,0,Cash loans,F,N,N,0,81000.0,679500.0,28786.5,679500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.007114,-21119,365243,-7438.0,-4571,,1,0,0,1,1,0,,2.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,0.8720267152353441,0.2629358786975408,0.7922644738669378,0.0773,0.1337,0.9891,0.8504,0.0827,0.0,0.2069,0.1667,0.2083,0.0,0.0614,0.0806,0.0077,0.0302,0.0788,0.1388,0.9891,0.8563,0.0834,0.0,0.2069,0.1667,0.2083,0.0,0.067,0.084,0.0078,0.032,0.0781,0.1337,0.9891,0.8524,0.0832,0.0,0.2069,0.1667,0.2083,0.0,0.0624,0.0821,0.0078,0.0309,reg oper account,block of flats,0.0828,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2564.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n207620,0,Cash loans,F,N,Y,0,315000.0,353241.0,20407.5,319500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.010032,-20032,-1659,-5422.0,-2100,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.6235658720411763,0.2750003523983893,,,0.9781,,,,,,,,,0.08900000000000001,,,,,0.9782,,,,,,,,,0.0927,,,,,0.9781,,,,,,,,,0.0906,,,,,0.07,,No,6.0,0.0,6.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n229246,0,Cash loans,F,Y,N,2,99000.0,130320.0,14868.0,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003813,-12756,365243,-654.0,-10,24.0,1,0,0,1,0,1,,4.0,2,2,FRIDAY,5,0,0,0,0,0,0,XNA,,0.2662291880469212,0.6512602186973006,,,0.9767,,,0.0,0.1379,0.1667,,,,,,,,,0.9767,,,0.0,0.1379,0.1667,,,,,,,,,0.9767,,,0.0,0.1379,0.1667,,,,,,,,,0.0468,Panel,No,0.0,0.0,0.0,0.0,-1761.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n165266,0,Cash loans,F,N,Y,0,41850.0,101880.0,10053.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23220,365243,-10908.0,-5029,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.7354619715070042,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2703.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n244646,0,Cash loans,F,Y,Y,0,180000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Office apartment,0.04622,-17360,-598,-3455.0,-908,10.0,1,1,0,1,0,0,Laborers,1.0,1,1,MONDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.5667186884404053,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-508.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n382507,0,Cash loans,M,Y,Y,3,180000.0,835380.0,40320.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-11939,-969,-1033.0,-1938,8.0,1,1,0,1,1,0,Drivers,5.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,,0.5062502196125132,0.6161216908872079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2002.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n181913,0,Revolving loans,F,N,N,1,135000.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.008473999999999999,-15769,-2295,-3981.0,-4143,,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6332775449604263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1497.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n412313,0,Cash loans,F,N,Y,2,315000.0,1772406.0,97254.0,1692000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-13497,-2271,-7362.0,-4556,,1,1,0,1,1,1,,4.0,1,1,SUNDAY,15,0,0,0,0,0,0,Realtor,0.7410980139747838,0.6896774196934142,0.5082869913916046,0.168,0.0919,0.9836,0.7756,0.0353,0.1864,0.1262,0.4442,0.0417,0.0,0.1356,0.1663,0.0064,0.0107,0.1513,0.0911,0.9791,0.7256,0.0,0.1611,0.1379,0.3333,0.0417,0.0,0.1286,0.1397,0.0,0.0,0.1499,0.0938,0.9791,0.7182,0.0,0.16,0.1379,0.3333,0.0417,0.0,0.1231,0.1432,0.0039,0.0146,,block of flats,0.1801,Panel,No,0.0,0.0,0.0,0.0,-1852.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n245569,0,Cash loans,F,Y,Y,2,157500.0,625356.0,20173.5,522000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-11700,-2226,-2123.0,-3513,12.0,1,1,0,1,0,0,High skill tech staff,4.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Other,0.5043230230578871,0.5770246147539441,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2212.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n247447,0,Cash loans,F,N,N,0,135000.0,225000.0,17905.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.020713,-21328,365243,-7859.0,-4787,,1,0,0,1,1,0,,1.0,3,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.3024030432581381,0.5352762504724826,0.0794,0.0798,0.9752,0.66,0.0086,0.0,0.1379,0.1667,0.2083,0.0633,0.0639,0.069,0.0039,0.0033,0.0809,0.0828,0.9752,0.6733,0.0087,0.0,0.1379,0.1667,0.2083,0.0648,0.0698,0.0719,0.0039,0.0034,0.0802,0.0798,0.9752,0.6645,0.0086,0.0,0.1379,0.1667,0.2083,0.0644,0.065,0.0702,0.0039,0.0033,not specified,block of flats,0.0597,Panel,No,1.0,0.0,1.0,0.0,-1873.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n423586,0,Cash loans,M,N,Y,2,225000.0,823657.5,34753.5,625500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-15016,-3411,-4430.0,-4482,,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 2,0.611766158536047,0.5971232390100172,0.6178261467332483,,,0.9687,,,,,,,,,0.0092,,,,,0.9687,,,,,,,,,0.0087,,,,,0.9687,,,,,,,,,0.0094,,,,block of flats,0.0078,,Yes,0.0,0.0,0.0,0.0,-2164.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,2.0,2.0\n442012,1,Revolving loans,M,N,Y,0,146812.5,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,With parents,0.031329,-10653,-250,-1002.0,-2752,,1,1,1,1,0,0,Laborers,1.0,2,2,THURSDAY,12,1,1,0,1,1,0,Trade: type 3,,0.013167035002413459,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-783.0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,,,,,,\n412349,0,Cash loans,F,N,Y,1,180000.0,1800000.0,47614.5,1800000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.005084,-19003,-3662,-4823.0,-2558,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,THURSDAY,17,0,0,0,0,1,1,Industry: type 10,,0.4671113935023904,0.2636468134452008,0.0247,0.0495,0.9921,,,,0.1034,0.125,,,,,,,0.0252,0.0514,0.9921,,,,0.1034,0.125,,,,,,,0.025,0.0495,0.9921,,,,0.1034,0.125,,,,,,,,block of flats,0.024,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n408207,0,Cash loans,F,Y,Y,0,130500.0,508495.5,24592.5,454500.0,Family,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.009549,-19758,365243,-4297.0,-3268,16.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.7716052709813608,0.5352762504724826,0.1526,0.1056,0.9861,0.8096,0.0277,0.16,0.1379,0.3333,0.375,0.0707,0.121,0.1529,0.0154,0.004,0.1555,0.1096,0.9861,0.8171,0.0279,0.1611,0.1379,0.3333,0.375,0.0723,0.1322,0.1593,0.0156,0.0042,0.1541,0.1056,0.9861,0.8121,0.0278,0.16,0.1379,0.3333,0.375,0.0719,0.1231,0.1556,0.0155,0.0041,,block of flats,0.1299,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n234271,0,Cash loans,M,N,N,0,315000.0,1686591.0,46507.5,1507500.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.010276,-15819,-4751,-2918.0,-4285,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Trade: type 7,,0.4514561709821101,0.2721336844147212,0.059000000000000004,0.0375,0.9821,0.7552,,0.055999999999999994,0.0552,0.3333,0.0417,0.2559,0.0303,0.0514,0.0,0.0,0.0378,0.0279,0.9826,0.7713,,0.0403,0.0345,0.3333,0.0417,0.2618,0.0331,0.024,0.0,0.0,0.0375,0.0269,0.9826,0.7652,,0.04,0.0345,0.3333,0.0417,0.2604,0.0308,0.04,0.0,0.0,reg oper spec account,block of flats,0.0303,Panel,No,0.0,0.0,0.0,0.0,-182.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n309348,0,Cash loans,F,N,Y,0,112500.0,761067.0,27468.0,657000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Co-op apartment,0.010643,-20378,365243,-12623.0,-3912,,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,0.8075981405538866,0.6563447994511425,0.5190973382084597,0.0186,,0.9836,,,,,0.0417,,,,,,,0.0189,,0.9836,,,,,0.0417,,,,,,,0.0187,,0.9836,,,,,0.0417,,,,,,,,block of flats,0.0152,,No,1.0,0.0,1.0,0.0,-685.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n119139,0,Cash loans,F,N,N,0,112500.0,225000.0,14377.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-23882,365243,-8834.0,-4451,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.6700976045864737,0.4543210601605785,0.1588,,0.9816,,,0.0,0.0345,0.1667,,,,0.0491,,0.0741,0.1618,,0.9816,,,0.0,0.0345,0.1667,,,,0.0511,,0.0785,0.1603,,0.9816,,,0.0,0.0345,0.1667,,,,0.05,,0.0757,,block of flats,0.0547,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2402.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n315803,0,Cash loans,M,N,Y,0,225000.0,377370.0,34740.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-20344,-1406,-3724.0,-1342,,1,1,0,1,0,0,Laborers,2.0,1,1,TUESDAY,10,0,0,0,0,0,0,Construction,,0.6816988025574287,0.2778856891082046,0.3701,0.1894,0.9767,0.6804,0.5027,0.4,0.3448,0.3333,0.375,0.0425,0.3009,0.2384,1.0,0.001,0.3771,0.1965,0.9767,0.6929,0.5073,0.4028,0.3448,0.3333,0.375,0.0435,0.3287,0.2484,1.0,0.0011,0.3737,0.1894,0.9767,0.6847,0.5059,0.4,0.3448,0.3333,0.375,0.0433,0.3061,0.2427,1.0,0.001,reg oper account,block of flats,0.2749,Panel,No,3.0,1.0,3.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n272600,0,Cash loans,F,N,N,0,198000.0,900000.0,39775.5,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.026392,-21299,365243,-6931.0,-4040,,1,0,0,1,1,1,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,0.7987112565397325,0.5983220774459101,0.3425288720742255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1776.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n440589,0,Cash loans,M,Y,Y,0,180000.0,178290.0,21289.5,157500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-8992,-189,-8961.0,-1043,1.0,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.15743406827907427,0.05458898646143365,0.2103502286944494,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n352050,0,Cash loans,F,N,N,0,83250.0,417024.0,16011.0,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010147,-13711,-677,-4976.0,-5892,,1,1,1,1,0,0,Laborers,2.0,2,2,WEDNESDAY,16,0,0,0,0,1,1,Self-employed,,0.5864368320720796,0.7700870700124128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-162.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n242169,0,Cash loans,F,N,Y,0,90000.0,103500.0,5746.5,103500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-22855,365243,-13359.0,-4702,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,0.6808155055258531,0.3038118858261645,0.7121551551910698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2290.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,1.0\n221106,0,Revolving loans,F,N,Y,2,315000.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.02461,-12509,-2325,-2834.0,-4290,,1,1,0,1,0,0,,4.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,0.4780617768973414,0.6713436282330717,0.8337869986815019,0.0701,0.0234,0.9767,0.6804,0.0077,0.0,0.1379,0.1667,0.2083,0.0341,0.0572,0.064,0.0,0.0077,0.0714,0.0242,0.9767,0.6929,0.0078,0.0,0.1379,0.1667,0.2083,0.0349,0.0624,0.0667,0.0,0.0082,0.0708,0.0234,0.9767,0.6847,0.0077,0.0,0.1379,0.1667,0.2083,0.0347,0.0581,0.0652,0.0,0.0079,reg oper account,block of flats,0.0562,\"Stone, brick\",No,6.0,1.0,6.0,1.0,-713.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n169096,1,Cash loans,M,Y,Y,1,202500.0,675000.0,21906.0,675000.0,Unaccompanied,Commercial associate,Incomplete higher,Separated,House / apartment,0.015221,-13229,-1178,-5365.0,-1388,23.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.17623419513719946,0.17456426555726348,0.2227,0.1213,0.9861,,,0.24,0.2069,0.3333,,0.1496,,0.2206,,,0.2269,0.1259,0.9861,,,0.2417,0.2069,0.3333,,0.1531,,0.2298,,,0.2248,0.1213,0.9861,,,0.24,0.2069,0.3333,,0.1522,,0.2246,,,,block of flats,0.2059,Panel,No,1.0,1.0,1.0,1.0,-1280.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n168949,0,Cash loans,F,Y,N,0,247500.0,1350000.0,37125.0,1350000.0,Unaccompanied,State servant,Higher education,Separated,House / apartment,0.032561,-13848,-1001,-7969.0,-2882,64.0,1,1,0,1,1,0,Core staff,1.0,1,1,SATURDAY,11,0,0,0,0,0,0,Other,,0.7762081628890036,0.5673792367572691,0.3526,0.034,0.9826,0.762,0.19399999999999998,0.48,0.2069,0.4583,0.5,0.1051,0.2858,0.2827,0.0077,0.14300000000000002,0.3592,0.0353,0.9826,0.7713,0.1957,0.4834,0.2069,0.4583,0.5,0.1075,0.3122,0.2161,0.0078,0.1514,0.35600000000000004,0.034,0.9826,0.7652,0.1952,0.48,0.2069,0.4583,0.5,0.1069,0.2907,0.2877,0.0078,0.146,reg oper account,block of flats,0.2815,Mixed,No,1.0,0.0,1.0,0.0,-1633.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n186372,0,Cash loans,F,N,N,0,130500.0,439074.0,21253.5,328500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.0105,-22548,365243,-10983.0,-4208,,1,0,0,1,0,0,,1.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.6225592705420568,0.6313545365850379,0.0227,0.0016,0.9851,,,0.0,0.1034,0.0417,,0.0227,,0.0183,,0.0,0.0231,0.0017,0.9851,,,0.0,0.1034,0.0417,,0.0232,,0.0191,,0.0,0.0229,0.0016,0.9851,,,0.0,0.1034,0.0417,,0.0231,,0.0187,,0.0,,block of flats,0.0157,\"Stone, brick\",No,2.0,1.0,2.0,0.0,-121.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n405014,0,Cash loans,M,Y,Y,0,81000.0,161730.0,9900.0,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20073,-1151,-6965.0,-3600,64.0,1,1,0,1,1,0,Security staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Other,,0.6779051605621741,0.6496203111237195,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,-829.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n239495,0,Cash loans,F,N,Y,0,101250.0,225000.0,8613.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.026392,-24673,365243,-6679.0,-1584,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.5721979751647406,,0.0825,,0.9781,,,0.0,,0.1667,,,,0.0476,,,0.084,,0.9782,,,0.0,,0.1667,,,,0.0496,,,0.0833,,0.9781,,,0.0,,0.1667,,,,0.0485,,,,block of flats,0.0558,Panel,No,11.0,0.0,11.0,0.0,-2542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n454312,0,Revolving loans,M,N,Y,0,157500.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.016612000000000002,-10360,-818,-409.0,-3025,,1,1,0,1,0,1,Cooking staff,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.16169541149759795,0.5817355029950602,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1729.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n125225,0,Cash loans,F,Y,N,0,135000.0,540000.0,17086.5,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010032,-9519,-1060,-3730.0,-1722,7.0,1,1,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.21784627356939834,0.603550392986473,0.3077366963789207,0.1485,0.0969,0.9841,0.7824,0.0678,0.16,0.1379,0.5,0.375,0.1104,0.1202,0.1598,0.0039,0.0038,0.1513,0.1005,0.9841,0.7909,0.0684,0.1611,0.1379,0.5,0.375,0.113,0.1313,0.1665,0.0039,0.004,0.1499,0.0969,0.9841,0.7853,0.0682,0.16,0.1379,0.5,0.375,0.1124,0.1223,0.1627,0.0039,0.0039,reg oper account,block of flats,0.1636,Panel,No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n146865,1,Cash loans,F,N,Y,0,99000.0,152820.0,10341.0,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-10801,-777,-4876.0,-3443,,1,1,1,1,1,0,,2.0,2,2,MONDAY,15,0,0,0,0,1,1,Self-employed,0.07182942881447373,0.1946363865451888,0.13426542355494275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-496.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n310740,0,Cash loans,F,N,Y,0,112500.0,454500.0,13153.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-22702,365243,-10054.0,-4602,,1,0,0,1,0,0,,2.0,2,2,MONDAY,17,0,0,0,0,0,0,XNA,,0.6650117637239039,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1570.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n346643,1,Cash loans,F,N,N,0,67500.0,162000.0,10822.5,162000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-18405,-2989,-3203.0,-403,,1,1,1,1,0,0,,2.0,2,2,FRIDAY,19,0,0,0,0,1,1,Other,0.3339168441653403,0.0916329077103269,0.032748039848638465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1726.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n265524,0,Cash loans,M,Y,Y,0,157500.0,254700.0,27153.0,225000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.00496,-13577,-1918,-4493.0,-4507,3.0,1,1,0,1,0,0,Drivers,1.0,2,2,MONDAY,12,0,0,0,1,1,0,Construction,,0.4943766850776106,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1011.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n173054,1,Cash loans,M,N,N,1,270000.0,790825.5,38173.5,639000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-12587,-867,-868.0,-1307,,1,1,0,1,0,0,Laborers,3.0,3,1,TUESDAY,7,0,0,0,0,0,0,Industry: type 1,,0.3301184670574001,0.11911906455945008,0.0371,0.0,0.9737,0.6396,0.0035,0.0,0.069,0.1667,0.0,0.0207,0.0303,0.0331,0.0,0.0,0.0378,0.0,0.9737,0.6537,0.0036,0.0,0.069,0.1667,0.0,0.0211,0.0331,0.0345,0.0,0.0,0.0375,0.0,0.9737,0.6444,0.0036,0.0,0.069,0.1667,0.0,0.021,0.0308,0.0337,0.0,0.0,reg oper account,block of flats,0.027999999999999997,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-225.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n310517,0,Cash loans,M,Y,N,0,90000.0,95940.0,10462.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.028663,-10134,-1491,-2285.0,-2546,6.0,1,1,1,1,1,0,Laborers,2.0,2,2,MONDAY,9,0,1,1,0,1,1,Business Entity Type 3,,0.3551907495058527,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n364756,0,Cash loans,F,N,N,0,81000.0,381528.0,14382.0,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010966,-19460,365243,-7255.0,-2924,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,19,0,0,0,0,0,0,XNA,,0.2007754035972499,0.5919766183185521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-464.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n224475,0,Cash loans,F,N,Y,2,67500.0,364896.0,23449.5,315000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019689,-11005,-2334,-699.0,-1031,,1,1,1,1,0,0,Sales staff,4.0,2,2,THURSDAY,13,0,0,0,0,0,0,Self-employed,0.6590687791866308,0.2979883622811077,0.3723336657058204,0.0247,0.036000000000000004,0.9985,0.9796,0.0133,0.0,0.069,0.0833,0.125,0.0076,0.0202,0.02,0.0,0.0,0.0252,0.0374,0.9985,0.9804,0.0134,0.0,0.069,0.0833,0.125,0.0077,0.022000000000000002,0.0209,0.0,0.0,0.025,0.036000000000000004,0.9985,0.9799,0.0134,0.0,0.069,0.0833,0.125,0.0077,0.0205,0.0204,0.0,0.0,reg oper account,block of flats,0.023,\"Stone, brick\",No,2.0,2.0,2.0,1.0,-906.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n402223,0,Cash loans,F,N,Y,0,135000.0,486000.0,31189.5,486000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-17023,-5634,-3704.0,-561,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Industry: type 7,,0.6820830196930634,0.12850437737240394,0.6227,0.5146,0.9786,,,0.0,1.0,0.1667,,0.894,,0.4603,,,0.6345,0.534,0.9786,,,0.0,1.0,0.1667,,0.9144,,0.4796,,,0.6287,0.5146,0.9786,,,0.0,1.0,0.1667,,0.9095,,0.4686,,,,block of flats,0.4259,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n212043,0,Cash loans,F,N,Y,0,135000.0,225000.0,24232.5,225000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.04622,-18476,-358,-912.0,-1338,,1,1,0,1,0,0,Laborers,1.0,1,1,WEDNESDAY,16,0,0,0,0,0,0,School,0.7561652705222868,0.7074160719814931,0.3185955240537633,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-86.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n190975,0,Cash loans,M,Y,Y,0,270000.0,2151009.0,59152.5,1795500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-16994,-2698,-6229.0,-532,3.0,1,1,0,1,1,0,Managers,2.0,1,1,WEDNESDAY,17,0,0,0,0,0,0,Construction,0.5590239849483555,0.751824347930314,0.5513812618027899,0.0299,0.0065,0.9478,0.28600000000000003,0.0144,0.0,0.2069,0.0833,0.0625,0.1074,0.0219,0.0482,0.0116,0.0177,0.0305,0.0,0.9479,0.314,0.0099,0.0,0.2069,0.0833,0.0,0.1099,0.0239,0.0502,0.0117,0.0187,0.0302,0.0065,0.9478,0.2955,0.0145,0.0,0.2069,0.0833,0.0625,0.1093,0.0222,0.049,0.0116,0.0181,reg oper spec account,block of flats,0.0521,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-2375.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n365148,0,Cash loans,F,N,N,1,121500.0,454500.0,29043.0,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009657,-14932,-2709,-4094.0,-4856,,1,1,1,1,0,0,Secretaries,3.0,2,2,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 2,,0.5394291002018563,0.15286622915504153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-427.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n239665,0,Cash loans,F,N,N,1,103500.0,339241.5,12919.5,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-23079,365243,-2174.0,-4071,,1,0,0,1,0,0,,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,0.7925033005063454,0.6539634944943902,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1389.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n329202,0,Cash loans,M,Y,N,2,180000.0,199467.0,17064.0,166500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-12338,-1055,-6367.0,-3087,7.0,1,1,0,1,0,0,Managers,4.0,2,2,TUESDAY,14,0,0,0,0,0,0,Self-employed,0.3157074060954013,0.7107721068622256,0.14375805349116522,0.1381,0.1273,0.9856,,,0.0,0.3448,0.1667,,0.1658,,0.0842,,,0.1408,0.1321,0.9856,,,0.0,0.3448,0.1667,,0.1696,,0.0878,,,0.1395,0.1273,0.9856,,,0.0,0.3448,0.1667,,0.1687,,0.0857,,,,block of flats,0.1129,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n361755,0,Cash loans,M,Y,Y,0,270000.0,463284.0,22662.0,382500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-12437,-605,-6386.0,-712,5.0,1,1,0,1,0,0,,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,0.38394201412252144,0.36197548910857824,0.1940678276718812,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,1.0,-437.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n432266,0,Cash loans,F,N,Y,0,135000.0,454500.0,30492.0,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.019689,-21776,-2917,-3678.0,-3998,,1,1,0,1,0,0,Accountants,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.9167727309182808,0.47506620430171503,0.6956219298394389,0.1227,0.1172,0.9786,,,0.0,0.2759,0.1667,,0.1317,,0.1124,,0.0044,0.125,0.1216,0.9786,,,0.0,0.2759,0.1667,,0.1347,,0.1171,,0.0046,0.1239,0.1172,0.9786,,,0.0,0.2759,0.1667,,0.134,,0.1144,,0.0044,,block of flats,0.0884,Panel,No,1.0,0.0,1.0,0.0,-1526.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,0.0\n413168,0,Cash loans,F,N,Y,1,157500.0,1002456.0,51313.5,810000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.02461,-16792,-536,-10830.0,-329,,1,1,0,1,1,0,Laborers,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.5897687296156876,0.6594055320683344,0.1907,0.1049,0.9826,,,0.2,0.1724,0.3333,,0.0952,,0.19899999999999998,,0.01,0.1943,0.1089,0.9826,,,0.2014,0.1724,0.3333,,0.0974,,0.2073,,0.0105,0.1926,0.1049,0.9826,,,0.2,0.1724,0.3333,,0.0969,,0.2025,,0.0102,,block of flats,0.1772,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2390.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n256376,1,Cash loans,F,N,N,0,67500.0,545040.0,25407.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-9389,-1219,-1463.0,-1845,,1,1,1,1,0,0,Laborers,2.0,2,2,SUNDAY,17,0,0,0,1,1,1,Business Entity Type 3,0.2809842574958532,0.5684522304388661,0.656158373001177,0.0,,0.0,,,,,,,,,,,,0.0,,0.0005,,,,,,,,,,,,0.0,,0.0,,,,,,,,,,,,,block of flats,0.0,,No,0.0,0.0,0.0,0.0,-875.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n426163,0,Cash loans,F,N,Y,2,45000.0,261000.0,15111.0,261000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-14094,-4884,-4631.0,-4883,,1,1,0,1,1,0,Medicine staff,4.0,3,3,WEDNESDAY,11,0,0,0,0,0,0,Medicine,0.5304899854698231,0.5289167912948914,0.375711009574066,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1762.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n256183,0,Cash loans,F,Y,Y,3,135000.0,78192.0,8973.0,67500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-12928,-1313,-2853.0,-3413,15.0,1,1,0,1,0,0,Accountants,5.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.6111235306255324,0.3687597060329724,0.4048783643353997,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-576.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n191673,0,Cash loans,F,Y,Y,0,135000.0,1323000.0,36513.0,1323000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-19748,365243,-4783.0,-3294,7.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.647043242831215,0.5226973172821112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2146.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n431311,0,Cash loans,M,N,Y,0,225000.0,1428408.0,87520.5,1350000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.025164,-9820,-2132,-3979.0,-1998,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.18952925747319205,0.5332515937848769,,0.0938,0.1,0.9767,0.6804,0.0,0.0,0.2069,0.1667,0.2083,0.1066,0.0756,0.0874,0.0039,0.0181,0.0956,0.1037,0.9767,0.6929,0.0,0.0,0.2069,0.1667,0.2083,0.109,0.0826,0.091,0.0039,0.0192,0.0947,0.1,0.9767,0.6847,0.0,0.0,0.2069,0.1667,0.2083,0.1085,0.077,0.0889,0.0039,0.0185,reg oper account,block of flats,0.0727,Block,No,0.0,0.0,0.0,0.0,-1045.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n149387,0,Cash loans,F,Y,Y,0,382500.0,1035832.5,30415.5,904500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-9951,-1942,-699.0,-2639,0.0,1,1,0,1,0,1,Core staff,2.0,1,1,TUESDAY,16,0,0,0,0,0,0,Bank,,0.7585873808486401,0.3108182544189319,0.1454,0.0994,0.9831,0.7688,0.0314,0.16,0.1379,0.3333,0.375,0.0,0.1185,0.13699999999999998,0.0,0.0,0.1481,0.1031,0.9831,0.7779,0.0317,0.1611,0.1379,0.3333,0.375,0.0,0.1295,0.1428,0.0,0.0,0.1468,0.0994,0.9831,0.7719,0.0316,0.16,0.1379,0.3333,0.375,0.0,0.1206,0.1395,0.0,0.0,reg oper account,block of flats,0.1078,Panel,No,2.0,0.0,2.0,0.0,-1722.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n437662,0,Cash loans,F,N,Y,2,135000.0,509400.0,37066.5,450000.0,Family,Commercial associate,Secondary / secondary special,Married,With parents,0.028663,-14213,-123,-2697.0,-4489,,1,1,1,1,1,0,,4.0,2,2,FRIDAY,10,0,1,1,0,1,1,Other,0.3996985831833866,0.6525270231165675,0.07647351918548448,0.0247,0.0517,0.9826,0.762,0.0035,0.0,0.1379,0.0417,0.0833,0.0415,0.0202,0.0232,0.0,0.0,0.0252,0.0536,0.9826,0.7713,0.0035,0.0,0.1379,0.0417,0.0833,0.0424,0.022000000000000002,0.0241,0.0,0.0,0.025,0.0517,0.9826,0.7652,0.0035,0.0,0.1379,0.0417,0.0833,0.0422,0.0205,0.0236,0.0,0.0,reg oper account,block of flats,0.0185,Panel,No,0.0,0.0,0.0,0.0,-585.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n355331,0,Cash loans,F,N,Y,0,135000.0,353241.0,34542.0,319500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-22375,365243,-7104.0,-2907,,1,0,0,1,0,0,,1.0,2,2,MONDAY,16,0,0,0,0,0,0,XNA,,0.4256231417282029,0.8716510650705064,0.0082,,0.9687,0.5716,0.0089,0.0,0.069,0.0,0.0417,0.0165,0.0067,0.0062,0.0,0.0,0.0084,,0.9687,0.5884,0.009000000000000001,0.0,0.069,0.0,0.0417,0.0168,0.0073,0.0065,0.0,0.0,0.0083,,0.9687,0.5773,0.009000000000000001,0.0,0.069,0.0,0.0417,0.0168,0.0068,0.0063,0.0,0.0,reg oper account,block of flats,0.0049,Block,No,0.0,0.0,0.0,0.0,-659.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n129455,0,Cash loans,F,N,Y,0,135000.0,485640.0,34668.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.072508,-21454,365243,-5669.0,-1813,,1,0,0,1,1,0,,1.0,1,1,THURSDAY,15,0,0,0,0,0,0,XNA,,0.4866603577241442,0.19633396621345675,0.1794,0.1347,0.9921,0.8912,0.0074,0.32,0.1379,0.4583,0.4167,0.0,0.1463,0.1285,0.0,0.0074,0.1828,0.1398,0.9921,0.8955,0.0075,0.3222,0.1379,0.4583,0.4167,0.0,0.1598,0.1338,0.0,0.0078,0.1811,0.1347,0.9921,0.8927,0.0075,0.32,0.1379,0.4583,0.4167,0.0,0.1488,0.1308,0.0,0.0076,reg oper account,block of flats,0.1711,Panel,No,0.0,0.0,0.0,0.0,-180.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n273517,0,Cash loans,M,N,N,1,94500.0,450000.0,21109.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-13938,-713,-276.0,-918,,1,1,1,1,1,0,,3.0,3,3,FRIDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.3578791922576905,0.472078723935541,,0.0371,0.0638,0.9732,0.6328,0.0028,0.0,0.1034,0.0833,0.0417,0.0129,0.0303,0.0301,0.0,0.0,0.0378,0.0662,0.9732,0.6472,0.0029,0.0,0.1034,0.0833,0.0417,0.0132,0.0331,0.0313,0.0,0.0,0.0375,0.0638,0.9732,0.6377,0.0028,0.0,0.1034,0.0833,0.0417,0.0131,0.0308,0.0306,0.0,0.0,reg oper account,block of flats,0.0252,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1181.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n384722,0,Cash loans,F,N,Y,0,202500.0,101880.0,11623.5,90000.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.018634,-12641,-3142,-2325.0,-4449,,1,1,1,1,0,0,Medicine staff,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,Medicine,0.29759929463436874,0.4765115337624806,0.7583930617144343,0.1495,,0.9811,,,,0.069,0.1667,,,,0.0784,,,0.1523,,0.9811,,,,0.069,0.1667,,,,0.0817,,,0.1509,,0.9811,,,,0.069,0.1667,,,,0.0798,,,,,0.0616,,No,6.0,0.0,6.0,0.0,-1670.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n423332,0,Revolving loans,F,Y,Y,1,90000.0,135000.0,6750.0,135000.0,Unaccompanied,State servant,Incomplete higher,Single / not married,House / apartment,0.005084,-9172,-1257,-1256.0,-1838,64.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,15,0,0,0,0,1,1,Other,0.3545142545452795,0.6190794523835058,,0.0784,0.0624,0.9757,,,0.0,0.1379,0.1667,,,,0.0602,,0.0109,0.0798,0.0647,0.9757,,,0.0,0.1379,0.1667,,,,0.0627,,0.0115,0.0791,0.0624,0.9757,,,0.0,0.1379,0.1667,,,,0.0613,,0.0111,,block of flats,0.0497,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-803.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n222733,0,Cash loans,M,N,Y,0,76500.0,162000.0,18450.0,162000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018209,-9955,-3006,-2850.0,-2614,,1,1,0,1,0,0,Laborers,1.0,3,3,THURSDAY,6,0,0,0,0,0,0,Self-employed,,0.2191381641344149,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-265.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n317781,0,Cash loans,F,N,Y,2,135000.0,199008.0,20520.0,180000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.0105,-14200,-2453,-325.0,-1736,,1,1,0,1,0,0,Security staff,4.0,3,3,MONDAY,15,0,0,0,0,0,0,Government,,0.7353768726317792,0.6754132910917112,,0.0236,0.9791,,,0.04,0.0345,0.3333,,,,0.024,,,,0.0245,0.9791,,,0.0403,0.0345,0.3333,,,,0.025,,,,0.0236,0.9791,,,0.04,0.0345,0.3333,,,,0.0245,,,,,0.0313,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2132.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n291649,0,Cash loans,F,Y,N,0,292500.0,555273.0,18040.5,463500.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.003122,-10076,-866,-4799.0,-2668,64.0,1,1,0,1,0,0,,1.0,3,3,FRIDAY,12,0,0,0,0,1,1,Other,,0.5954597731821383,0.2955826421513093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-684.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n291801,0,Cash loans,F,N,Y,1,112500.0,1255680.0,41629.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008019,-18336,-261,-1533.0,-1883,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,15,0,0,0,0,0,0,Trade: type 3,0.7815152728795653,0.5889854825001917,0.8482442999507556,0.1536,0.1689,0.9786,0.7076,0.2896,0.0,0.3448,0.1667,0.0417,0.0821,0.1252,0.1484,0.0,0.0,0.1565,0.1753,0.9786,0.7190000000000001,0.2923,0.0,0.3448,0.1667,0.0417,0.0839,0.1368,0.1546,0.0,0.0,0.1551,0.1689,0.9786,0.7115,0.2915,0.0,0.3448,0.1667,0.0417,0.0835,0.1274,0.151,0.0,0.0,reg oper account,block of flats,0.1583,Panel,No,1.0,0.0,1.0,0.0,-2244.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n312802,0,Cash loans,F,N,N,0,90000.0,651600.0,19885.5,562500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.010006,-22069,365243,-11027.0,-4904,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.734315925428378,,0.1131,0.0992,0.9851,0.7959999999999999,,0.1064,0.1379,0.3333,0.375,0.0585,0.0894,0.0784,0.0129,0.0879,0.1019,0.0926,0.9856,0.8105,,0.1611,0.1379,0.3333,0.375,0.0405,0.0882,0.0734,0.0039,0.0772,0.1166,0.0918,0.9856,0.8054,,0.16,0.1379,0.3333,0.375,0.0632,0.0898,0.0833,0.0078,0.0747,reg oper account,block of flats,0.0894,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,3.0\n433351,0,Cash loans,F,N,N,0,56902.5,364032.0,9733.5,288000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-16495,-984,-5663.0,-22,,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,16,0,1,1,0,1,1,Business Entity Type 2,,0.5576065658730079,0.7151031019926098,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-509.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n165504,1,Cash loans,M,N,Y,0,157500.0,521280.0,28408.5,450000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.032561,-20175,-838,-391.0,-3726,,1,1,0,1,0,0,Core staff,2.0,1,1,MONDAY,18,0,0,0,0,0,0,Postal,,0.08591399840494895,0.7688075728291359,0.0433,0.0333,0.9235,0.0,0.0197,0.0,0.2414,0.1667,0.125,0.0346,0.0277,0.0809,0.0154,0.0492,0.0441,0.0346,0.9235,0.0,0.0148,0.0,0.2414,0.1667,0.0417,0.0354,0.0257,0.0843,0.0156,0.0521,0.0437,0.0333,0.9235,0.0,0.0198,0.0,0.2414,0.1667,0.125,0.0352,0.0282,0.0823,0.0155,0.0503,reg oper spec account,block of flats,0.0878,\"Stone, brick\",No,1.0,1.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n435577,0,Cash loans,F,N,Y,1,58500.0,700830.0,22738.5,585000.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.008865999999999999,-16658,-831,-8871.0,-197,,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,10,0,0,0,1,1,0,Business Entity Type 3,,0.7241571320447721,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2348.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n335731,0,Cash loans,F,Y,Y,1,193500.0,545040.0,43060.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.007305,-10007,-214,-3784.0,-2636,1.0,1,1,0,1,0,0,Sales staff,3.0,3,3,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.4675852396269996,0.5574177560893995,0.324891229465852,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n271538,0,Cash loans,M,Y,Y,1,405000.0,512064.0,24903.0,360000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.072508,-17017,-3771,-90.0,-539,2.0,1,1,0,1,0,0,Managers,3.0,1,1,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.7110777003253653,0.5028782772082183,0.6103,0.2568,0.993,0.9048,0.3016,0.72,0.3103,0.6667,0.7083,,0.4833,0.653,0.0656,0.1013,0.6218,0.2665,0.993,0.9085,0.3044,0.725,0.3103,0.6667,0.7083,,0.528,0.6804,0.0661,0.1072,0.6162,0.2568,0.993,0.9061,0.3035,0.72,0.3103,0.6667,0.7083,,0.4917,0.6648,0.066,0.1034,reg oper account,block of flats,0.5357,Panel,No,3.0,0.0,3.0,0.0,-908.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n334257,0,Cash loans,M,N,N,0,180000.0,355500.0,28215.0,355500.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.006233,-10983,-2186,-2446.0,-3358,,1,1,1,1,1,0,Laborers,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Self-employed,,0.5604420721241251,0.2851799046358216,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1120.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n211594,0,Cash loans,F,N,N,0,225000.0,862560.0,22882.5,720000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00963,-23142,365243,-10790.0,-4285,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,0.7683598962148946,0.5749471109228941,0.28371188263500075,0.0722,0.0765,0.9801,,,,0.1379,0.1667,,0.0994,,0.0663,,,0.0735,0.0794,0.9801,,,,0.1379,0.1667,,0.1017,,0.0691,,,0.0729,0.0765,0.9801,,,,0.1379,0.1667,,0.1011,,0.0675,,,,block of flats,0.0557,Panel,No,2.0,0.0,2.0,0.0,-990.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n187649,0,Cash loans,F,Y,Y,0,103500.0,502497.0,29920.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.019689,-22635,365243,-7980.0,-4081,18.0,1,0,0,1,1,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.5863731884397793,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-1237.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n195287,0,Cash loans,F,Y,Y,0,112500.0,225000.0,22050.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-24562,365243,-12959.0,-4543,8.0,1,0,0,1,0,0,,2.0,3,3,THURSDAY,6,0,0,0,0,0,0,XNA,,0.433043396995951,0.7338145369642702,0.0701,0.0641,0.9791,0.7144,0.0,0.0,0.1379,0.1667,0.2083,0.0805,0.0572,0.0646,0.0,0.0,0.0714,0.0665,0.9791,0.7256,0.0,0.0,0.1379,0.1667,0.2083,0.0824,0.0624,0.0673,0.0,0.0,0.0708,0.0641,0.9791,0.7182,0.0,0.0,0.1379,0.1667,0.2083,0.0819,0.0581,0.0657,0.0,0.0,reg oper account,block of flats,0.0508,Panel,No,3.0,2.0,3.0,2.0,-476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n159320,0,Cash loans,F,Y,N,0,81000.0,258709.5,17289.0,234000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-10323,-2190,-2102.0,-2950,2.0,1,1,0,1,1,0,,2.0,2,2,SATURDAY,9,0,0,0,1,1,0,Business Entity Type 2,,0.4375694283911909,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-622.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n306413,0,Revolving loans,M,Y,Y,2,234000.0,270000.0,13500.0,270000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13691,-660,-7814.0,-4342,19.0,1,1,0,1,0,0,Drivers,4.0,2,2,SATURDAY,12,0,0,0,0,0,0,Self-employed,,0.6701217751197581,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2243.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n238735,0,Cash loans,M,Y,N,0,117000.0,227520.0,13059.0,180000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.030755,-22367,365243,-7389.0,-3541,18.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.5286715968722475,0.622922000268356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n339521,0,Cash loans,M,Y,Y,0,157500.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-8765,-871,-3647.0,-1440,15.0,1,1,0,1,1,0,Drivers,2.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,Transport: type 4,,0.5583131057184169,0.4418358231994413,0.0722,0.0831,0.9851,0.7959999999999999,0.0133,0.04,0.0345,0.3333,0.375,0.0523,0.0572,0.0601,0.0077,0.0292,0.0735,0.0862,0.9851,0.804,0.0134,0.0403,0.0345,0.3333,0.375,0.0535,0.0624,0.0626,0.0078,0.031,0.0729,0.0831,0.9851,0.7987,0.0134,0.04,0.0345,0.3333,0.375,0.0533,0.0581,0.0612,0.0078,0.0298,reg oper spec account,block of flats,0.0609,\"Stone, brick\",No,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n361664,0,Cash loans,F,N,Y,1,90000.0,97038.0,6880.5,81000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-16358,-3180,-5870.0,-1311,,1,1,0,1,1,0,Core staff,3.0,3,3,FRIDAY,7,0,0,0,0,0,0,Kindergarten,0.7921556789179686,0.13353174380825725,0.7675231046555077,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n124283,0,Cash loans,F,N,N,0,135000.0,225000.0,12564.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-24026,365243,-6930.0,-4397,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,0.8030597886579195,0.7153936862557085,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1199.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n224637,0,Cash loans,F,N,Y,1,112500.0,545040.0,26640.0,450000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13636,-2695,-928.0,-4047,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.5435198260943003,0.4288070038757747,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2580.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n396736,0,Cash loans,F,N,N,1,103500.0,536328.0,14746.5,351000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.010966,-13266,-315,-2516.0,-4091,,1,1,1,1,1,0,High skill tech staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Government,0.446034272466622,0.36153622836355903,0.3490552510751822,0.0722,0.0797,0.9816,0.7484,0.0077,0.0,0.1379,0.1667,0.2083,0.0,0.0588,0.0668,0.0,0.0,0.0735,0.0827,0.9816,0.7583,0.0078,0.0,0.1379,0.1667,0.2083,0.0,0.0643,0.0696,0.0,0.0,0.0729,0.0797,0.9816,0.7518,0.0077,0.0,0.1379,0.1667,0.2083,0.0,0.0599,0.068,0.0,0.0,reg oper account,block of flats,0.0568,Panel,No,1.0,0.0,1.0,0.0,-8.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n446434,0,Cash loans,M,Y,Y,0,112500.0,288873.0,19435.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-13443,-1014,-4845.0,-4300,17.0,1,1,0,1,0,0,Sales staff,2.0,3,3,WEDNESDAY,9,0,0,0,1,1,0,Self-employed,,0.56388140946307,0.36896873825284665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n234863,0,Cash loans,F,N,Y,0,76500.0,675000.0,20538.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.014519999999999996,-23648,365243,-2456.0,-6,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,6,0,0,0,0,0,0,XNA,0.7234349368382953,0.32527195705418105,0.7503751495159068,0.2392,0.1503,0.9791,0.7144,0.0399,0.16,0.1379,0.3333,0.375,0.1823,0.1942,0.221,0.0039,0.0012,0.2437,0.1559,0.9791,0.7256,0.0403,0.1611,0.1379,0.3333,0.375,0.1864,0.2121,0.2302,0.0039,0.0013,0.2415,0.1503,0.9791,0.7182,0.0402,0.16,0.1379,0.3333,0.375,0.1854,0.1975,0.2249,0.0039,0.0013,reg oper account,block of flats,0.1741,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-644.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n288011,0,Revolving loans,F,Y,Y,0,112500.0,202500.0,10125.0,202500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.015221,-16303,-290,-8993.0,-4436,7.0,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,13,0,0,0,0,1,1,Self-employed,0.6626407896172983,0.4872831606077812,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1566.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n419684,0,Cash loans,F,N,Y,1,202500.0,1066320.0,38430.0,900000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.035792000000000004,-13556,-861,-911.0,-4242,,1,1,1,1,0,0,,2.0,2,2,FRIDAY,23,0,0,0,0,0,0,Self-employed,0.5159173994263389,0.5993359660636699,0.4722533429586386,0.1526,0.0104,,0.9728,,0.0,0.1724,0.1667,,0.0619,0.1236,0.1139,0.0039,0.0763,0.1555,0.0108,,0.9739,,0.0,0.1724,0.1667,,0.0634,0.135,0.1186,0.0039,0.0807,0.1541,0.0104,,0.9732,,0.0,0.1724,0.1667,,0.063,0.1257,0.1159,0.0039,0.0779,org spec account,block of flats,0.1223,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-532.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n444999,0,Cash loans,F,Y,Y,2,90000.0,1078200.0,34780.5,900000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.008068,-15011,-7285,-7755.0,-4621,64.0,1,1,1,1,1,0,Core staff,4.0,3,3,TUESDAY,9,0,0,0,0,0,0,School,,0.4659249549439119,0.3962195720630885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-490.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n294516,0,Cash loans,F,N,N,0,130500.0,269550.0,13761.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.00733,-22137,-1510,-9487.0,-2978,,1,1,1,1,1,0,High skill tech staff,1.0,2,2,MONDAY,14,0,0,0,0,0,0,Transport: type 3,0.7776267591234323,0.6768872905469481,0.33125086459090186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2338.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n394545,0,Cash loans,M,Y,Y,2,36000.0,50940.0,5616.0,45000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.018209,-12474,-172,-4444.0,-4166,15.0,1,1,1,1,1,0,Security staff,4.0,3,3,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.5806549893074068,0.4561097392782771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n171701,0,Cash loans,M,Y,N,0,157500.0,286704.0,12145.5,247500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.010032,-8962,-819,-3302.0,-1580,17.0,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,8,0,0,0,0,0,0,Transport: type 2,0.2308097477664608,0.5671696756297933,,0.0124,0.0332,0.9866,,,,0.069,0.0417,,,0.0101,0.0119,0.0,0.0572,0.0126,0.0344,0.9866,,,,0.069,0.0417,,,0.011000000000000001,0.0124,0.0,0.0605,0.0125,0.0332,0.9866,,,,0.069,0.0417,,,0.0103,0.0122,0.0,0.0584,reg oper account,block of flats,0.0218,Panel,No,1.0,1.0,1.0,1.0,-685.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n129497,0,Cash loans,M,Y,Y,1,157500.0,675000.0,36747.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-10214,-305,-931.0,-2872,3.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.3683760080063134,0.4915672633169832,0.6212263380626669,0.2052,,0.9985,,,0.2,0.1724,0.375,,,,0.231,,0.0,0.209,,0.9985,,,0.2014,0.1724,0.375,,,,0.2406,,0.0,0.2071,,0.9985,,,0.2,0.1724,0.375,,,,0.2351,,0.0,,block of flats,0.1817,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n383739,0,Cash loans,M,Y,Y,0,90000.0,355536.0,18742.5,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.01885,-13358,-1170,-5521.0,-4822,28.0,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,18,0,0,0,1,1,0,Business Entity Type 2,,0.6773128022966716,0.5638350489514956,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-864.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n368911,0,Cash loans,F,Y,Y,1,202500.0,573408.0,24421.5,495000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.02461,-18766,-2448,-4019.0,-2298,10.0,1,1,0,1,1,1,Accountants,3.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Industry: type 12,0.833434109227805,0.6620776920431992,0.4902575124990026,,0.0506,0.9737,0.6396,0.0053,0.0,0.1034,0.1667,0.2083,0.042,0.0488,0.0485,,0.0051,,0.0525,0.9737,0.6537,0.0053,0.0,0.1034,0.1667,0.2083,0.0429,0.0533,0.0505,,0.0054,,0.0506,0.9737,0.6444,0.0053,0.0,0.1034,0.1667,0.2083,0.0427,0.0496,0.0493,,0.0052,reg oper account,block of flats,0.0421,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-776.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n325085,0,Cash loans,M,Y,N,0,675000.0,957901.5,36616.5,774000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.006233,-9399,-322,-1943.0,-2062,16.0,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,19,0,0,0,0,1,1,Business Entity Type 2,0.3840340141116301,0.6589394553549104,0.3606125659189888,0.0866,0.0946,0.9811,0.7416,0.0102,0.0,0.1379,0.1667,0.0417,0.0096,0.0706,0.0734,0.0,0.0633,0.0882,0.0982,0.9811,0.7517,0.0103,0.0,0.1379,0.1667,0.0417,0.0099,0.0771,0.0764,0.0,0.067,0.0874,0.0946,0.9811,0.7451,0.0103,0.0,0.1379,0.1667,0.0417,0.0098,0.0718,0.0747,0.0,0.0646,reg oper spec account,block of flats,0.077,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1727.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n388565,0,Cash loans,F,N,Y,0,117000.0,453514.5,23283.0,391500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.016612000000000002,-20748,365243,-10991.0,-3902,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,XNA,,0.5073925876299726,0.28961123838200553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1135.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n184468,0,Cash loans,M,N,Y,0,270000.0,675000.0,37692.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-19561,-4589,-7201.0,-3085,,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Self-employed,,0.5676312853238502,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n358666,0,Cash loans,M,N,Y,0,225000.0,518562.0,25078.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-16716,-4658,-8858.0,-227,,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.7048977663912922,0.2678689358444539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1222.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n413430,0,Cash loans,F,Y,N,0,110250.0,1026000.0,38151.0,1026000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.028663,-20526,-8701,-12716.0,-4089,14.0,1,1,1,1,0,0,Core staff,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Postal,,0.6769303331533043,0.5620604831738043,0.0227,0.0021,0.9816,0.7484,0.0041,0.0,0.1034,0.0417,0.0833,0.0524,0.0185,0.0186,0.0,0.0,0.0231,0.0022,0.9816,0.7583,0.0042,0.0,0.1034,0.0417,0.0833,0.0536,0.0202,0.0193,0.0,0.0,0.0229,0.0021,0.9816,0.7518,0.0042,0.0,0.1034,0.0417,0.0833,0.0533,0.0188,0.0189,0.0,0.0,reg oper account,block of flats,0.0146,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n421214,0,Cash loans,F,N,Y,0,67500.0,258709.5,14575.5,234000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.010147,-19903,365243,-7888.0,-2457,,1,0,0,1,0,0,,1.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.4476412695304625,0.7352209993926424,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n449304,0,Cash loans,M,Y,N,1,198000.0,1381113.0,36562.5,1206000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.005084,-14545,-466,-1591.0,-374,0.0,1,1,0,1,0,0,,3.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.51721317317476,0.7847163229342575,0.3825018041447388,0.1113,0.0673,0.9871,,,0.12,0.1034,0.3333,,,,0.1325,,0.102,0.1134,0.0699,0.9871,,,0.1208,0.1034,0.3333,,,,0.1381,,0.1079,0.1124,0.0673,0.9871,,,0.12,0.1034,0.3333,,,,0.1349,,0.1041,,block of flats,0.1264,Panel,No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n308530,0,Cash loans,F,N,Y,0,162000.0,297000.0,18981.0,297000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.026392,-23154,365243,-5413.0,-4868,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.7150507301289251,0.6785676886853644,0.0825,,0.9757,,,0.0,0.0345,0.1667,,,,0.0721,,0.0,0.084,,0.9757,,,0.0,0.0345,0.1667,,,,0.0751,,0.0,0.0833,,0.9757,,,0.0,0.0345,0.1667,,,,0.0734,,0.0,,block of flats,0.0567,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n350520,0,Cash loans,F,N,Y,0,261000.0,991818.0,42151.5,886500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.04622,-20502,-13549,-14533.0,-4060,,1,1,0,1,0,0,Cleaning staff,1.0,1,1,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 1,,0.5064251321629735,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1293.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n201401,0,Revolving loans,F,N,Y,0,135000.0,202500.0,10125.0,202500.0,Unaccompanied,Pensioner,Lower secondary,Married,House / apartment,0.018209,-21109,365243,-12875.0,-3382,,1,0,0,1,1,0,,2.0,3,3,SATURDAY,7,0,0,0,0,0,0,XNA,,0.09397834518403636,0.7121551551910698,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-773.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n244239,1,Cash loans,M,N,Y,0,180000.0,675000.0,53460.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.072508,-14851,-770,-761.0,-5969,,1,1,0,1,0,0,Laborers,2.0,1,1,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.4834546671080491,0.6761263774772632,,0.2959,0.1429,0.9762,0.6736,0.0,0.32,0.2759,0.3333,0.375,,0.2412,0.08900000000000001,0.0,0.0,0.3015,0.1483,0.9762,0.6864,0.0,0.3222,0.2759,0.3333,0.375,,0.2635,0.0928,0.0,0.0,0.2987,0.1429,0.9762,0.6779999999999999,0.0,0.32,0.2759,0.3333,0.375,,0.2454,0.0906,0.0,0.0,reg oper account,block of flats,0.07,Panel,No,0.0,0.0,0.0,0.0,-1081.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n225269,0,Cash loans,F,N,N,0,121500.0,278460.0,20947.5,225000.0,Other_B,Pensioner,Secondary / secondary special,Married,House / apartment,0.00496,-21254,365243,-13406.0,-4273,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.6418573315884261,0.3360615207658242,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.0,0.0,13.0,0.0,-1441.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n288612,0,Cash loans,M,Y,Y,0,180000.0,1113840.0,57001.5,900000.0,Other_B,Working,Secondary / secondary special,Married,House / apartment,0.011657,-21682,-2886,-4649.0,-4686,12.0,1,1,0,1,0,0,,2.0,1,1,TUESDAY,11,0,1,1,0,0,0,Industry: type 9,,0.5484446667251984,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2340.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n202313,0,Cash loans,M,N,N,0,157500.0,423000.0,22032.0,423000.0,Family,Working,Secondary / secondary special,Civil marriage,With parents,0.030755,-10093,-395,-4143.0,-2775,,1,1,0,1,1,1,Sales staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Trade: type 2,0.4306970179370129,0.5372388950110457,0.6925590674998008,0.0196,0.015,0.9672,0.5512,0.0037,0.0,0.069,0.125,,0.0095,,0.0211,,0.0162,0.02,0.0156,0.9672,0.5688,0.0038,0.0,0.069,0.125,,0.0097,,0.022000000000000002,,0.0171,0.0198,0.015,0.9672,0.5572,0.0037,0.0,0.069,0.125,,0.0096,,0.0215,,0.0165,reg oper spec account,block of flats,0.031,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-356.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n304209,1,Cash loans,F,N,Y,0,135000.0,675000.0,26284.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-10968,-1285,-2788.0,-1947,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Cleaning,,0.6572123736455789,0.5602843280409464,0.2577,0.0,0.9841,0.7824,0.0603,0.28,0.2414,0.3333,0.375,0.1182,0.2093,0.2747,0.0039,0.0054,0.2626,0.0,0.9841,0.7909,0.0608,0.282,0.2414,0.3333,0.375,0.1209,0.2287,0.2862,0.0039,0.0057,0.2602,0.0,0.9841,0.7853,0.0606,0.28,0.2414,0.3333,0.375,0.1203,0.2129,0.2797,0.0039,0.0055,org spec account,block of flats,0.2502,Panel,No,0.0,0.0,0.0,0.0,-1334.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n220029,0,Cash loans,F,N,Y,0,90000.0,270000.0,26433.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-12471,-5856,-1233.0,-3746,,1,1,0,1,1,0,Laborers,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 2,0.5140141935978086,0.6976923886538432,0.5620604831738043,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2972.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n153663,0,Cash loans,M,N,Y,0,112500.0,203985.0,22099.5,184500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.035792000000000004,-10522,-1924,-2360.0,-3103,,1,1,0,1,0,1,,1.0,2,2,THURSDAY,16,0,0,0,0,1,1,Business Entity Type 3,0.19200049723304785,0.5899750502233863,0.326475210066026,0.128,0.1044,0.9841,0.7892,0.1039,0.05,0.2483,0.2,0.25,0.0803,0.095,0.1264,0.0087,0.0209,0.084,0.0329,0.9836,0.7844,0.0502,0.0,0.2069,0.1667,0.2083,0.0427,0.0735,0.086,0.0,0.0,0.1135,0.0862,0.9841,0.792,0.094,0.0,0.2069,0.1667,0.2083,0.0553,0.0821,0.1266,0.0078,0.0087,reg oper spec account,block of flats,0.1888,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-428.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n228196,0,Revolving loans,F,N,N,0,81000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Lower secondary,Separated,House / apartment,0.010966,-14330,-591,-8292.0,-4635,,1,1,0,1,0,0,Sales staff,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,Trade: type 7,,0.5796749384493823,,0.0722,0.0688,0.9861,0.8096,0.0094,0.0,0.1379,0.1667,,0.059000000000000004,,0.0658,,0.0,0.0735,0.0714,0.9861,0.8171,0.0094,0.0,0.1379,0.1667,,0.0603,,0.0685,,0.0,0.0729,0.0688,0.9861,0.8121,0.0094,0.0,0.1379,0.1667,,0.06,,0.067,,0.0,org spec account,block of flats,0.0517,Panel,No,0.0,0.0,0.0,0.0,-294.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n365373,0,Revolving loans,F,N,Y,1,90000.0,270000.0,13500.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-9971,-1290,-2543.0,-2018,,1,1,1,1,1,0,Core staff,3.0,2,2,THURSDAY,11,0,0,0,1,1,0,Kindergarten,0.533857086014327,0.6290472124276889,0.6363761710860439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n329312,0,Cash loans,M,Y,N,0,157500.0,184500.0,13500.0,184500.0,Unaccompanied,Working,Lower secondary,Single / not married,With parents,0.018029,-14587,-1314,-1015.0,-1015,14.0,1,1,0,1,0,0,Drivers,1.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Transport: type 3,0.5221313259383927,0.4527399324840894,0.4956658291397297,0.0711,0.0595,0.9796,0.7212,0.0079,0.0,0.1379,0.1667,0.2083,0.0551,0.057999999999999996,0.0658,0.0,0.0,0.0725,0.0618,0.9796,0.7321,0.0079,0.0,0.1379,0.1667,0.2083,0.0564,0.0634,0.0686,0.0,0.0,0.0718,0.0595,0.9796,0.7249,0.0079,0.0,0.1379,0.1667,0.2083,0.0561,0.059000000000000004,0.067,0.0,0.0,reg oper account,block of flats,0.0561,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1411.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n131198,0,Cash loans,F,N,Y,0,90000.0,900000.0,32017.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-15564,-1508,-8462.0,-4326,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.7215153808083297,0.7610263695502636,,,0.9801,,,,,0.1667,,,,0.114,,0.001,,,0.9801,,,,,0.1667,,,,0.1188,,0.0011,,,0.9801,,,,,0.1667,,,,0.1161,,0.001,,block of flats,0.1208,,No,0.0,0.0,0.0,0.0,-2372.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n433037,0,Cash loans,F,N,N,0,157500.0,959598.0,28188.0,801000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-14612,-4104,-7443.0,-4291,,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,18,0,0,0,0,1,1,Business Entity Type 3,0.5614195819905254,0.5425568553519121,0.44539624156083396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-116.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n298656,0,Cash loans,F,N,N,0,112500.0,472500.0,44860.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.003818,-21823,365243,-14001.0,-4562,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.7410606036948293,,0.0371,0.0,0.9742,0.6464,0.0,0.0,0.1034,0.125,0.1667,0.0021,0.0303,0.03,0.0,0.0,0.0378,0.0,0.9742,0.6602,0.0,0.0,0.1034,0.125,0.1667,0.0022,0.0331,0.0312,0.0,0.0,0.0375,0.0,0.9742,0.6511,0.0,0.0,0.1034,0.125,0.1667,0.0022,0.0308,0.0305,0.0,0.0,reg oper account,block of flats,0.0236,Block,No,0.0,0.0,0.0,0.0,-2100.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n271949,0,Cash loans,F,N,Y,0,315000.0,1350000.0,37255.5,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.006670999999999999,-18521,-3416,-495.0,-2077,,1,1,0,1,0,0,Managers,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Self-employed,0.6868735939757837,0.757689199672516,0.8128226070575616,0.0825,0.0787,0.9841,0.7824,0.0342,0.0,0.2069,0.1667,,0.0785,0.0672,0.0776,0.0,0.0,0.084,0.0817,0.9841,0.7909,0.0345,0.0,0.2069,0.1667,,0.0803,0.0735,0.0808,0.0,0.0,0.0833,0.0787,0.9841,0.7853,0.0344,0.0,0.2069,0.1667,,0.0798,0.0684,0.079,0.0,0.0,reg oper account,block of flats,0.061,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-453.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n333143,0,Cash loans,M,Y,Y,0,126000.0,922500.0,35874.0,922500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-23128,-7057,-1152.0,-4512,6.0,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Self-employed,,0.6009260013347042,0.3893387918468769,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1067.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n173777,0,Cash loans,M,N,N,1,135000.0,723996.0,37017.0,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-10188,-1026,-1931.0,-2387,,1,1,1,1,0,0,Drivers,3.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.2699466174473558,0.6428168887188146,0.41534714488434,0.132,0.0642,0.997,0.9592,,0.08,0.0345,0.625,0.6667,,0.1076,0.1489,0.0,0.1333,0.1345,0.0666,0.997,0.9608,,0.0806,0.0345,0.625,0.6667,,0.1175,0.1552,0.0,0.1411,0.1332,0.0642,0.997,0.9597,,0.08,0.0345,0.625,0.6667,,0.1095,0.1516,0.0,0.1361,reg oper account,block of flats,0.1461,Block,No,0.0,0.0,0.0,0.0,-2611.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n365921,0,Revolving loans,F,N,Y,2,157500.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.04622,-10220,-1450,-4069.0,-2247,,1,1,0,1,0,0,Cleaning staff,4.0,1,1,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.36682753006238616,0.5638350489514956,0.2289,0.0926,0.9896,0.8572,,0.32,0.1379,0.5417,0.5833,0.1052,0.1866,0.1986,0.0,0.0,0.2332,0.096,0.9896,0.8628,,0.3222,0.1379,0.5417,0.5833,0.1076,0.2039,0.207,0.0,0.0,0.2311,0.0926,0.9896,0.8591,,0.32,0.1379,0.5417,0.5833,0.107,0.1898,0.2022,0.0,0.0,reg oper account,block of flats,0.1876,Panel,No,1.0,0.0,1.0,0.0,-621.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n111333,0,Cash loans,M,Y,N,0,157500.0,305221.5,20002.5,252000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008575,-11985,-243,-6863.0,-3983,3.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Government,,0.3121634126950472,0.6785676886853644,0.0515,0.0375,0.9871,,,0.04,0.0517,0.2708,,0.0076,,0.0508,,0.0028,0.021,0.018000000000000002,0.9841,,,0.0,0.0345,0.1667,,0.0,,0.0189,,0.0,0.052000000000000005,0.0375,0.9871,,,0.04,0.0517,0.2708,,0.0077,,0.0517,,0.0029,,block of flats,0.0657,Panel,No,2.0,0.0,2.0,0.0,-265.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n275344,0,Cash loans,F,N,Y,0,135000.0,675000.0,26284.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.026392,-19402,-10194,-6401.0,-2893,,1,1,0,1,0,0,Cleaning staff,1.0,2,2,FRIDAY,17,0,0,0,0,0,0,Industry: type 9,,0.7011678200111248,0.4311917977993083,0.1031,0.086,0.9786,,,0.0,0.2069,0.1667,,0.0695,,0.0912,,0.0,0.105,0.0892,0.9786,,,0.0,0.2069,0.1667,,0.0711,,0.095,,0.0,0.1041,0.086,0.9786,,,0.0,0.2069,0.1667,,0.0708,,0.0928,,0.0,,block of flats,0.0717,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-656.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,7.0,0.0,3.0\n410726,0,Cash loans,F,N,Y,1,90000.0,188460.0,10350.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.007273999999999998,-11186,-1385,-1029.0,-2307,,1,1,1,1,0,0,Cooking staff,3.0,2,2,MONDAY,10,0,0,0,0,0,0,School,,0.2805663032211406,,0.0773,0.0577,0.9876,0.83,0.0162,0.0,0.1724,0.1667,0.2083,0.0209,0.063,0.069,0.0,0.1512,0.0788,0.0598,0.9876,0.8367,0.0164,0.0,0.1724,0.1667,0.2083,0.0214,0.0689,0.0719,0.0,0.16,0.0781,0.0577,0.9876,0.8323,0.0163,0.0,0.1724,0.1667,0.2083,0.0212,0.0641,0.0703,0.0,0.1543,not specified,block of flats,0.096,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-235.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n446067,0,Cash loans,F,N,N,0,126000.0,1350000.0,39604.5,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-18660,-4081,-4637.0,-2187,,1,1,0,1,1,0,Managers,2.0,2,2,MONDAY,17,0,0,0,0,0,0,School,0.8242184052459447,0.7957306489441891,,0.0,,0.0,,,0.0,,,,,,,,,0.0,,0.0005,,,0.0,,,,,,,,,0.0,,0.0,,,0.0,,,,,,,,,,block of flats,0.0,,No,0.0,0.0,0.0,0.0,-1514.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n205394,0,Cash loans,F,N,Y,0,270000.0,612000.0,23845.5,612000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.010556,-12985,-689,-7064.0,-3504,,1,1,0,1,0,0,,1.0,3,3,TUESDAY,19,0,0,0,0,1,1,Business Entity Type 3,0.4646508080982569,0.6498298905155758,0.2910973802776635,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-420.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,3.0\n300973,1,Cash loans,F,N,N,0,103500.0,640080.0,29970.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-19187,-480,-578.0,-2728,,1,1,0,1,0,0,Security staff,2.0,3,2,SUNDAY,7,0,0,0,1,1,0,Industry: type 1,,0.15813312924984754,0.31703177858344445,0.0082,0.0,0.9613,,,,0.069,0.0417,,0.0,,0.0092,,0.0,0.0084,0.0,0.9613,,,,0.069,0.0417,,0.0,,0.0096,,0.0,0.0083,0.0,0.9613,,,,0.069,0.0417,,0.0,,0.0094,,0.0,,block of flats,0.0072,Wooden,No,0.0,0.0,0.0,0.0,-341.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n380451,0,Cash loans,F,N,Y,0,92250.0,135000.0,13279.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.011657,-22695,365243,-4122.0,-4248,,1,0,0,1,1,0,,2.0,1,1,MONDAY,17,0,0,0,0,0,0,XNA,,0.6767055220632967,0.6940926425266661,0.133,0.1527,0.9801,0.728,0.1739,0.0,0.2759,0.1667,0.2083,0.08800000000000001,0.1076,0.1204,0.0039,0.0047,0.1355,0.1585,0.9801,0.7387,0.1755,0.0,0.2759,0.1667,0.2083,0.09,0.1175,0.1254,0.0039,0.0049,0.1343,0.1527,0.9801,0.7316,0.175,0.0,0.2759,0.1667,0.2083,0.0895,0.1095,0.1226,0.0039,0.0048,reg oper account,block of flats,0.0951,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2210.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n226613,0,Revolving loans,F,N,Y,0,126000.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018634,-21904,-5410,-8869.0,-4298,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,Self-employed,,0.5064142001742044,0.475849908720221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,2.0,1.0,2.0,1.0,-1360.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n381052,0,Cash loans,M,Y,N,3,360000.0,1006920.0,51543.0,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.003540999999999999,-11256,-648,-1139.0,-3665,11.0,1,1,0,1,0,0,Laborers,5.0,1,1,WEDNESDAY,11,0,0,0,0,0,0,Transport: type 4,0.20500700410230085,0.4738502685116691,0.20915469884100693,0.0124,,0.9826,,,,,0.0417,,,,0.0147,,0.0065,0.0126,,0.9826,,,,,0.0417,,,,0.0153,,0.0069,0.0125,,0.9826,,,,,0.0417,,,,0.015,,0.0066,,block of flats,0.013000000000000001,,Yes,1.0,0.0,1.0,0.0,-320.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n133341,0,Cash loans,M,Y,Y,0,157500.0,518562.0,25078.5,463500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-22360,365243,-843.0,-4200,4.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.6274282049690126,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-803.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n445749,0,Cash loans,F,N,Y,0,81000.0,254700.0,17149.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.00702,-23853,365243,-6720.0,-4666,,1,0,0,1,1,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.655130417751193,0.3201633668633456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-20.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n376728,0,Cash loans,F,N,N,0,135000.0,1256400.0,34681.5,900000.0,,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-11898,-238,-5532.0,-1065,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.37133025467685177,0.6408815382271107,0.190705947811054,0.0718,0.0236,0.9776,0.6940000000000001,0.01,0.0,0.1607,0.1667,0.2083,0.20800000000000002,0.0583,0.0603,0.0013,0.003,0.0641,0.0,0.9777,0.706,0.0086,0.0,0.1379,0.1667,0.2083,0.0,0.0551,0.0532,0.0,0.0,0.0729,0.0,0.9776,0.6981,0.0089,0.0,0.1379,0.1667,0.2083,0.2553,0.0599,0.0525,0.0,0.0,reg oper account,block of flats,0.0448,Panel,No,1.0,0.0,1.0,0.0,-1497.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n337267,0,Cash loans,F,N,N,0,180000.0,239850.0,25447.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Rented apartment,0.032561,-8427,-1789,-2944.0,-1113,,1,1,1,1,1,0,Sales staff,1.0,1,1,MONDAY,13,0,1,1,0,1,1,Business Entity Type 3,0.3543827848454486,0.5339862879667728,,0.0928,0.0916,0.9737,,,,0.2069,0.1667,,,,0.0515,,,0.0945,0.0951,0.9737,,,,0.2069,0.1667,,,,0.0537,,,0.0937,0.0916,0.9737,,,,0.2069,0.1667,,,,0.0525,,,,block of flats,0.0646,,No,1.0,0.0,1.0,0.0,-1216.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n165599,0,Cash loans,F,N,Y,0,202500.0,894766.5,29700.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-16879,-5580,-10869.0,-436,,1,1,0,1,1,0,Security staff,2.0,1,1,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4494370506711652,0.6296742509538716,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1988.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n432395,0,Cash loans,F,N,Y,0,135000.0,521280.0,36409.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003122,-19606,-9769,-1988.0,-3147,,1,1,0,1,1,0,Accountants,2.0,3,3,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 2,,0.6302506527442796,0.3876253444214701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-813.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n416627,0,Cash loans,F,N,N,1,157500.0,976711.5,38866.5,873000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.00496,-15307,-8691,-9157.0,-4432,,1,1,0,1,0,0,,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Medicine,,0.4986570304477223,0.5064842396679806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-1076.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n344928,0,Cash loans,F,N,Y,0,180000.0,101880.0,12217.5,90000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.031329,-8252,-891,-4574.0,-937,,1,1,0,1,0,0,Accountants,1.0,2,2,THURSDAY,14,0,0,0,0,1,1,Construction,,0.486245178693936,,0.1031,0.1026,0.9796,0.7212,0.0119,0.0,0.2069,0.1667,0.2083,0.0847,0.0832,0.0876,0.0039,0.0128,0.105,0.1065,0.9796,0.7321,0.012,0.0,0.2069,0.1667,0.2083,0.0867,0.0909,0.0913,0.0039,0.0136,0.1041,0.1026,0.9796,0.7249,0.012,0.0,0.2069,0.1667,0.2083,0.0862,0.0847,0.0892,0.0039,0.0131,reg oper account,block of flats,0.0717,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-761.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n174564,0,Cash loans,F,N,Y,0,121500.0,270000.0,10179.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010276,-14205,-5953,-1096.0,-1135,,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 2,,0.5377498933942525,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-311.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n192260,1,Cash loans,M,Y,Y,0,157500.0,608076.0,23053.5,427500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.016612000000000002,-22553,-5809,-6862.0,-4452,16.0,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Other,,0.2531235041911428,0.1895952597360396,0.0691,0.1597,0.9901,0.8640000000000001,0.0281,0.0,0.2069,0.1667,0.0417,0.1025,0.0538,0.0621,0.0116,0.0414,0.0704,0.1658,0.9901,0.8693,0.0284,0.0,0.2069,0.1667,0.0417,0.1049,0.0588,0.0647,0.0117,0.0438,0.0697,0.1597,0.9901,0.8658,0.0283,0.0,0.2069,0.1667,0.0417,0.1043,0.0547,0.0633,0.0116,0.0423,reg oper account,block of flats,0.0733,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-755.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n179831,0,Cash loans,F,N,Y,2,157500.0,509400.0,31293.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.006852,-16757,-1232,-3180.0,-311,,1,1,0,1,0,0,Sales staff,3.0,3,3,THURSDAY,7,0,0,0,0,0,0,Self-employed,,0.12006412355261036,0.2955826421513093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-2019.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n139846,0,Cash loans,F,N,N,2,157500.0,983299.5,41791.5,904500.0,Family,Working,Higher education,Married,House / apartment,0.025164,-11432,-3488,-11388.0,-1556,,1,1,1,1,0,0,High skill tech staff,4.0,2,2,FRIDAY,13,0,0,0,0,0,0,Business Entity Type 2,0.5908346241462948,0.6707450637504054,0.6817058776720116,0.0268,0.0,0.9608,0.4696,0.0039,0.0,0.1034,0.125,0.1667,0.046,0.0219,0.0334,0.0,0.0,0.0273,0.0,0.9608,0.4904,0.004,0.0,0.1034,0.125,0.1667,0.0471,0.0239,0.0347,0.0,0.0,0.0271,0.0,0.9608,0.4767,0.0039,0.0,0.1034,0.125,0.1667,0.0468,0.0222,0.034,0.0,0.0,reg oper account,block of flats,0.0262,Block,No,0.0,0.0,0.0,0.0,-832.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n397274,0,Cash loans,F,N,Y,0,135000.0,1125000.0,32895.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-14508,-856,-5714.0,-4482,,1,1,0,1,0,0,Accountants,2.0,2,2,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6582560126784962,0.8128226070575616,0.1237,0.053,0.9856,0.8028,0.016,0.0,0.2759,0.1667,0.2083,0.1311,0.1009,0.1218,0.0,0.0,0.1261,0.055,0.9856,0.8105,0.0161,0.0,0.2759,0.1667,0.2083,0.1341,0.1102,0.1269,0.0,0.0,0.1249,0.053,0.9856,0.8054,0.0161,0.0,0.2759,0.1667,0.2083,0.1334,0.1026,0.124,0.0,0.0,reg oper spec account,block of flats,0.1046,Panel,No,6.0,2.0,6.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n317041,0,Cash loans,F,N,Y,0,157500.0,427045.5,21933.0,319500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-10239,-832,-10239.0,-2798,,1,1,0,1,0,1,High skill tech staff,2.0,2,2,MONDAY,17,0,0,0,0,1,1,Industry: type 11,0.2636235120496328,0.3292099718750139,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-471.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n251154,0,Cash loans,M,Y,Y,1,90000.0,545040.0,26509.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.028663,-10397,-1213,-4446.0,-3033,8.0,1,1,1,1,1,0,Sales staff,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Self-employed,,0.3677625798305245,0.18088797776707397,0.1031,0.1137,0.9776,0.6940000000000001,0.0098,0.0,0.2069,0.1667,0.2083,0.0605,0.0841,0.0892,0.0,0.0,0.105,0.11800000000000001,0.9777,0.706,0.0098,0.0,0.2069,0.1667,0.2083,0.0618,0.0918,0.09300000000000001,0.0,0.0,0.1041,0.1137,0.9776,0.6981,0.0098,0.0,0.2069,0.1667,0.2083,0.0615,0.0855,0.0908,0.0,0.0,reg oper account,block of flats,0.0755,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-310.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n304447,0,Cash loans,F,N,Y,0,171000.0,1113840.0,47322.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020713,-15526,-4316,-7584.0,-4656,,1,1,0,1,1,0,High skill tech staff,2.0,3,2,SATURDAY,9,0,0,0,0,0,0,Industry: type 11,,0.6476522814275232,0.5849900404894085,0.0825,0.0795,0.9747,0.6532,0.009000000000000001,0.0,0.1379,0.1667,0.2083,0.109,0.0672,0.0696,0.0,0.0,0.084,0.0825,0.9747,0.6668,0.0091,0.0,0.1379,0.1667,0.2083,0.1115,0.0735,0.0725,0.0,0.0,0.0833,0.0795,0.9747,0.6578,0.0091,0.0,0.1379,0.1667,0.2083,0.1109,0.0684,0.0708,0.0,0.0,reg oper account,block of flats,0.0596,Panel,No,0.0,0.0,0.0,0.0,-18.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n194385,1,Cash loans,F,Y,Y,0,157500.0,1128442.5,60120.0,1066500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.006852,-23205,365243,-6707.0,-4327,12.0,1,0,0,1,1,0,,1.0,3,3,TUESDAY,8,0,0,0,0,0,0,XNA,,0.2731936266061187,,0.0928,0.1023,0.9821,0.7552,0.0833,0.0,0.2069,0.1667,0.2083,0.0994,0.0756,0.0959,0.0,0.0,0.0945,0.1062,0.9821,0.7648,0.0841,0.0,0.2069,0.1667,0.2083,0.1017,0.0826,0.0999,0.0,0.0,0.0937,0.1023,0.9821,0.7585,0.0838,0.0,0.2069,0.1667,0.2083,0.1011,0.077,0.0976,0.0,0.0,not specified,block of flats,0.121,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-192.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n333986,0,Cash loans,F,N,Y,1,108000.0,71316.0,8590.5,63000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-10448,-1148,-3499.0,-2941,,1,1,0,1,1,0,Sales staff,3.0,3,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,0.2916237272915879,0.4736103891020086,,0.3186,0.2188,0.9891,0.8504,0.139,0.4,0.3448,0.375,0.4167,0.1833,0.258,0.3986,0.0077,0.0084,0.3246,0.22699999999999998,0.9891,0.8563,0.1403,0.4028,0.3448,0.375,0.4167,0.1875,0.2819,0.4153,0.0078,0.0089,0.3216,0.2188,0.9891,0.8524,0.1399,0.4,0.3448,0.375,0.4167,0.1865,0.2625,0.4058,0.0078,0.0086,reg oper account,block of flats,0.3913,Panel,No,5.0,1.0,5.0,0.0,-989.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n373079,0,Cash loans,M,N,Y,0,157500.0,254700.0,27558.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.0105,-9593,-281,-4389.0,-2244,,1,1,0,1,0,0,Cooking staff,2.0,3,3,MONDAY,11,0,0,0,0,1,1,Agriculture,,0.3575531795935343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-158.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n154432,0,Cash loans,F,Y,Y,0,202500.0,254700.0,24939.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-24788,365243,-15985.0,-4115,64.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.2631435910213423,0.8327850252992314,0.0742,,0.9747,,,0.0,0.1379,0.1667,,,,0.0635,,0.0,0.0756,,0.9747,,,0.0,0.1379,0.1667,,,,0.0661,,0.0,0.0749,,0.9747,,,0.0,0.1379,0.1667,,,,0.0646,,0.0,,block of flats,0.0499,Others,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n231581,0,Cash loans,F,N,Y,0,135000.0,573628.5,24435.0,463500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.003122,-19221,-6812,-8322.0,-2776,,1,1,0,1,1,0,High skill tech staff,2.0,3,3,TUESDAY,12,0,0,0,0,0,0,University,0.6147368922781912,0.5953912921893902,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1339.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n137003,0,Revolving loans,M,Y,Y,0,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.014519999999999996,-11938,-695,-5827.0,-4447,11.0,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6895416789003789,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-394.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n413236,0,Cash loans,M,N,N,1,135000.0,450000.0,27324.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-13174,-2956,-4752.0,-1742,,1,1,0,1,1,0,Laborers,3.0,3,3,TUESDAY,11,0,0,0,0,0,0,Industry: type 9,0.3182220470863743,0.5822994012366104,0.6706517530862718,0.0619,0.0635,0.9767,0.6804,0.0088,0.0,0.1379,0.1667,0.2083,0.0114,0.0504,0.0538,0.0,0.0,0.063,0.0659,0.9767,0.6929,0.0089,0.0,0.1379,0.1667,0.2083,0.0116,0.0551,0.055999999999999994,0.0,0.0,0.0625,0.0635,0.9767,0.6847,0.0089,0.0,0.1379,0.1667,0.2083,0.0116,0.0513,0.0547,0.0,0.0,,block of flats,0.0423,Panel,No,2.0,2.0,2.0,2.0,-1850.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n320745,0,Cash loans,F,N,Y,0,130500.0,284400.0,15880.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008575,-21709,365243,-3896.0,-4169,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.6837119919233701,0.622922000268356,0.0866,0.0699,0.9846,0.7824,0.075,0.06,0.1207,0.25,0.375,0.07400000000000001,0.0908,0.0759,0.0,0.0,0.063,0.0605,0.9846,0.7909,0.0757,0.0,0.1034,0.1667,0.375,0.0757,0.0992,0.0651,0.0,0.0,0.0874,0.0699,0.9846,0.7853,0.0755,0.06,0.1207,0.25,0.375,0.0753,0.0923,0.0644,0.0,0.0,reg oper account,block of flats,0.0492,Panel,No,0.0,0.0,0.0,0.0,-509.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n384771,0,Cash loans,F,N,N,0,675000.0,675000.0,32472.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-18307,-3893,-4338.0,-1863,,1,1,1,1,1,0,Managers,2.0,1,1,TUESDAY,14,0,1,1,0,1,1,Construction,0.7629484672788479,0.7231425747888868,,0.0124,,0.9702,0.5920000000000001,0.0192,0.0,0.069,0.0417,0.0833,0.0565,0.0101,0.0171,0.0,0.0484,0.0126,,0.9702,0.608,0.0194,0.0,0.069,0.0417,0.0833,0.0578,0.011000000000000001,0.0178,0.0,0.0512,0.0125,,0.9702,0.5975,0.0194,0.0,0.069,0.0417,0.0833,0.0574,0.0103,0.0174,0.0,0.0494,reg oper account,block of flats,0.024,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3760.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n362436,0,Cash loans,M,Y,Y,0,157500.0,531706.5,19228.5,459000.0,\"Spouse, partner\",Working,Secondary / secondary special,Single / not married,House / apartment,0.01885,-18067,-1720,-10251.0,-1628,25.0,1,1,0,1,0,0,,1.0,2,2,TUESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.5180457312376121,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-576.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n451227,0,Cash loans,F,Y,N,2,225000.0,971280.0,51745.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-11609,-4799,-5809.0,-3952,12.0,1,1,1,1,1,0,Accountants,4.0,2,2,SATURDAY,13,0,0,0,0,0,0,Self-employed,0.5270782425615665,0.5495167240068153,0.15193454904964762,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1684.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n285840,0,Cash loans,M,N,Y,1,144000.0,1057266.0,59166.0,945000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-18588,-7878,-11260.0,-2137,,1,1,0,1,0,1,Laborers,3.0,1,1,SATURDAY,17,0,1,1,0,0,0,Business Entity Type 2,0.2917113859384824,0.7370458513816548,0.633031641417419,0.0716,0.0976,0.9752,0.6668,0.0104,0.04,0.1283,0.1667,0.2083,,0.0462,0.0708,0.0039,0.0475,0.084,0.0866,0.9757,0.6798,0.0105,0.0,0.1379,0.1667,0.2083,,0.0505,0.0553,0.0039,0.0502,0.0833,0.0976,0.9757,0.6713,0.0105,0.04,0.1379,0.1667,0.2083,,0.047,0.0721,0.0039,0.0485,reg oper account,block of flats,0.0289,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1881.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n104271,0,Cash loans,F,N,N,0,90000.0,310500.0,15903.0,310500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018209,-21752,365243,-126.0,-133,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,13,0,0,0,0,0,0,XNA,,0.6356307497289548,,0.1289,0.1182,0.9717,0.6124,0.0,0.0,0.069,0.125,0.1667,0.1469,0.1051,0.0526,0.0,0.0,0.1313,0.1226,0.9717,0.6276,0.0,0.0,0.069,0.125,0.1667,0.1502,0.1148,0.0548,0.0,0.0,0.1301,0.1182,0.9717,0.6176,0.0,0.0,0.069,0.125,0.1667,0.1494,0.1069,0.0535,0.0,0.0,reg oper account,specific housing,0.0414,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-84.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n451631,0,Cash loans,F,N,Y,0,171000.0,364896.0,18760.5,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.022625,-22760,365243,-3943.0,-3746,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,,0.18799566116593527,0.16048893062734468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1174.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n416778,0,Cash loans,M,Y,Y,0,270000.0,702000.0,20655.0,702000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018634,-13249,-2410,-74.0,-4069,1.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,18,0,0,0,0,1,1,Business Entity Type 3,,0.6151163527873436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1041.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n404959,0,Cash loans,F,Y,Y,1,675000.0,355320.0,37831.5,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-13834,-1309,-224.0,-1590,4.0,1,1,0,1,0,0,Managers,3.0,2,2,SATURDAY,14,0,0,0,0,0,0,Self-employed,,0.3411523977071755,0.2340151665320674,0.2021,0.1073,0.9881,0.8368,,0.2,0.1724,0.3333,,0.1028,,0.2153,,0.0043,0.2059,0.1113,0.9881,0.8432,,0.2014,0.1724,0.3333,,0.1051,,0.2244,,0.0045,0.204,0.1073,0.9881,0.8390000000000001,,0.2,0.1724,0.3333,,0.1046,,0.2192,,0.0044,,block of flats,0.196,Panel,No,0.0,0.0,0.0,0.0,-939.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n293312,0,Cash loans,F,Y,Y,2,184500.0,894766.5,29700.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-16808,-3141,-1839.0,-341,6.0,1,1,1,1,1,0,Laborers,4.0,1,1,MONDAY,14,0,0,0,0,0,0,Industry: type 3,,0.6514652214871497,0.3233112448967859,0.3361,0.0937,0.9955,0.9388,0.2119,0.54,0.1552,0.875,0.7917,0.0,0.2732,0.4432,0.0039,0.341,0.2521,0.0194,0.995,0.9347,0.1569,0.4834,0.1379,0.875,0.7917,0.0,0.2195,0.3939,0.0039,0.0039,0.3393,0.0937,0.9955,0.9396,0.2133,0.54,0.1552,0.875,0.7917,0.0,0.2779,0.4511,0.0039,0.3482,reg oper account,block of flats,0.4006,Monolithic,No,0.0,0.0,0.0,0.0,-892.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n132619,0,Cash loans,M,N,Y,1,180000.0,265851.0,10530.0,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.00963,-10902,-359,-790.0,-3263,,1,1,0,1,1,0,Drivers,3.0,2,2,THURSDAY,10,0,0,0,0,1,1,Other,0.1346939246497156,0.5037302371203811,0.5298898341969072,0.0639,,0.9801,,,0.0,0.1379,0.1667,0.0417,0.0,,0.0553,,0.1347,0.0651,,0.9801,,,0.0,0.1379,0.1667,0.0417,0.0,,0.0576,,0.1426,0.0645,,0.9801,,,0.0,0.1379,0.1667,0.0417,0.0,,0.0563,,0.1376,,block of flats,0.0729,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-788.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n175804,0,Cash loans,M,N,N,0,135000.0,1024290.0,36418.5,855000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.001276,-15612,-2532,-4330.0,-110,,1,1,0,1,0,0,Laborers,1.0,2,2,SUNDAY,6,0,0,0,0,0,0,Self-employed,,0.7459314704499885,,0.0598,0.0418,0.9856,0.8028,0.0106,0.0,0.0345,0.1667,0.2083,0.0562,0.0488,0.0354,0.0,0.0623,0.0609,0.0434,0.9856,0.8105,0.0107,0.0,0.0345,0.1667,0.2083,0.0574,0.0533,0.0369,0.0,0.066,0.0604,0.0418,0.9856,0.8054,0.0107,0.0,0.0345,0.1667,0.2083,0.0571,0.0496,0.0361,0.0,0.0636,reg oper account,block of flats,0.0337,Panel,No,0.0,0.0,0.0,0.0,-427.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n443399,0,Cash loans,M,Y,Y,0,225000.0,254799.0,18670.5,193500.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.001276,-8952,-2276,-7712.0,-1117,8.0,1,1,0,1,0,1,,1.0,2,2,MONDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.26866986247622865,0.5155392618136185,0.4848514754962666,0.1144,0.1175,0.9871,0.8232,0.2239,0.0,0.2641,0.1667,0.0417,0.0744,0.0916,0.1148,0.0,0.0518,0.0914,0.0879,0.9871,0.8301,0.1774,0.0,0.2069,0.1667,0.0417,0.0576,0.0799,0.0943,0.0,0.0,0.1197,0.1129,0.9871,0.8256,0.2253,0.0,0.2759,0.1667,0.0417,0.0647,0.0932,0.1221,0.0,0.0113,reg oper spec account,block of flats,0.0961,Panel,No,1.0,1.0,1.0,1.0,-955.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,8.0\n248703,0,Revolving loans,M,N,N,0,450000.0,1237500.0,61875.0,1237500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-12720,-3619,-6730.0,-1016,,1,1,0,1,1,0,Managers,2.0,1,1,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.5597955289917338,,0.0825,0.0691,0.9737,,,0.0,0.1379,0.1667,,0.0,,0.0706,,0.0,0.084,0.0717,0.9737,,,0.0,0.1379,0.1667,,0.0,,0.0736,,0.0,0.0833,0.0691,0.9737,,,0.0,0.1379,0.1667,,0.0,,0.0719,,0.0,,block of flats,0.0555,Panel,No,0.0,0.0,0.0,0.0,-305.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n239388,0,Cash loans,F,N,Y,0,45000.0,755190.0,29947.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-18408,-2320,-2006.0,-1965,,1,1,0,1,1,0,Sales staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,0.8297451156093414,0.7535827772775467,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2375.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n161656,0,Cash loans,M,Y,N,2,180000.0,225000.0,12204.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-15604,-2656,-8968.0,-4304,10.0,1,1,1,1,1,0,Laborers,4.0,2,2,TUESDAY,10,0,0,0,0,1,1,Self-employed,,0.6329067382886214,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1679.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n195014,0,Cash loans,F,N,Y,0,292500.0,1467612.0,56029.5,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00963,-20462,365243,-5516.0,-4017,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.636152246685287,,0.0278,0.0519,0.9906,0.8708,0.0054,0.0,0.1034,0.0833,,0.0637,0.0227,0.0298,0.0,0.0,0.0284,0.0539,0.9906,0.8759,0.0054,0.0,0.1034,0.0833,,0.0652,0.0248,0.0311,0.0,0.0,0.0281,0.0519,0.9906,0.8725,0.0054,0.0,0.1034,0.0833,,0.0648,0.0231,0.0303,0.0,0.0,reg oper spec account,block of flats,0.0264,Panel,No,2.0,0.0,2.0,0.0,-1656.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n150816,0,Cash loans,F,Y,N,1,112500.0,1006920.0,51543.0,900000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.022625,-18544,-3754,-4093.0,-2086,8.0,1,1,0,1,1,0,,3.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Other,0.4699185567198437,0.373025268712207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-452.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n441832,0,Cash loans,F,N,N,1,67500.0,130824.0,8644.5,103500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Municipal apartment,0.018209,-15419,-151,-6980.0,-3943,,1,1,0,1,0,0,,3.0,3,3,SATURDAY,10,0,0,0,0,0,0,Construction,0.5822258023827213,0.5703716581598027,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-2611.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n336503,0,Cash loans,F,N,N,0,247500.0,1256400.0,36864.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-13539,-2244,-6828.0,-230,,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,Trade: type 7,0.6693985785747266,0.6681368139650165,0.28812959991785075,0.0928,0.1015,0.9851,0.7959999999999999,0.0119,0.0,0.2069,0.1667,0.2083,0.0537,0.0756,0.0897,0.0,0.0,0.0945,0.1053,0.9851,0.804,0.012,0.0,0.2069,0.1667,0.2083,0.055,0.0826,0.0934,0.0,0.0,0.0937,0.1015,0.9851,0.7987,0.012,0.0,0.2069,0.1667,0.2083,0.0547,0.077,0.0913,0.0,0.0,reg oper account,block of flats,0.0705,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n394263,0,Cash loans,F,N,Y,0,157500.0,265500.0,30024.0,265500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-15771,-1075,-9101.0,-3033,,1,1,1,1,1,0,Sales staff,2.0,3,2,THURSDAY,4,0,0,0,0,0,0,Business Entity Type 3,0.609670949098641,0.3768175849902693,0.6690566947824041,0.3165,0.0595,0.9861,0.8096,0.0744,0.08,0.0345,0.3333,0.375,0.0752,0.2564,0.078,0.0077,0.0156,0.3225,0.0617,0.9861,0.8171,0.0751,0.0806,0.0345,0.3333,0.375,0.0769,0.2801,0.0813,0.0078,0.0166,0.3196,0.0595,0.9861,0.8121,0.0749,0.08,0.0345,0.3333,0.375,0.0765,0.2608,0.0795,0.0078,0.016,,block of flats,0.1024,Panel,No,0.0,0.0,0.0,0.0,-758.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n167719,0,Cash loans,F,N,Y,1,180000.0,900000.0,26446.5,900000.0,Other_A,Working,Secondary / secondary special,Separated,With parents,0.028663,-10587,-1010,-981.0,-1306,,1,1,0,1,1,0,Sales staff,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.41430707828936303,0.3793164812299557,0.2650494299443805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-1926.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n397853,0,Revolving loans,F,N,Y,0,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.02461,-10860,-1922,-4796.0,-1681,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,14,0,0,0,1,1,0,Business Entity Type 3,,0.6399851958318798,0.6894791426446275,0.0082,0.0254,0.9856,0.8028,0.0,0.0,0.0345,0.0417,0.0833,0.0044,0.0067,0.0074,0.0,0.0201,0.0084,0.0264,0.9856,0.8105,0.0,0.0,0.0345,0.0417,0.0833,0.0045,0.0073,0.0078,0.0,0.0213,0.0083,0.0254,0.9856,0.8054,0.0,0.0,0.0345,0.0417,0.0833,0.0045,0.0068,0.0076,0.0,0.0205,reg oper spec account,block of flats,0.0102,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1114.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n366397,0,Revolving loans,F,N,Y,2,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.005084,-10340,-538,-158.0,-2686,,1,1,0,1,0,0,Sales staff,4.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Trade: type 7,0.3851240069708904,0.5987319373477202,0.511891801533151,0.1485,0.0001,0.9826,,,0.16,0.1379,0.3333,,0.0001,,0.0002,,0.0,0.1513,0.0001,0.9826,,,0.1611,0.1379,0.3333,,0.0001,,0.0002,,0.0,0.1499,0.0001,0.9826,,,0.16,0.1379,0.3333,,0.0001,,0.0002,,0.0,,block of flats,0.0001,Others,No,0.0,0.0,0.0,0.0,-796.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n235328,0,Cash loans,F,N,N,0,144000.0,679500.0,39006.0,679500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-20040,365243,-1725.0,-3280,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,8,0,0,0,0,0,0,XNA,0.8490900150307027,0.7361208933722787,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1047.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n327655,1,Cash loans,M,N,Y,1,180000.0,247500.0,22828.5,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-10522,-860,-2654.0,-2651,,1,1,0,1,0,1,,3.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Government,0.10455560384039224,0.24226137774836595,0.4992720153045617,0.0495,0.0531,0.9737,0.6396,0.0129,0.0,0.1034,0.125,0.0,0.0006,0.0403,0.0388,0.0,0.0,0.0504,0.0551,0.9737,0.6537,0.013000000000000001,0.0,0.1034,0.125,0.0,0.0006,0.0441,0.0404,0.0,0.0,0.05,0.0531,0.9737,0.6444,0.0129,0.0,0.1034,0.125,0.0,0.0006,0.040999999999999995,0.0395,0.0,0.0,reg oper account,block of flats,0.0375,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1256.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n376139,0,Revolving loans,F,Y,Y,1,45000.0,202500.0,10125.0,202500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-13579,-6766,-4718.0,-4774,65.0,1,1,0,1,0,0,Medicine staff,3.0,2,2,MONDAY,8,0,0,0,1,1,0,Medicine,,0.4693655515842838,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1678.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n134839,0,Revolving loans,F,N,Y,0,130500.0,405000.0,20250.0,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006008,-17165,-2036,-9587.0,-696,,1,1,0,1,1,0,Medicine staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.561830904243186,0.7259923137484607,,0.0825,0.0614,0.9786,0.7076,0.027999999999999997,0.0,0.1379,0.1667,0.2083,0.0534,0.0672,0.0668,0.0,0.0,0.084,0.0637,0.9786,0.7190000000000001,0.0282,0.0,0.1379,0.1667,0.2083,0.0546,0.0735,0.0696,0.0,0.0,0.0833,0.0614,0.9786,0.7115,0.0281,0.0,0.1379,0.1667,0.2083,0.0543,0.0684,0.068,0.0,0.0,reg oper account,block of flats,0.0572,Panel,No,0.0,0.0,0.0,0.0,-3170.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n301212,0,Cash loans,F,N,N,0,135000.0,397881.0,19480.5,328500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.025164,-21902,365243,-8060.0,-4024,,1,0,0,1,0,0,,1.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.5045064834718529,0.7136313997323308,0.4227,0.2587,0.9851,,,0.48,0.4138,0.3333,,0.069,,0.4347,,0.023,0.4307,0.2685,0.9851,,,0.4834,0.4138,0.3333,,0.0706,,0.4529,,0.0243,0.4268,0.2587,0.9851,,,0.48,0.4138,0.3333,,0.0702,,0.4425,,0.0234,,block of flats,0.4959,Panel,No,0.0,0.0,0.0,0.0,-2518.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n414009,0,Cash loans,M,Y,Y,0,315000.0,414792.0,18400.5,315000.0,Family,Working,Higher education,Married,Municipal apartment,0.020713,-23278,-2624,-1065.0,-5186,11.0,1,1,0,1,0,0,High skill tech staff,2.0,3,2,THURSDAY,11,0,0,0,0,0,0,Construction,,0.3871261525305866,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-907.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n439826,0,Cash loans,F,N,Y,0,157500.0,1319499.0,43011.0,1080000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-20516,-1586,-3960.0,-2595,,1,1,0,1,0,0,Realty agents,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Realtor,0.7700468603031623,0.596733891996564,0.24318648044201235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-632.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n331810,0,Cash loans,M,Y,Y,2,144000.0,1035832.5,33543.0,904500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.0105,-11709,-674,-1025.0,-4285,1.0,1,1,0,1,0,0,,4.0,3,3,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.6913956221793072,0.5532165971269888,0.5656079814115492,0.2116,0.1015,0.997,0.9592,0.0455,0.16,0.1121,0.3958,0.375,0.0848,0.1398,0.1611,0.0,0.0619,0.2153,0.1264,0.997,0.9608,0.0266,0.1611,0.1379,0.3333,0.375,0.0867,0.1882,0.0928,0.0,0.0,0.2134,0.1218,0.997,0.9597,0.0552,0.16,0.1207,0.3333,0.375,0.0863,0.1753,0.1503,0.0,0.0,reg oper account,block of flats,0.2846,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n446479,0,Cash loans,F,Y,N,0,270000.0,1724220.0,47542.5,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.009657,-20228,365243,-253.0,-3421,7.0,1,0,0,1,0,0,,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,XNA,,0.5430724022824696,0.4668640059537032,0.0515,0.0641,0.9752,0.66,0.0043,0.0,0.1034,0.1667,0.2083,0.0542,0.042,0.0505,0.0,0.0,0.0525,0.0665,0.9752,0.6733,0.0043,0.0,0.1034,0.1667,0.2083,0.0554,0.0459,0.0526,0.0,0.0,0.052000000000000005,0.0641,0.9752,0.6645,0.0043,0.0,0.1034,0.1667,0.2083,0.0551,0.0428,0.0514,0.0,0.0,reg oper spec account,block of flats,0.0398,Panel,No,0.0,0.0,0.0,0.0,-1680.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n432764,0,Cash loans,F,N,N,0,310500.0,299250.0,8023.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-15386,-4925,-6872.0,-4857,,1,1,1,1,1,0,Core staff,2.0,1,1,MONDAY,17,0,0,0,0,0,0,Kindergarten,,0.734422568736944,0.4135967602644276,0.0412,0.0664,0.9906,,,,0.069,0.1667,,0.0373,,0.0258,,0.0629,0.042,0.0689,0.9906,,,,0.069,0.1667,,0.0382,,0.0269,,0.0666,0.0416,0.0664,0.9906,,,,0.069,0.1667,,0.038,,0.0263,,0.0642,,block of flats,0.034,Panel,No,0.0,0.0,0.0,0.0,-420.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n318031,0,Cash loans,F,N,Y,0,112500.0,518562.0,25078.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-14276,-1258,-795.0,-4141,,1,1,0,1,0,0,Realty agents,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Services,0.4844878267421003,0.3397578836658222,0.7764098512142026,0.0928,0.0878,0.9776,0.6940000000000001,0.0435,0.0,0.2069,0.1667,0.2083,0.0801,0.0756,0.0869,0.0,0.0,0.0945,0.0911,0.9777,0.706,0.0439,0.0,0.2069,0.1667,0.2083,0.0819,0.0826,0.0905,0.0,0.0,0.0937,0.0878,0.9776,0.6981,0.0438,0.0,0.2069,0.1667,0.2083,0.0815,0.077,0.0884,0.0,0.0,reg oper account,block of flats,0.0921,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n193853,0,Cash loans,F,N,Y,12,337500.0,746280.0,59094.0,675000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-14093,-2710,-2066.0,-3908,,1,1,0,1,0,0,Core staff,14.0,1,1,FRIDAY,13,0,0,0,0,0,0,Kindergarten,0.6418171713075315,0.7032529880189342,0.13259749523614614,0.0062,0.0164,0.9727,0.626,0.012,0.0,0.0345,0.1667,0.2083,0.0,0.005,0.0524,0.0,0.0,0.0063,0.017,0.9727,0.6406,0.0121,0.0,0.0345,0.1667,0.2083,0.0,0.0055,0.0546,0.0,0.0,0.0062,0.0164,0.9727,0.631,0.0121,0.0,0.0345,0.1667,0.2083,0.0,0.0051,0.0534,0.0,0.0,org spec account,block of flats,0.0412,\"Stone, brick\",No,3.0,2.0,3.0,1.0,-357.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n101834,0,Cash loans,M,Y,Y,0,157500.0,343377.0,22486.5,283500.0,Family,Working,Secondary / secondary special,Married,Rented apartment,0.006852,-17039,-1333,-1734.0,-341,16.0,1,1,0,1,0,0,Laborers,2.0,3,3,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4307251946549311,0.013535442329965713,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n124934,0,Cash loans,F,N,N,1,112500.0,254700.0,25321.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018209,-11811,-474,-2569.0,-3704,,1,1,0,1,0,0,Sales staff,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,Trade: type 7,,0.4934802466435652,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3035.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n311229,0,Cash loans,F,Y,Y,2,108000.0,1170000.0,30861.0,1170000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-11440,-325,-683.0,-982,13.0,1,1,1,1,1,1,Managers,4.0,2,2,MONDAY,15,0,0,0,0,0,0,Trade: type 3,0.465446874777097,0.6901732977891835,0.3490552510751822,0.6186,0.4242,0.9851,0.7959999999999999,0.1618,0.72,0.6207,0.3333,0.375,0.6048,0.4858,0.7263,0.0849,0.1058,0.6303,0.4402,0.9851,0.804,0.1633,0.725,0.6207,0.3333,0.375,0.6186,0.5308,0.7567,0.0856,0.11199999999999999,0.6245,0.4242,0.9851,0.7987,0.1628,0.72,0.6207,0.3333,0.375,0.6153,0.4942,0.7393,0.0854,0.10800000000000001,reg oper account,block of flats,0.5943,Panel,No,0.0,0.0,0.0,0.0,-1773.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n343047,0,Cash loans,M,N,Y,2,144000.0,1125000.0,33025.5,1125000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.010006,-15097,-7070,-4030.0,-4221,,1,1,0,1,0,0,Laborers,4.0,2,2,MONDAY,8,0,0,0,0,0,0,Government,0.27901866115941915,0.1698542589574682,,0.1155,0.0369,0.9811,0.7416,,0.08,0.069,0.3333,0.375,0.0594,0.0916,0.0525,0.0116,0.0959,0.1176,0.0383,0.9811,0.7517,,0.0806,0.069,0.3333,0.375,0.0607,0.1001,0.0547,0.0117,0.1016,0.1166,0.0369,0.9811,0.7451,,0.08,0.069,0.3333,0.375,0.0604,0.0932,0.0534,0.0116,0.0979,reg oper account,block of flats,0.0679,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2340.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n415013,0,Cash loans,M,N,Y,0,270000.0,393354.0,46813.5,369000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.019689,-17517,-3616,-8959.0,-1045,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,0.28614707802942896,0.4314549086827289,0.524496446363472,,,0.9866,,,,,,,,,0.2248,,,,,0.9866,,,,,,,,,0.2342,,,,,0.9866,,,,,,,,,0.2288,,,,,0.19899999999999998,,No,0.0,0.0,0.0,0.0,-1724.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n121705,0,Revolving loans,M,Y,Y,1,675000.0,1350000.0,67500.0,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.009549,-12321,-116,-162.0,-3221,10.0,1,1,0,1,0,0,Drivers,3.0,2,2,FRIDAY,10,1,1,0,1,1,1,Business Entity Type 3,,0.6384369635082922,0.6626377922738201,0.0134,,0.9518,,,,0.1034,0.2083,,0.1265,,0.0628,,0.018000000000000002,0.0137,,0.9518,,,,0.1034,0.2083,,0.1294,,0.0654,,0.0191,0.0135,,0.9518,,,,0.1034,0.2083,,0.1287,,0.0639,,0.0184,,block of flats,0.0533,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-257.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n313762,0,Cash loans,F,Y,Y,1,157500.0,284400.0,16456.5,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010147,-12105,-2254,-1398.0,-3550,19.0,1,1,0,1,1,0,Laborers,3.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Industry: type 3,0.36179481382594497,0.3218260259846014,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n315270,0,Cash loans,F,N,Y,0,126000.0,545040.0,26640.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-8101,-488,-2694.0,-703,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.2849300625217421,,0.0062,0.0,0.9702,0.5920000000000001,0.0088,0.0,0.0345,0.0417,0.0833,0.0195,0.005,0.0095,0.0,0.0,0.0063,0.0,0.9702,0.608,0.0088,0.0,0.0345,0.0417,0.0833,0.0199,0.0055,0.0099,0.0,0.0,0.0062,0.0,0.9702,0.5975,0.0088,0.0,0.0345,0.0417,0.0833,0.0198,0.0051,0.0097,0.0,0.0,reg oper account,block of flats,0.0075,Block,No,1.0,0.0,1.0,0.0,-316.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n363783,0,Cash loans,M,N,Y,0,157500.0,338832.0,28984.5,292500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.014464,-8878,-578,-8878.0,-1557,,1,1,1,1,0,0,Drivers,2.0,2,2,FRIDAY,4,0,0,0,1,1,1,Military,,0.07509496101550771,0.24988506275045536,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-595.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n118936,0,Cash loans,M,N,Y,0,144000.0,781920.0,28215.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003122,-20280,365243,-1910.0,-3078,,1,0,0,1,1,0,,2.0,3,3,TUESDAY,15,0,0,0,0,0,0,XNA,,0.6456386413751065,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-480.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n153112,0,Cash loans,F,N,Y,0,157500.0,776803.5,83790.0,742500.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.04622,-11232,-1268,-2381.0,-2410,,1,1,1,1,1,1,,2.0,1,1,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.4508989784872449,0.7569537326056979,0.6577838002083306,0.0082,0.0,0.9677,0.5579999999999999,0.0075,0.0,0.069,0.0417,0.0833,0.02,0.0067,0.0078,,0.0189,0.0084,0.0,0.9677,0.5753,0.0076,0.0,0.069,0.0417,0.0833,0.0205,0.0073,0.0082,,0.02,0.0083,0.0,0.9677,0.5639,0.0076,0.0,0.069,0.0417,0.0833,0.0204,0.0068,0.008,,0.0193,reg oper spec account,block of flats,0.011000000000000001,Block,No,0.0,0.0,0.0,0.0,-1044.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n109262,0,Cash loans,F,N,N,0,202500.0,454500.0,44275.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.030755,-10369,-2438,-4125.0,-2957,,1,1,1,1,1,0,Core staff,1.0,2,2,SUNDAY,11,0,0,0,1,1,0,Self-employed,0.30253947054672525,0.5977175461776038,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1362.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n160046,0,Cash loans,M,N,N,0,225000.0,765000.0,22365.0,765000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-22157,-630,-3137.0,-3137,,1,1,1,1,1,0,,2.0,1,1,FRIDAY,17,1,1,0,1,1,0,Construction,,0.06271292877830402,0.4938628816996244,0.2789,0.1442,0.993,,0.0,0.32,0.1379,0.5833,,0.6041,,0.3988,,0.0938,0.1744,0.1497,0.9906,,0.0,0.2417,0.1034,0.5417,,0.6179,,0.4155,,0.0993,0.2816,0.1442,0.993,,0.0,0.32,0.1379,0.5833,,0.6146,,0.406,,0.0957,,block of flats,0.3987,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,5.0\n238350,0,Cash loans,F,Y,Y,0,180000.0,562491.0,27189.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00733,-19926,-4678,-9823.0,-3459,1.0,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5509562373662606,0.7001838506835805,0.1464,,0.9861,0.8096,,0.12,0.1034,0.4583,0.5,,,0.0999,,,0.1492,,0.9861,0.8171,,0.1208,0.1034,0.4583,0.5,,,0.1041,,,0.1478,,0.9861,0.8121,,0.12,0.1034,0.4583,0.5,,,0.1017,,,reg oper account,block of flats,0.1598,Panel,No,2.0,0.0,2.0,0.0,-592.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n173335,0,Cash loans,M,N,Y,0,135000.0,620325.0,31801.5,535500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.011703,-19367,-1695,-816.0,-1370,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,11,0,0,0,0,1,1,Trade: type 7,,0.5364668132639939,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,4.0,1.0,-750.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n420794,0,Cash loans,F,N,Y,0,90000.0,495000.0,20970.0,495000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.010276,-12849,-543,-6885.0,-3815,,1,1,1,1,1,0,Core staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,School,0.6739133700159678,0.6753693262883778,0.14287252304131962,0.0371,0.0238,0.9826,0.762,0.0003,0.04,0.0345,0.3333,0.375,0.0271,0.0303,0.0389,0.0,0.0008,0.0378,0.0239,0.9826,0.7713,0.0,0.0403,0.0345,0.3333,0.375,0.0205,0.0331,0.0404,0.0,0.0,0.0375,0.0232,0.9826,0.7652,0.0,0.04,0.0345,0.3333,0.375,0.0263,0.0308,0.0395,0.0,0.0,org spec account,block of flats,0.031,Panel,No,0.0,0.0,0.0,0.0,-1466.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n177272,0,Cash loans,F,N,Y,1,202500.0,953010.0,40509.0,837000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.004849,-14661,-4399,-1813.0,-4099,,1,1,0,1,0,1,Sales staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.6198239170908457,0.4027067971193988,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2314.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n324814,0,Revolving loans,F,Y,Y,0,180000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.01885,-12700,-2636,-379.0,-4568,6.0,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,19,0,0,0,0,0,0,Medicine,0.8887065859582438,0.5975229815419537,0.6195277080511546,0.0505,,0.9776,,,,0.1379,0.1667,,0.1368,,0.0647,,0.0858,0.0515,,0.9777,,,,0.1379,0.1667,,0.1399,,0.0674,,0.0908,0.051,,0.9776,,,,0.1379,0.1667,,0.1392,,0.0658,,0.0875,,block of flats,0.0695,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-519.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,1.0\n155243,0,Cash loans,M,Y,Y,1,180000.0,472500.0,34510.5,472500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-9974,-172,-309.0,-2644,6.0,1,1,0,1,0,0,Managers,3.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.579117819084851,0.7437162369097419,0.3842068130556564,0.0928,,0.995,,,0.0,0.069,0.1667,,0.1224,,0.1801,,0.1412,0.0945,,0.995,,,0.0,0.069,0.1667,,0.1252,,0.1877,,0.1495,0.0937,,0.995,,,0.0,0.069,0.1667,,0.1245,,0.1834,,0.1442,,block of flats,0.1724,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2406.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n400217,0,Cash loans,F,N,N,0,157500.0,573628.5,22878.0,463500.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.019689,-22774,-13499,-12433.0,-4160,,1,1,0,1,1,1,Core staff,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Kindergarten,0.7285046156052906,0.7365327148861609,,0.0825,0.0767,0.9747,0.6532,0.0498,0.0,0.1379,0.1667,0.2083,0.0574,0.0672,0.0628,0.0,0.0,0.084,0.0796,0.9747,0.6668,0.0502,0.0,0.1379,0.1667,0.2083,0.0587,0.0735,0.0654,0.0,0.0,0.0833,0.0767,0.9747,0.6578,0.0501,0.0,0.1379,0.1667,0.2083,0.0584,0.0684,0.0639,0.0,0.0,reg oper account,block of flats,0.0766,Panel,No,0.0,0.0,0.0,0.0,-1628.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n323306,0,Cash loans,M,Y,Y,2,180000.0,760131.0,24520.5,634500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006852,-15719,-459,-2548.0,-4070,18.0,1,1,0,1,0,0,Security staff,4.0,3,3,FRIDAY,10,0,0,0,0,0,0,Security,0.3859951565594783,0.2688818218391512,0.7121551551910698,0.066,0.0122,0.9965,0.9524,0.0,0.08,0.069,0.375,0.4167,0.1104,0.0538,0.0839,0.0,0.1739,0.0672,0.0127,0.9965,0.9543,0.0,0.0806,0.069,0.375,0.4167,0.113,0.0588,0.0874,0.0,0.184,0.0666,0.0122,0.9965,0.953,0.0,0.08,0.069,0.375,0.4167,0.1124,0.0547,0.0854,0.0,0.1775,not specified,block of flats,0.1165,Monolithic,No,4.0,0.0,4.0,0.0,-100.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n212888,0,Cash loans,F,Y,Y,1,157500.0,536917.5,30109.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-15249,-3223,-3884.0,-3901,0.0,1,1,0,1,0,0,Sales staff,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Self-employed,,0.6965149672860044,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-121.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n432600,0,Cash loans,F,N,Y,0,112500.0,472500.0,29034.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-15395,-567,-8435.0,-4357,,1,1,1,1,1,0,,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.5963023110394031,0.7623356180684377,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,3.0\n129975,0,Revolving loans,F,N,N,0,54000.0,157500.0,7875.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.02461,-15953,-3028,-10093.0,-4430,,1,1,0,1,1,0,Medicine staff,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.563510125974424,0.54948965746861,0.5190973382084597,0.2206,,0.9801,,,0.16,0.1379,0.3333,,,,0.2145,0.0039,0.0041,0.2248,,0.9801,,,0.1611,0.1379,0.3333,,,,0.2235,0.0039,0.0043,0.2228,,0.9801,,,0.16,0.1379,0.3333,,,,0.2184,0.0039,0.0042,,block of flats,0.1696,Panel,No,0.0,0.0,0.0,0.0,-461.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n357313,0,Cash loans,F,Y,Y,1,157500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-14455,-5197,-8531.0,-3926,16.0,1,1,0,1,0,0,,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Industry: type 11,0.4155624374247706,0.19794472146059647,0.4382813743111921,0.0021,,0.9717,0.6124,0.0,0.0,0.0345,0.0,0.0417,,0.0017,0.0013,0.0,0.0,0.0021,,0.9717,0.6276,0.0,0.0,0.0345,0.0,0.0417,,0.0018,0.0014,0.0,0.0,0.0021,,0.9717,0.6176,0.0,0.0,0.0345,0.0,0.0417,,0.0017,0.0014,0.0,0.0,not specified,terraced house,0.001,Mixed,No,5.0,0.0,5.0,0.0,-2216.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n402392,1,Cash loans,F,N,Y,0,112500.0,539230.5,23881.5,409500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-22795,365243,-1648.0,-1654,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,16,0,0,0,0,0,0,XNA,,0.3542247319929012,0.3296550543128238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1480.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n269841,0,Cash loans,F,Y,Y,1,270000.0,701730.0,70825.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002506,-13043,-2536,-2170.0,-3131,18.0,1,1,0,1,1,0,Sales staff,3.0,2,2,MONDAY,4,0,0,0,0,0,0,Government,,0.6585117662095962,0.7394117535524816,0.1175,,0.9796,,,0.0,0.2759,0.1667,0.2083,,0.0958,0.1094,,,0.1197,,0.9796,,,0.0,0.2759,0.1667,0.2083,,0.1047,0.1139,,,0.1187,,0.9796,,,0.0,0.2759,0.1667,0.2083,,0.0975,0.1113,,,reg oper account,block of flats,0.0903,Panel,No,0.0,0.0,0.0,0.0,-1671.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n335959,0,Cash loans,F,N,Y,0,103500.0,276277.5,19359.0,238500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-13328,-649,-7432.0,-1000,,1,1,0,1,1,0,Core staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,School,,0.6878304319911831,0.6161216908872079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1375.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n213125,0,Cash loans,F,N,Y,1,157500.0,237204.0,12550.5,198000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.032561,-14782,-319,-8867.0,-3707,,1,1,0,1,0,0,Private service staff,2.0,1,1,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 2,,0.3192542153043385,0.4329616670974407,0.0546,,0.9752,,,0.04,0.0345,0.3333,,0.0264,,0.0377,,0.0039,0.0557,,0.9752,,,0.0403,0.0345,0.3333,,0.027000000000000003,,0.0393,,0.0041,0.0552,,0.9752,,,0.04,0.0345,0.3333,,0.0269,,0.0384,,0.0039,,block of flats,0.0305,Panel,No,1.0,0.0,1.0,0.0,-761.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n239240,0,Cash loans,F,N,N,0,90000.0,675000.0,34465.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-8156,-1174,-1613.0,-836,,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 1,0.552605816335799,0.6269884839816091,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-971.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n350527,0,Cash loans,F,N,N,0,112500.0,490495.5,30136.5,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.007120000000000001,-10452,-1740,-759.0,-765,,1,1,1,1,0,0,Managers,2.0,2,2,FRIDAY,7,0,0,0,1,1,0,Transport: type 4,0.4616265523684001,0.368662996595146,0.18303516721781032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1797.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n417536,0,Cash loans,F,N,Y,0,112500.0,509922.0,28602.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010276,-15104,-3094,-9219.0,-1246,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,,0.21652638319253195,0.4956658291397297,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n438782,1,Cash loans,F,N,Y,0,121500.0,327249.0,26365.5,292500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.011657,-20241,365243,-9494.0,-2862,,1,0,0,1,0,0,,2.0,1,1,THURSDAY,16,0,0,0,0,0,0,XNA,,0.6343230790249411,,0.1485,0.1169,0.9871,0.8232,0.07,0.16,0.1379,0.3333,0.375,0.125,0.121,0.1615,0.0,0.0,0.1513,0.1213,0.9871,0.8301,0.0707,0.1611,0.1379,0.3333,0.375,0.1279,0.1322,0.1683,0.0,0.0,0.1499,0.1169,0.9871,0.8256,0.0705,0.16,0.1379,0.3333,0.375,0.1272,0.1231,0.1644,0.0,0.0,reg oper account,block of flats,0.127,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n136874,0,Revolving loans,M,Y,Y,0,315000.0,675000.0,33750.0,675000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.005002,-19286,-999,-11582.0,-2704,4.0,1,1,0,0,0,0,High skill tech staff,1.0,3,3,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.7718133060145226,0.7726311345553628,0.033,0.0299,0.9732,0.6328,0.002,0.0,0.069,0.125,0.1667,0.0268,0.0269,0.0256,0.0,0.0,0.0336,0.031,0.9732,0.6472,0.002,0.0,0.069,0.125,0.1667,0.0274,0.0294,0.0267,0.0,0.0,0.0333,0.0299,0.9732,0.6377,0.002,0.0,0.069,0.125,0.1667,0.0272,0.0274,0.0261,0.0,0.0,reg oper account,block of flats,0.0212,Block,No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n381223,0,Cash loans,F,N,N,1,108000.0,314100.0,16573.5,225000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.0105,-10117,-156,-4435.0,-2782,,1,1,0,1,0,0,,2.0,3,3,MONDAY,16,0,0,0,0,0,0,Insurance,0.4106718105467321,0.580181021919426,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1313.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n276788,0,Cash loans,F,N,N,0,144000.0,269550.0,18891.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-24217,365243,-3937.0,-3994,,1,0,0,1,1,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,,0.6461037890254693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1424.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n388447,0,Cash loans,M,Y,Y,1,135000.0,497520.0,39307.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-10992,-2607,-7535.0,-2627,11.0,1,1,1,1,1,0,Laborers,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Government,0.1471663709556919,0.6504367184119941,0.1852020815902493,0.0567,0.0,0.9781,0.7008,0.0794,0.0,0.1379,0.1667,0.2083,0.0,0.0462,0.0367,0.0,0.0001,0.0578,0.0,0.9782,0.7125,0.0802,0.0,0.1379,0.1667,0.2083,0.0,0.0505,0.0383,0.0,0.0001,0.0573,0.0,0.9781,0.7048,0.0799,0.0,0.1379,0.1667,0.2083,0.0,0.047,0.0374,0.0,0.0001,reg oper account,block of flats,0.0434,Panel,No,0.0,0.0,0.0,0.0,-694.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n116297,0,Cash loans,F,N,Y,0,180000.0,604152.0,28125.0,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010006,-20431,365243,-4173.0,-3907,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.3569054502913684,0.5316861425197883,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,6.0,0.0,-323.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n438692,0,Cash loans,M,Y,Y,1,157500.0,679500.0,36333.0,679500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008575,-9695,-384,-9674.0,-2379,15.0,1,1,0,1,0,0,Sales staff,3.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.5337228991137958,0.6436198258595928,0.6577838002083306,0.1031,0.0,0.9762,,,0.0,0.2069,0.1667,,0.0939,,0.0904,,0.0,0.105,0.0,0.9762,,,0.0,0.2069,0.1667,,0.096,,0.0942,,0.0,0.1041,0.0,0.9762,,,0.0,0.2069,0.1667,,0.0955,,0.0921,,0.0,,block of flats,0.0711,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n148330,0,Cash loans,F,N,N,0,112500.0,604152.0,24088.5,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.022625,-23273,365243,-12579.0,-4320,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,0.8031576310683989,0.4762006175343576,0.7136313997323308,0.0041,,0.9781,,,0.0,0.0345,0.0417,,,,0.0051,,,0.0042,,0.9782,,,0.0,0.0345,0.0417,,,,0.0053,,,0.0042,,0.9781,,,0.0,0.0345,0.0417,,,,0.0052,,,,block of flats,0.0044,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-424.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n171924,0,Cash loans,M,Y,N,3,171000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.006305,-15915,-167,-845.0,-4942,12.0,1,1,0,1,0,0,Laborers,4.0,3,3,SUNDAY,9,0,0,0,1,1,0,Business Entity Type 3,0.3529643171293781,0.5629562311501369,0.2622489709189549,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-844.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n427631,0,Cash loans,M,N,Y,2,337500.0,225000.0,24363.0,225000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.010006,-11591,-341,-186.0,-4218,,1,1,1,1,1,0,Laborers,4.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.4250342041152844,0.3623543473912628,0.1766525794312139,0.168,0.1221,0.9896,0.8572,0.0,0.16,0.1379,0.375,0.4167,0.0947,0.13699999999999998,0.1917,0.0,0.0226,0.1712,0.1267,0.9896,0.8628,0.0,0.1611,0.1379,0.375,0.4167,0.0968,0.1497,0.1997,0.0,0.0239,0.1697,0.1221,0.9896,0.8591,0.0,0.16,0.1379,0.375,0.4167,0.0963,0.1394,0.1951,0.0,0.023,reg oper account,block of flats,0.1926,Panel,No,5.0,0.0,5.0,0.0,-1913.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n202592,0,Cash loans,F,Y,Y,2,139500.0,855882.0,38803.5,765000.0,\"Spouse, partner\",Working,Incomplete higher,Married,House / apartment,0.018634,-10107,-188,-2449.0,-2672,2.0,1,1,1,1,1,1,Accountants,4.0,2,2,WEDNESDAY,11,0,0,0,1,1,0,Business Entity Type 1,,0.543284021216297,0.06825115445188665,0.0619,0.0644,0.9856,0.8028,0.0273,0.0,0.1379,0.1667,0.0417,0.0212,0.0504,0.0574,0.0,0.0509,0.063,0.0668,0.9856,0.8105,0.0276,0.0,0.1379,0.1667,0.0417,0.0216,0.0551,0.0598,0.0,0.0539,0.0625,0.0644,0.9856,0.8054,0.0275,0.0,0.1379,0.1667,0.0417,0.0215,0.0513,0.0585,0.0,0.052000000000000005,reg oper spec account,block of flats,0.0601,\"Stone, brick\",No,5.0,2.0,5.0,2.0,-904.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n388380,0,Cash loans,F,N,N,1,234000.0,95940.0,9472.5,90000.0,Unaccompanied,State servant,Higher education,Single / not married,Office apartment,0.020713,-14568,-5185,-181.0,-5146,,1,1,0,1,0,0,,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Other,0.5104761073461799,0.7613318624343021,0.4436153084085652,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1938.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n405564,0,Cash loans,F,N,Y,1,405000.0,1057266.0,47295.0,945000.0,Family,Pensioner,Higher education,Married,House / apartment,0.04622,-22662,365243,-8807.0,-4432,,1,0,0,1,0,0,,3.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.6887921778530354,0.4686596550493113,0.2598,0.1862,0.9876,0.83,0.0435,0.28,0.2414,0.3333,0.375,0.0331,0.2118,0.2731,0.0,0.0,0.2647,0.1932,0.9876,0.8367,0.0439,0.282,0.2414,0.3333,0.375,0.0338,0.2314,0.2845,0.0,0.0,0.2623,0.1862,0.9876,0.8323,0.0438,0.28,0.2414,0.3333,0.375,0.0336,0.2155,0.278,0.0,0.0,reg oper account,block of flats,0.2386,Panel,No,0.0,0.0,0.0,0.0,-545.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n305835,0,Cash loans,F,N,N,0,148500.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-19071,-5135,-4395.0,-2609,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Transport: type 4,0.6390264044991938,0.6545372995502279,0.3740208032583212,0.1113,0.0826,0.9916,0.8844,0.0,0.0,0.1034,0.3333,0.0417,0.0246,0.0908,0.0669,0.0,0.0,0.1134,0.0857,0.9916,0.8889,0.0,0.0,0.1034,0.3333,0.0417,0.0251,0.0992,0.0697,0.0,0.0,0.1124,0.0826,0.9916,0.8859,0.0,0.0,0.1034,0.3333,0.0417,0.025,0.0923,0.0681,0.0,0.0,reg oper account,block of flats,0.1018,Panel,No,6.0,0.0,6.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n187599,0,Cash loans,M,N,N,0,536026.5,2025000.0,53419.5,2025000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.018029,-10061,-427,-4262.0,-1329,,1,1,1,1,0,0,Managers,1.0,3,3,THURSDAY,11,1,1,0,1,1,0,Business Entity Type 2,,0.3703492805759385,0.25396280933631177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-412.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n380205,0,Cash loans,M,N,Y,0,63000.0,457717.5,26406.0,414000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-23571,365243,-3266.0,-4613,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,,0.7244889860592328,0.7106743858828587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1537.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n194609,0,Cash loans,F,N,Y,0,69750.0,225000.0,11781.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-11111,-1322,-5239.0,-3296,,1,1,1,1,0,0,Sales staff,2.0,2,2,FRIDAY,11,0,0,0,1,1,0,Self-employed,,0.7026319761530142,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-633.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n325108,0,Cash loans,F,N,Y,0,202500.0,701730.0,72036.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.016612000000000002,-18324,-247,-2181.0,-1880,,1,1,0,1,1,0,Managers,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.4596131670848855,0.6479768603302221,0.3701,0.2345,0.996,0.9456,0.0776,0.36,0.3103,0.375,0.4167,0.0411,0.2984,0.3437,0.0154,0.0156,0.3771,0.2434,0.996,0.9477,0.0783,0.3625,0.3103,0.375,0.4167,0.042,0.326,0.3581,0.0156,0.0165,0.3737,0.2345,0.996,0.9463,0.0781,0.36,0.3103,0.375,0.4167,0.0418,0.3036,0.3499,0.0155,0.016,reg oper account,block of flats,0.3161,Panel,No,0.0,0.0,0.0,0.0,-314.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n352512,0,Cash loans,M,Y,Y,1,315000.0,135000.0,10665.0,135000.0,Unaccompanied,State servant,Higher education,Married,Municipal apartment,0.04622,-11294,-4298,-574.0,-3393,3.0,1,1,0,1,0,0,Core staff,3.0,1,1,TUESDAY,17,0,0,0,1,1,0,Police,,0.7659692876059612,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1720.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n336587,0,Cash loans,F,N,Y,0,138748.5,270000.0,24984.0,270000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.032561,-17208,-3622,-2730.0,-767,,1,1,1,1,1,0,Core staff,1.0,1,1,MONDAY,18,0,0,0,0,0,0,Kindergarten,0.4730529753435709,0.6385278190986314,0.3656165070113335,0.16699999999999998,0.1438,0.9762,0.6736,0.0,0.08,0.2528,0.2221,0.2638,0.07400000000000001,0.1353,0.1143,0.0039,0.0037,0.1408,0.14400000000000002,0.9762,0.6864,0.0,0.0,0.2069,0.1667,0.2083,0.0501,0.1221,0.0,0.0039,0.0,0.1395,0.1438,0.9762,0.6779999999999999,0.0,0.0,0.2414,0.1667,0.2083,0.0846,0.1137,0.1221,0.0039,0.004,reg oper account,block of flats,0.1924,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n353383,1,Cash loans,M,N,N,0,180000.0,328365.0,35491.5,297000.0,Family,Working,Secondary / secondary special,Single / not married,With parents,0.020246,-9132,-289,-6292.0,-1810,,1,1,0,1,0,0,Cooking staff,1.0,3,3,MONDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.02543499756714028,0.266456808245056,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n347890,0,Cash loans,M,Y,Y,0,135000.0,755190.0,36459.0,675000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.010276,-15791,-402,-8054.0,-4238,27.0,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.28722566784386017,0.4507472818545589,0.0912,0.1169,0.9801,0.762,0.0165,0.04,0.1552,0.25,0.2083,0.0649,0.0883,0.1,0.0,0.0,0.0756,0.1213,0.9782,0.7713,0.0166,0.0,0.069,0.1667,0.2083,0.054000000000000006,0.0964,0.1042,0.0,0.0,0.0921,0.1169,0.9801,0.7652,0.0166,0.04,0.1552,0.25,0.2083,0.066,0.0898,0.1018,0.0,0.0,reg oper account,block of flats,0.0599,Panel,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n194199,0,Cash loans,F,N,N,0,90000.0,545040.0,16654.5,450000.0,Other_A,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.02461,-21612,365243,-15195.0,-4921,,1,0,0,1,1,0,,1.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.5916244696009136,0.5744466170995097,0.2052,0.1362,0.9836,0.7756,0.0187,0.1,0.2241,0.2708,0.3125,0.1028,,0.1199,0.0,0.0059,0.209,0.1201,0.9777,0.706,0.0189,0.0,0.1724,0.1667,0.2083,0.0876,,0.11800000000000001,0.0,0.0,0.2071,0.1362,0.9836,0.7786,0.0189,0.1,0.2241,0.2708,0.3125,0.1046,,0.1221,0.0,0.006,org spec account,block of flats,0.1593,Panel,No,3.0,1.0,3.0,0.0,-1902.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n141362,0,Cash loans,F,N,Y,0,135000.0,840951.0,43065.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-15517,-3136,-8068.0,-4198,,1,1,0,1,1,0,Managers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Industry: type 6,,0.6717295039196346,0.25670557243930026,0.0165,0.0,0.9717,0.6124,,0.0,0.069,0.0417,0.0417,0.0255,0.0134,0.0113,0.0,0.0,0.0168,0.0,0.9717,0.6276,,0.0,0.069,0.0417,0.0417,0.0261,0.0147,0.0118,0.0,0.0,0.0167,0.0,0.9717,0.6176,,0.0,0.069,0.0417,0.0417,0.0259,0.0137,0.0115,0.0,0.0,reg oper account,block of flats,0.0148,Others,No,0.0,0.0,0.0,0.0,-2492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n433331,0,Cash loans,F,Y,N,0,117000.0,755190.0,33264.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-10836,-3160,-126.0,-3262,12.0,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Government,0.4707487475906904,0.5203602717604611,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-106.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n259640,0,Cash loans,F,N,Y,0,274500.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-20521,-2466,-2084.0,-2081,,1,1,1,1,0,0,Managers,2.0,3,1,MONDAY,8,0,0,0,0,0,0,Self-employed,,0.19604219110713267,,0.1134,0.1235,0.9737,,0.0896,0.0,0.2069,0.1667,,0.0466,0.0925,0.0926,,0.03,0.1155,0.1282,0.9737,,0.0904,0.0,0.2069,0.1667,,0.0477,0.10099999999999999,0.0965,,0.0318,0.1145,0.1235,0.9737,,0.0902,0.0,0.2069,0.1667,,0.0474,0.0941,0.0943,,0.0307,reg oper account,block of flats,0.0794,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-424.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n342036,0,Cash loans,F,N,Y,0,112500.0,271957.5,20461.5,252000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-16731,-1367,-1532.0,-289,,1,1,0,1,0,0,Sales staff,1.0,2,2,SUNDAY,14,0,0,0,0,0,0,Self-employed,,0.6585511047278804,0.39449540531239935,0.0309,0.0,0.9707,0.5988,0.0062,0.0,0.0345,0.0417,0.0833,0.0244,0.0244,0.0151,0.0039,0.004,0.0315,0.0,0.9707,0.6145,0.0062,0.0,0.0345,0.0417,0.0833,0.025,0.0266,0.0158,0.0039,0.0042,0.0312,0.0,0.9707,0.6042,0.0062,0.0,0.0345,0.0417,0.0833,0.0248,0.0248,0.0154,0.0039,0.0041,reg oper account,block of flats,0.0162,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n233533,0,Cash loans,F,N,Y,0,180000.0,888840.0,32053.5,675000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.006233,-15848,-2851,-4456.0,-4255,,1,1,0,1,0,0,Medicine staff,1.0,2,2,FRIDAY,11,0,0,0,1,1,0,Other,,0.3953392436207215,0.7047064232963289,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n356537,0,Cash loans,F,Y,Y,1,180000.0,704844.0,29992.5,630000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018634,-13590,-2516,-5167.0,-3079,12.0,1,1,1,1,1,0,Sales staff,3.0,2,2,THURSDAY,15,0,0,0,0,0,0,Self-employed,0.5010790283506689,0.6522989128976899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2143.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n146332,1,Cash loans,M,Y,Y,0,270000.0,382050.0,30312.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-9145,-802,-8852.0,-308,16.0,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,12,0,0,0,1,1,0,Construction,,0.5175543342434865,0.44539624156083396,0.0227,0.0,0.9801,0.728,0.0013,0.0,0.1034,0.0417,0.0833,0.0127,0.0185,0.0172,0.0,0.0,0.0231,0.0,0.9801,0.7387,0.0014,0.0,0.1034,0.0417,0.0833,0.013000000000000001,0.0202,0.018000000000000002,0.0,0.0,0.0229,0.0,0.9801,0.7316,0.0014,0.0,0.1034,0.0417,0.0833,0.013000000000000001,0.0188,0.0175,0.0,0.0,reg oper account,block of flats,0.0143,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1235.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n398066,0,Cash loans,M,Y,N,0,112500.0,168102.0,18234.0,148500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21162,-4603,-10526.0,-4569,2.0,1,1,0,1,1,0,Drivers,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Industry: type 3,,0.2793188543666065,0.30162489168411943,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1396.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n210722,0,Cash loans,F,Y,N,0,112500.0,942300.0,30528.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008625,-17944,-5331,-5883.0,-1473,24.0,1,1,0,1,0,0,Secretaries,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.7905311826027741,0.6425306482183333,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n281013,0,Cash loans,F,N,Y,2,90000.0,582804.0,29884.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-10792,-2275,-5000.0,-3438,,1,1,0,1,0,0,Sales staff,3.0,2,2,THURSDAY,10,0,0,0,1,1,0,Self-employed,0.2041640696572312,0.5372878237917023,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n214533,0,Cash loans,F,N,Y,0,157500.0,562491.0,23962.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.018634,-22543,365243,-7964.0,-4925,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,0.5961128600890204,0.6662236668191654,0.7435593141311444,0.0825,0.0932,0.9891,,,0.0,0.2069,0.1667,,0.0175,,0.0853,,0.0978,0.084,0.0967,0.9891,,,0.0,0.2069,0.1667,,0.0179,,0.0889,,0.1036,0.0833,0.0932,0.9891,,,0.0,0.2069,0.1667,,0.0178,,0.0869,,0.0999,,block of flats,0.0884,Panel,No,0.0,0.0,0.0,0.0,-845.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n317870,0,Cash loans,M,Y,N,2,180000.0,781920.0,31522.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-14172,-4939,-600.0,-4933,6.0,1,1,0,1,1,0,Drivers,4.0,2,2,MONDAY,7,0,1,1,0,1,1,Industry: type 9,0.6474525516897912,0.6562066798440113,,,,0.9881,,,,,,,,,0.0257,,,,,0.9881,,,,,,,,,0.0268,,,,,0.9881,,,,,,,,,0.0262,,,,,0.0328,,No,0.0,0.0,0.0,0.0,-1481.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n284783,0,Cash loans,F,N,N,0,171000.0,1096020.0,52857.0,900000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.010006,-8777,-1917,-440.0,-567,,1,1,0,1,1,0,Sales staff,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,,0.2736410812487549,0.3425288720742255,0.0918,0.0839,0.9846,0.7892,0.1026,0.0,0.2069,0.1667,0.2083,0.0651,0.0748,0.0781,,0.0044,0.0935,0.0871,0.9846,0.7975,0.1036,0.0,0.2069,0.1667,0.2083,0.0666,0.0817,0.0814,,0.0046,0.0926,0.0839,0.9846,0.792,0.1033,0.0,0.2069,0.1667,0.2083,0.0662,0.0761,0.0795,,0.0045,reg oper account,block of flats,0.1185,Block,No,0.0,0.0,0.0,0.0,-429.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n275271,0,Cash loans,F,N,Y,0,202500.0,270000.0,14242.5,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-21221,365243,-5151.0,-4233,,1,0,0,1,1,0,,2.0,3,3,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.1761072454310267,0.7649392739475195,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1117.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n378452,0,Cash loans,F,Y,Y,0,135000.0,560664.0,16524.0,468000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-22029,365243,-4988.0,-4257,29.0,1,0,0,1,0,0,,2.0,3,3,TUESDAY,8,0,0,0,0,0,0,XNA,,0.438527596396812,0.6279908192952864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1007.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n168682,0,Cash loans,F,Y,Y,0,337500.0,956574.0,35577.0,855000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-18916,-3365,-2767.0,-2460,0.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.7688472550925838,0.7289533572108289,,0.0351,0.0367,0.9776,0.6940000000000001,0.0123,0.0,0.0345,0.0417,0.0833,0.0426,0.0286,0.0157,0.0,0.0,0.0357,0.0381,0.9777,0.706,0.0124,0.0,0.0345,0.0417,0.0833,0.0436,0.0312,0.0164,0.0,0.0,0.0354,0.0367,0.9776,0.6981,0.0124,0.0,0.0345,0.0417,0.0833,0.0434,0.0291,0.016,0.0,0.0,reg oper account,block of flats,0.0191,Panel,No,4.0,0.0,4.0,0.0,-2070.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n295759,0,Cash loans,M,N,Y,2,225000.0,1971072.0,68643.0,1800000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-17485,-4108,-9433.0,-1034,,1,1,0,1,0,0,,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.5346093212880558,0.6890639866062991,0.8171723992791566,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-3202.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n232254,0,Cash loans,F,Y,Y,2,180000.0,729792.0,43587.0,630000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010966,-14584,-3205,-3854.0,-4584,3.0,1,1,0,1,0,0,,4.0,2,2,MONDAY,14,0,0,0,0,0,0,Housing,0.6675571501250789,0.5712877372470216,0.33125086459090186,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n252693,0,Cash loans,F,N,N,0,202500.0,728460.0,68238.0,675000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.032561,-10391,-1959,-1312.0,-1444,,1,1,0,1,0,0,High skill tech staff,2.0,1,1,WEDNESDAY,18,0,0,0,0,0,0,Trade: type 6,0.6578024307172566,0.6223537114160583,0.7136313997323308,0.8619,,0.996,0.9456,,0.8079999999999999,0.2276,1.0,,0.23399999999999999,,1.0,,1.0,0.7248,,0.9945,0.9281,,0.5639,0.1379,1.0,,0.1462,,0.7773,,0.5189,0.7182,,0.9965,0.9463,,0.56,0.1379,1.0,,0.2381,,0.7595,,1.0,,block of flats,1.0,Mixed,No,0.0,0.0,0.0,0.0,-2278.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n159159,0,Cash loans,M,Y,Y,0,225000.0,450000.0,35685.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-20186,-189,-2100.0,-3732,10.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,17,0,0,0,0,1,1,Business Entity Type 2,0.4940496893513692,0.35215656470180673,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-1519.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n151433,0,Cash loans,F,Y,Y,0,211500.0,1078200.0,31653.0,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006852,-13830,-3955,-2028.0,-4002,11.0,1,1,0,1,0,0,Sales staff,2.0,3,3,SATURDAY,7,0,0,0,0,0,0,Self-employed,0.3926782009481728,0.24104713815846315,0.6127042441012546,0.132,0.1771,0.9831,0.7688,0.0,0.0,0.2759,0.1667,0.2083,0.1404,0.1076,0.1314,0.0,0.0,0.1345,0.1837,0.9831,0.7779,0.0,0.0,0.2759,0.1667,0.2083,0.1436,0.1175,0.1369,0.0,0.0,0.1332,0.1771,0.9831,0.7719,0.0,0.0,0.2759,0.1667,0.2083,0.1428,0.1095,0.1338,0.0,0.0,reg oper account,block of flats,0.1034,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2283.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n224172,0,Cash loans,F,Y,Y,1,157500.0,1293502.5,37948.5,1129500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-17410,-7076,-1549.0,-953,8.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Other,,0.7023211892855229,0.41184855592423975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.0,0.0,11.0,0.0,-792.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n219399,0,Cash loans,M,Y,N,2,270000.0,945000.0,27630.0,945000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-17559,-601,-3331.0,-1112,2.0,1,1,0,1,0,0,Managers,4.0,2,2,MONDAY,17,0,0,0,0,1,1,University,0.7976732493437109,0.6537358268807013,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-501.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n425633,0,Cash loans,M,Y,Y,0,202500.0,1078200.0,34911.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-18375,-2146,-8176.0,-1912,17.0,1,1,0,1,0,0,Drivers,2.0,1,1,FRIDAY,12,0,0,0,0,0,0,Transport: type 3,,0.7157942288532351,0.6879328378491735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2229.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n443094,0,Cash loans,F,Y,Y,0,315000.0,1595115.0,55444.5,1377000.0,Family,Working,Higher education,Married,House / apartment,0.018801,-16702,-2643,-2566.0,-244,8.0,1,1,0,1,1,0,,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,Government,0.5835776021081385,0.5241576718106354,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1383.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n418229,0,Cash loans,F,N,Y,0,31500.0,630747.0,18571.5,526500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-22213,365243,-568.0,-4741,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,XNA,,0.5059058560978968,0.6144143775673561,0.3742,0.1909,0.9856,0.8028,0.0797,0.4,0.3448,0.3333,0.375,0.2236,0.3043,0.3732,0.0039,0.0085,0.3813,0.1981,0.9856,0.8105,0.0805,0.4028,0.3448,0.3333,0.375,0.2287,0.3324,0.3889,0.0039,0.009000000000000001,0.3779,0.1909,0.9856,0.8054,0.0803,0.4,0.3448,0.3333,0.375,0.2275,0.3095,0.38,0.0039,0.0086,,block of flats,0.33899999999999997,Panel,No,0.0,0.0,0.0,0.0,-750.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n432134,0,Cash loans,F,N,Y,0,315000.0,630000.0,26820.0,630000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.026392,-15047,-4400,-1467.0,-4028,,1,1,0,1,0,0,Managers,1.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 2,,0.7199881468472245,0.5567274263630174,0.1299,0.0693,0.996,,,0.16,0.1379,0.3333,,,,0.066,,0.0,0.1324,0.0719,0.996,,,0.1611,0.1379,0.3333,,,,0.0688,,0.0,0.1312,0.0693,0.996,,,0.16,0.1379,0.3333,,,,0.0672,,0.0,,block of flats,0.0519,Panel,No,4.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n293754,0,Cash loans,F,Y,Y,0,256500.0,1129500.0,31189.5,1129500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.026392,-22916,-361,-1051.0,-4089,4.0,1,1,1,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.7416518231532658,0.6161216908872079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-3128.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n251041,0,Cash loans,F,Y,N,0,229500.0,675000.0,19737.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00702,-18004,-2896,-379.0,-1529,14.0,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Transport: type 4,0.4871287052789293,0.6035974837744178,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-2383.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n138248,0,Cash loans,F,N,N,0,144000.0,691020.0,19935.0,495000.0,Other_B,Working,Secondary / secondary special,Married,House / apartment,0.020246,-21105,-8977,-2332.0,-3942,,1,1,1,1,1,0,,2.0,3,3,TUESDAY,6,0,0,0,0,0,0,Industry: type 5,0.8370974447115437,0.037492386108253464,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1574.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n131891,0,Cash loans,M,N,Y,0,135000.0,550980.0,43659.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.032561,-14695,-963,-3592.0,-4866,,1,1,0,1,1,1,Cooking staff,2.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Construction,0.27869691712355715,0.4940213847105347,0.8193176922872417,0.2474,,0.9846,,,0.12,0.0345,0.5417,,,,0.1612,,0.0601,0.2521,,0.9846,,,0.1208,0.0345,0.5417,,,,0.1679,,0.0636,0.2498,,0.9846,,,0.12,0.0345,0.5417,,,,0.1641,,0.0614,,block of flats,0.1398,\"Stone, brick\",No,8.0,0.0,8.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n315601,0,Cash loans,F,N,N,0,90000.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-14737,-1293,-7688.0,-5144,,1,1,1,1,1,0,Accountants,2.0,3,3,FRIDAY,14,0,0,0,0,0,0,Government,0.4789860072141882,0.3101667015595683,0.3539876078507373,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n277410,0,Cash loans,M,N,Y,0,70200.0,343800.0,16852.5,225000.0,Family,Working,Incomplete higher,Single / not married,House / apartment,0.020713,-21064,-4063,-5050.0,-508,,1,1,1,1,0,0,Security staff,1.0,3,3,MONDAY,7,0,0,0,0,0,0,Self-employed,,0.2373620961210011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-250.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n449829,0,Revolving loans,M,Y,Y,0,279000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.026392,-12628,-4157,-6525.0,-4187,1.0,1,1,0,1,0,0,Managers,1.0,2,2,THURSDAY,17,0,0,0,0,0,0,Business Entity Type 2,0.43002524493191496,0.7087133756940833,0.6848276586890367,0.4351,0.2793,0.9916,0.7959999999999999,0.3901,0.46,0.4138,0.3542,0.375,0.3719,0.6438,0.488,0.1081,0.0785,0.0525,0.0282,0.9851,0.804,0.3937,0.0,0.0345,0.3333,0.375,0.0474,0.7034,0.0548,0.1089,0.0227,0.4393,0.2793,0.9916,0.7987,0.3926,0.46,0.4138,0.3542,0.375,0.3783,0.655,0.4967,0.1087,0.0801,reg oper account,block of flats,0.0603,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-663.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n415786,0,Cash loans,F,N,Y,0,211500.0,1282500.0,35397.0,1282500.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.030755,-18023,-2743,-3056.0,-1557,,1,1,0,1,0,0,Accountants,1.0,2,2,SATURDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.7924291578769848,0.6436198258595928,0.511891801533151,0.0742,0.1011,0.9796,0.7212,0.0285,0.0,0.1552,0.1667,0.2083,0.0276,0.0555,0.0597,0.0386,0.0,0.0945,0.0644,0.9796,0.7321,0.0224,0.0,0.1379,0.1667,0.2083,0.028999999999999998,0.0441,0.0447,0.0,0.0,0.0781,0.08800000000000001,0.9796,0.7249,0.0245,0.0,0.1379,0.1667,0.2083,0.0289,0.0513,0.0549,0.0,0.0,reg oper account,block of flats,0.0459,Panel,No,0.0,0.0,0.0,0.0,-868.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,1.0,0.0\n200261,0,Cash loans,F,Y,Y,0,112500.0,78192.0,8140.5,67500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006008,-17733,-1883,-10898.0,-1267,8.0,1,1,0,1,0,0,Accountants,2.0,2,2,MONDAY,12,0,0,0,0,1,1,Restaurant,,0.5624719511683713,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n249736,0,Cash loans,F,N,Y,0,90000.0,770328.0,25587.0,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-16926,-1496,-10238.0,-462,,1,1,0,1,1,0,Sales staff,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Self-employed,0.4863624166634912,0.7185619528937768,0.8214433128935934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-864.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n180936,0,Cash loans,M,N,N,0,121500.0,359725.5,23548.5,297000.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.007305,-11280,-2202,-4642.0,-3094,,1,1,0,1,0,0,Laborers,1.0,3,3,TUESDAY,15,0,0,0,0,0,0,Construction,0.23744912121031794,0.5265950746625168,0.6986675550534175,,,0.9846,,,,,,,,,0.2323,,,,,0.9846,,,,,,,,,0.242,,,,,0.9846,,,,,,,,,0.2364,,,,,0.1827,,No,0.0,0.0,0.0,0.0,-1075.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n301033,0,Revolving loans,F,N,Y,0,45000.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-15417,-1035,-1012.0,-4113,,1,1,0,1,0,0,Medicine staff,2.0,3,3,MONDAY,13,0,0,0,0,0,0,Medicine,,0.2833421557046579,0.656158373001177,0.2577,0.1758,0.9985,,,0.24,0.2069,0.375,,0.0552,,0.2691,,0.0,0.2626,0.1825,0.9985,,,0.2417,0.2069,0.375,,0.0565,,0.2803,,0.0,0.2602,0.1758,0.9985,,,0.24,0.2069,0.375,,0.0562,,0.2739,,0.0,,block of flats,0.2526,Panel,No,0.0,0.0,0.0,0.0,-618.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n173429,0,Cash loans,F,N,Y,2,162000.0,895500.0,35644.5,895500.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.010966,-14402,-4827,-4882.0,-4381,,1,1,0,1,0,1,Laborers,4.0,2,2,FRIDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.6474376749337114,0.7862666146611379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-510.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n220273,0,Cash loans,M,Y,N,1,162000.0,668304.0,24763.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-16895,-4026,-2503.0,-432,3.0,1,1,1,1,1,0,Laborers,3.0,2,2,THURSDAY,8,0,0,0,0,0,0,Industry: type 5,,0.5616376347425353,0.3962195720630885,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-484.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n122567,0,Cash loans,F,N,Y,0,90000.0,381528.0,24511.5,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.04622,-16338,-8996,-7807.0,-4136,,1,1,0,1,1,0,Laborers,1.0,1,1,SATURDAY,14,0,0,0,0,0,0,Telecom,,0.6991478689032922,,0.0247,,0.9727,,,0.0,0.069,0.0833,,0.027000000000000003,,,,,0.0252,,0.9727,,,0.0,0.069,0.0833,,0.0277,,,,,0.025,,0.9727,,,0.0,0.069,0.0833,,0.0275,,,,,,block of flats,0.0162,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1959.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n296228,0,Cash loans,M,N,N,0,112500.0,560664.0,16524.0,468000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.02461,-22473,365243,-3169.0,-4234,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.6930788948583436,,0.1464,0.0944,0.9955,0.9388,0.0,0.12,0.1034,0.375,0.4167,0.0394,0.1177,0.1567,0.0077,0.2116,0.1492,0.098,0.9955,0.9412,0.0,0.1208,0.1034,0.375,0.4167,0.0403,0.1286,0.1632,0.0078,0.2241,0.1478,0.0944,0.9955,0.9396,0.0,0.12,0.1034,0.375,0.4167,0.0401,0.1197,0.1595,0.0078,0.2161,org spec account,block of flats,0.1692,Panel,No,0.0,0.0,0.0,0.0,-1612.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n340444,0,Cash loans,M,Y,N,0,292500.0,225000.0,24363.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Rented apartment,0.010006,-19244,365243,-4527.0,-1834,6.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,,0.5942160035285584,,0.1085,0.0882,0.9940000000000001,0.9184,0.0,0.07,0.1379,0.25,0.2917,0.0588,0.0885,0.1115,0.0,0.0561,0.084,0.0,0.9935,0.9151,0.0,0.0,0.069,0.1667,0.2083,0.0,0.0735,0.0715,0.0,0.0253,0.1057,0.106,0.9935,0.9128,0.0,0.06,0.1207,0.25,0.2917,0.0643,0.0868,0.1227,0.0,0.0597,reg oper account,block of flats,0.1624,Panel,No,0.0,0.0,0.0,0.0,-296.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n243190,0,Cash loans,M,Y,Y,0,180000.0,450000.0,38623.5,450000.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.006233,-18936,-960,-1845.0,-2420,3.0,1,1,1,1,0,0,Drivers,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6398138730336619,0.3092753558842053,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-2110.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n118293,0,Revolving loans,F,N,Y,0,157500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00823,-8620,-1065,-8617.0,-772,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,14,0,0,0,1,1,0,Realtor,0.2457459367127265,0.007248902170073481,0.2822484337007223,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1009.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n360331,0,Cash loans,M,Y,Y,2,337500.0,1350000.0,39474.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-13146,-2442,-868.0,-4497,12.0,1,1,0,1,0,0,Drivers,4.0,2,2,MONDAY,8,0,0,0,1,1,0,Business Entity Type 3,0.384753572386904,0.5724441911125083,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2842.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n216744,0,Cash loans,F,N,Y,1,292500.0,603000.0,23364.0,603000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.04622,-15034,-1302,-876.0,-4578,,1,1,1,1,1,0,High skill tech staff,3.0,1,1,FRIDAY,13,0,1,1,0,1,1,Transport: type 2,0.30808699061880385,0.7144979150050929,0.05882590047568205,0.0567,0.0303,0.9781,0.7008,0.0192,0.04,0.0345,0.3333,0.375,0.0392,0.0462,0.0455,0.0,0.0,0.0578,0.0315,0.9782,0.7125,0.0194,0.0403,0.0345,0.3333,0.375,0.0401,0.0505,0.0474,0.0,0.0,0.0573,0.0303,0.9781,0.7048,0.0193,0.04,0.0345,0.3333,0.375,0.0399,0.047,0.0464,0.0,0.0,reg oper account,block of flats,0.0463,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1144.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n246958,0,Cash loans,F,Y,Y,0,175500.0,592560.0,31153.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011703,-20161,-410,-3412.0,-3527,7.0,1,1,0,1,1,0,,2.0,2,2,FRIDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.8027352485451951,0.6956591421460606,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-447.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n252669,0,Cash loans,F,Y,Y,1,157500.0,634482.0,20596.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-13212,-1149,-1638.0,-5173,14.0,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,18,0,0,0,0,0,0,Kindergarten,,0.7045974550654581,0.326475210066026,0.0825,0.0742,0.9866,0.8164,0.035,0.0,0.2069,0.1667,0.2083,0.0302,0.0672,0.0778,0.0,0.0,0.084,0.077,0.9866,0.8236,0.0353,0.0,0.2069,0.1667,0.2083,0.0309,0.0735,0.0811,0.0,0.0,0.0833,0.0742,0.9866,0.8189,0.0352,0.0,0.2069,0.1667,0.2083,0.0307,0.0684,0.0792,0.0,0.0,reg oper account,block of flats,0.0803,Panel,No,3.0,0.0,3.0,0.0,-1077.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n109248,0,Cash loans,M,Y,Y,0,247500.0,1006920.0,42790.5,900000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018029,-19762,-4005,-1453.0,-2753,22.0,1,1,0,1,0,0,Drivers,2.0,3,3,MONDAY,8,0,0,0,0,0,0,Self-employed,,0.4799491353445865,0.7366226976503176,0.2227,0.1773,0.9841,0.7824,0.0606,0.28,0.2069,0.5417,0.0417,,,0.2629,,0.0352,0.2269,0.184,0.9841,0.7909,0.0612,0.282,0.2069,0.5417,0.0417,,,0.2739,,0.0373,0.2248,0.1773,0.9841,0.7853,0.061,0.28,0.2069,0.5417,0.0417,,,0.2677,,0.036000000000000004,,block of flats,0.2476,Panel,No,4.0,0.0,4.0,0.0,-560.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n357662,0,Cash loans,M,Y,Y,0,135000.0,508495.5,24462.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-21017,-512,-7537.0,-4200,8.0,1,1,1,1,1,0,Drivers,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Agriculture,,0.76675237349755,0.7557400501752248,0.0495,0.063,0.9891,,,0.0,0.1034,0.1667,,0.0124,,0.0469,,0.0,0.0504,0.0653,0.9891,,,0.0,0.1034,0.1667,,0.0127,,0.0489,,0.0,0.05,0.063,0.9891,,,0.0,0.1034,0.1667,,0.0126,,0.0478,,0.0,,block of flats,0.0408,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1261.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,14.0,0.0,4.0\n262441,0,Cash loans,F,N,Y,0,87750.0,229500.0,6183.0,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.007120000000000001,-20545,365243,-7361.0,-3942,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.2280815294409257,,0.1124,,0.9796,,,0.0,0.2759,0.1667,,,,0.0785,,,0.1145,,0.9796,,,0.0,0.2759,0.1667,,,,0.0818,,,0.1135,,0.9796,,,0.0,0.2759,0.1667,,,,0.08,,,,block of flats,0.0915,Panel,No,0.0,0.0,0.0,0.0,-409.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n411869,0,Revolving loans,F,N,N,1,135000.0,450000.0,22500.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Office apartment,0.031329,-13584,-5343,-1343.0,-785,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Trade: type 3,0.4568454678183899,0.7195823744830012,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-372.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n380739,1,Cash loans,M,N,Y,0,112500.0,1155492.0,38317.5,877500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.018029,-8356,-933,-3185.0,-791,,1,1,0,1,0,0,Drivers,1.0,3,3,THURSDAY,6,0,0,0,1,1,0,Business Entity Type 2,,0.03105586733305672,0.42765737003502935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n196925,0,Cash loans,F,Y,Y,1,40500.0,269550.0,14242.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-10953,-1829,-4046.0,-3617,9.0,1,1,0,1,0,0,Core staff,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Kindergarten,0.3861376256276794,0.4085852369671596,0.7557400501752248,0.0691,0.0685,0.9861,0.8096,0.0091,0.0,0.1379,0.1667,0.2083,0.0446,0.0563,0.0654,0.0,0.0037,0.0704,0.0711,0.9861,0.8171,0.0092,0.0,0.1379,0.1667,0.2083,0.0456,0.0615,0.0681,0.0,0.004,0.0697,0.0685,0.9861,0.8121,0.0092,0.0,0.1379,0.1667,0.2083,0.0453,0.0573,0.0665,0.0,0.0038,reg oper account,block of flats,0.0572,Block,No,0.0,0.0,0.0,0.0,-338.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n290898,0,Cash loans,F,N,Y,1,180000.0,573408.0,20596.5,495000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.009657,-10199,-332,-994.0,-1465,,1,1,0,1,1,0,Accountants,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Trade: type 7,0.4480795239848076,0.5470145589669536,0.4740512892789932,0.0722,0.0842,0.9786,0.7076,0.0205,0.0,0.1379,0.1667,0.2083,0.051,0.0588,0.0669,0.0,0.0515,0.0735,0.0874,0.9786,0.7190000000000001,0.0207,0.0,0.1379,0.1667,0.2083,0.0522,0.0643,0.0697,0.0,0.0546,0.0729,0.0842,0.9786,0.7115,0.0206,0.0,0.1379,0.1667,0.2083,0.0519,0.0599,0.0681,0.0,0.0526,org spec account,block of flats,0.0639,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-89.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n237610,0,Cash loans,F,N,N,0,54000.0,193572.0,15421.5,171000.0,Unaccompanied,Working,Secondary / secondary special,Separated,With parents,0.04622,-16129,-1174,-6666.0,-4305,,1,1,0,1,1,0,Cleaning staff,1.0,1,1,THURSDAY,18,0,0,0,0,0,0,School,,0.4973395304885642,,0.2216,,0.9796,,,0.24,0.2069,0.3333,,,,0.2138,,,0.2258,,0.9796,,,0.2417,0.2069,0.3333,,,,0.2228,,,0.2238,,0.9796,,,0.24,0.2069,0.3333,,,,0.2177,,,,block of flats,0.1688,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n371036,0,Cash loans,M,N,Y,0,225000.0,519633.0,41184.0,481500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.030755,-22808,-1033,-4399.0,-4862,,1,1,0,1,1,0,Drivers,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,Self-employed,,0.5788488640504169,0.36896873825284665,0.1247,,0.9935,0.9116,,0.12,0.1034,0.375,0.4167,,0.1009,0.1469,0.0039,,0.1271,,0.9935,0.9151,,0.1208,0.1034,0.375,0.4167,,0.1102,0.1531,0.0039,,0.126,,0.9935,0.9128,,0.12,0.1034,0.375,0.4167,,0.1026,0.1495,0.0039,,org spec account,block of flats,0.1235,Panel,No,2.0,0.0,2.0,0.0,-831.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n222414,0,Cash loans,F,N,Y,0,112500.0,255960.0,17235.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.02461,-16022,-9052,-4658.0,-414,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,11,0,0,0,1,1,0,Trade: type 7,0.25671765757239035,0.4157110717590428,0.6363761710860439,0.0165,,0.9811,0.7416,0.0021,0.0,0.069,0.0417,0.0417,0.0105,0.0134,0.0143,0.0,0.0,0.0168,,0.9811,0.7517,0.0021,0.0,0.069,0.0417,0.0417,0.0107,0.0147,0.0149,0.0,0.0,0.0167,,0.9811,0.7451,0.0021,0.0,0.069,0.0417,0.0417,0.0107,0.0137,0.0145,0.0,0.0,reg oper account,block of flats,0.0124,\"Stone, brick\",No,2.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,8.0,0.0,0.0\n157138,0,Cash loans,F,N,Y,3,180000.0,835380.0,45445.5,675000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.072508,-17308,-2836,-4358.0,-850,,1,1,0,1,1,0,Core staff,5.0,1,1,SATURDAY,16,0,0,0,0,0,0,Government,,0.6252889911841029,0.0005272652387098817,0.3289,0.1493,0.9836,0.7756,0.2034,0.44,0.1897,0.5417,0.5833,0.0,0.2677,0.2058,0.0019,0.0011,0.2006,0.1038,0.9836,0.7844,0.0971,0.3222,0.1379,0.4583,0.5,0.0,0.1754,0.134,0.0,0.0,0.3321,0.1493,0.9836,0.7786,0.2047,0.44,0.1897,0.5417,0.5833,0.0,0.2723,0.2095,0.0019,0.0011,reg oper account,block of flats,0.3924,Panel,No,0.0,0.0,0.0,0.0,-630.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,7.0\n231163,0,Cash loans,M,Y,Y,0,450000.0,473760.0,53451.0,450000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.028663,-19428,365243,-5331.0,-2887,1.0,1,0,0,1,1,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,0.5626685194864559,0.6987751674988377,0.6178261467332483,0.2794,0.2064,0.9925,0.898,0.0575,0.32,0.2759,0.3333,0.375,0.17600000000000002,0.2185,0.3434,0.0425,0.0458,0.2847,0.2141,0.9926,0.902,0.0581,0.3222,0.2759,0.3333,0.375,0.1801,0.2388,0.3578,0.0428,0.0485,0.2821,0.2064,0.9925,0.8994,0.0579,0.32,0.2759,0.3333,0.375,0.1791,0.2223,0.3496,0.0427,0.0468,reg oper account,block of flats,0.3115,\"Stone, brick\",No,1.0,1.0,1.0,0.0,-1249.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n312471,0,Revolving loans,F,N,Y,0,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.007273999999999998,-14854,-2556,-1450.0,-4199,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,13,0,0,0,0,1,1,Other,0.6380021838649877,0.6129293040126128,0.2750003523983893,0.0722,0.0764,0.9861,,0.0,0.0,0.1379,0.1667,0.2083,0.0523,0.0588,0.0457,0.0,0.0,0.0735,0.0792,0.9861,,0.0,0.0,0.1379,0.1667,0.2083,0.0535,0.0643,0.0476,0.0,0.0,0.0729,0.0764,0.9861,,0.0,0.0,0.1379,0.1667,0.2083,0.0532,0.0599,0.0465,0.0,0.0,org spec account,block of flats,0.0518,Panel,No,0.0,0.0,0.0,0.0,-776.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n273288,0,Cash loans,F,Y,Y,0,112500.0,468333.0,28782.0,382500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.01885,-13287,-4193,-6413.0,-464,12.0,1,1,0,1,1,0,,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Industry: type 1,,0.7540697390785509,,0.1639,0.1129,0.9896,0.8572,0.0365,0.16,0.1379,0.375,0.4167,0.0433,0.1328,0.1721,0.0039,0.0346,0.16699999999999998,0.1171,0.9896,0.8628,0.0369,0.1611,0.1379,0.375,0.4167,0.0443,0.1451,0.1794,0.0039,0.0366,0.1655,0.1129,0.9896,0.8591,0.0368,0.16,0.1379,0.375,0.4167,0.0441,0.1351,0.1752,0.0039,0.0353,reg oper account,block of flats,0.1629,Panel,No,2.0,0.0,2.0,0.0,-3041.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n264688,0,Cash loans,F,Y,N,1,99000.0,738567.0,21726.0,616500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006233,-21826,-5119,-3174.0,-5060,27.0,1,1,0,1,1,0,High skill tech staff,3.0,2,2,FRIDAY,18,0,0,0,0,0,0,School,0.6569781950715079,0.7271479081458313,0.7091891096653581,0.0928,0.0027,0.9791,0.7144,0.0133,0.0,0.2069,0.1667,0.2083,0.0563,0.0756,0.0881,0.0,0.0021,0.0945,0.0028,0.9791,0.7256,0.0135,0.0,0.2069,0.1667,0.2083,0.0575,0.0826,0.0918,0.0,0.0023,0.0937,0.0027,0.9791,0.7182,0.0134,0.0,0.2069,0.1667,0.2083,0.0572,0.077,0.0897,0.0,0.0022,reg oper account,block of flats,0.0698,Panel,No,0.0,0.0,0.0,0.0,-1569.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,1.0,1.0\n408193,1,Cash loans,M,N,Y,0,337500.0,490500.0,23724.0,490500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-20562,-2178,-2188.0,-3897,,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,8,0,1,1,1,1,1,Business Entity Type 3,,0.5334094292529559,0.6058362647264226,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2321.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n312232,0,Cash loans,F,Y,Y,0,114750.0,599472.0,33471.0,517500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23038,365243,-5494.0,-42,8.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.3542247319929012,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-411.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n196989,0,Cash loans,F,Y,Y,0,180000.0,956574.0,43357.5,855000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.018634,-10654,-570,-4817.0,-3242,5.0,1,1,0,1,0,0,Realty agents,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Self-employed,0.3462012263324015,0.7070040250353513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1557.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n159958,0,Cash loans,F,N,Y,0,135000.0,585000.0,40716.0,585000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-23028,365243,-12988.0,-4337,,1,0,0,1,1,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.6272288226864605,,0.1948,0.0809,0.9811,0.7416,0.064,0.12,0.1207,0.25,0.2917,0.068,1.0,0.0976,0.9556,0.0289,0.1628,0.0,0.9806,0.7452,0.0,0.0,0.0345,0.1667,0.2083,0.0,0.1993,0.0515,0.0233,0.024,0.1967,0.0809,0.9811,0.7451,0.0644,0.12,0.1207,0.25,0.2917,0.0692,1.0,0.0994,0.9608,0.0295,reg oper account,block of flats,0.1703,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2543.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n357400,0,Cash loans,M,N,Y,0,90000.0,253737.0,14566.5,229500.0,Family,Pensioner,Incomplete higher,Married,House / apartment,0.025164,-23232,365243,-5398.0,-4078,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.3022416096870225,,0.0412,0.0561,0.9717,0.6124,0.0139,0.0,0.1379,0.125,0.1667,0.0545,0.0336,0.0478,0.0,0.0,0.042,0.0582,0.9717,0.6276,0.013999999999999999,0.0,0.1379,0.125,0.1667,0.0557,0.0367,0.0498,0.0,0.0,0.0416,0.0561,0.9717,0.6176,0.013999999999999999,0.0,0.1379,0.125,0.1667,0.0554,0.0342,0.0487,0.0,0.0,reg oper account,block of flats,0.0379,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-471.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n452608,0,Cash loans,F,N,Y,2,148500.0,942300.0,30528.0,675000.0,Children,Working,Secondary / secondary special,Married,Municipal apartment,0.020713,-15823,-389,-679.0,-4371,,1,1,0,1,0,0,Laborers,4.0,3,3,SATURDAY,11,0,0,0,0,0,0,Industry: type 9,0.4570779792945519,0.40778757096327306,0.25533177083329,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,0.0\n245817,0,Cash loans,F,N,Y,1,247500.0,1061599.5,34375.5,927000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.025164,-12294,-4235,-5233.0,-2572,,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 2,,0.7674907124526353,0.6041125998015721,0.0835,0.0552,0.9622,0.4832,0.0611,0.0,0.1724,0.125,0.1667,0.0856,0.0672,0.0562,0.0039,0.0052,0.0851,0.0573,0.9623,0.5034,0.0616,0.0,0.1724,0.125,0.1667,0.0875,0.0735,0.0585,0.0039,0.0055,0.0843,0.0552,0.9622,0.4901,0.0615,0.0,0.1724,0.125,0.1667,0.0871,0.0684,0.0572,0.0039,0.0053,reg oper account,block of flats,0.0787,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1841.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n272637,0,Cash loans,M,Y,N,0,427500.0,1984500.0,52479.0,1984500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-13821,-463,-2236.0,-4561,1.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Construction,,0.6403075890419354,0.4365064990977374,0.1443,,0.996,,,0.12,0.1034,0.375,,,,0.1443,,0.0981,0.1471,,0.996,,,0.1208,0.1034,0.375,,,,0.1504,,0.1039,0.1457,,0.996,,,0.12,0.1034,0.375,,,,0.1469,,0.1002,,block of flats,0.1348,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n218146,0,Cash loans,F,Y,Y,1,495000.0,1293502.5,35698.5,1129500.0,Family,Working,Higher education,Civil marriage,House / apartment,0.030755,-13177,-1529,-3717.0,-5268,8.0,1,1,0,1,0,0,High skill tech staff,3.0,2,2,FRIDAY,14,0,0,0,1,1,0,Business Entity Type 2,0.4543460816853528,0.7068137349557766,0.34741822720026416,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2279.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n443415,0,Cash loans,F,N,Y,0,157500.0,545040.0,20677.5,450000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.003069,-22484,365243,-12263.0,-4696,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,12,0,0,0,0,0,0,XNA,,0.3101292719186913,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-259.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n301278,0,Cash loans,F,N,N,0,202500.0,595273.5,27751.5,418500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018029,-10967,-1579,-5381.0,-1539,,1,1,0,1,1,0,Core staff,2.0,3,3,TUESDAY,15,0,0,0,0,1,1,Self-employed,,0.5002697613922344,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-172.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n171155,0,Cash loans,F,N,N,0,180000.0,1413801.0,56070.0,1300500.0,Unaccompanied,State servant,Higher education,Married,Municipal apartment,0.04622,-13946,-1193,-2015.0,-2015,,1,1,0,1,0,0,Sales staff,2.0,1,1,WEDNESDAY,9,1,1,0,1,1,0,Other,,0.6854779234048741,0.4083588531230431,0.0134,,0.9498,,,0.0,0.069,0.0,,,,0.0025,,,0.0137,,0.9499,,,0.0,0.069,0.0,,,,0.0026,,,0.0135,,0.9498,,,0.0,0.069,0.0,,,,0.0025,,,,block of flats,0.0019,Wooden,No,0.0,0.0,0.0,0.0,-956.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n213325,0,Cash loans,F,N,Y,0,90000.0,835380.0,40320.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.007305,-16312,-354,-1118.0,-1121,,1,1,0,1,0,0,Private service staff,2.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Services,0.7371009379523212,0.5440652475120783,0.6986675550534175,0.0619,0.0533,0.9811,0.7416,0.0265,0.0,0.1379,0.1667,0.2083,0.0566,0.0504,0.0535,0.0,0.0666,0.063,0.0553,0.9811,0.7517,0.0267,0.0,0.1379,0.1667,0.2083,0.0579,0.0551,0.0557,0.0,0.0705,0.0625,0.0533,0.9811,0.7451,0.0267,0.0,0.1379,0.1667,0.2083,0.0576,0.0513,0.0545,0.0,0.068,reg oper spec account,block of flats,0.0566,Panel,No,0.0,0.0,0.0,0.0,-508.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n447609,0,Revolving loans,F,N,Y,1,93600.0,202500.0,10125.0,202500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.008068,-10696,-2735,-790.0,-2021,,1,1,0,1,0,0,Sales staff,3.0,3,3,MONDAY,8,0,0,0,0,0,0,Trade: type 1,0.420683009733958,0.562256677338459,0.4848514754962666,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1954.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n451192,0,Cash loans,F,N,Y,0,135000.0,450000.0,23107.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-21260,365243,-8389.0,-2688,,1,0,0,1,0,1,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,XNA,,0.5979068227825868,0.35895122857839673,0.1309,,0.9876,,,0.16,0.1379,0.3333,,,,0.1427,,0.3827,0.1334,,0.9876,,,0.1611,0.1379,0.3333,,,,0.1486,,0.4051,0.1322,,0.9876,,,0.16,0.1379,0.3333,,,,0.1452,,0.3907,,block of flats,0.1954,Panel,No,3.0,0.0,3.0,0.0,-1688.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n376181,0,Cash loans,F,N,Y,0,139500.0,1575000.0,43443.0,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003540999999999999,-15345,-4274,-4307.0,-3561,,1,1,0,1,0,0,,2.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.1669359514132567,0.646329897706246,0.1113,0.0776,0.9841,0.7824,0.0422,0.12,0.1034,0.3333,,0.013999999999999999,0.0908,0.11199999999999999,0.0,0.0,0.1134,0.0805,0.9841,0.7909,0.0426,0.1208,0.1034,0.3333,,0.0143,0.0992,0.1167,0.0,0.0,0.1124,0.0776,0.9841,0.7853,0.0425,0.12,0.1034,0.3333,,0.0142,0.0923,0.114,0.0,0.0,reg oper account,block of flats,0.1112,Panel,No,5.0,0.0,5.0,0.0,-1612.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n377608,1,Cash loans,M,Y,Y,3,90000.0,250272.0,16294.5,198000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018209,-13483,-128,-345.0,-4199,14.0,1,1,0,1,0,0,Managers,4.0,3,3,THURSDAY,7,0,0,0,0,0,0,Self-employed,0.4910581871980794,0.060345623433389575,0.5424451438613613,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-306.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n257069,0,Cash loans,M,Y,Y,1,225000.0,541323.0,29493.0,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006008,-14986,-856,-754.0,-782,15.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,14,0,0,0,1,1,1,Self-employed,0.663332060447412,0.3591571608236768,0.4794489811780563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-672.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n349384,1,Cash loans,F,N,Y,1,180000.0,268659.0,28633.5,243000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-15124,-3268,-3124.0,-1902,,1,1,0,1,0,0,Laborers,3.0,3,3,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.08876193774484964,,,,0.9488,,,,,,,,,0.0106,,,,,0.9489,,,,,,,,,0.011000000000000001,,,,,0.9488,,,,,,,,,0.0108,,,,block of flats,0.0086,,Yes,0.0,0.0,0.0,0.0,-595.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n177172,1,Cash loans,M,N,Y,0,225000.0,728460.0,57685.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006852,-16962,-1171,-806.0,-500,,1,1,0,1,0,0,Laborers,2.0,3,3,SATURDAY,5,0,0,0,0,0,0,Self-employed,,0.27370628263730284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1018.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n123358,0,Cash loans,F,Y,N,1,135000.0,497520.0,53712.0,450000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.019689,-11133,-4119,-4432.0,-3228,2.0,1,1,0,1,0,0,Managers,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3956502875647717,0.5900173684881325,0.36896873825284665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2583.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,3.0\n222526,0,Cash loans,F,N,Y,0,202500.0,265851.0,17136.0,229500.0,Unaccompanied,Pensioner,Incomplete higher,Single / not married,House / apartment,0.0105,-22790,365243,-8767.0,-5144,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,12,0,0,0,0,0,0,XNA,0.7549081193961554,0.6417165677554837,0.7106743858828587,0.1186,0.0882,0.9876,0.83,0.0,0.12,0.1034,0.375,0.0417,0.0346,0.095,0.126,0.0077,0.0137,0.1208,0.0915,0.9876,0.8367,0.0,0.1208,0.1034,0.375,0.0417,0.0354,0.1038,0.1313,0.0078,0.0145,0.1197,0.0882,0.9876,0.8323,0.0,0.12,0.1034,0.375,0.0417,0.0352,0.0966,0.1283,0.0078,0.013999999999999999,reg oper account,block of flats,0.1021,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1631.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n105304,0,Cash loans,F,N,N,0,225000.0,453514.5,24732.0,391500.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.010006,-9018,-342,-4639.0,-291,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,8,0,0,0,1,1,0,Business Entity Type 3,0.4294208693846306,0.16368334628526646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-12.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n299156,0,Cash loans,F,N,Y,2,157500.0,848745.0,43465.5,675000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008625,-14318,-166,-2990.0,-1504,,1,1,1,1,0,0,,4.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4376555369115864,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2801.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n448169,0,Cash loans,F,N,Y,3,135000.0,1006920.0,39933.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007114,-10996,-425,-244.0,-583,,1,1,1,1,0,0,High skill tech staff,5.0,2,2,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.705697728081984,0.6313545365850379,0.0722,0.0,0.9786,0.7076,0.0077,0.0,0.1379,0.1667,0.2083,0.0038,0.0588,0.0656,0.0,0.0,0.0735,0.0,0.9786,0.7190000000000001,0.0078,0.0,0.1379,0.1667,0.2083,0.0038,0.0643,0.0684,0.0,0.0,0.0729,0.0,0.9786,0.7115,0.0077,0.0,0.1379,0.1667,0.2083,0.0038,0.0599,0.0668,0.0,0.0,reg oper account,block of flats,0.0516,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-328.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n215279,0,Revolving loans,F,Y,Y,0,90000.0,247500.0,12375.0,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-19197,-2500,-8292.0,-2710,4.0,1,1,1,1,1,0,,2.0,3,3,SATURDAY,8,0,0,0,0,0,0,Other,0.614179868687955,0.4559764616468202,0.4668640059537032,0.0742,0.0632,0.9886,,,0.08,0.069,0.3333,,,,0.0821,,,0.0756,0.0656,0.9886,,,0.0806,0.069,0.3333,,,,0.0855,,,0.0749,0.0632,0.9886,,,0.08,0.069,0.3333,,,,0.0836,,,,block of flats,0.0768,Panel,No,0.0,0.0,0.0,0.0,-444.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n185196,0,Cash loans,M,Y,N,1,360000.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-18914,-2622,-2556.0,-2444,31.0,1,1,1,1,1,0,Security staff,3.0,1,1,SUNDAY,20,0,0,0,0,0,0,Security,0.7958726266314669,0.6887265493321453,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2143.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n410440,0,Cash loans,M,Y,Y,0,180000.0,900000.0,40801.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-21737,-13720,-10897.0,-4632,6.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 2,,0.8035186550865046,0.6430255641096323,0.0732,0.0561,0.9851,0.7959999999999999,0.1063,0.08,0.069,0.3333,0.375,0.0733,0.0597,0.0739,0.0039,0.0066,0.0746,0.0582,0.9851,0.804,0.1073,0.0806,0.069,0.3333,0.375,0.075,0.0652,0.077,0.0039,0.006999999999999999,0.0739,0.0561,0.9851,0.7987,0.107,0.08,0.069,0.3333,0.375,0.0746,0.0607,0.0753,0.0039,0.0068,reg oper account,block of flats,0.0665,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n274492,1,Cash loans,F,N,Y,0,90000.0,555273.0,20529.0,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.0105,-20807,365243,-14922.0,-3844,,1,0,0,1,0,0,,2.0,3,3,SATURDAY,10,0,0,0,0,0,0,XNA,,,,0.0412,0.0309,0.9752,0.66,0.0243,0.0,0.069,0.1667,0.2083,0.0211,0.0336,0.0204,0.0,0.0,0.042,0.0321,0.9752,0.6733,0.0245,0.0,0.069,0.1667,0.2083,0.0216,0.0367,0.0212,0.0,0.0,0.0416,0.0309,0.9752,0.6645,0.0245,0.0,0.069,0.1667,0.2083,0.0215,0.0342,0.0207,0.0,0.0,reg oper account,block of flats,0.0247,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n453520,0,Cash loans,F,N,N,1,112500.0,450000.0,20979.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.0105,-13613,-6585,-4548.0,-1319,,1,1,0,1,0,0,,3.0,3,3,MONDAY,18,0,0,0,0,0,0,Medicine,,0.7436495430058341,0.6801388218428291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-845.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n215918,0,Cash loans,F,N,Y,0,76500.0,327024.0,21028.5,270000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.00733,-18109,-257,-7032.0,-1653,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,12,0,0,0,1,1,0,Trade: type 7,0.3102524931025755,0.4295516569235269,0.7850520263728172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-412.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n169452,0,Cash loans,F,N,Y,1,90000.0,506889.0,23760.0,418500.0,Other_A,Working,Higher education,Separated,Municipal apartment,0.022625,-14896,-1250,-9034.0,-3821,,1,1,0,1,0,0,Accountants,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Kindergarten,0.5202563068758725,0.5421877762403426,,0.0742,0.0575,0.9821,0.7552,0.0186,0.08,0.069,0.3333,0.375,0.0496,0.0597,0.0736,0.0039,0.0072,0.0756,0.0596,0.9821,0.7648,0.0188,0.0806,0.069,0.3333,0.375,0.0507,0.0652,0.0766,0.0039,0.0076,0.0749,0.0575,0.9821,0.7585,0.0188,0.08,0.069,0.3333,0.375,0.0505,0.0607,0.0749,0.0039,0.0073,reg oper account,block of flats,0.0755,Panel,No,0.0,0.0,0.0,0.0,-783.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n233188,0,Cash loans,F,N,Y,0,135000.0,454500.0,25506.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-17056,-1220,-9361.0,-115,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Government,,0.6584724255095251,,0.0814,0.0785,0.9752,0.66,0.0096,0.0,0.1379,0.1667,0.2083,0.0569,0.0664,0.064,0.0,0.0,0.083,0.0814,0.9752,0.6733,0.0097,0.0,0.1379,0.1667,0.2083,0.0582,0.0725,0.0666,0.0,0.0,0.0822,0.0785,0.9752,0.6645,0.0097,0.0,0.1379,0.1667,0.2083,0.0578,0.0676,0.0651,0.0,0.0,reg oper account,block of flats,0.0503,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1596.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n184376,0,Cash loans,F,N,Y,0,202500.0,299772.0,15651.0,247500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006629,-16444,-2929,-36.0,-1,,1,1,0,1,0,1,,2.0,2,2,FRIDAY,9,0,0,0,1,1,0,Other,,0.6881261657040989,0.7165702448010511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,3.0,8.0,0.0,-401.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n182141,0,Cash loans,F,N,Y,1,157500.0,834048.0,48897.0,720000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.003818,-14838,-985,-5172.0,-4265,,1,1,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Industry: type 3,,0.6547548321783898,0.4830501881366946,,,0.9767,,,,0.0,0.0417,,0.0,,0.0065,,,,,0.9767,,,,0.0,0.0417,,0.0,,0.0067,,,,,0.9767,,,,0.0,0.0417,,0.0,,0.0066,,,,block of flats,0.0055,Wooden,Yes,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n450606,0,Cash loans,M,N,Y,0,180000.0,1035832.5,33543.0,904500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.005084,-14848,-1371,-4778.0,-1709,,1,1,0,1,0,0,Drivers,1.0,2,2,FRIDAY,18,0,0,0,0,0,0,Self-employed,,0.540162455681543,,0.0392,0.0473,0.9737,,,0.0,0.1034,0.125,,,,0.0207,,,0.0399,0.0491,0.9737,,,0.0,0.1034,0.125,,,,0.0215,,,0.0396,0.0473,0.9737,,,0.0,0.1034,0.125,,,,0.021,,,,block of flats,0.0248,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-489.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n261404,0,Cash loans,M,N,Y,0,171000.0,1024740.0,52452.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-17664,-1121,-5457.0,-1192,,1,1,1,1,1,1,Laborers,2.0,3,3,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.5977930204074156,0.4584456770140592,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,6.0,0.0,6.0,0.0,-2591.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n243635,0,Cash loans,M,Y,N,0,247500.0,450000.0,20979.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018209,-15587,-471,-4320.0,-3906,21.0,1,1,1,1,1,0,Security staff,2.0,3,3,THURSDAY,18,0,0,0,0,0,0,Self-employed,,0.4037223728984408,0.501075160239048,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2612.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n385211,0,Revolving loans,F,N,Y,1,76500.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-11014,-2036,-3869.0,-2538,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.7511505113403039,0.7838324335199619,0.1,0.0436,0.9786,0.7076,0.0049,0.0,0.0345,0.1667,0.2083,0.0379,0.0815,0.0459,0.0,0.0,0.1019,0.0452,0.9786,0.7190000000000001,0.0049,0.0,0.0345,0.1667,0.2083,0.0388,0.0891,0.0478,0.0,0.0,0.10099999999999999,0.0436,0.9786,0.7115,0.0049,0.0,0.0345,0.1667,0.2083,0.0386,0.0829,0.0467,0.0,0.0,reg oper account,block of flats,0.0388,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n343651,0,Cash loans,M,N,Y,1,225000.0,728460.0,57555.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018634,-9867,-1113,-3276.0,-2450,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,16,0,0,0,1,1,0,Construction,,0.42792348172318495,0.3425288720742255,0.0608,0.1311,0.9856,0.8028,0.1977,0.0,0.2069,0.1667,0.0417,0.0372,0.0496,0.1048,0.0,0.1182,0.062,0.1361,0.9856,0.8105,0.1995,0.0,0.2069,0.1667,0.0417,0.038,0.0542,0.1092,0.0,0.1252,0.0614,0.1311,0.9856,0.8054,0.19899999999999998,0.0,0.2069,0.1667,0.0417,0.0378,0.0504,0.1067,0.0,0.1207,reg oper account,block of flats,0.1081,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-246.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,7.0\n290403,0,Cash loans,F,N,N,0,225000.0,664884.0,24012.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020246,-18080,-4409,-8806.0,-1632,,1,1,0,1,0,0,Sales staff,1.0,3,3,FRIDAY,5,0,0,0,0,0,0,Self-employed,0.46637730805869604,0.3082982717595054,0.21275630545434146,0.0722,0.0276,0.9846,0.7892,0.0151,0.0,0.1034,0.1667,0.0417,,0.0588,0.066,0.0,0.013999999999999999,0.0735,0.0287,0.9846,0.7975,0.0153,0.0,0.1034,0.1667,0.0417,,0.0643,0.0688,0.0,0.0149,0.0729,0.0276,0.9846,0.792,0.0152,0.0,0.1034,0.1667,0.0417,,0.0599,0.0672,0.0,0.0143,,block of flats,0.055,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-605.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,3.0\n244302,0,Cash loans,F,Y,Y,0,90000.0,270000.0,13117.5,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.002042,-16598,-3705,-8820.0,-139,9.0,1,1,1,1,0,0,Core staff,2.0,3,3,THURSDAY,10,0,0,0,0,1,1,Postal,,0.33751972321859897,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-532.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n393883,0,Cash loans,M,Y,Y,0,292500.0,677664.0,53671.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-12278,-1310,-6206.0,-4266,7.0,1,1,0,1,0,0,Drivers,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,,0.5284318378352644,0.3123653692278984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1284.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n145264,0,Cash loans,F,N,Y,0,202500.0,172021.5,19584.0,148500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.00702,-15177,-2473,-7489.0,-4970,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,11,0,0,0,0,1,1,Industry: type 3,0.1654321334579047,0.5255755480397455,0.8128226070575616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1732.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n455568,0,Cash loans,M,Y,Y,2,90000.0,450000.0,35685.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.019689,-11082,-375,-4226.0,-3426,9.0,1,1,1,1,1,0,Laborers,4.0,2,2,SATURDAY,10,0,0,0,1,1,0,Electricity,,0.3887409445462959,0.41184855592423975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-357.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n283268,0,Cash loans,F,N,N,0,270000.0,1183500.0,34047.0,1183500.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.04622,-8784,-278,-1358.0,-1476,,1,1,1,1,0,1,Medicine staff,2.0,1,1,THURSDAY,12,0,0,0,0,0,0,Other,0.16170619555044372,0.6893543966711283,,0.3222,0.1684,0.9925,0.898,0.2004,0.44,0.1897,0.5625,0.6042,0.3242,0.3404,0.3644,0.0,0.0737,0.2311,0.168,0.9921,0.8955,0.1498,0.4028,0.1724,0.4583,0.5,0.3289,0.3719,0.2714,0.0,0.0144,0.3253,0.1684,0.9925,0.8994,0.2016,0.44,0.1897,0.5625,0.6042,0.3299,0.3463,0.371,0.0,0.0752,reg oper account,block of flats,0.2339,Panel,No,0.0,0.0,0.0,0.0,-10.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n305572,0,Cash loans,F,N,Y,0,112500.0,509400.0,34173.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.009334,-11651,-1627,-1051.0,-35,,1,1,0,1,0,1,Sales staff,2.0,2,2,THURSDAY,18,0,0,0,1,1,0,Self-employed,0.6509452716642415,0.6339324201497528,0.3876253444214701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1777.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n369501,0,Cash loans,M,Y,N,0,360000.0,491211.0,46057.5,472500.0,,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014464,-14149,-5486,-3157.0,-4438,8.0,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,3,0,0,0,0,0,0,Self-employed,,0.6770881303940531,,0.0546,0.0918,0.9672,,,0.0,0.1379,0.1667,,,,0.0518,,0.0603,0.0557,0.0953,0.9672,,,0.0,0.1379,0.1667,,,,0.0539,,0.0638,0.0552,0.0918,0.9672,,,0.0,0.1379,0.1667,,,,0.0527,,0.0615,,block of flats,0.0619,Block,No,4.0,0.0,4.0,0.0,-2.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n116375,0,Cash loans,F,N,Y,0,108000.0,671652.0,49005.0,607500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.018029,-18873,-1659,-11934.0,-2291,,1,1,0,1,0,0,Core staff,2.0,3,3,WEDNESDAY,7,0,0,0,1,0,1,Kindergarten,,0.34238224276888424,0.6817058776720116,0.066,,0.9771,,,0.0,0.1379,0.125,,,,0.0619,,0.0,0.0672,,0.9772,,,0.0,0.1379,0.125,,,,0.0645,,0.0,0.0666,,0.9771,,,0.0,0.1379,0.125,,,,0.063,,0.0,,block of flats,0.0487,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1717.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n440221,0,Cash loans,F,N,Y,0,202500.0,1762110.0,46611.0,1575000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.010006,-16488,-533,-3336.0,-22,,1,1,0,1,0,0,Medicine staff,1.0,2,1,TUESDAY,15,0,0,0,0,1,1,Government,,0.5899221505190807,,0.0928,0.0946,0.9856,0.8028,0.0463,0.0,0.2069,0.1667,0.2083,0.0441,0.0756,0.08,0.0,0.0,0.0945,0.0982,0.9856,0.8105,0.0467,0.0,0.2069,0.1667,0.2083,0.0451,0.0826,0.0834,0.0,0.0,0.0937,0.0946,0.9856,0.8054,0.0466,0.0,0.2069,0.1667,0.2083,0.0449,0.077,0.0814,0.0,0.0,reg oper account,block of flats,0.0882,Panel,No,2.0,0.0,2.0,0.0,-2749.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n176136,0,Cash loans,F,N,Y,0,135000.0,314100.0,16573.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.009175,-13325,-903,-6054.0,-1338,,1,1,1,1,1,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.7856479631909433,0.718309813370774,,0.0485,0.0618,0.9737,0.6396,0.0071,0.0,0.1034,0.125,0.1667,0.0133,0.0387,0.0412,0.0039,0.0091,0.0494,0.0642,0.9737,0.6537,0.0072,0.0,0.1034,0.125,0.1667,0.0137,0.0422,0.0429,0.0039,0.0096,0.0489,0.0618,0.9737,0.6444,0.0072,0.0,0.1034,0.125,0.1667,0.0136,0.0393,0.0419,0.0039,0.0092,reg oper spec account,block of flats,0.0324,\"Stone, brick\",No,10.0,0.0,9.0,0.0,-266.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n326498,0,Cash loans,M,Y,Y,0,202500.0,47970.0,5166.0,45000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003540999999999999,-20085,-1030,-1057.0,-2315,3.0,1,1,1,1,1,0,Drivers,2.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,,0.7075654116150975,,0.0227,,0.9876,0.8504,0.0026,0.0,0.069,0.0417,,0.0372,0.0134,0.0133,0.0,0.0,0.0168,,0.9861,0.8563,0.0026,0.0,0.069,0.0417,,0.038,0.0147,0.0118,0.0,0.0,0.0229,,0.9876,0.8524,0.0026,0.0,0.069,0.0417,,0.0378,0.0137,0.0136,0.0,0.0,reg oper account,block of flats,0.0136,Wooden,No,1.0,0.0,1.0,0.0,-485.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n228541,0,Cash loans,F,N,Y,0,135000.0,369531.0,36126.0,351000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.006008,-20265,365243,-630.0,-3758,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.6752062964179772,0.2955826421513093,0.2227,0.0996,0.9990000000000001,,,0.16,0.1379,0.3333,,0.1049,0.1816,0.1773,0.0,0.0,0.2269,0.1034,0.9990000000000001,,,0.1611,0.1379,0.3333,,0.1073,0.1983,0.1848,0.0,0.0,0.2248,0.0996,0.9990000000000001,,,0.16,0.1379,0.3333,,0.1067,0.1847,0.1805,0.0,0.0,reg oper account,block of flats,0.2094,Panel,No,5.0,1.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n115782,0,Cash loans,F,N,N,2,135000.0,538389.0,38416.5,477000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-13389,-1744,-13146.0,-4160,,1,1,0,1,1,0,Core staff,4.0,2,2,FRIDAY,13,0,0,0,0,0,0,Kindergarten,0.44413050843729496,0.2141027330547528,0.646329897706246,0.0165,,0.9717,,,0.0,,0.0,,,,0.0077,,,0.0168,,0.9717,,,0.0,,0.0,,,,0.0081,,,0.0167,,0.9717,,,0.0,,0.0,,,,0.0079,,,,block of flats,0.0095,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n426502,0,Cash loans,F,Y,Y,0,202500.0,1075500.0,35671.5,1075500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-20406,365243,-11917.0,-2368,10.0,1,0,0,1,0,0,,2.0,3,3,THURSDAY,10,0,0,0,0,0,0,XNA,,0.5526706315656107,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n391432,0,Cash loans,F,N,N,0,58500.0,364896.0,15457.5,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.020246,-23501,365243,-1244.0,-4716,,1,0,0,1,0,0,,1.0,3,3,MONDAY,12,0,0,0,0,0,0,XNA,,0.19870613564252185,0.12217974403015852,0.1206,0.0727,0.9975,0.966,0.0752,0.12,0.1034,0.375,0.4167,0.0132,0.0983,0.1107,0.0,0.023,0.1229,0.0755,0.9975,0.9673,0.0758,0.1208,0.1034,0.375,0.4167,0.0135,0.1074,0.1154,0.0,0.0244,0.1218,0.0727,0.9975,0.9665,0.0756,0.12,0.1034,0.375,0.4167,0.0134,0.1,0.1127,0.0,0.0235,reg oper account,block of flats,0.0871,Panel,No,0.0,0.0,0.0,0.0,-1731.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n404793,1,Cash loans,M,Y,N,1,180000.0,203760.0,21748.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.035792000000000004,-8702,-461,-1808.0,-1396,4.0,1,1,1,1,0,0,,3.0,2,2,FRIDAY,15,0,1,1,0,1,1,Business Entity Type 3,,0.6108468600816664,0.09821859526287233,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-530.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n310557,0,Cash loans,F,N,Y,0,135000.0,1042560.0,40702.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-14575,-2511,-3003.0,-4969,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,13,0,0,0,1,1,1,Self-employed,,0.3551256437854799,0.1446481462546413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1510.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n205899,0,Cash loans,M,N,Y,0,450000.0,1147887.0,46881.0,1026000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.072508,-11774,-197,-5457.0,-3274,,1,1,0,1,1,1,Sales staff,1.0,1,1,TUESDAY,13,0,0,0,0,0,0,Trade: type 2,0.7309488191966605,0.5716625998753389,0.4382813743111921,0.132,0.0669,0.9821,0.7552,0.0799,0.16,0.069,0.625,0.0,0.0,0.1076,0.1647,0.0,0.0,0.1345,0.0694,0.9821,0.7648,0.0807,0.1611,0.069,0.625,0.0,0.0,0.1175,0.1716,0.0,0.0,0.1332,0.0669,0.9821,0.7585,0.0804,0.16,0.069,0.625,0.0,0.0,0.1095,0.1677,0.0,0.0,reg oper account,block of flats,0.1296,Panel,No,3.0,0.0,3.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n114036,0,Cash loans,F,N,Y,0,148500.0,779688.0,33165.0,630000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.010643,-14751,-7825,-1889.0,-2252,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Industry: type 7,,0.4718171389373914,0.6706517530862718,0.0773,0.0846,0.9861,0.8096,0.013999999999999999,0.0,0.1724,0.1667,0.2083,0.0455,0.063,0.0687,0.0,0.0,0.0788,0.0878,0.9861,0.8171,0.0141,0.0,0.1724,0.1667,0.2083,0.0465,0.0689,0.0716,0.0,0.0,0.0781,0.0846,0.9861,0.8121,0.0141,0.0,0.1724,0.1667,0.2083,0.0462,0.0641,0.07,0.0,0.0,reg oper account,block of flats,0.0617,Panel,No,0.0,0.0,0.0,0.0,-20.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n354304,0,Cash loans,F,N,N,0,112500.0,127350.0,13639.5,112500.0,Family,Working,Higher education,Married,Municipal apartment,0.010032,-16412,-2526,-2796.0,-4147,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,3,0,0,0,0,0,0,Kindergarten,0.8319069191728525,0.7678807083796908,0.746300213050371,0.1237,0.078,0.9861,0.8096,0.0277,0.16,0.1379,0.375,0.4167,0.1155,0.1009,0.152,0.0,0.0048,0.1261,0.0809,0.9861,0.8171,0.0279,0.1611,0.1379,0.375,0.4167,0.1182,0.1102,0.1584,0.0,0.0051,0.1249,0.078,0.9861,0.8121,0.0279,0.16,0.1379,0.375,0.4167,0.1176,0.1026,0.1548,0.0,0.0049,reg oper account,block of flats,0.1358,Panel,No,0.0,0.0,0.0,0.0,-2411.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n450273,1,Revolving loans,M,Y,Y,1,180000.0,180000.0,9000.0,180000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.015221,-16645,-910,-2207.0,-185,7.0,1,1,0,1,0,0,Managers,3.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.31727158131676125,0.296248413274097,,0.3753,0.27699999999999997,0.9866,0.8164,0.1926,0.4,0.3448,0.3333,0.375,0.1637,0.3009,0.3943,0.0232,0.0173,0.3824,0.2875,0.9866,0.8236,0.1943,0.4028,0.3448,0.3333,0.375,0.1675,0.3287,0.4108,0.0233,0.0183,0.3789,0.27699999999999997,0.9866,0.8189,0.1938,0.4,0.3448,0.3333,0.375,0.1666,0.3061,0.4014,0.0233,0.0176,reg oper account,block of flats,0.4192,Panel,No,0.0,0.0,0.0,0.0,-1530.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n193517,0,Cash loans,M,N,N,0,180000.0,848745.0,46174.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00702,-19426,-1914,-2866.0,-2979,,1,1,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.3915870229658845,0.6848276586890367,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-371.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n318054,0,Cash loans,F,N,Y,0,126000.0,640080.0,20227.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.007305,-19131,-2713,-8588.0,-2612,,1,1,1,1,1,0,Sales staff,2.0,3,3,THURSDAY,12,0,0,0,0,0,0,Trade: type 7,,0.3938974071740262,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-711.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n259113,0,Cash loans,F,N,N,0,225000.0,760225.5,34353.0,679500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-17105,-455,-2557.0,-635,,1,1,1,1,1,0,Managers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.8766205236248479,0.7228009568227035,0.1674084472266241,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2089.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n240237,0,Cash loans,F,N,Y,0,112500.0,252000.0,17037.0,252000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.018209,-18419,-3760,-6965.0,-1958,,1,1,0,1,0,0,Drivers,1.0,3,3,MONDAY,13,0,0,0,0,0,0,Government,,0.5474642609639604,,,,0.9593,,,,,,,,,0.0081,,,,,0.9593,,,,,,,,,0.0085,,,,,0.9593,,,,,,,,,0.0083,,,,block of flats,0.0067,,No,0.0,0.0,0.0,0.0,-241.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n355119,0,Cash loans,F,N,Y,0,67500.0,1078200.0,31653.0,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-17116,-2698,-2759.0,-659,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Kindergarten,0.615530026828951,0.6531514881138949,0.2793353208976285,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-620.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n217979,1,Cash loans,F,N,Y,0,247500.0,172512.0,9355.5,144000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Rented apartment,0.02461,-12076,-715,-428.0,-467,,1,1,1,1,1,0,Realty agents,2.0,2,2,WEDNESDAY,15,1,1,0,1,1,0,Services,0.2361152577626084,0.7059156799243899,0.2735646775174348,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-612.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n230533,0,Cash loans,F,N,N,1,90000.0,315000.0,12289.5,315000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Civil marriage,Municipal apartment,0.014519999999999996,-16580,365243,-3949.0,-124,,1,0,0,1,1,0,,3.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,XNA,0.40885032450890985,0.34898520446093906,,0.0165,,0.9707,,,,0.069,0.0417,,,,0.0082,,0.006999999999999999,0.0168,,0.9707,,,,0.069,0.0417,,,,0.0085,,0.0074,0.0167,,0.9707,,,,0.069,0.0417,,,,0.0083,,0.0072,,block of flats,0.011000000000000001,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-2651.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n393171,0,Cash loans,F,N,Y,0,85500.0,284400.0,16011.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.019689,-24344,365243,-16648.0,-4223,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.1253841717937459,0.5172965813614878,0.0247,0.0555,0.9712,0.6056,0.0072,0.0,0.1034,0.0833,0.125,0.0328,0.0202,0.0326,0.0,0.0135,0.0252,0.0576,0.9712,0.621,0.0072,0.0,0.1034,0.0833,0.125,0.0335,0.022000000000000002,0.0339,0.0,0.0143,0.025,0.0555,0.9712,0.6109,0.0072,0.0,0.1034,0.0833,0.125,0.0334,0.0205,0.0332,0.0,0.0138,reg oper spec account,block of flats,0.0318,Block,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n316943,0,Cash loans,M,N,N,0,112500.0,675000.0,28597.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-21765,-13902,-10513.0,-3978,,1,1,1,1,0,0,Laborers,2.0,2,2,MONDAY,18,0,0,0,0,0,0,Business Entity Type 2,0.6591561671435417,0.7231031705939309,0.12295541790885495,0.2196,0.1522,0.9856,0.8028,0.0382,0.24,0.2069,0.3333,0.375,0.1318,0.179,0.2207,0.0077,0.0162,0.2237,0.158,0.9856,0.8105,0.0386,0.2417,0.2069,0.3333,0.375,0.1348,0.1956,0.23,0.0078,0.0172,0.2217,0.1522,0.9856,0.8054,0.0385,0.24,0.2069,0.3333,0.375,0.1341,0.1821,0.2247,0.0078,0.0166,reg oper account,block of flats,0.198,Panel,No,2.0,0.0,2.0,0.0,-1288.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n251141,0,Cash loans,M,Y,N,1,54000.0,244584.0,19453.5,193500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018634,-16070,-3649,-4329.0,-4395,35.0,1,1,0,1,0,0,Drivers,3.0,2,2,THURSDAY,11,0,0,0,1,1,1,Government,,0.49434935407392,0.807273923277881,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1608.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n139380,0,Cash loans,M,Y,N,0,135000.0,452385.0,30361.5,373500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.003813,-16530,-2460,-1157.0,-70,16.0,1,1,0,1,0,0,Private service staff,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Self-employed,0.6635689069568202,0.4606561380790813,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n281718,1,Cash loans,M,Y,Y,0,112500.0,668304.0,32278.5,540000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-20986,-1233,-3038.0,-4458,23.0,1,1,0,1,0,1,Drivers,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.5608165922350232,0.2719059531978474,0.6986675550534175,0.1666,0.0392,0.9831,0.7688,0.0494,0.32,0.2621,0.3917,0.0729,0.1581,0.153,0.139,0.0116,0.0743,0.2269,0.0634,0.9816,0.7517,0.0266,0.4834,0.4138,0.25,0.0,0.0444,0.1983,0.0908,0.0,0.0,0.2248,0.0377,0.9831,0.7652,0.0534,0.48,0.4138,0.25,0.0,0.16,0.1847,0.1274,0.0097,0.0553,reg oper account,block of flats,0.1576,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n428589,0,Cash loans,F,N,Y,0,67500.0,1800000.0,47614.5,1800000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-21970,365243,-5918.0,-4224,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.7603769236768806,0.6380435278721609,,,0.9732,,,,,,,,,0.0052,,,,,0.9732,,,,,,,,,0.0055,,,,,0.9732,,,,,,,,,0.0053,,,,,0.005,,No,0.0,0.0,0.0,0.0,-569.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n195308,0,Cash loans,F,Y,Y,2,166500.0,796500.0,42565.5,796500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-14547,-1319,-7688.0,-4242,7.0,1,1,0,1,1,0,,4.0,2,2,FRIDAY,9,0,0,0,0,0,0,Government,0.5354669411703943,0.4280466119224716,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1859.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n313003,0,Cash loans,M,N,Y,0,157500.0,238500.0,15372.0,238500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-10288,-3633,-5124.0,-2847,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Self-employed,,0.6019534089554641,0.4418358231994413,0.0082,,0.9722,,,0.0,0.0345,0.0417,,0.0463,,0.0088,,0.0,0.0084,,0.9722,,,0.0,0.0345,0.0417,,0.0473,,0.0092,,0.0,0.0083,,0.9722,,,0.0,0.0345,0.0417,,0.0471,,0.009000000000000001,,0.0,,block of flats,0.0069,Mixed,No,1.0,1.0,1.0,1.0,-1575.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n184856,0,Cash loans,F,N,Y,0,315000.0,1288350.0,37800.0,1125000.0,Family,Working,Higher education,Married,House / apartment,0.006629,-15657,-570,-4722.0,-3334,,1,1,0,1,0,0,Realty agents,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.1601786840780995,0.7106743858828587,0.0928,0.1204,0.9871,0.8232,0.0158,0.0,0.2069,0.1667,0.0417,0.1007,0.0756,0.0914,0.0,0.0,0.0945,0.125,0.9871,0.8301,0.016,0.0,0.2069,0.1667,0.0417,0.10300000000000001,0.0826,0.0952,0.0,0.0,0.0937,0.1204,0.9871,0.8256,0.0159,0.0,0.2069,0.1667,0.0417,0.1025,0.077,0.09300000000000001,0.0,0.0,reg oper account,block of flats,0.0805,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n110921,0,Revolving loans,F,Y,N,1,112500.0,202500.0,10125.0,202500.0,Other_B,Working,Secondary / secondary special,Married,House / apartment,0.00963,-11046,-1156,-5587.0,-3577,7.0,1,1,0,1,1,0,Sales staff,3.0,2,2,FRIDAY,18,0,0,0,0,1,1,Self-employed,0.2433014423721057,0.3779839587139865,0.7281412993111438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n304587,0,Cash loans,F,N,Y,1,252000.0,1314117.0,36265.5,1147500.0,Family,State servant,Higher education,Married,House / apartment,0.025164,-12300,-3853,-3893.0,-3350,,1,1,0,1,0,0,Core staff,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Kindergarten,0.39386356284302615,0.5550373485359119,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1863.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n366724,0,Cash loans,M,Y,Y,0,202500.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010147,-16999,-1736,-9598.0,-534,9.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Other,0.4845893839213386,0.6766146177983713,0.7662336700704004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1599.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n228971,0,Cash loans,M,Y,Y,1,234000.0,2025000.0,55818.0,2025000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-15200,-1258,-3341.0,-3783,6.0,1,1,1,1,1,0,Managers,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,Transport: type 4,0.34909801564331205,0.6775278369730104,,0.1031,0.0909,0.9791,0.7144,0.0394,0.0,0.2069,0.1667,0.2083,0.0331,0.0841,0.0908,0.0,0.0,0.105,0.0943,0.9791,0.7256,0.0397,0.0,0.2069,0.1667,0.2083,0.0339,0.0918,0.0946,0.0,0.0,0.1041,0.0909,0.9791,0.7182,0.0396,0.0,0.2069,0.1667,0.2083,0.0337,0.0855,0.0924,0.0,0.0,reg oper account,block of flats,0.0929,Panel,No,0.0,0.0,0.0,0.0,-650.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n188390,0,Cash loans,F,N,Y,1,135000.0,467257.5,22855.5,328500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-9022,-1381,-909.0,-1710,,1,1,0,1,0,0,Medicine staff,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Medicine,,0.6933114299420283,0.4812493411434029,0.0608,0.0678,0.9891,0.8504,,0.0,0.1379,0.1667,0.2083,0.038,0.0496,0.0635,,0.0,0.062,0.0704,0.9891,0.8563,,0.0,0.1379,0.1667,0.2083,0.0389,0.0542,0.0662,,0.0,0.0614,0.0678,0.9891,0.8524,,0.0,0.1379,0.1667,0.2083,0.0387,0.0504,0.0647,,0.0,reg oper account,block of flats,0.05,Panel,No,3.0,0.0,3.0,0.0,-369.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n140985,0,Cash loans,F,N,N,0,103500.0,239850.0,23364.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.02461,-21943,365243,-9412.0,-3930,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,,0.5801970007623349,0.8027454758994178,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-753.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n360698,0,Cash loans,F,N,Y,0,94500.0,259794.0,25438.5,229500.0,Family,Pensioner,Secondary / secondary special,Single / not married,Office apartment,0.008625,-22224,365243,-8787.0,-1758,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.6688055946273893,0.7688075728291359,,,0.9881,,,,,,,,,0.1151,,,,,0.9881,,,,,,,,,0.12,,,,,0.9881,,,,,,,,,0.1172,,,,,0.0907,,No,0.0,0.0,0.0,0.0,-3203.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n244694,0,Revolving loans,F,N,Y,1,112500.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Widow,Municipal apartment,0.028663,-16606,-147,-7528.0,-103,,1,1,0,1,1,0,Cooking staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.5830491586874303,,0.0938,0.1008,0.9801,0.7552,,0.0,0.1931,0.1667,0.2083,0.0646,,0.0808,,0.0194,0.084,0.1046,0.9791,0.7648,,0.0,0.1379,0.1667,0.2083,0.0471,,0.0735,,0.0,0.0895,0.1008,0.9801,0.7585,,0.0,0.2069,0.1667,0.2083,0.0593,,0.0801,,0.0035,,block of flats,0.0555,Panel,No,4.0,0.0,4.0,0.0,-2307.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n127043,0,Cash loans,F,Y,Y,0,202500.0,781920.0,25969.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-14711,-6425,-4671.0,-4342,3.0,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,,0.724458430904524,0.5919766183185521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1275.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n324578,0,Cash loans,F,N,Y,0,90000.0,343800.0,13090.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.00733,-20084,-674,-2521.0,-2879,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.6900423535613192,0.6512602186973006,0.1041,,0.9826,0.762,,0.0,0.0345,0.1667,0.2083,,,0.0749,,,0.1061,,0.9826,0.7713,,0.0,0.0345,0.1667,0.2083,,,0.0781,,,0.1051,,0.9826,0.7652,,0.0,0.0345,0.1667,0.2083,,,0.0763,,,reg oper account,block of flats,0.0602,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-460.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n344130,0,Cash loans,F,N,N,0,101250.0,506889.0,16042.5,418500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006852,-21626,-2109,-9020.0,-4169,,1,1,0,1,0,0,Security staff,2.0,3,3,SATURDAY,6,0,0,0,0,0,0,Medicine,0.8150477070970088,0.463352052860304,0.5797274227921155,0.0598,0.0499,0.9742,,,0.0,0.1034,0.1667,,0.0,,0.0477,,0.0051,0.0609,0.0518,0.9742,,,0.0,0.1034,0.1667,,0.0,,0.0497,,0.0054,0.0604,0.0499,0.9742,,,0.0,0.1034,0.1667,,0.0,,0.0486,,0.0052,,block of flats,0.0387,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n318962,0,Cash loans,F,N,Y,0,94500.0,273636.0,14296.5,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.008019,-22180,365243,-598.0,-4091,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,XNA,,0.2410071351218125,0.4329616670974407,0.0928,0.1126,0.9846,0.7892,0.0507,0.0,0.2069,0.1667,0.2083,0.0585,0.0756,0.0899,0.0,0.0,0.0945,0.1169,0.9846,0.7975,0.0512,0.0,0.2069,0.1667,0.2083,0.0599,0.0826,0.0936,0.0,0.0,0.0937,0.1126,0.9846,0.792,0.051,0.0,0.2069,0.1667,0.2083,0.0595,0.077,0.0915,0.0,0.0,reg oper account,block of flats,0.0984,Panel,No,0.0,0.0,0.0,0.0,-693.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n174243,0,Cash loans,M,Y,Y,0,202500.0,981000.0,28813.5,981000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-18179,-4474,-826.0,-1714,21.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,19,0,0,0,0,0,0,Construction,,0.4865893389374672,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n284074,0,Cash loans,F,Y,Y,0,58500.0,180000.0,10048.5,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-23041,365243,-7171.0,-2640,20.0,1,0,0,1,1,0,,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,0.8444463464047924,0.5704895434457941,0.7862666146611379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n229836,0,Cash loans,F,Y,N,0,180000.0,1078200.0,28570.5,900000.0,Family,State servant,Higher education,Married,House / apartment,0.014464,-8530,-1677,-5066.0,-841,16.0,1,1,0,1,1,0,Realty agents,2.0,2,2,SUNDAY,4,0,0,0,0,0,0,Business Entity Type 3,0.27024286841739725,0.6487941804830302,0.2707073872651806,0.0309,,0.9737,0.6396,,0.0,0.069,0.0833,0.0417,,,0.0196,,0.0068,0.0315,,0.9737,0.6537,,0.0,0.069,0.0833,0.0417,,,0.0204,,0.0072,0.0312,,0.9737,0.6444,,0.0,0.069,0.0833,0.0417,,,0.02,,0.006999999999999999,,block of flats,0.0169,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-354.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n379750,0,Cash loans,M,N,N,1,180000.0,420588.0,32701.5,360000.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.010556,-14291,-4707,-4576.0,-4816,,1,1,0,1,0,0,Laborers,3.0,3,3,SATURDAY,11,0,0,0,0,1,1,Housing,,0.4987171658845558,0.722392890081304,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-288.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,7.0\n368838,0,Cash loans,F,N,N,0,135000.0,225000.0,16821.0,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.025164,-20045,-13404,-3563.0,-3573,,1,1,1,1,1,0,Core staff,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Kindergarten,0.5725219900484934,0.4924745366274182,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-437.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n291152,0,Cash loans,F,Y,Y,1,382500.0,641529.0,23697.0,535500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.009175,-17834,-1109,-6351.0,-1392,14.0,1,1,0,1,0,1,Laborers,3.0,2,2,THURSDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.6812905975537306,0.4614823912998385,0.1237,0.1206,0.9757,0.6668,0.0104,0.0,0.2069,0.1667,0.2083,0.0989,0.1009,0.0942,0.0,0.0,0.1261,0.1251,0.9757,0.6798,0.0104,0.0,0.2069,0.1667,0.2083,0.1012,0.1102,0.0981,0.0,0.0,0.1249,0.1206,0.9757,0.6713,0.0104,0.0,0.2069,0.1667,0.2083,0.1007,0.1026,0.0959,0.0,0.0,reg oper account,block of flats,0.0797,\"Stone, brick\",No,2.0,1.0,2.0,0.0,-680.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n112182,0,Cash loans,F,N,Y,0,180000.0,1223010.0,48631.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-22952,365243,-3720.0,-5007,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.7044563385025604,0.520897599048938,0.0412,,0.9881,,,,0.069,0.1667,,,,0.0362,,,0.042,,0.9881,,,,0.069,0.1667,,,,0.0377,,,0.0416,,0.9881,,,,0.069,0.1667,,,,0.0369,,,,block of flats,0.0285,Block,No,4.0,0.0,4.0,0.0,-2515.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n222502,0,Cash loans,F,N,N,0,157500.0,1258650.0,53455.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-22074,365243,-148.0,-2141,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.4257567953079256,0.6413682574954046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1426.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n244847,0,Revolving loans,M,N,Y,0,72000.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.01885,-18009,-543,-4423.0,-923,,1,1,1,1,1,0,Laborers,1.0,2,2,WEDNESDAY,11,0,0,0,0,1,1,Business Entity Type 2,,0.3474416869532951,0.3046721837533529,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-736.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n221999,0,Cash loans,F,N,Y,0,84150.0,101880.0,10566.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.018634,-21302,365243,-8278.0,-4506,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.5674380676015365,0.5531646987710016,,,0.9856,,,,0.1034,0.0417,,,,0.0563,,,,,0.9856,,,,0.1034,0.0417,,,,0.0586,,,,,0.9856,,,,0.1034,0.0417,,,,0.0573,,,,block of flats,0.0443,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n342856,0,Cash loans,F,N,Y,0,99000.0,463500.0,16645.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-8548,-1757,-732.0,-1208,,1,1,0,1,1,0,High skill tech staff,1.0,2,2,MONDAY,7,0,0,0,1,1,0,Other,0.07179584425610129,0.5720213199459027,0.31547215492577346,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-525.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n230150,0,Cash loans,F,N,N,0,157500.0,254700.0,24939.0,225000.0,Family,Working,Higher education,Married,House / apartment,0.030755,-17694,-8788,-4080.0,-1229,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,School,0.7547396235142806,0.6736890492739401,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2231.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n139066,0,Cash loans,M,Y,N,1,202500.0,754740.0,20038.5,630000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-12671,-4522,-3624.0,-4014,16.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,14,0,1,1,1,1,1,Other,,0.4498591440883211,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n375473,0,Revolving loans,M,Y,Y,2,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.009549,-13962,-2205,-712.0,-2497,10.0,1,1,0,1,0,0,Managers,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.5812671986410289,0.2778856891082046,0.0495,0.0127,0.9816,,,0.08,0.0345,0.4583,,0.0156,,0.0568,,0.0007,0.0504,0.0132,0.9816,,,0.0806,0.0345,0.4583,,0.016,,0.0592,,0.0007,0.05,0.0127,0.9816,,,0.08,0.0345,0.4583,,0.0159,,0.0578,,0.0007,,block of flats,0.0581,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-383.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n365779,0,Cash loans,M,Y,N,0,126000.0,540000.0,40374.0,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-22354,365243,-1479.0,-1832,2.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,0.5336437045535011,0.5449818266025064,0.7503751495159068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2896.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n372151,0,Cash loans,M,Y,Y,1,270000.0,443088.0,34420.5,382500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.011703,-15237,-886,-1403.0,-3546,1.0,1,1,0,1,0,1,Sales staff,3.0,2,2,FRIDAY,14,1,1,0,0,0,0,Business Entity Type 3,,0.5691226583886909,0.3506958432829587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-282.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n110812,1,Cash loans,F,N,N,0,76500.0,754740.0,24345.0,630000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-15472,-3074,-7568.0,-4771,,1,1,0,1,0,0,Laborers,2.0,3,3,TUESDAY,14,0,0,0,0,0,0,Industry: type 3,,0.24141137397883736,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n316637,0,Cash loans,F,N,N,1,180000.0,1118286.0,32179.5,976500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-15327,-4661,-4542.0,-4024,,1,1,0,1,0,0,,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.7448218804840493,0.6222868952223947,0.5424451438613613,0.0814,0.1017,0.9821,0.7552,0.0176,0.0,0.2069,0.1667,0.2083,0.0612,0.0664,0.0801,0.0,0.0,0.083,0.1056,0.9821,0.7648,0.0178,0.0,0.2069,0.1667,0.2083,0.0626,0.0725,0.0835,0.0,0.0,0.0822,0.1017,0.9821,0.7585,0.0177,0.0,0.2069,0.1667,0.2083,0.0623,0.0676,0.0816,0.0,0.0,not specified,block of flats,0.0726,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1310.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n167165,0,Cash loans,F,N,N,0,153000.0,286704.0,14787.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-19430,-113,-4596.0,-2967,,1,1,1,1,1,1,Laborers,2.0,3,3,MONDAY,12,0,0,0,0,1,1,Industry: type 11,0.8806925645530306,0.37937311532802903,0.2910973802776635,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1459.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n425417,0,Cash loans,F,N,Y,0,90000.0,283500.0,15957.0,283500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.015221,-22824,365243,-13933.0,-3951,,1,0,0,1,1,0,,2.0,2,2,MONDAY,16,0,0,0,0,0,0,XNA,,0.0019904000989791628,0.3825018041447388,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-359.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n344331,0,Cash loans,M,Y,Y,0,202500.0,284400.0,22599.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-10719,-290,-4521.0,-3147,13.0,1,1,0,1,0,1,Sales staff,2.0,2,2,TUESDAY,19,0,0,0,1,1,0,Business Entity Type 3,0.18959308319674506,0.03534581789522659,0.044130315852959436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-668.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n118634,0,Cash loans,M,Y,N,0,382500.0,684000.0,20128.5,684000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.072508,-13666,-122,-5914.0,-2685,10.0,1,1,0,1,0,0,Managers,1.0,1,1,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.679942795392825,0.8033563428072897,0.11761373170805695,0.0835,0.0844,0.9732,0.6328,0.0114,0.0,0.1379,0.1667,0.2083,0.0153,0.0661,0.0686,0.009000000000000001,0.0072,0.084,0.0875,0.9732,0.6472,0.0115,0.0,0.1379,0.1667,0.2083,0.015,0.0735,0.0696,0.0039,0.0034,0.0843,0.0844,0.9732,0.6377,0.0115,0.0,0.1379,0.1667,0.2083,0.0157,0.0684,0.0703,0.0078,0.004,reg oper account,block of flats,0.055,Panel,No,1.0,0.0,1.0,0.0,-3032.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n131928,1,Cash loans,F,N,Y,0,157500.0,503676.0,30564.0,382500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.025164,-21184,-2076,-1324.0,-1045,,1,1,0,1,0,0,Sales staff,1.0,2,2,FRIDAY,8,0,0,0,0,0,0,Trade: type 7,,0.15767370804607614,0.06003538286216455,0.2237,0.1637,0.9851,0.7959999999999999,0.0381,0.24,0.2069,0.3333,0.375,0.0,0.1807,0.24100000000000002,0.0077,0.0078,0.2279,0.1699,0.9851,0.804,0.0385,0.2417,0.2069,0.3333,0.375,0.0,0.1974,0.2511,0.0078,0.0083,0.2259,0.1637,0.9851,0.7987,0.0384,0.24,0.2069,0.3333,0.375,0.0,0.1838,0.2453,0.0078,0.008,reg oper account,block of flats,0.2121,Panel,No,13.0,0.0,13.0,0.0,-865.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n258913,0,Cash loans,F,N,Y,0,157500.0,346500.0,13189.5,346500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.006008,-20809,-892,-14461.0,-2835,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 1,,0.7016166571989102,0.3506958432829587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1218.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,6.0\n374574,0,Cash loans,F,N,Y,0,67500.0,327024.0,12456.0,270000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.007305,-14237,-7658,-2730.0,-4553,,1,1,0,1,0,0,Medicine staff,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,Medicine,,0.7068907658430509,0.7194907850918436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1458.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n315066,0,Cash loans,F,N,Y,0,117000.0,1005120.0,32553.0,720000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006233,-17642,-4588,-2511.0,-1161,,1,1,0,1,1,0,Managers,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Postal,,0.6176086733213232,0.6363761710860439,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-312.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n378210,0,Revolving loans,F,N,Y,0,270000.0,765000.0,38250.0,765000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-14754,-1383,-8860.0,-4451,,1,1,0,1,0,0,,2.0,1,1,WEDNESDAY,15,0,0,0,0,0,0,Other,0.6324351069311502,0.5913021479608719,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-420.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n305427,0,Cash loans,M,Y,Y,0,157500.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-12047,-205,-271.0,-4078,14.0,1,1,0,1,0,0,,2.0,3,3,MONDAY,10,0,0,0,0,1,1,Construction,,0.5053045732300359,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1626.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n267413,0,Cash loans,F,N,Y,0,157500.0,495000.0,16096.5,495000.0,Family,Commercial associate,Higher education,Civil marriage,House / apartment,0.018209,-13472,-5031,-2225.0,-4088,,1,1,1,1,1,0,Accountants,2.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Kindergarten,0.5010149421726375,0.408643363957384,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1359.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n116139,0,Cash loans,F,N,Y,0,360000.0,485640.0,34537.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.020246,-12204,-4829,-6252.0,-4608,,1,1,1,1,0,0,Core staff,2.0,3,3,FRIDAY,8,0,0,0,0,1,1,Police,0.5320176472169249,0.4416852175757556,0.8633633824101478,0.1546,0.2143,0.9846,0.7892,0.0238,0.0,0.3448,0.1667,0.2083,0.0836,0.1261,0.1744,0.0,0.0,0.1576,0.2224,0.9846,0.7975,0.024,0.0,0.3448,0.1667,0.2083,0.0855,0.1377,0.1817,0.0,0.0,0.1561,0.2143,0.9846,0.792,0.024,0.0,0.3448,0.1667,0.2083,0.0851,0.1283,0.1775,0.0,0.0,reg oper account,block of flats,0.1502,Panel,No,1.0,0.0,1.0,0.0,-407.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n286651,0,Revolving loans,M,N,Y,0,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.0228,-9024,-994,-3771.0,-1674,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Other,0.17572083647715425,0.6409821895518001,0.11987796089553485,0.1268,0.0591,0.9841,0.7824,0.0059,0.0,0.0345,0.1667,0.0417,0.0167,0.1034,0.0708,0.0077,0.0047,0.1292,0.0613,0.9841,0.7909,0.006,0.0,0.0345,0.1667,0.0417,0.0171,0.1129,0.0738,0.0078,0.005,0.128,0.0591,0.9841,0.7853,0.006,0.0,0.0345,0.1667,0.0417,0.017,0.1052,0.0721,0.0078,0.0048,reg oper account,block of flats,0.0589,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-803.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n133059,0,Cash loans,F,N,Y,0,247500.0,206271.0,22014.0,193500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008575,-9933,-1471,-3183.0,-2597,,1,1,0,1,1,0,Sales staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,0.4016216975820163,0.2547890202821796,,0.0722,0.0645,0.9821,0.7552,,0.0,0.1379,0.1667,0.2083,0.0302,0.0588,0.0419,0.0,,0.0735,0.067,0.9821,0.7648,,0.0,0.1379,0.1667,0.2083,0.0309,0.0643,0.0436,0.0,,0.0729,0.0645,0.9821,0.7585,,0.0,0.1379,0.1667,0.2083,0.0308,0.0599,0.0426,0.0,,reg oper account,block of flats,0.0491,\"Stone, brick\",No,2.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n362760,0,Cash loans,F,N,N,1,85500.0,339241.5,12919.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.008625,-17490,-2119,-4399.0,-1037,,1,1,0,1,0,0,Medicine staff,2.0,2,2,THURSDAY,9,0,0,0,0,1,1,Other,,0.5479951346075824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1779.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n311265,0,Revolving loans,F,N,Y,0,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-19168,-1140,-1188.0,-2679,,1,1,0,1,1,0,,2.0,2,2,WEDNESDAY,19,0,0,0,0,0,0,School,0.8510973375751837,0.6912150854389452,0.3791004853998145,0.0165,0.0,0.9727,0.626,,0.0,0.069,0.0417,0.0417,0.0238,0.0134,0.0114,0.0,0.0,0.0168,0.0,0.9727,0.6406,,0.0,0.069,0.0417,0.0417,0.0244,0.0147,0.0119,0.0,0.0,0.0167,0.0,0.9727,0.631,,0.0,0.069,0.0417,0.0417,0.0243,0.0137,0.0116,0.0,0.0,reg oper account,block of flats,0.0149,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-284.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,1.0,0.0,1.0,0.0,2.0\n159020,0,Cash loans,F,N,Y,1,67500.0,678456.0,22014.0,486000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.005313,-15705,-5658,-9749.0,-5805,,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Medicine,0.5216835819628963,0.6743040710694362,0.44539624156083396,0.0928,0.0847,0.9816,,,0.0,0.2069,0.1667,,0.0196,,0.0909,,0.0,0.0945,0.0879,0.9816,,,0.0,0.2069,0.1667,,0.0201,,0.0948,,0.0,0.0937,0.0847,0.9816,,,0.0,0.2069,0.1667,,0.02,,0.0926,,0.0,,block of flats,0.0806,Panel,No,1.0,0.0,1.0,0.0,-776.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n115679,0,Cash loans,F,N,N,1,157500.0,1506816.0,45814.5,1350000.0,Family,Working,Higher education,Married,House / apartment,0.010147,-11658,-924,-309.0,-3040,,1,1,0,1,1,0,Managers,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.6067747308717826,0.7440620289828325,0.4794489811780563,0.0722,0.0102,0.9757,,,0.0,0.1379,0.1667,,0.0766,,0.0719,,0.0176,0.0735,0.0106,0.9757,,,0.0,0.1379,0.1667,,0.0783,,0.075,,0.0186,0.0729,0.0102,0.9757,,,0.0,0.1379,0.1667,,0.0779,,0.0732,,0.018000000000000002,,block of flats,0.0567,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n336235,0,Cash loans,F,Y,Y,0,112500.0,894906.0,28998.0,747000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-11282,-678,-4929.0,-150,12.0,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 1,0.6462632236858639,0.621772769352509,,0.1227,,0.9811,,,0.0,0.069,0.1667,,,,0.044000000000000004,,,0.125,,0.9811,,,0.0,0.069,0.1667,,,,0.0459,,,0.1239,,0.9811,,,0.0,0.069,0.1667,,,,0.0448,,,,block of flats,0.0597,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1591.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n263069,0,Cash loans,F,N,Y,1,112500.0,1288350.0,37800.0,1125000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-13790,-5000,-2525.0,-5954,,1,1,0,1,0,0,Medicine staff,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Medicine,0.8297226370825196,0.5930290791742556,0.28812959991785075,0.0691,0.0187,0.9767,0.6804,0.0148,0.0,0.1379,0.1667,0.2083,0.0,0.0538,0.0519,0.0116,0.0813,0.0704,0.0194,0.9767,0.6929,0.0149,0.0,0.1379,0.1667,0.2083,0.0,0.0588,0.0541,0.0117,0.086,0.0697,0.0187,0.9767,0.6847,0.0149,0.0,0.1379,0.1667,0.2083,0.0,0.0547,0.0528,0.0116,0.083,reg oper account,block of flats,0.0666,Block,No,0.0,0.0,0.0,0.0,-842.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n242751,0,Cash loans,F,N,N,0,157500.0,675000.0,21775.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-13421,-395,-236.0,-4800,,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Other,0.5399669866334056,0.6655571472053153,0.4400578303966329,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1765.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,7.0,0.0,5.0\n245037,0,Cash loans,M,Y,N,0,225000.0,545040.0,26509.5,450000.0,Unaccompanied,Working,Incomplete higher,Married,Rented apartment,0.035792000000000004,-10398,-1635,-5134.0,-3068,9.0,1,1,1,1,0,0,Core staff,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Government,0.1995486051806313,0.6486297319604326,0.4884551844437485,0.2242,0.1997,0.9846,0.7892,0.0335,0.24,0.2069,0.3333,0.375,0.2643,0.1803,0.2593,0.0116,0.0015,0.1555,0.1356,0.9846,0.7975,0.0012,0.1611,0.1379,0.3333,0.375,0.27,0.1304,0.1767,0.0,0.0,0.2264,0.1997,0.9846,0.792,0.0337,0.24,0.2069,0.3333,0.375,0.2689,0.1834,0.264,0.0116,0.0015,org spec account,block of flats,0.2247,Block,No,0.0,0.0,0.0,0.0,-1594.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n377524,1,Cash loans,F,Y,Y,2,382500.0,755190.0,28894.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-13857,-1040,-2142.0,-572,3.0,1,1,0,1,1,1,HR staff,4.0,2,2,SATURDAY,13,0,0,0,0,0,0,Construction,,0.2858978721410488,0.06252447329197086,0.0206,,0.995,0.932,,0.0,0.1034,0.0833,0.0,,,0.0508,,0.0,0.021,,0.995,0.9347,,0.0,0.1034,0.0833,0.0,,,0.053,,0.0,0.0208,,0.995,0.9329,,0.0,0.1034,0.0833,0.0,,,0.0518,,0.0,,block of flats,0.04,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,3.0\n100476,0,Cash loans,M,Y,Y,1,225000.0,509922.0,31324.5,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008625,-18469,-304,-9289.0,-1992,3.0,1,1,0,1,1,0,Laborers,3.0,2,2,TUESDAY,10,0,1,1,0,1,1,Other,,0.3660203315419941,0.5280927512030451,0.1113,0.0744,0.9846,,,0.12,0.1034,0.3333,,0.0141,,0.1142,,0.0003,0.1134,0.0772,0.9846,,,0.1208,0.1034,0.3333,,0.0144,,0.11900000000000001,,0.0003,0.1124,0.0744,0.9846,,,0.12,0.1034,0.3333,,0.0143,,0.1162,,0.0003,,block of flats,0.1175,Panel,No,1.0,0.0,1.0,0.0,-271.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n359222,0,Cash loans,F,N,Y,0,112500.0,152820.0,15241.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009175,-23976,365243,-5767.0,-5785,,1,0,0,1,1,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.662449418193703,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-715.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n273186,1,Revolving loans,F,Y,Y,0,157500.0,450000.0,22500.0,450000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-13516,-295,-1881.0,-5143,0.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Self-employed,0.34576718361784137,0.633927345497653,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-744.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,7.0\n157688,0,Cash loans,F,N,Y,1,135000.0,545040.0,20677.5,450000.0,Children,Working,Secondary / secondary special,Separated,House / apartment,0.025164,-13868,-807,-4305.0,-4783,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 1,0.6662906438487073,0.5513186879858314,0.3706496323299817,0.0,,0.0,,,0.0,,,,,,0.0242,,,0.0,,0.0005,,,0.0,,,,,,0.0252,,,0.0,,0.0,,,0.0,,,,,,0.0246,,,,,0.0238,,No,4.0,0.0,4.0,0.0,-1221.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n293358,1,Cash loans,F,Y,N,0,180000.0,558855.0,40797.0,477000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,With parents,0.018029,-11786,-364,-131.0,-2400,5.0,1,1,0,1,0,1,Accountants,2.0,3,3,THURSDAY,9,0,0,0,0,0,0,Medicine,0.4654446754535242,0.5085184825397012,0.1595195404777181,0.2144,,0.9871,,,0.12,0.1034,0.3333,,,,0.1172,,0.0022,0.2185,,0.9871,,,0.1208,0.1034,0.3333,,,,0.1221,,0.0023,0.2165,,0.9871,,,0.12,0.1034,0.3333,,,,0.1193,,0.0022,,block of flats,0.0926,Panel,No,0.0,0.0,0.0,0.0,-183.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n265921,0,Cash loans,F,N,Y,1,36000.0,78192.0,8140.5,67500.0,Family,Pensioner,Lower secondary,Civil marriage,House / apartment,0.015221,-21002,365243,-11694.0,-4548,,1,0,0,1,0,0,,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.5395812176986448,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1196.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n260106,0,Revolving loans,F,N,N,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.00702,-8030,-1086,-2295.0,-713,,1,1,0,1,0,0,Waiters/barmen staff,1.0,2,2,FRIDAY,16,0,0,0,0,1,1,Business Entity Type 2,0.2333776824961661,0.12189423696253933,0.5744466170995097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,0.0,-754.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n439317,0,Revolving loans,F,Y,Y,0,225000.0,405000.0,20250.0,405000.0,Family,Working,Higher education,Separated,House / apartment,0.01885,-19817,-2987,-9401.0,-3356,12.0,1,1,0,1,0,1,,1.0,2,2,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.7617601887653267,0.6824622502232589,0.6610235391308081,0.0165,,0.9722,,,,0.069,0.0417,,,,0.0107,,,0.0168,,0.9722,,,,0.069,0.0417,,,,0.0111,,,0.0167,,0.9722,,,,0.069,0.0417,,,,0.0108,,,,block of flats,0.0084,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2023.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n435599,0,Cash loans,M,Y,Y,1,180000.0,816660.0,31774.5,585000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020246,-20545,-1032,-3765.0,-3710,9.0,1,1,0,1,0,0,Managers,3.0,3,3,WEDNESDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.7919118943418094,0.6769925032909132,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-699.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n244640,0,Cash loans,F,N,N,0,157500.0,900000.0,32017.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.014519999999999996,-21796,-2808,-431.0,-4176,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.8645496782186283,0.344191494398425,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1902.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n451888,0,Cash loans,F,N,Y,2,112500.0,824823.0,24246.0,688500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-15952,-4206,-4463.0,-5292,,1,1,0,1,0,0,,4.0,2,2,SATURDAY,15,0,0,0,0,0,0,Insurance,,0.2782548298153684,0.7958031328748403,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-914.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n376062,0,Cash loans,F,N,Y,0,90000.0,450000.0,27324.0,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.004849,-22440,365243,-6453.0,-3833,,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.2269324223303933,0.0938365970374978,0.0773,0.0495,0.9841,0.7824,0.0156,0.0,0.1724,0.1667,0.0417,,0.063,0.0679,0.0,0.0,0.0788,0.0513,0.9841,0.7909,0.0157,0.0,0.1724,0.1667,0.0417,,0.0689,0.0708,0.0,0.0,0.0781,0.0495,0.9841,0.7853,0.0157,0.0,0.1724,0.1667,0.0417,,0.0641,0.0692,0.0,0.0,reg oper spec account,block of flats,0.0619,Panel,No,0.0,0.0,0.0,0.0,-265.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n276785,0,Cash loans,M,Y,N,0,270000.0,1350000.0,73980.0,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-11533,-311,-9422.0,-3842,7.0,1,1,1,1,0,0,Managers,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.3906132089051349,0.7062051096536562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-465.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n421571,0,Cash loans,F,N,Y,0,202500.0,807984.0,26833.5,697500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.005084,-14957,-964,-9095.0,-4684,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.3758988500246272,0.7366226976503176,0.0825,0.085,0.9771,,,,0.1379,0.1667,,,,0.0754,,0.0,0.084,0.0882,0.9772,,,,0.1379,0.1667,,,,0.0786,,0.0,0.0833,0.085,0.9771,,,,0.1379,0.1667,,,,0.0768,,0.0,,block of flats,0.0593,Panel,No,0.0,0.0,0.0,0.0,-589.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n106069,0,Revolving loans,M,Y,Y,0,112500.0,405000.0,20250.0,405000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008625,-21032,-1156,-2382.0,-2387,14.0,1,1,0,1,0,0,,2.0,2,2,FRIDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.5778569921609762,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1109.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n111340,0,Cash loans,M,N,Y,0,148500.0,485190.0,26451.0,405000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.009334,-21940,365243,-2706.0,-4160,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.5485204836484299,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-441.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n185257,0,Cash loans,F,Y,N,0,247500.0,1546020.0,45333.0,1350000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,With parents,0.018209,-14062,-2233,-305.0,-2176,15.0,1,1,1,1,0,0,,2.0,3,3,THURSDAY,18,0,0,0,0,0,0,Self-employed,,0.3136726039980123,0.3723336657058204,0.0675,0.1266,0.9876,0.83,0.0044,0.08,0.069,0.3333,0.0417,0.0609,0.0546,0.0542,0.0019,0.0012,0.0683,0.1311,0.9876,0.8367,0.0033,0.0806,0.069,0.3333,0.0417,0.0623,0.0597,0.0546,0.0,0.0,0.0682,0.1266,0.9876,0.8323,0.0044,0.08,0.069,0.3333,0.0417,0.0619,0.0556,0.0551,0.0019,0.0012,reg oper account,block of flats,0.0435,Panel,No,5.0,2.0,5.0,2.0,-1608.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n316604,0,Cash loans,M,Y,Y,2,157500.0,911263.5,38610.0,814500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-10496,-292,-4669.0,-3073,24.0,1,1,0,1,0,0,Drivers,4.0,2,2,THURSDAY,10,0,0,0,1,1,0,Construction,0.15080643428092985,0.5300118210317709,0.5620604831738043,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1036.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n399202,0,Cash loans,F,N,Y,0,112500.0,523597.5,22311.0,468000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00733,-19167,-4950,-5511.0,-2703,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,,0.4887529099427945,,0.0082,0.0,0.9727,,0.0007,0.0,0.069,0.0417,,0.0,,0.0102,,0.0,0.0084,0.0,0.9727,,0.0007,0.0,0.069,0.0417,,0.0,,0.0106,,0.0,0.0083,0.0,0.9727,,0.0007,0.0,0.069,0.0417,,0.0,,0.0103,,0.0,,block of flats,0.008,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-584.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n407374,0,Cash loans,M,Y,Y,1,270000.0,943515.0,55296.0,814500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.072508,-8900,-673,-8813.0,-1532,1.0,1,1,0,1,1,1,Core staff,3.0,1,1,WEDNESDAY,18,0,0,0,0,0,0,Trade: type 6,0.3428436412981924,0.5667186884404053,0.25259869783397665,0.0925,0.0606,0.9816,0.7484,0.0467,0.0936,0.0759,0.3733,0.415,0.0006,0.0749,0.0606,0.0024,0.0246,0.0756,0.0514,0.9816,0.7583,0.0375,0.0806,0.069,0.3333,0.375,0.0,0.0661,0.0471,0.0,0.0002,0.0749,0.0496,0.9816,0.7518,0.0375,0.08,0.069,0.3333,0.375,0.0,0.0616,0.0461,0.0,0.0002,reg oper account,block of flats,0.0559,Panel,No,0.0,0.0,0.0,0.0,-587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n220284,0,Cash loans,F,N,Y,1,135000.0,251280.0,13284.0,180000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.003069,-14250,-1553,-787.0,-758,,1,1,0,1,1,0,Cleaning staff,3.0,3,3,FRIDAY,13,0,0,0,0,0,0,Industry: type 4,0.22396938189031515,0.3808983171922391,0.5154953751603267,0.1588,,0.9767,,,,0.0345,0.1667,,,,0.0523,,,0.1618,,0.9767,,,,0.0345,0.1667,,,,0.0545,,,0.1603,,0.9767,,,,0.0345,0.1667,,,,0.0532,,,,specific housing,0.0611,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-424.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n208829,0,Cash loans,F,Y,Y,0,225000.0,355536.0,18283.5,270000.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.00702,-17350,-1264,-7614.0,-908,65.0,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.5912651552045324,0.3825018041447388,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-524.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n269935,0,Cash loans,M,Y,Y,0,157500.0,787131.0,26145.0,679500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.011703,-20630,365243,-7902.0,-4142,35.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.7480845915959783,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2147.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n436762,0,Cash loans,M,N,Y,1,112500.0,640080.0,29839.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-20973,-10361,-5279.0,-4400,,1,1,0,1,1,0,Laborers,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 1,,0.7445780634818681,0.1852020815902493,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1784.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n161105,0,Cash loans,F,N,N,0,135000.0,521280.0,35392.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-17544,-1518,-5844.0,-1079,,1,1,0,1,0,0,Sales staff,2.0,3,3,THURSDAY,11,0,0,0,0,0,0,Self-employed,0.8183768428793065,0.5907260008361911,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n319882,0,Cash loans,M,N,N,1,112500.0,364896.0,19795.5,315000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.018029,-13410,-1275,-3763.0,-4650,,1,1,1,1,0,0,Laborers,3.0,3,3,THURSDAY,3,0,0,0,0,1,1,Construction,,0.16214456766623808,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-2561.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n136506,0,Cash loans,F,N,Y,0,63000.0,152820.0,8532.0,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.031329,-22609,365243,-5237.0,-3958,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.7016715902225946,0.2955826421513093,0.0361,0.1227,0.9816,0.7484,0.0021,0.0,0.1207,0.1042,0.0417,0.0236,0.0294,0.0335,0.0019,0.003,0.0231,0.0762,0.9816,0.7583,0.0015,0.0,0.1034,0.0833,0.0417,0.0173,0.0202,0.0209,0.0,0.0,0.0364,0.1227,0.9816,0.7518,0.0022,0.0,0.1207,0.1042,0.0417,0.024,0.0299,0.0341,0.0019,0.0031,reg oper spec account,block of flats,0.0158,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1612.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n149355,0,Cash loans,F,N,N,0,90000.0,254700.0,14220.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-21343,365243,-12565.0,-4829,,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,0.7485636048196758,0.7342732604536182,0.8027454758994178,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2544.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n382315,0,Cash loans,M,N,Y,0,144000.0,805536.0,34258.5,720000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.011703,-22818,365243,-6268.0,-4486,,1,0,0,1,1,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.6635096885539535,0.6006575372857061,0.0082,0.0,0.9717,0.6124,0.0,0.0,0.0345,0.0417,0.0417,0.0191,0.0067,0.008,0.0,0.0,0.0084,0.0,0.9717,0.6276,0.0,0.0,0.0345,0.0417,0.0417,0.0196,0.0073,0.0083,0.0,0.0,0.0083,0.0,0.9717,0.6176,0.0,0.0,0.0345,0.0417,0.0417,0.0195,0.0068,0.0081,0.0,0.0,reg oper account,block of flats,0.0063,Mixed,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n445923,0,Cash loans,F,N,Y,1,112500.0,808650.0,26217.0,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.015221,-18809,365243,-11670.0,-2342,,1,0,0,1,0,0,,3.0,2,2,MONDAY,8,0,0,0,0,0,0,XNA,0.6227855492141908,0.03466696615371038,0.6092756673894402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n375007,0,Cash loans,F,N,N,0,112500.0,110331.0,11043.0,103500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.020713,-23368,365243,-5147.0,-112,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,8,0,0,0,0,0,0,XNA,0.8958818362969102,0.1847559987129696,0.41885428862332175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-9.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n426615,0,Cash loans,M,Y,Y,0,270000.0,1542645.0,58887.0,1440000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007305,-11394,-2154,-979.0,-3351,12.0,1,1,1,1,1,0,,2.0,3,3,MONDAY,14,0,0,0,0,0,0,Industry: type 9,0.21869845191302795,0.6460487859569967,0.6178261467332483,0.2887,0.207,0.9901,0.8640000000000001,0.1454,0.28,0.2414,0.375,0.4167,0.1904,0.2353,0.3228,0.0,0.0,0.2941,0.2148,0.9901,0.8693,0.1468,0.282,0.2414,0.375,0.4167,0.1947,0.2571,0.3364,0.0,0.0,0.2915,0.207,0.9901,0.8658,0.1464,0.28,0.2414,0.375,0.4167,0.1937,0.2394,0.3286,0.0,0.0,reg oper account,block of flats,0.3336,Panel,No,3.0,1.0,3.0,1.0,-3252.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n281160,0,Cash loans,F,N,Y,0,126000.0,360000.0,9495.0,360000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.018209,-19936,-1764,-5877.0,-3500,,1,1,0,1,1,0,Accountants,2.0,3,3,FRIDAY,13,0,0,0,0,0,0,Government,,0.4568499429859815,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n263231,0,Cash loans,M,N,N,0,189000.0,571500.0,24340.5,571500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.04622,-20166,-849,-9782.0,-3469,,1,1,1,1,1,0,High skill tech staff,2.0,1,1,SATURDAY,16,0,1,1,1,1,1,Business Entity Type 3,,0.6123999930985298,0.3996756156233169,0.0732,0.0458,0.9866,0.8164,0.0294,0.08,0.069,0.3333,0.0417,0.0103,0.0597,0.0798,0.0,0.0,0.0746,0.0476,0.9866,0.8236,0.0297,0.0806,0.069,0.3333,0.0417,0.0106,0.0652,0.0831,0.0,0.0,0.0739,0.0458,0.9866,0.8189,0.0296,0.08,0.069,0.3333,0.0417,0.0105,0.0607,0.0812,0.0,0.0,reg oper spec account,block of flats,0.0788,Panel,No,0.0,0.0,0.0,0.0,-2328.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n241086,0,Revolving loans,M,Y,Y,0,112500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.015221,-20986,-5090,-9573.0,-4549,7.0,1,1,1,1,0,0,Drivers,1.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,,0.1121058265844465,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n101014,0,Cash loans,F,Y,N,0,225000.0,1002870.0,42619.5,922500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.04622,-14102,-3067,-8085.0,-4841,2.0,1,1,0,1,0,0,Laborers,2.0,1,1,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 2,,0.6092550336586996,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1473.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n395122,0,Revolving loans,M,N,N,3,135000.0,225000.0,11250.0,,,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-13455,-546,-330.0,-2803,,1,1,1,1,0,0,Laborers,5.0,2,2,SUNDAY,13,0,0,0,1,1,1,Transport: type 4,0.4204568283768766,0.5779370052262373,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-982.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n169338,0,Cash loans,F,N,Y,2,540000.0,649462.5,48690.0,580500.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.072508,-11440,-782,-5308.0,-3979,,1,1,0,1,1,0,Core staff,4.0,1,1,MONDAY,15,0,0,0,0,0,0,School,,0.6571384735005296,0.5673792367572691,0.4062,0.1045,0.9871,0.8232,0.0,0.48,0.1379,0.6667,0.7083,0.0048,0.3312,0.2518,0.0,0.0288,0.4139,0.1085,0.9871,0.8301,0.0,0.4834,0.1379,0.6667,0.7083,0.0049,0.3618,0.2624,0.0,0.0304,0.4101,0.1045,0.9871,0.8256,0.0,0.48,0.1379,0.6667,0.7083,0.0049,0.3369,0.2564,0.0,0.0294,reg oper account,block of flats,0.3429,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n167593,0,Cash loans,F,N,Y,1,135000.0,1325475.0,56290.5,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-14134,-757,-7252.0,-4952,,1,1,0,1,0,0,Accountants,3.0,1,1,SATURDAY,15,0,0,0,1,1,0,Other,0.7189113499377784,0.7350618544577412,0.6109913280868294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2771.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n357741,0,Cash loans,M,N,N,0,150750.0,254700.0,17019.0,225000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.019689,-9059,-829,-318.0,-1525,,1,1,1,1,1,0,High skill tech staff,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,,0.6856900419293415,,0.0371,0.062,0.9737,0.6396,0.0021,0.0,0.1034,0.0833,0.125,0.0214,0.0286,0.0287,0.0077,0.0089,0.0378,0.0644,0.9737,0.6537,0.0021,0.0,0.1034,0.0833,0.125,0.0218,0.0312,0.0299,0.0078,0.0095,0.0375,0.062,0.9737,0.6444,0.0021,0.0,0.1034,0.0833,0.125,0.0217,0.0291,0.0292,0.0078,0.0091,reg oper account,block of flats,0.0261,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-849.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n213769,0,Cash loans,M,Y,Y,0,180000.0,528633.0,39654.0,472500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.020246,-14029,-120,-1477.0,-2902,1.0,1,1,0,1,0,0,Managers,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.7401266899086136,0.2801736298113313,0.4812493411434029,0.0619,0.0631,0.9771,0.6872,0.0096,0.0,0.1379,0.1667,0.2083,0.0544,0.0504,0.0544,0.0,0.0,0.063,0.0655,0.9772,0.6994,0.0097,0.0,0.1379,0.1667,0.2083,0.0557,0.0551,0.0567,0.0,0.0,0.0625,0.0631,0.9771,0.6914,0.0097,0.0,0.1379,0.1667,0.2083,0.0554,0.0513,0.0554,0.0,0.0,reg oper account,block of flats,0.0481,Panel,No,0.0,0.0,0.0,0.0,-364.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n300362,0,Cash loans,F,N,Y,0,166500.0,679500.0,22585.5,679500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-22250,365243,-12467.0,-4864,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,XNA,,0.571635826695548,0.3556387169923543,0.1247,,0.9831,,,0.0,0.2759,0.1667,,,,0.1098,,0.0054,0.1271,,0.9831,,,0.0,0.2759,0.1667,,,,0.1144,,0.0058,0.126,,0.9831,,,0.0,0.2759,0.1667,,,,0.1118,,0.0056,,block of flats,0.0876,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1611.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n118742,0,Cash loans,M,Y,Y,0,292500.0,518562.0,38083.5,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008625,-19405,-3504,-4603.0,-2779,20.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Self-employed,0.6373077751865487,0.6446023243115201,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-969.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n351712,0,Cash loans,M,Y,N,1,405000.0,942300.0,26041.5,675000.0,,Working,Secondary / secondary special,Married,House / apartment,0.003540999999999999,-12046,-2043,-113.0,-4037,6.0,1,1,0,1,0,0,Drivers,3.0,1,1,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.038788670341723966,0.25259869783397665,0.0526,0.0892,0.9896,0.8572,,0.0,0.2069,0.1667,0.2083,0.0904,,0.0726,,0.0,0.0536,0.0926,0.9896,0.8628,,0.0,0.2069,0.1667,0.2083,0.0925,,0.0756,,0.0,0.0531,0.0892,0.9896,0.8591,,0.0,0.2069,0.1667,0.2083,0.092,,0.0739,,0.0,reg oper account,block of flats,0.0768,Panel,No,2.0,0.0,2.0,0.0,-1388.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n220800,0,Cash loans,M,Y,N,0,135000.0,592560.0,35937.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16686,-4002,-5754.0,-238,17.0,1,1,1,1,0,0,Low-skill Laborers,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,,0.6711506035902992,0.1624419982223248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,3.0,0.0,-2.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n244288,0,Cash loans,M,Y,N,1,382500.0,467257.5,22725.0,328500.0,Unaccompanied,State servant,Higher education,Married,Municipal apartment,0.04622,-10512,-3747,-4673.0,-2796,64.0,1,1,0,1,0,0,Core staff,3.0,1,1,TUESDAY,17,0,0,0,0,1,1,Police,,0.7335729313738113,0.3185955240537633,,,0.9821,,,,0.2069,0.1667,,,,0.0433,,,,,0.9821,,,,0.2069,0.1667,,,,0.0451,,,,,0.9821,,,,0.2069,0.1667,,,,0.0441,,,,,0.0698,,No,0.0,0.0,0.0,0.0,-547.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n297104,0,Cash loans,M,N,Y,0,157500.0,526500.0,25456.5,526500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-21071,-271,-11802.0,-4271,,1,1,0,1,1,0,Laborers,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Construction,,0.7012502885352407,0.7151031019926098,0.0825,0.0818,0.9757,,,0.0,0.1379,0.1667,,,,0.0731,,0.0,0.084,0.0849,0.9757,,,0.0,0.1379,0.1667,,,,0.0761,,0.0,0.0833,0.0818,0.9757,,,0.0,0.1379,0.1667,,,,0.0744,,0.0,,block of flats,0.0575,Panel,No,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n267372,0,Cash loans,F,N,Y,2,166500.0,971280.0,51876.0,900000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.018634,-16872,-6400,-6656.0,-431,,1,1,1,1,1,0,Core staff,4.0,2,2,MONDAY,10,0,0,0,0,0,0,Kindergarten,,0.3309894929040055,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1111.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n175391,0,Cash loans,F,Y,Y,2,202500.0,700830.0,22738.5,585000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00733,-14700,-2174,-1014.0,-1022,15.0,1,1,0,1,0,0,Core staff,4.0,2,2,MONDAY,11,0,0,0,0,0,0,Postal,,0.6456486474528675,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1545.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n450638,0,Cash loans,M,Y,N,0,135000.0,545040.0,36553.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.011703,-9325,-925,-4097.0,-1999,8.0,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,Self-employed,0.1943522410607296,0.5431646483490534,0.6785676886853644,0.0825,0.0644,0.9752,,,0.0,0.1379,0.1667,,0.0719,,0.0704,,0.0,0.084,0.0668,0.9752,,,0.0,0.1379,0.1667,,0.0735,,0.0733,,0.0,0.0833,0.0644,0.9752,,,0.0,0.1379,0.1667,,0.0731,,0.0717,,0.0,,block of flats,0.0601,Panel,No,0.0,0.0,0.0,0.0,-484.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n150599,0,Cash loans,F,N,N,0,135000.0,545040.0,26640.0,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.008068,-9979,-1644,-4407.0,-2550,,1,1,1,1,1,0,,1.0,3,3,FRIDAY,6,0,0,0,0,1,1,Other,,0.332191471618915,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-725.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n232137,0,Cash loans,F,N,Y,0,81000.0,490495.5,30136.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009175,-20908,365243,-10549.0,-4305,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.6603337875012921,0.39449540531239935,0.4825,0.3636,0.9851,,,0.52,0.4483,0.3333,,,,,,,0.4916,0.3774,0.9851,,,0.5236,0.4483,0.3333,,,,,,,0.4871,0.3636,0.9851,,,0.52,0.4483,0.3333,,,,,,,,block of flats,0.3951,Panel,No,0.0,0.0,0.0,0.0,-3039.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n277920,0,Cash loans,F,N,Y,0,265500.0,1113840.0,47322.0,900000.0,Family,Pensioner,Higher education,Married,House / apartment,0.04622,-22580,365243,-8201.0,-2260,,1,0,0,1,1,0,,2.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.6836268662422353,0.2276129150623945,0.0619,0.0599,0.9771,0.5784,0.0,0.0,0.1379,0.1667,0.2083,0.0097,0.0504,0.05,0.0,0.0231,0.063,0.0622,0.9772,0.5949,0.0,0.0,0.1379,0.1667,0.2083,0.0099,0.0551,0.0521,0.0,0.0244,0.0625,0.0599,0.9771,0.584,0.0,0.0,0.1379,0.1667,0.2083,0.0099,0.0513,0.0509,0.0,0.0236,reg oper account,block of flats,0.0443,Panel,No,0.0,0.0,0.0,0.0,-516.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n294498,0,Cash loans,M,N,Y,1,225000.0,481855.5,47070.0,463500.0,Family,Working,Higher education,Civil marriage,House / apartment,0.008575,-15610,-2910,-3547.0,-3547,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,WEDNESDAY,16,0,1,1,0,0,0,Other,,0.4017235887802191,0.28371188263500075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-702.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n391625,0,Cash loans,F,N,Y,1,112500.0,225000.0,11074.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018209,-12712,-505,-867.0,-881,,1,1,0,1,0,0,Sales staff,3.0,3,3,SUNDAY,9,0,0,0,0,0,0,Self-employed,0.7552236341482077,0.6124882300498562,0.6863823354047934,0.0495,0.0501,0.9836,0.7756,0.0044,0.0,0.069,0.125,0.0417,0.0325,0.0403,0.0325,0.0,0.0,0.0504,0.052000000000000005,0.9836,0.7844,0.0044,0.0,0.069,0.125,0.0417,0.0332,0.0441,0.0338,0.0,0.0,0.05,0.0501,0.9836,0.7786,0.0044,0.0,0.069,0.125,0.0417,0.0331,0.040999999999999995,0.033,0.0,0.0,reg oper account,block of flats,0.0279,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-2415.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n327292,1,Cash loans,M,Y,Y,1,225000.0,314100.0,21375.0,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-10907,-1704,-987.0,-2997,10.0,1,1,0,1,0,0,,3.0,2,2,FRIDAY,11,0,0,0,0,1,1,Trade: type 3,,0.14471506492646755,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-489.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n293089,1,Cash loans,M,N,N,3,135000.0,1125000.0,36423.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006852,-13819,-802,-1471.0,-4256,,1,1,1,1,1,0,Laborers,5.0,3,3,TUESDAY,14,0,0,0,0,1,1,Industry: type 7,,0.05532150044680402,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n228638,0,Cash loans,F,N,N,0,54000.0,508495.5,20164.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-22100,365243,-12872.0,-4177,,1,0,0,1,1,0,,2.0,3,3,FRIDAY,6,0,0,0,0,0,0,XNA,,0.30721279203650026,0.43473324875017305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n121953,0,Cash loans,M,Y,Y,0,157500.0,378117.0,38745.0,342000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009657,-23583,-779,-7267.0,-1588,13.0,1,1,0,1,1,0,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.4096266241285178,0.6626377922738201,0.2598,0.2017,0.9811,0.7416,0.0472,0.28,0.2414,0.3333,0.375,0.1826,0.21100000000000002,0.2958,0.0039,0.0049,0.2647,0.2093,0.9811,0.7517,0.0476,0.282,0.2414,0.3333,0.375,0.1868,0.2305,0.3082,0.0039,0.0052,0.2623,0.2017,0.9811,0.7451,0.0475,0.28,0.2414,0.3333,0.375,0.1858,0.2146,0.3011,0.0039,0.005,reg oper account,block of flats,0.2956,Panel,No,2.0,0.0,2.0,0.0,-1599.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n452632,0,Cash loans,M,N,N,0,180000.0,203760.0,22072.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-14436,-890,-7840.0,-4417,,1,1,0,1,0,0,Drivers,2.0,3,3,SATURDAY,10,0,0,0,1,1,1,Transport: type 3,,0.58611328035326,0.6161216908872079,0.134,0.0055,0.9821,0.7552,0.0889,0.16,0.1379,0.3333,0.375,0.0197,0.1093,0.1355,0.0,0.2313,0.1366,0.0057,0.9821,0.7648,0.0897,0.1611,0.1379,0.3333,0.375,0.0201,0.1194,0.1412,0.0,0.2449,0.1353,0.0055,0.9821,0.7585,0.0894,0.16,0.1379,0.3333,0.375,0.02,0.1112,0.138,0.0,0.2361,reg oper account,block of flats,0.2055,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2008.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,4.0\n452402,0,Cash loans,F,N,Y,0,450000.0,1214100.0,67923.0,1125000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.011657,-19024,-178,-3126.0,-2569,,1,1,0,1,0,0,Managers,1.0,1,1,TUESDAY,14,0,0,0,0,0,0,Self-employed,0.5261086472105664,0.6725487196858153,0.6397075677637197,0.0082,0.0,0.9732,0.6328,0.0011,0.0,0.0345,0.0417,0.0833,0.0061,0.0067,0.0073,0.0,0.0,0.0084,0.0,0.9732,0.6472,0.0012,0.0,0.0345,0.0417,0.0833,0.0063,0.0073,0.0076,0.0,0.0,0.0083,0.0,0.9732,0.6377,0.0011,0.0,0.0345,0.0417,0.0833,0.0062,0.0068,0.0074,0.0,0.0,reg oper account,block of flats,0.0063,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1865.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n352709,0,Cash loans,M,N,N,0,135000.0,900000.0,48825.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-11297,-616,-5105.0,-3323,,1,1,0,1,0,0,Security staff,2.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.37500864478401946,0.4076291519727456,0.5298898341969072,0.0928,,0.9816,,,0.0,0.2069,0.1667,,,,0.0844,,0.0,0.0945,,0.9816,,,0.0,0.2069,0.1667,,,,0.0879,,0.0,0.0937,,0.9816,,,0.0,0.2069,0.1667,,,,0.0859,,0.0,,block of flats,0.0664,Panel,No,0.0,0.0,0.0,0.0,-2974.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n410040,0,Cash loans,M,Y,Y,1,121500.0,1056447.0,31018.5,922500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.006305,-13313,-1137,-2159.0,-4735,13.0,1,1,0,1,0,0,Security staff,3.0,3,3,FRIDAY,9,0,0,0,0,0,0,Government,,0.7042969663309592,0.7194907850918436,0.0825,0.115,0.9796,0.7212,0.0112,0.0,0.1379,0.1667,0.2083,0.0966,,0.0712,,0.0758,0.084,0.1194,0.9796,0.7321,0.0113,0.0,0.1379,0.1667,0.2083,0.0988,,0.0741,,0.0803,0.0833,0.115,0.9796,0.7249,0.0113,0.0,0.1379,0.1667,0.2083,0.0983,,0.0724,,0.0774,reg oper account,block of flats,0.0786,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2300.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n302012,0,Cash loans,F,Y,N,0,157500.0,622188.0,20349.0,472500.0,,Working,Secondary / secondary special,Married,House / apartment,0.019101,-19965,-3584,-3238.0,-3485,6.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.25998430408279016,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-248.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n450821,0,Cash loans,M,Y,Y,0,135000.0,755190.0,32125.5,675000.0,Family,Commercial associate,Higher education,Single / not married,House / apartment,0.003069,-19817,-634,-8414.0,-3371,13.0,1,1,0,1,0,0,Managers,1.0,3,3,TUESDAY,15,0,0,0,0,1,1,Business Entity Type 3,0.5656396799825857,0.6499791555482656,0.5531646987710016,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-650.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n350325,0,Cash loans,F,N,Y,0,90000.0,263686.5,16258.5,238500.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.008625,-21478,365243,-454.0,-4369,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.4878240478494681,,0.0495,0.0,0.9737,0.6396,0.0041,0.0,0.1034,0.125,0.1667,0.0299,0.0403,0.0399,0.0,0.0,0.0504,0.0,0.9737,0.6537,0.0042,0.0,0.1034,0.125,0.1667,0.0306,0.0441,0.0416,0.0,0.0,0.05,0.0,0.9737,0.6444,0.0042,0.0,0.1034,0.125,0.1667,0.0304,0.040999999999999995,0.0407,0.0,0.0,reg oper account,block of flats,0.0337,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-631.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n265011,0,Cash loans,F,N,Y,0,121500.0,355536.0,17235.0,270000.0,Other_B,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.01885,-16607,-1219,-7896.0,-160,,1,1,1,1,1,0,Laborers,1.0,2,2,WEDNESDAY,15,0,1,1,0,1,1,Industry: type 7,0.405210635072114,0.5380271040828426,,0.0124,0.028999999999999998,0.9886,,,0.0,0.069,0.0417,,,,0.0113,,0.0036,0.0126,0.0301,0.9886,,,0.0,0.069,0.0417,,,,0.0117,,0.0038,0.0125,0.028999999999999998,0.9886,,,0.0,0.069,0.0417,,,,0.0115,,0.0037,,block of flats,0.0089,\"Stone, brick\",No,5.0,2.0,5.0,1.0,-659.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n197928,0,Cash loans,F,N,Y,0,148500.0,291915.0,17770.5,252000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00496,-19719,-1586,-13.0,-3245,,1,1,0,1,0,0,Cooking staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Industry: type 4,,0.6927671439004114,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-414.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n282303,0,Cash loans,M,N,Y,0,225000.0,332946.0,17127.0,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-23575,365243,-2285.0,-5743,,1,0,0,1,1,0,,2.0,1,1,SATURDAY,10,0,0,0,0,0,0,XNA,0.2588045626436927,0.7221388815121733,0.5388627065779676,0.0732,,0.9767,,,,0.1379,0.1667,,,,,,,0.0746,,0.9767,,,,0.1379,0.1667,,,,,,,0.0739,,0.9767,,,,0.1379,0.1667,,,,,,,,block of flats,0.045,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1477.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n359526,0,Cash loans,M,N,Y,0,112500.0,343800.0,16155.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00963,-9063,-1669,-3885.0,-1715,,1,1,0,1,0,0,High skill tech staff,1.0,2,2,THURSDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.7095664377006082,0.5117371517699516,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1356.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n250151,0,Cash loans,M,Y,Y,0,202500.0,468000.0,15228.0,468000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.026392,-19395,-971,-5609.0,-900,2.0,1,1,0,1,1,0,Low-skill Laborers,2.0,2,2,FRIDAY,11,0,0,0,1,1,0,Business Entity Type 3,,0.6048891120579138,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-889.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n188796,0,Cash loans,M,Y,Y,1,157500.0,284400.0,16456.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.018634,-13320,-1545,-6668.0,-4051,12.0,1,1,0,1,0,0,Drivers,3.0,2,2,SUNDAY,11,0,0,0,1,0,1,Business Entity Type 3,0.2915561676626228,0.0489377806133908,,0.1031,0.0436,0.9871,0.8232,0.0,0.08,0.069,0.3333,0.375,0.2035,0.0841,0.1022,0.0,0.0561,0.105,0.0452,0.9871,0.8301,0.0,0.0806,0.069,0.3333,0.375,0.2081,0.0918,0.1065,0.0,0.0594,0.1041,0.0436,0.9871,0.8256,0.0,0.08,0.069,0.3333,0.375,0.207,0.0855,0.10400000000000001,0.0,0.0573,,block of flats,0.1035,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n167266,0,Cash loans,M,Y,Y,0,247500.0,1113840.0,51745.5,900000.0,,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-23196,365243,-11596.0,-4303,29.0,1,0,0,1,1,0,,2.0,3,1,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.5381412428362743,0.6212263380626669,0.1113,0.0,0.9841,0.7824,0.027000000000000003,0.12,0.1034,0.3333,0.0,0.0538,0.0899,0.1124,0.0039,0.0066,0.1134,0.0,0.9841,0.7909,0.0273,0.1208,0.1034,0.3333,0.0,0.055,0.0983,0.1171,0.0039,0.0069,0.1124,0.0,0.9841,0.7853,0.0272,0.12,0.1034,0.3333,0.0,0.0547,0.0915,0.1144,0.0039,0.0067,reg oper account,block of flats,0.1046,Panel,No,2.0,0.0,2.0,0.0,-459.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n419231,0,Cash loans,F,N,N,0,85500.0,1288350.0,37800.0,1125000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.003813,-21520,365243,-4109.0,-4522,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,XNA,0.7176610605569108,0.6551402989690703,0.6058362647264226,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1649.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n446448,0,Revolving loans,F,N,Y,1,283500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-20372,-738,-12338.0,-1874,,1,1,0,1,0,0,,3.0,1,1,SATURDAY,17,0,0,0,0,0,0,Realtor,,0.6823674652668459,0.20915469884100693,0.1392,0.0491,0.9831,0.7688,0.1628,0.08,0.0345,0.625,0.6667,0.0,0.1051,0.1131,0.0386,0.2033,0.1418,0.0509,0.9831,0.7779,0.1643,0.0806,0.0345,0.625,0.6667,0.0,0.1148,0.1179,0.0389,0.2153,0.1405,0.0491,0.9831,0.7719,0.1638,0.08,0.0345,0.625,0.6667,0.0,0.1069,0.1152,0.0388,0.2076,reg oper account,block of flats,0.1117,Panel,No,0.0,0.0,0.0,0.0,-722.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n407956,0,Cash loans,F,N,N,0,126000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-16868,-10282,-5361.0,-424,,1,1,1,1,1,0,Medicine staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Medicine,,0.6313761375394793,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,4.0,0.0,-2487.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n267081,0,Cash loans,F,N,Y,0,189000.0,1092190.5,48852.0,927000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-23640,365243,-3456.0,-4724,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,12,0,0,0,0,0,0,XNA,,0.28542487097246394,,0.0608,0.0,0.9757,0.6668,,0.0,0.1034,0.1667,,0.0343,0.0496,0.031,,0.0235,0.062,0.0,0.9757,0.6798,,0.0,0.1034,0.1667,,0.0351,0.0542,0.0323,,0.0249,0.0614,0.0,0.9757,0.6713,,0.0,0.1034,0.1667,,0.0349,0.0504,0.0316,,0.024,,block of flats,0.0381,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n266388,0,Cash loans,F,N,Y,0,135000.0,755190.0,33394.5,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.00963,-17326,-380,-1675.0,-871,,1,1,0,1,1,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5762184993339008,0.6075573001388961,0.1856,0.1091,0.993,0.9048,0.0478,0.2,0.1724,0.3333,0.375,0.0047,0.1513,0.1936,0.0,0.0,0.1891,0.1132,0.993,0.9085,0.0482,0.2014,0.1724,0.3333,0.375,0.0048,0.1653,0.2018,0.0,0.0,0.1874,0.1091,0.993,0.9061,0.0481,0.2,0.1724,0.3333,0.375,0.0048,0.1539,0.1971,0.0,0.0,reg oper spec account,block of flats,0.1876,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n122866,0,Cash loans,F,N,Y,1,180000.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-8157,-443,-146.0,-454,,1,1,0,1,0,0,Sales staff,3.0,3,3,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.54554030268264,,0.0928,0.1005,0.9767,0.6804,0.0143,0.0,0.2069,0.1667,0.2083,0.0794,0.0756,0.0868,0.0,0.0,0.0945,0.1043,0.9767,0.6929,0.0144,0.0,0.2069,0.1667,0.2083,0.0812,0.0826,0.0904,0.0,0.0,0.0937,0.1005,0.9767,0.6847,0.0144,0.0,0.2069,0.1667,0.2083,0.0807,0.077,0.0884,0.0,0.0,reg oper spec account,block of flats,0.0761,Panel,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n240962,0,Cash loans,M,Y,Y,0,135000.0,450000.0,30442.5,450000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.018634,-12610,-5313,-1745.0,-4482,12.0,1,1,1,1,0,1,Laborers,2.0,2,2,THURSDAY,10,0,1,1,0,1,1,Business Entity Type 3,,0.17237268691215787,0.5280927512030451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-594.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n328622,0,Cash loans,F,N,Y,0,112500.0,258709.5,14850.0,234000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.010147,-20806,365243,-1496.0,-2673,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.6003857311808092,0.7062051096536562,0.1186,,0.9762,,,,0.1034,0.1667,,,,0.0543,,,0.1208,,0.9762,,,,0.1034,0.1667,,,,0.0566,,,0.1197,,0.9762,,,,0.1034,0.1667,,,,0.0553,,,,block of flats,0.0427,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-660.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n330777,0,Cash loans,F,N,Y,0,108000.0,942300.0,27679.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.003813,-19429,365243,-7208.0,-2982,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.4584022442459947,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1842.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n300746,0,Cash loans,F,Y,N,0,180000.0,1258650.0,53325.0,1125000.0,Unaccompanied,State servant,Higher education,Widow,House / apartment,0.00823,-21136,-5958,-2083.0,-4694,8.0,1,1,1,1,0,0,,1.0,2,2,FRIDAY,12,0,0,0,0,0,0,Other,0.8162081627349874,0.4461007972975477,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1754.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n295999,0,Cash loans,F,N,N,0,202500.0,742500.0,29574.0,742500.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.032561,-21909,365243,-9202.0,-4047,,1,0,0,1,1,0,,1.0,1,1,SATURDAY,12,0,0,0,0,0,0,XNA,,0.7939870350189359,,0.0649,0.0,0.9314,,,0.12,0.1034,0.2083,,,,0.079,,0.0454,0.0662,0.0,0.9315,,,0.1208,0.1034,0.2083,,,,0.0824,,0.0481,0.0656,0.0,0.9314,,,0.12,0.1034,0.2083,,,,0.0805,,0.0464,,block of flats,0.0721,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2364.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n171021,0,Cash loans,M,Y,N,2,135000.0,360000.0,18508.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-12331,-1114,-5526.0,-4743,11.0,1,1,0,1,1,0,Drivers,4.0,2,2,SUNDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.2427675323300304,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1326.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n106407,0,Cash loans,F,N,Y,2,225000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.005084,-13904,-633,-90.0,-4066,,1,1,0,1,0,0,Security staff,3.0,2,2,MONDAY,16,0,0,0,1,1,0,Trade: type 7,,0.5773821640462785,0.4632753280912678,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2202.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n303927,0,Cash loans,M,Y,Y,1,225000.0,545040.0,26509.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-9414,-162,-3847.0,-2084,12.0,1,1,0,1,0,0,Drivers,3.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Industry: type 4,0.1555461664489664,0.44544168162419395,0.8245949709919925,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1536.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n452138,0,Cash loans,F,N,N,0,67500.0,1056636.0,30892.5,882000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-21108,365243,-6662.0,-3463,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.3420278293317465,0.5567274263630174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1625.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n429163,0,Cash loans,M,N,Y,0,180000.0,113746.5,9247.5,103500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-17093,-4316,-9627.0,-633,,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.614769427878572,0.5046813193144684,0.0825,,0.9732,,,0.0,0.1379,0.1667,,,,0.0636,,,0.084,,0.9732,,,0.0,0.1379,0.1667,,,,0.0663,,,0.0833,,0.9732,,,0.0,0.1379,0.1667,,,,0.0647,,,,block of flats,0.05,Panel,No,2.0,0.0,2.0,0.0,-1892.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n389019,0,Cash loans,M,Y,N,0,247500.0,152820.0,16177.5,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010276,-16774,-1281,-4454.0,-318,24.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,,0.6071967855528649,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-1463.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n348821,0,Revolving loans,F,N,Y,0,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006305,-7890,-1199,-2599.0,-571,,1,1,0,1,0,0,Cooking staff,2.0,3,3,FRIDAY,8,0,1,1,0,1,1,Restaurant,0.22679658739548705,0.561944488657868,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-446.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n349069,0,Cash loans,F,N,Y,0,67500.0,195543.0,9981.0,148500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.030755,-18675,-6880,-1584.0,-2221,,1,1,1,1,0,0,,1.0,2,2,FRIDAY,18,0,0,0,0,0,0,Government,,0.7123653698798655,0.4956658291397297,0.0495,0.0,0.9737,0.6396,0.0042,0.0,0.1034,0.125,0.1667,0.0307,0.0403,0.0305,0.0,0.0,0.0504,0.0,0.9737,0.6537,0.0043,0.0,0.1034,0.125,0.1667,0.0314,0.0441,0.0318,0.0,0.0,0.05,0.0,0.9737,0.6444,0.0042,0.0,0.1034,0.125,0.1667,0.0312,0.040999999999999995,0.0311,0.0,0.0,reg oper account,block of flats,0.0263,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1739.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n225038,0,Cash loans,F,N,Y,0,193500.0,550980.0,40221.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-14934,-91,-2136.0,-654,,1,1,0,1,0,0,Cooking staff,2.0,2,2,THURSDAY,14,0,0,0,0,1,1,Industry: type 7,0.5047760104895018,0.6897101798211155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,0.0,-2548.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n277551,0,Cash loans,M,Y,Y,1,202500.0,143910.0,15399.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-11234,-2724,-1779.0,-3181,7.0,1,1,1,1,1,0,,3.0,2,2,FRIDAY,8,0,0,0,0,0,0,Other,,0.7625535342579832,0.4206109640437848,0.0485,0.0,0.9841,0.7824,0.0103,0.0,0.0345,0.1667,0.0417,0.0152,0.0395,0.0272,0.0,0.0,0.0494,0.0,0.9841,0.7909,0.0104,0.0,0.0345,0.1667,0.0417,0.0156,0.0432,0.0284,0.0,0.0,0.0489,0.0,0.9841,0.7853,0.0104,0.0,0.0345,0.1667,0.0417,0.0155,0.0402,0.0277,0.0,0.0,reg oper account,block of flats,0.0387,Block,No,3.0,0.0,3.0,0.0,-10.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n156446,0,Cash loans,M,Y,Y,2,225000.0,310671.0,24673.5,256500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-13773,-3575,-7364.0,-4270,11.0,1,1,0,1,0,0,Core staff,4.0,2,2,FRIDAY,10,0,0,0,0,0,0,Self-employed,0.7607400199764892,0.5637200729792761,0.5762088360175724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2409.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n125517,0,Cash loans,M,N,N,0,202500.0,312768.0,16096.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-17233,-1496,-1203.0,-198,,1,1,0,1,1,0,Low-skill Laborers,2.0,1,1,THURSDAY,15,0,0,0,0,0,0,Medicine,,0.6116003347889154,0.5531646987710016,0.1557,0.083,0.9786,0.7076,0.0406,0.0,0.1034,0.1667,0.2083,0.0938,0.1261,0.0568,0.0039,0.0027,0.1586,0.0861,0.9786,0.7190000000000001,0.0409,0.0,0.1034,0.1667,0.2083,0.0959,0.1377,0.0592,0.0039,0.0028,0.1572,0.083,0.9786,0.7115,0.0408,0.0,0.1034,0.1667,0.2083,0.0954,0.1283,0.0578,0.0039,0.0027,reg oper account,block of flats,0.0452,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n147492,0,Cash loans,M,Y,Y,0,301500.0,339948.0,24304.5,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008625,-23082,365243,-7872.0,-3745,7.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.7105517810122959,0.4938628816996244,0.1113,0.0902,0.9891,,,0.12,0.1034,0.3333,,0.0649,,0.1142,,,0.1134,0.0936,0.9891,,,0.1208,0.1034,0.3333,,0.0664,,0.1189,,,0.1124,0.0902,0.9891,,,0.12,0.1034,0.3333,,0.066,,0.1162,,,,block of flats,0.1075,Panel,No,0.0,0.0,0.0,0.0,-374.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n168664,0,Cash loans,F,N,N,0,157500.0,781920.0,23836.5,675000.0,\"Spouse, partner\",Pensioner,Higher education,Married,House / apartment,0.035792000000000004,-23584,365243,-8147.0,-4640,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,,0.6615197273418756,0.5460231970049609,0.0557,0.0727,0.9891,0.8504,0.0321,0.0,0.1379,0.1667,0.2083,0.0498,0.0437,0.0601,0.0077,0.0112,0.0567,0.0755,0.9891,0.8563,0.0324,0.0,0.1379,0.1667,0.2083,0.0509,0.0478,0.0626,0.0078,0.0119,0.0562,0.0727,0.9891,0.8524,0.0323,0.0,0.1379,0.1667,0.2083,0.0507,0.0445,0.0612,0.0078,0.0114,reg oper account,block of flats,0.0497,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n263230,0,Cash loans,F,N,N,0,72000.0,95940.0,10462.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.005313,-10941,-3255,-641.0,-3248,,1,1,0,1,0,0,Private service staff,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6465636631961561,0.7421816117614419,0.0021,0.0,0.9662,0.5376,0.0,0.0,0.0,0.0,0.0417,0.0136,0.0017,0.0011,0.0,0.0,0.0021,0.0,0.9662,0.5557,0.0,0.0,0.0,0.0,0.0417,0.0139,0.0018,0.0012,0.0,0.0,0.0021,0.0,0.9662,0.5438,0.0,0.0,0.0,0.0,0.0417,0.0138,0.0017,0.0011,0.0,0.0,not specified,block of flats,0.0009,Wooden,Yes,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n439412,0,Cash loans,F,N,N,1,67500.0,545040.0,25537.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.018029,-13563,-248,-2801.0,-4628,,1,1,1,1,1,0,,3.0,3,3,WEDNESDAY,5,0,0,0,0,0,0,Business Entity Type 3,,0.16297214507861668,,0.001,0.0,0.9811,,,,,0.0,,,,0.0008,,0.0,0.0011,0.0,0.9811,,,,,0.0,,,,0.0008,,0.0,0.001,0.0,0.9811,,,,,0.0,,,,0.0008,,0.0,,terraced house,0.0006,Wooden,Yes,3.0,0.0,3.0,0.0,-7.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n411758,0,Cash loans,F,N,Y,0,157500.0,675000.0,32602.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-15904,-1366,-1628.0,-4001,,1,1,0,1,0,0,Sales staff,1.0,2,2,SATURDAY,9,0,0,0,0,0,0,Government,,0.3833559306492068,0.4974688893052743,0.2067,0.1161,0.9881,0.8368,0.0243,0.11,0.0948,0.3438,0.3854,0.1339,0.1683,0.1572,0.0154,0.0327,0.1744,0.1087,0.9871,0.8301,0.0,0.0806,0.069,0.3333,0.375,0.0867,0.1524,0.1253,0.0,0.0072,0.1999,0.1075,0.9876,0.8323,0.0345,0.08,0.069,0.3333,0.375,0.1372,0.1637,0.1521,0.0097,0.0337,reg oper account,block of flats,0.1664,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-842.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n133035,0,Cash loans,F,Y,Y,1,103500.0,526491.0,29529.0,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-15413,-7377,-1170.0,-1783,6.0,1,1,0,1,0,0,Core staff,3.0,2,2,WEDNESDAY,11,0,0,0,0,1,1,School,,0.6731889034536185,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-683.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n184837,0,Cash loans,M,N,Y,1,180000.0,441616.5,35536.5,378000.0,\"Spouse, partner\",Working,Higher education,Married,With parents,0.020713,-15670,-1654,-1825.0,-1851,,1,1,0,1,0,0,Cooking staff,3.0,3,3,SUNDAY,8,0,0,0,1,1,0,Restaurant,0.2736521324315353,0.2487051809870233,0.44539624156083396,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-673.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n250256,0,Cash loans,F,N,N,0,90000.0,664569.0,29403.0,594000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-13719,-837,-4877.0,-4877,,1,1,0,1,0,0,,2.0,3,3,MONDAY,10,0,0,0,1,1,0,Business Entity Type 3,0.3714747133995899,0.4021546314465526,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1839.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n246605,0,Cash loans,F,Y,N,1,67500.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-17757,-153,-3881.0,-1284,12.0,1,1,1,1,1,0,,3.0,3,2,SUNDAY,13,0,0,0,0,0,0,Other,,0.5943952656395836,,0.0907,0.1045,0.9836,0.7756,0.0169,0.0,0.2069,0.1667,0.2083,0.0652,0.07400000000000001,0.0822,0.0,0.0,0.0924,0.1085,0.9836,0.7844,0.0171,0.0,0.2069,0.1667,0.2083,0.0667,0.0808,0.0856,0.0,0.0,0.0916,0.1045,0.9836,0.7786,0.017,0.0,0.2069,0.1667,0.2083,0.0663,0.0752,0.0836,0.0,0.0,reg oper account,block of flats,0.0739,Panel,No,1.0,0.0,1.0,0.0,-1616.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n421780,0,Cash loans,M,N,Y,0,171000.0,1002726.0,29448.0,837000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-23175,365243,-4320.0,-4353,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.6596467861933499,0.5919766183185521,0.0619,0.0474,0.9747,,,0.0,0.1379,0.125,,0.0317,,0.0444,,0.0,0.063,0.0492,0.9747,,,0.0,0.1379,0.125,,0.0324,,0.0463,,0.0,0.0625,0.0474,0.9747,,,0.0,0.1379,0.125,,0.0322,,0.0452,,0.0,,block of flats,0.0448,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-221.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n261206,0,Revolving loans,M,N,Y,0,112500.0,337500.0,16875.0,337500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-15628,-296,-9758.0,-2821,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.5879766347335327,0.5662031391793163,,0.034,0.021,0.9697,,,0.0,0.1379,0.125,,,,0.0234,,0.0283,0.0347,0.0218,0.9697,,,0.0,0.1379,0.125,,,,0.0244,,0.0299,0.0344,0.021,0.9697,,,0.0,0.1379,0.125,,,,0.0238,,0.0289,,,0.047,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-6.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n306282,0,Cash loans,F,N,Y,0,112500.0,755190.0,30078.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.005084,-23399,365243,-7554.0,-4913,,1,0,0,1,1,0,,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,XNA,,0.6716764597481939,0.4507472818545589,0.3639,,0.9856,0.8028,,0.4,0.3448,0.3333,,0.0517,,0.2534,,,0.3708,,0.9856,0.8105,,0.4028,0.3448,0.3333,,0.0529,,0.2641,,,0.3674,,0.9856,0.8054,,0.4,0.3448,0.3333,,0.0526,,0.258,,,reg oper account,block of flats,0.3278,,No,0.0,0.0,0.0,0.0,-331.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n315393,0,Cash loans,M,Y,Y,0,270000.0,521280.0,35392.5,450000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.010006,-19826,-1071,-719.0,-2783,6.0,1,1,0,1,0,0,Drivers,2.0,2,2,SUNDAY,9,0,0,0,0,0,0,Transport: type 3,,0.3729945832212916,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,2.0,0.0,2.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n130057,0,Cash loans,F,N,N,0,112500.0,576072.0,18216.0,405000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.020713,-14162,-153,-1574.0,-2713,,1,1,0,1,0,0,Sales staff,1.0,3,3,WEDNESDAY,5,0,0,0,0,0,0,Self-employed,,0.0973838342200956,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-154.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n195714,0,Cash loans,M,Y,Y,2,225000.0,835605.0,22041.0,697500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-10868,-1891,-5018.0,-3415,64.0,1,1,0,1,1,0,,4.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Transport: type 4,0.481258580031487,0.6372650515191888,0.7421816117614419,0.2206,0.1684,0.9816,0.7484,0.085,0.24,0.2069,0.3333,0.375,0.1071,0.1799,0.2157,0.0039,0.0073,0.2248,0.1747,0.9816,0.7583,0.0857,0.2417,0.2069,0.3333,0.375,0.1096,0.1965,0.2247,0.0039,0.0077,0.2228,0.1684,0.9816,0.7518,0.0855,0.24,0.2069,0.3333,0.375,0.109,0.183,0.2195,0.0039,0.0075,reg oper account,block of flats,0.2168,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-534.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n372514,0,Revolving loans,F,N,Y,1,81000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-13426,-408,-3607.0,-4196,,1,1,0,1,0,0,Sales staff,2.0,2,2,SUNDAY,10,0,0,0,0,1,1,Self-employed,,0.2890961456777387,0.23791607950711405,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-205.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n384988,0,Cash loans,F,N,Y,0,135000.0,168102.0,16506.0,148500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.011657,-20660,365243,-3101.0,-4098,,1,0,0,1,1,0,,1.0,1,1,FRIDAY,17,0,0,0,0,0,0,XNA,,0.6790500364753658,0.7992967832109371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3037.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n141251,0,Cash loans,M,Y,N,1,225000.0,835380.0,40189.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006629,-15357,-399,-1737.0,-4243,7.0,1,1,0,1,0,0,Managers,3.0,2,2,FRIDAY,4,0,0,0,0,0,0,Business Entity Type 3,,0.4134024226434351,0.4294236843421945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-600.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n235227,0,Cash loans,M,N,Y,1,360000.0,755190.0,38556.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-15691,-1960,-9451.0,-4209,,1,1,1,1,0,0,Managers,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.533621680193979,0.2735646775174348,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-605.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n112415,0,Cash loans,F,N,N,1,180000.0,1061599.5,28134.0,927000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.025164,-15517,-2023,-304.0,-4748,,1,1,0,1,0,0,Medicine staff,2.0,2,2,WEDNESDAY,10,0,0,0,1,1,0,Medicine,0.6142845952419053,0.1934633397842611,0.2940831009471255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2048.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n215667,0,Cash loans,F,N,Y,0,112500.0,781920.0,28215.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.030755,-20166,365243,-2647.0,-3528,,1,0,0,1,1,0,,1.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,XNA,0.7474856364027183,0.7468793444856168,0.4578995512067301,0.2639,0.0978,0.9816,0.7484,0.0499,0.08,0.069,0.3333,0.0417,0.0409,0.21100000000000002,0.0961,0.0193,0.0887,0.2689,0.1015,0.9816,0.7583,0.0504,0.0806,0.069,0.3333,0.0417,0.0418,0.2305,0.1001,0.0195,0.0939,0.2665,0.0978,0.9816,0.7518,0.0502,0.08,0.069,0.3333,0.0417,0.0416,0.2146,0.0978,0.0194,0.0906,reg oper account,specific housing,0.1054,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n325727,0,Cash loans,F,N,Y,0,270000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00733,-21656,-5052,-10762.0,-4609,,1,1,0,1,1,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Other,,0.7030932404343011,0.09322509537747448,0.2598,0.1958,0.9856,,,0.28,0.2414,0.3333,0.0417,0.2841,,0.2756,,0.0034,0.2647,0.2032,0.9856,,,0.282,0.2414,0.3333,0.0417,0.2906,,0.2871,,0.0036,0.2623,0.1958,0.9856,,,0.28,0.2414,0.3333,0.0417,0.2891,,0.2805,,0.0035,,block of flats,0.2776,Panel,No,0.0,0.0,0.0,0.0,-1812.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n266484,0,Cash loans,F,N,Y,2,54000.0,360000.0,18976.5,360000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.0228,-15477,-948,-6778.0,-4888,,1,1,1,1,1,0,Laborers,4.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Business Entity Type 1,0.4954439674806695,0.4533955986311956,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n174435,0,Cash loans,F,N,N,0,117000.0,225000.0,6822.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018209,-10392,-1216,-548.0,-3054,,1,1,1,1,1,0,Sales staff,1.0,3,3,WEDNESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.5359990001923877,0.5391302766181572,0.11104240226943142,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1170.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n371330,0,Cash loans,F,N,Y,0,81000.0,124380.0,12298.5,112500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-21512,365243,-5429.0,-4477,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,0.7706614278904325,0.4108964143848289,0.6863823354047934,0.0876,0.0843,0.9796,,,0.0,0.2069,0.1667,,0.0721,,0.0822,,0.0,0.0893,0.0875,0.9796,,,0.0,0.2069,0.1667,,0.0738,,0.0857,,0.0,0.0885,0.0843,0.9796,,,0.0,0.2069,0.1667,,0.0734,,0.0837,,0.0,,block of flats,0.0866,Panel,No,4.0,0.0,4.0,0.0,-414.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,5.0\n361898,0,Cash loans,F,N,Y,0,157500.0,547344.0,29290.5,472500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018801,-21876,-9475,-9335.0,-3979,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,12,0,0,0,0,0,0,School,0.7548099759621326,0.698517343876388,0.3706496323299817,0.3701,0.282,0.9831,0.7688,0.0,0.4,0.3448,0.3333,0.375,0.0,0.3018,0.3918,0.0,0.0,0.3771,0.2926,0.9831,0.7779,0.0,0.4028,0.3448,0.3333,0.375,0.0,0.3297,0.4082,0.0,0.0,0.3737,0.282,0.9831,0.7719,0.0,0.4,0.3448,0.3333,0.375,0.0,0.307,0.3988,0.0,0.0,reg oper account,block of flats,0.495,Panel,No,2.0,1.0,2.0,0.0,-769.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n165398,0,Cash loans,M,N,N,0,157500.0,168102.0,13279.5,148500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-10230,-960,-3449.0,-1631,,1,1,1,1,0,0,Laborers,2.0,2,2,TUESDAY,17,0,1,1,1,1,1,Business Entity Type 1,0.0758218319672177,0.7039871864878825,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-478.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n273804,0,Cash loans,M,N,Y,0,315000.0,755190.0,38740.5,675000.0,Family,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.009175,-16493,-1240,-3820.0,-6,,1,1,0,1,0,1,Laborers,2.0,2,2,TUESDAY,12,0,1,1,1,0,0,Business Entity Type 3,0.5288296023012687,0.5625472914549025,0.5046813193144684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n441456,0,Cash loans,F,N,N,0,90000.0,288873.0,16258.5,238500.0,Unaccompanied,Pensioner,Higher education,Civil marriage,House / apartment,0.022625,-22082,365243,-2784.0,-4062,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.5528706535645277,0.5867400085415683,0.2938,0.1577,0.9776,0.6940000000000001,0.1498,0.32,0.2759,0.3333,0.375,0.1978,0.2396,0.2803,0.0039,0.006999999999999999,0.2994,0.1636,0.9777,0.706,0.1512,0.3222,0.2759,0.3333,0.375,0.2023,0.2617,0.2921,0.0039,0.0074,0.2967,0.1577,0.9776,0.6981,0.1508,0.32,0.2759,0.3333,0.375,0.2013,0.2437,0.2854,0.0039,0.0072,reg oper account,block of flats,0.3037,Panel,No,0.0,0.0,0.0,0.0,-2018.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n183668,0,Cash loans,F,N,Y,0,90000.0,1125000.0,47794.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-21627,365243,-10676.0,-4789,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.6829501530825937,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-853.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n167832,0,Revolving loans,M,N,N,0,157500.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-14527,-1693,-3628.0,-4039,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6903182361886169,0.12610055883623253,,,0.9791,,,,,,,,,0.0775,,,,,0.9791,,,,,,,,,0.0808,,,,,0.9791,,,,,,,,,0.0789,,,,,0.0684,,No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,0.0\n446260,0,Cash loans,F,N,N,0,270000.0,1281712.5,50962.5,1179000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,Municipal apartment,0.072508,-19922,365243,-13957.0,-3437,,1,0,0,1,1,0,,1.0,1,1,WEDNESDAY,14,0,0,0,0,0,0,XNA,0.5593791451362995,0.6977338973841531,0.511891801533151,0.5753,0.3152,0.9806,0.7348,0.0,0.96,0.4138,0.4583,0.5,0.0,0.469,0.5822,0.0,0.0564,0.5861,0.3271,0.9806,0.7452,0.0,0.9667,0.4138,0.4583,0.5,0.0,0.5124,0.6066,0.0,0.0598,0.5808,0.3152,0.9806,0.7383,0.0,0.96,0.4138,0.4583,0.5,0.0,0.4771,0.5927,0.0,0.0576,reg oper account,block of flats,0.4702,Panel,No,0.0,0.0,0.0,0.0,-1493.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n366496,0,Cash loans,M,N,Y,0,270000.0,891072.0,37881.0,720000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.018634,-8771,-504,-2.0,-1409,,1,1,0,1,1,0,Laborers,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,Trade: type 7,0.3451775374163358,0.16996220564790532,,0.1856,0.0587,0.9891,0.8504,0.0164,0.12,0.1034,0.375,0.4167,0.0318,0.1513,0.1186,0.0,0.0,0.1891,0.0609,0.9891,0.8563,0.0166,0.1208,0.1034,0.375,0.4167,0.0326,0.1653,0.1236,0.0,0.0,0.1874,0.0587,0.9891,0.8524,0.0165,0.12,0.1034,0.375,0.4167,0.0324,0.1539,0.1207,0.0,0.0,,block of flats,0.1023,\"Stone, brick\",No,1.0,1.0,1.0,0.0,-734.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,6.0\n339610,0,Cash loans,F,N,Y,0,112500.0,535500.0,17401.5,535500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.0228,-20320,365243,-4326.0,-3846,,1,0,0,1,0,0,,1.0,2,2,MONDAY,8,0,0,0,0,0,0,XNA,,0.5980172206656823,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-299.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n399135,0,Cash loans,F,N,Y,1,162000.0,1546020.0,45333.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.006670999999999999,-16084,-886,-5596.0,-2749,,1,1,0,1,1,0,Cooking staff,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Medicine,,0.4885507390054287,0.7252764347002191,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n311178,0,Cash loans,F,Y,N,3,112500.0,835605.0,24430.5,697500.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.035792000000000004,-13212,-3471,-1459.0,-4456,28.0,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 1,,0.4973067300551824,0.22133520635446602,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-804.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n365761,0,Cash loans,F,N,N,0,58500.0,45000.0,4905.0,45000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-19462,365243,-4397.0,-2730,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.6691978227514547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1945.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n107888,0,Cash loans,M,Y,N,0,270000.0,1665000.0,43920.0,1665000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.072508,-17285,-1246,-8576.0,-846,7.0,1,1,0,1,0,0,Drivers,2.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Other,,0.6877271238280985,0.520897599048938,0.0825,,0.9796,,,,0.1379,0.1667,,,,0.0709,,,0.084,,0.9796,,,,0.1379,0.1667,,,,0.0739,,,0.0833,,0.9796,,,,0.1379,0.1667,,,,0.0722,,,,block of flats,0.0558,Panel,No,0.0,0.0,0.0,0.0,-2729.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,6.0,2.0,4.0\n233002,0,Cash loans,M,Y,Y,1,202500.0,265851.0,22873.5,229500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.009175,-17094,-273,-5912.0,-623,0.0,1,1,0,1,0,1,Laborers,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.4401732908469389,0.7029060443787546,0.33285056416487313,0.0021,0.0,0.9727,,,0.0,0.069,0.0,,0.0154,,0.0015,,0.0,0.0021,0.0,0.9727,,,0.0,0.069,0.0,,0.0157,,0.0015,,0.0,0.0021,0.0,0.9727,,,0.0,0.069,0.0,,0.0156,,0.0015,,0.0,,block of flats,0.0012,\"Stone, brick\",Yes,0.0,0.0,0.0,0.0,-1867.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n337968,0,Cash loans,F,N,Y,0,157500.0,609898.5,22036.5,526500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.030755,-23726,365243,-8532.0,-4049,,1,0,0,1,0,0,,1.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.4322382456797392,0.1510075350878296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1592.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n455610,0,Cash loans,F,N,Y,0,202500.0,835605.0,29736.0,697500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.008019,-13611,-5253,-5258.0,-4432,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.3870641489803312,0.7485088208842364,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1706.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n430424,0,Cash loans,M,Y,Y,0,382500.0,1066320.0,38430.0,900000.0,Unaccompanied,Working,Higher education,Married,With parents,0.010276,-14695,-1551,-8776.0,-1982,8.0,1,1,1,1,1,1,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Construction,0.6324638742364105,0.6369364206922925,0.31703177858344445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2965.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n296318,0,Cash loans,F,N,Y,0,112500.0,1282500.0,35266.5,1282500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.022625,-18874,-6888,-5221.0,-2412,,1,1,0,1,1,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.29743057325494376,0.7380196196295241,0.5773,0.3049,0.9806,0.7348,0.2122,0.64,0.5517,0.3333,0.375,0.3986,0.4707,0.5909,0.0,0.0,0.5882,0.3164,0.9806,0.7452,0.2142,0.6445,0.5517,0.3333,0.375,0.4077,0.5142,0.6156,0.0,0.0,0.5829,0.3049,0.9806,0.7383,0.2136,0.64,0.5517,0.3333,0.375,0.4056,0.4788,0.6015,0.0,0.0,reg oper account,block of flats,0.5786,Panel,No,1.0,0.0,1.0,0.0,-2488.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,1.0,1.0\n163461,0,Cash loans,F,N,Y,1,72000.0,343683.0,23247.0,261000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-8856,-230,-1678.0,-1523,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,10,0,0,0,0,0,0,University,0.22273809452594248,0.11756863321246973,0.2276129150623945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-326.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n303797,0,Revolving loans,M,N,Y,0,180000.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.009549,-12731,-475,-6405.0,-4112,,1,1,1,1,0,0,Managers,1.0,2,2,THURSDAY,17,1,1,0,1,1,0,Business Entity Type 1,0.1702281026995449,0.5127861640894961,0.5832379256761245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n341497,0,Revolving loans,F,Y,Y,1,90000.0,180000.0,9000.0,180000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.005084,-9013,-116,-9004.0,-864,1.0,1,1,1,1,0,0,Laborers,3.0,2,2,SATURDAY,12,0,0,0,1,1,1,Industry: type 4,,0.3590011491661472,0.622922000268356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-656.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n320088,0,Cash loans,F,N,Y,0,270000.0,1204047.0,67360.5,1129500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.018801,-22131,365243,-3849.0,-4203,,1,0,0,1,0,0,,1.0,2,2,MONDAY,7,0,0,0,0,0,0,XNA,0.8202676718321418,0.3463814448106152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2473.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n221098,0,Cash loans,F,N,Y,0,90000.0,651816.0,23409.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020713,-15929,-2973,-2003.0,-4363,,1,1,1,1,1,0,Cooking staff,1.0,3,3,THURSDAY,13,0,0,0,0,0,0,Self-employed,,0.4776680818160271,0.7675231046555077,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n167614,0,Cash loans,F,N,N,1,180000.0,265500.0,28723.5,265500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-10382,-783,-3177.0,-2973,,1,1,0,1,0,0,Sales staff,3.0,2,2,THURSDAY,9,0,0,0,0,1,1,Self-employed,0.3272212057437175,0.6722597044518989,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n338961,0,Cash loans,M,Y,Y,0,202500.0,491031.0,33075.0,463500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22670,365243,-363.0,-2835,19.0,1,0,0,1,1,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.1204991354823118,0.4596904504249018,0.0495,0.0596,0.9747,0.6532,0.0057,0.0,0.1034,0.125,0.1667,0.0478,0.0403,0.0405,0.0,0.0,0.0504,0.0619,0.9747,0.6668,0.0057,0.0,0.1034,0.125,0.1667,0.0489,0.0441,0.0422,0.0,0.0,0.05,0.0596,0.9747,0.6578,0.0057,0.0,0.1034,0.125,0.1667,0.0487,0.040999999999999995,0.0412,0.0,0.0,reg oper account,block of flats,0.0341,Panel,No,0.0,0.0,0.0,0.0,-2387.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n128941,0,Cash loans,F,N,Y,0,130500.0,746280.0,59094.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.002134,-14141,-1973,-1979.0,-1345,,1,1,0,1,0,0,High skill tech staff,2.0,3,3,SATURDAY,7,0,0,0,1,1,0,Medicine,,0.5838729170709761,0.21518240418475384,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,1.0,-1330.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,5.0\n433613,0,Cash loans,F,Y,Y,1,225000.0,468000.0,18265.5,468000.0,Unaccompanied,Commercial associate,Higher education,Married,With parents,0.02461,-10382,-583,-143.0,-2978,7.0,1,1,0,1,0,0,Accountants,3.0,2,2,FRIDAY,17,0,0,0,1,1,0,Trade: type 7,0.741352340515795,0.5359284272399136,0.1245194708919845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n399430,0,Cash loans,F,N,N,0,76500.0,130824.0,7434.0,103500.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.007120000000000001,-24515,-9942,-5326.0,-4371,,1,1,0,1,0,0,,1.0,2,2,MONDAY,14,0,0,0,0,0,0,Medicine,,0.026542470025678368,0.6212263380626669,,,0.9757,0.6668,,0.0,0.1034,0.1667,0.0417,,0.1336,0.0594,,,,,0.9757,0.6798,,0.0,0.1034,0.1667,0.0417,,0.146,0.0619,,,,,0.9757,0.6713,,0.0,0.1034,0.1667,0.0417,,0.136,0.0604,,,reg oper spec account,block of flats,0.0522,\"Stone, brick\",No,1.0,1.0,1.0,0.0,-300.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,2.0,3.0\n145489,0,Cash loans,F,N,N,1,360000.0,567000.0,18288.0,567000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-14750,-3379,-8871.0,-4340,,1,1,0,1,1,0,Private service staff,3.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Services,,0.6553724689738407,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-175.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,4.0\n304998,0,Cash loans,M,Y,Y,2,157500.0,472500.0,22860.0,472500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-12405,-5248,-1965.0,-4889,7.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Transport: type 2,,0.2953783727217784,0.3656165070113335,0.0619,0.0483,0.9861,0.8096,,,0.1379,0.1667,,,0.0504,0.0521,0.0,,0.063,0.0501,0.9861,0.8171,,,0.1379,0.1667,,,0.0551,0.0542,0.0,,0.0625,0.0483,0.9861,0.8121,,,0.1379,0.1667,,,0.0513,0.053,0.0,,reg oper account,block of flats,0.040999999999999995,Panel,No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n202427,0,Cash loans,M,Y,N,1,292500.0,178290.0,18850.5,157500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020713,-11558,-3720,-974.0,-3910,6.0,1,1,0,1,0,0,Sales staff,3.0,3,1,SATURDAY,8,0,0,0,0,0,0,Other,,0.3190593942777364,0.4135967602644276,0.2464,0.2266,0.9866,,0.2307,0.28,0.2414,0.3333,,0.075,0.2009,0.3062,,0.0341,0.2511,0.2352,0.9866,,0.2328,0.282,0.2414,0.3333,,0.0767,0.2195,0.3191,,0.0361,0.2488,0.2266,0.9866,,0.2322,0.28,0.2414,0.3333,,0.0763,0.2044,0.3118,,0.0349,reg oper account,block of flats,0.2457,Panel,No,2.0,0.0,2.0,0.0,-504.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n362195,0,Cash loans,F,N,Y,2,292500.0,1078200.0,31653.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-13178,-4719,-6122.0,-3585,,1,1,0,1,0,0,,4.0,1,1,FRIDAY,15,1,0,1,0,0,0,Business Entity Type 2,0.5761762481459015,0.7273908028261719,0.5884877883422673,0.2948,0.168,0.9826,,,0.48,0.2069,0.4583,,0.2445,,0.3284,,0.0028,0.3004,0.1743,0.9826,,,0.4834,0.2069,0.4583,,0.2501,,0.3421,,0.003,0.2977,0.168,0.9826,,,0.48,0.2069,0.4583,,0.2488,,0.3343,,0.0029,,block of flats,0.2589,Panel,No,5.0,0.0,5.0,0.0,-1797.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n278752,0,Cash loans,M,Y,N,0,139500.0,900000.0,46084.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-20292,-6284,-5352.0,-2863,16.0,1,1,1,1,1,0,Laborers,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.6032573405426612,0.4920600938649263,0.0577,0.064,0.9826,0.762,0.0105,0.0,0.1379,0.1667,0.2083,0.0298,0.0462,0.0536,0.0039,0.0486,0.0588,0.0664,0.9826,0.7713,0.0106,0.0,0.1379,0.1667,0.2083,0.0305,0.0505,0.0558,0.0039,0.0514,0.0583,0.064,0.9826,0.7652,0.0105,0.0,0.1379,0.1667,0.2083,0.0304,0.047,0.0545,0.0039,0.0496,reg oper account,block of flats,0.0584,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-654.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n373429,0,Cash loans,F,Y,Y,0,99000.0,315000.0,20259.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-13556,-4274,-38.0,-4287,7.0,1,1,1,1,1,0,,2.0,2,2,MONDAY,16,0,0,0,1,1,0,Government,,0.7176899409115689,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-456.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n156453,0,Cash loans,M,Y,N,0,137250.0,360000.0,13702.5,360000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,With parents,0.001417,-8436,-1088,-1077.0,-1080,64.0,1,1,0,1,0,0,Accountants,2.0,2,2,SUNDAY,13,0,0,0,1,1,1,Other,0.12897247294761974,0.3915818131005069,0.3201633668633456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-810.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n157119,0,Cash loans,F,N,Y,0,112500.0,468000.0,18265.5,468000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-14229,-4622,-3031.0,-5060,,1,1,0,1,1,0,,2.0,3,3,THURSDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.6292002813858718,0.5406544504453575,0.0577,0.0838,0.9801,0.728,,0.0,0.1379,0.1667,0.0417,0.0523,,0.0351,,0.1098,0.0588,0.087,0.9801,0.7387,,0.0,0.1379,0.1667,0.0417,0.0535,,0.0366,,0.1163,0.0583,0.0838,0.9801,0.7316,,0.0,0.1379,0.1667,0.0417,0.0532,,0.0357,,0.1121,reg oper account,block of flats,0.066,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1009.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n144303,0,Revolving loans,F,Y,Y,2,135000.0,405000.0,20250.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-13896,-3524,-7980.0,-4528,14.0,1,1,0,1,0,0,Managers,4.0,2,2,TUESDAY,12,0,0,0,0,0,0,School,,0.707035733016386,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-320.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n177736,0,Cash loans,F,N,Y,0,135000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.022625,-13061,-3065,-4813.0,-4118,,1,1,0,1,0,0,Cooking staff,1.0,2,2,SUNDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5072286158249464,0.0938365970374978,0.0,,0.0,,,0.0,,,,,,,,,0.0,,0.0005,,,0.0,,,,,,,,,0.0,,0.0,,,0.0,,,,,,,,,,block of flats,0.0,,No,0.0,0.0,0.0,0.0,-2075.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n305884,0,Cash loans,M,Y,Y,0,180000.0,634482.0,24583.5,454500.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.006305,-9336,-1839,-4137.0,-1968,14.0,1,1,0,1,0,0,Laborers,1.0,3,3,TUESDAY,6,0,0,0,0,1,1,Transport: type 4,0.17129328623258328,0.5856357732863157,0.6347055309763198,0.0021,0.0174,,,,,0.069,0.0,,,,0.0035,,,0.0021,0.0181,,,,,0.069,0.0,,,,0.0037,,,0.0021,0.0174,,,,,0.069,0.0,,,,0.0036,,,,block of flats,0.003,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-927.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n339819,0,Cash loans,F,Y,Y,1,135000.0,292500.0,28624.5,292500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-20327,365243,-11317.0,-3748,0.0,1,0,0,1,0,0,,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.6082289960090286,0.746300213050371,0.0021,0.0,0.9563,0.4016,0.0,0.0,0.069,0.0,,0.0,0.0017,0.003,0.0,0.0,0.0021,0.0,0.9563,0.425,0.0,0.0,0.069,0.0,,0.0,0.0018,0.0031,0.0,0.0,0.0021,0.0,0.9563,0.4096,0.0,0.0,0.069,0.0,,0.0,0.0017,0.003,0.0,0.0,reg oper account,block of flats,0.0023,Wooden,No,0.0,0.0,0.0,0.0,-1626.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n108133,0,Cash loans,F,Y,Y,1,472500.0,1458922.5,61942.5,1327500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-14855,-2250,-3646.0,-3021,4.0,1,1,0,1,1,0,,3.0,1,1,WEDNESDAY,19,0,1,1,0,0,0,Business Entity Type 3,0.6884247634025704,0.6952284041617003,0.6075573001388961,0.1291,0.0514,0.9861,0.7552,0.0464,0.08,0.0345,0.6458,0.6667,0.012,0.0955,0.1343,0.0,0.059000000000000004,0.1155,0.0422,0.9811,0.7517,0.045,0.0806,0.0345,0.625,0.6667,0.0122,0.10099999999999999,0.1152,0.0,0.0625,0.1202,0.0475,0.9826,0.7451,0.047,0.08,0.0345,0.625,0.6667,0.0122,0.0949,0.1389,0.0,0.0602,,block of flats,0.1724,Panel,No,0.0,0.0,0.0,0.0,-837.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n250751,0,Cash loans,M,Y,N,1,315000.0,971280.0,102123.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-12902,-1251,-6989.0,-4171,12.0,1,1,0,1,0,0,Managers,3.0,1,1,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6597940573964162,0.43473324875017305,0.16699999999999998,0.1012,0.9836,0.7756,0.0998,0.17,0.0776,0.4896,0.2708,0.0474,0.1395,0.1811,0.0077,0.0051,0.0294,0.0473,0.9841,0.7909,0.003,0.1611,0.069,0.4583,0.0417,0.0385,0.1084,0.1214,0.0078,0.0029,0.1738,0.0987,0.9841,0.7853,0.1004,0.16,0.069,0.4583,0.2708,0.0445,0.1419,0.16399999999999998,0.0078,0.0052,org spec account,block of flats,0.1636,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n390615,0,Cash loans,F,N,N,0,157500.0,715500.0,30442.5,715500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-18493,-10685,-9750.0,-2049,,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,Postal,0.7328481085452802,0.6051974228449336,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1972.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n239001,0,Cash loans,F,N,Y,0,58500.0,315000.0,12006.0,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.0228,-22746,365243,-2900.0,-3952,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,0.8484580378515465,0.7076604226388229,0.29708661164720285,0.0897,0.0817,0.9846,0.7892,0.0187,0.04,0.1724,0.25,0.0417,0.0669,0.0723,0.0888,0.0039,0.006,0.0567,0.0405,0.9826,0.7713,0.0134,0.0,0.069,0.1667,0.0417,0.0461,0.0487,0.0679,0.0039,0.0026,0.0906,0.0817,0.9846,0.792,0.0188,0.04,0.1724,0.25,0.0417,0.068,0.0735,0.0904,0.0039,0.0061,not specified,block of flats,0.0533,Panel,No,0.0,0.0,0.0,0.0,-1498.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n183166,0,Cash loans,F,Y,Y,0,225000.0,763870.5,39132.0,607500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020713,-18190,-3609,-6886.0,-1677,4.0,1,1,0,1,0,0,Sales staff,2.0,3,2,SUNDAY,13,0,0,0,0,0,0,Trade: type 7,,0.5205731234446901,0.6144143775673561,0.0897,0.1818,0.9627,0.49,0.0241,0.0,0.2759,0.1667,0.1667,0.0944,0.0647,0.0958,0.0386,0.12,0.0914,0.1887,0.9628,0.51,0.0243,0.0,0.2759,0.1667,0.1667,0.0965,0.0707,0.0998,0.0389,0.127,0.0906,0.1818,0.9627,0.4968,0.0243,0.0,0.2759,0.1667,0.1667,0.096,0.0658,0.0975,0.0388,0.1225,reg oper account,block of flats,0.1146,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1103.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n281004,0,Revolving loans,F,N,N,1,180000.0,360000.0,18000.0,360000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018801,-15175,-3745,-6484.0,-4782,,1,1,0,1,0,1,Managers,3.0,2,2,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.7412251975816806,0.5472042017397796,0.17352743046491467,0.1474,0.1121,0.9796,,,0.16,0.1379,0.3333,,0.1199,,0.0961,,0.3881,0.1502,0.1163,0.9796,,,0.1611,0.1379,0.3333,,0.1226,,0.1001,,0.4108,0.1489,0.1121,0.9796,,,0.16,0.1379,0.3333,,0.122,,0.0978,,0.3962,,block of flats,0.1599,\"Stone, brick\",No,8.0,0.0,8.0,0.0,-3077.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n361218,0,Cash loans,F,N,Y,2,54000.0,49752.0,5755.5,45000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-11161,-4382,-3283.0,-964,,1,1,1,1,0,0,Cleaning staff,4.0,2,2,TUESDAY,12,0,0,0,0,0,0,School,,0.6188267362218189,0.41184855592423975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-435.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,2.0\n221696,0,Cash loans,M,N,Y,0,157500.0,989473.5,43200.0,922500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.032561,-10729,-222,-360.0,-642,,1,1,0,1,0,0,Low-skill Laborers,2.0,1,1,MONDAY,15,0,1,1,0,1,1,Trade: type 7,,0.4988811718049153,0.3077366963789207,0.0811,0.0492,0.9975,0.966,0.0185,0.0932,0.0803,0.3333,0.3471,0.0524,0.0661,0.0879,0.0,0.0072,0.0378,0.0309,0.9975,0.9673,0.0093,0.1208,0.1034,0.2917,0.3333,0.0275,0.0331,0.0438,0.0,0.0063,0.0999,0.0567,0.9975,0.9665,0.0228,0.12,0.1034,0.3333,0.3333,0.0584,0.0821,0.10800000000000001,0.0,0.0073,reg oper account,block of flats,0.0381,Panel,No,0.0,0.0,0.0,0.0,-197.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n128476,0,Cash loans,F,N,N,0,126000.0,270000.0,13914.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.010556,-14786,-3867,-4996.0,-4252,,1,1,1,1,1,0,Laborers,1.0,3,3,FRIDAY,17,0,0,0,1,1,0,Business Entity Type 2,0.6773480247107314,0.454018902337008,0.6528965519806539,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3183.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n155498,0,Cash loans,F,N,Y,0,180000.0,973710.0,25686.0,697500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.030755,-18945,-1677,-358.0,-2478,,1,1,1,1,0,0,Managers,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6102176927431388,0.4014074137749511,0.5732,0.1809,0.9990000000000001,0.9864,0.3208,0.56,0.2414,0.7083,0.6667,0.2089,0.4606,0.4654,0.0309,0.0893,0.584,0.1878,0.9990000000000001,0.9869,0.3237,0.5639,0.2414,0.7083,0.6667,0.2137,0.5032,0.4849,0.0311,0.0945,0.5787,0.1809,0.9990000000000001,0.9866,0.3228,0.56,0.2414,0.7083,0.6667,0.2126,0.4686,0.4737,0.0311,0.0912,reg oper account,block of flats,0.3855,Panel,No,16.0,0.0,16.0,0.0,-1167.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n365442,0,Cash loans,F,N,Y,0,225000.0,479637.0,26167.5,396000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.007273999999999998,-8480,-163,-3306.0,-1043,,1,1,0,1,1,0,Core staff,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Trade: type 3,,0.14163550762043256,0.17560597946937906,0.0722,0.0634,0.9796,0.7212,0.0079,0.0,0.1379,0.1667,0.2083,0.0531,0.0588,0.0666,0.0,0.0,0.0735,0.0658,0.9796,0.7321,0.008,0.0,0.1379,0.1667,0.2083,0.0543,0.0643,0.0694,0.0,0.0,0.0729,0.0634,0.9796,0.7249,0.008,0.0,0.1379,0.1667,0.2083,0.0541,0.0599,0.0678,0.0,0.0,reg oper account,block of flats,0.0524,Panel,No,0.0,0.0,0.0,0.0,-154.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n102004,0,Cash loans,F,N,N,1,135000.0,202500.0,20025.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.031329,-13614,-4539,-4812.0,-4874,,1,1,0,1,1,1,,3.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.7656428166187343,0.6077442898030296,0.4382813743111921,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1904.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,4.0\n199750,0,Cash loans,F,Y,N,2,202500.0,990000.0,55408.5,990000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.026392,-14100,-389,-72.0,-3918,3.0,1,1,0,1,0,0,Sales staff,4.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.4217454288627565,0.6860434005398679,0.248535557339522,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-541.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n192736,0,Cash loans,M,Y,Y,0,193500.0,855000.0,81310.5,855000.0,Family,Working,Higher education,Married,House / apartment,0.014519999999999996,-18337,-4893,-3761.0,-1886,10.0,1,1,1,1,1,0,Laborers,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.6438467105433928,0.3389244647980185,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1911.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n317260,0,Revolving loans,F,N,Y,0,157500.0,450000.0,22500.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-15783,-254,-1727.0,-5916,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.4695156325414369,0.6054220712224818,0.6956219298394389,0.2588,0.1472,0.9786,0.7076,0.0501,0.28,0.2414,0.3333,0.375,0.1246,0.21100000000000002,0.2588,0.0,0.0,0.2637,0.1528,0.9786,0.7190000000000001,0.0506,0.282,0.2414,0.3333,0.375,0.1274,0.2305,0.2696,0.0,0.0,0.2613,0.1472,0.9786,0.7115,0.0504,0.28,0.2414,0.3333,0.375,0.1268,0.2146,0.2635,0.0,0.0,reg oper account,block of flats,0.2036,Panel,No,2.0,1.0,2.0,0.0,-374.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n286505,0,Cash loans,F,N,Y,0,148500.0,252000.0,25051.5,252000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.02461,-12744,-1586,-6857.0,-3962,,1,1,0,1,0,0,Laborers,1.0,2,2,SATURDAY,16,0,0,0,0,0,0,Hotel,0.6102745022121779,0.5939945274261405,0.7476633896301825,0.0825,0.0814,0.9752,0.66,0.0101,0.0,0.1379,0.1667,0.2083,0.0751,0.0672,0.0724,0.0,0.0,0.084,0.0844,0.9752,0.6733,0.0102,0.0,0.1379,0.1667,0.2083,0.0768,0.0735,0.0754,0.0,0.0,0.0833,0.0814,0.9752,0.6645,0.0102,0.0,0.1379,0.1667,0.2083,0.0764,0.0684,0.0737,0.0,0.0,,block of flats,0.0905,Panel,No,0.0,0.0,0.0,0.0,-680.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n234933,1,Cash loans,F,N,Y,1,103500.0,225000.0,18171.0,225000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-9706,-459,-2212.0,-1275,,1,1,1,1,0,0,Core staff,3.0,2,2,FRIDAY,14,0,0,0,0,1,1,Bank,0.2723959242060613,0.4306397440069677,0.17044612304522816,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-266.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n317032,1,Revolving loans,F,N,Y,0,90000.0,180000.0,9000.0,180000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006305,-8245,-778,-2992.0,-928,,1,1,1,1,1,0,Cooking staff,2.0,3,3,SUNDAY,8,0,0,0,0,0,0,Kindergarten,,0.15811566303388247,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-67.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n455353,0,Cash loans,F,Y,N,1,135000.0,522000.0,20358.0,522000.0,Unaccompanied,Commercial associate,Higher education,Married,Municipal apartment,0.009334,-14515,-3166,-2377.0,-4063,10.0,1,1,1,1,0,0,,3.0,2,2,TUESDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.6354382722549797,0.2925880073368475,0.0186,0.0,0.9846,0.7892,0.0025,0.0,0.1034,0.0417,0.0417,0.0118,0.0143,0.0178,0.0039,0.0042,0.0189,0.0,0.9846,0.7975,0.0026,0.0,0.1034,0.0417,0.0417,0.0121,0.0156,0.0186,0.0039,0.0044,0.0187,0.0,0.9846,0.792,0.0025,0.0,0.1034,0.0417,0.0417,0.0121,0.0145,0.0181,0.0039,0.0043,reg oper account,block of flats,0.0163,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1510.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n436010,0,Cash loans,M,N,N,0,225000.0,675000.0,32602.5,675000.0,Family,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.025164,-10406,-1434,-4995.0,-2295,,1,1,0,1,0,1,Managers,1.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5398908626479932,0.20915469884100693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-826.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n400477,0,Cash loans,M,N,N,2,135000.0,254700.0,27153.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.00963,-10163,-422,-8580.0,-608,,1,1,0,1,0,0,Drivers,4.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Self-employed,,0.6170765965612095,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1545.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n226912,0,Cash loans,F,N,Y,0,90000.0,188685.0,14238.0,157500.0,Unaccompanied,Pensioner,Lower secondary,Single / not married,House / apartment,0.011657,-23156,365243,-10518.0,-1825,,1,0,0,1,1,0,,1.0,1,1,TUESDAY,12,0,0,0,0,0,0,XNA,,0.6271879184061552,0.7194907850918436,0.0928,0.1209,0.9851,0.7959999999999999,0.0552,0.0,0.2069,0.1667,0.2083,0.1033,0.0748,0.0918,0.0039,0.0051,0.0945,0.1255,0.9851,0.804,0.0557,0.0,0.2069,0.1667,0.2083,0.1057,0.0817,0.0957,0.0039,0.0055,0.0937,0.1209,0.9851,0.7987,0.0555,0.0,0.2069,0.1667,0.2083,0.1051,0.0761,0.0935,0.0039,0.0053,reg oper account,block of flats,0.1035,Panel,No,3.0,1.0,3.0,1.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n328858,0,Cash loans,M,N,Y,0,135000.0,90000.0,9823.5,90000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.030755,-9670,-825,-1420.0,-2330,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.4742319196788891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-664.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n334380,0,Cash loans,F,Y,Y,0,517500.0,1711764.0,47200.5,1530000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.014464,-16274,-7172,-1338.0,-4916,17.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,4,0,0,0,0,1,1,Police,,0.6221532489001916,0.7826078370261895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n262888,0,Revolving loans,F,N,N,0,225000.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-17945,-6046,-3517.0,-1492,,1,1,0,1,0,0,Managers,2.0,3,3,THURSDAY,16,0,0,0,0,0,0,Trade: type 7,,0.7242968902088562,0.3876253444214701,0.1165,0.0595,0.995,0.9252,,0.1,0.0862,0.3542,0.4167,0.018000000000000002,,0.1268,,0.011000000000000001,0.0882,0.0,0.9945,0.9281,,0.0806,0.069,0.3333,0.4167,0.0141,,0.0976,,0.0,0.1176,0.0595,0.995,0.9262,,0.1,0.0862,0.3542,0.4167,0.0183,,0.1291,,0.0112,org spec account,block of flats,0.094,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-190.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n285920,0,Cash loans,F,N,N,1,81000.0,450000.0,21888.0,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.0105,-12468,-477,-952.0,-4546,,1,1,1,1,0,0,Sales staff,3.0,3,3,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3243515418148763,0.6538150236079107,0.0005272652387098817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-547.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n402393,0,Cash loans,M,Y,Y,0,202500.0,675000.0,19867.5,675000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-19798,-1842,-7274.0,-3321,5.0,1,1,1,1,1,0,Sales staff,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Self-employed,0.7389637816182962,0.7283999655151647,,0.0041,,0.9722,,,,0.069,0.0,,,,0.0013,,,0.0042,,0.9722,,,,0.069,0.0,,,,0.0014,,,0.0042,,0.9722,,,,0.069,0.0,,,,0.0013,,,,block of flats,0.0015,Wooden,No,0.0,0.0,0.0,0.0,-1575.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n429510,0,Cash loans,M,Y,N,0,225000.0,808650.0,31464.0,675000.0,Unaccompanied,Working,Higher education,Married,With parents,0.007120000000000001,-13525,-2812,-1526.0,-400,7.0,1,1,0,1,0,0,Managers,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,Self-employed,0.4915000282934453,0.6598676814217849,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-169.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n114449,0,Cash loans,F,N,N,0,126000.0,254700.0,14751.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-23910,365243,-12070.0,-5344,,1,0,0,1,0,0,,2.0,3,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.6650117637239039,0.7180328113294772,0.0794,0.0219,0.9836,0.7756,0.0375,0.08,0.0345,0.625,0.6667,0.0415,0.063,0.0853,0.0077,0.0798,0.0809,0.0227,0.9836,0.7844,0.0379,0.0806,0.0345,0.625,0.6667,0.0425,0.0689,0.0889,0.0078,0.0845,0.0802,0.0219,0.9836,0.7786,0.0378,0.08,0.0345,0.625,0.6667,0.0423,0.0641,0.0869,0.0078,0.0815,reg oper account,block of flats,0.105,Panel,No,0.0,0.0,0.0,0.0,-1413.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n108094,1,Revolving loans,F,N,N,0,90000.0,202500.0,10125.0,202500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-19739,-3193,-3243.0,-3290,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,15,0,0,0,0,1,1,Trade: type 7,,0.6309383352613674,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-2024.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n259674,0,Cash loans,M,Y,Y,1,315000.0,1113840.0,44302.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.007114,-12835,-647,-6218.0,-4762,7.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,17,0,0,0,0,1,1,Business Entity Type 3,,0.4123369454987437,0.248535557339522,0.0186,,0.9901,,,,0.069,0.0833,,0.033,,0.0169,,,0.0189,,0.9901,,,,0.069,0.0833,,0.0337,,0.0177,,,0.0187,,0.9901,,,,0.069,0.0833,,0.0336,,0.0172,,,,block of flats,0.0162,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-573.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n289903,0,Cash loans,F,N,Y,0,180000.0,545040.0,19575.0,450000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.018209,-22577,365243,-6399.0,-5024,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,12,0,0,0,0,0,0,XNA,,0.6210525332369636,0.3425288720742255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-286.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n257693,0,Cash loans,F,N,Y,1,90000.0,258709.5,14175.0,234000.0,Other_A,Working,Secondary / secondary special,Married,House / apartment,0.010643,-9122,-180,-3887.0,-1776,,1,1,0,1,0,1,,3.0,2,2,SATURDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.2982538149958738,0.3110798171249246,0.2822484337007223,0.0165,0.0,0.9791,0.7144,0.0017,0.0,0.069,0.0417,0.0833,0.025,0.0134,0.0147,0.0,0.0,0.0168,0.0,0.9791,0.7256,0.0017,0.0,0.069,0.0417,0.0833,0.0256,0.0147,0.0153,0.0,0.0,0.0167,0.0,0.9791,0.7182,0.0017,0.0,0.069,0.0417,0.0833,0.0254,0.0137,0.0149,0.0,0.0,reg oper account,block of flats,0.0115,\"Stone, brick\",No,12.0,0.0,12.0,0.0,-414.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n102789,1,Cash loans,F,N,Y,0,90000.0,593010.0,23107.5,495000.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.030755,-19032,-2022,-8725.0,-1162,,1,1,0,1,0,0,Cooking staff,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6624885355953175,0.4436153084085652,0.0505,0.0814,0.9876,0.83,0.0092,0.0,0.1379,0.1667,0.2083,0.0468,0.0403,0.0492,0.0039,0.006999999999999999,0.0515,0.0845,0.9876,0.8367,0.0093,0.0,0.1379,0.1667,0.2083,0.0479,0.0441,0.0512,0.0039,0.0074,0.051,0.0814,0.9876,0.8323,0.0093,0.0,0.1379,0.1667,0.2083,0.0476,0.040999999999999995,0.05,0.0039,0.0072,reg oper account,block of flats,0.0452,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-723.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n218929,0,Cash loans,F,N,N,0,135000.0,877500.0,24259.5,877500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-19202,-12276,-4546.0,-2743,,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Medicine,0.7625197495439211,0.09260401563603088,0.5814837058057234,0.1186,0.1273,0.9786,,,,0.2759,0.1667,,,,0.0748,,,0.1208,0.1321,0.9786,,,,0.2759,0.1667,,,,0.0779,,,0.1197,0.1273,0.9786,,,,0.2759,0.1667,,,,0.0761,,,,block of flats,0.0869,Panel,No,8.0,0.0,8.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n305704,1,Cash loans,M,N,N,2,121500.0,900000.0,31887.0,900000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.030755,-13373,-451,-7163.0,-4046,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.09167660132267848,0.1061556230153924,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-60.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n325720,0,Cash loans,F,N,Y,0,90000.0,123637.5,8923.5,112500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-12675,-3044,-6744.0,-4350,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Self-employed,,0.7059066004778441,0.5406544504453575,0.0165,0.0,0.9742,,,0.0,0.069,0.0417,,0.0,,0.0126,,0.0,0.0168,0.0,0.9742,,,0.0,0.069,0.0417,,0.0,,0.0131,,0.0,0.0167,0.0,0.9742,,,0.0,0.069,0.0417,,0.0,,0.0128,,0.0,,block of flats,0.0107,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-927.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n316264,0,Cash loans,M,Y,Y,0,157500.0,1042560.0,34587.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-11948,-1105,-2044.0,-4524,9.0,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Self-employed,0.2797002287610693,0.62362746616209,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1321.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n247681,1,Cash loans,F,Y,N,0,112500.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-11213,-274,-2541.0,-3398,13.0,1,1,1,1,0,0,Laborers,2.0,3,3,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.28935914187981715,0.486168700399622,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-529.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n297297,0,Cash loans,F,Y,Y,0,135000.0,532494.0,29862.0,454500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-22878,365243,-14399.0,-4198,28.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,XNA,,0.6486247481209687,0.7713615919194317,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-265.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n218386,0,Cash loans,M,Y,Y,0,360000.0,355536.0,21613.5,270000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.018029,-11528,-2832,-1178.0,-2895,2.0,1,1,0,1,0,0,Core staff,1.0,3,3,SATURDAY,10,0,0,0,1,1,1,Other,0.31975248086840163,0.4293212624017651,0.2176285202779586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-483.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n169692,0,Cash loans,F,N,Y,3,54000.0,192528.0,9103.5,126000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.002134,-11504,-477,-5117.0,-3319,,1,1,0,1,0,0,Medicine staff,5.0,3,3,THURSDAY,11,0,0,0,0,0,0,University,,0.37417159014000784,0.0963186927645401,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-971.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n288950,0,Cash loans,F,N,Y,0,130500.0,562500.0,16578.0,562500.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.0105,-22720,365243,-10483.0,-4582,,1,0,0,1,1,0,,2.0,3,3,TUESDAY,16,0,0,0,0,0,0,XNA,0.8998827745411603,0.475017124836905,0.7267112092725122,0.0928,0.0995,0.9841,0.7824,,,0.2069,0.1667,0.2083,0.0534,0.0756,0.0888,,0.0,0.0945,0.1032,0.9841,0.7909,,,0.2069,0.1667,0.2083,0.0546,0.0826,0.0926,,0.0,0.0937,0.0995,0.9841,0.7853,,,0.2069,0.1667,0.2083,0.0543,0.077,0.0904,,0.0,,block of flats,0.0699,Panel,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n276649,1,Cash loans,F,N,N,0,90000.0,566055.0,16681.5,472500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.010966,-13630,-967,-4410.0,-4492,,1,1,0,1,0,0,Accountants,1.0,2,2,MONDAY,16,0,0,0,0,1,1,Business Entity Type 3,0.13656336072794148,0.12542974210721686,0.5620604831738043,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1754.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n403234,0,Cash loans,M,Y,Y,1,166500.0,808650.0,26217.0,675000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.022625,-16422,365243,-2871.0,-4612,5.0,1,0,0,1,1,0,,3.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.7065916384823605,0.7136313997323308,0.1856,0.1096,0.9891,0.8504,0.0693,0.2,0.1724,0.3333,0.375,0.1234,0.1513,0.1915,0.0,0.0,0.1891,0.1137,0.9891,0.8563,0.0699,0.2014,0.1724,0.3333,0.375,0.1262,0.1653,0.1996,0.0,0.0,0.1874,0.1096,0.9891,0.8524,0.0697,0.2,0.1724,0.3333,0.375,0.1256,0.1539,0.195,0.0,0.0,not specified,block of flats,0.1885,Panel,No,2.0,0.0,2.0,0.0,-608.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n142146,0,Cash loans,M,Y,Y,0,270000.0,573628.5,27724.5,463500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.018634,-16699,-4319,-2195.0,-178,5.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Transport: type 2,0.3696500410217729,0.06424426785331487,,0.0041,,0.9762,,,0.0,,0.0,,,,0.0037,,,0.0042,,0.9762,,,0.0,,0.0,,,,0.0038,,,0.0042,,0.9762,,,0.0,,0.0,,,,0.0037,,,,block of flats,0.0029,Wooden,No,2.0,0.0,2.0,0.0,-1990.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n120358,1,Cash loans,M,Y,Y,3,90000.0,517788.0,34731.0,427500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-11308,-144,-1379.0,-3943,29.0,1,1,0,1,0,0,Laborers,5.0,2,2,THURSDAY,9,0,0,0,0,0,0,Trade: type 7,,0.1284174692434108,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-463.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n215254,0,Cash loans,F,N,Y,0,67500.0,254700.0,20250.0,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.030755,-13295,-5157,-4279.0,-4791,,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.495538984503921,0.4161891890374616,0.0005272652387098817,0.0722,0.07,0.9801,0.728,0.0,0.0,0.1379,0.1667,0.2083,0.0149,0.0588,0.0668,0.0,0.0,0.0735,0.0726,0.9801,0.7387,0.0,0.0,0.1379,0.1667,0.2083,0.0153,0.0643,0.0696,0.0,0.0,0.0729,0.07,0.9801,0.7316,0.0,0.0,0.1379,0.1667,0.2083,0.0152,0.0599,0.068,0.0,0.0,reg oper account,block of flats,0.0566,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-994.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n201854,0,Cash loans,F,Y,Y,0,198000.0,306000.0,19683.0,306000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.04622,-22625,365243,-13537.0,-4780,21.0,1,0,0,1,0,0,,2.0,1,1,THURSDAY,18,0,0,0,0,0,0,XNA,,0.6047375388618843,0.3842068130556564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n349912,0,Cash loans,M,Y,N,2,202500.0,781920.0,24682.5,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-16736,-2293,-9146.0,-286,1.0,1,1,0,1,0,0,Drivers,4.0,2,2,FRIDAY,11,0,1,1,0,1,1,Industry: type 9,,0.5597416412079451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-460.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n168397,0,Cash loans,M,N,Y,0,180000.0,961146.0,31135.5,688500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.072508,-16399,-845,-4828.0,-5174,,1,1,0,1,1,0,Laborers,1.0,1,1,WEDNESDAY,18,0,1,1,0,0,0,Business Entity Type 3,,0.7003057021018174,,0.14400000000000002,0.0,0.9826,0.762,0.0443,0.1732,0.1148,0.4442,0.4858,0.0,0.1157,0.1335,0.0077,0.0093,0.0725,0.0,0.9782,0.7125,0.0305,0.2417,0.0345,0.3333,0.375,0.0,0.0615,0.0512,0.0039,0.0007,0.1416,0.0,0.9801,0.7316,0.0349,0.24,0.1034,0.4583,0.5,0.0,0.1137,0.1463,0.0078,0.0024,reg oper account,block of flats,0.1639,Panel,No,0.0,0.0,0.0,0.0,-318.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n380882,0,Cash loans,M,Y,N,0,675000.0,675000.0,26154.0,675000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-17211,-1119,-5693.0,-752,1.0,1,1,1,1,1,0,Drivers,2.0,2,2,THURSDAY,17,0,0,0,0,1,1,Transport: type 3,,0.545957729463008,0.25946765482111994,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1116.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n392612,0,Cash loans,F,N,N,0,180000.0,1206000.0,33165.0,1206000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010643,-13914,-3094,-4051.0,-4201,,1,1,0,1,1,0,High skill tech staff,2.0,2,2,TUESDAY,10,0,1,1,0,1,1,Business Entity Type 3,,0.5267259071221184,0.7151031019926098,0.0052,0.0,0.9603,,,0.0,0.0345,0.0417,,0.0348,,0.005,,0.0,0.0053,0.0,0.9603,,,0.0,0.0345,0.0417,,0.0355,,0.0052,,0.0,0.0052,0.0,0.9603,,,0.0,0.0345,0.0417,,0.0354,,0.005,,0.0,,block of flats,0.0043,Wooden,No,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n254598,0,Cash loans,F,N,Y,0,135000.0,753840.0,24448.5,540000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.022625,-14447,-7347,-4195.0,-2407,,1,1,1,1,0,0,,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Other,0.5002834739511404,0.7777881873007747,,0.0825,0.0812,0.9757,0.6668,0.10800000000000001,0.0,0.1379,0.1667,0.2083,0.0854,0.0672,0.0479,0.0,0.0198,0.084,0.0843,0.9757,0.6798,0.109,0.0,0.1379,0.1667,0.2083,0.0874,0.0735,0.0499,0.0,0.0209,0.0833,0.0812,0.9757,0.6713,0.1087,0.0,0.1379,0.1667,0.2083,0.0869,0.0684,0.0487,0.0,0.0202,reg oper account,block of flats,0.059000000000000004,Panel,No,0.0,0.0,0.0,0.0,-2526.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n396068,0,Cash loans,F,Y,Y,0,112500.0,312768.0,24691.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008625,-9921,-298,-3848.0,-824,24.0,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,15,0,0,0,0,1,0,Self-employed,0.5229495242579661,0.2975402546666476,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-612.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n179041,0,Cash loans,F,N,N,0,180000.0,1256400.0,33273.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-20517,-9017,-70.0,-4001,,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,1,1,Transport: type 2,,0.6625569856968111,0.5919766183185521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n382463,0,Cash loans,M,Y,Y,2,225000.0,1319269.5,36409.5,1152000.0,\"Spouse, partner\",Working,Higher education,Married,House / apartment,0.010006,-13870,-6592,-2079.0,-4977,3.0,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.2868138162506301,0.3367720223574981,0.7238369900414456,0.1299,0.0518,0.9841,0.7824,,0.16,0.069,0.625,0.6667,0.0418,0.1059,0.0803,0.0,0.0,0.1313,0.0497,0.9841,0.7909,,0.1611,0.069,0.625,0.6667,0.0398,0.1148,0.0827,0.0,0.0,0.1312,0.0518,0.9841,0.7853,,0.16,0.069,0.625,0.6667,0.0426,0.1077,0.0817,0.0,0.0,reg oper account,block of flats,0.1083,Panel,No,7.0,0.0,7.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n393763,0,Revolving loans,M,Y,Y,0,225000.0,765000.0,38250.0,765000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.035792000000000004,-11989,-278,-41.0,-4639,0.0,1,1,0,1,1,0,Managers,2.0,2,2,MONDAY,18,0,0,0,0,0,0,Self-employed,,0.6634071534674316,0.6848276586890367,0.0165,0.0345,0.9717,0.6124,0.0024,0.0,0.069,0.0417,0.0833,0.0198,0.0134,0.0124,0.0,0.0,0.0168,0.0358,0.9717,0.6276,0.0024,0.0,0.069,0.0417,0.0833,0.0202,0.0147,0.0129,0.0,0.0,0.0167,0.0345,0.9717,0.6176,0.0024,0.0,0.069,0.0417,0.0833,0.0201,0.0137,0.0126,0.0,0.0,reg oper account,block of flats,0.0097,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2095.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n403431,0,Cash loans,M,Y,Y,0,135000.0,1255680.0,41629.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-16974,-1142,-366.0,-505,8.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Government,,0.31124855345049585,0.7380196196295241,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-845.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n416958,0,Cash loans,M,N,Y,1,180000.0,129357.0,14058.0,117000.0,Other_B,Working,Secondary / secondary special,Married,House / apartment,0.010032,-15976,-3855,-8070.0,-199,,1,1,0,1,0,0,,3.0,2,2,MONDAY,6,0,0,0,0,0,0,Business Entity Type 2,,0.2281546879731664,0.4135967602644276,0.08800000000000001,0.1109,0.9816,0.7484,,0.0,0.2069,0.1667,,0.0,,0.0517,,0.0246,0.083,0.1068,0.9801,0.7387,,0.0,0.2069,0.1667,,0.0,,0.0493,,0.0,0.0843,0.1144,0.9816,0.7518,,0.0,0.2069,0.1667,,0.0,,0.0519,,0.0128,reg oper account,block of flats,0.0564,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-185.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n337768,0,Cash loans,M,Y,Y,3,360000.0,1354500.0,37377.0,1354500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-15479,-2205,-2671.0,-4337,2.0,1,1,0,1,0,0,Drivers,5.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.4875703979060459,0.6134220499188654,0.7649392739475195,0.2033,0.9397,0.9955,0.9388,0.1605,0.22399999999999998,0.1931,0.3333,0.375,0.1242,0.1652,0.2173,0.0039,0.0122,0.2248,0.1473,0.996,0.9477,0.0,0.2417,0.2069,0.3333,0.375,0.0584,0.0643,0.0742,0.0,0.0,0.2228,0.1473,0.996,0.9463,0.2136,0.24,0.2069,0.3333,0.375,0.1357,0.183,0.253,0.0039,0.0031,reg oper account,block of flats,0.2047,Panel,No,0.0,0.0,0.0,0.0,-625.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n327077,0,Cash loans,M,Y,Y,0,180000.0,722290.5,44316.0,616500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019689,-16473,-448,-1391.0,-26,20.0,1,1,1,1,0,1,Laborers,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Transport: type 4,0.31152130004119105,0.4609930048424366,0.3092753558842053,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1034.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n194767,1,Revolving loans,F,N,N,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.018634,-8666,-157,-8652.0,-1189,,1,1,1,1,0,0,Sales staff,1.0,2,2,TUESDAY,19,0,0,0,1,1,0,Other,0.3976028155658854,0.32839438067383553,0.5280927512030451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1372.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n305031,0,Revolving loans,F,N,Y,0,202500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.032561,-17412,-921,-11098.0,-827,,1,1,1,1,1,0,Laborers,2.0,1,1,SATURDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.7236471163046143,0.6040839759993188,0.3723336657058204,0.0546,0.0429,0.9771,0.6872,,0.0,0.1379,0.1042,0.1458,,,0.0493,,,0.0168,0.0,0.9732,0.6472,,0.0,0.069,0.0417,0.0833,,,0.0128,,,0.0552,0.0429,0.9771,0.6914,,0.0,0.1379,0.1042,0.1458,,,0.0502,,,reg oper account,block of flats,0.0679,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-530.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n106259,0,Cash loans,M,N,Y,0,135000.0,422235.0,28692.0,364500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.006207,-20448,-907,-11691.0,-4004,,1,1,1,1,0,0,Security staff,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,School,,0.5263770124558126,0.3842068130556564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n122949,0,Cash loans,M,N,Y,3,225000.0,450000.0,35554.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14085,-1538,-2771.0,-2814,,1,1,1,1,0,0,,5.0,2,2,THURSDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.6234170030944411,0.3360615207658242,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1978.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,0.0\n222211,0,Cash loans,F,Y,Y,2,135000.0,352044.0,13401.0,247500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-16620,-893,-4108.0,-171,12.0,1,1,0,1,0,0,Sales staff,4.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.5182149821686272,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-686.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n108833,1,Cash loans,M,N,N,0,103500.0,225000.0,12577.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-13014,-278,-6556.0,-4032,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,12,0,0,0,1,1,0,Business Entity Type 1,,0.2297838363839477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,1.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n288208,0,Cash loans,F,N,Y,2,99000.0,1339884.0,43353.0,1170000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.004849,-12523,-197,-3415.0,-4235,,1,1,0,1,0,0,Managers,4.0,2,2,WEDNESDAY,16,0,0,0,1,1,0,Transport: type 4,0.28241157705001285,0.5997034881081342,0.5814837058057234,0.1237,0.0895,0.9861,0.8096,0.0546,0.0,0.2069,0.1667,0.0417,0.1451,0.1009,0.076,0.0,0.0,0.1261,0.0929,0.9861,0.8171,0.0551,0.0,0.2069,0.1667,0.0417,0.1485,0.1102,0.0792,0.0,0.0,0.1249,0.0895,0.9861,0.8121,0.0549,0.0,0.2069,0.1667,0.0417,0.1477,0.1026,0.0774,0.0,0.0,reg oper account,block of flats,0.1009,Mixed,No,5.0,0.0,5.0,0.0,-448.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n193680,0,Cash loans,F,Y,N,1,157500.0,225000.0,11488.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.007114,-15057,-1180,-205.0,-4608,16.0,1,1,1,1,1,0,Cooking staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Industry: type 11,0.6722834465488513,0.43925460305565706,0.2793353208976285,0.0412,0.0433,0.9767,0.6532,0.001,0.0,0.1034,0.125,0.0833,0.0366,0.0134,0.0313,0.0,0.0586,0.0168,0.0005,0.9747,0.6668,0.001,0.0,0.069,0.0417,0.0833,0.0374,0.0147,0.0132,0.0,0.0,0.0416,0.0433,0.9767,0.6578,0.001,0.0,0.1034,0.125,0.0833,0.0372,0.0137,0.0319,0.0,0.0598,reg oper account,block of flats,0.0648,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1487.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n211640,0,Cash loans,F,N,Y,2,202500.0,499221.0,25618.5,373500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.00823,-15683,-2146,-1689.0,-4887,,1,1,0,1,0,0,Laborers,4.0,2,2,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 2,0.6462854518929864,0.5787795602553449,0.6006575372857061,0.0701,0.0562,0.9811,0.7416,0.0253,0.0,0.1379,0.1667,0.2083,0.0135,0.0572,0.0602,0.0,0.0041,0.0714,0.0583,0.9811,0.7517,0.0255,0.0,0.1379,0.1667,0.2083,0.0138,0.0624,0.0627,0.0,0.0044,0.0708,0.0562,0.9811,0.7451,0.0255,0.0,0.1379,0.1667,0.2083,0.0138,0.0581,0.0612,0.0,0.0042,reg oper account,block of flats,0.0482,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1019.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n259915,0,Cash loans,F,N,Y,0,90000.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-10367,-1336,-363.0,-593,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.2658344593779077,0.2319427311585574,0.6058362647264226,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1901.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n158434,0,Revolving loans,F,Y,Y,0,112500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-8065,-1047,-1092.0,-738,1.0,1,1,0,1,0,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,Other,0.10540692598345668,0.5658539879288125,0.22383131747015547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-873.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n222230,0,Revolving loans,M,N,Y,1,135000.0,157500.0,7875.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019689,-14800,-307,-7223.0,-1006,,1,1,0,1,1,0,Security staff,2.0,2,2,MONDAY,8,0,0,0,0,0,0,Security,0.1945336204819559,0.17764497517020056,0.36227724703843145,0.0918,0.0976,0.9876,0.83,0.0578,0.0,0.2069,0.1667,0.0417,0.1024,0.0748,0.0927,0.0,0.0,0.0935,0.1013,0.9876,0.8367,0.0583,0.0,0.2069,0.1667,0.0417,0.1048,0.0817,0.0966,0.0,0.0,0.0926,0.0976,0.9876,0.8323,0.0582,0.0,0.2069,0.1667,0.0417,0.1042,0.0761,0.0944,0.0,0.0,,block of flats,0.1045,Panel,No,0.0,0.0,0.0,0.0,-582.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n411370,0,Cash loans,F,N,N,0,94500.0,808650.0,26086.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.008473999999999999,-13284,-4104,-4989.0,-3934,,1,1,1,1,1,0,Managers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Medicine,,0.7125490431202984,0.2650494299443805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1767.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n422500,0,Cash loans,F,N,Y,0,72000.0,343377.0,19300.5,283500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-21895,365243,-2935.0,-4073,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.5611476475194761,,0.1268,0.0318,0.9856,0.8028,0.0211,0.08,0.069,0.3333,0.375,0.0,0.1025,0.1233,0.0039,0.0086,0.1292,0.033,0.9856,0.8105,0.0213,0.0806,0.069,0.3333,0.375,0.0,0.11199999999999999,0.1285,0.0039,0.0091,0.128,0.0318,0.9856,0.8054,0.0212,0.08,0.069,0.3333,0.375,0.0,0.1043,0.1255,0.0039,0.0087,reg oper account,block of flats,0.1104,Block,No,0.0,0.0,0.0,0.0,-583.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n404958,0,Cash loans,M,Y,Y,0,270000.0,640080.0,31261.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006629,-19766,-3500,-3269.0,-3269,7.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Transport: type 3,,0.6692123452079385,0.21885908222837447,0.0082,0.0,0.9712,,,0.0,0.069,0.0417,,0.0055,,0.0104,,0.0,0.0084,0.0,0.9712,,,0.0,0.069,0.0417,,0.0057,,0.0108,,0.0,0.0083,0.0,0.9712,,,0.0,0.069,0.0417,,0.0056,,0.0106,,0.0,,block of flats,0.0097,Wooden,No,1.0,0.0,1.0,0.0,-724.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n227844,0,Cash loans,M,Y,N,0,135000.0,1350000.0,43681.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-19281,-920,-9248.0,-2676,7.0,1,1,0,1,1,0,Laborers,2.0,3,3,FRIDAY,13,0,0,0,1,1,0,Business Entity Type 2,,0.5489320218408594,0.5190973382084597,0.0907,0.0974,0.9816,,,0.0,0.2069,0.1667,,0.0481,,0.0791,,0.0063,0.0924,0.1011,0.9816,,,0.0,0.2069,0.1667,,0.0492,,0.0825,,0.0067,0.0916,0.0974,0.9816,,,0.0,0.2069,0.1667,,0.049,,0.0806,,0.0064,,block of flats,0.0622,Panel,No,0.0,0.0,0.0,0.0,-1635.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n214823,0,Cash loans,F,N,N,1,126000.0,76410.0,8235.0,67500.0,Unaccompanied,Working,Incomplete higher,Separated,Office apartment,0.00702,-11674,-692,-2221.0,-3904,,1,1,0,1,0,0,Sales staff,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Self-employed,,0.5448787943379104,,0.066,0.0773,0.9752,0.66,,,0.1379,0.125,,0.044000000000000004,,0.0503,,,0.0672,0.0802,0.9752,0.6733,,,0.1379,0.125,,0.045,,0.0524,,,0.0666,0.0773,0.9752,0.6645,,,0.1379,0.125,,0.0448,,0.0512,,,reg oper account,block of flats,0.0426,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-421.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n219843,0,Cash loans,M,Y,N,1,112500.0,1006920.0,42790.5,900000.0,Unaccompanied,State servant,Higher education,Married,Office apartment,0.018029,-11713,-3897,-3453.0,-3566,6.0,1,1,0,1,0,0,Core staff,3.0,3,3,SATURDAY,6,0,0,0,0,0,0,Military,0.2522534243919438,0.636455884003563,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1499.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n427393,0,Cash loans,F,N,N,0,171000.0,202500.0,19854.0,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-25175,365243,-12910.0,-4640,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.4934583827917893,0.8128226070575616,0.1495,0.0827,0.9816,0.7484,0.0599,0.16,0.1379,0.3333,0.0417,0.11199999999999999,0.1202,0.1478,0.0077,0.0242,0.1523,0.0858,0.9816,0.7583,0.0605,0.1611,0.1379,0.3333,0.0417,0.1146,0.1313,0.1539,0.0078,0.0256,0.1509,0.0827,0.9816,0.7518,0.0603,0.16,0.1379,0.3333,0.0417,0.114,0.1223,0.1504,0.0078,0.0247,org spec account,block of flats,0.1542,Panel,No,0.0,0.0,0.0,0.0,-240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n281930,0,Cash loans,F,N,Y,1,81000.0,689742.0,35347.5,616500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-10659,-458,-6493.0,-1688,,1,1,0,1,0,1,Sales staff,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Trade: type 3,0.3026719175408937,0.4573654760889351,0.3077366963789207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n112202,1,Cash loans,F,Y,N,4,180000.0,512338.5,24012.0,423000.0,Children,Commercial associate,Secondary / secondary special,Civil marriage,With parents,0.018801,-11566,-408,-2323.0,-2360,16.0,1,1,0,1,0,1,,6.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.2467204727254823,0.4451824098879531,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1330.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n223721,0,Cash loans,M,Y,N,1,292500.0,1639057.5,60871.5,1530000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.022625,-15370,-314,-6080.0,-2356,4.0,1,1,0,1,1,0,Drivers,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.7134037686013658,0.4596904504249018,0.0742,0.0,0.9866,0.8164,0.126,0.08,0.069,0.3333,0.375,0.0,0.0605,0.0537,0.0,0.0,0.0756,0.0,0.9866,0.8236,0.1272,0.0806,0.069,0.3333,0.375,0.0,0.0661,0.0559,0.0,0.0,0.0749,0.0,0.9866,0.8189,0.1268,0.08,0.069,0.3333,0.375,0.0,0.0616,0.0547,0.0,0.0,reg oper account,block of flats,0.0689,Block,No,3.0,0.0,3.0,0.0,-1592.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n268524,0,Cash loans,M,Y,Y,1,360000.0,790830.0,62613.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-15736,-472,-8347.0,-4309,6.0,1,1,0,1,1,1,IT staff,3.0,1,1,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.5094256947484037,0.6894791426446275,0.1644,0.079,0.9816,0.7484,0.0956,0.24,0.1034,0.5417,0.5833,0.0,0.1336,0.0888,0.0019,0.0012,0.1345,0.047,0.9806,0.7452,0.0839,0.1611,0.069,0.4583,0.5,0.0,0.1166,0.0845,0.0,0.0,0.166,0.079,0.9816,0.7518,0.0962,0.24,0.1034,0.5417,0.5833,0.0,0.136,0.0904,0.0019,0.0012,reg oper account,block of flats,0.1093,Panel,No,16.0,0.0,16.0,0.0,-285.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n454275,0,Cash loans,F,N,Y,0,81000.0,225000.0,15165.0,225000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.026392,-19439,-205,-4374.0,-2866,,1,1,1,1,1,1,Laborers,1.0,2,2,THURSDAY,9,0,0,0,0,1,1,Industry: type 3,0.6450599397748606,0.4769970357687582,0.8435435389318647,0.0072,,0.9752,,,0.0,,0.0,,,,,,,0.0074,,0.9752,,,0.0,,0.0,,,,,,,0.0073,,0.9752,,,0.0,,0.0,,,,,,,,terraced house,0.0035,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n256930,0,Cash loans,M,Y,Y,0,216000.0,701730.0,72036.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-13755,-1686,-546.0,-2539,15.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Self-employed,,0.29772310881779324,0.7407990879702335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n444195,0,Cash loans,M,Y,Y,0,202500.0,568057.5,24196.5,459000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.04622,-20379,-3494,-11051.0,-3927,0.0,1,1,0,1,1,0,High skill tech staff,2.0,1,1,WEDNESDAY,11,0,0,0,0,0,0,Industry: type 2,,0.7508192714454873,0.5280927512030451,0.0825,0.0815,0.9737,,,0.0,0.1379,0.1667,,0.0598,,0.0398,,0.03,0.084,0.0845,0.9737,,,0.0,0.1379,0.1667,,0.0611,,0.0414,,0.0317,0.0833,0.0815,0.9737,,,0.0,0.1379,0.1667,,0.0608,,0.0405,,0.0306,,block of flats,0.0593,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2243.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n379829,0,Cash loans,M,N,N,1,45000.0,135000.0,14454.0,135000.0,Unaccompanied,Commercial associate,Lower secondary,Single / not married,House / apartment,0.02461,-17022,-823,-2419.0,-520,,1,1,1,1,1,0,,2.0,2,2,SATURDAY,11,0,0,0,0,1,1,Agriculture,,0.32742088866273616,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-272.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n352006,0,Cash loans,F,N,Y,0,261000.0,490500.0,33039.0,490500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.028663,-20231,-1897,-5989.0,-3755,,1,1,0,1,0,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,Housing,0.7831289675879141,0.5382336379468899,,0.1629,0.1145,0.9816,0.7484,0.0757,0.0,0.0917,0.1667,0.2083,0.0684,0.1328,0.0746,0.0051,0.0122,0.1513,0.077,0.9791,0.7256,0.0343,0.0,0.069,0.1667,0.2083,0.0324,0.1322,0.0478,0.0,0.0,0.1561,0.0815,0.9796,0.7249,0.0958,0.0,0.069,0.1667,0.2083,0.0323,0.1283,0.0472,0.0039,0.0064,reg oper account,block of flats,0.052000000000000005,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,5.0\n288786,0,Cash loans,M,N,Y,2,135000.0,113760.0,7731.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010006,-13087,-5057,-7170.0,-4317,,1,1,1,1,1,0,Laborers,3.0,2,2,SATURDAY,16,0,0,0,0,0,0,Construction,0.3714561388475012,0.5083435916232788,0.33285056416487313,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-859.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n307705,0,Cash loans,F,N,Y,0,202500.0,545040.0,31419.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.008473999999999999,-18279,-779,-1566.0,-1790,,1,1,0,1,0,0,Sales staff,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.6921740228447352,0.6107376921579327,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-906.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n403496,0,Cash loans,M,Y,Y,0,67500.0,640080.0,31261.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.010276,-9894,-206,-4666.0,-1833,2.0,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Postal,0.5974507928615003,0.4007306675244545,,0.0134,,0.9717,0.6124,0.0088,,0.069,0.0417,0.0417,,0.0101,0.0142,0.0039,,0.0137,,0.9717,0.6276,0.0088,,0.069,0.0417,0.0417,,0.011000000000000001,0.0148,0.0039,,0.0135,,0.9717,0.6176,0.0088,,0.069,0.0417,0.0417,,0.0103,0.0145,0.0039,,,block of flats,0.016,,No,6.0,0.0,6.0,0.0,-163.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n108207,0,Cash loans,M,N,Y,1,315000.0,810000.0,41485.5,810000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-13026,-772,-1403.0,-4796,,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,9,0,1,1,0,1,1,Business Entity Type 3,0.4768113284427093,0.342470873632216,,0.0206,0.0,0.9826,,,0.0,0.1034,0.0417,,0.0,,0.0183,,0.0,0.021,0.0,0.9826,,,0.0,0.1034,0.0417,,0.0,,0.0191,,0.0,0.0208,0.0,0.9826,,,0.0,0.1034,0.0417,,0.0,,0.0186,,0.0,,block of flats,0.0249,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n276648,0,Cash loans,M,Y,Y,2,135000.0,1044000.0,41535.0,1044000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-13075,-1417,-3263.0,-2336,19.0,1,1,0,1,0,0,Drivers,4.0,3,3,TUESDAY,8,0,0,0,1,1,0,Self-employed,0.2337020465143332,0.2793804856991871,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-561.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n306833,0,Cash loans,F,N,N,0,60750.0,254700.0,14350.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.009549,-23828,365243,-4740.0,-4686,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,XNA,,0.6462837720317547,0.4776491548517548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n100647,0,Revolving loans,F,N,Y,0,99000.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-17142,-1051,-1203.0,-672,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,Medicine,,0.6241457248117958,0.4794489811780563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14.0,0.0,14.0,0.0,-733.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n281563,0,Cash loans,F,N,Y,0,90000.0,254700.0,14751.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-24036,365243,-4320.0,-4285,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,XNA,,0.29509172169293396,0.501075160239048,0.1031,0.086,0.9806,0.7348,0.0108,0.0,0.2069,0.1667,0.0417,0.0273,0.0841,0.0893,0.0,0.0,0.105,0.0892,0.9806,0.7452,0.0109,0.0,0.2069,0.1667,0.0417,0.027999999999999997,0.0918,0.09300000000000001,0.0,0.0,0.1041,0.086,0.9806,0.7383,0.0109,0.0,0.2069,0.1667,0.0417,0.0278,0.0855,0.0909,0.0,0.0,reg oper spec account,block of flats,0.0764,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1613.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n358330,0,Cash loans,F,N,Y,0,90000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-22119,365243,-12281.0,-4031,,1,0,0,1,1,0,,2.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.7686243314557059,0.6940926425266661,0.132,0.0725,0.9836,,,0.16,0.1379,0.3333,,0.1855,,0.1348,,0.0607,0.1345,0.0752,0.9836,,,0.1611,0.1379,0.3333,,0.1897,,0.1404,,0.0643,0.1332,0.0725,0.9836,,,0.16,0.1379,0.3333,,0.1887,,0.1372,,0.062,,block of flats,0.1792,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1316.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n267560,0,Cash loans,M,N,Y,0,225000.0,337923.0,22711.5,279000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16728,-5367,-130.0,-277,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.5436382529804697,0.5136056270687567,0.14734591802757252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-577.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n234329,0,Cash loans,M,Y,Y,0,261000.0,99000.0,6826.5,99000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-18951,-1180,-2008.0,-2514,5.0,1,1,1,1,0,0,Drivers,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Construction,0.6474525516897912,0.6407255041631312,0.2955826421513093,0.0619,0.0945,0.9886,,,0.0,0.1379,0.1667,,0.0505,,0.0621,,0.0,0.063,0.0981,0.9886,,,0.0,0.1379,0.1667,,0.0517,,0.0647,,0.0,0.0625,0.0945,0.9886,,,0.0,0.1379,0.1667,,0.0514,,0.0632,,0.0,reg oper account,block of flats,0.0488,Panel,No,1.0,0.0,1.0,0.0,-294.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n211058,0,Cash loans,F,N,Y,0,171000.0,256500.0,15822.0,256500.0,Unaccompanied,Working,Higher education,Widow,House / apartment,0.015221,-18274,-5863,-3578.0,-1821,,1,1,0,1,1,0,Managers,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Government,0.6301780652942293,0.4573003510559289,0.4614823912998385,0.2546,0.2567,0.9916,,,0.2,0.1724,0.3333,,0.252,,0.3886,,0.1562,0.2595,0.2663,0.9916,,,0.2014,0.1724,0.3333,,0.2578,,0.4049,,0.1654,0.2571,0.2567,0.9916,,,0.2,0.1724,0.3333,,0.2564,,0.3956,,0.1595,,block of flats,0.3396,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1174.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n441365,0,Cash loans,M,Y,Y,0,157500.0,135000.0,9018.0,135000.0,Unaccompanied,Working,Incomplete higher,Single / not married,Rented apartment,0.003813,-9019,-345,-9010.0,-1604,5.0,1,1,1,1,0,0,Sales staff,1.0,2,2,FRIDAY,6,1,1,0,1,1,0,Business Entity Type 3,,0.2858978721410488,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-863.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n147899,0,Cash loans,F,N,Y,0,180000.0,1102828.5,32373.0,963000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019101,-15659,-3155,-6491.0,-4233,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6107272946335631,0.7449321846094795,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2211.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n368486,0,Cash loans,M,Y,Y,0,225000.0,2028942.0,55926.0,1813500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-20811,-3913,-4400.0,-4084,1.0,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Other,0.7835850109415391,0.6399499264048614,0.6785676886853644,0.0619,0.0,0.9811,0.7416,0.0568,0.0,0.1379,0.1667,0.2083,0.0612,0.0504,0.0606,0.0,0.0,0.063,0.0,0.9811,0.7517,0.0573,0.0,0.1379,0.1667,0.2083,0.0626,0.0551,0.0631,0.0,0.0,0.0625,0.0,0.9811,0.7451,0.0571,0.0,0.1379,0.1667,0.2083,0.0622,0.0513,0.0616,0.0,0.0,reg oper spec account,block of flats,0.0476,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1677.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n372612,0,Cash loans,M,Y,Y,2,382500.0,1078200.0,38331.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010276,-13290,-1079,-3458.0,-5401,18.0,1,1,1,1,0,1,,4.0,2,2,FRIDAY,10,0,1,1,0,1,1,Security,0.5203423500588851,0.7463580962503779,0.0005272652387098817,,0.1182,0.9911,0.8708,0.0232,0.0,0.2069,0.1667,0.2083,,0.1009,0.08199999999999999,,0.0606,,0.1226,0.9911,0.8759,0.0235,0.0,0.2069,0.1667,0.2083,,0.1102,0.0854,,0.0642,,0.1182,0.9911,0.8725,0.0234,0.0,0.2069,0.1667,0.2083,,0.1026,0.0834,,0.0619,org spec account,block of flats,0.1053,Panel,No,0.0,0.0,0.0,0.0,-744.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n279959,0,Cash loans,F,Y,Y,1,135000.0,405000.0,22099.5,405000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.026392,-9915,-1412,-473.0,-1846,7.0,1,1,0,1,0,0,Core staff,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,Government,,0.6787687867110029,0.470456116119975,0.0361,0.0829,0.9732,,,0.0,0.1379,0.125,,,,0.0441,,0.0329,0.0368,0.086,0.9732,,,0.0,0.1379,0.125,,,,0.046,,0.0348,0.0364,0.0829,0.9732,,,0.0,0.1379,0.125,,,,0.0449,,0.0335,,block of flats,0.0436,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-807.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n297119,0,Cash loans,F,N,Y,0,257850.0,454500.0,27936.0,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-19423,-499,-1998.0,-2970,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Self-employed,,0.36316314972350094,0.7738956942145427,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,0.0,-236.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n271506,0,Cash loans,F,N,N,0,54000.0,808650.0,23643.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-20210,365243,-4929.0,-3661,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.6152405959499362,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2336.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n307845,0,Cash loans,F,N,Y,1,99000.0,1042560.0,34587.0,900000.0,Family,Working,Higher education,Married,House / apartment,0.020246,-10853,-3791,-164.0,-1783,,1,1,0,1,0,0,Accountants,3.0,3,3,MONDAY,14,0,0,0,0,0,0,Other,0.744610135530933,0.6759348058868231,0.5937175866150576,0.0825,0.0802,0.9747,0.6532,0.0077,0.0,0.1379,0.1667,0.2083,0.0543,0.0614,0.0411,0.027000000000000003,0.0755,0.084,0.0832,0.9747,0.6668,0.0078,0.0,0.1379,0.1667,0.2083,0.0555,0.067,0.0428,0.0272,0.08,0.0833,0.0802,0.9747,0.6578,0.0078,0.0,0.1379,0.1667,0.2083,0.0552,0.0624,0.0418,0.0272,0.0771,reg oper account,block of flats,0.0487,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1725.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n215566,0,Cash loans,M,Y,Y,0,360000.0,1467612.0,58203.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006629,-21258,-2057,-2078.0,-2074,10.0,1,1,1,1,1,0,High skill tech staff,2.0,2,2,THURSDAY,6,0,0,0,0,0,0,Construction,0.4698789230860687,0.7212516717738992,0.3606125659189888,0.001,,0.9627,,,,0.0345,0.0,,,,0.0018,,,0.0011,,0.9628,,,,0.0345,0.0,,,,0.0019,,,0.001,,0.9627,,,,0.0345,0.0,,,,0.0019,,,,terraced house,0.0015,Wooden,No,0.0,0.0,0.0,0.0,-804.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n120049,0,Cash loans,F,Y,N,1,229500.0,1006920.0,51412.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-12780,-666,-2868.0,-2883,7.0,1,1,1,1,1,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 2,0.5696530981862421,0.5202893195525753,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-686.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n109645,0,Cash loans,F,N,Y,0,225000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.003818,-23681,-629,-5571.0,-4048,,1,1,1,1,1,0,Medicine staff,1.0,2,2,THURSDAY,9,0,0,0,1,1,0,Medicine,,0.7679703428553109,0.6075573001388961,0.0595,0.0754,0.9911,0.8776,0.08900000000000001,0.0,0.1293,0.1458,0.0625,0.0155,0.0485,0.0412,0.0,0.0,0.0189,0.0357,0.9861,0.8171,0.0287,0.0,0.069,0.1667,0.0417,0.006999999999999999,0.0165,0.0206,0.0,0.0,0.0494,0.0592,0.9891,0.8524,0.0721,0.0,0.1207,0.1667,0.0417,0.0125,0.0406,0.0329,0.0,0.0,reg oper account,block of flats,0.0381,Block,No,0.0,0.0,0.0,0.0,-1562.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n184213,1,Cash loans,F,N,N,0,157500.0,187704.0,9256.5,148500.0,Unaccompanied,Working,Incomplete higher,Civil marriage,With parents,0.018029,-9693,-2427,-5360.0,-2338,,1,1,0,1,0,0,Private service staff,2.0,3,3,WEDNESDAY,11,0,0,0,1,1,0,Self-employed,0.30924705076299314,0.11403563080865378,0.14375805349116522,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-1492.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n118024,0,Cash loans,M,N,N,0,157500.0,679500.0,28786.5,679500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009657,-22775,365243,-4726.0,-4048,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.3838678272245281,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-365.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n305455,0,Cash loans,F,Y,N,0,177750.0,942300.0,27679.5,675000.0,Family,State servant,Higher education,Married,Municipal apartment,0.007114,-16022,-8791,-4845.0,-2199,4.0,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,School,,0.6745537524227595,0.6738300778602003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n437907,1,Cash loans,M,Y,N,3,225000.0,1264500.0,53703.0,1264500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0105,-14439,-3401,-3882.0,-4973,17.0,1,1,1,1,0,0,Laborers,5.0,3,3,FRIDAY,18,0,0,0,0,1,1,Business Entity Type 3,,0.549836086981709,0.4561097392782771,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2098.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n448248,0,Cash loans,M,N,N,0,135000.0,156384.0,12537.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009549,-13610,-2855,-4556.0,-5168,,1,1,0,1,1,0,Laborers,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Construction,0.5041683427598364,0.7357001551153914,0.34090642641523844,,,0.9518,,,,,,,,,0.021,,,,,0.9518,,,,,,,,,0.0219,,,,,0.9518,,,,,,,,,0.0214,,,,block of flats,0.0165,,Yes,10.0,1.0,10.0,0.0,-1468.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n247212,0,Cash loans,F,N,N,0,180000.0,67500.0,7267.5,67500.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.072508,-11000,-2305,-677.0,-2346,,1,1,0,1,1,1,Accountants,1.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Government,0.6072555020531305,0.3696917096610432,0.4794489811780563,0.068,0.0846,0.9747,0.6532,,0.0,0.1379,0.1667,0.2083,0.0636,,0.0565,,0.0798,0.0672,0.0876,0.9747,0.6668,,0.0,0.1379,0.1667,0.2083,0.0551,,0.0574,,0.0514,0.0687,0.0846,0.9747,0.6578,,0.0,0.1379,0.1667,0.2083,0.0647,,0.0575,,0.0815,,block of flats,0.055999999999999994,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1170.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n414434,0,Cash loans,F,N,Y,0,315000.0,521280.0,23089.5,450000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.035792000000000004,-23523,365243,-7136.0,-4778,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,,0.6026867225247658,0.5762088360175724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n414095,0,Cash loans,F,Y,N,1,157500.0,125361.0,9252.0,103500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.006008,-16968,-791,-7969.0,-506,24.0,1,1,0,1,0,0,Drivers,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Self-employed,,0.6939759375462756,0.25259869783397665,0.1031,0.0385,0.9771,0.6872,0.0116,0.0,0.1724,0.1667,0.2083,0.0153,0.0841,0.0881,0.0,0.0,0.105,0.04,0.9772,0.6994,0.0117,0.0,0.1724,0.1667,0.2083,0.0156,0.0918,0.0918,0.0,0.0,0.1041,0.0385,0.9771,0.6914,0.0117,0.0,0.1724,0.1667,0.2083,0.0155,0.0855,0.0897,0.0,0.0,reg oper account,block of flats,0.0756,Panel,No,2.0,0.0,2.0,0.0,-2264.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n198482,0,Cash loans,M,Y,Y,0,180000.0,1638000.0,60831.0,1638000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-20444,365243,-10627.0,-3849,11.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.7757215665960483,0.7700870700124128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1990.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n441532,0,Cash loans,M,N,N,1,90000.0,253737.0,25227.0,229500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,With parents,0.035792000000000004,-22988,365243,-49.0,-4732,,1,0,0,1,0,0,,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.5325439741235176,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-1546.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n203650,0,Cash loans,F,N,Y,0,99000.0,531706.5,17703.0,459000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.01885,-23556,365243,-13908.0,-4025,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.7091736214614321,0.6769925032909132,0.0206,0.0657,0.9717,0.6124,0.0045,0.0,0.1034,0.0833,0.125,0.0295,0.0168,0.0307,0.0,0.0,0.021,0.0682,0.9717,0.6276,0.0045,0.0,0.1034,0.0833,0.125,0.0302,0.0184,0.0319,0.0,0.0,0.0208,0.0657,0.9717,0.6176,0.0045,0.0,0.1034,0.0833,0.125,0.03,0.0171,0.0312,0.0,0.0,reg oper account,block of flats,0.0257,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-3372.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,1.0,5.0\n152937,0,Cash loans,F,N,Y,0,180000.0,314100.0,19111.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-20557,365243,-317.0,-3042,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.3788583809321707,0.3001077565791181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1063.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n350801,0,Cash loans,F,Y,Y,2,225000.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-11082,-1464,-5113.0,-821,8.0,1,1,0,1,1,0,Cooking staff,4.0,2,2,SATURDAY,8,0,0,0,0,0,0,Kindergarten,,0.11893897741417454,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-818.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n160491,0,Cash loans,M,Y,N,0,135000.0,225000.0,17775.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010556,-15406,365243,-7707.0,-4212,5.0,1,0,0,1,1,0,,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,XNA,0.5529794926332182,0.4468844188601144,0.19294222771695085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1279.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n296980,0,Cash loans,F,Y,Y,0,180000.0,1971072.0,68643.0,1800000.0,Family,Working,Higher education,Married,House / apartment,0.010147,-20389,-4475,-9310.0,-3689,9.0,1,1,0,1,1,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,Self-employed,0.7953580351762058,0.6883982930000149,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2535.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n385470,0,Cash loans,F,N,Y,2,90000.0,481176.0,26230.5,360000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.009175,-11365,-3172,-674.0,-2757,,1,1,0,1,0,0,Accountants,4.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4947611808277577,0.7710845994302343,0.6479768603302221,0.1856,0.0,0.9796,,,0.0,0.4138,0.1667,,,,,,,0.1891,0.0,0.9796,,,0.0,0.4138,0.1667,,,,,,,0.1874,0.0,0.9796,,,0.0,0.4138,0.1667,,,,,,,,block of flats,0.1415,Panel,No,5.0,0.0,5.0,0.0,-1423.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n371368,0,Cash loans,F,N,N,0,92700.0,310671.0,10053.0,256500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.010556,-23644,365243,-6278.0,-4376,,1,0,0,1,0,0,,1.0,3,3,TUESDAY,11,0,0,0,0,0,0,XNA,,0.6097650892663999,0.813917469762627,0.0289,0.0291,0.9841,0.7824,0.0,0.0,0.0345,0.125,0.1667,0.0587,0.0235,0.0185,0.0,0.0566,0.0294,0.0302,0.9841,0.7909,0.0,0.0,0.0345,0.125,0.1667,0.06,0.0257,0.0193,0.0,0.0599,0.0291,0.0291,0.9841,0.7853,0.0,0.0,0.0345,0.125,0.1667,0.0597,0.0239,0.0189,0.0,0.0578,reg oper account,block of flats,0.0269,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2090.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n325654,0,Revolving loans,F,N,N,1,119250.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006852,-15516,-142,-535.0,-3082,,1,1,1,1,1,0,Medicine staff,3.0,3,3,THURSDAY,13,0,0,0,0,0,0,Other,,0.18698296518502688,0.813917469762627,0.1113,0.153,0.9826,0.762,0.0288,0.0,0.2414,0.1667,0.2083,0.047,0.0899,0.1096,0.0039,0.0035,0.1134,0.1587,0.9826,0.7713,0.0291,0.0,0.2414,0.1667,0.2083,0.0481,0.0983,0.1142,0.0039,0.0037,0.1124,0.153,0.9826,0.7652,0.028999999999999998,0.0,0.2414,0.1667,0.2083,0.0478,0.0915,0.1116,0.0039,0.0036,reg oper account,block of flats,0.1043,Panel,No,0.0,0.0,0.0,0.0,-440.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,1.0\n121951,0,Cash loans,M,Y,N,0,157500.0,490495.5,27387.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-12868,-1360,-1052.0,-4374,1.0,1,1,1,1,0,0,Managers,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,Transport: type 4,0.4292107951807719,0.7644409628492667,0.6894791426446275,0.0309,0.0302,0.9737,,,0.0,0.1034,0.0833,,0.0274,,0.0358,,0.0,0.0315,0.0314,0.9737,,,0.0,0.1034,0.0833,,0.027999999999999997,,0.0373,,0.0,0.0312,0.0302,0.9737,,,0.0,0.1034,0.0833,,0.0279,,0.0365,,0.0,,block of flats,0.0282,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1966.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n184598,0,Cash loans,F,N,N,0,180000.0,687600.0,19039.5,450000.0,Unaccompanied,Working,Higher education,Married,With parents,0.005002,-8836,-742,-3750.0,-1516,,1,1,1,1,0,0,Core staff,2.0,3,3,FRIDAY,16,0,0,0,0,0,0,Kindergarten,0.5930125083146639,0.5086387188378594,,,,0.9632,,,,,,,,,0.0132,,,,,0.9633,,,,,,,,,0.0138,,,,,0.9632,,,,,,,,,0.0135,,,,block of flats,0.0104,,No,0.0,0.0,0.0,0.0,-4.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n256711,0,Cash loans,F,Y,Y,0,270000.0,547344.0,29821.5,472500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-20065,365243,-7483.0,-3613,4.0,1,0,0,1,0,0,,2.0,1,1,FRIDAY,14,0,0,0,0,0,0,XNA,,0.7962632964023024,0.7338145369642702,0.0144,,0.9757,,,,0.069,0.0417,,,,0.0125,,0.0042,0.0147,,0.9757,,,,0.069,0.0417,,,,0.013000000000000001,,0.0045,0.0146,,0.9757,,,,0.069,0.0417,,,,0.0127,,0.0043,,block of flats,0.0107,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-321.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,1.0,1.0\n235714,0,Cash loans,F,Y,N,0,225000.0,1056447.0,34209.0,922500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.010643,-17352,-2338,-9416.0,-884,1.0,1,1,0,1,1,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Trade: type 3,,0.5785023140586426,0.2225807646753351,0.1814,0.1696,0.9801,0.728,0.0281,0.0,0.4138,0.1667,0.2083,0.1699,0.1479,0.179,0.0039,0.0096,0.1849,0.17600000000000002,0.9801,0.7387,0.0284,0.0,0.4138,0.1667,0.2083,0.1738,0.1616,0.1865,0.0039,0.0101,0.1832,0.1696,0.9801,0.7316,0.0283,0.0,0.4138,0.1667,0.2083,0.1729,0.1505,0.1823,0.0039,0.0098,reg oper spec account,block of flats,0.1583,Panel,No,3.0,0.0,3.0,0.0,-1280.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n364486,0,Cash loans,F,N,Y,0,112500.0,898326.0,26397.0,643500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.006305,-22318,365243,-1525.0,-4422,,1,0,0,1,1,0,,1.0,3,3,SUNDAY,4,0,0,0,0,0,0,XNA,0.8421604529489515,0.1968741384902601,0.6313545365850379,0.0082,0.0,0.9925,,,0.0,0.0345,0.0417,,0.0095,,0.0101,,0.0,0.0084,0.0,0.9926,,,0.0,0.0345,0.0417,,0.0097,,0.0106,,0.0,0.0083,0.0,0.9925,,,0.0,0.0345,0.0417,,0.0096,,0.0103,,0.0,,block of flats,0.0086,Wooden,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n135775,0,Cash loans,F,N,Y,2,202500.0,215640.0,15466.5,180000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.006233,-14760,-2619,-5726.0,-4250,,1,1,0,1,0,0,Core staff,4.0,2,2,THURSDAY,13,0,0,0,0,1,1,Police,,0.6759970731243585,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1853.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n209521,0,Cash loans,M,Y,N,1,99000.0,420367.5,33835.5,382500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.020713,-10276,-145,-672.0,-2605,6.0,1,1,0,1,0,0,Laborers,3.0,3,3,THURSDAY,10,0,0,0,0,0,0,Industry: type 4,,0.7185177278212984,0.4812493411434029,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2360.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n328932,0,Cash loans,F,Y,N,0,112500.0,1125000.0,36292.5,1125000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21972,-3691,-4500.0,-4494,8.0,1,1,1,1,1,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Other,,0.5523894907640341,0.2176285202779586,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1590.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n124858,0,Cash loans,F,N,Y,0,157500.0,835380.0,40320.0,675000.0,Family,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.019689,-11886,-2822,-2.0,-106,,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.22994304664790866,0.475524302408169,0.4223696523543468,0.0825,0.0605,0.9752,0.66,0.009000000000000001,0.0,0.1379,0.1667,0.2083,0.147,0.0664,0.0634,0.0039,0.0047,0.084,0.0628,0.9752,0.6733,0.0091,0.0,0.1379,0.1667,0.2083,0.1503,0.0725,0.066,0.0039,0.005,0.0833,0.0605,0.9752,0.6645,0.0091,0.0,0.1379,0.1667,0.2083,0.1495,0.0676,0.0645,0.0039,0.0048,reg oper account,block of flats,0.0558,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1864.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n167289,0,Cash loans,M,N,N,0,135000.0,450000.0,24412.5,450000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,With parents,0.019101,-8237,-448,-2408.0,-532,,1,1,1,1,1,0,Drivers,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.12160233188458945,0.6464337262098849,0.3539876078507373,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,block of flats,0.0024,,No,4.0,0.0,4.0,0.0,-292.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n230969,0,Cash loans,M,Y,Y,0,270000.0,495882.0,36211.5,405000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018801,-17641,-1844,-11378.0,-1183,20.0,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,7,0,0,0,0,0,0,Self-employed,0.8490333732381306,0.6037649021302682,0.746300213050371,0.1031,0.0625,0.9757,0.6668,0.0312,0.0,0.1724,0.1667,0.2083,0.0784,0.0841,0.0673,0.0,0.0,0.105,0.0649,0.9757,0.6798,0.0314,0.0,0.1724,0.1667,0.2083,0.0802,0.0918,0.0701,0.0,0.0,0.1041,0.0625,0.9757,0.6713,0.0314,0.0,0.1724,0.1667,0.2083,0.0798,0.0855,0.0685,0.0,0.0,reg oper account,block of flats,0.07,Panel,No,0.0,0.0,0.0,0.0,-2638.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n275946,0,Revolving loans,F,N,N,1,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.009334,-10382,-972,-4794.0,-2686,,1,1,0,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Transport: type 4,,0.1886976790466218,0.7910749129911773,0.1031,0.1081,0.9781,0.7008,0.0,0.0,0.1724,0.1667,0.0417,0.0,0.0841,0.0618,0.0,0.0,0.105,0.1122,0.9782,0.7125,0.0,0.0,0.1724,0.1667,0.0417,0.0,0.0918,0.0644,0.0,0.0,0.1041,0.1081,0.9781,0.7048,0.0,0.0,0.1724,0.1667,0.0417,0.0,0.0855,0.063,0.0,0.0,reg oper account,block of flats,0.0752,Panel,No,0.0,0.0,0.0,0.0,-123.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n420176,0,Cash loans,F,N,N,0,90000.0,545040.0,25407.0,450000.0,Unaccompanied,State servant,Higher education,Married,With parents,0.032561,-15148,-1742,-2818.0,-3205,,1,1,0,1,1,0,,2.0,1,1,MONDAY,16,0,0,0,0,0,0,Other,0.6018879981380058,0.7426979197952767,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1598.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n328373,0,Cash loans,F,N,Y,3,135000.0,916470.0,26928.0,765000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009657,-14445,-2240,-3050.0,-4061,,1,1,0,1,0,0,Sales staff,5.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.6402000494737069,0.6605005290258881,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1055.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n340916,0,Cash loans,F,N,Y,0,135000.0,397881.0,15129.0,328500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-16641,-899,-5275.0,-187,,1,1,0,1,1,0,Managers,2.0,2,2,MONDAY,12,0,0,0,0,1,1,Kindergarten,0.4490590740231022,0.4459332990298269,0.3490552510751822,0.1392,0.0066,0.9921,0.8912,0.0308,0.0,0.2414,0.1667,0.2083,0.1505,0.1135,0.1542,0.0,0.0,0.1418,0.0069,0.9921,0.8955,0.0311,0.0,0.2414,0.1667,0.2083,0.154,0.124,0.1607,0.0,0.0,0.1405,0.0066,0.9921,0.8927,0.031,0.0,0.2414,0.1667,0.2083,0.1532,0.1154,0.157,0.0,0.0,reg oper spec account,block of flats,0.1381,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n378157,0,Revolving loans,F,N,Y,0,135000.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.022625,-19462,-2318,-2871.0,-2991,,1,1,0,1,0,0,,1.0,2,2,SATURDAY,12,0,0,0,0,0,0,Industry: type 11,,0.2762216977565205,0.6910212267577837,0.0711,0.0756,0.9871,0.8232,0.0142,0.04,0.0345,0.3333,0.375,0.0644,0.057999999999999996,0.0635,0.0,0.0498,0.0725,0.0784,0.9871,0.8301,0.0144,0.0403,0.0345,0.3333,0.375,0.0659,0.0634,0.0661,0.0,0.0527,0.0718,0.0756,0.9871,0.8256,0.0143,0.04,0.0345,0.3333,0.375,0.0655,0.059000000000000004,0.0646,0.0,0.0509,reg oper account,block of flats,0.0567,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1372.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n140590,0,Cash loans,F,N,Y,0,112500.0,514777.5,29682.0,477000.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.025164,-20152,365243,-6881.0,-3301,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.5015872779244256,0.633031641417419,0.1464,0.0849,0.9856,0.8028,0.0,0.16,0.1379,0.3333,0.375,0.0711,0.1194,0.1017,0.0,0.0032,0.1492,0.0881,0.9856,0.8105,0.0,0.1611,0.1379,0.3333,0.375,0.0529,0.1304,0.068,0.0,0.0,0.1478,0.0849,0.9856,0.8054,0.0,0.16,0.1379,0.3333,0.375,0.0724,0.1214,0.1035,0.0,0.0033,reg oper spec account,block of flats,0.1022,Panel,No,0.0,0.0,0.0,0.0,-2044.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n207070,0,Cash loans,F,Y,Y,0,157500.0,1762110.0,46611.0,1575000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-23085,365243,-3631.0,-2763,6.0,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,0.9199957121666186,0.7127639862696853,0.5779691187553125,0.0773,0.0989,0.9935,0.9116,0.0619,0.08,0.069,0.375,0.4167,,0.0614,0.1366,0.0077,0.0119,0.0788,0.1026,0.9935,0.9151,0.0625,0.0806,0.069,0.375,0.4167,,0.067,0.1423,0.0078,0.0126,0.0781,0.0989,0.9935,0.9128,0.0623,0.08,0.069,0.375,0.4167,,0.0624,0.1391,0.0078,0.0122,reg oper spec account,block of flats,0.1439,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1525.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n375019,1,Cash loans,F,N,Y,1,270000.0,808650.0,31464.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-14324,-686,-8379.0,-4246,,1,1,0,1,1,1,Waiters/barmen staff,3.0,1,1,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.2898719945620418,0.4794470101998216,0.13765446191826075,0.1639,0.0723,0.9821,0.7552,0.0,0.12,0.0517,0.625,0.3542,0.1505,0.1324,0.1564,0.0058,0.0065,0.1134,0.0462,0.9821,0.7648,0.0,0.0806,0.0345,0.625,0.0417,0.1292,0.0992,0.1079,0.0,0.0016,0.1655,0.0723,0.9821,0.7585,0.0,0.12,0.0517,0.625,0.3542,0.1531,0.1347,0.1592,0.0058,0.0066,reg oper account,block of flats,0.165,Block,No,0.0,0.0,0.0,0.0,-217.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n372714,1,Cash loans,F,N,Y,1,103500.0,427176.0,21942.0,306000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.009334,-11372,-2883,-3037.0,-110,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Self-employed,,0.6624005183150505,,0.1611,0.0804,0.9771,0.6872,0.0149,0.06,0.2241,0.2083,0.125,0.0252,0.1313,0.1489,0.0,0.0,0.1261,0.0,0.9767,0.6929,0.0129,0.0,0.2069,0.1667,0.0417,0.023,0.1102,0.1174,0.0,0.0,0.1457,0.0733,0.9767,0.6847,0.0131,0.0,0.2069,0.1667,0.125,0.0231,0.1197,0.13699999999999998,0.0,0.0,reg oper account,block of flats,0.1681,Panel,No,0.0,0.0,0.0,0.0,-12.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n433764,0,Cash loans,F,N,Y,0,180000.0,472500.0,27256.5,472500.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.022625,-23817,365243,-2396.0,-2231,,1,0,0,1,0,0,,1.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.5095841774268782,0.7850520263728172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-548.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n407660,0,Cash loans,F,Y,Y,1,67500.0,152820.0,12204.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.018209,-15279,-1237,-1379.0,-1408,8.0,1,1,1,1,1,0,Core staff,3.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,Government,,0.10683897852067603,0.2721336844147212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-405.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n322134,0,Cash loans,F,N,Y,0,76500.0,254700.0,14350.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.016612000000000002,-24171,365243,-13135.0,-4179,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.5951278691630065,0.475849908720221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1246.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n227120,0,Cash loans,F,N,Y,0,135000.0,454500.0,27805.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-17718,-4540,-4346.0,-1066,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Self-employed,,0.7364096362595253,0.5638350489514956,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-825.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n191368,0,Revolving loans,M,N,Y,0,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.022625,-22856,-4983,-257.0,-4413,,1,1,1,1,1,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Government,,0.7603051978851461,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-200.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n291102,0,Revolving loans,F,N,Y,0,54000.0,675000.0,33750.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006233,-18566,365243,-3928.0,-2112,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,XNA,,0.5571210214917685,0.12850437737240394,0.0412,0.0259,0.9747,0.6532,0.0107,0.0,0.069,0.1667,0.2083,0.0327,0.0336,0.0332,0.0,0.0205,0.042,0.0269,0.9747,0.6668,0.0108,0.0,0.069,0.1667,0.2083,0.0335,0.0367,0.0346,0.0,0.0217,0.0416,0.0259,0.9747,0.6578,0.0107,0.0,0.069,0.1667,0.2083,0.0333,0.0342,0.0338,0.0,0.0209,reg oper account,block of flats,0.0306,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-508.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n238959,0,Cash loans,F,Y,N,0,180000.0,269550.0,13761.0,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.032561,-13577,-1174,-7748.0,-4109,16.0,1,1,1,1,1,0,,1.0,1,1,MONDAY,19,0,0,0,0,0,0,Other,,0.7479279600178796,0.7032033049040319,0.4258,0.2715,0.9781,0.7008,0.0,0.44,0.3793,0.3333,0.375,0.1487,0.3463,0.4233,0.0039,0.0109,0.4338,0.2817,0.9782,0.7125,0.0,0.4431,0.3793,0.3333,0.375,0.1521,0.3783,0.441,0.0039,0.0116,0.4299,0.2715,0.9781,0.7048,0.0,0.44,0.3793,0.3333,0.375,0.1513,0.3523,0.4309,0.0039,0.0112,reg oper account,block of flats,0.364,Panel,No,1.0,0.0,1.0,0.0,-2983.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n253989,0,Cash loans,F,N,Y,0,157500.0,172512.0,7726.5,144000.0,Unaccompanied,Pensioner,Higher education,Civil marriage,House / apartment,0.019689,-23120,365243,-4335.0,-5025,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,XNA,,0.2460587926023757,0.3490552510751822,0.034,0.033,0.9707,,,0.0,0.1379,0.0833,,0.0521,,0.0442,,0.0267,0.0347,0.0343,0.9707,,,0.0,0.1379,0.0833,,0.0532,,0.0461,,0.0283,0.0344,0.033,0.9707,,,0.0,0.1379,0.0833,,0.053,,0.045,,0.0273,,block of flats,0.0388,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-804.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n316810,0,Revolving loans,F,N,N,0,112500.0,292500.0,14625.0,292500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-14868,-2888,-3625.0,-3635,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,School,0.5425020307035172,0.5512483659959135,0.8193176922872417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2383.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n167711,0,Cash loans,F,N,Y,0,202500.0,118602.0,9306.0,99000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-20325,365243,-10619.0,-3432,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.34304724195777403,0.7688075728291359,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,3.0,4.0,3.0,-1384.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n327328,0,Cash loans,F,Y,Y,1,351000.0,545040.0,34960.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-11218,-3486,-5499.0,-3834,6.0,1,1,0,1,0,0,,3.0,1,1,MONDAY,12,0,1,1,0,0,0,Security,0.21137443684606305,0.6706677892021078,0.5814837058057234,0.0619,,0.9861,,,,0.1379,0.1667,,0.0722,,0.0616,,,0.063,,0.9861,,,,0.1379,0.1667,,0.0738,,0.0641,,,0.0625,,0.9861,,,,0.1379,0.1667,,0.0734,,0.0627,,,,block of flats,0.0484,Others,No,0.0,0.0,0.0,0.0,-16.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n368966,0,Cash loans,F,N,Y,0,126000.0,284400.0,16456.5,225000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.022625,-24442,-2269,-16437.0,-5002,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Other,,0.7020193643210785,,0.033,0.0402,0.9747,0.6532,0.0,0.0,0.069,0.125,0.1667,0.0235,0.0269,0.0257,0.0,0.0,0.0336,0.0417,0.9747,0.6668,0.0,0.0,0.069,0.125,0.1667,0.024,0.0294,0.0267,0.0,0.0,0.0333,0.0402,0.9747,0.6578,0.0,0.0,0.069,0.125,0.1667,0.0239,0.0274,0.0261,0.0,0.0,reg oper account,block of flats,0.0217,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n175270,0,Cash loans,F,Y,Y,0,450000.0,1546020.0,40783.5,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-15676,-3324,-105.0,-4677,6.0,1,1,0,1,0,1,Medicine staff,2.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Other,0.6374262737662324,0.6895697658805202,0.7062051096536562,1.0,0.3112,0.9990000000000001,0.9864,0.0631,0.76,0.2069,1.0,1.0,0.1222,0.9490000000000001,1.0,0.0,0.0,1.0,0.32299999999999995,0.9990000000000001,0.9869,0.0637,0.7653,0.2069,1.0,1.0,0.125,1.0,1.0,0.0,0.0,1.0,0.3112,0.9990000000000001,0.9866,0.0635,0.76,0.2069,1.0,1.0,0.1243,0.9654,1.0,0.0,0.0,reg oper account,block of flats,0.9057,Monolithic,No,0.0,0.0,0.0,0.0,-583.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n269274,0,Cash loans,M,Y,Y,0,103500.0,314100.0,17167.5,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019689,-10012,-538,-5859.0,-2189,17.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4920646174904435,0.5298898341969072,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-118.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n186101,0,Cash loans,F,Y,Y,0,112500.0,332946.0,14796.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-17118,-1753,-9961.0,-660,20.0,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,15,0,0,0,0,1,1,Trade: type 3,0.7114645699012961,0.5967075803639218,0.6801388218428291,0.0124,0.0064,0.9727,,,0.0,0.069,0.0417,,0.0229,,0.0103,,0.0,0.0126,0.0066,0.9727,,,0.0,0.069,0.0417,,0.0234,,0.0107,,0.0,0.0125,0.0064,0.9727,,,0.0,0.069,0.0417,,0.0233,,0.0105,,0.0,,block of flats,0.0116,Block,No,4.0,0.0,3.0,0.0,-2206.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n156876,0,Cash loans,M,Y,Y,0,261000.0,473760.0,50400.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-11084,-1267,-1467.0,-1476,2.0,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Housing,0.2662746115327237,0.6016389913577186,0.3910549766342248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-423.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n369256,1,Cash loans,M,Y,Y,0,225000.0,1002456.0,51313.5,810000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.04622,-12237,-5635,-6086.0,-4157,3.0,1,1,0,1,0,0,Managers,2.0,1,1,FRIDAY,16,0,0,0,0,0,0,Police,,0.7137300418675729,,,,0.9627,,,,,,,,,,,,,,0.9628,,,,,,,,,,,,,,0.9627,,,,,,,,,,,,,block of flats,0.1129,,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n257870,1,Cash loans,M,Y,N,2,180000.0,675000.0,32472.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-15008,-182,-805.0,-4365,2.0,1,1,1,1,1,0,Managers,4.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6999613753422849,0.30620229831350426,0.5144,0.3182,0.9866,,,0.0,0.1724,0.1667,,0.0306,,0.2437,,0.2776,0.5242,0.3302,0.9866,,,0.0,0.1724,0.1667,,0.0313,,0.2539,,0.2939,0.5194,0.3182,0.9866,,,0.0,0.1724,0.1667,,0.0311,,0.2481,,0.2835,,block of flats,0.2874,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-791.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n426365,0,Cash loans,F,N,Y,2,180000.0,1007352.0,33421.5,765000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.004849,-16623,-1082,-7226.0,-155,,1,1,0,1,0,0,Managers,4.0,2,2,SATURDAY,12,0,0,0,0,1,1,Trade: type 3,,0.3989832209964019,0.4668640059537032,0.0021,,0.9876,,,0.0,,0.0,,,,0.0015,,0.001,0.0021,,0.9876,,,0.0,,0.0,,,,0.0016,,0.0011,0.0021,,0.9876,,,0.0,,0.0,,,,0.0015,,0.001,,block of flats,0.0014,Wooden,No,0.0,0.0,0.0,0.0,-602.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n273321,0,Cash loans,F,Y,Y,1,135000.0,276277.5,17653.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.04622,-10898,-401,-2190.0,-3497,1.0,1,1,0,1,0,0,,2.0,1,1,MONDAY,11,0,1,1,0,0,0,Security Ministries,0.35928202479034554,0.6787258727936563,0.4722533429586386,0.2753,,0.9955,0.9388,,0.32,0.1379,0.6667,0.7083,,,0.3305,,0.4006,0.2805,,0.9955,0.9412,,0.3222,0.1379,0.6667,0.7083,,,0.3443,,0.4241,0.2779,,0.9955,0.9396,,0.32,0.1379,0.6667,0.7083,,,0.3364,,0.409,reg oper account,block of flats,0.34700000000000003,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-958.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n368890,0,Cash loans,M,N,N,0,157500.0,1280794.5,42457.5,1147500.0,Unaccompanied,Working,Higher education,Married,With parents,0.003069,-14328,-705,-8168.0,-5089,,1,1,0,1,1,0,Security staff,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,Industry: type 9,,0.4762824358157909,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n132706,0,Cash loans,F,N,Y,0,135000.0,640080.0,31261.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.001276,-15617,-966,-897.0,-946,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,WEDNESDAY,7,0,0,0,0,0,0,Self-employed,,0.5343018892694713,,0.0565,0.0608,0.9851,0.7959999999999999,0.0113,0.0,0.1279,0.1667,0.1367,0.0606,0.045,0.053,0.0055,0.0061,0.0315,0.0316,0.9861,0.8171,0.006,0.0,0.069,0.1667,0.2083,0.0232,0.0275,0.0278,0.0,0.0,0.0312,0.0361,0.9861,0.8121,0.0101,0.0,0.069,0.1667,0.2083,0.0483,0.0257,0.0292,0.0,0.0,reg oper account,block of flats,0.0246,Panel,No,1.0,1.0,1.0,1.0,-776.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n107728,0,Cash loans,F,N,Y,1,90000.0,675000.0,19867.5,675000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.014519999999999996,-13865,-2532,-1639.0,-4568,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,Government,0.4393609685935961,0.7024080456312423,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1869.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n118274,0,Revolving loans,F,Y,Y,0,360000.0,382500.0,19125.0,382500.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.04622,-18563,-5090,-9204.0,-2097,3.0,1,1,0,1,0,0,Accountants,2.0,1,1,MONDAY,15,0,0,0,0,1,1,Self-employed,0.7167808031052054,0.7233001569574337,0.6986675550534175,0.1144,0.0311,0.9866,0.8164,0.06,0.08,0.0345,0.625,0.6667,0.01,0.0933,0.0633,0.0,0.0,0.1166,0.0323,0.9866,0.8236,0.0606,0.0806,0.0345,0.625,0.6667,0.0102,0.1019,0.066,0.0,0.0,0.1155,0.0311,0.9866,0.8189,0.0604,0.08,0.0345,0.625,0.6667,0.0102,0.0949,0.0645,0.0,0.0,reg oper account,block of flats,0.0826,Panel,No,2.0,0.0,2.0,0.0,-1892.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,8.0\n347403,0,Cash loans,M,Y,Y,0,180000.0,1305000.0,38155.5,1305000.0,Unaccompanied,Working,Higher education,Married,With parents,0.0228,-9951,-1270,-4794.0,-2625,7.0,1,1,1,1,1,0,Managers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Advertising,0.0847043431470346,0.4949834417172122,0.3360615207658242,0.1289,0.1388,0.9841,0.7824,0.0234,0.0,0.3448,0.1667,0.2083,0.066,0.1009,0.1293,0.0193,0.0234,0.1313,0.1441,0.9841,0.7909,0.0237,0.0,0.3448,0.1667,0.2083,0.0675,0.1102,0.1348,0.0195,0.0247,0.1301,0.1388,0.9841,0.7853,0.0236,0.0,0.3448,0.1667,0.2083,0.0672,0.1026,0.1317,0.0194,0.0239,reg oper account,block of flats,0.1193,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-662.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n261618,1,Cash loans,F,N,Y,0,90000.0,225000.0,17775.0,225000.0,Children,Pensioner,Secondary / secondary special,Widow,House / apartment,0.009175,-20292,365243,-758.0,-2354,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,,0.1252427543715856,,0.0371,0.0555,0.9985,,,0.0,0.1034,0.125,,,,0.0337,,0.0,0.0378,0.0575,0.9985,,,0.0,0.1034,0.125,,,,0.0351,,0.0,0.0375,0.0555,0.9985,,,0.0,0.1034,0.125,,,,0.0343,,0.0,,block of flats,0.0312,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-2155.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n405743,0,Cash loans,F,N,N,0,180000.0,1056447.0,31018.5,922500.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.010032,-17483,-7149,-6768.0,-1036,,1,1,0,1,0,0,Cooking staff,2.0,2,2,MONDAY,4,0,0,0,0,0,0,Medicine,,0.05550807603009497,0.6754132910917112,0.3711,0.2035,0.9806,0.7348,0.1348,0.4,0.3448,0.3333,0.375,0.3587,0.3026,0.3831,0.0,0.0,0.3782,0.2112,0.9806,0.7452,0.1361,0.4028,0.3448,0.3333,0.375,0.3669,0.3306,0.3992,0.0,0.0,0.3747,0.2035,0.9806,0.7383,0.1357,0.4,0.3448,0.3333,0.375,0.3649,0.3078,0.39,0.0,0.0,,block of flats,0.3751,Panel,No,2.0,0.0,2.0,0.0,-1713.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n130808,0,Cash loans,F,N,N,0,112500.0,688500.0,29299.5,688500.0,Other_A,Working,Secondary / secondary special,Married,House / apartment,0.030755,-14625,-1312,-2587.0,-2589,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,13,0,0,0,1,1,0,Business Entity Type 3,,0.6600934136090764,0.5100895276257282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1453.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n323282,0,Revolving loans,M,N,N,0,157500.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-16960,-1781,-2081.0,-513,,1,1,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Other,0.6651024644228598,0.7761663755461068,0.6769925032909132,0.0629,0.0502,0.9737,0.6396,,0.0,0.1034,0.1667,0.0,0.0349,,0.0334,,0.0135,0.0641,0.0521,0.9737,0.6537,,0.0,0.1034,0.1667,0.0,0.0357,,0.0348,,0.0142,0.0635,0.0502,0.9737,0.6444,,0.0,0.1034,0.1667,0.0,0.0355,,0.034,,0.0137,,specific housing,0.04,Panel,No,0.0,0.0,0.0,0.0,-590.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n450183,0,Cash loans,F,N,N,1,162000.0,1129500.0,33156.0,1129500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.022625,-10234,-549,-2553.0,-2553,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,19,0,0,0,1,1,0,Self-employed,0.3236545551337365,0.4755733868048963,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-281.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,8.0,2.0,2.0\n154667,0,Cash loans,F,N,Y,0,202500.0,703728.0,25407.0,607500.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.00496,-21281,365243,-9500.0,-4571,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.5975124636593346,0.4365064990977374,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-866.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n208310,0,Cash loans,M,N,N,1,225000.0,494118.0,39037.5,436500.0,Family,Commercial associate,Higher education,Married,House / apartment,0.04622,-12258,-2516,-6142.0,-3635,,1,1,0,1,0,0,Laborers,3.0,1,1,SUNDAY,9,0,0,0,0,1,1,Business Entity Type 3,0.4675544314895274,0.7327215650666891,0.4365064990977374,0.0825,,0.9836,,,0.0,0.2069,0.1667,,0.1258,,,,,0.084,,0.9836,,,0.0,0.2069,0.1667,,0.1287,,,,,0.0833,,0.9836,,,0.0,0.2069,0.1667,,0.128,,,,,,block of flats,0.0724,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1888.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n416516,0,Cash loans,F,Y,N,0,135000.0,1319269.5,36409.5,1152000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-17781,-403,-5247.0,-1312,13.0,1,1,0,1,1,0,,2.0,2,2,MONDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.6604024505929879,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2256.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n351218,0,Cash loans,M,Y,N,0,144000.0,700830.0,20214.0,585000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.022625,-10151,-228,-3869.0,-2811,6.0,1,1,0,1,0,0,,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.581618440684176,0.470456116119975,0.0979,0.0,0.9866,0.8164,0.1365,0.0,0.0,0.4583,0.5,0.0,0.0799,,0.0,0.0024,0.0998,0.0,0.9866,0.8236,0.1378,0.0,0.0,0.4583,0.5,0.0,0.0872,,0.0,0.0025,0.0989,0.0,0.9866,0.8189,0.1374,0.0,0.0,0.4583,0.5,0.0,0.0812,,0.0,0.0024,reg oper account,block of flats,0.0747,Panel,No,3.0,0.0,2.0,0.0,-1.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n318504,0,Cash loans,F,N,Y,0,135000.0,922266.0,39204.0,810000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.010643,-22724,365243,-7575.0,-4841,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.5509075458378659,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n357777,0,Cash loans,F,N,N,0,135000.0,270000.0,21330.0,270000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Co-op apartment,0.026392,-8702,-722,-5018.0,-1377,,1,1,1,1,1,0,Core staff,1.0,2,2,SUNDAY,14,0,0,0,1,1,0,Bank,,0.4931905526635762,0.6195277080511546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,2.0,0.0,-609.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n124471,0,Revolving loans,F,Y,N,2,225000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.020246,-15585,-2786,-3615.0,-4787,2.0,1,1,0,1,1,0,Accountants,4.0,3,3,MONDAY,9,0,0,0,0,0,0,Other,0.5507649840712039,0.4672311467253443,0.2608559142068693,0.1299,0.1111,0.9866,0.8164,0.0285,0.16,0.1379,0.3333,0.375,0.0985,0.1051,0.1526,0.0039,0.0074,0.1324,0.1153,0.9866,0.8236,0.0288,0.1611,0.1379,0.3333,0.375,0.1007,0.1148,0.159,0.0039,0.0078,0.1312,0.1111,0.9866,0.8189,0.0287,0.16,0.1379,0.3333,0.375,0.1002,0.1069,0.1553,0.0039,0.0075,reg oper account,block of flats,0.1372,Panel,No,0.0,0.0,0.0,0.0,-1347.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n414900,0,Cash loans,M,Y,N,0,202500.0,900000.0,31887.0,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018029,-15894,-2476,-943.0,-4865,10.0,1,1,1,1,0,0,Drivers,2.0,3,3,WEDNESDAY,13,0,0,0,0,0,0,Industry: type 9,0.4103894467607256,0.2448074120471529,0.4884551844437485,0.0825,0.0895,0.9762,0.6736,0.0654,0.0,0.1379,0.1667,0.2083,0.0554,0.0672,0.0767,0.0,0.0,0.084,0.0929,0.9762,0.6864,0.066,0.0,0.1379,0.1667,0.2083,0.0566,0.0735,0.0799,0.0,0.0,0.0833,0.0895,0.9762,0.6779999999999999,0.0659,0.0,0.1379,0.1667,0.2083,0.0563,0.0684,0.078,0.0,0.0,,block of flats,0.0961,Panel,No,0.0,0.0,0.0,0.0,-762.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n148352,1,Cash loans,F,Y,Y,0,135000.0,175896.0,10759.5,126000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.003122,-16856,-228,-2155.0,-344,13.0,1,1,0,1,0,0,,1.0,3,3,THURSDAY,10,0,0,0,0,1,1,Other,,0.4067792988027077,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-478.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n340120,0,Cash loans,F,N,N,0,202500.0,755190.0,31995.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-14012,-7097,-5292.0,-4802,,1,1,1,1,1,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,Other,,0.35728197361233577,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.0,0.0,13.0,0.0,-2463.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n388618,0,Cash loans,F,Y,Y,0,180000.0,225000.0,17410.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.022625,-14777,-209,-7254.0,-2657,5.0,1,1,1,1,1,1,Managers,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Industry: type 9,0.8384884890548948,0.7234795580699884,0.5226973172821112,0.0495,,0.9846,,,0.0,0.0345,0.4583,,,,0.0554,,,0.0504,,0.9846,,,0.0,0.0345,0.4583,,,,0.0577,,,0.05,,0.9846,,,0.0,0.0345,0.4583,,,,0.0564,,,,block of flats,0.0518,,No,3.0,1.0,3.0,1.0,-1917.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n232831,0,Cash loans,M,N,N,0,225000.0,1125000.0,47664.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-19124,-5064,-5358.0,-2664,,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Other,0.2975660360603197,0.4935677022992053,0.470456116119975,0.0371,0.0188,0.9796,0.7212,0.0095,0.04,0.0345,0.3333,0.0417,0.0376,0.0303,0.0396,0.0,0.0,0.0378,0.0195,0.9796,0.7321,0.0096,0.0403,0.0345,0.3333,0.0417,0.0385,0.0331,0.0412,0.0,0.0,0.0375,0.0188,0.9796,0.7249,0.0096,0.04,0.0345,0.3333,0.0417,0.0382,0.0308,0.0403,0.0,0.0,reg oper account,block of flats,0.0363,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2046.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n421894,0,Cash loans,M,Y,Y,0,270000.0,550980.0,36819.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.007305,-9741,-1020,-3200.0,-1746,20.0,1,1,0,1,0,0,Drivers,1.0,3,3,FRIDAY,7,0,0,0,0,1,1,Transport: type 4,0.10620476687914346,0.4183107305649452,,0.0722,,0.9762,,,,0.1379,0.1667,,,,0.0655,,,0.0735,,0.9762,,,,0.1379,0.1667,,,,0.0682,,,0.0729,,0.9762,,,,0.1379,0.1667,,,,0.0667,,,,block of flats,0.0515,Panel,No,0.0,0.0,0.0,0.0,-657.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n217928,0,Cash loans,F,Y,N,1,202500.0,302206.5,16524.0,229500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.003818,-15415,-235,-2094.0,-4040,17.0,1,1,0,1,1,0,Core staff,3.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Kindergarten,,0.4516240528017294,0.5673792367572691,0.0021,0.0,0.9712,,,,,,,,,0.0025,,,0.0021,0.0,0.9712,,,,,,,,,0.0026,,,0.0021,0.0,0.9712,,,,,,,,,0.0025,,,,,0.0019,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-389.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n296347,0,Cash loans,F,N,Y,0,180000.0,1436508.0,51727.5,1269000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.025164,-15708,-7293,-1233.0,-1437,,1,1,0,1,0,1,Managers,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Military,0.7777581873882415,0.6473777741248368,,0.0619,0.0621,0.9771,0.6872,0.0081,0.0,0.1379,0.1667,0.2083,0.0629,0.0504,0.0532,0.0,0.0,0.063,0.0644,0.9772,0.6994,0.0082,0.0,0.1379,0.1667,0.2083,0.0643,0.0551,0.0554,0.0,0.0,0.0625,0.0621,0.9771,0.6914,0.0082,0.0,0.1379,0.1667,0.2083,0.064,0.0513,0.0541,0.0,0.0,reg oper account,block of flats,0.0463,Block,No,4.0,0.0,4.0,0.0,-1923.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n402848,0,Cash loans,F,N,Y,0,90000.0,199080.0,6381.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.0228,-7860,-452,-3096.0,-534,,1,1,0,1,0,0,Sales staff,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5373095696859834,0.20915469884100693,0.2464,0.2712,0.9861,0.8096,0.0477,0.0,0.4138,0.1667,0.2083,0.098,0.2009,0.2633,0.0,0.0,0.2511,0.2814,0.9861,0.8171,0.0481,0.0,0.4138,0.1667,0.2083,0.1003,0.2195,0.2744,0.0,0.0,0.2488,0.2712,0.9861,0.8121,0.048,0.0,0.4138,0.1667,0.2083,0.0998,0.2044,0.2681,0.0,0.0,reg oper account,block of flats,0.2332,Panel,No,0.0,0.0,0.0,0.0,-407.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n188419,1,Cash loans,M,Y,N,0,292500.0,755190.0,29286.0,675000.0,,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-19041,365243,-1126.0,-2582,7.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.26886892555881203,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,0.0,-201.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n216732,0,Cash loans,M,Y,N,0,315000.0,269982.0,29205.0,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.01885,-13289,-1181,-902.0,-4125,4.0,1,1,0,1,0,0,Managers,2.0,2,2,FRIDAY,17,0,0,0,0,0,0,Self-employed,,0.5000510855944729,0.520897599048938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1758.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n275878,0,Cash loans,F,N,N,0,184500.0,781920.0,23836.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-21178,365243,-868.0,-4671,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.7834910112283557,0.4956658291397297,0.0124,0.0,0.9762,0.6736,0.0093,0.0,0.1034,0.0,0.0417,0.0,0.0101,0.0104,0.0,0.0,0.0126,0.0,0.9762,0.6864,0.0093,0.0,0.1034,0.0,0.0417,0.0,0.011000000000000001,0.0108,0.0,0.0,0.0125,0.0,0.9762,0.6779999999999999,0.0093,0.0,0.1034,0.0,0.0417,0.0,0.0103,0.0105,0.0,0.0,not specified,block of flats,0.0132,\"Stone, brick\",Yes,4.0,0.0,4.0,0.0,-1561.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n100558,0,Revolving loans,M,N,Y,0,157500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-19499,-1299,-7244.0,-3057,,1,1,1,1,1,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.19761164436272607,0.09885928035482196,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,-211.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n152786,0,Cash loans,M,Y,Y,0,180000.0,1035832.5,30415.5,904500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010006,-18353,-581,-1956.0,-1864,7.0,1,1,1,1,1,0,Drivers,2.0,2,2,FRIDAY,15,0,0,0,1,1,0,Self-employed,0.6007436624891047,0.4753443303397526,0.8193176922872417,0.0165,0.0,0.997,,,0.0,0.069,0.0417,,,,0.0149,,0.0061,0.0168,0.0,0.997,,,0.0,0.069,0.0417,,,,0.0155,,0.0065,0.0167,0.0,0.997,,,0.0,0.069,0.0417,,,,0.0151,,0.0063,,block of flats,0.0132,Others,No,1.0,0.0,1.0,0.0,-2472.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n391618,0,Cash loans,F,N,Y,0,180000.0,675000.0,19737.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Widow,With parents,0.018634,-16265,-2612,-7032.0,-4917,,1,1,1,1,1,0,Sales staff,1.0,2,2,SUNDAY,8,0,1,1,0,1,1,Self-employed,0.7786629204823751,0.7254307980336687,0.3740208032583212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,3.0,9.0,2.0,-2051.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,2.0\n415638,0,Cash loans,M,Y,N,0,225000.0,697500.0,29551.5,697500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-9327,-801,-3527.0,-1915,13.0,1,1,1,1,1,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.19915496970523955,0.6170765965612095,0.656158373001177,0.0825,,0.995,,,0.08,0.069,0.375,,,,0.0804,,0.0001,0.084,,0.995,,,0.0806,0.069,0.375,,,,0.0837,,0.0002,0.0833,,0.995,,,0.08,0.069,0.375,,,,0.0818,,0.0001,,block of flats,0.0632,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1262.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n415266,0,Cash loans,F,N,Y,0,63000.0,170640.0,9922.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-19438,-11944,-12209.0,-2537,,1,1,1,1,0,0,Medicine staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Agriculture,0.8308163429024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-475.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n195705,0,Revolving loans,F,N,N,0,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Civil marriage,Municipal apartment,0.009334,-10090,-515,-2226.0,-2445,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,12,0,0,0,1,1,1,Other,0.3292053013796717,0.6143705790353989,0.5638350489514956,0.0082,0.0,0.9846,,,0.0,0.069,0.0417,,0.0052,,0.0096,,0.0043,0.0084,0.0,0.9846,,,0.0,0.069,0.0417,,0.0054,,0.0101,,0.0045,0.0083,0.0,0.9846,,,0.0,0.069,0.0417,,0.0053,,0.0098,,0.0043,,block of flats,0.0085,Wooden,No,2.0,0.0,2.0,0.0,-619.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n427845,0,Cash loans,F,N,Y,0,135000.0,1448343.0,39955.5,1134000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018029,-20112,-10545,-5534.0,-3374,,1,1,1,1,1,0,Laborers,2.0,3,3,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.5900030941380016,0.511190749653581,0.3656165070113335,0.1031,0.1255,0.9876,,0.0362,0.0,0.1724,0.1667,,0.1136,,0.1083,,0.0,0.105,0.1302,0.9876,,0.0366,0.0,0.1724,0.1667,,0.1162,,0.1129,,0.0,0.1041,0.1255,0.9876,,0.0365,0.0,0.1724,0.1667,,0.1156,,0.1103,,0.0,,block of flats,0.105,Panel,No,13.0,0.0,13.0,0.0,-1889.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n411415,0,Cash loans,F,N,Y,0,135000.0,315000.0,8307.0,315000.0,Family,Pensioner,Higher education,Separated,House / apartment,0.00963,-20460,365243,-3171.0,-4010,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.5841172973971105,,0.0825,0.0688,0.9742,,,0.0,0.1379,0.1667,,0.0567,,0.0734,,0.0,0.084,0.0714,0.9742,,,0.0,0.1379,0.1667,,0.057999999999999996,,0.0765,,0.0,0.0833,0.0688,0.9742,,,0.0,0.1379,0.1667,,0.0577,,0.0747,,0.0,,block of flats,0.0577,Panel,No,9.0,0.0,8.0,0.0,-451.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n349392,0,Cash loans,F,N,Y,0,67500.0,195543.0,10737.0,148500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003813,-12242,-705,-255.0,-1214,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.3650922224328533,0.3979463219016906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-776.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n364687,0,Cash loans,F,N,N,0,202500.0,2013840.0,53122.5,1800000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.01885,-14455,-2686,-6639.0,-4343,,1,1,1,1,1,0,Medicine staff,2.0,2,2,MONDAY,19,1,1,0,1,1,0,Medicine,0.3768916996929792,0.7561522440234295,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2457.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n138062,0,Cash loans,F,Y,Y,0,144000.0,323388.0,34087.5,292500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-15320,-745,-4507.0,-5422,65.0,1,1,0,1,0,0,,2.0,1,1,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6830921851615133,0.7490217048463391,0.3278,0.15,0.9871,0.8232,0.1102,0.4,0.1724,0.4583,0.5,0.0894,0.248,0.3634,0.0888,0.0305,0.33399999999999996,0.1557,0.9871,0.8301,0.1112,0.4028,0.1724,0.4583,0.5,0.0914,0.2709,0.3786,0.0895,0.0322,0.331,0.15,0.9871,0.8256,0.1109,0.4,0.1724,0.4583,0.5,0.0909,0.2522,0.3699,0.0893,0.0311,org spec account,block of flats,0.2924,Panel,No,0.0,0.0,0.0,0.0,-363.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n179947,0,Cash loans,F,N,Y,2,76500.0,157500.0,13518.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-12850,-1428,-1485.0,-3807,,1,1,0,1,1,0,Sales staff,4.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,Self-employed,0.3501834338901165,0.43268908659330224,0.6006575372857061,0.0825,0.0634,0.9747,0.6532,0.0557,0.0,0.1379,0.1667,0.2083,0.0907,0.0672,0.0702,0.0,0.0,0.084,0.0658,0.9747,0.6668,0.0562,0.0,0.1379,0.1667,0.2083,0.0928,0.0735,0.0732,0.0,0.0,0.0833,0.0634,0.9747,0.6578,0.055999999999999994,0.0,0.1379,0.1667,0.2083,0.0923,0.0684,0.0715,0.0,0.0,reg oper account,block of flats,0.0857,Panel,No,5.0,0.0,5.0,0.0,-148.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n206031,0,Cash loans,F,N,Y,1,247500.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-9506,-597,-4073.0,-1079,,1,1,0,1,0,0,Accountants,3.0,2,2,THURSDAY,17,0,0,0,0,0,0,Self-employed,0.2519167741347341,0.4333816662713235,0.5567274263630174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-459.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n252310,0,Cash loans,F,N,Y,0,112500.0,562500.0,27189.0,562500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.015221,-15775,365243,-1945.0,-1103,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.39325544275015817,0.1047946227497676,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n184197,0,Cash loans,M,N,Y,0,126000.0,826398.0,29812.5,697500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-17914,-4822,-486.0,-1249,,1,1,0,1,1,0,Laborers,2.0,3,2,THURSDAY,11,0,0,0,0,0,0,Self-employed,0.6354402534052108,0.5274944731163074,,0.1227,0.1105,0.9771,0.6872,0.0565,0.0,0.2759,0.1667,0.2083,0.0775,0.0983,0.1113,0.0077,0.008,0.125,0.1146,0.9772,0.6994,0.0571,0.0,0.2759,0.1667,0.2083,0.0793,0.1074,0.11599999999999999,0.0078,0.0085,0.1239,0.1105,0.9771,0.6914,0.0569,0.0,0.2759,0.1667,0.2083,0.0789,0.1,0.1133,0.0078,0.0082,reg oper account,block of flats,0.1202,Panel,No,2.0,0.0,2.0,0.0,-692.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n327059,0,Cash loans,F,N,Y,1,67500.0,253737.0,26775.0,229500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020246,-10437,-1395,-1051.0,-1170,,1,1,0,1,0,0,Medicine staff,3.0,3,3,THURSDAY,13,0,0,0,0,0,0,Medicine,,0.4513370357462786,0.7295666907060153,0.0974,0.0865,0.9831,0.7688,0.0184,0.08,0.1379,0.25,0.2917,0.1111,0.079,0.0927,0.0019,0.0717,0.063,0.0649,0.9801,0.7387,0.0088,0.0,0.1379,0.1667,0.2083,0.05,0.0551,0.0556,0.0,0.0,0.0984,0.0865,0.9831,0.7719,0.0185,0.08,0.1379,0.25,0.2917,0.113,0.0804,0.0943,0.0019,0.0732,reg oper account,block of flats,0.042,Panel,No,2.0,0.0,2.0,0.0,-804.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n412533,0,Cash loans,M,N,Y,0,90000.0,284400.0,16456.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-24156,365243,-1460.0,-1656,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,1,0,0,XNA,,0.6708175002546979,0.7309873696832169,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n359995,0,Cash loans,F,N,Y,0,135000.0,247500.0,26118.0,247500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.015221,-24533,365243,-11010.0,-4276,,1,0,0,1,0,0,,1.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.4776080662496757,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-19.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n147433,0,Cash loans,F,N,N,0,121500.0,904608.0,25006.5,648000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.032561,-10740,-734,-4892.0,-3354,,1,1,0,1,1,0,Laborers,2.0,1,1,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.4005206296284076,0.2822484337007223,0.2979,0.1189,0.9866,0.8164,0.0581,0.28,0.2414,0.375,0.0417,0.1024,0.2429,0.3106,0.0,0.0,0.3036,0.1234,0.9866,0.8236,0.0586,0.282,0.2414,0.375,0.0417,0.1047,0.2654,0.3236,0.0,0.0,0.3008,0.1189,0.9866,0.8189,0.0584,0.28,0.2414,0.375,0.0417,0.1041,0.2471,0.3162,0.0,0.0,reg oper account,block of flats,0.2719,Panel,No,1.0,0.0,1.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n431881,0,Cash loans,M,Y,Y,0,180000.0,454500.0,16825.5,454500.0,Other_A,Working,Secondary / secondary special,Married,House / apartment,0.026392,-18410,-2924,-4139.0,-1956,6.0,1,1,0,1,0,0,Drivers,2.0,2,2,SUNDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.7402625340408109,0.42589289800515295,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,2.0,7.0,2.0,-755.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n122776,0,Cash loans,M,Y,Y,0,270000.0,753840.0,21604.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-13567,-525,-7400.0,-4093,19.0,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Other,,0.6290165955248841,0.524496446363472,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n183568,0,Revolving loans,F,N,Y,1,157500.0,337500.0,16875.0,337500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.009175,-13413,-162,-506.0,-4552,,1,1,0,1,1,0,Laborers,3.0,2,2,THURSDAY,17,0,0,0,0,0,0,Construction,0.5123954843072492,0.7099307372918501,0.501075160239048,0.16699999999999998,0.0746,0.9911,0.8776,0.0057,0.08,0.069,0.3542,0.2083,0.0608,0.1362,0.0995,0.1988,0.0147,0.10400000000000001,0.0666,0.9826,0.7713,0.0,0.0806,0.069,0.3333,0.0417,0.0165,0.0909,0.0957,0.0156,0.0,0.1686,0.0746,0.9911,0.8792,0.0058,0.08,0.069,0.3542,0.2083,0.0618,0.1385,0.1013,0.1999,0.015,reg oper account,block of flats,0.0723,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1236.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n189870,0,Cash loans,F,Y,Y,0,144000.0,521280.0,27423.0,450000.0,Family,Pensioner,Higher education,Married,House / apartment,0.006852,-20710,365243,-5334.0,-1927,28.0,1,0,0,1,1,0,,2.0,3,3,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.3401357005449033,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-319.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n426191,0,Cash loans,F,N,Y,0,202500.0,835380.0,35392.5,675000.0,Family,Pensioner,Secondary / secondary special,Widow,House / apartment,0.02461,-23165,365243,-2903.0,-2956,,1,0,0,1,0,0,,1.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.3390469639319284,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n225467,0,Cash loans,F,N,Y,0,112500.0,528633.0,25560.0,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-22535,365243,-2460.0,-4499,,1,0,0,1,0,0,,2.0,1,1,MONDAY,13,0,0,0,0,0,0,XNA,,0.7493804976814034,,0.0825,,0.9811,,,0.0,0.2069,0.1667,,0.0,,0.0632,,0.0,0.084,,0.9811,,,0.0,0.2069,0.1667,,0.0,,0.0659,,0.0,0.0833,,0.9811,,,0.0,0.2069,0.1667,,0.0,,0.0643,,0.0,,block of flats,0.0497,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-973.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n164013,0,Cash loans,F,Y,Y,1,270000.0,673875.0,21865.5,562500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.006670999999999999,-11725,-2761,-5608.0,-4232,12.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,15,0,0,0,0,1,1,Other,0.6427432767372016,0.7004112503403592,0.501075160239048,0.0082,0.0524,0.9742,0.6464,0.0015,0.0,0.069,0.0417,0.0417,0.034,0.0067,0.0129,0.0,0.0,0.0084,0.0543,0.9742,0.6602,0.0015,0.0,0.069,0.0417,0.0417,0.0348,0.0073,0.0134,0.0,0.0,0.0083,0.0524,0.9742,0.6511,0.0015,0.0,0.069,0.0417,0.0417,0.0346,0.0068,0.0131,0.0,0.0,reg oper account,block of flats,0.0101,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-508.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n258513,0,Cash loans,F,Y,N,0,135000.0,787500.0,20902.5,787500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-15592,-4424,-911.0,-3741,4.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 2,0.5503122129079783,0.7091691113307462,0.3876253444214701,0.134,0.1057,0.9901,0.8640000000000001,0.026000000000000002,0.1332,0.1148,0.3608,0.0417,0.0181,0.1093,0.1194,0.0154,0.1239,0.1366,0.1096,0.9906,0.8759,0.0262,0.1611,0.1379,0.375,0.0417,0.013000000000000001,0.1194,0.1456,0.0156,0.1968,0.1353,0.1057,0.9906,0.8725,0.0262,0.16,0.1379,0.375,0.0417,0.013000000000000001,0.1112,0.1423,0.0155,0.1898,org spec account,block of flats,0.0443,Panel,No,0.0,0.0,0.0,0.0,-834.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n293961,0,Cash loans,M,Y,N,1,157500.0,404325.0,22063.5,337500.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.008625,-18091,-3382,-3448.0,-1638,26.0,1,1,0,1,0,1,Laborers,3.0,2,2,MONDAY,10,0,0,0,0,1,1,Police,0.3140902603455827,0.436633242193628,0.21396685226179807,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2271.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n456107,0,Cash loans,M,Y,Y,1,225000.0,1437799.5,42169.5,1255500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003069,-14512,-2044,-1525.0,-4665,11.0,1,1,0,1,0,0,High skill tech staff,3.0,3,3,FRIDAY,10,0,0,0,0,0,0,Construction,0.6357514509803343,0.5943899935834603,0.6296742509538716,0.0206,,0.9742,,,,0.069,,,,,0.0107,,,0.021,,0.9742,,,,0.069,,,,,0.0112,,,0.0208,,0.9742,,,,0.069,,,,,0.0109,,,,block of flats,0.0156,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-937.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n326115,0,Cash loans,F,N,N,0,148500.0,454500.0,20020.5,454500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.007305,-21110,365243,-13537.0,-4586,,1,0,0,1,1,0,,2.0,3,3,MONDAY,12,0,0,0,0,0,0,XNA,0.502723152258468,0.3640638526288581,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-925.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n455448,0,Cash loans,F,N,N,0,225000.0,539100.0,25933.5,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.009175,-8538,-1323,-3318.0,-1143,,1,1,1,1,0,0,Secretaries,1.0,2,2,THURSDAY,14,0,1,1,1,1,1,Business Entity Type 3,0.6485010364420486,0.4439998632511177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-185.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n324960,1,Cash loans,M,Y,Y,3,171000.0,1125000.0,33025.5,1125000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-16859,-1832,-821.0,-407,1.0,1,1,1,1,1,0,,5.0,2,2,FRIDAY,14,0,0,0,0,0,0,Self-employed,,0.4031855403476313,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-252.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n303523,0,Cash loans,M,Y,N,1,180000.0,497520.0,25402.5,450000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.025164,-17590,-1136,-8903.0,-1037,21.0,1,1,0,1,0,0,Security staff,3.0,2,2,MONDAY,8,0,0,0,0,0,0,Security,,0.35866406459239797,0.7544061731797895,0.0979,0.0505,0.9856,0.8028,0.028999999999999998,0.08,0.0517,0.4583,0.5,0.0,0.0799,0.1016,0.0,0.0,0.0819,0.0493,0.9782,0.7125,0.0188,0.0806,0.0345,0.375,0.4167,0.0,0.0716,0.0985,0.0,0.0,0.0989,0.0505,0.9856,0.8054,0.0292,0.08,0.0517,0.4583,0.5,0.0,0.0812,0.1034,0.0,0.0,reg oper account,block of flats,0.0743,Panel,No,1.0,0.0,1.0,0.0,-1229.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n121430,0,Cash loans,M,N,Y,0,180000.0,296280.0,14382.0,225000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.020246,-23121,365243,-7855.0,-5048,,1,0,0,1,1,0,,1.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,XNA,0.4998260292630903,0.5384727660216198,0.5172965813614878,0.3536,0.2879,0.9831,0.7688,0.0656,0.4,0.3448,0.375,0.3333,0.2919,0.2799,0.3854,0.0386,0.0547,0.3603,0.2988,0.9831,0.7779,0.0662,0.4028,0.3448,0.375,0.3333,0.2986,0.3058,0.4015,0.0389,0.0579,0.35700000000000004,0.2879,0.9831,0.7719,0.066,0.4,0.3448,0.375,0.3333,0.297,0.2847,0.3923,0.0388,0.0559,reg oper account,block of flats,0.3509,Panel,No,2.0,1.0,2.0,1.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n212801,0,Cash loans,F,N,N,4,112500.0,199080.0,13302.0,157500.0,Unaccompanied,Working,Lower secondary,Civil marriage,Municipal apartment,0.009549,-12420,-1375,-9192.0,-1913,,1,1,0,1,0,0,,6.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.3348162422080977,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-224.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n279761,0,Cash loans,F,N,N,0,112500.0,101880.0,11101.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011703,-14778,-1177,-6528.0,-4490,,1,1,0,1,0,0,Medicine staff,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Agriculture,,0.5659130796679341,0.7194907850918436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-321.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,3.0,3.0\n278843,0,Cash loans,F,N,N,2,90000.0,314100.0,13963.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-14676,-415,-6331.0,-4740,,1,1,0,1,0,0,Sales staff,4.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.4916437782818617,0.7675231046555077,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n210360,0,Cash loans,F,N,Y,1,193500.0,436032.0,25159.5,360000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006629,-11654,-2204,-1030.0,-3709,,1,1,1,1,1,1,Core staff,3.0,2,2,THURSDAY,5,0,0,0,0,0,0,Other,0.394684768946424,0.6966305151295921,0.4884551844437485,0.0459,0.0,0.9796,0.7144,0.0152,0.0,0.0976,0.125,0.1804,0.0111,0.0471,0.0394,0.0,0.0239,0.0672,0.0,0.9801,0.7387,0.0,0.0,0.1379,0.125,0.1667,0.0102,0.0588,0.0213,0.0,0.0,0.0541,0.0,0.9796,0.7316,0.0,0.0,0.1034,0.125,0.1667,0.0115,0.0547,0.0415,0.0,0.0,reg oper spec account,block of flats,0.021,Panel,No,3.0,0.0,3.0,0.0,-1840.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n412885,0,Cash loans,F,N,N,0,247500.0,877500.0,23148.0,877500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.019689,-10621,-825,-345.0,-546,,1,1,1,1,0,0,Core staff,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Bank,0.6963935910298324,0.5624504248427435,0.5867400085415683,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n156684,0,Cash loans,F,N,N,0,171000.0,640080.0,31261.5,450000.0,Unaccompanied,State servant,Higher education,Married,Office apartment,0.003818,-13999,-425,-2215.0,-4482,,1,1,0,1,1,0,Sales staff,2.0,2,2,FRIDAY,7,0,0,0,0,0,0,Trade: type 7,,0.3445272222835176,0.6706517530862718,0.0915,0.0598,0.9781,0.7076,0.1301,0.0,0.1655,0.1583,0.0208,0.0334,0.1765,0.0627,0.0,0.0,0.063,0.0487,0.9782,0.7190000000000001,0.1312,0.0,0.2069,0.1667,0.0,0.0266,0.1873,0.0479,0.0,0.0,0.0999,0.0607,0.9781,0.7115,0.1309,0.0,0.2069,0.1667,0.0208,0.034,0.1796,0.0599,0.0,0.0,reg oper account,block of flats,0.0428,Block,No,0.0,0.0,0.0,0.0,-1530.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n156315,0,Cash loans,F,N,Y,2,112500.0,538704.0,26046.0,481500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-14738,-1253,-156.0,-275,,1,1,0,1,0,0,Managers,4.0,2,2,THURSDAY,13,0,0,0,0,1,1,Restaurant,,0.5467652934316535,0.5460231970049609,0.0454,0.0785,0.9945,0.9252,0.0113,0.0,0.1034,0.125,0.0417,0.052000000000000005,0.037000000000000005,0.059000000000000004,0.0,0.0,0.0462,0.0815,0.9945,0.9281,0.0114,0.0,0.1034,0.125,0.0417,0.0532,0.0404,0.0615,0.0,0.0,0.0458,0.0785,0.9945,0.9262,0.0113,0.0,0.1034,0.125,0.0417,0.0529,0.0376,0.0601,0.0,0.0,reg oper spec account,block of flats,0.0464,Panel,No,3.0,0.0,3.0,0.0,-147.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n389324,0,Cash loans,M,N,Y,0,247500.0,225000.0,24232.5,225000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.072508,-10675,-258,-135.0,-1393,,1,1,1,1,1,0,,1.0,1,1,FRIDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.6566949127387108,0.11465249430297055,0.0454,0.0381,0.9717,0.6124,0.0071,0.0,0.069,0.1667,0.2083,0.0,0.0336,0.0525,0.0154,0.0301,0.0462,0.0395,0.9717,0.6276,0.0072,0.0,0.069,0.1667,0.2083,0.0,0.0367,0.0547,0.0156,0.0319,0.0458,0.0381,0.9717,0.6176,0.0072,0.0,0.069,0.1667,0.2083,0.0,0.0342,0.0535,0.0155,0.0308,reg oper account,block of flats,0.0479,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-110.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n186184,0,Cash loans,F,Y,Y,1,135000.0,852088.5,36229.5,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005313,-14207,-7415,-9577.0,-698,8.0,1,1,0,1,0,0,Cooking staff,3.0,2,2,SATURDAY,15,0,0,0,0,0,0,School,,0.7199837382073645,0.24988506275045536,0.1191,0.0,0.9866,0.8164,0.036000000000000004,0.02,0.1207,0.25,0.0417,0.0888,0.0954,0.0908,0.0077,0.0118,0.0945,0.0,0.9861,0.8171,0.0179,0.0,0.0345,0.1667,0.0417,0.067,0.0826,0.0935,0.0,0.0,0.1202,0.0,0.9866,0.8189,0.0362,0.02,0.1207,0.25,0.0417,0.0904,0.0971,0.0924,0.0078,0.012,reg oper account,block of flats,0.081,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1365.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n184395,0,Cash loans,M,N,Y,0,157500.0,675000.0,21906.0,675000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.009175,-12530,-1281,-6568.0,-4326,,1,1,1,1,1,0,Managers,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.3300921260356906,0.7421124469222418,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2933.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,3.0\n339275,0,Cash loans,F,N,N,0,202500.0,324216.0,18234.0,256500.0,Family,Pensioner,Higher education,Married,House / apartment,0.00496,-20523,365243,-4082.0,-3473,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,0.7352725803979915,0.5914659605902063,0.7503751495159068,0.0653,0.0709,0.9811,0.7756,0.0157,0.0,0.1607,0.1667,0.2083,0.0566,0.05,0.068,0.0019,0.0043,0.0735,0.0604,0.9836,0.7844,0.0144,0.0,0.1379,0.1667,0.2083,0.0578,0.045,0.0617,0.0,0.0,0.0729,0.0709,0.9836,0.7786,0.0158,0.0,0.1379,0.1667,0.2083,0.0575,0.0509,0.0728,0.0019,0.0044,reg oper spec account,block of flats,0.0577,,No,0.0,0.0,0.0,0.0,-1850.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n308909,0,Cash loans,F,N,Y,0,135000.0,1125000.0,33025.5,1125000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.015221,-22316,-887,-12066.0,-4061,,1,1,0,1,1,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,,0.6538793646726128,0.7688075728291359,0.1402,0.1767,0.9831,0.7688,0.0724,0.16,0.1379,0.375,0.4167,0.0769,0.1093,0.1416,0.0232,0.2208,0.1429,0.1833,0.9831,0.7779,0.073,0.1611,0.1379,0.375,0.4167,0.0787,0.1194,0.1476,0.0233,0.2337,0.1416,0.1767,0.9831,0.7719,0.0728,0.16,0.1379,0.375,0.4167,0.0783,0.1112,0.1442,0.0233,0.2254,reg oper account,block of flats,0.19899999999999998,Panel,No,1.0,0.0,1.0,0.0,-1562.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n290698,0,Cash loans,F,Y,Y,0,90000.0,1321020.0,35554.5,1125000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.019689,-18722,-11104,-1940.0,-2259,13.0,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Government,0.4382159946470701,0.6058607906653767,0.4241303111942548,,,0.9896,,,,,,,,,0.0514,,,,,0.9896,,,,,,,,,0.0536,,,,,0.9896,,,,,,,,,0.0524,,,,,0.0457,,No,0.0,0.0,0.0,0.0,-2107.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n165612,0,Cash loans,F,N,N,0,202500.0,753840.0,24448.5,540000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-22151,365243,-6424.0,-5217,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.6344904501987171,0.6642482627052363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,0.0,-980.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n269338,0,Cash loans,F,Y,N,0,135000.0,808650.0,21460.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-11752,-522,-559.0,-2564,9.0,1,1,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3935618302777445,0.5537029930093265,0.4866531565147181,,0.0643,0.9786,0.7076,,0.0,0.1379,0.1667,0.2083,0.0562,,0.064,,0.0259,,0.0667,0.9786,0.7190000000000001,,0.0,0.1379,0.1667,0.2083,0.0575,,0.0667,,0.0274,,0.0643,0.9786,0.7115,,0.0,0.1379,0.1667,0.2083,0.0572,,0.0651,,0.0264,reg oper spec account,block of flats,0.0559,Panel,No,2.0,0.0,2.0,0.0,-2134.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n212671,0,Cash loans,F,N,N,0,112500.0,808650.0,31464.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.031329,-15914,-456,-2291.0,-2317,,1,1,0,1,0,1,Accountants,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.6277975918089763,0.5072340815770318,0.5064842396679806,0.1392,0.1238,0.9836,0.7756,0.0099,0.0,0.3103,0.1667,0.2083,0.1273,0.1126,0.07200000000000001,0.0039,0.0057,0.1418,0.1285,0.9836,0.7844,0.01,0.0,0.3103,0.1667,0.2083,0.1302,0.12300000000000001,0.075,0.0039,0.006,0.1405,0.1238,0.9836,0.7786,0.0099,0.0,0.3103,0.1667,0.2083,0.1295,0.1146,0.0733,0.0039,0.0058,reg oper account,block of flats,0.0959,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-126.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n141731,0,Cash loans,F,Y,Y,0,144000.0,1024290.0,30078.0,855000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-23167,365243,-4868.0,-4936,28.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,,0.6867447664971384,0.6817058776720116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,4.0,1.0,-1778.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n209695,1,Cash loans,M,N,Y,0,90000.0,122256.0,9787.5,108000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-18778,-220,-2535.0,-2325,,1,1,0,1,0,0,Laborers,2.0,3,3,TUESDAY,8,0,0,0,0,0,0,Self-employed,,0.5006524437396728,0.11987796089553485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,1.0,6.0,0.0,-1752.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n338260,0,Cash loans,F,N,N,0,202500.0,360000.0,17316.0,360000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.011703,-14625,-1007,-6416.0,-4365,,1,1,1,1,0,0,Managers,2.0,2,2,THURSDAY,16,1,0,1,1,0,1,Business Entity Type 3,0.4894610531794482,0.6443969021203142,0.09261717137485452,0.0928,0.085,0.9846,0.7892,0.0106,0.0,0.2069,0.1667,0.2083,0.0437,0.0756,0.0855,0.0,0.0,0.0945,0.0882,0.9846,0.7975,0.0107,0.0,0.2069,0.1667,0.2083,0.0446,0.0826,0.0891,0.0,0.0,0.0937,0.085,0.9846,0.792,0.0107,0.0,0.2069,0.1667,0.2083,0.0444,0.077,0.087,0.0,0.0,not specified,block of flats,0.0731,Panel,No,1.0,0.0,1.0,0.0,-1196.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n245531,0,Cash loans,F,N,N,1,247500.0,1546020.0,45202.5,1350000.0,Unaccompanied,State servant,Higher education,Married,Office apartment,0.014464,-9586,-1256,-600.0,-965,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,SUNDAY,13,0,0,0,0,1,1,Other,,0.1865279607490229,0.43473324875017305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-632.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,9.0\n338972,0,Cash loans,M,N,Y,0,157500.0,396706.5,31342.5,324000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010147,-15608,-1107,-3800.0,-3200,,1,1,0,1,1,0,Laborers,2.0,2,2,MONDAY,9,0,0,0,0,1,1,Agriculture,0.4672595661525424,0.5226957611581504,0.08281470424406495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-726.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n312236,0,Cash loans,F,Y,Y,1,180000.0,871029.0,44604.0,765000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-11992,-1923,-111.0,-1741,11.0,1,1,0,1,0,0,Managers,3.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.7249885803727333,0.7247159014600743,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-641.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n190037,0,Cash loans,F,N,Y,0,180000.0,840951.0,33480.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-18691,-677,-636.0,-2229,,1,1,0,1,0,0,Security staff,2.0,3,3,THURSDAY,10,0,0,0,0,0,0,Security,0.7788761317624574,0.5635802371143841,0.6894791426446275,0.1835,0.1207,0.9831,0.8164,0.0695,0.16,0.2069,0.25,0.375,0.0867,0.2421,0.1865,0.0,0.0042,0.0714,0.0879,0.9801,0.8236,0.0701,0.0,0.1379,0.1667,0.375,0.0,0.2645,0.0675,0.0,0.0,0.1853,0.1207,0.9831,0.8189,0.07,0.16,0.2069,0.25,0.375,0.0882,0.2463,0.1898,0.0,0.0042,reg oper account,block of flats,0.2424,Block,No,3.0,0.0,3.0,0.0,-1804.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n339132,0,Revolving loans,F,N,Y,0,112500.0,315000.0,15750.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.032561,-19144,-1358,-5320.0,-2245,,1,1,0,1,0,0,Sales staff,2.0,1,1,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.3675693899574872,0.25533177083329,0.2588,0.1703,0.9781,0.7008,0.0513,0.28,0.2414,0.3333,0.375,0.075,0.21100000000000002,0.2552,0.0,0.0,0.2637,0.1767,0.9782,0.7125,0.0518,0.282,0.2414,0.3333,0.375,0.0768,0.2305,0.2659,0.0,0.0,0.2613,0.1703,0.9781,0.7048,0.0516,0.28,0.2414,0.3333,0.375,0.0764,0.2146,0.2597,0.0,0.0,reg oper account,block of flats,0.2287,Panel,No,0.0,0.0,0.0,0.0,-151.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n386563,0,Cash loans,M,N,Y,0,135000.0,508495.5,24592.5,454500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-23526,365243,-4881.0,-4907,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.5699643567445468,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-350.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,3.0\n115635,0,Revolving loans,F,N,Y,0,67500.0,337500.0,16875.0,337500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-21829,365243,-444.0,-5114,,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.7542440758247836,0.7136313997323308,0.0742,0.0366,0.9757,0.6668,0.0,0.04,0.0345,0.3333,0.375,0.033,0.0605,0.0517,0.0,0.0,0.0756,0.038,0.9757,0.6798,0.0,0.0403,0.0345,0.3333,0.375,0.0338,0.0661,0.0538,0.0,0.0,0.0749,0.0366,0.9757,0.6713,0.0,0.04,0.0345,0.3333,0.375,0.0336,0.0616,0.0526,0.0,0.0,reg oper account,block of flats,0.0594,Panel,No,7.0,0.0,7.0,0.0,-298.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n314493,0,Cash loans,F,N,Y,3,211500.0,545040.0,26509.5,450000.0,Unaccompanied,Working,Higher education,Married,Co-op apartment,0.022625,-13037,-867,-6382.0,-2383,,1,1,0,1,1,0,Realty agents,5.0,2,2,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.34316236145854345,0.5876190154453372,0.2750003523983893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,2.0,1.0,-629.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n357336,0,Cash loans,F,N,N,0,67500.0,389844.0,19903.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.008625,-18789,-833,-7708.0,-2341,,1,1,1,1,0,0,Laborers,1.0,2,2,THURSDAY,13,0,0,0,0,0,0,Housing,,0.6260060242007348,0.5902333386185574,0.1794,,0.9786,,,,0.1034,0.1667,,0.135,,,,0.0,0.1828,,0.9786,,,,0.1034,0.1667,,0.1381,,,,0.0,0.1811,,0.9786,,,,0.1034,0.1667,,0.1373,,,,0.0,,,0.1156,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1479.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n258448,0,Cash loans,F,N,N,0,135000.0,679500.0,26442.0,679500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-9912,-616,-9845.0,-439,,1,1,1,1,1,0,Sales staff,2.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.3639447326079032,0.4651197017173759,0.2750003523983893,0.0897,,0.9747,0.6532,0.0092,0.0,0.1379,0.1667,0.0417,0.0152,0.0672,0.0614,0.027000000000000003,0.0172,0.0914,,0.9747,0.6668,0.0093,0.0,0.1379,0.1667,0.0417,0.0156,0.0735,0.064,0.0272,0.0182,0.0906,,0.9747,0.6578,0.0093,0.0,0.1379,0.1667,0.0417,0.0155,0.0684,0.0625,0.0272,0.0175,reg oper account,block of flats,0.0483,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-885.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n300500,0,Cash loans,F,N,Y,0,108000.0,447453.0,24403.5,373500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00496,-19500,-2530,-4448.0,-3048,,1,1,0,1,0,0,Laborers,2.0,2,2,SUNDAY,12,0,0,0,1,0,1,Business Entity Type 3,0.6077655669158287,0.4349664467632415,0.4083588531230431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-407.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n308293,0,Cash loans,F,Y,N,0,81000.0,136512.0,9018.0,108000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.018801,-12538,-373,-5773.0,-3788,7.0,1,1,0,1,0,0,Security staff,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,Self-employed,0.4344853145559599,0.5349384494514994,0.5190973382084597,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1438.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n448632,0,Cash loans,F,N,Y,0,157500.0,454500.0,20020.5,454500.0,Family,Pensioner,Higher education,Married,House / apartment,0.0105,-22489,365243,-8886.0,-4422,,1,0,0,1,0,0,,2.0,3,3,MONDAY,15,0,0,0,0,0,0,XNA,,0.5053756347659478,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-165.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n433231,0,Cash loans,F,N,Y,0,135000.0,1098000.0,35550.0,1098000.0,Family,Working,Higher education,Separated,House / apartment,0.014464,-20695,-33,-4083.0,-4083,,1,1,1,1,1,0,Accountants,1.0,2,2,SUNDAY,4,0,0,0,0,0,0,Housing,,0.6807918302590021,0.7636399214572418,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,5.0,0.0,-216.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n297498,0,Revolving loans,F,N,Y,0,112500.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006296,-14870,-2039,-6728.0,-3461,,1,1,1,1,0,0,,2.0,3,3,TUESDAY,17,0,0,0,0,1,1,Other,,0.2049408517355436,0.3296550543128238,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-280.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n112880,0,Cash loans,F,Y,Y,0,171000.0,625536.0,20803.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-16958,-5466,-1924.0,-503,13.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.3599175140812164,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2502.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n346518,0,Cash loans,F,N,Y,0,247500.0,573628.5,27724.5,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.008473999999999999,-18529,-7168,-12504.0,-2082,,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,19,0,0,0,0,0,0,Trade: type 7,,0.3641904323448479,0.7490217048463391,0.0528,0.0656,0.9791,0.7144,0.0284,0.0,0.1241,0.1667,0.0417,0.0536,0.0429,0.0469,0.0008,0.0061,0.0095,0.0139,0.9786,0.7190000000000001,0.0062,0.0,0.0345,0.1667,0.0417,0.0022,0.0083,0.0093,0.0,0.0,0.0604,0.0368,0.9786,0.7115,0.0196,0.0,0.1379,0.1667,0.0417,0.0323,0.0496,0.0515,0.0,0.0,reg oper account,,0.0589,Panel,No,0.0,0.0,0.0,0.0,-1471.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n209192,0,Cash loans,M,N,Y,1,157500.0,225000.0,18171.0,225000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,Office apartment,0.006629,-12495,-1118,-4769.0,-1809,,1,1,0,1,0,1,Core staff,3.0,2,2,FRIDAY,9,0,0,0,1,1,0,Religion,0.4171347887274272,0.3220408345937738,0.5884877883422673,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n275147,0,Cash loans,M,Y,Y,1,585000.0,2013840.0,53253.0,1800000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006233,-16305,-3227,-3962.0,-3959,7.0,1,1,0,1,0,0,,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Military,0.7184395340408709,0.6822868861647481,0.6863823354047934,0.0773,0.0848,0.9866,0.8164,0.0124,0.0,0.1724,0.1667,0.0417,0.0356,0.063,0.0704,0.0,0.0022,0.0788,0.08800000000000001,0.9866,0.8236,0.0125,0.0,0.1724,0.1667,0.0417,0.0364,0.0689,0.0734,0.0,0.0023,0.0781,0.0848,0.9866,0.8189,0.0125,0.0,0.1724,0.1667,0.0417,0.0362,0.0641,0.0717,0.0,0.0022,reg oper spec account,block of flats,0.0626,Panel,No,4.0,0.0,4.0,0.0,-1405.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n218747,0,Cash loans,F,N,Y,0,112500.0,266652.0,17172.0,202500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.018634,-17604,-7181,-6237.0,-1155,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,13,0,0,0,0,0,0,School,,0.7103493533520212,0.2512394458905693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2097.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n363599,0,Revolving loans,F,Y,Y,0,135000.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Separated,House / apartment,0.00733,-17300,-595,-10250.0,-841,14.0,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Construction,,0.4320772582262089,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,0.0,-126.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n151670,0,Cash loans,F,N,Y,2,180000.0,526491.0,22261.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-12366,-1620,-528.0,-4798,,1,1,0,1,0,0,Realty agents,4.0,1,1,SUNDAY,18,0,0,0,0,0,0,Services,0.6551012329136987,0.6589148825169824,0.8038850611746273,0.2495,0.1842,0.9836,0.7756,,0.22,0.1897,0.3333,,0.0825,,0.2398,,,0.1849,0.1323,0.9836,0.7844,,0.1611,0.1379,0.3333,,0.069,,0.1808,,,0.2519,0.1842,0.9836,0.7786,,0.22,0.1897,0.3333,,0.0839,,0.2441,,,reg oper account,block of flats,0.2407,Panel,No,0.0,0.0,0.0,0.0,-420.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n369607,0,Revolving loans,F,N,Y,0,162000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010147,-9837,-1334,-9837.0,-2497,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,17,0,0,0,0,1,1,Industry: type 7,0.31620794908016703,0.6408161082139685,0.5136937663039473,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-478.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n440642,0,Cash loans,F,N,N,0,180000.0,1256400.0,36004.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-18070,-4777,-4262.0,-1623,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Self-employed,,0.5561604019678831,0.4206109640437848,0.2227,0.1692,0.9831,,,0.24,0.2069,0.3333,,0.0209,,0.2366,,0.0,0.2269,0.1756,0.9831,,,0.2417,0.2069,0.3333,,0.0214,,0.2465,,0.0,0.2248,0.1692,0.9831,,,0.24,0.2069,0.3333,,0.0213,,0.2409,,0.0,,block of flats,0.1861,Panel,No,7.0,0.0,7.0,0.0,-3.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n406301,0,Cash loans,F,N,Y,0,216000.0,270000.0,12024.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-13265,-1612,-7088.0,-3931,,1,1,0,1,0,0,,2.0,3,3,SATURDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.08876193774484964,0.4686596550493113,0.4351,0.34,0.9816,0.7484,0.2205,0.48,0.4138,0.3333,0.375,0.1834,0.3387,0.462,0.0347,0.0303,0.4433,0.3528,0.9816,0.7583,0.2225,0.4834,0.4138,0.3333,0.375,0.1876,0.3701,0.4813,0.035,0.0321,0.4393,0.34,0.9816,0.7518,0.2219,0.48,0.4138,0.3333,0.375,0.1866,0.3446,0.4703,0.0349,0.031,reg oper account,block of flats,0.4839,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n194834,1,Cash loans,F,N,N,0,90000.0,675000.0,21775.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-14126,-243,-4084.0,-4172,,1,1,1,1,0,0,Core staff,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Postal,,0.4269120120990065,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-386.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n139431,0,Cash loans,F,N,N,0,180000.0,1007761.5,42826.5,927000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.025164,-16842,-1845,-10987.0,-367,,1,1,0,1,1,0,Sales staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6526360945733339,0.4311917977993083,0.5247,0.0,0.9796,0.7212,0.1128,0.4,0.3448,0.3333,0.375,0.1221,0.4236,0.4614,0.0193,0.301,0.5347,0.0,0.9796,0.7321,0.1139,0.4028,0.3448,0.3333,0.375,0.1249,0.4628,0.4807,0.0195,0.3186,0.5298,0.0,0.9796,0.7249,0.1136,0.4,0.3448,0.3333,0.375,0.1242,0.431,0.4697,0.0194,0.3073,reg oper account,block of flats,0.49,Panel,No,6.0,1.0,6.0,1.0,-1458.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n338280,0,Revolving loans,F,N,Y,0,225000.0,405000.0,20250.0,405000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-17273,-4728,-3692.0,-800,,1,1,0,1,1,0,,2.0,1,1,FRIDAY,16,0,0,0,0,0,0,Other,0.8213831001141226,0.7235976610155213,0.4436153084085652,0.2575,0.1478,0.9821,0.7552,0.6757,0.4,0.131,0.4583,0.0417,0.0602,0.3471,0.2535,0.0,0.0123,0.1239,0.1225,0.9821,0.7648,0.6819,0.3222,0.069,0.4583,0.0417,0.0291,0.3792,0.1398,0.0,0.005,0.2436,0.1183,0.9821,0.7585,0.68,0.32,0.1379,0.4583,0.0417,0.0289,0.3531,0.2702,0.0,0.0072,reg oper account,block of flats,0.1065,Panel,No,0.0,0.0,0.0,0.0,-457.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n250630,0,Cash loans,M,N,N,1,67500.0,450000.0,22977.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-20427,-508,-7732.0,-3546,,1,1,1,1,0,0,Laborers,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.699060468702073,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-189.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n342150,0,Cash loans,M,Y,N,0,112500.0,500211.0,32098.5,463500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.008473999999999999,-22526,365243,-342.0,-4255,16.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.3808158134418963,0.7136313997323308,0.0371,0.0288,0.9811,0.7416,0.027000000000000003,0.04,0.0345,0.3333,0.0,0.0,0.0303,0.0245,0.0,0.0,0.0378,0.0298,0.9811,0.7517,0.0273,0.0403,0.0345,0.3333,0.0,0.0,0.0331,0.0256,0.0,0.0,0.0375,0.0288,0.9811,0.7451,0.0272,0.04,0.0345,0.3333,0.0,0.0,0.0308,0.025,0.0,0.0,reg oper account,block of flats,0.0316,Panel,No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n456245,0,Cash loans,F,N,Y,3,81000.0,269550.0,11871.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009175,-12961,-1046,-1398.0,-3780,,1,1,1,1,0,0,Low-skill Laborers,5.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Industry: type 1,,0.5754814295263838,,0.0392,0.0827,0.9846,,,0.0,0.0345,0.0417,,0.0144,,0.0189,,0.0,0.0399,0.0858,0.9846,,,0.0,0.0345,0.0417,,0.0147,,0.0197,,0.0,0.0396,0.0827,0.9846,,,0.0,0.0345,0.0417,,0.0146,,0.0192,,0.0,,block of flats,0.0149,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-448.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n455365,0,Cash loans,M,Y,Y,0,180000.0,301896.0,27819.0,252000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.031329,-10974,-3542,-1161.0,-3650,13.0,1,1,0,1,0,1,Laborers,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Industry: type 5,0.5109863539499276,0.6287614143774005,0.3876253444214701,0.0928,0.0722,0.9747,0.6532,0.0227,0.0,0.2069,0.1667,0.2083,0.1106,0.0756,0.0783,0.0,0.0,0.0945,0.0749,0.9747,0.6668,0.0229,0.0,0.2069,0.1667,0.2083,0.1131,0.0826,0.0816,0.0,0.0,0.0937,0.0722,0.9747,0.6578,0.0229,0.0,0.2069,0.1667,0.2083,0.1125,0.077,0.0798,0.0,0.0,reg oper account,block of flats,0.07400000000000001,Panel,No,1.0,0.0,1.0,0.0,-2041.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n308619,0,Cash loans,F,N,N,0,39600.0,436032.0,13347.0,360000.0,Unaccompanied,Pensioner,Higher education,Civil marriage,House / apartment,0.007114,-18322,365243,-9817.0,-1864,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.69742019589036,0.5937175866150576,0.1278,0.1451,,0.7756,,0.16,0.1379,0.3333,0.375,0.1067,,0.131,,0.2296,0.1303,0.1505,,0.7844,,0.1611,0.1379,0.3333,0.375,0.1091,,0.1365,,0.2431,0.1291,0.1451,,0.7786,,0.16,0.1379,0.3333,0.375,0.1085,,0.1334,,0.2345,,block of flats,0.153,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n380267,0,Cash loans,F,N,Y,0,99000.0,225000.0,22050.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-24243,365243,-818.0,-4817,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,XNA,0.874400824027067,0.6695898158478278,0.7981372313187245,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n338373,0,Revolving loans,F,N,Y,1,135000.0,225000.0,11250.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-10714,-974,-3006.0,-3386,,1,1,0,1,0,0,Core staff,3.0,2,2,THURSDAY,9,0,0,0,0,1,1,Bank,,0.7346869261939216,0.5406544504453575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1532.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n309006,0,Cash loans,F,N,Y,1,112500.0,1093500.0,35275.5,1093500.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-11899,-2422,-963.0,-3607,,1,1,1,1,1,0,Sales staff,3.0,2,2,SATURDAY,9,0,0,0,0,0,0,Trade: type 7,0.7792747461604834,0.16219210595922867,,0.0082,0.0,0.9682,0.5648,0.0106,0.0,0.0345,0.0417,0.0417,0.019,0.0067,0.0073,0.0,0.0,0.0084,0.0,0.9682,0.5818,0.0107,0.0,0.0345,0.0417,0.0417,0.0194,0.0073,0.0077,0.0,0.0,0.0083,0.0,0.9682,0.5706,0.0106,0.0,0.0345,0.0417,0.0417,0.0193,0.0068,0.0075,0.0,0.0,reg oper account,block of flats,0.0058,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-1694.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n401554,0,Cash loans,F,N,Y,0,369000.0,834048.0,44568.0,720000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Widow,House / apartment,0.006629,-20508,-498,-12252.0,-1127,,1,1,0,1,0,0,Accountants,1.0,2,2,TUESDAY,5,0,0,0,0,0,0,Government,,0.0006120393644740464,0.5460231970049609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1617.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n167010,0,Cash loans,F,N,Y,0,225000.0,490495.5,26262.0,454500.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.003069,-19597,-3545,-7957.0,-3097,,1,1,0,1,0,0,Core staff,2.0,3,3,SATURDAY,11,0,0,0,0,0,0,Kindergarten,0.6385430150074645,0.4688699619676621,0.22383131747015547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1049.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n165162,0,Cash loans,M,Y,Y,3,180000.0,973710.0,28597.5,697500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.018801,-16367,-3728,-7057.0,-4496,14.0,1,1,0,1,0,0,,5.0,2,2,SATURDAY,7,0,0,0,0,1,1,Other,0.52761363656324,0.5045338157158507,0.5673792367572691,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-38.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n249020,0,Revolving loans,F,N,Y,1,112500.0,270000.0,13500.0,270000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020246,-15962,-2174,-88.0,-4592,,1,1,0,1,0,0,Laborers,3.0,3,3,FRIDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.5297285570340203,0.746300213050371,0.0845,0.0427,0.9826,,,0.08,0.0345,0.4583,,0.1659,,0.0735,,0.0013,0.0861,0.0443,0.9826,,,0.0806,0.0345,0.4583,,0.1697,,0.0766,,0.0014,0.0854,0.0427,0.9826,,,0.08,0.0345,0.4583,,0.1688,,0.0748,,0.0014,,block of flats,0.0581,Panel,No,1.0,0.0,0.0,0.0,-1615.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n338739,0,Cash loans,F,N,Y,0,148500.0,533668.5,23638.5,477000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-23007,365243,-5195.0,-4417,,1,0,0,1,1,0,,2.0,3,2,THURSDAY,13,0,0,0,0,0,0,XNA,,0.4825040299909481,0.6610235391308081,0.1485,0.0982,0.9871,0.8232,0.0358,0.16,0.1379,0.375,0.4167,0.0804,0.1042,0.1601,0.0772,0.1516,0.1513,0.1019,0.9871,0.8301,0.0361,0.1611,0.1379,0.375,0.4167,0.0822,0.1139,0.1668,0.0778,0.1605,0.1499,0.0982,0.9871,0.8256,0.036000000000000004,0.16,0.1379,0.375,0.4167,0.0818,0.106,0.163,0.0776,0.1548,reg oper account,block of flats,0.1785,Panel,No,0.0,0.0,0.0,0.0,-1595.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n406146,0,Cash loans,F,N,Y,1,112500.0,153000.0,16231.5,153000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-12342,-5654,-1033.0,-3089,,1,1,0,1,1,0,,3.0,2,2,SATURDAY,7,0,0,0,0,1,1,Transport: type 4,0.3782232828498197,0.34112290759888264,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-663.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n304826,0,Cash loans,F,N,N,0,225000.0,521280.0,28408.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018029,-17441,-2264,-38.0,-938,,1,1,0,1,0,0,,1.0,3,3,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 1,,0.39155576414419374,0.5585066276769286,,,0.9856,,,,,,,,,,,,,,0.9856,,,,,,,,,,,,,,0.9856,,,,,,,,,,,,,block of flats,0.0676,Panel,No,0.0,0.0,0.0,0.0,-1341.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n420653,0,Cash loans,F,N,Y,0,135000.0,213156.0,10494.0,139500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018634,-13210,-970,-7279.0,-1111,,1,1,0,1,0,0,,2.0,2,2,SUNDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.4744545840126597,0.5817940306990845,0.3031463744186309,0.0784,0.0539,0.9737,,,0.0,0.1379,0.1667,,0.0165,,0.0614,,0.0105,0.0798,0.0559,0.9737,,,0.0,0.1379,0.1667,,0.0169,,0.0639,,0.0112,0.0791,0.0539,0.9737,,,0.0,0.1379,0.1667,,0.0168,,0.0625,,0.0108,,block of flats,0.0759,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-670.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n243876,0,Cash loans,F,N,N,0,157500.0,254700.0,25834.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-10064,-2458,-3538.0,-1542,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Construction,0.7335484231427829,0.6600737876077739,0.4902575124990026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-340.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n396074,0,Cash loans,M,Y,Y,0,157500.0,979992.0,28782.0,702000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.018634,-10900,-1149,-10867.0,-3430,36.0,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,17,0,0,0,1,1,0,Business Entity Type 3,0.1971507691651904,0.6054481902382438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2502.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n249293,0,Cash loans,F,Y,Y,0,202500.0,828000.0,42408.0,828000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-19956,-2875,-9947.0,-3495,2.0,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,11,0,1,1,0,1,1,Business Entity Type 2,0.4868703243670273,0.5295978133337718,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1074.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n225929,0,Cash loans,F,N,Y,0,270000.0,755190.0,33394.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.072508,-20967,365243,-2987.0,-3056,,1,0,0,1,1,0,,1.0,1,1,MONDAY,16,0,0,0,0,0,0,XNA,,0.6945190264162702,0.2925880073368475,0.0474,0.0299,0.9712,0.6056,0.0005,0.0,0.1034,0.1667,0.2083,0.0183,0.0353,0.0633,0.0154,0.027000000000000003,0.0483,0.0311,0.9712,0.621,0.0005,0.0,0.1034,0.1667,0.2083,0.0187,0.0386,0.066,0.0156,0.0285,0.0479,0.0299,0.9712,0.6109,0.0005,0.0,0.1034,0.1667,0.2083,0.0186,0.0359,0.0645,0.0155,0.0275,reg oper account,block of flats,0.055999999999999994,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1354.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n346706,0,Cash loans,F,N,Y,0,67500.0,385164.0,18657.0,292500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-23240,365243,-2183.0,-4447,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,0.8020933777957531,0.03412012146322529,0.4812493411434029,0.0309,0.0292,0.9861,,,0.0,0.069,0.1667,,0.0255,,0.0189,,0.0,0.0315,0.0303,0.9861,,,0.0,0.069,0.1667,,0.0261,,0.0197,,0.0,0.0312,0.0292,0.9861,,,0.0,0.069,0.1667,,0.026000000000000002,,0.0193,,0.0,,block of flats,0.0285,Panel,No,0.0,0.0,0.0,0.0,-791.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n258436,0,Cash loans,F,N,Y,0,121500.0,144000.0,7942.5,144000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-14632,-3105,-6300.0,-4439,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Self-employed,0.3037401510113301,0.3856744387396969,0.36896873825284665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-519.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n443841,0,Cash loans,F,N,N,0,225000.0,360000.0,24057.0,360000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.032561,-14795,-1326,-2228.0,-4096,,1,1,0,1,1,0,,2.0,1,1,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.6304746677174327,0.7693119601602421,0.6642482627052363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1683.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n257544,0,Cash loans,F,Y,Y,1,225000.0,675000.0,32472.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.028663,-15860,-2881,-3541.0,-4690,9.0,1,1,1,1,1,0,,3.0,2,2,MONDAY,13,0,0,0,0,0,0,Housing,,0.5064360641455983,0.7476633896301825,0.0732,0.0883,0.9786,0.7076,0.0089,0.0,0.1379,0.1667,0.0417,0.0464,0.0588,0.0669,0.0039,0.0022,0.0746,0.0916,0.9786,0.7190000000000001,0.009000000000000001,0.0,0.1379,0.1667,0.0417,0.0475,0.0643,0.0697,0.0039,0.0023,0.0739,0.0883,0.9786,0.7115,0.009000000000000001,0.0,0.1379,0.1667,0.0417,0.0472,0.0599,0.0681,0.0039,0.0022,reg oper account,block of flats,0.0579,\"Stone, brick\",No,14.0,0.0,14.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n156670,0,Cash loans,F,N,Y,0,247500.0,814041.0,23931.0,679500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-18001,-2680,-864.0,-1557,,1,1,0,1,0,0,Sales staff,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6408261747108652,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-1673.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n285208,0,Cash loans,M,Y,Y,0,202500.0,359685.0,15970.5,310500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-16253,-115,-2570.0,-4626,12.0,1,1,1,1,0,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Government,0.44065038033945203,0.7748796585560404,0.7738956942145427,0.0124,0.0145,0.9578,,,0.0,0.069,0.0833,,0.0,,0.0188,,0.0166,0.0126,0.015,0.9578,,,0.0,0.069,0.0833,,0.0,,0.0196,,0.0176,0.0125,0.0145,0.9578,,,0.0,0.069,0.0833,,0.0,,0.0192,,0.017,,block of flats,0.0184,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-2356.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n125330,0,Revolving loans,M,N,Y,0,157500.0,202500.0,10125.0,202500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Municipal apartment,0.018634,-19235,-184,-1963.0,-2796,,1,1,1,1,1,0,Laborers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.2784038129963405,0.5856782248901687,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1032.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n240688,0,Cash loans,F,N,Y,0,126000.0,283419.0,10309.5,234000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.010032,-23607,365243,-7300.0,-4985,,1,0,0,1,0,0,,1.0,2,2,MONDAY,8,0,0,0,0,0,0,XNA,,0.2357705151393857,0.7421816117614419,0.366,0.2186,0.9881,0.8368,0.0,0.36,0.3103,0.375,0.4167,0.0,0.2984,0.4258,0.0,0.0,0.3729,0.2269,0.9881,0.8432,0.0,0.3625,0.3103,0.375,0.4167,0.0,0.326,0.4436,0.0,0.0,0.3695,0.2186,0.9881,0.8390000000000001,0.0,0.36,0.3103,0.375,0.4167,0.0,0.3036,0.4334,0.0,0.0,org spec account,block of flats,0.4262,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,0.0\n117519,0,Cash loans,M,N,N,2,225000.0,1350000.0,39604.5,1350000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.019689,-15054,-4121,-2971.0,-4023,,1,1,0,1,0,1,IT staff,4.0,2,2,FRIDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.4707818515716247,0.8128226070575616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-499.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n180927,0,Revolving loans,F,N,Y,0,180000.0,157500.0,7875.0,157500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,With parents,0.04622,-8681,-382,-8681.0,-925,,1,1,0,1,0,0,Sales staff,2.0,1,1,THURSDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.2371067748891673,0.5302678314775549,,0.0722,0.0,0.9791,0.7144,0.0077,0.0,0.1379,0.1667,0.0417,0.09300000000000001,0.0588,0.0636,0.0,0.0,0.0735,0.0,0.9791,0.7256,0.0078,0.0,0.1379,0.1667,0.0417,0.0951,0.0643,0.0663,0.0,0.0,0.0729,0.0,0.9791,0.7182,0.0078,0.0,0.1379,0.1667,0.0417,0.0946,0.0599,0.0648,0.0,0.0,,block of flats,0.0543,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-628.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n398080,0,Cash loans,F,N,Y,0,135000.0,234000.0,18238.5,234000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010966,-18790,-509,-1554.0,-2335,,1,1,0,1,0,0,Medicine staff,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Medicine,,0.5923692062134747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-753.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n187155,0,Cash loans,F,N,Y,0,247500.0,1360098.0,48852.0,1201500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.006233,-20174,-6838,-12199.0,-3669,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Military,,0.6606966445803164,0.5082869913916046,0.0653,0.0295,0.9831,0.7688,0.0102,0.04,0.0345,0.3333,0.375,0.0501,0.0532,0.0514,0.0,0.0,0.0473,0.0289,0.9831,0.7779,0.0088,0.0403,0.0345,0.3333,0.375,0.0339,0.0413,0.0504,0.0,0.0,0.0749,0.0303,0.9831,0.7719,0.0109,0.04,0.0345,0.3333,0.375,0.0337,0.0616,0.0539,0.0,0.0,reg oper spec account,block of flats,0.0428,Panel,No,6.0,0.0,6.0,0.0,-3165.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n221009,0,Cash loans,M,Y,Y,1,81000.0,528633.0,20538.0,472500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.006629,-15875,365243,-983.0,-486,17.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,5,0,0,0,0,0,0,XNA,,0.6332470736446594,0.622922000268356,0.1237,0.1767,0.9856,0.8028,0.0397,0.0,0.2241,0.1667,0.125,0.1212,0.0988,0.1391,0.0097,0.0094,0.1061,0.1382,0.9856,0.8105,0.0342,0.0,0.1724,0.1667,0.0417,0.0903,0.0882,0.1176,0.0,0.0,0.1249,0.1767,0.9856,0.8054,0.0399,0.0,0.2241,0.1667,0.125,0.1233,0.1005,0.1416,0.0097,0.0096,reg oper account,block of flats,0.1572,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n454372,1,Cash loans,M,N,Y,0,310500.0,331920.0,17077.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.002042,-10172,-1076,-1291.0,-2860,,1,1,0,1,0,0,Accountants,2.0,3,3,FRIDAY,16,0,0,0,0,0,0,Other,0.3181165778528032,0.31820008671947303,0.1997705696341145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-107.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n337146,0,Cash loans,M,N,Y,1,180000.0,448056.0,21919.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-13460,-772,-7540.0,-2668,,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,10,0,0,0,1,1,1,Construction,,0.5915822023932055,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-608.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n324879,0,Cash loans,F,N,Y,0,90000.0,560664.0,18216.0,468000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.0105,-20586,365243,-14638.0,-4130,,1,0,0,1,0,0,,1.0,3,3,TUESDAY,13,0,0,0,0,0,0,XNA,,0.5997769790693952,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1097,,No,2.0,0.0,2.0,0.0,-2564.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n370443,0,Cash loans,M,N,Y,0,180000.0,728460.0,44694.0,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-9171,-120,-6566.0,-1631,,1,1,0,1,0,0,Core staff,2.0,1,1,THURSDAY,15,0,0,0,0,0,0,Trade: type 3,0.2486872374999348,0.7665137256810628,0.38079968264891495,0.0818,0.0491,0.9767,0.6804,0.0257,0.0,0.1607,0.1667,0.2083,0.0202,0.0661,0.0685,0.0025,0.0036,0.063,0.0,0.9752,0.6733,0.0071,0.0,0.1379,0.1667,0.2083,0.0147,0.0551,0.057999999999999996,0.0,0.0,0.0812,0.0569,0.9762,0.6779999999999999,0.0307,0.0,0.1379,0.1667,0.2083,0.0199,0.065,0.0615,0.0,0.0,reg oper account,block of flats,0.0537,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1265.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n391778,0,Cash loans,F,N,Y,3,216000.0,562500.0,28849.5,562500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-13486,-5597,-4384.0,-4978,,1,1,0,1,0,0,Medicine staff,5.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Medicine,0.4411232209957506,0.4242177682335769,0.33928769990891394,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-635.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n224888,0,Cash loans,F,Y,Y,1,225000.0,550980.0,36949.5,450000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.006008,-14417,-389,-8127.0,-5515,21.0,1,1,0,1,0,1,Laborers,3.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Self-employed,0.7765119809330556,0.6300824720136619,0.6279908192952864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1479.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n260769,0,Cash loans,F,N,Y,1,81000.0,879480.0,25843.5,630000.0,Family,Pensioner,Lower secondary,Married,House / apartment,0.020713,-22469,365243,-4715.0,-3776,,1,0,0,1,0,0,,3.0,3,3,FRIDAY,13,0,0,0,1,0,0,XNA,,0.5821132321395489,0.8050196619153701,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-804.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n210713,0,Cash loans,F,N,Y,0,360000.0,1546020.0,42511.5,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.072508,-22676,365243,-14272.0,-4344,,1,0,0,1,1,0,,2.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.7451474030187221,,0.1345,0.1335,0.9796,0.7212,0.1684,0.14,0.1034,0.3958,0.4375,0.0,0.1088,0.1121,0.0039,0.0836,0.0861,0.1203,0.9791,0.7256,0.1461,0.0806,0.0345,0.3333,0.375,0.0,0.0744,0.0723,0.0039,0.0414,0.1358,0.1335,0.9796,0.7249,0.1694,0.14,0.1034,0.3958,0.4375,0.0,0.1107,0.1141,0.0039,0.0853,,block of flats,0.0822,Block,No,0.0,0.0,0.0,0.0,-3052.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n341396,0,Cash loans,F,N,Y,0,135000.0,427176.0,23301.0,306000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.010032,-18395,-1422,-1306.0,-1306,,1,1,0,1,0,1,,1.0,2,2,MONDAY,7,0,0,0,0,0,0,Other,0.8147943959067371,0.6853836236426393,,0.0619,0.0469,0.9747,0.6532,0.0207,0.0,0.1034,0.1667,0.2083,0.0525,0.0496,0.051,0.0039,0.0032,0.063,0.0486,0.9747,0.6668,0.0209,0.0,0.1034,0.1667,0.2083,0.0537,0.0542,0.0531,0.0039,0.0034,0.0625,0.0469,0.9747,0.6578,0.0209,0.0,0.1034,0.1667,0.2083,0.0534,0.0504,0.0519,0.0039,0.0032,reg oper account,block of flats,0.0521,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n443867,0,Cash loans,M,Y,Y,0,112500.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-18690,-3531,-12666.0,-2172,6.0,1,1,1,1,1,0,Drivers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Self-employed,,0.5575795940648379,0.08226850764912726,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-430.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n326148,0,Cash loans,F,N,Y,0,252000.0,969579.0,32175.0,837000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.007114,-20934,-2439,-6329.0,-4371,,1,1,0,1,0,0,Managers,1.0,2,2,FRIDAY,18,0,0,0,0,0,0,Trade: type 7,0.7079936599928833,0.2650475600360688,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1969.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n155127,0,Cash loans,F,N,Y,0,180000.0,390960.0,27337.5,337500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.011703,-18825,-9193,-9227.0,-2334,,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Postal,,0.7495529495169082,0.7252764347002191,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1953.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n145934,0,Cash loans,F,N,Y,0,103500.0,715095.0,48109.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-21967,365243,-4526.0,-4616,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,1,0,0,XNA,,0.3557719299487141,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-525.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n454741,0,Cash loans,F,N,N,0,157500.0,733176.0,23782.5,612000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,Municipal apartment,0.072508,-18703,365243,-10924.0,-2246,,1,0,0,1,1,0,,1.0,1,1,MONDAY,12,0,0,0,0,0,0,XNA,0.8306162083710896,0.5615676439044701,,0.1113,0.18600000000000005,0.9836,0.7756,0.0,0.04,0.0345,0.625,0.6667,0.0542,0.0908,0.1025,0.0,0.0122,0.1134,0.1931,0.9836,0.7844,0.0,0.0403,0.0345,0.625,0.6667,0.0554,0.0992,0.1068,0.0,0.0129,0.1124,0.18600000000000005,0.9836,0.7786,0.0,0.04,0.0345,0.625,0.6667,0.0551,0.0923,0.1043,0.0,0.0124,reg oper account,block of flats,0.0832,Panel,No,0.0,0.0,0.0,0.0,-1361.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n204584,0,Cash loans,F,Y,Y,2,90000.0,417024.0,18369.0,360000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.020713,-13761,-1065,-3871.0,-4291,31.0,1,1,1,1,1,0,Managers,4.0,3,3,FRIDAY,6,0,0,0,0,0,0,School,,0.06572975568353462,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-366.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n314303,0,Cash loans,F,N,Y,0,238500.0,1035000.0,37174.5,1035000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.01885,-20146,-5423,-7111.0,-3672,,1,1,1,1,1,0,Sales staff,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.2589538067110449,0.584181041990212,,0.1474,0.0739,0.9901,,,0.08,0.069,0.3333,,0.0134,,0.1047,,0.0,0.1502,0.0767,0.9901,,,0.0806,0.069,0.3333,,0.0137,,0.1091,,0.0,0.1489,0.0739,0.9901,,,0.08,0.069,0.3333,,0.0136,,0.1066,,0.0,,block of flats,0.0947,Panel,No,6.0,0.0,6.0,0.0,-1631.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,5.0\n284679,0,Cash loans,F,N,N,1,135000.0,916470.0,24304.5,765000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.008865999999999999,-11400,-1666,-5122.0,-3512,,1,1,0,1,0,0,Accountants,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Other,0.29029431897789704,0.28698845139104234,0.39449540531239935,0.0619,0.0486,0.9891,0.8504,0.0099,0.0,0.1379,0.1667,0.2083,0.0281,0.0504,0.0549,0.0,0.0,0.063,0.0504,0.9891,0.8563,0.01,0.0,0.1379,0.1667,0.2083,0.0288,0.0551,0.0572,0.0,0.0,0.0625,0.0486,0.9891,0.8524,0.01,0.0,0.1379,0.1667,0.2083,0.0286,0.0513,0.0559,0.0,0.0,reg oper account,block of flats,0.0432,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n101391,0,Cash loans,F,N,N,0,135000.0,455040.0,12132.0,360000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.019689,-12255,-270,-1133.0,-3536,,1,1,0,1,1,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Police,,0.6385883839883059,0.25533177083329,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-2410.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n228666,0,Cash loans,F,N,N,0,315000.0,1546020.0,40783.5,1350000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.032561,-20207,-2337,-2485.0,-2485,,1,1,1,1,0,0,Medicine staff,2.0,1,1,FRIDAY,17,0,0,0,0,0,0,Medicine,,0.4043647695083937,0.248535557339522,0.0642,,0.9707,0.5988,0.0095,,0.1131,0.1546,,,,0.0536,,0.0042,0.063,,0.9742,0.6602,0.0036,,0.1034,0.1667,,,,0.0227,,0.0,0.0625,,0.9742,0.6511,0.0076,,0.1034,0.1667,,,,0.0513,,0.0,,block of flats,0.0256,,No,1.0,0.0,1.0,0.0,-9.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n279928,0,Cash loans,F,N,Y,0,135000.0,835380.0,35523.0,675000.0,Family,State servant,Higher education,Married,House / apartment,0.04622,-20390,-6684,-6729.0,-3339,,1,1,0,1,0,0,Core staff,2.0,1,1,SATURDAY,10,0,0,0,0,0,0,School,0.34322412985193146,0.7493188885901375,0.7503751495159068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1667.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n447615,0,Cash loans,M,Y,Y,0,238500.0,699322.5,42781.5,648000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.00823,-9733,-506,-459.0,-2422,7.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,1,1,0,0,0,Construction,,0.2036930336170434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n375380,1,Cash loans,F,N,Y,1,202500.0,187704.0,9256.5,148500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.018801,-12928,-1260,-13.0,-5145,,1,1,0,1,0,0,Managers,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,,0.4966616599333015,,0.23199999999999998,0.0,1.0,1.0,0.0866,0.2,0.1724,0.375,0.4167,0.4017,0.1891,0.1891,0.0,0.0,0.2363,0.0,1.0,1.0,0.0874,0.2014,0.1724,0.375,0.4167,0.4108,0.2066,0.19699999999999998,0.0,0.0,0.2342,0.0,1.0,1.0,0.0872,0.2,0.1724,0.375,0.4167,0.4087,0.1924,0.1925,0.0,0.0,reg oper account,block of flats,0.1961,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1069.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n296614,0,Cash loans,M,Y,Y,1,315000.0,412614.0,32728.5,364500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018634,-10952,-2787,-132.0,-3640,2.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,13,0,0,0,1,0,1,Business Entity Type 2,0.3649869097607492,0.0023407250343263518,0.2807895743848605,0.0412,0.0726,0.9906,0.8708,0.0081,0.0,0.1379,0.1667,0.0,0.0277,0.0336,0.053,0.0,0.0,0.042,0.0753,0.9906,0.8759,0.0082,0.0,0.1379,0.1667,0.0,0.0283,0.0367,0.0552,0.0,0.0,0.0416,0.0726,0.9906,0.8725,0.0082,0.0,0.1379,0.1667,0.0,0.0282,0.0342,0.0539,0.0,0.0,reg oper account,block of flats,0.049,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,0.0\n296409,0,Cash loans,F,N,Y,1,292500.0,1107981.0,35869.5,967500.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.010276,-15533,-3760,-9234.0,-4078,,1,1,0,1,0,0,Cooking staff,3.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Medicine,,0.7855316255381081,0.5352762504724826,0.1536,,0.9786,,,0.0,0.3448,0.1667,,0.1656,,,,,0.1565,,0.9786,,,0.0,0.3448,0.1667,,0.1694,,,,,0.1551,,0.9786,,,0.0,0.3448,0.1667,,0.1685,,,,,,block of flats,0.1045,Panel,No,0.0,0.0,0.0,0.0,-2673.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n350267,0,Cash loans,M,Y,N,0,450000.0,1649844.0,85428.0,1575000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.031329,-15962,-2444,-92.0,-4801,15.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Government,0.7253497297577908,0.6682871065072882,0.6863823354047934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2904.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n236882,1,Cash loans,F,N,Y,0,157500.0,328405.5,16897.5,283500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-11647,-4766,-5810.0,-1083,,1,1,0,1,1,0,Laborers,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.16755181279631648,0.4059194565651129,0.3046721837533529,0.0546,0.0,0.9747,0.6532,0.0258,0.0,0.1034,0.1667,0.2083,0.0304,0.042,0.046,0.0116,0.0144,0.0557,0.0,0.9747,0.6668,0.026000000000000002,0.0,0.1034,0.1667,0.2083,0.031,0.0459,0.0479,0.0117,0.0152,0.0552,0.0,0.9747,0.6578,0.0259,0.0,0.1034,0.1667,0.2083,0.0309,0.0428,0.0468,0.0116,0.0147,reg oper account,block of flats,0.0534,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-639.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n350202,1,Cash loans,F,N,Y,0,270000.0,349258.5,17959.5,301500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-21351,365243,-12996.0,-4676,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.6019534089554641,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1477.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n414036,0,Cash loans,F,Y,N,0,135000.0,351000.0,17919.0,351000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-18065,-753,-8519.0,-1619,8.0,1,1,1,1,1,0,,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Other,,0.4962243304476609,,0.1742,0.1945,0.9846,0.7892,0.0,0.0,0.3448,0.2083,0.2083,0.0959,0.142,0.1554,0.0,0.0038,0.1775,0.2019,0.9846,0.7975,0.0,0.0,0.3448,0.2083,0.2083,0.0981,0.1552,0.1619,0.0,0.004,0.1759,0.1945,0.9846,0.792,0.0,0.0,0.3448,0.2083,0.2083,0.0976,0.1445,0.1582,0.0,0.0039,not specified,block of flats,0.1231,Panel,No,0.0,0.0,0.0,0.0,-1959.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n127217,0,Revolving loans,M,Y,Y,0,450000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.005002,-12583,-1347,-4623.0,-4070,8.0,1,1,1,1,0,0,,2.0,3,3,FRIDAY,12,0,0,0,1,1,0,Other,0.2658327342049411,0.6595633188155879,0.5082869913916046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-689.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n287443,0,Cash loans,F,N,Y,1,180000.0,584766.0,27225.0,472500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.022625,-14764,-2807,-3325.0,-3320,,1,1,0,1,0,0,Sales staff,3.0,2,2,FRIDAY,17,0,0,0,0,0,0,Trade: type 3,0.4606490977899297,0.6565864451277774,0.6594055320683344,0.3711,0.3644,0.9781,0.7008,0.1804,0.0,0.8276,0.1667,0.2083,0.3726,0.3026,0.3637,0.0,0.0,0.3782,0.3781,0.9782,0.7125,0.1821,0.0,0.8276,0.1667,0.2083,0.3811,0.3306,0.3789,0.0,0.0,0.3747,0.3644,0.9781,0.7048,0.1816,0.0,0.8276,0.1667,0.2083,0.3791,0.3078,0.3702,0.0,0.0,reg oper account,block of flats,0.3847,Panel,No,0.0,0.0,0.0,0.0,-235.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n182640,0,Cash loans,M,Y,Y,0,90000.0,135000.0,8919.0,135000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018209,-10648,-818,-428.0,-3318,16.0,1,1,0,1,1,0,Sales staff,2.0,3,3,THURSDAY,12,0,0,0,0,0,0,Self-employed,,0.42618990733632506,0.11761373170805695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-625.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n312604,0,Cash loans,M,N,Y,0,112500.0,497520.0,25888.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-14484,-2926,-5737.0,-4543,,1,1,0,1,0,1,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Transport: type 4,0.3930471734502491,0.5880428716604243,0.5442347412142162,0.2227,0.17600000000000002,0.9831,0.7688,0.3622,0.28,0.2414,0.3333,0.375,0.2119,0.1816,0.1581,0.0,0.0,0.2269,0.1826,0.9831,0.7779,0.3655,0.282,0.2414,0.3333,0.375,0.2167,0.1983,0.1647,0.0,0.0,0.2248,0.17600000000000002,0.9831,0.7719,0.3645,0.28,0.2414,0.3333,0.375,0.2156,0.1847,0.1609,0.0,0.0,reg oper account,block of flats,0.2244,Panel,No,2.0,0.0,2.0,0.0,-1072.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n206157,0,Cash loans,F,N,Y,0,135000.0,343377.0,16830.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.010643,-18421,-1635,-6434.0,-1976,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Self-employed,,0.5908370210876187,0.42589289800515295,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-318.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n131968,0,Cash loans,F,Y,Y,0,135000.0,553806.0,24525.0,495000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019689,-19695,-141,-312.0,-3232,7.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Industry: type 3,,0.6196053076367595,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-560.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,2.0,0.0\n364594,0,Cash loans,F,N,Y,1,76500.0,900000.0,26446.5,900000.0,Family,Working,Higher education,Married,House / apartment,0.025164,-9993,-214,-9758.0,-1465,,1,1,0,1,0,0,Accountants,3.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Self-employed,,0.14691028045226628,0.6940926425266661,0.1619,0.10800000000000001,0.9851,0.7959999999999999,0.0351,0.2,0.1724,0.3333,,0.0232,,0.1781,,0.0483,0.10400000000000001,0.0808,0.9846,0.7975,0.0207,0.1208,0.1034,0.3333,,0.0159,,0.1117,,0.0251,0.1634,0.10800000000000001,0.9851,0.7987,0.0353,0.2,0.1724,0.3333,,0.0236,,0.1813,,0.0493,reg oper account,block of flats,0.2118,Panel,No,0.0,0.0,0.0,0.0,-377.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n379248,0,Cash loans,M,N,N,0,202500.0,889515.0,26005.5,742500.0,Family,Commercial associate,Higher education,Single / not married,House / apartment,0.009334,-12956,-313,-4885.0,-4880,,1,1,0,1,1,1,High skill tech staff,1.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 1,0.2320172004501491,0.6057041246995528,0.3425288720742255,0.0082,0.0191,0.9747,0.6532,0.0,0.0,0.0345,0.0417,0.0417,0.0033,0.0067,0.0045,0.0,0.0,0.0084,0.0198,0.9747,0.6668,0.0,0.0,0.0345,0.0417,0.0417,0.0034,0.0073,0.0047,0.0,0.0,0.0083,0.0191,0.9747,0.6578,0.0,0.0,0.0345,0.0417,0.0417,0.0034,0.0068,0.0046,0.0,0.0,reg oper account,block of flats,0.0052,Wooden,No,0.0,0.0,0.0,0.0,-982.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n228795,0,Cash loans,M,Y,Y,0,247500.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-18705,-2711,-525.0,-2246,13.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5791580277094451,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1752.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n337721,0,Cash loans,F,N,Y,1,76500.0,462195.0,23728.5,351000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-15229,-1036,-6237.0,-2232,,1,1,0,1,0,0,Cooking staff,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.5549725397248405,0.4614823912998385,0.0505,0.0275,0.9806,0.7348,0.0125,0.08,0.0345,0.4583,0.5,0.0452,0.0403,0.0571,0.0039,0.0038,0.0515,0.0286,0.9806,0.7452,0.0126,0.0806,0.0345,0.4583,0.5,0.0462,0.0441,0.0595,0.0039,0.004,0.051,0.0275,0.9806,0.7383,0.0126,0.08,0.0345,0.4583,0.5,0.0459,0.040999999999999995,0.0581,0.0039,0.0039,reg oper account,block of flats,0.0568,\"Stone, brick\",Yes,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n135627,0,Cash loans,M,N,N,1,157500.0,360000.0,18418.5,360000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.022625,-11234,-119,-5281.0,-2110,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.4599090690642028,0.31792498974526,0.3774042489507649,0.0165,,0.9801,,,0.0,0.069,0.0417,,,,0.0151,,,0.0168,,0.9801,,,0.0,0.069,0.0417,,,,0.0157,,,0.0167,,0.9801,,,0.0,0.069,0.0417,,,,0.0154,,,,block of flats,0.0119,Block,No,2.0,1.0,2.0,1.0,-275.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n112096,0,Cash loans,F,N,Y,0,202500.0,516069.0,26478.0,445500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.011657,-22237,365243,-34.0,-4878,,1,0,0,1,0,0,,1.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.1720952162474257,0.3606125659189888,0.1227,0.1213,0.9762,0.6736,0.1901,0.0,0.2069,0.1667,0.0417,0.0476,0.1,0.0941,0.0,0.1375,0.125,0.1259,0.9762,0.6864,0.1918,0.0,0.2069,0.1667,0.0417,0.0487,0.1093,0.0981,0.0,0.1456,0.1239,0.1213,0.9762,0.6779999999999999,0.1913,0.0,0.2069,0.1667,0.0417,0.0485,0.1018,0.0958,0.0,0.1404,reg oper spec account,block of flats,0.1039,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-1069.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n266318,0,Cash loans,F,N,Y,2,112500.0,675000.0,34596.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.009334,-10747,-3889,-3855.0,-1954,,1,1,1,1,0,0,High skill tech staff,4.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Police,,0.7316967886486759,0.4311917977993083,0.0928,0.1006,0.9781,0.7008,0.012,0.0,0.2069,0.1667,0.0417,0.0634,0.0756,0.0873,0.0,0.0,0.0945,0.1044,0.9782,0.7125,0.0121,0.0,0.2069,0.1667,0.0417,0.0648,0.0826,0.0909,0.0,0.0,0.0937,0.1006,0.9781,0.7048,0.0121,0.0,0.2069,0.1667,0.0417,0.0645,0.077,0.0888,0.0,0.0,not specified,block of flats,0.0752,Panel,No,0.0,0.0,0.0,0.0,-1884.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n288349,0,Cash loans,M,N,N,0,202500.0,599472.0,38439.0,517500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-12908,-3842,-3165.0,-3911,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,0.3904311870901076,0.6127269518456179,,0.0722,,0.9767,,,0.0,0.1379,0.1667,,0.013999999999999999,,0.0625,,0.0,0.0735,,0.9767,,,0.0,0.1379,0.1667,,0.0143,,0.0651,,0.0,0.0729,,0.9767,,,0.0,0.1379,0.1667,,0.0143,,0.0636,,0.0,,block of flats,0.0532,\"Stone, brick\",No,4.0,1.0,4.0,1.0,-1097.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n326797,0,Revolving loans,F,N,Y,0,81000.0,180000.0,9000.0,180000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.001417,-19998,-5633,-1470.0,-3541,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,7,0,0,0,0,0,0,Postal,0.5594946135953542,0.20083155305519296,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-34.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n372849,0,Revolving loans,F,N,Y,1,108000.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Lower secondary,Civil marriage,House / apartment,0.01885,-18088,-3031,-7107.0,-1628,,1,1,0,1,0,0,,3.0,2,2,FRIDAY,10,0,0,0,0,0,0,School,,0.5228867049399664,0.5744466170995097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,1.0,0.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n253924,0,Cash loans,F,Y,Y,0,247500.0,219249.0,17451.0,166500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-19751,-2847,-1831.0,-3243,32.0,1,1,0,1,0,1,,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Other,0.487155206276568,0.2749382281368317,0.6496203111237195,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,2.0,10.0,2.0,-2531.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n320419,0,Cash loans,F,N,N,0,225000.0,1096020.0,56092.5,900000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.00496,-12580,-968,-2505.0,-1768,,1,1,0,1,0,0,Accountants,2.0,2,2,MONDAY,12,0,0,0,0,1,1,Government,0.7698057226095679,0.6848835797217973,0.22888341670067305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1754.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n122449,0,Cash loans,F,N,N,1,157500.0,415408.5,42696.0,373500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008625,-16757,-2379,-4984.0,-315,,1,1,0,1,0,1,Core staff,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,Insurance,,0.4452958376096058,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1639.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n234133,0,Revolving loans,F,N,Y,0,90000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00496,-18397,-3583,-9022.0,-1798,,1,1,0,1,0,0,Accountants,2.0,2,2,MONDAY,14,0,0,0,0,1,1,Trade: type 7,,0.7276595641883943,,0.2062,,,,,,,0.3333,,,,0.1109,,,0.2101,,,,,,,0.3333,,,,0.1156,,,0.2082,,,,,,,0.3333,,,,0.1129,,,,,0.1557,,No,0.0,0.0,0.0,0.0,-930.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n280313,0,Cash loans,F,Y,Y,0,112500.0,743031.0,39717.0,688500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019689,-18121,-4800,-4604.0,-1674,9.0,1,1,0,1,1,0,Laborers,1.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.587544827229703,,0.0227,,0.9841,0.7824,,0.0,0.1034,0.0417,0.0417,,,0.0184,,,0.0231,,0.9841,0.7909,,0.0,0.1034,0.0417,0.0417,,,0.0192,,,0.0229,,0.9841,0.7853,,0.0,0.1034,0.0417,0.0417,,,0.0188,,,reg oper account,block of flats,0.0145,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-365.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n355077,0,Cash loans,F,N,Y,0,76500.0,157500.0,8680.5,157500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018801,-20082,-1890,-10917.0,-3599,,1,1,1,1,1,0,Core staff,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Insurance,0.8529573451692241,0.5588091607295832,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1465.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n452259,0,Cash loans,M,N,Y,0,157500.0,291384.0,26725.5,270000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.010643,-10255,-745,-10233.0,-2938,,1,1,0,1,0,0,Core staff,1.0,2,2,MONDAY,12,0,1,1,1,0,0,Business Entity Type 3,0.2550110766446059,0.5772861139764767,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-278.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n254780,0,Cash loans,F,N,N,0,225000.0,360000.0,13059.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.035792000000000004,-23380,-1395,-2950.0,-2091,,1,1,0,1,1,0,Managers,1.0,2,2,SATURDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.5431952235189556,0.4810954407216913,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1135.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n106648,0,Cash loans,M,N,Y,0,247500.0,1354500.0,35730.0,1354500.0,Family,Working,Higher education,Married,House / apartment,0.010147,-9607,-1986,-8422.0,-1980,,1,1,0,1,1,0,Core staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Transport: type 2,,0.7069088890359162,0.6848276586890367,0.2227,0.1726,0.9881,0.8368,0.0511,0.24,0.2069,0.3333,0.375,,0.1807,0.2531,0.0039,0.0013,0.2269,0.1791,0.9881,0.8432,0.0516,0.2417,0.2069,0.3333,0.375,,0.1974,0.2637,0.0039,0.0013,0.2248,0.1726,0.9881,0.8390000000000001,0.0515,0.24,0.2069,0.3333,0.375,,0.1838,0.2576,0.0039,0.0013,reg oper spec account,block of flats,0.2273,Panel,No,1.0,0.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n428997,0,Cash loans,M,N,Y,0,81000.0,544068.0,19669.5,382500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.006008,-22889,365243,-10944.0,-5092,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,XNA,,0.7124863332959507,0.6528965519806539,0.0165,0.0,0.9737,0.6396,0.0013,0.0,0.069,0.0417,0.0833,,0.0134,0.0114,0.0,0.0,0.0168,0.0,0.9737,0.6537,0.0013,0.0,0.069,0.0417,0.0833,,0.0147,0.0118,0.0,0.0,0.0167,0.0,0.9737,0.6444,0.0013,0.0,0.069,0.0417,0.0833,,0.0137,0.0116,0.0,0.0,reg oper account,block of flats,0.0096,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-922.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n240289,0,Cash loans,F,N,N,0,274500.0,640080.0,24259.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21854,-2009,-9454.0,-4271,,1,1,0,1,0,0,,2.0,2,2,SUNDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.6757431753251181,0.42589289800515295,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1896.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n403508,0,Cash loans,M,N,Y,2,180000.0,592560.0,22090.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009334,-14405,-3036,-8464.0,-4876,,1,1,0,1,1,0,Laborers,4.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 2,,0.229838023599698,0.6986675550534175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,2.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n269591,0,Cash loans,F,N,N,0,90000.0,900000.0,26446.5,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018209,-21977,365243,-8278.0,-4390,,1,0,0,1,1,0,,2.0,3,3,SATURDAY,17,0,0,0,0,0,0,XNA,0.8310237368440629,0.4103301518445231,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-877.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n388027,0,Cash loans,F,N,N,0,180000.0,808650.0,26217.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.006852,-17824,-3584,-7741.0,-1355,,1,1,0,1,0,0,Managers,2.0,3,3,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.18179050500292848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2286.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n115379,0,Revolving loans,F,N,N,1,67500.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.007120000000000001,-12562,-538,-3014.0,-2057,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,8,0,0,0,0,0,0,Business Entity Type 2,0.3033664011427417,0.5949118213807982,0.6380435278721609,,,0.9821,,,,,,,,,0.1052,,,,,0.9821,,,,,,,,,0.1096,,,,,0.9821,,,,,,,,,0.1071,,,,,0.0907,,Yes,0.0,0.0,0.0,0.0,-13.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n284037,1,Cash loans,F,N,N,0,153000.0,296280.0,16069.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007305,-13940,-682,-4428.0,-4290,,1,1,0,1,1,0,Sales staff,2.0,3,3,TUESDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.6325152420784439,0.4026489395982208,0.2366108235287817,0.0814,0.0849,0.9836,,,,0.2069,0.1667,,,,0.0817,,,0.083,0.0881,0.9836,,,,0.2069,0.1667,,,,0.0851,,,0.0822,0.0849,0.9836,,,,0.2069,0.1667,,,,0.0831,,,,block of flats,0.0701,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-891.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n430347,1,Cash loans,M,N,Y,0,157500.0,781920.0,37615.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-16001,-2197,-3290.0,-3909,,1,1,1,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 1,,0.5498036112624728,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-767.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n288362,0,Cash loans,M,Y,N,1,189000.0,436032.0,28516.5,360000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-12847,-112,-6877.0,-4839,33.0,1,1,0,1,0,0,Drivers,3.0,2,2,THURSDAY,14,0,0,0,1,1,0,Medicine,,0.6001496140471702,0.7801436381572275,0.0124,,0.9707,,,,0.069,0.0417,,,,,,,0.0126,,0.9707,,,,0.069,0.0417,,,,,,,0.0125,,0.9707,,,,0.069,0.0417,,,,,,,,,0.0113,,No,0.0,0.0,0.0,0.0,-51.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,0.0\n207898,0,Cash loans,M,Y,N,0,211500.0,545040.0,25407.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.072508,-9917,-1482,-4648.0,-2181,12.0,1,1,0,1,1,0,Drivers,2.0,1,1,THURSDAY,14,0,0,0,0,0,0,Government,,0.7074432279684093,0.2851799046358216,0.0861,0.0778,0.9742,0.6464,,0.0,0.1552,0.1667,,0.0,,0.0689,,0.0193,0.063,0.0562,0.9737,0.6537,,0.0,0.1034,0.1667,,0.0,,0.0518,,0.0,0.0869,0.0778,0.9742,0.6511,,0.0,0.1552,0.1667,,0.0,,0.0701,,0.0197,,block of flats,0.0391,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1400.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n192063,0,Cash loans,M,N,Y,0,270000.0,269550.0,14242.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-20185,-1636,-8866.0,-3560,,1,1,0,1,0,0,,2.0,1,1,SATURDAY,12,0,0,0,0,0,0,Security,,0.7026639582927802,0.722392890081304,0.2014,0.0854,0.9886,0.8436,0.1091,0.24,0.0966,0.7083,0.75,0.0953,0.1634,0.2182,0.0039,0.0136,0.1418,0.0608,0.9876,0.8367,0.0904,0.2417,0.069,0.6667,0.7083,0.071,0.1827,0.1535,0.0,0.0,0.2082,0.0855,0.9876,0.8323,0.1156,0.24,0.1034,0.6667,0.7083,0.0969,0.1702,0.2209,0.0039,0.0169,reg oper account,block of flats,0.1743,Panel,No,2.0,0.0,2.0,0.0,-1459.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n171097,0,Cash loans,M,Y,Y,0,180000.0,1190434.5,38533.5,1039500.0,Unaccompanied,Working,Lower secondary,Married,House / apartment,0.003813,-15793,-1768,-6789.0,-4280,15.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.4412735936718563,0.4136039476194307,0.7801436381572275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1490.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n123391,0,Cash loans,M,N,Y,0,195750.0,1350000.0,57199.5,1350000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.04622,-13779,-199,-7882.0,-4543,,1,1,1,1,1,0,Managers,2.0,1,1,THURSDAY,16,0,1,1,0,1,1,Trade: type 3,,0.6679476875844433,0.5136937663039473,0.0619,0.0451,0.9757,0.6668,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0504,0.0341,0.0,0.0,0.063,0.0468,0.9757,0.6798,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0551,0.0355,0.0,0.0,0.0625,0.0451,0.9757,0.6713,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0513,0.0347,0.0,0.0,reg oper account,block of flats,0.0396,Panel,No,0.0,0.0,0.0,0.0,-3722.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n214166,0,Cash loans,F,N,Y,0,180000.0,528633.0,22396.5,472500.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.031329,-18346,-4507,-3094.0,-1874,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Trade: type 3,0.6562008743943935,0.5416775024738215,0.4543210601605785,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,0.0,0.0,-2081.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n288985,1,Cash loans,F,Y,Y,1,112500.0,1078200.0,38331.0,900000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-11706,-1858,-838.0,-2174,3.0,1,1,0,1,0,0,Laborers,3.0,2,2,SUNDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.26540166510156343,0.3766532766345116,0.6801388218428291,0.0196,0.0245,0.9593,0.4424,0.0281,0.0,0.1034,0.0833,0.125,0.0521,0.016,0.0229,,0.0,0.02,0.0254,0.9593,0.4642,0.0284,0.0,0.1034,0.0833,0.125,0.0533,0.0174,0.0239,,0.0,0.0198,0.0245,0.9593,0.4499,0.0283,0.0,0.1034,0.0833,0.125,0.053,0.0162,0.0234,,0.0,reg oper account,block of flats,0.0334,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-624.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n405103,0,Cash loans,F,N,N,0,202500.0,450000.0,17095.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.00733,-17164,-1242,-9244.0,-692,,1,1,1,1,0,0,Medicine staff,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,Other,,0.6634266851468241,0.511891801533151,,,0.9747,,,,,,,,,0.006999999999999999,,,,,0.9747,,,,,,,,,0.0073,,,,,0.9747,,,,,,,,,0.0071,,,,,0.0055,,No,0.0,0.0,0.0,0.0,-375.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n226017,1,Cash loans,M,Y,Y,0,270000.0,450000.0,22018.5,450000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-9448,-553,-4235.0,-1973,12.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,11,0,0,0,1,1,0,Business Entity Type 3,,0.31814790340047,0.06126811384928985,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1182.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n135192,0,Revolving loans,M,N,Y,0,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.015221,-18165,-9876,-1493.0,-1498,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,15,0,0,0,1,0,1,Business Entity Type 2,,0.7386236182773364,0.5762088360175724,0.0165,0.0,0.9762,,,0.0,0.069,0.0417,,,,0.0145,,,0.0168,0.0,0.9762,,,0.0,0.069,0.0417,,,,0.0151,,,0.0167,0.0,0.9762,,,0.0,0.069,0.0417,,,,0.0147,,,,block of flats,0.0119,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-941.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n399761,0,Cash loans,F,N,Y,0,67500.0,240660.0,8775.0,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.019101,-20791,365243,-5623.0,-4266,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,XNA,,0.4798290582169067,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n430814,0,Revolving loans,F,N,Y,0,166500.0,315000.0,15750.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-16809,-3487,-5446.0,-357,,1,1,0,1,0,0,Managers,1.0,2,2,FRIDAY,7,0,0,0,0,0,0,University,,0.6270242830868911,0.6195277080511546,0.0619,0.1276,0.9692,0.5784,0.0163,0.0,0.1724,0.125,0.1667,0.0636,0.0488,0.0642,0.0077,0.0971,0.063,0.1325,0.9692,0.5949,0.0164,0.0,0.1724,0.125,0.1667,0.0651,0.0533,0.0669,0.0078,0.1028,0.0625,0.1276,0.9692,0.584,0.0164,0.0,0.1724,0.125,0.1667,0.0647,0.0496,0.0654,0.0078,0.0992,reg oper account,block of flats,0.0716,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-965.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n172548,0,Cash loans,F,N,Y,0,112500.0,203760.0,19048.5,180000.0,Unaccompanied,State servant,Higher education,Married,Municipal apartment,0.035792000000000004,-22339,-4540,-6809.0,-4236,,1,1,0,1,0,0,Accountants,2.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Government,,0.7405651482320181,,0.1485,0.0794,0.9871,0.8232,0.0263,0.04,0.0345,0.3333,0.375,0.0314,0.121,0.0975,0.0,0.0,0.1513,0.0824,0.9871,0.8301,0.0266,0.0403,0.0345,0.3333,0.375,0.0321,0.1322,0.1016,0.0,0.0,0.1499,0.0794,0.9871,0.8256,0.0265,0.04,0.0345,0.3333,0.375,0.032,0.1231,0.0992,0.0,0.0,reg oper account,block of flats,0.0767,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-67.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n291289,0,Cash loans,M,Y,Y,0,67500.0,259794.0,26748.0,229500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009175,-15328,-1543,-3501.0,-4718,9.0,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.5214762403937454,0.764015423857138,0.7958031328748403,0.1485,0.1256,0.9806,,,0.16,0.1379,0.3333,,,,,,,0.1513,0.1304,0.9806,,,0.1611,0.1379,0.3333,,,,,,,0.1499,0.1256,0.9806,,,0.16,0.1379,0.3333,,,,,,,,block of flats,0.1222,Panel,No,0.0,0.0,0.0,0.0,-2782.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n125860,0,Cash loans,M,Y,Y,0,450000.0,604152.0,48568.5,540000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.072508,-10822,-2128,-5581.0,-1660,11.0,1,1,0,1,1,0,Sales staff,2.0,1,1,FRIDAY,13,0,1,1,0,0,0,Business Entity Type 3,,0.7090698780180215,0.2750003523983893,0.1563,0.0748,0.9911,0.8776,0.0883,0.19399999999999998,0.0786,0.6783,0.72,0.0,0.1262,0.1829,0.0055,0.0187,0.1366,0.0685,0.9911,0.8824,0.0676,0.1611,0.069,0.6667,0.7083,0.0,0.12300000000000001,0.1696,0.0039,0.0013,0.1405,0.0666,0.9911,0.8792,0.0689,0.16,0.069,0.6667,0.7083,0.0,0.1146,0.1731,0.0039,0.0091,reg oper account,block of flats,0.1629,Panel,No,0.0,0.0,0.0,0.0,-715.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n286613,0,Cash loans,F,Y,Y,1,90000.0,305640.0,13432.5,270000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.006008,-7941,-267,-1488.0,-572,9.0,1,1,0,1,0,0,,3.0,2,2,MONDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.22895220847963946,0.47274910514153995,0.5954562029091491,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-986.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n253456,0,Cash loans,F,N,Y,2,99000.0,134775.0,12492.0,112500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008068,-12009,-3660,-6013.0,-4166,,1,1,0,1,0,0,,4.0,3,3,TUESDAY,9,0,0,0,0,0,0,Medicine,0.2708516953817007,0.3388705720124365,0.4578995512067301,0.1021,,0.9806,,,,0.2069,0.1667,,,,0.0884,,,0.10400000000000001,,0.9806,,,,0.2069,0.1667,,,,0.0921,,,0.1031,,0.9806,,,,0.2069,0.1667,,,,0.09,,,,block of flats,0.0914,,No,4.0,0.0,4.0,0.0,-267.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n125492,0,Cash loans,F,Y,Y,0,382500.0,1303200.0,46809.0,1125000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.016612000000000002,-16757,-4246,-6766.0,-290,5.0,1,1,0,1,0,0,Accountants,1.0,2,2,FRIDAY,13,0,0,0,0,0,0,Insurance,0.8466259782119114,0.6022153593818508,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n302997,0,Cash loans,M,Y,Y,1,270000.0,521280.0,31630.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-14243,-918,-813.0,-406,65.0,1,1,0,1,0,0,,3.0,1,1,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.4178678416968796,0.6034352740925901,0.5352762504724826,0.0242,0.0,0.9419,,,0.0,0.1034,0.1042,,,,0.0471,,0.0205,0.0021,0.0,0.9419,,,0.0,0.0345,0.0417,,,,0.0024,,0.0,0.0245,0.0,0.9419,,,0.0,0.1034,0.1042,,,,0.0479,,0.0209,,block of flats,0.0018,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-776.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n363226,0,Cash loans,M,Y,Y,1,180000.0,1006920.0,42660.0,900000.0,Family,Working,Higher education,Married,With parents,0.02461,-8885,-1859,-3744.0,-1468,8.0,1,1,0,1,1,0,Laborers,3.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 2,,0.6822347409257103,0.43473324875017305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1248.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n442609,0,Cash loans,M,Y,Y,0,117000.0,463500.0,23796.0,463500.0,Other_B,Working,Secondary / secondary special,Separated,House / apartment,0.031329,-16487,-230,-8832.0,-25,29.0,1,1,0,1,0,0,Drivers,1.0,2,2,MONDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.5601134381900704,0.7267112092725122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2116.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n119356,0,Cash loans,F,N,Y,1,126000.0,239850.0,23850.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-14989,-1750,-2891.0,-4753,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6369465343193237,0.7981372313187245,0.0696,0.0459,0.9767,0.6804,0.0085,0.0,0.1379,0.1667,0.125,0.0,0.0563,0.0524,0.0019,0.0407,0.0672,0.0234,0.9767,0.6929,0.003,0.0,0.1379,0.1667,0.0417,0.0,0.0588,0.0524,0.0,0.0078,0.0703,0.0459,0.9767,0.6847,0.0085,0.0,0.1379,0.1667,0.125,0.0,0.0573,0.0533,0.0019,0.0416,org spec account,block of flats,0.048,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1881.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n425226,0,Cash loans,F,N,N,0,40500.0,454500.0,13288.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.014519999999999996,-19890,365243,-7669.0,-3182,,1,0,0,1,1,0,,1.0,2,2,FRIDAY,8,0,0,0,0,0,0,XNA,,0.2331056493322453,0.6971469077844458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1809.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n129874,0,Cash loans,F,N,N,2,157500.0,1183963.5,34618.5,927000.0,Family,Working,Higher education,Married,House / apartment,0.003813,-15920,-5820,-4520.0,-2071,,1,1,1,1,0,0,,4.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Medicine,,0.37414086662258494,0.5442347412142162,0.0495,0.0314,0.9856,,,0.0,0.0345,0.1667,,0.0354,,0.0214,,0.0,0.0504,0.0325,0.9856,,,0.0,0.0345,0.1667,,0.0362,,0.0223,,0.0,0.05,0.0314,0.9856,,,0.0,0.0345,0.1667,,0.036000000000000004,,0.0218,,0.0,,specific housing,0.0195,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n443311,1,Cash loans,F,N,N,0,45000.0,337500.0,15732.0,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-9380,-731,-2632.0,-1935,,1,1,1,1,1,0,Laborers,2.0,2,2,FRIDAY,14,0,0,0,1,0,1,Business Entity Type 3,,0.34112290759888264,0.3001077565791181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-474.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n422928,0,Revolving loans,M,N,Y,1,117000.0,180000.0,9000.0,,,Working,Secondary / secondary special,Married,House / apartment,0.009175,-12462,-1203,-3084.0,-1610,,1,1,0,1,1,1,Laborers,3.0,2,2,SATURDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.2145092050868094,0.630479939151501,0.5602843280409464,0.1557,0.0858,0.9851,0.7959999999999999,0.0557,0.08,0.069,0.3333,0.375,0.1484,0.1227,0.1698,0.0193,0.0078,0.1586,0.0891,0.9851,0.804,0.0562,0.0806,0.069,0.3333,0.375,0.1518,0.1341,0.177,0.0195,0.0083,0.1572,0.0858,0.9851,0.7987,0.0561,0.08,0.069,0.3333,0.375,0.151,0.1248,0.1729,0.0194,0.008,reg oper account,specific housing,0.1353,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1448.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,7.0\n433247,0,Cash loans,M,N,N,0,157500.0,579195.0,24669.0,468000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-20344,-327,-5061.0,-3761,,1,1,0,1,0,0,Laborers,2.0,1,1,FRIDAY,14,0,1,1,0,0,0,Business Entity Type 3,,0.7465484753234217,0.3842068130556564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-3239.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,7.0\n447746,0,Cash loans,M,Y,Y,2,202500.0,545040.0,25537.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-10800,-532,-130.0,-3466,12.0,1,1,1,1,0,0,Drivers,4.0,2,2,WEDNESDAY,16,0,0,0,1,0,1,Self-employed,0.5112226942504403,0.6380582920242341,0.4223696523543468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1536.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n162997,0,Cash loans,F,Y,N,1,99000.0,808650.0,31464.0,675000.0,Unaccompanied,Working,Higher education,Civil marriage,Office apartment,0.003813,-11317,-158,-2906.0,-3998,64.0,1,1,0,1,0,0,Sales staff,3.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,,0.27277265505469744,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-385.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n366652,0,Revolving loans,F,N,Y,0,180000.0,540000.0,27000.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.072508,-17787,-3155,-5127.0,-1042,,1,1,0,1,1,0,,2.0,1,1,MONDAY,18,0,0,0,0,0,0,Other,0.722549773429992,0.7203935602002216,,0.1158,0.0443,0.9925,0.898,0.0498,0.08,0.0345,0.625,0.0417,0.0072,0.09300000000000001,0.1115,0.0064,0.002,0.1176,0.0459,0.9926,0.902,0.0496,0.0806,0.0345,0.625,0.0417,0.0067,0.1019,0.115,0.0039,0.0005,0.1166,0.0443,0.9925,0.8994,0.0501,0.08,0.0345,0.625,0.0417,0.0072,0.0949,0.1138,0.0039,0.0005,org spec account,block of flats,0.0879,Panel,No,0.0,0.0,0.0,0.0,-2671.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n282761,0,Cash loans,F,N,N,0,103500.0,808650.0,26217.0,675000.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-17047,-10145,-7095.0,-608,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,TUESDAY,11,0,0,0,0,1,1,University,,0.1953537989258013,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,1.0,7.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n238242,0,Cash loans,M,Y,Y,2,292500.0,997974.0,42412.5,918000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.04622,-13198,-230,-7236.0,-4096,0.0,1,1,0,1,0,0,Drivers,4.0,1,1,WEDNESDAY,15,0,1,1,0,0,0,Transport: type 4,0.2358315835965588,0.7280147680047832,0.511891801533151,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-422.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n278373,0,Cash loans,M,Y,Y,0,90000.0,267102.0,31828.5,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-20980,-692,-4443.0,-4526,13.0,1,1,1,1,0,0,Security staff,2.0,3,3,TUESDAY,11,0,0,0,0,1,1,Medicine,,0.4190718231064069,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-394.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n309641,0,Revolving loans,M,Y,Y,0,159579.0,135000.0,6750.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-12687,-3111,-2411.0,-4272,11.0,1,1,1,1,0,0,IT staff,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Trade: type 2,0.459733420426793,0.7507251619921793,0.6925590674998008,0.068,0.0563,0.9866,0.8164,0.0205,0.12,0.069,0.3333,0.375,0.2087,0.0555,0.0785,0.0,0.0312,0.0693,0.0584,0.9866,0.8236,0.0207,0.1208,0.069,0.3333,0.375,0.2134,0.0606,0.0818,0.0,0.033,0.0687,0.0563,0.9866,0.8189,0.0206,0.12,0.069,0.3333,0.375,0.2123,0.0564,0.0799,0.0,0.0319,reg oper account,block of flats,0.0638,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-50.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n385647,0,Cash loans,M,Y,N,1,324000.0,1256400.0,36733.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006233,-16514,-1097,-3455.0,-49,8.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,14,0,0,0,0,1,1,Government,,0.6919846658360645,0.7662336700704004,0.0825,0.0615,0.9737,0.6396,0.0069,0.0,0.1379,0.1667,0.2083,0.0211,0.0672,0.0627,0.0,0.0,0.084,0.0639,0.9737,0.6537,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0216,0.0735,0.0654,0.0,0.0,0.0833,0.0615,0.9737,0.6444,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0214,0.0684,0.0639,0.0,0.0,reg oper account,block of flats,0.0531,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1512.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n317517,0,Revolving loans,M,Y,Y,2,171000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.010006,-9430,-898,-6013.0,-2080,3.0,1,1,1,1,0,0,Laborers,4.0,2,2,TUESDAY,14,0,0,0,0,0,0,Agriculture,,0.4851963911987498,0.475849908720221,0.1969,0.0727,0.9836,0.7756,0.1268,0.24,0.1034,0.625,0.6667,0.1009,0.1605,0.2082,,0.0037,0.2006,0.0755,0.9836,0.7844,0.128,0.2417,0.1034,0.625,0.6667,0.1032,0.1754,0.2169,,0.0039,0.1988,0.0727,0.9836,0.7786,0.1276,0.24,0.1034,0.625,0.6667,0.1027,0.1633,0.2119,,0.0038,reg oper account,block of flats,0.2339,Panel,No,0.0,0.0,0.0,0.0,-951.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n368121,0,Cash loans,F,N,Y,1,202500.0,158301.0,15786.0,148500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.031329,-17387,-2928,-6007.0,-923,,1,1,0,1,1,0,Private service staff,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Services,,0.7325202365130182,,0.0186,0.0232,0.9816,0.7484,0.0017,0.0,0.1034,0.0417,0.0833,0.0433,0.0151,0.0184,0.0,0.0043,0.0189,0.024,0.9816,0.7583,0.0017,0.0,0.1034,0.0417,0.0833,0.0443,0.0165,0.0192,0.0,0.0045,0.0187,0.0232,0.9816,0.7518,0.0017,0.0,0.1034,0.0417,0.0833,0.0441,0.0154,0.0187,0.0,0.0044,reg oper account,block of flats,0.015,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-981.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n421120,0,Cash loans,F,N,Y,0,67500.0,269550.0,11547.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019101,-21846,365243,-12326.0,-4316,,1,0,0,1,1,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.6912944246906746,0.7476633896301825,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n224018,0,Cash loans,M,Y,Y,0,157500.0,1575000.0,41679.0,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-14994,-2661,0.0,-3837,64.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Other,0.28157575747374897,0.5088026758129487,0.4740512892789932,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-2311.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n340651,0,Cash loans,M,Y,Y,0,216000.0,450000.0,48465.0,450000.0,Unaccompanied,Working,Higher education,Married,Rented apartment,0.011703,-12254,-947,-651.0,-1899,11.0,1,1,0,1,1,0,Cooking staff,2.0,2,2,THURSDAY,14,1,1,0,1,1,0,Business Entity Type 3,,0.5862458925219755,0.4418358231994413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-414.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n437937,0,Revolving loans,F,N,Y,0,90000.0,225000.0,11250.0,225000.0,Family,Pensioner,Higher education,Single / not married,House / apartment,0.008068,-20537,365243,-2460.0,-3890,,1,0,0,1,0,0,,1.0,3,3,MONDAY,5,0,0,0,0,0,0,XNA,0.8081346133474387,0.14785280234699236,0.1624419982223248,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2193.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n133882,0,Cash loans,F,N,Y,2,135000.0,533668.5,25803.0,477000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-13581,-4199,-6462.0,-4354,,1,1,0,1,0,0,Laborers,4.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.4667711083349699,0.2632411250133655,0.4241303111942548,0.0082,0.0,0.9682,0.5648,0.0009,0.0,0.0345,0.0417,0.0833,0.0084,0.0067,0.0078,0.0,0.0,0.0084,0.0,0.9682,0.5818,0.0009,0.0,0.0345,0.0417,0.0833,0.0086,0.0073,0.0081,0.0,0.0,0.0083,0.0,0.9682,0.5706,0.0009,0.0,0.0345,0.0417,0.0833,0.0085,0.0068,0.0079,0.0,0.0,reg oper account,block of flats,0.0061,Block,No,7.0,1.0,7.0,1.0,-2483.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n151830,0,Cash loans,M,Y,N,1,180000.0,585000.0,29866.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-14987,-5765,-1107.0,-4193,18.0,1,1,1,1,0,0,Laborers,3.0,2,2,TUESDAY,16,0,0,0,0,0,0,Transport: type 4,,0.4576422737098526,0.6863823354047934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2099.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n257113,0,Cash loans,F,N,Y,0,886500.0,247275.0,18616.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.00702,-22429,365243,-9651.0,-4984,,1,0,0,1,0,0,,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.3553760797358243,0.41184855592423975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-477.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n326702,0,Cash loans,F,Y,N,0,56250.0,508495.5,22396.5,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.00702,-21342,365243,-5914.0,-4676,20.0,1,0,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,,0.4211867953979823,0.16342569473134347,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1051.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n155946,0,Cash loans,F,N,Y,1,99000.0,225000.0,15165.0,225000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018634,-10103,-999,-1222.0,-2769,,1,1,1,1,0,0,Security staff,3.0,2,2,MONDAY,18,0,0,0,0,0,0,Security,0.4047910048724289,0.10578148625246007,0.5919766183185521,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,1.0,-788.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n280958,0,Cash loans,F,N,Y,0,157500.0,178290.0,17761.5,157500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.007273999999999998,-10758,-285,-4753.0,-3405,,1,1,0,1,0,1,Sales staff,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Trade: type 7,0.2991648602997312,0.5396355435906887,0.6075573001388961,0.0464,0.3417,0.9821,0.7552,0.0,0.0,0.1034,0.1667,0.0,0.0,0.0,0.0444,0.0,0.0,0.0473,0.3546,0.9821,0.7648,0.0,0.0,0.1034,0.1667,0.0,0.0,0.0,0.0463,0.0,0.0,0.0468,0.3417,0.9821,0.7585,0.0,0.0,0.1034,0.1667,0.0,0.0,0.0,0.0452,0.0,0.0,reg oper account,block of flats,0.0544,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-908.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n144255,1,Cash loans,F,N,Y,3,112500.0,337500.0,23620.5,337500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-16050,-1749,-6539.0,-4571,,1,1,0,1,0,0,Medicine staff,5.0,2,2,THURSDAY,15,0,0,0,0,0,0,Other,,0.5592619780457152,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n297911,0,Cash loans,M,N,Y,2,180000.0,220032.0,8424.0,144000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-15860,-2126,-3857.0,-4263,,1,1,0,1,0,0,Drivers,4.0,2,2,TUESDAY,8,0,0,0,0,0,0,Government,0.5212027146753639,0.5470145589669536,0.7032033049040319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2647.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n372977,0,Cash loans,M,Y,N,0,225000.0,348264.0,27513.0,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007114,-20930,-4427,-1263.0,-3999,1.0,1,1,1,1,0,0,Laborers,2.0,2,2,FRIDAY,12,1,1,0,1,1,1,Business Entity Type 1,0.4416724602624757,0.7531968059098835,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n281026,0,Cash loans,F,Y,Y,0,180000.0,1078200.0,38331.0,900000.0,Children,Working,Secondary / secondary special,Married,House / apartment,0.010032,-22846,-12485,-13322.0,-4379,7.0,1,1,0,1,1,0,Laborers,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 1,,0.5199618372620394,0.6848276586890367,0.1112,0.0837,0.9821,,,0.12,0.1034,0.3333,,0.0823,,0.1131,,0.0667,0.0756,0.0584,0.9816,,,0.0806,0.069,0.3333,,0.0594,,0.0792,,0.0467,0.1119,0.0836,0.9821,,,0.12,0.1034,0.3333,,0.077,,0.1149,,0.0671,,block of flats,0.0694,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n324261,0,Cash loans,M,N,Y,0,90000.0,315000.0,21924.0,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010006,-21163,365243,-7253.0,-4697,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.32808578742391986,,0.0031,0.0,0.9786,0.7076,0.0,0.0,0.0,0.0,0.0417,0.0,0.0025,0.0031,0.0,0.0,0.0032,0.0,0.9786,0.7190000000000001,0.0,0.0,0.0,0.0,0.0417,0.0,0.0028,0.0032,0.0,0.0,0.0031,0.0,0.9786,0.7115,0.0,0.0,0.0,0.0,0.0417,0.0,0.0026,0.0031,0.0,0.0,not specified,block of flats,0.0024,Wooden,No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n242856,0,Cash loans,F,N,N,0,202500.0,468733.5,21982.5,387000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-18432,-4984,-2455.0,-1985,,1,1,0,1,0,0,Cleaning staff,1.0,3,3,MONDAY,9,0,0,0,0,0,0,Other,,0.6508045572174066,0.6075573001388961,0.0825,0.0819,0.9747,,,0.0,0.1379,0.1667,,0.0709,,0.0693,,0.0,0.084,0.085,0.9747,,,0.0,0.1379,0.1667,,0.0725,,0.0722,,0.0,0.0833,0.0819,0.9747,,,0.0,0.1379,0.1667,,0.0721,,0.0706,,0.0,,block of flats,0.0545,Panel,No,2.0,0.0,2.0,0.0,-1495.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n408134,0,Cash loans,M,Y,Y,1,162000.0,144000.0,15637.5,144000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-19097,-11250,-8363.0,-2629,16.0,1,1,1,1,1,0,Laborers,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Transport: type 4,,0.608989501980215,0.6528965519806539,0.16699999999999998,0.0825,0.9881,,,0.12,0.1034,0.3333,,0.0143,,0.1158,,,0.1702,0.0856,0.9881,,,0.1208,0.1034,0.3333,,0.0146,,0.1206,,,0.1686,0.0825,0.9881,,,0.12,0.1034,0.3333,,0.0146,,0.1179,,,,block of flats,0.1103,Panel,No,0.0,0.0,0.0,0.0,-354.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n425358,0,Cash loans,F,N,Y,0,180000.0,589050.0,27423.0,526500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-22833,-11139,-70.0,-3861,,1,1,0,1,0,0,Secretaries,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Government,,0.5936727916700033,0.6496203111237195,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1233.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n205772,0,Cash loans,F,N,Y,0,168750.0,1125000.0,44748.0,1125000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.015221,-18540,-397,-6111.0,-2092,,1,1,0,1,0,0,Laborers,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,,0.5109940382768342,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,2.0,0.0,2.0,0.0,-1803.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n215250,0,Cash loans,F,Y,Y,0,202500.0,1042560.0,34587.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-15237,-2713,-7400.0,-3963,64.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Self-employed,0.3776891813559923,0.5600272303883109,0.30620229831350426,0.033,,0.9737,,,0.0,0.069,0.125,,,,0.0245,,,0.0336,,0.9737,,,0.0,0.069,0.125,,,,0.0255,,,0.0333,,0.9737,,,0.0,0.069,0.125,,,,0.0249,,,,block of flats,0.0207,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1047.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n290216,0,Cash loans,F,N,Y,2,180000.0,640080.0,31261.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-11320,-1359,-4937.0,-672,,1,1,0,1,0,0,Laborers,4.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Industry: type 3,,0.1855246689085004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-298.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n255804,0,Cash loans,F,Y,Y,0,90000.0,855882.0,34074.0,765000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-18199,-3518,-4455.0,-1736,30.0,1,1,0,1,1,0,Laborers,2.0,2,2,THURSDAY,18,0,0,0,0,1,1,Business Entity Type 3,0.7376179208265884,0.5309377405355606,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-609.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n226189,0,Cash loans,F,Y,Y,0,135000.0,526491.0,18909.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00823,-18158,-915,-4614.0,-1687,12.0,1,1,1,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,9,0,0,0,0,1,1,Self-employed,,0.5586689831256462,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-502.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n202954,0,Cash loans,F,Y,Y,0,73615.5,265500.0,13878.0,265500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018801,-10468,-163,-614.0,-3158,13.0,1,1,1,1,1,0,Sales staff,1.0,2,2,TUESDAY,14,0,0,0,0,1,1,Trade: type 3,0.4180033135944941,0.5827620627144693,0.5172965813614878,0.1639,0.0735,0.9811,0.7416,0.0339,0.0,0.069,0.1667,0.2083,0.0251,0.1278,0.0481,0.027000000000000003,0.0343,0.16699999999999998,0.0762,0.9811,0.7517,0.0342,0.0,0.069,0.1667,0.2083,0.0256,0.1396,0.0501,0.0272,0.0363,0.1655,0.0735,0.9811,0.7451,0.0341,0.0,0.069,0.1667,0.2083,0.0255,0.13,0.0489,0.0272,0.035,reg oper spec account,block of flats,0.0638,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-146.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n127848,0,Cash loans,M,N,Y,0,63000.0,254700.0,14751.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-23806,365243,-4473.0,-5057,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.7329870001228045,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-2637.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n370618,0,Cash loans,F,N,N,0,101250.0,1494486.0,39555.0,1305000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.011703,-18570,-11339,-11136.0,-2069,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SUNDAY,13,0,0,0,0,0,0,Medicine,,0.7183186626956156,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n225540,0,Cash loans,F,N,N,0,135000.0,589045.5,17986.5,508500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,Municipal apartment,0.02461,-19608,365243,-7838.0,-3128,,1,0,0,1,1,0,,1.0,2,2,MONDAY,14,0,0,0,0,0,0,XNA,,0.4770134017674324,0.25259869783397665,0.1402,0.1072,0.9896,0.8572,0.1053,0.12,0.1724,0.375,0.2083,0.0942,0.1143,0.1419,0.0,0.0,0.1429,0.1112,0.9896,0.8628,0.1062,0.1208,0.1724,0.375,0.2083,0.0964,0.1249,0.1479,0.0,0.0,0.1416,0.1072,0.9896,0.8591,0.1059,0.12,0.1724,0.375,0.2083,0.0959,0.1163,0.1445,0.0,0.0,reg oper account,block of flats,0.1116,Panel,No,0.0,0.0,0.0,0.0,-9.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,0.0\n224943,0,Cash loans,M,Y,Y,0,157500.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-9228,-291,-8520.0,-1890,6.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.18111493408984367,0.16342569473134347,0.2835,0.2271,0.9881,0.8368,0.0638,0.28,0.2414,0.375,0.0417,0.1723,0.2303,0.2966,0.0039,0.0474,0.2889,0.2357,0.9881,0.8432,0.0644,0.282,0.2414,0.375,0.0417,0.1762,0.2516,0.309,0.0039,0.0502,0.2863,0.2271,0.9881,0.8390000000000001,0.0642,0.28,0.2414,0.375,0.0417,0.1753,0.2343,0.3019,0.0039,0.0484,org spec account,block of flats,0.2784,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-615.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n195522,0,Cash loans,F,Y,Y,1,202500.0,1081179.0,43006.5,927000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019689,-10150,-869,-4567.0,-1011,3.0,1,1,0,1,0,0,,3.0,2,2,MONDAY,10,0,0,0,0,0,0,Transport: type 4,,0.56564446577937,0.4400578303966329,0.0412,0.0555,0.9791,0.7144,0.0,0.0,0.069,0.1667,0.2083,0.0288,0.0336,0.0356,0.0,0.0,0.042,0.0576,0.9791,0.7256,0.0,0.0,0.069,0.1667,0.2083,0.0294,0.0367,0.0371,0.0,0.0,0.0416,0.0555,0.9791,0.7182,0.0,0.0,0.069,0.1667,0.2083,0.0293,0.0342,0.0362,0.0,0.0,reg oper account,block of flats,0.0313,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-451.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n437768,0,Cash loans,M,Y,N,0,162000.0,1129500.0,43020.0,1129500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010147,-21016,-2711,-588.0,-4460,11.0,1,1,1,1,0,0,Laborers,2.0,2,2,WEDNESDAY,15,0,0,0,1,1,0,Business Entity Type 3,,0.4622158022483268,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n238969,0,Cash loans,F,N,Y,0,135000.0,781920.0,34573.5,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.00733,-22349,365243,-2260.0,-4520,,1,0,0,1,0,1,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,,0.18085885991227574,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-364.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n404890,0,Cash loans,M,N,Y,3,193500.0,491211.0,50463.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-15367,-5387,-4545.0,-1718,,1,1,0,1,0,0,Laborers,5.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.5990208581820523,0.6754132910917112,0.0124,0.0,0.9702,0.5920000000000001,0.0017,0.0,0.069,0.0417,0.0417,0.0147,0.0101,0.0115,0.0,0.0,0.0126,0.0,0.9702,0.608,0.0017,0.0,0.069,0.0417,0.0417,0.015,0.011000000000000001,0.012,0.0,0.0,0.0125,0.0,0.9702,0.5975,0.0017,0.0,0.069,0.0417,0.0417,0.015,0.0103,0.0117,0.0,0.0,reg oper account,block of flats,0.0091,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n244886,0,Cash loans,F,N,Y,0,180000.0,1255680.0,41629.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00496,-15307,-3034,-9237.0,-963,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,Trade: type 7,,0.6994697448219505,0.4507472818545589,,,0.9573,,,,,,,,,0.0105,,,,,0.9573,,,,,,,,,0.011000000000000001,,,,,0.9573,,,,,,,,,0.0107,,,,block of flats,0.011000000000000001,,No,0.0,0.0,0.0,0.0,-627.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n293324,0,Cash loans,F,Y,Y,0,202500.0,573408.0,25389.0,495000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-21464,365243,-1757.0,-4825,13.0,1,0,0,1,0,1,,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,0.7371300570368964,0.3424561010581985,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n401965,0,Cash loans,F,Y,Y,0,31500.0,269550.0,12964.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-20409,365243,-4721.0,-3134,2.0,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.4943930836959677,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2002.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n231814,0,Cash loans,F,N,Y,0,157500.0,983160.0,62964.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.008865999999999999,-19249,-206,-4773.0,-2775,,1,1,0,1,0,0,Laborers,1.0,2,2,THURSDAY,18,0,0,0,0,0,0,Self-employed,0.5794043469964199,0.23935498828071725,0.7338145369642702,0.0206,0.0697,0.9781,,,0.0,0.069,0.1667,,0.0619,,0.0772,,0.0,0.021,0.0723,0.9782,,,0.0,0.069,0.1667,,0.0633,,0.0805,,0.0,0.0208,0.0697,0.9781,,,0.0,0.069,0.1667,,0.063,,0.0786,,0.0,,block of flats,0.0607,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n339516,0,Cash loans,F,N,Y,0,135000.0,675000.0,24376.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-19985,-2829,-4452.0,-3243,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Self-employed,,0.5721819163593469,0.2032521136204725,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.0,3.0,11.0,3.0,-240.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n282716,1,Cash loans,F,N,N,1,180000.0,665892.0,25933.5,477000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.018029,-12534,-1181,-1167.0,-4618,,1,1,0,1,0,0,Sales staff,2.0,3,3,TUESDAY,10,0,0,0,0,1,1,Self-employed,0.4140175373980649,0.5164076108486942,0.6626377922738201,0.0619,,0.9737,,,0.0,0.1034,0.1667,,,,0.0508,,,0.063,,0.9737,,,0.0,0.1034,0.1667,,,,0.0529,,,0.0625,,0.9737,,,0.0,0.1034,0.1667,,,,0.0517,,,,block of flats,0.0625,Panel,No,0.0,0.0,0.0,0.0,-1530.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n406001,0,Revolving loans,F,N,N,0,121500.0,360000.0,18000.0,360000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.0105,-12505,-301,-48.0,-754,,1,1,0,1,0,0,Laborers,2.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.7264357964349251,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-445.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n223621,0,Cash loans,M,Y,Y,5,112500.0,472500.0,46161.0,454500.0,Family,Working,Higher education,Married,House / apartment,0.030755,-12846,-3532,-3651.0,-4359,16.0,1,1,0,1,0,0,Security staff,7.0,2,2,SATURDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.4805325917612671,0.5615568758657016,0.7324033228040929,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1891.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n216583,0,Cash loans,M,Y,Y,0,58500.0,325908.0,13936.5,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-20382,-263,-12189.0,-2304,38.0,1,1,1,1,1,0,Security staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,,0.39681430672320894,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,4.0,0.0,-79.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,1.0\n399841,0,Cash loans,F,N,Y,0,103500.0,871029.0,44473.5,765000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011703,-10848,-2371,-4689.0,-3527,,1,1,1,1,0,0,Laborers,2.0,2,2,MONDAY,16,0,1,1,0,1,1,Business Entity Type 3,0.30387102477616296,0.6432184578760498,0.5028782772082183,0.10099999999999999,0.1066,0.9796,,,0.0,0.2069,0.1667,,0.1366,,0.0615,,0.0284,0.1029,0.1107,0.9796,,,0.0,0.2069,0.1667,,0.1397,,0.0641,,0.0301,0.102,0.1066,0.9796,,,0.0,0.2069,0.1667,,0.1389,,0.0626,,0.028999999999999998,,block of flats,0.0715,Panel,No,7.0,1.0,7.0,0.0,-1714.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n376950,0,Revolving loans,F,N,Y,2,180000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.007114,-10789,-72,-2793.0,-3468,,1,1,1,1,1,0,,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,Business Entity Type 1,0.2731393882659228,0.2629358786975408,0.2764406945454034,0.0629,0.0868,0.9796,0.7212,,0.0,0.1379,0.1667,0.2083,,0.0513,0.0411,0.0,0.0905,0.0641,0.0901,0.9796,0.7321,,0.0,0.1379,0.1667,0.2083,,0.055999999999999994,0.0429,0.0,0.0958,0.0635,0.0868,0.9796,0.7249,,0.0,0.1379,0.1667,0.2083,,0.0522,0.0419,0.0,0.0924,reg oper spec account,block of flats,0.0627,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2564.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n296622,0,Cash loans,F,Y,Y,1,279000.0,657166.5,35779.5,531000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.04622,-17827,-7399,-11858.0,-1351,7.0,1,1,0,1,0,0,,3.0,1,1,THURSDAY,17,0,1,1,0,0,0,Business Entity Type 2,,0.7547382519648115,0.5495965024956946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,4.0,0.0,4.0,0.0,-2451.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n113588,0,Cash loans,M,Y,N,1,112500.0,225000.0,8482.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-17196,-1608,-8334.0,-746,35.0,1,1,1,1,0,0,Laborers,3.0,2,2,MONDAY,18,0,0,0,0,1,1,Industry: type 9,,0.4660501117001741,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-779.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n109616,0,Cash loans,F,N,Y,0,112500.0,673875.0,21865.5,562500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006008,-23454,365243,-11281.0,-4669,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.7112932979048959,,0.1814,0.0797,0.9841,0.7824,0.0962,0.1732,0.1952,0.2775,0.3192,0.1448,0.1003,0.1848,0.0141,0.0259,0.084,0.0282,0.9826,0.7713,0.0413,0.0,0.1379,0.3333,0.375,0.0766,0.0735,0.0861,0.0039,0.0,0.204,0.0884,0.9846,0.792,0.1145,0.24,0.2069,0.3333,0.375,0.1755,0.0701,0.1969,0.0155,0.0049,reg oper account,block of flats,0.1931,Panel,No,2.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n205982,0,Cash loans,F,N,N,0,94500.0,207000.0,13963.5,207000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-17352,-3576,-2630.0,-879,,1,1,0,1,1,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Trade: type 7,,0.030282299727655494,0.5226973172821112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-542.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n297108,0,Cash loans,F,N,Y,0,112500.0,715500.0,30312.0,715500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.026392,-23399,365243,-12799.0,-4651,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.7748643997622621,,0.0825,0.0703,0.9786,,,0.0,0.069,0.1667,,,,0.0703,,0.0,0.084,0.0729,0.9786,,,0.0,0.069,0.1667,,,,0.0733,,0.0,0.0833,0.0703,0.9786,,,0.0,0.069,0.1667,,,,0.0716,,0.0,,block of flats,0.0553,Panel,No,0.0,0.0,0.0,0.0,-1890.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n439563,0,Cash loans,F,N,Y,2,101250.0,526491.0,29529.0,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-11454,-2920,-1257.0,-4006,,1,1,0,1,1,0,Sales staff,4.0,2,2,SATURDAY,15,0,0,0,1,1,0,Self-employed,0.3939817459228296,0.7151754702066834,0.5762088360175724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n199674,0,Cash loans,F,N,Y,2,360000.0,247275.0,19264.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.04622,-13692,-6591,-208.0,-4835,,1,1,0,1,0,0,High skill tech staff,3.0,1,1,SUNDAY,16,0,0,0,0,0,0,Industry: type 11,0.6023475388100492,0.6248174982717155,0.31703177858344445,0.0866,0.0836,0.9816,0.7484,0.0142,0.0,0.2069,0.1667,0.2083,,0.0706,0.0829,0.0,0.0,0.0882,0.0867,0.9816,0.7583,0.0144,0.0,0.2069,0.1667,0.2083,,0.0771,0.0863,0.0,0.0,0.0874,0.0836,0.9816,0.7518,0.0143,0.0,0.2069,0.1667,0.2083,,0.0718,0.0844,0.0,0.0,reg oper account,block of flats,0.0652,Panel,No,2.0,1.0,2.0,0.0,-2280.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n395729,0,Revolving loans,M,Y,Y,1,135000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Married,With parents,0.035792000000000004,-10351,-525,-4518.0,-2505,3.0,1,1,0,1,0,0,Drivers,3.0,2,2,MONDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.20592333549484126,0.5532760509967526,0.2940831009471255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-291.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n348257,0,Cash loans,F,N,Y,0,157500.0,539100.0,27652.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.003540999999999999,-19347,365243,-1567.0,-2875,,1,0,0,1,0,0,,2.0,1,1,TUESDAY,12,0,0,0,0,0,0,XNA,,0.6385429607339289,,0.0661,0.0592,0.9911,0.8776,0.0176,0.04,0.0948,0.25,0.2917,0.0217,0.0342,0.0685,0.0013,0.0027,0.0315,0.0,0.9876,0.8367,0.0026,0.0,0.0345,0.1667,0.2083,0.0055,0.0312,0.0324,0.0,0.0,0.0463,0.0329,0.9886,0.8457,0.0204,0.02,0.069,0.25,0.2917,0.0167,0.0299,0.0429,0.0,0.0,not specified,block of flats,0.1964,Panel,No,2.0,0.0,2.0,0.0,-969.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n440684,0,Cash loans,F,Y,Y,0,256500.0,953460.0,68922.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-10424,-3689,-4935.0,-3098,6.0,1,1,1,1,1,0,Managers,2.0,1,1,FRIDAY,12,0,1,1,0,1,1,Business Entity Type 1,,0.3781022166255257,0.4543210601605785,0.16699999999999998,0.107,0.9816,0.7484,0.0707,0.24,0.1034,0.4583,0.5,0.0798,0.1345,0.1156,0.0077,0.0276,0.1702,0.111,0.9816,0.7583,0.0713,0.2417,0.1034,0.4583,0.5,0.0816,0.1469,0.1204,0.0078,0.0292,0.1686,0.107,0.9816,0.7518,0.0711,0.24,0.1034,0.4583,0.5,0.0812,0.1368,0.1177,0.0078,0.0282,reg oper account,block of flats,0.1431,Panel,No,0.0,0.0,0.0,0.0,-1414.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n345495,0,Cash loans,F,N,Y,0,202500.0,251280.0,16996.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.006305,-14419,-438,-620.0,-4882,,1,1,0,1,0,0,Laborers,1.0,3,3,MONDAY,4,0,0,0,0,0,0,Self-employed,,0.1396243918346621,0.44539624156083396,0.0247,,0.9801,0.728,0.0026,0.0,0.069,0.0833,0.0,0.0032,0.0202,0.022000000000000002,0.0,0.0,0.0252,,0.9801,0.7387,0.0026,0.0,0.069,0.0833,0.0,0.0032,0.022000000000000002,0.0229,0.0,0.0,0.025,,0.9801,0.7316,0.0026,0.0,0.069,0.0833,0.0,0.0032,0.0205,0.0224,0.0,0.0,reg oper account,block of flats,0.0188,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-688.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n415442,0,Cash loans,M,Y,Y,0,180000.0,2085120.0,72607.5,1800000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010032,-20751,-2502,-5291.0,-4149,8.0,1,1,0,1,0,1,Drivers,2.0,2,2,WEDNESDAY,6,0,0,0,0,0,0,Government,0.5337712949530407,0.6091092595614575,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1505.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n393604,0,Cash loans,F,N,Y,0,315000.0,675000.0,29731.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.003540999999999999,-19956,-371,-2940.0,-2971,,1,1,0,1,0,0,Sales staff,2.0,1,1,TUESDAY,12,0,0,0,0,0,0,Self-employed,0.7679915437746649,0.8006234275974431,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1984.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n167975,0,Cash loans,M,N,Y,0,270000.0,348264.0,27513.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009549,-12636,-914,-869.0,-1442,,1,1,1,1,1,1,Laborers,2.0,2,2,WEDNESDAY,10,0,1,1,0,1,1,Industry: type 10,,0.1962214732386609,0.2707073872651806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-793.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n315511,0,Cash loans,F,N,Y,0,135000.0,1762110.0,51651.0,1575000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018801,-20529,-2774,-3862.0,-4054,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,0.6715702620278621,0.5201747027197788,0.5280927512030451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-755.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n280602,0,Cash loans,M,N,Y,0,171000.0,857169.0,25191.0,715500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.04622,-11217,-1414,-6003.0,-3502,,1,1,0,1,0,0,Laborers,1.0,1,1,SATURDAY,11,0,0,0,0,1,1,Business Entity Type 1,,0.4570724249477075,,0.0619,0.0607,0.9806,,,0.0,0.1379,0.1667,,,,0.0518,,0.0221,0.063,0.063,0.9806,,,0.0,0.1379,0.1667,,,,0.054000000000000006,,0.0234,0.0625,0.0607,0.9806,,,0.0,0.1379,0.1667,,,,0.0528,,0.0226,,block of flats,0.0432,Panel,No,1.0,0.0,1.0,0.0,0.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n312254,0,Cash loans,F,N,Y,0,157500.0,675000.0,21775.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003122,-15033,-4610,-8203.0,-4440,,1,1,0,1,1,0,,2.0,3,3,FRIDAY,12,0,0,0,0,0,0,Medicine,,0.6340389809759129,0.3706496323299817,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-3012.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n424842,0,Cash loans,M,N,N,0,225000.0,268659.0,32013.0,243000.0,Family,Commercial associate,Secondary / secondary special,Civil marriage,Municipal apartment,0.035792000000000004,-14498,-204,-1698.0,-5267,,1,1,0,1,0,0,Drivers,2.0,2,2,SATURDAY,13,0,0,0,1,1,1,Business Entity Type 3,0.3415840955948566,0.5343290949962483,0.06733950871403091,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1618.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n385429,1,Cash loans,M,Y,Y,0,225000.0,1198548.0,50913.0,1102500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-21376,-1036,-5853.0,-4724,17.0,1,1,1,1,0,0,Managers,2.0,3,3,TUESDAY,6,0,0,0,0,0,0,Business Entity Type 3,0.4585239144354855,0.6667875005347976,0.4866531565147181,0.0959,0.0781,0.9851,0.7959999999999999,0.0785,0.08,0.1379,0.25,0.2917,0.0657,0.0756,0.1028,0.0116,0.0123,0.063,0.0577,0.9846,0.7975,0.0496,0.0,0.1379,0.1667,0.2083,0.0501,0.0505,0.0544,0.0039,0.0077,0.0968,0.0781,0.9851,0.7987,0.079,0.08,0.1379,0.25,0.2917,0.0669,0.077,0.1046,0.0116,0.0125,reg oper account,block of flats,0.0448,Block,No,0.0,0.0,0.0,0.0,-207.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n266883,0,Cash loans,M,Y,Y,1,135000.0,254700.0,27022.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020713,-18148,-922,-8672.0,-1370,9.0,1,1,0,1,0,0,,3.0,3,3,MONDAY,6,0,0,0,0,0,0,Medicine,0.6452218330397357,0.33911557286497024,0.6971469077844458,0.0928,0.1149,0.9881,0.8368,0.0867,0.0,0.2069,0.1667,0.2083,0.0442,0.0756,0.0986,0.0,0.0,0.0945,0.1192,0.9881,0.8432,0.0875,0.0,0.2069,0.1667,0.2083,0.0452,0.0826,0.1028,0.0,0.0,0.0937,0.1149,0.9881,0.8390000000000001,0.0872,0.0,0.2069,0.1667,0.2083,0.045,0.077,0.1004,0.0,0.0,reg oper account,block of flats,0.0776,Panel,No,2.0,0.0,2.0,0.0,-969.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n224540,0,Revolving loans,F,N,Y,0,315000.0,765000.0,38250.0,765000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,With parents,0.032561,-14922,-248,-3287.0,-238,,1,1,0,1,0,1,Sales staff,2.0,1,1,FRIDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.7913861894147737,0.5799359919637852,0.4135967602644276,0.2299,,0.9801,,,0.52,0.4483,0.3333,,,,0.1469,,,0.2342,,0.9801,,,0.5236,0.4483,0.3333,,,,0.1531,,,0.2321,,0.9801,,,0.52,0.4483,0.3333,,,,0.1496,,,,,0.1862,,No,1.0,0.0,1.0,0.0,-1150.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,7.0\n377677,0,Cash loans,F,N,N,2,157500.0,1161360.0,41845.5,900000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.035792000000000004,-14684,-3633,-1087.0,-5007,,1,1,0,1,0,0,High skill tech staff,4.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 1,0.7985122230684929,0.4728036120309435,0.6313545365850379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n157406,0,Cash loans,F,N,Y,0,126000.0,948816.0,27873.0,792000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-21070,365243,-2639.0,-3675,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,0.6919668071511187,0.6784063076587886,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2021.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n155466,0,Revolving loans,M,Y,Y,0,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Pensioner,Incomplete higher,Married,House / apartment,0.014464,-15645,365243,-8154.0,-5187,12.0,1,0,0,1,1,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,XNA,0.4956052756416121,0.7442909996292313,,0.0794,,0.9796,0.7212,,0.0,0.1379,0.1667,0.0417,,,0.077,,0.0251,0.0809,,0.9796,0.7321,,0.0,0.1379,0.1667,0.0417,,,0.0802,,0.0266,0.0802,,0.9796,0.7249,,0.0,0.1379,0.1667,0.0417,,,0.0784,,0.0256,reg oper account,block of flats,0.066,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1015.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n353777,0,Cash loans,F,N,Y,0,135000.0,247675.5,19989.0,229500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-9442,-806,-3856.0,-2078,,1,1,0,1,0,0,High skill tech staff,1.0,3,2,THURSDAY,5,0,0,0,0,0,0,Business Entity Type 3,0.5295189390722898,0.5590194095529287,,0.201,0.0941,0.9856,0.8028,0.0865,0.12,0.3103,0.3333,0.2083,0.1103,0.1605,0.1928,0.0154,0.0561,0.2048,0.0976,0.9856,0.8105,0.0873,0.1208,0.3103,0.3333,0.2083,0.1128,0.1754,0.2009,0.0156,0.0594,0.203,0.0941,0.9856,0.8054,0.0871,0.12,0.3103,0.3333,0.2083,0.1122,0.1633,0.1963,0.0155,0.0573,reg oper spec account,block of flats,0.2111,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-656.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n353091,0,Cash loans,F,N,N,0,121500.0,878733.0,25821.0,733500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.008865999999999999,-22400,365243,-11262.0,-4142,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.6305971072526092,0.4884551844437485,0.0928,0.0941,0.9806,0.7348,0.0141,0.0,0.2069,0.1667,0.2083,0.0889,0.0756,0.0792,0.0,0.0,0.0945,0.0977,0.9806,0.7452,0.0143,0.0,0.2069,0.1667,0.2083,0.0909,0.0826,0.0825,0.0,0.0,0.0937,0.0941,0.9806,0.7383,0.0142,0.0,0.2069,0.1667,0.2083,0.0904,0.077,0.0806,0.0,0.0,reg oper account,block of flats,0.07,Panel,No,1.0,0.0,1.0,0.0,-2448.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n236018,0,Revolving loans,F,Y,Y,0,270000.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-16784,-2952,-6929.0,-302,65.0,1,1,0,1,0,0,Managers,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Construction,0.8747334274311336,0.5143157693140837,0.6545292802242897,0.133,0.1231,0.9767,0.6804,0.0184,0.0,0.2759,0.1667,0.2083,0.0631,0.1084,0.1061,0.0,0.0,0.1355,0.1277,0.9767,0.6929,0.0186,0.0,0.2759,0.1667,0.2083,0.0645,0.1185,0.1105,0.0,0.0,0.1343,0.1231,0.9767,0.6847,0.0185,0.0,0.2759,0.1667,0.2083,0.0642,0.1103,0.10800000000000001,0.0,0.0,reg oper account,block of flats,0.0935,Panel,No,1.0,0.0,1.0,0.0,-1439.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n192132,0,Cash loans,M,N,N,0,270000.0,1006920.0,39528.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.01885,-12266,-1284,-6395.0,-4764,,1,1,0,1,0,0,,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Self-employed,0.08672565611868148,0.2133750939938857,0.3910549766342248,0.1608,,0.9896,,,0.16,0.1379,0.375,,0.0219,,0.1758,,0.0153,0.1639,,0.9896,,,0.1611,0.1379,0.375,,0.0224,,0.1832,,0.0161,0.1624,,0.9896,,,0.16,0.1379,0.375,,0.0223,,0.179,,0.0156,,block of flats,0.1553,Panel,No,3.0,1.0,3.0,0.0,-472.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n295515,0,Cash loans,M,N,Y,0,180000.0,1005120.0,29389.5,720000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.032561,-13695,-1052,-7748.0,-3933,,1,1,0,1,1,0,Laborers,1.0,1,1,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.7047203311505799,0.7380196196295241,0.1735,0.1338,0.9851,0.7959999999999999,,0.16,0.1148,0.5275,0.375,0.059000000000000004,,0.1808,,,0.1155,0.0508,0.9861,0.7909,,0.0806,0.0345,0.625,0.375,0.0193,,0.1181,,,0.1145,0.1338,0.9861,0.7987,,0.08,0.0345,0.625,0.375,0.06,,0.11800000000000001,,,reg oper account,block of flats,0.2502,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1137.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n242027,1,Cash loans,M,N,Y,0,180000.0,101880.0,10939.5,90000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.072508,-12068,-740,-5724.0,-1775,,1,1,0,1,1,0,Drivers,1.0,1,1,MONDAY,9,0,0,0,0,0,0,Transport: type 4,,0.6427214865316639,,0.0273,0.0437,0.9722,0.6192,0.0259,0.0,0.0862,0.1667,0.2083,0.0368,0.0206,0.044000000000000004,0.0077,0.031,0.0242,0.0454,0.9722,0.6341,0.0046,0.0,0.069,0.1667,0.2083,0.0376,0.0184,0.0366,0.0039,0.025,0.0276,0.0437,0.9722,0.6243,0.0261,0.0,0.0862,0.1667,0.2083,0.0374,0.0209,0.0448,0.0078,0.0317,reg oper account,block of flats,0.0327,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-594.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n221250,0,Cash loans,F,N,Y,0,166500.0,358344.0,20137.5,283500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-20364,365243,-7509.0,-3731,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.6612013882779811,0.3859146722745145,0.0804,0.0648,0.9896,0.8572,0.033,0.08,0.069,0.375,0.4167,0.0667,0.0656,0.1006,0.0,0.0,0.0819,0.0672,0.9896,0.8628,0.0333,0.0806,0.069,0.375,0.4167,0.0682,0.0716,0.1048,0.0,0.0,0.0812,0.0648,0.9896,0.8591,0.0332,0.08,0.069,0.375,0.4167,0.0678,0.0667,0.1024,0.0,0.0,reg oper account,block of flats,0.0971,Block,No,0.0,0.0,0.0,0.0,-1351.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n216079,0,Cash loans,M,N,Y,2,135000.0,974794.5,32346.0,841500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.006207,-10601,-315,-8855.0,-3276,,1,1,0,1,0,0,Laborers,4.0,2,2,FRIDAY,17,0,0,0,0,0,0,Construction,0.3690344077296873,0.6429474207327817,0.6545292802242897,0.1763,,0.9811,0.7416,,0.04,0.0345,0.3333,0.375,0.037000000000000005,,0.0811,,0.0149,0.1796,,0.9811,0.7517,,0.0403,0.0345,0.3333,0.375,0.0379,,0.0845,,0.0158,0.17800000000000002,,0.9811,0.7451,,0.04,0.0345,0.3333,0.375,0.0377,,0.0826,,0.0152,,block of flats,0.0638,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-214.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n315272,0,Revolving loans,F,N,Y,0,225000.0,630000.0,31500.0,630000.0,Other_B,Commercial associate,Higher education,Civil marriage,House / apartment,0.04622,-9798,-338,-4601.0,-2463,,1,1,1,1,1,1,Accountants,2.0,1,1,THURSDAY,19,0,0,0,0,1,1,Trade: type 7,0.31183420309128795,0.7306264916351966,0.5172965813614878,0.0454,0.08199999999999999,0.9737,,,0.0,0.1034,0.0971,,0.051,,0.045,,0.002,0.0252,0.0838,0.9697,,,0.0,0.069,0.0417,,0.0446,,0.0394,,0.0,0.0458,0.08199999999999999,0.9742,,,0.0,0.1034,0.0833,,0.0519,,0.0458,,0.0021,,block of flats,0.0297,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1349.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n297408,0,Cash loans,F,N,Y,2,72000.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-13756,-5305,-5465.0,-1234,,1,1,1,1,0,0,Core staff,4.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Government,0.4260952063503545,0.6632850678230939,0.36896873825284665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-754.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n403013,0,Cash loans,F,N,Y,0,225000.0,1078200.0,31653.0,900000.0,Family,Pensioner,Lower secondary,Married,House / apartment,0.025164,-20574,365243,-3534.0,-3534,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,XNA,,0.6748033346784756,0.34090642641523844,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-879.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n426959,0,Cash loans,F,N,N,0,112500.0,573408.0,27715.5,495000.0,Unaccompanied,Working,Higher education,Civil marriage,Office apartment,0.008575,-9907,-1700,-747.0,-1002,,1,1,0,1,0,0,Accountants,2.0,2,2,TUESDAY,16,0,0,0,1,1,0,Bank,0.7613556924739663,0.62710099076714,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-549.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n304715,0,Cash loans,M,Y,Y,1,292500.0,684652.5,30285.0,544500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006207,-15541,-1117,-10339.0,-4522,7.0,1,1,0,1,0,1,Drivers,3.0,2,2,THURSDAY,14,0,0,0,0,0,0,Transport: type 3,,0.34109341872003884,0.5797274227921155,0.0412,0.0632,0.9935,0.9116,0.0076,0.0,0.069,0.1667,0.0417,0.011000000000000001,0.0336,0.0517,0.0,0.0,0.042,0.0656,0.9935,0.9151,0.0077,0.0,0.069,0.1667,0.0417,0.0112,0.0367,0.0539,0.0,0.0,0.0416,0.0632,0.9935,0.9128,0.0077,0.0,0.069,0.1667,0.0417,0.0112,0.0342,0.0526,0.0,0.0,reg oper account,block of flats,0.042,Panel,No,0.0,0.0,0.0,0.0,-908.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n255012,0,Cash loans,M,Y,Y,0,69750.0,1078200.0,31653.0,900000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.008068,-22227,365243,-6363.0,-4451,15.0,1,0,0,1,0,0,,2.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,XNA,,0.4568879264949914,0.7151031019926098,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1666.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n428302,0,Cash loans,M,N,N,0,90000.0,1056447.0,30888.0,922500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010966,-20365,-5048,-12671.0,-3738,,1,1,0,1,0,0,Security staff,2.0,2,2,SATURDAY,8,0,1,1,0,0,0,Security,,0.2387344572957865,0.4525335592581747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1106.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n376146,0,Cash loans,F,N,Y,0,315000.0,755190.0,56592.0,675000.0,Unaccompanied,Commercial associate,Incomplete higher,Civil marriage,House / apartment,0.008019,-11152,-796,-3209.0,-3685,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Trade: type 7,0.37280480895524254,0.425163456530667,,0.2969,0.2319,0.9846,0.7892,0.45299999999999996,0.32,0.2759,0.3333,0.0417,0.128,0.2328,0.2988,0.0193,0.0579,0.3025,0.2406,0.9846,0.7975,0.4571,0.3222,0.2759,0.3333,0.0417,0.1309,0.2544,0.3114,0.0195,0.0613,0.2998,0.2319,0.9846,0.792,0.4558,0.32,0.2759,0.3333,0.0417,0.1302,0.2369,0.3042,0.0194,0.0591,reg oper account,block of flats,0.2476,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1339.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n356716,0,Cash loans,F,N,N,0,135000.0,405000.0,19611.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-17081,-5776,-10116.0,-621,,1,1,0,1,1,0,High skill tech staff,2.0,3,3,SATURDAY,11,0,0,0,0,0,0,Self-employed,0.6728499197843433,0.41598198546510506,0.6313545365850379,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1265.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n324670,0,Cash loans,F,N,Y,0,121500.0,427500.0,33903.0,427500.0,Family,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.026392,-11379,-206,-2080.0,-1590,,1,1,0,1,1,0,Sales staff,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,Trade: type 7,0.4275397794893448,0.5526706315656107,,0.1227,,0.9965,,,0.12,0.1034,0.375,,,,0.1435,,0.0056,0.125,,0.9965,,,0.1208,0.1034,0.375,,,,0.1495,,0.0059,0.1239,,0.9965,,,0.12,0.1034,0.375,,,,0.1461,,0.0057,,block of flats,0.1141,Panel,No,1.0,0.0,1.0,0.0,-2096.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n197310,0,Cash loans,M,Y,N,0,180000.0,441000.0,32089.5,441000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.028663,-13581,-1780,-7393.0,-4384,18.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.3462428340542353,0.2735646775174348,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1895.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n293239,0,Cash loans,F,Y,Y,1,234000.0,1886850.0,49905.0,1575000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.010966,-12683,-1290,-3478.0,-4390,7.0,1,1,0,1,0,0,Private service staff,3.0,2,2,THURSDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.4271222822264593,0.7548151534245531,0.8245949709919925,0.1093,0.1605,0.9791,0.7144,0.0291,0.24,0.2069,0.3333,0.375,0.1168,,0.2127,,0.0832,0.1113,0.1666,0.9791,0.7256,0.0294,0.2417,0.2069,0.3333,0.375,0.1195,,0.2216,,0.0881,0.1103,0.1605,0.9791,0.7182,0.0293,0.24,0.2069,0.3333,0.375,0.1189,,0.2165,,0.085,org spec account,block of flats,0.1859,\"Stone, brick\",No,3.0,1.0,3.0,0.0,-1214.0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.0,0.0,0.0,5.0,0.0,0.0\n182103,0,Cash loans,F,N,Y,0,202500.0,354276.0,17361.0,292500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011703,-17579,-3067,-8914.0,-1106,,1,1,0,1,1,0,Managers,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.7302863567969866,0.6863823354047934,0.066,0.0611,0.9737,0.6396,0.0055,0.0,0.1379,0.125,0.1667,0.0441,0.0538,0.0511,0.0,0.0,0.0672,0.0634,0.9737,0.6537,0.0056,0.0,0.1379,0.125,0.1667,0.0451,0.0588,0.0532,0.0,0.0,0.0666,0.0611,0.9737,0.6444,0.0056,0.0,0.1379,0.125,0.1667,0.0448,0.0547,0.052000000000000005,0.0,0.0,reg oper account,block of flats,0.0432,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-975.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n413654,0,Cash loans,F,N,Y,1,126000.0,474048.0,24331.5,360000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-10684,-2367,-1525.0,-1693,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Other,0.3525263672265171,0.7365369583183159,0.6144143775673561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1539.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n376268,0,Cash loans,F,N,Y,3,270000.0,792162.0,43101.0,630000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Municipal apartment,0.04622,-13043,-1596,-815.0,-5715,,1,1,0,1,0,0,,5.0,1,1,MONDAY,17,0,0,0,0,0,0,Kindergarten,0.4867996572996594,0.6763705456554348,0.2707073872651806,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-754.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n209810,0,Cash loans,M,Y,Y,1,270000.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-10864,-670,-1325.0,-3543,5.0,1,1,0,1,0,1,Managers,3.0,2,2,SUNDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.5276907457219263,0.5139388546763608,,0.0918,,0.9846,,,0.0,0.2069,0.1667,,0.0744,,0.0953,,0.0,0.0935,,0.9846,,,0.0,0.2069,0.1667,,0.076,,0.0993,,0.0,0.0926,,0.9846,,,0.0,0.2069,0.1667,,0.0756,,0.09699999999999999,,0.0,,block of flats,0.075,Panel,No,1.0,0.0,1.0,0.0,-797.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n347984,1,Cash loans,F,N,Y,0,135000.0,436032.0,21339.0,360000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.014464,-20457,365243,-872.0,-3983,,1,0,0,1,0,0,,1.0,2,2,MONDAY,4,0,0,0,0,0,0,XNA,,0.29074380114229154,0.6144143775673561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n160126,0,Cash loans,F,Y,N,0,225000.0,450000.0,22018.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-15523,-4922,-816.0,-4042,4.0,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Self-employed,,0.5832564707729591,0.0720131886405828,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,0.0,0.0,0.0,0.0,-505.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n406650,0,Cash loans,M,Y,N,1,445500.0,1467612.0,62181.0,1350000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-14548,-3615,-7385.0,-4000,2.0,1,1,1,1,1,0,Drivers,3.0,2,2,FRIDAY,21,0,0,0,0,0,0,Self-employed,,0.5931399049739212,0.2250868621163805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-384.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n189052,0,Cash loans,F,N,Y,0,157500.0,254700.0,30357.0,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.010147,-21735,365243,-12471.0,-4334,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,XNA,,0.19497596414263865,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-274.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n373364,0,Cash loans,F,N,N,0,190350.0,454500.0,21190.5,454500.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.011657,-21317,-3819,-5921.0,-4024,,1,1,0,1,0,0,Cleaning staff,1.0,1,1,THURSDAY,10,1,1,0,1,1,0,Housing,,0.5659614260608526,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3057.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n171871,0,Cash loans,F,N,N,1,112500.0,225000.0,24363.0,225000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Municipal apartment,0.010032,-8683,-1596,-4310.0,-355,,1,1,1,1,0,0,Core staff,3.0,2,2,TUESDAY,4,0,0,0,0,0,0,Kindergarten,0.2650330248032521,0.4662296918992436,,0.1938,0.0565,0.9851,,,0.08,0.069,0.3333,,0.0,,0.0899,,0.0021,0.1975,0.0586,0.9851,,,0.0806,0.069,0.3333,,0.0,,0.0937,,0.0022,0.1957,0.0565,0.9851,,,0.08,0.069,0.3333,,0.0,,0.0915,,0.0021,,block of flats,0.109,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-752.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n350353,0,Cash loans,M,Y,Y,1,225000.0,473760.0,51151.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.009549,-11897,-1512,-752.0,-786,2.0,1,1,0,1,0,0,Managers,3.0,2,2,SUNDAY,15,0,1,1,0,1,1,Other,,0.6042199477855558,0.6446794549585961,0.0794,0.0332,0.9791,0.7144,0.0349,0.08,0.0345,0.4583,0.5,0.0476,0.0647,0.0785,0.0,0.0,0.0809,0.0345,0.9791,0.7256,0.0352,0.0806,0.0345,0.4583,0.5,0.0487,0.0707,0.0818,0.0,0.0,0.0802,0.0332,0.9791,0.7182,0.0351,0.08,0.0345,0.4583,0.5,0.0484,0.0658,0.0799,0.0,0.0,reg oper account,block of flats,0.0751,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-736.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n111713,0,Cash loans,F,N,Y,0,90000.0,808650.0,23773.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-20967,-344,-404.0,-4079,,1,1,0,1,0,0,,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Other,,0.6686311956814324,0.7295666907060153,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n387592,0,Cash loans,F,N,Y,0,112500.0,568197.0,25159.5,490500.0,Family,Pensioner,Higher education,Separated,House / apartment,0.016612000000000002,-22059,365243,-12817.0,-2817,,1,0,0,1,0,0,,1.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,,0.1561607029295303,0.8599241760145402,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n361720,0,Cash loans,M,Y,N,0,157500.0,1346089.5,40941.0,1206000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-19843,-1922,-3205.0,-3403,14.0,1,1,0,1,0,0,Managers,2.0,2,2,THURSDAY,10,0,0,0,0,1,1,Self-employed,,0.4871575041522476,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n255866,0,Cash loans,F,Y,N,1,157500.0,696528.0,44644.5,630000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.019101,-13068,-1327,-1326.0,-3121,65.0,1,1,0,1,0,0,,3.0,2,2,MONDAY,9,0,0,0,0,0,0,Self-employed,0.1945308503656252,0.3700127887001421,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1004.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n408439,0,Cash loans,M,Y,N,0,225000.0,614574.0,16897.5,513000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-10594,-127,-1709.0,-2141,35.0,1,1,1,1,1,0,Drivers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.1975077930803872,0.3202292667671293,0.5082869913916046,0.0825,0.0589,0.9737,0.6396,0.0078,0.0,0.1379,0.1667,0.2083,0.0828,0.0756,0.063,0.0,0.0,0.084,0.0611,0.9737,0.6537,0.0078,0.0,0.1379,0.1667,0.2083,0.0847,0.0826,0.0657,0.0,0.0,0.0833,0.0589,0.9737,0.6444,0.0078,0.0,0.1379,0.1667,0.2083,0.0843,0.077,0.0642,0.0,0.0,reg oper account,block of flats,0.0538,Block,No,1.0,0.0,1.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n158863,0,Cash loans,F,N,Y,1,103500.0,314100.0,17167.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010966,-17409,-9216,-925.0,-936,,1,1,0,1,0,0,,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 2,,0.3133902111302968,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n294790,0,Cash loans,M,Y,Y,0,225000.0,298467.0,33804.0,283500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006305,-10104,-487,-2545.0,-2670,15.0,1,1,0,1,0,0,Drivers,2.0,3,3,SATURDAY,8,0,0,0,0,0,0,Government,,0.4231231731201178,0.5136937663039473,0.1454,0.2123,0.9866,0.8164,0.0913,0.0,0.3448,0.1667,0.2083,0.0368,0.1185,0.1075,0.0,0.0,0.1481,0.2203,0.9866,0.8236,0.0921,0.0,0.3448,0.1667,0.2083,0.0377,0.1295,0.11199999999999999,0.0,0.0,0.1468,0.2123,0.9866,0.8189,0.0918,0.0,0.3448,0.1667,0.2083,0.0375,0.1206,0.1094,0.0,0.0,reg oper account,block of flats,0.1344,Panel,No,0.0,0.0,0.0,0.0,-536.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n377366,0,Cash loans,F,N,Y,0,45000.0,371488.5,15736.5,371488.5,Unaccompanied,Pensioner,Incomplete higher,Single / not married,House / apartment,0.026392,-22695,365243,-3419.0,-4163,,1,0,0,1,1,0,,1.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,0.8287048964715192,0.4351491855187341,,0.0495,0.0612,0.9757,,,0.0,0.1034,0.125,,0.0735,,0.0404,,0.0114,0.0504,0.0635,0.9757,,,0.0,0.1034,0.125,,0.0752,,0.0421,,0.0121,0.05,0.0612,0.9757,,,0.0,0.1034,0.125,,0.0748,,0.0412,,0.0117,,block of flats,0.0343,Panel,No,2.0,1.0,2.0,1.0,-2534.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n186676,0,Cash loans,F,N,N,0,135000.0,407727.0,26185.5,333000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.018029,-10425,-786,-5238.0,-3113,,1,1,0,1,0,0,Laborers,1.0,3,3,SUNDAY,8,0,0,0,0,0,0,Other,0.16290322032706062,0.10606520468372284,,0.0082,0.0,0.9513,0.3336,0.0,0.0,0.069,0.0417,0.0417,0.0096,0.0067,0.0101,0.0,0.0,0.0084,0.0,0.9513,0.3597,0.0,0.0,0.069,0.0417,0.0417,0.0098,0.0073,0.0105,0.0,0.0,0.0083,0.0,0.9513,0.3425,0.0,0.0,0.069,0.0417,0.0417,0.0097,0.0068,0.0102,0.0,0.0,reg oper account,block of flats,0.0079,Wooden,No,0.0,0.0,0.0,0.0,-2178.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n389032,0,Cash loans,M,Y,Y,0,135000.0,545040.0,25537.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-11865,-477,-5974.0,-2543,23.0,1,1,1,1,0,0,Drivers,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,Self-employed,,0.518870107308416,0.3425288720742255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n209015,0,Cash loans,F,N,Y,0,81000.0,327024.0,25965.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-17011,-1220,-9324.0,-555,,1,1,1,1,1,0,Sales staff,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Self-employed,0.7381000746525489,0.3477590633767009,0.4418358231994413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-480.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n413991,0,Revolving loans,F,N,Y,0,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,Rented apartment,0.018209,-18742,-3604,-6103.0,-2295,,1,1,0,1,0,0,Laborers,1.0,3,3,TUESDAY,13,0,0,0,1,1,0,Self-employed,0.4883411976230448,0.5474534256793128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-876.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,0.0\n318457,0,Cash loans,F,N,Y,0,180000.0,284427.0,14652.0,216000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.005313,-19696,-1734,-11941.0,-3242,,1,1,1,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Hotel,0.7873119826607969,0.6477969824016335,0.3441550073724169,0.0371,0.0,0.9727,0.626,0.0009,0.0,0.1034,0.0833,0.125,0.0422,0.0303,0.0192,0.0,0.0,0.0378,0.0,0.9727,0.6406,0.001,0.0,0.1034,0.0833,0.125,0.0431,0.0331,0.02,0.0,0.0,0.0375,0.0,0.9727,0.631,0.0009,0.0,0.1034,0.0833,0.125,0.0429,0.0308,0.0195,0.0,0.0,reg oper account,block of flats,0.0171,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1842.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n355192,0,Cash loans,M,Y,Y,0,202500.0,231813.0,18333.0,193500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.025164,-14806,-2953,-7360.0,-4629,10.0,1,1,0,1,0,0,Managers,1.0,2,2,MONDAY,9,0,0,0,0,0,0,Self-employed,0.3583688919696228,0.7298079884154054,0.3425288720742255,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-544.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n337543,0,Cash loans,F,Y,N,0,157500.0,1007761.5,40095.0,927000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020246,-20001,-258,-9671.0,-3382,36.0,1,1,0,1,0,0,Cleaning staff,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,Government,,0.5245612618483826,0.5762088360175724,0.201,0.1386,0.9881,,,0.22,0.1724,0.4792,,0.0639,,0.2237,,0.0075,0.0809,0.033,0.9866,,,0.0806,0.0345,0.3333,,0.0252,,0.1003,,0.0051,0.203,0.1386,0.9881,,,0.22,0.1724,0.4792,,0.065,,0.2277,,0.0077,,block of flats,0.2771,Panel,No,0.0,0.0,0.0,0.0,-1960.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,2.0\n182283,0,Cash loans,M,Y,Y,0,135000.0,183784.5,13869.0,148500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018634,-19286,-284,-2459.0,-2844,12.0,1,1,0,1,1,0,,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Government,0.5607273257420392,0.6953071667837799,0.6023863442690867,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-314.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n200513,0,Cash loans,F,N,Y,1,67500.0,170640.0,11034.0,135000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-10897,-375,-1258.0,-77,,1,1,0,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Culture,0.28362232257879283,0.6290778282937192,0.6910212267577837,0.1155,0.1205,0.9816,0.7484,0.0196,0.0,0.2759,0.1667,0.2083,0.1048,0.0941,0.1026,0.0,0.0,0.1176,0.125,0.9816,0.7583,0.0198,0.0,0.2759,0.1667,0.2083,0.1072,0.1028,0.1069,0.0,0.0,0.1166,0.1205,0.9816,0.7518,0.0198,0.0,0.2759,0.1667,0.2083,0.1066,0.0958,0.1045,0.0,0.0,reg oper spec account,block of flats,0.0914,Panel,No,1.0,0.0,1.0,0.0,-1764.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n277515,1,Cash loans,M,N,Y,1,247500.0,835380.0,40320.0,675000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.018634,-12982,-401,-228.0,-4874,,1,1,0,1,0,0,Laborers,2.0,2,2,FRIDAY,11,0,0,0,0,0,0,Construction,0.6829209167372263,0.21488382082538127,0.7194907850918436,0.1474,0.0742,0.9856,,,0.04,0.0345,0.3333,,0.0646,,0.0988,,0.0,0.1502,0.077,0.9856,,,0.0403,0.0345,0.3333,,0.066,,0.1029,,0.0,0.1489,0.0742,0.9856,,,0.04,0.0345,0.3333,,0.0657,,0.1005,,0.0,,block of flats,0.0777,\"Stone, brick\",No,6.0,1.0,6.0,1.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n140008,1,Cash loans,M,N,Y,0,202500.0,419679.0,15948.0,346500.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.0105,-23027,365243,-8405.0,-4452,,1,0,0,1,0,0,,1.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.29398758450226137,0.392773860603134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n207155,0,Cash loans,F,Y,N,2,270000.0,2250000.0,61875.0,2250000.0,Unaccompanied,State servant,Incomplete higher,Married,House / apartment,0.006233,-12650,-4595,-1617.0,-1920,2.0,1,1,0,1,1,0,Core staff,4.0,2,2,TUESDAY,16,0,0,0,0,0,0,Kindergarten,0.5756969710510483,0.6468484492579611,0.6496203111237195,0.0619,,0.9831,,,0.0,0.1379,0.1667,,0.0,,0.0582,,0.0,0.063,,0.9831,,,0.0,0.1379,0.1667,,0.0,,0.0607,,0.0,0.0625,,0.9831,,,0.0,0.1379,0.1667,,0.0,,0.0593,,0.0,,block of flats,0.0608,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1476.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n432687,0,Cash loans,M,Y,Y,2,225000.0,450000.0,30204.0,450000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.026392,-14656,-1106,-6051.0,-4041,4.0,1,1,0,1,0,0,High skill tech staff,4.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.6878539085158113,0.5726825047161584,0.1206,,0.9811,,,0.0,0.2069,0.1667,,,,0.1021,,0.0,0.1229,,0.9811,,,0.0,0.2069,0.1667,,,,0.1063,,0.0,0.1218,,0.9811,,,0.0,0.2069,0.1667,,,,0.1039,,0.0,,block of flats,0.0803,Panel,No,1.0,1.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,1.0,5.0\n380752,0,Cash loans,M,N,Y,1,157500.0,417024.0,25330.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-14956,-1669,-4868.0,-4294,,1,1,0,1,0,0,Laborers,3.0,2,2,SUNDAY,13,0,0,0,0,1,1,Construction,,0.6653575491349416,0.2851799046358216,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n223140,0,Revolving loans,M,N,Y,0,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-12923,-1199,-6916.0,-3922,,1,1,0,0,0,0,Security staff,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Medicine,,0.6476772318053992,,0.0072,,0.9523,,,,0.0345,0.0417,,,,0.0031,,,0.0074,,0.9523,,,,0.0345,0.0417,,,,0.0032,,,0.0073,,0.9523,,,,0.0345,0.0417,,,,0.0032,,,,block of flats,0.004,\"Stone, brick\",No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n226431,1,Cash loans,F,N,Y,2,135000.0,528633.0,26991.0,472500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.019689,-14480,-1989,-7258.0,-4321,,1,1,0,1,0,0,Core staff,4.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Other,,0.6469533441719847,0.0785361445733672,0.0918,0.0922,0.9851,0.7959999999999999,0.0162,0.0,0.2069,0.1667,0.2083,0.195,0.0748,0.0775,0.0,0.0,0.0935,0.0957,0.9851,0.804,0.0163,0.0,0.2069,0.1667,0.2083,0.1994,0.0817,0.0807,0.0,0.0,0.0926,0.0922,0.9851,0.7987,0.0163,0.0,0.2069,0.1667,0.2083,0.1984,0.0761,0.0788,0.0,0.0,reg oper account,block of flats,0.0765,Panel,No,9.0,2.0,9.0,2.0,-67.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n232810,0,Cash loans,F,N,Y,0,67500.0,124380.0,13194.0,112500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.022625,-22483,365243,-3221.0,-3221,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,0.8020119807241739,0.41030898774923547,0.2650494299443805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1216.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n143020,0,Revolving loans,F,N,Y,0,130500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.04622,-19417,-142,-3444.0,-2279,,1,1,0,1,0,0,Cleaning staff,1.0,1,1,TUESDAY,16,0,0,0,0,1,1,Business Entity Type 3,,0.2551462596958946,0.08336419774067509,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-569.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n305664,0,Cash loans,M,Y,N,0,315000.0,573408.0,24232.5,495000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,Rented apartment,0.014464,-8531,-1127,-1559.0,-1158,10.0,1,1,0,1,0,0,Cooking staff,2.0,2,2,SATURDAY,9,0,0,0,0,1,1,Security Ministries,0.2341757048269633,0.19404406029613924,,0.0825,0.0796,0.9781,0.7008,0.0079,0.0,0.1379,0.1667,0.2083,0.0541,0.0664,0.0688,0.0039,0.004,0.084,0.0826,0.9782,0.7125,0.008,0.0,0.1379,0.1667,0.2083,0.0554,0.0725,0.0717,0.0039,0.0043,0.0833,0.0796,0.9781,0.7048,0.008,0.0,0.1379,0.1667,0.2083,0.0551,0.0676,0.07,0.0039,0.0041,reg oper account,block of flats,0.0593,Panel,No,2.0,0.0,2.0,0.0,-1127.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n417588,0,Cash loans,F,Y,N,0,180000.0,1006920.0,40063.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.019101,-15237,-4730,-2344.0,-2113,11.0,1,1,0,1,1,0,Managers,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Self-employed,0.5953911857564613,0.4882611493398778,0.6577838002083306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n274318,0,Cash loans,F,N,N,0,450000.0,1125000.0,36292.5,1125000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.04622,-19846,-1979,-2059.0,-2666,,1,1,1,1,1,0,Accountants,1.0,1,1,THURSDAY,12,0,1,1,0,1,1,Business Entity Type 1,0.7204342998884934,0.7120336850274888,0.324891229465852,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-477.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n299299,0,Cash loans,F,N,Y,1,99000.0,225000.0,10953.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.031329,-15964,-6144,-2796.0,-4903,,1,1,0,1,1,0,Laborers,3.0,2,2,MONDAY,13,0,0,0,0,1,1,Agriculture,,0.4395454789104932,0.5726825047161584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-441.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,5.0\n146143,0,Revolving loans,F,N,Y,0,157500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-7825,-928,-1074.0,-342,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,16,0,0,0,0,1,1,Business Entity Type 3,0.3071211800301792,0.6067064101527617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-320.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n287451,0,Cash loans,F,N,Y,0,135000.0,545040.0,20677.5,450000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-23758,365243,-11171.0,-4049,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.5880905473261308,0.5100895276257282,0.2227,0.0,0.9851,0.7959999999999999,0.0536,0.24,0.2069,0.3333,0.375,0.0303,0.1782,0.2136,0.0154,0.0383,0.2269,0.0,0.9851,0.804,0.0541,0.2417,0.2069,0.3333,0.375,0.031,0.1947,0.2226,0.0156,0.0406,0.2248,0.0,0.9851,0.7987,0.0539,0.24,0.2069,0.3333,0.375,0.0308,0.1813,0.2175,0.0155,0.0391,reg oper account,block of flats,0.2066,Panel,No,1.0,1.0,1.0,1.0,-1519.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n145030,0,Cash loans,M,N,Y,1,180000.0,840996.0,29925.0,702000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-14538,-5049,-7288.0,-5124,,1,1,0,1,0,0,Cooking staff,3.0,1,1,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,,0.6843028026934993,0.6006575372857061,0.2474,0.0,0.9871,0.8232,0.0,0.24,0.2069,0.375,0.4167,0.0,0.2017,0.2634,0.0,0.0,0.2521,0.0,0.9871,0.8301,0.0,0.2417,0.2069,0.375,0.4167,0.0,0.2204,0.2745,0.0,0.0,0.2498,0.0,0.9871,0.8256,0.0,0.24,0.2069,0.375,0.4167,0.0,0.2052,0.2682,0.0,0.0,reg oper account,block of flats,0.2072,Panel,No,0.0,0.0,0.0,0.0,-515.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n254574,0,Cash loans,M,N,Y,0,270000.0,314100.0,16033.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-19620,-12478,-1495.0,-3020,,1,1,0,1,0,0,,2.0,1,1,FRIDAY,18,0,0,0,0,0,0,Business Entity Type 1,,0.5331699521356871,0.324891229465852,0.0892,0.0612,0.9831,0.5988,0.0432,0.04,0.1379,0.25,0.2917,0.1031,0.0723,0.0886,0.0019,0.015,0.0756,0.0462,0.9767,0.5426,0.0384,0.0,0.069,0.1667,0.2083,0.0656,0.0661,0.0834,0.0,0.0,0.09,0.0612,0.9831,0.6042,0.0435,0.04,0.1379,0.25,0.2917,0.1049,0.0735,0.0902,0.0019,0.0153,reg oper account,block of flats,0.063,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n171958,0,Cash loans,M,Y,Y,2,90000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011703,-17206,-292,-6741.0,-754,2.0,1,1,0,1,1,0,Laborers,4.0,2,2,MONDAY,14,0,1,1,0,0,0,Construction,,0.6294706397870851,0.746300213050371,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2837.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n353792,0,Revolving loans,F,N,Y,0,112500.0,337500.0,16875.0,,,Working,Secondary / secondary special,Widow,House / apartment,0.003818,-23141,-5088,-10574.0,-4053,,1,1,1,1,1,0,Cleaning staff,1.0,2,2,TUESDAY,3,0,0,0,0,0,0,School,,0.34231331525544084,,0.0093,0.0224,0.9707,,,0.0,0.0345,0.0833,,0.0282,,0.0114,,0.0167,0.0095,0.0233,0.9707,,,0.0,0.0345,0.0833,,0.0288,,0.0119,,0.0176,0.0094,0.0224,0.9707,,,0.0,0.0345,0.0833,,0.0287,,0.0116,,0.017,,block of flats,0.0126,Block,No,0.0,0.0,0.0,0.0,-1138.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n391460,1,Cash loans,F,N,N,0,81000.0,268164.0,13171.5,175500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.008019,-12395,-1234,-6537.0,-2777,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.523388582264369,0.06643917173404808,0.0309,0.0656,0.9881,0.8368,0.0141,0.0,0.069,0.1667,0.0417,0.0277,0.0252,0.0176,0.0,0.0,0.0315,0.0681,0.9881,0.8432,0.0142,0.0,0.069,0.1667,0.0417,0.0283,0.0275,0.0183,0.0,0.0,0.0312,0.0656,0.9881,0.8390000000000001,0.0141,0.0,0.069,0.1667,0.0417,0.0282,0.0257,0.0179,0.0,0.0,reg oper account,block of flats,0.0233,Panel,No,0.0,0.0,0.0,0.0,-1203.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n373334,0,Cash loans,F,N,N,0,72000.0,808650.0,23643.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-19171,-594,-8770.0,-2693,,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,University,,0.029758256306035062,0.41184855592423975,0.0773,0.0782,0.9846,0.7892,0.0109,0.0,0.1724,0.1667,0.2083,0.0393,0.063,0.0689,0.0,0.0,0.0788,0.0811,0.9846,0.7975,0.011000000000000001,0.0,0.1724,0.1667,0.2083,0.0402,0.0689,0.0718,0.0,0.0,0.0781,0.0782,0.9846,0.792,0.011000000000000001,0.0,0.1724,0.1667,0.2083,0.0399,0.0641,0.0701,0.0,0.0,not specified,block of flats,0.0602,Panel,No,0.0,0.0,0.0,0.0,-56.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n339333,0,Cash loans,M,Y,Y,0,157500.0,879480.0,28498.5,630000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-18397,365243,-9612.0,-1920,8.0,1,0,0,1,0,0,,2.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.5503610519861771,0.5691487713619409,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2398.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n311430,1,Revolving loans,M,Y,N,2,225000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-11378,-1503,-2957.0,-164,3.0,1,1,0,1,0,0,Drivers,4.0,2,2,MONDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.2920170781187489,0.5549467685334323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1477.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n266450,0,Cash loans,M,Y,N,0,225000.0,675000.0,29731.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.011657,-21799,365243,-6088.0,-4173,5.0,1,0,0,1,1,0,,2.0,1,1,FRIDAY,14,0,0,0,0,0,0,XNA,,0.6440911755524878,0.6446794549585961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2380.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n322844,1,Cash loans,F,N,Y,1,135000.0,187704.0,11992.5,148500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00963,-11158,-1150,-5287.0,-3040,,1,1,0,1,1,0,Cooking staff,3.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.20356537249770892,0.633031641417419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-696.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n363029,0,Cash loans,F,N,N,1,135000.0,514777.5,28872.0,477000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.010276,-10880,-282,-1004.0,-2252,,1,1,0,1,0,0,,3.0,2,2,MONDAY,13,0,0,0,1,1,0,Business Entity Type 3,0.6461419677918899,0.5519623089613505,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,0.0,-787.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n158333,0,Revolving loans,F,N,Y,0,202500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-22340,-1474,-2726.0,-2736,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Cleaning,0.5783678552007768,0.5230176339561549,0.06126811384928985,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-361.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n387696,0,Cash loans,F,N,Y,0,121500.0,472500.0,44991.0,454500.0,\"Spouse, partner\",Pensioner,Incomplete higher,Married,House / apartment,0.008575,-21151,365243,-11875.0,-4203,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.7679235800558927,0.8327850252992314,0.1031,0.1217,0.9776,,,0.0,0.2069,0.1667,,0.0594,,0.0907,,0.0024,0.105,0.1137,0.9777,,,0.0,0.2069,0.1667,,0.0,,0.0928,,0.0,0.1041,0.1096,0.9776,,,0.0,0.2069,0.1667,,0.0602,,0.0931,,0.0,,block of flats,0.0716,Block,No,0.0,0.0,0.0,0.0,-1108.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n114806,0,Cash loans,F,N,Y,0,37350.0,117162.0,6669.0,103500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.031329,-20873,365243,-6298.0,-3015,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.6742320292007714,0.511891801533151,0.0619,0.0605,0.9821,0.7552,0.0095,0.0,0.1379,0.1667,0.0417,0.0308,0.0504,0.0516,0.0,0.0,0.063,0.0628,0.9821,0.7648,0.0095,0.0,0.1379,0.1667,0.0417,0.0315,0.0551,0.0538,0.0,0.0,0.0625,0.0605,0.9821,0.7585,0.0095,0.0,0.1379,0.1667,0.0417,0.0314,0.0513,0.0526,0.0,0.0,reg oper spec account,block of flats,0.0406,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n342979,0,Revolving loans,F,N,N,2,157500.0,202500.0,10125.0,202500.0,Family,Working,Higher education,Married,House / apartment,0.015221,-16785,-7317,-8808.0,-280,,1,1,0,1,0,0,Core staff,4.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,School,0.5979885362612725,0.4154189670005083,0.6910212267577837,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-577.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n139726,0,Revolving loans,F,Y,Y,1,90000.0,337500.0,16875.0,337500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.020713,-11295,-473,-4144.0,-1253,12.0,1,1,0,1,0,0,Accountants,3.0,3,3,THURSDAY,13,0,0,0,1,1,0,Business Entity Type 3,0.4950815800535465,0.5166096654745793,0.5334816299804352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-602.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n213060,0,Cash loans,F,N,Y,0,180000.0,315000.0,13473.0,315000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.018209,-17303,-1433,-11106.0,-842,,1,1,0,1,0,0,Managers,1.0,3,3,FRIDAY,14,0,0,0,0,0,0,Self-employed,,0.3057347613043059,,0.1454,0.1366,0.9767,0.6804,0.0596,0.0,0.2414,0.1667,0.2083,0.1118,0.1152,0.1188,0.0154,0.0193,0.1481,0.1417,0.9767,0.6929,0.0601,0.0,0.2414,0.1667,0.2083,0.1143,0.1258,0.1238,0.0156,0.0204,0.1468,0.1366,0.9767,0.6847,0.0599,0.0,0.2414,0.1667,0.2083,0.1137,0.1171,0.1209,0.0155,0.0197,,block of flats,0.1302,Panel,No,0.0,0.0,0.0,0.0,-925.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n229636,0,Cash loans,M,N,N,1,180000.0,417915.0,30411.0,378000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-10054,-571,-3435.0,-2718,,1,1,1,1,1,1,Drivers,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.5963256641259872,0.7745934340795098,0.646329897706246,0.0758,0.0698,0.9871,0.8232,0.0093,0.0,0.1379,0.1667,0.0417,0.1401,0.0618,0.0362,0.0,0.0,0.0725,0.0633,0.9871,0.8301,0.0088,0.0,0.1379,0.1667,0.0417,0.0532,0.0634,0.0376,0.0,0.0,0.0765,0.0698,0.9871,0.8256,0.0094,0.0,0.1379,0.1667,0.0417,0.1425,0.0628,0.0369,0.0,0.0,reg oper account,block of flats,0.0507,Panel,No,2.0,0.0,2.0,0.0,-419.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n290629,1,Cash loans,M,Y,Y,2,112500.0,595273.5,27882.0,418500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-11933,-463,-5615.0,-3107,8.0,1,1,0,1,0,0,Drivers,4.0,2,2,THURSDAY,15,0,0,0,0,0,0,Self-employed,,0.3542247319929012,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1734.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n118657,0,Cash loans,F,N,Y,0,225000.0,755190.0,33394.5,675000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.01885,-22536,365243,-14112.0,-4635,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,0.8854025482325139,0.6479765734622346,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-813.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n450499,0,Cash loans,M,Y,Y,0,202500.0,327024.0,23827.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-17823,-3133,-8249.0,-1363,23.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Self-employed,,0.7888541506785371,0.5814837058057234,0.1649,0.1027,0.9886,0.8436,0.0501,0.08,0.069,0.3333,0.375,0.1046,0.1345,0.1291,0.0,0.0103,0.1681,0.1065,0.9886,0.8497,0.0506,0.0806,0.069,0.3333,0.375,0.107,0.1469,0.1345,0.0,0.0109,0.1665,0.1027,0.9886,0.8457,0.0504,0.08,0.069,0.3333,0.375,0.1065,0.1368,0.1314,0.0,0.0106,,block of flats,0.1038,Panel,No,4.0,0.0,4.0,0.0,-271.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n226001,0,Cash loans,F,N,Y,0,144000.0,127350.0,13045.5,112500.0,Family,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.00963,-24570,365243,-11243.0,-4545,,1,0,0,1,1,0,,1.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.1222316876337766,0.5971924268337128,0.0722,0.0825,0.9846,,,0.0,0.1724,0.1667,,,,0.0708,,0.0,0.0735,0.0856,0.9846,,,0.0,0.1724,0.1667,,,,0.0738,,0.0,0.0729,0.0825,0.9846,,,0.0,0.1724,0.1667,,,,0.0721,,0.0,,block of flats,0.0557,Panel,No,3.0,0.0,3.0,0.0,-138.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n120376,0,Cash loans,F,N,Y,0,67500.0,95940.0,9616.5,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.020246,-24536,365243,-12859.0,-4633,,1,0,0,1,0,0,,1.0,3,3,THURSDAY,7,0,0,0,0,0,0,XNA,,0.6111015420013663,0.4014074137749511,0.0753,0.0811,0.9742,0.6464,0.0075,0.0,0.1379,0.1667,0.2083,0.064,0.0538,0.0296,0.0347,0.06,0.0767,0.0842,0.9742,0.6602,0.0076,0.0,0.1379,0.1667,0.2083,0.0655,0.0588,0.0308,0.035,0.0635,0.076,0.0811,0.9742,0.6511,0.0076,0.0,0.1379,0.1667,0.2083,0.0651,0.0547,0.0301,0.0349,0.0612,reg oper account,block of flats,0.0363,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-489.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n366695,0,Cash loans,F,Y,Y,0,265500.0,760225.5,30280.5,679500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.072508,-23063,365243,-796.0,-4162,8.0,1,0,0,1,1,0,,2.0,1,1,SUNDAY,11,1,0,0,0,0,0,XNA,,0.7324645328973032,0.6577838002083306,0.3289,0.1409,0.9871,0.8232,0.1863,0.4,0.1724,0.6667,0.0417,0.0,0.2681,0.3509,0.0,0.05,0.3351,0.1462,0.9871,0.8301,0.188,0.4028,0.1724,0.6667,0.0417,0.0,0.2929,0.3656,0.0,0.0529,0.3321,0.1409,0.9871,0.8256,0.1875,0.4,0.1724,0.6667,0.0417,0.0,0.2728,0.3572,0.0,0.0511,reg oper account,block of flats,0.3166,Panel,No,0.0,0.0,0.0,0.0,-1489.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n450165,1,Cash loans,F,N,N,0,130500.0,808650.0,26086.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.015221,-13484,-2030,-456.0,-2787,,1,1,1,1,0,0,Core staff,1.0,2,2,SUNDAY,11,0,0,0,0,0,0,Trade: type 3,,0.5321520051704889,0.1301285429480269,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-846.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n199375,0,Cash loans,M,Y,Y,0,112500.0,1311066.0,50067.0,1206000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-20945,365243,-10690.0,-4233,18.0,1,0,0,1,0,0,,2.0,3,3,TUESDAY,9,0,0,0,0,0,0,XNA,,0.5735196252732798,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-1327.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n193422,0,Cash loans,F,Y,Y,0,207000.0,840951.0,35761.5,679500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.072508,-20860,365243,-4117.0,-4388,3.0,1,0,0,1,0,0,,2.0,1,1,THURSDAY,14,0,0,0,0,0,0,XNA,,0.6408865710880006,0.5620604831738043,0.1918,0.1287,0.9935,0.9116,0.1816,0.36,0.1552,0.4583,0.5,0.1789,0.1538,0.2188,0.4266,0.0669,0.1712,0.1043,0.9935,0.9151,0.1615,0.3222,0.1379,0.4583,0.5,0.1286,0.1442,0.1986,0.0233,0.063,0.1936,0.1287,0.9935,0.9128,0.1827,0.36,0.1552,0.4583,0.5,0.1821,0.1565,0.2227,0.4289,0.0683,reg oper spec account,block of flats,0.1629,Panel,No,1.0,0.0,1.0,0.0,-2472.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n393922,0,Cash loans,M,N,N,0,225000.0,753264.0,73381.5,720000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006670999999999999,-10530,-1329,-1527.0,-3212,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,Government,,0.6215207476323418,0.8193176922872417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1039.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n414433,0,Cash loans,M,Y,Y,0,247500.0,521280.0,31630.5,450000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010147,-11315,-2479,-650.0,-2912,11.0,1,1,1,1,0,0,Laborers,2.0,2,2,WEDNESDAY,16,0,1,1,0,1,1,Transport: type 4,,0.6423447885238998,0.6545292802242897,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n406225,0,Cash loans,M,Y,Y,1,180000.0,276277.5,20785.5,238500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.020246,-12613,-2754,-828.0,-1073,11.0,1,1,0,1,0,0,Managers,3.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Military,,0.6581133409051391,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,2.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n280471,0,Cash loans,F,N,N,0,270000.0,450000.0,35685.0,450000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.032561,-13007,-2416,-3984.0,-4123,,1,1,1,1,1,0,,1.0,1,1,MONDAY,11,0,0,0,0,0,0,Business Entity Type 1,,0.6506703665397238,0.08064954432143595,0.3155,,0.9786,0.7144,0.0,0.04,0.2759,0.3333,0.375,0.0,0.2572,0.3147,0.0,0.0903,0.3214,,0.9782,0.7256,0.0,0.0403,0.2759,0.3333,0.375,0.0,0.281,0.3186,0.0,0.0956,0.3185,,0.9786,0.7182,0.0,0.04,0.2759,0.3333,0.375,0.0,0.2617,0.3204,0.0,0.0922,reg oper account,,0.2546,Panel,No,1.0,1.0,1.0,1.0,-1740.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n114536,0,Cash loans,M,N,Y,2,247500.0,787131.0,26145.0,679500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-12906,-731,-6601.0,-3203,,1,1,0,1,0,0,Drivers,4.0,2,2,THURSDAY,15,0,0,0,1,1,0,Self-employed,0.3991599836295081,0.28092838920213464,0.18629294965553744,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-325.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n121741,0,Cash loans,M,N,Y,1,252000.0,824823.0,26739.0,688500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.007114,-12206,-4348,-6209.0,-4206,,1,1,0,1,1,0,Managers,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Security Ministries,0.2769021961889053,0.4953824880333326,0.5136937663039473,0.0928,0.0925,0.9811,0.7416,0.0136,0.0,0.2069,0.1667,0.2083,0.0176,0.0756,0.0781,0.0,0.0,0.0945,0.096,0.9811,0.7517,0.0137,0.0,0.2069,0.1667,0.2083,0.018000000000000002,0.0826,0.0814,0.0,0.0,0.0937,0.0925,0.9811,0.7451,0.0137,0.0,0.2069,0.1667,0.2083,0.0179,0.077,0.0795,0.0,0.0,reg oper account,block of flats,0.0689,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n247807,0,Revolving loans,F,N,N,0,121500.0,270000.0,13500.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.003818,-20518,365243,-2343.0,-4054,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,3,0,0,0,0,0,0,XNA,,0.5941896393339847,0.7194907850918436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-224.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n301843,0,Cash loans,M,Y,Y,2,225000.0,481495.5,36130.5,454500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006296,-16217,-474,-6.0,-11,8.0,1,1,0,1,0,0,Laborers,4.0,3,3,FRIDAY,10,0,0,0,0,0,0,Transport: type 4,0.2960163557202561,0.6745201472412375,0.2735646775174348,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-889.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n197884,0,Cash loans,M,N,N,0,225000.0,1267735.5,37197.0,1107000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-16793,-616,-1012.0,-347,,1,1,0,1,1,0,Laborers,2.0,3,3,FRIDAY,13,0,0,0,0,0,0,Other,0.5854688658812975,0.3824930145972731,0.6058362647264226,0.0928,0.0874,0.9811,0.7416,0.0,0.0,0.2069,0.1667,0.2083,0.1017,0.0748,0.0871,0.0039,0.0045,0.0945,0.0907,0.9811,0.7517,0.0,0.0,0.2069,0.1667,0.2083,0.10400000000000001,0.0817,0.0908,0.0039,0.0047,0.0937,0.0874,0.9811,0.7451,0.0,0.0,0.2069,0.1667,0.2083,0.1035,0.0761,0.0887,0.0039,0.0046,reg oper spec account,block of flats,0.0772,Panel,No,0.0,0.0,0.0,0.0,-1752.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n332217,0,Cash loans,F,N,Y,0,202500.0,314100.0,19111.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-16560,-4776,-1783.0,-97,,1,1,0,1,0,0,Secretaries,2.0,2,2,MONDAY,11,0,0,0,0,0,0,Medicine,0.6843585720384526,0.6367492967493968,0.2471909382666521,0.5392,0.0,0.9876,0.83,0.0,0.52,0.4483,0.375,0.4167,0.0,0.4396,0.5699,0.0,0.0242,0.5494,0.0,0.9876,0.8367,0.0,0.5236,0.4483,0.375,0.4167,0.0,0.4803,0.5938,0.0,0.0256,0.5444,0.0,0.9876,0.8323,0.0,0.52,0.4483,0.375,0.4167,0.0,0.4472,0.5801,0.0,0.0247,reg oper account,block of flats,0.4535,Panel,No,5.0,1.0,5.0,0.0,-1568.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n240711,0,Cash loans,F,Y,Y,0,180000.0,1006920.0,45499.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-14567,-4848,-3089.0,-4220,5.0,1,1,1,1,1,0,Accountants,2.0,2,2,THURSDAY,12,0,0,0,0,0,0,Other,,0.6627525198687305,0.7407990879702335,0.0247,0.0,0.9732,0.6328,,0.0,0.069,0.0833,,0.0259,,0.0163,,0.0,0.0252,0.0,0.9732,0.6472,,0.0,0.069,0.0833,,0.0265,,0.017,,0.0,0.025,0.0,0.9732,0.6377,,0.0,0.069,0.0833,,0.0264,,0.0166,,0.0,,block of flats,0.0138,Block,No,1.0,0.0,1.0,0.0,-1498.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,4.0\n109441,0,Cash loans,M,N,Y,0,67500.0,117162.0,7825.5,103500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007305,-8654,-205,-32.0,-1082,,1,1,1,1,0,0,Laborers,2.0,3,3,SATURDAY,13,0,0,0,0,0,0,Self-employed,0.1260596165282635,0.16960772844741515,,0.0619,0.0611,0.9836,,,0.0,0.1379,0.1667,,0.0229,,0.0522,,,0.063,0.0634,0.9836,,,0.0,0.1379,0.1667,,0.0234,,0.0544,,,0.0625,0.0611,0.9836,,,0.0,0.1379,0.1667,,0.0233,,0.0532,,,,block of flats,0.0411,Panel,No,0.0,0.0,0.0,0.0,-587.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n433278,0,Cash loans,F,N,N,0,130500.0,942300.0,30528.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17295,-1290,-11335.0,-743,,1,1,0,1,1,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,Housing,0.8028080253525938,0.5869776861802833,0.6041125998015721,,,0.9781,,,,,,,,,0.1212,,,,,0.9782,,,,,,,,,0.1263,,,,,0.9781,,,,,,,,,0.1234,,,,,0.0953,,No,0.0,0.0,0.0,0.0,-1794.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n129710,0,Cash loans,F,N,Y,1,171000.0,1787283.0,49279.5,1597500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.006852,-10266,-1555,-938.0,-1603,,1,1,0,1,1,0,Sales staff,3.0,3,3,THURSDAY,5,0,0,0,0,0,0,Business Entity Type 3,0.4201230026717426,0.10636206390936352,,0.1021,0.1429,0.9836,0.7756,0.0626,0.0,0.2069,0.1667,0.2083,0.0871,0.0832,0.1063,0.0,0.0,0.10400000000000001,0.1483,0.9836,0.7844,0.0632,0.0,0.2069,0.1667,0.2083,0.0891,0.0909,0.1108,0.0,0.0,0.1031,0.1429,0.9836,0.7786,0.063,0.0,0.2069,0.1667,0.2083,0.0886,0.0847,0.1082,0.0,0.0,not specified,block of flats,0.1178,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n381231,0,Cash loans,M,Y,Y,0,180000.0,1317357.0,55944.0,1129500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-11343,-708,-5364.0,-3125,12.0,1,1,0,1,0,0,Laborers,2.0,1,1,FRIDAY,14,0,0,0,0,1,1,Industry: type 1,0.17242176403994555,0.7424596535755943,0.4992720153045617,0.0711,,0.9717,,,0.0,0.1379,0.1667,,,,0.08800000000000001,,,0.0725,,0.9717,,,0.0,0.1379,0.1667,,,,0.0917,,,0.0718,,0.9717,,,0.0,0.1379,0.1667,,,,0.0896,,,,block of flats,0.0692,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1449.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,6.0\n374352,0,Cash loans,M,Y,Y,0,225000.0,473760.0,51151.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018801,-9494,-163,-4074.0,-2171,18.0,1,1,0,1,1,0,Drivers,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,Construction,,0.5499389239561607,0.5154953751603267,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n437898,0,Cash loans,M,Y,Y,1,225000.0,521280.0,35392.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.04622,-14636,-852,-8593.0,-4667,9.0,1,1,0,1,0,0,Drivers,2.0,1,1,MONDAY,17,0,0,0,0,0,0,Business Entity Type 3,,0.6515049421608577,,0.0165,0.0,0.9687,0.5716,0.0022,0.0,0.069,0.0417,0.0833,,0.0134,0.0136,0.0,0.0,0.0168,0.0,0.9687,0.5884,0.0022,0.0,0.069,0.0417,0.0833,,0.0147,0.0142,0.0,0.0,0.0167,0.0,0.9687,0.5773,0.0022,0.0,0.069,0.0417,0.0833,,0.0137,0.0138,0.0,0.0,not specified,block of flats,0.0134,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-124.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n160007,0,Cash loans,M,N,Y,0,135000.0,538389.0,43294.5,477000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.020246,-11418,-1344,-5232.0,-3759,,1,1,0,1,0,0,Sales staff,1.0,3,3,MONDAY,8,0,0,0,1,1,0,Self-employed,0.06508959393656381,0.46261263047906304,0.8128226070575616,0.0371,0.0771,0.9801,0.728,0.0043,0.0,0.1034,0.0833,0.0417,0.0,0.0303,0.0352,0.0,0.0,0.0378,0.08,0.9801,0.7387,0.0043,0.0,0.1034,0.0833,0.0417,0.0,0.0331,0.0367,0.0,0.0,0.0375,0.0771,0.9801,0.7316,0.0043,0.0,0.1034,0.0833,0.0417,0.0,0.0308,0.0358,0.0,0.0,org spec account,block of flats,0.03,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-745.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n428649,0,Cash loans,F,N,Y,0,157500.0,1260702.0,39712.5,1129500.0,Unaccompanied,State servant,Secondary / secondary special,Widow,House / apartment,0.030755,-19306,-313,-8910.0,-2855,,1,1,0,1,0,0,Medicine staff,1.0,2,2,MONDAY,16,0,0,0,0,0,0,Medicine,,0.6102697039845575,0.8193176922872417,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-3042.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n143681,0,Revolving loans,F,N,Y,0,58500.0,180000.0,9000.0,180000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.009657,-22038,365243,-8321.0,-3798,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,17,0,0,0,0,0,0,XNA,,0.3634261774971311,0.07396514611722674,0.001,,0.9737,,,,0.0345,0.0,,,,0.0009,,,0.0011,,0.9737,,,,0.0345,0.0,,,,0.001,,,0.001,,0.9737,,,,0.0345,0.0,,,,0.0009,,,,,0.0007,Wooden,No,0.0,0.0,0.0,0.0,-2152.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n124069,0,Cash loans,F,N,Y,0,112500.0,157500.0,8181.0,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.018209,-22085,365243,-3744.0,-4679,,1,0,0,1,0,0,,1.0,3,3,MONDAY,12,0,0,0,0,0,0,XNA,0.7014085283133874,0.2298535073078563,0.363945238612397,0.0773,0.0752,0.9836,0.7756,0.0164,0.0,0.2069,0.1667,0.0417,0.0687,0.0622,0.0898,0.0039,0.0058,0.0788,0.078,0.9836,0.7844,0.0166,0.0,0.2069,0.1667,0.0417,0.0703,0.068,0.0936,0.0039,0.0061,0.0781,0.0752,0.9836,0.7786,0.0165,0.0,0.2069,0.1667,0.0417,0.0699,0.0633,0.0915,0.0039,0.0059,reg oper account,block of flats,0.0707,Panel,No,3.0,0.0,3.0,0.0,-2473.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n124115,0,Cash loans,F,N,Y,0,67500.0,545040.0,25537.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.0228,-8136,-1526,-682.0,-798,,1,1,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Transport: type 4,0.2622068505202944,,0.4776491548517548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-506.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n391069,0,Revolving loans,F,N,N,0,90000.0,247500.0,12375.0,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.016612000000000002,-17926,-913,-3851.0,-1475,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,0.7664254448820994,0.6566160288246851,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-244.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n355972,1,Cash loans,F,Y,N,0,90000.0,508495.5,24592.5,454500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.020246,-10802,-3069,-1388.0,-3487,2.0,1,1,0,1,0,0,Core staff,2.0,3,3,THURSDAY,9,0,0,0,1,1,0,Government,,0.5057364053069627,0.3556387169923543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-464.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n413202,0,Cash loans,F,N,Y,0,135000.0,585000.0,25897.5,585000.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-21066,365243,-12642.0,-4312,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,,0.2809372241237319,0.2353105173615833,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-112.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,9.0\n346344,0,Cash loans,M,Y,Y,0,157500.0,239850.0,28593.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-14877,-2742,-2410.0,-2421,12.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Trade: type 7,,0.6311521726579918,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1544.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n138349,0,Cash loans,F,Y,N,1,337500.0,582768.0,31176.0,540000.0,Unaccompanied,State servant,Secondary / secondary special,Widow,Municipal apartment,0.072508,-16588,-3268,-11089.0,-146,12.0,1,1,0,1,1,1,Cleaning staff,2.0,1,1,WEDNESDAY,16,0,0,0,0,0,0,Military,0.8162890371599185,0.7257442903283441,0.7662336700704004,0.4433,0.3027,0.9796,0.7212,0.0006,0.48,0.4138,0.3333,0.375,0.0,0.3602,0.4268,0.0058,0.0014,0.4517,0.3142,0.9796,0.7321,0.0001,0.4834,0.4138,0.3333,0.375,0.0,0.39299999999999996,0.4436,0.0039,0.0002,0.4476,0.3027,0.9796,0.7249,0.0006,0.48,0.4138,0.3333,0.375,0.0,0.3664,0.4345,0.0058,0.0015,reg oper account,block of flats,0.3355,Panel,No,3.0,0.0,3.0,0.0,-1866.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n329681,0,Cash loans,F,N,Y,0,207000.0,463941.0,17320.5,400500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-19282,-884,-5670.0,-2741,,1,1,0,1,0,0,High skill tech staff,2.0,1,1,TUESDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.7449604858107184,0.2866524828090694,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-471.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n406980,1,Cash loans,F,N,N,1,90000.0,98910.0,7789.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-10665,-1422,-2554.0,-3326,,1,1,1,1,0,0,Sales staff,3.0,3,3,SUNDAY,9,0,0,0,0,1,1,Self-employed,,0.6533545735246364,0.1168672659157136,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1067.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n319398,0,Revolving loans,F,Y,Y,1,67500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008068,-11010,-641,-568.0,-958,28.0,1,1,0,1,0,0,Cooking staff,3.0,3,3,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 2,0.2556245179798408,0.049793625945435606,0.375711009574066,0.066,,0.9752,,,0.0,0.1379,0.1667,,,,,,,0.0672,,0.9752,,,0.0,0.1379,0.1667,,,,,,,0.0666,,0.9752,,,0.0,0.1379,0.1667,,,,,,,,,0.0397,,No,2.0,0.0,2.0,0.0,-353.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n230010,0,Cash loans,F,N,Y,2,121500.0,1099350.0,39078.0,787500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-14464,-738,-3494.0,-4295,,1,1,1,1,1,0,Core staff,4.0,2,2,TUESDAY,14,0,1,1,0,1,1,Business Entity Type 1,,0.10221986327924036,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-280.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n269894,0,Cash loans,F,N,Y,0,135000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-16164,-2502,-7968.0,-4008,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,16,0,0,0,0,0,0,Self-employed,,0.5320104510479761,0.4525335592581747,0.121,0.0901,0.9861,,,0.08,0.1724,0.2221,,0.0446,,0.1241,,0.0552,0.0609,0.0656,0.9851,,,0.0,0.1379,0.1667,,0.0163,,0.0598,,0.0279,0.0749,0.0759,0.9856,,,0.0,0.1724,0.1667,,0.0202,,0.0728,,0.0333,,block of flats,0.2228,Panel,No,0.0,0.0,0.0,0.0,-761.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n263447,0,Revolving loans,M,Y,N,0,135000.0,405000.0,20250.0,405000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,With parents,0.007305,-22338,365243,-529.0,-611,0.0,1,0,0,1,0,0,,2.0,3,3,FRIDAY,13,0,0,0,0,0,0,XNA,,0.04674176277961516,0.22133520635446602,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-229.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n156326,0,Cash loans,F,N,Y,0,108000.0,225000.0,12694.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-23879,365243,-995.0,-4320,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.3553560419432075,0.8193176922872417,0.0186,0.043,0.9826,0.762,0.0584,0.0,0.1034,0.0417,0.0833,0.0368,0.0151,0.01,0.0,0.0,0.0189,0.0446,0.9826,0.7713,0.0589,0.0,0.1034,0.0417,0.0833,0.0376,0.0165,0.0104,0.0,0.0,0.0187,0.043,0.9826,0.7652,0.0588,0.0,0.1034,0.0417,0.0833,0.0374,0.0154,0.0101,0.0,0.0,reg oper account,block of flats,0.0238,\"Stone, brick\",No,2.0,0.0,2.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n127968,0,Cash loans,F,N,Y,2,180000.0,531265.5,19210.5,373500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.004849,-12676,-5271,-1048.0,-3734,,1,1,0,1,1,0,Accountants,4.0,2,2,SATURDAY,13,0,0,0,0,1,1,Business Entity Type 3,,0.2040728224984732,0.7826078370261895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.0,0.0,9.0,0.0,-203.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n341082,1,Cash loans,M,Y,Y,1,207000.0,297130.5,23953.5,256500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-11797,-414,-250.0,-1792,2.0,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.2047780347792544,0.7156474027027563,0.1301285429480269,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-420.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n427411,0,Cash loans,F,N,Y,0,405000.0,1793677.5,62478.0,1638000.0,Unaccompanied,Pensioner,Incomplete higher,Separated,House / apartment,0.072508,-20654,365243,-13107.0,-4190,,1,0,0,1,1,0,,1.0,1,1,FRIDAY,16,0,0,0,0,0,0,XNA,0.8093733075199504,0.7392690268638484,0.29859498978739724,0.2959,0.1956,0.9816,0.7484,0.0002,0.32,0.2759,0.3333,0.375,0.0,0.2404,0.2827,0.0039,0.0043,0.3015,0.203,0.9816,0.7583,0.0002,0.3222,0.2759,0.3333,0.375,0.0,0.2626,0.2946,0.0039,0.0045,0.2987,0.1956,0.9816,0.7518,0.0002,0.32,0.2759,0.3333,0.375,0.0,0.2446,0.2878,0.0039,0.0044,reg oper account,block of flats,0.2234,Panel,No,0.0,0.0,0.0,0.0,-1079.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n381144,0,Cash loans,F,N,Y,0,67500.0,225000.0,11619.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-17145,-3222,-8987.0,-677,,1,1,0,1,0,0,Medicine staff,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Medicine,,0.5011991313703716,0.2250868621163805,,,0.9712,,,,,,,,,0.0085,,,,,0.9712,,,,,,,,,0.0088,,,,,0.9712,,,,,,,,,0.0086,,,,block of flats,0.0067,,Yes,0.0,0.0,0.0,0.0,-31.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n108782,0,Cash loans,F,Y,N,0,180000.0,1260000.0,40774.5,1260000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-11032,-1359,-770.0,-3661,8.0,1,1,1,1,1,1,Accountants,2.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.5332257052639251,0.6831253210717081,0.7380196196295241,0.0825,,0.9752,,,0.0,0.1379,0.1667,,,,0.0245,,0.1434,0.084,,0.9752,,,0.0,0.1379,0.1667,,,,0.0255,,0.1518,0.0833,,0.9752,,,0.0,0.1379,0.1667,,,,0.0249,,0.1464,,block of flats,0.0504,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1168.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n427614,0,Cash loans,F,Y,Y,3,450000.0,746280.0,59094.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-15403,-1693,-9532.0,-4208,15.0,1,1,0,1,1,0,Drivers,5.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.7460267774252729,,0.0722,0.0,0.9786,0.7076,,0.0,0.1379,0.1667,0.2083,0.0423,,0.0678,,0.0,0.0735,0.0,0.9786,0.7190000000000001,,0.0,0.1379,0.1667,0.2083,0.0433,,0.0707,,0.0,0.0729,0.0,0.9786,0.7115,,0.0,0.1379,0.1667,0.2083,0.043,,0.0691,,0.0,reg oper account,block of flats,0.0582,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-642.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n443814,0,Cash loans,F,N,N,0,112500.0,1035873.0,39456.0,837000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.019689,-10256,-1120,-2768.0,-2841,,1,1,0,1,1,0,Laborers,1.0,2,2,MONDAY,10,0,0,0,0,0,0,Business Entity Type 1,,0.5395377563078328,0.3556387169923543,0.2227,0.1792,0.9801,0.728,0.0564,0.24,0.2069,0.3333,0.375,0.1193,0.1816,0.2235,0.0,0.0,0.2269,0.1859,0.9801,0.7387,0.0569,0.2417,0.2069,0.3333,0.375,0.122,0.1983,0.2328,0.0,0.0,0.2248,0.1792,0.9801,0.7316,0.0567,0.24,0.2069,0.3333,0.375,0.1213,0.1847,0.2275,0.0,0.0,reg oper account,block of flats,0.2066,Panel,No,5.0,3.0,5.0,2.0,-990.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n102127,0,Cash loans,F,Y,Y,1,112500.0,454500.0,19255.5,454500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.028663,-21244,365243,-4592.0,-4615,64.0,1,0,0,1,0,1,,3.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,0.8510715702818568,0.3004231098209273,0.622922000268356,0.0464,0.0499,0.9921,0.8912,0.0214,0.0,0.1034,0.1667,0.2083,0.0,0.0378,0.0455,0.0,0.0,0.0473,0.0518,0.9921,0.8955,0.0216,0.0,0.1034,0.1667,0.2083,0.0,0.0413,0.0475,0.0,0.0,0.0468,0.0499,0.9921,0.8927,0.0216,0.0,0.1034,0.1667,0.2083,0.0,0.0385,0.0464,0.0,0.0,reg oper account,block of flats,0.0475,Block,No,5.0,0.0,5.0,0.0,-1659.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n203566,0,Cash loans,F,N,N,0,54000.0,288873.0,14179.5,238500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.0105,-18357,-1496,-4019.0,-1870,,1,1,0,1,0,0,Accountants,2.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Transport: type 2,,0.7035495302460412,0.4525335592581747,,,0.9593,,,,,,,,,0.003,,,,,0.9593,,,,,,,,,0.0031,,,,,0.9593,,,,,,,,,0.003,,,,block of flats,0.0033,,Yes,2.0,0.0,2.0,0.0,-260.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n396366,0,Cash loans,M,Y,Y,0,225000.0,1113840.0,44302.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-18787,-2659,-5975.0,-2328,8.0,1,1,0,1,0,0,Managers,2.0,1,1,SATURDAY,17,0,0,0,0,0,0,Transport: type 4,,0.7463456769452527,0.5495965024956946,0.1845,0.1182,0.9891,0.8504,0.0444,0.32,0.1379,0.4583,0.5,0.1962,0.1488,0.2148,0.0077,0.0054,0.188,0.1226,0.9891,0.8563,0.0448,0.3222,0.1379,0.4583,0.5,0.2007,0.1625,0.2238,0.0078,0.0058,0.1863,0.1182,0.9891,0.8524,0.0447,0.32,0.1379,0.4583,0.5,0.1996,0.1513,0.2187,0.0078,0.0055,reg oper account,block of flats,0.1944,Panel,No,0.0,0.0,0.0,0.0,-336.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,0.0\n284802,0,Cash loans,F,Y,Y,0,270000.0,1006920.0,42790.5,900000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.035792000000000004,-23357,365243,-7002.0,-4072,6.0,1,0,0,1,1,0,,2.0,2,2,MONDAY,16,0,0,0,0,0,0,XNA,0.9038556212583247,0.6171024320213905,0.7106743858828587,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.0,0.0,12.0,0.0,-714.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n143760,0,Revolving loans,F,N,N,0,67500.0,180000.0,9000.0,180000.0,Unaccompanied,State servant,Higher education,Civil marriage,House / apartment,0.019101,-9201,-181,-4879.0,-1813,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,Government,0.13999272634207274,0.6041153555329881,0.2418614865234661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,3.0,1.0,-358.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n264132,1,Cash loans,F,N,N,0,90000.0,161730.0,9769.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.006670999999999999,-19306,365243,-7754.0,-2772,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.3868252747352083,0.06126811384928985,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-96.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n393332,0,Cash loans,M,Y,Y,0,135000.0,595903.5,26248.5,481500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-21299,-2402,-3484.0,-4132,4.0,1,1,0,1,0,0,Security staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.7048113313247231,0.8245949709919925,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-496.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n455680,0,Cash loans,M,Y,Y,0,202500.0,798408.0,61785.0,720000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.072508,-17053,-774,-9240.0,-307,19.0,1,1,0,1,1,1,Laborers,1.0,1,1,TUESDAY,10,0,0,0,0,0,0,Transport: type 4,,0.7508560904213369,,0.1144,0.0568,0.9806,0.7348,0.044000000000000004,0.08,0.0345,0.625,0.0417,0.0,0.2538,0.1377,0.0,0.0022,0.1166,0.0589,0.9806,0.7452,0.0444,0.0806,0.0345,0.625,0.0417,0.0,0.2773,0.1435,0.0,0.0023,0.1155,0.0568,0.9806,0.7383,0.0442,0.08,0.0345,0.625,0.0417,0.0,0.2582,0.1402,0.0,0.0022,reg oper account,block of flats,0.1088,Panel,No,2.0,0.0,2.0,0.0,-2580.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n115286,0,Cash loans,M,N,Y,0,234000.0,916299.0,46912.5,819000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-9423,-192,-6290.0,-2104,,1,1,0,1,0,0,Managers,2.0,1,1,MONDAY,13,0,1,1,1,0,0,Business Entity Type 3,0.4009760733815578,0.5750700197561606,0.28961123838200553,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-800.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n311581,0,Cash loans,F,N,Y,2,112500.0,622188.0,26284.5,472500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006629,-11870,-1362,-4569.0,-2991,,1,1,0,1,0,0,Sales staff,4.0,2,2,SATURDAY,9,0,0,0,1,1,0,Self-employed,,0.6748465215398061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2152.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n376759,0,Cash loans,F,N,Y,1,112500.0,215640.0,11826.0,180000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.018634,-21243,365243,-1589.0,-2048,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.5299900321775584,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1141.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,5.0\n388111,0,Cash loans,M,Y,Y,0,135000.0,528633.0,24624.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.010556,-14679,-174,-4279.0,-4510,18.0,1,1,0,1,0,0,Drivers,1.0,3,3,FRIDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.6761934135934345,0.2512394458905693,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1082.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n193020,0,Cash loans,F,Y,N,1,117000.0,555273.0,21514.5,463500.0,Family,Working,Higher education,Married,House / apartment,0.020713,-11408,-2719,-638.0,-1848,10.0,1,1,0,1,0,0,Accountants,3.0,3,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.2956904131084037,0.4904742372573642,0.8327850252992314,0.4237,0.2805,0.9975,0.966,0.1219,0.36,0.3103,0.375,0.4167,0.2001,0.3404,0.4181,0.0232,0.0442,0.4317,0.2911,0.9975,0.9673,0.12300000000000001,0.3625,0.3103,0.375,0.4167,0.2047,0.3719,0.4356,0.0233,0.0468,0.4278,0.2805,0.9975,0.9665,0.1227,0.36,0.3103,0.375,0.4167,0.2036,0.3463,0.4256,0.0233,0.0451,reg oper account,block of flats,0.3385,Panel,No,0.0,0.0,0.0,0.0,-1108.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n198843,0,Cash loans,M,N,Y,0,225000.0,432000.0,19156.5,432000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.026392,-18533,-731,-11891.0,-1842,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.13789984718250872,0.679221582692578,0.7238369900414456,0.0979,0.0483,0.9836,,,0.08,0.0345,0.5417,,,,0.08900000000000001,,0.0701,0.0998,0.0501,0.9836,,,0.0806,0.0345,0.5417,,,,0.0927,,0.0742,0.0989,0.0483,0.9836,,,0.08,0.0345,0.5417,,,,0.0906,,0.0716,,block of flats,0.0855,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1169.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n387265,0,Cash loans,M,N,Y,1,270000.0,1350000.0,39604.5,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010032,-11051,-1571,-2239.0,-3705,,1,1,1,1,1,0,Laborers,3.0,2,2,WEDNESDAY,2,0,0,0,0,0,0,Business Entity Type 3,0.2230535053363065,0.6987061199003388,0.633031641417419,0.2505,,0.9965,,,0.2,0.1724,0.375,,,,0.244,,0.0115,0.2553,,0.9965,,,0.2014,0.1724,0.375,,,,0.2542,,0.0122,0.2529,,0.9965,,,0.2,0.1724,0.375,,,,0.2484,,0.0117,,block of flats,0.1919,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1717.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n274150,0,Cash loans,F,N,Y,1,135000.0,814041.0,23931.0,679500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-18183,-1080,-3546.0,-1713,,1,1,0,1,0,0,Sales staff,3.0,2,2,SATURDAY,13,0,0,0,0,0,0,Self-employed,,0.5788915109956365,0.5567274263630174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-285.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n134643,0,Revolving loans,M,Y,N,0,180000.0,180000.0,9000.0,180000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007114,-18349,365243,-2938.0,-1890,14.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.6604122590556669,0.6397075677637197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-1463.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n324698,0,Cash loans,F,Y,Y,3,157500.0,592560.0,26230.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.009549,-13453,-523,-3959.0,-3984,13.0,1,1,0,1,1,0,Core staff,5.0,2,2,SATURDAY,10,0,0,0,0,1,1,Kindergarten,0.6634820727731767,0.6334146526907348,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2147.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n416223,0,Cash loans,F,N,Y,0,63000.0,450000.0,25834.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006296,-19724,-10517,-4722.0,-3246,,1,1,1,1,1,0,Cleaning staff,2.0,3,3,SATURDAY,13,0,0,0,0,0,0,School,,0.3977672966879481,0.4830501881366946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-244.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n296899,0,Cash loans,M,Y,Y,0,202500.0,307944.0,34956.0,292500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.007273999999999998,-10077,-1055,-2861.0,-2730,4.0,1,1,1,1,1,0,Managers,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Transport: type 4,0.20127105325595776,0.5925909608187199,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2039.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n355585,0,Cash loans,F,Y,Y,0,225000.0,1223010.0,48631.5,1125000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.008865999999999999,-17998,-1905,-6286.0,-1547,14.0,1,1,0,1,0,0,Managers,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.26097420501205804,0.4543210601605785,0.0165,,0.9742,,,,,0.0417,,0.006999999999999999,,0.0127,,,0.0168,,0.9742,,,,,0.0417,,0.0072,,0.0133,,,0.0167,,0.9742,,,,,0.0417,,0.0071,,0.0129,,,,block of flats,0.0108,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n178220,0,Cash loans,F,Y,Y,0,94500.0,1035000.0,34335.0,1035000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018209,-15252,-2591,-7486.0,-4970,12.0,1,1,0,1,0,0,Sales staff,2.0,3,3,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.6595949292951765,0.4962079307001587,0.8245949709919925,0.1227,0.0813,0.9896,,,0.12,0.1034,0.375,,0.0483,,0.0167,,0.0078,0.125,0.0843,0.9896,,,0.1208,0.1034,0.375,,0.0494,,0.0174,,0.0083,0.1239,0.0813,0.9896,,,0.12,0.1034,0.375,,0.0491,,0.017,,0.008,,block of flats,0.09699999999999999,Panel,No,1.0,0.0,1.0,0.0,-604.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n443297,0,Revolving loans,F,N,N,1,292500.0,180000.0,9000.0,180000.0,Family,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.006629,-8434,-877,-2074.0,-1091,,1,1,1,1,1,0,Managers,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 2,0.340535185236356,0.6570300728408036,0.6263042766749393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-717.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n449432,0,Cash loans,F,N,Y,0,135000.0,348264.0,34056.0,315000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-14360,-5148,-6216.0,-4109,,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,School,0.7976076171379167,0.268460745677134,0.3656165070113335,0.0227,0.0572,0.9801,0.728,0.0016,0.0,0.1034,0.0417,0.0417,0.0264,0.0185,0.0181,0.0,0.0,0.0231,0.0593,0.9801,0.7387,0.0016,0.0,0.1034,0.0417,0.0417,0.027000000000000003,0.0202,0.0188,0.0,0.0,0.0229,0.0572,0.9801,0.7316,0.0016,0.0,0.1034,0.0417,0.0417,0.0269,0.0188,0.0184,0.0,0.0,reg oper account,block of flats,0.0151,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-43.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n148465,0,Cash loans,F,N,Y,1,94500.0,225000.0,13045.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.019689,-9406,-1105,-1847.0,-2093,,1,1,1,1,0,0,Sales staff,3.0,2,2,MONDAY,8,0,0,0,0,0,0,Self-employed,,0.6119327335600919,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-810.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n193017,0,Cash loans,F,N,Y,0,90000.0,225000.0,15349.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.0228,-8422,-1413,-8360.0,-1086,,1,1,0,1,1,0,,2.0,2,2,TUESDAY,11,0,0,0,1,1,0,Business Entity Type 3,0.02815218084319965,0.23714049148457364,0.2103502286944494,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-239.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n287730,1,Cash loans,F,N,Y,2,121500.0,545040.0,26509.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-14395,-1279,-1745.0,-163,,1,1,0,1,1,0,,4.0,3,3,FRIDAY,6,0,0,0,0,0,0,Business Entity Type 3,,0.5240213166676334,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1383.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n345173,0,Cash loans,M,N,Y,0,67500.0,288873.0,16258.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.020713,-15579,-133,-9687.0,-4526,,1,1,0,1,0,0,Cleaning staff,1.0,3,2,TUESDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.6821920732386632,,0.0825,0.0795,0.9762,0.6736,0.0095,0.0,0.1379,0.1667,0.2083,0.0639,0.0672,0.0702,0.0,0.0,0.084,0.0825,0.9762,0.6864,0.0096,0.0,0.1379,0.1667,0.2083,0.0654,0.0735,0.0731,0.0,0.0,0.0833,0.0795,0.9762,0.6779999999999999,0.0095,0.0,0.1379,0.1667,0.2083,0.065,0.0684,0.0715,0.0,0.0,not specified,block of flats,0.0604,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n333010,0,Cash loans,M,N,N,0,67500.0,598500.0,19435.5,598500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.035792000000000004,-16180,-1622,-8728.0,-4763,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,1,1,Other,,0.5810968697854202,0.4776491548517548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1294.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n450709,0,Cash loans,F,N,Y,0,157500.0,521280.0,28408.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.026392,-12009,-1912,-6160.0,-4479,,1,1,0,1,1,0,Sales staff,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.4745736073755878,0.6190330399599243,0.1595195404777181,0.1485,,0.9836,,,0.04,0.0345,0.3333,,,,0.1113,,0.0,0.1513,,0.9836,,,0.0403,0.0345,0.3333,,,,0.11599999999999999,,0.0,0.1499,,0.9836,,,0.04,0.0345,0.3333,,,,0.1133,,0.0,,block of flats,0.0875,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1224.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,11.0,1.0,2.0\n108003,0,Cash loans,F,N,Y,0,90000.0,900000.0,26446.5,900000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-22466,365243,-12668.0,-4082,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,XNA,,0.0922499872964788,,0.4052,0.3003,0.9826,0.762,0.1956,0.44,0.3793,0.3333,0.375,0.309,0.3295,0.4241,0.0039,0.0,0.4128,0.3116,0.9826,0.7713,0.1974,0.4431,0.3793,0.3333,0.375,0.316,0.36,0.4419,0.0039,0.0,0.4091,0.3003,0.9826,0.7652,0.1968,0.44,0.3793,0.3333,0.375,0.3144,0.3352,0.4318,0.0039,0.0,reg oper spec account,block of flats,0.5498,Panel,No,3.0,0.0,3.0,0.0,-314.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n152742,0,Cash loans,F,N,N,0,177750.0,1971072.0,68643.0,1800000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.022625,-19296,365243,-744.0,-2839,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,XNA,0.7939314358934538,0.6364660033764806,0.6848276586890367,0.1031,0.1429,0.9861,0.8096,0.0213,0.0,0.1724,0.1667,0.2083,0.11199999999999999,0.0841,0.1052,0.0,0.0,0.105,0.1482,0.9861,0.8171,0.0215,0.0,0.1724,0.1667,0.2083,0.1146,0.0918,0.1096,0.0,0.0,0.1041,0.1429,0.9861,0.8121,0.0214,0.0,0.1724,0.1667,0.2083,0.1139,0.0855,0.1071,0.0,0.0,reg oper account,block of flats,0.0944,Panel,No,0.0,0.0,0.0,0.0,-740.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n400978,0,Revolving loans,M,N,Y,1,202500.0,585000.0,29250.0,585000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.018634,-11367,-3541,-3953.0,-3974,,1,1,0,1,0,0,Sales staff,3.0,2,2,MONDAY,19,0,0,0,0,0,0,Business Entity Type 3,,0.5924589685675292,0.2032521136204725,0.0732,0.0995,0.9692,0.5784,0.0459,0.0,0.1379,0.1667,0.2083,0.0631,0.0597,0.0741,0.0,0.0,0.0746,0.1033,0.9692,0.5949,0.0463,0.0,0.1379,0.1667,0.2083,0.0645,0.0652,0.0772,0.0,0.0,0.0739,0.0995,0.9692,0.584,0.0462,0.0,0.1379,0.1667,0.2083,0.0642,0.0607,0.0754,0.0,0.0,reg oper account,block of flats,0.0834,\"Stone, brick\",No,3.0,1.0,3.0,1.0,-2412.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n114967,1,Cash loans,F,N,Y,1,117000000.0,562491.0,26194.5,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010643,-12615,-922,-6762.0,-3643,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.4608094237235762,0.11316146741289584,0.1455428133497032,0.1031,0.0947,0.9791,,,0.0,0.2069,0.1667,,0.0688,,0.0615,,,0.105,0.0983,0.9791,,,0.0,0.2069,0.1667,,0.0704,,0.0641,,,0.1041,0.0947,0.9791,,,0.0,0.2069,0.1667,,0.07,,0.0626,,,,block of flats,0.0715,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n104074,0,Revolving loans,M,N,N,0,90000.0,180000.0,9000.0,180000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.007305,-19759,-960,-3574.0,-3234,,1,1,0,1,0,0,Low-skill Laborers,2.0,3,3,MONDAY,10,0,0,0,0,0,0,Other,,0.13869443444340476,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,1.0,-522.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n360348,0,Cash loans,M,Y,Y,0,189000.0,1350000.0,39604.5,1350000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.010643,-12499,-3664,-6570.0,-4095,64.0,1,1,0,1,1,0,Core staff,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Police,0.3145703584152767,0.6045127553422173,0.3441550073724169,0.0155,0.0169,0.9821,,,0.0,0.0345,0.1667,,0.0179,,0.0193,,0.0,0.0158,0.0175,0.9821,,,0.0,0.0345,0.1667,,0.0183,,0.0201,,0.0,0.0156,0.0169,0.9821,,,0.0,0.0345,0.1667,,0.0182,,0.0196,,0.0,reg oper account,block of flats,0.0161,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2377.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n226278,0,Cash loans,F,N,Y,0,180000.0,450000.0,29299.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018029,-18444,-1144,-4255.0,-1974,,1,1,1,1,0,0,Sales staff,1.0,3,3,MONDAY,11,0,0,0,0,1,1,Trade: type 7,0.1344931507964892,0.6372599966307261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23.0,0.0,23.0,0.0,-727.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n225873,0,Cash loans,M,N,N,0,171000.0,436032.0,21208.5,360000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-22410,365243,-6916.0,-4019,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,XNA,0.8292713196090479,0.565101748260907,0.5226973172821112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-465.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n283797,0,Revolving loans,F,N,Y,0,135000.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.010276,-7843,-207,-7281.0,-516,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,,0.6389264569538123,,0.0804,0.0,0.9791,0.7144,0.0975,0.0,0.1724,0.1667,0.2083,0.0692,0.0656,0.0678,0.0,0.0,0.0819,0.0,0.9791,0.7256,0.0984,0.0,0.1724,0.1667,0.2083,0.0708,0.0716,0.0706,0.0,0.0,0.0812,0.0,0.9791,0.7182,0.0981,0.0,0.1724,0.1667,0.2083,0.0704,0.0667,0.069,0.0,0.0,reg oper account,block of flats,0.0533,Panel,No,0.0,0.0,0.0,0.0,-678.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n192035,0,Cash loans,F,N,N,1,135000.0,1227901.5,46768.5,1129500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010643,-15556,-2543,-5104.0,-695,,1,1,1,1,0,0,Cooking staff,3.0,2,2,SUNDAY,16,0,0,0,0,1,1,Trade: type 7,,0.603665500411234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1188.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n349333,0,Cash loans,M,N,Y,0,46512.0,153504.0,16659.0,144000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-23100,365243,-4631.0,-4651,,1,0,0,1,1,0,,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,XNA,,0.19161730272717328,0.7886807751817684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n350583,0,Cash loans,F,N,N,2,135000.0,970380.0,28503.0,810000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.019101,-12099,-4853,-3258.0,-2500,,1,1,0,1,0,0,Cooking staff,4.0,2,2,FRIDAY,11,0,0,0,0,0,0,Medicine,,0.490539816356827,0.6690566947824041,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1113.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n293735,0,Cash loans,F,Y,N,1,121500.0,99504.0,10323.0,90000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00963,-11645,-949,-5063.0,-3102,11.0,1,1,1,1,0,0,Core staff,3.0,2,2,TUESDAY,17,1,1,0,1,1,0,Trade: type 3,0.21228206424140025,0.7061289998319146,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-919.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n132342,0,Cash loans,F,N,N,0,202500.0,540000.0,39424.5,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.001276,-15604,-2702,-3220.0,-4213,,1,1,0,1,0,0,,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.8560683250305094,0.19551196688129646,,0.0727,0.1132,0.9771,0.6872,0.0,0.0,0.1034,0.1667,0.0417,0.0645,0.0588,0.0864,0.0,0.028999999999999998,0.0641,0.0,0.9747,0.6668,0.0,0.0,0.069,0.1667,0.0417,0.0523,0.0551,0.0411,0.0,0.0,0.0734,0.1132,0.9771,0.6914,0.0,0.0,0.1034,0.1667,0.0417,0.0656,0.0599,0.08800000000000001,0.0,0.0296,reg oper account,block of flats,0.0461,Block,No,1.0,0.0,1.0,0.0,-75.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n258089,0,Revolving loans,M,N,N,0,157500.0,247500.0,12375.0,247500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.035792000000000004,-8659,-829,-3481.0,-1262,,1,1,0,1,0,0,Laborers,1.0,2,2,FRIDAY,10,1,1,0,1,1,0,Business Entity Type 3,0.1633001880258609,0.3443099688470891,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-604.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n106843,0,Revolving loans,F,N,Y,0,157500.0,225000.0,11250.0,225000.0,Family,Commercial associate,Higher education,Married,House / apartment,0.011703,-9419,-412,-5390.0,-1430,,1,1,0,1,0,0,IT staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.7609303110211864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-501.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n422893,0,Cash loans,F,Y,N,2,139500.0,1320975.0,43654.5,1183500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-10102,-1201,-3178.0,-832,18.0,1,1,1,1,0,0,Sales staff,4.0,2,2,THURSDAY,20,0,0,0,0,0,0,Self-employed,0.5868411944548011,0.4424996460495696,0.2866524828090694,0.0825,0.0,0.9776,0.6940000000000001,0.1008,0.0,0.1379,0.1667,0.2083,0.0,0.0672,0.0701,0.0,0.0,0.084,0.0,0.9777,0.706,0.1017,0.0,0.1379,0.1667,0.2083,0.0,0.0735,0.073,0.0,0.0,0.0833,0.0,0.9776,0.6981,0.1015,0.0,0.1379,0.1667,0.2083,0.0,0.0684,0.0713,0.0,0.0,reg oper account,block of flats,0.0551,Panel,No,0.0,0.0,0.0,0.0,-654.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n117742,0,Cash loans,F,N,Y,1,171000.0,239850.0,25578.0,225000.0,Unaccompanied,Commercial associate,Incomplete higher,Married,House / apartment,0.030755,-9907,-2755,-4615.0,-1795,,1,1,1,1,1,0,Medicine staff,3.0,2,2,MONDAY,11,0,0,0,0,0,0,Medicine,0.501284545844413,0.5930765771056854,0.12295541790885495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1949.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n403302,0,Revolving loans,F,N,Y,0,450000.0,1237500.0,61875.0,1237500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.072508,-21484,-5451,-293.0,-4696,,1,1,0,1,0,0,Managers,2.0,1,1,THURSDAY,17,1,1,0,0,0,0,Self-employed,,0.7425725344825241,0.3996756156233169,0.017,0.1315,0.9513,0.42200000000000004,0.0,0.02,0.0862,0.0625,0.125,0.0164,0.0168,0.0264,0.0077,0.1594,0.0116,0.0337,0.9454,0.4446,0.0,0.0,0.069,0.0417,0.125,0.0132,0.0184,0.0267,0.0078,0.0101,0.0172,0.1315,0.9513,0.4297,0.0,0.02,0.0862,0.0625,0.125,0.0167,0.0171,0.0269,0.0078,0.1628,reg oper account,block of flats,0.0234,\"Stone, brick\",No,,,,,-112.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n422867,1,Cash loans,M,N,N,0,180000.0,497520.0,53712.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.014464,-11925,-1233,-9295.0,-919,,1,1,1,1,0,0,Laborers,1.0,2,2,THURSDAY,6,0,0,0,0,0,0,Self-employed,0.2178779045433928,0.5820387579454502,,0.0588,0.0739,0.9856,0.8028,0.0307,0.0,0.1379,0.1667,0.0417,0.0131,0.0479,0.059000000000000004,0.0,0.0,0.0599,0.0767,0.9856,0.8105,0.031,0.0,0.1379,0.1667,0.0417,0.0134,0.0523,0.0614,0.0,0.0,0.0593,0.0739,0.9856,0.8054,0.0309,0.0,0.1379,0.1667,0.0417,0.0134,0.0487,0.06,0.0,0.0,reg oper account,block of flats,0.0505,Panel,No,3.0,0.0,3.0,0.0,-742.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n221359,0,Cash loans,F,N,Y,2,135000.0,227520.0,14670.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.030755,-15093,-4701,-4262.0,-2175,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,9,0,0,0,0,0,0,Business Entity Type 1,0.31648897482400073,0.4384360660501611,0.42765737003502935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2063.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n259138,0,Revolving loans,F,N,Y,0,90000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.0228,-17718,-128,-4562.0,-1275,,1,1,1,1,1,0,,1.0,2,2,TUESDAY,15,0,0,0,1,1,0,Restaurant,0.5313969678170432,0.6531514881138949,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1149.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n158608,0,Cash loans,F,N,Y,0,67500.0,123637.5,9909.0,112500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.00702,-22346,-15338,-8432.0,-4032,,1,1,1,1,1,0,Sales staff,1.0,2,2,MONDAY,16,0,0,0,0,1,1,Government,0.8075115943977504,0.6381794855793399,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1860.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n412203,0,Cash loans,F,N,N,0,180000.0,636138.0,18729.0,531000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-19637,365243,-9631.0,-3135,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,XNA,0.8682351558581888,0.6710878581334675,0.7338145369642702,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1651.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,8.0\n355007,0,Cash loans,M,N,Y,0,54000.0,381528.0,17784.0,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010556,-21383,365243,-9147.0,-1075,,1,0,0,1,0,0,,2.0,3,3,MONDAY,16,0,0,0,0,0,0,XNA,,0.12713069240349026,0.3842068130556564,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-710.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n103065,0,Cash loans,F,N,Y,1,112500.0,593010.0,16434.0,495000.0,,Working,Secondary / secondary special,Married,With parents,0.01885,-8967,-1801,-4433.0,-577,,1,1,0,1,0,0,Sales staff,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Trade: type 3,0.2644531666184384,0.7574000163217405,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1695.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n413222,1,Cash loans,F,N,Y,0,112500.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-15950,-2334,-5096.0,-4937,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,18,0,0,0,0,1,1,Self-employed,,0.5378966549125547,0.4489622731076524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2753.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n136793,0,Cash loans,F,N,Y,0,58500.0,152820.0,15016.5,135000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-21863,365243,-882.0,-4580,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.7136093913797698,0.7209441499436497,0.0515,0.0685,0.9846,,,0.0,0.1379,0.1667,0.2083,0.0713,0.042,0.0565,,0.0,0.0525,0.0711,0.9846,,,0.0,0.1379,0.1667,0.2083,0.0669,0.0459,0.0589,,0.0,0.052000000000000005,0.0685,0.9846,,,0.0,0.1379,0.1667,0.2083,0.0726,0.0428,0.0575,,0.0,,block of flats,0.0451,Panel,No,2.0,0.0,2.0,0.0,-1775.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,8.0\n362869,0,Revolving loans,M,Y,Y,2,162000.0,495000.0,24750.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.032561,-14456,-535,-14379.0,-3884,8.0,1,1,0,1,1,0,Drivers,4.0,1,1,THURSDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.2627664186689637,0.2628977389213844,0.4650692149562261,0.0722,,0.9767,,,,0.2414,0.1667,,0.0786,,0.0631,,0.0031,0.0735,,0.9767,,,,0.2414,0.1667,,0.0804,,0.0657,,0.0033,0.0729,,0.9767,,,,0.2414,0.1667,,0.0799,,0.0642,,0.0032,,block of flats,0.0503,Panel,No,1.0,0.0,1.0,0.0,-129.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n199834,0,Cash loans,M,Y,N,0,157500.0,86598.0,9454.5,76500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.035792000000000004,-11419,-963,-4934.0,-3739,10.0,1,1,0,1,0,0,Managers,1.0,2,2,THURSDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6691300470253838,,0.0649,0.035,0.9861,0.8096,0.0,0.04,0.0345,0.3333,0.375,0.0442,0.053,0.0617,0.0,0.0,0.0662,0.0363,0.9861,0.8171,0.0,0.0403,0.0345,0.3333,0.375,0.0452,0.0579,0.0643,0.0,0.0,0.0656,0.035,0.9861,0.8121,0.0,0.04,0.0345,0.3333,0.375,0.0449,0.0539,0.0628,0.0,0.0,reg oper account,block of flats,0.0579,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2406.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n383216,0,Cash loans,F,Y,Y,2,108000.0,314100.0,17167.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-10014,-290,-2003.0,-2581,21.0,1,1,1,1,1,0,Core staff,4.0,3,3,WEDNESDAY,12,0,0,0,0,0,0,Kindergarten,0.5579646838600949,0.28982475227689114,0.4101025731788671,0.1433,0.1652,0.9866,0.8164,0.04,0.16,0.1379,0.3333,0.375,0.0824,0.1168,0.1479,0.0,0.0489,0.146,0.1714,0.9866,0.8236,0.0403,0.1611,0.1379,0.3333,0.375,0.0843,0.1276,0.1541,0.0,0.0518,0.1447,0.1652,0.9866,0.8189,0.0402,0.16,0.1379,0.3333,0.375,0.0839,0.1189,0.1505,0.0,0.0499,reg oper account,block of flats,0.1488,Panel,No,3.0,0.0,3.0,0.0,-275.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n349506,0,Cash loans,M,Y,Y,1,157500.0,942300.0,30397.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-15598,-3040,-8035.0,-3969,15.0,1,1,0,1,0,0,Sales staff,3.0,2,2,WEDNESDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.6508910409055327,0.5860337070572998,0.4014074137749511,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1490.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n238707,0,Cash loans,M,Y,Y,0,135000.0,1350000.0,37255.5,1350000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010556,-16971,365243,-1077.0,-516,9.0,1,0,0,1,0,0,,2.0,3,3,MONDAY,16,0,0,0,1,0,0,XNA,0.2524118530399393,0.6267173884749245,0.6832688314232291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1648.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n235447,0,Cash loans,M,Y,N,0,180000.0,916470.0,24174.0,765000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17501,-230,-9665.0,-1036,16.0,1,1,1,1,0,0,,2.0,2,2,TUESDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.7033716254853928,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n196870,0,Cash loans,M,N,N,1,112500.0,1078200.0,38331.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007273999999999998,-11608,-2641,-5514.0,-2699,,1,1,0,1,0,0,Drivers,3.0,2,2,TUESDAY,10,0,0,0,1,1,1,Transport: type 4,0.2728920108698156,0.2652776501907447,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1907.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n331484,0,Cash loans,M,N,N,0,58500.0,98910.0,7011.0,90000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-24270,365243,-7226.0,-4637,,1,0,0,1,0,0,,2.0,2,2,SUNDAY,9,0,0,0,0,0,0,XNA,0.7820370545450313,0.6760497559205869,0.7421816117614419,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,0.0,15.0,0.0,-1819.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n222353,0,Cash loans,F,Y,Y,0,126000.0,270000.0,14778.0,270000.0,Unaccompanied,Working,Incomplete higher,Single / not married,With parents,0.028663,-8735,-876,-586.0,-1188,9.0,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,9,0,0,0,0,1,1,Other,,0.2367688329107237,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n346846,0,Cash loans,F,N,N,0,156600.0,862560.0,23850.0,720000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-19197,-234,-547.0,-633,,1,1,1,1,0,0,Sales staff,2.0,1,1,TUESDAY,15,0,1,1,1,1,1,Business Entity Type 1,0.5808512452710376,0.7376133675431231,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-336.0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,,,,,,\n373604,1,Cash loans,F,N,N,1,90000.0,314100.0,16573.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.031329,-10559,-1091,-5111.0,-574,,1,1,0,1,0,0,Laborers,3.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Industry: type 9,0.3348434941746921,0.7361761099034186,0.09322509537747448,0.0134,0.0103,0.9697,0.5852,0.0,0.0,0.069,0.0417,0.0833,0.0088,0.0109,0.0153,0.0,0.0,0.0137,0.0107,0.9697,0.6014,0.0,0.0,0.069,0.0417,0.0833,0.009000000000000001,0.0119,0.0159,0.0,0.0,0.0135,0.0103,0.9697,0.5907,0.0,0.0,0.069,0.0417,0.0833,0.0089,0.0111,0.0156,0.0,0.0,reg oper account,block of flats,0.012,Block,No,0.0,0.0,0.0,0.0,-944.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n339128,0,Cash loans,M,Y,Y,0,290250.0,957033.0,53568.0,904500.0,Family,Working,Higher education,Married,House / apartment,0.006629,-21406,-1143,-8177.0,-3509,15.0,1,1,0,1,0,0,Managers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Transport: type 4,,0.7528348431018916,,0.0742,0.0708,0.9886,0.8436,0.0242,0.08,0.069,0.3333,0.0417,0.0568,0.0605,0.0989,0.0,0.0,0.0756,0.0735,0.9886,0.8497,0.0245,0.0806,0.069,0.3333,0.0417,0.0581,0.0661,0.1031,0.0,0.0,0.0749,0.0708,0.9886,0.8457,0.0244,0.08,0.069,0.3333,0.0417,0.0578,0.0616,0.1007,0.0,0.0,reg oper account,block of flats,0.0911,Panel,No,1.0,0.0,1.0,0.0,-429.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n411908,0,Cash loans,F,N,Y,0,67500.0,790434.0,34816.5,706500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15354,-6392,-8027.0,-27,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,12,0,0,0,0,1,1,Kindergarten,,0.5787262475312671,,0.0464,0.0948,0.9846,0.8096,0.0715,0.0,0.1207,0.1667,0.2083,0.0632,0.0378,0.0913,0.0,0.0,0.0473,0.0479,0.9836,0.8171,0.0721,0.0,0.1034,0.1667,0.2083,0.0646,0.0413,0.0518,0.0,0.0,0.0468,0.0948,0.9846,0.8121,0.0719,0.0,0.1207,0.1667,0.2083,0.0643,0.0385,0.0929,0.0,0.0,reg oper spec account,block of flats,0.0432,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-624.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n130619,1,Cash loans,F,N,Y,0,135000.0,314055.0,16443.0,238500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-13715,-99,-868.0,-4477,,1,1,0,1,0,0,Realty agents,1.0,3,2,FRIDAY,10,0,0,0,0,0,0,Other,0.5835475279490279,0.10537880760290592,,0.3041,0.0663,0.9985,0.9796,0.07200000000000001,0.16,0.1379,0.375,0.4167,0.0104,0.2471,0.2168,0.0039,0.0049,0.3099,0.0688,0.9985,0.9804,0.0726,0.1611,0.1379,0.375,0.4167,0.0106,0.27,0.2258,0.0039,0.0052,0.3071,0.0663,0.9985,0.9799,0.0724,0.16,0.1379,0.375,0.4167,0.0105,0.2514,0.2206,0.0039,0.0051,reg oper account,block of flats,0.2098,Panel,No,2.0,0.0,2.0,0.0,-756.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n391671,0,Cash loans,M,N,Y,0,130500.0,281916.0,10755.0,184500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-15479,-1294,-3198.0,-4774,,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.2272932387728205,0.6832688314232291,0.0134,0.0,0.9578,0.4968,0.0022,0.0,0.1034,0.0417,0.0833,0.0084,0.0134,0.0065,0.0,0.0,0.0105,0.0,0.9523,0.5165,0.0022,0.0,0.069,0.0417,0.0833,0.0026,0.0147,0.0045,0.0,0.0,0.0135,0.0,0.9578,0.5035,0.0022,0.0,0.1034,0.0417,0.0833,0.0085,0.0137,0.0067,0.0,0.0,,block of flats,0.0049,Wooden,No,2.0,1.0,2.0,1.0,-48.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n182746,0,Cash loans,F,N,Y,0,67500.0,808650.0,23773.5,675000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22629,365243,-11397.0,-4776,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,XNA,,0.4893102648936198,0.3441550073724169,0.0722,0.0649,0.9846,0.7892,0.0,0.0,0.1379,0.1667,0.2083,,0.0588,0.0676,0.0,0.0,0.0735,0.0674,0.9846,0.7975,0.0,0.0,0.1379,0.1667,0.2083,,0.0643,0.0704,0.0,0.0,0.0729,0.0649,0.9846,0.792,0.0,0.0,0.1379,0.1667,0.2083,,0.0599,0.0688,0.0,0.0,reg oper spec account,block of flats,0.0532,Panel,No,4.0,2.0,4.0,2.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n345811,0,Cash loans,F,N,Y,0,54000.0,225000.0,14508.0,225000.0,\"Spouse, partner\",Working,Secondary / secondary special,Separated,House / apartment,0.00733,-12881,-1228,-1259.0,-2937,,1,1,0,1,0,0,Waiters/barmen staff,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,Business Entity Type 3,0.3783646510328153,0.5567594897311525,0.5100895276257282,0.1113,,0.9861,0.8096,,0.12,0.1034,0.3333,0.0417,,0.0908,0.1124,0.0,,0.1134,,0.9861,0.8171,,0.1208,0.1034,0.3333,0.0417,,0.0992,0.1171,0.0,,0.1124,,0.9861,0.8121,,0.12,0.1034,0.3333,0.0417,,0.0923,0.1144,0.0,,reg oper account,block of flats,,Panel,No,0.0,0.0,0.0,0.0,-724.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n394045,0,Cash loans,M,N,Y,1,225000.0,835380.0,45445.5,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-15789,-121,-4007.0,-4756,,1,1,0,1,1,1,Drivers,3.0,1,1,MONDAY,14,0,0,0,0,1,1,Trade: type 3,0.25682393406381243,0.7730510391350691,0.25396280933631177,0.0825,0.0805,0.9747,0.6532,0.0,0.0,0.1379,0.1667,0.2083,0.013999999999999999,0.0672,0.0712,0.0,0.0841,0.084,0.0836,0.9747,0.6668,0.0,0.0,0.1379,0.1667,0.2083,0.0143,0.0735,0.0741,0.0,0.08900000000000001,0.0833,0.0805,0.9747,0.6578,0.0,0.0,0.1379,0.1667,0.2083,0.0142,0.0684,0.0724,0.0,0.0858,reg oper account,block of flats,0.0742,Panel,No,2.0,1.0,2.0,1.0,-2096.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n195327,0,Cash loans,M,Y,Y,0,202500.0,450000.0,21109.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-10498,-935,-4219.0,-961,14.0,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Business Entity Type 3,0.3860559130402728,0.6356662013755796,0.6894791426446275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n106451,0,Revolving loans,F,N,Y,0,81000.0,225000.0,11250.0,225000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.007305,-19113,-532,-5722.0,-2644,,1,1,1,1,0,0,Sales staff,2.0,3,3,MONDAY,14,0,0,0,0,1,1,Self-employed,,0.598915804012189,0.6971469077844458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-734.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n152160,0,Cash loans,F,Y,Y,0,157500.0,231097.5,18387.0,175500.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.031329,-10989,-1549,-5302.0,-2756,0.0,1,1,0,1,0,0,High skill tech staff,1.0,2,2,MONDAY,12,0,0,0,0,0,0,Transport: type 2,0.48324716891106,0.6321493982845404,0.4974688893052743,0.0619,0.079,0.9742,0.6464,0.0135,0.0,0.1034,0.1667,0.2083,0.0,0.0504,0.0504,0.0,0.0,0.063,0.08199999999999999,0.9742,0.6602,0.0136,0.0,0.1034,0.1667,0.2083,0.0,0.0551,0.0525,0.0,0.0,0.0625,0.079,0.9742,0.6511,0.0136,0.0,0.1034,0.1667,0.2083,0.0,0.0513,0.0513,0.0,0.0,reg oper account,block of flats,0.0396,Others,No,0.0,0.0,0.0,0.0,-65.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n174474,0,Cash loans,M,N,N,0,180000.0,1271929.5,48447.0,1170000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-20141,-2773,-7219.0,-2887,,1,1,1,1,1,0,Security staff,2.0,2,2,THURSDAY,20,0,1,1,0,1,1,Business Entity Type 3,0.6556622384447778,0.7034126859097375,0.7801436381572275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-367.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,4.0,0.0,2.0\n173397,0,Cash loans,F,Y,Y,1,315000.0,1006920.0,51543.0,900000.0,Unaccompanied,Commercial associate,Higher education,Civil marriage,House / apartment,0.006670999999999999,-17316,-406,-4648.0,-807,24.0,1,1,0,1,1,0,Managers,3.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Kindergarten,0.6477269084532992,0.5252429286405303,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-576.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n353369,0,Revolving loans,F,Y,N,0,270000.0,720000.0,36000.0,720000.0,Unaccompanied,State servant,Higher education,Separated,House / apartment,0.022625,-21952,-3033,-6749.0,-2317,6.0,1,1,0,1,0,0,Managers,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Government,,0.7095658448332834,0.42589289800515295,0.2474,,0.0,,,0.24,0.2069,0.375,,,,0.1777,,,0.2521,,0.0005,,,0.2417,0.2069,0.375,,,,0.1852,,,0.2498,,0.0,,,0.24,0.2069,0.375,,,,0.1809,,,,block of flats,0.2286,Panel,No,2.0,0.0,2.0,0.0,-640.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,4.0\n110858,0,Cash loans,M,Y,N,0,193500.0,550467.0,20880.0,387000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.006207,-18549,-3330,-14405.0,-2077,17.0,1,1,0,1,0,1,Drivers,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,,0.6319510604806956,0.646329897706246,0.0938,0.11199999999999999,0.9806,0.7348,,0.0,0.2069,0.1667,0.2083,0.0311,0.0756,0.08800000000000001,0.0039,0.0195,0.0956,0.1162,0.9806,0.7452,,0.0,0.2069,0.1667,0.2083,0.0318,0.0826,0.0917,0.0039,0.0206,0.0947,0.11199999999999999,0.9806,0.7383,,0.0,0.2069,0.1667,0.2083,0.0316,0.077,0.0896,0.0039,0.0199,reg oper account,block of flats,0.081,Panel,No,0.0,0.0,0.0,0.0,-440.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,7.0\n385687,0,Cash loans,F,Y,Y,0,225000.0,505066.5,47353.5,468000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-20225,-604,-3494.0,-3753,2.0,1,1,0,1,0,0,Managers,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6740302681665611,,0.0928,,0.9781,,,0.0,0.2069,0.1667,,0.0,,0.0846,,,0.0945,,0.9782,,,0.0,0.2069,0.1667,,0.0,,0.0881,,,0.0937,,0.9781,,,0.0,0.2069,0.1667,,0.0,,0.0861,,,,block of flats,0.0906,Panel,No,0.0,0.0,0.0,0.0,-516.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n161435,0,Cash loans,F,N,Y,1,180000.0,156384.0,17815.5,135000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007114,-16985,-1019,-4955.0,-528,,1,1,0,1,1,0,Core staff,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Realtor,0.7887412862926177,0.7104393315518301,0.28371188263500075,0.2619,0.2033,0.9841,,,0.28,0.2414,0.3333,,,,0.2819,,0.0111,0.2668,0.2109,0.9841,,,0.282,0.2414,0.3333,,,,0.2937,,0.0117,0.2644,0.2033,0.9841,,,0.28,0.2414,0.3333,,,,0.287,,0.0113,,block of flats,0.2241,Panel,No,0.0,0.0,0.0,0.0,-929.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n375239,0,Cash loans,M,Y,N,1,315000.0,1276042.5,64953.0,1206000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.030755,-10227,-299,-5025.0,-2898,7.0,1,1,0,1,0,0,IT staff,3.0,2,2,MONDAY,18,0,1,1,0,0,0,Telecom,,0.3204958960505628,,0.0722,0.0609,0.9762,0.6736,0.0288,0.0,0.1379,0.1667,0.0417,0.0322,0.0588,0.0572,0.0,0.0,0.0735,0.0632,0.9762,0.6864,0.0291,0.0,0.1379,0.1667,0.0417,0.0329,0.0643,0.0596,0.0,0.0,0.0729,0.0609,0.9762,0.6779999999999999,0.028999999999999998,0.0,0.1379,0.1667,0.0417,0.0327,0.0599,0.0582,0.0,0.0,reg oper account,block of flats,0.0712,Panel,No,1.0,0.0,1.0,0.0,-3.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n416944,0,Cash loans,F,Y,Y,1,121500.0,323388.0,34956.0,292500.0,Unaccompanied,State servant,Higher education,Married,Office apartment,0.003818,-11579,-431,-419.0,-1666,9.0,1,1,1,1,0,0,Core staff,3.0,2,2,MONDAY,4,0,0,0,0,0,0,Kindergarten,0.5323169466712266,0.5791900066180766,0.4884551844437485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-167.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n116164,0,Cash loans,F,N,N,2,50850.0,568800.0,15133.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-10519,-198,-2835.0,-328,,1,1,0,1,0,0,Sales staff,4.0,3,3,FRIDAY,10,0,0,0,0,0,0,Trade: type 3,,0.010434532140997129,0.5797274227921155,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-163.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n213016,0,Cash loans,M,N,Y,0,270000.0,182178.0,11137.5,130500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.011657,-22606,365243,-1176.0,-4583,,1,0,0,1,0,0,,2.0,1,1,TUESDAY,13,0,0,0,0,0,0,XNA,,0.6805256515964139,0.7623356180684377,0.1124,0.1162,0.9861,,,0.0,0.3103,0.1667,,0.11900000000000001,,0.1242,,0.0042,0.1145,0.1206,0.9861,,,0.0,0.3103,0.1667,,0.1217,,0.1295,,0.0044,0.1135,0.1162,0.9861,,,0.0,0.3103,0.1667,,0.121,,0.1265,,0.0043,,block of flats,0.1006,Panel,No,1.0,0.0,1.0,0.0,-1000.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n136858,0,Cash loans,F,N,Y,0,180000.0,545040.0,39627.0,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.00496,-14979,-605,-3388.0,-4696,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,Self-employed,,0.5540109906190815,,0.0716,0.10800000000000001,0.9796,,,0.0,0.1724,0.1667,,0.0774,,0.0433,,0.0131,0.0588,0.0717,0.9796,,,0.0,0.1379,0.1667,,0.0453,,0.0372,,0.0098,0.0723,0.10800000000000001,0.9796,,,0.0,0.1724,0.1667,,0.0788,,0.044000000000000004,,0.0134,,block of flats,0.0421,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n173742,0,Cash loans,F,Y,Y,0,90000.0,315000.0,25015.5,315000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.035792000000000004,-18911,-579,-2873.0,-2456,10.0,1,1,0,1,0,0,Core staff,2.0,2,2,FRIDAY,21,0,0,0,0,0,0,Other,,0.3187554097491938,0.5954562029091491,0.1093,0.1327,0.9846,0.7892,0.0132,0.0,0.2414,0.1667,0.2083,0.0946,0.0891,0.0969,0.0,0.0,0.1113,0.1377,0.9846,0.7975,0.0133,0.0,0.2414,0.1667,0.2083,0.0967,0.0973,0.10099999999999999,0.0,0.0,0.1103,0.1327,0.9846,0.792,0.0133,0.0,0.2414,0.1667,0.2083,0.0962,0.0906,0.0986,0.0,0.0,reg oper account,block of flats,0.0762,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-228.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n129041,0,Cash loans,M,Y,Y,1,225000.0,544491.0,17694.0,454500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-12065,-593,-4737.0,-4753,4.0,1,1,0,1,0,0,Managers,3.0,2,2,MONDAY,8,0,0,0,1,1,0,Self-employed,,0.7282528518005728,0.5549467685334323,0.0639,,0.9831,,,0.0,0.0345,0.0417,,,,0.0188,,,0.0651,,0.9831,,,0.0,0.0345,0.0417,,,,0.0196,,,0.0645,,0.9831,,,0.0,0.0345,0.0417,,,,0.0192,,,,specific housing,0.0191,\"Stone, brick\",No,8.0,0.0,7.0,0.0,-2536.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n312133,0,Cash loans,F,N,Y,0,202500.0,731403.0,53658.0,684000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.00496,-21399,365243,-5898.0,-2678,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,0.7400297676762774,0.7721443454466688,,0.1003,,0.995,0.9524,,0.0,0.1379,0.2396,0.0417,,,0.0661,,,0.0315,,0.9975,0.9543,,0.0,0.0345,0.1667,0.0417,,,0.0251,,,0.0442,,0.9975,0.953,,0.0,0.1034,0.1667,0.0417,,,0.0673,,,reg oper account,block of flats,0.0313,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-1011.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n254929,0,Revolving loans,F,N,Y,0,202500.0,360000.0,18000.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-16432,-3097,-4231.0,-4323,,1,1,0,1,0,0,Drivers,2.0,2,2,SUNDAY,10,0,0,0,0,0,0,Self-employed,0.460745731626573,0.504861800483455,0.6986675550534175,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1157.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n177297,0,Cash loans,M,N,N,0,180000.0,269550.0,10404.0,225000.0,Family,Pensioner,Higher education,Married,House / apartment,0.022625,-21556,365243,-7405.0,-1112,,1,0,0,1,0,0,,2.0,2,2,MONDAY,10,0,0,0,0,0,0,XNA,0.7556890474644171,0.6124311363942415,0.6879328378491735,0.1003,0.2841,0.9901,0.8572,0.1162,0.12,0.1034,0.3646,0.375,0.0948,0.1647,0.0839,0.0077,0.7591,0.0672,0.2949,0.9896,0.8628,0.1173,0.0806,0.069,0.375,0.375,0.0969,0.18,0.0729,0.0078,0.8036,0.0666,0.2841,0.9896,0.8591,0.1169,0.08,0.069,0.375,0.375,0.0964,0.1676,0.0715,0.0078,0.775,,block of flats,0.1989,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1162.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n365959,0,Cash loans,M,Y,Y,0,157500.0,156384.0,18688.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-14319,-5809,-1306.0,-4510,8.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 2,0.5615632279536129,0.4951583653730064,0.6263042766749393,0.0619,0.0606,0.9776,0.6940000000000001,,0.0,0.1379,0.1667,,0.0715,,0.0594,,0.0226,0.063,0.0629,0.9777,0.706,,0.0,0.1379,0.1667,,0.0732,,0.0619,,0.024,0.0625,0.0606,0.9776,0.6981,,0.0,0.1379,0.1667,,0.0728,,0.0605,,0.0231,,block of flats,0.055999999999999994,Panel,No,3.0,1.0,3.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n225956,0,Cash loans,F,N,Y,0,90000.0,573628.5,22576.5,463500.0,Family,Pensioner,Secondary / secondary special,Separated,House / apartment,0.01885,-23117,365243,-1367.0,-2792,,1,0,0,1,0,0,,1.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.7641849150503032,0.5513812618027899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n423480,0,Cash loans,M,Y,Y,0,225000.0,1022436.0,49315.5,940500.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.018801,-10184,-2358,-2527.0,-2818,12.0,1,1,1,1,1,1,,1.0,2,2,MONDAY,7,0,0,0,0,1,1,Business Entity Type 3,,0.4246397869655096,0.2405414172860865,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1769.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n309553,0,Cash loans,F,N,Y,0,247500.0,1507869.0,52542.0,1377000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.022625,-19911,-3481,-4057.0,-3413,,1,1,0,1,0,0,Security staff,2.0,2,2,SATURDAY,9,0,0,0,0,1,1,Security,0.6498599156070044,0.5771740481759341,0.6161216908872079,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n418761,0,Revolving loans,M,Y,Y,0,180000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-13701,-1210,-1440.0,-3576,15.0,1,1,1,1,1,0,Laborers,2.0,2,2,SUNDAY,11,0,0,0,0,0,0,Self-employed,0.3109869185434527,0.7722212828240517,,0.2041,0.149,0.998,0.9728,0.2327,0.32,0.1379,0.4583,0.5,0.2359,0.1664,0.2925,0.0,0.0,0.20800000000000002,0.1546,0.998,0.9739,0.2349,0.3222,0.1379,0.4583,0.5,0.2412,0.1818,0.3047,0.0,0.0,0.2061,0.149,0.998,0.9732,0.2342,0.32,0.1379,0.4583,0.5,0.24,0.1693,0.2977,0.0,0.0,reg oper account,block of flats,0.3822,Block,No,0.0,0.0,0.0,0.0,-277.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n415791,0,Cash loans,F,N,Y,0,180000.0,254700.0,14350.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.04622,-24154,-3487,-5150.0,-5469,,1,1,0,1,0,0,Drivers,2.0,1,1,MONDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.7384335958886179,0.07749854649006513,0.2392,0.1504,0.9911,0.8776,0.0473,0.28,0.2414,0.3333,0.375,0.0413,0.1916,0.2434,0.0154,0.0,0.2437,0.1561,0.9911,0.8824,0.0478,0.282,0.2414,0.3333,0.375,0.0422,0.2094,0.2536,0.0156,0.0,0.2415,0.1504,0.9911,0.8792,0.0476,0.28,0.2414,0.3333,0.375,0.042,0.195,0.2477,0.0155,0.0,org spec account,block of flats,0.2173,\"Stone, brick\",No,1.0,1.0,1.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n436924,0,Cash loans,F,N,N,0,112500.0,810000.0,23683.5,810000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010643,-8649,-499,-8055.0,-1320,,1,1,1,1,0,0,,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.26302849872312845,0.6393249035080867,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-185.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,3.0\n425239,0,Cash loans,M,Y,Y,0,180000.0,889515.0,31644.0,742500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-18679,-1797,-7546.0,-2219,0.0,1,1,0,1,0,0,Drivers,2.0,2,2,WEDNESDAY,15,0,0,0,1,1,1,Self-employed,,0.5215008502344807,0.6413682574954046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2475.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n214656,0,Revolving loans,F,N,Y,1,81000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.009175,-9866,-1259,-4411.0,-2534,,1,1,1,1,0,0,Sales staff,3.0,2,2,THURSDAY,16,0,0,0,0,1,1,Self-employed,0.4495971205441254,0.6711602561956459,0.8106180215523969,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-583.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n194778,0,Cash loans,F,N,N,0,135000.0,450000.0,20979.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-15981,-651,-580.0,-4169,,1,1,1,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Self-employed,,0.11343827456767985,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-303.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n388333,0,Cash loans,F,Y,Y,0,225000.0,1258650.0,56277.0,1125000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,Municipal apartment,0.006207,-22243,365243,-36.0,-4438,10.0,1,0,0,1,0,1,,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,XNA,,0.6958396722560182,0.2778856891082046,0.0753,0.0,0.9831,0.7688,,0.08,0.069,0.3333,0.375,0.033,0.0614,0.0794,0.0,0.0101,0.0767,0.0,0.9831,0.7779,,0.0806,0.069,0.3333,0.375,0.0337,0.067,0.0827,0.0,0.0107,0.076,0.0,0.9831,0.7719,,0.08,0.069,0.3333,0.375,0.0335,0.0624,0.0808,0.0,0.0103,reg oper account,block of flats,0.0697,Panel,No,0.0,0.0,0.0,0.0,-1363.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n177207,0,Cash loans,F,Y,Y,0,81000.0,177768.0,9774.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-15678,-9012,-4336.0,-4555,7.0,1,1,1,1,1,0,Cooking staff,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,School,0.4420583208787067,0.5486775258203577,0.4329616670974407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2208.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n121654,0,Cash loans,M,Y,N,0,180000.0,531706.5,29817.0,459000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-19404,-1700,-10863.0,-2865,17.0,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,16,0,0,0,0,1,1,Other,,0.5705431250244671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1561.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n200600,0,Revolving loans,F,N,Y,0,81000.0,405000.0,20250.0,405000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.005002,-20770,365243,-9275.0,-4224,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,11,0,0,0,0,0,0,XNA,,0.6397886754991788,0.8083935912119442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0147,,No,1.0,0.0,1.0,0.0,-993.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n123979,0,Cash loans,M,Y,Y,0,171000.0,1046142.0,30717.0,913500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.01885,-15954,-288,-5735.0,-4480,0.0,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,10,0,1,1,0,0,0,Security,,0.6718934309216112,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1474.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,0.0\n106481,0,Cash loans,F,N,N,0,94500.0,225000.0,23053.5,225000.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.018634,-21982,365243,-9081.0,-4843,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,XNA,0.6115498924108337,0.19133293145177285,0.4561097392782771,0.1113,0.0897,0.9871,0.8232,0.0495,0.12,0.1034,0.3333,0.0417,0.063,0.0908,0.1156,0.0,0.0,0.1134,0.0931,0.9871,0.8301,0.05,0.1208,0.1034,0.3333,0.0417,0.0644,0.0992,0.1204,0.0,0.0,0.1124,0.0897,0.9871,0.8256,0.0498,0.12,0.1034,0.3333,0.0417,0.0641,0.0923,0.1177,0.0,0.0,org spec account,block of flats,0.11800000000000001,Panel,No,10.0,1.0,10.0,1.0,-198.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n437151,0,Cash loans,M,Y,Y,0,157500.0,599778.0,32665.5,477000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-20577,365243,-4576.0,-2563,19.0,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,0.648559467595132,0.7112708442837378,0.7517237147741489,0.1856,0.1236,0.9831,0.7688,0.0729,0.2,0.1724,0.3333,0.375,0.11,0.1513,0.1901,0.0,0.0,0.1891,0.1283,0.9831,0.7779,0.0736,0.2014,0.1724,0.3333,0.375,0.1125,0.1653,0.1981,0.0,0.0,0.1874,0.1236,0.9831,0.7719,0.0734,0.2,0.1724,0.3333,0.375,0.1119,0.1539,0.1935,0.0,0.0,reg oper account,block of flats,0.1894,Panel,No,0.0,0.0,0.0,0.0,-1495.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n393583,0,Cash loans,F,Y,N,2,144000.0,301500.0,15525.0,301500.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.008473999999999999,-14088,-2540,-3143.0,-4813,14.0,1,1,0,1,0,0,Laborers,4.0,2,2,FRIDAY,15,0,0,0,0,0,0,Business Entity Type 3,0.8772091988466437,0.6354737318890222,0.2079641743053816,0.0711,0.1208,0.9608,,0.0,0.0,0.2414,0.125,0.1667,0.0,0.0572,0.0858,0.0039,0.0041,0.0725,0.1254,0.9608,,0.0,0.0,0.2414,0.125,0.1667,0.0,0.0624,0.0894,0.0039,0.0043,0.0718,0.1208,0.9608,,0.0,0.0,0.2414,0.125,0.1667,0.0,0.0581,0.0873,0.0039,0.0042,reg oper account,block of flats,0.0769,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1666.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n171797,0,Cash loans,F,N,N,0,67500.0,254700.0,14350.5,225000.0,Group of people,Commercial associate,Secondary / secondary special,Married,Municipal apartment,0.018029,-19330,-2814,-8686.0,-2807,,1,1,0,1,0,0,Sales staff,2.0,3,3,THURSDAY,4,0,0,0,0,0,0,Other,0.6285738927453016,0.6599560200601262,0.4686596550493113,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n232484,1,Cash loans,M,N,N,0,202500.0,545040.0,26640.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020246,-18234,-295,-205.0,-1315,,1,1,0,1,0,0,Laborers,2.0,3,3,THURSDAY,10,0,0,0,0,1,1,Business Entity Type 3,0.4115856169395656,0.21522342676722184,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n277222,0,Cash loans,F,N,N,0,135000.0,755190.0,36459.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-18976,-7799,-10659.0,-2529,,1,1,0,1,0,0,Cleaning staff,2.0,2,2,SATURDAY,14,0,0,0,0,1,1,School,,0.5039325003774981,0.2721336844147212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-958.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n371313,0,Cash loans,F,Y,N,1,90000.0,225000.0,8932.5,225000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.030755,-10543,-243,-1883.0,-2323,6.0,1,1,1,1,0,0,High skill tech staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.12179886096249724,0.4980010103975307,0.4329616670974407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-950.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n101869,0,Cash loans,F,Y,Y,2,112500.0,450000.0,22977.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-12418,-732,-880.0,-3918,13.0,1,1,1,1,1,0,Medicine staff,4.0,2,2,TUESDAY,11,0,0,0,0,1,1,Medicine,0.4528716380229422,0.6654938664241413,0.3539876078507373,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-561.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n251883,0,Revolving loans,F,N,N,0,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-16103,-1145,-3455.0,-5185,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Self-employed,,0.1915258625230554,,0.1866,,0.9846,,,0.08,0.069,0.3333,,,,0.0606,,0.1643,0.1901,,0.9846,,,0.0806,0.069,0.3333,,,,0.0631,,0.1739,0.1884,,0.9846,,,0.08,0.069,0.3333,,,,0.0617,,0.1677,,block of flats,0.0834,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1214.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n240925,0,Cash loans,M,N,Y,0,67500.0,158148.0,7501.5,103500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006305,-19928,-371,-6868.0,-3241,,1,1,0,1,0,0,Laborers,2.0,3,3,THURSDAY,9,0,0,0,0,0,0,Other,0.4375654368472232,0.24394328479271304,,0.0722,0.0918,0.9876,0.83,0.0116,0.0,0.1379,0.1667,0.2083,0.0283,0.057999999999999996,0.0664,0.0039,0.0298,0.0735,0.0953,0.9876,0.8367,0.0118,0.0,0.1379,0.1667,0.2083,0.0289,0.0634,0.0692,0.0039,0.0315,0.0729,0.0918,0.9876,0.8323,0.0117,0.0,0.1379,0.1667,0.2083,0.0288,0.059000000000000004,0.0676,0.0039,0.0304,reg oper account,block of flats,0.0651,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-140.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n409830,0,Cash loans,F,Y,Y,0,90000.0,673875.0,19701.0,562500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.010966,-23475,365243,-7286.0,-4351,10.0,1,0,0,1,0,0,,1.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.2870421505243009,0.5902333386185574,0.2454,0.17800000000000002,0.9901,0.8640000000000001,0.0554,0.24,0.2069,0.375,0.0417,0.1549,0.1984,0.2572,0.0077,0.0272,0.25,0.1847,0.9901,0.8693,0.0559,0.2417,0.2069,0.375,0.0417,0.1585,0.2167,0.268,0.0078,0.0288,0.2477,0.17800000000000002,0.9901,0.8658,0.0558,0.24,0.2069,0.375,0.0417,0.1576,0.2018,0.2619,0.0078,0.0278,org spec account,block of flats,0.2386,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-710.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,2.0,3.0\n120707,0,Cash loans,F,N,Y,0,67500.0,71955.0,7879.5,67500.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-21759,365243,-9187.0,-4935,,1,0,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,XNA,,0.3365083226655035,0.7136313997323308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1263.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n127806,0,Cash loans,F,N,Y,0,81000.0,286704.0,12276.0,247500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-17926,-399,-3474.0,-1485,,1,1,0,1,0,0,Cooking staff,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Restaurant,0.4375958929740439,0.2631435910213423,0.31703177858344445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n272288,0,Cash loans,F,N,N,0,157500.0,281493.0,10561.5,243000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018634,-22438,365243,-546.0,-4696,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,0.7760959864927485,0.3956738436714012,0.7503751495159068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-449.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n351564,0,Cash loans,F,Y,N,0,135000.0,1205451.0,36549.0,1080000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.016612000000000002,-18836,-893,-2317.0,-2315,3.0,1,1,1,1,1,0,Security staff,2.0,2,2,TUESDAY,12,0,1,1,0,1,1,Security,,0.5290421075075158,0.6380435278721609,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-352.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n305980,0,Cash loans,F,N,Y,0,112500.0,1493086.5,52029.0,1363500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.020246,-20020,-6163,-9481.0,-3512,,1,1,0,1,0,0,Managers,2.0,3,3,MONDAY,10,0,0,0,0,0,0,Trade: type 7,0.6031095121842691,0.2958382616426805,0.5226973172821112,0.0928,0.1012,0.9762,0.6736,0.0145,0.0,0.2069,0.1667,0.2083,0.0577,0.0756,0.0873,0.0,0.0,0.0945,0.105,0.9762,0.6864,0.0146,0.0,0.2069,0.1667,0.2083,0.059000000000000004,0.0826,0.091,0.0,0.0,0.0937,0.1012,0.9762,0.6779999999999999,0.0146,0.0,0.2069,0.1667,0.2083,0.0587,0.077,0.0889,0.0,0.0,reg oper spec account,block of flats,0.0766,Panel,No,0.0,0.0,0.0,0.0,-577.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,1.0\n262168,0,Cash loans,F,N,N,0,54000.0,135000.0,7065.0,135000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.015221,-15133,-5844,-932.0,-3873,,1,1,1,1,1,0,,2.0,2,2,THURSDAY,20,0,0,0,0,0,0,Industry: type 11,0.4750585470929497,0.5942740028444657,0.7194907850918436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-1031.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n293085,0,Cash loans,F,Y,N,2,202500.0,225000.0,20637.0,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.025164,-14593,-1342,-1071.0,-515,8.0,1,1,1,1,1,1,Managers,4.0,2,2,THURSDAY,7,0,0,0,0,0,0,Other,,0.34613889311167945,0.18088797776707397,0.4515,0.2393,0.9866,0.8164,0.1634,0.44,0.3793,0.375,0.4167,0.1024,0.3682,0.4724,0.0,0.0,0.4601,0.2483,0.9866,0.8236,0.1649,0.4431,0.3793,0.375,0.4167,0.1047,0.4022,0.4922,0.0,0.0,0.4559,0.2393,0.9866,0.8189,0.1644,0.44,0.3793,0.375,0.4167,0.1041,0.3745,0.4809,0.0,0.0,not specified,block of flats,0.4609,Panel,No,0.0,0.0,0.0,0.0,-562.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n328726,0,Cash loans,F,N,Y,0,360000.0,781920.0,41656.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.0105,-18847,-4352,-9096.0,-2387,,1,1,0,1,1,0,,2.0,3,3,FRIDAY,13,0,0,0,0,0,0,Other,,0.6226106534403897,0.5478104658520093,,0.2434,0.9861,,,0.32,0.2759,0.3333,,,,0.3011,,0.1964,,0.2526,0.9861,,,0.3222,0.2759,0.3333,,,,0.3138,,0.2079,,0.2434,0.9861,,,0.32,0.2759,0.3333,,,,0.3066,,0.2005,,,0.2796,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-696.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n298720,0,Cash loans,F,Y,Y,0,135000.0,1032093.0,43857.0,922500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.010966,-23492,365243,-1548.0,-4751,7.0,1,0,0,1,0,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,,0.6989960593018631,0.6706517530862718,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1597.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n182083,0,Cash loans,F,N,Y,0,270000.0,1056447.0,31014.0,922500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.025164,-17465,-4019,-6867.0,-1019,,1,1,0,1,0,0,Core staff,1.0,2,2,TUESDAY,7,0,0,0,0,0,0,Security Ministries,,0.4690660121122938,0.6658549219640212,0.0649,0.0765,0.9806,0.7348,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.053,0.0582,0.0,0.0152,0.0662,0.0794,0.9806,0.7452,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0579,0.0606,0.0,0.0161,0.0656,0.0765,0.9806,0.7383,0.0,0.0,0.1379,0.1667,0.2083,0.0,0.0539,0.0592,0.0,0.0155,reg oper account,block of flats,0.049,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-531.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n365785,0,Cash loans,M,Y,Y,1,247500.0,1078200.0,38331.0,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.018801,-16158,-1641,-2828.0,-4032,15.0,1,1,0,1,0,1,,3.0,2,2,TUESDAY,10,0,0,0,0,1,1,Other,0.5169549193192325,0.5973757230247286,0.2392262794694045,0.099,0.091,0.9791,0.7144,0.040999999999999995,0.0,0.2069,0.1667,0.2083,0.1341,0.0807,0.0689,0.0,0.1102,0.1008,0.0944,0.9791,0.7256,0.0413,0.0,0.2069,0.1667,0.2083,0.1372,0.0882,0.064,0.0,0.1166,0.0999,0.091,0.9791,0.7182,0.0412,0.0,0.2069,0.1667,0.2083,0.1364,0.0821,0.0701,0.0,0.1125,reg oper spec account,block of flats,0.0908,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1197.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,17.0\n393782,0,Cash loans,F,N,N,1,135000.0,547344.0,19660.5,472500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Co-op apartment,0.0105,-17723,-3663,-3411.0,-1249,,1,1,0,1,0,0,Sales staff,3.0,3,3,FRIDAY,10,0,0,0,0,0,0,Trade: type 7,,0.7184558056189869,0.7091891096653581,0.0175,0.0002,0.9776,0.66,0.0,0.0,0.069,0.0625,0.1667,0.0331,0.0269,0.0134,0.0,0.0008,0.0021,0.0,0.9752,0.6733,0.0,0.0,0.069,0.0,0.1667,0.0194,0.0294,0.0012,0.0,0.0,0.0177,0.0002,0.9776,0.6645,0.0,0.0,0.069,0.0625,0.1667,0.0337,0.0274,0.0136,0.0,0.0008,org spec account,terraced house,0.0202,Wooden,No,1.0,1.0,1.0,1.0,-1610.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n329257,0,Cash loans,F,N,N,0,247500.0,298512.0,20079.0,270000.0,Unaccompanied,Commercial associate,Incomplete higher,Single / not married,House / apartment,0.010032,-10782,-714,-9679.0,-504,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,8,0,0,0,0,0,0,Self-employed,0.5332917082683089,0.5374074244526348,0.21275630545434146,0.1021,0.0916,0.9801,0.728,0.0406,0.0,0.2069,0.1667,0.25,0.0804,0.0832,0.0886,0.0116,0.0339,0.10400000000000001,0.0951,0.9801,0.7387,0.0409,0.0,0.2069,0.1667,0.25,0.0823,0.0909,0.0923,0.0117,0.0359,0.1031,0.0916,0.9801,0.7316,0.0408,0.0,0.2069,0.1667,0.25,0.0818,0.0847,0.0902,0.0116,0.0346,reg oper account,block of flats,0.0771,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-333.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n186348,0,Cash loans,F,N,Y,0,157500.0,848745.0,40963.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.010276,-22451,365243,-1598.0,-4557,,1,0,0,1,0,1,,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,XNA,,0.5941210898870689,0.41184855592423975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-162.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n381384,0,Cash loans,M,Y,Y,0,144000.0,592560.0,30384.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.009657,-8425,-1016,-3189.0,-947,7.0,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,15,1,1,0,1,1,0,Transport: type 4,0.14014817625665932,0.341767054270982,0.4206109640437848,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n301718,0,Cash loans,F,N,N,0,180000.0,679500.0,24534.0,679500.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.028663,-16128,-2027,-2439.0,-4225,,1,1,1,1,1,0,Core staff,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,School,0.6202966356972728,0.4970935278158654,0.5620604831738043,0.067,0.1038,0.9886,0.8436,0.0117,0.0,0.1724,0.125,0.1667,0.0531,0.0521,0.0899,0.0077,0.0146,0.0683,0.1077,0.9886,0.8497,0.0118,0.0,0.1724,0.125,0.1667,0.0543,0.0569,0.0937,0.0078,0.0154,0.0677,0.1038,0.9886,0.8457,0.0118,0.0,0.1724,0.125,0.1667,0.054000000000000006,0.053,0.0915,0.0078,0.0149,reg oper account,block of flats,0.0803,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1774.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n157181,0,Cash loans,F,N,Y,0,216000.0,670500.0,26235.0,670500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-21910,365243,-11258.0,-4111,,1,0,0,1,0,0,,2.0,2,2,FRIDAY,9,0,0,0,1,0,0,XNA,,0.642178985644672,0.4135967602644276,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n406704,0,Cash loans,M,N,Y,0,202500.0,1325475.0,56290.5,1125000.0,Unaccompanied,State servant,Secondary / secondary special,Single / not married,House / apartment,0.032561,-22652,-8597,-12013.0,-4800,,1,1,1,1,1,0,Medicine staff,1.0,1,1,WEDNESDAY,16,0,0,0,0,0,0,University,,0.6510877701599931,0.5691487713619409,0.2392,0.114,0.9747,0.6532,0.0393,0.16,0.1379,0.3333,0.375,0.0597,0.1942,0.2201,0.0039,0.0013,0.2437,0.1183,0.9747,0.6668,0.0396,0.1611,0.1379,0.3333,0.375,0.061,0.2121,0.2294,0.0039,0.0013,0.2415,0.114,0.9747,0.6578,0.0395,0.16,0.1379,0.3333,0.375,0.0607,0.1975,0.2241,0.0039,0.0013,reg oper account,block of flats,0.1949,\"Stone, brick\",No,9.0,0.0,9.0,0.0,-1076.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n179453,0,Cash loans,F,N,Y,0,180000.0,900000.0,26316.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.020713,-14107,-2338,-1950.0,-4717,,1,1,1,1,1,1,Accountants,2.0,3,2,TUESDAY,9,0,0,0,0,0,0,Self-employed,0.354627538375498,0.4589994999363088,0.475849908720221,0.1361,0.0636,0.9742,0.6464,0.0467,0.0,0.1034,0.1667,0.2083,0.0388,0.1101,0.0441,0.0039,0.0067,0.1387,0.066,0.9742,0.6602,0.0471,0.0,0.1034,0.1667,0.2083,0.0397,0.1203,0.046,0.0039,0.0071,0.1374,0.0636,0.9742,0.6511,0.047,0.0,0.1034,0.1667,0.2083,0.0395,0.11199999999999999,0.0449,0.0039,0.0068,reg oper account,block of flats,0.0361,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1884.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n448207,0,Cash loans,F,N,N,0,292500.0,983160.0,54895.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-21768,-5025,-5870.0,-4645,,1,1,1,1,1,0,Managers,2.0,2,2,THURSDAY,9,0,0,0,0,0,0,Other,0.8852284346114243,0.6782202151740777,0.392773860603134,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2623.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n276702,0,Cash loans,F,N,N,0,76500.0,105534.0,12654.0,99000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,Municipal apartment,0.003818,-16091,-3290,-7946.0,-4506,,1,1,1,1,0,0,Medicine staff,1.0,2,2,WEDNESDAY,6,0,0,0,0,0,0,Medicine,,0.5622782057778712,0.656158373001177,0.0619,0.0708,0.9821,,,0.0,0.1034,0.1667,,0.0,,0.0556,,0.0988,0.063,0.0735,0.9821,,,0.0,0.1034,0.1667,,0.0,,0.0579,,0.1046,0.0625,0.0708,0.9821,,,0.0,0.1034,0.1667,,0.0,,0.0566,,0.1009,,block of flats,0.0765,Panel,No,0.0,0.0,0.0,0.0,-1289.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n144277,0,Cash loans,F,N,Y,1,72000.0,327024.0,21982.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018029,-11108,-678,-3688.0,-2250,,1,1,0,1,0,0,Laborers,3.0,3,3,MONDAY,8,0,0,0,0,0,0,Postal,0.3427858884265507,0.22010885955137405,0.07958643615986162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,0.0,4.0,0.0,-406.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n444179,0,Cash loans,F,N,Y,0,130500.0,808650.0,23305.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.014464,-22532,365243,-14454.0,-4500,,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,0.8055377720869972,0.6416210355253198,,0.0515,0.0604,0.9613,0.4696,0.0285,0.0,0.1034,0.1667,0.2083,,0.042,0.0485,,,0.0525,0.0627,0.9613,0.4904,0.0288,0.0,0.1034,0.1667,0.2083,,0.0459,0.0506,,,0.052000000000000005,0.0604,0.9613,0.4767,0.0287,0.0,0.1034,0.1667,0.2083,,0.0428,0.0494,,,reg oper account,block of flats,0.0538,Block,No,0.0,0.0,0.0,0.0,-1726.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n390264,0,Cash loans,F,N,Y,0,202500.0,481855.5,46354.5,463500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.025164,-15654,-2134,-5172.0,-4185,,1,1,0,1,0,0,,1.0,2,2,TUESDAY,14,0,0,0,0,0,0,Business Entity Type 3,0.8278323112366961,0.5929551900463176,0.5028782772082183,0.0412,0.0643,0.9603,0.456,0.0588,0.0,0.1724,0.125,0.1667,0.0275,0.0336,0.0623,0.0,0.0,0.042,0.0667,0.9603,0.4773,0.0593,0.0,0.1724,0.125,0.1667,0.0282,0.0367,0.0649,0.0,0.0,0.0416,0.0643,0.9603,0.4633,0.0592,0.0,0.1724,0.125,0.1667,0.027999999999999997,0.0342,0.0634,0.0,0.0,reg oper account,block of flats,0.0811,\"Stone, brick\",No,2.0,1.0,2.0,1.0,-2148.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n149297,0,Cash loans,F,N,Y,0,337500.0,1467612.0,56029.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-21213,-727,-13030.0,-4651,,1,1,0,1,0,0,Cleaning staff,2.0,1,1,FRIDAY,10,0,0,0,0,0,0,Cleaning,0.7892313498793881,0.4752025386728375,0.7922644738669378,0.0495,0.0811,0.9697,0.5852,0.0077,0.0,0.1379,0.125,0.1667,0.0,0.0403,0.0485,0.0,0.0,0.0504,0.0842,0.9697,0.6014,0.0078,0.0,0.1379,0.125,0.1667,0.0,0.0441,0.0506,0.0,0.0,0.05,0.0811,0.9697,0.5907,0.0077,0.0,0.1379,0.125,0.1667,0.0,0.040999999999999995,0.0494,0.0,0.0,reg oper account,block of flats,0.0424,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-551.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n339415,0,Cash loans,M,Y,Y,1,112500.0,640080.0,31261.5,450000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.006852,-18569,-2311,-7958.0,-1863,41.0,1,1,0,1,0,0,Laborers,3.0,3,3,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.4729235294679335,0.0969483170508572,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-447.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n182426,0,Cash loans,F,N,N,0,270000.0,1223010.0,47853.0,1125000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.04622,-22882,-1840,-13348.0,-4078,,1,1,0,1,0,0,Cleaning staff,1.0,1,1,MONDAY,19,0,0,0,0,1,1,Business Entity Type 3,,0.7402793519500342,0.2314393514998941,0.0753,0.0453,0.9856,0.8028,0.0315,0.0,0.1262,0.1667,0.2083,0.0589,0.0614,0.0619,0.0,0.0,0.063,0.0118,0.9816,0.7583,0.0,0.0,0.1379,0.1667,0.2083,0.0487,0.0551,0.0547,0.0,0.0,0.0625,0.0615,0.9816,0.7518,0.047,0.0,0.1379,0.1667,0.2083,0.06,0.0513,0.0548,0.0,0.0,reg oper account,block of flats,0.0668,Panel,No,3.0,0.0,3.0,0.0,-1707.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n285604,0,Revolving loans,M,N,Y,0,90000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.003122,-8379,-332,-2854.0,-1064,,1,1,0,1,0,0,Drivers,2.0,3,3,TUESDAY,15,0,0,0,0,1,1,Other,,0.6681998440427737,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-499.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n255476,0,Cash loans,F,N,N,0,225000.0,430582.5,24849.0,355500.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.010643,-20411,365243,-1148.0,-3943,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.551864965647536,0.7801436381572275,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-702.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,1.0,3.0\n170484,0,Cash loans,F,Y,Y,0,103500.0,454500.0,18022.5,454500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.02461,-16104,-5425,-943.0,-605,7.0,1,1,1,1,1,0,Core staff,2.0,2,2,TUESDAY,11,0,0,0,1,1,1,Government,0.7875886471721557,0.5334203142114855,0.6545292802242897,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-867.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n117708,0,Cash loans,F,N,Y,1,202500.0,900000.0,26446.5,900000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-14527,-7468,-8648.0,-3994,,1,1,0,1,0,0,Medicine staff,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Medicine,,0.6501035206413784,0.7557400501752248,0.0371,0.0309,0.9866,0.8164,0.0097,0.04,0.0345,0.3333,0.375,0.0126,0.0303,0.0399,0.0,0.0049,0.0378,0.0321,0.9866,0.8236,0.0097,0.0403,0.0345,0.3333,0.375,0.0129,0.0331,0.0415,0.0,0.0052,0.0375,0.0309,0.9866,0.8189,0.0097,0.04,0.0345,0.3333,0.375,0.0128,0.0308,0.0406,0.0,0.005,reg oper account,block of flats,0.0324,Panel,No,0.0,0.0,0.0,0.0,-432.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n351752,0,Cash loans,F,N,Y,0,90000.0,312768.0,17595.0,270000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006852,-21971,365243,-4201.0,-4208,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,5,0,0,0,0,0,0,XNA,,0.18930100842774086,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-384.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n399473,0,Cash loans,F,N,N,0,157500.0,1002870.0,39901.5,922500.0,Unaccompanied,State servant,Secondary / secondary special,Married,Office apartment,0.007120000000000001,-22425,-15181,-7161.0,-4715,,1,1,0,1,0,0,Medicine staff,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Medicine,,0.5145014894055968,0.5154953751603267,0.1134,0.1136,0.9806,0.7348,0.0538,0.0,0.2759,0.1667,0.2083,0.0697,0.0925,0.11699999999999999,0.0,0.0,0.1155,0.1179,0.9806,0.7452,0.0543,0.0,0.2759,0.1667,0.2083,0.0712,0.10099999999999999,0.1219,0.0,0.0,0.1145,0.1136,0.9806,0.7383,0.0541,0.0,0.2759,0.1667,0.2083,0.0709,0.0941,0.1191,0.0,0.0,reg oper account,block of flats,0.1214,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n129113,0,Cash loans,M,Y,Y,0,157500.0,454500.0,27936.0,454500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-17159,-2097,-3688.0,-706,30.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.6119487956403028,0.6884451983618574,0.6479768603302221,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-602.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n178177,0,Cash loans,F,N,Y,0,180000.0,675000.0,23071.5,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.030755,-21294,365243,-1746.0,-4602,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,XNA,,0.3810942911508809,0.6413682574954046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-638.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n158045,0,Cash loans,F,Y,Y,2,157500.0,900000.0,26446.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-16745,-1253,-4747.0,-274,16.0,1,1,0,1,0,0,Managers,4.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,Self-employed,0.6559774888861033,0.5792433033222801,0.4170996682522097,0.0577,0.0846,0.9811,0.7416,0.0064,0.0,0.1379,0.1667,0.0417,0.0526,0.0462,0.0527,0.0039,0.0548,0.0588,0.0877,0.9811,0.7517,0.0065,0.0,0.1379,0.1667,0.0417,0.0538,0.0505,0.0549,0.0039,0.057999999999999996,0.0583,0.0846,0.9811,0.7451,0.0064,0.0,0.1379,0.1667,0.0417,0.0535,0.047,0.0537,0.0039,0.055999999999999994,not specified,block of flats,0.0534,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-562.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n156379,0,Revolving loans,F,N,Y,0,112500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-16953,-5489,-1465.0,-502,,1,1,0,1,0,0,Private service staff,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,Services,,0.4999198801010818,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-197.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n325103,0,Cash loans,M,Y,Y,0,135000.0,513531.0,24835.5,459000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-8997,-263,-1458.0,-1643,19.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,11,0,0,0,1,1,0,Other,,0.5872003298987594,0.28371188263500075,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-1285.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,2.0\n314266,0,Cash loans,M,Y,N,0,157500.0,270000.0,21330.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,With parents,0.035792000000000004,-10379,-167,-3093.0,-2887,16.0,1,1,0,1,0,0,Cleaning staff,1.0,2,2,TUESDAY,16,0,0,0,1,1,0,Self-employed,,0.34074947270235817,0.4920600938649263,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-15.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,1.0,0.0,0.0,1.0\n397921,0,Cash loans,M,Y,Y,0,112500.0,808650.0,26086.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019101,-15911,-1094,-2685.0,-4294,12.0,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.297001212320088,0.8224987619370829,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1159.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n447516,0,Cash loans,F,N,Y,1,180000.0,137538.0,14724.0,121500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.025164,-11158,-3473,-2051.0,-3381,,1,1,0,1,0,0,,3.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.5581621093051902,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,1.0,0.0,-2440.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n134426,0,Cash loans,M,Y,Y,1,225000.0,1190340.0,63549.0,1125000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.00496,-10000,-822,-4513.0,-2685,14.0,1,1,0,1,0,0,,3.0,2,2,MONDAY,13,0,0,0,0,1,1,Military,0.1928549087210812,0.19181045253061774,0.4992720153045617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-757.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n115645,1,Cash loans,F,N,Y,1,157500.0,450000.0,17095.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-8752,-1474,-3884.0,-1406,,1,1,0,1,0,0,Sales staff,3.0,2,2,SUNDAY,13,0,0,0,1,1,0,Self-employed,,0.4939667236425896,0.41534714488434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n230151,0,Cash loans,F,N,N,0,202500.0,1762110.0,46611.0,1575000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.072508,-18672,-1505,-5474.0,-2206,,1,1,0,1,1,0,Managers,2.0,1,1,WEDNESDAY,10,0,0,0,0,0,0,Other,,0.7582748791051239,0.4974688893052743,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n395965,0,Cash loans,M,N,Y,0,157500.0,514777.5,33025.5,477000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Rented apartment,0.010032,-12868,-1279,-2092.0,-2372,,1,1,0,1,0,0,Sales staff,1.0,2,2,TUESDAY,4,0,0,0,1,1,0,Trade: type 7,,0.3035575589190972,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-557.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n262129,0,Cash loans,M,Y,N,0,135000.0,900000.0,45954.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018801,-13800,-5160,-2040.0,-4598,19.0,1,1,1,1,1,0,Core staff,2.0,2,2,MONDAY,14,0,0,0,1,1,0,Self-employed,,0.5297067667001445,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-329.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n130310,0,Cash loans,M,N,N,0,135000.0,990000.0,41944.5,990000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-23547,365243,-6360.0,-4505,,1,0,0,1,1,0,,2.0,1,1,SATURDAY,14,0,0,0,0,0,0,XNA,,0.3722175591401516,0.6785676886853644,0.0928,0.0,0.9901,0.8640000000000001,0.0503,0.0,0.2069,0.1667,0.2083,0.0914,0.0756,0.0502,0.0,0.0,0.0945,0.0,0.9901,0.8693,0.0508,0.0,0.2069,0.1667,0.2083,0.0935,0.0826,0.0523,0.0,0.0,0.0937,0.0,0.9901,0.8658,0.0506,0.0,0.2069,0.1667,0.2083,0.09300000000000001,0.077,0.0511,0.0,0.0,reg oper account,block of flats,0.067,Block,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n162779,1,Cash loans,F,N,N,0,76500.0,450000.0,21109.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.018029,-9453,-1765,0.0,-2130,,1,1,0,1,0,0,Cooking staff,2.0,3,3,FRIDAY,11,0,0,0,0,0,0,Other,,0.1915258625230554,0.4294236843421945,0.0082,,0.9727,,,,0.0345,0.0417,,,,0.0069,,,0.0084,,0.9727,,,,0.0345,0.0417,,,,0.0072,,,0.0083,,0.9727,,,,0.0345,0.0417,,,,0.006999999999999999,,,,block of flats,0.0054,Wooden,No,2.0,1.0,2.0,0.0,-483.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n373665,0,Cash loans,F,Y,N,0,67500.0,473760.0,51021.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.015221,-10851,-1321,-2351.0,-2553,17.0,1,1,1,1,1,0,Private service staff,2.0,2,2,SATURDAY,16,0,0,0,0,0,0,Self-employed,,0.561336118307776,0.6940926425266661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1145.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n300471,0,Cash loans,F,N,Y,2,63000.0,101880.0,10939.5,90000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.020713,-10118,-1291,-3925.0,-2700,,1,1,1,1,0,0,Cleaning staff,3.0,3,3,WEDNESDAY,5,0,0,0,0,0,0,School,,0.480691481210096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-693.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n100324,0,Cash loans,F,Y,Y,1,112500.0,152820.0,18265.5,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.003069,-10471,-1753,-4922.0,-2885,1.0,1,1,0,1,0,0,Sales staff,2.0,3,3,THURSDAY,17,0,0,0,0,0,0,Self-employed,0.4704602541955576,0.659140920402117,,0.1649,0.0928,0.9901,,,0.12,0.1034,0.375,,0.0145,,0.077,,0.0,0.1681,0.0963,0.9901,,,0.1208,0.1034,0.375,,0.0149,,0.0803,,0.0,0.1665,0.0928,0.9901,,,0.12,0.1034,0.375,,0.0148,,0.0784,,0.0,,block of flats,0.1047,Panel,No,0.0,0.0,0.0,0.0,-1957.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n202403,0,Cash loans,F,N,N,1,112500.0,225000.0,12204.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008625,-19881,-1644,-1362.0,-3393,,1,1,1,1,0,0,Core staff,3.0,2,2,MONDAY,14,0,0,0,0,0,0,Government,,0.5654993982357964,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-239.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n129153,0,Cash loans,F,N,N,2,126000.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.02461,-15997,-6433,-1781.0,-4587,,1,1,1,1,0,0,Laborers,4.0,2,2,TUESDAY,7,0,0,0,0,1,1,Business Entity Type 2,,0.6551452395277262,0.15193454904964762,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2523.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n448118,0,Cash loans,M,N,Y,0,90000.0,521280.0,28408.5,450000.0,\"Spouse, partner\",Pensioner,Secondary / secondary special,Married,House / apartment,0.006207,-21926,365243,-12322.0,-4622,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,12,0,0,0,0,0,0,XNA,,0.2158297769216068,0.6413682574954046,,,,,,,,,,,,0.0619,,,,,,,,,,,,,,0.0645,,,,,,,,,,,,,,0.063,,,,,0.0487,,No,0.0,0.0,0.0,0.0,-1345.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n250289,0,Cash loans,F,N,Y,1,157500.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.026392,-14418,-2693,-4339.0,-4185,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,15,0,0,0,0,0,0,Industry: type 9,0.5228811619371548,0.6973786627652306,0.520897599048938,0.1814,0.1135,0.993,,,0.2,0.1724,0.375,,0.174,,0.2111,,0.0233,0.1849,0.1178,0.993,,,0.2014,0.1724,0.375,,0.17800000000000002,,0.2199,,0.0247,0.1832,0.1135,0.993,,,0.2,0.1724,0.375,,0.177,,0.2149,,0.0238,,block of flats,0.1711,Panel,No,0.0,0.0,0.0,0.0,-735.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,0.0\n440929,0,Cash loans,F,N,N,0,157500.0,495801.0,34636.5,468000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.006207,-17302,-5250,-998.0,-836,,1,1,0,1,0,0,Sales staff,1.0,2,2,SATURDAY,10,0,0,0,0,1,1,Agriculture,0.6422377060746737,0.698153415226322,0.7801436381572275,0.066,0.006,0.9806,0.7348,0.008,0.0,0.1379,0.1667,0.2083,0.0523,0.053,0.0569,0.0039,0.0478,0.0672,0.0063,0.9806,0.7452,0.0081,0.0,0.1379,0.1667,0.2083,0.0535,0.0579,0.0593,0.0039,0.0506,0.0666,0.006,0.9806,0.7383,0.0081,0.0,0.1379,0.1667,0.2083,0.0533,0.0539,0.0579,0.0039,0.0488,reg oper account,block of flats,0.0552,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-1673.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n426653,0,Cash loans,M,Y,Y,2,218700.0,213948.0,21289.5,189000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-11446,-2968,-419.0,-4005,6.0,1,1,0,1,0,0,Core staff,4.0,2,2,TUESDAY,12,0,0,0,0,0,0,Military,,0.6564976867440595,0.7557400501752248,0.0309,,0.993,,,0.0,0.069,0.1667,,0.0214,,0.0442,,0.0434,0.0315,,0.993,,,0.0,0.069,0.1667,,0.0219,,0.046,,0.046,0.0312,,0.993,,,0.0,0.069,0.1667,,0.0217,,0.0449,,0.0443,,block of flats,0.0356,\"Stone, brick\",No,4.0,0.0,4.0,0.0,-338.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n258720,0,Revolving loans,F,Y,Y,0,112500.0,495000.0,24750.0,495000.0,\"Spouse, partner\",Commercial associate,Higher education,Married,House / apartment,0.015221,-10812,-765,-5290.0,-3434,7.0,1,1,0,1,1,0,Core staff,2.0,2,2,MONDAY,13,1,1,0,1,1,0,Bank,,0.7661769830409292,0.8297501422395771,0.0825,0.0633,0.9906,0.8708,0.0574,0.08,0.069,0.375,0.4167,0.0389,0.0672,0.0934,0.0,0.0,0.084,0.0657,0.9906,0.8759,0.0579,0.0806,0.069,0.375,0.4167,0.0398,0.0735,0.0973,0.0,0.0,0.0833,0.0633,0.9906,0.8725,0.0577,0.08,0.069,0.375,0.4167,0.0396,0.0684,0.095,0.0,0.0,,block of flats,0.1048,Panel,No,0.0,0.0,0.0,0.0,-176.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n349512,0,Revolving loans,M,N,N,1,450000.0,585000.0,29250.0,585000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.028663,-12383,-316,-1653.0,-4302,,1,1,0,1,0,0,Managers,3.0,2,2,WEDNESDAY,15,1,1,0,1,1,0,Industry: type 9,0.3650442765810887,0.7686904370380906,0.8425892767430772,0.2134,0.1463,0.9921,0.8912,0.0924,0.24,0.2069,0.375,0.3333,0.1522,0.174,0.2293,0.0,0.0,0.2174,0.1518,0.9921,0.8955,0.0932,0.2417,0.2069,0.375,0.3333,0.1557,0.1901,0.2389,0.0,0.0,0.2155,0.1463,0.9921,0.8927,0.0929,0.24,0.2069,0.375,0.3333,0.1548,0.177,0.2334,0.0,0.0,reg oper account,block of flats,0.1803,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2560.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n416725,0,Cash loans,F,N,N,1,90000.0,1078200.0,28570.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.006670999999999999,-13992,-970,-3961.0,-4735,,1,1,0,1,0,0,Cleaning staff,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Business Entity Type 3,0.3816885368418951,0.6182178905543536,0.2301588968634147,0.1031,0.1088,0.9796,,,0.0,0.2069,0.1667,,,,0.0903,,0.0983,0.105,0.1129,0.9796,,,0.0,0.2069,0.1667,,,,0.0941,,0.1041,0.1041,0.1088,0.9796,,,0.0,0.2069,0.1667,,,,0.092,,0.1004,,block of flats,0.0924,Block,No,1.0,0.0,1.0,0.0,-6.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n258427,0,Cash loans,M,Y,Y,0,180000.0,576072.0,21717.0,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-18302,-3089,-11853.0,-1828,64.0,1,1,0,1,1,0,,2.0,2,2,FRIDAY,15,0,0,0,1,1,0,Construction,0.5157694862708382,0.5518433332600051,0.6195277080511546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1720.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n300912,0,Cash loans,F,N,Y,0,76500.0,450000.0,22018.5,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Rented apartment,0.030755,-10906,-1805,-5201.0,-3548,,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,15,0,0,0,1,1,0,Business Entity Type 3,0.7015103398210738,0.4600857200402014,0.6690566947824041,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-789.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n404148,0,Cash loans,F,N,N,0,54000.0,445500.0,16924.5,445500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.028663,-17409,-10236,-2942.0,-936,,1,1,1,1,0,0,Medicine staff,2.0,2,2,SUNDAY,9,0,0,0,0,0,0,Medicine,,0.5060534411665178,0.326475210066026,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n211514,0,Cash loans,F,Y,Y,0,202500.0,945000.0,27630.0,945000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-19572,-2580,-2252.0,-3103,2.0,1,1,0,1,1,0,Core staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Kindergarten,,0.7726594760764038,0.7136313997323308,0.0722,0.048,0.9781,0.7008,0.0163,0.0,0.1379,0.1667,0.0417,0.0197,0.0588,0.0519,0.0,0.0,0.0735,0.0498,0.9782,0.7125,0.0164,0.0,0.1379,0.1667,0.0417,0.0201,0.0643,0.0541,0.0,0.0,0.0729,0.048,0.9781,0.7048,0.0164,0.0,0.1379,0.1667,0.0417,0.02,0.0599,0.0529,0.0,0.0,reg oper account,block of flats,0.0408,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2083.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n321981,0,Cash loans,F,N,Y,0,157500.0,491823.0,29848.5,373500.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.006305,-14969,-1729,-3490.0,-4484,,1,1,0,1,0,0,,1.0,3,3,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.4022072079869671,0.8435435389318647,0.0691,0.0714,0.9866,0.8164,0.0593,0.0,0.0345,0.1667,0.2083,0.032,0.0546,0.0475,0.0077,0.0103,0.0704,0.07400000000000001,0.9866,0.8236,0.0598,0.0,0.0345,0.1667,0.2083,0.0327,0.0597,0.0495,0.0078,0.0109,0.0697,0.0714,0.9866,0.8189,0.0597,0.0,0.0345,0.1667,0.2083,0.0326,0.0556,0.0484,0.0078,0.0105,reg oper account,block of flats,0.0396,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1261.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n434597,0,Cash loans,F,Y,Y,0,90000.0,1255680.0,41499.0,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-19815,-4726,-9553.0,-3266,7.0,1,1,1,1,0,0,,2.0,2,2,SUNDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.6531415800811785,0.6212263380626669,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1820.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n339904,0,Cash loans,F,N,Y,1,135000.0,481855.5,47070.0,463500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-18317,-776,-2909.0,-1874,,1,1,0,1,0,0,Sales staff,3.0,2,2,TUESDAY,16,0,0,0,0,0,0,Self-employed,0.5683003785658957,0.7130504278184864,0.6127042441012546,0.1392,0.1293,0.9841,0.932,0.0579,0.2,0.0493,0.0283,0.0417,0.0017,0.1135,0.0161,0.0,0.1457,0.1418,0.1342,0.9916,0.9347,0.0585,0.2014,0.0345,0.0,0.0417,0.0,0.124,0.0013,0.0,0.1542,0.1405,0.1293,0.9916,0.9329,0.0583,0.2,0.0345,0.0,0.0417,0.0,0.1154,0.002,0.0,0.1488,reg oper spec account,block of flats,0.001,Panel,Yes,0.0,0.0,0.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n132511,0,Cash loans,F,Y,N,0,90000.0,555273.0,14778.0,463500.0,,Working,Higher education,Married,House / apartment,0.018634,-12515,-2442,-1866.0,-4732,8.0,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,Other,0.32710056540117777,0.5261098725477281,0.6109913280868294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,1.0,5.0,1.0,-3.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n212833,0,Cash loans,F,N,Y,0,157500.0,679500.0,39726.0,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009334,-9155,-159,-6152.0,-1755,,1,1,1,1,0,0,,2.0,2,2,TUESDAY,17,0,0,0,0,0,0,Self-employed,,0.6130019337489677,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1271.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n444993,1,Cash loans,F,N,Y,0,144000.0,188685.0,8311.5,157500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020246,-23474,365243,-9371.0,-4807,,1,0,0,1,0,0,,2.0,3,3,TUESDAY,10,0,0,0,0,0,0,XNA,0.8373480131152733,0.06707039721858178,0.7981372313187245,0.001,0.0,0.9831,0.7688,0.0,0.0,0.0,0.0,0.0,0.0,0.0008,0.0008,0.0,0.0,0.0011,0.0,0.9831,0.7779,0.0,0.0,0.0,0.0,0.0,0.0,0.0009,0.0008,0.0,0.0,0.001,0.0,0.9831,0.7719,0.0,0.0,0.0,0.0,0.0,0.0,0.0009,0.0008,0.0,0.0,not specified,block of flats,0.0006,Wooden,No,1.0,0.0,1.0,0.0,-272.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n153483,0,Cash loans,F,N,Y,0,360000.0,1288350.0,37800.0,1125000.0,Unaccompanied,Pensioner,Higher education,Separated,House / apartment,0.01885,-21700,365243,-6533.0,-4827,,1,0,0,1,0,0,,1.0,2,2,SUNDAY,11,0,0,0,0,0,0,XNA,,0.4157801233636881,0.4489622731076524,0.2433,0.1137,0.9911,,,0.2,0.1724,0.3333,,0.0914,,0.2083,,0.2113,0.2479,0.11800000000000001,0.9911,,,0.2014,0.1724,0.3333,,0.0935,,0.217,,0.2237,0.2457,0.1137,0.9911,,,0.2,0.1724,0.3333,,0.09300000000000001,,0.2121,,0.2158,,block of flats,0.1638,Panel,No,0.0,0.0,0.0,0.0,-436.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n409811,0,Revolving loans,M,N,N,2,144000.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,Rented apartment,0.006233,-19435,-141,-7817.0,-2449,,1,1,0,1,0,0,,4.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.2562866351500153,,0.2031,0.2037,0.9856,0.8028,0.0217,0.0,0.069,0.1667,0.2083,0.271,0.1656,0.0742,0.0,0.0,0.2069,0.2114,0.9856,0.8105,0.0219,0.0,0.069,0.1667,0.2083,0.2772,0.1809,0.0773,0.0,0.0,0.2051,0.2037,0.9856,0.8054,0.0218,0.0,0.069,0.1667,0.2083,0.2757,0.1685,0.0755,0.0,0.0,not specified,specific housing,0.0702,Panel,No,4.0,0.0,4.0,0.0,-173.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n300691,0,Cash loans,M,Y,Y,1,288000.0,450000.0,27324.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-16434,-699,-3768.0,-4315,12.0,1,1,0,1,0,0,Laborers,3.0,3,3,THURSDAY,10,0,0,0,0,1,1,Other,0.3293029098471917,0.6237814339770127,0.3077366963789207,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-882.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n115695,0,Cash loans,F,N,Y,0,270000.0,906615.0,30091.5,688500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.018029,-19642,-2651,-754.0,-3163,,1,1,0,1,0,0,,2.0,3,3,WEDNESDAY,9,0,0,0,0,0,0,Self-employed,,0.1888249255855544,0.7338145369642702,0.0082,0.0,0.9722,,,0.0,0.0345,0.0417,,0.0082,,0.0054,,0.0,0.0084,0.0,0.9722,,,0.0,0.0345,0.0417,,0.0084,,0.0056,,0.0,0.0083,0.0,0.9722,,,0.0,0.0345,0.0417,,0.0084,,0.0055,,0.0,,block of flats,0.0064,Wooden,No,1.0,0.0,1.0,0.0,-2064.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n409498,0,Revolving loans,F,N,N,0,270000.0,765000.0,38250.0,765000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.072508,-10167,-1443,-4925.0,-2814,,1,1,0,1,1,0,Managers,1.0,1,1,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.7506637734726628,0.8106180215523969,0.2079,0.1058,0.9891,0.8504,0.0018,0.2664,0.1148,0.5971,0.0417,0.0,0.1437,0.2412,0.0,0.0117,0.1008,0.0766,0.9891,0.8563,0.0008,0.3222,0.1379,0.6667,0.0417,0.0,0.0882,0.1266,0.0,0.0,0.2561,0.0956,0.9891,0.8524,0.0018,0.32,0.1379,0.6667,0.0417,0.0,0.1462,0.2951,0.0,0.0,reg oper account,block of flats,0.2356,Panel,No,0.0,0.0,0.0,0.0,-185.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n415432,0,Cash loans,F,N,Y,0,135000.0,271066.5,23395.5,234000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.009549,-22548,365243,-12917.0,-4047,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,,0.5089720960307708,0.4135967602644276,,,0.9513,,,,0.0345,0.0,,,,0.003,,,,,0.9513,,,,0.0345,0.0,,,,0.0031,,,,,0.9513,,,,0.0345,0.0,,,,0.003,,,,,0.0023,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-54.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n104555,0,Cash loans,F,N,Y,0,360000.0,405000.0,32125.5,405000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-14563,-1876,-2695.0,-5033,,1,1,1,1,0,0,Laborers,2.0,2,2,FRIDAY,12,0,0,0,0,1,1,Transport: type 2,0.4260887215714386,0.3487169697880438,0.326475210066026,0.0082,0.0,0.9692,0.5784,0.0013,0.0,0.069,0.0417,0.0833,0.0118,0.0067,0.0076,0.0,0.0,0.0084,0.0,0.9692,0.5949,0.0013,0.0,0.069,0.0417,0.0833,0.0121,0.0073,0.008,0.0,0.0,0.0083,0.0,0.9692,0.584,0.0013,0.0,0.069,0.0417,0.0833,0.0121,0.0068,0.0078,0.0,0.0,reg oper account,block of flats,0.0067,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n386637,0,Cash loans,F,Y,Y,0,247500.0,1971072.0,68643.0,1800000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-14462,-1871,-1030.0,-2976,2.0,1,1,0,1,1,1,,2.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 1,0.6997935499967477,0.7678378311780938,0.29859498978739724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2354.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,3.0\n349467,0,Cash loans,M,N,Y,0,112500.0,284400.0,10849.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018801,-22002,365243,-4913.0,-4928,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,12,0,0,0,1,0,0,XNA,,0.4344881849886724,0.4294236843421945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1518.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n197960,0,Cash loans,F,N,N,0,157500.0,414229.5,21172.5,342000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-17622,-683,-9919.0,-1166,,1,1,0,1,0,0,,2.0,2,2,SATURDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.712844565207856,0.5884877883422673,0.4454,0.2835,0.9871,0.8232,0.0675,0.48,0.4138,0.3333,0.375,0.2561,0.3631,0.4684,0.0,0.0,0.4538,0.2942,0.9871,0.8301,0.0681,0.4834,0.4138,0.3333,0.375,0.2619,0.3967,0.4881,0.0,0.0,0.4497,0.2835,0.9871,0.8256,0.0679,0.48,0.4138,0.3333,0.375,0.2605,0.3694,0.4769,0.0,0.0,reg oper account,block of flats,0.4275,Panel,No,0.0,0.0,0.0,0.0,-2499.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n324050,0,Revolving loans,F,Y,N,0,112500.0,225000.0,11250.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-19971,-1686,-3233.0,-3152,17.0,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Postal,,0.6937018676007985,0.2750003523983893,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-714.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n299494,0,Cash loans,F,N,Y,0,112500.0,225000.0,22252.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.030755,-25133,365243,-15360.0,-3897,,1,0,0,1,0,0,,1.0,2,2,SATURDAY,14,0,0,0,0,0,0,XNA,,0.6655474120881619,0.5989262182569273,0.1072,0.1976,0.9712,0.5104,0.0275,0.0,0.3103,0.2083,0.2083,0.0644,0.0832,1.0,0.0193,0.1595,0.1092,0.205,0.9712,0.5296,0.0277,0.0,0.3103,0.2083,0.2083,0.0658,0.0909,1.0,0.0195,0.1688,0.1083,0.1976,0.9712,0.5169,0.0276,0.0,0.3103,0.2083,0.2083,0.0655,0.0847,1.0,0.0194,0.1628,reg oper spec account,block of flats,0.1658,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-488.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n276718,0,Cash loans,M,Y,Y,1,157500.0,263686.5,27819.0,238500.0,Unaccompanied,State servant,Incomplete higher,Married,House / apartment,0.035792000000000004,-13841,-3712,-7882.0,-4503,18.0,1,1,0,1,0,0,Core staff,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Police,,0.7848271292943221,0.5709165417729987,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-493.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n355750,0,Cash loans,F,N,Y,0,90000.0,76752.0,9238.5,72000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-16030,-1270,-4005.0,-4480,,1,1,0,1,0,0,Sales staff,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Business Entity Type 1,0.552846199746293,0.6727413238334873,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1584.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n264646,0,Cash loans,F,N,Y,0,81000.0,50940.0,5166.0,45000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00702,-8780,-1410,-119.0,-199,,1,1,1,1,0,0,Waiters/barmen staff,2.0,2,2,TUESDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.233994215612468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n221040,0,Cash loans,F,N,Y,0,202500.0,1125000.0,33025.5,1125000.0,Family,State servant,Secondary / secondary special,Married,House / apartment,0.030755,-22493,-6163,-664.0,-5010,,1,1,1,1,0,0,Accountants,2.0,2,2,TUESDAY,16,1,1,0,1,1,0,Military,,0.7208514208182348,0.36227724703843145,0.0072,0.0,0.9354,,,0.0,0.0345,0.0417,,0.0096,,0.0051,,0.0,0.0074,0.0,0.9355,,,0.0,0.0345,0.0417,,0.0098,,0.0053,,0.0,0.0073,0.0,0.9354,,,0.0,0.0345,0.0417,,0.0097,,0.0052,,0.0,,block of flats,0.0055,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-540.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n136548,0,Cash loans,F,N,Y,2,81000.0,835380.0,40320.0,675000.0,Unaccompanied,State servant,Secondary / secondary special,Married,Municipal apartment,0.020713,-9555,-2306,-9555.0,-1134,,1,1,0,1,0,0,,4.0,3,3,THURSDAY,15,0,0,0,1,1,0,Other,0.4006109380309968,0.5534435946077892,0.4866531565147181,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-577.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n242322,1,Cash loans,M,Y,Y,0,180000.0,314055.0,19107.0,238500.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.00702,-13982,-2326,-2434.0,-2547,4.0,1,1,0,1,0,0,Core staff,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Religion,,0.5132505351662536,0.3740208032583212,0.0113,,,,,0.0,0.0345,0.0,,,,,,,0.0116,,,,,0.0,0.0345,0.0,,,,,,,0.0115,,,,,0.0,0.0345,0.0,,,,,,,,,0.0314,Wooden,No,0.0,0.0,0.0,0.0,-989.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n191591,0,Cash loans,F,N,Y,0,112500.0,571446.0,18562.5,477000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.009657,-21997,365243,-12021.0,-616,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,10,0,0,0,0,0,0,XNA,,0.6105089233721966,0.6347055309763198,0.0165,0.0375,0.9816,0.7484,0.0022,0.0,0.069,0.0417,0.0417,0.03,0.0134,0.0144,0.0,0.0352,0.0168,0.0389,0.9816,0.7583,0.0022,0.0,0.069,0.0417,0.0417,0.0307,0.0147,0.015,0.0,0.0372,0.0167,0.0375,0.9816,0.7518,0.0022,0.0,0.069,0.0417,0.0417,0.0306,0.0137,0.0147,0.0,0.0359,reg oper account,block of flats,0.0191,Panel,No,0.0,0.0,0.0,0.0,-762.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n155291,0,Cash loans,M,Y,Y,1,157500.0,250614.0,19525.5,202500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.031329,-11460,-431,-5535.0,-2021,13.0,1,1,0,1,0,0,Drivers,3.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Transport: type 4,,0.6456886704890787,0.656158373001177,0.0165,,0.9722,,,0.0,0.069,0.0417,,,,0.0129,,0.0,0.0168,,0.9722,,,0.0,0.069,0.0417,,,,0.0134,,0.0,0.0167,,0.9722,,,0.0,0.069,0.0417,,,,0.0131,,0.0,,block of flats,0.0101,,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n340153,0,Cash loans,M,N,Y,0,270000.0,411813.0,22338.0,355500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019101,-10416,-737,-1333.0,-3092,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,0.3633637965098708,0.6820640518196789,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n273734,0,Cash loans,F,Y,Y,0,135000.0,531706.5,28458.0,459000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018634,-16990,-319,-5332.0,-525,2.0,1,1,0,1,0,0,Sales staff,1.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.6732020083330592,0.4571483983248818,0.7662336700704004,0.4866,0.3195,0.9891,,,0.48,0.4138,0.375,,0.2373,,0.5273,,0.5203,0.4958,0.3316,0.9891,,,0.4834,0.4138,0.375,,0.2427,,0.5494,,0.5508,0.4913,0.3195,0.9891,,,0.48,0.4138,0.375,,0.2414,,0.5368,,0.5312,,block of flats,0.4658,Panel,No,1.0,0.0,1.0,0.0,-571.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,1.0,1.0\n113900,1,Cash loans,F,N,Y,0,90000.0,284400.0,22599.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003813,-16347,-4557,-999.0,-2287,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,0.5067248625197678,0.53854884851985,0.11392239629221085,0.1675,0.0483,0.9871,,,0.0,0.0862,0.1667,,0.0692,,0.0691,,0.0393,0.125,0.0501,0.9871,,,0.0,0.069,0.1667,,0.0708,,0.0586,,0.0416,0.1691,0.0483,0.9871,,,0.0,0.0862,0.1667,,0.0704,,0.0703,,0.0401,,block of flats,0.0644,Panel,No,0.0,0.0,0.0,0.0,-1234.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,3.0\n148748,0,Revolving loans,F,N,Y,0,157500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.00702,-8374,-1239,-4775.0,-1045,,1,1,0,1,1,0,Accountants,1.0,2,2,SUNDAY,10,0,0,0,0,1,1,Government,,0.2345748106559173,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-563.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n354091,0,Cash loans,M,Y,N,0,441000.0,755190.0,32125.5,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.014464,-12413,-1890,-1030.0,-4228,11.0,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,3,0,0,0,0,0,0,Other,0.2764546319045804,0.6886609132270598,0.5531646987710016,0.0309,0.016,0.9856,0.8028,0.0056,0.0,0.0345,0.1667,0.2083,0.0631,0.0252,0.0077,0.0,0.0,0.0315,0.0166,0.9856,0.8105,0.0056,0.0,0.0345,0.1667,0.2083,0.0645,0.0275,0.0081,0.0,0.0,0.0312,0.016,0.9856,0.8054,0.0056,0.0,0.0345,0.1667,0.2083,0.0642,0.0257,0.0079,0.0,0.0,not specified,block of flats,0.0096,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-402.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n146808,0,Cash loans,F,N,Y,1,112500.0,206271.0,21789.0,193500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010032,-15067,-1378,-9.0,-3705,,1,1,0,1,0,0,High skill tech staff,3.0,2,2,THURSDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.5440198170979679,0.374499370738744,0.5744466170995097,0.1106,0.1051,0.9816,0.7484,,0.0,0.1838,0.1667,0.0417,,0.0799,0.0659,0.0077,0.11699999999999999,0.1019,0.1023,0.9777,0.706,,0.0,0.0345,0.1667,0.0417,,0.0872,0.0362,0.0078,0.1239,0.1031,0.0988,0.9816,0.7518,,0.0,0.2069,0.1667,0.0417,,0.0812,0.0746,0.0078,0.1195,reg oper account,block of flats,0.0528,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-521.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n166747,0,Cash loans,F,N,N,0,72000.0,255960.0,9648.0,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.020713,-22753,365243,-3878.0,-3878,,1,0,0,1,1,0,,1.0,3,3,FRIDAY,9,0,0,0,0,0,0,XNA,0.6253715567308726,0.4748862486261609,,0.0082,0.0,0.9732,0.6328,,,0.0345,0.0417,,,,0.0073,,,0.0084,0.0,0.9732,0.6472,,,0.0345,0.0417,,,,0.0076,,,0.0083,0.0,0.9732,0.6377,,,0.0345,0.0417,,,,0.0074,,,,block of flats,0.0057,Wooden,No,0.0,0.0,0.0,0.0,-552.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n206182,0,Cash loans,M,N,N,0,202500.0,423000.0,22963.5,423000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.011657,-21839,-724,-5883.0,-4360,,1,1,1,1,0,0,,1.0,1,1,FRIDAY,18,0,1,1,0,1,1,Other,,0.7475773662426943,0.08391700365753578,0.0041,0.0,0.9513,,,0.0,0.0345,0.0417,,,,0.0057,,0.0232,0.0042,0.0,0.9513,,,0.0,0.0345,0.0417,,,,0.0059,,0.0245,0.0042,0.0,0.9513,,,0.0,0.0345,0.0417,,,,0.0058,,0.0237,,block of flats,0.0095,\"Stone, brick\",No,7.0,1.0,7.0,0.0,-589.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n451361,0,Cash loans,M,Y,Y,0,202500.0,805882.5,29074.5,612000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.020246,-20675,-13964,-7077.0,-4208,12.0,1,1,0,1,0,0,,2.0,3,3,TUESDAY,11,0,0,0,0,0,0,Industry: type 2,,0.2781538332133105,0.5971924268337128,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-336.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n103903,0,Cash loans,F,N,Y,2,405000.0,852088.5,33921.0,688500.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.04622,-13445,-185,-4366.0,-4709,,1,1,0,1,0,0,Accountants,4.0,1,1,TUESDAY,18,0,0,0,1,1,1,Business Entity Type 3,0.7917551646477281,0.7595831383007074,0.2910973802776635,0.2546,,0.9925,,,0.4,0.1724,0.5417,,,,,,,0.2595,,0.9926,,,0.4028,0.1724,0.5417,,,,,,,0.2571,,0.9925,,,0.4,0.1724,0.5417,,,,,,,,block of flats,0.2698,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-870.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,2.0\n171406,0,Cash loans,F,N,N,2,135000.0,135000.0,14175.0,135000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.008625,-13298,-1086,-4004.0,-2583,,1,1,1,1,0,0,Secretaries,4.0,2,2,THURSDAY,15,0,0,0,0,0,0,Other,0.5602439122448117,0.4117965689535414,0.6801388218428291,0.0825,0.1051,0.9737,,,,0.1724,0.125,,0.0495,,0.0667,,,0.084,0.109,0.9737,,,,0.1724,0.125,,0.0506,,0.0695,,,0.0833,0.1051,0.9737,,,,0.1724,0.125,,0.0504,,0.0679,,,,block of flats,0.065,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-757.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n401750,1,Cash loans,F,N,Y,0,270000.0,754740.0,24475.5,630000.0,Unaccompanied,Pensioner,Secondary / secondary special,Civil marriage,House / apartment,0.030755,-22089,365243,-2122.0,-4631,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.5416503577936894,0.5442347412142162,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-1418.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n166531,1,Cash loans,M,Y,Y,1,315000.0,1081143.0,45805.5,994500.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.02461,-9988,-297,-104.0,-2673,10.0,1,1,0,1,1,0,Laborers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Trade: type 3,0.3209014167144898,0.17117183514391268,0.266456808245056,0.2412,0.2228,0.9826,0.762,0.1345,0.28,0.2414,0.3333,0.375,0.1339,0.1958,0.263,0.0347,0.0783,0.2458,0.2312,0.9826,0.7713,0.1358,0.282,0.2414,0.3333,0.375,0.1369,0.214,0.27399999999999997,0.035,0.0829,0.2436,0.2228,0.9826,0.7652,0.1354,0.28,0.2414,0.3333,0.375,0.1362,0.1992,0.2677,0.0349,0.0799,reg oper account,block of flats,0.2945,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-1856.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,10.0,0.0,4.0\n131378,1,Cash loans,F,Y,N,2,90000.0,690048.0,20173.5,576000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.0105,-13719,-2833,-5243.0,-4982,8.0,1,1,0,1,1,0,Medicine staff,4.0,3,3,MONDAY,16,0,0,0,0,0,0,Medicine,,0.6337852431743857,0.6413682574954046,0.0825,0.0786,0.9776,0.6940000000000001,,0.0,0.1379,0.1667,0.2083,0.0138,,0.0709,,0.0,0.084,0.0816,0.9777,0.706,,0.0,0.1379,0.1667,0.2083,0.0141,,0.0739,,0.0,0.0833,0.0786,0.9776,0.6981,,0.0,0.1379,0.1667,0.2083,0.0141,,0.0722,,0.0,org spec account,block of flats,0.0623,Panel,No,2.0,0.0,2.0,0.0,-980.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n187490,0,Cash loans,M,Y,Y,0,157500.0,760225.5,38947.5,679500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-21115,-86,-4263.0,-2770,9.0,1,1,0,1,1,0,Security staff,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Security,,0.6892888332297292,0.21275630545434146,0.2969,0.1926,0.9826,,,0.24,0.2759,0.3333,,0.2051,,0.1892,,0.0,0.3025,0.1999,0.9826,,,0.2417,0.2759,0.3333,,0.2098,,0.1971,,0.0,0.2998,0.1926,0.9826,,,0.24,0.2759,0.3333,,0.2087,,0.1926,,0.0,,block of flats,0.2449,Panel,No,0.0,0.0,0.0,0.0,-2712.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n176208,0,Cash loans,M,Y,N,0,135000.0,450000.0,22977.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.031329,-12187,-540,-3594.0,-4458,16.0,1,1,1,1,1,0,,2.0,2,2,TUESDAY,11,0,0,0,1,0,1,Self-employed,0.5407617487153463,0.2624615038901207,0.2707073872651806,0.1381,0.152,0.9911,0.8776,0.033,0.0,0.2414,0.1667,0.2083,0.0296,,0.1362,,0.0,0.1408,0.1577,0.9911,0.8824,0.0333,0.0,0.2414,0.1667,0.2083,0.0302,,0.1419,,0.0,0.1395,0.152,0.9911,0.8792,0.0332,0.0,0.2414,0.1667,0.2083,0.0301,,0.1387,,0.0,reg oper account,block of flats,0.1072,Panel,No,1.0,0.0,1.0,0.0,-1900.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n223905,0,Cash loans,M,Y,Y,0,315000.0,1319269.5,38704.5,1152000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-22654,-420,-3497.0,-4295,1.0,1,1,0,1,0,0,Drivers,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.5815758702911179,0.41184855592423975,0.0825,0.0051,0.9742,0.6464,0.0204,0.0,0.1379,0.1667,0.2083,0.063,0.0672,0.0661,0.0039,0.0223,0.084,0.0053,0.9742,0.6602,0.0205,0.0,0.1379,0.1667,0.2083,0.0644,0.0735,0.0689,0.0039,0.0236,0.0833,0.0051,0.9742,0.6511,0.0205,0.0,0.1379,0.1667,0.2083,0.0641,0.0684,0.0673,0.0039,0.0227,reg oper account,block of flats,0.0528,Panel,No,0.0,0.0,0.0,0.0,-341.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n248130,0,Revolving loans,F,N,Y,0,202500.0,180000.0,9000.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-10391,-563,-4043.0,-3068,,1,1,0,1,0,0,,1.0,2,2,MONDAY,7,0,0,0,1,1,0,Business Entity Type 3,0.5945604306303096,0.6266764614770457,,0.0082,,0.9767,,,,0.1034,0.0417,,,,0.0076,,0.0067,0.0084,,0.9767,,,,0.1034,0.0417,,,,0.0079,,0.0071,0.0083,,0.9767,,,,0.1034,0.0417,,,,0.0078,,0.0068,,block of flats,0.0074,Block,No,,,,,-147.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n434694,0,Cash loans,F,N,N,1,202500.0,495351.0,48384.0,459000.0,Unaccompanied,State servant,Higher education,Married,Municipal apartment,0.003818,-18644,-8358,-8645.0,-2196,,1,1,0,1,1,0,Managers,3.0,2,2,TUESDAY,3,0,0,0,0,0,0,School,,0.1736931565655777,0.6940926425266661,0.0021,,0.9866,,,,,0.0,,0.0285,,0.0015,,,0.0021,,0.9866,,,,,0.0,,0.0292,,0.0016,,,0.0021,,0.9866,,,,,0.0,,0.028999999999999998,,0.0015,,,,block of flats,0.0023,Wooden,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n133104,0,Cash loans,M,Y,Y,0,135000.0,239850.0,23850.0,225000.0,Unaccompanied,Pensioner,Higher education,Married,Co-op apartment,0.072508,-24561,365243,-14220.0,-5330,11.0,1,0,0,1,1,0,,2.0,1,1,FRIDAY,16,0,0,0,0,0,0,XNA,0.9158195162707916,0.7469537506821877,,0.2959,,0.9806,,,0.48,0.2069,0.4583,,,,0.2932,,0.0032,0.3015,,0.9806,,,0.4834,0.2069,0.4583,,,,0.3055,,0.0033,0.2987,,0.9806,,,0.48,0.2069,0.4583,,,,0.2985,,0.0032,,block of flats,0.2313,Panel,No,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n431394,0,Cash loans,M,Y,N,1,90000.0,508495.5,34249.5,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.005144,-13537,-4261,-12980.0,-4328,13.0,1,1,0,1,1,0,,3.0,2,2,FRIDAY,15,0,0,0,0,0,0,Industry: type 11,,0.4501351673222748,0.7001838506835805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1053.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n285408,0,Cash loans,F,N,Y,1,121500.0,239418.0,17478.0,211500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-21592,365243,-582.0,-4688,,1,0,0,1,0,0,,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,XNA,,0.6785017177418924,0.7165702448010511,0.0649,0.0784,0.9742,0.6464,0.0056,0.0,0.1379,0.125,0.1667,0.0,0.053,0.0492,0.0039,0.0041,0.0662,0.0813,0.9742,0.6602,0.0057,0.0,0.1379,0.125,0.1667,0.0,0.0579,0.0513,0.0039,0.0044,0.0656,0.0784,0.9742,0.6511,0.0057,0.0,0.1379,0.125,0.1667,0.0,0.0539,0.0501,0.0039,0.0042,reg oper account,block of flats,0.0427,\"Stone, brick\",No,9.0,0.0,9.0,0.0,-2330.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n139314,0,Revolving loans,M,Y,Y,1,270000.0,270000.0,13500.0,270000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.035792000000000004,-16290,-285,-445.0,-4234,1.0,1,1,0,1,0,0,Security staff,3.0,2,2,MONDAY,9,0,0,0,0,1,1,Security,0.525985229886634,0.7660398400426855,0.5989262182569273,0.1866,0.0909,0.9881,0.8368,,0.2,0.1724,0.3333,0.375,0.1017,0.1521,0.2073,0.0,0.0,0.1901,0.0943,0.9881,0.8432,,0.2014,0.1724,0.3333,0.375,0.10400000000000001,0.1662,0.2159,0.0,0.0,0.1884,0.0909,0.9881,0.8390000000000001,,0.2,0.1724,0.3333,0.375,0.1035,0.1548,0.21100000000000002,0.0,0.0,reg oper account,block of flats,0.163,Panel,No,3.0,0.0,3.0,0.0,-261.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,2.0\n343540,0,Cash loans,F,N,Y,0,193500.0,627277.5,23778.0,441000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.072508,-23455,365243,-5772.0,-3931,,1,0,0,1,1,0,,1.0,1,1,MONDAY,16,0,0,0,0,0,0,XNA,,0.5436258341515299,0.5478104658520093,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-44.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n242840,0,Cash loans,M,Y,Y,0,135000.0,545040.0,39627.0,450000.0,Family,Commercial associate,Secondary / secondary special,Married,With parents,0.031329,-16772,-636,-111.0,-334,20.0,1,1,0,1,0,0,Low-skill Laborers,2.0,2,2,TUESDAY,9,0,0,0,0,1,1,Housing,0.4006066929193279,0.615644282368553,0.5190973382084597,0.0227,,0.9771,0.6872,,0.0,0.1034,0.0417,0.0833,0.1756,0.0185,0.0119,0.0,0.0204,0.0231,,0.9772,0.6994,,0.0,0.1034,0.0417,0.0833,0.1796,0.0202,0.0124,0.0,0.0216,0.0229,,0.9771,0.6914,,0.0,0.1034,0.0417,0.0833,0.1786,0.0188,0.0121,0.0,0.0208,reg oper account,block of flats,0.0138,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-640.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n167181,1,Cash loans,F,N,Y,0,202500.0,640080.0,31261.5,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.019689,-10232,-1868,-201.0,-896,,1,1,1,1,1,1,Core staff,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Other,,0.19378428063633726,0.03390980579201828,0.1325,0.0499,0.9841,0.7824,0.0327,0.16,0.1034,0.3125,0.3542,0.0,0.1063,0.1139,0.0077,0.0177,0.0651,0.0,0.9831,0.7779,0.024,0.0,0.069,0.1667,0.2083,0.0,0.055999999999999994,0.0388,0.0039,0.0145,0.1338,0.0499,0.9841,0.7853,0.0329,0.16,0.1034,0.3125,0.3542,0.0,0.1082,0.1159,0.0078,0.0181,reg oper account,block of flats,0.1773,Block,No,0.0,0.0,0.0,0.0,-1342.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n236275,0,Cash loans,F,N,N,3,180000.0,349258.5,27724.5,301500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-12488,-3844,-3255.0,-4651,,1,1,0,1,0,1,Sales staff,5.0,2,2,THURSDAY,15,0,0,0,0,0,0,Self-employed,,0.5156648779801506,0.2622489709189549,0.0742,,0.9856,,,0.08,0.069,0.3333,,0.0574,,0.0758,,0.0381,0.0756,,0.9856,,,0.0806,0.069,0.3333,,0.0587,,0.079,,0.0403,0.0749,,0.9856,,,0.08,0.069,0.3333,,0.0584,,0.0772,,0.0389,,block of flats,0.0601,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,6.0\n424478,0,Cash loans,M,N,N,0,405000.0,651816.0,26298.0,495000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018634,-19048,-4174,-8265.0,-2595,,1,1,0,1,0,0,Laborers,2.0,2,2,MONDAY,9,0,0,0,0,0,0,Business Entity Type 3,0.3502377456577073,0.3978982626542243,0.6279908192952864,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1214.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n121003,0,Cash loans,F,N,Y,0,112500.0,481495.5,31833.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.009657,-21325,365243,-9126.0,-4654,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,XNA,,0.499914413205638,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1796.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n429909,0,Cash loans,F,N,Y,0,157500.0,2517300.0,66532.5,2250000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.072508,-16996,-1149,-10776.0,-548,,1,1,0,1,1,0,Cooking staff,2.0,1,1,SUNDAY,14,0,0,0,0,0,0,Trade: type 7,,0.6617840825173673,0.7922644738669378,0.2206,0.11800000000000001,0.9826,0.762,0.3767,0.24,0.2069,0.3333,0.375,,0.1799,0.2122,0.0,0.0038,0.2248,0.1225,0.9826,0.7713,0.3801,0.2417,0.2069,0.3333,0.375,,0.1965,0.2211,0.0,0.004,0.2228,0.11800000000000001,0.9826,0.7652,0.3791,0.24,0.2069,0.3333,0.375,,0.183,0.21600000000000005,0.0,0.0039,reg oper account,block of flats,0.1677,Panel,No,0.0,0.0,0.0,0.0,-2571.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n394236,0,Cash loans,M,Y,Y,2,225000.0,280332.0,22603.5,234000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14465,-536,-8516.0,-4621,2.0,1,1,0,1,0,0,Laborers,4.0,2,2,TUESDAY,9,0,0,0,0,1,1,Government,,0.4763315273959953,0.3201633668633456,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-259.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n188371,0,Cash loans,F,N,Y,2,90000.0,225000.0,17775.0,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-10813,-2057,-4606.0,-3413,,1,1,1,1,0,0,Sales staff,4.0,2,2,TUESDAY,16,0,0,0,1,1,0,Trade: type 7,0.6734897589804394,0.3476697875753758,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,-1694.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n228217,0,Revolving loans,F,Y,N,2,162000.0,495000.0,24750.0,495000.0,Unaccompanied,Commercial associate,Higher education,Married,Rented apartment,0.003540999999999999,-16717,-919,-1964.0,-267,12.0,1,1,0,1,1,0,,4.0,1,1,FRIDAY,13,0,0,0,0,1,1,Government,,0.01924104755675731,0.622922000268356,0.0216,0.0,0.9866,0.8164,0.0012,0.0,0.1034,0.0417,0.0417,0.0383,0.0177,0.0218,0.0,0.0137,0.0221,0.0,0.9866,0.8236,0.0012,0.0,0.1034,0.0417,0.0417,0.0392,0.0193,0.0227,0.0,0.0145,0.0219,0.0,0.9866,0.8189,0.0012,0.0,0.1034,0.0417,0.0417,0.039,0.018000000000000002,0.0222,0.0,0.013999999999999999,not specified,block of flats,0.0202,Wooden,No,10.0,0.0,10.0,0.0,-1110.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n115785,0,Cash loans,F,N,N,0,225000.0,1036980.0,32688.0,787500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.032561,-19280,-1383,-9928.0,-2627,,1,1,0,1,0,0,Sales staff,2.0,1,1,THURSDAY,19,0,0,0,0,1,1,Self-employed,0.6995130641580433,0.6989270405209734,0.6347055309763198,0.1567,0.1126,0.9816,0.7484,,0.16,0.1379,0.3333,,0.0878,,0.161,,0.0097,0.1597,0.1168,0.9816,0.7583,,0.1611,0.1379,0.3333,,0.0898,,0.1678,,0.0103,0.1582,0.1126,0.9816,0.7518,,0.16,0.1379,0.3333,,0.0894,,0.1639,,0.0099,,block of flats,0.1288,Panel,No,0.0,0.0,0.0,0.0,-417.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n454898,0,Cash loans,F,N,N,2,135000.0,970380.0,28503.0,810000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.020713,-15582,-1920,-8805.0,-2126,,1,1,0,1,0,0,Laborers,4.0,3,3,SUNDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.6343563221815863,0.4642167287797906,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n315331,0,Cash loans,M,N,Y,0,112500.0,801000.0,23418.0,801000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.019689,-15680,-3240,-1420.0,-4089,,1,1,0,1,1,0,,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Business Entity Type 2,,0.6340491289420283,0.5495965024956946,0.1165,0.0986,0.9771,0.6872,0.0559,0.0,0.2759,0.1667,0.2083,0.1244,0.095,0.1032,0.0,0.0,0.1187,0.1024,0.9772,0.6994,0.0564,0.0,0.2759,0.1667,0.2083,0.1272,0.1038,0.1075,0.0,0.0,0.1176,0.0986,0.9771,0.6914,0.0563,0.0,0.2759,0.1667,0.2083,0.1266,0.0966,0.105,0.0,0.0,reg oper account,block of flats,0.1117,Panel,No,3.0,0.0,3.0,0.0,-2589.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n382298,0,Cash loans,F,N,Y,0,87750.0,213322.5,14386.5,162000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-19508,-1086,-4247.0,-3039,,1,1,0,1,1,0,Private service staff,2.0,2,2,WEDNESDAY,15,0,0,0,0,0,0,Self-employed,0.8248144099098627,0.26390327285655385,0.7544061731797895,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.0,0.0,8.0,0.0,-316.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n304541,1,Cash loans,F,N,N,0,67500.0,315000.0,19165.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-15847,-3195,-8137.0,-4360,,1,1,1,1,0,0,Cleaning staff,2.0,2,2,TUESDAY,14,0,0,0,0,1,1,Hotel,,0.497683936367637,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n277948,0,Cash loans,F,N,N,0,67500.0,339241.5,15943.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020246,-14655,-5048,-2564.0,-4417,,1,1,0,1,0,0,Cleaning staff,2.0,3,3,THURSDAY,7,0,0,0,0,0,0,Construction,,0.4366708961865809,0.8245949709919925,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2256.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n303724,0,Cash loans,M,N,Y,0,112500.0,943425.0,27715.5,787500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-22442,365243,-6323.0,-3990,,1,0,0,1,1,0,,2.0,3,2,FRIDAY,11,0,0,0,0,0,0,XNA,,0.5841332338092928,0.06126811384928985,0.4247,0.2805,0.9851,0.7959999999999999,0.1093,0.48,0.4138,0.3333,0.375,0.2841,0.3446,0.4731,0.0077,0.009000000000000001,0.4328,0.2911,0.9851,0.804,0.1103,0.4834,0.4138,0.3333,0.375,0.2905,0.3765,0.4929,0.0078,0.0095,0.4289,0.2805,0.9851,0.7987,0.11,0.48,0.4138,0.3333,0.375,0.289,0.3506,0.4816,0.0078,0.0091,reg oper spec account,block of flats,0.374,Panel,No,4.0,0.0,4.0,0.0,-322.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,5.0,3.0\n284051,0,Cash loans,F,N,Y,0,360000.0,781920.0,34573.5,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-23643,365243,-12457.0,-4695,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,0.5065326369365345,0.4722912730433736,0.6430255641096323,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n298235,0,Cash loans,M,N,Y,2,225000.0,254700.0,27558.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006629,-15796,-290,-124.0,-691,,1,1,1,1,0,0,Laborers,4.0,2,2,TUESDAY,8,0,0,0,0,0,0,Other,,0.1919359099293164,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-652.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n205962,0,Cash loans,F,N,Y,0,180000.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Incomplete higher,Civil marriage,House / apartment,0.02461,-14493,-4226,-8609.0,-5279,,1,1,0,1,0,0,Medicine staff,2.0,2,2,THURSDAY,17,0,0,0,0,1,1,Industry: type 3,0.631962366332011,0.6272237097509878,0.4170996682522097,0.1,0.0692,0.9796,0.7212,0.0332,0.0,0.2069,0.1667,0.0417,0.1177,0.0799,0.0866,0.0077,0.0163,0.1019,0.0718,0.9796,0.7321,0.0335,0.0,0.2069,0.1667,0.0417,0.1204,0.0872,0.0902,0.0078,0.0172,0.10099999999999999,0.0692,0.9796,0.7249,0.0334,0.0,0.2069,0.1667,0.0417,0.1197,0.0812,0.0881,0.0078,0.0166,reg oper account,block of flats,0.0692,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n246252,0,Cash loans,F,Y,N,1,234000.0,405000.0,18895.5,405000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.018209,-10653,-956,-2206.0,-2661,16.0,1,1,0,1,0,0,Sales staff,2.0,3,3,WEDNESDAY,12,0,0,0,0,1,1,Business Entity Type 3,0.3014512940013636,0.015700869463239817,0.363945238612397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-83.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n356804,0,Cash loans,M,N,Y,0,180000.0,315000.0,22918.5,315000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.031329,-13163,-367,-4509.0,-4509,,1,1,0,1,1,0,Drivers,2.0,2,2,TUESDAY,8,0,0,0,0,0,0,Business Entity Type 3,,0.5934987043658262,0.10822632266971416,0.0464,0.0532,0.9866,0.8164,0.0109,0.0,0.1034,0.1667,0.2083,0.0581,0.0378,0.0432,0.0,0.0,0.0473,0.0552,0.9866,0.8236,0.011000000000000001,0.0,0.1034,0.1667,0.2083,0.0594,0.0413,0.045,0.0,0.0,0.0468,0.0532,0.9866,0.8189,0.011000000000000001,0.0,0.1034,0.1667,0.2083,0.0591,0.0385,0.044000000000000004,0.0,0.0,reg oper account,block of flats,0.034,Panel,No,0.0,0.0,0.0,0.0,-1361.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n395925,0,Cash loans,F,N,Y,0,135000.0,835380.0,35523.0,675000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-23098,365243,-2973.0,-4464,,1,0,0,1,0,0,,2.0,2,2,MONDAY,11,0,0,0,0,0,0,XNA,,0.4224401004339979,0.2340151665320674,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2862.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n153911,0,Cash loans,F,Y,Y,0,139500.0,900000.0,38263.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-18331,-727,-778.0,-1842,1.0,1,1,0,1,0,0,Security staff,2.0,2,2,THURSDAY,11,0,0,0,0,0,0,Security,0.6852019359018715,0.6375127038617406,0.5797274227921155,0.1247,0.0764,0.9856,0.8028,0.0168,0.0,0.2759,0.1667,0.2083,0.1141,0.1009,0.1104,0.0039,0.0097,0.1271,0.0792,0.9856,0.8105,0.017,0.0,0.2759,0.1667,0.2083,0.1167,0.1102,0.115,0.0039,0.0103,0.126,0.0764,0.9856,0.8054,0.0169,0.0,0.2759,0.1667,0.2083,0.11599999999999999,0.1026,0.1124,0.0039,0.0099,reg oper account,block of flats,0.0889,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-332.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n414535,0,Cash loans,F,N,Y,0,157500.0,582768.0,54058.5,540000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.022625,-19250,-8922,-7260.0,-2800,,1,1,0,1,0,0,Core staff,2.0,2,2,THURSDAY,15,0,0,0,0,0,0,Government,0.8560400044621077,0.6108884449394203,,0.1072,0.147,0.4948,0.8640000000000001,0.0939,0.28,0.2414,0.375,0.4167,0.1609,0.1748,0.2432,0.0,0.0,0.0,0.1526,0.0005,0.8693,0.0948,0.282,0.2414,0.375,0.4167,0.1646,0.191,0.2533,0.0,0.0,0.1083,0.147,0.4948,0.8658,0.0945,0.28,0.2414,0.375,0.4167,0.1637,0.1779,0.2475,0.0,0.0,not specified,block of flats,0.0,Panel,No,0.0,0.0,0.0,0.0,-642.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n154572,0,Cash loans,M,N,N,0,90000.0,381528.0,11695.5,315000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.006670999999999999,-22507,365243,-14383.0,-3806,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,XNA,,0.5603935884757426,0.4830501881366946,0.0186,0.0317,0.9657,0.5308,0.0137,0.0,0.1034,0.0833,0.125,0.0088,0.0151,0.0227,0.0,0.002,0.0189,0.0329,0.9657,0.5492,0.0138,0.0,0.1034,0.0833,0.125,0.009000000000000001,0.0165,0.0236,0.0,0.0022,0.0187,0.0317,0.9657,0.5371,0.0138,0.0,0.1034,0.0833,0.125,0.0089,0.0154,0.0231,0.0,0.0021,reg oper spec account,block of flats,0.0253,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n210479,1,Cash loans,F,N,N,0,225000.0,1102171.5,46827.0,945000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,Municipal apartment,0.072508,-8824,-1149,-8805.0,-1360,,1,1,0,1,0,0,Sales staff,2.0,1,1,WEDNESDAY,19,0,0,0,0,0,0,Self-employed,,0.39365206031765415,0.17352743046491467,0.0825,0.0959,0.9737,,,0.0,0.1379,0.1667,,0.0,,0.0687,,0.0352,0.084,0.0995,0.9737,,,0.0,0.1379,0.1667,,0.0,,0.0716,,0.0372,0.0833,0.0959,0.9737,,,0.0,0.1379,0.1667,,0.0,,0.0699,,0.0359,,block of flats,0.054000000000000006,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-60.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,3.0\n267637,0,Cash loans,M,N,Y,0,144000.0,474048.0,28773.0,360000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.02461,-19089,-574,-3681.0,-2621,,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,11,0,0,0,0,0,0,Self-employed,0.2761488473905696,0.6720765938735093,0.4170996682522097,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-786.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n141828,0,Cash loans,F,N,N,0,175500.0,376920.0,18261.0,270000.0,Family,Working,Secondary / secondary special,Married,Municipal apartment,0.072508,-16864,-3876,-7835.0,-407,,1,1,0,1,0,0,Laborers,2.0,1,1,FRIDAY,15,0,0,0,0,0,0,Industry: type 3,,0.686528328329161,0.6754132910917112,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-24.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n197222,0,Cash loans,M,Y,Y,0,189000.0,1762110.0,48586.5,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.0228,-20522,-1179,-5175.0,-3919,2.0,1,1,0,1,1,0,Drivers,2.0,2,2,MONDAY,8,0,0,0,0,1,1,Business Entity Type 3,,0.056601475836464174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-12.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n206286,0,Cash loans,F,N,Y,0,180000.0,597339.0,34420.5,553500.0,Family,Pensioner,Higher education,Married,House / apartment,0.01885,-21910,365243,-10157.0,-5034,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,0.7325745824478386,0.5466731676181352,0.15759499866631024,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1638.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n366504,0,Cash loans,M,Y,Y,2,202500.0,755190.0,38740.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-15281,-4619,-2113.0,-4754,1.0,1,1,0,1,0,0,Managers,4.0,2,2,TUESDAY,11,0,0,0,0,1,1,Business Entity Type 3,,0.40414357946476737,0.4241303111942548,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-2194.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n431270,0,Cash loans,F,N,Y,0,225000.0,612612.0,31410.0,495000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.011657,-20900,365243,-301.0,-4407,,1,0,0,1,1,1,,2.0,1,1,TUESDAY,11,0,0,0,0,0,0,XNA,0.7037174908843196,0.7404516947577268,0.5989262182569273,0.1232,0.0895,0.9896,0.8572,0.0231,0.12,0.1034,0.375,0.4167,0.0383,0.1004,0.1335,0.0,0.033,0.1239,0.0928,0.9896,0.8628,0.0222,0.1208,0.1034,0.375,0.4167,0.0389,0.1084,0.1389,0.0,0.0055,0.1244,0.0895,0.9896,0.8591,0.0232,0.12,0.1034,0.375,0.4167,0.039,0.1022,0.1359,0.0,0.0337,reg oper account,block of flats,0.1172,Panel,No,13.0,1.0,12.0,1.0,-5.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n433659,0,Cash loans,F,N,Y,0,90000.0,562491.0,27189.0,454500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.030755,-19010,-187,-8153.0,-1835,,1,1,0,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,14,0,0,0,0,0,0,Self-employed,,0.6954322368530303,0.6658549219640212,0.0804,0.0008,0.9767,0.6804,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.043,,0.0611,,0.005,0.0819,0.0008,0.9767,0.6929,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.044000000000000004,,0.0637,,0.0053,0.0812,0.0008,0.9767,0.6847,0.006999999999999999,0.0,0.1379,0.1667,0.2083,0.0438,,0.0622,,0.0051,,block of flats,0.0492,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-734.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n336886,0,Cash loans,M,Y,Y,1,112500.0,144000.0,11304.0,144000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-14933,-3982,-9030.0,-4540,2.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,17,0,0,0,0,1,1,Industry: type 1,,0.5116060177707921,0.4650692149562261,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1990.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n374597,0,Revolving loans,F,N,N,0,112500.0,337500.0,16875.0,337500.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.007120000000000001,-14228,-5416,-2175.0,-2607,,1,1,0,1,0,0,Medicine staff,2.0,2,2,SATURDAY,12,0,1,1,0,1,1,Medicine,0.18966779377956844,0.5500201080273096,0.4170996682522097,0.08800000000000001,,0.9955,0.9388,,0.016,0.1241,0.2417,,,0.0656,0.0684,,,0.0819,,0.9955,0.9412,,0.0,0.1379,0.2083,,,0.0716,0.0608,,,0.0812,,0.9955,0.9396,,0.0,0.1379,0.2083,,,0.0667,0.0594,,,reg oper account,block of flats,0.0458,\"Stone, brick\",No,8.0,1.0,8.0,0.0,-591.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n389868,0,Revolving loans,F,Y,Y,0,360000.0,450000.0,22500.0,450000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.072508,-19249,365243,-5629.0,-2800,1.0,1,0,0,1,1,0,,2.0,1,1,WEDNESDAY,12,0,0,0,0,0,0,XNA,0.8707249023967555,0.7205697148095835,0.5280927512030451,0.0412,0.0102,0.9707,0.5988,0.0,0.0,0.1034,0.1667,0.0417,0.0854,0.0336,0.0549,0.0,0.0999,0.042,0.0106,0.9707,0.6145,0.0,0.0,0.1034,0.1667,0.0417,0.0873,0.0367,0.0572,0.0,0.1058,0.0416,0.0102,0.9707,0.6042,0.0,0.0,0.1034,0.1667,0.0417,0.0868,0.0342,0.0559,0.0,0.102,not specified,block of flats,0.0432,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-142.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n369328,0,Cash loans,F,N,Y,0,202500.0,238500.0,12937.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,With parents,0.0105,-13305,-1224,-6023.0,-4731,,1,1,0,1,1,0,Accountants,2.0,3,3,MONDAY,13,0,0,0,0,0,0,Self-employed,0.06979160537653263,0.38374887876181424,0.2250868621163805,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-468.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n341379,0,Cash loans,F,N,N,0,112500.0,254700.0,14620.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.006233,-12345,-4406,-2858.0,-4269,,1,1,1,1,1,0,Core staff,1.0,2,2,SATURDAY,18,0,0,0,0,0,0,Medicine,,0.6129189279259468,0.4920600938649263,0.133,0.1075,0.9801,0.728,0.0181,0.0,0.2759,0.2083,0.25,0.0572,0.1084,0.1237,0.0,0.0766,0.1355,0.1116,0.9801,0.7387,0.0182,0.0,0.2759,0.2083,0.25,0.0585,0.1185,0.1289,0.0,0.0811,0.1343,0.1075,0.9801,0.7316,0.0182,0.0,0.2759,0.2083,0.25,0.0582,0.1103,0.1259,0.0,0.0782,reg oper spec account,block of flats,0.1238,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1744.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n343858,0,Cash loans,M,N,N,0,180000.0,675000.0,21906.0,675000.0,Unaccompanied,Pensioner,Incomplete higher,Married,House / apartment,0.030755,-19883,365243,-9051.0,-3382,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,12,0,0,0,0,0,0,XNA,,0.4691585946701569,0.622922000268356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2177.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n448060,0,Cash loans,M,Y,Y,1,247500.0,1223010.0,58837.5,1125000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006629,-9376,-460,-4096.0,-1977,18.0,1,1,0,1,0,0,Core staff,3.0,2,2,FRIDAY,3,0,0,0,1,1,0,School,,0.4837818288055005,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-376.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n416054,0,Cash loans,F,N,Y,0,81000.0,254700.0,14350.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-23996,365243,-4022.0,-4682,,1,0,0,1,0,0,,2.0,2,2,MONDAY,8,0,0,0,0,0,0,XNA,0.8215179347900932,0.7090292768175291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1309.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n378791,0,Cash loans,M,Y,N,0,157500.0,1078200.0,31522.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.018801,-10020,-2286,-7234.0,-1226,19.0,1,1,0,1,0,0,Laborers,1.0,2,2,WEDNESDAY,16,0,0,0,0,0,0,Industry: type 12,0.18523168597306933,0.13814416507023178,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-836.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n328803,0,Cash loans,F,N,Y,0,112500.0,544491.0,16047.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-20712,365243,-6836.0,-4190,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,13,0,0,0,0,0,0,XNA,,0.3878475720117273,0.4507472818545589,0.1392,0.1279,0.9906,0.8708,0.0336,0.16,0.1379,0.3333,0.375,0.154,0.1135,0.1537,0.0,0.0,0.1418,0.1327,0.9906,0.8759,0.0339,0.1611,0.1379,0.3333,0.375,0.1575,0.124,0.1601,0.0,0.0,0.1405,0.1279,0.9906,0.8725,0.0338,0.16,0.1379,0.3333,0.375,0.1567,0.1154,0.1564,0.0,0.0,reg oper account,block of flats,0.1392,Panel,No,5.0,0.0,5.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n167820,0,Cash loans,F,N,Y,0,270000.0,1006920.0,67693.5,900000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,House / apartment,0.016612000000000002,-9780,-1294,-3209.0,-2434,,1,1,0,1,0,0,Managers,1.0,2,2,MONDAY,16,0,0,0,0,0,0,Industry: type 11,,0.2422453211117744,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n139179,0,Cash loans,M,Y,Y,0,67500.0,225000.0,22383.0,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.022625,-14582,-201,-8733.0,-4547,12.0,1,1,0,1,1,0,Drivers,1.0,2,2,TUESDAY,10,0,0,0,0,0,0,Trade: type 7,,0.6400758817030231,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n427594,0,Cash loans,F,N,N,0,270000.0,762453.0,42705.0,706500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.018801,-21210,365243,-13579.0,-4394,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,XNA,,0.5705270507243844,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1828.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,9.0\n244350,0,Cash loans,M,Y,Y,0,157500.0,1350000.0,42390.0,1350000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.014519999999999996,-12232,-1494,-5500.0,-4202,7.0,1,1,0,1,1,0,High skill tech staff,1.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Telecom,0.3299435863420322,0.6337091073510512,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1107.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n138268,0,Cash loans,F,N,Y,0,180000.0,949500.0,27891.0,949500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.026392,-16675,-1715,-10833.0,-206,,1,1,1,1,1,0,,1.0,2,2,TUESDAY,15,0,0,0,0,0,0,Business Entity Type 3,,0.6381946335381412,0.7001838506835805,0.2701,,0.9861,,,0.2,0.1724,0.3333,,0.0832,,0.2289,,0.1154,0.2752,,0.9861,,,0.2014,0.1724,0.3333,,0.0851,,0.2384,,0.1222,0.2727,,0.9861,,,0.2,0.1724,0.3333,,0.0846,,0.233,,0.1178,,block of flats,0.2122,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1474.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n115997,0,Cash loans,M,Y,Y,0,157500.0,270000.0,13914.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010556,-10337,-503,-4595.0,-696,9.0,1,1,0,1,0,0,Drivers,2.0,3,3,SATURDAY,14,0,0,0,0,1,1,Emergency,0.3870557604766571,0.5085567396512206,0.7338145369642702,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-531.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n354719,1,Cash loans,M,N,Y,1,279000.0,269550.0,14242.5,225000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-12473,-643,-1891.0,-176,,1,1,1,1,0,0,Drivers,3.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,Self-employed,,0.4313851760621161,0.2445163919946749,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1396.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n162783,0,Cash loans,F,N,Y,1,202500.0,755190.0,35122.5,675000.0,Family,State servant,Higher education,Married,House / apartment,0.016612000000000002,-10820,-1841,-8199.0,-507,,1,1,0,1,1,0,Core staff,3.0,2,2,THURSDAY,16,0,0,0,0,0,0,Medicine,0.35633888379610723,0.2487215252572837,0.31547215492577346,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-351.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,2.0\n315461,0,Cash loans,F,Y,Y,1,117000.0,733176.0,23782.5,612000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00733,-13851,-741,-7794.0,-3956,8.0,1,1,0,1,1,0,Sales staff,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Trade: type 7,,0.09297585927237424,0.4884551844437485,0.134,0.151,0.9816,0.7484,0.2294,0.0,0.2759,0.1667,0.0417,0.1318,0.1059,0.11599999999999999,0.0154,0.1571,0.1366,0.1567,0.9816,0.7583,0.2315,0.0,0.2759,0.1667,0.0417,0.1348,0.1157,0.1208,0.0156,0.1664,0.1353,0.151,0.9816,0.7518,0.2309,0.0,0.2759,0.1667,0.0417,0.1341,0.1077,0.1181,0.0155,0.1604,reg oper spec account,block of flats,0.1254,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2157.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n435662,0,Cash loans,F,Y,Y,0,135000.0,417024.0,21964.5,360000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.025164,-13047,-4598,-1895.0,-2701,6.0,1,1,0,1,0,0,Accountants,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Medicine,,0.6494167710874135,0.2707073872651806,0.2588,0.1984,0.9786,0.7076,0.1271,0.2,0.1724,0.3333,0.375,0.0524,0.2085,0.2256,0.0116,0.1587,0.2637,0.2058,0.9786,0.7190000000000001,0.1283,0.2014,0.1724,0.3333,0.375,0.0536,0.2277,0.235,0.0117,0.168,0.2613,0.1984,0.9786,0.7115,0.128,0.2,0.1724,0.3333,0.375,0.0533,0.2121,0.2296,0.0116,0.1621,reg oper account,block of flats,0.2814,\"Stone, brick\",No,5.0,0.0,5.0,0.0,-2635.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,6.0,0.0,3.0\n426524,0,Cash loans,F,Y,Y,0,171000.0,298512.0,31491.0,270000.0,Unaccompanied,Working,Incomplete higher,Married,House / apartment,0.010032,-10692,-447,-11.0,-1470,10.0,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,6,0,0,0,0,0,0,Self-employed,0.5652052712314649,0.5453505420379522,0.6769925032909132,0.2041,0.1076,0.9851,,,0.24,0.2069,0.3333,,0.2074,,0.2253,,0.0,0.20800000000000002,0.1117,0.9851,,,0.2417,0.2069,0.3333,,0.2121,,0.2347,,0.0,0.2061,0.1076,0.9851,,,0.24,0.2069,0.3333,,0.21100000000000002,,0.2293,,0.0,,block of flats,0.1983,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-8.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n304337,0,Cash loans,F,N,N,0,63000.0,265500.0,11376.0,265500.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.018209,-23702,365243,-885.0,-4320,,1,0,0,1,0,0,,1.0,3,3,FRIDAY,9,0,0,0,0,0,0,XNA,,0.6335111229621574,0.3859146722745145,0.0928,0.0978,0.9806,0.7348,0.0428,0.0,0.2069,0.1667,0.2083,0.0651,0.0756,0.0879,0.0,0.0,0.0945,0.1015,0.9806,0.7452,0.0432,0.0,0.2069,0.1667,0.2083,0.0666,0.0826,0.0916,0.0,0.0,0.0937,0.0978,0.9806,0.7383,0.0431,0.0,0.2069,0.1667,0.2083,0.0663,0.077,0.0895,0.0,0.0,org spec account,block of flats,0.0692,Panel,No,3.0,0.0,3.0,0.0,-1378.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n218890,0,Cash loans,M,Y,Y,0,198000.0,497520.0,28692.0,450000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.010032,-13935,-3786,-209.0,-4740,22.0,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Government,,0.6772315474387064,0.4866531565147181,0.0124,,0.9647,,,0.0,0.069,0.0417,,0.01,,0.0085,,,0.0126,,0.9647,,,0.0,0.069,0.0417,,0.0103,,0.0088,,,0.0125,,0.9647,,,0.0,0.069,0.0417,,0.0102,,0.0086,,,,block of flats,0.0112,Wooden,No,1.0,1.0,1.0,1.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n408812,0,Cash loans,F,N,Y,0,202500.0,1546020.0,50004.0,1350000.0,Unaccompanied,Working,Incomplete higher,Single / not married,House / apartment,0.031329,-9296,-487,-7275.0,-1954,,1,1,0,1,0,0,Sales staff,1.0,2,2,FRIDAY,7,0,0,0,0,1,1,Self-employed,0.5599716695997967,0.504457285364891,0.5352762504724826,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1688.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n242189,0,Cash loans,F,N,N,0,225000.0,547344.0,17608.5,472500.0,Unaccompanied,Pensioner,Higher education,Married,House / apartment,0.018634,-21670,365243,-4312.0,-3756,,1,0,0,1,0,0,,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,XNA,,0.4596077358622203,0.6801388218428291,0.0454,0.0502,0.9816,,,0.0,0.1034,0.1667,,0.0204,,0.0393,,0.0363,0.0462,0.0521,0.9816,,,0.0,0.1034,0.1667,,0.0209,,0.040999999999999995,,0.0385,0.0458,0.0502,0.9816,,,0.0,0.1034,0.1667,,0.0208,,0.04,,0.0371,,block of flats,0.0508,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-782.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n275624,0,Cash loans,M,Y,Y,0,180000.0,508495.5,23692.5,454500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-18778,-1513,-3544.0,-2319,20.0,1,1,1,1,1,0,Drivers,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.595997930868044,0.7734536208543162,0.28961123838200553,0.1165,0.1166,0.9826,0.762,0.0501,0.0,0.2414,0.1667,0.2083,0.0315,0.0941,0.1062,0.0039,0.0085,0.1187,0.121,0.9826,0.7713,0.0506,0.0,0.2414,0.1667,0.2083,0.0322,0.1028,0.1106,0.0039,0.009000000000000001,0.1176,0.1166,0.9826,0.7652,0.0505,0.0,0.2414,0.1667,0.2083,0.032,0.0958,0.1081,0.0039,0.0087,reg oper spec account,block of flats,0.1128,Mixed,No,0.0,0.0,0.0,0.0,-2663.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n164055,0,Revolving loans,F,Y,Y,1,225000.0,630000.0,31500.0,630000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-21302,-6449,-76.0,-4654,10.0,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,12,0,0,0,0,0,0,Other,,0.7176146143667855,0.5867400085415683,0.1091,0.0725,0.9866,,,0.1064,0.0803,0.375,,,,0.1231,,0.0005,0.0756,0.0335,0.9861,,,0.0806,0.0345,0.3333,,,,0.08,,0.0,0.0749,0.0486,0.9861,,,0.08,0.069,0.3333,,,,0.1253,,0.0,,block of flats,0.1738,Mixed,No,0.0,0.0,0.0,0.0,-657.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,2.0\n451072,0,Cash loans,F,N,N,1,360000.0,143910.0,17077.5,135000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010556,-14770,-2544,-276.0,-4966,,1,1,1,1,0,0,High skill tech staff,3.0,3,3,THURSDAY,16,0,0,0,0,0,0,Business Entity Type 3,,0.5749043578515318,0.2735646775174348,0.0722,0.0439,0.9737,0.6396,,0.0,0.1724,0.1667,0.2083,0.0,,0.0508,,0.0477,0.0735,0.0456,0.9737,0.6537,,0.0,0.1724,0.1667,0.2083,0.0,,0.0529,,0.0505,0.0729,0.0439,0.9737,0.6444,,0.0,0.1724,0.1667,0.2083,0.0,,0.0517,,0.0487,reg oper account,block of flats,0.0399,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-454.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n274999,0,Cash loans,M,N,Y,1,315000.0,472500.0,56205.0,472500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-14194,-1968,-2847.0,-4262,,1,1,0,1,1,0,Laborers,3.0,2,2,MONDAY,11,0,1,1,0,1,1,Industry: type 9,,0.3659746635024689,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n387522,0,Cash loans,M,N,Y,1,162000.0,668304.0,34254.0,540000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018634,-11568,-662,-389.0,-1012,,1,1,0,1,0,0,,3.0,2,2,TUESDAY,13,0,0,0,0,1,1,Business Entity Type 3,0.6196926817174454,0.3261940940879746,0.190705947811054,0.3021,0.1656,0.9990000000000001,0.9864,0.2412,0.28,0.2414,0.375,0.4167,0.0,0.2463,0.3129,0.0,0.0,0.3078,0.1719,0.9990000000000001,0.9869,0.2434,0.282,0.2414,0.375,0.4167,0.0,0.2691,0.326,0.0,0.0,0.305,0.1656,0.9990000000000001,0.9866,0.2427,0.28,0.2414,0.375,0.4167,0.0,0.2505,0.3185,0.0,0.0,not specified,block of flats,0.37799999999999995,Panel,No,0.0,0.0,0.0,0.0,-375.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n313107,0,Cash loans,F,N,N,0,157500.0,719284.5,25965.0,535500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.005313,-18618,-1759,-8324.0,-2174,,1,1,0,1,0,0,Core staff,2.0,2,2,SATURDAY,12,0,0,0,0,0,0,Kindergarten,0.6324638742364105,0.6976877763732987,0.3656165070113335,0.1613,0.0545,0.9876,,,0.06,0.0345,0.3333,,0.1013,,0.0827,,0.003,0.1103,0.0565,0.9836,,,0.0403,0.0345,0.3333,,0.0704,,0.0664,,0.0,0.1629,0.0545,0.9876,,,0.06,0.0345,0.3333,,0.1031,,0.0842,,0.003,,block of flats,0.0726,\"Stone, brick\",No,10.0,0.0,10.0,0.0,-1631.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n141584,0,Cash loans,F,N,Y,0,67500.0,593010.0,19260.0,495000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.020246,-18758,-2413,-4305.0,-2306,,1,1,0,1,0,0,,2.0,3,3,THURSDAY,11,0,0,0,0,0,0,Other,,0.4702915394735486,0.6610235391308081,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-540.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n308835,0,Cash loans,F,N,N,0,135000.0,239850.0,22324.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-24405,365243,-14376.0,-5043,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.7563779694822421,0.6956219298394389,0.0639,0.0478,0.9786,0.7076,0.006999999999999999,0.02,0.0862,0.25,0.125,0.0338,0.0521,0.0507,0.0,0.0,0.0567,0.0447,0.9767,0.6929,0.0056,0.0,0.0345,0.1667,0.0417,0.0103,0.0496,0.0502,0.0,0.0,0.0645,0.0478,0.9786,0.7115,0.006999999999999999,0.02,0.0862,0.25,0.125,0.0343,0.053,0.0516,0.0,0.0,reg oper spec account,block of flats,0.0379,\"Stone, brick\",No,1.0,1.0,1.0,0.0,-1613.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,5.0\n356479,1,Cash loans,M,Y,Y,1,270000.0,825588.0,48402.0,765000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-12247,-1158,-4705.0,-4809,14.0,1,1,0,1,0,1,Drivers,3.0,2,2,THURSDAY,13,0,1,1,1,1,1,Construction,0.3149116215142205,0.052046370350931466,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1779.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n280952,0,Cash loans,F,N,Y,0,67500.0,180000.0,17932.5,180000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.01885,-20235,365243,-268.0,-3723,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,0.6432525867852579,0.7200542713549211,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1030.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n118506,1,Cash loans,F,N,N,0,90000.0,272520.0,16672.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.015221,-21013,365243,-5301.0,-4369,,1,0,0,1,1,0,,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,XNA,0.7612111152955968,0.6203214625163875,0.05609207884829402,0.0954,0.1291,0.9811,,,0.2,0.1207,0.1875,,0.0682,,0.0935,,0.0239,0.0095,0.134,0.9707,,,0.2014,0.069,0.0417,,0.0224,,0.0115,,0.0224,0.0963,0.1291,0.9811,,,0.2,0.1207,0.1875,,0.0693,,0.0951,,0.0244,reg oper account,block of flats,0.1639,Block,No,0.0,0.0,0.0,0.0,-1793.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n122026,0,Cash loans,F,N,N,0,103500.0,513531.0,20223.0,459000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.025164,-21917,365243,-660.0,-2750,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,8,0,0,0,0,0,0,XNA,0.8060896641265397,0.015556545969301682,0.7136313997323308,0.0825,0.0727,0.9786,,,0.0,0.1379,0.1667,,0.0153,,0.0763,,0.0035,0.084,0.0754,0.9786,,,0.0,0.1379,0.1667,,0.0156,,0.0794,,0.0037,0.0833,0.0727,0.9786,,,0.0,0.1379,0.1667,,0.0155,,0.0776,,0.0036,,block of flats,0.0607,Panel,No,1.0,0.0,0.0,0.0,-2511.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n205942,1,Cash loans,M,N,Y,1,157500.0,659610.0,21406.5,472500.0,Unaccompanied,Commercial associate,Lower secondary,Civil marriage,House / apartment,0.030755,-19971,-445,-223.0,-509,,1,1,0,1,0,0,Laborers,3.0,2,2,MONDAY,14,0,0,0,0,1,1,Business Entity Type 3,,0.5153699499635696,0.1873887653772306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n283595,0,Cash loans,F,N,N,0,202500.0,942300.0,27135.0,675000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.005313,-17556,-302,-6603.0,-1099,,1,1,0,1,0,0,,2.0,2,2,MONDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6937854966909746,0.6817058776720116,0.1392,0.0,0.9831,0.7688,0.0251,0.0,0.3103,0.1667,0.0417,0.1156,0.1135,0.1357,0.0,0.0012,0.1418,0.0,0.9831,0.7779,0.0254,0.0,0.3103,0.1667,0.0417,0.1182,0.124,0.1414,0.0,0.0013,0.1405,0.0,0.9831,0.7719,0.0253,0.0,0.3103,0.1667,0.0417,0.1176,0.1154,0.1381,0.0,0.0013,reg oper account,block of flats,0.1224,Panel,No,3.0,0.0,3.0,0.0,-1098.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n403374,0,Cash loans,F,N,Y,0,157500.0,291384.0,23359.5,270000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.02461,-10737,-3967,-4627.0,-288,,1,1,0,1,0,0,Sales staff,1.0,2,2,FRIDAY,9,0,0,0,0,0,0,Self-employed,0.09336459021706348,0.5411291321786956,0.042929206557984725,0.1031,0.0954,0.9786,,,0.0,0.2069,0.1667,,,,0.0922,,0.0034,0.105,0.099,0.9786,,,0.0,0.2069,0.1667,,,,0.0961,,0.0036,0.1041,0.0954,0.9786,,,0.0,0.2069,0.1667,,,,0.0939,,0.0035,,block of flats,0.0797,Panel,No,0.0,0.0,0.0,0.0,-268.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n303508,0,Cash loans,F,N,N,0,157500.0,1035832.5,30285.0,904500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.018029,-14378,-1225,-5364.0,-4222,,1,1,1,1,0,0,,2.0,3,3,THURSDAY,14,0,0,0,0,0,0,Self-employed,,0.4770134017674324,0.5531646987710016,,0.0876,0.9821,0.7552,0.0551,0.08,0.0345,0.3333,0.375,0.0881,,0.1029,,0.1765,,0.0909,0.9821,0.7648,0.0556,0.0806,0.0345,0.3333,0.375,0.0901,,0.1072,,0.1869,,0.0876,0.9821,0.7585,0.0555,0.08,0.0345,0.3333,0.375,0.0896,,0.1048,,0.1802,reg oper account,block of flats,0.1113,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n271905,0,Cash loans,F,N,Y,0,135000.0,640080.0,31261.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Separated,Municipal apartment,0.072508,-16433,-875,-6447.0,-2409,,1,1,0,1,0,0,Laborers,1.0,1,1,FRIDAY,12,0,0,0,0,0,0,Construction,,0.7382181293618759,0.6347055309763198,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1073,,No,0.0,0.0,0.0,0.0,-753.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n229231,0,Cash loans,M,N,Y,0,81000.0,276277.5,22279.5,238500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.00733,-18060,-2998,-10698.0,-1494,,1,1,0,1,0,0,Laborers,2.0,2,2,THURSDAY,18,0,0,0,0,0,0,Trade: type 7,0.3587348356730044,0.31383739695895096,0.3962195720630885,0.3588,0.2911,0.9826,,,0.2,0.1724,0.3333,,0.0,,0.3655,,0.1836,0.3655,0.302,0.9826,,,0.2014,0.1724,0.3333,,0.0,,0.3808,,0.1944,0.3622,0.2911,0.9826,,,0.2,0.1724,0.3333,,0.0,,0.3721,,0.1875,,block of flats,0.3625,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-2055.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,4.0\n261452,1,Cash loans,M,N,N,0,238500.0,298512.0,31671.0,270000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.002506,-8706,-369,-8362.0,-1362,,1,1,0,1,0,0,,2.0,2,2,TUESDAY,7,0,0,0,0,0,0,Business Entity Type 3,,0.5000674862810227,,0.0974,0.1635,0.9856,0.8028,0.0,0.0,0.2069,0.1667,0.125,0.0658,0.0794,0.0604,0.0,0.0023,0.0315,0.1697,0.9821,0.7648,0.0,0.0,0.069,0.1667,0.0417,0.0336,0.0275,0.0282,0.0,0.0,0.0984,0.1635,0.9856,0.8054,0.0,0.0,0.2069,0.1667,0.125,0.067,0.0808,0.0614,0.0,0.0024,reg oper spec account,block of flats,0.0223,Panel,No,0.0,0.0,0.0,0.0,-15.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n299162,0,Cash loans,M,Y,Y,0,112500.0,521280.0,20799.0,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.019689,-16929,-220,-9037.0,-425,2.0,1,1,1,1,1,0,Drivers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.5998819583680394,,0.0825,0.0742,0.9752,0.66,0.0101,0.0,0.1379,0.1667,0.2083,0.0804,0.0651,0.0692,0.0097,0.0076,0.084,0.077,0.9752,0.6733,0.0101,0.0,0.1379,0.1667,0.2083,0.0594,0.0735,0.0662,0.0,0.0,0.0833,0.0742,0.9752,0.6645,0.0101,0.0,0.1379,0.1667,0.2083,0.077,0.068,0.0718,0.0019,0.0016,reg oper account,block of flats,0.0608,Panel,No,1.0,0.0,1.0,0.0,-1627.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n422588,0,Cash loans,M,N,Y,0,180000.0,225000.0,21582.0,225000.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.031329,-9872,-295,-9825.0,-2541,,1,1,0,1,0,0,Sales staff,2.0,2,2,FRIDAY,14,0,0,0,0,0,0,Trade: type 2,,0.6623124897813153,0.6986675550534175,0.0309,0.0481,0.9608,,,0.0,0.1724,0.0833,,0.0519,,0.0327,,0.0,0.0315,0.0499,0.9608,,,0.0,0.1724,0.0833,,0.053,,0.034,,0.0,0.0312,0.0481,0.9608,,,0.0,0.1724,0.0833,,0.0528,,0.0333,,0.0,,block of flats,0.0288,\"Stone, brick\",No,19.0,0.0,19.0,0.0,-116.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,4.0,2.0\n195676,0,Cash loans,F,Y,Y,0,157500.0,781920.0,25101.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.025164,-21862,365243,-4423.0,-4438,7.0,1,0,0,1,0,0,,1.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.3780816489901773,0.2866524828090694,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-676.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n376452,0,Cash loans,F,N,Y,0,540000.0,1433520.0,60867.0,1237500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.04622,-21179,365243,-9040.0,-4628,,1,0,0,1,0,0,,2.0,1,1,FRIDAY,16,0,0,0,0,0,0,XNA,0.839237660645718,0.7306350991283137,0.5549467685334323,0.2887,0.1441,0.9876,0.83,0.0673,0.28,0.2414,0.375,0.4167,,0.2353,0.2879,0.0,0.0,0.2941,0.1495,0.9876,0.8367,0.0679,0.282,0.2414,0.375,0.4167,,0.2571,0.3,0.0,0.0,0.2915,0.1441,0.9876,0.8323,0.0678,0.28,0.2414,0.375,0.4167,,0.2394,0.2931,0.0,0.0,reg oper account,block of flats,0.2633,Panel,No,0.0,0.0,0.0,0.0,-905.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n389936,0,Cash loans,F,N,N,0,45000.0,94230.0,4941.0,67500.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,With parents,0.007120000000000001,-11538,365243,-2567.0,-2466,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,12,0,0,0,0,0,0,XNA,0.1958653389010296,0.04973985180391259,0.38079968264891495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-536.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n341093,0,Cash loans,F,N,Y,1,112500.0,110331.0,11704.5,103500.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.01885,-18056,-2459,-7700.0,-1614,,1,1,1,1,1,0,Managers,3.0,2,2,MONDAY,10,0,0,0,0,1,1,Postal,,0.5973389056505819,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-1450.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n200704,0,Cash loans,M,N,N,0,157500.0,814041.0,23800.5,679500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.010276,-11998,-1389,-4463.0,-4462,,1,1,1,1,0,0,Drivers,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Business Entity Type 3,,0.6865612698141095,,0.0794,0.1359,0.9707,0.5988,0.012,0.0,0.1724,0.125,0.1667,0.1125,0.0538,0.081,0.0502,0.0795,0.0809,0.141,0.9707,0.6145,0.0121,0.0,0.1724,0.125,0.1667,0.1151,0.0588,0.0844,0.0506,0.0842,0.0802,0.1359,0.9707,0.6042,0.0121,0.0,0.1724,0.125,0.1667,0.1145,0.0547,0.0825,0.0505,0.0812,reg oper account,block of flats,0.081,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-561.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n313212,0,Cash loans,M,Y,Y,0,135000.0,269550.0,14242.5,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.009657,-8601,-429,-1113.0,-1279,11.0,1,1,0,1,0,0,Laborers,1.0,2,2,MONDAY,13,0,0,0,0,0,0,Industry: type 7,,0.35378466196999353,0.6610235391308081,0.0454,0.0227,0.9781,0.7008,0.0728,0.04,0.0345,0.3333,0.0417,0.0363,0.0,0.038,0.0,0.0,0.0462,0.0236,0.9782,0.7125,0.0734,0.0403,0.0345,0.3333,0.0417,0.0371,0.0,0.0396,0.0,0.0,0.0458,0.0227,0.9781,0.7048,0.0732,0.04,0.0345,0.3333,0.0417,0.0369,0.0,0.0387,0.0,0.0,reg oper account,block of flats,0.0299,Block,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n408892,0,Cash loans,F,N,Y,0,225000.0,1144764.0,61123.5,1030500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.072508,-15662,-3143,-7572.0,-4559,,1,1,0,1,0,0,,2.0,1,1,WEDNESDAY,17,0,0,0,0,0,0,Business Entity Type 3,0.7905428923554042,0.5136711810057546,0.6925590674998008,0.0928,0.065,0.9891,0.8504,0.0475,0.15,0.0862,0.4479,0.375,0.0,0.0731,0.0972,0.0039,0.0016,0.0777,0.0437,0.9891,0.8563,0.048,0.0806,0.069,0.4583,0.375,0.0,0.0799,0.0872,0.0039,0.0,0.08800000000000001,0.0675,0.9891,0.8524,0.0478,0.14,0.0862,0.4583,0.375,0.0,0.0744,0.0933,0.0039,0.0016,org spec account,block of flats,0.0658,Panel,No,0.0,0.0,0.0,0.0,-113.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,5.0\n408725,0,Cash loans,M,Y,Y,0,90000.0,755190.0,36459.0,675000.0,Family,Working,Higher education,Married,House / apartment,0.006296,-13945,-191,-1630.0,-4771,13.0,1,1,0,1,0,0,Sales staff,2.0,3,3,TUESDAY,16,0,0,0,0,0,0,Self-employed,,0.39216547132903545,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-343.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n272677,1,Cash loans,F,N,Y,0,81000.0,545040.0,25537.5,450000.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,House / apartment,0.018029,-13374,-3184,-3341.0,-621,,1,1,0,1,1,0,Core staff,2.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Kindergarten,0.4850684858996971,0.2858978721410488,,,,0.9806,,,,,,,,,,,,,,0.9806,,,,,,,,,,,,,,0.9806,,,,,,,,,,,,,block of flats,0.0415,Panel,No,3.0,0.0,3.0,0.0,-426.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n231997,0,Cash loans,F,Y,N,0,360000.0,1350000.0,87291.0,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.02461,-10535,-1904,-442.0,-639,5.0,1,1,0,1,1,0,High skill tech staff,2.0,2,2,THURSDAY,17,0,0,0,0,0,0,Self-employed,0.7557069987775511,0.2858978721410488,0.5656079814115492,0.0175,0.0,0.9702,0.5920000000000001,,0.0,0.069,0.0417,0.0417,0.0328,0.0143,0.0145,0.0,0.0,0.0179,0.0,0.9702,0.608,,0.0,0.069,0.0417,0.0417,0.0335,0.0156,0.0151,0.0,0.0,0.0177,0.0,0.9702,0.5975,,0.0,0.069,0.0417,0.0417,0.0334,0.0145,0.0148,0.0,0.0,reg oper account,block of flats,0.0206,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-494.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n347584,0,Cash loans,F,N,Y,1,247500.0,592560.0,32274.0,450000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.010556,-10029,-1167,-854.0,-1943,,1,1,0,1,0,0,Laborers,3.0,3,3,SATURDAY,16,0,0,0,0,0,0,Self-employed,0.6395482304575351,0.27909881044963564,0.17249546677733105,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1340.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n127649,0,Cash loans,F,Y,Y,0,99000.0,971280.0,54364.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.008068,-21797,-1078,-770.0,-5115,16.0,1,1,0,1,0,0,Security staff,2.0,3,3,WEDNESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.4167259306843416,0.7136313997323308,0.0206,,0.9851,0.7959999999999999,,0.0,0.0345,0.1667,,,,,,,0.021,,0.9851,0.804,,0.0,0.0345,0.1667,,,,,,,0.0208,,0.9851,0.7987,,0.0,0.0345,0.1667,,,,,,,reg oper account,block of flats,0.0233,,No,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n385507,0,Cash loans,M,N,Y,1,247500.0,998010.0,77364.0,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.04622,-16574,-1880,-8163.0,-80,,1,1,0,1,0,0,Laborers,3.0,1,1,FRIDAY,16,0,0,0,0,1,1,Self-employed,,0.4325280591442895,0.656158373001177,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-702.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n329327,0,Cash loans,M,Y,N,1,270000.0,573628.5,27724.5,463500.0,Unaccompanied,Working,Secondary / secondary special,Married,Municipal apartment,0.04622,-17298,-392,-4129.0,-839,5.0,1,1,0,1,0,0,Laborers,3.0,1,1,WEDNESDAY,18,0,0,0,0,0,0,Self-employed,,0.7151086492515001,,0.2216,,0.9806,,,0.24,0.2069,0.3333,,,,0.2061,,,0.2258,,0.9806,,,0.2417,0.2069,0.3333,,,,0.2147,,,0.2238,,0.9806,,,0.24,0.2069,0.3333,,,,0.2098,,,,block of flats,0.1675,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n336278,0,Cash loans,M,N,Y,0,157500.0,552555.0,30105.0,477000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.022625,-18709,-4396,-2034.0,-2237,,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,Business Entity Type 3,,0.1171902925185262,,0.1732,0.1073,0.997,0.9592,0.0457,0.16,0.1379,0.375,0.4167,0.0,0.1412,0.2291,0.0,0.0,0.1765,0.1113,0.997,0.9608,0.0461,0.1611,0.1379,0.375,0.4167,0.0,0.1543,0.2387,0.0,0.0,0.1749,0.1073,0.997,0.9597,0.046,0.16,0.1379,0.375,0.4167,0.0,0.1437,0.2332,0.0,0.0,reg oper account,block of flats,0.2052,Panel,No,3.0,0.0,3.0,0.0,-557.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n303193,0,Cash loans,F,N,Y,0,202500.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-15904,-5943,-9750.0,-4089,,1,1,0,1,0,0,Core staff,2.0,2,2,TUESDAY,18,0,0,0,0,0,0,Telecom,0.2944750355759813,0.6671712178284699,0.5513812618027899,0.0825,0.0765,0.9767,,,0.0,0.1379,0.1667,,0.08800000000000001,,0.0666,,0.0,0.084,0.0794,0.9767,,,0.0,0.1379,0.1667,,0.09,,0.0694,,0.0,0.0833,0.0765,0.9767,,,0.0,0.1379,0.1667,,0.0896,,0.0678,,0.0,,block of flats,0.0524,Others,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,3.0,0.0,1.0\n190623,0,Cash loans,F,N,N,0,67500.0,225000.0,6952.5,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.007273999999999998,-20385,365243,-5817.0,-2703,,1,0,0,1,1,0,,1.0,2,2,TUESDAY,16,0,0,0,0,0,0,XNA,,0.2620426497366743,0.43473324875017305,0.0619,0.0728,0.9856,0.7959999999999999,0.0375,0.0,0.1379,0.1667,0.2083,0.0836,0.0504,0.0588,0.0,0.0,0.063,0.0756,0.9856,0.804,0.0378,0.0,0.1379,0.1667,0.2083,0.0855,0.0551,0.0612,0.0,0.0,0.0625,0.0728,0.9856,0.7987,0.0377,0.0,0.1379,0.1667,0.2083,0.0851,0.0513,0.0598,0.0,0.0,reg oper spec account,block of flats,0.0541,Panel,No,0.0,0.0,0.0,0.0,-2.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n416667,0,Cash loans,F,N,N,1,90000.0,573741.0,15133.5,478917.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.019689,-12711,-522,-4119.0,-4124,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,11,0,0,0,0,0,0,Industry: type 1,,0.5520434253481197,0.5280927512030451,0.2052,0.1224,0.9876,0.8164,0.0,0.0,0.4483,0.1667,0.2083,0.0611,0.1673,0.1748,0.0,0.079,0.209,0.127,0.9876,0.8236,0.0,0.0,0.4483,0.1667,0.2083,0.0625,0.1827,0.1822,0.0,0.0836,0.2071,0.1224,0.9876,0.8189,0.0,0.0,0.4483,0.1667,0.2083,0.0621,0.1702,0.17800000000000002,0.0,0.0806,not specified,block of flats,0.1547,Panel,No,0.0,0.0,0.0,0.0,-1849.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n114299,0,Cash loans,M,Y,Y,2,225000.0,675000.0,29862.0,675000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-19111,365243,-6036.0,-2646,17.0,1,0,0,1,0,0,,4.0,2,2,WEDNESDAY,10,0,0,0,0,0,0,XNA,,0.4662405758294487,0.4596904504249018,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,2.0,3.0,2.0,-1905.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,1.0,0.0,0.0,0.0,0.0\n254834,0,Cash loans,F,N,Y,0,157500.0,265536.0,13554.0,180000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,House / apartment,0.016612000000000002,-15531,-705,-7857.0,-4538,,1,1,1,1,0,0,Sales staff,1.0,2,2,MONDAY,13,1,1,0,1,1,0,Business Entity Type 3,,0.6651481509812618,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-631.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n333266,0,Cash loans,M,Y,Y,0,180000.0,1255680.0,45234.0,1125000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.030755,-20328,-741,-2064.0,-3558,2.0,1,1,0,1,0,0,Drivers,2.0,2,2,FRIDAY,9,0,0,0,0,0,0,Self-employed,,0.7743871927504159,0.807273923277881,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1522.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n240154,0,Revolving loans,F,Y,Y,0,315000.0,900000.0,45000.0,900000.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.072508,-18540,-955,-6265.0,-1778,23.0,1,1,0,1,1,0,,1.0,1,1,WEDNESDAY,18,0,0,0,0,0,0,University,0.8816422234233198,0.7170426218220837,,0.0505,0.0758,0.9712,0.6056,0.0743,0.08,0.069,0.25,0.2917,0.0489,0.0387,0.0655,0.0116,0.0915,0.0515,0.0787,0.9712,0.621,0.075,0.0806,0.069,0.25,0.2917,0.05,0.0422,0.0682,0.0117,0.0969,0.051,0.0758,0.9712,0.6109,0.0748,0.08,0.069,0.25,0.2917,0.0497,0.0393,0.0667,0.0116,0.0934,reg oper account,block of flats,0.0714,\"Stone, brick\",No,12.0,0.0,12.0,0.0,-877.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n186029,0,Cash loans,F,N,Y,0,180000.0,646920.0,20997.0,540000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.025164,-20410,-6923,-12869.0,-3903,,1,1,0,1,1,0,Managers,2.0,2,2,FRIDAY,13,0,0,0,0,0,0,Other,,0.6976047485050194,0.6279908192952864,0.0619,0.0701,0.9727,0.626,0.0323,0.0,0.1379,0.1667,0.2083,0.0562,0.0504,0.0716,0.0,0.0,0.063,0.0728,0.9727,0.6406,0.0326,0.0,0.1379,0.1667,0.2083,0.0575,0.0551,0.0746,0.0,0.0,0.0625,0.0701,0.9727,0.631,0.0325,0.0,0.1379,0.1667,0.2083,0.0572,0.0513,0.0729,0.0,0.0,reg oper account,block of flats,0.0612,Mixed,No,6.0,1.0,6.0,1.0,-465.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n303216,0,Cash loans,F,N,Y,1,85500.0,594000.0,23148.0,594000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-13568,-1704,-3063.0,-5113,,1,1,0,1,0,0,,3.0,2,2,SUNDAY,9,0,0,0,0,0,0,Medicine,0.6349937328774462,0.5071138346340023,0.2314393514998941,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n321034,0,Cash loans,F,N,N,2,135000.0,545040.0,25537.5,450000.0,Family,Working,Higher education,Married,House / apartment,0.025164,-11987,-1328,-729.0,-1017,,1,1,0,1,0,0,Sales staff,4.0,2,2,THURSDAY,8,0,0,0,0,0,0,Business Entity Type 3,0.3632554264486772,0.2973985872353381,0.6626377922738201,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-2041.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,6.0\n431192,0,Cash loans,F,Y,Y,0,162000.0,247275.0,17338.5,225000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.018634,-24047,365243,-7404.0,-4561,15.0,1,0,0,1,0,0,,1.0,2,2,MONDAY,15,0,0,0,0,0,0,XNA,,0.7398250203370831,0.8528284668510541,0.0598,0.0764,0.9811,0.7416,0.0323,0.0,0.1379,0.1667,0.2083,0.0334,0.0471,0.0521,0.0077,0.0792,0.0609,0.0792,0.9811,0.7517,0.0326,0.0,0.1379,0.1667,0.2083,0.0342,0.0514,0.0543,0.0078,0.0838,0.0604,0.0764,0.9811,0.7451,0.0325,0.0,0.1379,0.1667,0.2083,0.034,0.0479,0.0531,0.0078,0.0808,reg oper account,block of flats,0.0759,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-598.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n250476,0,Cash loans,F,N,N,0,270000.0,314100.0,13833.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.072508,-17795,365243,-8426.0,-1337,,1,0,0,1,1,0,,1.0,1,1,SUNDAY,12,0,0,0,0,0,0,XNA,,0.5929076876716473,0.6075573001388961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-3285.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n145180,0,Cash loans,F,Y,Y,0,157500.0,994500.0,40504.5,994500.0,Family,Working,Higher education,Married,House / apartment,0.010966,-11676,-3648,-594.0,-4142,4.0,1,1,0,1,0,1,Core staff,2.0,2,2,WEDNESDAY,18,0,0,0,0,0,0,Trade: type 2,0.5863460240019401,0.6794073698090554,0.8050196619153701,0.3979,0.2579,0.9985,,,0.32,0.2759,0.375,,0.3035,,0.3638,,0.0082,0.4055,0.2677,0.9985,,,0.3222,0.2759,0.375,,0.3104,,0.379,,0.0087,0.4018,0.2579,0.9985,,,0.32,0.2759,0.375,,0.3088,,0.3703,,0.0084,,block of flats,0.4025,Mixed,No,0.0,0.0,0.0,0.0,-1329.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n294910,1,Cash loans,F,N,Y,2,94500.0,675000.0,21906.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-13762,-2181,-343.0,-1514,,1,1,1,1,0,0,Sales staff,4.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Self-employed,,0.545659574035148,0.35895122857839673,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-573.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n444223,0,Cash loans,F,N,Y,0,180000.0,157500.0,8172.0,157500.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.028663,-16079,-3881,-294.0,-3375,,1,1,0,1,0,0,High skill tech staff,2.0,2,2,FRIDAY,15,0,0,0,0,1,1,Business Entity Type 2,0.5705565011035539,0.4732669466367975,0.21640296051521946,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1445.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n259717,0,Cash loans,M,N,Y,0,103500.0,534141.0,20263.5,441000.0,Unaccompanied,Pensioner,Higher education,Single / not married,House / apartment,0.04622,-23483,365243,-4623.0,-1266,,1,0,0,1,0,0,,1.0,1,1,FRIDAY,10,0,0,0,0,0,0,XNA,,0.3737978563219818,0.1510075350878296,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-696.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n366597,0,Cash loans,M,Y,Y,0,337500.0,900000.0,46084.5,900000.0,\"Spouse, partner\",State servant,Secondary / secondary special,Married,House / apartment,0.072508,-20485,-1135,-1705.0,-4008,7.0,1,1,0,1,1,0,Drivers,2.0,1,1,MONDAY,17,0,0,0,0,0,0,University,0.8853469286892427,0.7158876398719309,0.5989262182569273,0.4299,0.2523,0.9821,0.7552,0.0,0.6,0.3448,0.3958,0.4375,0.0,0.0,0.4176,0.0,0.0054,0.375,0.2594,0.9821,0.7648,0.0,0.4028,0.3448,0.3333,0.375,0.0,0.0,0.3696,0.0,0.0033,0.4341,0.2523,0.9821,0.7585,0.0,0.6,0.3448,0.3958,0.4375,0.0,0.0,0.4251,0.0,0.0055,reg oper account,block of flats,0.2807,Panel,No,1.0,0.0,1.0,0.0,-1088.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n242909,0,Revolving loans,F,N,Y,0,67500.0,202500.0,10125.0,202500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.019689,-22912,365243,-871.0,-3779,,1,0,0,1,0,0,,2.0,2,2,MONDAY,12,0,0,0,0,0,0,XNA,,0.3980659204241564,0.5316861425197883,0.0186,0.0314,0.9876,,,0.0,0.069,0.0833,,0.0147,,0.0166,,0.0018,0.0189,0.0325,0.9876,,,0.0,0.069,0.0833,,0.015,,0.0173,,0.0019,0.0187,0.0314,0.9876,,,0.0,0.069,0.0833,,0.0149,,0.0169,,0.0018,,block of flats,0.0134,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-1826.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n128006,1,Cash loans,F,Y,Y,0,112500.0,382500.0,18531.0,382500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17037,-2037,-998.0,-518,12.0,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,15,0,0,0,0,0,0,Self-employed,0.5373529597229328,0.6156494568027608,0.8555235867964663,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1137.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n410762,0,Cash loans,F,Y,Y,0,270000.0,398016.0,31576.5,360000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.04622,-10256,-3472,-4629.0,-2933,13.0,1,1,0,1,1,0,Medicine staff,2.0,1,1,THURSDAY,18,0,0,0,0,0,0,Medicine,0.4487747885527095,0.20223205475390355,0.6178261467332483,0.0021,0.0,0.9558,0.3948,0.0026,0.0,0.069,0.0,0.0417,0.0023,0.0017,0.0018,0.0,0.0,0.0021,0.0,0.9558,0.4185,0.0026,0.0,0.069,0.0,0.0417,0.0024,0.0018,0.0019,0.0,0.0,0.0021,0.0,0.9558,0.4029,0.0026,0.0,0.069,0.0,0.0417,0.0024,0.0017,0.0018,0.0,0.0,reg oper spec account,block of flats,0.0014,Wooden,No,0.0,0.0,0.0,0.0,-1534.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n123432,0,Cash loans,M,Y,Y,0,171000.0,450000.0,22018.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.026392,-19615,-4791,-7189.0,-370,5.0,1,1,0,1,1,0,,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.54783805066772,0.2721336844147212,0.0351,,0.9757,,,0.0,0.069,0.1667,,,,0.0281,,0.0166,0.0357,,0.9757,,,0.0,0.069,0.1667,,,,0.0293,,0.0175,0.0354,,0.9757,,,0.0,0.069,0.1667,,,,0.0286,,0.0169,,block of flats,0.0257,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-952.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,9.0\n264637,0,Cash loans,F,N,Y,1,270000.0,1078200.0,31522.5,900000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.008068,-13403,-1183,-4768.0,-362,,1,1,0,1,1,0,Sales staff,3.0,3,3,WEDNESDAY,6,0,0,0,0,0,0,Self-employed,0.5675563808278425,0.15759240488962162,0.4722533429586386,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1336.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n190176,0,Revolving loans,F,N,Y,0,315000.0,900000.0,45000.0,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010147,-16998,-1513,-9141.0,-548,,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,,0.5984954965225675,0.6479768603302221,0.0629,0.0656,0.9816,0.7484,0.0099,0.0,0.2069,0.1667,0.2083,0.0379,0.0504,0.053,0.0039,0.0348,0.0641,0.0681,0.9816,0.7583,0.01,0.0,0.2069,0.1667,0.2083,0.0387,0.0551,0.0551,0.0039,0.0369,0.0635,0.0656,0.9816,0.7518,0.01,0.0,0.2069,0.1667,0.2083,0.0385,0.0513,0.0539,0.0039,0.0356,org spec account,block of flats,0.0416,Panel,No,,,,,-611.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n163300,0,Cash loans,F,N,N,0,180000.0,553806.0,21798.0,495000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.010276,-9043,-382,-7710.0,-685,,1,1,0,1,0,1,,2.0,2,2,WEDNESDAY,13,0,0,0,0,1,1,Government,0.3736857151123108,0.6175363687325887,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1867.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n132783,0,Cash loans,F,N,Y,0,135000.0,813195.0,27004.5,702000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.00733,-20279,365243,-8588.0,-3599,,1,0,0,1,0,0,,1.0,2,2,THURSDAY,14,0,0,0,0,0,0,XNA,0.7068080510713307,0.28232640421904515,0.4722533429586386,0.2227,0.162,0.9886,0.8436,0.0953,0.12,0.1034,0.3333,0.375,0.1338,0.1816,0.2407,0.0,0.0,0.2269,0.1681,0.9886,0.8497,0.0962,0.1208,0.1034,0.3333,0.375,0.1368,0.1983,0.2508,0.0,0.0,0.2248,0.162,0.9886,0.8457,0.0959,0.12,0.1034,0.3333,0.375,0.1361,0.1847,0.245,0.0,0.0,reg oper account,block of flats,0.2344,Panel,No,0.0,0.0,0.0,0.0,-306.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n330599,0,Cash loans,M,N,Y,0,202500.0,355536.0,18742.5,270000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.009549,-9751,-766,-9694.0,-2421,,1,1,0,1,0,0,Laborers,2.0,2,2,TUESDAY,14,0,0,0,0,1,1,Business Entity Type 3,0.2916547712899928,0.7530423036013465,,0.1485,0.0795,0.9851,0.7959999999999999,0.0332,0.16,0.1379,0.3333,0.375,0.0705,0.1202,0.1425,0.0039,0.0009,0.1513,0.0825,0.9851,0.804,0.0335,0.1611,0.1379,0.3333,0.375,0.0721,0.1313,0.1485,0.0039,0.001,0.1499,0.0795,0.9851,0.7987,0.0334,0.16,0.1379,0.3333,0.375,0.0717,0.1223,0.145,0.0039,0.0009,reg oper spec account,block of flats,0.1304,Panel,No,1.0,0.0,1.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n350053,0,Revolving loans,F,Y,Y,1,144000.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00733,-14146,-1371,-2989.0,-4276,11.0,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,16,0,0,0,0,1,1,Self-employed,0.6683002185406554,0.5521569836360225,0.6006575372857061,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-174.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n173887,0,Cash loans,F,N,Y,0,400500.0,1113840.0,44302.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.04622,-19107,-4571,-9431.0,-2656,,1,1,0,1,0,0,Cooking staff,2.0,1,1,MONDAY,14,0,0,0,0,0,0,Other,0.6356286233790309,0.6567540699984526,0.6380435278721609,0.0124,,0.9866,0.8164,0.008,0.0,0.0345,0.0833,0.0417,,0.0101,0.008,0.0,0.0,0.0126,,0.9866,0.8236,0.0081,0.0,0.0345,0.0833,0.0417,,0.011000000000000001,0.0083,0.0,0.0,0.0125,,0.9866,0.8189,0.0081,0.0,0.0345,0.0833,0.0417,,0.0103,0.0081,0.0,0.0,,block of flats,0.0107,Panel,No,2.0,0.0,2.0,0.0,-839.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,0.0,0.0,1.0\n349904,0,Cash loans,F,Y,N,0,85500.0,531000.0,25542.0,531000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-21902,-1801,-963.0,-1239,3.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Self-employed,,0.5929129657990798,0.22888341670067305,0.2206,0.0747,0.9975,0.966,0.049,0.16,0.069,0.625,0.6667,,0.1799,0.1766,0.0,0.0,0.2248,0.0776,0.9975,0.9673,0.0494,0.1611,0.069,0.625,0.6667,,0.1965,0.184,0.0,0.0,0.2228,0.0747,0.9975,0.9665,0.0493,0.16,0.069,0.625,0.6667,,0.183,0.1798,0.0,0.0,reg oper account,block of flats,0.1657,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-702.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n406215,0,Cash loans,M,Y,N,0,180000.0,1288350.0,34114.5,1125000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.018801,-14015,-394,-1158.0,-4254,14.0,1,1,1,1,0,0,Drivers,2.0,2,2,FRIDAY,8,0,0,0,0,0,0,Other,0.4123693653830224,0.462427800447858,0.7530673920730478,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-12.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n342664,0,Cash loans,F,N,Y,0,157500.0,1169532.0,42138.0,945000.0,Family,Pensioner,Higher education,Civil marriage,House / apartment,0.020246,-21779,365243,-2283.0,-4743,,1,0,0,1,0,0,,2.0,3,3,FRIDAY,9,0,0,0,0,0,0,XNA,0.8345307034571978,0.476418802445023,,0.0948,0.1192,0.9861,0.8096,0.0169,0.0,0.1379,0.1667,0.2083,0.1646,0.0773,0.0907,0.0,0.0,0.0966,0.1237,0.9861,0.8171,0.017,0.0,0.1379,0.1667,0.2083,0.1684,0.0845,0.0945,0.0,0.0,0.0958,0.1192,0.9861,0.8121,0.017,0.0,0.1379,0.1667,0.2083,0.1675,0.0787,0.0924,0.0,0.0,reg oper account,block of flats,0.0714,Block,No,0.0,0.0,0.0,0.0,-2258.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n221601,0,Revolving loans,F,Y,Y,0,202500.0,405000.0,20250.0,405000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.008865999999999999,-19889,-4928,-9240.0,-3298,6.0,1,1,0,1,0,0,,2.0,2,2,THURSDAY,10,0,0,0,0,0,0,Industry: type 3,,0.6480962775957331,,0.1619,0.094,0.9876,,,0.16,0.1379,0.375,,0.1065,,0.17800000000000002,,0.4335,0.1649,0.0976,0.9876,,,0.1611,0.1379,0.375,,0.109,,0.1855,,0.4589,0.1634,0.094,0.9876,,,0.16,0.1379,0.375,,0.1084,,0.1812,,0.4425,,block of flats,0.2343,Panel,No,0.0,0.0,0.0,0.0,-201.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n162432,0,Cash loans,M,Y,N,1,315000.0,1359985.5,54067.5,1251000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.030755,-14448,-1115,-3009.0,-1161,4.0,1,1,0,1,0,0,Drivers,3.0,2,2,THURSDAY,15,0,0,0,0,1,1,Business Entity Type 3,,0.7361506262718016,0.23791607950711405,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,0.0,6.0,0.0,-1608.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n158819,0,Cash loans,F,N,N,0,157500.0,253737.0,25227.0,229500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Single / not married,With parents,0.031329,-11312,-2960,-4971.0,-3078,,1,1,1,1,1,0,Sales staff,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Self-employed,0.14686264595544965,0.5956967940571597,0.7597121819739279,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1517.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n301579,1,Cash loans,F,Y,Y,0,31500.0,873000.0,28039.5,873000.0,Unaccompanied,Pensioner,Higher education,Widow,House / apartment,0.014519999999999996,-21474,365243,-8205.0,-4883,64.0,1,0,0,1,0,0,,1.0,2,2,TUESDAY,13,0,0,0,0,0,0,XNA,,0.3718344019744929,,0.2227,,0.9801,0.728,,0.096,0.1655,0.2333,0.375,0.0571,0.1782,0.0835,0.0154,0.0782,0.2269,,0.9796,0.7387,,0.0,0.1379,0.1667,0.375,0.0524,0.1947,0.0558,0.0156,0.0,0.2248,,0.9801,0.7316,,0.0,0.1379,0.1667,0.375,0.0524,0.1813,0.0645,0.0155,0.0281,reg oper account,block of flats,0.1769,Panel,No,0.0,0.0,0.0,0.0,-1617.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n216337,0,Cash loans,M,N,Y,0,202500.0,284400.0,22599.0,225000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.009657,-11499,-817,-5543.0,-4097,,1,1,0,1,0,0,,1.0,2,2,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.7381716411743717,0.2764406945454034,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1424.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n241712,1,Cash loans,F,Y,Y,1,90000.0,788472.0,52821.0,688500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.020713,-12577,-2507,-510.0,-3116,22.0,1,1,0,1,0,0,Laborers,3.0,3,3,FRIDAY,7,0,0,0,0,0,0,Business Entity Type 3,0.2962521953132468,0.3573874317525013,0.5460231970049609,0.1113,0.0675,0.9831,0.7688,0.0275,0.12,0.1034,0.3333,,0.08900000000000001,,0.1138,,,0.1134,0.0701,0.9831,0.7779,0.0278,0.1208,0.1034,0.3333,,0.0911,,0.1186,,,0.1124,0.0675,0.9831,0.7719,0.0277,0.12,0.1034,0.3333,,0.0906,,0.1159,,,reg oper account,block of flats,0.1046,Panel,No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n242531,0,Cash loans,F,N,Y,2,90000.0,360000.0,18508.5,360000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.015221,-14876,-563,-1992.0,-565,,1,1,0,1,1,1,Cleaning staff,3.0,2,2,FRIDAY,13,0,0,0,0,0,0,Construction,0.6076623082704503,0.2675641319203065,0.5280927512030451,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-697.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,0.0\n236355,0,Cash loans,F,N,N,0,157500.0,497520.0,39307.5,450000.0,Unaccompanied,Commercial associate,Incomplete higher,Separated,House / apartment,0.072508,-10374,-1618,-3643.0,-3056,,1,1,1,1,0,0,Private service staff,1.0,1,1,WEDNESDAY,14,0,1,1,0,1,1,Business Entity Type 3,,0.7081849188331246,,0.0371,0.0025,0.9722,0.6192,0.0,0.0,0.1034,0.125,0.1667,0.0,0.0303,0.0379,0.0,0.0012,0.0378,0.0026,0.9722,0.6341,0.0,0.0,0.1034,0.125,0.1667,0.0,0.0331,0.0395,0.0,0.0012,0.0375,0.0025,0.9722,0.6243,0.0,0.0,0.1034,0.125,0.1667,0.0,0.0308,0.0386,0.0,0.0012,reg oper account,block of flats,0.0301,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1848.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n213017,1,Cash loans,M,N,Y,1,193500.0,521280.0,31630.5,450000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.031329,-15367,-1175,-306.0,-4322,,1,1,0,1,0,0,Drivers,2.0,2,2,MONDAY,11,0,0,0,0,1,1,Business Entity Type 3,0.2676844819161528,0.4326407768638117,0.07105512466873665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1649.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n423108,0,Cash loans,M,Y,Y,0,153000.0,835380.0,40320.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.010147,-22180,-7283,-3.0,-421,10.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Industry: type 7,,0.5130210840314396,0.5656079814115492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-374.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n372248,0,Cash loans,F,N,Y,2,157500.0,314055.0,16033.5,238500.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.025164,-11329,-882,-810.0,-215,,1,1,0,1,0,0,Core staff,4.0,2,2,SATURDAY,10,0,0,0,0,0,0,Kindergarten,0.4044396449902776,0.4279395416780212,0.6363761710860439,0.1577,0.0338,0.9786,0.7076,0.0306,0.04,0.0345,0.3333,0.375,0.0333,0.1278,0.0713,0.0039,0.0223,0.1607,0.0351,0.9786,0.7190000000000001,0.0308,0.0403,0.0345,0.3333,0.375,0.034,0.1396,0.0743,0.0039,0.0236,0.1593,0.0338,0.9786,0.7115,0.0308,0.04,0.0345,0.3333,0.375,0.0338,0.13,0.0726,0.0039,0.0228,reg oper account,block of flats,0.0777,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n131422,0,Cash loans,F,Y,Y,2,135000.0,942300.0,24984.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010276,-11611,-1689,-4988.0,-996,9.0,1,1,1,1,1,0,Sales staff,4.0,2,2,MONDAY,13,0,1,1,0,1,1,Business Entity Type 3,,0.3261652568923096,0.09141199381412644,0.0701,0.0669,0.9881,,,0.0,0.1379,0.1667,,0.0602,,0.0414,,0.0,0.0714,0.0694,0.9881,,,0.0,0.1379,0.1667,,0.0615,,0.0431,,0.0,0.0708,0.0669,0.9881,,,0.0,0.1379,0.1667,,0.0612,,0.0421,,0.0,,block of flats,0.0542,\"Stone, brick\",No,7.0,0.0,7.0,0.0,-109.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n294294,0,Cash loans,F,Y,Y,0,90000.0,675000.0,21775.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.022625,-19262,-1072,-8622.0,-2722,16.0,1,1,1,1,0,0,Sales staff,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.6144327474154886,0.6109913280868294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1554.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,2.0,2.0,0.0,0.0,2.0\n454583,0,Cash loans,F,N,N,0,315000.0,412542.0,43447.5,387000.0,Unaccompanied,State servant,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-12632,-1329,-1615.0,-2627,,1,1,1,1,1,0,Accountants,2.0,2,2,MONDAY,14,0,0,0,0,0,0,Police,0.43868391850876,0.18483176632893286,0.06917422671336917,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1906.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n272317,0,Revolving loans,M,Y,Y,1,180000.0,270000.0,13500.0,270000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.010032,-14443,-2918,-1239.0,-3349,32.0,1,1,0,1,1,0,Drivers,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Self-employed,,0.4800582983803864,0.7557400501752248,0.0928,0.0867,0.9767,0.6804,0.0392,0.0,0.2069,0.1667,0.2083,0.0845,0.0756,0.0884,0.0,0.0,0.0945,0.09,0.9767,0.6929,0.0395,0.0,0.2069,0.1667,0.2083,0.0864,0.0826,0.0921,0.0,0.0,0.0937,0.0867,0.9767,0.6847,0.0394,0.0,0.2069,0.1667,0.2083,0.086,0.077,0.09,0.0,0.0,reg oper spec account,block of flats,0.091,Panel,No,0.0,0.0,0.0,0.0,-1057.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n426708,0,Revolving loans,M,Y,N,1,153000.0,270000.0,13500.0,270000.0,Family,Working,Higher education,Single / not married,With parents,0.015221,-10828,-880,-4960.0,-3470,9.0,1,1,0,1,0,0,Managers,2.0,2,2,SATURDAY,14,0,0,0,0,0,0,Other,,0.3476400313536334,,0.0742,0.045,0.9841,0.7824,0.0318,0.08,0.069,0.3333,0.375,0.042,0.0605,0.0799,0.0,0.0,0.0756,0.0467,0.9841,0.7909,0.0321,0.0806,0.069,0.3333,0.375,0.043,0.0661,0.0833,0.0,0.0,0.0749,0.045,0.9841,0.7853,0.032,0.08,0.069,0.3333,0.375,0.0427,0.0616,0.0814,0.0,0.0,reg oper account,block of flats,0.0803,Panel,No,1.0,0.0,1.0,0.0,-693.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n407720,0,Cash loans,M,N,Y,1,112500.0,225000.0,15660.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-10815,-2806,-4625.0,-3284,,1,1,0,1,0,0,Managers,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,,0.6152871832623411,0.3825018041447388,0.0186,0.059000000000000004,0.9707,0.5988,0.0766,0.0,0.069,0.0833,0.125,0.0361,0.0151,0.0165,0.0,0.0,0.0189,0.0613,0.9707,0.6145,0.0773,0.0,0.069,0.0833,0.125,0.0369,0.0165,0.0172,0.0,0.0,0.0187,0.059000000000000004,0.9707,0.6042,0.0771,0.0,0.069,0.0833,0.125,0.0367,0.0154,0.0168,0.0,0.0,reg oper account,block of flats,0.0326,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1598.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,4.0\n200885,0,Cash loans,M,Y,Y,1,157500.0,506902.5,34006.5,414000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-17582,-296,-1803.0,-1117,25.0,1,1,0,1,1,0,Drivers,3.0,2,2,TUESDAY,10,0,0,0,0,0,0,Business Entity Type 2,0.2553756633833622,0.4474304080258458,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-296.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,1.0\n236660,0,Revolving loans,F,N,N,0,54000.0,157500.0,7875.0,157500.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,With parents,0.035792000000000004,-8270,-288,-249.0,-951,,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Self-employed,0.2089329338155432,0.2852019204146127,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-325.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n244111,0,Revolving loans,M,Y,Y,0,135000.0,180000.0,9000.0,180000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-8313,-832,-8248.0,-862,18.0,1,1,0,1,0,0,Drivers,1.0,2,2,SATURDAY,16,0,0,0,0,0,0,Self-employed,,0.24655811233240105,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,No,4.0,0.0,4.0,0.0,-170.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n428038,0,Revolving loans,F,Y,Y,0,90000.0,382500.0,19125.0,382500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.028663,-19753,-11943,-6452.0,-3309,8.0,1,1,0,1,0,0,Laborers,2.0,2,2,SATURDAY,10,0,0,0,0,0,0,Government,0.8728501081113068,0.5376955356147627,0.7165702448010511,0.1124,0.0916,0.9891,0.8504,0.0245,0.12,0.1034,0.3333,0.375,0.0681,0.0908,0.1246,0.0039,0.0006,0.1145,0.0951,0.9891,0.8563,0.0247,0.1208,0.1034,0.3333,0.375,0.0697,0.0992,0.1298,0.0039,0.0006,0.1135,0.0916,0.9891,0.8524,0.0246,0.12,0.1034,0.3333,0.375,0.0693,0.0923,0.1269,0.0039,0.0006,reg oper account,block of flats,0.1115,Panel,No,0.0,0.0,0.0,0.0,-301.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n288243,0,Cash loans,F,N,Y,0,135000.0,509602.5,30924.0,387000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.0105,-16309,-546,-4665.0,-4474,,1,1,0,1,1,0,Sales staff,1.0,3,3,THURSDAY,14,0,0,0,0,0,0,Trade: type 7,,0.091531028756502,0.5585066276769286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n235450,0,Cash loans,F,N,N,1,225000.0,278613.0,16123.5,252000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,Municipal apartment,0.003818,-13747,-4234,-5068.0,-4460,,1,1,0,1,1,0,Managers,2.0,2,2,FRIDAY,7,0,0,0,0,0,0,Self-employed,,0.6148108578892578,0.8256357449717892,,,0.9732,,,,0.0,0.0417,,0.0,,0.0066,,,,,0.9732,,,,0.0,0.0417,,0.0,,0.0069,,,,,0.9732,,,,0.0,0.0417,,0.0,,0.0067,,,,block of flats,0.0052,Wooden,Yes,3.0,0.0,3.0,0.0,-486.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n296187,0,Cash loans,F,N,Y,0,112500.0,540000.0,19395.0,540000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.026392,-17416,-488,-8749.0,-923,,1,1,1,1,1,0,Laborers,2.0,2,2,THURSDAY,12,0,0,0,0,1,1,Business Entity Type 3,,0.056863604880677175,0.1455428133497032,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-115.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n304872,0,Cash loans,F,N,Y,0,99000.0,352044.0,13270.5,247500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-13715,-467,-852.0,-861,,1,1,0,1,1,0,Cleaning staff,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,School,0.2323765104211449,0.6873747966972311,,0.0825,0.0799,0.9767,0.6804,0.0079,0.0,0.1379,0.1667,0.2083,0.0537,0.0672,0.0701,0.0,0.0,0.084,0.0829,0.9767,0.6929,0.008,0.0,0.1379,0.1667,0.2083,0.0549,0.0735,0.073,0.0,0.0,0.0833,0.0799,0.9767,0.6847,0.0079,0.0,0.1379,0.1667,0.2083,0.0546,0.0684,0.0713,0.0,0.0,reg oper account,block of flats,0.0594,Panel,No,2.0,1.0,2.0,1.0,-1177.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n205234,0,Cash loans,M,Y,Y,0,180000.0,573408.0,20727.0,495000.0,Family,Working,Secondary / secondary special,Single / not married,House / apartment,0.028663,-17756,-2474,-4779.0,-1294,9.0,1,1,1,1,1,0,Drivers,1.0,2,2,TUESDAY,13,0,1,1,0,1,1,Business Entity Type 3,,0.4866494317218325,0.4101025731788671,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-570.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n222726,0,Cash loans,F,Y,N,2,148500.0,484789.5,18094.5,418500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.014464,-14702,-1631,-5220.0,-4588,12.0,1,1,0,1,0,0,Managers,4.0,2,2,SATURDAY,3,0,0,0,0,0,0,Kindergarten,,0.5987792200033096,0.4596904504249018,0.0866,0.0095,0.9762,0.6736,0.0101,0.0,0.1379,0.1667,0.2083,0.0148,0.0706,0.0684,0.0,0.0,0.0882,0.0099,0.9762,0.6864,0.0102,0.0,0.1379,0.1667,0.2083,0.0151,0.0771,0.0713,0.0,0.0,0.0874,0.0095,0.9762,0.6779999999999999,0.0101,0.0,0.1379,0.1667,0.2083,0.015,0.0718,0.0696,0.0,0.0,reg oper account,block of flats,0.0608,\"Stone, brick\",No,2.0,0.0,2.0,0.0,-3.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n426333,0,Cash loans,F,N,Y,0,99000.0,254700.0,24939.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.032561,-24682,365243,-2701.0,-4672,,1,0,0,1,0,0,,1.0,1,1,THURSDAY,11,0,0,0,1,0,0,XNA,,0.6843311466559674,,0.0371,0.0617,0.9727,,,0.0,,0.0833,,0.0,,0.0255,,0.0323,0.0378,0.0641,0.9727,,,0.0,,0.0833,,0.0,,0.0234,,0.0342,0.0375,0.0617,0.9727,,,0.0,,0.0833,,0.0,,0.026000000000000002,,0.033,,block of flats,0.0247,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1509.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n418509,0,Cash loans,F,Y,Y,0,157500.0,384048.0,18810.0,270000.0,Unaccompanied,Working,Higher education,Civil marriage,House / apartment,0.006305,-10908,-2614,-2823.0,-3574,5.0,1,1,0,1,0,0,Accountants,2.0,3,3,SATURDAY,8,0,0,0,0,0,0,Business Entity Type 1,0.4111297071424266,0.3108314910896068,0.4830501881366946,0.0278,,,,,,0.1034,0.0833,0.125,,0.0227,0.0258,,,0.0284,,,,,,0.1034,0.0833,0.125,,0.0248,0.0269,,,0.0281,,,,,,0.1034,0.0833,0.125,,0.0231,0.0263,,,reg oper account,block of flats,,,No,1.0,0.0,1.0,0.0,-1028.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n116363,0,Cash loans,M,Y,N,1,180000.0,675000.0,28507.5,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.014519999999999996,-15163,-2218,-6511.0,-4292,21.0,1,1,0,1,0,0,Laborers,3.0,2,2,THURSDAY,9,0,0,0,0,0,0,Industry: type 11,,0.12828780440466472,0.6610235391308081,0.1082,0.0875,0.9831,0.7688,0.0369,0.0,0.1607,0.1667,0.2083,0.0566,0.0863,0.0735,0.0051,0.0313,0.0966,0.0652,0.9816,0.7583,0.0202,0.0,0.2069,0.1667,0.2083,0.0285,0.0817,0.0515,0.0,0.0,0.1031,0.0922,0.9836,0.7786,0.0371,0.0,0.2069,0.1667,0.2083,0.0707,0.0847,0.0782,0.0039,0.0048,reg oper spec account,block of flats,0.0567,\"Stone, brick\",No,3.0,0.0,3.0,0.0,-2575.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n433865,0,Cash loans,F,N,Y,1,180000.0,213948.0,21289.5,189000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.035792000000000004,-11736,-896,-145.0,-776,,1,1,0,1,0,0,Managers,3.0,2,2,TUESDAY,18,0,0,0,0,0,0,Business Entity Type 3,0.5648620174639524,0.5470470701273984,0.6956219298394389,0.3649,0.2013,1.0,1.0,0.0806,0.16,0.1379,0.375,0.4167,0.0785,1.0,0.2918,1.0,0.124,0.2962,0.1208,1.0,1.0,0.0509,0.0806,0.069,0.3333,0.375,0.0662,0.259,0.2127,1.0,0.0692,0.3685,0.2013,1.0,1.0,0.0811,0.16,0.1379,0.375,0.4167,0.0799,1.0,0.2971,1.0,0.1266,not specified,block of flats,0.2024,\"Stone, brick\",No,0.0,0.0,0.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n298664,0,Revolving loans,F,N,Y,0,202500.0,675000.0,33750.0,675000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.00823,-18386,-1453,-4566.0,-1916,,1,1,0,1,0,0,,2.0,2,2,SUNDAY,9,0,0,0,0,0,0,Business Entity Type 3,,0.503675571162869,0.3740208032583212,0.0773,,0.9786,,,,,0.1667,,,,0.0612,,,0.0788,,0.9786,,,,,0.1667,,,,0.0637,,,0.0781,,0.9786,,,,,0.1667,,,,0.0623,,,,block of flats,0.0488,Panel,No,3.0,0.0,3.0,0.0,-2422.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n125572,0,Cash loans,M,Y,Y,1,135000.0,577147.5,25551.0,459000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.01885,-15072,-3229,-3798.0,-4791,6.0,1,1,0,1,0,1,Laborers,3.0,2,2,SATURDAY,10,0,0,0,0,0,0,Business Entity Type 3,0.3630366839281577,0.7367745204480496,0.5814837058057234,0.2227,0.1686,0.9821,0.7552,0.0807,0.24,0.2069,0.3333,0.375,0.1185,0.1816,0.2373,0.0,0.0,0.2269,0.1749,0.9821,0.7648,0.0815,0.2417,0.2069,0.3333,0.375,0.1212,0.1983,0.2473,0.0,0.0,0.2248,0.1686,0.9821,0.7585,0.0813,0.24,0.2069,0.3333,0.375,0.1205,0.1847,0.2416,0.0,0.0,reg oper account,block of flats,0.2308,Panel,No,0.0,0.0,0.0,0.0,-3198.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,2.0\n129769,1,Cash loans,F,N,Y,0,189000.0,1125000.0,33025.5,1125000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.028663,-16651,-4997,-1899.0,-204,,1,1,0,1,1,0,Laborers,2.0,2,2,WEDNESDAY,13,0,0,0,0,0,0,Transport: type 4,,0.6345766590370713,0.42765737003502935,0.1216,0.0516,0.9796,0.7212,0.0,0.04,0.0345,0.3333,0.375,0.1403,0.0975,0.0347,0.0077,0.0031,0.1218,0.0508,0.9796,0.7321,0.0,0.0403,0.0345,0.3333,0.375,0.07400000000000001,0.1065,0.0361,0.0078,0.0,0.1228,0.0516,0.9796,0.7249,0.0,0.04,0.0345,0.3333,0.375,0.1427,0.0992,0.0353,0.0078,0.0032,reg oper account,block of flats,0.0407,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-128.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,2.0\n132436,0,Cash loans,F,N,Y,0,292500.0,1762110.0,46611.0,1575000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.022625,-16815,-5877,-5740.0,-366,,1,1,0,1,1,0,,2.0,2,2,THURSDAY,16,0,0,0,0,1,1,Other,0.7798595613407845,0.6578820534802856,0.7583930617144343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-778.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n136622,0,Cash loans,F,Y,N,1,202500.0,832500.0,26856.0,832500.0,Unaccompanied,Working,Higher education,Married,With parents,0.035792000000000004,-10098,-1139,-4411.0,-788,4.0,1,1,0,1,0,0,Core staff,3.0,2,2,WEDNESDAY,15,0,0,0,0,1,1,Security Ministries,0.5144580189401445,0.6589689416330766,0.6658549219640212,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n267953,0,Cash loans,F,N,Y,1,76500.0,152820.0,15241.5,135000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005084,-16423,-132,-3758.0,-4888,,1,1,1,1,0,0,,3.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Construction,,0.4151906355699462,0.5028782772082183,0.0928,0.0927,0.9811,,,0.0,0.2069,0.1667,,0.1126,,0.0797,,0.0,0.0945,0.0962,0.9811,,,0.0,0.2069,0.1667,,0.1152,,0.083,,0.0,0.0937,0.0927,0.9811,,,0.0,0.2069,0.1667,,0.1146,,0.0811,,0.0,,block of flats,0.0701,Panel,No,0.0,0.0,0.0,0.0,-264.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n308560,1,Cash loans,M,N,N,1,112500.0,685012.5,29020.5,553500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.003813,-15738,-3380,-8292.0,-4655,,1,1,0,1,0,0,Laborers,3.0,2,2,TUESDAY,7,0,0,0,0,0,0,Industry: type 3,,0.05616170722479972,0.5620604831738043,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1841.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n309281,0,Cash loans,F,N,Y,0,225000.0,900000.0,26446.5,900000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Separated,House / apartment,0.003540999999999999,-18214,-568,-6758.0,-1757,,1,1,0,1,0,0,Sales staff,1.0,1,1,TUESDAY,9,0,0,0,0,0,0,Self-employed,,0.6797311703330493,0.12850437737240394,0.1237,0.0642,0.9861,0.8096,,0.08,0.0345,0.2917,0.3333,0.0384,,0.051,,,0.1261,0.0666,0.9861,0.8171,,0.0806,0.0345,0.2917,0.3333,0.0392,,0.0531,,,0.1249,0.0642,0.9861,0.8121,,0.08,0.0345,0.2917,0.3333,0.039,,0.0519,,,reg oper account,block of flats,0.0762,Panel,No,1.0,0.0,1.0,0.0,-1722.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n165702,0,Revolving loans,M,Y,Y,1,247500.0,270000.0,13500.0,270000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.008625,-15621,-1022,-1673.0,-2526,6.0,1,1,0,1,0,0,Laborers,2.0,2,2,WEDNESDAY,12,1,1,0,1,1,0,Business Entity Type 3,0.6263216327894546,0.7122488579168413,0.6212263380626669,0.2227,0.1289,0.9821,0.7348,0.3305,0.24,0.2069,0.3333,0.375,0.0381,,0.1407,,0.2096,0.2269,0.1338,0.9821,0.7452,0.3335,0.2417,0.2069,0.3333,0.375,0.039,,0.1465,,0.2219,0.2248,0.1289,0.9821,0.7383,0.3326,0.24,0.2069,0.3333,0.375,0.0387,,0.1432,,0.214,reg oper account,block of flats,0.1807,Panel,No,,,,,-325.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n365441,0,Cash loans,M,Y,Y,0,81000.0,140166.0,7600.5,117000.0,Family,State servant,Higher education,Married,Municipal apartment,0.002042,-10150,-1170,-4132.0,-2755,9.0,1,1,0,1,0,0,Medicine staff,2.0,3,3,SUNDAY,13,0,0,0,0,0,0,Medicine,,0.3197106285761425,0.324891229465852,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1080.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n253971,0,Cash loans,F,Y,Y,0,270000.0,1520451.0,41940.0,1359000.0,Unaccompanied,State servant,Secondary / secondary special,Civil marriage,House / apartment,0.035792000000000004,-16249,-2378,-199.0,-5555,6.0,1,1,0,1,0,0,Secretaries,2.0,2,2,FRIDAY,11,0,0,0,0,1,1,Other,0.6887090984493986,0.7343457883093811,0.5937175866150576,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-740.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n316113,0,Cash loans,M,Y,Y,1,135000.0,675000.0,21775.5,675000.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.035792000000000004,-15640,-2626,-6164.0,-4879,16.0,1,1,1,1,1,0,Laborers,2.0,2,2,TUESDAY,13,0,0,0,0,0,0,Self-employed,0.14707763844104266,0.5082288183468503,0.6127042441012546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-3268.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n436055,0,Cash loans,F,N,Y,0,184500.0,1096020.0,56092.5,900000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.010032,-23032,-1654,-2913.0,-2962,,1,1,0,1,0,0,Core staff,1.0,2,2,FRIDAY,8,0,0,0,0,0,0,School,,0.6148574646619929,0.7449321846094795,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-962.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,0.0,0.0,0.0,0.0,2.0\n427284,0,Cash loans,F,N,Y,0,225000.0,1350000.0,39604.5,1350000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.010643,-15265,-3248,-8119.0,-4793,,1,1,0,1,1,0,Core staff,1.0,2,2,TUESDAY,10,0,1,1,0,1,1,School,,0.4988647712014631,,0.0982,0.081,0.9876,0.8436,0.0204,0.0132,0.2155,0.2188,0.2775,0.0498,0.0786,0.0969,0.0068,0.0561,0.0851,0.0402,0.9816,0.7583,0.0115,0.0,0.2069,0.1667,0.2083,0.032,0.0735,0.0513,0.0039,0.0029,0.0843,0.0845,0.9851,0.8189,0.0232,0.0,0.2069,0.1667,0.2083,0.0507,0.0684,0.0769,0.0039,0.0347,reg oper account,block of flats,0.0648,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-272.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n263654,0,Cash loans,F,Y,Y,2,112500.0,808650.0,26217.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.030755,-11342,-131,-225.0,-614,21.0,1,1,0,1,0,0,Core staff,4.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,0.43788307383744,0.4618951130162571,0.28961123838200553,,,0.9791,,,,,,,,,0.0623,,,,,0.9791,,,,,,,,,0.0649,,,,,0.9791,,,,,,,,,0.0634,,,,,0.0637,,No,0.0,0.0,0.0,0.0,-385.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n113796,0,Revolving loans,F,Y,Y,0,315000.0,675000.0,33750.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Civil marriage,House / apartment,0.018209,-17859,-2401,-3782.0,-1399,4.0,1,1,0,1,0,0,,2.0,3,3,THURSDAY,13,0,0,0,0,0,0,Business Entity Type 3,,0.29168263883223355,0.42765737003502935,0.1216,0.1304,0.9801,0.728,0.0574,0.0,0.2759,0.1667,0.2083,0.0903,0.0958,0.1145,0.0154,0.0131,0.1239,0.1353,0.9801,0.7387,0.0579,0.0,0.2759,0.1667,0.2083,0.0924,0.1047,0.1193,0.0156,0.0138,0.1228,0.1304,0.9801,0.7316,0.0577,0.0,0.2759,0.1667,0.2083,0.0919,0.0975,0.1165,0.0155,0.0133,reg oper account,block of flats,0.1214,Panel,No,3.0,1.0,3.0,1.0,-1025.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n345512,0,Cash loans,M,Y,N,0,157500.0,521280.0,35262.0,450000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,With parents,0.018634,-10897,-280,-8751.0,-2434,16.0,1,1,1,1,0,0,Core staff,1.0,2,2,TUESDAY,17,0,0,0,0,0,0,Self-employed,,0.22597094958558525,,0.1443,0.0937,0.9881,0.8368,0.0968,0.16,0.1379,0.3333,0.375,0.1035,0.1177,0.14800000000000002,0.0,0.0,0.1471,0.0973,0.9881,0.8432,0.0977,0.1611,0.1379,0.3333,0.375,0.1059,0.1286,0.1542,0.0,0.0,0.1457,0.0937,0.9881,0.8390000000000001,0.0974,0.16,0.1379,0.3333,0.375,0.1053,0.1197,0.1506,0.0,0.0,reg oper account,block of flats,0.1693,Panel,No,4.0,0.0,4.0,0.0,-2323.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n418956,0,Cash loans,F,N,Y,1,135000.0,450000.0,21109.5,450000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.006233,-13578,-4146,-904.0,-4087,,1,1,0,1,1,0,Sales staff,3.0,2,2,THURSDAY,13,0,0,0,0,0,0,Self-employed,0.5997446480536885,0.6347642602160306,0.4776491548517548,0.1227,0.0015,0.9791,,,0.0,0.2759,0.1667,,0.0866,,0.1127,,0.0422,0.125,0.0016,0.9791,,,0.0,0.2759,0.1667,,0.0886,,0.1174,,0.0447,0.1239,0.0015,0.9791,,,0.0,0.2759,0.1667,,0.0881,,0.1147,,0.0431,,block of flats,0.0978,Panel,No,8.0,2.0,8.0,1.0,-611.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n243457,0,Cash loans,M,Y,Y,0,47250.0,454500.0,13288.5,454500.0,Children,Pensioner,Secondary / secondary special,Married,House / apartment,0.030755,-20016,365243,-3860.0,-3563,40.0,1,0,0,1,1,0,,2.0,2,2,TUESDAY,9,0,0,0,0,0,0,XNA,,0.2961435654275993,0.7091891096653581,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1696.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n412173,0,Cash loans,M,Y,N,1,94500.0,891000.0,26050.5,891000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.025164,-12529,-4833,-498.0,-4469,21.0,1,1,1,1,0,0,Core staff,3.0,2,2,THURSDAY,16,0,0,0,0,0,0,Security Ministries,0.16967071204110587,0.0786495199505203,0.2418614865234661,0.066,0.0675,0.9737,0.6396,0.0,0.0,0.1379,0.125,0.1667,,0.0538,0.049,0.0,0.0,0.0672,0.07,0.9737,0.6537,0.0,0.0,0.1379,0.125,0.1667,,0.0588,0.0511,0.0,0.0,0.0666,0.0675,0.9737,0.6444,0.0,0.0,0.1379,0.125,0.1667,,0.0547,0.0499,0.0,0.0,reg oper spec account,block of flats,0.0386,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1211.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,2.0,4.0\n277045,0,Cash loans,M,Y,Y,0,112500.0,225000.0,24232.5,225000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.026392,-14794,-1134,-3307.0,-4201,14.0,1,1,0,1,1,0,Managers,2.0,2,2,THURSDAY,13,0,0,0,0,0,0,Construction,,0.5422094880621511,0.6785676886853644,0.0557,0.0359,0.9806,,,0.04,0.0345,0.3333,,0.0573,,0.0467,,0.02,0.0567,0.0373,0.9806,,,0.0403,0.0345,0.3333,,0.0586,,0.0487,,0.0211,0.0562,0.0359,0.9806,,,0.04,0.0345,0.3333,,0.0583,,0.0475,,0.0204,,block of flats,0.0412,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-727.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n300549,0,Cash loans,M,Y,Y,0,135000.0,1256400.0,44644.5,900000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.006852,-9020,-805,-2105.0,-1608,22.0,1,1,0,1,0,0,,2.0,3,3,MONDAY,6,0,0,0,1,1,0,School,,0.14283875606733876,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-869.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n209343,0,Cash loans,F,N,Y,0,58500.0,545040.0,20677.5,450000.0,Unaccompanied,State servant,Secondary / secondary special,Separated,House / apartment,0.006305,-17285,-889,-9961.0,-831,,1,1,0,1,0,0,Medicine staff,1.0,3,3,TUESDAY,10,0,0,0,0,0,0,Other,,0.667229484625056,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-489.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n232068,0,Cash loans,M,Y,Y,1,135000.0,251280.0,17127.0,180000.0,Unaccompanied,Working,Higher education,Married,House / apartment,0.018029,-10722,-283,-1468.0,-3334,13.0,1,1,0,1,0,0,Laborers,3.0,3,2,MONDAY,12,0,0,0,1,0,1,Business Entity Type 2,,0.6300569873292073,0.16342569473134347,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2190.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n162873,0,Cash loans,M,N,Y,1,360000.0,450000.0,21888.0,450000.0,Unaccompanied,Working,Higher education,Separated,House / apartment,0.04622,-14879,-993,-5657.0,-5676,,1,1,1,1,0,0,Laborers,2.0,1,1,WEDNESDAY,13,0,0,0,0,0,0,Business Entity Type 2,,0.7184734985220679,0.6195277080511546,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1905.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n262069,0,Revolving loans,F,Y,Y,1,900000.0,1350000.0,67500.0,1350000.0,Unaccompanied,Commercial associate,Higher education,Married,House / apartment,0.04622,-14453,-1539,-8567.0,-5295,3.0,1,1,0,1,0,0,Managers,3.0,1,1,WEDNESDAY,10,0,1,1,0,0,0,Advertising,0.7261256417244574,0.5863997069706343,0.7076993447402619,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-562.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,6.0,0.0,0.0\n455701,0,Cash loans,F,N,N,0,90000.0,135000.0,11547.0,135000.0,Unaccompanied,Working,Higher education,Single / not married,House / apartment,0.025164,-9382,-645,-7150.0,-2052,,1,1,0,1,0,0,,1.0,2,2,WEDNESDAY,14,0,0,0,1,1,0,Business Entity Type 3,0.17388601801545378,0.08310743007117005,0.3233112448967859,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-439.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,5.0\n376930,0,Revolving loans,M,N,Y,2,225000.0,270000.0,13500.0,270000.0,Unaccompanied,Commercial associate,Higher education,Separated,House / apartment,0.006670999999999999,-15597,-425,-5638.0,-5271,,1,1,0,1,0,1,Laborers,3.0,2,2,FRIDAY,11,0,0,0,0,0,0,Construction,,0.6108936429332107,0.4614823912998385,0.0695,0.062,0.9871,0.8232,0.0345,0.0,0.1569,0.1667,0.2083,0.0618,0.0564,0.063,0.0,0.0,0.0788,0.0709,0.9871,0.8301,0.0242,0.0,0.1724,0.1667,0.2083,0.0496,0.0689,0.0432,0.0,0.0,0.0781,0.0676,0.9871,0.8256,0.0357,0.0,0.1724,0.1667,0.2083,0.0595,0.0641,0.0703,0.0,0.0,reg oper account,block of flats,0.0918,Panel,No,0.0,0.0,0.0,0.0,-735.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n237070,0,Cash loans,M,Y,Y,2,531000.0,781920.0,47965.5,675000.0,Unaccompanied,State servant,Higher education,Married,House / apartment,0.006629,-15173,-7916,-2125.0,-133,15.0,1,1,0,1,0,0,Managers,4.0,2,2,MONDAY,7,0,0,0,0,0,0,Police,0.4856778901651417,0.6574980491395829,0.7726311345553628,0.1052,,0.9955,,,0.12,0.1034,0.3333,,,,0.0824,,,0.1071,,0.9955,,,0.1208,0.1034,0.3333,,,,0.0859,,,0.1062,,0.9955,,,0.12,0.1034,0.3333,,,,0.0839,,,,block of flats,0.1091,Panel,No,3.0,0.0,3.0,0.0,0.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n428190,0,Cash loans,M,Y,N,0,270000.0,884844.0,49536.0,810000.0,Unaccompanied,Commercial associate,Higher education,Married,Municipal apartment,0.04622,-16226,-794,-5492.0,-4314,7.0,1,1,0,1,0,1,Core staff,2.0,1,1,MONDAY,15,0,0,0,0,1,1,Security Ministries,,0.772259744643695,0.4311917977993083,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,3.0,0.0,-1824.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,1.0\n161192,0,Cash loans,F,N,Y,0,121500.0,225000.0,25578.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.008625,-8315,-283,-1931.0,-1001,,1,1,1,1,1,0,Sales staff,1.0,2,2,WEDNESDAY,14,0,0,0,0,1,1,Trade: type 7,0.24240423992945195,0.4319484785911499,0.5172965813614878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-955.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n387309,0,Revolving loans,F,N,N,0,157500.0,202500.0,10125.0,202500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.02461,-19581,-4091,-5712.0,-3131,,1,1,0,1,1,0,Cleaning staff,2.0,2,2,FRIDAY,9,0,0,0,0,1,1,Business Entity Type 3,,0.3703594792670537,0.22009464485041005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1444.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n323532,0,Cash loans,F,N,Y,0,67500.0,727785.0,21406.5,607500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.025164,-22276,365243,-4385.0,-4390,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.5641771578732413,0.6512602186973006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-771.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n387670,0,Cash loans,F,Y,N,3,101250.0,888840.0,32053.5,675000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,Municipal apartment,0.031329,-16023,-6548,-1.0,-4972,15.0,1,1,0,1,0,0,Core staff,5.0,2,2,FRIDAY,13,0,0,0,0,0,0,Agriculture,,0.26525634018619443,0.7366226976503176,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.0,0.0,5.0,0.0,-1632.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n379339,0,Revolving loans,M,N,Y,1,112500.0,180000.0,9000.0,180000.0,\"Spouse, partner\",Working,Secondary / secondary special,Married,House / apartment,0.020246,-7907,-280,-7306.0,-579,,1,1,0,1,0,0,Laborers,3.0,3,3,SUNDAY,12,0,0,0,0,0,0,Business Entity Type 3,0.08794881048519228,0.251082593555036,,0.3969,0.1137,0.9866,0.8164,0.0,0.08,0.069,0.3333,0.375,0.1835,0.3236,0.1554,0.0,0.0,0.4044,0.11800000000000001,0.9866,0.8236,0.0,0.0806,0.069,0.3333,0.375,0.1877,0.3535,0.1619,0.0,0.0,0.4008,0.1137,0.9866,0.8189,0.0,0.08,0.069,0.3333,0.375,0.1867,0.3292,0.1582,0.0,0.0,not specified,block of flats,0.1222,Panel,No,0.0,0.0,0.0,0.0,-183.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n332982,0,Revolving loans,F,N,N,0,270000.0,180000.0,9000.0,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.016612000000000002,-10572,-644,-5028.0,-731,,1,1,0,1,0,0,Sales staff,2.0,2,2,THURSDAY,9,1,1,0,1,1,0,Self-employed,0.4461041658400619,0.5965918025717393,,0.1216,0.1283,0.9876,,,0.0,0.2069,0.1667,,0.1096,,0.1093,,0.0825,0.1239,0.1331,0.9876,,,0.0,0.2069,0.1667,,0.1121,,0.1138,,0.0874,0.1228,0.1283,0.9876,,,0.0,0.2069,0.1667,,0.1115,,0.1112,,0.0843,,block of flats,0.1019,\"Stone, brick\",No,1.0,1.0,1.0,1.0,-422.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n419210,0,Cash loans,F,N,Y,0,90000.0,373500.0,16452.0,373500.0,Unaccompanied,Working,Secondary / secondary special,Widow,House / apartment,0.02461,-22934,-1828,-4596.0,-4554,,1,1,0,1,1,0,,1.0,2,2,WEDNESDAY,17,0,0,0,0,0,0,Medicine,,0.5553235650301274,0.5779691187553125,0.1227,0.0993,0.9747,,,0.0,0.2069,0.1667,,0.0927,,0.0995,,0.0,0.125,0.1031,0.9747,,,0.0,0.2069,0.1667,,0.0948,,0.1037,,0.0,0.1239,0.0993,0.9747,,,0.0,0.2069,0.1667,,0.0943,,0.1013,,0.0,,block of flats,0.084,\"Stone, brick\",No,1.0,0.0,1.0,0.0,-2485.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n351056,0,Cash loans,F,N,Y,0,180000.0,668484.0,21694.5,558000.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.020713,-22012,365243,-4200.0,-4003,,1,0,0,1,0,0,,2.0,3,3,THURSDAY,10,0,0,0,0,0,0,XNA,,0.5786942590156351,0.28961123838200553,0.1206,0.1227,0.9841,,,0.0,0.2069,0.1667,,0.1391,,0.1118,,0.0358,0.1229,0.1273,0.9841,,,0.0,0.2069,0.1667,,0.1423,,0.1165,,0.0379,0.1218,0.1227,0.9841,,,0.0,0.2069,0.1667,,0.1416,,0.1138,,0.0365,,block of flats,0.0896,Panel,No,1.0,1.0,1.0,1.0,-1241.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n371600,0,Cash loans,M,Y,Y,0,135000.0,1971072.0,68643.0,1800000.0,\"Spouse, partner\",State servant,Higher education,Married,House / apartment,0.007120000000000001,-19406,-789,-4579.0,-2959,7.0,1,1,0,1,0,0,Core staff,2.0,2,2,WEDNESDAY,8,0,0,0,0,0,0,Other,,0.2221501723668026,0.6144143775673561,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,1.0,0.0,-1.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n207090,0,Cash loans,F,N,Y,1,112500.0,383089.5,27891.0,346500.0,Family,Working,Higher education,Married,House / apartment,0.006207,-11637,-2288,-3853.0,-4073,,1,1,0,1,0,0,Sales staff,3.0,2,2,FRIDAY,15,0,0,0,0,1,1,Self-employed,0.4941734253492276,0.37975419371095626,,0.0619,,0.9831,,,0.0,0.1379,0.1667,,,,0.0621,,,0.063,,0.9831,,,0.0,0.1379,0.1667,,,,0.0647,,,0.0625,,0.9831,,,0.0,0.1379,0.1667,,,,0.0632,,,,block of flats,0.0489,Block,No,0.0,0.0,0.0,0.0,-2091.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n120624,0,Cash loans,M,N,Y,2,225000.0,755190.0,36459.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.025164,-14074,-1321,-507.0,-2859,,1,1,0,1,1,0,Core staff,4.0,2,2,MONDAY,13,0,0,0,1,1,0,Advertising,0.4966814217345125,0.2632411250133655,0.4223696523543468,0.0103,0.0,0.9578,0.42200000000000004,0.0014,0.0,0.069,0.0417,0.0833,0.0047,0.0084,0.006999999999999999,0.0,0.0106,0.0105,0.0,0.9578,0.4446,0.0014,0.0,0.069,0.0417,0.0833,0.0049,0.0092,0.0073,0.0,0.0112,0.0104,0.0,0.9578,0.4297,0.0014,0.0,0.069,0.0417,0.0833,0.0048,0.0086,0.0071,0.0,0.0108,reg oper account,block of flats,0.0086,Wooden,No,6.0,0.0,6.0,0.0,0.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,3.0\n340663,0,Cash loans,F,N,Y,0,112500.0,490500.0,19449.0,490500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.026392,-22022,365243,-12812.0,-4255,,1,0,0,1,1,0,,2.0,2,2,FRIDAY,15,0,0,0,0,0,0,XNA,,0.6228932101035792,0.6413682574954046,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,0.0,-2278.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,9.0\n377752,0,Revolving loans,M,N,Y,0,157500.0,225000.0,11250.0,225000.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.028663,-20434,365243,-3621.0,-3633,,1,0,0,1,1,0,,2.0,2,2,WEDNESDAY,11,0,0,0,0,0,0,XNA,,0.6643830524009862,0.13595104417329515,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-691.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n377206,0,Cash loans,M,N,N,0,225000.0,1546020.0,49873.5,1350000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.011657,-14268,-1951,-7958.0,-4510,,1,1,1,1,1,0,Drivers,2.0,1,1,MONDAY,12,0,1,1,0,1,1,Business Entity Type 3,0.5531258875194237,0.6818506216250942,0.7503751495159068,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1722.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n383258,0,Cash loans,M,Y,Y,1,225000.0,354276.0,17361.0,292500.0,Unaccompanied,Working,Secondary / secondary special,Separated,House / apartment,0.010276,-8867,-372,-6958.0,-1446,10.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,15,0,0,0,1,1,1,Business Entity Type 3,,0.6388608714570507,0.39449540531239935,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-572.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n423396,0,Cash loans,M,N,N,0,112500.0,585000.0,22279.5,585000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.018634,-7713,-696,-7666.0,-392,,1,1,0,1,1,0,Sales staff,1.0,2,2,THURSDAY,17,0,0,0,0,0,0,Trade: type 2,,0.4273721822797851,,0.0742,0.0615,0.9896,0.8572,0.0651,0.12,0.1034,0.2917,0.3333,0.0381,0.0605,0.0976,0.0,0.0,0.0756,0.0638,0.9896,0.8628,0.0657,0.1208,0.1034,0.2917,0.3333,0.039,0.0661,0.1017,0.0,0.0,0.0749,0.0615,0.9896,0.8591,0.0655,0.12,0.1034,0.2917,0.3333,0.0388,0.0616,0.0994,0.0,0.0,reg oper account,block of flats,0.1124,Panel,No,0.0,0.0,0.0,0.0,-757.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n128561,0,Revolving loans,F,Y,N,1,67500.0,337500.0,16875.0,337500.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,With parents,0.0105,-8113,-43,-395.0,-733,14.0,1,1,0,1,1,0,Laborers,3.0,3,3,THURSDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.19036376085506707,0.4365064990977374,,,0.9771,,,,,,,,,0.0766,,,,,0.9772,,,,,,,,,0.0798,,,,,0.9771,,,,,,,,,0.0779,,,,,0.0602,,No,0.0,0.0,0.0,0.0,-262.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n277299,0,Cash loans,M,Y,Y,0,135000.0,916470.0,25330.5,765000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.022625,-11643,-3684,-4005.0,-4005,3.0,1,1,0,1,0,0,,2.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,Trade: type 7,,0.6985265540225621,0.5334816299804352,0.1763,0.0934,0.9826,0.762,0.0308,0.0,0.0345,0.3333,0.375,0.1255,0.1437,0.0842,0.0,0.0,0.1796,0.0969,0.9826,0.7713,0.0311,0.0,0.0345,0.3333,0.375,0.1284,0.157,0.0877,0.0,0.0,0.17800000000000002,0.0934,0.9826,0.7652,0.031,0.0,0.0345,0.3333,0.375,0.1277,0.1462,0.0857,0.0,0.0,reg oper account,block of flats,0.0991,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-685.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n220149,0,Cash loans,F,N,N,0,99000.0,263844.0,16164.0,189000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.003813,-14145,-2119,-361.0,-1316,,1,1,0,1,0,0,Accountants,2.0,2,2,FRIDAY,7,0,0,0,0,1,1,Self-employed,,0.4982087494427066,0.5136937663039473,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-883.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,5.0\n388544,0,Cash loans,F,N,Y,0,67500.0,273636.0,15835.5,247500.0,Family,Pensioner,Secondary / secondary special,Married,House / apartment,0.007120000000000001,-21784,365243,-4769.0,-4825,,1,0,0,1,0,0,,2.0,2,2,SATURDAY,11,0,0,0,0,0,0,XNA,,0.16538169659645452,0.6956219298394389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-302.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,0.0\n410878,0,Cash loans,M,Y,Y,0,225000.0,1575000.0,62464.5,1575000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-19709,-3730,-4860.0,-3221,6.0,1,1,0,1,0,0,Drivers,2.0,2,2,THURSDAY,8,0,0,0,0,0,0,Transport: type 4,,0.5100814724876398,,0.1113,0.0765,0.9861,0.8096,0.0398,0.12,0.1034,0.3333,,0.1034,0.0908,0.1089,0.0,0.1001,0.1134,0.0794,0.9861,0.8171,0.0402,0.1208,0.1034,0.3333,,0.1057,0.0992,0.1134,0.0,0.106,0.1124,0.0765,0.9861,0.8121,0.0401,0.12,0.1034,0.3333,,0.1052,0.0923,0.1108,0.0,0.1022,org spec account,block of flats,0.1073,Panel,No,3.0,0.0,3.0,0.0,-1959.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0\n126327,0,Cash loans,M,Y,N,1,315000.0,582768.0,37242.0,540000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.001333,-18627,-899,-7074.0,-1289,20.0,1,1,1,1,0,0,Laborers,3.0,3,3,MONDAY,8,0,0,0,0,0,0,Trade: type 7,,0.6057302372036348,0.5779691187553125,0.068,0.0451,0.9826,0.762,,0.0,0.0345,0.1667,0.2083,0.0299,0.0538,0.0424,0.0077,0.0137,0.0693,0.0468,0.9826,0.7713,,0.0,0.0345,0.1667,0.2083,0.0305,0.0588,0.0442,0.0078,0.0145,0.0687,0.0451,0.9826,0.7652,,0.0,0.0345,0.1667,0.2083,0.0304,0.0547,0.0432,0.0078,0.013999999999999999,reg oper account,block of flats,0.0451,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-601.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n215291,0,Cash loans,F,N,N,0,121500.0,900000.0,26446.5,900000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.010147,-12406,-842,-2751.0,-5032,,1,1,1,1,1,0,Laborers,2.0,2,2,TUESDAY,10,0,0,0,0,0,0,Industry: type 3,0.13831335958268082,0.2442297520333025,0.5989262182569273,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-657.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n427706,0,Cash loans,F,Y,Y,1,76500.0,203760.0,13396.5,180000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005313,-13731,-334,-5897.0,-4182,28.0,1,1,1,1,0,0,Core staff,3.0,2,2,FRIDAY,16,0,0,0,0,1,1,Government,,0.6627280811403536,0.470456116119975,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-682.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n195807,0,Cash loans,F,N,N,0,112500.0,225000.0,10953.0,225000.0,Unaccompanied,Pensioner,Secondary / secondary special,Separated,House / apartment,0.020713,-20681,365243,-4521.0,-3883,,1,0,0,1,1,0,,1.0,3,3,SATURDAY,15,0,0,0,0,0,0,XNA,,0.11415498819158255,0.16146308000577247,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,0.0,3.0,0.0,-2343.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,3.0\n151336,0,Cash loans,F,Y,Y,0,243000.0,500211.0,26779.5,463500.0,Unaccompanied,Working,Higher education,Widow,House / apartment,0.0105,-22821,-2846,-1738.0,-4879,6.0,1,1,0,1,0,0,Accountants,1.0,3,3,FRIDAY,17,0,0,0,0,0,0,Housing,0.8985618598450911,0.5114311699566826,0.7713615919194317,0.0825,,0.9771,0.6872,,0.0,0.1379,0.1667,0.2083,0.0677,0.0672,0.0763,0.0,,0.084,,0.9772,0.6994,,0.0,0.1379,0.1667,0.2083,0.0692,0.0735,0.0795,0.0,,0.0833,,0.9771,0.6914,,0.0,0.1379,0.1667,0.2083,0.0689,0.0684,0.0777,0.0,,org spec account,block of flats,0.06,Panel,No,1.0,0.0,1.0,0.0,-514.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n400734,0,Cash loans,F,N,Y,0,112500.0,257391.0,18432.0,238500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.01885,-21197,365243,-3577.0,-4666,,1,0,0,1,0,0,,1.0,2,2,FRIDAY,16,0,0,0,0,0,0,XNA,,0.6960016351232778,0.11033242816628903,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-38.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,2.0,0.0\n216223,0,Cash loans,M,Y,Y,1,126000.0,142200.0,11362.5,112500.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.009549,-9231,-1393,-1779.0,-1767,9.0,1,1,0,1,0,0,Laborers,3.0,2,2,SATURDAY,12,0,0,0,0,0,0,Business Entity Type 3,,0.5916561691175296,0.4992720153045617,0.0742,0.040999999999999995,0.9871,0.8232,0.0178,0.08,0.069,0.3333,0.375,0.0468,0.0605,0.0753,0.0,0.0,0.0756,0.0425,0.9871,0.8301,0.0179,0.0806,0.069,0.3333,0.375,0.0478,0.0661,0.0785,0.0,0.0,0.0749,0.040999999999999995,0.9871,0.8256,0.0179,0.08,0.069,0.3333,0.375,0.0476,0.0616,0.0767,0.0,0.0,reg oper spec account,block of flats,0.069,Panel,No,0.0,0.0,0.0,0.0,-857.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,1.0,0.0,4.0\n106127,0,Revolving loans,M,N,N,0,112500.0,135000.0,6750.0,135000.0,Unaccompanied,Commercial associate,Higher education,Single / not married,Rented apartment,0.014519999999999996,-10189,-434,-4983.0,-2825,,1,1,0,1,0,0,Low-skill Laborers,1.0,2,2,SUNDAY,8,0,0,0,1,1,1,Business Entity Type 3,,0.5951647516890751,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-1977.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n374960,0,Cash loans,F,N,Y,0,90000.0,481495.5,32436.0,454500.0,Unaccompanied,Pensioner,Secondary / secondary special,Married,House / apartment,0.035792000000000004,-21968,365243,-1714.0,-4963,,1,0,0,1,0,0,,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,XNA,,0.6983054663831425,0.8128226070575616,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-592.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,1.0\n250227,0,Cash loans,M,N,N,0,135000.0,47970.0,5692.5,45000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.018209,-9460,-626,-3790.0,-2027,,1,1,0,1,1,0,Drivers,2.0,3,3,WEDNESDAY,14,0,0,0,1,1,0,Industry: type 3,0.18216450951459776,0.5428607678009856,0.178760465484575,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-950.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,2.0,0.0,5.0\n325639,0,Cash loans,F,N,Y,0,135000.0,305640.0,31446.0,270000.0,Unaccompanied,Pensioner,Incomplete higher,Civil marriage,House / apartment,0.02461,-13947,365243,-1023.0,-4319,,1,0,0,1,0,0,,2.0,2,2,WEDNESDAY,14,0,0,0,1,0,0,XNA,,0.7098316575198929,,0.1113,0.0453,0.9891,0.8504,0.0204,0.0,0.0345,0.3333,0.375,0.0,0.0908,0.07400000000000001,0.0,0.0,0.1134,0.047,0.9891,0.8563,0.0206,0.0,0.0345,0.3333,0.375,0.0,0.0992,0.0771,0.0,0.0,0.1124,0.0453,0.9891,0.8524,0.0205,0.0,0.0345,0.3333,0.375,0.0,0.0923,0.0754,0.0,0.0,not specified,block of flats,0.0582,Panel,No,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n370875,0,Cash loans,F,N,N,0,270000.0,728460.0,74772.0,675000.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.003069,-16264,-2911,-3858.0,-4920,,1,1,0,1,0,0,Managers,2.0,3,3,WEDNESDAY,11,0,0,0,0,0,0,Self-employed,,0.30721279203650026,0.6674577419214722,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,0.0,2.0,0.0,-410.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n334361,0,Cash loans,F,Y,Y,0,112500.0,1006920.0,42790.5,900000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.0228,-13834,-934,-4958.0,-2511,4.0,1,1,0,1,0,0,Sales staff,2.0,2,2,MONDAY,12,0,0,0,0,0,0,Trade: type 3,0.7617746263433283,0.7527900813756453,0.6879328378491735,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.0,0.0,7.0,0.0,-1861.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n403052,0,Cash loans,F,N,Y,0,67500.0,101880.0,5976.0,90000.0,Unaccompanied,Pensioner,Secondary / secondary special,Single / not married,House / apartment,0.018029,-22935,365243,-10558.0,-4359,,1,0,0,1,1,0,,1.0,3,3,SUNDAY,8,0,0,0,0,0,0,XNA,,0.4597761082200014,0.44539624156083396,0.0928,0.1257,0.9806,0.7348,0.0118,,0.2069,0.1667,0.0417,,,0.0865,,0.0,0.0945,0.1305,0.9806,0.7452,0.0119,,0.2069,0.1667,0.0417,,,0.0901,,0.0,0.0937,0.1257,0.9806,0.7383,0.0118,,0.2069,0.1667,0.0417,,,0.08800000000000001,,0.0,,block of flats,0.0745,Panel,No,0.0,0.0,0.0,0.0,-419.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,7.0\n208104,0,Cash loans,F,N,Y,1,135000.0,188460.0,9621.0,135000.0,Unaccompanied,State servant,Higher education,Single / not married,House / apartment,0.006629,-11860,-1578,-2595.0,-468,,1,1,0,1,0,0,Accountants,2.0,2,2,MONDAY,6,0,0,0,0,0,0,Government,0.4952892893126015,0.08674363616360717,,0.021,,0.9771,,,,0.069,0.0417,,,,0.0102,,,0.0168,,0.9772,,,,0.069,0.0417,,,,0.0106,,,0.0167,,0.9771,,,,0.069,0.0417,,,,0.0104,,,,block of flats,0.0078,Wooden,Yes,0.0,0.0,0.0,0.0,-1101.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n368906,0,Cash loans,F,N,Y,0,112500.0,315000.0,11875.5,315000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.015221,-13214,-5934,-1538.0,-1936,,1,1,1,1,0,0,Medicine staff,2.0,2,2,MONDAY,10,0,0,0,0,0,0,Medicine,0.5699802835913794,0.6054116234475714,0.6894791426446275,0.068,0.0,0.9846,0.7892,0.0082,0.0,0.1379,0.1667,0.2083,0.0199,0.0504,0.0547,0.0232,0.0207,0.0693,0.0,0.9846,0.7975,0.0083,0.0,0.1379,0.1667,0.2083,0.0204,0.0551,0.057,0.0233,0.022000000000000002,0.0687,0.0,0.9846,0.792,0.0083,0.0,0.1379,0.1667,0.2083,0.0203,0.0513,0.0557,0.0233,0.0212,reg oper account,block of flats,0.0476,\"Stone, brick\",No,6.0,0.0,6.0,0.0,-2490.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n260059,0,Cash loans,F,N,Y,0,36000.0,86346.0,8667.0,81000.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.014464,-22843,365243,-13464.0,-4702,,1,0,0,1,0,0,,1.0,2,2,MONDAY,5,0,0,0,0,0,0,XNA,,0.0657418425836809,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-400.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,\n208631,0,Cash loans,F,N,Y,0,112500.0,58500.0,4288.5,58500.0,Unaccompanied,Pensioner,Secondary / secondary special,Widow,House / apartment,0.015221,-20962,365243,-11810.0,-3997,,1,0,0,1,0,0,,1.0,2,2,WEDNESDAY,9,0,0,0,0,0,0,XNA,0.5793548008770282,0.5529193049792199,0.4507472818545589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,1.0,4.0,0.0,-1172.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,6.0\n254763,0,Cash loans,M,Y,Y,0,225000.0,1762110.0,70015.5,1575000.0,\"Spouse, partner\",Commercial associate,Secondary / secondary special,Married,House / apartment,0.02461,-19539,-3365,-2163.0,-3069,6.0,1,1,1,1,0,0,Laborers,2.0,2,2,THURSDAY,16,0,0,0,0,0,0,Self-employed,,0.7007002517588631,0.3490552510751822,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-2990.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,7.0,0.0,0.0\n389082,0,Cash loans,M,Y,Y,0,112500.0,101880.0,10809.0,90000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,House / apartment,0.031329,-16370,-553,-5019.0,-4186,16.0,1,1,1,1,1,0,Drivers,1.0,2,2,FRIDAY,14,0,0,0,0,0,0,Transport: type 3,,0.748175243526057,0.7180328113294772,0.2959,,0.9866,,,0.28,0.2414,0.3333,,0.1821,,0.1691,,,0.3015,,0.9866,,,0.282,0.2414,0.3333,,0.1863,,0.1762,,,0.2987,,0.9866,,,0.28,0.2414,0.3333,,0.1853,,0.1721,,,,block of flats,0.2304,Panel,No,0.0,0.0,0.0,0.0,-1357.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n368877,0,Cash loans,M,Y,N,0,112500.0,284400.0,22599.0,225000.0,Unaccompanied,Working,Secondary / secondary special,Single / not married,With parents,0.018634,-7984,-1109,-4632.0,-665,10.0,1,1,0,1,0,0,,1.0,2,2,THURSDAY,15,0,0,0,0,0,0,Military,0.1990379793316916,0.3273631040635219,0.4992720153045617,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,1.0,2.0,1.0,-935.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,1.0,2.0\n206526,0,Cash loans,F,N,N,0,202500.0,592560.0,26230.5,450000.0,Unaccompanied,Pensioner,Lower secondary,Single / not married,House / apartment,0.0228,-21588,365243,-2765.0,-3896,,1,0,0,1,1,0,,1.0,2,2,MONDAY,16,0,0,0,0,0,0,XNA,,0.6007844008202533,0.392773860603134,0.0825,0.1182,0.9732,0.6328,0.0079,0.0,0.1724,0.125,0.1667,0.0,0.0647,0.0615,0.0116,0.0106,0.084,0.1226,0.9732,0.6472,0.008,0.0,0.1724,0.125,0.1667,0.0,0.0707,0.0641,0.0117,0.0113,0.0833,0.1182,0.9732,0.6377,0.008,0.0,0.1724,0.125,0.1667,0.0,0.0658,0.0626,0.0116,0.0108,reg oper account,block of flats,0.055,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1769.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,6.0\n134394,0,Cash loans,F,Y,Y,1,112500.0,824823.0,29353.5,688500.0,Family,Commercial associate,Secondary / secondary special,Married,House / apartment,0.00702,-12259,-5479,-389.0,-4516,13.0,1,1,0,1,0,0,Cooking staff,3.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,Business Entity Type 3,,0.6515943059778407,0.7910749129911773,0.0909,,0.998,0.9388,0.0247,0.16,0.1666,0.325,0.3125,0.1467,0.087,0.0801,0.0,0.0589,0.0756,,0.9975,0.9216,0.0249,0.1611,0.1379,0.3333,0.25,0.22399999999999998,0.0661,0.0165,0.0,0.0,0.0843,,0.9975,0.9396,0.0249,0.16,0.1379,0.3333,0.3125,0.2228,0.0885,0.0938,0.0,0.0278,reg oper account,block of flats,0.0727,Panel,No,5.0,0.0,5.0,0.0,-470.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,4.0\n380244,0,Cash loans,M,Y,Y,0,157500.0,728460.0,40806.0,675000.0,Unaccompanied,Working,Secondary / secondary special,Civil marriage,House / apartment,0.00702,-21043,-451,-12962.0,-4527,7.0,1,1,1,1,1,0,Drivers,2.0,2,2,WEDNESDAY,10,0,0,0,0,1,1,School,,0.1361233209632966,0.8277026681625442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13.0,0.0,13.0,0.0,-1460.0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,3.0\n252595,0,Cash loans,M,Y,Y,1,202500.0,851778.0,25033.5,711000.0,Unaccompanied,Working,Secondary / secondary special,Married,House / apartment,0.005313,-13025,-108,-4341.0,-2571,13.0,1,1,0,1,0,0,Laborers,3.0,2,2,FRIDAY,13,0,0,0,1,1,1,Business Entity Type 3,,0.4541110551619218,0.21275630545434146,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,-88.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,1.0,1.0,0.0,2.0\n153218,0,Cash loans,F,N,Y,0,112500.0,942300.0,30528.0,675000.0,Unaccompanied,Commercial associate,Secondary / secondary special,Married,House / apartment,0.011703,-15435,-1516,-5177.0,-4233,,1,1,0,1,0,0,Sales staff,2.0,2,2,TUESDAY,11,0,0,0,0,0,0,Industry: type 3,,0.7280667247028426,,0.0402,0.0846,0.9742,0.6464,0.0,0.0,0.0862,0.1042,0.1458,0.0073,0.0319,0.0377,0.0039,0.0019,0.0084,0.0878,0.9692,0.5949,0.0,0.0,0.0345,0.0417,0.0833,0.0,0.0073,0.0087,0.0,0.0,0.0406,0.0846,0.9742,0.6511,0.0,0.0,0.0862,0.1042,0.1458,0.0075,0.0325,0.0384,0.0039,0.0019,reg oper account,block of flats,0.0536,Others,No,0.0,0.0,0.0,0.0,-2802.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,1.0\n429449,0,Cash loans,F,Y,Y,0,270000.0,1147500.0,33682.5,1147500.0,Family,Working,Secondary / secondary special,Married,House / apartment,0.026392,-13573,-1796,-7629.0,-4786,3.0,1,1,0,1,0,0,Core staff,2.0,2,2,MONDAY,16,0,0,0,0,0,0,Kindergarten,,0.71642994695213,0.4650692149562261,0.2701,0.1883,0.9811,,,0.2,0.1724,0.3333,,0.12,,0.2333,,0.1112,0.2752,0.1954,0.9811,,,0.2014,0.1724,0.3333,,0.1227,,0.2431,,0.1177,0.2727,0.1883,0.9811,,,0.2,0.1724,0.3333,,0.1221,,0.2375,,0.1135,,block of flats,0.2077,\"Stone, brick\",No,0.0,0.0,0.0,0.0,-1594.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,3.0,9.0\n196142,1,Revolving loans,F,N,Y,1,135000.0,270000.0,13500.0,270000.0,Family,Working,Secondary / secondary special,Civil marriage,House / apartment,0.003540999999999999,-11120,-202,-912.0,-1008,,1,1,1,1,1,0,Core staff,3.0,1,1,FRIDAY,10,0,0,0,0,0,0,Bank,,0.3237572192618898,0.07347264041181442,0.0021,,0.9831,,,,0.069,0.0,,0.0106,,0.0025,,0.0,0.0021,,0.9831,,,,0.069,0.0,,0.0108,,0.0026,,0.0,0.0021,,0.9831,,,,0.069,0.0,,0.0107,,0.0025,,0.0,,block of flats,0.002,,No,0.0,0.0,0.0,0.0,-592.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,2.0\n"
  },
  {
    "path": "9.feature-engineering/FeatureSelectorUsage/feature_selector/__init__.py",
    "content": "from .feature_selector import FeatureSelector\n"
  },
  {
    "path": "9.feature-engineering/FeatureSelectorUsage/feature_selector/feature_selector.py",
    "content": "# numpy and pandas for data manipulation\nimport pandas as pd\nimport numpy as np\n\n# model used for feature importances\nimport lightgbm as lgb\n\n# utility for early stopping with a validation set\nfrom sklearn.model_selection import train_test_split\n\n# visualizations\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# memory management\nimport gc\n\n# utilities\nfrom itertools import chain\n\nclass FeatureSelector():\n    \"\"\"\n    Class for performing feature selection for machine learning or data preprocessing.\n    \n    Implements five different methods to identify features for removal \n    \n        1. Find columns with a missing percentage greater than a specified threshold\n        2. Find columns with a single unique value\n        3. Find collinear variables with a correlation greater than a specified correlation coefficient\n        4. Find features with 0.0 feature importance from a gradient boosting machine (gbm)\n        5. Find low importance features that do not contribute to a specified cumulative feature importance from the gbm\n        \n    Parameters\n    --------\n        data : dataframe\n            A dataset with observations in the rows and features in the columns\n\n        labels : array or series, default = None\n            Array of labels for training the machine learning model to find feature importances. These can be either binary labels\n            (if task is 'classification') or continuous targets (if task is 'regression').\n            If no labels are provided, then the feature importance based methods are not available.\n        \n    Attributes\n    --------\n    \n    ops : dict\n        Dictionary of operations run and features identified for removal\n        \n    missing_stats : dataframe\n        The fraction of missing values for all features\n    \n    record_missing : dataframe\n        The fraction of missing values for features with missing fraction above threshold\n        \n    unique_stats : dataframe\n        Number of unique values for all features\n    \n    record_single_unique : dataframe\n        Records the features that have a single unique value\n        \n    corr_matrix : dataframe\n        All correlations between all features in the data\n    \n    record_collinear : dataframe\n        Records the pairs of collinear variables with a correlation coefficient above the threshold\n        \n    feature_importances : dataframe\n        All feature importances from the gradient boosting machine\n    \n    record_zero_importance : dataframe\n        Records the zero importance features in the data according to the gbm\n    \n    record_low_importance : dataframe\n        Records the lowest importance features not needed to reach the threshold of cumulative importance according to the gbm\n    \n    \n    Notes\n    --------\n    \n        - All 5 operations can be run with the `identify_all` method.\n        - If using feature importances, one-hot encoding is used for categorical variables which creates new columns\n    \n    \"\"\"\n    \n    def __init__(self, data, labels=None):\n        \n        # Dataset and optional training labels\n        self.data = data\n        self.labels = labels\n\n        if labels is None:\n            print('No labels provided. Feature importance based methods are not available.')\n        \n        self.base_features = list(data.columns)\n        self.one_hot_features = None\n        \n        # Dataframes recording information about features to remove\n        self.record_missing = None\n        self.record_single_unique = None\n        self.record_collinear = None\n        self.record_zero_importance = None\n        self.record_low_importance = None\n        \n        self.missing_stats = None\n        self.unique_stats = None\n        self.corr_matrix = None\n        self.feature_importances = None\n        \n        # Dictionary to hold removal operations\n        self.ops = {}\n        \n        self.one_hot_correlated = False\n        \n    def identify_missing(self, missing_threshold):\n        \"\"\"Find the features with a fraction of missing values above `missing_threshold`\"\"\"\n        \n        self.missing_threshold = missing_threshold\n\n        # Calculate the fraction of missing in each column \n        missing_series = self.data.isnull().sum() / self.data.shape[0]\n        self.missing_stats = pd.DataFrame(missing_series).rename(columns = {'index': 'feature', 0: 'missing_fraction'})\n\n        # Sort with highest number of missing values on top\n        self.missing_stats = self.missing_stats.sort_values('missing_fraction', ascending = False)\n\n        # Find the columns with a missing percentage above the threshold\n        record_missing = pd.DataFrame(missing_series[missing_series > missing_threshold]).reset_index().rename(columns = \n                                                                                                               {'index': 'feature', \n                                                                                                                0: 'missing_fraction'})\n\n        to_drop = list(record_missing['feature'])\n\n        self.record_missing = record_missing\n        self.ops['missing'] = to_drop\n        \n        print('%d features with greater than %0.2f missing values.\\n' % (len(self.ops['missing']), self.missing_threshold))\n        \n    def identify_single_unique(self):\n        \"\"\"Finds features with only a single unique value. NaNs do not count as a unique value. \"\"\"\n\n        # Calculate the unique counts in each column\n        unique_counts = self.data.nunique()\n        self.unique_stats = pd.DataFrame(unique_counts).rename(columns = {'index': 'feature', 0: 'nunique'})\n        self.unique_stats = self.unique_stats.sort_values('nunique', ascending = True)\n        \n        # Find the columns with only one unique count\n        record_single_unique = pd.DataFrame(unique_counts[unique_counts == 1]).reset_index().rename(columns = {'index': 'feature', \n                                                                                                                0: 'nunique'})\n\n        to_drop = list(record_single_unique['feature'])\n    \n        self.record_single_unique = record_single_unique\n        self.ops['single_unique'] = to_drop\n        \n        print('%d features with a single unique value.\\n' % len(self.ops['single_unique']))\n    \n    def identify_collinear(self, correlation_threshold, one_hot=False):\n        \"\"\"\n        Finds collinear features based on the correlation coefficient between features. \n        For each pair of features with a correlation coefficient greather than `correlation_threshold`,\n        only one of the pair is identified for removal. \n\n        Using code adapted from: https://chrisalbon.com/machine_learning/feature_selection/drop_highly_correlated_features/\n        \n        Parameters\n        --------\n\n        correlation_threshold : float between 0 and 1\n            Value of the Pearson correlation cofficient for identifying correlation features\n\n        one_hot : boolean, default = False\n            Whether to one-hot encode the features before calculating the correlation coefficients\n\n        \"\"\"\n        \n        self.correlation_threshold = correlation_threshold\n        self.one_hot_correlated = one_hot\n        \n         # Calculate the correlations between every column\n        if one_hot:\n            \n            # One hot encoding\n            features = pd.get_dummies(self.data)\n            self.one_hot_features = [column for column in features.columns if column not in self.base_features]\n\n            # Add one hot encoded data to original data\n            self.data_all = pd.concat([features[self.one_hot_features], self.data], axis = 1)\n            \n            corr_matrix = pd.get_dummies(features).corr()\n\n        else:\n            corr_matrix = self.data.corr()\n        \n        self.corr_matrix = corr_matrix\n    \n        # Extract the upper triangle of the correlation matrix\n        upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k = 1).astype(np.bool))\n        \n        # Select the features with correlations above the threshold\n        # Need to use the absolute value\n        to_drop = [column for column in upper.columns if any(upper[column].abs() > correlation_threshold)]\n\n        # Dataframe to hold correlated pairs\n        record_collinear = pd.DataFrame(columns = ['drop_feature', 'corr_feature', 'corr_value'])\n\n        # Iterate through the columns to drop to record pairs of correlated features\n        for column in to_drop:\n\n            # Find the correlated features\n            corr_features = list(upper.index[upper[column].abs() > correlation_threshold])\n\n            # Find the correlated values\n            corr_values = list(upper[column][upper[column].abs() > correlation_threshold])\n            drop_features = [column for _ in range(len(corr_features))]    \n\n            # Record the information (need a temp df for now)\n            temp_df = pd.DataFrame.from_dict({'drop_feature': drop_features,\n                                             'corr_feature': corr_features,\n                                             'corr_value': corr_values})\n\n            # Add to dataframe\n            record_collinear = record_collinear.append(temp_df, ignore_index = True)\n\n        self.record_collinear = record_collinear\n        self.ops['collinear'] = to_drop\n        \n        print('%d features with a correlation magnitude greater than %0.2f.\\n' % (len(self.ops['collinear']), self.correlation_threshold))\n\n    def identify_zero_importance(self, task, eval_metric=None, \n                                 n_iterations=10, early_stopping = True):\n        \"\"\"\n        \n        Identify the features with zero importance according to a gradient boosting machine.\n        The gbm can be trained with early stopping using a validation set to prevent overfitting. \n        The feature importances are averaged over `n_iterations` to reduce variance. \n        \n        Uses the LightGBM implementation (http://lightgbm.readthedocs.io/en/latest/index.html)\n\n        Parameters \n        --------\n\n        eval_metric : string\n            Evaluation metric to use for the gradient boosting machine for early stopping. Must be\n            provided if `early_stopping` is True\n\n        task : string\n            The machine learning task, either 'classification' or 'regression'\n\n        n_iterations : int, default = 10\n            Number of iterations to train the gradient boosting machine\n            \n        early_stopping : boolean, default = True\n            Whether or not to use early stopping with a validation set when training\n        \n        \n        Notes\n        --------\n        \n        - Features are one-hot encoded to handle the categorical variables before training.\n        - The gbm is not optimized for any particular task and might need some hyperparameter tuning\n        - Feature importances, including zero importance features, can change across runs\n\n        \"\"\"\n\n        if early_stopping and eval_metric is None:\n            raise ValueError(\"\"\"eval metric must be provided with early stopping. Examples include \"auc\" for classification or\n                             \"l2\" for regression.\"\"\")\n            \n        if self.labels is None:\n            raise ValueError(\"No training labels provided.\")\n        \n        # One hot encoding\n        features = pd.get_dummies(self.data)\n        self.one_hot_features = [column for column in features.columns if column not in self.base_features]\n\n        # Add one hot encoded data to original data\n        self.data_all = pd.concat([features[self.one_hot_features], self.data], axis = 1)\n\n        # Extract feature names\n        feature_names = list(features.columns)\n\n        # Convert to np array\n        features = np.array(features)\n        labels = np.array(self.labels).reshape((-1, ))\n\n        # Empty array for feature importances\n        feature_importance_values = np.zeros(len(feature_names))\n        \n        print('Training Gradient Boosting Model\\n')\n        \n        # Iterate through each fold\n        for _ in range(n_iterations):\n\n            if task == 'classification':\n                model = lgb.LGBMClassifier(n_estimators=1000, learning_rate = 0.05, verbose = -1)\n\n            elif task == 'regression':\n                model = lgb.LGBMRegressor(n_estimators=1000, learning_rate = 0.05, verbose = -1)\n\n            else:\n                raise ValueError('Task must be either \"classification\" or \"regression\"')\n                \n            # If training using early stopping need a validation set\n            if early_stopping:\n                \n                train_features, valid_features, train_labels, valid_labels = train_test_split(features, labels, test_size = 0.15, stratify=labels)\n\n                # Train the model with early stopping\n                model.fit(train_features, train_labels, eval_metric = eval_metric,\n                          eval_set = [(valid_features, valid_labels)],\n                          early_stopping_rounds = 100, verbose = -1)\n                \n                # Clean up memory\n                gc.enable()\n                del train_features, train_labels, valid_features, valid_labels\n                gc.collect()\n                \n            else:\n                model.fit(features, labels)\n\n            # Record the feature importances\n            feature_importance_values += model.feature_importances_ / n_iterations\n\n        feature_importances = pd.DataFrame({'feature': feature_names, 'importance': feature_importance_values})\n\n        # Sort features according to importance\n        feature_importances = feature_importances.sort_values('importance', ascending = False).reset_index(drop = True)\n\n        # Normalize the feature importances to add up to one\n        feature_importances['normalized_importance'] = feature_importances['importance'] / feature_importances['importance'].sum()\n        feature_importances['cumulative_importance'] = np.cumsum(feature_importances['normalized_importance'])\n\n        # Extract the features with zero importance\n        record_zero_importance = feature_importances[feature_importances['importance'] == 0.0]\n        \n        to_drop = list(record_zero_importance['feature'])\n\n        self.feature_importances = feature_importances\n        self.record_zero_importance = record_zero_importance\n        self.ops['zero_importance'] = to_drop\n        \n        print('\\n%d features with zero importance after one-hot encoding.\\n' % len(self.ops['zero_importance']))\n    \n    def identify_low_importance(self, cumulative_importance):\n        \"\"\"\n        Finds the lowest importance features not needed to account for `cumulative_importance` fraction\n        of the total feature importance from the gradient boosting machine. As an example, if cumulative\n        importance is set to 0.95, this will retain only the most important features needed to \n        reach 95% of the total feature importance. The identified features are those not needed.\n\n        Parameters\n        --------\n        cumulative_importance : float between 0 and 1\n            The fraction of cumulative importance to account for \n\n        \"\"\"\n\n        self.cumulative_importance = cumulative_importance\n        \n        # The feature importances need to be calculated before running\n        if self.feature_importances is None:\n            raise NotImplementedError(\"\"\"Feature importances have not yet been determined. \n                                         Call the `identify_zero_importance` method first.\"\"\")\n            \n        # Make sure most important features are on top\n        self.feature_importances = self.feature_importances.sort_values('cumulative_importance')\n\n        # Identify the features not needed to reach the cumulative_importance\n        record_low_importance = self.feature_importances[self.feature_importances['cumulative_importance'] > cumulative_importance]\n\n        to_drop = list(record_low_importance['feature'])\n\n        self.record_low_importance = record_low_importance\n        self.ops['low_importance'] = to_drop\n    \n        print('%d features required for cumulative importance of %0.2f after one hot encoding.' % (len(self.feature_importances) -\n                                                                            len(self.record_low_importance), self.cumulative_importance))\n        print('%d features do not contribute to cumulative importance of %0.2f.\\n' % (len(self.ops['low_importance']),\n                                                                                               self.cumulative_importance))\n        \n    def identify_all(self, selection_params):\n        \"\"\"\n        Use all five of the methods to identify features to remove.\n        \n        Parameters\n        --------\n            \n        selection_params : dict\n           Parameters to use in the five feature selection methhods.\n           Params must contain the keys ['missing_threshold', 'correlation_threshold', 'eval_metric', 'task', 'cumulative_importance']\n        \n        \"\"\"\n        \n        # Check for all required parameters\n        for param in ['missing_threshold', 'correlation_threshold', 'eval_metric', 'task', 'cumulative_importance']:\n            if param not in selection_params.keys():\n                raise ValueError('%s is a required parameter for this method.' % param)\n        \n        # Implement each of the five methods\n        self.identify_missing(selection_params['missing_threshold'])\n        self.identify_single_unique()\n        self.identify_collinear(selection_params['correlation_threshold'])\n        self.identify_zero_importance(task = selection_params['task'], eval_metric = selection_params['eval_metric'])\n        self.identify_low_importance(selection_params['cumulative_importance'])\n        \n        # Find the number of features identified to drop\n        self.all_identified = set(list(chain(*list(self.ops.values()))))\n        self.n_identified = len(self.all_identified)\n        \n        print('%d total features out of %d identified for removal after one-hot encoding.\\n' % (self.n_identified, \n                                                                                                  self.data_all.shape[1]))\n        \n    def check_removal(self, keep_one_hot=True):\n        \n        \"\"\"Check the identified features before removal. Returns a list of the unique features identified.\"\"\"\n        \n        self.all_identified = set(list(chain(*list(self.ops.values()))))\n        print('Total of %d features identified for removal' % len(self.all_identified))\n        \n        if not keep_one_hot:\n            if self.one_hot_features is None:\n                print('Data has not been one-hot encoded')\n            else:\n                one_hot_to_remove = [x for x in self.one_hot_features if x not in self.all_identified]\n                print('%d additional one-hot features can be removed' % len(one_hot_to_remove))\n        \n        return list(self.all_identified)\n        \n    \n    def remove(self, methods, keep_one_hot = True):\n        \"\"\"\n        Remove the features from the data according to the specified methods.\n        \n        Parameters\n        --------\n            methods : 'all' or list of methods\n                If methods == 'all', any methods that have identified features will be used\n                Otherwise, only the specified methods will be used.\n                Can be one of ['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance']\n            keep_one_hot : boolean, default = True\n                Whether or not to keep one-hot encoded features\n                \n        Return\n        --------\n            data : dataframe\n                Dataframe with identified features removed\n                \n        \n        Notes \n        --------\n            - If feature importances are used, the one-hot encoded columns will be added to the data (and then may be removed)\n            - Check the features that will be removed before transforming data!\n        \n        \"\"\"\n        \n        \n        features_to_drop = []\n      \n        if methods == 'all':\n            \n            # Need to use one-hot encoded data as well\n            data = self.data_all\n                                          \n            print('{} methods have been run\\n'.format(list(self.ops.keys())))\n            \n            # Find the unique features to drop\n            features_to_drop = set(list(chain(*list(self.ops.values()))))\n            \n        else:\n            # Need to use one-hot encoded data as well\n            if 'zero_importance' in methods or 'low_importance' in methods or self.one_hot_correlated:\n                data = self.data_all\n                \n            else:\n                data = self.data\n                \n            # Iterate through the specified methods\n            for method in methods:\n                \n                # Check to make sure the method has been run\n                if method not in self.ops.keys():\n                    raise NotImplementedError('%s method has not been run' % method)\n                    \n                # Append the features identified for removal\n                else:\n                    features_to_drop.append(self.ops[method])\n        \n            # Find the unique features to drop\n            features_to_drop = set(list(chain(*features_to_drop)))\n            \n        features_to_drop = list(features_to_drop)\n            \n        if not keep_one_hot:\n            \n            if self.one_hot_features is None:\n                print('Data has not been one-hot encoded')\n            else:\n                             \n                features_to_drop = list(set(features_to_drop) | set(self.one_hot_features))\n       \n        # Remove the features and return the data\n        data = data.drop(columns = features_to_drop)\n        self.removed_features = features_to_drop\n        \n        if not keep_one_hot:\n        \tprint('Removed %d features including one-hot features.' % len(features_to_drop))\n        else:\n        \tprint('Removed %d features.' % len(features_to_drop))\n        \n        return data\n    \n    def plot_missing(self):\n        \"\"\"Histogram of missing fraction in each feature\"\"\"\n        if self.record_missing is None:\n            raise NotImplementedError(\"Missing values have not been calculated. Run `identify_missing`\")\n        \n        self.reset_plot()\n        \n        # Histogram of missing values\n        plt.style.use('seaborn-white')\n        plt.figure(figsize = (7, 5))\n        plt.hist(self.missing_stats['missing_fraction'], bins = np.linspace(0, 1, 11), edgecolor = 'k', color = 'red', linewidth = 1.5)\n        plt.xticks(np.linspace(0, 1, 11));\n        plt.xlabel('Missing Fraction', size = 14); plt.ylabel('Count of Features', size = 14); \n        plt.title(\"Fraction of Missing Values Histogram\", size = 16);\n        \n    \n    def plot_unique(self):\n        \"\"\"Histogram of number of unique values in each feature\"\"\"\n        if self.record_single_unique is None:\n            raise NotImplementedError('Unique values have not been calculated. Run `identify_single_unique`')\n        \n        self.reset_plot()\n\n        # Histogram of number of unique values\n        self.unique_stats.plot.hist(edgecolor = 'k', figsize = (7, 5))\n        plt.ylabel('Frequency', size = 14); plt.xlabel('Unique Values', size = 14); \n        plt.title('Number of Unique Values Histogram', size = 16);\n        \n    \n    def plot_collinear(self, plot_all = False):\n        \"\"\"\n        Heatmap of the correlation values. If plot_all = True plots all the correlations otherwise\n        plots only those features that have a correlation above the threshold\n        \n        Notes\n        --------\n            - Not all of the plotted correlations are above the threshold because this plots\n            all the variables that have been idenfitied as having even one correlation above the threshold\n            - The features on the x-axis are those that will be removed. The features on the y-axis\n            are the correlated features with those on the x-axis\n        \n        Code adapted from https://seaborn.pydata.org/examples/many_pairwise_correlations.html\n        \"\"\"\n        \n        if self.record_collinear is None:\n            raise NotImplementedError('Collinear features have not been idenfitied. Run `identify_collinear`.')\n        \n        if plot_all:\n        \tcorr_matrix_plot = self.corr_matrix\n        \ttitle = 'All Correlations'\n        \n        else:\n\t        # Identify the correlations that were above the threshold\n\t        # columns (x-axis) are features to drop and rows (y_axis) are correlated pairs\n\t        corr_matrix_plot = self.corr_matrix.loc[list(set(self.record_collinear['corr_feature'])), \n\t                                                list(set(self.record_collinear['drop_feature']))]\n\n\t        title = \"Correlations Above Threshold\"\n\n       \n        f, ax = plt.subplots(figsize=(10, 8))\n        \n        # Diverging colormap\n        cmap = sns.diverging_palette(220, 10, as_cmap=True)\n\n        # Draw the heatmap with a color bar\n        sns.heatmap(corr_matrix_plot, cmap=cmap, center=0,\n                    linewidths=.25, cbar_kws={\"shrink\": 0.6})\n\n        # Set the ylabels \n        ax.set_yticks([x + 0.5 for x in list(range(corr_matrix_plot.shape[0]))])\n        ax.set_yticklabels(list(corr_matrix_plot.index), size = int(160 / corr_matrix_plot.shape[0]));\n\n        # Set the xlabels \n        ax.set_xticks([x + 0.5 for x in list(range(corr_matrix_plot.shape[1]))])\n        ax.set_xticklabels(list(corr_matrix_plot.columns), size = int(160 / corr_matrix_plot.shape[1]));\n        plt.title(title, size = 14)\n        \n    def plot_feature_importances(self, plot_n = 15, threshold = None):\n        \"\"\"\n        Plots `plot_n` most important features and the cumulative importance of features.\n        If `threshold` is provided, prints the number of features needed to reach `threshold` cumulative importance.\n\n        Parameters\n        --------\n        \n        plot_n : int, default = 15\n            Number of most important features to plot. Defaults to 15 or the maximum number of features whichever is smaller\n        \n        threshold : float, between 0 and 1 default = None\n            Threshold for printing information about cumulative importances\n\n        \"\"\"\n        \n        if self.record_zero_importance is None:\n            raise NotImplementedError('Feature importances have not been determined. Run `idenfity_zero_importance`')\n            \n        # Need to adjust number of features if greater than the features in the data\n        if plot_n > self.feature_importances.shape[0]:\n            plot_n = self.feature_importances.shape[0] - 1\n\n        self.reset_plot()\n        \n        # Make a horizontal bar chart of feature importances\n        plt.figure(figsize = (10, 6))\n        ax = plt.subplot()\n\n        # Need to reverse the index to plot most important on top\n        # There might be a more efficient method to accomplish this\n        ax.barh(list(reversed(list(self.feature_importances.index[:plot_n]))), \n                self.feature_importances['normalized_importance'][:plot_n], \n                align = 'center', edgecolor = 'k')\n\n        # Set the yticks and labels\n        ax.set_yticks(list(reversed(list(self.feature_importances.index[:plot_n]))))\n        ax.set_yticklabels(self.feature_importances['feature'][:plot_n], size = 12)\n\n        # Plot labeling\n        plt.xlabel('Normalized Importance', size = 16); plt.title('Feature Importances', size = 18)\n        plt.show()\n\n        # Cumulative importance plot\n        plt.figure(figsize = (6, 4))\n        plt.plot(list(range(1, len(self.feature_importances) + 1)), self.feature_importances['cumulative_importance'], 'r-')\n        plt.xlabel('Number of Features', size = 14); plt.ylabel('Cumulative Importance', size = 14); \n        plt.title('Cumulative Feature Importance', size = 16);\n\n        if threshold:\n\n            # Index of minimum number of features needed for cumulative importance threshold\n            # np.where returns the index so need to add 1 to have correct number\n            importance_index = np.min(np.where(self.feature_importances['cumulative_importance'] > threshold))\n            plt.vlines(x = importance_index + 1, ymin = 0, ymax = 1, linestyles='--', colors = 'blue')\n            plt.show();\n\n            print('%d features required for %0.2f of cumulative importance' % (importance_index + 1, threshold))\n\n    def reset_plot(self):\n        plt.rcParams = plt.rcParamsDefault\n"
  },
  {
    "path": "9.feature-engineering/README.md",
    "content": "## 一、面向机器学习的特征工程\n\n- [一、引言](1.引言.ipynb)\n- [二、简单数字的奇特技巧](2.简单数字的奇特技巧.ipynb)\n- [三、文本数据：展开、过滤和分块](3.文本数据.ipynb)\n- [四、特征缩放的效果：从词袋到 TF-IDF](4.特征缩放的效果：从词袋到_TF-IDF.ipynb)\n- [五、类别特征：机器鸡时代的鸡蛋计数](5.类别特征.ipynb)\n- [六、降维：使用 PCA 压缩数据集](6.降维：用_PCA_压缩数据集.ipynb)\n- [七、非线性特征提取和模型堆叠](7.非线性特征提取和模型堆叠.ipynb)\n- [八、自动化特征提取器：图像特征提取和深度学习](8.自动化特征提取器：图像特征提取和深度学习.ipynb)\n- [九、回到特征：将它们放到一起(备注：代码还在测试)](9.回到特征：将它们放到一起.ipynb)\n- [附录、线性模型和线性代数基础](附录.线性模型和线性代数基础.ipynb)\n\n**内容简介**\n\n第 1 章从数字数据的基本特征工程开始：过滤，合并，缩放，日志转换和能量转换以及交互功能。\n\n第 2 章和第 3 章深入探讨了自然文本的特征工程：bag-of-words，n-gram 和短语检测。\n\n第 4 章将 tf-idf 作为特征缩放的例子，并讨论它的工作原理。\n\n第 5 章讨论分类变量的高效编码技术，包括特征哈希和 bin-counting。\n\n第 6 章中进行主成分分析，我们深入机器学习的领域。\n\n第 7 章将 k-means 看作一种特征化技术，它说明了模型堆叠的有效理论。\n\n第 8 章都是关于图像的，在特征提取方面比文本数据更具挑战性。在得出深度学习是最新图像特征提取技术的解释之前，我们着眼于两种手动特征提取技术 SIFT 和 HOG。\n\n第 9 章中完成了一个端到端示例中的几种不同技术，为学术论文数据集创建了一个推荐器。\n\n------\n\n[原版（英文）图书地址](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/)\n\n**翻译**：[apachecn](https://github.com/apachecn)，[翻译版本地址](https://github.com/apachecn/feature-engineering-for-ml-zh)\n\n**代码修改和整理**：[黄海广](https://github.com/fengdu78)，原文修改成jupyter notebook格式，并增加和修改了部分代码，测试全部通过，所有数据集已经放在[百度云](data/README.md)下载。\n\n## 二、特征工程的神器-基于Python的特征自动化选择代码\n\n[FeatureSelectorUsage](FeatureSelectorUsage/)\n\n该选择器基于python编写，有五种方法来标识要删除的特征：\n\n- 缺失值\n- 唯一值\n- 共线特征\n- 零重要性特征\n- 低重要性特征\n\n如果需要引用这个Repo:\n\n格式： `fengdu78, Data-Science-Notes, (2019), GitHub repository, https://github.com/fengdu78/Data-Science-Notes`"
  },
  {
    "path": "9.feature-engineering/data/README.md",
    "content": "# 数据下载说明\n原书的数据下载比较麻烦，有些比较大，我对数据进行了下载和处理，并上传到百度云。运行代码前，请解压数据到`data`目录即可。\n\n\n\n**百度云下载地址：**\n\n链接：https://pan.baidu.com/s/1uDXt5jWUOfI0fS7hD91vBQ \n提取码：8p5d \n\n**内容截图:**\n\n![](../images/data.png)"
  },
  {
    "path": "9.feature-engineering/images/Appendix/pic",
    "content": "\n"
  },
  {
    "path": "9.feature-engineering/附录.线性模型和线性代数基础.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[原版（英文）图书地址](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/)\\n\",\n    \"\\n\",\n    \"**翻译**：[apachecn](https://github.com/apachecn)，[翻译版本地址](https://github.com/apachecn/feature-engineering-for-ml-zh)\\n\",\n    \"\\n\",\n    \"**代码修改和整理**：[黄海广](https://github.com/fengdu78)，原文修改成jupyter notebook格式，并增加和修改了部分代码，测试全部通过，所有数据集已经放在[百度云](data/README.md)下载。\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 附录、线性模型和线性代数基\\n\",\n    \"\\n\",\n    \"> 译者：[@Sherlock-kid](https://github.com/Sherlock-kid)\\n\",\n    \"\\n\",\n    \"## 线性分类概述\\n\",\n    \"\\n\",\n    \"当我们有一个已经标记的数据集时，特征空间散布着来自不同类别的数据点。分类器的工作是将不同类别的数据点分开。它可以通过生成一个数据点与另一个数据点非常不同的输出来实现。例如，当这里只有两个类别的时候，一个好的分类器应该为一个类别产生大量的输出，而另一个则为小的输出。作为一个类别而不是另一个类别的点就形成了一个决策平面。\\n\",\n    \"\\n\",\n    \"![avatar](images/Appendix/FigureA_1.png)\\n\",\n    \"\\n\",\n    \"图 A-1：简单的二元分类找到了一个分离两类数据点的曲面\\n\",\n    \"\\n\",\n    \"许多函数能被当作分类器。这是一个寻找能完全分离不同类的简单函数的好方法。首先，相比于寻找最复杂的分类器，寻找最简单的分类器更容易。而且，简单函数常常能更好地适应新数据，要将它们与训练数据（特别是过度拟合）相比。一个简单的模型也许会出错，就像上图一些数据点被分到了错误的一边。但是我们牺牲了一些训练的准确性，以便有一个更简单的决策平面，可以达到更好的测试精度。最大限度减少复杂性和最大限度增加精度被叫做“奥卡姆剃刀”，广泛适用于科学与工程。\\n\",\n    \"\\n\",\n    \"最简单的函数是一条直线。一个一元线性函数是我们最熟悉的。\\n\",\n    \"\\n\",\n    \"![avatar](images/Appendix/FigureA_2.png)\\n\",\n    \"\\n\",\n    \"图 A-2：一元线性函数\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"二元线性函数可以显示为 3D 中的平面或 2D 中的等高线图（如图 A-3）。与拓扑地理图一样，等高线图的每一行代表输入空间中具有相同输出的点。\\n\",\n    \"\\n\",\n    \"![avatar](images/Appendix/FigureA_3.png)\\n\",\n    \"\\n\",\n    \"图 A-3：二维线性函数的等高线图\\n\",\n    \"\\n\",\n    \"可视化更高维度的线性函数很难，它们被称作超平面。但是写出它们的代数公式还是很容易的。一个多维的线性模型有一个输入集合$x_1,x_2,…,x_n$和一个权重参数集合$w_0,w_1,…,w_n$：$fw(x_1,x_2,…,x_n) = w_0+w_1*x_1+w_2*x_2+…+w_n*x_n$。这个公式能被写成更简洁的向量形式 $fw(X)=X^T W$。 我们遵循通常的数学符号惯例，它使用粗体来表示一个向量和非粗体来表示一个标量。向量$x$在开始处用一个额外的 1 填充，作为截距项$w_0$的占位符。如果所有输入特征都为零，那么函数的输出是$w_0$。 所以$w_0$也被称为偏差或截距项。\\n\",\n    \"\\n\",\n    \"训练线性分类器相当于在类之间挑出最佳分离超平面。这意味着找到在空间中精确定位的最佳向量`w`。由于每个数据点都有一个目标标签$y$，我们可以找到一个$w$，它试图直接模拟目标的标签 $x^T w=y$。\\n\",\n    \"\\n\",\n    \"由于通常有多个数据点，我们需要一个$w$同时使所有的预测都接近目标标签：\\n\",\n    \"\\n\",\n    \"方程 A-1：线性模型公式$Aw=y$\\n\",\n    \"\\n\",\n    \"这里，$A$被称为数据矩阵（在统计中也被称为设计矩阵）。它包含特定形式的数据：每行是数据点，每列都是一个特征。（有时人们也会看到它的转置，其中的特征以行的形式显示，数据以列的形式显示。）\\n\",\n    \"\\n\",\n    \"## 矩阵的解析\\n\",\n    \"\\n\",\n    \"为了解决方程 A-1，我们需要一些线性代数的基本知识。为了系统地介绍这个主题，我们强烈推荐 Gilbert Strang 的书《Linear Algebra and Its Applications》（线性代数及其应用）。\\n\",\n    \"\\n\",\n    \"方程 A-1 指出，当某个矩阵乘以某个向量时，会有一定的结果。一个矩阵也被叫做一个线性算子，这个名称使得矩阵更像一台小机器。该机器将一个向量作为输入，并使用多个关键操作的组合来推导出另一个向量：一个向量方向的旋转，添加或减去维度，以及拉伸或压缩其长度。这种组合在输入空间中操纵形状时非常有用（见图 A-4）。\\n\",\n    \"\\n\",\n    \"![avatar](images/Appendix/FigureA_4.png)\\n\",\n    \"\\n\",\n    \"图 A-4：`3 x 2`矩阵可以将 2D 中的正方形区域转换为 3D 中的菱形区域。 它通过将输入空间中的每个向量旋转并拉伸到输出空间中的新向量来实现。\\n\",\n    \"\\n\",\n    \"## 从向量到子空间\\n\",\n    \"\\n\",\n    \"为了理解线性算子，我们必须看看它如何将输入变为输出。幸运的是，我们不必一次分析一个输入向量。向量可以组织成子空间，而线性算子则可以处理向量子空间。子空间是一组满足两个标准的向量：（1）如果它包含一个向量，那么它包含通过原点和该点的直线，以及（2）如果它包含两个点，则它包含所有线性组合 的这两个向量。 线性组合是两种操作类型的组合：将向量与标量相乘，并将两个向量相加在一起。\\n\",\n    \"\\n\",\n    \"子空间的一个重要属性是它的秩或维度，它是这个空间中自由度的度量。一条线的秩为 1，2D 平面的秩为 2，依此类推。如果你可以想象在我们的多维空间中的多维鸟，那么子空间的秩告诉我们鸟可以飞行多少个“独立”的方向。这里的“独立”意思是“线性独立性”：如果一个不是另一个的常数倍，那么两个向量是线性独立的，即它们并不指向完全相同或相反的方向。\\n\",\n    \"\\n\",\n    \"子空间可以被定义为一组基向量的范围。（跨度是描述一组向量的所有线性组合的技术术语。）一组向量的跨度在线性组合下是不变的（因为它是以这种方式定义的）。因此，如果我们有一组基向量，那么我们可以将这些向量乘以任何非零常数或者添加向量来获得另一个基。\\n\",\n    \"\\n\",\n    \"有一个更独特和可识别的基来描述一个子空间将是很好的。标准正交基包含具有单位长度且彼此正交的向量。正交性是另一个技术术语。（所有数学和科学中至少有50％是由技术术语组成的，如果你不相信我，请在本书中做一个袋装词）。如果两个向量的内积是 零。 对于所有密集的目的，我们可以将正交向量看作相互成 90 度角。（这在欧几里得空间中是真实的，这与我们的物理三维现实非常相似。）将这些向量标准化为单位长度将它们变成一组统一的测量棒。\\n\",\n    \"\\n\",\n    \"总而言之，一个子空间就像一个帐篷，正交基向量是支撑帐篷所需的直角杆数。秩等于正交基向量的总数。\\n\",\n    \"\\n\",\n    \"![avatar](images/Appendix/FigureA_5.png)\\n\",\n    \"\\n\",\n    \"图 A-5：四个有用的线性代数概念的插图：内积，线性组合，基向量和正交基向量。\\n\",\n    \" \\n\",\n    \"对于那些在数学中思考的人来说，这里有一些数学来描述我们的描述。\\n\",\n    \"\\n\",\n    \"#### 有用的线性代数定义\\n\",\n    \"\\n\",\n    \"+   标量：一个数$c$，不是向量\\n\",\n    \"+   向量：$x=(x_1,x_2,…,x_n)$\\n\",\n    \"+   线性组合：$ax+by=ax_1+by_1,ax_2+by_2,…,ax_n+by_n$\\n\",\n    \"+   向量组的范围：向量组 $u=a_1 v_1+⋯+a_k v_k$，$a_1,…,a_k$ 任意\\n\",\n    \"+   线性独立性：$x$和$y$相互独立，如果$x≠cy$，$c$是任何标量常量\\n\",\n    \"+   内在产品：$x,y=x_1 y_1+x_2 y_2+⋯+x_n y_n$\\n\",\n    \"+   正交向量：如果$<x,y>=0$，两个向量$x$和$y$正交\\n\",\n    \"+   子空间：包含更大的向量空间中的向量子集，满足这三个标准：\\n\",\n    \"    +   包含 0 向量\\n\",\n    \"    +   如果包含一个向量$v$，那么也包含所有向量$cv$，其中$c$是一个标量。\\n\",\n    \"    +   如果包含两个向量$u$和$v$，那么也包含$u+v$\\n\",\n    \"+   基：一组跨向子空间的向量\\n\",\n    \"+   正交基：对于任意$i,j$，当$<v_i,v_j>=0$时，$\\\\{v_1,v_2,…,v_d\\\\}$为正交基\\n\",\n    \"+   子空间的秩：跨越子空间的线性无关基向量的最小数目。\\n\",\n    \"+   \\n\",\n    \"## 奇异值分解（SVD）\\n\",\n    \"\\n\",\n    \"矩阵对输入向量执行线性变换。线性变换非常简单且受到限制。因此，矩阵不能无情地操纵子空间。线性代数最迷人的定理之一证明了每一个方阵，无论它包含什么数字，都必须将某一组向量映射回自己，并进行一些缩放。在矩形矩阵的一般情况下，它将一组输入向量映射为相应的一组输出向量，其转置将这些输出映射回原始输入。技术术语是正方形矩阵具有特征值的特征向量，矩形矩阵具有奇异值的左右奇异向量。\\n\",\n    \"\\n\",\n    \"#### 特征向量和奇异向量\\n\",\n    \"\\n\",\n    \"设$A$是一个$n*n$的矩阵。如果这里有一个向量$v$和一个标量$\\\\lambda$，使$Av=\\\\lambda v$，$v$称作特征向量，$\\\\lambda$称为$A$的一个特征值。\\n\",\n    \"\\n\",\n    \"让$A$成为一个长方形矩阵。如果这里有一个向量$u$和一个向量$v$和一个标量$\\\\sigma$，那么$Av=\\\\sigma u$，$A^T u=\\\\sigma v$，$u$和$v$被叫做左右奇异向量，$\\\\sigma$是$A$的奇异值。\\n\",\n    \"\\n\",\n    \"代数上，矩阵的 SVD 看起来像这样：\\n\",\n    \"\\n\",\n    \"$A=U \\\\Sigma V^T$\\n\",\n    \"\\n\",\n    \"其中矩阵$U$和$V$的列分别形成输入和输出空间的正交基。$\\\\Sigma$是一个一个包含奇异值的对角矩阵。\\n\",\n    \"\\n\",\n    \"在几何学上，矩阵执行以下的变换序列（见图 A-6）：\\n\",\n    \"\\n\",\n    \"+   将输入向量映射到右奇异向量基$V$上\\n\",\n    \"+   通过相应的奇异值缩放每个坐标\\n\",\n    \"+   将此分数与每个左奇异向量相乘\\n\",\n    \"+   总结结果\\n\",\n    \"\\n\",\n    \"当`A`是实矩阵（即所有元素都是实值）时，所有的奇异值和奇异向量都是实值的。奇异值可以是正数，负数或零。矩阵的有序奇异值集称为谱，它揭示了很多矩阵。奇异值之间的差距会影响解的稳定性，最大绝对奇异值与最小绝对奇异值之间的比值（条件数）会影响迭代求解器的求解速度。这两个属性都对可找到的解决方案的质量产生显着影响。\\n\",\n    \"\\n\",\n    \"![avatar](images/Appendix/FigureA_6.png)\\n\",\n    \"\\n\",\n    \"图 A-6：矩阵分解成三个小机器：旋转，缩放，旋转。\\n\",\n    \"\\n\",\n    \"操作从右到左进行矩阵向量乘法。最右边的机器旋转并潜在地将输入投影到较低维空间中。在这个例子中，输入立方体变成了一个扁平的正方形，并且也被旋转了。下一台机器在一个方向挤压正方形，并将其拉伸到另一个方向；正方形变成矩形。最后一个最左边的机器再次旋转矩形，并将其投影回可能更高的空间。但它仍然是一个扁平的矩形，而不是一些更高维的对象。\\n\",\n    \"\\n\",\n    \"## 数据矩阵的四个基本子空间\\n\",\n    \"\\n\",\n    \"解析矩阵的另一种有用方法是通过四个基本子空间：列空间，行空间，右空间和左空间。这四个子空间完全刻画了涉及$A$或 $A^T$ 的线性系统的解决方案。因此它们被称为四个基本子空间。对于数据矩阵，可以根据数据和特征理解四个基本子空间。让我们更详细地看看它们。\\n\",\n    \"\\n\",\n    \"#### 数据矩阵\\n\",\n    \"\\n\",\n    \"$A$：行是数据点，列是特征。\\n\",\n    \"\\n\",\n    \"### 列空间\\n\",\n    \"\\n\",\n    \"数学定义：\\n\",\n    \"\\n\",\n    \"当我们改变权值向量$w$时，输出向量$s$的集合，其中$s=Aw$\\n\",\n    \"\\n\",\n    \"数学解释：\\n\",\n    \"\\n\",\n    \"所有可能的列的组合。\\n\",\n    \"\\n\",\n    \"数据解释：\\n\",\n    \"\\n\",\n    \"根据观察到的特征，所有结果都是线性可预测的，向量$w$包含每个特征的权重。\\n\",\n    \"\\n\",\n    \"基本原理：\\n\",\n    \"\\n\",\n    \"对应非零奇异值的左奇异向量（$U$列的子集）。\\n\",\n    \"\\n\",\n    \"### 行空间\\n\",\n    \"\\n\",\n    \"数学定义：\\n\",\n    \"\\n\",\n    \"当我们改变权值向量$u$时，输出向量的集合 $r=u^T A$\\n\",\n    \"\\n\",\n    \"数学解释：\\n\",\n    \"\\n\",\n    \"所有可能的行线性组合\\n\",\n    \"\\n\",\n    \"数据解释：\\n\",\n    \"\\n\",\n    \"行空间中的向量可以表示为现有数据点的线性组合。因此，这可以解释为已有数据的空间。向量`u`包含线性组合中每个数据点的权重。\\n\",\n    \"\\n\",\n    \"基本原理：\\n\",\n    \"\\n\",\n    \"对应非零奇异值的右奇异向量（$V$列的子集）\\n\",\n    \"\\n\",\n    \"### 零空间\\n\",\n    \"\\n\",\n    \"数学定义：\\n\",\n    \"\\n\",\n    \"输入向量$w$的集合，其中$Aw=0$。\\n\",\n    \"\\n\",\n    \"数学解释：\\n\",\n    \"\\n\",\n    \"正交于$A$的所有行的向量。零空间被矩阵压缩为 0。这是“fluff”，它增加了$Aw=y$的解空间的体积。\\n\",\n    \"\\n\",\n    \"数据解释：\\n\",\n    \"\\n\",\n    \"新数据点，它不能被表示为现有数据点的任何线性组合\\n\",\n    \"\\n\",\n    \"基本原理：\\n\",\n    \"\\n\",\n    \"对应零奇异值的右奇异向量（$V$的其余列）\\n\",\n    \"\\n\",\n    \"### 左零空间\\n\",\n    \"\\n\",\n    \"数学定义：\\n\",\n    \"\\n\",\n    \"输入向量$u$的集合，其中 $u^T A=0$\\n\",\n    \"\\n\",\n    \"数学解释：\\n\",\n    \"\\n\",\n    \"正交于$A$的所有列的向量。左零空间正交于列空间。\\n\",\n    \"\\n\",\n    \"数据解释：\\n\",\n    \"\\n\",\n    \"不能用现有特征的线性组合来表示的新特征向量。\\n\",\n    \"\\n\",\n    \"基本原理：\\n\",\n    \"\\n\",\n    \"对应零奇异值的左奇异向量（$U$的其余列）\\n\",\n    \"\\n\",\n    \"列空间和行空间包含根据观察到的数据和特性所能表示的内容。列空间中的这些向量是非新特征。那些位于行空间中向量都是非新的数据点。\\n\",\n    \"\\n\",\n    \"对于建模和预测的目的，非新奇性是好的。完整的列空间意味着特征集包含足够的信息来建模我们的任何目标向量的愿望。一个完整的行空间意味着不同的数据点包含足够的变化来覆盖特性空间的所有可能的角落。我们要担心的是新的数据点和特征，它们分别包含在零空间和左零空间中。\\n\",\n    \"\\n\",\n    \"在建立数据线性模型的应用，零空间中也可以看作是“新”数据点的子空间。在这种情况下，新奇并不是一件好事。新数据点是训练集不能线性表示的虚数据，同样，左零空间包含的新特征不能用现有特征的线性组合表示。\\n\",\n    \"\\n\",\n    \"零空间正交于行空间。原因很简单，零空间的定义是$w$的内积为 0，每一个行向量都是$A$。因此，$w$正交于由这些行向量张成的空间即行空间。类似地，左零空间正交于列空间。\\n\",\n    \"\\n\",\n    \"### 求解一个线性系统\\n\",\n    \"\\n\",\n    \"让我们把所有这些数学问题都归结到手头的问题上：训练一个线性分类器，它与解决线性系统的任务密切相关。我们仔细看一个矩阵是如何运作的，因为我们要逆向设计它。为了建立一个线性模型，我们必须找到输入权向量$w$映射到观察到的输出目标$y$在系统$Aw=y$，其中$A$是数据矩阵。让我们试着把线性算子的机器反过来转动。如果我们有$A$的 SVD 分解，那么我们可以把$y$映射到左奇异向量（$U$的列），反转比例因子（乘以非零奇异值的逆）最后把它们映射回正确的奇异向量（$V$的列），很简单，是吧？\\n\",\n    \"\\n\",\n    \"这实际上是计算$a$的伪逆的过程，它利用了一个标准正交基的一个关键性质：转置就是逆。这就是 SVD 如此强大的原因。（在实践中，真正的线性系统求解器不使用 SVD，因为它们计算是相当昂贵。还有其他更便宜的方法来分解矩阵，比如 QR、LU 或乔列斯基分解）。\\n\",\n    \"\\n\",\n    \"然而，我们在匆忙中漏掉了一个小细节。如果奇异值为零会怎样？我们不能取零为倒数，因为$1/0=∞$。这就是为什么它叫伪逆。（矩形矩阵没有真实逆的定义，只有方阵（只要所有特征值非零）。无论输入什么，它的奇异值都为零；没有办法重新跟踪它的步骤并得出原始的输入。\\n\",\n    \"\\n\",\n    \"好吧，让我们倒回来看这个小细节。让我们带着我们学到的，再往前走看看能不能把机器拆开。假设我们得到了$Aw=y$的答案，我们称它为特定的$w$，因为它特别适合$y$。假设还有一堆输入向量$A$被压到 0。让我们选一个，称它为$W_{sad-trumpet}$。那么当我们把 $W_{particular}$ 添加到 $W_{sad-trumpet}$，您认为会发生什么呢？\\n\",\n    \"\\n\",\n    \"$$W_{particular}+W_{sad-trumpet}=y$$\\n\",\n    \"\\n\",\n    \"神奇！这也是一个解。事实上，任何被压缩到零的输入都可以被添加到一个特定的解中，并解决方案是这样的：\\n\",\n    \"\\n\",\n    \"$$W_{general}=W_{particular}+W_{homogeneous}=y$$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Wparticular 是方程$Aw=y$的精确解，可能有也可能没有这样的解。如果没有，那么系统只能近似地解决。如果有，那么$y$属于已知的$a$的列空间，列空间是$A$可以映射到的向量集合，通过它的列的线性组合。\\n\",\n    \"\\n\",\n    \"Whomogeneous 是方程$Aw = 0$的解。（ $W_{sad-trumpet}$ 的完整名称是“$W_{homogeneous}$”。）这一点现在应该很熟悉了。所有的广义向量的集合构成了$a$的零空间，这是具有奇异值为 0 的右奇异向量张成的空间。\\n\",\n    \"\\n\",\n    \"“零空间”这个名字听起来像是一场生死攸关的危机的结局。如果零空间包含除零向量之外的任何向量，那么就有方程$Aw = y$有无穷多个解，有太多的解可供选择\\n\",\n    \"\\n\",\n    \"来自本身并不是一件坏事。有时任何解决方案都可以。\\n\",\n    \"\\n\",\n    \"但是如果有许多可能的答案，那么就有许多对分类任务有用的特性集。很难理解哪些是真正重要的。\\n\",\n    \"解决大零空间问题的一种方法是通过添加来调节模型\\n\",\n    \"额外的约束:\\n\",\n    \"$$Aw = y$$，其中$$w^Tw = c$$\\n\",\n    \"这种正则化形式使得权向量具有一定的范数。这种正则化的强度是由正则化参数控制的，正则化参数必须进行调整，就像我们在实验中所做的那样。\\n\",\n    \"\\n\",\n    \"一般来说，特性选择方法处理选择最有用的特性来减少计算负担，减少模型的混乱程度，并使学习的模式更独特。这是“特性选择”的重点。\\n\",\n    \"\\n\",\n    \"另一个问题是数据矩阵的“不均匀性”。当我们训练一个线性分类器时，我们不仅关心线性系统有一个通解，而且关心我们能轻易地找到它。通常，训练过程使用一个求解器，它通过计算损失函数的梯度和以小步骤下坡来工作。当某些奇异值非常大，而另一些非常接近于零时，求解者需要仔细地遍历较长的奇异向量(那些对应于较大奇异值的向量)，并花费大量时间挖掘较短的奇异向量以找到真正的答案。在光谱中这种“不均匀性”是由矩阵的条件数来衡量的，它基本上是最大和最小绝对值之间的比值。\\n\",\n    \"\\n\",\n    \"综上所述，为了有一个相对独特的好的线性模型，为了让它容易被发现，我们希望:\\n\",\n    \"\\n\",\n    \"1、标签向量可以通过特征子集(列向量)的线性组合很好地逼近。更好的是，特性集应该是线性无关的。\\n\",\n    \"2、为了使零空间很小，行空间必须很大。(这是因为这两个子空间是正交的。)数据点(行向量)的集合越线性无关，零空间就越小。\\n\",\n    \"\\n\",\n    \"•为了便于求解，数据矩阵的条件数——最大奇异值与最小奇异值之比——应该很小。\\n\",\n    \"\\n\",\n    \"## 分类器的概述\\n\",\n    \"使用统计分类或分类器的方法来识别一个或者一组新的观察将属于哪个类别。书中引用的大多数方法都属于监督学习领域，在监督学习中，分类是通过使用预先确定的类别标记的训练数据进行算法确定的。我们将在这里给出这些算法的高级视图，仅仅足以理解它们在文本中的应用。(实际上，维基百科是你最好的朋友，因为它在每一种方法中都能找到很好的参考文献。)\\n\",\n    \"\\n\",\n    \"### 支持向量机的径向基函数核\\n\",\n    \"支持向量机径向基函数核(RBF SVM)\\n\",\n    \"\\n\",\n    \"在Scikit Learn的`sklearn.gaussian_process.kernes.RBF`包\\n\",\n    \"\\n\",\n    \"### K-最近邻\\n\",\n    \"K-最近邻(kNN)\\n\",\n    \"\\n\",\n    \"Scikit Learn的`sklearn-neighbors`包\\n\",\n    \"\\n\",\n    \"### 随机森林\\n\",\n    \"\\n\",\n    \"随机森林（RF）\\n\",\n    \"\\n\",\n    \"随机森林分类器在Scikit Learn的`sklearn.ensembles`包中\\n\",\n    \"\\n\",\n    \"### 梯度提升树\\n\",\n    \"\\n\",\n    \"梯度提升树（GBT）\\n\",\n    \"\\n\",\n    \"梯度提升树分类器在Scikit Learn的 `sklearn.ensembles`包中\\n\",\n    \"\\n\",\n    \"严格地说，这里给出的公式是线性回归，而不是线性分类。不同的是，回归允许实值目标变量，而分类目标通常是表示不同类的整数。一个回归子可以通过非线性的变换转化为一个分类器。例如，logistic回归分类器通过logistic函数传递输入的线性变换。这些模型被称为广义线性模型，其核心是线性函数。虽然这个例子是关于分类的，但是我们使用线性回归公式作为教学工具，因为它更容易分析。直觉容易映射到广义线性分类器\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"实际上，它比那要复杂一些。$y$可能不在它的列空间中，所以这个方程可能没有解。统计机器学习不是放弃，而是寻找一个近似的解决方案。它定义了一个量化解决方案质量的损失函数。如果解是精确的，那么损失是0。小错误,小损失;大错误，大损失等等。然后，训练过程寻找最佳参数来最小化这个损失函数。在普通线性回归中，损失函数称为平方剩余损失，它本质上是将$y$映射到列空间中$A$的最近点。逻辑回归最小化了日志丢失。在这两种情况下，以及一般的线性模型中，线性系统$Aw = y$通常位于核心。因此，我们的分析是非常相关的。\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "README.md",
    "content": "# Data-Science-Notes\n数据科学的笔记以及资料搜集，目前尚在更新，部分内容来源于github搜集。\n\n[0.math](0.math) （数学基础）\n\n[1.python-basic](1.python-basic) （python基础）\n\n[2.numpy](2.numpy)（numpy基础）\n\n[3.pandas](3.pandas)（pandas基础）\n\n[4.scipy](4.scipy)（scipy基础）\n\n[5.data-visualization](5.data-visualization)（数据可视化基础，包含matplotlib和seaborn）\n\n[6.scikit-learn](6.scikit-learn)（scikit-learn基础）\n\n[7.machine-learning](7.machine-learning)（机器学习基础）\n\n[8.deep-learning](8.deep-learning)（深度学习基础）\n\n[9.feature-engineering](9.feature-engineering)（特征工程基础）\n\n\n\n## 参考\n\n- 《统计学习方法》李航\n- https://github.com/donnemartin/data-science-ipython-notebooks\n- https://github.com/apachecn/feature-engineering-for-ml-zh\n- https://github.com/datawhalechina/pumpkin-book\n- https://github.com/Doraemonzzz/Learning-from-data\n- https://github.com/wzyonggege/statistical-learning-method\n- https://github.com/WenDesi/lihang_book_algorithm\n- https://www.coursera.org/course/ml \n- https://mooc.guokr.com/note/12/ [小小人_V](https://mooc.guokr.com/user/2133483357/) \n- 《python科学计算》\n\n\n## 关于作者\n\n微信公众号：机器学习初学者 ![gongzhong](images/gongzhong.jpg)\n知识星球：黄博的机器学习圈子![xingqiu](images/zhishixingqiu1.jpg)\n\n[我的知乎](https://www.zhihu.com/people/fengdu78/activities)\n\n机器学习qq群：704220115（我们有11个群，加过一个就不需要加了）\n\n**注意：github下载太慢的话，关注我的公众号：“机器学习初学者”，回复“学习路线”即可下载本仓库的镜像文件，整个仓库压缩成一个iso。**\n\n如果需要引用这个Repo:\n\n格式： `fengdu78, Data-Science-Notes, (2019), GitHub repository, https://github.com/fengdu78/Data-Science-Notes`\n\n"
  }
]